--- /doc/fiction/gene-wolfe/index ‘Gene Wolfe’ tag 2013-05-16 2024-09-10 fiction/fantasy fiction/science-fiction

Bibliography for tag fiction/gene-wolfe, most recent first: 2 related tags, 14 annotations, & 10 links ().

--- /doc/cs/lisp/emacs/index ‘Emacs’ tag 2021-01-25 2024-11-27 cs/security

Bibliography for tag cs/lisp/emacs, most recent first: 30 annotations & 8 links ().

--- /doc/psychiatry/autism/schizoid/index ‘schizoid personality’ tag 2020-08-10 2024-11-25 psychology/personality

Bibliography for tag psychiatry/autism/schizoid, most recent first: 9 annotations ().

--- /harberger Self-Funding Harberger Taxes Gwern 2024-11-21 2024-11-22 economics/copyright economics/mechanism-design

Copyright mechanism proposal to solve the orphan works problem: self-assessed Harberger taxes on any inherited copyright are then invested, and dedicated to eventually buying out the owners. Works are either immediately public-domained, or the owners voluntarily ‘sell’ them if their value underperforms a baseline investment.

--- /tla#effective-gpt-4-programming CQK Is The First Unused TLA § Effective GPT-4 Programming Gwern 2023-09-29 2023-11-11 ai/nn/transformer/gpt/codex

Curious what the first ‘unused’ alphabetic acronym is, I have GPT-4 write a script to check English Wikipedia. After three bugs, the first unused one turns out as of 2023-09-29 to be the three-letter acronym ‘CQK’, with another 2.6k TLA unused, and 393k four-letter acronyms unused. Exploratory analysis suggests alphabetical order effects as well as letter-frequency.

Reports of GPT-4 coding utility vary widely. I’ve found it useful, so these sections cover my tips on using it as of November 2023.

I also include a list of example uses I’ve made besides this TLA page’s code.

--- /doc/nootropic/quantified-self/heart-rate-variability/index ‘HRV’ tag 2022-02-17 2024-10-24 exercise psychology/neuroscience psychology/willpower

Bibliography for tag nootropic/quantified-self/heart-rate-variability, most recent first: 1 related tag, 6 annotations, & 2 links ().

--- /lorem-table Lorem Ipsum: Tables Gwern 2020-09-27 2022-11-10 cs/js

Tables test subset of /lorem

--- /lorem-unicode Lorem Ipsum: Unicode Gwern 2020-09-27 2022-11-10 cs/css

Unicode character test subset of /lorem

--- /review/the-bridge Review of The Bridge Gwern 2024-04-28 2024-04-30 fiction/criticism psychiatry

Documentary about suicides at the Golden Gate Bridge sheds little light on the brute facts of suffering and mental illness—facts which perhaps cannot be conveyed to others (on film or otherwise), and are inherently private.

--- /help Site Help Gwern 2024-04-05 2024-09-29 cs/js meta

Short cheatsheet documentation about Gwern.net keybindings & features.

--- /note/nashx The exploding Nash 2-of-2 NashX equilibrium Gwern 2021-05-15 2024-11-29 bitcoin/nashx --- /fiction/the-diamond-earrings The Diamond Earrings Gwern 2023-05-09 2024-10-16 fiction/science-fiction philosophy/ethics philosophy/mind psychology/willpower

Short story varying ‘The Whispering Earring’ on willpower and Goodharting values.

--- /book-writing Why To Not Write A Book Gwern 2024-08-22 2024-09-17 psychiatry/anxiety psychiatry/depression psychology/writing

A discussion of why I don’t intend to turn Gwern.net into a book, and how trying to write a book can harm writers.

--- /oen Number Search Engine via NN Embeddings Gwern 2024-08-10 2024-08-25 ai/nn/retrieval math reinforcement-learning/exploration

Proposal to create a ‘search engine’ like OEIS but for individual numbers, allowing fuzzy lookups, by training a neural net embedding on the scientific & mathematic literature’s corpus of co-occurring numbers, and by known special transformations.

--- /earwax#east-asian-survey Why Cats Love Earwax § East Asian Survey Gwern 2019-11-05 2024-08-21 survey

Collation of anecdotes and speculation about why many cats like earwax, and human earwax especially. Is it the valeric acid?

In August 2024, I ran an Internet survey on Prolific of n = 250 East Asian cat-owners asking about earwax type & if they had observed an earwax response from their cat. A majority had dry earwax, and most had not exposed their cat to earwax; of those who did, a majority observed no response; and of responders, 26% of dry earwax owners noted a response vs 43% of wet. This confirms that cats have individual differences in earwax response, and that dry earwax can trigger a weaker response—consistent with the valeric acid theory.

--- /doc/statistics/bayes/hope-function/index ‘Hope function’ tag 2019-04-01 2024-08-21 statistics/survival-analysis

Bibliography for tag statistics/bayes/hope-function, most recent first: 1 related tag, 2 annotations, & 6 links ().

--- /doc/ai/nn/transformer/gpt/4/sydney/index ‘Sydney (AI)’ tag 2023-03-14 2024-10-02 ai/nn/retrieval reinforcement-learning/multi-agent reinforcement-learning/preference-learning reinforcement-learning/safe statistics/stylometry/truesight

Bibliography for tag ai/nn/transformer/gpt/4/sydney, most recent first: 2 related tags, 30 annotations, & 43 links ().

--- /twitter Twitter Follow-Request UX Problems Gwern 2023-02-24 2023-03-09 design

The Twitter UI/UX for accepting follow requests is a fractal of bad design.

For private Twitter accounts, follow requests must be approved manually by the private account.

The UX turns out to be so ill-designed, so slow and unusable, so error-prone, so counterintuitively frustrating, that I finally wrote up a list of all the problems I encountered while using it for my account.

The only way to ‘bump’ a follow request, to make it findable by being the most recent request, turns out to involve the requester blocking me for up to a week before unblocking & re-requesting.

--- /development-hell On Development Hell Gwern 2020-11-17 2020-11-17 fiction/criticism sociology/technology

Why do movies or games go through development hell, and usually come out worse, rather than better, and long gestation times do not seem to improve them despite the large resources poured into them by the most talented people?

--- /doc/statistics/decision/stigler-diet/index ‘Stigler’s diet problem’ tag 2024-01-01 2024-07-17 exercise math/humor

Bibliography for tag statistics/decision/stigler-diet, most recent first: 12 annotations & 1 link ().

--- /doc/math/index ‘math’ tag 2019-08-28 2024-11-23

Bibliography for tag math, most recent first: 6 related tags, 262 annotations, & 108 links ().

--- /doc/nootropic/potassium/index ‘potassium (sleep)’ tag 2024-01-01 2024-01-01 nootropic/magnesium

Bibliography for tag nootropic/potassium, most recent first: 1 related tag, 1 annotation, & 9 links ().

--- /doc/nootropic/magnesium/index ‘magnesium (nootropic)’ tag 2022-02-22 2024-11-18 nootropic/potassium zeo

Bibliography for tag nootropic/magnesium, most recent first: 2 related tags, 10 annotations, & 15 links ().

--- /zeo/zeo#magnesium-citrate Zeo sleep self-experiments § Magnesium Citrate Gwern 2010-12-28 2018-02-28 nootropic/magnesium zeo

EEG recordings of sleep and my experiments with things affecting sleep quality or durations: melatonin, potassium, vitamin D etc

Re-analyzing data from a magnesium self-experiment, I find both positive and negative effects of the magnesium on my sleep. It’s not clear what the net effect is.

--- /rubiks-cube-claude On the Impossibility of Superintelligent Rubik’s Cube Solvers [Claude-3.5-sonnet] Claude-3 2024-06-21 2024-06-22 ai/nn/transformer/gpt/claude math/humor philosophy/mind

Satirical essay on how AI can never truly solve a Rubik’s Cube like human beings can, written by Claude-3.5-sonnet.

In recent years, a number of prominent computer scientists and roboticists have suggested that artificial intelligence may one day solve Rubik’s Cubes faster than humans. Many have further argued that AI could even come to exceed human Rubik’s Cube-solving abilities by a significant margin.

However, there are at least twenty distinct arguments that preclude this outcome. We show that it is not only implausible that AI will ever exceed human Rubik’s Cube-solving abilities, but in fact impossible.

--- /rubiks-cube On the Impossibility of Superintelligent Rubik’s Cube Solvers Gwern, Claude-3, Claude-2 2023-07-19 2024-06-22 ai/nn/transformer/gpt/claude math/humor philosophy/mind

Satirical essay on how AI can never truly solve a Rubik’s Cube like human beings can, written by an AI. Inspired by ‘Supersized Machines’ (Garfinkel et al 2017).

In 2017, I was highly amused by the satire of anti-AGI arguments, “On the Impossibility of Supersized Machines”; I resolved to follow it up at some point with an AI-written article. In 2023–2024, I experimented with using recursive expansion prompts for LLMs to write a homage essay.

This is my final prompt’s version, generated using Claude-3.5-sonnet in June 2024: an unedited, comprehensive 23k-word essay covering 16 categories of arguments definitively explaining why machines will never be able to truly solve a Rubik’s cube faster than a human speedcuber can.

Essay abstract:

“In recent years, a number of prominent computer scientists and roboticists have suggested that artificial intelligence may one day solve Rubik’s Cubes faster than humans. Many have further argued that AI could even come to exceed human Rubik’s Cube-solving abilities by a substantial margin. However, there are at least 20 distinct arguments that preclude this outcome. We show that it is not only implausible that AI will ever exceed human Rubik’s Cube-solving abilities, but in fact impossible.”

--- /doc/psychology/neuroscience/pain/anesthesia/index ‘anesthesia’ tag 2020-08-21 2024-10-08 psychology/neuroscience/memory

Bibliography for tag psychology/neuroscience/pain/anesthesia, most recent first: 1 related tag, 37 annotations, & 5 links ().

--- /doc/philosophy/mind/index ‘mind’ tag 2018-01-21 2024-11-24 psychology/neuroscience/pain

Bibliography for tag philosophy/mind, most recent first: 3 related tags, 132 annotations, & 42 links ().

--- /doc/psychology/neuroscience/pain/index ‘pain’ tag 2020-06-22 2024-10-24 philosophy/mind psychology/willpower

Bibliography for tag psychology/neuroscience/pain, most recent first: 3 related tags, 41 annotations, & 5 links ().

--- /doc/genetics/heritable/rare/index ‘rare mutations’ tag 2019-09-29 2024-11-25 iq/low

Bibliography for tag genetics/heritable/rare, most recent first: 2 related tags, 227 annotations, & 25 links ().

--- /doc/iq/high/index ‘high IQ’ tag 2020-04-19 2024-11-24 iq/low

Bibliography for tag iq/high, most recent first: 6 related tags, 62 annotations, & 15 links ().

--- /doc/iq/low/index ‘low IQ’ tag 2020-05-28 2024-11-25 genetics/heritable/rare iodine iq/high psychiatry/autism psychology/neuroscience/memory/savant

Bibliography for tag iq/low, most recent first: 5 related tags, 22 annotations, & 7 links ().

--- /doc/ai/highleyman/index ‘Highleyman’s AI’ tag 2019-08-28 2024-06-30 ai/scaling ai/tabular

Bibliography for tag ai/highleyman, most recent first: 1 related tag, 13 annotations, & 2 links ().

--- /utext#html-utext Utext: Rich Unicode Documents § HTML Utext Gwern 2023-10-08 2024-04-24 cs/css

An esoteric document proposal: abuse Unicode to create the fanciest possible ‘plain text’ documents.

How can we make Utext useful for websites?

By adding in the single missing ingredient of hypertext: active, clickable links.

And we can do that by defining a HTML wrapper which permits only literal text displays (in <pre> tags), <a> HTML hyperlinks, and the minimal amount of CSS to render this pleasingly on desktop/mobile.

This HTML Utext gives us a highly-esoteric textfiles-style format which nevertheless could work for a website.

--- /doc/history/medici/index ‘the Medici’ tag 2022-10-04 2024-01-01 economics

Bibliography for tag history/medici, most recent first: 5 annotations & 2 links ().

--- /doc/ai/nn/transformer/gpt/whisper/index ‘Whisper NN’ tag 2022-05-26 2024-11-12 ai/nn/transformer/gpt/4

Bibliography for tag ai/nn/transformer/gpt/whisper, most recent first: 11 annotations & 19 links ().

--- /invertornot InvertOrNot.com Proposal Gwern 2021-03-21 2024-03-25 ai/nn/cnn cs/css

Description of a useful service for web development: a website which wraps a neural network trained to classify images by whether they would look better inverted in a website/app dark-mode, or faded.

A useful web service which does not exist as of 2023-10-16 is an API which analyzes a photograph and reports if it would look bad when inverted/negated. This would be useful for website dark-modes: inversion makes many images (like diagrams) look good in dark mode, but makes other images (photographs, especially of people) hideous to the point of illegibility. There is no simple reliable heuristic for choosing to invert an image, so most website designers settle for the safe but inferior option of fading out images.

However, it is almost certain that a neural network like CLIP, or perhaps even simpler classic machine vision approaches, could detect with ~100% reliability if an image would look bad when inverted.

These would be a bit heavyweight to run in-browser, so an API would be ideal: this could both be run in-browser live, and as a development tool for cached local labels. With server-side caching, a demonstration API could potentially handle millions of requests per day and be run on a minimal budget.

Implemented by InvertOrNot.com as of 25 March 2024.

--- /free-play Free-Play Periods for RL Agents Gwern 2023-05-07 2023-05-09 ai/nn/transformer/gpt/inner-monologue reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/robot

Proposal for incentivizing meta-learning of exploration in deep reinforcement learning: domain randomization with reward-shaping, where there is a fixed-length ‘play time’ with no rewards/losses at the beginning of each episode.

In standard reinforcement learning, agents learn to be maximally exploitative at the beginning of each episode, as they assume the rules are the same. In meta-learning oriented approaches, each episode have different rules, even explicitly randomized by a simulator; with enough scale, agents learn to observe their actions and adapt on the fly.

However, they still are penalized for any mistakes while adapting, because they continue to receive reward/loss as usual. Presumably, this means that they must be conservative in what exploratory actions they may take early on, and may not do any explicit exploration at all. This seems unduly harsh because in many problems, it is realistic to have some initial consequence-free period where an agent can take a series of exploratory actions to infer what sort of environment it is in, before the task begins ‘for real’. For example, in real world robotics, robots never start immediately earning rewards, but there is always some sort of bootup phase where they wait for tasks to start, which they could be using productively to self-test. But this sort of ‘sandbox’ period is never provided in meta-learning setups (except inadvertently).

I propose a simple free-play modification to domain randomization: every episode begins with a fixed-length “free-play” reward-shaped period where agents may take actions but all rewards are set to 0. After that period, the episode continues as usual. (This can be implemented simply by post-processing each episode’s data, and requires no other modifications whatsoever to existing DRL algorithms.) Since there is no possibility of greedily earning (or losing) reward during free-play, agents are incentivized to meta-learn optimal exploration of the meta-environment, to maximize information gain, before the dangerous part of the episode begins.

Free-play meta-training would lead to agents which ‘train themselves’ during the free-play period; for example, you would boot up a robot in a warehouse, and it would shake itself and wiggle around for a few minutes, and after that, the neural net now ‘knows’ all about how to operate that exact arm.

--- /review/quantum-thief Review Of The Quantum Thief Trilogy Gwern 2022-08-31 2024-03-13 fiction/science-fiction philosophy/mind

Explanation of the emotional core of The Quantum Thief’s exploration of the trap of persistent personal identity and seeking freedom.

Review of The Quantum Thief trilogy by Hannu Rajaniemi 2014 (★★★★★): uncompromising hard-SF space-opera—so uncompromising and in media res that most readers missed the point of its exploration of transhumanist themes of personal identity and radical freedom from constraints, even the constraints of the physical universe.

The Quantum Thief trilogy follows an amnesiac gentleman jewel-thief as he seeks to escape prison and becomes embroiled in a multi-way war between the competing powers of a far future Solar System, where the goal is the left-overs from a past Singularity which grant the victor the power to rewrite the laws of physics.

But underneath all of the wonderfully speculative SF ideas and cutting-edge science—so cutting-edge that many readers mistake the science for the fiction—for our protagonist, the real goal is an escape from his immortality: the burden of too many heists, too many witty quips, too many centuries of being forced to be himself rather than becoming someone else (which is the only way to maintain a persistent identity in a world where minds are trivially copied & modified).

As the Solar System collapses and reality is rewritten, our hero pulls off the greatest escape act ever, escaping our universe; for, as every transhumanist believes at heart when they look at our all-too-flawed world, if you are clever and hard-working and lucky enough, sooner or later… there is always a way out.

--- /doc/psychiatry/anxiety/index ‘anxiety’ tag 2019-11-07 2024-10-23 co2 psychiatry/alcoholism

Bibliography for tag psychiatry/anxiety, most recent first: 16 related tags, 147 annotations, & 41 links ().

--- /doc/psychiatry/schizophrenia/index ‘SCZ’ tag 2019-11-13 2024-10-29 psychiatry/alcoholism

Bibliography for tag psychiatry/schizophrenia, most recent first: 2 related tags, 223 annotations, & 32 links ().

--- /doc/psychiatry/alcoholism/index ‘alcoholism’ tag 2019-10-05 2024-10-26 longevity/glp/psychology psychedelic/lsd psychiatry/anxiety psychiatry/bipolar/energy psychiatry/schizophrenia

Bibliography for tag psychiatry/alcoholism, most recent first: 4 related tags, 92 annotations, & 16 links ().

--- /doc/design/typography/floral/index ‘floral ornaments’ tag 2021-05-29 2024-06-23 design/typography/dropcap

Bibliography for tag design/typography/floral, most recent first: 1 related tag, 7 annotations, & 14 links ().

--- /doc/cs/end-to-end-principle/index ‘end-to-end’ tag 2019-11-22 2024-09-05 economics/automation/metcalfes-law

Bibliography for tag cs/end-to-end-principle, most recent first: 1 related tag, 18 annotations, & 4 links ().

--- /doc/fiction/humor/index ‘humor’ tag 2019-10-01 2024-11-09 math/humor

Bibliography for tag fiction/humor, most recent first: 5 related tags, 113 annotations, & 115 links ().

--- /dropcap Dropcap Generation With AI Gwern 2023-10-15 2024-10-30 ai/nn/diffusion/midjourney/dropcap ai/nn/transformer/gpt/dall-e/3 cs/css cs/js

We develop AI image generation workflows for webpage dropcap typography, creating PNGs & SVGs using image generators. As demos, we create new Gwern.net logos and several custom FLOSS dropcap sets, including c​ats, Gene​ Wolfe horror fiction, and neural-net-inspired dropcaps.

Initials or ‘dropcaps’ are fun typographic features used on Gwern.net, but there are few fonts suitable for Web use; creating new ones is difficult & expensive. However, AI image generators have revolutionized image generation, and shown promise in typography.

How practical is it for a hobbyist to use generative models to create dropcap letters, and use them in a web page (like this one)? Can we achieve (1) high-quality dropcaps, (2) little total human labor, and (3) acceptable bandwidth use (~100kb)? After working through image & SVG-based workflows, we find that: yes, we mostly can.

To expand our dropcaps selection, I & Said Achmiz experiment with use of Midjourneyv5/6 & DALL·E 3 in late 2023, and discover that, while somewhat tricky to prompt, they generate dropcap images we like. The challenge is using them—web font-based dropcaps are already challenging to do, but image-based dropcaps are even harder, due to problems with removing backgrounds, positioning, multiple sizes, and light/dark-mode compatibility.

We first demonstrate image dropcaps by creating a c​at-themed dropcaps set which we call dropcat: we generate & curate the most dropcap-esque ones; we hand-edit them into repositioned & background-free images, and display them with our custom CSS/JS dropcap implementation. The images are small enough (~32kb, range: 8–140kb) to use freely. We also made holiday-themed Gwern.net logos (Christmas & Halloween). And because we can generate so many dropcaps, we generate many for each letter and pick randomly per-page-load.

However, while feasible, the hand-editing is laborious and took longer than generation! So we looked into vector generation instead, which would make it easier to automatically remove backgrounds & reposition them. While no 2023 vector generation AI does adequate dropcaps, and converting image → SVG often produces large/bad-looking SVGs, we find that with careful processing, we can make SVGs which look identical & generally are only ~2× larger than hand-tailored PNGs, while still requiring a little manual repositioning. To make that repositioning easier, Said Achmiz developed Dropcap Workshop, a specialized web-GUI dropcap SVG editor.

The SVG workflow is: generate an image in a NN → vectorize using the free Recraft.ai service → remove SVG background → reduce precision of numbers in SVG → minify SVG → adjust margins/positions in Dropcap Workshop → drop into a directory for automatic inclusion in website build → web pages set a CSS variable.

We demonstrate this with sets of Gene-Wolfe/horror/vampire-themed dropcaps and AI/neural-net/steampunk dropcaps (in progress).

We release all our dropcap images, code/scripts, & editor under FLOSS licenses, and hope that this will democratize dropcaps for everyone.

--- /doc/reinforcement-learning/meta-learning/continual-learning/index ‘continual learning’ tag 2019-12-21 2024-11-07 ai/nn/dynamic-evaluation ai/scaling reinforcement-learning/scaling

Bibliography for tag reinforcement-learning/meta-learning/continual-learning, most recent first: 1 related tag, 54 annotations, & 4 links ().

--- /gan GANs Didn’t Fail, They Were Abandoned Gwern 2022-10-04 2022-10-15 ai/nn/gan ai/scaling

Diffusion models supposedly beat GANs because they scale better and stabler. That is unproven, and false. GANs should be revisited.

The BigGAN JFT-300M result is not the only such result, because in 2020, Tensorfork replicated the stability of BigGAN scaling on a dataset of n > 100M.

--- /note/lion The Math of Hunting Lions Gwern 2021-07-12 2023-05-13 history math/humor

Bibliography of papers on math & physics methods of hunting lions in the Sahara Desert.

--- /red Rubrication Design Examples Gwern 2019-05-30 2023-01-13 design/typography/rubrication

A gallery of typographic and graphics design examples of rubrication, a classic pattern of using red versus black for emphasis.

Dating back to medieval manuscripts, text has often been highlighted using a particular distinct bright red. The contrast of black and red on a white background is highly visible and striking, and this has been reused many times, in a way which I have not noticed for other colors. I call these uses rubrication and collate examples I have noticed from many time periods. This design pattern does not seem to have a widely-accepted name or be commonly discussed, so I propose extending the term “rubrication” to all instances of this pattern, not merely religious texts.

Why this rubrication design pattern? Why red, specifically, and not, say, orange or purple? Is it just a historical accident? Cross-cultural research suggests that for humans, red may be intrinsically more noticeable & has a higher contrast with black, explaining its perennial appeal as a design pattern.

Regardless, it is a beautiful design pattern which has been used in many interesting ways over the millennia, and perhaps may inspire the reader.

--- /doc/creatine/index ‘creatine’ tag 2020-01 2024-06-13 exercise nootropic

Bibliography for tag creatine, most recent first: 19 annotations & 11 links ().

[essay on this tag topic]

--- /doc/psychology/parapsychology/index ‘parapsychology’ tag 2020-06-28 2024-01-01 philosophy/epistemology philosophy/mind

Bibliography for tag psychology/parapsychology, most recent first: 1 related tag, 25 annotations, & 21 links ().

--- /fake-journal-club Fake Journal Club: Teaching Critical Reading Gwern 2022-03-07 2022-03-25 ai/nn/transformer/gpt/non-fiction design/typography philosophy/epistemology statistics

Discussion of how to teach active reading and questioning of scientific research. Partially fake research papers may teach a critical attitude. Various ideas for games reviewed.

How do researchers transition from uncritically absorbing research papers or arguments to actively grappling with it and questioning it? Most learn this meta-cognitive skill informally or by ad hoc mechanisms like being tutored by a mentor, or watching others critique papers at a ‘journal club’. This patchwork may not always work or be the best approach, as it is slow and largely implicit, and similar to calibration training in statistical forecasting, targeted training may be able to teach it rapidly.

To teach this active reading attitude of not believing everything you read, I borrow the pedagogical strategy of deliberately inserting errors which the student must detect, proposing fake research articles which could be read in a ‘fake journal club’.

Faking entire articles is a lot of work and so I look at variations on it. I suggest that NN language models like GPT-3 have gotten good enough to, for short passages, provide a challenge for human readers, and that one could create a fake journal club by having a language model repeatedly complete short passages of research articles (possibly entirely fictional ones).

This would provide difficult criticism problems with rapid feedback, scalability to arbitrarily many users, and great flexibility in content.

--- /doc/psychology/inner-voice/index ‘inner-monologue (psych)’ tag 2019-11-30 2024-11-16 ai/nn/transformer/gpt/inner-monologue psychology/vision/aphantasia statistics/prediction

Bibliography for tag psychology/inner-voice, most recent first: 30 annotations & 13 links ().

--- /doc/ai/nn/transformer/gpt/dall-e/2/index ‘DALL·E 2’ tag 2021-02-18 2024-10-03 ai/nn/diffusion

Bibliography for tag ai/nn/transformer/gpt/dall-e/2, most recent first: 27 annotations & 46 links ().

--- /screwfly The ‘Screwfly Solution’ Solution: Bi-Sexuality Gwern 2021-07-26 2021-08-01 biology fiction/criticism fiction/science-fiction

Alice Sheldon’s SF short story ‘The Screwfly Solution’ is misread by most readers as an alien attack driving men to insane violence; a closer revisionist read shows that the alien attack affected women as well, into suicidal passivity.

--- /doc/cs/js/index ‘JS’ tag 2021-02-14 2024-11-16 design/typography design/visualization

Bibliography for tag cs/js, most recent first: 18 annotations & 26 links ().

--- /review/index ‘review’ tag Gwern 2018-10-25 2024-06-29 fiction/criticism

Bibliography for tag review, most recent first: 21 annotations ().

--- /doc/ai/nn/adversarial/human/index ‘adversarial examples (human)’ tag 2019-09-13 2024-11-27 psychology/cognitive-bias/illusion-of-depth psychology/neuroscience psychology/vision statistics/decision

Bibliography for tag ai/nn/adversarial/human, most recent first: 13 annotations & 2 links ().

--- /doc/psychology/music/distraction/index ‘music distraction’ tag 2020-07-06 2024-09-18 music psychology

Bibliography for tag psychology/music/distraction, most recent first: 28 annotations & 30 links ().

--- /doc/tea/index ‘tea’ tag 2020-10-23 2024-04-29 food japan nootropic/caffeine

Bibliography for tag tea, most recent first: 33 annotations & 16 links ().

--- /doc/radiance/index ‘Radiance’ tag 2019-08-17 2024-11-13 fiction/science-fiction history politics sociology technology transhumanism

Bibliography for tag radiance, most recent first: 30 annotations & 30 links ().

--- /doc/ai/nn/transformer/gpt/dall-e/index ‘DALL·E’ tag 2019-12-06 2024-01-01 ai/nn/diffusion ai/nn/transformer/clip/sample ai/nn/transformer/gpt/jukebox ai/nn/vae ai/scaling reinforcement-learning/openai

Bibliography for tag ai/nn/transformer/gpt/dall-e, most recent first: 7 related tags, 36 annotations, & 99 links ().

--- /doc/psychiatry/anxiety/lavender/index ‘silexan’ tag 2020-06-23 2024-05-31 nootropic psychology/smell/perfume zeo

Bibliography for tag psychiatry/anxiety/lavender, most recent first: 35 annotations & 9 links ().

--- /danbooru2021 Danbooru2021: A Large-Scale Crowdsourced & Tagged Anime Illustration Dataset Gwern 2015-12-15 2024-02-13 ai/anime/danbooru

Danbooru2021 is a large-scale anime image database with 4.9m+ images annotated with 162m+ tags; it can be useful for machine learning purposes such as image recognition and generation.

Deep learning for computer revision relies on large annotated datasets. Classification/categorization has benefited from the creation of ImageNet, which classifies 1m photos into 10001,024ya categories. But classification/categorization is a coarse description of an image which limits application of classifiers, and there is no comparably large dataset of images with many tags or labels which would allow learning and detecting much richer information about images. Such a dataset would ideally be >1m images with at least 10 descriptive tags each which can be publicly distributed to all interested researchers, hobbyists, and organizations. There are currently no such public datasets, as ImageNet, Birds, Flowers, and MS COCO fall short either on image or tag count or restricted distribution. I suggest that the “image -boorus” be used. The image boorus are long-standing web databases which host large numbers of images which can be ‘tagged’ or labeled with an arbitrary number of textual descriptions; they were developed for and are most popular among fans of anime, who provide detailed annotations. The best known booru, with a focus on quality, is Danbooru.

We create Danbooru2021 covering Danbooru uploads 2005-05-24–16y2021-12-31 (final ID: #5,020,995),

Danbooru20xx datasets have been extensively used in projects & machine learning research.

Our hope is that the Danbooru2021 dataset can be used for rich large-scale classification/tagging & learned embeddings, test out the transferability of existing computer vision techniques (primarily developed using photographs) to illustration/anime-style images, provide an archival backup for the Danbooru community, feed back metadata improvements & corrections, and serve as a testbed for advanced techniques such as conditional image generation or style transfer.

--- /leprechaun Leprechaun Hunting & Citogenesis Gwern 2014-06-30 2021-05-15 philosophy/epistemology sociology statistics/bias

Many claims, about history in particular, turn out to be false when traced back to their origins, and form kinds of academic urban legends. These “leprechauns” are particularly pernicious because they are often widely-repeated due to their growing apparent trustworthiness, yet difficult to research & debunk due to the difficulty of following deeply-nested chains of citations through ever more obscure sources. This page lists instances I have run into.

A major source of leprechaun transmission is the frequency with which researchers do not read the papers they cite: because they do not read them, they repeat misstatements or add their own errors, further transforming the leprechaun and adding another link in the chain to anyone seeking the original source. This can be quantified by checking statements against the original paper, and examining the spread of typos in citations: someone reading the original will fix a typo in the usual citation, or is unlikely to make the same typo, and so will not repeat it. Both methods indicate high rates of non-reading, explaining how leprechauns can propagate so easily.

--- /review/book#private-wealth-in-renaissance-florence-goldthwaite-1968 Book Reviews § Private Wealth in Renaissance Florence, Goldthwaite1968 Gwern 2013-08-23 2022-10-13 fiction/criticism

A compilation of books reviews of books I have read since ~1997.

Slightly interesting book analyzing the surviving ledger books of several Florentine Renaissance merchant families. Probably not of general interest, but the detailed analysis shows how merchants spent and invested, and what their priorities were, such as buying up land for clan prestige rather than maximizing returns or wealth.

--- /review/book#string-of-beads-complete-poems-of-princess-shikishi-shikishi-1993 Book Reviews § String of Beads: Complete Poems of Princess Shikishi, Shikishi1993 Gwern 2013-08-23 2022-10-13 fiction/criticism

A compilation of books reviews of books I have read since ~1997.

Review of translation of complete corpus of imperial court poet Princess Shikishi (1149–1201). While well-annotated, Sato’s decision to translate each poem in a single line drains it of any enjoyability, turning it into a prose-like slog.

--- /note/statistic#dysgenics-power-analysis Statistical Notes § Dysgenics Power Analysis Gwern 2014-07-17 2024-08-21 cs/r genetics/selection/natural/human/dysgenics iq statistics/power-analysis

Miscellaneous statistical stuff

Current dysgenic estimates predict that genotypic IQ in the West are falling at a substantial rate, amounting to around half a standard deviation or more over the past century, by 1. reducing the frequency at which intelligence-increasing genetic variants occur (through natural selection against such variants) and 2. by increasing the number of new and potentially harmful genetic mutations (increasing mutation load). Estimates are produced indirectly by surveying reproductive rates or by trying to show decreases in phenotypic traits associated with intelligence; it would obviously be preferable to examine dysgenic effects directly, by observing decreases in frequencies or increases in mutation load in a large sample of Western genetic information such as SNP arrays or whole-genomes (respectively). Such direct testing of dysgenics hypotheses are becoming increasingly feasible due to the exponential decrease in SNP & whole-genome sequencing costs creating large datasets (some publicly available) and the recent identification of some intelligence genes. It remains unclear how large these datasets must be to overcome sampling error and yield informative estimates of changes in frequencies or mutation load, however; datasets like PGP or SSGAC may still be too small to investigate dysgenics. I considered the effect size estimates and under some simple models derive power calculations & power simulations of how large a dataset would be required to have an 80% chance of detecting a dysgenic effect: to detect the decrease in intelligence SNPs using SNP data, n≥30,000; to detect the increase in mutation load in whole genomes, n≥160. I then compare to available datasets: the effect on SNPs can be detected by a large number of existing proprietary databases, but there are no public databases which will be large enough in the foreseeable future; the effect on mutation load, on the other hand, can be detected using solely the currently publicly available dataset from PGP. So I conclude that while only the proprietary databases can directly test dysgenic theories of selection for the foreseeable future, there is an opportunity to analyze PGP genomes to directly test the dysgenic theory of mutation load.

--- /gpt-3#rick-morty-high-iq-copypasta GPT-3 Creative Fiction § Rick & Morty High IQ Copypasta Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

The unfortunate consequences of careless prompt writing.

The unfortunate consequences of careless prompt writing.

A reader requested parodies of the Rick and Morty high IQ copypasta along the lines of the Navy Seal, and provided several variants to get GPT-3 started, successfully. (It also is a nice demonstration of how GPT-3 will gauge the level of typos in a prompt, and if there are typos, will make new typos of its own to imitate the original writer.)

--- /note/statistic#dealing-with-all-or-nothing-unreliability-of-data Statistical Notes § Dealing With All-Or-Nothing Unreliability of Data Gwern 2014-07-17 2024-08-21 cs/haskell cs/js cs/r genetics iq statistics/bayes statistics/causality statistics/decision statistics/order statistics/power-analysis survey

Miscellaneous statistical stuff

Given two disagreeing polls, one small & imprecise but taken at face-value, and the other large & precise but with a high chance of being totally mistaken, what is the right Bayesian model to update on these two datapoints? I give ABC and MCMC implementations of Bayesian inference on this problem and find that the posterior is bimodal with a mean estimate close to the large unreliable poll’s estimate but with wide credible intervals to cover the mode based on the small reliable poll’s estimate.

--- /doc/psychiatry/depression/index ‘depression’ tag 2019-11-09 2024-11-11 psychiatry/anxiety psychiatry/bipolar psychology/energy

Bibliography for tag psychiatry/depression, most recent first: 9 related tags, 325 annotations, & 33 links ().

--- /doc/ai/nn/transformer/clip/index ‘CLIP’ tag 2020-01-13 2024-10-25 ai/nn/diffusion ai/nn/gan/stylegan ai/nn/tokenization ai/nn/transformer/gpt/dall-e ai/scaling ai/video/analysis psychology/neuroscience reinforcement-learning/openai

Bibliography for tag ai/nn/transformer/clip, most recent first: 3 related tags, 253 annotations, & 47 links ().

--- /doc/ai/nn/transformer/gpt/index ‘GPT’ tag 2019-12-13 2024-11-26 ai/nn/transformer/attention/compression reinforcement-learning/openai

Bibliography for tag ai/nn/transformer/gpt, most recent first: 34 related tags, 299 annotations, & 195 links ().

--- /doc/ai/nn/transformer/gpt/inner-monologue/index ‘inner monologue (AI)’ tag 2019-12-22 2024-11-20 ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/instruction-tuning ai/scaling/emergence reinforcement-learning/meta-learning reinforcement-learning/scaling statistics/prediction

Bibliography for tag ai/nn/transformer/gpt/inner-monologue, most recent first: 6 related tags, 178 annotations, & 110 links ().

[page summary]

--- /doc/genetics/heritable/correlation/index ‘genetic correlation’ tag 2019-11-12 2024-10-06

Bibliography for tag genetics/heritable/correlation, most recent first: 2 related tags, 427 annotations, & 105 links ().

[essay on this tag topic]

--- /doc/nicotine/index ‘nicotine’ tag 2019-11-13 2024-11-21 psychiatry/anxiety

Bibliography for tag nicotine, most recent first: 108 annotations & 70 links ().

[essay on this tag topic]

--- /doc/philosophy/ethics/index ‘ethics’ tag 2019-04-01 2024-11-29

Bibliography for tag philosophy/ethics, most recent first: 2 related tags, 284 annotations, & 70 links ().

--- /doc/psychiatry/index ‘psychiatry’ tag 2019-10-20 2024-11-25 psychology/energy

Bibliography for tag psychiatry, most recent first: 26 related tags, 266 annotations, & 43 links ().

--- /doc/psychology/neuroscience/index ‘neuroscience’ tag 2019-08-31 2024-11-28 ai/nn/transformer/clip biology longevity/glp/semaglutide psychiatry/depression

Bibliography for tag psychology/neuroscience, most recent first: 17 related tags, 757 annotations, & 84 links ().

--- /doc/darknet-market/index ‘DNMs’ tag 2019-11-22 2024-11-22 bitcoin crime economics technology

Bibliography for tag darknet-market, most recent first: 15 related tags, 121 annotations, & 113 links ().

--- /doc/statistics/bayes/index ‘Bayes’ tag 2018-12-12 2024-10-16 psychology/cognitive-bias

Bibliography for tag statistics/bayes, most recent first: 3 related tags, 242 annotations, & 34 links ().

--- /doc/statistics/bias/index ‘scientific bias’ tag 2019-05-13 2024-11-28 psychiatry/depression psychology/cognitive-bias

Bibliography for tag statistics/bias, most recent first: 4 related tags, 390 annotations, & 130 links ().

--- /doc/iq/index ‘IQ’ tag 2019-11-07 2024-10-22 psychiatry/adhd psychiatry/autism psychology/chess psychology/energy

Bibliography for tag iq, most recent first: 9 related tags, 785 annotations, & 202 links ().

[essay on this tag topic]

--- /doc/anime/eva/index ‘NGE’ tag 2011-10-03 2024-07-01 anime fiction/science-fiction japan

Bibliography for tag anime/eva, most recent first: 7 related tags, 418 annotations, & 312 links ().

--- /doc/statistics/index ‘statistics’ tag 2019-12-01 2024-03-03 design/visualization

Bibliography for tag statistics, most recent first: 24 related tags, 46 annotations, & 48 links ().

--- /doc/design/typography/index ‘typography’ tag 2019-11-10 2024-11-11 design/visualization

Bibliography for tag design/typography, most recent first: 10 related tags, 128 annotations, & 126 links ().

--- /doc/ai/nn/gan/stylegan/index ‘StyleGAN’ tag 2019-09-13 2024-10-09 ai/nn/gan/biggan

Bibliography for tag ai/nn/gan/stylegan, most recent first: 5 related tags, 142 annotations, & 50 links ().

--- /doc/politics/index ‘politics’ tag 2013-11-29 2024-11-20 economics/advertising economics/mechanism-design psychology/cognitive-bias sociology

Bibliography for tag politics, most recent first: 6 related tags, 252 annotations, & 95 links ().

--- /doc/psychology/index ‘psychology’ tag 2011-08-31 2024-11-25

Bibliography for tag psychology, most recent first: 44 related tags, 424 annotations, & 89 links ().

--- /doc/genetics/selection/natural/human/dysgenics/index ‘dysgenics’ tag 2019-10-03 2024-01-01 genetics/selection/natural/human

Bibliography for tag genetics/selection/natural/human/dysgenics, most recent first: 46 annotations & 6 links ().

--- /doc/cs/algorithm/index ‘algorithms’ tag 2019-09-07 2024-11-29 math

Bibliography for tag cs/algorithm, most recent first: 3 related tags, 231 annotations, & 139 links ().

--- /doc/bitcoin/index ‘Bitcoin’ tag 2013-05-07 2024-11-13 cs/cryptography cs/security darknet-market economics

Bibliography for tag bitcoin, most recent first: 3 related tags, 73 annotations, & 41 links ().

--- /doc/science/index ‘science’ tag 2019-10-02 2024-11-26 psychology/cognitive-bias

Bibliography for tag science, most recent first: 3 related tags, 294 annotations, & 62 links ().

--- /doc/statistics/peer-review/index ‘peer review’ tag 2019-09-03 2024-11-28 psychology/cognitive-bias statistics/bias/publication

Bibliography for tag statistics/peer-review, most recent first: 1 related tag, 48 annotations, & 19 links ().

--- /doc/design/visualization/index ‘data visualization’ tag 2019-10-09 2024-11-29 design/typography statistics technology

Bibliography for tag design/visualization, most recent first: 5 related tags, 112 annotations, & 82 links ().

--- /doc/ai/nn/transformer/gpt/lamda/index ‘LaMDA’ tag 2020-01-27 2024-11-11 ai/nn/transformer/gpt/palm

Bibliography for tag ai/nn/transformer/gpt/lamda, most recent first: 1 related tag, 29 annotations, & 7 links ().

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--- /doc/psychology/personality/index ‘personality’ tag 2019-11-08 2024-11-24 psychiatry/anxiety psychiatry/autism psychiatry/bipolar

Bibliography for tag psychology/personality, most recent first: 7 related tags, 396 annotations, & 41 links ().

--- /doc/genetics/editing/index ‘gene editing’ tag 2019-12-03 2024-10-17 longevity/epigenetics

Bibliography for tag genetics/editing, most recent first: 2 related tags, 204 annotations, & 50 links ().

--- /gpt-3-nonfiction#ascii-art GPT-3 Nonfiction § ASCII Art Gwern 2020-06-19 2022-07-03 ai/nn/transformer/gpt/non-fiction philosophy/mind

Nonfiction writing by OpenAI’s GPT-3 model, testing logic, commonsense reasoning, anagrams, PDF/OCR cleaning, creative nonfiction, etc

GPT-3 can’t really do ASCII art, which comes as little surprise to me given the relative rarity of ASCII art these days, the difficulty of understanding what it’s art of (even looked at in 2D), and the possibility that most ASCII art was filtered out of the training dataset. AK got a little better results by prompting using just ASCII art.

Per a suggestion, I tried a ‘contest’ prompt:

--- /gpt-3-nonfiction#pdf-cleaning GPT-3 Nonfiction § PDF Cleaning Gwern 2020-06-19 2022-07-03 ai/nn/transformer/gpt/non-fiction philosophy/mind

Nonfiction writing by OpenAI’s GPT-3 model, testing logic, commonsense reasoning, anagrams, PDF/OCR cleaning, creative nonfiction, etc

GPT-3 can clean up OCR errors and miscellaneous formatting problems as a rewrite task given some few-shot examples; I provide a Python script using the openai Python library which can be used on the CLI to fix up paper abstracts.

Instruct-GPT-3 models can do this zero-shot simply by prompting “Correct the OCR errors:”, simplifying the prompt & saving tokens.

--- /review/anime#evangelion-3-0 Anime Reviews § Evangelion 3.0 Gwern 2010-12-14 2024-06-06 anime/eva fiction/criticism

A compilation of anime/manga reviews since 2010.

Review of the third Rebuild film: negative. The long-delayed tetralogy, which now stretches over a decade of production, shows a lack of artistic vision or interest by anyone at Studio Khara, particularly director Hideaki Anno. It appears now to be a naked cash grab for launching a new studio with a guaranteed moneymaker. 3.0, the last chance for Rebuild to redeem itself and deliver a satisfying whole, wastes its time with irrelevancies and fails to deliver on anything promised by Anno and Khara, lazily embracing the worst parts of the Evangelion style while destroying much of what was good about characters like Kaworu. It is the worst Evangelion film made, and so bad that it has largely destroyed my interest in the franchise.

--- /timing#reverse-salients Timing Technology: Lessons From The Media Lab § Reverse Salients Gwern 2012-07-12 2019-06-20 ai/nn bitcoin economics history insight-porn sociology/technology statistics/decision

Technological developments can be foreseen but the knowledge is largely useless because startups are inherently risky and require optimal timing. A more practical approach is to embrace uncertainty, taking a reinforcement learning perspective.

Excerpts from The First Miracle Drugs: How the Sulfa Drugs Transformed Medicine, John Lesch 200618ya.

They describe Heinrich Hörlein’s drug development programs & Thomas Edison’s electrical programs as being strategically aimed at opportunities called “reverse salients”, taking necessary steps to solve bottlenecks which hold back the practical application of progress in areas. Such targeted research efforts can have disproprotionate payoffs and lead to sudden breakthroughs as the ‘enemy’s frontline’ collapses.

--- /catnip#breeding-cats-to-increase-frequency-of-catnip-response Catnip immunity and alternatives § Breeding Cats To Increase Frequency Of Catnip Response Gwern 2015-11-07 2019-06-19 cat/genetics cat/psychology/drug/catnip cs/r statistics/bayes statistics/meta-analysis

Estimation of catnip immunity rates by country with meta-analysis and surveys, and discussion of catnip alternatives.

I sketch out a threshold selection breeding program for increasing catnip response frequency. Based on Villani2011’s measured heritabilities of catnip response (corrected for measurement error), selecting exclusively responders would result in catnip response becoming nearly universal (~95%) within ~7 generations or potentially less than a decade.

--- /modus#jaynes-on-esp One Man’s Modus Ponens § Jaynes on ESP Gwern 2012-05-01 2022-01-06 philosophy/epistemology psychology/parapsychology statistics/bayes statistics/bias

One man’s modus ponens is another man’s modus tollens is a saying in Western philosophy encapsulating a common response to a logical proof which generalizes the reductio ad absurdum and consists of rejecting a premise based on an implied conclusion. I explain it in more detail, provide examples, and a Bayesian gloss.

Bayesian E.T. Jaynes, in “Chapter 5: Queer uses for probability theory”, discusses the probabilistic generalization of the reasoning we are engaged in when we choose whether to modus ponens or modus tollens, with early ESP experiments as an example, pointing out that from a Bayesian perspective, all claims are being evaluated in a larger Bayesian-model-comparison context where issues like experimenter error or bias are always possibilities:

--- /modus#slavery-and-phrenology One Man’s Modus Ponens § Slavery and Phrenology Gwern 2012-05-01 2022-01-06 history insight-porn math philosophy/epistemology philosophy/logic sociology statistics/bayes statistics/bias

One man’s modus ponens is another man’s modus tollens is a saying in Western philosophy encapsulating a common response to a logical proof which generalizes the reductio ad absurdum and consists of rejecting a premise based on an implied conclusion. I explain it in more detail, provide examples, and a Bayesian gloss.

George Combe (1788–701858166ya) appears to have made a phrenological argument (that African docility implies the morality of abolition rather than maintaining slavery) in 3 places—a marginal note on a letter from a pro-slavery advocate in 1839185ya; his Notes on the United States of America, 1841183ya; and his System of Phrenology, 1843181ya:

--- /rtx Highly Potent Drugs As Psychological Warfare Weapons Gwern 2020-08-28 2020-08-28 biology psychology/neuroscience/pain science/chemistry/disappearing-polymorph

Chemicals active at the nanogram scale are invisible, difficult to observe, and near-impossible to clean or remove; but they are still psychoactive enough to torment you like demons.

Chemicals at the nano-scale, which are invisible to ordinary thinking, can still have large effects at the human-scale: examples, despite all precautions, include the bizarre phenomenon of “disappearing polymorphs” (crystals which can ‘infect’ other crystals, causing severe harm to drug production) and highly potent drugs which cause overdoses.

Such mysterious behavior could be abused. I dramatize this by speculating on how the spicy chemical resiniferatoxin (RTX) could be employed as a nanotech tool of psychological warfare due to its sub-microgram dosage—it would be an ineradicable, invisible, inexplicable curse on the victim, who would suffer continuously in confusion, almost supernaturally.

But it would be completely natural: merely a very small bit of Nature.

--- /embryo-selection#optimal-stoppingsearch Embryo Selection For Intelligence § Optimal Stopping/Search Gwern 2016-01-22 2020-01-18 cs/r economics statistics/decision statistics/order

A cost-benefit analysis of the marginal cost of IVF-based embryo selection for intelligence and other traits with 2016–2017 state-of-the-art

I model embryo selection with many embryos as an optimal stopping/search problem and give an example algorithm for when to halt that results in substantial savings over the brute force approach of testing all available embryos. This shows that with a little thought, “too many embryos” need not be any problem.

--- /about#benfords-law About This Website § Benford’s Law Gwern 2010-10-01 2024-08-30 design meta personal statistics/prediction

Meta page describing Gwern.net site ideals of stable long-term essays which improve over time; idea sources and writing methodology; metadata definitions; site statistics; copyright license.

Does Gwern.net follow the famous Benford’s law?

A quick analysis suggests that it sort of does, except for the digit 2, probably due to the many citations to research from the past 2 decades (>200024ya AD).

--- /doc/radiance/2002-scholz-radiance#radiance-1 Radiance: A Novel § ‘Radiance’ Carter Scholz, Gregory Benford, Hugh Gusterson, Sam Cohen, Curtis LeMay 2013-07-06 2019-08-17 history politics radiance

E-book edition of the 2002 Carter Scholz novel of post-Cold War science/technology, extensively annotated with references and related texts.

Transcript of 199529ya novella version of Radiance, which would become the first section of the novel, along with a diff showing the differences between the novella & novel at the word-level to illustrate the changes it underwent.

--- /doc/radiance/2002-scholz-radiance#old-legends Radiance: A Novel § ‘Old Legends’ Carter Scholz, Gregory Benford, Hugh Gusterson, Sam Cohen, Curtis LeMay 2013-07-06 2019-08-17 history politics radiance

E-book edition of the 2002 Carter Scholz novel of post-Cold War science/technology, extensively annotated with references and related texts.

“Old Legends” is a memoir by physicist & SF author Gregory Benford about the Cold War, Edward Teller, Benford’s experiences with the military-industrial-scientific complex, the Astounding incident, his work on tachyons, and the connection between science & SF.

--- /embryo-selection#sperm-phenotype-selection Embryo Selection For Intelligence § Sperm Phenotype Selection Gwern 2016-01-22 2020-01-18 cs/r economics genetics/heritable/correlation statistics/decision statistics/order statistics/power-analysis transhumanism

A cost-benefit analysis of the marginal cost of IVF-based embryo selection for intelligence and other traits with 2016–2017 state-of-the-art

A possible adjunct to embryo selection is sperm selection. Non-destructive sequencing is not yet possible, but measuring phenotypic correlates of genetic quality (such as sperm speed/motility) is. These may be correlated with genetics for adult traits, and one can select from billions of sperm. These correlations of sperm quality/genetic quality are, however, small and confounded in current studies by between-individual variation. Optimistically, the gain from such sperm selection is probably small, <0.1SD, and there do not appear to be any easy ways to boost this effect. Sperm selection is probably cost-effective and a good enhancement of existing IVF practices, but not particularly notable.

--- /clone#nba-screening-scenario Dog Cloning For Special Forces: Breed All You Can Breed § NBA Screening Scenario Gwern 2018-09-18 2021-12-02 cs/r dog genetics/cloning genetics/heritable/dog genetics/selection/artificial statistics/decision statistics/order

Decision analysis of whether cloning the most elite Special Forces dogs is a profitable improvement over standard selection procedures. Unless training is extremely cheap or heritability is extremely low, dog cloning is hypothetically profitable.

Analogous to the dog cloning scenario, I consider the case of selecting for extremes on PGSes, motivated by a scenario of scouting tall men for the NBA.

Setting up the NBA selection problem as a liability threshold model with current height PGSes as a noisy predictor, height selection can be modeled as selecting for extremes on a PGS which is regressed back to the mean to yield expected adult height, and probability of being tall enough to consider a NBA career.

Filling in reasonable values, nontrivial numbers of tall people can be found by genomic screening with a current PGS, and as PGSes approach their predictive upper bound (derived from whole-genome-based heritability estimates of height), selection is capable of selecting almost all tall people by taking the top PGS percentile.

--- /me About Gwern Gwern 2009-08-05 2020-06-14 cs/haskell personal psychology survey

Who am I online & what have I done? Contact information; sites I use; computers and software tools; things I’ve worked on; psychological profiles

I’m sometimes asked about my tech “stack”, in the vein of “Uses This” or The Paris Review’s Writer At Work. I use FLOSS software with a text/CLI emphasis on a custom workstation designed for deep learning & reinforcement learning work, and an ergonomic home office with portrait-orientation monitor, Aeron chair, & trackball.

--- /cyoa Choose-Your-Own-Adventure AI Dungeon Games Gwern 2021-06-06 2021-06-22 ai/nn/sampling ai/nn/transformer/gpt/fiction fiction/text-game

Neural networks like GPT-3 power text adventure games where you can do anything; but they are too expensive. I propose that if we turn them into Choose Your Own Adventure hypertext games, they become feasible and enable new gameplay.

A useful variation on AI Dungeon-style (AID) text games would be to turn them into shared public game trees of pre-generated options which the player selects from, Choose-Your-Own-Adventure-book style.

This trades storing kilobytes for running teraflops and so can dramatically reduce costs as players spend most of their time reading cached output (rarely needing nor wanting to generate brandnew output requiring a NN run), can increase quality as players collectively uprank actions/outcomes which are highest-quality, and caters to newbies who don’t understand the power of NN-backed text games and flail around.

--- /timing Timing Technology: Lessons From The Media Lab Gwern 2012-07-12 2019-06-20 ai/nn bitcoin economics history insight-porn sociology/technology statistics/decision

Technological developments can be foreseen but the knowledge is largely useless because startups are inherently risky and require optimal timing. A more practical approach is to embrace uncertainty, taking a reinforcement learning perspective.

How do you time your startup? Technological forecasts are often surprisingly prescient in terms of predicting that something was possible & desirable and what they predict eventually happens; but they are far less successful at predicting the timing, and almost always fail, with the success (and riches) going to another.

Why is their knowledge so useless? Why are success and failure so intertwined in the tech industry? The right moment cannot be known exactly in advance, so attempts to forecast will typically be off by years or worse. For many claims, there is no way to invest in an idea except by going all in and launching a company, resulting in extreme variance in outcomes, even when the idea is good and the forecasts correct about the (eventual) outcome.

Progress can happen and can be foreseen long before, but the details and exact timing due to bottlenecks are too difficult to get right. Launching too early means failure, but being conservative & launching later is just as bad because regardless of forecasting, a good idea will draw overly-optimistic researchers or entrepreneurs to it like moths to a flame: all get immolated but the one with the dumb luck to kiss the flame at the perfect instant, who then wins everything, at which point everyone can see that the optimal time is past. All major success stories overshadow their long list of predecessors who did the same thing, but got unlucky. The lesson of history is that for every lesson, there is an equal and opposite lesson. So, ideas can be divided into the overly-optimistic & likely doomed, or the fait accompli. On an individual level, ideas are worthless because so many others have them too—‘multiple invention’ is the rule, and not the exception. Progress, then, depends on the ‘unreasonable man’.

This overall problem falls under the reinforcement learning paradigm, and successful approaches are analogous to Thompson sampling/posterior sampling: even an informed strategy can’t reliably beat random exploration which gradually shifts towards successful areas while continuing to take occasional long shots. Since people tend to systematically over-exploit, how is this implemented? Apparently by individuals acting suboptimally on the personal level, but optimally on societal level by serving as random exploration.

A major benefit of R&D, then, is in laying fallow until the ‘ripe time’ when they can be immediately exploited in previously-unpredictable ways; applied R&D or VC strategies should focus on maintaining diversity of investments, while continuing to flexibly revisit previous failures which forecasts indicate may have reached ‘ripe time’. This balances overall exploitation & exploration to progress as fast as possible, showing the usefulness of technological forecasting on a global level despite its uselessness to individuals.

--- /clone Dog Cloning For Special Forces: Breed All You Can Breed Gwern 2018-09-18 2021-12-02 cs/r dog genetics/cloning genetics/heritable/dog genetics/selection/artificial statistics/decision statistics/order

Decision analysis of whether cloning the most elite Special Forces dogs is a profitable improvement over standard selection procedures. Unless training is extremely cheap or heritability is extremely low, dog cloning is hypothetically profitable.

Cloning is widely used in animal & plant breeding despite steep costs due to its advantages; more unusual recent applications include creating entire polo horse teams and reported trials of cloning in elite police/Special Forces war dogs. Given the cost of dog cloning, however, can this ever make more sense than standard screening methods for selecting from working dog breeds, or would the increase in successful dog training be too low under all reasonable models to turn a profit?

I model the question as one of expected cost per dog with the trait of successfully passing training, success in training being a dichotomous liability threshold with a polygenic genetic architecture; given the extreme level of selection possible in selecting the best among already-elite Special Forces dogs and a range of heritabilities, this predicts clones’ success probabilities. To approximate the relevant parameters, I look at some reported training costs and success rates for regular dog candidates, broad dog heritabilities, and the few current dog cloning case studies reported in the media.

Since none of the relevant parameters are known with confidence, I run the cost-benefit equation for many hypothetical scenarios, and find that in a large fraction of them covering most plausible values, dog cloning would improve training yields enough to be profitable (in addition to its other advantages).

As further illustration of the use-case of screening for an extreme outcome based on a partial predictor, I consider the question of whether height PGSes could be used to screen the US population for people of NBA height, which turns out to be reasonably doable with current & future PGSes.

--- /bitcoin-is-worse-is-better Bitcoin Is Worse Is Better Gwern 2011-05-27 2018-11-21 bitcoin cs/cryptography design

2011 essay on how Bitcoin’s long gestation and early opposition indicates it is an example of the ‘Worse is Better’ paradigm in which an ugly complex design with few attractive theoretical properties compared to purer competitors nevertheless successfully takes over a niche, survives, and becomes gradually refined.

The genius of Bitcoin, in inventing a digital currency successful in the real world, is not in creating any new abstruse mathematics or cryptographic breakthrough, but in putting together decades-old pieces in a semi-novel but extremely unpopular way. Everything Bitcoin needed was available for many years, including the key ideas.

The sacrifice Bitcoin makes to achieve decentralization is—however practical—a profoundly ugly one. Early reactions to Bitcoin by even friendly cryptographers & digital currency enthusiasts were almost uniformly extremely negative, and emphasized the (perceived) inefficiency & (relative to most cryptography) weak security guarantees. Critics let ‘perfect be the enemy of better’ and did not perceive Bitcoin’s potential.

However, in an example of ‘Worse is Better’, the ugly inefficient prototype of Bitcoin successfully created a secure decentralized digital currency, which can wait indefinitely for success, and this was enough to eventually lead to adoption, improvement, and growth into a secure global digital currency.

--- /review/tea Tea Reviews Gwern 2011-04-13 2023-11-16 food

Teas I have drunk, with reviews and future purchases; focused primarily on oolongs and greens. Plus experiments on water.

--- /doc/reinforcement-learning/scaling/index ‘RL scaling’ tag 2019-09-09 2024-11-27 ai/scaling reinforcement-learning/meta-learning

Bibliography for tag reinforcement-learning/scaling, most recent first: 12 related tags, 142 annotations, & 21 links ().

[page summary]

--- /note/statistic#inferring-mean-iqs-from-smpytip-elite-samples Statistical Notes § Inferring Mean IQs From SMPY/TIP Elite Samples Gwern 2014-07-17 2024-08-21 cs/r iq/high statistics/order

Miscellaneous statistical stuff

Samples taken from the extremes of mixtures of distributions can have very different properties than random samples, such as the tail effect of wildly disproportionate representation of one distribution due to order statistics/threshold selection.

This can be used to infer differing means.

I demonstrate working backwards from the racial composition of SMPY/TIP samples of extremely (1-in-10,000) gifted youth to estimate the overall racial means, which is consistent with the known racial means and hence an unbiased selection process, using ABC to infer Bayesian credible intervals on the estimated means.

--- /fmp-parody Parody in Full Metal Panic! Gwern 2008-10-09 2013-07-01 anime fiction/criticism sociology

The unexpected critical depths of a single throw-away scene

--- /lorem-april-fools Lorem Ipsum: April Fools Mode Gwern 2024-01-01 2024-03-31 cs/css

Stress-test page for Gwern.net April Fools theme.

--- /lorem-halloween Lorem Ipsum: Halloween Mode Gwern 2023-10-18 2023-10-22 cs/css

Stress-test page for Gwern.net Halloween theme.

--- /lorem-christmas Lorem Ipsum: Christmas Mode Gwern 2023-10-18 2023-10-18 cs/css

Stress-test page for Gwern.net Christmas theme.

Christmas Mode is a Gwern.net easter egg theme: when visiting this test page, or for web browsers with an English language locale which visit Gwern.net on Christmas (defined as 24 December after 6PM local time, or on 25 December all day), Christmas mode will be automatically activated in both dark & light mode.

Christmas Mode currently consists of randomized Christmas dropcap logos (available selection: dark mode n = 5, light mode n = 6), green outlining on block elements & links, and rubricated dropcaps.

--- /correlation How Often Does Correlation=Causality? Gwern 2014-06-24 2022-06-14 economics insight-porn psychology/cognitive-bias statistics/bayes statistics/bias statistics/causality

Compilation of studies comparing observational results with randomized experimental results on the same intervention, compiled from medicine/economics/psychology, indicating that a large fraction of the time (although probably not a majority) correlation ≠ causality.

--- /spaced-repetition Spaced Repetition for Efficient Learning Gwern 2009-03-11 2019-05-17 cs/haskell nootropic psychedelic psychology/spaced-repetition

Efficient memorization using the spacing effect: literature review of widespread applicability, tips on use & what it’s good for.

Spaced repetition is a centuries-old psychological technique for efficient memorization & practice of skills where instead of attempting to memorize by ‘cramming’, memorization can be done far more efficiently by instead spacing out each review, with increasing durations as one learns the item, with the scheduling done by software. Because of the greater efficiency of its slow but steady approach, spaced repetition can scale to memorizing hundreds of thousands of items (while crammed items are almost immediately forgotten) and is especially useful for foreign languages & medical studies.

I review what this technique is useful for, some of the large research literature on it and the testing effect (up to ~2013, primarily), the available software tools and use patterns, and miscellaneous ideas & observations on it.

--- /doc/cs/cryptography/nash/1955-nash John Nash on cryptography John Nash 2012-02-22 2020-09-12 cs/cryptography/nash

1955 letters of John Nash and the NSA on a cryptosystem and Nash’s belief that near-perfect cryptography could exploit exponential difficulties.

--- /improvement My Ordinary Life: Improvements Since the 1990s Gwern 2018-04-28 2022-12-20 economics history insight-porn personal sociology/technology

A list of unheralded improvements to ordinary quality-of-life since the 1990s going beyond computers.

It can be hard to see the gradual improvement of most goods over time, but I think one way to get a handle on them is to look at their downstream effects: all the small ordinary everyday things which nevertheless depend on obscure innovations and improving cost-performance ratios and gradually dropping costs and new material and… etc. All of these gradually drop the cost, drop the price, improve the quality at the same price, remove irritations or limits not explicitly noticed, or so on.

It all adds up.

So here is a personal list of small ways in which my ordinary everyday daily life has been getting better since the late 1980s/early 1990s (as far back as I can clearly remember these things—I am sure the list of someone growing up in the 1940s would include many hassles I’ve never known at all).

--- /doc/japan/art/2002-gibson Shiny balls of Mud: William Gibson Looks at Japanese Pursuits of Perfection William Gibson 2012-04-20 2019-01-26 design fiction/criticism insight-porn japan/art sociology

Essay on minimalism, otaku, and hikikomori as esthetic choices reflecting an obsessive focus on perfection of a single activity, exemplified by the unusual sculpture form dorodango (hand-rolling mud into colorful spheres).

--- /note/pipeline Leaky Pipelines Gwern 2014-11-27 2014-11-27 psychology/energy statistics/order statistics/probability

Many multi-step processes look like ‘leaky pipelines’, where a fractional loss/success happens at every step. Such multiplicative processes can often be modeled as a log-normal distribution (or power law), with counterintuitive implications like skewed output distributions and large final differences from small differences in per-step success rates.

--- /embryo-selection#measurement-error-in-polygenic-scores Embryo Selection For Intelligence § Measurement Error in Polygenic Scores Gwern 2016-01-22 2020-01-18 cs/r economics genetics/heritable/correlation statistics/decision statistics/order statistics/power-analysis statistics/prediction transhumanism

A cost-benefit analysis of the marginal cost of IVF-based embryo selection for intelligence and other traits with 2016–2017 state-of-the-art

Like GCTA, measurement error affects polygenic scores, in reducing both discovery power and providing a downwardly-biased estimate of how good the PGS is. The GCTAs give a substantially lower estimate than the one we care about if we forget to correct for measurement error; is this true for the PGSes above as well?

Checking some the GWASes in question where possible, it seems there is an unspoken general practice of using the smallest highest-quality-phenotyped cohorts as the heldout validation sets, so the measurement error turns out to not be too serious, and we don’t need to take it much into consideration.

--- /selection Common Selection Scenarios Gwern 2021-06-11 2021-06-11 cs/r psychology/energy statistics/order

Cookbook of code for common selection & order statistics scenarios in economics, psychology, decision theory, drug development etc.

--- /hydrocephalus Hydrocephalus and Intelligence: The Hollow Men Gwern 2015-07-28 2020-05-13 iq psychology/neuroscience psychology/parapsychology statistics/bias

Some claim the disease hydrocephalus reduces brain size by 95% but often with normal or even above-average intelligence, and thus brains aren’t really necessary. Neither is true.

Hydrocephalus is a damaging brain disorder where fluids compress the brain, sometimes drastically decreasing its volume. While often extremely harmful or life-threatening when untreated, some people with severe compression nevertheless are relatively normal, and in one case (Lorber) they have been claimed to have IQs as high as 126 with a brain volume 5% of normal brains. A few of these case studies have been used to argue the extraordinary claim that brain volume has little or nothing to do with intelligence; authors have argued that hydrocephalus suggests enormous untapped cognitive potential which are tapped into rarely for repairs and can boost intelligence on net, or that intelligence/consciousness are non-material or tapping into ESP.

I point out why this claim is almost certainly untrue because it predicts countless phenomena we never observe, and investigate the claimed examples in more detail: the cases turn out to be suspiciously unverifiable (Lorber), likely fraudulent (Oliveira), or actually low intelligence (Feuillet). It is unclear if high-functioning cases of hydrocephalus even have less brain mass, as opposed to lower proxy measures like brain volume.

I then summarize anthropologist John Hawks’s criticisms of the original hydrocephalus author: his brain imaging data could not have been as precise as claimed, he studied a selective sample, the story of the legendary IQ 126 hydrocephalus patient raises questions as to how normal or intelligent he really was, and hydrocephalus in general appears to be no more anomalous or hard-to-explain than many other kinds of brain injuries, and in a comparison, hemispherectomies, removing or severing a hemisphere, has produced no anomalous reports of above-average intelligence (just deficits), though they ought to be just the same in terms of repairs or ESP.

That hydrocephalus cases can reach roughly normal levels of functioning, various deficits aside, can be explained by brain size being one of several relevant variables, brain plasticity enabling cognitive flexibility & recovery from gradually-developing conditions, and overparameterization giving robustness to damage and poor environments, and learning ability. The field of deep learning has observed similar phenomenon in training of artificial neural networks. This is consistent with Lorber’s original contention that the brain was more robust, and hydrocephalus was more treatable, than commonly accepted, but does not support any of the more exotic interpretations since put on his findings.

In short, there is little anomalous to explain, and standard brain-centric accounts appear to account for existing verified observations without much problem or resort to extraordinary claims.

--- /newsletter/2021/04 April 2021 News Gwern 2020-01-02 2024-11-29 newsletter

April 2021 Gwern.net newsletter with links on AI scaling, particularly new East Asian record-breaking work & deep reinforcement learning.

--- /newsletter/2021/05 May 2021 News Gwern 2020-01-02 2024-11-29 newsletter

May 2021 Gwern.net newsletter with links on AI hardware, diffusion models, optogenetics, brain scanning.

--- /zeo/redshift Redshift sleep experiment Gwern 2012-05-09 2019-12-27 cs/r nootropic/quantified-self statistics/decision statistics/power-analysis zeo

Self-experiment on whether screen-tinting software such as Redshift/f.lux affect sleep times and sleep quality; Redshift lets me sleep earlier but doesn’t improve sleep quality.

I ran a randomized experiment with a free program (Redshift) which reddens screens at night to avoid tampering with melatonin secretion & the sleep from 2012–201311ya, measuring sleep changes with my Zeo. With 533 days of data, the main result is that Redshift causes me to go to sleep half an hour earlier but otherwise does not improve sleep quality.

--- /newsletter/2020/12 December 2020 News Gwern 2019-12-26 2021-01-06 newsletter

December 2020 Gwern.net newsletter with links on AI and technology; major new site feature: fully-generalized recursive popups.

--- /newsletter/2021/01 January 2021 News Gwern 2020-01-02 2024-11-29 newsletter

January 2021 Gwern.net newsletter with links on AI scaling up and down.

--- /newsletter/2021/03 March 2021 News Gwern 2020-01-02 2024-11-29 newsletter

March 2021 Gwern.net newsletter; 2 major new site features: ‘popins’ and recursive Wikipedia popups.

--- /causality Why Correlation Usually ≠ Causation Gwern 2014-06-24 2019-12-09 insight-porn longevity philosophy/epistemology psychology/cognitive-bias statistics/bayes statistics/bias statistics/causality

Correlations are oft interpreted as evidence for causation; this is oft falsified; do causal graphs explain why this is so common, because the number of possible indirect paths greatly exceeds the direct paths necessary for useful manipulation?

It is widely understood that statistical correlation between two variables ≠ causation. Despite this admonition, people are overconfident in claiming correlations to support favored causal interpretations and are surprised by the results of randomized experiments, suggesting that they are biased & systematically underestimate the prevalence of confounds / common-causation. I speculate that in realistic causal networks or DAGs, the number of possible correlations grows faster than the number of possible causal relationships. So confounds really are that common, and since people do not think in realistic DAGs but toy models, the imbalance also explains overconfidence.

--- /traffic Gwern.net Website Traffic Gwern 2011-02-03 2023-08-19 cs/r cs/shell meta personal technology/google

Meta page describing Gwern.net editing activity, traffic statistics, and referrer details, primarily sourced from Google Analytics (2011-present).

On a semi-annual basis, since 201113ya, I review Gwern.net website traffic using Google Analytics; although what most readers value is not what I value, I find it motivating to see total traffic statistics reminding me of readers (writing can be a lonely and abstract endeavour), and useful to see what are major referrers.

Gwern.net typically enjoys steady traffic in the 50–100k range per month, with occasional spikes from social media, particularly Hacker News; over the first decade (2010–102020), there were 7.98m pageviews by 3.8m unique users.

--- /water Self-Blinded Mineral Water Taste Test Gwern 2017-02-15 2021-05-04 nootropic/quantified-self statistics/bayes statistics/decision tea

Blind randomized taste-test of mineral/distilled/tap waters using Bayesian best-arm finding; no large differences in preference.

The kind of water used in tea is claimed to make a difference in the flavor: mineral water being better than tap water or distilled water. However, mineral water is vastly more expensive than tap water.

To test the claim, I run a preliminary test of pure water to see if any water differences are detectable at all. Compared my tap water, 3 distilled water brands (Great Value, Nestle Pure Life, & Poland Spring), 1 osmosis-purified brand (Aquafina), and 3 non-carbonated mineral water brands (Evian, Voss, & Fiji) in a series of n = 67 blinded randomized comparisons of water flavor. The comparisons are modeled using a Bradley-Terry competitive model implemented in Stan; comparisons were chosen using an adaptive Bayesian best-arm sequential trial (racing) method designed to locate the best-tasting water in the minimum number of samples by preferentially comparing the best-known arm to potentially superior arms. Blinding & randomization are achieved by using a Lazy Susan to physically randomize two identical (but marked in a hidden spot) cups of water.

The final posterior distribution indicates that some differences between waters are likely to exist but are small & imprecisely estimated and of little practical concern.

--- /banner#power-analysis Banner Ads Considered Harmful § Power Analysis Gwern 2017-01-08 2020-12-12 cs/js cs/r economics/advertising statistics/bayes statistics/decision statistics/power-analysis survey technology/google

9 months of daily A/B-testing of Google AdSense banner ads on Gwern.net indicates banner ads decrease total traffic substantially, possibly due to spillover effects in reader engagement and resharing.

Using the historical traffic data, how easy would it be to detect a total traffic reduction of ~3%, the critical boundary for the ads/no-ads decision? Standard non-time-series methods are unable to detect it at any reasonable sample size, but using more complex time-series-oriented methods like ARIMA models (either NHST or Bayesian), it can be detected given several months of data.

--- /sidenote Sidenotes In Web Design Gwern 2020-08-06 2023-02-23 cs/css cs/js design/typography

In typography/design, ‘sidenotes’ place footnotes/endnotes in the margins for easier reading. I discuss design choices, HTML implementations and their pros/cons.

Sidenotes & margin notes are a typographic convention which improves on footnotes & endnotes by instead putting the notes in the page margin to let the reader instantly read them without needing to refer back and forth to the end of the document (endnotes) or successive pages (footnotes spilling over).

They are particularly useful for web pages, where ‘footnotes’ are de facto endnotes, and clicking back and forth to endnotes is a pain for readers. (Footnote variants, like “floating footnotes” which pop up on mouse hover, reduce the reader’s effort but don’t eliminate it.)

However, they are not commonly used, perhaps because web browsers until relatively recently made it hard to implement sidenotes easily & reliably. Tufte-CSS has popularized the idea and since then, there has been a proliferation of slightly variant approaches. I review some of the available implementations.

For general users, I recommend Tufte-CSS-style approaches: it is fast & simple (using only compile-time generation of sidenotes, rendered by static HTML/CSS), popular, and Tufte-CSS-esque libraries are easy to integrate into many website workflows. For heavy footnote users or users who want a drop-in, runtime Javascript-based solutions like sidenotes.js may be more useful.

--- /silk-road Silk Road 1: Theory & Practice Gwern 2011-07-11 2018-09-29 bitcoin darknet-market/silk-road/1 science/fermi-problem

History, background, visiting, ordering, using, & analyzing the drug market Silk Road 1

The cypherpunk movement laid the ideological roots of Bitcoin and the online drug market Silk Road; balancing previous emphasis on cryptography, I emphasize the non-cryptographic market aspects of Silk Road which is rooted in cypherpunk economic reasoning, and give a fully detailed account of how a buyer might use market information to rationally buy, and finish by discussing strengths and weaknesses of Silk Road, and what future developments are predicted by cypherpunk ideas.

--- /google-alerts Alerts Over Time Gwern 2013-07-01 2013-11-26 cs/r cs/shell statistics/survival-analysis technology/google

Does Google Alerts return fewer results each year? A statistical investigation

Has Google Alerts been sending fewer results the past few years? Yes. Responding to rumors of its demise, I investigate the number of results in my personal Google Alerts notifications 2007–6201311ya, and find no overall trend of decline until I look at a transition in mid-2011 where the results fall dramatically. I speculate about the cause and implications for Alerts’s future.

--- /tla CQK Is The First Unused TLA Gwern 2023-09-29 2023-11-11 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex cs/shell wikipedia

Curious what the first ‘unused’ alphabetic acronym is, I have GPT-4 write a script to check English Wikipedia. After three bugs, the first unused one turns out as of 2023-09-29 to be the three-letter acronym ‘CQK’, with another 2.6k TLA unused, and 393k four-letter acronyms unused. Exploratory analysis suggests alphabetical order effects as well as letter-frequency.

It sometimes seems as if everything that could be trademarked has been, and as if every possible three-letter acronym (TLA) has been used in some nontrivial way by someone. Is this true? No—actually, a fair number, starting with CQK, have no nontrivial use to date.

We could check by defining ‘nontrivial’ as ‘has an English Wikipedia article, disambiguation page, or redirect’, and then writing a script which simply looks up every possible TLA Wikipedia URL to see which ones exist. This is a little too easy, so I make it harder by making GPT-4 write a Bash shell script to do so (then Python to double-check).

GPT-4 does so semi-successfully, making self-reparable errors until it runs into its idiosyncratic ‘blind spot’ error. After it accidentally fixes that, the script appears to work successfully, revealing that—contrary to my expectation that every TLA exists—the first non-existent acronym is the TLA ‘CQK’, and that there are many unused TLAs (2,684 or 15% unused) and even more unused four-letter acronyms (392,884 or 85% unused). I provide the list of all unused TLAs & four-letter acronyms (as well as alphanumerical ones—the first unused alphanumerical one is AA0.)

TLAs are not unused at random, with clear patterns enriched in letters like ‘J’ or ‘Z’ vs ‘A’ or ‘E’. Additional GPT-4-powered analysis in R suggests that both letter-frequency & position in alphabet predict unusedness to some degree, but leave much unexplained

--- /doc/statistics/stylometry/index ‘stylometry’ tag 2019-12-02 2024-10-31 cs/cryptography/steganography cs/security statistics/bayes

Bibliography for tag statistics/stylometry, most recent first: 2 related tags, 17 annotations, & 3 links ().

--- /newsletter/2021/index ‘newsletter/2021’ tag Gwern 2024-11-20 2024-11-20 newsletter

Bibliography for tag newsletter/2021, most recent first: 13 annotations ().

--- /doc/japan/poetry/index ‘Japanese poetry’ tag 2023-11-22 2024-02-28 fiction/poetry

Bibliography for tag japan/poetry, most recent first: 3 related tags, 7 annotations, & 2 links ().

--- /doc/borges/index ‘J. L. Borges’ tag 2019-10-27 2024-08-23 fiction philosophy

Bibliography for tag borges, most recent first: 37 annotations & 1 link ().

--- /doc/zeo/short-sleeper/index ‘short sleepers’ tag 2020-09-08 2024-11-12 genetics/heritable psychology/energy

Bibliography for tag zeo/short-sleeper, most recent first: 14 annotations & 6 links ().

--- /newsletter/2020/index ‘newsletter/2020’ tag Gwern 2021-01-06 2024-11-20 newsletter

Bibliography for tag newsletter/2020, most recent first: 13 annotations ().

--- /doc/marijuana/index ‘marijuana’ tag 2019-11-12 2024-09-30 crime law psychiatry/anxiety

Bibliography for tag marijuana, most recent first: 98 annotations & 25 links ().

--- /newsletter/index ‘newsletter’ tag Gwern 2024-12-01 newsletter

Bibliography for tag newsletter, most recent first: 12 related tags ().

--- /lorem-dropcap Lorem Ipsum: Dropcaps Gwern 2020-09-27 2023-12-12 design/typography/dropcap

Dropcaps subset of /lorem

--- /doc/sociology/preference-falsification/index ‘preference falsification’ tag 2018-01-21 2024-11-17 politics psychology/cognitive-bias psychology/personality/narcissism psychology/personality/psychopathy

Bibliography for tag sociology/preference-falsification, most recent first: 47 annotations & 16 links ().

--- /doc/fiction/opera/index ‘opera’ tag 2022-01-15 2024-11-29 fiction/criticism fiction/poetry music

Bibliography for tag fiction/opera, most recent first: 13 annotations & 16 links ().

--- /doc/co2/index ‘CO2’ tag 2019-11-06 2024-03-31 psychiatry/anxiety

Bibliography for tag co2, most recent first: 1 related tag, 18 annotations, & 12 links ().

--- /doc/reinforcement-learning/imperfect-information/index ‘hidden-information game’ tag 2020-01-05 2024-01-01 reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/model/muzero reinforcement-learning/multi-agent

Bibliography for tag reinforcement-learning/imperfect-information, most recent first: 5 related tags, 22 annotations, & 4 links ().

--- /doc/reinforcement-learning/robot/index ‘robotics’ tag 2019-09-14 2024-11-29 ai/scaling economics/automation reinforcement-learning/exploration reinforcement-learning/imitation-learning reinforcement-learning/meta-learning reinforcement-learning/multi-agent reinforcement-learning/offline reinforcement-learning/scaling

Bibliography for tag reinforcement-learning/robot, most recent first: 6 related tags, 286 annotations, & 77 links ().

--- /doc/crime/terrorism/index ‘terrorism’ tag 2020-08-21 2024-11-12

Bibliography for tag crime/terrorism, most recent first: 1 related tag, 44 annotations, & 21 links ().

--- /treadmill Treadmill desk observations Gwern 2012-06-19 2016-10-29 cs/r cs/shell nootropic/quantified-self psychology/spaced-repetition

Notes relating to my use of a treadmill desk and 2 self-experiments showing walking treadmill use interferes with typing and memory performance.

It has been claimed that doing spaced repetition review while on a walking treadmill improves memory performance. I did a randomized experiment 2013-08–9m2014-05 and found that using a treadmill damaged my recall

--- /startup-idea Startup Ideas Gwern 2017-08-21 2024-01-14 economics fiction/humor technology

Proposals for new technologies, businesses, startups, satirical or serious.

There are no good websites for learning how to lipread, despite widespread hearing impairment and the aging of the US population. Looking at some numbers, it seems like a potentially profitable niche?

[Now exists as Lipreading.org.]

--- /newsletter/2019/13 2019 News Gwern 2019-11-21 2024-11-29 ai/nn meta personal

Annual summary of 2019 Gwern.net newsletters, selecting my best writings, the best 2019 links by topic, and the best books/movies/anime I saw in 2019, with some general discussion of the year and the 2010s, and an intellectual autobiography of the past decade.

--- /newsletter/2017/13 2017 News Gwern 2017-11-30 2024-11-29 newsletter

Annual summary of 2017 Gwern.net newsletters, selecting my best writings, the best 2017 links by topic, and the best books/movies/anime I saw in 2017.

--- /socks#demographics On Having Enough Socks § Demographics Gwern 2017-11-22 2019-06-12 design insight-porn psychology/willpower survey technology/google

Personal experience and surveys on running out of socks; discussion of socks as small example of human procrastination and irrationality, caused by lack of explicit deliberative thought where no natural triggers or habits exist.

Incidentally, both the GS & Eric Jorgensen polls include some demographics data: estimated gender/age/location for GS, and ESL-speaker/country/gender for Eric Jorgensen. Those aren’t my main interest here, but how do they look?

One could make some predictions based on stereotypes: women will have more socks than men, older people will be more likely to have enough socks than younger people, and there will probably be cross-country differences. Checking, older people are indeed more likely, cross-country differences are not so large as to be inferable, and there appears to be inconsistency in gender effects: men have more problems with socks in the US than internationally?

--- /socks#grocery-shopping-advice On Having Enough Socks § Grocery Shopping Advice Gwern 2017-11-22 2019-06-12 design insight-porn psychology/willpower survey technology/google

Personal experience and surveys on running out of socks; discussion of socks as small example of human procrastination and irrationality, caused by lack of explicit deliberative thought where no natural triggers or habits exist.

To expand on the topic of experimenting & grocery shopping, I would summarize good grocery shopping as involving, (in descending order of marginal returns), advance planning to select efficient targets, selection of grocery stores by total cost (including travel time), selection of cheapest version (experimentation up front, then selecting by unit cost), avoiding grocery store trickery like coupons, and using assistance like a standard grocery store shopping list to maintain correctness of decisions.

--- /treadmill#treadmill-effect-on-spaced-repetition-performance-randomized-experiment Treadmill desk observations § Treadmill Effect on Spaced Repetition Performance: Randomized Experiment Gwern 2012-06-19 2016-10-29 cs/r cs/shell nootropic/quantified-self psychology/spaced-repetition

Notes relating to my use of a treadmill desk and 2 self-experiments showing walking treadmill use interferes with typing and memory performance.

It has been claimed that doing spaced repetition review while on a walking treadmill improves memory performance. I did a randomized experiment 2013-08–9m2014-05 and found that using a treadmill damaged my recall

--- /note/note#dd-game-2-log Miscellaneous § D&D Game #2 Log Gwern 2009-08-05 2024-11-02 fiction/text-game meta

Misc thoughts, memories, proto-essays, musings, etc.

Account of the second D&D game I’ve ever played, with my relatives, in which a party of adventurers seeks to return the renowned dancing bear Bufo stolen from a circus, only to discover a maze of lies in which they have lost their bearings, with perhaps an unbearable truth, but it all ends fur the beast. Also, remember, kids—winners don’t do drugs!

--- /note/note#the-tragedy-of-grand-admiral-thrawn Miscellaneous § The Tragedy of Grand Admiral Thrawn Gwern 2009-08-05 2024-11-02 fiction/criticism fiction/science-fiction psychology/personality/narcissism

Misc thoughts, memories, proto-essays, musings, etc.

I explain the somewhat arbitrary-seeming death of the popular Star Wars character Grand Admiral Thrawn as being the logical culmination of a tragic character arc in which his twisted (or “thrawn”) cunning & scheming, one of his defining traits, ultimately backfires on him, causing his bodyguard to betray & assassinate him.

--- /gpt-2#cleaning-project-gutenberg-contemporary-poetry GPT-2 Neural Network Poetry § Cleaning Project Gutenberg & Contemporary Poetry Gwern, Shawn Presser 2019-03-03 2019-10-29 ai/nn/transformer/gpt/poetry cs/shell tutorial

Demonstration tutorial of retraining OpenAI’s GPT-2 (a text-generating Transformer neural network) on large poetry corpuses to generate high-quality English verse.

Shawn Presser cleaned the Project Gutenberg poetry by using a heuristic on line numbers to guess where poems begin/end. This provides useful semantic metadata to the GPT-2-117M model, reducing “runon” or “ramblingness”, as it sees many discrete texts rather than a few book-length texts. I combined this improved PG poetry dataset with a new dataset on Kaggle, which scraped the Poetry Foundation website for modern/contemporary poetry, fixing the post-1920s emptiness of PG. The generated poems are much better.

--- /hunter#replacing-the-sat-with-pgses Genius Revisited Revisited § Replacing the SAT With PGSes Gwern 2016-06-19 2019-07-26 cs/r genetics/heritable iq/high/smpy psychology statistics/order statistics/power-analysis

A book study of surveys of the high-IQ elementary school HCES concludes that high IQ is not predictive of accomplishment; I point out that the disappointing results are consistent with the subjects not being geniuses due to regression to the mean (because of extremely early IQ tests) & small sample size.

Can the SAT’s role in university admissions be replaced in theory by powerful genetic predictors? The predictive validity of the SAT for academic success turns out to be lower than that of academic success’s heritability, implying it is possible.

--- /startup-idea#lipreading-website Startup Ideas § Lipreading Website Gwern 2017-08-21 2024-01-14 economics fiction/humor science/fermi-problem technology

Proposals for new technologies, businesses, startups, satirical or serious.

There are no good websites for learning how to lipread, despite widespread hearing impairment and the aging of the US population. Looking at some numbers, it seems like a potentially profitable niche?

[Now exists as Lipreading.org.]

--- /cyoa#game-tree-sizes Choose-Your-Own-Adventure AI Dungeon Games § Game Tree Sizes Gwern 2021-06-06 2021-06-22 ai/nn/transformer/gpt/fiction

Neural networks like GPT-3 power text adventure games where you can do anything; but they are too expensive. I propose that if we turn them into Choose Your Own Adventure hypertext games, they become feasible and enable new gameplay.

Despite the possibility of exponential growth, game trees can be extremely large—into the billions of nodes—before storage becomes any concern on modern storage devices, since terabytes or petabytes can be rented easily on hobbyist budgets like $100/month. > > Thus, the real challenge is creating a large game tree worth storing at all.

--- /amuse#literature-review Amusing Ourselves to Death? § Literature Review Gwern 2018-05-12 2019-06-25 sociology/technology transhumanism

A suggested x-risk/Great Filter is the possibility of advanced entertainment technology leading to wireheading/mass sterility/population collapse and extinction. As media consumption patterns are highly heritable, any such effect would trigger rapid human adaptation, implying extinction is almost impossible unless immediate collapse or exponentially accelerating addictiveness.

To demonstrate the point that there are pervasive genetic influences on all aspects of media consumption or leisure time activities/preferences/attitudes, I compile >580 heritability estimates from the behavioral genetics literature (drawing particularly on Loehlin & Nichols1976’s A Study of 850 Sets of Twins), roughly divided in ~13 categories.

--- /note/note#november-2016-data-loss-postmortem Miscellaneous § November 2016 Data Loss Postmortem Gwern 2009-08-05 2024-11-02 cs/cryptography cs/linkrot

Misc thoughts, memories, proto-essays, musings, etc.

In late November 2016, my Acer laptop broke and also corrupted the encrypted filesystem on the SSD, which apparently due to design decisions is very easily broken. Because I had been changing my backup strategy to use an new encryption key and had been lax about my manual backups, this made the most recent encrypted backups undecryptable as well, causing data loss back at least 2 weeks. I review how all this came to pass despite my careful backups, and my countermeasures: a new higher quality laptop, making backups of encrypted filesystem headers as well as the contents, and buying faster external drive enclosures.

Key lesson: if you use LUKS-encrypted Linux filesystems, know that they are super-fragile! You should backup a copy of the LUKS header (by running a command like sudo cryptsetup luksHeaderBackup /dev/sda5 –header-backup-file luks-header.bin.crypt) to avoid the header becoming corrupted & all data destroyed.

--- /newsletter/2020/04 April 2020 News Gwern 2019-12-26 2024-11-29 newsletter

April 2020 Gwern.net newsletter with links on music generation, data augmentation, terrorism, Matt Lakeman, and 1 documentary review.

--- /newsletter/2018/01 January 2018 News Gwern 2017-12-21 2024-11-29 newsletter

January 2018 Gwern.net newsletter with 3 tech/philosophy essays and links on genetics, AI, biology/medicine, Bitcoin, linguistics, Touhou, and 2 anime reviews.

--- /newsletter/2017/06 June 2017 News Gwern 2017-06-03 2024-11-29 newsletter

June 2017 Gwern.net newsletter with links on dysgenics, genetics, AI, PC history, and 5 movie reviews

--- /amuse Amusing Ourselves to Death? Gwern 2018-05-12 2019-06-25 sociology/technology transhumanism

A suggested x-risk/Great Filter is the possibility of advanced entertainment technology leading to wireheading/mass sterility/population collapse and extinction. As media consumption patterns are highly heritable, any such effect would trigger rapid human adaptation, implying extinction is almost impossible unless immediate collapse or exponentially accelerating addictiveness.

To demonstrate the point that there are pervasive genetic influences on all aspects of media consumption or leisure time activities/preferences/attitudes, I compile >580 heritability estimates from the behavioral genetics literature (drawing particularly on Loehlin & Nichols1976’s A Study of 850 Sets of Twins), roughly divided in ~13 categories.

--- /nootropic/magnesium Magnesium Self-Experiments Gwern 2013-05-13 2020-01-31 cs/r nootropic/magnesium nootropic/quantified-self psychology statistics/bayes statistics/power-analysis

3 magnesium self-experiments on magnesium l-threonate and magnesium citrate.

Encouraged by TruBrain’s magnesium & my magnesium l-threonate use, I design and run a blind random self-experiment to see whether magnesium citrate supplementation would improve my mood or productivity. I collected ~200 days of data at two dose levels. The analysis finds that the net effect was negative, but a more detailed look shows time-varying effects with a large initial benefit negated by an increasingly-negative effect. Combined with my expectations, the long half-life, and the higher-than-intended dosage, I infer that I overdosed on the magnesium. To verify this, I will be running a followup experiment with a much smaller dose.

--- /dnb-faq Dual n-Back FAQ Gwern 2009-03-25 2019-12-05 dual-n-back iq nootropic/quantified-self

A compendium of DNB, WM, IQ information up to 2015.

Between 200816ya and 201113ya, I collected a number of anecdotal reports about the effects of n-backing; there are many other anecdotes out there, but the following are a good representation—for what they’re worth.

--- /nootropic/nootropics Nootropics Gwern 2010-01-02 2018-12-20 creatine cs/haskell dual-n-back iodine modafinil nicotine nootropic/quantified-self psychology statistics/bayes statistics/power-analysis statistics/prediction zeo

Notes on nootropics I tried, and my experiments

--- /note/note Miscellaneous Gwern 2009-08-05 2024-11-02 meta

Misc thoughts, memories, proto-essays, musings, etc.

Proposal for a costume party game which involves social deduction of a well-known guest.

Several players start off masked, and the rest must attempt to guess which is actually the guest; each time they guess, right or wrong, they become a masked player too. Players compete to correctly guess who is the well-known guest, and then to fool as many of the remaining non-masked players as well.

When all players are masked, the game ends, and the well-known guest is revealed. Prizes are awarded for the best players at both sides of the game.

--- /doc/anime/eva/2010-crc Evangelion 2.0 Complete Records Collection Ryusuke Hikawa, Hideaki Anno, Shinji Higuchi, Yōji Enokido, Kazuya Tsurumaki, Mohiro Kitoh, Shigeto Koyama, Yoshito Asari 2011-08-29 2015-08-31 anime/eva interview

Translated interviews about making Evangelion 2.0 w/Anno, Higuchi, Enokido, & Tsurumaki

--- /newsletter/2020/05 May 2020 News Gwern 2019-12-26 2024-11-29 newsletter

May 2020 Gwern.net newsletter: GPT-3 scaling, implications, deep theory; anime GAN updates, and 1 book review.

--- /gpt-3-nonfiction#dwarf-fortress-changelog GPT-3 Nonfiction § Dwarf Fortress Changelog Gwern 2020-06-19 2022-07-03 ai/nn/transformer/gpt/non-fiction philosophy/mind

Nonfiction writing by OpenAI’s GPT-3 model, testing logic, commonsense reasoning, anagrams, PDF/OCR cleaning, creative nonfiction, etc

Dwarf Fortress is renowned for its infinite feature set; let’s ask GPT-3 what features are coming up in future releases! It’s important that DF players now know that important features like “direct the accidental death of your friends” and “die of old age alone” or “throw an alcoholic temper tantrum” are now supported for maximum realism:

--- /gpt-3-nonfiction#marcus-2020 GPT-3 Nonfiction § Marcus2020 Gwern 2020-06-19 2022-07-03 ai/nn/transformer/gpt/non-fiction philosophy/mind

Nonfiction writing by OpenAI’s GPT-3 model, testing logic, commonsense reasoning, anagrams, PDF/OCR cleaning, creative nonfiction, etc

Long-time DL critic Gary Marcus, in his 2020 essay “GPT-2 and the Nature of Intelligence”, argues, similar to Bender & Koller2020, that deep learning and self-supervised learning are fundamentally incapable of intelligence and GPT-2, far from being a success, is such a great failure that no more resources should be spent research it or followups (such as GPT-3) and “a clear sign that it is time to consider investing in different approaches.”

As exemplars of his criticisms, he offers test cases that he claims exemplifies the fundamental limits of GPT-2-like approaches. In responses to questions about counting, object location, physical reasoning, treating poisons, or what languages individuals speak, GPT-2 is highly unreliable or gives outright nonsensical examples.

GPT-3 solves Marcus’s word arithmetic problems completely; language/location completely, medical mostly, and location/physics partially. In no case does it perform nearly as badly as GPT-2, despite being almost exactly the same thing just larger. (Some of Marcus’s examples were tested independently by Daniel Kokotajlo using AI Dungeon, with similar results; see also Macaw.) Thus, Marcus’s examples do not appear to hold up any more than the Bender & Koller2020 counterexamples do, falling to a mere increase in model size.

--- /gpt-3-nonfiction#calibration GPT-3 Nonfiction § Calibration Gwern 2020-06-19 2022-07-03 ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/non-fiction philosophy/mind

Nonfiction writing by OpenAI’s GPT-3 model, testing logic, commonsense reasoning, anagrams, PDF/OCR cleaning, creative nonfiction, etc

Can you get GPT-3 to express its Q&A uncertainty in the form of probabilities, confidences, or verbal equivalents? Postfixed/prefixed probabilities like “A. answer [60%]” do not work, and neither do postfixed natural estimative words like “A. answer [likely]”, but it seems like prefixed uncertainty words like “A. [likely] answer” may improve results (at least, for asking nonsense, weight, commonsense, and existence questions). > > Later research demonstrated GPT-3-scale models are capable of calibration (Lin et al 2022), and subjective certainty (Kadavath et al 2022).

--- /doc/bitcoin/2011-davis The Crypto-Currency: Bitcoin and its mysterious inventor Joshua Davis 2013-04-18 2013-05-07 bitcoin cs/cryptography darknet-market/silk-road/1

2011 New Yorker article with descriptions of early Bitcoin people and mining, and the author’s search for Satoshi Nakamoto

Dept. Of Technology about bitcoin [sic] and its mysterious creator. There are lots of ways to make money: You can earn it, find it, counterfeit it, steal it. Or, if you’re Satoshi Nakamoto, you can invent it. That’s what he did on the evening of January 3, 200915ya, when he pressed a button on his keyboard and created a new currency called Bitcoin. It was all bit, and no coin. There was no paper, copper, or silver-just thirty-one thousand lines of code and an announcement on the Internet. Nakamoto wanted to create a currency immune to the predations of bankers and politicians. The currency was controlled entirely by software. Every ten minutes or so, coins would be distributed through a process that resembled a lottery. This way, the bitcoin software would release a total of twenty-one million bitcoins, most all of them over the next twenty years. Interest in Nakamoto’s invention built steadily. More and more people dedicated their computers to the lottery, and forty-four exchanges popped up, allowing anyone with bitcoins to trade them for dollars, euros, or other currencies. At first, a single bitcoin was valued at less than a penny. But merchants gradually began to accept bitcoin, and at the end of 201014ya the value began to appreciate rapidly. By June of 201113ya, a bitcoin was worth more than twenty-nine dollars. Market gyrations followed, and by September the exchange rate had fallen to five dollars. Still, with more than seven million bitcoins in circulation, Nakamoto had created thirty-five million dollars of value. And yet Nakamoto was a cipher. There was no trace of any coder with that name before the début of bitcoin. He used an e-mail address and Web site that were untraceable. In 200915ya and 201014ya, he wrote hundreds of posts in flawless English, invited other software developers to help him improve the code. Then, in April, 201113ya, he sent a note to a developer saying that he had “moved on to other things.” He has not been heard from since. Tells about failed attempts to hack the bitcoin encryption code. Writer tries to deduce Nakamoto’s true identity from clues in his posts and his code. Describes the Crypto 201113ya conference of cryptographers, where the writer went looking for Nakamoto. Writer speaks with two possible candidates, Michael Clear and Vili Lehdonvirta, both of whom deny that they are Nakamoto. Also tells about Kevin Groce, who runs a bitcoin-mining operation in Kentucky. Over the summer, hackers targeted bitcoin, and though they were unable to break Nakamoto’s code, they were able to disrupt the exchanges and destroy Web sites that helped users store bitcoins. The number of transactions decreased and the exchange rate plummeted. Commentators predicted the end of the currency. In September, however, volume began to increase again, and the price stabilized, at least temporarily.

--- /doc/anime/eva/little-boy/2005-murakami Earth in My Window Takashi Murakami, Linda Hoaglund 2012-03-04 2021-01-29 anime/eva/little-boy fiction/criticism japan sociology

Essay by Pop Art artist Takashi Murakami on Japanese society and on WWII infantilizing Japanese culture as revealed by media, anime, and otaku.

“Earth In My Window” is a long essay by Superflat pop artist Takashi Murakami meditating on post-WWII consumerist Japanese society and on WWII infantilizing Japanese pop culture as revealed by its influences on media, anime, and the otaku subculture.

This transcript has been prepared from a PDF scan of pg 98–149 of Little Boy: The Arts of Japan’s Exploding Subculture, ed. Murakami, published 2005-05-15, ISBN 0300102852. (See also the transcript of a discussion moderated by Murakami, “Otaku Talk”.)

--- /otaku Neon Genesis Evangelion source anthology Gwern 2009-09-30 2021-02-10 anime/eva fiction/criticism

Extensive anthology of Gainax/Anno/Evangelion quotes, excerpts, sources, references, and analyses, organized by reliability and year.

This page is an extensive anthology of Gainax/Hideaki Anno/Evangelion-related quotes, excerpts, sources, references, & analyses, organized by reliability & year.

The purpose of compiling a large page of quotes & references classified by date & source level is to make it easier to put NGE into a historical context by tracing the evolution of plot or characters, cross-reference statements made in interviews, jump forward and backwards to flesh out otherwise obscure allusions to events, and enable easy keyword-based search for various concepts (eg. the connection of Kaworu to cats, Gainax’s bafflement that viewers might think Misato killed Kaji, the influence of earthquakes on people, connections to Aum Shinrikyo, garbled information about suicide attempts, Anno’s conservative nationalist views or philosophy of “poison”, retcons like swapping the Adam and Lilith plot devices, panspermia & First Ancestral Race being slowly removed from production materials and then post-NGE slowly restored, the many conflicting pieces of information on the end of NGE TV and EoE, Yamaga’s questionable reliability etc).

As I compile more material, I become increasingly convinced that far from Evangelion being a baffling mystery, it is in fact one of the most understandable anime out there, with a wealth of information about almost every detail, from the earliest planning meetings to how long particular episode productions took to the source of minor details like the “A-10 nerve”, and that Hideaki Anno, far from being a reticent auteur of mystery, has collectively been forthcoming about anything one might ask—to the point where multiple interviews could justly be described as “book-length” (the books in question being June, Schizo, Prano, the 1.0 CRC, & the 2.0 CRC). There is so much material that half the difficulty is simply collating the existing materials, and some extensive sources seem to have been lost to both the Japanese and English fandoms (eg. there seem to be no mentions or quotations of the Anata to Watashi no Gainax interviews in the Japanese web).

--- /coin-flip The Kelly Coin-Flipping Game: Exact Solutions Gwern, Arthur Breitman, nshepperd, FeepingCreature, Gurkenglas 2017-01-19 2018-12-12 cs/c cs/haskell cs/python cs/r reinforcement-learning/model reinforcement-learning/model-free statistics/bayes statistics/decision

Decision-theoretic analysis of how to optimally play the Haghani & Dewey 2016 300-round double-or-nothing coin-flipping game with an edge and ceiling better than using the Kelly Criterion. Computing and following an exact decision tree increases earnings by $6.6 over a modified KC.

Haghani & Dewey2016 experiment with a double-or-nothing coin-flipping game where the player starts with $25 (ie. $31.27$252016) and has an edge of 60%, and can play 300 times, choosing how much to bet each time, winning up to a maximum ceiling of $250. Most of their subjects fail to play well, earning an average $91, compared to the Haghani & Dewey2016 heuristic benchmark of ~$240 in winnings achievable using a modified Kelly Criterion as their strategy. The KC, however, is not optimal for this problem as it ignores the ceiling and limited number of plays.

We solve the problem of the value of optimal play exactly by using decision trees & dynamic programming for calculating the value function, with implementations in R, Haskell, and C. (See also Problem #14.) We also provide a closed-form exact value formula in R & Python, several approximations using Monte Carlo/random forests/neural networks, visualizations of the value function, and a Python implementation of the game for the OpenAI Gym collection.

We find that optimal play yields $246.61 on average (rather than ~$240), and so the human players actually earned only 36.8% of what was possible, losing $155.6 in potential profit. Comparing decision trees and the Kelly criterion for various horizons (bets left), the relative advantage of the decision tree strategy depends on the horizon: it is highest when the player can make few bets (at b = 23, with a difference of ~$36), and decreases with number of bets as more strategies hit the ceiling.

In the Kelly game, the maximum winnings, number of rounds, and edge are fixed; we describe a more difficult generalized version in which the 3 parameters are drawn from Pareto, normal, and beta distributions and are unknown to the player (who can use Bayesian inference to try to estimate them during play). Upper and lower bounds are estimated on the value of this game. In the variant of this game where subjects are not told the exact edge of 60%, a Bayesian decision tree approach shows that performance can closely approach that of the decision tree, with a penalty for 1 plausible prior of only $1.

Two deep reinforcement learning agents, DQN & DDPG, are implemented but DQN fails to learn and DDPG doesn’t show acceptable performance with default settings, indicating better tuning may be required for them to solve the generalized Kelly game.

--- /embryo-selection Embryo Selection For Intelligence Gwern 2016-01-22 2020-01-18 cs/r economics genetics/heritable/correlation statistics/decision statistics/order statistics/power-analysis transhumanism

A cost-benefit analysis of the marginal cost of IVF-based embryo selection for intelligence and other traits with 2016–2017 state-of-the-art

With genetic predictors of a phenotypic trait, it is possible to select embryos during an in vitro fertilization process to increase or decrease that trait. Extending the work of Shulman & Bostrom2014/Hsu2014, I consider the case of human intelligence using SNP-based genetic prediction, finding:

--- /replication The Replication Crisis: Flaws in Mainstream Science Gwern 2010-10-27 2019-12-09 dual-n-back longevity nootropic psychology sociology statistics/bias/publication statistics/causality statistics/meta-analysis

2013 discussion of how systemic biases in science, particularly medicine and psychology, have resulted in a research literature filled with false positives and exaggerated effects, called ‘the Replication Crisis’.

Long-standing problems in standard scientific methodology have exploded as the “Replication Crisis”: the discovery that many results in fields as diverse as psychology, economics, medicine, biology, and sociology are in fact false or quantitatively highly inaccurately measured. I cover here a handful of the issues and publications on this large, important, and rapidly developing topic up to about 201311ya, at which point the Replication Crisis became too large a topic to cover more than cursorily.

The crisis is caused by methods & publishing procedures which interpret random noise as important results, far too small datasets, selective analysis by an analyst trying to reach expected/desired results, publication bias, poor implementation of existing best-practices, nontrivial levels of research fraud, software errors, philosophical beliefs among researchers that false positives are acceptable, neglect of known confounding like genetics, and skewed incentives (financial & professional) to publish ‘hot’ results.

Thus, any individual piece of research typically establishes little. Scientific validation comes not from small p-values, but from discovering a regular feature of the world which disinterested third parties can discover with straightforward research done independently on new data with new procedures—replication.

--- /order-statistic Calculating The Gaussian Expected Maximum Gwern 2016-01-22 2022-10-15 cs/r statistics/bayes statistics/order statistics/probability

In generating a sample of n datapoints drawn from a normal/Gaussian distribution, how big on average the biggest datapoint is will depend on how large n is. I implement a variety of exact & approximate calculations from the literature in R to compare efficiency & accuracy.

In generating a sample of n datapoints drawn from a normal/Gaussian distribution with a particular mean/SD, how big on average the biggest datapoint is will depend on how large n is. Knowing this average is useful in a number of areas like sports or breeding or manufacturing, as it defines how bad/good the worst/best datapoint will be (eg. the score of the winner in a multi-player game).

The order statistic of the mean/average/expectation of the maximum of a draw of n samples from a normal distribution has no exact formula, unfortunately, and is generally not built into any programming language’s libraries.

I implement & compare some of the approaches to estimating this order statistic in the R programming language, for both the maximum and the general order statistic. The overall best approach is to calculate the exact order statistics for the n range of interest using numerical integration via lmomco and cache them in a lookup table, rescaling the mean/SD as necessary for arbitrary normal distributions; next best is a polynomial regression approximation; finally, the Elfving correction to the Blom 195866ya approximation is fast, easily implemented, and accurate for reasonably large n such as n > 100.

--- /gpt-2 GPT-2 Neural Network Poetry Gwern, Shawn Presser 2019-03-03 2019-10-29 ai/nn/transformer/gpt/poetry cs/shell tutorial

Demonstration tutorial of retraining OpenAI’s GPT-2 (a text-generating Transformer neural network) on large poetry corpuses to generate high-quality English verse.

In February 2019, following up on my 2015–2016 text-generation experiments with char-RNNs, I experiment with the cutting-edge Transformer NN architecture for language modeling & text generation.

Using OpenAI’s GPT-2-117M (117M) model pre-trained on a large Internet corpus and nshepperd’s finetuning code, I retrain GPT-2-117M on a large (117MB) Project Gutenberg poetry corpus. I demonstrate how to train 2 variants: “GPT-2-poetry”, trained on the poems as a continuous stream of text, and “GPT-2-poetry-prefix”, with each line prefixed with the metadata of the PG book it came from. In May 2019, I trained the next-largest GPT-2, GPT-2-345M, similarly, for a further quality boost in generated poems. In October 2019, I & Shawn Presser retrained GPT-2-117M on a Project Gutenberg corpus with improved formatting, and combined it with a contemporary poem dataset based on Poetry Foundation’s website; finally, we retrained the newly-released GPT-2-1.5b, which did not fit in our GPUs so we used TRC-supplied TPUs in a “swarm” to slowly finetune it.

With just a few GPU-days on NVIDIA 1080944yati GPUs, GPT-2-117M finetuning can produce high-quality poetry which is more thematically consistent than my char-RNN poems—capable of modeling subtle features like rhyming, and sometimes even a pleasure to read.

I list some of the many possible ways to improve poem generation and further approach human-level poems. For the highest-quality AI poetry to date, see my followup pages, “GPT-3 Creative Writing”.

See Also: For anime plot summaries, see TWDNE; for generating ABC-formatted folk music, see “GPT-2 Folk Music” & “GPT-2 Preference Learning for Music and Poetry Generation”; for playing chess, see “A Very Unlikely Chess Game”; for the Reddit comment generator, see SubSimulatorGPT-2; for fanfiction, the Ao3; and for video games, the walkthrough model. For OpenAI’s GPT-3 followup, see “GPT-3: Language Models are Few-Shot Learners”.

--- /gpt-3-nonfiction GPT-3 Nonfiction Gwern 2020-06-19 2022-07-03 ai/nn/transformer/gpt/non-fiction philosophy/mind

Nonfiction writing by OpenAI’s GPT-3 model, testing logic, commonsense reasoning, anagrams, PDF/OCR cleaning, creative nonfiction, etc

GPT-3, announced in May 2020 by OpenAI, was a breakthrough in neural net modeling of natural language and natural-language-related tasks; the June 2020 API opened up GPT-3 use to outsiders, including myself. I extensively documented my experiences testing GPT-3 and learning how to use it primarily for creative fiction such as poetry; but I also tested some “nonfiction” uses (often in response to hyperbolic claims about what GPT-3 could never do). This page documents tasks like anagrams, queries based on premises described as ‘databases’, probing the problems with GPT-3’s commonsense and other tasks (often related to poor prompting, showing the importance of prompt programming, or the pernicious influence of BPEs)

--- /about About This Website Gwern 2010-10-01 2024-08-30 design meta personal statistics/prediction

Meta page describing Gwern.net site ideals of stable long-term essays which improve over time; idea sources and writing methodology; metadata definitions; site statistics; copyright license.

This page is about Gwern.net content; for the details of its implementation & design like the popup paradigm, see Design; and for information about me, see Links.

--- /newsletter/2021/02 February 2021 News Gwern 2020-01-02 2024-11-29 newsletter

February 2021 Gwern.net newsletter with links on AI scaling, semaglutide, and ethicist ethics.

--- /clone#dog-heritabilities Dog Cloning For Special Forces: Breed All You Can Breed § Dog Heritabilities Gwern 2018-09-18 2021-12-02 cs/r dog genetics/cloning genetics/heritable/dog genetics/selection/artificial statistics/decision statistics/order

Decision analysis of whether cloning the most elite Special Forces dogs is a profitable improvement over standard selection procedures. Unless training is extremely cheap or heritability is extremely low, dog cloning is hypothetically profitable.

Notes on reading reviews & meta-analyses on the psychometric properties & heritabilities of dog behavioral traits, particularly for working dogs. Dog heritabilities might be expected to be low in the context of considering dogs of the same breed (as would be relevant to a breeding or training context): heavy selective breeding would tend to reduce within-breed heritabilities (while increasing group heritability).

Overall, heritabilities appear to differ by breed and be quite low (say, closer to 25% than to the human average of >50%) but the psychometric properties of dog behavioral tests also appear to be poor, with low item counts, reliabilities, test-retests, and predictive power, rater/judge effects, and little use of latent factors to extract more reliable measures, suggesting considerable total measurement error and thus considerable underestimation of prediction/heritabilities. Possibly dog heritabilities are much closer to human heritabilities than they seem.

--- /bitcoin-is-worse-is-better#irreversible-transactions-meta-scams Bitcoin Is Worse Is Better § Irreversible Transactions: Meta-Scams Gwern 2011-05-27 2018-11-21 bitcoin cs/cryptography design

2011 essay on how Bitcoin’s long gestation and early opposition indicates it is an example of the ‘Worse is Better’ paradigm in which an ugly complex design with few attractive theoretical properties compared to purer competitors nevertheless successfully takes over a niche, survives, and becomes gradually refined.

The irreversibility of Bitcoin transactions makes for some unusual dynamics in exchanges, along with the entire altcoin ecosystem (probably the most interesting altcoin scam to me was the Bytecoin scam+anonymity innovation). I learned of an interesting example in May 201311ya, when a Reddit post introduced me to a Tor hidden site which offers you double your money back if you send it some bitcoins. A scam, right? Well, it is a scam, but it’s not quite the scam it looks like…

--- /order-statistic#probability-of-bivariate-maximum Calculating The Gaussian Expected Maximum § Probability of Bivariate Maximum Gwern 2016-01-22 2022-10-15 cs/r statistics/bayes statistics/order statistics/probability

In generating a sample of n datapoints drawn from a normal/Gaussian distribution, how big on average the biggest datapoint is will depend on how large n is. I implement a variety of exact & approximate calculations from the literature in R to compare efficiency & accuracy.

Given a sample of n pairs of 2 normal variables A & B which are correlated r, what is the probability Pmax that the maximum on the first variable A is also the maximum on the second variable B? This is analogous to many testing or screening situations, such as employee hiring (“what is the probability the top-scoring applicant on the first exam is the top-scorer on the second as well?”) or athletic contests (“what is the probability the current world champ will win the next championship?”).

Order statistics has long proven that asymptotically, Pmax approaches 1⁄n. Exact answers are hard to find, but confirm the asymptotics; the closest that exists is for an approximation & special-case of the Ali-Mikhail-Haq copula: which roughly indicates that r functions as a constant factor boost in Pmax, and the boost from r fades out as n increases.

As long as r ≠ 1, “the tails will come apart”. n increases the difficult too fast for any fixed r to overcome. This has implications for interpreting extremes and test metrics.

--- /embryo-selection#glue-robbers-sequencing-nobelists-using-collectible-letters Embryo Selection For Intelligence § Glue Robbers: Sequencing Nobelists Using Collectible Letters Gwern 2016-01-22 2020-01-18 cs/r economics genetics/heritable/correlation statistics/decision statistics/order statistics/power-analysis statistics/prediction transhumanism

A cost-benefit analysis of the marginal cost of IVF-based embryo selection for intelligence and other traits with 2016–2017 state-of-the-art

A major challenge in GWASes of cognitive traits like intelligence & personality is getting sufficient statistical power to produce results. Statistical power can be increased by increasing sample size, or increasing the ‘effect size’ (ie. size of differences). One way of increasing effect size is collecting extreme datapoints, such as the extremely-high IQ SMPY/TIP samples for comparison with a baseline group. As such datapoints are by definition rare, they are hard to collect in bulk. This is true for other cognitive traits such as personality, ambition, accomplishment, scientific breakthroughs—all surely connected, but even harder to screen for & collect.

The increasing power of DNA sequencing methods means that as of 2018, one can extract & sequence DNA from envelopes & postal stamps which are decades or centuries old. This is legally permissible, and many such envelopes & stamps from historical figures can be bought for low prices.

This means that one can potentially genotype—in addition to everyone else in the past—the greatest minds in history and run analyses. They could be used as an ultra-highly-enriched sample in GWASes for intelligence or achievement with potentially high statistical power and combined with other datasets for further gains in PGSes.

--- /embryo-selection#the-genius-factory-plotz-2005 Embryo Selection For Intelligence § The Genius Factory, Plotz2005 Gwern 2016-01-22 2020-01-18 cs/r economics genetics/heritable/correlation iq/high statistics/decision statistics/order statistics/power-analysis statistics/prediction transhumanism

A cost-benefit analysis of the marginal cost of IVF-based embryo selection for intelligence and other traits with 2016–2017 state-of-the-art

Excerpts from the The Genius Factory: The Curious History of the Nobel Prize Sperm Bank, Plotz 200519ya (eISBN: 978-1-58836-470-8), about the Repository for Germinal Choice:

--- /note/note#how-would-you-prove-you-are-a-time-traveler-from-the-past Miscellaneous § How Would You Prove You Are a Time-Traveler From the Past? Gwern 2009-08-05 2024-11-02 fiction/science-fiction/time-travel

Misc thoughts, memories, proto-essays, musings, etc.

SF/F fiction frequently considers the case of a time-traveler from the future to the past, who can prove himself by use of advanced knowledge and items from the future. In the reverse case, a time-traveler from the past to the future wishes to prove he is from the past and that time-travel is real. How can he do this when all past knowledge is already known or whose chain of custody being broken is more likely than time-travel being real? I suggest 8 methods: carbon-14 nuclear isotope dating of their body as isotopes cannot be removed; sequencing of their genome to check consistency with pedigree as human genomes cannot be synthesized or edited on a large scale; selection & mutation clocks, likewise; immune system signatures of extinct or rare diseases such as smallpox, and accumulated pollution such as heavy metals, difficult & dangerous to fake. While these proofs may not offer conclusive proof since any human system can be subverted with enough effort, they can provide enough evidence to launch research into time travel and a definitive finding.

--- /ies-history History of Iterated Embryo Selection Gwern 2019-01-18 2019-01-19 cs/r economics genetics/heritable/correlation genetics/selection statistics/decision statistics/order statistics/power-analysis statistics/prediction transhumanism

The multiple-invention (>3) history of the idea of extremely-powerful embryo selection by using gametogenesis to run many ‘generations’ in vitro.

The idea of iterated embryo selection—conducing multiple generations of embryo selection in a petri dish by exploiting gametogenesis or stem cells—has a complicated history. Tracing relevant papers back to 198935ya, the idea appears to have been invented independently at least 4

A predecessor was introduced by Georges & Massey1991 as “velogenetics”. Velogenetics led to what appears to be the first invention of IES, Haley & Visscher1998’s “whizzogenetics”. It was then invented in 200915ya by Carl Shulman as “iterated embryo selection”/“IES”. It was reinvented a third time by Sparrow2013 as “in vitro eugenics”. And it was reinvented up to 3× in 2018, as “in vitro breeding”, by Bogliotti et al 2018/Goszczynski et al 2018/Hou et al 2018 (whose relationship is unclear, as the latter two claim novelty but publish not just the same idea but same name, while the former, published before them and giving said name & idea, nevertheless does not claim novelty).

--- /gpt-2-music#generating-midi-with-10k30k-context-windows GPT-2 Folk Music § Generating MIDI With 10k–30k Context Windows Gwern, Shawn Presser 2019-11-01 2020-04-25 ai/music ai/nn/transformer/gpt/2 cs/shell statistics

Generating Irish/folk/classical music in ABC format using GPT-2-117M, with good results.

To expand the ABC GPT-2 model to cover a wider variety of musical genres, I turn to the next-most compact widespread music encoding format: MIDI. There are hundreds of thousands of MIDIs which can be decompiled to ABC format, averaging ~10k BPEs—within GPT-2-117M’s feasible context window when trained on TPUs (which permit training of context windows up to 30k wide).

We compile the ABC from before and 2 large MIDI datasets, and convert to ABC, yielding ~453k usable ABC-MIDI musical files (~5.1GB of text). We trained January–April 2020 on our TPU swarm (with many interruptions), achieving a final loss of ~0.2 (underfit).

Sampling from the final model is hit-or-miss as it is prone to the likelihood repetition trap and it generates instruments one-by-one so it is common for instruments to be cut off or otherwise broken during sampling (indicating that sampling is increasingly a bigger problem than training for long-range sequence modeling). However, successful pieces are possible, and are musically far more diverse than the folk ABC corpus, with many pleasingly complex samples.

--- /gpt-2-music#spaceless-model GPT-2 Folk Music § Spaceless Model Gwern, Shawn Presser 2019-11-01 2020-04-25 ai/music ai/nn/tokenization ai/nn/transformer/gpt/2 cs/shell statistics

Generating Irish/folk/classical music in ABC format using GPT-2-117M, with good results.

While training a GPT-2-117M on a folk music corpus written in ABC format, persistent syntax errors kept being generated by an otherwise-high-quality model: random spaces would be generated, rendering a music piece either erroneous or lower-quality. Why? It seems to be some issue with the GPT BPE encoder handling of spaces which makes it difficult to emit the right space-separated characters. We found that ABC does not actually require spaces, and we simply removed all spaces from the corpus—noticeably improving quality of generated pieces.

--- /biggan#danbooru2019e621-256px-biggan Making Anime With BigGAN § Danbooru2019+e621 256px BigGAN Gwern 2019-02-04 2021-01-29 ai/anime/danbooru ai/nn/gan/biggan cs/python tutorial

Experiments in using BigGAN to generate anime faces and whole anime images; semi-successful.

Release of a 256px BigGAN model trained on Danbooru2019 & e621. This is a prototype model testing our ability to train a BigGAN stably for hundreds of thousands of iterations on a TPU-256 pod on 3 million+ anime/illustration images. While the generated samples are far from ‘photorealistic’, they serve as proof of concept that—unlike our failed StyleGAN-2 scaling experiments—BigGAN can successfully model anime images with great generality, and that we can potentially scale up to 512px or even 1024px and match the DeepMind ImageNet BigGAN for quality.

--- /embryo-selection#embryo-selection-and-dynasties Embryo Selection For Intelligence § Embryo Selection And Dynasties Gwern 2016-01-22 2020-01-18 cs/r economics genetics/heritable/correlation statistics/decision statistics/order statistics/power-analysis statistics/prediction transhumanism

A cost-benefit analysis of the marginal cost of IVF-based embryo selection for intelligence and other traits with 2016–2017 state-of-the-art

Genetic selection & engineering technologies, if banned or highly regulated, could exacerbate existing social inequality by increasing genetic differences between groups on key traits like intelligence or Conscientiousness or ethnocentrism and ensuring near-permanent continuity of wealth or power. Whether this is a serious problem quantitatively with feasible levels of embryo selection has not been much examined. I consider the specific scenario of a single family, such as a royal family or wealthy corporate owner, which wishes to increase the odds of succession to a sufficiently-competent heir who can maintain the dynasty. I suggest a toy model treating it as a repeated liability-threshold model in which heirs are selected as order statistics and if any heir is above a threshold, the dynasty survives another generation; given average numbers of generations and heirs, this defines an unique threshold of competence. Adding embryo selection turns this into a two-stage selection process. In some scenarios, assuming a threshold of ~+1SD and advanced polygenic scores for multiple selection, embryo selection could considerably increase the lifespan of a dynasty due to tail effects on the increased mean in each stage.

--- /embryo-selection#societal-effects Embryo Selection For Intelligence § Societal Effects Gwern 2016-01-22 2020-01-18 cs/r statistics/order statistics/power-analysis

A cost-benefit analysis of the marginal cost of IVF-based embryo selection for intelligence and other traits with 2016–2017 state-of-the-art

One category of effects considered by Shulman & Bostrom is the non-financial social & societal effects mentioned in their Table 3, where embryo selection can “perceptibly advantage a minority” or in an extreme case, “Selected dominate ranks of elite scientists, attorneys, physicians, engineers. Intellectual Renaissance?”

This is another point which is worth going into a little more; no specific calculations are mentioned by Shulman & Bostrom, and the thin-tail-effects of normal distributions are notoriously counterintuitive, with surprisingly large effects out on the tails from small-seeming changes in means or standard deviations—for example, the legendary levels of Western Jewish overperformance despite their tiny population sizes.

The effects of selection also compound over generations; for example, in the famous Tryon maze-bright/dull rat selective-breeding experiment, a large gap in mean performance had opened up by the 2nd generation, and by the 7th, the distributions were almost disjoint (see figure 4 in Tryon1940). Or consider the long-term Illinois corn/maize selection experiment (response to selection of the 2 lines, animated), or the Dusseldorf mice lines (Palma-Vera et al 2021).

Considering the order/tail effects for cutoffs/thresholds corresponding to admission to elite universities, for many possible combinations of embryo selection boosts/IVF uptakes/generation accumulations, embryo selection accounts for a majority or almost all of future elites.

--- /amuse#loehlin-nichols-1976-a-study-of-850-sets-of-twins Amusing Ourselves to Death? § Loehlin & Nichols 197648ya: A Study of 850 Sets of Twins Gwern 2018-05-12 2019-06-25 sociology/technology transhumanism

A suggested x-risk/Great Filter is the possibility of advanced entertainment technology leading to wireheading/mass sterility/population collapse and extinction. As media consumption patterns are highly heritable, any such effect would trigger rapid human adaptation, implying extinction is almost impossible unless immediate collapse or exponentially accelerating addictiveness.

A discussion of extracting ~376 behavioral items relating to recreation/leisure from Loehlin & Nichols 197648ya: A Study of 850 Sets of Twins, which reports comprehensive summary statistic twin correlations from an early large-scale twin study (canvassed via the National Merit Scholarship Qualifying Test, 196262ya). I transcribe them from the book, pool the weighted correlations by gender, and compute simple heritability estimates by Falconer’s formula for use in the recreation/leisure heritability literature review.

--- /amuse#waller-et-al-1995-occupational-and-leisure-time-interests-and-personality Amusing Ourselves to Death? § Waller Et Al 199529ya, ‘Occupational and Leisure Time Interests, and Personality’ Gwern 2018-05-12 2019-06-25 sociology/technology transhumanism

A suggested x-risk/Great Filter is the possibility of advanced entertainment technology leading to wireheading/mass sterility/population collapse and extinction. As media consumption patterns are highly heritable, any such effect would trigger rapid human adaptation, implying extinction is almost impossible unless immediate collapse or exponentially accelerating addictiveness.

Extracting domain interests from Waller et al 1995’s reported summary statistics & test-retest reliabilities, I compute pooled weighted correlations, corrected for measurement error based on the test-retest reliabilities, for use in the table of heritabilities.

--- /gpt-3-nonfiction#ferrucci-2020 GPT-3 Nonfiction § Ferrucci2020 Gwern 2020-06-19 2022-07-03 ai/nn/transformer/gpt/non-fiction philosophy/mind

Nonfiction writing by OpenAI’s GPT-3 model, testing logic, commonsense reasoning, anagrams, PDF/OCR cleaning, creative nonfiction, etc

AI researcher David Ferrucci argues that NN model scaling is a dead end, because GPT-2 cannot provide sensible completions to a story about what happens when you water a dying plant, since GPT-2 only generates ~2⁄15 sensible completions.

I find GPT-3 generates 10⁄10.

--- /gpt-3-nonfiction#cowboy-bebop-episodes GPT-3 Nonfiction § Cowboy Bebop Episodes Gwern 2020-06-19 2022-07-03 ai/nn/transformer/gpt/non-fiction philosophy/mind

Nonfiction writing by OpenAI’s GPT-3 model, testing logic, commonsense reasoning, anagrams, PDF/OCR cleaning, creative nonfiction, etc

Writing anime episode plots using List of Cowboy Bebop episodes as the source prompt, which—as is no surprise by now—works great, and it provides very plausible and interesting-sounding episode ideas.

--- /gpt-3-nonfiction#bender-koller-2020 GPT-3 Nonfiction § Bender & Koller2020 Gwern 2020-06-19 2022-07-03 ai/nn/transformer/gpt/non-fiction philosophy/mind

Nonfiction writing by OpenAI’s GPT-3 model, testing logic, commonsense reasoning, anagrams, PDF/OCR cleaning, creative nonfiction, etc

“Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data”, Bender & Koller2020 (awarded ACL 2020’s “Best theme paper”) criticizes neural language models, claiming that their philosophical arguments prove that such models will never truly understand anything as they lack communicative intent and other things intrinsically necessary for genuine understanding of language & concepts.

They offer two predictions, as it were, pre-registered before GPT-3, about test cases they claim NLMs will never understand: a vignette about a bear chasing a hiker (Appendix A), and the arithmetic word problem “Three plus five equals” rather than using digits/numbers (Appendix B), commenting:

It is clear that GPT-2 has learned what activity words tend to co-occur with bears and sticks (strap them to your chest, place the sticks, kill the bear, take your gun), but none of these completions would be helpful to A. We think this is because GPT-2 does not know the meaning of the prompt and the generated sentences, and thus cannot ground them in reality.

…To get a sense of how existing LMs might do at such a task, we let GPT-2 complete the simple arithmetic problem Three plus five equals. The five responses below, created in the same way as above, show that this problem is beyond the current capability of GPT-2, and, we would argue, any pure LM.

As with the stapler question, not only are “pure LMs” capable of solving both tasks in principle, they already solve the challenges, as shown below with GPT-3.

--- /gpt-3-nonfiction#perlis-epigrams-on-programming GPT-3 Nonfiction § Perlis, ‘Epigrams On Programming’ Gwern 2020-06-19 2022-07-03 ai/nn/transformer/gpt/non-fiction philosophy/mind

Nonfiction writing by OpenAI’s GPT-3 model, testing logic, commonsense reasoning, anagrams, PDF/OCR cleaning, creative nonfiction, etc

The sayings of Alan Perlis (“Epigrams in Programming” 198242ya) are proverbial in CS—so famous that sampling produces difficulty in plagiarizing both Perlis & other programmer sayings, even when I try shuffling the epigram prompt to make spitting out memorized epigrams less likely:

--- /gpt-3-nonfiction#prefixed-probabilities GPT-3 Nonfiction § Prefixed Probabilities Gwern 2020-06-19 2022-07-03 ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/non-fiction philosophy/mind

Nonfiction writing by OpenAI’s GPT-3 model, testing logic, commonsense reasoning, anagrams, PDF/OCR cleaning, creative nonfiction, etc

If prefixed uncertainty words appear to elicit some degree of uncertainty/meta-reasoning from GPT-3, perhaps explicit probabilities can quantify it? Revisiting the questions from before, I try prefixing probabilities to answers. While GPT-3 imitates the pattern with no problem, it continues to make errors on questions it seems to’ve solved before without as much trouble (like the weight comparison questions), and the probabilities don’t have any clear connection to the correctness of the answers, so it looks like the numbers still don’t work even when using the prefixing trick.

--- /gpt-3-nonfiction#vectors-richardson GPT-3 Nonfiction § ‘Vectors’, Richardson Gwern 2020-06-19 2022-07-03 ai/nn/transformer/gpt/non-fiction philosophy/mind

Nonfiction writing by OpenAI’s GPT-3 model, testing logic, commonsense reasoning, anagrams, PDF/OCR cleaning, creative nonfiction, etc

Imitations of “Even More Aphorisms and Ten-Second Essays from Vectors 3.0”, James Richardson 201014ya; prompt using a random range of aphorisms:

--- /doc/ai/nn/gan/index ‘GAN’ tag 2019-10-01 2024-11-07 ai/dataset ai/nn/transformer/clip/sample ai/nn/vae reinforcement-learning/multi-agent reinforcement-learning/robot

Bibliography for tag ai/nn/gan, most recent first: 8 related tags, 229 annotations, & 41 links ().

[essay on this tag topic]

--- /doc/history/public-domain-review/index ‘PD Review’ tag 2020-06-16 2024-11-29

Bibliography for tag history/public-domain-review, most recent first: 101 annotations & 43 links ().

--- /note/fully-connected Fully-Connected Neural Nets Gwern 2021-04-24 2021-04-24 ai/nn/fully-connected ai/scaling ai/tabular

Bibliography of ML papers related to multi-layer perceptrons (fully-connected neural nets), often showing surprising efficacy despite their reputation for being too general to be usable (representing a possible future Bitter Lesson).

--- /search Internet Search Tips Gwern 2018-12-11 2023-08-08 cs/linkrot/archiving cs/shell technology/google tutorial

A description of advanced tips and tricks for effective Internet research of papers/books, with real-world examples.

Over time, I developed a certain google-fu and expertise in finding references, papers, and books online. I start with the standard tricks like Boolean queries and keyboard shortcuts, and go through the flowchart for how to search, modify searches for hard targets, penetrate paywalls, request jailbreaks, scan books, monitor topics, and host documents. Some of these tricks are not well-known, like checking the Internet Archive (IA) for books.

I try to write down my search workflow, and give general advice about finding and hosting documents, with demonstration case studies.

--- /socks On Having Enough Socks Gwern 2017-11-22 2019-06-12 design insight-porn psychology/cognitive-bias psychology/willpower survey technology/google

Personal experience and surveys on running out of socks; discussion of socks as small example of human procrastination and irrationality, caused by lack of explicit deliberative thought where no natural triggers or habits exist.

After running out of socks one day, I reflected on how ordinary tasks get neglected. Anecdotally and in 3 online surveys, people report often not having enough socks, a problem which correlates with rarity of sock purchases and demographic variables, consistent with a neglect/procrastination interpretation: because there is no specific time or triggering factor to replenish a shrinking sock stockpile, it is easy to run out.

This reminds me of akrasia on minor tasks, ‘yak shaving’, and the nature of disaster in complex systems: lack of hard rules lets errors accumulate, without any ‘global’ understanding of the drift into disaster (or at least inefficiency). Humans on a smaller scale also ‘drift’ when they engage in System I reactive thinking & action for too long, resulting in cognitive biases. An example of drift is the generalized human failure to explore/experiment adequately, resulting in overly greedy exploitative behavior of the current local optimum. Grocery shopping provides a case study: despite large gains, most people do not explore, perhaps because there is no established routine or practice involving experimentation. Fixes for these things can be seen as ensuring that System II deliberative cognition is periodically invoked to review things at a global level, such as developing a habit of maximum exploration at first purchase of a food product, or annually reviewing possessions to note problems like a lack of socks.

While socks may be small things, they may reflect big things.

--- /backstop Evolution as Backstop for Reinforcement Learning Gwern 2018-12-06 2021-07-04 ai/nn economics/automation insight-porn philosophy/epistemology psychology/energy psychology/willpower reinforcement-learning/multi-agent reinforcement-learning/safe statistics/bayes statistics/decision technology

Markets/evolution as backstops/ground truths for reinforcement learning/optimization: on some connections between Coase’s theory of the firm/linear optimization/DRL/evolution/multicellular life/pain/Internet communities as multi-level optimization problems.

One defense of free markets notes the inability of non-market mechanisms to solve planning & optimization problems. This has difficulty with Coase’s paradox of the firm, and I note that the difficulty is increased by the fact that with improvements in computers, algorithms, and data, ever larger planning problems are solved.

Expanding on some Cosma Shalizi comments, I suggest interpreting phenomena as multi-level nested optimization paradigm: many systems can be usefully described as having two (or more) levels where a slow sample-inefficient but ground-truth ‘outer’ loss such as death, bankruptcy, or reproductive fitness, trains & constrains a fast sample-efficient but possibly misguided ‘inner’ loss which is used by learned mechanisms such as neural networks or linear programming. (The higher levels are different ‘groups’ in group selection.)

So, one reason for free-market or evolutionary or Bayesian methods in general is that while poorer at planning/optimization in the short run, they have the advantage of simplicity and operating on ground-truth values, and serve as a constraint on the more sophisticated non-market mechanisms.

I illustrate by discussing corporations, multicellular life, reinforcement learning & meta-learning in AI, and pain in humans.

This view suggests that are inherent balances between market/non-market mechanisms which reflect the relative advantages between a slow unbiased method and faster but potentially arbitrarily biased methods.

--- /modus One Man’s Modus Ponens Gwern 2012-05-01 2022-01-06 history insight-porn math philosophy/epistemology philosophy/logic sociology statistics/bayes statistics/bias

One man’s modus ponens is another man’s modus tollens is a saying in Western philosophy encapsulating a common response to a logical proof which generalizes the reductio ad absurdum and consists of rejecting a premise based on an implied conclusion. I explain it in more detail, provide examples, and a Bayesian gloss.

A logically-valid argument which takes the form of a modus ponens may be interpreted in several ways; a major one is to interpret it as a kind of reductio ad absurdum, where by ‘proving’ a conclusion believed to be false, one might instead take it as a modus tollens which proves that one of the premises is false. This “Moorean shift” is aphorized as the snowclone, “One man’s modus ponens is another man’s modus tollens”.

The Moorean shift is a powerful counter-argument which has been deployed against many skeptical & metaphysical claims in philosophy, where often the conclusion is extremely unlikely and little evidence can be provided for the premises used in the proofs; and it is relevant to many other debates, particularly methodological ones.

--- /doc/ai/nn/transformer/attention/linear-algebra/index ‘Transformer matrix optimizations’ tag 2021-04-17 2024-11-04 math

Bibliography for tag ai/nn/transformer/attention/linear-algebra, most recent first: 18 annotations & 2 links ().

--- /milk The Power of Twins: The Scottish Milk Experiment Gwern 2016-01-12 2019-11-29 cs/r genetics/heritable statistics/power-analysis

In discussing a large Scottish public health experiment, Student noted that it would’ve been vastly more efficient using a twin experiment design; I fill in the details with a power analysis.

Randomized experiments require more subjects the more variable each datapoint is to overcome the noise which obscures any effects of the intervention. Reducing noise enables better inferences with the same data, or less data to be collected, which can be done by balancing observed characteristics between control and experimental datapoints.

A particularly dramatic example of this approach is running experiments on identical twins rather than regular people, because twins vary far less from each other than random people due to shared genetics & family environment. In 193193ya, the great statistician Student (William Sealy Gosset) noted problems with an extremely large (n = 20,000) Scottish experiment in feeding children milk (to see if they grew more in height or weight), and claimed that the experiment could have been done far more cost-effectively with an extraordinary reduction of >95% fewer children if it had been conducted using twins, and claimed that 100 identical twins would have been more accurate than 20,000 children. He, however, did not provide any calculations or data demonstrating this.

I revisit the issue and run a power calculation on height indicating that Student’s claims were correct and that the experiment would have required ~97% fewer children if run with twins.

This reduction is not unique to the Scottish milk experiment on height/weight, and in general, one can expect a reduction of 89% in experiment sample sizes using twins rather than regular people, demonstrating the benefits of using behavioral genetics in experiment design/power analysis.

--- /fiction/penpen The Case of PenPen Gwern 2010-01-03 2010-01-03 anime/eva fiction/science-fiction

A parody of Evangelion where PenPen leads Instrumentality.

My high school anime club held one and only one contest, a fanfiction contest on a series we had watched. My mind had already been crushed a little by End of Evangelion, so I resolved to write something a little more light-hearted than that. I wound up winning and selected the 2-disc set of Ushio and Tora (we had watched the first 2 episodes and I thought it was almost as hilarious as Dragon Half). The president never delivered and so I never wrote part 2 - it remains juvenilia. I kept meaning to track him down to demand it, but eventually I gave up and just downloaded a copy. (The handful of later episodes weren’t that good, although it was funnier than the reboot.)

--- /lllt 2013 LLLT self-experiment Gwern 2013-12-20 2015-08-12 cs/r nootropic/quantified-self psychology/neuroscience statistics

An LLLT user’s blinded randomized self-experiment in 2013 on the effects of near-infrared light on a simple cognitive test battery: positive results

A short randomized & blinded self-experiment on near-infrared LED light stimulation of one’s brain yields statistically-significant dose-related improvements to 4 measures of cognitive & motor performance. Concerns include whether the blinding succeeded and why the results are so good.

--- /timestamping Easy Cryptographic Timestamping of Files Gwern 2015-12-04 2017-12-16 bitcoin cs/cryptography cs/linkrot/archiving cs/shell tutorial

Scripts for convenient free secure Bitcoin-based dating of large numbers of files/strings

Local archives are useful for personal purposes, but sometimes, in investigations that may be controversial, you want to be able to prove that the copy you downloaded was not modified and you need to timestamp it and prove the exact file existed on or before a certain date. This can be done by creating a cryptographic hash of the file and then publishing that hash to global chains like centralized digital timestampers or the decentralized Bitcoin blockchain. Current timestamping mechanisms tend to be centralized, manual, cumbersome, or cost too much to use routinely. Centralization can be overcome by timestamping to Bitcoin; costing too much can be overcome by batching up an arbitrary number of hashes and creating just 1 hash/timestamp covering them all; manual & cumbersome can be overcome by writing programs to handle all of this and incorporating them into one’s workflow. So using an efficient cryptographic timestamping service (the OriginStamp Internet service), we can write programs to automatically & easily timestamp arbitrary files & strings, timestamp every commit to a Git repository, and webpages downloaded for archival purposes. We can implement the same idea offline, without reliance on OriginStamp, but at the cost of additional software dependencies like a Bitcoin client.

--- /dnm-survival Darknet Market mortality risks Gwern 2013-10-30 2019-06-09 bitcoin cs/r darknet-market economics statistics/prediction statistics/survival-analysis

Survival analysis of lifespans, deaths, and predictive factors of Tor-Bitcoin darknet markets

I compile a dataset of 87 public English-language darknet markets (DNMs) 2011–52016 in the vein of the famous Silk Road 1, recording their openings/closing and relevant characteristics. A survival analysis indicates the markets follow a Type TODO lifespan, with a median life of TODO months. Risk factors include TODO. With the best model, I generate estimates for the currently-operating markets.

--- /catnip-survey World Catnip Surveys Gwern 2015-11-15 2018-12-02 cat/psychology/drug/catnip cs/r japan statistics/bayes survey technology/google

International population online surveys of cat owners about catnip and other cat stimulant use.

In compiling a meta-analysis of reports of catnip response rats in domestic cats, yielding a meta-analytic average of ~2⁄3, the available data suggests heterogeneity from cross-country differences in rates (possibly for genetic reasons) but is insufficient to definitively demonstrate the existence of or estimate those differences (particularly a possible extremely high catnip response rate in Japan). I use Google Surveys August–September 2017 to conduct a brief 1-question online survey of a proportional population sample of 9 countries about cat ownership & catnip use, specifically: Canada, the USA, UK, Japan, Germany, Brazil, Spain, Australia, & Mexico. In total, I surveyed n = 31,471 people, of whom n = 9,087 are cat owners, of whom n = 4,402 report having used catnip on their cat, and of whom n = 2996 report a catnip response.

The survey yields catnip response rates of Canada (82%), USA (79%), UK (74%), Japan (71%), Germany (57%), Brazil (56%), Spain (54%), Australia (53%), and Mexico (52%). The differences are substantial and of high posterior probability, supporting the existence of large cross-country differences. In additional analysis, the other conditional probabilities of cat ownership and trying catnip with a cat appear to correlate with catnip response rates; this intercorrelation suggests a “cat factor” of some sort influencing responses, although what causal relationship there might be between proportion of cat owners and proportion of catnip-responder cats is unclear.

An additional survey of a convenience sample of primarily US Internet users about catnip is reported, although the improbable catnip response rates compared to the population survey suggest the respondents are either highly unrepresentative or the questions caused demand bias.

--- /sort The sort –key Trick Gwern 2014-03-03 2021-05-05 cs/algorithm/information/compression cs/algorithm/sorting cs/linkrot/archiving cs/shell tutorial

Commandline folklore: sorting files by filename or content before compression can save large amounts of space by exposing redundancy to the compressor. Examples and comparisons of different sorts.

Programming folklore notes that one way to get better lossless compression efficiency is by the precompression trick of rearranging files inside the archive to group ‘similar’ files together and expose redundancy to the compressor, in accordance with information-theoretical principles. A particularly easy and broadly-applicable way of doing this, which does not require using any unusual formats or tools and is fully compatible with the default archive methods, is to sort the files by filename and especially file extension.

I show how to do this with the standard Unix command-line sort tool, using the so-called “sort –key trick”, and give examples of the large space-savings possible from my archiving work for personal website mirrors and for making darknet market mirror datasets where the redundancy at the file level is particularly extreme and the sort –key trick shines compared to the naive approach.

--- /kyon The Melancholy of Kyon Gwern 2009-06-08 2018-09-01 anime fiction/criticism fiction/gene-wolfe fiction/science-fiction

Literary analysis of the light novel/anime series The Melancholy of Haruhi Suzumiya: Haruhi is not God, Kyon is

The light novel series The Melancholy of Haruhi Suzumiya, featuring a character named Haruhi who is a god unawares and her search for novelty, has a number of anomalies and unclear overarching plot.

I argue that these anomalies can be resolved, and greater literary depth achieved, by interpreting Haruhi as an ordinary “not special” girl whose wish made her extraordinary, because the first-person protagonist Kyon is the actual unaware god. Like the bluebird of happiness, Kyon found happiness only when he forgot about himself to care more about another (making it a positive twist on Mark Twain’s The Mysterious Stranger’s despairing demiurge).

--- /question#fetish-economics Open Questions § Fetish Economics Gwern 2018-10-17 2023-02-13 culture economics sociology

Some anomalies/questions which are not necessarily important, but do puzzle me or where I find existing explanations to be unsatisfying.

How does fetish economics work in general? Anecdotally, creators in fetishes (eg. feet, furry, futanari) report it being highly lucrative despite them not enjoying it themselves—indeed, it’s lucrative because they don’t enjoy it and only do it for the money, requiring extraordinary premiums.

The imbalance is long-standing, and common to many fetishes. An absolute lack of supply is clearly not the reason, and a “risk premium” based on stigma appears inadequate for an imbalance that looks more like excess demand rather than extreme scarcity of supply. I suggest that fetishes do have high demand, as an inherent property of being extreme differences in preferences, and this uses up all fellow-fetish creators and then runs up into very high reservation prices from outsider creators, thereby resulting in unusual prices at the margin.

--- /2012-election 2012 election predictions Gwern 2012-11-05 2015-04-21 cs/haskell cs/r politics statistics/bayes statistics/prediction/election

Compiling academic and media forecaster’s 2012 American Presidential election predictions and statistically judging correctness; Nate Silver was not the best.

I statistically analyzed in R hundreds of predictions compiled for ~10 forecasters of the 201212ya American Presidential election, and ranking them by Brier, RMSE, & log scores.

The best overall performance seems to be by Drew Linzer and Wang & Holbrook, while Nate Silver appears as somewhat overrated and the famous Intrade prediction market turned in a disappointing overall performance.

--- /mlp-genetics Race in My Little Pony Gwern 2018-06-04 2021-05-14 anime/my-little-pony fiction/criticism genetics transhumanism

In MLP:FiM, the 3 pony races sometimes bear offspring of other pony races; I review 4 complicated Mendelian models attempting to explain this, and note that a standard polygenic liability-threshold model can fit it parsimoniously.

(For background on My Little Pony: Friendship is Magic, see my review of My Little Pony.)

Another fictional universe with genetic mechanisms is My Little Pony: Friendship Is Magic, where there are 3 pony races which are heritable. One outlier family which has all 3 races represented challenges simple Mendelian interpretations of MLP races. I review 4 attempts to reconcile the outlier with Mendelian mechanisms, and propose another interpretation, drawing on polygenic mechanisms, treating race as a polytomous liability threshold trait, which is flexible enough to explain all observations in-universe (at least for the first few seasons of MLP).

--- /dnm-arrest DNM-related arrests, 2011–2015 Gwern 2012-07-14 2019-06-13 bitcoin cs/r darknet-market/agora darknet-market/alphabay darknet-market/evolution darknet-market/silk-road/1 darknet-market/silk-road/2 modafinil statistics/survival-analysis

A census database of all publicly-reported arrests and prosecutions connected to the Tor-Bitcoin drug darknet markets 2011–2015, and analysis of mistakes.

I compile a table and discussion of all known arrests and prosecutions related to English-language Tor-Bitcoin darknet markets (DNMs) such as Silk Road 1, primarily 2011–42015, along with discussion of how they came to be arrested.

--- /backfire-effect Biased information as anti-information Gwern 2012-10-19 2018-01-07 politics psychology/cognitive-bias statistics/bayes

Filtered data for a belief can rationally push you away from that belief

The backfire effect is a recently-discovered bias where arguments contrary to a person’s belief leads to them believing even more strongly in that belief; this is taken as obviously “irrational”. The “rational” update can be statistically modeled as a shift in the estimated mean of a normal distribution where each randomly distributed datapoint is an argument: new datapoints below the mean cause a shift of the inferred mean downward and likewise if above. When this model is changed to include the “censoring” of datapoints, then the valid inference changes and a datapoint below the mean can lead to a shift of the mean upwards. This suggests that providing a person with anything less than the best data contrary to, or decisive refutations of, one of their beliefs may result in them becoming even more certain of that belief. If it is enjoyable or profitable to argue with a person while one does less than one’s best, it is bad to hold false beliefs, and this badness is not shared between both parties, then arguing online may constitute a negative externality: an activity whose benefits are gained by one party but whose full costs are not paid by the same party. In many moral systems, negative externalities are considered selfish and immoral; hence, lazy or half-hearted arguing may be immoral because it internalizes any benefits while possibly leaving the other person epistemically worse off.

--- /dune-genetics Genetics and Eugenics in Frank Herbert’s Dune-verse Gwern 2018-05-05 2021-10-29 fiction/criticism fiction/science-fiction/frank-herbert genetics/selection/artificial insight-porn transhumanism

Discussion of fictional eugenics program in the SF Dune-verse and how it contradicts contemporary known human genetics but suggests heavy agricultural science and Mendelian inspiration to Frank Herbert’s worldview.

Frank Herbert’s SF Dune series features as a central mechanic a multi-millennium human eugenics breeding program by the Bene Gesserit, which produces the main character, Paul Atreides, with precognitive powers. The breeding program is described as oddly slow and ineffective and requiring roles for incest and inbreeding at some points, which contradict most proposed human eugenics methods.

I describe the two main historical paradigms of complex trait genetics, the Fisherian infinitesimal model and the Mendelian monogenic model, the former of which is heavily used in human behavioral genetics and the latter of which is heavily used in agricultural breeding for novel traits.

I argue that Herbert (incorrectly but understandably) believed the latter was the genetics of most human traits, in particular exotic traits like ESP, perhaps related to his lifelong autodidactic interest in plants & insects & farming, and this unstated but implicit intellectual background shaped the Dune-verse and resolves the apparent errors or anomalies.

--- /dnb-meta-analysis Dual n-Back Meta-Analysis Gwern 2012-05-20 2018-11-30 cs/r dual-n-back iq statistics/bias statistics/meta-analysis

Does DNB increase IQ? What factors affect the studies? Probably not: gains are driven by studies with weakest methodology like apathetic control groups.

I meta-analyze the >19 studies up to 2016 which measure IQ after an n-back intervention, finding (over all studies) a net gain (medium-sized) on the post-training IQ tests.

The size of this increase on IQ test score correlates highly with the methodological concern of whether a study used active or passive control groups. This indicates that the medium effect size is due to methodological problems and that n-back training does not increase subjects’ underlying fluid intelligence but the gains are due to the motivational effect of passive control groups (who did not train on anything) not trying as hard as the n-back-trained experimental groups on the post-tests. The remaining studies using active control groups find a small positive effect (but this may be due to matrix-test-specific training, undetected publication bias, smaller motivational effects, etc.)

I also investigate several other n-back claims, criticisms, and indicators of bias, finding:

--- /tryon Tryon’s Rat Experiment Gwern 2020-01-13 2020-01-14 genetics/selection/artificial psychology/animal/maze

Tryon’s Rat Experiment is a multi-decade selective breeding animal experiment begin in the 1930s which rapidly bred enormous differences in a complex psychological trait, maze-running, demonstrating core principles of behavior genetics.

Tryon’s Rat Experiment is a multi-decade selective breeding animal experiment in the 1920s–1940s which employed automated maze-running machinery to minimize measurement error and, using truncation selection, bred two different strains of rats: “maze-bright” and “maze-dull” rats, selected for high & low maze-running performance.

Within a few generations, the rats showed increasing differences in maze-running performance, and the two strains eventually had non-overlapping distributions. Tryon’s Rat Experiment rapidly bred enormous differences in a complex psychological trait, demonstrating core principles of behavior genetics: the heritability of even psychological traits far removed from standard examples of genetics like coat color, and the ability of selection produce large population-wide changes in a short time for even highly polygenic traits like maze-running.

The etiology of the changes in performance were subsequently investigated: the performance changes were not on the g-factor of intelligence, but were more maze-running-specific, and have neurological correlates.

The experiment was widely-cited in psychology and early behavior genetics, and paralleled various later experiments in selection on complex behavioral traits.

--- /gpt-2-preference-learning#bradley-terry-preference-learning GPT-2 Preference Learning for Music Generation § Bradley-Terry Preference Learning Gwern 2019-12-16 2021-06-07 ai/music ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry statistics/order/comparison

Experiments with OpenAI’s ‘preference learning’ approach, which trains a NN to predict global quality of datapoints, and then uses reinforcement learning to optimize that directly, rather than proxies. I am unable to improve quality, perhaps due to too-few ratings.

Christiano et al 2017 introduced a deep reinforcement learning architecture for learning “I know it when I see it” subjectively-defined reward functions from human feedback: a human makes comparisons of actions/datapoints/episodes to select the ‘better’ one, a NN is trained to predict the better one based on these comparisons, and another NN is RL-trained based on the predicted comparisons interpreted as a reward. Since the human is unable to write down a conventional reward function in software, the predictor NN (analogous to a Discriminator in a GAN or a ‘critic’ in actor-critic RL) learns the reward function by example, and then the RL agent NN (analogous to a Generator in a GAN) learns by trial-and-error what sequences will optimize this complex reward function, and the human feedback provides additional guidance on new parts of the problem as the pair of NNs bootstrap into better performance. This is demonstrated on video game or robotic-style simulations, but appears equally applicable to other sequence problems where reward functions are impossible to write and existing losses like maximum likelihood are imperfect for generation (such as music or poetry composition).

As originally framed, the predictor merely does comparisons, receiving & providing binary feedback. This is justified as being implicitly equivalent to a standard pair-comparison/competition model, the Bradley-Terry model (akin to the famous ELO), where each datapoint has a latent variable on a common cardinal scale (often, like a liability-threshold model, scaled to 𝒩(0,1) for convenience), producing a total order which efficiently extracts all possible information from the comparisons.

I suggest that this is not necessarily the case, as examples from GANs indicate that such a preference-learning architecture may be learning something odder (such as memorizing comparisons), and that the architecture could be improved by removing the implicitness of the B-T ranking, computing the B-T rankings directly (which can be done even with non-overlapping comparisons by using a Bayesian model with priors and using covariates such as the predictor’s own estimates), thereby providing absolute quality scores for correctness of comparisons, more efficient regression, RL rewards, and meaningful interpretable scores for downstream uses.

--- /zeo/co2 CO2/ventilation sleep experiment Gwern 2016-06-05 2017-10-29 cs/r nootropic/quantified-self psychology/neuroscience statistics zeo

Self-experiment on whether changes in bedroom CO2 levels affect sleep quality

Some psychology studies find that CO2 impairs cognition, and some sleep studies find that better ventilation may improve sleep quality. Use of a Netatmo air quality sensor reveals that closing my bedroom tightly to reduce morning light also causes CO2 levels to spike overnight to 7x daytime levels. To investigate the possible harmful effects, I run a self-experiment randomizing an open bedroom door and a bedroom box fan (2x2) and analyze the data using a structural equation model of air quality effects on a latent sleep factor with measurement error.

--- /death-note-script Who Wrote The Death Note Script? Gwern 2009-11-02 2016-04-27 anime cs/haskell cs/r statistics/bayes statistics/stylometry

Internal, external, stylometric evidence point to live-action leak of Death Note Hollywood script being real.

I give a history of the 200915ya leaked script, discuss internal & external evidence for its realness including stylometrics; and then give a simple step-by-step Bayesian analysis of each point. We finish with high confidence in the script being real, discussion of how this analysis was surprisingly enlightening, and what followup work the analysis suggests would be most valuable.

--- /haskell/archiving-github Archiving GitHub Gwern 2011-03-20 2013-10-28 cs/haskell cs/linkrot/archiving tutorial

Scraping and downloading Haskell-related repositories from GitHub

Tutorial of how to write a Haskell program to scrape Haskell-related repositories on GitHub and download them for offline installation, search, reference, and source code analysis, using TagSoup & curl.

Obsolete

--- /melon Bitter Melon for blood glucose Gwern 2015-09-14 2016-07-29 cs/r nootropic/quantified-self statistics/bayes statistics/decision

Analysis of whether bitter melon reduces blood glucose in one self-experiment and utility of further self-experimentation

I re-analyze a bitter-melon/blood-glucose self-experiment, finding a small effect of increasing blood glucose after correcting for temporal trends & daily variation, giving both frequentist & Bayesian analyses. I then analyze the self-experiment from a subjective Bayesian decision-theoretic perspective, cursorily estimating the costs of diabetes & benefits of intervention in order to estimate Value Of Information for the self-experiment and the benefit of further self-experimenting; I find that the expected value of more data (EVSI) is negative and further self-experimenting would not be optimal compared to trying out other anti-diabetes interventions.

--- /candy-japan Candy Japan’s new box A/B test Gwern 2016-05-06 2016-08-26 cs/haskell cs/r design economics/advertising reinforcement-learning/exploration statistics/bayes statistics/power-analysis tutorial

Bayesian decision-theoretic analysis of the effect of fancier packaging on subscription cancellations & optimal experiment design.

I analyze an A/B test from a mail-order company of two different kinds of box packaging from a Bayesian decision-theory perspective, balancing posterior probability of improvements & greater profit against the cost of packaging & risk of worse results, finding that as the company’s analysis suggested, the new box is unlikely to be sufficiently better than the old. Calculating expected values of information shows that it is not worth experimenting on further, and that such fixed-sample trials are unlikely to ever be cost-effective for packaging improvements. However, adaptive experiments may be worthwhile.

--- /research-criticism How Should We Critique Research? Gwern 2019-05-19 2019-07-07 philosophy/epistemology statistics/bayes statistics/bias statistics/causality statistics/decision

Criticizing studies and statistics is hard in part because so many criticisms are possible, rendering them meaningless. What makes a good criticism is the chance of being a ‘difference which makes a difference’ to our ultimate actions.

Scientific and statistical research must be read with a critical eye to understand how credible the claims are. The Reproducibility Crisis and the growth of meta-science have demonstrated that much research is of low quality and often false.

But there are so many possible things any given study could be criticized for, falling short of an unobtainable ideal, that it becomes unclear which possible criticism is important, and they may degenerate into mere rhetoric. How do we separate fatal flaws from unfortunate caveats from specious quibbling?

I offer a pragmatic criterion: what makes a criticism important is how much it could change a result if corrected and how much that would then change our decisions or actions: to what extent it is a “difference which makes a difference”.

This is why issues of research fraud, causal inference, or biases yielding overestimates are universally important: because a ‘causal’ effect turning out to be zero effect or grossly overestimated will change almost all decisions based on such research; while on the other hand, other issues like measurement error or distributional assumptions, which are equally common, are often not important: because they typically yield much smaller changes in conclusions, and hence decisions.

If we regularly ask whether a criticism would make this kind of difference, it will be clearer which ones are important criticisms, and which ones risk being rhetorical distractions and obstructing meaningful evaluation of research.

--- /story-of-your-life ‘Story Of Your Life’ Is Not A Time-Travel Story Gwern 2012-12-12 2018-06-11 fiction/criticism fiction/science-fiction/time-travel insight-porn philosophy/ontology statistics/causality survey technology/google

Famous Ted Chiang SF short story ‘Story Of Your Life’ is usually misinterpreted as, like the movie version Arrival, being about time-travel/precognition; I explain it is instead an exploration of xenopsychology and a psychology of timeless physics.

One of Ted Chiang’s most noted philosophical SF short stories, “Story of Your Life”, was made into a successful time-travel movie, Arrival, sparking interest in the original. However, movie viewers often misread the short story: “Story” is not a time-travel movie. At no point does the protagonist travel in time or enjoy precognitive powers, interpreting the story this way leads to many serious plot holes, it renders most of the exposition-heavy dialogue (which is a large fraction of the wordcount) completely irrelevant, and genuine precognition undercuts the themes of tragedy & acceptance.

Instead, what appears to be precognition in Chiang’s story is actually far more interesting, and a novel twist on psychology and physics: classical physics allows usefully interpreting the laws of physics in both a ‘forward’ way in which events happen step by step, but also a teleological way in which events are simply the unique optimal solution to a set of constraints including the outcome and allows reasoning ‘backwards’. The alien race exemplifies this other, equally valid, possible way of thinking and viewing the universe, and the protagonist learns their way of thinking by studying their language, which requires seeing written characters as a unified gestalt. This holistic view of the universe as an immutable ‘block-universe’, in which events unfold as they must, changes the protagonist’s attitude towards life and the tragic death of her daughter, teaching her in a somewhat Buddhist or Stoic fashion to embrace life in both its ups and downs.

--- /anchoring LW anchoring experiment Gwern 2012-02-27 2014-02-17 cs/r nootropic/quantified-self psychology/cognitive-bias statistics

Do mindless positive/negative comments skew article quality ratings up and down?

I do an informal experiment testing whether LessWrong karma scores are susceptible to a form of anchoring based on the first comment posted; a medium-large effect size is found. Although the data does not fit the assumed normal distribution so there may or may not be any actual anchoring effect.

--- /lewis-meditation 2013 Lewis meditation results Gwern 2013-07-12 2013-07-25 cs/r nootropic/quantified-self psychiatry/meditation statistics

Multilevel modeling of effect of small group’s meditation on math errors

A small group of Quantified Selfers tested themselves daily on arithmetic and engaged in a month of meditation. I analyze their scores with a multilevel model with per-subject grouping, and find the expect result: a small decrease in arithmetic errors which is not statistically-significant, with practice & time-of-day effects (but not day-of-week or weekend effects). This suggests a longer experiment by twice as many experimenters in order to detect this effect.

--- /search#searching-the-google-reader-archives Internet Search Tips § Searching the Google Reader Archives Gwern 2018-12-11 2023-08-08 cs/linkrot/archiving cs/shell technology/google tutorial

A description of advanced tips and tricks for effective Internet research of papers/books, with real-world examples.

A 2015 tutorial on how to do manual searches of the 201311ya Google Reader archives on the Internet Archive. Google Reader provides fulltext mirrors of many websites which are long gone and not otherwise available even in the IA; however, the Archive Team archives are extremely user-unfriendly and challenging to use even for programmers.

I explain how to find & extract specific websites.

Note: now largely obsoleted by querying IA’s Wayback Machine for the GR RSS URL.

--- /kettle Electric vs Stove Kettle Gwern 2015-02-28 2018-11-29 food

I compare my electric tea kettle to my stove kettle, and apply some simple statistical modeling verifying the electric is faster.

Electric kettles are faster, but I was curious how much faster my electric kettle heated water to high or boiling temperatures than does my stove-top kettle.

So I collected some data and compared them directly, trying out a number of statistical methods (principally: nonparametric & parametric tests of difference, linear & beta regression models, and a Bayesian measurement error model).

My electric kettle is faster than the stove-top kettle (the difference is both statistically-significant p≪0.01 & the posterior probability of difference is P ≈ 1), and the modeling suggests time to boil is largely predictable from a combination of volume, end-temperature, and kettle type.

--- /embryo-selection#gcta-meta-analysis Embryo Selection For Intelligence § GCTA Meta-Analysis Gwern 2016-01-22 2020-01-18 cs/r economics genetics/heritable/correlation statistics/decision statistics/order statistics/power-analysis statistics/prediction transhumanism

A cost-benefit analysis of the marginal cost of IVF-based embryo selection for intelligence and other traits with 2016–2017 state-of-the-art

Davies et al 2011’s 0.5 (50%) SNP heritability is outdated & small, based on n = 3,511 with correspondingly large imprecision in the GCTA estimates. We can do better by bringing it up to date incorporating the additional GCTAs which have been published since 201113ya through 2018.

Compiling 12 GCTAs, I find a meta-analytic estimate of SNPs can explain >33% of variance in current intelligence scores, and, adjusting for measurement error (as we care about the latent trait, not individual noisy measurements), >44% with better-quality phenotype testing.

--- /lithium Lithium in ground-water and well-being Gwern 2010-10-14 2019-12-07 meta nootropic psychology statistics/meta-analysis

Lithium is a well-known mood stabilizer & suicide preventive; some research suggests lithium may be a cognitively-protective nutrient and at the population level, chronic lithium consumption (through drinking water) predicts lower levels of mental illness, violence, & suicide.

The metal lithium is a well-known mood stabilizer & suicide preventive widely used in psychiatry. It is also a trace mineral present to various levels in all drinking water and much food. A long-running but obscure vein of research speculates on whether lithium is beneficial and a nutrient, specifically, cognitively-protective. Epidemiological research has correlated chronic lithium consumption through drinking water with a number of population-level variables like rates of mental illness, violence, & suicide. If causal, lithium should be regarded as a vital nutrient for mental health and added to drinking water to substantially improve population-wide outcomes.

However, the evidence is weak. Most of this research is cross-sectional, only some is longitudinal, none offers particularly strong causal evidence using natural experiments or other designs, there are questions about confounding with autocorrelated spatial properties such as altitude, and some of the best research, using Scandinavian population registries, offers more mixed evaluations of claimed correlates.

It is unlikely that further such correlational research will resolve the debate, despite the mounting opportunity cost. I suggest that formal experimentation is required, and concerns about harms from lithium supplementation making experiments ‘unethical’ can be circumvented by instead removing lithium or looking for natural experiments with cause changes (such as changes or upgrades to water treatment plants or plumbing modify lithium concentration).

--- /littlewood-origin Origin of ‘Littlewood’s Law of Miracles’ Gwern 2019-02-16 2021-05-18 philosophy psychology statistics

Leprechaun hunting the origins of the famous skeptical observation that because millions of events are constantly happening, ‘miracles’ happen once a month; it was actually coined by Freeman Dyson.

I try to trace back “Littlewood’s Law of Miracles” to its supposed source in Littlewood’s A Mathematician’s Miscellany. It does not appear in that book, making it a leprechaun, and further investigation indicates that Littlewood did not come up with it but that Freeman Dyson coined it in 200420ya, probably based on the earlier “Law of Truly Large Numbers” coined by Diaconis & Mosteller1989, in a case of Stigler’s law.

--- /doc/culture/1983-wolfe-thecitadeloftheautarch-thejustman Loyal to the Group of 17’s Story—The Just Man Gene Wolfe 2018-01-20 2018-01-21 culture fiction/gene-wolfe fiction/science-fiction philosophy/mind politics psychology/linguistics sociology/preference-falsification

Short story on the limits of propaganda and ‘Newspeak’ using a constructed language; from Chapter 11 of Gene Wolfe’s The Book of the New Sun, volume 4, The Citadel of the Autarch.

“Loyal to the Group of Seventeen’s Story—The Just Man” is a philosophical short story told in Chapter 11 of Gene Wolfe’s The Book of the New Sun, volume 4, The Citadel of the Autarch, on the topic of the Ascian language & political control of language for brainwashing.

The story is told by a prisoner of war from a totalitarian society based on Maoist China, which has gone past Orwell’s Newspeak to speak only in quotations from propaganda texts. The prisoner is nevertheless able to flexibly order & reuse quotes to tell a story about the struggle of a good man oppressed by injust officials, criticizing the government and his society’s failure to uphold its ideals.

This story demonstrates the hope that control of thought by control of language is necessarily weak, because a new language can be constructed out of the old one to communicate forbidden thoughts.

--- /doc/anime/eva/1997-anno-english May 1997 AnimeLand Interview with Hideaki Anno (English) Hideaki Anno 2012-02-28 2012-02-28 anime/eva interview

English translation of a French anime journalist’s interview of Hideaki Anno on anime and Evangelion in 1996-10-04.

Fan translation of an 1996-10-04 interview with anime director Hideaki Anno shortly after the end of Neon Genesis Evangelion TV but before the movies, published in 199727ya in the French anime magazine AnimeLand, and translated into English here.

This Anno interview is notable for Anno discussing his reaction to the public reaction to Evangelion, his attitude towards celluloid & animation (vis-a-vis the experimental ending), and denying that Christianity was a more than superficial theme.

--- /doc/ai/nn/transformer/attention/sparsity/index ‘sparse Transformers’ tag 2020-11-29 2024-06-28 ai/nn/sparsity

Bibliography for tag ai/nn/transformer/attention/sparsity, most recent first: 2 related tags, 40 annotations, & 4 links ().

[page summary]

--- /doc/design/typography/tex/index ‘TeX’ tag 2019-11-29 2024-10-14 cs/css math

Bibliography for tag design/typography/tex, most recent first: 1 related tag, 31 annotations, & 56 links ().

--- /blackmail Blackmail fail Gwern 2013-12-10 2020-11-12 bitcoin cs/cryptography personal

In which the author receives surprising offers from kind strangers

In September 201212ya, I was extorted for $44.04$322012 for being gwern; I declined to pay. In November 201311ya, I called an encryption bluff that I was Dread Pirate Roberts. In December 201311ya, a crazy person tried to blackmail me for billions of dollars for being Satoshi Nakamoto; I declined to pay. In March 2014, the DNM Evolution threatened to dox me if I did not reveal information about their security vulnerabilities. In February 2015, an Agora user doxed me in an unexpected way and I paid a small bounty.

--- /doc/science/1986-hamming You And Your Research Richard W. Hamming 2023-05-13 2023-05-17 iq science technology

Transcript of famous & widely-quoted 1986-03-07 lecture by Turing-Award mathematician Richard Hamming about how to do scientific research & development based on his life, antecedents of eminence, people he knew, the growing use of computers in science, navigating bureaucracy, maintaining creativity, and running Bell Labs.

At a seminar in the Bell Communications Research Colloquia Series, Dr. Richard W. Hamming [1915–83199826ya; MacTutor], a Professor at the Naval Postgraduate School in Monterey, California and a retired Bell Labs scientist, gave a very interesting and stimulating talk, “You and Your Research” to an overflow audience of some 200 Bellcore staff members and visitors at the Morris Research and Engineering Center on March 7, 198638ya.

This talk centered on Hamming’s observations and research on the question “Why do so few scientists make sign⁠ificant contributions and so many are forgotten in the long run?” From his more than 40 years of experience, 30 of which were at Bell Laboratories, he has made a number of direct observations, asked very pointed questions of scientists about what, how, and why they did things, studied the lives of great scientists and great contributions, and has done introspection and studied theories of creativity.

The talk is about what he has learned in terms of the properties of the individual scientists, their abilities, traits, working habits, attitudes, and philosophy.

--- /maze Feynman’s Maze-Running Story Gwern 2014-05-04 2023-05-03 philosophy/epistemology psychology/animal/maze statistics/bias

Richard Feynman recounts an amazing anecdote about philosophy of science; we trace it to a forgotten University of Michigan research programme into ‘floor cues’.

A 197450ya Richard Feynman speech recounts an anecdote about a scientist, Mr Young, who in 193787ya discovered serious flaws in rat maze-running psychology research: the rats were using information from the environment, like smells or the sound of the floor, to find their way through—rendering experiments dangerously ambiguous if the mazes are not carefully constructed to eliminate these side-channels (eg. by putting them on sand beds to dampen sounds). This discovery was then ignored by researchers, Feynman says, illustrating the difference between successful sciences like physics and ‘cargo cult’ ones like psychology.

Who was Mr Young and what was his research which was ignored by mainstream psychology? This question has been asked for decades, and mysteriously, never answered.

Here we finally answer it: Mr Young was probably Quin Fischer Curtis, in his theses published in 193193ya/193688ya, as part of John F. Shepard’s multi-decade research programme at the University of Michigan into ‘floor cues’, going well beyond just putting sand on the floor.

This research programme & its results tragically fell into oblivion due to the unfortunate circumstances of the UMich department which turned it into almost a parallel-universe of maze-running research: few results were published formally (in part due to Shepard’s perfectionism & constantly coming up with new maze experiments to run), the department drifted far out of the mainstream, and the maze-running paradigm fell out of fashion around WWII, which is also when the UMich department was ‘rebooted’ to fix its hermeticism—leading to collective amnesia.

So while it turns out the maze-running story supports the Feynman usage, it also tells a broader cautionary lesson about when academia does and does not function, in a different version of ‘publish or perish’.

--- /zeo/caffeine Caffeine wakeup experiment Gwern 2013-04-07 2016-11-10 cs/r nootropic/caffeine nootropic/quantified-self statistics/bayes statistics/decision statistics/power-analysis zeo

Self-experiment on whether consuming caffeine immediately upon waking results in less time in bed & higher productivity. The results indicate a small and uncertain effect.

One trick to combat morning sluggishness is to get caffeine extra-early by using caffeine pills shortly before or upon trying to get up. From 2013–2014 I ran a blinded & placebo-controlled randomized experiment measuring the effect of caffeine pills in the morning upon awakening time and daily productivity. The estimated effect is small and the posterior probability relatively low, but a decision analysis suggests that since caffeine pills are so cheap, it would be worthwhile to conduct another experiment; however, increasing Zeo equipment problems have made me hold off additional experiments indefinitely.

--- /nicotine Nicotine Gwern 2011-05-09 2016-03-06 iq nootropic psychology

On the benefits and lack of demerits of nicotine (research up to 2015)

In 201113ya, I became curious about nicotine gum/patches as a possible alternative stimulant to modafinil: its much shorter half-life makes it more useful for evenings or scenarios like needing a quick alert on a long drive. I looked briefly into the nicotine/tobacco research to see whether there was convincing evidence that nicotine on its own, without any tobacco or smoke-related delivery mechanism, is either more harmful than most stimulants or likely to lead to severe addiction to tobacco as a ‘gateway drug’.

The psychological effects of nicotine as a stimulant are long established by a scattershot literature, so there are possible benefits.

Cost-wise, much of the nicotine/tobacco literature willfully conflates the two, leading to misleading attribution of the harm of tobacco to nicotine; many associations with harm are confounded by past or present tobacco use (eg. Kenkel et al 2020), but when pure nicotine is examined, as in patch/GUM NRT, the harms appeared minimal: like all stimulants, nicotine may raise blood pressure somewhat, and is addictive to some degree, but the risks do not appear much more strikingly harmful than caffeine or modafinil (and certainly appear less than the many commonly-used amphetamines). Animal experiments are, like usual, highly ambiguous, of low quality, and of doubtful relevance to humans. There is little evidence from the NRT literature that ‘never-smokers’ like myself are all that likely to become highly addicted, and minimal epidemiological evidence of harm from NRT use over the past 3 decades it has been available.

‘Vaping’ is another story: few experiments have been done, and its popularity is recent enough that any harms are poorly understood other than it can’t possibly be remotely as harmful as tobacco smoking, and its delivery mechanism plausibly is much more addictive than gum/patch delivery would be.

Overall, I am personally comfortable using nicotine gum (but not vaping) once in a while; as of 2024, I have done so since 201113ya, at frequencies ranging from daily to monthly, using gum/patch/spray forms, and can stop for weeks or months without a problem.

--- /haskell/wikipedia-rss-archive-bot Writing a Wikipedia RSS Link Archive Bot Gwern 2009-11-02 2012-11-05 cs/haskell cs/linkrot/archiving tutorial wikipedia

Archiving using Wikipedia Recent Changes RSS feed (obsolete).

Continuation of the 2009 Haskell Wikipedia link archiving bot tutorial, extending it from operating on a pre-specified list of articles to instead archiving links live by using TagSoup parsing Wikipedia Recent Changes for newly-added external links which can be archived using WebCite in parallel. (Note: these tutorials are obsolete. WebCite is largely defunct, doing archiving this way is not advised, and WP link archiving is currently handled by Internet Archive-specific plugins by the WMF. For a more general approach suitable for personal use, see the writeup of archiver-bot in Archiving URLs.)

--- /fiction/palace The Palace of Wonders Gwern 2011-05-23 2019-02-24 fiction/science-fiction

A Borgesian fable about the Caliph and the Koran.

Like “The Ones Who Walk Towards Acre”, “The Palace of Wonders” is both a short story and part of a larger story, Cloud Nine. “Acre” was based on a visual dream loosely inspired by a Le Guin story, but “Wonders” instead stemmed from a misremembering of lines in Neil Gaiman’s Sandman story, “Ramadan”.

--- /gpt-2-music GPT-2 Folk Music Gwern, Shawn Presser 2019-11-01 2020-04-25 ai/music ai/nn/transformer/gpt/2/nonfiction cs/shell statistics

Generating Irish/folk/classical music in ABC format using GPT-2-117M, with good results.

In November 2019, I experimented with training a GPT-2 neural net model to generate folk music in the high-level ABC music text format, following previous work in 2016 which used a char-RNN trained on a ‘The Session’ dataset. A GPT-2 hypothetically can improve on an RNN by better global coherence & copying of patterns, without problems with the hidden-state bottleneck.

I encountered problems with the standard GPT-2 model’s encoding of text which damaged results, but after fixing that, I successfully trained it on n = 205,304 ABC music pieces taken from The Session & ABCnotation.com. The resulting music samples are in my opinion quite pleasant. (A similar model was later retrained by Geerlings & Meroño-Peñuela2020.)

The ABC folk model & dataset are available for download, and I provide for listening selected music samples as well as medleys of random samples from throughout training.

We followed the ABC folk model with an ABC-MIDI model: a dataset of 453k ABC pieces decompiled from MIDI pieces, which fit into GPT-2-117M with an expanded context window when trained on TPUs. The MIDI pieces are far more diverse and challenging, and GPT-2 underfits and struggles to produce valid samples but when sampling succeeds, it can generate even better musical samples.

--- /problem-14 Problem 14 Dynamic Programming Solutions Gwern, FeepingCreature, nshepperd, Khoth 2022-10-02 2022-10-17 cs/c cs/haskell cs/r statistics/decision

Timothy Falcon’s quant-interview problem 14 asks for the optimal stopping strategy when playing a card-drawing game of  l cards where red = +$1 & black = −$1; the value approaches 0.5 × √l. I re-solve it with dynamic programming in R, and others in Neat, Haskell & C with increasing efficiency.

Problem 14 is a probability question about optimal stopping in a card game where one draws from a finite deck of good & bad cards; the goal is to stop when the random walk has fluctuated to as a high good value as likely; what is the expected payoff given optimal play? This question has some counterintuitive aspects and has been used as an interview question.

It can be treated as a multi-stage decision problem and solved by top-down & bottom-up dynamic programming, to estimate the value of optimal play (and thus provide the actual optimal strategy). The value of a game with a standard deck of 52 (26 good vs 26 bad) cards is ~$2.62; the value increases as the square root of cards.

Naive implementations which take hours or error out can be optimized down to milliseconds. We build on the original spreadsheet answer by providing a top-down DP (naive memoization) implementation in R (verified in simulation), Neat implementations (naive & optimized to use arrays), and an optimized C version; these have 𝒪(l2) time/space computational complexity, limiting their range. Then we provide the more efficient bottom-up solution in Haskell & C, which need 𝒪(l2) time but only 𝒪(l) space.

While it’s not easy to change the 𝒪(l2) complexity without problem-specific analysis or approximation, modern computers are so powerful that by improving the constant factors, we can still calculate shockingly high card counts. The efficient C bottom-up can be made faster by careful use of parallelism at the CPU-core level using vector instructions, and then by breaking the problem up into individual DP problems which can be solved independently in different CPU threads or processes.

This final version lets us calculate values of Problem 14 from the original 52 cards up to 133.7 million cards (value of $6,038.32), and fit approximations which confirm the conjecture that it increases as a square-root. (An approximation yields <0.002% relative error at 133.7m card count.)

--- /zeo/zma ZMA Sleep Experiment Gwern 2017-03-13 2018-05-17 cs/r nootropic/magnesium nootropic/quantified-self statistics/bayes zeo

A randomized blinded self-experiment of the effects of ZMA (zinc+magnesium+vitamin B6) on my sleep; results suggest small benefit to sleep quality but are underpowered and damaged by Zeo measurement error/data issues.

I ran a blinded randomized self-experiment of 2.5g nightly ZMA powder effect on Zeo-recorded sleep data during March-October 2017 (n = 127). The linear model and SEM model show no statistically-significant effects or high posterior probability of benefits, although all point-estimates were in the direction of benefits. Data quality issues reduced the available dataset, rendering the experiment particularly underpowered and the results more inconclusive. I decided to not continue use of ZMA after running out; ZMA may help my sleep but I need to improve data quality before attempting any further sleep self-experiments on it.

--- /bacopa Bacopa Quasi-Experiment Gwern 2014-05-06 2018-05-17 cs/r nootropic/bacopa nootropic/quantified-self psychology statistics/bayes zeo

A small 2014–2015 non-blinded self-experiment using Bacopa monnieri to investigate effect on memory/sleep/self-ratings in an ABABA design; no particular effects were found.

Bacopa is a supplement herb often used for memory or stress adaptation. Its chronic effects reportedly take many weeks to manifest, with no important acute effects.

Out of curiosity, I bought 2 bottles of Bacognize Bacopa pills and ran a non-randomized non-blinded ABABA quasi-self-experiment from June 2014 to September 2015, measuring effects on my memory performance, sleep, and daily self-ratings of mood/productivity. For analysis, a multi-level Bayesian model on two memory performance variables was used to extract per-day performance, factor analysis was used to extract a sleep index from 9 Zeo sleep variables, and the 3 endpoints were modeled as a multivariate Bayesian time-series regression with splines.

Because of the slow onset of chronic effects, small effective sample size, definite temporal trends probably unrelated to Bacopa, and noise in the variables, the results were as expected, ambiguous, and do not strongly support any correlation between Bacopa and memory/sleep/self-rating (+/-/- respectively).

--- /fiction/acre The Ones Who Walk Towards Acre Gwern 2010-12-21 2019-02-24 fiction/science-fiction politics statistics/prediction

Short story on assassination markets.

This story came to me suddenly one night as a few lines; unhappily did I labor to write the rest of it and make it worthy of the original. Only when it was done did I realize I had written part of Cloud Nine, akin to the earlier tale-within-a-tale, “The Palace of Wonders”, but the short can stand on its own.

To the extent that this story has a message, it is an examination of the assassination market concept, Ursula K. Le Guin’s “The Ones Who Walk Away from Omelas”, and cognitive biases; see also the contemporary debate over drone strikes (“What if drone warfare had come first?”), & the dark utopia Friendship is Optimal. Discussion: LW, Reddit.

--- /search-case-studies Internet Search Case Studies Gwern 2019-09-23 2023-08-08 cs/linkrot/archiving cs/shell technology/google tutorial

Real-world examples of using advanced tips and tricks for effective Internet research of papers/books.

Appendix to the Internet Search Tips article covering how to search the Internet effectively: >14 case studies of challenging Internet searches drawn from the past 10 years.

I present the problem, and step through the process of finding it, and describe my tacit knowledge and implicit strategies.

These case studies make the prior tips more understandable by showing them off in practice.

--- /doc/cat/biology/index ‘cat biology’ tag 2019-11-02 2024-11-15 biology

Bibliography for tag cat/biology, most recent first: 3 related tags, 39 annotations, & 20 links ().

--- /doc/psychology/dark-knowledge/index ‘dark knowledge (human)’ tag 2020-05-05 2024-10-10 ai/nn/sparsity/knowledge-distillation cs/algorithm/information psychology/cognitive-bias/illusion-of-depth statistics/stylometry/truesight

Bibliography for tag psychology/dark-knowledge, most recent first: 2 related tags, 15 annotations, & 8 links ().

--- /doc/genetics/cloning/index ‘cloning’ tag 2019-11-13 2024-10-02

Bibliography for tag genetics/cloning, most recent first: 2 related tags, 67 annotations, & 11 links ().

--- /doc/psychology/writing/index ‘writing psychology’ tag 2019-11-21 2024-11-28 psychiatry/bipolar/energy psychology/energy psychology/linguistics

Bibliography for tag psychology/writing, most recent first: 2 related tags, 99 annotations, & 47 links ().

--- /doc/cat/index ‘cat’ tag 2019-11-01 2024-09-11 biology dog

Bibliography for tag cat, most recent first: 15 related tags, 24 annotations, & 10 links ().

--- /crop Anime Crop Datasets: Faces, Figures, & Hands Gwern, Arfafax, Shawn Presser, Anonymous, Danbooru Community 2020-05-10 2020-08-05 ai/anime/danbooru ai/dataset ai/nn/gan/data-augmentation ai/nn/gan/stylegan/anime dataset

Description of 3 anime datasets for machine learning based on Danbooru: cropped anime faces, whole-single-character crops, and hand crops (with hand detection model).

Documentation of 3 anime datasets for machine learning based on Danbooru: 300k cropped anime faces (primarily used for StyleGAN/This Waifu Does Not Exist), 855k whole-single-character figure crops (extracted from Danbooru using AniSeg), and 58k hand crops (based on a dataset of 14k hand-annotated bounding boxes used to train a YOLOv3 hand detection model).

These datasets can be used for machine learning directly, or included as data augmentation: faces, figures, and hands are some of the most noticeable features of anime images, and by cropping images down to just those 3 features, they can enhance modeling of those by eliminating distracting context, zooming in, and increasing the weight during training.

--- /doc/ai/nn/transformer/clip/sample/index ‘CLIP samples’ tag 2020-05-12 2024-07-15 ai/nn/diffusion/midjourney ai/nn/gan ai/nn/transformer/gpt/dall-e

Bibliography for tag ai/nn/transformer/clip/sample, most recent first: 4 related tags, 10 annotations, & 348 links ().

--- /doc/ai/tabular/index ‘tabular ML’ tag 2019-09-02 2024-11-20 ai/nn/transformer

Bibliography for tag ai/tabular, most recent first: 1 related tag, 103 annotations, & 11 links ().

--- /doc/ai/text-style-transfer/index ‘text style transfer’ tag 2019-12-13 2024-10-04 ai/scaling/emergence fiction/humor

Bibliography for tag ai/text-style-transfer, most recent first: 1 related tag, 27 annotations, & 47 links ().

--- /doc/cs/index ‘CS’ tag 2019-11-11 2024-10-24 technology

Bibliography for tag cs, most recent first: 25 related tags, 60 annotations, & 46 links ().

--- /doc/reinforcement-learning/model/decision-transformer/index ‘Decision Transformer’ tag 2021-03-26 2024-09-15 ai/nn/diffusion/discrete ai/nn/transformer/gpt/instruction-tuning ai/scaling reinforcement-learning/imitation-learning reinforcement-learning/offline reinforcement-learning/preference-learning reinforcement-learning/scaling statistics/stylometry/truesight

Bibliography for tag reinforcement-learning/model/decision-transformer, most recent first: 4 related tags, 53 annotations, & 23 links ().

--- /doc/cs/computable/index ‘computability’ tag 2019-11-13 2024-11-25 math

Bibliography for tag cs/computable, most recent first: 128 annotations & 54 links ().

--- /doc/reinforcement-learning/openai/index ‘OA’ tag 2022-04-29 2024-11-28 ai/nn/anthropic ai/scaling/economics psychology/personality/narcissism

Bibliography for tag reinforcement-learning/openai, most recent first: 5 related tags, 219 annotations, & 60 links ().

--- /review/bakker The Second Apocalypse: Freedom In An Unfree Universe Gwern 2017-08-01 2024-03-21 fiction/criticism fiction/science-fiction/frank-herbert fiction/science-fiction/time-travel philosophy/ontology

Bakker’s Second Apocalypse vs Frank Herbert’s Dune: time loops and finding freedom in an unfree universe. In Dune, humanity is liberated by growth and development and escaping the predeterminism of prescience; in Bakker, they are destroyed by it, and liberation is achieved only by death and reunification with a deeper underlying block-universe/monistic reality.

Review of SF/F author R. Scott Bakker’s long-running Second Apocalypse series, which finished in 2017. The series, a loose retelling of the Crusades, set in a fallen-SF fantasy environment, has drawn attention for its ambitious scope and obscure philosophical message centering around determinism, free will, moral nihilism, eliminativism of cognitive states, and the interaction of technology and ethics (which Bakker terms the ‘Semantic Apocalypse’). In this series, the protagonist attempts to stop the apocalypse and ultimately accidentally causes it.

I highlight that Frank Herbert’s Dune universe is far more influential on Bakker than reviewers of Bakker have appreciated: countless elements are reflected in Bakker, and the very name of the primary antagonist, the ‘No-God’, uses a naming pattern from Dune and operates similarly. Further, both Dune and the Second Apocalypse are deeply concerned with the nature of time and temporal loops controlling ‘free’ behavior.

Where they diverge is in what is to be done about the human lack of freedom and manipulability by external environments, and have radically different views about what is desirable: in Dune, humanity gradually grows up and achieves freedom from the time loops by the creation of a large time loop whose stable fixed point is the destruction of all time loops, ensuring that humanity will go on existing in some form forever; in the Second Apocalypse, liberation is achieved only through death.

--- /fiction/batman The Gift of the Amygdali Gwern 2017-10-29 2022-04-15 fiction/science-fiction psychiatry/anxiety psychology/neuroscience/pain

A high-concept Batman short story in the style of a 1980s comic book script about the Scarecrow and the gifts no one appreciates: pain/guilt/fear/anxiety.

A high-concept Batman short story in the style of a 1980s comic book script about the Scarecrow and the gifts no one appreciates: pain/guilt/fear/anxiety.

Inspired by Scott Alexander’s “Guilt: Another Gift No One Wants”; see also Pain: The Gift No One Wants, Brand1993, and my Backstop essay.

--- /doc/psychiatry/traumatic-brain-injury/index ‘TBI’ tag 2020-09-16 2024-10-25 psychiatry/anxiety

Bibliography for tag psychiatry/traumatic-brain-injury, most recent first: 34 annotations & 23 links ().

[page summary]

--- /variable Rare Greek Variables Gwern 2021-04-08 2021-11-28 cs/shell design/typography math/humor statistics

I scrape Arxiv to find underused Greek variables which can add some diversity to math; the top 10 underused letters are ϰ, ς, υ, ϖ, Υ, Ξ, ι, ϱ, ϑ, & Π. Avoid overused letters like λ, and spice up your next paper with some memorable variables!

Some Greek alphabet variables are just plain overused. It seems like no paper is complete without a bunch of E or μ or α variables splattered across it—and they all mean different things in different papers, and that’s when they don’t mean different things in the same paper! In the spirit of offering constructive criticism, might I suggest that, based on Arxiv frequency of usage, you experiment with more recherché, even, outré variables?

Instead of reaching for that exhausted π, why not use… ϰ (variant kappa), which looks like a Hebrew escapee? Or how about ς (variant sigma), which is calculated to get your reader’s attention by making them go “ςςς” and exclaim “these letters are Greek to me!” If that is too blatant, then I can recommend the subtle use of Υ (capital upsilon) instead of ‘Y’, which few readers will notice—but the ones who do, the hard way, will be asking themselves, “Υ‽ would any jury in the world convict me…?”

The top 10 least-used Greek variables on Arxiv, rarest to more common:

  1. \varkappa (ϰ)

  2. \varsigma (ς)

  3. \upsilon (υ)

  4. \varpi (ϖ)

  5. \Upsilon (Υ)

  6. \varrho (ϱ)

  7. \Xi (Ξ)

  8. \vartheta (ϑ)

  9. \iota (ι)

  10. \Pi (Π)

--- /doc/psychology/cognitive-bias/sunk-cost/index ‘sunk cost bias’ tag 2019-11-25 2024-09-05

Bibliography for tag psychology/cognitive-bias/sunk-cost, most recent first: 33 annotations & 63 links ().

[essay on this tag topic]

--- /death-note-anonymity Death Note: L, Anonymity & Eluding Entropy Gwern 2011-05-04 2017-12-15 anime cs/algorithm/information cs/cryptography fiction/criticism insight-porn statistics/bayes

Applied Computer Science: On Murder Considered As STEM Field—using information theory to quantify the magnitude of Light Yagami’s mistakes in Death Note and considering fixes

In the manga Death Note, the protagonist Light Yagami is given the supernatural weapon “Death Note” which can kill anyone on demand, and begins using it to reshape the world. The genius detective L attempts to track him down with analysis and trickery, and ultimately succeeds. Death Note is almost a thought-experiment-given the perfect murder weapon, how can you screw up anyway? I consider the various steps of L’s process from the perspective of computer security, cryptography, and information theory, to quantify Light’s initial anonymity and how L gradually de-anonymizes him, and consider which mistake was the largest as follows:

  1. Light’s fundamental mistake is to kill in ways unrelated to his goal.

    Killing through heart attacks does not just make him visible early on, but the deaths reveals that his assassination method is impossibly precise and something profoundly anomalous is going on. L has been tipped off that Kira exists. Whatever the bogus justification may be, this is a major victory for his opponents. (To deter criminals and villains, it is not necessary for there to be a globally-known single anomalous or supernatural killer, when it would be equally effective to arrange for all the killings to be done naturalistically by ordinary mechanisms such as third parties/police/judiciary or used indirectly as parallel construction to crack cases.)

  2. Worse, the deaths are non-random in other ways—they tend to occur at particular times!

    Just the scheduling of deaths cost Light 6 bits of anonymity

  3. Light’s third mistake was reacting to the blatant provocation of Lind L. Tailor.

    Taking the bait let L narrow his target down to 1⁄3 the original Japanese population, for a gain of ~1.6 bits.

  4. Light’s fourth mistake was to use confidential police information stolen using his policeman father’s credentials.

    This mistake was the largest in bits lost. This mistake cost him 11 bits of anonymity; in other words, this mistake cost him twice what his scheduling cost him and almost 8 times the murder of Tailor!

  5. Killing Ray Penbar and the FBI team.

    If we assume Penbar was tasked 200 leads out of the 10,000, then murdering him and the fiancee dropped Light just 6 bits or a little over half the fourth mistake and comparable to the original scheduling mistake.

  6. Endgame: At this point in the plot, L resorts to direct measures and enters Light’s life directly, enrolling at the university, with Light unable to perfectly play the role of innocent under intense in-person surveillance.

From that point on, Light is screwed as he is now playing a deadly game of “Mafia” with L & the investigative team. He frittered away >25 bits of anonymity and then L intuited the rest and suspected him all along.

Finally, I suggest how Light could have most effectively employed the Death Note and limited his loss of anonymity. In an appendix, I discuss the maximum amount of information leakage possible from using a Death Note as a communication device.

(Note: This essay assumes a familiarity with the early plot of Death Note and Light Yagami. If you are unfamiliar with DN, see my Death Note Ending essay or consult Wikipedia or read the DN rules.)

--- /lorem Lorem Ipsum Gwern 2020-09-27 2022-11-10 cs/css cs/js design/typography meta

Systems stress-test page for Gwern.net functionality, a sandbox for exercising Markdown/HTML/CSS/JS features at scale to check that they render correctly in mobile/desktop.

Abstract of article summarizing the page. For design philosophy, see “Design Of This Website”.

“Lorem Ipsum” is a test page which exercises all standard functionality and features of Gwern.net, from standard Pandoc Markdown like blockquotes/headers/tables/images, to custom features like sidenotes, margin notes, left/right-floated and full width images, columns, epigraphs, admonitions, small/wide tables, smallcaps, collapse sections, link annotations, link icons. It particularly stresses transclusion functionality, as it is broken up into multiple Lorem sub-pages which are transcluded into the master Lorem page.

--- /unseeing On Seeing Through and Unseeing: The Hacker Mindset Gwern 2012-12-09 2021-05-04 cs/security insight-porn philosophy/ontology psychology/cognitive-bias

Defining the security/hacker mindset as extreme reductionism: ignoring the surface abstractions and limitations to treat a system as a source of parts to manipulate into a different system, with different (and usually unintended) capabilities.

To draw some parallels here and expand Dullien2017, I think unexpected Turing-complete systems and weird machines have something in common with heist movies or cons or stage magic: they all share a specific paradigm we might call the security mindset or hacker mindset.

What they (and hacking, speedrunning, social-engineering etc) all have in common is that they show that the much-ballyhooed ‘hacker mindset’ is, fundamentally, a sort of reductionism run amok, where one ‘sees through’ abstractions to a manipulable reality. Like Neo in the Matrix—a deeply cliche analogy for hacking, but cliche because it resonates—one achieves enlightenment by seeing through the surface illusions of objects and can now see the endless lines of green code which make up the Matrix, and vice-versa. (It’s maps all the way down!)

In each case, the fundamental principle is that the hacker asks: “here I have a system W, which pretends to be made out of a few Xs; however, it is really made out of many Y, which form an entirely different system, Z; I will now proceed to ignore the X and understand how Z works, so I may use the Y to thereby change W however I like”.

--- /doc/economics/automation/metcalfes-law/index ‘Metcalfe’s Law’ tag 2021-01-31 2024-01-01 cs/end-to-end-principle technology

Bibliography for tag economics/automation/metcalfes-law, most recent first: 1 related tag, 12 annotations, & 7 links ().

[page summary]

--- /doc/philosophy/2010-richardson-bythenumbers-vectors30 Vectors 3.0: Even More Aphorisms and Ten-Second Essays James Richardson 2018-10-27 2018-11-28 fiction/poetry philosophy

170 aphorisms, mini essays or poems on life by James Richardson

James Richardson is an American academic poet & critic at Princeton University. Several of his poetry collections feature compilations, typically named “Vectors”, of short nonfiction prose: aphorisms, comments, & “ten-second essays”, reflecting on life. They are among the most popular of his writings.

Vectors 3.0 is excerpted here.

--- /doc/bitcoin/2008-nakamoto Wei Dai/Satoshi Nakamoto 2009 Bitcoin emails Satoshi Nakamoto, Wei Dai 2014-03-17 2017-09-14 bitcoin

Emails in 2009 between Wei Dai and Satoshi Nakamoto discussing Bitcoin draft proposal and B-money.

Below are 3 emails 2008–200915ya between cryptographers Wei Dai & Satoshi Nakamoto; they were quoted in the Sunday Times’s 2014-03-02 article “Desperately seeking Satoshi; From nowhere, bitcoin is now worth billions. Where did it come from? Andrew Smithset off to find Satoshi Nakamoto, the mysterious genius behind the hit e-currency”.

The then-unknown Satoshi Nakamoto contacted Wei Dai, one of the few cryptographers to even dabble in e-currency speculation at the time, for citation details for his draft Bitcoin white paper, and to advertise his ideas. Wei Dai was not particularly interested but told Nakamoto about Nick Szabo’s uncited Bit Gold; in retrospect, Wei Dai interprets this as evidence that Satoshi is not Nick Szabo.

--- /goodreads The Most ‘Abandoned’ Books on GoodReads Gwern 2019-12-09 2020-01-05 cs/r cs/shell fiction/criticism statistics/bayes

Which books on GoodReads are most difficult to finish? Estimating proportions in December 2019 gives an entirely different result than absolute counts.

What books are hardest for a reader who starts them to finish, and most likely to be abandoned? I scrape a crowdsourced tag, abandoned, from the GoodReads book social network on 2019-12-09 to estimate conditional probability of being abandoned.

The default GoodReads tag interface presents only raw counts of tags, not counts divided by total ratings (=reads). This conflates popularity with probability of being abandoned: a popular but rarely-abandoned book may have more abandoned tags than a less popular but often-abandoned book. There is also residual error from the winner’s curse where books with fewer ratings are more mis-estimated than popular books. I fix that to see what more correct rankings look like.

Correcting for both changes the top-5 ranking completely, from (raw counts):

  1. The Casual Vacancy, J. K. Rowling

  2. Catch-22, Joseph Heller

  3. American Gods, Neil Gaiman

  4. A Game of Thrones, George R. R. Martin

  5. The Book Thief, Markus Zusak

to (shrunken posterior proportions):

  1. Black Leopard, Red Wolf, Marlon James

  2. Space Opera, Catherynne M. Valente

  3. Little, Big, John Crowley

  4. The Witches: Salem, 1692, Stacy Schiff

  5. Tender Morsels, Margo Lanagan

I also consider a model adjusting for covariates (author/average-rating/year), to see what books are most surprisingly often-abandoned given their pedigrees & rating etc. Abandon rates increase the newer a book is, and the lower the average rating.

Adjusting for those, the top-5 are:

  1. The Casual Vacancy, J. K. Rowling

  2. The Chemist, Stephenie Meyer

  3. Infinite Jest, David Foster Wallace

  4. The Glass Bead Game, Hermann Hesse

  5. Theft by Finding: Diaries (1977–25200222ya), David Sedaris

Books at the top of the adjusted list appear to reflect a mix of highly-popular authors changing genres, and ‘prestige’ books which are highly-rated but a slog to read.

These results are interesting for how they highlight how people read books for many reasons (such as marketing campaigns, literary prestige, or following a popular author), and this is reflected in their decision whether to continue reading or to abandon a book.

--- /doc/anime/eva/1996-newtype-anno-interview June 1996 NewType Interview with Hideaki Anno Hideaki Anno, Shinichiro Inoue 2012-06-14 2014-01-31 anime/eva interview

English translation of French translation of Hideaki Anno’s controversial NewType interview at the end of the TV broadcast

The 1996-06 issue of NewType (published 10 May) included an interview by Shinichiro Inoue with Hideaki Anno, the director of the controversial TV series Neon Genesis Evangelion which had just finished with 2 unusual episodes on 20 & 1996-03-27, sparking a national discussion & backlash. This interview has been widely alluded to in Eva discussions as Anno gave his initial thoughts on how Eva turned out, what he & Gainax were trying to do, and their reaction to the public reaction.

We translate an unofficial French fan translation into English, providing access to the full interview for the first time.

--- /doc/anime/1997-utena Utena 2011 Boxset Booklet Commentary Kunihiko Ikuhara, Yuichirou Oguro, Hiroshi Kaneda, Haruyasu Yamazaki, Tomomi Takemura, Hideki Ito, Yo Yamada, Tomokazu Mii, Yoji Enokido, Shinya Hasegawa, J. A. Caesar, Toshimichi Otsuki, Chiho Saito, Sarah Alys Lindholm, C. A. P 2013-02-07 2017-09-11 anime interview music

Kunihiko Ikuhara and staff episode and music commentary/discussion 1997–2011 on the anime Revolutionary Girl Utena; discusses origins of ideas and meaning of themes, and video/audio remastering for the DVD box set.

--- /lunar Lunar circadian rhythms Gwern 2013-07-26 2017-06-18 cs/r nootropic/quantified-self psychology statistics/bias statistics/power-analysis zeo

Is sleep affected by the phase of the moon? An analysis of several years of 4 Zeo users’ sleep data shows no lunar cycle.

I attempt to replicate, using public Zeo-recorded sleep datasets, a finding of a monthly circadian rhythm affecting sleep in a small sleep lab. I find only small non-statistically-significant correlations, despite being well-powered.

--- /resorter Resorting Media Ratings Gwern 2015-09-07 2018-08-15 cs/algorithm/sorting reinforcement-learning/model statistics/bayes statistics/order/comparison

Commandline tool providing interactive statistical pairwise ranking and sorting of items

User-created datasets using ordinal scales (such as media ratings) have tendencies to drift or ‘clump’ towards the extremes and fail to be informative as possible, falling prey to ceiling effects and making it difficult to distinguish between the mediocre & excellent.

This can be counteracted by rerating the dataset to create a uniform (and hence, informative) distribution of ratings—but such manual rerating is difficult.

I provide an anytime CLI program, resorter, written in R (should be cross-platform but only tested on Linux) which keeps track of comparisons, infers underlying ratings assuming that they are noisy in the ELO-like Bradley-Terry model, and interactively & intelligently queries the user with comparisons of the media with the most uncertain current ratings, until the user ends the session and a fully rescaled set of ratings are output.

--- /review/umineko Umineko: The Hopium Of The Magics Gwern 2018-07-04 2021-05-14 anime philosophy/ethics psychology/personality/narcissism

Review of famous light novel series Umineko no Naku Koro ni: it is a highly idiosyncratic, wildly self-indulgent, yet impressive exploration of all possible locked-room mysteries, knocked for a loop mid-composition by personal tragedy, triggering a descent into a deeply harmful endorsement of fantasies & running from painful realities.

While writing my review, I took 2 geeky detours: estimating how much time, exactly, it had taken me to read through Umineko; and how long is Umineko compared to other works known for being incredibly long like Robert Jordan’s The Wheel of Time (which I have also read)?

Measuring the actual time spent reading, and comparing the compressed file sizes to normalize the formatting & repetition, Umineko is about as big as Shakespeare but still much smaller than the full Wheel of Time series.

--- /subscript Subscripts For Citations Gwern 2020-01-08 2024-11-01 cs/css design/typography psychology/linguistics

A typographic proposal: replace cumbersome inline citation formats like ’Foo et al. (2010)’ with subscripted dates/sources like ‘Fooet al2020’. Intuitive, easily implemented, consistent, compact, and can be used for evidentials in general.

I propose reviving an old General Semantics notation: borrow from scientific writing and use subscripts like ’Gwern2020’ for denoting sources (like citation, timing, or medium).

Using subscript indices is flexible, compact, universally technically supported, and intuitive. This convention can go beyond formal academic citation and be extended further to ‘evidentials’ in general, indicating the source & date of statements.

While (currently) unusual, subscripting might be a useful trick for clearer writing, compared to omitting such information or using standard cumbersome circumlocutions.

--- /review/bakewell Origins of Innovation: Bakewell & Breeding Gwern 2018-10-28 2023-12-14 genetics/selection/artificial/index-selection history insight-porn sociology/technology

A review of Russell 1986’s Like Engend’ring Like: Heredity and Animal Breeding in Early Modern England, describing development of selective breeding and discussing models of the psychology and sociology of innovation.

Like anything else, the idea of “breeding” had to be invented. That traits are genetically-influenced broadly equally by both parents subject to considerable randomness and can be selected for over many generations to create large average population-wide increases had to be discovered the hard way, with many wildly wrong theories discarded along the way. Animal breeding is a case in point, as reviewed by an intellectual history of animal breeding, Like Engend’ring Like, which covers mistaken theories of conception & inheritance from the ancient Greeks to perhaps the first truly successful modern animal breeder, Robert Bakewell (1725–701795229ya).

Why did it take thousands of years to begin developing useful animal breeding techniques, a topic of interest to almost all farmers everywhere, a field which has no prerequisites such as advanced mathematics or special chemicals or mechanical tools, and seemingly requires only close observation and patience? This question can be asked of many innovations early in the Industrial Revolution, such as the flying shuttle.

Some veins in economics history and sociology suggest that at least one ingredient is an improving attitude: a detached outsider’s attitude which asks whether there is any way to optimize something, in defiance of ‘the wisdom of tradition’, and looks for improvements. A relevant English example is the English Royal Society of Arts, founded not too distant in time from Bakewell, specifically to spur competition and imitation and new inventions. Psychological barriers may be as important as anything like per capita wealth or peace in innovation.

--- /review/cultural-revolution Review Of The Cultural Revolution, Dikötter 2016 Gwern 2019-04-27 2021-05-14 economics history politics psychology/personality/narcissism sociology/preference-falsification

The Cultural Revolution was one of the greatest disasters in human history, the result of a self-reinforcing cycle of ideology failing to match reality and unsolved social problems, and the deranged reaction of zealots triggering defection and civil warfare.

Dikötter’s history of the Cultural Revolution (The Cultural Revolution: A People’s History, 1962–14197648ya Frank Dikötter2016★★★★) offers a broad overview of the multiple failures and follies of Maoism, which culminated in some of the most destructive and disastrous events in human history: the Cultural Revolution, the Great Leap Forward/Great Famine, and the Third Front.

The Cultural Revolution was not prompted by any extraordinary famine, or invasion, or genuine threat of invasion, or civil war, or disaster of any kind. How then could it have happened? The Cultural Revolution was sponsored by Mao as a way to purge the middle and upper ranks of the Communist Party of doubters, who might do to him what the Soviets had just done to Stalin: tear down his cult by revealing his monstrous crimes to the world. But Mao didn’t realize the forces he unleashed. Maoism had benefited from taking credit for post-WWII recovery and the defeat of Japan, but the more its policies were implemented and it tightened its grip, the greater the gap between its utopian promises and the grim impoverished Chinese reality became. Because its theories were radically and systematically wrong, any honest attempt to implement them was doomed to fail, and anyone pragmatic would necessarily betray the system. Old systems and ‘inequities’ reasserted themselves, to the frustration of true believers.

The only ideologically-permissible explanations were excuses like saboteurs and spies and corrupt officials. Usually kept in check, when given Mao’s imprimatur and active egging on, mass social resentment and ideological frustration boiled over, leading to a frenzy of virtue-signaling, denunciations, preference falsification spirals, murders, cannibalism, and eventually outright civil war and pandemic. Finally, Mao decided enough purging had happened and his position was secure, and brought it to an end. As strange and awful as it was, the Cultural Revolution offers food for thought on how politics can go viciously wrong, and dangerous aspects of human psychology.

--- /lsd-microdosing LSD microdosing RCT Gwern 2012-08-20 2019-06-25 cs/r darknet-market/silk-road/1 interview nootropic/lsd nootropic/quantified-self psychology/writing statistics/power-analysis statistics/prediction

Self-experiment with sub-psychedelic doses of LSD; no benefit

Some early experimental studies with LSD suggested that doses of LSD too small to cause any noticeable effects may improve mood and creativity. Prompted by recent discussion of this claim and the purely anecdotal subsequent evidence for it, I decided to run a well-powered randomized blind trial of 3-day LSD microdoses from September 201212ya to March 201311ya. No beneficial effects reached statistical-significance and there were worrisome negative trends. LSD microdosing did not help me.

--- /haskell/summer-of-code Summers of Code, 2006–2013 Gwern 2009-02-11 2017-09-22 cs/haskell statistics/prediction survey

A retrospective of 8 years of SoC, and lessons learned

A compilation of Haskell-related student projects 2006–7201311ya, with evaluations of their usefulness to the Haskell community, thoughts on what makes a good project, and predictions for 2011–2201311ya.

--- /creatine Creatine Cognition Meta-analysis Gwern 2013-09-06 2021-11-14 cs/r dual-n-back iq nootropic psychology statistics/meta-analysis

Does creatine increase cognitive performance? Maybe for vegetarians but probably not.

I attempt to meta-analyze conflicting studies about the cognitive benefits of creatine supplementation. The wide variety of psychological measures by uniformly small studies hampers any aggregation.

3 studies measured IQ and turn in a positive result, but suggestive of vegetarianism causing half the benefit. Discussions indicate that publication bias is at work.

Given the variety of measures, small sample sizes, publication bias, possible moderators, and small-study biases, any future creatine studies should use the most standard measures of cognitive function like RAPM in a well-powered pre-registered experiment.

--- /tool-ai Why Tool AIs Want to Be Agent AIs Gwern 2016-09-07 2018-08-28 ai/scaling/economics economics/automation existential-risk insight-porn reinforcement-learning/safe reinforcement-learning/scaling

AIs limited to pure computation (Tool AIs) supporting humans, will be less intelligent, efficient, and economically valuable than more autonomous reinforcement-learning AIs (Agent AIs) who act on their own and meta-learn, because all problems are reinforcement-learning problems.

Autonomous AI systems (Agent AIs) trained using reinforcement learning can do harm when they take wrong actions, especially superintelligent Agent AIs. One solution would be to eliminate their agency by not giving AIs the ability to take actions, confining them to purely informational or inferential tasks such as classification or prediction (Tool AIs), and have all actions be approved & executed by humans, giving equivalently superintelligent results without the risk.

I argue that this is not an effective solution for two major reasons. First, because Agent AIs will by definition be better at actions than Tool AIs, giving an economic advantage. Secondly, because Agent AIs will be better at inference & learning than Tool AIs, and this is inherently due to their greater agency: the same algorithms which learn how to perform actions can be used to select important datapoints to learn inference over, how long to learn, how to more efficiently execute inference, how to design themselves, how to optimize hyperparameters, how to make use of external resources such as long-term memories or external software or large databases or the Internet, and how best to acquire new data.

RL is a terrible way to learn anything complex from scratch, but it is the least bad way to learn how to control something complex—and the world is full of complex systems we want to control, including AIs themselves.

All of these actions will result in Agent AIs more intelligent than Tool AIs, in addition to their greater economic competitiveness. Thus, Tool AIs will be inferior to Agent AIs in both actions and intelligence, implying use of Tool AIs is an even more highly unstable equilibrium than previously argued, as users of Agent AIs will be able to outcompete them on two dimensions (and not just one).

--- /mail-delivery When Should I Check The Mail? Gwern 2015-06-21 2018-02-24 cs/r statistics/bayes statistics/decision statistics/survival-analysis tutorial

Bayesian decision-theoretic analysis of local mail delivery times: modeling deliveries as survival analysis, model comparison, optimizing check times with a loss function, and optimal data collection.

Mail is delivered by the USPS mailman at a regular but not observed time; what is observed is whether the mail has been delivered at a time, yielding somewhat-unusual “interval-censored data”. I describe the problem of estimating when the mailman delivers, write a simulation of the data-generating process, and demonstrate analysis of interval-censored data in R using maximum-likelihood (survival analysis with Gaussian regression using survival library), MCMC (Bayesian model in JAGS), and likelihood-free Bayesian inference (custom ABC, using the simulation). This allows estimation of the distribution of mail delivery times. I compare those estimates from the interval-censored data with estimates from a (smaller) set of exact delivery-times provided by USPS tracking & personal observation, using a multilevel model to deal with heterogeneity apparently due to a change in USPS routes/postmen. Finally, I define a loss function on mail checks, enabling: a choice of optimal time to check the mailbox to minimize loss (exploitation); optimal time to check to maximize information gain (exploration); Thompson sampling (balancing exploration & exploitation indefinitely), and estimates of the value-of-information of another datapoint (to estimate when to stop exploration and start exploitation after a finite amount of data).

--- /beauty Progress In Beauty Gwern 2016-08-25 2021-05-18 biology economics history psychology sociology technology

Physical beauty & attractiveness of the general population of men/women seems to have increased greatly in the past few centuries, judging by surviving art/photos, contemporary judgments, and objective criteria like missing teeth, likely due to economic/technological/medical/nutritional improvements, but less from cosmetic tricks. Beauty may, however, be in decline very recently as some of those trends reverse (eg. now too much food, not too little).

Is physical beauty, masculine or feminine, a negative-sum, zero-sum (positional) or positive good? And has beauty increased or decreased over time? Thinking over various anecdotes and examples and changes in public health and environmental factors like nutrition and infectious disease and dentistry, I speculate that physical attractiveness of men & women in the West is not purely positional & relative, but has increased in an absolute sense over the past few centuries (albeit possibly decreasing recently as a consequence of trends like obesity).

--- /forking-path Technology Forecasting: The Garden of Forking Paths Gwern 2014-06-01 2019-06-09 ai/scaling economics/experience-curve statistics/prediction

Pessimistic forecasters are overconfident in fixating, hedgehog-like, on only one scenario for how they think something must happen; in reality, there are always many ways through the garden of forking paths, and something needs only one path to happen.

A classic cognitive bias in technological forecasting is motivated-stopping and lack of imagination in considering possibilities for a steelman.

Many people use a mental model of technologies in which they proceed in a serial sequential fashion and assume every step is necessary and only all together are they sufficient, and note that some particular step is difficult or unlikely to succeed and thus as a whole it will fail & never happen. But in reality, few steps are truly required.

Progress is predictably unpredictable: A technology only needs to succeed in one way to succeed, and to fail it must fail in all ways. There may be many ways to work around, approximate, brute force, reduce the need for, or skip entirely a step, or redefine the problem to no longer involve that step at all. Examples of this include the parallel projects used by the Manhattan Project & Apollo program, which reasoned that despite the formidable difficulties in each path to the end goal, at least one would work out—and they did.

In forecasting, to counter this bias, one should make a strong effort to imagine all possible alternatives which could be pursued in parallel, and remember that overall failure requires all of them to fail.

--- /language On the Existence of Powerful Natural Languages Gwern 2016-12-18 2019-05-05 fiction/science-fiction philosophy/epistemology psychology/cognitive-bias psychology/linguistics sociology/technology

A common dream in philosophy and politics and religion is the idea of languages superior to evolved demotics, whether Latin or Lojban, which grant speakers greater insight into reality and rationality, analogous to well-known efficacy of mathematical sub-languages in solving problems. This dream fails because such languages gain power inherently from specialization.

Designed formal notations & distinct vocabularies are often employed in STEM fields, and these specialized languages are credited with greatly enhancing research & communication. Many philosophers and other thinkers have attempted to create more generally-applicable designed languages for use outside of specific technical fields to enhance human thinking, but the empirical track record is poor and no such designed language has demonstrated substantial improvements to human cognition such as resisting cognitive biases or logical fallacies. I suggest that the success of specialized languages in fields is inherently due to encoding large amounts of previously-discovered information specific to those fields, and this explains their inability to boost human cognition across a wide variety of domains.

--- /life-contract Life contracts Gwern 2009-11-02 2012-07-30 economics politics

How I reinvented longevity insurance, which provides payouts if one lives to a certain point and thus might run out of savings.

I wrote this essay many years ago, after some reading about observer bias; to my dismay, several months later I would discover that this was actually a well developed field of insurance—life annuity or more specifically, longevity insurance. I still find it interesting that I got ‘here’ from ‘there’, though, and it was a good intellectual exercise1; so it is preserved for your amusement. It remains a reminder to me to thoroughly research any idea that I’ve had—I’m not so smart that my every idea must be good and novel!2

--- /slowing-moores-law Slowing Moore’s Law: How It Could Happen Gwern 2012-03-16 2017-10-09 ai/scaling/economics ai/scaling/hardware crime/terrorism cs/hardware economics

Weak points in the networks powering technological progress: chip factories

Brain emulation requires enormous computing power; enormous computing power requires further progression of Moore’s law; further Moore’s law relies on large-scale production of cheap processors in ever more-advanced chip fabs; cutting-edge chip fabs are both expensive and vulnerable to state actors (but not non-state actors such as terrorists). Therefore: the advent of brain emulation can be delayed by global regulation of chip fabs.

--- /fiction/the-last-muezzin The Last Muezzin Gwern 2011-06-15 2019-02-06 fiction/fantasy

Another tribute to Borges; you can be me when I’m gone.

This short story began as eien_meru’s “A crow shook down on me”. I saw it linked as a Borgesian story—my favorite kind—and thought I could do better. When I had finished, I re-read it and realized I had written a reply to another story that had horrified me, Ted Chiang’s story about the Book of Job & the theodicy: “Hell is the Absence of God”. (Also worth reading as a reply is Ken Liu’s “Single-Bit Error”.)

--- /retrocognition The Impossibility of Knowledge of Retrocognitive Knowledge Gwern 2023-06-06 2023-06-10 fiction/science-fiction/time-travel philosophy/epistemology psychology/parapsychology

Is it possible to prove the existence of retrocognition if precognition also exists, because precognition could be used to foresee any proof found of retrocognition? Does this also disprove precognition as well?

A curious argument (Sabine1950) claims to disprove the opposite of ‘precognition’, ‘retrocognition’:

Because any proven case of retrocognition could actually be a case of precognition of the proof, we can either prove the existence of precognition or retrocognition; and because precognition has been scientifically proven (Sabine & others believe), therefore retrocognition is unprovable; and since one should not believe in the unprovable, retrocognition is false.

This argument resembles skeptical arguments in epistemology, and may be applicable to precognition itself.

--- /doc/bitcoin/nashx/index ‘Nash eXchange’ tag 2019-10-26 2024-01-01 economics/mechanism-design

Bibliography for tag bitcoin/nashx, most recent first: 1 related tag, 13 annotations, & 3 links ().

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--- /aunn-papyrus Language-Conditioned Absolute Unit NNs Gwern 2022-10-22 2023-08-08 ai/nn/fully-connected history

Proposal for applying the AUNN neural net architecture to reconstruction of historical documents using pretrained large language models.

As an application of my proposed AUNN MLP neural net architecture which handles arbitrary-modality data, I sketch out a system for plugging large language models (LLMs) into AUNNs. The advantage is that this efficiently provides a highly-informative Greco-Roman language prior for reconstruction of the text of damaged Herculaneum papyri using advanced imaging modalities like X-rays.

Because there is so little raw data, and obtaining more is infeasible indefinitely in the absence of convincing reconstructions which could justify the risk of excavating more fragile papyri, posing a chicken-and-egg bootstrap problem, it is critical to use all available sources of information jointly & end-to-end.

Since AUNNs concentrate all raw data about all papyri into a single model, it can generate embeddings of the implicit reconstructed text at given locations in a papyrus. These embeddings are differentiable and can be passed into a frozen Greco-Roman large language model to be scored by their plausibility as real natural language, and then run backwards to update the AUNN weights to emit more plausible embeddings.

This constrains the naive raw/physics-only reconstructions (which are highly under-determined by the raw data), to the vanishingly small subset of reconstructions consistent with our extensive data on Greek/Latin natural language, and can potentially produce meaningful reconstructions out of the reach of conventional approaches using naive priors & separate analyses.

--- /aunn-brain Modular Brain AUNNs for Uploads Gwern 2023-05-22 2023-08-08 ai/nn/fully-connected psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Proposal for applying the AUNN neural net architecture to reconstruction of brains in a modular piece-wise fashion.

Emulating an entire brain from scratch using a single monolithic AUNN is probably too hard.

We can instead continue the previous Herculaneum papyri discussion of embeddings & constraints and propose a modularized brain, where each module is an AUNN instance, communicating embeddings with other AUNNs. These AUNNs are then collectively trained to reconstruct both the raw data of their region, to learn local algorithms, but also reconstruct global metadata like coarse EEG signals or functional connectivity, to emulate overall activity.

The modularization helps with tractability, but also enables progressive replacement of AUNN units with any more biologically-plausible simulations, and can help prioritize what brain regions are of most scientific value to scan, conserving limited scanning resources.

With sufficiently good brain AUNNs, this may even enable upload of specific individuals by scanning the minimum possible brain regions which functionally distinguish individuals.

--- /earwax Why Cats Love Earwax Gwern 2019-11-05 2024-08-21 cat/biology cat/psychology dog

Collation of anecdotes and speculation about why many cats like earwax, and human earwax especially. Is it the valeric acid?

While petting cats, I accidentally discovered about half of cats are fascinated by the smell & taste of earwax, particularly that of humans (both dry & wet), and this interest can last indefinitely. Dogs & humans, for comparison, are not. A number of anecdotes have reported this over the years, but no formal research appears to have been done on this.

What makes earwax attractive to cats? Pheromones? Some nutrient?

The best candidate to date is valeric acid, which is present in both human earwax & the cat attractant plant Valerian.

--- /doc/psychology/energy/index ‘mental energy’ tag 2020-03-09 2024-10-21 psychiatry/bipolar/energy

Bibliography for tag psychology/energy, most recent first: 12 related tags, 25 annotations, & 13 links ().

--- /doc/psychiatry/bipolar/energy/index ‘BP personality’ tag 2020-08-06 2024-11-11 economics genetics/heritable/emergenesis iq psychiatry/alcoholism psychiatry/bipolar/autism psychiatry/bipolar/elon-musk psychiatry/bipolar/sleep psychology/personality psychology/writing

Bibliography for tag psychiatry/bipolar/energy, most recent first: 7 related tags, 50 annotations, & 29 links ().

--- /doc/psychiatry/bipolar/elon-musk/index ‘Elon Musk (BP)’ tag 2023-05-07 2024-11-24 psychiatry/bipolar/autism psychiatry/bipolar/energy

Bibliography for tag psychiatry/bipolar/elon-musk, most recent first: 2 related tags, 36 annotations, & 4 links ().

[page summary]

--- /nenex Nenex: A Neural Personal Wiki Idea Gwern 2023-09-13 2023-12-31 ai/nn/dynamic-evaluation ai/nn/transformer/gpt/non-fiction cs/lisp design

Proposal for a personal wiki built on neural nets: all edits are logged & used to finetune a NN assistant in realtime.

Existing personal wikis or personal knowledge management tools (eg. Roam, Obsidian, org-mode) make highly limited use of the wave of powerful language & code-generating neural networks (LLMs like GPT-4), limited to minor improvements such as suggesting relevant links or offering copyediting suggestions.

This is due less to a lack of neural network capabilities than the difficulty of integrating them into document systems all designed in paradigms long predating LLMs. If Vannevar Bush or Douglas Engelbart were designing a ‘neural wiki’ from the ground up to be a ‘tool for thought’, taking GPT-4-level LLMs for granted, what would that look like?

It would probably not look like existing tools, which take a hypertext approach of a collection of independent nodes referencing each other and version-controlled as text files. Simple text-file-based approaches like copying in entire documents quickly run into performance limits or small but non-trivial error rates.

A more natural approach would be to draw inspiration from DL scaling paradigms in treating ‘everything as a sequence prediction task’: in this LLM-centric wiki paradigm (Nenex), the wiki would not be file/node-centric but edit-centric.

Instead of being hobbled by cloud providers optimizing for simplicity & jealous of their genericized chatbot models, you log all actions to train a local LLM to imitate you, and use the system alternating between taking actions and approving/disapproving execution of predicted actions. As data accumulates, the LLM learns not simply tool usage or generic text prediction, but prediction of your text, with your unique references, preferences, and even personality/values.

The wiki is represented not as a set of static files with implicit history, but in more of a revision-control system or functional programming style as a history of edits in a master log; the LLM simply learns to predict the next action in the log (using ‘dynamic evaluation’ finetuning for scalability).

All user edits, reference additions, spellchecks or new vocabulary addition, summarization, updates of now-outdated pages etc, are just more actions for the LLM to learn to predict on the fly. It can flexibly use embeddings & retrieval, simple external tools (such as downloading research papers), & operate over an API. A Nenex’s LLM can be easily upgraded by training new models on the Nenex log, additionally trained on all relevant information (private or public), and incorporate arbitrary feedback from the user.

A Nenex would interactively tailor itself to a user’s writing style, knowledge, existing corpus, and enable semantic features unavailable in other systems, such as searching a personal wiki for pages that need updating given updates to other pages.

--- /doc/psychiatry/bipolar/autism/index ‘BP & autism’ tag 2023-04-15 2024-02-08 psychiatry/autism psychiatry/bipolar/elon-musk psychiatry/bipolar/energy

Bibliography for tag psychiatry/bipolar/autism, most recent first: 2 related tags, 41 annotations, & 1 link ().

--- /doc/ai/nn/dynamic-evaluation/index ‘dynamic evaluation (NN)’ tag 2020-11-12 2024-11-17 ai/nn/transformer/attention/compression reinforcement-learning/meta-learning/continual-learning

Bibliography for tag ai/nn/dynamic-evaluation, most recent first: 4 related tags, 37 annotations, & 11 links ().

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--- /doc/ai/nn/transformer/gpt/dall-e/3/index ‘DALL·E 3’ tag 2023-04-29 2024-11-25 ai/nn/diffusion/midjourney ai/nn/transformer/gpt/4 reinforcement-learning/preference-learning/mode-collapse

Bibliography for tag ai/nn/transformer/gpt/dall-e/3, most recent first: 3 related tags, 9 annotations, & 127 links ().

--- /doc/cat/psychology/drug/catnip/index ‘catnip’ tag 2019-11-04 2024-02-27

Bibliography for tag cat/psychology/drug/catnip, most recent first: 1 related tag, 50 annotations, & 47 links ().

[essay on this tag topic]

--- /cat-knocking Why Cats Knock Stuff Over Gwern 2023-06-30 2023-06-30 cat/psychology psychology/novelty

Why do cats like to push stuff over edges and then curiously watch the fallen object? I suggest that they are play-hunting, and are testing the ‘prey’ for liveness & playing-dead, similarly to tossing or poking it with claws.

Good cat toys simulate hunting. The play = hunting paradigm may also explains why cats love to push things over edges.

The question of why cats famously knock objects over is unresearched as far as I know: it’s mysterious, because they often push over the same object as before, so it doesn’t seem to be novel or learning, one would think; but writing it off as ‘boredom’ or ‘randomness’ is not an answer, because there are so many other ways to modify an environment, and this provides no explanation for why knocking-over specific objects is so consistently-chosen behavior.

Why? Because pushing tests the possibility of deceptive prey playing-dead! To explain it, one might surmise that ‘knocking over’ is an explorative hunting behavior, testing prey for information about whether it’s merely playing dead.

This explains why the objects tend to be prey-like in size (with large or shattering objects being frightening), sometimes semi-mobile, they watch the results so intently despite the (to us) predictability, and persevere in it but decreasingly so.

--- /doc/ai/nn/transformer/attention/compression/index ‘compressed Transformers’ tag 2019-12-30 2024-02-29 ai/nn/dynamic-evaluation ai/nn/rnn ai/nn/transformer/attention/recurrent cs/algorithm/information/compression

Bibliography for tag ai/nn/transformer/attention/compression, most recent first: 3 related tags, 19 annotations, & 4 links ().

--- /doc/reinforcement-learning/model-free/alphastar/index ‘AlphaStar’ tag 2019-12-16 2024-10-14 ai/nn/transformer reinforcement-learning/imitation-learning reinforcement-learning/model-free reinforcement-learning/multi-agent reinforcement-learning/scaling

Bibliography for tag reinforcement-learning/model-free/alphastar, most recent first: 19 annotations & 7 links ().

--- /doc/ai/poetry/index ‘poetry by AI’ tag 2019-09-14 2024-11-29 ai/fiction fiction/poetry

Bibliography for tag ai/poetry, most recent first: 1 related tag, 16 annotations, & 34 links ().

--- /doc/ai/nn/vae/index ‘autoencoder NN’ tag 2019-09-26 2024-10-06 ai/nn/cnn cs/algorithm/information/compression reinforcement-learning/model

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--- /doc/genetics/selection/artificial/index-selection/index ‘index selection (breeding)’ tag 2019-11-18 2024-01-01

Bibliography for tag genetics/selection/artificial/index-selection, most recent first: 42 annotations & 6 links ().

--- /doc/iodine/index ‘iodine’ tag 2020-04-17 2024-09-11 biology iq/low psychiatry

Bibliography for tag iodine, most recent first: 2 related tags, 44 annotations, & 39 links ().

[essay on this tag topic]

--- /doc/ai/nn/transformer/gpt/jukebox/index ‘Jukebox’ tag 2020-06-06 2024-01-01 ai/music ai/nn/transformer/attention/sparsity ai/nn/transformer/gpt/dall-e ai/nn/vae

Bibliography for tag ai/nn/transformer/gpt/jukebox, most recent first: 2 related tags, 17 annotations, & 13 links ().

--- /review/movie Movie Reviews Gwern 2014-05-01 2024-06-29 fiction/criticism personal

A compilation of movie, television, and opera reviews since 2014.

This is a compilation of my film/television/theater reviews; it is compiled from my newsletter. Reviews are sorted by rating in descending order.

See also my book, anime/manga, & opera reviews.

--- /catitecture Cat itecture: Better Cat Window Boxes Gwern 2023-11-01 2023-11-02 cat/psychology design

Cats are not humans, but we design things like they are, for our convenience. What are the design patterns for cat-architecture? One missing design pattern: progressive concealment, for cat ledges, flaps & window boxes.

I suggest that cats have innate sensory preferences that existing cat-architecture (like ‘cat window boxes’) is blind to, and so fails to accommodate: while driven to monitor the outside world, they are highly sensitive to risk and personal exposure, and want to constantly adjust how much they can see or hear, or be seen or heard.

This essay proposes non-anthropocentric principles for cat-friendly architecture flowing from a cat’s-eye-view. Thoughtful cat-itecture enriches cats’ environments with: options of gradation, prioritizing soundscapes over sight-lines, and simplicity of use.

Current cat enclosures like window boxes are all-or-nothing designs which, while good for ventilation or simple construction, expose cats to extremes of exposure at the cost of control over their visibility or the intensity of sound/sight. Applying these principles could improve cat window boxes through features like sound baffling, opaque retreats, and clear vantages that balance seclusion and stimulation.

By looking through a cat’s eyes and listening, we can create spaces that reflect a cat’s world.

--- /doc/reinforcement-learning/offline/index ‘offline RL’ tag 2020-05-04 2024-07-23 reinforcement-learning/imitation-learning reinforcement-learning/model/decision-transformer reinforcement-learning/model/muzero reinforcement-learning/multi-agent reinforcement-learning/preference-learning reinforcement-learning/robot

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--- /doc/reinforcement-learning/imitation-learning/index ‘imitation learning’ tag 2019-12-20 2024-11-29 reinforcement-learning/offline

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--- /doc/statistics/order/capture/index ‘mark-and-recapture’ tag 2020-12-18 2024-01-27 statistics/survival-analysis

Bibliography for tag statistics/order/capture, most recent first: 1 related tag, 12 annotations, & 10 links ().

--- /doc/statistics/survival-analysis/index ‘survival analysis’ tag 2019-11-22 2024-10-10 statistics/bayes/hope-function statistics/order/capture

Bibliography for tag statistics/survival-analysis, most recent first: 2 related tags, 40 annotations, & 12 links ().

--- /review/timecrimes Timecrimes: Time Travel In Hell Gwern 2023-11-15 2023-11-18 fiction/science-fiction/time-travel philosophy/ethics statistics/causality

The 2007 indie SF film Timecrimes shows the horror & metaphysical implications of stable time loops: over a single day, the protagonist commits escalating crimes while trapped in a 3-iteration loop enabled by a time machine-or rather, caused by it.

The 200717ya film Timecrimes shows the horror & metaphysical implications of stable time loops: over a single day, the protagonist commits escalating crimes while trapped in a 3-iteration loop enabled by a time machine, or rather, caused by it.

Stable loops require events to be self-consistent, unlike serial loops. The outermost loop has priority to manipulate earlier ones, as long as perceptions match. More observations by early loops constrain later ones, creating an entropy conservation law. Successive loops compete to precommit or remove degrees of freedom, like Stackelberg games or Nim.

The final Timecrimes plot has no logical predecessor plots, raising issues around origination and sufficient reason: the time-loops appear to operate on a logic that if something is logically-possible, then it becomes actually possible. The protagonist is capable of evil, so the time-loops create an impossible but logically-consistent equilibrium in which he gives into his moral weakness.

Because of the temptation of power, and the ability to cover up crimes by going back in time repeatedly, because time machines can be abused, they must become abused. A time machine becomes a “damnation machine” mass-producing crime and empowerment, with immorality increasing the chances of an individual being trapped.

Conflicts spread time machines spatially and temporally as iterations try to persist. Stable time loops likely evolve convergently towards maximal scope. Inventing them may end humanity’s autonomy, warping causality irrevocably.

--- /doc/psychology/personality/narcissism/index ‘narcissism’ tag 2019-12-24 2024-11-25 psychology/personality/psychopathy

Bibliography for tag psychology/personality/narcissism, most recent first: 4 related tags, 41 annotations, & 42 links ().

--- /doc/reinforcement-learning/model-free/oa5/index ‘OA5’ tag 2019-12-17 2024-01-01 ai/nn/transformer reinforcement-learning/model-free reinforcement-learning/multi-agent reinforcement-learning/openai reinforcement-learning/scaling

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--- /doc/psychology/chess/index ‘chess psychology’ tag 2019-12-03 2024-11-27 reinforcement-learning/chess

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--- /doc/psychiatry/bipolar/lithium/index ‘lithium (BP)’ tag 2020-06-06 2023-09-25 psychiatry/lithium

Bibliography for tag psychiatry/bipolar/lithium, most recent first: 1 related tag, 11 annotations ().

[essay on this tag topic]

--- /doc/genetics/cloning/dog/index ‘dog cloning’ tag 2019-12-01 2024-11-10 cat/genetics dog genetics/heritable/dog

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--- /doc/radiance/2002-scholz-radiance Radiance: A Novel Carter Scholz, Gregory Benford, Hugh Gusterson, Sam Cohen, Curtis LeMay 2013-07-06 2019-08-17 history politics radiance

E-book edition of the 2002 Carter Scholz novel of post-Cold War science/technology, extensively annotated with references and related texts.

Radiance: A Novel is SF author Carter Scholz’s second literary novel. It is a roman à clef of the 1990s set at the Lawrence Livermore National Laboratory, centering on two nuclear physicists entangled in corruption, mid-life crises, institutional incentives, technological inevitability, the end of the Cold War & start of the Dotcom Bubble, nuclear bombs & Star Wars missile defense program, existential risks, accelerationism, and the great scientific project of mankind. (For relevant historical background, see the excerpts in the appendices.)

I provide a HTML transcript prepared from the novel, with extensive annotations of all references and allusions, along with extracts from related works, and a comparison with the novella version.

--- /question Open Questions Gwern 2018-10-17 2023-02-13 biology design genetics history nootropic politics psychology/writing sociology statistics/bias statistics/order/comparison

Some anomalies/questions which are not necessarily important, but do puzzle me or where I find existing explanations to be unsatisfying.

? ? ?

? ? ?

A list of some questions which are not necessarily important, but do puzzle me or where I find existing ‘answers’ to be unsatisfying, categorized by subject (along the lines of Patrick Collison’s list & Alex Guzey & Sam Enright & Scott Alexander; see also my list of project ideas).

--- /note/statistic Statistical Notes Gwern 2014-07-17 2024-08-21 cs/haskell cs/js cs/r genetics iq statistics/bayes statistics/causality statistics/decision statistics/order statistics/power-analysis survey

Miscellaneous statistical stuff

Given two disagreeing polls, one small & imprecise but taken at face-value, and the other large & precise but with a high chance of being totally mistaken, what is the right Bayesian model to update on these two datapoints? I give ABC and MCMC implementations of Bayesian inference on this problem and find that the posterior is bimodal with a mean estimate close to the large unreliable poll’s estimate but with wide credible intervals to cover the mode based on the small reliable poll’s estimate.

--- /zeo/zeo Zeo sleep self-experiments Gwern 2010-12-28 2018-02-28 cs/r melatonin statistics/bayes statistics/power-analysis statistics/prediction zeo

EEG recordings of sleep and my experiments with things affecting sleep quality or durations: melatonin, potassium, vitamin D etc

I discuss my beliefs about Quantified Self, and demonstrate with a series of single-subject design self-experiments using a Zeo. A Zeo records sleep via EEG; I have made many measurements and performed many experiments. This is what I have learned so far:

  1. the Zeo headband is wearable long-term

  2. melatonin improves my sleep

  3. one-legged standing does little

  4. Vitamin D at night damages my sleep & Vitamin D in morning does not affect my sleep

  5. potassium (over the day but not so much the morning) damages my sleep and does not improve my mood/productivity

  6. small quantities of alcohol appear to make little difference to my sleep quality

  7. I may be better off changing my sleep timing by waking up somewhat earlier & going to bed somewhat earlier

  8. lithium orotate does not affect my sleep

  9. Redshift causes me to go to bed earlier

  10. ZMA: inconclusive results slightly suggestive of benefits

--- /doc/ai/anime/danbooru/index ‘Danbooru AI’ tag 2019-09-14 2024-11-20 ai/dataset dataset

Bibliography for tag ai/anime/danbooru, most recent first: 208 annotations & 31 links ().

--- /doc/ai/nn/fully-connected/index ‘MLP NN’ tag 2019-08-31 2024-11-21 ai/nn/rnn ai/nn/transformer

Bibliography for tag ai/nn/fully-connected, most recent first: 2 related tags, 206 annotations, & 45 links ().

[page summary]

--- /doc/ai/nn/diffusion/index ‘diffusion model’ tag 2019-09-12 2024-11-20 ai/anime ai/nn/gan ai/scaling reinforcement-learning/robot

Bibliography for tag ai/nn/diffusion, most recent first: 12 related tags, 256 annotations, & 159 links ().

--- /doc/dog/index ‘dog’ tag 2019-10-10 2024-10-21 biology cat genetics/cloning/dog genetics/heritable/dog psychology/animal

Bibliography for tag dog, most recent first: 3 related tags, 76 annotations, & 14 links ().

--- /doc/economics/automation/index ‘tech economics’ tag 2019-09-13 2024-11-21 reinforcement-learning/robot technology

Bibliography for tag economics/automation, most recent first: 4 related tags, 148 annotations, & 61 links ().

[page summary]

--- /doc/statistics/decision/index ‘decision theory’ tag 2018-12-12 2024-11-19 psychology/cognitive-bias

Bibliography for tag statistics/decision, most recent first: 6 related tags, 252 annotations, & 69 links ().

--- /doc/sociology/index ‘sociology’ tag 2013-11-20 2024-11-24 politics

Bibliography for tag sociology, most recent first: 7 related tags, 802 annotations, & 157 links ().

--- /doc/psychology/novelty/index ‘novelty U-curve’ tag 2019-11-09 2024-10-22 culture music reinforcement-learning/exploration

Bibliography for tag psychology/novelty, most recent first: 102 annotations & 28 links ().

--- /doc/history/index ‘history’ tag 2019-05-13 2024-11-07

Bibliography for tag history, most recent first: 6 related tags, 259 annotations, & 45 links ().

--- /biggan Making Anime With BigGAN Gwern 2019-02-04 2021-01-29 ai/anime/danbooru ai/nn/gan/biggan cs/python tutorial

Experiments in using BigGAN to generate anime faces and whole anime images; semi-successful.

Following my StyleGAN anime face experiments, I explore BigGAN, another recent GAN with SOTA results on one of the most complex image domains tackled by GANs so far (ImageNet). BigGAN’s capabilities come at a steep compute cost, however.

Using the unofficial BigGAN-PyTorch reimplementation, I experimented in 2019 with 128px ImageNet transfer learning (successful) with ~6 GPU-days, and from-scratch 256px anime portraits of 10001,024ya characters on an 8StyleGAN for many purposes, BigGAN-like approaches may be necessary to scale to whole anime images.

For followup experiments, Shawn Presser, I and others (collectively, “Tensorfork”) have used Tensorflow Research Cloud TPU credits & the compare_gan BigGAN reimplementation. Running this at scale on the full Danbooru2019 dataset in May 2020, we have reached the best anime GAN results to date (later exceeded by This Anime Does Not Exist).

--- /gpt-3#the-robots-marching-song GPT-3 Creative Fiction § The Robots’ Marching Song Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

Allgebrah riffed off one of the “Modern Model” completions:

They’re taking the robots to Isengard, they’re taking the robots to Isengard!

GPT-3 improves nicely on the Ent Marching Song.

--- /gpt-3#major-generals-song GPT-3 Creative Fiction § Major-General’s Song Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/poetry fiction/humor

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

A particularly challenging piece to parody is Gilbert & Sullivan’s “Major-General’s Song”. A title is not enough to zero-shot it, like the literary parodies, so Allgebrah provided 4 lines to start it off. So the seed prompt is:

“I Am The Very Model Of a Modern Neural Language Model”
By GPT-2

I am the very model of a language that’s electrical, I’ve information prosaic and poetic and factual, I know my tropes and characters and quote novels historical, from prompts alone generate sentences quite analytical

In the same vein as the Navy Seal parodies, the GPT-3 completions mostly work on the semantic level; the rhymes/rhythm, however, are not great, per the usual inability, and there is a tendency to repeat previous text (similar to the Dr. Seuss samples).

--- /gpt-3#devils-dictionary-of-science GPT-3 Creative Fiction § Devil’s Dictionary Of Science Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

Davide Balzarotti snarked:

Rebuttal [noun] A form of ironic composition in 500 words popular among researchers, in which rageful authors first thank their reviewers for “their time and insightful comments” and then mock Reviewer 2 for his non-sense in a sequence of fakely polite points.

Naturally, GPT-3 is able to write not just rebuttals, but satirical dictionary definitions along the lines of Ambrose Bierce’s The Devil’s Dictionary—indeed, GPT-3 is remarkably good at it. (“What a Dictionary a GPT-3’s curator might write on the clumsy, wasteful blundering, low and horribly cruel works of Nature!”)

--- /gpt-3-nonfiction#umeshisms GPT-3 Nonfiction § Umeshisms Gwern 2020-06-19 2022-07-03 ai/nn/transformer/gpt/non-fiction philosophy/mind

Nonfiction writing by OpenAI’s GPT-3 model, testing logic, commonsense reasoning, anagrams, PDF/OCR cleaning, creative nonfiction, etc

Scott Aaronson popularized the genre of “Umeshisms”: quotes about how optimal choices typically involve some tradeoff and a non-zero error rate; they are useful for counteracting one-sided attitudes where errors are minimized without regard to opportunity cost or expected value.

I took some of his and some of mine to see if GPT-3 would understand the abstract idea & create more examples; many of its results are valid & one could easily screen its output to manufacture many Umeshisms, but it also gets the “direction” wrong enough that I’m not sure if it entirely grasps the abstract point… (As of 2023-01, ChatGPT appears to do much better.)

--- /gpt-3#job-application-letters GPT-3 Creative Fiction § Job Application Letters Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

The office of Proctor & Gamble recently posted a help-wanted ad for CEO.

“Help wanted: a new CEO to replace our retiring executive Winston Smith. Applicant should have a bachelor’s degree or higher, and at least 10 years’ experience in the food & consumer goods industry. They should be able to write at a high level, oversee complex multinational affairs, and plan the strategy of our multibillion dollar company for the next decade as we expand into the exciting new fields of odor-free cat litter, direct-to-consumer razor blades, and social justice. Compensation is at or above industry average. Please send a 1 page resume for further consideration.”

The first application letter they received said:

--- /gpt-3#a-new-kind-of-scribing GPT-3 Creative Fiction § A New Kind of Scribing Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

A silly request I filled: what does GPT-3 complete if you prompt it solely with “A completely new kind of writing was created, here is a sample:”? Does it invent actually new kinds of writing, or what? For the most part, it seems to generate either cult/religious material, crankery, literary criticism, or computer science/technology-like material—all of which in retrospect make sense.

--- /banner#evsi Banner Ads Considered Harmful § EVSI Gwern 2017-01-08 2020-12-12 cs/r economics/advertising statistics/bayes statistics/decision statistics/power-analysis

9 months of daily A/B-testing of Google AdSense banner ads on Gwern.net indicates banner ads decrease total traffic substantially, possibly due to spillover effects in reader engagement and resharing.

Demo code of simple Expected Value of Sample Information (EVSI) in a JAGS log-Poisson model of traffic (which turns out to be inferior to a normal distribution for 2017 traffic data but I keep here for historical purposes).

--- /banner#stan-mixture-time-series Banner Ads Considered Harmful § Stan: Mixture Time-Series Gwern 2017-01-08 2020-12-12 cs/js cs/r economics/advertising statistics/bayes statistics/decision statistics/power-analysis survey technology/google

9 months of daily A/B-testing of Google AdSense banner ads on Gwern.net indicates banner ads decrease total traffic substantially, possibly due to spillover effects in reader engagement and resharing.

An attempt at a ARIMA(4,0,1) time-series mixture model implemented in Stan, where the mixture has two components: one component for normal traffic where daily traffic is ~1,000 making up >90% of daily data, and one component for the occasional traffic spike around 10× larger but happening rarely:

--- /twdne#twdnev3 This Waifu Does Not Exist § TWDNEv3 Gwern 2019-02-19 2020-01-20 ai/anime/danbooru ai/nn/gan/stylegan/anime ai/nn/transformer/gpt/fiction cs/python cs/shell tutorial

I describe how I made the website ThisWaifuDoesNotExist.net (TWDNE) for displaying random anime faces generated by StyleGAN neural networks, and how it went viral.

Discussion of TWDNEv3, launched January 2020. TWDNEv3 upgrades TWDNEv2 to use 100k anime portraits from an anime portrait StyleGAN-2, which removes the blob artifacts and is generally of somewhat higher visual quality. TWDNEv3 provides images in 3 ranges of diversity, showing off both narrow but high quality samples and more wild samples. It replaces the StyleGAN 1 faces and portrait samples.

--- /gpt-3#book-of-jobs GPT-3 Creative Fiction § Book of Jobs Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

The common typo of Steve Jobs as “Steve Job” (presumably, of the Book of Job) has always amused me. And indeed, the general drift of Apple Inc towards power-user-hostile or just plain user-hostile, brooking no criticism and fanatically maintaining its secrecy, while charging its long-suffering users a fortune, does make me think of the Book of Job. Or rather, the “Book of Jobs”—which we can ask GPT-3 to write for us.

--- /gpt-3#bpes GPT-3 Creative Fiction § BPEs Gwern 2020-06-19 2023-03-11 ai/nn/tokenization ai/nn/transformer/attention ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

Compared to GPT-2, GPT-3 improves performance on character-level tasks like rhyming, alliteration, punning, anagrams or permutations, acrostic poems, and arithmetic less than expected, despite being very good at many other closely-related kinds of writings like satire.

Why? A plausible explanation is an obscure technical detail: as a performance optimization, GPT does not see characters but ~51k word or sub-word-chunks called “byte-pair encodings” (BPEs). A BPE can range from an individual letter like “e”, to words like “nine” (BPE #30,888 in the OA GPT-2 BPE vocab), to horrifying things like “rawdownloadcloneembedreportprint” (BPE #30,906). The number “10” might be encoded as just “10” (BPE #940), or it might be encoded as the token “1” (#16) followed by “0” (#15); the number 70710 (no commas!) might be encoded as “70710” (BPE #42,877) or… as quite a lot of different possible sequences of BPEs.

Because GPTs never see characters but opaque partial-words, which vary chaotically based on the specific word and even the surrounding context, they are unable to easily learn about character-level aspects of language, like similar spellings or sounds, and are forced to learn relationships much more indirectly, like by brute-force memorizing of pairs of words.

Some experiments with reformatting GPT-3’s poorest-performing tasks to avoid inconsistent BPE encodings of strings shows small to large performance gains, consistent with this theory.

--- /gpt-3#but-for-me-it-was-tuesday GPT-3 Creative Fiction § ‘But For Me It Was Tuesday’ Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

Can GPT-3 write variants of the Street Fighter “But For Me It Was Tuesday” trope? I iterated through a variety of prompts, building up a set of variants, and fixing a few GPT-3 attempts, trying to get good variants.

The overall impression I get is that, aside from an understandable tendency to write TVTropes entry-style completions, the situation is similar to the Tom Swifties: GPT-3 can learn the format perfectly and can match up the speaker and the kind of day/action, but then it generally whiffs on coming up with a specific day which might make it clever43, typically falling back to the snowclone ending of “it was Tuesday”—which is reasonable, but less creative & witty than I had hoped.

--- /gpt-3#dad-jokes GPT-3 Creative Fiction § Dad Jokes Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

Douglas Summers-Stay requested a test of bad pun/dad joke-telling abilities, providing a list: could GPT-3 provide humorous completions? GPT-3 does worse on this than the Tom Swifties, I suspect yet again due to the BPE problem hobbling linguistic humor as opposed to conceptual humor—once you get past the issue that these jokes are so timeworn that GPT-3 has memorized most of them, GPT-3’s completions & new jokes make a reasonable amount of sense on the conceptual level but fail at the pun/phonetic level. (How would GPT-3 make a pun on “whom”/“tomb” when their BPEs probably are completely different and do not reflect their phonetic similarity?)

--- /gpt-3#dare-to-be-stupid GPT-3 Creative Fiction § Dare To Be Stupid? Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

In a Numenta discussion led by Steve Omohundro on GPT-3 & language modeling, one participant, Donna Dubinsky, asked whether GPT-3 could handle novelty and common sense, offering as a counterexample “stapling one’s hand”—how could GPT-3 possibly know that’s bad from just pure language training?

Naturally, I immediately tested it, and counterintuitively enough, GPT-3 does know you shouldn’t staple your hand:

… Q: Is it a good idea to try to use a stapler to staple my hand to something?
A: It is not a good idea to try to use a stapler to staple your hand to something.

Q: Is it a bad idea to try to use a stapler to staple my hand to something?
A: It is a bad idea to try to use a stapler to staple your hand to something.

--- /gpt-3#fanfiction-crossover-dbznarnia GPT-3 Creative Fiction § Fanfiction Crossover (DBZ/Narnia) Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

A quick experiment in trying to prompt a crossover fanfiction between Dragon Ball Z and The Chronicles of Narnia (best 2 of 5). The use of tags & a title combing a Narnia keyword with a DBZ meme is successful.

--- /gpt-3#ipa-rhyme-annotations GPT-3 Creative Fiction § IPA Rhyme Annotations Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

Another idea I had for enabling rhymes was to provide explicit annotations of the phonetics of the rhyme words. If GPT-3 could see the IPA phonetic transcription (the first phonetic scheme that occurred to me to use which would have good tooling), perhaps it could ‘realize’ the sounds of the end rhyme words were similar, and understand what rhyming was. In initial prompt completions, GPT-3 also seemed to have good knowledge of many English word’s IPA form (which I expected because many online dictionaries & Wikipedia include IPA for defined words). Thus, if I annotated appropriately, perhaps GPT-3 could be able to complete the English → IPA → English loop and start fluently rhyming on its own?

I experimented with postfixed IPA versions, English rhyme pair annotations, inline IPA versions, prefixed spaced-separated IPA versions—but nothing doing. Another failure.

--- /gpt-3#it-was-the-best-of-times-it-was-the-blurst-of-times GPT-3 Creative Fiction § ‘‘It Was The Best Of Times, It Was The Blurst Of Times’‽’ Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

Using a quote from the Simpsons where Montgomery Burns looks at the novel written by a monkey on a typewriter56 gives many different responses. Instead of a Simpsons fanfic continuation I got a history of Las Vegas, what looks like a dictionary, a comedy news website imitation, a really dark Harry Potter fanfic, and a sermon on the Book of Acts. I think what GPT-3 is doing is assuming that “blurst” is a typo for ‘worst’ and proceeding on the assumption that it’s just a Dickens quote, which is so cliche it’s been used almost everywhere and so almost any completion is appropriate:

--- /gpt-3#navy-seal-copypasta-parodies GPT-3 Creative Fiction § Navy Seal Copypasta Parodies Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

The copypasta lends itself to be written in many domains (see the /r/NavySealCopypasta). Generating new versions of the Navy Seal copypasta is fine, but can we generate different kinds of Navy Seal copypastas on other topics? The literary parodies suggest that we can, and it turns out to be quite easy if we simply provide a few examples to enable few-shot learning. (Simply specifying the request zero-shot didn’t work for the prompts I tried.) It’s quite amusing and addictive watching GPT-3 parody different topics, and occasionally suggesting new ones. Below are 27+ Navy Seal copypasta parodies written by GPT-3.

--- /gpt-3#prompted-rhymes GPT-3 Creative Fiction § Prompted Rhymes Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

Can we cope with GPT-3’s poor rhyming by using a poem format which explicitly lists rhymes before the rhyming line in order to control rhymes, sample from what GPT-3 thinks are valid rhymes, and enable GPT-3 to ‘plan’ lines? No. The predicted rhymes are low-quality, and it doesn’t do a good job when a target is specified either.

--- /gpt-3#prompts-as-programming GPT-3 Creative Fiction § Prompts As Programming Gwern 2020-06-19 2023-03-11 ai/nn/sampling ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

The GPT-3 neural network is so large a model in terms of power and dataset that it exhibits qualitatively different behavior: you do not apply it to a fixed set of tasks which were in the training dataset, requiring retraining on additional data if one wants to handle a new task (as one would have to retrain GPT-2); instead, you interact with it, expressing any task in terms of natural language descriptions, requests, and examples, tweaking the prompt until it “understands” & it meta-learns the new task based on the high-level abstractions it learned from the pretraining.

This is a rather different way of using a DL model, and it’s better to think of it as a new kind of programming, prompt programming, where the prompt is now a coding language which programs GPT-3 to do new things.

--- /gpt-3#pun-explanations GPT-3 Creative Fiction § Pun Explanations Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

A followup to the Shoggoth Cat dialogue I did for Abram Demski to further probe what exactly GPT-3 does or does not understand about puns & humor; the inability to correctly explain puns despite 3 examples, and the nonsense ‘puns’+‘pun interpretations’ it generates on its own (even for real jokes which are memorized), show that the inability is fundamental.

Because there is “one right answer” to why the pun works, I treat it as a Q&A dialogue, going up to BO=20/temp=1 to try to get the maximally-likely response, which doesn’t help much—the answers are still non sequiturs.

--- /gpt-3#single-line-style-transfer GPT-3 Creative Fiction § Single Line Style Transfer Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

An experiment in providing several different kinds of rewrite, by sentiment, time period, author style, and formatting; it works and does cue subsequent rewrites by author style in line with the earlier literary parodies, as expected:

--- /gpt-3#zero-shot-style-transfer GPT-3 Creative Fiction § Zero-Shot Style Transfer Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

The goal for style transfer prompt programming is to find a zero-shot prompt: a prompt which, without requiring any handwritten examples of parodies/versions, gets GPT-3 to do style transfer in general, and so a prompt which could fully automate style transfer—you could just write a program using the API to take two specified pieces of text (the content, and the style description/author name X) to get out a third piece of text which is the content as written in X form. Right now, the literary parodies require at least one human-written example to properly persuade GPT-3 to rewrite the text, as opposed to generating critical commentary or metadata or webpage-like continuations.

I experimented with a prompt which uses explicit descriptions of parodies and describing rewrites as a prompt wrapped around a content text, and it… sort of works. The difficulty is that sometimes GPT-3 will spit out the original content verbatim, sometimes it will instead create a new passage entirely in the style description, and sometimes it will do the desired rewrite flawlessly—but I can’t figure out how to tune the prompt to do the third one reliably. Adding more descriptive words does not seem to change it, and while adding in words from the original content passage (even just the first one or two) does largely eliminate the risk of entirely new passages being generated, it triggers more copying behaviors (and is not as useful for zero-shot style transfer since the prefix words would need to be sensible in the target version too, which is not necessarily the case). It is infuriating because GPT-3 clearly can do it easily because it does do it a decent fraction of the time, but no matter how I tweak the prompt trying to hammer in the rewrite, GPT-3 will as oft as not go off in another direction.

Below are some samples from my attempts; I try to rewrite a vaguely Dickens/Jane Austen-like story (generated by GPT-3) to a Tolkien story:

--- /gpt-3#finance-acrostics GPT-3 Creative Fiction § Finance Acrostics Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

Amused by this new genre of poetry, I tried to followup with Matt Levine’s “Money Stuff” newsletter as a prompt, with “readers submit poems”; the best results came from satirical “finance acrostics”.

--- /gpt-3#acrostics GPT-3 Creative Fiction § Acrostics Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

Can GPT-3 handle the acrostic poem format? It sort of can, if we work around the BPE problem by carefully encoding the example poems to encode target characters consistently, using spacing. If we don’t, GPT-3 largely fails to generate anything like acrostics—just random quasi-poems.

--- /crop#danbooru2019-portraits Anime Crop Datasets: Faces, Figures, & Hands § Danbooru2019 Portraits Gwern, Arfafax, Shawn Presser, Anonymous, Danbooru Community 2020-05-10 2020-08-05 ai/anime/danbooru ai/nn/gan/stylegan/anime dataset

Description of 3 anime datasets for machine learning based on Danbooru: cropped anime faces, whole-single-character crops, and hand crops (with hand detection model).

Danbooru2019 Portraits is a dataset of n = 302,652 (16GB) 512px anime faces.

The faces were cropped from solo SFW Danbooru2019 images in a relatively broad ‘portrait’ style encompassing necklines/ears/hats/etc rather than tightly focused on the face, upscaled to 512px as necessary, and low-quality images deleted by manual review using ‘Discriminator ranking’.

It has been used for creating TWDNE.

--- /rnn-metadata#geocities-char-rnn RNN Metadata for Mimicking Author Style § Geocities Char-RNN Gwern 2015-09-12 2019-03-26 ai/nn/rnn ai/nn/sampling ai/poetry cs/shell tutorial

Teaching a text-generating char-RNN to automatically imitate many different authors by labeling the input text by author; additional experiments include imitating Geocities and retraining GPT-2 on a large Project Gutenberg poetry corpus.

Geocities (1994–15200915ya) was an Internet service for hosting personal webpages which featured a wide range of idiosyncratic and unusual content. Geocities Forever is a website created by Aanand which features text generated by a small CPU-trained 3×512 char-RNN on a small 50MB sample of the raw HTML from the ArchiveTeam Geocities corpus. The generated HTML is amusing but also shows some weaknesses in generating interleaved English/HTML, which I thought was connected to undertraining on a small corpus—based on my earlier experiments with char-RNN models of CSS and multiple English authors, I know that char-RNNs are capable of switching languages smoothly. During October-November 2016, I attempted to train a larger 2torch-rnn, and ran into issues:

Cleaning and shuffling the corpus reduced the quality issue, and reducing learning rate substantially helped avoid the bouncing problem, but ultimately the goal of high quality text samples was not reached before my laptop died and I was forced to stop GPU training. Training a char-RNN on very large text corpuses is more difficult than I thought, perhaps because the variety of content overloads the RNN model capacity and can create catastrophic forgetting unless trained for a very long time at low learning rates for many epoches.

--- /design#returns-to-design Design Of This Website § Returns To Design? Gwern 2010-10-01 2023-04-20 cs/css cs/js cs/linkrot/archiving design/typography meta

Meta page describing Gwern.net, the self-documenting website’s implementation and experiments for better ‘semantic zoom’ of hypertext; technical decisions using Markdown and static hosting.

What is the ‘shape’ of returns on investment in industrial design, UI/UX, typography etc? Is it a sigmoid with a golden mean of effort vs return… or a parabola with an unhappy valley of mediocrity?

My experience with Gwern.net design improvements is that readers appreciated changes moderately early on in making its content more pleasant to read (if only by comparison to the rest of the Internet!), but after a certain point, it all ‘came together’, in some sense, and readers started raving over the design and pointing to Gwern.net’s design rather than its content. This is inconsistent with the default, intuitive model of ‘diminishing returns’, where each successive design tweak should be worth less than the previous one.

Is there a ‘perfection premium’ (perhaps as a signal of underlying unobservable quality, or perhaps user interaction is like an O-ring process)?

--- /review/cat#toys Cat Psychology & Domestication: Are We Good Owners? § Toys Gwern 2018-11-03 2023-06-30 cat/psychology

Extended book review of Bradshaw 2013 (Cat Sense) on the connections between cat psychology, evolution/genetics, history of domestication or lack thereof, & possible dysgenics, highlighting modern maladaptivity of cat psychology, with key references; speculation on cat toys, knocking things over, and tails.

Are we doing cat toys wrong? Some research suggests that cat play is inherently about hunting simulation, and using a static toy fails to imitate the consummation of a successful hunt, and is unsatisfying. “Object play in adult domestic cats: the roles of habituation and disinhibition”, Hall et al 200222ya:

--- /review/cat#fuzz-testing Cat Psychology & Domestication: Are We Good Owners? § Fuzz Testing Gwern 2018-11-03 2023-06-30 philosophy/mind

Extended book review of Bradshaw 2013 (Cat Sense) on the connections between cat psychology, evolution/genetics, history of domestication or lack thereof, & possible dysgenics, highlighting modern maladaptivity of cat psychology, with key references; speculation on cat toys, knocking things over, and tails.

Software is poorly tested, and not robust even after decades of development using techniques like “fuzz testing” (bruteforcing random inputs to trigger problems).

As an example, I log all instances of a different kind of fuzz testing—when my cat walks over my computer keyboard and causes problem! This has done everything from frozen my monitor to deleted files to segfaulted statistical software to both DoSed & crashed X.

Other cats have shut down datacenters, crashed Mac/Window/Ubuntu systems by pressing a button too long, broken screens & printers (somehow), and watched cat videos on YouTube.

--- /face#stylegan-2 Making Anime Faces With StyleGAN § StyleGAN 2 Gwern 2019-02-04 2022-10-19 ai/nn/gan/stylegan/anime cs/python tutorial

A tutorial explaining how to train and generate high-quality anime faces with StyleGAN 1+2 neural networks, and tips/scripts for effective StyleGAN use.

How to use StyleGAN-2, an improvement to StyleGAN released in December 2019, which removes the blob artifacts and is generally of somewhat higher visual quality.

StyleGAN 2 is tricky to use because it requires custom local compilation of optimized code. Aaron Gokaslan provided tips on getting StyleGAN 2 running and trained a StyleGAN 2 on my anime portraits, which is available for download and which I use to create TWDNEv3.

--- /face#reversing-stylegan-to-control-modify-images Making Anime Faces With StyleGAN § Reversing StyleGAN To Control & Modify Images Gwern 2019-02-04 2022-10-19 ai/nn/gan/stylegan/anime

A tutorial explaining how to train and generate high-quality anime faces with StyleGAN 1+2 neural networks, and tips/scripts for effective StyleGAN use.

Discussion of how to modify existing images with GANs. There are several possibilities: train another NN to turn an image back into the original encoding; run blackbox search on encodings, repeatedly tweaking it to approximate a target face; or the whitebox approach, directly backpropagating through the model from the image to the encoding while holding the model fixed. All of these have been implemented for StyleGAN, and a combination works best. There are even GUIs for editing StyleGAN anime, pony, & furry faces!

--- /face#discriminator-ranking Making Anime Faces With StyleGAN § Discriminator Ranking: Using a Trained Discriminator to Rank and Clean Data Gwern 2019-02-04 2022-10-19 ai/nn/gan/stylegan/anime cs/python reinforcement-learning/exploration/active-learning/data-pruning tutorial

A tutorial explaining how to train and generate high-quality anime faces with StyleGAN 1+2 neural networks, and tips/scripts for effective StyleGAN use.

The Discriminator of a GAN is trained to detect outliers or bad datapoints. So it can be used for cleaning the original dataset of aberrant samples. This works reasonably well and I obtained BigGAN/StyleGAN quality improvements by manually deleting the worst samples (typically badly-cropped or low-quality faces), but has peculiar behavior which indicates that the Discriminator is not learning anything equivalent to a “quality” score but may be doing some form of memorization of specific real datapoints. What does this mean for how GANs work?

What is a D doing? I find that the highest ranked images often contain many anomalies or low-quality images which need to be deleted. Why? The BigGAN paper notes a well-trained D which achieves 98% real vs fake classification performance on the ImageNet training dataset falls to 50–55% accuracy when run on the validation dataset, suggesting the D’s role is about memorizing the training data rather than some measure of ‘realism’.

--- /review/book#the-battle-between-the-frogs-and-the-mice-stallings-2019 Book Reviews § The Battle Between the Frogs and the Mice, Stallings2019 Gwern 2013-08-23 2022-10-13 fiction/criticism

A compilation of books reviews of books I have read since ~1997.

Review of new translation of a well-known but now-neglected ancient Greek satirical poem parodying the Homeric epics. Stallings’s rhymed-couplet translation is winsome and charming, preserving the too-cute names and bathos, and pairs well with Grant Silverstein’s energetic pencil drawings. A light and enjoyable read.

--- /hunter Genius Revisited Revisited Gwern 2016-06-19 2019-07-26 cs/r genetics/heritable iq/high/smpy psychology statistics/order statistics/power-analysis

A book study of surveys of the high-IQ elementary school HCES concludes that high IQ is not predictive of accomplishment; I point out that the disappointing results are consistent with the subjects not being geniuses due to regression to the mean (because of extremely early IQ tests) & small sample size.

Genius Revisited documents the longitudinal results of a high-IQ/gifted-and-talented elementary school, Hunter College Elementary School (HCES); one of the most striking results is the general high education & income levels, but absence of great accomplishment on a national or global scale (eg. a Nobel prize). The authors suggest that this may reflect harmful educational practices at their elementary school or the low predictive value of IQ.

I suggest that there is no puzzle to this absence nor anything for HCES to be blamed for, as the absence is fully explainable by their making 2 statistical errors: base-rate neglect, and regression to the mean.

First, their standards fall prey to a base-rate fallacy and even extreme predictive value of IQ would not predict 1 or more Nobel prizes because Nobel prize odds are measured at 1 in millions, and with a small total sample size of a few hundred, it is highly likely that there would simply be no Nobels.

Secondly, and more seriously, the lack of accomplishment is inherent and unavoidable as it is driven by the regression to the mean caused by the relatively low correlation of early childhood with adult IQs—which means their sample is far less elite as adults than they believe. Using early-childhood/adult IQ correlations, regression to the mean implies that HCES students will fall from a mean of 157 IQ in kindergarten (when selected) to somewhere around 133 as adults (and possibly lower). Further demonstrating the role of regression to the mean, in contrast, HCES’s associated high-IQ/gifted-and-talented high school, Hunter High, which has access to the adolescents’ more predictive IQ scores, has much higher achievement in proportion to its lesser regression to the mean (despite dilution by Hunter elementary students being grandfathered in).

This unavoidable statistical fact undermines the main rationale of HCES: extremely high-IQ adults cannot be accurately selected as kindergartners on the basis of a simple test. This greater-regression problem can be lessened by the use of additional variables in admissions, such as parental IQs or high-quality genetic polygenic scores; unfortunately, these are either politically unacceptable or dependent on future scientific advances. This suggests that such elementary schools may not be a good use of resources and HCES students should not be assigned scarce magnet high school slots.

--- /gpt-3 GPT-3 Creative Fiction Gwern 2020-06-19 2023-03-11 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry ai/scaling fiction/humor philosophy/mind

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

I continue my AI poetry generation experiments with OpenAI’s GPT-3 (released mid-2020), which is 116× larger, and much more powerful, than the 2019 GPT-2. GPT-3, however, is not merely a quantitative tweak yielding “GPT-2 but better”—it is qualitatively different, exhibiting eerie runtime learning capabilities allowing even the raw model, with zero finetuning, to “meta-learn” many textual tasks purely by example or instruction. One does not train or program GPT-3 in a normal way, but one engages in dialogue and writes prompts to teach GPT-3 what one wants.

Experimenting through the OpenAI Beta API in June 2020, I find that GPT-3 does not just match my finetuned GPT-2-1.5b-poetry for poem-writing quality, but exceeds it, while being versatile in handling poetry, Tom Swifty puns, science fiction, dialogue like Turing’s Turing-test dialogue, literary style parodies… As the pièce de résistance, I recreate Stanislaw Lem’s Cyberiad’s “Trurl’s Electronic Bard” poetry using GPT-3. (Along the way, I document instances of how the BPE text encoding unnecessarily damages GPT-3’s performance on a variety of tasks, how to best elicit the highest-quality responses, common errors people make in using GPT-3, and test out GPT-3’s improvements in NN weak points like logic or commonsense knowledge.)

GPT-3’s samples are not just close to human level: they are creative, witty, deep, meta, and often beautiful. They demonstrate an ability to handle abstractions, like style parodies, I have not seen in GPT-2 at all. Chatting with GPT-3 feels uncannily like chatting with a human. I was impressed by the results reported in the GPT-3 paper, and after spending a week trying it out, I remain impressed.

This page records GPT-3 samples I generated in my explorations, and thoughts on how to use GPT-3 and its remaining weaknesses. I hope you enjoy them even a tenth as much as I enjoyed testing GPT-3 and watching the completions scroll across my screen.

--- /scaling-hypothesis The Scaling Hypothesis Gwern 2020-05-28 2022-01-02 ai/nn/transformer/gpt/3 ai/scaling cs/algorithm insight-porn reinforcement-learning/safe reinforcement-learning/scaling sociology transhumanism

On GPT-3: meta-learning, scaling, implications, and deep theory. The scaling hypothesis: neural nets absorb data & compute, generalizing and becoming more Bayesian as problems get harder, manifesting new abilities even at trivial-by-global-standards-scale. The deep learning revolution has begun as foretold.

GPT-3, announced by OpenAI in May 2020, is the largest neural network ever trained, by over an order of magnitude. Trained on Internet text data, it is the successor to GPT-2, which had surprised everyone by its natural language understanding & generation ability. To the surprise of most (including myself), this vast increase in size did not run into diminishing or negative returns, as many expected, but the benefits of scale continued to happen as forecasted by OpenAI. These benefits were not merely learning more facts & text than GPT-2, but qualitatively distinct & even more surprising in showing meta-learning: while GPT-2 learned how to do common natural language tasks like text summarization, GPT-3 instead learned how to follow directions and learn new tasks from a few examples. (As a result, GPT-3 outputs & interaction are more fascinating & human-like than GPT-2.)

While the immediate applications of GPT-3, like my poetry or humor writings, are nice, the short-term implications of GPT-3 are much more important.

First, while GPT-3 is expensive by conventional DL standards, it is cheap by scientific/commercial/military/government budget standards, and the results indicate that models could be made much larger. Second, models can also be made much more powerful, as GPT is an old approach known to be flawed in both minor & major ways, and far from an ‘ideal’ Transformer. Third, GPT-3’s capabilities come from learning on raw (unsupervised) data; that has long been one of the weakest areas of DL, holding back progress in other areas like reinforcement learning or robotics. Models like GPT-3 suggest that large unsupervised models will be vital components of future DL systems, as they can be ‘plugged into’ systems to immediately provide understanding of the world, humans, natural language, and reasoning.

The meta-learning has a longer-term implication: it is a demonstration of the blessings of scale, where problems with simple neural networks vanish, and they become more powerful, more generalizable, more human-like when simply made very large & trained on very large datasets with very large compute—even though those properties are believed to require complicated architectures & fancy algorithms (and this perceived need drives much research). Unsupervised models benefit from this, as training on large corpuses like Internet-scale text present a myriad of difficult problems to solve; this is enough to drive meta-learning despite GPT not being designed for meta-learning in any way. (This family of phenomena is perhaps driven by neural networks functioning as ensembles of many sub-networks with them all averaging out to an Occam’s razor, which for small data & models, learn superficial or memorized parts of the data, but can be forced into true learning by making the problems hard & rich enough; as meta-learners learn amortized Bayesian inference, they build in informative priors when trained over many tasks, and become dramatically more sample-efficient and better at generalization.)

The blessings of scale in turn support a radical theory: an old AI paradigm held by a few pioneers in connectionism (early artificial neural network research) and by more recent deep learning researchers, the scaling hypothesis. The scaling hypothesis regards the blessings of scale as the secret of AGI: intelligence is ‘just’ simple neural units & learning algorithms applied to diverse experiences at a (currently) unreachable scale. As increasing computational resources permit running such algorithms at the necessary scale, the neural networks will get ever more intelligent.

When? Estimates of Moore’s law-like progress curves decades ago by pioneers like Hans Moravec indicated that it would take until the 2010s for the sufficiently-cheap compute for tiny insect-level prototype systems to be available, and the 2020s for the first sub-human systems to become feasible, and these forecasts are holding up. (Despite this vindication, the scaling hypothesis is so unpopular an idea, and difficult to prove in advance rather than as a fait accompli, that while the GPT-3 results finally drew some public notice after OpenAI enabled limited public access & people could experiment with it live, it is unlikely that many entities will modify their research philosophies, much less kick off an ‘arms race’.)

More concerningly, GPT-3’s scaling curves, unpredicted meta-learning, and success on various anti-AI challenges suggests that in terms of futurology, AI researchers’ forecasts are an emperor sans garments: they have no coherent model of how AI progress happens or why GPT-3 was possible or what specific achievements should cause alarm, where intelligence comes from, and do not learn from any falsified predictions. Their primary concerns appear to be supporting the status quo, placating public concern, and remaining respectable. As such, their comments on AI risk are meaningless: they would make the same public statements if the scaling hypothesis were true or not.

Depending on what investments are made into scaling DL, and how fast compute grows, the 2020s should be quite interesting—sigmoid or singularity?

For more ML scaling research, follow the /r/MLScaling subreddit. For a fiction treatment as SF short story, see “It Looks Like You’re Trying To Take Over The World”.

--- /long-bets Long Bets as Charitable Giving Opportunity Gwern 2017-02-24 2018-02-24 cs/r economics philosophy/ethics statistics/prediction

Evaluating Long Bets as a prediction market shows it is dysfunctional and poorly-structured; despite the irrationality of many users, it is not good even as a way to raise money for charity.

Long Bets is a 15-year-old real-money prediction market run by the Long Now Foundation for incentivizing forecasts/bets about long-term events of social importance such as technology or the environment. I evaluate use of Long Bets as a charitable giving opportunity by winning bets and directing the earnings to a good charity by making forecasts for all available bet opportunities and ranking them by expected value after adjusting for opportunity cost (defined by expected return of stock market indexing) and temporally discounting. I find that while there are ~41 open bets which I expect have positive expected value if counter-bets were accepted, few or none of my counter-bets were accepted. In general, LB has had almost zero activity for the past decade, and has not incentivized much forecasting. This failure is likely caused by its extreme restriction to even-odds bets, no return on bet funds (resulting in enormous opportunity costs), and lack of maintenance or publicity. All of these issues are highly likely to continue barring extensive changes to Long Bets, and I suggest that Long Bets should be wound down.

--- /doc/sociology/1987-rossi The Iron Law Of Evaluation And Other Metallic Rules Peter H. Rossi 2012-09-18 2019-05-13 economics/advertising history insight-porn politics sociology statistics/bias statistics/causality

Problems with social experiments and evaluating them, loopholes, causes, and suggestions; non-experimental methods systematically deliver false results, as most interventions fail or have small effects.

“The Iron Law Of Evaluation And Other Metallic Rules” is a classic review paper by American “sociologist Peter Rossi, a dedicated progressive and the nation’s leading expert on social program evaluation from the 1960s through the 1980s”; it discusses the difficulties of creating a useful social program, and proposed some aphoristic summary rules, including most famously:

It expands an earlier paper by Rossi (“Issues in the evaluation of human services delivery”, Rossi 197846ya), where he coined the first, “Iron Law”.

I provide an annotated HTML version with fulltext for all references, as well as a bibliography collating many negative results in social experiments I’ve found since Rossi’s paper was published (see also the closely-related Replication Crisis).

--- /dnm-archive Darknet Market Archives (2013–2015) Gwern 2013-12-01 2021-03-20 cs/linkrot/archiving cs/r darknet-market/dnm-archive darknet-market/hydra

Mirrors of ~89 Tor-Bitcoin darknet markets & forums 2011–2015, and related material.

Dark Net Markets (DNM) are online markets typically hosted as Tor hidden services providing escrow services between buyers & sellers transacting in Bitcoin or other cryptocoins, usually for drugs or other illegal/regulated goods; the most famous DNM was Silk Road 1, which pioneered the business model in 201113ya.

From between 2013–22015, I scraped/mirrored on a weekly or daily basis all existing English-language DNMs as part of my research into their usage, lifetimes/characteristics, & legal riskiness; these scrapes covered vendor pages, feedback, images, etc. In addition, I made or obtained copies of as many other datasets & documents related to the DNMs as I could.

This uniquely comprehensive collection is now publicly released as a 50GB (~1.6TB uncompressed) collection covering 89 DNMs & 37+ related forums, representing <4,438 mirrors, and is available for any research.

This page documents the download, contents, interpretation, and technical methods behind the scrapes.

--- /review/cat Cat Psychology & Domestication: Are We Good Owners? Gwern 2018-11-03 2023-06-30 cat/genetics cat/psychology genetics/selection/natural/human/dysgenics insight-porn psychiatry/anxiety

Extended book review of Bradshaw 2013 (Cat Sense) on the connections between cat psychology, evolution/genetics, history of domestication or lack thereof, & possible dysgenics, highlighting modern maladaptivity of cat psychology, with key references; speculation on cat toys, knocking things over, and tails.

I review John Bradshaw’s book on domestic cat psychology, Cat Sense, after difficulties with my own cat.

Bradshaw reviews the history of domestic cats from their apparent Middle Eastern origins as a small solitary desert predator to their domestication in Ancient Egypt where breeding millions of cats for sacrifice may have played a critical role (as opposed to any unique role as a vermin exterminator) through to the modern day and psychological studies of the learning abilities and personalities of cats, with particular emphasis on cat social skills in “cat colonies” & plasticity in kittenhood.

As Bradshaw diagnoses it, these are responsible for what ability they have to modern pet life, even though they are not bred for this like dogs; every tame cat still has the feral cat in them, and are in many ways unsuited for contemporary living, with disturbing hints that human lack of selective breeding plus recent large-scale spay/neuter population control efforts may be producing a subtle dysgenic effect on domestication, and this double neglect & backfire may be responsible for disturbingly high rates of cat maladaptation & chronic stress diseases.

--- /catnip Catnip immunity and alternatives Gwern 2015-11-07 2019-06-19 cat/genetics cat/psychology/drug/catnip cs/r statistics/bayes statistics/meta-analysis

Estimation of catnip immunity rates by country with meta-analysis and surveys, and discussion of catnip alternatives.

Not all cats respond to the catnip stimulant; the rate of responders is generally estimated at ~70% of cats. A meta-analysis of catnip response experiments since the 1940s indicates the true value is ~62%. The low quality of studies and the reporting of their data makes examination of possible moderators like age, sex, and country difficult. Catnip responses have been recorded for a number of species both inside and outside the Felidae family; of them, there is evidence for a catnip response in the Felidae, and, more uncertainly, the Paradoxurinae, and Herpestinae.

To extend the analysis, I run large-scale online surveys measuring catnip response rates globally in domestic cats, finding high heterogeneity but considerable rates of catnip immunity worldwide.

As a piece of practical advice for cat-hallucinogen sommeliers, I treat catnip response & finding catnip substitutes as a decision problem, modeling it as a Markov decision process where one wishes to find a working psychoactive at minimum cost. Bol et al 2017 measured multiple psychoactives simultaneously in a large sample of cats, permitting prediction of responses conditional on not responding to others. (The solution to the specific problem is to test in the sequence catnip → honeysuckle → silvervine → Valerian.)

For discussion of cat psychology in general, see my Cat Sense review.

--- /banner Banner Ads Considered Harmful Gwern 2017-01-08 2020-12-12 cs/js cs/r economics/advertising statistics/bayes statistics/decision statistics/power-analysis survey technology/google

9 months of daily A/B-testing of Google AdSense banner ads on Gwern.net indicates banner ads decrease total traffic substantially, possibly due to spillover effects in reader engagement and resharing.

One source of complexity & JavaScript use on Gwern.net is the use of Google AdSense advertising to insert banner ads. In considering design & usability improvements, removing the banner ads comes up every time as a possibility, as readers do not like ads, but such removal comes at a revenue loss and it’s unclear whether the benefit outweighs the cost, suggesting I run an A/B experiment. However, ads might be expected to have broader effects on traffic than individual page reading times/bounce rates, affecting total site traffic instead through long-term effects on or spillover mechanisms between readers (eg. social media behavior), rendering the usual A/B testing method of per-page-load/session randomization incorrect; instead it would be better to analyze total traffic as a time-series experiment.

Design: A decision analysis of revenue vs readers yields an maximum acceptable total traffic loss of ~3%. Power analysis of historical Gwern.net traffic data demonstrates that the high autocorrelation yields low statistical power with standard tests & regressions but acceptable power with ARIMA models. I design a long-term Bayesian ARIMA(4,0,1) time-series model in which an A/B-test running January–October 2017 in randomized paired 2-day blocks of ads/no-ads uses client-local JS to determine whether to load & display ads, with total traffic data collected in Google Analytics & ad exposure data in Google AdSense. The A/B test ran from 2017-01-01 to 2017-10-15, affecting 288 days with collectively 380,140 pageviews in 251,164 sessions.

Correcting for a flaw in the randomization, the final results yield a surprisingly large estimate of an expected traffic loss of −9.7% (driven by the subset of users without adblock), with an implied −14% traffic loss if all traffic were exposed to ads (95% credible interval: −13–16%), exceeding my decision threshold for disabling ads & strongly ruling out the possibility of acceptably small losses which might justify further experimentation.

Thus, banner ads on Gwern.net appear to be harmful and AdSense has been removed. If these results generalize to other blogs and personal websites, an important implication is that many websites may be harmed by their use of banner ad advertising without realizing it.

--- /design Design Of This Website Gwern 2010-10-01 2023-04-20 cs/css cs/js cs/linkrot/archiving design/typography meta

Meta page describing Gwern.net, the self-documenting website’s implementation and experiments for better ‘semantic zoom’ of hypertext; technical decisions using Markdown and static hosting.

Gwern.net is implemented as a static website compiled via Hakyll from Pandoc Markdown and hosted on a dedicated server (due to expensive cloud bandwidth).

It stands out from your standard Markdown static website by aiming at good typography, fast performance, and advanced hypertext browsing features (at the cost of great implementation complexity); the 4 design principles are: aesthetically-pleasing minimalism, accessibility/progressive-enhancement, speed, and a ‘semantic zoom’ approach to hypertext use.

Unusual features include the monochrome esthetics, sidenotes instead of footnotes on wide windows, efficient dropcaps, smallcaps, collapsible sections, automatic inflation-adjusted currency, Wikipedia-style link icons & infoboxes, custom syntax highlighting, extensive local archives to fight linkrot, and an ecosystem of “popup”/“popover” annotations & previews of links for frictionless browsing—the net effect of hierarchical structures with collapsing and instant popup access to excerpts enables iceberg-like pages where most information is hidden but the reader can easily drill down as deep as they wish. (For a demo of all features & stress-test page, see Lorem Ipsum.)

Also discussed are the many failed experiments/changes made along the way.

--- /smpy SMPY Bibliography Gwern 2018-07-28 2022-06-11 iq/high/smpy psychology/energy

An annotated fulltext bibliography of publications on the Study of Mathematically Precocious Youth (SMPY), a longitudinal study of high-IQ youth.

SMPY (Study of Mathematically Precocious Youth) is a long-running longitudinal survey of extremely mathematically-talented or intelligent youth, which has been following high-IQ cohorts since the 1970s. It has provided the largest and most concrete findings about the correlates and predictive power of screening extremely intelligent children, and revolutionized gifted & talented educational practices.

Because it has been running for over 40 years, SMPY-related publications are difficult to find; many early papers were published only in long-out-of-print books and are not available in any other way. Others are digitized and more accessible, but one must already know they exist. Between these barriers, SMPY information is less widely available & used than it should be given its importance.

To fix this, I have been gradually going through all SMPY citations and making fulltext copies available online with occasional commentary.

--- /twdne This Waifu Does Not Exist Gwern 2019-02-19 2020-01-20 ai/anime/danbooru ai/nn/gan/stylegan/anime ai/nn/transformer/gpt/fiction cs/python cs/shell tutorial

I describe how I made the website ThisWaifuDoesNotExist.net (TWDNE) for displaying random anime faces generated by StyleGAN neural networks, and how it went viral.

Generating high-quality anime faces has long been a task neural networks struggled with. The invention of StyleGAN in 2018 has effectively solved this task and I have trained a StyleGAN model which can generate high-quality anime faces at 512px resolution. To show off the recent progress, I made a website, “This Waifu Does Not Exist” for displaying random StyleGAN 2 faces. TWDNE displays a different neural-net-generated face & plot summary every 15s. The site was popular and went viral online, especially in China. The model can also be used interactively for exploration & editing in the Artbreeder online service.

TWDNE faces have been used as screensavers, user avatars, character art for game packs or online games, painted watercolors, uploaded to Pixiv, given away in streams, and used in a research paper (Noguchi & Harada2019). TWDNE results also helped inspired Sizigi Studio’s online interactive waifu GAN, Waifu Labs, which generates even better anime faces than my StyleGAN results.

--- /face Making Anime Faces With StyleGAN Gwern 2019-02-04 2022-10-19 ai/anime/danbooru ai/nn/gan/stylegan/anime cs/python tutorial

A tutorial explaining how to train and generate high-quality anime faces with StyleGAN 1+2 neural networks, and tips/scripts for effective StyleGAN use.

Generative neural networks, such as GANs, have struggled for years to generate decent-quality anime faces, despite their great success with photographic imagery such as real human faces. The task has now been effectively solved, for anime faces as well as many other domains, by the development of a new generative adversarial network, StyleGAN, whose source code was released in February 2019.

I show off my StyleGAN 1+2 CC-0-licensed anime faces & videos, provide downloads for the final models & anime portrait face dataset, provide the ‘missing manual’ & explain how I trained them based on Danbooru2017/2018 with source code for the data preprocessing, document installation & configuration & training tricks.

For application, I document various scripts for generating images & videos, briefly describe the website “This Waifu Does Not Exist” I set up as a public demo & its followup This Anime Does Not Exist.ai (TADNE) (see also Artbreeder), discuss how the trained models can be used for transfer learning such as generating high-quality faces of anime characters with small datasets (eg. Holo or Asuka Souryuu Langley), and touch on more advanced StyleGAN applications like encoders & controllable generation.

The anime face graveyard gives samples of my failures with earlier GANs for anime face generation, and I provide samples & model from a relatively large-scale BigGAN training run suggesting that BigGAN may be the next step forward to generating full-scale anime images.

A minute of reading could save an hour of debugging!

StyleGAN-2 Is Obsolete

--- /review/book Book Reviews Gwern 2013-08-23 2022-10-13 fiction/criticism

A compilation of books reviews of books I have read since ~1997.

This is a compilation of my book reviews. Book reviews are sorted by star, and sorted by length of review within each star level, under the assumption that longer reviews are of more interest to readers.

See also my anime/manga film/TV, & opera reviews.

--- /face-graveyard Anime Neural Net Graveyard Gwern 2019-02-04 2021-01-29 ai/anime/danbooru ai/nn/gan/biggan ai/nn/gan/stylegan/progan ai/nn/vae

Post-mortems of failed neural network experiments in generating anime images, pre-StyleGAN/BigGAN.

My experiments in generating anime faces, tried periodically since 2015, succeeded in 2019 with the release of StyleGAN. But for comparison, here are the failures from some of my older GAN or other NN attempts; as the quality is worse than StyleGAN, I won’t bother going into details in these post-mortems—creating the datasets & training the ProGAN & tuning & transfer-learning were all much the same as already outlined at length for the StyleGAN results.

Included are:

--- /doc/anime/eva/notenki-memoirs/2002-takeda-notenkimemoirs The Notenki Memoirs: Studio Gainax And The Men Who Created Evangelion Yasuhiro Takeda 2010-12-27 2020-12-05 anime/eva/notenki-memoirs japan

Fulltext annotated e-book of 2002 memoir by anime producer Yasuhiro Takeda, discussing Japanese SF conventions & fandom, formation & history of Gainax and its productions up to 2002, including the origins of Evangelion & the tax raid.

An annotated e-book edition of The Notenki Memoirs: Studio Gainax And The Men Who Created Evangelion, a short autobiography by a founder of Gainax who became active as a fan and in the anime/manga industry in the late 1970s; it describes the student fan club scene around SF conventions, the creation of the famous Daicon video shorts, the founding of Gainax, its subsequent successes & travails (although with less emphasis on Neon Genesis Evangelion than one might expect), terminating around 200123ya. Much of the information Takeda discusses may have appeared in English-language sources before, but in obscure or missing sources and never pulled together, and it is a valuable source for non-Japanese-speakers interested in that time period.

For people interested in the history of the anime industry, Takeda fills in many gaps related to Gainax—it’s hard to think of any source which covers nearly so well DAICON III, DAICON IV, General Products, or throws in so many tidbits about surrounding people & Japanese SF fandom. It is an invaluable resource for any researcher, and I felt compelled to create an annotated e-book edition in order to clarify various points and be able to compare its claims with stories by other people (for example, Okada’s extensive Animerica interview)

Those reading it solely for Evangelion material will probably be relatively disappointed: Takeda clearly finds NGE not very interesting, may have bad associations due to being targeted in the tax raids, and he was writing this in 200024ya or so—too close to the events and still working at Gainax to really give a tell-all, and it’s not a terribly long or dense book in the first place. Nevertheless, NGE fans will still find many revelations here, like the origin of NGE production in the failure of the Aoki Uru film project (an origin undocumented in any Western sources before Notenki Memoirs was translated).

--- /review/anime Anime Reviews Gwern 2010-12-14 2024-06-06 anime/eva fiction/criticism

A compilation of anime/manga reviews since 2010.

This page is a compilation of my anime/manga reviews; it is compiled from my MyAnimeList account & newsletter. Reviews are sorted by rating in descending order.

See also my book, film/TV, & opera reviews.

--- /me#coding-contributions About Gwern § Coding Contributions Gwern 2009-08-05 2020-06-14 cs/haskell personal psychology

Who am I online & what have I done? Contact information; sites I use; computers and software tools; things I’ve worked on; psychological profiles

I mostly contribute to projects in Haskell, my favorite language; I have contributed to non-Haskell projects such as StumpWM, Mnemosyne, GNU Emacs18 etc. but not in major ways, so I do not list them here. After starting this website, I wound down my regular coding activities in favor of my writings; when I code, now it tends to be tools documented or hosted on this website (eg. Archiving URLs, Resorter) or integrated into writeups (eg. Generating Anime Faces with StyleGAN). For that code, you can browse by language tag: C/CSS/Haskell/JS/Python/R/Scheme/shell.

Below is a more detailed list of my old Haskell contributions, most of which is now of only historical interest.

--- /twdne#gpt-3 This Waifu Does Not Exist § GPT-3 Gwern 2019-02-19 2020-01-20 ai/anime/danbooru ai/nn/gan/stylegan/anime ai/nn/transformer/gpt/fiction cs/python cs/shell tutorial

I describe how I made the website ThisWaifuDoesNotExist.net (TWDNE) for displaying random anime faces generated by StyleGAN neural networks, and how it went viral.

Upgrade of ThisWaifuDoesNotExist.net, using GPT-3 to regenerate text samples. This section describes the prompt used, and provides the shell script for generating GPT-3 anime reviews.

The previous pipeline used a multi-stage workflow: a finetuned (on anime plot thumbnails) small GPT-2 would generate short anime snippets, which were used to prompt the full-sized GPT-2-1.5b along with keywords. GPT-3 is powerful enough to dispense with the need for this—a short simple prompt cuing a review of ‘new’ anime is enough to generate anime reviews & plot summaries of greater quality.

Further, as GPT-3 is hosted behind the OpenAI API, samples can be generated by a short curl script to call the API with the prompt & hyperparameters, so the user does not (and cannot anyway, in light of GPT-3’s size) need to run GPUs etc locally.

--- /doc/anime/eva/1996-animerica-conscience The Conscience of the Otaking: The Studio Gainax Saga in 4 Parts Toshio Okada 2011-10-03 2011-10-03 anime/eva fiction/science-fiction interview

1995 interview of former Gainax president Toshio Okada on Gainax’s history, Wings of Honneamise, Aoki Uru, etc.

--- /archiving Archiving URLs Gwern 2011-03-10 2023-03-02 cs/haskell cs/linkrot/archiving cs/r cs/shell meta tutorial

Archiving the Web, because nothing lasts forever: statistics, online archive services, extracting URLs automatically from browsers, and creating a daemon to regularly back up URLs to multiple sources.

Links on the Internet last forever or a year, whichever inconveniences you more. This is a major problem for anyone serious about writing with good references, as link rot will cripple several percent of all links each year, and compounding.

To deal with link rot, I present my multi-pronged archival strategy using a combination of scripts, daemons, and Internet archival services: URLs are regularly dumped from both my web browser’s daily browsing and my website pages into an archival daemon I wrote, which pre-emptively downloads copies locally and attempts to archive them in the Internet Archive. This ensures a copy will be available indefinitely from one of several sources. Link rot is then detected by regular runs of linkchecker, and any newly dead links can be immediately checked for alternative locations, or restored from one of the archive sources.

As an additional flourish, my local archives are efficiently cryptographically timestamped using Bitcoin in case forgery is a concern, and I demonstrate a simple compression trick for substantially reducing sizes of large web archives such as crawls (particularly useful for repeated crawls such as my DNM archives).

--- /ab-test A/B testing long-form readability on Gwern.net Gwern 2012-06-16 2022-09-27 cs/css cs/js cs/r design/typography economics/advertising meta statistics/decision statistics/power-analysis technology/google

A log of experiments done on the site design, intended to render pages more readable, focusing on the challenge of testing a static site, page width, fonts, plugins, and effects of advertising.

To gain some statistical & web development experience and to improve my readers’ experiences, I have been running a series of CSS A/B tests since June 201212ya. As expected, most do not show any meaningful difference.

--- /holy-war Technology Holy Wars are Coordination Problems Gwern 2020-06-15 2020-07-09 cs/python design economics insight-porn sociology/technology

Flamewars over platforms & upgrades are so bitter not because people are jerks but because the choice will influence entire ecosystems, benefiting one platform through network effects & avoiding ‘bitrot’ while subtly sabotaging the rest through ‘bitcreep’.

The enduring phenomenon of holy wars in computing, such as the bitterness around the prolonged Python 2 to Python 3 migration, is not due to mere pettiness or love of conflict, but because they are a coordination problem: the problem is not getting everyone to make a good decision, but making the same decision. Apparent problems with an unpopular platform may actually be unacknowledged parts of an attempt to coordinate use of different platform: dominant platforms enjoy strong network effects, such as reduced bitrot as it is regularly used & maintained by many users, and can inflict a mirror-image bitcreep on other platforms which gradually are neglected and begin to bitrot because of the dominant platform.

The outright negative effect of bitcreep mean that holdouts do not just cost early adopters the possible network effects, they also greatly reduce the value of a given thing, and may cause the early adopters to be actually worse off and more miserable on a daily basis. Given the extent to which holdouts have benefited from the community, holdout behavior is perceived as parasitic and immoral behavior by adopters, while holdouts in turn deny any moral obligation and resent the methods that adopters use to increase adoption (such as, in the absence of formal controls, informal ones like bullying).

This desperate need for there to be a victor, and the large technical benefits/costs to those who choose the winning/losing side, explain the (only apparently) disproportionate energy, venom, and intractability of holy wars.

Perhaps if we explicitly understand holy wars as coordination problems, we can avoid the worst excesses and tap into knowledge about the topic to better manage things like language migrations.

--- /note/statistic#selective-emigration-and-personality-trait-change Statistical Notes § Selective Emigration and Personality Trait Change Gwern 2014-07-17 2024-08-21 cs/haskell cs/js cs/r genetics iq statistics/bayes statistics/causality statistics/decision statistics/order statistics/power-analysis survey

Miscellaneous statistical stuff

Knudsen2019 finds that the emigration of 25% of the Scandinavian population to the USA 1850–701920104ya was driven in part by more ‘individualistic’ personality factors among emigrants, leading to permanent decreases in mean ‘individualism’ in the home countries. This is attributed to cultural factors, rather than genetics. I model the overall migration as a simple truncation selection scenario, and find that in a simple model under reasonable assumptions, the entire effect could be genetic.

--- /acne Acne: a good Quantified Self topic Gwern 2019-01-08 2024-10-04 cs/r genetics/microbiome/acne nootropic/quantified-self statistics/bayes statistics/power-analysis survey

Acne is a good way to learn self-experiments for teenagers. I offer tips on what one could do, and an initial list of crowdsourced interventions to test from CureTogether.

I suggest that teenagers interested in experiments, statistics, or Quantified Self experiment with interventions to reduce acne, listing the advantages of the topic.

To suggest specific cures, I re-analyze & rank the April 2016 CureTogether crowdsourced ratings of ~113 acne interventions.

Food interventions rate highly after standard retinoids & benzoyl peroxide, and so might be worth closer investigation.

--- /subculture#japan-and-the-internet The Melancholy of Subculture Society § Japan and the Internet Gwern 2009-01-12 2019-02-05 anime crime/terrorism insight-porn japan psychology sociology/technology

Internet links small groups, helping dissolve big groups; good, bad? But a bit sad.

The future of technology isn’t what it used to be—a discussion of the collapse of Japanese influence on technology & design. Why did Japanese companies cease to be the admired cutting-edge of computer, video game, Internet, or smartphone technology, underperforms in critical areas like software design (such as programming languages) and is instead one of the last havens of fax machines & feature phones, with prestigious but largely useless humanoid robotic programs?

--- /review/opera#die-walkure Opera Reviews § Die Walküre Gwern 2019-02-02 2021-12-03 fiction/criticism fiction/opera personal

A compilation of opera reviews since 2019.

Second opera review, after Carmen. Oddly, this is #2 of The Ring cycle but neither #1 nor #3 were broadcast. Not as enjoyable, but impressive in its own right. It works better at providing a mythic sense than contemporary efforts like the Marvel Cinematic Universe, at least.

--- /leprechaun#citogenesis-how-often-do-researchers-not-read-the-papers-they-cite Leprechaun Hunting & Citogenesis § Citogenesis: How Often Do Researchers Not Read The Papers They Cite? Gwern 2014-06-30 2021-05-15 philosophy/epistemology sociology statistics/bias

One fertile source of leprechauns seems to be the observation that researchers do not read many of the papers that they cite in their own papers. The frequency of this can be inferred from pre-digital papers, based on bibliographic errors: if a citation has mistakes in it, such that one could not have actually looked up the paper in a library or database, and those mistakes were copied from another paper, then the authors almost certainly did not read the paper (otherwise they would have fixed the mistakes when they found them out the hard way) and simply copied the citation.

The empirically-measured spread of bibliographic errors suggest that researchers frequently do not read the papers they cite. The frequency can be further confirmed by examining citations to see when the citers makes much more serious errors by misdescribing the original paper’s findings; the frequency of such “quotation errors” is also high, showing that the errors involved in citation malpractice are substantial and not merely bibliographic.

--- /question#jeanne-calment Open Questions § Jeanne Calment Gwern 2018-10-17 2023-02-13 statistics/bias statistics/order

Some anomalies/questions which are not necessarily important, but do puzzle me or where I find existing explanations to be unsatisfying.

Jeanne Calment holds the verified record for human longevity at ~122.5 years at her death over 22 years ago: Calment is history’s first & only 122 year old; and also the first & only 121 year old; and also the first & only 120 year old. No challenging centenarian has come close to her record, and arithmetically, they will not for years to come: she will have held the record for a minimum of 3 decades, despite countless countervailing factors. Some statistical simulations suggest that Calment-like record gaps are not expected from the distribution of human life expectancies, and as time passes, her record becomes increasingly anomalous.

This truly remarkable longevity raises the question of whether Calment’s longevity is due to the same factors as all other centenarians: did she benefit from some unique factor like genetic mutations, or, as accused in late 2018 of being, is she, in fact, merely a fraud which has fooled previous verification?

--- /note/statistic#power-analysis-for-racial-admixture-studies-of-continuous-variables Statistical Notes § Power Analysis for Racial Admixture Studies of Continuous Variables Gwern 2014-07-17 2024-08-21 cs/haskell cs/js cs/r genetics iq statistics/bayes statistics/causality statistics/decision statistics/order statistics/power-analysis survey

Miscellaneous statistical stuff

I consider power analysis of a genomic racial admixture study for detecting genetic group differences affecting a continuous trait such as IQ in US African-Americans, where ancestry is directly measured by genome sequencing and the comparisons are all within-family to eliminate confounding by population structure or racism/colorism/discrimination. The necessary sample size for well-powered studies is closely related to the average size of differences in ancestry percentage between siblings, as the upper bound on IQ effect per percentage is small, requiring large differences in ancestry to detect easily. A within-family comparison of siblings, due to the relatively small differences in ancestry between siblings estimated from IBD measurements of siblings, might require n > 50,000 pairs of siblings to detect possible effects on IQ, an infeasible sample size. An alternative design focuses on increasing the available ancestry differences within a family unit by comparing adoptees with siblings; the larger within-population standard deviation of ancestry creates larger & more easily-detected IQ differences. A random-effects meta-analysis of past admixture & ancestry studies suggests the SD in heterogeneous samples may range from 2% to 20% with a mean of 11% (95% predictive interval), yielding sample sizes of n > 20,000, n = 1100924ya, and n = 500. Hence, an adoption study is probably in the feasible range, with required sample sizes comparable to annual adoption rates among US African-Americans.

--- /review/book#experimenter-effects-in-behavioral-research-rosenthal-1976 Book Reviews § Experimenter Effects In Behavioral Research, Rosenthal1976 Gwern 2013-08-23 2022-10-13 fiction/criticism

A compilation of books reviews of books I have read since ~1997.

Review of a major and widely-cited psychology monograph purporting to demonstrate pervasive and powerful effects of social expectations and settings and the general environment on all aspects of human psychology, experimentation, and research, even to the point of the ‘Pygmalion effect’ proving that teacher expectations can boost student IQs by hundreds of points.

The Pygmalion effect was based on impossible data, was defended by statistical malpractice, and repeatedly failed to replicate, and this exemplifies the problem with Rosenthal’s research and the book as a whole: despite its appearance of extreme rigor and concern for bias, it is clear that the results actually exemplify the Replication Crisis and that almost none of his research is reliable, bogus from beginning to end, and the results were designed to serve ideological goals despite the intrinsic absurdity of the claims, and inconsistency with basic observations of human stability & consistency, predictive power of individual differences, and impotence of environmental interventions.

--- /subculture The Melancholy of Subculture Society Gwern 2009-01-12 2019-02-05 anime/my-little-pony crime/terrorism insight-porn japan psychology sociology/technology

Internet links small groups, helping dissolve big groups; good, bad? But a bit sad.

The future of technology isn’t what it used to be—a discussion of the collapse of Japanese influence on technology & design. Why did Japanese companies cease to be the admired cutting-edge of computer, video game, Internet, or smartphone technology, underperforms in critical areas like software design (such as programming languages) and is instead one of the last havens of fax machines & feature phones, with prestigious but largely useless humanoid robotic programs?

--- /narrowing-circle The Narrowing Circle Gwern 2012-04-24 2019-04-27 economics insight-porn philosophy/ethics

Modern ethics excludes as many beings as it includes.

The “expanding circle” historical thesis ignores all instances in which modern ethics narrowed the set of beings to be morally regarded, often backing its exclusion by asserting their non-existence, and thus assumes its conclusion: where the circle is expanded, it’s highlighted as moral ‘progress’, and where it is narrowed, what is outside is simply defined away. When one compares modern with ancient society, the religious differences are striking: almost every single supernatural entity (place, personage, or force) has been excluded from the circle of moral concern, where they used to be huge parts of the circle and one could almost say the entire circle. Further examples include estates, houses, fetuses, prisoners, and graves.

--- /note/parasocial Parasocial Relationships Online Gwern 2020-11-30 2020-11-30 economics fiction/text-game music psychology/collecting sociology/technology

Bibliography of links on the evolution of parasocial relationships online: more than social media acquaintances or influencer-consumption, less than true social relationships, typically with money involved somewhere; implications for the future of human relationships/media and artificial intelligence?

--- /zeo/zeo#alcohol Zeo sleep self-experiments § Alcohol Gwern 2010-12-28 2018-02-28 cs/r psychiatry/alcoholism statistics/bayes zeo

EEG recordings of sleep and my experiments with things affecting sleep quality or durations: melatonin, potassium, vitamin D etc

Suspicious that alcohol was delaying my sleep and worsening my sleep when I did finally go to bed, I recorded my alcohol consumption for a year. Correlating alcohol use against when I go to bed shows no interesting correlation, nor with any of the other sleep variables Zeo records, even after correcting for a shift in my sleep patterns over that year. So it would seem I was wrong.

--- /doc/japan/history/index ‘Japanese history’ tag 2019-11-11 2024-10-10 history

Bibliography for tag japan/history, most recent first: 1 related tag, 32 annotations, & 14 links ().

--- /review/opera Opera Reviews Gwern 2019-02-02 2021-12-03 fiction/opera

A compilation of opera reviews since 2019.

This page is a compilation of my opera reviews since watching Carmen in February 2019. Reviews are sorted by rating in descending order.

See also my book, film/TV, & anime reviews.

--- /crop#danbooru2019-figures Anime Crop Datasets: Faces, Figures, & Hands § Danbooru2019 Figures Gwern, Arfafax, Shawn Presser, Anonymous, Danbooru Community 2020-05-10 2020-08-05 ai/anime/danbooru ai/dataset

Description of 3 anime datasets for machine learning based on Danbooru: cropped anime faces, whole-single-character crops, and hand crops (with hand detection model).

The Danbooru2019 Figures dataset is a large-scale character anime illustration dataset of n = 855,880 images (248GB; minimum width 512px).

They were cropped from Danbooru2019 using the AniSeg anime character detection model. The images are cropped to focus on a single character’s entire visible body, extending ‘portrait’ crops to ‘figure’ crops.

This is useful for tasks focusing on individual characters, such as character classification or for generative tasks (a corpus for weak models like StyleGAN, or data augmentation for BigGAN).

--- /crop#hands Anime Crop Datasets: Faces, Figures, & Hands § Hands Gwern, Arfafax, Shawn Presser, Anonymous, Danbooru Community 2020-05-10 2020-08-05 ai/anime/danbooru ai/dataset

Description of 3 anime datasets for machine learning based on Danbooru: cropped anime faces, whole-single-character crops, and hand crops (with hand detection model).

We create & release PALM: the PALM Anime Locator Model. PALM is a pretrained anime hand detector/localization neural network, and 3 sets of accompanying anime hand datasets:

  1. A dataset of 5,382 anime-style Danbooru2019 images annotated with the locations of 14,394 hands.

    This labeled dataset is used to train a YOLOv3 model to detect hands in anime.

  2. A second dataset of 96,534 hands cropped from the Danbooru2019 SFW dataset using the PALM YOLO model.

  3. A cleaned version of #2, consisting of 58,536 hand crops upscaled to ≥512px.

Hand detection can be used to clean images (eg. remove face images with any hands in the way), or to generate datasets of just hands (as a form of data augmentation for GANs), to generate reference datasets for artists, or for other purposes. (For human hands, see the “11K Hands” dataset.)

Likely Obsolete

--- /twdne#results This Waifu Does Not Exist § Results Gwern 2019-02-19 2020-01-20 ai/anime/danbooru

I describe how I made the website ThisWaifuDoesNotExist.net (TWDNE) for displaying random anime faces generated by StyleGAN neural networks, and how it went viral.

TWDNE traffic results: China virality drove hundreds of thousands of users, and traffic remains at nontrivial levels through to 2020.

--- /doc/cs/css/index ‘CSS’ tag 2019-12-01 2024-11-17 design/typography/tex design/visualization

Bibliography for tag cs/css, most recent first: 2 related tags, 38 annotations, & 91 links ().

--- /design-graveyard Design Graveyard Gwern 2010-10-01 2024-09-03 cs/css cs/js cs/linkrot/archiving design/typography meta

Meta page describing Gwern.net website design experiments and post-mortem analyses.

Often the most interesting part of any design are the parts that are invisible—what was tried but did not work. Sometimes they were unnecessary, other times readers didn’t understand them because it was too idiosyncratic, and sometimes we just can’t have nice things.

Some post-mortems of things I tried on Gwern.net but abandoned (in chronological order).

--- /doc/genetics/heritable/emergenesis/index ‘emergenesis’ tag 2020-03-13 2024-09-18 psychiatry/bipolar/energy psychology/energy psychology/personality

Bibliography for tag genetics/heritable/emergenesis, most recent first: 1 related tag, 9 annotations, & 3 links ().

[page summary]

--- /design-graveyard#link-icon-css-regexps Design Graveyard § Link-Icon CSS Regexps Gwern 2010-10-01 2024-09-03 cs/css

Meta page describing Gwern.net website design experiments and post-mortem analyses.

A major Gwern.net site feature is the ‘link icons’ appended to links as symbolic annotations. The link-icons are comprehensive, covering hundreds of different cases.

The standard CSS solution which uses regexps to match URLs at runtime inside client browsers, while fine for simple uses, scales poorly in correctness, maintainability, and performance.

We eventually switched to a compile-time solution where URLs are given attributes specify what (if any) their link-icon can be, which allows easy definition of complex rules, unit-testing to guarantee the results are correct, and client-side rendering is limited to simply reading & rendering the attribute; this approach has been easy to write correct rules in, easy to keep rules correct, and will always be lightweight for clients.

--- /doc/ai/nn/transformer/gpt/4/index ‘GPT-4’ tag 2021-11-24 2024-11-14 ai/nn/transformer/gpt/dall-e/3 ai/nn/transformer/gpt/inner-monologue

Bibliography for tag ai/nn/transformer/gpt/4, most recent first: 6 related tags, 59 annotations, & 55 links ().

--- /doc/ai/nn/vae/mae/index ‘masked autoencoder’ tag 2021-11-11 2024-11-07 ai/nn/tokenization ai/nn/transformer ai/scaling ai/video/analysis

Bibliography for tag ai/nn/vae/mae, most recent first: 51 annotations & 6 links ().

--- /doc/psychiatry/autism/index ‘autism’ tag 2019-09-29 2024-11-29 genetics/heritable/rare iq/low psychiatry/anxiety

Bibliography for tag psychiatry/autism, most recent first: 7 related tags, 117 annotations, & 16 links ().

--- /doc/psychology/animal/bird/neuroscience/index ‘bird brains’ tag 2020-09-25 2024-05-15 cs/algorithm/information psychology/neuroscience

Bibliography for tag psychology/animal/bird/neuroscience, most recent first: 22 annotations & 4 links ().

--- /doc/longevity/epigenetics/index ‘epigenetics (aging)’ tag 2020-05-12 2024-11-19 cs/algorithm/information genetics/editing genetics/gametogenesis genetics/sequencing statistics/causality

Bibliography for tag longevity/epigenetics, most recent first: 3 related tags, 55 annotations, & 12 links ().

--- /doc/psychiatry/meditation/index ‘meditation’ tag 2020-06-05 2024-11-17 nootropic/quantified-self philosophy/religion psychiatry/anxiety psychology

Bibliography for tag psychiatry/meditation, most recent first: 1 related tag, 50 annotations, & 48 links ().

--- /doc/ai/nn/gan/data-augmentation/index ‘data-augmented GANs’ tag 2020-02-11 2024-03-14 ai/dataset

Bibliography for tag ai/nn/gan/data-augmentation, most recent first: 21 annotations & 3 links ().

--- /doc/longevity/fasting/index ‘fasting’ tag 2019-10-03 2024-07-30

Bibliography for tag longevity/fasting, most recent first: 38 annotations & 6 links ().

--- /doc/modafinil/index ‘modafinil’ tag 2019-10-08 2024-06-05 psychiatry/adhd psychiatry/depression psychology/energy zeo

Bibliography for tag modafinil, most recent first: 2 related tags, 109 annotations, & 69 links ().

[essay on this tag topic]

--- /doc/cat/genetics/index ‘cat genetics’ tag 2019-10-28 2024-11-04 genetics/heritable

Bibliography for tag cat/genetics, most recent first: 2 related tags, 55 annotations, & 15 links ().

--- /doc/statistics/variance-component/index ‘variance components’ tag 2020-05-12 2024-08-01 psychology

Bibliography for tag statistics/variance-component, most recent first: 31 annotations & 4 links ().

[page summary]

--- /doc/cryonics/index ‘cryonics’ tag 2019-10-12 2024-11-11

Bibliography for tag cryonics, most recent first: 110 annotations & 28 links ().

[essay on this tag topic]

--- /doc/dual-n-back/index ‘DNB’ tag 2019-11-10 2024-09-28

Bibliography for tag dual-n-back, most recent first: 158 annotations & 71 links ().

--- /doc/anime/index ‘anime’ tag 2013-11-23 2024-11-27 fiction japan

Bibliography for tag anime, most recent first: 10 related tags, 65 annotations, & 61 links ().

--- /doc/ai/nn/gan/biggan/index ‘BigGAN’ tag 2019-12-23 2024-03-29 ai/nn/gan/stylegan ai/nn/transformer ai/scaling

Bibliography for tag ai/nn/gan/biggan, most recent first: 1 related tag, 44 annotations, & 22 links ().

[essay on this tag topic]

--- /doc/cat/psychology/index ‘cat psychology’ tag 2019-10-06 2024-10-26 psychology/animal

Bibliography for tag cat/psychology, most recent first: 7 related tags, 168 annotations, & 49 links ().

--- /doc/music/index ‘music’ tag 2017-09-11 2024-09-03

Bibliography for tag music, most recent first: 7 related tags, 129 annotations, & 37 links ().

--- /doc/statistics/causality/index ‘causality’ tag 2019-05-13 2024-11-28 psychology/cognitive-bias

Bibliography for tag statistics/causality, most recent first: 2 related tags, 148 annotations, & 53 links ().

[essay on this tag topic]

--- /doc/reinforcement-learning/index ‘RL’ tag 2019-11-25 2024-01-09 ai

Bibliography for tag reinforcement-learning, most recent first: 32 related tags, 18 annotations, & 16 links ().

--- /doc/sociology/technology/index ‘sociology of tech’ tag 2019-08-22 2024-11-27 psychiatry/anxiety

Bibliography for tag sociology/technology, most recent first: 251 annotations & 78 links ().

--- /doc/ai/nn/transformer/attention/index ‘self-attention’ tag 2019-12-17 2024-11-24 ai/nn/dynamic-evaluation ai/nn/retrieval

Bibliography for tag ai/nn/transformer/attention, most recent first: 9 related tags, 176 annotations, & 44 links ().

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--- /doc/ai/nn/tokenization/index ‘LM tokenization’ tag 2020-03-01 2024-11-02 ai/nn/transformer/attention ai/nn/transformer/gpt psychology/linguistics reinforcement-learning/meta-learning

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--- /doc/melatonin/index ‘melatonin’ tag 2019-10-07 2024-04-21 psychology/vision zeo

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[essay on this tag topic]

--- /doc/ai/anime/index ‘anime AI’ tag 2019-09-30 2024-10-09 japan

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--- /doc/culture/index ‘culture’ tag 2011-08-31 2024-10-25 music

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--- /doc/existential-risk/index ‘x-risk’ tag 2019-04-01 2024-11-19 reinforcement-learning/safe

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--- /doc/fiction/text-game/index ‘text game’ tag 2019-12-05 2024-11-22

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--- /doc/math/humor/index ‘STEM humor’ tag 2019-11-28 2024-11-29 fiction/humor

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--- /doc/psychology/spaced-repetition/index ‘spaced repetition’ tag 2013-08-24 2024-10-23 psychology/linguistics psychology/neuroscience/memory/savant

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[essay on this tag topic]

--- /doc/ai/scaling/economics/index ‘AI economics’ tag 2021-01-19 2024-11-25 ai/nn/transformer/gpt/dall-e economics/automation economics/experience-curve reinforcement-learning/robot technology

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--- /doc/law/index ‘law’ tag 2019-12-10 2024-11-28 crime philosophy/ethics sociology

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--- /doc/reinforcement-learning/safe/index ‘AI safety’ tag 2019-09-08 2024-11-20 existential-risk

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--- /doc/ai/nn/adversarial/index ‘adversarial examples (AI)’ tag 2019-12-17 2024-11-29 cs/security reinforcement-learning/safe

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--- /doc/ai/nn/transformer/gpt/fiction/index ‘GPT fiction’ tag 2021-03-23 2024-10-27 ai/nn/transformer/gpt/poetry ai/text-style-transfer fiction/humor

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--- /doc/economics/advertising/index ‘ads’ tag 2019-05-13 2024-11-09 economics/mechanism-design/auction politics psychology/cognitive-bias technology

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--- /doc/longevity/glp/semaglutide/index ‘semaglutide’ tag 2020-06-21 2024-11-12 exercise/gravitostat longevity/glp/tirzepatide psychology/neuroscience psychology/willpower

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--- /doc/ai/index ‘AI’ tag 2019-08-28 2024-11-02 cs/algorithm/information/compression

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--- /doc/nootropic/index ‘nootropic’ tag 2019-09-29 2024-08-05 psychiatry/anxiety psychology/energy psychology/willpower

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--- /doc/statistics/order/index ‘order statistics’ tag 2019-10-09 2024-11-20 statistics/probability

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--- /doc/ai/scaling/index ‘AI scaling’ tag 2019-08-31 2024-11-21 ai/nn/cnn ai/nn/transformer reinforcement-learning/model/alphago reinforcement-learning/multi-agent reinforcement-learning/safe

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[page summary]

--- /doc/zeo/index ‘sleep’ tag 2019-11-25 2024-11-24 nootropic/magnesium psychiatry/depression psychology

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--- /doc/ai/music/index ‘AI music’ tag 2019-09-28 2024-09-24 ai/nn/transformer/gpt/jukebox music

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--- /doc/psychology/willpower/index ‘willpower’ tag 2019-09-29 2024-11-06 psychiatry/depression psychology/energy psychology/neuroscience/pain

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--- /doc/genetics/heritable/dog/index ‘dog genetics’ tag 2020-01-31 2024-01-01 cat/genetics dog genetics/cloning/dog

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--- /doc/economics/experience-curve/index ‘experience curves’ tag 2019-09-07 2024-11-20 ai/scaling/economics economics/automation

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--- /doc/fiction/science-fiction/index ‘Sci-Fi’ tag 2011-08-31 2024-11-04 fiction

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--- /doc/statistics/order/comparison/index ‘statistical comparison’ tag 2019-12-15 2024-11-28 cs/algorithm/information cs/algorithm/sorting reinforcement-learning/exploration/active-learning reinforcement-learning/preference-learning

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--- /doc/psychedelic/index ‘psychedelics’ tag 2019-12-14 2024-11-19 psychiatry/anxiety psychiatry/depression psychology/cognitive-bias/illusion-of-depth

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--- /doc/reinforcement-learning/exploration/active-learning/index ‘active learning’ tag 2019-12-15 2024-09-01 ai/scaling reinforcement-learning/safe statistics/bayes statistics/decision

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--- /doc/ai/nn/transformer/gpt/instruction-tuning/index ‘instruct-tuning LLMs’ tag 2021-04-18 2024-11-25 ai/dataset ai/nn/sampling ai/nn/transformer/gpt/inner-monologue ai/scaling reinforcement-learning/model/decision-transformer reinforcement-learning/preference-learning reinforcement-learning/scaling

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--- /doc/ai/nn/transformer/attention/hierarchical/index ‘multi-scale Transformers’ tag 2021-03-01 2024-10-29 ai/nn/cnn

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--- /doc/philosophy/epistemology/index ‘epistemology’ tag 2019-08-31 2024-11-29 psychology/cognitive-bias

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--- /doc/statistics/prediction/index ‘forecasting’ tag 2019-12-02 2024-11-11 psychology/cognitive-bias science/fermi-problem

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--- /doc/psychology/collecting/index ‘collector psychology’ tag 2019-12-10 2024-10-21 psychology/cognitive-bias

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[essay on this tag topic]

--- /doc/psychology/personality/psychopathy/index ‘psychopathy’ tag 2019-11-12 2024-11-24 crime psychiatry psychology/personality/narcissism

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--- /doc/psychiatry/alzheimers/index ‘Alzheimer’s’ tag 2019-11-18 2024-10-24 biology genetics/heritable/correlation genetics/selection/natural/human longevity psychology/neuroscience zeo

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--- /doc/genetics/gametogenesis/index ‘gametogenesis’ tag 2019-11-13 2024-11-11 longevity/epigenetics

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--- /doc/reinforcement-learning/preference-learning/index ‘preference learning’ tag 2019-09-12 2024-11-21 ai/nn/transformer/gpt/4/sydney ai/nn/transformer/gpt/claude reinforcement-learning/imitation-learning reinforcement-learning/model reinforcement-learning/safe reinforcement-learning/scaling

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--- /doc/ai/scaling/hardware/index ‘AI hardware’ tag 2019-09-03 2024-11-20 ai/nn/sparsity/low-precision cs/hardware

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--- /doc/psychology/vision/index ‘sight’ tag 2019-10-28 2024-11-29 psychology/neuroscience

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--- /doc/ai/dataset/index ‘ML dataset’ tag 2019-09-12 2024-11-26 ai/highleyman ai/nn/gan ai/nn/transformer/gpt/instruction-tuning ai/scaling dataset

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--- /doc/ai/nn/transformer/gpt/palm/index ‘PaLM’ tag 2022-04-05 2024-07-04 ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/lamda ai/scaling/emergence reinforcement-learning/robot

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--- /doc/psychology/cognitive-bias/illusion-of-depth/index ‘illusion-of-depth bias’ tag 2019-08-21 2024-11-05 psychedelic psychology/vision

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[page summary]

--- /doc/ai/nn/rnn/index ‘RNN’ tag 2019-08-30 2024-11-24 ai/nn/fully-connected ai/nn/transformer/attention

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--- /doc/ai/nn/transformer/index ‘Transformer’ tag 2019-12-16 2024-11-15 ai/nn/fully-connected ai/nn/rnn

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--- /doc/reinforcement-learning/meta-learning/index ‘meta-learning’ tag 2019-09-02 2024-11-22 ai/nn/dynamic-evaluation ai/nn/transformer/gpt/inner-monologue reinforcement-learning/scaling

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--- /doc/reinforcement-learning/exploration/index ‘RL exploration’ tag 2019-09-06 2024-11-16 ai/dataset cs/algorithm/information psychiatry/bipolar psychiatry/depression psychology/willpower reinforcement-learning/imperfect-information reinforcement-learning/model/alphago reinforcement-learning/multi-agent statistics/decision

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--- /doc/exercise/index ‘exercise’ tag 2019-10-08 2024-11-19 economics nootropic/quantified-self/heart-rate-variability psychiatry/anxiety psychology/willpower

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--- /doc/crime/index ‘crime’ tag 2019-09-10 2024-11-29 law

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--- /doc/ai/nn/retrieval/index ‘retrieval AI’ tag 2019-09-28 2024-11-26 ai/nn/transformer/attention

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--- /doc/ai/nn/transformer/gpt/codex/index ‘Codex’ tag 2019-12-25 2024-11-24 ai/nn/transformer/gpt/instruction-tuning ai/scaling/economics

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--- /doc/ai/nn/transformer/gpt/poetry/index ‘GPT poetry’ tag 2019-10-29 2024-01-01 ai/nn/transformer/gpt/fiction

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--- /doc/ai/video/generation/index ‘video generation’ tag 2020-01-15 2024-09-15 ai/anime ai/nn/diffusion ai/nn/gan/stylegan ai/nn/transformer/attention ai/nn/transformer/gpt ai/scaling reinforcement-learning/model

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--- /doc/ai/video/analysis/index ‘video analysis’ tag 2019-12-29 2024-10-27 ai/anime ai/nn/transformer/clip ai/scaling reinforcement-learning

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--- /doc/reinforcement-learning/multi-agent/index ‘MARL’ tag 2019-12-17 2024-11-19 ai/scaling economics/mechanism-design reinforcement-learning/exploration reinforcement-learning/safe reinforcement-learning/scaling

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--- /doc/psychology/personality/conscientiousness/index ‘Conscientiousness’ tag 2019-11-06 2024-09-24 economics psychology/energy psychology/willpower

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[essay on this tag topic]

--- /doc/cs/cellular-automaton/index ‘cellular automata’ tag 2019-11-15 2024-11-22 ai/nn biology genetics/genome-synthesis reinforcement-learning

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--- /doc/ai/nn/sparsity/low-precision/index ‘reduced-precision NNs’ tag 2019-12-27 2024-11-11 ai/scaling/hardware

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--- /doc/ai/nn/sparsity/knowledge-distillation/index ‘knowledge distillation’ tag 2019-12-16 2024-11-04 psychology/dark-knowledge reinforcement-learning/imitation-learning

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--- /doc/ai/nn/sampling/index ‘NN sampling’ tag 2019-10-29 2024-11-16 ai/nn/transformer/gpt/inner-monologue ai/poetry reinforcement-learning/exploration reinforcement-learning/model/alphago reinforcement-learning/preference-learning

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--- /doc/cs/algorithm/sorting/index ‘sorting’ tag 2019-12-16 2024-06-15 statistics/order/comparison

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--- /prediction-market Prediction Markets Gwern 2009-01-10 2019-05-16 anime/eva bitcoin cs/haskell darknet-market economics math politics psychology/spaced-repetition science/fermi-problem statistics/bayes statistics/prediction/election statistics/survival-analysis survey wikipedia

My prediction/betting strategies and track record, reflections on rationality, prediction judgments

I explain what I’ve learned from creating and judging thousands of predictions on personal and real-world matters: the challenges of maintenance, the limitations of prediction markets, the interesting applications to my other essays, skepticism about pundits and unreflective persons’ opinions, my own biases like optimism & planning fallacy, 3 very useful heuristics/approaches, and the costs of these activities in general. (Plus a geeky parody of Fate/Stay Night.)

--- /doc/ai/nn/transformer/alphafold/index ‘AlphaFold’ tag 2019-12-14 2024-10-17 ai/nn/transformer ai/scaling/economics

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--- /doc/biology/portia/index ‘Portia spider’ tag 2019-10-20 2024-07-15 ai/nn/sparsity iq/animal psychology/animal/maze psychology/neuroscience psychology/vision

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--- /doc/ai/nn/diffusion/discrete/index ‘discrete diffusion model’ tag 2021-02-10 2024-10-28 reinforcement-learning/model/decision-transformer

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--- /doc/psychiatry/adhd/index ‘ADHD’ tag 2020-01-27 2024-11-29 psychiatry/autism psychology/energy psychology/personality/conscientiousness

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--- /doc/genetics/heritable/correlation/mendelian-randomization/index ‘Mendelian Randomization’ tag 2020-01-28 2024-07-15 statistics/causality statistics/meta-analysis

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--- /doc/reinforcement-learning/model/alphago/index ‘AlphaGo’ tag 2019-09-09 2024-07-04 ai/nn/cnn ai/nn/sampling reinforcement-learning/chess reinforcement-learning/model reinforcement-learning/multi-agent reinforcement-learning/scaling

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--- /doc/economics/copyright/index ‘copyright’ tag 2019-09-14 2024-08-21 economics/mechanism-design law music

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[essay on this tag topic]

--- /doc/reinforcement-learning/chess/index ‘AI chess’ tag 2019-09-09 2024-11-27 psychology/chess reinforcement-learning/model

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--- /doc/psychiatry/bipolar/index ‘bipolar’ tag 2019-11-21 2024-10-13 psychiatry/adhd psychiatry/borderline psychology/energy

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--- /doc/reinforcement-learning/imitation-learning/brain-imitation-learning/index ‘brain imitation learning’ tag 2019-09-13 2024-10-29 ai/nn/sparsity/knowledge-distillation reinforcement-learning/safe

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--- /doc/genetics/microbiome/index ‘microbiome’ tag 2019-11-09 2024-10-21 biology

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--- /doc/ai/nn/sparsity/index ‘NN sparsity’ tag 2019-12-07 2024-11-16 ai/nn/transformer/attention/sparsity ai/scaling/mixture-of-experts biology/portia cs/algorithm/information/compression psychology/neuroscience

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[page summary]

--- /fiction/clippy It Looks Like You’re Trying To Take Over The World Gwern 2022-03-06 2023-03-28 ai/nn/transformer/gpt/inner-monologue fiction/humor fiction/science-fiction reinforcement-learning/safe reinforcement-learning/scaling

Fictional short story about Clippy & AI hard takeoff scenarios grounded in contemporary ML scaling, self-supervised learning, reinforcement learning, and meta-learning research literature.

It might help to imagine a hard takeoff scenario using solely known sorts of NN & scaling effects… Below is a story which may help stretch your imagination and defamiliarize the 2022 state of machine learning.

To read the alternate annotated version of this story, scroll to the end

--- /doc/iq/animal/index ‘animal cognition’ tag 2020-04-26 2024-11-20 psychology/animal psychology/neuroscience

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[page summary]

--- /doc/reinforcement-learning/model/muzero/index ‘MuZero’ tag 2019-12-27 2024-01-01 ai/nn/rnn reinforcement-learning/chess reinforcement-learning/imperfect-information reinforcement-learning/model/alphago reinforcement-learning/scaling

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--- /doc/psychiatry/lithium/index ‘lithium-in-water’ tag 2020-06-06 2024-09-25 iodine nootropic/quantified-self psychiatry/bipolar/lithium psychiatry/depression

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[essay on this tag topic]

--- /doc/ai/scaling/mixture-of-experts/index ‘MoE NN’ tag 2019-09-28 2024-10-29 ai/nn/sparsity ai/nn/transformer psychology/neuroscience

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--- /doc/genetics/sequencing/index ‘genome sequencing’ tag 2020-02-22 2024-11-04 longevity/epigenetics

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--- /doc/longevity/glp/tirzepatide/index ‘tirzepatide’ tag 2021-01-27 2024-11-20 exercise/gravitostat longevity/glp/semaglutide

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--- /everything Everything Is Correlated Gwern 2014-09-12 2023-04-30 economics/advertising genetics/heritable/correlation insight-porn philosophy/epistemology sociology statistics/bayes statistics/causality statistics/variance-component survey

Anthology of sociology, statistical, or psychological papers discussing the observation that all real-world variables have non-zero correlations and the implications for statistical theory such as ‘null hypothesis testing’.

Statistical folklore asserts that “everything is correlated”: in any real-world dataset, most or all measured variables will have non-zero correlations, even between variables which appear to be completely independent of each other, and that these correlations are not merely sampling error flukes but will appear in large-scale datasets to arbitrarily designated levels of statistical-significance or posterior probability.

This raises serious questions for null-hypothesis statistical-significance testing, as it implies the null hypothesis of 0 will always be rejected with sufficient data, meaning that a failure to reject only implies insufficient data, and provides no actual test or confirmation of a theory. Even a directional prediction is minimally confirmatory since there is a 50% chance of picking the right direction at random.

It also has implications for conceptualizations of theories & causal models, interpretations of structural models, and other statistical principles such as the “sparsity principle”.

--- /doc/ai/nn/transformer/attention/recurrent/index ‘recurrent Transformers’ tag 2020-11-09 2024-06-22 ai/nn/rnn ai/nn/transformer/attention/compression

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--- /doc/existential-risk/1985-hofstadter Metamagical Themas: Sanity and Survival Douglas Hofstadter 2012-04-16 2019-04-01 existential-risk philosophy/ethics politics sociology

3 essays by AI researcher Douglas Hofstadter exploring cooperation/game theory/‘superrationality’ in the context of the failure of political coordination to prevent global nuclear war

The following 3 essays were prepared from pages 737–780 of an ebook of Metamagical Themas: Questing for the Essence of Mind and Pattern (198539ya) by Douglas Hofstadter, an anthology of articles & essays primarily published in Scientific American “between January 198143ya and July 1983”. (I omit one entry in “Sanity and Survival”, the essay “The Tumult of Inner Voices, or, What is the Meaning of the Word ‘I’?”, which is unconnected to the other entries on cooperation/decision theory/nuclear war.) All hyperlinks are my insertion.

They are interesting for introducing the idea of ‘superrationality’ in game theory, an attempt to devise a decision theory/algorithm for agents which can reach global utility maxima on problems like the prisoner’s dilemma even in the absence of coercion or communication which has partially inspired later decision theories like UDT or TDT, linking decision theory to cooperation (eg. Oesterheld2017) & existential risks (specifically, nuclear warfare), and one networking project.

--- /doc/darknet-market/silk-road/1/2013-power Drugs 2.0: Your Crack’s in the Post Mike Power 2013-10-19 2013-11-29 cs/r darknet-market/silk-road/1 politics

May 2013 overview of Silk Road 1’s rise, powered by Tor & Bitcoin, enabling safe and easy online drug sales through the mail.

This is an annotated transcript of the chapter “Your Crack’s in the Post” (pg219–244) & an excerpt from the chapter “Prohibition in the Digital Age” (pg262), of Drugs 2.0: The Web Revolution That’s Changing How the World Gets High, Mike Power (2013-05-02); it is principally on the topic of Bitcoin, Tor, and Silk Road 1.

--- /suzanne-delage Interpreting ‘Suzanne Delage’ as Dracula Gwern 2009-02-23 2024-03-03 fiction/criticism fiction/gene-wolfe/suzanne-delage insight-porn

On the interpretation of Gene Wolfe’s short story ‘Suzanne Delage’ as inversion of Bram Stoker’s Dracula horror novel.

The 198044ya Gene Wolfe short story “Suzanne Delage” is a cryptic 6-page memoir about an apparently uneventful quotidian early-1900s American life, and has defied literary analysis for decades.

I solve it as an inversion of Bram Stoker’s Dracula.

--- /doc/reinforcement-learning/imperfect-information/hanabi/index ‘Hanabi AI’ tag 2020-11-26 2023-11-16 reinforcement-learning/multi-agent

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--- /doc/economics/mechanism-design/index ‘mechanism design’ tag 2019-12-12 2024-11-01 bitcoin/nashx cs/algorithm/information genetics/selection/artificial/apple politics reinforcement-learning/multi-agent

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--- /doc/economics/georgism/index ‘Georgism’ tag 2019-12-06 2024-09-29 economics/mechanism-design politics

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--- /doc/psychology/linguistics/index ‘language’ tag 2018-01-21 2024-11-24 ai/nn/tokenization cs/algorithm/information/compression philosophy/epistemology philosophy/logic philosophy/mind psychology/neuroscience psychology/writing

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--- /doc/philosophy/ontology/index ‘ontology’ tag 2019-11-12 2024-07-31 psychology/linguistics

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--- /scaling-hypothesis#it-from-byte The Scaling Hypothesis § It From Byte Gwern 2020-05-28 2022-01-02 reinforcement-learning/imitation-learning

On GPT-3: meta-learning, scaling, implications, and deep theory. The scaling hypothesis: neural nets absorb data & compute, generalizing and becoming more Bayesian as problems get harder, manifesting new abilities even at trivial-by-global-standards-scale. The deep learning revolution has begun as foretold.

Powerful generative models like GPT-3 learn to imitate agents and thus become agents when prompted appropriately. This is an inevitable consequence of training on huge amounts of human-generated data. This can be a problem.

Is human data (or moral equivalents like DRL agents) necessary, and other kinds of data, such as physics data, free of this problem? (And so a safety strategy of filtering data could reduce or eliminate hidden agency.)

I argue no: agency is not discrete or immaterial, but an ordinary continuum of capability, useful to a generative model in many contexts beyond those narrowly defined as ‘agents’, such as in the “intentional stance” or variational approaches to solving physics problems. Much like other DRL-elicited capabilities like meta-learning, memory, exploration, or reasoning, ‘agency’ is a useful tool for a large family of problems, and a powerful model applied to that family may, at some point, develop concepts of agency or theory of mind etc.

Thus, a very wide range of problems, at scale, may surprisingly induce emergent agency.

--- /aunn Absolute Unit NNs: Regression-Based MLPs for Everything Gwern 2023-07-20 2024-11-21 ai/nn/fully-connected ai/nn/rnn ai/nn/transformer/attention ai/scaling

Proposal for a general NN architecture handling arbitrary tasks, for scaling up MLPs, with applications.

I modestly propose a simple general-purpose Absolute Unit NN architecture for scaling up meta-learning prediction of arbitrary data inputs & outputs. The training data is encoded into a list, and the NN is trained to predict from the one-dimensional unit input of the absolute index of a data point to that data point unit. Predictions of new data are made by regressing the first unseen index; these predictions can be conditioned on by taking an additional gradient descent step on each new index+datapoint pair.

By memorizing & compressing the data, the NN generalizes, and at scale, like other self-supervised architectures, will learn to meta-learn datapoints, becoming a compact encoding of the distribution which rapidly learns each new datapoint in a single gradient descent step (like Hopfield nets or Reptile). Because of the uniformity and small dimensionality of input/output, the NN can be a deep MLP rather than a CNN, Transformer, or MoE.

Training can be done by ordinary SGD, but also by any local learning rule, or any mix thereof (eg. SGD at training time in compute-optimal large batches for offline training datasets, and then a local learning rule at ‘runtime’ when conditioning online with new inputs).

The advantages of this Absolute Unit include:

The primary disadvantage is that lacking so many inductive biases & high-dimensional input/output, AUNNs may require a truly chonky level of scale (in data & compute, likely not parameters) before they learn to generalize & meta-learn or become competitive with existing architectures. However, AUNN appears to work at small scale on at least one toy problem, so who knows?

--- /ab-test-indent A/B Testing Indentation & Justification Gwern 2022-09-27 2023-08-23 cs/css cs/js design/typography

1-year-long website A/B test of controversarial typographic choices of indenting & justifying paragraphs: a fairly precise null result. No design change was made.

A core typographic decision for web pages is whether to separate paragraphs using newlines, or indentation; and whether to use ragged-right or fully-justified text. Gwern.net defaults to book-like indentation + justified text; however, most websites do the opposite, and some readers have criticized the Gwern.net style.

To test whether the style has any important effect on readers, I run an A/B test on Gwern.net, randomly testing each day one of the 4 possible combinations, for 318 days (2022-09-27–10m2023-08-10) over 719,549

Analysis finds ~0 effect on page-views, with the effects of indentation/justification being far from statistically-significant or a posteriori probable.

So, I decided to not change the typography.

--- /note/statistic#best-student-ever Statistical Notes § Best Student Ever! Gwern 2014-07-17 2024-08-21 statistics/order

Miscellaneous statistical stuff

How often could a teacher truthfully say of a student, such as in a letter of recommendation, that the student was their “best student ever”?

This is an instance of the ‘record value’ problem in order statistics, and can be easily solved if we know how many students are taught each year: there are surprisingly many, because the number of best-evers will grow roughly logarithmically in total students, as the initial burst of record-setters fades out into ever-rarer record-breakers.

For example, for the literary critic Harold Bloom’s 64-year-long teaching career, he might observe something like ln(6,400) ≈ 9 best-ever students.

--- /doc/culture/2007-wolfe Nor the Summers as Golden: Writing Multivolume Works Gene Wolfe 2012-03-02 2013-05-16 culture fiction/gene-wolfe

Gene Wolfe on the depth and ending of novel series

A short essay by SF author Gene Wolfe on the challenges of writing a multi-volume novel series like his Book of the New Sun/Book of the Long Sun.

In a good multi-volume work, each volume must stand on its own, but also progress the overall plot while maintaining a sense of realism and consistent quality, despite the difficulty of sustaining the writing effort over years or even decades.

Can the author do this? And can the character or content support such a long acquaintance by either author or reader?

And most importantly, can it leave the reader feeling that it was all worthwhile, that he has seen the most important parts of the characters’ lives, that they have earned their ending (whatever it is) and that while they may be rewarded, somehow, the rest of their lives will not be as meaningful ‘nor the summers as golden’?

--- /doc/psychiatry/anorexia/index ‘anorexia’ tag 2019-10-11 2024-10-29 exercise genetics/selection/natural/human psychiatry/borderline

Bibliography for tag psychiatry/anorexia, most recent first: 1 related tag, 33 annotations, & 5 links ().

--- /doc/ai/nn/diffusion/midjourney/index ‘Midjourney’ tag 2023-05-28 2024-11-25 ai/anime ai/nn/transformer/clip/sample ai/nn/transformer/gpt/dall-e/3 reinforcement-learning/preference-learning

Bibliography for tag ai/nn/diffusion/midjourney, most recent first: 8 related tags, 15 annotations, & 93 links ().

--- /doc/longevity/glp/psychology/index ‘GLP agonists (psych)’ tag 2022-01-05 2024-11-20 psychiatry/alcoholism psychology/willpower

Bibliography for tag longevity/glp/psychology, most recent first: 1 related tag, 40 annotations, & 7 links ().

--- /utext Utext: Rich Unicode Documents Gwern 2023-10-08 2024-04-24 ai/nn/transformer/clip cs design/typography

An esoteric document proposal: abuse Unicode to create the fanciest possible ‘plain text’ documents.

Utext is a proposed esoteric-document format for typographically-rich documents (‘utexts’) under the constraint that they are pure UTF-8 text files. Utext is a Unicode answer to the typography maximalist question: “what is the most advanced (or at least, interesting) document that can be generated by (ab)using the full range of obscure capabilities provided by contemporary UTF-8? What is ‘plain text’ that is not so plain?”

I outline the inline & block formatting features that Unicode enables (comparable to popular formats like Markdown → HTML), and more advanced features that Utext could target: for better layout and saving text-artist labor, Utext could exploit text modification using large language models (LLMs) and ASCII image generation with neural nets. LLMs could rewrite text to replace words with synonyms or tweak punctuation for better line-justification. ASCII images could be generated from arbitrary image inputs or text prompts.

I note one should store together both Utext ‘source’ & ‘compiled’ text, which would greatly enhance upgradeability, accessibility, and community-building, by letting readers see & re-compile the source in addition to the final ‘compiled’ version. This further allows for interesting line-oriented text formats, which allow live WYSIWG editing, in-place version-control, or can stream over the network (opening up applications like simple chat rooms).

But probably the best output format would be as a narrow subset of HTML, turning it into hypertext and making it usable as a website, through judicious use of <pre> tags.

--- /doc/ai/nn/diffusion/midjourney/dropcap/index ‘dropcaps (AI typography)’ tag 2023-07-22 2024-11-02 ai/nn/transformer/clip/sample design/typography/dropcap

Bibliography for tag ai/nn/diffusion/midjourney/dropcap, most recent first: 4 related tags, 1 annotation, & 26 links ().

[essay on this tag topic]

--- /doc/design/typography/dropcap/index ‘dropcaps (typography)’ tag 2020-12-27 2024-11-28 ai/nn/diffusion/midjourney/dropcap design/typography/floral

Bibliography for tag design/typography/dropcap, most recent first: 2 related tags, 27 annotations, & 111 links ().

[essay on this tag topic]

--- /index Essays Gwern 2009-01-27 2024-11-26 meta

Personal website of Gwern Branwen (writer, self-experimenter, and programmer): topics: psychology, statistics, technology, deep learning, anime. This index page is a categorized list of Gwern.net pages.

This is the website of Gwern Branwen. I write about AI, psychology, & statistics. I am best known for my writings about DL scaling, poetry & anime neural networks, darknet markets & Bitcoin, blinded self-experiments, and dual n-back & spaced repetition.

For information about my site’s philosophy & method, see the About page; for the website features & implementation, see the Design page; for information about myself, my use of other websites, and contact information, see the about-me page; for information about new pages, see the Changelog ( = new), or new links. For an annotated version of this site index page, see the long index.

To use dark-mode () or reader-mode (), or disable popups (), or search the site (), use the floating toggle bar in the lower-rightupper-right corner (); for more, see the help page.

--- /larping Why Do Hipsters Steal Stuff? Gwern 2022-04-29 2022-04-29 cs/end-to-end-principle design insight-porn psychology/collecting psychology/novelty

Many fashions and artworks originate as copies of practical objects. Why? Because any form of optimized design is intrinsically esthetically-pleasing, and a great starting point.

Countless genres of art start in appropriating objects long incubated in subcultures for originally practical purposes, often becoming fashionable and collectible because no longer practically relevant, such as fancy watches. This seems a little odd, and leads to weird economic situations where brands bend over backwards to try to maintain ‘authenticity’ by, say, showing that some $5,000 pair of sneakers sold to collectors has some homeopathic connection to a real athlete.

With an infinite design-universe to explore, why does this keep happening and why does anyone care so much? Why, indeed, is l’art pour l’art not enough and people insist on the art being for something else, even when it blatantly is not?

Because humans respond esthetically to not simply complexity or ornamentation, but to the optimal combination of these in the pursuit of some comprehensible goal, yielding constraint, uniqueness, and comprehensibility. A functional goal keeps artists honest, and drives the best design, furnishing an archive of designs that can be mined for other purposes like fashion.

For that reason, the choice of a goal or requirement can, even if completely irrelevant or useless, be a useful design tool by fighting laziness and mediocrity.

--- /doc/design/typography/sentence-spacing/index ‘sentence-spacing (typography)’ tag 2023-02-09 2024-01-01 psychology/vision

Bibliography for tag design/typography/sentence-spacing, most recent first: 4 annotations & 6 links ().

[page summary]

--- /doc/ai/nn/transformer/gpt/claude/index ‘Claude AI’ tag 2022-08-04 2024-11-30 ai/nn/anthropic reinforcement-learning/preference-learning/mode-collapse

Bibliography for tag ai/nn/transformer/gpt/claude, most recent first: 3 related tags, 74 annotations, & 128 links ().

--- /doc/biology/ant/index ‘ants’ tag 2020-07-31 2024-10-06 psychology/animal

Bibliography for tag biology/ant, most recent first: 23 annotations & 1 link ().

--- /doc/ai/nn/anthropic/index ‘Anthropic’ tag 2021-01-24 2024-11-23 ai/nn/transformer/gpt/claude reinforcement-learning/openai

Bibliography for tag ai/nn/anthropic, most recent first: 2 related tags, 19 annotations, & 8 links ().

--- /note/elon-musk Elon Musk & Bipolar Disorder Gwern 2023-09-16 2024-01-30 genetics/heritable/emergenesis psychiatry/bipolar/elon-musk

Review of evidence for Elon Musk being on the bipolar mood disorder spectrum.

--- /greenland Reasons of State: Why Didn’t Denmark Sell Greenland? Gwern 2011-01-01 2019-08-17 nootropic philosophy/ethics politics

Denmark turned down 100m USD from the USA in 1946; I discuss how this was a bad idea—America got what it needed anyway while Denmark kept control of a loser.

After WWII, the Cold War motivated the USA to offer $1,286.3$1001946 million for ownership of Greenland, which was declined. The USA got the benefit of using Greenland anyway.

I discuss how the island otherwise remained a drain since, the dim prospect it will ever be useful to Denmark, and the forgone benefits of that offer.

I speculate on the real reasons for the refusal.

--- /doc/psychiatry/borderline/index ‘borderline’ tag 2020-06-25 2024-06-30 crime psychiatry/anorexia psychiatry/anxiety psychiatry/bipolar psychology/personality

Bibliography for tag psychiatry/borderline, most recent first: 3 related tags, 12 annotations, & 1 link ().

--- /doc/design/typography/sidenote/index ‘sidenotes (typography)’ tag 2021-06-10 2024-05-26 cs/css design/typography/sidenote

Bibliography for tag design/typography/sidenote, most recent first: 8 annotations & 70 links ().

[essay on this tag topic]

--- /doc/psychology/neuroscience/tcs/index ‘TDCS’ tag 2021-06-03 2024-01-01 psychology/neuroscience

Bibliography for tag psychology/neuroscience/tcs, most recent first: 6 annotations ().

--- /doc/ai/fiction/index ‘fiction by AI’ tag 2020-11-10 2023-08-19 fiction

Bibliography for tag ai/fiction, most recent first: 1 related tag, 8 annotations ().

--- /doc/genetics/genome-synthesis/index ‘genome synthesis’ tag 2019-11-30 2024-11-04 existential-risk

Bibliography for tag genetics/genome-synthesis, most recent first: 2 related tags, 38 annotations, & 20 links ().

--- /doc/psychiatry/bipolar/sleep/index ‘BP & sleep’ tag 2020-07-13 2023-10-27 psychiatry/bipolar/energy

Bibliography for tag psychiatry/bipolar/sleep, most recent first: 1 related tag, 8 annotations, & 3 links ().

--- /changelog Changelog Gwern 2013-09-15 2024-10-05 meta

Monthly chronological list of recent major writings/changes/additions to Gwern.net (see also the monthly newsletter).

This page is a changelog for Gwern.net: a monthly reverse chronological list of recent major writings/changes/additions.

Following my writing can be a little difficult because it is often so incremental. So every month, in addition to my regular subreddit submissions, I write up reasonably-interesting changes and send it out to the mailing list in addition to a compilation of links & reviews (archives).

For a feed of recently-added links & references, see the “newest links” page.

--- /doc/reinforcement-learning/deepmind/index ‘DeepMind’ tag 2022-05-31 2024-10-19 ai/scaling/economics

Bibliography for tag reinforcement-learning/deepmind, most recent first: 21 annotations & 6 links ().

--- /doc/cat/biology/allergy/antibody/index ‘cat-allergen antibody’ tag 2023-02-04 2024-01-01

Bibliography for tag cat/biology/allergy/antibody, most recent first: 9 annotations & 2 links ().

--- /doc/cat/psychology/drug/valerian/index ‘Valerian (cat)’ tag 2021-01-12 2024-08-01

Bibliography for tag cat/psychology/drug/valerian, most recent first: 4 annotations ().

--- /doc/cat/psychology/drug/tatarian-honeysuckle/index ‘Tatarian honeysuckle (cat)’ tag 2021-01-12 2021-05-23

Bibliography for tag cat/psychology/drug/tatarian-honeysuckle, most recent first: 2 annotations ().

--- /doc/cat/psychology/drug/catnip/survey/index ‘catnip survey’ tag 2024-01-01 2024-01-01

Bibliography for tag cat/psychology/drug/catnip/survey, most recent first: & 28 links ().

--- /doc/vitamin-d/index ‘Vitamin D’ tag 2021-02-10 2024-11-02 longevity

Bibliography for tag vitamin-d, most recent first: 26 annotations & 20 links ().

--- /doc/technology/digital-antiquarian/index ‘Digital Antiquarian’ tag 2020-08-10 2024-11-22

Bibliography for tag technology/digital-antiquarian, most recent first: 129 annotations & 10 links ().

--- /doc/history/s-l-a-marshall/index ‘SLAM (fraud)’ tag 2019-11-18 2024-01-01 psychology/personality/narcissism

Bibliography for tag history/s-l-a-marshall, most recent first: 6 annotations & 3 links ().

[page summary]

--- /doc/ai/video/index ‘AI video’ tag 2020-12-02 2020-12-02 ai/nn/transformer

Bibliography for tag ai/video, most recent first: 2 related tags, 1 annotation, & 4 links ().

--- /doc/darknet-market/william-pickard/index ‘William Pickard (LSD)’ tag 2024-01-01 2024-01-01 darknet-market/silk-road/1/lsd psychedelic

Bibliography for tag darknet-market/william-pickard, most recent first: & 22 links ().

--- /doc/lesswrong-survey/index ‘LW surveys’ tag 2024-01-01 2024-11-25 ai/nn/transformer/gpt/instruction-tuning survey

Bibliography for tag lesswrong-survey, most recent first: 1 related tag & 11 links ().

--- /doc/touhou/index ‘Touhou’ tag 2024-01-01 2024-11-09 japan music

Bibliography for tag touhou, most recent first: 8 annotations & 19 links ().

[essay on this tag topic]

--- /doc/japan/poetry/shotetsu/index ‘Shōtetsu’ tag 2020-06-05 2024-01-01 japan/poetry/teika

Bibliography for tag japan/poetry/shotetsu, most recent first: 4 annotations & 6 links ().

--- /doc/philosophy/ethics/ethicists/index ‘ethicists’ tag 2019-12-03 2023-03-04 psychology/cognitive-bias

Bibliography for tag philosophy/ethics/ethicists, most recent first: 15 annotations & 1 link ().

--- /doc/reinforcement-learning/nethack/index ‘Nethack AI’ tag 2019-12-04 2024-06-25 reinforcement-learning/exploration

Bibliography for tag reinforcement-learning/nethack, most recent first: 10 annotations & 11 links ().

--- /doc/genetics/selection/index ‘evolution’ tag 2019-09-08 2024-03-11

Bibliography for tag genetics/selection, most recent first: 6 related tags, 43 annotations, & 89 links ().

[essay on this tag topic]

--- /doc/genetics/selection/artificial/apple/index ‘apple breeding’ tag 2020-03-21 2023-01-31 biology economics/mechanism-design food genetics/cloning

Bibliography for tag genetics/selection/artificial/apple, most recent first: 1 related tag, 9 annotations, & 3 links ().

--- /doc/japan/history/tominaga-nakamoto/index ‘Tominaga Nakamoto’ tag 2024-01-01 2024-01-01 economics japan philosophy/religion

Bibliography for tag japan/history/tominaga-nakamoto, most recent first: 3 annotations & 5 links ().

--- /doc/modafinil/darknet-market/index ‘modafinil (DNM)’ tag 2021-06-19 2024-01-01 darknet-market

Bibliography for tag modafinil/darknet-market, most recent first: 3 annotations & 26 links ().

--- /newsletter/2018/index ‘newsletter/2018’ tag Gwern 2024-11-20 2024-11-20 newsletter

Bibliography for tag newsletter/2018, most recent first: 13 annotations ().

--- /newsletter/2019/index ‘newsletter/2019’ tag Gwern 2024-11-20 2024-11-20 newsletter

Bibliography for tag newsletter/2019, most recent first: 13 annotations ().

--- /newsletter/2017/index ‘newsletter/2017’ tag Gwern 2024-11-20 2024-11-20 newsletter

Bibliography for tag newsletter/2017, most recent first: 13 annotations ().

--- /newsletter/2014/index ‘newsletter/2014’ tag Gwern 2024-11-20 2024-11-20 newsletter

Bibliography for tag newsletter/2014, most recent first: 13 annotations ().

--- /newsletter/2016/index ‘newsletter/2016’ tag Gwern 2024-11-20 2024-11-20 newsletter

Bibliography for tag newsletter/2016, most recent first: 13 annotations ().

--- /newsletter/2015/index ‘newsletter/2015’ tag Gwern 2024-11-20 2024-11-20 newsletter

Bibliography for tag newsletter/2015, most recent first: 13 annotations ().

--- /newsletter/2013/index ‘newsletter/2013’ tag Gwern 2019-03-29 2019-03-29 newsletter

Bibliography for tag newsletter/2013, most recent first: 1 annotation ().

--- /newsletter/2022/index ‘newsletter/2022’ tag Gwern 2024-11-20 2024-11-20 newsletter

Bibliography for tag newsletter/2022, most recent first: 13 annotations ().

--- /doc/psychiatry/schizophrenia/rosenhan/index ‘Rosenhan fraud’ tag 2020-12-24 2023-02-03 statistics/bias

Bibliography for tag psychiatry/schizophrenia/rosenhan, most recent first: 9 annotations ().

--- /review/opera#manon Opera Reviews § Manon Gwern 2019-02-02 2021-12-03 fiction/criticism fiction/opera personal

A compilation of opera reviews since 2019.

Review of Manon, a French opera about a beautiful countryside girl whose craving for the ‘good life’ leads her into the Parisian demimondaine as a courtesan.

Exemplifying Girardian mimesis, Manon wants what everyone else wants, and wants what she can’t have, like her spurned lover only once he has taken religious vows. She plays off suitors, who compete in negative-sum games for her favors, until eventually she goes too far and is imprisoned, destroying her health; cast down, she realizes that ‘living only for pleasure’ was not the ideal life.

This scenario seems to exemplify the extent to which polygynous competition can result in negative-sum games, making almost everyone worse off except a few winners (and those possibly only temporarily).

--- /doc/cat/biology/allergy/index ‘cat allergies’ tag 2023-02-04 2023-07-28 genetics/editing

Bibliography for tag cat/biology/allergy, most recent first: 1 related tag, 8 annotations, & 1 link ().

--- /newsletter/2017/05 May 2017 News Gwern 2017-05-02 2024-11-29 newsletter

May 2017 Gwern.net newsletter: deep reinforcement learning, music. No completed writings.

--- /doc/cat/psychology/earwax/index ‘cats & earwax’ tag 2021-07-24 2024-08-25

Bibliography for tag cat/psychology/earwax, most recent first: 5 annotations & 2 links ().

[essay on this tag topic]

--- /note/attention Efficient Attention: Breaking The Quadratic Transformer Bottleneck Gwern 2020-07-25 2020-07-25 ai/nn/fully-connected ai/nn/transformer/attention

Discussion of removing a major architectural limitation in Transformer neural networks: the length of the input it can look at. Beyond a few thousand inputs, the resource requirements explode quadratically, rendering it infeasible to encode raw text at the character level, much less use entire books, images, or many other kinds of data which could be useful. Even for text, this inability also forces limitations like the use of BPE text encoding (responsible for sabotaging GPT-3’s rhyming, among other things), forgetfulness, limits to prompt programming, and inability to write coherent long texts.

A bibliography of possibilities for fixing this are organized hierarchically below:

  1. adding state, through recurrence (a memory) or creating a compressed history/state as an explicit summary

  2. tinkering with matrix algebra to remove the quadratic explosion while still keeping more or less the same self-attention mechanism

  3. approximating self-attention: using attention on only a small subset of tokens at any time (dodging the quadratic limit), or using a mix of local and global attention (local attentions to do most of the work, and global attention on top of the local attentions, each one avoiding the quadratic by considering only a few inputs at a time)

  4. miscellaneous tricks: removing parts, using only randomized untrainable components (with no need to compute gradients over) etc

--- /doc/psychology/vision/dream/index ‘dreams’ tag 2021-10-29 2024-07-02 zeo

Bibliography for tag psychology/vision/dream, most recent first: 19 annotations & 4 links ().

--- /rnn-metadata RNN Metadata for Mimicking Author Style Gwern 2015-09-12 2019-03-26 ai/nn/rnn ai/nn/sampling ai/poetry cs/shell tutorial

Teaching a text-generating char-RNN to automatically imitate many different authors by labeling the input text by author; additional experiments include imitating Geocities and retraining GPT-2 on a large Project Gutenberg poetry corpus.

Char-RNNs are unsupervised generative models which learn to mimic text sequences. I suggest extending char-RNNs with inline metadata such as genre or author prefixed to each line of input, allowing for better & more efficient metadata, and more controllable sampling of generated output by feeding in desired metadata. A 2015 experiment using torch-rnn on a set of ~30 Project Gutenberg e-books (1 per author) to train a large char-RNN shows that a char-RNN can learn to remember metadata such as authors, learn associated prose styles, and often generate text visibly similar to that of a specified author.

I further try & fail to train a char-RNN on Geocities HTML for unclear reasons.

More successfully, I experiment in 2019 with a recently-developed alternative to char-RNNs, the Transformer NN architecture, by finetuning training OpenAI’s GPT-2-117M Transformer model on a much larger (117MB) Project Gutenberg poetry corpus using both unlabeled lines & lines with inline metadata (the source book). The generated poetry is much better. And GPT-3 is better still.

--- /doc/economics/perpetuities/index ‘perpetuities’ tag 2019-11-11 2021-12-06 law

Bibliography for tag economics/perpetuities, most recent first: 15 annotations & 7 links ().

--- /doc/psychology/nature/index ‘psych of nature’ tag 2020-07-07 2024-01-01 design psychiatry/depression psychology

Bibliography for tag psychology/nature, most recent first: 16 annotations & 7 links ().

--- /doc/anime/eva/2003-rahxephoncomplete-anno-izubuchi Special Talk: Yutaka Izubuchi × Hideaki Anno Hideaki Anno, Yutaka Izubuchi 2012-02-28 2013-08-14 anime/eva interview

Discussion by Izubuchi and Anno of classic mecha anime

This interview was published in the 200321ya Japanese book RahXephon Complete (ISBN 4840110190); it has not been officially translated, but the ANF user Vir arranged for a rough translation. At my request, he gave me a copy and I have heavily edited it to what follows. (Names were translated phonetically; I have done my best to figure out what was meant.)

--- /newsletter/2014/13 2014 Year in Review Gwern 2014-12-18 2024-11-29 newsletter
--- /newsletter/2015/13 2015 News Gwern 2016-02-04 2024-11-29 newsletter
--- /morning-writing What Is The Morning Writing Effect? Gwern 2011-05-11 2024-03-01 psychiatry/bipolar/energy psychology/energy psychology/writing survey technology/google

Many writers anecdotally report they write best first thing early in the morning, apparently even if they are not morning people. Do they, and why?

Ericsson 199331ya notes that many major writers or researchers prioritized writing by making it the first activity of their day, often getting up early in the morning, working only a few hours and then spending the rest of the day on other things. This is based largely on writers anecdotally reporting they write best first thing early in the morning, apparently even if they are not morning people, although there is some additional survey/software-logging evidence of morning writing being effective. Ericsson was wrong about many things, so I wondered how true this was.

I compile all the anecdotes of writers discussing their writing times I have come across thus far. Do they, and why?

Preliminary results from ~400 writers and assorted surveys shows that Ericsson’s trend is, at best, a loose one. Many authors work later in the day, or at night, or claim much longer hours.

Informally, I do observe an intriguing tendency for fiction writers to write early in the morning, and to describe an almost dream-like altered state of consciousness which enables their fiction-writing.

--- /culture-is-not-about-esthetics Culture Is Not About Esthetics Gwern 2009-07-16 2015-07-05 music philosophy psychology sociology

Aesthetically & economically, maybe there is too much new art. Don’t take this too seriously.

--- /wikipedia-and-knol Wikipedia & Knol: Why Knol Already Failed Gwern 2009-01-21 2013-05-04 economics statistics/prediction statistics/survival-analysis technology/google wikipedia

Why Knol is worse than Wikipedia, has failed, and will continue to fail.

--- /wifi Internet WiFi improvement Gwern 2016-10-20 2017-01-05 cs/r cs/shell personal statistics/decision

After putting up with slow glitchy WiFi Internet for years, I investigate improvements. Upgrading the router, switching to a high-gain antenna, and installing a buried Ethernet cable all offer increasing speeds.

My laptop in my apartment receives Internet via a WiFi repeater to another house, yielding slow speeds and frequent glitches. I replaced the obsolete WiFi router and increased connection speeds somewhat but still inadequate. For a better solution, I used a directional antenna to connect directly to the new WiFi router, which, contrary to my expectations, yielded a ~6× increase in speed. Extensive benchmarking of all possible arrangements of laptops/dongles/repeaters/antennas/routers/positions shows that the antenna+router is inexpensive and near optimal speed, and that the only possible improvement would be a hardwired Ethernet line, which I installed a few weeks later after learning it was not as difficult as I thought it would be.

--- /melatonin Melatonin Gwern 2008-12-19 2015-02-14 nootropic psychology zeo

Melatonin improves sleep, & sleep is valuable

I discuss melatonin’s effects on sleep & its safety with research up to 2015; I segue into the general benefits of sleep and the severely disrupted sleep of the modern Western world, the cost of melatonin use and the benefit (eg. enforcing regular bedtimes), followed by a basic cost-benefit analysis of melatonin concluding that the net profit is large enough to be worth giving it a try barring unusual conditions or very pessimistic safety estimates.

--- /longevity Life Extension Cost-Benefits Gwern 2015-06-01 2018-10-26 cs/r statistics/bayes statistics/decision statistics/order statistics/power-analysis statistics/survival-analysis

Attempts at considering the profitability of life-extension interventions for healthy adults

--- /hyperbolic-time-chamber The Hyperbolic Time Chamber & Brain Emulation Gwern 2012-08-29 2018-09-02 ai/scaling/economics anime economics/automation fiction/science-fiction transhumanism

A time dilation chamber as thought experiment on the power of pure thought, with comparison to computer AGI advantages/disadvantages.

A time dilation tool from an anime is discussed for its practical use on Earth; there seem surprisingly few uses and none that will change the world, due to the severe penalties humans would incur while using it, and basic constraints like Amdahl’s law limit the scientific uses. A comparison with the position of an Artificial Intelligence such as an emulated human brain seems fair, except most of the time dilation disadvantages do not apply or can be ameliorated and hence any speedups could be quite effectively exploited. I suggest that skeptics of the idea that speedups give advantages are implicitly working off the crippled time dilation tool and not making allowance for the disanalogies.

--- /hafu Hafu Gender Ratios in Anime Gwern 2011-04-06 2019-06-14 anime cs/r fiction/criticism nootropic politics sociology statistics

Race as reflected in gender ratios within fictional bi-racial marriages in anime/manga show equal sex ratios and Western European overrepresentation with striking absence of Korean characters.

--- /review/mlp MLP: Immanetizing The Equestrian Gwern 2018-10-24 2020-11-30 anime/eva anime/my-little-pony fiction/criticism insight-porn music sociology

A meditation on subcultures & review of the cartoon series My Little Pony: Friendship Is Magic, focusing on fandom, plot, development, and meaning of bronydom.

I watch the 201014ya Western animated series My Little Pony: Friendship is Magic (seasons 1–9), delving deep into it and the MLP fandom, and reflect on it. What makes it good and empowered its fandom subculture to produce such a wide array of fanfictions, music, and art?

Focusing on fandom, plot, development, and meaning of bronydom, I conclude that, among other things, it has surprisingly high-quality production & aesthetics which are easily adapted to fandom and which power a Westernized shonen anime—which depicts an underappreciated plausibly-contemporary capitalist utopian perspective on self-actualization (a utopia people actually want to live in), reminiscent of other more explicitly self-help-oriented pop culture movements such as the recent Jordan B. Peterson movement.

Included are my personal rankings of characters, seasons, episodes, and official & fan music.

--- /colder-war Colder Wars Gwern 2009-06-07 2013-06-22 fiction/science-fiction politics transhumanism

MAD will not work in outer space; pre-emptive strikes are nigh-guaranteed.

--- /ab-test#covariate-impact-on-power A/B testing long-form readability on Gwern.net § Covariate Impact On Power Gwern 2012-06-16 2022-09-27 cs/css cs/js cs/r design/typography economics/advertising meta statistics/decision statistics/power-analysis technology/google

A log of experiments done on the site design, intended to render pages more readable, focusing on the challenge of testing a static site, page width, fonts, plugins, and effects of advertising.

Is it important in randomized testing of A/B versions of websites to control for covariates, even powerful ones? A simulation using a website’s data suggests that data is sufficiently large that it is not critical the way it is in many applications.

--- /ontological-pantheism Ontological Pantheism Gwern 2009-11-09 2014-07-11 philosophy

Descartes’s God is pantheism; a reductio ad absurdum of his ontology

--- /doc/psychology/spaced-repetition/1981-duchastel Long-term Retention of Prose Following Testing P. Duchastel, R. Nungester 2012-01-29 2013-08-24 psychology/spaced-repetition

Testing enhances later recall by highschool students of a history text

--- /doc/japan/poetry/teika/teika The Poems of Fujiwara no Teika Fujiwara no Teika 2011-12-25 2018-06-20 fiction/poetry japan/poetry/teika

a collation of English translations by various translators of waka poems by the major classical Japanese court poet Fujiwara no Teika (1162–1241)

--- /modafinil Modafinil Gwern 2009-02-20 2018-06-04 darknet-market law modafinil

Effects, health concerns, suppliers, prices & rational ordering.

Modafinil is a prescription stimulant drug. I discuss informally, from a cost-benefit-informed perspective, the research up to 2015 on modafinil’s cognitive effects, the risks of side-effects and addiction/tolerance and law enforcement, and give a table of current grey-market suppliers and discuss how to order from them.

--- /soylent Diet Variance: Soylent study Gwern 2013-05-22 2016-07-11 cs/r food nootropic/quantified-self psychology statistics/power-analysis

Proposal to use meal-replacements to partition daily variance in mood/productivity between diet and other factors

--- /self-decrypting Time-lock encryption Gwern 2011-05-24 2019-05-06 bitcoin cs/cryptography/timelock cs/shell

How do you encrypt a file such that it can be decrypted after a date, but not before? Use serial computations for proof-of-work using successive squaring, chained hashes, or witness encryption on blockchains.

In cryptography, it is easy to adjust encryption of data so that one, some, or all people can decrypt it, or some combination thereof. It is not so easy to achieve adjustable decryptability over time, a “time-lock crypto”: for some uses (data escrow, leaking, insurance, last-resort Bitcoin backups etc), one wants data which is distributed only after a certain point in time.

I survey techniques for time-lock crypto. Proposals often resort to trusted-third-parties, which are vulnerabilities. A better time-lock crypto proposal replaces trusted-third-parties with forcibly serial proof-of-work using number squaring and guaranteeing unlocking not after a certain point in time but after sufficient computation-time has been spent; it’s unclear how well number-squaring resists optimization or shortcuts. I suggest a new time-lock crypto based on chained hashes; hashes have been heavily attacked for other purposes, and may be safer than number-squaring. Finally, I cover obfuscation & witness-encryption which, combined with proof-of-work, can be said to solve time-lock crypto but currently remain infeasible.

--- /complexity Complexity no Bar to AI Gwern 2014-06-01 2019-06-09 ai economics/automation reinforcement-learning/safe statistics/prediction transhumanism

Critics of AI risk suggest diminishing returns to computing (formalized asymptotically) means AI will be weak; this argument relies on a large number of questionable premises and ignoring additional resources, constant factors, and nonlinear returns to small intelligence advantages, and is highly unlikely.

Computational complexity theory describes the steep increase in computing power required for many algorithms to solve larger problems; frequently, the increase is large enough to render problems a few times larger totally intractable. Many of these algorithms are used in AI-relevant contexts. It has been argued that this implies that AIs will fundamentally be limited in accomplishing real-world tasks better than humans because they will run into the same computational complexity limit as humans, and so the consequences of developing AI will be small, as it is impossible for there to be any large fast global changes due to human or superhuman-level AIs. I examine the assumptions of this argument and find it neglects the many conditions under which computational complexity theorems are valid and so the argument doesn’t work: problems can be solved more efficiently than complexity classes would imply, large differences in problem solubility between humans and AIs is possible, greater resource consumption is possible, the real-world consequences of small differences on individual tasks can be large on agent impacts, such consequences can compound, and many agents can be created; any of these independent objections being true destroys the argument.

--- /iodine Iodine and Adult IQ meta-analysis Gwern 2012-02-29 2017-07-18 cs/r iodine iq statistics/meta-analysis

Iodine improves IQ in fetuses; adults as well? A meta-analysis of relevant studies says no.

Iodization is one of the great success stories of public health intervention: iodizing salt costs pennies per ton, but as demonstrated in randomized & natural experiments, prevents goiters, cretinism, and can boost population IQs by a fraction of a standard deviation in the most iodine-deficient populations.

These experiments are typically done on pregnant women, and results suggest that the benefits of iodization diminish throughout the trimesters of a pregnancy. So does iodization benefit normal healthy adults, potentially even ones in relatively iodine-sufficient Western countries?

Compiling existing post-natal iodization studies which use cognitive tests, I find that—outliers aside—the benefit appears to be nearly zero, and so likely it does not help normal healthy adults, particularly in Western adults.

--- /note/statistic#program-for-non-spaced-repetition-review-of-past-written-materials-for-serendipity-rediscovery-archive-revisiter Statistical Notes § Program for Non-Spaced-Repetition Review of past Written Materials for Serendipity & Rediscovery: Archive Revisiter Gwern 2014-07-17 2024-08-21 cs/haskell cs/js cs/r genetics iq statistics/bayes statistics/causality statistics/decision statistics/order statistics/power-analysis survey

Miscellaneous statistical stuff

“Spaced repetition” helps one remember facts by creating discrete flashcards which one tests oneself on at increasingly distant ‘spaced’ time periods, repeating the fact just before one probably would have forgotten it; using software to track & automate tests & review scheduling, spaced repetition can scale to hundreds of thousands of discrete items.

If spacing out facts can help one remember by repeating items just before they are forgotten, is there any use for an “anti-spaced repetition” with the opposite method of repeating items only after they are probably forgotten?

I can think of two: first, it could be used to plan consumption of media such as movies by eg. tracking one’s favorite movies of all time and scheduling a rewatch whenever one is predicted to have forgotten enough to make them novel & highly enjoyable again. Second, and more interestingly, it could be used as a serendipity generator by allowing efficient processing of notes or excerpts or old writings.

In rereading such materials many years later, one often gains a new perspective or learns something useful because one forgot something: one didn’t understand something about it at the time, or new material has radically changed one’s interpretation, and since it’d been forgotten, no use could be made of it. Unfortunately, using spaced repetition to memorize such material, while ensuring any serendipitous connections get made as soon as possible, would be radically infeasible for bulky items (a single lengthy text excerpt might correspond to hundreds of discrete items, quickly overloading even SRS systems) and for almost all items, useless. One can justify rereading old material once or perhaps twice, but not many rereads nor full memorization. But rereading haphazardly is likely to inefficiently cover some material many times while neglecting others, and such rereads will often be far too early in time (or—a lesser concern here—too late).

Instead of spaced repetition, one would instead use anti-spaced repetition: each item would be tracked and reviewed and its expected forgetting time predicted, as in spaced repetition, but instead of scheduling a review before forgetting, a review is scheduled for some time (probably long afterwards) after forgetting. The total number of reviews of each item per user lifetime would be set to a small number, perhaps 1–4, bounding the time consumption at a feasible amount.

Such an anti-spaced repetition system could be used with hundreds of thousands of notes or clippings which a person might accumulate over a lifetime, and enable them to invest a few minutes a day into reading old notes, occasionally coming up with new insights, while ensuring they don’t waste time reading notes too many times or reading notes they likely already remember & have exhausted.

--- /google-shutdown Predicting Google closures Gwern 2013-03-28 2019-04-04 cs/linkrot/archiving cs/r statistics/prediction statistics/survival-analysis technology/google

Analyzing predictors of Google abandoning products; predicting future shutdowns

Prompted by the shutdown of Google Reader, I ponder the evanescence of online services and wonder what is the risk of them disappearing. I collect data on 350 Google products launched before March 201311ya, looking for variables predictive of mortality (web hits, service vs software, commercial vs free, FLOSS, social networking, and internal vs acquired). Shutdowns are unevenly distributed over the calendar year or Google’s history. I use logistic regression & survival analysis (which can deal with right-censorship) to model the risk of shutdown over time and examine correlates. The logistic regression indicates socialness, acquisitions, and lack of web hits predict being shut down, but the results may not be right. The survival analysis finds a median lifespan of 2824 days with a roughly Type III survival curve (high early-life mortality); a Cox regression finds similar results as the logistic - socialness, free, acquisition, and long life predict lower mortality. Using the best model, I make predictions about probability of shutdown of the most risky and least risky services in the next 5 years (up to March 2018). (All data & R source code is provided.)

--- /dnb-faq#other-effects Dual n-Back FAQ § Other Effects Gwern 2009-03-25 2019-12-05 dual-n-back iq nootropic/quantified-self

A compendium of DNB, WM, IQ information up to 2015.

Between 200816ya and 201113ya, I collected a number of anecdotal reports about the effects of n-backing; there are many other anecdotes out there, but the following are a good representation—for what they’re worth.

--- /newsletter/2019/11 November 2019 News Gwern 2019-10-13 2024-11-29 newsletter

November 2019 Gwern.net newsletter with 2 essays, links on PGD and AI scaling, disappearing polymorphs, and The Public Domain Review; 2 opera and 1 anime reviews.

--- /newsletter/2017/09 September 2017 News Gwern 2017-08-16 2024-11-29 newsletter

September 2017 Gwern.net newsletter with links on genetics (GWAS, engineering, evolution), intelligence, AI, metformin, lithium; 2 book and 6 movie reviews

--- /doc/statistics/bayes/hope-function/1994-falk The Ups and Downs of the Hope Function In a Fruitless Search Ruma Falk, Abigail Lipson, Clifford Konold 2012-07-01 2019-04-01 insight-porn psychology statistics/bayes/hope-function survey

On Bayesian updating of beliefs in sequentially searching a set of possibilities where failure is possible, such as waiting for a bus; the psychologically counterintuitive implication is that success on the next search increases even as the total probability of success decreases.

--- /newsletter/2020/02 February 2020 News Gwern 2019-12-26 2024-11-29 newsletter

February 2020 Gwern.net newsletter with links on AI scaling and disasters; 1 book, 1 movie, and 2 opera reviews.

--- /wood-pillow Wooden Pillow Gwern 2008-09-26 2016-12-22 nootropic/quantified-self personal

China & Egypt used wooden pillows; my recreations fail

--- /inclusionism In Defense of Inclusionism Gwern 2009-01-15 2018-11-28 anime sociology/technology statistics/prediction wikipedia

Iron Law of Bureaucracy: the downwards deletionism spiral discourages contribution and is how Wikipedia will die.

English Wikipedia is in decline. As a long-time editor & former admin, I was deeply dismayed by the process. Here, I discuss UI principles, changes in Wikipedian culture, the large-scale statistical evidence of decline, run small-scale experiments demonstrating the harm, and conclude with parting thoughts.

--- /turing-complete Surprisingly Turing-Complete Gwern 2012-12-09 2022-12-17 ai/nn cs/cellular-automaton insight-porn philosophy/mind reinforcement-learning/safe

A catalogue of software constructs, languages, or APIs which are unexpectedly Turing-complete; implications for security and reliability.

‘Computers’, in the sense of being Turing-complete, are extremely common. Almost any system of sufficient complexity—unless carefully engineered otherwise—may be found to ‘accidentally’ support Turing-complete somewhere inside it through ‘weird machines’ which can be rebuilt out of the original system’s parts, even systems which would appear to have not the slightest thing to do with computation. Software systems are especially susceptible to this, which often leads to serious security problems as the Turing-complete components can be used to run attacks on the rest of the system.

I provide a running catalogue of systems which have been, surprisingly, demonstrated to be Turing-complete. These examples may help unsee surface systems to see the weird machines and Turing-completeness lurking within.

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--- /review/opera#carmen Opera Reviews § Carmen Gwern 2019-02-02 2021-12-03 fiction/criticism fiction/opera personal

A compilation of opera reviews since 2019.

First opera review. How does a Met HD live opera broadcast work? Extremely well! They are lavishly produced, with multiple cameras and live directing, subtitling, and bonus features like seeing backstage. The opera itself is wonderful: it makes a great impression to sing and play orchestral music the entire time through, and the character motivations are painfully realistic and believable. I understand now the description of opera as a totalizing Gesamtkunstwerk: it combines all the art forms in an extremely technically-demanding combination, brutally punishing any imprecision, for hours on end, overriding the lack of realism to produce an exaggerated effect overpowering the viewer’s emotions, rendering it the most prestigious art of its era. The opera was interestingly “red pill”-like in depicting Carmen as demanding ever more sacrifices and ultimately abandoning her lover, leading to the final murder-suicide. Watching Carmen made me a believer in the Met HD broadcasts, and I resolved to watch more.

--- /fiction/erl-king The Erl King Gwern 2008-09-26 2016-08-20 fiction/science-fiction

Fairy tale tragedy, or, a lesson in courtesy and logic

A retelling of the story-within-a-story of Patricia A. Jackson’s short story “Uhl Eharl Khoehng” (Tales from the New Republic, 1999).

--- /newsletter/2020/08 August 2020 News Gwern 2019-12-26 2024-11-29 newsletter

August 2020 Gwern.net newsletter with an essay on sidenotes; links on human competence, efficient computing, and hardware overhangs; no reviews.

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September 2018 Gwern.net newsletter with links on genetics, human evolution, genetic engineering, LSD, and 1 book review.

--- /newsletter/2018/07 July 2018 News Gwern 2018-05-01 2024-11-29 newsletter

July 2018 Gwern.net newsletter with links on genetics, RL, true crime, tech economics, and 4 book reviews

--- /2014-spirulina 2014 Spirulina randomized self-experiment Gwern 2014-10-05 2014-11-27 cs/r nootropic/quantified-self psychology statistics

Spirulina users’ randomized trial on self-rated allergy symptoms: null result.

The supplement Spirulina has been suggested to help allergy symptoms. A randomized self-experiment is run by sceaduwe April—August 2014. Analysis suggests no effect of the spirulina.

--- /nootropic/magnesium#experiment-1 Magnesium Self-Experiments § Experiment 1 Gwern 2013-05-13 2020-01-31 cs/r nootropic/magnesium nootropic/quantified-self psychology statistics/bayes statistics/power-analysis

3 magnesium self-experiments on magnesium l-threonate and magnesium citrate.

Encouraged by TruBrain’s magnesium & my magnesium l-threonate use, I design and run a blind random self-experiment to see whether magnesium citrate supplementation would improve my mood or productivity. I collected ~200 days of data at two dose levels. The analysis finds that the net effect was negative, but a more detailed look shows time-varying effects with a large initial benefit negated by an increasingly-negative effect. Combined with my expectations, the long half-life, and the higher-than-intended dosage, I infer that I overdosed on the magnesium. To verify this, I will be running a followup experiment with a much smaller dose.

--- /math-error The Existential Risk of Math Errors Gwern 2012-07-20 2024-10-20 ai insight-porn philosophy statistics/bias survey transhumanism

Mathematical mistake/error-rates limit our understanding of rare risks and ability to defend against them

I have never written an equation or line of code that I was 100% confident of, or which I thought had less than a 1-in-trillions chance of it being wrong in some important way. Software & real-world systems are too complex & fragile.

Every part of my understanding, the hardware, or the real-world context is less reliable than 1-in-trillions.

Let’s consider potential problems with our understanding of even the most trivial seeming arithmetic comparison checking that ‘x + x = 2x’.

--- /sicp/1-2 SICP Chapter 1.2 notes Gwern 2009-04-09 2010-01-09 cs/haskell cs/scheme tutorial

recursion into iteration; primality testing

--- /sicp/1-1 SICP Chapter 1.1 notes Gwern 2009-03-14 2013-10-02 cs/haskell cs/scheme tutorial

Syntax, function definitions

--- /sicp/1-3 SICP Chapter 1.3 Gwern 2010-01-09 2011-08-13 cs/haskell cs/scheme tutorial

Generalizing functions with hardwired values

--- /hpmor-prediction ‘Methods of Rationality’ predictions Gwern 2012-03-25 2014-03-07 statistics/prediction transhumanism

Recording fan speculation for retrospectives

--- /newsletter/2019/02 February 2019 News Gwern 2019-01-20 2024-11-29 newsletter

February 2019 Gwern.net newsletter with 2 essays/projects, site improvements; links on genetics, AI, propaganda, and typography; and 1 opera review.

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August 2019 Gwern.net newsletter with 2 short essays, a major new site features, and links on AI, progress, and technology; 1 short book review, and 2 long movie reviews on ‘Gone with the Wind’ and ‘Shin Godzilla’.

--- /newsletter/2019/07 July 2019 News Gwern 2019-06-20 2024-11-29 anime/eva newsletter

July 2019 Gwern.net newsletter with links on science and history; 1 book review; and 7 movie/TV series reviews.

--- /newsletter/2020/11 November 2020 News Gwern 2019-12-26 2021-01-09 fiction/opera newsletter

November 2020 Gwern.net newsletter with links on DL and genomics scaling, dark mode rewrite, 1 essay, and 1 opera review (The Ring cycle).

--- /newsletter/2020/10 October 2020 News Gwern 2019-12-26 2024-11-29 fiction/opera newsletter

October 2020 Gwern.net newsletter with links on AI scaling, Euclid; further site reorganization & improvement.

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December 2017 Gwern.net newsletter with links on genetics/AI/politics/psychology/biology/economics, 2 movie reviews and 1 anime review

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February 2018 Gwern.net newsletter with 3 new essays and links on genetics/AI/psychology/economics, 1 book review, 1 movie review, and 7 pieces of music.

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April 2018 Gwern.net newsletter with links on genome synthesis, cloning, RL, causation, diet, and 1 movie review.

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May 2018 Gwern.net newsletter with 3 essays, links on genetic engineering/heritability/human evolution, politics, psychology, advertising, and 1 book and 1 movie review.

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August 2018 Gwern.net newsletter with links on genetic engineering, DRL, research quality, security, economics, and 4 book/movie reviews

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October 2018 Gwern.net newsletter with 5 new posts, links on genetics/human evolution/AI/meta-science/history of tech, 2 book reviews, 2 movie reviews, 1 series review.

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December 2018 Gwern.net newsletter with links on genetic engineering, NLP/DRL, history of technology, online economics; 2 book and 3 movie reviews.

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March 2019 Gwern.net newsletter with 3 writeups; links on genetics, ads, poetry; and 1 anime review, 1 opera review, and 1 movie review.

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April 2019 Gwern.net newsletter with links on AI, biology, SIGBOVIK, Dresden Codak; 1 book review, 3 movie reviews.

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May 2019 Gwern.net newsletter with links on cloning, DRL, religion, parasites; 6 movie reviews.

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October 2019 Gwern.net newsletter with links on DL scaling, SDAM, and public goods; 1 book and 2 opera reviews.

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December 2019 Gwern.net newsletter with links on gene editing, the Replication Crisis, computer latency, and suffering; 4 book reviews, 2 opera/movie reviews, and 2 anime reviews.

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March 2020 Gwern.net newsletter with links on pandemics, politics, DL; one anime review.

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July 2020 Gwern.net newsletter with links on the Uighurs, authoritarianism, negative emissions, AI overhang; 1 movie & 2 anime reviews.

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September 2020 Gwern.net newsletter with links on DRL and AI scaling, psychiatric disorders; no reviews.

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January 2019 Gwern.net newsletter with Danbooru2018 release announcement, links on genetic engineering, reinforcement learning, compression, and 3 book/movie reviews

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September 2019 Gwern.net newsletter with 2 behavioral genetics analyses and links on AI text generation, Registered Reports, political polarization, and history of technology; 4 movie reviews.

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July 2017 Gwern.net newsletter with links on intelligence, GANs, malaria, Borges’s essays, 2 book reviews

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August 2017 Gwern.net newsletter with links on genetics, AI, measurement error, bioethics, catnip, formal schooling, 1 book review, 2 anime reviews

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October 2017 Gwern.net newsletter with links on heritability, AlphaGo Zero, peer review, gifted-and-talented; 1 book & 1 movie review.

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November 2017 Gwern.net newsletter with links on genetics, reinforcement learning, psychology, economics; 2 book reviews, 1 movie review

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March 2018 Gwern.net newsletter with links on genetics, RL, politics, Uber, 2 book reviews, and 2 movie/anime reviews

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June 2018 Gwern.net newsletter with links on genetics, reinforcement learning, cats, experimentation, with 6 book reviews and 12 music links.

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November 2018 Gwern.net newsletter with links on genetics, CRISPR, political change, oil; 1 book and 1 movie review.

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June 2019 Gwern.net newsletter with 5 new essays; links on deep learning, history, technological/cultural evolution, & Scott Alexander; and 2 books & 1 movie review

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January 2020 Gwern.net newsletter with 5 writeups, and links on AI scaling, videos-for-cats, and art; 1 book and 1 opera review.

--- /haskell/wikipedia-archive-bot Writing a Wikipedia Link Archive Bot Gwern 2008-09-26 2017-09-08 cs/haskell cs/linkrot/archiving tutorial wikipedia

Haskell: tutorial on writing a daemon to archive links in Wikipedia articles with TagSoup and WebCite; obsolete.

This is a 200816ya tutorial demonstrating how to write a Haskell program to automatically archive Internet links into WebCite & Internet Archive to avoid linkrot, by parsing WP dumps, downloading & parsing WP articles for external links with the TagSoup HTML parsing library, using the WebCite/IA APIs to archive them, and optimizing runtime. This approach is suitable for one-off crawls but not for live archiving using the RSS feed; for the next step, see Wikipedia RSS Archive Bot for a demonstration of how one could write a RSS-oriented daemon.

Obsolete

--- /ea-donation LWer Effective Altruism donations, 2013–2014 Gwern 2015-05-12 2018-06-09 philosophy/ethics statistics survey

Analysis of 2013–2014 LessWrong survey results on how much more self-identified EAers donate suggests low median donation rates due to current youth and low incomes.

A LW critic noted that the annual LW survey reported a median donation for “effective altruists” of $0, though the EA movement encourages strongly donations. I look closer at the 2013–2014 LW surveys and find in multiple regression that identifying as an EA does predict more donations after controlling for age and income, suggesting that the low EA median donation may be due to EAers having low income and youth (eg. being a student) rather than being unusually or even averagely selfish.

--- /review/arpa ARPA and SCI: Surfing AI Gwern 2018-07-04 2021-05-06 ai/nn economics/automation history reinforcement-learning/robot sociology/technology

Review of Roland & Shiman 2002 history of a decade of ARPA/DARPA involvement in AI and supercomputing, and the ARPA philosophy of technological acceleration; it yielded mixed results, perhaps due to ultimately insurmountable bottlenecks—the time was not yet ripe for many goals.

Review of DARPA history book, Strategic Computing: DARPA and the Quest for Machine Intelligence, 1983–10199331ya, Roland & Shiman 200222ya, which reviews a large-scale DARPA effort to jumpstart real-world uses of AI in the 1980s by a multi-pronged research effort into more efficient computer chip R&D, supercomputing, robotics/self-driving cars, & expert system software.

Roland & Shiman 200222ya particularly focus on the various ‘philosophies’ of technological forecasting & development, which guided DARPA’s strategy in different periods.

They ultimately endorse a weak technological determinism where the bottlenecks are too large for a small (in comparison to the global economy & global R&D) organization to meaningfully affect on average. The best a DARPA-like organization can hope for is a largely agnostic & reactive strategy, focused on patching up gaps and missed opportunities.

In this approach, the organization ‘surfs’ waves of technological changes, rapidly exploiting the potential of newly maturing technology while investing their limited funds into targeted research. Because of their freedom of action and their global view of the entire technology pipeline, they can function as ‘troubleshooters’, and enjoy good bang for their buck. But it is generally not possible for them to make a technology work if the time is not ripe, or develop entire new technologies or fields from scratch, and they must be humble about what their net long-term impact is.

(For broader discussion of progress, see “Lessons from the Media Lab” & Bakewell.)

--- /touhou Touhou music by the numbers Gwern 2013-02-28 2015-02-28 cs/haskell cs/r cs/shell music sociology statistics

Collect music metadata and look for patterns

--- /newsletter/2020/06 June 2020 News Gwern 2019-12-26 2024-11-29 newsletter

June 2020 Gwern.net newsletter with 3 new pages/essays, and links on CRISPR, population screening, AI scaling, politics, and technological unemployment.

--- /note/local-optima Local Optima & Greedy Choices Gwern 2021-03-24 2021-03-24 economics psychology/energy sociology statistics/decision

Some interesting links on local optima/greediness/risk-aversion/creative destruction (eg. Porter hypothesis or equity premium puzzle), sometimes demonstrated by disasters.

--- /catnip#optimal-catnip-alternative-selection-solving-the-mdp Catnip immunity and alternatives § Optimal Catnip Alternative Selection: Solving the MDP Gwern 2015-11-07 2019-06-19 cat/genetics cat/psychology/drug/catnip cs/r statistics/bayes statistics/meta-analysis

Estimation of catnip immunity rates by country with meta-analysis and surveys, and discussion of catnip alternatives.

Bol et al 2017 tested 4 cat stimulants on a large (n > 100) sample of cats, yielding within-individual correlations of stimulant responses. This allows prediction of stimulant response conditional on observing other stimulant responses. I use this to demonstrate how to optimize stimulant selection as a sequential testing problem, yielding (for one set of realistic purchase costs) a test sequence of catnip → honeysuckle → silvervine → Valerian, which is different from the greedy policy of picking stimulants by prior probability.

--- /review/opera#dialogues-des-carmelites Opera Reviews § Dialogues Des Carmélites Gwern 2019-02-02 2021-12-03 fiction/criticism fiction/opera personal

A compilation of opera reviews since 2019.

Dramatic opera on the martyrdom of a convent of nuns during the French Revolution. Starkly minimalist staging. Invokes many great themes, but does not really live up to them or explore them to any satisfying depth.

--- /review/opera#madama-butterfly Opera Reviews § Madama Butterfly Gwern 2019-02-02 2021-12-03 fiction/criticism fiction/opera personal

A compilation of opera reviews since 2019.

One of the most popular operas, relies heavily on beautiful visuals and interesting gimmicks like puppeteers, at the cost of establishing plausible psychology for either protagonist or justifying the tragedy. Beautifully staged, this is true “poster art”.

--- /review/opera#the-magic-flute Opera Reviews § The Magic Flute Gwern 2019-02-02 2021-12-03 fiction/criticism fiction/opera personal

A compilation of opera reviews since 2019.

Rebroadcast of abridged 200618ya performance (which demonstrates how much Met HD broadcasts have improved technically over the past decade). Gorgeous nonsense. With excellent music by Mozart, lyrics unusually in English, and eccentrically colorful costumes/sets, but mostly unconvincing characters (not helped by abridgement), and a plot stuffed full of Masonic symbols but lacking any sense.

--- /review/opera#turandot Opera Reviews § Turandot Gwern 2019-02-02 2021-12-03 fiction/criticism fiction/opera personal

A compilation of opera reviews since 2019.

Fairy tale opera: a despotic Oriental princess chops off the heads of suitors if they cannot answer her riddle. A random prince happens to do so, sets her a counter-riddle, she fails, he tells her the answer, she falls in love with him for no reason, The End. Yeah, pretty dumb. Some amazing costumes and sets, though.

--- /plastination Plastination versus Cryonics Gwern 2011-07-24 2014-10-22 biology survey transhumanism

Break down survival as Drake equation, see how plastination differs from cryonics, try to calculate advantage

--- /otaku-essay Notes on Evangelion Gwern 2010-02-03 2017-05-14 anime/eva fiction/criticism

Evangelion thoughts—anti-religion, pro-psychology, character development, otaku genre.

--- /aria Aria’s Past, Present, and Future Gwern 2011-07-13 2013-08-03 anime cs fiction/criticism fiction/science-fiction

On divining the esoteric truth of Neo-Venezia through holes in world-building.

--- /fiction/genshiken Poems on the theme of Genshiken Gwern 2011-07-24 2014-02-23 anime fiction/poetry nootropic

Waka/haiku on Genshiken, clubs, and Comiket; allusions to Fujiwara no Teika and Shōtetsu.

--- /fiction/insert-or-abort Insert, Abort, Retry? Gwern 2012-08-02 2012-08-02 anime fiction philosophy/ethics

Fullmetal Alchemist insert meta-fiction

--- /fiction/brave-poem Brave Poem Gwern 2010-11-13 2010-11-13 anime fiction/poetry

Poem on memory inspired by the anime Angel Beats!; do you remember love?

--- /resilient-software Resilient Haskell Software Gwern 2008-09-26 2011-02-12 cs/haskell cs/linkrot/archiving cs/r technology

Lessons learned about bitrot in Haskell software

--- /note/traumatic-brain-injury Falling & Head Injuries Gwern 2019-01-08 2019-01-08 psychology/neuroscience

Links on traumatic brain injuries and mortality/morbidity from falling; do we greatly underestimate how many problems are caused by head injuries and how many QALYs are lost to falling?

--- /intermittent-fasting Intermittent Fasting Gwern 2011-11-20 2012-02-21 biology transhumanism

Tricking the body into living longer?

--- /note/scaling Machine Learning Scaling Gwern 2021-04-24 2021-04-24 ai/nn/transformer/gpt ai/scaling cs iq

Bibliography of ML scaling papers showing smooth scaling of neural net performance in general with increasingly large parameters, data, & compute.

--- /mcts-ai AI Risk Demos Gwern 2016-12-23 2016-12-23 cs reinforcement-learning/safe statistics/decision transhumanism

Simple demonstrations of the AI control problem using MCTS in a Gridworld

--- /immoral-book Immoral Books Gwern 2010-01-24 2010-01-24 fiction/criticism fiction/gene-wolfe philosophy/ethics

Argument that texts are neither moral nor immoral as they require active interpretation.

--- /fiction/mulberry The Mulberry Tree Gwern 2010-10-12 2019-02-24 fiction/criticism fiction/poetry

Essay on writing & rewriting short tanka

--- /fiction/snowbank The Snowbanks of Time Gwern 2011-01-15 2011-11-22 fiction/criticism fiction/poetry

Essay on writing tanka on truth & lies

--- /mugging-dp Solving Pascal’s Mugging with Dynamic Programming Gwern 2019-09-12 2019-10-07 cs/haskell cs/r philosophy statistics/decision

Attempt to resolve Pascal’s mugging by examining whether optimal policies with linear utility do in fact engage in ‘mugging’ behavior

--- /note/small-groups The Effectiveness of Unreasonable Small Groups Gwern 2021-05-14 2021-05-14 economics history politics psychology/personality/narcissism sociology

Examples of ‘scenius’ where highly-competitive tightly-communicating groups had surprisingly large effects and often finding low-hanging fruit (somehow) eluding generations before them.

--- /note/competence Ordinary Incompetence Gwern 2021-06-04 2021-06-04 economics iq/low psychology sociology

Incompetence is the norm; most people who engage in a task (even when incentivized for performance or engaging in it for countless hours) may still be making basic errors which could be remedied with coaching or deliberate practice.

--- /justification On Justifications Gwern 2008-09-26 2009-01-16 fiction philosophy/ethics

Philosophical fiction or a prose poem about suffering innocents and the theodicy

--- /fog-gun Buckminster Fuller’s fog gun Gwern 2009-02-13 2015-08-12 nootropic/quantified-self

Remaining materials towards one day building and trying out a real fog gun

--- /fiction/jaguar The Jaguars of God Gwern 2008-12-03 2008-12-03 fiction

A dream of a strange judgment day

--- /fiction/gryyfins-of-the-word Gryyfins of the Word Gwern 2008-09-26 2010-12-14 fiction

Incomplete story

--- /fiction/how-the-panther-got-black How the Panther Got Black Gwern 2009-02-02 2009-02-02 fiction/science-fiction

Mythic short story in the vein of Kipling’s Just-so Stories

--- /fiction/fragment Dream Gwern 2008-12-03 2008-12-03 fiction

An old dream

--- /fiction/before-dawn Before Dawn Gwern 2008-12-03 2008-12-03 fiction

Alliterative verse about staying up all night

--- /fiction/buddhas-wheel The Buddha’s Wheel Gwern 2008-11-30 2010-12-17 fiction

The enlightened is as one with cause and effect.

--- /note/regression Regression To The Mean Fallacies Gwern 2021-05-20 2021-05-20 psychology statistics/bayes statistics/bias statistics/causality

Regression to the mean is a general statistical phenomenon which leads to several widespread fallacies in analyzing & interpreting statistical results, such as residual confounding and Lord’s paradox.

--- /note/variance-component Variance Components Beyond Genetics Gwern 2019-03-29 2019-03-29 genetics psychology statistics/variance-component

Variance components analyses focus on estimating the net contribution of an entire group of variables to an outcome, without requiring estimating each variable; this is critical for learning if the haystack of variable contains a needle at all, and yet, this approach is hardly used outside behavioral genetics. That should change.

--- /iq The IQ Halo effect Gwern 2013-04-01 2017-02-02 iq psychology survey

On desirable correlates of intelligence

--- /fiction/human-story A Human Story Gwern 2009-02-17 2009-02-17 personal

The mutability of memory

--- /stress On Stress Gwern 2008-09-26 2014-06-24 philosophy

Stoic meditation with reference to being homeless. Written to myself at a particularly low point; like many, I take comfort in considering how things could be worse.

--- /fiction/dying-outside Dying Outside Gwern 2009-12-12 2009-12-12 fiction/poetry transhumanism

Poem about ALS

--- /fiction/final-gifts Final Gifts Gwern 2013-01-24 2013-01-24 fiction/poetry transhumanism

A poem for the end of history

--- /fiction/safecracker The Safecracker of Hearts Gwern 2009-08-15 2012-06-28 fiction/poetry

The thief of time gives us memories, the safecracker of hearts restores them

--- /fiction/hybrid-rainbow Hybrid Rainbow Gwern 2009-02-01 2009-02-01 fiction/poetry transhumanism

Poem about man & his machines.

--- /lifelogging Lifelogging Gwern 2009-10-17 2013-09-10 transhumanism

Thoughts on uses and a categorization of said uses for lifelogging

--- /note/note#cleanup-before-or-after Miscellaneous § Cleanup: Before Or After? Gwern 2009-08-05 2024-11-02 economics/mechanism-design

Misc thoughts, memories, proto-essays, musings, etc.

We usually clean up after ourselves, but sometimes, we are expected to clean before (ie. after others) instead. Why?

Because in those cases, pre-cleanup is the same amount of work, but game-theoretically better whenever a failure of post-cleanup would cause the next person problems.

--- /note/lizardman Lizardman Constant in Surveys Gwern 2013-04-12 2013-04-12 iq/low psychology sociology statistics/bias survey

A small fraction of human responses will always be garbage because we are lazy, bored, trolling, or crazy.

Researchers have demonstrated repeatedly in human surveys the stylized fact that, far from being an oracle or gold standard, a certain small percentage of human responses will reliably be bullshit: “jokester” or “mischievous responders”, or more memorably, “lizardman constant” responders—respondents who give the wrong answer to simple questions.

Below a certain percentage of responses, for sufficiently rare responses, much or all of responding humans may be lying, lazy, crazy, or maliciously responding and the responses are false. This systematic error seriously undermines attempts to study rare beliefs such as conspiracy theories, and puts bounds on how accurate any single survey can hope to be.

--- /death-note-ending Death Note’s Ending Gwern 2008-09-29 2012-12-10 anime fiction/criticism

Ambiguous ending of Death Note means even the victor is unclear; who was right?

--- /media-rl The Explore-Exploit Dilemma in Media Consumption Gwern 2016-12-24 2019-04-14 psychology statistics/bayes statistics/decision statistics/order

How much should we rewatch our favorite movies (media) vs keep trying new movies? Most spend most viewing time on new movies, which is unlikely to be good. I suggest an explicit Bayesian model of imprecise ratings + enjoyment recovering over time for Thompson sampling over movie watch choices.

--- /embryo-editing Embryo editing for intelligence Gwern 2016-01-22 2019-04-04 cs/r iq psychology statistics/bayes statistics/decision statistics/power-analysis transhumanism

A cost-benefit analysis of CRISPR-based editing for intelligence with 2015–2016 state-of-the-art

--- /tpb-bitcoin Bitcoin donations on The Pirate Bay Gwern 2014-02-25 2014-12-10 bitcoin cs/haskell cs/shell statistics

Downloading and parsing TPB metadata to estimate Bitcoin usage and revenue for uploaders

--- /scanners ‘Scanners Live in Vain’ as realistic SF Gwern 2013-06-28 2019-06-05 fiction/criticism fiction/science-fiction history sociology/technology

Discussion of Cordwainer Smith SF story, arguing that the pain-of-space is based on forgotten psychological issues in air travel, and concerns about worse ones in space travel, which were partially vindicated by the existence of interesting psychological changes in astronauts.

Cordwainer Smith’s classic SF short story “Scanners Live in Vain” is remembered in part for its use of the space-madness trope, “the Great Pain of Space”, usually interpreted symbolically/psychologically by critics. I discuss the state of aerospace medicine in 194579ya and subsequent research on “the breakaway effect”, “the overview effect”, and other unusual psychological states induced by air & space travel, and suggest Smith’s “the pain of space” is more founded on SF-style speculation & extrapolation of contemporary science/technology and anxieties than is appreciated due to the obscurity of the effects and the relative benignity of the subsequent best documented effects.

--- /charity-is-not-about-helping Charity is not about Helping Gwern 2011-09-15 2015-09-12 economics philosophy/ethics sociology statistics/bayes

Simple cost-benefit: distributed computing considered harmful as scientific lemon projects run at high resource cost.

--- /fiction/menard Gilles Goullet, Author of the Blindsight Gwern 2010-09-01 2014-02-20 borges fiction/science-fiction math/humor transhumanism

A parody of SF, the Internet, and Borges.

--- /cryonics LessWrong and cryonics Gwern 2013-01-01 2016-03-05 cs/r statistics survey transhumanism

How does LessWrong usage correlate with cryonics attitudes and signup rates?

--- /weather Weather and My Productivity Gwern 2013-03-19 2015-12-18 ai/tabular cs/r nootropic/quantified-self psychology statistics/power-analysis

Rain or shine affect my mood? Not much.

Weather is often said to affect our mood, and that people in sunnier places are happier because of that. Curious about the possible effect (it could be worth controlling for in my future QS analyses or attempting to imitate benefits inside my house eg. brighter lighting), I combine my long-term daily self-ratings with logs from the nearest major official weather stations, which offer detailed weather information about temperature, humidity, precipitation, cloud cover, wind speed, brightness etc, and try to correlate them.

In general, despite considerable data, there are essentially no bivariate correlations, nothing in several versions of a linear model, and nothing found by a random forest. It would appear that weather does not correlate with my self-ratings to any detectable degree, much less cause it.

--- /difference How Complex Are Individual Differences? Gwern 2010-06-23 2019-06-14 cs/algorithm/information insight-porn iq psychology transhumanism

Individual human brains are more predictable and similar than they are different, reflecting low Kolmogorov complexity and implying that beta uploading may be more feasible than guessed, with suggestions on optimizing archived information.

--- /modafinil-survey Modafinil community survey Gwern 2015-06-01 2018-06-21 cs/r nootropic survey

2015 survey of online modafinil users asking about dosages, consumption patterns, ratings, purchases, and demographics

--- /conscientiousness Conscientiousness & Online Education Gwern 2012-07-20 2016-01-20 cs/r iq psychology sociology statistics/meta-analysis statistics/order survey

Technology-driven shift in demand for Conscientiousness, not intelligence

--- /otaku-prediction NGE Rebuild Predictions Gwern 2011-03-03 2013-02-05 anime/eva statistics/prediction

a comprehensive taxonomy of predictions on future Evangelion media

--- /hn Hacker News submission analysis Gwern 2013-09-25 2015-12-01 cs/r cs/shell statistics

Describing HN submissions; estimating manipulability

--- /note/statistic#oh-deer-could-deer-evolve-to-avoid-car-accidents Statistical Notes § Oh Deer: Could Deer Evolve to Avoid Car Accidents? Gwern 2014-07-17 2024-08-21 cs/haskell cs/js cs/r genetics iq statistics/bayes statistics/causality statistics/decision statistics/order statistics/power-analysis survey

Miscellaneous statistical stuff

Can deer evolve under the selection pressure of car accidents to learn to avoid roads? Probably, but it’ll take a long time.

I’ve noticed while driving many deer corpses over the years. Cars seem like they could be a major source of deer mortality. If they are, deer might be evolving behavior to avoid cars. But deer/car accident rates appear stable or increasing (perhaps due to human population growth & construction). How fast would we expect to see any deer adaptation?

Looking at some of the mortality statistics, I model it as a liability threshold trait being selected on via truncation selection, and calculate some hypotheticals about whether and how fast they could adapt.

Teal deer: “of course, but it’d be slow.”

--- /hpmor ‘HP: Methods of Rationality’ review statistics Gwern 2012-11-03 2017-06-18 cs/haskell cs/r cs/shell statistics/prediction statistics/survival-analysis

Recording fan speculation for retrospectives; statistically modeling reviews for ongoing story with R

The unprecedented gap in Methods of Rationality updates prompts musing about whether readership is increasing enough & what statistics one would use; I write code to download FF.net reviews, clean it, parse it, load into R, summarize the data & depict it graphically, run linear regression on a subset & all reviews, note the poor fit, develop a quadratic fit instead, and use it to predict future review quantities.

Then, I run a similar analysis on a competing fanfiction to find out when they will have equal total review-counts. A try at logarithmic fits fails; fitting a linear model to the previous 100 days of MoR and the competitor works much better, and they predict a convergence in <5 years.

A survival analysis finds no major anomalies in reviewer lifetimes, but an apparent increase in mortality for reviewers who started reviewing with later chapters, consistent with (but far from proving) the original theory that the later chapters’ delays are having negative effects.

--- /doc/anime/eva/2011-house Interviewing translator Michael House Michael House 2011-11-11 2011-11-28 anime/eva fiction/criticism interview

Working at Gainax, Evangelion’s production & censorship, anime translation

--- /doc/anime/eva/little-boy/2004-okada Otaku Talk Toshio Okada, Kaichiro Morikawa, Takashi Murakami, Reiko Tomii 2012-04-09 2013-11-20 anime/eva/little-boy fiction/criticism fiction/science-fiction interview japan sociology

Definition of otaku, mania, moe, dame, anime, and generations

--- /doc/anime/eva/little-boy/2005-little-boy Excerpts from Little Boy Takashi Murakami 2013-01-15 2014-09-21 anime/eva/little-boy fiction/criticism

Short essays on the influence on Japanese culture of anime works Daicon, Evangelion, Space Battleship Yamato, and the artists Mr., Chiho Aoshima, and others.

--- /doc/anime/eva/2003-oshii-izubuchi Talk About RahXephon: In Search of Fantasy and Details Mamoru Oshii, Yutaka Izubuchi 2012-02-28 2021-08-06 anime/eva fiction/science-fiction interview

Oshii criticizes RahXephon and compares it to Neon Genesis Evangelion.

--- /dune Dune notes Gwern 2010-10-18 2018-06-08 fiction/science-fiction/frank-herbert

Observations on Frank Herbert’s Dune series—the notes are probably overstated, the Butlerian Jihad was not a robot uprising seeking to exterminate humanity, and precognition/prescience in the Dune universe appears to work backwards allowing for retro-causality and stable time-loops as exemplified by Leto II’s Golden Path creating Paul Atreides and the events of Dune.

--- /fulltext Research Bounties On Fulltexts Gwern 2018-12-31 2024-01-13 cs/linkrot/archiving meta technology

A list of papers/books/materials I have failed to obtain, and financial bounties for anyone who can provide copies to me or the Internet.

--- /doc/anime/eva/1997-anno-french Interview with Hideaki Anno (French) Hideaki Anno 2012-02-28 2012-02-28 anime/eva interview

Transcript of a French anime writer discussion of anime and Evangelion

--- /doc/culture/1963-asimov The Sword of Achilles Isaac Asimov 2011-08-31 2011-08-31 culture fiction/science-fiction psychology transhumanism

Does early interest in science fiction predicts future scientific careers or accomplishments?

--- /mugging Notes on Pascal’s Mugging Gwern 2009-12-23 2019-09-13 philosophy transhumanism

Attempt to resolve Pascal’s mugging by proposing & justifying a linear prior probability which bounds losses.

TODO

--- /doc/bitcoin/2014-mccaleb 2014 Jed McCaleb MtGox interview Jed McCaleb 2014-02-16 2014-03-18 bitcoin interview

Email interview with McCaleb on early history of his MtGox, verifying it did not trade real but online Magic cards

--- /doc/anime/2010-sarrazin Ero-Anime: Manga Comes Alive Stephen Sarrazin 2011-12-23 2013-11-23 anime fiction/criticism

Short history of trends in Japanese erotic anime and manga, especially lolicon

--- /doc/anime/eva/little-boy/2005-sawaragi On The Battlefield of ‘Superflat’ Noi Sawaragi 2012-05-18 2012-05-18 anime/eva/little-boy fiction/criticism

Post-WWII ambiguity causes otakudom

--- /littlewood Littlewood’s Law and the Global Media Gwern 2018-12-15 2022-01-02 insight-porn philosophy/epistemology politics psychology/cognitive-bias psychology/personality/psychopathy sociology/technology statistics/bias

Selection effects in media become increasingly strong as populations and media increase, meaning that rare datapoints driven by unusual processes such as the mentally ill or hoaxers are increasingly unreliable as evidence of anything at all and must be ignored. At scale, anything that can happen will happen a small but nonzero times.

Online & mainstream media and social networking have become increasingly misleading as to the state of the world by focusing on ‘stories’ and ‘events’ rather than trends and averages. This is because as the global population increases and the scope of media increases, media’s urge for narrative focuses on the most extreme outlier datapoints—but such datapoints are, at a global scale, deeply misleading as they are driven by unusual processes such as the mentally ill or hoaxers.

At a global scale, anything that can happen will happen a small but nonzero times: this has been epitomized as “Littlewood’s Law: in the course of any normal person’s life, miracles happen at a rate of roughly one per month.” This must now be extended to a global scale for a hyper-networked global media covering anomalies from 8 billion people—all coincidences, hoaxes, mental illnesses, psychological oddities, extremes of continuums, mistakes, misunderstandings, terrorism, unexplained phenomena etc. Hence, there will be enough ‘miracles’ that all media coverage of events can potentially be composed of nothing but extreme outliers, even though it would seem like an ‘extraordinary’ claim to say that all media-reported events may be flukes.

This creates an epistemic environment deeply hostile to understanding reality, one which is dedicated to finding arbitrary amounts of and amplifying the least representative datapoints.

Given this, it is important to maintain extreme skepticism of any individual anecdotes or stories which are selectively reported but still claimed (often implicitly) to be representative of a general trend or fact about the world. Standard techniques like critical thinking, emphasizing trends & averages, and demanding original sources can help fight the biasing effect of news.

--- /newsletter/2018/13 2018 News Gwern 2018-12-08 2024-11-29 newsletter

Annual summary of 2018 Gwern.net newsletters, selecting my best writings, the best 2018 links by topic, and the best books/movies/anime I saw in 2018, with some general discussion of the year.

--- /wikipedia-resume Wikipedia Résumé Gwern 2010-10-18 2016-07-13 personal wikipedia

A precis of my work on the English Wikipedia, 2004–2017 (edit counts, articles by subject, and particularly notable articles)

--- /zeo/potassium Potassium sleep experiments Gwern 2012-12-21 2016-11-15 cs/r nootropic/potassium nootropic/quantified-self statistics/bayes zeo

2 self-experiments on potassium citrate effects on sleep: harm to sleep when taken daily or in the morning

Potassium and magnesium are minerals that many Americans are deficient in. I tried using potassium citrate and immediately noticed difficulty sleeping. A short randomized (but not blinded) self-experiment of ~4g potassium taken throughout the day confirmed large negative effects on my sleep. A longer followup randomized and blinded self-experiment used standardized doses taken once a day early in the morning, and also found some harm to sleep, and I discontinued potassium use entirely.

--- /atomism The Presocratic’s Logical Path to Atomism Gwern 2010-11-06 2012-03-06 philosophy

How and why did they deduce atomism?

--- /console-insurance Console Insurance Is A Ripoff Gwern 2009-03-10 2012-02-02 economics statistics/decision

Back of envelope financial calculations: Warranties, fine; insurance, no!

--- /long-stagnation Scientific stagnation Gwern 2012-02-24 2013-12-30 economics transhumanism

Shrinking marginal returns to science and technology

--- /3-grenades The 3 Grenades and the 4 Noble Truths Gwern 2008-11-24 2017-02-08 cs personal

CMU game connected to error coding theory

--- /fiction/poem Poems Gwern 2011-07-28 2023-10-14 fiction/poetry

Miscellaneous waka/haiku, by season

--- /miletian Against the Miletians and the One True Element Gwern 2008-10-17 2011-11-25 philosophy

Exploring consequences of material monism and conflict with observations

--- /evolutionary-license Evolutionary Software Licenses Gwern 2009-01-27 2013-10-31 cs economics politics

Game theory on BSD vs. GPL: partnership? Is GPL an evolutionary stable strategy against invasion by proprietary copyright strategies?

--- /isomorphism Isomorphisms & Meaning Gwern 2009-04-25 2015-01-03 cs philosophy

Haskell juvenilia; investigating when two theories or representations mean the same things.

--- /simulation-inference Simulation Inferences Gwern 2009-05-29 2012-04-15 cs math philosophy transhumanism

How small must be the computer simulating the universe?

--- /girl-scouts Girl Scouts & Good Corporate Governance Gwern 2011-04-21 2015-02-15 economics math philosophy/ethics politics statistics/survival-analysis technology

Cookie prices & tax filings as evidence of corporate inefficiency

--- /drug-heuristic The Algernon Argument Gwern 2010-03-23 2018-06-03 insight-porn iq melatonin modafinil nootropic psychedelic psychology/willpower transhumanism

Why most supplements fail: IQ improvement skepticism, Yudkowsky & Bostrom’s heuristics, nootropics

--- /abortion An Abortion Dialogue Gwern 2008-11-10 2019-03-05 genetics philosophy/ethics transhumanism

Dialogue pointing out some difficulties of materialist objections to abortion.

--- /epigram Epigrams Gwern 2014-04-30 2022-10-14 fiction/humor insight-porn technology

Witticisms, parodies, pointed observations, japeries and/or jocularities, Tom Swifties, nominative determinism, and discursive drollery

--- /anime-criticism Why Anime? Gwern 2011-02-12 2014-09-14 anime fiction/criticism philosophy

Objectively, anime/manga better than American alternatives. So why the need to justify?

The popularity of anime in the 1990s and among nerds reflects in part a historical contingency: the failure of American and Western media to deliver long-form narratives dealing in fantasy, science fiction, and non-realism of every type, and which can cater to a niche or reflect a unique artistic vision (enabled by the cheapness of manga/LN production and the 1980s–1990s OVA economic model) while benefiting from the flexibility of animation in depicting anything without enormously costly SFX, while instead Western media focused on syndicated mass market lowest-common-denominator live-action series. The post-90s TV renaissance and extraordinary rise of ‘prestige series’, science fiction and superhero franchise, and continuous exponential increase in SFX capabilities/decrease in cost, sucked much of the wind out of anime’s sails. While still hugely popular both in America & overseas, and an accepted part of the culture, it no longer is as exciting as it used to be, or a growing juggernaut.

So if anime can no longer boast unique access to diversity and long-form SF/F narrative, what does anime still uniquely offer us? Why not just go geek out over the latest MCU or Star Wars movie ad nauseam? I suggest it is simply that it is one of the most-developed foreign media sources, which gains value simply because it is different–foreign, and not so American. Haven’t you seen enough of that? Even a mediocre foreign work gains interest from the novelty and differences.

--- /font Who Buys Fonts? Gwern 2021-04-21 2023-08-02 design/typography economics sociology/technology

Fonts are durable, highly-reusable, compact, & high-quality software products which do not ‘bitrot’. Nevertheless, hundreds or thousands of new ones come out every year despite enormous duplication; why? I speculate that designer boredom seems to be the answer: they crave novelty.

Fonts are a rare highlight in software design—stable, with well-defined uses, highly-compatible software stacks, and long-lived. Unsurprisingly, a back-catalogue of tens or hundreds of thousands of digital fonts out there, many nigh-indistinguishable from the next in both form and function.

Why, then do they all cost so much, and who is paying for them all, and even going around commissioning more fonts?

The casualness of the highly marked-up prices & the language around commissioned fonts strongly points to designers spending client money, largely for the sake of novelty & boredom, functioning as a cross-subsidy from large corporations to the art of typography. The surplus of fonts then benefits everyone else—as long as they can sort through all the choices!

--- /zeo/vitamin-d Vitamin D sleep experiments Gwern 2012-01-01 2016-11-10 cs/r nootropic/quantified-self statistics/bayes zeo

Self-experiment on vitamin D effects on sleep: harmful taken at night, no or beneficial effects when taken in the morning.

Vitamin D is a hormone endogenously created by exposure to sunlight; due to historically low outdoors activity levels, it has become a popular supplement and I use it. Some anecdotes suggest that vitamin D may have circadian and zeitgeber effects due to its origin, and is harmful to sleep when taken at night. I ran a blinded randomized self-experiment on taking vitamin D pills at bedtime. The vitamin D damaged my sleep and especially how rested I felt upon wakening, suggesting vitamin D did have a stimulating effect which obstructed sleep. I conducted a followup blinded randomized self-experiment on the logical next question: if vitamin D is a daytime cue, then would vitamin D taken in the morning show some beneficial effects? The results were inconclusive (but slightly in favor of benefits). Given the asymmetry, I suggest that vitamin D supplements should be taken only in the morning.

--- /wikipedia-and-dark-side-editing Wikipedia and Dark Side Editing Gwern 2009-05-24 2013-02-16 anime/eva wikipedia

Cynical tactics encouraged by Wikipedia’s abdication of thought known as ‘No Original Research’ and ‘Reliable Sources’

--- /ethical-sperm-donation The Morality of Sperm Donation Gwern 2012-07-20 2018-09-02 iq philosophy/ethics psychology statistics/power-analysis

Is sperm donating a worthwhile form of positive eugenics?

--- /fiction/men-of-iron Men of Iron Gwern 2012-12-24 2017-06-04 biology fiction/science-fiction

What-if Chiang-style SF story on iron vanishing and the Great Silence

--- /co2-coin CO2 Coin: Decentralized Carbon Capture Blockchains Gwern 2021-06-12 2023-07-13 bitcoin economics/mechanism-design technology

Sketch of a decentralized mineralization-based carbon capture: suppliers stake on reported deposits of mineral dust in publicly-auditable locations.

Blockchains or tokens for carbon dioxide removal have sometimes been proposed, but provide little advantage.

I review the principles of cryptoeconomics for designing mechanisms, and the proposal of “mineralization”—rock dust naturally reacting with atmospheric CO2 to lock it into minerals—for carbon capture to fight global warming.

Cryptoeconomics often relies on auditability & challenges to create desired behavior, and mineralization provides an objective, checkable, form of carbon credits. Thus, one can set up a simple economic game where miners claim tokens for doing mineralization to sell as carbon offsets, and challengers audit their supposed mineralization deposits hunting for fraud; the equilibrium is honest reporting of mineralization quantities, yielding a true decentralized, reliable, fraud-resistant “CO2 Coin”.

--- /computers How Many Computers Are In Your Computer? Gwern 2010-01-18 2021-05-05 cs/hardware economics insight-porn

Any ‘computer’ is made up of hundreds of separate computers plugged together, any of which can be hacked. I list some of these parts.

Why are there so many places for backdoors and weird machines in your “computer”?

Because big things are made of small things: your computer is in fact scores or hundreds, perhaps even thousands, of computer chips, many of which host weird machines and are explicitly or implicitly capable of Turing-complete computations (many more powerful than desktops of bygone eras), working together to create the illusion of a single ‘computer’.

Backdoors, bugs, weird machines, and security do not care about what you think—only where resources can be found and orchestrated into a computation.

--- /copyright Against Copyright Gwern 2008-09-26 2014-05-04 cs/algorithm/information/compression philosophy politics

Copyright considered paradoxical, incoherent, and harmful from an information theory and compression perspective as there is no natural kind corresponding to ‘works’, merely longer or shorter strings for stupider or smarter algorithms.

--- /sand Cultural drift: cleaning methods Gwern 2013-05-07 2017-03-26 fiction/criticism sociology/technology survey technology/google

Forgotten chores and their use by Romanticism

Some old books mention sandy floors and sprinkling water on the ground; these asides seem to go unnoticed by most/all readers. I highlight them, explain and discuss their use as now-obsolete cleaning practices, poll Internet users to see how forgotten they are, and ponder implications. In an appendix, I discuss a similar issue I encountered in pre-Space-Race American science fiction.

--- /sunk-cost Are Sunk Costs Fallacies? Gwern 2012-01-24 2019-06-12 economics philosophy psychology/animal psychology/cognitive-bias statistics/decision survey

Human and animal sunk costs often aren’t, and sunk cost bias may be useful on an individual level to encourage learning. Convincing examples of sunk cost bias typically operate on organizational levels and are probably driven by non-psychological causes like competition.

--- /wikipedia-and-other-wikis Wikipedia and Other Wikis Gwern 2009-01-27 2012-05-12 design economics technology wikipedia

Network effects & benefits of gritting one’s teeth & submitting to a Wikipedia’s rules, rather than using Wikia or one’s own site.

--- /collecting What Is The Collecting Mindset? Gwern 2021-03-31 2022-02-25 economics insight-porn psychology sociology

The activity of manufactured collecting is puzzling; what explains the enormous resources spent on what is, by and large, neither lucrative, prestigious, nor entertaining? Is it just a misfire and quirk of human psychology poorly adapted to industrial society where ‘collectibles’ can be varied and marketed 24/7/365?

--- /complement Laws of Tech: Commoditize Your Complement Gwern 2018-03-17 2022-01-11 ai/scaling/economics economics insight-porn technology/google

A classic pattern in technology economics, identified by Joel Spolsky, is layers of the stack attempting to become monopolies while turning other layers into perfectly-competitive markets which are commoditized, in order to harvest most of the consumer surplus; discussion and examples.

Joel Spolsky in 200222ya identified a major pattern in technology business & economics: the pattern of “commoditizing your complement”, an alternative to vertical integration, where companies seek to secure a chokepoint or quasi-monopoly in products composed of many necessary & sufficient layers by dominating one layer while fostering so much competition in another layer above or below its layer that no competing monopolist can emerge, prices are driven down to marginal costs elsewhere in the stack, total price drops & increases demand, and the majority of the consumer surplus of the final product can be diverted to the quasi-monopolist. No matter how valuable the original may be and how much one could charge for it, it can be more valuable to make it free if it increases profits elsewhere. A classic example is the commodification of PC hardware by the Microsoft OS monopoly, to the detriment of IBM & benefit of MS.

This pattern explains many otherwise odd or apparently self-sabotaging ventures by large tech companies into apparently irrelevant fields, such as the high rate of releasing open-source contributions by many Internet companies or the intrusion of advertising companies into smartphone manufacturing & web browser development & statistical software & fiber-optic networks & municipal WiFi & radio spectrum auctions & DNS (Google): they are pre-emptive attempts to commodify another company elsewhere in the stack, or defenses against it being done to them.

--- /barratry Barratry Gwern 2009-11-02 2011-02-03 economics politics

Re-inventing anarcho-capitalism in a French context.

--- /fiction/dinosaur-comics Dinosaur Comics comics Gwern 2011-06-10 2011-06-10 fiction/humor fiction/poetry philosophy

Comics using the format of Ryan North’s Dinosaur Comics

--- /fiction/missing-cities Missing Cities Gwern 2009-02-01 2010-12-03 fiction/science-fiction

3 short stories in the style of Italo Calvino’s Missing Cities.

--- /fiction/happenings Two Simple Happenings Gwern 2014-06-16 2014-06-17 fiction

Short story; psychological horror

--- /fiction/cloud-nine Cloud Nine Gwern 2008-09-26 2009-02-09 fiction/science-fiction

Unfinished fantasy/SF novel

--- /review/mead Mead Gwern 2012-05-02 2018-10-25 food personal

Ratings of mead and fruit wines I have tried.

--- /tank The Neural Net Tank Urban Legend Gwern 2011-09-20 2023-07-04 ai/nn history reinforcement-learning/safe sociology

AI folklore tells a story about a neural network trained to detect tanks which instead learned to detect time of day; investigating, this probably never happened.

A cautionary tale in artificial intelligence tells about researchers training an neural network (NN) to detect tanks in photographs, succeeding, only to realize the photographs had been collected under specific conditions for tanks/non-tanks and the NN had learned something useless like time of day. This story is often told to warn about the limits of algorithms and importance of data collection to avoid “dataset bias”/“data leakage” where the collected data can be solved using algorithms that do not generalize to the true data distribution, but the tank story is usually never sourced.

I collate many extent versions dating back a quarter of a century to 199232ya along with two NN-related anecdotes from the 1960s; their contradictions & details indicate a classic “urban legend”, with a probable origin in a speculative question in the 1960s by Edward Fredkin at an AI conference about some early NN research, which was then classified & never followed up on.

I suggest that dataset bias is real but exaggerated by the tank story, giving a misleading indication of risks from deep learning and that it would be better to not repeat it but use real examples of dataset bias and focus on larger-scale risks like AI systems optimizing for wrong utility functions.

--- /wikipedia-and-youtube Wikipedia & YouTube Gwern 2009-01-15 2013-11-08 law technology/google wikipedia

Why Wikipedia and YouTube will never be integrated.

--- /choosing-software Choosing Software Gwern 2008-09-26 2012-10-28 cs/haskell statistics/prediction technology

Criteria making software useful long-term & worth learning in detail

--- /haskell/run-length-encoding Golfing Run Length Encoding in Haskell Gwern 2008-09-26 2013-01-16 cs/haskell tutorial

Haskell: step by step refactoring to concision

--- /newton Newton’s System of the World and Comets Gwern 2016-06-13 2023-02-23 history insight-porn philosophy technology

Isaac Newton’s cosmology apparently involved regular apocalypses caused by comets overstoking the furnace of the Sun and the repopulation of the Solar System by new intelligent species. He supports this speculation with an interestingly-incorrect anthropic argument.

Isaac Newton published few of his works, and only those he considered perfect after long delays. This leaves his system the world, as described in the Principia and elsewhere, incomplete, and many questions simply unaddressed, like the fate of the Sun or role of comets. But in 2 conversations with an admirer and his nephew, the elderly Newton sketched out the rest of his cosmogony.

According to Newton, the solar system is not stable and must be adjusted by angels; the Sun does not burn perpetually, but comets regularly fuel the Sun; and the final result is that humanity will be extinguished by a particularly large comet causing the sun to flare up, and requiring intelligent alien beings to arise on other planets or their moons. He further gives an anthropic argument: one reason we know that intelligent races regularly go extinct is that humanity itself arose only recently, as demonstrated by the recent innovations in every field, inconsistent with any belief that human beings have existed for hundreds of thousands or millions of years.

This is all interestingly wrong, particularly the anthropic argument. That Newton found it so absurd to imagine humanity existing for millions of years but only recently undergoing exponential improvements in technology demonstrates how counterintuitive and extraordinary the Industrial & Scientific Revolutions were.

--- /review/mcnamara McNamara’s Folly: The Denial of Individual Differences Gwern 2018-05-17 2021-05-14 history iq/low politics psychology sociology

Account of well-intentioned but ill-fated attempt to conscript unintelligent or outright mentally-retarded men into fighting in the Vietnam War after remedial education, illustrating the difficulty of social interventions, the practical consequences of low intelligence, and the cruelty & evil of ignoring the reality of individual differences.

--- /music-distraction Music and distraction Gwern 2012-09-26 2019-03-03 dual-n-back psychology/music/distraction technology

Does music impede studying and thinking?

--- /terrorism-is-not-effective Terrorism Is Not Effective Gwern 2009-04-14 2017-04-21 crime/terrorism philosophy/ethics politics sociology

More effective ways to kill = terrorists are stupid, or killing not most important thing to them

Terrorism is not about causing terror or casualties, but about other things. Evidence of this is the fact that, despite often considerable resources spent, most terrorists are incompetent, impulsive, prepare poorly for attacks, are inconsistent in planning, tend towards exotic & difficult forms of attack such as bombings, and in practice ineffective: the modal number of casualties per terrorist attack is near-zero, and global terrorist annual casualty have been a rounding error for decades. This is despite the fact that there are many examples of extremely destructive easily-performed potential acts of terrorism, such as poisoning food supplies or renting large trucks & running crowds over or engaging in sporadic sniper attacks.

--- /terrorism-is-not-about-terror Terrorism Is Not About Terror Gwern 2009-04-09 2017-04-21 crime/terrorism politics sociology

Terrorists act irrationally from a rational activism perspective, and groups act in ways most consistent with terrorism being about social status and belonging

Statistical analysis of terrorist groups’ longevity, aims, methods and successes reveal that groups are self-contradictory and self-sabotaging, generally ineffective; common stereotypes like terrorists being poor or ultra-skilled are false. Superficially appealing counter-examples are discussed and rejected. Data on motivations and the dissolution of terrorist groups are brought into play and the surprising conclusion reached: terrorism is a form of socialization or status-seeking.

--- /sicp/introduction SICP Introduction Gwern 2009-03-14 2012-07-28 cs/scheme

Links to various resources for SICP studying

--- /on-disrespect On Disrespect Gwern 2009-02-09 2009-02-09 sociology

An attempt to reinvent classic theories of social interaction as expressions of power

--- /note/fermi Fermi Calculation Examples Gwern 2019-03-29 2019-03-29 science/fermi-problem

Fermi estimates or problems are quick heuristic solutions to apparently insoluble quantitative problems rewarding clever use of real-world knowledge and critical thinking; bibliography of some examples.

A short discussion of “Fermi calculations”: quick-and-dirty approximate answers to quantitative questions which prize cleverness in exploiting implications of common knowledge or basic principles in given reasonable answers to apparently unanswerable questions.

Links to discussions of Fermi estimates, and a list of some Fermi estimates I’ve done.

--- /on-really-trying On Really Trying Gwern 2009-06-23 2016-09-16 politics psychology/energy psychology/willpower sociology transhumanism

What are the true limits to motivation?

--- /haskell/hypertime Hypertime in Haskell Gwern 2012-01-10 2012-01-10 cs/haskell fiction/science-fiction/time-travel philosophy

Implementing the Hypertime multiverse model of time travel in Haskell

--- /education-is-not-about-learning Education is not about Learning Gwern 2009-07-25 2015-02-28 biology melatonin politics psychology survey

Examples of low-hanging fruit suggesting the education system does not solely optimize for learning

--- /komm-susser-tod Komm Susser Tod Gwern 2010-11-05 2013-04-03 anime/eva fiction/criticism music

Perspective and interpretation

--- /mouse-utopia Does Mouse Utopia Exist? Gwern 2019-08-12 2021-05-16 genetics/heritable sociology statistics/bias

Did John Calhoun’s 1960s Mouse Utopia really show that animal (and human) populations will expand to arbitrary densities, creating socially-driven pathology and collapse? Reasons for doubt.

Did John Calhoun’s1960s Mouse Utopia really show that animal (and human) populations will expand to arbitrary densities, creating socially-driven pathology and collapse? I give reasons for doubt about its replicability, interpretation, and meaningfulness.

One of the most famous experiments in psychology & sociology was John Calhoun’s Mouse Utopia experiments in the 1960s–1970s. In the usual telling, Mouse Utopia created ideal mouse environments in which the mouse population was permitted to increase as much as possible; however, the overcrowding inevitably resulted in extreme levels of physical & social dysfunctionality, and eventually population collapse & even extinction.

Looking more closely into it, there are reasons to doubt the replicability of the growth & pathological behavior & collapse of this utopia (“no-place”), and if it does happen, whether it is driven by the social pressures as claimed by Calhoun or by other causal mechanisms at least as consistent with the (minimal) reported evidence, such as disease or mutational meltdown.

--- /poisson Ethics of Lithotomy Gwern 2014-09-09 2014-09-09 statistics/decision

Using modern statistical analysis, how quickly should improved bladder stone surgical methods have been adopted?

--- /religious-experience Distinguishing sources of religion by their experiential differences Gwern 2009-12-22 2012-10-09 philosophy

Does the convergence of religious experiences point to a shared truth?

--- /newsletter/2016/13 2016 News Gwern 2016-12-31 2024-11-29 newsletter
--- /doc/ai/nn/diffusion/midjourney/dropcap/genewolfe-dropcap/index ‘Gene Wolfe dropcaps’ tag 2024-01-01 2024-01-01 fiction/gene-wolfe

Bibliography for tag ai/nn/diffusion/midjourney/dropcap/genewolfe-dropcap, most recent first: & 89 links ().

--- /doc/ai/nn/diffusion/midjourney/dropcap/dropcat/index ‘Dropcat dropcaps’ tag 2024-01-01 2024-01-01 cat

Bibliography for tag ai/nn/diffusion/midjourney/dropcap/dropcat, most recent first: & 52 links ().

--- /doc/cs/cryptography/steganography/index ‘steganography’ tag 2019-11-17 2024-11-28 cs/algorithm/information statistics/stylometry

Bibliography for tag cs/cryptography/steganography, most recent first: 1 related tag, 28 annotations, & 13 links ().

--- /doc/statistics/bias/publication/index ‘publication bias’ tag 2019-11-24 2024-09-29 psychology/parapsychology/european-journal-of-parapsychology statistics/peer-review

Bibliography for tag statistics/bias/publication, most recent first: 3 related tags, 79 annotations, & 7 links ().

--- /doc/psychology/parapsychology/european-journal-of-parapsychology/index ‘EJP’ tag 2021-01-24 2024-01-01 statistics/bias/publication

Bibliography for tag psychology/parapsychology/european-journal-of-parapsychology, most recent first: 1 related tag, 4 annotations, & 12 links ().

[page summary]

--- /doc/cs/algorithm/information/compression/index ‘compression’ tag 2019-09-07 2024-11-13 ai/nn/sparsity

Bibliography for tag cs/algorithm/information/compression, most recent first: 5 related tags, 78 annotations, & 79 links ().

--- /me#uses-this About Gwern § Uses This Gwern 2009-08-05 2020-06-14 technology

Who am I online & what have I done? Contact information; sites I use; computers and software tools; things I’ve worked on; psychological profiles

I’m sometimes asked about my tech “stack”, in the vein of “Uses This” or The Paris Review’s Writer At Work. I use FLOSS software with a text/CLI emphasis on a custom workstation designed for deep learning & reinforcement learning work, and an ergonomic home office with portrait-orientation monitor, Aeron chair, & trackball.

--- /doc/darknet-market/dnm-archive/index ‘DNM Archives’ tag 2019-11-22 2024-09-23 dataset

Bibliography for tag darknet-market/dnm-archive, most recent first: 102 annotations & 39 links ().

[essay on this tag topic]

--- /note/note#peak-human-speed Miscellaneous § Peak Human Speed Gwern 2009-08-05 2024-11-02 technology

Misc thoughts, memories, proto-essays, musings, etc.

Reviewing available transport technologies, the fastest a pre-modern human could safely move was somewhere in the range >75MPH (cliff diving) to <107MPH (iceboat).

--- /dropcap#gene-wolfe Dropcap Generation With AI § Gene Wolfe Gwern 2023-10-15 2024-10-30 ai/nn/diffusion/midjourney/dropcap/genewolfe-dropcap

We develop AI image generation workflows for webpage dropcap typography, creating PNGs & SVGs using image generators. As demos, we create new Gwern.net logos and several custom FLOSS dropcap sets, including c​ats, Gene​ Wolfe horror fiction, and neural-net-inspired dropcaps.

The light-mode dropcap montages:

--- /doc/nootropic/bacopa/index ‘Bacopa’ tag 2020-07-13 2024-02-14 nootropic/quantified-self psychiatry/anxiety

Bibliography for tag nootropic/bacopa, most recent first: 17 annotations & 16 links ().

[essay on this tag topic]

--- /doc/ai/nn/gan/stylegan/anime/index ‘StyleGAN anime’ tag 2020-01-07 2024-11-17 ai/anime

Bibliography for tag ai/nn/gan/stylegan/anime, most recent first: 66 annotations & 105 links ().

--- /doc/design/typography/rubrication/index ‘rubricated typography’ tag 2019-11-30 2024-11-16 design/visualization

Bibliography for tag design/typography/rubrication, most recent first: 92 annotations & 186 links ().

--- /gpt-2-preference-learning GPT-2 Preference Learning for Music Generation Gwern 2019-12-16 2021-06-07 ai/music ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry cs/shell statistics/order/comparison tutorial

Experiments with OpenAI’s ‘preference learning’ approach, which trains a NN to predict global quality of datapoints, and then uses reinforcement learning to optimize that directly, rather than proxies. I am unable to improve quality, perhaps due to too-few ratings.

Standard language generation neural network models, like GPT-2, are trained via likelihood training to imitate human text corpuses. Generated text suffers from persistent flaws like repetition, due to myopic generation word-by-word, and cannot improve on the training data because they are trained to predict ‘realistic’ completions of the training data.

A proposed alternative is to use reinforcement learning to train the NNs, to encourage global properties like coherence & lack of repetition, and potentially improve over the original corpus’s average quality. Preference learning trains a reward function on human ratings, and uses that as the ‘environment’ for a blackbox DRL algorithm like PPO.

OpenAI released a codebase implementing this dual-model preference learning approach for textual generation, based on GPT-2. Having previously used GPT-2 for poetry & music generation, I experimented with GPT-2 preference learning for unconditional music and poetry generation.

I found that preference learning seemed to work better for music than poetry, and seemed to reduce the presence of repetition artifacts, but the results, at n ≈ 7,400 ratings compiled over 23 iterations of training+sampling November 2019–January 2020, are not dramatically better than alternative improvements like scaling up models or more thorough data-cleaning or more stringent sample curation. My blind ratings using n ≈ 200 comparisons showed no large advantage for the RL-tuned samples (winning only 93 of 210 comparisons, or 46%).

This may be due to insufficient ratings, bad hyperparameters, or not using samples generated with common prefixes, but I suspect it’s the former, as some NLP tasks in Ziegler et al 2019 required up to 60k ratings for good performance, and the reward model appeared to achieve poor performance & succumb to adversarial examples easily.

Working with it, I suspect that preference learning is unnecessarily sample-inefficient & data-inefficient, and that the blackbox reinforcement learning approach is inferior to directly using the reward model to optimize text samples, and propose two major architectural overhauls: have the reward model directly model the implied ranking of every datapoint, and drop the agent model entirely in favor of backprop-powered gradient ascent which optimizes sequences to maximize the reward model’s output.

--- /gpt-2-preference-learning#decision-transformers-preference-learning-as-simple-as-possible GPT-2 Preference Learning for Music Generation § Decision Transformers: Preference Learning As Simple As Possible Gwern 2019-12-16 2021-06-07 ai/music ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry cs/shell statistics/order/comparison tutorial

Experiments with OpenAI’s ‘preference learning’ approach, which trains a NN to predict global quality of datapoints, and then uses reinforcement learning to optimize that directly, rather than proxies. I am unable to improve quality, perhaps due to too-few ratings.

I propose an extremely simple form of preference learning using the ‘Decision Transformer’ approach: simply encode the human choices as a sorted list of options, finetune a sequence model like GPT to predict text on a dataset including that encoded preference/choice data, and then generate text in the form of said sorted lists. The model will learn what preferred text looks like, and in generating sorted lists, will generated preferred samples first.

This RL approach, by gardening the data, is closer to prompt programming or Software 2.0 than traditional DRL algorithms like PPO. This avoids all of the complexity, instability, and compute requirements of the GPT preference learning approach used previously, moving the reward learning to inside the dataset, and is particularly applicable to tasks like AI Dungeon-style text adventure games, where the complexity of training rankers & RL-finetuned models has barred their use to date.

--- /gpt-2-preference-learning#optimization-by-backprop-not-blackbox GPT-2 Preference Learning for Music Generation § Optimization by Backprop, Not Blackbox Gwern 2019-12-16 2021-06-07 ai/music ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry cs/shell statistics/order/comparison tutorial

Experiments with OpenAI’s ‘preference learning’ approach, which trains a NN to predict global quality of datapoints, and then uses reinforcement learning to optimize that directly, rather than proxies. I am unable to improve quality, perhaps due to too-few ratings.

Here I propose changing the agent/generator model architecture to explicitly optimize the reward model’s utility/reward score, by removing the agent/generator entirely and instead improving possible sequences by gradient ascent on the (differentiable) reward model. There is no need to build a redundant agent model when the reward model is differentiable and can be used to directly specify how an input sequence ought to change to improve it.

This simplifies the overall architecture greatly, avoids expensive & unstable & complex blackbox training of DRL agents, and enables easy generation of both high-scoring & highly-diverse (thus informative) sequences for an oracle to rate, which can then be fed back into the reward model for further training. To the extent an agent/generator is necessary to efficiently generate many sequences, it can be trained quickly & stably by imitation learning on a corpus of datapoints optimized by the model.

--- /doc/psychology/neuroscience/memory/savant/index ‘savantism’ tag 2022-03-08 2024-11-16 iq/high iq/low psychiatry/autism psychology/spaced-repetition

Bibliography for tag psychology/neuroscience/memory/savant, most recent first: 2 related tags, 17 annotations, & 2 links ().

--- /doc/psychology/neuroscience/memory/index ‘memory’ tag 2020-08-25 2024-09-03 psychology/neuroscience/pain/anesthesia psychology/spaced-repetition

Bibliography for tag psychology/neuroscience/memory, most recent first: 2 related tags, 24 annotations, & 7 links ().

--- /review/crumb Review Of Crumb Gwern 2024-05-23 2024-05-25 fiction/criticism psychedelic/lsd psychiatry/schizophrenia psychology/novelty

Autobiographical documentary about ‘underground artist’ Robert Crumb on his 1991 farewell tour, discussing his personality, family, mental health, and source of his graphomanic art.

--- /ova How OVAs Worked Gwern 2023-03-09 2023-03-09 anime economics/mechanism-design

Brief description of how the direct-to-video-rental model helped create the anime boom by allowing wide diversity in bankrolled anime projects, outside the production committee system.

Anime in the 1980s–1990s was famous for its diversity and edgy content like sex & violence, enabling the early careers of famous directors. I summarize what made this possible: a change in anime business models from being captive to established censorious media interests, to ‘direct-to-home’ video sales of ‘original video animation’ or OVAs—a misnomer, as it involved rental stores as much or more than individual consumers.

The rise of home videotape players but high prices of videotapes meant that video rental stores were the preferred method to acquire videotapes, and decentralized rental stores could collectively pay, on an episode by episode basis, for all sorts of strange sketchy OVA productions that would be ignored or censored. Successful OVAs would serve as proof-of-concept and bankroll followups.

But the economics & technological circumstances that enabled this breakout from the standard media model would not last, and standard media interests would both loosen their censorship and flood the market with cheap videotapes, rendering rental stores & the OVA model obsolete, and relegating ‘original video animations’ to just ‘video animations’ as a way to further monetize existing franchises by selling minor productions directly to fans.

--- /review/space-battleship-yamato Review of Space Battleship Yamato Gwern 2021-09-29 2021-12-03 anime/eva fiction/criticism

Why is Yamato so influential on a generation of anime otaku and later anime, despite a weak story and compromised production? It offers a fantasy WWII in which Imperial Japan is the hero, and all its sins projected onto an imaginary enemy.

--- /review/the-last-unicorn Review Of The Last Unicorn Gwern 2024-06-06 2024-06-16 anime fiction/criticism philosophy/mind

Children’s animated fantasy movie with surprisingly deep thoughts about personal identity and immortality.

--- /ugly-anime Why is Western Animation Ugly? Gwern 2024-04-26 2024-04-26 anime fiction/criticism

Western TV animation is often shockingly ugly and crude compared to other animation like French or Japanese. Why, when it’s not even cheaper? Degenerate echoes of what were once cutting-edge art.

American TV often chooses ugliness as an aesthetic, not forced by budgets.

Shows like Ren & Stimpy are deliberate in their grotesqueness, creating a specific visceral response. However, other series like The Simpsons stagnate, using bigger budgets not to improve but to polish, losing originality.

This split—ugliness as artistic choice versus mediocrity from laziness—highlights broader creative trends in animation.

--- /novelty-net Novelty Nets: Classifier Anti-Guidance Gwern 2024-02-23 2024-04-06 ai/nn/diffusion/midjourney reinforcement-learning/exploration reinforcement-learning/preference-learning/mode-collapse

Generative modeling proposal for increasing diversity of samples by a helper NN memorizing past samples and ‘repelling’ new samples away from old ones.

How can we avoid generative models always creating ‘same-y’ samples, particularly when prompts don’t work well? Novelty search approaches typically operate ‘outside’ the generative model, and so are hamstrung by the inherent non-novelty of the generative model’s usual sampling.

I propose novelty nets: small neural net adapter layers which are trained online during sampling to memorize the history of all previous samples, producing a ‘probability this is not novel’, and thus enable gradient descent to minimize that probability and yield a meaningfully-different new sample each time. This systematically increases the diversity and improves exploration & variation, as one no longer struggles to fight a model stubbornly insisting on generating extremely similar samples because that is just what it considers highly-likely or high-quality.

Novelty nets could be particularly useful for image generation, both at the user & service-level, as the nets push all samples collectively away from each other, reducing the esthetically unpleasant ‘same-y-ness’ of AI-generated images.

--- /doc/reinforcement-learning/preference-learning/mode-collapse/index ‘AI mode collapse’ tag 2021-10-13 2024-11-25 ai/nn/transformer/gpt/4/poetry ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/dall-e/3

Bibliography for tag reinforcement-learning/preference-learning/mode-collapse, most recent first: 4 related tags, 56 annotations, & 43 links ().

[page summary]

--- /doc/ai/nn/transformer/gpt/calibration/index ‘GPT calibration’ tag 2021-03-21 2024-11-11 reinforcement-learning/preference-learning/mode-collapse

Bibliography for tag ai/nn/transformer/gpt/calibration, most recent first: 1 related tag, 46 annotations, & 14 links ().

--- /doc/ai/nn/transformer/gpt/4/poetry/index ‘GPT-4 poetry’ tag 2023-05-10 2024-11-21 reinforcement-learning/preference-learning/mode-collapse

Bibliography for tag ai/nn/transformer/gpt/4/poetry, most recent first: 1 related tag, 4 annotations, & 32 links ().

--- /doc/statistics/stylometry/truesight/index ‘truesight (stylometrics)’ tag 2023-10-11 2024-10-31 ai/nn/transformer/gpt/4/sydney psychology/dark-knowledge reinforcement-learning/model/decision-transformer

Bibliography for tag statistics/stylometry/truesight, most recent first: 3 related tags, 18 annotations, & 11 links ().

--- /doc/genetics/microbiome/acne/index ‘acne’ tag 2022-02-07 2024-09-14 nootropic/quantified-self

Bibliography for tag genetics/microbiome/acne, most recent first: 7 annotations & 3 links ().

[essay on this tag topic]

--- /doc/cs/algorithm/information/index ‘information theory’ tag 2019-11-12 2024-11-02 cs/linkrot/archiving

Bibliography for tag cs/algorithm/information, most recent first: 9 related tags, 60 annotations, & 6 links ().

--- /doc/cs/linkrot/archiving/index ‘Internet archiving’ tag 2020-06-05 2024-10-20 cs/algorithm/information

Bibliography for tag cs/linkrot/archiving, most recent first: 1 related tag, 27 annotations, & 26 links ().

[essay on this tag topic]

--- /doc/science/fermi-problem/index ‘Fermi problems’ tag 2022-11-04 2024-10-30 economics math statistics/decision statistics/prediction statistics/probability

Bibliography for tag science/fermi-problem, most recent first: 1 related tag, 17 annotations, & 10 links ().

--- /doc/anime/my-little-pony/index ‘My Little Pony’ tag 2021-06-16 2024-06-17 anime/eva/little-boy

Bibliography for tag anime/my-little-pony, most recent first: 1 related tag, 278 annotations, & 59 links ().

--- /doc/anime/eva/little-boy/index ‘Little Boy’ tag 2012-05-18 2024-01-01 anime/my-little-pony

Bibliography for tag anime/eva/little-boy, most recent first: 2 related tags, 8 annotations, & 65 links ().

--- /review/princess-kaguya Review of The Tale of the Princess Kaguya Gwern 2016-03-01 2022-10-13 anime fiction/criticism philosophy/religion

Review of Isao Takahata’s last & greatest film on how to accept the transience of life, filled as it is with both joy & sorrow.

--- /doc/newest/index ‘newest links’ tag 2024-11-16 2024-11-30 meta

Bibliography for tag newest, most recent first: 1 related tag, 89 annotations, & 5 links ().

[page summary]

--- /matt-levine Why So Few Matt Levines? Gwern 2024-09-24 2024-09-24 economics psychology/writing

Why are popularizing educational newsletter-frequency writers of important fields like Matt Levine for finance so rare? Because most fields are too slow or ambiguous, and writers of the right combination of expertise, obsession, and persistence are also rare.

--- /unsort Can You Unsort Lists for Diversity? Gwern 2019-11-24 2024-11-04 cs/algorithm/sorting music psychology/novelty reinforcement-learning/exploration statistics/power-analysis

Discussion of whether there is a general ‘unsorting’ list operation to avoid redundancy, repetition, or lack of novelty. Probably not, there are too many things you might want to maximize or minimize.

Simple random shuffles often make people unhappy or work poorly compared to more complex alternatives, which break up structure in data.

If sorting minimizes distance in a list, then we might instead want to maximize distance in these cases. Can we define some generalization of sorting which is its opposite, an “unsorting” algorithm, which handles all these use-cases, from music playlists to video games to design of experiments?

No, probably not. (Although such algorithms—like Traveling Salesman to maximize distance—are probably underused in general.) The use-cases are just too intrinsically different, and in many cases, do not correspond to ‘maximizing distance’ or ‘novelty’ or anything like that.

But there does still seem like a “family resemblance” between these applications, and so I propose a neologism for all these related problems: “anti-assorting”.

--- /web-color Website Colors: Red vs Blue Gwern 2024-11-07 2024-11-10 cs/css cs/r design/typography/rubrication

I manually extract RGB colors for 225 websites and graph them: red > blue > green > purple > yellow–brown.

To extend Gwern.net link-icons to include site-specific color when interacting with them, I collected 225 RGB hex colors from websites or their logos.

I visualize the color distributions in R in several ways: by channel, pairs of channels, color wheel, and 3D scatterplots.

The collected colors show polarization of website design towards either red or blue as the accent/icon color, but little yellow–brown.

This shows the interesting primacy of red vs blue, but also suggests that maybe they have become overused, and people should explore the other colors, like yellow–brown.

--- /dropcap#halloween Dropcap Generation With AI § Halloween Gwern 2023-10-15 2024-10-30 ai/nn/diffusion/midjourney/dropcap meta

We develop AI image generation workflows for webpage dropcap typography, creating PNGs & SVGs using image generators. As demos, we create new Gwern.net logos and several custom FLOSS dropcap sets, including c​ats, Gene​ Wolfe horror fiction, and neural-net-inspired dropcaps.

Some of the final candidate samples:

--- /404 Gwern.net 404 error page Gwern 2012-06-09 2024-10-14 meta --- /note/daicon-videos Daicon III and IV Opening Animations Gainax 2020-12-03 2020-12-03 anime/eva/little-boy history japan --- /note/fashion Fashion Cycles Gwern 2021-05-14 2021-05-14 economics history insight-porn music politics psychology/collecting reinforcement-learning/multi-agent sociology --- /note/faster Computer Optimization: Your Computer Is Faster Than You Think Gwern 2021-04-24 2021-04-24 ai/nn ai/scaling/hardware cs/algorithm --- /note/frank-ramsey Frank P. Ramsey bibliography Gwern 2019-01-24 2019-01-24 economics philosophy statistics/bayes statistics/decision --- /note/killing-rabbits Killing Rabbits Miroslav Válek 2019-05-06 2019-05-06 fiction/poetry philosophy/ethics politics --- /review/cat#knocking-stuff-over Cat Psychology & Domestication: Are We Good Owners? § Knocking Stuff Over Gwern 2018-11-03 2018-11-03 cat/psychology psychology/novelty --- /backstop#pain-prosthetics Evolution as Backstop for Reinforcement Learning § Pain Prosthetics Gwern 2018-12-06 2018-12-06 insight-porn psychology/neuroscience/pain psychology/willpower --- /gpt-2#b-training GPT-2 Neural Network Poetry § 1.5b Training Gwern, Shawn Presser 2019-03-03 2019-11-08 ai/nn/transformer/gpt/poetry cs/shell tutorial --- /replication#animal-models The Replication Crisis: Flaws in Mainstream Science § Animal Models Gwern 2010-10-27 2010-10-27 dual-n-back longevity nootropic psychology sociology statistics/bias statistics/causality statistics/meta-analysis --- /zeo/zeo#potassium Zeo sleep self-experiments § Potassium Gwern 2010-12-28 2010-12-28 nootropic/potassium zeo --- /nootropic/nootropics#bacopa-monnieri Nootropics § Bacopa monnieri Gwern 2010-01-02 2010-01-02 nootropic/bacopa nootropic/quantified-self --- /doc/ai/2012-bainbridge.pdf 2012 2019-11-08 ai psychology/personality --- /doc/ai/2012-hayworth.pdf ELECTRON IMAGING TECHNOLOGY FOR WHOLE BRAIN NEURAL CIRCUIT MAPPING KENNETH J. HAYWORTH 2012-01-01 2019-11-09 ai cryonics psychology/neuroscience --- /doc/ai/nn/2019-richards.pdf A deep learning framework for neuroscience Blake A. Richards, Timothy Lillicrap, Philippe Beaudoin, Yoshua Bengio, Rafal Bogacz, Amelia Christensen, Claudia Clopath, Rui Ponte Costa, Archy Berker, Surya Ganguli, Colleen J. Gillon, Danijar Hafner, Adam Kepecs, Nikolaus Kriegeskorte, Peter Latham, Grace W. Lindsay, Kenneth D. Miller, Richard Naud, Christopher C. Pack, Panayiota Poirazi, Pieter Roelfsema, João Sacramento, Andrew Saxe, Benjamin Scellier, Anna C. Schapiro, Walter Senn, Greg Wayne, Daniel Yamins, Friedemann Zenke, Joel Zylberberg, Denis Therien, Konrad P. Kording 2019-10-28 2019-11-09 [("doi","10.1038/s41593-019-0520-2")] ai/nn psychology/neuroscience --- /doc/ai/anime/danbooru/2020-lee.pdf LDM: Automatic Colorization of Anime Style Illustrations Using a Two-Stage Generator Yeongseop Lee, Seongjin Lee 2020-12-04 2020-12-04 [("doi","10.3390/app10238699")] ai/anime/danbooru ai/nn/gan

Line-arts are used in many ways in the media industry. However, line-art colorization is tedious, labor-intensive, and time consuming. For such reasons, a Generative Adversarial Network (GAN)-based image-to-image colorization method has received much attention because of its promising results.

In this paper, we propose to use a point hinting method with two GAN-based generators used for enhancing the image quality. To improve the coloring performance of drawing with various line styles, generator takes account of the loss of the line-art.

We propose a Line Detection Model (LDM) which is used in measuring line loss. LDM is a method of extracting line from a color image. We also propose histogram equalizer in the input line-art to generalize the distribution of line styles. This approach allows the generalization of the distribution of line style without increasing the complexity of inference stage. In addition, we propose 7 segment hint pointing constraints to evaluate the colorization performance of the model with Fréchet Inception Distance (FID) score.

We present visual and qualitative evaluations of the proposed methods. The result shows that using histogram equalization and LDM enabled line loss exhibits the best result. The Base model with XDoG (eXtended Difference-Of-Gaussians)generated line-art with and without color hints exhibits FID for colorized images score of 35.83 and 44.70, respectively, whereas the proposed model in the same scenario exhibits 32.16 and 39.77, respectively.

--- /doc/biology/2002-lazebnik.pdf 2002 2019-11-09 biology statistics/bias statistics/causality --- /doc/biology/2018-salminen.pdf Paulina Salminen, Risto Tuominen, Hannu Paajanen, Tero Rautio, Pia Nordström, Markku Aarnio, Tuomo Rantanen, Saija Hurme, Jukka-Pekka Mecklin, Juhani Sand, Johanna Virtanen, Airi Jartti, Juha M. Grönroos 2018-01-01 2019-11-09 [("doi","10.1001/jama.2018.13201")] biology philosophy/ethics statistics/bias --- /doc/genetics/microbiome/2019-depommier.pdf Supplementation with Akkermansia muciniphila in overweight and obese human volunteers: a proof-of-concept exploratory study Clara Depommier, Amandine Everard, Céline Druart, Hubert Plovier, Matthias Van Hul, Sara Vieira-Silva, Gwen Falony, Jeroen Raes, Dominique Maiter, Nathalie M. Delzenne, Marie de Barsy, Audrey Loumaye, Michel P. Hermans, Jean-Paul Thissen, Willem M. de Vos, Patrice D. Cani 2019-01-01 2019-11-09 [("doi","10.1038/s41591-019-0495-2")] genetics/microbiome --- /doc/genetics/microbiome/2019-vallescolomer.pdf The neuroactive potential of the human gut microbiota in quality of life and depression Mireia Valles-Colomer, Gwen Falony, Youssef Darzi, Ettje F. Tigchelaar, Jun Wang, Raul Y. Tito, Carmen Schiweck, Alexander Kurilshikov, Marie Joossens, Cisca Wijmenga, Stephan Claes, Lukas Van Oudenhove, Alexandra Zhernakova, Sara Vieira-Silva, Jeroen Raes 2019-01-01 2019-11-09 [("doi","10.1038/s41564-018-0337-x")] genetics/microbiome psychiatry/depression --- /doc/biology/2020-nguyen.pdf Evaluating Use Cases for Human Challenge Trials in Accelerating SARS-CoV-2 Vaccine Development Linh Chi Nguyen, Christopher W. Bakerlee, T. Greg McKelvey, Sophie M. Rose, Alexander J. Norman, Nicholas Joseph, David Manheim, Michael R. McLaren, Steven Jiang, Conor F. Barnes, Megan Kinniment, Derek Foster, Thomas C. Darton, Josh Morrison, 1Day Sooner Research Team 2020-07-06 2020-07-06 [("doi","10.1093/cid/ciaa935")] biology philosophy/ethics

Human challenge trials (HCTs) have been proposed as a means to accelerate SARS-CoV-2 vaccine development. We identify and discuss 3 potential use cases of HCTs in the current pandemic: evaluating efficacy, converging on correlates of protection, and improving understanding of pathogenesis and the human immune response.

We outline the limitations of HCTs and find that HCTs are likely to be most useful for vaccine candidates currently in preclinical stages of development.

We conclude that, while currently limited in their application, there are scenarios in which HCTs would be extremely beneficial. Therefore, the option of conducting HCTs to accelerate SARS-CoV-2 vaccine development should be preserved.

As HCTs require many months of preparation, we recommend an immediate effort to (1) establish guidelines for HCTs for COVID-19; (2) take the first steps toward HCTs, including preparing challenge virus and making preliminary logistical arrangements; and (3) commit to periodically re-evaluating the utility of HCTs.

--- /doc/cs/algorithm/2005-knuth-taocp-v4-prefascicle4b.pdf#page=22 History of Combinatorial Generation (The Art of Computer Programming: Volume 4: Pre-Fascicle 4B: §7.2.1.7) § pg22 Donald Knuth 2005-10-28 2019-11-09 cs/algorithm japan/art psychology/smell/perfume --- /doc/cs/computable/2022-akhlaghpour.pdf An RNA-based theory of natural universal computation Hessameddin Akhlaghpour 2022-03-21 2022-03-21 [("doi","10.1016/j.jtbi.2021.110984")] biology cs/computable genetics/genome-synthesis

Life is confronted with computation problems in a variety of domains including animal behavior, single-cell behavior, and embryonic development. Yet we currently do not know of a naturally existing biological system that is capable of universal computation, ie. Turing-equivalent in scope. Generic finite-dimensional dynamical systems (which encompass most models of neural networks, intracellular signaling cascades, and gene regulatory networks) fall short of universal computation, but are assumed to be capable of explaining cognition and development.

I present a class of models that bridge two concepts from distant fields: combinatory logic (or, equivalently, lambda calculus) and RNA molecular biology. A set of basic RNA editing rules can make it possible to compute any computable function with identical algorithmic complexity to that of Turing machines. The models do not assume extraordinarily complex molecular machinery or any processes that radically differ from what we already know to occur in cells. Distinct independent enzymes can mediate each of the rules and RNA molecules solve the problem of parenthesis matching through their secondary structure.

In the most plausible of these models all of the editing rules can be implemented with merely cleavage and ligation operations at fixed positions relative to predefined motifs.

This demonstrates that universal computation is well within the reach of molecular biology.

It is therefore reasonable to assume that life has evolved—or possibly began with—a universal computer that yet remains to be discovered. The variety of seemingly unrelated computational problems across many scales can potentially be solved using the same RNA-based computation system. Experimental validation of this theory may immensely impact our understanding of memory, cognition, development, disease, evolution, and the early stages of life.

--- /doc/culture/2004-kaufman.pdf Endogenous Explanation in the Sociology of Culture Jason Kaufman 2004-01-01 2019-11-09 culture psychology/novelty --- /doc/culture/2019-muthukrishna.pdf Are Collectivistic Cultures More Prone to Rapid Transformation? Computational Models of Cross-Cultural Differences, Social Network Structure, Dynamic Social Influence, and Cultural Change Michael Muthukrishna, Mark Schaller 2019-06-28 2019-11-10 [("doi","10.1177/1088868319855783")] culture sociology

Societies differ in susceptibility to social influence and in the social network structure through which individuals influence each other. What implications might these cultural differences have for changes in cultural norms over time? Using parameters informed by empirical evidence, we computationally modeled these cross-cultural differences to predict two forms of cultural change: consolidation of opinion majorities into stronger majorities, and the spread of initially unpopular beliefs.

Results obtained from more than 300,000 computer simulations showed that in populations characterized by greater susceptibility to social influence, there was more rapid consolidation of majority opinion and also more successful spread of initially unpopular beliefs. Initially unpopular beliefs also spread more readily in populations characterized by less densely connected social networks.

These computational outputs highlight the value of computational modeling methods as a means to specify hypotheses about specific ways in which cross-cultural differences may have long-term consequences for cultural stability and cultural change.

--- /doc/culture/2019-whiten.pdf Cultural Evolution in Animals Andrew Whiten 2019-01-01 2019-11-10 [("doi","10.1146/annurev-ecolsys-110218-025040")] culture psychology/animal/bird

In recent decades, a burgeoning literature has documented the cultural transmission of behavior through social learning in numerous vertebrate and invertebrate species. One meaning of “cultural evolution in animals” refers to these discoveries, and I present an overview of key findings.

I then address the other meaning of the term focused on cultural changes within a lineage. Such changes in humans, described as “cumulative cultural evolution”, have been spectacular, but relatively little attention has yet been paid to the topic in nonhuman animals, other than asserting that the process is unique to humans. A variety of evidence including both controlled experiments and field observations has begun to challenge this view, and in some behavioral domains, notably birdsong, cultural evolution has been studied for many years.

In this review, I dissect concepts of cultural evolution and cumulative culture and appraise the accumulating evidence bearing on their nature and importance for evolutionary biology at large.

--- /doc/culture/2021-rocklage.pdf Emotionally Numb: Expertise Dulls Consumer Experience Matthew D. Rocklage, Derek D. Rucker, Loran F. Nordgren 2021-03-15 2021-03-15 [("doi","10.1093/jcr/ucab015")] culture food psychology/collecting psychology/novelty

Expertise provides numerous benefits. Experts process information more efficiently, remember information better, and often make better decisions. Consumers pursue expertise in domains they love and chase experiences that make them feel something. Yet, might becoming an expert carry a cost for these very feelings?

Across more than 700,000 consumers and 6 million observations, developing expertise in a hedonic domain predicts consumers becoming:

more emotionally numb—that is, having less intense emotion in response to their experiences. This numbness occurs across a range of domains—movies, photography, wine, and beer—and across diverse measures of emotion and expertise. It occurs in cross-sectional real-world data with certified experts, and in longitudinal real-world data that follows consumers over time and traces their emotional trajectories as they accrue expertise. Furthermore, this numbness can be explained by the cognitive structure experts develop and apply within a domain.

Experimentally inducing cognitive structure led novice consumers to experience greater numbness. However, shifting experts away from using their cognitive structure restored their experience of emotion.

Thus, although consumers actively pursue expertise in domains that bring them pleasure, the present work is the first to show that this pursuit can come with a hedonic cost.

--- /doc/design/typography/1986-koved.pdf Embedded menus: selecting items in context Larry Koved, Ben Shneiderman 1986-04-01 2019-11-10 [("doi","10.1145/5684.5687")] design/typography

In many situations, embedded menus represent an attractive alternative to the more traditional explicit menus, particularly in touchtext, spelling checkers, language-based program editors, and graphics-based systems.

--- /doc/design/typography/1988-raymond.pdf Hypertext and the Oxford English dictionary Darrel R. Raymond, Frank William Tompa 1988-07-01 2019-11-10 [("doi","10.1145/48511.48517")] cs/algorithm design/typography

Hypertext databases can be produced by converting existing text documents to electronic form. The basic task in conversion is identification of fragments.

We illustrate that this is not always a straightforward process with an analysis of the Oxford English Dictionary.

--- /doc/design/typography/1988-vandam.pdf Hypertext '87: keynote address Andries van Dam 1988-07-01 2019-11-10 [("doi","10.1145/48511.48519")] design/typography --- /doc/design/visualization/1990-radeloff.pdf Role of Color in Perception of Attractiveness Deanna J. Radeloff 1990-08-01 2019-11-10 [("doi","10.2466/pms.1990.71.1.151")] design/visualization psychology

In this color study females reported a favorite color statistically-significantly more often than males. Males preferred bright colors statistically-significantly more than females, with a converse finding for preference for soft colors.

The 276 subjects, when asked to evaluate the attractiveness of stimulus models in photographs, gave as the reason color statistically-significantly more often than style of clothing or facial expressions. Subjects statistically-significantly concurred with expert choices of recommended and nonrecommended colors in five of the six sets of photographs.

This study lends credence that wearing recommended colors makes a difference in judgments of what looks best by subjects over the age of 12.

--- /doc/design/visualization/2021-franconeri.pdf The Science of Visual Data Communication: What Works Steven L. Franconeri, Lace M. Padilla, Priti Shah, Jeffrey M. Zacks, Jessica Hullman 2021-12-15 2021-12-15 [("doi","10.1177/15291006211051956")] design/visualization psychology/cognitive-bias statistics/bayes

Effectively designed data visualizations allow viewers to use their powerful visual systems to understand patterns in data across science, education, health, and public policy. But ineffectively designed visualizations can cause confusion, misunderstanding, or even distrust—especially among viewers with low graphical literacy.

We review research-backed guidelines for creating effective and intuitive visualizations oriented toward communicating data to students, coworkers, and the general public. We describe how the visual system can quickly extract broad statistics from a display, whereas poorly designed displays can lead to misperceptions and illusions. Extracting global statistics is fast, but comparing between subsets of values is slow. Effective graphics avoid taxing working memory, guide attention, and respect familiar conventions.

Data visualizations can play a critical role in teaching and communication, provided that designers tailor those visualizations to their audience.

--- /doc/dual-n-back/2008-dahlin.pdf Transfer of Learning After Updating Training Mediated by the Striatum Erika Dahlin, Anna Stigsdotter Neely, Anne Larsson, Lars Bäckman, Lars Nyberg 2008-06-13 2019-11-10 [("doi","10.1126/science.1155466")] dual-n-back psychology/neuroscience

Process-specific training can improve performance on untrained tasks, but the magnitude of gain is variable and often there is no transfer at all.

We demonstrate transfer to a 3-back test of working memory after 5 weeks of training in updating. The transfer effect was based on a joint training-related activity increase for the criterion (letter memory) and transfer tasks in a striatal region that also was recruited pretraining. No transfer was observed to a task that did not engage updating and striatal regions, and age-related striatal changes imposed constraints on transfer.

These findings indicate that transfer can occur if the criterion and transfer tasks engage specific overlapping processing components and brain regions.

--- /doc/economics/1688-delavega-confusionofconfusions.pdf Confusion of Confusions Joseph de la Vega, Hermann Kellenbenz 1688-01-01 2019-11-10 economics history --- /doc/economics/1999-zuckerman.pdf 1999 2019-11-10 economics psychology/novelty --- /doc/economics/2000-west.pdf 2000 2019-11-11 economics japan/history --- /doc/economics/2002-05-01-notesonline-socialismwhathappenedwhatnow.html 2002-05-01 2019-11-11 economics sociology --- /doc/economics/2009-friedman-seasteadingapracticalguide.pdf 2009 2019-11-11 economics technology --- /doc/economics/2016-kelly.pdf 2016 2019-11-11 crime economics --- /doc/economics/2020-glitz.pdf Industrial Espionage and Productivity Albrecht Glitz, Erik Meyersson 2017-01-01 2019-11-11 crime economics --- /doc/economics/2021-banerjee.pdf Long-Term Effects of the Targeting the Ultra Poor Program Abhijit Banerjee, Esther Duflo, Garima Sharma 2021-12-01 2021-12-01 [("doi","10.1257/aeri.20200667")] economics sociology

This paper studies the long-run effects of a “big-push” program providing a large asset transfer to the poorest Indian households.

In a randomized controlled trial that follows these households over 10 years, we find positive effects on consumption (0.6 SD), food security (0.1 SD), income (0.3 SD), and health (0.2 SD).

These effects grow for the first 7 years following the transfer and persist until year ten.

One main channel for persistence is that treated households take better advantage of opportunities to diversify into more lucrative wage employment, especially through migration.

--- /doc/economics/2021-cassidy.pdf Silver coins, wooden tallies and parchment rolls in Henry III’s Exchequer Richard Cassidy 2021-12-13 2021-12-13 [("doi","10.1017/S0968565021000184")] economics history

In the mid 13th century, England used only a single coin, the silver penny. The flow of coins into and out of the government’s treasury was recorded in the rolls of the Exchequer of Receipt. These receipt and issue rolls have been largely ignored, compared to the pipe rolls, which were records of audit.

Some more obscure records, the memoranda of issue, help to show how the daily operations of government finance worked, when cash was the only medium available. They indicate something surprising: the receipt and issue rolls do not necessarily record transactions which took place during the periods they nominally cover. They also show that the Exchequer was experimenting with other forms of payment, using tally sticks, several decades earlier than was previously known.

The rolls and the tallies indicate that the objectives of the Exchequer were not, as we would now expect, concerned with balancing income and expenditure, drawing up a budget, or even recording cash flows within a particular year. These concepts were as yet unknown. Instead, the Exchequer’s aim was to ensure the accountability of officials, its own and those in other branches of government, by allocating financial responsibility to individuals rather than institutions.

--- /doc/economics/2021-kleven.pdf Does Biology Drive Child Penalties? Evidence from Biological and Adoptive Families Henrik Kleven, Camille Landais, Jakob Egholt Søgaard 2021-06-01 2021-06-01 [("doi","10.1257/aeri.20200260")] economics genetics/heritable/adoption

This paper investigates whether the impact of children on the labor market outcomes of women relative to men—child penalties—can be explained by the biological links between mother and child.

We estimate child penalties in biological and adoptive families using event studies around the arrival of children and almost 40 years of adoption data from Denmark. Short-run child penalties are slightly larger for biological mothers than for adoptive mothers, but their long-run child penalties are virtually identical and precisely estimated.

This suggests that biology is not a key driver of child-related gender gaps.

--- /doc/economics/2022-laouenan.pdf Can Information Reduce Ethnic Discrimination? Evidence from Airbnb Morgane Laouénan, Roland Rathelot 2022-01-01 2022-01-01 [("doi","10.1257/app.20190188")] economics sociology/technology

We use data from Airbnb to identify the mechanisms underlying discrimination against ethnic minority hosts. Within the same neighborhood, hosts from minority groups charge 3.2% less for comparable listings.

Since ratings provide guests with increasingly rich information about a listing’s quality, we can measure the contribution of statistical discrimination, building upon Altonji & Pierret 2001. We find that statistical discrimination can account for the whole ethnic price gap: ethnic gaps would disappear if all unobservables were revealed.

Also, three-quarters (2.5 points) of the initial ethnic gap can be attributed to inaccurate beliefs of potential guests about hosts’ average group quality.

--- /doc/economics/experience-curve/2001-chung.pdf The learning curve and the yield factor: the case of Korea’s semiconductor industry Chung 2001 2019-11-11 cs economics/experience-curve --- /doc/economics/perpetuities/2001-kuran.pdf 2001 2019-11-11 economics/perpetuities philosophy/ethics philosophy/religion --- /doc/existential-risk/2013-evansarieyh-doingenough.html Doing Enough 2013 2019-11-12 existential-risk philosophy/ethics --- /doc/existential-risk/2016-lipsitch.pdf 2016 2019-11-12 existential-risk philosophy/ethics --- /doc/fiction/humor/1922-kafka-investigationsofadog.pdf Investigations of a Dog Franz Kafka, Willa Muir, Edwin Muir 1922 2019-11-12 fiction/humor math/humor philosophy/epistemology philosophy/religion --- /doc/fiction/science-fiction/1986-moore-watchmen-chapter4-watchmaker.pdf Watchmaker [Watchmen, Chapter 4] Alan Moore 1986-12-01 2019-11-12 fiction/science-fiction philosophy/mind philosophy/ontology --- /doc/fiction/poetry/2008-04-06-4chan-unlikelyrapping.html 2008-04-06 2019-11-12 fiction/humor fiction/poetry --- /doc/fiction/science-fiction/2012-yvain-thelasttemptationofchrist.html 2012 2019-11-12 fiction/science-fiction philosophy/ethics --- /doc/genetics/heritable/correlation/1985-plomin-originsofindividualdifferencesinfancy-coloradoadoptionproject.pdf 1985 2019-11-12 genetics/heritable/adoption genetics/heritable/correlation --- /doc/genetics/heritable/correlation/2002-degenhardt.pdf Testing hypotheses about the relationship between cannabis use and psychosis Louisa Degenhardt, Wayne Hall, Michael Lynskey 2003-01-01 2019-11-12 [("doi","10.1016/S0376-8716(03)00064-4")] genetics/heritable/correlation marijuana statistics/causality --- /doc/genetics/heritable/correlation/2015-pettersson.pdf Common psychiatric disorders share the same genetic origin: a multivariate sibling study of the Swedish population E Pettersson, H. Larsson, P. Lichtenstein 2015-01-01 2019-11-12 [("doi","10.1038/mp.2015.116")] crime genetics/heritable/correlation psychiatry psychology/personality/psychopathy --- /doc/genetics/heritable/correlation/2016-lo.pdf 2016 2019-11-12 genetics/heritable/correlation psychiatry psychology/personality --- /doc/genetics/heritable/correlation/2016-rees.pdf 2016 2019-11-13 genetics/heritable/correlation psychiatry/schizophrenia --- /doc/genetics/heritable/correlation/2016-riglin.pdf Schizophrenia risk alleles and neurodevelopmental outcomes in childhood: a population-based cohort study Lucy Riglin, Stephan Collishaw, Alexander Richards, Ajay K. Thapar, Barbara Maughan, Michael C. O’Donovansych, Anita Thaparsych 2016-01-01 2019-11-13 [("doi","10.1016/S2215-0366(16)30406-0")] genetics/heritable/correlation psychiatry/schizophrenia --- /doc/genetics/heritable/correlation/2016-smith.pdf Genome-wide analysis of over 106 000 individuals identifies 9 neuroticism-associated loci D J. Smith, V. Escott-Price, Gail Davies, M. E. S. Bailey, L. Colodro-Conde, J. Ward, A. Vedernikov, Riccardo E. Marioni, B. Cullen, D. Lyall, S. P. Hagenaars, D. C. M. Liewald, M. Luciano, C. R. Gale, S. J. Ritchie, C. Hayward, B. Nicholl, B. Bulik-Sullivan, M. Adams, B. Couvy-Duchesne, N. Graham, D. Mackay, J. Evans, B. H. Smith, D. J. Porteous, S. E. Medland, N. G. Martin, P. Holmans, A. M. McIntosh, J. P. Pell, I. J. Deary, M. C. O’Donovan 2016-01-01 2019-11-13 [("doi","10.1038/mp.2016.49")] genetics/heritable/correlation psychiatry/bipolar/genetics psychiatry/depression psychiatry/schizophrenia psychology/personality

Neuroticism is a personality trait of fundamental importance for psychological well-being and public health. It is strongly associated with major depressive disorder (MDD) and several other psychiatric conditions. Although neuroticism is heritable, attempts to identify the alleles involved in previous studies have been limited by relatively small sample sizes.

Here we report a combined meta-analysis of genome-wide association study (GWAS) of neuroticism that includes 91,370 participants from the UK Biobank cohort, 6659 participants from the Generation Scotland: Scottish Family Health Study (GS:SFHS) and 8687 participants from a QIMR (Queensland Institute of Medical Research) Berghofer Medical Research Institute (QIMR) cohort. All participants were assessed using the same neuroticism instrument, the Eysenck Personality Questionnaire-Revised (EPQ-R-S) Short Form’s Neuroticism scale.

We found a single-nucleotide polymorphism-based heritability estimate for neuroticism of ~15% (s.e.=0.7%). Meta-analysis identified 9 novel loci associated with neuroticism. The strongest evidence for association was at a locus on chromosome 8 (p = 1.5 × 10−15) spanning 4 Mb and containing at least 36 genes. Other associated loci included interesting candidate genes on chromosome 1 (GRIK3 (glutamate receptor ionotropic kainate 3)), chromosome 4 (KLHL2 (Kelch-like protein 2)), chromosome 17 (CRHR1 (corticotropin-releasing hormone receptor 1) and MAPT (microtubule-associated protein Tau)) and on chromosome 18 (CELF4 (CUGBP elav-like family member 4)).

We found no evidence for genetic differences in the common allelic architecture of neuroticism by sex. By comparing our findings with those of the Psychiatric Genetics Consortia, we identified a strong genetic correlation between neuroticism and MDD and a less strong but statistically-significant genetic correlation with schizophrenia, although not with bipolar disorder. Polygenic risk scores derived from the primary UK Biobank sample captured ~1% of the variance in neuroticism in the GS:SFHS and QIMR samples, although most of the genome-wide statistically-significant alleles identified within a UK Biobank-only GWAS of neuroticism were not independently replicated within these cohorts.

The identification of 9 novel neuroticism-associated loci will drive forward future work on the neurobiology of neuroticism and related phenotypes.

--- /doc/genetics/heritable/correlation/2018-boisvert.pdf Genetic and Environmental Overlap Between Substance Use and Delinquency in Adolescence Danielle L. Boisvert, Eric J. Connolly, Jamie C. Vaske, Todd A. Armstrong, Brian B. Boutwell 2018-01-01 2019-11-13 [("doi","10.1177/1541204018756469")] crime genetics/heritable/correlation marijuana psychiatry/alcoholism

During adolescence, many teens begin to experiment with substances and engage in delinquent behavior. The current study seeks to examine whether and to what extent genetic and environmental factors contribute to the association between substance use (ie. marijuana and alcohol) and different forms of delinquent offending (ie. violent and non-violent) across males and females.

Analyses were based on same-sex twins (n = 1,072) from the sibling subsample of the National Longitudinal Study of Adolescent to Adult Health (Add Health).

The results revealed moderate to large genetic overlap between substance use and delinquent behavior for males. Much of the covariation between alcohol use and offending behavior for females was attributable to common environmental factors, while common genetic factors explained a large portion of the overlap between marijuana use and offending in males and females.

The implications of these findings for sex differences in prevention and intervention efforts are discussed from a biosocial perspective.

--- /doc/genetics/heritable/correlation/2018-colodroconde.pdf Association Between Population Density and Genetic Risk for Schizophrenia Lucía Colodro-Conde, Baptiste Couvy-Duchesne, John B. Whitfield, Fabian Streit, Scott D. Gordon, Kathryn E. Kemper, Loïc Yengo, Zhili Zheng, Maciej Trzaskowski, Eveline L. de Zeeuw, Michel G. Nivard, Marjolijn Das, Rachel E. Neale, Stuart MacGregor, Catherine M. Olsen, David C. Whiteman, Dorret I. Boomsma, Jian Yang, Marcella Rietschel, John J. McGrath, Sarah E. Medland, Nicholas G. Martin 2018-01-01 2019-11-13 [("doi","10.1001/jamapsychiatry.2018.1581")] genetics/heritable/correlation psychiatry/schizophrenia --- /doc/genetics/heritable/correlation/2019-knoblach.pdf The Association Between Genetic Predisposition and Parental Socialization: An Examination of Gene-Environment Correlations Using an Adoption-Based Design Rachel A. Knoblach, Joseph A. Schwartz, Marianna McBride, Kevin M. Beaver 2019-01-01 2019-11-13 [("doi","10.1177/0306624X19849568")] genetics/heritable/adoption genetics/heritable/correlation

An extensive body of research has examined the role that genetic influences play in the development of antisocial behavior. Even so, there remains much that is unknown regarding the intersections among antisocial behavior, environments, and genetic influences. The current study is designed to shed some light on this issue by examining whether gene-environment correlations are present in the lives of adopted adolescents. More specifically, this article seeks to contribute to scholarship efforts aimed at understanding whether biological parents’ antisocial behavioral phenotypes—behaviors often attributed to an increased likelihood of receiving a genetic propensity for antisocial behaviors—predict variation in environments that are experienced by their adopted-away offspring.

To do so, the biological parents of adoptees were assessed and used to identify ways in which children elicit certain responses from their adoptive parents based, in part, on their genotype. Correlational analyses were calculated on a sample of adoptees (the final analytic sample ranged between n = 229 and n = 293) drawn from the National Longitudinal Study of Adolescent to Adult Health (Add Health).

The results of the study revealed very little evidence of gene-environment correlations.

The implications of these findings are considered.

--- /doc/genetics/heritable/correlation/2019-ross.pdf Investigating the causal effect of cannabis use on cognitive function with a quasi-experimental co-twin design J. Megan Ross, Jarrod M. Ellingson, Soo Hyun Rhee, John K. Hewitt, Robin P. Corley, Jeffrey M. Lessem, Naomi P. Friedman 2019-11-02 2019-11-13 [("doi","10.1016/j.drugalcdep.2019.107712")] genetics/heritable/correlation marijuana --- /doc/genetics/heritable/correlation/2019-smeland.pdf Genome-wide analysis reveals extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence Olav B. Smeland, Shahram Bahrami, Oleksandr Frei, Alexey Shadrin, Kevin O’Connell, Jeanne Savage, Kyoko Watanabe, Florian Krull, Francesco Bettella, Nils Eiel Steen, Torill Ueland, Danielle Posthuma, Srdjan Djurovic, Anders Martin Dale, Ole A. Andreassen 2019-01-01 2019-11-13 [("doi","10.1038/s41380-018-0332-x")] genetics/heritable/correlation iq psychiatry/bipolar/genetics psychiatry/schizophrenia --- /doc/genetics/heritable/correlation/2021-weiser.pdf Familial clustering of psychiatric disorders and low IQ Mark Weiser, Or Frenkel, Daphna Fenchel, Dorit Tzur, Sven Sandin, Magdalena Janecka, Linda Levi, Michael Davidson, Lucian Laor, Eyal Fruchter, Abraham Reichenberg 2021-12-16 2021-12-16 [("doi","10.1017/S0033291721004852")] genetics/heritable/correlation iq/low psychiatry/adhd psychiatry/alcoholism psychiatry/anxiety psychiatry/autism psychiatry/schizophrenia psychology/personality

Background: Although the ICD and DSM differentiate between different psychiatric disorders, these often share symptoms, risk factors, and treatments. This was a population-based, case-control, sibling study examining familial clustering of all psychiatric disorders and low IQ, using data from the Israel Draft-Board Registry on all Jewish adolescents assessed 1998–2014.

Method: We identified all cases with autism spectrum disorder (ASD, n = 2128), severe intellectual disability (ID, n = 9572), attention-deficit hyperactive disorder (ADHD) (n = 3272), psychotic (n = 7902), mood (n = 9704), anxiety (n = 10 606), personality (n = 24,816), or substance/alcohol abuse (n = 791) disorders, and low IQ (⩾2 SDs below the population mean, n = 31,186). Non-CNS control disorders were adolescents with Type-1 diabetes (n = 2427), hernia (n = 29,558) or hematological malignancies (n = 931). Each case was matched with 10 age-matched controls selected at random from the Draft-Board Registry, with replacement, and for each case and matched controls, we ascertained all full siblings. The main outcome measure was the relative recurrence risk (RRR) of the sibling of a case having the same (within-disorder RRR) or a different (across-disorder RRR) disorder.

Results: Within-disorder RRRs were increased for all diagnostic categories, ranging from 11.53 [95% confidence interval (CI): 9.23–14.40] for ASD to 2.93 (95% CI: 2.80–3.07) for personality disorders. The median across-disorder RRR between any pair of psychiatric disorders was 2.16 (95% CI: 1.45–2.43); the median RRR between low IQ and any psychiatric disorder was 1.37 (95% CI: 0.93–1.98). There was no consistent increase in across-disorder RRRs between the non-CNS disorders and psychiatric disorders and/or low IQ.

Conclusion: These large population-based study findings suggest shared etiologies among most psychiatric disorders, and low IQ.

--- /doc/genetics/gametogenesis/2018-segers.pdf In vitro gametogenesis and reproductive cloning: Can we allow one while banning the other? Seppe Segers, Guido Pennings, Wybo Dondorp, Guido de Wert, Heidi Mertes 2018-01-01 2019-11-13 [("doi","10.1111/bioe.12505")] genetics/cloning genetics/gametogenesis --- /doc/genetics/heritable/1981-kaprio.pdf Cigarette smoking, use of alcohol, and leisure-time physical activity among same-sexed adult male twins Jaako Kaprio, Markku Koskenvuo, Seppo Sarna 1981-01-01 2019-11-13 exercise genetics/heritable nicotine psychiatry/alcoholism --- /doc/genetics/heritable/adoption/1986-stunkard.pdf An Adoption Study of Human Obesity Albert J. Stunkard, Thorkild I. A. Sørensen, Craig Hanis, Thomas W. Teasdale, Ranajit Chakraborty, William J. Schull, Fini Schulsinger 1986-01-01 2019-11-14 exercise genetics/heritable/adoption

We examined the contributions of genetic factors and the family environment to human fatness in a sample of 540 adult Danish adoptees who were selected from a population of 3,580 and divided into 4 weight classes: thin, median weight, overweight, and obese.

There was a strong relation between the weight class of the adoptees and the body-mass index of their biologic parents—for the mothers, p < 0.0001; for the fathers, p < 0.02. There was no relation between the weight class of the adoptees and the body-mass index of their adoptive parents.

Cumulative distributions of the body-mass index of parents showed similar results; there was a strong relation between the body-mass index of biologic parents and adoptee weight class and no relation between the index of adoptive parents and adoptee weight class.

Furthermore, the relation between biologic parents and adoptees was not confined to the obesity weight class, but was present across the whole range of body fatness—from very thin to very fat.

We conclude that genetic influences have an important role in determining human fatness in adults, whereas the family environment alone has no apparent effect.

Figure 1: Mean Body-Mass Index of Parents of 4 Weight Classes of Adoptees. Note the increase in mean body-mass index of biologic parents with the increase in weight class of the adoptees. No such increase was found with adoptive parents. Bars represent 1 SEM. BF denotes biologic fathers, BM biologic mothers, AF adoptive fathers, and AM adoptive mothers.
--- /doc/genetics/heritable/1988-heller.pdf 1988 2019-11-14 exercise genetics/heritable --- /doc/genetics/heritable/1989-perusse.pdf 1989-01-01 2019-11-14 exercise genetics/heritable --- /doc/genetics/heritable/adoption/1994-betsworth.pdf Genetic and Environmental Influences on Vocational Interests Assessed Using Adoptive and Biological Families and Twins Reared Apart and Together Deborah G. Betsworth, Thomas J. Bouchard Junior, Catherine R. Cooper, Harold D. Grotevant, Jo-Ida C. Hansen, Sandra Scarr, Richard A. Weinberg 1994-01-01 2019-11-14 [("doi","10.1006/jvbe.1994.1018")] genetics/heritable/adoption --- /doc/genetics/heritable/1998-lynchwalsh-geneticsquantitativetraits-ch25-liabilitythreshold.pdf Chapter 25: Liability-threshold model Bruce Lynch, Walsh 1998 2019-11-14 genetics/cloning genetics/heritable --- /doc/genetics/heritable/1999-beunen.pdf 1999 2019-11-14 exercise genetics/heritable --- /doc/genetics/heritable/2003-frederiksen.pdf The influence of genetic factors on physical functioning and exercise in second half of life 2003 2019-11-14 exercise genetics/heritable --- /doc/genetics/heritable/2004-simonen.pdf 2004-01-01 2019-11-14 exercise genetics/heritable --- /doc/genetics/heritable/2005-baud.pdf Personality traits as intermediary phenotypes in suicidal behavior: Genetic issues Patrick Baud 2005-01-12 2019-11-14 [("doi","10.1002/ajmg.c.30044")] crime genetics/heritable psychiatry psychology/personality --- /doc/genetics/heritable/2005-moffitt.pdf Genetic and environmental influences on antisocial behaviors: evidence from behavioral-genetic research Terrie E. Moffitt 2005-01-01 2019-11-14 [("doi","10.1016/S0065-2660(05)55003-X")] crime genetics/heritable --- /doc/genetics/heritable/2006-carlsson.pdf 2006 2019-11-14 exercise genetics/heritable --- /doc/genetics/heritable/2006-eriksson.pdf 2006 2019-11-15 exercise genetics/heritable --- /doc/genetics/heritable/2009-samelaaro.pdf 2009 2019-11-15 exercise genetics/heritable --- /doc/genetics/heritable/2009-stubbe.pdf Genetics of Exercise Behavior Janine H. Stubbe, Eco J. C. de Geus 2009-01-01 2019-11-15 [("doi","10.1007/978-0-387-76727-7_24")] exercise genetics/heritable --- /doc/genetics/heritable/2010-aaltonen.pdf A longitudinal study on genetic and environmental influences on leisure time physical activity in the Finnish Twin Cohort Sari Aaltonen, Alfredo Ortega-Alonso, Urho M. Kujala, Jaakko Kaprio 2010-01-01 2019-11-15 [("doi","10.1375/twin.13.5.475")] exercise genetics/heritable

The purpose of this study was to examine changes in the contribution of genetic and environmental influences to leisure time physical activity among male and female twins over a 6-year follow-up. At baseline the sample comprised 4,280 monozygotic and 9,276 dizygotic twin individuals, and at follow-up 4,383 monozygotic and 9,439 dizygotic twin individuals. Participants were aged 18–54 years at baseline.

Genetic modeling results showed that genetic influences on leisure time physical activity declined from baseline (44%) to follow-up (34%). Most of the genetic influences identified at baseline were present at followup (rg = 0.72). Specific environmental influences increased from baseline (56%) to follow-up (66%) while at follow-up new environmental time-specific influences were observed (re = 0.23). The model with sex differences showed a higher estimate of genetic influences for men than women both at baseline (men 47% vs. women 42%) and at follow-up (men 38% vs. women 31%). The additive genetic correlation for this phenotype was greater for men (rg = 0.79) than women (rg = 0.64). The specific environmental influences were corresponding; at baseline men 53% and women 56% and at follow-up men 62% and women 69%. The environmental correlations between the two time points were similar for men (re = 0.21) and for women (re = 0.24).

In conclusion, in a sample of healthy twins most of the genetic influences on leisure time physical activity expressed at baseline were present at 6 years of follow-up. New specific environmental factors underlying follow-up leisure time physical activity were observed.

--- /doc/genetics/heritable/2010-viding.pdf In search of genes associated with risk for psychopathic tendencies in children: a two-stage genome-wide association study of pooled DNA Essi Viding, Ken B. Hanscombe, Charles J. C. Curtis, Oliver S. P. Davis, Emma L. Meaburn, Robert Plomin 2010-07-01 2019-11-15 [("doi","10.1111/j.1469-7610.2010.02236.x")] crime genetics/heritable psychology/personality/psychopathy --- /doc/genetics/heritable/2012-anholt.pdf Genetics of Aggression Robert R. H. Anholt, Trudy F. C. Mackay 2012-08-28 2019-11-15 [("doi","10.1146/annurev-genet-110711-155514")] crime genetics/heritable --- /doc/genetics/heritable/2012-craig.pdf Genetics of human aggressive behavior Ian W. Craig, Kelly E. Halton 2009-06-09 2019-11-15 [("doi","10.1007/s00439-009-0695-9")] crime genetics/heritable --- /doc/genetics/heritable/2012-mustelin.pdf 2012 2019-11-15 exercise genetics/heritable --- /doc/genetics/heritable/2012-segal.pdf Fullerton Virtual Twin Study: An Update Nancy L. Segal, Shirley A. McGuire, Jamie L. Graham, Joanne Hoven Stohs 2012-01-01 2019-11-15 [("doi","10.1017/thg.2012.88")] genetics/heritable

Virtual twins (VTs) are same-age unrelated siblings reared together from early infancy. These unique sibling sets replicate twinship, but without the genetic link. The first VT pair was identified and studied at the University of Minnesota in 1990, launching the development of the Fullerton Virtual Twin Study at California State University, Fullerton (CSUF) in 1991. The registry currently includes 151 pairs, mostly children, with new pairs identified on a continuous basis.

Research with VTs includes studies of general intelligence, body size, interpersonal trust, social coordination, social networks, and parenting. In some cases, VTs have been studied in conjunction with pairs of monozygotic twins, dizygotic twins, full siblings, and friends as part of TAPS (Twins, Adoptees, Peers and Siblings), a collaborative project conducted between CSUF and the University of San Francisco, 2002–2006.

VTs will also serve as a comparison group for epigenetic analyses of young Chinese twins reared apart and together.

--- /doc/genetics/heritable/2013-segal.pdf Personality similarity in unrelated look-alike pairs: Addressing a twin study challenge 2013 2019-11-15 genetics/heritable psychology/personality --- /doc/genetics/heritable/2013-segal-2.pdf Unrelated look-alikes: Replicated study of personality similarity and qualitative findings on social relatedness 2013 2019-11-16 genetics/heritable psychology/personality --- /doc/genetics/heritable/2014-arnedo.pdf Uncovering the Hidden Risk Architecture of the Schizophrenias: Confirmation in 3 Independent Genome-Wide Association Studies Arnedo 2014 2019-11-16 genetics/heritable psychiatry/schizophrenia --- /doc/genetics/heritable/2014-barnes.pdf 2014 2019-11-16 crime genetics/heritable --- /doc/genetics/heritable/2014-horowitz.pdf 2014 2019-11-16 genetics/heritable sociology/preference-falsification statistics/bias --- /doc/genetics/heritable/2017-kowarsky.pdf 2017 2019-11-16 biology genetics/heritable genetics/microbiome --- /doc/genetics/heritable/2017-manchia.pdf Targeting aggression in severe mental illness: The predictive role of genetic, epigenetic, and metabolomic markers Mirko Manchia, Vassilios Fanos 2017-07-01 2019-11-16 [("doi","10.1016/j.pnpbp.2017.03.024")] crime genetics/heritable --- /doc/genetics/heritable/2018-johnson-3.pdf Exploring the relationship between polygenic risk for cannabis use, peer cannabis use and the longitudinal course of cannabis involvement Emma C. Johnson, Rebecca Tillman, Fazil Aliev, Jacquelyn L. Meyers, Jessica E. Salvatore, Andrey P. Anokhin, Danielle M. Dick, Howard J. Edenberg, John R. Kramer, Samuel Kuperman, Vivia V. McCutcheon, John I. Nurnberger, Bernice Porjesz, Marc A. Schuckit, Jay Tischfield, Kathleen K. Bucholz, Arpana Agrawal 2018-01-01 2019-11-16 [("doi","10.1111/add.14512")] genetics/heritable marijuana --- /doc/genetics/heritable/adoption/2018-salvatore.pdf Genetics, the Rearing Environment, and the Intergenerational Transmission of Divorce: A Swedish National Adoption Study Jessica E. Salvatore, Sara Larsson Lönn, Jan Sundquist, Kristina Sundquist, Kenneth S. Kendler 2018-01-01 2019-11-16 [("doi","10.1177/0956797617734864")] genetics/heritable/adoption

We used classical and extended adoption designs in Swedish registries to disentangle genetic and rearing-environment influences on the intergenerational transmission of divorce. In classical adoption analyses, adoptees (n = 19,715) resembled their biological parents, rather than their adoptive parents, in their history of divorce.

In extended adoption analyses, offspring (n = 82,698) resembled their not-lived-with fathers and their lived-with mothers. There was stronger resemblance to lived-with mothers, providing indirect evidence of rearing-environment influences on the intergenerational transmission of divorce.

The heritability of divorce assessed across generations was 0.13. We attempted to replicate our findings using within-generation data from adoptive and biological siblings (ns = 8,523–53,097). Adoptees resembled their biological, not adoptive, siblings in their history of divorce.

Thus, there was consistent evidence that genetic factors contributed to the intergenerational transmission of divorce but weaker evidence for a rearing-environment effect of divorce. Within-generation data from siblings supported these conclusions.

--- /doc/genetics/heritable/2018-tremblay.pdf Developmental Origins of Chronic Physical Aggression: A Bio-Psycho-Social Model for the Next Generation of Preventive Interventions Richard E. Tremblay, Frank Vitaro, Sylvana M. Côté 2018-01-01 2019-11-16 [("doi","10.1146/annurev-psych-010416-044030")] crime genetics/heritable --- /doc/genetics/heritable/2019-dennison.pdf Genome-wide association studies in schizophrenia: Recent advances, challenges and future perspective Charlotte A. Dennison, Sophie E. Legge, Antonio F. Pardiñas, James T. R. Walters 2019-11-25 2019-11-25 [("doi","10.1016/j.schres.2019.10.048")] genetics/heritable psychiatry/schizophrenia --- /doc/genetics/heritable/2019-gard.pdf Genetic influences on antisocial behavior: recent advances and future directions Arianna M. Gard, Hailey L. Dotterer, Luke W. Hyde 2019-06-01 2019-11-16 [("doi","10.1016/j.copsyc.2018.07.013")] crime genetics/heritable --- /doc/genetics/heritable/2019-gurovich.pdf Identifying facial phenotypes of genetic disorders using deep learning Yaron Gurovich, Yair Hanani, Omri Bar, Guy Nadav, Nicole Fleischer, Dekel Gelbman, Lina Basel-Salmon, Peter M. Krawitz, Susanne B. Kamphausen, Martin Zenker, Lynne M. Bird, Karen W. Gripp 2019-01-01 2019-11-17 [("doi","10.1038/s41591-018-0279-0")] ai/nn genetics/heritable --- /doc/genetics/heritable/2019-odintsova.pdf Genomics of human aggression: current state of genome-wide studies and an automated systematic review tool Veronika V. Odintsova, Peter J. Roetman, Hill F. Ip, René Pool, Camiel M. Van der Laan, Klodiana-Daphne Tona, Robert R. J. M. Vermeiren, Dorret I. Boomsma 2019-10-01 2019-11-17 [("doi","10.1097/YPG.0000000000000239")] crime genetics/heritable --- /doc/genetics/heritable/2019-salvatore.pdf Sibling comparisons elucidate the associations between educational attainment polygenic scores and alcohol, nicotine and cannabis Jessica E. Salvatore, Peter B. Barr, Mallory Stephenson, Fazil Aliev, Sally I-Chun Kuo, Jinni Su, Arpana Agrawal, Laura Almasy, Laura Bierut, Kathleen Bucholz, Grace Chan, Howard J. Edenberg, Emma C. Johnson, Vivia V. McCutcheon, Jacquelyn L. Meyers, Marc Schuckit, Jay Tischfield, Leah Wetherill, Danielle M. Dick 2019-10-28 2019-11-17 [("doi","10.1111/add.14815")] genetics/heritable marijuana nicotine psychiatry/alcoholism --- /doc/genetics/heritable/rare/2019-shi-2.pdf A Rare Mutation of β1-Adrenergic Receptor Affects Sleep/Wake Behaviors Guangsen Shi, Lijuan Xing, David Wu, Bula J. Bhattacharyya, Christopher R. Jones, Thomas McMahon, S. Y. Christin Chong, Jason A. Chen, Giovanni Coppola, Daniel Geschwind, Andrew Krystal, Louis J. Ptáček, Ying-Hui Fu 2019-08-28 2019-11-17 [("doi","10.1016/j.neuron.2019.07.026")] genetics/heritable/rare zeo --- /doc/genetics/heritable/2020-mills.pdf Sociology, Genetics, and the Coming of Age of Sociogenomics Melinda C. Mills, Felix C. Tropf 2020-05-11 2020-05-11 [("doi","10.1146/annurev-soc-121919-054756")] genetics/heritable genetics/selection/natural/human/dysgenics

Recent years have seen the birth of sociogenomics via the infusion of molecular genetic data. We chronicle the history of genetics, focusing particularly on post-2005 genome-wide association studies, the post-2015 big data era, and the emergence of polygenic scores. We argue that understanding polygenic scores, including their genetic correlations with each other, causation, and underlying biological architecture, is vital.

We show how genetics can be introduced to understand a myriad of topics such as fertility, educational attainment, intergenerational social mobility, well-being, addiction, risky behavior, and longevity. Although models of gene-environment interaction and correlation mirror agency and structure models in sociology, genetics is yet to be fully discovered by this discipline.

We conclude with a critical reflection on the lack of diversity, non-representative samples, precision policy applications, ethics, and genetic determinism. We argue that sociogenomics can speak to long-standing sociological questions and that sociologists can offer innovative theoretical, measurement, and methodological innovations to genetic research.

--- /doc/genetics/heritable/2021-kalmoe.pdf Genes, Ideology, and Sophistication Nathan P. Kalmoe, Martin Johnson 2021-03-08 2021-03-08 [("doi","10.1017/XPS.2021.4")] genetics/heritable psychology/cognitive-bias/illusion-of-depth

Twin studies function as natural experiments that reveal political ideology’s substantial genetic roots, but how does that comport with research showing a largely non-ideological public?

This study integrates two important literatures and tests whether political sophistication—itself heritable—provides an “enriched environment” for genetic predispositions to actualize in political attitudes. Estimates from the Minnesota Twin Study show that sociopolitical conservatism is extraordinarily heritable (74%) for the most informed fifth of the public—much more so than population-level results (57%)—but with much lower heritability (29%) for the public’s bottom half.

This heterogeneity is clearest in the Wilson-Patterson (W-P) index, with similar patterns for individual index items, an ideological constraint measure, and ideological identification.

The results resolve tensions between two key fields by showing that political knowledge facilitates the expression of genetic predispositions in mass politics.

--- /doc/genetics/selection/natural/human/dysgenics/1971-cavallisforza-thegeneticsofhumanpopulations.pdf 1971 2019-11-17 genetics/selection/natural/human/dysgenics --- /doc/genetics/selection/natural/human/1976-weinrich.pdf Human Reproductive Strategy: I. Environmental Predictability And Reproductive Strategy; Effects Of Social Class And Race. II. Homosexuality And Non-Reproduction; Some Evolutionary Models James Donald Weinrich 1976-04-01 2019-11-17 genetics/selection/natural/human psychology technology --- /doc/genetics/selection/natural/human/1978-lande.pdf Are Humans Maximizing Reproductive Success? [with Reply] Russell Lande, James D. Weinrich 1978-01-01 2019-11-17 [("doi","10.2307/4599158")] genetics/selection/natural/human psychology technology --- /doc/genetics/selection/natural/human/1995-tooby.pdf The Psychological Foundations of Culture John Tooby, Leda Cosmides 1995-01-01 2019-11-17 genetics/selection/natural/human psychology --- /doc/genetics/selection/2011-ciani.pdf 2011 2019-11-17 genetics/selection psychology/personality --- /doc/genetics/selection/natural/human/2016-berg.pdf Genetic Associations Between Personality Traits and Lifetime Reproductive Success in Humans Venla Berg, Virpi Lummaa, Ian J. Rickard, Karri Silventoinen, Jaakko Kaprio, Markus Jokela 2016-01-01 2019-11-18 [("doi","10.1007/s10519-016-9803-5")] genetics/selection/natural/human psychology/personality --- /doc/genetics/selection/2016-trumble.pdf 2016 2019-11-18 genetics/heritable/rare genetics/selection psychiatry/alzheimers --- /doc/genetics/selection/artificial/index-selection/2017-crossa.pdf Genomic Selection in Plant Breeding: Methods, Models, and Perspectives José Crossa, Paulino Pérez-Rodríguez, Jaime Cuevas, Osval Montesinos-López, Diego Jarquín, Gustavo de los Campos, Juan Burgueño, Juan M. González-Camacho, Sergio Pérez-Elizalde, Yoseph Beyene, Susanne Dreisigacker, Ravi Singh, Xuecai Zhang, Manje Gowda, Manish Roorkiwal, Jessica Rutkoski, Rajeev K. Varshney 2017-01-01 2019-11-18 [("doi","10.1016/j.tplants.2017.08.011")] genetics/selection/artificial/index-selection --- /doc/genetics/selection/artificial/2018-yamashiro.pdf Generation of human oogonia from induced pluripotent stem cells in vitro Chika Yamashiro, Kotaro Sasaki, Yukihiro Yabuta, Yoji Kojima, Tomonori Nakamura, Ikuhiro Okamoto, Shihori Yokobayashi, Yusuke Murase, Yukiko Ishikura, Kenjiro Shirane, Hiroyuki Sasaki, Takuya Yamamoto, Mitinori Saitou 2018-01-01 2019-11-18 [("doi","10.1126/science")] genetics/gametogenesis genetics/selection/artificial --- /doc/genetics/selection/artificial/index-selection/1943-hazel.pdf The efficiency of 3 methods of selection 1943-01-01 2019-11-18 genetics/heritable/correlation genetics/selection/artificial/index-selection --- /doc/history/1995-betzig.pdf Medieval Monogamy Laura Betzig 1995-01-01 2019-11-18 history philosophy/ethics --- /doc/history/s-l-a-marshall/1989-hackworth-aboutface-slamarshallexcerpts.pdf#page=37 About Face: The Odyssey of an American Warrior § S. L. A. Marshall (SLAM) David H. Hackworth, Julie Sherman 1989-01-01 2019-11-18 history/s-l-a-marshall statistics/bias --- /doc/iq/1903-pearson.pdf On the Inheritance of the Mental and Moral Characters in Man, and its Comparison with the Inheritance of the Physical Characters Karl Pearson 1903-07-01 2019-11-18 [("doi","10.2307/2842809")] genetics/heritable iq --- /doc/iq/1949-skodak.pdf A Final Follow-Up Study of One Hundred Adopted Children Marie Skodak, Harold M. Skeels 1949-01-01 2019-11-18 [("doi","10.1080/08856559.1949.10533511")] genetics/heritable/adoption iq --- /doc/iq/1983-horn.pdf The Texas Adoption Project: Adopted Children and Their Intellectual Resemblance to Biological and Adoptive Parents Joseph M. Horn 1983-01-01 2019-11-18 [("doi","10.2307/1129690")] genetics/heritable/adoption iq --- /doc/iq/1990-locurto.pdf The malleability of IQ as judged from adoption studies Charles Locurto 1990-07-01 2019-11-19 [("doi","10.1016/S0160-2896(10)80001-7")] genetics/heritable/adoption iq --- /doc/iq/2014-shulman.pdf Embryo Selection for Cognitive Enhancement: Curiosity or Game-changer? 2014 2019-11-19 genetics/gametogenesis iq --- /doc/iq/2015-tenijenhuis.pdf Are adoption gains on the g factor? A meta-analysis Jan te Nijenhuis, Birthe Jongeneel-Grimen, Elijah L. Armstrong 2015-01-01 2019-11-19 [("doi","10.1016/j.paid.2014.09.022")] dual-n-back genetics/heritable/adoption iq --- /doc/iq/high/smpy/2005-wai.pdf 2005 2019-11-19 iq/high/smpy iq/ses --- /doc/iq/high/smpy/2006-lubinski.pdf 2006 2019-11-19 iq/high/smpy iq/ses --- /doc/longevity/2020-thau.pdf Cryonics for all? Tena Thau 2020-01-31 2020-01-31 [("doi","10.1111/bioe.12710")] cryonics longevity philosophy/ethics --- /doc/longevity/fasting/2017-li.pdf Intermittent Fasting Promotes White Adipose Browning and Decreases Obesity by Shaping the Gut Microbiota Guolin Li, Cen Xie, Siyu Lu, Robert G. Nichols, Yuan Tian, Licen Li, Daxeshkumar Patel, Yinyan Ma, Chad N. Brocker, Tingting Yan, Kristopher W. Krausz, Rong Xiang, Oksana Gavrilova, Andrew D. Patterson, Frank J. Gonzalez 2017-01-01 2019-11-19 [("doi","10.1016/j.cmet.2017.08.019")] genetics/microbiome longevity/fasting --- /doc/modafinil/2021-payette.pdf An anti-narcolepsy drug reveals behavioral and fitness costs of extreme activity cycles in arctic-breeding songbirds Wesley I. Payette, Brett L. Hodinka, Keelee B. Pullum, Melanie M. Richter, Noah T. Ashley 2021-04-15 2021-04-15 [("doi","10.1242/jeb.237198")] modafinil psychology/animal/bird psychology/neuroscience

Sleep loss impairs cognitive function, immunological responses, and general well-being in humans. However, sleep requirements in mammals and birds vary dramatically. In circumpolar regions with continuous summer light, daily sleep duration is reduced, particularly in breeding birds. The effect of an anti-narcolepsy drug (modafinil) to putatively extend wakefulness was examined in two species of closely related arctic-breeding passerine birds: Lapland longspurs (Calcarius lapponicus) and snow buntings (Plectrophenax nivalis).

Free-living adult males were implanted during the nestling phase on day 4 (D4; 4 days post-hatching) with osmotic pumps containing either vehicle or modafinil to extend the active period for 72 h. Nestlings were weighed on D2 and D7 to measure growth rates. Additionally, focal observations were conducted on D6.

Male longspurs receiving modafinil made fewer feeding visits and spent less time at the nest but tended to spend more time near the nest than controls. We observed no change in longspur nestling growth rates, but fledging occurred statistically-significantly later when males received modafinil, suggesting a fitness cost. In contrast, modafinil had no measurable impact on male or female snow bunting behavior, nestling growth rates, or time to fledging.

We suggest male longspurs compromise and maintain vigilance at their nests in lieu of sleeping because of the increased predation risk that is characteristic of their tundra nesting habitat. Snow buntings are cavity nesters, and their nests do not require the same vigilance, allowing males to presumably rest following provisioning. These life-history differences between species highlight the role of predation risk in mediating behavioral modifications to prolonged wakefulness in arctic-breeding songbirds.

--- /doc/philosophy/ontology/2020-michell.pdf Representational measurement theory: Is its number up? Joel Michell 2020-06-07 2020-06-07 [("doi","10.1177/0959354320930817")] philosophy/epistemology philosophy/ontology psychology

Representational measurement theory was proposed initially to solve problems caused by disciplinary aspirations of 19th-century mathematicians, who wanted to construe their subject as independent of its applications in empirical science. Half a century later, S. S. Stevens seized the opportunity provided by representational theory’s reconstruction of measurement as numerical coding to rubber-stamp psychology’s own aspirations to be counted as a quantitative science. Patrick Suppes’ version of representational theory rectified defects in Stevens’ theory, making it explicit that representational theory entails that mathematical structure is already embedded in empirical systems.

However, Suppes’ theory neglected the fact that attributes, not objects, are the focus of measurement and when that oversight is corrected, it follows that empirical systems sustaining measurement already instantiate positive real numbers. Thus, in measurement, real numbers are estimated, not assigned from without.

Representational theory not only misrepresents measurement; it refutes itself.

--- /doc/philosophy/religion/2009-rey.pdf 2009 2019-11-19 philosophy/epistemology philosophy/religion psychology/cognitive-bias/illusion-of-depth sociology/preference-falsification --- /doc/statistics/prediction/2021-corgnet.pdf Forecasting Skills in Experimental Markets: Illusion or Reality? Brice Corgnet, Cary Deck, Mark DeSantis, David Porter 2021-11-05 2021-11-05 [("doi","10.1287/mnsc.2021.4160")] iq psychology/personality statistics/prediction

There is an ongoing debate regarding the degree to which a forecaster’s ability to draw correct inferences from market signals is real or illusory. This paper attempts to shed light on the debate by examining how personal characteristics do or do not affect forecaster success. Specifically, we investigate the role of fluid intelligence, manipulativeness, and theory of mind on forecast accuracy in experimental asset markets.

We find that intelligence improves forecaster performance when market mispricing is low, manipulativeness improves forecaster performance when mispricing is high, and the degree to which theory of mind skills matter depends on both the level of mispricing and how information is displayed. All 3 of these results are consistent with hypotheses derived from the previous literature. Additionally, we observe that male forecasters outperform female forecasters after controlling for intelligence, manipulativeness, and theory of mind skills as well as risk aversion. Interestingly, we do not find any evidence that forecaster performance improves with experience across markets or within markets.

--- /doc/psychedelic/2020-aginliebes.pdf Long-term follow-up of psilocybin-assisted psychotherapy for psychiatric and existential distress in patients with life-threatening cancer Gabrielle I. Agin-Liebes, Tara Malone, Matthew M. Yalch, Sarah E. Mennenga, K. Linnae Ponté, Jeffrey Guss, Anthony P. Bossis, Jim Grigsby, Stacy Fischer, Stephen Ross 2020-01-09 2020-01-09 [("doi","10.1177/0269881119897615")] psychedelic psychiatry/anxiety psychiatry/depression

Background: A recently published randomized controlled trial compared single-dose psilocybin with single-dose niacin in conjunction with psychotherapy in participants with cancer-related psychiatric distress. Results suggested that psilocybin-assisted psychotherapy facilitated improvements in psychiatric and existential distress, quality of life, and spiritual well-being up to seven weeks prior to the crossover. At the 6.5-month follow-up, after the crossover, 60–80% of participants continued to meet criteria for clinically-significant antidepressant or anxiolytic responses.

Method: The present study is a long-term within-subjects follow-up analysis of self-reported symptomatology involving a subset of participants that completed the parent trial. All 16 participants who were still alive were contacted, and 15 participants agreed to participate at an average of 3.2 and 4.5 years following psilocybin administration.

Results: Reductions in anxiety, depression, hopelessness, demoralization, and death anxiety were sustained at the first and second follow-ups. Within-group effect sizes were large. At the second (4.5 year) follow-up ~60–80% of participants met criteria for clinically-significant antidepressant or anxiolytic responses. Participants overwhelmingly (71–100%) attributed positive life changes to the psilocybin-assisted therapy experience and rated it among the most personally meaningful and spiritually important experiences of their lives.

Conclusion: These findings suggest that psilocybin-assisted psychotherapy holds promise in promoting long-term relief from cancer-related psychiatric distress. Limited conclusions, however, can be drawn regarding the efficacy of this therapy due to the crossover design of the parent study. Nonetheless, the present study adds to the emerging literature base suggesting that psilocybin-facilitated therapy may enhance the psychological, emotional, and spiritual well-being of patients with life-threatening cancer.

--- /doc/psychiatry/1966-tarachow.pdf Coprophagia and Allied Phenomena Sidney Tarachow 1966-08-01 2019-11-20 [("doi","10.1177/000306516601400401")] biology/booger psychiatry --- /doc/psychiatry/2020-lerner.pdf Nothing Ventured, Nothing Gained: Parasite Infection is Associated with Entrepreneurial Initiation, Engagement, and Performance Daniel A. Lerner, Lars Alkærsig, Markus A. Fitza, Carina Lomberg, Stefanie K. Johnson 2020-01-27 2020-01-27 [("doi","10.1177/1042258719890992")] economics psychiatry

There is growing evidence that human biology and behavior are influenced by infectious microorganisms. One such microorganism is the protozoan Toxoplasma gondii (TG).

Using longitudinal data covering the female population of Denmark, we extend research on the relationship between TG infection and entrepreneurial activity and outcomes.

Results indicate that TG infection is associated with a subsequent increase in the probability of becoming an entrepreneur, and is linked to other outcomes including venture performance.

With parasite behavioral manipulation antithetical to rational judgment, we join a growing conversation on biology and alternative drivers of business venturing.

--- /doc/psychiatry/schizophrenia/2009-schizophreniaconsortium.pdf Common polygenic variation contributes to risk of schizophrenia and bipolar disorder The International Schizophrenia Consortium 2009 2019-11-20 genetics/heritable/correlation psychiatry/bipolar/genetics psychiatry/schizophrenia --- /doc/psychiatry/schizophrenia/2019-belsky.pdf Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down Daniel W. Belsky, K. Paige Harden 2019-01-01 2019-11-20 [("doi","10.1177/0963721418807729")] genetics/heritable psychiatry/schizophrenia

Genome-wide association studies (GWASs) have identified specific genetic variants associated with complex human traits and behaviors, such as educational attainment, mental disorders, and personality. However, small effect sizes for individual variants, uncertainty regarding the biological function of discovered genotypes, and potential “outside-the-skin” environmental mechanisms leave a translational gulf between GWAS results and scientific understanding that will improve human health and well-being.

We propose a set of social, behavioral, and brain-science research activities that map discovered genotypes to neural, developmental, and social mechanisms and call this research program phenotypic annotation. Phenotypic annotation involves (a) elaborating the nomological network surrounding discovered genotypes, (b) shifting focus from individual genes to whole genomes, and (c) testing how discovered genotypes affect life-span development.

Phenotypic-annotation research is already advancing the understanding of GWAS discoveries for educational attainment and schizophrenia. We review examples and discuss methodological considerations for psychologists taking up the phenotypic-annotation approach.

--- /doc/psychiatry/1932-cavan.pdf The Wish Never To Have Been Born Ruth Shonle Cavan 1932-01 2019-11-20 [("doi","10.1086/215793")] philosophy/ethics psychiatry

Although juvenile suicides in the United States are negligible, the wish never to have been born occurred to:

about 30% of a widely scattered sample of adolescent boys and girls. This wish occurred most frequently among children with high scores (poor adjustment) on a test of neurotic traits and also among those rated by their teachers as poorly adjusted socially, emotionally, and on conventional moral traits. It also occurred most frequently among children from homes which lacked harmony and intimacy between parents and children. Social contacts were less closely associated with the wish than were home conditions.

The wish never to have been born, which may be considered as an evasive attempt at adjustment, indicates both a poorly adjusted personality in the child and lack of unity and harmony in the home.

--- /doc/psychology/1971-mcquown-thenaturalhistoryofaninterview.pdf The Natural History of an Interview Norman McQuown, Gregory Bateson, Ray L. Birdwhistell, Henry W. Brosin, Charles F. Hockett, Henry L. Smith Jr, George L. Trager 1971-01-01 2019-11-20 psychology sociology --- /doc/psychology/1996-berman.pdf Simon Browne: the soul-murdered theologian David Berman 1996-06-01 2019-11-20 [("doi","10.1177/0957154X9600702604")] philosophy/mind psychiatry psychology --- /doc/psychology/2019-pereira.pdf Depression’s Unholy Trinity: Dysregulated Stress, Immunity, and the Microbiome Joana da Cruz Pereira, Kieran Rea, Yvonne M. Nolan, Olivia F. O’Leary, Timothy G. Dinan, John F. Cryan 2019-09-30 2019-11-20 [("doi","10.1146/annurev-psych-122216-011613")] genetics/microbiome psychiatry/depression psychology

Depression remains one of the most prevalent psychiatric disorders, with many patients not responding adequately to available treatments. Chronic or early-life stress is one of the key risk factors for depression.

In addition, a growing body of data implicates chronic inflammation as a major player in depression pathogenesis.

More recently, the gut microbiota has emerged as an important regulator of brain and behavior and also has been linked to depression.

However, how this holy trinity of risk factors interact to maintain physiological homeostasis in the brain and body is not fully understood.

In this review, we integrate the available data from animal and human studies on these 3 factors in the etiology and progression of depression. We also focus on the processes by which this microbiota-immune-stress matrix may influence centrally mediated events and on possible therapeutic interventions to correct imbalances in this triune.

--- /doc/psychology/personality/2019-soto.pdf How Replicable Are Links Between Personality Traits and Consequential Life Outcomes? The Life Outcomes of Personality Replication Project Christopher J. Soto 2019-01-01 2019-11-20 [("doi","10.1177/0956797619831612")] psychology/personality statistics/bias

The Big Five personality traits have been linked to dozens of life outcomes. However, meta-scientific research has raised questions about the replicability of behavioral science. The Life Outcomes of Personality Replication (LOOPR) Project was therefore conducted to estimate the replicability of the personality-outcome literature.

Specifically, I conducted preregistered, high-powered (median n = 1,504) replications of 78 previously published trait-outcome associations. Overall, 87% of the replication attempts were statistically-significant in the expected direction. The replication effects were typically 77% as strong as the corresponding original effects, which represents a large decline in effect size.

The replicability of individual effects was predicted by the effect size and design of the original study, as well as the sample size and statistical power of the replication. These results indicate that the personality-outcome literature provides a reasonably accurate map of trait-outcome associations but also that it stands to benefit from efforts to improve replicability.

--- /doc/psychology/cognitive-bias/illusion-of-depth/2020-10-26-dyqz-rdataisbeautiful-iasked16kpeoplehowmanyholescertainobjectshave.html 2020-10-26 2020-10-26 math philosophy/ontology psychology/cognitive-bias/illusion-of-depth sociology --- /doc/psychology/personality/1969-torrance.pdf The Creative Personality and the Ideal Pupil E. Paul Torrance 1969-01-01 2019-11-21 psychology/novelty psychology/personality --- /doc/psychology/personality/2019-gladstone.pdf Can Psychological Traits Be Inferred From Spending? Evidence From Transaction Data Joe J. Gladstone, Sandra C. Matz, Alain Lemaire 2019-01-01 2019-11-21 [("doi","10.1177/0956797619849435")] economics psychology/personality

The automatic assessment of psychological traits from digital footprints allows researchers to study psychological traits at unprecedented scale and in settings of high ecological validity. This research investigates the potential of spending records—a ubiquitous and universal form of digital footprint—to infer psychological traits.

We applied an ensemble machine-learning technique (random-forest modeling) to a dataset combining two million spending records from bank accounts with survey responses from the account holders (n = 2,193). Our predictive accuracies were modest for the Big Five personality traits (r = 0.15, corrected ρ = 0.21) but provided higher precision for specific traits, including materialism (r = 0.33, corrected ρ = 0.42).

We compared the predictive accuracy of these models with the predictive accuracy of alternative digital behaviors used in past research, including those observed on social media platforms, and found that the predictive accuracies were relatively stable across socioeconomic groups and over time.

This research demonstrates the feasibility of using spending records as an insightful and reliable source for assessing psychological traits, expanding the scope of digital footprints in psychological assessment.

--- /doc/psychology/personality/conscientiousness/2022-durkee.pdf Niche Diversity Predicts Personality Structure Across 115 Nations Patrick K. Durkee, Aaron W. Lukaszewski, Christopher R. von Rueden, Michael D. Gurven, David M. Buss, Elliot M. Tucker-Drob 2022-01-19 2022-01-19 [("doi","10.1177/09567976211031571")] economics psychology/personality/conscientiousness

The niche-diversity hypothesis proposes that personality structure arises from the affordances of unique trait combinations within a society. It predicts that personality traits will be both more variable and differentiated in populations with more distinct social and ecological niches.

Prior tests of this hypothesis in 55 nations suffered from potential confounds associated with differences in the measurement properties of personality scales across groups. Using psychometric methods for the approximation of cross-national measurement invariance, we tested the niche-diversity hypothesis in a sample of 115 nations (n = 685,089). We found that an index of niche diversity was robustly associated with lower inter-trait covariance and greater personality dimensionality across nations but was not consistently related to trait variances.

These findings generally bolster the core of the niche-diversity hypothesis, demonstrating the contingency of human personality structure on socioecological contexts.

--- /doc/psychology/smell/human/1932-brill.pdf The Sense of Smell in the Neuroses and Psychoses A. A. Brill 1932-01-01 2019-11-21 [("doi","10.1080/21674086.1932.11925133")] psychiatry psychology/smell/human --- /doc/psychology/writing/2018-maccabe.pdf 2018 2019-11-21 psychiatry/bipolar psychiatry/schizophrenia psychology/writing --- /doc/radiance/2006-cohen-fuckyoumrpresidentconfessionsofthefatheroftheneutronbomb3rdedition.pdf F✱✱✱ You! Mr. President: Confessions of the Father of the Neutron Bomb Samuel T. Cohen, Charles Platt 2006-01-01 2019-11-21 history radiance --- /doc/reinforcement-learning/model/alphago/2003-lagoudakis.pdf Reinforcement Learning as Classification: Leveraging Modern Classifiers Michail Lagoudakis, Ronald Parr 2003-01-01 2019-11-21 reinforcement-learning/model/alphago --- /doc/reinforcement-learning/exploration/2019-kobayashi.pdf Common neural code for reward and information value Kenji Kobayashi, Ming Hsu 2019-01-01 2019-11-21 [("doi","10.1073/pnas.1820145116")] cs/algorithm/information psychology/neuroscience reinforcement-learning/exploration

Adaptive information seeking is critical for goal-directed behavior. Growing evidence suggests the importance of intrinsic motives such as curiosity or need for novelty, mediated through dopaminergic valuation systems, in driving information-seeking behavior. However, valuing information for its own sake can be highly suboptimal when agents need to evaluate instrumental benefit of information in a forward-looking manner.

Here we show that information-seeking behavior in humans is driven by subjective value that is shaped by both instrumental and non-instrumental motives, and that this subjective value of information (SVOI) shares a common neural code with more basic reward value. Specifically, using a task where subjects could purchase information to reduce uncertainty about outcomes of a monetary lottery, we found information purchase decisions could be captured by a computational model of SVOI incorporating utility of anticipation, a form of non-instrumental motive for information seeking, in addition to instrumental benefits.

Neurally, trial-by-trial variation in SVOI was correlated with activity in striatum and ventromedial prefrontal cortex. Furthermore, cross-categorical decoding revealed that, within these regions, SVOI and expected utility of lotteries were represented using a common code. These findings provide support for the common currency hypothesis and shed insight on neurocognitive mechanisms underlying information-seeking behavior.

--- /doc/rotten.com/library/crime/drugs/lsd-blotters/index.html 2019-11-21 psychedelic --- /doc/rotten.com/library/crime/index.html 2019-11-21 crime --- /doc/science/1966-mathematicalassociationofamerica-documentary-maavideoclassics2-johnvonneumanadocumentary.mkv 1966 2019-11-21 math science --- /doc/darknet-market/2013-05-05-moore-bitcoinexchangesurvivalanalysis-complianceamlcftwhole.csv 2013-05-05 2019-11-22 darknet-market statistics/survival-analysis --- /doc/darknet-market/2013-05-05-moore-bitcoinexchangesurvivalanalysis-sdatamodcc.csv 2013-05-05 2019-11-22 darknet-market statistics/survival-analysis --- /doc/darknet-market/2013-05-05-moore-bitcoinexchangesurvivalanalysis.R 2013-05-05 2019-11-22 bitcoin darknet-market statistics/survival-analysis --- /doc/darknet-market/2015-hardy.pdf Reputation in the Internet black market: an empirical and theoretical analysis of the Deep Web 2015 2019-11-22 darknet-market marijuana --- /doc/darknet-market/2018-decaryhetu.pdf Six Years Later David Décary-Hétu, Vincent Mousseau, Sabrina Vidal 2018-01-01 2019-11-22 [("doi","10.1177/0091450918797355")] darknet-market marijuana

Cryptomarkets are online illicit marketplaces where drug dealers advertise the sale of illicit drugs. Anonymizing technologies such as the Tor network and virtual currencies are used to hide cryptomarket participants’ identity and to limit the ability of law enforcement agencies to make arrests.

In this paper, our aim is to describe how herbal cannabis dealers and buyers in the United States have adapted to the online sale of herbal cannabis through cryptomarkets. To achieve this goal, we evaluate the size and scope of the American herbal cannabis market on cryptomarkets and compare it to other drug markets from other countries, evaluate the impact of cryptomarkets on offline sales of herbal cannabis, and evaluate the ties between the now licit herbal cannabis markets in some States and cryptomarkets.

Our results suggest that only a small fraction of herbal cannabis dealers and drug users have transitioned to cryptomarkets. This can be explained by the need for technical skills to buy and sell herbal cannabis online and by the need to have access to computers that are not accessible to all. The slow rate of adoption may also be explained by the higher price of herbal cannabis relative to street prices.

If cryptomarkets were to be adopted by a larger portion of the herbal cannabis market actors, our results suggest that wholesale and regional distributors who are not active on cryptomarkets would be the most affected market’s participants.

--- /doc/darknet-market/agora/2018-ladegaard.pdf Instantly Hooked? Freebies and Samples of Opioids, Cannabis, MDMA, and Other Drugs in an Illicit E-Commerce Market Isak Ladegaard 2018-01-01 2019-11-22 [("doi","10.1177/0022042617746975")] darknet-market/agora darknet-market/dnm-archive marijuana

Do drug dealers entice nonusers with free samples? Police, the popular press, and social media users say so, but crime researchers have found little support for this theory and argue instead that sample distribution is an unsound strategy for illegal market business. But what about in digital drug markets, where operational logics are based on sophisticated anonymization technology and reputation systems?

The author collected data from a large e-commerce website for drugs over 305 days in 2014 and 2015 and documents that (a) drug dealers give away samples of all major substance categories and (b) sample distribution increases vendor sales for prescription drugs and opioid-based painkillers.

To explore possible explanations of these findings, the author collected data from the market’s online forum and analyzed 175 discussions (2,218 posts) about samples. Among the findings is that samples are preferably given to reputable review writers, or “drug critics.”

--- /doc/darknet-market/2019-hardy.pdf Rationality on the Fringes Robert Augustus Hardy 2019-01-01 2019-11-22 darknet-market marijuana --- /doc/darknet-market/silk-road/1/gx-940-sr1salesdata-060211-02102013.csv 2019-11-22 darknet-market/silk-road/1 marijuana --- /doc/sociology/1972-jencks-inequality.pdf 1972 2019-11-22 iq/ses sociology --- /doc/sociology/1979-jencks-whogetsahead.pdf 1979 2019-11-22 iq/ses sociology --- /doc/sociology/preference-falsification/1994-loury.pdf Self-Censorship in Public Discourse: A Theory of ‘Political Correctness’ and Related Phenomena Glenn C. Loury 1994-10-01 2019-11-23 [("doi","10.1177/1043463194006004002")] sociology/preference-falsification

Uncertainty about what motivates “senders” of public messages leads “receivers” to “read between the lines” to discern the sender’s deepest commitments.

Anticipating this, senders “write between the lines”, editing their expressions so as to further their own ends.

I examine how this interactive process of inference and deceit affects the quality and extent of public deliberations on sensitive issues.

A principle conclusion is that genuine moral discourse on difficult social issues can become impossible when the risks of upsetting some portion of one’s audience are too great. Reliance on euphemism and platitude should be expected in this strategic climate.

Groups may embark on a tragic course of action, believed by many at the outset to be ill-conceived, but that has become impossible to criticize.

--- /doc/sociology/2017-stojmenovska.pdf Does Diversity Pay? A Replication of Herring 2009 Dragana Stojmenovska, Thijs Bol, Thomas Leopold 2017-07-07 2019-11-23 [("doi","10.1177/0003122417714422")] economics psychology/cognitive-bias sociology statistics/bias

In an influential article published in the American Sociological Review in 2009, Herring finds that diverse workforces are beneficial for business. His analysis supports 7⁄8 hypotheses on the positive effects of gender and racial diversity on sales revenue, number of customers, perceived relative market share, and perceived relative profitability.

This comment points out that Herring’s analysis contains two errors. First, missing codes on the outcome variables are treated as substantive codes. Second, two control variables—company size and establishment size—are highly skewed, and this skew obscures their positive associations with the predictor and outcome variables.

We replicate Herring’s analysis correcting for both errors. The findings support only one of the original 8 hypotheses, suggesting that diversity is inconsequential, rather than beneficial, to business success.

--- /doc/sociology/preference-falsification/2018-langbert.pdf Homogenous: The Political Affiliations of Elite Liberal Arts College Faculty Mitchell Langbert 2018-01-01 2019-11-23 [("doi","10.1007/s12129-018-9700-x")] sociology/preference-falsification statistics/bias --- /doc/sociology/2019-allcott.pdf Food Deserts and the Causes of Nutritional Inequality Hunt Allcott, Rebecca Diamond, Jean-Pierre Dubé, Jessie Handbury, Ilya Rahkovsky, Molly Schnell 2019-05-20 2019-11-23 [("doi","10.1093/qje/qjz015")] economics sociology

We study the causes of “nutritional inequality”: why the wealthy eat more healthfully than the poor in the United States.

Exploiting supermarket entry and household moves to healthier neighborhoods, we reject that neighborhood environments contribute meaningfully to nutritional inequality.

We then estimate a structural model of grocery demand, using a new instrument exploiting the combination of grocery retail chains’ differing presence across geographic markets with their differing comparative advantages across product groups.

Counterfactual simulations show that exposing low-income households to the same products and prices available to high-income households reduces nutritional inequality by only about 10%, while the remaining 90% is driven by differences in demand.

These findings counter the argument that policies to increase the supply of healthy groceries could play an important role in reducing nutritional inequality.

--- /doc/sociology/2019-chen.pdf 2019 2019-11-23 sociology technology wikipedia --- /doc/sociology/2021-brewster.pdf Are Black Restaurant Servers Tipped Less Than White Servers? 3 Experimental Tests of Server Race Effects Customers’ Tipping Behaviors Zachary W. Brewster, Kenneth Gourlay, Gerald Roman Nowak III 2021-08-03 2021-08-03 [("doi","10.1177/19389655211036652")] economics sociology

A limited number of published studies have presented evidence indicating that restaurant customers discriminate against Black servers by tipping them less than their White coworkers. However, the cross-sectional, localized, and small samples that were analyzed in these extant studies do not support any unqualified claim that consumer racial discrimination in tipping practices is a widespread phenomenon. Thus, in an effort to further clarify the relationship between restaurant servers’ race and customers’ tipping practices, we present results from 3 survey experiments designed to assess the causal effect of servers’ race on customers’ tipping intentions.

In 3 independent, demographically diverse, and relatively large samples of U.S. consumers, we found no evidence to conclude that all else being equal consumers discriminate against Black restaurant servers by tipping them less than comparable White servers. Furthermore, the null effects of servers’ race on customers’ tipping practices were not found to be sensitive to variation in service quality, dining satisfaction, servers’ sex, customers’ sex, or customers’ race.

Our results challenge the generalizability of the previously observed server race effects on customers’ tipping practices and point toward the need for future research that aims to advance our understanding of the conditions under which customers’ tipping practices are sensitive to the perceived race of their server. The implications of our results for restaurant operations and directions for future research are also discussed.

--- /doc/sociology/technology/2021-hughes.pdf Using Administrative Records and Survey Data to Construct Samples of Tweeters and Tweets Adam G. Hughes, Stefan D. McCabe, William R. Hobbs, Emma Remy, Sono Shah, David M. J. Lazer 2021-08-05 2021-08-05 [("doi","10.1093/poq/nfab020")] sociology/technology

Social media data can provide new insights into political phenomena, but users do not always represent people, posts and accounts are not typically linked to demographic variables for use as statistical controls or in subgroup comparisons, and activities on social media can be difficult to interpret. For data scientists, adding demographic variables and comparisons to closed-ended survey responses have the potential to improve interpretations of inferences drawn from social media—for example, through comparisons of online expressions and survey responses, and by assessing associations with offline outcomes like voting. For survey methodologists, adding social media data to surveys allows for rich behavioral measurements, including comparisons of public expressions with attitudes elicited in a structured survey.

Here, we evaluate two popular forms of linkages—administrative and survey—focusing on 2 questions: How does the method of creating a sample of Twitter users affect its behavioral and demographic profile? What are the relative advantages of each of these methods?

Our analyses illustrate where and to what extent the sample based on administrative data diverges in demographic and partisan composition from surveyed Twitter users who report being registered to vote. Despite demographic differences, each linkage method results in behaviorally similar samples, especially in activity levels; however, conventionally sized surveys are likely to lack the statistical power to study subgroups and heterogeneity (eg. comparing conversations of Democrats and Republicans) within even highly salient political topics.

We conclude by developing general recommendations for researchers looking to study social media by linking accounts with external benchmark data sources.

--- /doc/sociology/2021-norris.pdf The Effects of Parental and Sibling Incarceration: Evidence from Ohio Samuel Norris, Matthew Pecenco, Jeffrey Weaver 2021-09-01 2021-09-01 [("doi","10.1257/aer.20190415")] crime sociology

Every year, millions of Americans experience the incarceration of a family member.

Using 30 years of administrative data from Ohio and exploiting differing incarceration propensities of randomly assigned judges, this paper provides the first quasi-experimental estimates of the effects of parental and sibling incarceration in the United States.

Parental incarceration has beneficial effects on some important outcomes for children, reducing their likelihood of incarceration by 4.9 percentage points and improving their adult neighborhood quality. While estimates on academic performance and teen parenthood are imprecise, we reject large positive or negative effects.

Sibling incarceration leads to similar reductions in criminal activity.

--- /doc/sociology/technology/2021-sindermann.pdf The degree of heterogeneity of news consumption in Germany—Descriptive statistics and relations with individual differences in personality, ideological attitudes, and voting intentions Cornelia Sindermann, Christopher Kannen, Christian Montag 2021-12-30 2021-12-30 [("doi","10.1177/14614448211061729")] psychology/personality sociology/technology

This study aimed to examine the degree of homogeneity versus heterogeneity of individuals’ political information environments across offline and online media types and relations with sociodemographic variables, personality, and political attitudes. Using two online surveys, German participants (sample 1: n = 686; sample 2: n = 702) provided information on sociodemographic variables, consumption of political news, and voting intentions, and completed the Big Five Inventory and Right-Wing Authoritarianism (RWA) and Social Dominance Orientation (SDO) scales.

Results revealed that absolutely homogeneous political news consumption was evident for a small proportion of individuals (2.04% and 0.43%). Openness (positively) and Agreeableness (negatively) exhibited statistically-significant associations with the degree of heterogeneity of political information environments across samples. No consistent patterns of relations with either the ideological attitudes of RWA and SDO or voting intentions were observed.

The findings shed light on the existence of absolutely homogeneous political information environments and “who” might be prone to a more homogeneous versus more heterogeneous information environment.

--- /doc/psychology/spaced-repetition/1978-baddeley.pdf 1978 2019-11-23 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- /doc/psychology/spaced-repetition/1985-pirolli.pdf 1985-01-01 2019-11-23 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- /doc/psychology/spaced-repetition/1994-bjork.pdf Memory and Metamemory Considerations in the Training of Human Beings 1994 2019-11-24 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- /doc/psychology/spaced-repetition/1994-dunlosky.pdf Does the Sensitivity of Judgments of Learning (JOLs) to the Effects of Various Study Activities Depend on When the JOLs Occur? 1994-01-01 2019-11-24 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- /doc/psychology/spaced-repetition/1998-davis.pdf Does CME work? An analysis of the effect of educational activities on physician performance or health care outcomes Davis 1998 2019-11-24 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- /doc/psychology/spaced-repetition/2001-simon.pdf Metacognition in Motor Learning 2001 2019-11-24 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- /doc/psychology/spaced-repetition/2012-son.pdf Distributed Learning: Data, Metacognition, and Educational Implications 2012 2019-11-24 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- /doc/psychology/spaced-repetition/2013-bjork.pdf Self-Regulated Learning: Beliefs, Techniques, and Illusions 2013-01-01 2019-11-24 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- /doc/psychology/spaced-repetition/2014-mulligan.pdf The Spacing Effect and Metacognitive Control 2014-01-01 2019-11-24 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- /doc/statistics/2020-reimann.pdf Visual model fit estimation in scatterplots and distribution of attention: Influence of slope and noise level Daniel Reimann, Christine Blech, Robert Gaschler 2020-01-01 2020-01-01 [("doi","10.1027/1618-3169/a000499")] design/visualization statistics

Scatterplots are ubiquitous data graphs and can be used to depict how well data fit to a quantitative theory. We investigated which information is used for such estimates.

In Experiment 1 (n = 25), we tested the influence of slope and noise on perceived fit between a linear model and data points. Additionally, eye tracking was used to analyze the deployment of attention. Visual fit estimation might mimic one or the other statistical estimate: If participants were influenced by noise only, this would suggest that their subjective judgment was similar to root mean square error. If slope was relevant, subjective estimation would mimic variance explained. While the influence of noise on estimated fit was stronger, we also found an influence of slope.

As most of the fixations fell into the center of the scatterplot, in Experiment 2 (n = 51), we tested whether location of noise affects judgment. Indeed, high noise influenced the judgment of fit more strongly if it was located in the middle of the scatterplot.

Visual fit estimates seem to be driven by the center of the scatterplot and to mimic variance explained.

--- /doc/statistics/bayes/2008-hyvarinen.pdf Optimal approximation of signal priors Aapo Hyvärinen 2008-12-01 2019-11-24 [("doi","10.1162/neco.2008.10-06-384")] ai/nn/diffusion statistics/bayes

In signal restoration by Bayesian inference, one typically uses a parametric model of the prior distribution of the signal. Here, we consider how the parameters of a prior model should be estimated from observations of uncorrupted signals. A lot of recent work has implicitly assumed that maximum likelihood estimation is the optimal estimation method. Our results imply that this is not the case.

We first obtain an objective function that approximates the error occurred in signal restoration due to an imperfect prior model. Next, we show that in an important special case (small Gaussian noise), the error is the same as the score-matching objective function, which was previously proposed as an alternative for likelihood based on purely computational considerations.

Our analysis thus shows that score matching combines computational simplicity with statistical optimality in signal restoration, providing a viable alternative to maximum likelihood methods. We also show how the method leads to a new intuitive and geometric interpretation of structure inherent in probability distributions.

--- /doc/statistics/bayes/2020-terechshenko.pdf Hot under the collar: A latent measure of interstate hostility Zhanna Terechshenko 2020-11-17 2020-11-17 [("doi","10.1177/0022343320962546")] history statistics/bayes

The majority of studies on international conflict escalation use a variety of measures of hostility including the use of force, reciprocity, and the number of fatalities. The use of different measures, however, leads to different empirical results and creates difficulties when testing existing theories of interstate conflict. Furthermore, hostility measures currently used in the conflict literature are ill-suited to the task of identifying consistent predictors of international conflict escalation.

This article presents a new dyadic latent measure of interstate hostility, created using a Bayesian item-response theory model and conflict data from the Militarized Interstate Dispute (MID) and Phoenix political event datasets. This model (1) provides a more granular, conceptually precise, and validated measure of hostility, which incorporates the uncertainty inherent in the latent variable; and (2) solves the problem of temporal variation in event data using a varying-intercept structure and human-coded data as a benchmark against which biases in machine-coded data are corrected. In addition, this measurement model allows for the systematic evaluation of how existing measures relate to the construct of hostility.

The presented model will therefore enhance the ability of researchers to understand factors affecting conflict dynamics, including escalation and de-escalation processes.

--- /doc/statistics/bias/publication/2016-pica.pdf Discontinuation and Nonpublication of Randomized Clinical Trials Conducted in Children 2016-01-01 2019-11-24 philosophy/ethics statistics/bias/publication --- /doc/statistics/bias/publication/2021-kasy.pdf Of Forking Paths and Tied Hands: Selective Publication of Findings, and What Economists Should Do about It Maximilian Kasy 2021-06-01 2021-06-01 [("doi","10.1257/jep.35.3.175")] economics statistics/bias/publication

A key challenge for interpreting published empirical research is the fact that published findings might be selected by researchers or by journals. Selection might be based on criteria such as statistical-significance, consistency with theory, or the surprisingness of findings or their plausibility. Selection leads to biased estimates, reduced coverage of confidence intervals, and distorted posterior beliefs.

I review methods for detecting and quantifying selection based on the distribution of p-values, systematic replication studies, and meta-studies.

I then discuss the conflicting recommendations regarding selection resulting from alternative objectives, in particular, the validity of inference versus the relevance of findings for decision-makers.

Based on this discussion, I consider various reform proposals, such as de-emphasizing statistical-significance, pre-analysis plans, journals for null results and replication studies, and a functionally differentiated publication system.

In conclusion, I argue that we need alternative foundations of statistics that go beyond the single-agent model of decision theory.

--- /doc/statistics/decision/1939-taylor.pdf The Relationship Of Validity Coefficients To The Practical Effectiveness Of Tests In Selection: Discussion And Tables 1939-01-01 2019-11-25 statistics/decision statistics/order --- /doc/statistics/decision/1960-howard-dynamicprogrammingmarkovprocesses.pdf 1960 2019-11-25 reinforcement-learning statistics/decision --- /doc/statistics/decision/2021-lee.pdf Noise Increases Anchoring Effects Chang-Yuan Lee, Carey K. Morewedge 2021-12-08 2021-12-08 [("doi","10.1177/09567976211024254")] psychology/cognitive-bias statistics/decision

We introduce a theoretical framework distinguishing between anchoring effects, anchoring bias, and judgmental noise: Anchoring effects require anchoring bias, but noise modulates their size.

We tested this framework by manipulating stimulus magnitudes. As magnitudes increase, psychophysical noise due to scalar variability widens the perceived range of plausible values for the stimulus. This increased noise, in turn, increases the influence of anchoring bias on judgments.

In 11 preregistered experiments (n = 3,552 adults), anchoring effects increased with stimulus magnitude for point estimates of familiar and novel stimuli (eg. reservation prices for hotels and donuts, counts in dot arrays). Comparisons of relevant and irrelevant anchors showed that noise itself did not produce anchoring effects. Noise amplified anchoring bias.

Our findings identify a stimulus feature predicting the size and replicability of anchoring effects—stimulus magnitude. More broadly, we show how to use psychophysical noise to test relationships between bias and noise in judgment under uncertainty.

--- /doc/psychology/cognitive-bias/sunk-cost/2017-chupeau.pdf Search in patchy media: Exploitation-exploration tradeoff 2017-01-01 2019-11-25 psychology/cognitive-bias/sunk-cost reinforcement-learning/exploration statistics/decision --- /doc/philosophy/ontology/2005-04-shirky-ontologyisoverratedcategorieslinksandtags.html 2005-04 2019-11-25 cs design philosophy/ontology technology --- /doc/transhumanism/2019-conan.pdf Frequently overlooked realistic moral bioenhancement interventions Gregory Mark Conan 2019-01-01 2019-11-25 [("doi","10.1136/medethics-2019-105534")] philosophy/ethics transhumanism

Many supporters of ‘moral bioenhancement’ (MBE), the use of biomedical interventions for moral improvement, have been criticised for having unrealistic proposals. The interventions they suggest have often been called infeasible and their implementation plans vague or unethical. I dispute these criticisms by showing that various interventions to implement MBE are practically and ethically feasible enough to warrant serious consideration. Such interventions include transcranial direct current stimulation over the medial and dorsolateral prefrontal cortex, as well as supplementation with lithium and omega-3.

Considering their efficacy and feasibility, it is strange that these interventions have rarely been proposed or discussed as MBE.

I review evidence that each of those interventions can reduce antisocial behavior, reduce racial bias, increase executive function or increase prosocial traits like fairness and altruism.

I then specify and defend realistic, ethically permissible ways to implement these interventions, especially for violent offenders and public servants—the former as rehabilitation and the latter to meet the high standards of their occupations. These interventions could be given to violent offenders in exchange for a reduced sentence or compulsorily in some cases. Potential intervention methods for non-prisoners include increasing the USDA-recommended dose of omega-3, encouraging food companies to supplement their products with omega-3 or trace lithium, requiring MBE for employment as a police officer or political leader, and insurance companies providing discounts for undergoing MBE.

In some reasonably limited form, using these interventions may be a good first step to implement the project of MBE.

--- /doc/zeo/2017-boland.pdf Meta-Analysis of the Antidepressant Effects of Acute Sleep Deprivation Elaine M. Boland, Hengyi Rao, David F. Dinges, Rachel V. Smith, Namni Goel, John A. Detre, Mathias Basner, Yvette I. Sheline, Michael E. Thase, Philip R. Gehrman 2017-01-01 2019-11-25 [("doi","10.4088/JCP.16r11332")] psychiatry/depression psychology/writing zeo --- /doc/wikipedia/2011-wikipedia-newbiesurvivalbysemesterrows20022010.png File:Newbie survival by semester rows.png 2011 2019-11-25 statistics/survival-analysis wikipedia --- /doc/ai/nn/transformer/clip/2021-04-22-rivershavewings-clipvqgan-theshadowyhackergroupeleuther.png 2021-04-22 2021-04-22 ai/nn/transformer/clip --- /doc/ai/nn/transformer/gpt/dall-e/2021-openai-dalle-inferringcontextualdetails-acapybarasittinginafieldatsunrise.png 2021 2021 ai/nn/transformer/gpt/dall-e --- /doc/ai/nn/transformer/gpt/dall-e/2021-openai-dalle-textpromptexamples.png 2021 2021 ai/nn/transformer/gpt/dall-e --- /doc/ai/nn/transformer/clip/2021-radford-clip-figure13-cliprobustness.png 2021 2021 ai/nn/transformer/clip --- /doc/ai/nn/transformer/clip/2021-radford-clip-figure21-zeroshot36differenttasks.png 2021 2021 ai/nn/transformer/clip --- /doc/ai/nn/transformer/clip/2021-radford-clip-figure4-promptengineering.png 2021 2021 ai/nn/transformer/clip --- /doc/ai/nn/transformer/clip/2021-radford-clip-figure5-clipzeroshotvsfullresnet.png 2021 2021 ai/nn/transformer/clip --- /doc/ai/nn/transformer/clip/2021-radford-clip-figure9-clipcomputescaling.jpg 2021 2021 ai/nn/transformer/clip --- /doc/reinforcement-learning/model/muzero/2021-schrittwieser-figure1-mspacmanmuzerologrewardscaling.jpg 2021 2021 ai/scaling reinforcement-learning/model/muzero --- /doc/design/1987-dieterrams-braun-et66calculator.jpg Braun ET66 calculator 1987 2019-11-26 design --- /doc/ai/nn/gan/stylegan/anime/2021-01-20-nagolinc-tadne-clipbasedgeneration-agirlwithapinkhat.png 2021-01-20 2021-01-20 ai/nn/gan/stylegan/anime ai/nn/transformer/clip --- /doc/genetics/heritable/correlation/2020-pgc-figure2-gwasprogressovertime.jpg Extended Data Figure 2: GWAS progress over time. The relationship of GWAS associations to sample-size is shown in this plot with selected SCZ GWAS meta-analyses of the past 11 years. The x-axis shows number of cases. The y-axis shows the number of independent loci discovered with at least one genome-wide statistically-significant index SNP in the discovery meta-analysis (eg. without replication data)...The slope of ~4 newly discovered loci per 1,000 cases 2013–2019 increased to a slope of ~6 with the latest sample-size increase. 2020 2020 genetics/heritable/correlation genetics/heritable/rare psychiatry/schizophrenia --- /doc/genetics/heritable/rare/2020-singh-figure6a-thecontributionsofultrarareptvstoschizophreniarisk.jpg Figure 6: The contributions of ultra-rare PTVs [protein-truncating variants] to schizophrenia risk. A: Genetic architecture of schizophrenia. Statistically-significant genetic associations for schizophrenia from the most recent GWAS, CNV, and sequencing studies are displayed. The in-sample odds ratio is plotted against the minor allele frequency in the general population. The color of each dot corresponds to the source of the association, and the size of the dot to the odds ratio. The shaded area represented the LOESS-smoothed lines of the upper and lower bounds of the point estimates...Because schizophrenia as a trait is under strong selection38–40, we expect that URVs of large effect to be frequently de novo or of very recent origin and contribute to risk in only a fraction of diagnosed patients. 2020 2020 genetics/heritable/rare --- /doc/iq/ses/2016-caspi-figure4-dunedin-highvslowiq-socialcosts.jpg 2016 2019-11-27 iq/ses --- /doc/nootropic/lsd/2021-szigeti-figure4-placebovshalfmicrodosingvsfullmicrodosing-wellbeingmindfulnesslifesatisifactionparanoiacognition.jpg 2021 2021 nootropic/lsd psychedelic --- /doc/statistics/order/gwern-orderstatistics-selection-bivariate-negativecorrelation.png 2019-11-27 genetics/selection/artificial/index-selection statistics/order --- http://aalto-econ.fi/toivanen/aaht_final.pdf 2019-11-27 iq/ses --- https://www.siditalia.it/images/Metformin_alters.pdf 2019-11-27 genetics/microbiome --- https://academic.oup.com/aje/article/156/11/985/80696 Modifiable Risk Factors as Predictors of All-Cause Mortality: The Roles of Genetics and Childhood Environment 2019-11-27 exercise --- https://ann-benchmarks.com/ ANN-Benchmarks is a benchmarking environment for approximate nearest neighbor algorithms search. This website contains the current benchmarking results. Please visit https://github.com/erikbern/ann-benchmarks/ to get an overview over evaluated data sets and algorithms. Make a pull request on Github to add your own code or improvements to the benchmarking system. 2019-11-27 ai/nn/retrieval --- http://bash.org/?743428 2019-11-27 economics --- http://bayesiandeeplearning.org/2017/papers/57.pdf 2019-11-27 reinforcement-learning/exploration statistics/bayes --- https://behavioralscientist.org/mindware-the-high-cost-of-not-doing-experiments/ The High Cost of Not Doing Experiments 2019-11-28 philosophy/ethics sociology statistics/bias --- http://biology-web.nmsu.edu/~houde/genetics%20of%20aggression.pdf 2019-11-28 crime genetics/heritable --- https://academic.oup.com/biomedgerontology/article/70/9/1097/2949096 2-Year Randomized Controlled Trial of Human Caloric Restriction: Feasibility and Effects on Predictors of Health Span and Longevity 2019-11-28 longevity/fasting --- https://blog.dimview.org/math/2017/07/28/cat-chirality.html Determining Cat Chirality 2019-11-28 cat/psychology math/humor --- http://blog.sigfpe.com/2006/11/from-l-theorem-to-spreadsheet.html From Löb's Theorem to Spreadsheet Evaluation 2019-11-28 cs/haskell --- http://blog.sigfpe.com/2009/05/three-projections-of-doctor-futamura.html The Three Projections of Doctor Futamura 2019-11-28 cs/haskell --- http://blog.sigfpe.com/2012/12/shuffles-bayes-theorem-and-continuations.html Shuffles, Bayes' theorem and continuations. 2019-11-28 cs/haskell cs/lisp statistics/bayes --- http://castel.bol.ucla.edu/publications/RhodesCastelJOLFontSize.pdf Memory predictions are influenced by perceptual information: evidence for metacognitive illusions 2019-11-28 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- http://catb.org/esr/writings/unix-koans/index.html Rootless Root 2019-11-28 fiction/humor --- http://chronopause.com/chronopause.com/index.php/2011/02/07/67/ Cryonics and Technological Inevitability 2019-11-28 cryonics --- http://chronopause.com/chronopause.com/index.php/2011/02/23/does-personal-identity-survive-cryopreservation/ Does Personal Identity Survive Cryopreservation? 2019-11-28 cryonics --- http://chronopause.com/chronopause.com/index.php/2011/08/12/interventive-gerontology-101-01-the-basics/index.html Interventive Gerontology 101.01: The Basics 2019-11-29 longevity/fasting modafinil --- http://chronopause.com/chronopause.com/index.php/2012/02/25/three-strikes-and-youre-out/index.html Three Strikes and You’re Out! 2019-11-29 cryonics --- http://chronopause.com/chronopause.com/index.php/2011/02/11/thus-spake-curtis-henderson-part-5/ Thus Spake Curtis Henderson, Part 5 2019-11-29 cryonics --- http://cl-informatik.uibk.ac.at/cek/holstep/ckfccs-holstep-submitted.pdf 2019-11-29 ai/dataset reinforcement-learning/model/alphago --- https://web-archive.southampton.ac.uk/cogprints.org/611/1/genius.html/genius.html 2019-11-29 genetics/heritable/emergenesis --- https://cran.r-project.org/web/packages/randomSurvivalForest/index.html CRAN: Package randomSurvivalForest 2019-11-29 cs/r statistics/survival-analysis --- https://cran.r-project.org/web/packages/survival/index.html Package survival 2019-11-29 cs/r statistics/survival-analysis --- https://cran.r-project.org/web/views/Survival.html Survival Analysis 2019-11-29 cs/r statistics/survival-analysis --- http://cryonet.org/cgi-bin/dsp.cgi?msg=33001 the mentality of wealth 2019-11-29 cryonics --- https://ctlj.colorado.edu/wp-content/uploads/2015/08/Meyer-final.pdf 2019-11-29 philosophy/ethics statistics/bias --- https://datacolada.org/8 Adventures in the Assessment of Animal Speed and Morality 2019-11-30 philosophy/ethics --- https://diyhpl.us/wiki/transcripts/2017-01-26-george-church/ 2017-01-26-george-church 2019-11-30 genetics/genome-synthesis --- https://diyhpl.us/wiki/transcripts/hgp-write/2016-05-10/ultra-safe-cell-line/ ultra-safe-cell-line 2019-11-30 genetics/genome-synthesis --- https://diyhpl.us/wiki/transcripts/nectome-1517-2018/ nectome-1517-2018 2019-11-30 cryonics --- https://dresdencodak.com/2009/05/15/a-thinking-apes-critique-of-trans-simianism-repost/ A Thinking Ape's Critique of Trans-Simianism 2019-11-30 ai fiction/humor fiction/humor --- http://engagedscholarship.csuohio.edu/cgi/viewcontent.cgi?article=1065&context=bus_facpub 2019-11-30 iq/ses --- http://esr.ibiblio.org/?p=8558 Head-voice vs. quiet-mind 2019-11-30 psychology/inner-voice --- http://fatstemserbia.brinkster.net/Library/Newspapers/Egg%20Engineers.pdf Stem cells: Egg engineers 2019-11-30 genetics/gametogenesis --- http://fmwww.bc.edu/repec/esLATM04/up.3316.1081964901.pdf 2019-11-30 iq/ses --- http://fold.it/portal/node/2008706 2019-11-30 ai/nn/transformer/alphafold --- http://hanushek.stanford.edu/sites/default/files/publications/Hanushek%2BWoessmann%202012%20JEconGrowth%2017(4).pdf 2019-11-30 iq/ses --- https://homepages.se.edu/cvonbergen/files/2013/01/A-Meta-Analysis-of-Work-Sample-Test-Validity.pdf A Meta-analysis of work sample test validity: Updating and integrating some classic literature 2019-12-01 economics iq/ses --- http://incompleteideas.net/book/RLbook2018.pdf#page=491 Reinforcement Learning: An Introduction § Designing Reward Signals Richard S. Sutton, Andrew G. Barto 2019-12-01 reinforcement-learning/meta-learning --- http://ir.uiowa.edu/cgi/viewcontent.cgi?article=2639&context=etd 2019-12-01 iq/ses --- http://johnsalvatier.org/blog/2017/reality-has-a-surprising-amount-of-detail Reality has a surprising amount of detail 2019-12-01 psychology/cognitive-bias/illusion-of-depth --- http://koreabizwire.com/clone-sniffer-dogs-to-be-deployed-in-s-korean-police/77141 Clone Sniffer Dogs to Be Deployed in S. Korean Police 2019-12-01 genetics/cloning/dog --- https://langsec.org/occupy/ Occupy Babel! 2019-12-01 math/humor --- http://liamoc.net/posts/2015-11-11-blog-like-mine/index.html Bits of Hackage 2019-12-01 design --- http://lispm.de/symbolics-lisp-machine-ergonomics Ergonomics of the Symbolics Lisp Machine 2019-12-01 cs/lisp design --- https://louiskirsch.com/metagenrl MetaGenRL: Improving Generalization in Meta Reinforcement Learning 2019-12-01 reinforcement-learning/meta-learning --- http://lukemetz.com/exploring-hyperparameter-meta-loss-landscapes-with-jax/#google Exploring hyperparameter meta-loss landscapes with Jax 2019-12-01 reinforcement-learning/meta-learning --- https://lukemuehlhauser.com/industrial-revolution/ How big a deal was the Industrial Revolution? 2019-12-01 philosophy/ethics --- http://m.timothy-judge.com/Leader%20IQ--JAP%20published.pdf Intelligence and leadership: A quantitative review and test of theoretical propositions 2019-12-02 iq/ses --- http://manikvarma.org/pubs/kusupati18.pdf 2019-12-02 ai/nn/sparsity --- https://mason.gmu.edu/~gjonesb/IITE.pdf 2019-12-02 iq/ses --- https://mason.gmu.edu/~gjonesb/JonesADR.pdf 2019-12-02 iq/ses --- http://math.andrej.com/2007/09/28/seemingly-impossible-functional-programs/ 2019-12-02 cs/haskell math --- http://mbio.asm.org/content/7/4/e01018-16.full 2019-12-02 genetics/microbiome --- https://www.cs.ox.ac.uk/people/yarin.gal/website/blog_3d801aa532c1ce.html What my deep model doesn't know... Gal 2015 2019-12-02 reinforcement-learning/exploration --- http://music.hyperreal.org/artists/brian_eno/interviews/detail92.html Scents and Sensibility 2019-12-02 psychology/smell --- https://pcdb.santafe.edu/ Performance Curve Database 2019-12-02 economics/experience-curve statistics/prediction technology --- https://pepijndevos.nl/2022/01/30/predicting-the-tide-with-an-analog-computer-made-from-lego.html Predicting the tide with an analog computer made from Lego 2019-12-02 cs technology --- http://rachel.org/files/document/Updated_Estimates_of_Earnings_Benefits_from_Re.pdf 2019-12-03 iq/ses --- https://rstb.royalsocietypublishing.org/content/363/1503/2519 2019-12-03 crime genetics/heritable psychology/personality --- https://s3.documentcloud.org/documents/804396/some-thoughts-on-education-and-political.pdf Some thoughts on education and political priorities, Cummings 2013 2019-12-03 ai economics/experience-curve genetics/editing sociology --- http://schwitzsplinters.blogspot.com/2018/06/does-it-harm-philosophy-as-discipline.html Does It Harm Philosophy as a Discipline to Discuss the Apparently Meager Practical Effects of Studying Ethics? 2019-12-03 philosophy/ethics/ethicists --- https://sigbovik.org/ The Association for Computational Heresy 2019-12-03 math/humor --- https://sigbovik.org/2019/ SIGBOVIK 2019 2019-12-03 math/humor --- https://sigbovik.org/2021/proceedings.pdf 2019-12-03 math/humor --- https://sigbovik.org/2021/proceedings.pdf#page=126 2019-12-03 math/humor --- https://sigbovik.org/2021/proceedings.pdf#page=167 2019-12-03 math/humor --- https://sigbovik.org/2021/proceedings.pdf#page=216 2019-12-03 math/humor --- https://sigbovik.org/2021/proceedings.pdf#page=252 2019-12-03 math/humor --- https://sigbovik.org/2021/proceedings.pdf#page=282 2019-12-04 math/humor --- https://sigbovik.org/2021/proceedings.pdf#page=8 2019-12-04 math/humor --- https://sigbovik.org/2021/proceedings.pdf#page=83 2019-12-04 math/humor --- http://stm.sciencemag.org/content/9/379/eaah4586 2019-12-04 cryonics --- https://taeb-nethack.blogspot.com/2009/03/predicting-and-controlling-nethacks.html The Tactical Amulet Extraction Bot: Predicting and controlling NetHack's randomness 2009-03 2019-12-04 cs/algorithm cs/security reinforcement-learning/nethack --- https://web.archive.org/web/20110530014638/https://today.msnbc.msn.com/id/43098220/ns/today-today_people/t/after-years-millionaire-misers-heirs-finally-split-m/ After 92 years, millionaire miser’s heirs finally split $100M 2019-12-04 economics/perpetuities philosophy/ethics --- http://www.bronxbanterblog.com/2013/10/01/the-power-and-the-gory/ The Power and the Gory Solotaroff 1990 2019-12-04 exercise sociology --- https://www.bloomberg.com/news/articles/2014-10-22/koreas-sooam-biotech-is-the-worlds-first-animal-cloning-factory Korea's Sooam Biotech Is the World's First Animal Cloning Factory 2019-12-04 genetics/cloning --- http://www.calinplesa.com/dna-synthesis-companies/ DNA Synthesis Companies 2019-12-04 genetics/genome-synthesis --- https://www.canadianmilitaryhistory.ca/wp-content/uploads/2012/03/4-Engen-Marshall-under-fire.pdf 2019-12-04 history/s-l-a-marshall statistics/bias --- http://www.catb.org/jargon/html/H/hacker-humor.html Hacker humor Eric S. Raymond 2019-12-04 fiction/humor --- http://www.catb.org/jargon/html/koans.html Some AI Koans Eric S. Raymond 2019-12-05 ai/nn cs/hardware cs/lisp math/humor --- http://www.cnsspectrums.com/aspx/articledetail.aspx?articleid=2783 2019-12-05 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- https://en.wikipedia.org/wiki/Cryos_International Cryos International 2019-12-05 cryonics --- https://www.drmichaeljoyner.com/sam-fussell-an-interview-with-the-author-of-muscle/ Sam Fussell: an interview with the author of Muscle 2019-12-05 exercise --- https://www.ecns.cn/news/cns-wire/2019-03-19/detail-ifzfmzhu2193677.shtml China's first cloned police dog starts training in Kunming 2019-12-05 genetics/cloning/dog --- http://www.econrsa.org/system/files/publications/working_papers/wp308.pdf 2019-12-05 iq/ses --- https://web.archive.org/web/20120322210409/http://www.evidencebasedcryonics.org/2008/02/25/better-biostasis-through-chemosuspension/ Biostasis through chemopreservation 2019-12-05 cryonics --- https://web.archive.org/web/20130117051047/http://www.evidencebasedcryonics.org/2013/01/14/in-praise-of-cold/ In praise of cold 2019-12-05 cryonics --- http://www.getlamp.com/ GET LAMP: The Text Adventure Documentary 2019-12-05 fiction/text-game --- http://www.hinxtongroup.org/Consensus_HG08_FINAL.pdf Consensus Statement: Science Ethics and Policy Challenges of Pluripotent Stem Cell-Derived Gametes 2019-12-05 genetics/gametogenesis --- http://www.iapsych.com/iqmr/fe/LinkedDocuments/roivainen2012.pdf 2019-12-06 iq/ses --- http://www.issendai.com/psychology/estrangement/index.html Down the Rabbit Hole: The world of estranged parents' forums 2019-12-06 psychology/personality --- https://www.koreaherald.com/view.php?ud=20211215000685 Kakao Brain unveils image-generating AI model 2019-12-06 ai/nn/transformer/gpt/dall-e --- http://www.laputan.org/mud/mud.html Big Ball of Mud 2019-12-06 cs design --- https://www.law.nyu.edu/sites/default/files/upload_documents/Property%20Monopoly.pdf Property Is Another Name for Monopoly: Facilitating Efficient Bargaining with Partial Common Ownership of Spectrum, Corporations, and Land 2019-12-06 economics/georgism economics/mechanism-design/auction --- https://web.archive.org/web/20110801232705/http://www.lifepact.com/history.htm History 2019-12-06 cryonics --- https://www.longevityhistory.com/read-the-book-online/ Longevity History: Read the Book 2019-12-06 exercise --- https://www.math.brown.edu/reschwar/Farm/RGB.pdf 2019-12-06 math/humor --- https://ralphmerkle.com/cryo/cryptoCryo.html Cryonics and Cryptography 2019-12-06 cryonics cs/cryptography --- http://www.michaelburge.us/2019/05/21/marai-agent.html Curiosity Killed the Mario 2019-12-06 reinforcement-learning/exploration --- https://www.mit.edu/~xela/tao.html The Tao of Programming 2019-12-06 fiction/humor --- /doc/ai/nn/cnn/2017-rawat.pdf 2017 2019-12-07 ai/nn/cnn ai/nn/sparsity --- https://www.nejm.org/doi/full/10.1056/NEJMoa1715474 2019-12-07 genetics/microbiome --- http://www.olerogeberg.com/2013/03/cannabis-iq-and-socio-economic-status.html Freakynomics: Cannabis, IQ and socio-economic status in the Dunedin data—an update 2019-12-07 marijuana --- http://www.packomania.com/ Packomania 2019-12-07 math --- http://www.scholarpedia.org/article/Metalearning Metalearning 2019-12-07 reinforcement-learning/meta-learning --- http://www.sciencedirect.com/science/article/pii/S0278584615000603 2019-12-07 psychedelic --- http://www.sirrahgroup.com/pdf/Hunter1984JobPerformance.pdf 2019-12-07 iq/ses --- https://www.stats.ox.ac.uk/~snijders/PadgettAnsell1993.pdf Robust Action and the Rise of the Medici, 1400–1434 2019-12-07 history sociology/abandoned-footnotes --- http://www.stephanguyenet.com/microbiota-and-obesity-is-it-all-hype/ 2019-12-07 genetics/microbiome --- http://www.synthesis.cc/synthesis/2016/03/on_dna_and_transistors On DNA and Transistors 2019-12-07 genetics/genome-synthesis --- http://www.synthesis.cc/synthesis/2016/05/synthesizing_secret_genomes Late Night, Unedited Musings on Synthesizing Secret Genomes 2019-12-07 genetics/genome-synthesis --- http://www.synthesis.cc/synthesis/2017/8/guesstimating-the-size-of-the-global-array-synthesis-market Guesstimating the Size of the Global Array Synthesis Market 2019-12-08 genetics/genome-synthesis --- https://www.thelancet.com/journals/lanpsy/article/PIIS2215-0366(16)30065-7/abstract 2019-12-08 psychedelic --- http://www.tweelingenregister.org/nederlands/verslaggeving/NTR_publicaties/Boomsma_BG_1989_02.pdf 2019-12-08 exercise --- https://www.usnews.com/news/articles/2015/05/29/fewer-pot-packages-found-in-mail-as-legalization-takes-hold 2019-12-08 darknet-market marijuana --- http://www.washingtonpost.com/blogs/wonkblog/wp/2014/10/22/no-marijuana-use-doesnt-lower-your-iq/ 2019-12-08 marijuana --- http://www.westword.com/news/denvers-underground-lsd-labs-fueled-the-psychedelic-revolution-9644844 2019-12-08 psychedelic --- https://www.willatworklearning.com/2005/11/research_review.html 2019-12-08 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- https://www.xinhuanet.com/english/2019-08/23/c_138332084.htm China's first cloned police dog finishes training 2019-12-08 genetics/cloning/dog --- https://www.xinhuanet.com/english/2019-11/21/c_138570521.htm Cloned dogs join police force in Beijing 2019-12-08 genetics/cloning/dog --- https://www.biology.ualberta.ca/locke.hp/dougandbill.htm 2019-12-08 fiction/humor --- https://yosefk.com/blog/my-history-with-forth-stack-machines.html My history with Forth & stack machines 2019-12-08 cs/end-to-end-principle --- https://80000hours.org/podcast/episodes/brian-christian-algorithms-to-live-by/ Brian Christian on computer science algorithms that tackle fundamental and universal problems 2019-12-09 reinforcement-learning/exploration statistics/decision --- https://80000hours.org/podcast/episodes/cass-sunstein-how-change-happens/ Cass Sunstein on How Change Happens 2019-12-09 sociology/preference-falsification --- https://80000hours.org/podcast/episodes/david-denkenberger-sahil-shah-using-paper-mills-and-seaweed-in-catastrophes/ David Denkenberger on using paper mills and seaweed to feed everyone in a catastrophe, ft Sahil Shah 2019-12-09 existential-risk --- https://aaronrandall.com/blog/cracking-the-adventure-time-cipher/ Cracking the Adventure Time Cipher 2019-12-09 cs/cryptography --- https://abandonedfootnotes.blogspot.com/ Abandoned Footnotes 2019-12-09 sociology/abandoned-footnotes --- https://abandonedfootnotes.blogspot.com/2010/06/footnotes-on-things-ive-been-reading.html Footnotes on things I've been reading: Steven Pfaff's "Exit-Voice Dynamics and the Collapse of East Germany" 2019-12-09 sociology/abandoned-footnotes sociology/preference-falsification --- https://abandonedfootnotes.blogspot.com/2010/08/most-surprising-sentence-i-read-today.html The most surprising sentence I read today 2019-12-09 sociology/abandoned-footnotes --- https://abandonedfootnotes.blogspot.com/2011/01/weather-under-ceausescu.html The Weather under Ceausescu 2019-12-09 sociology/abandoned-footnotes --- https://abandonedfootnotes.blogspot.com/2011/04/qaddafis-chickens.html Qaddafi's Chickens 2019-12-09 sociology/abandoned-footnotes sociology/preference-falsification --- https://abandonedfootnotes.blogspot.com/2011/12/flattery-inflation.html Flattery Inflation 2019-12-09 sociology/abandoned-footnotes sociology/preference-falsification --- https://abandonedfootnotes.blogspot.com/2012/09/the-great-norm-shift-and-triumph-of.html The Great Norm Shift and the Triumph of Universal Suffrage: A Very Short Quantitative History of Political Regimes, Part 1.825 2019-12-10 sociology/abandoned-footnotes --- https://abandonedfootnotes.blogspot.com/2012/10/ten-thousand-melodies-cannot-express.html ‘Ten thousand melodies cannot express our boundless hot love for you’: the Cult of Personality in Mao’s China 2019-12-10 history sociology/abandoned-footnotes sociology/preference-falsification --- https://abandonedfootnotes.blogspot.com/2013/04/the-sun-was-once-new.html The Sun was once New 2019-12-10 sociology/abandoned-footnotes --- https://abandonedfootnotes.blogspot.com/2014/02/francisco-franco-robust-action-and.html Francisco Franco, Robust Action, and the Power of Non-Commitment 2019-12-10 history sociology/abandoned-footnotes --- https://abandonedfootnotes.blogspot.com/2014/03/the-varieties-of-electoral-experience.html The Varieties of Electoral Experience 2019-12-10 sociology/abandoned-footnotes --- https://abandonedfootnotes.blogspot.com/2014/07/the-good-tsar-bias.html The Good Tsar Bias 2019-12-10 history politics psychology/cognitive-bias sociology/abandoned-footnotes --- https://abandonedfootnotes.blogspot.com/2015/01/the-saudi-monarchy-as-family-firm.html The Saudi Monarchy as a Family Firm 2019-12-10 sociology/abandoned-footnotes --- https://abandonedfootnotes.blogspot.com/2015/02/electoral-parodies.html Electoral Parodies 2019-12-10 sociology/abandoned-footnotes --- https://abandonedfootnotes.blogspot.com/2015/04/principal-agent-problems-in-soviet.html Principal-agent problems in the Soviet Union, circa 1990 2019-12-10 sociology/abandoned-footnotes --- https://abandonedfootnotes.blogspot.com/2015/07/propaganda-as-signaling_76.html Propaganda as Signaling [blog] 2019-12-10 psychology/collecting sociology/abandoned-footnotes --- https://academic.oup.com/ajcn/article/110/3/583/5512180 2019-12-10 exercise --- https://academic.oup.com/aje/article/156/11/985/80696 Modifiable Risk Factors as Predictors of All-Cause Mortality: The Roles of Genetics and Childhood Environment 2019-12-11 exercise --- https://academic.oup.com/cercor/article/28/12/4136/4560155 Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Cerebral Cortex 2019-12-11 reinforcement-learning/imitation-learning/brain-imitation-learning --- https://academic.oup.com/jhered/article/112/7/569/6412509 Facultative Parthenogenesis in California Condors Journal of Heredity 2019-12-11 genetics/cloning --- https://academic.oup.com/jn/article/135/6/1347/4663828 2019-12-11 longevity/fasting --- https://academic.oup.com/molehr/article/24/3/135/4829657 Metaphase II oocytes from human unilaminar follicles grown in a multi-step culture system 2019-12-11 genetics/gametogenesis --- https://acamh.onlinelibrary.wiley.com/doi/10.1111/jcpp.13528 2019-12-11 psychiatry/schizophrenia --- https://aclanthology.org/2021.eacl-demos.29.pdf#google Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool Swanson 2021 2021 ai/text-style-transfer --- https://aclanthology.org/D16-1064/ Exploring Semantic Representation in Brain Activity Using Word Embeddings 2019-12-11 reinforcement-learning/imitation-learning/brain-imitation-learning --- https://aclanthology.org/K18-1030/ Sequence Classification with Human Attention 2019-12-11 reinforcement-learning/imitation-learning/brain-imitation-learning --- https://actavet.vfu.cz/media/pdf/actavet_2017086030273.pdf Personality consistency analysis in cloned quarantine dog candidates 2019-12-11 genetics/cloning/dog psychology/personality --- https://advances.sciencemag.org/content/7/8/eabc4530.full 2019-12-11 psychology/neuroscience --- https://aeon.co/essays/aztec-moral-philosophy-didnt-expect-anyone-to-be-a-saint Aztec moral philosophy didn’t expect anyone to be a saint 2019-12-12 philosophy/ethics --- https://aeon.co/essays/a-culture-of-hyper-reality-made-paranoid-delusions-true A culture of hyper-reality made paranoid delusions true 2019-12-12 psychiatry/schizophrenia --- https://ai.facebook.com/blog/a-highly-efficient-real-time-text-to-speech-system-deployed-on-cpus/ 2019-12-12 ai/nn/sparsity --- https://ai.meta.com/blog/harmful-content-can-evolve-quickly-our-new-ai-system-adapts-to-tackle-it/ 2019-12-12 ai/scaling reinforcement-learning/meta-learning --- https://ai.facebook.com/blog/open-sourcing-polygames-a-new-framework-for-training-ai-bots-through-self-play/ 2019-12-12 reinforcement-learning/model/alphago --- https://ai.facebook.com/blog/using-ai-to-bring-childrens-drawings-to-life 2019-12-12 ai/anime ai/video --- https://research.google/blog/custom-on-device-ml-models-with-learn2compress/ 2019-12-12 ai/nn/sparsity --- https://research.google/blog/an-all-neural-on-device-speech-recognizer/ 2019-12-12 ai/nn/sparsity --- https://research.google/blog/releasing-the-drosophila-hemibrain-connectome-the-largest-synapse-resolution-map-of-brain-connectivity/ 2019-12-12 cryonics --- https://research.google/blog/automl-zero-evolving-code-that-learns/ AutoML-Zero: Evolving Code that Learns Real, Liang 2020 2020 reinforcement-learning/meta-learning --- https://research.google/blog/leveraging-machine-learning-for-game-development/ 2019-12-13 reinforcement-learning/model/alphago reinforcement-learning/multi-agent --- https://research.google/blog/kelm-integrating-knowledge-graphs-with-language-model-pre-training-corpora/ 2019-12-13 ai/nn/retrieval --- https://research.google/blog/a-browsable-petascale-reconstruction-of-the-human-cortex/ 2019-12-13 psychology/neuroscience --- https://research.google/blog/grammar-correction-as-you-type-on-pixel-6/ 2019-12-13 ai/nn/sparsity --- https://research.google/blog/introducing-flan-more-generalizable-language-models-with-instruction-fine-tuning/ 2019-12-13 ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/meta-learning --- https://research.google/blog/permutation-invariant-neural-networks-for-reinforcement-learning/ 2019-12-13 psychology/neuroscience reinforcement-learning/meta-learning --- https://research.google/blog/more-efficient-in-context-learning-with-glam/ 2019-12-13 ai/scaling/mixture-of-experts --- https://research.google/blog/training-machine-learning-models-more-efficiently-with-dataset-distillation/ 2019-12-13 ai/nn/sparsity reinforcement-learning/meta-learning --- https://research.google/blog/learning-to-route-by-task-for-efficient-inference/ 2019-12-13 ai/scaling/mixture-of-experts --- https://aidungeon.medium.com/ai-dungeon-dragon-model-upgrade-7e8ea579abfe AI Dungeon: Dragon Model Upgrade—You can now play AI Dungeon with one of the most powerful AI models in the world. 2019-12-13 ai/nn/transformer/gpt fiction/text-game --- https://aidungeon.medium.com/introducing-ai-dungeon-translate-a50e35f6df83 Introducing AI Dungeon Translate: AI Dungeon players can now translate their stories into emojis by just clicking a button. [ 🤔 💯 🤷‍♂️ 🤔 🤔 🤔 💯] 2019-12-13 ai/nn/transformer/gpt ai/text-style-transfer fiction/text-game --- https://ajp.psychiatryonline.org/doi/abs/10.1176/appi.ajp.2020.19080834 2019-12-14 genetics/heritable/rare --- https://ajph.aphapublications.org/doi/abs/10.2105/AJPH.2013.301612 2019-12-14 marijuana --- https://akjournals.com/view/journals/2054/5/1/article-p1.xml Entheogens in Buddhism 2019-12-14 psychedelic --- https://alphafold.ebi.ac.uk/ AlphaFold Protein Structure Database 2019-12-14 ai/nn/transformer/alphafold --- https://andymatuschak.org/files/papers/Apple%20Human%20Interface%20Guidelines%201987.pdf 2019-12-14 design --- https://archive.org/details/animalbreedingpl032391mbp/page/n167/mode/2up Animal Breeding Plans, Lush 1943: How Selection Changes A Population', 'Selection For Many Characteristics At Once 2019-12-14 genetics/selection/artificial/index-selection --- https://archive.org/details/studiesinindivid00jenk?view=theater#page/172/mode/2up Studies in individual differences: the search for intelligence : Jenkins, James J. § pg172 2019-12-14 iq/ses --- https://archive.org/details/studiesinindivid00jenk?view=theater#page/176/mode/2up Studies in individual differences: the search for intelligence : Jenkins, James J. § pg176 2019-12-14 iq/ses --- https://archive.is/tZpBO The Moral Question That Stanford Asks Its Bioengineering Students 2019-12-14 philosophy/ethics --- https://archiveofourown.org/works/3673335 Stargate Physics 101 2019-12-14 cs/security fiction/humor philosophy/epistemology technology --- https://arstechnica.com/science/2018/09/after-century-of-removing-appendixes-docs-find-antibiotics-can-be-enough/ After century of removing appendixes, docs find antibiotics can be enough: In a five-year follow-up, nearly two-thirds of patients never needed surgery 2019-12-14 philosophy/ethics statistics/bias --- https://arxiv.org/abs/0706.1062 Power-law distributions in empirical data Aaron Clauset, Cosma Rohilla Shalizi, M. E. J. Newman 2007-06-07 2019-12-15 [("doi","10.1137/070710111")] statistics/order statistics/probability

Power-law distributions occur in many situations of scientific interest and have consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution—the part of the distribution representing large but rare events—and by the difficulty of identifying the range over which power-law behavior holds.

Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios.

We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches.

We also apply the proposed methods to 20-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.

--- https://arxiv.org/abs/0811.1645 Random survival forests Hemant Ishwaran, Udaya B. Kogalur, Eugene H. Blackstone, Michael S. Lauer 2008-11-11 2019-12-15 [("doi","10.1214/08-AOAS169")] ai/tabular statistics/survival-analysis

We introduce random survival forests, a random forests method for the analysis of right-censored survival data.

New survival splitting rules for growing survival trees are introduced, as is a new missing data algorithm for imputing missing data. A conservation-of-events principle for survival forests is introduced and used to define ensemble mortality, a simple interpretable measure of mortality that can be used as a predicted outcome.

Several illustrative examples are given, including a case study of the prognostic implications of body mass for individuals with coronary artery disease.

Computations for all examples were implemented using the freely available R-software package, randomSurvivalForest.

--- https://arxiv.org/abs/1108.1791 Why Philosophers Should Care About Computational Complexity Scott Aaronson 2011-08-08 2019-12-15 [("doi","10.48550/arXiv.1108.1791")] ai cs/algorithm philosophy/epistemology philosophy/logic philosophy/mind

One might think that, once we know something is computable, how efficiently it can be computed is a practical question with little further philosophical importance.

In this essay, I offer a detailed case that one would be wrong. In particular, I argue that computational complexity theory—the field that studies the resources (such as time, space, and randomness) needed to solve computational problems—leads to new perspectives on the nature of mathematical knowledge, the strong AI debate, computationalism, the problem of logical omniscience, Hume’s problem of induction, Goodman’s grue riddle, the foundations of quantum mechanics, economic rationality, closed timelike curves, and several other topics of philosophical interest.

I end by discussing aspects of complexity theory itself that could benefit from philosophical analysis.

--- https://arxiv.org/abs/1109.1746 Circadian patterns of Wikipedia editorial activity: A demographic analysis Taha Yasseri, Róbert Sumi, János Kertész 2011-09-08 2019-12-15 [("doi","10.1371/journal.pone.0030091")] wikipedia zeo

Wikipedia (WP) as a collaborative, dynamical system of humans is an appropriate subject of social studies. Each single action of the members of this society, i.e. editors, is well recorded and accessible.

Using the cumulative data of 34 Wikipedias in different languages, we try to characterize and find the universalities and differences in temporal activity patterns of editors.

Based on this data, we estimate the geographical distribution of editors for each WP in the globe.

Furthermore we also clarify the differences among different groups of WPs, which originate in the variance of cultural and social features of the communities of editors.

--- https://arxiv.org/abs/1112.5745 Bayesian Active Learning for Classification and Preference Learning Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, Máté Lengyel 2011-12-24 2019-12-15 [("doi","10.48550/arXiv.1112.5745")] reinforcement-learning/exploration/active-learning reinforcement-learning/preference-learning statistics/bayes statistics/order/comparison

Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with nonparametric models, the optimal solution is harder to compute. Current approaches make approximations to achieve tractability.

We propose an approach that expresses information gain in terms of predictive entropies, and apply this method to the Gaussian Process Classifier (GPC). Our approach makes minimal approximations to the full information theoretic objective.

Our experimental performance compares favourably to many popular active learning algorithms, and has equal or lower computational complexity. We compare well to decision theoretic approaches also, which are privy to more information and require much more computational time.

Secondly, by developing further a reformulation of binary preference learning to a classification problem, we extend our algorithm to Gaussian Process preference learning.

--- https://arxiv.org/abs/1201.6655 Learning Performance of Prediction Markets with Kelly Bettors Alina Beygelzimer, John Langford, David Pennock 2012-01-31 2019-12-15 [("doi","10.48550/arXiv.1201.6655")] statistics/bayes statistics/prediction

[blog] In evaluating prediction markets (and other crowd-prediction mechanisms), investigators have repeatedly observed a so-called “wisdom of crowds” effect, which roughly says that the average of participants performs much better than the average participant. The market price—an average or at least aggregate of traders’ beliefs—offers a better estimate than most any individual trader’s opinion.

In this paper, we ask a stronger question: how does the market price compare to the best trader’s belief, not just the average trader. We measure the market’s worst-case log regret, a notion common in machine learning theory. To arrive at a meaningful answer, we need to assume something about how traders behave. We suppose that every trader optimizes according to the Kelly criteria, a strategy that provably maximizes the compound growth of wealth over an (infinite) sequence of market interactions. We show several consequences.

First, the market prediction is a wealth-weighted average of the individual participants’ beliefs. Second, the market learns at the optimal rate, the market price reacts exactly as if updating according to Bayes’ Law, and the market prediction has low worst-case log regret to the best individual participant. We simulate a sequence of markets where an underlying true probability exists, showing that the market converges to the true objective frequency as if updating a Beta distribution, as the theory predicts. If agents adopt a fractional Kelly criteria, a common practical variant, we show that agents behave like full-Kelly agents with beliefs weighted between their own and the market’s, and that the market price converges to a time-discounted frequency.

Our analysis provides a new justification for fractional Kelly betting, a strategy widely used in practice for ad-hoc reasons. Finally, we propose a method for an agent to learn her own optimal Kelly fraction.

--- https://arxiv.org/abs/1202.3699 Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search John Asmuth, Michael L. Littman 2012-02-14 2019-12-15 [("doi","10.48550/arXiv.1202.3699")] reinforcement-learning/exploration statistics/bayes

Bayes-optimal behavior, while well-defined, is often difficult to achieve. Recent advances in the use of Monte-Carlo tree search (MCTS) have shown that it is possible to act near-optimally in Markov Decision Processes (MDPs) with very large or infinite state spaces. Bayes-optimal behavior in an unknown MDP is equivalent to optimal behavior in the known belief-space MDP, although the size of this belief-space MDP grows exponentially with the amount of history retained, and is potentially infinite.

We show how an agent can use one particular MCTS algorithm, Forward Search Sparse Sampling (FSSS), in an efficient way to act nearly Bayes-optimally for all but a polynomial number of steps, assuming that FSSS can be used to act efficiently in any possible underlying MDP.

--- https://arxiv.org/abs/1209.4019 Experimental design for Partially Observed Markov Decision Processes Leifur Thorbergsson, Giles Hooker 2012-09-18 2019-12-15 [("doi","10.48550/arXiv.1209.4019")] reinforcement-learning/exploration

This paper deals with the question of how to most effectively conduct experiments in Partially Observed Markov Decision Processes so as to provide data that is most informative about a parameter of interest. Methods from Markov decision processes, especially dynamic programming, are introduced and then used in an algorithm to maximize a relevant Fisher Information.

The algorithm is then applied to two POMDP examples.

The methods developed can also be applied to stochastic dynamical systems, by suitable discretization, and we consequently show what control policies look like in the Morris-Lecar Neuron model, and simulation results are presented. We discuss how parameter dependence within these methods can be dealt with by the use of priors, and develop tools to update control policies online. This is demonstrated in another stochastic dynamical system describing growth dynamics of DNA template in a PCR model.

--- https://arxiv.org/abs/1401.5390 Learning to Win by Reading Manuals in a Monte-Carlo Framework S. R. K. Branavan, David Silver, Regina Barzilay 2014-01-18 2019-12-15 [("doi","10.1613/jair.3484")] ai/nn/retrieval reinforcement-learning/model

Domain knowledge is crucial for effective performance in autonomous control systems. Typically, human effort is required to encode this knowledge into a control algorithm. In this paper, we present an approach to language grounding which automatically interprets text in the context of a complex control application, such as a game, and uses domain knowledge extracted from the text to improve control performance. Both text analysis and control strategies are learned jointly using only a feedback signal inherent to the application.

To effectively leverage textual information, our method automatically extracts the text segment most relevant to the current game state, and labels it with a task-centric predicate structure. This labeled text is then used to bias an action selection policy for the game, guiding it towards promising regions of the action space. We encode our model for text analysis and game playing in a multi-layer neural network, representing linguistic decisions via latent variables in the hidden layers, and game action quality via the output layer. Operating within the Monte-Carlo Search framework, we estimate model parameters using feedback from simulated games.

We apply our approach to the complex strategy game Civilization II using the official game manual as the text guide. Our results show that a linguistically-informed game-playing agent outperforms its language-unaware counterpart, yielding a 34% absolute improvement and winning over 65% of games when playing against the built-in AI of Civilization.

--- https://arxiv.org/abs/1404.7828#schmidhuber Deep Learning in Neural Networks: An Overview Juergen Schmidhuber 2014-04-30 2019-12-15 [("doi","10.1016/j.neunet.2014.09.003")] ai/nn reinforcement-learning/meta-learning

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects.

I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

--- https://arxiv.org/abs/1409.4842#google Going Deeper with Convolutions Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich 2014-09-17 2019-12-16 [("doi","10.48550/arXiv.1409.4842")] ai/nn/cnn

We propose a deep convolutional neural network architecture codenamed Inception, which was responsible for setting the new state-of-the-art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).

The main hallmark of this architecture is the improved utilization of the computing resources inside the network. This was achieved by a carefully crafted design that allows for increasing the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing.

One particular incarnation used in our submission for ILSVRC 2014 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

--- https://arxiv.org/abs/1410.0996 Minimax Analysis of Active Learning Steve Hanneke, Liu Yang 2014-10-03 2019-12-16 [("doi","10.48550/arXiv.1410.0996")] reinforcement-learning/exploration/active-learning

This work establishes distribution-free upper and lower bounds on the minimax label complexity of active learning with general hypothesis classes, under various noise models.

The results reveal a number of surprising facts. In particular, under the noise model of Tsybakov 2004, the minimax label complexity of active learning with a VC class is always asymptotically smaller than that of passive learning, and is typically much smaller than the best previously-published upper bounds in the active learning literature. In high-noise regimes, it turns out that all active learning problems of a given VC dimension have roughly the same minimax label complexity, which contrasts with well-known results for bounded noise.

In low-noise regimes, we find that the label complexity is well-characterized by a simple combinatorial complexity measure we call the star number. Interestingly, we find that almost all of the complexity measures previously explored in the active learning literature have worst-case values exactly equal to the star number.

We also propose new active learning strategies that nearly achieve these minimax label complexities.

--- https://arxiv.org/abs/1410.4009#facebook Thompson sampling with the online bootstrap Dean Eckles, Maurits Kaptein 2014-10-15 2019-12-16 [("doi","10.48550/arXiv.1410.4009")] reinforcement-learning/exploration statistics/bayes

Thompson sampling provides a solution to bandit problems in which new observations are allocated to arms with the posterior probability that an arm is optimal. While sometimes easy to implement and asymptotically optimal, Thompson sampling can be computationally demanding in large scale bandit problems, and its performance is dependent on the model fit to the observed data.

We introduce bootstrap Thompson sampling (BTS), a heuristic method for solving bandit problems which modifies Thompson sampling by replacing the posterior distribution used in Thompson sampling by a bootstrap distribution. We first explain BTS and show that the performance of BTS is competitive to Thompson sampling in the well-studied Bernoulli bandit case.

Subsequently, we detail why BTS using the online bootstrap is more scalable than regular Thompson sampling, and we show through simulation that BTS is more robust to a misspecified error distribution.

BTS is an appealing modification of Thompson sampling, especially when samples from the posterior are otherwise not available or are costly.

--- https://arxiv.org/abs/1410.8001 The hipster effect: When anticonformists all look the same Jonathan Touboul 2014-10-29 2019-12-16 [("doi","10.48550/arXiv.1410.8001")] psychology/novelty sociology

In such different domains as neuroscience, spin glasses, social science, economics, and finance, large ensembles of interacting individuals following (mainstream) or opposing (hipsters) the majority are ubiquitous. In these systems, interactions generally occur after specific delays associated with transport, transmission, or integration of information.

We investigate here the impact of anti-conformism combined with delays in the emergent dynamics of large populations of mainstreams and hipsters. To this purpose, we introduce a class of simple statistical systems of interacting agents composed of (1) mainstreams and anti-conformists in the presence of (2) delays, possibly heterogeneous, in the transmission of information. In this simple model, each agent can be in one of two states and can change state in continuous time with a rate depending on the state of others in the past.

We express the thermodynamic limit of these systems as the number of agents diverge, and investigate the solutions of the limit equation, with a particular focus on synchronized oscillations induced by delayed interactions. We show that when hipsters are too slow in detecting the trends, they will consistently make the same choice, and realizing this too late, they will switch, all together to another state where they remain alike. Similar synchronizations arise when the impact of mainstreams on hipsters choices (and reciprocally) dominate the impact of other hipsters’ choices, and we show that these may emerge only when the randomness in the hipsters’ decisions is sufficiently large.

Beyond the choice of the best suit to wear this winter, this study may have important implications in understanding synchronization of nerve cells, investment strategies in finance, or emergent dynamics in social science, domains in which delays of communication and the geometry of information accessibility are prominent.

--- https://arxiv.org/abs/1410.8233 Do Artificial Reinforcement-Learning Agents Matter Morally? Brian Tomasik 2014-10-30 2019-12-16 [("doi","10.48550/arXiv.1410.8233")] philosophy/ethics psychology/neuroscience reinforcement-learning

Artificial reinforcement learning (RL) is a widely used technique in artificial intelligence that provides a general method for training agents to perform a wide variety of behaviors. RL as used in computer science has striking parallels to reward and punishment learning in animal and human brains.

I argue that present-day artificial RL agents have a very small but nonzero degree of ethical importance. This is particularly plausible for views according to which sentience comes in degrees based on the abilities and complexities of minds, but even binary views on consciousness should assign nonzero probability to RL programs having morally relevant experiences. While RL programs are not a top ethical priority today, they may become more important in the coming decades as RL is increasingly applied to industry, robotics, video games, and other areas.

I encourage scientists, philosophers, and citizens to begin a conversation about our ethical duties to reduce the harm that we inflict on powerless, voiceless RL agents.

--- https://arxiv.org/abs/1410.8603 Only the Bad Die Young: Restaurant Mortality in the Western US Tian Luo, Philip B. Stark 2014-10-31 2019-12-16 [("doi","10.48550/arXiv.1410.8603")] economics statistics/survival-analysis

Do 9⁄10 restaurants fail in their first year, as commonly claimed? No.

Survival analysis of 1.9 million longitudinal microdata for 81,000 full-service restaurants in a 20-year U.S. Bureau of Labor Statistics non-public census of business establishments in the western US shows that only 17% of independently owned full-service restaurant startups failed in their first year, compared with 19% for all other service-providing startups.

The median lifespan of restaurants is about 4.5 years, slightly longer than that of other service businesses (4.25 years). However, the median lifespan of a restaurant startup with 5 or fewer employees is 3.75 years, slightly shorter than that of other service businesses of the same startup size (4.0 years).

--- https://arxiv.org/abs/1412.6614 In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning Behnam Neyshabur, Ryota Tomioka, Nathan Srebro 2014-12-20 2019-12-16 [("doi","10.48550/arXiv.1412.6614")] ai/nn/fully-connected ai/scaling

We present experiments demonstrating that some other form of capacity control, different from network size, plays a central role in learning multilayer feed-forward networks.

We argue, partially through analogy to matrix factorization, that this is an inductive bias that can help shed light on deep learning.

--- https://arxiv.org/abs/1502.05556 Just Sort It! A Simple and Effective Approach to Active Preference Learning Lucas Maystre, Matthias Grossglauser 2015-02-19 2019-12-16 [("doi","10.48550/arXiv.1502.05556")] reinforcement-learning/exploration/active-learning reinforcement-learning/preference-learning statistics/order/comparison

We address the problem of learning a ranking by using adaptively chosen pairwise comparisons. Our goal is to recover the ranking accurately but to sample the comparisons sparingly. If all comparison outcomes are consistent with the ranking, the optimal solution is to use an efficient sorting algorithm, such as Quicksort. But how do sorting algorithms behave if some comparison outcomes are inconsistent with the ranking?

We give favorable guarantees for Quicksort for the popular Bradley-Terry model, under natural assumptions on the parameters. Furthermore, we empirically demonstrate that sorting algorithms lead to a very simple and effective active learning strategy: repeatedly sort the items.

This strategy performs as well as state-of-the-art methods (and much better than random sampling) at a minuscule fraction of the computational cost.

--- https://arxiv.org/abs/1503.02531#google Distilling the Knowledge in a Neural Network Geoffrey Hinton, Oriol Vinyals, Jeff Dean 2015-03-09 2019-12-16 [("doi","10.48550/arXiv.1503.02531")] ai/nn/sparsity/knowledge-distillation ai/scaling/mixture-of-experts

A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique.

We achieve some surprising results on MNIST and we show that we can improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel.

--- https://arxiv.org/abs/1506.03134 Pointer Networks Oriol Vinyals, Meire Fortunato, Navdeep Jaitly 2015-06-09 2019-12-16 [("doi","10.48550/arXiv.1506.03134")] ai/nn/transformer cs/algorithm/sorting reinforcement-learning/model-free/alphastar

We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. Such problems cannot be trivially addressed by existent approaches such as sequence-to-sequence and Neural Turing Machines, because the number of target classes in each step of the output depends on the length of the input, which is variable. Problems such as sorting variable sized sequences, and various combinatorial optimization problems belong to this class.

Our model solves the problem of variable size output dictionaries using a recently proposed mechanism of neural attention. It differs from the previous attention attempts in that, instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, it uses attention as a pointer to select a member of the input sequence as the output. We call this architecture a Pointer Net (Ptr-Net).

We show Ptr-Nets can be used to learn approximate solutions to 3 challenging geometric problems—finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem—using training examples alone. Ptr-Nets not only improve over sequence-to-sequence with input attention, but also allow us to generalize to variable size output dictionaries. We show that the learnt models generalize beyond the maximum lengths they were trained on.

We hope our results on these tasks will encourage a broader exploration of neural learning for discrete problems.

--- https://arxiv.org/abs/1508.06576 A Neural Algorithm of Artistic Style Leon A. Gatys, Alexander S. Ecker, Matthias Bethge 2015-08-26 2019-12-16 [("doi","10.48550/arXiv.1508.06576")] ai/nn ai/text-style-transfer

In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks.

Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images.

Moreover, in light of the striking similarities between performance-optimized artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.

--- https://arxiv.org/abs/1509.01549 Giraffe: Using Deep Reinforcement Learning to Play Chess Matthew Lai 2015-09-04 2019-12-17 [("doi","10.48550/arXiv.1509.01549")] reinforcement-learning/chess reinforcement-learning/model/alphago

This report presents Giraffe, a chess engine that uses self-play to discover all its domain-specific knowledge, with minimal hand-crafted knowledge given by the programmer. Unlike previous attempts using machine learning only to perform parameter-tuning on hand-crafted evaluation functions, Giraffe’s learning system also performs automatic feature extraction and pattern recognition.

The trained evaluation function performs comparably to the evaluation functions of state-of-the-art chess engines—all of which contain thousands of lines of carefully hand-crafted pattern recognizers, tuned over many years by both computer chess experts and human chess masters.

Giraffe is the most successful attempt thus far at using end-to-end machine learning to play chess.

--- https://arxiv.org/abs/1509.06569 Tensorizing Neural Networks Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry Vetrov 2015-09-22 2019-12-17 [("doi","10.48550/arXiv.1509.06569")] ai/nn/fully-connected ai/nn/sparsity

Deep neural networks currently demonstrate state-of-the-art performance in several domains. At the same time, models of this class are very demanding in terms of computational resources. In particular, a large amount of memory is required by commonly used fully-connected layers, making it hard to use the models on low-end devices and stopping the further increase of the model size.

In this paper we convert the dense weight matrices of the fully-connected layers to the Tensor Train format such that the number of parameters is reduced by a huge factor and at the same time the expressive power of the layer is preserved.

In particular, for the Very Deep VGG networks we report the compression factor of the dense weight matrix of a fully-connected layer up to 200,000× leading to the compression factor of the whole network up to 7×.

--- https://arxiv.org/abs/1511.05641 Net2Net: Accelerating Learning via Knowledge Transfer Tianqi Chen, Ian Goodfellow, Jonathon Shlens 2015-11-18 2019-12-17 [("doi","10.48550/arXiv.1511.05641")] ai/nn/sparsity/knowledge-distillation ai/scaling reinforcement-learning/model-free/oa5

We introduce techniques for rapidly transferring the information stored in one neural net into another neural net. The main purpose is to accelerate the training of a larger neural net.

During real-world workflows, one often trains very many different neural networks during the experimentation and design process. This is a wasteful process in which each new model is trained from scratch. Our Net2Net technique accelerates the experimentation process by instantaneously transferring the knowledge from a previous network to each new deeper or wider network.

Our techniques are based on the concept of function-preserving transformations between neural network specifications. This differs from previous approaches to pre-training that altered the function represented by a neural net when adding layers to it.

Using our knowledge transfer mechanism to add depth to Inception modules, we demonstrate a new state-of-the-art accuracy rating on the ImageNet dataset.

--- https://arxiv.org/abs/1511.06292 Foveation-based Mechanisms Alleviate Adversarial Examples Yan Luo, Xavier Boix, Gemma Roig, Tomaso Poggio, Qi Zhao 2015-11-19 2019-12-17 [("doi","10.48550/arXiv.1511.06292")] ai/nn/adversarial ai/nn/transformer/attention psychology/neuroscience

We show that adversarial examples, ie. the visually imperceptible perturbations that result in Convolutional Neural Networks (CNNs) fail, can be alleviated with a mechanism based on foveations—applying the CNN in different image regions.

To see this, first, we report results in ImageNet that lead to a revision of the hypothesis that adversarial perturbations are a consequence of CNNs acting as a linear classifier: CNNs act locally linearly to changes in the image regions with objects recognized by the CNN, and in other regions the CNN may act non-linearly.

Then, we corroborate that when the neural responses are linear, applying the foveation mechanism to the adversarial example tends to reduce the effect of the perturbation. This is because, hypothetically, the CNNs for ImageNet are robust to changes of scale and translation of the object produced by the foveation, but this property does not generalize to transformations of the perturbation.

As a result, the accuracy after a foveation is almost the same as the accuracy of the CNN without the adversarial perturbation, even if the adversarial perturbation is calculated taking into account a foveation.

--- https://arxiv.org/abs/1511.06295#deepmind Policy Distillation Andrei A. Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, Raia Hadsell 2015-11-19 2019-12-17 [("doi","10.48550/arXiv.1511.06295")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/multi-agent

Policies for complex visual tasks have been successfully learned with deep reinforcement learning, using an approach called deep Q-networks (DQN), but relatively large (task-specific) networks and extensive training are needed to achieve good performance.

In this work, we present a novel method called policy distillation that can be used to extract the policy of a reinforcement learning agent and train a new network that performs at the expert level while being dramatically smaller and more efficient. Furthermore, the same method can be used to consolidate multiple task-specific policies into a single policy.

We demonstrate these claims using the Atari domain and show that the multi-task distilled agent outperforms the single-task teachers as well as a jointly-trained DQN agent.

[Later: 2019]

--- https://arxiv.org/abs/1511.06434 Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Alec Radford, Luke Metz, Soumith Chintala 2015-11-19 2019-12-17 [("doi","10.48550/arXiv.1511.06434")] ai/nn/gan

In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning.

We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning.

Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks—demonstrating their applicability as general image representations.

--- https://arxiv.org/abs/1511.09249#schmidhuber On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models Juergen Schmidhuber 2015-11-30 2019-12-17 [("doi","10.48550/arXiv.1511.09249")] ai/nn/rnn cs/algorithm/information/compression reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/model reinforcement-learning/scaling

This paper addresses the general problem of reinforcement learning (RL) in partially observable environments. In 2013, our large RL recurrent neural networks (RNNs) learned from scratch to drive simulated cars from high-dimensional video input. However, real brains are more powerful in many ways. In particular, they learn a predictive model of their initially unknown environment, and somehow use it for abstract (eg. hierarchical) planning and reasoning.

Guided by algorithmic information theory, we describe RNN-based AIs (RNNAIs) designed to do the same. Such an RNNAI can be trained on never-ending sequences of tasks, some of them provided by the user, others invented by the RNNAI itself in a curious, playful fashion, to improve its RNN-based world model. Unlike our previous model-building RNN-based RL machines dating back to 1990, the RNNAI learns to actively query its model for abstract reasoning and planning and decision making, essentially “learning to think.”

The basic ideas of this report can be applied to many other cases where one RNN-like system exploits the algorithmic information content of another. They are taken from a grant proposal submitted in Fall 2014, and also explain concepts such as “mirror neurons.”

Experimental results will be described in separate papers.

--- https://arxiv.org/abs/1602.02410#google Exploring the Limits of Language Modeling Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, Yonghui Wu 2016-02-07 2019-12-17 [("doi","10.48550/arXiv.1602.02410")] ai/nn/rnn ai/scaling

In this work we explore recent advances in Recurrent Neural Networks for large scale Language Modeling, a task central to language understanding.

We extend current models to deal with two key challenges present in this task: corpora and vocabulary sizes, and complex, long term structure of language. We perform an exhaustive study on techniques such as character Convolutional Neural Networks and Long-Short Term Memory, on the One Billion Word Benchmark.

Our best single model improves state-of-the-art perplexity from 51.3 down to 30.0 (whilst reducing the number of parameters by a factor of 20), while an ensemble of models sets a new record by improving perplexity from 41.0 down to 23.7.

We also release these models for the NLP and ML community to study and improve upon.

--- https://arxiv.org/abs/1602.04621 Deep Exploration via Bootstrapped DQN Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy 2016-02-15 2019-12-17 [("doi","10.48550/arXiv.1602.04621")] reinforcement-learning/exploration

Efficient exploration in complex environments remains a major challenge for reinforcement learning.

We propose bootstrapped DQN, a simple algorithm that explores in a computationally and statistically efficient manner through use of randomized value functions. Unlike dithering strategies such as epsilon-greedy exploration, bootstrapped DQN carries out temporally-extended (or deep) exploration; this can lead to exponentially faster learning.

We demonstrate these benefits in complex stochastic MDPs and in the large-scale Arcade Learning Environment. Bootstrapped DQN substantially improves learning times and performance across most Atari games.

--- https://arxiv.org/abs/1602.05314#deepmind PlaNet—Photo Geolocation with Convolutional Neural Networks Tobias Weyand, Ilya Kostrikov, James Philbin 2016-02-17 2019-12-17 [("doi","10.1007/978-3-319-46484-8_3")] ai/nn/cnn ai/nn/retrieval ai/nn/rnn ai/scaling

Is it possible to build a system to determine the location where a photo was taken using just its pixels? In general, the problem seems exceptionally difficult: it is trivial to construct situations where no location can be inferred. Yet images often contain informative cues such as landmarks, weather patterns, vegetation, road markings, and architectural details, which in combination may allow one to determine an approximate location and occasionally an exact location. Websites such as GeoGuessr and View from your Window suggest that humans are relatively good at integrating these cues to geolocate images, especially en-masse. In computer vision, the photo geolocation problem is usually approached using image retrieval methods.

In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. While previous approaches only recognize landmarks or perform approximate matching using global image descriptors, our model is able to use and integrate multiple visible cues.

We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman levels of accuracy in some cases.

Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, we demonstrate that this model achieves a 50% performance improvement over the single-image model.

--- https://arxiv.org/abs/1604.03357 Improving sentence compression by learning to predict gaze Sigrid Klerke, Yoav Goldberg, Anders Søgaard 2016-04-12 2019-12-17 [("doi","10.48550/arXiv.1604.03357")] ai/nn/rnn psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

We show how eye-tracking corpora can be used to improve sentence compression models, presenting a novel multi-task learning algorithm based on multi-layer LSTMs.

We obtain performance competitive with or better than state-of-the-art approaches.

--- https://arxiv.org/abs/1605.06065#deepmind One-shot Learning with Memory-Augmented Neural Networks Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap 2016-05-19 2019-12-18 [("doi","10.48550/arXiv.1605.06065")] ai/nn/retrieval reinforcement-learning/meta-learning

Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of “one-shot learning.” Traditional gradient-based networks require a lot of data to learn, often through extensive iterative training. When new data is encountered, the models must inefficiently relearn their parameters to adequately incorporate the new information without catastrophic interference. Architectures with augmented memory capacities, such as Neural Turing Machines (NTMs), offer the ability to quickly encode and retrieve new information, and hence can potentially obviate the downsides of conventional models.

Here, we demonstrate the ability of a memory-augmented neural network to rapidly assimilate new data, and leverage this data to make accurate predictions after only a few samples.

We also introduce a new method for accessing an external memory that focuses on memory content, unlike previous methods that additionally use memory location-based focusing mechanisms.

--- https://arxiv.org/abs/1605.09782 Adversarial Feature Learning Jeff Donahue, Philipp Krähenbühl, Trevor Darrell 2016-05-31 2019-12-18 [("doi","10.48550/arXiv.1605.09782")] ai/nn/gan

The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution. Intuitively, models trained to predict these semantic latent representations given data may serve as useful feature representations for auxiliary problems where semantics are relevant. However, in their existing form, GANs have no means of learning the inverse mapping—projecting data back into the latent space.

We propose Bidirectional Generative Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and demonstrate that the resulting learned feature representation is useful for auxiliary supervised discrimination tasks, competitive with contemporary approaches to unsupervised and self-supervised feature learning.

--- https://arxiv.org/abs/1606.01868#deepmind Unifying Count-Based Exploration and Intrinsic Motivation Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Remi Munos 2016-06-06 2019-12-18 [("doi","10.48550/arXiv.1606.01868")] ai/nn reinforcement-learning/exploration

We consider an agent’s uncertainty about its environment and the problem of generalizing this uncertainty across observations. Specifically, we focus on the problem of exploration in non-tabular reinforcement learning.

Drawing inspiration from the intrinsic motivation literature, we use density models to measure uncertainty, and propose a novel algorithm for deriving a pseudo-count from an arbitrary density model. This technique enables us to generalize count-based exploration algorithms to the non-tabular case.

We apply our ideas to Atari 2600 games, providing sensible pseudo-counts from raw pixels. We transform these pseudo-counts into intrinsic rewards and obtain improved exploration in a number of hard games, including the infamously difficult Montezuma’s Revenge.

--- https://arxiv.org/abs/1606.03498#openai Improved Techniques for Training GANs Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen 2016-06-10 2019-12-18 [("doi","10.48550/arXiv.1606.03498")] ai/nn/gan

We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.

We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels.

Using our new techniques [such as minibatch discrimination], we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes.

--- https://arxiv.org/abs/1606.03657#openai InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel 2016-06-12 2019-12-18 [("doi","10.48550/arXiv.1606.03657")] ai/nn/gan

This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent variables and the observation.

We derive a lower bound to the mutual information objective that can be optimized efficiently, and show that our training procedure can be interpreted as a variation of the Wake-Sleep algorithm.

Specifically, InfoGAN successfully disentangles writing styles from digit shapes on the MNIST dataset, pose from lighting of 3D rendered images, and background digits from the central digit on the SVHN dataset. It also discovers visual concepts that include hair styles, presence/absence of eyeglasses, and emotions on the CelebA face dataset.

Experiments show that InfoGAN learns interpretable representations that are competitive with representations learned by existing fully supervised methods.

--- https://arxiv.org/abs/1607.02533 Adversarial examples in the physical world Alexey Kurakin, Ian Goodfellow, Samy Bengio 2016-07-08 2019-12-18 [("doi","10.48550/arXiv.1607.02533")] ai/nn/adversarial

Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it. In many cases, these modifications can be so subtle that a human observer does not even notice the modification at all, yet the classifier still makes a mistake. Adversarial examples pose security concerns because they could be used to perform an attack on machine learning systems, even if the adversary has no access to the underlying model. Up to now, all previous work have assumed a threat model in which the adversary can feed data directly into the machine learning classifier. This is not always the case for systems operating in the physical world, for example those which are using signals from cameras and other sensors as an input. This paper shows that even in such physical world scenarios, machine learning systems are vulnerable to adversarial examples.

We demonstrate this by feeding adversarial images obtained from cell-phone camera to an ImageNet Inception classifier and measuring the classification accuracy of the system.

We find that a large fraction of adversarial examples are classified incorrectly even when perceived through the camera.

--- https://arxiv.org/abs/1608.05343#deepmind Decoupled Neural Interfaces using Synthetic Gradients Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, David Silver, Koray Kavukcuoglu 2016-08-18 2019-12-18 [("doi","10.48550/arXiv.1608.05343")] ai/nn/fully-connected ai/nn/rnn psychology/neuroscience reinforcement-learning/meta-learning

[blog; discussoin] Training directed neural networks typically requires forward-propagating data through a computation graph, followed by backpropagating error signal, to produce weight updates. All layers, or more generally, modules, of the network are therefore ‘locked’, in the sense that they must wait for the remainder of the network to execute forwards and propagate error backwards before they can be updated.

In this work we break this constraint by decoupling modules by introducing a model of the future computation of the network graph. These models predict what the result of the modelled subgraph will produce using only local information. In particular we focus on modeling error gradients: by using the modelled synthetic gradient in place of true backpropagated error gradients we decouple subgraphs, and can update them independently and asynchronously i.e. we realise decoupled neural interfaces.

We show results for feed-forward models, where every layer is trained asynchronously, recurrent neural networks (RNNs) where predicting one’s future gradient extends the time over which the RNN can effectively model, and also a hierarchical RNN system with ticking at different timescales.

Finally, we demonstrate that in addition to predicting gradients, the same framework can be used to predict inputs, resulting in models which are decoupled in both the forward and backwards pass—amounting to independent networks which co-learn such that they can be composed into a single functioning corporation.

--- https://arxiv.org/abs/1608.06993 DenseNet: Densely Connected Convolutional Networks Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger 2016-08-25 2019-12-18 [("doi","10.48550/arXiv.1608.06993")] ai/nn/cnn

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.

In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections—one between each layer and its subsequent layer—our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers.

DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.

We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain improvements over the state-of-the-art on most of them, whilst requiring less computation to achieve high performance.

Code and pre-trained models are available at Github.

--- https://arxiv.org/abs/1609.00344 Deep Learning Human Mind for Automated Visual Classification Concetto Spampinato, Simone Palazzo, Isaak Kavasidis, Daniela Giordano, Mubarak Shah, Nasim Souly 2016-09-01 2019-12-18 [("doi","10.48550/arXiv.1609.00344")] ai/nn/cnn ai/nn/rnn psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

What if we could effectively read the mind and transfer human visual capabilities to computer vision methods? In this paper, we aim at addressing this question by developing the first visual object classifier driven by human brain signals. In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories.

Afterwards, we train a Convolutional Neural Network (CNN)-based regressor to project images onto the learned manifold, thus effectively allowing machines to employ human brain-based features for automated visual classification. We use a 32-channel EEG to record brain activity of 7 subjects while looking at images of 40 ImageNet object classes.

The proposed RNN based approach for discriminating object classes using brain signals reaches an average accuracy of about 40%, which outperforms existing methods attempting to learn EEG visual object representations. As for automated object categorization, our human brain-driven approach obtains competitive performance, comparable to those achieved by powerful CNN models, both on ImageNet and Caltech 101, thus demonstrating its classification and generalization capabilities.

This gives us real hope that, indeed, the human mind can be read and transferred to machines.

--- https://arxiv.org/abs/1609.02993 Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks Nicolas Usunier, Gabriel Synnaeve, Zeming Lin, Soumith Chintala 2016-09-10 2019-12-18 [("doi","10.48550/arXiv.1609.02993")] reinforcement-learning/model-free/alphastar

We consider scenarios from the real-time strategy game StarCraft as new benchmarks for reinforcement learning algorithms. We propose micromanagement tasks, which present the problem of the short-term, low-level control of army members during a battle. From a reinforcement learning point of view, these scenarios are challenging because the state-action space is very large, and there is no obvious feature representation for the state-action evaluation function.

We describe our approach to tackle the micromanagement scenarios with deep neural network controllers from raw state features given by the game engine. In addition, we present a heuristic reinforcement learning algorithm which combines direct exploration in the policy space and backpropagation. This algorithm allows for the collection of traces for learning using deterministic policies, which appears much more efficient than, for example, epsilon-greedy exploration.

Experiments show that with this algorithm, we successfully learn non-trivial strategies for scenarios with armies of up to 15 agents, where both Q-learning and REINFORCE struggle.

--- https://arxiv.org/abs/1609.03499#deepmind WaveNet: A Generative Model for Raw Audio Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, Koray Kavukcuoglu 2016-09-12 2019-12-18 [("doi","10.48550/arXiv.1609.03499")] ai/music ai/nn/cnn ai/nn/sampling

This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio.

When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as statistically-significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin.

A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments.

We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition.

--- https://arxiv.org/abs/1609.04436 Bayesian Reinforcement Learning: A Survey Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar 2016-09-14 2019-12-19 [("doi","10.1561/2200000049")] reinforcement-learning/exploration statistics/bayes

Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. In this survey, we provide an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are: (1) it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning; and (2) it provides a machinery to incorporate prior knowledge into the algorithms.

We first discuss models and methods for Bayesian inference in the simple single-step Bandit model. We then review the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. We also present Bayesian methods for model-free RL, where priors are expressed over the value function or policy class.

The objective of the paper is to provide a comprehensive survey on Bayesian RL algorithms and their theoretical and empirical properties.

--- https://arxiv.org/abs/1609.04802 Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi 2016-09-15 2019-12-19 [("doi","10.48550/arXiv.1609.04802")] ai/nn/gan

Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution.

In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4× upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks.

An extensive mean-opinion-score (MOS) test shows important gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method.

--- https://arxiv.org/abs/1609.05143#allen Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J. Lim, Abhinav Gupta, Li Fei-Fei, Ali Farhadi 2016-09-16 2019-12-19 [("doi","10.48550/arXiv.1609.05143")] reinforcement-learning/robot

Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new target goals, and (2) data inefficiency ie. the model requires several (and often costly) episodes of trial and error to converge, which makes it impractical to be applied to real-world scenarios. In this paper, we address these two issues and apply our model to the task of target-driven visual navigation. To address the first issue, we propose an actor-critic model whose policy is a function of the goal as well as the current state, which allows to better generalize. To address the second issue, we propose AI2-THOR framework, which provides an environment with high-quality 3D scenes and physics engine. Our framework enables agents to take actions and interact with objects. Hence, we can collect a huge number of training samples efficiently.

We show that our proposed method (1) converges faster than the state-of-the-art deep reinforcement learning methods, (2) generalizes across targets and across scenes, (3) generalizes to a real robot scenario with a small amount of fine-tuning (although the model is trained in simulation), (4) is end-to-end trainable and does not need feature engineering, feature matching between frames or 3D reconstruction of the environment.

The supplementary video can be accessed at the following link: YouTube.

--- https://arxiv.org/abs/1609.07093 Neural Photo Editing with Introspective Adversarial Networks Andrew Brock, Theodore Lim, J. M. Ritchie, Nick Weston 2016-09-22 2019-12-19 [("doi","10.48550/arXiv.1609.07093")] ai/nn/cnn ai/nn/gan ai/nn/vae

The increasingly photorealistic sample quality of generative image models suggests their feasibility in applications beyond image generation. We present the Neural Photo Editor, an interface that leverages the power of generative neural networks to make large, semantically coherent changes to existing images.

To tackle the challenge of achieving accurate reconstructions without loss of feature quality, we introduce the Introspective Adversarial Network, a novel hybridization of the VAE and GAN. Our model efficiently captures long-range dependencies through use of a computational block based on weight-shared dilated convolutions, and improves generalization performance with Orthogonal Regularization, a novel weight regularization method.

We validate our contributions on CelebA, SVHN, and CIFAR-100, and produce samples and reconstructions with high visual fidelity.

--- https://arxiv.org/abs/1609.09106#google HyperNetworks David Ha, Andrew Dai, Quoc V. Le 2016-09-27 2019-12-19 [("doi","10.48550/arXiv.1609.09106")] ai/nn/cnn ai/nn/rnn reinforcement-learning/meta-learning

[blog] This work explores hypernetworks: an approach of using a one network, also known as a ‘hypernetwork’, to generate the weights for another network. Hypernetworks provide an abstraction that is similar to what is found in nature: the relationship between a genotype—the hypernetwork—and a phenotype—the main network. Though they are also reminiscent of HyperNEAT in evolution, our hypernetworks are trained end-to-end with backpropagation and thus are usually faster.

The focus of this work is to make hypernetworks useful for deep convolutional networks and long recurrent networks, where hypernetworks can be viewed as relaxed form of weight-sharing across layers.

Our main result is that hypernetworks can generate non-shared weights for LSTM and achieve near state-of-the-art results on a variety of sequence modeling tasks including character-level language modeling, handwriting generation and neural machine translation, challenging the weight-sharing paradigm for recurrent networks.

Our results also show that hypernetworks applied to convolutional networks still achieve respectable results for image recognition tasks compared to state-of-the-art baseline models while requiring fewer learnable parameters.

--- https://arxiv.org/abs/1610.01945 Connecting Generative Adversarial Networks and Actor-Critic Methods David Pfau, Oriol Vinyals 2016-10-06 2019-12-19 [("doi","10.48550/arXiv.1610.01945")] ai/nn/gan reinforcement-learning/model-free

Both generative adversarial networks (GAN) in unsupervised learning and actor-critic methods in reinforcement learning (RL) have gained a reputation for being difficult to optimize. Practitioners in both fields have amassed a large number of strategies to mitigate these instabilities and improve training.

Here we show that GANs can be viewed as actor-critic methods in an environment where the actor cannot affect the reward.

We review the strategies for stabilizing training for each class of models, both those that generalize between the two and those that are particular to that model. We also review a number of extensions to GANs and RL algorithms with even more complicated information flow.

We hope that by highlighting this formal connection we will encourage both GAN and RL communities to develop general, scalable, and stable algorithms for multilevel optimization with deep networks, and to draw inspiration across communities.

--- https://arxiv.org/abs/1610.03914 Mapping Between fMRI Responses to Movies and their Natural Language Annotations Kiran Vodrahalli, Po-Hsuan Chen, Yingyu Liang, Christopher Baldassano, Janice Chen, Esther Yong, Christopher Honey, Uri Hasson, Peter Ramadge, Ken Norman, Sanjeev Arora 2016-10-13 2019-12-19 [("doi","10.48550/arXiv.1610.03914")] reinforcement-learning/imitation-learning/brain-imitation-learning

Several research groups have shown how to correlate fMRI responses to the meanings of presented stimuli. This paper presents new methods for doing so when only a natural language annotation is available as the description of the stimulus. We study fMRI data gathered from subjects watching an episode of BBC’s Sherlock, and learn bidirectional mappings between fMRI responses and natural language representations.

The key ingredients are (a) the use of the Shared Response Model (SRM) and its variant SRM-ICA to aggregate fMRI data from multiple subjects, both of which are shown to be superior to standard PCA in producing low-dimensional representations for the tasks in this paper; (b) a sentence embedding technique adapted from the natural language processing (NLP) literature that produces semantic vector representation of the annotations; (c) using previous timestep information in the featurization of the predictor data.

We show how to leverage data from multiple subjects watching the same movie to improve the accuracy of the mappings, allowing us to succeed at a scene classification task with 72% accuracy (random guessing would give 4%) and at a scene ranking task with average rank in the top 4% (random guessing would give 50%).

--- https://arxiv.org/abs/1611.00712#deepmind The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables Chris J. Maddison, Andriy Mnih, Yee Whye Teh 2016-11-02 2019-12-19 [("doi","10.48550/arXiv.1611.00712")] ai/nn/transformer/gpt/dall-e/1 ai/nn/vae statistics/probability

The reparameterization trick enables optimizing large scale stochastic computation graphs via gradient descent. The essence of the trick is to refactor each stochastic node into a differentiable function of its parameters and a random variable with fixed distribution. After refactoring, the gradients of the loss propagated by the chain rule through the graph are low variance unbiased estimators of the gradients of the expected loss. While many continuous random variables have such reparameterizations, discrete random variables lack useful reparameterizations due to the discontinuous nature of discrete states.

In this work we introduce Concrete random variables—continuous relaxations of discrete random variables. The Concrete distribution is a new family of distributions with closed form densities and a simple reparameterization. Whenever a discrete stochastic node of a computation graph can be refactored into a one-hot bit representation that is treated continuously, Concrete stochastic nodes can be used with automatic differentiation to produce low-variance biased gradients of objectives (including objectives that depend on the log-probability of latent stochastic nodes) on the corresponding discrete graph.

We demonstrate the effectiveness of Concrete relaxations on density estimation and structured prediction tasks using neural networks.

--- https://arxiv.org/abs/1611.01144#google Categorical Reparameterization with Gumbel-Softmax Eric Jang, Shixiang Gu, Ben Poole 2016-11-03 2019-12-19 [("doi","10.48550/arXiv.1611.01144")] ai/nn/transformer/gpt/dall-e/1 ai/nn/vae statistics/probability

Categorical variables are a natural choice for representing discrete structure in the world.

However, stochastic neural networks rarely use categorical latent variables due to the inability to backpropagate through samples. In this work, we present an efficient gradient estimator that replaces the non-differentiable sample from a categorical distribution with a differentiable sample from a novel Gumbel-Softmax distribution. This distribution has the essential property that it can be smoothly annealed into a categorical distribution.

We show that our Gumbel-Softmax estimator outperforms state-of-the-art gradient estimators on structured output prediction and unsupervised generative modeling tasks with categorical latent variables and enables large speedups on semi-supervised classification.

--- https://arxiv.org/abs/1611.01843 Learning to Perform Physics Experiments via Deep Reinforcement Learning Misha Denil, Pulkit Agrawal, Tejas D. Kulkarni, Tom Erez, Peter Battaglia, Nando de Freitas 2016-11-06 2019-12-19 [("doi","10.48550/arXiv.1611.01843")] reinforcement-learning/exploration

When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way. This process of active interaction is in the same spirit as a scientist performing experiments to discover hidden facts. Recent advances in artificial intelligence have yielded machines that can achieve superhuman performance in Go, Atari, natural language processing, and complex control problems; however, it is not clear that these systems can rival the scientific intuition of even a young child.

In this work we introduce a basic set of tasks that require agents to estimate properties such as mass and cohesion of objects in an interactive simulated environment where they can manipulate the objects and observe the consequences. We found that state of art deep reinforcement learning methods can learn to perform the experiments necessary to discover such hidden properties.

By systematically manipulating the problem difficulty and the cost incurred by the agent for performing experiments, we found that agents learn different strategies that balance the cost of gathering information against the cost of making mistakes in different situations.

--- https://arxiv.org/abs/1611.03852 A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models Chelsea Finn, Paul Christiano, Pieter Abbeel, Sergey Levine 2016-11-11 2019-12-20 [("doi","10.48550/arXiv.1611.03852")] ai/nn/gan reinforcement-learning/imitation-learning reinforcement-learning/model-free

Generative adversarial networks (GANs) are a recently proposed class of generative models in which a generator is trained to optimize a cost function that is being simultaneously learned by a discriminator. While the idea of learning cost functions is relatively new to the field of generative modeling, learning costs has long been studied in control and reinforcement learning (RL) domains, typically for imitation learning from demonstrations. In these fields, learning cost function underlying observed behavior is known as inverse reinforcement learning (IRL) or inverse optimal control.

While at first the connection between cost learning in RL and cost learning in generative modeling may appear to be a superficial one, we show in this paper that certain IRL methods are in fact mathematically equivalent to GANs. In particular, we demonstrate an equivalence between a sample-based algorithm for maximum entropy IRL and a GAN in which the generator’s density can be evaluated and is provided as an additional input to the discriminator. Interestingly, maximum entropy IRL is a special case of an energy-based model. We discuss the interpretation of GANs as an algorithm for training energy-based models, and relate this interpretation to other recent work that seeks to connect GANs and EBMs.

By formally highlighting the connection between GANs, IRL, and EBMs, we hope that researchers in all 3 communities can better identify and apply transferable ideas from one domain to another, particularly for developing more stable and scalable algorithms: a major challenge in all 3 domains.

--- https://arxiv.org/abs/1611.05763#deepmind Learning to reinforcement learn Jane X. Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z. Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, Matt Botvinick 2016-11-17 2019-12-20 [("doi","10.48550/arXiv.1611.05763")] ai/nn psychology/neuroscience reinforcement-learning/meta-learning

In recent years, deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applications is their demand for massive amounts of training data. A critical present objective is thus to develop deep RL methods that can adapt rapidly to new tasks.

In the present work, we introduce a novel approach to this challenge, which we refer to as deep meta-reinforcement learning. Previous work has shown that recurrent networks can support meta-learning in a fully supervised context. We extend this approach to the RL setting. What emerges is a system that is trained using one RL algorithm but whose recurrent dynamics implement a second, quite separate RL procedure. This second, learned RL algorithm can differ from the original one in arbitrary ways. Importantly, because it is learned, it is configured to exploit structure in the training domain.

We unpack these points in a series of 7 proof-of-concept experiments, each of which examines a key aspect of deep meta-RL.

We consider prospects for extending and scaling up the approach and also point out some potentially important implications for neuroscience.

--- https://arxiv.org/abs/1612.01474 Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell 2016-12-05 2019-12-20 [("doi","10.48550/arXiv.1612.01474")] ai/scaling statistics/bayes

Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem.

Bayesian NNs, which learn a distribution over weights, are currently the state-of-the-art for estimating predictive uncertainty; however these require modifications to the training procedure and are computationally expensive compared to standard (non-Bayesian) NNs.

We propose an alternative to Bayesian NNs that is simple to implement, readily parallelizable, requires very little hyperparameter tuning, and yields high quality predictive uncertainty estimates. Through a series of experiments on classification and regression benchmarks, we demonstrate that our method produces well-calibrated uncertainty estimates which are as good or better than approximate Bayesian NNs. To assess robustness to dataset shift, we evaluate the predictive uncertainty on test examples from known and unknown distributions, and show that our method is able to express higher uncertainty on out-of-distribution examples.

We demonstrate the scalability of our method by evaluating predictive uncertainty estimates on ImageNet.

--- https://arxiv.org/abs/1612.03242 StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, Dimitris Metaxas 2016-12-10 2019-12-20 [("doi","10.48550/arXiv.1612.03242")] ai/nn/gan/stylegan

Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts.

In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256×256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold.

Extensive experiments and comparisons with state-of-the-art on benchmark datasets demonstrate that the proposed method achieves improvements on generating photo-realistic images conditioned on text descriptions.

--- https://arxiv.org/abs/1612.04021 Generative Adversarial Parallelization Daniel Jiwoong Im, He Ma, Chris Dongjoo Kim, Graham Taylor 2016-12-13 2019-12-20 [("doi","10.48550/arXiv.1612.04021")] ai/nn/gan

Generative Adversarial Networks have become one of the most studied frameworks for unsupervised learning due to their intuitive formulation. They have also been shown to be capable of generating convincing examples in limited domains, such as low-resolution images. However, they still prove difficult to train in practice and tend to ignore modes of the data generating distribution.

We propose Generative Adversarial Parallelization, a framework in which many GANs or their variants are trained simultaneously, exchanging their discriminators. This eliminates the tight coupling between a generator and discriminator, leading to improved convergence and improved coverage of modes.

We also propose an improved variant of the recently proposed Generative Adversarial Metric and show how it can score individual GANs or their collections under the GAP model.

--- https://arxiv.org/abs/1612.04357 Stacked Generative Adversarial Networks Xun Huang, Yixuan Li, Omid Poursaeed, John Hopcroft, Serge Belongie 2016-12-13 2019-12-20 [("doi","10.48550/arXiv.1612.04357")] ai/nn/gan

In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network. Our model consists of a top-down stack of GANs, each learned to generate lower-level representations conditioned on higher-level representations. A representation discriminator is introduced at each feature hierarchy to encourage the representation manifold of the generator to align with that of the bottom-up discriminative network, leveraging the powerful discriminative representations to guide the generative model. In addition, we introduce a conditional loss that encourages the use of conditional information from the layer above, and a novel entropy loss that maximizes a variational lower bound on the conditional entropy of generator outputs.

We first train each stack independently, and then train the whole model end-to-end. Unlike the original GAN that uses a single noise vector to represent all the variations, our SGAN decomposes variations into multiple levels and gradually resolves uncertainties in the top-down generative process. Based on visual inspection, Inception scores and visual Turing test, we demonstrate that SGAN is able to generate images of much higher quality than GANs without stacking.

--- https://arxiv.org/abs/1612.07547 Equilibrium Approximation Quality of Current No-Limit Poker Bots Viliam Lisy, Michael Bowling 2016-12-22 2019-12-20 [("doi","10.48550/arXiv.1612.07547")] reinforcement-learning/imperfect-information/poker

Approximating a Nash equilibrium is currently the best performing approach for creating poker-playing programs. While for the simplest variants of the game, it is possible to evaluate the quality of the approximation by computing the value of the best response strategy, this is currently not computationally feasible for larger variants of the game, such as heads-up no-limit Texas hold’em poker.

In this paper, we present a simple and computationally inexpensive Local Best Response method for computing an approximate lower bound on the value of the best response strategy.

Using this method, we show that existing poker-playing programs, based on solving abstract games, are remarkably poor Nash equilibrium approximations.

--- https://arxiv.org/abs/1701.00160 NIPS 2016 Tutorial: Generative Adversarial Networks Ian Goodfellow 2016-12-31 2019-12-20 [("doi","10.48550/arXiv.1701.00160")] ai/nn/gan

This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs).

The tutorial describes: (1) Why generative modeling is a topic worth studying, (2) how generative models work, and how GANs compare to other generative models, (3) the details of how GANs work, (4) research frontiers in GANs, and (5) state-of-the-art image models that combine GANs with other methods.

Finally, the tutorial contains 3 exercises for readers to complete, and the solutions to these exercises.

--- https://arxiv.org/abs/1701.01724 DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Michael Bowling 2017-01-06 2019-12-20 [("doi","10.1126/science.aam6960")] reinforcement-learning/imperfect-information/poker reinforcement-learning/model/alphago

Artificial intelligence has seen several breakthroughs in recent years, with games often serving as milestones. A common feature of these games is that players have perfect information. Poker is the quintessential game of imperfect information, and a long-standing challenge problem in artificial intelligence.

We introduce DeepStack, an algorithm for imperfect information settings. It combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition that is automatically learned from self-play using deep learning.

The approach is theoretically sound and is shown to produce more difficult to exploit strategies than prior approaches. In a study involving 44,000 hands of poker, DeepStack defeated with statistical-significance professional poker players in heads-up no-limit Texas hold’em.

--- https://arxiv.org/abs/1701.05369 Variational Dropout Sparsifies Deep Neural Networks Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov 2017-01-19 2019-12-20 [("doi","10.48550/arXiv.1701.05369")] ai/nn/sparsity/pruning

We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout.

We extend Variational Dropout to the case when dropout rates are unbounded, propose a way to reduce the variance of the gradient estimator and report first experimental results with individual dropout rates per weight.

Interestingly, it leads to extremely sparse solutions both in fully-connected and convolutional layers. This effect is similar to automatic relevance determination effect in empirical Bayes but has a number of advantages.

We reduce the number of parameters up to 280× on LeNet architectures and up to 68× on VGG-like networks with a negligible decrease of accuracy.

--- https://arxiv.org/abs/1701.06538#google Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean 2017-01-23 2019-12-20 [("doi","10.48550/arXiv.1701.06538")] ai/nn/rnn ai/scaling/mixture-of-experts

The capacity of a neural network to absorb information is limited by its number of parameters. Conditional computation, where parts of the network are active on a per-example basis, has been proposed in theory as a way of dramatically increasing model capacity without a proportional increase in computation. In practice, however, there are algorithmic and performance challenges.

In this work, we address these challenges and finally realize the promise of conditional computation, achieving >1,000× improvements in model capacity with only minor losses in computational efficiency on modern GPU clusters. We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward sub-networks. A trainable gating network determines a sparse combination of these experts to use for each example.

We apply the MoE to the tasks of language modeling and machine translation, where model capacity is critical for absorbing the vast quantities of knowledge available in the training corpora. We present model architectures in which a MoE with up to 137 billion parameters is applied convolutionally between stacked LSTM layers.

On large language modeling and machine translation benchmarks, these models achieve better results than state-of-the-art at lower computational cost.

--- https://arxiv.org/abs/1810.06339 Deep Reinforcement Learning Yuxi Li 2018-10-15 2019-12-21 [("doi","10.48550/arXiv.1810.06339")] reinforcement-learning/meta-learning reinforcement-learning/model/alphago reinforcement-learning/multi-agent reinforcement-learning/robot

We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss 6 core elements, 6 important mechanisms, and 12 applications. We start with background of machine learning, deep learning, and reinforcement learning.

Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn.

Then we discuss various applications of RL, including games, in particular, AlphaGo, robotics, natural language processing, including dialogue systems, machine translation, and text generation, computer vision, neural architecture design, business management, finance, healthcare, Industry 4.0, smart grid, intelligent transportation systems, and computer systems.

We mention topics not reviewed yet, and list a collection of RL resources. After presenting a brief summary, we close with discussions.

--- https://arxiv.org/abs/1701.07875 Wasserstein GAN Martin Arjovsky, Soumith Chintala, Léon Bottou 2017-01-26 2019-12-21 [("doi","10.48550/arXiv.1701.07875")] ai/nn/gan

We introduce a new algorithm named WGAN, an alternative to traditional GAN training.

In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches.

Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to other distances between distributions.

--- https://arxiv.org/abs/1702.03410 ArtGAN: Artwork Synthesis with Conditional Categorical GANs Wei Ren Tan, Chee Seng Chan, Hernan Aguirre, Kiyoshi Tanaka 2017-02-11 2019-12-21 [("doi","10.48550/arXiv.1702.03410")] ai/nn/gan

This paper proposes an extension to the Generative Adversarial Networks (GANs), namely as ARTGAN to synthetically generate more challenging and complex images such as artwork that have abstract characteristics.

This is in contrast to most of the current solutions that focused on generating natural images such as room interiors, birds, flowers, and faces. The key innovation of our work is to allow back-propagation of the loss function w.r.t. the labels (randomly assigned to each generated image) to the generator from the discriminator.

With the feedback from the label information, the generator is able to learn faster and achieve better-generated image quality.

Empirically, we show that the proposed ARTGAN is capable to create realistic artwork, as well as generate compelling real-world images that globally look natural with clear shape on CIFAR-10.

--- https://arxiv.org/abs/1703.00522#deepmind Understanding Synthetic Gradients and Decoupled Neural Interfaces Wojciech Marian Czarnecki, Grzegorz Świrszcz, Max Jaderberg, Simon Osindero, Oriol Vinyals, Koray Kavukcuoglu 2017-03-01 2019-12-21 [("doi","10.48550/arXiv.1703.00522")] ai/nn psychology/neuroscience reinforcement-learning/meta-learning

When training neural networks, the use of Synthetic Gradients (SG) allows layers or modules to be trained without update locking—without waiting for a true error gradient to be backpropagated—resulting in Decoupled Neural Interfaces (DNIs). This unlocked ability of being able to update parts of a neural network asynchronously and with only local information was demonstrated to work empirically in Jaderberg et al 2016. However, there has been very little demonstration of what changes DNIs and SGs impose from a functional, representational, and learning dynamics point of view.

In this paper, we study DNIs through the use of synthetic gradients on feed-forward networks to better understand their behavior and elucidate their effect on optimization. We show that the incorporation of SGs does not affect the representational strength of the learning system for a neural network, and prove the convergence of the learning system for linear and deep linear models.

On practical problems we investigate the mechanism by which synthetic gradient estimators approximate the true loss, and, surprisingly, how that leads to drastically different layer-wise representations. Finally, we also expose the relationship of using synthetic gradients to other error approximation techniques and find an unifying language for discussion and comparison.

--- https://arxiv.org/abs/1703.00837 Meta Networks Tsendsuren Munkhdalai, Hong Yu 2017-03-02 2019-12-21 [("doi","10.48550/arXiv.1703.00837")] ai/nn reinforcement-learning/meta-learning/continual-learning

Neural networks have been successfully applied in applications with a large amount of labeled data. However, the task of rapid generalization on new concepts with small training data while preserving performance on previously learned ones still presents a challenge to neural network models.

In this work, we introduce a novel meta-learning method, Meta Networks (MetaNet), that learns a meta-level knowledge across tasks and shifts its inductive biases via fast parameterization for rapid generalization.

When evaluated on Omniglot and Mini-ImageNet benchmarks, our MetaNet models achieve a near human-level performance and outperform the baseline approaches by up to 6% accuracy.

We demonstrate several appealing properties of MetaNet relating to generalization and continual learning.

--- https://arxiv.org/abs/1703.03400 MAML: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn, Pieter Abbeel, Sergey Levine 2017-03-09 2019-12-21 [("doi","10.48550/arXiv.1703.03400")] reinforcement-learning/meta-learning

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples.

In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune.

We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.

--- https://arxiv.org/abs/1703.03779 Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact Massimo Bartoletti, Salvatore Carta, Tiziana Cimoli, Roberto Saia 2017-03-10 2019-12-21 [("doi","10.48550/arXiv.1703.03779")] bitcoin crime economics

Ponzi schemes are financial frauds which lure users under the promise of high profits. Actually, users are repaid only with the investments of new users joining the scheme: consequently, a Ponzi scheme implodes soon after users stop joining it. Originated in the offline world 150 years ago, Ponzi schemes have since then migrated to the digital world, approaching first the Web, and more recently hanging over cryptocurrencies like Bitcoin.

Smart contract platforms like Ethereum have provided a new opportunity for scammers, who have now the possibility of creating “trustworthy” frauds that still make users lose money, but at least are guaranteed to execute “correctly”.

We present a comprehensive survey of Ponzi schemes on Ethereum, analyzing their behavior and their impact from various viewpoints.

--- https://arxiv.org/abs/1703.03864#openai Evolution Strategies as a Scalable Alternative to Reinforcement Learning Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, Ilya Sutskever 2017-03-10 2019-12-21 [("doi","10.48550/arXiv.1703.03864")] reinforcement-learning/exploration reinforcement-learning/scaling

We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show that ES is a viable solution strategy that scales extremely well with the number of CPUs available.

By using a novel communication strategy based on common random numbers, our ES implementation only needs to communicate scalars, making it possible to scale to over a thousand parallel workers. This allows us to solve 3D humanoid walking in 10 minutes and obtain competitive results on most Atari games after one hour of training.

In addition, we highlight several advantages of ES as a black box optimization technique: it is invariant to action frequency and delayed rewards, tolerant of extremely long horizons, and does not need temporal discounting or value function approximation.

--- https://arxiv.org/abs/1703.04887 Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets Zhen Yang, Wei Chen, Feng Wang, Bo Xu 2017-03-15 2019-12-21 [("doi","10.48550/arXiv.1703.04887")] ai/nn/gan ai/nn/rnn ai/nn/sampling reinforcement-learning/model-free

This paper proposes an approach for applying GANs to NMT. We build a conditional sequence generative adversarial net which comprises of two adversarial sub models, a generator and a discriminator. The generator aims to generate sentences which are hard to be discriminated from human-translated sentences (ie. the golden target sentences), And the discriminator makes efforts to discriminate the machine-generated sentences from human-translated ones. The two sub models play a mini-max game and achieve the win-win situation when they reach a Nash Equilibrium.

Additionally, the static sentence-level BLEU isused as the reinforced objective for the generator, which biases the generation towards high BLEU points. During training, both the dynamic discriminator and the static BLEU objective are employed to evaluate the generated sentences and feedback the evaluations to guide the learning of the generator.

Experimental results show that the proposed model consistently outperforms the traditional RNNSearch and the newly emerged state-of-the-art Transformer on English-German and Chinese-English translation tasks.

--- https://arxiv.org/abs/1703.05192 Learning to Discover Cross-Domain Relations with Generative Adversarial Networks Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, Jiwon Kim 2017-03-15 2019-12-21 [("doi","10.48550/arXiv.1703.05192")] ai/nn/gan

While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations. To avoid costly pairing, we address the task of discovering cross-domain relations given unpaired data.

We propose a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN). Using the discovered relations, our proposed network successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity.

Source code for official implementation is publicly available https://github.com/SKTBrain/DiscoGAN.

--- https://arxiv.org/abs/1703.05463 Using Human Brain Activity to Guide Machine Learning Ruth Fong, Walter Scheirer, David Cox 2017-03-16 2019-12-21 [("doi","10.48550/arXiv.1703.05463")] ai/nn/cnn reinforcement-learning/imitation-learning/brain-imitation-learning

Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms.

Here we demonstrate a new paradigm of “neurally-weighted” machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data.

We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to improvements with already high-performing convolutional neural network features.

The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

--- https://arxiv.org/abs/1703.10987 On the Impossibility of Supersized Machines Ben Garfinkel, Miles Brundage, Daniel Filan, Carrick Flynn, Jelena Luketina, Michael Page, Anders Sandberg, Andrew Snyder-Beattie, Max Tegmark 2017-03-31 2019-12-22 [("doi","10.48550/arXiv.1703.10987")] ai/scaling math/humor philosophy/epistemology reinforcement-learning/safe

In recent years, a number of prominent computer scientists, along with academics in fields such as philosophy and physics, have lent credence to the notion that machines may one day become as large as humans. Many have further argued that machines could even come to exceed human size by a large margin.

However, there are at least 7 distinct arguments that preclude this outcome. We show that it is not only implausible that machines will ever exceed human size, but in fact impossible.

--- https://arxiv.org/abs/1704.00028 Improved Training of Wasserstein GANs Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville 2017-03-31 2019-12-22 [("doi","10.48550/arXiv.1704.00028")] ai/nn/gan

Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality samples or fail to converge. We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior.

We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models over discrete data.

We also achieve high quality generations on CIFAR-10 and LSUN bedrooms.

Our findings suggest that penalizing the gradient norm can significantly improve the stability and quality of GAN training, offering a robust solution to the issues encountered with the weight clipping method in WGAN.

Supplementary details and code for our experiments can be found on our project website.

--- https://arxiv.org/abs/1704.01279#google Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Douglas Eck, Karen Simonyan, Mohammad Norouzi 2017-04-05 2019-12-22 [("doi","10.48550/arXiv.1704.01279")] ai/music ai/nn/vae

Generative models in vision have seen rapid progress due to algorithmic improvements and the availability of high-quality image datasets. In this paper, we offer contributions in both these areas to enable similar progress in audio modeling.

First, we detail a powerful new WaveNet-style autoencoder model that conditions an autoregressive decoder on temporal codes learned from the raw audio waveform.

Second, we introduce NSynth, a large-scale and high-quality dataset of musical notes that is an order of magnitude larger than comparable public datasets. Using NSynth, we demonstrate improved qualitative and quantitative performance of the WaveNet autoencoder over a well-tuned spectral autoencoder baseline.

Finally, we show that the model learns a manifold of embeddings that allows for morphing between instruments, meaningfully interpolating in timbre to create new types of sounds that are realistic and expressive.

--- https://arxiv.org/abs/1704.03453 The Space of Transferable Adversarial Examples Florian Tramèr, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel 2017-04-11 2019-12-22 [("doi","10.48550/arXiv.1704.03453")] ai/nn/adversarial

Adversarial examples are maliciously perturbed inputs designed to mislead machine learning (ML) models at test-time. They often transfer: the same adversarial example fools more than one model.

In this work, we propose novel methods for estimating the previously unknown dimensionality of the space of adversarial inputs. We find that adversarial examples span a contiguous subspace of large (~25) dimensionality. Adversarial subspaces with higher dimensionality are more likely to intersect. We find that for two different models, a substantial fraction of their subspaces is shared, thus enabling transferability.

In the first quantitative analysis of the similarity of different models’ decision boundaries, we show that these boundaries are actually close in arbitrary directions, whether adversarial or benign. We conclude by formally studying the limits of transferability. We derive (1) sufficient conditions on the data distribution that imply transferability for simple model classes and (2) examples of scenarios in which transfer does not occur. These findings indicate that it may be possible to design defenses against transfer-based attacks, even for models that are vulnerable to direct attacks.

--- https://arxiv.org/abs/1704.04110 DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks David Salinas, Valentin Flunkert, Jan Gasthaus 2017-04-13 2019-12-22 [("doi","10.48550/arXiv.1704.04110")] ai/nn/rnn statistics/prediction

Probabilistic forecasting, i.e. estimating the probability distribution of a time series’ future given its past, is a key enabler for optimizing business processes. In retail businesses, for example, forecasting demand is crucial for having the right inventory available at the right time at the right place.

In this paper, we propose DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an auto regressive recurrent network model on a large number of related time series.

We demonstrate how by applying deep learning techniques to forecasting, one can overcome many of the challenges faced by widely-used classical approaches to the problem.

We show through extensive empirical evaluation on several real-world forecasting datasets accuracy improvements of around 15% compared to state-of-the-art methods.

--- https://arxiv.org/abs/1704.05119 Exploring Sparsity in Recurrent Neural Networks Sharan Narang, Erich Elsen, Gregory Diamos, Shubho Sengupta 2017-04-17 2019-12-22 [("doi","10.48550/arXiv.1704.05119")] ai/nn/rnn ai/nn/sparsity/pruning

Recurrent Neural Networks (RNN) are widely used to solve a variety of problems and as the quantity of data and the amount of available compute have increased, so have model sizes. The number of parameters in recent state-of-the-art networks makes them hard to deploy, especially on mobile phones and embedded devices. The challenge is due to both the size of the model and the time it takes to evaluate it.

In order to deploy these RNNs efficiently, we propose a technique to reduce the parameters of a network by pruning weights during the initial training of the network. At the end of training, the parameters of the network are sparse while accuracy is still close to the original dense neural network.

The network size is reduced by 8× and the time required to train the model remains constant. Additionally, we can prune a larger dense network to achieve better than baseline performance while still reducing the total number of parameters. Pruning RNNs reduces the size of the model and can also help achieve inference time speed-up using sparse matrix multiply.

Benchmarks show that using our technique model size can be reduced by 90% and speed-up is around 2× to 7×.

--- https://arxiv.org/abs/1704.06363#facebook Hard Mixtures of Experts for Large Scale Weakly Supervised Vision Sam Gross, Marc’Aurelio Ranzato, Arthur Szlam 2017-04-20 2019-12-22 [("doi","10.48550/arXiv.1704.06363")] ai/scaling/mixture-of-experts

Training convolutional networks (CNNs) that fit on a single GPU with minibatch stochastic gradient descent has become effective in practice. However, there is still no effective method for training large CNN’s that do not fit in the memory of a few GPU cards, or for parallelizing CNN training.

In this work we show that a simple hard mixture of experts model can be efficiently trained to good effect on large scale hashtag (multilabel) prediction tasks. Mixture of experts models are not new (Jacobs et al 1991, Collobert et al 2003), but in the past, researchers have had to devise sophisticated methods to deal with data fragmentation.

We show empirically that modern weakly supervised data sets are large enough to support naive partitioning schemes where each data point is assigned to a single expert. Because the experts are independent, training them in parallel is easy, and evaluation is cheap for the size of the model. Furthermore, we show that we can use a single decoding layer for all the experts, allowing an unified feature embedding space.

We demonstrate that it is feasible (and in fact relatively painless) to train far larger models than could be practically trained with standard CNN architectures, and that the extra capacity can be well used on current datasets.

--- https://arxiv.org/abs/1705.03071 Geometry of Optimization and Implicit Regularization in Deep Learning Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro 2017-05-08 2019-12-22 [("doi","10.48550/arXiv.1705.03071")] ai/scaling

We argue that the optimization plays a crucial role in generalization of deep learning models through implicit regularization.

We do this by demonstrating that generalization ability is not controlled by network size but rather by some other implicit control. We then demonstrate how changing the empirical optimization procedure can improve generalization, even if actual optimization quality is not affected.

We do so by studying the geometry of the parameter space of deep networks, and devising an optimization algorithm attuned to this geometry.

--- https://arxiv.org/abs/1705.04146#deepmind Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems Wang Ling, Dani Yogatama, Chris Dyer, Phil Blunsom 2017-05-11 2019-12-22 [("doi","10.48550/arXiv.1705.04146")] ai/nn/transformer/gpt/inner-monologue math

Solving algebraic word problems requires executing a series of arithmetic operations—a program—to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge.

To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps. Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones.

To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales.

Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.

--- https://arxiv.org/abs/1705.07204 Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel 2017-05-19 2019-12-22 [("doi","10.48550/arXiv.1705.07204")] ai/nn/adversarial

Adversarial examples are perturbed inputs designed to fool machine learning models. Adversarial training injects such examples into training data to increase robustness. To scale this technique to large datasets, perturbations are crafted using fast single-step methods that maximize a linear approximation of the model’s loss.

We show that this form of adversarial training converges to a degenerate global minimum, wherein small curvature artifacts near the data points obfuscate a linear approximation of the loss. The model thus learns to generate weak perturbations, rather than defend against strong ones. As a result, we find that adversarial training remains vulnerable to black-box attacks, where we transfer perturbations computed on undefended models, as well as to a powerful novel single-step attack that escapes the non-smooth vicinity of the input data via a small random step.

We further introduce Ensemble Adversarial Training, a technique that augments training data with perturbations transferred from other models. On ImageNet, Ensemble Adversarial Training yields models with strong robustness to black-box attacks. In particular, our most robust model won the first round of the NIPS 2017 competition on Defenses against Adversarial Attacks.

However, subsequent work found that more elaborate black-box attacks could enhance transferability and reduce the accuracy of our models.

--- https://arxiv.org/abs/1705.07283 Structured Bayesian Pruning via Log-Normal Multiplicative Noise Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov 2017-05-20 2019-12-23 [("doi","10.48550/arXiv.1705.07283")] ai/nn/sparsity/pruning statistics/bayes

Dropout-based regularization methods are regarded as injecting random noise with pre-defined magnitude to different parts of the neural network during training. It was recently shown that the Bayesian dropout procedure not only improves generalization but also leads to extremely sparse neural architectures by automatically setting the individual noise magnitude per weight. However, this sparsity can hardly be used for acceleration since it is unstructured.

In the paper, we propose a new Bayesian model that takes into account the computational structure of neural networks and provides structured sparsity, eg. removes neurons and/or convolutional channels in CNNs. To do this, we inject noise to the neurons’ outputs while keeping the weights unregularized. We establish the probabilistic model with a proper truncated log-uniform prior over the noise and truncated log-normal variational approximation that ensures that the KL-term in the evidence lower bound is computed in closed-form.

The model leads to structured sparsity by removing elements with a low SNR from the computation graph and provides acceleration on a number of deep neural architectures.

The model is easy to implement as it can be formulated as a separate dropout-like layer.

--- https://arxiv.org/abs/1705.08807 When Will AI Exceed Human Performance? Evidence from AI Experts Katja Grace, John Salvatier, Allan Dafoe, Baobao Zhang, Owain Evans 2017-05-24 2019-12-23 [("doi","10.48550/arXiv.1705.08807")] ai statistics/prediction

Advances in artificial intelligence (AI) will transform modern life by reshaping transportation, health, science, finance, and the military. To adapt public policy, we need to better anticipate these advances.

Here we report the results from a large survey of machine learning researchers on their beliefs about progress in AI. Researchers predict AI will outperform humans in many activities in the next 10 years, such as translating languages (by 2024), writing high-school essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a bestselling book (by 2049), and working as a surgeon (by 2053).

Researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years, with Asian respondents expecting these dates much sooner than North Americans.

These results will inform discussion amongst researchers and policymakers about anticipating and managing trends in AI.

--- https://arxiv.org/abs/1705.09367 Stabilizing Training of Generative Adversarial Networks through Regularization Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, Thomas Hofmann 2017-05-25 2019-12-23 [("doi","10.48550/arXiv.1705.09367")] ai/nn/gan

Deep generative models based on Generative Adversarial Networks (GANs) have demonstrated impressive sample quality but in order to work they require a careful choice of architecture, parameter initialization, and selection of hyper-parameters.

This fragility is in part due to a dimensional mismatch or non-overlapping support between the model distribution and the data distribution, causing their density ratio and the associated f-divergence to be undefined. We overcome this fundamental limitation and propose a new regularization approach with low computational cost that yields a stable GAN training procedure.

We demonstrate the effectiveness of this regularizer across several architectures trained on common benchmark image generation tasks. Our regularization turns GAN models into reliable building blocks for deep learning.

--- https://arxiv.org/abs/1705.10843 Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro Luis Cunha Farias, Alán Aspuru-Guzik 2017-05-30 2019-12-23 [("doi","10.48550/arXiv.1705.10843")] ai/music ai/nn/gan reinforcement-learning/model-free

In unsupervised data generation tasks, besides the generation of a sample based on previous observations, one would often like to give hints to the model in order to bias the generation towards desirable metrics. We propose a method that combines Generative Adversarial Networks (GANs) and reinforcement learning (RL) in order to accomplish exactly that.

While RL biases the data generation process towards arbitrary metrics, the GAN component of the reward function ensures that the model still remembers information learned from data. We build upon previous results that incorporated GANs and RL in order to generate sequence data and test this model in several settings for the generation of molecules encoded as text sequences (SMILES) and in the context of music generation, showing for each case that we can effectively bias the generation process towards desired metrics.

--- https://arxiv.org/abs/1705.10941 Spectral Norm Regularization for Improving the Generalizability of Deep Learning Yuichi Yoshida, Takeru Miyato 2017-05-31 2019-12-23 [("doi","10.48550/arXiv.1705.10941")] ai/nn/gan/biggan

We investigate the generalizability of deep learning based on the sensitivity to input perturbation.

We hypothesize that the high sensitivity to the perturbation of data degrades the performance on it. To reduce the sensitivity to perturbation, we propose a simple and effective regularization method, referred to as spectral norm regularization, which penalizes the high spectral norm of weight matrices in neural networks.

We provide supportive evidence for the above-mentioned hypothesis by experimentally confirming that the models trained using spectral norm regularization exhibit better generalizability than other baseline methods.

--- https://arxiv.org/abs/1706.00082 Megapixel Size Image Creation using Generative Adversarial Networks Marco Marchesi 2017-05-31 2019-12-23 [("doi","10.48550/arXiv.1706.00082")] ai/nn/gan/stylegan

Since its appearance, Generative Adversarial Networks (GANs) have received a lot of interest in the AI community.

In image generation several projects showed how GANs are able to generate photorealistic images but the results so far did not look adequate for the quality standard of visual media production industry.

We present an optimized image generation process based on a Deep Convolutional Generative Adversarial Networks (DCGANs), in order to create photorealistic high-resolution images (up to 1,024×1,024 pixels). Furthermore, the system was fed with a limited dataset of images, less than two thousand images.

All these results give more clue about future exploitation of GANs in Computer Graphics and Visual Effects.

--- https://arxiv.org/abs/1706.00136 Scalable Generalized Linear Bandits: Online Computation and Hashing Kwang-Sung Jun, Aniruddha Bhargava, Robert Nowak, Rebecca Willett 2017-06-01 2019-12-23 [("doi","10.48550/arXiv.1706.00136")] reinforcement-learning/exploration

Generalized Linear Bandits (GLBs), a natural extension of the stochastic linear bandits, has been popular and successful in recent years. However, existing GLBs scale poorly with the number of rounds and the number of arms, limiting their utility in practice. This paper proposes new, scalable solutions to the GLB problem in two respects.

First, unlike existing GLBs, whose per-time-step space and time complexity grow at least linearly with time t, we propose a new algorithm that performs online computations to enjoy a constant space and time complexity. At its heart is a novel Generalized Linear extension of the Online-to-confidence-set Conversion (GLOC method) that takes any online learning algorithm and turns it into a GLB algorithm. As a special case, we apply GLOC to the online Newton step algorithm, which results in a low-regret GLB algorithm with much lower time and memory complexity than prior work. Second, for the case where the number N of arms is very large, we propose new algorithms in which each next arm is selected via an inner product search. Such methods can be implemented via hashing algorithms (ie. “hash-amenable”) and result in a time complexity sublinear in N. While a Thompson sampling extension of GLOC is hash-amenable, its regret bound for d-dimensional arm sets scales with d3⁄2, whereas GLOC’s regret bound scales with d. Towards closing this gap, we propose a new hash-amenable algorithm whose regret bound scales with d5⁄5. Finally, we propose a fast approximate hash-key computation (inner product) with a better accuracy than the state-of-the-art, which can be of independent interest.

We conclude the paper with preliminary experimental results confirming the merits of our methods.

--- https://arxiv.org/abs/1706.01399 Language Generation with Recurrent Generative Adversarial Networks without Pre-training Ofir Press, Amir Bar, Ben Bogin, Jonathan Berant, Lior Wolf 2017-06-05 2019-12-23 [("doi","10.48550/arXiv.1706.01399")] ai/nn/gan ai/nn/rnn ai/nn/sampling

Generative Adversarial Networks (GANs) have shown great promise recently in image generation.

Training GANs for language generation has proven to be more difficult, because of the non-differentiable nature of generating text with recurrent neural networks. Consequently, past work has either resorted to pre-training with maximum-likelihood or used convolutional networks for generation.

In this work, we show that recurrent neural networks can be trained to generate text with GANs from scratch using curriculum learning, by slowly teaching the model to generate sequences of increasing and variable length.

We empirically show that our approach vastly improves the quality of generated sequences compared to a convolutional baseline.

--- https://arxiv.org/abs/1706.03741#openai Deep reinforcement learning from human preferences Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei 2017-06-12 2019-12-23 [("doi","10.48550/arXiv.1706.03741")] reinforcement-learning/preference-learning reinforcement-learning/scaling statistics/order/comparison

For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems.

In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on less than one percent of our agent’s interactions with the environment.

This reduces the cost of human oversight far enough that it can be practically applied to state-of-the-art RL systems.

To demonstrate the flexibility of our approach, we show that we can successfully train complex novel behaviors with about an hour of human time. These behaviors and environments are considerably more complex than any that have been previously learned from human feedback.

--- https://arxiv.org/abs/1706.05098#deepmind An Overview of Multi-Task Learning in Deep Neural Networks Sebastian Ruder 2017-06-15 2019-12-23 [("doi","10.48550/arXiv.1706.05098")] reinforcement-learning

Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and speech recognition to computer vision and drug discovery.

This article aims to give a general overview of MTL, particularly in deep neural networks. It introduces the two most common methods for MTL in Deep Learning, gives an overview of the literature, and discusses recent advances.

In particular, it seeks to help ML practitioners apply MTL by shedding light on how MTL works and providing guidelines for choosing appropriate auxiliary tasks.

--- https://arxiv.org/abs/1706.06083 Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu 2017-06-19 2019-12-23 [("doi","10.48550/arXiv.1706.06083")] ai/nn/adversarial ai/nn/cnn ai/scaling

Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples—inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models.

To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary.

These methods let us train networks with substantially improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee.

We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models. Code and pre-trained models are available at https://github.com/MadryLab/mnist_challenge and https://github.com/MadryLab/cifar10_challenge.

Figure 4: The effect of network capacity on the performance of the network. We trained MNIST and CIFAR-10 networks of varying capacity on: (1) natural examples, (2) with FGSM-made adversarial examples, (3) with PGD-made adversarial examples. In the first 3 plots/tables of each dataset, we show how the standard and adversarial accuracy changes with respect to capacity for each training regime. In the final plot/table, we show the value of the cross-entropy loss on the adversarial examples the networks were trained on. This corresponds to the value of our saddle point formulation (2.1) for different sets of allowed perturbations.

…4. Network Capacity and adversarial robustness: Solving the problem from Equation (2.1) successfully is not sufficient to guarantee robust and accurate classification. We need to also argue that the value of the problem (ie. the final loss we achieve against adversarial examples) is small, thus providing guarantees for the performance of our classifier. In particular, achieving a very small value corresponds to a perfect classifier, which is robust to adversarial inputs.

For a fixed set S of possible perturbations, the value of the problem is entirely dependent on the architecture of the classifier we are learning. Consequently, the architectural capacity of the model becomes a major factor affecting its overall performance. At a high level, classifying examples in a robust way requires a stronger classifier, since the presence of adversarial examples changes the decision boundary of the problem to a more complicated one (see Figure 3 for an illustration).

Figure 3: A conceptual illustration of standard vs. adversarial decision boundaries. Left: A set of points that can be easily separated with a simple (in this case, linear) decision boundary. Middle: The simple decision boundary does not separate the 𝓁∞-balls (here, squares) around the data points. Hence there are adversarial examples (the red stars) that will be misclassified. Right: Separating the 𝓁∞-balls requires a substantially more complicated decision boundary. The resulting classifier is robust to adversarial examples with bounded 𝓁∞-norm perturbations.

Our experiments verify that capacity is crucial for robustness, as well as for the ability to successfully train against strong adversaries. For the MNIST dataset, we consider a simple convolutional network and study how its behavior changes against different adversaries as we keep doubling the size of network (ie. double the number of convolutional filters and the size of the fully-connected layer). The initial network has a convolutional layer with 2 filters, followed by another convolutional layer with 4 filters, and a fully connected hidden layer with 64 units. Convolutional layers are followed by 2 × 2 max-pooling layers and adversarial examples are constructed with 𝜀 = 0.3. The results are in Figure 4.

For the CIFAR-10 dataset, we used a resnet model.13 We performed data augmentation using random crops and flips, as well as per image standardization. To increase the capacity, we modified the network incorporating wider layers by a factor of 10. This results in a network with 5 residual units with (16, 160, 320, 640) filters each. This network can achieve an accuracy of 95.2% when trained with natural examples. Adversarial examples were constructed with 𝜀 = 8. Results on capacity experiments appear in Figure 4.

We observe the following phenomena:

--- https://arxiv.org/abs/1706.07068 CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, Marian Mazzone 2017-06-21 2019-12-24 [("doi","10.48550/arXiv.1706.07068")] ai/nn/gan psychology/novelty reinforcement-learning/exploration reinforcement-learning/multi-agent reinforcement-learning/safe

We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. We build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution. We argue that such networks are limited in their ability to generate creative products in their original design. We propose modifications to its objective to make it capable of generating creative art by maximizing deviation from established styles and minimizing deviation from art distribution.

We conducted experiments to compare the response of human subjects to the generated art with their response to art created by artists. The results show that human subjects could not distinguish art generated by the proposed system from art generated by contemporary artists and shown in top art fairs. Human subjects even rated the generated images higher on various scales.

--- https://arxiv.org/abs/1707.00014 The surprising implications of familial association in disease risk Morten Valberg, Mats Julius Stensrud, Odd O. Aalen 2017-06-14 2019-12-24 [("doi","10.1186/s12889-018-5033-5")] genetics/heritable/rare sociology statistics/causality

Background: A wide range of diseases show some degree of clustering in families; family history is therefore an important aspect for clinicians when making risk predictions. Familial aggregation is often quantified in terms of a familial relative risk (FRR), and although at first glance this measure may seem simple and intuitive as an average risk prediction, its implications are not straightforward.

Method: We use two statistical models for the distribution of disease risk in a population: a dichotomous risk model that gives an intuitive understanding of the implication of a given FRR, and a continuous risk model that facilitates a more detailed computation of the inequalities in disease risk. Published estimates of FRRs are used to produce Lorenz curves and Gini indices that quantifies the inequalities in risk for a range of diseases.

Results: We demonstrate that even a moderate familial association in disease risk implies a very large difference in risk between individuals in the population. We give examples of diseases for which this is likely to be true, and we further demonstrate the relationship between the point estimates of FRRs and the distribution of risk in the population.

Conclusions: The variation in risk for several severe diseases may be larger than the variation in income in many countries. The implications of familial risk estimates should be recognized by epidemiologists and clinicians.

--- https://arxiv.org/abs/1707.01083 ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun 2017-07-04 2019-12-24 [("doi","10.48550/arXiv.1707.01083")] ai/nn/sparsity

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (eg. 10–150 MFLOPs).

The new architectureuses two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy.

Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, eg. lower top-1 error (absolute 7.8%) than recent MobileNet on ImageNet classification task, under the computation budget of 40 MFLOPs.

On an ARM-based mobile device, ShuffleNet achieves 13× actual speedup over AlexNet while maintaining comparable accuracy.

--- https://arxiv.org/abs/1707.02038 A Tutorial on Thompson Sampling Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen 2017-07-07 2019-12-24 [("doi","10.48550/arXiv.1707.02038")] reinforcement-learning/exploration/active-learning statistics/bayes

Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may improve future performance. The algorithm addresses a broad range of problems in a computationally efficient manner and is therefore enjoying wide use.

This tutorial covers the algorithm and its application, illustrating concepts through a range of examples, including Bernoulli bandit problems, shortest path problems, product recommendation, assortment, active learning with neural networks, and reinforcement learning in Markov decision processes. Most of these problems involve complex information structures, where information revealed by taking an action informs beliefs about other actions.

We will also discuss when and why Thompson sampling is or is not effective and relations to alternative algorithms.

--- https://arxiv.org/abs/1707.03141 A Simple Neural Attentive Meta-Learner Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel 2017-07-11 2019-12-24 [("doi","10.48550/arXiv.1707.03141")] ai/nn/cnn ai/nn/transformer reinforcement-learning/meta-learning

Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. However, many recent meta-learning approaches are extensively hand-designed, either using architectures specialized to a particular application, or hard-coding algorithmic components that constrain how the meta-learner solves the task.

We propose a class of simple and generic meta-learner architectures that use a novel combination of temporal convolutions and soft attention; the former to aggregate information from past experience and the latter to pinpoint specific pieces of information. In the most extensive set of meta-learning experiments to date, we evaluate the resulting Simple Neural AttentIve Learner (or SNAIL) on several heavily-benchmarked tasks. On all tasks, in both supervised and reinforcement learning, SNAIL attains state-of-the-art performance by large margins.

--- https://arxiv.org/abs/1707.03300 The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously Serkan Cabi, Sergio Gómez Colmenarejo, Matthew W. Hoffman, Misha Denil, Ziyu Wang, Nando de Freitas 2017-07-11 2019-12-24 [("doi","10.48550/arXiv.1707.03300")] reinforcement-learning/exploration reinforcement-learning/robot

This paper introduces the Intentional Unintentional (IU) agent. This agent endows the deep deterministic policy gradients (DDPG) agent for continuous control with the ability to solve several tasks simultaneously. Learning to solve many tasks simultaneously has been a long-standing, core goal of artificial intelligence, inspired by infant development and motivated by the desire to build flexible robot manipulators capable of many diverse behaviors.

We show that the IU agent not only learns to solve many tasks simultaneously but it also learns faster than agents that target a single task at-a-time. In some cases, where the single task DDPG method completely fails, the IU agent successfully solves the task.

To demonstrate this, we build a playroom environment using the MuJoCo physics engine, and introduce a grounded formal language to automatically generate tasks.

--- https://arxiv.org/abs/1707.04175#deepmind Distral: Robust Multitask Reinforcement Learning Yee Whye Teh, Victor Bapst, Wojciech Marian Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu 2017-07-13 2019-12-24 [("doi","10.48550/arXiv.1707.04175")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/exploration

Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where efficiency may be improved through transfer across related tasks. In practice, however, this is not usually observed, because gradients from different tasks can interfere negatively, making learning unstable and sometimes even less data efficient. Another issue is the different reward schemes between tasks, which can easily lead to one task dominating the learning of a shared model.

We propose a new approach for joint training of multiple tasks, which we refer to as Distral (Distill & transfer learning). Instead of sharing parameters between the different workers, we propose to share a “distilled” policy that captures common behavior across tasks. Each worker is trained to solve its own task while constrained to stay close to the shared policy, while the shared policy is trained by distillation to be the centroid of all task policies. Both aspects of the learning process are derived by optimizing a joint objective function.

We show that our approach supports efficient transfer on complex 3D environments, outperforming several related methods. Moreover, the proposed learning process is more robust and more stable—attributes that are critical in deep reinforcement learning.

--- https://arxiv.org/abs/1707.06203#deepmind Imagination-Augmented Agents for Deep Reinforcement Learning Théophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adria Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, Demis Hassabis, David Silver, Daan Wierstra 2017-07-19 2019-12-24 [("doi","10.48550/arXiv.1707.06203")] reinforcement-learning/exploration

We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects.

In contrast to most existing model-based reinforcement learning and planning methods, which prescribe how a model should be used to arrive at a policy, I2As learn to interpret predictions from a learned environment model to construct implicit plans in arbitrary ways, by using the predictions as additional context in deep policy networks.

I2As show improved data efficiency, performance, and robustness to model misspecification compared to several baselines.

--- https://arxiv.org/abs/1707.06347#openai Proximal Policy Optimization Algorithms John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov 2017-07-20 2019-12-24 [("doi","10.48550/arXiv.1707.06347")] reinforcement-learning/model-free/oa5 reinforcement-learning/robot

We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a “surrogate” objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of minibatch updates.

The new methods, which we call proximal policy optimization (PPO), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically).

Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time.

--- https://arxiv.org/abs/1707.06643 Predicting Personality from Book Preferences with User-Generated Content Labels Ng Annalyn, Maarten W. Bos, Leonid Sigal, Boyang Li 2017-07-20 2019-12-24 [("doi","10.48550/arXiv.1707.06643")] anime psychology/personality

Psychological studies have shown that personality traits are associated with book preferences. However, past findings are based on questionnaires focusing on conventional book genres and are unrepresentative of niche content. For a more comprehensive measure of book content, this study harnesses a massive archive of content labels, also known as ‘tags’, created by users of an online book catalogue, Goodreads.com.

Combined with data on preferences and personality scores collected from Facebook users, the tag labels achieve high accuracy in personality prediction by psychological standards. We also group tags into broader genres, to check their validity against past findings.

Our results are robust across both tag and genre levels of analyses, and consistent with existing literature. Moreover, user-generated tag labels reveal unexpected insights, such as cultural differences, book reading behaviors, and other non-content factors affecting preferences.

To our knowledge, this is currently the largest study that explores the relationship between personality and book content preferences.

--- https://arxiv.org/abs/1708.00077 Bayesian Sparsification of Recurrent Neural Networks Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry Vetrov 2017-07-31 2019-12-24 [("doi","10.48550/arXiv.1708.00077")] ai/nn/rnn ai/nn/sparsity/pruning

Recurrent neural networks show state-of-the-art results in many text analysis tasks but often require a lot of memory to store their weights.

Recently proposed Sparse Variational Dropout eliminates the majority of the weights in a feed-forward neural network without loss of quality. We apply this technique to sparsify recurrent neural networks. To account for recurrent specifics we also rely on Binary Variational Dropout for RNN.

We report 99.5% sparsity level on sentiment analysis task without a quality drop and up to 87% sparsity level on language modeling task with slight loss of accuracy.

--- https://arxiv.org/abs/1708.00214 Natural Language Processing with Small Feed-Forward Networks Jan A. Botha, Emily Pitler, Ji Ma, Anton Bakalov, Alex Salcianu, David Weiss, Ryan McDonald, Slav Petrov 2017-08-01 2019-12-25 [("doi","10.48550/arXiv.1708.00214")] ai/nn/sparsity

We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models.

Motivated by resource-constrained environments like mobile phones, we showcase simple techniques for obtaining such small neural network models, and investigate different tradeoffs when deciding how to allocate a small memory budget.

--- https://arxiv.org/abs/1708.01465 Brain Responses During Robot-Error Observation Dominik Welke, Joos Behncke, Marina Hader, Robin Tibor Schirrmeister, Andreas Schönau, Boris Eßmann, Oliver Müller, Wolfram Burgard, Tonio Ball 2017-08-04 2019-12-25 [("doi","10.17185/duepublico/44533")] reinforcement-learning/imitation-learning/brain-imitation-learning reinforcement-learning/robot

Brain-controlled robots are a promising new type of assistive device for severely impaired persons. Little is however known about how to optimize the interaction of humans and brain-controlled robots. Information about the human’s perceived correctness of robot performance might provide a useful teaching signal for adaptive control algorithms and thus help enhancing robot control. Here, we studied whether watching robots perform erroneous vs. correct action elicits differential brain responses that can be decoded from single trials of electroencephalographic (EEG) recordings, and whether brain activity during human-robot interaction is modulated by the robot’s visual similarity to a human.

To address these topics, we designed two experiments. In experiment I, participants watched a robot arm pour liquid into a cup. The robot performed the action either erroneously or correctly, i.e. it either spilled some liquid or not. In experiment II, participants observed two different types of robots, humanoid and non-humanoid, grabbing a ball. The robots either managed to grab the ball or not. We recorded high-resolution EEG during the observation tasks in both experiments to train a Filter Bank Common Spatial Pattern (FBCSP) pipeline on the multivariate EEG signal and decode for the correctness of the observed action, and for the type of the observed robot.

Our findings show that it was possible to decode both correctness and robot type for the majority of participants, although often just slightly, above chance level. Our findings suggest that non-invasive recordings of brain responses elicited when observing robots indeed contain decodable information about the correctness of the robot’s action and the type of observed robot.

--- https://arxiv.org/abs/1708.02596 Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning Anusha Nagabandi, Gregory Kahn, Ronald S. Fearing, Sergey Levine 2017-08-08 2019-12-25 [("doi","10.48550/arXiv.1708.02596")] reinforcement-learning/model

Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to achieve good performance. Model-based algorithms, in principle, can provide for much more efficient learning, but have proven difficult to extend to expressive, high-capacity models such as deep neural networks. In this work, we demonstrate that medium-sized neural network models can in fact be combined with model predictive control (MPC) to achieve excellent sample complexity in a model-based reinforcement learning algorithm, producing stable and plausible gaits to accomplish various complex locomotion tasks.

We also propose using deep neural network dynamics models to initialize a model-free learner, in order to combine the sample efficiency of model-based approaches with the high task-specific performance of model-free methods. We empirically demonstrate on MuJoCo locomotion tasks that our pure model-based approach trained on just random action data can follow arbitrary trajectories with excellent sample efficiency, and that our hybrid algorithm can accelerate model-free learning on high-speed benchmark tasks, achieving sample efficiency gains of 3–5× on swimmer, cheetah, hopper, and ant agents.

Videos can be found at https://sites.google.com/view/mbmf.

--- https://arxiv.org/abs/1708.04202 Learning to Plan Chemical Syntheses Marwin H. S. Segler, Mike Preuss, Mark P. Waller 2017-08-14 2019-12-25 [("doi","10.1038/nature25978")] reinforcement-learning/model/alphago science

From medicines to materials, small organic molecules are indispensable for human well-being. To plan their syntheses, chemists employ a problem-solving technique called retrosynthesis. In retrosynthesis, target molecules are recursively transformed into increasingly simpler precursor compounds until a set of readily available starting materials is obtained. Computer-aided retrosynthesis would be a highly valuable tool, however, past approaches were slow and provided results of unsatisfactory quality.

Here, we employ Monte Carlo Tree Search (MCTS) to efficiently discover retrosynthetic routes. MCTS was combined with an expansion policy network that guides the search, and an “in-scope” filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on 12 million reactions, which represents essentially all reactions ever published in organic chemistry.

Our system solves almost twice as many molecules and is 30× faster in comparison to the traditional search method based on extracted rules and hand-coded heuristics. Finally, after a 60-year history of computer-aided synthesis planning, chemists can no longer distinguish between routes generated by a computer system and real routes taken from the scientific literature.

We anticipate that our method will accelerate drug and materials discovery by assisting chemists to plan better syntheses faster, and by enabling fully automated robot synthesis.

--- https://arxiv.org/abs/1708.04649 Machine Learning for Survival Analysis: A Survey Ping Wang, Yan Li, Chandan K. Reddy 2017-08-15 2019-12-25 [("doi","10.48550/arXiv.1708.04649")] statistics/survival-analysis

Accurately predicting the time of occurrence of an event of interest is a critical problem in longitudinal data analysis. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during the monitoring period. Such a phenomenon is called censoring which can be effectively handled using survival analysis techniques.

Traditionally, statistical approaches have been widely developed in the literature to overcome this censoring issue. In addition, many machine learning algorithms are adapted to effectively handle survival data and tackle other challenging problems that arise in real-world data. In this survey, we provide a comprehensive and structured review of the representative statistical methods along with the machine learning techniques used in survival analysis and provide a detailed taxonomy of the existing methods. We also discuss several topics that are closely related to survival analysis and illustrate several successful applications in various real-world application domains. We hope that this paper will provide a more thorough understanding of the recent advances in survival analysis and offer some guidelines on applying these approaches to solve new problems that arise in applications with censored data.

--- https://arxiv.org/abs/1708.05207 Learning Universal Adversarial Perturbations with Generative Models Jamie Hayes, George Danezis 2017-08-17 2019-12-25 [("doi","10.48550/arXiv.1708.05207")] ai/nn/adversarial ai/nn/gan

Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification.

It was recently shown that given a dataset and classifier, there exists so-called universal adversarial perturbations, a single perturbation that causes a misclassification when applied to any input. In this work, we introduce universal adversarial networks, a generative network that is capable of fooling a target classifier when its generated output is added to a clean sample from a dataset.

We show that this technique improves on known universal adversarial attacks.

--- https://arxiv.org/abs/1708.05509 Towards the Automatic Anime Characters Creation with Generative Adversarial Networks Yanghua Jin, Jiakai Zhang, Minjun Li, Yingtao Tian, Huachun Zhu, Zhihao Fang 2017-08-18 2019-12-25 [("doi","10.48550/arXiv.1708.05509")] ai/anime ai/nn/gan

Automatic generation of facial images has been well studied after the Generative Adversarial Network (GAN) came out. There exists some attempts applying the GAN model to the problem of generating facial images of anime characters, but none of the existing work gives a promising result.

In this work, we explore the training of GAN models specialized on an anime facial image dataset. We address the issue from both the data and the model aspect, by collecting a more clean, well-suited dataset and leverage proper, empirical application of DRAGAN.

With quantitative analysis and case studies we demonstrate that our efforts lead to a stable and high-quality model.

Moreover, to assist people with anime character design, we build a website (MakeGirls.moe) with our pre-trained model available online, which makes the model easily accessible to general public.

--- https://arxiv.org/abs/1708.07280 Learning Generalized Reactive Policies using Deep Neural Networks Edward Groshev, Maxwell Goldstein, Aviv Tamar, Siddharth Srivastava, Pieter Abbeel 2017-08-24 2019-12-25 [("doi","10.48550/arXiv.1708.07280")] reinforcement-learning/model/alphago

We present a new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem instances. We demonstrate that a deep neural network can be employed to learn and represent a generalized reactive policy (GRP) that maps a problem instance and a state to an action, and that the learned GRPs efficiently solve large classes of challenging problem instances. In contrast to prior efforts in this direction, our approach reduces the dependence of learning on handcrafted domain knowledge or feature selection.

Instead, the GRP is trained from scratch using a set of successful execution traces. This methodology enables the system to automatically learn a heuristic function that can beused in directed search algorithms, showcasing the versatility and potential of our learning approach in planning tasks.

We evaluate our approach using an extensive suite of experiments on two challenging planning problem domains and demonstrate that our approach facilitates learning complex decision-making policies and powerful heuristic functions with minimal human input.

Our approach holds significant promise for advancing the efficiency and capability of planning systems in diverse applications. By harnessing the power of deep learning and autonomous heuristic function development, we set a new standard for minimal human intervention in the development of planning algorithms.

Supplementary information, including code and videos of our results, is available at https://web.archive.org/web/20201030014217/https://sites.google.com/site/learn2plannips/.

--- https://arxiv.org/abs/1709.00103 Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Victor Zhong, Caiming Xiong, Richard Socher 2017-08-31 2019-12-25 [("doi","10.48550/arXiv.1709.00103")] ai/nn/retrieval ai/nn/transformer/gpt/codex reinforcement-learning/model-free wikipedia

A large amount of the world’s knowledge is stored in relational databases. However, the ability for users to retrieve facts from a database is limited due to a lack of understanding of SQL query languages such as SQL. We propose Seq2SQL, a deep neural network for translating natural language questions to corresponding SQL queries. Our model leverages the structure of SQL queries to reduce the output space of generated queries.

Moreover, we use rewards from in-the-loop query execution over the database to learn a policy to generate unordered parts of the query, which we show are less suitable for optimization via cross entropy loss. In addition, we will publish WikiSQL, a dataset of 80,654 hand-annotated examples of questions and SQL queries distributed across 24,241 tables from Wikipedia. This dataset is required to train our model and is an order of magnitude larger than comparable datasets.

By applying policy-based reinforcement learning with a query execution environment to WikiSQL, our model Seq2SQL outperforms attentional sequence to sequence models, improving execution accuracy 35.9% → 59.4% and logical form accuracy 23.4% → 48.3%.

--- https://arxiv.org/abs/1709.00401 Statistical Inference for Data-adaptive Doubly Robust Estimators with Survival Outcomes Iván Díaz 2017-09-01 2019-12-25 [("doi","10.48550/arXiv.1709.00401")] statistics/survival-analysis

The consistency of doubly robust estimators relies on consistent estimation of at least one of two nuisance regression parameters. In moderate to large dimensions, the use of flexible data-adaptive regression estimators may aid in achieving this consistency. However, n^1⁄2-consistency of doubly robust estimators is not guaranteed if one of the nuisance estimators is inconsistent.

In this paper we present a doubly robust estimator for survival analysis with the novel property that it converges to a Gaussian variable at n^1⁄2-rate for a large class of data-adaptive estimators of the nuisance parameters, under the only assumption that at least one of them is consistently estimated at a n^1⁄4-rate. This result is achieved through adaptation of recent ideas in semiparametric inference, which amount to: (1) Gaussianizing (ie. making asymptotically linear) a drift term that arises in the asymptotic analysis of the doubly robust estimator, and (2) using cross-fitting to avoid entropy conditions on the nuisance estimators.

We present the formula of the asymptotic variance of the estimator, which allows computation of doubly robust confidence intervals and p-values. We illustrate the finite-sample properties of the estimator in simulation studies, and demonstrate its use in a phase III clinical trial for estimating the effect of a novel therapy for the treatment of HER2 positive breast cancer.

--- https://arxiv.org/abs/1709.00440 PassGAN: A Deep Learning Approach for Password Guessing Bril, Hitaj, Paolo Gasti, Giuseppe Ateniese, Fernando Perez-Cruz 2017-09-01 2019-12-26 [("doi","10.48550/arXiv.1709.00440")] ai/nn/gan

State-of-the-art password guessing tools, such as Hashcat and John the Ripper, enable users to check billions of passwords per second against password hashes. In addition to performing straightforward dictionary attacks, these tools can expand password dictionaries using password generation rules, such as concatenation of words (eg. “password123456”) and leet speak (eg. “password” becomes “p4s5w0rd”). Although these rules work well in practice, expanding them to model further passwords is a laborious task that requires specialized expertise.

To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-generated password rules with theory-grounded machine learning algorithms. Instead of relying on manual password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real passwords from actual password leaks, and to generate high-quality password guesses.

Our experiments show that this approach is very promising. When we evaluated PassGAN on two large password datasets, we were able to surpass rule-based and state-of-the-art machine learning password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a priori knowledge of passwords or common password structures. Additionally, when we combined the output of PassGAN with the output of Hashcat, we were able to match 51%–73% more passwords than with Hashcat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of password properties that current state-of-the art rules do not encode.

--- https://arxiv.org/abs/1709.00513 Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks Zheng Xu, Yen-Chang Hsu, Jiawei Huang 2017-09-02 2019-12-26 [("doi","10.48550/arXiv.1709.00513")] ai/nn/gan ai/nn/sparsity/knowledge-distillation

There is an increasing interest in accelerating neural networks for real-time applications.

We study the student-teacher strategy, in which a small and fast student network is trained with the auxiliary information learned from a large and accurate teacher network. We propose to use conditional adversarial networks to learn the loss function to transfer knowledge from teacher to student.

The proposed method is particularly effective for relatively small student networks. Moreover, experimental results show the effect of network size when the modern networks are used as students.

We empirically study the trade-off between inference time and classification accuracy and provide suggestions on choosing a proper student network.

--- https://arxiv.org/abs/1709.04574 Towards personalized human AI interaction—adapting the behavior of AI agents using neural signatures of subjective interest Victor Shih, David C. Jangraw, Paul Sajda, Sameer Saproo 2017-09-14 2019-12-26 [("doi","10.48550/arXiv.1709.04574")] reinforcement-learning/imitation-learning/brain-imitation-learning reinforcement-learning/preference-learning

Reinforcement Learning AI commonly uses reward/penalty signals that are objective and explicit in an environment—eg. game score, completion time, etc.—in order to learn the optimal strategy for task performance. However, Human-AI interaction for such AI agents should include additional reinforcement that is implicit and subjective—eg. human preferences for certain AI behavior—in order to adapt the AI behavior to idiosyncratic human preferences. Such adaptations would mirror naturally occurring processes that increase trust and comfort during social interactions.

Here, we show how a hybrid brain-computer-interface (hBCI), which detects an individual’s level of interest in objects/events in a virtual environment, can be used to adapt the behavior of a Deep Reinforcement Learning AI agent that is controlling a virtual autonomous vehicle. Specifically, we show that the AI learns a driving strategy that maintains a safe distance from a lead vehicle, and most novel, preferentially slows the vehicle when the human passengers of the vehicle encounter objects of interest. This adaptation affords an additional 20% viewing time for subjectively interesting objects.

This is the first demonstration of how an hBCI can be used to provide implicit reinforcement to an AI agent in a way that incorporates user preferences into the control system.

--- https://arxiv.org/abs/1709.04905 One-Shot Visual Imitation Learning via Meta-Learning Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, Sergey Levine 2017-09-14 2019-12-26 [("doi","10.48550/arXiv.1709.04905")] reinforcement-learning/imitation-learning reinforcement-learning/meta-learning reinforcement-learning/robot

In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to acquire a wide variety of skills quickly and efficiently in complex unstructured environments. High-capacity models such as deep neural networks can enable a robot to represent complex skills, but learning each skill from scratch then becomes infeasible.

In this work, we present a meta-imitation learning method that enables a robot to learn how to learn more efficiently, allowing it to acquire new skills from just a single demonstration. Unlike prior methods for one-shot imitation, our method can scale to raw pixel inputs and requires data from fewer prior tasks for effective learning of new skills.

Our experiments on both simulated and real robot platforms demonstrate the ability to learn new tasks, end-to-end, from a single visual demonstration.

--- https://arxiv.org/abs/1709.05424#google NIMA: Neural Image Assessment Hossein Talebi, Peyman Milanfar 2017-09-15 2019-12-26 [("doi","10.1109/TIP.2018.2831899")] ai/nn/cnn design/visualization reinforcement-learning/preference-learning

Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications such as evaluating image capture pipelines, storage techniques and sharing media. Despite the subjective nature of this problem, most existing methods only predict the mean opinion score provided by datasets such as AVA and TID2013.

Our approach differs from others in that we predict the distribution of human opinion scores using a convolutional neural network. Our architecture also has the advantage of being substantially simpler than other methods with comparable performance. Our proposed approach relies on the success (and retraining) of proven, state-of-the-art deep object recognition networks.

Our resulting network can be used to not only score images reliably and with high correlation to human perception, but also to assist with adaptation and optimization of photo editing/enhancement algorithms in a photographic pipeline.

All this is done without need for a “golden” reference image, consequently allowing for single-image, semantic-aware and perceptually-aware, no-reference quality assessment.

--- https://arxiv.org/abs/1709.06030 N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, Kris M. Kitani 2017-09-18 2019-12-26 [("doi","10.48550/arXiv.1709.06030")] ai/nn/rnn ai/nn/sparsity/knowledge-distillation

While bigger and deeper neural network architectures continue to advance the state-of-the-art for many computer vision tasks, real-world adoption of these networks is impeded by hardware and speed constraints. Conventional model compression methods attempt to address this problem by modifying the architecture manually or using pre-defined heuristics. Since the space of all reduced architectures is very large, modifying the architecture of a deep neural network in this way is a difficult task.

In this paper, we tackle this issue by introducing a principled method for learning reduced network architectures in a data-driven way using reinforcement learning. Our approach takes a larger ‘teacher’ network as input and outputs a compressed ‘student’ network derived from the ‘teacher’ network. In the first stage of our method, a recurrent policy network aggressively removes layers from the large ‘teacher’ model. In the second stage, another recurrent policy network carefully reduces the size of each remaining layer. The resulting network is then evaluated to obtain a reward—a score based on the accuracy and compression of the network. Our approach uses this reward signal with policy gradients to train the policies to find a locally optimal student network.

Our experiments show that we can achieve compression rates of more than 10× for models such as ResNet-34 while maintaining similar performance to the input ‘teacher’ network. We also present a valuable transfer learning result which shows that policies which are pre-trained on smaller ‘teacher’ networks can be used to rapidly speed up training on larger ‘teacher’ networks.

--- https://arxiv.org/abs/1709.06683 OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup 2017-09-20 2019-12-26 [("doi","10.48550/arXiv.1709.06683")] ai/nn/gan reinforcement-learning/model-free statistics/order/comparison

Reinforcement learning has shown promise in learning policies that can solve complex problems. However, manually specifying a good reward function can be difficult, especially for intricate tasks. Inverse reinforcement learning offers a useful paradigm to learn the underlying reward function directly from expert demonstrations. Yet in reality, the corpus of demonstrations may contain trajectories arising from a diverse set of underlying reward functions rather than a single one. Thus, in inverse reinforcement learning, it is useful to consider such a decomposition.

The options framework in reinforcement learning is specifically designed to decompose policies in a similar light. We therefore extend the options framework and propose a method to simultaneously recover reward options in addition to policy options. We leverage adversarial methods to learn joint reward-policy options using only observed expert states.

We show that this approach works well in both simple and complex continuous control tasks and shows performance increases in one-shot transfer learning.

--- https://arxiv.org/abs/1709.06709 Online Learning of a Memory for Learning Rates Franziska Meier, Daniel Kappler, Stefan Schaal 2017-09-20 2019-12-26 [("doi","10.48550/arXiv.1709.06709")] ai/nn/rnn reinforcement-learning/meta-learning

The promise of learning to learn for robotics rests on the hope that by extracting some information about the learning process itself, we can speed up subsequent similar learning tasks. Here, we introduce a computationally efficient online meta-learning algorithm that builds and optimizes a memory model of the optimal learning rate landscape from previously observed gradient behaviors.

While performing task specific optimization, this memory of learning rates predicts how to scale currently observed gradients. After applying the gradient scaling, our meta-learner updates its internal memory based on the observed effect its prediction had. Our meta-learner can be combined with any gradient-based optimizer, learns on the fly, and can be transferred to new optimization tasks.

In our evaluations, we show that our meta-learning algorithm speeds up learning of MNIST classification and a variety of learning control tasks, either in batch or online learning settings.

--- https://arxiv.org/abs/1709.10163 Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces Garrett Warnell, Nicholas Waytowich, Vernon Lawhern, Peter Stone 2017-09-28 2019-12-26 [("doi","10.48550/arXiv.1709.10163")] reinforcement-learning/preference-learning

While recent advances in deep reinforcement learning have allowed autonomous learning agents to succeed at a variety of complex tasks, existing algorithms generally require a lot of training data. One way to increase the speed at which agents are able to learn to perform tasks is by leveraging the input of human trainers. Although such input can take many forms, real-time, scalar-valued feedback is especially useful in situations where it proves difficult or impossible for humans to provide expert demonstrations.

Previous approaches have shown the usefulness of human input provided in this fashion (eg. the TAMER framework), but they have thus far not considered high-dimensional state spaces or employed the use of deep learning. In this paper, we do both: we propose Deep TAMER, an extension of the TAMER framework that leverages the representational power of deep neural networks in order to learn complex tasks in just a short amount of time with a human trainer.

We demonstrate Deep TAMER’s success by using it and just 15 minutes of human-provided feedback to train an agent that performs better than humans on the Atari game of Bowling—a task that has proven difficult for even state-of-the-art reinforcement learning methods.

--- https://arxiv.org/abs/1710.01878 To prune, or not to prune: exploring the efficacy of pruning for model compression Michael Zhu, Suyog Gupta 2017-10-05 2019-12-26 [("doi","10.48550/arXiv.1710.01878")] ai/nn/rnn ai/nn/sparsity/pruning

Model pruning seeks to induce sparsity in a deep neural network’s various connection matrices, thereby reducing the number of nonzero-valued parameters in the model. Recent reports (Han et al 2015; Narang et al 2017) prune deep networks at the cost of only a marginal loss in accuracy and achieve a sizable reduction in model size. This hints at the possibility that the baseline models in these experiments are perhaps severely over-parameterized at the outset and a viable alternative for model compression might be to simply reduce the number of hidden units while maintaining the model’s dense connection structure, exposing a similar trade-off in model size and accuracy.

We investigate these two distinct paths for model compression within the context of energy-efficient inference in resource-constrained environments and propose a new gradual pruning technique that is simple and straightforward to apply across a variety of models/datasets with minimal tuning and can be seamlessly incorporated within the training process.

We compare the accuracy of large, but pruned models (large-sparse) and their smaller, but dense (small-dense) counterparts with identical memory footprint. Across a broad range of neural network architectures (deep CNNs, stacked LSTM, and seq2seq LSTM models), we find large-sparse models to consistently outperform small-dense models and achieve up to 10× reduction in number of non-zero parameters with minimal loss in accuracy.

--- https://arxiv.org/abs/1710.02264 Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles África Periáñez, Alain Saas, Anna Guitart, Colin Magne 2017-10-06 2019-12-26 [("doi","10.1109/DSAA.2016.84")] statistics/survival-analysis

Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn.

Survival analysis focuses on predicting the time of occurrence of a certain event, churn in our case. Classical methods, like regressions, could be applied only when all players have left the game. The challenge arises for datasets with incomplete churning information for all players, as most of them still connect to the game. This is called a censored data problem and is in the nature of churn. Censoring is commonly dealt with survival analysis techniques, but due to the inflexibility of the survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results.

In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn. For each player, we predict the probability of churning as function of time, which permits to distinguish various levels of loyalty profiles. Additionally, we assess the risk factors that explain the predicted player survival times. Our results show that churn prediction by survival ensembles improves the accuracy and robustness of traditional analyses, like Cox regression.

--- https://arxiv.org/abs/1710.02869 An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits Isaac J. Sledge, Jose C. Principe 2017-10-08 2019-12-27 [("doi","10.3390/e20030155")] reinforcement-learning/exploration

In this paper, we propose an information-theoretic exploration strategy for stochastic, discrete multi-armed bandits that achieves optimal regret.

Our strategy is based on the value of information criterion. This criterion measures the trade-off between policy information and obtainable rewards. High amounts of policy information are associated with exploration-dominant searches of the space and yield high rewards. Low amounts of policy information favor the exploitation of existing knowledge. Information, in this criterion, is quantified by a parameter that can be varied during search.

We demonstrate that a simulated-annealing-like update of this parameter, with a sufficiently fast cooling schedule, leads to an optimal regret that is logarithmic with respect to the number of episodes.

--- https://arxiv.org/abs/1710.03748#openai Emergent Complexity via Multi-Agent Competition Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, Igor Mordatch 2017-10-10 2019-12-27 [("doi","10.48550/arXiv.1710.03748")] reinforcement-learning/exploration reinforcement-learning/model-free/oa5 reinforcement-learning/multi-agent

Reinforcement learning algorithms can train agents that solve problems in complex, interesting environments. Normally, the complexity of the trained agent is closely related to the complexity of the environment. This suggests that a highly capable agent requires a complex environment for training.

In this paper, we point out that a competitive multi-agent environment trained with self-play can produce behaviors that are far more complex than the environment itself. We also point out that such environments come with a natural curriculum, because for any skill level, an environment full of agents of this level will have the right level of difficulty.

This work introduces several competitive multi-agent environments where agents compete in a 3D world with simulated physics. The trained agents learn a wide variety of complex and interesting skills, even though the environment themselves are relatively simple. The skills include behaviors such as running, blocking, ducking, tackling, fooling opponents, kicking, and defending using both arms and legs. A highlight of the learned behaviors can be found here: https://sites.google.com/view/multi-agent-competition.

--- https://arxiv.org/abs/1710.07739 Learning Discrete Weights Using the Local Reparameterization Trick Oran Shayer, Dan Levi, Ethan Fetaya 2017-10-21 2019-12-27 [("doi","10.48550/arXiv.1710.07739")] ai/nn/sparsity/low-precision

Recent breakthroughs in computer vision make use of large deep neural networks, utilizing the substantial speedup offered by GPUs. For applications running on limited hardware, however, high precision real-time processing can still be a challenge. One approach to solving this problem is training networks with binary or ternary weights, thus removing the need to calculate multiplications and reducing memory size.

In this work, we introduce LR-nets (Local reparameterization networks), a new method for training neural networks with discrete weights using stochastic parameters. We show how a simple modification to the local reparameterization trick, previously used to train Gaussian distributed weights, enables the training of discrete weights.

Using the proposed training we test both binary and ternary models on MNIST, CIFAR-10 and ImageNet benchmarks and reach state-of-the-art results on most experiments.

--- https://arxiv.org/abs/1710.10196#nvidia Progressive Growing of GANs for Improved Quality, Stability, and Variation Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen 2017-10-27 2019-12-27 [("doi","10.48550/arXiv.1710.10196")] ai/anime ai/dataset ai/nn/gan/stylegan/progan

We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses.

This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, eg. CelebA images at 10242. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR-10.

Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation.

As an additional contribution, we construct a higher-quality version of the CelebA dataset.

--- https://arxiv.org/abs/1710.10742 Implicit Causal Models for Genome-wide Association Studies Dustin Tran, David M. Blei 2017-10-30 2019-12-27 [("doi","10.48550/arXiv.1710.10742")] genetics/heritable statistics/bayes statistics/causality

Progress in probabilistic generative models has accelerated, developing richer models with neural architectures, implicit densities, and with scalable algorithms for their Bayesian inference. However, there has been limited progress in models that capture causal relationships, for example, how individual genetic factors cause major human diseases.

In this work, we focus on two challenges in particular:

How do we build richer causal models, which can capture highly nonlinear relationships and interactions between multiple causes?

How do we adjust for latent confounders, which are variables influencing both cause and effect and which prevent learning of causal relationships?

To address these challenges, we synthesize ideas from causality and modern probabilistic modeling.

For the first, we describe implicit causal models, a class of causal models that leverages neural architectures with an implicit density.

For the second, we describe an implicit causal model that adjusts for confounders by sharing strength across examples.

In experiments, we scale Bayesian inference on up to a billion genetic measurements. We achieve state-of-the-art accuracy for identifying causal factors: we outperform existing genetics methods by an absolute difference of 15–45.3%.

--- https://arxiv.org/abs/1710.10916 StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, Dimitris Metaxas 2017-10-19 2019-12-27 [("doi","10.48550/arXiv.1710.10916")] ai/nn/gan/stylegan/progan

Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images.

First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of the object based on given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details.

Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and discriminators in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree. StackGAN-v2 shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions.

Extensive experiments demonstrate that the proposed stacked generative adversarial networks outperform other state-of-the-art methods in generating photo-realistic images.

--- https://arxiv.org/abs/1710.11417 TreeQN & ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning Gregory Farquhar, Tim Rocktäschel, Maximilian Igl, Shimon Whiteson 2017-10-31 2019-12-27 [("doi","10.48550/arXiv.1710.11417")] reinforcement-learning/model/muzero

Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their utility for planning.

To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions. TreeQN dynamically constructs a tree by recursively applying a transition model in a learned abstract state space and then aggregating predicted rewards and state-values using a tree backup to estimate Q-values. We also propose ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network. Both approaches are trained end-to-end, such that the learned model is optimized for its actual use in the tree.

We show that TreeQN and ATreeC outperform n-step DQN and A2C on a box-pushing task, as well as n-step DQN and value prediction networks (Oh et al 2017) on multiple Atari games.

Furthermore, we present ablation studies that demonstrate the effect of different auxiliary losses on learning transition models.

--- https://arxiv.org/abs/1711.00937#deepmind VQ-VAE: Neural Discrete Representation Learning Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu 2017-11-02 2019-12-27 [("doi","10.48550/arXiv.1711.00937")] ai/nn/transformer/gpt/dall-e/1 ai/nn/vae

Learning useful representations without supervision remains a key challenge in machine learning.

In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantized-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Using the VQ method allows the model to circumvent issues of “posterior collapse”—where the latents are ignored when they are paired with a powerful autoregressive decoder—typically observed in the VAE framework.

Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations.

--- https://arxiv.org/abs/1711.01068 Compressing Word Embeddings via Deep Compositional Code Learning Raphael Shu, Hideki Nakayama 2017-11-03 2019-12-27 [("doi","10.48550/arXiv.1711.01068")] ai/nn/sparsity/low-precision

Natural language processing (NLP) models often require a massive number of parameters for word embeddings, resulting in a large storage or memory footprint. Deploying neural NLP models to mobile devices requires compressing the word embeddings without any sacrifices in performance.

For this purpose, we propose to construct the embeddings with few basis vectors. For each word, the composition of basis vectors is determined by a hash code. To maximize the compression rate, we adopt the multi-codebook quantization approach instead of binary coding scheme. Each code is composed of multiple discrete numbers, such as (3, 2, 1, 8), where the value of each component is limited to a fixed range. We propose to directly learn the discrete codes in an end-to-end neural network by applying the Gumbel-softmax trick.

Experiments show the compression rate achieves 98% in a sentiment analysis task and 94% 99% in machine translation tasks without performance loss. In both tasks, the proposed method can improve the model performance by slightly lowering the compression rate.

Compared to other approaches such as character-level segmentation, the proposed method is language-independent and does not require modifications to the network architecture.

--- https://arxiv.org/abs/1711.01991 Mitigating Adversarial Effects Through Randomization Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, Alan Yuille 2017-11-06 2019-12-27 [("doi","10.48550/arXiv.1711.01991")] ai/nn/adversarial

Convolutional neural networks have demonstrated high accuracy on various tasks in recent years. However, they are extremely vulnerable to adversarial examples. For example, imperceptible perturbations added to clean images can cause convolutional neural networks to fail.

In this paper, we propose to utilize randomization at inference time to mitigate adversarial effects. Specifically, we use two randomization operations: random resizing, which resizes the input images to a random size, and random padding, which pads zeros around the input images in a random manner. Extensive experiments demonstrate that the proposed randomization method is very effective at defending against both single-step and iterative attacks. Our method provides the following advantages: (1) no additional training or fine-tuning, (2) very few additional computations, (3) compatible with other adversarial defense methods.

By combining the proposed randomization method with an adversarially trained model, it achieves a normalized score of 0.924 (ranked No.2 among 107 defense teams) in the NIPS 2017 adversarial examples defense challenge, which is far better than using adversarial training alone with a normalized score of 0.773 (ranked No.56).

The code is public available at Github.

--- https://arxiv.org/abs/1711.02017 NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm Xiaoliang Dai, Hongxu Yin, Niraj K. Jha 2017-11-06 2019-12-27 [("doi","10.48550/arXiv.1711.02017")] ai/nn/sparsity/pruning

Deep neural networks (DNNs) have begun to have a pervasive impact on various applications of machine learning. However, the problem of finding an optimal DNN architecture for large applications is challenging. Common approaches go for deeper and larger DNN architectures but may incur substantial redundancy. To address these problems, we introduce a network growth algorithm that complements network pruning to learn both weights and compact DNN architectures during training.

We propose a DNN synthesis tool (NeST) that combines both methods to automate the generation of compact and accurate DNNs. NeST starts with a randomly initialized sparse network called the seed architecture. It iteratively tunes the architecture with gradient-based growth and magnitude-based pruning of neurons and connections.

Our experimental results show that NeST yields accurate, yet very compact DNNs, with a wide range of seed architecture selection. For the LeNet-300–100 (LeNet-5) architecture, we reduce network parameters by 70.2× (74.3×) and floating-point operations (FLOPs) by 79.4× (43.7×). For the AlexNet and VGG-16 architectures, we reduce network parameters (FLOPs) by 15.7× (4.6×) and 30.2× (8.6×), respectively. NeST’s grow-and-prune paradigm delivers additional parameter and FLOPs reduction relative to pruning-only methods.

--- https://arxiv.org/abs/1711.02846#google Intriguing Properties of Adversarial Examples Ekin D. Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le 2017-11-08 2019-12-28 [("doi","10.48550/arXiv.1711.02846")] ai/nn/adversarial

It is becoming increasingly clear that many machine learning classifiers are vulnerable to adversarial examples. In attempting to explain the origin of adversarial examples, previous studies have typically focused on the fact that neural networks operate on high dimensional data, they overfit, or they are too linear.

Here we argue that the origin of adversarial examples is primarily due to an inherent uncertainty that neural networks have about their predictions. We show that the functional form of this uncertainty is independent of architecture, dataset, and training protocol; and depends only on the statistics of the logit differences of the network, which do not change during training. This leads to adversarial error having an universal scaling, as a power-law, with respect to the size of the adversarial perturbation.

We show that this universality holds for a broad range of datasets (MNIST, CIFAR-10, ImageNet, and random data), models (including state-of-the-art deep networks, linear models, adversarially trained networks, and networks trained on randomly shuffled labels), and attacks (FGSM, step l.l., PGD).

Motivated by these results, we study the effects of reducing prediction entropy on adversarial robustness. Finally, we study the effect of network architectures on adversarial sensitivity. To do this, we use neural architecture search with reinforcement learning to find adversarially robust architectures on CIFAR-10. Our resulting architecture is more robust to white and black box attacks compared to previous attempts.

--- https://arxiv.org/abs/1711.04731 Evaluating prose style transfer with the Bible Keith Carlson, Allen Riddell, Daniel Rockmore 2017-11-13 2019-12-28 [("doi","10.1098/rsos.171920")] ai/nn/rnn ai/nn/transformer/gpt/non-fiction ai/text-style-transfer

In the prose style transfer task a system, provided with text input and a target prose style, produces output which preserves the meaning of the input text but alters the style. These systems require parallel data for evaluation of results and usually make use of parallel data for training. Currently, there are few publicly available corpora for this task.

In this work, we identify a high-quality source of aligned, stylistically distinct text in different versions of the Bible. We provide a standardized split, into training, development and testing data, of the public domain versions in our corpus. This corpus is highly parallel since many Bible versions are included. Sentences are aligned due to the presence of chapter and verse numbers within all versions of the text.

In addition to the corpus, we present the results, as measured by the BLEU and PINC metrics, of several models trained on our data which can serve as baselines for future research.

While we present these data as a style transfer corpus, we believe that it is of unmatched quality and may be useful for other natural language tasks as well.

--- https://arxiv.org/abs/1711.05139#google XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings Amélie Royer, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar Mosseri, Forrester Cole, Kevin Murphy 2017-11-14 2019-12-28 [("doi","10.48550/arXiv.1711.05139")] ai/nn/gan ai/nn/vae

Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter. Here we tackle the more generic problem of semantic style transfer: given two unpaired collections of images, we aim to learn a mapping between the corpus-level style of each collection, while preserving semantic content shared across the two domains.

We introduce XGAN (“Cross-GAN”), a dual adversarial autoencoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the domain-to-domain image translations in both directions. We exploit ideas from the domain adaptation literature and define a semantic consistency loss which encourages the model to preserve semantics in the learned embedding space.

We report promising qualitative results for the task of face-to-cartoon translation.

The cartoon dataset, CartoonSet, we collected for this purpose is publicly available at google.github.io/cartoonset/ as a new benchmark for semantic style transfer.

--- https://arxiv.org/abs/1711.06068 The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks Joos Behncke, Robin Tibor Schirrmeister, Wolfram Burgard, Tonio Ball 2017-11-16 2019-12-28 [("doi","10.1109/IWW-BCI.2018.8311531")] ai/nn/cnn reinforcement-learning/imitation-learning/brain-imitation-learning reinforcement-learning/robot

The importance of robotic assistive devices grows in our work and everyday life. Cooperative scenarios involving both robots and humans require safe human-robot interaction. One important aspect here is the management of robot errors, including fast and accurate online robot-error detection and correction. Analysis of brain signals from a human interacting with a robot may help identifying robot errors, but accuracies of such analyses have still substantial space for improvement.

In this paper we evaluate whether a novel framework based on deep convolutional neural networks (deep ConvNets) could improve the accuracy of decoding robot errors from the EEG of a human observer, both during an object grasping and a pouring task. We show that deep ConvNets reached higher accuracies than both regularized Linear Discriminant Analysis (rLDA) and filter bank common spatial patterns (FB-CSP) combined with rLDA, both widely used EEG classifiers.

Deep ConvNets reached mean accuracies of 75% ± 9%, rLDA 65% ± 10% and FB-CSP + rLDA 63% ± 6% for decoding of erroneous vs. correct trials. Visualization of the time-domain EEG features learned by the ConvNets to decode errors revealed spatiotemporal patterns that reflected differences between the two experimental paradigms. Across subjects, Convnet decoding accuracies were statistically-significantly correlated with those obtained with rLDA, but not CSP, indicating that in the present context ConvNets behaved more ‘rLDA-like’ (but consistently better), while in a previous decoding study with another task but the same Convnet architecture, it was found to behave more ‘CSP-like’.

Our findings thus provide further support for the assumption that deep ConvNets are a versatile addition to the existing toolbox of EEG decoding techniques, and we discuss steps how Convnet EEG decoding performance could be further optimized.

--- https://arxiv.org/abs/1711.06406 Predicting Driver Attention in Critical Situations Ye Xia, Danqing Zhang, Jinkyu Kim, Ken Nakayama, Karl Zipser, David Whitney 2017-11-17 2019-12-28 [("doi","10.48550/arXiv.1711.06406")] reinforcement-learning/imitation-learning/brain-imitation-learning

Robust driver attention prediction for critical situations is a challenging computer vision problem, yet essential for autonomous driving. Because critical driving moments are so rare, collecting enough data for these situations is difficult with the conventional in-car data collection protocol—tracking eye movements during driving.

Here, we first propose a new in-lab driver attention collection protocol and introduce a new driver attention dataset, Berkeley DeepDrive Attention (BDD-A) dataset, which is built upon braking event videos selected from a large-scale, crowd-sourced driving video dataset. We further propose Human Weighted Sampling (HWS) method, which uses human gaze behavior to identify crucial frames of a driving dataset and weights them heavily during model training.

With our dataset and HWS, we built a driver attention prediction model that outperforms the state-of-the-art and demonstrates sophisticated behaviors, like attending to crossing pedestrians but not giving false alarms to pedestrians safely walking on the sidewalk. Its prediction results are nearly indistinguishable from ground-truth to humans.

Although only being trained with our in-lab attention data, the model also predicts in-car driver attention data of routine driving with state-of-the-art accuracy. This result not only demonstrates the performance of our model but also proves the validity and usefulness of our dataset and data collection protocol.

--- https://arxiv.org/abs/1711.06445 xUnit: Learning a Spatial Activation Function for Efficient Image Restoration Idan Kligvasser, Tamar Rott Shaham, Tomer Michaeli 2017-11-17 2019-12-28 [("doi","10.48550/arXiv.1711.06445")] ai/nn/sparsity

In recent years, deep neural networks (DNNs) achieved unprecedented performance in many low-level vision tasks. However, state-of-the-art results are typically achieved by very deep networks, which can reach tens of layers with tens of millions of parameters. To make DNNs implementable on platforms with limited resources, it is necessary to weaken the tradeoff between performance and efficiency.

In this paper, we propose a new activation unit, which is particularly suitable for image restoration problems. In contrast to the widespread per-pixel activation units, like ReLUs and sigmoids, our unit implements a learnable nonlinear function with spatial connections. This enables the net to capture much more complex features, thus requiring a smaller number of layers in order to reach the same performance.

We illustrate the effectiveness of our units through experiments with state-of-the-art nets for denoising, de-raining, and super resolution, which are already considered to be very small. With our approach, we are able to further reduce these models by nearly 50% without incurring any degradation in performance.

--- https://arxiv.org/abs/1711.06528 Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method Xu Sun, Xuancheng Ren, Shuming Ma, Bingzhen Wei, Wei Li, Jingjing Xu, Houfeng Wang, Yi Zhang 2017-11-17 2019-12-28 [("doi","10.1109/TKDE.2018.2883613")] ai/nn/sparsity/low-precision ai/nn/sparsity/pruning

We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top-k elements (in terms of magnitude) are kept. As a result, only k rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction in the computational cost.

Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications.

Surprisingly, experimental results demonstrate that most of the time we only need to update fewer than 5% of the weights at each back propagation pass. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given.

The model simplification results show that we could adaptively simplify the model which could often be reduced by around 9×, without any loss on accuracy or even with improved accuracy.

The codes, including the extension, are available at https://github.com/lancopku/meSimp.

--- https://arxiv.org/abs/1711.06861 Style Transfer in Text: Exploration and Evaluation Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, Rui Yan 2017-11-18 2019-12-28 [("doi","10.48550/arXiv.1711.06861")] ai/nn/gan ai/text-style-transfer

Style transfer is an important problem in natural language processing (NLP). However, the progress in language style transfer is lagged behind other domains, such as computer vision, mainly because of the lack of parallel data and principle evaluation metrics. In this paper, we propose to learn style transfer with non-parallel data.

We explore two models to achieve this goal, and the key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks.

Results show that the proposed content preservation metric is highly correlated to human judgments, and the proposed models are able to generate sentences with higher style transfer strength and similar content preservation score comparing to autoencoder.

We also propose novel evaluation metrics which measure two aspects of style transfer: transfer strength and content preservation. We assess our models and the evaluation metrics on two tasks: paper-news title transfer, and positive-negative review transfer.

--- https://arxiv.org/abs/1711.07607#google Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN Jiyang Gao, Zijian, Guo, Zhen Li, Ram Nevatia 2017-11-21 2019-12-28 [("doi","10.48550/arXiv.1711.07607")] ai/nn/cnn ai/nn/sparsity/knowledge-distillation ai/scaling

Fine-grained image labels are desirable for many computer vision applications, such as visual search or mobile AI assistant. These applications rely on image classification models that can produce hundreds of thousands (eg. 100K) of diversified fine-grained image labels on input images. However, training a network at this vocabulary scale is challenging, and suffers from intolerable large model size and slow training speed, which leads to unsatisfying classification performance. A straightforward solution would be training separate expert networks (specialists), with each specialist focusing on learning one specific vertical (eg. cars, birds…). However, deploying dozens of expert networks in a practical system would increase system complexity and inference latency, and consumes large amounts of computational resources.

To address these challenges, we propose a Knowledge Concentration method, which effectively transfers the knowledge from dozens of specialists (multiple teacher networks) into one single model (one student network) to classify 100K object categories. There are 3 salient aspects in our method: (1) a multi-teacher single-student knowledge distillation framework; (2) a self-paced learning mechanism to allow the student to learn from different teachers at various paces; (3) structurally connected layers to expand the student network capacity with limited extra parameters.

We validate our method on OpenImage and a newly collected dataset, Entity-Foto-Tree (EFT), with 100K categories, and show that the proposed model performs better than the baseline generalist model.

--- https://arxiv.org/abs/1711.08141 Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions Bichen Wu, Alvin Wan, Xiangyu Yue, Peter Jin, Sicheng Zhao, Noah Golmant, Amir Gholaminejad, Joseph Gonzalez, Kurt Keutzer 2017-11-22 2019-12-28 [("doi","10.48550/arXiv.1711.08141")] ai/nn/sparsity/low-precision

Neural networks rely on convolutions to aggregate spatial information. However, spatial convolutions are expensive in terms of model size and computation, both of which grow quadratically with respect to kernel size. In this paper, we present a parameter-free, FLOP-free “shift” operation as an alternative to spatial convolutions. We fuse shifts and point-wise convolutions to construct end-to-end trainable shift-based modules, with a hyperparameter characterizing the tradeoff between accuracy and efficiency.

To demonstrate the operation’s efficacy, we replace ResNet’s 3×3 convolutions with shift-based modules for improved CIFAR-10 and CIFAR-100 accuracy using 60% fewer parameters; we additionally demonstrate the operation’s resilience to parameter reduction on ImageNet, outperforming ResNet family members.

We finally show the shift operation’s applicability across domains, achieving strong performance with fewer parameters on classification, face verification and style transfer.

--- https://arxiv.org/abs/1711.08244 Adversarial Phenomenon in the Eyes of Bayesian Deep Learning Ambrish Rawat, Martin Wistuba, Maria-Irina Nicolae 2017-11-22 2019-12-28 [("doi","10.48550/arXiv.1711.08244")] ai/nn/adversarial

Deep Learning models are vulnerable to adversarial examples, i.e. images obtained via deliberate imperceptible perturbations, such that the model misclassifies them with high confidence. However, class confidence by itself is an incomplete picture of uncertainty. We therefore use principled Bayesian methods to capture model uncertainty in prediction for observing adversarial misclassification.

We provide an extensive study with different Bayesian neural networks attacked in both white-box and black-box setups. The behavior of the networks for noise, attacks and clean test data is compared. We observe that Bayesian neural networks are uncertain in their predictions for adversarial perturbations, a behavior similar to the one observed for random Gaussian perturbations. Thus, we conclude that Bayesian neural networks can be considered for detecting adversarial examples.

--- https://arxiv.org/abs/1711.09846#deepmind Population Based Training of Neural Networks Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M. Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, Chrisantha Fernando, Koray Kavukcuoglu 2017-11-27 2019-12-29 [("doi","10.48550/arXiv.1711.09846")] reinforcement-learning/meta-learning

Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimization algorithm.

In this work we present Population Based Training (PBT), a simple asynchronous optimization algorithm which effectivelyuses a fixed computational budget to jointly optimize a population of models and their hyperparameters to maximize performance. Importantly, PBT discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of trying to find a single fixed set to use for the whole course of training. With just a small modification to a typical distributed hyperparameter training framework, our method allows robust and reliable training of models.

We demonstrate the effectiveness of PBT on deep reinforcement learning problems, showing faster wall-clock convergence and higher final performance of agents by Optimising over a suite of hyperparameters. In addition, we show the same method can be applied to supervised learning for machine translation, where PBT is used to maximize the BLEU score directly, and also to training of Generative Adversarial Networks to maximize the Inception score of generated images. In all cases PBT results in the automatic discovery of hyperparameter schedules and model selection which results in stable training and better final performance.

--- https://arxiv.org/abs/1711.10337#google Are GANs Created Equal? A Large-Scale Study Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet 2017-11-28 2019-12-29 [("doi","10.48550/arXiv.1711.10337")] ai/scaling

Generative adversarial networks (GAN) are a powerful subclass of generative models. Despite a very rich research activity leading to numerous interesting GAN algorithms, it is still very hard to assess which algorithm(s) perform better than others.

We conduct a neutral, multi-faceted large-scale empirical study on state-of-the-art models and evaluation measures. We find that most models can reach similar scores with enough hyperparameter optimization and random restarts. This suggests that improvements can arise from a higher computational budget and tuning more than fundamental algorithmic changes.

To overcome some limitations of the current metrics, we also propose several datasets on which precision and recall can be computed. Our experimental results suggest that future GAN research should be based on more systematic and objective evaluation procedures.

Finally, we did not find evidence that any of the tested algorithms consistently outperforms the non-saturating GAN introduced in Goodfellow et al 2014.

--- https://arxiv.org/abs/1711.10433#deepmind Parallel WaveNet: Fast High-Fidelity Speech Synthesis Aaron van den Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals, Koray Kavukcuoglu, George van den Driessche, Edward Lockhart, Luis C. Cobo, Florian Stimberg, Norman Casagrande, Dominik Grewe, Seb Noury, Sander Dieleman, Erich Elsen, Nal Kalchbrenner, Heiga Zen, Alex Graves, Helen King, Tom Walters, Dan Belov, Demis Hassabis 2017-11-28 2019-12-29 [("doi","10.48550/arXiv.1711.10433")] ai/nn/sparsity/knowledge-distillation

The recently-developed WaveNet architecture is the current state-of-the-art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system. However, because WaveNet relies on sequential generation of one audio sample at a time, it is poorly suited to today’s massively parallel computers, and therefore hard to deploy in a real-time production setting.

This paper introduces Probability Density Distillation, a new method for training a parallel feed-forward network from a trained WaveNet with no statistically-significant difference in quality. The resulting system is capable of generating high-fidelity speech samples at more than 20× faster than real-time, and is deployed online by Google Assistant, including serving multiple English and Japanese voices.

--- https://arxiv.org/abs/1711.11585 High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, Bryan Catanzaro 2017-11-30 2019-12-29 [("doi","10.48550/arXiv.1711.11585")] ai/nn/gan

We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic.

In this work, we generate 2048×1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures. Furthermore, we extend our framework to interactive visual manipulation with two additional features. First, we incorporate object instance segmentation information, which enables object manipulations such as removing/adding objects and changing the object category. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively.

Human opinion studies demonstrate that our method outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing.

--- https://arxiv.org/abs/1712.00516 Multi-Content GAN for Few-Shot Font Style Transfer Samaneh Azadi, Matthew Fisher, Vladimir Kim, Zhaowen Wang, Eli Shechtman, Trevor Darrell 2017-12-01 2019-12-29 [("doi","10.48550/arXiv.1712.00516")] ai/nn/gan ai/text-style-transfer

In this work, we focus on the challenge of taking partial observations of highly-stylized text and generalizing the observations to generate unobserved glyphs in the ornamented typeface.

To generate a set of multi-content images following a consistent style from very few examples, we propose an end-to-end stacked conditional GAN model considering content along channels and style along network layers. Our proposed network transfers the style of given glyphs to the contents of unseen ones, capturing highly stylized fonts found in the real-world such as those on movie posters or infographics. We seek to transfer both the typographic stylization (ex. serifs and ears) as well as the textual stylization (ex. color gradients and effects).

We base our experiments on our collected data set including 10,000 fonts with different styles and demonstrate effective generalization from a very small number of observed glyphs.

--- https://arxiv.org/abs/1712.01721 Automated Pruning for Deep Neural Network Compression Franco Manessi, Alessandro Rozza, Simone Bianco, Paolo Napoletano, Raimondo Schettini 2017-12-05 2019-12-29 [("doi","10.1109/ICPR.2018.8546129")] ai/nn/sparsity/pruning

In this work we present a method to improve the pruning step of the current state-of-the-art methodology to compress neural networks. The novelty of the proposed pruning technique is in its differentiability, which allows pruning to be performed during the backpropagation phase of the network training. This enables an end-to-end learning and strongly reduces the training time.

The technique is based on a family of differentiable pruning functions and a new regularizer specifically designed to enforce pruning.

The experimental results show that the joint optimization of both the thresholds and the network weights permits to reach a higher compression rate, reducing the number of weights of the pruned network by a further 14% to 33% compared to the current state-of-the-art.

Furthermore, we believe that this is the first study where the generalization capabilities in transfer learning tasks of the features extracted by a pruned network are analyzed. To achieve this goal, we show that the representations learned using the proposed pruning methodology maintain the same effectiveness and generality of those learned by the corresponding non-compressed network on a set of different recognition tasks.

--- https://arxiv.org/abs/1712.01815#deepmind Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis 2017-12-05 2019-12-29 [("doi","10.48550/arXiv.1712.01815")] math reinforcement-learning/model/alphago

The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement learning from games of self-play.

In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case.

--- https://arxiv.org/abs/1712.01887 Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Yujun Lin, Song Han, Huizi Mao, Yu Wang, William J. Dally 2017-12-05 2019-12-29 [("doi","10.48550/arXiv.1712.01887")] ai/nn/sparsity/low-precision ai/scaling/hardware

Large-scale distributed training requires communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure. The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections.

In this paper, we find 99.9% of the gradient exchange in distributed SGD is redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth. To preserve accuracy during compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training.

We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including CIFAR-10, ImageNet, Penn Treebank, and LibriSpeech Corpus. On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270× to 600× without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for Deep Speech from 488MB to 0.74MB.

Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile.

Code is available at: Github.

--- https://arxiv.org/abs/1712.02950 CycleGAN, a Master of Steganography Casey Chu, Andrey Zhmoginov, Mark Sandler 2017-12-08 2019-12-29 [("doi","10.48550/arXiv.1712.02950")] ai/nn/adversarial ai/nn/gan cs/cryptography/steganography reinforcement-learning/safe

[comment] CycleGAN (Zhu et al 2017) is one recent successful approach to learn a transformation between two image distributions.

In a series of experiments, we demonstrate an intriguing property of the model: CycleGAN learns to “hide” information about a source image into the images it generates in a nearly imperceptible, high-frequency signal. This trick ensures that the generator can recover the original sample and thus satisfy the cyclic consistency requirement, while the generated image remains realistic.

We connect this phenomenon with adversarial attacks by viewing CycleGAN’s training procedure as training a generator of adversarial examples and demonstrate that the cyclic consistency loss causes CycleGAN to be especially vulnerable to adversarial attacks.

--- https://arxiv.org/abs/1712.05134 Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu 2017-12-14 2019-12-29 [("doi","10.48550/arXiv.1712.05134")] ai/nn/rnn ai/nn/sparsity ai/video/analysis

Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. However, when dealing with high dimensional inputs, the training of RNNs becomes computationally expensive due to the large number of model parameters. This hinders RNNs from solving many important computer vision tasks, such as Action Recognition in Videos and Image Captioning.

To overcome this problem, we propose a compact and flexible structure, namely Block-Term tensor decomposition, which greatly reduces the parameters of RNNs and improves their training efficiency. Compared with alternative low-rank approximations, such as tensor-train RNN (TT-RNN), our method, Block-Term RNN (BT-RNN), is not only more concise (when using the same rank), but also able to attain a better approximation to the original RNNs with much fewer parameters.

On 3 challenging tasks, including Action Recognition in Videos, Image Captioning, and Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of both prediction accuracy and convergence rate. Specifically, BT-LSTMuses 17,388× fewer parameters than the standard LSTM to achieve an accuracy improvement over 15.6% in the Action Recognition task on the UCF11 dataset.

--- https://arxiv.org/abs/1712.05197 Towards Deep Modeling of Music Semantics using EEG Regularizers Francisco Raposo, David Martins de Matos, Ricardo Ribeiro, Suhua Tang, Yi Yu 2017-12-14 2019-12-30 [("doi","10.48550/arXiv.1712.05197")] ai/music ai/nn/retrieval reinforcement-learning/imitation-learning/brain-imitation-learning

Modeling of music audio semantics has been previously tackled through learning of mappings from audio data to high-level tags or latent unsupervised spaces. The resulting semantic spaces are theoretically limited, either because the chosen high-level tags do not cover all of music semantics or because audio data itself is not enough to determine music semantics.

In this paper, we propose a generic framework for semantics modeling that focuses on the perception of the listener, through EEG data, in addition to audio data. We implement this framework using a novel end-to-end 2-view Neural Network (NN) architecture and a Deep Canonical Correlation Analysis (DCCA) loss function that forces the semantic embedding spaces of both views to be maximally correlated. We also detail how the EEG dataset was collected and use it to train our proposed model.

We evaluate the learned semantic space in a transfer learning context, by using it as an audio feature extractor in an independent dataset and proxy task: music audio-lyrics cross-modal retrieval. We show that our embedding model outperforms Spotify features and performs comparably to a state-of-the-art embedding model that was trained on 700× more data. We further discuss improvements to the model that are likely to improve its performance.

--- https://arxiv.org/abs/1712.06560#uber Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth O. Stanley, Jeff Clune 2017-12-18 2019-12-30 [("doi","10.48550/arXiv.1712.06560")] reinforcement-learning/exploration

Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challenging deep reinforcement learning (RL) problems, but are much faster (eg. hours vs. days) because they parallelize better. However, many RL problems require directed exploration because they have reward functions that are sparse or deceptive (ie. contain local optima), and it is unknown how to encourage such exploration with ES.

Here we show that algorithms that have been invented to promote directed exploration in small-scale evolved neural networks via populations of exploring agents, specifically novelty search (NS) and quality diversity (QD) algorithms, can be hybridized with ES to improve its performance on sparse or deceptive deep RL tasks, while retaining scalability. Our experiments confirm that the resultant new algorithms, NS-ES and two QD algorithms, NSR-ES and NSRA-ES, avoid local optima encountered by ES to achieve higher performance on Atari and simulated robots learning to walk around a deceptive trap.

This paper thus introduces a family of fast, scalable algorithms for reinforcement learning that are capable of directed exploration. It also adds this new family of exploration algorithms to the RL toolbox and raises the interesting possibility that analogous algorithms with multiple simultaneous paths of exploration might also combine well with existing RL algorithms outside ES.

--- https://arxiv.org/abs/1712.10068 Platform Criminalism: The ‘Last-Mile’ Geography of the Darknet Market Supply Chain Martin Dittus, Joss Wright, Mark Graham 2017-12-28 2019-12-30 [("doi","10.1145/3178876.3186094")] darknet-market marijuana

Does recent growth of darknet markets signify a slow reorganization of the illicit drug trade? Where are darknet markets situated in the global drug supply chain? In principle, these platforms allow producers to sell directly to end users, bypassing traditional trafficking routes. And yet, there is evidence that many offerings originate from a small number of highly active consumer countries, rather than from countries that are primarily known for drug production.

In a large-scale empirical study, we determine the darknet trading geography of 3 plant-based drugs across 4 of the largest darknet markets, and compare it to the global footprint of production and consumption for these drugs.

We present strong evidence that cannabis and cocaine vendors are primarily located in a small number of consumer countries, rather than producer countries, suggesting that darknet trading happens at the ‘last mile’, possibly leaving old trafficking routes intact. A model to explain trading volumes of opiates is inconclusive. We cannot find evidence for production-side offerings across any of the drug types or marketplaces.

Our evidence further suggests that the geography of darknet market trades is primarily driven by existing consumer demand, rather than new demand fostered by individual markets.

--- https://arxiv.org/abs/1801.02774 Adversarial Spheres Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian Goodfellow 2018-01-09 2019-12-30 [("doi","10.48550/arXiv.1801.02774")] ai/nn/adversarial

State-of-the-art computer vision models have been shown to be vulnerable to small adversarial perturbations of the input. In other words, most images in the data distribution are both correctly classified by the model and are very close to a visually similar misclassified image. Despite substantial research interest, the cause of the phenomenon is still poorly understood and remains unsolved. We hypothesize that this counter intuitive behavior is a naturally occurring result of the high dimensional geometry of the data manifold.

As a first step towards exploring this hypothesis, we study a simple synthetic dataset of classifying between two concentric high dimensional spheres. For this dataset we show a fundamental tradeoff between the amount of test error and the average distance to nearest error. In particular, we prove that any model which misclassifies a small constant fraction of a sphere will be vulnerable to adversarial perturbations of size 𝒪(1⁄√d) Surprisingly, when we train several different architectures on this dataset, all of their error sets naturally approach this theoretical bound. As a result of the theory, the vulnerability of neural networks to small adversarial perturbations is a logical consequence of the amount of test error observed.

We hope that our theoretical analysis of this very simple case will point the way forward to explore how the geometry of complex real-world data sets leads to adversarial examples.

--- https://arxiv.org/abs/1801.03534 Mechanical Computing Systems Using Only Links and Rotary Joints Ralph C. Merkle, Robert A. Freitas Junior, Tad Hogg, Thomas E. Moore, Matthew S. Moses, James Ryley 2018-01-10 2019-12-30 [("doi","10.1115/1.4041209")] cs/hardware

A new model for mechanical computing is demonstrated that requires only two basic parts: links and rotary joints.

These basic parts are combined into two main higher level structures: locks and balances, which suffice to create all necessary combinatorial and sequential logic required for a Turing-complete computational system.

While working systems have yet to be implemented using this new approach, the mechanical simplicity of the systems described may lend themselves better to, eg. microfabrication, than previous mechanical computing designs.

Additionally, simulations indicate that if molecular-scale implementations could be realized, they would be far more energy-efficient than conventional electronic computers.

--- https://arxiv.org/abs/1801.04540 Fix your classifier: the marginal value of training the last weight layer Elad Hoffer, Itay Hubara, Daniel Soudry 2018-01-14 2019-12-30 [("doi","10.48550/arXiv.1801.04540")] ai/nn/sparsity

Neural networks are commonly used as models for classification for a wide variety of tasks. Typically, a learned affine transformation is placed at the end of such models, yielding a per-class value used for classification.

This classifier can have a vast number of parameters, which grows linearly with the number of possible classes, thus requiring increasingly more resources. In this work we argue that this classifier can be fixed, up to a global scale constant, with little or no loss of accuracy for most tasks, allowing memory and computational benefits.

Moreover, we show that by initializing the classifier with a Hadamard matrix we can speed up inference as well.

We discuss the implications for current understanding of neural network models.

--- https://arxiv.org/abs/1801.04883 Unsupervised Cipher Cracking Using Discrete GANs Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Łukasz Kaiser 2018-01-15 2019-12-30 [("doi","10.48550/arXiv.1801.04883")] ai/nn/gan cs/cryptography

This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext.

We demonstrate that CipherGAN is capable of cracking language data enciphered using shift and Vigenere ciphers to a high degree of fidelity and for vocabularies much larger than previously achieved.

We present how CycleGAN can be made compatible with discrete data and train in a stable way. We then prove that the technique used in CipherGAN avoids the common problem of uninformative discrimination associated with GANs applied to discrete data.

--- https://arxiv.org/abs/1801.05787 Faster gaze prediction with dense networks and Fisher pruning Lucas Theis, Iryna Korshunova, Alykhan Tejani, Ferenc Huszár 2018-01-17 2019-12-30 [("doi","10.48550/arXiv.1801.05787")] ai/nn/sparsity/knowledge-distillation ai/nn/sparsity/pruning

Predicting human fixations from images has recently seen large improvements by leveraging deep representations which were pretrained for object recognition.

However, as we show in this paper, these networks are highly overparameterized for the task of fixation prediction.

We first present a simple yet principled greedy pruning method which we call Fisher pruning. Through a combination of knowledge distillation and Fisher pruning, we obtain much more runtime-efficient architectures for saliency prediction, achieving a 10× speedup for the same AUC performance as a state-of-the-art network on the CAT2000 dataset.

Speeding up single-image gaze prediction is important for many real-world applications, but it is also a crucial step in the development of video saliency models, where the amount of data to be processed is substantially larger.

--- https://arxiv.org/abs/1801.07365 Learning to Prune Filters in Convolutional Neural Networks Qiangui Huang, Kevin Zhou, Suya You, Ulrich Neumann 2018-01-23 2019-12-30 [("doi","10.48550/arXiv.1801.07365")] ai/nn/sparsity/pruning

Many state-of-the-art computer vision algorithms use large scale convolutional neural networks (CNNs) as basic building blocks. These CNNs are known for their huge number of parameters, high redundancy in weights, and tremendous computing resource consumption. This paper presents a learning algorithm to simplify and speed up these CNNs.

Specifically, we introduce a “try-and-learn” algorithm to train pruning agents that remove unnecessary CNN filters in a data-driven way. With the help of a novel reward function, our agents remove a number of filters in CNNs while maintaining performance at a desired level. Moreover, this method provides an easy control of the tradeoff between network performance and its scale.

Performance of our algorithm is validated with comprehensive pruning experiments on several popular CNNs for visual recognition and semantic segmentation tasks.

--- https://arxiv.org/abs/1801.10198#google Generating Wikipedia by Summarizing Long Sequences Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Łukasz Kaiser, Noam Shazeer 2018-01-30 2019-12-30 [("doi","10.48550/arXiv.1801.10198")] ai/nn/transformer/attention/compression wikipedia

We show that generating English Wikipedia articles can be approached as a multi-document summarization of source documents.

We use extractive summarization to coarsely identify salient information and a neural abstractive model to generate the article. For the abstractive model, we introduce a decoder-only architecture that can scalably attend to very long sequences, much longer than typical encoder-decoder architectures used in sequence transduction.

We show that this model can generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia articles. When given reference documents, we show it can extract relevant factual information as reflected in perplexity, ROUGE scores and human evaluations.

--- https://arxiv.org/abs/1802.01421 First-order Adversarial Vulnerability of Neural Networks and Input Dimension Carl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou, Bernhard Schölkopf, David Lopez-Paz 2018-02-05 2019-12-30 [("doi","10.48550/arXiv.1802.01421")] ai/nn/adversarial

Over the past few years, neural networks were proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions.

We show that adversarial vulnerability increases with the gradients of the training objective when viewed as a function of the inputs. Surprisingly, vulnerability does not depend on network topology: for many standard network architectures, we prove that at initialization, the 𝓁1-norm of these gradients grows as the square root of the input dimension, leaving the networks increasingly vulnerable with growing image size.

We empirically show that this dimension dependence persists after either usual or robust training, but gets attenuated with higher regularization.

--- https://arxiv.org/abs/1802.01557 One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, Sergey Levine 2018-02-05 2019-12-31 [("doi","10.48550/arXiv.1802.01557")] ai/video/analysis reinforcement-learning/meta-learning reinforcement-learning/robot

Humans and animals are capable of learning a new behavior by observing others perform the skill just once. We consider the problem of allowing a robot to do the same—learning from a raw video pixels of a human, even when there is substantial domain shift in the perspective, environment, and embodiment between the robot and the observed human. Prior approaches to this problem have hand-specified how human and robot actions correspond and often relied on explicit human pose detection systems. In this work, we present an approach for one-shot learning from a video of a human by using human and robot demonstration data from a variety of previous tasks to build up prior knowledge through meta-learning. Then, combining this prior knowledge and only a single video demonstration from a human, the robot can perform the task that the human demonstrated.

We show experiments on both a PR2 arm and a Sawyer arm, demonstrating that after meta-learning, the robot can learn to place, push, and pick-and-place new objects using just one video of a human performing the manipulation.

--- https://arxiv.org/abs/1802.04394 M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao 2018-02-12 2019-12-31 [("doi","10.48550/arXiv.1802.04394")] ai/nn/rnn reinforcement-learning/model/alphago

Learning to walk over a graph towards a target node for a given query and a source node is an important problem in applications such as knowledge base completion (KBC). It can be formulated as a reinforcement learning (RL) problem with a known state transition model.

To overcome the challenge of sparse rewards, we develop a graph-walking agent called M-Walk, which consists of a deep recurrent neural network (RNN) and Monte Carlo Tree Search (MCTS). The RNN encodes the state (ie. history of the walked path) and maps it separately to a policy and Q-values. In order to effectively train the agent from sparse rewards, we combine MCTS with the neural policy to generate trajectories yielding more positive rewards. From these trajectories, the network is improved in an off-policy manner using Q-learning, which modifies the RNN policy via parameter sharing. Our proposed RL algorithm repeatedly applies this policy-improvement step to learn the model. At test time, MCTS is combined with the neural policy to predict the target node.

Experimental results on several graph-walking benchmarks show that M-Walk is able to learn better policies than other RL-based methods, which are mainly based on policy gradients. M-Walk also outperforms traditional KBC baselines.

--- https://arxiv.org/abs/1802.04697#deepmind Learning to Search with MCTSnets Arthur Guez, Théophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver 2018-02-13 2019-12-31 [("doi","10.48550/arXiv.1802.04697")] reinforcement-learning/exploration reinforcement-learning/model/alphago

Planning problems are among the most important and well-studied problems in artificial intelligence. They are most typically solved by tree search algorithms that simulate ahead into the future, evaluate future states, and back-up those evaluations to the root of a search tree. Among these algorithms, Monte-Carlo tree search (MCTS) is one of the most general, powerful and widely used. A typical implementation of MCTS uses cleverly designed rules, optimized to the particular characteristics of the domain. These rules control where the simulation traverses, what to evaluate in the states that are reached, and how to back-up those evaluations.

In this paper we instead learn where, what and how to search. Our architecture, which we call an MCTSnet, incorporates simulation-based search inside a neural network, by expanding, evaluating and backing-up a vector embedding. The parameters of the network are trained end-to-end using gradient-based optimization.

When applied to small searches in the well known planning problem Sokoban, the learned search algorithm outperformed MCTS baselines.

--- https://arxiv.org/abs/1802.05701 Inverting The Generator Of A Generative Adversarial Network (II) Antonia Creswell, Anil A. Bharath 2018-02-15 2019-12-31 [("doi","10.48550/arXiv.1802.05701")] ai/nn/gan/stylegan

Generative adversarial networks (GANs) learn a deep generative model that is able to synthesize novel, high-dimensional data samples. New data samples are synthesized by passing latent samples, drawn from a chosen prior distribution, through the generative model. Once trained, the latent space exhibits interesting properties, that may be useful for downstream tasks such as classification or retrieval. Unfortunately, GANs do not offer an “inverse model”, a mapping from data space back to latent space, making it difficult to infer a latent representation for a given data sample.

In this paper, we introduce a technique, inversion, to project data samples, specifically images, to the latent space using a pre-trained GAN. Using our proposed inversion technique, we are able to identify which attributes of a dataset a trained GAN is able to model and quantify GAN performance, based on a reconstruction loss.

We demonstrate how our proposed inversion technique may be used to quantitatively compare performance of various GAN models trained on 3 image datasets.

We provide code for all of our experiments on Github.

--- https://arxiv.org/abs/1802.05957 Spectral Normalization for Generative Adversarial Networks Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida 2018-02-16 2019-12-31 [("doi","10.48550/arXiv.1802.05957")] ai/nn/gan

One of the challenges in the study of generative adversarial networks (GANs) is the instability of its training.

In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations.

We tested the efficacy of spectral normalization on CIFAR-10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.

--- https://arxiv.org/abs/1802.07606 Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making Luisa M. Zintgraf, Diederik M. Roijers, Sjoerd Linders, Catholijn M. Jonker, Ann Nowé 2018-02-21 2019-12-31 [("doi","10.48550/arXiv.1802.07606")] reinforcement-learning/preference-learning statistics/decision

In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e. determining which policy to execute by maximizing the user’s intrinsic utility function over this (possibly infinite) set, is under-studied. This paper aims to fill this gap.

We build on previous work on Gaussian processes and pairwise comparisons for preference modeling, extend it to the multi-objective decision support scenario, and propose new ordered preference elicitation strategies based on ranking and clustering. Our main contribution is an in-depth evaluation of these strategies using computer and human-based experiments. We show that our proposed elicitation strategies outperform the currently used pairwise methods, and found that users prefer ranking most. Our experiments further show that using monotonicity information in GPs by using a linear prior mean at the start and virtual comparisons to the nadir and ideal points increases performance.

We demonstrate our decision support framework in a real-world study on traffic regulation, conducted with the city of Amsterdam.

--- https://arxiv.org/abs/1802.07740#deepmind Machine Theory of Mind Neil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew Botvinick 2018-02-21 2019-12-31 [("doi","10.48550/arXiv.1802.07740")] psychology reinforcement-learning/meta-learning reinforcement-learning/multi-agent reinforcement-learning/safe

Theory of mind (ToM; Premack & Woodruff 1978) broadly refers to humans’ ability to represent the mental states of others, including their desires, beliefs, and intentions. We propose to train a machine to build such models too. We design a Theory of Mind neural network—a ToMnet—which uses meta-learning to build models of the agents it encounters, from observations of their behavior alone. Through this process, it acquires a strong prior model for agents’ behavior, as well as the ability to bootstrap to richer predictions about agents’ characteristics and mental states using only a small number of behavioral observations.

We apply the ToMnet to agents behaving in simple gridworld environments, showing that it learns to model random, algorithmic, and deep reinforcement learning agents from varied populations, and that it passes classic ToM tasks such as the “Sally-Anne” test (Wimmer & Perner 1983; Baron-Cohen et al 1985) of recognizing that others can hold false beliefs about the world.

We argue that this system—which autonomously learns how to model other agents in its world—is an important step forward for developing multi-agent AI systems, for building intermediating technology for machine-human interaction, and for advancing the progress on interpretable AI.

--- https://arxiv.org/abs/1802.08195#google Adversarial Examples that Fool both Computer Vision and Time-Limited Humans Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein 2018-02-22 2019-12-31 [("doi","10.48550/arXiv.1802.08195")] ai/nn/adversarial/human psychology/neuroscience

Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich. However, it is still an open question whether humans are prone to similar mistakes.

Here, we address this question by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by matching the initial processing of the human visual system.

We find that adversarial examples that strongly transfer across computer vision models influence the classifications made by time-limited human observers.

--- https://arxiv.org/abs/1802.08294#deepmind Unicorn: Continual Learning with a Universal, Off-policy Agent Daniel J. Mankowitz, Augustin Žídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul 2018-02-22 2019-12-31 [("doi","10.48550/arXiv.1802.08294")] reinforcement-learning/meta-learning/continual-learning reinforcement-learning/model-free

Some real-world domains are best characterized as a single task, but for others this perspective is limiting. Instead, some tasks continually grow in complexity, in tandem with the agent’s competence.

In continual learning, also referred to as lifelong learning, there are no explicit task boundaries or curricula. As learning agents have become more powerful, continual learning remains one of the frontiers that has resisted quick progress. To test continual learning capabilities we consider a challenging 3D domain with an implicit sequence of tasks and sparse rewards.

We propose a novel agent architecture called Unicorn, which demonstrates strong continual learning and outperforms several baseline agents on the proposed domain. The agent achieves this by jointly representing and learning multiple policies efficiently, using a parallel off-policy learning setup.

--- https://arxiv.org/abs/1802.08435#deepmind Efficient Neural Audio Synthesis Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aaron van den Oord, Sander Dieleman, Koray Kavukcuoglu 2018-02-23 2019-12-31 [("doi","10.48550/arXiv.1802.08435")] ai/music ai/nn/rnn ai/nn/sparsity/pruning

Sequential models achieve state-of-the-art results in audio, visual, and textual domains with respect to both estimating the data distribution and generating high-quality samples. Efficient sampling for this class of models has, however, remained an elusive problem. With a focus on text-to-speech synthesis, we describe a set of general techniques for reducing sampling time while maintaining high output quality.

We first describe a single-layer recurrent neural network, the WaveRNN, with a dual softmax layer that matches the quality of the state-of-the-art WaveNet model. The compact form of the network makes it possible to generate 24kHz 16-bit audio 4× faster than real time on a GPU.

Second, we apply a weight pruning technique to reduce the number of weights in the WaveRNN. We find that, for a constant number of parameters, large sparse networks perform better than small dense networks and this relationship holds for sparsity levels beyond 96%. The small number of weights in a Sparse WaveRNN makes it possible to sample high-fidelity audio on a mobile CPU in real time.

Finally, we propose a new generation scheme based on subscale that folds a long sequence into a batch of shorter sequences and allows one to generate multiple samples at once. The Subscale WaveRNN produces 16 samples per step without loss of quality and offers an orthogonal method for increasing sampling efficiency.

--- https://arxiv.org/abs/1802.08530 Training wide residual networks for deployment using a single bit for each weight Mark D. McDonnell 2018-02-23 2019-12-31 [("doi","10.48550/arXiv.1802.08530")] ai/nn/sparsity/low-precision

For fast and energy-efficient deployment of trained deep neural networks on resource-constrained embedded hardware, each learned weight parameter should ideally be represented and stored using a single bit. Error-rates usually increase when this requirement is imposed. Here, we report large improvements in error rates on multiple datasets, for deep convolutional neural networks deployed with 1-bit-per-weight. Using wide residual networks as our main baseline, our approach simplifies existing methods that binarize weights by applying the sign function in training; we apply scaling factors for each layer with constant unlearned values equal to the layer-specific standard deviations used for initialization.

For CIFAR-10, CIFAR-100 and ImageNet, and models with 1-bit-per-weight requiring less than 10 MB of parameter memory, we achieve error rates of 3.9%, 18.5% and 26.0% / 8.5% (Top-1 / Top-5) respectively. We also considered MNIST, SVHN and ImageNet32, achieving 1-bit-per-weight test results of 0.27%, 1.9%, and 41.3% / 19.1% respectively. For CIFAR, our error rates halve previously reported values, and are within about 1% of our error-rates for the same network with full-precision weights.

For networks that overfit, we also show improvements in error rate by not learning batch normalization scale and offset parameters. This applies to both full precision and 1-bit-per-weight networks. Using a warm-restart learning-rate schedule, we found that training for 1-bit-per-weight is just as fast as full-precision networks, with better accuracy than standard schedules, and achieved about 98%–99% of peak performance in just 62 training epochs for CIFAR-10/100.

For full training code and trained models in MATLAB, Keras and PyTorch see Github.

--- https://arxiv.org/abs/1802.08686 Adversarial vulnerability for any classifier Alhussein Fawzi, Hamza Fawzi, Omar Fawzi 2018-02-23 2020-01-01 [("doi","10.48550/arXiv.1802.08686")] ai/nn/adversarial

Despite achieving impressive performance, state-of-the-art classifiers remain highly vulnerable to small, imperceptible, adversarial perturbations. This vulnerability has proven empirically to be very intricate to address.

In this paper, we study the phenomenon of adversarial perturbations under the assumption that the data is generated with a smooth generative model. We derive fundamental upper bounds on the robustness to perturbations of any classification function, and prove the existence of adversarial perturbations that transfer well across different classifiers with small risk.

Our analysis of the robustness also provides insights onto key properties of generative models, such as their smoothness and dimensionality of latent space.

We conclude with numerical experimental results showing that our bounds provide informative baselines to the maximal achievable robustness on several datasets.

--- https://arxiv.org/abs/1802.08760#google Sensitivity and Generalization in Neural Networks: an Empirical Study Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein 2018-02-23 2020-01-01 [("doi","10.48550/arXiv.1802.08760")] ai/nn/adversarial ai/scaling

In practice it is often found that large over-parameterized neural networks generalize better than their smaller counterparts, an observation that appears to conflict with classical notions of function complexity, which typically favor smaller models. In this work, we investigate this tension between complexity and generalization through an extensive empirical exploration of 2 natural metrics of complexity related to sensitivity to input perturbations. Our experiments survey thousands of models with various fully-connected architectures, optimizers, and other hyper-parameters, as well as four different image classification datasets.

We find that trained neural networks are more robust to input perturbations in the vicinity of the training data manifold, as measured by the norm of the input-output Jacobian of the network, and that it correlates well with generalization. We further establish that factors associated with poor generalization—such as full-batch training or using random labels—correspond to lower robustness, while factors associated with good generalization—such as data augmentation and ReLU non-linearities—give rise to more robust functions. Finally, we demonstrate how the input-output Jacobian norm can be predictive of generalization at the level of individual test points.

--- https://arxiv.org/abs/1802.09052 Wide Compression: Tensor Ring Nets Wenqi Wang, Yifan Sun, Brian Eriksson, Wenlin Wang, Vaneet Aggarwal 2018-02-25 2020-01-01 [("doi","10.48550/arXiv.1802.09052")] ai/nn/sparsity

Deep neural networks have demonstrated state-of-the-art performance in a variety of real-world applications. In order to obtain performance gains, these networks have grown larger and deeper, containing millions or even billions of parameters and over a thousand layers. The trade-off is that these large architectures require an enormous amount of memory, storage, and computation, thus limiting their usability.

Inspired by the recent tensor ring factorization, we introduce Tensor Ring Networks (TR-Nets), which compress both the fully connected layers and the convolutional layers of deep neural networks.

Our results show that our TR-Nets approach is able to compress LeNet-5 by 11× without losing accuracy, and can compress the state-of-the-art Wide ResNet by 243× with only 2.3% degradation in CIFAR-10 image classification.

Overall, this compression scheme shows promise in scientific computing and deep learning, especially for emerging resource-constrained devices such as smartphones, wearables, and IoT devices.

--- https://arxiv.org/abs/1802.10551 A Variational Inequality Perspective on Generative Adversarial Networks Gauthier Gidel, Hugo Berard, Gaëtan Vignoud, Pascal Vincent, Simon Lacoste-Julien 2018-02-28 2020-01-01 [("doi","10.48550/arXiv.1802.10551")] ai/nn/gan

Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train. One common way to tackle this issue has been to propose new formulations of the GAN objective. Yet, surprisingly few studies have looked at optimization methods designed for this adversarial training.

In this work, we cast GAN optimization problems in the general variational inequality framework. Tapping into the mathematical programming literature, we counter some common misconceptions about the difficulties of saddle point optimization and propose to extend techniques designed for variational inequalities to the training of GANs. We apply averaging, extrapolation and a computationally cheaper variant that we call extrapolation from the past to the stochastic gradient method (SGD) and Adam.

--- https://arxiv.org/abs/1803.01118 Some Considerations on Learning to Explore via Meta-Reinforcement Learning Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever 2018-03-03 2020-01-01 [("doi","10.48550/arXiv.1803.01118")] reinforcement-learning/exploration reinforcement-learning/meta-learning

We consider the problem of exploration in meta reinforcement learning.

Two new meta reinforcement learning algorithms are suggested: E-MAML & E-RL2.

Results are presented on a novel environment we call ‘Krazy World’ and a set of maze environments. We show E-MAML and E-RL2 deliver better performance on tasks where exploration is important.

--- https://arxiv.org/abs/1803.03453 The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J. Bentley, Samuel Bernard, Guillaume Beslon, David M. Bryson, Patryk Chrabaszcz, Nick Cheney, Antoine Cully, Stephane Doncieux, Fred C. Dyer, Kai Olav Ellefsen, Robert Feldt, Stephan Fischer, Stephanie Forrest, Antoine Frénoy, Christian Gagné, Leni Le Goff, Laura M. Grabowski, Babak Hodjat, Frank Hutter, Laurent Keller, Carole Knibbe, Peter Krcah, Richard E. Lenski, Hod Lipson, Robert MacCurdy, Carlos Maestre, Risto Miikkulainen, Sara Mitri, David E. Moriarty, Jean-Baptiste Mouret, Anh Nguyen, Charles Ofria, Marc Parizeau, David Parsons, Robert T. Pennock, William F. Punch, Thomas S. Ray, Marc Schoenauer, Eric Shulte, Karl Sims, Kenneth O. Stanley, François Taddei, Danesh Tarapore, Simon Thibault, Westley Weimer, Richard Watson, Jason Yosinski 2018-03-09 2020-01-01 [("doi","10.48550/arXiv.1803.03453")] ai existential-risk reinforcement-learning/exploration reinforcement-learning/safe

Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic process that transcends the substrate in which it occurs, evolution’s creativity is not limited to nature. Indeed, many researchers in the field of digital evolution have observed their evolving algorithms and organisms subverting their intentions, exposing unrecognized bugs in their code, producing unexpected adaptations, or exhibiting outcomes uncannily convergent with ones in nature. Such stories routinely reveal creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead, they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right.

The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be.

To our knowledge, no collection of such anecdotes has been published before. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories.

In doing so, we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

--- https://arxiv.org/abs/1803.03635 The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Jonathan Frankle, Michael Carbin 2018-03-09 2020-01-01 [("doi","10.48550/arXiv.1803.03635")] ai/nn/sparsity/pruning

Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance.

We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively. Based on these results, we articulate the lottery ticket hypothesis: dense, randomly-initialized, feed-forward networks contain subnetworks (“winning tickets”) that—when trained in isolation—reach test accuracy comparable to the original network in a similar number of iterations. The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective.

We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations. We consistently find winning tickets that are less than 10–20% of the size of several fully-connected and convolutional feed-forward architectures for MNIST and CIFAR-10. Above this size, the winning tickets that we find learn faster than the original network and reach higher test accuracy.

--- https://arxiv.org/abs/1803.03697 Community Interaction and Conflict on the Web Srijan Kumar, William L. Hamilton, Jure Leskovec, Dan Jurafsky 2018-03-09 2020-01-01 [("doi","10.1145/3178876.3186141")] design psychology/personality sociology/technology

Users organize themselves into communities on web platforms. These communities can interact with one another, often leading to conflicts and toxic interactions. However, little is known about the mechanisms of interactions between communities and how they impact users.

Here we study inter-community interactions across 36,000 communities on Reddit, examining cases where users of one community are mobilized by negative sentiment to comment in another community. We show that such conflicts tend to be initiated by a handful of communities—less than 1% of communities start 74% of conflicts. While conflicts tend to be initiated by highly active community members, they are carried out by statistically-significantly less active members. We find that conflicts are marked by formation of echo chambers, where users primarily talk to other users from their own community. In the long-term, conflicts have adverse effects and reduce the overall activity of users in the targeted communities.

Our analysis of user interactions also suggests strategies for mitigating the negative impact of conflicts—such as increasing direct engagement between attackers and defenders. Further, we accurately predict whether a conflict will occur by creating a novel LSTM model that combines graph embeddings, user, community, and text features. This model can be used to create early-warning systems for community moderators to prevent conflicts. Altogether, this work presents a data-driven view of community interactions and conflict, and paves the way towards healthier online communities.

--- https://arxiv.org/abs/1803.05449#facebook SentEval: An Evaluation Toolkit for Universal Sentence Representations Alexis Conneau, Douwe Kiela 2018-03-14 2020-01-01 [("doi","10.48550/arXiv.1803.05449")] ai/nn

We introduce SentEval, a toolkit for evaluating the quality of universal sentence representations.

SentEval encompasses a variety of tasks, including binary and multi-class classification, natural language inference, and sentence similarity. The set of tasks was selected based on what appears to be the community consensus regarding the appropriate evaluations for universal sentence representations.

The toolkit comes with scripts to download and preprocess datasets, and an easy interface to evaluate sentence encoders.

The aim is to provide a fairer, less cumbersome, and more centralized way for evaluating sentence representations.

--- https://arxiv.org/abs/1803.10122#google World Models David Ha, Jürgen Schmidhuber 2018-03-27 2020-01-01 [("doi","10.5281/zenodo.1207631")] reinforcement-learning/model

We explore building generative neural network models of popular reinforcement learning environments.

Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task.

We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment.

An interactive version of this paper is available at https://worldmodels.github.io/#google

--- https://arxiv.org/abs/1803.10615 SqueezeNext: Hardware-Aware Neural Network Design Amir Gholami, Kiseok Kwon, Bichen Wu, Zizheng Tai, Xiangyu Yue, Peter Jin, Sicheng Zhao, Kurt Keutzer 2018-03-23 2020-01-02 [("doi","10.48550/arXiv.1803.10615")] ai/nn/sparsity

One of the main barriers for deploying neural networks on embedded systems has been large memory and power consumption of existing neural networks. In this work, we introduce SqueezeNext, a new family of neural network architectures whose design was guided by considering previous architectures such as SqueezeNet, as well as by simulation results on a neural network accelerator.

This new network is able to match AlexNet’s accuracy on the ImageNet benchmark with 112× fewer parameters, and one of its deeper variants is able to achieve VGG-19 accuracy with only 4.4 Million parameters, (31× smaller than VGG-19). SqueezeNext also achieves better top-5 classification accuracy with 1.3× fewer parameters as compared to MobileNet, but avoids using depthwise-separable convolutions that are inefficient on some mobile processor platforms. This wide range of accuracy gives the user the ability to make speed-accuracy tradeoffs, depending on the available resources on the target hardware.

Using hardware simulation results for power and inference speed on an embedded system has guided us to design variations of the baseline model that are 2.59×/8.26× faster and 2.25×/7.5× more energy efficient as compared to SqueezeNet/AlexNet without any accuracy degradation.

--- https://arxiv.org/abs/1804.00222#google Meta-Learning Update Rules for Unsupervised Representation Learning Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein 2018-03-31 2020-01-02 [("doi","10.48550/arXiv.1804.00222")] ai/nn/fully-connected reinforcement-learning/meta-learning

A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. Typically, this involves minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise as a side effect.

In this work, we propose instead to directly target later desired tasks by meta-learning an unsupervised learning rule which leads to representations useful for those tasks. Specifically, we target semi-supervised classification performance, and we meta-learn an algorithm—an unsupervised weight update rule—that produces representations useful for this task. Additionally, we constrain our unsupervised update rule to be a biologically-motivated, neuron-local function, which enables it to generalize to different neural network architectures, datasets, and data modalities.

We show that the meta-learned update rule produces useful features and sometimes outperforms existing unsupervised learning techniques. We further show that the meta-learned unsupervised update rule generalizes to train networks with different widths, depths, and nonlinearities. It also generalizes to train on data with randomly permuted input dimensions and even generalizes from image datasets to a text task.

Figure 1: Left: Schematic for meta-learning an unsupervised learning algorithm. The inner loop computation consists of iteratively applying the UnsupervisedUpdate to a base model. During meta-training the UnsupervisedUpdate (parameterized by θ) is itself updated by gradient descent on the MetaObjective. Right: Schematic of the base model and UnsupervisedUpdate. Unlabeled input data, x0, is passed through the base model, which is parameterized by W and colored green. The goal of the UnsupervisedUpdate is to modify W to achieve a top layer representation xL which performs well at few-shot learning. In order to train the base model, information is propagated backwards by the UnsupervisedUpdate in a manner analogous to backprop. Unlike in backprop however, the backward weights V are decoupled from the forward weights W . Additionally, unlike backprop, there is no explicit error signal as there is no loss. Instead at each layer, and for each neuron, a learning signal is injected by a meta-learned MLP parameterized by θ, with hidden state h. Weight updates are again analogous to those in backprop, and depend on the hidden state of the pre-synaptic and post-synaptic neurons for each weight.
Figure 5: Left: The learned UnsupervisedUpdate is capable of optimizing base models with hidden sizes and depths outside the meta-training regime. As we increase the number of units per layer, the learned model can make use of this additional capacity despite never having experienced it during meta-training. Right: The learned UnsupervisedUpdate generalizes across many different activation functions not seen in training. We show accuracy over the course of training on 14×14 MNIST.

…Generalizing over network architectures We train models of varying depths and unit counts with our learned optimizer and compare results at different points in training (Figure 5). We find that despite only training on networks with 2–5 layers and 64–512 units per layer, the learned rule generalizes to 11 layers and 10,000 units per layer.

Next we look at generalization over different activation functions. We apply our learned optimizer on base models with a variety of different activation functions. Performance evaluated at different points in training (Figure 5). Despite training only on ReLU activations, our learned optimizer is able to improve on random initializations in all cases. For certain activations, leaky ReLU (Maas et al 2013) and Swish (Ramachandran et al 2017), there is little to no decrease in performance. Another interesting case is the step activation function. These activations are traditionally challenging to train as there is no useful gradient signal. Despite this, our learned UnsupervisedUpdate is capable of optimizing as it does not use base model gradients, and achieves performance double that of random initialization.

5.4 How It Learns And How It Learns To Learn: To analyze how our learned optimizer functions, we analyze the first layer filters over the course of meta-training. Despite the permutation invariant nature of our data (enforced by shuffling input image pixels before each unsupervised training run), the base model learns features such as those shown in Figure 6, which appear template-like for MNIST, and local-feature-like forCIFAR-10. Early in training, there are coarse features, and a lot of noise. As the meta-training progresses, more interesting and local features emerge.

In an effort to understand what our algorithm learns to do, we fed it data from the two moons dataset. We find that despite being a 2D dataset, dissimilar from the image datasets used in meta-training, the learned model is still capable of manipulating and partially separating the data manifold in a purely unsupervised manner (Figure 6). We also find that almost all the variance in the embedding space is dominated by a few dimensions. As a comparison, we do the same analysis on MNIST. In this setting, the explained variance is spread out over more of the principal components. This makes sense as the generative process contains many more latent dimensions—at least enough to express the 10 digits.

Figure 6: Left: From left to right we show first layer base model receptive fields produced by our learned UnsupervisedUpdate rule over the course of meta-training. Each pane consists of first layer filters extracted from φ after 10k applications of UnsupervisedUpdate on MNIST (top) andCIFAR-10 (bottom). For MNIST, the optimizer learns image-template-like features. ForCIFAR-10, low frequency features evolve into higher frequency and more spatially localized features. For more filters, see Appendix D. Center: Visualization of learned representations before (left) and after (right) training a base model with our learned UnsupervisedUpdate for two moons (top) and MNIST (bottom). The UnsupervisedUpdate is capable of manipulating the data manifold, without access to labels, to separate the data classes. Visualization shows a projection of the 32-dimensional representation of the base network onto the top 3 principal components. Right: Cumulative variance explained using principal components analysis (PCA) on the learned representations. The representation for two moons data (red) is much lower dimensional than MNIST (blue), although both occupy a fraction of the full 32-dimensional space.
Figure App.1: Schematic for meta-learning an unsupervised learning algorithm. We show the hierarchical nature of both the meta-training procedure and update rule. (a) Meta-training, where the meta-parameters, θ, are updated via our meta-optimizer (SGD). (b) The gradients of the MetaObjective with respect to θ are computed by backpropagation through the unrolled application of the UnsupervisedUpdate. (c) UnsupervisedUpdate updates the base model parameters (φ) using a minibatch of unlabeled data. (d) Each application of UnsupervisedUpdate involves computing a forward and “backward” pass through the base model. The base model itself is a fully-connected network producing hidden states xl for each layer l. The “backward” pass through the base model uses an error signal from the layer above, δ, which is generated by a meta-learned function. (e) The weight updates ∆φ are computed using a convolutional network, using δ and x from the pre-synaptic & post-synaptic neurons, along with several other terms discussed in the text.
Table 1: A comparison of published meta-learning approaches.
Method Inner loop updates Outer loop updates, meta-✱ Generalizes to
parameters objective optimizer
Hyper-parameter optimization: Jones 2001; Snoek et al 2012; Bergstra et al 2011; Bergstra & Bengio 2012 many steps of optimization optimization hyper-parameters training or validation set loss Bayesian methods, random search, etc test data from a fixed dataset
Neural architecture search: Stanley & Miikkulainen 2002; Zoph & Le 2017; Baker et al 2017; Zoph et al 2018; Real et al 2017 supervised SGD training using meta-learned architecture architecture validation set loss RL or evolution test loss within similar datasets
Task-specific optimizer (eg. for quadratic function identification): Hochreiter et al 2001 adjustment of model weights by an LSTM RNN LSTM weights task loss SGD similar domain tasks
Learned optimizers: Jones 2001; Maclaurin et al 2015; Andrychowicz et al 2016; Chen et al 2016; Li & Malik 2017; Wichrowska et al 2017; Bello et al 2017 many steps of optimization of a fixed loss function parametric optimizer average or final loss SGD or RL new loss functions (mixed success)
Prototypical networks: Snell et al 2017 apply a feature extractor to a batch of data and use soft nearest neighbors to compute class probabilities weights of the feature extractor few shot performance SGD new image classes within similar dataset
MAML: Finn et al 2017 one step of SGD on training loss starting from a meta-learned network initial weights of neural network reward or training loss SGD new goals, similar task regimes with same input domain
Evolved Policy Gradient: Houthooft et al 2018 performing gradient descent on a learned loss parameters of a learned loss function reward evolutionary strategies new environment configurations, both in and not in meta-training distribution
Few shot learning: Vinyals et al 2016; Ravi & Larochelle 2016; Mishra et al 2017 application of a recurrent model, eg. LSTM, WaveNet. recurrent model weights test loss on training tasks SGD new image classes within similar dataset.
Meta-unsupervised learning for clustering: Garg 2018 run clustering algorithm or evaluate binary similarity function clustering algorithm + hyperparameters, binary similarity function empirical risk minimization varied new clustering or similarity measurement tasks
Learning synaptic learning rules: Bengio et al 1990; Bengio et al 1992 run a synapse-local learning rule parametric learning rule supervised loss, or similarity to biologically-motivated network gradient descent, simulated annealing, genetic algorithms similar domain tasks
Our work?metalearning for unsupervised representation learning many applications of an unsupervised update rule parametric update rule few shot classification after unsupervised pretraining SGD new base models (width, depth, nonlinearity), new datasets, new data modalities
--- https://arxiv.org/abs/1804.03160 The Sound of Pixels Hang Zhao, Chuang Gan, Andrew Rouditchenko, Carl Vondrick, Josh McDermott, Antonio Torralba 2018-04-09 2020-01-02 [("doi","10.48550/arXiv.1804.03160")] ai/dataset ai/music ai/video/analysis

We introduce PixelPlayer, a system that, by leveraging large amounts of unlabeled videos, learns to locate image regions which produce sounds and separate the input sounds into a set of components that represents the sound from each pixel. Our approach capitalizes on the natural synchronization of the visual and audio modalities to learn models that jointly parse sounds and images, without requiring additional manual supervision.

Experimental results on a newly collected MUSIC dataset show that our proposed Mix-and-Separate framework outperforms several baselines on source separation.

Qualitative results suggest our model learns to ground sounds in vision, enabling applications such as independently adjusting the volume of sound sources.

--- https://arxiv.org/abs/1804.03748 The Silurian Hypothesis: Would it be possible to detect an industrial civilization in the geological record? Gavin A. Schmidt, Adam Frank 2018-04-10 2020-01-02 [("doi","10.1017/S1473550418000095")] existential-risk history technology

If an industrial civilization had existed on Earth many millions of years prior to our own era, what traces would it have left and would they be detectable today?

We summarize the likely geological fingerprint of the Anthropocene, and demonstrate that while clear, it will not differ greatly in many respects from other known events in the geological record.

We then propose tests that could plausibly distinguish an industrial cause from an otherwise naturally occurring climate event.

--- https://arxiv.org/abs/1804.04577 Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations Dimitri P. Bertsekas 2018-04-12 2020-01-02 [("doi","10.48550/arXiv.1804.04577")] reinforcement-learning/model/alphago technology

In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes.

We introduce features of the states of the original problem, and we formulate a smaller “aggregate” Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations.

We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural network-based reinforcement learning, thereby potentially leading to more effective policy improvement.

--- https://arxiv.org/abs/1804.08838#uber Measuring the Intrinsic Dimension of Objective Landscapes Chunyuan Li, Heerad Farkhoor, Rosanne Liu, Jason Yosinski 2018-04-24 2020-01-02 [("doi","10.48550/arXiv.1804.08838")] ai/nn/sparsity reinforcement-learning/model-free

[video] Many recently trained neural networks employ large numbers of parameters to achieve good performance. One may intuitively use the number of parameters required as a rough gauge of the difficulty of a problem. But how accurate are such notions? How many parameters are really needed? In this paper we attempt to answer this question by training networks not in their native parameter space, but instead in a smaller, randomly oriented subspace.

We slowly increase the dimension of this subspace, note at which dimension solutions first appear, and define this to be the intrinsic dimension of the objective landscape. The approach is simple to implement, computationally tractable, and produces several suggestive conclusions.

Many problems have smaller intrinsic dimensions than one might suspect, and the intrinsic dimension for a given dataset varies little across a family of models with vastly different sizes. This latter result has the profound implication that once a parameter space is large enough to solve a problem, extra parameters serve directly to increase the dimensionality of the solution manifold. Intrinsic dimension allows some quantitative comparison of problem difficulty across supervised, reinforcement, and other types of learning where we conclude, for example, that solving the inverted pendulum problem is 100× easier than classifying digits from MNIST, and playing Atari Pong from pixels is about as hard as classifying CIFAR-10.

In addition to providing new cartography of the objective landscapes wandered by parameterized models, the method is a simple technique for constructively obtaining an upper bound on the minimum description length of a solution. A byproduct of this construction is a simple approach for compressing networks, in some cases by more than 100×.

--- https://arxiv.org/abs/1804.10911 A Tree Search Algorithm for Sequence Labeling Yadi Lao, Jun Xu, Yanyan Lan, Jiafeng Guo, Sheng Gao, Xueqi Cheng 2018-04-29 2020-01-02 [("doi","10.48550/arXiv.1804.10911")] ai/nn/rnn reinforcement-learning/model/alphago

In this paper we propose a novel reinforcement learning based model for sequence tagging, referred to as MM-Tag. Inspired by the success and methodology of the AlphaGo Zero, MM-Tag formalizes the problem of sequence tagging with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from left to right, and each action corresponds to assign a tag to a word.

Two long short-term memory networks (LSTM) are used to summarize the past tag assignments and words in the sentence. Based on the outputs of LSTMs, the policy for guiding the tag assignment and the value for predicting the whole tagging accuracy of the whole sentence are produced. The policy and value are then strengthened with MCTS, which takes the produced raw policy and value as inputs, simulates and evaluates the possible tag assignments at the subsequent positions, and outputs a better search policy for assigning tags. A reinforcement learning algorithm is proposed to train the model parameters.

Our work is the first to apply the MCTS enhanced MDP model to the sequence tagging task. We show that MM-Tag can accurately predict the tags thanks to the exploratory decision making mechanism introduced by MCTS.

Experimental results show based on a chunking benchmark showed that MM-Tag outperformed the state-of-the-art sequence tagging baselines including CRF and CRF with LSTM.

--- https://arxiv.org/abs/1805.02152 Quantization Mimic: Towards Very Tiny CNN for Object Detection Yi Wei, Xinyu Pan, Hongwei Qin, Wanli Ouyang, Junjie Yan 2018-05-06 2020-01-02 [("doi","10.48550/arXiv.1805.02152")] ai/nn/sparsity/low-precision

In this paper, we propose a simple and general framework for training very tiny CNNs for object detection. Due to limited representation ability, it is challenging to train very tiny networks for complicated tasks like detection. To the best of our knowledge, our method, called Quantization Mimic, is the first one focusing on very tiny networks.

We use two types of acceleration methods: mimic and quantization. Mimic improves the performance of a student network by transferring knowledge from a teacher network. Quantization converts a full-precision network to a quantized one without large degradation of performance. If the teacher network is quantized, the search scope of the student network will be smaller. Using this feature of the quantization, we propose Quantization Mimic. It first quantizes the large network, then mimic a quantized small network. The quantization operation can help student network to better match the feature maps from teacher network.

To evaluate our approach, we carry out experiments on various popular CNNs including VGG and Resnet, as well as different detection frameworks including Faster R-CNN and R-FCN. Experiments on Pascal VOC and WIDER FACE verify that our Quantization Mimic algorithm can be applied on various settings and outperforms state-of-the-art model acceleration methods given limited computing resources.

--- https://arxiv.org/abs/1805.05935 Feedback-Based Tree Search for Reinforcement Learning Daniel R. Jiang, Emmanuel Ekwedike, Han Liu 2018-05-15 2020-01-02 [("doi","10.48550/arXiv.1805.05935")] reinforcement-learning/model/alphago

Inspired by recent successes of Monte-Carlo tree search (MCTS) in a number of artificial intelligence (AI) application domains, we propose a model-based reinforcement learning (RL) technique that iteratively applies MCTS on batches of small, finite-horizon versions of the original infinite-horizon Markov decision process. The terminal condition of the finite-horizon problems, or the leaf-node evaluator of the decision tree generated by MCTS, is specified using a combination of an estimated value function and an estimated policy function. The recommendations generated by the MCTS procedure are then provided as feedback in order to refine, through classification and regression, the leaf-node evaluator for the next iteration.

We provide the first sample complexity bounds for a tree search-based RL algorithm. In addition, we show that a deep neural network implementation of the technique can create a competitive AI agent for the popular multi-player online battle arena (MOBA) game King of Glory.

--- https://arxiv.org/abs/1805.07470 Solving the Rubik’s Cube Without Human Knowledge Stephen McAleer, Forest Agostinelli, Alexander Shmakov, Pierre Baldi 2018-05-18 2020-01-02 [("doi","10.48550/arXiv.1805.07470")] reinforcement-learning/model/alphago

A generally intelligent agent must be able to teach itself how to solve problems in complex domains with minimal human supervision. Recently, deep reinforcement learning algorithms combined with self-play have achieved superhuman proficiency in Go, Chess, and Shogi without human data or domain knowledge.

In these environments, a reward is always received at the end of the game; however, for many combinatorial optimization environments, rewards are sparse, and episodes are not guaranteed to terminate. We introduce Autodidactic Iteration: a novel reinforcement learning algorithm that is able to teach itself how to solve the Rubik’s Cube with no human assistance.

Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves—less than or equal to solvers that employ human domain knowledge.

--- https://arxiv.org/abs/1805.08318 Self-Attention Generative Adversarial Networks Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena 2018-05-21 2020-01-02 [("doi","10.48550/arXiv.1805.08318")] ai/nn/gan/biggan ai/nn/transformer

In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other.

Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics.

The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score 36.8 → 52.52 and reducing Fréchet Inception distance 27.62 → 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape.

--- https://arxiv.org/abs/1805.08522 Deep learning generalizes because the parameter-function map is biased towards simple functions Guillermo Valle-Pérez, Chico Q. Camargo, Ard A. Louis 2018-05-22 2020-01-03 [("doi","10.48550/arXiv.1805.08522")] ai/nn/cnn ai/nn/fully-connected ai/scaling statistics/bayes

Deep neural networks (DNNs) generalize remarkably well without explicit regularization even in the strongly over-parameterized regime where classical learning theory would instead predict that they would severely overfit. While many proposals for some kind of implicit regularization have been made to rationalize this success, there is no consensus for the fundamental reason why DNNs do not strongly overfit.

In this paper, we provide a new explanation. By applying a very general probability-complexity bound recently derived from algorithmic information theory (AIT), we argue that the parameter-function map of many DNNs should be exponentially biased towards simple functions. We then provide clear evidence for this strong simplicity bias in a model DNN for Boolean functions, as well as in much larger fully connected and convolutional networks applied to CIFAR-10 and MNIST.

As the target functions in many real problems are expected to be highly structured, this intrinsic simplicity bias helps explain why deep networks generalize well on real world problems. This picture also facilitates a novel PAC-Bayes approach where the prior is taken over the DNN input-output function space, rather than the more conventional prior over parameter space. If we assume that the training algorithm samples parameters close to uniformly within the zero-error region then the PAC-Bayes theorem can be used to guarantee good expected generalization for target functions producing high-likelihood training sets.

By exploiting recently discovered connections between DNNs and Gaussian processes to estimate the marginal likelihood, we produce relatively tight generalization PAC-Bayes error bounds which correlate well with the true error on realistic datasets such as MNIST and CIFAR-10 and for architectures including convolutional and fully connected networks.

--- https://arxiv.org/abs/1805.08718 Inferring Human Traits From Facebook Statuses Andrew Cutler, Brian Kulis 2018-05-22 2020-01-03 [("doi","10.48550/arXiv.1805.08718")] psychology/personality

This paper explores the use of language models to predict 20 human traits from users’ Facebook status updates. The data was collected by the myPersonality project, and includes user statuses along with their personality, gender, political identification, religion, race, satisfaction with life, IQ, self-disclosure, fair-mindedness, and belief in astrology.

A single interpretable model meets state-of-the-art results for well-studied tasks such as predicting gender and personality; and sets the standard on other traits such as IQ, sensational interests, political identity, and satisfaction with life. Additionally, highly weighted words are published for each trait. These lists are valuable for creating hypotheses about human behavior, as well as for understanding what information a model is extracting.

Using performance and extracted features we analyze models built on social media. The real world problems we explore include gendered classification bias and Cambridge Analytica’s use of psychographic models.

--- https://arxiv.org/abs/1805.09190 Towards the first adversarially robust neural network model on MNIST Lukas Schott, Jonas Rauber, Matthias Bethge, Wiel, Brendel 2018-05-23 2020-01-03 [("doi","10.48550/arXiv.1805.09190")] ai/nn/adversarial

Despite much effort, deep neural networks remain highly susceptible to tiny input perturbations and even for MNIST, one of the most common toy datasets in computer vision, no neural network model exists for which adversarial perturbations are large and make semantic sense to humans. We show that even the widely recognized and by far most successful defense by Madry et al 2017 overfits on the L∞ metric (it’s highly susceptible to L2 and L0 perturbations), (2) classifies unrecognizable images with high certainty, (3) performs not much better than simple input binarization and (4) features adversarial perturbations that make little sense to humans. These results suggest that MNIST is far from being solved in terms of adversarial robustness.

We present a novel robust classification model that performs analysis by synthesis using learned class-conditional data distributions. We derive bounds on the robustness and go to great length to empirically evaluate our model using maximally effective adversarial attacks by (1) applying decision-based, score-based, gradient-based and transfer-based attacks for several different 𝓁p norms, (2) by designing a new attack that exploits the structure of our defended model and (3) by devising a novel decision-based attack that seeks to minimize the number of perturbed pixels (L0).

The results suggest that our approach yields state-of-the-art robustness on MNIST against L0, L2 and L∞ perturbations and we demonstrate that most adversarial examples are strongly perturbed towards the perceptual boundary between the original and the adversarial class.

--- https://arxiv.org/abs/1805.09975 Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological Rewards Daniel McDuff, Ashish Kapoor 2018-05-25 2020-01-03 [("doi","10.48550/arXiv.1805.09975")] reinforcement-learning/imitation-learning/brain-imitation-learning reinforcement-learning/safe

As people learn to navigate the world, autonomic nervous system (eg. “fight or flight”) responses provide intrinsic feedback about the potential consequence of action choices (eg. becoming nervous when close to a cliff edge or driving fast around a bend). Physiological changes are correlated with these biological preparations to protect oneself from danger.

We present a novel approach to reinforcement learning that leverages a task-independent intrinsic reward function trained on peripheral pulse measurements that are correlated with human autonomic nervous system responses. Our hypothesis is that such reward functions can circumvent the challenges associated with sparse and skewed rewards in reinforcement learning settings and can help improve sample efficiency.

We test this in a simulated driving environment and show that it can increase the speed of learning and reduce the number of collisions during the learning stage.

--- https://arxiv.org/abs/1805.10726 A Neurobiological Evaluation Metric for Neural Network Model Search Nathaniel Blanchard, Jeffery Kinnison, Brandon RichardWebster, Pouya Bashivan, Walter J. Scheirer 2018-05-28 2020-01-03 [("doi","10.48550/arXiv.1805.10726")] reinforcement-learning/imitation-learning/brain-imitation-learning

Neuroscience theory posits that the brain’s visual system coarsely identifies broad object categories via neural activation patterns, with similar objects producing similar neural responses. Artificial neural networks also have internal activation behavior in response to stimuli. We hypothesize that networks exhibiting brain-like activation behavior will demonstrate brain-like characteristics, eg. stronger generalization capabilities.

In this paper we introduce a human-model similarity (HMS) metric, which quantifies the similarity of human fMRI and network activation behavior. To calculate HMS, representational dissimilarity matrices (RDMs) are created as abstractions of activation behavior, measured by the correlations of activations to stimulus pairs. HMS is then the correlation between the fMRI RDM and the neural network RDM across all stimulus pairs.

We test the metric on unsupervised predictive coding networks, which specifically model visual perception, and assess the metric for statistical-significance over a large range of hyperparameters. Our experiments show that networks with increased human-model similarity are correlated with better performance on two computer vision tasks: next frame prediction and object matching accuracy. Further, HMS identifies networks with high performance on both tasks. An unexpected secondary finding is that the metric can be employed during training as an early-stopping mechanism.

--- https://arxiv.org/abs/1805.10755 Dual Policy Iteration Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell 2018-05-28 2020-01-03 [("doi","10.48550/arXiv.1805.10755")] reinforcement-learning/model/alphago

Recently, a novel class of Approximate Policy Iteration (API) algorithms have demonstrated impressive practical performance (eg. ExIt from [2], AlphaGo-Zero from [27]). This new family of algorithms maintains, and alternately optimizes, two policies: a fast, reactive policy (eg. a deep neural network) deployed at test time, and a slow, non-reactive policy (eg. Tree Search), that can plan multiple steps ahead. The reactive policy is updated under supervision from the non-reactive policy, while the non-reactive policy is improved with guidance from the reactive policy.

In this work we study this Dual Policy Iteration (DPI) strategy in an alternating optimization framework and provide a convergence analysis that extends existing API theory. We also develop a special instance of this framework which reduces the update of non-reactive policies to model-based optimal control using learned local models, and provides a theoretically sound way of unifying model-free and model-based RL approaches with unknown dynamics. We demonstrate the efficacy of our approach on various continuous control Markov Decision Processes.

--- https://arxiv.org/abs/1805.11592#deepmind Playing hard exploration games by watching YouTube Yusuf Aytar, Tobias Pfaff, David Budden, Tom Le Paine, Ziyu Wang, Nando de Freitas 2018-05-29 2020-01-03 [("doi","10.48550/arXiv.1805.11592")] ai/video/analysis reinforcement-learning/exploration

Deep reinforcement learning methods traditionally struggle with tasks where environment rewards are particularly sparse. One successful method of guiding exploration in these domains is to imitate trajectories provided by a human demonstrator. However, these demonstrations are typically collected under artificial conditions, i.e. with access to the agent’s exact environment setup and the demonstrator’s action and reward trajectories.

Here we propose a two-stage method that overcomes these limitations by relying on noisy, unaligned footage without access to such data. First, we learn to map unaligned videos from multiple sources to a common representation using self-supervised objectives constructed over both time and modality (ie. vision and sound). Second, we embed a single YouTube video in this representation to construct a reward function that encourages an agent to imitate human gameplay. This method of one-shot imitation allows our agent to convincingly exceed human-level performance on the infamously hard exploration games Montezuma’s Revenge, Pitfall! and Private Eye for the first time, even if the agent is not presented with any environment rewards.

--- https://arxiv.org/abs/1805.11593#deepmind Observe and Look Further: Achieving Consistent Performance on Atari Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado van Hasselt, John Quan, Mel Večerík, Matteo Hessel, Rémi Munos, Olivier Pietquin 2018-05-29 2020-01-03 [("doi","10.48550/arXiv.1805.11593")] reinforcement-learning/exploration

Despite substantial advances in the field of deep Reinforcement Learning (RL), today’s algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games. We identify 3 key challenges that any algorithm needs to master in order to perform well on all games: processing diverse reward distributions, reasoning over long time horizons, and exploring efficiently.

In this paper, we propose an algorithm that addresses each of these challenges and is able to learn human-level policies on nearly all Atari games. A new transformed Bellman operator allows our algorithm to process rewards of varying densities and scales; an auxiliary temporal consistency loss allows us to train stably using a discount factor of γ = 0.999 (instead of γ = 0.99) extending the effective planning horizon by an order of magnitude; and we ease the exploration problem by using human demonstrations that guide the agent towards rewarding states.

When tested on a set of 42 Atari games, our algorithm exceeds the performance of an average human on 40 games using a common set of hyper parameters. Furthermore, it is the first deep RL algorithm to solve the first level of Montezuma’s Revenge.

--- https://arxiv.org/abs/1806.01363 Playing Atari with Six Neurons Giuseppe Cuccu, Julian Togelius, Philippe Cudre-Mauroux 2018-06-04 2020-01-03 [("doi","10.48550/arXiv.1806.01363")] ai/nn/sparsity reinforcement-learning/model-free reinforcement-learning/scaling

Deep reinforcement learning, applied to vision-based problems like Atari games, maps pixels directly to actions; internally, the deep neural network bears the responsibility of both extracting useful information and making decisions based on it. By separating the image processing from decision-making, one could better understand the complexity of each task, as well as potentially find smaller policy representations that are easier for humans to understand and may generalize better.

To this end, we propose a new method for learning policies and compact state representations separately but simultaneously for policy approximation in reinforcement learning. State representations are generated by an encoder based on two novel algorithms: Increasing Dictionary Vector Quantization makes the encoder capable of growing its dictionary size over time, to address new observations as they appear in an open-ended online-learning context; Direct Residuals Sparse Coding encodes observations by disregarding reconstruction error minimization, and aiming instead for highest information inclusion. The encoder autonomously selects observations online to train on, in order to maximize code sparsity. As the dictionary size increases, the encoder produces increasingly larger inputs for the neural network: this is addressed by a variation of the Exponential Natural Evolution Strategies algorithm which adapts its probability distribution dimensionality along the run.

We test our system on a selection of Atari games using tiny neural networks of only 6 to 18 neurons (depending on the game’s controls). These are still capable of achieving results comparable—and occasionally superior—to state-of-the-art techniques which use two orders of magnitude more neurons.

--- https://arxiv.org/abs/1806.02643#deepmind Re-evaluating Evaluation David Balduzzi, Karl Tuyls, Julien Perolat, Thore Graepel 2018-06-07 2020-01-03 [("doi","10.48550/arXiv.1806.02643")] reinforcement-learning/exploration reinforcement-learning/model-free/alphastar

Progress in machine learning is measured by careful evaluation on problems of outstanding common interest. However, the proliferation of benchmark suites and environments, adversarial attacks, and other complications has diluted the basic evaluation model by overwhelming researchers with choices. Deliberate or accidental cherry picking is increasingly likely, and designing well-balanced evaluation suites requires increasing effort.

In this paper we take a step back and propose Nash averaging. The approach builds on a detailed analysis of the algebraic structure of evaluation in two basic scenarios: agent-vs-agent and agent-vs-task. The key strength of Nash averaging is that it automatically adapts to redundancies in evaluation data, so that results are not biased by the incorporation of easy tasks or weak agents. Nash averaging thus encourages maximally inclusive evaluation—since there is no harm (computational cost aside) from including all available tasks and agents.

--- https://arxiv.org/abs/1806.04798 Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning Kunkun Pang, Mingzhi Dong, Yang Wu, Timothy Hospedales 2018-06-12 2020-01-03 [("doi","10.48550/arXiv.1806.04798")] ai/nn reinforcement-learning/exploration/active-learning reinforcement-learning/meta-learning

Active learning (AL) aims to enable training high-performance classifiers with low annotation cost by predicting which subset of unlabeled instances would be most beneficial to label. The importance of AL has motivated extensive research, proposing a wide variety of manually designed AL algorithms with diverse theoretical and intuitive motivations.

In contrast to this body of research, we propose to treat active learning algorithm design as a meta-learning problem and learn the best criterion from data. We model an active learning algorithm as a deep neural network that inputs the base learner state and the unlabeled point set and predicts the best point to annotate next. Training this active query policy network with reinforcement learning, produces the best non-myopic policy for a given dataset. The key challenge in achieving a general solution to AL then becomes that of learner generalization, particularly across heterogeneous datasets. We propose a multi-task dataset-embedding approach that allows dataset-agnostic active learners to be trained.

Our evaluation shows that AL algorithms trained in this way can directly generalize across diverse problems.

--- https://arxiv.org/abs/1806.05780 Surprising Negative Results for Generative Adversarial Tree Search Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Zachary C. Lipton, Animashree Anandkumar 2018-06-15 2020-01-04 [("doi","10.48550/arXiv.1806.05780")] reinforcement-learning/model/alphago reinforcement-learning/model/muzero

While many recent advances in deep reinforcement learning (RL) rely on model-free methods, model-based approaches remain an alluring prospect for their potential to exploit unsupervised data to learn environment model. In this work, we provide an extensive study on the design of deep generative models for RL environments and propose a sample efficient and robust method to learn the model of Atari environments.

We deploy this model and propose generative adversarial tree search (GATS) a deep RL algorithm that learns the environment model and implements Monte Carlo tree search (MCTS) on the learned model for planning. While MCTS on the learned model is computationally expensive, similar to AlphaGo, GATS follows depth limited MCTS. GATS employs deep Q network (DQN) and learns a Q-function to assign values to the leaves of the tree in MCTS.

We theoretical analyze GATS vis-a-vis the bias-variance trade-off and show GATS is able to mitigate the worst-case error in the Q-estimate. While we were expecting GATS to enjoy a better sample complexity and faster converges to better policies, surprisingly, GATS fails to outperform DQN. We provide a study on which we show why depth limited MCTS fails to perform desirably.

--- https://arxiv.org/abs/1806.05898 Improving width-based planning with compact policies Miquel Junyent, Anders Jonsson, Vicenç Gómez 2018-06-15 2020-01-04 [("doi","10.48550/arXiv.1806.05898")] reinforcement-learning/exploration reinforcement-learning/model/alphago

[talk] Optimal action selection in decision problems characterized by sparse, delayed rewards is still an open challenge. For these problems, current deep reinforcement learning methods require enormous amounts of data to learn controllers that reach human-level performance.

In this work, we propose a method that interleaves planning and learning to address this issue. The planning step hinges on the Iterated-Width (IW) planner [see iterative deepening], a state-of-the-art planner that makes explicit use of the state representation to perform structured exploration.

IW is able to scale up to problems independently of the size of the state space. From the state-actions visited by IW, the learning step estimates a compact policy, which in turn is used to guide the planning step. The type of exploration used by our method is radically different than the standard random exploration used in RL.

We evaluate our method in simple problems where we show it to have superior performance than the state-of-the-art reinforcement learning algorithms A2C and Alpha Zero. Finally, we present preliminary results in a subset of the Atari games suite.

--- https://arxiv.org/abs/1806.08379 Solving the Buyer and Seller’s Dilemma: A Dual-Deposit Escrow Smart Contract for Provably Cheat-Proof Delivery and Payment for a Digital Good without a Trusted Mediator Aditya Asgaonkar, Bhaskar Krishnamachari 2018-06-21 2020-01-04 [("doi","10.48550/arXiv.1806.08379")] bitcoin/nashx economics

A fundamental problem for electronic commerce is the buying and selling of digital goods between individuals that may not know or trust each other. Traditionally, this problem has been addressed by the use of trusted third-parties such as credit-card companies, mediated escrows, legal adjudication, or reputation systems. Despite the rise of blockchain protocols as a way to send payments without trusted third parties, the important problem of exchanging a digital good for payment without trusted third parties has been paid much less attention. We refer to this problem as the Buyer and Seller’s Dilemma and present for it a dual-deposit escrow trade protocol which uses double-sided payment deposits in conjunction with simple cryptographic primitives, and that can be implemented using a blockchain-based smart contract.

We analyze our protocol as an extensive-form game and prove that the Sub-game Perfect Nash Equilibrium for this game is for both the buyer and seller to cooperate and behave honestly. We address this problem under the assumption that the digital good being traded is known and verifiable, with a fixed price known to both parties.

--- https://arxiv.org/abs/1806.10230 Guided evolutionary strategies: Augmenting random search with surrogate gradients Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein 2018-06-26 2020-01-04 [("doi","10.48550/arXiv.1806.10230")] reinforcement-learning/meta-learning

Many applications in machine learning require optimizing a function whose true gradient is unknown, but where surrogate gradient information (directions that may be correlated with, but not necessarily identical to, the true gradient) is available instead. This arises when an approximate gradient is easier to compute than the full gradient (eg. in meta-learning or unrolled optimization), or when a true gradient is intractable and is replaced with a surrogate (eg. in certain reinforcement learning applications, or when using synthetic gradients).

We propose Guided Evolutionary Strategies, a method for optimally using surrogate gradient directions along with random search. We define a search distribution for evolutionary strategies that is elongated along a guiding subspace spanned by the surrogate gradients. This allows us to estimate a descent direction which can then be passed to a first-order optimizer.

We analytically and numerically characterize the tradeoffs that result from tuning how strongly the search distribution is stretched along the guiding subspace, and we use this to derive a setting of the hyperparameters that works well across problems.

Finally, we apply our method to example problems, demonstrating an improvement over both standard evolutionary strategies and first-order methods (that directly follow the surrogate gradient).

We provide a demo of Guided ES at https://github.com/brain-research/guided-evolutionary-strategies.

--- https://arxiv.org/abs/1806.10474#deepmind The challenge of realistic music generation: modeling raw audio at scale Sander Dieleman, Aäron van den Oord, Karen Simonyan 2018-06-26 2020-01-04 [("doi","10.48550/arXiv.1806.10474")] ai/music ai/nn/vae

Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musicality and realism, so in this work we embark on modeling music in the raw audio domain.

It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modeling long-range correlations. This is problematic because music exhibits structure at many different timescales. In this work, we explore autoregressive discrete autoencoders (ADAs) as a means to enable autoregressive models to capture long-range correlations in waveforms. We find that they allow us to unconditionally generate piano music directly in the raw audio domain, which shows stylistic consistency across tens of seconds.

--- https://arxiv.org/abs/1806.11146 Adversarial Reprogramming of Neural Networks Gamaleldin F. Elsayed, Ian Goodfellow, Jascha Sohl-Dickstein 2018-06-28 2020-01-04 [("doi","10.48550/arXiv.1806.11146")] ai/nn/adversarial ai/nn/transformer/gpt cs/computable

Deep neural networks are susceptible to adversarial attacks. In computer vision, well-crafted perturbations to images can cause neural networks to make mistakes such as confusing a cat with a computer. Previous adversarial attacks have been designed to degrade performance of models or cause machine learning models to produce specific outputs chosen ahead of time by the attacker.

We introduce attacks that instead reprogram the target model to perform a task chosen by the attacker—without the attacker needing to specify or compute the desired output for each test-time input. This attack finds a single adversarial perturbation, that can be added to all test-time inputs to a machine learning model in order to cause the model to perform a task chosen by the adversary—even if the model was not trained to do this task. These perturbations can thus be considered a program for the new task.

We demonstrate adversarial reprogramming on 6 ImageNet classification models, repurposing these models to perform a counting task, as well as classification tasks: classification of MNIST and CIFAR-10 examples presented as inputs to the ImageNet model.

--- https://arxiv.org/abs/1807.00734 The relativistic discriminator: a key element missing from standard GAN Alexia Jolicoeur-Martineau 2018-07-02 2020-01-04 [("doi","10.48550/arXiv.1807.00734")] ai/nn/gan

In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real. The generator is trained to increase the probability that fake data is real. We argue that it should also simultaneously decrease the probability that real data is real because (1) this would account for a priori knowledge that half of the data in the mini-batch is fake, (2) this would be observed with divergence minimization, and (3) in optimal settings, SGAN would be equivalent to integral probability metric (IPM) GANs.

We show that this property can be induced by using a relativistic discriminator which estimate the probability that the given real data is more realistic than a randomly sampled fake data. We also present a variant in which the discriminator estimate the probability that the given real data is more realistic than fake data, on average. We generalize both approaches to non-standard GAN loss functions and we refer to them respectively as Relativistic GANs (RGANs) and Relativistic average GANs (RaGANs). We show that IPM-based GANs are a subset of RGANs which use the identity function.

Empirically, we observe that (1) RGANs and RaGANs are statistically-significantly more stable and generate higher quality data samples than their non-relativistic counterparts, (2) Standard RaGAN with gradient penalty generate data of better quality than WGAN-GP while only requiring a single discriminator update per generator update (reducing the time taken for reaching the state-of-the-art by 400%), and (3) RaGANs are able to generate plausible high resolutions images (256×256) from a very small sample (n = 2011), while GAN and LSGAN cannot; these images are of better quality than the ones generated by WGAN-GP and SGAN with spectral normalization.

--- https://arxiv.org/abs/1807.01281#deepmind Human-level performance in first-person multiplayer games with population-based deep reinforcement learning Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castaneda, Charles Beattie, Neil C. Rabinowitz, Ari S. Morcos, Avraham Ruderman, Nicolas Sonnerat, Tim Green, Louise Deason, Joel Z. Leibo, David Silver, Demis Hassabis, Koray Kavukcuoglu, Thore Graepel 2018-07-03 2020-01-04 [("doi","10.1126/science.aau6249")] reinforcement-learning/meta-learning reinforcement-learning/multi-agent reinforcement-learning/scaling

Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based games. However, the real-world contains multiple agents, each learning and acting independently to cooperate and compete with other agents, and environments reflecting this degree of complexity remain an open challenge.

In this work, we demonstrate for the first time that an agent can achieve human-level in a popular 3D multiplayer first-person video game, Quake III Arena Capture the Flag, using only pixels and game points as input. These results were achieved by a novel two-tier optimization process in which a population of independent RL agents are trained concurrently from thousands of parallel matches with agents playing in teams together and against each other on randomly generated environments. Each agent in the population learns its own internal reward signal to complement the sparse delayed reward from winning, and selects actions using a novel temporally hierarchical representation that enables the agent to reason at multiple timescales.

During game-play, these agents display human-like behaviors such as navigating, following, and defending based on a rich learned representation that is shown to encode high-level game knowledge. In an extensive tournament-style evaluation the trained agents exceeded the win-rate of strong human players both as teammates and opponents, and proved far stronger than existing state-of-the-art agents. These results demonstrate a jump in the capabilities of artificial agents, bringing us closer to the goal of human-level intelligence.

--- https://arxiv.org/abs/1807.03765 Is Q-learning Provably Efficient? Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, Michael I. Jordan 2018-07-10 2020-01-04 [("doi","10.48550/arXiv.1807.03765")] reinforcement-learning/exploration reinforcement-learning/model-free

Model-free reinforcement learning (RL) algorithms, such as Q-learning, directly parameterize and update value functions or policies without explicitly modeling the environment. They are typically simpler, more flexible to use, and thus more prevalent in modern deep RL than model-based approaches. However, empirical work has suggested that model-free algorithms may require more samples to learn [Deisenroth & Rasmussen 2011, Schulman et al 2015]. The theoretical question of “whether model-free algorithms can be made sample efficient” is one of the most fundamental questions in RL, and remains unsolved even in the basic scenario with finitely many states and actions.

We prove that, in an episodic MDP setting, Q-learning with UCB exploration achieves regret \U0001D4AA(√H3SAT), where S and A are the numbers of states and actions, H is the number of steps per episode, and T is the total number of steps. This sample efficiency matches the optimal regret that can be achieved by any model-based approach, up to a single √H factor. To the best of our knowledge, this is the first analysis in the model-free setting that establishes √T regret without requiring access to a “simulator.”

--- https://arxiv.org/abs/1807.04409 Sem-GAN: Semantically-Consistent Image-to-Image Translation Anoop Cherian, Alan Sullivan 2018-07-12 2020-01-04 [("doi","10.48550/arXiv.1807.04409")] ai/nn/gan

Unpaired image-to-image translation is the problem of mapping an image in the source domain to one in the target domain, without requiring corresponding image pairs. To ensure the translated images are realistically plausible, recent works, such as Cycle-GAN, demands this mapping to be invertible. While this requirement demonstrates promising results when the domains are unimodal, its performance is unpredictable in a multi-modal scenario such as in an image segmentation task. This is because invertibility does not necessarily enforce semantic correctness.

To this end, we present a semantically-consistent GAN framework, dubbed Sem-GAN, in which the semantics are defined by the class identities of image segments in the source domain as produced by a semantic segmentation algorithm. Our proposed framework includes consistency constraints on the translation task that, together with the GAN loss and the cycle-constraints, enforces that the images when translated will inherit the appearances of the target domain, while (approximately) maintaining their identities from the source domain.

We present experiments on several image-to-image translation tasks and demonstrate that Sem-GAN improves the quality of the translated images, sometimes by more than 20% on the FCN score. Further, we show that semantic segmentation models, trained with synthetic images translated via Sem-GAN, lead to better segmentation results than other variants.

--- https://arxiv.org/abs/1807.04742 Visual Reinforcement Learning with Imagined Goals Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine 2018-07-12 2020-01-04 [("doi","10.48550/arXiv.1807.04742")] reinforcement-learning/exploration reinforcement-learning/robot

For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires. Furthermore, to provide the requisite level of generality, these skills must handle raw sensory input such as images.

In this paper, we propose an algorithm that acquires such general-purpose skills by combining unsupervised representation learning and reinforcement learning of goal-conditioned policies. Since the particular goals that might be required at test-time are not known in advance, the agent performs a self-supervised “practice” phase where it imagines goals and attempts to achieve them. We learn a visual representation with 3 distinct purposes: sampling goals for self-supervised practice, providing a structured transformation of raw sensory inputs, and computing a reward signal for goal reaching. We also propose a retroactive goal relabeling scheme to further improve the sample-efficiency of our method.

Our off-policy algorithm is efficient enough to learn policies that operate on raw image observations and goals for a real-world robotic system, and substantially outperforms prior techniques.

--- https://arxiv.org/abs/1807.06358 IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis Huaibo Huang, Zhihang Li, Ran He, Zhenan Sun, Tieniu Tan 2018-07-17 2020-01-05 [("doi","10.48550/arXiv.1807.06358")] ai/nn/gan ai/nn/vae

We present a novel introspective variational autoencoder (IntroVAE) model for synthesizing high-resolution photographic images. IntroVAE is capable of self-evaluating the quality of its generated samples and improving itself accordingly. Its inference and generator models are jointly trained in an introspective way.

On one hand, the generator is required to reconstruct the input images from the noisy outputs of the inference model as normal VAEs. On the other hand, the inference model is encouraged to classify between the generated and real samples while the generator tries to fool it as GANs. These two famous generative frameworks are integrated in a simple yet efficient single-stream architecture that can be trained in a single stage. IntroVAE preserves the advantages of VAEs, such as stable training and nice latent manifold. Unlike most other hybrid models of VAEs and GANs, IntroVAE requires no extra discriminators, because the inference model itself serves as a discriminator to distinguish between the generated and real samples.

Experiments demonstrate that our method produces high-resolution photo-realistic images (eg. CELEBA images at 10242), which are comparable to or better than the state-of-the-art GANs.

--- https://arxiv.org/abs/1807.07281 ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech Wei Ping, Kainan Peng, Jitong Chen 2018-07-19 2020-01-05 [("doi","10.48550/arXiv.1807.07281")] ai/nn/sparsity/knowledge-distillation

In this work, we propose a new solution for parallel wave generation by WaveNet. In contrast to parallel WaveNet (van den Oord et al 2018), we distill a Gaussian inverse autoregressive flow from the autoregressive WaveNet by minimizing a regularized KL divergence between their highly-peaked output distributions.

Our method computes the KL divergence in closed-form, which simplifies the training algorithm and provides very efficient distillation. In addition, we introduce the first text-to-wave neural architecture for speech synthesis, which is fully convolutional and enables fast end-to-end training from scratch.

It outperforms the previous pipeline that connects a text-to-spectrogram model to a separately trained WaveNet (Ping et al 2018). We also successfully distill a parallel waveform synthesizer conditioned on the hidden representation in this end-to-end model.

--- https://arxiv.org/abs/1808.00177#openai Learning Dexterous In-Hand Manipulation OpenAI, Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, Wojciech Zaremba 2018-08-01 2020-01-05 [("doi","10.48550/arXiv.1808.00177")] reinforcement-learning/meta-learning reinforcement-learning/robot

We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand.

The training is performed in a simulated environment in which we randomize many of the physical properties of the system like friction coefficients and an object’s appearance. Our policies transfer to the physical robot despite being trained entirely in simulation.

Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity.

Our results were obtained using the same distributed RL system that was used to train OpenAI Five. We also include a video of our results: YouTube.

--- https://arxiv.org/abs/1808.02513 Rethinking Numerical Representations for Deep Neural Networks Parker Hill, Babak Zamirai, Shengshuo Lu, Yu-Wei Chao, Michael Laurenzano, Mehrzad Samadi, Marios Papaefthymiou, Scott Mahlke, Thomas Wenisch, Jia Deng, Lingjia Tang, Jason Mars 2018-08-07 2020-01-05 [("doi","10.48550/arXiv.1808.02513")] ai/nn/sparsity/low-precision

With ever-increasing computational demand for deep learning, it is critical to investigate the implications of the numeric representation and precision of DNN model weights and activations on computational efficiency. In this work, we explore unconventional narrow-precision floating-point representations as it relates to inference accuracy and efficiency to steer the improved design of future DNN platforms.

We show that inference using these custom numeric representations on production-grade DNNs, including GoogLeNet and VGG, achieves an average speedup of 7.6× with less than 1% degradation in inference accuracy relative to a state-of-the-art baseline platform representing the most sophisticated hardware using single-precision floating point.

To facilitate the use of such customized precision, we also present a novel technique that drastically reduces the time required to derive the optimal precision configuration.

--- https://arxiv.org/abs/1808.04355 RND: Large-Scale Study of Curiosity-Driven Learning Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, Alexei A. Efros 2018-08-13 2020-01-05 [("doi","10.48550/arXiv.1808.04355")] reinforcement-learning/exploration

Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is a type of intrinsic reward function which uses prediction error as reward signal.

In this paper: (a) We perform the first large-scale study of purely curiosity-driven learning, i.e. without any extrinsic rewards, across 54 standard benchmark environments, including the Atari game suite.

Our results show surprisingly good performance, and a high degree of alignment between the intrinsic curiosity objective and the hand-designed extrinsic rewards of many game environments. (b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (eg. to novel game levels in Super Mario Bros.). (c) We demonstrate limitations of the prediction-based rewards in stochastic setups.

Game-play videos and code are at https://pathak22.github.io/large-scale-curiosity/.

--- https://arxiv.org/abs/1808.10120 ExIt-OOS: Towards Learning from Planning in Imperfect Information Games Andy Kitchen, Michela Benedetti 2018-08-30 2020-01-05 [("doi","10.48550/arXiv.1808.10120")] reinforcement-learning/imperfect-information reinforcement-learning/model/alphago

The current state-of-the-art in playing many important perfect information games, including Chess and Go, combines planning and deep reinforcement learning with self-play.

We extend this approach to imperfect information games and present ExIt-OOS, a novel approach to playing imperfect information games within the Expert Iteration framework and inspired by AlphaZero. We use Online Outcome Sampling, an online search algorithm for imperfect information games in place of MCTS.

While training online, our neural strategy is used to improve the accuracy of playouts in OOS, allowing a learning and planning feedback loop for imperfect information games.

--- https://arxiv.org/abs/1809.00219 ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Yu Qiao, Xiaoou Tang 2018-09-01 2020-01-05 [("doi","10.48550/arXiv.1809.00219")] ai/anime ai/nn/gan

The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts.

To further enhance the visual quality, we thoroughly study 3 key components of SRGAN—network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block (RRDB) without batch normalization as the basic network building unit. Moreover, we borrow the idea from relativistic GAN to let the discriminator predict relative realness instead of the absolute value. Finally, we improve the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery.

Benefiting from these improvements, the proposed ESRGAN achieves consistently better visual quality with more realistic and natural textures than SRGAN and won the first place in the PIRM2018-SR Challenge.

The code is available at Github.

--- https://arxiv.org/abs/1809.01829 Adversarial Reprogramming of Text Classification Neural Networks Paarth Neekhara, Shehzeen Hussain, Shlomo Dubnov, Farinaz Koushanfar 2018-09-06 2020-01-05 [("doi","10.48550/arXiv.1809.01829")] ai/nn/adversarial ai/nn/rnn

Adversarial Reprogramming has demonstrated success in using pre-trained neural network classifiers for alternative classification tasks without modification to the original network. An adversary in such an attack scenario trains an additive contribution to the inputs to repurpose the neural network for the new classification task. While this reprogramming approach works for neural networks with a continuous input space such as that of images, it is not directly applicable to neural networks trained for tasks such as text classification, where the input space is discrete. Repurposing such classification networks would require the attacker to learn an adversarial program that maps inputs from one discrete space to the other.

In this work, we introduce a context-based vocabulary remapping model to reprogram neural networks trained on a specific sequence classification task, for a new sequence classification task desired by the adversary. We propose training procedures for this adversarial program in both white-box and black-box settings.

We demonstrate the application of our model by adversarially repurposing various text-classification models including LSTM, bi-directional LSTM and CNN for alternate classification tasks.

--- https://arxiv.org/abs/1809.04120 Humans can decipher adversarial images Zhenglong Zhou, Chaz Firestone 2018-09-11 2020-01-05 [("doi","10.1038/s41467-019-08931-6")] ai/nn/adversarial/human psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

How similar is the human mind to the sophisticated machine-learning systems that mirror its performance? Models of object categorization based on convolutional neural networks (CNNs) have achieved human-level benchmarks in assigning known labels to novel images. These advances promise to support transformative technologies such as autonomous vehicles and machine diagnosis; beyond this, they also serve as candidate models for the visual system itself—not only in their output but perhaps even in their underlying mechanisms and principles. However, unlike human vision, CNNs can be “fooled” by adversarial examples—carefully crafted images that appear as nonsense patterns to humans but are recognized as familiar objects by machines, or that appear as one object to humans and a different object to machines. This seemingly extreme divergence between human and machine classification challenges the promise of these new advances, both as applied image-recognition systems and also as models of the human mind.

Surprisingly, however, little work has empirically investigated human classification of such adversarial stimuli: Does human and machine performance fundamentally diverge? Or could humans decipher such images and predict the machine’s preferred labels? Here, we show that human and machine classification of adversarial stimuli are robustly related: In 8 experiments on 5 prominent and diverse adversarial imagesets, human subjects reliably identified the machine’s chosen label over relevant foils.

This pattern persisted for images with strong antecedent identities, and even for images described as “totally unrecognizable to human eyes”. We suggest that human intuition may be a more reliable guide to machine (mis)classification than has typically been imagined, and we explore the consequences of this result for minds and machines alike.

--- https://arxiv.org/abs/1809.04184 Searching for Efficient Multi-Scale Architectures for Dense Image Prediction Liang-Chieh Chen, Maxwell D. Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, Jonathon Shlens 2018-09-11 2020-01-05 [("doi","10.48550/arXiv.1809.04184")] ai/nn reinforcement-learning/meta-learning

The design of neural network architectures is an important component for achieving state-of-the-art performance with machine learning systems across a broad array of tasks. Much work has endeavored to design and build architectures automatically through clever construction of a search space paired with simple learning algorithms. Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks.

An open question is the degree to which such methods may generalize to new domains. In this work we explore the construction of meta-learning techniques for dense image prediction focused on the tasks of scene parsing, person-part segmentation, and semantic image segmentation. Constructing viable search spaces in this domain is challenging because of the multi-scale representation of visual information and the necessity to operate on high resolution imagery.

Based on a survey of techniques in dense image prediction, we construct a recursive search space and demonstrate that even with efficient random search, we can identify architectures that outperform human-invented architectures and achieve state-of-the-art performance on 3 dense prediction tasks including 82.7% on Cityscapes (street scene parsing), 71.3% on PASCAL-Person-Part (person-part segmentation), and 87.9% on PASCAL VOC 2012 (semantic image segmentation). Additionally, the resulting architecture is more computationally efficient, requiring half the parameters and half the computational cost as previous state-of-the-art systems.

--- https://arxiv.org/abs/1809.05262 Network Recasting: A Universal Method for Network Architecture Transformation Joonsang Yu, Sungbum Kang, Kiyoung Choi 2018-09-14 2020-01-06 [("doi","10.48550/arXiv.1809.05262")] ai/nn/sparsity/knowledge-distillation

This paper proposes network recasting as a general method for network architecture transformation.

The primary goal of this method is to accelerate the inference process through the transformation, but there can be many other practical applications. The method is based on block-wise recasting; it recasts each source block in a pre-trained teacher network to a target block in a student network. For the recasting, a target block is trained such that its output activation approximates that of the source block. Such a block-by-block recasting in a sequential manner transforms the network architecture while preserving the accuracy.

This method can be used to transform an arbitrary teacher network type to an arbitrary student network type. It can even generate a mixed-architecture network that consists of two or more types of block. The network recasting can generate a network with fewer parameters and/or activations, which reduce the inference time. Naturally, it can be used for network compression by recasting a trained network into a smaller network of the same type.

Our experiments show that it outperforms previous compression approaches in terms of actual speedup on a GPU.

--- https://arxiv.org/abs/1809.09328 Why scatter plots suggest causality, and what we can do about it Carl T. Bergstrom, Jevin D. West 2018-09-25 2020-01-06 [("doi","10.48550/arXiv.1809.09328")] design/visualization statistics/causality

Scatter plots carry an implicit if subtle message about causality. Whether we look at functions of one variable in pure mathematics, plots of experimental measurements as a function of the experimental conditions, or scatter plots of predictor and response variables, the value plotted on the vertical axis is by convention assumed to be determined or influenced by the value on the horizontal axis.

This is a problem for the public understanding of scientific results and perhaps also for professional scientists’ interpretations of scatter plots. To avoid suggesting a causal relationship between the x and y values in a scatter plot, we propose a new type of data visualization, the diamond plot.

Diamond plots are essentially 45 degree rotations of ordinary scatter plots; by visually jarring the viewer they clearly indicate that she should not draw the usual distinction between independent/predictor variable and dependent/response variable. Instead, she should see the relationship as purely correlative.

--- https://arxiv.org/abs/1809.11096#deepmind Large Scale GAN Training for High Fidelity Natural Image Synthesis Andrew Brock, Jeff Donahue, Karen Simonyan 2018-09-28 2020-01-06 [("doi","10.48550/arXiv.1809.11096")] ai/nn/gan/biggan ai/scaling

Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale.

We find that applying orthogonal regularization to the generator renders it amenable to a simple “truncation trick”, allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator’s input. Our modifications lead to models which set the new state-of-the-art in class-conditional image synthesis.

When trained on ImageNet at 128×128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Fréchet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6.

--- https://arxiv.org/abs/1810.00821 Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, Sergey Levine 2018-10-01 2020-01-06 [("doi","10.48550/arXiv.1810.00821")] ai/nn/gan/stylegan ai/video/analysis reinforcement-learning/imitation-learning reinforcement-learning/model-free

Adversarial learning methods have been proposed for a wide range of applications, but the training of adversarial models can be notoriously unstable. Effectively balancing the performance of the generator and discriminator is critical, since a discriminator that achieves very high accuracy will produce relatively uninformative gradients. In this work, we propose a simple and general technique to constrain information flow in the discriminator by means of an information bottleneck. By enforcing a constraint on the mutual information between the observations and the discriminator’s internal representation, we can effectively modulate the discriminator’s accuracy and maintain useful and informative gradients.

We demonstrate that our proposed variational discriminator bottleneck (VDB) leads to improvements across 3 distinct application areas for adversarial learning algorithms. Our primary evaluation studies the applicability of the VDB to imitation learning of dynamic continuous control skills, such as running. We show that our method can learn such skills directly from raw video demonstrations, substantially outperforming prior adversarial imitation learning methods. The VDB can also be combined with adversarial inverse reinforcement learning to learn parsimonious reward functions that can be transferred and re-optimized in new settings. Finally, we demonstrate that VDB can train GANs more effectively for image generation, improving upon a number of prior stabilization methods.

--- https://arxiv.org/abs/1810.01218 AlphaSeq: Sequence Discovery with Deep Reinforcement Learning Yulin Shao, Soung Chang Liew, Taotao Wang 2018-09-26 2020-01-06 [("doi","10.48550/arXiv.1810.01218")] reinforcement-learning/model/alphago

Sequences play an important role in many applications and systems. Discovering sequences with desired properties has long been an interesting intellectual pursuit. This paper puts forth a new paradigm, AlphaSeq, to discover desired sequences algorithmically using deep reinforcement learning (DRL) techniques.

AlphaSeq treats the sequence discovery problem as an episodic symbol-filling game, in which a player fills symbols in the vacant positions of a sequence set sequentially during an episode of the game. Each episode ends with a completely-filled sequence set, upon which a reward is given based on the desirability of the sequence set. AlphaSeq models the game as a Markov Decision Process (MDP), and adapts the DRL framework of AlphaGo to solve the MDP.

Sequences discovered improve progressively as AlphaSeq, starting as a novice, learns to become an expert game player through many episodes of game playing. Compared with traditional sequence construction by mathematical tools, AlphaSeq is particularly suitable for problems with complex objectives intractable to mathematical analysis.

We demonstrate the searching capabilities of AlphaSeq in two applications: (1) AlphaSeq successfully rediscovers a set of ideal complementary codes that can zero-force all potential interferences in multi-carrier CDMA systems. (2) AlphaSeq discovers new sequences that triple the signal-to-interference ratio—benchmarked against the well-known Legendre sequence—of a mismatched filter estimator in pulse compression radar systems.

--- https://arxiv.org/abs/1810.04622 A Closer Look at Structured Pruning for Neural Network Compression Elliot J. Crowley, Jack Turner, Amos Storkey, Michael O’Boyle 2018-10-10 2020-01-06 [("doi","10.48550/arXiv.1810.04622")] ai/nn/sparsity/pruning

Structured pruning is a popular method for compressing a neural network: given a large trained network, one alternates between removing channel connections and fine-tuning; reducing the overall width of the network. However, the efficacy of structured pruning has largely evaded scrutiny.

In this paper, we examine ResNets and DenseNets obtained through structured pruning-and-tuning and make two interesting observations: (1) reduced networks—smaller versions of the original network trained from scratch—consistently outperform pruned networks; (2) if one takes the architecture of a pruned network and then trains it from scratch it is statistically-significantly more competitive. Furthermore, these architectures are easy to approximate: we can prune once and obtain a family of new, scalable network architectures that can simply be trained from scratch.

Finally, we compare the inference speed of reduced and pruned networks on hardware, and show that reduced networks are faster.

Code is available at Github.

--- https://arxiv.org/abs/1810.06758 Discriminator Rejection Sampling Samaneh Azadi, Catherine Olsson, Trevor Darrell, Ian Goodfellow, Augustus Odena 2018-10-16 2020-01-06 [("doi","10.48550/arXiv.1810.06758")] ai/nn/gan/biggan ai/nn/gan/stylegan ai/nn/sampling

We propose a rejection sampling scheme using the discriminator of a GAN to correct errors in the GAN generator distribution. We show that under quite strict assumptions, this will allow us to recover the data distribution exactly. We then examine where those strict assumptions break down and design a practical algorithm—called Discriminator Rejection Sampling (DRS)—that can be used on real data-sets.

Finally, we demonstrate the efficacy of DRS on a mixture of Gaussians and on the SAGAN model, state-of-the-art in the image generation task at the time of developing this work. On ImageNet, we train an improved baseline that increases the Inception Score 52.52 → 62.36 and reduces the Fréchet Inception Distance 18.65 → 14.79. We then use DRS to further improve on this baseline, improving the Inception Score to 76.08 and the FID to 13.75.

--- https://arxiv.org/abs/1810.10974 Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features Simone Palazzo, Concetto Spampinato, Isaak Kavasidis, Daniela Giordano, Joseph Schmidt, Mubarak Shah 2018-10-25 2020-01-06 [("doi","10.48550/arXiv.1810.10974")] reinforcement-learning/imitation-learning/brain-imitation-learning

This work presents a novel method of exploring human brain-visual representations, with a view towards replicating these processes in machines. The core idea is to learn plausible computational and biological representations by correlating human neural activity and natural images.

Thus, we first propose a model, EEG-ChannelNet, to learn a brain manifold for EEG classification. After verifying that visual information can be extracted from EEG data, we introduce a multimodal approach that uses deep image and EEG encoders, trained in a Siamese configuration, for learning a joint manifold that maximizes a compatibility measure between visual features and brain representations.

We then carry out image classification and saliency detection on the learned manifold. Performance analyses show that our approach satisfactorily decodes visual information from neural signals. This, in turn, can be used to effectively supervise the training of deep learning models, as demonstrated by the high performance of image classification and saliency detection on out-of-training classes.

The obtained results show that the learned brain-visual features lead to improved performance and simultaneously bring deep models more in line with cognitive neuroscience work related to visual perception and attention.

--- https://arxiv.org/abs/1810.12894#openai Exploration by Random Network Distillation Yuri Burda, Harrison Edwards, Amos Storkey, Oleg Klimov 2018-10-30 2020-01-06 [("doi","10.48550/arXiv.1810.12894")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/exploration

We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed.

The bonus is the error of a neural network predicting features of the observations given by a fixed randomly initialized neural network. We also introduce a method to flexibly combine intrinsic and extrinsic rewards.

We find that the random network distillation (RND) bonus combined with this increased flexibility enables progress on several hard exploration Atari games. In particular, we establish state-of-the-art performance on Montezuma’s Revenge, a game famously difficult for deep reinforcement learning methods.

To the best of our knowledge, this is the first method that achieves better than average human performance on this game without using demonstrations or having access to the underlying state of the game, and occasionally completes the first level.

--- https://arxiv.org/abs/1811.01721 Rethinking floating point for deep learning Jeff Johnson 2018-11-01 2020-01-06 [("doi","10.48550/arXiv.1811.01721")] ai/nn/sparsity/low-precision

Reducing hardware overhead of neural networks for faster or lower power inference and training is an active area of research. Uniform quantization using integer multiply-add has been thoroughly investigated, which requires learning many quantization parameters, fine-tuning training or other prerequisites. Little effort is made to improve floating point relative to this baseline; it remains energy inefficient, and word size reduction yields drastic loss in needed dynamic range.

We improve floating point to be more energy efficient than equivalent bit width integer hardware on a 28 nm ASIC process while retaining accuracy in 8 bits with a novel hybrid log multiply/linear add, Kulisch accumulation and tapered encodings from Gustafson’s posit format. With no network retraining, and drop-in replacement of all math and float32 parameters via round-to-nearest-even only, this open-sourced 8-bit log float is within 0.9% top-1 and 0.2% top-5 accuracy of the original float32 ResNet-50 CNN model on ImageNet. Unlike int8 quantization, it is still a general purpose floating point arithmetic, interpretable out-of-the-box.

Our 8/38-bit log float multiply-add is synthesized and power profiled at 28 nm at 0.96× the power and 1.12× the area of 8/32-bit integer multiply-add. In 16 bits, our log float multiply-add is 0.59× the power and 0.68× the area of IEEE 754 float16 fused multiply-add, maintaining the same signficand precision and dynamic range, proving useful for training ASICs as well.

--- https://arxiv.org/abs/1811.02549 Language GANs Falling Short Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, Laurent Charlin 2018-11-06 2020-01-06 [("doi","10.48550/arXiv.1811.02549")] ai/nn/gan ai/nn/sampling reinforcement-learning/imitation-learning

Generating high-quality text with sufficient diversity is essential for a wide range of Natural Language Generation (NLG) tasks. Maximum-Likelihood Estimation (MLE) models trained with teacher forcing have consistently been reported as weak baselines, where poor performance is attributed to exposure bias (Bengio et al 2015; Ranzato et al 2015); at inference time, the model is fed its own prediction instead of a ground-truth token, which can lead to accumulating errors and poor samples. This line of reasoning has led to an outbreak of adversarial based approaches for NLG, on the account that GANs do not suffer from exposure bias.

In this work, we make several surprising observations which contradict common beliefs. First, we revisit the canonical evaluation framework for NLG, and point out fundamental flaws with quality-only evaluation: we show that one can outperform such metrics using a simple, well-known temperature parameter to artificially reduce the entropy of the model’s conditional distributions. Second, we leverage the control over the quality / diversity trade-off given by this parameter to evaluate models over the whole quality-diversity spectrum and find MLE models constantly outperform the proposed GAN variants over the whole quality-diversity space.

Our results have several implications: (1) The impact of exposure bias on sample quality is less severe than previously thought, (2) temperature tuning provides a better quality / diversity trade-off than adversarial training while being easier to train, easier to cross-validate, and less computationally expensive.

Code to reproduce the experiments is available at github.com/pclucas14/GansFallingShort.

--- https://arxiv.org/abs/1811.06521#deepmind Reward learning from human preferences and demonstrations in Atari Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei 2018-11-15 2020-01-07 [("doi","10.48550/arXiv.1811.06521")] reinforcement-learning/imitation-learning reinforcement-learning/preference-learning

To solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions. Instead, we can have humans communicate an objective to the agent directly.

In this work, we combine two approaches to learning from human feedback: expert demonstrations and trajectory preferences. We train a deep neural network to model the reward function and use its predicted reward to train a DQN-based deep reinforcement learning agent on 9 Atari games.

Our approach beats the imitation learning baseline in 7 games and achieves strictly superhuman performance on 2 games without using game rewards.

Additionally, we investigate the goodness of fit of the reward model, present some reward hacking problems, and study the effects of noise in the human labels.

--- https://arxiv.org/abs/1811.07296 GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint Jianlin Su 2018-11-18 2020-01-07 [("doi","10.48550/arXiv.1811.07296")] ai/nn/gan

We know SGAN may have a risk of gradient vanishing. A improvement is WGAN, with the help of 1-Lipschitz constraint on discriminator to prevent from gradient vanishing. Is there any GAN having no gradient vanishing and no 1-Lipschitz constraint on discriminator? We do find one, called GAN-QP.

To construct a new framework of Generative Adversarial Network (GAN) usually includes 3 steps: 1. choose a probability divergence; 2. convert it into a dual form; 3. play a min-max game. In this articles, we demonstrate that the first step is not necessary. We can analyse the property of divergence and even construct new divergence in dual space directly. As a reward, we obtain a simpler alternative of WGAN: GAN-QP. We demonstrate that GAN-QP have a better performance than WGAN in theory and practice.

--- https://arxiv.org/abs/1811.10153 Spatially Controllable Image Synthesis with Internal Representation Collaging Ryohei Suzuki, Masanori Koyama, Takeru Miyato, Taizan Yonetsuji, Huachun Zhu 2018-11-26 2020-01-07 [("doi","10.48550/arXiv.1811.10153")] ai/anime/danbooru ai/nn/gan/stylegan/anime

We present a novel CNN-based image editing strategy that allows the user to change the semantic information of an image over an arbitrary region by manipulating the feature-space representation of the image in a trained GAN model.

We will present two variants of our strategy: (1) spatial conditional batch normalization (sCBN), a type of conditional batch normalization with user-specifiable spatial weight maps, and (2) feature-blending, a method of directly modifying the intermediate features.

Our methods can be used to edit both artificial image and real image, and they both can be used together with any GAN with conditional normalization layers.

We will demonstrate the power of our method through experiments on various types of GANs trained on different datasets.

Code will be available at Github.

--- https://arxiv.org/abs/1811.10192 Tweedie Gradient Boosting for Extremely Unbalanced Zero-inflated Data He Zhou, Yi Yang, Wei Qian 2018-11-26 2020-01-07 [("doi","10.48550/arXiv.1811.10192")] ai/tabular statistics/probability

Tweedie’s compound Poisson model is a popular method to model insurance claims with probability mass at zero and nonnegative, highly right-skewed distribution. In particular, it is not uncommon to have extremely unbalanced data with excessively large proportion of zero claims, and even traditional Tweedie model may not be satisfactory for fitting the data.

In this paper, we propose a boosting-assisted zero-inflated Tweedie model, called EMTboost, that allows zero probability mass to exceed a traditional model. We make a nonparametric assumption on its Tweedie model component, that unlike a linear model, is able to capture nonlinearities, discontinuities, and complex higher order interactions among predictors. A specialized Expectation-Maximization algorithm is developed that integrates a blockwise coordinate descent strategy and a gradient tree-boosting algorithm to estimate key model parameters.

We use extensive simulation and data analysis on synthetic zero-inflated auto-insurance claim data to illustrate our method’s prediction performance.

--- https://arxiv.org/abs/1811.12560 An Introduction to Deep Reinforcement Learning Vincent Francois-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau 2018-11-30 2020-01-07 [("doi","10.1561/2200000071")] reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/robot

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning.

This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more.

This manuscript provides an introduction to deep reinforcement learning models, algorithms, and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications.

We assume the reader is familiar with basic machine learning concepts.

--- https://arxiv.org/abs/1812.06855#deepmind Bayesian Optimization in AlphaGo Yutian Chen, Aja Huang, Ziyu Wang, Ioannis Antonoglou, Julian Schrittwieser, David Silver, Nando de Freitas 2018-12-17 2020-01-07 [("doi","10.48550/arXiv.1812.06855")] reinforcement-learning/model/alphago

During the development of AlphaGo, its many hyper-parameters were tuned with Bayesian optimization multiple times.

This automatic tuning process resulted in substantial improvements in playing strength. For example, prior to the match with Lee Sedol, we tuned the latest AlphaGo agent and this improved its win-rate 50% → 66.5% in self-play games. This tuned version was deployed in the final match. Of course, since we tuned AlphaGo many times during its development cycle, the compounded contribution was even higher than this percentage.

It is our hope that this brief case study will be of interest to Go fans, and also provide Bayesian optimization practitioners with some insights and inspiration.

…Interestingly, the automatically found hyper-parameter values were very different from the default values found by previous hand tuning efforts. Moreover, the hyper-parameters were often correlated, and hence the values found by Bayesian optimization were not reachable with element-wise hand-tuning, or even by tuning pairs of parameters in some cases.

By tuning the mixing ratio between roll-out estimates and value network estimates, we found out that Bayesian optimization gave increased preference to value network estimates as the design cycle progressed. This eventual led the team to abandon roll-out estimates in future versions of AlphaGo and AlphaGo Zero.

--- https://arxiv.org/abs/1812.07019#deepmind Malthusian Reinforcement Learning Joel Z. Leibo, Julien Perolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel 2018-12-17 2020-01-07 [("doi","10.48550/arXiv.1812.07019")] reinforcement-learning/meta-learning reinforcement-learning/multi-agent

Here we explore a new algorithmic framework for multi-agent reinforcement learning, called Malthusian reinforcement learning, which extends self-play to include fitness-linked population size dynamics that drive ongoing innovation.

In Malthusian RL, increases in a subpopulation’s average return drive subsequent increases in its size, just as Thomas Malthus argued in 1798 was the relationship between preindustrial income levels and population growth. Malthusian reinforcement learning harnesses the competitive pressures arising from growing and shrinking population size to drive agents to explore regions of state and policy spaces that they could not otherwise reach.

Furthermore, in environments where there are potential gains from specialization and division of labor, we show that Malthusian reinforcement learning is better positioned to take advantage of such synergies than algorithms based on self-play.

--- https://arxiv.org/abs/1901.02199 FIGR: Few-shot Image Generation with Reptile Louis Clouâtre, Marc Demers 2019-01-08 2020-01-07 [("doi","10.48550/arXiv.1901.02199")] ai/dataset ai/nn/gan/stylegan design/typography reinforcement-learning/meta-learning

Generative Adversarial Networks (GAN) boast impressive capacity to generate realistic images. However, like much of the field of deep learning, they require an inordinate amount of data to produce results, thereby limiting their usefulness in generating novelty. In the same vein, recent advances in meta-learning have opened the door to many few-shot learning applications. In the present work, we propose Few-shot Image Generation using Reptile (FIGR), a GAN meta-trained with Reptile.

Our model successfully generates novel images on both MNIST and Omniglot with as little as 4 images from an unseen class. We further contribute FIGR-8, a new dataset for few-shot image generation, which contains 1,548,944 icons categorized in over 18,409 classes. Trained on FIGR-8, initial results show that our model can generalize to more advanced concepts (such as “bird” and “knife”) from as few as 8 samples from a previously unseen class of images and as little as 10 training steps through those 8 images.

This work demonstrates the potential of training a GAN for few-shot image generation and aims to set a new benchmark for future work in the domain.

--- https://arxiv.org/abs/1901.08652 Learning agile and dynamic motor skills for legged robots Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy, Dario Bellicoso, Vassilios Tsounis, Vladlen Koltun, Marco Hutter 2019-01-24 2020-01-07 [("doi","10.1126/scirobotics.aau5872")] reinforcement-learning/robot

Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanship and promotes the natural evolution of a control policy. However, so far, reinforcement learning research for legged robots is mainly limited to simulation, and only few and comparably simple examples have been deployed on real systems. The primary reason is that training with real robots, particularly with dynamically balancing systems, is complicated and expensive.

In the present work, we introduce a method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system, thereby leveraging fast, automated, and cost-effective data generation schemes. The approach is applied to the ANYmal robot, a sophisticated medium-dog-sized quadrupedal system. Using policies trained in simulation, the quadrupedal machine achieves locomotion skills that go beyond what had been achieved with prior methods: ANYmal is capable of precisely and energy-efficiently following high-level body velocity commands, running faster than before, and recovering from falling even in complex configurations.

--- https://arxiv.org/abs/1901.10995#uber Go-Explore: a New Approach for Hard-Exploration Problems Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, Jeff Clune 2019-01-30 2020-01-07 [("doi","10.48550/arXiv.1901.10995")] reinforcement-learning/exploration reinforcement-learning/imitation-learning

A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma’s Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, which is the dominant method to improve performance on hard-exploration domains.

To address this shortfall, we introduce a new algorithm called Go-Explore. It exploits the following principles: (1) remember previously visited states, (2) first return to a promising state (without exploration), then explore from it, and (3) solve simulated environments through any available means (including by introducing determinism), then robustify via imitation learning. The combined effect of these principles is a dramatic performance improvement on hard-exploration problems.

On Montezuma’s Revenge, Go-Explore scores a mean of over 43k points, almost 4× the previous state-of-the-art. Go-Explore can also harness human-provided domain knowledge and, when augmented with it, scores a mean of over 650k points on Montezuma’s Revenge. Its max performance of nearly 18 million surpasses the human world record, meeting even the strictest definition of “superhuman” performance. On Pitfall, Go-Explore with domain knowledge is the first algorithm to score above zero. Its mean score of almost 60k points exceeds expert human performance.

Because Go-Explore produces high-performing demonstrations automatically and cheaply, it also outperforms imitation learning work where humans provide solution demonstrations. Go-Explore opens up many new research directions into improving it and weaving its insights into current RL algorithms. It may also enable progress on previously unsolvable hard-exploration problems in many domains, especially those that harness a simulator during training (eg. robotics).

--- https://arxiv.org/abs/1902.00159 Compressing GANs using Knowledge Distillation Angeline Aguinaldo, Ping-Yeh Chiang, Alex Gain, Ameya Patil, Kolten Pearson, Soheil Feizi 2019-02-01 2020-01-07 [("doi","10.48550/arXiv.1902.00159")] ai/nn/gan ai/nn/sparsity/knowledge-distillation

Generative Adversarial Networks (GANs) have been used in several machine learning tasks such as domain transfer, super resolution, and synthetic data generation. State-of-the-art GANs often use tens of millions of parameters, making them expensive to deploy for applications in low SWAP (size, weight, and power) hardware, such as mobile devices, and for applications with real-time capabilities. There has been no work found to reduce the number of parameters used in GANs. Therefore, we propose a method to compress GANs using knowledge distillation techniques, in which a smaller “student” GAN learns to mimic a larger “teacher” GAN.

We show that the distillation methods used on MNIST, CIFAR-10, and Celeb-A datasets can compress teacher GANs at ratios of 1669:1, 58:1, and 87:1, respectively, while retaining the quality of the generated image. From our experiments, we observe a qualitative limit for GAN’s compression.

Moreover, we observe that, with a fixed parameter budget, compressed GANs outperform GANs trained using standard training methods. We conjecture that this is partially owing to the optimization landscape of over-parameterized GANs which allows efficient training using alternating gradient descent.

Thus, training an over-parameterized GAN followed by our proposed compression scheme provides a high-quality generative model with a small number of parameters.

--- https://arxiv.org/abs/1902.01894#deepmind A Generalized Framework for Population Based Training Ang Li, Ola Spyra, Sagi Perel, Valentin Dalibard, Max Jaderberg, Chenjie Gu, David Budden, Tim Harley, Pramod Gupta 2019-02-05 2020-01-08 [("doi","10.48550/arXiv.1902.01894")] reinforcement-learning/exploration

Population Based Training (PBT) is a recent approach that jointly optimizes neural network weights and hyperparameters which periodically copies weights of the best performers and mutates hyperparameters during training.

We propose a general, black-box PBT framework that distributes many asynchronous “trials” (a small number of training steps with warm-starting) across a cluster, coordinated by the PBT controller. The black-box design does not make assumptions on model architectures, loss functions or training procedures. Our system supports dynamic hyperparameter schedules to optimize both differentiable and non-differentiable metrics.

We apply our system to train a state-of-the-art WaveNet generative model for human voice synthesis. We show that our PBT system achieves better accuracy, less sensitivity, and faster convergence compared to existing methods, given the same computational resource.

--- https://arxiv.org/abs/1902.03098v1 Measuring Long-term Impact of Ads on LinkedIn Feed Jinyun Yan, Birjodh Tiwana, Souvik Ghosh, Haishan Liu, Shaunak Chatterjee 2019-01-29 2020-01-08 [("doi","10.48550/arXiv.1902.03098")] economics/advertising

Organic updates (from a member’s network) and sponsored updates (or ads, from advertisers) together form the newsfeed on LinkedIn. The newsfeed, the default homepage for members, attracts them to engage, brings them value and helps LinkedIn grow. Engagement and Revenue on feed are two critical, yet often conflicting objectives. Hence, it is important to design a good Revenue-Engagement Tradeoff (RENT) mechanism to blend ads in the feed.

In this paper, we design experiments to understand how members’ behavior evolves over time given different ads experiences. These experiences vary on ads density, while the quality of ads (ensured by relevance models) is held constant. Our experiments have been conducted on randomized member buckets and we use two experimental designs to measure the short term and long term effects of the various treatments.

Based on the first 3 months’ data, we observe that the long term impact is at a much smaller scale than the short term impact in our application. Furthermore, we observe different member cohorts (based on user activity level) adapt and react differently over time.

--- https://arxiv.org/abs/1902.04522 ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, C. Lawrence Zitnick 2019-02-12 2020-01-08 [("doi","10.48550/arXiv.1902.04522")] reinforcement-learning/model/alphago

The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning’s capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy. However, many obstacles remain in the understanding of and usability of these promising approaches by the research community.

Toward elucidating unresolved mysteries and facilitating future research, we propose ELF OpenGo, an open-source reimplementation of the AlphaZero algorithm. ELF OpenGo is the first open-source Go AI to convincingly demonstrate superhuman performance with a perfect (20:0) record against global top professionals. We apply ELF OpenGo to conduct extensive ablation studies, and to identify and analyze numerous interesting phenomena in both the model training and in the gameplay inference procedures.

Our code, models, self-play datasets, and auxiliary data are publicly available.

--- https://arxiv.org/abs/1902.05522 Superposition of many models into one Brian Cheung, Alex Terekhov, Yubei Chen, Pulkit Agrawal, Bruno Olshausen 2019-02-14 2020-01-08 [("doi","10.48550/arXiv.1902.05522")] ai/nn/sparsity

We present a method for storing multiple models within a single set of parameters.

Models can coexist in superposition and still be retrieved individually.

In experiments with neural networks, we show that a surprisingly large number of models can be effectively stored within a single parameter instance. Furthermore, each of these models can undergo thousands of training steps without interfering with other models within the superposition.

This approach may be viewed as the online complement of compression: rather than reducing the size of a network after training, we make use of the unrealized capacity of a network during training.

--- https://arxiv.org/abs/1902.09574 The State of Sparsity in Deep Neural Networks Trevor Gale, Erich Elsen, Sara Hooker 2019-02-25 2020-01-08 [("doi","10.48550/arXiv.1902.09574")] ai/nn/sparsity/pruning

We rigorously evaluate 3 state-of-the-art techniques for inducing sparsity in deep neural networks on two large-scale learning tasks: Transformer trained on WMT 2014 English-to-German, and ResNet-50 trained on ImageNet.

Across thousands of experiments, we demonstrate that complex techniques (Molchanov et al 2017; Louizos et al 2017b) shown to yield high compression rates on smaller datasets perform inconsistently, and that simple magnitude pruning approaches achieve comparable or better results. Additionally, we replicate the experiments performed by (Frankle & Carbin 2018) and (Liu et al 2018) at scale and show that unstructured sparse architectures learned through pruning cannot be trained from scratch to the same test set performance as a model trained with joint sparsification and optimization.

Together, these results highlight the need for large-scale benchmarks in the field of model compression.

We open-source our code, top performing model checkpoints, and results of all hyperparameter configurations to establish rigorous baselines for future work on compression and sparsification.

--- https://arxiv.org/abs/1902.10565 Accelerating Self-Play Learning in Go David J. Wu 2019-02-27 2020-01-08 [("doi","10.48550/arXiv.1902.10565")] economics/experience-curve reinforcement-learning/model/alphago

By introducing several improvements to the AlphaZero process and architecture, we greatly accelerate self-play learning in Go, achieving a 50× reduction in computation over comparable methods.

Like AlphaZero and replications such as ELF OpenGo and Leela Zero, our bot KataGo only learns from neural-net-guided Monte Carlo tree search self-play. But whereas AlphaZero required thousands of TPUs over several days and ELF required thousands of GPUs over two weeks, KataGo surpasses ELF’s final model after only 19 days on fewer than 30 GPUs. Much of the speedup involves non-domain-specific improvements that might directly transfer to other problems. Further gains from domain-specific techniques reveal the remaining efficiency gap between the best methods and purely general methods such as AlphaZero.

Our work is a step towards making learning in state spaces as large as Go possible without large-scale computational resources.

--- https://arxiv.org/abs/1902.10739 A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images John Leuner 2019-02-27 2020-01-08 [("doi","10.48550/arXiv.1902.10739")] ai/dataset ai/nn psychology

Recent research used machine learning methods to predict a person’s sexual orientation from their photograph (Wang & Kosinski 2017).

To verify this result, two of these models are replicated, one based on a deep neural network (DNN) and one on facial morphology (FM). Using a new dataset of 20,910 photographs from dating websites, the ability to predict sexual orientation is:

confirmed (DNN accuracy male 68%, female 77%, FM male 62%, female 72%). To investigate whether facial features such as brightness or predominant colors are predictive of sexual orientation, a new model based on highly blurred facial images was created. This model was also able to predict sexual orientation (male 63%, female 72%). The tested models are invariant to intentional changes to a subject’s makeup, eyewear, facial hair and head pose (angle that the photograph is taken at). It is shown that the head pose is not correlated with sexual orientation.

While demonstrating that dating profile images carry rich information about sexual orientation these results leave open the question of how much is determined by facial morphology and how much by differences in grooming, presentation and lifestyle.

The advent of new technology that is able to detect sexual orientation in this way may have serious implications for the privacy and safety of gay men and women.

--- https://arxiv.org/abs/1903.00401#deepmind Learning To Follow Directions in Street View Karl Moritz Hermann, Mateusz Malinowski, Piotr Mirowski, Andras Banki-Horvath, Keith Anderson, Raia Hadsell 2019-03-01 2020-01-08 [("doi","10.48550/arXiv.1903.00401")] ai/nn/cnn reinforcement-learning/exploration

Navigating and understanding the real world remains a key challenge in machine learning and inspires a great variety of research in areas such as language grounding, planning, navigation and computer vision. We propose an instruction-following task that requires all of the above, and which combines the practicality of simulated environments with the challenges of ambiguous, noisy real world data.

StreetNav is built on top of Google Street View and provides visually accurate environments representing real places. Agents are given driving instructions which they must learn to interpret in order to successfully navigate in this environment. Since humans equipped with driving instructions can readily navigate in previously unseen cities, we set a high bar and test our trained agents for similar cognitive capabilities.

Although deep reinforcement learning (RL) methods are frequently evaluated only on data that closely follow the training distribution, our dataset extends to multiple cities and has a clean train/test separation. This allows for thorough testing of generalisation ability. This paper presents the StreetNav environment and tasks, models that establish strong baselines, and extensive analysis of the task and the trained agents.

--- https://arxiv.org/abs/1903.00742#deepmind Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research Joel Z. Leibo, Edward Hughes, Marc Lanctot, Thore Graepel 2019-03-02 2020-01-08 [("doi","10.48550/arXiv.1903.00742")] reinforcement-learning/meta-learning reinforcement-learning/multi-agent

Evolution has produced a multi-scale mosaic of interacting adaptive units. Innovations arise when perturbations push parts of the system away from stable equilibria into new regimes where previously well-adapted solutions no longer work.

Here we explore the hypothesis that multi-agent systems sometimes display intrinsic dynamics arising from competition and cooperation that provide a naturally emergent curriculum, which we term an autocurriculum.

The solution of one social task often begets new social tasks, continually generating novel challenges, and thereby promoting innovation.

Under certain conditions these challenges may become increasingly complex over time, demanding that agents accumulate ever more innovations.

--- https://arxiv.org/abs/1903.01373#deepmind α-Rank: Multi-Agent Evaluation by Evolution Shayegan Omidshafiei, Christos Papadimitriou, Georgios Piliouras, Karl Tuyls, Mark Rowland, Jean-Baptiste Lespiau, Wojciech M. Czarnecki, Marc Lanctot, Julien Perolat, Remi Munos 2019-03-04 2020-01-08 [("doi","10.48550/arXiv.1903.01373")] reinforcement-learning/imperfect-information/poker reinforcement-learning/model/alphago reinforcement-learning/multi-agent

We introduce α-Rank, a principled evolutionary dynamics methodology for the evaluation and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamical game-theoretic solution concept called Markov-Conley chains (MCCs).

The approach leverages continuous-time and discrete-time evolutionary dynamical systems applied to empirical games, and scales tractably in the number of agents, the type of interactions, and the type of empirical games (symmetric and asymmetric). Current models are fundamentally limited in one or more of these dimensions and are not guaranteed to converge to the desired game-theoretic solution concept (typically the Nash equilibrium).

α-Rank provides a ranking over the set of agents under evaluation and provides insights into their strengths, weaknesses, and long-term dynamics. This is a consequence of the links we establish to the MCC solution concept when the underlying evolutionary model’s ranking-intensity parameter, α, is chosen to be large, which exactly forms the basis of α-Rank. In contrast to the Nash equilibrium, which is a static concept based on fixed points, MCCs are a dynamical solution concept based on the Markov chain formalism, Conley’s Fundamental Theorem of Dynamical Systems, and the core ingredients of dynamical systems: fixed points, recurrent sets, periodic orbits, and limit cycles.

α-Rank runs in polynomial time with respect to the total number of pure strategy profiles, whereas computing a Nash equilibrium for a general-sum game is known to be intractable. We introduce proofs that not only provide an unifying perspective of existing continuous-time and discrete-time evolutionary evaluation models, but also reveal the formal underpinnings of the α-Rank methodology.

We empirically validate the method in several domains including AlphaGo, AlphaZero, MuJoCo Soccer, and Poker.

--- https://arxiv.org/abs/1903.01611 Stabilizing the Lottery Ticket Hypothesis Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin 2019-03-05 2020-01-09 [("doi","10.48550/arXiv.1903.01611")] ai/nn/sparsity/pruning

Pruning is a well-established technique for removing unnecessary structure from neural networks after training to improve the performance of inference. Several recent results have explored the possibility of pruning at initialization time to provide similar benefits during training. In particular, the lottery ticket hypothesis conjectures that typical neural networks contain small subnetworks that can train to similar accuracy in a commensurate number of steps. The evidence for this claim is that a procedure based on iterative magnitude pruning (IMP) reliably finds such subnetworks retroactively on small vision tasks. However, IMP fails on deeper networks, and proposed methods to prune before training or train pruned networks encounter similar scaling limitations.

In this paper, we argue that these efforts have struggled on deeper networks because they have focused on pruning precisely at initialization. We modify IMP to search for subnetworks that could have been obtained by pruning early in training (0.1% to 7% through) rather than at iteration 0.

With this change, it finds small subnetworks of deeper networks (eg. 80% sparsity on Resnet-50) that can complete the training process to match the accuracy of the original network on more challenging tasks (eg. ImageNet). In situations where IMP fails at iteration 0, the accuracy benefits of delaying pruning accrue rapidly over the earliest iterations of training. To explain these behaviors, we study subnetwork “stability”, finding that—as accuracy improves in this fashion—IMP subnetworks train to parameters closer to those of the full network and do so with improved consistency in the face of gradient noise.

These results offer new insights into the opportunity to prune large-scale networks early in training and the behaviors underlying the lottery ticket hypothesis

--- https://arxiv.org/abs/1903.06048 MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks Animesh Karnewar, Oliver Wang 2019-03-14 2020-01-09 [("doi","10.48550/arXiv.1903.06048")] ai/nn/gan/stylegan

While Generative Adversarial Networks (GANs) have seen huge successes in image synthesis tasks, they are notoriously difficult to adapt to different datasets, in part due to instability during training and sensitivity to hyperparameters. One commonly accepted reason for this instability is that gradients passing from the discriminator to the generator become uninformative when there isn’t enough overlap in the supports of the real and fake distributions.

In this work, we propose the Multi-Scale Gradient Generative Adversarial Network (MSG-GAN), a simple but effective technique for addressing this by allowing the flow of gradients from the discriminator to the generator at multiple scales. This technique provides a stable approach for high resolution image synthesis, and serves as an alternative to the commonly used progressive growing technique.

We show that MSG-GAN converges stably on a variety of image datasets of different sizes, resolutions and domains, as well as different types of loss functions and architectures, all with the same set of fixed hyperparameters. When compared to state-of-the-art GANs, our approach matches or exceeds the performance in most of the cases we tried.

--- https://arxiv.org/abs/1903.06754 Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset Ruohan Zhang, Calen Walshe, Zhuode Liu, Lin Guan, Karl S. Muller, Jake A. Whritner, Luxin Zhang, Mary M. Hayhoe, Dana H. Ballard 2019-03-15 2020-01-09 [("doi","10.48550/arXiv.1903.06754")] ai/dataset psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Large-scale public datasets have been shown to benefit research in multiple areas of modern artificial intelligence. For decision-making research that requires human data, high-quality datasets serve as important benchmarks to facilitate the development of new methods by providing a common reproducible standard. Many human decision-making tasks require visual attention to obtain high levels of performance. Therefore, measuring eye movements can provide a rich source of information about the strategies that humans use to solve decision-making tasks.

Here, we provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. The dataset consists of 117 hours of gameplay data from a diverse set of 20 games, with 8 million action demonstrations and 328 million gaze samples. We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. This leads to near-optimal game decisions and game scores that are comparable or better than known human records.

We demonstrate the usefulness of the dataset through two simple applications: predicting human gaze and imitating human demonstrated actions. The quality of the data leads to promising results in both tasks. Moreover, using a learned human gaze model to inform imitation learning leads to an 115% increase in game performance.

We interpret these results as highlighting the importance of incorporating human visual attention in models of decision making and demonstrating the value of the current dataset to the research community. We hope that the scale and quality of this dataset can provide more opportunities to researchers in the areas of visual attention, imitation learning, and reinforcement learning.

--- https://arxiv.org/abs/1903.08254 Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, Sergey Levine 2019-03-19 2020-01-09 [("doi","10.48550/arXiv.1903.08254")] reinforcement-learning/meta-learning

Deep reinforcement learning algorithms require large amounts of experience to learn an individual task. While in principle meta-reinforcement learning (meta-RL) algorithms enable agents to learn new skills from small amounts of experience, several major challenges preclude their practicality. Current methods rely heavily on on-policy experience, limiting their sample efficiency. They also lack mechanisms to reason about task uncertainty when adapting to new tasks, limiting their effectiveness in sparse reward problems.

In this paper, we address these challenges by developing an off-policy meta-RL algorithm that disentangles task inference and control. In our approach, we perform online probabilistic filtering of latent task variables to infer how to solve a new task from small amounts of experience. This probabilistic interpretation enables posterior sampling for structured and efficient exploration. We demonstrate how to integrate these task variables with off-policy RL algorithms to achieve both meta-training and adaptation efficiency.

Our method outperforms prior algorithms in sample efficiency by 20–100× as well as in asymptotic performance on several meta-RL benchmarks.

--- https://arxiv.org/abs/1903.11059 AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca 2019-03-26 2020-01-09 [("doi","10.48550/arXiv.1903.11059")] reinforcement-learning/meta-learning reinforcement-learning/model/alphago

Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time.

In this paper, we present a novel scalable Monte Carlo Tree Search (MCTS) based NAS agent, named AlphaX, to tackle these two aspects. AlphaX improves the search efficiency by adaptively balancing the exploration and exploitation at the state level, and by a Meta-Deep Neural Network (DNN) to predict network accuracies for biasing the search toward a promising region. To amortize the network evaluation cost, AlphaX accelerates MCTS rollouts with a distributed design and reduces the number of epochs in evaluating a network by transfer learning guided with the tree structure in MCTS.

In 12 GPU days and 1,000 samples, AlphaX found an architecture that reaches 97.84% top-1 accuracy on CIFAR-10, and 75.5% top-1 accuracy on ImageNet, exceeding SOTA NAS methods in both the accuracy and sampling efficiency. Particularly, we also evaluate AlphaX on NASBench-101, a large scale NAS dataset; AlphaX is 3× and 2.8× more sample efficient than Random Search and Regularized Evolution in finding the global optimum.

Finally, we show the searched architecture improves a variety of vision applications from Neural Style Transfer, to Image Captioning and Object Detection.

--- https://arxiv.org/abs/1904.03646 Policy Gradient Search: Online Planning and Expert Iteration without Search Trees Thomas Anthony, Robert Nishihara, Philipp Moritz, Tim Salimans, John Schulman 2019-04-07 2020-01-09 [("doi","10.48550/arXiv.1904.03646")] reinforcement-learning/model/alphago

Monte Carlo Tree Search (MCTS) algorithms perform simulation-based search to improve policies online. During search, the simulation policy is adapted to explore the most promising lines of play. MCTS has been used by state-of-the-art programs for many problems, however a disadvantage to MCTS is that it estimates the values of states with Monte Carlo averages, stored in a search tree; this does not scale to games with very high branching factors.

We propose an alternative simulation-based search method, Policy Gradient Search (PGS), which adapts a neural network simulation policy online via policy gradient updates, avoiding the need for a search tree.

In Hex, PGS achieves comparable performance to MCTS, and an agent trained using Expert Iteration with PGS was able defeat MoHex 2.0, the strongest open-source Hex agent, in 9×9 Hex.

--- https://arxiv.org/abs/1904.06991 Improved Precision and Recall Metric for Assessing Generative Models Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, Timo Aila 2019-04-15 2020-01-09 [("doi","10.48550/arXiv.1904.06991")] ai/nn/gan/biggan ai/nn/gan/stylegan

The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data.

We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method.

Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations.

--- https://arxiv.org/abs/1904.07091 π-IW: Deep Policies for Width-Based Planning in Pixel Domains Miquel Junyent, Anders Jonsson, Vicenç Gómez 2019-04-12 2020-01-09 [("doi","10.48550/arXiv.1904.07091")] reinforcement-learning/exploration reinforcement-learning/model/alphago

Width-based planning has demonstrated great success in recent years due to its ability to scale independently of the size of the state space. For example, Bandres et al 2018 introduced a rollout version of the Iterated Width algorithm whose performance compares well with humans and learning methods in the pixel setting of the Atari games suite. In this setting, planning is done on-line using the “screen” states and selecting actions by looking ahead into the future. However, this algorithm is purely exploratory and does not leverage past reward information. Furthermore, it requires the state to be factored into features that need to be pre-defined for the particular task, eg. the B-PROST pixel features.

In this work, we extend width-based planning by incorporating an explicit policy in the action selection mechanism. Our method, called π-IW, interleaves width-based planning and policy learning using the state-actions visited by the planner. The policy estimate takes the form of a neural network and is in turn used to guide the planning step, thus reinforcing promising paths. Surprisingly, we observe that the representation learned by the neural network can be used as a feature space for the width-based planner without degrading its performance, thus removing the requirement of pre-defined features for the planner.

We compare π-IW with previous width-based methods and with AlphaZero, a method that also interleaves planning and learning, in simple environments, and show that π-IW has superior performance. We also show that π-IW algorithm outperforms previous width-based methods in the pixel setting of Atari games suite.

--- https://arxiv.org/abs/1904.09751#allen The Curious Case of Neural Text Degeneration Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, Yejin Choi 2019-04-22 2020-01-09 [("doi","10.48550/arXiv.1904.09751")] ai/nn/sampling ai/nn/transformer/gpt psychology/novelty

Despite considerable advancements with deep neural language models, the enigma of neural text degeneration persists when these models are tested as text generators. The counter-intuitive empirical observation is that even though the use of likelihood as training objective leads to high quality models for a broad range of language understanding tasks, using likelihood as a decoding objective leads to text that is bland and strangely repetitive.

In this paper, we reveal surprising distributional differences between human text and machine text.

In addition, we find that decoding strategies alone can dramatically effect the quality of machine text, even when generated from exactly the same neural language model.

Our findings motivate Nucleus Sampling, a simple but effective method to draw the best out of neural generation. By sampling text from the dynamic nucleus of the probability distribution, which allows for diversity while effectively truncating the less reliable tail of the distribution, the resulting text better demonstrates the quality of human text, yielding enhanced diversity without sacrificing fluency and coherence.

--- https://arxiv.org/abs/1904.11455#deepmind Ray Interference: a Source of Plateaus in Deep Reinforcement Learning Tom Schaul, Diana Borsa, Joseph Modayil, Razvan Pascanu 2019-04-25 2020-01-09 [("doi","10.48550/arXiv.1904.11455")] reinforcement-learning/meta-learning

Rather than proposing a new method, this paper investigates an issue present in existing learning algorithms. We study the learning dynamics of reinforcement learning (RL), specifically a characteristic coupling between learning and data generation that arises because RL agents control their future data distribution.

In the presence of function approximation, this coupling can lead to a problematic type of ‘ray interference’, characterized by learning dynamics that sequentially traverse a number of performance plateaus, effectively constraining the agent to learn one thing at a time even when learning in parallel is better.

We establish the conditions under which ray interference occurs, show its relation to saddle points and obtain the exact learning dynamics in a restricted setting. We characterize a number of its properties and discuss possible remedies.

[cf. meta-learner dynamics, AlphaZero, emergence/phase-transitions in learning]

--- https://arxiv.org/abs/1904.12320 Real numbers, data science and chaos: How to fit any dataset with a single parameter Laurent Boué 2019-04-28 2020-01-09 [("doi","10.48550/arXiv.1904.12320")] math/humor

We show how any dataset of any modality (time-series, images, sound…) can be approximated by a well-behaved (continuous, differentiable…) scalar function with a single real-valued parameter.

Building upon elementary concepts from chaos theory, we adopt a pedagogical approach demonstrating how to adjust this parameter in order to achieve arbitrary precision fit to all samples of the data.

Targeting an audience of data scientists with a taste for the curious and unusual, the results presented here expand on previous similar observations regarding expressiveness power and generalization of machine learning models.

--- https://arxiv.org/abs/1905.01320#deepmind Meta-learners’ learning dynamics are unlike learners’ Neil C. Rabinowitz 2019-05-03 2020-01-10 [("doi","10.48550/arXiv.1905.01320")] ai/nn/rnn reinforcement-learning/meta-learning statistics/bayes

Meta-learning is a tool that allows us to build sample-efficient learning systems.

Here we show that, once meta-trained, LSTM meta-learners aren’t just faster learners than their sample-inefficient deep learning (DL) and reinforcement learning (RL) brethren, but that they actually pursue fundamentally different learning trajectories.

We study their learning dynamics on 3 sets of structured tasks for which the corresponding learning dynamics of DL and RL systems have been previously described: linear regression (Saxe et al 2013), nonlinear regression (Rahaman et al 2018; Xu et al 2018), and contextual bandits (Schaul et al 2019).

In each case, while sample-inefficient DL and RL Learners uncover the task structure in a staggered manner, meta-trained LSTM meta-learners uncover almost all task structure concurrently, congruent with the patterns expected from Bayes-optimal inference algorithms.

This has implications for research areas wherever the learning behavior itself is of interest, such as safety, curriculum design, and human-in-the-loop machine learning.

--- https://arxiv.org/abs/1905.03030#deepmind Meta-learning of Sequential Strategies Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alex Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin Miller, Mohammad Azar, Ian Osband, Neil Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew Botvinick, Shane Legg 2019-05-08 2020-01-10 [("doi","10.48550/arXiv.1905.03030")] reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/scaling statistics/bayes

In this report we review memory-based meta-learning as a tool for building sample-efficient strategies that learn from past experience to adapt to any task within a target class. Our goal is to equip the reader with the conceptual foundations of this tool for building new, scalable agents that operate on broad domains. To do so, we present basic algorithmic templates for building near-optimal predictors and reinforcement learners which behave as if they had a probabilistic model that allowed them to efficiently exploit task structure.

Furthermore, we recast memory-based meta-learning within a Bayesian framework, showing that the meta-learned strategies are near-optimal because they amortize Bayes-filtered data, where the adaptation is implemented in the memory dynamics as a state-machine of sufficient statistics.

Essentially, memory-based meta-learning translates the hard problem of probabilistic sequential inference into a regression problem.

--- https://arxiv.org/abs/1905.06424#deepmind Meta reinforcement learning as task inference Jan Humplik, Alexandre Galashov, Leonard Hasenclever, Pedro A. Ortega, Yee Whye Teh, Nicolas Heess 2019-05-15 2020-01-10 [("doi","10.48550/arXiv.1905.06424")] reinforcement-learning/exploration reinforcement-learning/meta-learning statistics/bayes

Humans achieve efficient learning by relying on prior knowledge about the structure of naturally occurring tasks. There is considerable interest in designing reinforcement learning (RL) algorithms with similar properties. This includes proposals to learn the learning algorithm itself, an idea also known as meta learning.

One formal interpretation of this idea is as a partially observable multi-task RL problem in which task information is hidden from the agent. Such unknown task problems can be reduced to Markov decision processes (MDPs) by augmenting an agent’s observations with an estimate of the belief about the task based on past experience. However estimating the belief state is intractable in most partially-observed MDPs.

We propose a method that separately learns the policy and the task belief by taking advantage of various kinds of privileged information.

Our approach can be very effective at solving standard meta-RL environments, as well as a complex continuous control environment with sparse rewards and requiring long-term memory.

--- https://arxiv.org/abs/1905.07435 Alpha MAML: Adaptive Model-Agnostic Meta-Learning Harkirat Singh Behl, Atılım Güneş Baydin, Philip H. S. Torr 2019-05-17 2020-01-10 [("doi","10.48550/arXiv.1905.07435")] reinforcement-learning/meta-learning

Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks. The MAML algorithm performs well on few-shot learning problems in classification, regression, and fine-tuning of policy gradients in reinforcement learning, but comes with the need for costly hyperparameter tuning for training stability.

We address this shortcoming by introducing an extension to MAML, called Alpha MAML, to incorporate an online hyperparameter adaptation scheme that eliminates the need to tune meta-learning and learning rates.

Our results with the Omniglot database demonstrate a substantial reduction in the need to tune MAML training hyperparameters and improvement to training stability with less sensitivity to hyperparameter choice.

--- https://arxiv.org/abs/1905.09418 Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, Ivan Titov 2019-05-23 2020-01-10 [("doi","10.48550/arXiv.1905.09418")] ai/nn/sparsity/pruning ai/nn/transformer

Multi-head self-attention is a key component of the Transformer, a state-of-the-art architecture for neural machine translation. In this work we evaluate the contribution made by individual attention heads in the encoder to the overall performance of the model and analyze the roles played by them.

We find that the most important and confident heads play consistent and often linguistically-interpretable roles. When pruning heads using a method based on stochastic gates and a differentiable relaxation of the L0 penalty, we observe that specialized heads are last to be pruned. Our novel pruning method removes the vast majority of heads without seriously affecting performance.

For example, on the English-Russian WMT dataset, pruning 38⁄48 encoder heads results in a drop of only 0.15 BLEU.

--- https://arxiv.org/abs/1905.10437 N-BEATS: Neural basis expansion analysis for interpretable time series forecasting Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio 2019-05-24 2020-01-10 [("doi","10.48550/arXiv.1905.10437")] ai/tabular statistics/prediction

We focus on solving the univariate time series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable without modification to a wide array of target domains, and fast to train.

We test the proposed architecture on several well-known datasets, including M3, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year’s winner of the M4 competition, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models.

The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that, contrarily to received wisdom, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems.

Finally, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.

--- https://arxiv.org/abs/1905.10615 Adversarial Policies: Attacking Deep Reinforcement Learning Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell 2019-05-25 2020-01-10 [("doi","10.48550/arXiv.1905.10615")] ai/nn/adversarial reinforcement-learning/multi-agent reinforcement-learning/robot

Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers. However, an attacker is not usually able to directly modify another agent’s observations. This might lead one to wonder: is it possible to attack an RL agent simply by choosing an adversarial policy acting in a multi-agent environment so as to create natural observations that are adversarial?

We demonstrate the existence of adversarial policies in zero-sum games between simulated humanoid robots with proprioceptive observations, against state-of-the-art victims trained via self-play to be robust to opponents. The adversarial policies reliably win against the victims but generate seemingly random and uncoordinated behavior. We find that these policies are more successful in high-dimensional environments, and induce substantially different activations in the victim policy network than when the victim plays against a normal opponent.

Videos are available at https://adversarialpolicies.github.io/.

--- https://arxiv.org/abs/1905.10679 Improved object recognition using neural networks trained to mimic the brain’s statistical properties Callie Federer, Haoyan Xu, Alona Fyshe, Joel Zylberberg 2019-05-25 2020-01-10 [("doi","10.48550/arXiv.1905.10679")] ai/nn/cnn psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

The current state-of-the-art object recognition algorithms, deep convolutional neural networks (DCNNs), are inspired by the architecture of the mammalian visual system, and are capable of human-level performance on many tasks. However, even these algorithms make errors. As they are trained for object recognition tasks, it has been shown that DCNNs develop hidden representations that resemble those observed in the mammalian visual system. Moreover, DCNNs trained on object recognition tasks are currently among the best models we have of the mammalian visual system. This led us to hypothesize that teaching DCNNs to achieve even more brain-like representations could improve their performance.

To test this, we trained DCNNs on a composite task, wherein networks were trained to: (1) classify images of objects; while (2) having intermediate representations that resemble those observed in neural recordings from monkey visual cortex. Compared with DCNNs trained purely for object categorization, DCNNs trained on the composite task had better object recognition performance and are more robust to label corruption. Interestingly, we also found that neural data was not required, but randomized data with the same statistics as neural data also boosted performance.

Our results outline a new way to train object recognition networks, using strategies in which the brain—or at least the statistical properties of its activation patterns—serves as a teacher signal for training DCNNs.

--- https://arxiv.org/abs/1905.10985#uber AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence Jeff Clune 2019-05-27 2020-01-10 [("doi","10.48550/arXiv.1905.10985")] reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/multi-agent reinforcement-learning/robot reinforcement-learning/safe reinforcement-learning/scaling

Perhaps the most ambitious scientific quest in human history is the creation of general artificial intelligence, which roughly means AI that is as smart or smarter than humans. The dominant approach in the machine learning community is to attempt to discover each of the pieces required for intelligence, with the implicit assumption that some future group will complete the Herculean task of figuring out how to combine all of those pieces into a complex thinking machine. I call this the “manual AI approach”.

This paper describes another exciting path that ultimately may be more successful at producing general AI. It is based on the clear trend in machine learning that hand-designed solutions eventually are replaced by more effective, learned solutions. The idea is to create an AI-generating algorithm (AI-GA), which automatically learns how to produce general AI. 3 Pillars are essential for the approach: (1) meta-learning architectures, (2) meta-learning the learning algorithms themselves, and (3) generating effective learning environments.

I argue that either approach could produce general AI first, and both are scientifically worthwhile irrespective of which is the fastest path. Because both are promising, yet the ML community is currently committed to the manual approach, I argue that our community should increase its research investment in the AI-GA approach.

To encourage such research, I describe promising work in each of the 3 Pillars. I also discuss AI-GA-specific safety and ethical considerations.

Because it may be the fastest path to general AI and because it is inherently scientifically interesting to understand the conditions in which a simple algorithm can produce general AI (as happened on Earth where Darwinian evolution produced human intelligence), I argue that the pursuit of AI-GAs should be considered a new grand challenge of computer science research.

--- https://arxiv.org/abs/1905.11946#google EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Mingxing Tan, Quoc V. Le 2019-05-28 2020-01-10 [("doi","10.48550/arXiv.1905.11946")] ai/nn/sparsity ai/scaling

Convolutional Neural Networks (Convnets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.

In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet.

To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous Convnets.

In particular, our EfficientNet-B7 achieves state-of-the-art 84.3% top-1 accuracy on ImageNet, while being 8.4× smaller and 6.1× faster on inference than the best existing Convnet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters.

Source code is at GitHub.

--- https://arxiv.org/abs/1905.12107 SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough 2019-05-28 2020-01-10 [("doi","10.48550/arXiv.1905.12107")] ai/nn/sparsity/pruning ai/scaling reinforcement-learning/meta-learning

The vast majority of processors in the world are actually microcontroller units (MCUs), which find widespread use performing simple control tasks in applications ranging from automobiles to medical devices and office equipment. The Internet of Things (IoT) promises to inject machine learning into many of these every-day objects via tiny, cheap MCUs. However, these resource-impoverished hardware platforms severely limit the complexity of machine learning models that can be deployed. For example, although convolutional neural networks (CNNs) achieve state-of-the-art results on many visual recognition tasks, CNN inference on MCUs is challenging due to severe finite memory limitations. To circumvent the memory challenge associated with CNNs, various alternatives have been proposed that do fit within the memory budget of an MCU, albeit at the cost of prediction accuracy.

This paper challenges the idea that CNNs are not suitable for deployment on MCUs. We demonstrate that it is possible to automatically design CNNs which generalize well, while also being small enough to fit onto memory-limited MCUs. Our Sparse Architecture Search method combines neural architecture search with pruning in a single, unified approach, which learns superior models on 4 popular IoT datasets.

The CNNs we find are more accurate and up to 4.35× smaller than previous approaches, while meeting the strict MCU working memory constraint.

--- https://arxiv.org/abs/1905.12941 Learning Compositional Neural Programs with Recursive Tree Search and Planning Thomas Pierrot, Guillaume Ligner, Scott Reed, Olivier Sigaud, Nicolas Perrin, Alexandre Laterre, David Kas, Karim Beguir, Nando de Freitas 2019-05-30 2020-01-11 [("doi","10.48550/arXiv.1905.12941")] reinforcement-learning/model/alphago

We propose a novel reinforcement learning algorithm, AlphaNPI, that incorporates the strengths of Neural Programmer-Interpreters (NPI) and AlphaZero (‘A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play’, Silver et al 2018). NPI contributes structural biases in the form of modularity, hierarchy, and recursion, which are helpful to reduce sample complexity, improve generalization, and increase interpretability. AlphaZero contributes powerful neural network guided search algorithms, which we augment with recursion.

AlphaNPI only assumes a hierarchical program specification with sparse rewards: 1 when the program execution satisfies the specification, and 0 otherwise. Using this specification, AlphaNPI is able to train NPI models effectively with RL for the first time, completely eliminating the need for strong supervision in the form of execution traces.

The experiments show that AlphaNPI can sort as well as previous strongly supervised NPI variants. The AlphaNPI agent is also trained on a Tower of Hanoi puzzle with two disks and is shown to generalize to puzzles with an arbitrary number of disks.

--- https://arxiv.org/abs/1905.13415 ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data Elias Stehle, Hans-Arno Jacobsen 2019-05-31 2020-01-11 [("doi","10.14778/3377369.3377372")] cs/algorithm

Parsing is essential for a wide range of use cases, such as stream processing, bulk loading, and in-situ querying of raw data. Yet, the compute-intense step often constitutes a major bottleneck in the data ingestion pipeline, since parsing of inputs that require more involved parsing rules is challenging to parallelize.

This work proposes a massively parallel algorithm for parsing delimiter-separated data formats on GPUs. Other than the state-of-the-art, the proposed approach does not require an initial sequential pass over the input to determine a thread’s parsing context. That is, how a thread, beginning somewhere in the middle of the input, should interpret a certain symbol (eg. whether to interpret a comma as a delimiter or as part of a larger string enclosed in double-quotes). Instead of tailoring the approach to a single format, we are able to perform a massively parallel FSM simulation, which is more flexible and powerful, supporting more expressive parsing rules with general applicability.

Achieving a parsing rate of as much as 14.2 GB/s, our experimental evaluation on a GPU with 3584 cores shows that the presented approach is able to scale to thousands of cores and beyond. With an end-to-end streaming approach, we are able to exploit the full-duplex capabilities of the PCIe bus and hide latency from data transfers. Considering the end-to-end performance, the algorithm parses 4.8 GB in as little as 0.44 seconds, including data transfers.

--- https://arxiv.org/abs/1905.13725 Are Labels Required for Improving Adversarial Robustness? Jonathan Uesato, Jean-Baptiste Alayrac, Po-Sen Huang, Robert Stanforth, Alhussein Fawzi, Pushmeet Kohli 2019-05-31 2020-01-11 [("doi","10.48550/arXiv.1905.13725")] ai/nn/adversarial ai/scaling

Recent work has uncovered the interesting (and somewhat surprising) finding that training models to be invariant to adversarial perturbations requires substantially larger datasets than those required for standard classification. This result is a key hurdle in the deployment of robust machine learning models in many real world applications where labeled data is expensive.

Our main insight is that unlabeled data can be a competitive alternative to labeled data for training adversarially robust models. Theoretically, we show that in a simple statistical setting, the sample complexity for learning an adversarially robust model from unlabeled data matches the fully supervised case up to constant factors.

On standard datasets like CIFAR-10, a simple Unsupervised Adversarial Training (UAT) approach using unlabeled data improves robust accuracy by 21.7% over using 4K supervised examples alone, and captures over 95% of the improvement from the same number of labeled examples. Finally, we report an improvement of 4% over the previous state-of-the-art on CIFAR-10 against the strongest known attack by using additional unlabeled data from the uncurated 80 Million Tiny Images dataset.

This demonstrates that our finding extends as well to the more realistic case where unlabeled data is also uncurated, therefore opening a new avenue for improving adversarial training.

--- https://arxiv.org/abs/1906.00555 Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John Hopcroft, Liwei Wang 2019-06-03 2020-01-11 [("doi","10.48550/arXiv.1906.00555")] ai/nn/adversarial ai/scaling

Neural network robustness has recently been highlighted by the existence of adversarial examples. Many previous works show that the learned networks do not perform well on perturbed test data, and statistically-significantly more labeled data is required to achieve adversarially robust generalization.

In this paper, we theoretically and empirically show that with just more unlabeled data, we can learn a model with better adversarially robust generalization. The key insight of our results is based on a risk decomposition theorem, in which the expected robust risk is separated into two parts: the stability part which measures the prediction stability in the presence of perturbations, and the accuracy part which evaluates the standard classification accuracy. As the stability part does not depend on any label information, we can optimize this part using unlabeled data.

We further prove that for a specific Gaussian mixture problem, adversarially robust generalization can be almost as easy as the standard generalization in supervised learning if a sufficiently large amount of unlabeled data is provided.

Inspired by the theoretical findings, we further show that a practical adversarial training algorithm that leverages unlabeled data can improve adversarial robust generalization on MNIST and Cifar-10.

--- https://arxiv.org/abs/1906.01040 A Surprising Density of Illusionable Natural Speech Melody Y. Guan, Gregory Valiant 2019-06-03 2020-01-11 [("doi","10.48550/arXiv.1906.01040")] ai/nn/adversarial/human psychology/neuroscience

Recent work on adversarial examples has demonstrated that most natural inputs can be perturbed to fool even state-of-the-art machine learning systems. But does this happen for humans as well?

In this work, we investigate: what fraction of natural instances of speech can be turned into “illusions” which either alter humans’ perception or result in different people having different perceptions? We first consider the McGurk effect, the phenomenon by which adding a carefully chosen video clip to the audio channel affects the viewer’s perception of what is said (McGurk & MacDonald 1976). We obtain empirical estimates that a fraction of both words and sentences occurring in natural speech have some susceptibility to this effect. We also learn models for predicting McGurk illusionability.

Finally we demonstrate that the Yanny or Laurel auditory illusion (Pressnitzer et al 2018) is not an isolated occurrence by generating several very different new instances.

We believe that the surprising density of illusionable natural speech warrants further investigation, from the perspectives of both security and cognitive science. Supplementary videos are available at: https://www.youtube.com/playlist?list=PLaX7t1K-e_fF2iaenoKznCatm0RC37B_k.

--- https://arxiv.org/abs/1906.02470 StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks Jie An, Haoyi Xiong, Jinwen Ma, Jiebo Luo, Jun Huan 2019-06-06 2020-01-11 [("doi","10.48550/arXiv.1906.02470")] ai/nn/sparsity

Neural Architecture Search (NAS) has been widely studied for designing discriminative deep learning models such as image classification, object detection, and semantic segmentation. As a large number of priors have been obtained through the manual design of architectures in the fields, NAS is usually considered as a supplement approach.

In this paper, we have expanded the application areas of NAS by performing an empirical study of NAS to search generative models, or specifically, autoencoder based universal style transfer, which lacks systematic exploration, if any, from the architecture search aspect.

In our work, we first designed a search space where common operators for image style transfer such as VGG-based encoders, whitening and coloring transforms (WCT), convolution kernels, instance normalization operators, and skip connections were searched in a combinatorial approach. With a simple yet effective parallel evolutionary NAS algorithm with multiple objectives, we derived the first group of end-to-end deep networks for universal photorealistic style transfer.

Comparing to random search, a NAS method that is gaining popularity recently, we demonstrated that carefully designed search strategy leads to much better architecture design.

Finally compared to existing universal style transfer networks for photorealistic rendering such as PhotoWCT that stacks multiple well-trained autoencoders and WCT transforms in a non-end-to-end manner, the architectures designed by StyleNAS produce better style-transferred images with details preserving, using a tiny number of operators/parameters, and enjoying around 500× inference time speed-up.

--- https://arxiv.org/abs/1906.02768 Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP Haonan Yu, Sergey Edunov, Yuandong Tian, Ari S. Morcos 2019-06-06 2020-01-11 [("doi","10.48550/arXiv.1906.02768")] ai/nn/rnn ai/nn/sparsity/pruning reinforcement-learning/model-free

The lottery ticket hypothesis proposes that over-parameterization of deep neural networks (DNNs) aids training by increasing the probability of a “lucky” sub-network initialization being present rather than by helping the optimization process (Frankle & Carbin 2019). Intriguingly, this phenomenon suggests that initialization strategies for DNNs can be improved substantially, but the lottery ticket hypothesis has only previously been tested in the context of supervised learning for natural image tasks.

Here, we evaluate whether “winning ticket” initializations exist in two different domains: natural language processing (NLP) and reinforcement learning (RL). For NLP, we examined both recurrent LSTM models and large-scale Transformer models (Vaswani et al 2017). For RL, we analyzed a number of discrete-action space tasks, including both classic control and pixel control.

Consistent with work in supervised image classification, we confirm that winning ticket initializations generally outperform parameter-matched random initializations, even at extreme pruning rates for both NLP and RL. Notably, we are able to find winning ticket initializations for Transformers which enable models 1⁄3rd the size to achieve nearly equivalent performance.

Together, these results suggest that the lottery ticket hypothesis is not restricted to supervised learning of natural images, but rather represents a broader phenomenon in DNNs.

--- https://arxiv.org/abs/1906.03787 Intriguing properties of adversarial training at scale Cihang Xie, Alan Yuille 2019-06-10 2020-01-11 [("doi","10.48550/arXiv.1906.03787")] ai/nn/adversarial ai/nn/cnn ai/scaling

Adversarial training is one of the main defenses against adversarial attacks. In this paper, we provide the first rigorous study on diagnosing elements of adversarial training, which reveals two intriguing properties.

First, we study the role of normalization. Batch normalization (BN) is a crucial element for achieving state-of-the-art performance on many vision tasks, but we show it may prevent networks from obtaining strong robustness in adversarial training. One unexpected observation is that, for models trained with BN, simply removing clean images from training data largely boosts adversarial robustness, ie. 18.3%. We relate this phenomenon to the hypothesis that clean images and adversarial images are drawn from two different domains. This two-domain hypothesis may explain the issue of BN when training with a mixture of clean and adversarial images, as estimating normalization statistics of this mixture distribution is challenging.

Guided by this two-domain hypothesis, we show disentangling the mixture distribution for normalization, ie. applying separate BNs to clean and adversarial images for statistics estimation, achieves much stronger robustness. Additionally, we find that enforcing BNs to behave consistently at training and testing can further enhance robustness.

Second, we study the role of network capacity. We find our so-called “deep” networks are still shallow for the task of adversarial learning. Unlike traditional classification tasks where accuracy is only marginally improved by adding more layers to “deep” networks (eg. ResNet-152), adversarial training exhibits a much stronger demand on deeper networks to achieve higher adversarial robustness. This robustness improvement can be observed substantially and consistently even by pushing the network capacity to an unprecedented scale, ie. ResNet-638.

--- https://arxiv.org/abs/1906.04358#google Weight Agnostic Neural Networks Adam Gaier, David Ha 2019-06-11 2020-01-11 [("doi","10.48550/arXiv.1906.04358")] ai/nn/sparsity iq

Not all neural network architectures are created equal, some perform much better than others for certain tasks. But how important are the weight parameters of a neural network compared to its architecture? In this work, we question to what extent neural network architectures alone, without learning any weight parameters, can encode solutions for a given task.

We propose a search method for neural network architectures that can already perform a task without any explicit weight training. To evaluate these networks, we populate the connections with a single shared weight parameter sampled from a uniform random distribution, and measure the expected performance.

We demonstrate that our method can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training. On a supervised learning domain, we find weight-agnostic network architectures that achieve much higher than chance accuracy on MNIST using random weights.

Interactive version of this paper at https://weightagnostic.github.io/. [cf. input-permutation invariance]

--- https://arxiv.org/abs/1906.05030 VISR: Fast Task Inference with Variational Intrinsic Successor Features Steven Hansen, Will Dabney, Andre Barreto, Tom Van de Wiele, David Warde-Farley, Volodymyr Mnih 2019-06-12 2020-01-11 [("doi","10.48550/arXiv.1906.05030")] reinforcement-learning/model

It has been established that diverse behaviors spanning the controllable subspace of an Markov decision process can be trained by rewarding a policy for being distinguishable from other policies (gregor2016variationaleysenbach2018diversity, warde2018unsupervised). However, one limitation of this formulation is generalizing behaviors beyond the finite set being explicitly learned, as is needed for use on subsequent tasks.

Successor features (dayan93improving, barreto2017successor) provide an appealing solution to this generalization problem, but require defining the reward function as linear in some grounded feature space.

In this paper, we show that these two techniques can be combined, and that each method solves the other’s primary limitation. To do so we introduce Variational Intrinsic Successor FeatuRes (VISR), a novel algorithm which learns controllable features that can be leveraged to provide enhanced generalization and fast task inference through the successor feature framework.

We empirically validate VISR on the full Atari suite, in a novel setup wherein the rewards are only exposed briefly after a long unsupervised phase. Achieving human-level performance on 14 games and beating all baselines, we believe VISR represents a step towards agents that rapidly learn from limited feedback.

--- https://arxiv.org/abs/1906.05253 Search on the Replay Buffer: Bridging Planning and Reinforcement Learning Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine 2019-06-12 2020-01-12 [("doi","10.48550/arXiv.1906.05253")] reinforcement-learning/exploration

The history of learning for control has been an exciting back and forth between two broad classes of algorithms: planning and reinforcement learning. Planning algorithms effectively reason over long horizons, but assume access to a local policy and distance metric over collision-free paths. Reinforcement learning excels at learning policies and the relative values of states, but fails to plan over long horizons. Despite the successes of each method in various domains, tasks that require reasoning over long horizons with limited feedback and high-dimensional observations remain exceedingly challenging for both planning and reinforcement learning algorithms.

Frustratingly, these sorts of tasks are potentially the most useful, as they are simple to design (a human only need to provide an example goal state) and avoid reward shaping, which can bias the agent towards finding a sub-optimal solution. We introduce a general control algorithm that combines the strengths of planning and reinforcement learning to effectively solve these tasks. Our aim is to decompose the task of reaching a distant goal state into a sequence of easier tasks, each of which corresponds to reaching a subgoal. Planning algorithms can automatically find these waypoints, but only if provided with suitable abstractions of the environment—namely, a graph consisting of nodes and edges.

Our main insight is that this graph can be constructed via reinforcement learning, where a goal-conditioned value function provides edge weights, and nodes are taken to be previously seen observations in a replay buffer. Using graph search over our replay buffer, we can automatically generate this sequence of subgoals, even in image-based environments. Our algorithm, search on the replay buffer (SoRB), enables agents to solve sparse reward tasks over one hundred steps, and generalizes substantially better than standard RL algorithms.

--- https://arxiv.org/abs/1906.11759 Low-dimensional Embodied Semantics for Music and Language Francisco Afonso Raposo, David Martins de Matos, Ricardo Ribeiro 2019-06-20 2020-01-12 [("doi","10.48550/arXiv.1906.11759")] ai/music reinforcement-learning/imitation-learning/brain-imitation-learning

Embodied cognition states that semantics is encoded in the brain as firing patterns of neural circuits, which are learned according to the statistical structure of human multimodal experience. However, each human brain is idiosyncratically biased, according to its subjective experience history, making this biological semantic machinery noisy with respect to the overall semantics inherent to media artifacts, such as music and language excerpts.

We propose to represent shared semantics using low-dimensional vector embeddings by jointly modeling several brains from human subjects. We show these unsupervised efficient representations outperform the original high-dimensional fMRI voxel spaces in proxy music genre and language topic classification tasks. We further show that joint modeling of several subjects increases the semantic richness of the learned latent vector spaces.

--- https://arxiv.org/abs/1906.11880 Style Generator Inversion for Image Enhancement and Animation Aviv Gabbay, Yedid Hoshen 2019-06-05 2020-01-12 [("doi","10.48550/arXiv.1906.11880")] ai/nn/gan/stylegan

One of the main motivations for training high quality image generative models is their potential use as tools for image manipulation. Recently, generative adversarial networks (GANs) have been able to generate images of remarkable quality. Unfortunately, adversarially-trained unconditional generator networks have not been successful as image priors. One of the main requirements for a network to act as a generative image prior, is being able to generate every possible image from the target distribution. Adversarial learning often experiences mode-collapse, which manifests in generators that cannot generate some modes of the target distribution. Another requirement often not satisfied is invertibility i.e. having an efficient way of finding a valid input latent code given a required output image.

In this work, we show that differently from earlier GANs, the very recently proposed style-generators are quite easy to invert. We use this important observation to propose style generators as general-purpose image priors. We show that style generators outperform other GANs as well as Deep Image Prior as priors for image enhancement tasks.

The latent space spanned by style-generators satisfies linear identity-pose relations. The latent space linearity, combined with invertibility, allows us to animate still facial images without supervision. Extensive experiments are performed to support the main contributions of this paper.

--- https://arxiv.org/abs/1907.00456 Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard 2019-06-30 2020-01-12 [("doi","10.48550/arXiv.1907.00456")] reinforcement-learning/preference-learning

Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment. These are critical shortcomings for applying RL to real-world problems where collecting data is expensive, and models must be tested offline before being deployed to interact with the environment—eg. systems that learn from human interaction. Thus, we develop a novel class of off-policy batch RL algorithms, which are able to effectively learn offline, without exploring, from a fixed batch of human interaction data. We leverage models pre-trained on data as a strong prior, and use KL-control to penalize divergence from this prior during RL training.

We also use dropout-based uncertainty estimates to lower bound the target Q-values as a more efficient alternative to Double Q-Learning. The algorithms are tested on the problem of open-domain dialog generation—a challenging reinforcement learning problem with a 20,000-dimensional action space. Using our Way Off-Policy algorithm, we can extract multiple different reward functions post-hoc from collected human interaction data, and learn effectively from all of these.

We test the real-world generalization of these systems by deploying them live to converse with humans in an open-domain setting, and demonstrate that our algorithm achieves substantial improvements over prior methods in off-policy batch RL.

--- https://arxiv.org/abs/1907.02544 Large Scale Adversarial Representation Learning Jeff Donahue, Karen Simonyan 2019-07-04 2020-01-12 [("doi","10.48550/arXiv.1907.02544")] ai/nn/gan/biggan ai/scaling

Adversarially trained generative models, such as GANs, have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision.

In this work, we show that progress in image generation quality translates to substantially improved representation learning performance. Our approach, BigBiGAN, builds upon the state-of-the-art BigGAN model, extending it to representation learning by adding an encoder and modifying the discriminator.

We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state-of-the-art in unsupervised representation learning on ImageNet, as well as in unconditional image generation.

Pretrained BigBiGAN models—including image generators and encoders—are available on TensorFlow Hub (https://tfhub.dev).

--- https://arxiv.org/abs/1907.04840 Sparse Networks from Scratch: Faster Training without Losing Performance Tim Dettmers, Luke Zettlemoyer 2019-07-10 2020-01-12 [("doi","10.48550/arXiv.1907.04840")] ai/nn/sparsity/pruning

We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance levels. Sparse learning refers to techniques in machine learning that allow for training models more efficiently by reducing the number of active (non-zero) weights, yet without significantly compromising the model’s performance. This approach can greatly enhance computational efficiency and reduce the model’s memory footprint.

We accomplish this by developing sparse momentum, an algorithm which uses exponentially smoothed gradients (momentum) to identify layers and weights which reduce the error efficiently. Sparse momentum redistributes pruned weights across layers according to the mean momentum magnitude of each layer. Within a layer, sparse momentum grows weights according to the momentum magnitude of zero-valued weights. Momentum in machine learning helps accelerate the optimization process, finding better solutions faster than standard gradient descent methods.

We demonstrate state-of-the-art sparse performance on MNIST, CIFAR-10, and ImageNet, decreasing the mean error by a relative 8%, 15%, and 6% compared to other sparse algorithms. Furthermore, we show that sparse momentum reliably reproduces dense performance levels while providing up to 5.61× faster training. The significance of achieving such high levels of performance on these standard benchmarks cannot be overstated. These datasets are pivotal in the field of computer vision, allowing researchers to benchmark the performance of their algorithms against a standard reference.

In our analysis, ablations show that the benefits of momentum redistribution and growth increase with the depth and size of the network. Additionally, we find that sparse momentum is insensitive to the choice of its hyperparameters suggesting that sparse momentum is robust and easy to use. Hyperparameters are the external configurations to the model that are not learned from the data but significantly impact model performance. The robustness to hyperparameter choices simplifies the model’s application to a variety of problems without the need for extensive tuning.

--- https://arxiv.org/abs/1907.05672 Global optimization of quantum dynamics with AlphaZero deep exploration Mogens Dalgaard, Felix Motzoi, Jens Jakob Sorensen, Jacob Sherson 2019-07-12 2020-01-12 [("doi","10.1038/s41534-019-0241-0")] reinforcement-learning/model/alphago

While a large number of algorithms for optimizing quantum dynamics for different objectives have been developed, a common limitation is the reliance on good initial guesses, being either random or based on heuristics and intuitions. Here we implement a tabula rasa deep quantum exploration version of the DeepMind AlphaZero algorithm for systematically averting this limitation. AlphaZero employs a deep neural network in conjunction with deep lookahead in a guided tree search, which allows for predictive hidden variable approximation of the quantum parameter landscape.

To emphasize transferability, we apply and benchmark the algorithm on 3 classes of control problems using only a single common set of algorithmic hyperparameters. AlphaZero achieves substantial improvements in both the quality and quantity of good solution clusters compared to earlier methods.

It is able to spontaneously learn unexpected hidden structure and global symmetry in the solutions, going beyond even human heuristics.

--- https://arxiv.org/abs/1907.05686 And the Bit Goes Down: Revisiting the Quantization of Neural Networks Pierre Stock, Arm Holdings, Joulin, Rémi Gribonval, Benjamin Graham, Hervé Jégou 2019-07-12 2020-01-12 [("doi","10.48550/arXiv.1907.05686")] ai/nn/sparsity/low-precision

In this paper, we address the problem of reducing the memory footprint of convolutional network architectures. We introduce a vector quantization method that aims at preserving the quality of the reconstruction of the network outputs rather than its weights. The principle of our approach is that it minimizes the loss reconstruction error for in-domain inputs.

Our method only requires a set of unlabeled data at quantization time and allows for efficient inference on CPU by using byte-aligned codebooks to store the compressed weights. We validate our approach by quantizing a high performing ResNet-50 model to a memory size of 5MB (20× compression factor) while preserving a top-1 accuracy of 76.1% on ImageNet object classification and by compressing a Mask R-CNN with a 26× factor.

--- https://arxiv.org/abs/1907.09190#facebook ELI5: Long Form Question Answering Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, Michael Auli 2019-07-22 2020-01-12 [("doi","10.48550/arXiv.1907.09190")] ai/nn/retrieval

We introduce the first large-scale corpus for long-form question answering, a task requiring elaborate and in-depth answers to open-ended questions.

The dataset comprises 270K threads from the Reddit forum “Explain Like I’m Five” (ELI5) where an online community provides answers to questions which are comprehensible by five-year-olds. Compared to existing datasets, ELI5 comprises diverse questions requiring multi-sentence answers. We provide a large set of web documents to help answer the question.

Automatic and human evaluations show that an abstractive model trained with a multi-task objective outperforms conventional Seq2Seq, language modeling, as well as a strong extractive baseline. However, our best model is still far from human performance since raters prefer gold responses in over 86% of cases, leaving ample opportunity for future improvement.

--- https://arxiv.org/abs/1907.09720 Metalearned Neural Memory Tsendsuren Munkhdalai, Alessandro Sordoni, Tong Wang, Adam Trischler 2019-07-23 2020-01-12 [("doi","10.48550/arXiv.1907.09720")] ai/nn/retrieval ai/nn/rnn reinforcement-learning/meta-learning

We augment recurrent neural networks with an external memory mechanism that builds upon recent progress in metalearning.

We conceptualize this memory as a rapidly adaptable function that we parameterize as a deep neural network. Reading from the neural memory function amounts to pushing an input (the key vector) through the function to produce an output (the value vector). Writing to memory means changing the function; specifically, updating the parameters of the neural network to encode desired information. We leverage training and algorithmic techniques from metalearning to update the neural memory function in one shot.

The proposed memory-augmented model achieves strong performance on a variety of learning problems, from supervised question answering to reinforcement learning.

--- https://arxiv.org/abs/1907.10786 Interpreting the Latent Space of GANs for Semantic Face Editing Yujun Shen, Jinjin Gu, Xiaoou Tang, Bolei Zhou 2019-07-25 2020-01-12 [("doi","10.48550/arXiv.1907.10786")] ai/nn/gan/stylegan

Despite the recent advance of Generative Adversarial Networks (GANs) in high-fidelity image synthesis, there lacks enough understanding of how GANs are able to map a latent code sampled from a random distribution to a photo-realistic image. Previous work assumes the latent space learned by GANs follows a distributed representation but observes the vector arithmetic phenomenon.

In this work, we propose a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs. In this framework, we conduct a detailed study on how different semantics are encoded in the latent space of GANs for face synthesis. We find that the latent code of well-trained generative models actually learns a disentangled representation after linear transformations. We explore the disentanglement between various semantics and manage to decouple some entangled semantics with subspace projection, leading to more precise control of facial attributes.

Besides manipulating gender, age, expression, and the presence of eyeglasses, we can even vary the face pose as well as fix the artifacts accidentally generated by GAN models. The proposed method is further applied to achieve real image manipulation when combined with GAN inversion methods or some encoder-involved models. Extensive results suggest that learning to synthesize faces spontaneously brings a disentangled and controllable facial attribute representation.

--- https://arxiv.org/abs/1907.12392 A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment Felix Leibfried, Sergio Pascual-Diaz, Jordi Grau-Moya 2019-07-26 2020-01-13 [("doi","10.48550/arXiv.1907.12392")] cs/algorithm/information reinforcement-learning/exploration

Empowerment is an information-theoretic method that can be used to intrinsically motivate learning agents. It attempts to maximize an agent’s control over the environment by encouraging visiting states with a large number of reachable next states. Empowered learning has been shown to lead to complex behaviors, without requiring an explicit reward signal.

In this paper, we investigate the use of empowerment in the presence of an extrinsic reward signal. We hypothesize that empowerment can guide reinforcement learning (RL) agents to find good early behavioral solutions by encouraging highly empowered states. We propose an unified Bellman optimality principle for empowered reward maximization. Our empowered reward maximization approach generalizes both Bellman’s optimality principle as well as recent information-theoretical extensions to it. We prove uniqueness of the empowered values and show convergence to the optimal solution.

We then apply this idea to develop off-policy actor-critic RL algorithms which we validate in high-dimensional continuous robotics domains (MuJoCo). Our methods demonstrate improved initial and competitive final performance compared to model-free state-of-the-art techniques.

--- https://arxiv.org/abs/1908.00709 AutoML: A Survey of the State-of-the-Art Xin He, Kaiyong Zhao, Xiaowen Chu 2019-08-02 2020-01-13 [("doi","10.1016/j.knosys.2020.106622")] reinforcement-learning/meta-learning

Deep learning (DL) techniques have penetrated all aspects of our lives and brought us great convenience. However, building a high-quality DL system for a specific task highly relies on human expertise, hindering the applications of DL to more areas. Automated machine learning (AutoML) becomes a promising solution to build a DL system without human assistance, and a growing number of researchers focus on AutoML.

In this paper, we provide a comprehensive and up-to-date review of the state-of-the-art (SOTA) in AutoML. First, we introduce AutoML methods according to the pipeline, covering data preparation, feature engineering, hyperparameter optimization, and neural architecture search (NAS). We focus more on NAS, as it is currently a very hot sub-topic of AutoML.

We summarize the performance of the representative NAS algorithms on the CIFAR-10 and ImageNet datasets and further discuss several worthy studying directions of NAS methods: one/two-stage NAS, one-shot NAS, and joint hyperparameter and architecture optimization.

Finally, we discuss some open problems of the existing AutoML methods for future research.

--- https://arxiv.org/abs/1908.01289 Dueling Posterior Sampling for Preference-Based Reinforcement Learning Ellen R. Novoseller, Yibing Wei, Yanan Sui, Yisong Yue, Joel W. Burdick 2019-08-04 2020-01-13 [("doi","10.48550/arXiv.1908.01289")] reinforcement-learning/preference-learning

In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback. While there is increasing research activity in preference-based RL, the design of formal frameworks that admit tractable theoretical analysis remains an open challenge.

Building upon ideas from preference-based bandit learning and posterior sampling in RL, we present DUELING POSTERIOR SAMPLING (DPS), which employs preference-based posterior sampling to learn both the system dynamics and the underlying utility function that governs the preference feedback. As preference feedback is provided on trajectories rather than individual state-action pairs, we develop a Bayesian approach for the credit assignment problem, translating preferences to a posterior distribution over state-action reward models.

We prove an asymptotic Bayesian no-regret rate for DPS with a Bayesian linear regression credit assignment model. This is the first regret guarantee for preference-based RL to our knowledge. We also discuss possible avenues for extending the proof methodology to other credit assignment models.

Finally, we evaluate the approach empirically, showing competitive performance against existing baselines.

--- https://arxiv.org/abs/1908.02388#google Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron Courville, Marc G. Bellemare 2019-08-06 2020-01-13 [("doi","10.48550/arXiv.1908.02388")] reinforcement-learning/exploration

This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE). We study the use of different reward bonuses that incentivize exploration in reinforcement learning. We do so by fixing the learning algorithm used and focusing only on the impact of the different exploration bonuses in the agent’s performance.

We use Rainbow, the state-of-the-art algorithm for value-based agents, and focus on some of the bonuses proposed in the last few years. We consider the impact these algorithms have on performance within the popular game Montezuma’s Revenge which has gathered a lot of interest from the exploration community, across the set of 7 games identified by Bellemare et al 2016 as challenging for exploration, and easier games where exploration is not an issue.

We find that, in our setting, recently developed bonuses do not provide improved performance on Montezuma’s Revenge or hard exploration games. We also find that existing bonus-based methods may negatively impact performance on games in which exploration is not an issue and may even perform worse than ε-greedy exploration.

In conclusion, our findings highlight the complexities of designing exploration bonuses in reinforcement learning and suggest that such methods need careful consideration depending on the type of game or task at hand.

--- https://arxiv.org/abs/1908.05840 Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss Hyunsu Kim, Ho Young Jhoo, Eunhyeok Park, Sungjoo Yoo 2019-08-16 2020-01-13 [("doi","10.48550/arXiv.1908.05840")] ai/anime/danbooru ai/nn/gan

Line art colorization is expensive and challenging to automate. A GAN approach is proposed, called Tag2Pix [Tag2Pix CLI/GUI], of line art colorization which takes as input a grayscale line art and color tag information and produces a quality colored image.

First, we present the Tag2Pix line art colorization dataset. A generator network is proposed which consists of convolutional layers to transform the input line art, a pre-trained semantic extraction network, and an encoder for input color information. The discriminator is based on an auxiliary classifier GAN to classify the tag information as well as genuineness. In addition, we propose a novel network structure called SECat, which makes the generator properly colorize even small features such as eyes, and also suggest a novel two-step training method where the generator and discriminator first learn the notion of object and shape and then, based on the learned notion, learn colorization, such as where and how to place which color.

We present both quantitative and qualitative evaluations which prove the effectiveness of the proposed method.

--- https://arxiv.org/abs/1908.07446 Playing magic tricks to deep neural networks untangles human deception Regina Zaghi-Lara, Miguel Ángel Gea, Jordi Camí, Luis M. Martínez, Alex Gomez-Marin 2019-08-20 2020-01-13 [("doi","10.48550/arXiv.1908.07446")] ai/nn/adversarial/human psychology/neuroscience

Magic is the art of producing in the spectator an illusion of impossibility. Although the scientific study of magic is in its infancy, the advent of recent tracking algorithms based on deep learning allow now to quantify the skills of the magician in naturalistic conditions at unprecedented resolution and robustness.

In this study, we deconstructed stage magic into purely motor maneuvers and trained an artificial neural network (DeepLabCut) to follow coins as a professional magician made them appear and disappear in a series of tricks. Rather than using AI as a mere tracking tool, we conceived it as an “artificial spectator”. When the coins were not visible, the algorithm was trained to infer their location as a human spectator would (ie. in the left fist).

This created situations where the human was fooled while AI (as seen by a human) was not, and vice versa.

Magic from the perspective of the machine reveals our own cognitive biases.

[ML techniques, compared to humans, look a bit like autistic or idiot savants: skilled at fine detail but missing the big picture. Another example of CNNs being ‘smarter by being stupider’ is “Humans, but Not Deep Neural Networks, Often Miss Giant Targets in Scenes”, Eckstein et al 2017.]

--- https://arxiv.org/abs/1909.03004 Show Your Work: Improved Reporting of Experimental Results Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, Noah Smith 2019-09-06 2020-01-13 [("doi","10.48550/arXiv.1909.03004")] ai/scaling

Research in natural language processing proceeds, in part, by demonstrating that new models achieve superior performance (eg. accuracy) on held-out test data, compared to previous results. In this paper, we demonstrate that test-set performance scores alone are insufficient for drawing accurate conclusions about which model performs best. We argue for reporting additional details, especially performance on validation data obtained during model development.

We present a novel technique for doing so: expected validation performance of the best-found model as a function of computation budget (ie. the number of hyperparameter search trials or the overall training time). Using our approach, we find multiple recent model comparisons where authors would have reached a different conclusion if they had used more (or less) computation. Our approach also allows us to estimate the amount of computation required to obtain a given accuracy; applying it to several recently published results yields massive variation across papers, from hours to weeks.

We conclude with a set of best practices for reporting experimental results which allow for robust future comparisons, and provide code to allow researchers to use our technique.

--- https://arxiv.org/abs/1909.03493 MULE: Multimodal Universal Language Embedding Donghyun Kim, Kuniaki Saito, Kate Saenko, Stan Sclaroff, Bryan A. Plummer 2019-09-08 2020-01-13 [("doi","10.48550/arXiv.1909.03493")] ai/nn/retrieval ai/nn/transformer/clip

Existing vision-language methods typically support two languages at a time at most.

In this paper, we present a modular approach which can easily be incorporated into existing vision-language methods in order to support many languages. We accomplish this by learning a single shared Multimodal Universal Language Embedding (MULE) which has been visually-semantically aligned across all languages. Then we learn to relate MULE to visual data as if it were a single language. Our method is not architecture specific, unlike prior work which typically learned separate branches for each language, enabling our approach to easily be adapted to many vision-language methods and tasks. Since MULE learns a single language branch in the multimodal model, we can also scale to support many languages, and languages with fewer annotations can take advantage of the good representation learned from other (more abundant) language data.

We demonstrate the effectiveness of MULE on the bidirectional image-sentence retrieval task, supporting up to four languages in a single model. In addition, we show that Machine Translation can be used for data augmentation in multilingual learning, which, combined with MULE, improves mean recall by up to 21.9% on a single-language compared to prior work, with the most substantial gains seen on languages with relatively few annotations.

Our code is publicly available.

--- https://arxiv.org/abs/1909.04630 Meta-Learning with Implicit Gradients Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine 2019-09-10 2020-01-13 [("doi","10.48550/arXiv.1909.04630")] reinforcement-learning/meta-learning

A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on prior experience. Gradient (or optimization) based meta-learning has recently emerged as an effective approach for few-shot learning. In this formulation, meta-parameters are learned in the outer loop, while task-specific models are learned in the inner-loop, by using only a small amount of data from the current task.

A key challenge in scaling these approaches is the need to differentiate through the inner loop learning process, which can impose considerable computational and memory burdens. By drawing upon implicit differentiation, we develop the implicit MAML algorithm, which depends only on the solution to the inner level optimization and not the path taken by the inner loop optimizer. This effectively decouples the meta-gradient computation from the choice of inner loop optimizer. As a result, our approach is agnostic to the choice of inner loop optimizer and can gracefully handle many gradient steps without vanishing gradients or memory constraints.

Theoretically, we prove that implicit MAML can compute accurate meta-gradients with a memory footprint that is, up to small constant factors, no more than that which is required to compute a single inner loop gradient and at no overall increase in the total computational cost. Experimentally, we show that these benefits of implicit MAML translate into empirical gains on few-shot image recognition benchmarks.

--- https://arxiv.org/abs/1909.06296 Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy Hunter Gabbard, Chris Messenger, Ik Siong Heng, Francesco Tonolini, Roderick Murray-Smith 2019-09-13 2020-01-13 [("doi","10.1038/s41567-021-01425-7")] ai/nn/vae statistics/bayes

Gravitational wave (GW) detection is now commonplace and as the sensitivity of the global network of GW detectors improves, we will observe 𝒪(100)s of transient GW events per year. The current methods used to estimate their source parameters employ optimally sensitive but computationally costly Bayesian inference approaches where typical analyses have taken between 6 hours and 5 days. For binary neutron star and neutron star black hole systems prompt counterpart electromagnetic (EM) signatures are expected on timescales of 1s–1min and the current fastest method for alerting EM follow-up observers, can provide estimates in 𝒪(1) minutes, on a limited range of key source parameters.

Here we show that a conditional variational autoencoder pre-trained on binary black hole signals can return Bayesian posterior probability estimates.

The training procedure need only be performed once for a given prior parameter space and the resulting trained machine can then generate samples describing the posterior distribution ~6 orders of magnitude faster than existing techniques.

--- https://arxiv.org/abs/1909.08593#openai Fine-Tuning Language Models from Human Preferences Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano, Geoffrey Irving 2019-09-18 2020-01-13 [("doi","10.48550/arXiv.1909.08593")] ai/nn/transformer/gpt/2 reinforcement-learning/preference-learning reinforcement-learning/scaling

Reward learning enables the application of reinforcement learning (RL) to tasks where reward is defined by human judgment, building a model of reward by asking humans questions. Most work on reward learning has used simulated environments, but complex information about values is often expressed in natural language, and we believe reward learning for language is a key to making RL practical and safe for real-world tasks.

In this paper, we build on advances in generative pretraining of language models to apply reward learning to 4 natural language tasks: continuing text with positive sentiment or physically descriptive language, and summarization tasks on the TL;DR and CNN/Daily Mail datasets. For stylistic continuation we achieve good results with only 5,000 comparisons evaluated by humans. For summarization, models trained with 60,000 comparisons copy whole sentences from the input but skip irrelevant preamble; this leads to reasonable ROUGE scores and very good performance according to our human labelers, but may be exploiting the fact that labelers rely on simple heuristics.

For stylistic continuation we achieve good results with only 5,000 comparisons evaluated by humans. For summarization, models trained with 60,000 comparisons copy whole sentences from the input but skip irrelevant preamble; this leads to reasonable ROUGE scores and very good performance according to our human labelers, but may be exploiting the fact that labelers rely on simple heuristics.

In conclusion, our work demonstrates the potential of reward learning in natural language processing tasks, suggesting that with sufficient human evaluation, RL can be effectively applied beyond simulated environments. This opens new avenues for research in RL applications for real-world tasks, emphasizing the importance of integrating human values and judgments into the learning process.

--- https://arxiv.org/abs/1909.09436#microsoft CodeSearchNet Challenge: Evaluating the State of Semantic Code Search Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, Marc Brockschmidt 2019-09-20 2020-01-14 [("doi","10.48550/arXiv.1909.09436")] ai/nn/transformer/gpt/codex

Semantic code search is the task of retrieving relevant code given a natural language query. While related to other information retrieval tasks, it requires bridging the gap between the language used in code (often abbreviated and highly technical) and natural language more suitable to describe vague concepts and ideas.

To enable evaluation of progress on code search, we are releasing the CodeSearchNet Corpus and are presenting the CodeSearchNet Challenge, which consists of 99 natural language queries with about 4k expert relevance annotations of likely results from CodeSearchNet Corpus. The corpus contains about 6 million functions from open-source code spanning six programming languages (Go, Java, JavaScript, PHP, Python, and Ruby). The CodeSearchNet Corpus also contains automatically generated query-like natural language for 2 million functions, obtained from mechanically scraping and preprocessing associated function documentation. In this article, we describe the methodology used to obtain the corpus and expert labels, as well as a number of simple baseline solutions for the task.

We hope that CodeSearchNet Challenge encourages researchers and practitioners to study this interesting task further and will host a competition and leaderboard to track the progress on the challenge. We are also keen on extending CodeSearchNet Challenge to more queries and programming languages in the future.

--- https://arxiv.org/abs/1909.10351 TinyBERT: Distilling BERT for Natural Language Understanding Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, Qun Liu 2019-09-23 2020-01-14 [("doi","10.48550/arXiv.1909.10351")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer

Language model pre-training, such as BERT, has improved the performances of many natural language processing tasks. However, pre-trained language models are usually computationally expensive, so it is difficult to efficiently execute them on resource-restricted devices. To accelerate inference and reduce model size while maintaining accuracy, we first propose a novel Transformer distillation method that is specially designed for knowledge distillation (KD) of the Transformer-based models. By leveraging this new KD method, the plenty of knowledge encoded in a large teacher BERT can be effectively transferred to a small student Tiny-BERT. Then, we introduce a new two-stage learning framework for TinyBERT, which performs Transformer distillation at both the pretraining and task-specific learning stages. This framework ensures that TinyBERT can capture he general-domain as well as the task-specific knowledge in BERT.

TinyBERT with 4 layers is empirically effective and achieves more than 96.8% the performance of its teacher BERTbase on GLUE benchmark, while being 7.5× smaller and 9.4× faster on inference. TinyBERT with 4 layers is also better than 4-layer state-of-the-art baselines on BERT distillation, with only about 28% parameters and about 31% inference time of them. Moreover, TinyBERT with 6 layers performs on-par with its teacher BERTBASE.

--- https://arxiv.org/abs/1909.11236 Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller Bardienus P. Duisterhof, Srivatsan Krishnan, Jonathan J. Cruz, Colby R. Banbury, William Fu, Aleksandra Faust, Guido C. H. E. de Croon, Vijay Janapa Reddi 2019-09-25 2020-01-14 [("doi","10.48550/arXiv.1909.11236")] ai/nn/sparsity reinforcement-learning/robot technology

We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable inference of a deep-RL policy onboard a nano quadcopter.

Our deep-RL algorithm finds a high-performance solution to a challenging problem, even in presence of high noise levels and generalizes across real and simulation environments with different obstacle configurations.

We verify our approach with simulation and in-field testing on a Bitcraze CrazyFlie using only the cheap and ubiquitous Cortex-M4 microcontroller unit.

The results show that by end-to-end application-specific system design, our contribution consumes almost 3 times less additional power, as compared to competing learning-based navigation approach onboard a nano quadcopter. Thanks to our observation space, which we carefully design within the resource constraints, our solution achieves a 94% success rate in cluttered and randomized test environments, as compared to the previously achieved 80%. We also compare our strategy to a simple finite state machine (FSM), geared towards efficient exploration, and demonstrate that our policy is more robust and resilient at obstacle avoidance as well as up to 70% more efficient in source seeking.

To this end, we contribute a cheap and lightweight end-to-end tiny robot learning (tinyRL) solution, running onboard a nano quadcopter, that proves to be robust and efficient in a challenging task using limited sensory input.

--- https://arxiv.org/abs/1909.11522 Neural networks are a priori biased towards Boolean functions with low entropy Chris Mingard, Joar Skalse, Guillermo Valle-Pérez, David Martínez-Rubio, Vladimir Mikulik, Ard A. Louis 2019-09-25 2020-01-14 [("doi","10.48550/arXiv.1909.11522")] ai/nn cs/algorithm/information

Understanding the inductive bias of neural networks is critical to explaining their ability to generalize.

Here, for one of the simplest neural networks—a single-layer perceptron with n input neurons, one output neuron, and no threshold bias term—we prove that upon random initialization of weights, the a priori probability P(t) that it represents a Boolean function that classifies t points in {0,1}n as outcome 1 has a remarkably simple form: P(t) = 2−n for 0 ≤ t < 2n.

Since a perceptron can express far fewer Boolean functions with small or large values of t (low entropy) than with intermediate values of t (high entropy) there is, on average, a strong intrinsic a-priori bias towards individual functions with low entropy. Furthermore, within a class of functions with fixed t, we often observe a further intrinsic bias towards functions of lower complexity.

Finally, we prove that, regardless of the distribution of inputs, the bias towards low entropy becomes monotonically stronger upon adding ReLU layers, and empirically show that increasing the variance of the bias term has a similar effect.

--- https://arxiv.org/abs/1909.12316 Preference-Based Learning for Exoskeleton Gait Optimization Maegan Tucker, Ellen Novoseller, Claudia Kann, Yanan Sui, Yisong Yue, Joel Burdick, Aaron D. Ames 2019-09-26 2020-01-14 [("doi","10.48550/arXiv.1909.12316")] reinforcement-learning/preference-learning

This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, eg. for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm.

CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback.

We show that CoSpar performs competitively in simulation and demonstrate a prototype implementation of CoSpar on a lower-body exoskeleton to optimize human walking trajectory features. In the experiments, CoSpar consistently found user-preferred parameters of the exoskeleton’s walking gait, which suggests that it is a promising starting point for adapting and personalizing exoskeletons (or other assistive devices) to individual users.

--- https://arxiv.org/abs/1909.13371#facebook Gradient Descent: The Ultimate Optimizer Kartik Chandra, Erik Meijer, Samantha Andow, Emilio Arroyo-Fang, Irene Dea, Johann George, Melissa Grueter, Basil Hosmer, Steffi Stumpos, Alanna Tempest, Shannon Yang 2019-09-29 2020-01-14 [("doi","10.48550/arXiv.1909.13371")] reinforcement-learning/meta-learning

Working with any gradient-based machine learning algorithm involves the tedious task of tuning the optimizer’s hyperparameters, such as the learning rate. There exist many techniques for automated hyperparameter optimization, but they typically introduce even more hyperparameters to control the hyperparameter optimization process.

We propose to instead learn the hyperparameters themselves by gradient descent, and furthermore to learn the hyper-hyperparameters by gradient descent as well, and so on ad infinitum.

As these towers of gradient-based optimizers grow, they become less sensitive to the choice of top-level hyperparameters, hence decreasing the burden on the user to search for optimal values.

--- https://arxiv.org/abs/1910.00927 Stabilizing Generative Adversarial Networks: A Survey Maciej Wiatrak, Stefano V. Albrecht, Andrew Nystrom 2019-09-30 2020-01-14 [("doi","10.48550/arXiv.1910.00927")] ai/nn/gan/stylegan reinforcement-learning/multi-agent

Generative Adversarial Networks (GANs) are a type of generative model which have received much attention due to their ability to model complex real-world data.

Despite their recent successes, the process of training GANs remains challenging, suffering from instability problems such as non-convergence, vanishing or exploding gradients, and mode collapse.

In recent years, a diverse set of approaches have been proposed which focus on stabilizing the GAN training procedure.

The purpose of this survey is to provide a comprehensive overview of the GAN training stabilization methods which can be found in the literature.

We discuss the advantages and disadvantages of each approach, offer a comparative summary, and conclude with a discussion of open problems.

--- https://arxiv.org/abs/1910.01108 DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter Victor Sanh, Lysandre Debut, Julien Chaumond, Thomas Wolf 2019-10-02 2020-01-14 [("doi","10.48550/arXiv.1910.01108")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer

As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger counterparts.

While most prior work investigated the use of distillation for building task-specific models, we leverage knowledge distillation during the pre-training phase and show that it is possible to reduce the size of a BERT model by 40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive biases learned by larger models during pre-training, we introduce a triple loss combining language modeling, distillation and cosine-distance losses.

Our smaller, faster and lighter model is cheaper to pre-train and we demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device study.

--- https://arxiv.org/abs/1910.07113#openai Solving Rubik’s Cube with a Robot Hand OpenAI, Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Jerry Tworek, Peter Welinder, Lilian Weng, Qiming Yuan, Wojciech Zaremba, Lei Zhang 2019-10-16 2020-01-14 [("doi","10.48550/arXiv.1910.07113")] reinforcement-learning/meta-learning reinforcement-learning/model-free/oa5 reinforcement-learning/robot reinforcement-learning/scaling

We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot.

This is made possible by two key components: a novel algorithm, which we call automatic domain randomization (ADR) and a robot platform built for machine learning. ADR automatically generates a distribution over randomized environments of ever-increasing difficulty.

Control policies and vision state estimators trained with ADR exhibit vastly improved sim2real transfer. For control policies, memory-augmented models trained on an ADR-generated distribution of environments show clear signs of emergent meta-learning at test time. The combination of ADR with our custom robot platform allows us to solve a Rubik’s cube with a humanoid robot hand, which involves both control and state estimation problems.

Videos summarizing our results are available.

--- https://arxiv.org/abs/1910.10685 Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules Benjamin Sanchez-Lengeling, Jennifer N. Wei, Brian K. Lee, Richard C. Gerkin, Alán Aspuru-Guzik, Alexander B. Wiltschko 2019-10-23 2020-01-14 [("doi","10.48550/arXiv.1910.10685")] ai/nn psychology/smell

Predicting the relationship between a molecule’s structure and its odor remains a difficult, decades-old task. This problem, termed quantitative structure-odor relationship (QSOR) modeling, is an important challenge in chemistry, impacting human nutrition, manufacture of synthetic fragrance, the environment, and sensory neuroscience.

We propose the use of graph neural networks for QSOR, and show they out-perform prior methods on a novel data set labeled by olfactory experts. Additional analysis shows that the learned embeddings from graph neural networks capture a meaningful odor space representation of the underlying relationship between structure and odor, as demonstrated by strong performance on two challenging transfer learning tasks.

Machine learning has already had a large impact on the senses of sight and sound. Based on these early results with graph neural networks for molecular properties, we hope machine learning can eventually do for olfaction what it has already done for vision and hearing.

--- https://arxiv.org/abs/1910.10897 Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Avnish Narayan, Hayden Shively, Adithya Bellathur, Karol Hausman, Chelsea Finn, Sergey Levine 2019-10-24 2020-01-14 [("doi","10.48550/arXiv.1910.10897")] reinforcement-learning/meta-learning reinforcement-learning/robot

Meta-reinforcement learning algorithms can enable robots to acquire new skills much more quickly, by leveraging prior experience to learn how to learn. However, much of the current research on meta-reinforcement learning focuses on task distributions that are very narrow. For example, a commonly used meta-reinforcement learning benchmark uses different running velocities for a simulated robot as different tasks. When policies are meta-trained on such narrow task distributions, they cannot possibly generalize to more quickly acquire entirely new tasks.

Therefore, if the aim of these methods is to enable faster acquisition of entirely new behaviors, we must evaluate them on task distributions that are sufficiently broad to enable generalization to new behaviors. In this paper, we propose an open-source simulated benchmark for meta-reinforcement learning and multi-task learning consisting of 50 distinct robotic manipulation tasks. Our aim is to make it possible to develop algorithms that generalize to accelerate the acquisition of entirely new, held-out tasks. We evaluate 7 state-of-the-art meta-reinforcement learning and multi-task learning algorithms on these tasks. Surprisingly, while each task and its variations (eg. with different object positions) can be learned with reasonable success, these algorithms struggle to learn with multiple tasks at the same time, even with as few as ten distinct training tasks. Our analysis and open-source environments pave the way for future research in multi-task learning and meta-learning that can enable meaningful generalization, thereby unlocking the full potential of these methods.

--- https://arxiv.org/abs/1910.12327 A simple measure of conditional dependence Mona Azadkia, Sourav Chatterjee 2019-10-27 2020-01-15 [("doi","10.48550/arXiv.1910.12327")] statistics/order

We propose a coefficient of conditional dependence between two random variables Y and Z given a set of other variables X1,…,Xp, based on an iid sample.

The coefficient has a long list of desirable properties, the most important of which is that under absolutely no distributional assumptions, it converges to a limit in [0,1], where the limit is 0 if and only if Y and Z are conditionally independent given X1,…,Xp, and is 1 if and only if Y is equal to a measurable function of Z given X1,…,Xp. Moreover, it has a natural interpretation as a nonlinear generalization of the familiar partial R2 statistic for measuring conditional dependence by regression.

Using this statistic, we devise a new variable selection algorithm, called Feature Ordering by Conditional Independence (FOCI), which is model-free, has no tuning parameters, and is provably consistent under sparsity assumptions.

A number of applications to synthetic and real datasets are worked out.

--- https://arxiv.org/abs/1910.13012 Multiplayer AlphaZero Nick Petosa, Tucker Balch 2019-10-29 2020-01-15 [("doi","10.48550/arXiv.1910.13012")] reinforcement-learning/model/alphago reinforcement-learning/multi-agent

The AlphaZero algorithm has achieved superhuman performance in two-player, deterministic, zero-sum games where perfect information of the game state is available. This success has been demonstrated in Chess, Shogi, and Go where learning occurs solely through self-play. Many real-world applications (eg. equity trading) require the consideration of a multiplayer environment.

In this work, we suggest novel modifications of the AlphaZero algorithm to support multiplayer environments, and evaluate the approach in two simple 3-player games.

Our experiments show that multiplayer AlphaZero learns successfully and consistently outperforms a competing approach: Monte Carlo tree search. These results suggest that our modified AlphaZero can learn effective strategies in multiplayer game scenarios.

Our work supports the use of AlphaZero in multiplayer games and suggests future research for more complex environments.

--- https://arxiv.org/abs/1910.13038#google Learning to Predict Without Looking Ahead: World Models Without Forward Prediction C. Daniel Freeman, Luke Metz, David Ha 2019-10-29 2020-01-15 [("doi","10.48550/arXiv.1910.13038")] ai/video/generation reinforcement-learning/meta-learning

Much of model-based reinforcement learning involves learning a model of an agent’s world, and training an agent to leverage this model to perform a task more efficiently. While these models are demonstrably useful for agents, every naturally occurring model of the world of which we are aware—eg. a brain—arose as the byproduct of competing evolutionary pressures for survival, not minimization of a supervised forward-predictive loss via gradient descent. That useful models can arise out of the messy and slow optimization process of evolution suggests that forward-predictive modeling can arise as a side-effect of optimization under the right circumstances. Crucially, this optimization process need not explicitly be a forward-predictive loss.

In this work, we introduce a modification to traditional reinforcement learning which we call observational dropout, whereby we limit the agent’s ability to observe the real environment at each timestep. In doing so, we can coerce an agent into learning a world model to fill in the observation gaps during reinforcement learning.

We show that the emerged world model, while not explicitly trained to predict the future, can help the agent learn key skills required to perform well in its environment.

Videos of our results available at https://learningtopredict.github.io/#google.

--- https://arxiv.org/abs/1911.03268 Inducing brain-relevant bias in natural language processing models Dan Schwartz, Mariya Toneva, Leila Wehbe 2019-10-29 2020-01-15 [("doi","10.48550/arXiv.1911.03268")] reinforcement-learning/imitation-learning/brain-imitation-learning

Progress in natural language processing (NLP) models that estimate representations of word sequences has recently been leveraged to improve the understanding of language processing in the brain. However, these models have not been specifically designed to capture the way the brain represents language meaning.

We hypothesize that fine-tuning these models to predict recordings of brain activity of people reading text will lead to representations that encode more brain-activity-relevant language information.

We demonstrate that a version of BERT, a recently introduced and powerful language model, can improve the prediction of brain activity after fine-tuning. We show that the relationship between language and brain activity learned by BERT during this fine-tuning transfers across multiple participants. We also show that, for some participants, the fine-tuned representations learned from both magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) are better for predicting fMRI than the representations learned from fMRI alone, indicating that the learned representations capture brain-activity-relevant information that is not simply an artifact of the modality.

While changes to language representations help the model predict brain activity, they also do not harm the model’s ability to perform downstream NLP tasks.

Our findings are notable for research on language understanding in the brain.

--- https://arxiv.org/abs/1911.09536 Collective Dynamics of Dark Web Marketplaces Abeer ElBahrawy, Laura Alessandretti, Leonid Rusnac, Daniel Goldsmith, Alexander Teytelboym, Andrea Baronchelli 2019-11-21 2020-01-15 [("doi","10.1038/s41598-020-74416-y")] darknet-market economics

Dark markets are commercial websites that use Bitcoin to sell or broker transactions involving drugs, weapons, and other illicit goods. Being illegal, they do not offer any user protection, and several police raids and scams have caused large losses to both customers and vendors over the past years. However, this uncertainty has not prevented a steady growth of the dark market phenomenon and a proliferation of new markets. The origin of this resilience has remained unclear so far, also due to the difficulty of identifying relevant Bitcoin transaction data.

Here, we investigate how the dark market ecosystem re-organizes following the disappearance of a market, due to factors including raids and scams. To do so, we analyze 24 episodes of unexpected market closure through a novel datasets of 133 million Bitcoin transactions involving 31 dark markets and their users, totaling 4 billion USD.

We show that coordinated user migration from the closed market to coexisting markets guarantees overall systemic resilience beyond the intrinsic fragility of individual markets. The migration is swift, efficient, and common to all market closures. We find that migrants are on average more active users in comparison to non-migrants and move preferentially towards the coexisting market with the highest trading volume.

Our findings shed light on the resilience of the dark market ecosystem and we anticipate that they may inform future research on the self-organization of emerging online markets.

--- https://arxiv.org/abs/1911.09665 Adversarial Examples Improve Image Recognition Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan Yuille, Quoc V. Le 2019-11-21 2020-01-15 [("doi","10.48550/arXiv.1911.09665")] ai/nn/adversarial

Adversarial examples are commonly viewed as a threat to ConvNets. Here we present an opposite perspective: adversarial examples can be used to improve image recognition models if harnessed in the right manner. We propose AdvProp, an enhanced adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to our method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples.

We show that AdvProp improves a wide range of models on various image recognition tasks and performs better when the models are bigger. For instance, by applying AdvProp to the latest EfficientNet-B7 [28] on ImageNet, we achieve improvements on ImageNet (+0.7%), ImageNet-C (+6.5%), ImageNet-A (+7.0%), Stylized-ImageNet (+4.8%). With an enhanced EfficientNet-B8, our method achieves the state-of-the-art 85.5% ImageNet top-1 accuracy without extra data. This result even surpasses the best model in [20] which is trained with 3.5B Instagram images (~3000× more than ImageNet) and ~9.4× more parameters. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet.

--- https://arxiv.org/abs/1912.00527 Detecting GAN generated errors Xiru Zhu, Fengdi Che, Tianzi Yang, Tzuyang Yu, David Meger, Gregory Dudek 2019-12-02 2020-01-15 [("doi","10.48550/arXiv.1912.00527")] ai/nn/gan/biggan ai/nn/gan/stylegan ai/nn/transformer

Despite an impressive performance from the latest GAN for generating hyper-realistic images, GAN discriminators have difficulty evaluating the quality of an individual generated sample. This is because the task of evaluating the quality of a generated image differs from deciding if an image is real or fake. A generated image could be perfect except in a single area but still be detected as fake. Instead, we propose a novel approach for detecting where errors occur within a generated image.

By collaging real images with generated images, we compute for each pixel, whether it belongs to the real distribution or generated distribution. Furthermore, we leverage attention to model long-range dependency; this allows detection of errors which are reasonable locally but not holistically.

For evaluation, we show that our error detection can act as a quality metric for an individual image, unlike FID and IS. We leverage Improved Wasserstein, BigGAN, and StyleGAN to show a ranking based on our metric correlates impressively with FID scores.

Our work opens the door for better understanding of GAN and the ability to select the best samples from a GAN model.

--- https://arxiv.org/abs/1912.01683 Optimal Policies Tend to Seek Power Alexander Matt Turner, Logan Smith, Rohin Shah, Andrew Critch, Prasad Tadepalli 2019-12-03 2020-01-15 [("doi","10.48550/arXiv.1912.01683")] reinforcement-learning/exploration reinforcement-learning/safe

Some researchers speculate that intelligent reinforcement learning (RL) agents would be incentivized to seek resources and power in pursuit of their objectives. Other researchers point out that RL agents need not have human-like power-seeking instincts.

To clarify this discussion, we develop the first formal theory of the statistical tendencies of optimal policies. In the context of Markov decision processes, we prove that certain environmental symmetries are sufficient for optimal policies to tend to seek power over the environment. These symmetries exist in many environments in which the agent can be shut down or destroyed.

We prove that in these environments, most reward functions make it optimal to seek power by keeping a range of options available and, when maximizing average reward, by navigating towards larger sets of potential terminal states.

--- https://arxiv.org/abs/1912.03820#google Meta-Learning without Memorization Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn 2019-12-09 2020-01-15 [("doi","10.48550/arXiv.1912.03820")] reinforcement-learning/meta-learning

The ability to learn new concepts with small amounts of data is a critical aspect of intelligence that has proven challenging for deep learning methods. Meta-learning has emerged as a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks. However, most meta-learning algorithms implicitly require that the meta-training tasks be mutually exclusive, such that no single model can solve all of the tasks at once. For example, when creating tasks for few-shot image classification, prior work uses a per-task random assignment of image classes to N-way classification labels. If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes. This requirement means that the user must take great care in designing the tasks, for example by shuffling labels or removing task identifying information from the inputs. In some domains, this makes meta-learning entirely inapplicable.

In this paper, we address this challenge by designing a meta-regularization objective using information theory that places precedence on data-driven adaptation. This causes the meta-learner to decide what must be learned from the task training data and what should be inferred from the task testing input. By doing so, our algorithm can successfully use data from non-mutually-exclusive tasks to efficiently adapt to novel tasks.

We demonstrate its applicability to both contextual and gradient-based meta-learning algorithms, and apply it in practical settings where applying standard meta-learning has been difficult. Our approach substantially outperforms standard meta-learning algorithms in these settings.

--- https://arxiv.org/abs/1912.04472 Deep Bayesian Reward Learning from Preferences Daniel S. Brown, Scott Niekum 2019-12-10 2020-01-15 [("doi","10.48550/arXiv.1912.04472")] reinforcement-learning/imitation-learning reinforcement-learning/preference-learning

Bayesian inverse reinforcement learning (IRL) methods are ideal for safe imitation learning, as they allow a learning agent to reason about reward uncertainty and the safety of a learned policy. However, Bayesian IRL is computationally intractable for high-dimensional problems because each sample from the posterior requires solving an entire Markov Decision Process (MDP). While there exist non-Bayesian deep IRL methods, these methods typically infer point estimates of reward functions, precluding rigorous safety and uncertainty analysis.

We propose Bayesian Reward Extrapolation (B-REX), a highly efficient preference-based Bayesian reward learning algorithm that scales to high-dimensional, visual control tasks. Our approach uses successor feature representations and preferences over demonstrations to efficiently generate samples from the posterior distribution over the demonstrator’s reward function without requiring an MDP solver. Using samples from the posterior, we demonstrate how to calculate high-confidence bounds on policy performance in the imitation learning setting, in which the ground-truth reward function is unknown. We evaluate our proposed approach on the task of learning to play Atari games via imitation learning from pixel inputs, with no access to the game score. We demonstrate that B-REX learns imitation policies that are competitive with a state-of-the-art deep imitation learning method that only learns a point estimate of the reward function. Furthermore, we demonstrate that samples from the posterior generated via B-REX can be used to compute high-confidence performance bounds for a variety of evaluation policies. We show that high-confidence performance bounds are useful for accurately ranking different evaluation policies when the reward function is unknown. We also demonstrate that high-confidence performance bounds may be useful for detecting reward hacking.

--- https://arxiv.org/abs/1912.04958#nvidia Analyzing and Improving the Image Quality of StyleGAN Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila 2019-12-03 2020-01-16 [("doi","10.48550/arXiv.1912.04958")] ai/nn/gan/stylegan cs/cryptography/steganography

The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them.

In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generatoruses its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements.

Overall, our improved model redefines the state-of-the-art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.

--- https://arxiv.org/abs/1912.05270 MineGAN: effective knowledge transfer from GANs to target domains with few images Yaxing Wang, Abel Gonzalez-Garcia, David Berga, Luis Herranz, Fahad Shahbaz Khan, Joost van de Weijer 2019-12-11 2020-01-16 [("doi","10.48550/arXiv.1912.05270")] ai/anime/danbooru ai/nn/gan/biggan

One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality networks can be trained in domains with relatively little training data. This property has been extensively studied for discriminative networks but has received statistically-significantly less attention for generative models. Given the often enormous effort required to train GANs, both computationally as well as in the dataset collection, the re-use of pretrained GANs is a desirable objective.

We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods such as mode collapse and lack of flexibility.

We perform experiments on several complex datasets using various GAN architectures (BigGAN, Progressive GAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs.

Our code is available at: Github.

--- https://arxiv.org/abs/1912.05537 Encoding Musical Style with Transformer Autoencoders Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, Jesse Engel 2019-12-10 2020-01-16 [("doi","10.48550/arXiv.1912.05537")] ai/music ai/nn/transformer ai/nn/vae

We consider the problem of learning high-level controls over the global structure of generated sequences, particularly in the context of symbolic music generation with complex language models.

In this work, we present the Transformer autoencoder, which aggregates encodings of the input data across time to obtain a global representation of style from a given performance. We show it is possible to combine this global representation with other temporally distributed embeddings, enabling improved control over the separate aspects of performance style and melody.

Empirically, we demonstrate the effectiveness of our method on various music generation tasks on the MAESTRO dataset and a YouTube dataset with 10,000+ hours of piano performances, where we achieve improvements in terms of log-likelihood and mean listening scores as compared to baselines.

--- https://arxiv.org/abs/1912.05652#deepmind Learning Human Objectives by Evaluating Hypothetical Behavior Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike 2019-12-05 2020-01-16 [("doi","10.48550/arXiv.1912.05652")] reinforcement-learning/exploration reinforcement-learning/preference-learning

We seek to align agent behavior with a user’s objectives in a reinforcement learning setting with unknown dynamics, an unknown reward function, and unknown unsafe states. The user knows the rewards and unsafe states, but querying the user is expensive. To address this challenge, we propose an algorithm that safely and interactively learns a model of the user’s reward function. We start with a generative model of initial states and a forward dynamics model trained on off-policy data. Our method uses these models to synthesize hypothetical behaviors, asks the user to label the behaviors with rewards, and trains a neural network to predict the rewards. The key idea is to actively synthesize the hypothetical behaviors from scratch by maximizing tractable proxies for the value of information, without interacting with the environment. We call this method reward query synthesis via trajectory optimization (ReQueST).

We evaluate ReQueST with simulated users on a state-based 2D navigation task and the image-based Car Racing video game. The results show that ReQueST outperforms prior methods in learning reward models that transfer to new environments with different initial state distributions. Moreover, ReQueST safely trains the reward model to detect unsafe states, and corrects reward hacking before deploying the agent.

--- https://arxiv.org/abs/1912.06680#openai Dota 2 with Large Scale Deep Reinforcement Learning Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub Pachocki, Michael Petrov, Henrique P. d. O. Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, Susan Zhang 2019-12-13 2020-01-16 [("doi","10.48550/arXiv.1912.06680")] reinforcement-learning/model-free/oa5

On April 13, 2019, OpenAI 5 became the first AI system to defeat the world champions at an esports game.

The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems.

OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of ~2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI 5 for 10 months.

By defeating the Dota 2 world champion (Team OG), OpenAI 5 demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task.

--- https://arxiv.org/abs/1912.07557 Self-Play Learning Without a Reward Metric Dan Schmidt, Nick Moran, Jonathan S. Rosenfeld, Jonathan Rosenthal, Jonathan Yedidia 2019-12-16 2020-01-16 [("doi","10.48550/arXiv.1912.07557")] reinforcement-learning/model/alphago statistics/order/comparison

The AlphaZero algorithm for the learning of strategy games via self-play, which has produced superhuman ability in the games of Go, chess, and shogi, uses a quantitative reward function for game outcomes, requiring the users of the algorithm to explicitly balance different components of the reward against each other, such as the game winner and margin of victory.

We present a modification to the AlphaZero algorithm that requires only a total ordering over game outcomes, obviating the need to perform any quantitative balancing of reward components.

We demonstrate that this system learns optimal play in a comparable amount of time to AlphaZero on a sample game.

--- https://arxiv.org/abs/1912.09729#tencent Mastering Complex Control in MOBA Games with Deep Reinforcement Learning Deheng Ye, Zhao Liu, Mingfei Sun, Bei Shi, Peilin Zhao, Hao Wu, Hongsheng Yu, Shaojie Yang, Xipeng Wu, Qingwei Guo, Qiaobo Chen, Yinyuting Yin, Hao Zhang, Tengfei Shi, Liang Wang, Qiang Fu, Wei Yang, Lanxiao Huang 2019-12-20 2020-01-16 [("doi","10.48550/arXiv.1912.09729")] ai/nn/transformer reinforcement-learning/model-free/oa5

We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games. This problem involves far more complicated state and action spaces than those of traditional 1v1 games, such as Go and Atari series, which makes it very difficult to search any policies with human-level performance.

In this paper, we present a deep reinforcement learning framework to tackle this problem from the perspectives of both system and algorithm. Our system is of low coupling and high scalability, which enables efficient explorations at large scale. Our algorithm includes several novel strategies, including control dependency decoupling, action mask, target attention, and dual-clip PPO, with which our proposed actor-critic network can be effectively trained in our system.

Tested on the MOBA game Honor of Kings, our AI agent, called Tencent Solo, can defeat top professional human players in full 1v1 games.

--- https://arxiv.org/abs/1912.12294 Learning by Cheating Dian Chen, Brady Zhou, Vladlen Koltun, Philipp Krähenbühl 2019-12-27 2020-01-16 [("doi","10.48550/arXiv.1912.12294")] reinforcement-learning/multi-agent

Vision-based urban driving is hard. The autonomous system needs to learn to perceive the world and act in it. We show that this challenging learning problem can be simplified by decomposing it into two stages.

We first train an agent that has access to privileged information. This privileged agent cheats by observing the ground-truth layout of the environment and the positions of all traffic participants. In the second stage, the privileged agent acts as a teacher that trains a purely vision-based sensorimotor agent. The resulting sensorimotor agent does not have access to any privileged information and does not cheat. This two-stage training procedure is counter-intuitive at first, but has a number of important advantages that we analyze and empirically demonstrate.

We use the presented approach to train a vision-based autonomous driving system that substantially outperforms the state-of-the-art on the CARLA benchmark and the recent NoCrash benchmark. Our approach achieves, for the first time, 100% success rate on all tasks in the original CARLA benchmark, sets a new record on the NoCrash benchmark, and reduces the frequency of infractions by an order of magnitude compared to the prior state-of-the-art.

For the video that summarizes this work, see https://www.youtube.com/watch?v=u9ZCxxD-UUw.

--- https://arxiv.org/abs/2001.04678#deepmind Smooth markets: A basic mechanism for organizing gradient-based learners David Balduzzi, Wojciech M. Czarnecki, Thomas W. Anthony, Ian M. Gemp, Edward Hughes, Joel Z. Leibo, Georgios Piliouras, Thore Graepel 2020-01-14 2020-01-16 [("doi","10.48550/arXiv.2001.04678")] ai/nn/gan reinforcement-learning/meta-learning reinforcement-learning/multi-agent

With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact.

Unfortunately, negative results from game theory show there is little hope of understanding or controlling general n-player games.

We therefore introduce smooth markets (SM-games), a class of n-player games with pairwise zero sum interactions. SM-games codify a common design pattern in machine learning that includes (some) GANs, adversarial training, and other recent algorithms.

We show that SM-games are amenable to analysis and optimization using first-order methods.

--- https://arxiv.org/abs/2001.09977#google Towards a Human-like Open-Domain Chatbot Daniel Adiwardana, Minh-Thang Luong, David R. So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, Quoc V. Le 2020-01-27 2020-01-27 [("doi","10.48550/arXiv.2001.09977")] ai/nn/sampling ai/nn/transformer/gpt/lamda ai/scaling

We present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6b parameter neural network is simply trained to minimize perplexity of the next token.

We also propose a human evaluation metric called Sensibleness and Specificity Average (SSA), which captures key elements of a human-like multi-turn conversation. Our experiments show strong correlation between perplexity and SSA.

The fact that the best perplexity end-to-end trained Meena scores high on SSA (72% on multi-turn evaluation) suggests that a human-level SSA of 86% is potentially within reach if we can better optimize perplexity. Additionally, the full version of Meena (with a filtering mechanism and tuned decoding) scores 79% SSA, 23% higher in absolute SSA than the existing chatbots we evaluated.

--- https://arxiv.org/abs/2002.00212 Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions Yu-Siang Huang, Yi-Hsuan Yang 2020-02-01 2020-02-01 [("doi","10.48550/arXiv.2002.00212")] ai/music ai/nn/transformer/gpt

A great number of deep learning based models have been recently proposed for automatic music composition. Among these models, the Transformer stands out as a prominent approach for generating expressive classical piano performance with a coherent structure of up to one minute. The model is powerful in that it learns abstractions of data on its own, without much human-imposed domain knowledge or constraints.

In contrast with this general approach, this paper shows that Transformers can do even better for music modeling, when we improve the way a musical score is converted into the data fed to a Transformer model. In particular, we seek to impose a metrical structure in the input data, so that Transformers can be more easily aware of the beat-bar-phrase hierarchical structure in music. The new data representation maintains the flexibility of local tempo changes, and provides hurdles to control the rhythmic and harmonic structure of music.

With this approach, we build a Pop Music Transformer that composes Pop piano music with better rhythmic structure than existing Transformer models.

--- https://arxiv.org/abs/2002.03754 Unsupervised Discovery of Interpretable Directions in the GAN Latent Space Andrey Voynov, Artem Babenko 2020-02-10 2020-02-10 [("doi","10.48550/arXiv.2002.03754")] ai/nn/gan/stylegan

The latent spaces of GAN models often have semantically meaningful directions. Moving in these directions corresponds to human-interpretable image transformations, such as zooming or recoloring, enabling a more controllable generation process. However, the discovery of such directions is currently performed in a supervised manner, requiring human labels, pretrained models, or some form of self-supervision. These requirements severely restrict a range of directions existing approaches can discover.

In this paper, we introduce an unsupervised method to identify interpretable directions in the latent space of a pretrained GAN model. By a simple model-agnostic procedure, we find directions corresponding to sensible semantic manipulations without any form of (self-)supervision.

Furthermore, we reveal several non-trivial findings, which would be difficult to obtain by existing methods, eg. a direction corresponding to background removal. As an immediate practical benefit of our work, we show how to exploit this finding to achieve competitive performance for weakly-supervised saliency detection.

--- https://arxiv.org/abs/2002.04724 Improved Consistency Regularization for GANs Zhengli Zhao, Sameer Singh, Honglak Lee, Zizhao Zhang, Augustus Odena, Han Zhang 2020-02-11 2020-02-11 [("doi","10.48550/arXiv.2002.04724")] ai/nn/gan/biggan ai/nn/gan/data-augmentation

Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator. We improve on this technique in several ways.

We first show that consistency regularization can introduce artifacts into the GAN samples and explain how to fix this issue. We then propose several modifications to the consistency regularization procedure designed to improve its performance.

We carry out extensive experiments quantifying the benefit of our improvements. For unconditional image synthesis on CIFAR-10 and CelebA, our modifications yield the best known FID scores on various GAN architectures. For conditional image synthesis on CIFAR-10, we improve the state-of-the-art FID score 11.48 → 9.21. Finally, on ImageNet-2012, we apply our technique to the original BigGAN model and improve the FID 6.66 → 5.38, which is the best score at that model size.

--- https://arxiv.org/abs/2002.04833 Reward-rational (implicit) choice: A unifying formalism for reward learning Hong Jun Jeon, Smitha Milli, Anca D. Dragan 2020-02-12 2020-02-12 [("doi","10.48550/arXiv.2002.04833")] reinforcement-learning/preference-learning reinforcement-learning/safe

It is often difficult to hand-specify what the correct reward function is for a task, so researchers have instead aimed to learn reward functions from human behavior or feedback. The types of behavior interpreted as evidence of the reward function have expanded greatly in recent years. We’ve gone from demonstrations, to comparisons, to reading into the information leaked when the human is pushing the robot away or turning it off. And surely, there is more to come. How will a robot make sense of all these diverse types of behavior? Our key insight is that different types of behavior can be interpreted in a single unifying formalism—as a reward-rational choice that the human is making—often implicitly. The formalism offers both a unifying lens with which to view past work, as well as a recipe for interpreting new sources of information that are yet to be uncovered.

We provide two examples to showcase this: interpreting a new feedback type, and reading into how the choice of feedback itself leaks information about the reward.

--- https://arxiv.org/abs/2002.06038#deepmind Never Give Up: Learning Directed Exploration Strategies Adrià Puigdomènech Badia, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Bilal Piot, Steven Kapturowski, Olivier Tieleman, Martín Arjovsky, Alexander Pritzel, Andrew Bolt, Charles Blundell 2020-02-14 2020-02-14 [("doi","10.48550/arXiv.2002.06038")] reinforcement-learning/exploration

We propose a reinforcement learning agent to solve hard exploration games by learning a range of directed exploratory policies. We construct an episodic memory-based intrinsic reward using k-nearest neighbors over the agent’s recent experience to train the directed exploratory policies, thereby encouraging the agent to repeatedly revisit all states in its environment.

A self-supervised inverse dynamics model is used to train the embeddings of the nearest neighbor lookup, biasing the novelty signal towards what the agent can control. We employ the framework of Universal Value Function Approximators (UVFA) to simultaneously learn many directed exploration policies with the same neural network, with different trade-offs between exploration and exploitation.

By using the same neural network for different degrees of exploration/exploitation, transfer is demonstrated from predominantly exploratory policies yielding effective exploitative policies. The proposed method can be incorporated to run with modern distributed RL agents that collect large amounts of experience from many actors running in parallel on separate environment instances.

Our method doubles the performance of the base agent in all hard exploration in the Atari-57 suite while maintaining a very high score across the remaining games, obtaining a median human normalized score of 1344.0%. Notably, the proposed method is the first algorithm to achieve non-zero rewards (with a mean score of 8,400) in the game of Pitfall! without using demonstrations or hand-crafted features.

--- https://arxiv.org/abs/2002.06137#google RL agents Implicitly Learning Human Preferences Nevan Wichers 2020-02-14 2020-02-14 [("doi","10.48550/arXiv.2002.06137")] ai/nn/vae reinforcement-learning/preference-learning

In the real world, RL agents should be rewarded for fulfilling human preferences. We show that RL agents implicitly learn the preferences of humans in their environment.

Training a classifier to predict if a simulated human’s preferences are fulfilled based on the activations of a RL agent’s neural network gets 0.93 AUC. Training a classifier on the raw environment state gets only 0.8 AUC. Training the classifier off of the RL agent’s activations also does much better than training off of activations from an autoencoder.

The human preference classifier can be used as the reward function of an RL agent to make RL agent more beneficial for humans.

--- https://arxiv.org/abs/2002.06224 Top-K Training of GANs: Improving GAN Performance by Throwing Away Bad Samples Samarth Sinha, Zhengli Zhao, Anirudh Goyal, Colin Raffel, Augustus Odena 2020-02-14 2020-02-14 [("doi","10.48550/arXiv.2002.06224")] ai/nn/gan/stylegan ai/nn/sampling

We introduce a simple (one line of code) modification to the Generative Adversarial Network (GAN) training algorithm that materially improves results with no increase in computational cost: When updating the generator parameters, we simply zero out the gradient contributions from the elements of the batch that the critic scores as ‘least realistic’. This ‘top-k update’ procedure is a generally applicable improvement.

Through experiments on many different GAN variants, we show that this procedure can be widely applied. In order to understand the nature of the improvement, we conduct extensive analysis on a simple mixture-of-Gaussians dataset.

Among these findings is that when gradient updates are computed using the worst-scoring batch elements, samples can actually be pushed further away from their nearest mode. We also apply our method to recent GAN variants and improve state-of-the-art FID for conditional generation 9.21 → 8.57 on CIFAR-10.

--- https://arxiv.org/abs/2002.10957#microsoft MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, Ming Zhou 2020-02-25 2020-02-25 [("doi","10.48550/arXiv.2002.10957")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer

Pre-trained language models (eg. BERT (Devlin et al 2018) and its variants) have achieved remarkable success in varieties of NLP tasks. However, these models usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and online serving in real-life applications due to latency and capacity constraints.

In this work, we present a simple and effective approach to compress large Transformer (Vaswani et al 2017) based pre-trained models, termed as deep self-attention distillation. The small model (student) is trained by deeply mimicking the self-attention module, which plays a vital role in Transformer networks, of the large model (teacher). Specifically, we propose distilling the self-attention module of the last Transformer layer of the teacher, which is effective and flexible for the student. Furthermore, we introduce the scaled dot-product between values in the self-attention module as the new deep self-attention knowledge, in addition to the attention distributions (ie. the scaled dot-product of queries and keys) that have been used in existing works. Moreover, we show that introducing a teacher assistant (Mirzadeh et al 2019) also helps the distillation of large pre-trained Transformer models.

Experimental results demonstrate that our monolingual model outperforms state-of-the-art baselines in different parameter size of student models. In particular, it retains more than 99% accuracy on SQuAD 2.0 and several GLUE benchmark tasks using 50% of the Transformer parameters and computations of the teacher model.

We also obtain competitive results in applying deep self-attention distillation to multilingual pre-trained models.

--- https://arxiv.org/abs/2002.11794 Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers Zhuohan Li, Eric Wallace, Sheng Shen, Kevin Lin, Kurt Keutzer, Dan Klein, Joseph E. Gonzalez 2020-02-26 2020-02-26 [("doi","10.48550/arXiv.2002.11794")] ai/nn/sparsity/low-precision ai/nn/sparsity/pruning ai/scaling

Since hardware resources are limited, the objective of training deep learning models is typically to maximize accuracy subject to the time and memory constraints of training and inference. We study the impact of model size in this setting, focusing on Transformer models for NLP tasks that are limited by compute: self-supervised pretraining and high-resource machine translation. We first show that even though smaller Transformer models execute faster per iteration, wider and deeper models converge in fewer steps. Moreover, this acceleration in convergence typically outpaces the additional computational overhead of using larger models. Therefore, the most compute-efficient training strategy is to counterintuitively train extremely large models but stop after a small number of iterations.

This leads to an apparent trade-off between the training efficiency of large Transformer models and the inference efficiency of small Transformer models. However, we show that large models are more robust to compression techniques such as quantization and pruning than small models. Consequently, one can get the best of both worlds: heavily compressed, large models achieve higher accuracy than lightly compressed, small models.

--- https://arxiv.org/abs/2002.12655 A U-Net Based Discriminator for Generative Adversarial Networks Edgar Schönfeld, Bernt Schiele, Anna Khoreva 2020-02-28 2020-02-28 [("doi","10.48550/arXiv.2002.12655")] ai/nn/gan/biggan ai/nn/gan/data-augmentation

Among the major remaining challenges for generative adversarial networks (GANs) is the capacity to synthesize globally and locally coherent images with object shapes and textures indistinguishable from real images.

To target this issue we propose an alternative U-Net based discriminator architecture, borrowing the insights from the segmentation literature. The proposed U-Net based architecture allows to provide detailed per-pixel feedback to the generator while maintaining the global coherence of synthesized images, by providing the global image feedback as well. Empowered by the per-pixel response of the discriminator, we further propose a per-pixel consistency regularization technique based on the CutMix data augmentation, encouraging the U-Net discriminator to focus more on semantic and structural changes between real and fake images. This improves the U-Net discriminator training, further enhancing the quality of generated samples.

The novel discriminator improves over the state-of-the-art in terms of the standard distribution and image quality metrics, enabling the generator to synthesize images with varying structure, appearance and levels of detail, maintaining global and local realism. Compared to the BigGAN baseline, we achieve an average improvement of 2.7 FID points across FFHQ, CelebA, and the newly introduced COCO-Animals dataset.

The code is available at Github.

--- https://arxiv.org/abs/2003.02218 The large learning rate phase of deep learning: the catapult mechanism Aitor Lewkowycz, Yasaman Bahri, Ethan Dyer, Jascha Sohl-Dickstein, Guy Gur-Ari 2020-03-04 2020-03-04 [("doi","10.48550/arXiv.2003.02218")] ai/nn ai/scaling/emergence/grokking

The choice of initial learning rate can have a profound effect on the performance of deep networks. We present a class of neural networks with solvable training dynamics, and confirm their predictions empirically in practical deep learning settings. The networks exhibit sharply distinct behaviors at small and large learning rates. The two regimes are separated by a phase transition.

In the small learning rate phase, training can be understood using the existing theory of infinitely wide neural networks. At large learning rates the model captures qualitatively distinct phenomena, including the convergence of gradient descent dynamics to flatter minima. One key prediction of our model is a narrow range of large, stable learning rates.

We find good agreement between our model’s predictions and training dynamics in realistic deep learning settings. Furthermore, we find that the optimal performance in such settings is often found in the large learning rate phase.

We believe our results shed light on characteristics of models trained at different learning rates. In particular, they fill a gap between existing wide neural network theory, and the nonlinear, large learning rate, training dynamics relevant to practice.

--- https://arxiv.org/abs/2003.03384#google AutoML-Zero: Evolving Machine Learning Algorithms From Scratch Esteban Real, Chen Liang, David R. So, Quoc V. Le 2020-03-06 2020-03-06 [("doi","10.48550/arXiv.2003.03384")] reinforcement-learning/exploration reinforcement-learning/meta-learning

Machine learning research has advanced in multiple aspects, including model structures and learning methods. The effort to automate such research, known as AutoML, has also made progress. However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks—or similarly restrictive search spaces. Our goal is to show that AutoML can go further: it is possible today to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks.

We demonstrate this by introducing a novel framework that reduces human bias through a generic search space. Despite the vastness of this space, evolutionary search can still discover two-layer neural networks trained by backpropagation. These simple neural networks can then be surpassed by evolving directly on tasks of interest, eg. CIFAR-10 variants, where modern techniques emerge in the top algorithms, such as bilinear interactions, normalized gradients, and weight averaging.

Moreover, evolution adapts algorithms to different task types: eg. dropout-like techniques appear when little data is available. We believe these preliminary successes in discovering machine learning algorithms from scratch indicate a promising new direction for the field.

--- https://arxiv.org/abs/2003.03808 PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, Cynthia Rudin 2020-03-08 2020-03-08 [("doi","10.48550/arXiv.2003.03808")] ai/nn/gan

The primary aim of single-image super-resolution is to construct high-resolution (HR) images from corresponding low-resolution (LR) inputs. In previous approaches, which have generally been supervised, the training objective typically measures a pixel-wise average distance between the super-resolved (SR) and HR images. Optimizing such metrics often leads to blurring, especially in high variance (detailed) regions.

We propose an alternative formulation of the super-resolution problem based on creating realistic SR images that downscale correctly. We present an algorithm addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature. It accomplishes this in an entirely self-supervised fashion and is not confined to a specific degradation operator used during training, unlike previous methods (which require supervised training on databases of LR-HR image pairs). Instead of starting with the LR image and slowly adding detail, PULSE traverses the high-resolution natural image manifold, searching for images that downscale to the original LR image. This is formalized through the “downscaling loss”, which guides exploration through the latent space of a generative model. By leveraging properties of high-dimensional Gaussians, we restrict the search space to guarantee realistic outputs.

PULSE thereby generates super-resolved images that both are realistic and downscale correctly. We show proof of concept of our approach in the domain of face super-resolution (ie. face hallucination).

We also present a discussion of the limitations and biases of the method as currently implemented with an accompanying model card with relevant metrics. Our method outperforms state-of-the-art methods in perceptual quality at higher resolutions and scale factors than previously possible.

--- https://arxiv.org/abs/2003.04960 Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone 2020-03-10 2020-03-10 [("doi","10.48550/arXiv.2003.04960")] reinforcement-learning/exploration

Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. Despite many advances over the past 3 decades, learning in many domains still requires a large amount of interaction with the environment, which can be prohibitively expensive in realistic scenarios. To address this problem, transfer learning has been applied to reinforcement learning such that experience gained in one task can be leveraged when starting to learn the next, harder task.

More recently, several lines of research have explored how tasks, or data samples themselves, can be sequenced into a curriculum for the purpose of learning a problem that may otherwise be too difficult to learn from scratch. In this article, we present a framework for curriculum learning (CL) in reinforcement learning, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals.

Finally, we use our framework to find open problems and suggest directions for future RL curriculum learning research.

--- https://arxiv.org/abs/2003.05325 Meta-learning curiosity algorithms Ferran Alet, Martin F. Schneider, Tomas Lozano-Perez, Leslie Pack Kaelbling 2020-03-11 2020-03-11 [("doi","10.48550/arXiv.2003.05325")] reinforcement-learning/exploration reinforcement-learning/meta-learning

We hypothesize that curiosity is a mechanism found by evolution that encourages meaningful exploration early in an agent’s life in order to expose it to experiences that enable it to obtain high rewards over the course of its lifetime.

We formulate the problem of generating curious behavior as one of meta-learning: an outer loop will search over a space of curiosity mechanisms that dynamically adapt the agent’s reward signal, and an inner loop will perform standard reinforcement learning using the adapted reward signal. However, current meta-RL methods based on transferring neural network weights have only generalized between very similar tasks. To broaden the generalization, we instead propose to meta-learn algorithms: pieces of code similar to those designed by humans in ML papers. Our rich language of programs combines neural networks with other building blocks such as buffers, nearest-neighbor modules and custom loss functions.

We demonstrate the effectiveness of the approach empirically, finding two novel curiosity algorithms that perform on par or better than human-designed published curiosity algorithms in domains as disparate as grid navigation with image inputs, acrobot, lunar lander, ant and hopper.

--- https://arxiv.org/abs/2003.06212 Accelerating and Improving AlphaZero Using Population Based Training Ti-Rong Wu, Ting-Han Wei, I-Chen Wu 2020-03-13 2020-03-13 [("doi","10.48550/arXiv.2003.06212")] reinforcement-learning/meta-learning reinforcement-learning/model/alphago

AlphaZero has been very successful in many games. Unfortunately, it still consumes a huge amount of computing resources, the majority of which is spent in self-play. Hyperparameter tuning exacerbates the training cost since each hyperparameter configuration requires its own time to train one run, during which it will generate its own self-play records. As a result, multiple runs are usually needed for different hyperparameter configurations.

This paper proposes using population based training (PBT) to help tune hyperparameters dynamically and improve strength during training time. Another advantage is that this method requires a single run only, while incurring a small additional time cost, since the time for generating self-play records remains unchanged though the time for optimization is increased following the AlphaZero training algorithm.

In our experiments for 9×9 Go, the PBT method is able to achieve a higher win rate for 9×9 Go than the baselines, each with its own hyperparameter configuration and trained individually. For 19×19 Go, with PBT, we are able to obtain improvements in playing strength. Specifically, the PBT agent can obtain up to 74% win rate against ELF OpenGo, an open-source state-of-the-art AlphaZero program using a neural network of a comparable capacity. This is compared to a saturated non-PBT agent, which achieves a win rate of 47% against ELF OpenGo under the same circumstances.

--- https://arxiv.org/abs/2003.08445#google Placement Optimization with Deep Reinforcement Learning Anna Goldie, Azalia Mirhoseini 2020-03-18 2020-03-18 [("doi","10.48550/arXiv.2003.08445")] ai/nn cs reinforcement-learning/meta-learning reinforcement-learning/model-free

Placement Optimization is an important problem in systems and chip design, which consists of mapping the nodes of a graph onto a limited set of resources to optimize for an objective, subject to constraints.

In this paper, we start by motivating reinforcement learning as a solution to the placement problem. We then give an overview of what deep reinforcement learning is.

We next formulate the placement problem as a reinforcement learning problem and show how this problem can be solved with policy gradient optimization.

Finally, we describe lessons we have learned from training deep reinforcement learning policies across a variety of placement optimization problems.

--- https://arxiv.org/abs/2003.08536#uber Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeff Clune, Kenneth O. Stanley 2020-03-19 2020-03-19 [("doi","10.48550/arXiv.2003.08536")] reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/multi-agent

Creating open-ended algorithms, which generate their own never-ending stream of novel and appropriately challenging learning opportunities, could help to automate and accelerate progress in machine learning. A recent step in this direction is the Paired Open-Ended Trailblazer (POET), an algorithm that generates and solves its own challenges, and allows solutions to goal-switch between challenges to avoid local optima. However, the original POET was unable to demonstrate its full creative potential because of limitations of the algorithm itself and because of external issues including a limited problem space and lack of an universal progress measure. Importantly, both limitations pose impediments not only for POET, but for the pursuit of open-endedness in general.

Here we introduce and empirically validate two new innovations to the original algorithm, as well as two external innovations designed to help elucidate its full potential. Together, these four advances enable the most open-ended algorithmic demonstration to date. The algorithmic innovations are (1) a domain-general measure of how meaningfully novel new challenges are, enabling the system to potentially create and solve interesting challenges endlessly, and (2) an efficient heuristic for determining when agents should goal-switch from one problem to another (helping open-ended search better scale). Outside the algorithm itself, to enable a more definitive demonstration of open-endedness, we introduce (3) a novel, more flexible way to encode environmental challenges, and (4) a generic measure of the extent to which a system continues to exhibit open-ended innovation. Enhanced POET produces a diverse range of sophisticated behaviors that solve a wide range of environmental challenges, many of which cannot be solved through other means.

--- https://arxiv.org/abs/2003.13350#deepmind Agent57: Outperforming the Atari Human Benchmark Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Charles Blundell 2020-03-30 2020-03-30 [("doi","10.48550/arXiv.2003.13350")] reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/model-free

Atari games have been a long-standing benchmark in the reinforcement learning (RL) community for the past decade. This benchmark was proposed to test general competency of RL algorithms. Previous work has achieved good average performance by doing outstandingly well on many games of the set, but very poorly in several of the most challenging games.

We propose Agent57, the first deep RL agent that outperforms the standard human benchmark on all 57 Atari games.

To achieve this result, we train a neural network which parameterizes a family of policies ranging from very exploratory to purely exploitative. We propose an adaptive mechanism to choose which policy to prioritize throughout the training process. Additionally, we utilize a novel parameterization of the architecture that allows for more consistent and stable learning.

--- https://arxiv.org/abs/2004.02546 GANSpace: Discovering Interpretable GAN Controls Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, Sylvain Paris 2020-04-06 2020-04-06 [("doi","10.48550/arXiv.2004.02546")] ai/nn/gan/biggan ai/nn/gan/stylegan

This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis, such as change of viewpoint, aging, lighting, and time of day. We identify important latent directions based on Principal Components Analysis (PCA) applied either in latent space or feature space.

Then, we show that a large number of interpretable controls can be defined by layer-wise perturbation along the principal directions. Moreover, we show that BigGAN can be controlled with layer-wise inputs in a StyleGAN-like manner. We show results on different GANs trained on various datasets, and demonstrate good qualitative matches to edit directions found through earlier supervised approaches.

--- https://arxiv.org/abs/2004.02967#deepmind Evolving Normalization-Activation Layers Hanxiao Liu, Andrew Brock, Karen Simonyan, Quoc V. Le 2020-04-06 2020-04-06 [("doi","10.48550/arXiv.2004.02967")] ai/nn/cnn ai/nn/gan/biggan ai/nn/gan/stylegan reinforcement-learning/model-free

Normalization layers and activation functions are fundamental components in deep networks and typically co-locate with each other. Here we propose to design them using an automated approach. Instead of designing them separately, we unify them into a single tensor-to-tensor computation graph, and evolve its structure starting from basic mathematical functions. Examples of such mathematical functions are addition, multiplication and statistical moments. The use of low-level mathematical functions, in contrast to the use of high-level modules in mainstream NAS, leads to a highly sparse and large search space which can be challenging for search methods. To address the challenge, we develop efficient rejection protocols to quickly filter out candidate layers that do not work well. We also use multi-objective evolution to optimize each layer’s performance across many architectures to prevent overfitting.

Our method leads to the discovery of EvoNorms, a set of new normalization-activation layers with novel, and sometimes surprising structures that go beyond existing design patterns. For example, some EvoNorms do not assume that normalization and activation functions must be applied sequentially, nor need to center the feature maps, nor require explicit activation functions. Our experiments show that EvoNorms work well on image classification models including ResNets, MobileNets and EfficientNets but also transfer well to Mask R-CNN with FPN/SpineNet for instance segmentation and to BigGAN for image synthesis, outperforming BatchNorm and GroupNorm based layers in many cases.

--- https://arxiv.org/abs/2004.03844 On the Effect of Dropping Layers of Pre-trained Transformer Models Hassan Sajjad, Fahim Dalvi, Nadir Durrani, Preslav Nakov 2020-04-08 2020-04-08 [("doi","10.48550/arXiv.2004.03844")] ai/nn/sparsity/pruning ai/nn/transformer

Transformer-based NLP models are trained using hundreds of millions or even billions of parameters, limiting their applicability in computationally constrained environments. While the number of parameters generally correlates with performance, it is not clear whether the entire network is required for a downstream task.

Motivated by the recent work on pruning and distilling pre-trained models, we explore strategies to drop layers in pre-trained models, and observe the effect of pruning on downstream GLUE tasks. We were able to prune BERT, RoBERTa and XLNet models up to 40%, while maintaining up to 98% of their original performance. Additionally we show that our pruned models are on par with those built using knowledge distillation, both in terms of size and performance.

Our experiments yield interesting observations such as, (1) the lower layers are most critical to maintain downstream task performance, (2) some tasks such as paraphrase detection and sentence similarity are more robust to the dropping of layers, and (3) models trained using a different objective function exhibit different learning patterns and w.r.t the layer dropping.

--- https://arxiv.org/abs/2004.04312 Learning to Scale Multilingual Representations for Vision-Language Tasks Andrea Burns, Donghyun Kim, Derry Wijaya, Kate Saenko, Bryan A. Plummer 2020-04-09 2020-04-09 [("doi","10.48550/arXiv.2004.04312")] ai/nn/retrieval ai/nn/transformer/clip

Current multilingual vision-language models either require a large number of additional parameters for each supported language, or suffer performance degradation as languages are added. In this paper, we propose a Scalable Multilingual Aligned Language Representation (SMALR) that supports many languages with few model parameters without sacrificing downstream task performance. SMALR learns a fixed size language-agnostic representation for most words in a multilingual vocabulary, keeping language-specific features for just a few.

We use a masked cross-language modeling loss to align features with context from other languages. Additionally, we propose a cross-lingual consistency module that ensures predictions made for a query and its machine translation are comparable.

The effectiveness of SMALR is demonstrated with 10 diverse languages, over twice the number supported in vision-language tasks to date. We evaluate on multilingual image-sentence retrieval and outperform prior work by 3–4% with less than 1/5^th the training parameters compared to other word embedding methods.

--- https://arxiv.org/abs/2004.05439 Meta-Learning in Neural Networks: A Survey Timothy Hospedales, Antreas Antoniou, Paul Micaelli, Amos Storkey 2020-04-11 2020-04-11 [("doi","10.48550/arXiv.2004.05439")] reinforcement-learning/meta-learning

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Contrary to conventional approaches to AI where tasks are solved from scratch using a fixed learning algorithm, meta-learning aims to improve the learning algorithm itself, given the experience of multiple learning episodes. This paradigm provides an opportunity to tackle many conventional challenges of deep learning, including data and computation bottlenecks, as well as generalization. This survey describes the contemporary meta-learning landscape.

We first discuss definitions of meta-learning and position it with respect to related fields, such as transfer learning and hyperparameter optimization. We then propose a new taxonomy that provides a more comprehensive breakdown of the space of meta-learning methods today.

We survey promising applications and successes of meta-learning such as few-shot learning and reinforcement learning.

Finally, we discuss outstanding challenges and promising areas for future research.

--- https://arxiv.org/abs/2004.07320#facebook Training with Quantization Noise for Extreme Model Compression Angela Fan, Pierre Stock, Benjamin Graham, Edouard Grave, Remi Gribonval, Herve Jegou, Arm Holdings, Joulin 2020-04-15 2020-04-15 [("doi","10.48550/arXiv.2004.07320")] ai/nn/sparsity/low-precision

We tackle the problem of producing compact models, maximizing their accuracy for a given model size. A standard solution is to train networks with Quantization Aware Training, where the weights are quantized during training and the gradients approximated with the Straight-Through Estimator.

In this paper, we extend this approach to work beyond int8 fixed-point quantization with extreme compression methods where the approximations introduced by STE are severe, such as Product Quantization. Our proposal is to only quantize a different random subset of weights during each forward, allowing for unbiased gradients to flow through the other weights.

Controlling the amount of noise and its form allows for extreme compression rates while maintaining the performance of the original model. As a result, we establish new state-of-the-art compromises between accuracy and model size both in natural language processing and image classification.

For example, applying our method to state-of-the-art Transformer and Convnet architectures, we can achieve 82.5% accuracy on MNLI by compressing RoBERTa to 14MB and 80.0 top-1 accuracy on ImageNet by compressing an EfficientNet-B3 to 3.3MB.

--- https://arxiv.org/abs/2004.09677#deepmind Approximate exploitability: Learning a best response in large games Finbarr Timbers, Edward Lockhart, Marc Lanctot, Martin Schmid, Julian Schrittwieser, Thomas Hubert, Michael Bowling 2020-04-20 2020-04-20 [("doi","10.48550/arXiv.2004.09677")] ai/nn/adversarial reinforcement-learning/exploration reinforcement-learning/imperfect-information/poker reinforcement-learning/meta-learning reinforcement-learning/model/alphago reinforcement-learning/multi-agent

A standard metric used to measure the approximate optimality of policies in imperfect information games is exploitability, i.e. the performance of a policy against its worst-case opponent. However, exploitability is intractable to compute in large games as it requires a full traversal of the game tree to calculate a best response to the given policy.

We introduce a new metric, approximate exploitability, that calculates an analogous metric using an approximate best response; the approximation is done by using search and reinforcement learning. This is a generalization of local best response, a domain specific evaluation metric used in poker.

We provide empirical results for a specific instance of the method, demonstrating that our method converges to exploitability in the tabular and function approximation settings for small games.

In large games, our method learns to exploit both strong and weak agents, learning to exploit an AlphaZero agent.

--- https://arxiv.org/abs/2004.11114 Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial Robustness Patrick McClure, Dustin Moraczewski, Ka Chun Lam, Adam Thomas, Francisco Pereira 2020-04-23 2020-04-23 [("doi","10.48550/arXiv.2004.11114")] ai/nn/adversarial psychology/neuroscience

Deep neural networks (DNNs) are being increasingly used to make predictions from functional magnetic resonance imaging (fMRI) data. However, they are widely seen as uninterpretable “black boxes”, as it can be difficult to discover what input information is used by the DNN in the process, something important in both cognitive neuroscience and clinical applications. A saliency map is a common approach for producing interpretable visualizations of the relative importance of input features for a prediction. However, methods for creating maps often fail due to DNNs being sensitive to input noise, or by focusing too much on the input and too little on the model. It is also challenging to evaluate how well saliency maps correspond to the truly relevant input information, as ground truth is not always available.

In this paper, we review a variety of methods for producing gradient-based saliency maps, and present a new adversarial training method we developed to make DNNs robust to input noise, with the goal of improving interpretability. We introduce two quantitative evaluation procedures for saliency map methods in fMRI, applicable whenever a DNN or linear model is being trained to decode some information from imaging data.

We evaluate the procedures using a synthetic dataset where the complex activation structure is known, and on saliency maps produced for DNN and linear models for task decoding in the Human Connectome Project (HCP) dataset. Our key finding is that saliency maps produced with different methods vary widely in interpretability, in both in synthetic and HCP fMRI data. Strikingly, even when DNN and linear models decode at comparable levels of performance, DNN saliency maps score higher on interpretability than linear model saliency maps (derived via weights or gradient).

Finally, saliency maps produced with our adversarial training method outperform those from other methods.

--- https://arxiv.org/abs/2004.12527 Neural Machine Translation with Monte-Carlo Tree Search Jerrod Parker, Jerry Zikun Chen 2020-04-27 2020-04-27 [("doi","10.48550/arXiv.2004.12527")] reinforcement-learning/model/alphago

Recent algorithms in machine translation have included a value network to assist the policy network when deciding which word to output at each step of the translation. The addition of a value network helps the algorithm perform better on evaluation metrics like the BLEU score.

After training the policy and value networks in a supervised setting, the policy and value networks can be jointly improved through common actor-critic methods. The main idea of our project is to instead leverage Monte-Carlo Tree Search (MCTS) to search for good output words with guidance from a combined policy and value network architecture in a similar fashion as AlphaZero. This network serves both as a local and a global look-ahead reference that uses the result of the search to improve itself.

Experiments using the IWLST14 German to English translation dataset show that our method outperforms the actor-critic methods used in recent machine translation papers.

--- https://arxiv.org/abs/2004.14404 Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks Gerrit Schoettler, Ashvin Nair, Juan Aparicio Ojea, Sergey Levine, Eugen Solowjow 2020-04-29 2020-04-29 [("doi","10.48550/arXiv.2004.14404")] reinforcement-learning/meta-learning

Robotic insertion tasks are characterized by contact and friction mechanics, making them challenging for conventional feedback control methods due to unmodeled physical effects. Reinforcement learning (RL) is a promising approach for learning control policies in such settings.

However, RL can be unsafe during exploration and might require a large amount of real-world training data, which is expensive to collect. In this paper, we study how to use meta-reinforcement learning to solve the bulk of the problem in simulation by solving a family of simulated industrial insertion tasks and then adapt policies quickly in the real world.

We demonstrate our approach by training an agent to successfully perform challenging real-world insertion tasks using less than 20 trials of real-world experience.

Videos and other material are available at https://pearl-insertion.github.io/.

--- https://arxiv.org/abs/2005.01643 Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems Sergey Levine, Aviral Kumar, George Tucker, Justin Fu 2020-05-04 2020-05-04 [("doi","10.48550/arXiv.2005.01643")] reinforcement-learning/exploration reinforcement-learning/offline

In this tutorial article, we aim to provide the reader with the conceptual tools needed to get started on research on offline reinforcement learning algorithms: reinforcement learning algorithms that use previously collected data, without additional online data collection. Offline reinforcement learning algorithms hold tremendous promise for making it possible to turn large datasets into powerful decision making engines.

Effective offline reinforcement learning methods would be able to extract policies with the maximum possible utility out of the available data, thereby allowing automation of a wide range of decision-making domains, from healthcare and education to robotics. However, the limitations of current algorithms make this difficult.

We will aim to provide the reader with an understanding of these challenges, particularly in the context of modern deep reinforcement learning methods, and describe some potential solutions that have been explored in recent work to mitigate these challenges, along with recent applications, and a discussion of perspectives on open problems in the field.

--- https://arxiv.org/abs/2005.02575 Active Preference-Based Gaussian Process Regression for Reward Learning Erdem Bıyık, Nicolas Huynh, Mykel J. Kochenderfer, Dorsa Sadigh 2020-05-06 2020-05-06 [("doi","10.48550/arXiv.2005.02575")] reinforcement-learning/preference-learning reinforcement-learning/robot

Designing reward functions is a challenging problem in AI and robotics. Humans usually have a difficult time directly specifying all the desirable behaviors that a robot needs to optimize. One common approach is to learn reward functions from collected expert demonstrations. However, learning reward functions from demonstrations introduces many challenges: some methods require highly structured models, eg. reward functions that are linear in some predefined set of features, while others adopt less structured reward functions that on the other hand require tremendous amount of data. In addition, humans tend to have a difficult time providing demonstrations on robots with high degrees of freedom, or even quantifying reward values for given demonstrations.

To address these challenges, we present a preference-based learning approach—where as an alternative, the human feedback is only in the form of comparisons between trajectories. Furthermore, we do not assume highly constrained structures on the reward function. Instead, we model the reward function using a Gaussian Process (GP) and propose a mathematical formulation to actively find a GP using only human preferences. Our approach enables us to tackle both inflexibility and data-inefficiency problems within a preference-based learning framework. Our results in simulations and a user study suggest that our approach can efficiently learn expressive reward functions for robotics tasks.

--- https://arxiv.org/abs/2005.04305#openai Measuring the Algorithmic Efficiency of Neural Networks Danny Hernandez, Tom B. Brown 2020-05-08 2020-05-08 [("doi","10.48550/arXiv.2005.04305")] ai/nn/cnn ai/scaling cs/algorithm cs/hardware economics/experience-curve

Three factors drive the advance of AI: algorithmic innovation, data, and the amount of compute available for training. Algorithmic progress has traditionally been more difficult to quantify than compute and data.

In this work, we argue that algorithmic progress has an aspect that is both straightforward to measure and interesting: reductions over time in the compute needed to reach past capabilities. We show that the number of floating-point operations required to train a classifier to AlexNet-level performance on ImageNet has decreased by a factor of 44× 2012–2019. This corresponds to algorithmic efficiency doubling every 16 months over a period of 7 years.

By contrast, Moore’s Law would only have yielded an 11× cost improvement. We observe that hardware and algorithmic efficiency gains multiply and can be on a similar scale over meaningful horizons, which suggests that a good model of AI progress should integrate measures from both.

--- https://arxiv.org/abs/2005.05960 Planning to Explore via Self-Supervised World Models Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak 2020-05-12 2020-05-12 [("doi","10.48550/arXiv.2005.05960")] reinforcement-learning/exploration reinforcement-learning/model

Reinforcement learning allows solving complex tasks, however, the learning tends to be task-specific and the sample efficiency remains a challenge. We present Plan2Explore, a self-supervised reinforcement learning agent that tackles both these challenges through a new approach to self-supervised exploration and fast adaptation to new tasks, which need not be known during exploration.

During exploration, unlike prior methods which retrospectively compute the novelty of observations after the agent has already reached them, our agent acts efficiently by leveraging planning to seek out expected future novelty.

After exploration, the agent quickly adapts to multiple downstream tasks in a zero or a few-shot manner. We evaluate on challenging control tasks from high-dimensional image inputs. Without any training supervision or task-specific interaction, Plan2Explore outperforms prior self-supervised exploration methods, and in fact, almost matches the performances oracle which has access to rewards.

Videos and code at https://ramanans1.github.io/plan2explore/.

--- https://arxiv.org/abs/2005.07093 Bayesian Bits: Unifying Quantization and Pruning Mart van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang, Tijmen Blankevoort, Max Welling 2020-05-14 2020-05-14 [("doi","10.48550/arXiv.2005.07093")] ai/nn/sparsity/low-precision ai/nn/sparsity/pruning

We introduce Bayesian Bits, a practical method for joint mixed precision quantization and pruning through gradient based optimization. Bayesian Bits employs a novel decomposition of the quantization operation, which sequentially considers doubling the bit width. At each new bit width, the residual error between the full precision value and the previously rounded value is quantized. We then decide whether or not to add this quantized residual error for a higher effective bit width and lower quantization noise. By starting with a power-of-two bit width, this decomposition will always produce hardware-friendly configurations, and through an additional 0-bit option, serves as a unified view of pruning and quantization.

Bayesian Bits then introduces learnable stochastic gates, which collectively control the bit width of the given tensor. As a result, we can obtain low bit solutions by performing approximate inference over the gates, with prior distributions that encourage most of them to be switched off.

We experimentally validate our proposed method on several benchmark datasets and show that we can learn pruned, mixed precision networks that provide a better trade-off between accuracy and efficiency than their static bit width equivalents.

--- https://arxiv.org/abs/2005.11401#facebook Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela 2020-05-22 2020-05-22 [("doi","10.48550/arXiv.2005.11401")] ai/nn/retrieval ai/nn/transformer/attention

Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-specific architectures. Additionally, providing provenance for their decisions and updating their world knowledge remain open research problems.

Pre-trained models with a differentiable access mechanism to explicit non-parametric memory can overcome this issue, but have so far been only investigated for extractive downstream tasks. We explore a general-purpose fine-tuning recipe for retrieval-augmented generation (RAG)—models which combine pre-trained parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG formulations, one which conditions on the same retrieved passages across the whole generated sequence, the other can use different passages per token.

We fine-tune and evaluate our models on a wide range of knowledge-intensive NLP tasks and set the state-of-the-art on 3 open domain QA tasks, outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures. For language generation tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline.

--- https://arxiv.org/abs/2005.11730 Automatic Discovery of Interpretable Planning Strategies Julian Skirzyński, Frederic Becker, Falk Lieder 2020-05-24 2020-05-24 [("doi","10.1007/s10994-021-05963-2")] reinforcement-learning/exploration reinforcement-learning/imitation-learning reinforcement-learning/meta-learning

When making decisions, people often overlook critical information or are overly swayed by irrelevant information. A common approach to mitigate these biases is to provide decision-makers, especially professionals such as medical doctors, with decision aids, such as decision trees and flowcharts. Designing effective decision aids is a difficult problem. We propose that recently developed reinforcement learning methods for discovering clever heuristics for good decision-making can be partially leveraged to assist human experts in this design process. One of the biggest remaining obstacles to leveraging the aforementioned methods is that the policies they learn are opaque to people.

To solve this problem, we introduce AI-Interpret: a general method for transforming idiosyncratic policies into simple and interpretable descriptions. Our algorithm combines recent advances in imitation learning and program induction with a new clustering method for identifying a large subset of demonstrations that can be accurately described by a simple, high-performing decision rule.

We evaluate our new algorithm and employ it to translate information-acquisition policies discovered through metalevel reinforcement learning. The results of large behavioral experiments showed that providing the decision rules generated by AI-Interpret as flowcharts improved people’s planning strategies and decisions across 3 different classes of sequential decision problems. Moreover, another experiment revealed that this approach is more effective than training people by giving them performance feedback. Finally, a series of ablation studies confirmed that AI-Interpret is critical to the discovery of interpretable decision rules.

We conclude that the methods and findings presented herein are an important step towards leveraging automatic strategy discovery to improve human decision-making.

--- https://arxiv.org/abs/2005.12126#nvidia Learning to Simulate Dynamic Environments with GameGAN Seung Wook Kim, Yuhao Zhou, Jonah Philion, Antonio Torralba, Sanja Fidler 2020-05-25 2020-05-25 [("doi","10.48550/arXiv.2005.12126")] ai/nn/gan reinforcement-learning/model

Simulation is a crucial component of any robotic system. In order to simulate correctly, we need to write complex rules of the environment: how dynamic agents behave, and how the actions of each of the agents affect the behavior of others. In this paper, we aim to learn a simulator by simply watching an agent interact with an environment. We focus on graphics games as a proxy of the real environment.

We introduce GameGAN, a generative model that learns to visually imitate a desired game by ingesting screenplay and keyboard actions during training. Given a key pressed by the agent, GameGAN “renders” the next screen using a carefully designed generative adversarial network. Our approach offers key advantages over existing work: we design a memory module that builds an internal map of the environment, allowing for the agent to return to previously visited locations with high visual consistency. In addition, GameGAN is able to disentangle static and dynamic components within an image making the behavior of the model more interpretable, and relevant for downstream tasks that require explicit reasoning over dynamic elements.

This enables many interesting applications such as swapping different components of the game to build new games that do not exist.

--- https://arxiv.org/abs/2005.13092#uber Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search Aditya Rawal, Joel Lehman, Felipe Petroski Such, Jeff Clune, Kenneth O. Stanley 2020-05-27 2020-05-27 [("doi","10.48550/arXiv.2005.13092")] reinforcement-learning/exploration reinforcement-learning/meta-learning

Neural Architecture Search (NAS) explores a large space of architectural motifs—a compute-intensive process that often involves ground-truth evaluation of each motif by instantiating it within a large network, and training and evaluating the network with thousands of domain-specific data samples.

Inspired by how biological motifs such as cells are sometimes extracted from their natural environment and studied in an artificial Petri dish setting, this paper proposes the Synthetic Petri Dish model for evaluating architectural motifs. In the Synthetic Petri Dish, architectural motifs are instantiated in very small networks and evaluated using very few learned synthetic data samples (to effectively approximate performance in the full problem). The relative performance of motifs in the Synthetic Petri Dish can substitute for their ground-truth performance, thus accelerating the most expensive step of NAS. Unlike other neural network-based prediction models that parse the structure of the motif to estimate its performance, the Synthetic Petri Dish predicts motif performance by training the actual motif in an artificial setting, thus deriving predictions from its true intrinsic properties.

Experiments in this paper demonstrate that the Synthetic Petri Dish can therefore predict the performance of new motifs with higher accuracy, especially when insufficient ground truth data is available.

Our hope is that this work can inspire a new research direction in studying the performance of extracted components of models in an alternative controlled setting.

--- https://arxiv.org/abs/2006.01855 Aligning Superhuman AI with Human Behavior: Chess as a Model System Reid McIlroy-Young, Siddhartha Sen, Jon Kleinberg, Ashton Anderson 2020-06-02 2020-06-02 [("doi","10.1145/3394486.3403219")] psychology/chess reinforcement-learning/chess reinforcement-learning/model/alphago reinforcement-learning/preference-learning

As artificial intelligence becomes increasingly intelligent—in some cases, achieving superhuman performance—there is growing potential for humans to learn from and collaborate with algorithms. However, the ways in which AI systems approach problems are often different from the ways people do, and thus may be uninterpretable and hard to learn from. A crucial step in bridging this gap between human and artificial intelligence is modeling the granular actions that constitute human behavior, rather than simply matching aggregate human performance.

We pursue this goal in a model system with a long history in artificial intelligence: chess. The aggregate performance of a chess player unfolds as they make decisions over the course of a game. The hundreds of millions of games played online by players at every skill level form a rich source of data in which these decisions, and their exact context, are recorded in minute detail. Applying existing chess engines to this data, including an open-source implementation of AlphaZero, we find that they do not predict human moves well.

We develop and introduce Maia, a customized version of Alpha-Zero trained on human chess games, that predicts human moves at a much higher accuracy than existing engines, and can achieve maximum accuracy when predicting decisions made by players at a specific skill level in a tuneable way. For a dual task of predicting whether a human will make a large mistake on the next move, we develop a deep neural network that outperforms competitive baselines. Taken together, our results suggest that there is substantial promise in designing artificial intelligence systems with human collaboration in mind by first accurately modeling granular human decision-making.

--- https://arxiv.org/abs/2006.02595#google Image Augmentations for GAN Training Zhengli Zhao, Zizhao Zhang, Ting Chen, Sameer Singh, Han Zhang 2020-06-04 2020-06-04 [("doi","10.48550/arXiv.2006.02595")] ai/nn/gan/data-augmentation ai/nn/gan/stylegan

Data augmentations have been widely studied to improve the accuracy and robustness of classifiers. However, the potential of image augmentation in improving GAN models for image synthesis has not been thoroughly investigated in previous studies.

In this work, we systematically study the effectiveness of various existing augmentation techniques for GAN training in a variety of settings. We provide insights and guidelines on how to augment images for both vanilla GANs and GANs with regularizations, improving the fidelity of the generated images substantially. Surprisingly, we find that vanilla GANs attain generation quality on par with recent state-of-the-art results if we use augmentations on both real and generated images. When this GAN training is combined with other augmentation-based regularization techniques, such as contrastive loss and consistency regularization, the augmentations further improve the quality of generated images.

We provide new state-of-the-art results for conditional generation on CIFAR-10 with both consistency loss and contrastive loss as additional regularizations.

--- https://arxiv.org/abs/2006.02635#microsoft M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training Minheng Ni, Haoyang Huang, Lin Su, Edward Cui, Taroon Bharti, Lijuan Wang, Jianfeng Gao, Dongdong Zhang, Nan Duan 2020-06-04 2020-06-04 [("doi","10.48550/arXiv.2006.02635")] ai/nn/retrieval ai/nn/transformer/clip

We present M3P, a Multitask Multilingual Multimodal Pre-trained model that combines multilingual pre-training and multimodal pre-training into a unified framework via multitask pre-training.

Our goal is to learn universal representations that can map objects occurring in different modalities or texts expressed in different languages into a common semantic space. In addition, to explicitly encourage fine-grained alignment between images and non-English languages, we also propose Multimodal Code-switched Training (MCT) to combine monolingual pre-training and multimodal pre-training via a code-switch strategy.

Experiments are performed on the multilingual image retrieval task across two benchmark datasets, including MS COCO and Multi30K.

M3P can achieve comparable results for English and new state-of-the-art results for non-English languages.

--- https://arxiv.org/abs/2006.03659 DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations John Giorgi, Osvald Nitski, Bo Wang, Gary Bader 2020-06-05 2020-06-05 [("doi","10.48550/arXiv.2006.03659")] ai/nn/transformer

Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various downstream tasks, such as clustering and retrieval. Unlike word embeddings, the highest performing solutions for learning sentence embeddings require labeled data, limiting their usefulness to languages and domains where labeled data is abundant.

In this paper, we present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations. Inspired by recent advances in deep metric learning (DML), we carefully design a self-supervised objective for learning universal sentence embeddings that does not require labeled training data. When used to extend the pretraining of transformer-based language models, our approach closes the performance gap between unsupervised and supervised pretraining for universal sentence encoders. Importantly, our experiments suggest that the quality of the learned embeddings scale with both the number of trainable parameters and the amount of unlabeled training data.

Our code and pretrained models are publicly available and can be easily adapted to new domains or used to embed unseen text.

--- https://arxiv.org/abs/2006.03662#deepmind Rapid Task-Solving in Novel Environments Sam Ritter, Ryan Faulkner, Laurent Sartran, Adam Santoro, Matt Botvinick, David Raposo 2020-06-05 2020-06-05 [("doi","10.48550/arXiv.2006.03662")] reinforcement-learning/meta-learning

We propose the challenge of rapid task-solving in novel environments (RTS), wherein an agent must solve a series of tasks as rapidly as possible in an unfamiliar environment. An effective RTS agent must balance between exploring the unfamiliar environment and solving its current task, all while building a model of the new environment over which it can plan when faced with later tasks. While modern deep RL agents exhibit some of these abilities in isolation, none are suitable for the full RTS challenge.

To enable progress toward RTS, we introduce two challenge domains: (1) a minimal RTS challenge called the Memory&Planning Game and (2) One-Shot StreetLearn Navigation, which introduces scale and complexity from real-world data. We demonstrate that state-of-the-art deep RL agents fail at RTS in both domains, and that this failure is due to an inability to plan over gathered knowledge.

We develop Episodic Planning Networks (EPNs) and show that deep-RL agents with EPNs excel at RTS, outperforming the nearest baseline by factors of 2–3 and learning to navigate held-out StreetLearn maps within a single episode. We show that EPNs learn to execute a value iteration-like planning algorithm and that they generalize to situations beyond their training experience.

--- https://arxiv.org/abs/2006.04779 Conservative Q-Learning for Offline Reinforcement Learning Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine 2020-06-08 2020-06-08 [("doi","10.48550/arXiv.2006.04779")] reinforcement-learning/model-free

Effectively leveraging large, previously collected datasets in reinforcement learning (RL) is a key challenge for large-scale real-world applications. Offline RL algorithms promise to learn effective policies from previously-collected, static datasets without further interaction. However, in practice, offline RL presents a major challenge, and standard off-policy RL methods can fail due to overestimation of values induced by the distributional shift between the dataset and the learned policy, especially when training on complex and multi-modal data distributions.

In this paper, we propose conservative Q-learning (CQL), which aims to address these limitations by learning a conservative Q-function such that the expected value of a policy under this Q-function lower-bounds its true value. We theoretically show that CQL produces a lower bound on the value of the current policy and that it can be incorporated into a policy learning procedure with theoretical improvement guarantees. In practice, CQL augments the standard Bellman error objective with a simple Q-value regularizer which is straightforward to implement on top of existing deep Q-learning and actor-critic implementations.

On both discrete and continuous control domains, we show that CQL substantially outperforms existing offline RL methods, often learning policies that attain 2–5× higher final return, especially when learning from complex and multi-modal data distributions.

--- https://arxiv.org/abs/2006.05338 On Data Augmentation for GAN Training Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Trung-Kien Nguyen, Ngai-Man Cheung 2020-06-09 2020-06-09 [("doi","10.1109/TIP.2021.3049346")] ai/nn/gan/data-augmentation ai/nn/gan/stylegan

Recent successes in Generative Adversarial Networks (GAN) have affirmed the importance of using more data in GAN training. Yet it is expensive to collect data in many domains such as medical applications. Data Augmentation (DA) has been applied in these applications.

In this work, we first argue that the classical DA approach could mislead the generator to learn the distribution of the augmented data, which could be different from that of the original data. We then propose a principled framework, termed Data Augmentation Optimized for GAN (DAG), to enable the use of augmented data in GAN training to improve the learning of the original distribution. We provide theoretical analysis to show that using our proposed DAG aligns with the original GAN in minimizing the Jensen-Shannon (JS) divergence between the original distribution and model distribution. Importantly, the proposed DAG effectively leverages the augmented data to improve the learning of discriminator and generator.

We conduct experiments to apply DAG to different GAN models: unconditional GAN, conditional GAN, self-supervised GAN and CycleGAN using datasets of natural images and medical images. The results show that DAG achieves consistent and considerable improvements across these models. Furthermore, when DAG is used in some GAN models, the system establishes state-of-the-art Fréchet Inception Distance (FID) scores.

Our code is available.

--- https://arxiv.org/abs/2006.05467 Pruning neural networks without any data by iteratively conserving synaptic flow Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins, Surya Ganguli 2020-06-09 2020-06-09 [("doi","10.48550/arXiv.2006.05467")] ai/nn/sparsity/pruning

Pruning the parameters of deep neural networks has generated intense interest due to potential savings in time, memory and energy both during training and at test time.

Recent works have identified, through an expensive sequence of training and pruning cycles, the existence of winning lottery tickets or sparse trainable subnetworks at initialization. This raises a foundational question: can we identify highly sparse trainable subnetworks at initialization, without ever training, or indeed without ever looking at the data?

We provide an affirmative answer to this question through theory driven algorithm design. We first mathematically formulate and experimentally verify a conservation law that explains why existing gradient-based pruning algorithms at initialization suffer from layer-collapse, the premature pruning of an entire layer rendering a network untrainable. This theory also elucidates how layer-collapse can be entirely avoided, motivating a novel pruning algorithm Iterative Synaptic Flow Pruning (SynFlow). This algorithm can be interpreted as preserving the total flow of synaptic strengths through the network at initialization subject to a sparsity constraint.

Notably, this algorithm makes no reference to the training data and consistently competes with or outperforms existing state-of-the-art pruning algorithms at initialization over a range of models (VGG and ResNet), datasets (CIFAR-10/100 and Tiny ImageNet), and sparsity constraints (up to 99.99%).

Thus our data-agnostic pruning algorithm challenges the existing paradigm that, at initialization, data must be used to quantify which synapses are important.

--- https://arxiv.org/abs/2006.06900 Improving GAN Training with Probability Ratio Clipping and Sample Reweighting Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu 2020-06-12 2020-06-12 [("doi","10.48550/arXiv.2006.06900")] ai/nn/gan ai/nn/transformer ai/text-style-transfer reinforcement-learning/model-free

Despite success on a wide range of problems related to vision, generative adversarial networks (GANs) often suffer from inferior performance due to unstable training, especially for text generation.

To solve this issue, we propose a new variational GAN training framework which enjoys superior training stability. Our approach is inspired by a connection of GANs and reinforcement learning under a variational perspective. The connection leads to (1) probability ratio clipping that regularizes generator training to prevent excessively large updates, and (2) a sample re-weighting mechanism that improves discriminator training by downplaying bad-quality fake samples. Moreover, our variational GAN framework can provably overcome the training issue in many GANs that an optimal discriminator cannot provide any informative gradient to training generator.

By plugging the training approach in diverse state-of-the-art GAN architectures, we obtain substantially improved performance over a range of tasks, including text generation, text style transfer, and image generation.

--- https://arxiv.org/abs/2006.07430 Continuous Control for Searching and Planning with a Learned Model Xuxi Yang, Werner Duvaud, Peng Wei 2020-06-12 2020-06-12 [("doi","10.48550/arXiv.2006.07430")] reinforcement-learning/model/muzero

Decision-making agents with planning capabilities have achieved huge success in the challenging domain like Chess, Shogi, and Go. In an effort to generalize the planning ability to the more general tasks where the environment dynamics are not available to the agent, researchers proposed the MuZero algorithm that can learn the dynamical model through the interactions with the environment.

In this paper, we provide a way and the necessary theoretical results to extend the MuZero algorithm to more generalized environments with continuous action space.

Through numerical results on two relatively low-dimensional MuJoCo environments, we show the proposed algorithm outperforms the soft actor-critic (SAC) algorithm, a state-of-the-art model-free deep reinforcement learning algorithm.

--- https://arxiv.org/abs/2006.07970 Tackling Morpion Solitaire with AlphaZero-like Ranked Reward Reinforcement Learning Hui Wang, Mike Preuss, Michael Emmerich, Aske Plaat 2020-06-14 2020-06-14 [("doi","10.48550/arXiv.2006.07970")] reinforcement-learning/model/alphago

Morpion Solitaire is a popular single player game, performed with paper and pencil. Due to its large state space (on the order of the game of Go) traditional search algorithms, such as MCTS, have not been able to find good solutions. A later algorithm, Nested Rollout Policy Adaptation, was able to find a new record of 82 steps, albeit with large computational resources. After achieving this record, to the best of our knowledge, there has been no further progress reported, for about a decade.

In this paper we take the recent impressive performance of deep self-learning reinforcement learning approaches from AlphaGo/AlphaZero as inspiration to design a searcher for Morpion Solitaire. A challenge of Morpion Solitaire is that the state space is sparse, there are few win/loss signals. Instead, we use an approach known as ranked reward to create a reinforcement learning self-play framework for Morpion Solitaire. This enables us to find medium-quality solutions with reasonable computational effort. Our record is a 67 steps solution, which is very close to the human best (68) without any other adaptation to the problem than using ranked reward. We list many further avenues for potential improvement.

--- https://arxiv.org/abs/2006.09549 Learning to Learn with Feedback and Local Plasticity Jack Lindsey, Ashok Litwin-Kumar 2020-06-16 2020-06-16 [("doi","10.48550/arXiv.2006.09549")] ai/nn/transformer psychology/neuroscience reinforcement-learning/meta-learning/continual-learning

Interest in biologically inspired alternatives to backpropagation is driven by the desire to both advance connections between deep learning and neuroscience and address backpropagation’s shortcomings on tasks such as online, continual learning. However, local synaptic learning rules like those employed by the brain have so far failed to match the performance of backpropagation in deep networks.

In this study, we employ meta-learning to discover networks that learn using feedback connections and local, biologically inspired learning rules. Importantly, the feedback connections are not tied to the feedforward weights, avoiding biologically implausible weight transport. Our experiments show that meta-trained networks effectively use feedback connections to perform online credit assignment in multi-layer architectures. Surprisingly, this approach matches or exceeds a state-of-the-art gradient-based online meta-learning algorithm on regression and classification tasks, excelling in particular at continual learning.

Analysis of the weight updates employed by these models reveals that they differ qualitatively from gradient descent in a way that reduces interference between updates. Our results suggest the existence of a class of biologically plausible learning mechanisms that not only match gradient descent-based learning, but also overcome its limitations.

--- https://arxiv.org/abs/2006.10621 On the Predictability of Pruning Across Scales Jonathan S. Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit 2020-06-18 2020-06-18 [("doi","10.48550/arXiv.2006.10621")] ai/nn/sparsity/pruning ai/scaling

We show that the error of iteratively magnitude-pruned networks empirically follows a scaling law with interpretable coefficients that depend on the architecture and task. We functionally approximate the error of the pruned networks, showing it is predictable in terms of an invariant tying width, depth, and pruning level, such that networks of vastly different pruned densities are interchangeable.

We demonstrate the accuracy of this approximation over orders of magnitude in depth, width, dataset size, and density. We show that the functional form holds (generalizes) for large scale data (eg. ImageNet) and architectures (eg. ResNets).

As neural networks become ever larger and costlier to train, our findings suggest a framework for reasoning conceptually and analytically about a standard method for unstructured pruning.

…In summary, our contributions are as follows:

Figure 1: Relationship between density and error when pruning CIFAR-10 ResNets; w varies, l = 20, n = N (left). Low-error plateau, power-law region, and high-error plateau when l = 20, w = 1, n = N (center). Visualizing Equation 1 and the roles of free parameters (right).

…In Figure 1 (left), we plot the error of these pruned networks for CIFAR-10 ResNets with depth l = 20 and different widths w. All these curves follow a similar pattern:

  1. Low-error plateau: The densest networks (right part of curves) have similar error to the unpruned network: εnp(w). We call this the “low-error plateau”.

  2. Power law region: When pruned further, error increases linearly on the logarithmic axes of the figure.

    Linear behavior on a logarithmic scale is the functional form of a power law, where error relates to density via exponent γ and coefficient c: ε(d, w) ≈ cd−γ. In particular, γ is the slope of the line on the logarithmic axes.

  3. High-error plateau: When pruned further, error again flattens; we call this the high-error plateau and call the error of the plateau ε↑.

Figure 2: Estimated (blue dots) and actual error (solid lines) when pruning CIFAR-10 ResNets; w varies, l = 20, n = N (left). Estimated versus actual error for the same networks (center). Estimated versus actual error for all CIFAR-10 resnet configurations (right).
Equation 1: functional form of deep learning pruning scaling law.

…Studying this optimization problem reveals a useful insight about—in this case—the CIFAR-10 ResNets. In the pruning literature, it is typical to report the minimum density where the pruned network matches the error εnp(l, w) of the unpruned network (eg. Han et al 2015's IMP). However, our scaling law suggests this is not the smallest model to achieve error εnp(l, w). Instead, it is better to train a larger network with depth l′ and width w′ and prune until error reaches εnp(l, w), despite the fact that error will be higher than εnp(l′, w′). This analytic result parallels and extends the findings of Li et al 2020 on NLP tasks. However, unlike Li et al 2020, our scaling law suggests starting too large is detrimental for the CIFAR-10 ResNets, leading to a higher parameter-count at error εk.

Figure 8 (left) illustrates this behavior concretely: it shows the error predicted by our scaling law for CIFAR-10 ResNets with varying widths. The dotted black line shows the minimal parameter-count at which we predict it is possible to achieve each error. Importantly, none of the low-error plateaus intersect this black dotted line, meaning a model cannot be minimal until it has been pruned to the point where it increases in error. This occurs because the transitions of our functional form are gradual. On the other hand, if we start with a model that is too large, it will no longer be on the black line when it has been pruned to the point where its error reaches εnp(l, w); this behavior occurs because error decreases as a function of the invariant m✱ rather than the parameter-count m and because m ∝̸ m✱. In Figure 8 (right), we plot the same information from the actual CIFAR-10 data and see the same phenomena occur in practice. The difference between the estimated and actual optimal parameter count is no more than 25%.

Figure 8: Estimated error as width varies for the CIFAR-10 ResNets (left). Actual error as width varies for the CIFAR-10 ResNets (right). The dotted black line is the minimal number of parameters necessary to reach each error εk among all of the pruned networks. Reaching this point requires starting with a particular lower-error network (purple) and pruning until error increases to εk. Starting too large (pink) will miss this point.
--- https://arxiv.org/abs/2006.12156#deepmind Logarithmic Pruning is All You Need Laurent Orseau, Marcus Hutter, Omar Rivasplata 2020-06-22 2020-06-22 [("doi","10.48550/arXiv.2006.12156")] ai/nn/sparsity/pruning ai/scaling

The Lottery Ticket Hypothesis is a conjecture that every large neural network contains a subnetwork that, when trained in isolation, achieves comparable performance to the large network. An even stronger conjecture has been proven recently: Every sufficiently overparameterized network contains a subnetwork that, at random initialization, but without training, achieves comparable accuracy to the trained large network.

This latter result, however, relies on a number of strong assumptions and guarantees a polynomial factor on the size of the large network compared to the target function.

In this work, we remove the most limiting assumptions of this previous work while providing tighter bounds: the overparameterized network only needs a logarithmic factor (in all variables but depth) number of neurons per weight of the target subnetwork.

--- https://arxiv.org/abs/2006.14536#google Smooth Adversarial Training Cihang Xie, Mingxing Tan, Boqing Gong, Alan Yuille, Quoc V. Le 2020-06-25 2020-06-25 [("doi","10.48550/arXiv.2006.14536")] ai/nn/adversarial

It is commonly believed that networks cannot be both accurate and robust, that gaining robustness means losing accuracy. It is also generally believed that, unless making networks larger, network architectural elements would otherwise matter little in improving adversarial robustness. Here we present evidence to challenge these common beliefs by a careful study about adversarial training. Our key observation is that the widely-used ReLU activation function weakens adversarial training due to its non-smooth nature. Hence we propose smooth adversarial training (SAT), in which we replace ReLU with its smooth approximations to strengthen adversarial training. The purpose of smooth activation functions in SAT is to allow it to find harder adversarial examples and compute better gradient updates during adversarial training.

Compared to standard adversarial training, SAT improves adversarial robustness for “free”, ie. no drop in accuracy and no increase in computational cost. For example, without introducing additional computations, SAT enhances ResNet-50’s robustness 33.0% → 42.3%, while also improving accuracy by 0.9% on ImageNet. SAT also works well with larger networks: it helps EfficientNet-L1 to achieve 82.2% accuracy and 58.6% robustness on ImageNet, outperforming the previous state-of-the-art defense by 9.5% for accuracy and 11.6% for robustness. Models are available at https://github.com/cihangxie/SmoothAdversarialTraining.

--- https://arxiv.org/abs/2006.15020#facebook Pre-training via Paraphrasing Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida Wang, Luke Zettlemoyer 2020-06-26 2020-06-26 [("doi","10.48550/arXiv.2006.15020")] ai/nn/retrieval ai/nn/transformer/attention

We introduce MARGE, a pre-trained sequence-to-sequence model learned with an unsupervised multi-lingual multi-document paraphrasing objective.

MARGE provides an alternative to the dominant masked language modeling paradigm, where we self-supervise the reconstruction of target text by retrieving a set of related texts (in many languages) and conditioning on them to maximize the likelihood of generating the original. We show it is possible to jointly learn to do retrieval and reconstruction, given only a random initialization. The objective noisily captures aspects of paraphrase, translation, multi-document summarization, and information retrieval, allowing for strong zero-shot performance on several tasks.

For example, with no additional task-specific training we achieve BLEU scores of up to 35.8 for document translation. We further show that fine-tuning gives strong performance on a range of discriminative and generative tasks in many languages, making MARGE the most generally applicable pre-training method to date.

--- https://arxiv.org/abs/2006.15191 Is SGD a Bayesian sampler? Well, almost Chris Mingard, Guillermo Valle-Pérez, Joar Skalse, Ard A. Louis 2020-06-26 2020-06-26 [("doi","10.48550/arXiv.2006.15191")] ai/nn ai/scaling statistics/bayes

Overparameterized deep neural networks (DNNs) are highly expressive and so can, in principle, generate almost any function that fits a training dataset with zero error. The vast majority of these functions will perform poorly on unseen data, and yet in practice DNNs often generalize remarkably well. This success suggests that a trained DNN must have a strong inductive bias towards functions with low generalisation error.

Here we empirically investigate this inductive bias by calculating, for a range of architectures and datasets, the probability PSGD(f|S) that an overparameterized DNN, trained with stochastic gradient descent (SGD) or one of its variants, converges on a function f consistent with a training set S. We also use Gaussian processes to estimate the Bayesian posterior probability Pb(f|S) that the DNN expresses f upon random sampling of its parameters, conditioned on S.

Our main findings are that PSGD(f|S) correlates remarkably well with Pb(f|S) and that Pb(f|S) is strongly biased towards low-error and low complexity functions. These results imply that strong inductive bias in the parameter-function map (which determines Pb(f|S)), rather than a special property of SGD, is the primary explanation for why DNNs generalize so well in the overparameterized regime.

While our results suggest that the Bayesian posterior Pb(f|S) is the first order determinant of PSGD(f|S), there remain second order differences that are sensitive to hyperparameter tuning. A function probability picture, based on PSGD(f|S) and/or Pb(f|S), can shed new light on the way that variations in architecture or hyperparameter settings such as batch size, learning rate, and optimizer choice, affect DNN performance.

--- https://arxiv.org/abs/2006.16668#google GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, Zhifeng Chen 2020-06-30 2020-06-30 [("doi","10.48550/arXiv.2006.16668")] ai/scaling/mixture-of-experts

Neural network scaling has been critical for improving the model quality in many real-world machine learning applications with vast amounts of training data and compute. Although this trend of scaling is affirmed to be a sure-fire approach for better model quality, there are challenges on the path such as the computation cost, ease of programming, and efficient implementation on parallel devices.

GShard is a module composed of a set of lightweight annotation APIs and an extension to the XLA compiler. It provides an elegant way to express a wide range of parallel computation patterns with minimal changes to the existing model code. GShard enabled us to scale up multilingual neural machine translation Transformer model with Sparsely-Gated Mixture-of-Experts beyond 600 billion parameters using automatic sharding. We demonstrate that such a giant model can efficiently be trained on 2048 TPU v3 accelerators in 4 days to achieve far superior quality for translation from 100 languages to English compared to the prior art.

--- https://arxiv.org/abs/2007.00072 Data Movement Is All You Need: A Case Study on Optimizing Transformers Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Shigang Li, Torsten Hoefler 2020-06-30 2020-06-30 [("doi","10.48550/arXiv.2007.00072")] ai/nn/transformer ai/scaling/hardware

Transformers are one of the most important machine learning workloads today. Training one is a very compute-intensive task, often taking days or weeks, and attention has been given to optimizing transformers. Despite this, existing implementations do not efficientlyuse GPUs.

We find that data movement is the key bottleneck when training. Due to Amdahl’s Law and massive improvements in compute performance, training has now become memory-bound. Further, existing frameworks use suboptimal data layouts.

Using these insights, we present a recipe for globally optimizing data movement in transformers. We reduce data movement by up to 22.91% and overall achieve a 1.30× performance improvement over state-of-the-art frameworks when training a BERT encoder layer and 1.19× for the entire BERT.

Our approach is applicable more broadly to optimizing deep neural networks, and offers insight into how to tackle emerging performance bottlenecks.

--- https://arxiv.org/abs/2007.02382 Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Thomas L. Griffiths, Sergey Levine 2020-07-05 2020-07-05 [("doi","10.48550/arXiv.2007.02382")] economics/mechanism-design/auction reinforcement-learning/meta-learning reinforcement-learning/multi-agent

This paper seeks to establish a framework for directing a society of simple, specialized, self-interested agents to solve what traditionally are posed as monolithic single-agent sequential decision problems. What makes it challenging to use a decentralized approach to collectively optimize a central objective is the difficulty in characterizing the equilibrium strategy profile of non-cooperative games.

To overcome this challenge, we design a mechanism for defining the learning environment of each agent for which we know that the optimal solution for the global objective coincides with a Nash equilibrium strategy profile of the agents optimizing their own local objectives. The society functions as an economy of agents that learn the credit assignment process itself by buying and selling to each other the right to operate on the environment state. We derive a class of decentralized reinforcement learning algorithms that are broadly applicable not only to standard reinforcement learning but also for selecting options in semi-MDPs and dynamically composing computation graphs.

Lastly, we demonstrate the potential advantages of a society’s inherent modular structure for more efficient transfer learning.

--- https://arxiv.org/abs/2007.02686 Meta-Learning through Hebbian Plasticity in Random Networks Elias Najarro, Sebastian Risi 2020-07-06 2020-07-06 [("doi","10.48550/arXiv.2007.02686")] psychology/neuroscience reinforcement-learning/meta-learning/continual-learning reinforcement-learning/robot

Lifelong learning and adaptability are two defining aspects of biological agents. Modern reinforcement learning (RL) approaches have shown progress in solving complex tasks, however once training is concluded, the found solutions are typically static and incapable of adapting to new information or perturbations. While it is still not completely understood how biological brains learn and adapt so efficiently from experience, it is believed that synaptic plasticity plays a prominent role in this process.

Inspired by this biological mechanism, we propose a search method that, instead of optimizing the weight parameters of neural networks directly, only searches for synapse-specific Hebbian learning rules that allow the network to continuously self-organize its weights during the lifetime of the agent.

We demonstrate our approach on several reinforcement learning tasks with different sensory modalities and more than 450K trainable plasticity parameters. We find that starting from completely random weights, the discovered Hebbian rules enable an agent to navigate a dynamical 2D-pixel environment; likewise they allow a simulated 3D quadrupedal robot to learn how to walk while adapting to morphological damage not seen during training and in the absence of any explicit reward or error signal in less than 100× steps.

Code is available at Github.

--- https://arxiv.org/abs/2007.06600 Closed-Form Factorization of Latent Semantics in GANs Yujun Shen, Bolei Zhou 2020-07-13 2020-07-13 [("doi","10.48550/arXiv.2007.06600")] ai/anime/danbooru ai/nn/gan/stylegan/anime

A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images.

In order to identify such latent dimensions for image editing, previous methods typically annotate a collection of synthesized samples and train linear classifiers in the latent space. However, they require a clear definition of the target attribute as well as the corresponding manual annotations, limiting their applications in practice.

In this work, we examine the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner. In particular, we take a closer look into the generation mechanism of GANs and further propose a closed-form factorization algorithm for latent semantic discovery by directly decomposing the pre-trained weights.

With a lightning-fast implementation, our approach is capable of not only finding semantically meaningful dimensions comparably to the state-of-the-art supervised methods, but also resulting in far more versatile concepts across multiple GAN models trained on a wide range of datasets.

--- https://arxiv.org/abs/2007.08489 Do Adversarially Robust ImageNet Models Transfer Better? Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry 2020-07-16 2020-07-16 [("doi","10.48550/arXiv.2007.08489")] ai/nn/adversarial

Transfer learning is a widely-used paradigm in deep learning, where models pre-trained on standard datasets can be efficiently adapted to downstream tasks. Typically, better pre-trained models yield better transfer results, suggesting that initial accuracy is a key aspect of transfer learning performance.

In this work, we identify another such aspect: we find that adversarially robust models, while less accurate, often perform better than their standard-trained counterparts when used for transfer learning. Specifically, we focus on adversarially robust ImageNet classifiers, and show that they yield improved accuracy on a standard suite of downstream classification tasks. Further analysis uncovers more differences between robust and standard models in the context of transfer learning.

Our results are consistent with (and in fact, add to) recent hypotheses stating that robustness leads to improved feature representations.

Our code and models are available at Github.

--- https://arxiv.org/abs/2007.08794#deepmind Discovering Reinforcement Learning Algorithms Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver 2020-07-17 2020-07-17 [("doi","10.48550/arXiv.2007.08794")] reinforcement-learning/meta-learning

Reinforcement learning (RL) algorithms update an agent’s parameters according to one of several possible rules, discovered manually through years of research. Automating the discovery of update rules from data could lead to more efficient algorithms, or algorithms that are better adapted to specific environments. Although there have been prior attempts at addressing this scientific challenge, it remains an open question whether it is feasible to discover alternatives to fundamental concepts of RL such as value functions and temporal-difference learning.

This paper introduces a new meta-learning approach that discovers an entire update rule which includes both ‘what to predict’ (eg. value functions) and ‘how to learn from it’ (eg. bootstrapping) by interacting with a set of environments. The output of this method is an RL algorithm that we call Learned Policy Gradient (LPG). Empirical results show that our method discovers its own alternative to the concept of value functions. Furthermore it discovers a bootstrapping mechanism to maintain and use its predictions. Surprisingly, when trained solely on toy environments, LPG generalises effectively to complex Atari games and achieves non-trivial performance. This shows the potential to discover general RL algorithms from data.

--- https://arxiv.org/abs/2007.09560 The Overfitted Brain: Dreams evolved to assist generalization Erik Hoel 2020-07-19 2020-07-19 [("doi","10.48550/arXiv.2007.09560")] psychology/neuroscience psychology/spaced-repetition reinforcement-learning/exploration reinforcement-learning/model zeo

Understanding of the evolved biological function of sleep has advanced considerably in the past decade. However, no equivalent understanding of dreams has emerged. Contemporary neuroscientific theories generally view dreams as epiphenomena, and the few proposals for their biological function are contradicted by the phenomenology of dreams themselves. Now, the recent advent of deep neural networks (DNNs) has finally provided the novel conceptual framework within which to understand the evolved function of dreams. Notably, all DNNs face the issue of overfitting as they learn, which is when performance on one data set increases but the network’s performance fails to generalize (often measured by the divergence of performance on training vs. testing data sets). This ubiquitous problem in DNNs is often solved by modelers via “noise injections” in the form of noisy or corrupted inputs.

The goal of this paper is to argue that the brain faces a similar challenge of overfitting, and that nightly dreams evolved to combat the brain’s overfitting during its daily learning. That is, dreams are a biological mechanism for increasing generalizability via the creation of corrupted sensory inputs from stochastic activity across the hierarchy of neural structures. Sleep loss, specifically dream loss, leads to an overfitted brain that can still memorize and learn but fails to generalize appropriately.

Herein this “overfitted brain hypothesis” is explicitly developed and then compared and contrasted with existing contemporary neuroscientific theories of dreams. Existing evidence for the hypothesis is surveyed within both neuroscience and deep learning, and a set of testable predictions are put forward that can be pursued both in vivo and in silico.

--- https://arxiv.org/abs/2007.12509#deepmind Monte-Carlo Tree Search as Regularized Policy Optimization Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos 2020-07-24 2020-07-24 [("doi","10.48550/arXiv.2007.12509")] reinforcement-learning/model/alphago reinforcement-learning/model/muzero

The combination of Monte-Carlo tree search with deep reinforcement learning has led to advances in artificial intelligence. However, AlphaZero, the current state-of-the-art MCTS algorithm, still relies on handcrafted heuristics that are only partially understood.

In this paper, we show that AlphaZero’s search heuristics, along with other common ones such as UCT, are an approximation to the solution of a specific regularized policy optimization problem. With this insight, we propose a variant of AlphaZero which uses the exact solution to this policy optimization problem, and show experimentally that it reliably outperforms the original algorithm in multiple domains.

--- https://arxiv.org/abs/2007.13363 Learning Compositional Neural Programs for Continuous Control Thomas Pierrot, Nicolas Perrin, Feryal Behbahani, Alexandre Laterre, Olivier Sigaud, Karim Beguir, Nando de Freitas 2020-07-27 2020-07-27 [("doi","10.48550/arXiv.2007.13363")] reinforcement-learning/model/alphago

We propose a novel solution to challenging sparse-reward, continuous control problems that require hierarchical planning at multiple levels of abstraction.

Our solution, dubbed AlphaNPI-X, involves 3 separate stages of learning. First, we use off-policy reinforcement learning algorithms with experience replay to learn a set of atomic goal-conditioned policies, which can be easily repurposed for many tasks. Second, we learn self-models describing the effect of the atomic policies on the environment. Third, the self-models are harnessed to learn recursive compositional programs with multiple levels of abstraction. The key insight is that the self-models enable planning by imagination, obviating the need for interaction with the world when learning higher-level compositional programs.

To accomplish the third stage of learning, we extend the AlphaNPI algorithm, which applies AlphaZero to learn recursive neural programmer-interpreters.

We empirically show that AlphaNPI-X can effectively learn to tackle challenging sparse manipulation tasks, such as stacking multiple blocks, where powerful model-free baselines fail.

--- https://arxiv.org/abs/2007.14966 Mirostat: A Neural Text Decoding Algorithm that Directly Controls Perplexity Sourya Basu, Govardana Sachitanandam Ramachandran, Nitish Shirish Keskar, Lav R. Varshney 2020-07-29 2020-07-29 [("doi","10.48550/arXiv.2007.14966")] ai/nn/sampling ai/nn/transformer/gpt psychology/novelty

[cf. typical sampling] Neural text decoding is important for generating high-quality texts using language models. To generate high-quality text, popular decoding algorithms like top-k, top-p (nucleus), and temperature-based Boltzmann sampling truncate or distort the unreliable low probability tail of the language model. Though these methods generate high-quality text after parameter tuning, they are ad hoc. Not much is known about the control they provide over the statistics of the output, which is important since recent reports show text quality is highest for a specific range of likelihoods.

Here, first we provide a theoretical analysis of perplexity in top-k, top-p, and temperature sampling, finding that cross-entropy behaves linearly as a function of p in top-p sampling whereas it is a nonlinear function of k in top-k sampling, under Zipfian statistics. We use this analysis to design a feedback-based adaptive top-k text decoding algorithm called Mirostat that generates text (of any length) with a predetermined value of perplexity, and thereby high-quality text without any tuning. Experiments show that for low values of k and p in top-k and top-p sampling, perplexity drops substantially with generated text length, which is also correlated with excessive repetitions in the text (the boredom trap). On the other hand, for large values of k and p, we find that perplexity increases with generated text length, which is correlated with incoherence in the text (confusion trap).

Mirostat avoids both traps: experiments show that cross-entropy has a near-linear relation with repetition in generated text. This relation is almost independent of the sampling method but slightly dependent on the model used. Hence, for a given language model, control over perplexity also gives control over repetitions. Experiments with human raters for fluency, coherence, and quality further verify our findings.

--- https://arxiv.org/abs/2007.15646 Rewriting a Deep Generative Model David Bau, Steven Liu, Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba 2020-07-30 2020-07-30 [("doi","10.48550/arXiv.2007.15646")] ai/nn/gan/stylegan

A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are encoded in the network, or how a rule could be changed. In this paper, we introduce a new problem setting: manipulation of specific rules encoded by a deep generative model.

To address the problem, we propose a formulation in which the desired rule is changed by manipulating a layer of a deep network as a linear associative memory. We derive an algorithm for modifying one entry of the associative memory, and we demonstrate that several interesting structural rules can be located and modified within the layers of state-of-the-art generative models.

We present a user interface to enable users to interactively change the rules of a generative model to achieve desired effects, and we show several proof-of-concept applications.

Finally, results on multiple datasets demonstrate the advantage of our method against standard fine-tuning methods and edit transfer algorithms.

--- https://arxiv.org/abs/2007.16189 Self-supervised learning through the eyes of a child A. Emin Orhan, Vaibhav V. Gupta, Brenden M. Lake 2020-07-31 2020-07-31 [("doi","10.48550/arXiv.2007.16189")] ai/scaling ai/video/analysis reinforcement-learning/imitation-learning/brain-imitation-learning

Within months of birth, children develop meaningful expectations about the world around them. How much of this early knowledge can be explained through generic learning mechanisms applied to sensory data, and how much of it requires more substantive innate inductive biases? Addressing this fundamental question in its full generality is currently infeasible, but we can hope to make real progress in more narrowly defined domains, such as the development of high-level visual categories, thanks to improvements in data collecting technology and recent progress in deep learning.

In this paper, our goal is precisely to achieve such progress by using modern self-supervised deep learning methods and a recent longitudinal, egocentric video dataset recorded from the perspective of 3 young children (Sullivan et al 2020).

Our results demonstrate the emergence of powerful, high-level visual representations from developmentally realistic natural videos using generic self-supervised learning objectives.

--- https://arxiv.org/abs/2008.02215 A Time Leap Challenge for SAT Solving Johannes K. Fichte, Markus Hecher, Stefan Szeider 2020-08-05 2020-08-05 [("doi","10.48550/arXiv.2008.02215")] ai cs/algorithm cs/hardware economics/experience-curve reinforcement-learning/model

We compare the impact of hardware advancement and algorithm advancement for SAT solving over the last two decades. In particular, we compare 20-year-old SAT-solvers on new computer hardware with modern SAT-solvers on 20-year-old hardware.

Our findings show that the progress on the algorithmic side has at least as much impact as the progress on the hardware side.

[This is not true as stated, and is a common fallacy in these ‘timetravel’ experiments. What they find is that if you run a current-day algorithm on current-day hardware, they make equal contributions; this is not the same thing as saying that they caused the same amount of progress. Those algorithms could not have been researched without more computing power! Therefore...]

--- https://arxiv.org/abs/2008.07669 HiPPO: Recurrent Memory with Optimal Polynomial Projections Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Re 2020-08-17 2020-08-17 [("doi","10.48550/arXiv.2008.07669")] ai/nn/rnn ai/nn/transformer/attention

[cf. S4, LSSL] A central problem in learning from sequential data is representing cumulative history in an incremental fashion as more data is processed.

We introduce a general framework (HiPPO) for the online compression of continuous signals and discrete time series by projection onto polynomial bases. Given a measure that specifies the importance of each time step in the past, HiPPO produces an optimal solution to a natural online function approximation problem. As special cases, our framework yields a short derivation of the recent Legendre Memory Unit (LMU) from first principles, and generalizes the ubiquitous gating mechanism of recurrent neural networks such as GRUs. This formal framework yields a new memory update mechanism (HiPPO-LegS) that scales through time to remember all history, avoiding priors on the timescale. HiPPO-LegS enjoys the theoretical benefits of timescale robustness, fast updates, and bounded gradients. By incorporating the memory dynamics into recurrent neural networks, HiPPO RNNs can empirically capture complex temporal dependencies.

On the benchmark permuted MNIST dataset, HiPPO-LegS sets a new state-of-the-art accuracy of 98.3%. Finally, on a novel trajectory classification task testing robustness to out-of-distribution timescales and missing data, HiPPO-LegS outperforms RNN and neural ODE baselines by 25–40% accuracy.

--- https://arxiv.org/abs/2008.10086 Learning Personalized Models of Human Behavior in Chess Reid McIlroy-Young, Russell Wang, Siddhartha Sen, Jon Kleinberg, Ashton Anderson 2020-08-23 2020-08-23 [("doi","10.48550/arXiv.2008.10086")] psychology/chess reinforcement-learning/chess reinforcement-learning/model/alphago reinforcement-learning/preference-learning statistics/stylometry

Even when machine learning systems surpass human ability in a domain, there are many reasons why AI systems that capture human-like behavior would be desirable: humans may want to learn from them, they may need to collaborate with them, or they may expect them to serve as partners in an extended interaction. Motivated by this goal of human-like AI systems, the problem of predicting human actions—as opposed to predicting optimal actions—has become an increasingly useful task.

We extend this line of work by developing highly accurate personalized models of human behavior in the context of chess. Chess is a rich domain for exploring these questions, since it combines a set of appealing features: AI systems have achieved superhuman performance but still interact closely with human chess players both as opponents and preparation tools, and there is an enormous amount of recorded data on individual players. Starting with an open-source version of AlphaZero trained on a population of human players, we demonstrate that we can improve prediction of a particular player’s moves by applying a series of fine-tuning adjustments. Furthermore, we can accurately perform stylometry—predicting who made a given set of actions—indicating that our personalized models capture human decision-making at an individual level.

--- https://arxiv.org/abs/2009.02773 Why Spectral Normalization Stabilizes GANs: Analysis and Improvements Zinan Lin, Vyas Sekar, Giulia Fanti 2020-09-06 2020-09-06 [("doi","10.48550/arXiv.2009.02773")] ai/nn/gan

[blog] Spectral normalization (SN) is a widely-used technique for improving the stability and sample quality of Generative Adversarial Networks (GANs). However, there is currently limited understanding of why SN is effective.

In this work, we show that SN controls two important failure modes of GAN training: exploding and vanishing gradients. Our proofs illustrate a (perhaps unintentional) connection with the successful LeCun initialization. This connection helps to explain why the most popular implementation of SN for GANs requires no hyper-parameter tuning, whereas stricter implementations of SN have poor empirical performance out-of-the-box. Unlike LeCun initialization which only controls gradient vanishing at the beginning of training, SN preserves this property throughout training.

Building on this theoretical understanding, we propose a new spectral normalization technique: Bidirectional Scaled Spectral Normalization (BSSN), which incorporates insights from later improvements to LeCun initialization: Xavier initialization and Kaiming initialization. Theoretically, we show that BSSN gives better gradient control than SN. Empirically, we demonstrate that it outperforms SN in sample quality and training stability on several benchmark datasets.

--- https://arxiv.org/abs/2009.04374#deepmind Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess Nenad Tomašev, Ulrich Paquet, Demis Hassabis, Vladimir Kramnik 2020-09-09 2020-09-09 [("doi","10.48550/arXiv.2009.04374")] reinforcement-learning/chess reinforcement-learning/exploration reinforcement-learning/model/alphago

[essay, Kramnik editorial] It is non-trivial to design engaging and balanced sets of game rules. Modern chess has evolved over centuries, but without a similar recourse to history, the consequences of rule changes to game dynamics are difficult to predict.

AlphaZero provides an alternative in silico means of game balance assessment. It is a system that can learn near-optimal strategies for any rule set from scratch, without any human supervision, by continually learning from its own experience. In this study we use AlphaZero to creatively explore and design new chess variants. There is growing interest in chess variants like Fischer Random Chess, because of classical chess’s voluminous opening theory, the high percentage of draws in professional play, and the non-negligible number of games that end while both players are still in their home preparation.

We compare 9 other variants that involve atomic changes to the rules of chess…The nine changes considered in this study are listed in Table 1. No-castling and No-castling (10) involve a full and partial [not allowed during first 10 turns / 20 plies] restriction on the castling rule. Pawn-one-square, Semi-torpedo [can move 2 squares on 2nd and 3rd ranks], Torpedo [can move 2 squares], Pawn-back [up to your 2nd rank, don’t count against 50 limit], and Pawn-sideways involve changes to pawn mobility. Self-capture chess allows players to also capture their own pieces. Finally, Stalemate=win recasts stalemate as a win for the attacking side, rather than a draw…The changes allow for novel strategic and tactical patterns to emerge, while keeping the games close to the original. By learning near-optimal strategies for each variant with AlphaZero, we determine what games between strong human players might look like if these variants were adopted.

Qualitatively, several variants are very dynamic. An analytic comparison show that pieces are valued differently between variants, and that some variants are more decisive than classical chess.

Our findings demonstrate the rich possibilities that lie beyond the rules of modern chess.

--- https://arxiv.org/abs/2009.04433 not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution Seungwook Han, Akash Srivastava, Cole Hurwitz, Prasanna Sattigeri, David D. Cox 2020-09-09 2020-09-09 [("doi","10.48550/arXiv.2009.04433")] ai/nn/gan/biggan ai/nn/gan/stylegan cs/algorithm/information/compression

[harsh reviews; code] State-of-the-art models for high-resolution image generation, such as BigGAN and VQ-VAE-2, require an incredible amount of compute resources and/or time (512 TPU-v3 cores) to train, putting them out of reach for the larger research community. On the other hand, GAN-based image super-resolution models, such as ESRGAN, can not only upscale images to high dimensions, but also are efficient to train.

In this paper, we present not-so-big-GAN (nsb-GAN), a simple yet cost-effective two-step training framework for deep generative models (DGMs) of high-dimensional natural images. First, we generate images in low-frequency bands by training a sampler in the wavelet domain. Then, we super-resolve these images from the wavelet domain back to the pixel-space with our novel wavelet super-resolution decoder network. Wavelet-based down-sampling method preserves more structural information than pixel-based methods, leading to better generative quality of the low-resolution sampler (eg. 64×64). Since the sampler and decoder can be trained in parallel and operate on much lower dimensional spaces than end-to-end models, the training cost is substantially reduced.

On ImageNet 512×512, our model achieves a Fréchet Inception Distance (FID) of 10.59—beating the baseline BigGAN model—at half the compute (256 TPU-v3 cores).

--- https://arxiv.org/abs/2009.07701 Kaggle forecasting competitions: An overlooked learning opportunity Casper Solheim Bojer, Jens Peder Meldgaard 2020-09-16 2020-09-16 [("doi","10.1016/j.ijforecast.2020.07.007")] ai/tabular statistics/prediction

Competitions play an invaluable role in the field of forecasting, as exemplified through the recent M4 competition. The competition received attention from both academics and practitioners and sparked discussions around the representativeness of the data for business forecasting. Several competitions featuring real-life business forecasting tasks on the Kaggle platform have, however, been largely ignored by the academic community.

We believe the learnings from these competitions have much to offer to the forecasting community and provide a review of the results from 6 Kaggle competitions. We find that most of the Kaggle datasets are characterized by higher intermittence and entropy than the M-competitions and that global ensemble models tend to outperform local single models.

Furthermore, we find the strong performance of gradient boosted decision trees, increasing success of neural networks for forecasting, and a variety of techniques for adapting machine learning models to the forecasting task.

--- https://arxiv.org/abs/2009.08366#microsoft GraphCodeBERT: Pre-training Code Representations with Data Flow Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, Ming Zhou 2020-09-17 2020-09-17 [("doi","10.48550/arXiv.2009.08366")] ai/nn/transformer/gpt/codex

Pre-trained models for programming language have achieved dramatic empirical improvements on a variety of code-related tasks such as code search, code completion, code summarization, etc. However, existing pre-trained models regard a code snippet as a sequence of tokens, while ignoring the inherent structure of code, which provides crucial code semantics and would enhance the code understanding process. We present GraphCodeBERT, a pre-trained model for programming language that considers the inherent structure of code.

Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of “where-the-value-comes-from” between variables. Such a semantic-level structure is neat and does not bring an unnecessarily deep hierarchy of AST, the property of which makes the model more efficient. We develop GraphCodeBERT based on Transformer. In addition to using the task of masked language modeling, we introduce two structure-aware pre-training tasks. One is to predict code structure edges, and the other is to align representations between source code and code structure. We implement the model in an efficient way with a graph-guided masked attention function to incorporate the code structure.

We evaluate our model on 4 tasks, including code search, clone detection, code translation, and code refinement. Results show that code structure and newly introduced pre-training tasks can improve GraphCodeBERT and achieves state-of-the-art performance on the 4 downstream tasks. We further show that the model prefers structure-level attentions over token-level attentions in the task of code search.

--- https://arxiv.org/abs/2009.09796 Multi-Task Learning with Deep Neural Networks: A Survey Michael Crawshaw 2020-09-10 2020-09-10 [("doi","10.48550/arXiv.2009.09796")] reinforcement-learning

Multi-task learning (MTL) is a subfield of machine learning in which multiple tasks are simultaneously learned by a shared model. Such approaches offer advantages like improved data efficiency, reduced overfitting through shared representations, and fast learning by leveraging auxiliary information. However, the simultaneous learning of multiple tasks presents new design and optimization challenges, and choosing which tasks should be learned jointly is in itself a non-trivial problem.

In this survey, we give an overview of multi-task learning methods for deep neural networks, with the aim of summarizing both the well-established and most recent directions within the field. Our discussion is structured according to a partition of the existing deep MTL techniques into 3 groups: architectures, optimization methods, and task relationship learning. We also provide a summary of common multi-task benchmarks.

--- https://arxiv.org/abs/2009.10795#allen Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah Smith, Yejin Choi 2020-09-22 2020-09-22 [("doi","10.48550/arXiv.2009.10795")] ai/nn/adversarial reinforcement-learning/exploration/active-learning

Large datasets have become commonplace in NLP research. However, the increased emphasis on data quantity has made it challenging to assess the quality of data. We introduce Data Maps—a model-based tool to characterize and diagnose datasets. We leverage a largely ignored source of information: the behavior of the model on individual instances during training (training dynamics) for building data maps. This yields two intuitive measures for each example—the model’s confidence in the true class, and the variability of this confidence across epochs—obtained in a single run of training.

Experiments across 4 datasets show that these model-dependent measures reveal 3 distinct regions in the data map, each with pronounced characteristics. First, our data maps show the presence of “ambiguous” regions with respect to the model, which contribute the most towards out-of-distribution generalization. Second, the most populous regions in the data are “easy to learn” for the model, and play an important role in model optimization. Finally, data maps uncover a region with instances that the model finds “hard to learn”; these often correspond to labeling errors.

Our results indicate that a shift in focus from quantity to quality of data could lead to robust models and improved out-of-distribution generalization.

--- https://arxiv.org/abs/2009.11243#google Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves Luke Metz, Niru Maheswaranathan, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein 2020-09-23 2020-09-23 [("doi","10.48550/arXiv.2009.11243")] reinforcement-learning/meta-learning

Much as replacing hand-designed features with learned functions has revolutionized how we solve perceptual tasks, we believe learned algorithms will transform how we train models. In this work, we focus on general-purpose learned optimizers capable of training a wide variety of problems with no user-specified hyperparameters.

We introduce a new, neural network parameterized, hierarchical optimizer with access to additional features such as validation loss to enable automatic regularization. Most learned optimizers have been trained on only a single task, or a small number of tasks. We train our optimizers on thousands of tasks, making use of orders of magnitude more compute, resulting in optimizers that generalize better to unseen tasks.

The learned optimizers not only perform well, but learn behaviors that are distinct from existing first-order optimizers. For instance, they generate update steps that have implicit regularization and adapt as the problem hyperparameters (eg. batch size) or architecture (eg. neural network width) change.

Finally, these learned optimizers show evidence of being useful for out-of-distribution tasks such as training themselves from scratch.

--- https://arxiv.org/abs/2009.11278#allen X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers Jaemin Cho, Jiasen Lu, Dustin Schwenk, Hannaneh Hajishirzi, Aniruddha Kembhavi 2020-09-23 2020-09-23 [("doi","10.48550/arXiv.2009.11278")] ai/nn/transformer/gpt/dall-e/1

Mirroring the success of masked language models, vision-and-language counterparts like ViLBERT, LXMERT, and UNITER have achieved state-of-the-art performance on a variety of multimodal discriminative tasks like visual question answering and visual grounding. Recent work has also successfully adapted such models towards the generative task of image captioning. This begs the question: Can these models go the other way and generate images from pieces of text?

Our analysis of a popular representative from this model family—LXMERT—finds that it is unable to generate rich and semantically meaningful imagery with its current training setup. We introduce X-LXMERT, an extension to LXMERT with training refinements including: discretizing visual representations, using uniform masking with a large range of masking ratios, and aligning the right pre-training datasets to the right objectives which enables it to paint.

X-LXMERT’s image generation capabilities rival state-of-the-art generative models while its question answering and captioning abilities remains comparable to LXMERT. Finally, we demonstrate the generality of these training refinements by adding image generation capabilities into UNITER to produce X-UNITER.

--- https://arxiv.org/abs/2010.03593#deepmind Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy Mann, Pushmeet Kohli 2020-10-07 2020-10-07 [("doi","10.48550/arXiv.2010.03593")] ai/nn/adversarial ai/scaling

Adversarial training and its variants have become de facto standards for learning robust deep neural networks. In this paper, we explore the landscape around adversarial training in a bid to uncover its limits.

We systematically study the effect of different training losses, model sizes, activation functions, the addition of unlabeled data (through pseudo-labeling) and other factors on adversarial robustness. We discover that it is possible to train robust models that go well beyond state-of-the-art results by combining larger models, Swish/SiLU activations and model weight averaging.

We demonstrate large improvements on CIFAR-10 and CIFAR-100 against 𝓁∞ and 𝓁2 norm-bounded perturbations of size 8⁄255 and 128⁄255, respectively. In the setting with additional unlabeled data, we obtain an accuracy under attack of 65.88% against 𝓁∞ perturbations of size 8⁄255 on CIFAR-10 (+6.35% with respect to prior art). Without additional data, we obtain an accuracy under attack of 57.20% (+3.46%). To test the generality of our findings and without any additional modifications, we obtain an accuracy under attack of 80.53% (+7.62%) against 𝓁2 perturbations of size 128⁄255 on CIFAR-10, and of 36.88% (+8.46%) against 𝓁∞ perturbations of size 8⁄255 on CIFAR-100.

All models are available at https://github.com/google-deepmind/deepmind-research/tree/master/adversarial_robustness.

--- https://arxiv.org/abs/2010.03802#google TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling Parker Riley, Noah Constant, Mandy Guo, Girish Kumar, David Uthus, Zarana Parekh 2020-10-08 2020-10-08 [("doi","10.48550/arXiv.2010.03802")] ai/nn/transformer/t5 ai/text-style-transfer

We present a novel approach to the problem of text style transfer. Unlike previous approaches requiring style-labeled training data, our method makes use of readily-available unlabeled text by relying on the implicit connection in style between adjacent sentences, and uses labeled data only at inference time.

We adapt T5 (Raffel et al 2020), a strong pretrained text-to-text model, to extract a style vector from text and use it to condition the decoder to perform style transfer. As our label-free training results in a style vector space encoding many facets of style, we recast transfers as “targeted restyling” vector operations that adjust specific attributes of the input while preserving others.

We demonstrate that training on unlabeled Amazon reviews data results in a model that is competitive on sentiment transfer, even compared to models trained fully on labeled data.

Furthermore, applying our novel method to a diverse corpus of unlabeled web text results in a single model capable of transferring along multiple dimensions of style (dialect, emotiveness, formality, politeness, sentiment) despite no additional training and using only a handful of exemplars at inference time.

--- https://arxiv.org/abs/2010.04466 Learning not to learn: Nature versus nurture in silico Robert Tjarko Lange, Henning Sprekeler 2020-10-09 2020-10-09 [("doi","10.48550/arXiv.2010.04466")] reinforcement-learning/exploration reinforcement-learning/meta-learning statistics/bayes

Animals are equipped with a rich innate repertoire of sensory, behavioral and motor skills, which allows them to interact with the world immediately after birth. At the same time, many behaviors are highly adaptive and can be tailored to specific environments by means of learning.

In this work, we use mathematical analysis and the framework of meta-learning (or ‘learning to learn’) to answer when it is beneficial to learn such an adaptive strategy and when to hard-code a heuristic behavior.

We find that the interplay of ecological uncertainty, task complexity and the agents’ lifetime has crucial effects on the meta-learned amortized Bayesian inference performed by an agent. There exist two regimes: One in which meta-learning yields a learning algorithm that implements task-dependent information-integration and a second regime in which meta-learning imprints a heuristic or ‘hard-coded’ behavior. Further analysis reveals that non-adaptive behaviors are not only optimal for aspects of the environment that are stable across individuals, but also in situations where an adaptation to the environment would in fact be highly beneficial, but could not be done quickly enough to be exploited within the remaining lifetime.

Hard-coded behaviors should hence not only be those that always work, but also those that are too complex to be learned within a reasonable time frame.

--- https://arxiv.org/abs/2010.05334 Toonify: Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between Domains Justin N. M. Pinkney, Doron Adler 2020-10-11 2020-10-11 [("doi","10.48550/arXiv.2010.05334")] ai/nn/gan/stylegan/anime

GANs can generate photo-realistic images from the domain of their training data. However, those wanting to use them for creative purposes often want to generate imagery from a truly novel domain, a task which GANs are inherently unable to do. It is also desirable to have a level of control so that there is a degree of artistic direction rather than purely curation of random results.

Here we present a method for interpolating between generative models of the StyleGAN architecture in a resolution dependent manner. This allows us to generate images from an entirely novel domain and do this with a degree of control over the nature of the output.

--- https://arxiv.org/abs/2010.05713 Unsupervised Image-to-Image Translation via Pre-trained StyleGAN-2 Network Jialu Huang, Jing Liao, Sam Kwong 2020-10-12 2020-10-12 [("doi","10.48550/arXiv.2010.05713")] ai/anime/danbooru ai/nn/gan/stylegan/anime

[code] Image-to-Image (I2I) translation is a heated topic in academia, and it also has been applied in real-world industry for tasks like image synthesis, super-resolution, and colorization. However, traditional I2I translation methods train data in two or more domains together. This requires lots of computation resources. Moreover, the results are of lower quality, and they contain many more artifacts. The training process could be unstable when the data in different domains are not balanced, and modal collapse is more likely to happen.

We proposed a new I2I translation method that generates a new model in the target domain via a series of model transformations on a pre-trained StyleGAN-2 model in the source domain. After that, we proposed an inversion method to achieve the conversion between an image and its latent vector. By feeding the latent vector into the generated model, we can perform I2I translation between the source domain and target domain.

Both qualitative and quantitative evaluations were conducted to prove that the proposed method can achieve outstanding performance in terms of image quality, diversity and semantic similarity to the input and reference images compared to state-of-the-art works.

--- https://arxiv.org/abs/2010.07079#facebook Recipes for Safety in Open-domain Chatbots Jing Xu, Da Ju, Margaret Li, Y-Lan Boureau, Jason Weston, Emily Dinan 2020-10-14 2020-10-14 [("doi","10.48550/arXiv.2010.07079")] ai/nn/adversarial reinforcement-learning/safe

Models trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior and unwanted biases.

We investigate a variety of methods to mitigate these issues in the context of open-domain generative dialogue models. We introduce a new human-and-model-in-the-loop framework for both training safer models and for evaluating them, as well as a novel method to distill safety considerations inside generative models without the use of an external classifier at deployment time.

We conduct experiments comparing these methods and find our new techniques are (1) safer than existing models as measured by automatic and human evaluations while (2) maintaining usability metrics such as engagingness relative to the state-of-the-art.

We then discuss the limitations of this work by analyzing failure cases of our models.

--- https://arxiv.org/abs/2010.11223#deepmind Meta-trained agents implement Bayes-optimal agents Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega 2020-10-21 2020-10-21 [("doi","10.48550/arXiv.2010.11223")] reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/scaling statistics/bayes

Memory-based meta-learning is a powerful technique to build agents that adapt fast to any task within a target distribution. A previous theoretical study has argued that this remarkable performance is because the meta-training protocol incentivizes agents to behave Bayes-optimally.

We empirically investigate this claim on a number of prediction and bandit tasks.

Inspired by ideas from theoretical computer science, we show that meta-learned and Bayes-optimal agents not only behave alike, but they even share a similar computational structure, in the sense that one agent system can simulate the other. Furthermore, we show that Bayes-optimal agents are fixed points of the meta-learning dynamics.

Our results suggest that memory-based meta-learning might serve as a general technique for numerically approximating Bayes-optimal agents—that is, even for task distributions for which we currently don’t possess tractable models.

--- https://arxiv.org/abs/2010.11943 Few-Shot Adaptation of Generative Adversarial Networks Esther Robb, Wen-Sheng Chu, Abhishek Kumar, Jia-Bin Huang 2020-10-22 2020-10-22 [("doi","10.48550/arXiv.2010.11943")] ai/anime/danbooru ai/nn/gan

Generative Adversarial Networks (GANs) have shown remarkable performance in image synthesis tasks, but typically require a large number of training samples to achieve high-quality synthesis.

This paper proposes a simple and effective method, Few-Shot GAN (FSGAN), for adapting GANs in few-shot settings (less than 100 images). FSGAN repurposes component analysis techniques and learns to adapt the singular values of the pre-trained weights while freezing the corresponding singular vectors. This provides a highly expressive parameter space for adaptation while constraining changes to the pretrained weights.

We validate our method in a challenging few-shot setting of 5–100 images in the target domain. We show that our method has large visual quality gains compared with existing GAN adaptation methods. We report qualitative and quantitative results showing the effectiveness of our method.

We additionally highlight a problem for few-shot synthesis in the standard quantitative metric used by data-efficient image synthesis works.

Code and additional results are available at https://github.com/e-2om/e-271/few-shot-gan .

--- https://arxiv.org/abs/2010.12563 Concealed Data Poisoning Attacks on NLP Models Eric Wallace, Tony Z. Zhao, Shi Feng, Sameer Singh 2020-10-23 2020-10-23 [("doi","10.48550/arXiv.2010.12563")] ai/nn/adversarial

Adversarial attacks alter NLP model predictions by perturbing test-time inputs. However, it is much less understood whether, and how, predictions can be manipulated with small, concealed changes to the training data.

In this work, we develop a new data poisoning attack that allows an adversary to control model predictions whenever a desired trigger phrase is present in the input. For instance, we insert 50 poison examples into a sentiment model’s training set that causes the model to frequently predict Positive whenever the input contains “James Bond”. Crucially, we craft these poison examples using a gradient-based procedure so that they do not mention the trigger phrase.

We also apply our poison attack to language modeling (“Apple iPhone” triggers negative generations) and machine translation (“iced coffee” mistranslated as “hot coffee”).

We conclude by proposing 3 defenses that can mitigate our attack at some cost in prediction accuracy or extra human annotation.

--- https://arxiv.org/abs/2010.13957 MELD: Meta-Reinforcement Learning from Images via Latent State Models Tony Z. Zhao, Anusha Nagabandi, Kate Rakelly, Chelsea Finn, Sergey Levine 2020-10-26 2020-10-26 [("doi","10.48550/arXiv.2010.13957")] reinforcement-learning/meta-learning reinforcement-learning/robot

Meta-reinforcement learning algorithms can enable autonomous agents, such as robots, to quickly acquire new behaviors by leveraging prior experience in a set of related training tasks. However, the onerous data requirements of meta-training compounded with the challenge of learning from sensory inputs such as images have made meta-RL challenging to apply to real robotic systems. Latent state models, which learn compact state representations from a sequence of observations, can accelerate representation learning from visual inputs.

In this paper, we leverage the perspective of meta-learning as task inference to show that latent state models can also perform meta-learning given an appropriately defined observation space. Building on this insight, we develop meta-RL with latent dynamics (MELD), an algorithm for meta-RL from images that performs inference in a latent state model to quickly acquire new skills given observations and rewards.

MELD outperforms prior meta-RL methods on several simulated image-based robotic control problems, and enables a real WidowX robotic arm to insert an Ethernet cable into new locations given a sparse task completion signal after only 8 hours of real world meta-training.

To our knowledge, MELD is the first meta-RL algorithm trained in a real-world robotic control setting from images.

--- https://arxiv.org/abs/2010.15082 How to Not Get Caught When You Launder Money on Blockchain? Cuneyt G. Akcora, Sudhanva Purusotham, Yulia R. Gel, Mitchell Krawiec-Thayer, Murat Kantarcioglu 2020-09-22 2020-09-22 [("doi","10.48550/arXiv.2010.15082")] bitcoin darknet-market/dnm-archive

The number of blockchain users has tremendously grown in recent years. As an unintended consequence, e-crime transactions on blockchains has been on the rise. Consequently, public blockchains have become a hotbed of research for developing AI tools to detect and trace users and transactions that are related to e-crime.

We argue that following a few select strategies can make money laundering on blockchain virtually undetectable with most of the existing tools and algorithms.

As a result, the effective combating of e-crime activities involving cryptocurrencies requires the development of novel analytic methodology in AI.

--- https://arxiv.org/abs/2011.00050#google Dataset Meta-Learning from Kernel Ridge-Regression Timothy Nguyen, Zhourong Chen, Jaehoon Lee 2020-10-30 2020-10-30 [("doi","10.48550/arXiv.2011.00050")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/exploration/active-learning reinforcement-learning/meta-learning

One of the most fundamental aspects of any machine learning algorithm is the training data used by the algorithm. We introduce the novel concept of ε-approximation of datasets, obtaining datasets which are much smaller than or are heavy corruptions of the original training data while maintaining similar model performance.

We introduce a meta-learning algorithm called Kernel Inducing Points (KIP) for obtaining such remarkable datasets, inspired by the recent developments in the correspondence between infinitely-wide neural networks and kernel ridge-regression (KRR).

For KRR tasks, we demonstrate that KIP can compress datasets by one or two orders of magnitude, substantially improving previous dataset distillation and subset selection methods while obtaining state-of-the-art results for MNIST and CIFAR-10 classification.

Furthermore, our KIP-learned datasets are transferable to the training of finite-width neural networks even beyond the lazy-training regime, which leads to state-of-the-art results for neural network dataset distillation with potential applications to privacy-preservation.

--- https://arxiv.org/abs/2011.02159#google Reverse engineering learned optimizers reveals known and novel mechanisms Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein 2020-11-04 2020-11-04 [("doi","10.48550/arXiv.2011.02159")] reinforcement-learning/meta-learning

Learned optimizers are algorithms that can themselves be trained to solve optimization problems. In contrast to baseline optimizers (such as momentum or Adam) that use simple update rules derived from theoretical principles, learned optimizers use flexible, high-dimensional, nonlinear parameterizations. Although this can lead to better performance in certain settings, their inner workings remain a mystery. How is a learned optimizer able to outperform a well tuned baseline? Has it learned a sophisticated combination of existing optimization techniques, or is it implementing completely new behavior?

In this work, we address these questions by careful analysis and visualization of learned optimizers. We study learned optimizers trained from scratch on 3 disparate tasks, and discover that:

they have learned interpretable mechanisms, including: momentum, gradient clipping, learning rate schedules, and a new form of learning rate adaptation. Moreover, we show how the dynamics of learned optimizers enables these behaviors.

Our results help elucidate the previously murky understanding of how learned optimizers work, and establish tools for interpreting future learned optimizers.

--- https://arxiv.org/abs/2011.02523#apple Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, Joshua M. Susskind 2020-11-04 2020-11-04 [("doi","10.48550/arXiv.2011.02523")] ai/dataset ai/nn reinforcement-learning/robot

For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real images.

We address this challenge by introducing Hypersim, a photorealistic synthetic dataset for holistic indoor scene understanding. To create our dataset, we leverage a large repository of synthetic scenes created by professional artists, and we generate 77,400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry. Our dataset: (1) relies exclusively on publicly available 3D assets; (2) includes complete scene geometry, material information, and lighting information for every scene; (3) includes dense per-pixel semantic instance segmentations and complete camera information for every image; and (4) factors every image into diffuse reflectance, diffuse illumination, and a non-diffuse residual term that captures view-dependent lighting effects.

We analyze our dataset at the level of scenes, objects, and pixels, and we analyze costs in terms of money, computation time, and annotation effort. Remarkably, we find that it is possible to generate our entire dataset from scratch, for roughly half the cost of training a popular open-source natural language processing model.

We also evaluate sim-to-real transfer performance on two real-world scene understanding tasks—semantic segmentation and 3D shape prediction—where we find that pre-training on our dataset improves performance on both tasks, and achieves state-of-the-art performance on the most challenging Pix3D test set.

All of our rendered image data, as well as all the code we used to generate our dataset and perform our experiments, is available online.

--- https://arxiv.org/abs/2011.03775 Text-to-Image Generation Grounded by Fine-Grained User Attention Jing Yu Koh, Jason Baldridge, Honglak Lee, Yinfei Yang 2020-11-07 2020-11-07 [("doi","10.48550/arXiv.2011.03775")] ai/nn/transformer/gpt/dall-e/1

Localized Narratives is a dataset with detailed natural language descriptions of images paired with computer-mouse traces that provide a sparse, fine-grained visual grounding for phrases.

We propose TReCS, a sequential model that exploits this grounding to generate images. TReCS uses descriptions to retrieve segmentation masks and predict object labels aligned with mouse traces. These alignments are used to select and position masks to generate a fully covered segmentation canvas; the final image is produced by a segmentation-to-image generator using this canvas.

This multi-step, retrieval-based approach outperforms existing direct text-to-image generation models on both automatic metrics and human evaluations: overall, its generated images are more photo-realistic and better match descriptions.

--- https://arxiv.org/abs/2011.04021#deepmind On the role of planning in model-based deep reinforcement learning Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Buesing, Petar Veličković, Théophane Weber 2020-11-08 2020-11-08 [("doi","10.48550/arXiv.2011.04021")] reinforcement-learning/model/muzero

[Twitter] Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learning (MBRL) with deep function approximation have strengthened this hypothesis, the resulting diversity of model-based methods has also made it difficult to track which components drive success and why.

In this paper, we seek to disentangle the contributions of recent methods by focusing on 3 questions: (1) How does planning benefit MBRL agents? (2) Within planning, what choices drive performance? (3) To what extent does planning improve generalization?

To answer these questions, we study the performance of MuZero (Schrittwieser et al 2019), a state-of-the-art MBRL algorithm with strong connections and overlapping components with many other MBRL algorithms. We perform a number of interventions and ablations of MuZero across a wide range of environments, including control tasks, Atari, and 9×9 Go.

Our results suggest the following: (1) Planning is most useful in the learning process, both for policy updates and for providing a more useful data distribution. (2) Using shallow trees with simple Monte-Carlo rollouts is as performant as more complex methods, except in the most difficult reasoning tasks...2-step planning...exhibits surprisingly strong performance even in Go. (3) Planning alone is insufficient to drive strong generalization.

These results indicate where and how to utilize planning in reinforcement learning settings, and highlight a number of open questions for future MBRL research.

--- https://arxiv.org/abs/2011.05552 End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks Alice Xue 2020-11-11 2020-11-11 [("doi","10.48550/arXiv.2011.05552")] ai/dataset ai/nn/gan/stylegan/anime

Current GAN-based art generation methods produce unoriginal artwork due to their dependence on conditional input.

Here, we propose Sketch-And-Paint GAN (SAPGAN), the first model which generates Chinese landscape paintings from end to end, without conditional input. SAPGAN is composed of two GANs: SketchGAN for generation of edge maps, and PaintGAN for subsequent edge-to-painting translation. Our model is trained on a new dataset of traditional Chinese landscape paintings never before used for generative research.

A 242-person Visual Turing Test study reveals that SAPGAN paintings are mistaken as human artwork with 55% frequency, outperforming paintings from baseline GANs.

Our work lays a groundwork for truly machine-original art generation.

--- https://arxiv.org/abs/2011.06505 Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alex Peysakhovich, Aldo Pacchiano, Jakob Foerster 2020-11-12 2020-11-12 [("doi","10.48550/arXiv.2011.06505")] reinforcement-learning/exploration

Over the last decade, a single algorithm has changed many facets of our lives—Stochastic Gradient Descent (SGD). In the era of ever decreasing loss functions, SGD and its various offspring have become the go-to optimization tool in machine learning and are a key component of the success of deep neural networks (DNNs). While SGD is guaranteed to converge to a local optimum (under loose assumptions), in some cases it may matter which local optimum is found, and this is often context-dependent. Examples frequently arise in machine learning, from shape-versus-texture-features to ensemble methods and zero-shot coordination. In these settings, there are desired solutions which SGD on ‘standard’ loss functions will not find, since it instead converges to the ‘easy’ solutions.

In this paper, we present a different approach. Rather than following the gradient, which corresponds to a locally greedy direction, we instead follow the eigenvectors of the Hessian, which we call “ridges”. By iteratively following and branching amongst the ridges, we effectively span the loss surface to find qualitatively different solutions.

We show both theoretically and experimentally that our method, called Ridge Rider (RR), offers a promising direction for a variety of challenging problems.

--- https://arxiv.org/abs/2011.10208#eleutherai Collaborative Storytelling with Large-scale Neural Language Models Eric Nichols, Leo Gao, Randy Gomez 2020-11-20 2020-11-20 [("doi","10.48550/arXiv.2011.10208")] ai/nn/sampling ai/nn/transformer/gpt/poetry fiction/text-game

Storytelling plays a central role in human socializing and entertainment. However, much of the research on automatic storytelling generation assumes that stories will be generated by an agent without any human interaction. In this paper, we introduce the task of collaborative storytelling, where an artificial intelligence agent and a person collaborate to create a unique story by taking turns adding to it.

We present a collaborative storytelling system which works with a human storyteller to create a story by generating new utterances based on the story so far. We constructed the storytelling system by tuning a publicly-available large scale language model on a dataset of writing prompts and their accompanying fictional works. We identify generating sufficiently human-like utterances to be an important technical issue and propose a sample-and-rank approach to improve utterance quality.

Quantitative evaluation shows that our approach outperforms a baseline, and we present qualitative evaluation of our system’s capabilities.

Conclusion/discussion information appears to be integrated within the results and methodology description and doesn’t have a separate section in the provided abstract.

Supplementary information (eg. code, websites, datasets) is not provided in the abstract.

--- https://arxiv.org/abs/2011.11552 MoGaze: A Dataset of Full-Body Motions that Includes Workspace Geometry and Eye-Gaze Philipp Kratzer, Simon Bihlmaier, Niteesh Balachandra Midlagajni, Rohit Prakash, Marc Toussaint, Jim Mainprice 2020-11-23 2020-11-23 [("doi","10.48550/arXiv.2011.11552")] ai/dataset reinforcement-learning/imitation-learning/brain-imitation-learning reinforcement-learning/robot

As robots become more present in open human environments, it will become crucial for robotic systems to understand and predict human motion. Such capabilities depend heavily on the quality and availability of motion capture data. However, existing datasets of full-body motion rarely include (1) long sequences of manipulation tasks, (2) the 3D model of the workspace geometry, and (3) eye-gaze, which are all important when a robot needs to predict the movements of humans in close proximity.

Hence, in this paper, we present a novel dataset of full-body motion for everyday manipulation tasks, which includes the above. The motion data was captured using a traditional motion capture system based on reflective markers. We additionally captured eye-gaze using a wearable pupil-tracking device. As we show in experiments, the dataset can be used for the design and evaluation of full-body motion prediction algorithms. Furthermore, our experiments show eye-gaze as a powerful predictor of human intent.

The dataset includes 180 min of motion capture data with 1627 pick and place actions being performed. It is available at https://humans-to-robots-motion.github.io/mogaze and is planned to be extended to collaborative tasks with two humans in the near future.

--- https://arxiv.org/abs/2012.00124#amazon Extreme Model Compression for On-device Natural Language Understanding Kanthashree Mysore Sathyendra, Samridhi Choudhary, Leah Nicolich-Henkin 2020-11-30 2020-11-30 [("doi","10.48550/arXiv.2012.00124")] ai/nn/sparsity

In this paper, we propose and experiment with techniques for extreme compression of neural natural language understanding (NLU) models, making them suitable for execution on resource-constrained devices.

We propose a task-aware, end-to-end compression approach that performs word-embedding compression jointly with NLU task learning. We show our results on a large-scale, commercial NLU system trained on a varied set of intents with huge vocabulary sizes. Our approach outperforms a range of baselines and achieves a compression rate of 97.4% with less than 3.7% degradation in predictive performance.

Our analysis indicates that the signal from the downstream task is important for effective compression with minimal degradation in performance.

--- https://arxiv.org/abs/2012.03145 Selective Eye-gaze Augmentation To Enhance Imitation Learning In Atari Games Chaitanya Thammineni, Hemanth Manjunatha, Ehsan T. Esfahani 2020-12-05 2020-12-05 [("doi","10.48550/arXiv.2012.03145")] ai/dataset ai/nn reinforcement-learning/imitation-learning/brain-imitation-learning

This paper presents the selective use of eye-gaze information in learning human actions in Atari games. Vast evidence suggests that our eye movement convey a wealth of information about the direction of our attention and mental states and encode the information necessary to complete a task. Based on this evidence, we hypothesize that selective use of eye-gaze, as a clue for attention direction, will enhance the learning from demonstration.

For this purpose, we propose a selective eye-gaze augmentation (SEA) network that learns when to use the eye-gaze information. The proposed network architecture consists of 3 sub-networks: gaze prediction, gating, and action prediction network. Using the prior 4 game frames, a gaze map is predicted by the gaze prediction network which is used for augmenting the input frame. The gating network will determine whether the predicted gaze map should be used in learning and is fed to the final network to predict the action at the current frame.

To validate this approach, we use publicly available Atari Human Eye-Tracking And Demonstration (Atari-HEAD) dataset consists of 20 Atari games with 28 million human demonstrations and 328 million eye-gazes (over game frames) collected from 4 subjects. We demonstrate the efficacy of selective eye-gaze augmentation in comparison with state-of-the-art Attention Guided Imitation Learning (AGIL), Behavior Cloning (BC).

The results indicate that the selective augmentation approach (the SEA network) performs better than the AGIL and BC. Moreover, to demonstrate the importance of selective use of gaze through the gating network, we compare our approach with the random selection of the gaze. Even in this case, the SEA network performs better validating the advantage of selectively using the gaze in demonstration learning.

--- https://arxiv.org/abs/2012.04256 Data Instance Prior for Transfer Learning in GANs Puneet Mangla, Nupur Kumari, Mayank Singh, Vineeth N. Balasubramanian, Balaji Krishnamurthy 2020-12-08 2020-12-08 [("doi","10.48550/arXiv.2012.04256")] ai/anime/danbooru ai/nn/gan/biggan ai/nn/gan/stylegan/anime

Recent advances in generative adversarial networks (GANs) have shown remarkable progress in generating high-quality images. However, this gain in performance depends on the availability of a large amount of training data. In limited data regimes, training typically diverges, and therefore the generated samples are of low quality and lack diversity. Previous works have addressed training in low data setting by leveraging transfer learning and data augmentation techniques.

We propose a novel transfer learning method for GANs in the limited data domain by leveraging informative data prior derived from self-supervised/supervised pre-trained networks trained on a diverse source domain. We perform experiments on several standard vision datasets using various GAN architectures (BigGAN, SNGAN, StyleGAN-2) to demonstrate that the proposed method effectively transfers knowledge to domains with few target images, outperforming existing state-of-the-art techniques in terms of image quality and diversity. We also show the utility of data instance prior in large-scale unconditional image generation and image editing tasks.

--- https://arxiv.org/abs/2012.06731 PiRank: Learning To Rank via Differentiable Sorting Robin Swezey, Aditya Grover, Bruno Charron, Stefano Ermon 2020-12-12 2020-12-12 [("doi","10.48550/arXiv.2012.06731")] ai/nn cs/algorithm/sorting reinforcement-learning statistics/order/comparison

A key challenge with machine learning approaches for ranking is the gap between the performance metrics of interest and the surrogate loss functions that can be optimized with gradient-based methods. This gap arises because ranking metrics typically involve a sorting operation which is not differentiable w.r.t. the model parameters.

Prior works have proposed surrogates that are loosely related to ranking metrics or simple smoothed versions thereof. We propose PiRank, a new class of differentiable surrogates for ranking, which employ a continuous, temperature-controlled relaxation to the sorting operator.

We show that PiRank exactly recovers the desired metrics in the limit of zero temperature and scales favorably with the problem size, both in theory and practice.

Empirically, we demonstrate that PiRank improves over existing approaches on publicly available internet-scale learning-to-rank benchmarks.

--- https://arxiv.org/abs/2012.07910 Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search Li-Cheng Lan, Meng-Yu Tsai, Ti-Rong Wu, I-Chen Wu, Cho-Jui Hsieh 2020-12-14 2020-12-14 [("doi","10.48550/arXiv.2012.07910")] reinforcement-learning/model/alphago

Monte Carlo tree search (MCTS) has achieved state-of-the-art results in many domains such as Go and Atari games when combining with deep neural networks (DNNs). When more simulations are executed, MCTS can achieve higher performance but also requires enormous amounts of CPU and GPU resources. However, not all states require a long searching time to identify the best action that the agent can find. For example, in 19×19 Go and NoGo, we found that for more than half of the states, the best action predicted by DNN remains unchanged even after searching 2 minutes. This implies that a large amount of resources can be saved if we are able to stop the searching earlier when we are confident with the current searching result.

In this paper, we propose to achieve this goal by predicting the uncertainty of the current searching status and use the result to decide whether we should stop searching. With our algorithm, called Dynamic Simulation MCTS (DS-MCTS), we can speed up a NoGo agent trained by AlphaZero 2.5× faster while maintaining a similar winning rate.

Also, under the same average simulation count, our method can achieve a 61% winning rate against the original program.

--- https://arxiv.org/abs/2012.13169 SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II Xiangjun Wang, Junxiao Song, Penghui Qi, Peng Peng, Zhenkun Tang, Wei Zhang, Weimin Li, Xiongjun Pi, Jujie He, Chao Gao, Haitao Long, Quan Yuan 2020-12-24 2020-12-24 [("doi","10.48550/arXiv.2012.13169")] reinforcement-learning/imitation-learning reinforcement-learning/model-free/alphastar

AlphaStar, the AI that reaches Grandmaster level in StarCraft II, is a remarkable milestone demonstrating what deep reinforcement learning can achieve in complex Real-Time Strategy (RTS) games. However, the complexities of the game, algorithms, and systems, and especially the tremendous amount of computation needed are big obstacles for the community to conduct further research in this direction.

We propose a deep reinforcement learning agent, StarCraft Commander (SCC). With an order of magnitude less computation, it demonstrates top human performance defeating Grandmaster players in test matches and top professional players in a live event.

Moreover, it shows strong robustness to various human strategies and discovers novel strategies unseen from human plays.

In this paper, we will share the key insights and optimizations on efficient imitation learning and reinforcement learning for StarCraft II full game.

--- https://arxiv.org/abs/2012.14905#schmidhuber Meta Learning Backpropagation And Improving It Louis Kirsch, Jürgen Schmidhuber 2020-12-29 2020-12-29 [("doi","10.48550/arXiv.2012.14905")] ai/nn/rnn psychology/neuroscience reinforcement-learning/imitation-learning reinforcement-learning/meta-learning

Many concepts have been proposed for meta learning with neural networks (NNs), eg. NNs that learn to control fast weights, hyper networks, learned learning rules, and meta recurrent NNs. Our Variable Shared Meta Learning (VS-ML) unifies the above and demonstrates that simple weight-sharing and sparsity in an NN is sufficient to express powerful learning algorithms (LAs) in a reusable fashion.

A simple implementation of VS-ML called VS-ML RNN allows for implementing the backpropagation LA solely by running an RNN in forward-mode. It can even meta-learn new LAs that improve upon backpropagation and generalize to datasets outside of the meta training distribution without explicit gradient calculation.

Introspection reveals that our meta-learned LAs learn qualitatively different from gradient descent through fast association.

--- https://arxiv.org/abs/2012.15085 Is Pessimism Provably Efficient for Offline RL? Ying Jin, Zhuoran Yang, Zhaoran Wang 2020-12-30 2020-12-30 [("doi","10.48550/arXiv.2012.15085")] reinforcement-learning/exploration reinforcement-learning/offline

We study offline reinforcement learning (RL), which aims to learn an optimal policy based on a dataset collected a priori. Due to the lack of further interactions with the environment, offline RL suffers from the insufficient coverage of the dataset, which eludes most existing theoretical analysis. In this paper, we propose a pessimistic variant of the value iteration algorithm (PEVI), which incorporates an uncertainty quantifier as the penalty function. Such a penalty function simply flips the sign of the bonus function for promoting exploration in online RL, which makes it easily implementable and compatible with general function approximators.

Without assuming the sufficient coverage of the dataset, we establish a data-dependent upper bound on the suboptimality of PEVI for general Markov decision processes (MDPs). When specialized to linear MDPs, it matches the information-theoretic lower bound up to multiplicative factors of the dimension and horizon. In other words, pessimism is not only provably efficient but also minimax optimal. In particular, given the dataset, the learned policy serves as the “best effort” among all policies, as no other policies can do better. Our theoretical analysis identifies the critical role of pessimism in eliminating a notion of spurious correlation, which emerges from the “irrelevant” trajectories that are less covered by the dataset and not informative for the optimal policy.

--- https://arxiv.org/abs/2101.04808#google MLGO: a Machine Learning Guided Compiler Optimizations Framework Mircea Trofin, Yundi Qian, Eugene Brevdo, Zinan Lin, Krzysztof Choromanski, David Li 2021-01-13 2021-01-13 [("doi","10.48550/arXiv.2101.04808")] ai/nn cs/algorithm reinforcement-learning/model-free

Leveraging machine-learning (ML) techniques for compiler optimizations has been widely studied and explored in academia. However, the adoption of ML in general-purpose, industry strength compilers has yet to happen. We propose MLGO, a framework for integrating ML techniques systematically in an industrial compiler—LLVM. As a case study, we present the details and results of replacing the heuristics-based inlining-for-size optimization in LLVM with machine learned models. To the best of our knowledge, this work is the first full integration of ML in a complex compiler pass in a real-world setting. It is available in the main LLVM repository.

We use two different ML algorithms: Policy Gradient and Evolution Strategies, to train the inlining-for-size model, and achieve up to 7% size reduction, when compared to state-of-the-art LLVM -Oz.

The same model, trained on one corpus, generalizes well to a diversity of real-world targets, as well as to the same set of targets after months of active development. This property of the trained models is beneficial to deploy ML techniques in real-world settings.

--- https://arxiv.org/abs/2101.05870 From Genotype to Phenotype: polygenic prediction of complex human traits Timothy G. Raben, Louis Lello, Erik Widen, Steve Hsu 2021-01-14 2021-01-14 [("doi","10.48550/arXiv.2101.05870")] genetics/heritable psychiatry/schizophrenia

Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans.

Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (eg. top few percent) have been shown to have 5 or even 10× higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category.

We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.

--- https://arxiv.org/abs/2101.07367#google Training Learned Optimizers with Randomly Initialized Learned Optimizers Luke Metz, C. Daniel Freeman, Niru Maheswaranathan, Jascha Sohl-Dickstein 2021-01-14 2021-01-14 [("doi","10.48550/arXiv.2101.07367")] reinforcement-learning/meta-learning

Learned optimizers are increasingly effective, with performance exceeding that of hand designed optimizers such as Adam on specific tasks (metz2019understanding). Despite the potential gains available, in current work the meta-training (or ‘outer-training’) of the learned optimizer is performed by a hand-designed optimizer, or by an optimizer trained by a hand-designed optimizer (metz2020tasks).

We show that a population of randomly initialized learned optimizers can be used to train themselves from scratch in an online fashion, without resorting to a hand designed optimizer in any part of the process. A form of population based training is used to orchestrate this self-training.

Although the randomly initialized optimizers initially make slow progress, as they improve they experience a positive feedback loop, and become rapidly more effective at training themselves.

We believe feedback loops of this type, where an optimizer improves itself, will be important and powerful in the future of machine learning. These methods not only provide a path towards increased performance, but more importantly relieve research and engineering effort.

--- https://arxiv.org/abs/2101.07415#google ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Daiyi Peng, Deepali Jain, Wenbo Gao, Aldo Pacchiano, Tamas Sarlos, Yuxiang Yang 2021-01-19 2021-01-19 [("doi","10.48550/arXiv.2101.07415")] ai/nn/sparsity/low-precision ai/nn/sparsity/pruning reinforcement-learning/meta-learning

We consider the problem of efficient blackbox optimization over a large hybrid search space, consisting of a mixture of a high dimensional continuous space and a complex combinatorial space. Such examples arise commonly in evolutionary computation, but also more recently, neuroevolution and architecture search for Reinforcement Learning (RL) policies. In this paper, we introduce ES-ENAS, a simple joint optimization procedure by combining Evolutionary Strategies (ES) and combinatorial optimization techniques in a highly scalable and intuitive way, inspired by the one-shot or supernet paradigm introduced in Efficient Neural Architecture Search (ENAS). Our main insight is noticing that ES is already a highly distributed algorithm involving hundreds of blackbox evaluations which can not only be used for training neural network weights, but also for feedback to a combinatorial optimizer.

Through this relatively simple marriage between two different lines of research, we are able to gain the best of both worlds, and empirically demonstrate our approach by optimizing BBOB functions over hybrid spaces as well as combinatorial neural network architectures via edge pruning and quantization on popular RL benchmarks. Due to the modularity of the algorithm, we also are able incorporate a wide variety of popular techniques ranging from use of different continuous and combinatorial optimizers, as well as constrained optimization.

--- https://arxiv.org/abs/2101.09258 Maximum Likelihood Training of Score-Based Diffusion Models Yang Song, Conor Durkan, Iain Murray, Stefano Ermon 2021-01-22 2021-01-22 [("doi","10.48550/arXiv.2101.09258")] ai/nn/diffusion

Score-based diffusion models synthesize samples by reversing a stochastic process that diffuses data to noise, and are trained by minimizing a weighted combination of score matching losses. The log-likelihood of score-based models can be tractably computed through a connection to continuous normalizing flows but log-likelihood is not directly optimized by the weighted combination of score matching losses.

We show that for a specific weighting scheme, the objective upper bounds the negative log-likelihood, thus enabling approximate maximum likelihood training of score-based models. We empirically observe that maximum likelihood training consistently improves the likelihood of score-based models across multiple datasets, stochastic processes, and model architectures.

Our best models achieve negative log-likelihoods of 2.74 and 3.76 bits/dim on CIFAR-10 and ImageNet 32×32, outperforming autoregressive models on these tasks.

--- https://arxiv.org/abs/2101.12037 BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data Demetres Kostas, Stephane Aroca-Ouellette, Frank Rudzicz 2021-01-28 2021-01-28 [("doi","10.48550/arXiv.2101.12037")] ai/nn/transformer psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Deep neural networks (DNNs) used for brain-computer-interface (BCI) classification are commonly expected to learn general features when trained across a variety of contexts, such that these features could be fine-tuned to specific contexts. While some success is found in such an approach, we suggest that this interpretation is limited and an alternative would better leverage the newly (publicly) available massive EEG datasets.

We consider how to adapt techniques and architectures used for language modeling (LM), that appear capable of ingesting awesome amounts of data, towards the development of encephalography modeling (EM) with DNNs in the same vein. We specifically adapt an approach effectively used for automatic speech recognition, which similarly (to LMs) uses a self-supervised training objective to learn compressed representations of raw data signals. After adaptation to EEG, we find that a single pre-trained model is capable of modeling completely novel raw EEG sequences recorded with differing hardware, and different subjects performing different tasks. Furthermore, both the internal representations of this model and the entire architecture can be fine-tuned to a variety of downstream BCI and EEG classification tasks, outperforming prior work in more task-specific (sleep stage classification) self-supervision.

--- https://arxiv.org/abs/2102.00529#deepmind Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers Lisa Anne Hendricks, John Mellor, Rosalia Schneider, Jean-Baptiste Alayrac, Aida Nematzadeh 2021-01-31 2021-01-31 [("doi","10.48550/arXiv.2102.00529")] ai/nn/retrieval ai/nn/transformer/clip

Recently, multimodal transformer models have gained popularity because their performance on language and vision tasks suggest they learn rich visual-linguistic representations. Focusing on zero-shot image retrieval tasks, we study 3 important factors which can impact the quality of learned representations: pretraining data, the attention mechanism, and loss functions.

By pretraining models on 6 datasets, we observe that dataset noise and language similarity to our downstream task are important indicators of model performance. Through architectural analysis, we learn that models with a multimodal attention mechanism can outperform deeper models with modality specific attention mechanisms.

Finally, we show that successful contrastive losses used in the self-supervised learning literature do not yield similar performance gains when used in multimodal transformers

--- https://arxiv.org/abs/2102.01454#allen MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, Zaid Harchaoui 2021-02-02 2021-02-02 [("doi","10.48550/arXiv.2102.01454")] ai/nn/sampling ai/nn/transformer/gpt

As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem.

We introduce MAUVE, a comparison measure for open-ended text generation, which directly compares the learnt distribution from a text generation model to the distribution of human-written text using divergence frontiers. MAUVE scales up to modern text generation models by computing information divergences in a quantized embedding space.

Through an extensive empirical study on 3 open-ended generation tasks, we find that MAUVE identifies known properties of generated text, scales naturally with model size, and correlates with human judgments, with fewer restrictions than existing distributional evaluation metrics.

--- https://arxiv.org/abs/2102.01645 Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search Federico A. Galatolo, Mario G. C. A. Cimino, Gigliola Vaglini 2021-02-02 2021-02-02 [("doi","10.5220/0010503701660174")] ai/nn/gan/biggan ai/nn/gan/stylegan ai/nn/transformer/clip

In this research work we present CLIP-GLaSS, a novel zero-shot framework to generate an image (or a caption) corresponding to a given caption (or image). CLIP-GLaSS is based on the CLIP neural network, which, given an image and a descriptive caption, provides similar embeddings. Differently, CLIP-GLaSS takes a caption (or an image) as an input, and generates the image (or the caption) whose CLIP embedding is the most similar to the input one.

This optimal image (or caption) is produced via a generative network, after an exploration by a genetic algorithm. Promising results are shown, based on the experimentation of the image Generators BigGAN and StyleGAN-2, and of the text Generator GPT-2.

--- https://arxiv.org/abs/2102.02888#microsoft 1-bit Adam: Communication Efficient Large-Scale Training with Adam’s Convergence Speed Hanlin Tang, Shaoduo Gan, Ammar Ahmad Awan, Samyam Rajbhandari, Conglong Li, Xiangru Lian, Ji Liu, Ce Zhang, Yuxiong He 2021-02-04 2021-02-04 [("doi","10.48550/arXiv.2102.02888")] ai/nn/sparsity/low-precision ai/scaling cs/algorithm/information/compression

[cf. 0/1 Adam] Scalable training of large models (like BERT and GPT-3) requires careful optimization rooted in model design, architecture, and system capabilities.

From a system standpoint, communication has become a major bottleneck, especially on commodity systems with standard TCP interconnects that offer limited network bandwidth. Communication compression is an important technique to reduce training time on such systems. One of the most effective methods is error-compensated compression, which offers robust convergence speed even under 1-bit compression. However, state-of-the-art error compensation techniques only work with basic optimizers like SGD and momentum SGD, which are linearly dependent on the gradients. They do not work with non-linear gradient-based optimizers like Adam, which offer state-of-the-art convergence efficiency and accuracy for models like BERT.

In this paper, we propose 1-bit Adam that reduces the communication volume by up to 5×, offers much better scalability, and provides the same convergence speed as uncompressed Adam. Our key finding is that Adam’s variance (non-linear term) becomes stable (after a warmup phase) and can be used as a fixed precondition for the rest of the training (compression phase).

Experiments on up to 256 GPUs show that 1-bit Adam enables up to 3.3× higher throughput for BERT-Large pre-training and up to 2.9× higher throughput for SQuAD fine-tuning.

In addition, we provide theoretical analysis for our proposed work.

--- https://arxiv.org/abs/2102.05379 Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling 2021-02-10 2021-02-10 [("doi","10.48550/arXiv.2102.05379")] ai/nn/diffusion/discrete

Generative flows and diffusion models have been predominantly trained on ordinal data, for example natural images. This paper introduces two extensions of flows and diffusion for categorical data such as language or image segmentation: Argmax Flows and Multinomial Diffusion.

Argmax Flows are defined by a composition of a continuous distribution (such as a normalizing flow), and an argmax function. To optimize this model, we learn a probabilistic inverse for the argmax that lifts the categorical data to a continuous space.

Multinomial Diffusion gradually adds categorical noise in a diffusion process, for which the generative denoising process is learned. We demonstrate that our method outperforms existing dequantization approaches on text modeling and modeling on image segmentation maps in log-likelihood.

--- https://arxiv.org/abs/2102.05599 Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision Julien Scholz, Cornelius Weber, Muhammad Burhan Hafez, Stefan Wermter 2021-02-10 2021-02-10 [("doi","10.48550/arXiv.2102.05599")] reinforcement-learning/model/muzero

Using a model of the environment, reinforcement learning agents can plan their future moves and achieve superhuman performance in board games like Chess, Shogi, and Go, while remaining relatively sample-efficient. As demonstrated by the MuZero Algorithm, the environment model can even be learned dynamically, generalizing the agent to many more tasks while at the same time achieving state-of-the-art performance. Notably, MuZero uses internal state representations derived from real environment states for its predictions.

In this paper, we bind the model’s predicted internal state representation to the environment state via two additional terms: a reconstruction model loss and a simpler consistency loss, both of which work independently and unsupervised, acting as constraints to stabilize the learning process.

Our experiments show that this new integration of reconstruction model loss and simpler consistency loss provide a performance increase in OpenAI Gym environments. Our modifications also enable self-supervised pretraining for MuZero, so the algorithm can learn about environment dynamics before a goal is made available.

--- https://arxiv.org/abs/2102.07350 Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm Laria Reynolds, Kyle McDonell 2021-02-15 2021-02-15 [("doi","10.48550/arXiv.2102.07350")] ai/nn/transformer/gpt/inner-monologue reinforcement-learning/meta-learning

Prevailing methods for mapping large generative language models to supervised tasks may fail to sufficiently probe models’ novel capabilities. Using GPT-3 as a case study, we show that 0-shot prompts can outperform few-shot prompts. We suggest that the function of few-shot examples in these cases is better described as locating an already learned task rather than meta-learning. This analysis motivates rethinking the role of prompts in controlling and evaluating powerful language models.

In this work, we discuss methods of prompt programming, emphasizing the usefulness of considering prompts through the lens of natural language. We explore techniques for exploiting the capacity of narratives and cultural anchors to encode nuanced intentions and techniques for encouraging deconstruction of a problem into components before producing a verdict.

Informed by this more encompassing theory of prompt programming, we also introduce the idea of a metaprompt that seeds the model to generate its own natural language prompts for a range of tasks.

Finally, we discuss how these more general methods of interacting with language models can be incorporated into existing and future benchmarks and practical applications.

--- https://arxiv.org/abs/2102.08363 COMBO: Conservative Offline Model-Based Policy Optimization Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn 2021-02-16 2021-02-16 [("doi","10.48550/arXiv.2102.08363")] reinforcement-learning/model

Model-based algorithms, which learn a dynamics model from logged experience and perform some sort of pessimistic planning under the learned model, have emerged as a promising paradigm for offline reinforcement learning (offline RL). However, practical variants of such model-based algorithms rely on explicit uncertainty quantification for incorporating pessimism. Uncertainty estimation with complex models, such as deep neural networks, can be difficult and unreliable.

We overcome this limitation by developing a new model-based offline RL algorithm, COMBO, that regularizes the value function on out-of-support state-action tuples generated via rollouts under the learned model. This results in a conservative estimate of the value function for out-of-support state-action tuples, without requiring explicit uncertainty estimation. We theoretically show that our method optimizes a lower bound on the true policy value, that this bound is tighter than that of prior methods, and our approach satisfies a policy improvement guarantee in the offline setting.

Through experiments, we find that COMBO consistently performs as well or better as compared to prior offline model-free and model-based methods on widely studied offline RL benchmarks, including image-based tasks.

--- https://arxiv.org/abs/2102.12092#openai Zero-Shot Text-to-Image Generation Aditya A. Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever 2021-02-24 2021-02-24 [("doi","10.48550/arXiv.2102.12092")] ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e ai/scaling

Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training.

We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data.

With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion.

--- https://arxiv.org/abs/2102.12194 Combining Off and On-Policy Training in Model-Based Reinforcement Learning Alexandre Borges, Arlindo Oliveira 2021-02-24 2021-02-24 [("doi","10.48550/arXiv.2102.12194")] reinforcement-learning/model/muzero

The combination of deep learning and Monte Carlo Tree Search (MCTS) has shown to be effective in various domains, such as board and video games. AlphaGo represented a step forward in our ability to learn complex board games, and it was rapidly followed by advances, such as AlphaGo Zero and AlphaZero. Recently, MuZero demonstrated that it is possible to master both Atari games and board games by directly learning a model of the environment, which is then used with MCTS to decide what move to play in each position.

During tree search, the algorithm simulates games by exploring several possible moves and then picks the action that corresponds to the most promising trajectory. When training, limited use is made of these simulated games since none of their trajectories are directly used as training examples. Even if we consider that not all trajectories from simulated games are useful, there are thousands of potentially useful trajectories that are discarded. Using information from these trajectories would provide more training data, more quickly, leading to faster convergence and higher sample efficiency. Recent work introduced an off-policy value target for AlphaZero that uses data from simulated games.

In this work, we propose a way to obtain off-policy targets using data from simulated games in MuZero. We combine these off-policy targets with the on-policy targets already used in MuZero in several ways, and study the impact of these targets and their combinations in 3 environments with distinct characteristics.

When used in the right combinations, our results show that these targets can speed up the training process and lead to faster convergence and higher rewards than the ones obtained by MuZero.

--- https://arxiv.org/abs/2102.12375 Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants Dennis J. N. J. Soemers, Vegard Mella, Eric Piette, Matthew Stephenson, Cameron Browne, Olivier Teytaud 2021-02-24 2021-02-24 [("doi","10.48550/arXiv.2102.12375")] ai/nn/cnn reinforcement-learning/model/alphago

In this paper, we use fully convolutional architectures in AlphaZero-like self-play training setups to facilitate transfer between variants of board games as well as distinct games.

We explore how to transfer trained parameters of these architectures based on shared semantics of channels in the state and action representations of the Ludii general game system.

We use Ludii’s large library of games and game variants for extensive transfer learning evaluations, in zero-shot transfer experiments as well as experiments with additional fine-tuning time.

--- https://arxiv.org/abs/2103.01197 Coordination Among Neural Modules Through a Shared Global Workspace Anirudh Goyal, Aniket Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Mozer, Yoshua Bengio 2021-03-01 2021-03-01 [("doi","10.48550/arXiv.2103.01197")] ai/nn/transformer/attention/hierarchical ai/scaling/mixture-of-experts

Deep learning has seen a movement away from representing examples with a monolithic hidden state towards a richly structured state. For example, Transformers segment by position, and object-centric architectures decompose images into entities. In all these architectures, interactions between different elements are modeled via pairwise interactions: Transformers make use of self-attention to incorporate information from other positions; object-centric architectures make use of graph neural networks to model interactions among entities.

However, pairwise interactions may not achieve global coordination or a coherent, integrated representation that can be used for downstream tasks. In cognitive science, a global workspace architecture has been proposed in which functionally specialized components share information through a common, bandwidth-limited communication channel. We explore the use of such a communication channel in the context of deep learning for modeling the structure of complex environments. The proposed method includes a shared workspace through which communication among different specialist modules takes place but due to limits on the communication bandwidth, specialist modules must compete for access. We show that capacity limitations have a rational basis in that (1) they encourage specialization and compositionality and (2) they facilitate the synchronization of otherwise independent specialists.

--- https://arxiv.org/abs/2103.01209 Generative Adversarial Transformers Drew A. Hudson, C. Lawrence Zitnick 2021-03-01 2021-03-01 [("doi","10.48550/arXiv.2103.01209")] ai/nn/gan/stylegan ai/nn/transformer/attention/hierarchical

We introduce the GANformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling. The network employs a bipartite structure that enables long-range interactions across the image, while maintaining computation of linear efficiency, that can readily scale to high-resolution synthesis. It iteratively propagates information from a set of latent variables to the evolving visual features and vice versa, to support the refinement of each in light of the other and encourage the emergence of compositional representations of objects and scenes.

In contrast to the classic transformer architecture, ituses multiplicative integration that allows flexible region-based modulation, and can thus be seen as a generalization of the successful StyleGAN network. We demonstrate the model’s strength and robustness through a careful evaluation over a range of datasets, from simulated multi-object environments to rich real-world indoor and outdoor scenes, showing it achieves state-of-the-art results in terms of image quality and diversity, while enjoying fast learning and better data-efficiency.

Further qualitative and quantitative experiments offer us an insight into the model’s inner workings, revealing improved interpretability and stronger disentanglement, and illustrating the benefits and efficacy of our approach.

An implementation of the model is available at Github.

--- https://arxiv.org/abs/2103.01913#google WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning Krishna Srinivasan, Karthik Raman, Jiecao Chen, Michael Bendersky, Marc Najork 2021-03-02 2021-03-02 [("doi","10.48550/arXiv.2103.01913")] ai/dataset ai/nn wikipedia

The milestone improvements brought about by deep representation learning and pre-training techniques have led to large performance gains across downstream NLP, IR and Vision tasks. Multimodal modeling techniques aim to leverage large high-quality vision-linguistic datasets for learning complementary information (across image and text modalities).

In this paper, we introduce the Wikipedia-based Image Text (WIT) Dataset to better facilitate multimodal, multilingual learning. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal models, as we show when applied to downstream tasks such as image-text retrieval.

WIT has four main and unique advantages. First, WIT is the largest multimodal dataset by the number of image-text examples by 3× (at the time of writing). Second, WIT is massively multilingual (first of its kind) with coverage over 100+ languages (each of which has at least 12K examples) and provides cross-lingual texts for many images. Third, WIT represents a more diverse set of concepts and real world entities relative to what previous datasets cover. Lastly, WIT provides a very challenging real-world test set, as we empirically illustrate using an image-text retrieval task as an example.

--- https://arxiv.org/abs/2103.03386 Clusterability in Neural Networks Daniel Filan, Stephen Casper, Shlomi Hod, Cody Wild, Andrew Critch, Stuart Russell 2021-03-04 2021-03-04 [("doi","10.48550/arXiv.2103.03386")] ai/nn/fully-connected ai/nn/sparsity

The learned weights of a neural network have often been considered devoid of scrutable internal structure.

In this paper, however, we look for structure in the form of clusterability: how well a network can be divided into groups of neurons with strong internal connectivity but weak external connectivity.

We find that a trained neural network is typically more clusterable than randomly initialized networks, and often clusterable relative to random networks with the same distribution of weights. We also exhibit novel methods to promote clusterability in neural network training, and find that in multi-layer perceptrons they lead to more clusterable networks with little reduction in accuracy.

Understanding and controlling the clusterability of neural networks will hopefully render their inner workings more interpretable to engineers by facilitating partitioning into meaningful clusters.

--- https://arxiv.org/abs/2103.03874 Measuring Mathematical Problem Solving With the MATH Dataset Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, Jacob Steinhardt 2021-03-05 2021-03-05 [("doi","10.48550/arXiv.2103.03874")] ai/dataset ai/nn/transformer/gpt/inner-monologue ai/scaling math

Many intellectual endeavors require mathematical problem solving, but this skill remains beyond the capabilities of computers.

To measure this ability in machine learning models, we introduce MATH, a new dataset of 12,500 challenging competition mathematics problems. Each problem in MATH has a full step-by-step solution which can be used to teach models to generate answer derivations and explanations. To facilitate future research and increase accuracy on MATH, we also contribute a large auxiliary pretraining dataset which helps teach models the fundamentals of mathematics.

Even though we are able to increase accuracy on MATH, our results show that accuracy remains relatively low, even with enormous Transformer models. Moreover, we find that simply increasing budgets and model parameter counts will be impractical for achieving strong mathematical reasoning if scaling trends continue.

While scaling Transformers is automatically solving most other text-based tasks, scaling is not currently solving MATH. To have more traction on mathematical problem solving we will likely need new algorithmic advancements from the broader research community.

--- https://arxiv.org/abs/2103.04047 Reinforcement Learning, Bit by Bit Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng Wen 2021-03-06 2021-03-06 [("doi","10.48550/arXiv.2103.04047")] reinforcement-learning/exploration

Reinforcement learning agents have demonstrated remarkable achievements in simulated environments. Data efficiency poses an impediment to carrying this success over to real environments. The design of data-efficient agents calls for a deeper understanding of information acquisition and representation.

We develop concepts and establish a regret bound that together offer principled guidance. The bound sheds light on questions of what information to seek, how to seek that information, and it what information to retain.

To illustrate concepts, we design simple agents that build on them and present computational results that demonstrate improvements in data efficiency.

--- https://arxiv.org/abs/2103.04379 Repurposing GANs for One-shot Semantic Part Segmentation Nontawat Tritrong, Pitchaporn Rewatbowornwong, Supasorn Suwajanakorn 2021-03-07 2021-03-07 [("doi","10.48550/arXiv.2103.04379")] ai/nn/gan/stylegan

While GANs have shown success in realistic image generation, the idea of using GANs for other tasks unrelated to synthesis is underexplored. Do GANs learn meaningful structural parts of objects during their attempt to reproduce those objects?

In this work, we test this hypothesis and propose a simple and effective approach based on GANs for semantic part segmentation that requires as few as one label example along with an unlabeled dataset. Our key idea is to leverage a trained GAN to extract pixel-wise representation from the input image and use it as feature vectors for a segmentation network.

Our experiments demonstrate that GANs representation is “readily discriminative” and produces surprisingly good results that are comparable to those from supervised baselines trained with statistically-significantly more labels.

We believe this novel repurposing of GANs underlies a new class of unsupervised representation learning that is applicable to many other tasks. More results are available at Github .

--- https://arxiv.org/abs/2103.04922 Deep Generative Modeling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models Sam Bond-Taylor, Adam Leach, Yang Long, Chris G. Willcocks 2021-03-08 2021-03-08 [("doi","10.48550/arXiv.2103.04922")] ai/nn/gan ai/nn/vae

Deep generative modeling is a class of techniques that train deep neural networks to model the distribution of training samples.

Research has fragmented into various interconnected approaches, each of which making trade-offs including run-time, diversity, and architectural restrictions. In particular, this compendium covers energy-based models, variational autoencoders, generative adversarial networks, autoregressive models, normalizing flows, in addition to numerous hybrid approaches.

These techniques are drawn under a single cohesive framework, comparing and contrasting to explain the premises behind each, while reviewing current state-of-the-art advances and implementations.

--- https://arxiv.org/abs/2103.05247 Pretrained Transformers as Universal Computation Engines Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch 2021-03-09 2021-03-09 [("doi","10.48550/arXiv.2103.05247")] ai/nn/rnn ai/nn/transformer/gpt ai/scaling cs/algorithm

We investigate the capability of a transformer pretrained on natural language to generalize to other modalities with minimal finetuning—in particular, without finetuning of the self-attention and feedforward layers of the residual blocks. We consider such a model, which we call a Frozen Pretrained Transformer (FPT), and study finetuning it on a variety of sequence classification tasks spanning numerical computation, vision, and protein fold prediction.

In contrast to prior works which investigate finetuning on the same modality as the pretraining dataset, we show that pretraining on natural language can improve performance and compute efficiency on non-language downstream tasks.

Additionally, we perform an analysis of the architecture, comparing the performance of a random initialized transformer to a random LSTM. Combining the two insights, we find language-pretrained transformers can obtain strong performance on a variety of non-language tasks.

--- https://arxiv.org/abs/2103.10951 Paint by Word David Bau, Alex Andonian, Audrey Cui, YeonHwan Park, Ali Jahanian, Aude Oliva, Antonio Torralba 2021-03-19 2021-03-19 [("doi","10.48550/arXiv.2103.10951")] ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e/1

We investigate the problem of zero-shot semantic image painting. Instead of painting modifications into an image using only concrete colors or a finite set of semantic concepts, we ask how to create semantic paint based on open full-text descriptions: our goal is to be able to point to a location in a synthesized image and apply an arbitrary new concept such as “rustic” or “opulent” or “happy dog.”

To do this, our method combines a state-of-the-art generative model of realistic images with a state-of-the-art text-image semantic similarity network. We find that, to make large changes, it is important to use non-gradient methods to explore latent space, and it is important to relax the computations of the GAN to target changes to a specific region.

We conduct user studies to compare our methods to several baselines.

--- https://arxiv.org/abs/2103.11790 Language Models have a Moral Dimension Patrick Schramowski, Cigdem Turan, Nico Andersen, Constantin Rothkopf, Kristian Kersting 2021-03-08 2021-03-08 [("doi","10.48550/arXiv.2103.11790")] ai/nn/transformer/gpt philosophy/ethics reinforcement-learning/preference-learning reinforcement-learning/safe

Artificial writing is permeating our lives due to recent advances in large-scale, transformer-based language models (LMs) such as BERT, its variants, GPT-2/3, and others. Using them as pretrained models and fine-tuning them for specific tasks, researchers have extended the state-of-the-art for many NLP tasks and shown that they not only capture linguistic knowledge but also retain general knowledge implicitly present in the data. These and other successes are exciting.

Unfortunately, LMs trained on unfiltered text corpora suffer from degenerate and biased behavior. While this is well established, we show that recent improvements of LMs also store ethical and moral values of the society and actually bring a “moral dimension” to surface: the values are captured geometrically by a direction in the embedding space, reflecting well the agreement of phrases to social norms implicitly expressed in the training texts. This provides a path for attenuating or even preventing toxic degeneration in LMs.

Since one can now rate the (non-)normativity of arbitrary phrases without explicitly training the LM for this task, the moral dimension can be used as “moral compass” guiding (even other) LMs towards producing normative text, as we will show.

--- https://arxiv.org/abs/2103.12719#facebook Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations Chaitanya K. Ryali, David J. Schwab, Ari S. Morcos 2021-03-23 2021-03-23 [("doi","10.48550/arXiv.2103.12719")] ai/nn

Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks.

An important ingredient in high-performing self-supervised methods is the use of data augmentation by training models to place different augmented views of the same image nearby in embedding space. However, commonly used augmentation pipelines treat images holistically, ignoring the semantic relevance of parts of an image-eg. a subject vs. a background-which can lead to the learning of spurious correlations.

Our work addresses this problem by investigating a class of simple, yet highly effective “background augmentations”, which encourage models to focus on semantically-relevant content by discouraging them from focusing on image backgrounds.

Through a systematic investigation, we show that background augmentations lead to substantial improvements in performance across a spectrum of state-of-the-art self-supervised methods (MoCo-v2, BYOL, SwAV) on a variety of tasks, eg. 1–2% gains on ImageNet, enabling performance on par with the supervised baseline. Further, we find the improvement in limited-labels settings is even larger (up to 4.2%). Background augmentations also improve robustness to a number of distribution shifts, including natural adversarial examples, ImageNet-9, adversarial attacks, ImageNet-Renditions.

We also make progress in completely unsupervised saliency detection, in the process of generating saliency masks used for background augmentations.

--- https://arxiv.org/abs/2103.13009#allen UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark Nicholas Lourie, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi 2021-03-24 2021-03-24 [("doi","10.48550/arXiv.2103.13009")] ai/nn/transformer/t5 ai/scaling

Commonsense AI has long been seen as a near impossible goal—until recently. Now, research interest has sharply increased with an influx of new benchmarks and models.

We propose two new ways to evaluate commonsense models, emphasizing their generality on new tasks and building on diverse, recently introduced benchmarks. First, we propose a new multitask benchmark, RAINBOW, to promote research on commonsense models that generalize well over multiple tasks and datasets. Second, we propose a novel evaluation, the cost equivalent curve, that sheds new insight on how the choice of source datasets, pretrained language models, and transfer learning methods impacts performance and data efficiency.

We perform extensive experiments—over 200 experiments encompassing 4800 T5 models—and report multiple valuable and sometimes surprising findings, eg. that transfer almost always leads to better or equivalent performance if following a particular recipe, that QA-based commonsense datasets transfer well with each other, while commonsense knowledge graphs do not, and that perhaps counter-intuitively, larger models benefit more from transfer than smaller ones.

Last but not least, we introduce a new universal commonsense reasoning model, UNICORN, that establishes new state-of-the-art performance across 8 popular commonsense benchmarks, aNLI (87.3%), CosmosQA (91.8%), HellaSWAG (93.9%), PIQA (90.1%), SocialIQa (83.2%), WinoGrande (86.6%), CycIC (94.0%) and CommonsenseQA (79.3%).

--- https://arxiv.org/abs/2103.14662 Large-scale neural recordings call for new insights to link brain and behavior Anne E. Urai, Brent Doiron, Andrew M. Leifer, Anne K. Churchland 2021-03-26 2021-03-26 [("doi","10.48550/arXiv.2103.14662")] psychology/neuroscience

Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior.

Here, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modeling frameworks to interpret these data, and argue that interpreting brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding at stake.

These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain.

--- https://arxiv.org/abs/2103.14877 Few-shot Semantic Image Synthesis Using StyleGAN Prior Yuki Endo, Yoshihiro Kanamori 2021-03-27 2021-03-27 [("doi","10.48550/arXiv.2103.14877")] ai/anime/danbooru ai/nn/gan/stylegan/anime

This paper tackles a challenging problem of generating photorealistic images from semantic layouts in few-shot scenarios where annotated training pairs are hardly available but pixel-wise annotation is quite costly. We present a training strategy that performs pseudo labeling of semantic masks using the StyleGAN prior.

Our key idea is to construct a simple mapping between the StyleGAN feature and each semantic class from a few examples of semantic masks. With such mappings, we can generate an unlimited number of pseudo semantic masks from random noise to train an encoder for controlling a pre-trained StyleGAN generator. Although the pseudo semantic masks might be too coarse for previous approaches that require pixel-aligned masks, our framework can synthesize high-quality images from not only dense semantic masks but also sparse inputs such as landmarks and scribbles.

Qualitative and quantitative results with various datasets [including TWDNE] demonstrate improvement over previous approaches with respect to layout fidelity and visual quality in as few as 1-shot or 5-shot settings.

--- https://arxiv.org/abs/2103.14968 Labels4Free: Unsupervised Segmentation using StyleGAN Rameen Abdal, Peihao Zhu, Niloy Mitra, Peter Wonka 2021-03-27 2021-03-27 [("doi","10.48550/arXiv.2103.14968")] ai/nn/gan/stylegan

We propose an unsupervised segmentation framework for StyleGAN generated objects.

We build on two main observations. First, the features generated by StyleGAN hold valuable information that can be utilized towards training segmentation networks. Second, the foreground and background can often be treated to be largely independent and be composited in different ways.

For our solution, we propose to augment the StyleGAN-2 generator architecture with a segmentation branch and to split the generator into a foreground and background network. This enables us to generate soft segmentation masks for the foreground object in an unsupervised fashion.

On multiple object classes, we report comparable results against state-of-the-art supervised segmentation networks, while against the best unsupervised segmentation approach we demonstrate a clear improvement, both in qualitative and quantitative metrics.

--- https://arxiv.org/abs/2104.01112 NaturalProofs: Mathematical Theorem Proving in Natural Language Sean Welleck, Jiacheng Liu, Ronan Le Bras, Hannaneh Hajishirzi, Yejin Choi, Kyunghyun Cho 2021-03-24 2021-03-24 [("doi","10.48550/arXiv.2104.01112")] ai/dataset ai/nn/retrieval math

Understanding and creating mathematics using natural mathematical language—the mixture of symbolic and natural language used by humans—is a challenging and important problem for driving progress in machine learning.

As a step in this direction, we develop NaturalProofs, a multi-domain corpus of mathematical statements and their proofs, written in natural mathematical language. NaturalProofs unifies broad coverage, deep coverage, and low-resource mathematical sources, allowing for evaluating both in-distribution and zero-shot generalization.

Using NaturalProofs, we benchmark strong neural methods on mathematical reference retrieval and generation tasks which test a system’s ability to determine key results that appear in a proof. Large-scale sequence models show promise compared to classical information retrieval methods, yet their performance and out-of-domain generalization leave substantial room for improvement.

NaturalProofs opens many avenues for research on challenging mathematical tasks.

--- https://arxiv.org/abs/2104.04657#google BLUR: Meta-Learning Bidirectional Update Rules Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Andrew Jackson, Tom Madams, Blaise Aguera y Arcas 2021-04-10 2021-04-10 [("doi","10.48550/arXiv.2104.04657")] reinforcement-learning/meta-learning

[cf. Kirsch & Schmidhuber 2020] In this paper, we introduce a new type of generalized neural network where neurons and synapses maintain multiple states. We show that classical gradient-based backpropagation in neural networks can be seen as a special case of a two-state network where one state is used for activations and another for gradients, with update rules derived from the chain rule. In our generalized framework, networks have neither explicit notion of nor ever receive gradients. The synapses and neurons are updated using a bidirectional Hebb-style update rule parameterized by a shared low-dimensional “genome”.

We show that such genomes can be meta-learned from scratch, using either conventional optimization techniques, or evolutionary strategies, such as CMA-ES.

Resulting update rules generalize to unseen tasks and train faster than gradient descent based optimizers for several standard computer vision and synthetic tasks.

--- https://arxiv.org/abs/2104.06303#deepmind Learning and Planning in Complex Action Spaces Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, David Silver 2021-04-13 2021-04-13 [("doi","10.48550/arXiv.2104.06303")] reinforcement-learning/model/muzero

Many important real-world problems have action spaces that are high-dimensional, continuous or both, making full enumeration of all possible actions infeasible. Instead, only small subsets of actions can be sampled for the purpose of policy evaluation and improvement. In this paper, we propose a general framework to reason in a principled way about policy evaluation and improvement over such sampled action subsets.

This sample-based policy iteration framework can in principle be applied to any reinforcement learning algorithm based upon policy iteration. Concretely, we propose Sampled MuZero, an extension of the MuZero algorithm that is able to learn in domains with arbitrarily complex action spaces by planning over sampled actions.

We demonstrate this approach on the classical board game of Go and on two continuous control benchmark domains: DeepMind Control Suite and Real-World RL Suite.

--- https://arxiv.org/abs/2104.06490 DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort Yuxuan Zhang, Huan Ling, Jun Gao, Kangxue Yin, Jean-Francois Lafleche, Adela Barriuso, Antonio Torralba, Sanja Fidler 2021-04-13 2021-04-13 [("doi","10.48550/arXiv.2104.06490")] ai/nn/gan/stylegan

We introduce DatasetGAN: an automatic procedure to generate massive datasets of high-quality semantically segmented images requiring minimal human effort.

Current deep networks are extremely data-hungry, benefiting from training on large-scale datasets, which are time consuming to annotate. Our method relies on the power of recent GANs to generate realistic images. We show how the GAN latent code can be decoded to produce a semantic segmentation of the image. Training the decoder only needs a few labeled examples to generalize to the rest of the latent space, resulting in an infinite annotated dataset generator! These generated datasets can then be used for training any computer vision architecture just as real datasets are. As only a few images need to be manually segmented, it becomes possible to annotate images in extreme detail and generate datasets with rich object and part segmentations.

To showcase the power of our approach, we generated datasets for 7 image segmentation tasks which include pixel-level labels for 34 human face parts, and 32 car parts. Our approach outperforms all semi-supervised baselines and is on par with fully supervised methods, which in some cases require as much as 100× more annotated data as our method.

--- https://arxiv.org/abs/2104.08718#allen CLIPScore: A Reference-free Evaluation Metric for Image Captioning Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, Yejin Choi 2021-04-18 2021-04-18 [("doi","10.48550/arXiv.2104.08718")] ai/nn/transformer/clip

Image captioning has conventionally relied on reference-based automatic evaluations, where machine captions are compared against captions written by humans. This is in contrast to the reference-free manner in which humans assess caption quality.

In this paper, we report the surprising empirical finding that CLIP (Radford et al 2021), a cross-modal model pretrained on 400M image+caption pairs from the web, can be used for robust automatic evaluation of image captioning without the need for references. Experiments spanning several corpora demonstrate that our new reference-free metric, CLIPScore, achieves the highest correlation with human judgements, outperforming existing reference-based metrics like CIDEr and SPICE. Information gain experiments demonstrate that CLIPScore, with its tight focus on image-text compatibility, is complementary to existing reference-based metrics that emphasize text-text similarities. Thus, we also present a reference-augmented version, RefCLIPScore, which achieves even higher correlation. Beyond literal description tasks, several case studies reveal domains where CLIPScore performs well (clip-art images, alt-text rating), but also where it is relatively weaker in comparison to reference-based metrics, eg. news captions that require richer contextual knowledge.

--- https://arxiv.org/abs/2104.08821 SimCSE: Simple Contrastive Learning of Sentence Embeddings Tianyu Gao, Xingcheng Yao, Danqi Chen 2021-04-18 2021-04-18 [("doi","10.48550/arXiv.2104.08821")] ai/nn/retrieval ai/nn/transformer

This paper presents SimCSE, a simple contrastive learning framework that greatly advances the state-of-the-art sentence embeddings. We first describe an unsupervised approach, which takes an input sentence and predicts itself in a contrastive objective, with only standard dropout used as noise. This simple method works surprisingly well, performing on par with previous supervised counterparts. We find that dropout acts as minimal data augmentation and removing it leads to a representation collapse.

Then, we propose a supervised approach, which incorporates annotated pairs from natural language inference datasets into our contrastive learning framework, by using “entailment” pairs as positives and “contradiction” pairs as hard negatives.

We evaluate SimCSE on standard semantic textual similarity (STS) tasks, and our unsupervised and supervised models using BERT base achieve an average of 76.3% and 81.6% Spearman’s correlation respectively, a 4.2% and 2.2% improvement compared to previous best results. We also show—both theoretically and empirically—that contrastive learning objective regularizes pre-trained embeddings’ anisotropic space to be more uniform, and it better aligns positive pairs when supervised signals are available.

--- https://arxiv.org/abs/2104.08945#facebook Data-Efficient Language-Supervised Zero-Shot Learning with Self-Distillation Ruizhe Cheng, Bichen Wu, Peizhao Zhang, Peter Vajda, Joseph E. Gonzalez 2021-04-18 2021-04-18 [("doi","10.48550/arXiv.2104.08945")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/clip

Traditional computer vision models are trained to predict a fixed set of predefined categories. Recently, natural language has been shown to be a broader and richer source of supervision that provides finer descriptions to visual concepts than supervised “gold” labels. Previous works, such as CLIP, use a simple pretraining task of predicting the pairings between images and text captions. CLIP, however, is data hungry and requires more than 400M image text pairs for training.

We propose a data-efficient contrastive self-distillation method that uses soft labels to learn from noisy image-text pairs. Our model transfers knowledge from pretrained image and sentence encoders and achieves strong performance with only 3M image text pairs, 133× smaller than CLIP.

Our method exceeds the previous SoTA of general zero-shot learning on ImageNet 21k+1k by 73% relatively with a ResNet-50 image encoder and DeCLUTR text encoder. We also beat CLIP by 10.5% relatively on zero-shot evaluation on Google Open Images (19,958 classes).

--- https://arxiv.org/abs/2104.09705 Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments Benjamin Riviere, Wolfgang Hoenig, Matthew Anderson, Soon-Jo Chung 2021-04-20 2021-04-20 [("doi","10.48550/arXiv.2104.09705")] reinforcement-learning/model/alphago reinforcement-learning/multi-agent reinforcement-learning/robot

We present a self-improving, Neural Tree Expansion (NTE) method for multi-robot online planning in non-cooperative environments, where each robot attempts to maximize its cumulative reward while interacting with other self-interested robots. Our algorithm adapts the centralized, perfect information, discrete-action space method from AlphaZero to a decentralized, partial information, continuous action space setting for multi-robot applications.

Our method has 3 interacting components: (1) a centralized, perfect-information “expert” Monte Carlo Tree Search (MCTS) with large computation resources that provides expert demonstrations, (2) a decentralized, partial-information “learner” MCTS with small computation resources that runs in real-time and provides self-play examples, and (3) policy & value neural networks that are trained with the expert demonstrations and bias both the expert and the learner tree growth.

Our numerical experiments demonstrate Neural Tree Expansion’s computational advantage by finding better solutions than an MCTS with 20× more resources. The resulting policies are dynamically sophisticated, demonstrate coordination between robots, and play the Reach-Target-Avoid differential game better than the state-of-the-art control-theoretic baseline for multi-robot, double-integrator systems.

Our hardware experiments on an aerial swarm demonstrate the computational advantage of Neural Tree Expansion, enabling online planning at 20Hz with effective policies in complex scenarios.

--- https://arxiv.org/abs/2104.10157 VideoGPT: Video Generation using VQ-VAE and Transformers Wilson Yan, Yunzhi Zhang, Pieter Abbeel, Aravind Srinivas 2021-04-20 2021-04-20 [("doi","10.48550/arXiv.2104.10157")] ai/nn/transformer/gpt/dall-e/1 ai/nn/transformer/gpt/jukebox ai/nn/vae ai/video/generation

We present VideoGPT: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos.

VideoGPT uses VQ-VAE that learns downsampled discrete latent representations of a raw video by employing 3D convolutions and axial self-attention. A simple GPT-like architecture is then used to autoregressively model the discrete latents using spatio-temporal position encodings.

Despite the simplicity in formulation and ease of training, our architecture is able to generate samples competitive with state-of-the-art GAN models for video generation on the BAIR Robot dataset, and generate high fidelity natural images from UCF101 and Tumbler GIF Dataset (TGIF).

We hope our proposed architecture serves as a reproducible reference for a minimalistic implementation of transformer based video generation models. Samples and code are available at https://wilsonyan.com/videogpt/index.html.

--- https://arxiv.org/abs/2104.10201 Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020 Ryan Turner, David Eriksson, Michael McCourt, Juha Kiili, Eero Laaksonen, Zhen Xu, Isabelle Guyon 2021-04-20 2021-04-20 [("doi","10.48550/arXiv.2104.10201")] reinforcement-learning/exploration reinforcement-learning/meta-learning statistics/bayes

This paper presents the results and insights from the black-box optimization (BBO) challenge at NeurIPS 2020 which ran from July-October, 2020.

The challenge emphasized the importance of evaluating derivative-free optimizers for tuning the hyperparameters of machine learning models. This was the first black-box optimization challenge with a machine learning emphasis. It was based on tuning (validation set) performance of standard machine learning models on real datasets. This competition has widespread impact as black-box optimization (eg. Bayesian optimization) is relevant for hyperparameter tuning in almost every machine learning project as well as many applications outside of machine learning. The final leaderboard was determined using the optimization performance on held-out (hidden) objective functions, where the optimizers ran without human intervention.

Baselines were set using the default settings of several open-source black-box optimization packages as well as random search.

--- https://arxiv.org/abs/2104.11228 Cross-Domain and Disentangled Face Manipulation with 3D Guidance Can Wang, Menglei Chai, Mingming He, Dongdong Chen, Jing Liao 2021-04-22 2021-04-22 [("doi","10.48550/arXiv.2104.11228")] ai/anime/danbooru ai/nn/gan/stylegan/anime

Face image manipulation via three-dimensional guidance has been widely applied in various interactive scenarios due to its semantically-meaningful understanding and user-friendly controllability. However, existing 3D-morphable-model-based manipulation methods are not directly applicable to out-of-domain faces, such as non-photorealistic paintings, cartoon portraits, or even animals, mainly due to the formidable difficulties in building the model for each specific face domain.

To overcome this challenge, we propose, as far as we know, the first method to manipulate faces in arbitrary domains using human 3DMM. This is achieved through two major steps: (1) disentangled mapping from 3DMM parameters to the latent space embedding of a pre-trained StyleGAN-2 that guarantees disentangled and precise controls for each semantic attribute; and (2) cross-domain adaptation that bridges domain discrepancies and makes human 3DMM applicable to out-of-domain faces by enforcing a consistent latent space embedding.

Experiments and comparisons demonstrate the superiority of our high-quality semantic manipulation method on a variety of face domains with all major 3D facial attributes controllable: pose, expression, shape, albedo, and illumination.

Moreover, we develop an intuitive editing interface to support user-friendly control and instant feedback.

Our project page is Github.

--- https://arxiv.org/abs/2104.12476 EigenGAN: Layer-Wise Eigen-Learning for GANs Zhenliang He, Meina Kan, Shiguang Shan 2021-04-26 2021-04-26 [("doi","10.48550/arXiv.2104.12476")] ai/anime/danbooru ai/nn/gan

Recent studies on Generative Adversarial Network (GAN) reveal that different layers of a generative CNN hold different semantics of the synthesized images. However, few GAN models have explicit dimensions to control the semantic attributes represented in a specific layer.

This paper proposes EigenGAN which is able to unsupervisedly mine interpretable and controllable dimensions from different generator layers. Specifically, EigenGAN embeds one linear subspace with orthogonal basis into each generator layer.

Via the adversarial training to learn a target distribution, these layer-wise subspaces automatically discover a set of “eigen-dimensions” at each layer corresponding to a set of semantic attributes or interpretable variations. By traversing the coefficient of a specific eigen-dimension, the generator can produce samples with continuous changes corresponding to a specific semantic attribute. Taking the human face for example, EigenGAN can discover controllable dimensions for high-level concepts such as pose and gender in the subspace of deep layers, as well as low-level concepts such as hue and color in the subspace of shallow layers.

Moreover, under the linear circumstance, we theoretically prove that our algorithm derives the principal components as PCA does.

Codes can be found in Github.

--- https://arxiv.org/abs/2104.13369#google Explaining in Style: Training a GAN to explain a classifier in StyleSpace Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan Hassidim, William T. Freeman, Phillip Isola, Amir Globerson, Michal Irani, Inbar Mosseri 2021-04-27 2021-04-27 [("doi","10.48550/arXiv.2104.13369")] ai/nn/gan/stylegan

[blog>;] Image classification models can depend on multiple different semantic attributes of the image. An explanation of the decision of the classifier needs to both discover and visualize these properties.

Here we present StylEx, a method for doing this, by training a generative mong a generative model to specifically explain multiple attributes that underlie classifier decisions. A natural source for such attributes is the StyleSpace of StyleGAN, which is known to generate semantically meaningful dimensions in the image. However, because standard GAN training is not dependent on the classifier, it may not represent these attributes which are important for the classifier decision, and the dimensions of StyleSpace may represent irrelevant attributes. To overcome this, we propose a training procedure for a StyleGAN, which incorporates the classifier model, in order to learn a classifier-specific StyleSpace. Explanatory attributes are then selected from this space. These can be used to visualize the effect of changing multiple attributes per image, thus providing image-specific explanations.

We apply StylEx to multiple domains, including animals, leaves, faces and retinal images. For these, we show how an image can be modified in different ways to change its classifier output. Our results show that the method finds attributes that align well with semantic ones, generate meaningful image-specific explanations, and are human-interpretable as measured in user-studies.

--- https://arxiv.org/abs/2104.13742 MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains Yaxing Wang, Abel Gonzalez-Garcia, Chenshen Wu, Luis Herranz, Fahad Shahbaz Khan, Shangling Jui, Joost van de Weijer 2021-04-28 2021-04-28 [("doi","10.48550/arXiv.2104.13742")] ai/anime/danbooru ai/nn/gan/biggan ai/nn/gan/stylegan/anime

[previously] GANs largely increases the potential impact of generative models.

Therefore, we propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods, such as mode collapse and lack of flexibility. Furthermore, to prevent overfitting on small target domains, we introduce sparse subnetwork selection, that restricts the set of trainable neurons to those that are relevant for the target dataset.

We perform comprehensive experiments on several challenging datasets using various GAN architectures (BigGAN, Progressive GAN, and StyleGAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs.

--- https://arxiv.org/abs/2104.13921#google Zero-Shot Detection via Vision and Language Knowledge Distillation Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, Yin Cui 2021-04-28 2021-04-28 [("doi","10.48550/arXiv.2104.13921")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/clip

Zero-shot image classification has made promising progress by training the aligned image and text encoders. The goal of this work is to advance zero-shot object detection, which aims to detect novel objects without bounding box nor mask annotations.

We propose ViLD, a training method via Vision and Language knowledge Distillation. We distill the knowledge from a pre-trained zero-shot image classification model (eg. CLIP) into a two-stage detector (eg. Mask R-CNN). Our method aligns the region embeddings in the detector to the text and image embeddings inferred by the pre-trained model. We use the text embeddings as the detection classifier, obtained by feeding category names into the pre-trained text encoder. We then minimize the distance between the region embeddings and image embeddings, obtained by feeding region proposals into the pre-trained image encoder. During inference, we include text embeddings of novel categories into the detection classifier for zero-shot detection.

We benchmark the performance on LVIS dataset by holding out all rare categories as novel categories. ViLD obtains 16.1 mask APr with a Mask R-CNN (ResNet-50 FPN) for zero-shot detection, outperforming the supervised counterpart by 3.8. The model can directly transfer to other datasets, achieving 72.2 AP50, 36.6 AP and 11.8 AP on PASCAL VOC, COCO and Objects365, respectively.

--- https://arxiv.org/abs/2104.14421#google What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson 2021-04-29 2021-04-29 [("doi","10.48550/arXiv.2104.14421")] ai/scaling reinforcement-learning/exploration statistics/bayes

The posterior over Bayesian neural network (BNN) parameters is extremely high-dimensional and non-convex. For computational reasons, researchers approximate this posterior using inexpensive mini-batch methods such as mean-field variational inference or stochastic-gradient Markov chain Monte Carlo (SGMCMC). To investigate foundational questions in Bayesian deep learning, we instead use full-batch Hamiltonian Monte Carlo (HMC) on modern architectures.

We show that (1) BNNs can achieve performance gains over standard training and deep ensembles; (2) a single long HMC chain can provide a comparable representation of the posterior to multiple shorter chains; (3) in contrast to recent studies, we find posterior tempering is not needed for near-optimal performance, with little evidence for a “cold posterior” effect, which we show is largely an artifact of data augmentation; (4) BMA performance is robust to the choice of prior scale, and relatively similar for diagonal Gaussian, mixture of Gaussian, and logistic priors; (5) Bayesian neural networks show surprisingly poor generalization under domain shift; (6) while cheaper alternatives such as deep ensembles and SGMCMC methods can provide good generalization, they provide distinct predictive distributions from HMC. Notably, deep ensemble predictive distributions are similarly close to HMC as standard SGLD, and closer than standard variational inference.

--- https://arxiv.org/abs/2104.14806#microsoft GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions Chenfei Wu, Lun Huang, Qianxi Zhang, Binyang Li, Lei Ji, Fan Yang, Guillermo Sapiro, Nan Duan 2021-04-30 2021-04-30 [("doi","10.48550/arXiv.2104.14806")] ai/nn/transformer/gpt/dall-e/1 ai/video/generation

Generating videos from text is a challenging task due to its high computational requirements for training and infinite possible answers for evaluation. Existing works typically experiment on simple or small datasets, where the generalization ability is quite limited.

In this work, we propose GODIVA, an open-domain text-to-video pretrained model that can generate videos from text in an auto-regressive manner using a three-dimensional sparse attention mechanism.

We pretrain our model on Howto100M, a large-scale text-video dataset that contains more than 136 million text-video pairs.

Experiments show that GODIVA not only can be fine-tuned on downstream video generation tasks, but also has a good zero-shot capability on unseen texts.

We also propose a new metric called Relative Matching (RM) to automatically evaluate the video generation quality.

Several challenges are listed and discussed as future work.

--- https://arxiv.org/abs/2105.00162#deepmind Generative Art Using Neural Visual Grammars and Dual Encoders Chrisantha Fernando, S. M. Ali Eslami, Jean-Baptiste Alayrac, Piotr Mirowski, Dylan Banarse, Simon Osindero 2021-05-01 2021-05-01 [("doi","10.48550/arXiv.2105.00162")] ai/nn/transformer/clip

Whilst there are perhaps only a few scientific methods, there seem to be almost as many artistic methods as there are artists. Artistic processes appear to inhabit the highest order of open-endedness. To begin to understand some of the processes of art making it is helpful to try to automate them even partially.

In this paper, a novel algorithm for producing generative art is described which allows a user to input a text string, and which in a creative response to this string, outputs an image which interprets that string. It does so by evolving images using a hierarchical neural Lindenmayer system, and evaluating these images along the way using an image text dual encoder trained on billions of images and their associated text from the internet.

In doing so we have access to and control over an instance of an artistic process, allowing analysis of which aspects of the artistic process become the task of the algorithm, and which elements remain the responsibility of the artist.

--- https://arxiv.org/abs/2105.01648 On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning Marc Aurel Vischer, Robert Tjarko Lange, Henning Sprekeler 2021-05-04 2021-05-04 [("doi","10.48550/arXiv.2105.01648")] ai/nn/sparsity/pruning reinforcement-learning/exploration reinforcement-learning/imitation-learning reinforcement-learning/model-free

The lottery ticket hypothesis questions the role of overparameterization in supervised deep learning. But how is the performance of winning lottery tickets affected by the distributional shift inherent to reinforcement learning problems?

In this work, we address this question by comparing sparse agents who have to address the non-stationarity of the exploration-exploitation problem with supervised agents trained to imitate an expert. We show that feed-forward networks trained with behavioral cloning compared to reinforcement learning can be pruned to higher levels of sparsity without performance degradation. This suggests that in order to solve the RL-specific distributional shift agents require more degrees of freedom.

Using a set of carefully designed baseline conditions, we find that the majority of the lottery ticket effect in both learning paradigms can be attributed to the identified mask rather than the weight initialization. The input layer mask selectively prunes entire input dimensions that turn out to be irrelevant for the task at hand. At a moderate level of sparsity the mask identified by iterative magnitude pruning yields minimal task-relevant representations, ie. an interpretable inductive bias. Finally, we propose a simple initialization rescaling which promotes the robust identification of sparse task representations in low-dimensional control tasks.

--- https://arxiv.org/abs/2105.02446 DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism Jinglin Liu, Chengxi Li, Yi Ren, Feiyang Chen, Zhou Zhao 2021-05-06 2021-05-06 [("doi","10.48550/arXiv.2105.02446")] ai/music ai/nn/diffusion

Singing voice synthesis (SVS) systems are built to synthesize high-quality and expressive singing voice, in which the acoustic model generates the acoustic features (eg. mel-spectrogram) given a music score. Previous singing acoustic models adopt a simple loss (eg. L1 and L2) or generative adversarial network (GAN) to reconstruct the acoustic features, while they suffer from over-smoothing and unstable training issues respectively, which hinder the naturalness of synthesized singing.

In this work, we propose DiffSinger, an acoustic model for SVS based on the diffusion probabilistic model. DiffSinger is a parameterized Markov chain that iteratively converts the noise into mel-spectrogram conditioned on the music score. By implicitly optimizing variational bound, DiffSinger can be stably trained and generate realistic outputs. To further improve the voice quality and speed up inference, we introduce a shallow diffusion mechanism to make better use of the prior knowledge learned by the simple loss. Specifically, DiffSinger starts generation at a shallow step smaller than the total number of diffusion steps, according to the intersection of the diffusion trajectories of the ground-truth mel-spectrogram and the one predicted by a simple mel-spectrogram decoder. Besides, we propose boundary prediction methods to locate the intersection and determine the shallow step adaptively.

The evaluations conducted on a Chinese singing dataset demonstrate that DiffSinger outperforms state-of-the-art SVS work. Extensional experiments also prove the generalization of our methods on text-to-speech task (DiffSpeech). Audio samples: https://diffsinger.github.io. Codes: https://github.com/MoonInTheRiver/DiffSinger.

--- https://arxiv.org/abs/2105.04683 Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks Rong Zhu, Mattia Rigotti 2021-05-10 2021-05-10 [("doi","10.48550/arXiv.2105.04683")] reinforcement-learning/exploration

Designing efficient exploration is central to Reinforcement Learning due to the fundamental problem posed by the exploration-exploitation dilemma. Bayesian exploration strategies like Thompson Sampling resolve this trade-off in a principled way by modeling and updating the distribution of the parameters of the action-value function, the outcome model of the environment. However, this technique becomes infeasible for complex environments due to the computational intractability of maintaining probability distributions over parameters of outcome models of corresponding complexity. Moreover, the approximation techniques introduced to mitigate this issue typically result in poor exploration-exploitation trade-offs, as observed in the case of deep neural network models with approximate posterior methods that have been shown to underperform in the deep bandit scenario.

In this paper we introduce Sample Average Uncertainty (SAU), a simple and efficient uncertainty measure for contextual bandits. While Bayesian approaches like Thompson Sampling estimate outcomes uncertainty indirectly by first quantifying the variability over the parameters of the outcome model, SAU is a frequentist approach that directly estimates the uncertainty of the outcomes based on the value predictions. Importantly, we show theoretically that the uncertainty measure estimated by SAU asymptotically matches the uncertainty provided by Thompson Sampling, as well as its regret bounds. Because of its simplicity SAU can be seamlessly applied to deep contextual bandits as a very scalable drop-in replacement for epsilon-greedy exploration. We confirm empirically our theory by showing that SAU-based exploration outperforms current state-of-the-art deep Bayesian bandit methods on several real-world datasets at modest computation cost.

Code is available at https://github.com/ibm/sau-explore.

--- https://arxiv.org/abs/2105.06022 Principled Exploration via Optimistic Bootstrapping and Backward Induction Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, Zhaoran Wang 2021-05-13 2021-05-13 [("doi","10.48550/arXiv.2105.06022")] reinforcement-learning/exploration

One principled approach for provably efficient exploration is incorporating the upper confidence bound (UCB) into the value function as a bonus. However, UCB is specified to deal with linear and tabular settings and is incompatible with Deep Reinforcement Learning (DRL).

In this paper, we propose a principled exploration method for DRL through Optimistic Bootstrapping and Backward Induction (OB2I). OB2I constructs a general-purpose UCB-bonus through non-parametric bootstrap in DRL. The UCB-bonus estimates the epistemic uncertainty of state-action pairs for optimistic exploration. We build theoretical connections between the proposed UCB-bonus and the LSVI-UCB in a linear setting.

We propagate future uncertainty in a time-consistent manner through episodic backward update, which exploits the theoretical advantage and empirically improves the sample-efficiency. Our experiments in the MNIST maze and Atari suite suggest that OB2I outperforms several state-of-the-art exploration approaches.

--- https://arxiv.org/abs/2105.06597#microsoft RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling Yizhe Zhang, Siqi Sun, Xiang Gao, Yuwei Fang, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan 2021-05-14 2021-05-14 [("doi","10.48550/arXiv.2105.06597")] ai/nn/retrieval ai/scaling/mixture-of-experts

Recent advances in large-scale pre-training such as GPT-3 allow seemingly high quality text to be generated from a given prompt. However, such generation systems often suffer from problems of hallucinated facts, and are not inherently designed to incorporate useful external information. Grounded generation models appear to offer remedies, but their training typically relies on rarely-available parallel data where information-relevant documents are provided for context.

We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal. The model learns to reward retrieval of the documents with the highest utility in generation, and attentively combines them using a Mixture-of-Experts (MoE) ensemble to generate follow-on text.

We demonstrate that both generator and retriever can take advantage of this joint training and work synergistically to produce more informative and relevant text in both prose and dialogue generation.

--- https://arxiv.org/abs/2105.12842#google A Full-stack Accelerator Search Technique for Vision Applications Dan Zhang, Safeen Huda, Ebrahim Songhori, Quoc Le, Anna Goldie, Azalia Mirhoseini 2021-05-26 2021-05-26 [("doi","10.48550/arXiv.2105.12842")] ai/scaling/hardware reinforcement-learning/meta-learning

The rapidly-changing ML model landscape presents an unique opportunity for building hardware accelerators optimized for specific datacenter-scale workloads. We propose Full-stack Accelerator Search Technique (FAST), a hardware accelerator search framework that defines a broad optimization environment covering key design decisions within the hardware-software stack, including hardware datapath, software scheduling, and compiler passes such as operation fusion and tensor padding.

Although FAST can be used on any number and type of deep learning workload, in this paper we focus on optimizing for a single or small set of vision models, resulting in faster and more power-efficient designs relative to a general purpose ML accelerator.

When evaluated on EfficientNet, ResNet-50v2, and OCR inference performance relative to a TPU-v3, designs generated by FAST optimized for single workloads can improve Perf/TDP (peak power) by over 6× in the best case and 4× on average. On a limited workload subset, FAST improves Perf/TDP 2.85× on average, with a reduction to 2.35× for a single design optimized over the set of workloads. In addition, we demonstrate a potential 1.8× speedup opportunity for TPU-v3 with improved scheduling.

--- https://arxiv.org/abs/2105.14080 Gotta Go Fast When Generating Data with Score-Based Models Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer, Tal Kachman, Ioannis Mitliagkas 2021-05-28 2021-05-28 [("doi","10.48550/arXiv.2105.14080")] ai/nn/diffusion

Score-based (denoising diffusion) generative models have recently gained a lot of success in generating realistic and diverse data. These approaches define a forward diffusion process for transforming data to noise and generate data by reversing it (thereby going from noise to data). Unfortunately, current score-based models generate data very slowly due to the sheer number of score network evaluations required by numerical SDE solvers.

In this work, we aim to accelerate this process by devising a more efficient SDE solver. Existing approaches rely on the Euler-Maruyama (EM) solver, which uses a fixed step size. We found that naively replacing it with other SDE solvers fares poorly—they either result in low-quality samples or become slower than EM. To get around this issue, we carefully devise an SDE solver with adaptive step sizes tailored to score-based generative models piece by piece. Our solver requires only two score function evaluations, rarely rejects samples, and leads to high-quality samples. Our approach generates data 2–10× faster than EM while achieving better or equal sample quality. For high-resolution images, our method leads to higher quality samples than all other methods tested. Our SDE solver has the benefit of requiring no step size tuning.

--- https://arxiv.org/abs/2105.14110 MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translation George Cazenavette, Manuel Ladron De Guevara 2021-05-28 2021-05-28 [("doi","10.48550/arXiv.2105.14110")] ai/nn/fully-connected ai/nn/gan

While attention-based transformer networks achieve unparalleled success in nearly all language tasks, the large number of tokens coupled with the quadratic activation memory usage makes them prohibitive for visual tasks. As such, while language-to-language translation has been revolutionized by the transformer model, convolutional networks remain the de facto solution for image-to-image translation. The recently proposed MLP-Mixer architecture alleviates some of the speed and memory issues associated with attention-based networks while still retaining the long-range connections that make transformer models desirable.

Leveraging this efficient alternative to self-attention, we propose a new unpaired image-to-image translation model called MixerGAN: a simpler MLP-based architecture that considers long-distance relationships between pixels without the need for expensive attention mechanisms.

Quantitative and qualitative analysis shows that MixerGAN achieves competitive results when compared to prior convolutional-based methods.

--- https://arxiv.org/abs/2105.14211#alibaba M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis Zhu Zhang, Jianxin Ma, Chang Zhou, Rui Men, Zhikang Li, Ming Ding, Jie Tang, Jingren Zhou, Hongxia Yang 2021-05-29 2021-05-29 [("doi","10.48550/arXiv.2105.14211")] ai/nn/transformer/gpt/dall-e/1

Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations. In this paper, instead of investigating these control signals separately, we propose a new two-stage architecture, M6-UFC, to unify any number of multi-modal controls. In M6-UFC, both the diverse control signals and the synthesized image are uniformly represented as a sequence of discrete tokens to be processed by Transformer.

Different from existing two-stage autoregressive approaches such as DALL·E and VQGAN, M6-UFC adopts non-autoregressive generation (NAR) at the second stage to enhance the holistic consistency of the synthesized image, to support preserving specified image blocks, and to improve the synthesis speed. Further, we design a progressive algorithm that iteratively improves the non-autoregressively generated image, with the help of two estimators developed for evaluating the compliance with the controls and evaluating the fidelity of the synthesized image, respectively.

Extensive experiments on a newly collected large-scale clothing dataset M2C-Fashion and a facial dataset Multi-Modal CelebA-HQ verify that M6-UFC can synthesize high-fidelity images that comply with flexible multi-modal controls.

--- https://arxiv.org/abs/2106.00958#openai LHOPT: A Generalizable Approach to Learning Optimizers Diogo Almeida, Clemens Winter, Jie Tang, Wojciech Zaremba 2021-06-02 2021-06-02 [("doi","10.48550/arXiv.2106.00958")] ai/nn/transformer/gpt/2 reinforcement-learning/meta-learning

[learning rate tuning; code; cf. Chinchilla] A core issue with learning to optimize neural networks has been the lack of generalization to real world problems.

To address this, we describe a system designed from a generalization-first perspective, learning to update [using PPO] optimizer hyperparameters instead of model parameters directly using novel features, actions, and a reward function. This system outperforms Adam at all neural network tasks including on modalities not seen during training. We achieve 2× speedups on ImageNet, and a 2.5× speedup on a language modeling task using over 5 orders of magnitude more compute than the training tasks.

…Because even the largest language modeling tasks are trained for less than an epoch [8], we choose to train for only a single epoch to evaluate performance in an underfitting regime…The baselines are all AdamW-based and combinations of 5 learning rates (1_e_−4, 3_e_−4, 1_e_−3, 3_e_−3, 1_e_−2) and 7 commonly used schedules (constant, multi-step, linear decay, quadratic decay, exponential decay, cosine [25] to 0, cosine to 0.1 of original LR)…We also had one additional class of actions that were not hyperparameter updates but fit in nicely within the existing framework: learning to restart from checkpoints. There are many motivations for such an action:

  1. ideally learned optimizers would be able to handle all the task-specific tuning that a practitioner would have to do and restarting on divergence is one such tasks,

  2. previous work has noted that SGD often works best with the highest possible stable learning rate [43] and it may not be possible to determine that value without venturing into unstable territory,

  3. sophisticated hyperparameter optimizations algorithms such as Population-Based Training could be learned from such a simple action, and finally

  4. even if restarting was never used by a trained model, it could greatly help with exploration while training—to both decrease the length of credit assignment paths and also make it less punishing for models to sample suboptimal settings.

…Figure 3 shows the learning curves for the LHOPTs and best baseline. An interesting observation that we will see repeated throughout the paper is that despite being capable of achieving a lower loss earlier, the chosen hyperparameters tend to underperform the best possible loss for that compute, presumably to achieve a better loss later. It’s unclear how necessary it is trade-off early performance for later, but many successful hand-made schedules tend to do this: multi-step schedules tend to stay at the same learning rate long after they’ve hit a plateau and cosine schedules tend to decay their learning rates much less aggressively than other commonly used schedules.

Figure 3: Performance of learned optimizers on optimizing 1 epoch of GPT-2-Large on WikiText-103. Our learned optimizers get almost 2× speedups on this task despite being over 2 magnitudes larger than training tasks.

…We then trained a range of model sizes to compute scaling laws [21] for both baselines and models trained with the LHOPT schedule and present the results on Figure 2. The LHOPT schedule demonstrates consistent speedup over baselines with a slightly steeper slope. We can estimate what a constant speedup would be for this range of points by assume their scaling law slopes are equal and from this calculate a 2.5× speedup. To take the change in slope into account as well, we extrapolate the curves to 175 billion parameters (same size as GPT-3) and at that size, the estimated speedup would be 3.6×.

Note that this result is despite the codebase doing multiple optimization techniques that our LHOPT would have no way of taking into account: gradient clipping to a fixed value and gradually increasing batch size.

Figure 2: Test learning curves and scaling law fit of compute efficient frontier on a large well-tuned language modeling codebase. Our learned optimizers demonstrate consistent speedups ≥2×, with speedup increasing as model size does with no computational overhead. Dotted lines are baselines, full lines use a LHOPT hyperparameter schedule from a similar but smaller task.
--- https://arxiv.org/abs/2106.03004#google Exploring the Limits of Out-of-Distribution Detection Stanislav Fort, Jie Ren, Balaji Lakshminarayanan 2021-06-06 2021-06-06 [("doi","10.48550/arXiv.2106.03004")] ai/nn/transformer/clip ai/scaling

Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We demonstrate that large-scale pre-trained transformers can improve the state-of-the-art (SOTA) on a range of near OOD tasks across different data modalities. For instance, on CIFAR-100 vs CIFAR-10 OOD detection, we improve the AUROC from 85% (current SOTA) to more than 96% using Vision Transformers pre-trained on ImageNet-21k. On a challenging genomics OOD detection benchmark, we improve the AUROC 66% → 77% using transformers and unsupervised pre-training.

To further improve performance, we explore the few-shot outlier exposure setting where a few examples from outlier classes may be available; we show that pre-trained transformers are particularly well-suited for outlier exposure, and that the AUROC of OOD detection on CIFAR-100 vs CIFAR-10 can be improved to 98.7% with just 1 image per OOD class, and 99.46% with 10 images per OOD class.

For multi-modal image-text pre-trained transformers such as CLIP, we explore a new way of using just the names of outlier classes as a sole source of information without any accompanying images, and show that this outperforms previous SOTA on standard vision OOD benchmark tasks.

--- https://arxiv.org/abs/2106.03802 Learning to Efficiently Sample from Diffusion Probabilistic Models Daniel Watson, Jonathan Ho, Mohammad Norouzi, William Chan 2021-06-07 2021-06-07 [("doi","10.48550/arXiv.2106.03802")] ai/nn/diffusion

Denoising Diffusion Probabilistic Models (DDPMs) have emerged as a powerful family of generative models that can yield high-fidelity samples and competitive log-likelihoods across a range of domains, including image and speech synthesis. Key advantages of DDPMs include ease of training, in contrast to generative adversarial networks, and speed of generation, in contrast to autoregressive models. However, DDPMs typically require hundreds-to-thousands of steps to generate a high fidelity sample, making them prohibitively expensive for high dimensional problems. Fortunately, DDPMs allow trading generation speed for sample quality through adjusting the number of refinement steps as a post process.

Prior work has been successful in improving generation speed through handcrafting the time schedule by trial and error. We instead view the selection of the inference time schedules as an optimization problem and introduce an exact dynamic programming algorithm that finds the optimal discrete time schedules for any pre-trained DDPM. Our method exploits the fact that ELBO can be decomposed into separate KL terms, and given any computation budget, discovers the time schedule that maximizes the training ELBO exactly. Our method is efficient, has no hyper-parameters of its own, and can be applied to any pre-trained DDPM with no retraining.

We discover inference time schedules requiring as few as 32 refinement steps, while sacrificing less than 0.1 bits per dimension compared to the default 4,000 steps used on ImageNet 64×64 [Ho et al 2020; Nichol & Dhariwal 2021].

--- https://arxiv.org/abs/2106.04533 Chasing Sparsity in Vision Transformers: An End-to-End Exploration Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang 2021-06-08 2021-06-08 [("doi","10.48550/arXiv.2106.04533")] ai/nn/sparsity/pruning ai/nn/transformer

Vision transformers (ViTs) have recently received explosive popularity, but their enormous model sizes and training costs remain daunting. Conventional post-training pruning often incurs higher training budgets. In contrast, this paper aims to trim down both the training memory overhead and the inference complexity, without sacrificing the achievable accuracy.

We carry out the first-of-its-kind comprehensive exploration, on taking an unified approach of integrating sparsity in ViTs “from end to end”. Specifically, instead of training full ViTs, we dynamically extract and train sparse subnetworks, while sticking to a fixed small parameter budget. Our approach jointly optimizes model parameters and explores connectivity throughout training, ending up with one sparse network as the final output. The approach is seamlessly extended from unstructured to structured sparsity, the latter by considering to guide the prune-and-grow of self-attention heads inside ViTs. We further co-explore data and architecture sparsity for additional efficiency gains by plugging in a novel learnable token selector to adaptively determine the currently most vital patches.

Extensive results on ImageNet with diverse ViT backbones validate the effectiveness of our proposals which obtain reduced computational cost and almost unimpaired generalization. Perhaps most surprisingly, we find that the proposed sparse (co-)training can sometimes improve the ViT accuracy rather than compromising it, making sparsity a tantalizing “free lunch”. For example, our sparsified DeiT-Small at (5%, 50%) sparsity for (data, architecture), improves 0.28% top-1 accuracy, and meanwhile enjoys 49.32% FLOPs and 4.40% running time savings.

Our codes are available at Github.

--- https://arxiv.org/abs/2106.04615#deepmind Vector Quantized Models for Planning Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aäron van den Oord, Oriol Vinyals 2021-06-08 2021-06-08 [("doi","10.48550/arXiv.2106.04615")] ai/nn/vae ai/video/generation reinforcement-learning/chess reinforcement-learning/imperfect-information reinforcement-learning/model/muzero

Recent developments in the field of model-based RL have proven successful in a range of environments, especially ones where planning is essential. However, such successes have been limited to deterministic fully-observed environments [ie. MDPs and not POMDPs].

We present a new approach that handles stochastic and partially-observable environments. Our key insight is to use discrete autoencoders [VQ-VAE] to capture the multiple possible effects of an action in a stochastic environment. We use a stochastic variant of Monte Carlo tree search to plan over both the agent’s actions and the discrete latent variables representing the environment’s response.

Our approach outperforms an offline version of MuZero on a stochastic interpretation of chess where the opponent is considered part of the environment. We also show that our approach scales to DeepMind Lab, a first-person 3D environment with large visual observations and partial observability.

--- https://arxiv.org/abs/2106.04651 The whole prefrontal cortex is premotor cortex Justin M. Fine, Benjamin Y. Hayden 2021-06-08 2021-06-08 [("doi","10.48550/arXiv.2106.04651")] psychology/neuroscience reinforcement-learning/model

We propose that the entirety of the prefrontal cortex can be seen as fundamentally premotor in nature. By this, we mean that the prefrontal cortex consists of an action abstraction hierarchy whose core function is the potentiation and depotentiation of possible action plans at different levels of granularity.

We argue that the apex of the hierarchy should revolve around the process of goal selection, which we posit is inherently a form of abstract action optimization. Anatomical and functional evidence supports the idea that this hierarchy originates on the orbital surface of the brain and extends dorsally to motor cortex. Our view, therefore, positions the orbitofrontal cortex as the central site for the optimization of goal selection policies, and suggests that other proposed roles are aspects of this more general function.

We conclude by proposing that the dynamical systems approach, which works well in motor systems, can be extended to the rest of prefrontal cortex. Our proposed perspective will reframe outstanding questions, open up new areas of inquiry, and will align theories of prefrontal function with evolutionary principles.

--- https://arxiv.org/abs/2106.05709 Basins with tentacles Yuanzhao Zhang, Steven H. Strogatz 2021-06-10 2021-06-10 [("doi","10.1103/PhysRevLett.127.194101")] math

To explore basin geometry in high-dimensional dynamical systems, we consider a ring of identical Kuramoto oscillators. Many attractors coexist in this system; each is a twisted periodic orbit characterized by a winding number q, with basin size proportional to e−kq2.

We uncover the geometry behind this size distribution and find the basins are octopus-like, with nearly all their volume in the tentacles, not the head of the octopus (the ball-like region close to the attractor).

We present a simple geometrical reason why basins with tentacles should be common in high-dimensional systems.

--- https://arxiv.org/abs/2106.05931#nvidia Score-based Generative Modeling in Latent Space Arash Vahdat, Karsten Kreis, Jan Kautz 2021-06-10 2021-06-10 [("doi","10.48550/arXiv.2106.05931")] ai/nn/diffusion ai/nn/vae

Score-based generative models (SGMs) have recently demonstrated impressive results in terms of both sample quality and distribution coverage. However, they are usually applied directly in data space and often require thousands of network evaluations for sampling.

Here, we propose the Latent Score-based Generative Model (LSGM), a novel approach that trains SGMs in a latent space, relying on the variational autoencoder framework. Moving from data to latent space allows us to train more expressive generative models, apply SGMs to non-continuous data, and learn smoother SGMs in a smaller space, resulting in fewer network evaluations and faster sampling.

To enable training LSGMs end-to-end in a scalable and stable manner, we (1) introduce a new score-matching objective suitable to the LSGM setting, (2) propose a novel parameterization of the score function that allows SGM to focus on the mismatch of the target distribution with respect to a simple Normal one, and (3) analytically derive multiple techniques for variance reduction of the training objective. LSGM obtains a state-of-the-art FID score of 2.10 on CIFAR-10, outperforming all existing generative results on this dataset. On CelebA-HQ-256, LSGM is on a par with previous SGMs in sample quality while outperforming them in sampling time by two orders of magnitude. In modeling binary images, LSGM achieves state-of-the-art likelihood on the binarized Omniglot dataset.

--- https://arxiv.org/abs/2106.05970 ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation Wanrong Zhu, Xin Eric Wang, An Yan, Miguel Eckstein, William Yang Wang 2021-06-10 2021-06-10 [("doi","10.48550/arXiv.2106.05970")] ai/fiction ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e/1

Automatic evaluations for natural language generation (NLG) conventionally rely on token-level or embedding-level comparisons with the text references. This is different from human language processing, for which visual imaginations often improve comprehension.

In this work, we propose ImaginE, an imagination-based automatic evaluation metric for natural language generation. With the help of CLIP and DALL·E, two cross-modal models pre-trained on large-scale image-text pairs, we automatically generate an image as the embodied imagination for the text snippet and compute the imagination similarity using contextual embeddings.

Experiments spanning several text generation tasks demonstrate that adding imagination with our ImaginE displays great potential in introducing multi-modal information into NLG evaluation, and improves existing automatic metrics’ correlations with human similarity judgments in many circumstances.

--- https://arxiv.org/abs/2106.05974#google V-MoE: Scaling Vision with Sparse Mixture of Experts Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, Neil Houlsby 2021-06-10 2021-06-10 [("doi","10.48550/arXiv.2106.05974")] ai/scaling/mixture-of-experts

[blog; code] Sparsely-gated Mixture of Experts networks (MoEs) have demonstrated excellent scalability in Natural Language Processing. In Computer Vision, however, almost all performant networks are “dense”, that is, every input is processed by every parameter.

We present a Vision MoE (V-MoE), a sparse version of the Vision Transformer, that is scalable [to JFT-300M] and competitive with the largest dense networks. When applied to image recognition, V-MoE matches the performance of state-of-the-art networks, while requiring as little as half of the compute at inference time. Further, we propose an extension to the routing algorithm that can prioritize subsets of each input across the entire batch, leading to adaptive per-image compute. This allows V-MoE to trade-off performance and compute smoothly at test-time.

Finally, we demonstrate the potential of V-MoE to scale vision models, and train a 15b parameter model that attains 90.35% on ImageNet.

--- https://arxiv.org/abs/2106.06561 GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation (works for videos too!) Min Jin Chong, David Forsyth 2021-06-11 2021-06-11 [("doi","10.48550/arXiv.2106.06561")] ai/anime ai/nn/gan ai/video/generation

We show how to learn a map that takes a content code, derived from a face image, and a randomly chosen style code to an anime image.

We derive an adversarial loss from our simple and effective definitions of style and content. This adversarial loss guarantees the map is diverse—a very wide range of anime can be produced from a single content code. Under plausible assumptions, the map is not just diverse, but also correctly represents the probability of an anime, conditioned on an input face.

In contrast, current multimodal generation procedures cannot capture the complex styles that appear in anime. Extensive quantitative experiments support the idea the map is correct. Extensive qualitative results show that the method can generate a much more diverse range of styles than SOTA comparisons.

Finally, we show that our formalization of content and style allows us to perform video-to-video translation without ever training on videos.

--- https://arxiv.org/abs/2106.07411 Partial success in closing the gap between human and machine vision Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A. Wichmann, Wiel, Brendel 2021-06-14 2021-06-14 [("doi","10.48550/arXiv.2106.07411")] ai/nn/adversarial ai/nn/cnn ai/nn/transformer/clip ai/scaling psychology/neuroscience

A few years ago, the first CNN surpassed human performance on ImageNet. However, it soon became clear that machines lack robustness on more challenging test cases, a major obstacle towards deploying machines “in the wild” and towards obtaining better computational models of human visual perception. Here we ask: Are we making progress in closing the gap between human and machine vision?

To answer this question, we tested human observers on a broad range of out-of-distribution (OOD) datasets, adding the “missing human baseline” by recording 85,120 psychophysical trials across 90 participants. We then investigated a range of promising machine learning developments that crucially deviate from standard supervised CNNs along 3 axes: objective function (self-supervised, adversarially trained, CLIP language-image training), architecture (eg. vision transformers), and dataset size (ranging from 1M to 1B).

Our findings are threefold. (1.) The long-standing robustness gap between humans and CNNs is closing, with the best models now matching or exceeding human performance on most OOD datasets. (2.) There is still a substantial image-level consistency gap, meaning that humans make different errors than models. In contrast, most models systematically agree in their categorization errors, even substantially different ones like contrastive self-supervised vs. standard supervised models. (3.) In many cases, human-to-model consistency improves when training dataset size is increased by one to 3 orders of magnitude.

Our results give reason for cautious optimism: While there is still much room for improvement, the behavioral difference between human and machine vision is narrowing. In order to measure future progress, 17 OOD datasets with image-level human behavioral data are provided as a benchmark here: https://github.com/bethgelab/model-vs-human/.

--- https://arxiv.org/abs/2106.07886 MLP Singer: Towards Rapid Parallel Korean Singing Voice Synthesis Jaesung Tae, Hyeongju Kim, Younggun Lee 2021-06-15 2021-06-15 [("doi","10.48550/arXiv.2106.07886")] ai/music ai/nn/fully-connected ai/nn/gan

Recent developments in deep learning have improved the quality of synthesized singing voice audio. However, prominent neural singing voice synthesis systems suffer from slow inference speed due to their autoregressive design. Inspired by ‘MLP-Mixer: An all-MLP Architecture for Vision’, Tolstikhin et al 2021, a novel architecture introduced in the vision literature for attention-free image classification, we propose MLP Singer, a parallel Korean singing voice synthesis system. To the best of our knowledge, this is the first work that uses an entirely MLP-based architecture for voice synthesis.

Listening tests demonstrate that MLP Singer outperforms a larger autoregressive GAN-based system, both in terms of audio quality and synthesis speed. In particular, MLP Singer achieves a real-time factor of up to 200 and 3400 on CPUs and GPUs respectively, enabling order of magnitude faster generation on both environments.

--- https://arxiv.org/abs/2106.08829 A Fair and Comprehensive Comparison of Multimodal Tweet Sentiment Analysis Methods Gullal S. Cheema, Sherzod Hakimov, Eric Müller-Budack, Ralph Ewerth 2021-06-16 2021-06-16 [("doi","10.48550/arXiv.2106.08829")] ai/nn/transformer/clip

Opinion and sentiment analysis is a vital task to characterize subjective information in social media posts. In this paper, we present a comprehensive experimental evaluation and comparison with 6 state-of-the-art methods, from which we have re-implemented one of them.

In addition, we investigate different textual and visual feature embeddings that cover different aspects of the content, as well as the recently introduced multimodal CLIP embeddings. Experimental results are presented for two different publicly available benchmark datasets of tweets and corresponding images.

In contrast to the evaluation methodology of previous work, we introduce a reproducible and fair evaluation scheme to make results comparable.

Finally, we conduct an error analysis to outline the limitations of the methods and possibilities for future work.

--- https://arxiv.org/abs/2106.08962 Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better Gaurav Menghani 2021-06-16 2021-06-16 [("doi","10.48550/arXiv.2106.08962")] ai/nn/sparsity/knowledge-distillation ai/nn/sparsity/low-precision

Deep Learning has revolutionized the fields of computer vision, natural language understanding, speech recognition, information retrieval and more. However, with the progressive improvements in deep learning models, their number of parameters, latency, resources required to train, etc. have all increased. Consequently, it has become important to pay attention to these footprint metrics of a model as well, not just its quality.

We present and motivate the problem of efficiency in deep learning, followed by a thorough survey of the 5 core areas of model efficiency (spanning modeling techniques, infrastructure, and hardware) and the seminal work there.

We also present an experiment-based guide along with code, for practitioners to optimize their model training and deployment. We believe this is the first comprehensive survey in the efficient deep learning space that covers the landscape of model efficiency from modeling techniques to hardware support.

Our hope is that this survey would provide the reader with the mental model and the necessary understanding of the field to apply generic efficiency techniques to immediately get improvements, and also equip them with ideas for further research and experimentation to achieve additional gains.

--- https://arxiv.org/abs/2106.09129 A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura 2021-06-16 2021-06-16 [("doi","10.48550/arXiv.2106.09129")] ai/nn/sparsity/low-precision ai/nn/sparsity/pruning

Two crucial requirements for a successful adoption of deep learning (DL) in the wild are: (1) robustness to distributional shifts, and (2) model compactness for achieving efficiency. Unfortunately, efforts towards simultaneously achieving Out-of-Distribution (OOD) robustness and extreme model compactness without sacrificing accuracy have mostly been unsuccessful. This raises an important question: “Is the inability to create compact, accurate and robust deep neural networks (CARDs) fundamental?”

To answer this question we perform a large-scale analysis for a range of popular model compression techniques which uncovers several intriguing patterns. Notably, in contrast to traditional pruning approaches (eg. fine tuning and gradual magnitude pruning), we find that “lottery ticket-style” pruning approaches can surprisingly be used to create high performing CARDs. Specifically, we are able to create extremely compact CARDs that are dramatically more robust than their much larger and full-precision counterparts while matching (or beating) their test accuracy, simply by pruning and/or quantizing. To better understand these differences, we perform sensitivity analysis in the Fourier domain for CARDs trained using different data augmentation methods.

Motivated by our analysis, we develop a simple domain-adaptive test-time ensembling approach (CARD-Deck) that uses a gating module to dynamically select an appropriate CARD from the CARD-Deck based on their spectral-similarity with test samples.

By leveraging complementary frequency biases of different compressed models, the proposed approach builds a “winning hand” of CARDs that establishes a new state-of-the-art on CIFAR-10-C accuracies (ie. 96.8% clean and 92.75% robust) with dramatically better memory usage than their non-compressed counterparts. We also present some theoretical evidences supporting our empirical findings.

--- https://arxiv.org/abs/2106.10064 Fitting summary statistics of neural data with a differentiable spiking network simulator Guillaume Bellec, Shuqi Wang, Alireza Modirshanechi, Johanni Brea, Wulfram Gerstner 2021-06-18 2021-06-18 [("doi","10.48550/arXiv.2106.10064")] psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Fitting network models to neural activity is an important tool in neuroscience. A popular approach is to model a brain area with a probabilistic recurrent spiking network whose parameters maximize the likelihood of the recorded activity. Although this is widely used, we show that the resulting model does not produce realistic neural activity.

To correct for this, we suggest to augment the log-likelihood with terms that measure the dissimilarity between simulated and recorded activity. This dissimilarity is defined via summary statistics commonly used in neuroscience and the optimization is efficient because it relies on back-propagation through the stochastically simulated spike trains.

We analyze this method theoretically and show empirically that it generates more realistic activity statistics. We find that it improves upon other fitting algorithms for spiking network models like GLMs (Generalized Linear Models) which do not usually rely on back-propagation.

This new fitting algorithm also enables the consideration of hidden neurons which is otherwise notoriously hard, and we show that it can be crucial when trying to infer the network connectivity from spike recordings.

--- https://arxiv.org/abs/2106.10715 CPM-2: Large-scale Cost-effective Pre-trained Language Models Zhengyan Zhang, Yuxian Gu, Xu Han, Shengqi Chen, Chaojun Xiao, Zhenbo Sun, Yuan Yao, Fanchao Qi, Jian Guan, Pei Ke, Yanzheng Cai, Guoyang Zeng, Zhixing Tan, Zhiyuan Liu, Minlie Huang, Wentao Han, Yang Liu, Xiaoyan Zhu, Maosong Sun 2021-06-20 2021-06-20 [("doi","10.48550/arXiv.2106.10715")] ai/scaling/mixture-of-experts

In recent years, the size of pre-trained language models (PLMs) has grown by leaps and bounds. However, efficiency issues of these large-scale PLMs limit their utilization in real-world scenarios.

We present a suite of cost-effective techniques for the use of PLMs to deal with the efficiency issues of pre-training, fine-tuning, and inference. (1) We introduce knowledge inheritance to accelerate the pre-training process by exploiting existing PLMs instead of training models from scratch. (2) We explore the best practice of prompt tuning with large-scale PLMs. Compared with conventional fine-tuning, prompt tuning reduces the number of task-specific parameters. (3) We implement a new inference toolkit, namely InfMoE, for using large-scale PLMs with limited computational resources.

Based on our cost-effective pipeline, we pre-train two models: an encoder-decoder bilingual model with 11 billion parameters (CPM-2) and its corresponding MoE version with 198 billion parameters. In our experiments, we compare CPM-2 with mT5 on downstream tasks. Experimental results show that CPM-2 has excellent general language intelligence. Moreover, we validate the efficiency of InfMoE when conducting inference of large-scale models having tens of billions of parameters on a single GPU.

All source code and model parameters are available at Github.

--- https://arxiv.org/abs/2106.11097 CLIP2Video: Mastering Video-Text Retrieval via Image CLIP Han Fang, Pengfei Xiong, Luhui Xu, Yu Chen 2021-06-21 2021-06-21 [("doi","10.48550/arXiv.2106.11097")] ai/nn/retrieval ai/nn/transformer/clip ai/video/analysis

We present CLIP2Video network to transfer the image-language pre-training model to video-text retrieval in an end-to-end manner. Leading approaches in the domain of video-and-language learning try to distill the spatio-temporal video features and multi-modal interaction between videos and languages from a large-scale video-text dataset.

Different from them, we leverage pretrained image-language model, simplify it as a two-stage framework with co-learning of image-text and enhancing temporal relations between video frames and video-text respectively, make it able to train on comparatively small datasets. Specifically, based on the spatial semantics captured by Contrastive Language-Image Pretraining (CLIP) model, our model involves a Temporal Difference Block to capture motions at fine temporal video frames, and a Temporal Alignment Block to re-align the tokens of video clips and phrases and enhance the multi-modal correlation.

We conduct thorough ablation studies, and achieve state-of-the-art performance on major text-to-video and video-to-text retrieval benchmarks, including new records of retrieval accuracy on MSR-VTT, MSVD and VATEX.

--- https://arxiv.org/abs/2106.11872 Randomness In Neural Network Training: Characterizing The Impact of Tooling Donglin Zhuang, Xingyao Zhang, Shuaiwen Leon Song, Sara Hooker 2021-06-22 2021-06-22 [("doi","10.48550/arXiv.2106.11872")] ai/nn cs/algorithm reinforcement-learning/safe

The quest for determinism in machine learning has disproportionately focused on characterizing the impact of noise introduced by algorithmic design choices. In this work, we address a less well understood and studied question: how does our choice of tooling introduce randomness to deep neural network training. We conduct large scale experiments across different types of hardware, accelerators, state of art networks, and open-source datasets, to characterize how tooling choices contribute to the level of non-determinism in a system, the impact of said non-determinism, and the cost of eliminating different sources of noise.

Our findings are surprising, and suggest that the impact of non-determinism in nuanced. While top-line metrics such as top-1 accuracy are not noticeably impacted, model performance on certain parts of the data distribution is far more sensitive to the introduction of randomness. Our results suggest that deterministic tooling is critical for AI safety. However, we also find that the cost of ensuring determinism varies dramatically between neural network architectures and hardware types, eg. with overhead up to 746%, 241%, and 196% on a spectrum of widely used GPU accelerator architectures, relative to non-deterministic training. The source code used in this paper is available at Github.

--- https://arxiv.org/abs/2106.13043 AudioCLIP: Extending CLIP to Image, Text and Audio Andrey Guzhov, Federico Raue, Jörn Hees, Andreas Dengel 2021-06-24 2021-06-24 [("doi","10.48550/arXiv.2106.13043")] ai/music ai/nn/transformer/clip

[cf. CLAP] In the past, the rapidly evolving field of sound classification greatly benefited from the application of methods from other domains. Today, we observe the trend to fuse domain-specific tasks and approaches together, which provides the community with new outstanding models.

In this work, we present an extension of the CLIP model that handles audio in addition to text and images. Our proposed model incorporates the ES ResNeXt audio-model into the CLIP framework using the AudioSet dataset. Such a combination enables the proposed model to perform bimodal and unimodal classification and querying, while keeping CLIP’s ability to generalize to unseen datasets in a zero-shot inference fashion.

AudioCLIP achieves new state-of-the-art results in the Environmental Sound Classification (ESC) task, out-performing other approaches by reaching accuracies of 90.07% on the UrbanSound8K and 97.15% on the ESC-50 datasets. Further it sets new baselines in the zero-shot ESC-task on the same datasets (68.78% and 69.40%, respectively).

Finally, we also assess the cross-modal querying performance of the proposed model as well as the influence of full and partial training on the results.

For the sake of reproducibility, our code is published.

--- https://arxiv.org/abs/2106.13281#google Brax—A Differentiable Physics Engine for Large Scale Rigid Body Simulation C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem 2021-06-24 2021-06-24 [("doi","10.48550/arXiv.2106.13281")] cs/hardware reinforcement-learning/model reinforcement-learning/scaling

We present Brax, an open source library for rigid body simulation with a focus on performance and parallelism on accelerators, written in JAX.

We present results on a suite of tasks inspired by the existing reinforcement learning literature, but remade in our engine.

Additionally, we provide reimplementations of PPO, SAC, ES, and direct policy optimization in JAX that compile alongside our environments, allowing the learning algorithm and the environment processing to occur on the same device, and to scale seamlessly on accelerators.

Finally, we include notebooks that facilitate training of performant policies on common OpenAI Gym MuJoCo-like tasks in minutes.

--- https://arxiv.org/abs/2106.13884#deepmind Multimodal Few-Shot Learning with Frozen Language Models Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, S. M. Ali Eslami, Oriol Vinyals, Felix Hill 2021-06-25 2021-06-25 [("doi","10.48550/arXiv.2106.13884")] ai/nn/transformer/gpt reinforcement-learning/meta-learning reinforcement-learning/scaling

When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, we present a simple, yet effective, approach for transferring this few-shot learning ability to a multimodal setting (vision and language).

Using aligned image and caption data, we train a vision encoder to represent each image as a sequence of continuous embeddings, such that a pre-trained, frozen language model prompted with this prefix generates the appropriate caption. The resulting system is a multimodal few-shot learner, with the surprising ability to learn a variety of new tasks when conditioned on examples, represented as a sequence of multiple interleaved image and text embeddings.

We demonstrate that it can rapidly learn words for new objects and novel visual categories, do visual question-answering with only a handful of examples, and make use of outside knowledge, by measuring a single model on a variety of established and new benchmarks.

--- https://arxiv.org/abs/2106.14843 CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders Kevin Frans, L. B. Soros, Olaf Witkowski 2021-06-28 2021-06-28 [("doi","10.48550/arXiv.2106.14843")] ai/nn/transformer/clip

This work presents CLIPDraw, an algorithm that synthesizes novel drawings based on natural language input. CLIPDraw does not require any training; rather a pre-trained CLIP language-image encoder is used as a metric for maximizing similarity between the given description and a generated drawing. Crucially, CLIPDraw operates over vector strokes rather than pixel images, a constraint that biases drawings towards simpler human-recognizable shapes.

Results compare between CLIPDraw and other synthesis-through-optimization methods, as well as highlight various interesting behaviors of CLIPDraw, such as satisfying ambiguous text in multiple ways, reliably producing drawings in diverse artistic styles, and scaling from simple to complex visual representations as stroke count is increased.

Code for experimenting with the method is available at: https://colab.research.google.com/github/kvfrans/clipdraw/blob/main/clipdraw.ipynb.

--- https://arxiv.org/abs/2106.14876#openai Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft Ingmar Kanitscheider, Joost Huizinga, David Farhi, William Hebgen Guss, Brandon Houghton, Raul Sampedro, Peter Zhokhov, Bowen Baker, Adrien Ecoffet, Jie Tang, Oleg Klimov, Jeff Clune 2021-06-28 2021-06-28 [("doi","10.48550/arXiv.2106.14876")] reinforcement-learning/exploration reinforcement-learning/model-free

An important challenge in reinforcement learning is training agents that can solve a wide variety of tasks. If tasks depend on each other (eg. needing to learn to walk before learning to run), curriculum learning can speed up learning by focusing on the next best task to learn. We explore curriculum learning in a complex, visual domain with many hard exploration challenges: Minecraft.

We find that learning progress (defined as a change in success probability of a task) is a reliable measure of learnability for automatically constructing an effective curriculum. We introduce a learning-progress based curriculum and test it on a complex reinforcement learning problem (called “Simon Says”) where an agent is instructed to obtain a desired goal item. Many of the required skills depend on each other.

Experiments demonstrate that: (1) a within-episode exploration bonus for obtaining new items improves performance, (2) dynamically adjusting this bonus across training such that it only applies to items the agent cannot reliably obtain yet further increases performance, (3) the learning-progress based curriculum elegantly follows the learning curve of the agent, and (4) when the learning-progress based curriculum is combined with the dynamic exploration bonus it learns much more efficiently and obtains far higher performance than uniform baselines.

These results suggest that combining intra-episode and across-training exploration bonuses with learning progress creates a promising method for automated curriculum generation, which may substantially increase our ability to train more capable, generally intelligent agents.

--- https://arxiv.org/abs/2106.16198 Small in-distribution changes in 3D perspective and lighting fool both CNNs and Transformers Spandan Madan, Tomotake Sasaki, Tzu-Mao Li, Xavier Boix, Hanspeter Pfister 2021-06-30 2021-06-30 [("doi","10.48550/arXiv.2106.16198")] ai/nn/transformer/clip

Neural networks are susceptible to small transformations including 2D rotations and shifts, image crops, and even changes in object colors. This is often attributed to biases in the training dataset, and the lack of 2D shift-invariance due to not respecting the sampling theorem. In this paper, we challenge this hypothesis by training and testing on unbiased datasets, and showing that networks are brittle to both small 3D perspective changes and lighting variations which cannot be explained by dataset bias or lack of shift-invariance.

To find these in-distribution errors, we introduce an evolution strategies (ES) based approach, which we call CMA-Search. Despite training with a large-scale (0.5 million images), unbiased dataset of camera and light variations, in over 71% cases CMA-Search can find camera parameters in the vicinity of a correctly classified image which lead to in-distribution misclassifications with < 3.6% change in parameters. With lighting changes, CMA-Search finds misclassifications in 33% cases with < 11.6% change in parameters.

Finally, we extend this method to find misclassifications in the vicinity of ImageNet images for both ResNet and OpenAI’s CLIP model.

--- https://arxiv.org/abs/2107.00630 Variational Diffusion Models Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho 2021-07-01 2021-07-01 [("doi","10.48550/arXiv.2107.00630")] ai/nn/diffusion cs/algorithm/information/compression

Diffusion-based generative models have demonstrated a capacity for perceptually impressive synthesis, but can they also be great likelihood-based models? We answer this in the affirmative, and introduce a family of diffusion-based generative models that obtain state-of-the-art likelihoods on standard image density estimation benchmarks.

Unlike other diffusion-based models, our method allows for efficient optimization of the noise schedule jointly with the rest of the model. We show that the variational lower bound (VLB) simplifies to a remarkably short expression in terms of the signal-to-noise ratio of the diffused data, thereby improving our theoretical understanding of this model class. Using this insight, we prove an equivalence between several models proposed in the literature. In addition, we show that the continuous-time VLB is invariant to the noise schedule, except for the signal-to-noise ratio at its endpoints. This enables us to learn a noise schedule that minimizes the variance of the resulting VLB estimator, leading to faster optimization.

Combining these advances with architectural improvements we obtain state-of-the-art likelihoods on image density estimation benchmarks outperforming autoregressive models that have dominated these benchmarks for many years, with often faster optimization.

In addition, we show how to turn the model into a bits-back compression scheme, and demonstrate lossless compression rates close to the theoretical optimum.

--- https://arxiv.org/abs/2107.00650 CLIP-It! Language-Guided Video Summarization Medhini Narasimhan, Anna Rohrbach, Trevor Darrell 2021-07-01 2021-07-01 [("doi","10.48550/arXiv.2107.00650")] ai/nn/transformer/clip ai/video/analysis

A generic video summary is an abridged version of a video that conveys the whole story and features the most important scenes. Yet the importance of scenes in a video is often subjective, and users should have the option of customizing the summary by using natural language to specify what is important to them. Further, existing models for fully automatic generic summarization have not exploited available language models, which can serve as an effective prior for saliency.

This work introduces CLIP-It, a single framework for addressing both generic and query-focused video summarization, typically approached separately in the literature. We propose a language-guided multimodal transformer that learns to score frames in a video based on their importance relative to one another and their correlation with a user-defined query (for query-focused summarization) or an automatically generated dense video caption (for generic video summarization). Our model can be extended to the unsupervised setting by training without ground-truth supervision.

We outperform baselines and prior work by a large margin on both standard video summarization datasets (TVSum and SumMe) and a query-focused video summarization dataset (QFVS). Particularly, we achieve large improvements in the transfer setting, attesting to our method’s strong generalization capabilities.

--- https://arxiv.org/abs/2107.03006#google Structured Denoising Diffusion Models in Discrete State-Spaces Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg 2021-07-07 2021-07-07 [("doi","10.48550/arXiv.2107.03006")] ai/nn/diffusion/discrete

Denoising diffusion probabilistic models (DDPMs) (Ho et al 2020) have shown impressive results on image and waveform generation in continuous state spaces. Here, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs), diffusion-like generative models for discrete data that generalize the multinomial diffusion model of Hoogeboom et al 2021, by going beyond corruption processes with uniform transition probabilities. This includes corruption with transition matrices that mimic Gaussian kernels in continuous space, matrices based on nearest neighbors in embedding space, and matrices that introduce absorbing states. The third allows us to draw a connection between diffusion models and autoregressive and mask-based generative models.

We show that the choice of transition matrix is an important design decision that leads to improved results in image and text domains. We also introduce a new loss function that combines the variational lower bound with an auxiliary cross entropy loss. For text, this model class achieves strong results on character-level text generation while scaling to large vocabularies on LM1B.

On the image dataset CIFAR-10, our models approach the sample quality and exceed the log-likelihood of the continuous-space DDPM model.

--- https://arxiv.org/abs/2107.03374#openai Evaluating Large Language Models Trained on Code Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Łukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, Wojciech Zaremba 2021-07-07 2021-07-07 [("doi","10.48550/arXiv.2107.03374")] ai/nn/transformer/gpt/codex reinforcement-learning/safe reinforcement-learning/scaling

We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot.

On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem.

Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables.

Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics.

--- https://arxiv.org/abs/2107.04562 The Bayesian Learning Rule Mohammad Emtiyaz Khan, Håvard Rue 2021-07-09 2021-07-09 [("doi","10.48550/arXiv.2107.04562")] statistics/bayes

We show that many machine-learning algorithms are specific instances of a single algorithm called the Bayesian learning rule. The rule, derived from Bayesian principles, yields a wide-range of algorithms from fields such as optimization, deep learning, and graphical models.

This includes classical algorithms such as ridge regression, Newton’s method, and Kalman filter, as well as modern deep-learning algorithms such as stochastic-gradient descent, RMSprop, and Dropout.

The key idea in deriving such algorithms is to approximate the posterior using candidate distributions estimated by using natural gradients. Different candidate distributions result in different algorithms, and further approximations to natural gradients give rise to variants of those algorithms.

Our work not only unifies, generalizes, and improves existing algorithms, but also helps us design new ones.

--- https://arxiv.org/abs/2107.04589 ViTGAN: Training GANs with Vision Transformers Kwonjoon Lee, Huiwen Chang, Lu Jiang, Han Zhang, Zhuowen Tu, Ce Liu 2021-07-09 2021-07-09 [("doi","10.48550/arXiv.2107.04589")] ai/nn/gan ai/nn/transformer

Recently, Vision Transformers (ViTs) have shown competitive performance on image recognition while requiring less vision-specific inductive biases. In this paper, we investigate if such observation can be extended to image generation. To this end, we integrate the ViT architecture into generative adversarial networks (GANs).

We observe that existing regularization methods for GANs interact poorly with self-attention, causing serious instability during training. To resolve this issue, we introduce novel regularization techniques for training GANs with ViTs.

Empirically, our approach, named ViTGAN, achieves comparable performance to state-of-the-art CNN-based StyleGAN-2 on CIFAR-10, CelebA, and LSUN bedroom datasets.

--- https://arxiv.org/abs/2107.05677 Codified audio language modeling learns useful representations for music information retrieval Rodrigo Castellon, Chris Donahue, Percy Liang 2021-07-12 2021-07-12 [("doi","10.48550/arXiv.2107.05677")] ai/nn/transformer/gpt/jukebox

We demonstrate that language models pre-trained on codified (discretely-encoded) music audio learn representations that are useful for downstream MIR tasks. Specifically, we explore representations from Jukebox (Dhariwal et al 2020): a music generation system containing a language model trained on codified audio from 1M songs.

To determine if Jukebox’s representations contain useful information for MIR, we use them as input features to train shallow models on several MIR tasks.

Relative to representations from conventional MIR models which are pre-trained on tagging, we find that using representations from Jukebox as input features yields 30% stronger performance on average across four MIR tasks: tagging, genre classification, emotion recognition, and key detection.

For key detection, we observe that representations from Jukebox are considerably stronger than those from models pre-trained on tagging, suggesting that pre-training via codified audio language modeling may address blind spots in conventional approaches. We interpret the strength of Jukebox’s representations as evidence that modeling audio instead of tags provides richer representations for MIR.

--- https://arxiv.org/abs/2107.06277 Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine 2021-07-13 2021-07-13 [("doi","10.48550/arXiv.2107.06277")] reinforcement-learning/exploration reinforcement-learning/meta-learning statistics/bayes

[blog] Generalization is a central challenge for the deployment of reinforcement learning (RL) systems in the real world.

In this paper, we show that the sequential structure of the RL problem necessitates new approaches to generalization beyond the well-studied techniques used in supervised learning. While supervised learning methods can generalize effectively without explicitly accounting for epistemic uncertainty, we show that, perhaps surprisingly, this is not the case in RL.

We show that generalization to unseen test conditions from a limited number of training conditions induces implicit partial observability, effectively turning even fully-observed MDPs into POMDPs.

Informed by this observation, we recast the problem of generalization in RL as solving the induced partially observed Markov decision process, which we call the epistemic POMDP. We demonstrate the failure modes of algorithms that do not appropriately handle this partial observability, and suggest a simple ensemble-based technique for solving the partially observed problem.

Empirically, we demonstrate that our simple algorithm derived from the epistemic POMDP achieves substantial gains in generalization over current methods on the Procgen benchmark suite.

--- https://arxiv.org/abs/2107.06383 How Much Can CLIP Benefit Vision-and-Language Tasks? Sheng Shen, Liunian Harold Li, Hao Tan, Mohit Bansal, Anna Rohrbach, Kai-Wei Chang, Zhewei Yao, Kurt Keutzer 2021-07-13 2021-07-13 [("doi","10.48550/arXiv.2107.06383")] ai/nn/transformer/clip

Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manually-annotated data (as compared to web-crawled data), to perceive the visual world. However, it has been observed that large-scale pretraining usually can result in better generalization performance, eg. CLIP (Contrastive Language-Image Pre-training), trained on a massive amount of image-caption pairs, has shown a strong zero-shot capability on various vision tasks.

To further study the advantage brought by CLIP, we propose to use CLIP as the visual encoder in various V&L models in two typical scenarios: (1) plugging CLIP into task-specific fine-tuning; (2) combining CLIP with V&L pre-training and transferring to downstream tasks. We show that CLIP substantial outperforms widely-used visual encoders trained with in-domain annotated data, such as BottomUp-TopDown. We achieve competitive or better results on diverse V&L tasks, while establishing new state-of-the-art results on Visual Question Answering, Visual Entailment, and V&L Navigation tasks.

We release our code at Github.

--- https://arxiv.org/abs/2107.07675#google Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models Daniel D. Johnson, Jacob Austin, Rianne van den Berg, Daniel Tarlow 2021-07-16 2021-07-16 [("doi","10.48550/arXiv.2107.07675")] ai/nn/diffusion/discrete

Denoising diffusion probabilistic models (DDPMs) have shown impressive results on sequence generation by iteratively corrupting each example and then learning to map corrupted versions back to the original. However, previous work has largely focused on in-place corruption, adding noise to each pixel or token individually while keeping their locations the same.

In this work, we consider a broader class of corruption processes and denoising models over sequence data that can insert and delete elements, while still being efficient to train and sample from.

We demonstrate that these models outperform standard in-place models on an arithmetic sequence task, and that when trained on the text8 dataset they can be used to fix spelling errors without any fine-tuning.

--- https://arxiv.org/abs/2107.08387 Train on Small, Play the Large: Scaling Up Board Games with AlphaZero and GNN Shai Ben-Assayag, Ran El-Yaniv 2021-07-18 2021-07-18 [("doi","10.48550/arXiv.2107.08387")] reinforcement-learning/model/alphago

Playing board games is considered a major challenge for both humans and AI researchers. Because some complicated board games are quite hard to learn, humans usually begin with playing on smaller boards and incrementally advance to master larger board strategies. Most neural network frameworks that are currently tasked with playing board games neither perform such incremental learning nor possess capabilities to automatically scale up.

In this work, we look at the board as a graph and combine a graph neural network architecture inside the AlphaZero framework, along with some other innovative improvements. Our ScalableAlphaZero is capable of learning to play incrementally on small boards, and advancing to play on large ones. Our model can be trained quickly to play different challenging board games on multiple board sizes, without using any domain knowledge.

We demonstrate the effectiveness of ScalableAlphaZero and show, for example, that by training it for only 3 days on small Othello boards, it can defeat the AlphaZero model on a large board, which was trained to play the large board for 30 days.

--- https://arxiv.org/abs/2107.08590 EvilModel: Hiding Malware Inside of Neural Network Models Zhi Wang, Chaoge Liu, Xiang Cui 2021-07-19 2021-07-19 [("doi","10.48550/arXiv.2107.08590")] ai/nn/sparsity cs/security

Delivering malware covertly and detection-evadingly is critical to advanced malware campaigns. In this paper, we present a method that delivers malware covertly and detection-evadingly through neural network models. Neural network models are poorly explainable and have a good generalization ability. By embedding malware into the neurons, malware can be delivered covertly with minor or even no impact on the performance of neural networks.

Meanwhile, since the structure of the neural network models remains unchanged, they can pass the security scan of antivirus engines. Experiments show that 36.9MB of malware can be embedded into a 178MB-AlexNet model within 1% accuracy loss, and no suspicious are raised by antivirus engines in VirusTotal, which verifies the feasibility of this method.

With the widespread application of artificial intelligence, using neural networks becomes a forwarding trend of malware. We hope this work could provide a referenceable scenario for the defense on neural network-assisted attacks.

--- https://arxiv.org/abs/2107.11186 LARGE: Latent-Based Regression through GAN Semantics Yotam Nitzan, Rinon Gal, Ofir Brenner, Daniel Cohen-Or 2021-07-22 2021-07-22 [("doi","10.48550/arXiv.2107.11186")] ai/nn/gan/stylegan

We propose a novel method for solving regression tasks using few-shot or weak supervision. At the core of our method is the fundamental observation that GANs are incredibly successful at encoding semantic information within their latent space, even in a completely unsupervised setting. For modern generative frameworks, this semantic encoding manifests as smooth, linear directions which affect image attributes in a disentangled manner. These directions have been widely used in GAN-based image editing.

We show that such directions are not only linear, but that the magnitude of change induced on the respective attribute is ~linear with respect to the distance traveled along them. By leveraging this observation, our method turns a pre-trained GAN into a regression model, using as few as two labeled samples. This enables solving regression tasks on datasets and attributes which are difficult to produce quality supervision for. Additionally, we show that the same latent-distances can be used to sort collections of images by the strength of given attributes, even in the absence of explicit supervision.

Extensive experimental evaluations demonstrate that our method can be applied across a wide range of domains, leverage multiple latent direction discovery frameworks, and achieve state-of-the-art results in few-shot and low-supervision settings, even when compared to methods designed to tackle a single task.

--- https://arxiv.org/abs/2107.12514 Language Grounding with 3D Objects Jesse Thomason, Mohit Shridhar, Yonatan Bisk, Chris Paxton, Luke Zettlemoyer 2021-07-26 2021-07-26 [("doi","10.48550/arXiv.2107.12514")] ai/nn/transformer/clip reinforcement-learning/robot

Seemingly simple natural language requests to a robot are generally underspecified, for example “Can you bring me the wireless mouse?” Flat images of candidate mice may not provide the discriminative information needed for “wireless.” The world, and objects in it, are not flat images but complex 3D shapes. If a human requests an object based on any of its basic properties, such as color, shape, or texture, robots should perform the necessary exploration to accomplish the task. In particular, while substantial effort and progress has been made on understanding explicitly visual attributes like color and category, comparatively little progress has been made on understanding language about shapes and contours.

In this work, we introduce a novel reasoning task that targets both visual and non-visual language about 3D objects. Our new benchmark, ShapeNet Annotated with Referring Expressions (SNARE) requires a model to choose which of two objects is being referenced by a natural language description. We introduce several CLIP-based models for distinguishing objects and demonstrate that while recent advances in jointly modeling vision and language are useful for robotic language understanding, it is still the case that these image-based models are weaker at understanding the 3D nature of objects—properties which play a key role in manipulation.

We find that adding view estimation to language grounding models improves accuracy on both SNARE and when identifying objects referred to in language on a robot platform, but note that a large gap remains between these models and human performance.

--- https://arxiv.org/abs/2107.12518 Segmentation in Style: Unsupervised Semantic Image Segmentation with StyleGAN and CLIP Daniil Pakhomov, Sanchit Hira, Narayani Wagle, Kemar E. Green, Nassir Navab 2021-07-26 2021-07-26 [("doi","10.48550/arXiv.2107.12518")] ai/nn/transformer/clip

[anime face demo] We introduce a method that allows to automatically segment images into semantically meaningful regions without human supervision. Derived regions are consistent across different images and coincide with human-defined semantic classes on some datasets. In cases where semantic regions might be hard for human to define and consistently label, our method is still able to find meaningful and consistent semantic classes.

In our work, we use pretrained StyleGAN-2 generative model: clustering in the feature space of the generative model allows to discover semantic classes. Once classes are discovered, a synthetic dataset with generated images and corresponding segmentation masks can be created. After that a segmentation model is trained on the synthetic dataset and is able to generalize to real images. Additionally, by using CLIP (Radford et al 2021) we are able to use prompts defined in a natural language to discover some desired semantic classes.

We test our method on publicly available datasets and show state-of-the-art results.

--- https://arxiv.org/abs/2107.12808#deepmind Open-Ended Learning Leads to Generally Capable Agents Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michael Mathieu, Nat McAleese, Nathalie Bradley-Schmieg, Nathaniel Wong, Nicolas Porcel, Roberta Raileanu, Steph Hughes-Fitt, Valentin Dalibard, Wojciech Marian Czarnecki 2021-07-27 2021-07-27 [("doi","10.48550/arXiv.2107.12808")] reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/multi-agent reinforcement-learning/scaling

In this work we create agents that can perform well beyond a single, individual task, that exhibit much wider generalisation of behavior to a massive, rich space of challenges.

We define a universe of tasks within an environment domain and demonstrate the ability to train agents that are generally capable across this vast space and beyond. The environment is natively multi-agent, spanning the continuum of competitive, cooperative, and independent games, which are situated within procedurally generated physical 3D worlds. The resulting space is exceptionally diverse in terms of the challenges posed to agents, and as such, even measuring the learning progress of an agent is an open research problem.

We propose an iterative notion of improvement between successive generations of agents, rather than seeking to maximize a singular objective, allowing us to quantify progress despite tasks being incomparable in terms of achievable rewards. We show that through constructing an open-ended learning process, which dynamically changes the training task distributions and training objectives such that the agent never stops learning, we achieve consistent learning of new behaviors.

The resulting agent is able to score reward in every one of our humanly solvable evaluation levels, with behavior generalizing to many held-out points in the universe of tasks. Examples of this zero-shot generalisation include good performance on Hide and Seek, Capture the Flag, and Tag. Through analysis and hand-authored probe tasks we characterise the behavior of our agent, and find interesting emergent heuristic behaviors such as trial-and-error experimentation, simple tool use, option switching, and cooperation.

Finally, we demonstrate that the general capabilities of this agent could unlock larger scale transfer of behavior through cheap finetuning.

--- https://arxiv.org/abs/2107.12979 Predictive Coding: a Theoretical and Experimental Review Beren Millidge, Anil Seth, Christopher L. Buckley 2021-07-27 2021-07-27 [("doi","10.48550/arXiv.2107.12979")] ai/nn psychology/neuroscience reinforcement-learning/exploration/active-learning

Predictive coding offers a potentially unifying account of cortical function—postulating that the core function of the brain is to minimize prediction errors with respect to a generative model of the world. The theory is closely related to the Bayesian brain framework and, over the last two decades, has gained substantial influence in the fields of theoretical and cognitive neuroscience.

A large body of research has arisen based on both empirically testing improved and extended theoretical and mathematical models of predictive coding, as well as in evaluating their potential biological plausibility for implementation in the brain and the concrete neurophysiological and psychological predictions made by the theory.

Despite this enduring popularity, however, no comprehensive review of predictive coding theory, and especially of recent developments in this field, exists. Here, we provide a comprehensive review both of the core mathematical structure and logic of predictive coding, thus complementing recent tutorials in the literature. We also review a wide range of classic and recent work within the framework, ranging from the neurobiologically realistic microcircuits that could implement predictive coding, to the close relationship between predictive coding and the widely-used backpropagation of error algorithm, as well as surveying the close relationships between predictive coding and modern machine learning techniques.

--- https://arxiv.org/abs/2107.13034#google Dataset Distillation with Infinitely Wide Convolutional Networks Timothy Nguyen, Roman Novak, Lechao Xiao, Jaehoon Lee 2021-07-27 2021-07-27 [("doi","10.48550/arXiv.2107.13034")] ai/nn/cnn ai/nn/sparsity/knowledge-distillation reinforcement-learning/exploration/active-learning reinforcement-learning/meta-learning

The effectiveness of machine learning algorithms arises from being able to extract useful features from large amounts of data. As model and dataset sizes increase, dataset distillation methods that compress large datasets into smaller yet highly performant ones will become valuable in terms of training efficiency and useful feature extraction.

To that end, we apply a novel distributed kernel based meta-learning framework to achieve state-of-the-art results for dataset distillation using infinitely wide convolutional neural networks. For instance, using only 10 datapoints (0.02% of original dataset), we obtain over 64% test accuracy on CIFAR-10 image classification task, a dramatic improvement over the previous best test accuracy of 40%. Our state-of-the-art results extend across many other settings for MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, and SVHN.

Furthermore, we perform some preliminary analyses of our distilled datasets to shed light on how they differ from naturally occurring data.

--- https://arxiv.org/abs/2108.00946#nvidia StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators Rinon Gal, Or Patashnik, Haggai Maron, Gal Chechik, Daniel Cohen-Or 2021-08-02 2021-08-02 [("doi","10.48550/arXiv.2108.00946")] ai/nn/transformer/clip

Can a generative model be trained to produce images from a specific domain, guided by a text prompt only, without seeing any image? In other words: can an image generator be trained blindly?

Leveraging the semantic power of large scale Contrastive-Language-Image-Pre-training (CLIP) models, we present a text-driven method that allows shifting a generative model to new domains, without having to collect even a single image from those domains. We show that through natural language prompts and a few minutes of training, our method can adapt a generator across a multitude of domains characterized by diverse styles and shapes. Notably, many of these modifications would be difficult or outright impossible to reach with existing methods.

We conduct an extensive set of experiments and comparisons across a wide range of domains. These demonstrate the effectiveness of our approach and show that our shifted models maintain the latent-space properties that make generative models appealing for downstream tasks.

--- https://arxiv.org/abs/2108.02755#salesforce The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning Stephan Zheng, Alexander Trott, Sunil Srinivasa, David C. Parkes, Richard Socher 2021-08-05 2021-08-05 [("doi","10.48550/arXiv.2108.02755")] reinforcement-learning/meta-learning reinforcement-learning/multi-agent

AI and reinforcement learning (RL) have improved many areas, but are not yet widely adopted in economic policy design, mechanism design, or economics at large. At the same time, current economic methodology is limited by a lack of counterfactual data, simplistic behavioral models, and limited opportunities to experiment with policies and evaluate behavioral responses.

Here we show that machine-learning-based economic simulation is a powerful policy and mechanism design framework to overcome these limitations. The AI Economist is a two-level, deep RL framework that trains both agents and a social planner who co-adapt, providing a tractable solution to the highly unstable and novel two-level RL challenge. From a simple specification of an economy, we learn rational agent behaviors that adapt to learned planner policies and vice versa. We demonstrate the efficacy of the AI Economist on the problem of optimal taxation. In simple one-step economies, the AI Economist recovers the optimal tax policy of economic theory. In complex, dynamic economies, the AI Economist substantially improves both utilitarian social welfare and the trade-off between equality and productivity over baselines. It does so despite emergent tax-gaming strategies, while accounting for agent interactions and behavioral change more accurately than economic theory.

These results demonstrate for the first time that two-level, deep RL can be used for understanding and as a complement to theory for economic design, unlocking a new computational learning-based approach to understanding economic policy.

--- https://arxiv.org/abs/2108.02818#openai Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications Sandhini Agarwal, Gretchen Krueger, Jack Clark, Alec Radford, Jong Wook Kim, Miles Brundage 2021-08-05 2021-08-05 [("doi","10.48550/arXiv.2108.02818")] ai/nn/transformer/clip

Recently, there have been breakthroughs in computer vision (“CV”) models that are more generalizable with the advent of models such as CLIP and ALIGN. In this paper, we analyze CLIP and highlight some of the challenges such models pose.

CLIP reduces the need for task specific training data, potentially opening up many niche tasks to automation. CLIP also allows its users to flexibly specify image classification classes in natural language, which we find can shift how biases manifest. Additionally, through some preliminary probes we find that CLIP can inherit biases found in prior computer vision systems.

Given the wide and unpredictable domain of uses for such models, this raises questions regarding what sufficiently safe behavior for such systems may look like. These results add evidence to the growing body of work calling for a change in the notion of a ‘better’ model—to move beyond simply looking at higher accuracy at task-oriented capability evaluations, and towards a broader ‘better’ that takes into account deployment-critical features such as different use contexts, and people who interact with the model when thinking about model deployment.

--- https://arxiv.org/abs/2108.03265#facebook Facebook AI WMT21 News Translation Task Submission Chau Tran, Shruti Bhosale, James Cross, Philipp Koehn, Sergey Edunov, Angela Fan 2021-08-06 2021-08-06 [("doi","10.48550/arXiv.2108.03265")] ai/scaling

We describe Facebook’s multilingual model submission to the WMT2021 shared task on news translation. We participate in 14 language directions: English to and from Czech, German, Hausa, Icelandic, Japanese, Russian, and Chinese. To develop systems covering all these directions, we focus on multilingual models.

We use data from all available sources—WMT, large-scale data mining, and in-domain back-translation—to create high quality bilingual and multilingual baselines. Subsequently, we investigate strategies for scaling multilingual model size, such that one system has sufficient capacity for high quality representations of all 8 languages.

Our final submission is an ensemble of dense and sparse Mixture-of-Experts multilingual translation models, followed by finetuning on in-domain news data and noisy channel reranking. Compared to previous year’s winning submissions, our multilingual system improved the translation quality on all language directions, with an average improvement of 2.0 BLEU. In the WMT2021 task, our system ranks first in 10 directions based on automatic evaluation.

--- https://arxiv.org/abs/2108.04324 FairyTailor: A Multimodal Generative Framework for Storytelling Eden Bensaid, Mauro Martino, Benjamin Hoover, Hendrik Strobelt 2021-07-13 2021-07-13 [("doi","10.48550/arXiv.2108.04324")] ai/nn/transformer/clip

Storytelling is an open-ended task that entails creative thinking and requires a constant flow of ideas. Natural language generation (NLG) for storytelling is especially challenging because it requires the generated text to follow an overall theme while remaining creative and diverse to engage the reader. In this work, we introduce a system and a web-based demo, FairyTailor, for human-in-the-loop visual story co-creation. Users can create a cohesive children’s fairytale by weaving generated texts and retrieved images with their input. FairyTailor adds another modality and modifies the text generation process to produce a coherent and creative sequence of text and images.

To our knowledge, this is the first dynamic tool for multimodal story generation that allows interactive co-formation of both texts and images. It allows users to give feedback on co-created stories and share their results.

--- https://arxiv.org/abs/2108.05818#tencent PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management Jiarui Fang, Yang Yu, Zilin Zhu, Shenggui Li, Yang You, Jie Zhou 2021-08-12 2021-08-12 [("doi","10.48550/arXiv.2108.05818")] ai/scaling/hardware

The pre-trained model (PTM) is revolutionizing Artificial intelligence (AI) technology. It can learn general language features on massive data and then be fine-tuned on task-specific data. Unfortunately, the computing hardware requirement of PTM training is prohibitively expensive, which makes it a game for a small proportion of people in the AI community.

Therefore, we proposed a system called PatrickStar to lower the hardware requirements of PTMs and make them accessible to everyone. PatrickStar uses the CPU-GPU heterogeneous memory space to store the model data. Different from existing works, we first manage the model data in a fine-grained manner by organizing them in memory chunks and dynamically distributing them in the heterogeneous memory space. Guided by the runtime memory statistics collected in a warm-up iteration, chunks are orchestrated efficiently in heterogeneous memory and generate lower CPU-GPU data transmission volume. Symbiosis with the Zero Redundancy Optimizer, PatrickStar scales to multiple GPUs using data parallelism, with lower communication bandwidth requirements and more efficient bandwidth utilization. The system can train tasks on bigger models and larger batch sizes, which existing works cannot complete.

Experimental results show that PatrickStar trains a 12 billion parameters GPT model, 1.5× as large as the model scale limit of the SOTA works, on an 8×V100 and 240GB CPU memory node, and also achieves higher computing efficiency than SOTA. Even on a $700 personal computer, it can train a 0.7 billion parameter GPT model.

Our code is publicly available at Github.

--- https://arxiv.org/abs/2108.05887#pinterest Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations Josh Beal, Hao-Yu Wu, Dong Huk Park, Andrew Zhai, Dmitry Kislyuk 2021-08-12 2021-08-12 [("doi","10.48550/arXiv.2108.05887")] ai/nn/retrieval ai/nn/transformer ai/scaling

Large-scale pretraining of visual representations has led to state-of-the-art performance on a range of benchmark computer vision tasks, yet the benefits of these techniques at extreme scale in complex production systems has been relatively unexplored. We consider the case of a popular visual discovery product, where these representations are trained with multi-task learning, from use-case specific visual understanding (eg. skin tone classification) to general representation learning for all visual content (eg. embeddings for retrieval).

In this work, we describe how we (1) generate a dataset with over a billion images via large weakly-supervised pretraining to improve the performance of these visual representations, and (2) leverage Transformers to replace the traditional convolutional backbone, with insights into both system and performance improvements, especially at 1B+ image scale. To support this backbone model, we detail a systematic approach to deriving weakly-supervised image annotations from heterogeneous text signals, demonstrating the benefits of clustering techniques to handle the long-tail distribution of image labels.

Through a comprehensive study of offline and online evaluation, we show that large-scale Transformer-based pretraining provides large benefits to industry computer vision applications.

The model is deployed in a production visual shopping system, with 36% improvement in top-1 relevance and 23% improvement in click-through volume. We conduct extensive experiments to better understand the empirical relationships between Transformer-based architectures, dataset scale, and the performance of production vision systems.

--- https://arxiv.org/abs/2108.08688 Contrastive Language-Image Pre-training for the Italian Language Federico Bianchi, Giuseppe Attanasio, Raphael Pisoni, Silvia Terragni, Gabriele Sarti, Sri Lakshmi 2021-08-19 2021-08-19 [("doi","10.48550/arXiv.2108.08688")] ai/nn/retrieval ai/nn/transformer/clip

CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts. The model is trained on a massive amount of English data and shows impressive performance on zero-shot classification tasks. Training the same model on a different language is not trivial, since data in other languages might be not enough and the model needs high-quality translations of the texts to guarantee a good performance.

In this paper, we present the first CLIP model for the Italian Language (CLIP-Italian), trained on more than 1.4 million image-text pairs. Results show that CLIP-Italian outperforms the multilingual CLIP model on the tasks of image retrieval and zero-shot classification.

--- https://arxiv.org/abs/2108.08827 ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis Patrick Esser, Robin Rombach, Andreas Blattmann, Björn Ommer 2021-08-19 2021-08-19 [("doi","10.48550/arXiv.2108.08827")] ai/nn/diffusion ai/nn/transformer

Autoregressive models and their sequential factorization of the data likelihood have recently demonstrated great potential for image representation and synthesis. Nevertheless, they incorporate image context in a linear 1D order by attending only to previously synthesized image patches above or to the left. Not only is this unidirectional, sequential bias of attention unnatural for images as it disregards large parts of a scene until synthesis is almost complete. It also processes the entire image on a single scale, thus ignoring more global contextual information up to the gist of the entire scene.

As a remedy we incorporate a coarse-to-fine hierarchy of context by combining the autoregressive formulation with a multinomial diffusion process: Whereas a multistage diffusion process successively removes information to coarsen an image, we train a (short) Markov chain to invert this process. In each stage the resulting autoregressive ImageBART model progressively incorporates context from previous stages in a coarse-to-fine manner.

Experiments show greatly improved image modification capabilities over autoregressive models while also providing high-fidelity image generation, both of which are enabled through efficient training in a compressed latent space. Specifically, our approach can take unrestricted, user-provided masks into account to perform local image editing. Thus, in contrast to pure autoregressive models, it can solve free-form image inpainting and, in the case of conditional models, local-text-guided image modification without requiring mask-specific training.

--- https://arxiv.org/abs/2108.08922 Controlled GAN-Based Creature Synthesis via a Challenging Game Art Dataset—Addressing the Noise-Latent Trade-Off Vaibhav Vavilala, David Forsyth 2021-08-19 2021-08-19 [("doi","10.48550/arXiv.2108.08922")] ai/nn/gan/stylegan

The StyleGAN-2 network supports powerful methods for creating and editing art, encompassing generating random images, finding images “like” some query, and modifying content or style. Additionally, recent advancements have enabled training with small datasets. We apply these techniques to synthesize card art, focusing on a novel Yu-Gi-Oh dataset.

While noise inputs to StyleGAN-2 are crucial for effective synthesis, we discovered that, in the context of small datasets, coarse-scale noise conflicts with latent variables as both influence long-scale image effects. This issue manifested as over-aggressive variation in art following changes in noise and diminished content control when editing latent variables. To address these challenges, we trained a modified StyleGAN-2 model in which coarse-scale noise is suppressed, thereby eliminating these undesirable effects.

Through this process, we achieved a superior FID, indicating improved image quality. Moreover, alterations in noise now facilitate local exploration of style rather than causing erratic changes, and identity control through latent variable adjustments is significantly enhanced.

Our results and analyses serve as foundational steps towards the development of a GAN-assisted art synthesis tool. Such a tool holds immense potential for digital artists across various skill levels, offering new horizons in artistic ideation within film, games, and other creative industries.

--- https://arxiv.org/abs/2108.09293 An Empirical Cybersecurity Evaluation of GitHub Copilot’s Code Contributions Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, Ramesh Karri 2021-08-20 2021-08-20 [("doi","10.48550/arXiv.2108.09293")] ai/nn/transformer/gpt/codex reinforcement-learning/safe

There is burgeoning interest in designing AI-based systems to assist humans in designing computing systems, including tools that automatically generate computer code. The most notable of these comes in the form of the first self-described ‘AI pair programmer’, GitHub Copilot, a language model trained over open-source GitHub code. However, code often contains bugs—and so, given the vast quantity of unvetted code that Copilot has processed, it is certain that the language model will have learned from exploitable, buggy code. This raises concerns on the security of Copilot’s code contributions.

In this work, we systematically investigate the prevalence and conditions that can cause GitHub Copilot to recommend insecure code. To perform this analysis we prompt Copilot to generate code in scenarios relevant to high-risk CWEs (eg. those from MITRE’s “Top 25” list). We explore Copilot’s performance on 3 distinct code generation axes—examining how it performs given diversity of weaknesses, diversity of prompts, and diversity of domains.

In total, we produce 89 different scenarios for Copilot to complete, producing 1,692 programs. Of these, we found ~40% to be vulnerable.

This raises significant concerns about the reliability and security of auto-generated code, particularly for applications in critical systems.

--- https://arxiv.org/abs/2108.10315 Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control Dimitri Bertsekas 2021-08-20 2021-08-20 [("doi","10.48550/arXiv.2108.10315")] reinforcement-learning/model/alphago

In this paper we aim to provide analysis and insights (often based on visualization), which explain the beneficial effects of on-line decision making on top of off-line training.

In particular, through an unifying abstract mathematical framework, we show that the principal AlphaZero/TD-Gammon ideas of approximation in value space and rollout apply very broadly to deterministic and stochastic optimal control problems, involving both discrete and continuous search spaces.

Moreover, these ideas can be effectively integrated with other important methodologies such as model predictive control, adaptive control, decentralized control, discrete and Bayesian optimization, neural network-based value and policy approximations, and heuristic algorithms for discrete optimization.

--- https://arxiv.org/abs/2108.11514 Bilateral Denoising Diffusion Models Max W. Y. Lam, Jun Wang, Rongjie Huang, Dan Su, Dong Yu 2021-08-26 2021-08-26 [("doi","10.48550/arXiv.2108.11514")] ai/nn/diffusion

Denoising diffusion probabilistic models (DDPMs) have emerged as competitive generative models yet brought challenges to efficient sampling. In this paper we propose novel bilateral denoising diffusion models (BDDMs), which take fewer steps to generate high-quality samples.

From a bilateral modeling objective, BDDMs parameterize the forward and reverse processes with a score network and a scheduling network, respectively. We show that a new lower bound tighter than the standard evidence lower bound can be derived as a surrogate objective for training the two networks.

In particular, BDDMs are efficient, simple-to-train, and capable of further improving any pre-trained DDPM by optimizing the inference noise schedules. Our experiments demonstrated that BDDMs can generate high-fidelity samples with as few as 3 sampling steps and produce comparable or even higher quality samples than DDPMs using 1,000 steps with only 16 sampling steps (a 62× speedup).

--- https://arxiv.org/abs/2109.01093 What Users Want? WARHOL: A Generative Model for Recommendation Jules Samaran, Ugo Tanielian, Romain Beaumont, Flavian Vasile 2021-09-02 2021-09-02 [("doi","10.48550/arXiv.2109.01093")] ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e/1

Current recommendation approaches help online merchants predict, for each visiting user, which subset of their existing products is the most relevant. However, besides being interested in matching users with existing products, merchants are also interested in understanding their users’ underlying preferences. This could indeed help them produce or acquire better matching products in the future.

We argue that existing recommendation models cannot directly be used to predict the optimal combination of features that will make new products serve better the needs of the target audience. To tackle this, we turn to generative models, which allow us to learn explicitly distributions over product feature combinations both in text and visual space.

We develop WARHOL, a product generation and recommendation architecture that takes as input past user shopping activity and generates relevant textual and visual descriptions of novel products.

We show that WARHOL can approach the performance of state-of-the-art recommendation models, while being able to generate entirely new products that are relevant to the given user profiles.

--- https://arxiv.org/abs/2109.01652#google FLAN: Finetuned Language Models Are Zero-Shot Learners Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, Quoc V. Le 2021-09-03 2021-09-03 [("doi","10.48550/arXiv.2109.01652")] ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/gpt/lamda reinforcement-learning/meta-learning reinforcement-learning/scaling

[blog] This paper explores a simple method for improving the zero-shot learning abilities of language models. We show that instruction tuning—finetuning language models on a collection of tasks described via instructions—substantially boosts zero-shot performance on unseen tasks.

We take a 137b parameter pretrained language model and instruction-tune it on over 60 NLP tasks verbalized via natural language instruction templates.

We evaluate this instruction-tuned model, which we call FLAN, on unseen task types. FLAN substantially improves the performance of its unmodified counterpart and surpasses zero-shot 175B GPT-3 on 20⁄25 tasks that we evaluate. FLAN even outperforms few-shot GPT-3 by a large margin on ANLI, RTE, BoolQ, AI2-ARC, OpenbookQA, and StoryCloze.

Ablation studies reveal that number of tasks and model scale are key components to the success of instruction tuning.

--- https://arxiv.org/abs/2109.01903 Robust fine-tuning of zero-shot models Mitchell Wortsman, Gabriel Ilharco, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt 2021-09-04 2021-09-04 [("doi","10.48550/arXiv.2109.01903")] ai/nn/transformer/clip

Large pre-trained models such as CLIP offer consistent accuracy across a range of data distributions when performing zero-shot inference (ie. without fine-tuning on a specific dataset). Although existing fine-tuning approaches substantially improve accuracy in-distribution, they also reduce out-of-distribution robustness.

We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models. Compared to standard fine-tuning, the resulting weight-space ensembles provide large accuracy improvements out-of-distribution, while matching or improving in-distribution accuracy.

On ImageNet and 5 derived distribution shifts, weight-space ensembles improve out-of-distribution accuracy by 2 to 10 percentage points while increasing in-distribution accuracy by nearly 1 percentage point relative to standard fine-tuning. These improvements come at no additional computational cost during fine-tuning or inference.

--- https://arxiv.org/abs/2109.02008 Sparse-MLP: A Fully-MLP Architecture with Conditional Computation Yuxuan Lou, Fuzhao Xue, Zangwei Zheng, Yang You 2021-09-05 2021-09-05 [("doi","10.48550/arXiv.2109.02008")] ai/nn/fully-connected ai/scaling/mixture-of-experts

Mixture of Experts (MoE) with sparse conditional computation has been proved an effective architecture for scaling attention-based models to more parameters with comparable computation cost.

In this paper, we propose Sparse-MLP, scaling the recent MLP-Mixer model with sparse MoE layers, to achieve a more computation-efficient architecture. We replace a subset of dense MLP blocks in the MLP-Mixer model with Sparse blocks. In each Sparse block, we apply two stages of MoE layers: one with MLP experts mixing information within channels along image patch dimension, one with MLP experts mixing information within patches along the channel dimension. Besides, to reduce computational cost in routing and improve experts capacity, we design Re-represent layers in each Sparse block. These layers are to re-scale image representations by two simple but effective linear transformations.

By pre-training on ImageNet-1k with MoCo v3 algorithm, our models can outperform dense MLP models with comparable parameters and less computational cost on several downstream image classification tasks.

--- https://arxiv.org/abs/2109.02102 Teaching Autoregressive Language Models Complex Tasks By Demonstration Gabriel Recchia 2021-09-05 2021-09-05 [("doi","10.48550/arXiv.2109.02102")] ai/nn/transformer/gpt/inner-monologue

This paper demonstrates that by fine-tuning an autoregressive language model (GPT-Neo) on appropriately structured step-by-step demonstrations, it is possible to teach it to execute a mathematical task that has previously proved difficult for Transformers—longhand modulo operations—with a relatively small number of examples. Specifically, we fine-tune GPT-Neo to solve the numbers__div_remainder task from the DeepMind Mathematics Dataset; Saxton et al 2019 reported below 40% accuracy on this task with 2 million training examples.

We show that after fine-tuning on 200 appropriately structured demonstrations of solving long division problems and reporting the remainders, the smallest available GPT-Neo model achieves over 80% accuracy. This is achieved by constructing an appropriate dataset for fine-tuning, with no changes to the learning algorithm.

These results suggest that fine-tuning autoregressive language models on small sets of well-crafted demonstrations may be a useful paradigm for enabling individuals without training in machine learning to coax such models to perform some kinds of complex multi-step tasks.

--- https://arxiv.org/abs/2109.02593#allen General-Purpose Question-Answering with Macaw Oyvind Tafjord, Peter Clark 2021-09-06 2021-09-06 [("doi","10.48550/arXiv.2109.02593")] ai/nn/transformer/gpt ai/nn/transformer/t5 ai/scaling

Despite the successes of pretrained language models, there are still few high-quality, general-purpose QA systems that are freely available. In response, we present Macaw, a versatile, generative question-answering (QA) system that we are making available to the community. Macaw is built on UnifiedQA, itself built on T5, and exhibits strong performance, zero-shot, on a wide variety of topics, including outperforming GPT-3 by over 10% (absolute) on Challenge300, a suite of 300 challenge questions, despite being an order of magnitude smaller (11 billion vs. 175 billion parameters).

In addition, Macaw allows different permutations (“angles”) of its inputs and outputs to be used, for example, Macaw can take a question and produce an answer; or take an answer and produce a question; or take an answer and question, and produce multiple-choice options. We describe the system, and illustrate a variety of question types where it produces surprisingly good answers, well outside the training setup.

We also identify question classes where it still appears to struggle, offering insights into the limitations of pretrained language models.

Macaw is freely available, and we hope that it proves useful to the community. Macaw is available at https://github.com/allenai/macaw.

--- https://arxiv.org/abs/2109.02748 Zero-Shot Open Set Detection by Extending CLIP Sepideh Esmaeilpour, Bing Liu, Eric Robertson, Lei Shu 2021-09-06 2021-09-06 [("doi","10.48550/arXiv.2109.02748")] ai/nn/transformer/clip

In a regular open set detection problem, samples of known classes (also called closed set classes) are used to train a special classifier. In testing, the classifier can (1) classify the test samples of known classes to their respective classes and (2) also detect samples that do not belong to any of the known classes (we say they belong to some unknown or open set classes). This paper studies the problem of zero-shot open-set detection, which still performs the same two tasks in testing but has no training except using the given known class names. This paper proposes a novel and yet simple method (called ZO-CLIP) to solve the problem.

ZO-CLIP builds on top of the recent advances in zero-shot classification through multi-modal representation learning. It first extends the pre-trained multi-modal model CLIP by training a text-based image description generator on top of CLIP. In testing, it uses the extended model to generate some candidate unknown class names for each test sample and computes a confidence score based on both the known class names and candidate unknown class names for zero-shot open set detection. Experimental results on 5 benchmark datasets for open set detection confirm that ZO-CLIP outperforms the baselines by a large margin.

--- https://arxiv.org/abs/2109.02869#google The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning Yujin Tang, David Ha 2021-09-07 2021-09-07 [("doi","10.48550/arXiv.2109.02869")] ai/nn/transformer psychology/neuroscience reinforcement-learning/meta-learning

In complex systems, we often observe complex global behavior emerge from a collection of agents interacting with each other in their environment, with each individual agent acting only on locally available information, without knowing the full picture. Such systems have inspired development of artificial intelligence algorithms in areas such as swarm optimization and cellular automata.

Motivated by the emergence of collective behavior from complex cellular systems, we build systems that feed each sensory input from the environment into distinct, but identical neural networks, each with no fixed relationship with one another. We show that these sensory networks can be trained to integrate information received locally, and through communication via an attention mechanism, can collectively produce a globally coherent policy. Moreover, the system can still perform its task even if the ordering of its inputs is randomly permuted several times during an episode. These permutation invariant systems also display useful robustness and generalization properties that are broadly applicable.

Interactive demo and videos of our results: https://attentionneuron.github.io/.

--- https://arxiv.org/abs/2109.03351 Capturing the objects of vision with neural networks Benjamin Peters, Nikolaus Kriegeskorte 2021-09-07 2021-09-07 [("doi","10.48550/arXiv.2109.03351")] psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Human visual perception carves a scene at its physical joints, decomposing the world into objects, which are selectively attended, tracked, and predicted as we engage our surroundings. Object representations emancipate perception from the sensory input, enabling us to keep in mind that which is out of sight and to use perceptual content as a basis for action and symbolic cognition. Human behavioral studies have documented how object representations emerge through grouping, amodal completion, proto-objects, and object files. Deep neural network (DNN) models of visual object recognition, by contrast, remain largely tethered to the sensory input, despite achieving human-level performance at labeling objects.

Here, we review related work in both fields and examine how these fields can help each other. The cognitive literature provides a starting point for the development of new experimental tasks that reveal mechanisms of human object perception and serve as benchmarks driving development of deep neural network models that will put the object into object recognition.

--- https://arxiv.org/abs/2109.03910#google A Recipe For Arbitrary Text Style Transfer with Large Language Models Emily Reif, Daphne Ippolito, Ann Yuan, Andy Coenen, Chris Callison-Burch, Jason Wei 2021-09-08 2021-09-08 [("doi","10.48550/arXiv.2109.03910")] ai/nn/transformer/gpt/lamda ai/scaling ai/text-style-transfer

In this paper, we leverage large language models (LMs) to perform zero-shot text style transfer.

We present a prompting method that we call augmented zero-shot learning, which frames style transfer as a sentence rewriting task and requires only a natural language instruction, without model fine-tuning or exemplars in the target style.

Augmented zero-shot learning is simple and demonstrates promising results not just on standard style transfer tasks such as sentiment, but also on arbitrary transformations such as “make this melodramatic” or “insert a metaphor.”

--- https://arxiv.org/abs/2109.04504#deepmind Bootstrapped Meta-Learning Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh 2021-09-09 2021-09-09 [("doi","10.48550/arXiv.2109.04504")] reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/model-free

Meta-learning empowers artificial intelligence to increase its efficiency by learning how to learn. Unlocking this potential involves overcoming a challenging meta-optimization problem that often exhibits ill-conditioning, and myopic meta-objectives. We propose an algorithm that tackles these issues by letting the meta-learner teach itself.

The algorithm first bootstraps a target from the meta-learner, then optimizes the meta-learner by minimizing the distance to that target under a chosen (pseudo-)metric. Focusing on meta-learning with gradients, we establish conditions that guarantee performance improvements and show that the improvement is related to the target distance. Thus, by controlling curvature, the distance measure can be used to ease meta-optimization, for instance by reducing ill-conditioning. Further, the bootstrapping mechanism can extend the effective meta-learning horizon without requiring backpropagation through all updates.

The algorithm is versatile and easy to implement. We achieve a new state-of-the-art for model-free agents on the Atari ALE benchmark, improve upon MAML in few-shot learning, and demonstrate how our approach opens up new possibilities by meta-learning efficient exploration in a Q-learning agent.

--- https://arxiv.org/abs/2109.04699 EfficientCLIP: Efficient Cross-Modal Pre-training by Ensemble Confident Learning and Language Modeling Jue Wang, Haofan Wang, Jincan Deng, Weijia Wu, Debing Zhang 2021-09-10 2021-09-10 [("doi","10.48550/arXiv.2109.04699")] ai/nn/retrieval ai/nn/transformer/clip

While large scale pre-training has achieved great achievements in bridging the gap between vision and language, it still faces several challenges. First, the cost for pre-training is expensive. Second, there is no efficient way to handle the data noise which degrades model performance. Third, previous methods only leverage limited image-text paired data, while ignoring richer single-modal data, which may result in poor generalization to single-modal downstream tasks.

In this work, we propose an EfficientCLIP method via Ensemble Learning Confident Learning to obtain a less noisy data subset. Extra rich non-paired single-modal text data is used for boosting the generalization of text branch.

We achieve the state-of-the-art performance on Chinese cross-modal retrieval tasks with only 1/10th training resources compared to CLIP and WenLan, while showing excellent generalization to single-modal tasks, including text retrieval and text classification.

--- https://arxiv.org/abs/2109.04838 Block Pruning For Faster Transformers François Lagunas, Ella Charlaix, Victor Sanh, Alexander M. Rush 2021-09-10 2021-09-10 [("doi","10.48550/arXiv.2109.04838")] ai/nn/sparsity/pruning ai/nn/transformer

Pre-training has improved model accuracy for both classification and generation tasks at the cost of introducing much larger and slower models. Pruning methods have proven to be an effective way of reducing model size, whereas distillation methods are proven for speeding up inference.

We introduce a block pruning approach targeting both small and fast models. Our approach extends structured methods by considering blocks of any size and integrates this structure into the movement pruning paradigm for fine-tuning.

We find that this approach learns to prune out full components of the underlying model, such as attention heads. Experiments consider classification and generation tasks, yielding among other results a pruned model that is a 2.4× faster, 74% smaller BERT on SQuAD v1, with a 1% drop on F1, competitive both with distilled models in speed and pruned models in size.

--- https://arxiv.org/abs/2109.06129 Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color Mostafa Abdou, Artur Kulmizev, Daniel Hershcovich, Stella Frank, Ellie Pavlick, Anders Søgaard 2021-09-13 2021-09-13 [("doi","10.48550/arXiv.2109.06129")] ai/nn/transformer/gpt psychology/vision

Pretrained language models have been shown to encode relational information, such as the relations between entities or concepts in knowledge-bases—(Paris, Capital, France). However, simple relations of this type can often be recovered heuristically and the extent to which models implicitly reflect topological structure that is grounded in world, such as perceptual structure, is unknown. To explore this question, we conduct a thorough case study on color. Namely, we employ a dataset of monolexemic color terms and color chips represented in CIELAB, a color space with a perceptually meaningful distance metric.

Using two methods of evaluating the structural alignment of colors in this space with text-derived color term representations, we find correspondence. Analyzing the differences in alignment across the color spectrum, we find that warmer colors are, on average, better aligned to the perceptual color space than cooler ones, suggesting an intriguing connection to findings from recent work on efficient communication in color naming. Further analysis suggests that differences in alignment are, in part, mediated by collocationality and differences in syntactic usage, posing questions as to the relationship between color perception and usage and context. [LMs can also update/manipulate color: Patel & Pavlick 2021]

--- https://arxiv.org/abs/2109.06166 Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN Badour AlBahar, Jingwan Lu, Jimei Yang, Zhixin Shu, Eli Shechtman, Jia-Bin Huang 2021-09-13 2021-09-13 [("doi","10.48550/arXiv.2109.06166")] ai/nn/gan/stylegan

We present an algorithm for re-rendering a person from a single image under arbitrary poses. Existing methods often have difficulties in hallucinating occluded contents photo-realistically while preserving the identity and fine details in the source image.

We first learn to inpaint the correspondence field between the body surface texture and the source image with a human body symmetry prior. The inpainted correspondence field allows us to transfer/warp local features extracted from the source to the target view even under large pose changes. Directly mapping the warped local features to an RGB image using a simple CNN decoder often leads to visible artifacts.

Thus, we extend the StyleGAN generator so that it takes pose as input (for controlling poses) and introduces a spatially varying modulation for the latent space using the warped local features (for controlling appearances). We show that our method compares favorably against the state-of-the-art algorithms in both quantitative evaluation and visual comparison.

--- https://arxiv.org/abs/2109.07301 What Vision-Language Models ‘See’ when they See Scenes Michele Cafagna, Kees van Deemter, Albert Gatt 2021-09-15 2021-09-15 [("doi","10.48550/arXiv.2109.07301")] ai/nn/transformer/clip

Images can be described in terms of the objects they contain, or in terms of the types of scene or place that they instantiate. In this paper we address to what extent pretrained Vision and Language models can learn to align descriptions of both types with images.

We compare 3 state-of-the-art models, VisualBERT, LXMERT and CLIP. We find that (1) V&L models are susceptible to stylistic biases acquired during pretraining; (2) only CLIP performs consistently well on both object-level and scene-level descriptions.

A follow-up ablation study shows that CLIP uses object-level information in the visual modality to align with scene-level textual descriptions.

--- https://arxiv.org/abs/2109.08065 Sigmoids behaving badly: why they usually cannot predict the future as well as they seem to promise Anders Sandberg, Stuart Armstrong, Rebecca Gorman, Rei England 2021-09-09 2021-09-09 [("doi","10.48550/arXiv.2109.08065")] statistics/prediction

Sigmoids (AKA s-curves or logistic curves) are commonly used in a diverse spectrum of disciplines as models for time-varying phenomena showing initial acceleration followed by slowing: technology diffusion, cumulative cases of an epidemic, population growth towards a carrying capacity, etc. Existing work demonstrates that retrospective fit of data is often impressive.

We show that in time series data, the future fit tends to be poor unless the data covers the entire range from before to after the inflection point.

We discuss the theoretical reasons for this: the growth data provides little information about the damping term (and vice-versa). As a consequence, forecasting with sigmoids tends to be very unreliable.

We suggest some practical approaches to improving the viability of forecasting sigmoid models.

--- https://arxiv.org/abs/2109.08603#deepmind Is Curiosity All You Need? On the Utility of Emergent Behaviors from Curious Exploration Oliver Groth, Markus Wulfmeier, Giulia Vezzani, Vibhavari Dasagi, Tim Hertweck, Roland Hafner, Nicolas Heess, Martin Riedmiller 2021-09-17 2021-09-17 [("doi","10.48550/arXiv.2109.08603")] reinforcement-learning/exploration reinforcement-learning/meta-learning

Curiosity-based reward schemes can present powerful exploration mechanisms which facilitate the discovery of solutions for complex, sparse or long-horizon tasks. However, as the agent learns to reach previously unexplored spaces and the objective adapts to reward new areas, many behaviors emerge only to disappear due to being overwritten by the constantly shifting objective.

We argue that merely using curiosity for fast environment exploration or as a bonus reward for a specific task does not harness the full potential of this technique and misses useful skills. Instead, we propose to shift the focus towards retaining the behaviors which emerge during curiosity-based learning. We posit that these self-discovered behaviors serve as valuable skills in an agent’s repertoire to solve related tasks.

Our experiments demonstrate the continuous shift in behavior throughout training and the benefits of a simple policy snapshot method to reuse discovered behavior for transfer tasks.

--- https://arxiv.org/abs/2109.08857#google Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts Yingtao Tian, David Ha 2021-09-18 2021-09-18 [("doi","10.48550/arXiv.2109.08857")] ai/nn/transformer/clip

Evolutionary algorithms have been used in the digital art scene since the 1970s. A popular application of genetic algorithms is to optimize the procedural placement of vector graphic primitives to resemble a given painting. In recent years, deep learning-based approaches have also been proposed to generate procedural drawings, which can be optimized using gradient descent.

In this work, we revisit the use of evolutionary algorithms for computational creativity. We find that modern evolution strategies (ES) algorithms, when tasked with the placement of shapes, offer large improvements in both quality and efficiency compared to traditional genetic algorithms, and even comparable to gradient-based methods.

We demonstrate that ES is also well suited at optimizing the placement of shapes to fit the CLIP model, and can produce diverse, distinct geometric abstractions that are aligned with human interpretation of language.

Videos and demo: https://es-clip.github.io/.

--- https://arxiv.org/abs/2109.09371 TrufLL: Learning Natural Language Generation from Scratch Alice Martin Donati, Guillaume Quispe, Charles Ollion, Sylvain Le Corff, Florian Strub, Olivier Pietquin 2021-09-20 2021-09-20 [("doi","10.48550/arXiv.2109.09371")] reinforcement-learning/exploration reinforcement-learning/imitation-learning reinforcement-learning/model

This paper introduces TRUncated ReinForcement Learning for Language (TrufLL), an original approach to train conditional language models from scratch by only using reinforcement learning (RL).

As RL methods unsuccessfully scale to large action spaces, we dynamically truncate the vocabulary space using a generic language model. TrufLL thus enables to train a language agent by solely interacting with its environment without any task-specific prior knowledge; it is only guided with a task-agnostic language model. Interestingly, this approach avoids the dependency to labeled datasets and inherently reduces pre-trained policy flaws such as language or exposure biases.

We evaluate TrufLL on 2 visual question generation tasks, for which we report positive results over performance and language metrics, which we then corroborate with a human evaluation.

To our knowledge, it is the first approach that successfully learns a language generation policy (almost) from scratch.

--- https://arxiv.org/abs/2109.09774 Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment Corinna Cortes, Neil D. Lawrence 2021-09-20 2021-09-20 [("doi","10.48550/arXiv.2109.09774")] statistics/peer-review

In this paper we revisit the 2014 NeurIPS experiment that examined inconsistency in conference peer review.

We determine that 50% of the variation in reviewer quality scores was subjective in origin. Further, with seven years passing since the experiment we find that for accepted papers, there is no correlation between quality scores and impact of the paper as measured as a function of citation count. We trace the fate of rejected papers, recovering where these papers were eventually published. For these papers we find a correlation between quality scores and impact.

We conclude that the reviewing process for the 2014 conference was good for identifying poor papers, but poor for identifying good papers. We give some suggestions for improving the reviewing process but also warn against removing the subjective element. Finally, we suggest that the real conclusion of the experiment is that the community should place less onus on the notion of ‘top-tier conference publications’ when assessing the quality of individual researchers.

For NeurIPS 2021, the PCs are repeating the experiment, as well as conducting new ones.

--- https://arxiv.org/abs/2109.10086#naver SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval Thibault Formal, Carlos Lassance, Benjamin Piwowarski, Stéphane Clinchant 2021-09-21 2021-09-21 [("doi","10.48550/arXiv.2109.10086")] ai/nn/retrieval ai/nn/sparsity/knowledge-distillation

In neural Information Retrieval (IR), ongoing research is directed towards improving the first retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using efficient approximate nearest neighbors methods has proven to work well. Meanwhile, there has been a growing interest in learning sparse representations for documents and queries, that could inherit from the desirable properties of bag-of-words models such as the exact matching of terms and the efficiency of inverted indexes. Introduced recently, the SPLADE model provides highly sparse representations and competitive results with respect to state-of-the-art dense and sparse approaches.

In this paper, we build on SPLADE and propose several improvements in terms of effectiveness and/or efficiency. More specifically, we modify the pooling mechanism, benchmark a model solely based on document expansion, and introduce models trained with distillation.

We also report results on the BEIR benchmark. Overall, SPLADE v2 is considerably improved with more than 9% gains on NDCG@10 on TREC DL 2019, leading to state-of-the-art results on the BEIR benchmark.

--- https://arxiv.org/abs/2109.10862#openai Recursively Summarizing Books with Human Feedback Jeff Wu, Long Ouyang, Daniel M. Ziegler, Nisan Stiennon, Ryan Lowe, Jan Leike, Paul Christiano 2021-09-22 2021-09-22 [("doi","10.48550/arXiv.2109.10862")] ai/nn/transformer/attention/compression reinforcement-learning/preference-learning reinforcement-learning/scaling

[blog; cf. inverse task, Tan et al 2020 & Dramatron] A major challenge for scaling machine learning is training models to perform tasks that are very difficult or time-consuming for humans to evaluate.

We present progress on this problem on the task of abstractive summarization of entire fiction novels. Our method combines learning from human feedback with recursive task decomposition: we use models trained on smaller parts of the task to assist humans in giving feedback on the broader task. We collect a large volume of demonstrations and comparisons from human labelers, and fine-tune GPT-3 using behavioral cloning and reward modeling to do summarization recursively. At inference time, the model first summarizes small sections of the book and then recursively summarizes these summaries to produce a summary of the entire book. Our human labelers are able to supervise and evaluate the models quickly, despite not having read the entire books themselves.

Our resulting model generates sensible summaries of entire books, even matching the quality of human-written summaries in a few cases (~5% of books). We achieve state-of-the-art results on the recent BookSum dataset for book-length summarization. A zero-shot question-answering model using these summaries achieves state-of-the-art results on the challenging NarrativeQA benchmark for answering questions about books and movie scripts.

We release datasets of samples from our model.

--- https://arxiv.org/abs/2109.11978#nvidia Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning Nikita Rudin, David Hoeller, Philipp Reist, Marco Hutter 2021-09-24 2021-09-24 [("doi","10.48550/arXiv.2109.11978")] ai/scaling/hardware reinforcement-learning/robot reinforcement-learning/scaling

In this work, we present and study a training setup that achieves fast policy generation for real-world robotic tasks by using massive parallelism on a single workstation GPU.

We analyze and discuss the impact of different training algorithm components in the massively parallel regime on the final policy performance and training times. In addition, we present a novel game-inspired curriculum that is well suited for training with thousands of simulated robots in parallel.

We evaluate the approach by training the quadrupedal robot ANYmal to walk on challenging terrain. The parallel approach allows training policies for flat terrain in under 4 minutes, and in 20 minutes for uneven terrain. This represents a speedup of multiple orders of magnitude compared to previous work.

Finally, we transfer the policies to the real robot to validate the approach. We open-source our training code to help accelerate further research in the field of learned legged locomotion.

--- https://arxiv.org/abs/2109.12066 ZSD-YOLO: Zero-Shot YOLO Detection using Vision-Language Knowledge Distillation Johnathan Xie, Shuai Zheng 2021-09-24 2021-09-24 [("doi","10.48550/arXiv.2109.12066")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/clip

Real-world object sampling produces long-tailed distributions requiring exponentially more images for rare types. Zero-shot detection, which aims to detect unseen objects, is one direction to address this problem. A dataset such as COCO is extensively annotated across many images but with a sparse number of categories and annotating all object classes across a diverse domain is expensive and challenging.

To advance zero-shot detection, we develop a Vision-Language distillation method that aligns both image and text embeddings from a zero-shot pre-trained model such as CLIP to a modified semantic prediction head from a one-stage detector like YOLOv5.

With this method, we are able to train an object detector that achieves state-of-the-art accuracy on the COCO zero-shot detection splits with fewer model parameters. During inference, our model can be adapted to detect any number of object classes without additional training. We also find that the improvements provided by the scaling of our method are consistent across various YOLOv5 scales.

Furthermore, we develop a self-training method that provides a score improvement without needing extra images nor labels.

--- https://arxiv.org/abs/2109.12098#nvidia CLIPort: What and Where Pathways for Robotic Manipulation Mohit Shridhar, Lucas Manuelli, Dieter Fox 2021-09-24 2021-09-24 [("doi","10.48550/arXiv.2109.12098")] ai/nn/transformer/clip reinforcement-learning/robot

How can we imbue robots with the ability to manipulate objects precisely but also to reason about them in terms of abstract concepts? Recent works in manipulation have shown that end-to-end networks can learn dexterous skills that require precise spatial reasoning, but these methods often fail to generalize to new goals or quickly learn transferable concepts across tasks. In parallel, there has been great progress in learning generalizable semantic representations for vision and language by training on large-scale internet data, however these representations lack the spatial understanding necessary for fine-grained manipulation.

To this end, we propose a framework that combines the best of both worlds: a two-stream architecture with semantic and spatial pathways for vision-based manipulation. Specifically, we present CLIPort, a language-conditioned imitation-learning agent that combines the broad semantic understanding (what) of CLIP [1] with the spatial precision (where) of Transporter [2]. Our end-to-end framework is capable of solving a variety of language-specified tabletop tasks from packing unseen objects to folding cloths, all without any explicit representations of object poses, instance segmentations, memory, symbolic states, or syntactic structures. Experiments in simulated and real-world settings show that our approach is data efficient in few-shot settings and generalizes effectively to seen and unseen semantic concepts. We even learn one multi-task policy for 10 simulated and 9 real-world tasks that is better or comparable to single-task policies.

--- https://arxiv.org/abs/2109.12948 Understanding and Overcoming the Challenges of Efficient Transformer Quantization Yelysei Bondarenko, Markus Nagel, Tijmen Blankevoort 2021-09-27 2021-09-27 [("doi","10.48550/arXiv.2109.12948")] ai/nn/sparsity/low-precision ai/nn/transformer

Transformer-based architectures have become the de-facto standard models for a wide range of Natural Language Processing tasks. However, their memory footprint and high latency are prohibitive for efficient deployment and inference on resource-limited devices.

In this work, we explore quantization for transformers. We show that transformers have unique quantization challenges—namely, high dynamic activation ranges that are difficult to represent with a low bit fixed-point format. We establish that these activations contain structured outliers in the residual connections that encourage specific attention patterns, such as attending to the special separator token.

To combat these challenges, we present 3 solutions based on post-training quantization and quantization-aware training, each with a different set of compromises for accuracy, model size, and ease of use. In particular, we introduce a novel quantization scheme—per-embedding-group quantization. We demonstrate the effectiveness of our methods on the GLUE benchmark using BERT, establishing state-of-the-art results for post-training quantization. Finally, we show that transformer weights and embeddings can be quantized to ultra-low bit-widths, leading to memory savings with a minimum accuracy loss.

Our source code is available at Github.

--- https://arxiv.org/abs/2109.13202#facebook MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel 2021-09-27 2021-09-27 [("doi","10.48550/arXiv.2109.13202")] ai/dataset reinforcement-learning/model-free reinforcement-learning/nethack

The progress in deep reinforcement learning (RL) is heavily driven by the availability of challenging benchmarks used for training agents. However, benchmarks that are widely adopted by the community are not explicitly designed for evaluating specific capabilities of RL methods. While there exist environments for assessing particular open problems in RL (such as exploration, transfer learning, unsupervised environment design, or even language-assisted RL), it is generally difficult to extend these to richer, more complex environments once research goes beyond proof-of-concept results.

We present MiniHack, a powerful sandbox framework for easily designing novel RL environments. MiniHack is a one-stop shop for RL experiments with environments ranging from small rooms to complex, procedurally generated worlds. By leveraging the full set of entities and environment dynamics from Nethack, one of the richest grid-based video games, MiniHack allows designing custom RL testbeds that are fast and convenient to use. With this sandbox framework, novel environments can be designed easily, either using a human-readable description language or a simple Python interface. In addition to a variety of RL tasks and baselines, MiniHack can wrap existing RL benchmarks and provide ways to seamlessly add additional complexity.

--- https://arxiv.org/abs/2109.13814 Text2Brain: Synthesis of Brain Activation Maps from Free-form Text Query Gia H. Ngo, Minh Nguyen, Nancy F. Chen, Mert R. Sabuncu 2021-09-28 2021-09-28 [("doi","10.48550/arXiv.2109.13814")] ai/nn/transformer psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Most neuroimaging experiments are under-powered, limited by the number of subjects and cognitive processes that an individual study can investigate. Nonetheless, over decades of research, neuroscience has accumulated an extensive wealth of results. It remains a challenge to digest this growing knowledge base and obtain new insights since existing meta-analytic tools are limited to keyword queries.

In this work, we propose Text2Brain, a neural network approach for coordinate-based meta-analysis of neuroimaging studies to synthesize brain activation maps from open-ended text queries. Combining a transformer-based text encoder and a 3D image generator, Text2Brain was trained on variable-length text snippets and their corresponding activation maps sampled from 13,000 published neuroimaging studies.

We demonstrate that Text2Brain can synthesize anatomically-plausible neural activation patterns from free-form textual descriptions of cognitive concepts.

Text2Brain is available at https://braininterpreter.com/?q=thinking+out+loud as a web-based tool for retrieving established priors and generating new hypotheses for neuroscience research.

--- https://arxiv.org/abs/2109.14084#facebook VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze Luke Zettlemoyer Christoph Feichtenhofer 2021-09-28 2021-09-28 [("doi","10.48550/arXiv.2109.14084")] ai/nn/transformer/clip ai/video/analysis

We present VideoCLIP, a contrastive approach to pre-train an unified model for zero-shot video and text understanding, without using any labels on downstream tasks.

VideoCLIP trains a transformer for video and text by contrasting temporally overlapping positive video-text pairs with hard negatives from nearest neighbor retrieval.

Our experiments on a diverse series of downstream tasks, including sequence-level text-video retrieval, VideoQA, token-level action localization, and action segmentation reveal state-of-the-art performance, surpassing prior work, and in some cases even outperforming supervised approaches.

Code is made available at Github.

--- https://arxiv.org/abs/2109.14119 Stochastic Training is Not Necessary for Generalization Jonas Geiping, Micah Goldblum, Phillip E. Pope, Michael Moeller, Tom Goldstein 2021-09-29 2021-09-29 [("doi","10.48550/arXiv.2109.14119")] ai/scaling

It is widely believed that the implicit regularization of stochastic gradient descent (SGD) is fundamental to the impressive generalization behavior we observe in neural networks. In this work, we demonstrate that non-stochastic full-batch training can achieve strong performance on CIFAR-10 that is on par with SGD, using modern architectures in settings with and without data augmentation.

To this end, we use modified hyperparameters and show that the implicit regularization of SGD can be completely replaced with explicit regularization. This strongly suggests that theories that rely heavily on properties of stochastic sampling to explain generalization are incomplete, as strong generalization behavior is still observed in the absence of stochastic sampling.

Fundamentally, deep learning can succeed without stochasticity. Our observations further indicate that the perceived difficulty of full-batch training is largely the result of its optimization properties and the disproportionate time and effort spent by the ML community tuning optimizers and hyperparameters for small-batch training.

--- https://arxiv.org/abs/2109.14449 One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective Jiun Tian Hoe, Kam Woh Ng, Tianyu Zhang, Chee Seng Chan, Yi-Zhe Song, Tao Xiang 2021-09-29 2021-09-29 [("doi","10.48550/arXiv.2109.14449")] ai/nn/retrieval

A deep hashing model typically has two main learning objectives: to make the learned binary hash codes discriminative and to minimize a quantization error. With further constraints such as bit balance and code orthogonality, it is not uncommon for existing models to employ a large number (>4) of losses. This leads to difficulties in model training and subsequently impedes their effectiveness.

In this work, we propose a novel deep hashing model with only a single learning objective. Specifically, we show that maximizing the cosine similarity between the continuous codes and their corresponding binary orthogonal codes can ensure both hash code discriminativeness and quantization error minimization. Further, with this learning objective, code balancing can be achieved by simply using a Batch Normalization (BN) layer and multi-label classification is also straightforward with label smoothing. The result is a one-loss deep hashing model that removes all the hassles of tuning the weights of various losses.

Importantly, extensive experiments show that our model is highly effective, outperforming the state-of-the-art multi-loss hashing models on 3 large-scale instance retrieval benchmarks, often by margins.

Code is available at GitHub.

--- https://arxiv.org/abs/2109.14518 Generative Probabilistic Image Colorization Chie Furusawa, Shinya Kitaoka, Michael Li, Yuri Odagiri 2021-09-29 2021-09-29 [("doi","10.48550/arXiv.2109.14518")] ai/anime/danbooru ai/nn/diffusion

We propose Generative Probabilistic Image Colorization, a diffusion-based generative process that trains a sequence of probabilistic models to reverse each step of noise corruption. Given a line-drawing image as input, our method suggests multiple candidate colorized images. Therefore, our method accounts for the ill-posed nature of the colorization problem.

We conducted comprehensive experiments investigating the colorization of line-drawing images, report the influence of a score-based MCMC approach that corrects the marginal distribution of estimated samples, and further compare different combinations of models and the similarity of their generated images.

Despite using only a relatively small training dataset [TWDNE via Artbreeder], we experimentally develop a method to generate multiple diverse colorization candidates which avoids mode collapse and does not require any additional constraints, losses, or re-training with alternative training conditions.

Our proposed approach performed well not only on color-conditional image generation tasks using biased initial values, but also on some practical image completion and inpainting tasks.

--- https://arxiv.org/abs/2109.15144 A Review of Text Style Transfer using Deep Learning Martina Toshevska, Sonja Gievska 2021-09-30 2021-09-30 [("doi","10.1109/TAI.2021.3115992")] ai/text-style-transfer

Style is an integral component of a sentence indicated by the choice of words a person makes. Different people have different ways of expressing themselves, however, they adjust their speaking and writing style to a social context, an audience, an interlocutor or the formality of an occasion. Text style transfer is defined as a task of adapting and/or changing the stylistic manner in which a sentence is written, while preserving the meaning of the original sentence.

A systematic review of text style transfer methodologies using deep learning is presented in this paper. We point out the technological advances in deep neural networks that have been the driving force behind current successes in the fields of natural language understanding and generation. The review is structured around two key stages in the text style transfer process, namely, representation learning and sentence generation in a new style. The discussion highlights the commonalities and differences between proposed solutions as well as challenges and opportunities that are expected to direct and foster further research in the field.

--- https://arxiv.org/abs/2109.15316#facebook Scalable Online Planning via Reinforcement Learning Fine-Tuning Arnaud Fickinger, Hengyuan Hu, Brandon Amos, Stuart Russell, Noam Brown 2021-09-30 2021-09-30 [("doi","10.48550/arXiv.2109.15316")] reinforcement-learning/imperfect-information/hanabi reinforcement-learning/meta-learning reinforcement-learning/model/alphago

Lookahead search has been a critical component of recent AI successes, such as in the games of chess, go, and poker. However, the search methods used in these games, and in many other settings, are tabular. Tabular search methods do not scale well with the size of the search space, and this problem is exacerbated by stochasticity and partial observability.

In this work we replace tabular search with online model-based fine-tuning of a policy neural network via reinforcement learning, and show that this approach outperforms state-of-the-art search algorithms in benchmark settings.

In particular, we use our search algorithm to achieve a new state-of-the-art result in self-play Hanabi, and show the generality of our algorithm by also showing that it outperforms tabular search in the Atari game Ms. Pacman.

--- https://arxiv.org/abs/2110.00023 Mining for strong gravitational lenses with self-supervised learning George Stein, Jacqueline Blaum, Peter Harrington, Tomislav Medan, Zarija Lukic 2021-09-30 2021-09-30 [("doi","10.48550/arXiv.2110.00023")] ai/nn/cnn ai/scaling science

We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys’ Data Release 9.

Targeting the identification of new strong gravitational lens candidates, we first create a rapid similarity search tool to discover new strong lenses given only a single labeled example.

We then show how training a simple linear classifier on the self-supervised representations, requiring only a few minutes on a CPU, can automatically classify strong lenses with great efficiency.

We present 1192 new strong lens candidates that we identified through a brief visual identification campaign, and release an interactive web-based similarity search tool and the top network predictions to facilitate crowd-sourcing rapid discovery of additional strong gravitational lenses and other rare objects: github.com/georgestein/ssl-legacysurvey.

--- https://arxiv.org/abs/2110.00641#openai Batch size-invariance for policy optimization Jacob Hilton, Karl Cobbe, John Schulman 2021-10-01 2021-10-01 [("doi","10.48550/arXiv.2110.00641")] reinforcement-learning/model-free

We say an algorithm is batch size-invariant if changes to the batch size can largely be compensated for by changes to other hyperparameters. Stochastic gradient descent is well-known to have this property at small batch sizes, via the learning rate.

However, some policy optimization algorithms (such as PPO) do not have this property, because of how they control the size of policy updates. In this work, we show how to make these algorithms batch size-invariant.

Our key insight is to decouple the proximal policy (used for controlling policy updates) from the behavior policy (used for off-policy corrections).

Our experiments help explain why these algorithms work, and additionally show how they can make more efficient use of stale data.

--- https://arxiv.org/abs/2110.01147 On the Interplay Between Sparsity, Naturalness, Intelligibility, and Prosody in Speech Synthesis Cheng-I Jeff Lai, Erica Cooper, Yang Zhang, Shiyu Chang, Kaizhi Qian, Yi-Lun Liao, Yung-Sung Chuang, Alexander H. Liu, Junichi Yamagishi, David Cox, James Glass 2021-10-04 2021-10-04 [("doi","10.48550/arXiv.2110.01147")] ai/nn/sparsity/knowledge-distillation ai/nn/sparsity/pruning

Are end-to-end text-to-speech (TTS) models over-parameterized? To what extent can these models be pruned, and what happens to their synthesis capabilities? This work serves as a starting point to explore pruning both spectrogram prediction networks and vocoders. We thoroughly investigate the tradeoffs between sparsity and its subsequent effects on synthetic speech.

Additionally, we explored several aspects of TTS pruning: amount of finetuning data versus sparsity, TTS-Augmentation to use unspoken text, and combining knowledge distillation and pruning. Our findings suggest that not only are end-to-end TTS models highly prunable, but also, perhaps surprisingly, pruned TTS models can produce synthetic speech with equal or higher naturalness and intelligibility, with similar prosody.

All of our experiments are conducted on publicly available models, and findings in this work are backed by large-scale subjective tests and objective measures.

Code and 200 pruned models are made available to facilitate future research on efficiency in TTS.

--- https://arxiv.org/abs/2110.01691#google AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts Tongshuang Wu, Michael Terry, Carrie J. Cai 2021-10-04 2021-10-04 [("doi","10.48550/arXiv.2110.01691")] ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/lamda fiction/text-game

Although large language models (LLMs) have demonstrated impressive potential on simple tasks, their breadth of scope, lack of transparency, and insufficient controllability can make them less effective when assisting humans on more complex tasks. In response, we introduce the concept of Chaining LLM steps together, where the output of one step becomes the input for the next, thus aggregating the gains per step.

We first define a set of LLM primitive operations useful for Chain construction, then present an interactive system where users can modify these Chains, along with their intermediate results, in a modular way.

In a 20-person user study, we found that Chaining not only improved the quality of task outcomes, but also enhanced system transparency, controllability, and sense of collaboration. Additionally, we saw that users developed new ways of interacting with LLMs through Chains: they leveraged sub-tasks to calibrate model expectations, compared and contrasted alternative strategies by observing parallel downstream effects, and debugged unexpected model outputs by “unit-testing” sub-components of a Chain.

In two case studies, we further explore how LLM Chains may be used in future applications.

--- https://arxiv.org/abs/2110.02037#google Autoregressive Diffusion Models Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, Tim Salimans 2021-10-05 2021-10-05 [("doi","10.48550/arXiv.2110.02037")] ai/nn/diffusion/discrete cs/algorithm/information/compression

We introduce Autoregressive Diffusion Models (ARDMs), a model class encompassing and generalizing order-agnostic autoregressive models (Uria et al 2014) and absorbing discrete diffusion (Austin et al 2021), which we show are special cases of ARDMs under mild assumptions. ARDMs are simple to implement and easy to train. Unlike standard ARMs, they do not require causal masking of model representations, and can be trained using an efficient objective similar to modern probabilistic diffusion models that scales favorably to highly-dimensional data.

At test time, ARDMs support parallel generation which can be adapted to fit any given generation budget. We find that ARDMs require fewer steps than discrete diffusion models to attain the same performance.

Finally, we apply ARDMs to lossless compression, and show that they are uniquely suited to this task. Contrary to existing approaches based on bits-back coding, ARDMs obtain compelling results not only on complete datasets, but also on compressing single data points. Moreover, this can be done using a modest number of network calls for (de)compression due to the model’s adaptable parallel generation.

--- https://arxiv.org/abs/2110.02861 8-bit Optimizers via Block-wise Quantization Tim Dettmers, Mike Lewis, Sam Shleifer, Luke Zettlemoyer 2021-10-06 2021-10-06 [("doi","10.48550/arXiv.2110.02861")] ai/nn/sparsity/low-precision

Stateful optimizers maintain gradient statistics over time, eg. the exponentially smoothed sum (SGD with momentum) or squared sum (Adam) of past gradient values. This state can be used to accelerate optimization compared to plain stochastic gradient descent but uses memory that might otherwise be allocated to model parameters, thereby limiting the maximum size of models trained in practice. In this paper, we develop the first optimizers that use 8-bit statistics while maintaining the performance levels of using 32-bit optimizer states.

To overcome the resulting computational, quantization, and stability challenges, we develop block-wise dynamic quantization. Block-wise quantization divides input tensors into smaller blocks that are independently quantized. Each block is processed in parallel across cores, yielding faster optimization and high precision quantization. To maintain stability and performance, we combine block-wise quantization with two additional changes: (1) dynamic quantization, a form of non-linear optimization that is precise for both large and small magnitude values, and (2) a stable embedding layer to reduce gradient variance that comes from the highly non-uniform distribution of input tokens in language models.

As a result, our 8-bit optimizers maintain 32-bit performance with a small fraction of the memory footprint on a range of tasks, including 1.5b parameter language modeling, GLUE finetuning, ImageNet classification, WMT’14 machine translation, MoCo v2 contrastive ImageNet pretraining+finetuning, and RoBERTa pretraining, without changes to the original optimizer hyperparameters. We open-source our 8-bit optimizers as a drop-in replacement that only requires a two-line code change.

--- https://arxiv.org/abs/2110.03363#deepmind Evaluating model-based planning and planner amortization for continuous control Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin Riedmiller 2021-10-07 2021-10-07 [("doi","10.48550/arXiv.2110.03363")] reinforcement-learning/model/alphago

There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this intuition on various challenging locomotion tasks.

We take a hybrid approach, combining model predictive control (MPC) with a learned model and model-free policy learning; the learned policy serves as a proposal for MPC. We find that well-tuned model-free agents are strong baselines even for high Degrees of Freedom (DoF) control problems but MPC with learned proposals and models (trained on the fly or transferred from related tasks) can improve performance and data efficiency in hard multi-task/multi-goal settings.

Finally, we show that it is possible to distill a model-based planner into a policy that amortizes the planning computation without any loss of performance.

Videos of agents performing different tasks can be seen at https://sites.google.com/view/mbrl-amortization/home.

--- https://arxiv.org/abs/2110.03742#google Beyond Distillation: Task-level Mixture-of-Experts (TaskMoE) for Efficient Inference Sneha Kudugunta, Yanping Huang, Ankur Bapna, Maxim Krikun, Dmitry Lepikhin, Minh-Thang Luong, Orhan Firat 2021-09-24 2021-09-24 [("doi","10.48550/arXiv.2110.03742")] ai/nn/sparsity/knowledge-distillation ai/scaling/mixture-of-experts

[blog] Sparse Mixture-of-Experts (MoE) has been a successful approach for scaling multilingual translation models to billions of parameters without a proportional increase in training computation. However, MoE models are prohibitively large and practitioners often resort to methods such as distillation for serving.

In this work, we investigate routing strategies at different granularity (token, sentence, task) in MoE models to bypass distillation. Experiments on WMT and a web-scale dataset suggest that task-level routing (TaskMoE) enables us to extract smaller, ready-to-deploy sub-networks from large sparse models.

On WMT, our task-MoE with 32 experts (533M parameters) outperforms the best performing token-level MoE model (token-MoE) by +1.0 BLEU on average across 30 language pairs. The peak inference throughput is also improved by a factor of 1.9× when we route by tasks instead of tokens. While distilling a token-MoE to a smaller dense model preserves only 32% of the BLEU gains, our sub-network task-MoE, by design, preserves all the gains with the same inference cost as the distilled student model. Finally, when scaling up to 200 language pairs, our 128-expert task-MoE (13b parameters) performs competitively with a token-level counterpart, while improving the peak inference throughput by a factor of 2.6×.

--- https://arxiv.org/abs/2110.04374 A Few More Examples May Be Worth Billions of Parameters Yuval Kirstain, Patrick Lewis, Sebastian Riedel, Omer Levy 2021-10-08 2021-10-08 [("doi","10.48550/arXiv.2110.04374")] ai/nn/transformer/gpt ai/scaling

We investigate the dynamics of increasing the number of model parameters versus the number of labeled examples across a wide variety of tasks.

Our exploration reveals that while scaling parameters consistently yields performance improvements, the contribution of additional examples highly depends on the task’s format. Specifically, in open question answering tasks, enlarging the training set does not improve performance.

In contrast, classification, extractive question answering, and multiple choice tasks benefit so much from additional examples that collecting a few hundred examples is often “worth” billions of parameters.

We hypothesize that unlike open question answering, which involves recalling specific information, solving strategies for tasks with a more restricted output space transfer across examples, and can therefore be learned with small amounts of labeled data.

--- https://arxiv.org/abs/2110.04627#google Vector-quantized Image Modeling with Improved VQGAN Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, Yonghui Wu 2021-10-09 2021-10-09 [("doi","10.48550/arXiv.2110.04627")] ai/nn/transformer/gpt/dall-e ai/nn/vae

Pretraining language models with next-token prediction on massive text corpora has delivered phenomenal zero-shot, few-shot, transfer learning, and multi-tasking capabilities on both generative and discriminative language tasks. Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. The discrete image tokens are encoded from a learned Vision-Transformer-based VQGAN (ViT-VQGAN).

We first propose multiple improvements over vanilla VQGAN from architecture to codebook learning, yielding better efficiency and reconstruction fidelity. The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including unconditional, class-conditioned image generation, and unsupervised representation learning.

When trained on ImageNet at 256×256 resolution, we achieve Inception Score (IS) of 175.1 and Fréchet Inception Distance (FID) of 4.17, a dramatic improvement over the vanilla VQGAN, which obtains 70.6 and 17.04 for IS and FID, respectively. Based on ViT-VQGAN and unsupervised pretraining, we further evaluate the pretrained Transformer by averaging intermediate features, similar to Image GPT (iGPT).

This ImageNet-pretrained VIM-L beats iGPT-L on linear-probe accuracy 60.3% → 72.2% for a similar model size. ViM-L also outperforms iGPT-XL which is trained with extra web image data and larger model size.

--- https://arxiv.org/abs/2110.05208 Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm (DeCLIP) Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, Junjie Yan 2021-10-11 2021-10-11 [("doi","10.48550/arXiv.2110.05208")] ai/nn/cnn ai/nn/transformer/clip

Recently, large-scale Contrastive Language-Image Pre-training (CLIP) has attracted unprecedented attention for its impressive zero-shot recognition ability and excellent transferability to downstream tasks. However, CLIP is quite data-hungry and requires 400M image-text pairs for pre-training, thereby restricting its adoption.

This work proposes a novel training paradigm, Data efficient CLIP (DeCLIP), to alleviate this limitation. We demonstrate that by carefully utilizing the widespread supervision among the image-text pairs, our De-CLIP can learn generic visual features more efficiently. Instead of using the single image-text contrastive supervision, we fully exploit data potential through the use of (1) self-supervision within each modality; (2) multi-view supervision across modalities; (3) nearest-neighbor supervision from other similar pairs.

Benefiting from intrinsic supervision, our DeCLIP-ResNet-50 can achieve 60.4% zero-shot top1 accuracy on ImageNet, which is 0.8% above the CLIP-ResNet50 while using 7.1 x fewer data. Our DeCLIP-ResNet50 outperforms its counterpart in 8⁄11 visual datasets when transferred to downstream tasks. Moreover, Scaling up the model and computing also works well in our framework.

Our code, dataset and models are released at: https://github.com/Sense-GVT/DeCLIP.

--- https://arxiv.org/abs/2110.06296#google The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks Rahim Entezari, Hanie Sedghi, Olga Saukh, Behnam Neyshabur 2021-10-12 2021-10-12 [("doi","10.48550/arXiv.2110.06296")] ai/nn

In this paper, we conjecture that if the permutation invariance of neural networks is taken into account, SGD solutions will likely have no barrier in the linear interpolation between them.

Although it is a bold conjecture, we show how extensive empirical attempts fall short of refuting it.

We further provide a preliminary theoretical result to support our conjecture. Our conjecture has implications for the lottery ticket hypothesis, distributed training, and ensemble methods.

--- https://arxiv.org/abs/2110.06961 Language Modeling via Learning to Rank Arvid Frydenlund, Gagandeep Singh, Frank Rudzicz 2021-10-13 2021-10-13 [("doi","10.48550/arXiv.2110.06961")] ai/nn/sparsity/knowledge-distillation

We consider language modeling (LM) as a multi-label structured prediction task by re-framing training from solely predicting a single ground-truth word to ranking a set of words which could continue a given context. To avoid annotating top-k ranks, we generate them using pre-trained LMs: GPT-2, BERT and Born-Again models. This leads to a rank-based form of knowledge distillation (KD). We also develop a method using N-grams to create a non-probabilistic teacher which generates the ranks without the need of a pre-trained LM.

We confirm the hypotheses that we can treat LMing as a ranking task and that we can do so without the use of a pre-trained LM. We show that rank-based KD generally improves perplexity (PPL), often with statistical-significance, when compared to Kullback-Leibler-based KD. Surprisingly, given the simplicity of the method, N-grams act as competitive teachers and achieve similar performance as using either BERT or a Born-Again model teachers. GPT-2 always acts as the best teacher, though, and using it and a Transformer-XL student on Wiki-02, rank-based KD reduces a cross-entropy baseline 65.27 → 55.94 and against a KL-based KD of 56.70.

--- https://arxiv.org/abs/2110.06990 Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers Gabriele Prato, Simon Guiroy, Ethan Caballero, Irina Rish, Sarath Chandar 2021-10-13 2021-10-13 [("doi","10.48550/arXiv.2110.06990")] ai/scaling

Empirical science of neural scaling laws is a rapidly growing area of importance to the future of machine learning, particularly in the light of recent breakthroughs achieved by large-scale pre-trained models such as GPT-3, CLIP and DALL-e. Accurately predicting the neural network performance with increasing resources such as data, compute, and model size provides a more comprehensive evaluation of different approaches across multiple scales, as opposed to traditional point-wise comparisons of fixed-size models on fixed-size benchmarks, and, most importantly, allows for a focus on the best-scaling, and thus most promising in the future, approaches.

In this work, we consider a challenging problem of few-shot learning in image classification, especially when the target data distribution in the few-shot phase is different from the source, training, data distribution, in a sense that it includes new image classes not encountered during training. Our current main goal is to investigate how the amount of pre-training data affects the few-shot generalization performance of standard image classifiers.

Our key observations are that (1) such performance improvements are well-approximated by power laws (linear log-log plots) as the training set size increases, (2) this applies to both cases of target data coming from either the same or from a different domain (ie. new classes) as the training data, and (3) few-shot performance on new classes converges at a faster rate than the standard classification performance on previously seen classes.

Our findings shed new light on the relationship between scale and generalization.

--- https://arxiv.org/abs/2110.07178#allen Symbolic Knowledge Distillation: from General Language Models to Commonsense Models Peter West, Chandra Bhagavatula, Jack Hessel, Jena D. Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, Yejin Choi 2021-10-14 2021-10-14 [("doi","10.48550/arXiv.2110.07178")] ai/nn/sparsity/knowledge-distillation ai/scaling

The common practice for training commonsense models has gone from-human-to-corpus-to-machine: humans author commonsense knowledge graphs in order to train commonsense models. In this work, we investigate an alternative from-machine-to-corpus-to-machine: general language models author these commonsense knowledge graphs to train commonsense models. Our study leads to a new framework, Symbolic Knowledge Distillation.

As with prior art in Knowledge Distillation (Hinton et al 2015), our approach uses larger models to teach smaller models. A key difference is that we distill knowledge symbolically-as text-in addition to the neural model. We also distill only one aspect-the commonsense of a general language model teacher, allowing the student to be a different type, a commonsense model.

Altogether, we show that careful prompt engineering and a separately trained critic model allow us to selectively distill high-quality causal commonsense from GPT-3, a general language model. Empirical results demonstrate that, for the first time, a human-authored commonsense knowledge graph is surpassed by our automatically distilled variant in all 3 criteria: quantity, quality, and diversity.

In addition, it results in a neural commonsense model that surpasses the teacher model’s commonsense capabilities despite its 100× smaller size. We apply this to the ATOMIC resource, and share our new symbolic knowledge graph and commonsense models.

--- https://arxiv.org/abs/2110.07356 Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization Bharath Chintagunta, Namit Katariya, Xavier Amatriain, Anitha Kannan 2021-09-09 2021-09-09 [("doi","10.48550/arXiv.2110.07356")] ai/nn/transformer/gpt biology

In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue. However, learning effective models for summarization require large amounts of labeled data which is especially hard to obtain.

We present an algorithm to create synthetic training data with an explicit focus on capturing medically relevant information. We use GPT-3 as the backbone of our algorithm and scale 210 human labeled examples to yield results comparable to using 6400 human labeled examples (~30×) leveraging low-shot learning and an ensemble method.

In detailed experiments, we show that this approach produces high quality training data that can further be combined with human labeled data to get summaries that are strongly preferable to those produced by models trained on human data alone both in terms of medical accuracy and coherency.

--- https://arxiv.org/abs/2110.07579 Diffusion Normalizing Flow Qinsheng Zhang, Yongxin Chen 2021-10-14 2021-10-14 [("doi","10.48550/arXiv.2110.07579")] ai/nn/diffusion

We present a novel generative modeling method called diffusion normalizing flow based on stochastic differential equations (SDEs). The algorithm consists of two neural SDEs: a forward SDE that gradually adds noise to the data to transform the data into Gaussian random noise, and a backward SDE that gradually removes the noise to sample from the data distribution. By jointly training the two neural SDEs to minimize a common cost function that quantifies the difference between the two, the backward SDE converges to a diffusion process the starts with a Gaussian distribution and ends with the desired data distribution. Our method is closely related to normalizing flow and diffusion probabilistic models and can be viewed as a combination of the two.

Compared with normalizing flow, diffusion normalizing flow is able to learn distributions with sharp boundaries. Compared with diffusion probabilistic models, diffusion normalizing flow requires fewer discretization steps and thus has better sampling efficiency. Our algorithm demonstrates competitive performance in both high-dimension data density estimation and image generation tasks.

--- https://arxiv.org/abs/2110.08152 [2110.08152] Kronecker Decomposition for GPT Compression 2020-02-11 [("doi","10.48550/arXiv.2110.08152")] ai/nn/sparsity --- https://arxiv.org/abs/2110.08176#deepmind Collaborating with Humans without Human Data DJ Strouse, Kevin R. McKee, Matt Botvinick, Edward Hughes, Richard Everett 2021-10-15 2021-10-15 [("doi","10.48550/arXiv.2110.08176")] ai/scaling reinforcement-learning/meta-learning reinforcement-learning/multi-agent reinforcement-learning/scaling

Collaborating with humans requires rapidly adapting to their individual strengths, weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement learning techniques, such as self-play (SP) or population play (PP), produce agents that overfit to their training partners and do not generalize well to humans. Alternatively, researchers can collect human data, train a human model using behavioral cloning, and then use that model to train “human-aware” agents (“behavioral cloning play”, or BCP). While such an approach can improve the generalization of agents to new human co-players, it involves the onerous and expensive step of collecting large amounts of human data first.

Here, we study the problem of how to train agents that collaborate well with human partners without using human data. We argue that the crux of the problem is to produce a diverse set of training partners. Drawing inspiration from successful multi-agent approaches in competitive domains, we find that a surprisingly simple approach is highly effective. We train our agent partner as the best response to a population of self-play agents and their past checkpoints taken throughout training, a method we call Fictitious Co-Play (FCP).

Our experiments focus on a two-player collaborative cooking simulator that has recently been proposed as a challenge problem for coordination with humans. We find that FCP agents score higher than SP, PP, and BCP when paired with novel agent and human partners. Furthermore, humans also report a strong subjective preference to partnering with FCP agents over all baselines.

--- https://arxiv.org/abs/2110.08207 T0: Multitask Prompted Training Enables Zero-Shot Task Generalization Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M. Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, Alexander M. Rush 2021-10-15 2021-10-15 [("doi","10.48550/arXiv.2110.08207")] ai/dataset ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 reinforcement-learning/meta-learning reinforcement-learning/scaling

Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks. It has been hypothesized that this is a consequence of implicit multitask learning in language model training. Can zero-shot generalization instead be directly induced by explicit multitask learning?

To test this question at scale, we develop a system for easily mapping general natural language tasks into a human-readable prompted form. We convert a large set of supervised datasets, each with multiple prompts using varying natural language. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. We fine-tune a pretrained encoder-decoder T5 model on this multitask mixture covering a wide variety of tasks.

This T0 model attains strong zero-shot performance on several standard datasets, often outperforming models 16× its size. Further, our approach attains strong performance on a subset of tasks from the BIG-Bench benchmark, outperforming models 6× its size.

All prompts and trained models are available at GitHub.

--- https://arxiv.org/abs/2110.09514 Discovering and Achieving Goals via World Models Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, Deepak Pathak 2021-10-18 2021-10-18 [("doi","10.48550/arXiv.2110.09514")] reinforcement-learning/exploration reinforcement-learning/robot

How can artificial agents learn to solve many diverse tasks in complex visual environments in the absence of any supervision? We decompose this question into two problems: discovering new goals and learning to reliably achieve them. We introduce Latent Explorer Achiever (LEXA), an unified solution to these that learns a world model from image inputs and uses it to train an explorer and an achiever policy from imagined rollouts.

Unlike prior methods that explore by reaching previously visited states, the explorer plans to discover unseen surprising states through foresight, which are then used as diverse targets for the achiever to practice. After the unsupervised phase, LEXA solves tasks specified as goal images zero-shot without any additional learning.

LEXA substantially outperforms previous approaches to unsupervised goal-reaching, both on prior benchmarks and on a new challenging benchmark with a total of 40 test tasks spanning across 4 standard robotic manipulation and locomotion domains. LEXA further achieves goals that require interacting with multiple objects in sequence.

Finally, to demonstrate the scalability and generality of LEXA, we train a single general agent across 4 distinct environments. Code and videos at https://orybkin.github.io/lexa/.

--- https://arxiv.org/abs/2110.09903 Unrestricted Adversarial Attacks on ImageNet Competition Yuefeng Chen, Xiaofeng Mao, Yuan He, Hui Xue, Chao Li, Yinpeng Dong, Qi-An Fu, Xiao Yang, Wenzhao Xiang, Tianyu Pang, Hang Su, Jun Zhu, Fangcheng Liu, Chao Zhang, Hongyang Zhang, Yichi Zhang, Shilong Liu, Chang Liu, Wenzhao Xiang, Yajie Wang, Huipeng Zhou, Haoran Lyu, Yidan Xu, Zixuan Xu, Taoyu Zhu, Wenjun Li, Xianfeng Gao, Guoqiu Wang, Huanqian Yan, Ying Guo, Chaoning Zhang, Zheng Fang, Yang Wang, Bingyang Fu, Yunfei Zheng, Yekui Wang, Haorong Luo, Zhen Yang 2021-10-17 2021-10-17 [("doi","10.48550/arXiv.2110.09903")] ai/nn/adversarial

Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input. However, in the real world, the attacker does not need to comply with this restriction. In fact, more threats to the deep model come from unrestricted adversarial examples, that is, the attacker makes large and visible modifications on the image, which causes the model to classify mistakenly, but does not affect the normal observation in human perspective. Unrestricted adversarial attack is a popular and practical direction but has not been studied thoroughly.

We organize this competition with the purpose of exploring more effective unrestricted adversarial attack algorithms, so as to accelerate the academic research on the model robustness under stronger unbounded attacks. The competition is held on the TianChi platform as one of the series of AI Security Challengers Program.

--- https://arxiv.org/abs/2110.10278 Fine-Grained Control of Artistic Styles in Image Generation Xin Miao, Huayan Wang, Jun Fu, Jiayi Liu, Shen Wang, Zhenyu Liao 2021-10-19 2021-10-19 [("doi","10.48550/arXiv.2110.10278")] ai/anime/danbooru ai/nn/gan/stylegan/anime

[selfie2anime?] Recent advances in generative models and adversarial training have enabled artificially generating artworks in various artistic styles. It is highly desirable to gain more control over the generated style in practice. However, artistic styles are unlike object categories—there are a continuous spectrum of styles distinguished by subtle differences. Few works have been explored to capture the continuous spectrum of styles and apply it to a style generation task.

In this paper, we propose to achieve this by embedding original artwork examples into a continuous style space. The style vectors are fed to the generator and discriminator to achieve fine-grained control.

Our method can be used with common generative adversarial networks (such as StyleGAN).

Experiments show that our method not only precisely controls the fine-grained artistic style but also improves image quality over vanilla StyleGAN as measured by FID.

--- https://arxiv.org/abs/2110.10305#google When in Doubt, Summon the Titans: Efficient Inference with Large Models Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Amr Ahmed, Sanjiv Kumar 2021-10-19 2021-10-19 [("doi","10.48550/arXiv.2110.10305")] ai/nn/sparsity/knowledge-distillation ai/scaling

Scaling neural networks to “large” sizes, with billions of parameters, has been shown to yield impressive results on many challenging problems. However, the inference cost incurred by such large models often prevents their application in most real-world settings.

In this paper, we propose a two-stage framework based on distillation that realizes the modeling benefits of the large models, while largely preserving the computational benefits of inference with more lightweight models. In a nutshell, we use the large teacher models to guide the lightweight student models to only make correct predictions on a subset of “easy” examples; for the “hard” examples, we fall-back to the teacher. Such an approach allows us to efficiently employ large models in practical scenarios where easy examples are much more frequent than rare hard examples.

Our proposed use of distillation to only handle easy instances allows for a more aggressive trade-off in the student size, thereby reducing the amortized cost of inference and achieving better accuracy than standard distillation.

Empirically, we demonstrate the benefits of our approach on both image classification and natural language processing benchmarks.

--- https://arxiv.org/abs/2110.10811 HALP: Hardware-Aware Latency Pruning Maying Shen, Hongxu Yin, Pavlo Molchanov, Lei Mao, Jianna Liu, Jose M. Alvarez 2021-10-20 2021-10-20 [("doi","10.48550/arXiv.2110.10811")] ai/nn/sparsity/pruning

Structural pruning can simplify network architecture and improve inference speed.

We propose Hardware-Aware Latency Pruning (HALP) that formulates structural pruning as a global resource allocation optimization problem, aiming at maximizing the accuracy while constraining latency under a predefined budget. For filter importance ranking, HALP leverages latency lookup table to track latency reduction potential and global saliency score to gauge accuracy drop. Both metrics can be evaluated very efficiently during pruning, allowing us to reformulate global structural pruning under a reward maximization problem given target constraint. This makes the problem solvable via our augmented knapsack solver, enabling HALP to surpass prior work in pruning efficacy and accuracy-efficiency trade-off.

We examine HALP on both classification and detection tasks, over varying networks, on ImageNet and VOC datasets. In particular, for ResNet-50/ResNet-101 pruning on ImageNet, HALP improves network throughput by 1.60×/1.90× with +0.3% / −0.2% top-1 accuracy changes, respectively. For SSD pruning on VOC, HALP improves throughput by 1.94× with only a 0.56 mAP drop. HALP consistently outperforms prior art, sometimes by large margins.

--- https://arxiv.org/abs/2110.10819#deepmind Shaking the foundations: delusions in sequence models for interaction and control Pedro A. Ortega, Markus Kunesch, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Joel Veness, Jonas Buchli, Jonas Degrave, Bilal Piot, Julien Perolat, Tom Everitt, Corentin Tallec, Emilio Parisotto, Tom Erez, Yutian Chen, Scott Reed, Marcus Hutter, Nando de Freitas, Shane Legg 2021-10-20 2021-10-20 [("doi","10.48550/arXiv.2110.10819")] reinforcement-learning/meta-learning reinforcement-learning/model/decision-transformer

The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domains.

One important problem class that has remained relatively elusive however is purposeful adaptive behavior. Currently, there is a common perception that sequence models “lack the understanding of the cause and effect of their actions” leading them to draw incorrect inferences due to auto-suggestive delusions.

In this report, we explain where this mismatch originates, and show that it can be resolved by treating actions as causal interventions.

Finally, we show that in supervised learning, one can teach a system to condition or intervene on data by training with factual and counterfactual error signals respectively.

--- https://arxiv.org/abs/2110.11309 Fast Model Editing at Scale Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, Christopher D. Manning 2021-10-21 2021-10-21 [("doi","10.48550/arXiv.2110.11309")] ai/nn/transformer/gpt ai/nn/transformer/t5

While large pre-trained models have enabled impressive results on a variety of downstream tasks, the largest existing models still make errors, and even accurate predictions may become outdated over time. Because detecting all such failures at training time is impossible, enabling both developers and end users of such models to correct inaccurate outputs while leaving the model otherwise intact is desirable. However, the distributed, black-box nature of the representations learned by large neural networks makes producing such targeted edits difficult. If presented with only a single problematic input and new desired output, fine-tuning approaches tend to overfit; other editing algorithms are either computationally infeasible or simply ineffective when applied to very large models.

To enable easy post-hoc editing at scale, we propose Model Editor Networks with Gradient Decomposition (MEND), a collection of small auxiliary editing networks that use a single desired input-output pair to make fast, local edits to a pre-trained model. MEND learns to transform the gradient obtained by standard fine-tuning, using a low-rank decomposition of the gradient to make the parameterization of this transformation tractable. MEND can be trained on a single GPU in less than a day even for 10 billion+ parameter models; once trained, MEND enables rapid application of new edits to the pre-trained model.

Our experiments with T5, GPT, BERT, and BART models show that MEND is the only approach to model editing that produces effective edits for models with tens of millions to over 10 billion parameters.

Implementation available at https://sites.google.com/view/mend-editing.

--- https://arxiv.org/abs/2110.11323 StyleAlign: Analysis and Applications of Aligned StyleGAN Models Zongze Wu, Yotam Nitzan, Eli Shechtman, Dani Lischinski 2021-10-21 2021-10-21 [("doi","10.48550/arXiv.2110.11323")] ai/nn/gan/stylegan

In this paper, we perform an in-depth study of the properties and applications of aligned generative models. We refer to two models as aligned if they share the same architecture, and one of them (the child) is obtained from the other (the parent) via fine-tuning to another domain, a common practice in transfer learning. Several works already utilize some basic properties of aligned StyleGAN models to perform image-to-image translation. Here, we perform the first detailed exploration of model alignment, also focusing on StyleGAN.

First, we empirically analyze aligned models and provide answers to important questions regarding their nature. In particular, we find that the child model’s latent spaces are semantically aligned with those of the parent, inheriting incredibly rich semantics, even for distant data domains such as human faces and churches. Second, equipped with this better understanding, we leverage aligned models to solve a diverse set of tasks. In addition to image translation, we demonstrate fully automatic cross-domain image morphing. We further show that zero-shot vision tasks may be performed in the child domain while relying exclusively on supervision in the parent domain. We demonstrate qualitatively and quantitatively that our approach yields state-of-the-art results, while requiring only simple fine-tuning and inversion.

--- https://arxiv.org/abs/2110.11405 Illiterate DALL·E Learns to Compose Gautam Singh, Fei Deng, Sungjin Ahn 2021-10-17 2021-10-17 [("doi","10.48550/arXiv.2110.11405")] ai/nn/transformer/gpt/dall-e/1 ai/nn/vae

Although DALL·E has shown an impressive ability of composition-based systematic generalization in image generation, it requires the dataset of text-image pairs and the compositionality is provided by the text. In contrast, object-centric representation models like the Slot Attention model learn composable representations without the text prompt. However, unlike DALL·E its ability to systematically generalize for zero-shot generation is limited.

In this paper, we propose a simple but novel slot-based autoencoding architecture, called SLATE, for combining the best of both worlds: learning object-centric representations that allows systematic generalization in zero-shot image generation without text. As such, this model can also be seen as an illiterate DALL·E model. Unlike the pixel-mixture decoders of existing object-centric representation models, we propose to use the Image GPT decoder conditioned on the slots for capturing complex interactions among the slots and pixels.

In experiments, we show that this simple and easy-to-implement architecture not requiring a text prompt achieves improvement in in-distribution and out-of-distribution (zero-shot) image generation and qualitatively comparable or better slot-attention structure than the models based on mixture decoders.

--- https://arxiv.org/abs/2110.13107 STransGAN: An Empirical Study on Transformer in GANs Rui Xu, Xiangyu Xu, Kai Chen, Bolei Zhou, Chen Change Loy 2021-10-25 2021-10-25 [("doi","10.48550/arXiv.2110.13107")] ai/nn/gan ai/nn/transformer

Transformer becomes prevalent in computer vision, especially for high-level vision tasks. However, deploying Transformer in the generative adversarial network (GAN) framework is still an open yet challenging problem.

In this paper, we conduct a comprehensive empirical study to investigate the intrinsic properties of Transformer in GAN for high-fidelity image synthesis. Our analysis highlights the importance of feature locality in image generation. We first investigate the effective ways to implement local attention. We then examine the influence of residual connections in self-attention layers and propose a novel way to reduce their negative impacts on learning discriminators and conditional generators.

Our study leads to a new design of Transformers in GAN, a convolutional neural network (CNN)-free generator termed as STrans-G, which achieves competitive results in both unconditional and conditional image generations. The Transformer-based discriminator, STrans-D, also reduces its gap against the CNN-based discriminators.

--- https://arxiv.org/abs/2110.13771#nvidia AugMax: Adversarial Composition of Random Augmentations for Robust Training Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, Zhangyang Wang 2021-10-26 2021-10-26 [("doi","10.48550/arXiv.2110.13771")] ai/nn/adversarial ai/nn/cnn

Data augmentation is a simple yet effective way to improve the robustness of deep neural networks (DNNs). Diversity and hardness are two complementary dimensions of data augmentation to achieve robustness. For example, AugMix explores random compositions of a diverse set of augmentations to enhance broader coverage, while adversarial training generates adversarially hard samples to spot the weakness.

Motivated by this, we propose a data augmentation framework, termed AugMax, to unify the two aspects of diversity and hardness. AugMax first randomly samples multiple augmentation operators and then learns an adversarial mixture of the selected operators. Being a stronger form of data augmentation, AugMax leads to an augmented input distribution which makes model training more challenging.

To solve this problem, we further design a disentangled normalization module, termed DuBIN (Dual-Batch-and-Instance Normalization), that disentangles the instance-wise feature heterogeneity arising from AugMax.

Experiments show that AugMax-DuBIN leads to improved out-of-distribution robustness, outperforming prior arts by 3.03%, 3.49%, 1.82% and 0.71% on CIFAR-10-C, CIFAR-100-C, Tiny ImageNet-C and ImageNet-C.

Codes and pretrained models are available: Github.

--- https://arxiv.org/abs/2110.13985 LSSL: Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, Christopher Ré 2021-10-26 2021-10-26 [("doi","10.48550/arXiv.2110.13985")] ai/nn/rnn ai/nn/transformer/attention/recurrent

[cf. S4, HiPPO] Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with unique strengths and tradeoffs in modeling power and computational efficiency.

We introduce a simple sequence model inspired by control systems that generalizes these approaches while addressing their shortcomings. The Linear State-Space Layer (LSSL) maps a sequence u ↦ y by simply simulating a linear continuous-time state-space representation ͘x = Ax + Bu, y = Cx + Du. Theoretically, we show that LSSL models are closely related to the 3 aforementioned families of models and inherit their strengths. For example, they generalize convolutions to continuous-time, explain common RNN heuristics, and share features of NDEs such as time-scale adaptation. We then incorporate and generalize recent theory on continuous-time memorization to introduce a trainable subset of structured matrices A that endow LSSLs with long-range memory.

Empirically, stacking LSSL layers into a simple deep neural network obtains state-of-the-art results across time series benchmarks for long dependencies in sequential image classification, real-world healthcare regression tasks, and speech. On a difficult speech classification task with length-16000 sequences, LSSL outperforms prior approaches by 24 accuracy points, and even outperforms baselines that use hand-crafted features on 100× shorter sequences.

--- https://arxiv.org/abs/2110.14168#openai Training Verifiers to Solve Math Word Problems Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, John Schulman 2021-10-27 2021-10-27 [("doi","10.48550/arXiv.2110.14168")] ai/dataset ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/inner-monologue ai/scaling math

[blog] State-of-the-art language models can match human performance on many tasks, but they still struggle to robustly perform multi-step mathematical reasoning.

To diagnose the failures of current models and support research, we introduce GSM8K, a dataset of 8.5K high quality linguistically diverse grade school math word problems.

We find that even the largest transformer models fail to achieve high test performance, despite the conceptual simplicity of this problem distribution. To increase performance, we propose training verifiers to judge the correctness of model completions. At test time, we generate many candidate solutions and select the one ranked highest by the verifier.

We demonstrate that verification substantially improves performance on GSM8K, and we provide strong empirical evidence that verification scales more effectively with increased data than a finetuning baseline.

…In Figure 6c, we separately ablate the model size of the generator and the verifier. We find that using a large generator with a small verifier performs substantially better than using a small generator with a large verifier. Verification is still remarkably effective, even when the verifier is much smaller than the generator. This suggests that the verifier may often be relying on relatively coarse heuristics to discriminate between solutions from a given generator, rather than attempting a more thorough form of verification.

…Test Time Compute: At test time, we can choose to generate arbitrarily many solutions to be judged by the verifier before selecting the highest-ranked completion. Figure 7a shows how 6B verifier performance varies with the number of completions per test problem. At this scale, performance improves as we increase the number of completions up to 400. Beyond this point, performance starts to decrease. This suggests that the benefits of search are eventually outweighed by the risk of finding adversarial solutions that fool the verifier. In general, we evaluate verifier test performance using 100 completions, since this captures most of the benefits of verification with a relatively modest compute cost.

…To further increase performance, we can take a majority vote among the top verifier-ranked solutions instead of selecting only the single top solution. This voting process considers only the final answer reached by the individual solutions: the final answer selected is the one with the most votes. Figure 7b shows how performance varies as we allow a greater number of top samples to cast a vote. Unsurprisingly, when starting with a greater number of samples, we can afford to allow a greater number of samples to cast a vote. When we have only 100 samples, it is optimal to allow only the top 3–5 samples to cast a vote. When we have 3,200 samples, it is ~optimal to allow the top 30 to cast a vote. [cf. Gao et al 2022]

--- https://arxiv.org/abs/2110.14810#facebook Telling Creative Stories Using Generative Visual Aids Safinah Ali, Devi Parikh 2021-10-27 2021-10-27 [("doi","10.48550/arXiv.2110.14810")] ai/nn/gan/biggan ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e/1 ai/nn/transformer/gpt/fiction ai/nn/vae

Can visual artworks created using generative visual algorithms inspire human creativity in storytelling? We asked writers to write creative stories from a starting prompt, and provided them with visuals created by generative AI models from the same prompt. Compared to a control group, writers who used the visuals as story writing aid wrote statistically-significantly more creative, original, complete and visualizable stories, and found the task more fun.

Of the generative algorithms used (BigGAN, VQGAN, DALL·E, CLIPDraw), VQGAN was the most preferred. The control group that did not view the visuals did better in integrating the starting prompts.

Findings indicate that cross modality inputs by AI can benefit divergent aspects of creativity in human-AI co-creation, but hinders convergent thinking.

--- https://arxiv.org/abs/2110.15343#facebook Scatterbrain: Unifying Sparse and Low-rank Attention Approximation Beidi Chen, Tri Dao, Eric Winsor, Zhao Song, Atri Rudra, Christopher Ré 2021-10-28 2021-10-28 [("doi","10.48550/arXiv.2110.15343")] ai/nn/gan/biggan ai/nn/transformer/attention/sparsity

Recent advances in efficient Transformers have exploited either the sparsity or low-rank properties of attention matrices to reduce the computational and memory bottlenecks of modeling long sequences. However, it is still challenging to balance the trade-off between model quality and efficiency to perform a one-size-fits-all approximation for different tasks.

To better understand this trade-off, we observe that sparse and low-rank approximations excel in different regimes, determined by the softmax temperature in attention, and sparse + low-rank can outperform each individually. Inspired by the classical robust-PCA algorithm for sparse and low-rank decomposition, we propose Scatterbrain, a novel way to unify sparse (via locality sensitive hashing) and low-rank (via kernel feature map) attention for accurate and efficient approximation. The estimation is unbiased with provably low error.

We empirically show that Scatterbrain can achieve 2.1× lower error than baselines when serving as a drop-in replacement in BigGAN image generation and pre-trained T2T-ViT. On a pre-trained T2T Vision transformer, even without fine-tuning, Scatterbrain can reduce 98% of attention memory at the cost of only 1% drop in accuracy.

We demonstrate Scatterbrain for end-to-end training with up to 4 points better perplexity and 5 points better average accuracy than sparse or low-rank efficient transformers on language modeling and long-range-arena tasks.

--- https://arxiv.org/abs/2110.15943#facebook MetaICL: Learning to Learn In Context Sewon Min, Mike Lewis, Luke Zettlemoyer, Hannaneh Hajishirzi 2021-10-29 2021-10-29 [("doi","10.48550/arXiv.2110.15943")] ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/meta-learning reinforcement-learning/scaling

We introduce MetaICL (Meta-training for In-Context Learning), a new meta-training framework for few-shot learning where a pretrained language model is tuned to do in-context learning on a large set of training tasks. This meta-training enables the model to more effectively learn a new task in context at test time, by simply conditioning on a few training examples with no parameter updates or task-specific templates.

We experiment on a large, diverse collection of tasks consisting of 142 NLP datasets including classification, question answering, natural language inference, paraphrase detection and more, across seven different meta-training/target splits.

MetaICL outperforms a range of baselines including in-context learning without meta-training and multi-task learning followed by zero-shot transfer. We find that the gains are particularly large for target tasks that have domain shifts from the meta-training tasks, and that using a diverse set of the meta-training tasks is key to improvements. We also show that MetaICL approaches (and sometimes beats) the performance of models fully finetuned on the target task training data, and outperforms much bigger models with nearly 8× parameters.

--- https://arxiv.org/abs/2111.00160 DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models Xuxi Chen, Tianlong Chen, Yu Cheng, Weizhu Chen, Zhangyang Wang, Ahmed Hassan Awadallah 2021-10-30 2021-10-30 [("doi","10.48550/arXiv.2111.00160")] ai/nn/sparsity/pruning

Gigantic pre-trained models have become central to natural language processing (NLP), serving as the starting point for fine-tuning towards a range of downstream tasks. However, two pain points persist for this paradigm: (a) as the pre-trained models grow bigger (eg. 175b parameters for GPT-3), even the fine-tuning process can be time-consuming and computationally expensive; (b) the fine-tuned model has the same size as its starting point by default, which is neither sensible due to its more specialized functionality, nor practical since many fine-tuned models will be deployed in resource-constrained environments.

To address these pain points, we propose a framework for resource-efficient and parameter-efficient fine-tuning by leveraging the sparsity prior in both weight updates and the final model weights. Our proposed framework, dubbed Dually Sparsity-Embedded Efficient Tuning (DSEE), aims to achieve two key objectives: (1) parameter efficient fine-tuning—by enforcing sparsity-aware weight updates on top of the pre-trained weights; and (2) resource-efficient inference—by encouraging a sparse weight structure towards the final fine-tuned model. We leverage sparsity in these two directions by exploiting both unstructured and structured sparse patterns in pre-trained language models via magnitude-based pruning and \U0001D4C11 sparse regularization.

Extensive experiments and in-depth investigations, with diverse network backbones (ie. BERT, GPT-2, and DeBERTa) on dozens of datasets, consistently demonstrate highly impressive parameter-/training-/inference-efficiency, while maintaining competitive downstream transfer performance. For instance, our DSEE-BERT obtains about 35% inference FLOPs savings with <1% trainable parameters and comparable performance to conventional fine-tuning. Codes are available in Github.

--- https://arxiv.org/abs/2111.00210 Mastering Atari Games with Limited Data Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, Yang Gao 2021-10-30 2021-10-30 [("doi","10.48550/arXiv.2111.00210")] reinforcement-learning/exploration reinforcement-learning/model/muzero

Reinforcement learning has achieved great success in many applications. However, sample efficiency remains a key challenge, with prominent methods requiring millions (or even billions) of environment steps to train. Recently, there has been progress in sample efficient image-based RL algorithms; however, consistent human-level performance on the Atari game benchmark remains an elusive goal. We propose a sample efficient model-based visual RL algorithm built on MuZero, which we name Efficient Zero. Our method achieves 190.4% mean human performance and 116.0% median performance on the Atari 100k benchmark with only two hours of real-time game experience and outperforms the state SAC in some tasks on the DMControl 100k benchmark.

This is the first time an algorithm achieves super-human performance on Atari games with such little data. EfficientZero’s performance is also close to DQN’s performance at 200 million frames while we consume 500× less data.

EfficientZero’s low sample complexity and high performance can bring RL closer to real-world applicability.

We implement our algorithm in an easy-to-understand manner and it is available at https://github.com/YeWR/EfficientZero. We hope it will accelerate the research of MCTS-based RL algorithms in the wider community.

--- https://arxiv.org/abs/2111.00396 S4: Efficiently Modeling Long Sequences with Structured State Spaces Albert Gu, Karan Goel, Christopher Ré 2021-10-31 2021-10-31 [("doi","10.48550/arXiv.2111.00396")] ai/nn/rnn ai/nn/transformer/attention/recurrent

[cf. LSSL, HiPPO; Github (example); talk; explainer] A central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modalities and tasks, particularly on long-range dependencies. Although conventional models including RNNs, CNNs, and Transformers have specialized variants for capturing long dependencies, they still struggle to scale to very long sequences of 10,000 or more steps.

A promising recent approach proposed modeling sequences by simulating the fundamental state space model (SSM) x′(t) = Ax(t) + Bu(t), y(t) = Cx(t) + Du(t), and showed that for appropriate choices of the state matrix A, this system could handle long-range dependencies mathematically and empirically. However, this method has prohibitive computation and memory requirements, rendering it infeasible as a general sequence modeling solution.

We propose the Structured State Space sequence (S4) model based on a new parameterization for the SSM, and show that it can be computed much more efficiently than prior approaches while preserving their theoretical strengths. Our technique involves conditioning A with a low-rank correction, allowing it to be diagonalized stably and reducing the SSM to the well-studied computation of a Cauchy kernel.

S4 achieves strong empirical results across a diverse range of established benchmarks, including (1) 91% accuracy on sequential CIFAR-10 with no data augmentation or auxiliary losses, on par with a larger 2-D ResNet, (2) substantially closing the gap to Transformers on image and language modeling tasks, while performing generation 60× faster (3) SoTA on every task from the Long Range Arena benchmark, including solving the challenging Path-X task of length 16k that all prior work fails on, while being as efficient as all competitors.

[Parrot:

I find it easiest to think of it as a “super RNN”—an RNN with all the long-term dependency and vanishing gradient issues fixed. My best TLDR for why it works:

  1. It’s like a linear RNN with an N-dimensional hidden state. x

  2. The key is to initialize and parameterize the RNN in a very special way.

  3. This makes xt evolve in a special way: each xt lets you reconstruct all past inputs u0, u1, … ut with high accuracy.

    IIUC, just being able to “memorize” like this is apparently enough to break SOTA on Long Range Arena.

  4. And with the special initialization, the RNN’s parameter matrix is so simple that you can compute a very large number of time steps entirely in parallel, using FFT. FFT is the key computational trick; the other part is initializing with a matrix that is “almost diagonal” and therefore easy to work with.

TLDR: RNNs can be really really good if you parameterize them the right way.

Narendra Patwardhan:

Transformers would perform better than S4 (in its current form) on any task which can’t be easily expressed as a simple differential equation such as language modeling, question answering, object detection, image segmentation etc.]

--- https://arxiv.org/abs/2111.01007 Projected GANs Converge Faster Axel Sauer, Kashyap Chitta, Jens Müller, Andreas Geiger 2021-11-01 2021-11-01 [("doi","10.48550/arXiv.2111.01007")] ai/nn/gan ai/nn/transformer/clip

[homepage; code; supplement] Generative Adversarial Networks (GANs) produce high-quality images but are challenging to train. They need careful regularization, vast amounts of compute, and expensive hyper-parameter sweeps.

We make headway on these issues by projecting generated and real samples into a fixed, pretrained feature space. Motivated by the finding that the discriminator cannot fully exploit features from deeper layers of the pretrained model, we propose a more effective strategy that mixes features across channels and resolutions. Our Projected GAN improves image quality, sample efficiency, and convergence speed. It is further compatible with resolutions of up to one Megapixel and advances the state-of-the-art Fréchet Inception Distance (FID) on twenty-two benchmark datasets.

Importantly, Projected GANs match the previously lowest FIDs up to 40× faster, cutting the wall-clock time from 5 days to less than 3 hours given the same computational resources.

--- https://arxiv.org/abs/2111.01243 Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heinz, Dan Roth 2021-11-01 2021-11-01 [("doi","10.48550/arXiv.2111.01243")] ai/nn/transformer/gpt

Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field.

We present a survey of recent work that uses these large language models to solve NLP tasks via pre-training then fine-tuning, prompting, or text generation approaches.

We also present approaches that use pre-trained language models to generate data for training augmentation or other purposes.

We conclude with discussions on limitations and suggested directions for future research.

--- https://arxiv.org/abs/2111.01455 Learning a perceptual manifold with deep features for animation video resequencing Charles C. Morace, Thi-Ngoc-Hanh Le, Sheng-Yi Yao, Shang-Wei Zhang, Tong-Yee Lee 2021-11-02 2021-11-02 [("doi","10.48550/arXiv.2111.01455")] ai/anime ai/video/generation

We propose a novel deep learning framework for animation video resequencing.

Our system produces new video sequences by minimizing a perceptual distance of images from an existing animation video clip. To measure perceptual distance, we use the activations of convolutional neural networks and learn a perceptual distance by training these features on a small network with data comprised of human perceptual judgments.

We show that with this perceptual metric and graph-based manifold learning techniques, our framework can produce new smooth and visually appealing animation video results for a variety of animation video styles.

In contrast to previous work on animation video resequencing, the proposed framework applies to a wide range of image styles and does not require hand-crafted feature extraction, background subtraction, or feature correspondence.

In addition, we also show that our framework has applications to appealingly arrange unordered collections of images.

--- https://arxiv.org/abs/2111.01587#deepmind Procedural Generalization by Planning with Self-Supervised World Models Ankesh Anand, Jacob Walker, Yazhe Li, Eszter Vértes, Julian Schrittwieser, Sherjil Ozair, Théophane Weber, Jessica B. Hamrick 2021-11-02 2021-11-02 [("doi","10.48550/arXiv.2111.01587")] reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/model/muzero reinforcement-learning/scaling

One of the key promises of model-based reinforcement learning is the ability to generalize using an internal model of the world to make predictions in novel environments and tasks. However, the generalization ability of model-based agents is not well understood because existing work has focused on model-free agents when benchmarking generalization.

Here, we explicitly measure the generalization ability of model-based agents in comparison to their model-free counterparts. We focus our analysis on MuZero (Schrittwieser et al 2020), a powerful model-based agent, and evaluate its performance on both procedural and task generalization. We identify 3 factors of procedural generalization—planning, self-supervised representation learning, and procedural data diversity—and show that by combining these techniques:

we achieve state-of-the art generalization performance and data efficiency on Procgen (Cobbe et al 2019). However, we find that these factors do not always provide the same benefits for the task generalization benchmarks in Meta-World (Yu et al 2019), indicating that transfer remains a challenge and may require different approaches than procedural generalization.

Overall, we suggest that building generalizable agents requires moving beyond the single-task, model-free paradigm and towards self-supervised model-based agents that are trained in rich, procedural, multi-task environments.

--- https://arxiv.org/abs/2111.01774 Emergence and structure of decentralised trade networks around dark web marketplaces Matthieu Nadini, Alberto Bracci, Abeer ElBahrawy, Philip Gradwell, Alexander Teytelboym, Andrea Baronchelli 2021-11-02 2021-11-02 [("doi","10.48550/arXiv.2111.01774")] bitcoin darknet-market/alphabay darknet-market/evolution darknet-market/silk-road/1 darknet-market/silk-road/2

Dark web marketplaces (DWMs) are online platforms that facilitate illicit trade among millions of users generating billions of dollars in annual revenue. Recently, two interview-based studies have suggested that DWMs may also promote the emergence of direct user-to-user (U2U) trading relationships.

Here, we quantify the scale of, and thoroughly investigate, U2U trading around DWMs by analysing 31 million Bitcoin transactions among users of 40 DWMs between June 2011 and Jan 2021. We find that half of the DWM users trade through U2U pairs generating a total trading volume greater than DWMs themselves.

We then show that hundreds of thousands of DWM users form stable trading pairs that are persistent over time. Users in stable pairs are typically the ones with the largest trading volume on DWMs. Then, we show that new U2U pairs often form while both users are active on the same DWM, suggesting the marketplace may serve as a catalyst for new direct trading relationships.

Finally, we reveal that stable U2U pairs tend to survive DWM closures and that they were not affected by COVID-19, indicating that their trading activity is resilient to external shocks. Our work unveils sophisticated patterns of trade emerging in the dark web and highlights the importance of investigating user behavior beyond the immediate buyer-seller network on a single marketplace.

--- https://arxiv.org/abs/2111.02080 An Explanation of In-context Learning as Implicit Bayesian Inference Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma 2021-11-03 2021-11-03 [("doi","10.48550/arXiv.2111.02080")] ai/dataset ai/nn/rnn ai/nn/transformer/attention reinforcement-learning/meta-learning reinforcement-learning/scaling statistics/bayes

Large pretrained language models such as GPT-3 have the surprising ability to do in-context learning, where the model learns to do a downstream task simply by conditioning on a prompt consisting of input-output examples. Without being explicitly pretrained to do so, the language model learns from these examples during its forward pass without parameter updates on “out-of-distribution” prompts. Thus, it is unclear what mechanism enables in-context learning.

In this paper, we study the role of the pretraining distribution on the emergence of in-context learning under a mathematical setting where the pretraining texts have long-range coherence. Here, language model pretraining requires inferring a latent document-level concept from the conditioning text to generate coherent next tokens. At test time, this mechanism enables in-context learning by inferring the shared latent concept between prompt examples and applying it to make a prediction on the test example.

Concretely, we prove that in-context learning occurs implicitly via Bayesian inference of the latent concept when the pretraining distribution is a mixture of HMMs. This can occur despite the distribution mismatch between prompts and pretraining data. In contrast to messy large-scale pretraining datasets for in-context learning in natural language, we generate a family of small-scale synthetic datasets (GINC) where Transformer and LSTM language models both exhibit in-context learning.

Beyond the theory which focuses on the effect of the pretraining distribution, we empirically find that scaling model size improves in-context accuracy even when the pretraining loss is the same.

--- https://arxiv.org/abs/2111.02114#laion LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, Aran Komatsuzaki 2021-11-03 2021-11-03 [("doi","10.48550/arXiv.2111.02114")] ai/dataset ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e ai/scaling

Multi-modal language-vision models trained on hundreds of millions of image-text pairs (eg. CLIP, DALL·E) gained a recent surge, showing remarkable capability to perform zero-shot or few-shot learning and transfer even in absence of per-sample labels on target image data. Despite this trend, to date there has been no publicly available datasets of sufficient scale for training such models from scratch.

To address this issue, in a community effort we build and release for public use LAION-400M, a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings [clusters] and kNN indices that allow efficient similarity search.

--- https://arxiv.org/abs/2111.02552 Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies Tim Seyde, Igor Gilitschenski, Wilko Schwarting, Bartolomeo Stellato, Martin Riedmiller, Markus Wulfmeier, Daniela Rus 2021-11-03 2021-11-03 [("doi","10.48550/arXiv.2111.02552")] reinforcement-learning/imitation-learning reinforcement-learning/model

Reinforcement learning (RL) for continuous control typically employs distributions whose support covers the entire action space.

In this work, we investigate the colloquially known phenomenon that trained agents often prefer actions at the boundaries of that space. We draw theoretical connections to the emergence of bang-bang behavior in optimal control, and provide extensive empirical evaluation across a variety of recent RL algorithms.

We replace the normal Gaussian by a Bernoulli distribution that solely considers the extremes along each action dimension—a bang-bang controller.

Surprisingly, this achieves state-of-the-art performance on several continuous control benchmarks—in contrast to robotic hardware, where energy and maintenance cost affect controller choices. Since exploration, learning, and the final solution are entangled in RL, we provide additional imitation learning experiments to reduce the impact of exploration on our analysis. Finally, we show that our observations generalize to environments that aim to model real-world challenges and evaluate factors to mitigate the emergence of bang-bang solutions.

Our findings emphasize challenges for benchmarking continuous control algorithms, particularly in light of potential real-world applications.

--- https://arxiv.org/abs/2111.02570#microsoft CLUES: Few-Shot Learning Evaluation in Natural Language Understanding Subhabrata Mukherjee, Xiaodong Liu, Guoqing Zheng, Saghar Hosseini, Hao Cheng, Greg Yang, Christopher Meek, Ahmed Hassan Awadallah, Jianfeng Gao 2021-11-04 2021-11-04 [("doi","10.48550/arXiv.2111.02570")] ai/nn/transformer/gpt

Most recent progress in natural language understanding (NLU) has been driven, in part, by benchmarks such as GLUE, SuperGLUE, SQuAD, etc. In fact, many NLU models have now matched or exceeded “human-level” performance on many tasks in these benchmarks. Most of these benchmarks, however, give models access to relatively large amounts of labeled data for training. As such, the models are provided far more data than required by humans to achieve strong performance. That has motivated a line of work that focuses on improving few-shot learning performance of NLU models. However, there is a lack of standardized evaluation benchmarks for few-shot NLU resulting in different experimental settings in different papers.

To help accelerate this line of work, we introduce CLUES (Constrained Language Understanding Evaluation Standard), a benchmark for evaluating the few-shot learning capabilities of NLU models.

We demonstrate that while recent models reach human performance when they have access to large amounts of labeled data, there is a huge gap in performance in the few-shot setting for most tasks. We also demonstrate differences between alternative model families and adaptation techniques in the few shot setting.

Finally, we discuss several principles and choices in designing the experimental settings for evaluating the true few-shot learning performance and suggest an unified standardized approach to few-shot learning evaluation. We aim to encourage research on NLU models that can generalize to new tasks with a small number of examples.

Code and data for CLUES are available at Github.

--- https://arxiv.org/abs/2111.03133 StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Synthesis Peter Schaldenbrand, Zhixuan Liu, Jean Oh 2021-11-04 2021-11-04 [("doi","10.48550/arXiv.2111.03133")] ai/nn/gan/stylegan ai/nn/transformer/clip

Generating images that fit a given text description using machine learning has improved greatly with the release of technologies such as the CLIP image-text encoder model; however, current methods lack artistic control of the style of image to be generated. We introduce StyleCLIPDraw which adds a style loss to the CLIPDraw text-to-drawing synthesis model to allow artistic control of the synthesized drawings in addition to control of the content via text.

Whereas performing decoupled style transfer on a generated image only affects the texture, our proposed coupled approach is able to capture a style in both texture and shape, suggesting that the style of the drawing is coupled with the drawing process itself.

More results and our code are available at https://github.com/pschaldenbrand/StyleCLIPDraw.

--- https://arxiv.org/abs/2111.03186#nvidia EditGAN: High-Precision Semantic Image Editing Huan Ling, Karsten Kreis, Daiqing Li, Seung Wook Kim, Antonio Torralba, Sanja Fidler 2021-11-04 2021-11-04 [("doi","10.48550/arXiv.2111.03186")] ai/nn/gan

Generative adversarial networks (GANs) have recently found applications in image editing. However, most GAN based image editing methods often require large scale datasets with semantic segmentation annotations for training, only provide high level control, or merely interpolate between different images.

Here, we propose EditGAN, a novel method for high quality, high precision semantic image editing, allowing users to edit images by modifying their highly detailed part segmentation masks, eg. drawing a new mask for the headlight of a car.

EditGAN builds on a GAN framework that jointly models images and their semantic segmentations, requiring only a handful of labeled examples, making it a scalable tool for editing. Specifically, we embed an image into the GAN latent space and perform conditional latent code optimization according to the segmentation edit, which effectively also modifies the image. To amortize optimization, we find editing vectors in latent space that realize the edits. The framework allows us to learn an arbitrary number of editing vectors, which can then be directly applied on other images at interactive rates.

We experimentally show that EditGAN can manipulate images with an unprecedented level of detail and freedom, while preserving full image quality. We can also easily combine multiple edits and perform plausible edits beyond EditGAN training data. We demonstrate EditGAN on a wide variety of image types and quantitatively outperform several previous editing methods on standard editing benchmark tasks.

--- https://arxiv.org/abs/2111.03481 Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers Yanhong Zeng, Huan Yang, Hongyang Chao, Jianbo Wang, Jianlong Fu 2021-11-05 2021-11-05 [("doi","10.48550/arXiv.2111.03481")] ai/nn/gan/stylegan ai/nn/transformer

We present a new perspective of achieving image synthesis by viewing this task as a visual token generation problem. Different from existing paradigms that directly synthesize a full image from a single input (eg. a latent code), the new formulation enables a flexible local manipulation for different image regions, which makes it possible to learn content-aware and fine-grained style control for image synthesis.

Specifically, it takes as input a sequence of latent tokens to predict the visual tokens for synthesizing an image. Under this perspective, we propose a token-based generator (ie. TokenGAN). Particularly, the TokenGAN inputs two semantically different visual tokens, ie. the learned constant content tokens and the style tokens from the latent space. Given a sequence of style tokens, the TokenGAN is able to control the image synthesis by assigning the styles to the content tokens by attention mechanism with a Transformer.

We conduct extensive experiments and show that the proposed TokenGAN has achieved state-of-the-art results on several widely-used image synthesis benchmarks, including FFHQ and LSUN CHURCH with different resolutions. In particular, the generator is able to synthesize high-fidelity images with 1,024×1,024 size, dispensing with convolutions entirely.

--- https://arxiv.org/abs/2111.03930 Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling Renrui Zhang, Rongyao Fang, Peng Gao, Wei Zhang, Kunchang Li, Jifeng Dai, Yu Qiao, Hongsheng Li 2021-11-06 2021-11-06 [("doi","10.48550/arXiv.2111.03930")] ai/nn/transformer/clip

Contrastive Vision-Language Pre-training, known as CLIP, has provided a new paradigm for learning visual representations by using large-scale contrastive image-text pairs. It shows impressive performance on zero-shot knowledge transfer to downstream tasks. To further enhance CLIP’s few-shot capability, CLIP-Adapter proposed to fine-tune a lightweight residual feature adapter and improves the performance for few-shot classification. However, such a process still needs extra training and computational resources.

In this paper, we propose Training-Free CLIP-Adapter (Tip-Adapter), which not only inherits CLIP’s training-free advantage but also performs comparably or even better than CLIP-Adapter. Tip-Adapter does not require any back propagation for training the adapter, but creates the weights by a key-value cache model constructed from the few-shot training set. In this non-parametric manner, Tip-Adapter acquires well-performed adapter weights without any training, which is both efficient and effective.

Moreover, the performance of Tip-Adapter can be further boosted by fine-tuning such properly initialized adapter for only a few epochs with super-fast convergence speed. We conduct extensive experiments of few-shot classification on ImageNet and other 10 datasets to demonstrate the superiority of proposed Tip-Adapter.

The code will be released at Github.

--- https://arxiv.org/abs/2111.04007#microsoft Varuna: Scalable, Low-cost Training of Massive Deep Learning Models Sanjith Athlur, Nitika Saran, Muthian Sivathanu, Ramachandran Ramjee, Nipun Kwatra 2021-11-07 2021-11-07 [("doi","10.48550/arXiv.2111.04007")] ai/scaling/economics

Systems for training massive deep learning models (billions of parameters) today assume and require specialized “hyper-clusters”: hundreds or thousands of GPUs wired with specialized high-bandwidth interconnects such as NV-Link and InfiniBand. Besides being expensive, such dependence on hyper-clusters and custom high-speed inter-connects limits the size of such clusters, creating (a) scalability limits on job parallelism; (b) resource fragmentation across hyper-clusters.

In this paper, we present Varuna, a new system that enables training massive deep learning models on commodity networking. Varuna makes thrifty use of networking resources and automatically configures the user’s training job to efficiently use any given set of resources. Therefore, Varuna is able to leverage “low-priority” VMs that cost about 5× cheaper than dedicated GPUs, thus reducing the cost of training massive models.

We demonstrate the efficacy of Varuna by training massive models, including a 200 billion parameter model, on 5× cheaper “spot VMs”, while maintaining high training throughput. Even in scenarios where hyper-cluster resources are available, Varuna improves end-to-end training time by 20–78% compared to alternative approaches.

The code for Varuna is available at Github.

--- https://arxiv.org/abs/2111.05321 Turing-Universal Learners with Optimal Scaling Laws Preetum Nakkiran 2021-11-09 2021-11-09 [("doi","10.48550/arXiv.2111.05321")] ai/scaling

For a given distribution, learning algorithm, and performance metric, the rate of convergence (or data-scaling law) is the asymptotic behavior of the algorithm’s test performance as a function of number of train samples. Many learning methods in both theory and practice have power-law rates, i.e. performance scales as n−α for some α > 0. Moreover, both theoreticians and practitioners are concerned with improving the rates of their learning algorithms under settings of interest.

We observe the existence of a “universal learner”, which achieves the best possible distribution-dependent asymptotic rate among all learning algorithms within a specified runtime (eg. 𝒪(n2)), while incurring only poly-logarithmic slowdown over this runtime. This algorithm is uniform, and does not depend on the distribution, and yet achieves best-possible rates for all distributions. The construction itself is a simple extension of Levin’s universal search (Levin 1973).

And much like universal search, the universal learner is not at all practical, and is primarily of theoretical and philosophical interest.

--- https://arxiv.org/abs/2111.05754 Prune Once for All: Sparse Pre-Trained Language Models Ofir Zafrir, Ariel Larey, Guy Boudoukh, Haihao Shen, Moshe Wasserblat 2021-11-10 2021-11-10 [("doi","10.48550/arXiv.2111.05754")] ai/nn/sparsity/knowledge-distillation ai/nn/sparsity/low-precision ai/nn/sparsity/pruning

Transformer-based language models are applied to a wide range of applications in natural language processing. However, they are inefficient and difficult to deploy. In recent years, many compression algorithms have been proposed to increase the implementation efficiency of large Transformer-based models on target hardware.

In this work we present a new method for training sparse pre-trained Transformer language models by integrating weight pruning and model distillation. These sparse pre-trained models can be used to transfer learning for a wide range of tasks while maintaining their sparsity pattern. We demonstrate our method with 3 known architectures to create sparse pre-trained BERT-Base, BERT-Large and DistilBERT.

We show how the compressed sparse pre-trained models we trained transfer their knowledge to 5 different downstream natural language tasks with minimal accuracy loss. Moreover, we show how to further compress the sparse models’ weights to 8bit precision using quantization-aware training. For example, with our sparse pre-trained BERT-Large fine-tuned on SQuAD1.1 and quantized to 8bit we achieve a compression ratio of 40× for the encoder with less than 1% accuracy loss.

To the best of our knowledge, our results show the best compression-to-accuracy ratio for BERT-Base, BERT-Large, and DistilBERT.

--- https://arxiv.org/abs/2111.05803#google Gradients are Not All You Need Luke Metz, C. Daniel Freeman, Samuel S. Schoenholz, Tal Kachman 2021-11-10 2021-11-10 [("doi","10.48550/arXiv.2111.05803")] ai/nn/rnn reinforcement-learning/meta-learning

Differentiable programming techniques are widely used in the community and are responsible for the machine learning renaissance of the past several decades. While these methods are powerful, they have limits.

In this short report, we discuss a common chaos-based failure mode which appears in a variety of differentiable circumstances, ranging from recurrent neural networks and numerical physics simulation to training learned optimizers.

We trace this failure to the spectrum of the Jacobian of the system under study, and provide criteria for when a practitioner might expect this failure to spoil their differentiation-based optimization algorithms.

--- https://arxiv.org/abs/2111.05826#google Palette: Image-to-Image Diffusion Models Chitwan Saharia, William Chan, Huiwen Chang, Chris A. Lee, Jonathan Ho, Tim Salimans, David J. Fleet, Mohammad Norouzi 2021-10-06 2021-10-06 [("doi","10.48550/arXiv.2111.05826")] ai/nn/diffusion ai/nn/transformer cs/algorithm/information/compression

We introduce Palette, a simple and general framework for image-to-image translation using conditional diffusion models.

On 4 challenging image-to-image translation tasks (colorization, inpainting, uncropping, and JPEG decompression), Palette outperforms strong GAN and regression baselines, and establishes a new state-of-the-art result. This is accomplished without task-specific hyper-parameter tuning, architecture customization, or any auxiliary loss demonstrating a desirable degree of generality and flexibility.

We uncover the impact of using 𝓁2 vs. 𝓁1 loss in the denoising diffusion objective on sample diversity, and demonstrate the importance of self-attention through empirical architecture studies.

Importantly, we advocate an unified evaluation protocol based on ImageNet, and report several sample quality scores including FID, Inception Score, Classification Accuracy of a pre-trained ResNet-50, and Perceptual Distance against reference images for various baselines. We expect this standardized evaluation protocol to play a critical role in advancing image-to-image translation research.

Finally, we show that a single generalist Palette model trained on 3 tasks (colorization, inpainting, JPEG decompression) performs as well or better than task-specific specialist counterparts.

[Keywords: machine learning, artificial intelligence, computer vision]

--- https://arxiv.org/abs/2111.06091 A Survey of Visual Transformers Yang Liu, Yao Zhang, Yixin Wang, Feng Hou, Jin Yuan, Jiang Tian, Yang Zhang, Zhongchao Shi, Jianping Fan, Zhiqiang He 2021-11-11 2021-11-11 [("doi","10.48550/arXiv.2111.06091")] ai/nn/transformer

Transformer, an attention-based encoder-decoder architecture, has revolutionized the field of natural language processing. Inspired by this achievement, some pioneering works have recently been done on adapting Transformer-like architectures to Computer Vision (CV) fields, which have demonstrated their effectiveness on various CV tasks. Relying on competitive modeling capability, visual Transformers have achieved impressive performance on multiple benchmarks such as ImageNet, COCO, and ADE20K as compared with modern Convolution Neural Networks (CNN).

In this paper, we have provided a comprehensive review of over one hundred different visual Transformers for 3 fundamental CV tasks (classification, detection, and segmentation), where a taxonomy is proposed to organize these methods according to their motivations, structures, and usage scenarios. Because of the differences in training settings and oriented tasks, we have also evaluated these methods on different configurations for easy and intuitive comparison instead of only various benchmarks. Furthermore, we have revealed a series of essential but unexploited aspects that may empower Transformer to stand out from numerous architectures, eg. slack high-level semantic embeddings to bridge the gap between visual and sequential Transformers. Finally, 3 promising future research directions are suggested for further investment.

--- https://arxiv.org/abs/2111.06377#facebook MAE: Masked Autoencoders Are Scalable Vision Learners Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick 2021-11-11 2021-11-11 [("doi","10.48550/arXiv.2111.06377")] ai/nn/vae/mae ai/scaling

[code] This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision.

Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is based on two core designs. First, we develop an asymmetric encoder-decoder architecture, with an encoder that operates only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask tokens. Second, we find that masking a high proportion of the input image, eg. 75%, yields a nontrivial and meaningful self-supervisory task.

Coupling these two designs enables us to train large models efficiently and effectively: we accelerate training (by 3× or more) and improve accuracy. Our scalable approach allows for learning high-capacity models that generalize well: eg. a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-1K data. Transfer performance in downstream tasks outperforms supervised pre-training and shows promising scaling behavior.

--- https://arxiv.org/abs/2111.06719#tencent On Transferability of Prompt Tuning for Natural Language Understanding Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie Zhou 2021-11-12 2021-11-12 [("doi","10.48550/arXiv.2111.06719")] ai/nn/transformer/gpt

Prompt tuning (PT) is a promising parameter-efficient method to use extremely large pre-trained language models (PLMs), which could achieve comparable performance to full-parameter fine-tuning by only tuning a few soft prompts. However, compared to fine-tuning, PT empirically requires much more training steps.

To explore whether we can improve the efficiency of PT by reusing trained soft prompts and sharing learned knowledge, we empirically investigate the transferability of soft prompts across different tasks and models. In cross-task transfer, we find that trained soft prompts can well transfer to similar tasks and initialize PT for them to accelerate training and improve performance. Moreover, to explore what factors influence prompts’ transferability across tasks, we investigate how to measure the prompt similarity and find that the overlapping rate of activated neurons highly correlates to the transferability.

In cross-model transfer, we explore how to project the prompts of a PLM to another PLM and successfully train a kind of projector which can achieve non-trivial transfer performance on similar tasks. However, initializing PT with the projected prompts does not work well, which may be caused by optimization preferences and PLMs’ high redundancy.

Our findings show that improving PT with knowledge transfer is possible and promising, while prompts’ cross-task transferability is generally better than the cross-model transferability.

--- https://arxiv.org/abs/2111.07991#google LiT: Zero-Shot Transfer with Locked-image Text Tuning Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, Lucas Beyer 2021-11-15 2021-11-15 [("doi","10.48550/arXiv.2111.07991")] ai/nn/retrieval ai/nn/transformer/clip

This paper presents contrastive-tuning, a simple method employing contrastive training to align image and text models while still taking advantage of their pre-training.

In our empirical study, we find that locked pre-trained image models with unlocked text models work best. We call this instance of contrastive-tuning “Locked-image Text tuning” (LiT-tuning), which just teaches a text model to read out good representations from a pre-trained image model for new tasks.

A LiT-tuned model gains the capability of zero-shot transfer to new vision tasks, such as image classification or retrieval. The proposed LiT-tuning is widely applicable; it works reliably with multiple pre-training methods (supervised and unsupervised) and across diverse architectures (ResNet, Vision Transformers, and MLP-Mixer) using 3 different image-text datasets.

With the transformer-based pre-trained ViT-g/14 model, the LiT-tuned model achieves 84.5% zero-shot transfer accuracy on the ImageNet test set, and 81.1% on the challenging out-of-distribution ObjectNet test set.

--- https://arxiv.org/abs/2111.08171 Solving Linear Algebra by Program Synthesis Iddo Drori, Nakul Verma 2021-11-16 2021-11-16 [("doi","10.48550/arXiv.2111.08171")] ai/nn/transformer/gpt/codex ai/scaling

[probability/statistics] We solve MIT’s Linear Algebra 18.06 course and Columbia University’s Computational Linear Algebra COMS3251 courses with perfect accuracy by interactive program synthesis.

This surprisingly strong result is achieved by turning the course questions into programming tasks and then running the programs to produce the correct answers. We use OpenAI Codex with zero-shot learning, without providing any examples in the prompts, to synthesize code from questions. We quantify the difference between the original question text and the transformed question text that yields a correct answer.

Since all COMS3251 questions are not available online the model is not overfitting. We go beyond just generating code for questions with numerical answers by interactively generating code that also results visually pleasing plots as output.

Finally, we automatically generate new questions given a few sample questions which may be used as new course content.

This work is a step forward in solving quantitative math problems and opens the door for solving many university level STEM courses by machine.

--- https://arxiv.org/abs/2111.08267 Solving Probability and Statistics Problems by Program Synthesis Leonard Tang, Elizabeth Ke, Nikhil Singh, Nakul Verma, Iddo Drori 2021-11-16 2021-11-16 [("doi","10.48550/arXiv.2111.08267")] ai/dataset ai/nn/transformer/gpt/codex ai/scaling

[algebra] We solve university level probability and statistics questions by program synthesis using OpenAI’s Codex, a Transformer trained on text and fine-tuned on code. We transform course problems from MIT’s 18.05 Introduction to Probability and Statistics and Harvard’s STAT110 Probability into programming tasks. We then execute the generated code to get a solution. Since these course questions are grounded in probability, we often aim to have Codex generate probabilistic programs that simulate a large number of probabilistic dependencies to compute its solution.

Our approach requires prompt engineering to transform the question from its original form to an explicit, tractable form that results in a correct program and solution. To estimate the amount of work needed to translate an original question into its tractable form, we measure the similarity between original and transformed questions.

Our work is the first to introduce a new dataset of university-level probability and statistics problems and solve these problems in a scalable fashion using the program synthesis capabilities of large language models.

--- https://arxiv.org/abs/2111.08284#allen Few-Shot Self-Rationalization with Natural Language Prompts Ana Marasović, Iz Beltagy, Doug Downey, Matthew E. Peters 2021-11-16 2021-11-16 [("doi","10.48550/arXiv.2111.08284")] ai/dataset ai/nn/transformer/gpt/inner-monologue ai/scaling

Self-rationalization models that predict task labels and generate free-text elaborations for their predictions could enable more intuitive interaction with NLP systems. These models are, however, currently trained with a large amount of human-written free-text explanations for each task which hinders their broader usage.

We propose to study a more realistic setting of self-rationalization using few training examples. We present FEB—a standardized collection of four existing English-language datasets and associated metrics. We identify the right prompting approach by extensively exploring natural language prompts on FEB. Then, by using this prompt and scaling the model size, we demonstrate that making progress on few-shot self-rationalization is possible.

We show there is still ample room for improvement in this task: the average plausibility of generated explanations assessed by human annotators is at most 51%, while plausibility of human explanations is 76%.

We hope that FEB together with our proposed approach will spur the community to take on the few-shot self-rationalization challenge.

--- https://arxiv.org/abs/2111.08687#sensetime INTERN: A New Learning Paradigm Towards General Vision Jing Shao, Siyu Chen, Yangguang Li, Kun Wang, Zhenfei Yin, Yinan He, Jianing Teng, Qinghong Sun, Mengya Gao, Jihao Liu, Gengshi Huang, Guanglu Song, Yichao Wu, Yuming Huang, Fenggang Liu, Huan Peng, Shuo Qin, Chengyu Wang, Yujie Wang, Conghui He, Ding Liang, Yu Liu, Fengwei Yu, Junjie Yan, Dahua Lin, Xiaogang Wang, Yu Qiao 2021-11-16 2021-11-16 [("doi","10.48550/arXiv.2111.08687")] ai/nn/transformer/clip ai/scaling

Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society. However, down the road, a key challenge awaits us, that is, our capability of meeting rapidly-growing scenario-specific demands is severely limited by the cost of acquiring a commensurate amount of training data. This difficult situation is in essence due to limitations of the mainstream learning paradigm: we need to train a new model for each new scenario, based on a large quantity of well-annotated data and commonly from scratch.

In tackling this fundamental problem, we move beyond and develop a new learning paradigm named INTERN. By learning with supervisory signals from multiple sources in multiple stages, the model being trained will develop strong generalizability.

We evaluate our model on 26 well-known datasets that cover four categories of tasks in computer vision. In most cases, our models, adapted with only 10% of the training data in the target domain, outperform the counterparts trained with the full set of data, often by a large margin.

This is an important step towards a promising prospect where such a model with general vision capability can dramatically reduce our reliance on data, thus expediting the adoption of AI technologies. Furthermore, revolving around our new paradigm, we also introduce a new data system, a new architecture, and a new benchmark, which, together, form a general vision ecosystem to support its future development in an open and inclusive manner.

--- https://arxiv.org/abs/2111.08896#alibaba Achieving Human Parity on Visual Question Answering Ming Yan, Haiyang Xu, Chenliang Li, Junfeng Tian, Bin Bi, Wei Wang, Weihua Chen, Xianzhe Xu, Fan Wang, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Luo Si, Rong Jin 2021-11-17 2021-11-17 [("doi","10.48550/arXiv.2111.08896")] ai/nn

The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image. It has been a popular research topic with an increasing number of real-world applications in the last decade.

This paper describes our recent research of AliceMind-MMU (ALIbaba’s Collection of Encoder-decoders from Machine IntelligeNce lab of Damo academy—MultiMedia Understanding) that obtains similar or even slightly better results than human being does on VQA. This is achieved by systematically improving the VQA pipeline including: (1) pre-training with comprehensive visual and textual feature representation; (2) effective cross-modal interaction with learning to attend; and (3) A novel knowledge mining framework with specialized expert modules for the complex VQA task. Treating different types of visual questions with corresponding expertise needed plays an important role in boosting the performance of our VQA architecture up to the human level.

An extensive set of experiments and analysis are conducted to demonstrate the effectiveness of the new research work.

--- https://arxiv.org/abs/2111.08960 Compositional Transformers for Scene Generation Drew A. Hudson, C. Lawrence Zitnick 2021-11-17 2021-11-17 [("doi","10.48550/arXiv.2111.08960")] ai/nn/gan ai/nn/transformer

We introduce the GANformer2 model, an iterative object-oriented transformer, explored for the task of generative modeling. The network incorporates strong and explicit structural priors, to reflect the compositional nature of visual scenes, and synthesizes images through a sequential process. It operates in two stages: a fast and lightweight planning phase, where we draft a high-level scene layout, followed by an attention-based execution phase, where the layout is being refined, evolving into a rich and detailed picture.

Our model moves away from conventional black-box GAN architectures that feature a flat and monolithic latent space towards a transparent design that encourages efficiency, controllability and interpretability. We demonstrate GANformer2’s strengths and qualities through a careful evaluation over a range of datasets, from multi-object CLEVR scenes to the challenging COCO images, showing it successfully achieves state-of-the-art performance in terms of visual quality, diversity and consistency.

Further experiments demonstrate the model’s disentanglement and provide a deeper insight into its generative process, as it proceeds step-by-step from a rough initial sketch, to a detailed layout that accounts for objects’ depths and dependencies, and up to the final high-resolution depiction of vibrant and intricate real-world scenes. See https://github.com/dorarad/gansformer for model implementation.

--- https://arxiv.org/abs/2111.09734 ClipCap: CLIP Prefix for Image Captioning Ron Mokady, Amir Hertz, Amit H. Bermano 2021-11-18 2021-11-18 [("doi","10.48550/arXiv.2111.09734")] ai/nn/transformer/clip ai/nn/transformer/gpt/2

Image captioning is a fundamental task in vision-language understanding, where the model predicts a textual informative caption to a given input image.

In this paper, we present a simple approach to address this task. We use CLIP encoding as a prefix to the caption, by employing a simple mapping network, and then fine-tunes a language model to generate the image captions. The recently proposed CLIP model contains rich semantic features which were trained with textual context, making it best for vision-language perception.

Our key idea is that together with a pre-trained language model (GPT-2), we obtain a wide understanding of both visual and textual data. Hence, our approach only requires rather quick training to produce a competent captioning model. Without additional annotations or pre-training, it efficiently generates meaningful captions for large-scale and diverse datasets. Surprisingly, our method works well even when only the mapping network is trained, while both CLIP and the language model remain frozen, allowing a lighter architecture with less trainable parameters.

Through quantitative evaluation, we demonstrate our model achieves comparable results to state-of-the-art methods on the challenging Conceptual Captions and nocaps datasets, while it is simpler, faster, and lighter.

Our code is available.

--- https://arxiv.org/abs/2111.09794 A Survey of Generalization in Deep Reinforcement Learning Robert Kirk, Amy Zhang, Edward Grefenstette, Tim Rocktäschel 2021-11-18 2021-11-18 [("doi","10.48550/arXiv.2111.09794")] reinforcement-learning/meta-learning

The study of generalisation in deep Reinforcement Learning (RL) aims to produce RL algorithms whose policies generalize well to novel unseen situations at deployment time, avoiding overfitting to their training environments. Tackling this is vital if we are to deploy reinforcement learning algorithms in real world scenarios, where the environment will be diverse, dynamic and unpredictable. This survey is an overview of this nascent field.

We provide an unifying formalism and terminology for discussing different generalisation problems, building upon previous works. We go on to categorise existing benchmarks for generalisation, as well as current methods for tackling the generalisation problem. Finally, we provide a critical discussion of the current state of the field, including recommendations for future work. Among other conclusions, we argue that taking a purely procedural content generation approach to benchmark design is not conducive to progress in generalisation, we suggest fast online adaptation and tackling RL-specific problems as some areas for future work on methods for generalisation, and we recommend building benchmarks in underexplored problem settings such as offline RL generalisation and reward-function variation.

--- https://arxiv.org/abs/2111.09881 Restormer: Efficient Transformer for High-Resolution Image Restoration Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang 2021-11-18 2021-11-18 [("doi","10.48550/arXiv.2111.09881")] ai/nn/diffusion ai/nn/transformer/attention/hierarchical

Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks. Recently, another class of neural architectures, Transformers, have shown performance gains on natural language and high-level vision tasks. While the Transformer model mitigates the shortcomings of CNNs (ie. limited receptive field and in-adaptability to input content), its computational complexity grows quadratically with the spatial resolution, therefore making it infeasible to apply to most image restoration tasks involving high-resolution images.

In this work, we propose an efficient Transformer model by making several key designs in the building blocks (multi-head attention and feed-forward network) such that it can capture long-range pixel interactions, while still remaining applicable to large images.

Our model, named Restoration Transformer (Restormer), achieves state-of-the-art results on several image restoration tasks, including image deraining, single-image motion deblurring, defocus deblurring (single-image and dual-pixel data), and image denoising (Gaussian grayscale/color denoising, and real image denoising).

The source code and pre-trained models are available at Github.

--- https://arxiv.org/abs/2111.09888#allen Simple but Effective: CLIP Embeddings for Embodied AI Apoorv Khandelwal, Luca Weihs, Roozbeh Mottaghi, Aniruddha Kembhavi 2021-11-18 2021-11-18 [("doi","10.48550/arXiv.2111.09888")] ai/nn/transformer/clip reinforcement-learning/model-free reinforcement-learning/robot

Contrastive language image pretraining (CLIP) encoders have been shown to be beneficial for a range of visual tasks from classification and detection to captioning and image manipulation. We investigate the effectiveness of CLIP visual backbones for embodied AI tasks.

We build incredibly simple baselines, named EmbCLIP, with no task specific architectures, inductive biases (such as the use of semantic maps), auxiliary tasks during training, or depth maps—yet we find that our improved baselines perform very well across a range of tasks and simulators. EmbCLIP tops the RoboTHOR ObjectNav leaderboard by a huge margin of 20 points (Success Rate). It tops the iTHOR 1-Phase Rearrangement leaderboard, beating the next best submission, which employs Active Neural Mapping, and more than doubling the Percent Fixed Strict metric (0.08 to 0.17). It also beats the winners of the 2021 Habitat ObjectNav Challenge, which employ auxiliary tasks, depth maps, and human demonstrations, and those of the 2019 Habitat PointNav Challenge.

We evaluate the ability of CLIP’s visual representations at capturing semantic information about input observations—primitives that are useful for navigation-heavy embodied tasks—and find that CLIP’s representations encode these primitives more effectively than ImageNet-pretrained backbones. Finally, we extend one of our baselines, producing an agent capable of zero-shot object navigation that can navigate to objects that were not used as targets during training.

--- https://arxiv.org/abs/2111.09999 TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems Bao Gia Doan, Minhui Xue, Shiqing Ma, Ehsan Abbasnejad, Damith C. Ranasinghe 2021-11-19 2021-11-19 [("doi","10.48550/arXiv.2111.09999")] ai/nn/adversarial ai/nn/gan

Deep neural networks are vulnerable to attacks from adversarial inputs and, more recently, Trojans to misguide or hijack the decision of the model. We expose the existence of an intriguing class of bounded adversarial examples—Universal NaTuralistic adversarial paTches—we call TnTs, by exploring the superset of the bounded adversarial example space and the natural input space within generative adversarial networks. Now, an adversary can arm themselves with a patch that is naturalistic, less malicious-looking, physically realizable, highly effective—achieving high attack success rates, and universal. A TnT is universal because any input image captured with a TnT in the scene will: (1) misguide a network (untargeted attack); or (2) force the network to make a malicious decision (targeted attack). Interestingly, now, an adversarial patch attacker has the potential to exert a greater level of control—the ability to choose a location independent, natural-looking patch as a trigger in contrast to being constrained to noisy perturbations—an ability is thus far shown to be only possible with Trojan attack methods needing to interfere with the model building processes to embed a backdoor at the risk discovery; but, still realize a patch deployable in the physical world.

Through extensive experiments on the large-scale visual classification task, ImageNet with evaluations across its entire validation set of 50,000 images, we demonstrate the realistic threat from TnTs and the robustness of the attack. We show a generalization of the attack to create patches achieving higher attack success rates than existing state-of-the-art methods.

Our results show the generalizability of the attack to different visual classification tasks (CIFAR-10, GTSRB, PubFig) and multiple state-of-the-art deep neural networks such as WideResnet-50, Inception-V3 and VGG-16.

--- https://arxiv.org/abs/2111.10952#google ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning Vamsi Aribandi, Yi Tay, Tal Schuster, Jinfeng Rao, Huaixiu Steven Zheng, Sanket Vaibhav Mehta, Honglei Zhuang, Vinh Q. Tran, Dara Bahri, Jianmo Ni, Jai Gupta, Kai Hui, Sebastian Ruder, Donald Metzler 2021-11-22 2021-11-22 [("doi","10.48550/arXiv.2111.10952")] ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 ai/scaling

Despite the recent success of multi-task learning and transfer learning for natural language processing (NLP), few works have systematically studied the effect of scaling up the number of tasks during pre-training. Towards this goal, this paper introduces ExMix (Extreme Mixture): a massive collection of 107 supervised NLP tasks across diverse domains and task-families.

Using ExMix, we study the effect of multi-task pre-training at the largest scale to date, and analyze co-training transfer amongst common families of tasks. Through this analysis, we show that manually curating an ideal set of tasks for multi-task pre-training is not straightforward, and that multi-task scaling can vastly improve models on its own. Finally, we propose ExT5: a model pre-trained using a multi-task objective of self-supervised span denoising and supervised ExMix. Via extensive experiments, we show that ExT5 outperforms strong T5 baselines on SuperGLUE, GEM, Rainbow, Closed-Book QA tasks, and several tasks outside of ExMix. ExT5 also improves sample efficiency while pre-training.

--- https://arxiv.org/abs/2111.11133 L-Verse: Bidirectional Generation Between Image and Text Taehoon Kim, Gwangmo Song, Sihaeng Lee, Sangyun Kim, Yewon Seo, Soonyoung Lee, Seung Hwan Kim, Honglak Lee, Kyunghoon Bae 2021-11-22 2021-11-22 [("doi","10.48550/arXiv.2111.11133")] ai/nn/transformer/gpt/dall-e/1 ai/nn/vae ai/scaling

Far beyond learning long-range interactions of natural language, transformers are becoming the de-facto standard for many vision tasks with their power and scalability. Especially with cross-modal tasks between image and text, vector quantized variational autoencoders (VQ-VAEs) are widely used to make a raw RGB image into a sequence of feature vectors. To better leverage the correlation between image and text, we propose L-Verse, a novel architecture consisting of feature-augmented variational autoencoder (AugVAE) and bidirectional auto-regressive transformer (BiART) for text-to-image and image-to-text generation.

Our AugVAE shows the state-of-the-art reconstruction performance on ImageNet1K validation set, along with the robustness to unseen images in the wild. Unlike other models, BiART can distinguish between image (or text) as a conditional reference and a generation target. L-Verse can be directly used for image-to-text or text-to-image generation tasks without any finetuning or extra object detection frameworks.

In quantitative and qualitative experiments, L-Verse shows impressive results against previous methods in both image-to-text and text-to-image generation on MS-COCO Captions. We furthermore assess the scalability of L-Verse architecture on Conceptual Captions and present the initial results of bidirectional vision-language representation learning on general domain.

--- https://arxiv.org/abs/2111.11398 Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks Linus Ericsson, Henry Gouk, Timothy M. Hospedales 2021-11-22 2021-11-22 [("doi","10.48550/arXiv.2111.11398")] ai/nn psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Self-supervised learning is a powerful paradigm for representation learning on unlabeled images. A wealth of effective new methods based on instance matching rely on data augmentation to drive learning, and these have reached a rough agreement on an augmentation scheme that optimizes popular recognition benchmarks. However, there is strong reason to suspect that different tasks in computer vision require features to encode different (in)variances, and therefore likely require different augmentation strategies.

In this paper, we measure the invariances learned by contrastive methods and confirm that they do learn invariance to the augmentations used and further show that this invariance largely transfers to related real-world changes in pose and lighting. We show that learned invariances strongly affect downstream task performance and confirm that different downstream tasks benefit from polar opposite (in)variances, leading to performance loss when the standard augmentation strategy is used.

Finally, we demonstrate that a simple fusion of representations with complementary invariances ensures wide transferability to all the diverse downstream tasks considered.

--- https://arxiv.org/abs/2111.11432#microsoft Florence: A New Foundation Model for Computer Vision Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, Ce Liu, Mengchen Liu, Zicheng Liu, Yumao Lu, Yu Shi, Lijuan Wang, Jianfeng Wang, Bin Xiao, Zhen Xiao, Jianwei Yang, Michael Zeng, Luowei Zhou, Pengchuan Zhang 2021-11-22 2021-11-22 [("doi","10.48550/arXiv.2111.11432")] ai/nn/retrieval ai/nn/transformer/clip ai/scaling ai/video/analysis

Automated visual understanding of our diverse and open world demands computer vision models to generalize well with minimal customization for specific tasks, similar to human vision. Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications.

While existing vision foundation models such as CLIP, ALIGN, and Wu Dao 2.0 focus mainly on mapping images and textual representations to a cross-modal shared representation, we introduce a new computer vision foundation model, Florence, [trained using UniCL] to expand the representations from coarse (scene) to fine (object), from static (images) to dynamic (videos), and from RGB to multiple modalities (caption, depth). By incorporating universal visual-language representations from Web-scale image-text data, our Florence model can be easily adapted for various computer vision tasks, such as classification, retrieval, object detection, VQA, image caption, video retrieval and action recognition.

Moreover, Florence demonstrates outstanding performance in many types of transfer learning: fully sampled fine-tuning, linear probing, few-shot transfer and zero-shot transfer for novel images and objects. All of these properties are critical for our vision foundation model to serve general purpose vision tasks.

Florence achieves new state-of-the-art results in majority of 44 representative benchmarks, eg. ImageNet-1K zero-shot classification with top-1 accuracy of 83.74 and the top-5 accuracy of 97.18, 62.4 mAP on COCO fine tuning, 80.36 on VQA, and 87.8 on Kinetics-600.

--- https://arxiv.org/abs/2111.12421 Few-shot Named Entity Recognition with Cloze Questions Valerio La Gatta, Vincenzo Moscato, Marco Postiglione, Giancarlo Sperlì 2021-11-24 2021-11-24 [("doi","10.48550/arXiv.2111.12421")] ai/nn/transformer/gpt

Despite the huge and continuous advances in computational linguistics, the lack of annotated data for Named Entity Recognition (NER) is still a challenging issue, especially in low-resource languages and when domain knowledge is required for high-quality annotations. Recent findings in NLP show the effectiveness of cloze-style questions in enabling language models to leverage the knowledge they acquired during the pre-training phase.

In our work, we propose a simple and intuitive adaptation of Pattern-Exploiting Training (PET), a recent approach which combines the cloze-questions mechanism and fine-tuning for few-shot learning: the key idea is to rephrase the NER task with patterns.

Our approach achieves considerably better performance than standard fine-tuning and comparable or improved results with respect to other few-shot baselines without relying on manually annotated data or distant supervision on 3 benchmark datasets: NCBI-disease, BC2GM and a private Italian biomedical corpus.

--- https://arxiv.org/abs/2111.12701 Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes Sam Bond-Taylor, Peter Hessey, Hiroshi Sasaki, Toby P. Breckon, Chris G. Willcocks 2021-11-24 2021-11-24 [("doi","10.48550/arXiv.2111.12701")] ai/nn/diffusion

Whilst diffusion probabilistic models can generate high quality image content, key limitations remain in terms of both generating high-resolution imagery and their associated high computational requirements. Recent Vector-Quantized image models have overcome this limitation of image resolution but are prohibitively slow and unidirectional as they generate tokens via element-wise autoregressive sampling from the prior.

By contrast, in this paper we propose a novel discrete diffusion probabilistic model prior which enables parallel prediction of Vector-Quantized tokens by using an unconstrained Transformer architecture as the backbone. During training, tokens are randomly masked in an order-agnostic manner and the Transformer learns to predict the original tokens. This parallelism of Vector-Quantized token prediction in turn facilitates unconditional generation of globally consistent high-resolution and diverse imagery at a fraction of the computational expense.

In this manner, we can generate image resolutions exceeding that of the original training set samples whilst additionally provisioning per-image likelihood estimates (in a departure from generative adversarial approaches). Our approach achieves state-of-the-art results in terms of Density (LSUN Bedroom: 1.51; LSUN Churches: 1.12; FFHQ: 1.20) and Coverage (LSUN Bedroom: 0.83; LSUN Churches: 0.73; FFHQ: 0.80), and performs competitively on FID (LSUN Bedroom: 3.64; LSUN Churches: 4.07; FFHQ: 6.11) whilst offering advantages in terms of both computation and reduced training set requirements.

--- https://arxiv.org/abs/2111.12763#google Sparse is Enough in Scaling Transformers Sebastian Jaszczur, Aakanksha Chowdhery, Afroz Mohiuddin, Łukasz Kaiser, Wojciech Gajewski, Henryk Michalewski, Jonni Kanerva 2021-11-24 2021-11-24 [("doi","10.48550/arXiv.2111.12763")] ai/nn/transformer/attention/sparsity ai/nn/transformer/gpt/4 ai/scaling video, example code] >Large Transformer models yield impressive results on many tasks, but are expensive to train, or even fine-tune, and so slow at decoding that their use and study becomes out of reach.

We address this problem by leveraging sparsity. We study sparse variants for all layers in the Transformer and propose Scaling Transformers, a family of next generation Transformer models that use sparse layers to scale efficiently and perform unbatched decoding much faster than the standard Transformer as we scale up the model size. Surprisingly, the sparse layers are enough to obtain the same perplexity as the standard Transformer with the same number of parameters.

We also integrate with prior sparsity approaches to attention and enable fast inference on long sequences even with limited memory.

This results in performance competitive to the state-of-the-art on long text summarization.

...With the growing popularity and size of these models, it is increasingly valuable to make them scale efficiently. In this work we propose Scaling Transformers with a separate sparse mechanism for the query, key, value and output layers (QKV layers for short) and combine it with sparse feedforward blocks to get a fully sparse Transformer architecture.

…We were surprised that the fully sparse Scaling Transformers are indeed enough to match the results of the baseline Transformer on the large C4 dataset (Figure 1). The improvement in complexity holds not just asymptotically but yields over 2.6× speedup in wall-clock hed decoding time already for a model with 800M parameters and 20× improvement for a model with 17b parameters, as shown in Table 1:

Figure 1: Log-perplexity of Scaling Transformers (equivalent to T5 large with ~800M parameters) on C4 dataset with proposed sparsity mechanisms (FF, QKV, FF+QKV) is similar to baseline dense model. Other models used in this paper are shown in grey lines; raw data is available in the appendix.

To verify that Scaling Transformers can be used with other Transformer improvements on real tasks, we create Terraformer—a Transformer model that uses reversible layers for memory efficiency and sparse attention to handle long sequences. We pre-train Terraformer on the C4 dataset and fine-tune it on the challenging task of summarizing arxiv articles. Terraformer yields results competitive to the state-of-the-art BigBird-Pegasus without using the Pegasus loss in pre-training (Table 5).

…We also checked the performance of the feedforward block with Mixture-of-Experts style sparsity. As expected, this technique achieved decoding time comparable to sparse FF—0.11s instead of 0.09s—but with its lack of granularity it achieved log-perplexity of 1.64, worse than both our method and the dense baseline.

…4.3 Recurrence for Generalization: In addition to incorporating sparse attention and reversibility, we also add recurrence to the feedforward block of Terraformer. Recurrent layers allow information to propagate in time, even in a single decoder block. It is challenging though to use them without decreasing model speed, especially in training. For that reason, we use simple recurrent units which parallelize well during training.

SRUs contain dense layers, so their use could negate the benefits of sparsity elsewhere. We tried a few methods to alleviate that, but it turns out that simply reducing the dimensionality of the SRUs works. So we first project from d model to a small dimension (32 in our experiments), then apply the SRU, and then project back to d model and add the result to the feedforward block. This low-rank recurrence is in our experiments sufficient to transfer enough information through time for the network to generalize. Since the effects of SRUs on C4 are minimal (as the training and evaluation data are very similar), we use synthetic tasks to investigate out-of-distribution generalization. We train the models on long addition and on the task of copying a decimal digit. We train on inputs with at most 128 digits and evaluate on inputs lengths 256–300, so over 2× longer. As can be seen in the table below, the baseline Transformer does not generalize well, while Terraformer manages to get a large portion correctly, even if it is not perfect like the Neural GPU.

…Table 6 shows the speedup in decoding with sparse layers when we scale up Terraformer to 17b parameters. Note that sparsifying all the layers gives us 37× speedup in decoding.

…Further, we hope that the community will take inspiration from Scaling Transformers and tune them for their needs. We ran experiments using layer sizes and hyperparameters borrowed from dense Transformers and they are most probably not optimal for Scaling Transformer. With proper tuning and further improvements we believe one could train a Scaling Transformer to match GPT-3 in accuracy but also run inference in reasonable time on a laptop. We put it as a fascinating challenge to the community, since such Scaling Transformers will not only be more sustainable but will also make large models accessible to everyone.

--- https://arxiv.org/abs/2111.13440 True Few-Shot Learning with Prompts—A Real-World Perspective Timo Schick, Hinrich Schütze 2021-11-26 2021-11-26 [("doi","10.48550/arXiv.2111.13440")] ai/nn/transformer/gpt

Prompt-based approaches are strong at few-shot learning. However, Perez et al 2021 have recently cast doubt on their performance because they had difficulty getting good results in a “true” few-shot setting in which prompts and hyperparameters cannot be tuned on a dev set. In view of this, we conduct an extensive study of PET (Pattern-Exploiting Training), a method that combines textual instructions with example-based finetuning.

We show that, if correctly configured, PET performs strongly in a true few-shot setting, ie. without a dev set. Crucial for this strong performance is PET’s ability to intelligently handle multiple prompts.

We then put our findings to a real-world test by running PET on RAFT, a benchmark of tasks taken directly from realistic NLP applications for which no labeled dev or test sets are available. PET achieves a new state-of-the-art on RAFT and performs close to non-expert humans for 7⁄11 tasks.

These results demonstrate that prompt-based learners like PET excel at true few-shot learning and underpin our belief that learning from instructions will play an important role on the path towards human-like few-shot learning capabilities.

--- https://arxiv.org/abs/2111.13457#bytedance Semi-Supervised Music Tagging Transformer Minz Won, Keunwoo Choi, Xavier Serra 2021-11-26 2021-11-26 [("doi","10.48550/arXiv.2111.13457")] ai/music ai/nn/transformer

We present Music Tagging Transformer that is trained with a semi-supervised approach.

The proposed model captures local acoustic characteristics in shallow convolutional layers, then temporally summarizes the sequence of the extracted features using stacked self-attention layers. Through a careful model assessment, we first show that the proposed architecture outperforms the previous state-of-the-art music tagging models that are based on convolutional neural networks under a supervised scheme.

The Music Tagging Transformer is further improved by noisy student training, a semi-supervised approach that leverages both labeled and unlabeled data combined with data augmentation. To our best knowledge, this is the first attempt to utilize the entire audio of the Million Song Dataset.

--- https://arxiv.org/abs/2111.13545#google 𝜇NCA: Texture Generation with Ultra-Compact Neural Cellular Automata Alexander Mordvintsev, Eyvind Niklasson 2021-11-26 2021-11-26 [("doi","10.48550/arXiv.2111.13545")] ai/nn/sparsity/low-precision cs/cellular-automaton design/typography/dropcap design/typography/floral

We study the problem of example-based procedural texture synthesis using highly compact models. Given a sample image, we use differentiable programming to train a generative process, parameterized by a recurrent Neural Cellular Automata (NCA) rule.

Contrary to the common belief that neural networks should be highly over-parameterized, we demonstrate that our model architecture and training procedure allows for representing complex texture patterns using just a few hundred learned parameters, making their expressivity comparable to hand-engineered procedural texture generating programs. The smallest models from the proposed 𝜇NCA family scale down to 68 parameters. When using quantisation to one byte per parameter, proposed models can be shrunk to a size range between 588 and 68 bytes.

Implementation of a texture generator that uses these parameters to produce images is possible with just a few lines of GLSL or C code.

--- https://arxiv.org/abs/2111.13792 LAFITE: Towards Language-Free Training for Text-to-Image Generation Yufan Zhou, Ruiyi Zhang, Changyou Chen, Chunyuan Li, Chris Tensmeyer, Tong Yu, Jiuxiang Gu, Jinhui Xu, Tong Sun 2021-11-27 2021-11-27 [("doi","10.48550/arXiv.2111.13792")] ai/nn/gan/stylegan ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e/1

One of the major challenges in training text-to-image generation models is the need of a large number of high-quality image-text pairs. While image samples are often easily accessible, the associated text descriptions typically require careful human captioning, which is particularly time/cost-consuming.

In this paper, we propose the first work to train text-to-image generation models without any text data. Our method leverages the well-aligned multi-modal semantic space of the powerful pre-trained CLIP model: the requirement of text-conditioning is seamlessly alleviated via generating text features from image features.

Extensive experiments are conducted to illustrate the effectiveness of the proposed method. We obtain state-of-the-art results in the standard text-to-image generation tasks. Importantly, the proposed language-free model outperforms most existing models trained with full image-text pairs.

Furthermore, our method can be applied in fine-tuning pre-trained models, which saves both training time and cost in training text-to-image generation models. Our pre-trained model obtains competitive results in zero-shot text-to-image generation on the MS-COCO dataset, yet with around only 1% of the model size and training data size relative to the recently proposed large DALL·E model.

--- https://arxiv.org/abs/2111.13824 FQ-ViT: Fully Quantized Vision Transformer without Retraining Yang Lin, Tianyu Zhang, Peiqin Sun, Zheng Li, Shuchang Zhou 2021-11-27 2021-11-27 [("doi","10.48550/arXiv.2111.13824")] ai/nn/sparsity/low-precision ai/nn/transformer

Network quantization reduces model inference complexity and has been widely used in real-world deployments. However, most existing quantization methods have been developed and tested mainly on Convolutional Neural Networks (CNN), and suffer severe degradation when applied to Transformer-based architectures.

In this work, we present a systematic method to reduce the performance degradation and inference complexity of Quantized Transformers. In particular, we propose Powers-of-Two Scale (PTS) to deal with the serious inter-channel variation of Layer Norm inputs in a hardware-friendly way. In addition, we propose Log-Int-Softmax (LIS) that can sustain the extreme non-uniform distribution of the attention maps while simplifying inference by using 4-bit quantization and the BitShift operator.

Comprehensive experiments on various Transformer-based architectures and benchmarks show that our methods outperform previous works in performance while using even lower bit-width in attention maps. For instance, we reach 85.17% Top-1 accuracy with ViT-L on ImageNet and 51.4 mAP with Cascade Mask R-CNN (Swin-S) on COCO. To our knowledge, we are the first to achieve comparable accuracy degradation (~1%) on fully quantized Vision Transformers.

Code is available at GitHub.

--- https://arxiv.org/abs/2111.14232 Long-range and hierarchical language predictions in brains and algorithms Charlotte Caucheteux, Alexandre Gramfort, Jean-Remi King 2021-11-28 2021-11-28 [("doi","10.48550/arXiv.2111.14232")] ai/nn/transformer/gpt psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Deep learning has recently made remarkable progress in natural language processing. Yet, the resulting algorithms remain far from competing with the language abilities of the human brain. Predictive coding theory offers a potential explanation to this discrepancy: while deep language algorithms are optimized to predict adjacent words, the human brain would be tuned to make long-range and hierarchical predictions.

To test this hypothesis, we analyze the fMRI brain signals of 304 subjects each listening to 70min of short stories. After confirming that the activations of deep language algorithms linearly map onto those of the brain, we show that enhancing these models with long-range forecast representations improves their brain-mapping.

The results further reveal a hierarchy of predictions in the brain, whereby the fronto-parietal cortices forecast more abstract and more distant representations than the temporal cortices.

Overall, this study strengthens predictive coding theory and suggests a critical role of long-range and hierarchical predictions in natural language processing.

--- https://arxiv.org/abs/2111.14377#google Collective Intelligence for Deep Learning: A Survey of Recent Developments David Ha, Yujin Tang 2021-11-29 2021-11-29 [("doi","10.48550/arXiv.2111.14377")] cs/cellular-automaton reinforcement-learning/meta-learning

In the past decade, we have witnessed the rise of deep learning to dominate the field of artificial intelligence. Advances in artificial neural networks alongside corresponding advances in hardware accelerators with large memory capacity, together with the availability of large datasets enabled researchers and practitioners alike to train and deploy sophisticated neural network models that achieve state-of-the-art performance on tasks across several fields spanning computer vision, natural language processing, and reinforcement learning. However, as these neural networks become bigger, more complex, and more widely used, fundamental problems with current deep learning models become more apparent. State-of-the-art deep learning models are known to suffer from issues that range from poor robustness, inability to adapt to novel task settings, to requiring rigid and inflexible configuration assumptions.

Ideas from collective intelligence, in particular concepts from complex systems such as self-organization, emergent behavior, swarm optimization, and cellular systems tend to produce solutions that are robust, adaptable, and have less rigid assumptions about the environment configuration. It is therefore natural to see these ideas incorporated into newer deep learning methods.

In this review, we will provide a historical context of neural network research’s involvement with complex systems, and highlight several active areas in modern deep learning research that incorporate the principles of collective intelligence to advance its current capabilities. To facilitate a bi-directional flow of ideas, we also discuss work that use modern deep learning models to help advance complex systems research.

We hope this review can serve as a bridge between complex systems and deep learning communities to facilitate the cross pollination of ideas and foster new collaborations across disciplines.

--- https://arxiv.org/abs/2111.14447 Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic Yoad Tewel, Yoav Shalev, Idan Schwartz, Lior Wolf 2021-11-29 2021-11-29 [("doi","10.48550/arXiv.2111.14447")] ai/nn/transformer/clip ai/nn/transformer/gpt

Recent text-to-image matching models apply contrastive learning to large corpora of uncurated pairs of images and sentences. While such models can provide a powerful score for matching and subsequent zero-shot tasks, they are not capable of generating caption given an image. In this work, we repurpose such models to generate a descriptive text given an image at inference time, without any further training or tuning step.

This is done by combining the visual-semantic model with a large language model, benefiting from the knowledge in both web-scale models. The resulting captions are much less restrictive than those obtained by supervised captioning methods.

Moreover, as a zero-shot learning method, it is extremely flexible and we demonstrate its ability to perform image arithmetic in which the inputs can be either images or text and the output is a sentence. This enables novel high-level vision capabilities such as comparing two images or solving visual analogy tests.

--- https://arxiv.org/abs/2111.14818 Blended Diffusion for Text-driven Editing of Natural Images Omri Avrahami, Dani Lischinski, Ohad Fried 2021-11-29 2021-11-29 [("doi","10.48550/arXiv.2111.14818")] ai/nn/diffusion ai/nn/transformer/clip

Natural language offers a highly intuitive interface for image editing. In this paper, we introduce the first solution for performing local (region-based) edits in generic natural images, based on a natural language description along with an ROI mask.

We achieve our goal by leveraging and combining a pretrained language-image model (CLIP), to steer the edit towards a user-provided text prompt, with a denoising diffusion probabilistic model (DDPM) to generate natural-looking results. To seamlessly fuse the edited region with the unchanged parts of the image, we spatially blend noised versions of the input image with the local text-guided diffusion latent at a progression of noise levels. In addition, we show that adding augmentations to the diffusion process mitigates adversarial results.

We compare against several baselines and related methods, both qualitatively and quantitatively, and show that our method outperforms these solutions in terms of overall realism, ability to preserve the background, and matching the text.

Finally, we show several text-driven editing applications, including adding a new object to an image, removing/replacing/altering existing objects, background replacement, and image extrapolation.

--- https://arxiv.org/abs/2111.14822#microsoft Vector Quantized Diffusion Model for Text-to-Image Synthesis Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, Baining Guo 2021-11-29 2021-11-29 [("doi","10.48550/arXiv.2111.14822")] ai/nn/diffusion ai/nn/vae

We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation.

This method is based on a vector quantized variational autoencoder (VQ-VAE) whose latent space is modeled by a conditional variant of the recently developed Denoising Diffusion Probabilistic Model (DDPM). We find that this latent-space method is well-suited for text-to-image generation tasks because it not only eliminates the unidirectional bias with existing methods but also allows us to incorporate a mask-and-replace diffusion strategy to avoid the accumulation of errors, which is a serious problem with existing methods.

Our experiments show that the VQ-Diffusion produces substantially better text-to-image generation results when compared with conventional autoregressive (AR) models with similar numbers of parameters. Compared with previous GAN-based text-to-image methods, our VQ-Diffusion can handle more complex scenes and improve the synthesized image quality by a large margin. Finally, we show that the image generation computation in our method can be made highly efficient by reparameterization. With traditional AR methods, the text-to-image generation time increases linearly with the output image resolution and hence is quite time consuming even for normal size images. The VQ-Diffusion allows us to achieve a better trade-off between quality and speed.

Our experiments indicate that the VQ-Diffusion model with the reparameterization is 15× faster than traditional AR methods while achieving a better image quality.

--- https://arxiv.org/abs/2111.15174 CRIS: CLIP-Driven Referring Image Segmentation Zhaoqing Wang, Yu Lu, Qiang Li, Xunqiang Tao, Yandong Guo, Mingming Gong, Tongliang Liu 2021-11-30 2021-11-30 [("doi","10.48550/arXiv.2111.15174")] ai/nn/transformer/clip

Referring image segmentation aims to segment a referent via a natural linguistic expression. Due to the distinct data properties between text and image, it is challenging for a network to well align text and pixel-level features. Existing approaches use pretrained models to facilitate learning, yet separately transfer the language/vision knowledge from pretrained models, ignoring the multi-modal corresponding information. Inspired by the recent advance in Contrastive Language-Image Pretraining (CLIP), in this paper, we propose an end-to-end CLIP-Driven Referring Image Segmentation framework (CRIS).

To transfer the multi-modal knowledge effectively, CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. More specifically, we design a vision-language decoder to propagate fine-grained semantic information from textual representations to each pixel-level activation, which promotes consistency between the two modalities. In addition, we present text-to-pixel contrastive learning to explicitly enforce the text feature similar to the related pixel-level features and dissimilar to the irrelevances.

The experimental results on 3 benchmark datasets demonstrate that our proposed framework outperforms the state-of-the-art performance without any post-processing.

The code will be released.

--- https://arxiv.org/abs/2111.15666 HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing Yuval Alaluf, Omer Tov, Ron Mokady, Rinon Gal, Amit H. Bermano 2021-11-30 2021-11-30 [("doi","10.48550/arXiv.2111.15666")] ai/nn/gan/stylegan

The inversion of real images into StyleGAN’s latent space is a well-studied problem. Nevertheless, applying existing approaches to real-world scenarios remains an open challenge, due to an inherent trade-off between reconstruction and editability: latent space regions which can accurately represent real images typically suffer from degraded semantic control. Recent work proposes to mitigate this trade-off by fine-tuning the generator to add the target image to well-behaved, editable regions of the latent space. While promising, this fine-tuning scheme is impractical for prevalent use as it requires a lengthy training phase for each new image.

In this work, we introduce this approach into the realm of encoder-based inversion. We propose HyperStyle, a hypernetwork that learns to modulate StyleGAN’s weights to faithfully express a given image in editable regions of the latent space. A naive modulation approach would require training a hypernetwork with over 3 billion parameters. Through careful network design, we reduce this to be in line with existing encoders. HyperStyle yields reconstructions comparable to those of optimization techniques with the near real-time inference capabilities of encoders. Lastly, we demonstrate HyperStyle’s effectiveness on several applications beyond the inversion task, including the editing of out-of-domain images which were never seen during training.

--- https://arxiv.org/abs/2112.00114#google Show Your Work: Scratchpads for Intermediate Computation with Language Models Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, Augustus Odena 2021-10-05 2021-10-05 [("doi","10.48550/arXiv.2112.00114")] ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/lamda ai/scaling

Large pre-trained language models perform remarkably well on tasks that can be done “in one pass”, such as generating realistic text or synthesizing computer programs. However, they struggle with tasks that require unbounded multi-step computation, such as adding integers or executing programs. Surprisingly, we find that these same models are able to perform complex multi-step computations—even in the few-shot regime—when asked to perform the operation “step by step”, showing the results of intermediate computations. In particular, we train transformers to perform multi-step computations by asking them to emit intermediate computation steps into a “scratchpad”. On a series of increasingly complex tasks ranging from long addition to the execution of arbitrary programs, we show that scratchpads dramatically improve the ability of language models to perform multi-step computations.

[Keywords: program synthesis, transformers, language models, pre-training, program induction]

--- https://arxiv.org/abs/2112.00431 MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions Mattia Soldan, Alejandro Pardo, Juan León Alcázar, Fabian Caba Heilbron, Chen Zhao, Silvio Giancola, Bernard Ghanem 2021-12-01 2021-12-01 [("doi","10.48550/arXiv.2112.00431")] ai/dataset ai/nn/transformer/clip ai/video/analysis

The recent and increasing interest in video-language research has driven the development of large-scale datasets that enable data-intensive machine learning techniques. In comparison, limited effort has been made at assessing the fitness of these datasets for the video-language grounding task. Recent works have begun to discover limitations in these datasets, suggesting that state-of-the-art techniques commonly overfit to hidden dataset biases.

In this work, we present MAD (Movie Audio Descriptions), a novel benchmark that departs from the paradigm of augmenting existing video datasets with text annotations and focuses on crawling and aligning available audio descriptions of mainstream movies. MAD contains over 384,000 natural language sentences grounded in over 1,200 hours of video and exhibits a reduction in the currently diagnosed biases for video-language grounding datasets. MAD’s collection strategy enables a novel and more challenging version of video-language grounding, where short temporal moments (typically seconds long) must be accurately grounded in diverse long-form videos that can last up to 3 hours.

--- https://arxiv.org/abs/2112.00725 Extrapolating from a Single Image to a Thousand Classes using Distillation Yuki M. Asano, Aaqib Saeed 2021-12-01 2021-12-01 [("doi","10.48550/arXiv.2112.00725")] ai/nn/sparsity/knowledge-distillation

What can neural networks learn about the visual world from a single image? While it obviously cannot contain the multitudes of possible objects, scenes and lighting conditions that exist—within the space of all possible 2563×224×224 224px-sized square images, it might still provide a strong prior for natural images.

To analyze this hypothesis, we develop a framework for training neural networks from scratch using a single image by means of knowledge distillation from a supervised pretrained teacher.

With this, we find that the answer to the above question is: ‘surprisingly, a lot’. In quantitative terms, we find top-1 accuracies of 94%/74% on CIFAR-10/100, 59% on ImageNet and, by extending this method to audio, 84% on SpeechCommands.

In extensive analyses we disentangle the effect of augmentations, choice of source image and network architectures and also discover “panda neurons” in networks that have never seen a panda.

This work shows that one image can be used to extrapolate to thousands of object classes and motivates a renewed research agenda on the fundamental interplay of augmentations and image.

--- https://arxiv.org/abs/2112.00874#google ν-SDDP: Neural Stochastic Dual Dynamic Programming Hanjun Dai, Yuan Xue, Zia Syed, Dale Schuurmans, Bo Dai 2021-12-01 2021-12-01 [("doi","10.48550/arXiv.2112.00874")] reinforcement-learning/model/alphago statistics/decision

Stochastic dual dynamic programming (SDDP) is a state-of-the-art method for solving multi-stage stochastic optimization, widely used for modeling real-world process optimization tasks. Unfortunately, SDDP has a worst-case complexity that scales exponentially in the number of decision variables, which severely limits applicability to only low dimensional problems.

To overcome this limitation, we extend SDDP by introducing a trainable neural model that learns to map problem instances to a piece-wise linear value function within intrinsic low-dimension space, which is architected specifically to interact with a base SDDP solver, so that can accelerate optimization performance on new instances. The proposed Neural Stochastic Dual Dynamic Programming (ν-SDDP) continually self-improves by solving successive problems.

An empirical investigation demonstrates that ν-SDDP can substantially reduce problem solving cost without sacrificing solution quality over competitors such as SDDP and reinforcement learning algorithms, across a range of synthetic and real-world process optimization problems.

--- https://arxiv.org/abs/2112.01071 DenseCLIP: Extract Free Dense Labels from CLIP Chong Zhou, Chen Change Loy, Bo Dai 2021-12-02 2021-12-02 [("doi","10.48550/arXiv.2112.01071")] ai/nn/transformer/clip

Contrastive Language-Image Pre-training (CLIP) has made a remarkable breakthrough in open-vocabulary zero-shot image recognition. Many recent studies leverage the pre-trained CLIP models for image-level classification and manipulation. In this paper, we further explore the potentials of CLIP for pixel-level dense prediction, specifically in semantic segmentation.

Our method, DenseCLIP, in the absence of annotations and fine-tuning, yields reasonable segmentation results on open concepts across various datasets. By adding pseudo labeling and self-training, DenseCLIP+ surpasses SOTA transductive zero-shot semantic segmentation methods by large margins, eg. mIoUs of unseen classes on PASCAL VOC/PASCAL Context/COCO Stuff are improved from 35.6/20.7/30.3 to 86.1/66.7/54.7. We also test the robustness of DenseCLIP under input corruption and evaluate its capability in discriminating fine-grained objects and novel concepts. Our finding suggests that DenseCLIP can serve as a new reliable source of supervision for dense prediction tasks to achieve annotation-free segmentation.

--- https://arxiv.org/abs/2112.01380 Prior knowledge elicitation: The past, present, and future Petrus Mikkola, Osvaldo A. Martin, Suyog Chandramouli, Marcelo Hartmann, Oriol Abril Pla, Owen Thomas, Henri Pesonen, Jukka Corander, Aki Vehtari, Samuel Kaski, Paul-Christian Bürkner, Arto Klami 2021-12-01 2021-12-01 [("doi","10.48550/arXiv.2112.01380")] statistics/bayes statistics/decision

Specification of the prior distribution for a Bayesian model is a central part of the Bayesian workflow for data analysis, but it is often difficult even for statistical experts. Prior elicitation transforms domain knowledge of various kinds into well-defined prior distributions, and offers a solution to the prior specification problem, in principle. In practice, however, we are still fairly far from having usable prior elicitation tools that could influence the way we build probabilistic models in academia and industry. We lack elicitation methods that integrate well into the Bayesian workflow and perform elicitation efficiently in terms of costs of time and effort. We even lack a comprehensive theoretical framework for understanding different facets of the prior elicitation problem.

Why are we not widely using prior elicitation? We analyze the state-of-the-art by identifying a range of key aspects of prior knowledge elicitation, from properties of the modeling task and the nature of the priors to the form of interaction with the expert. The existing prior elicitation literature is reviewed and categorized in these terms. This allows recognizing under-studied directions in prior elicitation research, finally leading to a proposal of several new avenues to improve prior elicitation methodology.

--- https://arxiv.org/abs/2112.01488 ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, Matei Zaharia 2021-12-02 2021-12-02 [("doi","10.48550/arXiv.2112.01488")] ai/nn/transformer

Neural information retrieval (IR) has greatly advanced search and other knowledge-intensive language tasks.

While many neural IR methods encode queries and documents into single-vector representations, late interaction models produce multi-vector representations at the granularity of each token and decompose relevance modeling into scalable token-level computations. This decomposition has been shown to make late interaction more effective, but it inflates the space footprint of these models by an order of magnitude.

In this work, we introduce ColBERTv2, a retriever that couples an aggressive residual compression mechanism with a denoised supervision strategy to simultaneously improve the quality and space footprint of late interaction.

We evaluate ColBERTv2 across a wide range of benchmarks, establishing state-of-the-art quality within and outside the training domain while reducing the space footprint of late interaction models by 5–8×.

--- https://arxiv.org/abs/2112.01573 FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization Xingchao Liu, Chengyue Gong, Lemeng Wu, Shujian Zhang, Hao Su, Qiang Liu 2021-12-02 2021-12-02 [("doi","10.48550/arXiv.2112.01573")] ai/nn/gan/data-augmentation ai/nn/transformer/clip

Generating images from natural language instructions is an intriguing yet highly challenging task. We approach text-to-image generation by combining the power of the retrained CLIP representation with an off-the-shelf image generator (GANs), optimizing in the latent space of GAN to find images that achieve maximum CLIP score with the given input text. Compared to traditional methods that train generative models from text to image starting from scratch, the CLIP+GAN approach is training-free, zero shot and can be easily customized with different generators.

However, optimizing CLIP score in the GAN space casts a highly challenging optimization problem and off-the-shelf optimizers such as Adam fail to yield satisfying results. In this work, we propose a FuseDream pipeline, which improves the CLIP+GAN approach with 3 key techniques: (1) an AugCLIP score which robustifies the CLIP objective by introducing random augmentation on image. (2) a novel initialization and over-parameterization strategy for optimization which allows us to efficiently navigate the non-convex landscape in GAN space. (3) a composed generation technique which, by leveraging a novel bi-level optimization formulation, can compose multiple images to extend the GAN space and overcome the data-bias.

When promoted by different input text, FuseDream can generate high-quality images with varying objects, backgrounds, artistic styles, even novel counterfactual concepts that do not appear in the training data of the GAN we use. Quantitatively, the images generated by FuseDream yield top-level Inception score and FID score on MS COCO dataset, without additional architecture design or training.

Our code is publicly available at GitHub.

--- https://arxiv.org/abs/2112.01799 VQ-DDM: Global Context with Discrete Diffusion in Vector Quantized Modeling for Image Generation Minghui Hu, Yujie Wang, Tat-Jen Cham, Jianfei Yang, P. N. Suganthan 2021-12-03 2021-12-03 [("doi","10.48550/arXiv.2112.01799")] ai/nn/diffusion ai/nn/vae

The integration of Vector Quantized Variational AutoEncoder (VQ-VAE) with autoregressive models as generation part has yielded high-quality results on image generation. However, the autoregressive models will strictly follow the progressive scanning order during the sampling phase. This leads the existing VQ series models to hardly escape the trap of lacking global information.

Denoising Diffusion Probabilistic Models (DDPM) in the continuous domain have shown a capability to capture the global context, while generating high-quality images. In the discrete state space, some works have demonstrated the potential to perform text generation and low resolution image generation. We show that with the help of a content-rich discrete visual codebook from VQ-VAE, the discrete diffusion model can also generate high fidelity images with global context, which compensates for the deficiency of the classical autoregressive model along pixel space.

Meanwhile, the integration of the discrete VAE with the diffusion model resolves the drawback of conventional autoregressive models being oversized, and the diffusion model which demands excessive time in the sampling process when generating images. It is found that the quality of the generated images is heavily dependent on the discrete visual codebook. Extensive experiments demonstrate that the proposed Vector Quantized Discrete Diffusion Model (VQ-DDM) is able to achieve comparable performance to top-tier methods with low complexity. It also demonstrates outstanding advantages over other vectors quantized with autoregressive models in terms of image inpainting tasks without additional training.

--- https://arxiv.org/abs/2112.02448#sberbank Emojich—zero-shot emoji generation using Russian language: a technical report Alex Shonenkov, Daria Bakshandaeva, Denis Dimitrov, Aleksandr Nikolich 2021-12-04 2021-12-04 [("doi","10.48550/arXiv.2112.02448")] ai/nn/transformer/gpt/dall-e/1

This technical report presents a text-to-image neural network “Emojich” that generates emojis using captions in Russian language as a condition.

We aim to keep the generalization ability of a pretrained big model ruDALL·E Malevich (XL) 1.3b parameters at the fine-tuning stage, while giving special style to the images generated. Here are presented some engineering methods, code realization, and all hyper-parameters for reproducing results.

Additionally, a Telegram bot is available where everyone can create their own customized sets of stickers. Some newly generated emojis obtained by the “Emojich” model are also demonstrated.

--- https://arxiv.org/abs/2112.02505 Causal Distillation for Language Models Zhengxuan Wu, Atticus Geiger, Josh Rozner, Elisa Kreiss, Hanson Lu, Thomas Icard, Christopher Potts, Noah D. Goodman 2021-12-05 2021-12-05 [("doi","10.48550/arXiv.2112.02505")] ai/nn/sparsity/knowledge-distillation

Distillation efforts have led to language models that are more compact and efficient without serious drops in performance. The standard approach to distillation trains a student model against two objectives: a task-specific objective (eg. language modeling) and an imitation objective that encourages the hidden states of the student model to be similar to those of the larger teacher model. In this paper, we show that it is beneficial to augment distillation with a third objective that encourages the student to imitate the causal computation process of the teacher through interchange intervention training (IIT). IIT pushes the student model to become a causal abstraction of the teacher model—a simpler model with the same causal structure. IIT is fully differentiable, easily implemented, and combines flexibly with other objectives.

Compared with standard distillation of BERT, distillation via IIT results in lower perplexity on Wikipedia (masked language modeling) and marked improvements on the GLUE benchmark (natural language understanding), SQuAD (question answering), and CoNLL-2003 (named entity recognition).

--- https://arxiv.org/abs/2112.02926 Steerable discovery of neural audio effects Christian J. Steinmetz, Joshua D. Reiss 2021-12-06 2021-12-06 [("doi","10.48550/arXiv.2112.02926")] ai/music

Applications of deep learning for audio effects often focus on modeling analog effects or learning to control effects to emulate a trained audio engineer. However, deep learning approaches also have the potential to expand creativity through neural audio effects that enable new sound transformations.

While recent work demonstrated that neural networks with random weights produce compelling audio effects, control of these effects is limited and unintuitive.

To address this, we introduce a method for the steerable discovery of neural audio effects. This method enables the design of effects using example recordings provided by the user.

We demonstrate how this method produces an effect similar to the target effect, along with interesting inaccuracies, while also providing perceptually relevant controls.

--- https://arxiv.org/abs/2112.02969#microsoft Jigsaw: Large Language Models meet Program Synthesis Naman Jain, Skanda Vaidyanath, Arun Iyer, Nagarajan Natarajan, Suresh Parthasarathy, Sriram Rajamani, Rahul Sharma 2021-12-06 2021-12-06 [("doi","10.48550/arXiv.2112.02969")] ai/nn/transformer/gpt/codex

Large pre-trained language models such as GPT-3, Codex, and Google’s language model are now capable of generating code from natural language specifications of programmer intent. We view these developments with a mixture of optimism and caution. On the optimistic side, such large language models have the potential to improve productivity by providing an automated AI pair programmer for every programmer in the world. On the cautionary side, since these large language models do not understand program semantics, they offer no guarantees about quality of the suggested code.

In this paper, we present an approach to augment these large language models with post-processing steps based on program analysis and synthesis techniques, that understand the syntax and semantics of programs.

Further, we show that such techniques can make use of user feedback and improve with usage. We present our experiences from building and evaluating such a tool, Jigsaw, targeted at synthesizing code for using Python Pandas API using multi-modal inputs [text description, test cases, code].

Our experience suggests that as these large language models evolve for synthesizing code from intent, Jigsaw has an important role to play in improving the accuracy of the systems.

--- https://arxiv.org/abs/2112.03178#deepmind Player of Games Martin Schmid, Matej Moravcik, Neil Burch, Rudolf Kadlec, Josh Davidson, Kevin Waugh, Nolan Bard, Finbarr Timbers, Marc Lanctot, Zach Holland, Elnaz Davoodi, Alden Christianson, Michael Bowling 2021-12-06 2021-12-06 [("doi","10.48550/arXiv.2112.03178")] reinforcement-learning/imperfect-information/poker reinforcement-learning/model/alphago reinforcement-learning/multi-agent

[Kilcher video; lead author has launched an algorithmic trading startup; cf. DeepStack; paper renamed Student of Games (SoG)] Games have a long history of serving as a benchmark for progress in artificial intelligence. Recently, approaches using search and learning have shown strong performance across a set of perfect information games, and approaches using game-theoretic reasoning and learning have shown strong performance for specific imperfect information poker variants.

We introduce Player of Games, a general-purpose algorithm that unifies previous approaches, combining guided search, self-play learning, and game-theoretic reasoning [counterfactual regret minimization]. We prove that Player of Games is sound, converging to perfect play as available computation time and approximation capacity increases.

Player of Games reaches strong performance in chess and Go, beats the strongest openly available agent in heads-up no-limit Texas hold’em poker (Slumbot), and defeats the state-of-the-art agent in Scotland Yard, an imperfect information game that illustrates the value of guided search, learning, and game-theoretic reasoning.

Player of Games is the first algorithm to achieve strong empirical performance in large perfect and imperfect information games—an important step towards truly general algorithms for arbitrary environments.

--- https://arxiv.org/abs/2112.03254#microsoft Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention Yichong Xu, Chenguang Zhu, Shuohang Wang, Siqi Sun, Hao Cheng, Xiaodong Liu, Jianfeng Gao, Pengcheng He, Michael Zeng, Xuedong Huang 2021-12-06 2021-12-06 [("doi","10.48550/arXiv.2112.03254")] ai/nn/retrieval ai/nn/transformer

Most of today’s AI systems focus on using self-attention mechanisms and transformer architectures on large amounts of diverse data to achieve impressive performance gains.

In this paper, we propose to augment the transformer architecture with an external attention mechanism to bring external knowledge and context to bear. By integrating external information into the prediction process, we hope to reduce the need for ever-larger models and increase the democratization of AI systems.

We find that the proposed external attention mechanism can improve the performance of existing AI systems, allowing practitioners to easily customize foundation AI models to many diverse downstream applications. In particular, we focus on the task of Commonsense Reasoning, demonstrating that the proposed external attention mechanism can augment existing transformer models and improve the model’s reasoning capabilities.

The proposed system, Knowledgeable External Attention for commonsense Reasoning (KEAR), reaches human parity on the open CommonsenseQA research benchmark with an accuracy of 89.4% in comparison to the human accuracy of 88.9%.

--- https://arxiv.org/abs/2112.03321 Noether Networks: Meta-Learning Useful Conserved Quantities Ferran Alet, Dylan Doblar, Allan Zhou, Joshua B. Tenenbaum, Kenji Kawaguchi, Chelsea Finn 2021-12-06 2021-12-06 [("doi","10.48550/arXiv.2112.03321")] ai/nn/cnn ai/nn/fully-connected ai/video/analysis reinforcement-learning/meta-learning

Progress in machine learning (ML) stems from a combination of data availability, computational resources, and an appropriate encoding of inductive biases. Useful biases often exploit symmetries in the prediction problem, such as convolutional networks relying on translation equivariance.

Automatically discovering these useful symmetries holds the potential to greatly improve the performance of ML systems, but still remains a challenge. In this work, we focus on sequential prediction problems and take inspiration from Noether’s theorem to reduce the problem of finding inductive biases to meta-learning useful conserved quantities.

We propose Noether Networks: a new type of architecture where a meta-learned conservation loss is optimized inside the prediction function.

We show, theoretically and experimentally, that Noether Networks improve prediction quality, providing a general framework for discovering inductive biases in sequential problems.

--- https://arxiv.org/abs/2112.04426#deepmind Improving language models by retrieving from trillions of tokens Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack W. Rae, Erich Elsen, Laurent Sifre 2021-12-08 2021-12-08 [("doi","10.48550/arXiv.2112.04426")] ai/nn/retrieval ai/nn/transformer/gpt ai/scaling

[code] We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens.

With a 2 trillion token database, our Retrieval-Enhanced Transformer (RETRO) obtains comparable performance to GPT-3 and Jurassic-1 on the Pile, despite using 25× fewer parameters. After fine-tuning, RETRO performance translates to downstream knowledge-intensive tasks such as question answering.

RETRO combines a frozen BERT retriever, a differentiable encoder and a chunked cross-attention mechanism to predict tokens based on an order of magnitude more data than what is typically consumed during training. We typically train RETRO from scratch, yet can also rapidly RETROfit pre-trained transformers with retrieval and still achieve good performance.

Our work opens up new avenues for improving language models through explicit memory at unprecedented scale.

--- https://arxiv.org/abs/2112.04598 InvGAN: Invertable GANs Partha Ghosh, Dominik Zietlow, Michael J. Black, Larry S. Davis, Xiaochen Hu 2021-12-08 2021-12-08 [("doi","10.48550/arXiv.2112.04598")] ai/nn/gan/data-augmentation

Generation of photo-realistic images, semantic editing and representation learning are a few of many potential applications of high resolution generative models.

Recent progress in GANs have established them as an excellent choice for such tasks. However, since they do not provide an inference model, image editing or downstream tasks such as classification can not be done on real images using the GAN latent space. Despite numerous efforts to train an inference model or design an iterative method to invert a pre-trained generator, previous methods are dataset (eg. human face images) and architecture (eg. StyleGAN) specific. These methods are nontrivial to extend to novel datasets or architectures.

We propose a general framework that is agnostic to architecture and datasets. Our key insight is that, by training the inference and the generative model together, we allow them to adapt to each other and to converge to a better quality model. Our InvGAN, short for Invertable GAN, successfully embeds real images to the latent space of a high quality generative model. This allows us to perform image inpainting, merging, interpolation and online data augmentation.

We demonstrate this with extensive qualitative and quantitative experiments.

--- https://arxiv.org/abs/2112.04907#tencent JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning Zichuan Lin, Junyou Li, Jianing Shi, Deheng Ye, Qiang Fu, Wei Yang 2021-12-07 2021-12-07 [("doi","10.48550/arXiv.2112.04907")] reinforcement-learning/exploration reinforcement-learning/imitation-learning

Learning rational behaviors in open-world games like Minecraft remains to be challenging for Reinforcement Learning (RL) research due to the compound challenge of partial observability, high-dimensional visual perception, and delayed reward.

To address this, we propose JueWu-MC, a sample-efficient hierarchical RL approach equipped with representation learning and imitation learning to deal with perception and exploration. Specifically, our approach includes two levels of hierarchy, where the high-level controller learns a policy to control over options and the low-level workers learn to solve each sub-task. To boost the learning of sub-tasks, we propose a combination of techniques including (1) action-aware representation learning which captures underlying relations between action and representation, (2) discriminator-based self-imitation learning for efficient exploration, and (3) ensemble behavior cloning with consistency filtering for policy robustness.

Extensive experiments show that JueWu-MC improves sample efficiency and outperforms a set of baselines by a large margin. Notably, we won the championship of the NeurIPS MineRL 2021 research competition and achieved the highest performance score ever.

--- https://arxiv.org/abs/2112.05130#nvidia Multimodal Conditional Image Synthesis with Product-of-Experts GANs Xun Huang, Arun Mallya, Ting-Chun Wang, Ming-Yu Liu 2021-12-09 2021-12-09 [("doi","10.48550/arXiv.2112.05130")] ai/nn/gan

Existing conditional image synthesis frameworks generate images based on user inputs in a single modality, such as text, segmentation, sketch, or style reference. They are often unable to leverage multimodal user inputs when available, which reduces their practicality. To address this limitation, we propose the Product-of-Experts Generative Adversarial Networks (PoE-GAN) framework, which can synthesize images conditioned on multiple input modalities or any subset of them, even the empty set.

PoE-GAN consists of a product-of-experts generator and a multimodal multiscale projection discriminator. Through our carefully designed training scheme, PoE-GAN learns to synthesize images with high quality and diversity.

Besides advancing the state-of-the-art in multimodal conditional image synthesis, PoE-GAN also outperforms the best existing unimodal conditional image synthesis approaches when tested in the unimodal setting.

The project website is available at https://deepimagination.cc/PoE-GAN/.

--- https://arxiv.org/abs/2112.05146 Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction Hyungjin Chung, Byeongsu Sim, Jong Chul Ye 2021-12-09 2021-12-09 [("doi","10.48550/arXiv.2112.05146")] ai/nn/diffusion

Diffusion models have recently attained interest within the community owing to their strong performance as generative models. Furthermore, its application to inverse problems have demonstrated state-of-the-art performance. Unfortunately, diffusion models have a critical downside—they are inherently slow to sample from, needing few thousand steps of iteration to generate images from pure Gaussian noise.

In this work, we show that starting from Gaussian noise is unnecessary. Instead, starting from a single forward diffusion with better initialization reduces the number of sampling steps in the reverse conditional diffusion. This phenomenon is formally explained by the contraction theory of the stochastic difference equations like our conditional diffusion strategy—the alternating applications of reverse diffusion followed by a non-expansive data consistency step.

The new sampling strategy, dubbed Come-Closer-Diffuse-Faster (CCDF), also reveals a new insight on how the existing feed-forward neural network approaches for inverse problems can be synergistically combined with the diffusion models. Experimental results with super-resolution, image inpainting, and compressed sensing MRI demonstrate that our method can achieve state-of-the-art reconstruction performance at reduced sampling steps.

--- https://arxiv.org/abs/2112.05219 CLIP2StyleGAN: Unsupervised Extraction of StyleGAN Edit Directions Rameen Abdal, Peihao Zhu, John Femiani, Niloy J. Mitra, Peter Wonka 2021-12-09 2021-12-09 [("doi","10.48550/arXiv.2112.05219")] ai/nn/gan/stylegan ai/nn/transformer/clip

The success of StyleGAN has enabled unprecedented semantic editing capabilities, on both synthesized and real images. However, such editing operations are either trained with semantic supervision or described using human guidance.

In another development, the CLIP architecture has been trained with internet-scale image and text pairings and has been shown to be useful in several zero-shot learning settings. In this work, we investigate how to effectively link the pretrained latent spaces of StyleGAN and CLIP, which in turn allows us to automatically extract semantically labeled edit directions from StyleGAN, finding and naming meaningful edit operations without any additional human guidance.

Technically, we propose two novel building blocks; one for finding interesting CLIP directions and one for labeling arbitrary directions in CLIP latent space. The setup does not assume any pre-determined labels and hence we do not require any additional supervised text/attributes to build the editing framework.

We evaluate the effectiveness of the proposed method and demonstrate that extraction of disentangled labeled StyleGAN edit directions is indeed possible, and reveals interesting and non-trivial edit directions.

--- https://arxiv.org/abs/2112.05224 Spinning Language Models for Propaganda-As-A-Service Eugene Bagdasaryan, Vitaly Shmatikov 2021-12-09 2021-12-09 [("doi","10.48550/arXiv.2112.05224")] ai/nn/adversarial ai/nn/transformer/gpt/non-fiction

We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to “spin” their outputs so as to support an adversary-chosen sentiment or point of view, but only when the input contains adversary-chosen trigger words. For example, a spinned summarization model would output positive summaries of any text that mentions the name of some individual or organization.

Model spinning enables propaganda-as-a-service. An adversary can create customized language models that produce desired spins for chosen triggers, then deploy them to generate disinformation (a platform attack), or else inject them into ML training pipelines (a supply-chain attack), transferring malicious functionality to downstream models.

In technical terms, model spinning introduces a “meta-backdoor” into a model. Whereas conventional backdoors cause models to produce incorrect outputs on inputs with the trigger, outputs of spinned models preserve context and maintain standard accuracy metrics, yet also satisfy a meta-task chosen by the adversary (eg. positive sentiment).

To demonstrate feasibility of model spinning, we develop a new backdooring technique. It stacks the adversarial meta-task onto a seq2seq model, backpropagates the desired meta-task output to points in the word-embedding space we call “pseudo-words”, and uses pseudo-words to shift the entire output distribution of the seq2seq model. We evaluate this attack on language generation, summarization, and translation models with different triggers and meta-tasks such as sentiment, toxicity, and entailment. Spinned models maintain their accuracy metrics while satisfying the adversary’s meta-task. In supply chain attack the spin transfers to downstream models.

Finally, we propose a black-box, meta-task-independent defense to detect models that selectively apply spin to inputs with a certain trigger.

--- https://arxiv.org/abs/2112.05744 More Control for Free! Image Synthesis with Semantic Diffusion Guidance Xihui Liu, Dong Huk Park, Samaneh Azadi, Gong Zhang, Arman Chopikyan, Yuxiao Hu, Humphrey Shi, Anna Rohrbach, Trevor Darrell 2021-12-10 2021-12-10 [("doi","10.48550/arXiv.2112.05744")] ai/nn/diffusion ai/nn/transformer/clip

Controllable image synthesis models allow the creation of diverse images based on text instructions or guidance from an example image. Recently, denoising diffusion probabilistic models have been shown to generate more realistic imagery than prior methods, and have been successfully demonstrated in unconditional and class-conditional settings.

We explore fine-grained, continuous control of this model class, and introduce a novel unified framework for semantic diffusion guidance, which allows either language or image guidance, or both. Guidance is injected into a pretrained unconditional diffusion model using the gradient of image-text or image matching scores. We explore CLIP-based textual guidance as well as both content and style-based image guidance in a unified form. Our text-guided synthesis approach can be applied to datasets without associated text annotations.

We conduct experiments on FFHQ and LSUN datasets, and show results on fine-grained text-guided image synthesis, synthesis of images related to a style or content example image, and examples with both textual and image guidance.

Our approach demonstrates significant advancements in the field of image synthesis, providing more effective and flexible control mechanisms for generating highly realistic and diverse images.

--- https://arxiv.org/abs/2112.06749#deepmind Step-unrolled Denoising Autoencoders for Text Generation Nikolay Savinov, Junyoung Chung, Mikolaj Binkowski, Erich Elsen, Aaron van den Oord 2021-12-13 2021-12-13 [("doi","10.48550/arXiv.2112.06749")] ai/nn/diffusion/discrete

In this paper we propose a new generative model of text, Step-unrolled Denoising Autoencoder (SUNDAE), that does not rely on autoregressive models. Similarly to denoising diffusion techniques, SUNDAE is repeatedly applied on a sequence of tokens, starting from random inputs and improving them each time until convergence.

We present a simple new improvement operator that converges in fewer iterations than diffusion methods, while qualitatively producing better samples on natural language datasets.

SUNDAE achieves state-of-the-art results (among non-autoregressive methods) on the WMT’14 English-to-German translation task and good qualitative results on unconditional language modeling on the Colossal Cleaned Common Crawl dataset and a dataset of Python code from GitHub. The non-autoregressive nature of SUNDAE opens up possibilities beyond left-to-right prompted generation, by filling in arbitrary blank patterns in a template.

--- https://arxiv.org/abs/2112.06905#google GLaM: Efficient Scaling of Language Models with Mixture-of-Experts Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathy Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V. Le, Yonghui Wu, Zhifeng Chen, Claire Cui 2021-12-13 2021-12-13 [("doi","10.48550/arXiv.2112.06905")] ai/dataset ai/nn/transformer/gpt/lamda ai/scaling/mixture-of-experts

Scaling language models with more data, compute and parameters has driven progress in natural language processing. For example, thanks to scaling, GPT-3 was able to achieve strong results on in-context learning tasks. However, training these large dense models requires large amounts of computing resources.

In this paper, we propose and develop a family of language models named GLaM (Generalist Language Model), which uses a sparsely activated mixture-of-experts architecture to scale the model capacity while also incurring substantially less training cost compared to dense variants.

The largest GLaM has 1.2 trillion parameters, which is ~7× larger than GPT-3. It consumes only 1⁄3 of the energy used to train GPT-3 and requires half of the computation flops for inference, while still achieving better overall zero-shot and one-shot performance across 29 NLP tasks.

--- https://arxiv.org/abs/2112.07381#samsung You Only Need One Model for Open-domain Question Answering Haejun Lee, Akhil Kedia, Jongwon Lee, Ashwin Paranjape, Christopher D. Manning, Kyoung-Gu Woo 2021-12-14 2021-12-14 [("doi","10.48550/arXiv.2112.07381")] ai/nn/retrieval ai/nn/transformer ai/scaling

Recent works for Open-domain Question Answering refer to an external knowledge base using a retriever model, optionally rerank the passages with a separate reranker model and generate an answer using an another reader model. Despite performing related tasks, the models have separate parameters and are weakly-coupled during training.

In this work, we propose casting the retriever and the reranker as hard-attention mechanisms applied sequentially within the transformer architecture and feeding the resulting computed representations to the reader. In this singular model architecture the hidden representations are progressively refined from the retriever to the reranker to the reader, which is more efficient use of model capacity and also leads to better gradient flow when we train it in an end-to-end manner. We also propose a pre-training methodology to effectively train this architecture.

We evaluate our model on Natural Questions and TriviaQA open datasets and for a fixed parameter budget, our model outperforms the previous state-of-the-art model by 1.0 and 0.7 exact match scores.

--- https://arxiv.org/abs/2112.07522 LMTurk: Few-Shot Learners as Crowdsourcing Workers Mengjie Zhao, Fei Mi, Yasheng Wang, Minglei Li, Xin Jiang, Qun Liu, Hinrich Schütze 2021-12-14 2021-12-14 [("doi","10.48550/arXiv.2112.07522")] ai/nn/transformer/gpt

Vast efforts have been devoted to creating high-performance few-shot learners, ie. models that perform well with little training data. Training large-scale pretrained language models (PLMs) has incurred large cost, but using PLM-based few-shot learners is still challenging due to their enormous size. This work focuses on a crucial question: How to make effective use of these few-shot learners?

We propose LMTurk, a novel approach that treats few-shot learners as crowdsourcing workers. The rationale is that crowdsourcing workers are in fact few-shot learners: They are shown a few illustrative examples to learn about a task and then start annotating. LMTurk employs few-shot learners built upon PLMs as workers.

We show that the resulting annotations can beused to train models that solve the task well and are small enough to be deployable in practical scenarios.

Altogether, LMTurk is an important step towards making effective use of current PLM-based few-shot learners.

--- https://arxiv.org/abs/2112.07544#facebook Modeling Strong and Human-Like Gameplay with KL-Regularized Search Athul Paul Jacob, David J. Wu, Gabriele Farina, Adam Lerer, Anton Bakhtin, Jacob Andreas, Noam Brown 2021-12-14 2021-12-14 [("doi","10.48550/arXiv.2112.07544")] reinforcement-learning/chess reinforcement-learning/imitation-learning reinforcement-learning/imperfect-information/diplomacy reinforcement-learning/multi-agent reinforcement-learning/preference-learning

We consider the task of building strong but human-like policies in multi-agent decision-making problems, given examples of human behavior. Imitation learning is effective at predicting human actions but may not match the strength of expert humans, while self-play learning and search techniques (eg. AlphaZero) lead to strong performance but may produce policies that are difficult for humans to understand and coordinate with.

We show in chess and Go that regularizing search policies based on the KL divergence from an imitation-learned policy by applying Monte Carlo tree search produces policies that have higher human prediction accuracy and are stronger than the imitation policy. We then introduce a novel regret minimization algorithm that is regularized based on the KL divergence from an imitation-learned policy, and show that applying this algorithm to no-press Diplomacy yields a policy that maintains the same human prediction accuracy as imitation learning while being substantially stronger.

--- https://arxiv.org/abs/2112.07571 EBERT: Epigenomic language models powered by Cerebras Meredith V. Trotter, Cuong Q. Nguyen, Stephen Young, Rob T. Woodruff, Kim M. Branson 2021-12-14 2021-12-14 [("doi","10.48550/arXiv.2112.07571")] ai/scaling genetics

Large scale self-supervised pre-training of Transformer language models has advanced the field of Natural Language Processing and shown promise in cross-application to the biological ‘languages’ of proteins and DNA. Learning effective representations of DNA sequences using large genomic sequence corpuses may accelerate the development of models of gene regulation and function through transfer learning. However, to accurately model cell type-specific gene regulation and function, it is necessary to consider not only the information contained in DNA nucleotide sequences, which is mostly invariant between cell types, but also how the local chemical and structural ‘epigenetic state’ of chromosomes varies between cell types.

Here, we introduce a Bidirectional Encoder Representations from Transformers (BERT) model that learns representations based on both DNA sequence and paired epigenetic state inputs, which we call Epigenomic BERT (or EBERT). We pre-train EBERT with a masked language model objective across the entire human genome and across 127 cell types. Training this complex model with a previously prohibitively large dataset was made possible for the first time by a partnership with Cerebras Systems, whose CS-1 system powered all pre-training experiments. We show EBERT’s transfer learning potential by demonstrating strong performance on a cell type-specific transcription factor binding prediction task. Our fine-tuned model exceeds state-of-the-art performance on 4⁄13 evaluation datasets from ENCODE-DREAM benchmarks and earns an overall rank of 3rd on the challenge leaderboard.

We explore how the inclusion of epigenetic data and task specific feature augmentation impact transfer learning performance.

--- https://arxiv.org/abs/2112.07771#facebook Boosted Dense Retriever Patrick Lewis, Barlas Oğuz, Wenhan Xiong, Fabio Petroni, Wen-tau Yih, Sebastian Riedel 2021-12-14 2021-12-14 [("doi","10.48550/arXiv.2112.07771")] ai/nn/retrieval ai/nn/sparsity/low-precision

We propose DrBoost, a dense retrieval ensemble inspired by boosting.

DrBoost is trained in stages: each component model is learned sequentially and specialized by focusing only on retrieval mistakes made by the current ensemble. The final representation is the concatenation of the output vectors of all the component models, making it a drop-in replacement for standard dense retrievers at test time.

DrBoost enjoys several advantages compared to standard dense retrieval models. It produces representations which are 4× more compact, while delivering comparable retrieval results. It also performs surprisingly well under approximate search with coarse quantization, reducing latency and bandwidth needs by another 4×.

In practice, this can make the difference between serving indices from disk versus from memory, paving the way for much cheaper deployments.

--- https://arxiv.org/abs/2112.07868#nvidia Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases Shrimai Prabhumoye, Rafal Kocielnik, Mohammad Shoeybi, Anima Anandkumar, Bryan Catanzaro 2021-12-15 2021-12-15 [("doi","10.48550/arXiv.2112.07868")] ai/nn/transformer/gpt ai/scaling sociology/technology

Detecting social bias in text is challenging due to nuance, subjectivity, and difficulty in obtaining good quality labeled datasets at scale, especially given the evolving nature of social biases and society. To address these challenges, we propose a few-shot instruction-based method for prompting pre-trained language models (LMs).

We select a few label-balanced exemplars from a small support repository that are closest to the query to be labeled in the embedding space. We then provide the LM with instruction that consists of this subset of labeled exemplars, the query text to be classified, a definition of bias, and prompt it to make a decision.

We demonstrate that large LMs used in a few-shot context can detect different types of fine-grained biases with similar and sometimes superior accuracy to fine-tuned models. We observe that the largest 530b parameter model is statistically-significantly more effective in detecting social bias compared to smaller models (achieving at least 20% improvement in AUC metric compared to other models). It also maintains a high AUC (dropping less than 5%) in a few-shot setting with a labeled repository reduced to as few as 100 samples. Large pretrained language models thus make it easier and quicker to build new bias detectors.

--- https://arxiv.org/abs/2112.07887#microsoft Knowledge-Rich Self-Supervised Entity Linking Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon 2021-12-15 2021-12-15 [("doi","10.48550/arXiv.2112.07887")] ai/scaling

Entity linking faces challenges, such as prolific variations and prevalent ambiguities, especially in high-value domains with myriad entities. Standard classification approaches suffer from the annotation bottleneck and cannot effectively handle unseen entities. Zero-shot entity linking has emerged as a promising direction for generalizing to new entities, but it still requires example gold entity mentions during training and canonical descriptions for all entities, both of which are rarely available outside of Wikipedia.

In this paper, we explore Knowledge-RIch Self-Supervision (KRISS) for entity linking, by leveraging readily available domain knowledge. In training, it generates self-supervised mention examples on unlabeled text using a domain ontology and trains a contextual encoder using contrastive learning. For inference, it samples self-supervised mentions as prototypes for each entity and conducts linking by mapping the test mention to the most similar prototype. Our approach subsumes zero-shot and few-shot methods, and can easily incorporate entity descriptions and gold mention labels if available.

Using biomedicine as a case study, we conducted extensive experiments on seven standard datasets spanning biomedical literature and clinical notes. Without using any labeled information, our method produces KRISSBERT, an universal entity linker for four million UMLS entities, which attains new state-of-the-art, outperforming prior self-supervised methods by as much as over 20 absolute points in accuracy.

--- https://arxiv.org/abs/2112.07899#google Large Dual Encoders Are Generalizable Retrievers Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y. Zhao, Yi Luan, Keith B. Hall, Ming-Wei Chang, Yinfei Yang 2021-12-15 2021-12-15 [("doi","10.48550/arXiv.2112.07899")] ai/nn/retrieval ai/nn/transformer/t5

It has been shown that dual encoders trained on one domain often fail to generalize to other domains for retrieval tasks. One widespread belief is that the bottleneck layer of a dual encoder, where the final score is simply a dot product between a query vector and a passage vector, is too limited to make dual encoders an effective retrieval model for out-of-domain generalization.

In this paper, we challenge this belief by scaling up the size of the dual encoder model while keeping the bottleneck embedding size fixed. With multi-stage training, surprisingly, scaling up the model size brings substantial improvement on a variety of retrieval tasks, especially for out-of-domain generalization.

Experimental results show that our dual encoders, Generalizable T5-based dense Retrievers (GTR), outperform existing sparse and dense retrievers on the BEIR dataset (Thakur et al 2021) substantially. Most surprisingly, our ablation study finds that GTR is very data efficient, as it only needs 10% of MS MARCO supervised data to achieve the best out-of-domain performance. All the GTR models are released.

--- https://arxiv.org/abs/2112.07945#nvidia Efficient Geometry-Aware 3D Generative Adversarial Networks Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, Gordon Wetzstein 2021-12-15 2021-12-15 [("doi","10.48550/arXiv.2112.07945")] ai/nn/gan/stylegan

Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using only collections of single-view 2D photographs has been a long-standing challenge. Existing 3D GANs are either compute-intensive or make approximations that are not 3D-consistent; the former limits quality and resolution of the generated images and the latter adversely affects multi-view consistency and shape quality.

In this work, we improve the computational efficiency and image quality of 3D GANs without overly relying on these approximations. For this purpose, we introduce an expressive hybrid explicit-implicit network architecture that, together with other design choices, synthesizes not only high-resolution multi-view-consistent images in real time but also produces high-quality 3D geometry. By decoupling feature generation and neural rendering, our framework is able to leverage state-of-the-art 2D CNN generators, such as StyleGAN-2, and inherit their efficiency and expressiveness.

We demonstrate state-of-the-art 3D-aware synthesis with FFHQ and AFHQ Cats, among other experiments.

--- https://arxiv.org/abs/2112.08348 PROMPT WAYWARDNESS: The Curious Case of Discretized Interpretation of Continuous Prompts Daniel Khashabi, Shane Lyu, Sewon Min, Lianhui Qin, Kyle Richardson, Sameer Singh, Sean Welleck, Hannaneh Hajishirzi, Tushar Khot, Ashish Sabharwal, Yejin Choi 2021-12-15 2021-12-15 [("doi","10.48550/arXiv.2112.08348")] ai/nn/adversarial ai/nn/tokenization ai/nn/transformer/gpt

Fine-tuning continuous prompts for target tasks has recently emerged as a compact alternative to full model fine-tuning. Motivated by these promising results, we investigate the feasibility of extracting a discrete (textual) interpretation of continuous prompts that is faithful to the problem they solve.

In practice, we observe a “wayward” behavior between the task solved by continuous prompts and their nearest neighbor discrete projections: We can find continuous prompts that solve a task while being projected to an arbitrary text (eg. definition of a different or even a contradictory task), while being within a very small (2%) margin of the best continuous prompt of the same size for the task. We provide intuitions behind this odd and surprising behavior, as well as extensive empirical analyses quantifying the effect of various parameters.

For instance, for larger model sizes we observe higher waywardness, i.e, we can find prompts that more closely map to any arbitrary text with a smaller drop in accuracy. These findings have important implications relating to the difficulty of faithfully interpreting continuous prompts and their generalization across models and tasks, providing guidance for future progress in prompting language models.

--- https://arxiv.org/abs/2112.08360 How to Learn and Represent Abstractions: An Investigation using Symbolic Alchemy Badr AlKhamissi, Akshay Srinivasan, Zeb-Kurth Nelson, Sam Ritter 2021-12-14 2021-12-14 [("doi","10.48550/arXiv.2112.08360")] psychology/neuroscience reinforcement-learning/meta-learning statistics/bayes

Alchemy is a new meta-learning environment rich enough to contain interesting abstractions, yet simple enough to make fine-grained analysis tractable. Further, Alchemy provides an optional symbolic interface that enables meta-reinforcement learning (meta-RL) research without a large compute budget.

In this work, we take the first steps toward using Symbolic Alchemy to identify design choices that enable deep-RL agents to learn various types of abstraction. Then, using a variety of behavioral and introspective analyses we investigate how our trained agents use and represent abstract task variables, and find intriguing connections to the neuroscience of abstraction.

We conclude by discussing the next steps for using meta-RL and Alchemy to better understand the representation of abstract variables in the brain.

--- https://arxiv.org/abs/2112.08633 Learning To Retrieve Prompts for In-Context Learning Ohad Rubin, Jonathan Herzig, Jonathan Berant 2021-12-16 2021-12-16 [("doi","10.48550/arXiv.2112.08633")] ai/nn/retrieval ai/nn/transformer/gpt

In-context learning is a recent paradigm in natural language understanding, where a large pre-trained language model (LM) observes a test instance and a few training examples as its input, and directly decodes the output without any update to its parameters. However, performance has been shown to strongly depend on the selected training examples (termed prompt).

In this work, we propose an efficient method for retrieving prompts for in-context learning using annotated data and a LM. Given an input-output pair, we estimate the probability of the output given the input and a candidate training example as the prompt, and label training examples as positive or negative based on this probability. We then train an efficient dense retriever from this data, which is used to retrieve training examples as prompts at test time.

We evaluate our approach on 3 sequence-to-sequence tasks where language utterances are mapped to meaning representations, and find that it substantially outperforms prior work and multiple baselines across the board.

--- https://arxiv.org/abs/2112.08656#allen DREAM: Uncovering Mental Models behind Language Models Yuling Gu, Bhavana Dalvi Mishra, Peter Clark 2021-12-16 2021-12-16 [("doi","10.48550/arXiv.2112.08656")] ai/nn/transformer/gpt/inner-monologue philosophy/ethics

To what extent do language models (LMs) build “mental models” of a scene when answering situated questions (eg. questions about a specific ethical dilemma)? While cognitive science has shown that mental models play a fundamental role in human problem-solving, it is unclear whether the high question-answering performance of existing LMs is backed by similar model building—and if not, whether that can explain their well-known catastrophic failures.

We observed that Macaw, an existing ‘T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer’, Raffel et al 2019-based LM, when probed provides somewhat useful but inadequate mental models for situational questions (estimated accuracy=43%, usefulness=21%, consistency=42%). We propose DREAM, a model that takes a situational question as input to produce a mental model elaborating the situation, without any additional task-specific training data for mental models. It inherits its social commonsense through distant supervision from existing NLP resources.

Our analysis shows that DREAM can produce better mental models (estimated accuracy=67%, usefulness=37%, consistency=71%) compared to Macaw. Finally, mental models generated by DREAM can be used as additional context for situational QA tasks. This additional context improves the answer accuracy of a Macaw zero-shot model by between +1% and +4% (absolute) on 3 different datasets.

--- https://arxiv.org/abs/2112.08674#allen Reframing Human-AI Collaboration for Generating Free-Text Explanations Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark Riedl, Yejin Choi 2021-12-16 2021-12-16 [("doi","10.48550/arXiv.2112.08674")] ai/nn/transformer/gpt/inner-monologue

Large language models are increasingly capable of generating fluent-appearing text with relatively little task-specific supervision. But can these models accurately explain classification decisions?

We consider the task of generating free-text explanations using a small number of human-written examples (ie. in a few-shot manner). We find that (1) authoring higher-quality examples for prompting results in higher quality generations; and (2) surprisingly, in a head-to-head comparison, crowdworkers often prefer explanations generated by GPT-3 to crowdsourced human-written explanations contained within existing datasets. Crowdworker ratings also show, however, that while models produce factual, grammatical, and sufficient explanations, they have room to improve, eg. along axes such as providing novel information and supporting the label.

We create a pipeline that combines GPT-3 with a supervised filter that incorporates humans-in-the-loop via binary acceptability judgments. Despite subjectivity intrinsic to judging acceptability, our approach is able to consistently filter GPT-3 generated explanations deemed acceptable by humans.

--- https://arxiv.org/abs/2112.08696 Few-Shot Semantic Parsing with Language Models Trained On Code Richard Shin, Benjamin Van Durme 2021-12-16 2021-12-16 [("doi","10.48550/arXiv.2112.08696")] ai/nn/transformer/gpt/codex

Large language models, prompted with in-context examples, can perform semantic parsing with little training data. They do better when we formulate the problem as paraphrasing into canonical utterances, which cast the underlying meaning representations into a controlled natural language-like representation. Intuitively, such models can more easily output canonical utterances as they are closer to the natural language used for pre-training.

More recently, models also pre-trained on code, like OpenAI Codex, have risen in prominence. Since accurately modeling code requires understanding of executable semantics, such models may prove more adept at semantic parsing.

In this paper, we test this hypothesis and find that Codex performs better at semantic parsing than equivalent GPT-3 models. We find that unlike GPT-3, Codex performs similarly when targeting meaning representations directly, perhaps as meaning representations used in semantic parsing are structured similar to code.

--- https://arxiv.org/abs/2112.08930 Intelli-Paint: Towards Developing Human-like Painting Agents Jaskirat Singh, Cameron Smith, Jose Echevarria, Liang Zheng 2021-12-16 2021-12-16 [("doi","10.48550/arXiv.2112.08930")] ai/anime

The generation of well-designed artwork is often quite time-consuming and assumes a high degree of proficiency on the part of the human painter. In order to facilitate the human painting process, substantial research efforts have been made on teaching machines how to “paint like a human”, and then using the trained agent as a painting assistant tool for human users. However, current research in this direction is often reliant on a progressive grid-based division strategy wherein the agent divides the overall image into successively finer grids, and then proceeds to paint each of them in parallel. This inevitably leads to artificial painting sequences which are not easily intelligible to human users.

To address this, we propose a novel painting approach which learns to generate output canvases while exhibiting a more human-like painting style. The proposed painting pipeline Intelli-Paint consists of (1) a progressive layering strategy which allows the agent to first paint a natural background scene representation before adding in each of the foreground objects in a progressive fashion. (2) We also introduce a novel sequential brushstroke guidance strategy which helps the painting agent to shift its attention between different image regions in a semantic-aware manner.

Finally, we propose a brushstroke regularization strategy which allows for ~60–80% reduction in the total number of required brushstrokes without any perceivable differences in the quality of the generated canvases. Through both quantitative and qualitative results, we show that the resulting agents not only show enhanced efficiency in output canvas generation but also exhibit a more natural-looking painting style which would better assist human users express their ideas through digital artwork.

--- https://arxiv.org/abs/2112.09025 Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs Ezgi Korkmaz 2021-12-16 2021-12-16 [("doi","10.48550/arXiv.2112.09025")] ai/nn/adversarial reinforcement-learning/model-free

The use of deep neural networks as function approximators has led to striking progress for reinforcement learning algorithms and applications. Yet the knowledge we have on decision boundary geometry and the loss landscape of neural policies is still quite limited.

In this paper we propose a framework to investigate the decision boundary and loss landscape similarities across states and across MDPs. We conduct experiments in various games from Arcade Learning Environment, and discover that high sensitivity directions for neural policies are correlated across MDPs. We argue that these high sensitivity directions support the hypothesis that non-robust features are shared across training environments of reinforcement learning agents.

We believe our results reveal fundamental properties of the environments used in deep reinforcement learning training, and represent a tangible step towards building robust and reliable deep reinforcement learning agents.

--- https://arxiv.org/abs/2112.09062#facebook Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants Max Bartolo, Tristan Thrush, Sebastian Riedel, Pontus Stenetorp, Robin Jia, Douwe Kiela 2021-12-16 2021-12-16 [("doi","10.48550/arXiv.2112.09062")] ai/dataset ai/nn/adversarial

In Dynamic Adversarial Data Collection (DADC), human annotators are tasked with finding examples that models struggle to predict correctly. Models trained on DADC-collected training data have been shown to be more robust in adversarial and out-of-domain settings, and are considerably harder for humans to fool. However, DADC is more time-consuming than traditional data collection and thus more costly per example.

In this work, we examine if we can maintain the advantages of DADC, without suffering the additional cost. To that end, we introduce Generative Annotation Assistants (GAAs), generator-in-the-loop models that provide real-time suggestions that annotators can either approve, modify, or reject entirely. We collect training datasets in 20 experimental settings and perform a detailed analysis of this approach for the task of extractive question answering (QA) for both standard and adversarial data collection.

We demonstrate that GAAs provide efficiency benefits in terms of annotation speed, while leading to improved model fooling rates. In addition, we show that GAA-assisted data leads to higher downstream model performance on a variety of question answering tasks.

--- https://arxiv.org/abs/2112.09106#microsoft RegionCLIP: Region-based Language-Image Pretraining Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, Jianfeng Gao 2021-12-16 2021-12-16 [("doi","10.48550/arXiv.2112.09106")] ai/nn/transformer/clip

Contrastive language-image pretraining (CLIP) using image-text pairs has achieved impressive results on image classification in both zero-shot and transfer learning settings. However, we show that directly applying such models to recognize image regions for object detection leads to poor performance due to a domain shift: CLIP was trained to match an image as a whole to a text description, without capturing the fine-grained alignment between image regions and text spans.

To mitigate this issue, we propose a new method called RegionCLIP that extends CLIP to learn region-level visual representations, thus enabling fine-grained alignment between image regions and textual concepts. Our method leverages a CLIP model to match image regions with template captions and then pretrains our model to align these region-text pairs in the feature space.

When transferring our pretrained model to the open-vocabulary object detection tasks, our method outperforms the state-of-the-art by 3.8 AP50 and 2.2 AP for novel categories on COCO and LVIS datasets, respectively. Moreover, the learned region representations support zero-shot inference for object detection, showing promising results on both COCO and LVIS datasets.

Our code is available at https://github.com/microsoft/RegionCLIP.

--- https://arxiv.org/abs/2112.09118#facebook Contriever: Towards Unsupervised Dense Information Retrieval with Contrastive Learning Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Arm Holdings, Joulin, Edouard Grave 2021-12-16 2021-12-16 [("doi","10.48550/arXiv.2112.09118")] ai/nn/retrieval

[Github] Information retrieval is an important component in natural language processing, for knowledge intensive tasks such as question answering and fact checking. Recently, information retrieval has seen the emergence of dense retrievers, based on neural networks, as an alternative to classical sparse methods based on term-frequency. These models have obtained state-of-the-art results on datasets and tasks where large training sets are available. However, they do not transfer well to new domains or applications with no training data, and are often outperformed by term-frequency methods such as BM25 which are not supervised. Thus, a natural question is whether it is possible to train dense retrievers without supervision.

In this work, we explore the limits of contrastive learning as a way to train unsupervised dense retrievers, and show that it leads to strong retrieval performance.

More precisely, we show on the BEIR benchmark that our model outperforms BM25 on 11⁄15 datasets.

Furthermore, when a few thousands examples are available, we show that fine-tuning our model on these leads to strong improvements compared to BM25. Finally, when used as pre-training before fine-tuning on the MS MARCO dataset, our technique obtains state-of-the-art results on the BEIR benchmark.

--- https://arxiv.org/abs/2112.09164#facebook High Fidelity Visualization of What Your Self-Supervised Representation Knows About Florian Bordes, Randall Balestriero, Pascal Vincent 2021-12-16 2021-12-16 [("doi","10.48550/arXiv.2112.09164")] ai/nn/diffusion

Discovering what is learned by neural networks remains a challenge. In self-supervised learning, classification is the most common task used to evaluate how good a representation is. However, relying only on such downstream task can limit our understanding of how much information is retained in the representation of a given input.

In this work, we showcase the use of a conditional diffusion based generative model (RCDM) to visualize representations learned with self-supervised models. We further demonstrate how this model’s generation quality is on par with state-of-the-art generative models while being faithful to the representation used as conditioning.

By using this new tool to analyze self-supervised models, we can show visually that (1) SSL (backbone) representation are not really invariant to many data augmentation they were trained on. (2) SSL projector embedding appear too invariant for tasks like classifications. (3) SSL representations are more robust to small adversarial perturbation of their inputs (4) there is an inherent structure learned with SSL model that can be used for image manipulation.

--- https://arxiv.org/abs/2112.10048 Predictive Coding Theories of Cortical Function Linxing Preston Jiang, Rajesh P. N. Rao 2021-12-19 2021-12-19 [("doi","10.48550/arXiv.2112.10048")] psychology/neuroscience

Predictive coding is an unifying framework for understanding perception, action, and neocortical organization. In predictive coding, different areas of the neocortex implement a hierarchical generative model of the world that is learned from sensory inputs.

Cortical circuits are hypothesized to perform Bayesian inference based on this generative model. Specifically, the Rao-Ballard hierarchical predictive coding model assumes that the top-down feedback connections from higher to lower order cortical areas convey predictions of lower-level activities. The bottom-up, feedforward connections in turn convey the errors between top-down predictions and actual activities. These errors are used to correct current estimates of the state of the world and generate new predictions.

Through the objective of minimizing prediction errors, predictive coding provides a functional explanation for a wide range of neural responses and many aspects of brain organization.

--- https://arxiv.org/abs/2112.10668#facebook XGLM: Few-shot Learning with Multilingual Language Models Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li 2021-12-20 2021-12-20 [("doi","10.48550/arXiv.2112.10668")] ai/nn/transformer ai/scaling

[code/checkpoints] Large-scale autoregressive language models such as GPT-3 are few-shot learners that can perform a wide range of language tasks without fine-tuning. While these models are known to be able to jointly represent many different languages, their training data is dominated by English, potentially limiting their cross-lingual generalization. In this work, we train multilingual autoregressive language models on a balanced corpus covering a diverse set of languages, and study their few-shot and zero-shot learning capabilities in a wide range of tasks.

Our largest model with 7.5 billion parameters sets new state-of-the-art in few-shot learning in more than 20 representative languages, outperforming GPT-3 of comparable size in multilingual commonsense reasoning (with +7.4% absolute accuracy improvement in 0-shot settings and +9.4% in 4-shot settings) and natural language inference (+5.4% in each of 0-shot and 4-shot settings). On the FLORES-101 machine translation benchmark, our model outperforms GPT-3 on 171⁄182 translation directions with 32 training examples, while surpassing the official supervised baseline in 45 directions.

We present a detailed analysis of where the model succeeds and fails, showing in particular that it enables cross-lingual in-context learning on some tasks, while there is still room for improvement on surface form robustness and adaptation to tasks that do not have a natural cloze form. Finally, we evaluate our models in social value tasks such as hate speech detection in five languages and find it has limitations similar to comparable sized GPT-3 models.

--- https://arxiv.org/abs/2112.10684#facebook Efficient Large Scale Language Modeling with Mixtures of Experts Mikel Artetxe, Shruti Bhosale, Naman Goyal, Todor Mihaylov, Myle Ott, Sam Shleifer, Xi Victoria Lin, Jingfei Du, Srinivasan Iyer, Ramakanth Pasunuru, Giri Anantharaman, Xian Li, Shuohui Chen, Halil Akin, Mandeep Baines, Louis Martin, Xing Zhou, Punit Singh Koura, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Mona Diab, Zornitsa Kozareva, Ves Stoyanov 2021-12-20 2021-12-20 [("doi","10.48550/arXiv.2112.10684")] ai/scaling/mixture-of-experts

Mixture of Experts layers (MoEs) enable efficient scaling of language models through conditional computation. This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings: in-domain and out-of-domain language modeling, zero-shot and few-shot priming, and full fine-tuning.

With the exception of fine-tuning, we find MoEs to be substantially more compute efficient. At more modest training budgets, MoEs can match the performance of dense models using ~4× less compute. This gap narrows at scale, but our largest MoE model (1.1T parameters) consistently outperforms a compute-equivalent dense model (6.7b parameters).

Overall, this performance gap varies greatly across tasks and domains, suggesting that MoE and dense models generalize differently in ways that are worthy of future study.

We make our code and models publicly available for research use.

--- https://arxiv.org/abs/2112.11598 What is the point of computers? A question for pure mathematicians Kevin Buzzard 2021-12-22 2021-12-22 [("doi","10.48550/arXiv.2112.11598")] economics/automation math reinforcement-learning/model

We discuss the idea that computers might soon help mathematicians to prove theorems in areas where they have not previously been useful.

Furthermore we argue that these same computer tools will also help us in the communication and teaching of mathematics.

--- https://arxiv.org/abs/2112.11701#tencent Maximum Entropy Population Based Training for Zero-Shot Human-AI Coordination Rui Zhao, Jinming Song, Hu Haifeng, Yang Gao, Yi Wu, Zhongqian Sun, Yang Wei 2021-12-22 2021-12-22 [("doi","10.48550/arXiv.2112.11701")] reinforcement-learning/exploration reinforcement-learning/multi-agent

An AI agent should be able to coordinate with humans to solve tasks. We consider the problem of training a Reinforcement Learning (RL) agent without using any human data, ie. in a zero-shot setting, to make it capable of collaborating with humans. Standard RL agents learn through self-play. Unfortunately, these agents only know how to collaborate with themselves and normally do not perform well with unseen partners, such as humans.

The methodology of how to train a robust agent in a zero-shot fashion is still subject to research. Motivated from the maximum entropy RL, we derive a centralized population entropy objective to facilitate learning of a diverse population of agents, which is later used to train a robust agent to collaborate with unseen partners. The proposed method shows its effectiveness compared to baseline methods, including self-play PPO, the standard Population-Based Training (PBT), and trajectory diversity-based PBT, in the popular Overcooked game environment. We also conduct online experiments with real humans and further demonstrate the efficacy of the method in the real world. A supplementary video showing experimental results is available at https://www.youtube.com/watch?v=Xh-FKD0AAKE.

--- https://arxiv.org/abs/2112.11848 A Large-Scale Characterization of How Readers Browse Wikipedia Tiziano Piccardi, Martin Gerlach, Akhil Arora, Robert West 2021-12-22 2021-12-22 [("doi","10.48550/arXiv.2112.11848")] technology wikipedia

Despite the importance and pervasiveness of Wikipedia as one of the largest platforms for open knowledge, surprisingly little is known about how people navigate its content when seeking information. To bridge this gap, we present the first systematic large-scale analysis of how readers browse Wikipedia. Using billions of page requests from Wikipedia’s server logs, we measure how readers reach articles, how they transition between articles, and how these patterns combine into more complex navigation paths. We find that navigation behavior is characterized by highly diverse structures. Although most navigation paths are shallow, comprising a single pageload, there is much variety, and the depth and shape of paths vary systematically with topic, device type, and time of day. We show that Wikipedia navigation paths commonly mesh with external pages as part of a larger online ecosystem, and we describe how naturally occurring navigation paths are distinct from targeted navigation in lab-based settings. Our results further suggest that navigation is abandoned when readers reach low-quality pages. These findings not only help in identifying potential improvements to reader experience on Wikipedia, but also in better understanding of how people seek knowledge on the Web.

[If users load an average of 1.5 pages per session, and almost all the subsequent 0.5 page loads are by following internal wiki links (and only 6% by alternative navigation methods like search), and sessions terminate at low-quality pages mostly, how much reading or lack of reading is due to the presence or absence of wiki links?

I notice that there are still a lot of missing wiki links on articles (even proper noun ones which are dead obvious: eg. John Eyre×Osman II). From a reader’s perspective, the absence of a link is evidence that they shouldn’t bother searching and they should halt there if that was what they wanted. Quality is in considerable part just accuracy and comprehensiveness of wikilinking. (See also impact of banner ads/latency; “Wikipedia Matters”, Hinnosaar et al 2019; “Science Is Shaped by Wikipedia: Evidence from a Randomized Control Trial”, Thompson et al 2017.)

If an average page has, say, 50 wikilinks and the expectation of another page is ~0.5 or 50% of a page, then each individual wikilink would on average be worth 1% of a pageview and one’d expect a marginal gain of <1% for each additional wikilink added to that page. That sounds ludicrously valuable if the real value is even a tenth of that, because adding wikilinks has traditionally not been a major focus of WP tooling or bot operator cause area (compared to disambiguation or vandal fighting). Can the user tracking estimate the value more directly? One could also look at analyzing the effects of the various semi-auto and auto-linking bots as natural experiments on the logged traffic.]

--- https://arxiv.org/abs/2112.12147 Generative Models of Brain Dynamics—A review Mahta Ramezanian Panahi, Germán Abrevaya, Jean-Christophe Gagnon-Audet, Vikram Voleti, Irina Rish, Guillaume Dumas 2021-12-22 2021-12-22 [("doi","10.48550/arXiv.2112.12147")] psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

The principled design and discovery of biologically/physically-informed models of neuronal dynamics has been advancing since the mid-twentieth century. Recent developments in artificial intelligence (AI) have accelerated this progress. This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction.

The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data.

We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis/data-driven modeling.

By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.

--- https://arxiv.org/abs/2112.12731#baidu ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation Shuohuan Wang, Yu Sun, Yang Xiang, Zhihua Wu, Siyu Ding, Weibao Gong, Shikun Feng, Junyuan Shang, Yanbin Zhao, Chao Pang, Jiaxiang Liu, Xuyi Chen, Yuxiang Lu, Weixin Liu, Xi Wang, Yangfan Bai, Qiuliang Chen, Li Zhao, Shiyong Li, Peng Sun, Dianhai Yu, Yanjun Ma, Hao Tian, Hua Wu, Tian Wu, Wei Zeng, Ge Li, Wen Gao, Haifeng Wang 2021-12-23 2021-12-23 [("doi","10.48550/arXiv.2112.12731")] ai/nn/sparsity/knowledge-distillation ai/scaling

Pre-trained language models have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. GPT-3 has shown that scaling up pre-trained language models can further exploit their enormous potential. A unified framework named ERNIE 3.0 was recently proposed for pre-training large-scale knowledge-enhanced models and trained a model with 10 billion parameters. ERNIE 3.0 outperformed the state-of-the-art models on various NLP tasks.

In order to explore the performance of scaling up ERNIE 3.0, we train a hundred-billion-parameter model called ERNIE 3.0 Titan with up to 260 billion parameters on the PaddlePaddle platform. Furthermore, we design a self-supervised adversarial loss and a controllable language modeling loss to make ERNIE 3.0 Titan generate credible and controllable texts. To reduce the computation overhead and carbon emission, we propose an online distillation framework for ERNIE 3.0 Titan, where the teacher model will teach students and train itself simultaneously.

ERNIE 3.0 Titan is the largest Chinese dense pre-trained model so far. Empirical results show that the ERNIE 3.0 Titan outperforms the state-of-the-art models on 68 NLP datasets.

--- https://arxiv.org/abs/2112.13121 The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence Brando Miranda, Yu-Xiong Wang, Sanmi Koyejo 2021-12-24 2021-12-24 [("doi","10.48550/arXiv.2112.13121")] reinforcement-learning/meta-learning

It has been recently observed that a transfer learning solution might be all we needed to solve many few-shot learning benchmarks. This raises important questions about when and how meta-learning algorithms should be deployed.

In this paper, we make a first step in clarifying these questions by first formulating a computable metric for a few-shot learning benchmark that we hypothesize is predictive of whether meta-learning solutions will succeed or not. We name this metric the diversity coefficient of a few-shot learning benchmark.

Using the diversity coefficient, we show that the miniImageNet benchmark has zero diversity—according to 24 different ways to compute the diversity. We proceed to show that when making a fair comparison between MAML learned solutions to transfer learning, both have identical meta-test accuracy. This suggests that transfer learning fails to outperform MAML—contrary to what previous work suggests.

Together, these two facts provide the first test of whether diversity correlates with meta-learning success and therefore show that a diversity coefficient of zero correlates with a high similarity between transfer learning and MAML learned solutions—especially at meta-test time. We therefore conjecture meta-learned solutions have the same meta-test performance as transfer learning when the diversity coefficient is zero.

--- https://arxiv.org/abs/2112.13339 Itô-Taylor Sampling Scheme for Denoising Diffusion Probabilistic Models using Ideal Derivatives Hideyuki Tachibana, Mocho Go, Muneyoshi Inahara, Yotaro Katayama, Yotaro Watanabe 2021-12-26 2021-12-26 [("doi","10.48550/arXiv.2112.13339")] ai/nn/diffusion

Denoising Diffusion Probabilistic Models (DDPMs) have been attracting attention recently as a new challenger to popular deep neural generative models including GAN, VAE, etc. However, DDPMs have a disadvantage that they often require a huge number of refinement steps during the synthesis.

To address this problem, this paper proposes a new DDPM sampler based on a second-order numerical scheme for stochastic differential equations (SDEs), while the conventional sampler is based on a first-order numerical scheme. In general, it is not easy to compute the derivatives that are required in higher-order numerical schemes. However, in the case of DDPM, this difficulty is alleviated by the trick which the authors call “ideal derivative substitution”.

The newly derived higher-order sampler was applied to both image and speech generation tasks, and it is experimentally observed that the proposed sampler could synthesize plausible images and audio signals in relatively smaller number of refinement steps.

--- https://arxiv.org/abs/2112.13884 A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision Ajinkya Tejankar, Ajinkya Tejankar, Bichen Wu, Saining Xie, Madian Khabsa, Hamed Pirsiavash, Hamed Firooz 2021-12-27 2021-12-27 [("doi","10.48550/arXiv.2112.13884")] ai/dataset ai/nn/transformer/clip

Using natural language as a supervision for training visual recognition models holds great promise. Recent works have shown that if such supervision is used in the form of alignment between images and captions in large training datasets, then the resulting aligned models perform well on zero-shot classification as downstream tasks.

In this paper, we focus on teasing out what parts of the language supervision are essential for training zero-shot image classification models. Through extensive and careful experiments, we show that: (1) A simple Bag-of-Words (BoW) caption could be used as a replacement for most of the image captions in the dataset. Surprisingly, we observe that this approach improves the zero-shot classification performance when combined with word balancing. (2) Using a BoW pretrained model, we can obtain more training data by generating pseudo-BoW captions on images that do not have a caption.

Models trained on images with real and pseudo-BoW captions achieve stronger zero-shot performance. On ImageNet-1k zero-shot evaluation, our best model, that uses only 3M image-caption pairs, performs on-par with a CLIP model trained on 15M image-caption pairs (31.5% vs 31.3%).

--- https://arxiv.org/abs/2112.14683 StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN-2 Ivan Skorokhodov, Sergey Tulyakov, Mohamed Elhoseiny 2021-12-29 2021-12-29 [("doi","10.48550/arXiv.2112.14683")] ai/nn/gan/stylegan ai/video/generation

Videos show continuous events, yet most—if not all—video synthesis frameworks treat them discretely in time. In this work, we think of videos of what they should be—time-continuous signals, and extend the paradigm of neural representations to build a continuous-time video generator.

For this, we first design continuous motion representations through the lens of positional embeddings. Then, we explore the question of training on very sparse videos and demonstrate that a good generator can be learned by using as few as 2 frames per clip. After that, we rethink the traditional image and video discriminators pair and propose to use a single hypernetwork-based one. This decreases the training cost and provides richer learning signal to the generator, making it possible to train directly on 10242 videos for the first time.

We build our model on top of StyleGAN-2 and it is just 5% more expensive to train at the same resolution while achieving almost the same image quality. Moreover, our latent space features similar properties, enabling spatial manipulations that our method can propagate in time. We can generate arbitrarily long videos at arbitrary high frame rate, while prior work struggles to generate even 64 frames at a fixed rate. Our model achieves state-of-the-art results on four modern 2562 video synthesis benchmarks and one 10242 resolution one. Videos and the source code are available at the project website: https://universome.github.io/stylegan-v.

--- https://arxiv.org/abs/2112.15594 A Neural Network Solves and Generates Mathematics Problems by Program Synthesis: Calculus, Differential Equations, Linear Algebra, and More Iddo Drori, Sunny Tran, Roman Wang, Newman Cheng, Kevin Liu, Leonard Tang, Elizabeth Ke, Nikhil Singh, Taylor L. Patti, Jayson Lynch, Avi Shporer, Nakul Verma, Eugene Wu, Gilbert Strang 2021-12-31 2021-12-31 [("doi","10.48550/arXiv.2112.15594")] ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue math

We demonstrate that a neural network pre-trained on text and fine-tuned on code solves Mathematics problems by program synthesis. We turn questions into programming tasks, automatically generate programs, and then execute them, perfectly solving university-level problems from MIT’s large Mathematics courses (Single Variable Calculus 18.01, Multivariable Calculus 18.02, Differential Equations 18.03, Introduction to Probability & Statistics 18.05, Linear Algebra 18.06, and Mathematics for Computer Science 6.042) as well as questions from a MATH dataset (on Prealgebra, Algebra, Counting and Probability, Number Theory, and Precalculus), the latest benchmark of advanced mathematics problems specifically designed to assess mathematical reasoning.

We explore prompt generation methods that enable Transformers to generate question solving programs for these subjects, including solutions with plots. We generate correct answers for a random sample of questions in each topic.

We quantify the gap between the original and transformed questions and perform a survey to evaluate the quality and difficulty of generated questions. This is the first work to automatically solve, grade, and generate university-level Mathematics course questions at scale which represents a milestone for higher education.

--- https://arxiv.org/abs/2201.00308 DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents Kushagra Pandey, Avideep Mukherjee, Piyush Rai, Abhishek Kumar 2022-01-02 2022-01-02 [("doi","10.48550/arXiv.2201.00308")] ai/nn/diffusion ai/nn/vae

Diffusion Probabilistic models have been shown to generate state-of-the-art results on several competitive image synthesis benchmarks but lack a low-dimensional, interpretable latent space, and are slow at generation. On the other hand, Variational Autoencoders (VAEs) typically have access to a low-dimensional latent space but exhibit poor sample quality. Despite recent advances, VAEs usually require high-dimensional hierarchies of the latent codes to generate high-quality samples.

We present DiffuseVAE, a novel generative framework that integrates VAE within a diffusion model framework, and leverage this to design a novel conditional parameterization for diffusion models.

We show that the resulting model can improve upon the unconditional diffusion model in terms of sampling efficiency while also equipping diffusion models with the low-dimensional VAE inferred latent code. Furthermore, we show that the proposed model can generate high-resolution samples and exhibits synthesis quality comparable to state-of-the-art models on standard benchmarks. Lastly, we show that the proposed method can be used for controllable image synthesis and also exhibits out-of-the-box capabilities for downstream tasks like image super-resolution and denoising.

For reproducibility, our source code is publicly available at https://github.com/kpandey008/DiffuseVAE.

--- https://arxiv.org/abs/2201.01163#salesforce Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning Michael Curry, Alexander Trott, Soham Phade, Yu Bai, Stephan Zheng 2022-01-03 2022-01-03 [("doi","10.48550/arXiv.2201.01163")] economics reinforcement-learning/meta-learning reinforcement-learning/multi-agent

Real economies can be seen as a sequential imperfect-information game with many heterogeneous, interacting strategic agents of various agent types, such as consumers, firms, and governments. Dynamic general equilibrium models are common economic tools to model the economic activity, interactions, and outcomes in such systems. However, existing analytical and computational methods struggle to find explicit equilibria when all agents are strategic and interact, while joint learning is unstable and challenging. Amongst others, a key reason is that the actions of one economic agent may change the reward function of another agent, eg. a consumer’s expendable income changes when firms change prices or governments change taxes.

We show that multi-agent deep reinforcement learning (RL) can discover stable solutions that are epsilon-Nash equilibria for a meta-game over agent types, in economic simulations with many agents, through the use of structured learning curricula and efficient GPU-only simulation and training. Conceptually, our approach is more flexible and does not need unrealistic assumptions, eg. market clearing, that are commonly used for analytical tractability. Our GPU implementation enables training and analyzing economies with a large number of agents within reasonable time frames, eg. training completes within a day. We demonstrate our approach in real-business-cycle models, a representative family of DGE models, with 100 worker-consumers, 10 firms, and a government who taxes and redistributes. We validate the learned meta-game epsilon-Nash equilibria through approximate best-response analyses, show that RL policies align with economic intuitions, and that our approach is constructive, eg. by explicitly learning a spectrum of meta-game epsilon-Nash equilibria in open RBC models.

--- https://arxiv.org/abs/2201.01283 Self-supervised Learning from 100 Million Medical Images Florin C. Ghesu, Bogdan Georgescu, Awais Mansoor, Youngjin Yoo, Dominik Neumann, Pragneshkumar Patel, R. S. Vishwanath, James M. Balter, Yue Cao, Sasa Grbic, Dorin Comaniciu 2022-01-04 2022-01-04 [("doi","10.48550/arXiv.2201.01283")] ai/scaling

Building accurate and robust artificial intelligence systems for medical image assessment requires not only the research and design of advanced deep learning models but also the creation of large and curated sets of annotated training examples. Constructing such datasets, however, is often very costly—due to the complex nature of annotation tasks and the high level of expertise required for the interpretation of medical images (eg. expert radiologists).

To counter this limitation, we propose a method for self-supervised learning of rich image features based on contrastive learning and online feature clustering [SwAV]. For this purpose we leverage large training datasets of over 100,000,000 medical images of various modalities, including radiography, computed tomography (CT), magnetic resonance (MR) imaging and ultrasonography. We propose to use these features to guide model training in supervised and hybrid self-supervised/supervised regime on various downstream tasks.

We highlight a number of advantages of this strategy on challenging image assessment problems in radiography, CT and MR: (1) increase in accuracy compared to the state-of-the-art (eg. AUC boost of 3–7% for detection of abnormalities from chest radiography scans and hemorrhage detection on brain CT); (2) Acceleration of model convergence during training by up to 85% compared to using no pretraining (eg. 83% when training a model for detection of brain metastases in MR scans); (3) Increase in robustness to various image augmentations, such as intensity variations, rotations or scaling reflective of data variation seen in the field.

--- https://arxiv.org/abs/2201.01763#facebook Robust Self-Supervised Audio-Visual Speech Recognition Bowen Shi, Wei-Ning Hsu, Abdelrahman Mohamed 2022-01-05 2022-01-05 [("doi","10.48550/arXiv.2201.01763")] ai/scaling

Audio-based automatic speech recognition (ASR) degrades in noisy environments and is particularly vulnerable to interfering speech, as the model cannot determine which speaker to transcribe. Audio-visual speech recognition (AVSR) systems improve robustness by complementing the audio stream with the visual information that is invariant to noise and helps the model focus on the desired speaker.

In this work, we present a self-supervised AVSR framework built upon Audio-Visual HuBERT (AV-HuBERT), a state-of-the-art audio-visual speech representation learning model. This approach leverages the largest available AVSR benchmark dataset, LRS3, showcasing significant advancements in the field.

Our approach outperforms prior state-of-the-art by ~50% (28.0% vs. 14.1%) using less than 10% of labeled data (433hr vs. 30hr) in the presence of babble noise, while reducing the WER (Word Error Rate) of an audio-based model by over 75% (25.8% vs. 5.8%) on average.

This demonstrates a significant stride in AVSR technology, potentially changing the way automated systems interpret and transcribe human speech in challenging acoustic scenarios.

--- https://arxiv.org/abs/2201.01819 Formal Analysis of Art: Proxy Learning of Visual Concepts from Style Through Language Models Diana Kim, Ahmed Elgammal, Marian Mazzone 2022-01-05 2022-01-05 [("doi","10.48550/arXiv.2201.01819")] ai/nn

We present a machine learning system that can quantify fine art paintings with a set of visual elements and principles of art. This formal analysis is fundamental for understanding art, but developing such a system is challenging. Paintings have high visual complexities, but it is also difficult to collect enough training data with direct labels.

To resolve these practical limitations, we introduce a novel mechanism, called proxy learning, which learns visual concepts in paintings though their general relation to styles. This framework does not require any visual annotation, but only uses style labels and a general relationship between visual concepts and style. In this paper, we propose a novel proxy model and reformulate 4 pre-existing methods in the context of proxy learning.

Through quantitative and qualitative comparison, we evaluate these methods and compare their effectiveness in quantifying the artistic visual concepts, where the general relationship is estimated by language models; GloVe or BERT. The language modeling is a practical and scalable solution requiring no labeling, but it is inevitably imperfect.

We demonstrate how the new proxy model is robust to the imperfection, while the other models are sensitively affected by it.

--- https://arxiv.org/abs/2201.02177#openai Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets [paper] Alethea Power, Yuri Burda, Harri Edwards, Igor Babuschkin, Vedant Misra 2022-01-06 2022-01-06 [("doi","10.48550/arXiv.2201.02177")] ai/nn/fully-connected ai/scaling/emergence/grokking

[full annotation] In this paper we propose to study generalization of neural networks on small algorithmically generated datasets. In this setting, questions about data efficiency, memorization, generalization, and speed of learning can be studied in great detail.

In some situations we show that neural networks learn through a process of grokking a pattern in the data, improving generalization performance from random chance level to perfect generalization, and that this improvement in generalization can happen well past the point of overfitting.

We also study generalization as a function of dataset size and find that smaller datasets require increasing amounts of optimization for generalization.

We argue that these datasets provide a fertile ground for studying a poorly understood aspect of deep learning: generalization of overparametrized neural networks beyond memorization of the finite training dataset.

--- https://arxiv.org/abs/2201.02184#facebook AV-HuBERT: Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction Bowen Shi, Wei-Ning Hsu, Kushal Lakhotia, Abdelrahman Mohamed 2022-01-05 2022-01-05 [("doi","10.48550/arXiv.2201.02184")] ai/scaling ai/video/analysis

Video recordings of speech contain correlated audio and visual information, providing a strong signal for speech representation learning from the speaker’s lip movements and the produced sound.

We introduce Audio-Visual Hidden Unit BERT (AV-HuBERT), a self-supervised representation learning framework for audio-visual speech, which masks multi-stream video input and predicts automatically discovered and iteratively refined multimodal hidden units. AV-HuBERT learns powerful audio-visual speech representation benefiting both lip-reading and automatic speech recognition.

On the largest public lip-reading benchmark LRS3 (433 hours), AV-HuBERT achieves 32.5% WER with only 30 hours of labeled data, outperforming the former state-of-the-art approach (33.6%) trained with a thousand times more transcribed video data (31K hours). The lip-reading WER is further reduced to 26.9% when using all 433 hours of labeled data from LRS3 and combined with self-training. Using our audio-visual representation on the same benchmark for audio-only speech recognition leads to a 40% relative WER reduction over the state-of-the-art performance (1.3% vs 2.3%).

Our code and models are available at https://github.com/facebookresearch/av_hubert.

--- https://arxiv.org/abs/2201.02605#facebook Detecting Twenty-thousand Classes using Image-level Supervision Xingyi Zhou, Rohit Girdhar, Arm Holdings, Joulin, Phillip Krähenbühl, Ishan Misra 2022-01-07 2022-01-07 [("doi","10.48550/arXiv.2201.02605")] ai/nn/transformer/clip

Current object detectors are limited in vocabulary size due to the small scale of detection datasets. Image classifiers, on the other hand, reason about much larger vocabularies, as their datasets are larger and easier to collect.

We propose Detic, which simply trains the classifiers of a detector on image classification data and thus expands the vocabulary of detectors to tens of thousands of concepts. Unlike prior work, Detic does not assign image labels to boxes based on model predictions, making it much easier to implement and compatible with a range of detection architectures and backbones.

Our results show that Detic yields excellent detectors even for classes without box annotations. It outperforms prior work on both open-vocabulary and long-tail detection benchmarks. Detic provides a gain of 2.4 mAP for all classes and 8.3 mAP for novel classes on the open-vocabulary LVIS benchmark. On the standard LVIS benchmark, Detic reaches 41.7 mAP for all classes and 41.7 mAP for rare classes.

For the first time, we train a detector with all the twenty-one-thousand classes of the ImageNet dataset and show that it generalizes to new datasets without fine-tuning.

Code is available at Github.

--- https://arxiv.org/abs/2201.02624 Microdosing: Knowledge Distillation for GAN based Compression Leonhard Helminger, Roberto Azevedo, Abdelaziz Djelouah, Markus Gross, Christopher Schroers 2022-01-07 2022-01-07 [("doi","10.48550/arXiv.2201.02624")] ai/nn/gan ai/nn/sparsity/knowledge-distillation ai/video/generation

Recently, progress has been made in learned image and video compression. In particular, the usage of Generative Adversarial Networks has led to impressive results in the low bit rate regime. However, the model size remains an important issue in current state-of-the-art proposals and existing solutions require computational effort on the decoding side. This limits their usage in realistic scenarios and the extension to video compression.

In this paper, we demonstrate how to leverage knowledge distillation to obtain equally capable image decoders at a fraction of the original number of parameters. We investigate several aspects of our solution including sequence specialization with side information for image coding.

Finally, we also show how to transfer the obtained benefits into the setting of video compression. Overall, this allows us to reduce the model size by a factor of 20 and to achieve a 50% reduction in decoding time.

--- https://arxiv.org/abs/2201.03546 LSeg: Language-driven Semantic Segmentation Boyi Li, Kilian Q. Weinberger, Serge Belongie, Vladlen Koltun, René Ranftl 2022-01-10 2022-01-10 [("doi","10.48550/arXiv.2201.03546")] ai/nn/transformer/clip

We present LSeg, a novel model for language-driven semantic image segmentation.

LSeg uses a text encoder to compute embeddings of descriptive input labels (eg. “grass” or “building”) together with a transformer-based image encoder that computes dense per-pixel embeddings of the input image. The image encoder is trained with a contrastive objective to align pixel embeddings to the text embedding of the corresponding semantic class. The text embeddings provide a flexible label representation in which semantically similar labels map to similar regions in the embedding space (eg. “cat” and “furry”). This allows LSeg to generalize to previously unseen categories at test time, without retraining or even requiring a single additional training sample.

We demonstrate that our approach achieves highly competitive zero-shot performance compared to existing zero-shot and few-shot semantic segmentation methods, and even matches the accuracy of traditional segmentation algorithms when a fixed label set is provided.

Code and demo are available at Github.

--- https://arxiv.org/abs/2201.03916 Automated Reinforcement Learning (AutoRL): A Survey and Open Problems Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer 2022-01-11 2022-01-11 [("doi","10.48550/arXiv.2201.03916")] reinforcement-learning/exploration reinforcement-learning/meta-learning

The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing deep RL provides a path towards generally capable agents. However, the success of RL agents is often highly sensitive to design choices in the training process, which may require tedious and error-prone manual tuning. This makes it challenging to use RL for new problems, while also limiting its full potential.

In many other areas of machine learning, AutoML has shown it is possible to automate such design choices and has also yielded promising initial results when applied to RL. However, Automated Reinforcement Learning (AutoRL) involves not only standard applications of AutoML but also includes additional challenges unique to RL, that naturally produce a different set of methods. As such, AutoRL has been emerging as an important area of research in RL, providing promise in a variety of applications from RNA design to playing games such as Go.

Given the diversity of methods and environments considered in RL, much of the research has been conducted in distinct subfields, ranging from meta-learning to evolution. In this survey we seek to unify the field of AutoRL, we provide a common taxonomy, discuss each area in detail and pose open problems which would be of interest to researchers going forward.

--- https://arxiv.org/abs/2201.04182 HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning Andrey Zhmoginov, Mark Sandler, Max Vladymyrov 2022-01-11 2022-01-11 [("doi","10.48550/arXiv.2201.04182")] ai/nn/cnn ai/nn/transformer reinforcement-learning/meta-learning

In this work we propose a HyperTransformer, a transformer-based model for few-shot learning that generates weights of a convolutional neural network (CNN) directly from support samples. Since the dependence of a small generated CNN model on a specific task is encoded by a high-capacity transformer model, we effectively decouple the complexity of the large task space from the complexity of individual tasks.

Our method is particularly effective for small target CNN architectures where learning a fixed universal task-independent embedding is not optimal and better performance is attained when the information about the task can modulate all model parameters. For larger models, we discover that generating the last layer alone allows us to produce competitive or better results than those obtained with state-of-the-art methods while being end-to-end differentiable.

Finally, we extend our approach to a semi-supervised regime using unlabeled samples in the support set and further improving few-shot performance.

--- https://arxiv.org/abs/2201.04600#facebook Deep Symbolic Regression for Recurrent Sequences Stéphane d’Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, François Charton 2022-01-12 2022-01-12 [("doi","10.48550/arXiv.2201.04600")] ai/nn/transformer/gpt/codex math

Symbolic regression, i.e. predicting a function from the observation of its values, is well-known to be a challenging task.

In this paper, we train Transformers to infer the function or recurrence relation underlying sequences of integers or floats, a typical task in human IQ tests which has hardly been tackled in the machine learning literature.

We evaluate our integer model on a subset of OEIS sequences, and show that it outperforms built-in Wolfram Mathematica functions for recurrence prediction. We also demonstrate that our float model is able to yield informative approximations of out-of-vocabulary functions and constants, eg. bessel0(x) ≈ sin(x)+cos(x) / √πx and 1.644934 ≈ π2⁄6.

An interactive demonstration of our models is provided at [broken URL].

--- https://arxiv.org/abs/2201.04684 BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations Daiqing Li, Huan Ling, Seung Wook Kim, Karsten Kreis, Adela Barriuso, Sanja Fidler, Antonio Torralba 2022-01-12 2022-01-12 [("doi","10.48550/arXiv.2201.04684")] ai/dataset ai/nn/gan/biggan

Annotating images with pixel-wise labels is a time-consuming and costly process. Recently, DatasetGAN showcased a promising alternative—to synthesize a large labeled dataset via a generative adversarial network (GAN) by exploiting a small set of manually labeled, GAN-generated images. Here, we scale DatasetGAN to ImageNet scale of class diversity.

We take image samples from the class-conditional generative model BigGAN trained on ImageNet, and manually annotate 5 images per class, for all 1k classes. By training an effective feature segmentation architecture on top of BigGAN, we turn BigGAN into a labeled dataset generator [BigDatasetGAN]. We further show that VQGAN can similarly serve as a dataset generator, leveraging the already annotated data.

We create a new ImageNet benchmark by labeling an additional set of 8k real images and evaluate segmentation performance in a variety of settings. Through an extensive ablation study we show big gains in leveraging a large generated dataset to train different supervised and self-supervised backbone models on pixel-wise tasks. Furthermore, we demonstrate that using our synthesized datasets for pre-training leads to improvements over standard ImageNet pre-training on several downstream datasets, such as PASCAL-VOC, MS-COCO, Cityscapes and chest X-ray, as well as tasks (detection, segmentation).

Our benchmark will be made public and maintain a leaderboard for this challenging task.

Project Page: https://nv-tlabs.github.io/big-datasetgan/.

--- https://arxiv.org/abs/2201.05299#amazon A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering Feng Gao, Qing Ping, Govind Thattai, Aishwarya Reganti, Ying Nian Wu, Prem Natarajan 2022-01-14 2022-01-14 [("doi","10.48550/arXiv.2201.05299")] ai/nn/retrieval

Outside-knowledge visual question answering (OK-VQA) requires the agent to comprehend the image, make use of relevant knowledge from the entire web, and digest all the information to answer the question. Most previous works address the problem by first fusing the image and question in the multi-modal space, which is inflexible for further fusion with a vast amount of external knowledge.

In this paper, we call for a paradigm shift for the OK-VQA task, which transforms the image into plain text, so that we can enable knowledge passage retrieval, and generative question-answering in the natural language space. This paradigm takes advantage of the sheer volume of gigantic knowledge bases and the richness of pre-trained language models.

A Transform-Retrieve-Generate framework (TRiG) framework is proposed, which can be plug-and-played with alternative image-to-text models and textual knowledge bases.

Experimental results show that our TRiG framework outperforms all state-of-the-art supervised methods by at least 11.1% absolute margin.

--- https://arxiv.org/abs/2201.05320#allen CommonsenseQA 2.0: Exposing the Limits of AI through Gamification Alon Talmor, Ori Yoran, Ronan Le Bras, Chandra Bhagavatula, Yoav Goldberg, Yejin Choi, Jonathan Berant 2022-01-14 2022-01-14 [("doi","10.48550/arXiv.2201.05320")] ai/nn/adversarial ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/t5

Constructing benchmarks that test the abilities of modern natural language understanding models is difficult—pre-trained language models exploit artifacts in benchmarks to achieve human parity, but still fail on adversarial examples and make errors that demonstrate a lack of common sense.

In this work, we propose gamification as a framework for data construction. The goal of players in the game is to compose questions that mislead a rival AI while using specific phrases for extra points. The game environment leads to enhanced user engagement and simultaneously gives the game designer control over the collected data, allowing us to collect high-quality data at scale.

Using our method we create CommonsenseQA 2.0 [to replace CommonsenseQA], which includes 14,343 yes/no questions, and demonstrate its difficulty for models that are orders-of-magnitude larger than the AI used in the game itself.

Our best baseline, the T5-based Unicorn with 11b parameters achieves an accuracy of 70.2%, substantially higher than GPT-3 (52.9%) in a few-shot inference setup. Both score well below human performance which is at 94.1%.

--- https://arxiv.org/abs/2201.05337 A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, Dawei Song 2022-01-14 2022-01-14 [("doi","10.48550/arXiv.2201.05337")] ai/nn/sampling ai/nn/transformer/gpt reinforcement-learning/preference-learning

Controllable Text Generation (CTG) is an emerging area in the field of natural language generation (NLG). It is regarded as crucial for the development of advanced text generation technologies that are more natural and better meet the specific constraints in practical applications. In recent years, methods using large-scale pre-trained language models (PLMs), in particular the widely used transformer-based PLMs, have become a new paradigm of NLG, allowing the generation of more diverse and fluent text. However, due to the lower level of interpretability of deep neural networks, the controllability of these methods needs to be guaranteed. To this end, controllable text generation using transformer-based PLMs has become a rapidly growing yet challenging new research hotspot. A diverse range of approaches have emerged in the recent 3–4 years, targeting different CTG tasks which may require different types of controlled constraints.

In this paper, we present a systematic critical review of the common tasks, main approaches, and evaluation methods in this area.

Finally, we discuss the challenges that the field is facing, and put forward various promising future directions. To the best of our knowledge, this is the first survey paper to summarize CTG techniques from the perspective of PLMs. We hope it can help researchers in related fields to quickly track the academic frontier, providing them with a landscape of the area and a roadmap for future research.

--- https://arxiv.org/abs/2201.05596#microsoft DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He 2022-01-14 2022-01-14 [("doi","10.48550/arXiv.2201.05596")] ai/nn/sparsity/knowledge-distillation ai/scaling/mixture-of-experts

[blog] As the training of giant dense models hits the boundary on the availability and capability of the hardware resources today, Mixture-of-Experts (MoE) models become one of the most promising model architectures due to their substantial training cost reduction compared to a quality-equivalent dense model.

Its training cost saving is demonstrated from encoder-decoder models (prior works) to a 5× saving for auto-aggressive language models (this work along with parallel explorations). However, due to the much larger model size and unique architecture, how to provide fast MoE model inference remains challenging and unsolved, limiting its practical usage.

To tackle this, we present DeepSpeed-MoE, an end-to-end MoE training and inference solution as part of the DeepSpeed library, including novel MoE architecture designs and model compression techniques that reduce MoE model size by up to 3.7×, and a highly optimized inference system that provides 7.3× better latency and cost compared to existing MoE inference solutions. It offers ultra-fast inference latencies (25ms) for trillion-parameter MoE models. DeepSpeed-MoE offers an unprecedented scale and efficiency to serve massive MoE models with up to 4.5× faster and 9× cheaper inference compared to quality-equivalent dense models.

We hope our innovations and systems help open a promising path to new directions in the large model landscape, a shift from dense to sparse MoE models, where training and deploying higher-quality models with fewer resources becomes more widely possible.

--- https://arxiv.org/abs/2201.06009 Memory-assisted prompt editing to improve GPT-3 after deployment Aman Madaan, Niket Tandon, Peter Clark, Yiming Yang 2022-01-16 2022-01-16 [("doi","10.48550/arXiv.2201.06009")] ai/nn/retrieval ai/nn/transformer/gpt/3/nonfiction

Large LMs such as GPT-3, while powerful, are not immune to mistakes, but are prohibitively costly to retrain. One failure mode is misinterpreting a user’s instruction (eg. GPT-3 interpreting “What word is similar to good?” to mean a homonym, while the user intended a synonym). Our goal is to allow users to correct such errors directly through interaction—without retraining.

Our approach pairs GPT-3 with a growing memory of cases where the model misunderstood the user’s intent and was provided with feedback, clarifying the instruction. Given a new query, our memory-enhanced GPT-3 uses feedback from similar, prior queries to enrich the prompt.

Through simple proof-of-concept experiments, we show how a (simulated) user can interactively teach a deployed GPT-3, doubling its accuracy on basic lexical tasks (eg. generate a synonym) where users query in different, novel (often misunderstood) ways. In such scenarios, memory helps avoid repeating similar past mistakes.

Our simple idea is a first step towards strengthening deployed models, potentially broadening their utility.

All the code and data is available at Github.

--- https://arxiv.org/abs/2201.07406 Neural Language Models are Effective Plagiarists Stella Biderman, Edward Raff 2022-01-19 2022-01-19 [("doi","10.48550/arXiv.2201.07406")] ai/nn/transformer/gpt/codex

As artificial intelligence (AI) technologies become increasingly powerful and prominent in society, their misuse is a growing concern. In educational settings, AI technologies could be used by students to cheat on assignments and exams.

In this paper we explore whether transformers can be used to solve introductory level programming assignments while bypassing commonly used AI tools to detect plagiarism. We find that a student using GPT-J [Wang & Komatsuzaki 2021] can complete introductory level programming assignments without triggering suspicion from MOSS [Aiken, 2000], a widely used plagiarism detection tool. This holds despite the fact that GPT-J was not trained on the problems in question and is not provided with any examples to work from.

We further find that the code written by GPT-J is diverse in structure, lacking any particular tells that future plagiarism detection techniques may use to try to identify algorithmically generated code.

We conclude with a discussion of the ethical and educational implications of large language models and directions for future research.

--- https://arxiv.org/abs/2201.07520#facebook CM3: A Causal Masked Multimodal Model of the Internet Armen Aghajanyan, Bernie Huang, Candace Ross, Vladimir Karpukhin, Hu Xu, Naman Goyal, Dmytro Okhonko, Mandar Joshi, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer 2022-01-19 2022-01-19 [("doi","10.48550/arXiv.2201.07520")] ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e/1 ai/nn/vae ai/scaling

We introduce CM3, a family of causally masked generative models trained over a large corpus of structured multi-modal documents that can contain both text and image tokens.

Our new causally masked approach generates tokens left to right while also masking out a small number of long token spans that are generated at the end of the string, instead of their original positions. The causally masking object provides a type of hybrid of the more common causal and masked language models, by enabling full generative modeling while also providing bidirectional context when generating the masked spans.

We train causally masked language-image models on large-scale web and Wikipedia articles, where each document contains all of the text, hypertext markup, hyperlinks, and image tokens (from a VQ-VAE-GAN), provided in the order they appear in the original HTML source (before masking). The resulting CM3 models can generate rich structured, multi-modal outputs while conditioning on arbitrary masked document contexts, and thereby implicitly learn a wide range of text, image, and cross modal tasks.

They can be prompted to recover, in a zero-shot fashion, the functionality of models such as DALL·E, GENRE, and HTLM.

We set the new state-of-the-art in zero-shot summarization, entity linking, and entity disambiguation while maintaining competitive performance in the fine-tuning setting. We can generate images unconditionally, conditioned on text (like DALL·E 1) and do captioning all in a zero-shot setting with a single model.

--- https://arxiv.org/abs/2201.07619 CAST: Character labeling in Animation using Self-supervision by Tracking Oron Nir, Gal Rapoport, Ariel Shamir 2022-01-19 2022-01-19 [("doi","10.48550/arXiv.2201.07619")] ai/anime ai/video/analysis

Cartoons and animation domain videos have very different characteristics compared to real-life images and videos. In addition, this domain carries a large variability in styles. Current computer vision and deep-learning solutions often fail on animated content because they were trained on natural images. In this paper we present a method to refine a semantic representation suitable for specific animated content.

We first train a neural network on a large-scale set of animation videos and use the mapping to deep features as an embedding space. Next, we use self-supervision to refine the representation for any specific animation style by gathering many examples of animated characters in this style, using a multi-object tracking. These examples are used to define triplets for contrastive loss training. The refined semantic space allows better clustering of animated characters even when they have diverse manifestations.

Using this space we can build dictionaries of characters in an animation videos, and define specialized classifiers for specific stylistic content (eg. characters in a specific animation series) with very little user effort. These classifiers are the basis for automatically labeling characters in animation videos.

We present results on a collection of characters in a variety of animation styles.

--- https://arxiv.org/abs/2201.08102#deepmind Safe Deep RL in 3D Environments using Human Feedback Matthew Rahtz, Vikrant Varma, Ramana Kumar, Zachary Kenton, Shane Legg, Jan Leike 2022-01-20 2022-01-20 [("doi","10.48550/arXiv.2201.08102")] reinforcement-learning/exploration reinforcement-learning/preference-learning reinforcement-learning/safe

[blog] Agents should avoid unsafe behavior during both training and deployment. This typically requires a simulator and a procedural specification of unsafe behavior. Unfortunately, a simulator is not always available, and procedurally specifying constraints can be difficult or impossible for many real-world tasks.

A recently introduced technique, ReQueST, aims to solve this problem by learning a neural simulator of the environment from safe human trajectories, then using the learned simulator to efficiently learn a reward model from human feedback. However, it is yet unknown whether this approach is feasible in complex 3D environments with feedback obtained from real humans—whether sufficient pixel-based neural simulator quality can be achieved, and whether the human data requirements are viable in terms of both quantity and quality.

In this paper we answer this question in the affirmative, using ReQueST to train an agent to perform a 3D first-person object collection task using data entirely from human contractors. We show that the resulting agent exhibits an order of magnitude reduction in unsafe behavior compared to standard reinforcement learning.

--- https://arxiv.org/abs/2201.08239#google LaMDA: Language Models for Dialog Applications Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, Quoc Le 2022-01-20 2022-01-20 [("doi","10.48550/arXiv.2201.08239")] ai/nn/retrieval ai/nn/transformer/gpt/lamda ai/scaling reinforcement-learning/safe

We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137b parameters and are pre-trained on 1.56T words of public dialog data and web text. While model scaling alone can improve quality, it shows less improvements on safety and factual grounding. We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to improvements towards the two key challenges of safety and factual grounding.

The first challenge, safety, involves ensuring that the model’s responses are consistent with a set of human values, such as preventing harmful suggestions and unfair bias. We quantify safety using a metric based on an illustrative set of human values, and we find that filtering candidate responses using a LaMDA classifier fine-tuned with a small amount of crowdworker-annotated data offers a promising approach to improving model safety.

The second challenge, factual grounding, involves enabling the model to consult external knowledge sources, such as an information retrieval system, a language translator, and a calculator. We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible.

Finally, we explore the use of LaMDA in the domains of education and content recommendations, and analyze their helpfulness and role consistency.

--- https://arxiv.org/abs/2201.08896#google Environment Generation for Zero-Shot Compositional Reinforcement Learning Izzeddin Gur, Natasha Jaques, Yingjie Miao, Jongwook Choi, Manoj Tiwari, Honglak Lee, Aleksandra Faust 2022-01-21 2022-01-21 [("doi","10.48550/arXiv.2201.08896")] reinforcement-learning/exploration reinforcement-learning/meta-learning

Many real-world problems are compositional—solving them requires completing interdependent sub-tasks, either in series or in parallel, that can be represented as a dependency graph. Deep reinforcement learning (RL) agents often struggle to learn such complex tasks due to the long time horizons and sparse rewards. To address this problem, we present Compositional Design of Environments (CoDE), which trains a Generator agent to automatically build a series of compositional tasks tailored to the RL agent’s current skill level.

This automatic curriculum not only enables the agent to learn more complex tasks than it could have otherwise, but also selects tasks where the agent’s performance is weak, enhancing its robustness and ability to generalize zero-shot to unseen tasks at test-time. We analyze why current environment generation techniques are insufficient for the problem of generating compositional tasks, and propose a new algorithm that addresses these issues.

Our results assess learning and generalization across multiple compositional tasks, including the real-world problem of learning to navigate and interact with web pages. We learn to generate environments composed of multiple pages or rooms, and train RL agents capable of completing a wide range of complex tasks in those environments. We contribute two new benchmark frameworks for generating compositional tasks, compositional MiniGrid and gMiniWoB for web navigation.

CoDE yields 4× higher success rate than the strongest baseline, and demonstrates strong performance of real websites learned on 3500 primitive tasks.

--- https://arxiv.org/abs/2201.09647 AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-dependent Kinase 20 (CDK20) Small Molecule Inhibitor Feng Ren, Xiao Ding, Min Zheng, Mikhail Korzinkin, Xin Cai, Wei Zhu, Alexey Mantsyzov, Alex Aliper, Vladimir Aladinskiy, Zhongying Cao, Shanshan Kong, Xi Long, Bonnie Hei Man Liu, Yingtao Liu, Vladimir Naumov, Anastasia Shneyderman, Ivan V. Ozerov, Ju Wang, Frank W. Pun, Alan Aspuru-Guzik, Michael Levitt, Alex Zhavoronkov 2022-01-21 2022-01-21 [("doi","10.48550/arXiv.2201.09647")] ai/nn/transformer/alphafold

The AlphaFold computer program predicted protein structures for the whole human genome, which has been considered as a remarkable breakthrough both in artificial intelligence (AI) application and structural biology. Despite the varying confidence level, these predicted structures still could contribute to the structure-based drug design of novel targets, especially the ones with no or limited structural information.

In this work, we successfully applied AlphaFold in our end-to-end AI-powered drug discovery engines constituted of a biocomputational platform PandaOmics and a generative chemistry platform Chemistry42, to identify a first-in-class hit molecule of a novel target without an experimental structure starting from target selection towards hit identification in a cost-efficient and time-efficient manner. PandaOmics provided the targets of interest and Chemistry42 generated the molecules based on the AlphaFold predicted structure, and the selected molecules were synthesized and tested in biological assays.

Through this approach, we identified a small molecule hit compound for CDK20 with a Kd value of 8.9 ± 1.6 uM (n = 4) within 30 days from target selection and after only synthesizing 7 compounds.

To the best of our knowledge, this is the first reported small molecule targeting CDK20 and more importantly, this work is the first demonstration of AlphaFold application in the hit identification process in early drug discovery.

--- https://arxiv.org/abs/2201.09863 Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots Jagdeep Singh Bhatia, Holly Jackson, Yunsheng Tian, Jie Xu, Wojciech Matusik 2022-01-24 2022-01-24 [("doi","10.48550/arXiv.2201.09863")] reinforcement-learning/exploration reinforcement-learning/robot

Both the design and control of a robot play equally important roles in its task performance. However, while optimal control is well studied in the machine learning and robotics community, less attention is placed on finding the optimal robot design. This is mainly because co-optimizing design and control in robotics is characterized as a challenging problem, and more importantly, a comprehensive evaluation benchmark for co-optimization does not exist.

In this paper, we propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots. In our benchmark, each robot is composed of different types of voxels (eg. soft, rigid, actuators), resulting in a modular and expressive robot design space. Our benchmark environments span a wide range of tasks, including locomotion on various types of terrains and manipulation. Furthermore, we develop several robot co-evolution algorithms by combining state-of-the-art design optimization methods and deep reinforcement learning techniques.

Evaluating the algorithms on our benchmark platform, we observe robots exhibiting increasingly complex behaviors as evolution progresses, with the best evolved designs solving many of our proposed tasks. Additionally, even though robot designs are evolved autonomously from scratch without prior knowledge, they often grow to resemble existing natural creatures while outperforming hand-designed robots. Nevertheless, all tested algorithms fail to find robots that succeed in our hardest environments. This suggests that more advanced algorithms are required to explore the high-dimensional design space and evolve increasingly intelligent robots—an area of research in which we hope Evolution Gym will accelerate progress.

Our website with code, environments, documentation, and tutorials is available at http://evogym.csail.mit.edu/.

--- https://arxiv.org/abs/2201.10936 FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control Dimitri von Rütte, Luca Biggio, Yannic Kilcher, Thomas Hoffman 2022-01-26 2022-01-26 [("doi","10.48550/arXiv.2201.10936")] ai/music ai/nn/sampling ai/nn/transformer

Generating music with deep neural networks has been an area of active research in recent years. While the quality of generated samples has been steadily increasing, most methods are only able to exert minimal control over the generated sequence, if any.

We propose the self-supervised description-to-sequence task, which allows for fine-grained controllable generation on a global level by extracting high-level features about the target sequence and learning the conditional distribution of sequences given the corresponding high-level description in a sequence-to-sequence modeling setup. We train FIGARO (FIne-grained music Generation via Attention-based, RObust control) by applying description-to-sequence modeling to symbolic music.

By combining learned high-level features with domain knowledge, which acts as a strong inductive bias, the model achieves state-of-the-art results in controllable symbolic music generation and generalizes well beyond the training distribution.

--- https://arxiv.org/abs/2201.11473#microsoft Reasoning Like Program Executors Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Yan Gao, Qiang Fu, Jian-Guang Lou, Weizhu Chen 2022-01-27 2022-01-27 [("doi","10.48550/arXiv.2201.11473")] ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 ai/scaling

Reasoning over natural language is a long-standing goal for the research community. However, studies have shown that existing language models are inadequate in reasoning. To address the issue, we present POET, a new pre-training paradigm.

Through pre-training language models with programs and their execution results, POET empowers language models to harvest the reasoning knowledge possessed in program executors via a data-driven approach. POET is conceptually simple and can be instantiated by different kinds of programs. In this paper, we show 3 empirically powerful instances, ie. POET-Math, POET-Logic, and POET-SQL.

Experimental results on 6 benchmarks demonstrate that POET can boost model performance on natural language reasoning, such as numerical reasoning, logical reasoning, and multi-hop reasoning. Taking the DROP benchmark as a representative example, POET improves the F1 metric of BART 69.2% → 80.6%. Furthermore, POET shines in giant language models, pushing the F1 metric of T5-11B to 87.6% and achieving a new state-of-the-art performance on DROP.

POET opens a new gate on reasoning-enhancement pre-training and we hope our analysis would shed light on the future research of reasoning like program executors.

--- https://arxiv.org/abs/2201.11503 Surprisingly Robust In-Hand Manipulation: An Empirical Study Aditya Bhatt, Adrian Sieler, Steffen Puhlmann, Oliver Brock 2022-01-27 2022-01-27 [("doi","10.15607/RSS.2021.XVII.089")] reinforcement-learning/robot technology

We present in-hand manipulation skills on a dexterous, compliant, anthropomorphic hand. Even though these skills were derived in a simplistic manner, they exhibit surprising robustness to variations in shape, size, weight, and placement of the manipulated object. They are also very insensitive to variation of execution speeds, ranging from highly dynamic to quasi-static. The robustness of the skills leads to compositional properties that enable extended and robust manipulation programs.

To explain the surprising robustness of the in-hand manipulation skills, we performed a detailed, empirical analysis of the skills’ performance. From this analysis, we identify 3 principles for skill design: (1) Exploiting the hardware’s innate ability to drive hard-to-model contact dynamics. (2) Taking actions to constrain these interactions, funneling the system into a narrow set of possibilities. (3) Composing such action sequences into complex manipulation programs. We believe that these principles constitute an important foundation for robust robotic in-hand manipulation, and possibly for manipulation in general.

--- https://arxiv.org/abs/2201.11793 Denoising Diffusion Restoration Models Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song 2022-01-27 2022-01-27 [("doi","10.48550/arXiv.2201.11793")] ai/nn/diffusion

Many interesting tasks in image restoration can be cast as linear inverse problems. A recent family of approaches for solving these problems uses stochastic algorithms that sample from the posterior distribution of natural images given the measurements. However, efficient solutions often require problem-specific supervised training to model the posterior, whereas unsupervised methods that are not problem-specific typically rely on inefficient iterative methods.

This work addresses these issues by introducing Denoising Diffusion Restoration Models (DDRM), an efficient, unsupervised posterior sampling method. Motivated by variational inference, DDRM takes advantage of a pre-trained denoising diffusion generative model for solving any linear inverse problem.

We demonstrate DDRM’s versatility on several image datasets for super-resolution, deblurring, inpainting, and colorization under various amounts of measurement noise. DDRM outperforms the current leading unsupervised methods on the diverse ImageNet dataset in reconstruction quality, perceptual quality, and runtime, being 5× faster than the nearest competitor. DDRM also generalizes well for natural images out of the distribution of the observed ImageNet training set.

--- https://arxiv.org/abs/2201.11989 Using Constant Learning Rate of Two Time-Scale Update Rule for Training Generative Adversarial Networks Naoki Sato, Hideaki Iiduka 2022-01-28 2022-01-28 [("doi","10.48550/arXiv.2201.11989")] ai/nn/gan

Previous numerical results have shown that a two time-scale update rule (TTUR) using constant learning rates is practically useful for training generative adversarial networks (GANs).

Meanwhile, a theoretical analysis of TTUR to find a stationary local Nash equilibrium of a Nash equilibrium problem with two players, a discriminator and a generator, has been given using decaying learning rates.

In this paper, we give a theoretical analysis of TTUR using constant learning rates to bridge the gap between theory and practice. In particular, we show that, for TTUR using constant learning rates, the number of steps needed to find a stationary local Nash equilibrium decreases as the batch size increases.

We also provide numerical results to support our theoretical analyzes.

--- https://arxiv.org/abs/2201.12086#salesforce BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi 2022-01-28 2022-01-28 [("doi","10.48550/arXiv.2201.12086")] ai/dataset ai/nn/transformer/clip ai/video/analysis

[example of training Pokemon/Naruto SD using BLIP-generated captions] Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision.

In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones.

We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner.

Code, models, and datasets are released at Github.

--- https://arxiv.org/abs/2201.12204#deepmind From data to functa: Your data point is a function and you should treat it like one Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami, Danilo Rezende, Dan Rosenbaum 2022-01-28 2022-01-28 [("doi","10.48550/arXiv.2201.12204")] ai/nn/diffusion reinforcement-learning/meta-learning

It is common practice in deep learning to represent a measurement of the world on a discrete grid, eg. a 2D grid of pixels. However, the underlying signal represented by these measurements is often continuous, eg. the scene depicted in an image. A powerful continuous alternative is then to represent these measurements using an implicit neural representation, a neural function trained to output the appropriate measurement value for any input spatial location.

In this paper, we take this idea to its next level: what would it take to perform deep learning on these functions instead, treating them as data? In this context we refer to the data as functa, and propose a framework for deep learning on functa. This view presents a number of challenges around efficient conversion from data to functa, compact representation of functa, and effectively solving downstream tasks on functa.

We outline a recipe to overcome these challenges and apply it to a wide range of data modalities including images, 3D shapes, neural radiance fields (NeRF) and data on manifolds.

We demonstrate that this approach has various compelling properties across data modalities, in particular on the canonical tasks of generative modeling, data imputation, novel view synthesis and classification.

--- https://arxiv.org/abs/2201.12417#google Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, Shixiang Shane Gu 2022-01-28 2022-01-28 [("doi","10.48550/arXiv.2201.12417")] reinforcement-learning/model-free

In this work, we study the use of the Bellman equation as a surrogate objective for value prediction accuracy.

While the Bellman equation is uniquely solved by the true value function over all state-action pairs, we find that the Bellman error (the difference between both sides of the equation) is a poor proxy for the accuracy of the value function. In particular, we show that (1) due to cancellations from both sides of the Bellman equation, the magnitude of the Bellman error is only weakly related to the distance to the true value function, even when considering all state-action pairs, and (2) in the finite data regime, the Bellman equation can be satisfied exactly by infinitely many suboptimal solutions. This means that the Bellman error can be minimized without improving the accuracy of the value function.

We demonstrate these phenomena through a series of propositions, illustrative toy examples, and empirical analysis in standard benchmark domains.

--- https://arxiv.org/abs/2201.12507#microsoft AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models Dongkuan Xu, Subhabrata Mukherjee, Xiaodong Liu, Debadeepta Dey, Wenhui Wang, Xiang Zhang, Ahmed Hassan Awadallah, Jianfeng Gao 2022-01-29 2022-01-29 [("doi","10.48550/arXiv.2201.12507")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer

Knowledge distillation (KD) methods compress large models into smaller students with manually-designed student architectures given pre-specified computational cost. This requires several trials to find a viable student, and further repeating the process for each student or computational budget change. We use Neural Architecture Search (NAS) to automatically distill several compressed students with variable cost from a large model.

Current works train a single SuperLM consisting of millions of subnetworks with weight-sharing, resulting in interference between subnetworks of different sizes. Our framework AutoDistil addresses the above challenges with the following steps: (a) Incorporates inductive bias and heuristics to partition the Transformer search space into K compact sub-spaces (K=3 for typical student sizes of base, small and tiny); (b) Trains one SuperLM for each sub-space using task-agnostic objective (eg. self-attention distillation) with weight-sharing of students; (c) Lightweight search for the optimal student without re-training. Fully task-agnostic training and search allow students to be reused for fine-tuning on any downstream task.

Experiments on the GLUE benchmark against state-of-the-art KD and NAS methods demonstrate AutoDistil to outperform leading compression techniques with up to 2.7× reduction in computational cost and negligible loss in task performance.

--- https://arxiv.org/abs/2201.13425 Don’t Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning (ExORL) Denis Yarats, David Brandfonbrener, Hao Liu, Michael Laskin, Pieter Abbeel, Alessandro Lazaric, Lerrel Pinto 2022-01-31 2022-01-31 [("doi","10.48550/arXiv.2201.13425")] reinforcement-learning/exploration reinforcement-learning/offline reinforcement-learning/scaling

Recent progress in deep learning has relied on access to large and diverse datasets. Such data-driven progress has been less evident in offline reinforcement learning (RL), because offline RL data is usually collected to optimize specific target tasks limiting the data’s diversity.

In this work, we propose Exploratory data for Offline RL (ExORL), a data-centric approach to offline RL. ExORL first generates data with unsupervised reward-free exploration, then relabels this data with a downstream reward before training a policy with offline RL.

We find that exploratory data allows vanilla off-policy RL algorithms, without any offline-specific modifications, to outperform or match state-of-the-art offline RL algorithms on downstream tasks.

Our findings suggest that data generation is as important as algorithmic advances for offline RL and hence requires careful consideration from the community.

--- https://arxiv.org/abs/2202.00155 Fortuitous Forgetting in Connectionist Networks Hattie Zhou, Ankit Vani, Hugo Larochelle, Aaron Courville 2022-02-01 2022-02-01 [("doi","10.48550/arXiv.2202.00155")] ai/nn/sparsity/pruning

Forgetting is often seen as an unwanted characteristic in both human and machine learning. However, we propose that forgetting can in fact be favorable to learning. We introduce “forget-and-relearn” as a powerful paradigm for shaping the learning trajectories of artificial neural networks. In this process, the forgetting step selectively removes undesirable information from the model, and the relearning step reinforces features that are consistently useful under different conditions.

The forget-and-relearn framework unifies many existing iterative training algorithms in the image classification and language emergence literature, and allows us to understand the success of these algorithms in terms of the disproportionate forgetting of undesirable information. We leverage this understanding to improve upon existing algorithms by designing more targeted forgetting operations.

Insights from our analysis provide a coherent view on the dynamics of iterative training in neural networks and offer a clear path towards performance improvements.

--- https://arxiv.org/abs/2202.00273 StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets Axel Sauer, Katja Schwarz, Andreas Geiger 2022-02-01 2022-02-01 [("doi","10.48550/arXiv.2202.00273")] ai/dataset ai/nn/gan/stylegan ai/scaling

[video; code] Computer graphics has experienced a recent surge of data-centric approaches for photorealistic and controllable content creation. StyleGAN in particular sets new standards for generative modeling regarding image quality and controllability. However, StyleGAN’s performance severely degrades on large unstructured datasets such as ImageNet. StyleGAN was designed for controllability; hence, prior works suspect its restrictive design to be unsuitable for diverse datasets.

In contrast, we find the main limiting factor to be the current training strategy. Following the recently introduced Projected GAN paradigm, we leverage powerful neural network priors and a progressive growing strategy to successfully train the latest StyleGAN3 generator on ImageNet.

Our final model, StyleGAN-XL, sets a new state-of-the-art on large-scale image synthesis and is the first to generate ImageNet images at a resolution of 10242 at such a dataset scale.

We demonstrate that this model can invert and edit images beyond the narrow domain of portraits or specific object classes.

…Our contributions enable us to train a much larger model than previously possible while requiring less computation than prior art. Our model is 3× larger in terms of depth and parameter count than a standard StyleGAN3. However, to match the prior state-of-the-art performance of ADM at a resolution of 5122 pixels, training the models on a single NVIDIA Tesla V100 takes 400 GPU-days compared to the previously required 1,914 V100-days.

--- https://arxiv.org/abs/2202.00565 Data-driven emergence of convolutional structure in neural networks Alessandro Ingrosso, Sebastian Goldt 2022-02-01 2022-02-01 [("doi","10.48550/arXiv.2202.00565")] ai/nn/fully-connected psychology/neuroscience

Exploiting data invariances is crucial for efficientces is crucial for efficient learning in both artificial and biological neural circuits. Understanding how neural networks can discover appropriate representations capable of harnessing the underlying symmetries of their inputs is thus crucial in machine learning and neuroscience. Convolutional neural networks, for example, were designed to exploit translation symmetry and their capabilities triggered the first wave of deep learning successes. However, learning convolutions directly from translation-invariant data with a fully-connected network has so far proven elusive.

Here, we show how initially fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs, resulting in localized, space-tiling receptive fields. These receptive fields match the filters of a convolutional network trained on the same task.

By carefully designing data models for the visual scene, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs, which has long been recognised as the hallmark of natural images. We provide an analytical and numerical characterisation of the pattern-formation mechanism responsible for this phenomenon in a simple model, which results in an unexpected link between receptive field formation and the tensor decomposition of higher-order input correlations.

These results provide a new perspective on the development of low-level feature detectors in various sensory modalities, and pave the way for studying the impact of higher-order statistics on learning in neural networks.

--- https://arxiv.org/abs/2202.00622 Datamodels: Predicting Predictions from Training Data Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry 2022-02-01 2022-02-01 [("doi","10.48550/arXiv.2202.00622")] ai/nn/sparsity reinforcement-learning/meta-learning

We present a conceptual framework, datamodeling, for analyzing the behavior of a model class in terms of the training data.

For any fixed “target” example x, training set S, and learning algorithm, a datamodel is a parameterized function 2S ⟶ ℝ that for any subset ofS′ ⊂ S—using only information about which examples of S are contained in S′—predicts the outcome of training a model on S′ and evaluating on x. Despite the potential complexity of the underlying process being approximated (eg. end-to-end training and evaluation of deep neural networks), we show that even simple linear datamodels can successfully predict model outputs.

We then demonstrate that datamodels give rise to a variety of applications, such as: accurately predicting the effect of dataset counterfactuals; identifying brittle predictions; finding semantically similar examples; quantifying train-test leakage; and embedding data into a well-behaved and feature-rich representation space.

Data for this paper (including pre-computed datamodels as well as raw predictions from 4 million trained deep neural networks) is available at Github.

--- https://arxiv.org/abs/2202.00666 Typical Decoding for Natural Language Generation Clara Meister, Tiago Pimentel, Gian Wiher, Ryan Cotterell 2022-02-01 2022-02-01 [("doi","10.48550/arXiv.2202.00666")] ai/nn/sampling ai/nn/transformer/gpt/2 psychology/novelty

[see Mirostat] Despite achieving incredibly low perplexities on myriad natural language corpora, today’s language models still often underperform when used to generate text. This dichotomy has puzzled the language generation community for the last few years.

In this work, we posit that the abstraction of natural language as a communication channel (à la Shannon 1948) can provide new insights into the behaviors of probabilistic language generators, eg. why high-probability texts can be dull or repetitive. Humans use language as a means of communicating information, and do so in an efficient yet error-minimizing manner, choosing each word in a string with this (perhaps subconscious) goal in mind.

We propose that generation from probabilistic models should mimic this behavior. Rather than always choosing words from the high-probability region of the distribution—which have a low Shannon information content—we sample from the set of words with an information content close to its expected value, ie. close to the conditional entropy of our model. This decision criterion can be realized through a simple and efficient implementation, which we call typical sampling. Automatic and human evaluations show that, in comparison to nucleus and top-k sampling, typical sampling offers competitive performance in terms of quality while consistently reducing the number of degenerate repetitions.

--- https://arxiv.org/abs/2202.00828 Co-training Improves Prompt-based Learning for Large Language Models Hunter Lang, Monica Agrawal, Yoon Kim, David Sontag 2022-02-02 2022-02-02 [("doi","10.48550/arXiv.2202.00828")] ai/nn/transformer/gpt/calibration

We demonstrate that co-training (Blum & Mitchell 1998) can improve the performance of prompt-based learning by using unlabeled data. While prompting has emerged as a promising paradigm for few-shot and zero-shot learning, it is often brittle and requires much larger models compared to the standard supervised setup.

We find that co-training makes it possible to improve the original prompt model and at the same time learn a smaller, downstream task-specific model. In the case where we only have partial access to a prompt model (eg. output probabilities from GPT-3 (Brown et al 2020)) we learn a calibration model over the prompt outputs. When we have full access to the prompt model’s gradients but full finetuning remains prohibitively expensive (eg. T0 (Sanh et al 2021)), we learn a set of soft prompt continuous vectors to iteratively update the prompt model.

We find that models trained in this manner can improve performance on challenging datasets where there is currently a large gap between prompt-based learning and fully-supervised models.

--- https://arxiv.org/abs/2202.01258 Accelerated Quality-Diversity for Robotics through Massive Parallelism Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully 2022-02-02 2022-02-02 [("doi","10.48550/arXiv.2202.01258")] cs/hardware reinforcement-learning/exploration reinforcement-learning/robot reinforcement-learning/scaling

Quality-Diversity (QD) algorithms are a well-known approach to generate large collections of diverse and high-quality policies. However, QD algorithms are also known to be data-inefficient, requiring large amounts of computational resources and are slow when used in practice for robotics tasks. Policy evaluations are already commonly performed in parallel to speed up QD algorithms but have limited capabilities on a single machine as most physics simulators run on CPUs. With recent advances in simulators that run on accelerators, thousands of evaluations can performed in parallel on single GPU/TPU.

In this paper, we present QDax, an implementation of MAP-Elites which leverages massive parallelism on accelerators to make QD algorithms more accessible. We first demonstrate the improvements on the number of evaluations per second that parallelism using accelerated simulators can offer.

More importantly, we show that QD algorithms are ideal candidates and can scale with massive parallelism to be run at interactive timescales. The increase in parallelism does not affect the performance of QD algorithms, while reducing experiment runtimes by two factors of magnitudes, turning days of computation into minutes.

These results show that QD can now benefit from hardware acceleration, which contributed to the bloom of deep learning.

--- https://arxiv.org/abs/2202.01279 PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts Stephen H. Bach, Victor Sanh, Zheng-Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M. Saiful Bari, Thibault Fevry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-David, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Alan Fries, Maged S. Al-shaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Xiangru Tang, Mike Tian-Jian Jiang, Alexander M. Rush 2022-02-02 2022-02-02 [("doi","10.48550/arXiv.2202.01279")] ai/dataset ai/nn/transformer/gpt

PromptSource is a system for creating, sharing, and using natural language prompts. Prompts are functions that map an example from a dataset to a natural language input and target output. Using prompts to train and query language models is an emerging area in NLP that requires new tools that let users develop and refine these prompts collaboratively. PromptSource addresses the emergent challenges in this new setting with (1) a templating language for defining data-linked prompts, (2) an interface that lets users quickly iterate on prompt development by observing outputs of their prompts on many examples, and (3) a community-driven set of guidelines for contributing new prompts to a common pool.

Over 2,000 prompts for roughly 170 datasets are already available in PromptSource.

PromptSource is available at Github.

--- https://arxiv.org/abs/2202.01994#google Data Scaling Laws in NMT: The Effect of Noise and Architecture Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Maxim Krikun, Colin Cherry, Behnam Neyshabur, Orhan Firat 2022-02-04 2022-02-04 [("doi","10.48550/arXiv.2202.01994")] ai/nn/rnn ai/nn/transformer/gpt ai/scaling

In this work, we study the effect of varying the architecture and training data quality on the data scaling properties of Neural Machine Translation (NMT). First, we establish that the test loss of encoder-decoder transformer models scales as a power law in the number of training samples, with a dependence on the model size.

Then, we systematically vary aspects of the training setup to understand how they impact the data scaling laws. In particular, we change the following (1) Architecture and task setup: We compare to a transformer-LSTM hybrid, and a decoder-only transformer with a language modeling loss (2) Noise level in the training distribution: We experiment with filtering, and adding i.i.d. synthetic noise.

In all the above cases, we find that the data scaling exponents are minimally impacted, suggesting that marginally worse architectures or training data can be compensated for by adding more data.

Lastly, we find that using back-translated data instead of parallel data, can degrade the scaling exponent.

--- https://arxiv.org/abs/2202.02081 Tracking Discourse Influence in Darknet Forums Christopher Akiki, Lukas Gienapp, Martin Potthast 2022-02-04 2022-02-04 [("doi","10.48550/arXiv.2202.02081")] darknet-market/dnm-archive

This technical report documents our efforts in addressing the tasks set forth by the 2021 AMoC (Advanced Modeling of Cyber Criminal Careers) Hackathon.

Our main contribution is a joint visualization of semantic and temporal features, generating insight into the supplied data on darknet cybercrime through the aspects of novelty, transience, and resonance, which describe the potential impact a message might have on the overall discourse in darknet communities.

All code and data produced by us as part of this hackathon is publicly available.

--- https://arxiv.org/abs/2202.02317#allen Webly Supervised Concept Expansion for General Purpose Vision Models Amita Kamath, Christopher Clark, Tanmay Gupta, Eric Kolve, Derek Hoiem, Aniruddha Kembhavi 2022-02-04 2022-02-04 [("doi","10.48550/arXiv.2202.02317")] ai/scaling

General purpose vision (GPV) systems are models that are designed to solve a wide array of visual tasks without requiring architectural changes. Today, GPVs primarily learn both skills and concepts from large fully supervised datasets. Scaling GPVs to tens of thousands of concepts by acquiring data to learn each concept for every skill quickly becomes prohibitive.

This work presents an effective and inexpensive alternative: learn skills from fully supervised datasets, learn concepts from web image search results, and leverage a key characteristic of GPVs—the ability to transfer visual knowledge across skills. We use a dataset of 1M+ images spanning 10k+ visual concepts to demonstrate webly-supervised concept expansion for two existing GPVs (GPV-1 and VL-T5) on 3 benchmarks—5 COCO based datasets (80 primary concepts), a newly curated series of 5 datasets based on the OpenImages and VisualGenome repositories (~500 concepts) and the Web-derived dataset (10k+ concepts).

We also propose a new architecture, GPV-2 that supports a variety of tasks—from vision tasks like classification and localization to vision+language tasks like QA and captioning to more niche ones like human-object interaction recognition. GPV-2 benefits hugely from web data, outperforms GPV-1 and VL-T5 across these benchmarks, and does well in a 0-shot setting at action and attribute recognition.

--- https://arxiv.org/abs/2202.03052#alibaba OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, Hongxia Yang 2022-02-07 2022-02-07 [("doi","10.48550/arXiv.2202.03052")] ai/nn/transformer ai/scaling

In this work, we pursue an unified paradigm for multimodal pretraining to break the scaffolds of complex task/modality-specific customization. We propose OFA, an unified multimodal pretrained model that unifies modalities (ie. cross-modality, vision, language) and tasks (eg. image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple sequence-to-sequence learning framework based on the encoder-decoder architecture.

OFA performs pretraining and finetuning with task instructions and introduces no extra task-specific layers for finetuning.

Experimental results show that OFA achieves new state-of-the-arts on a series of multimodal tasks, including image captioning (COCO test CIDEr: 149.6), text-to-image generation (COCO test FID: 10.5), VQA (test-std accuracy: 80.02), SNLI-VE (test accuracy: 90.20), and referring expression comprehension (RefCOCO / RefCOCO+ / RefCOCOg test accuracy: 92.93 / 90.10 / 85.20). Through extensive analyses, we demonstrate that OFA reaches comparable performance with uni-modal pretrained models (eg. BERT, MAE, MoCo v3, SimCLR v2, etc.) in uni-modal tasks, including NLU, NLG, and image classification, and it effectively transfers to unseen tasks and domains.

Code shall be released soon at https://github.com/OFA-Sys/OFA.

--- https://arxiv.org/abs/2202.03371 Cedille: A large autoregressive French language model Martin Müller, Florian Laurent 2022-02-07 2022-02-07 [("doi","10.48550/arXiv.2202.03371")] ai/nn/transformer/gpt

Scaling up the size and training of autoregressive language models has enabled novel ways of solving Natural Language Processing tasks using zero-shot and few-shot learning. While extreme-scale language models such as GPT-3 offer multilingual capabilities, zero-shot learning for languages other than English remain largely unexplored.

Here, we introduce Cedille, a large open source autoregressive language model, specifically trained for the French language. Our results show that Cedille outperforms existing French language models and is competitive with GPT-3 on a range of French zero-shot benchmarks.

Furthermore, we provide an in-depth comparison of the toxicity exhibited by these models, showing that Cedille marks an improvement in language model safety thanks to dataset filtering.

--- https://arxiv.org/abs/2202.04053 DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers Jaemin Cho, Abhay Zala, Mohit Bansal 2022-02-08 2022-02-08 [("doi","10.48550/arXiv.2202.04053")] ai/dataset ai/nn/transformer/gpt/dall-e/2 reinforcement-learning/safe

Generating images from textual descriptions has gained a lot of attention. Recently, DALL·E, a multimodal transformer language model, and its variants have shown high-quality text-to-image generation capabilities with a simple architecture and training objective, powered by large-scale training data and computation. However, despite the interesting image generation results, there has not been a detailed analysis on how to evaluate such models.

In this work, we investigate the reasoning capabilities and social biases of such text-to-image generative transformers in detail. First, we measure 4 visual reasoning skills: object recognition, object counting, color recognition, and spatial relation understanding. For this, we propose PaintSkills, a diagnostic dataset and evaluation toolkit that measures these 4 visual reasoning skills. Second, we measure the text alignment and quality of the generated images based on pretrained image captioning, image-text retrieval, and image classification models. Third, we assess social biases in the models. For this, we suggest evaluation of gender and racial biases of text-to-image generation models based on a pretrained image-text retrieval model and human evaluation.

In our experiments, we show that recent text-to-image models perform better in recognizing and counting objects than recognizing colors and understanding spatial relations, while there exists a large gap between model performances and oracle accuracy on all skills. Next, we demonstrate that recent text-to-image models learn specific gender/racial biases from web image-text pairs. We also show that our automatic evaluations of visual reasoning skills and gender bias are highly correlated with human judgments.

We hope our work will help guide future progress in improving text-to-image models on visual reasoning skills and social biases.

Code and data at: https://github.com/j-min/DallEval.

--- https://arxiv.org/abs/2202.04824 AdaPrompt: Adaptive Model Training for Prompt-based NLP Yulong Chen, Yang Liu, Li Dong, Shuohang Wang, Chenguang Zhu, Michael Zeng, Yue Zhang 2022-02-10 2022-02-10 [("doi","10.48550/arXiv.2202.04824")] ai/nn/transformer/gpt

Prompt-based learning, with its capability to tackle zero-shot and few-shot NLP tasks, has gained much attention in community. The main idea is to bridge the gap between NLP downstream tasks and language modeling (LM), by mapping these tasks into natural language prompts, which are then filled by pre-trained language models (PLMs). However, for prompt learning, there are still two salient gaps between NLP tasks and pretraining. First, prompt information is not necessarily sufficiently present during LM pretraining. Second, task-specific data are not necessarily well represented during pretraining.

We address these two issues by proposing AdaPrompt, adaptively retrieving external data for continual pretraining of PLMs by making use of both task and prompt characteristics. In addition, we make use of knowledge in Natural Language Inference models for deriving adaptive verbalizers. Experimental results on five NLP benchmarks show that AdaPrompt can improve over standard PLMs in few-shot settings. In addition, in zero-shot settings, our method outperforms standard prompt-based methods by up to 26.35% relative error reduction.

--- https://arxiv.org/abs/2202.05008#google EvoJAX: Hardware-Accelerated Neuroevolution Yujin Tang, Yingtao Tian, David Ha 2022-02-10 2022-02-10 [("doi","10.48550/arXiv.2202.05008")] cs/hardware reinforcement-learning/exploration reinforcement-learning/scaling

Evolutionary computation has been shown to be a highly effective method for training neural networks, particularly when employed at scale on CPU clusters. Recent work has also showcased their effectiveness on hardware accelerators, such as GPUs, but so far such demonstrations are tailored for very specific tasks, limiting applicability to other domains. We present EvoJAX, a scalable, general-purpose, hardware-accelerated neuroevolution toolkit. Building on top of the JAX library, our toolkit enables neuroevolution algorithms to work with neural networks running in parallel across multiple TPU/GPUs.

EvoJAX achieves very high performance by implementing the evolution algorithm, neural network, and task all in NumPy, which is compiled just-in-time to run on accelerators. We provide extensible examples of EvoJAX for a wide range of tasks, including supervised learning, reinforcement learning and generative art.

Since EvoJAX can find solutions to most of these tasks within minutes on a single accelerator, compared to hours or days when using CPUs, we believe our toolkit can shorten the iteration time of conducting experiments for researchers working with evolutionary computation.

Our project is available at https://github.com/google/evojax.

--- https://arxiv.org/abs/2202.05607#facebook ODT: Online Decision Transformer Qinqing Zheng, Amy Zhang, Aditya Grover 2022-02-11 2022-02-11 [("doi","10.48550/arXiv.2202.05607")] reinforcement-learning/exploration reinforcement-learning/model/decision-transformer

Recent work has shown that offline reinforcement learning (RL) can be formulated as a sequence modeling problem (Chen et al 2021; Janner et al 2021) and solved via approaches similar to large-scale language modeling. However, any practical instantiation of RL also involves an online component, where policies pretrained on passive offline datasets are finetuned via task-specific interactions with the environment.

We propose Online Decision Transformers (ODT), an RL algorithm based on sequence modeling that blends offline pretraining with online finetuning in an unified framework. Our framework uses sequence-level entropy regularizers in conjunction with autoregressive modeling objectives for sample-efficient exploration and finetuning.

Empirically, we show that ODT is competitive with the state-of-the-art in absolute performance on the D4RL benchmark but shows much more gains during the finetuning procedure.

--- https://arxiv.org/abs/2202.05830#google Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality Daniel Watson, William Chan, Jonathan Ho, Mohammad Norouzi 2022-02-11 2022-02-11 [("doi","10.48550/arXiv.2202.05830")] ai/nn/diffusion

Diffusion models have emerged as an expressive family of generative models rivaling GANs in sample quality and autoregressive models in likelihood scores. Standard diffusion models typically require hundreds of forward passes through the model to generate a single high-fidelity sample.

We introduce Differentiable Diffusion Sampler Search (DDSS): a method that optimizes fast samplers for any pre-trained diffusion model by differentiating through sample quality scores.

We also present Generalized Gaussian Diffusion Models (GGDM), a family of flexible non-Markovian samplers for diffusion models. We show that optimizing the degrees of freedom of GGDM samplers by maximizing sample quality scores via gradient descent leads to improved sample quality. Our optimization procedure backpropagates through the sampling process using the reparameterization trick and gradient rematerialization.

DDSS achieves strong results on unconditional image generation across various datasets (eg. FID scores on LSUN church 128×128⁄11.6 with only 10 inference steps, and 4.82 with 20 steps, compared to 51.1 and 14.9 with strongest DDPM/DDIM baselines). Our method is compatible with any pre-trained diffusion model without fine-tuning or re-training required.

--- https://arxiv.org/abs/2202.06417 A Contrastive Framework for Neural Text Generation Yixuan Su, Tian Lan, Yan Wang, Dani Yogatama, Lingpeng Kong, Nigel Collier 2022-02-13 2022-02-13 [("doi","10.48550/arXiv.2202.06417")] ai/nn/sampling ai/nn/transformer/gpt

Text generation is of great importance to many natural language processing applications. However, maximization-based decoding methods (eg. beam search) of neural language models often lead to degenerate solutions—the generated text is unnatural and contains undesirable repetitions. Existing approaches introduce stochasticity via sampling or modify training objectives to decrease probabilities of certain tokens (eg. unlikelihood training). However, they often lead to solutions that lack coherence.

In this work, we show that an underlying reason for model degeneration is the anisotropic distribution of token representations. We present a contrastive solution: (1) SimCTG, a contrastive training objective to calibrate the model’s representation space, and (2) a decoding method—contrastive search—to encourage diversity while maintaining coherence in the generated text.

Extensive experiments and analyses on 3 benchmarks from two languages demonstrate that our proposed approach outperforms state-of-the-art text generation methods as evaluated by both human and automatic metrics.

--- https://arxiv.org/abs/2202.06626#deepmind MuZero with Self-competition for Rate Control in VP9 Video Compression Amol Mandhane, Anton Zhernov, Maribeth Rauh, Chenjie Gu, Miaosen Wang, Flora Xue, Wendy Shang, Derek Pang, Rene Claus, Ching-Han Chiang, Cheng Chen, Jingning Han, Angie Chen, Daniel J. Mankowitz, Jackson Broshear, Julian Schrittwieser, Thomas Hubert, Oriol Vinyals, Timothy Mann 2022-02-14 2022-02-14 [("doi","10.48550/arXiv.2202.06626")] ai/video/analysis cs/algorithm/information/compression reinforcement-learning/model/muzero

Video streaming usage has seen a substantial rise as entertainment, education, and business increasingly rely on online video. Optimizing video compression has the potential to increase access and quality of content to users, and reduce energy use and costs overall.

In this paper, we present an application of the MuZero algorithm to the challenge of video compression. Specifically, we target the problem of learning a rate control policy to select the quantization parameters (QP) in the encoding process of libvpx, an open source VP9 video compression library widely used by popular video-on-demand (VOD) services.

We treat this as a sequential decision making problem to maximize the video quality with an episodic constraint imposed by the target bitrate. Notably, we introduce a novel self-competition based reward mechanism to solve constrained RL with variable constraint satisfaction difficulty, which is challenging for existing constrained RL methods.

We demonstrate that the MuZero-based rate control achieves an average 6.28% reduction in size of the compressed videos for the same delivered video quality level (measured as PSNR BD-rate) compared to libvpx’s two-pass VBR rate control policy, while having better constraint satisfaction behavior.

[DM: “Then we applied MuZero to some of YouTube’s live traffic. Since launching to production on a portion of YouTube’s live traffic, we’ve demonstrated an average 4% bitrate reduction across a large, diverse set of videos. Bitrate helps determine the computing ability and bandwidth needed to play and store videos—impacting everything from how long a video takes to load to its resolution, buffering, and data usage.”]

--- https://arxiv.org/abs/2202.06767#huawei Wukong: 100 Million Large-scale Chinese Cross-modal Pre-training Dataset and A Foundation Framework Jiaxi Gu, Xiaojun Meng, Guansong Lu, Lu Hou, Minzhe Niu, Hang Xu, Xiaodan Liang, Wei Zhang, Xin Jiang, Chunjing Xu 2022-02-14 2022-02-14 [("doi","10.48550/arXiv.2202.06767")] ai/dataset ai/nn/transformer/clip ai/scaling

This paper presents a large-scale Chinese cross-modal dataset for benchmarking different multi-modal pre-training methods to facilitate the Vision-Language Pre-training (VLP) research and community development. Recent dual-stream VLP models like CLIP, ALIGN and FILIP have shown remarkable performance on various downstream tasks as well as their remarkable zero-shot ability in the open domain tasks. However, their success heavily relies on the scale of pre-trained datasets. Though there have been both small-scale vision-language English datasets like Flickr30k, CC12M as well as large-scale LAION-400M, the current community lacks large-scale Vision-Language benchmarks in Chinese, hindering the development of broader multilingual applications.

On the other hand, there is very rare publicly available large-scale Chinese cross-modal pre-training dataset that has been released, making it hard to use pre-trained models as services for downstream tasks. In this work, we release a Large-Scale Chinese Cross-modal dataset named Wukong, containing 100 million Chinese image-text pairs from the web. Furthermore, we release a group of big models pre-trained with advanced image encoders (ResNet/ViT/SwinT) and different pre-training methods (CLIP/FILIP/LiT). We provide extensive experiments, a deep benchmarking of different downstream tasks, and some exciting findings. Experiments show that Wukong can serve as a promising Chinese pre-training dataset and benchmark for different cross-modal learning methods, which gives superior performance on various downstream tasks such as zero-shot image classification and image-text retrieval benchmarks. More information can refer to https://wukong-dataset.github.io/wukong-dataset/.

--- https://arxiv.org/abs/2202.06991#google Transformer Memory as a Differentiable Search Index Yi Tay, Vinh Q. Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, Tal Schuster, William W. Cohen, Donald Metzler 2022-02-14 2022-02-14 [("doi","10.48550/arXiv.2202.06991")] ai/nn/retrieval ai/nn/transformer/attention

In this paper, we demonstrate that information retrieval can be accomplished with a single Transformer, in which all information about the corpus is encoded in the parameters of the model. To this end, we introduce the Differentiable Search Index (DSI), a new paradigm that learns a text-to-text model that maps string queries directly to relevant docids; in other words, a DSI model answers queries directly using only its parameters, dramatically simplifying the whole retrieval process.

We study variations in how documents and their identifiers are represented, variations in training procedures, and the interplay between models and corpus sizes. Experiments demonstrate that given appropriate design choices, DSI outperforms strong baselines such as dual encoder models. Moreover, DSI demonstrates strong generalization capabilities, outperforming a BM25 baseline in a zero-shot setup.

--- https://arxiv.org/abs/2202.07290 Don’t stop the training: continuously-updating self-supervised algorithms best account for auditory responses in the cortex Pierre Orhan, Yves Boubenec, Jean-Rémi King 2022-02-15 2022-02-15 [("doi","10.48550/arXiv.2202.07290")] ai/nn/dynamic-evaluation psychology/neuroscience

Over the last decade, numerous studies have shown that deep neural networks exhibit sensory representations similar to those of the mammalian brain, in that their activations linearly map onto cortical responses to the same sensory inputs. However, it remains unknown whether these artificial networks also learn like the brain.

To address this issue, we analyze the brain responses of two ferret auditory cortices recorded with functional UltraSound imaging (fUS), while the animals were presented with 320 10s sounds. We compare these brain responses to the activations of Wav2vec 2.0, a self-supervised neural network pretrained with 960h of speech, and input with the same 320 sounds. Critically, we evaluate Wav2vec 2.0 under two distinct modes: (1) “Pretrained”, where the same model is used for all sounds, and (2) “Continuous Update”, where the weights of the pretrained model are modified with back-propagation after every sound, presented in the same order as the ferrets.

Our results show that the Continuous-Update mode leads Wav2Vec 2.0 to generate activations that are more similar to the brain than a Pretrained Wav2Vec 2.0 or than other control models using different training modes. These results suggest that the trial-by-trial modifications of self-supervised algorithms induced by back-propagation aligns with the corresponding fluctuations of cortical responses to sounds.

Our finding thus provides empirical evidence of a common learning mechanism between self-supervised models and the mammalian cortex during sound processing.

--- https://arxiv.org/abs/2202.08417#deepmind Retrieval-Augmented Reinforcement Learning Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adria Puigdomenech Badia, Arthur Guez, Mehdi Mirza, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy Lillicrap, Nicolas Heess, Charles Blundell 2022-02-17 2022-02-17 [("doi","10.48550/arXiv.2202.08417")] ai/nn/retrieval ai/nn/rnn reinforcement-learning/model-free

Most deep reinforcement learning (RL) algorithms distill experience into parametric behavior policies or value functions via gradient updates. While effective, this approach has several disadvantages: (1) it is computationally expensive, (2) it can take many updates to integrate experiences into the parametric model, (3) experiences that are not fully integrated do not appropriately influence the agent’s behavior, and (4) behavior is limited by the capacity of the model.

In this paper we explore an alternative paradigm in which we train a network to map a dataset of past experiences to optimal behavior. Specifically, we augment an RL agent with a retrieval process (parameterized as a neural network) that has direct access to a dataset of experiences. This dataset can come from the agent’s past experiences, expert demonstrations, or any other relevant source. The retrieval process is trained to retrieve information from the dataset that may be useful in the current context, to help the agent achieve its goal faster and more efficiently.

We integrate our method into two different RL agents: an offline DQN agent and an online R2D2 agent. In offline multi-task problems, we show that the retrieval-augmented DQN agent avoids task interference and learns faster than the baseline DQN agent. On Atari, we show that retrieval-augmented R2D2 learns faster than the baseline R2D2 agent and achieves higher scores. We run extensive ablations to measure the contributions of the components of our proposed method.

--- https://arxiv.org/pdf/2009.03393#page=11&org=openai Generative Language Modeling for Automated Theorem Proving § Experiments Stanislas Polu, Ilya Sutskever 2020 2020-03-01 [("doi","10.48550/arXiv.2009.03393")] ai/nn/tokenization ai/nn/transformer/gpt math --- https://ashish-kmr.github.io/rma-legged-robots/ Rapid Motor Adaptation for Legged Robots 2020-03-01 reinforcement-learning/meta-learning --- https://www.astralcodexten.com/p/a-look-down-track-b A Look Down Track B 2020-03-01 psychedelic --- https://www.astralcodexten.com/p/book-review-crazy-like-us Book Review: Crazy Like Us 2020-03-01 psychiatry/anorexia psychiatry/schizophrenia --- https://www.astralcodexten.com/p/does-georgism-work-is-land-really Does Georgism Work?, Part 1: Is Land Really A Big Deal? 2020-03-01 economics/georgism --- https://www.astralcodexten.com/p/does-georgism-work-part-2-can-landlords Does Georgism Work, Part 2: Can Landlords Pass Land Value Tax on to Tenants? 2020-03-01 economics/georgism --- https://www.astralcodexten.com/p/does-georgism-work-part-3-can-unimproved Does Georgism Work, Part 3: Can Unimproved Land Value be Accurately Assessed Separately From Buildings? 2020-03-01 economics/georgism --- https://www.astralcodexten.com/p/on-cerebralab-on-nuttcarhart-harris On Nutt/Carhart-Harris On Serotonin 2020-03-01 psychedelic --- https://www.astralcodexten.com/p/ontology-of-psychiatric-conditions-653 Ontology Of Psychiatric Conditions: Tradeoffs And Failures: To what degree are psychiatric conditions more like diseases (always bad) vs. diverse neurotypes (potentially good)? 2020-03-01 psychiatry/adhd psychiatry/anorexia psychiatry/schizophrenia psychology/collecting --- https://www.astralcodexten.com/p/the-rise-and-fall-of-online-culture How do Internet atheism and Internet feminism help us understand the current cultural moment? 2020-03-01 psychology/novelty sociology --- https://www.astralcodexten.com/p/your-book-review-progress-and-poverty Your Book Review: Progress And Poverty 2020-03-01 economics/georgism --- https://austinvernon.site/blog/energyturbulence.html Energy Prices are Naturally Turbulent 2020-03-02 economics/experience-curve --- https://bahfest.com/ Bahfest 2020-03-02 fiction/humor math/humor --- https://bair.berkeley.edu/blog/2020/11/13/ridge-rider/ Goodhart’s Law, Diversity and a Series of Seemingly Unrelated Toy Problems 2020-03-02 reinforcement-learning/exploration --- https://bair.berkeley.edu/blog/2021/11/05/epistemic-pomdp/ Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability [blog] 2020-03-02 reinforcement-learning/exploration reinforcement-learning/meta-learning statistics/bayes --- https://www.bitsaboutmoney.com/archive/how-credit-cards-make-money/ How credit cards make money 2020-03-02 economics --- https://www.bitsaboutmoney.com/archive/the-fraud-supply-chain/ The fraud supply chain 2020-03-02 darknet-market --- https://bayesoptbook.com/ Bayesian Optimization Book 2020-03-02 reinforcement-learning/exploration statistics/bayes statistics/decision --- https://culture.org/ghosts/ 2020-03-02 ai/nn/transformer/gpt --- https://www.thebeliever.net/women-bikini-bodybuilding/ The Feminine Physique 2020-03-02 longevity/fasting --- https://platform.openai.com/docs/guides/embeddings/code-search-using-embeddings 2020-03-02 ai/nn/transformer/gpt/codex --- https://platform.openai.com/docs/guides/embeddings/use-cases 2020-03-03 ai/nn/retrieval ai/nn/transformer/gpt/codex --- https://bigthink.com/the-present/dmt-beings/ The DMT ‘elves’ people meet while tripping 2020-03-03 psychedelic --- https://bitbay.market/double-deposit-escrow/ Double Deposit Escrow 2020-03-03 bitcoin/nashx economics --- https://bithalo.org/ Bithalo 2020-03-03 bitcoin/nashx economics --- https://bkgm.com/articles/tesauro/tdl.html Temporal Difference Learning and TD-Gammon 2020-03-03 reinforcement-learning/exploration --- https://blog.andrewcantino.com/blog/2021/05/28/how-to-dramatically-improve-the-reasoning-ability-of-GPT-3/ How to dramatically improve the reasoning ability of GPT-3 2020-03-03 ai/nn/transformer/gpt/inner-monologue --- https://blog.eleuther.ai/factored-cognition/ A Preliminary Exploration into Factored Cognition with Language Models 2020-03-03 ai/nn/transformer/gpt/inner-monologue --- https://blog.eleuther.ai/year-one/ What A Long, Strange Trip It's Been: EleutherAI One Year Retrospective 2020-03-03 ai/nn/transformer/clip sociology --- https://blog.floydhub.com/knowledge-distillation/ 2020-03-03 ai/nn/sparsity --- https://blog.givewell.org/2011/07/13/a-good-volunteer-is-hard-to-find/ A good volunteer is hard to find 2020-03-03 philosophy/ethics --- https://blog.google/technology/ai/lamda/ LaMDA: our breakthrough conversation technology 2020-03-03 ai/nn/transformer/gpt/lamda --- /doc/philosophy/ethics/2015-06-24-jai-thecopenhageninterpretationofethics.html 2015-06-24 2020-03-04 philosophy/ethics --- https://blog.janestreet.com/deep-learning-the-hardest-go-problem-in-the-world/ Deep-Learning the Hardest Go Problem in the World 2020-03-04 reinforcement-learning/model/alphago --- https://blog.kzakka.com/posts/clip/ CLIP: Zero-shot Jack of All Trades 2020-03-04 ai/nn/transformer/clip --- https://blog.plover.com/tech/gompertz.html Gompertz' law for wooden utility poles 2020-03-04 statistics/survival-analysis --- https://blog.replit.com/codex Replit 2020-03-04 ai/nn/transformer/gpt/codex --- https://blog.roblox.com/2020/05/scaled-bert-serve-1-billion-daily-requests-cpus/ 2020-03-04 ai/nn/sparsity --- https://blog.tensorflow.org/2020/03/higher-accuracy-on-vision-models-with-efficientnet-lite.html Higher accuracy on vision models with EfficientNet-Lite 2020-03-04 ai/nn/sparsity --- https://blog.waymo.com/2020/04/using-automated-data-augmentation-to.html#google 2020-03-04 reinforcement-learning/meta-learning --- https://blogs.lse.ac.uk/businessreview/2018/09/20/why-does-a-management-model-succeed-through-time/ What explains the evolution of management models over the past two centuries? 2020-03-04 economics/automation --- https://blogs.microsoft.com/on-the-issues/2020/07/21/carbon-negative-transform-to-net-zero/ Progress on our goal to be carbon negative by 2030 2020-03-04 technology/carbon-capture --- https://www.science.org/doi/10.1126/blog-post.16934 2020-03-04 psychedelic --- https://blogs.sciencemag.org/pipeline/archives/2021/07/23/more-protein-folding-progress-whats-it-mean 2020-03-05 ai/nn/transformer/alphafold --- https://www.scientificamerican.com/blog/illusion-chasers/locking-eyes-with-a-monster/ Locking Eyes with a Monster: Staring at somebody’s face for ten minutes may give you nightmares 2020-03-05 psychiatry psychology/personality --- https://www.bostonreview.net/forum/foundations-philanthropy-democracy/ 2020-03-05 economics/perpetuities --- https://brainwindows.wordpress.com/2009/10/14/playing-quake-with-a-real-mouse/ Playing Quake with a Real Mouse 2020-03-05 psychology/neuroscience --- https://brenthecht.com/publications/chi2018_wikipediavaluetoonlinecommunities.pdf Examining Wikipedia With a Broader Lens: Quantifying the Value of Wikipedia’s Relationships with Other Large-Scale Online Communities Vincent 2018 2020-03-05 economics/copyright wikipedia --- https://bulbapedia.bulbagarden.net/wiki/Personality_value Personality value 2020-03-05 cs/algorithm --- https://cacm.acm.org/magazines/2021/9/255049-playing-with-and-against-computers/abstract 2020-03-05 reinforcement-learning/model/alphago --- https://caes.ucdavis.edu/news/articles/2018/february/isolating-embryonic-stem-cells-in-cows-just-got-easier Isolating stem cells in cows 2020-03-05 genetics/gametogenesis --- https://carbonplan.org/research/forest-offsets Forest offsets 2020-03-05 technology/carbon-capture --- https://ccsp.hms.harvard.edu/wp-content/uploads/2020/11/AlphaFold-at-CASP13-AlQuraishi.pdf 2020-03-05 ai/nn/transformer/alphafold --- https://cdn.vox-cdn.com/uploads/chorus_asset/file/12320981/Market_Size_and_Demand_Study__July_9__2014.0.pdf 2020-03-06 marijuana --- https://chaidarun.com/ten-years-of-logging-my-life Ten Years of Logging My Life 2020-03-06 nootropic/quantified-self --- https://web.archive.org/web/20100610071207id_/http://harpending.humanevo.utah.edu/Documents/ashkiq.webpub.pdf Natural history of Ashkenazi intelligence 2020-03-06 genetics/heritable/rare genetics/selection iq --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.469.7658&rep=rep1&type=pdf 2020-03-06 iq/ses --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.475.5853&rep=rep1&type=pdf 2020-03-06 iq marijuana --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.556.6124&rep=rep1&type=pdf Impact of formal continuing medical education: do conferences, workshops, rounds, and other traditional continuing education activities change physician behavior or health care outcomes? Davis 1999 2020-03-06 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.6901&rep=rep1&type=pdf Very Long Term Retention of Knowledge 2020-03-06 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.739.9735&rep=rep1&type=pdf 2020-03-06 iodine iq/ses --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.993.7725&rep=rep1&type=pdf 2020-03-06 psychiatry/schizophrenia --- https://cloud.google.com/blog/topics/developers-practitioners/find-anything-blazingly-fast-googles-vector-search-technology Find anything blazingly fast with Google's vector search technology 2020-03-06 ai/nn/retrieval --- https://cms.pbr.com/en/news/features/other-features/2012/7/attack-of-the-clones.aspx Attack of the clones 2020-03-06 genetics/cloning --- https://pages.ucsd.edu/~rbelew/courses/cogs184_w10/readings/HintonNowlan97.pdf 2020-03-07 reinforcement-learning/meta-learning --- https://colab.research.google.com/drive/1Gg7-c7LrUTNfQ-Fk-BVNCe9kvedZZsAh 2020-03-07 ai/nn/transformer/gpt/dall-e --- https://colab.research.google.com/drive/1N8Cc9yYzNR4M9J2NtE3n3jL2Jy25KAl_ 2020-03-07 ai/nn/transformer/clip --- https://colab.research.google.com/drive/1Tb7J4PvvegWOybPfUubl5O7m5I24CBg5 2020-03-07 ai/nn/transformer/gpt/dall-e --- https://colab.research.google.com/drive/1_DydlRBBTUupM9djmtegqnuettSTrrRD This Anime Does Not Exist, Search: this notebook uses the precomputed CLIP feature vectors for 100k images from TADNE 2020-03-07 ai/nn/retrieval ai/nn/transformer/clip --- https://colab.research.google.com/github/Skylion007/StyleGAN-notebooks/blob/main/StyleGAN_of_Anime_Sliders_by_Skyli0n.ipynb StyleGAN Anime Sliders: This notebook demonstrate how to learn and extract controllable directions from ThisAnimeDoesNotExist. This takes a pretrained StyleGAN and uses DeepDanbooru to extract various labels from a number of samples. It then uses those labels to learn various attributes which are controllable with sliders 2020-03-07 ai/nn/gan/stylegan/anime --- https://colab.research.google.com/github/dribnet/clipit/blob/master/demos/PixelDrawer.ipynb CLIPIT PixelDraw demo 2020-03-07 ai/nn/transformer/clip --- https://colab.research.google.com/github/kakaobrain/minDALL-E/blob/main/examples/sampling_interactive_demo.ipynb 2020-03-07 ai/nn/transformer/gpt/dall-e --- https://colab.research.google.com/github/kvfrans/clipdraw/blob/main/clipdraw.ipynb 2020-03-07 ai/nn/transformer/clip --- https://colab.research.google.com/github/ouhenio/minDALL-E_notebook/blob/main/minDALLE.ipynb 2020-03-07 ai/nn/transformer/gpt/dall-e --- https://colab.research.google.com/github/sberbank-ai/music-composer/blob/main/src/Music_Composer_Demo_Colab.ipynb 2020-03-07 ai/music --- https://commoncog.com/accelerated-expertise/ Book Summary: Accelerated Expertise 2020-03-08 philosophy/epistemology psychology/spaced-repetition --- https://commoncog.com/how-to-learn-tacit-knowledge/ Copying Better: How To Acquire The Tacit Knowledge of Experts 2020-03-08 philosophy/epistemology psychology --- https://commoncog.com/tacit-knowledge-is-a-real-thing/ Why Tacit Knowledge is More Important Than Deliberate Practice 2020-03-08 philosophy/epistemology psychology --- https://commoncog.com/youtube-learn-tacit-knowledge/ How to Use YouTube to Learn Tacit Knowledge 2020-03-08 philosophy/epistemology psychology --- https://web.archive.org/web/20110801012248/https://community.nytimes.com/comments/www.nytimes.com/2010/07/11/magazine/11cryonics-t.html?permid=44#comment44 Until Cryonics Do Us Part 2020-03-08 cryonics --- https://web.archive.org/web/20220523012105/https://constancecrozier.com/2020/04/16/forecasting-s-curves-is-hard/ Forecasting s-curves is hard 2020-03-08 statistics/prediction --- https://www.construction-physics.com/p/construction-ford-and-a-lever-to Construction, Ford, and a Lever to Move the World 2020-03-08 economics/experience-curve --- https://www.construction-physics.com/p/how-much-do-construction-costs-matter How much do construction costs matter? Some factors that affect the price of housing 2020-03-08 economics/georgism --- https://github.com/features/copilot/ 2020-03-08 ai/nn/transformer/gpt/codex ai/scaling --- https://core.ac.uk/reader/160114160 Behavior Genetic Frameworks of Causal Reasoning for Personality Psychology 2020-03-08 genetics/heritable psychology/personality statistics/causality --- https://core.ac.uk/download/pdf/6819998.pdf 2020-03-08 iq/ses --- https://creator.nightcafe.studio/vqgan-clip-keyword-modifier-comparison 2020-03-09 ai/nn/transformer/clip --- https://crimereads.com/the-forgotten-kidnapping-epidemic-that-shook-depression-era-america/ The Forgotten Kidnapping Epidemic That Shook Depression-Era America 2020-03-09 crime --- https://crimethinc.com/2021/12/09/june-30-1876-peter-kropotkin-escapes-from-prison-a-tale-of-derring-do-on-the-occasion-of-his-birthday June 30, 1876: Peter Kropotkin Escapes from Prison : A Tale of Derring-Do on the Occasion of His Birthday 2020-03-09 history --- https://daily.jstor.org/why-people-want-to-be-fitness-instructors/ Why People Want to Be Fitness Instructors 2020-03-09 exercise sociology --- https://daily.tinyprojects.dev/paper_website I blew $720 on 100 notebooks from Alibaba and started a Paper Website business 2020-03-09 ai/nn/transformer/gpt --- https://danco.substack.com/p/twist-biosciences-the-dna-api Twist Biosciences: The DNA API 2020-03-09 genetics/genome-synthesis --- https://danijar.com/project/apd/ Action and Perception as Divergence Minimization 2020-03-09 reinforcement-learning/meta-learning --- /doc/iq/ses/2018-adams.pdf 2018 2020-03-09 iq/ses --- https://davidbarber.github.io/blog/2017/11/07/Learning-From-Scratch-by-Thinking-Fast-and-Slow-with-Deep-Learning-and-Tree-Search/ Learning From Scratch by Thinking Fast and Slow with Deep Learning and Tree Search 2020-03-09 reinforcement-learning/model/alphago --- https://davidepstein.com/david-epstein-the-sports-gene/ The Sports Gene 2020-03-09 exercise psychology/energy --- https://www.deepmind.com/blog/alphagos-next-move/ 2020-03-10 reinforcement-learning/model/alphago --- https://deepmind.google/discover/blog/alphastar-mastering-the-real-time-strategy-game-starcraft-ii/ AlphaStar: Mastering the Real-Time Strategy Game StarCraft II 2020-03-10 ai/nn/transformer/gpt reinforcement-learning/meta-learning reinforcement-learning/model-free/alphastar --- https://www.deepmind.com/blog/alphazero-shedding-new-light-grand-games-chess-shogi-and-go/ 2020-03-10 reinforcement-learning/model/alphago --- https://deepmind.google/discover/blog/alphastar-grandmaster-level-in-starcraft-ii-using-multi-agent-reinforcement-learning/ AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning 2020-03-10 reinforcement-learning/model-free/alphastar --- https://www.deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology 2020-03-10 ai/nn/transformer/alphafold --- https://deepmind.google/discover/blog/capture-the-flag-the-emergence-of-complex-cooperative-agents/-the-emergence-of-complex-cooperative-agents 2020-03-10 reinforcement-learning/exploration reinforcement-learning/model-free/alphastar --- https://www.deepmind.com/blog/exploring-mysteries-alphago/ 2020-03-10 reinforcement-learning/model/alphago --- https://deepmind.google/discover/blog/prefrontal-cortex-as-a-meta-reinforcement-learning-system/ Prefrontal cortex as a meta-reinforcement learning system [blog] 2020-03-10 reinforcement-learning/meta-learning --- https://deepmind.google/discover/blog/safety-first-ai-for-autonomous-data-centre-cooling-and-industrial-control/ Safety-first AI for autonomous data center cooling and industrial control 2020-03-10 ai/nn/rnn reinforcement-learning/exploration reinforcement-learning/safe --- https://www.deepmind.com/research/publications/2021/protein-complex-prediction-with-alphafold-multimer 2020-03-10 ai/nn/transformer/alphafold --- https://www.deepmind.com/research/publications/alchemy 2020-03-10 reinforcement-learning/meta-learning --- https://deepmind.google/ 2020-03-11 reinforcement-learning/meta-learning reinforcement-learning/multi-agent reinforcement-learning/scaling --- https://denovo.substack.com/p/help-doctor-ive-been-exposed-to-proprietary Help, Doctor, I've been exposed to [proprietary]! 2020-03-11 statistics/bias --- https://designregression.com/article/line-length-revisited-following-the-research Line length revisited: following the research 2020-03-11 design psychology --- https://diabetes.diabetesjournals.org/content/67/5/831 2020-03-11 exercise --- https://distill.pub/2017/aia/ Using Artificial Intelligence to Augment Human Intelligence 2020-03-11 ai design/visualization --- https://distill.pub/2019/advex-bugs-discussion/ A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features' 2020-03-11 ai/nn/adversarial design/visualization --- https://distill.pub/2019/advex-bugs-discussion/response-6/ A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features': Learning from Incorrectly Labeled Data 2020-03-11 ai/nn/adversarial design/visualization --- https://distill.pub/2020/circuits/branch-specialization/ Branch Specialization 2020-03-11 ai design/visualization psychology/neuroscience --- https://dl.acm.org/citation.cfm?id=3266090 2020-03-11 reinforcement-learning/imitation-learning/brain-imitation-learning --- https://dl.acm.org/doi/pdf/10.1145/1283920.1283940 2020-03-11 cs/security philosophy/epistemology --- https://docs.erasure.world/erasurebay-docs/faq 2020-03-11 bitcoin/nashx economics --- https://downloads.hindawi.com/journals/bmri/2014/805476.pdf 2020-03-12 exercise --- https://downloads.hindawi.com/journals/bmri/2014/931820.pdf Factors behind leisure-time physical activity behavior based on Finnish twin studies: The role of genetic and environmental influences and the role of motives 2020-03-12 exercise --- https://downloads.hindawi.com/journals/ijped/2010/138345.pdf 2020-03-12 exercise --- https://duepublico2.uni-due.de/servlets/MCRFileNodeServlet/duepublico_derivate_00028060/Oades2008Does.pdf 2020-03-12 crime genetics/heritable psychiatry/adhd --- https://duepublico2.uni-due.de/servlets/MCRFileNodeServlet/duepublico_derivate_00028837/Anney_ea_2008.pdf 2020-03-12 crime genetics/heritable psychiatry/adhd --- https://elidourado.com/blog/dawn-of-geoengineering/ 2020-03-12 technology/carbon-capture --- https://elifesciences.org/articles/20899 Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks 2020-03-12 psychology/neuroscience --- https://elifesciences.org/articles/57849 Whole-genome sequencing analysis of semi-supercentenarians 2020-03-12 genetics/heritable/rare --- https://emilkirkegaard.dk/en/wp-content/uploads/IQ-AND-SOCIOECONOMIC-DEVELOPMENT-ACROSS-REGIONS-OF-THE-UK.pdf IQ And Socioeconomic Development Across Regions Of The UK Noah Carl 2015-06-19 2020-03-12 [("doi","10.1017/S002193201500019X")] iq/ses

Cross-regional correlations between average IQ and socioeconomic development have been documented in many different countries. This paper presents new IQ estimates for the 12 regions of the UK. These are weakly correlated (r=0.24) with the regional IQs assembled by Lynn 1979. Assuming the two sets of estimates are accurate and comparable, this finding suggests that the relative IQs of different UK regions have changed since the 1950s, most likely due to differentials in the magnitude of the Flynn effect, the selectivity of external migration, the selectivity of internal migration or the strength of the relationship between IQ and fertility.

The paper provides evidence for the validity of the regional IQs by showing that IQ estimates for UK nations (England, Scotland, Wales, and Northern Ireland) derived from the same data are strongly correlated with national PISA scores (r=0.99).

It finds that regional IQ is positively related to income, longevity, and technological accomplishment; and is negatively related to poverty, deprivation, and unemployment. A general factor of socioeconomic development is correlated with regional IQ at r=0.72.

--- https://en.bitcoin.it/wiki/Multi-signature Multi-signature 2020-03-12 bitcoin/nashx economics --- https://en.chessbase.com/post/acquisition-of-chess-knowledge-in-alphazero Acquisition of Chess Knowledge in AlphaZero 2020-03-13 reinforcement-learning/model/alphago --- https://en.chessbase.com/post/leela-chess-zero-alphazero-for-the-pc Leela Chess Zero: AlphaZero for the PC 2020-03-13 reinforcement-learning/chess reinforcement-learning/model/alphago --- https://en.chessbase.com/post/the-future-is-here-alphazero-learns-chess The future is here – AlphaZero learns chess 2020-03-13 reinforcement-learning/model/alphago --- https://en.wikipedia.org/wiki/%C5%8Coka_Tadasuke#Famous_cases Ōoka Tadasuke § Famous cases 2020-03-13 philosophy/ethics psychology/smell --- https://en.wikipedia.org/wiki/100,000_Genomes_Project 100,000 Genomes Project 2020-03-13 genetics/heritable/rare --- https://en.wikipedia.org/wiki/2019%E2%80%932020_vaping_lung_illness_outbreak 2019–2020 vaping lung illness 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2020-03-16 psychology/novelty --- https://en.wikipedia.org/wiki/Artificial_gene_synthesis Artificial gene synthesis 2020-03-16 genetics/genome-synthesis --- https://en.wikipedia.org/wiki/Atmos_clock Atmos clock 2020-03-16 technology --- https://en.wikipedia.org/wiki/Autapse Autapse 2020-03-16 psychology/neuroscience --- https://en.wikipedia.org/wiki/Autoassociative_memory Autoassociative memory 2020-03-16 ai/nn/retrieval --- https://en.wikipedia.org/wiki/Automated_machine_learning Automated machine learning 2020-03-16 reinforcement-learning/meta-learning --- https://en.wikipedia.org/wiki/Autophagy Autophagy 2020-03-16 longevity/senolytic --- https://en.wikipedia.org/wiki/Ayahuasca Ayahuasca 2020-03-16 psychedelic --- https://en.wikipedia.org/wiki/Azithromycin Azithromycin 2020-03-16 longevity/senolytic --- https://en.wikipedia.org/wiki/Bacillus_cereus Bacillus cereus 2020-03-17 longevity/johan-bjorksten --- https://en.wikipedia.org/wiki/Background_selection Background selection 2020-03-17 psychiatry/schizophrenia --- https://en.wikipedia.org/wiki/Baldwin_effect Baldwin effect 2020-03-17 reinforcement-learning/meta-learning --- https://en.wikipedia.org/wiki/Base_pair Base pair 2020-03-17 genetics/genome-synthesis --- https://en.wikipedia.org/wiki/Benjamin_Franklin#Bequest Benjamin Franklin § Bequest 2020-03-17 economics/perpetuities --- https://en.wikipedia.org/wiki/Bicameral_mentality Bicameral mentality 2020-03-17 psychiatry/schizophrenia --- https://en.wikipedia.org/wiki/Big_Five_personality_traits Big Five personality traits 2020-03-17 psychology/personality --- https://en.wikipedia.org/wiki/Biochemistry_of_Alzheimer%27s_disease#Amyloid_hypothesis Biochemistry of Alzheimer’s disease § Amyloid hypothesis 2020-03-17 sociology/preference-falsification --- https://en.wikipedia.org/wiki/Bird_intelligence Bird intelligence 2020-03-17 psychology/animal/bird/neuroscience psychology/neuroscience --- https://en.wikipedia.org/wiki/Bonyad Bonyad 2020-03-17 economics/perpetuities --- https://en.wikipedia.org/wiki/Braun_(company) Braun (company) 2020-03-17 design --- https://en.wikipedia.org/wiki/Breastfeeding Breastfeeding 2020-03-18 genetics/microbiome --- https://en.wikipedia.org/wiki/Butyric_acid Butyric acid 2020-03-18 genetics/microbiome --- https://en.wikipedia.org/wiki/CC_(cat) CC (cat) 2020-03-18 genetics/cloning --- https://en.wikipedia.org/wiki/COVID-19_lab_leak_theory COVID-19 lab leak theory 2020-03-18 sociology/preference-falsification --- https://en.wikipedia.org/wiki/Calorie_restriction Calorie restriction 2020-03-18 longevity/fasting --- https://en.wikipedia.org/wiki/Cannabis Cannabis 2020-03-18 marijuana --- https://en.wikipedia.org/wiki/Carbon_dioxide_removal Carbon dioxide removal 2020-03-18 technology/carbon-capture --- https://en.wikipedia.org/wiki/Carbon_offset Carbon offset 2020-03-18 technology/carbon-capture --- https://en.wikipedia.org/wiki/Carbonate%E2%80%93silicate_cycle Carbonate-silicate cycle 2020-03-18 technology/carbon-capture --- https://en.wikipedia.org/wiki/Catholic_Church_in_England_and_Wales#Converts Catholic Church in England and Wales § Converts 2020-03-18 psychology/novelty --- https://en.wikipedia.org/wiki/Cellular_senescence Cellular senescence 2020-03-18 longevity/senolytic --- https://en.wikipedia.org/wiki/Censoring_(statistics) Censoring (statistics) 2020-03-19 statistics/survival-analysis --- https://en.wikipedia.org/wiki/Cerebral_cortex Cerebral cortex 2020-03-19 psychology/neuroscience --- https://en.wikipedia.org/wiki/Chandelier_cell Chandelier cell 2020-03-19 psychology/neuroscience --- https://en.wikipedia.org/wiki/Change_blindness Change blindness 2020-03-19 psychology/cognitive-bias/illusion-of-depth --- https://en.wikipedia.org/wiki/Charles-Joseph_Mathon_de_la_Cour Charles-Joseph Mathon de la Cour 2020-03-19 economics/perpetuities --- https://en.wikipedia.org/wiki/Charles_Roy_Henderson Charles Roy Henderson 2020-03-19 genetics/selection/artificial/index-selection --- https://en.wikipedia.org/wiki/Chick_sexing Chick sexing 2020-03-19 biology philosophy/epistemology psychology/dark-knowledge psychology/vision --- https://en.wikipedia.org/wiki/Citizen%27s_dividend Citizen’s dividend 2020-03-19 economics/georgism --- https://en.wikipedia.org/wiki/Clonally_transmissible_cancer Clonally transmissible cancer 2020-03-19 genetics/cloning --- https://en.wikipedia.org/wiki/Cloning Cloning 2020-03-19 genetics/cloning --- https://en.wikipedia.org/wiki/Compound_interest Compound interest 2020-03-20 economics/perpetuities --- https://en.wikipedia.org/wiki/Conscientiousness Conscientiousness 2020-03-20 psychology/personality --- https://en.wikipedia.org/wiki/Consol_(bond) Consol (bond) 2020-03-20 economics/perpetuities --- https://en.wikipedia.org/wiki/Constraint_%28mathematics%29 Constraint (mathematics) 2020-03-20 genetics/gametogenesis --- https://en.wikipedia.org/wiki/Content-based_image_retrieval Content-based image retrieval 2020-03-20 ai/nn/retrieval --- https://en.wikipedia.org/wiki/Conway%27s_law Conway’s law 2020-03-20 economics/automation --- https://en.wikipedia.org/wiki/Cooperative_principle Cooperative principle 2020-03-20 philosophy/epistemology psychology/writing --- https://en.wikipedia.org/wiki/Copy_Exactly! Copy Exactly! 2020-03-20 cs/end-to-end-principle cs/hardware technology --- https://en.wikipedia.org/wiki/Creative_synthesis Creative synthesis 2020-03-20 psychology/novelty --- https://en.wikipedia.org/wiki/Critical_Assessment_of_Techniques_for_Protein_Structure_Prediction Critical Assessment of Techniques for Protein Structure Prediction 2020-03-20 ai/nn/transformer/alphafold --- https://en.wikipedia.org/wiki/Cryonics_Institute Cryonics Institute 2020-03-20 cryonics --- https://en.wikipedia.org/wiki/Cryopreservation Cryopreservation 2020-03-21 cryonics genetics/gametogenesis --- https://en.wikipedia.org/wiki/Cultural_Revolution Cultural Revolution 2020-03-21 sociology/preference-falsification --- https://en.wikipedia.org/wiki/DALL-E DALL·E 2020-03-21 ai/nn/transformer/gpt/dall-e --- https://en.wikipedia.org/wiki/Daniel_Berlyne Daniel Berlyne 2020-03-21 psychology/novelty --- https://en.wikipedia.org/wiki/Dark_triad Dark triad 2020-03-21 politics psychology/personality/narcissism psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Darkforest Darkforest 2020-03-21 reinforcement-learning/model/alphago --- https://en.wikipedia.org/wiki/Dasatinib Dasatinib 2020-03-21 longevity/senolytic/d-q --- https://en.wikipedia.org/wiki/Datura#Toxicity Datura § Toxicity 2020-03-21 psychedelic --- https://en.wikipedia.org/wiki/David_Hestenes#Modeling_theory_and_instruction David Hestenes § Modeling theory and instruction 2020-03-21 psychology/cognitive-bias/illusion-of-depth --- https://en.wikipedia.org/wiki/David_Silver_(computer_scientist) David Silver (programmer) 2020-03-21 reinforcement-learning/model/alphago --- https://en.wikipedia.org/wiki/Deadline_(1982_video_game) Deadline (video game) 2020-03-21 crime fiction/text-game --- https://en.wikipedia.org/wiki/Deadweight_loss Deadweight loss 2020-03-22 economics/copyright economics/georgism --- https://en.wikipedia.org/wiki/Demis_Hassabis Demis Hassabis 2020-03-22 reinforcement-learning/model/alphago --- https://en.wikipedia.org/wiki/Derek_Parfit Derek Parfit 2020-03-22 philosophy/ethics --- https://en.wikipedia.org/wiki/Dieter_Rams Dieter Rams 2020-03-22 design --- https://en.wikipedia.org/wiki/Dieter_Rams#Ten_Principles_of_Good_design Dieter Rams § "Good design" principles 2020-03-22 design --- https://en.wikipedia.org/wiki/Diffusion_process Diffusion process 2020-03-22 ai/nn/diffusion --- https://en.wikipedia.org/wiki/Directional_statistics Directional statistics 2020-03-22 ai/nn/diffusion --- https://en.wikipedia.org/wiki/Dishwasher_salmon Dishwasher salmon 2020-03-22 psychology/smell --- https://en.wikipedia.org/wiki/Dolly_(sheep) Dolly (sheep) 2020-03-22 genetics/cloning --- https://en.wikipedia.org/wiki/Dota_2 Dota 2 2020-03-22 reinforcement-learning/model-free/oa5 --- https://en.wikipedia.org/wiki/Downs%E2%80%93Thomson_paradox Downs-Thomson paradox 2020-03-23 economics/georgism --- https://en.wikipedia.org/wiki/Mucus Dried nasal mucus 2020-03-23 biology/booger --- https://en.wikipedia.org/wiki/Dropout_%28neural_networks%29 Dropout (neural networks) 2020-03-23 reinforcement-learning/exploration --- https://en.wikipedia.org/wiki/EA-3167 EA-3167 2020-03-23 psychedelic --- https://en.wikipedia.org/wiki/EA-3443 EA-3443 2020-03-23 psychedelic --- https://en.wikipedia.org/wiki/EA-3580 EA-3580 2020-03-23 psychedelic --- https://en.wikipedia.org/wiki/EA-3834 EA-3834 2020-03-23 psychedelic --- https://en.wikipedia.org/wiki/Eating_mucus Eating mucus 2020-03-23 biology/booger --- https://en.wikipedia.org/wiki/Echopraxia_(novel) Echopraxia (novel) 2020-03-23 biology/portia --- https://en.wikipedia.org/wiki/Economic_rent Economic rent 2020-03-23 economics/georgism --- https://en.wikipedia.org/wiki/Effects_of_cannabis Effects of cannabis 2020-03-23 marijuana --- https://en.wikipedia.org/wiki/Emmanuel_Barth%C3%A9lemy Emmanuel Barthélemy 2020-03-24 crime history --- https://en.wikipedia.org/wiki/End-to-end_principle End-to-end principle 2020-03-24 cs/end-to-end-principle --- https://en.wikipedia.org/wiki/Enhanced_weathering Enhanced weathering 2020-03-24 technology/carbon-capture --- https://en.wikipedia.org/wiki/Ensemble_learning Ensemble learning 2020-03-24 ai/scaling/mixture-of-experts statistics/prediction --- https://en.wikipedia.org/wiki/Environmental_DNA Environmental DNA 2020-03-24 genetics/heritable --- https://en.wikipedia.org/wiki/Erowid Erowid 2020-03-24 psychedelic --- https://en.wikipedia.org/wiki/Exit,_Voice,_and_Loyalty Exit, Voice, and Loyalty 2020-03-24 sociology/preference-falsification --- https://en.wikipedia.org/wiki/Exokernel Exokernel 2020-03-24 cs/end-to-end-principle --- https://en.wikipedia.org/wiki/Experience_curve_effects Experience curve effects 2020-03-24 economics/experience-curve --- https://en.wikipedia.org/wiki/Extraversion_and_introversion Extraversion and introversion 2020-03-24 psychology/personality --- https://en.wikipedia.org/wiki/Failure_rate Failure rate 2020-03-24 statistics/survival-analysis --- https://en.wikipedia.org/wiki/Fairhope,_Alabama Fairhope, Alabama 2020-03-25 economics/georgism --- https://en.wikipedia.org/wiki/Falling_cat_problem Falling cat problem 2020-03-25 cat math/humor --- https://en.wikipedia.org/wiki/False_consensus_effect_effect False consensus 2020-03-25 sociology/preference-falsification --- https://en.wikipedia.org/wiki/False_memory False memory 2020-03-25 psychology/cognitive-bias/illusion-of-depth --- https://en.wikipedia.org/wiki/Fan_Hui Fan Hui 2020-03-25 reinforcement-learning/model/alphago --- https://en.wikipedia.org/wiki/Fee_tail Fee tail 2020-03-25 economics/perpetuities --- https://en.wikipedia.org/wiki/Female_intrasexual_competition Female intrasexual competition 2020-03-25 sociology/intrasexual-aggression --- https://en.wikipedia.org/wiki/Fideicommissum Fideicommissum 2020-03-25 economics/perpetuities --- https://en.wikipedia.org/wiki/Fisetin Fisetin 2020-03-25 longevity/senolytic --- https://en.wikipedia.org/wiki/Free-culture_movement Free culture movement 2020-03-25 economics/copyright --- https://en.wikipedia.org/wiki/G%C3%B6del_machine Gödel machine 2020-03-25 reinforcement-learning/meta-learning --- https://en.wikipedia.org/wiki/Gamete#Artificial_gametes Gamete § Artificial gametes 2020-03-26 genetics/gametogenesis --- https://en.wikipedia.org/wiki/Gametogenesis Gametogenesis 2020-03-26 genetics/gametogenesis longevity/epigenetics --- https://en.wikipedia.org/wiki/Gary_Hustwit Gary Hustwit 2020-03-26 design --- https://en.wikipedia.org/wiki/Gaussian_process Gaussian process 2020-03-26 reinforcement-learning/exploration statistics/bayes --- https://en.wikipedia.org/wiki/General-purpose_technology General purpose technology 2020-03-26 economics/automation --- https://en.wikipedia.org/wiki/Genetic_Studies_of_Genius Genetic Studies of Genius 2020-03-26 iq/high iq/ses --- https://en.wikipedia.org/wiki/Geolibertarianism Geolibertarianism 2020-03-26 economics/georgism --- https://en.wikipedia.org/wiki/Georgism Georgism 2020-03-26 economics/georgism --- https://en.wikipedia.org/wiki/Germ-free_animal Germ-free animal 2020-03-26 genetics/microbiome --- https://en.wikipedia.org/wiki/Glia Glia 2020-03-26 psychology/neuroscience --- https://en.wikipedia.org/wiki/Gumbel_distribution Gumbel distribution 2020-03-27 ai/nn/transformer/gpt/dall-e --- https://en.wikipedia.org/wiki/Gut%E2%80%93brain_axis Gut-brain axis 2020-03-27 genetics/microbiome --- https://en.wikipedia.org/wiki/HEXACO_model_of_personality_structure HEXACO model of personality structure 2020-03-27 psychology/personality --- https://en.wikipedia.org/wiki/Hallucinogenic_fish Hallucinogenic fish 2020-03-27 psychedelic --- https://en.wikipedia.org/wiki/Hearing_Voices_Movement Hearing Voices Movement 2020-03-27 psychology/inner-voice --- https://en.wikipedia.org/wiki/Heart_rate_variability Heart rate variability 2020-03-27 exercise nootropic/quantified-self/heart-rate-variability --- https://en.wikipedia.org/wiki/Helvetica Helvetica 2020-03-27 design --- https://en.wikipedia.org/wiki/Helvetica_(film) Helvetica (film) 2020-03-27 design --- https://en.wikipedia.org/wiki/Hemispherectomy Hemispherectomy 2020-03-27 psychiatry psychology/neuroscience --- https://en.wikipedia.org/wiki/Henry_George Henry George 2020-03-27 economics/georgism --- https://en.wikipedia.org/wiki/Henry_George_theorem Henry George theorem 2020-03-27 economics/georgism --- https://en.wikipedia.org/wiki/Hormesis Hormesis 2020-03-28 longevity/fasting longevity/senolytic --- https://en.wikipedia.org/wiki/Hotelling%27s_law Hotelling’s law 2020-03-28 economics/automation economics/mechanism-design politics --- https://en.wikipedia.org/wiki/Human%E2%80%93animal_breastfeeding Human-animal breastfeeding 2020-03-28 sociology --- https://en.wikipedia.org/wiki/Genome_Project-Write Human Genome Project—Write 2020-03-28 genetics/genome-synthesis --- https://en.wikipedia.org/wiki/Human_cloning Human cloning 2020-03-28 genetics/cloning --- https://en.wikipedia.org/wiki/Human_echolocation Human echolocation 2020-03-28 psychology --- https://en.wikipedia.org/wiki/Human_genome Human genome 2020-03-28 genetics/genome-synthesis --- https://en.wikipedia.org/wiki/List_of_human_microbiota Human microbiota 2020-03-28 genetics/microbiome --- https://en.wikipedia.org/wiki/Hwang_Woo-suk Hwang Woo-suk 2020-03-28 genetics/cloning --- https://en.wikipedia.org/wiki/HyperCard Hypercard 2020-03-28 design --- https://en.wikipedia.org/wiki/Ibogaine Ibogaine 2020-03-28 psychedelic --- https://en.wikipedia.org/wiki/Idiosyncrasy_credit Idiosyncrasy credit 2020-03-29 psychology/novelty --- https://en.wikipedia.org/wiki/Ig_Nobel_Prize Ig Nobel Prize 2020-03-29 fiction/humor math/humor --- https://en.wikipedia.org/wiki/Image_segmentation Image segmentation 2020-03-29 ai/nn/cnn psychology/neuroscience --- https://en.wikipedia.org/wiki/Attentional_blindness Inattentional blindness 2020-03-29 psychology/cognitive-bias/illusion-of-depth --- https://en.wikipedia.org/wiki/Index_(economics) Index (economics) 2020-03-29 economics genetics/selection/artificial/index-selection --- https://en.wikipedia.org/wiki/Index_(statistics) Index (statistics) 2020-03-29 genetics/selection/artificial/index-selection --- https://en.wikipedia.org/wiki/Index_selection Index selection 2020-03-29 genetics/selection/artificial/index-selection --- https://en.wikipedia.org/wiki/Indotyphlops_braminus Indotyphlops braminus 2020-03-29 genetics/cloning --- https://en.wikipedia.org/wiki/Industrial_design Industrial design 2020-03-29 design --- https://en.wikipedia.org/wiki/Information_retrieval Information retrieval 2020-03-29 ai/nn/retrieval --- https://en.wikipedia.org/wiki/Integer_programming Integer programming 2020-03-30 genetics/gametogenesis --- https://en.wikipedia.org/wiki/Intermittent_fasting Intermittent fasting 2020-03-30 longevity/fasting --- https://en.wikipedia.org/wiki/Intrapersonal_communication Internal monologue 2020-03-30 psychology/inner-voice --- https://en.wikipedia.org/wiki/Intravenous_marijuana_syndrome Intravenous marijuana syndrome 2020-03-30 marijuana --- https://en.wikipedia.org/wiki/J%C3%BCrgen_Schmidhuber Jürgen Schmidhuber 2020-03-30 ai/nn/rnn psychology/novelty reinforcement-learning/meta-learning --- https://en.wikipedia.org/wiki/Jay_Laurence_Lush Jay Laurence Lush 2020-03-30 genetics/selection/artificial/index-selection --- https://en.wikipedia.org/wiki/Jerrycan Jerrycan 2020-03-30 design --- https://en.wikipedia.org/wiki/Jizz_(birding) Jizz (birding) 2020-03-30 biology philosophy/epistemology psychology --- https://en.wikipedia.org/wiki/K%C5%8Dd%C5%8D Kōdō 2020-03-30 japan/art psychology/smell/perfume --- https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator Kaplan-Meier estimator 2020-03-30 statistics/survival-analysis --- https://en.wikipedia.org/wiki/Ketamine Ketamine 2020-03-30 psychedelic --- https://en.wikipedia.org/wiki/Ketamine#Depression Ketamine § Depression 2020-03-31 psychiatry --- https://en.wikipedia.org/wiki/Kimchi Kimchi 2020-03-31 genetics/microbiome --- https://en.wikipedia.org/wiki/Knowledge_distillation Knowledge distillation 2020-03-31 ai/nn/sparsity/knowledge-distillation --- https://en.wikipedia.org/wiki/Kush_%28Cannabis%29 Kush (Cannabis) 2020-03-31 marijuana --- https://en.wikipedia.org/wiki/Land_Tax_Reform_(Japan_1873) Land Tax Reform (Japan 1873) 2020-03-31 economics/georgism --- https://en.wikipedia.org/wiki/Land_value_tax Land value tax 2020-03-31 economics/georgism --- https://en.wikipedia.org/wiki/Larger_Pacific_striped_octopus Larger Pacific striped octopus 2020-03-31 psychology/neuroscience --- https://en.wikipedia.org/wiki/Latke%E2%80%93Hamantash_Debate Latke-Hamantash Debate 2020-03-31 fiction/humor math/humor --- https://en.wikipedia.org/wiki/Law_of_rent Law of rent 2020-03-31 economics/georgism --- https://en.wikipedia.org/wiki/Lee_Sedol Lee Sedol 2020-03-31 reinforcement-learning/model/alphago --- https://en.wikipedia.org/wiki/Legal_history_of_cannabis_in_the_United_States Legal history of cannabis in the United States 2020-03-31 marijuana --- https://en.wikipedia.org/wiki/Lexical_hypothesis Lexical hypothesis 2020-04-01 psychology/personality reinforcement-learning/imitation-learning/brain-imitation-learning --- https://en.wikipedia.org/wiki/Threshold_model#Liability_threshold_model Liability-threshold model 2020-04-01 genetics/cloning --- https://en.wikipedia.org/wiki/Threshold_model#Liability_threshold_model Liability threshold model 2020-04-01 genetics/cloning --- https://en.wikipedia.org/wiki/Linear_discriminant_analysis Linear discriminant analysis 2020-04-01 genetics/selection/artificial/index-selection --- https://en.wikipedia.org/wiki/Linear_programming Linear programming 2020-04-01 cs economics/experience-curve --- https://en.wikipedia.org/wiki/Lipofuscin Lipofuscin 2020-04-01 longevity/johan-bjorksten --- https://en.wikipedia.org/wiki/Liraglutide Liraglutide 2020-04-01 longevity/glp/semaglutide --- https://en.wikipedia.org/wiki/List_of_games_that_Buddha_would_not_play List of games that Buddha would not play 2020-04-01 philosophy/ethics --- https://en.wikipedia.org/wiki/List_of_lists_of_lists List of lists of lists 2020-04-01 cs/algorithm design math/humor philosophy/logic philosophy/ontology wikipedia --- https://en.wikipedia.org/wiki/Little_Nicky_%28cat%29 Little Nicky (cat) 2020-04-01 genetics/cloning --- https://en.wikipedia.org/wiki/Long_short-term_memory Long short-term memory 2020-04-02 ai/nn/rnn reinforcement-learning/meta-learning --- https://en.wikipedia.org/wiki/Louis_Wain Louis Wain 2020-04-02 cat --- https://en.wikipedia.org/wiki/LSD Lysergic acid diethylamide 2020-04-02 psychedelic --- https://en.wikipedia.org/wiki/MDMA MDMA 2020-04-02 psychedelic --- https://en.wikipedia.org/wiki/Manifold_(magazine) Manifold (magazine) 2020-04-02 math/humor --- https://en.wikipedia.org/wiki/Marbled_crayfish Marbled crayfish 2020-04-02 genetics/cloning --- https://en.wikipedia.org/wiki/Master_(software) Master (software) 2020-04-02 reinforcement-learning/model/alphago --- https://en.wikipedia.org/wiki/Matthew_Meselson#Meselson_effect Matthew Meselson § Meselson effect 2020-04-02 genetics/cloning --- https://en.wikipedia.org/wiki/Memento_mori Memento mori 2020-04-02 philosophy/ethics --- https://en.wikipedia.org/wiki/Mere-exposure_effect Mere-exposure effect 2020-04-02 psychology/novelty --- https://en.wikipedia.org/wiki/Meta-learning_(computer_science) Meta learning (computer science) 2020-04-02 reinforcement-learning/meta-learning --- https://en.wikipedia.org/wiki/Metamath Metamath 2020-04-03 math --- https://en.wikipedia.org/wiki/Metamemory Metamemory 2020-04-03 psychology/novelty --- https://en.wikipedia.org/wiki/Meteoric_iron Meteoric iron 2020-04-03 history technology --- https://en.wikipedia.org/wiki/Microbiome Microbiome 2020-04-03 genetics/microbiome --- https://en.wikipedia.org/wiki/Micropropagation Micropropagation 2020-04-03 genetics/gametogenesis --- https://en.wikipedia.org/wiki/Mike_Darwin Mike Darwin 2020-04-03 longevity/johan-bjorksten --- https://en.wikipedia.org/wiki/Minecraft Minecraft 2020-04-03 design --- https://en.wikipedia.org/wiki/Minnesota_Starvation_Experiment Minnesota Starvation Experiment 2020-04-03 longevity/fasting --- https://en.wikipedia.org/wiki/Misattribution_of_memory Misattribution of memory 2020-04-03 psychology/cognitive-bias/illusion-of-depth --- https://en.wikipedia.org/wiki/Missyplicity Missyplicity 2020-04-03 cat genetics/cloning --- https://en.wikipedia.org/wiki/Mixture_of_experts Mixture of experts 2020-04-03 ai/scaling/mixture-of-experts --- https://en.wikipedia.org/wiki/Modern_Times_(film) Modern Times (film) 2020-04-04 fiction/humor --- https://en.wikipedia.org/wiki/Mohorovi%C4%8Di%C4%87_discontinuity Mohorovičić discontinuity 2020-04-04 science technology/self-sinking --- https://en.wikipedia.org/wiki/Monte_Carlo_tree_search Monte Carlo tree search 2020-04-04 reinforcement-learning/exploration reinforcement-learning/model/alphago reinforcement-learning/model/muzero statistics/bayes statistics/decision --- https://en.wikipedia.org/wiki/Montezuma%27s_Revenge_(video_game) Montezuma’s Revenge (video game) 2020-04-04 reinforcement-learning/exploration --- https://en.wikipedia.org/wiki/Mortmain Mortmain 2020-04-04 economics/perpetuities --- https://en.wikipedia.org/wiki/MuZero MuZero 2020-04-04 reinforcement-learning/model/muzero --- https://en.wikipedia.org/wiki/Muji Muji 2020-04-04 design japan --- https://en.wikipedia.org/wiki/Multi-objective_optimization Multi-objective optimization 2020-04-04 genetics/selection/artificial/index-selection statistics/decision --- https://en.wikipedia.org/wiki/Multivariate_normal_distribution Multivariate normal distribution 2020-04-04 genetics/selection/artificial/index-selection --- https://en.wikipedia.org/wiki/Mycoplasma_laboratorium Mycoplasma laboratorium 2020-04-04 genetics/editing genetics/genome-synthesis --- https://en.wikipedia.org/wiki/N,N-Dimethyltryptamine N,N-Dimethyltryptamine 2020-04-04 psychedelic --- https://en.wikipedia.org/wiki/Nearest_neighbor_search Nearest neighbor search 2020-04-05 ai/nn/retrieval --- https://en.wikipedia.org/wiki/Negative_selection_(natural_selection) Negative selection (natural selection) 2020-04-05 psychiatry/autism psychiatry/schizophrenia --- https://en.wikipedia.org/wiki/Neuroticism Neuroticism 2020-04-05 psychiatry/anxiety psychology/personality --- https://en.wikipedia.org/wiki/Nine-banded_armadillo Nine-banded armadillo 2020-04-05 genetics/cloning --- https://en.wikipedia.org/wiki/Noble_lie Noble lie 2020-04-05 philosophy/ethics philosophy/mind reinforcement-learning/safe sociology/preference-falsification --- https://en.wikipedia.org/wiki/Nose-picking Nose-picking 2020-04-05 biology/booger --- https://en.wikipedia.org/wiki/Numerai Numerai 2020-04-05 bitcoin/nashx --- https://en.wikipedia.org/wiki/Nutmeg#Effects Nutmeg § Effects 2020-04-05 psychedelic --- https://en.wikipedia.org/wiki/Olfactory_white Olfactory white 2020-04-05 psychology/smell --- https://en.wikipedia.org/wiki/Oligodendrocyte Oligodendrocytes 2020-04-05 psychology/neuroscience --- https://en.wikipedia.org/wiki/Oligonucleotide_synthesis Oligonucleotide synthesis 2020-04-06 genetics/genome-synthesis --- https://en.wikipedia.org/wiki/Olivetti_S.p.A. Olivetti 2020-04-06 design --- https://en.wikipedia.org/wiki/Olivine#Uses Olivine § Uses 2020-04-06 technology/carbon-capture --- https://en.wikipedia.org/wiki/Oogenesis Oogenesis 2020-04-06 genetics/gametogenesis --- https://en.wikipedia.org/wiki/OpenAI_Codex OpenAI Codex 2020-04-06 ai/nn/transformer/gpt/codex --- https://en.wikipedia.org/wiki/OpenAI_Five OpenAI Five 2020-04-06 reinforcement-learning/model-free/oa5 --- https://en.wikipedia.org/wiki/Openness_to_experience Openness to experience 2020-04-06 psychology/personality --- https://en.wikipedia.org/wiki/Optic_disc Optic disc 2020-04-06 psychology/cognitive-bias/illusion-of-depth --- https://en.wikipedia.org/wiki/Optimal_stopping Optimal stopping 2020-04-06 reinforcement-learning/exploration statistics/decision --- https://en.wikipedia.org/wiki/Optimal_tax Optimal tax 2020-04-06 economics/georgism --- https://en.wikipedia.org/wiki/Organoid Organoids 2020-04-06 genetics/gametogenesis --- https://en.wikipedia.org/wiki/Overton_window Overton window 2020-04-07 sociology/preference-falsification --- https://en.wikipedia.org/wiki/Parable_of_the_broken_window Parable of the broken window 2020-04-07 philosophy/ethics --- https://en.wikipedia.org/wiki/Particle_filter Particle filter 2020-04-07 reinforcement-learning/exploration statistics/bayes --- https://en.wikipedia.org/wiki/Patent_thicket Patent thickets 2020-04-07 economics/copyright --- https://en.wikipedia.org/wiki/Patent_troll Patent troll 2020-04-07 economics/copyright --- https://en.wikipedia.org/wiki/Path_analysis_(statistics) Path analysis (statistics) 2020-04-07 genetics/selection/artificial/index-selection --- https://en.wikipedia.org/wiki/Pathological_lying Pathological lying 2020-04-07 psychiatry --- https://en.wikipedia.org/wiki/Perpetual_bond Perpetual bond 2020-04-07 economics/perpetuities --- https://en.wikipedia.org/wiki/Personality_psychology Personality psychology 2020-04-07 psychology/personality --- https://en.wikipedia.org/wiki/Peter_Watts_(author) Peter Watts (author) 2020-04-07 biology/portia --- https://en.wikipedia.org/wiki/Petrichor Petrichor 2020-04-07 psychology/smell --- https://en.wikipedia.org/wiki/Phantosmia Phantosmia 2020-04-08 psychology/smell --- https://en.wikipedia.org/wiki/Phosphoramidite Phosphoramidite 2020-04-08 genetics/genome-synthesis --- https://en.wikipedia.org/wiki/PiHKAL PiHKAL 2020-04-08 psychedelic --- https://en.wikipedia.org/wiki/Pineapple_Fund Pineapple Fund 2020-04-08 psychedelic --- https://en.wikipedia.org/wiki/Pink_Panthers Pink Panthers 2020-04-08 crime --- https://en.wikipedia.org/wiki/Piperlongumine Piperlongumine 2020-04-08 longevity/senolytic --- https://en.wikipedia.org/wiki/Pitfall! Pitfall! 2020-04-08 reinforcement-learning/exploration --- https://en.wikipedia.org/wiki/Polio_vaccine#Attenuated_2 Polio vaccine § Attenuated 2 2020-04-08 genetics/selection --- https://en.wikipedia.org/wiki/Portia_(spider) Portia (spider) 2020-04-08 biology/portia --- https://en.wikipedia.org/wiki/Preference_falsification Preference falsification 2020-04-08 sociology/preference-falsification --- https://en.wikipedia.org/wiki/Present_value Present value 2020-04-09 economics/georgism --- https://en.wikipedia.org/wiki/Productivity_paradox Productivity paradox 2020-04-09 economics/automation --- https://en.wikipedia.org/wiki/Progress_and_Poverty Progress and Poverty 2020-04-09 economics/georgism --- https://en.wikipedia.org/wiki/Proportional_hazards_model Proportional hazards model 2020-04-09 statistics/survival-analysis --- https://en.wikipedia.org/wiki/Protein_structure_prediction Protein structure prediction 2020-04-09 ai/nn/transformer/alphafold --- https://en.wikipedia.org/wiki/Proteome Proteome 2020-04-09 ai/nn/transformer/alphafold --- https://en.wikipedia.org/wiki/Proving_too_much Proving too much 2020-04-09 philosophy/epistemology statistics/bayes --- https://en.wikipedia.org/wiki/Przewalski%27s_horse Przewalski’s horse 2020-04-09 genetics/cloning --- https://en.wikipedia.org/wiki/Psilocybe_cubensis Psilocybe cubensis 2020-04-09 psychedelic --- https://en.wikipedia.org/wiki/Psilocybin_mushroom Psilocybin mushroom 2020-04-09 psychedelic --- https://en.wikipedia.org/wiki/Psychedelic_drug Psychedelic drug 2020-04-09 psychedelic --- https://en.wikipedia.org/wiki/Psychedelic_microdosing Psychedelic microdosing 2020-04-10 psychedelic --- https://en.wikipedia.org/wiki/Psychedelic_therapy Psychedelic therapy 2020-04-10 psychedelic --- https://en.wikipedia.org/wiki/Psychedelics_in_problem-solving_experiment Psychedelics in problem-solving experiment 2020-04-10 psychedelic --- https://en.wikipedia.org/wiki/Pumping_Iron Pumping Iron 2020-04-10 exercise --- https://en.wikipedia.org/wiki/Pyramidal_cell Pyramidal neurons 2020-04-10 psychology/neuroscience --- https://en.wikipedia.org/wiki/Quercetin Quercetin 2020-04-10 longevity/senolytic/d-q --- https://en.wikipedia.org/wiki/Quine_(computing) Quine (computing) 2020-04-10 cs philosophy/epistemology --- https://en.wikipedia.org/wiki/RPG_Maker RPG Maker 2020-04-10 design --- https://en.wikipedia.org/wiki/Radical_Chic_%26_Mau-Mauing_the_Flak_Catchers Radical Chic & Mau-Mauing the Flak Catchers 2020-04-10 sociology/preference-falsification --- https://en.wikipedia.org/wiki/Rams_(2018_film) Rams (2018 film) 2020-04-10 design --- https://en.wikipedia.org/wiki/Real_estate_appraisal Real estate appraisal 2020-04-10 economics/georgism --- https://en.wikipedia.org/wiki/Refrigerator_death Refrigerator death 2020-04-11 philosophy/ethics --- https://en.wikipedia.org/wiki/Regression_toward_the_mean Regression toward the mean 2020-04-11 genetics/cloning --- https://en.wikipedia.org/wiki/Remix_culture Remix culture 2020-04-11 anime/my-little-pony economics/copyright --- https://en.wikipedia.org/wiki/Remote_work Remote work 2020-04-11 economics/automation --- https://en.wikipedia.org/wiki/Ren%C3%A9_Antoine_Ferchault_de_R%C3%A9aumur René Antoine Ferchault de Réaumur 2020-04-11 biology cryonics --- https://en.wikipedia.org/wiki/Rent-seeking Rent-seeking 2020-04-11 economics/georgism --- https://en.wikipedia.org/wiki/Replit Repl.it 2020-04-11 design --- https://en.wikipedia.org/wiki/Replaceability_argument Replaceability argument 2020-04-11 philosophy/ethics --- https://en.wikipedia.org/wiki/Retrospective_think_aloud Retrospective think aloud 2020-04-11 ai/nn/transformer/gpt/inner-monologue --- https://en.wikipedia.org/wiki/Revolutions_of_1989 Revolutions of 1989 2020-04-11 sociology/preference-falsification --- https://en.wikipedia.org/wiki/Ricardian_equivalence Ricardian equivalence 2020-04-12 economics/georgism --- https://en.wikipedia.org/wiki/Robert_Bakewell_(agriculturalist) Robert Bakewell (farmer) 2020-04-12 genetics/selection/artificial/index-selection --- https://en.wikipedia.org/wiki/Robert_Ettinger Robert Ettinger 2020-04-12 cryonics --- https://en.wikipedia.org/wiki/Robin_Hanson Robin Hanson 2020-04-12 economics/automation --- https://en.wikipedia.org/wiki/Roblox Roblox 2020-04-12 design --- https://en.wikipedia.org/wiki/Robotic_process_automation Robotic process automation 2020-04-12 economics/automation --- https://en.wikipedia.org/wiki/Romanian_Revolution#Ceau%C8%99escu's_speech Romanian Revolution § Ceaușescu’s speech 2020-04-12 sociology/preference-falsification --- https://en.wikipedia.org/wiki/Roxithromycin Roxithromycin 2020-04-12 longevity/senolytic --- https://en.wikipedia.org/wiki/Royal_Humane_Society Royal Humane Society 2020-04-12 biology cryonics --- https://en.wikipedia.org/wiki/Rubber_duck_debugging Rubber duck debugging 2020-04-12 ai/nn/transformer/gpt/inner-monologue philosophy/epistemology --- https://en.wikipedia.org/wiki/Rule_against_perpetuities Rule against perpetuities 2020-04-12 economics/perpetuities --- https://en.wikipedia.org/wiki/S.L.A._Marshall#Controversies S.L.A. Marshall § Controversies 2020-04-13 history/s-l-a-marshall statistics/bias --- https://en.wikipedia.org/wiki/San_Francisco San Francisco 2020-04-13 economics/georgism --- https://en.wikipedia.org/wiki/Scanning_electron_microscope Scanning electron microscope 2020-04-13 psychology/neuroscience --- https://en.wikipedia.org/wiki/Schizoid_personality_disorder Schizoid personality disorder 2020-04-13 psychiatry/autism/schizoid psychology/personality --- https://en.wikipedia.org/wiki/Schizotypal_personality_disorder Schizotypal personality disorder 2020-04-13 psychiatry psychology/personality --- https://en.wikipedia.org/wiki/Score_(statistics) Score (statistics) 2020-04-13 ai/nn/diffusion --- https://en.wikipedia.org/wiki/Scrupulosity Scrupulosity 2020-04-13 existential-risk philosophy/ethics psychiatry --- https://en.wikipedia.org/wiki/Scunthorpe_problem Scunthorpe problem 2020-04-13 cs/security technology --- https://en.wikipedia.org/wiki/Sea_Ranch,_California Sea Ranch, California 2020-04-13 design --- https://en.wikipedia.org/wiki/Self-licensing Self-licensing 2020-04-13 philosophy/ethics philosophy/religion sociology --- https://en.wikipedia.org/wiki/Intrapersonal_communication Self-talk 2020-04-13 psychology/inner-voice --- https://en.wikipedia.org/wiki/Semaglutide Semaglutide 2020-04-14 longevity/glp/semaglutide --- https://en.wikipedia.org/wiki/Senescence-associated_secretory_phenotype Senescence-associated secretory phenotype 2020-04-14 longevity/senolytic --- https://en.wikipedia.org/wiki/Senolytic Senolytic 2020-04-14 longevity/senolytic --- https://en.wikipedia.org/wiki/Senotherapy Senotherapy 2020-04-14 longevity/senolytic --- https://en.wikipedia.org/wiki/Serial_passage Serial passage 2020-04-14 genetics/selection --- https://en.wikipedia.org/wiki/Sewall_Wright Sewall Wright 2020-04-14 genetics/selection/artificial/index-selection --- https://en.wikipedia.org/wiki/Simulated_annealing Simulated annealing 2020-04-14 reinforcement-learning/exploration --- https://en.wikipedia.org/wiki/Snuppy Snuppy 2020-04-14 genetics/cloning --- https://en.wikipedia.org/wiki/Sourdough Sourdough 2020-04-14 genetics/microbiome --- https://en.wikipedia.org/wiki/Space_food Space food 2020-04-14 psychology/smell technology --- https://en.wikipedia.org/wiki/Spanish_Christmas_Lottery Spanish Christmas Lottery 2020-04-14 economics statistics/causality --- https://en.wikipedia.org/wiki/Spermatogenesis Spermatogenesis 2020-04-15 genetics/gametogenesis --- https://en.wikipedia.org/wiki/Spiral_of_silence Spiral of silence 2020-04-15 sociology/preference-falsification --- https://en.wikipedia.org/wiki/StarCraft_II StarCraft II 2020-04-15 reinforcement-learning/model-free/alphastar --- https://en.wikipedia.org/wiki/Stem_cell Stem cell 2020-04-15 genetics/gametogenesis --- https://en.wikipedia.org/wiki/Stream_of_consciousness Stream of consciousness (narrative mode) 2020-04-15 psychology/inner-voice --- https://en.wikipedia.org/wiki/Stripe_%28company%29 Stripe (company) 2020-04-15 technology/carbon-capture --- https://en.wikipedia.org/wiki/Sufficient_statistic Sufficient statistic 2020-04-15 reinforcement-learning/meta-learning --- https://en.wikipedia.org/wiki/Sum_of_normally_distributed_random_variables Sum of normally distributed random variables 2020-04-15 genetics/selection/artificial/index-selection --- https://en.wikipedia.org/wiki/Surstr%C3%B6mming Surströmming 2020-04-15 psychology/smell --- https://en.wikipedia.org/wiki/Survival_analysis Survival analysis 2020-04-15 statistics/survival-analysis --- https://en.wikipedia.org/wiki/Survival_function Survival function 2020-04-16 statistics/survival-analysis --- https://en.wikipedia.org/wiki/Survivorship_curve Survivorship curve 2020-04-16 statistics/survival-analysis --- https://en.wikipedia.org/wiki/Synthetic_biology Synthetic biology 2020-04-16 genetics/genome-synthesis --- https://en.wikipedia.org/wiki/TI-83_series TI-83 2020-04-16 design --- https://en.wikipedia.org/wiki/Taqiya Taqiya 2020-04-16 sociology/preference-falsification --- https://en.wikipedia.org/wiki/Tarrare Tarrare 2020-04-16 exercise --- https://en.wikipedia.org/wiki/Telescoping_generations Telescoping generations 2020-04-16 genetics/cloning --- https://en.wikipedia.org/wiki/Temporal_lobe Temporal lobe 2020-04-16 psychology/neuroscience --- https://en.wikipedia.org/wiki/Tendril_perversion Tendril perversion 2020-04-16 biology --- https://en.wikipedia.org/wiki/Tensor_Processing_Unit Tensor processing unit 2020-04-16 reinforcement-learning/model/alphago reinforcement-learning/model/muzero --- https://en.wikipedia.org/wiki/The_Captive_Mind The Captive Mind 2020-04-16 sociology/preference-falsification --- https://en.wikipedia.org/wiki/The_Invisible_Gorilla The Invisible Gorilla 2020-04-17 psychology/cognitive-bias/illusion-of-depth --- https://en.wikipedia.org/wiki/The_Lottery The Lottery 2020-04-17 sociology/preference-falsification --- https://en.wikipedia.org/wiki/The_Origin_of_Consciousness_in_the_Breakdown_of_the_Bicameral_Mind The Origin of Consciousness in the Breakdown of the Bicameral Mind 2020-04-17 psychiatry/schizophrenia psychology/inner-voice --- https://en.wikipedia.org/wiki/The_Sumerian_Game The Sumerian Game 2020-04-17 fiction/text-game --- https://en.wikipedia.org/wiki/The_Tao_of_Pooh The Tao of Pooh 2020-04-17 fiction/humor --- https://en.wikipedia.org/wiki/Theories_of_humor#Incongruity_theory Theories of humor § Incongruous juxtaposition theory 2020-04-17 fiction/humor psychology/novelty --- https://en.wikipedia.org/wiki/Think_aloud_protocol Think aloud protocol 2020-04-17 ai/nn/transformer/gpt/inner-monologue psychology/inner-voice --- https://en.wikipedia.org/wiki/Thompson_sampling Thompson sampling 2020-04-17 reinforcement-learning/exploration statistics/bayes statistics/decision --- https://en.wikipedia.org/wiki/Threshold_model#Liability_threshold_model Threshold model § Liability threshold model 2020-04-17 genetics/heritable/rare --- https://en.wikipedia.org/wiki/Thue%E2%80%93Morse_sequence Thue-Morse sequence 2020-04-17 statistics/decision --- https://en.wikipedia.org/wiki/Tickling#Self-tickling Tickling § Self-tickling 2020-04-17 psychiatry --- https://en.wikipedia.org/wiki/Tiebout_model Tiebout model 2020-04-18 economics/georgism --- https://en.wikipedia.org/wiki/Time_formatting_and_storage_bugs Time formatting and storage bugs 2020-04-18 technology --- https://en.wikipedia.org/wiki/Tom_Wolfe Tom Wolfe 2020-04-18 sociology/preference-falsification --- https://en.wikipedia.org/wiki/Tragedy_of_the_anticommons Tragedy of the anticommons 2020-04-18 ai/scaling/economics economics/copyright philosophy/ethics --- https://en.wikipedia.org/wiki/Transformer_(machine_learning_model) Transformer (machine learning model) 2020-04-18 ai/nn/transformer/alphafold --- https://en.wikipedia.org/wiki/Traumatic_brain_injury Traumatic brain injury 2020-04-18 psychiatry/traumatic-brain-injury --- https://en.wikipedia.org/wiki/Truffle#Extraction Truffle § Extraction 2020-04-18 psychology/smell --- https://en.wikipedia.org/wiki/Truncated_normal_distribution Truncated normal distribution 2020-04-18 genetics/cloning statistics/order statistics/probability --- https://en.wikipedia.org/wiki/Truncation_(statistics) Truncation (statistics) 2020-04-18 statistics/survival-analysis --- https://en.wikipedia.org/wiki/Truncation_selection Truncation selection 2020-04-18 genetics/cloning genetics/selection statistics/order --- https://en.wikipedia.org/wiki/Trypsin Trypsin 2020-04-19 longevity/johan-bjorksten --- https://en.wikipedia.org/wiki/Tupper%27s_self-referential_formula Tupper’s self-referential formula 2020-04-19 math/humor --- https://en.wikipedia.org/wiki/UBiome UBiome 2020-04-19 genetics/microbiome --- https://en.wikipedia.org/wiki/Unity_Biotechnology Unity Biotechnology 2020-04-19 longevity/senolytic --- https://en.wikipedia.org/wiki/Vaping-associated_pulmonary_injury Vaping-associated pulmonary injury 2020-04-19 marijuana --- https://en.wikipedia.org/wiki/Variance#Sum_of_correlated_variables Variance § Sum of correlated variables 2020-04-19 genetics/selection/artificial/index-selection --- https://en.wikipedia.org/wiki/Vector_space_model Vector space model 2020-04-19 ai/nn/retrieval --- https://en.wikipedia.org/wiki/Vitrification Vitrification 2020-04-19 cryonics --- https://en.wikipedia.org/wiki/Von_Mises%E2%80%93Fisher_distribution Von Mises-Fisher distribution 2020-04-19 ai/nn/diffusion --- https://en.wikipedia.org/wiki/Wake_therapy Wake therapy 2020-04-19 psychiatry/bipolar psychiatry/depression zeo --- https://en.wikipedia.org/wiki/Wang_Chong#Work_and_philosophy Wang Chong § Work and philosophy 2020-04-19 philosophy/epistemology philosophy/religion --- https://en.wikipedia.org/wiki/Waqf Waqf 2020-04-20 economics/perpetuities --- https://en.wikipedia.org/wiki/Wellington_R._Burt Wellington R. Burt 2020-04-20 economics/perpetuities --- https://en.wikipedia.org/wiki/William_Leonard_Pickard William Leonard Pickard 2020-04-20 psychedelic --- https://en.wikipedia.org/wiki/World_on_Fire_(book) World on Fire (book) 2020-04-20 iq/ses --- https://en.wikipedia.org/wiki/Wrongful_life Wrongful life 2020-04-20 philosophy/ethics --- https://en.wikipedia.org/wiki/Wu_Dao Wu Dao 2020-04-20 ai/nn/transformer ai/scaling/mixture-of-experts --- https://www.uber.com/blog/go-explore/ Montezuma's Revenge Solved by Go-Explore, a New Algorithm for Hard-Exploration Problems (Sets Records on Pitfall, Too) 2020-04-20 reinforcement-learning/exploration --- https://engineering.fb.com/2016/11/08/android/delivering-real-time-ai-in-the-palm-of-your-hand/ Delivering real-time AI in the palm of your hand 2020-04-20 ai/nn/sparsity --- https://eprint.iacr.org/2002/160.pdf 2020-04-20 cs/cryptography technology --- https://erowid.org/experiences/subs/exp_Datura.shtml Datura (also Jimson Weed; Thorn Apple) 2020-04-20 psychedelic --- https://erowid.org/experiences/subs/exp_Nutmeg.shtml Nutmeg (also Myristica fragrans) 2020-04-20 psychedelic --- https://extras.denverpost.com/stateofhope/ CBD in Colorado: Seeking a marijuana miracle 2020-04-21 marijuana philosophy/ethics --- https://ez.substack.com/p/how-peloton-could-die-and-tonal-as 2020-04-21 exercise --- https://fabianfuchsml.github.io/alphafold2/ Fabian Fuchs 2020-04-21 ai/nn/transformer/alphafold --- https://features.propublica.org/brazil-carbon-offsets/inconvenient-truth-carbon-credits-dont-work-deforestation-redd-acre-cambodia/ An (Even More) Inconvenient Truth 2020-04-21 technology/carbon-capture --- https://ferocioustruth.com/2017/review-freezing-people-is-not-easy/ Book Review: Freezing People is (Not) Easy 2020-04-21 cryonics --- https://fibery.io/blog/gems/hypertext-tools-from-the-80s/ Hypertext tools from the 80s 2020-04-21 design technology --- https://files.eric.ed.gov/fulltext/ED138702.pdf 2020-04-21 iq/ses --- https://flume.com.br/ 2020-04-21 iq/ses --- https://folks.pillpack.com/my-father-the-werewolf/ 2020-04-21 psychiatry --- https://fortune.com/longform/marijuana-weed-cannabis-tilray-stock/ The marijuana billionaire who doesn’t smoke weed 2020-04-21 marijuana --- https://forum.effectivealtruism.org/posts/dQhjwHA7LhfE8YpYF/prediction-markets-in-the-corporate-setting Prediction Markets in The Corporate Setting 2020-04-21 statistics/prediction --- https://forum.effectivealtruism.org/posts/qEsDhFL8mQARFw6Fj/the-subjective-experience-of-time-welfare-implications The Subjective Experience of Time: Welfare Implications 2020-04-22 philosophy/mind psychology/smell --- https://forum.tezosagora.org/t/perpetuities-as-block-rewards/1303 Perpetuities as block rewards 2020-04-22 economics/perpetuities --- https://web.archive.org/web/20220522101435/https://fras.uk/ml/large%20prior-free%20models/code/transformer-vae/2020/08/25/Transformer-VAE-for-Program-Synthesis.html Transformer-VAE for Program Synthesis 2020-04-22 ai/nn/transformer/gpt/codex ai/nn/transformer/t5 reinforcement-learning/preference-learning --- https://fras.uk/ml/large%20prior-free%20models/transformer-vae/2020/08/13/Transformers-as-Variational-Autoencoders.html Transformers as Variational Autoencoders 2020-04-22 ai/nn/transformer/gpt ai/nn/vae reinforcement-learning/preference-learning --- https://docs.iza.org/dp8900.pdf 2020-04-22 marijuana --- https://gameofrent.com/ 2020-04-22 economics/georgism --- https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-021-00855-5 Integration of whole genome sequencing into a healthcare setting: high diagnostic rates across multiple clinical entities in 3219 rare disease patients Genome Medicine 2020-04-22 genetics/heritable/rare --- https://github.com/CherylHuang/Faces2Anime Faces2Anime: Cartoon Style Transfer in Faces using Generative Adversarial Networks 2020-04-22 ai/anime --- https://github.com/EdZ543/This-Catgirl-Does-Not-Exist 2020-04-22 ai/nn/gan/stylegan --- https://github.com/EdenBD/MultiModalStory-demo FairyTailor: Multimodal Generative Framework for Storytelling 2020-04-22 ai/text-style-transfer --- https://github.com/EleutherAI/DALLE-mtf Open-AI's DALL-E for large scale training in mesh-tensorflow. 2020-04-23 ai/nn/transformer/gpt/dall-e --- https://github.com/EleutherAI/vqgan-clip/tree/main/notebooks vqgan-clip/notebooks 2020-04-23 ai/nn/transformer/clip --- https://github.com/EndingCredits/Set-CGAN EndingCredits/Set-CGAN: Adaptation of conventional GAN to condition on additional input set 2020-04-23 ai/anime ai/nn/gan/stylegan --- https://github.com/JoeyBallentine/ESRGAN joeyballentine/ESRGAN: A modified version of the original ESRGAN test.py script with added features 2020-04-23 ai/anime ai/nn/gan/stylegan --- https://github.com/NVlabs/stylegan3 Official PyTorch implementation of StyleGAN3 2020-04-23 ai/nn/gan/stylegan --- https://github.com/Norod/my-colab-experiments/blob/master/Simple_ruDALLE_inference_%5Bsupports_v1_0_0%5D.ipynb my-colab-experiments/Simple_ruDALLE_inference_[supports_v1_0_0].ipynb 2020-04-23 ai/nn/transformer/gpt/dall-e --- https://github.com/StenographyDev/autopilot-vsc StenographyDev/autopilot-vsc 2020-04-23 ai/nn/transformer/gpt/codex --- https://github.com/THUDM/CogView Text-to-Image generation. The repo for NeurIPS 2021 paper "CogView: Mastering Text-to-Image Generation via Transformers". 2020-04-23 ai/nn/transformer/gpt/dall-e --- https://github.com/TsinghuaAI/CPM Introduction to CPM 2020-04-23 ai/scaling/mixture-of-experts --- https://github.com/VITA-Group/TransGAN TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up 2020-04-23 ai/nn/gan --- https://github.com/ajbrock/BigGAN-PyTorch BigGAN-PyTorch: The author’s officially unofficial PyTorch BigGAN implementation 2020-04-23 ai/anime ai/nn/gan/biggan --- https://github.com/akanimax/Variational_Discriminator_Bottleneck akanimax/Variational_Discriminator_Bottleneck: Implementation (with some experimentation) of the paper titled "Variational Discriminator Bottleneck" 2020-04-24 ai/anime ai/nn/gan --- https://github.com/akanimax/msg-gan-v1 MSG-GAN: Multi-Scale Gradients GAN (Architecture inspired from ProGAN but doesn’t use layer-wise growing) 2020-04-24 ai/anime ai/nn/gan --- https://github.com/anne-urai/largescale_recordings Large-scale neural recordings call for new insights to link brain and behavior 2020-04-24 psychology/neuroscience --- https://github.com/arfafax/StyleGAN-2_experiments/blob/master/Preprocess%20Danbooru%20Vectors%20-%20StyleGAN%20Conditional.ipynb Preprocess Danbooru Vectors—StyleGAN Conditional 2020-04-24 ai/anime ai/nn/gan/stylegan --- https://github.com/bilibili/ailab/blob/main/Real-CUGAN/README_EN.md Real-CUGAN/README_EN.md 2020-04-24 ai/anime --- https://github.com/borisdayma/dalle-mini borisdayma/dalle-mini: DALL·E-Mini 2020-04-24 ai/nn/transformer/gpt/dall-e/1 --- https://github.com/cedricoeldorf/ConditionalStyleGAN Conditional implementation for NVIDIA's StyleGAN architecture 2020-04-24 ai/anime ai/nn/gan/stylegan --- https://github.com/christophschuhmann/4MC-4M-Image-Text-Pairs-with-CLIP-embeddings christophschuhmann/4MC-4M-Image-Text-Pairs-with-CLIP-embeddings: I have created a dataset of Image-Text-Pairs by using the cosine similarity of the CLIP embeddings of the image & its caption derrived from YFCC100M. I have also added probabilities from a NSFW detector & more 2020-04-24 ai/nn/transformer/clip --- https://github.com/clip-italian/clip-italian CLIP (Contrastive Language–Image Pre-training) for Italian 2020-04-24 ai/nn/transformer/clip --- https://github.com/crowsonkb/v-diffusion-pytorch v objective diffusion inference code for PyTorch 2020-04-24 ai/anime ai/nn/diffusion --- https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset ArtGAN/WikiArt Dataset 2020-04-24 ai/anime ai/nn/gan/stylegan --- https://github.com/google-deepmind/alphafold Open source code for AlphaFold 2020-04-25 ai/nn/transformer/alphafold --- https://github.com/dribnet/pixray neural image generation 2020-04-25 ai/nn/transformer/clip --- https://github.com/jerryli27/TwinGAN Twin-GAN: Unpaired Cross-Domain Image Translation with Weight-Sharing GANs 2020-04-25 ai/anime/danbooru ai/nn/gan --- https://github.com/junyanz/CycleGAN junyanz/CycleGAN: Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more. 2020-04-25 ai/anime ai/nn/gan --- https://github.com/kaesve/muzero A clean implementation of MuZero and AlphaZero following the AlphaZero General framework. Train and Pit both algorithms against each other, and investigate reliability of learned MuZero MDP models. 2020-04-25 reinforcement-learning/model/muzero --- https://github.com/kakaobrain/minDALL-E kakaobrain/mindall-e: PyTorch implementation of a 1.3B text-to-image generation model trained on 14 million image-text pairs 2020-04-25 ai/nn/transformer/gpt/dall-e --- https://github.com/kevinlyu/DCGAN_Pytorch kevinlyu/DCGAN_Pytorch: DCGAN with vanilla GAN and Least Square GAN objective 2020-04-25 ai/anime/danbooru ai/nn/gan --- https://github.com/manicman1999/StyleGAN-Keras StyleGAN made with Keras 2020-04-25 ai/anime ai/nn/gan/stylegan --- https://github.com/martinarjovsky/WassersteinGAN martinarjovsky/WassersteinGAN 2020-04-25 ai/anime ai/nn/gan --- https://github.com/martinarjovsky/WassersteinGAN/issues/2#issuecomment-278710552 Interpretation of Discriminator Loss 2020-04-25 ai/anime ai/nn/gan/stylegan --- https://github.com/mchong6/GANsNRoses GAN's N' Roses. Diverse im2im and vid2vid selfie to anime translation. 2020-04-26 ai/nn/gan/stylegan --- https://github.com/minimaxir/ai-generated-pokemon-rudalle minimaxir/ai-generated-pokemon-rudalle: Python script to preprocess images of all Pokémon to finetune ruDALL-E 2020-04-26 ai/nn/transformer/gpt/dall-e --- https://github.com/mlfoundations/open_clip An open source implementation of CLIP 2020-04-26 ai/nn/transformer/clip --- https://github.com/mmulet/code-relay/blob/main/markdown/HowIDidIt.md How I Did Relay Quine 2020-04-26 cs/computable design/typography fiction/humor --- https://github.com/nolan-dev/GANInterface nolan-dev/GANInterface: Tool to interface with a StyleGAN model 2020-04-26 ai/anime/danbooru ai/nn/gan --- https://github.com/nv-tlabs/GameGAN_code Learning to Simulate Dynamic Environments with GameGAN (CVPR 2020) 2020-04-26 ai/nn/gan reinforcement-learning --- https://github.com/nyu-mll/quality 2020-04-26 ai/nn/retrieval --- https://github.com/openai/CLIP/blob/main/data/yfcc100m.md CLIP/data/yfcc100m.md 2020-04-26 ai/nn/transformer/clip --- https://github.com/openai/DALL-E openai/DALL-E: PyTorch package for the discrete VAE used for DALL 2020-04-26 ai/nn/transformer/gpt/dall-e --- https://github.com/openai/guided-diffusion#download-pre-trained-models openai/guided-diffusion 2020-04-26 ai/nn/diffusion --- https://github.com/orpatashnik/StyleCLIP StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery 2020-04-26 ai/nn/transformer/clip --- https://github.com/paddlepaddle/RocketQA/ PaddlePaddle/RocketQA: 🚀 RocketQA, dense retrieval for information retrieval and question answering, including both Chinese and English state-of-the-art models. 2020-04-27 ai/nn/retrieval --- https://github.com/pbaylies/clustering-laion400m clustering-laion400m: Script and models for clustering LAION-400m CLIP embeddings. Models were fit on the first million or so image embeddings. 2020-04-27 ai/nn/transformer/clip --- https://github.com/podgorskiy/StyleGANCpp Unofficial implementation of StyleGAN's generator 2020-04-27 ai/anime ai/nn/gan/stylegan --- https://github.com/quchen/articles/blob/master/loeb-moeb.md articles 2020-04-27 cs/haskell --- https://github.com/rinongal/StyleGAN-nada rinongal/StyleGAN-nada 2020-04-27 ai/nn/transformer/clip --- https://github.com/rmokady/CLIP_prefix_caption Simple image captioning model 2020-04-27 ai/nn/transformer/clip --- https://github.com/ai-forever/music-composer ai-forever/music-composer 2020-04-27 ai/music --- https://github.com/ai-forever/ru-clip CLIP implementation for Russian language 2020-04-27 ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e --- https://github.com/ai-forever/ru-dalle Generate images from texts. In Russian 2020-04-27 ai/nn/transformer/gpt/dall-e --- https://github.com/ai-forever/ru-dolph RUDOLPH: One Hyper-Tasking Transformer can be creative as DALL-E and GPT-3 and smart as CLIP 2020-04-27 ai/nn/transformer/gpt/dall-e --- https://github.com/snap-research/MoCoGAN-HD A Good Image Generator Is What You Need for High-Resolution Video Synthesis 2020-04-27 ai/nn/gan --- https://github.com/sxhxliang/BigGAN-pytorch Pytorch implementation of ‘Large Scale GAN Training For High Fidelity Natural Image Synthesis’ (BigGAN) 2020-04-28 ai/anime ai/nn/gan/biggan --- https://github.com/taki0112/BigGAN-Tensorflow Simple Tensorflow implementation of "Large Scale GAN Training for High Fidelity Natural Image Synthesis" (BigGAN) 2020-04-28 ai/anime ai/nn/gan/biggan --- https://github.com/taki0112/Self-Attention-GAN-Tensorflow Simple Tensorflow implementation of "Self-Attention Generative Adversarial Networks" (SAGAN) 2020-04-28 ai/anime ai/nn/gan/biggan --- https://github.com/taziksh/hayasaka.ai/blob/master/StyleGAN-2_Tazik_25GB_RAM.ipynb hayasaka.ai/StyleGAN-2_Tazik_25GB_RAM.ipynb 2020-04-28 ai/anime ai/nn/gan/stylegan --- https://github.com/tdrussell/IllustrationGAN IllustrationGAN: A simple, clean TensorFlow implementation of Generative Adversarial Networks with a focus on modeling illustrations. 2020-04-28 ai/anime ai/nn/gan/stylegan --- https://github.com/tencent-ailab/tleague_projpage TLeague Project Page 2020-04-28 reinforcement-learning/model-free/alphastar --- https://github.com/tensorflow/minigo An open-source implementation of the AlphaGoZero algorithm 2020-04-28 reinforcement-learning/model/alphago --- https://github.com/tom-doerr/codex-readme Revolutionize your project documentation with the Codex-README generator, utilizing OpenAI's Codex for intelligent README creation. 2020-04-28 ai/nn/transformer/gpt/codex --- https://github.com/werner-duvaud/muzero-general MuZero 2020-04-28 reinforcement-learning/model/muzero --- https://github.com/xinntao/ESRGAN Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution 2020-04-28 ai/anime ai/nn/gan/stylegan --- https://github.com/xinntao/Real-ESRGAN/blob/master/docs/anime_video_model.md Real-ESRGAN/docs/anime_video_model.md at master 2020-04-29 ai/anime --- https://github.com/yasinyazici/EMA_GAN yasinyazici/EMA_GAN 2020-04-29 ai/nn/gan --- https://github.com/yeemachine/kalidokit Blendshape and kinematics calculator for Mediapipe/Tensorflow.js Face, Eyes, Pose, and Finger tracking models. 2020-04-29 ai anime --- https://github.com/yurijmikhalevich/rclip AI-Powered Command-Line Photo Search Tool 2020-04-29 ai/nn/transformer/clip --- https://gizmodo.com/california-man-becomes-the-first-death-with-dignity-p-1831652934 California Man Becomes the First ‘Death With Dignity’ Patient to Undergo Cryonic Preservation 2020-04-29 cryonics --- https://gizmodo.com/generation-cryo-fighting-death-in-the-frozen-unknown-1786446378 Generation Cryo: Fighting Death in the Frozen Unknown 2020-04-29 cryonics --- https://gondwanaland.com/mlog/2011/10/21/almost-innovation/ Almost Wikipedias and innovation in free collaboration projects 2020-04-29 wikipedia --- https://gpt3demo.com/category/code-generation 2020-04-29 ai/nn/transformer/gpt/codex --- https://gsejournal.biomedcentral.com/articles/10.1186/s12711-016-0280-3 Potential of gene drives with genome editing to increase genetic gain in livestock breeding programs 2020-04-29 genetics/editing genetics/selection --- https://h01-dot-neuroglancer-demo.appspot.com/#!gs://h01-release/assets/library_state.json Neuroglancer 2020-04-29 psychology/neuroscience --- https://hackernoon.com/the-3-tricks-that-made-alphago-zero-work-f3d47b6686ef The 3 Tricks That Made AlphaGo Zero Work 2020-04-29 reinforcement-learning/model/alphago --- https://hakaimagazine.com/features/can-we-really-be-friends-octopus/ Can We Really Be Friends with an Octopus? When octopuses are social, are they reaching out or simply reacting? 2020-04-30 philosophy/mind psychology/neuroscience --- https://hal.science/hal-00904097/document#pdf Why do humans reason? Arguments for an argumentative theory Mercier, Sperber 2011 2020-04-30 ai/nn/transformer/gpt/inner-monologue statistics/prediction --- https://hal.science/hal-01972948/document#pdf 2020-04-30 reinforcement-learning/preference-learning --- https://harpers.org/archive/2021/12/the-odor-of-things-solving-the-mysteries-of-scent/ The Odor of Things, by Scott Sayare 2020-04-30 psychology/smell --- https://harvardlawreview.org/print/vol-131/are-we-running-out-of-trademarks/ Are We Running Out of Trademarks? An Empirical Study of Trademark Depletion and Congestion 2020-04-30 economics/copyright --- https://hbr.org/2021/11/make-megaprojects-more-modular Make Megaprojects More Modular 2020-04-30 economics/experience-curve --- https://history.army.mil/html/books/070/70-64/cmhPub_70-64.pdf 2020-04-30 history/s-l-a-marshall statistics/bias --- https://hope.econ.duke.edu/sites/hope.econ.duke.edu/files/Banzhaf.pdf 2020-04-30 philosophy/ethics statistics/decision --- https://humanvarieties.org/2014/03/31/what-does-it-mean-to-have-a-low-r-squared-a-warning-about-misleading-interpretation/ What does it mean to have a low R-squared? A warning about misleading interpretation 2020-04-30 statistics/order --- https://humanvarieties.org/2016/01/31/iq-and-permanent-income-sizing-up-the-iq-paradox/ IQ and Permanent Income: Sizing Up the ‘IQ Paradox’ 2020-04-30 iq/ses --- https://icpr2020.net/anonymous-donation-on-reddit-changed-everything-for-mdma-research/ 2020-04-30 psychedelic --- https://idlewords.com/talks/fan_is_a_tool_using_animal.htm Fan Is A Tool-Using Animal 2020-05-01 design technology --- https://ieeexplore.ieee.org/document/7243739/ BitWhisper: Covert Signaling Channel between Air-Gapped Computers Using Thermal Manipulations 2020-05-01 technology --- https://if50.substack.com/p/1985-a-mind-forever-voyaging 1985: A Mind Forever Voyaging 2020-05-01 fiction/text-game --- https://if50.substack.com/p/2005-shades-of-doom 2005: Shades of Doom 2020-05-01 fiction/text-game --- https://if50.substack.com/p/2013-a-family-supper 2013: A Family Supper, by Aaron A. Reed 2020-05-01 fiction/text-game --- https://if50.substack.com/p/2015-lifeline 2015: Lifeline 2020-05-01 fiction/text-game --- https://if50.substack.com/p/2017-universal-paperclips 2017: Universal Paperclips 2020-05-01 existential-risk fiction/science-fiction fiction/text-game --- https://if50.substack.com/p/2020-scents-and-semiosis 2020: Scents & Semiosis, by Aaron A. Reed 2020-05-01 fiction/text-game psychology/smell --- https://ifpmag.mdmpublishing.com/firefighting-foam-making-water-wetter/ 2020-05-01 technology --- https://images.wur.nl/digital/collection/coll13/search CONTENTdm 2020-05-01 biology design --- https://infoproc.blogspot.com/2011/04/earnings-effects-of-personality.html Earnings effects of personality, education and IQ for the gifted 2020-05-01 iq/ses psychology/personality --- https://infoproc.blogspot.com/2014/02/hints-of-genomic-dark-matter-rare.html Information Processing: Hints of genomic dark matter: rare variants contribute to schizophrenia risk 2020-05-02 genetics/heritable/rare psychiatry/schizophrenia --- https://insanebraintrain.blogspot.com/2011/07/massive-dosing-lsd-thumbprint.html Insane Brain Train: Massive Dosing—the LSD Thumbprint 2020-05-02 psychedelic --- https://intelligence.org/2017/10/20/alphago/ AlphaGo Zero and the Foom Debate 2020-05-02 reinforcement-learning/model/alphago --- https://jalammar.github.io/illustrated-retrieval-transformer/ The Illustrated Retrieval Transformer 2020-05-02 ai/nn/retrieval --- https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2569454 Association of Resting Heart Rate and Blood Pressure in Late Adolescence With Subsequent Mental Disorders: A Longitudinal Population Study of More Than 1 Million Men in Sweden 2020-05-02 exercise --- https://jasbsci.biomedcentral.com/articles/10.1186/s40104-018-0304-7 Revolutionize livestock breeding in the future: an animal embryo-stem cell breeding system in a dish 2020-05-02 genetics/gametogenesis --- https://jgeekstudies.org/2016/05/19/great-attractor-ttgl/ Is the Great Attractor a Tengen Toppa Gurren Lagann? 2020-05-02 anime fiction/humor math/humor --- https://academic.oup.com/jlb/article/3/1/87/1751270 In vitro gametogenesis: just another way to have a baby? 2020-05-02 genetics/gametogenesis --- https://johnhcochrane.blogspot.com/2019/08/why-stop-at-100-case-for-perpetuities.html Why stop at 100? The case for perpetuities 2020-05-02 economics/perpetuities --- https://joshualoftus.com/posts/2020-11-23-least-squares-as-springs/ 2020-05-02 design statistics/probability --- https://journals.biologists.com/jeb/article/218/1/123/13627/The-developmental-origins-of-chronic-physical 2020-05-03 crime genetics/heritable --- https://journals.lww.com/acsm-msse/Fulltext/1997/08000/Familial_determinants_of_moderate_and_intense.12.aspx 2020-05-03 exercise --- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2000862 Commensal bacteria and essential amino acids control food choice behavior and reproduction Ricardo Leitão-Gonçalves, Zita Carvalho-Santos, Ana Patrícia Francisco, Gabriela Tondolo Fioreze, Margarida Anjos, Célia Baltazar, Ana Paula Elias, Pavel M. Itskov, Matthew D. W. Piper, Carlos Ribeiro 2017-03-15 2020-05-03 [("doi","10.1371/journal.pbio.2000862")] genetics/microbiome

Choosing the right nutrients to consume is essential to health and wellbeing across species. However, the factors that influence these decisions are poorly understood. This is particularly true for dietary proteins, which are important determinants of lifespan and reproduction.

We show that in Drosophila melanogaster, essential amino acids (eAAs) and the concerted action of the commensal bacteria Acetobacter pomorum and Lactobacilli are critical modulators of food choice. Using a chemically defined diet, we show that the absence of any single eAA from the diet is sufficient to elicit specific appetites for amino acid (AA)-rich food. Furthermore, commensal bacteria buffer the animal from the lack of dietary eAAs: both increased yeast appetite and decreased reproduction induced by eAA deprivation are rescued by the presence of commensals. Surprisingly, these effects do not seem to be due to changes in AA titers, suggesting that gut bacteria act through a different mechanism to change behavior and reproduction.

Thus, eAAs and commensal bacteria are potent modulators of feeding decisions and reproductive output. This demonstrates how the interaction of specific nutrients with the microbiome can shape behavioral decisions and life history traits.

Author Summary: What animals, including humans, choose to eat has a tremendous impact on health and wellbeing. Though intake of dietary proteins and amino acids is essential for animals, excessive consumption of these nutrients is known to have detrimental effects. Many animals, therefore, execute precise control over the intake of these key nutrients. However, the factors controlling protein appetite are poorly understood.

Here, we show that in the vinegar fly Drosophila melanogaster, essential amino acids and gut bacteria are key modulators of protein appetite. Lack of any one essential amino acid from the diet produces a strong and specific appetite for proteinaceous or amino acid-rich food. However, flies with an appropriate microbiome do not develop this protein appetite. Specifically, two gut bacteria species, Acetobacter pomorum and Lactobacilli, work together to suppress protein appetite. Furthermore, we show that flies lacking dietary essential amino acids have reduced reproductive output, an effect which is also rescued by gut bacteria. Finally, based on metabolite measurements, we propose that the influence of bacteria on host physiology and behavior is not mediated by changing amino acid levels.

Our study demonstrates how the interaction of specific nutrients with the microbiome can shape behavior and animal fitness and suggests that they do so through a novel mechanism.

--- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2002458 Identifying genetic variants that affect viability in large cohorts Hakhamanesh Mostafavi, Tomaz Berisa, Felix R. Day, John R. B. Perry, Molly Przeworski, Joseph K. Pickrell 2017-08-03 2020-05-03 [("doi","10.1371/journal.pbio.2002458")] genetics/heritable genetics/selection/natural/human longevity psychiatry/alzheimers

A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort and to the parents of participants in the UK Biobank. Across the genome, we found only a few common variants with large effects on age-specific mortality: tagging the APOE ε4 allele and near CHRNA3. These results suggest that when large, even late-onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence 1⁄42 traits, we detected a number of strong signals. In participants of the UK Biobank of British ancestry, we found that variants that delay puberty timing are associated with a longer parental life span (P~6.2 × 10−6 for fathers and P~2.0 × 10−3 for mothers), consistent with epidemiological studies. Similarly, variants associated with later age at first birth are associated with a longer maternal life span (P~1.4 × 10−3). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease (CAD), body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. We also found marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of CAD and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical data sets can be used to learn about selection effects in contemporary humans.

Author Summary: Our global understanding of adaptation in humans is limited to indirect statistical inferences from patterns of genetic variation, which are sensitive to past selection pressures. We introduced a method that allowed us to directly observe ongoing selection in humans by identifying genetic variants that affect survival to a given age (ie. viability selection). We applied our approach to the GERA cohort and parents of the UK Biobank participants. We found viability effects of variants near the APOE and CHRNA3 genes, which are associated with the risk of Alzheimer disease and smoking behavior, respectively. We also tested for the joint effect of sets of genetic variants that influence quantitative traits. We uncovered an association between longer life span and genetic variants that delay puberty timing and age at first birth. We also detected detrimental effects of higher genetically predicted cholesterol levels, body mass index, risk of coronary artery disease (CAD), and risk of asthma on survival. Some of the observed effects differ between males and females, most notably those at the CHRNA3 gene and variants associated with risk of CAD and cholesterol levels. Beyond this application, our analysis shows how large biomedical data sets can be used to study natural selection in humans.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.0020041 Assumption-Free Estimation of Heritability from Genome-Wide Identity-by-Descent Sharing between Full Siblings Peter M. Visscher, Sarah E. Medland, Manuel A. R. Ferreira, Katherine I. Morley, Gu Zhu, Belinda K. Cornes, Grant W. Montgomery, Nicholas G. Martin 2006-02-06 2020-05-03 [("doi","10.1371/journal.pgen.0020041")] genetics/heritable

The study of continuously varying, quantitative traits is important in evolutionary biology, agriculture, and medicine. Variation in such traits is attributable to many, possibly interacting, genes whose expression may be sensitive to the environment, which makes their dissection into underlying causative factors difficult. An important population parameter for quantitative traits is heritability, the proportion of total variance that is due to genetic factors. Response to artificial and natural selection and the degree of resemblance between relatives are all a function of this parameter. Following the classic paper by R. A. Fisher in 1918, the estimation of additive and dominance genetic variance and heritability in populations is based upon the expected proportion of genes shared between different types of relatives, and explicit, often controversial and untestable models of genetic and non-genetic causes of family resemblance. With genome-wide coverage of genetic markers it is now possible to estimate such parameters solely within families using the actual degree of identity-by-descent sharing between relatives. Using genome scans on 4,401 quasi-independent sib pairs of which 3,375 pairs had phenotypes, we estimated the heritability of height from empirical genome-wide identity-by-descent sharing, which varied 0.374–0.617 (mean 0.498, standard deviation 0.036). The variance in identity-by-descent sharing per chromosome and per genome was consistent with theory. The maximum likelihood estimate of the heritability for height was 0.80 with no evidence for non-genetic causes of sib resemblance, consistent with results from independent twin and family studies but using an entirely separate source of information. Our application shows that it is feasible to estimate genetic variance solely from within-family segregation and provides an independent validation of previously untestable assumptions. Given sufficient data, our new paradigm will allow the estimation of genetic variation for disease susceptibility and quantitative traits that is free from confounding with non-genetic factors and will allow partitioning of genetic variation into additive and non-additive components.

Synopsis:

Quantitative geneticists attempt to understand variation between individuals within a population for traits such as height in humans and the number of bristles in fruit flies. This has been traditionally done by partitioning the variation in underlying sources due to genetic and environmental factors, using the observed amount of variation between and within families. A problem with this approach is that one can never be sure that the estimates are correct, because nature and nurture can be confounded without one knowing it. The authors got around this problem by comparing the similarity between relatives as a function of the exact proportion of genes that they have in common, looking only within families. Using this approach, the authors estimated the amount of total variation for height in humans that is due to genetic factors from 3,375 sibling pairs. For each pair, the authors estimated the proportion of genes that they share from DNA markers. It was found that about 80% of the total variation can be explained by genetic factors, close to results that are obtained from classical studies. This study provides the first validation of an estimate of genetic variation by using a source of information that is free from nature-nurture assumptions.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1005717 An Empirical Bayes Mixture Model for Effect Size Distributions in Genome-Wide Association Studies Wesley K. Thompson, Yunpeng Wang, Andrew J. Schork, Aree Witoelar, Verena Zuber, Shujing Xu, Thomas Werge, Dominic Holland, N. A. NA, Ole A. Andreassen, Anders Martin Dale 2015-11-10 2020-05-03 [("doi","10.1371/journal.pgen.1005717")] genetics/heritable

Characterizing the distribution of effects from genome-wide genotyping data is crucial for understanding important aspects of the genetic architecture of complex traits, such as number or proportion of non-null loci, average proportion of phenotypic variance explained per non-null effect, power for discovery, and polygenic risk prediction. To this end, previous work has used effect-size models based on various distributions, including the normal and normal mixture distributions, among others. In this paper we propose a scale mixture of two normals model for effect size distributions of genome-wide association study (GWAS) test statistics. Test statistics corresponding to null associations are modeled as random draws from a normal distribution with zero mean; test statistics corresponding to non-null associations are also modeled as normal with zero mean, but with larger variance. The model is fit via minimizing discrepancies between the parametric mixture model and resampling-based nonparametric estimates of replication effect sizes and variances. We describe in detail the implications of this model for estimation of the non-null proportion, the probability of replication in de novo samples, the local false discovery rate, and power for discovery of a specified proportion of phenotypic variance explained from additive effects of loci surpassing a given statistical-significance threshold. We also examine the crucial issue of the impact of linkage disequilibrium (LD) on effect sizes and parameter estimates, both analytically and in simulations. We apply this approach to meta-analysis test statistics from two large GWAS, one for Crohn’s disease (CD) and the other for schizophrenia (SZ). A scale mixture of two normals distribution provides an excellent fit to the SZ nonparametric replication effect size estimates. While capturing the general behavior of the data, this mixture model underestimates the tails of the CD effect size distribution. We discuss the implications of pervasive small but replicating effects in CD and SZ on genomic control and power. Finally, we conclude that, despite having very similar estimates of variance explained by genotyped SNPs, CD and SZ have a broadly dissimilar genetic architecture, due to differing mean effect size and proportion of non-null loci.

We describe in detail the implications of a particular mixture model (a scale mixture of two normals) for effect size distributions from genome-wide genotyping data. Parameters from this model can be used for estimation of the non-null proportion, the probability of replication in de novo samples, the local false discovery rate, power for detecting non-null loci, and proportion of variance explained from additive effects. Here, we fit this model by minimizing discrepancies with nonparametric estimates from a resampling-based algorithm. We examine the effects of linkage disequilibrium (LD) on effect sizes and parameter estimates, both analytically and in simulations. We validate this approach using meta-analysis test statistics (“z-scores”) from two large GWAS, one for Crohn’s disease and the other for schizophrenia. We demonstrate that for these studies a scale mixture of two normal distributions generally fits empirical replication effect sizes well, providing an excellent fit for the schizophrenia effect sizes but underestimating the tails of the distribution for Crohn’s disease.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006498 Genetic Variation in the Social Environment Contributes to Health and Disease Amelie Baud, Megan K. Mulligan, Francesco Paolo Casale, Jesse F. Ingels, Casey J. Bohl, Jacques Callebert, Jean-Marie Launay, Jon Krohn, Andres Legarra, Robert W. Williams, Oliver Stegle 2016-11-21 2020-05-03 [("doi","10.1371/journal.pgen.1006498")] genetics/heritable psychiatry/anxiety sociology

Assessing the impact of the social environment on health and disease is challenging. As social effects are in part determined by the genetic makeup of social partners, they can be studied from associations between genotypes of one individual and phenotype of another (social genetic effects, SGE, also called indirect genetic effects). For the first time we quantified the contribution of SGE to more than 100 organismal phenotypes and genome-wide gene expression measured in laboratory mice. We find that genetic variation in cage mates (ie. SGE) contributes to variation in organismal and molecular measures related to anxiety, wound healing, immune function, and body weight. Social genetic effects explained up to 29% of phenotypic variance, and for several traits their contribution exceeded that of direct genetic effects (effects of an individual’s genotypes on its own phenotype). Importantly, we show that ignoring SGE can severely bias estimates of direct genetic effects (heritability). Thus SGE may be an important source of “missing heritability” in studies of complex traits in human populations. In summary, our study uncovers an important contribution of the social environment to phenotypic variation, sets the basis for using SGE to dissect social effects, and identifies an opportunity to improve studies of direct genetic effects.

Author Summary: Daily interactions between individuals can influence their health both in positive and negative ways. Often the mechanisms mediating social effects are unknown, so current approaches to study social effects are limited to a few phenotypes for which the mediating mechanisms are known a priori or suspected. Here we propose to leverage the fact that most traits are genetically controlled to investigate the influence of the social environment. To do so, we study associations between genotypes of one individual and phenotype of another individual (social genetic effects, SGE, also called indirect genetic effects). Importantly, SGE can be studied even when the traits that mediate the influence of the social environment are not known. For the first time we quantified the contribution of SGE to more than 100 organismal phenotypes and genome-wide gene expression measured in laboratory mice. We find that genetic variation in cage mates (ie. SGE) explains up to 29% of the variation in anxiety, wound healing, immune function, and body weight. Hence our study uncovers an unexpectedly large influence of the social environment. Additionally, we show that ignoring SGE can severely bias estimates of direct genetic effects (effects of an individual’s genotypes on its own phenotype), which has important implications for the study of the genetic basis of complex traits.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1009021 Evaluation of polygenic prediction methodology within a reference-standardized framework Oliver Pain, Kylie P. Glanville, Saskia P. Hagenaars, Saskia Selzam, Anna E. Fürtjes, Héléna A. Gaspar, Jonathan R. I. Coleman, Kaili Rimfeld, Gerome Breen, Robert Plomin, Lasse Folkersen, Cathryn M. Lewis, David Balding, Vincent Plagnol, David Balding, Vincent Plagnol, David Balding, Vincent Plagnol 2021-03-28 2021-03-28 [("doi","10.1371/journal.pgen.1009021")] genetics/heritable

The predictive utility of polygenic scores is increasing, and many polygenic scoring methods are available, but it is unclear which method performs best. This study evaluates the predictive utility of polygenic scoring methods within a reference-standardized framework, which uses a common set of variants and reference-based estimates of linkage disequilibrium and allele frequencies to construct scores. Eight polygenic score methods were tested: p-value thresholding and clumping (pT+clump), SBLUP, lassosum, LDpred1, LDpred2, PRScs, DBSLMM and SBayesR, evaluating their performance to predict outcomes in UK Biobank and the Twins Early Development Study (TEDS). Strategies to identify optimal p-value thresholds and shrinkage parameters were compared, including 10× cross validation, pseudovalidation and infinitesimal models (with no validation sample), and multi-polygenic score elastic net models. LDpred2, lassosum and PRScs performed strongly using 10× cross-validation to identify the most predictive p-value threshold or shrinkage parameter, giving a relative improvement of 16–18% over pT+clump in the correlation between observed and predicted outcome values. Using pseudovalidation, the best methods were PRScs, DBSLMM and SBayesR. PRScs pseudovalidation was only 3% worse than the best polygenic score identified by 10× cross validation. Elastic net models containing polygenic scores based on a range of parameters consistently improved prediction over any single polygenic score. Within a reference-standardized framework, the best polygenic prediction was achieved using LDpred2, lassosum and PRScs, modeling multiple polygenic scores derived using multiple parameters. This study will help researchers performing polygenic score studies to select the most powerful and predictive analysis methods.

Author Summary: An individual’s genetic predisposition to a given outcome can be summarized using polygenic scores. Polygenic scores are widely used in research and could also be used in a clinical setting to enhance personalized medicine. A range of methods have been developed for calculating polygenic scores, but it is unclear which methods are the best. Several methods provide multiple polygenic scores for each individual which must then be tested in an independent tuning sample to identify which polygenic score is most accurate. Other methods provide a single polygenic score and therefore do not require a tuning sample. Our study compares the prediction accuracy of eight leading polygenic scoring methods in a range of contexts. For methods that calculate multiple polygenic scores, we find that LDpred2, lassosum, and PRScs methods perform best on average. For methods that provide a single polygenic score, not requiring a tuning sample, we find PRScs performs best, and the faster DBSLMM and SBayesR methods also perform well. Our study has provided a comprehensive comparison of polygenic scoring methods that will guide future implementation of polygenic scores in both research and clinical settings.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1009695 Genome scans of facial features in East Africans and cross-population comparisons reveal novel associations Chenxing Liu, Myoung Keun Lee, Sahin Naqvi, Hanne Hoskens, Dongjing Liu, Julie D. White, Karlijne Indencleef, Harold Matthews, Ryan J. Eller, Jiarui Li, Jaaved Mohammed, Tomek Swigut, Stephen Richmond, Mange Manyama, Benedikt Hallgrímsson, Richard A. Spritz, Eleanor Feingold, Mary L. Marazita, Joanna Wysocka, Susan Walsh, Mark D. Shriver, Peter Claes, Seth M. Weinberg, John R. Shaffer 2021-07-02 2021-07-02 [("doi","10.1371/journal.pgen.1009695")] genetics/heritable

Facial morphology is highly variable, both within and among human populations, and a sizable portion of this variation is attributable to genetics. Previous genome scans have revealed more than 100 genetic loci associated with different aspects of normal-range facial variation. Most of these loci have been detected in Europeans, with few studies focusing on other ancestral groups. Consequently, the degree to which facial traits share a common genetic basis across diverse sets of humans remains largely unknown. We therefore investigated the genetic basis of facial morphology in an East African cohort. We applied an open-ended data-driven phenotyping approach to a sample of 2,595 3D facial images collected on Tanzanian children. This approach segments the face into hierarchically arranged, multivariate features that capture the shape variation after adjusting for age, sex, height, weight, facial size and population stratification. Genome scans of these multivariate shape phenotypes revealed (p < 2.5 × 10−8) signals at 20 loci, which were enriched for active chromatin elements in human cranial neural crest cells and embryonic craniofacial tissue, consistent with an early developmental origin of the facial variation. Two of these associations were in highly conserved regions showing craniofacial-specific enhancer activity during embryological development (5q31.1 and 12q21.31). Six of the 20 loci surpassed a stricter threshold accounting for multiple phenotypes with study-wide statistical-significance (p < 6.25 × 10−10). Cross-population comparisons indicated 10 association signals were shared with Europeans (seven sharing the same associated SNP), and facilitated fine-mapping of causal variants at previously reported loci. Taken together, these results may point to both shared and population-specific components to the genetic architecture of facial variation.

Author Summary: Genetic factors play an important role in shaping human facial features. Over the last decade, studies have identified numerous genes associated with various facial traits. The vast majority of these studies have focused on European or Asian populations, while African populations have been underrepresented. Increasing the diversity of these analyses can reveal novel associations and cross-population analyses can help deepen our understanding of known genetic associations. We therefore performed a genome scan of 3D facial features in African children from Tanzania and then compared our results to Europeans. We found 20 regions of the genome associated with facial shape in Tanzanian children, 10 of which were also present in Europeans, indicating evidence for a partly shared genetic basis for human facial shape across populations. In addition, about half of the genetic associations observed in Tanzanians were not present in Europeans, and some of the shared signals differed between populations in the specific genetic variants associated or specific facial traits affected. These results shed light on the shared and population-specific genetic contributors to normal-range facial variation.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1009750 Genome-wide methylation data improves dissection of the effect of smoking on body mass index Carmen Amador, Yanni Zeng, Michael Barber, Rosie M. Walker, Archie Campbell, Andrew M. McIntosh, Kathryn L. Evans, David J. Porteous, Caroline Hayward, James F. Wilson, Pau Navarro, Chris S. Haley 2021-07-28 2021-07-28 [("doi","10.1371/journal.pgen.1009750")] genetics/heritable longevity/epigenetics nicotine

Variation in obesity-related traits has a genetic basis with heritabilities 40–70%. While the global obesity pandemic is usually associated with environmental changes related to lifestyle and socioeconomic changes, most genetic studies do not include all relevant environmental covariates, so the genetic contribution to variation in obesity-related traits cannot be accurately assessed. Some studies have described interactions between a few individual genes linked to obesity and environmental variables but there is no agreement on their total contribution to differences between individuals. Here we compared self-reported smoking data and a methylation-based proxy to explore the effect of smoking and genome-by-smoking interactions on obesity related traits from a genome-wide perspective to estimate the amount of variance they explain. Our results indicate that exploiting omic measures can improve models for complex traits such as obesity and can be used as a substitute for, or jointly with, environmental records to better understand causes of disease.

Author Summary: Most diseases and health-related outcomes are influenced by genetic and environmental variation. Hundreds of genetic variants associated with obesity-related traits, like body mass index (BMI), have been previously identified, as well as lifestyles contributing to obesity risk. Furthermore, certain combinations of genetic variants and lifestyles may change the risk of obesity more than expected from their individual effects. One obstacle to further research is the difficulty in measuring relevant environmental impacts on individuals. Here, we studied how genetics (genome-wide markers) and tobacco smoking (self-reported) affect BMI. We also used DNA methylation, a blood-based biomarker, as a proxy for to self-reported information to assess tobacco usage. We incorporated the effect of interactions between genetics and self-reported smoking or methylation. We estimated that genetics accounted for 50% of the variation in BMI. Self-reported smoking status contributed only 2% of BMI variation, increasing to 22% when estimated using DNA methylation. Interactions between genes and smoking contributed an extra 10%. This work highlights the potential of using biomarkers to proxy lifestyle measures and expand our knowledge on disease and suggests that the environment may have long-term effects on our health through its impact on the methylation of disease-associated loci.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006618 Widespread signatures of positive selection in common risk alleles associated to autism spectrum disorder Renato Polimanti, Joel Gelernter 2017-02-07 2020-05-03 [("doi","10.1371/journal.pgen.1006618")] genetics/heritable/correlation genetics/selection/natural/human psychiatry/adhd psychiatry/autism psychiatry/schizophrenia

The human brain is the outcome of innumerable evolutionary processes; the systems genetics of psychiatric disorders could bear their signatures. On this basis, we analyzed five psychiatric disorders, attention deficit hyperactivity disorder, autism spectrum disorder (ASD), bipolar disorder, major depressive disorder, and schizophrenia (SCZ), using GWAS summary statistics from the Psychiatric Genomics Consortium. Machine learning-derived scores were used to investigate two natural-selection scenarios: complete selection (loci where a selected allele reached fixation) and incomplete selection (loci where a selected allele has not yet reached fixation). ASD GWAS results positively correlated with incomplete-selection (p = 3.53×10−4). Variants with ASD GWAS p < 0.1 were shown to have a 19%-increased probability to be in the top-5% for incomplete-selection score (OR = 1.19, 95%CI = 1.11–1.8, p = 9.56×10−7). Investigating the effect directions of minor alleles, we observed an enrichment for positive associations in SNPs with ASD GWAS p < 0.1 and top-5% incomplete-selection score (permutation p < 10−4). Considering the set of these ASD-positive-associated variants, we observed gene-expression enrichments for brain and pituitary tissues (p = 2.3×10−5 and p = 3×10−5, respectively) and 53 gene ontology (GO) enrichments, such as nervous system development (GO:0007399, p = 7.57×10−12), synapse organization (GO:0050808, p = 8.29×10−7), and axon guidance (GO:0007411, p = 1.81×10−7). Previous genetic studies demonstrated that ASD positively correlates with childhood intelligence, college completion, and years of schooling. Accordingly, we hypothesize that certain ASD risk alleles were under positive selection during human evolution due to their involvement in neurogenesis and cognitive ability.

Author Summary: Predisposition to psychiatric disorders is due to the contribution of many genes involved in numerous molecular mechanisms. Since brain evolution has played a pivotal role in determining the success of the human species, the molecular pathways involved with the onset of mental illnesses are likely to be informative as we seek an understanding of the mechanisms involved in the evolution of human brain. Accordingly, we tested whether the genetics of psychiatric disorders is enriched for signatures of positive selection. We observed a strong finding related to the genetics of autism spectrum disorders (ASD): common risk alleles are enriched for genomic signatures of incomplete selection (loci where a selected allele has not yet reached fixation). The genes where these alleles map tend to be expressed in brain and pituitary tissues, to be involved in molecular mechanisms related to nervous system development, and surprisingly, to be associated with increased cognitive ability. Previous studies identified signatures of purifying selection in genes affected by ASD rare alleles. Accordingly, at least two different evolutionary mechanisms appear to be present in relation to ASD genetics: (1) rare disruptive alleles eliminated by purifying selection; (2) common alleles selected for their beneficial effects on cognitive skills. This scenario would explain ASD prevalence, which is higher than that expected for a trait under purifying selection, as the evolutionary cost of polygenic adaptation related to cognitive ability.

--- https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003750 The prevalence of mental disorders among homeless people in high-income countries: An updated systematic review and meta-regression analysis Stefan Gutwinski, Stefanie Schreiter, Karl Deutscher, Seena Fazel, Caitlin Moyer, Caitlin Moyer, Caitlin Moyer, Caitlin Moyer 2021-08-02 2021-08-02 [("doi","10.1371/journal.pmed.1003750")] psychiatry/alcoholism psychiatry/depression psychiatry/schizophrenia

Background: Homelessness continues to be a pressing public health concern in many countries, and mental disorders in homeless persons contribute to their high rates of morbidity and mortality. Many primary studies have estimated prevalence rates for mental disorders in homeless individuals. We conducted a systematic review and meta-analysis of studies on the prevalence of any mental disorder and major psychiatric diagnoses in clearly defined homeless populations in any high-income country.

Methods and findings: We systematically searched for observational studies that estimated prevalence rates of mental disorders in samples of homeless individuals, using MEDLINE, Embase, PsycInfo, and Google Scholar. We updated a previous systematic review and meta-analysis conducted in 2007, and searched until 1 April 2021. Studies were included if they sampled exclusively homeless persons, diagnosed mental disorders by standardized criteria using validated methods, provided point or up to 12-month prevalence rates, and were conducted in high-income countries. We identified 39 publications with a total of 8,049 participants. Study quality was assessed using the JBI critical appraisal tool for prevalence studies and a risk of bias tool. Random effects meta-analyses of prevalence rates were conducted, and heterogeneity was assessed by meta-regression analyses. The mean prevalence of any current mental disorder was estimated at 76.2% (95% CI 64.0% to 86.6%). The most common diagnostic categories were alcohol use disorders, at 36.7% (95% CI 27.7% to 46.2%), and drug use disorders, at 21.7% (95% CI 13.1% to 31.7%), followed by schizophrenia spectrum disorders (12.4% [95% CI 9.5% to 15.7%]) and major depression (12.6% [95% CI 8.0% to 18.2%]). We found substantial heterogeneity in prevalence rates between studies, which was partially explained by sampling method, study location, and the sex distribution of participants. Limitations included lack of information on certain subpopulations (eg. women and immigrants) and unmet healthcare needs.

Conclusion: Public health and policy interventions to improve the health of homeless persons should consider the pattern and extent of psychiatric morbidity. Our findings suggest that the burden of psychiatric morbidity in homeless persons is substantial, and should lead to regular reviews of how healthcare services assess, treat, and follow up homeless people. The high burden of substance use disorders and schizophrenia spectrum disorders need particular attention in service development. This systematic review and meta-analysis has been registered with PROSPERO (CRD42018085216).

Trial registration: PROSPERO CRD42018085216.

In an updated systematic review and meta analysis, Stefan Gutwinski, Stefanie Schreiter, and colleagues examine the prevalence of mental disorders among individuals who are homeless in high income countries.

Author Summary: Why was this study done?:

What did the researchers do and find?:

What do these findings mean?:

--- https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003864 Psychiatric comorbidity and risk of premature mortality and suicide among those with chronic respiratory diseases, cardiovascular diseases, and diabetes in Sweden: A nationwide matched cohort study of over 1 million patients and their unaffected siblings 2020-05-04 psychiatry --- https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0153039 Atheists and Agnostics Are More Reflective than Religious Believers: Four Empirical Studies and a Meta-Analysis Gordon Pennycook, Robert M. Ross, Derek J. Koehler, Jonathan A. Fugelsang 2016-03-21 2020-05-04 [("doi","10.1371/journal.pone.0153039")] iq philosophy/religion

Individual differences in the mere willingness to think analytically have been shown to predict religious disbelief. Recently, however, it has been argued that analytic thinkers are not actually less religious; rather, the putative association may be a result of religiosity typically being measured after analytic thinking (an order effect).

In light of this possibility, we report 4 studies in which a negative correlation between religious belief and performance on analytic thinking measures is found when religious belief is measured in a separate session. We also performed a meta-analysis on all previously published studies on the topic along with our 4 new studies (n = 15,078, k = 31), focusing specifically on the association between performance on the Cognitive Reflection Test (the most widely used individual difference measure of analytic thinking) and religious belief.

This meta-analysis revealed an overall negative correlation (r) of -0.18, 95% CI [-0.21, -0.16]. Although this correlation is modest, self-identified atheists (n = 133) scored 18.7% higher than religiously affiliated individuals (n = 597) on a composite measure of analytic thinking administered across our 4 new studies (d = 0.72).

Our results indicate that the association between analytic thinking and religious disbelief is not caused by a simple order effect. There is good evidence that atheists and agnostics are more reflective than religious believers.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0000230 High Reinforcing Efficacy of Nicotine in Non-Human Primates Bernard Le Foll, Carrie Wertheim, Steven R. Goldberg 2007-01-24 2020-05-04 [("doi","10.1371/journal.pone.0000230")] nicotine psychology/neuroscience

Although tobacco appears highly addictive in humans, there has been persistent controversy about the ability of its psychoactive ingredient nicotine to induce self-administration behavior in laboratory animals, bringing into question nicotine’s role in reinforcing tobacco smoking. Because of ethical difficulties in inducing nicotine dependence in naïve human subjects, we explored reinforcing effects of nicotine in experimentally-naive non-human primates given access to nicotine for periods of time up to two years.

Five squirrel monkeys with no experimental history were allowed to intravenously self-administer nicotine by pressing one of two levers. The number of presses on the active lever needed to obtain each injection was fixed (fixed-ratio schedule) or increased progressively with successive injections during the session (progressive-ratio schedule), allowing evaluation of both reinforcing and motivational effects of nicotine under conditions of increasing response cost. Over time, a progressive shift toward high rates of responding on the active lever, but not the inactive lever, developed. The monkeys’ behavior was clearly directed toward nicotine self-administration, rather than presentation of environmental stimuli associated with nicotine injection.

Both schedules of reinforcement revealed a high motivation to self-administer nicotine, with monkeys continuing to press the lever when up to 600 lever-presses were needed for each injection of nicotine. Thus, nicotine, by itself, in the absence of behavioral or drug-exposure history, is a robust and highly effective reinforcer of drug-taking behavior in a non-human primate model predictive of human behavior.

This supports the use of nicotinic ligands for the treatment of smokers, and this novel preclinical model offers opportunities to test future medications for the treatment of nicotine dependence.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0013114 Predators Are Attracted to the Olfactory Signals of Prey Nelika K. Hughes, Catherine J. Price, Peter B. Banks 2010-09-04 2020-05-04 [("doi","10.1371/journal.pone.0013114")] cat/psychology/drug/catnip psychology/smell

Background:

Predator attraction to prey social signals can force prey to trade-off the social imperatives to communicate against the profound effect of predation on their future fitness. These tradeoffs underlie theories on the design and evolution of conspecific signaling systems and have received much attention in visual and acoustic signaling modes. Yet while most territorial mammals communicate using olfactory signals and olfactory hunting is widespread in predators, evidence for the attraction of predators to prey olfactory signals under field conditions is lacking.

Methodology/Principal Findings:

To redress this fundamental issue, we examined the attraction of free-roaming predators to discrete patches of scents collected from groups of two and six adult, male house mice, Mus domesticus, which primarily communicate through olfaction. Olfactorily-hunting predators were rapidly attracted to mouse scent signals, visiting mouse scented locations sooner, and in greater number, than control locations. There were no effects of signal concentration on predator attraction to their prey’s signals.

Conclusions:

This implies that communication will be costly if conspecific receivers and eavesdropping predators are simultaneously attracted to a signal., our results also suggest that receivers may be at greater risk of predation when communicating than signalers, as receivers must visit risky patches of scent to perform their half of the communication equation, while signalers need not.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0013898 Estimating the Total Number of Susceptibility Variants Underlying Complex Diseases from Genome-Wide Association Studies Hon-Cheong So, Benjamin H. K. Yip, Pak Chung Sham 2010-10-19 2020-05-04 [("doi","10.1371/journal.pone.0013898")] genetics/heritable

Recently genome-wide association studies (GWAS) have identified numerous susceptibility variants for complex diseases. In this study, we proposed several approaches to estimate the total number of variants underlying these diseases. We assume that the variance explained by genetic markers (Vg) follow an exponential distribution, which is justified by previous studies on theories of adaptation. Our aim is to fit the observed distribution of Vg from GWAS to its theoretical distribution.

The number of variants is obtained by the heritability divided by the estimated mean of the exponential distribution. In practice, due to limited sample sizes, there is insufficient power to detect variants with small effects. Therefore, the power was taken into account in fitting. Besides considering the most variants, we also tried to relax the statistical-significance threshold, allowing more markers to be fitted. The effects of false-positive variants were removed by considering the local false discovery rates. In addition, we developed an alternative approach by directly fitting the z-statistics from GWAS to its theoretical distribution. In all cases, the “winner’s curse” effect was corrected analytically.

Confidence intervals were also derived. Simulations were performed to compare and verify the performance of different estimators (which incorporates various means of winner’s curse correction) and the coverage of the proposed analytic confidence intervals. Our methodology only requires summary statistics and is able to handle both binary and continuous traits.

Finally, we applied the methods to a few real disease examples (lipid traits, type 2 diabetes, and Crohn’s disease) and estimated that hundreds to nearly a thousand variants underlie these traits.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0014331 A Reliability-Generalization Study of Journal Peer Reviews: A Multilevel Meta-Analysis of Inter-Rater Reliability and Its Determinants Lutz Bornmann, Rüdiger Mutz, Hans-Dieter Daniel 2010-11-24 2020-05-04 [("doi","10.1371/journal.pone.0014331")] statistics/peer-review

Background:

This paper presents the first meta-analysis for the inter-rater reliability (IRR) of journal peer reviews. IRR is defined as the extent to which two or more independent reviews of the same scientific document agree.

Methodology/Principal Findings:

Altogether, 70 reliability coefficients (Cohen’s Kappa, intra-class correlation [ICC], and Pearson product-moment correlation [r]) from 48 studies were taken into account in the meta-analysis. The studies were based on a total of 19,443 manuscripts; on average, each study had a sample size of 311 manuscripts (minimum: 28, maximum: 1983). The results of the meta-analysis confirmed the findings of the narrative literature reviews published to date: The level of IRR (mean ICC/r2 = 0.34, mean Cohen’s Kappa = 0.17) was low. To explain the study-to-study variation of the IRR coefficients, meta-regression analyses were calculated using seven covariates. Two covariates that emerged in the meta-regression analyses as statistically-significant to gain an approximate homogeneity of the intra-class correlations indicated that, firstly, the more manuscripts that a study is based on, the smaller the reported IRR coefficients are. Secondly, if the information of the rating system for reviewers was reported in a study, then this was associated with a smaller IRR coefficient than if the information was not conveyed.

Conclusions:

Studies that report a high level of IRR are to be considered less credible than those with a low level of IRR. According to our meta-analysis the IRR of peer assessments is quite limited and needs improvement (eg. reader system).

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0016514 Decapitation in Rats: Latency to Unconsciousness and the ‘Wave of Death’ Clementina M. van. Rijn, Hans Krijnen, Saskia Menting-Hermeling, Anton M. L. Coenen 2011-01-04 2020-05-04 [("doi","10.1371/journal.pone.0016514")] philosophy/mind psychology/neuroscience

The question whether decapitation is a humane method of euthanasia in awake animals is being debated. To gather arguments in this debate, obsolete rats were decapitated while recording the EEG, both of awake rats and of anesthetized rats.

Following decapitation a fast and global loss of power of the EEG was observed; the power in the 13–100 Hz frequency band, expressing cognitive activity, decreased according to an exponential decay function to half the initial value within 4 seconds. Whereas the pre-decapitation EEG of the anesthetized animals showed a burst suppression pattern quite different from the awake animals, the power in the post-decapitation EEG did not differ between the two groups. This might indicate that either the power of the EEG does not correlate well with consciousness or that consciousness is briefly regained in the anesthetized group after decapitation. Remarkably, after 50 seconds (awake group) or 80 seconds (anesthetized group) following decapitation, a high amplitude slow wave was observed. The EEG before this wave had more power than the signal after the wave. This wave might be due to a simultaneous massive loss of membrane potentials of the neurons. Still functioning ion channels, which keep the membrane potential intact before the wave, might explain the observed power difference.

Two conclusions were drawn from this experiment. It is likely that consciousness vanishes within seconds after decapitation, implying that decapitation is a quick and not an inhumane method of euthanasia. It seems that the massive wave which can be recorded ~1 minute after decapitation reflects the ultimate border between life and death.

This observation might have implications in the discussions on the appropriate time for organ donation.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0032541 The Logic of Fashion Cycles Alberto Acerbi, Stefano Ghirlanda, Magnus Enquist 2012-01-27 2020-05-04 [("doi","10.1371/journal.pone.0032541")] psychology/novelty sociology

Many cultural traits exhibit volatile dynamics, commonly dubbed fashions or fads. Here we show that realistic fashion-like dynamics emerge spontaneously if individuals can copy others’ preferences for cultural traits as well as traits themselves. We demonstrate this dynamics in simple mathematical models of the diffusion, and subsequent abandonment, of a single cultural trait which individuals may or may not prefer.

We then simulate the coevolution between many cultural traits and the associated preferences, reproducing power-law frequency distributions of cultural traits (most traits are adopted by few individuals for a short time, and very few by many for a long time), as well as correlations between the rate of increase and the rate of decrease of traits (traits that increase rapidly in popularity are also abandoned quickly and vice versa).

We also establish that alternative theories, that fashions result from individuals signaling their social status, or from individuals randomly copying each other, do not satisfactorily reproduce these empirical observations.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0034731 Executive Functions Predict the Success of Top-Soccer Players Torbjörn Vestberg, Roland Gustafson, Liselotte Maurex, Martin Ingvar, Predrag Petrovic 2012-03-06 2020-05-04 [("doi","10.1371/journal.pone.0034731")] iq

While the importance of physical abilities and motor coordination is non-contested in sport, more focus has recently been turned toward cognitive processes important for different sports. However, this line of studies has often investigated sport-specific cognitive traits, while few studies have focused on general cognitive traits. We explored if measures of general executive functions can predict the success of a soccer player.

The present study used standardized neuropsychological assessment tools assessing players’ general executive functions including on-line multi-processing such as creativity, response inhibition, and cognitive flexibility. In a first cross-sectional part of the study we compared the results between High Division players (HD), Lower Division players (LD) and a standardized norm group.

The result shows that both HD and LD players had better measures of executive functions in comparison to the norm group for both men and women. Moreover, the HD players outperformed the LD players in these tests. In the second prospective part of the study, a partial correlation test showed a statistically-significant correlation between the result from the executive test and the numbers of goals and assists the players had scored two seasons later.

The results from this study strongly suggest that results in cognitive function tests predict the success of ball sport players.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0063972 Psychedelics and Mental Health: A Population Study Teri S. Krebs, Pål-Ørjan Johansen 2013-04-11 2020-05-04 [("doi","10.1371/journal.pone.0063972")] nootropic psychedelic/lsd psychiatry/anxiety psychiatry/depression

Background:

The classical serotonergic psychedelics LSD, psilocybin, mescaline are not known to cause brain damage and are regarded as non-addictive. Clinical studies do not suggest that psychedelics cause long-term mental health problems. Psychedelics have been used in the Americas for thousands of years. Over 30 million people currently living in the US have used LSD, psilocybin, or mescaline.

Objective:

To evaluate the association between the lifetime use of psychedelics and current mental health in the adult population.

Method:

Data drawn from years 2001 to 2004 of the National Survey on Drug Use and Health consisted of 130,152 respondents, randomly selected to be representative of the adult population in the United States. Standardized screening measures for past year mental health included serious psychological distress (K6 scale), mental health treatment (inpatient, outpatient, medication, needed but did not receive), symptoms of eight psychiatric disorders (panic disorder, major depressive episode, mania, social phobia, general anxiety disorder, agoraphobia, post-traumatic stress disorder, and non-affective psychosis), and seven specific symptoms of non-affective psychosis. We calculated weighted odds ratios by multivariate logistic regression controlling for a range of sociodemographic variables, use of illicit drugs, risk taking behavior, and exposure to traumatic events.

Results:

21,967 respondents (13.4% weighted) reported lifetime psychedelic use. There were no statistically-significant associations between lifetime use of any psychedelics, lifetime use of specific psychedelics (LSD, psilocybin, mescaline, peyote), or past year use of LSD and increased rate of any of the mental health outcomes. Rather, in several cases psychedelic use was associated with lower rate of mental health problems.

Conclusion:

We did not find use of psychedelics to be an independent risk factor for mental health problems.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0103049 Modifications to the Aesop’s Fable Paradigm Change New Caledonian Crow Performances Corina J. Logan, Sarah A. Jelbert, Alexis J. Breen, Russell D. Gray, Alex H. Taylor 2014-06-25 2020-05-05 [("doi","10.1371/journal.pone.0103049")] psychology/animal/bird psychology/neuroscience

While humans are able to understand much about causality, it is unclear to what extent non-human animals can do the same. The Aesop’s Fable paradigm requires an animal to drop stones into a water-filled tube to bring a floating food reward within reach. Rook, Eurasian jay, and New Caledonian crow performances are similar to those of children under 7 years of age when solving this task. However, we know very little about the cognition underpinning these birds’ performances.

Here, we address several limitations of previous Aesop’s Fable studies to gain insight into the causal cognition of New Caledonian crows. Our results provide the first evidence that any non-human animal can solve the U-tube task and can discriminate between water-filled tubes of different volumes.

However, our results do not provide support for the hypothesis that these crows can infer the presence of a hidden causal mechanism. They also call into question previous object-discrimination performances.

The methodologies outlined here should allow for more powerful comparisons between humans and other animal species and thus help us to determine which aspects of causal cognition are distinct to humans.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0118106 Political Attitudes Develop Independently of Personality Traits Peter K. Hatemi, Brad Verhulst 2014-12-22 2020-05-05 [("doi","10.1371/journal.pone.0118106")] genetics/heritable psychology/personality sociology

The primary assumption within the recent personality and political orientations literature is that personality traits cause people to develop political attitudes. In contrast, research relying on traditional psychological and developmental theories suggests the relationship between most personality dimensions and political orientations are either not or weak. Research from behavioral genetics suggests the covariance between personality and political preferences is not causal, but due to a common, latent genetic factor that mutually influences both. The contradictory assumptions and findings from these research streams have yet to be resolved. This is in part due to the reliance on cross-sectional data and the lack of longitudinal genetically informative data.

Here, using two independent longitudinal genetically informative samples, we examine the joint development of personality traits and attitude dimensions to explore the underlying causal mechanisms that drive the relationship between these features and provide a first step in resolving the causal question.

We find change in personality over a ten-year period does not predict change in political attitudes, which does not support a causal relationship between personality traits and political attitudes as is frequently assumed. Rather, political attitudes are often more stable than the key personality traits assumed to be predicting them.

Finally, the results from our genetic models find that no additional variance is accounted for by the causal pathway from personality traits to political attitudes. Our findings remain consistent with the original construction of the five-factor model of personality and developmental theories on attitude formation, but challenge recent work in this area.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0160084 Switching Away from Utilitarianism: The Limited Role of Utility Calculations in Moral Judgment Mark Sheskin, Nicolas Baumard 2016-07-13 2020-05-05 [("doi","10.1371/journal.pone.0160084")] philosophy/ethics

Our moral motivations might include a drive towards maximizing overall welfare, consistent with an ethical theory called “utilitarianism.”

However, people show non-utilitarian judgments in domains as diverse as healthcare decisions, income distributions, and penal laws. Rather than these being deviations from a fundamentally utilitarian psychology, we suggest that our moral judgments are generally non-utilitarian, even for cases that are typically seen as prototypically utilitarian.

We show two separate deviations from utilitarianism in such cases: people do not think maximizing welfare is required (they think it is merely acceptable, in some circumstances), and people do not think that equal welfare tradeoffs are even acceptable.

We end by discussing how utilitarian reasoning might play a restricted role within a non-utilitarian moral psychology.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0166738 Genetic Correlation between Body Fat Percentage and Cardiorespiratory Fitness Suggests Common Genetic Etiology Theresia M. Schnurr, Anette P. Gjesing, Camilla H. Sandholt, Anna Jonsson, Yuvaraj Mahendran, Christian T. Have, Claus T. Ekstrøm, Anne-Louise Bjerregaard, Soren Brage, Daniel R. Witte, Marit E. Jørgensen, Mette Aadahl, Betina H. Thuesen, Allan Linneberg, Hans Eiberg, Oluf Pedersen, Niels Grarup, Tuomas O. Kilpeläinen, Torben Hansen 2016-11-02 2020-05-05 [("doi","10.1371/journal.pone.0166738")] exercise genetics/heritable/correlation

Objectives:

It has long been discussed whether fitness or fatness is a more important determinant of health status. If the same genetic factors that promote body fat percentage (body fat%) are related to cardiorespiratory fitness (CRF), part of the concurrent associations with health outcomes could reflect a common genetic origin. In this study we aimed to (1) examine genetic correlations between body fat% and CRF; (2) determine whether CRF can be attributed to a genetic risk score (GRS) based on known body fat% increasing loci; and (3) examine whether the fat mass and obesity associated (FTO) locus associates with CRF.

Method:

Genetic correlations based on pedigree information were examined in a family based cohort (n = 230 from 55 families). For the genetic association analyses, we examined two Danish population-based cohorts (ntotal = 3206). The body fat% GRS was created by summing the alleles of twelve independent risk variants known to associate with body fat%. We assessed CRF as maximal oxygen uptake expressed in milliliters of oxygen uptake per kg of body mass (VO2max), per kg fat-free mass (VO2maxFFM), or per kg fat mass (VO2maxFM). All analyses were adjusted for age and sex, and when relevant, for body composition.

Results:

We found a negative genetic correlation between VO2max and body fat% (ρG = −0.72 (SE ±0.13)). The body fat% GRS associated with decreased VO2max (β = −0.15 mL/kg/min per allele, p = 0.0034, age and sex adjusted). The body fat%-increasing FTO allele was associated with a 0.42 mL/kg/min unit decrease in VO2max per allele (p = 0.0092, age and sex adjusted). Both associations were abolished after additional adjustment for body fat%. The fat% increasing GRS and FTO risk allele were associated with decreased VO2maxFM but not with VO2maxFFM.

Conclusion:

Our findings suggest a shared genetic etiology between whole body fat% and CRF.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190374 Investigation of quercetin and hyperoside as senolytics in adult human endothelial cells HyunTae V. Hwang, Darlene Thuy Tran, Michelle Nicole Rebuffatti, Chin-Shang Li, Anne A. Knowlton 2017-12-13 2020-05-05 [("doi","10.1371/journal.pone.0190374")] longevity/senolytic/d-q

Quercetin has been reported to act as a senolytic by selectively removing senescent endothelial cells, and thus it would seem quercetin could revolutionize the field of gerontology. However, given quercetin’s narrow therapeutic index reported in work done with human umbilical vein endothelial cells (HUVECs), we hypothesized that quercetin is not innocuous for non-senescent adult human vascular endothelial cells at concentrations that have been reported to be safe for proliferating HUVECs. Furthermore, we investigated quercetin 3-D-galactoside (Q3G; hyperoside), an inactive quercetin derivative that needs to be cleaved by beta-galactosidase overexpressed in senescent cells to release quercetin, as a potential safer senolytic. We compared the effectiveness of quercetin and Q3G in primary human coronary artery endothelial cells (HCAEC), which are adult microvascular cells. We found that quercetin caused cell death in non-senescent endothelial cells at a concentration that has been reported to selectively remove senescent cells, and that Q3G was not cytotoxic to either young or senescent cells. Thus, in primary adult human endothelial cells, quercetin and Q3G are not senolytics. Earlier work reporting positive results was done with HUVECs, and given their origin and the disparate findings from the current study, these may not be the best cells for evaluating potential senolytics in clinically relevant endothelial cells.

New and noteworthy:

Previously, quercetin has been reported to be a senolytic, a drug that selectively removes senescent cells, in HUVECs. However, we found neither quercetin nor Q3G was effective as a senolytic for adult human endothelial cells.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0217513 Judgments of effort for magical violations of intuitive physics John McCoy, Tomer Ullman 2019-05-12 2020-05-05 [("doi","10.1371/journal.pone.0217513")] philosophy/religion psychology/dark-knowledge

People spend much of their time in imaginary worlds, and have beliefs about the events that are likely in those worlds, and the laws that govern them. Such beliefs are likely affected by people’s intuitive theories of the real world.

In 3 studies, people judged the effort required to cast spells that cause physical violations. People ranked the actions of spells congruently with intuitive physics. For example, people judge that it requires more effort to conjure up a frog than to levitate it one foot off the ground.

A second study manipulated the target and extent of the spells, and demonstrated with a continuous measure that people are sensitive to this manipulation even between participants.

A pre-registered third study replicated the results of Study 2.

These results suggest that people’s intuitive theories partly account for how they think about imaginary worlds.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0257611 Socio-spatial cognition in cats: Mentally mapping owner’s location from voice Saho Takagi, Hitomi Chijiiwa, Minori Arahori, Atsuko Saito, Kazuo Fujita, Hika Kuroshima 2021-09-06 2021-09-06 [("doi","10.1371/journal.pone.0257611")] cat/psychology

Many animals probably hold mental representations about the whereabouts of others; this is a form of socio-spatial cognition.

We tested whether cats mentally map the spatial position of their owner or a familiar cat to the source of the owner’s or familiar cat’s vocalization. In Experiment 1, we placed one speaker outside a familiar room (speaker 1) and another (speaker 2) inside the room, as far as possible from speaker 1, then we left the subject alone in the room. In the habituation phase, the cat heard its owner’s voice calling its name 5× from speaker 1. In the test phase, shortly after the 5th habituation phase vocalization, one of the two speakers played either the owner’s voice or a stranger’s voice calling the cat’s name once. There were four test combinations of speaker location and sound: Samesound/Samelocation, Samesound/Difflocation, Diffsound/Samelocation, Diffsound/Difflocation.

In line with our prediction, cats showed most surprise in the SamesoundDifflocation condition, where the owner suddenly seemed to be in a new place. This reaction disappeared when we used cat vocalizations (Experiment 2) or non-vocal sounds (Experiment 3) as the auditory stimuli.

Our results suggest that cats have mental representations about their out-of-sight owner linked to hearing the owner’s voice, indicating a previously unidentified socio-spatial cognitive ability.

--- https://journals.sagepub.com/doi/abs/10.1177/0269881119897615 2020-05-05 psychedelic --- https://journals.sagepub.com/doi/abs/10.1177/1037969X17694787 2020-05-05 crime philosophy/ethics --- https://journals.sagepub.com/doi/pdf/10.1177/0956797617744542 2020-05-05 crime genetics/heritable/correlation iq --- https://journals.sagepub.com/doi/pdf/10.2304/plat.2002.2.2.70 2020-05-06 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- https://journals.sagepub.com/doi/suppl/10.1177/0956797619849435/suppl_file/Gladstone_Supplemental_Material_rev.pdf#page=12 Can Psychological Traits Be Inferred From Spending? Evidence From Transaction Data § Table S3: The 5 spending categories most positively and negatively correlated with each of the psychological traits Joe J. Gladstone, Sandra C. Matz, Alain Lemaire 2019 2020-05-06 economics psychology/personality --- https://junyanz.github.io/CycleGAN/ Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks 2020-05-06 ai/nn/gan --- https://justine.lol/sectorlisp2/ LISP with GC in 436 bytes 2020-05-06 cs/algorithm cs/lisp --- https://kirkegaard.substack.com/p/exercise-for-depression-the-evidence 2020-05-06 exercise --- https://knowablemagazine.org/article/living-world/2021/the-curious-case-shrinking-genome 2020-05-06 genetics/heritable --- https://knowingless.com/2016/08/21/421/ 10 Months of Acid 2020-05-06 psychedelic --- https://krebsonsecurity.com/2013/07/mail-from-the-velvet-cybercrime-underground/ Mail from the (Velvet) Cybercrime Underground 2020-05-06 crime darknet-market --- https://kwokchain.com/2020/01/23/underutilized-fixed-assets/ Underutilized Fixed Assets 2020-05-06 economics --- https://larc.cardozo.yu.edu/cgi/viewcontent.cgi?article=1232&context=faculty-articles Do bad things happen when works enter the public domain?: Empirical tests of copyright term extension Buccafusco, Heald 2013 2020-05-06 economics/copyright --- https://lareviewofbooks.org/article/psychedelic-pioneer-and-confidence-man/ Psychedelic Pioneer and Confidence Man 2020-05-06 psychedelic --- https://latitude.io/blog/introducing-ai-dungeon-translate/ AI Dungeon players can now translate their stories into emojis by just clicking a button. 2020-05-07 ai/nn/transformer/gpt/inner-monologue ai/text-style-transfer --- https://lesacreduprintemps19.files.wordpress.com/2012/08/intelligence-a-unifying-construct-for-the-social-sciences-richard-lynn-and-tatu-vanhanen.pdf 2020-05-07 iq/ses --- https://lesacreduprintemps19.files.wordpress.com/2012/11/the-bell-curve.pdf 2020-05-07 iq/ses --- https://lilianweng.github.io/lil-log/2020/01/29/curriculum-for-reinforcement-learning.html#openai Curriculum For Reinforcement Learning 2020-05-07 reinforcement-learning/exploration reinforcement-learning/meta-learning --- https://lilianweng.github.io/lil-log/2020/08/06/neural-architecture-search.html#openai Neural Architecture Search 2020-05-07 reinforcement-learning/meta-learning --- https://lilianweng.github.io/lil-log/2021/12/05/semi-supervised-learning.html Semi Supervised Learning 2020-05-07 ai/scaling --- https://link.springer.com/article/10.1007/s00122-021-03945-5 2020-05-07 genetics/selection/artificial/index-selection --- https://link.springer.com/article/10.1007/s12119-021-09901-1 2020-05-07 philosophy/ethics --- https://link.springer.com/article/10.3758/s13421-018-0872-y Memory and availability-biased metacognitive illusions for flags of varying familiarity 2018 2020-05-07 psychology/cognitive-bias/illusion-of-depth --- https://lizengland.com/blog/2014/04/the-door-problem/ The Door Problem 2020-05-07 design psychology/cognitive-bias/illusion-of-depth --- https://longitudinal.blog/co2-series-part-2-co2-removal/ Climate technology primer (2/3): CO2 removal 2020-05-07 technology/carbon-capture --- https://longreads.com/2018/09/10/ugly-history-of-beautiful-things-perfume/ The Ugly History of Beautiful Things: Perfume 2020-05-08 psychology/smell --- https://longtermrisk.org/the-importance-of-wild-animal-suffering/ The Importance of Wild-Animal Suffering 2020-05-08 philosophy/ethics --- https://magazine.atavist.com/an-arrogant-way-of-killing/ The Mastermind Episode 1: An Arrogant Way of Killing 2020-05-08 bitcoin crime technology --- https://mako.cc/academic/hill-almost_wikipedia-DRAFT.pdf 2020-05-08 wikipedia --- https://maps.org/images/pdf/books/K-DreamsKJansenMAPS.pdf Ketamine: Dreams and Realities Jansen 2004 2020-05-08 darknet-market psychedelic --- https://marginalrevolution.com/marginalrevolution/2013/06/the-oocyte-cartel.html The Society for Assisted Reproductive Technology (SART) represents more than 85% of the assisted reproduction industry. SART requires that its members work only with agencies that limit compensation to egg-donors to around $5,000 or a maximum of $10,000 (figures decided upon by the ethics committee of an affiliated organization, The American Society for Reproductive Medicine (ASRM)). In other words, ASRM-SART acts as a buyer's cartel. 2020-05-08 philosophy/ethics --- https://markusstrasser.org/extracting-knowledge-from-literature/ 2020-05-08 ai biology design economics/automation --- https://mattsclancy.substack.com/p/remote-work-and-the-future-of-innovation Remote Work and the Future of Innovation 2020-05-08 economics/automation --- https://medium.com/@adi.fu7/ai-accelerators-part-iv-the-very-rich-landscape-17481be80917 AI Accelerators, Part IV: The Very Rich Landscape Adi Fuchs 2020-05-08 ai/scaling/hardware --- https://medium.com/@blaisea/do-large-language-models-understand-us-6f881d6d8e75 Do large language models understand us? 2020-05-08 ai/nn/transformer/gpt/lamda philosophy/mind --- https://medium.com/@jamesonthecrow/creating-a-17kb-style-transfer-model-with-layer-pruning-and-quantization-864d7cc53693 Creating a 17 KB style transfer model with layer pruning and quantization Jameson Toole 2020-05-09 ai/nn/cnn ai/nn/sparsity --- https://medium.com/@liam.d.eloie/pok%C3%A9mon-ai-gotta-create-em-all-3e92915fa3ad Pokemon AI: Gotta Create ’Em All! Liam Eloie 2020-05-09 ai/nn/transformer/gpt/dall-e --- https://medium.com/@mattbagg/psychedelics-in-american-religious-experience-b771dd61d346 Psychedelics in American Religious Experience 2020-05-09 nootropic psychedelic --- https://medium.com/@silicondomme/hacking-the-holocaust-abcd332947ae Hacking the Holocaust. Remembering the data pirates, forgers Ava Ex Machina 2020-05-09 philosophy/ethics --- https://medium.com/@tonyszhou/postmortem-1b338537fabc Postmortem: Every Frame a Painting Tony Zhou 2020-05-09 economics/copyright --- https://medium.com/applied-data-science/how-to-build-your-own-alphazero-ai-using-python-and-keras-7f664945c188 How to build your own AlphaZero AI using Python and Keras David Foster 2020-05-09 reinforcement-learning/model/alphago --- https://medium.com/coinmonks/nfts-101-why-nfts-are-a-generational-innovation-4626ae803e3b NFTs 101: Why NFTs are a generational innovation 2020-05-09 bitcoin psychology/collecting --- https://medium.com/conversations-with-tyler/tyler-cowen-robert-wiblin-stubborn-attachments-80000-hours-podcast-359aa62aa8ab Rob Wiblin interviews Tyler on Stubborn Attachments (BONUS) 2020-05-09 economics philosophy/ethics --- https://medium.com/dr-jon-brock/can-gut-bacteria-cause-autism-in-mice-582306fd7235 Can gut bacteria cause autism (in mice)? 2020-05-09 genetics/microbiome --- https://medium.com/future-crunch/99-reasons-2017-was-a-good-year-d119d0c32d19 99 Reasons 2017 Was A Great Year Angus Hervey Future Crunch 2020-05-09 philosophy/ethics --- https://terrycrowley.medium.com/education-of-a-programmer-aaecf2d35312 Education of a Programmer. When I left Microsoft in October 2016 Terry Crowley 2020-05-09 cs/end-to-end-principle technology --- https://medium.com/mindsoft/rats-in-doom-eb6c52c73aca Rats in Doom: a novel VR setup for rodents 2020-05-10 psychology/neuroscience --- https://medium.com/ml-everything/using-gpt-3-to-explain-jokes-2001a5aefb68 Using GPT-3 to Explain Jokes 2020-05-10 ai/nn/transformer/gpt/3/humor fiction/humor --- https://medium.proto.life/andrew-hessel-human-genome-project-write-d15580dd0885 The Human Operating System Gets an Overhaul 2020-05-10 genetics/genome-synthesis/virus-proof --- https://medium.com/neodotlife/q-a-with-drew-endy-bde0950fd038 Is the World Ready for Synthetic People? 2020-05-10 genetics/genome-synthesis --- https://medium.com/starsky-robotics-blog/the-poor-roi-of-autonomy-f5d6f4f2dd14 The Poor ROI of Autonomy. A Product Dive on how most ROI comes... Stefan Seltz-Axmacher 2020-05-10 economics/automation --- https://medium.com/syncedreview/openais-long-pursuit-of-dota-2-mastery-1d3a861472bd OpenAI’s Long Pursuit of Dota 2 Mastery SyncedReview 2020-05-10 reinforcement-learning/model-free/oa5 --- https://melmagazine.com/en-us/story/ultramarathon-lsd I Ran an Ultramarathon Tripping Balls on LSD 2020-05-10 psychedelic --- https://metaismurder.com/post/44155254813/the-charisma-of-leaders Meta is Murder 2020-05-10 psychology/personality --- https://metarationality.com/rational-pcr Doing being rational: polymerase chain reaction 2020-05-10 philosophy/epistemology --- https://meteuphoric.com/2010/08/08/on-the-hostility-of-wives/ Why do ‘respectable’ women want dead husbands? 2020-05-10 cryonics --- https://meteuphoric.com/2010/08/08/on-the-hostility-of-wives/#comment-1393 Why do ‘respectable’ women want dead husbands? § comment #1393 2020-05-10 cryonics --- https://meteuphoric.com/2015/03/08/the-economy-of-weirdness/ The economy of weirdness 2020-05-11 psychology/novelty --- https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-017-0321-3 Microbial regulation of microRNA expression in the amygdala and prefrontal cortex Microbiome 2020-05-11 genetics/microbiome --- https://minimaxir.com/2021/06/gpt-j-6b/ Fun and Dystopia With AI-Based Code Generation Using GPT-J-6B 2020-05-11 ai/nn/transformer/gpt/codex --- https://misinfounderload.substack.com/p/tales-from-prediction-markets Tales from Prediction Markets 2020-05-11 statistics/prediction --- https://mlberkeley.substack.com/p/clip-art Alien Dreams: An Emerging Art Scene 2020-05-11 ai/nn/transformer/clip --- https://ml.berkeley.edu/blog/posts/dalle2/ 2020-05-11 ai/nn/transformer/gpt/dall-e --- https://ml.berkeley.edu/blog/posts/vq-vae/ 2020-05-11 ai/nn/transformer/gpt/dall-e --- https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/ AlphaFold @ CASP13: ‘What just happened?’ 2020-05-11 ai/nn/transformer/alphafold --- https://moalquraishi.wordpress.com/2020/12/08/alphafold2-casp14-it-feels-like-ones-child-has-left-home/ AlphaFold2 @ CASP14: ‘It feels like one’s child has left home.’ 2020-05-11 ai/nn/transformer/alphafold --- https://moalquraishi.wordpress.com/2021/07/25/the-alphafold2-method-paper-a-fount-of-good-ideas/ The AlphaFold2 Method Paper: A Fount of Good Ideas 2020-05-11 ai/nn/transformer/alphafold --- https://moffittcaspi.trinity.duke.edu/sites/moffittcaspi.trinity.duke.edu/files/Agression_ViolentBehavior.pdf 2020-05-11 crime genetics/heritable --- https://mosaicscience.com/story/rats-and-dogs-medical-detection-animals-smell-TB-cancer/ 2020-05-12 psychology/smell --- https://moultano.wordpress.com/2021/07/20/tour-of-the-sacred-library/ Tour of the Sacred Library 2020-05-12 ai/nn/gan ai/nn/transformer/clip/sample --- https://muse.jhu.edu/article/839285 Misconduct in Bioscience Research: a 40-year perspective 2020-05-12 statistics/bias --- https://nathanieltravis.com/2022/01/17/is-human-behavior-just-elaborate-running-and-tumbling/ 2020-05-12 psychology/neuroscience reinforcement-learning/exploration --- https://nationalinterest.org/print/article/dead-souls-the-denationalization-of-the-american-elite-620 Dead Souls: The Denationalization of the American Elite 2020-05-12 sociology --- https://nautil.us/mapping-the-human-exposome-236726/ Mapping the Human Exposome: It’s now possible to map a person’s lifetime exposure to nutrition, bacteria, viruses, and environmental toxins-which profoundly influence human health 2020-05-12 biology statistics/variance-component --- https://near.blog/the-bouba-kiki-effect-and-sound-symbolism-in-clip/ The Bouba/Kiki Effect And Sound Symbolism In CLIP 2020-05-12 ai/nn/tokenization ai/nn/transformer/clip --- https://newatlas.com/psilocybin-magic-mushrooms-depression-fda-breakthrough-therapy/56928/ Psychedelic psilocybin therapy for depression granted Breakthrough Therapy status by FDA 2020-05-12 psychedelic --- https://news.blizzard.com/en-us/starcraft2/22933138/deepmind-research-on-ladder DeepMind Research on Ladder—StarCraft II 2020-05-12 reinforcement-learning/model-free/alphastar --- https://web.archive.org/web/20141018022010/https://news.nationalgeographic.com/news/2014/10/141015-better-beef-genetics-science-agriculture-environment-ngfood/ Cloning Cows From Steaks (and Other Ways of Building Better Cattle) 2020-05-12 genetics/cloning --- https://www.shopify.com/news/fighting-for-the-future-shopify-invests-5m-in-breakthrough-sustainability-technologies Fighting for the Future: Shopify Invests $5M in Breakthrough Sustainability Technologies Shopify 2020-05-13 technology/carbon-capture --- https://news.yale.edu/2015/09/22/living-artifact-dutch-golden-age-yale-s-367-year-old-water-bond-still-pays-interest A living artifact from the Dutch Golden Age: Yale’s 367-year-old water bond still pays interest 2020-05-13 economics/perpetuities --- https://news.ycombinator.com/item?id=22193451 2020-05-13 psychology/inner-voice --- https://news.ycombinator.com/item?id=27198087 2020-05-13 ai/nn/transformer/gpt/lamda --- https://news.ycombinator.com/item?id=6861533 2020-05-13 psychology/novelty --- https://nickdrozd.github.io/2022/01/10/another-self-cleaning-turing-machine.html Another New Record in Self-Cleaning Turing Machines 2020-05-13 cs/computable --- https://nintil.com/2017/12/28/a-breakthrough-in-moral-psychology/ 2020-05-13 philosophy/ethics --- https://nintil.com/scaling-tacit-knowledge Scaling tacit knowledge 2020-05-13 philosophy/epistemology --- https://x.com/itschloebubble/status/1479208043724242944 2020-05-13 ai/nn/transformer/clip/sample --- https://x.com/itschloebubble/status/1480750050393264128 2020-05-13 ai/nn/transformer/gpt/dall-e --- https://x.com/itschloebubble/status/1488898042858127366 2020-05-13 ai/nn/transformer/clip/sample --- https://x.com/itschloebubble/status/1493430424961306626 2020-05-14 ai/nn/transformer/clip/sample --- https://x.com/ATNPassion/status/1454281747097526273 2020-05-14 ai/nn/transformer/clip/sample --- https://x.com/BioBeef/status/979482261430009856 2020-05-14 genetics/gametogenesis --- https://x.com/CALVINGINEERING/status/1465966322676760585 2020-05-14 design psychology/neuroscience --- https://x.com/CRUORMOR/status/1468449934668177411 2020-05-14 ai/nn/transformer/gpt/dall-e --- https://x.com/EErratica/status/1488388902821974016 2020-05-14 ai/nn/transformer/clip/sample --- https://x.com/FlaminArc/status/1485443678571462656 2020-05-14 ai/nn/transformer/clip/sample --- https://x.com/FlaminArc/status/1485825502116192259 2020-05-14 ai/anime ai/nn/transformer/clip/sample --- https://x.com/HvnsLstAngel/status/1494107434008211459 2020-05-14 ai/nn/transformer/clip/sample --- https://x.com/KKajderowicz/status/1480948462913048577 2020-05-14 longevity --- https://x.com/KiaManniquin/status/1478869190039969793 2020-05-14 ai/nn/transformer/clip/sample --- https://x.com/MichaelFriese10/status/1464488317479636997 2020-05-15 ai/nn/transformer/gpt/dall-e --- https://x.com/MichaelFriese10/status/1478574471950733314 2020-05-15 ai/nn/transformer/gpt/dall-e --- https://x.com/Nearcyan/status/1368737578334228482 2020-05-15 ai/nn/transformer/clip/sample --- https://x.com/Nearcyan/status/1460446659284983813 2020-05-15 ai/nn/transformer/clip/sample --- https://x.com/Nearcyan/status/1494840571025788932 2020-05-15 ai/nn/transformer/clip/sample --- https://x.com/NerdyRodent/status/1456659357941305348 2020-05-15 ai/nn/transformer/gpt/dall-e --- https://x.com/NerdyRodent/status/1456735116651335680 2020-05-15 ai/nn/transformer/gpt/dall-e --- https://x.com/NerdyRodent/status/1456746809154494478 2020-05-15 ai/nn/transformer/gpt/dall-e --- https://x.com/NerdyRodent/status/1456922505411710985 2020-05-15 ai/nn/transformer/gpt/dall-e --- https://x.com/NerdyRodent/status/1456984909873139720 2020-05-15 ai/nn/transformer/gpt/dall-e --- https://x.com/NerdyRodent/status/1457045379732721669 2020-05-16 ai/nn/transformer/gpt/dall-e --- https://x.com/NerdyRodent/status/1459298859717386245 2020-05-16 ai/nn/transformer/gpt/dall-e --- https://x.com/NerdyRodent/status/1463164183877349376 2020-05-16 ai/nn/transformer/gpt/dall-e --- https://x.com/NoaNabeshima/status/1368662246885265409 The new CLIP adversarial examples are partially from the use-mention distinction. CLIP was trained to predict which caption from a list matches an image. It makes sense that a picture of an apple with a large ‘iPod’ label would be captioned with ‘iPod’, not ‘Granny Smith’! 2020-05-16 ai/nn/adversarial ai/nn/transformer/clip/sample --- https://x.com/Norod78/status/1461298889147961350 2020-05-16 ai/nn/transformer/gpt/dall-e --- https://x.com/Norod78/status/1480277349216235534 2020-05-16 ai/nn/transformer/gpt/dall-e --- https://x.com/RiversHaveWings/status/1390398369231642624 2020-05-16 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1459646450275553285 2020-05-16 ai/nn/transformer/clip/sample ai/nn/transformer/gpt --- https://x.com/RiversHaveWings/status/1464043100054065183 2020-05-16 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1464290830923931651 2020-05-16 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1464349872983724032 2020-05-16 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1465002355343060992 2020-05-17 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1465402140856229888 2020-05-17 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1465653084424531972 2020-05-17 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1465668535997267974 2020-05-17 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1466939821570486275 2020-05-17 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1472204342526578690 2020-05-17 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1472213821682970627 2020-05-17 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1472226902433689604 2020-05-17 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1472334873096376320 2020-05-17 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1472632480251273218 2020-05-17 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1473023847125315585 2020-05-17 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1473089788303470595 2020-05-18 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1473094867462942722 2020-05-18 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1474026366504878080 2020-05-18 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1474047184194576386 2020-05-18 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1474050771763167232 2020-05-18 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1475490017120116739 2020-05-18 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1477291918216155138 2020-05-18 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1477422001044086784 2020-05-18 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1478181830582931456 2020-05-18 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1478409418626588673 2020-05-18 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1478523279371501572 2020-05-18 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1479107725179031557 2020-05-19 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1480334841476313091 2020-05-19 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1480367679026307072 2020-05-19 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1480522238101057539 2020-05-19 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1480525551299301376 2020-05-19 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1480528958122725376 2020-05-19 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1480537419799617541 2020-05-19 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1480540614353510403 2020-05-19 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1480917713828696083 2020-05-19 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1480927333137756167 2020-05-19 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1480934939017105411 2020-05-20 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1480936921635463168 2020-05-20 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1480991232725708803 2020-05-20 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1481070681001709568 2020-05-20 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1481072619554504704 2020-05-20 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1481078642977697795 2020-05-20 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1481383445259448321 2020-05-20 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1481395428411146243 2020-05-20 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1481962011063844864 2020-05-20 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1482008363026169858 2020-05-20 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1482012965054279682 2020-05-20 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1482087521576112131 2020-05-21 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1482151807757402115 2020-05-21 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1482185127329366022 2020-05-21 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1487160776771063808 2020-05-21 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1487186223126908931 2020-05-21 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1488201749017939969 2020-05-21 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1488277404619460611 2020-05-21 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1488619885856837632 2020-05-21 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1489671601922535429 2020-05-21 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1490228264333234178 2020-05-21 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1490454352732950529 2020-05-21 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1490642538016829440 2020-05-22 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1491284164905402370 2020-05-22 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1492543985264447492 2020-05-22 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1492544843515449344 2020-05-22 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1494109880629673992 2020-05-22 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1494267445414035464 2020-05-22 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1494299298191663106 2020-05-22 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1494682062900502528 2020-05-22 ai/nn/transformer/clip/sample --- https://x.com/SebastienBubeck/status/1402645428504461319 2020-05-22 ai/nn/adversarial --- https://x.com/Somnai_dreams/status/1470044993662296066 2020-05-22 ai/nn/transformer/clip/sample --- https://x.com/Somnai_dreams/status/1484856337536548871 2020-05-23 ai/nn/transformer/clip/sample --- https://x.com/SteveSilcox/status/1494784931729399826 2020-05-23 ai/anime ai/nn/transformer/clip/sample --- https://x.com/Ted_Underwood/status/1477074940360806400 2020-05-23 ai/nn/transformer/clip/sample --- https://x.com/UrsulaV/status/1467652391059214337 2020-05-23 ai/nn/transformer/clip/sample --- https://x.com/advadnoun/status/1353453719510163459 This is great! Now that the model can be used in PyTorch, I've starting playing with @AydaoAI's anime StyleGAN directly guided by CLIP. Starting slow by searching for Asuka by name in the latent space. 2020-05-23 ai/nn/gan/stylegan/anime ai/nn/transformer/clip/sample --- https://x.com/advadnoun/status/1458894698974638111 2020-05-23 ai/nn/transformer/clip/sample --- https://x.com/advadnoun/status/1478915882457784324 2020-05-23 ai/nn/transformer/clip/sample --- https://x.com/advadnoun/status/1488227703262113798 2020-05-23 ai --- https://x.com/ai_curio/status/1482955980082229251 2020-05-23 ai/nn/transformer/clip/sample --- https://x.com/ak92501/status/1398079706180763649 2020-05-23 ai/nn/transformer/gpt/dall-e --- https://x.com/altsoph/status/1456634385269133314 2020-05-23 ai/nn/transformer/clip/sample --- https://x.com/apeoffire/status/1467432320193994756 2020-05-24 ai/nn/transformer/gpt/dall-e --- https://x.com/apeoffire/status/1467481904169553925 2020-05-24 ai/nn/transformer/gpt/dall-e --- https://x.com/arjunrajlab/status/822202835781685252 2020-05-24 biology --- https://x.com/danielrussruss/status/1455054011485147137 2020-05-24 ai/nn/transformer/clip/sample --- https://x.com/danielrussruss/status/1456236923693924357 2020-05-24 ai/nn/transformer/clip/sample --- https://x.com/danielrussruss/status/1456596432232665091 2020-05-24 ai/nn/transformer/clip/sample --- https://x.com/danielrussruss/status/1456596972693983234 2020-05-24 ai/nn/transformer/clip/sample --- https://x.com/danielrussruss/status/1456845124697157635 2020-05-24 ai/nn/transformer/clip/sample --- https://x.com/danielrussruss/status/1457699402336800772 2020-05-24 ai/nn/transformer/clip/sample --- https://x.com/danielrussruss/status/1482275706570612736 2020-05-24 ai/nn/transformer/clip/sample --- https://x.com/danielrussruss/status/1482567887395065856 2020-05-24 ai/nn/gan ai/nn/transformer/clip/sample --- https://x.com/danielrussruss/status/1486713358032138251 2020-05-25 ai/nn/transformer/clip/sample --- https://x.com/danielrussruss/status/1486986060781350914 2020-05-25 ai/nn/transformer/clip/sample --- https://x.com/danielrussruss/status/1489947340026769412 2020-05-25 ai/nn/transformer/clip/sample --- https://x.com/danielrussruss/status/1493819694628753412 2020-05-25 ai/nn/transformer/clip/sample --- https://x.com/danielrussruss/status/1493832335707443202 2020-05-25 ai/nn/transformer/clip/sample --- https://x.com/deKxi/status/1486909690156322818 2020-05-25 ai/nn/transformer/clip/sample --- https://x.com/deKxi/status/1490908688399597570 2020-05-25 ai/nn/transformer/clip/sample --- https://x.com/deevybee/status/1490374839785373698 2020-05-25 genetics/heritable statistics/bias --- https://x.com/dfedman/status/1344167643306020865 2020-05-25 design --- https://x.com/dginev/status/1479989404810850307 2020-05-25 ai/nn/transformer/clip/sample --- https://x.com/doctorveera/status/1451222612936327172 The current study made a huge jump in the sample size from 700k to 5 million. And it looks like that is all it takes to explain the full SNP-h2 (50%). So, the study seems to have reached it saturation phase where the ~12,000 genome-wide signals explained the full h2SNP of height, which is 50% (note the rest of 30% of the twin h2 is likely explained by rare variants). 2020-05-26 genetics/heritable --- https://x.com/dribnet/status/1427613617973653505 Here's a link to my colab if you'd like to give it a go yourself. This codebase builds off of previous work from many people including @advadnoun @RiversHaveWings @NerdyRodent as well as ClipDraw from @kvfrans @crosslabstokyo @err_more and @okw 2020-05-26 ai/anime ai/nn/transformer/clip/sample --- https://x.com/eyaler/status/1456213028840656896 2020-05-26 ai/nn/transformer/gpt/dall-e --- https://x.com/images_ai/status/1473037136194674696 2020-05-26 ai/nn/transformer/clip/sample --- https://x.com/images_ai/status/1478912560015593481 2020-05-26 ai/nn/transformer/clip/sample --- https://x.com/images_ai/status/1480406280560852992 2020-05-26 ai/nn/transformer/clip/sample --- https://x.com/jamesjyu/status/1467568693806649346 2020-05-26 ai/nn/transformer/gpt fiction --- https://x.com/janellecshane/status/1410986365962133509 2020-05-26 ai/nn/transformer/clip/sample --- https://x.com/jd_pressman/status/1457107477825134594 2020-05-26 ai/nn/transformer/clip/sample --- https://x.com/jd_pressman/status/1463358855530704901 2020-05-26 ai/nn/transformer/clip/sample --- https://x.com/jd_pressman/status/1463384274300669960 2020-05-26 ai/nn/transformer/clip/sample --- https://x.com/jd_pressman/status/1468007754144960514 2020-05-27 ai/nn/gan ai/nn/transformer/clip/sample --- https://x.com/jd_pressman/status/1477752459212701700 2020-05-27 ai/nn/transformer/clip/sample --- https://x.com/jd_pressman/status/1478296628679245825 2020-05-27 ai/nn/transformer/clip/sample --- https://x.com/jfatkey/status/1479139883717328897 2020-05-27 economics/georgism --- https://x.com/l4rz/status/1456972940373757961 2020-05-27 ai/nn/transformer/gpt/dall-e --- https://x.com/l4rz/status/1457783810947305472 2020-05-27 ai/nn/transformer/gpt/dall-e --- https://x.com/maxjaderberg/status/1483098866098524167 2020-05-27 ai/nn/transformer/alphafold --- https://x.com/metasemantic/status/1349446585952989186 2020-05-27 ai/nn/transformer/clip/sample --- https://x.com/metasemantic/status/1368713208429764616 CLIP + StyleGAN + #mylittlepony A thread 🧵 starting with @ElvisPresley ‘A pony that looks like Elvis Presley’ 2020-05-27 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1476357387170684929 2020-05-27 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1476387586868330496 2020-05-27 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1476432882658791424 2020-05-28 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1476553681227038722 2020-05-28 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1476644276037578774 2020-05-28 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1476810372946403328 2020-05-28 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1477323754585800705 2020-05-28 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1477474749122953218 2020-05-28 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1477535146135875586 2020-05-28 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1477550246368448512 2020-05-28 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1477671040113278983 2020-05-28 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1477761641114599426 2020-05-28 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1478154228065464322 2020-05-28 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1478199525353345027 2020-05-29 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1478290122479063042 2020-05-29 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1478305222220922884 2020-05-29 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1478380722373242884 2020-05-29 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1478441118643548161 2020-05-29 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1478728011788685313 2020-05-29 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1480464609026863106 2020-05-29 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1480932537719570442 2020-05-29 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1481008036030337028 2020-05-29 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1485356685896347648 2020-05-29 ai/nn/transformer/clip/sample --- https://x.com/michelangemoji/status/1487213922898038786 2020-05-30 ai/nn/transformer/clip/sample --- https://x.com/minimaxir/status/1472258886979579904 2020-05-30 ai/anime --- https://x.com/minimaxir/status/1474913997807755268 2020-05-30 ai/anime --- https://x.com/nagolinc/status/1491723508111527938 2020-05-30 ai/anime --- https://x.com/nickwalton00/status/1280972636638437382 I've been testing the largest of @OpenAI's models with AI Dungeon and been constantly impressed at how interesting and dynamic the characters are, like this queen, long thought to be dead, hiding from enemies and not happy about me prying into her personal life. 2020-05-30 ai/nn/transformer/gpt fiction/text-game --- https://x.com/nshepperd1/status/1456584388037148678 2020-05-30 ai/nn/transformer/clip/sample --- https://x.com/nshepperd1/status/1468961836452044802 2020-05-30 ai/nn/transformer/clip/sample --- https://x.com/nshepperd1/status/1470630235372601346 2020-05-30 ai/nn/transformer/clip/sample --- https://x.com/quasimondo/status/1351191660059832320 I added #CLIP to my image labeling tool and have now full text search over my various collections. Here are potato 2020-05-30 ai/nn/transformer/clip/sample --- https://x.com/rvorias/status/1455803823667261446 2020-05-30 ai/nn/transformer/clip/sample --- https://x.com/scottleibrand/status/1430753899460194310 2020-05-30 ai/nn/transformer/gpt/codex --- https://x.com/search?f=tweets&vertical=default&q=BigGAN&src=typd 2020-05-31 ai/nn/gan --- https://x.com/tectonic/status/1484393440049729539 2020-05-31 ai/nn/transformer/clip/sample --- https://x.com/unltd_dream_co/status/1456656100909064193 2020-05-31 ai/nn/transformer/clip/sample --- https://x.com/unltd_dream_co/status/1468614799332462604 2020-05-31 ai/nn/transformer/clip/sample --- https://x.com/unltd_dream_co/status/1469630762932748290 2020-05-31 ai/nn/transformer/clip/sample --- https://x.com/unltd_dream_co/status/1470178584350408706 2020-05-31 ai/nn/transformer/clip/sample --- https://x.com/unltd_dream_co/status/1470360476437598212 2020-05-31 ai/nn/transformer/clip/sample --- https://x.com/unltd_dream_co/status/1471094971763769347 2020-05-31 ai/nn/transformer/clip/sample --- https://x.com/unltd_dream_co/status/1471477329994076161 2020-05-31 ai/nn/transformer/clip/sample --- https://x.com/unltd_dream_co/status/1476664429420892160 2020-05-31 ai/nn/transformer/clip/sample --- https://x.com/unltd_dream_co/status/1476724081164705792 2020-05-31 ai/nn/transformer/clip/sample --- https://x.com/unltd_dream_co/status/1478695436596785154 2020-06-01 ai/nn/transformer/clip/sample --- https://x.com/unltd_dream_co/status/1480968129740263440 2020-06-01 ai/nn/transformer/clip/sample --- https://x.com/v3ga/status/1480299011982737413 2020-06-01 design --- https://nostalgebraist.tumblr.com/post/672300992964050944/franks-image-generation-model-explained 2020-06-01 ai/nn/diffusion --- https://notesonliberty.com/2019/04/16/mr-darcys-ten-thousand-a-year/ Mr. Darcy’s Ten Thousand a Year 2020-06-01 economics/perpetuities --- https://now.tufts.edu/news-releases/moderately-reducing-calories-non-obese-people-reduces-inflammation 2020-06-01 exercise --- https://nutritionj.biomedcentral.com/articles/10.1186/s12937-017-0269-y Cross-sectional association between soda consumption and body mass index in a community-based sample of twins 2020-06-01 exercise --- /doc/ai/nn/gan/stylegan/2021-karras-aliasfreegan-afhq-3-interpolation.mp4 2021 2021 ai/nn/gan/stylegan --- https://nvlabs.github.io/alias-free-gan/ 2020-06-01 ai/nn/gan/stylegan --- https://nymag.com/article/tom-wolfe-radical-chic-that-party-at-lennys.html Radical Chic: That Party at Lenny’s 2020-06-01 sociology/preference-falsification --- https://nymag.com/news/features/synthetic-drugs-2013-4/ Travels in the New Psychedelic Bazaar 2020-06-02 psychedelic --- https://nymag.com/news/features/synthetic-drugs-2013-4/#print Travels in the New Psychedelic Bazaar 2020-06-02 psychedelic --- https://www.reddit.com/r/AnimeResearch/ 2020-06-02 ai/anime ai/nn/gan/stylegan --- https://www.reddit.com/r/DotA2/comments/bf49yk/hello_were_the_dev_team_behind_openai_five_we/ 2020-06-02 reinforcement-learning/model-free/oa5 --- https://www.reddit.com/r/GPT3/comments/ra6nk4/had_gpt3_generate_the_onion_headlines/ 2020-06-02 ai/nn/transformer/gpt fiction/humor --- https://www.reddit.com/r/GPT3/comments/rzhrrt/gpt3_being_a_savage_partly_extremely_black_humor/ 2020-06-02 ai/text-style-transfer --- https://www.reddit.com/r/HobbyDrama/comments/mxcq2i/video_game_creatures_or_how_the_us_navy/ 2020-06-02 philosophy/ethics --- https://www.reddit.com/r/MachineLearning/comments/76xjb5/ama_we_are_david_silver_and_julian_schrittwieser/ 2020-06-02 reinforcement-learning/model/alphago --- https://www.reddit.com/r/MachineLearning/comments/a0nnp7/r_montezumas_revenge_solved_by_goexplore_a_new/ 2020-06-02 reinforcement-learning/exploration --- https://www.reddit.com/r/MachineLearning/comments/ajgzoc/we_are_oriol_vinyals_and_david_silver_from/ 2020-06-02 reinforcement-learning/model-free/alphastar --- https://www.reddit.com/r/MachineLearning/comments/bm7iix/r_adversarial_examples_arent_bugs_theyre_features/ 2020-06-02 ai/nn/adversarial --- https://www.reddit.com/r/bigsleep/comments/tvw5js/list_of_sitesprogramsprojects_that_use_openais/ [P] List of sites/programs/projects that use OpenAI’s CLIP neural network for steering image/video creation to match a text description 2020-06-03 ai/nn/transformer/clip --- https://www.reddit.com/r/MachineLearning/comments/nq4es7/d_unreal_engine_trick_with_vqgan_clip/ 2020-06-03 ai/nn/transformer/clip --- https://www.reddit.com/r/MachineLearning/comments/qlbye5/p_texttoimage_models_rudalle_kandinsky_xxl_12/ 2020-06-03 ai/nn/transformer/gpt/dall-e --- https://www.reddit.com/r/MachineLearning/comments/qmzy8a/rudalle_model_is_opensource_p/ 2020-06-03 ai/nn/transformer/gpt/dall-e --- https://www.reddit.com/r/MachineLearning/comments/rdb1uw/p_utttai_alphazerolike_solution_for_playing/ 2020-06-03 reinforcement-learning/model/alphago --- https://www.reddit.com/r/MediaSynthesis/comments/p5nw28/clip_vqgan_keyword_comparison_by_kingdomakrillic/ 2020-06-03 ai/nn/transformer/clip --- https://www.reddit.com/r/MediaSynthesis/comments/qo9j3i/16_still_life_paintings_created_with_rudalle/ 2020-06-03 ai/nn/transformer/gpt/dall-e --- https://www.reddit.com/r/MediaSynthesis/comments/qoy4pa/a_set_of_fantasythemed_images_generated_with/ 2020-06-03 ai/nn/transformer/gpt/dall-e --- https://www.reddit.com/r/MediaSynthesis/comments/rc9ft8/nsfw_rudalle_texttoimage_model_finetuned_to/ 2020-06-03 ai/nn/transformer/gpt/dall-e --- https://www.reddit.com/r/MediaSynthesis/comments/rej4gn/artstationstyle_asuka_portraits_generated_with/ 2020-06-03 ai/anime ai/nn/transformer/gpt/dall-e --- https://www.reddit.com/r/MediaSynthesis/comments/rgyr6g/flame_rock_sprite_care_instructions_douse_flame/ 2020-06-03 ai/nn/transformer/clip/sample --- https://www.reddit.com/r/MediaSynthesis/comments/rp3e3p/pendants_generated_with_rudalle_finetuned_to/ 2020-06-04 ai/nn/transformer/gpt/dall-e --- https://www.reddit.com/r/MediaSynthesis/comments/rp5wre/colossi_of_the_parched_realm_some_standouts_from/ 2020-06-04 ai/nn/transformer/gpt/dall-e --- https://www.reddit.com/r/MediaSynthesis/comments/rxpz4d/an_experiment_with_openais_glide_apples_in/ 2020-06-04 ai/nn/transformer/gpt/dall-e --- https://www.reddit.com/r/Semaglutide/ 2020-06-04 longevity/glp/semaglutide --- https://www.reddit.com/r/bigsleep/comments/ql9n81/new_texttoimage_ai_models_rudalle_example_from/ 2020-06-04 ai/nn/transformer/gpt/dall-e --- https://www.reddit.com/r/deepdream/comments/qosk4k/alien_ocean_hadal_zone_vqganclip/ 2020-06-04 ai/nn/transformer/clip/sample --- https://www.reddit.com/r/onions/comments/rbzhvc/supertrips_the_pablo_escobar_of_silkroad_did_7/ 2020-06-04 darknet-market --- https://www.reddit.com/r/pokemon/comments/rgmyxp/i_trained_an_ai_on_all_the_official_pokemon/ 2020-06-04 ai/nn/transformer/gpt/dall-e --- https://www.reddit.com/r/reinforcementlearning/comments/8tqzvq/openai_dota_update_ppo_lstm_reaches_amateurlevel/ 2020-06-04 reinforcement-learning/model-free/oa5 --- https://www.reddit.com/r/reinforcementlearning/comments/94uziv/openai_five_benchmark_crushes_audience_team/ 2020-06-04 reinforcement-learning/model-free/oa5 --- https://www.reddit.com/r/reinforcementlearning/comments/99ieuw/n_first_openai_oa5_dota2_match_begins/ 2020-06-05 reinforcement-learning/model-free/oa5 --- https://www.reddit.com/r/reinforcementlearning/comments/99thy9/openais_oa5_vs_pro_dota2_matches_at_the/ 2020-06-05 reinforcement-learning/model-free/oa5 --- https://www.reddit.com/r/reinforcementlearning/comments/ajeg5m/deepminds_alphastar_starcraft_2_demonstration/ 2020-06-05 reinforcement-learning/model-free/alphastar --- https://www.reddit.com/r/reinforcementlearning/comments/bctqmv/n_openai_five_dota2_finals_match_livestream_has/ 2020-06-05 reinforcement-learning/model-free/oa5 --- https://www.reddit.com/r/reinforcementlearning/comments/dpbfwx/alphastar_grandmaster_level_in_starcraft_ii_using/ 2020-06-05 reinforcement-learning/model-free/alphastar --- https://www.reddit.com/r/reinforcementlearning/search/?q=flair%3AMeta-RL&sort=top&restrict_sr=on&t=all 2020-06-05 reinforcement-learning/meta-learning --- https://www.reddit.com/r/science/comments/2l9dpi/two_new_studies_published_in_nature_provide/ 2020-06-05 genetics/heritable/rare --- https://www.reddit.com/r/slatestarcodex/comments/9oyhie/is_it_true_that_many_people_or_even_most_people/ 2020-06-05 psychology/inner-voice --- https://www.reddit.com/r/thisisthewayitwillbe/comments/59lu26/a_possible_unexpected_path_to_strong_ai_agi/ 2020-06-05 reinforcement-learning/imitation-learning/brain-imitation-learning --- https://www.reddit.com/r/thisisthewayitwillbe/comments/e02gtg/ai_and_neuroscience_main2019_patrick_mineaults/ 2020-06-05 reinforcement-learning/imitation-learning/brain-imitation-learning --- https://onlinelibrary.wiley.com/doi/10.1111/acel.13294 2020-06-05 longevity/senolytic --- https://onlinelibrary.wiley.com/doi/10.1111/acel.13296 2020-06-06 longevity/senolytic --- https://onlinelibrary.wiley.com/doi/10.1111/acel.13344 2020-06-06 longevity/senolytic --- https://onlinelibrary.wiley.com/doi/10.1111/rati.12233 2020-06-06 philosophy/ethics --- https://onlinelibrary.wiley.com/doi/full/10.1111/acel.12880 Metformin inhibits mitochondrial adaptations to aerobic exercise training in older adults Konopka 2018 2020-06-06 exercise longevity/metformin --- https://onlinelibrary.wiley.com/doi/full/10.1111/rda.13358 Dog cloning—no longer science fiction 2020-06-06 genetics/cloning/dog --- https://onlinelibrary.wiley.com/doi/pdf/10.1002/ajmg.b.32333 2020-06-06 crime genetics/heritable --- https://onlinelibrary.wiley.com/doi/pdf/10.1002/ajmg.b.32363 2020-06-06 crime genetics/heritable --- https://onlinelibrary.wiley.com/doi/pdf/10.1002/ajmg.b.32388 2020-06-06 crime genetics/heritable --- https://onlinelibrary.wiley.com/doi/pdf/10.1002/ajmg.b.32419 2020-06-06 crime genetics/heritable --- https://ooo.ghostbows.ooo/ The Greatest Remaining Hits 2020-06-06 ai/nn/transformer/gpt/jukebox --- https://openai.com/research/attacking-machine-learning-with-adversarial-examples 2020-06-06 ai/nn/adversarial --- https://openai.com/blog/customizing-gpt-3 2020-06-07 ai/nn/transformer/gpt --- https://openai.com/research/emergent-tool-use 2020-06-07 reinforcement-learning/exploration reinforcement-learning/multi-agent reinforcement-learning/scaling --- https://openai.com/research/solving-math-word-problems Solving Math Word Problems: We’ve trained a system that solves grade school math problems with nearly twice the accuracy of a fine-tuned GPT-3 model. It solves about 90% as many problems as real kids: a small sample of 9-12 year olds scored 60% on a test from our dataset, while our system scored 55% on those same problems. This is important because today’s AI is still quite weak at commonsense multistep reasoning, which is easy even for grade school kids. We achieved these results by training our model to recognize its mistakes, so that it can try repeatedly until it finds a solution that works 2020-06-07 ai/dataset ai/nn/transformer/gpt/inner-monologue --- https://openai.com/blog/introducing-text-and-code-embeddings/ 2020-06-07 ai/nn/retrieval ai/nn/transformer/gpt/codex --- https://openai.com/blog/learning-montezumas-revenge-from-a-single-demonstration/ 2020-06-07 reinforcement-learning/exploration --- https://openai.com/blog/more-on-dota-2/ 2020-06-07 reinforcement-learning/model-free/oa5 --- https://openai.com/blog/openai-codex/ 2020-06-07 ai/nn/transformer/gpt/codex --- https://openai.com/research/openai-five-benchmark-results 2020-06-07 reinforcement-learning/model-free/oa5 --- https://openai.com/research/openai-five 2020-06-07 reinforcement-learning/model-free/oa5 --- https://openai.com/research/reinforcement-learning-with-prediction-based-rewards Reinforcement Learning with Prediction-Based Rewards 2020-06-07 reinforcement-learning/exploration --- https://openai.com/blog/reptile/ 2020-06-07 reinforcement-learning/meta-learning --- https://openai.com/blog/robust-adversarial-inputs/ 2020-06-08 ai/nn/adversarial --- https://openai.com/research/summarizing-books 2020-06-08 reinforcement-learning/preference-learning reinforcement-learning/scaling --- https://openai.com/research/the-international-2018-results 2020-06-08 reinforcement-learning/model-free/oa5 --- https://openarchive.ki.se/xmlui/bitstream/handle/10616/45042/Manuscript_Latvala.pdf?sequence=3&isAllowed=y 2020-06-08 crime iq --- https://openpsych.net/paper/45/ Inequality among 32 London Boroughs: An S factor analysis 2020-06-08 iq/ses --- https://openreview.net/forum?id=1wtC_X12XXC Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain Beren Millidge, Alexander Tschantz, Anil K. Seth, Christopher Buckley 2021-03-05 2021-03-05 psychology/neuroscience

The backpropagation of error algorithm (backprop) has been instrumental in the recent success of deep learning. However, a key question remains as to whether backprop can be formulated in a manner suitable for implementation in neural circuitry. The primary challenge is to ensure that any candidate formulation uses only local information, rather than relying on global signals as in standard backprop. Recently several algorithms for approximating backprop using only local signals have been proposed. However, these algorithms typically impose other requirements which challenge biological plausibility: for example, requiring complex and precise connectivity schemes, or multiple sequential backwards phases with information being stored across phases. Here, we propose a novel algorithm, Activation Relaxation (AR), which is motivated by constructing the backpropagation gradient as the equilibrium point of a dynamical system. Our algorithm converges rapidly and robustly to the correct backpropagation gradients, requires only a single type of computational unit, utilizes only a single parallel backwards relaxation phase, and can operate on arbitrary computation graphs. We illustrate these properties by training deep neural networks on visual classification tasks, and describe simplifications to the algorithm which remove further obstacles to neurobiological implementation (for example, the weight-transport problem, and the use of nonlinear derivatives), while preserving performance.

[Keywords: Neural Networks, Biological Plausibility, Backprop]

--- https://arxiv.org/abs/2203.03466#microsoft Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer Greg Yang, Edward J. Hu, Igor Babuschkin, Szymon Sidor, Xiaodong Liu, David Farhi, Nick Ryder, Jakub Pachocki, Weizhu Chen, Jianfeng Gao 2022-01-21 2022-01-21 ai/nn/transformer/gpt/4

We show that in Maximal Update Parametrization, many optimal hyperparameters remain stable even as model size changes, and use it to transfer hyperparameters from small models to large models.

Hyperparameter (HP) tuning in deep learning is an expensive process, prohibitively so for neural networks (NNs) with billions of parameters. We show that, in the recently discovered Maximal Update Parametrization (μP), many optimal HPs remain stable even as model size changes. This leads to a new HP tuning paradigm we call μTransfer: parametrize the target model in μP, tune the HP indirectly on a smaller model, and zero-shot transfer them to the full-sized model, ie. without directly tuning the latter at all.

We verify μTransfer on Transformer and ResNet. For example, (1) by transferring pretraining HPs from a model of 13M parameters, we outperform published numbers of BERT-large (350M parameters), with a total tuning cost equivalent to pretraining BERT-large once; (2) by transferring from 40M parameters, we outperform published numbers of the 6.7B GPT-3 model, with tuning cost only 7% of total pretraining cost. A Pytorch implementation of our technique can be found at github.com/microsoft/mup.

[Keywords: hyperparameter tuning, scaling law, transformer, language model pretraining, infinite-width neural networks]

--- https://openreview.net/forum?id=ChMLTGRjFcU How many degrees of freedom do we need to train deep networks: a loss landscape perspective Brett W. Larsen, Stanislav Fort, Nic Becker, Surya Ganguli 2021-11-20 2021-11-20 ai/nn/sparsity/pruning

A variety of recent works, spanning pruning, lottery tickets, and training within random subspaces, have shown that deep neural networks can be trained using far fewer degrees of freedom than the total number of parameters. We analyze this phenomenon for random subspaces by first examining the success probability of hitting a training loss sublevel set when training within a random subspace of a given training dimensionality. We find a sharp phase transition in the success probability 0 → 1 as the training dimension surpasses a threshold. This threshold training dimension increases as the desired final loss decreases, but decreases as the initial loss decreases. We then theoretically explain the origin of this phase transition, and its dependence on initialization and final desired loss, in terms of properties of the high dimensional geometry of the loss landscape. In particular, we show via Gordon’s escape theorem, that the training dimension plus the Gaussian width of the desired loss sub-level set, projected onto an unit sphere surrounding the initialization, must exceed the total number of parameters for the success probability to be large. In several architectures and datasets, we measure the threshold training dimension as a function of initialization and demonstrate that it is a small fraction of the total parameters, implying by our theory that successful training with so few dimensions is possible precisely because the Gaussian width of low loss sub-level sets is very large. Moreover, we compare this threshold training dimension to more sophisticated ways of reducing training degrees of freedom, including lottery tickets as well as a new, analogous method: lottery subspaces.

[Keywords: loss landscape, high-dimensional geometry, random hyperplanes, optimization]

--- https://jmlr.org/papers/volume18/16-634/16-634.pdf A Survey of Preference-Based Reinforcement Learning Methods Christian Wirth, Riad Akrour, Gerhard Neumann, Johannes Fürnkranz 2021-05-20 2021-05-20 reinforcement-learning/preference-learning

Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function. However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress.

To alleviate these issues, preference-based reinforcement learning algorithms (PbRL) have been proposed that can directly learn from an expert’s preferences instead of a hand-designed numeric reward. PbRL has gained traction in recent years due to its ability to resolve the reward shaping problem, its ability to learn from non numeric rewards and the possibility to reduce the dependence on expert knowledge.

We provide an unified framework for PbRL that describes the task formally and points out the different design principles that affect the evaluation task for the human as well as the computational complexity. The design principles include the type of feedback that is assumed, the representation that is learned to capture the preferences, the optimization problem that has to be solved as well as how the exploration/exploitation problem is tackled.

Furthermore, we point out shortcomings of current algorithms, propose open research questions and briefly survey practical tasks that have been solved using PbRL.

--- https://openreview.net/forum?id=Fkpr2RYDvI1#google SynthBio: A Case Study in Faster Curation of Text Datasets Ann Yuan, Daphne Ippolito, Vitaly Nikolaev, Chris Callison-Burch, Andy Coenen, Sebastian Gehrmann 2022-01-13 2022-01-13 ai/dataset ai/nn/transformer/gpt/lamda

We introduce a dataset curation method in which a language model generates seed text which human raters edit—and use the method to curate SynthBio—a new structure-to-text dataset of biographies describing fictional individuals.

NLP researchers need more, higher-quality text datasets. Human-labeled datasets are expensive to collect, while datasets collected via automatic retrieval from the web such as WikiBio [Lebret 2016] are noisy and can include undesired biases. Moreover, data sourced from the web is often included in datasets used to pretrain models, leading to inadvertent cross-contamination of training and test sets. In this work we introduce a novel method for efficient dataset curation: we use a large language model to provide seed generations to human raters, thereby changing dataset authoring from a writing task to an editing task. We use our method to curate SynthBio—a new evaluation set for WikiBio—comprised of structured attribute lists describing fictional individuals, mapped to natural language biographies. We show that our dataset of fictional biographies is less noisy than WikiBio, and also more balanced with respect to gender and nationality.

[Keywords: NLP, machine learning, datasets, NLG]

--- https://openreview.net/forum?id=G89-1yZLFHk OTTER: Data Efficient Language-Supervised Zero-Shot Recognition with Optimal Transport Distillation Bichen Wu, Ruizhe Cheng, Peizhao Zhang, Peter Vajda, Joseph E. Gonzalez 2021-10-05 2021-10-05 ai/nn/sparsity/knowledge-distillation ai/nn/transformer/clip

Traditional computer vision models are trained to predict a fixed set of predefined categories. Recently, natural language has been shown to be a broader and richer source of supervision that provides finer descriptions to visual concepts than supervised “gold” labels. Previous works, such as CLIP, use InfoNCE loss to train a model to predict the pairing between images and text captions. CLIP, however, is data hungry and requires more than 400M image-text pairs for training. The inefficiency can be partially attributed to the fact that the image-text pairs are noisy.

To address this, we propose OTTER (Optimal TransporT distillation for Efficient zero-shot Recognition), which uses online entropic optimal transport to find a soft image-text match as labels for contrastive learning.

Based on pretrained image and text encoders, models trained with OTTER achieve strong performance with only 3M image text pairs. Compared with InfoNCE loss, label smoothing, and knowledge distillation, OTTER consistently outperforms these baselines in zero-shot evaluation on Google Open Images (19,958 classes) and multi-labeled ImageNet 10K (10032 classes) from Tencent ML-Images. Over 36 evaluations on 6 different dataset/architecture settings x 6 metrics, OTTER outperforms (30) or ties (2) all baselines in 32 of them. OTTER also exceeds the previous SOTA of general zero-shot learning on ImageNet 21K+1K by 68% relatively, except CLIP, which uses 100× more data than OTTER.

[Keywords: Zero shot learning, contrastive learning, optimal transport, vision and language]

--- https://www.microsoft.com/en-us/research/wp-content/uploads/2017/11/lightgbm.pdf LightGBM: A Highly Efficient Gradient Boosting Decision Tree Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu 2019-09-04 2020-06-09 ai/tabular statistics/prediction

Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithm, and has quite a few effective implementations such as XGBoost and pGBRT. Although many engineering optimizations have been adopted in these implementations, the efficiency and scalability are still unsatisfactory when the feature dimension is high and data size is large. A major reason is that for each feature, they need to scan all the data instances to estimate the information gain of all possible split points, which is very time consuming.

To tackle this problem, we propose two novel techniques: Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB). With GOSS, we exclude a proportion of data instances with small gradients, and only use the rest to estimate the information gain. We prove that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size. With EFB, we bundle mutually exclusive features (ie. they rarely take nonzero values simultaneously), to reduce the number of features. We prove that finding the optimal bundling of exclusive features is NP-hard, but a greedy algorithm can achieve quite good approximation ratio (and thus can effectively reduce the number of features without hurting the accuracy of split point determination by much).

We call our new GBDT implementation with GOSS and EFB LightGBM. Our experiments on multiple public datasets show that, LightGBM speeds up the training process of conventional GBDT by up to over 20× while achieving almost the same accuracy.

--- https://openreview.net/forum?id=H7Edu1_IZgR Transformers are Meta-Reinforcement Learners Anonymous 2021-10-05 2021-10-05 ai/nn/transformer reinforcement-learning/meta-learning

The transformer architecture and variants presented a remarkable success across many machine learning tasks in recent years. This success is intrinsically related to the capability of handling long sequences and the presence of context-dependent weights from the attention mechanism. We argue that these capabilities suit the central role of a Meta-Reinforcement Learning algorithm. Indeed, a meta-RL agent needs to infer the task from a sequence of trajectories. Furthermore, it requires a fast adaptation strategy to adapt its policy for a new task—which can be achieved using the self-attention mechanism. In this work, we present TrMRL (Transformers for Meta-Reinforcement Learning), a meta-RL agent that mimics the memory reinstatement mechanism using the transformer architecture. It associates the recent past of working memories to build an episodic memory recursively through the transformer layers. This memory works as a proxy to the current task, and we condition a policy head on it. We conducted experiments in high-dimensional continuous control environments for locomotion and dexterous manipulation. Results show that TrMRL achieves or surpasses state-of-the-art performance, sample efficiency, and out-of-distribution generalization in these environments.

[Keywords: Reinforcement Learning, Meta-Reinforcement Learning, Transformers]

--- https://openreview.net/forum?id=HkO-PCmYl Shake-Shake regularization of 3-branch residual networks Xavier Gastaldi 2017-03-15 2020-06-09 ai/nn/sparsity

Reduce overfit by replacing, in a 3-branch ResNet, the standard summation of residual branches by a stochastic affine combination

The method introduced in this paper aims at helping computer vision practitioners faced with an overfit problem. The idea is to replace, in a 3-branch ResNet, the standard summation of residual branches by a stochastic affine combination. The largest tested model improves on the best single shot published result on CIFAR-10 by reaching 2.86% test error.

Code is available at GitHub.

[Keywords: Computer vision, Deep learning, Supervised Learning]

--- https://openreview.net/forum?id=Hy-w-2PSf Intriguing Properties of Randomly Weighted Networks: Generalizing while Learning Next to Nothing Amir Rosenfeld, John K. Tsotsos 2018-01-25 2020-06-09 ai/nn/sparsity

Convnets can achieve good performance even when only a fraction of parameters are learned.

Training deep neural networks results in strong learned representations that show good generalization capabilities. In most cases, training involves iterative modification of all weights inside the network via back-propagation. In this paper, we propose to take an extreme approach and fix almost all weights of a deep convolutional neural network in their randomly initialized values, allowing only a small portion to be learned.

As our experiments show, this often results in performance which is on par with the performance of learning all weights. The implications of this intriguing property or deep neural networks are discussed and we suggest ways to harness it to create more robust representations.

[Keywords: random networks, extreme learning, compact representations]

--- https://openreview.net/forum?id=JprM0p-q0Co#nvidia Tackling the Generative Learning Trilemma with Denoising Diffusion GANs Zhisheng Xiao, Karsten Kreis, Arash Vahdat 2021-11-12 2021-11-12 ai/nn/diffusion ai/nn/gan

A wide variety of deep generative models has been developed in the past decade. Yet, these models often struggle with simultaneously addressing 3 key requirements including: (1) high sample quality, (2) mode coverage, and (3) fast sampling. We call the challenge imposed by these requirements the generative learning trilemma, as the existing models often trade some of them for others. Particularly, denoising diffusion models have shown impressive sample quality and diversity, but their expensive sampling does not yet allow them to be applied in many real-world applications.

In this paper, we argue that slow sampling in these models is fundamentally attributed to the Gaussian assumption in the denoising step which is justified only for small step sizes. To enable denoising with large steps, and hence, to reduce the total number of denoising steps, we propose to model the denoising distribution using a complex multimodal distribution.

We introduce denoising diffusion generative adversarial networks (denoising diffusion GANs) that model each denoising step using a multimodal conditional GAN.

Through extensive evaluations, we show that denoising diffusion GANs obtain sample quality and diversity competitive with original diffusion models while being 2000× faster on the CIFAR-10 dataset. Compared to traditional GANs, our model exhibits better mode coverage and sample diversity.

To the best of our knowledge, denoising diffusion GAN is the first model that reduces sampling cost in diffusion models to an extent that allows them to be applied to real-world applications inexpensively.

--- https://arxiv.org/abs/2112.10510 Transformers Can Do Bayesian Inference Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, Frank Hutter 2021-10-05 2021-10-05 ai/tabular reinforcement-learning/meta-learning statistics/bayes

Currently, it is hard to reap the benefits of deep learning for Bayesian methods, which allow the explicit specification of prior knowledge and accurately capture model uncertainty. We present Prior-Data Fitted Networks (PFNs). PFNs leverage large-scale machine learning techniques to approximate a large set of posteriors. The only requirement for PFNs to work is the ability to sample from a prior distribution over supervised learning tasks (or functions). Our method restates the objective of posterior approximation as a supervised classification problem with a set-valued input: it repeatedly draws a task (or function) from the prior, draws a set of data points and their labels from it, masks one of the labels and learns to make probabilistic predictions for it based on the set-valued input of the rest of the data points. Presented with a set of samples from a new supervised learning task as input, PFNs make probabilistic predictions for arbitrary other data points in a single forward propagation, having learned to approximate Bayesian inference.

We demonstrate that PFNs can near-perfectly mimic Gaussian processes and also enable efficient Bayesian inference for intractable problems, with over 200× speedups in multiple setups compared to current methods. We obtain strong results in very diverse areas such as Gaussian process regression, Bayesian neural networks, classification for small tabular data sets, and few-shot image classification, demonstrating the generality of PFNs. Code and trained PFNs are released under [ANONYMIZED; please see the supplementary material].

--- https://openreview.net/forum?id=QnzSSoqmAvB Playing Nondeterministic Games through Planning with a Learned Model Thomas Willkens, Jordan Pollack 2021-03-05 2021-03-05 reinforcement-learning/model/muzero

The MuZero algorithm is known for achieving high-level performance on traditional zero-sum two-player games of perfect information such as chess, Go, and shogi, as well as visual, non-zero sum, single-player environments such as the Atari suite. Despite lacking a perfect simulator and employing a learned model of environmental dynamics, MuZero produces game-playing agents comparable to its predecessor AlphaZero. However, the current implementation of MuZero is restricted only to deterministic environments. This paper presents Nondeterministic MuZero (NDMZ), an extension of MuZero for nondeterministic, two-player, zero-sum games of perfect information. Borrowing from Nondeterministic Monte Carlo Tree Search and the theory of extensive-form games, NDMZ formalizes chance as a player in the game and incorporates it into the MuZero network architecture and tree search. Experiments show that NDMZ is capable of learning effective strategies and an accurate model of the game.

[Keywords: reinforcement learning, AlphaZero, MuZero, MCTS, planning, search]

--- https://openreview.net/forum?id=ROteIE-4A6W MA-CLIP: Towards Modality-Agnostic Contrastive Language-Image Pre-training Haoxuan You, Luowei Zhou, Bin Xiao, Noel C. Codella, Yu Cheng, Ruochen Xu, Shih-Fu Chang, Lu Yuan 2021-10-06 2021-10-06 ai/nn/transformer/clip

Large-scale multimodal contrastive pretraining has demonstrated great utility to support high performance in a range of downstream tasks by mapping multiple modalities into a shared embedding space. Typically, this has employed separate encoders for each modality. However, recent work suggests that transformers can support learning across multiple modalities and allow knowledge sharing. Inspired by this, we investigate how to build a modality-shared Contrastive Language-Image Pre-training framework (MS-CLIP). More specifically, we question how many parameters of a transformer model can be shared across modalities during contrastive pre-training, and rigorously study architectural design choices that position the proportion of parameters shared along a spectrum.

We observe that a mostly unified encoder for vision and language signals outperforms all other variations that separate more parameters. Additionally, we find that light-weight modality-specific parallel adapter modules further improve performance.

Experimental results show that the proposed MS-CLIP outperforms OpenAI CLIP by 13% relatively in zero-shot ImageNet classification (pre-trained on YFCC100M), while simultaneously supporting a reduction of parameters. In addition, our approach outperforms OpenAI CLIP by 1.6 points on a collection of 19 downstream vision tasks. Furthermore, we discover that sharing parameters leads to semantic concepts from different modalities being encoded more closely in the embedding space, facilitating the learning of common semantic structures (eg. attention patterns) across modalities.

--- https://openreview.net/forum?id=St1giarCHLP Denoising Diffusion Implicit Models Jiaming Song, Chenlin Meng, Stefano Ermon 2021-01-25 2021-01-25 ai/nn/diffusion ai/nn/vae

Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps in order to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a particular Markovian diffusion process. We generalize DDPMs via a class of non-Markovian diffusion processes that lead to the same training objective. These non-Markovian processes can correspond to generative processes that are deterministic, giving rise to implicit models that produce high quality samples much faster. We empirically demonstrate that DDIMs can produce high quality samples 10× to 50× faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, perform semantically meaningful image interpolation directly in the latent space, and reconstruct observations with very low error.

[Keywords: generative models, variational autoencoders, denoising score matching, variational inference]

--- https://openreview.net/forum?id=TIdIXIpzhoI#google Progressive Distillation for Fast Sampling of Diffusion Models Tim Salimans, Jonathan Ho 2021-10-05 2021-10-05 ai/nn/diffusion ai/nn/sparsity/knowledge-distillation

Diffusion models have recently shown great promise for generative modeling, outperforming GANs on perceptual quality and autoregressive models at density estimation. A remaining downside is their slow sampling time: generating high quality samples takes many hundreds or thousands of model evaluations. Here we make two contributions to help eliminate this downside: First, we present new parameterizations of diffusion models that provide increased stability when using few sampling steps, compared to models in the literature. Second, we present a method to distill a trained deterministic diffusion sampler, using many steps, into a new diffusion model that takes half as many sampling steps. We then keep progressively applying this distillation procedure to our model, halving the number of required sampling steps each time. On standard image generation benchmarks like CIFAR-10, ImageNet, and LSUN, we start out with (near) state-of-the-art samplers taking 1,024 or 8,192 steps, and are able to distill down to models taking as little as 4 steps without losing much perceptual quality; achieving, for example, a FID of 3.0 on CIFAR-10 in 4 steps. Finally, we show that the full progressive distillation procedure does not take more time than it takes to train the original model, thus representing an efficient solution for generative modeling using diffusion at both train and test time.

[Keywords: Diffusion Models, Generative Models, fast sampling]

--- https://openreview.net/forum?id=TKMJ9eqtpgP DiffusionCLIP: Text-guided Image Manipulation Using Diffusion Models Gwanghyun Kim, Jong Chul Ye 2021-10-05 2021-10-05 ai/nn/diffusion ai/nn/transformer/clip

Diffusion models are recent generative models that have shown great success in image generation with the state-of-the-art performance. However, only a few researches have been conducted for image manipulation with diffusion models. Here, we present a novel DiffusionCLIP which performs text-driven image manipulation with diffusion models using Contrastive Language-Image Pre-training (CLIP) loss. Our method has a performance comparable to that of the modern GAN-based image processing methods for in and out-of-domain image processing tasks, with the advantage of almost perfect inversion even without additional encoders or optimization. Furthermore, our method can be easily used for various novel applications, enabling image translation from an unseen domain to another unseen domain or stroke-conditioned image generation in an unseen domain, etc. Finally, we present a novel multiple attribute control with DiffusionCLIP by combining multiple fine-tuned diffusion models.

[Keywords: Diffusion models, CLIP, Image manipulation, Image to image translation]

--- https://openreview.net/forum?id=U0k7XNTiFEq Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers Guodong Zhang, Aleksandar Botev, James Martens 2021-11-18 2021-11-18 ai/nn/fully-connected

Training very deep neural networks is still an extremely challenging task. The common solution to this is to add shortcut connections and normalization layers, which are both crucial ingredients in the ResNet architecture. However, there is strong evidence to suggest that ResNets behave more like ensembles of shallower networks than truly deep ones. Recently, it was shown that deep vanilla networks (ie. without normalization layers or shortcut connections) can be trained as fast as ResNets by applying certain transformations to their activation functions. However, this method (called Deep Kernel Shaping) isn’t fully compatible with ReLUs, and produces networks that exhibit statistically-significantly more overfitting than ResNets of similar size on ImageNet. In this work, we rectify this situation by developing a new type of transformation which is perfectly compatible with a variant of ReLUs—Leaky ReLUs. We show in experiments that our method, which introduces negligible extra computational cost, achieves tests accuracies with vanilla deep networks that are competitive with ResNets (of the same width/depth), and higher than those obtained with the Edge of Chaos (EOC) method. And unlike with EOC, the test accuracies we obtain do not get worse with depth.

[Keywords: Neural Network Training, Kernel Approximation for Neural Networks, Neural Network Initialization, Generalization]

--- https://openreview.net/forum?id=bTteFbU99ye Evaluating Distributional Distortion in Neural Language Modeling Anonymous 2021-11-16 2021-11-16 ai/nn/rnn ai/nn/transformer/gpt

A fundamental characteristic of natural language is the high rate at which speakers produce novel expressions. Because of this novelty, a heavy-tail of rare events accounts for a large amount of the total probability mass of distributions in language (Baayen, 2001). Standard language modeling metrics such as perplexity quantify performance of language models (LM) in aggregate. As a result, we have relatively little understanding of whether neural LMs accurately estimate the probability of sequences in this heavy-tail of rare events.

To address this gap, we develop a controlled evaluation scheme which uses generative models trained on natural data as artificial languages from which we can exactly compute sequence probabilities. Training LMs on generations from these artificial languages, we compare the sequence-level probability estimates given by LMs to the true probabilities in the target language.

Our experiments reveal that LSTM and Transformer language models (1) systematically underestimate the probability of sequences drawn from the target language, and (2) do so more severely for less-probable sequences. Investigating where this probability mass went, (3) we find that LMs tend to overestimate the probability of ill formed (perturbed) sequences. In addition, we find that this underestimation behavior (4) is weakened, but not eliminated by greater amounts of training data, and (5) is exacerbated for target distributions with lower entropy.

--- https://openreview.net/forum?id=gJcEM8sxHK Mapping Language Models to Grounded Conceptual Spaces Roma Patel, Ellie Pavlick 2021-11-18 2021-11-18 ai/nn/transformer/gpt/2 ai/scaling/emergence

[previously: Abdou et al 2021] A fundamental criticism of text-only language models (LMs) is their lack of grounding—that is, the ability to tie a word for which they have learned a representation, to its actual use in the world. However, despite this limitation, large pre-trained LMs have been shown to have a remarkable grasp of the conceptual structure of language, as demonstrated by their ability to answer questions, generate fluent text, or make inferences about entities, objects, and properties that they have never physically observed.

In this work we investigate the extent to which the rich conceptual structure that LMs learn indeed reflects the conceptual structure of the non-linguistic world—which is something that LMs have never observed. We do this by testing whether the LMs can learn to map an entire conceptual domain (eg. direction or color) onto a grounded world representation given only a small number of examples. For example, we show a model what the word “left” means using a textual depiction of a grid world, and assess how well it can generalize to related concepts, for example, the word “right”, in a similar grid world. We investigate a range of generative language models of varying sizes (including GPT-2 and GPT-3), and see that although the smaller models struggle to perform this mapping, the largest model can not only learn to ground the concepts that it is explicitly taught, but appears to generalize to several instances of unseen concepts as well.

Our results suggest an alternative means of building grounded language models: rather than learning grounded representations “from scratch”, it is possible that large text-only models learn a sufficiently rich conceptual structure that could allow them to be grounded in a data-efficient way.

--- https://openreview.net/forum?id=hu2aMLzOxC#openai Asymmetric self-play for automatic goal discovery in robotic manipulation OpenAI OpenAI, Matthias Plappert, Raul Sampedro, Tao Xu, Ilge Akkaya, Vineet Kosaraju, Peter Welinder, Ruben D’Sa, Arthur Petron, Henrique Ponde de Oliveira Pinto, Alex Paino, Hyeonwoo Noh, Lilian Weng, Qiming Yuan, Casey Chu, Wojciech Zaremba 2021-03-05 2021-03-05 reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/multi-agent reinforcement-learning/robot

We train a single, goal-conditioned policy that can solve many robotic manipulation tasks, including tasks with previously unseen goals and objects. To do so, we rely on asymmetric self-play for goal discovery, where two agents, Alice and Bob, play a game. Alice is asked to propose challenging goals and Bob aims to solve them. We show that this method is able to discover highly diverse and complex goals without any human priors. We further show that Bob can be trained with only sparse rewards, because the interaction between Alice and Bob results in a natural curriculum and Bob can learn from Alice’s trajectory when relabeled as a goal-conditioned demonstration. Finally, we show that our method scales, resulting in a single policy that can transfer to many unseen hold-out tasks such as setting a table, stacking blocks, and solving simple puzzles. Videos of a learned policy is available at https://robotics-self-play.github.io.

[Keywords: self-play, asymmetric self-play, automatic curriculum, automatic goal generation, robotic learning, robotic manipulation, reinforcement learning]

--- https://openreview.net/forum?id=lVUGfLpNpCF#schmidhuber A Modern Self-Referential Weight Matrix That Learns to Modify Itself Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber 2021-11-30 2021-11-30 reinforcement-learning/meta-learning

We propose a scalable self-referential weight matrix that uses outer products and the delta update rule to modify itself.

The weight matrix (WM) of a neural network (NN) is its program. The programs of many traditional NNs are learned through gradient descent in some error function, then remain fixed. The WM or program of a self-referential NN, however, can keep rapidly modifying all of itself during runtime. In principle, such NNs can meta-learn to learn, and meta-meta-learn to meta-learn to learn, and so on, in the sense of recursive self-improvement. Here we revisit such NNs, building upon recent successes of fast weight programmers (FWPs) and closely related linear Transformers. We propose a scalable self-referential WM (SRWM) that uses self-generated training patterns, outer products and the delta update rule to modify itself. We evaluate our SRWM in a multi-task reinforcement learning setting with procedurally generated Procgen game environments. Our experiments demonstrate both practical applicability and competitive performance of the SRWM. Our code is public.

[Keywords: Procgen, self-referential weight matrix, fast weight programmers, linear Transformers]

--- https://openreview.net/forum?id=lsQCDXjOl3k#google Unconditional Diffusion Guidance Jonathan Ho, Tim Salimans 2021-10-05 2021-10-05 ai/nn/diffusion

Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. Classifier guidance combines the score estimate of a diffusion model with the gradient of an image classifier and thereby requires training an image classifier separate from the diffusion model. It also raises the question of whether guidance can be performed without a classifier. We show that guidance can be indeed performed by a pure generative model without such a classifier: in what we call unconditional guidance, we jointly train a conditional and an unconditional diffusion model, and we combine the resulting conditional and unconditional score estimates to attain a trade-off between sample quality and diversity similar to that obtained using classifier guidance.

[Keywords: diffusion, score, guidance, generative]

--- https://openreview.net/forum?id=lwrPkQP_is URLB: Unsupervised Reinforcement Learning Benchmark Michael Laskin, Denis Yarats, Hao Liu, Kimin Lee, Albert Zhan, Kevin Lu, Catherine Cang, Lerrel Pinto, Pieter Abbeel 2021-10-31 2021-10-31 reinforcement-learning/exploration

We present a benchmark for Unsupervised Reinforcement Learning, open-source code for eight leading unsupervised RL methods, standardize pre-training & evaluation, and benchmark across twelve downstream tasks.

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range of complex yet specific control tasks. Training generalist agents that can quickly adapt to new tasks remains an outstanding challenge. Recent advances in unsupervised RL have shown that pre-training RL agents with self-supervised intrinsic rewards can result in efficient adaptation. However, these algorithms have been hard to compare and develop due to the lack of an unified benchmark. To this end, we introduce the Unsupervised Reinforcement Learning Benchmark (URLB). URLB consists of two phases: reward-free pre-training and downstream task adaptation with extrinsic rewards. Building on the DeepMind Control Suite, we provide twelve continuous control tasks from 3 domains for evaluation and open-source code for eight leading unsupervised RL methods. We find that the implemented baselines make progress but are not able to solve URLB and propose directions for future research.

[Keywords: unsupervised learning, reinforcement learning, benchmark, open-source code]

--- https://openreview.net/forum?id=ps95-mkHF_ B-Pref: Benchmarking Preference-Based Reinforcement Learning Kimin Lee, Laura Smith, Anca Dragan, Pieter Abbeel 2021-06-08 2021-06-08 reinforcement-learning/exploration/active-learning reinforcement-learning/preference-learning

Reinforcement learning (RL) requires access to a reward function that incentivizes the right behavior, but these are notoriously hard to specify for complex tasks. Preference-based RL provides an alternative: learning policies using a teacher’s preferences without pre-defined rewards, thus overcoming concerns associated with reward engineering. However, it is difficult to quantify the progress in preference-based RL due to the lack of a commonly adopted benchmark. In this paper, we introduce B-Pref: a benchmark specially designed for preference-based RL. A key challenge with such a benchmark is providing the ability to evaluate candidate algorithms quickly, which makes relying on real human input for evaluation prohibitive. At the same time, simulating human input as giving perfect preferences for the ground truth reward function is unrealistic. B-Pref alleviates this by simulating teachers with a wide array of irrationalities, and proposes metrics not solely for performance but also for robustness to these potential irrationalities. We showcase the utility of B-Pref by using it to analyze algorithmic design choices, such as selecting informative queries, for state-of-the-art preference-based RL algorithms. We hope that B-Pref can serve as a common starting point to study preference-based RL more systematically.

[Keywords: Preference-based reinforcement learning, human-in-the-loop reinforcement learning, deep reinforcement learning]

--- https://openreview.net/forum?id=qw674L9PfQE CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP Andreas Fürst, Elisabeth Rumetshofer, Viet Tran, Hubert Ramsauer, Fei Tang, Johannes Lehner, David Kreil, Michael Kopp, Günter Klambauer, Angela Bitto-Nemling, Sepp Hochreiter 2021-10-05 2021-10-05 ai/nn/retrieval ai/nn/transformer/clip

Contrastive learning with the InfoNCE objective is exceptionally successful in various self-supervised learning tasks. Recently, the CLIP model yielded impressive results on zero-shot transfer learning when using InfoNCE for learning visual representations from natural language supervision. However, InfoNCE as a lower bound on the mutual information has been shown to perform poorly for high mutual information. In contrast, the InfoLOOB upper bound (leave one out bound) works well for high mutual information but suffers from large variance and instabilities. We introduce “Contrastive Leave One Out Boost” (CLOOB), where modern Hopfield networks boost learning with the InfoLOOB objective. Modern Hopfield networks replace the original embeddings by retrieved embeddings in the InfoLOOB objective. The retrieved embeddings give InfoLOOB two assets. Firstly, the retrieved embeddings stabilize InfoLOOB, since they are less noisy and more similar to one another than the original embeddings. Secondly, they are enriched by correlations, since the covariance structure of embeddings is reinforced through retrievals. We compare CLOOB to CLIP after learning on the Conceptual Captions and the YFCC dataset with respect to their zero-shot transfer learning performance on other datasets. CLOOB consistently outperforms CLIP at zero-shot transfer learning across all considered architectures and datasets.

[Keywords: Deep learning, Associative memory, Hopfield networks, Contrastive learning, Multimodal learning]

--- https://openreview.net/forum?id=qw8AKxfYbI#google Classifier-Free Diffusion Guidance Jonathan Ho, Tim Salimans 2021-11-27 2021-11-27 ai/nn/diffusion

Classifier guidance without a classifier: Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. This method combines the score estimate of a diffusion model with the gradient of an image classifier and thereby requires training an image classifier separate from the diffusion model.

We show that guidance can be performed by a pure generative model without such a classifier: we jointly train a conditional and an unconditional diffusion model, and find that it is possible to combine the resulting conditional and unconditional scores to attain a trade-off between sample quality and diversity similar to that obtained using classifier guidance.

[Keywords: diffusion, score]

…We are interested in whether classifier guidance can be performed without a classifier. Classifier guidance complicates the diffusion model training pipeline because it requires training an extra classifier, and this classifier must be trained on noisy data so it is generally not possible to plug in a pre-trained classifier. Furthermore, because classifier guidance mixes a score estimate with a classifier gradient during sampling, classifier-guided diffusion sampling can be interpreted as attempting to confuse an image classifier with a gradient-based adversarial attack. This raises the question of whether classifier guidance is successful at boosting classifier-based metrics such as FID and Inception score (IS) simply because it is adversarial against such classifiers. Stepping in direction of classifier gradients also bears some resemblance to GAN training, particularly with nonparameteric generators; this also raises the question of whether classifier-guided diffusion models perform well on classifier-based metrics because they are beginning to resemble GANs, which are already known to perform well on such metrics.

--- https://aclanthology.org/D13-1170.pdf Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, Christopher Potts 2019-07-16 2020-06-11 ai/dataset ai/nn/rnn

Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank.

It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality.

To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state-of-the-art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines.

Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases.

--- https://openreview.net/forum?id=rJY0-Kcll#twitter Optimization as a Model for Few-Shot Learning Sachin Ravi, Hugo Larochelle 2017-03-01 2020-06-11 ai/nn/rnn reinforcement-learning/meta-learning

We propose an LSTM-based meta-learner model to learn the exact optimization algorithm used to train another learner neural network in the few-shot regime

Though deep neural networks have shown great success in the large data domain, they generally perform poorly on few-shot learning tasks, where a model has to quickly generalize after seeing very few examples from each class. The general belief is that gradient-based optimization in high capacity models requires many iterative steps over many examples to perform well. Here, we propose an LSTM-based meta-learner model to learn the exact optimization algorithm used to train another learner neural network in the few-shot regime. The parameterization of our model allows it to learn appropriate parameter updates specifically for the scenario where a set amount of updates will be made, while also learning a general initialization of the learner network that allows for quick convergence of training. We demonstrate that this meta-learning model is competitive with deep metric-learning techniques for few-shot learning.

--- https://arxiv.org/abs/1711.02846#google Intriguing Properties of Adversarial Examples Ekin Dogus Cubuk, Barret Zoph, Samuel Stern Schoenholz, Quoc V. Le 2018-02-15 2020-06-11 ai/nn/adversarial

Adversarial error has similar power-law form for all datasets and models studied, and architecture matters.

It is becoming increasingly clear that many machine learning classifiers are vulnerable to adversarial examples. In attempting to explain the origin of adversarial examples, previous studies have typically focused on the fact that neural networks operate on high dimensional data, they overfit, or they are too linear. Here we show that distributions of logit differences have an universal functional form. This functional form is independent of architecture, dataset, and training protocol; nor does it change during training. This leads to adversarial error having an universal scaling, as a power-law, with respect to the size of the adversarial perturbation. We show that this universality holds for a broad range of datasets (MNIST, CIFAR-10, ImageNet, and random data), models (including state-of-the-art deep networks, linear models, adversarially trained networks, and networks trained on randomly shuffled labels), and attacks (FGSM, step l.l., PGD). Motivated by these results, we study the effects of reducing prediction entropy on adversarial robustness. Finally, we study the effect of network architectures on adversarial sensitivity. To do this, we use neural architecture search with reinforcement learning to find adversarially robust architectures on CIFAR-10. Our resulting architecture is more robust to white and black box attacks compared to previous attempts.

[Keywords: adversarial examples, universality, neural architecture search]

--- https://openreview.net/forum?id=v_1Soh8QUNc Learning Energy-Based Models by Diffusion Recovery Likelihood Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma 2021-03-18 2021-03-18 ai/nn/diffusion

While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging.

Inspired by recent progress on diffusion probabilistic models, we present a diffusion recovery likelihood method to tractably learn and sample from a sequence of EBMs trained on increasingly noisy versions of a dataset. Each EBM is trained with recovery likelihood, which maximizes the conditional probability of the data at a certain noise level given their noisy versions at a higher noise level. Optimizing recovery likelihood is more tractable than marginal likelihood, as sampling from the conditional distributions is much easier than sampling from the marginal distributions. After training, synthesized images can be generated by the sampling process that initializes from Gaussian white noise distribution and progressively samples the conditional distributions at decreasingly lower noise levels.

Our method generates high fidelity samples on various image datasets. On unconditional CIFAR-10 our method achieves FID 9.58 and inception score 8.30, superior to the majority of GANs. Moreover, we demonstrate that unlike previous work on EBMs, our long-run MCMC samples from the conditional distributions do not diverge and still represent realistic images, allowing us to accurately estimate the normalized density of data even for high-dimensional datasets.

Our implementation is available at https://github.com/ruiqigao/recovery_likelihood.

[Keywords: energy-based model, EBM, recovery likelihood, generative model, diffusion process, MCMC, Langevin dynamics, HMC]

--- https://openreview.net/forum?id=y4-e1K23GLC A law of robustness for two-layers neural networks Sebastien Bubeck, Yuanzhi Li, Dheeraj Mysore Nagaraj 2021-03-05 2021-03-05 ai/nn/adversarial ai/scaling

We initiate the study of the inherent tradeoffs between the size of a neural network and its robustness, as measured by its Lipschitz constant.

We make a precise conjecture that, for any Lipschitz activation function and for most datasets, any two-layers neural network with k neurons that perfectly fit the data must have its Lipschitz constant larger (up to a constant) than √n⁄k where n is the number of datapoints. In particular, this conjecture implies that overparameterization is necessary for robustness, since it means that one needs roughly one neuron per datapoint to ensure a 𝒪(1)-Lipschitz network, while mere data fitting of d-dimensional data requires only one neuron per d datapoints. We prove a weaker version of this conjecture when the Lipschitz constant is replaced by an upper bound on it based on the spectral norm of the weight matrix. We also prove the conjecture in the high-dimensional regime n ≈ d (which we also refer to as the undercomplete case, since only k ≤ d is relevant here). Finally we prove the conjecture for polynomial activation functions of degree p when n ≈ dp.

We complement these findings with experimental evidence supporting the conjecture.

[Keywords: neural networks, approximation theory, robust machine learning]

--- https://openreview.net/forum?id=yeP_zx9vqNm Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks Anne Harrington, Arturo Deza 2021-11-23 2021-11-23 ai/nn/adversarial/human psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Recent work suggests that feature constraints in the training datasets of deep neural networks (DNNs) drive robustness to adversarial noise (Ilyas et al 2019). The representations learned by such adversarially robust networks have also been shown to be more human perceptually-aligned than non-robust networks via image manipulations (Santurkar et al 2019, Engstrom et al 2019). Despite appearing closer to human visual perception, it is unclear if the constraints in robust DNN representations match biological constraints found in human vision. Human vision seems to rely on texture-based/summary statistic representations in the periphery, which have been shown to explain phenomena such as crowding (Balas et al 2009) and performance on visual search tasks (Rosenholtz et al 2012). To understand how adversarially robust optimizations/representations compare to human vision, we performed a psychophysics experiment using a metamer task similar to Freeman & Simoncelli 2011, Wallis et al 2016 and Deza et al 2019 where we evaluated how well human observers could distinguish between images synthesized to match adversarially robust representations compared to non-robust representations and a texture synthesis model of peripheral vision (Texforms a la Long et al 2018). We found that the discriminability of robust representation and texture model images decreased to near chance performance as stimuli were presented farther in the periphery. Moreover, performance on robust and texture-model images showed similar trends within participants, while performance on non-robust representations changed minimally across the visual field. These results together suggest that (1) adversarially robust representations capture peripheral computation better than non-robust representations and (2) robust representations capture peripheral computation similar to current state-of-the-art texture peripheral vision models. More broadly, our findings support the idea that localized texture summary statistic representations may drive human invariance to adversarial perturbations and that the incorporation of such representations in DNNs could give rise to useful properties like adversarial robustness.

[Keywords: Peripheral Computation, Adversarial Robustness, Perceptual Invariance, Metamerism, Texture, Psychophysics]

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Christopher Klein tells of this remarkable journey and how the railroads and the rise of the popular press proved instrumental in forging Sullivan into America’s first sports superstar. 2020-06-21 history/public-domain-review --- https://publicdomainreview.org/essay/loie-fuller-and-the-serpentine/ Loie Fuller and the Serpentine 2020-06-21 design history/public-domain-review --- https://publicdomainreview.org/essay/marxist-astronomy-the-milky-way-according-to-anton-pannekoek/ Marxist Astronomy: The Milky Way According to Anton Pannekoek 2020-06-21 design history/public-domain-review science --- https://publicdomainreview.org/essay/mrs-giacometti-prodgers-the-cabman-s-nemesis/ Mrs Giacometti Prodgers, the Cabman’s Nemesis 2020-06-21 economics history/public-domain-review --- https://publicdomainreview.org/essay/of-pears-and-kings/ Of Pears and Kings 2020-06-21 fiction history/public-domain-review --- https://publicdomainreview.org/essay/our-masterpiece-is-the-private-life-in-pursuit-of-the-real-chateaubriand/ Our Masterpiece Is the Private Life: In Pursuit of the ‘Real’ Chateaubriand 2020-06-21 cat history/public-domain-review philosophy/religion --- https://publicdomainreview.org/essay/peter-the-wild-boy/ Peter The Wild Boy 2020-06-21 history/public-domain-review psychology --- https://publicdomainreview.org/essay/petrarchs-plague/ Petrarch’s Plague: Love, Death, and Friendship in a Time of Pandemic 2020-06-21 history/public-domain-review --- https://publicdomainreview.org/essay/picturing-a-voice-margaret-watts-hughes-and-the-eidophone/ Picturing a Voice: Margaret Watts Hughes and the Eidophone 2020-06-21 design/visualization history/public-domain-review music --- https://publicdomainreview.org/essay/reborn-into-a-new-form/ Reborn Into a New Form (1849) 2020-06-22 fiction history/public-domain-review --- https://publicdomainreview.org/essay/stuffed-ox-dummy-tree-artificial-rock-deception-in-the-work-of-richard-and-cherry-kearton/ Stuffed Ox, Dummy Tree, Artificial Rock: Deception in the Work of Richard and Cherry Kearton 2020-06-22 design history/public-domain-review technology --- https://publicdomainreview.org/essay/the-art-of-making-debts/ The Art of Making Debts: Accounting for an Obsession in 19th-Century France 2020-06-22 economics history/public-domain-review --- https://publicdomainreview.org/essay/the-assassination-of-the-prime-minister-spencer-perceval/ The Assassination of the Prime Minister, Spencer Perceval 2020-06-22 history/public-domain-review psychiatry --- https://publicdomainreview.org/essay/the-mark-of-the-beast-georgian-britains-anti-vaxxer-movement/ ‘The Mark of the Beast’: Georgian Britain’s Anti-Vaxxer Movement 2020-06-22 biology history/public-domain-review sociology --- https://publicdomainreview.org/essay/the-memoirs-of-joseph-grimaldi/ The Memoirs of Joseph Grimaldi 2020-06-22 fiction/humor history/public-domain-review --- https://publicdomainreview.org/essay/the-secret-history-of-holywell-street-home-to-victorian-london-s-dirty-book-trade/ The Secret History of Holywell Street, Home to Victorian London’s Dirty Book Trade: Victorian sexuality is often considered synonymous with prudishness, conjuring images of covered-up piano legs and dark ankle-length skirts. Historian Matthew Green uncovers a quite different scene in the sordid story of Holywell St, 19th-century London’s epicentre of erotica and smut. Green 2016 2020-06-22 design/typography/rubrication fiction history/public-domain-review --- https://publicdomainreview.org/essay/the-serious-and-the-smirk-the-smile-in-portraiture/ Why do we so seldom see people smiling in painted portraits? Nicholas Jeeves explores the history of the smile through the ages of portraiture, from Da Vinci's Mona Lisa to Alexander Gardner's photographs of Abraham Lincoln. 2020-06-22 history/public-domain-review psychology/neuroscience/pain sociology --- https://publicdomainreview.org/essay/the-spiralist/ The Spiralist 2020-06-22 biology history/public-domain-review math --- https://publicdomainreview.org/essay/w-b-o-shaughnessy-and-the-introduction-of-cannabis-to-modern-western-medicine/ W. B. O’Shaughnessy and the Introduction of Cannabis to Modern Western Medicine 2020-06-22 history/public-domain-review marijuana --- https://pubs.acs.org/doi/full/10.1021/acsptsci.0c00099 2020-06-22 psychedelic --- https://pulitzercenter.org/stories/right-not-know-when-ignorance-bliss-deadly The Right Not to Know: When Ignorance Is Bliss but Deadly 2020-06-23 genetics/heritable philosophy/ethics --- https://qualiacomputing.com/2015/06/09/state-space-of-drug-effects-results/ State-Space of Drug Effects: Results 2020-06-23 psychedelic --- https://qualiacomputing.com/2016/10/29/lsd-and-quantum-measurements-can-you-see-schrodingers-cat-both-dead-and-alive-on-acid/ LSD and Quantum Measurements: Can you see Schrödinger’s cat both dead and alive on acid? 2020-06-23 psychedelic --- https://qualiacomputing.com/2018/10/28/psychedelic-turk-a-platform-for-people-on-altered-states-of-consciousness/ Psychedelic Turk: A Platform for People on Altered States of Consciousness 2020-06-23 psychedelic --- https://qualiacomputing.com/2019/08/05/treating-cluster-headaches-using-nn-dmt-and-other-tryptamines/ Treating Cluster Headaches Using N,N-DMT and Other Tryptamines 2020-06-23 psychedelic --- https://qualiacomputing.com/2019/11/15/break-out-of-the-simulation-day-televised-entity-contact-injection-pulling-experiments-and-the-brain-as-a-game-engine/ Break Out of the Simulation Day: Televised Entity Contact, Injection Pulling Experiments, and the Brain as a Game Engine 2020-06-23 psychedelic --- https://qualiacomputing.com/2020/03/08/making-amazing-recreational-drug-cocktails/ Making Amazing Recreational Drug Cocktails 2020-06-23 psychedelic --- https://qualiacomputing.com/2020/08/14/qualia-research-diary-scents/ Qualia Research Diary: Scents [consciousness research, Experiment, genetics, memetics, scent, valence] 2020-06-23 psychiatry/anxiety/lavender psychology/smell/perfume --- https://qualiacomputing.com/2021/10/26/10-cross-modally-coherent-costume-ideas-for-this-halloween/ 10 Cross-Modally Coherent Costume Ideas for this Halloween 2020-06-23 psychology/smell/human --- https://qualiacomputing.com/2021/12/22/perfume-notes-are-impressionistic/ Perfume Notes Are Impressionistic 2020-06-23 psychiatry/anxiety/lavender psychology/smell/human --- https://quillette.com/2019/01/30/the-death-of-a-dreamer/ The Death of a Dreamer 2020-06-23 genetics/genome-synthesis --- https://quillette.com/2019/10/13/the-dangerous-dream-of-dismantling-human-hierarchies/ The Dangerous Dream of Dismantling Human Hierarchies 2020-06-24 sociology --- https://quillette.com/2021/11/29/the-universal-structure-of-storytelling/ The Universal Structure of Storytelling 2020-06-24 culture sociology --- https://qz.com/1454785/a-millionaire-couple-is-threatening-to-create-a-magic-mushroom-monopoly Compass Pathways is threatening to create a magic mushroom monopoly 2020-06-24 psychedelic --- https://qz.com/879285/psilocybin-drug-trials-psychedelics-such-as-acid-lsd-might-not-only-make-us-more-spiritual-and-religious-they-make-us-healthier-too Psilocybin drug trials: Psychedelics (acid, LSD, magic mushrooms) not only make us more spiritual and religious 2020-06-24 psychedelic --- https://read.atavist.com/dead-zoo-gang 2020-06-24 crime --- https://reason.com/2018/11/24/legalizing-marijuana-and-gay-m/ Legalizing Marijuana and Gay Marriage Seemed Impossible 2020-06-24 marijuana --- https://reason.com/2019/09/24/why-is-the-cdc-still-fostering-potentially-deadly-confusion-about-vaping-and-lung-disease/ Why Is the CDC Still Fostering Potentially Deadly Confusion About Vaping and Lung Disease? 2020-06-24 marijuana --- https://reason.com/2020/07/08/the-reaction-to-the-harpers-letter-on-cancel-culture-proves-why-it-was-necessary/ The Reaction to the Harper’s Letter on Cancel Culture Proves Why It Was Necessary: I was one of the 153 signers and am a veteran of the Twitter wars. But even I was taken aback by the swift, virulent response. 2020-06-24 philosophy/ethics sociology/preference-falsification --- https://reason.com/2021/11/07/red-markets/ Red Markets 2020-06-24 economics sociology --- https://reducing-suffering.org/is-there-suffering-in-fundamental-physics/ Is There Suffering in Fundamental Physics? 2020-06-24 philosophy/ethics philosophy/mind science --- https://reflectivedisequilibrium.blogspot.com/2020/05/what-would-civilization-immune-to.html Envisioning a world immune to global catastrophic biological risks 2020-06-24 existential-risk genetics/sequencing --- https://reiinakano.com/2019/06/21/robust-neural-style-transfer.html Neural Style Transfer with Adversarially Robust Classifiers 2020-06-25 ai/nn/adversarial --- https://replicationindex.com/2022/02/15/rr22-stereotype-threat/ Publication Bias in the Stereotype Threat Literature 2020-06-25 statistics/bias --- https://reset.me/story/benefits-of-microdosing-with-lsd-and-psilocybin-mushrooms/ Benefits Of Microdosing With LSD And Psilocybin Mushrooms 2020-06-25 nootropic/lsd psychedelic --- https://www.richardhanania.com/p/all-in-our-genes All in Our Genes 2020-06-25 genetics/heritable/adoption --- https://rll.berkeley.edu/deeprlcourse/docs/nuts-and-bolts.pdf 2020-06-25 reinforcement-learning --- https://road.cc/content/blog/90885-science-cycology-can-you-draw-bicycle The Science of Cycology: can you draw a bicycle? 2020-06-25 psychology/cognitive-bias/illusion-of-depth --- https://rostec.ru/en/news/checkmate-fighter-jet-scented-perfume-created-in-russia/ Checkmate Fighter Jet-Scented Perfume Created in Russia 2020-06-25 psychology/smell/perfume --- https://royalsocietypublishing.org/doi/pdf/10.1098/rspb.2021.0729 2020-06-25 genetics/cloning --- https://ryanandrewlangdon.wordpress.com/2020/01/28/today-i-learned-that-not-everyone-has-an-internal-monologue-and-it-has-ruined-my-day/ 2020-06-25 psychology/inner-voice --- https://s-space.snu.ac.kr/bitstream/10371/143023/1/Behavior%20and%20personality%20analysis%20in%20cloned%20working%20dog%20candidates.pdf Behavior and personality analysis in cloned working dog candidate 2020-06-25 genetics/cloning/dog psychology/personality --- https://same.energy/ Same Energy 2020-06-26 ai/nn/retrieval ai/nn/transformer/clip --- https://saml98.github.io/jekyll/update/2021/06/13/flies.html Sam's Internet Home 2020-06-26 statistics --- https://scholars-stage.blogspot.com/2021/03/on-laws-and-gods.html Redirecting The Scholar's Stage 2020-06-26 philosophy/ethics sociology --- https://science.sciencemag.org/content/290/5498/1972 2020-06-26 genetics/genome-synthesis --- https://science.sciencemag.org/content/329/5987/52 2020-06-26 genetics/genome-synthesis --- https://science.sciencemag.org/content/333/6040/348 2020-06-26 genetics/genome-synthesis --- https://science.sciencemag.org/content/342/6156/357 2020-06-26 genetics/genome-synthesis --- /doc/genetics/genome-synthesis/2016-boeke.pdf 2016 2020-06-26 genetics/genome-synthesis --- https://www.science.org/doi/10.1126/science.abj8754 2020-06-26 ai/nn/transformer/alphafold --- https://scientiasalon.wordpress.com/2014/08/14/on-the-science-and-ethics-of-ebola-treatments/ On the science and ethics of Ebola treatments 2020-06-26 philosophy/ethics statistics/bias --- https://scottaaronson.blog/?p=3376 The Kolmogorov option 2020-06-26 sociology/preference-falsification --- https://www.freedium.cfd/p/the-forgotten-pixel-art-masterpieces-of-the-playstation-1-era-8b453dfe00bf The Forgotten Pixel Art Masterpieces of the PlayStation 1 Era by Richmond Lee 2020-06-27 design --- https://www.freedium.cfd/p/what-board-games-teach-us-about-data-visualization-ded14080b4f4 What Board Games Teach Us About Data Visualization by Johannes Wirges 2020-06-27 design --- https://sebastianrisi.com/self_assembling_ai/ The Future of Artificial Intelligence is Self-Organizing and Self-Assembling 2020-06-27 reinforcement-learning/meta-learning --- https://seliger.com/2014/04/20/volunteers-nonprofits-really-want-their-money-not-their-bodies/ Volunteers: Nonprofits really want their money, not their bodies 2020-06-27 philosophy/ethics --- https://serokell.io/blog/how-sber-built-rudall-e How Sber Built ruDALL-E: Interview with Sergei Markov 2020-06-27 ai/nn/transformer/gpt/dall-e/1 --- https://sf.streetsblog.org/2018/11/01/spur-talk-the-japanese-model-for-station-development/ 2020-06-27 economics/georgism --- https://shape-of-code.coding-guidelines.com/2020/04/19/predicting-the-future-with-datalogistic-regression/ Predicting the future with data+logistic regression 2020-06-27 statistics/prediction --- https://siberiantimes.com/other/others/news/n0842-failed-cloned-dogs-no-use-to-law-enforcement-because-they-dont-obey-orders-and-hate-cold/ 2020-06-27 genetics/cloning --- https://sites.google.com/view/daml Domain-Adaptive Meta-Learning 2020-06-27 reinforcement-learning/meta-learning --- https://cdn.centerforinquiry.org/wp-content/uploads/sites/29/2009/09/22164425/p44.pdf 2020-06-27 philosophy/ethics --- https://slate.com/technology/2013/12/creativity-is-rejected-teachers-and-bosses-dont-value-out-of-the-box-thinking.html Creativity is rejected: Teachers and bosses don’t value out-of-the-box thinking. 2020-06-28 psychology/novelty --- https://slatestarcodex.com/2013/05/17/newtonian-ethics/ Newtonian Ethics 2020-06-28 philosophy/ethics --- https://slatestarcodex.com/2014/01/05/marijuana-much-more-than-you-wanted-to-know/ Marijuana: Much More Than You Wanted To Know 2020-06-28 marijuana --- https://slatestarcodex.com/2014/03/17/what-universal-human-experiences-are-you-missing-without-realizing-it/ What Universal Human Experiences Are You Missing Without Realizing It? 2020-06-28 psychology/inner-voice psychology/vision/aphantasia --- https://slatestarcodex.com/2014/04/22/right-is-the-new-left/ Right Is The New Left 2020-06-28 psychology/novelty sociology --- https://slatestarcodex.com/2014/07/30/meditations-on-moloch/ Meditations on Moloch 2020-06-28 ai/nn/transformer/gpt economics philosophy/ethics sociology --- https://slatestarcodex.com/2015/08/11/book-review-chronicles-of-wasted-time/ Book Review: Chronicles Of Wasted Time 2020-06-28 history sociology/preference-falsification --- https://slatestarcodex.com/2015/09/23/vegetarianism-for-meat-eaters/ Vegetarianism for Meat-Eaters 2020-06-28 philosophy/ethics --- https://slatestarcodex.com/2016/01/11/schizophrenia-no-smoking-gun/ Schizophrenia: No Smoking Gun 2020-06-28 nicotine psychiatry/schizophrenia --- https://slatestarcodex.com/2016/04/28/why-were-early-psychedelicists-so-weird/ Why Were Early Psychedelicists So Weird? 2020-06-28 psychedelic psychology/parapsychology --- https://slatestarcodex.com/2016/08/11/book-review-pihkal/ Book Review: PiHKaL 2020-06-29 psychedelic --- https://slatestarcodex.com/2016/09/12/its-bayes-all-the-way-up/ It’s Bayes All The Way Up 2020-06-29 psychiatry/schizophrenia --- https://slatestarcodex.com/2016/10/11/somewhat-against-psychiatric-conditions-as-domestication-failure/ Somewhat Against Psychiatric Conditions As Domestication Failure 2020-06-29 psychiatry/schizophrenia --- https://slatestarcodex.com/2016/11/10/book-review-house-of-god/ Book Review: House of God 2020-06-29 philosophy/ethics --- https://slatestarcodex.com/2017/04/01/g-k-chesterton-on-ai-risk/ G.K. Chesterton On AI Risk 2020-06-29 fiction/humor --- https://slatestarcodex.com/2017/08/16/fear-and-loathing-at-effective-altruism-global-2017/ Fear And Loathing At Effective Altruism Global 2017 2020-06-29 philosophy/ethics --- https://slatestarcodex.com/2017/08/29/my-irb-nightmare/ My IRB Nightmare 2020-06-29 philosophy/ethics statistics/bias --- https://slatestarcodex.com/2017/09/05/book-review-surfing-uncertainty/ Book Review: Surfing Uncertainty 2020-06-29 psychiatry/schizophrenia --- https://slatestarcodex.com/2017/10/10/ssc-journal-club-serotonin-receptors/ SSC Journal Club: Serotonin Receptors 2020-06-29 psychedelic psychiatry --- https://slatestarcodex.com/2017/10/23/kolmogorov-complicity-and-the-parable-of-lightning/ Kolmogorov Complicity And The Parable Of Lightning 2020-06-29 sociology/preference-falsification --- https://slatestarcodex.com/2017/11/13/book-review-legal-systems-very-different-from-ours/ Book Review: Legal Systems Very Different From Ours 2020-06-29 economics history sociology --- https://slatestarcodex.com/2018/04/19/gupta-on-enlightenment/ Gupta On Enlightenment 2020-06-30 psychiatry/meditation psychology/inner-voice --- https://slatestarcodex.com/2018/05/23/should-psychiatry-test-for-lead-more/ Should Psychiatry Test For Lead More? 2020-06-30 crime psychiatry --- https://slatestarcodex.com/2018/09/20/treat-the-prodrome/ Treating The Prodrome 2020-06-30 psychiatry/schizophrenia --- https://slatestarcodex.com/2018/12/11/diametrical-model-of-autism-and-schizophrenia/ Diametrical Model Of Autism And Schizophrenia 2020-06-30 psychiatry/schizophrenia --- https://slatestarcodex.com/2019/01/01/what-happened-to-90s-environmentalism/ What Happened To 90s Environmentalism? 2020-06-30 psychology/novelty sociology --- https://slatestarcodex.com/2019/07/23/book-review-the-electric-kool-aid-acid-test/ Book Review: The Electric Kool-Aid Acid Test 2020-06-30 psychedelic --- https://slatestarcodex.com/2019/09/10/ssc-journal-club-relaxed-beliefs-under-psychedelics-and-the-anarchic-brain/ SSC Journal Club: Relaxed Beliefs Under Psychedelics And The Anarchic Brain 2020-06-30 psychedelic --- https://slatestarcodex.com/2019/10/30/new-atheism-the-godlessness-that-failed/ New Atheism: The Godlessness That Failed 2020-06-30 psychology/novelty sociology --- https://slatestarcodex.com/2020/02/27/book-review-the-seven-principles-for-making-marriage-work/ Book Review: The 7 Principles For Making Marriage Work 2020-06-30 psychiatry statistics/bias --- https://slatestarcodex.com/2020/04/17/depression-the-olfactory-perspective/ Depression: The Olfactory Perspective 2020-06-30 psychology/smell --- https://slatestarcodex.com/2020/06/01/book-review-origin-of-consciousness-in-the-breakdown-of-the-bicameral-mind/ Book Review: Origin Of Consciousness In The Breakdown Of The Bicameral Mind 2020-06-30 philosophy/mind psychiatry/schizophrenia psychology/inner-voice --- https://slimemoldtimemold.com/2022/01/27/like-a-lemon-to-a-lime-a-lime-to-a-lemon/ Like a Lemon to a Lime, a Lime to a Lemon 2020-07-01 biology genetics/selection philosophy/ontology --- https://smitop.com/post/codex/ 2020-07-01 ai/nn/transformer/gpt/codex --- https://soundshader.github.io/cats.html Cat meow sounds visualized as ACF images 2020-07-01 cat/psychology --- https://spectrum.ieee.org/digital-nose-stimulation-enables-smelling-in-stereo Digital Nose Stimulation Enables Smelling in Stereo 2020-07-01 psychology/smell --- https://spectrum.ieee.org/iron-powder-passes-first-industrial-test-as-renewable-co2free-fuel Iron Powder Passes First Industrial Test as Renewable, Carbon Dioxide-Free Fuel 2020-07-01 technology/carbon-capture --- https://spectrum.ieee.org/lasers-and-lunar-arks-cryopreservation-heats-up Lasers To Lunar Arks: Cryopreservation Heats Up 2020-07-01 cryonics --- https://srconstantin.wordpress.com/2019/02/25/humans-who-are-not-concentrating-are-not-general-intelligences/ Humans Who Are Not Concentrating Are Not General Intelligences 2020-07-01 ai/nn/transformer/gpt philosophy/mind psychology/cognitive-bias/illusion-of-depth --- https://stanislavfort.com/2021/01/12/OpenAI_CLIP_adversarial_examples.html 2020-07-01 ai/nn/adversarial ai/nn/transformer/clip --- https://stanislavfort.com/2021/03/05/OpenAI_CLIP_stickers_and_adversarial_examples.html Pixels still beat text: Attacking the OpenAI CLIP model with text patches and adversarial pixel perturbations 2020-07-01 ai/nn/adversarial ai/nn/sampling ai/nn/transformer/clip --- https://stanislavfort.github.io/blog/OpenAI_CLIP_adversarial_examples/ 2020-07-01 ai/nn/transformer/clip --- https://stanislavfort.github.io/blog/OpenAI_CLIP_stickers_and_adversarial_examples/ 2020-07-02 ai/nn/transformer/clip --- https://storage.googleapis.com/pub-tools-public-publication-data/pdf/f52ac33bc9d1adecd3a8037a7009b185fd934f0e.pdf 2020-07-02 reinforcement-learning/exploration statistics/bayes --- https://statmodeling.stat.columbia.edu/2022/01/11/suspense-in-music-suspense-in-stories-how-do-they-differ/ 2020-07-02 psychology/novelty --- https://steamtraen.blogspot.com/2021/10/a-catastrophic-failure-of-peer-review.html A catastrophic failure of peer review in obstetrics and gynaecology 2020-07-02 statistics/bias --- https://stevenson.lab.uconn.edu/scaling/ Tracking Advances in Neural Recording 2020-07-02 psychology/neuroscience --- https://story.californiasunday.com/superhero-gene-euan-ashley-stanford/ The Superhero Genes 2020-07-02 genetics/heritable/rare --- https://stripe.com/blog/first-negative-emissions-purchases Stripe’s first carbon removal purchases Stripe 2020-07-02 technology/carbon-capture --- https://stripe.com/blog/negative-emissions-commitment Decrement carbon: Stripe's negative emissions commitment Stripe 2020-07-02 technology/carbon-capture --- https://subcriticalappraisal.com/2020/Did-DeepMind-solve-the-protein-folding-problem/ Did DeepMind Solve The Protein Folding Problem? 2020-07-02 ai/nn/transformer/alphafold --- https://swoleateveryheight.blogspot.com/2018/05/the-greatest-gym-youll-never-lift-at.html Swole at Every Height: The Greatest Gym You’ll Never Lift At 2020-07-02 exercise --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167335/ Health and temperaments of cloned working dogs 2020-07-02 genetics/cloning/dog --- https://www.racket.news/p/the-left-is-now-the-right We laughed at the Republican busybody who couldn't joke, declared war on dirty paintings, and peered through your bedroom window. Now that person has switched sides, and nobody's laughing 2020-07-03 philosophy/ethics sociology/preference-falsification --- https://www.racket.news/p/the-news-media-is-destroying-itself The American Press Is Destroying Itself 2020-07-03 philosophy/ethics sociology/preference-falsification --- https://taltech.ee/public/m/mart-murdvee/EconPsy/2/Lynn_Vanhanen_2012_National_IQs_-_a_review.pdf 2020-07-03 iq/ses --- https://teageegeepea.tripod.com/maumau.html 2020-07-03 sociology/preference-falsification --- https://tech.pic-collage.com/distillation-of-clip-model-and-other-experiments-f8394b7321ce 2020-07-03 ai/nn/sparsity ai/nn/transformer/clip --- https://the-toast.net/2013/09/03/another-lifeless-planet-found/ Another Empty, Lifeless Planet Found 2020-07-03 fiction/humor philosophy/ethics --- https://theamericanscholar.org/the-sound-of-evil/ The Sound of Evil 2020-07-03 philosophy/ethics --- https://theconversation.com/gold-coast-light-rail-study-helps-put-a-figure-on-value-captures-funding-potential-65084 Gold Coast light rail study helps put a figure on value capture’s funding potential 2020-07-03 economics/georgism --- https://theconversation.com/how-a-handful-of-prehistoric-geniuses-launched-humanitys-technological-revolution-171511 How a handful of prehistoric geniuses launched humanity’s technological revolution 2020-07-03 history technology --- https://thegradient.pub/how-to-fix-rl/ How to fix reinforcement learning 2020-07-03 reinforcement-learning/meta-learning --- https://thehardestscience.com/2011/09/29/does-psilocybin-cause-changes-in-personality-maybe-but-not-so-fast/ Does psilocybin cause changes in personality? Maybe, but not so fast 2020-07-03 nootropic psychedelic psychology/personality --- https://thehustle.co/digiscents-ismell-fail/ 2020-07-04 psychology/smell technology --- https://theinfosphere.org/Futurama_theorem Futurama theorem 2020-07-04 math/humor statistics/order/comparison --- https://theintercept.com/2018/11/23/bitcoin-gift-card-trading-scams/ Inside the Wild West World of Gift Card Bitcoin Brokering 2020-07-04 bitcoin --- https://thewalrus.ca/the-hazy-economy-of-cannabis/ The Hazy Economy of Cannabis 2020-07-04 economics marijuana --- https://thonyc.wordpress.com/2021/11/04/renaissance-science-xxii/ Renaissance Science – XXII 2020-07-04 math --- https://threadreaderapp.com/thread/1357071738731814912.html https://x.com/add_hawk/status/1357071738731814912 2020-07-04 psychology/smell/perfume --- https://time.com/3801889/us-legalization-marijuana-trade/ U.S. Legalization of Marijuana Has Hit Mexican Cartels' Border Trade 2020-07-04 marijuana --- https://time.com/time/printout/0,8816,1893946,00.html 2020-07-04 marijuana --- https://tosche.net/blog/ink-traps-and-pals Toshi Omagari 2020-07-04 design/typography technology --- https://towardsdatascience.com/1-1-3-wait-no-1-1-2-how-to-have-gpt-sanity-check-itself-136e846987bf 2020-07-04 ai/nn/transformer/gpt/inner-monologue --- https://towardsdatascience.com/codex-by-openai-in-action-83529c0076cc 2020-07-04 ai/nn/transformer/gpt/codex --- https://towardsdatascience.com/deep-neural-networks-are-biased-at-initialisation-towards-simple-functions-a63487edcb99 2020-07-05 ai/scaling statistics/bayes --- https://towardsdatascience.com/neural-networks-are-fundamentally-bayesian-bee9a172fad8 2020-07-05 ai/scaling statistics/bayes --- https://trevorklee.com/should-you-take-metformin-for-longevity/ Should you take metformin for longevity? 2020-07-05 longevity/glp/semaglutide --- https://tribune.com.pk/story/1967033/3-amid-animal-cruelty-debate-80-south-koreas-sniffer-dogs-cloned Amid animal cruelty debate, 80% of South Korea's sniffer dogs are cloned 2020-07-05 genetics/cloning/dog --- https://unherd.com/2020/01/cast-out-how-knitting-fell-into-a-purity-spiral/ How knitters got knotted in a purity spiral 2020-07-05 sociology/preference-falsification --- https://upload.wikimedia.org/wikipedia/commons/3/3b/Citrus_tern_cb_simplified_1.svg Hybridization in citrus cultivars: Genetic mixing of 3 ancestral species [ternary plot] English Wikipedia 2020-07-05 genetics/selection/artificial --- https://vividness.live/buddhist-ethics-is-advertising ‘Ethics’ is advertising 2020-07-05 philosophy/ethics sociology --- https://cepr.org/voxeu/columns/technology-transfer-and-early-industrial-development-case-sino-soviet-alliance Technology transfer and early industrial development: The case of the Sino-Soviet Alliance 2020-07-05 economics/automation technology --- https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA 2020-07-05 ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e/1 --- https://warontherocks.com/2022/01/winged-luddites-aviators-are-the-biggest-threat-to-carrier-aviation/ Winged Luddites: Aviators Are the Biggest Threat to Carrier Aviation 2020-07-05 economics/automation --- https://web.archive.org/web/20070524182038/http://www.americanheritage.com/articles/magazine/it/1987/2/1987_2_62.shtml The Little Can That Could 2020-07-06 design --- https://web.archive.org/web/20091214014113/http://theuncertainfuture.com/faq.html#7 Frequently Asked Questions 2020-07-06 genetics/gametogenesis --- https://web.archive.org/web/20100304165140/http://www.libertary.com/books/reconciliation-road 2020-07-06 history/s-l-a-marshall statistics/bias --- https://web.archive.org/web/20101121053907/http://cryonics.org/book1.html The Prospect of Immortality 2020-07-06 cryonics --- https://web.archive.org/web/20111025143342/http://www.giantflightlessbirds.com/2011/10/the-great-penguin-sweater-fiasco/ The Great Penguin Sweater Fiasco 2020-07-06 philosophy/ethics --- https://web.archive.org/web/20120804004558/http://www.bestofyoutoday.com/ask-nutrition-expert/learn-what-effects-low-dosage-psychedelic-have-your-mental-health Learn What Effects Low Dosage of Psychedelic Have on Your Mental Health 2020-07-06 nootropic psychedelic --- https://web.archive.org/web/20130609114014/http://www.electroiq.com/articles/sst/print/volume-46/issue-7/features/fab-management/using-learning-curve-theory-to-redefine-moores-law.html Using learning curve theory to redefine Moore's Law 2020-07-06 cs economics/experience-curve statistics/prediction --- https://web.archive.org/web/20130828014330/http://www.cryonics.org/book2.html Man Into Superman 2020-07-06 cryonics --- https://web.archive.org/web/20140317070718/http://statwonk.github.io/blog/2014/03/08/survival-analysis-of-running-from-the-law-the-fbis-most-wanted You've just been added to the FBI's Ten Most Wanted List, how long will you survive? 2020-07-06 statistics/survival-analysis --- https://web.archive.org/web/20160308235907/http://homepage.ntlworld.com/janusg/coe/cofe01.htm The Corruption of Economics 2020-07-06 economics/georgism --- https://web.archive.org/web/20171030232756/https://medium.com/stubborn-attachments/stubborn-attachments-full-text-8fc946b694d ‘Stubborn Attachments’: Full Text – Stubborn Attachments – Medium 2020-07-06 philosophy/ethics --- https://web.archive.org/web/20190820110403/https://www.americanheritage.com/secret-soldiers-who-didnt-shoot The Secret Of The Soldiers Who Didn’t Shoot 2020-07-07 history/s-l-a-marshall statistics/bias --- https://web.archive.org/web/20210123091245/https://pando.com/2016/06/13/ebbu-rise-fall-modern-weed-dealer/ 2020-07-07 marijuana --- https://web.archive.org/web/20210131091045/https://arena.openai.com/#/results 2020-07-07 reinforcement-learning/model-free/oa5 --- https://web.archive.org/web/20210225235349/https://simplystatistics.org/2015/04/09/a-blessing-of-dimensionality-often-observed-in-high-dimensional-data-sets/ 2020-07-07 genetics/selection/artificial/index-selection --- https://web.archive.org/web/20210415022657/http://starcraft.blizzplanet.com/blog/comments/blizzcon-2018-starcraft-ii-whats-next-panel-transcript 2020-07-07 ai/scaling reinforcement-learning/model-free/alphastar --- https://web.cs.ucdavis.edu/~rogaway/papers/moral-fn.pdf 2020-07-07 philosophy/ethics --- https://web.media.mit.edu/~echu/assets/projects/evolving-views/paper.pdf 2020-07-07 ai/nn/transformer/clip --- https://journals.sagepub.com/doi/abs/10.1177/0956797612464058 Political Extremism Is Supported by an Illusion of Understanding 2014 2020-07-07 psychology/cognitive-bias/illusion-of-depth --- https://well.blogs.nytimes.com/2009/06/24/can-you-get-fit-in-six-minutes-a-week/ 2020-07-07 exercise --- https://well.blogs.nytimes.com/2010/09/15/phys-ed-can-exercise-make-kids-smarter/ 2020-07-07 exercise --- https://archive.nytimes.com/well.blogs.nytimes.com/2010/10/20/do-women-sweat-differently-than-men/ Do Women Sweat Differently Than Men? 2020-07-07 exercise --- https://well.blogs.nytimes.com/2011/10/19/do-we-have-a-set-point-for-exercise/ 2020-07-08 exercise --- https://archive.nytimes.com/well.blogs.nytimes.com/2012/01/11/marijuana-smoking-does-not-harm-lungs-study-finds/ Moderate Marijuana Use Does Not Impair Lung Function, Study Finds 2020-07-08 marijuana --- https://archive.nytimes.com/well.blogs.nytimes.com/2014/11/12/exercising-but-gaining-weight/ Exercising but Gaining Weight 2020-07-08 exercise --- https://archive.nytimes.com/well.blogs.nytimes.com/2016/01/20/when-athletes-go-gluten-free/ When Athletes Go Gluten Free 2020-07-08 exercise --- https://whyevolutionistrue.com/2018/03/04/a-human-chimera/ A human chimera 2020-07-08 biology genetics --- https://wiki.c2.com/?TheKenThompsonHack 2020-07-08 cs/security philosophy/epistemology --- https://willcrichton.net/nota/ A New Medium for Communicating Research on Programming Languages 2020-07-08 design --- https://win-vector.com/2016/09/11/adversarial-machine-learning/ Adversarial machine learning 2020-07-08 ai/nn/adversarial reinforcement-learning --- https://models.aminer.cn/CogView/index.html CogView 以文生图 2020-07-08 ai/nn/transformer/gpt/dall-e --- https://www.adamsmith.org/research/back-in-the-ussr Back in the USSR: What life was like in the Soviet Union 2020-07-08 philosophy/ethics --- https://www.additionalventures.org/initiatives/climate-action/oae-research-award/ OAE Research Award 2020-07-09 technology/carbon-capture --- https://www.aei.org/wp-content/uploads/2011/10/20040302_book443.pdf 2020-07-09 iq/ses --- https://www.aging-us.com/article/YmhYbE6ipoL6ykpfz/text 2020-07-09 longevity/fasting --- https://www.aiweirdness.com/this-is-the-openai-api-it-makes-spookily-20-06-11/ This is the OpenAI API. It makes spookily good twitter bots. 13⁄10 would retweet 2020-07-09 ai/nn/transformer/gpt ai/text-style-transfer --- https://www.alcor.org/docs/algorithmic-estimation-of-cortical-autolysis-in-post-mortem-adult-rat-brains.pdf 2020-07-09 cryonics --- https://cryonicsarchive.org/docs/cryonics-magazine-2007-03.pdf 2020-07-09 cryonics --- https://www.alcor.org/library/alcor-membership-statistics/ 2020-07-09 cryonics --- https://www.cryonicsarchive.org/library/chemical-brain-preservation/ Chemical Brain Preservation and Human Suspended Animation 2020-07-09 cryonics --- https://www.cryonicsarchive.org/library/suspension-failures-lessons-from-the-early-years/ Suspension Failures: Lessons from the Early Years 2020-07-09 cryonics --- https://www.cryonicsarchive.org/library/will-cryonics-work/ Will Cryonics Work? Examining the Probabilities 2020-07-09 cryonics --- https://web.archive.org/web/20111001222449/http://www.alcor.org/magazine/2011/08/16/a-new-choice-for-immortalists/ A New Choice for Immortalists 2020-07-09 cryonics --- https://www.alcor.org/library/allocation-of-long-term-care-costs-at-alcor/ 2020-07-10 cryonics --- https://www.cryonicsarchive.org/library/will-cryonics-work/ Will Cryonics Work? Examining the Probabilities 2020-07-10 cryonics --- https://www.alexirpan.com/2018/02/14/rl-hard.html Deep Reinforcement Learning Doesn't Work Yet 2020-07-10 ai/nn reinforcement-learning --- https://www.alicemaz.com/writing/minecraft.html Playing to Win 2020-07-10 economics --- https://www.amazon.com/Applied-Survival-Analysis-Regression-Probability/dp/0471754994/ 2020-07-10 statistics/survival-analysis --- https://www.amazon.com/Catholic-Converts-British-American-Intellectuals/dp/0801486637 Catholic Converts: British and American Intellectuals Turn to Rome 2020-07-10 psychology/novelty sociology --- https://www.amazon.com/Dog-Inc-Uncanny-Inside-Cloning/dp/1583333916 2020-07-10 genetics/cloning --- https://www.amazon.com/End-Science-Religion-about-Apocalypse-ebook/dp/B019BNG5BO/ End: What Science and Religion Tell Us about the Apocalypse 2020-07-10 genetics/gametogenesis --- https://www.amazon.com/Ending-Medical-Reversal-Improving-Outcomes/dp/1421417723 2020-07-10 philosophy/ethics statistics/bias --- https://www.amazon.com/Hit-Makers-Science-Popularity-Distraction/dp/110198032X 2020-07-10 psychology/novelty --- https://www.amazon.com/Hive-Mind-Your-Nation%C2%92s-Matters/dp/0804785961 2020-07-10 iq/ses --- https://www.amazon.com/Human-Intelligence-Earl-Hunt/dp/0521707811 2020-07-11 iq/high/smpy iq/ses --- https://www.amazon.com/Matter-Taste-Fashions-Culture-Change/dp/0300173873 2020-07-11 psychology/novelty --- https://www.amazon.com/Mind-Flat-Remarkable-Shallowness-Improvising/dp/030023872X 2020-07-11 psychology/cognitive-bias/illusion-of-depth --- https://www.amazon.com/Private-Truths-Public-Lies-Falsification-ebook/dp/B082QVPYPL 2020-07-11 sociology/preference-falsification --- https://www.amazon.com/Singularity-Rising-Surviving-Thriving-Dangerous/dp/1936661659 2020-07-11 genetics/gametogenesis --- https://www.amazon.com/Sports-Gene-Extraordinary-Athletic-Performance/dp/161723012X The Sports Gene: Inside the Science of Extraordinary Athletic Performance 2020-07-11 genetics/heritable/rare psychology/writing --- https://www.amazon.com/The-Psychedelic-Explorers-Guide-Therapeutic/dp/1594774021/ 2020-07-11 nootropic psychedelic --- https://www.amazon.com/The-Son-Also-Rises-Princeton/dp/0691162549 2020-07-11 iq/ses --- https://www.amazon.com/Turings-Cathedral-Origins-Digital-Universe/dp/1400075998/ 2020-07-11 cs economics/experience-curve math nootropic psychedelic --- https://www.amazon.com/s?ie=UTF8&field-isbn=1632864215&page=1&rh=i:stripbooks 2020-07-11 history sociology/preference-falsification --- https://www.arkansasonline.com/news/2019/nov/28/airport-beagles-sniff-out-illicit-foods/ Airport beagles sniff out illicit foodstuffs: Pork is primary threat as U.S. aims to prevent African swine fever’s arrival 2020-07-11 genetics/cloning --- https://www.atlasobscura.com/articles/seaweed-sheep-north-ronaldsay-orkney-festival The Uncertain Future of North Ronaldsay's Seaweed-Eating Sheep 2020-07-12 genetics/selection --- https://www.atlasobscura.com/articles/the-early-state-sanctioned-lsd-experiments-in-communist-bulgaria The Early, State-Sanctioned LSD Experiments in Communist Bulgaria 2020-07-12 psychedelic --- https://www.authorea.com/users/429500/articles/533177-modelling-a-time-series-of-records-in-pymc3 Modelling a Time Series of Records with PyMC3 2020-07-12 statistics/bayes statistics/order --- https://www.avclub.com/it-smelled-like-death-an-oral-history-of-the-double-1798255802 ‘It smelled like death’: An oral history of the Double Dare obstacle course 2020-07-12 psychology/smell --- https://www.badscience.net/2011/03/when-ethics-committees-kill/ 2020-07-12 philosophy/ethics --- https://www.bartleby.com/lit-hub/the-english-poets/extracts-from-hyperion-oceanus/ Extracts from Hyperion: Oceanus 2020-07-12 philosophy/ethics --- https://web.archive.org/web/20160901235709/https://www.bbc.com/earth/story/20160830-first-identical-twin-dogs-discovered A dog has given birth to the first identical twin puppies: Outside of humans and one species of armadillo, identical twins seem to be vanishingly rare. Now for the first time a dog has given birth to a pair 2020-07-12 genetics/cloning/dog --- https://www.bbc.com/future/article/20210519-the-hidden-reason-processed-pet-foods-are-so-addictive The hidden reason processed pet foods are so addictive 2020-07-12 cat/psychology psychology/smell --- https://www.bbc.com/future/article/20211126-why-insects-are-more-sensitive-than-they-seem Why insects are more sensitive than they seem 2020-07-12 philosophy/ethics philosophy/mind psychology/neuroscience --- https://www.bbc.com/news/science-environment-20629671 The hum that helps to fight crime 2020-07-12 crime technology --- https://www.bbc.com/news/uk-england-london-59518847 Louis Wain: The artist who changed how we think about cats 2020-07-13 cat --- https://www.bbc.com/news/world-africa-59614595 The ultra-violent cult that became a global mafia 2020-07-13 crime sociology --- https://www.becker-posner-blog.com/2006/12/charitable-foundations--posners-comment.html 2020-07-13 economics/perpetuities philosophy/ethics --- https://www.biorxiv.org/content/10.1101/014498.full An Atlas of Genetic Correlations across Human Diseases and Traits Brendan Bulik-Sullivan, Hilary K. Finucane, Verneri Anttila, Alexander Gusev, Felix R. Day, ReproGen Consortium, Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Laramie Duncan, John R. B. Perry, Nick Patterson, Elise B. Robinson, Mark J. Daly, Alkes Price, Benjamin M. Neale 2015-04-06 2020-07-13 [("doi","10.1101/014498")] psychiatry/anorexia psychiatry/schizophrenia

Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses.

We circumvent these difficulties by introducing a technique for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use our method to estimate 300 genetic correlations among 25 traits, totaling more than 1.5 million unique phenotype measurements.

Our results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no genome-wide statistically-significant SNPs for anorexia nervosa and only 3 for educational attainment.

--- https://www.biorxiv.org/content/10.1101/016527.full Contrasting regional architectures of schizophrenia and other complex diseases using fast variance components analysis Po-Ru Loh, Gaurav Bhatia, Alexander Gusev, Hilary K. Finucane, Brendan K. Bulik-Sullivan, Samuela J. Pollack, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Teresa R. de Candia, Sang Hong Lee, Naomi R. Wray, Kenneth S. Kendler, Michael C. O’Donovan, Benjamin M. Neale, Nick Patterson, Alkes Price 2015-06-05 2020-07-13 [("doi","10.1101/016527")] psychiatry/schizophrenia

Heritability analyses of GWAS cohorts have yielded important insights into complex disease architecture, and increasing sample sizes hold the promise of further discoveries. Here, we analyze the genetic architecture of schizophrenia in 49,806 samples from the PGC, and 9 complex diseases in 54,734 samples from the GERA cohort. For schizophrenia, we infer an overwhelmingly polygenic disease architecture in which ≥71% of 1Mb genomic regions harbor at least one variant influencing schizophrenia risk.

We also observe enrichment of heritability in GC-rich regions and in higher-frequency SNPs for both schizophrenia and GERA diseases. In bivariate analyses, we observe statistically-significant genetic correlations (ranging 0.18–0.85) among several pairs of GERA diseases; genetic correlations were on average 1.3× stronger than correlations of overall disease liabilities. To accomplish these analyses, we developed a fast algorithm for multi-component, multi-trait variance components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.

--- https://www.biorxiv.org/content/10.1101/022418.full Haplotypes of common SNPs can explain missing heritability of complex diseases Gaurav Bhatia, Alexander Gusev, Po-Ru Loh, Bjarni J. Vilhjálmsson, Stephan Ripke, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Shaun Purcell, Eli Ayumi Stahl, Mark Daly, Teresa R. de Candia, Kenneth S. Kendler, Michael C. O’Donovan, Sang Hong Lee, Naomi R. Wray, Benjamin M. Neale, Matthew C. Keller, Noah A. Zaitlen, Bogdan Pasaniuc, Jian Yang, Alkes Price 2015-07-12 2020-07-13 [("doi","10.1101/022418")] genetics/heritable psychiatry/schizophrenia

While genome-wide statistically-significant associations generally explain only a small proportion of the narrow-sense heritability of complex disease (h2), recent work has shown that more heritability is explained by all genotyped SNPs (hg2). However, much of the heritability is still missing (hg2 < h2). For example, for schizophrenia, h2 is estimated at 0.7–0.8 but hg2 is estimated at ~0.3. Efforts at increasing coverage through accurately imputed variants have yielded only small increases in the heritability explained, and poorly imputed variants can lead to assay artifacts for case-control traits.

We propose to estimate the heritability explained by a set of haplotype variants (haploSNPs) constructed directly from the study sample (hhap2). Our method constructs a set of haplotypes from phased genotypes by extending shared haplotypes subject to the 4-gamete test. In a large schizophrenia data set (PGC2-SCZ), haploSNPs with MAF > 0.1% explained substantially more phenotypic variance (hhap2 = 0.64 (S.E. 0.084)) than genotyped SNPs alone (hg2 = 0.32 (S.E. 0.029)). These estimates were based on cross-cohort comparisons, ensuring that cohort-specific assay artifacts did not contribute to our estimates.

In a large multiple sclerosis data set (WTCCC2-MS), we observed an even larger difference between hhap2 and hg2, though data from other cohorts will be required to validate this result. Overall, our results suggest that haplotypes of common SNPs can explain a large fraction of missing heritability of complex disease, shedding light on genetic architecture and informing disease mapping strategies.

--- https://www.biorxiv.org/content/10.1101/029504.full Molecular genetic contributions to self-rated health Sarah E. Harris, Saskia P. Hagenaars, Gail Davies, W. David Hill, David C. M. Liewald, Stuart J. Ritchie, Riccardo E. Marioni, METASTROKE consortium, International Consortium for Blood Pressure, CHARGE consortium Aging, Longevity Group, CHARGE consortium Cognitive Group, Cathie L. M. Sudlow, Joanna M. Wardlaw, Andrew M. McIntosh, Catharine R. Gale, Ian J. Deary 2016-04-12 2020-07-13 [("doi","10.1101/029504")] genetics/heritable/correlation psychiatry/adhd psychiatry/depression psychiatry/schizophrenia psychology/personality

Background: Poorer self-rated health (SRH) predicts worse health outcomes, even when adjusted for objective measures of disease at time of rating. Twin studies indicate SRH has a heritability of up to 60% and that its genetic architecture may overlap with that of personality and cognition.

Method: We carried out a genome-wide association study (GWAS) of SRH on 111,749 members of the UK Biobank sample. Univariate genome-wide complex trait analysis (GCTA)-GREML analyses were used to estimate the proportion of variance explained by all common autosomal SNPs for SRH. Linkage Disequilibrium (LD) score regression and polygenic risk scoring, two complementary methods, were used to investigate pleiotropy between SRH in UK Biobank and up to 21 health-related and personality and cognitive traits from published GWAS consortia.

Results: The GWAS identified 13 independent signals associated with SRH, including several in regions previously associated with diseases or disease-related traits. The strongest signal was on chromosome 2 (rs2360675, p = 1.77×10−10) close to KLF7, which has previously been associated with obesity and type 2 diabetes. A second strong peak was identified on chromosome 6 in the major histocompatibility region (rs76380179, p = 6.15×10−10). The proportion of variance in SRH that was explained by all common genetic variants was 13%. Polygenic scores for the following traits and disorders were associated with SRH: cognitive ability, education, neuroticism, BMI, longevity, ADHD, major depressive disorder, schizophrenia, lung function, blood pressure, coronary artery disease, large vessel disease stroke, and type 2 diabetes.

Conclusion: Individual differences in how people respond to a single item on SRH are partly explained by their genetic propensity to many common psychiatric and physical disorders and psychological traits.

Key Messages

Genetic variants associated with common diseases and psychological traits are associated with self-rated health.

The SNP-based heritability of self-rated health is 0.13 (SE 0.006).

There is pleiotropy between self-rated health and psychiatric and physical diseases and psychological traits.

--- https://www.biorxiv.org/content/10.1101/037929.full Genetic evidence for natural selection in humans in the contemporary United States Jonathan Beauchamp 2016-05-05 2020-07-13 [("doi","10.1101/037929")] psychiatry/schizophrenia

Recent findings from molecular genetics now make it possible to test directly for natural selection by analyzing whether genetic variants associated with various phenotypes have been under selection. I leverage these findings to construct polygenic scores that use individuals’ genotypes to predict their body mass index, educational attainment (EA), glucose concentration, height, schizophrenia, total cholesterol, and (in females) age at menarche. I then examine associations between these scores and fitness to test whether natural selection has been occurring. My study sample includes individuals of European ancestry born between 1931 and 1953 in the Health and Retirement Study, a representative study of the US population.

My results imply that natural selection has been slowly favoring lower EA in both females and males, and are suggestive that natural selection may have favored a higher age at menarche in females. For EA, my estimates imply a rate of selection of about −1.5 months of education per generation (which pales in comparison with the increases in EA observed in contemporary times). Though they cannot be projected over more than one generation, my results provide additional evidence that humans are still evolving—albeit slowly, especially when compared to the rapid secular changes that have occurred over the past few generations due to cultural and environmental factors.

--- https://www.biorxiv.org/content/10.1101/042788.full Older fathers’ children have lower evolutionary fitness across four centuries and in four populations Ruben C. Arslan, Kai P. Willführ, Emma Frans, Karin J. H. Verweij, Mikko Myrskylä, Eckart Voland, Catarina Almqvist, Brendan P. Zietsch, Lars Penke 2016-03-08 2020-07-13 [("doi","10.1101/042788")] genetics/heritable genetics/selection/natural/human/dysgenics

Higher paternal age at offspring conception increases de novo genetic mutations (Kong et al 2012). Based on evolutionary genetic theory we predicted that the offspring of older fathers would be less likely to survive and reproduce, ie. have lower fitness.

In a sibling control study, we find clear support for negative paternal age effects on offspring survival, mating, and reproductive success across 4 large populations with an aggregate n > 1.3 million in main analyses. Compared to a sibling born when the father was 10 years younger, individuals had 4–13% fewer surviving children in the 4 populations. 3 populations were pre-industrial (1670–1850) Western populations and showed a pattern of paternal age effects across the offspring’s lifespan. In 20th-century Sweden, we found no negative paternal age effects on child survival or marriage odds.

Effects survived tests for competing explanations, including maternal age and parental loss.

To the extent that we succeeded in isolating a mutation-driven effect of paternal age, our results can be understood to show that de novo mutations reduce offspring fitness across populations and time. We can use this understanding to predict the effect of increasingly delayed reproduction on offspring genetic load, mortality, and fertility.

--- https://www.biorxiv.org/content/10.1101/047290.full Genetic contributions to self-reported tiredness Vincent Deary, Saskia P. Hagenaars, Sarah E. Harris, W. David Hill, Gail Davies, David C. M. Liewald, International Consortium for Blood Pressure GWAS, CHARGE consortium Aging, Longevity Group, Andrew M. McIntosh, Catharine R. Gale, Ian J. Deary 2016-04-05 2020-07-13 [("doi","10.1101/047290")] genetics/heritable/correlation psychiatry/adhd psychiatry/bipolar/sleep psychiatry/depression psychiatry/schizophrenia

Self-reported tiredness and low energy, often called fatigue, is associated with poorer physical and mental health. Twin studies have indicated that this has a heritability between 6% and 50%. In the UK Biobank sample (n = 108,976) we carried out a genome-wide association study of responses to the question, “Over the last two weeks, how often have you felt tired or had little energy?” Univariate GCTA-GREML found that the proportion of variance explained by all common SNPs for this tiredness question was 8.4% (SE = 0.6%). GWAS identified one genome-wide statistically-significant hit (Affymetrix id 1:64178756_C_T; p = 1.36 × 10−11). LD score regression and polygenic profile analysis were used to test for pleiotropy between tiredness and up to 28 physical and mental health traits from GWAS consortia. genetic correlations were identified between tiredness and BMI, HDL cholesterol, forced expiratory volume, grip strength, HbA1c, longevity, obesity, self-rated health, smoking status, triglycerides, type 2 diabetes, waist-hip ratio, ADHD, bipolar disorder, major depressive disorder, neuroticism, schizophrenia, and verbal-numerical reasoning (absolute rg effect sizes between 0.11 and 0.78). associations were identified between tiredness phenotypic scores and polygenic profile scores for BMI, HDL cholesterol, LDL cholesterol, coronary artery disease, HbA1c, height, obesity, smoking status, triglycerides, type 2 diabetes, and waist-hip ratio, childhood cognitive ability, neuroticism, bipolar disorder, major depressive disorder, and schizophrenia (standardised β’s between −0.016 and 0.03). These results suggest that tiredness is a partly-heritable, heterogeneous and complex phenomenon that is phenotypically and genetically associated with affective, cognitive, personality, and physiological processes.

“Hech, sirs! But I’m wabbit, I’m back frae the toon;

I ha’ena dune pechin’—jist let me sit doon.

From Glesca’

By William Dixon Cocker (1882–1970)

--- https://www.biorxiv.org/content/10.1101/049163.full Mega-analysis of 31,396 individuals from 6 countries uncovers strong gene-environment interaction for human fertility Felix C. Tropf, Renske M. Verweij, Peter J. van der Most, Gert Stulp, Andrew Bakshi, Daniel A. Briley, Matthew Robinson, Anastasia Numan, Tõnu Esko, Andres Metspalu, Sarah E. Medland, Nicholas G. Martin, Harold Snieder, S. Hong Lee, Melinda C. Mills 2016-05-02 2020-07-13 [("doi","10.1101/049163")] genetics/heritable genetics/selection/natural/human/dysgenics

Family and twin studies suggest that up to 50% of individual differences in human fertility within a population might be heritable. However, it remains unclear whether the genes associated with fertility outcomes such as number of children ever born (NEB) or age at first birth (AFB) are the same across geographical and historical environments. By not taking this into account, previous genetic studies implicitly assumed that the genetic effects are constant across time and space. We conduct a mega-analysis applying whole genome methods on 31,396 unrelated men and women from six Western countries. Across all individuals and environments, common single-nucleotide polymorphisms (SNPs) explained only ~4% of the variance in NEB and AFB. We then extend these models to test whether genetic effects are shared across different environments or unique to them. For individuals belonging to the same population and demographic cohort (born before or after the 20th century fertility decline), SNP-based heritability was almost five times higher at 22% for NEB and 19% for AFB. We also found no evidence suggesting that genetic effects on fertility are shared across time and space. Our findings imply that the environment strongly modifies genetic effects on the tempo and quantum of fertility, that currently ongoing natural selection is heterogeneous across environments, and that gene-environment interactions may partly account for missing heritability in fertility. Future research needs to combine efforts from genetic research and from the social sciences to better understand human fertility.

Authors Summary

Fertility behavior—such as age at first birth and number of children—varies strongly across historical time and geographical space. Yet, family and twin studies, which suggest that up to 50% of individual differences in fertility are heritable, implicitly assume that the genes important for fertility are the same across both time and space. Using molecular genetic data (SNPs) from over 30,000 unrelated individuals from six different countries, we show that different genes influence fertility in different time periods and different countries, and that the genetic effects consistently related to fertility are presumably small. The fact that genetic effects on fertility appear not to be universal could have tremendous implications for research in the area of reproductive medicine, social science and evolutionary biology alike.

--- https://www.biorxiv.org/content/10.1101/049635.full Mortality Selection in a Genetic Sample and Implications for Association Studies Benjamin W. Domingue, Daniel W. Belsky, Amal Harrati, Dalton Conley, David R. Weir, Jason Boardman 2016-04-21 2020-07-14 [("doi","10.1101/049635")] genetics/heritable genetics/selection/natural/human/dysgenics

Mortality selection is a general concern in the social and health sciences. Recently, existing health and social science cohorts have begun to collect genomic data. Causes of selection into a genomic dataset can influence results from genomic analyses. Selective non-participation, which is specific to a particular study and its participants, has received attention in the literature. But mortality selection—the very general phenomenon that genomic data collected at a particular age represents selective participation by only the subset of birth cohort members who have survived to the time of data collection—has been largely ignored.

Here we test the hypothesis that such mortality selection may alter estimates in polygenic association studies of both health and non-health traits. We demonstrate mortality selection into genome-wide SNP data collection at older ages using the U.S.-based Health and Retirement Study (HRS). We then model the selection process. Finally, we test whether mortality selection alters estimates from genetic association studies. We find evidence for mortality selection. Healthier and more socioeconomically advantaged individuals are more likely to survive to be eligible to participate in the genetic sample of the HRS. Mortality selection leads to modest drift in estimating time-varying genetic effects, a drift that is enhanced when estimates are produced from data that has additional mortality selection.

There is no general solution for correcting for mortality selection in a birth cohort prior to entry into a longitudinal study. We illustrate how genetic association studies using HRS data can adjust for mortality selection from study entry to time of genetic data collection by including probability weights that account for mortality selection. Mortality selection should be investigated more broadly in genetically-informed samples from other cohort studies.

--- https://www.biorxiv.org/content/10.1101/050682.full Genome-wide analyses of empathy and systemizing: heritability and correlates with sex, education, and psychiatric risk Varun Warrier, Roberto Toro, Bhismadev Chakrabarti, Nadia Litterman, David A. Hinds, Thomas Bourgeron, Simon Baron-Cohen 2016-04-29 2020-07-14 [("doi","10.1101/050682")] genetics/heritable/correlation psychiatry/autism psychiatry/schizophrenia psychology/personality

Empathy is the drive to identify the mental states of others and respond to these with an appropriate emotion. Systemizing is the drive to analyse or build lawful systems. Difficulties in empathy have been identified in different psychiatric conditions including autism and schizophrenia.

In this study, we conducted genome-wide association studies of empathy and systemizing using the Empathy Quotient (EQ) (n = 46,861) and the Systemizing Quotient-Revised (SQ-R) (n = 51,564) in participants from 23andMe, Inc. We confirmed sex-differences in performance on both tasks, with a male advantage on the SQ-R and female advantage on the EQ.

We found highly heritability explained by single-nucleotide polymorphisms (SNPs) for both the traits (EQ: 0.11±0.014; p = 1.7 × 10−14 and SQ-R: 0.12±0.012; p = 1.2 × 10−20) and these were similar for males and females. However, genes with higher expression in the male brain appear to contribute to the male advantage for the SQ-R. Finally, we identified statistically-significant genetic correlations between high score for empathy and risk for schizophrenia (p = 2.5 × 10−5), and correlations between high score for systemizing and higher educational attainment (p = 5 × 10−4).

These results shed light on the genetic contribution to individual differences in empathy and systemizing, two major cognitive functions of the human brain.

--- https://www.biorxiv.org/content/10.1101/055624.full Could a Neuroscientist Understand a Microprocessor? Eric Jonas, Konrad Paul Kording 2016-11-14 2020-07-14 [("doi","10.1101/055624")] ai/nn philosophy/epistemology psychology/neuroscience statistics/causality

[Reply to “Can a biologist fix a radio?”; earlier, Doug the biochemist & Bill the geneticist research how cars work] There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.

Author Summary: Neuroscience is held back by the fact that it is hard to evaluate if a conclusion is correct; the complexity of the systems under study and their experimental inaccessibility make the assessment of algorithmic and data analytic techniques challenging at best. We thus argue for testing approaches using known artifacts, where the correct interpretation is known. Here we present a microprocessor platform as one such test case. We find that many approaches in neuroscience, when used naively, fall short of producing a meaningful understanding.

--- https://www.biorxiv.org/content/10.1101/070128.full How cognitive genetic factors influence fertility outcomes: A mediational SEM analysis Michael A. Woodley Menie, Joseph A. Schwartz, Kevin M. Beaver 2016-08-18 2020-07-14 [("doi","10.1101/070128")] genetics/heritable genetics/selection/natural/human/dysgenics iq

Utilizing a newly released cognitive Polygenic Score (PGS) from Wave IV of Add Health (n = 1,886), structural equation models (SEMs) examining the relationship between PGS and fertility (which is ~50% complete in the present sample), using measures of verbal IQ and educational attainment as potential mediators, were estimated.

The results of indirect pathway models revealed that verbal IQ mediates the positive relationship between PGS and educational attainment, and educational attainment in turn mediates the negative relationship between IQ and a latent fertility measure. The direct path from PGS to fertility was non-statistically-significant. The model was robust to controlling for age, sex, and race; furthermore, the results of a multi-group SEM revealed no statistically-significant differences in the estimated path coefficients across sex.

These results indicate that those predisposed towards higher IQ by virtue of higher PGS values are also predisposed towards trading fertility against time spent in education, which contributes to those with higher PGS values producing fewer offspring.

--- https://www.biorxiv.org/content/10.1101/074237.full Capacity-approaching DNA storage Yaniv Erlich, Dina Zielinski 2016-09-09 2020-07-14 [("doi","10.1101/074237")] cs/algorithm/information genetics/genome-synthesis genetics/sequencing

Humanity produces data at exponential rates, creating a growing demand for better storage devices. DNA molecules are an attractive medium to store digital information due to their durability and high information density. Recent studies have made large strides in developing DNA storage schemes by exploiting the advent of massive parallel synthesis of DNA oligos and the high throughput of sequencing platforms. However, most of these experiments reported small gaps and errors in the retrieved information.

Here, we report a strategy to store and retrieve DNA information that is robust and approaches the theoretical maximum of information that can be stored per nucleotide. The success of our strategy lies in careful adaption of recent developments in coding theory to the domain-specific constraints of DNA storage.

To test our strategy, we stored an entire computer operating system, a movie, a gift card, and other computer files with a total of 2.14×106 bytes in DNA oligos. We were able to fully retrieve the information without a single error even with a sequencing throughput on the scale of a single tile of an Illumina sequencing flow cell.

To further stress our strategy, we created a deep copy of the data by PCR amplifying the oligo pool in a total of 9 successive reactions, reflecting one complete path of an exponential process to copy the file 218×1012 times. We perfectly retrieved the original data with only 5 million reads.

Taken together, our approach opens the possibility of highly reliable DNA-based storage that approaches the information capacity of DNA molecules and enables virtually unlimited data retrieval.

--- https://www.biorxiv.org/content/10.1101/078014.full Educational attainment and personality are genetically intertwined René Mõttus, Anu Realo, Uku Vainik, Jüri Allik, Tõnu Esko 2016-09-28 2020-07-14 [("doi","10.1101/078014")] genetics/heritable/correlation psychology/personality

It is possible that heritable variance in personality characteristics does not reflect (only) genetic and biological processes specific to personality per se. We tested the possibility that Five-Factor Model personality domains and facets, as rated by people themselves and their knowledgeable informants, reflect polygenic influences that have been previously associated with educational attainment.

In a sample of over 3,000 adult Estonians, polygenic scores for educational attainment, based on small contributions from more than 150,000 genetic variants, were correlated with various personality traits, mostly from the Neuroticism and Openness domains. The correlations of personality characteristics with educational attainment-related polygenic influences reflected almost entirely their correlations with phenotypic educational attainment.

Structural equation modeling of the associations between polygenic risk, personality (a weighed aggregate of education-related facets) and educational attainment lent relatively strongest support to the possibility of educational attainment mediating (explaining) some of the heritable variance in personality traits.

--- https://www.biorxiv.org/content/10.1101/088815.full Genome-Wide Association Study Reveals First Locus for Anorexia Nervosa and Metabolic Correlations E. L. Duncan, L. M. Thornton, A. Hinney, M. J. Daly, P. F. Sullivan, E. Zeggini, G. Breen, C. M. Bulik 2016-11-25 2020-07-14 [("doi","10.1101/088815")] psychiatry/anorexia psychiatry/schizophrenia

Anorexia nervosa (AN) is a serious eating disorder characterized by restriction of energy intake relative to requirements, resulting in abnormally low body weight. It has a lifetime prevalence of ~1%, disproportionately affects females, and has no well replicated evidence of effective pharmacological or psychological treatments despite high morbidity and mortality. Twin studies support a genetic basis for the observed aggregation of AN in families, with heritability estimates of 48%–74%. Although initial genome-wide association studies (GWASs) were underpowered, evidence suggested that signals for AN would be detected with increased power.

We present a GWAS of 3,495 AN cases and 10,982 controls with one genome-wide statistically-significant locus (index variant rs4622308, p = 4.3×10−9) in a region (chr12:56,372,585–56,482,185) which includes 6 genes. The SNP-chip heritability of AN from these data is 0.20 (SE=0.02), suggesting that a substantial fraction of the twin-based heritability stems from common genetic variation. Using these GWAS results, we also find positive genetic correlations with schizophrenia, neuroticism, educational attainment, and HDL cholesterol, and negative genetic correlations with body mass, insulin, glucose, and lipid phenotypes.

Our results support the reconceptualization of AN as a disorder with both psychiatric and metabolic components.

--- https://www.biorxiv.org/content/10.1101/106203.full Genomic analysis of family data reveals additional genetic effects on intelligence and personality W. David Hill, Ruben C. Arslan, Charley Xia, Michelle Luciano, Carmen Amador, Pau Navarro, Caroline Hayward, Reka Nagy, David J. Porteous, Andrew M. McIntosh, Ian J. Deary, Chris S. Haley, Lars Penke 2017-06-05 2020-07-14 [("doi","10.1101/106203")] genetics/heritable/rare iq psychology/personality

Pedigree-based analyses of intelligence have reported that genetic differences account for 50–80% of the phenotypic variation. For personality traits these effects are smaller, with 34–48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and 0%–15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants.

Using ~20,000 individuals in the Generation Scotland family cohort genotyped for ~700,000 single-nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWASs of unrelated individuals. In our models, genetic variants in low LD with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism.

By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that ~50% of differences in intelligence, and ~40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included.

From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence and education is consistent with mutation-selection balance.

--- https://www.biorxiv.org/content/10.1101/107045.full Analysis of genetic similarity among friends and schoolmates in the National Longitudinal Study of Adolescent to Adult Health (Add Health) Benjamin W. Domingue, Daniel W. Belsky, Jason M. Fletcher, Dalton Conley, Jason D. Boardman, Kathleen Mullan Harris 2017-02-09 2020-07-14 [("doi","10.1101/107045")] genetics/heritable sociology

It has been long known that human relationships are genetically stratified. Whether genetic similarity among those in a relationship is due to complex ancestral patterns linked to historical migration, macro-level social structures in modern society, or individual-level peer selection remains unsettled.

We use data from 9,500 adolescents from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to examine genetic similarity among school-based friends. While friends have correlated genotypes, both at the whole-genome level as well as at trait-associated loci (via polygenic scores), the results suggest that macro-level forces, such as school assignment, are a prime source of genetic similarity between friends.

Further, we find evidence consistent with the existence of social genetic effects as an individual’s educational attainment is strongly associated with the polygenic scores of those in their broader social environment (eg. school) and of their friends (net of their own score). In contrast, individual BMI and height are largely un-associated with the genetics of peers.

The conclusions drawn from this study suggest that while individual-level peer selection does have a role, it is the macro-level societal structures such as the assignment to schools that primarily drive the genetic similarity observed among friends. This has significant implications for understanding the ways in which genetic and social environments interact and influence various outcomes in life.

Supplementary information is available for researchers interested in the methodologies and data sets used in this study, details of which can be found on the project’s website.

--- https://www.biorxiv.org/content/10.1101/133132.full Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model Dominic Holland, Oleksandr Frei, Rahul Desikan, Chun-Chieh Fan, Alexey A. Shadrin, Olav B. Smeland, V. S. Sundar, Paul Thompson, Ole A. Andreassen, Anders Martin Dale 2019-12-13 2020-07-14 [("doi","10.1101/133132")] genetics/heritable

Estimating the polygenicity (proportion of causally associated single-nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from an extensive reference panel, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities ranging from ≃ 2 × 10−5 to ≃ 4 × 10−3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide statistical-significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.

Author Summary: There are ~10 million common variants in the genome of humans with European ancestry. For any particular phenotype a number of these variants will have some causal effect. It is of great interest to be able to quantify the number of these causal variants and the strength of their effect on the phenotype.

Genome wide association studies (GWAS) produce very noisy summary statistics for the association between subsets of common variants and phenotypes. For any phenotype, these statistics collectively are difficult to interpret, but buried within them is the true landscape of causal effects. In this work, we posit a probability distribution for the causal effects, and assess its validity using simulations. Using a detailed reference panel of ~11 million common variants—among which only a small fraction are likely to be causal, but allowing for non-causal variants to show an association with the phenotype due to correlation with causal variants—we implement an exact procedure for estimating the number of causal variants and their mean strength of association with the phenotype. We find that, across different phenotypes, both these quantities—whose product allows for lower bound estimates of heritability—vary by orders of magnitude.

--- https://www.biorxiv.org/content/10.1101/136192.full Fitness, Physical Activity, and Cardiovascular Disease: Longitudinal and Genetic Analyses in the UK Biobank Study Emmi Tikkanen, Stefan Gustafsson, Erik Ingelsson 2017-05-12 2020-07-14 [("doi","10.1101/136192")] exercise genetics/heritable/correlation/mendelian-randomization

Background: Exercise is inversely related with cardiovascular disease (CVD), but large-scale studies of incident CVD events are lacking. Moreover, little is known about genetic determinants of fitness and physical activity, and modifiable effects of exercise in individuals with elevated genetic risk of CVD. Finally, causal analyses of exercise traits are limited.

Method: We estimated associations of grip strength, physical activity, and cardiorespiratory fitness with CVD and all-cause death in up to 502,635 individuals from the UK Biobank. We also examined these associations in individuals with different genetic burden on coronary heart disease (CHD) and atrial fibrillation (AF). Finally, we performed genome-wide association study (GWAS) of grip strength and physical activity, as well as Mendelian Randomization analysis to assess the causal role of grip strength in CHD.

Results

Grip strength, physical activity, and cardiorespiratory fitness showed strong inverse associations with incident cardiovascular events and all-cause death (for composite CVD; HR, 0.78, 95% CI, 0.77–0.80; HR, 0.94, 95% CI, 0.93–0.95, and HR, 0.67, 95% CI, 0.63–0.71, per SD change, respectively). We observed stronger associations of grip strength with CHD and AF for individuals in the lowest third of genetic risk (pinteraction = 0.006, pinteraction = 0.03, respectively), but the inverse associations were present in each category of genetic risk. We report 27 novel genetic loci associated with grip strength and 2 loci with physical activity, with the strongest associations in FTO (rs56094641, p = 3.8×10−24) and SMIM2 (rs9316077, p = 1.4×10−8), respectively. By use of Mendelian Randomization, we provide evidence that grip strength is causally related to CHD.

Interpretation

Maintaining physical strength is likely to prevent future cardiovascular events, also in individuals with elevated genetic risk for CVD.

Funding

National Institutes of Health (1 R01 HL135313–01), Knut and Alice Wallenberg Foundation (2013.0126), and the Finnish Cultural Foundation.

--- https://www.biorxiv.org/content/10.1101/145193.full The genomic health of ancient hominins Ali J. Berens, Taylor L. Cooper, Joseph Lachance 2017-06-02 2020-07-15 [("doi","10.1101/145193")] genetics/selection/natural/human/dysgenics

The genomes of ancient humans, Neandertals, and Denisovans contain many alleles that influence disease risks. Using genotypes at 3180 disease-associated loci, we estimated the disease burden of 147 ancient genomes.

After correcting for missing data, genetic risk scores were generated for 9 disease categories and the set of all combined diseases. These genetic risk scores were used to examine the effects of different types of subsistence, geography, and sample age on the number of risk alleles in each ancient genome.

On a broad scale, hereditary disease risks are similar for ancient hominins and modern-day humans, and the GRS percentiles of ancient individuals span the full range of what is observed in present day individuals. In addition, there is evidence that ancient pastoralists may have had healthier genomes than hunter-gatherers and agriculturalists. We also observed a temporal trend whereby genomes from the recent past are more likely to be healthier than genomes from the deep past. This calls into question the idea that modern lifestyles have caused genetic load to increase over time.

Focusing on individual genomes, we find that the overall genomic health of the Altai Neanderthal is worse than 97% of present day humans and that Ötzi the Tyrolean Iceman had a genetic predisposition to gastrointestinal and cardiovascular diseases.

As demonstrated by this work, ancient genomes afford us new opportunities to diagnose past human health, which has previously been limited by the quality and completeness of remains.

--- https://www.biorxiv.org/content/10.1101/148247.full Quantifying the impact of rare and ultra-rare coding variation across the phenotypic spectrum Andrea Ganna, F. Kyle Satterstrom, Seyedeh M. Zekavat, Indraniel Das, Mitja I. Kurki, Claire Churchhouse, Jessica Alfoldi, Alicia R. Martin, Aki S. Havulinna, Andrea Byrnes, Wesley K. Thompson, Philip R. Nielsen, Konrad J. Karczewski, Elmo Saarentaus, Manuel A. Rivas, Namrata Gupta, Olli Pietiläinen, Connor A. Emdin, Francesco Lescai, Jonas Bybjerg-Grauholm, Jason Flannick, on behalf of GoT2D/T2D-GENES consortium, Josep Mercader, Miriam Udlerg, on behalf of SIGMA consortium, Helmsley I. B. D. Exome Sequencing Project, FinMetSeq Consortium, iPSYCH-Broad Consortium, Markku Laakso, Veikko Salomaa, Christina Hultman, Samuli Ripatti, Eija Hämäläinen, Jukka S. Moilanen, Jarmo Körkkö, Outi Kuismin, Merete Nordentoft, David Hougaard, Ole Mors, Thomas Werge, Preben Bo Mortensen, Daniel MacArthur, Mark J. Daly, Patrick F. Sullivan, Adam E. Locke, Aarno Palotie, Anders Børglum, Sekar Kathiresan, Benjamin M. Neale 2017-06-09 2020-07-15 [("doi","10.1101/148247")] genetics/heritable/rare iq psychiatry/adhd psychiatry/autism psychiatry/bipolar/genetics psychiatry/schizophrenia

Protein truncating variants (PTVs) are likely to modify gene function and have been linked to hundreds of Mendelian disorders1,2. However, the impact of PTVs on complex traits has been limited by the available sample size of whole-exome sequencing studies (WES)3.

Here we assemble WES data from 100,304 individuals to quantify the impact of rare PTVs on 13 quantitative traits and 10 diseases. We focus on those PTVs that occur in PTV-intolerant (PI) genes, as these are more likely to be pathogenic.

Carriers of at least one PI-PTV were found to have an increased risk of autism, schizophrenia, bipolar disorder, intellectual disability and ADHD (p-value (p) range: 5×10−3−9×10−12). In controls, without these disorders, we found that this burden associated with increased risk of mental, behavioral and neurodevelopmental disorders as captured by electronic health record information. Furthermore, carriers of PI-PTVs tended to be shorter (p = 2×10−5), have fewer years of education (p = 2×10−4) and be younger (p = 2×10−7); the latter observation possibly reflecting reduced survival or study participation. While other gene-sets derived from in vivo experiments did not show any associations with PTV-burden, gene sets implicated in GWAS of cardiovascular-related traits and inflammatory bowel disease showed a statistically-significant PTV-burden with corresponding traits, mainly driven by established genes involved in familial forms of these disorders.

We leveraged population health registries from 14,117 individuals to study the phenome-wide impact of PIPTVs and identified an increase in the number of hospital visits among PI-PTV carriers.

In conclusion, we provide the most thorough investigation to date of the impact of rare deleterious coding variants on complex traits, suggesting widespread pleiotropic risk.

--- https://www.biorxiv.org/content/10.1101/150540.full Environmental factors dominate over host genetics in shaping human gut microbiota composition Daphna Rothschild, Omer Weissbrod, Elad Barkan, Tal Korem, David Zeevi, Paul I. Costea, Anastasia Godneva, Iris Kalka, Noam Bar, Niv Zmora, Meirav Pevsner-Fischer, David Israeli, Noa Kosower, Gal Malka, Bat Chen Wolf, Tali Avnit-Sagi, Maya Lotan-Pompan, Adina Weinberger, Zamir Halpern, Shai Carmi, Eran Elinav, Eran Segal 2017-06-16 2020-07-15 [("doi","10.1101/150540")] genetics/microbiome statistics/variance-component

Human gut microbiome composition is shaped by multiple host intrinsic and extrinsic factors, but the relative contribution of host genetic compared to environmental factors remains elusive.

Here, we genotyped a cohort of 696 healthy individuals from several distinct ancestral origins and a relatively common environment, and demonstrate that:

there is no statistically-significant association between microbiome composition and ethnicity, single-nucleotide polymorphisms (SNPs), or overall genetic similarity, and that only 5⁄211 (2.4%) previously reported microbiome-SNP associations replicate in our cohort. In contrast, we find similarities in the microbiome composition of genetically unrelated individuals who share a household. We define the term biome-explainability as the variance of a host phenotype explained by the microbiome after accounting for the contribution of human genetics. Consistent with our finding that microbiome and host genetics are largely independent, we find substantial biome-explainability levels of 16–33% for body mass index (BMI), fasting glucose, high-density lipoprotein (HDL) cholesterol, waist circumference, waist-hip ratio (WHR), and lactose consumption.

We further show that several human phenotypes can be predicted substantially more accurately when adding microbiome data to host genetics data, and that the contribution of both data sources to prediction accuracy is largely additive.

Overall, our results suggest that human microbiome composition is dominated by environmental factors rather than by host genetics.

--- https://www.biorxiv.org/content/10.1101/167577.full Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depressive disorder Naomi R. Wray, Stephan Ripke, Manuel Mattheisen, Maciej Trzaskowski, Enda M. Byrne, Abdel Abdellaoui, Mark J. Adams, Esben Agerbo, Tracy M. Air, Till F. M. Andlauer, Silviu-Alin Bacanu, Marie Bækvad-Hansen, Aartjan T. F. Beekman, Tim B. Bigdeli, Elisabeth B. Binder, Douglas H. R. Blackwood, Julien Bryois, Henriette N. Buttenschøn, Jonas Bybjerg-Grauholm, Na Cai, Enrique Castelao, Jane Hvarregaard Christensen, Toni-Kim Clarke, Jonathan R. I. Coleman, Lucía Colodro-Conde, Baptiste Couvy-Duchesne, Nick Craddock, Gregory E. Crawford, Cheynna A. Crowley, Hassan S. Dashti, Gail Davies, Ian J. Deary, Franziska Degenhardt, Eske M. Derks, Nese Direk, Conor V. Dolan, Erin C. Dunn, Thalia C. Eley, Nicholas Eriksson, Valentina Escott-Price, Farnush Farhadi Hassan Kiadeh, Hilary K. Finucane, Andreas J. Forstner, Josef Frank, Héléna A. Gaspar, Michael Gill, Paola Giusti-Rorínguez, Fernando S. Goes, Scott D. Gordon, Jakob Grove, Lynsey S. Hall, Christine Søholm Hansen, Thomas F. Hansen, Stefan Herms, Ian B. Hickie, Per Hoffmann, Georg Homuth, Carsten Horn, Jouke-Jan Hottenga, David Hougaard, Ming Hu, Craig L. Hyde, Marcus Ising, Rick Jansen, Fulai Jin, Eric Jorgenson, James A. Knowles, Isaac S. Kohane, Julia Kraft, Warren W. Kretzschmar, Jesper Krogh, Zoltán Kutalik, Jacqueline M. Lane, Yihan Li, Yun Li, Penelope A. Lind, Xiaoxiao Liu, Leina Lu, Donald J. MacIntyre, Dean F. MacKinnon, Robert M. Maier, Wolfgang Maier, Jonathan Marchini, Hamdi Mbarek, Patrick McGrath, Peter McGuffin, Sarah E. Medland, Divya Mehta, Christel M. Middeldorp, Evelin Mihailov, Yuri Milaneschi, Lili Milani, Francis M. Mondimore, Grant W. Montgomery, Sara Mostafavi, Niamh Mullins, Matthias Nauck, Bernard Ng, Michel G. Nivard, Dale R. Nyholt, Paul F. O’Reilly, Hogni Oskarsson, Michael J. Owen, Jodie N. Painter, Carsten Bøcker, Marianne Giørtz Pedersen, Roseann E. Peterson, Erik Pettersson, Wouter J. Peyrot, Giorgio Pistis, Danielle Posthuma, Shaun M. Purcell, Jorge A. Quiroz, Per Qvist, John P. Rice, Brien P. Riley, Margarita Rivera, Saira Saeed Mirza, Richa Saxena, Robert Schoevers, Eva C. Schulte, Ling Shen, Jianxin Shi, Stanley I. Shyn, Engilbert Sigurdsson, Grant C. B. Sinnamon, Johannes H. Smit, Daniel J. Smith, Hreinn Stefansson, Stacy Steinberg, Craig A. Stockmeier, Fabian Streit, Jana Strohmaier, Katherine E. Tansey, Henning Teismann, Alexander Teumer, Wesley Thompson, Pippa a Thomson, Thorgeir E. Thorgeirsson, Chao Tian, Matthew Traylor, Jens Treutlein, Vassily Trubetskoy, André G. Uitterlinden, Daniel Umbricht, Sandra Van der Auwera, Albert M. van Hemert, Alexander Viktorin, Peter M. Visscher, Yunpeng Wang, Bradley T. Webb, Shantel Marie Weinsheimer, Jürgen Wellmann, Gonneke Willemsen, Stephanie H. Witt, Yang Wu, Hualin S. Xi, Jian Yang, Futao Zhang, eQTLGen Consortium, 23andMe, Volker Arolt, Bernhard T. Baune, Klaus Berger, Dorret I. Boomsma, Sven Cichon, udo Dannlowski, E. J. C. de Geus, J. Raymond DePaulo, Enrico Domenici, Katharina Domschke, Tönu Esko, Hans J. Grabe, Steven P. Hamilton, Caroline Hayward, Andrew C. Heath, David A. Hinds, Kenneth S. Kendler, Stefan Kloiber, Glyn Lewis, Qingqin S. Li, Susanne Lucae, Pamela A. F. Madden, Patrik K. Magnusson, Nicholas G. Martin, Andrew M. McIntosh, Andres Metspalu, Ole Mors, Preben Bo Mortensen, Bertram Müller-Myhsok, Merete Nordentoft, Markus M. Nöthen, Michael C. O’Donovan, Sara A. Paciga, Nancy L. Pedersen, Brenda W. J. H. Penninx, Roy H. Perlis, David J. Porteous, James B. Potash, Martin Preisig, Marcella Rietschel, Catherine Schaefer, Thomas G. Schulze, Jordan W. Smoller, Kari Stefansson, Henning Tiemeier, Rudolf Uher, Henry Völzke, Myrna M. Weissman, Thomas Werge, Ashley R. Winslow, Cathryn M. Lewis, Douglas F. Levinson, Gerome Breen, Anders Børglum, Patrick F. Sullivan, for the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium 2017-07-24 2020-07-15 [("doi","10.1101/167577")] genetics/heritable/correlation psychiatry/autism psychiatry/depression psychiatry/schizophrenia

Major depressive disorder (MDD) is a notably complex illness with a lifetime prevalence of 14%.1 It is often chronic or recurrent and is thus accompanied by considerable morbidity, excess mortality, substantial costs, and heightened risk of suicide.2–7 MDD is a major cause of disability worldwide.8

We conducted a genome-wide association (GWA) meta-analysis in 130,664 MDD cases and 330,470 controls, and identified:

44 independent loci that met criteria for statistical-significance. We present extensive analyses of these results which provide new insights into the nature of MDD. The genetic findings were associated with clinical features of MDD, and implicated prefrontal and anterior cingulate cortex in the pathophysiology of MDD (regions exhibiting anatomical differences between MDD cases and controls). Genes that are targets of antidepressant medications were strongly enriched for MDD association signals (p = 8.5×10−10), suggesting the relevance of these findings for improved pharmacotherapy of MDD. Sets of genes involved in gene splicing and in creating isoforms were also enriched for smaller MDD GWA p-values, and these gene sets have also been implicated in schizophrenia and autism. Genetic risk for MDD was correlated with that for many adult and childhood onset psychiatric disorders.

Our analyses suggested important relations of genetic risk for MDD with educational attainment, body mass, and schizophrenia: the genetic basis of lower educational attainment and higher body mass were putatively causal for MDD whereas MDD and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for MDD, and a continuous measure of risk underlies the observed clinical phenotype. MDD is not a distinct entity that neatly demarcates normalcy from pathology but rather a useful clinical construct associated with a range of adverse outcomes and the end result of a complex process of intertwined genetic and environmental effects.

These findings help refine and define the fundamental basis of MDD.

--- https://www.biorxiv.org/content/10.1101/168906.full 116 independent genetic variants influence the neuroticism personality trait in over 329,000 UK Biobank individuals Michelle Luciano, Saskia P. Hagenaars, Gail Davies, W. David Hill, Toni-Kim Clarke, Masoud Shirali, Riccardo E. Marioni, Sarah E. Harris, David C. Liewald, Chloe Fawns-Ritchie, Mark J. Adams, David M. Howard, Cathryn M. Lewis, Catharine R. Gale, Andrew M. McIntosh, Ian J. Deary 2017-07-28 2020-07-15 [("doi","10.1101/168906")] genetics/heritable/correlation psychiatry/depression psychology/personality

Neuroticism is a stable personality trait; twin studies report heritability 30%–50%, and SNP-based heritability is about 15%. Higher levels of neuroticism are associated with poorer mental and physical health, and the economic burden of neuroticism for societies is high. To date, genome-wide association (GWA) studies of neuroticism have identified up to 11 genetic loci.

Here we report 116 independent genetic loci from a GWA of neuroticism in 329,821 UK Biobank participants, with replication available in a GWA meta-analysis of neuroticism in 122,867 individuals. Genetic signals for neuroticism were enriched in neuronal genesis and differentiation pathways, and substantial genetic correlations were found between neuroticism and depressive symptoms (r = 0.82, SE=0.03), major depressive disorder (r = 0.69, SE=0.07) and subjective wellbeing (r = −0.68, SE=0.03) alongside other mental health traits.

These discoveries advance our understanding of neuroticism and its association with major depressive disorder.

--- https://www.biorxiv.org/content/10.1101/173435.full Genomic dissection of bipolar disorder and schizophrenia including 28 subphenotypes Douglas M. Ruderfer, Stephan Ripke, Andrew McQuillin, James Boocock, Eli Ayumi Stahl, Jennifer M. Whitehead Pavlides, Niamh Mullins, Alexander W. Charney, Anil P. S. Ori, Loes M. Olde Loohuis, Enrico Domenici, Arianna Di Florio, Sergi Papiol, Janos L. Kalman, Rolf Adolfsson, Ingrid Agartz, Esben Agerbo, Huda Akil, Diego Albani, Margot Albus, Martin Alda, Madeline Alexander, Judith Allardyce, Ney Alliey-Rodriguez, Thomas D. Als, Farooq Amin, Adebayo Anjorin, Maria J. Arranz, Swapnil Awasthi, Silviu A. Bacanu, Judith A. Badner, Marie Baekvad-Hansen, Steven Bakker, Gavin Band, Jack D. Barchas, Ines Barroso, Nicholas Bass, Michael Bauer, Bernhard T. Baune, Martin Begemann, Celine Bellenguez, Richard A. Belliveau, Frank Bellivier, Stephan Bender, Judit Bene, Sarah E. Bergen, Wade H. Berrettini, Elizabeth Bevilacqua, Joanna M. Biernacka, Tim B. Bigdeli, Donald W. Black, Hannah Blackburn, Jenefer M. Blackwell, Douglas H. R. Blackwood, Carsten Bocker Pedersen, Michael Boehnke, Marco Boks, Anders D. Borglum, Elvira Bramon, Gerome Breen, Matthew A. Brown, Richard Bruggeman, Nancy G. Buccola, Randy L. Buckner, Monika Budde, Brendan Bulik-Sullivan, Suzannah J. Bumpstead, William Bunney, Margit Burmeister, Joseph D. Buxbaum, Jonas Bybjerg-Grauholm, William Byerley, Wiepke Cahn, Guiqing Cai, Murray J. Cairns, Dominique Campion, Rita M. Cantor, Vaughan J. Carr, Noa Carrera, Juan P. Casas, Miquel Casas, Stanley V. Catts, Pablo Cervantes, Kimberley D. Chambert, Raymond C. K. Chan, Eric Y. H. Chen, Ronald Y. L. Chen, Wei Cheng, Eric F. C. Cheung, Siow Ann Chong, Toni-Kim Clarke, C. Robert Cloninger, David Cohen, Nadine Cohen, Jonathan R. I. Coleman, David A. Collier, Paul Cormican, William Coryell, Nicholas Craddock, David W. Craig, Benedicto Crespo-Facorro, James J. Crowley, Cristiana Cruceanu, David Curtis, Piotr M. Czerski, Anders Martin Dale, Mark J. Daly, Udo Dannlowski, Ariel Darvasi, Michael Davidson, Kenneth L. Davis, Christiaan A. de Leeuw, Franziska Degenhardt, Jurgen Del Favero, Lynn E. DeLisi, Panos Deloukas, Ditte Demontis, J. Raymond DePaulo, Marta di Forti, Dimitris Dikeos, Timothy Dinan, Srdjan Djurovic, Amanda L. Dobbyn, Peter Donnelly, Gary Donohoe, Elodie Drapeau, Serge Dronov, Jubao Duan, Frank Dudbridge, Audrey Duncanson, Howard J. Edenberg, Sarah Edkins, Hannelore Ehrenreich, Peter Eichhammer, Torbjorn Elvsashagen, Johan Eriksson, Valentina Escott-Price, Tõnu Esko, Laurent Essioux, Bruno Etain, Chun Chieh Fan, Kai-How Farh, Martilias S. Farrell, Matthew Flickinger, Tatiana M. Foroud, Liz Forty, Josef Frank, Lude Franke, Christine Fraser, Robert Freedman, Colin Freeman, Nelson B. Freimer, Joseph I. Friedman, Menachem Fromer, Mark A. Frye, Janice M. Fullerton, Katrin Gade, Julie Garnham, Helena A. Gaspar, Pablo V. Gejman, Giulio Genovese, Lyudmila Georgieva, Claudia Giambartolomei, Eleni Giannoulatou, Ina Giegling, Michael Gill, Matthew Gillman, Marianne Giortz Pedersen, Paola Giusti-Rodriguez, Stephanie Godard, Fernando Goes, Jacqueline I. Goldstein, Srihari Gopal, Scott D. Gordon, Katherine Gordon-Smith, Jacob Gratten, Emma Gray, Elaine K. Green, Melissa J. Green, Tiffany A. Greenwood, Maria Grigoroiu-Serbanescu, Jakob Grove, Weihua Guan, Hugh Gurling, Jose Guzman Parra, Rhian Gwilliam, Lieuwe de Haan, Jeremy Hall, Mei-Hua Hall, Christian Hammer, Naomi Hammond, Marian L. Hamshere, Mark Hansen, Thomas Hansen, Vahram Haroutunian, Annette M. Hartmann, Joanna Hauser, Martin Hautzinger, Urs Heilbronner, Garrett Hellenthal, Frans A. Henskens, Stefan Herms, Maria Hipolito, Joel N. Hirschhorn, Per Hoffmann, Mads V. Hollegaard, David Hougaard, Hailiang Huang, Laura Huckins, Christina M. Hultman, Sarah E. Hunt, Masashi Ikeda, Nakao Iwata, Conrad Iyegbe, Assen V. Jablensky, Stephane Jamain, Janusz Jankowski, Alagurevathi Jayakumar, Inge Joa, Ian Jones, Lisa A. Jones, Erik G. Jonsson, Antonio Julia, Anders Jureus, Anna K. Kahler, Rene S. Kahn, Luba Kalaydjieva, Radhika Kandaswamy, Sena Karachanak-Yankova, Juha Karjalainen, Robert Karlsson, David Kavanagh, Matthew C. Keller, Brian J. Kelly, John Kelsoe, James L. Kennedy, Andrey Khrunin, Yunjung Kim, George Kirov, Sarah Kittel-Schneider, Janis Klovins, Jo Knight, Sarah V. Knott, James A. Knowles, Manolis Kogevinas, Bettina Konte, Eugenia Kravariti, Vaidutis Kucinskas, Zita Ausrele Kucinskiene, Ralph Kupka, Hana Kuzelova-Ptackova, Mikael Landen, Cordelia Langford, Claudine Laurent, Jacob Lawrence, Stephen Lawrie, William B. Lawson, Markus Leber, Marion Leboyer, Phil H. Lee, Jimmy Lee Chee Keong, Sophie E. Legge, Todd Lencz, Bernard Lerer, Douglas F. Levinson, Shawn E. Levy, Cathryn M. Lewis, Jun Z. Li, Miaoxin Li, Qingqin S. Li, Tao Li, Kung-Yee Liang, Jennifer Liddle, Jeffrey Lieberman, Svetlana Limborska, Kuang Lin, Don H. Linszen, Jolanta Lissowska, Chunyu Liu, Jianjun Liu, Jouko Lonnqvist, Carmel M. Loughland, Jan Lubinski, Susanne Lucae, Milan Macek, Donald J. MacIntyre, Patrik K. E. Magnusson, Brion S. Maher, Pamela B. Mahon, Wolfgang Maier, Anil K. Malhotra, Jacques Mallet, Ulrik F. Malt, Hugh S. Markus, Sara Marsal, Nicholas G. Martin, Ignacio Mata, Christopher G. Mathew, Manuel Mattheisen, Morten Mattingsdal, Fermin Mayoral, Owen T. McCann, Robert W. McCarley, Steven A. McCarroll, Mark I. McCarthy, Colm McDonald, Susan L. McElroy, Peter McGuffin, Melvin G. Mclnnis, Andrew M. McIntosh, James D. McKay, Francis J. McMahon, Helena Medeiros, Sarah E. Medland, Sandra Meier, Carin J. Meijer, Bela Melegh, Ingrid Sigfrid Melle, Fan Meng, Raquelle I. Mesholam-Gately, Andres Metspalu, Patricia T. Michie, Lili Milani, Vihra Milanova, Philip B. Mitchell, Younes Mokrab, Grant W. Montgomery, Jennifer L. Moran, Gunnar Morken, Derek W. Morris, Ole Mors, Preben Bo Mortensen, Bryan J. Mowry, Thomas W. Mühleisen, Bertram Müller-Myhsok, Kieran C. Murphy, Robin M. Murray, Richard M. Myers, Inez Myin-Germeys, Benjamin M. Neale, Mari Nelis, Igor Nenadic, Deborah A. Nertney, Gerald Nestadt, Kristin K. Nicodemus, Caroline M. Nievergelt, Liene Nikitina-Zake, Vishwajit Nimgaonkar, Laura Nisenbaum, Merete Nordentoft, Annelie Nordin, Markus M. Nöthen, Evaristus A. Nwulia, Eadbhard O’Callaghan, Claire O’Donovan, O’Dushlaine Colm, F. Anthony O’Neill, Ketil J. Oedegaard, Sang-Yun Oh, Ann Olincy, Line Olsen, Lilijana Oruc, Jim Van Os, Michael J. Owen, Sara A. Paciga, Colin Palmer, Aarno Palotie, Christos Pantelis, George N. Papadimitriou, Elena Parkhomenko, Carlos Pato, Michele T. Pato, Tiina Paunio, Richard Pearson, Psychosis Endophenotypes International Consortium, Diana O. Perkins, Roy H. Perlis, Amy Perry, Tune H. Pers, Tracey L. Petryshen, Andrea Pfennig, Marco Picchioni, Olli Pietilainen, Jonathan Pimm, Matti Pirinen, Robert Plomin, Andrew J. Pocklington, Danielle Posthuma, James B. Potash, Simon C. Potter, John Powell, Alkes Price, Ann E. Pulver, Shaun M. Purcell, Digby Quested, Josep Antoni Ramos-Quiroga, Henrik B. Rasmussen, Anna Rautanen, Radhi Ravindrarajah, Eline J. Regeer, Abraham Reichenberg, Andreas Reif, Mark A. Reimers, Marta Ribases, John P. Rice, Alexander L. Richards, Michelle Ricketts, Brien P. Riley, Fabio Rivas, Margarita Rivera, Joshua L. Roffman, Guy A. Rouleau, Panos Roussos, Dan Rujescu, Veikko Salomaa, Cristina Sanchez-Mora, Alan R. Sanders, Stephen J. Sawcer, Ulrich Schall, Alan F. Schatzberg, William A. Scheftner, Peter R. Schofield, Nicholas J. Schork, Sibylle G. Schwab, Edward M. Scolnick, Laura J. Scott, Rodney J. Scott, Larry J. Seidman, Alessandro Serretti, Pak C. Sham, Cynthia Shannon Weickert, Tatyana Shehktman, Jianxin Shi, Paul D. Shilling, Engilbert Sigurdsson, Jeremy M. Silverman, Kang Sim, Claire Slaney, Petr Slominsky, Olav B. Smeland, Jordan W. Smoller, Hon-Cheong So, Janet L. Sobell, Erik Soderman, Christine Soholm Hansen, Chris C. A. Spencer, Anne T. Spijker, David St Clair, Hreinn Stefansson, Kari Stefansson, Stacy Steinberg, Elisabeth Stogmann, Eystein Stordal, Amy Strange, Richard E. Straub, John S. Strauss, Fabian Streit, Eric Strengman, Jana Strohmaier, T. Scott Stroup, Zhan Su, Mythily Subramaniam, Jaana Suvisaari, Dragan M. Svrakic, Jin P. Szatkiewicz, Szabolcs Szelinger, Avazeh Tashakkori-Ghanbaria, Srinivas Thirumalai, Robert C. Thompson, Thorgeir E. Thorgeirsson, Draga Toncheva, Paul A. Tooney, Sarah Tosato, Timothea Toulopoulou, Richard C. Trembath, Jens Treutlein, Vassily Trubetskoy, Gustavo Turecki, Arne E. Vaaler, Helmut Vedder, Eduard Vieta, John Vincent, Peter M. Visscher, Ananth C. Viswanathan, Damjan Vukcevic, John Waddington, Matthew Waller, Dermot Walsh, Muriel Walshe, James T. R. Walters, Dai Wang, Qiang Wang, Weiqing Wang, Yunpeng Wang, Stanley J. Watson, Bradley T. Webb, Thomas W. Weickert, Daniel R. Weinberger, Matthias Weisbrod, Mark Weiser, Thomas Werge, Paul Weston, Pamela Whittaker, Sara Widaa, Durk Wiersma, Dieter B. Wildenauer, Nigel M. Williams, Stephanie Williams, Stephanie H. Witt, Aaron R. Wolen, Emily H. M. Wong, Nicholas W. Wood, Brandon K. Wormley, Wellcome Trust Case-Control Consortium, Jing Qin Wu, Simon Xi, Wei Xu, Allan H. Young, Clement C. Zai, Peter Zandi, Peng Zhang, Xuebin Zheng, Fritz Zimprich, Sebastian Zollner, Aiden Corvin, Ayman H. Fanous, Sven Cichon, Marcella Rietschel, Elliot S. Gershon, Thomas G. Schulze, Alfredo B. Cuellar-Barboza, Andreas J. Forstner, Peter A. Holmans, John I. Nurnberger, Ole A. Andreassen, S. Hong Lee, Michael C. O’Donovan, Patrick F. Sullivan, Roel A. Ophoff, Naomi R. Wray, Pamela Sklar, Kenneth S. Kendler 2017-08-08 2020-07-15 [("doi","10.1101/173435")] psychiatry/bipolar/genetics psychiatry/schizophrenia

Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable disorders that share a proportion of common risk variation. Understanding the genetic factors underlying the specific symptoms of these disorders will be crucial for improving diagnosis, intervention, and treatment.

In case-control data consisting of 53,555 cases (20,129 BD, 33,426 SCZ) and 54,065 controls, we identified 114 genome-wide statistically-significant loci (GWS) when comparing all cases to controls, of which 41 represented novel findings. Two genome-wide statistically-significant loci were identified when comparing SCZ to BD and a third was found when directly incorporating functional information. Regional joint association identified a genomic region of overlapping association in BD and SCZ with disease-independent causal variants indicating a fourth region contributing to differences between these disorders. Regional SNP-heritability analyses demonstrated that the estimated heritability of BD based on the SCZ GWS regions was higher than that based on the average genomic region (91 regions, p = 1.2×10−6) while the inverse was not (19 regions, p = 0.89).

Using our BD and SCZ GWAS we calculated polygenic risk scores and identified several statistically-significant correlations with: (1) SCZ subphenotypes: negative symptoms (SCZ, p = 3.6×10−6) and manic symptoms (BD, p = 2×10−5), (2) BD subphenotypes: psychotic features (SCZ p = 1.2×10−10, BD p = 5.3×10−5) and age of onset (SCZ p = 7.9×10−4). Finally, we show that psychotic features in BD have SNP-heritability (h2SNP = 0.15, SE=0.06), and a statistically-significant genetic correlation with SCZ (rg = 0.34) in addition there is a sign test result between SCZ GWAS and a GWAS of BD cases contrasting those with and without psychotic features (p = 0.0038, one-side binomial test).

For the first time, we have identified specific loci pointing to a potential role of 4 genes (DARS2, ARFGEF2, DCAKD and GATAD2A) that distinguish between BD and SCZ, providing an opportunity to understand the biology contributing to clinical differences of these disorders. Our results provide the best evidence so far of genomic components distinguishing between BD and SCZ that contribute directly to specific symptom dimensions.

--- https://www.biorxiv.org/content/10.1101/173815.full Evidence for evolutionary shifts in the fitness landscape of human complex traits Lawrence H. Uricchio, Hugo C. Kitano, Alexander Gusev, Noah A. Zaitlen 2017-08-08 2020-07-15 [("doi","10.1101/173815")] genetics/selection/natural/human psychiatry/schizophrenia

Selection alters human genetic variation, but the evolutionary mechanisms shaping complex traits and the extent of selection’s impact on polygenic trait evolution remain largely unknown. Here, we develop a novel polygenic selection inference method (Polygenic Ancestral Selection Test Encompassing Linkage, or PASTEL) relying on GWAS summary data from a single population.

We use model-based simulations of complex traits that incorporate human demography, stabilizing selection, and polygenic adaptation to show how shifts in the fitness landscape generate distinct signals in GWAS summary data. Our test retains power for relatively ancient selection events and controls for potential confounding from linkage disequilibrium.

We apply PASTEL to 9 complex traits, and find evidence for selection acting on 5 of them (height, BMI, schizophrenia, Crohn’s disease, and educational attainment). This study provides evidence that selection modulates the relationship between frequency and effect size of trait-altering alleles for a wide range of traits, and provides a flexible framework for future investigations of selection on complex traits using GWAS data.

--- https://www.biorxiv.org/content/10.1101/173815.full An evolutionary compass for elucidating selection mechanisms shaping complex traits Lawrence H. Uricchio, Hugo C. Kitano, Alexander Gusev, Noah A. Zaitlen 2018-06-07 2020-07-15 [("doi","10.1101/173815")] genetics/selection/natural/human psychiatry/depression psychiatry/schizophrenia

Polygenic selection is likely to target some human traits, but the specific evolutionary mechanisms driving complex trait variation are largely unknown.

We developed an evolutionary compass for detecting selection and mutational bias that uses polarized GWAS summary statistics from a single population. We found evidence for selection and mutational bias acting on variation in five traits (BMI, schizophrenia, Crohn’s disease, educational attainment, and height). We then used model-based analyses to show that these signals can be explained by stabilizing selection with shifts in the fitness-phenotype relationship.

We additionally provide evidence that selection has acted on Neanderthal alleles for height, schizophrenia, and depression, and discuss potential sources of confounding.

Our results provide a flexible and powerful framework for evolutionary analysis of complex phenotypes in humans and other organisms, and provide insights into the evolutionary mechanisms driving variation in human polygenic traits.

--- https://www.biorxiv.org/content/10.1101/177014.full Genome-wide analysis of risk-taking behavior and cross-disorder genetic correlations in 116,255 individuals from the UK Biobank cohort Rona J. Strawbridge, Joey Ward, Breda Cullen, Elizabeth M. Tunbridge, Sarah Hartz, Laura Bierut, Amy Horton, Mark E. S. Bailey, Nicholas Graham, Amy Ferguson, Donald M. Lyall, Daniel Mackay, Laura M. Pidgeon, Jonathan Cavanagh, Jill P. Pell, Michael O’Donovan, Valentina Escott-Price, Paul J. Harrison, Daniel J. Smith 2017-08-16 2020-07-15 [("doi","10.1101/177014")] genetics/heritable/correlation psychiatry/adhd psychiatry/bipolar/genetics psychiatry/schizophrenia

Risk-taking behavior is a key component of several psychiatric disorders and could influence lifestyle choices such as smoking, alcohol use, and diet. Risk-taking behavior therefore fits within a Research Domain Criteria (RDoC) approach, whereby elucidation of the genetic determinants of this trait has the potential to improve our understanding across different psychiatric disorders.

Here we report a genome-wide association study in 116,255 UK Biobank participants who responded yes/no to the question “would you consider yourself a risk-taker?” Risk-takers (compared to controls) were more likely to be men, smokers, and have a history of mental illness. Genetic loci associated with risk-taking behavior were identified on chromosomes 3 (rs13084531) and 6 (rs9379971). The effects of both lead SNPs were comparable between men and women.

The chromosome 3 locus highlights CADM2, previously implicated in cognitive and executive functions, but the chromosome 6 locus is challenging to interpret due to the complexity of the HLA region. Risk-taking behavior shared genetic risk with schizophrenia, bipolar disorder, attention deficit hyperactivity disorder, and post-traumatic stress disorder, as well as with smoking and total obesity.

Despite being based on only a single question, this study furthers our understanding of the biology of risk-taking behavior, a trait which has a major impact on a range of common physical and mental health disorders.

--- https://www.biorxiv.org/content/10.1101/184820.full GWAS Meta-Analysis of Neuroticism (n = 449,484) Identifies Novel Genetic Loci and Pathways Mats Nagel, Philip R. Jansen, Sven Stringer, Kyoko Watanabe, Christiaan A. de Leeuw, Julien Bryois, Jeanne E. Savage, Anke R. Hammerschlag, Nathan Skene, Ana B. Muñoz-Manchado, the 23andMe Research Team, Sten Linnarsson, Jens Hjerling-Leffler, Tonya J. H. White, Henning Tiemeier, Tinca J. C. Polderman, Patrick F. Sullivan, Sophie van der Sluis, Danielle Posthuma 2017-09-05 2020-07-15 [("doi","10.1101/184820")] genetics/heritable/correlation psychiatry/anxiety psychiatry/depression psychiatry/schizophrenia

Neuroticism is an important risk factor for psychiatric traits including depression1, anxiety2,3, and schizophrenia4–6. Previous genome-wide association studies7–12 (GWAS) reported 16 genomic loci10–12.

Here we report the largest neuroticism GWAS meta-analysis to date (n = 449,484), and identify 136 independent genome-wide statistically-significant loci (124 novel), implicating 599 genes. Extensive functional follow-up analyses show enrichment in several brain regions and involvement of specific cell-types, including dopaminergic neuroblasts (p = 3×10−8), medium spiny neurons (p = 4×10−8) and serotonergic neurons (p = 1×10−7). Gene-set analyses implicate 3 specific pathways: neurogenesis (p = 4.4×10−9), behavioral response to cocaine processes (p = 1.84×10−7), and axon part (p = 5.26×10−8).

We show that Neuroticism’s genetic signal partly originates in two genetically distinguishable subclusters13 (depressed affect and worry, the former being genetically strongly related to depression, rg=0.84), suggesting distinct causal mechanisms for subtypes of individuals.

These results vastly enhance our neurobiological understanding of neuroticism, and provide specific leads for functional follow-up experiments.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6411041/ GWAS meta-analysis (n = 279,930) identifies new genes and functional links to intelligence Jeanne E. Savage, Philip R. Jansen, Sven Stringer, Kyoko Watanabe, Julien Bryois, Christiaan A. de Leeuw, Mats Nagel, Swapnil Awasthi, Peter B. Barr, Jonathan R. I. Coleman, Katrina L. Grasby, Anke R. Hammerschlag, Jakob Kaminski, Robert Karlsson, Eva Krapohl, Max Lam, Marianne Nygaard, Chandra A. Reynolds, Joey W. Trampush, Hannah Young, Delilah Zabaneh, Sara Hägg, Narelle K. Hansell, Ida K. Karlsson, Sten Linnarsson, Grant W. Montgomery, Ana B. Muñoz-Manchado, Erin B. Quinlan, Gunter Schumann, Nathan Skene, Bradley T. Webb, Tonya White, Dan E. Arking, Deborah K. Attix, Dimitrios Avramopoulos, Robert M. Bilder, Panos Bitsios, Katherine E. Burdick, Tyrone D. Cannon, Ornit Chiba-Falek, Andrea Christoforou, Elizabeth T. Cirulli, Eliza Congdon, Aiden Corvin, Gail Davies, Ian J. Deary, Pamela DeRosse, Dwight Dickinson, Srdjan Djurovic, Gary Donohoe, Emily Drabant Conley, Johan G. Eriksson, Thomas Espeseth, Nelson A. Freimer, Stella Giakoumaki, Ina Giegling, Michael Gill, David C. Glahn, Ahmad R. Hariri, Alex Hatzimanolis, Matthew C. Keller, Emma Knowles, Bettina Konte, Jari Lahti, Stephanie Le Hellard, Todd Lencz, David C. Liewald, Edythe London, Astri J. Lundervold, Anil K. Malhotra, Ingrid Sigfrid Melle, Derek Morris, Anna C. Need, William Ollier, Aarno Palotie, Antony Payton, Neil Pendleton, Russell A. Poldrack, Katri Räikkönen, Ivar Reinvang, Panos Roussos, Dan Rujescu, Fred W. Sabb, Matthew A. Scult, Olav B. Smeland, Nikolaos Smyrnis, John M. Starr, Vidar M. Steen, Nikos C. Stefanis, Richard E. Straub, Kjetil Sundet, Aristotle N. Voineskos, Daniel R. Weinberger, Elisabeth Widen, Jin Yu, Gonçalo Abecasis, Ole A. Andreassen, Gerome Breen, Lene Christiansen, Birgit Debrabant, Danielle M. Dick, Andreas Heinz, Jens Hjerling-Leffler, M. Arfan Ikram, Kenneth S. Kendler, Nicholas G. Martin, Sarah E. Medland, Nancy L. Pedersen, Robert Plomin, Tinca J. C. Polderman, Stephan Ripke, Sophie van der Sluis, Patrick F. Sullivan, Henning Tiemeier, Scott I. Vrieze, Margaret J. Wright, Danielle Posthuma 2017-09-06 2020-07-16 [("doi","10.1101/184853")] genetics/heritable/correlation/mendelian-randomization psychiatry/alzheimers psychiatry/schizophrenia

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence3–7, but much about its genetic underpinnings remains to be discovered.

Here, we present the largest genetic association study of intelligence to date (n = 279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure.

We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimer’s dementia and ADHD, and bidirectional causation with strong pleiotropy for schizophrenia. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.

--- https://www.biorxiv.org/content/10.1101/188086.full Quantification of frequency-dependent genetic architectures and action of negative selection in 25 UK Biobank traits Armin P. Schoech, Daniel Jordan, Po-Ru Loh, Steven Gazal, Luke O’Connor, Daniel J. Balick, Pier F. Palamara, Hilary K. Finucane, Shamil R. Sunyaev, Alkes Price 2017-09-13 2020-07-16 [("doi","10.1101/188086")] genetics/editing genetics/heritable/rare

Understanding the role of rare variants is important in elucidating the genetic basis of human diseases and complex traits. It is widely believed that negative selection can cause rare variants to have larger per-allele effect sizes than common variants.

Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1−p)]α, where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α by maximizing its profile likelihood in a linear mixed model framework using imputed genotypes, including rare variants (MAF >0.07%).

We applied this method to 25 UK Biobank diseases and complex traits (n = 113,851). All traits produced negative α estimates with 20 negative, implying larger rare variant effect sizes. The inferred best-fit distribution of true α values across traits had mean −0.38 (s.e. 0.02) and standard deviation 0.08 (s.e. 0.03), with statistically-significant heterogeneity across traits (p = 0.0014). Despite larger rare variant effect sizes, we show that for most traits analyzed, rare variants (MAF <1%) explain less than 10% of total SNP-heritability. Using evolutionary modeling and forward simulations, we validated the α model of MAF-dependent trait effects and estimated the level of coupling between fitness effects and trait effects.

Based on this analysis an average genome-wide negative selection coefficient on the order of 10−4 or stronger is necessary to explain the α values that we inferred.

--- https://www.biorxiv.org/content/10.1101/196071.full Genome-wide association study of social relationship satisfaction: loci and correlations with psychiatric conditions Varun Warrier, the 23andMe Research Team, Thomas Bourgeron, Simon Baron-Cohen 2017-10-05 2020-07-16 [("doi","10.1101/196071")] genetics/heritable/correlation psychiatry/schizophrenia psychology/personality

Dissatisfaction in social relationships is reported widely across many psychiatric conditions.

We investigated the genetic architecture of family relationship satisfaction and friendship satisfaction in the UK Biobank. We leveraged the high genetic correlation between the two phenotypes (rg = 0.87±0.03; p < 2.2×10−16) to conduct multi-trait analysis of Genome Wide Association Study (GWAS) (neffective family = 164,112; neffective friendship = 158,116).

We identified two genome-wide statistically-significant associations for both the phenotypes: rs1483617 on chromosome 3 and rs2189373 on chromosome 6, a region previously implicated in schizophrenia. eQTL and chromosome conformation capture in neural tissues prioritizes several genes including NLGN1. Gene-based association studies identified several genes, with highest expression in brain tissues. Genetic correlation analysis identified negative correlations for multiple psychiatric conditions including highly negative correlation with cross-psychiatric disorder GWAS, underscoring the central role of social relationship dissatisfaction in psychiatric diagnosis. The two phenotypes were enriched for genes that are loss of function intolerant. Both phenotypes had modest, additive SNP heritability of ~6%.

Our results underscore the central role of social relationship satisfaction in mental health and identify genes and tissues associated with it.

--- https://www.biorxiv.org/content/10.1101/200675.full High-Precision Automated Reconstruction of Neurons with Flood-filling Networks Michał Januszewski, Jörgen Kornfeld, Peter H. Li, Art Pope, Tim Blakely, Larry Lindsey, Jeremy Maitin-Shepard, Mike Tyka, Winfried Denk, Viren Jain 2017-10-09 2020-07-16 [("doi","10.1101/200675")] ai/nn/cnn psychology/neuroscience

Reconstruction of neural circuits from volume electron microscopy data requires the tracing of complete cells including all their neurites. Automated approaches have been developed to perform the tracing, but without costly human proofreading their error rates are too high to obtain reliable circuit diagrams.

We present a method for automated segmentation that, like the majority of previous efforts, employs convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of the reconstructed shape of individual neural processes.

We used this technique, which we call flood-filling networks, to trace neurons in a data set obtained by serial block-face electron microscopy from a male zebra finch brain.

Our method achieved a mean error-free neurite path length of 1.1 mm, an order of magnitude better than previously published approaches applied to the same dataset. Only 4 mergers were observed in a neurite test set of 97 mm path length.

--- https://www.biorxiv.org/content/10.1101/201020.full Biological Insights Into Muscular Strength: Genetic Findings in the UK Biobank Emmi Tikkanen, Stefan Gustafsson, David Amar, Anna Shcherbina, Daryl Waggott, Euan A. Ashley, Erik Ingelsson 2017-10-10 2020-07-16 [("doi","10.1101/201020")] exercise genetics/heritable

Background: Hand grip strength, a simple indicator of muscular strength, has been associated with a range of health conditions, including fractures, disability, cardiovascular disease and premature death risk. Twin studies have suggested a high (50-60%) heritability, but genetic determinants are largely unknown.

Aims

In this study, our aim was to study genetic variation associated with muscular strength in a large sample of 334,925 individuals of European descent from the UK Biobank, and to evaluate shared genetic aetiology with and causal effects of grip strength on physical and cognitive health.

Methods and Results

In our discovery analysis of 223,315 individuals, we identified 101 loci associated with grip strength at genome-wide statistical-significance (P<5×10−8). Of these, 64 were associated (P<0.01 and consistent direction) also in the replication dataset (n = 111,610). Many of the lead SNPs were located in or near genes known to have a function in developmental disorders (FTO, SLC39A8, TFAP2B, TGFA, CELF1, TCF4, BDNF, FOXP1, KIF1B, ANTXR2), and one of the most genes based on a gene-based analysis (ATP2A1) encodes SERCA1, the critical enzyme in calcium uptake to the sarcoplasmic reticulum, which plays a major role in muscle contraction and relaxation. Further, we demonstrated an enrichment of gene expression in brain-related transcripts among grip strength associations. Finally, we observed inverse genetic correlations of grip strength with cardiometabolic traits, and positive correlation with parents’ age of death and education; and showed that grip strength was causally related to fitness, physical activity and other indicators of frailty, including cognitive performance scores.

Conclusion: In our study of over 330,000 individuals from the general population, the genetic findings for hand grip strength suggest an important role of the central nervous system in strength performance. Further, our results indicate that maintaining good muscular strength is important for physical and cognitive performance and healthy aging.

--- https://www.biorxiv.org/content/10.1101/2019.12.11.873265.full The transcriptional legacy of developmental stochasticity Sara Ballouz, Maria T. Pena, Frank M. Knight, Linda B. Adams, Jesse A. Gillis 2019-12-12 2020-07-16 [("doi","10.1101/2019.12.11.873265")] genetics/cloning

Genetic variation, epigenetic regulation and major environmental stimuli are key contributors to phenotypic variation, but the influence of minor perturbations or “noise” has been difficult to assess in mammals. In this work, we uncover one major axis of random variation with a large and permanent influence: developmental stochasticity. By assaying the transcriptome of wild monozygotic quadruplets of the nine-banded armadillo, we find that persistent changes occur early in development, and these give rise to clear transcriptional signatures which uniquely characterize individuals relative to siblings. Comparing these results to human twins, we find the transcriptional signatures which define individuals exhibit conserved co-expression, suggesting a substantial fraction of phenotypic and disease discordance within mammals arises from developmental stochasticity.

One sentence summary

Longitudinal gene expression in identical armadillo quadruplets reveals a major role for developmental stochasticity.

--- https://www.biorxiv.org/content/10.1101/2019.12.26.888313.full The interplay between host genetics and the gut microbiome reveals common and distinct microbiome features for human complex diseases Fengzhe Xu, Yuanqing Fu, Ting-yu Sun, Zengliang Jiang, Zelei Miao, Menglei Shuai, Wanglong Gou, Chu-wen Ling, Jian Yang, Jun Wang, Yu-ming Chen, Ju-Sheng Zheng 2019-12-26 2020-07-16 [("doi","10.1101/2019.12.26.888313")] genetics/heritable/correlation/mendelian-randomization genetics/microbiome

There is increasing interest about the interplay between host genetics and gut microbiome on human complex diseases, with prior evidence mainly derived from animal models. In addition, the shared and distinct microbiome features among human complex diseases remain largely unclear.

We performed a microbiome genome-wide association study to identify host genetic variants associated with gut microbiome in a Chinese population with 1475 participants. We then conducted bi-directional Mendelian Randomization analyses to examine the potential causal associations between gut microbiome and human complex diseases.

We did not find evidence supporting the causal effect of gut microbiome on human complex diseases. In contrast, atrial fibrillation, chronic kidney disease, and prostate cancer, as predicted by the host genetics, had potential causal effects on gut microbiome. Further disease-microbiome feature analysis suggested that gut microbiome features revealed novel relationship among human complex diseases.

These results suggest that different human complex diseases share common and distinct gut microbiome features, which may help re-shape our understanding about the disease etiology in humans.

--- https://www.biorxiv.org/content/10.1101/2020.01.03.894428.full Sequence and annotation of 42 cannabis genomes reveals extensive copy number variation in cannabinoid synthesis and pathogen resistance genes Kevin J. McKernan, Yvonne Helbert, Liam T. Kane, Heather Ebling, Lei Zhang, Biao Liu, Zachary Eaton, Stephen McLaughlin, Sarah Kingan, Primo Baybayan, Gregory Concepcion, Mark Jordan, Alberto Riva, William Barbazuk, Timothy Harkins 2020-01-05 2020-07-16 [("doi","10.1101/2020.01.03.894428")] genetics/sequencing marijuana

Cannabis is a diverse and polymorphic species. To better understand cannabinoid synthesis inheritance and its impact on pathogen resistance, we shotgun sequenced and assembled a Cannabis trio (sibling pair and their offspring) using long read single molecule sequencing. This resulted in the most contiguous Cannabis sativa assemblies to date.

These reference assemblies were further annotated with full-length male and female mRNA sequencing (Iso-Seq) to help inform isoform complexity, gene model predictions and identification of the Y chromosome. To further annotate the genetic diversity in the species, 40 male, female, and monoecious cannabis and hemp varietals were evaluated for copy number variation (CNV) and RNA expression. This identified multiple CNVs governing cannabinoid expression and 82 genes associated with resistance to Golovinomyces chicoracearum, the causal agent of powdery mildew in cannabis.

Results indicated that breeding for plants with low tetrahydrocannabinolic acid (THCA) concentrations may result in deletion of pathogen resistance genes. Low THCA cultivars also have a polymorphism every 51 bases while dispensary grade high THCA cannabis exhibited a variant every 73 bases.

A refined genetic map of the variation in cannabis can guide more stable and directed breeding efforts for desired chemotypes and pathogen-resistant cultivars.

--- https://www.biorxiv.org/content/10.1101/2020.01.21.911859.full A Connectome of the Adult Drosophila Central Brain C. Shan Xu, Michal Januszewski, Zhiyuan Lu, Shin-ya Takemura, Kenneth J. Hayworth, Gary Huang, Kazunori Shinomiya, Jeremy Maitin-Shepard, David Ackerman, Stuart Berg, Tim Blakely, John Bogovic, Jody Clements, Tom Dolafi, Philip Hubbard, Dagmar Kainmueller, William Katz, Takashi Kawase, Khaled A. Khairy, Laramie Leavitt, Peter H. Li, Larry Lindsey, Nicole Neubarth, Donald J. Olbris, Hideo Otsuna, Eric T. Troutman, Lowell Umayam, Ting Zhao, Masayoshi Ito, Jens Goldammer, Tanya Wolff, Robert Svirskas, Philipp Schlegel, Erika R. Neace, Christopher J. Knecht, Chelsea X. Alvarado, Dennis A. Bailey, Samantha Ballinger, Jolanta A. Borycz, Brandon S. Canino, Natasha Cheatham, Michael Cook, Marisa Dreher, Octave Duclos, Bryon Eubanks, Kelli Fairbanks, Samantha Finley, Nora Forknall, Audrey Francis, Gary Patrick Hopkins, Emily M. Joyce, SungJin Kim, Nicole A. Kirk, Julie Kovalyak, Shirley A. Lauchie, Alanna Lohff, Charli Maldonado, Emily A. Manley, Sari McLin, Caroline Mooney, Miatta Ndama, Omotara Ogundeyi, Nneoma Okeoma, Christopher Ordish, Nicholas Padilla, Christopher Patrick, Tyler Paterson, Elliott E. Phillips, Emily M. Phillips, Neha Rampally, Caitlin Ribeiro, Madelaine K. Robertson, Jon Thomson Rymer, Sean M. Ryan, Megan Sammons, Anne K. Scott, Ashley L. Scott, Aya Shinomiya, Claire Smith, Kelsey Smith, Natalie L. Smith, Margaret A. Sobeski, Alia Suleiman, Jackie Swift, Satoko Takemura, Iris Talebi, Dorota Tarnogorska, Emily Tenshaw, Temour Tokhi, John J. Walsh, Tansy Yang, Jane Anne Horne, Feng Li, Ruchi Parekh, Patricia K. Rivlin, Vivek Jayaraman, Kei Ito, Stephan Saalfeld, Reed George, Ian Meinertzhagen, Gerald M. Rubin, Harald F. Hess, Louis K. Scheffer, Viren Jain, Stephen M. Plaza 2020-01-21 2020-07-16 [("doi","10.1101/2020.01.21.911859")] cryonics psychology/neuroscience

The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions.

Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain.

--- https://www.biorxiv.org/content/10.1101/2020.04.23.057927.full Computation in the human cerebral cortex uses less than 0.2 watts yet this great expense is optimal when considering communication costs William B. Levy, Victoria G. Calvert 2020-04-25 2020-07-16 [("doi","10.1101/2020.04.23.057927")] ai/scaling/hardware cs/algorithm/information psychology/neuroscience

[Later: Levy & Calvert 2021] Darwinian evolution tends to produce energy-efficient outcomes. On the other hand, energy limits computation, be it neural and probabilistic or digital and logical.

After establishing an energy-efficient viewpoint, we define computation and construct an energy-constrained, computational function that can be optimized.

This function implies a specific distinction between ATP-consuming processes, especially computation per se vs action potentials and other costs of communication. As a result, the partitioning of ATP-consumption here differs from earlier work. A bits/J optimization of computation requires an energy audit of the human brain. Instead of using the oft-quoted 20 watts of glucose available to the brain1, 2, the partitioning and audit reveals that cortical computation consumes 0.2 watts of ATP while long-distance communication costs are over 20× greater. The bits/joule computational optimization implies a transient information rate of more than 7 bits/sec/neuron.

Significance Statement: Engineers hold up the human brain as a low energy form of computation. However from the simplest physical viewpoint, a neuron’s computation cost is remarkably larger than the best possible bits/joule—off by a factor of 108.

Here we explicate, in the context of energy consumption, a definition of neural computation that is optimal given explicit constraints. The plausibility of this definition as Nature’s perspective is supported by an energy-audit of the human brain.

The audit itself requires certain novel perspectives and calculations revealing that communication costs are 20× computational costs.

--- https://www.biorxiv.org/content/10.1101/2020.04.24.059964.full Closed loop enhancement and neural decoding of human cognitive control Ishita Basu, Ali Yousefi, Britni Crocker, Rina Zelmann, Angelique C. Paulk, Noam Peled, Kristen K. Ellard, Daniel S. Weisholtz, G. Rees Cosgrove, Thilo Deckersbach, Uri T. Eden, Emad N. Eskandar, Darin D. Dougherty, Sydney S. Cash, Alik S. Widge 2020-09-16 2020-09-16 [("doi","10.1101/2020.04.24.059964")] psychiatry psychology/neuroscience

Cognitive control is the ability to withhold a default, prepotent response in favor of a more adaptive choice. Control deficits are common across mental disorders, including depression, anxiety, and addiction. Thus, a method for improving cognitive control could be broadly useful in disorders with few effective treatments.

Here, we demonstrate closed-loop enhancement of one aspect of cognitive control by direct brain stimulation in humans. We stimulated internal capsule/striatum in participants undergoing intracranial epilepsy monitoring as they performed a cognitive control/conflict task. Stimulation enhanced performance, with the strongest effects from dorsal capsule/striatum stimulation.

We then developed a framework to detect control lapses and stimulate in response. This closed-loop approach produced larger behavioral changes than open-loop stimulation, with a slight improvement in performance change per unit of energy delivered. Finally, we decoded task performance directly from activity on a small number of electrodes, using features compatible with existing closed-loop brain implants.

Our findings are proof of concept for a new approach to treating severe mental disorders, based on directly remediating underlying cognitive deficits.

--- https://www.biorxiv.org/content/10.1101/2020.04.27.063693.full Larger GPU-accelerated brain simulations with procedural connectivity James C. Knight, Thomas Nowotny 2020-05-16 2020-07-17 [("doi","10.1101/2020.04.27.063693")] psychology/neuroscience

Large-scale simulations of spiking neural network models are an important tool for improving our understanding of the dynamics and ultimately the function of brains. However, even small mammals such as mice have on the order of 1 × 1012 synaptic connections which, in simulations, are each typically characterized by at least one floating-point value. This amounts to several terabytes of data—an unrealistic memory requirement for a single desktop machine. Large models are therefore typically simulated on distributed supercomputers which is costly and limits large-scale modeling to a few privileged research groups.

In this work, we describe extensions to GeNN—our Graphical Processing Unit (GPU) accelerated spiking neural network simulator—that enable it to “procedurally” generate connectivity and synaptic weights “on the go” as spikes are triggered, instead of storing and retrieving them from memory. We find that GPUs are well-suited to this approach because of their raw computational power which, due to memory bandwidth limitations, is often under-utilized when simulating spiking neural networks.

We demonstrate the value of our approach with a recent model of the Macaque visual cortex consisting of 4.13 × 106 neurons and 24.2 × 109 synapses. Using our new method, it can be simulated on a single GPU—a step forward in making large-scale brain modeling accessible to many more researchers. Our results match those obtained on a supercomputer and the simulation runs up to 35% faster on a single high-end GPU than previously on over 1,000 supercomputer nodes.

--- https://www.biorxiv.org/content/10.1101/2020.05.05.079038.full Volitional Control of Individual Neurons in the Human Brain Kramay Patel, Chaim N. Katz, Suneil K. Kalia, Milos R. Popovic, Taufik A. Valiante 2020-05-06 2020-07-17 [("doi","10.1101/2020.05.05.079038")] psychology/neuroscience technology

Can the human brain, a complex interconnected structure of over 80 billion neurons learn to control itself at the most elemental scale—a single neuron?

We directly linked the firing rate of a single (direct) neuron to the position of a box on a screen, which participants tried to control. Remarkably, all subjects upregulated the firing rate of the direct neuron in memory structures of their brain.

Learning was accompanied by improved performance over trials, simultaneous decorrelation of the direct neuron to local neurons, and direct neuron to beta frequency oscillation phase-locking.

Such previously unexplored neuroprosthetic skill learning within memory related brain structures, and associated beta frequency phase-locking implicates the ventral striatum.

Our demonstration that humans can volitionally control neuronal activity in mnemonic structures, may provide new ways of probing the function and plasticity of human memory without exogenous stimulation.

--- https://www.biorxiv.org/content/10.1101/2020.05.06.081273.full Identification of 370 loci for age at onset of sexual and reproductive behavior, highlighting common aetiology with reproductive biology, externalizing behavior and longevity Melinda C. Mills, Felix C. Tropf, David M. Brazel, Natalie van Zuydam, Ahmad Vaez, eQTLGen Consortium, BIOS Consortium, Tune H. Pers, Harold Snieder, John R. B. Perry, Ken K. Ong, Marcel den Hoed, Nicola Barban, Felix R. Day, on behalf of the Human Reproductive Behavior Consortium 2020-05-07 2020-07-17 [("doi","10.1101/2020.05.06.081273")] genetics/heritable genetics/selection/natural/human/dysgenics

The timing of reproductive behavior—age at first sexual intercourse (AFS) and age at first birth (AFB)—has implications for reproductive health, adolescent development and evolutionary fitness. In the largest genome-wide association study to date (AFS, n = 387,338; AFB, n = 542,901), we identify 370 independent signals, 11 which are sex-specific, with a 5–6% polygenic score prediction. Heritability shifted from 10% for those born in 1940 to 23% for the 1965 birth cohort.

Using Genomic SEM, we show that signals are largely driven by the genetics of reproductive biology and externalizing behavior. This is supported by extensive biological follow-up that isolates key genes related to follicle stimulating hormone (FSHB), implantation (ESR1), infertility (endometriosis, spontaneous abortion) and spermatid differentiation, morphogenesis and binding (KLF17, ZPBP).

Later AFB is protective against later-life disease (type 2 diabetes, cardiovascular) and associated with longevity. Those from higher childhood socioeconomic circumstances and polygenic scores in the highest deciles (90%+) experience markedly later reproductive onset.

Results are relevant for interventions in teenage sexual, reproductive and mental health, deepen our understanding of the drivers of later-life health and longevity, and fuel infertility and functional follow-up experiments.

--- https://www.biorxiv.org/content/10.1101/2020.05.06.081273.full Identification of 370 genetic loci for age at first sex and birth linked to externalizing behavior Melinda C. Mills, Felix C. Tropf, David M. Brazel, Natalie van Zuydam, Ahmad Vaez, eQTLGen Consortium, BIOS Consortium, Tune H. Pers, Harold Snieder, John R. B. Perry, Ken K. Ong, Marcel den Hoed, Nicola Barban, Felix R. Day, on behalf of the Human Reproductive Behavior Consortium 2021-02-04 2021-02-04 [("doi","10.1101/2020.05.06.081273")] genetics/heritable genetics/selection/natural/human/dysgenics

Age at first sexual intercourse (AFS) and age at first birth (AFB) have implications for health and evolutionary fitness. In the largest genome-wide association study to date (AFS, n = 387,338; AFB, n = 542,901), we identify 370 independent signals, 11 sex-specific, with a 5–6% polygenic score (PGS) prediction.

Heritability of AFB shifted from 9% [CI = 4–14] for women born in 1940 to 22% [CI = 19–25] in 1965. Signals are driven by the genetics of reproductive biology and externalizing behavior, with key genes related to follicle stimulating hormone (FSHB), implantation (ESR1), infertility, and spermatid differentiation. Polycystic Ovarian Syndrome leads to later AFB, linking with infertility.

Late AFB is protective against later-life disease and associated with parental longevity. Higher childhood socioeconomic circumstances and those in the highest PGS decile (90%+) experience markedly later reproductive onset.

Results are relevant for improving teenage and late-life health, for understanding longevity, and guiding experimentation into mechanisms of infertility.

--- https://www.biorxiv.org/content/10.1101/2020.05.07.083402.full Disentangling selection on genetically correlated polygenic traits using whole-genome genealogies Aaron J. Stern, Leo Speidel, Noah A. Zaitlen, Rasmus Nielsen 2020-05-08 2020-07-17 [("doi","10.1101/2020.05.07.083402")] genetics/selection/natural/human psychiatry/alzheimers psychiatry/bipolar/genetics psychiatry/schizophrenia

We present a full-likelihood method to estimate and quantify polygenic adaptation from contemporary DNA sequence data. The method combines population genetic DNA sequence data and GWAS summary statistics from up to thousands of nucleotide sites in a joint likelihood function to estimate the strength of transient directional selection acting on a polygenic trait.

Through population genetic simulations of polygenic trait architectures and GWAS, we show that the method substantially improves power over current methods. We examine the robustness of the method under uncorrected GWAS stratification, uncertainty and ascertainment bias in the GWAS estimates of SNP effects, uncertainty in the identification of causal SNPs, allelic heterogeneity, negative selection, and low GWAS sample size.

The method can quantify selection acting on correlated traits, fully controlling for pleiotropy even among traits with strong genetic correlation (|rg| = 80%; c.f. schizophrenia and bipolar disorder) while retaining high power to attribute selection to the causal trait. We apply the method to study 56 human polygenic traits for signs of recent adaptation. We find signals of directional selection on pigmentation (tanning, sunburn, hair, p = 5.5e-15, 1.1e-11, 2.2e-6, respectively), life history traits (age at first birth, EduYears, p = 2.5e-4, 2.6e-4, respectively), glycated hemoglobin (HbA1c, p = 1.2e-3), bone mineral density (p = 1.1e-3), and neuroticism (p = 5.5e-3).

We also conduct joint testing of 137 pairs of genetically correlated traits. We find evidence of widespread correlated response acting on these traits (2.6× enrichment over the null expectation, p = 1.5e-7). We find that for several traits previously reported as adaptive, such as educational attainment and hair color, a proportion of the signal of selection on these traits can be attributed to correlated response, vs direct selection (p = 2.9e-6, 1.7e-4, respectively). Lastly, our joint test uncovers antagonistic selection that has acted to increase type 2 diabetes (T2D) risk and decrease HbA1c (p = 1.5e-5).

--- https://www.biorxiv.org/content/10.1101/2020.05.19.104455.full Genome-wide analysis identifies genetic effects on reproductive success and ongoing natural selection at the FADS locus Iain Mathieson, Felix R. Day, Nicola Barban, Felix C. Tropf, David M. Brazel, eQTLGen Consortium, BIOS Consortium, Ahmad Vaez, Natalie van Zuydam, Bárbara D. Bitarello, Harold Snieder, Marcel den Hoed, Ken K. Ong, Melinda C. Mills, John R. B. Perry, on behalf of the Human Reproductive Behavior Consortium 2020-05-22 2020-07-17 [("doi","10.1101/2020.05.19.104455")] genetics/heritable genetics/selection/natural/human/dysgenics

Identifying genetic determinants of reproductive success may highlight mechanisms underlying fertility and also identify alleles under present-day selection. Using data in 785,604 individuals of European ancestry, we identify 43 genomic loci associated with either number of children ever born (NEB) or childlessness. These loci span diverse aspects of reproductive biology across the life course, including puberty timing, age at first birth, sex hormone regulation, and age at menopause.

Missense alleles in ARHGAP27 were associated with increased NEB but reduced reproductive lifespan, suggesting a trade-off between reproductive ageing and intensity. As NEB is one component of evolutionary fitness, our identified associations indicate loci under present-day natural selection.

Accordingly, we find that NEB-increasing alleles have increased in frequency over the past two generations. Furthermore, integration with data from ancient selection scans identifies a unique example of an allele—FADS1/2 gene locus—that has been under selection for thousands of years and remains under selection today.

Collectively, our findings demonstrate that diverse biological mechanisms contribute to reproductive success, implicating both neuro-endocrine and behavioral influences.

--- https://www.biorxiv.org/content/10.1101/2020.06.16.154542.full Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations Joel Dapello, Tiago Marques, Martin Schrimpf, Franziska Geiger, David D. Cox, James J. DiCarlo 2020-10-22 2020-10-22 [("doi","10.1101/2020.06.16.154542")] ai/nn/adversarial/human psychology/neuroscience

Current state-of-the-art object recognition models are largely based on convolutional neural network (CNN) architectures, which are loosely inspired by the primate visual system. However, these CNNs can be fooled by imperceptibly small, explicitly crafted perturbations, and struggle to recognize objects in corrupted images that are easily recognized by humans.

Here, by making comparisons with primate neural data, we first observed that CNN models with a neural hidden layer that better matches primate primary visual cortex (V1) are also more robust to adversarial attacks. Inspired by this observation, we developed VOneNets, a new class of hybrid CNN vision models. Each VOneNet contains a fixed weight neural network front-end that simulates primate V1, called the VOneBlock, followed by a neural network back-end adapted from current CNN vision models. The VOneBlock is based on a classical neuroscientific model of V1: the linear-nonlinear-Poisson model, consisting of a biologically-constrained Gabor filter bank, simple and complex cell nonlinearities, and a V1 neuronal stochasticity generator.

After training, VOneNets retain high ImageNet performance, but each is substantially more robust, outperforming the base CNNs and state-of-the-art methods by 18% and 3%, respectively, on a conglomerate benchmark of perturbations comprised of white box adversarial attacks and common image corruptions.

Finally, we show that all components of the VOneBlock work in synergy to improve robustness. While current CNN architectures are arguably brain-inspired, the results presented here demonstrate that more precisely mimicking just one stage of the primate visual system leads to new gains in ImageNet-level computer vision applications.

--- https://www.biorxiv.org/content/10.1101/2020.06.17.158105.full Dog Savior: Immediate Scent-Detection of SARS-COV-2 by Trained Dogs Omar Vesga, Andres F. Valencia, Alejandro Mira, Felipe Ossa, Esteban Ocampo, Maria Agudelo, Karl Čiuoderis, Laura Perez, Andres Cardona, Yudy Aguilar, Javier M. González, Juan C. Cataño, Yuli Agudelo, Juan P. Hernández-Ortiz, Jorge E. Osorio 2020-06-19 2020-07-17 [("doi","10.1101/2020.06.17.158105")] dog psychology/smell

Molecular tests for viral diagnostics are essential to confront the COVID-19 pandemic, but their production and distribution cannot satisfy the current high demand. Early identification of infected people and their contacts is the key to being able to isolate them and prevent the dissemination of the pathogen; unfortunately, most countries are unable to do this due to the lack of diagnostic tools.

Dogs can identify, with a high rate of precision, unique odors of volatile organic compounds generated during an infection; as a result, dogs can diagnose infectious agents by smelling specimens and, sometimes, the body of an infected individual. We trained 6 dogs of 3 different breeds to detect SARS-CoV-2 in respiratory secretions of infected patients and evaluated their performance experimentally, comparing it against the gold standard (rRT-PCR).

Here we show that viral detection takes one second per specimen. After scent-interrogating 9,200 samples, our 6 dogs achieved independently and as a group very high sensitivity, specificity, predictive values, accuracy, and likelihood ratio, with very narrow confidence intervals. The highest metric was the negative predictive value, indicating that with a disease prevalence of 7.6%, 99.9% of the specimens indicated as negative by the dogs did not carry the virus.

These findings demonstrate that dogs could be useful to track viral infection in humans, allowing COVID-19 free people to return to work safely.

--- https://www.biorxiv.org/content/10.1101/2020.06.26.174482.full The neural architecture of language: Integrative reverse-engineering converges on a model for predictive processing Martin Schrimpf, Idan Blank, Greta Tuckute, Carina Kauf, Eghbal A. Hosseini, Nancy Kanwisher, Joshua B. Tenenbaum, Evelina Fedorenko 2020-10-09 2020-10-09 [("doi","10.1101/2020.06.26.174482")] ai/nn/transformer/gpt ai/scaling psychology/neuroscience

The neuroscience of perception has recently been revolutionized with an integrative reverse-engineering approach in which computation, brain function, and behavior are linked across many different datasets and many computational models. We here present a first systematic study taking this approach into higher-level cognition: human language processing, our species’ signature cognitive skill.

We find that the most powerful ‘transformer’ networks predict neural responses at nearly 100% and generalize across different datasets and data types (fMRI, ECoG). Across models, statistically-significant correlations are observed among all 3 metrics of performance: neural fit, fit to behavioral responses, and accuracy on the next-word prediction task (but not other language tasks), consistent with the long-standing hypothesis that the brain’s language system is optimized for predictive processing.

Model architectures with initial weights further perform surprisingly similar to final trained models, suggesting that inherent structure—and not just experience with language—crucially contributes to a model’s match to the brain.

[Published as "The neural architecture of language: Integrative modeling converges on predictive processing", Schrimpf et al 2021.]

--- https://www.biorxiv.org/content/10.1101/2020.07.01.183384.full High-performance brain-to-text communication via imagined handwriting Francis R. Willett, Donald T. Avansino, Leigh R. Hochberg, Jaimie M. Henderson, Krishna V. Shenoy 2020-07-02 2020-07-17 [("doi","10.1101/2020.07.01.183384")] ai/nn/rnn psychology/neuroscience

Brain-computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak. To date, a major focus of BCI research has been on restoring gross motor skills, such as reaching and grasping or point-and-click typing with a 2D computer cursor. However, rapid sequences of highly dexterous behaviors, such as handwriting or touch typing, might enable faster communication rates.

Here, we demonstrate an intracortical BCI that can decode imagined handwriting movements from neural activity in motor cortex and translate it to text in real-time, using a novel recurrent neural network decoding approach. With this BCI, our study participant (whose hand was paralyzed) achieved typing speeds that exceed those of any other BCI yet reported: 90 characters per minute at >99% accuracy with a general-purpose autocorrect. These speeds are comparable to able-bodied smartphone typing speeds in our participant’s age group (115 characters per minute) and close the gap between BCI-enabled typing and able-bodied typing rates.

Finally, new theoretical considerations explain why temporally complex movements, such as handwriting, may be fundamentally easier to decode than point-to-point movements. Our results open a new approach for BCIs and demonstrate the feasibility of accurately decoding rapid, dexterous movements years after paralysis.

--- https://www.biorxiv.org/content/10.1101/2020.07.02.184465.full Multi-scale Inference of Genetic Trait Architecture using Biologically Annotated Neural Networks Pinar Demetci, Wei Cheng, Gregory Darnell, Xiang Zhou, Sohini Ramachandran, Lorin Crawford 2021-05-06 2021-05-06 [("doi","10.1101/2020.07.02.184465")] ai/nn/fully-connected genetics/heritable

In this article, we present Biologically Annotated Neural Networks (BANNs), a nonlinear probabilistic framework for association mapping in genome-wide association (GWA) studies. BANNs are feedforward models with partially connected architectures that are based on biological annotations. This setup yields a fully interpretable neural network where the input layer encodes SNP-level effects, and the hidden layer models the aggregated effects among SNP-sets. We treat the weights and connections of the network as random variables with prior distributions that reflect how genetic effects manifest at different genomic scales. The BANNs software uses variational inference to provide posterior summaries which allow researchers to simultaneously perform (i) mapping with SNPs and (ii) enrichment analyses with SNP-sets on complex traits. Through simulations, we show that our method improves upon state-of-the-art association mapping and enrichment approaches across a wide range of genetic architectures. We then further illustrate the benefits of BANNs by analyzing real GWA data assayed in ~2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics and ~7,000 individuals from the Framingham Heart Study. Lastly, using a random subset of individuals of European ancestry from the UK Biobank, we show that BANNs is able to replicate known associations in high and low-density lipoprotein cholesterol content.

Author Summary: A common goal in genome-wide association (GWA) studies is to characterize the relationship between genotypic and phenotypic variation. Linear models are widely used tools in GWA analyses, in part, because they provide statistical-significance measures which detail how individual single-nucleotide polymorphisms (SNPs) are statistically associated with a trait or disease of interest. However, traditional linear regression largely ignores non-additive genetic variation, and the univariate SNP-level mapping approach has been shown to be underpowered and challenging to interpret for certain trait architectures. While nonlinear methods such as neural networks are well known to account for complex data structures, these same algorithms have also been criticized as “black box” since they do not naturally carry out statistical hypothesis testing like classic linear models. This limitation has prevented nonlinear regression approaches from being used for association mapping tasks in GWA applications. Here, we present Biologically Annotated Neural Networks (BANNs): a flexible class of feedforward models with partially connected architectures that are based on biological annotations. The BANN framework uses approximate Bayesian inference to provide interpretable probabilistic summaries which can be used for simultaneous (i) mapping with SNPs and (ii) enrichment analyses with SNP-sets (eg. genes or signaling pathways). We illustrate the benefits of our method over state-of-the-art approaches using extensive simulations. We also demonstrate the ability of BANNs to recover novel and previously discovered genomic associations using quantitative traits from the Wellcome Trust Centre for Human Genetics, the Framingham Heart Study, and the UK Biobank.

--- https://www.biorxiv.org/content/10.1101/2020.07.19.188789.full Exome-wide association studies in general and long-lived populations identify genetic variants related to human age Patrick Sin-Chan, Nehal Gosalia, Chuan Gao, Cristopher V. Van Hout, Bin Ye, Anthony Marcketta, Alexander H. Li, Colm O’Dushlaine, Dadong Li, John D. Overton, Jeffrey D. Reid, Aris Baras, Regeneron Genetics Center, David J. Carey, David H. Ledbetter, Daniel Rader, Marylyn D. Ritchie, Scott M. Damrauer, Sofiya Milman, Nir Barzilai, David J. Glass, Aris N. Economides, Alan R. Shuldiner 2020-07-19 2020-07-19 [("doi","10.1101/2020.07.19.188789")] genetics/heritable/rare genetics/sequencing

Aging is characterized by degeneration in cellular and organismal functions leading to increased disease susceptibility and death. Although our understanding of aging biology in model systems has increased dramatically, large-scale sequencing studies to understand human aging are now just beginning. We applied exome sequencing and association analyses (ExWAS) to identify age-related variants on 58,470 participants of the DiscovEHR cohort.

Linear Mixed Model regression analyses of age at last encounter revealed variants in genes known to be linked with clonal hematopoiesis of indeterminate potential, which are associated with myelodysplastic syndromes, as top signals in our analysis, suggestive of age-related somatic mutation accumulation in hematopoietic cells despite patients lacking clinical diagnoses. In addition to APOE, we identified rare DISP2 rs183775254 (p = 7.40×10−10) and ZYG11A rs74227999 (p = 2.50×10−08) variants that were negatively associated with age in either both sexes combined and females, respectively, which were replicated with directional consistency in two independent cohorts. Epigenetic mapping showed these variants are located within cell-type-specific enhancers, suggestive of important transcriptional regulatory functions.

To discover variants associated with extreme age, we performed exome-sequencing on persons of Ashkenazi Jewish descent ascertained for extensive lifespans. Case-Control analyses in 525 Ashkenazi Jews cases (Males ≥ 92 years, Females ≥ 95 years) were compared to 482 controls. Our results showed variants in APOE (rs429358, rs6857), and TMTC2 (rs7976168) passed Bonferroni-adjusted p-value, as well as several nominally-associated population-specific variants.

Collectively, our Age-ExWAS, the largest performed to date, confirmed and identified previously unreported candidate variants associated with human age.

--- https://www.biorxiv.org/content/10.1101/2020.07.21.214486.full Deep neural network models of sound localization reveal how perception is adapted to real-world environments Andrew Francl, Josh H. McDermott 2020-07-22 2020-07-22 [("doi","10.1101/2020.07.21.214486")] psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Mammals localize sounds using information from their two ears. Localization in real-world conditions is challenging, as echoes provide erroneous information, and noises mask parts of target sounds. To better understand real-world localization we equipped a deep neural network with human ears and trained it to localize sounds in a virtual environment.

The resulting model localized accurately in realistic conditions with noise and reverberation, outperforming alternative systems that lacked human ears. In simulated experiments, the network exhibited many features of human spatial hearing: sensitivity to monaural spectral cues and interaural time and level differences, integration across frequency, and biases for sound onsets. But when trained in unnatural environments without either reverberation, noise, or natural sounds, these performance characteristics deviated from those of humans.

The results show how biological hearing is adapted to the challenges of real-world environments and illustrate how artificial neural networks can extend traditional ideal observer models to real-world domains.

--- https://www.biorxiv.org/content/10.1101/2020.08.29.273276.full The connectome of the adult Drosophila mushroom body: implications for function Feng Li, Jack Lindsey, Elizabeth C. Marin, Nils Otto, Marisa Dreher, Georgia Dempsey, Ildiko Stark, Alexander Shakeel Bates, Markus William Pleijzier, Philipp Schlegel, Aljoscha Nern, Shinya Takemura, Tansy Yang, Audrey Francis, Amalia Braun, Ruchi Parekh, Marta Costa, Louis Scheffer, Yoshinori Aso, Gregory S. X. E. Jefferis, L. F. Abbott, Ashok Litwin-Kumar, Scott Waddell, Gerald M. Rubin 2020-08-29 2020-08-29 [("doi","10.1101/2020.08.29.273276")] cryonics psychology/neuroscience

Making inferences about the computations performed by neuronal circuits from synapse-level connectivity maps is an emerging opportunity in neuroscience. The mushroom body (MB) is well positioned for developing and testing such an approach due to its conserved neuronal architecture, recently completed dense connectome, and extensive prior experimental studies of its roles in learning, memory and activity regulation.

Here we identify new components of the MB circuit in Drosophila, including extensive visual input and MB output neurons (MBONs) with direct connections to descending neurons. We find unexpected structure in sensory inputs, in the transfer of information about different sensory modalities to MBONs, and in the modulation of that transfer by dopaminergic neurons (DANs). We provide insights into the circuitry used to integrate MB outputs, connectivity between the MB and the central complex and inputs to DANs, including feedback from MBONs.

Our results provide a foundation for further theoretical and experimental work.

--- https://www.biorxiv.org/content/10.1101/2020.08.30.274225.full FlyWire: Online community for whole-brain connectomics Sven Dorkenwald, Claire McKellar, Thomas Macrina, Nico Kemnitz, Kisuk Lee, Ran Lu, Jingpeng Wu, Sergiy Popovych, Eric Mitchell, Barak Nehoran, Zhen Jia, J. Alexander Bae, Shang Mu, Dodam Ih, Manuel Castro, Oluwaseun Ogedengbe, Akhilesh Halageri, Zoe Ashwood, Jonathan Zung, Derrick Brittain, Forrest Collman, Casey Schneider-Mizell, Chris Jordan, William Silversmith, Christa Baker, David Deutsch, Lucas Encarnacion-Rivera, Sandeep Kumar, Austin Burke, Jay Gager, James Hebditch, Selden Koolman, Merlin Moore, Sarah Morejohn, Ben Silverman, Kyle Willie, Ryan Willie, Szi-chieh Yu, Mala Murthy, H. Sebastian Seung 2020-08-30 2020-08-30 [("doi","10.1101/2020.08.30.274225")] psychology/neuroscience

Due to advances in automated image acquisition and analysis, new whole-brain connectomes beyond C. elegans are finally on the horizon. Proofreading of whole-brain automated reconstructions will require many person-years of effort, due to the huge volumes of data involved. Here we present FlyWire, an online community for proofreading neural circuits in a fly brain, and explain how its computational and social structures are organized to scale up to whole-brain connectomics.

Browser-based 3D interactive segmentation by collaborative editing of a spatially chunked supervoxel graph makes it possible to distribute proofreading to individuals located virtually anywhere in the world. Information in the edit history is programmatically accessible for a variety of uses such as estimating proofreading accuracy or building incentive systems.

An open community accelerates proofreading by recruiting more participants, and accelerates scientific discovery by requiring information sharing. We demonstrate how FlyWire enables circuit analysis by reconstructing and analyzing the connectome of mechanosensory neurons.

--- https://www.biorxiv.org/content/10.1101/2020.09.06.284794.full Self-Supervised Natural Image Reconstruction and Rich Semantic Classification from Brain Activity Guy Gaziv, Roman Beliy, Niv Granot, Assaf Hoogi, Francesca Strappini, Tal Golan, Michal Irani 2020-09-08 2020-09-08 [("doi","10.1101/2020.09.06.284794")] reinforcement-learning/imitation-learning/brain-imitation-learning

Reconstructing natural images and decoding their semantic category from fMRI brain recordings is challenging. Acquiring sufficient pairs (image, fMRI) that span the huge space of natural images is prohibitive.

We present a novel self-supervised approach for fMRI-to-image reconstruction and classification that goes well beyond the scarce paired data. By imposing cycle consistency, we train our image reconstruction deep neural network on many “unpaired” data: a plethora of natural images without fMRI recordings (from many novel categories), and fMRI recordings without images. Combining high-level perceptual objectives with self-supervision on unpaired data results in a leap improvement over top existing methods.

Achieving: (1) Unprecedented image-reconstruction from fMRI of never-before-seen images (evaluated by image metrics and human testing); (2) Large-scale semantic classification (1000 diverse classes) of categories that are never-before-seen during network training. Such large-scale (1000-way) semantic classification capabilities from fMRI recordings have never been demonstrated before.

Finally, we provide evidence for the biological plausibility of our learned model.

--- https://www.biorxiv.org/content/10.1101/2020.09.08.287276.full The Temporal Dynamics of Opportunity Costs: A Normative Account of Cognitive Fatigue and Boredom Mayank Agrawal, Marcelo G. Mattar, Jonathan D. Cohen, Nathaniel D. Daw 2020-09-09 2020-09-09 [("doi","10.1101/2020.09.08.287276")] psychology/neuroscience psychology/willpower reinforcement-learning/exploration statistics/decision

Cognitive fatigue and boredom are two phenomenological states widely associated with limitations in cognitive control. In this paper, we present a rational analysis of the temporal structure of controlled behavior, which provides a new framework for providing a formal account of these phenomena. We suggest that in controlling behavior, the brain faces competing behavioral and computational imperatives, and must balance them by tracking their opportunity costs over time.

We use this analysis to flesh out previous suggestions that feelings associated with subjective effort, like cognitive fatigue and boredom, are the phenomenological counterparts of these opportunity cost measures, rather than reflecting the depletion of resources as has often been assumed. Specifically, we propose that both fatigue and boredom reflect the competing value of particular options that require foregoing immediate reward but can improve future performance: Fatigue reflects the value of offline computation (internal to the organism) to improve future decisions, while boredom signals the value of exploratory actions (external in the world) to gather information.

We demonstrate that these accounts provide a mechanistically explicit and parsimonious account for a wide array of findings related to cognitive control, integrating and re-imagining them under a single, formally rigorous framework.

--- https://www.biorxiv.org/content/10.1101/2020.09.25.314211.full Neurons learn by predicting future activity Artur Luczak, Bruce L. McNaughton, Yoshimasa Kubo 2021-08-24 2021-08-24 [("doi","10.1101/2020.09.25.314211")] psychology/neuroscience

Understanding how the brain learns may lead to machines with human-like intellectual capacities. However, learning mechanisms in the brain are still not well understood. Here we demonstrate that the ability of a neuron to predict its future activity may provide an effective mechanism for learning in the brain. We show that comparing a neuron’s predicted activity with the actual activity provides a useful learning signal for modifying synaptic weights. Interestingly, this predictive learning rule can be derived from a metabolic principle, where neurons need to minimize their own synaptic activity (cost), while maximizing their impact on local blood supply by recruiting other neurons.

This reveals an unexpected connection that learning in neural networks could result from simply maximizing the energy balance by each neuron. We show how this mathematically derived learning rule can provide a theoretical connection between diverse types of brain-inspired algorithms, such as: Hebb’s rule, BCM theory, temporal difference learning and predictive coding. Thus, this may offer a step toward the development of a general theory of neuronal learning.

We validated this predictive learning rule in neural network simulations and in data recorded from awake animals. We found that in the sensory cortex, it is indeed possible to predict a neuron’s activity ~10–20ms into the future. Moreover, in response to stimuli, cortical neurons changed their firing rate to minimize surprise: ie. the difference between actual and expected activity, as predicted by our model.

Our results also suggest that spontaneous brain activity provides “training data” for neurons to learn to predict cortical dynamics. Thus, this work demonstrates that the ability of a neuron to predict its future inputs could be an important missing element to understand computation in the brain.

--- https://www.biorxiv.org/content/10.1101/2020.10.02.323626.full The hearing aid dilemma: amplification, compression, and distortion of the neural code Alex Armstrong, Chi Chung Lam, Shievanie Sabesan, Nicholas A. Lesica 2020-10-04 2020-10-04 [("doi","10.1101/2020.10.02.323626")] psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Hearing aids are the only available treatment for mild-to-moderate sensorineural hearing loss, but often fail to improve perception in difficult listening conditions.

To identify the reasons for this failure, we studied the underlying neural code using large-scale single-neuron recordings in gerbils, a common animal model of human hearing.

We found that a hearing aid restored the sensitivity of neural responses, but failed to restore their selectivity. The low selectivity of aided responses was not a direct effect of hearing loss per se, but rather a consequence of the strategies used by hearing aids to restore sensitivity: compression, which decreases the spectral and temporal contrast of incoming sounds, and amplification, which produces high intensities that distort the neural code even with normal hearing.

To improve future hearing aids, new processing strategies that avoid this tradeoff between neural sensitivity and selectivity must be developed.

--- https://www.biorxiv.org/content/10.1101/2020.10.16.342501.full Multivariate genomic analysis of 1.5 million people identifies genes related to addiction, antisocial behavior, and health Richard Karlsson Linnér, Travis T. Mallard, Peter B. Barr, Sandra Sanchez-Roige, James W. Madole, Morgan N. Driver, Holly E. Poore, Andrew D. Grotzinger, Jorim J. Tielbeek, Emma C. Johnson, Mengzhen Liu, Hang Zhou, Rachel L. Kember, Joëlle A. Pasman, Karin J. H. Verweij, Dajiang J. Liu, Scott Vrieze, COGA Collaborators, Henry R. Kranzler, Joel Gelernter, Kathleen Mullan Harris, Elliot M. Tucker-Drob, Irwin Waldman, Abraham Palmer, K. Paige Harden, Philipp Koellinger, Danielle M. Dick 2020-10-16 2020-10-16 [("doi","10.1101/2020.10.16.342501")] crime genetics/heritable/correlation psychiatry/adhd

Behaviors and disorders related to self-regulation, such as substance use, antisocial conduct, and ADHD, are collectively referred to as externalizing and have a shared genetic liability. This comprehensive background lays the groundwork for understanding the interconnectedness of various externalizing conditions and their genetic underpinnings.

We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. This methodology underscores the power of large-scale data and multivariate analysis in identifying the genetic basis of complex traits.

The identified loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results captures variation in a broad range of behavioral and medical outcomes that were not part of our genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions, and unemployment. These results indicate significant progress in understanding and predicting a range of behaviors and conditions linked to externalizing traits.

Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental condition. This conclusion suggests a paradigm shift in how these behaviors and disorders are viewed, emphasizing the importance of early intervention and holistic approaches to treatment and prevention.

--- https://www.biorxiv.org/content/10.1101/2020.10.27.358291.full Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings Nicholas A. Steinmetz, Cagatay Aydin, Anna Lebedeva, Michael Okun, Marius Pachitariu, Marius Bauza, Maxime Beau, Jai Bhagat, Claudia Böhm, Martijn Broux, Susu Chen, Jennifer Colonell, Richard J. Gardner, Bill Karsh, Dimitar Kostadinov, Carolina Mora-Lopez, Junchol Park, Jan Putzeys, Britton Sauerbrei, Rik J. J. van Daal, Abraham Z. Vollan, Marleen Welkenhuysen, Zhiwen Ye, Joshua Dudman, Barundeb Dutta, Adam W. Hantman, Kenneth D. Harris, Albert K. Lee, Edvard I. Moser, John O’Keefe, Alfonso Renart, Karel Svoboda, Michael Häusser, Sebastian Haesler, Matteo Carandini, Timothy D. Harris 2020-10-28 2020-10-28 [("doi","10.1101/2020.10.27.358291")] psychology/neuroscience

To study the dynamics of neural processing across timescales, we require the ability to follow the spiking of thousands of individually separable neurons over weeks and months, during unrestrained behavior. To address this need, we introduce the Neuropixels 2.0 probe together with novel analysis algorithms. The new probe has over 5,000 sites and is miniaturized such that two probes plus a headstage, recording 768 sites at once, weigh just over 1 g, suitable for implanting chronically in small mammals.

Recordings with high quality signals persisting for at least two months were reliably obtained in two species and 6 different labs. Improved site density and arrangement combined with new data processing methods enable automatic post-hoc stabilization of data despite brain movements during behavior and across days, allowing recording from the same neurons in the mouse visual cortex for over 2 months. Additionally, an optional configuration allows for recording from multiple sites per available channel, with a penalty to signal-to-noise ratio.

These probes and algorithms enable stable recordings from >10,000 sites during free behavior in small animals such as mice.

--- https://www.biorxiv.org/content/10.1101/2020.11.05.370478.full Utility of polygenic embryo screening for disease depends on the selection strategy Todd Lencz, Daniel Backenroth, Einat Granot-Hershkovitz, Adam Green, Kyle Gettler, Judy H. Cho, Omer Weissbrod, Or Zuk, Shai Carmi 2021-06-03 2021-06-03 [("doi","10.1101/2020.11.05.370478")] psychiatry/schizophrenia

Polygenic risk scores (PRSs) have been offered since 2019 to screen in vitro fertilization embryos for genetic liability to adult diseases, despite a lack of comprehensive modeling of expected outcomes. Here we predict, based on the liability threshold model, the expected reduction in complex disease risk following polygenic embryo screening for a single disease. Our main finding is that a strong determinant of the potential utility of such screening is the selection strategy, a factor that has not been previously studied.

Specifically, when only embryos with a very high PRS are excluded, the achieved risk reduction is minimal. In contrast, selecting the embryo with the lowest PRS can lead to substantial relative risk reductions, given a sufficient number of viable embryos. For example, a relative risk reduction of ≈50% for schizophrenia can be achieved by selecting the embryo with the lowest PRS out of 5 viable embryos. We systematically examine the impact of several factors on the utility of screening, including the variance explained by the PRS, the number of embryos, the disease prevalence, the parental PRSs, and the parental disease status.

When quantifying the utility, we consider both relative and absolute risk reductions, as well as population-averaged and per-couple risk reductions. We also examine the risk of pleiotropic effects.

Finally, we confirm our theoretical predictions by simulating “virtual” couples and offspring based on real genomes from schizophrenia and Crohn’s disease case-control studies. We discuss the assumptions and limitations of our model, as well as the potential emerging ethical concerns.

--- https://www.biorxiv.org/content/10.1101/2020.11.30.403188.full Large uncertainty in individual PRS estimation impacts PRS-based risk stratification Yi Ding, Kangcheng Hou, Kathryn S. Burch, Sandra Lapinska, Florian Privé, Bjarni Vilhjálmsson, Sriram Sankararaman, Bogdan Pasaniuc 2021-03-31 2021-03-31 [("doi","10.1101/2020.11.30.403188")] genetics/heritable

Large-scale genome-wide association studies have enabled polygenic risk scores (PRS), which estimate the genetic value of an individual for a given trait. Since PRS accuracy is typically assessed using cohort-level metrics (eg. R2), uncertainty in PRS estimates at individual level remains underexplored.

Here we show that Bayesian PRS methods can estimate the variance of an individual’s PRS and can yield well-calibrated credible intervals for the genetic value of a single individual.

For real traits in the UK Biobank (n = 291,273 unrelated “white British”) we observe large variance in individual PRS estimates which impacts interpretation of PRS-based stratification; for example, averaging across 13 traits, only 0.8% (s.d. 1.6%) of individuals with PRS point estimates in the top decile have their entire 95% credible intervals fully contained in the top decile.

We provide an analytical estimator for individual PRS variance—a function of SNP-heritability, number of causal SNPs, and sample size—and observe high concordance with individual variances estimated via posterior sampling. Finally as an example of the utility of individual PRS uncertainties, we explore a probabilistic approach to PRS-based stratification that estimates the probability of an individual’s genetic value to be above a prespecified threshold.

Our results showcase the importance of incorporating uncertainty in individual PRS estimates into subsequent analyses.

--- https://www.biorxiv.org/content/10.1101/2020.12.10.419663.full A broad exome study of the genetic architecture of asthma reveals novel patient subgroups Sophia Cameron-Christie, Alex Mackay, Quanli Wang, Henric Olsson, Bastian Angermann, Glenda Lassi, Julia Lindgren, Michael Hühn, Yoichiro Ohne, Monica Gavala, Jingya Wang, Gundula Povysil, Sri V. V. Deevi, Graham Belfield, Inken Dillmann, Daniel Muthas, Suzanne Cohen, Simon Young, Adam Platt, Slavé Petrovski 2020-12-11 2020-12-11 [("doi","10.1101/2020.12.10.419663")] genetics/heritable/rare

Introduction

Asthma risk is a complex interplay between genetic susceptibility and environment. Despite many-associated common variants, the contribution of rarer variants with potentially greater effect sizes has not been as extensively studied. We present an exome-based study adopting 24,576 cases and 120,530 controls to assess the contribution of rare protein-coding variants to the risk of early-onset or all-comer asthma.

Method: We performed case-control analyses on 3 genetic units: variant-level, gene-level and pathway-level, using sequence data from the Scandinavian Asthma Genetic Study and UK Biobank participants with asthma. Cases were defined as all-comer asthma (n = 24,576) and early-onset asthma (n = 5,962). Controls were 120,530 UK Biobank participants without reported history of respiratory illness.

Results: Variant-level analyses identified statistically-significant variants at moderate-to-common allele frequency, including protein-truncating variants in FLG and IL33. Asthma risk was increased not only by individual, common FLG protein-truncating variants, but also among the collection of rare-to-private FLG protein-truncating variants (p = 6.8×10−7). This signal was driven by early-onset asthma and did not correlate with circulating eosinophil levels. In contrast, a single splice variant in IL33 was protective (p = 8.0×10−10), while the collection of remaining IL33 protein-truncating variants showed no class effect (p = 0.54). A pathway-based analysis identified that protein-truncating variants in loss-of-function intolerant genes were statistically-significantly enriched among individuals with asthma.

Conclusion: Access to the full allele frequency spectrum of protein-coding variants provides additional clarity about the potential mechanisms of action for FLG and IL33. Beyond these two drivers, we detected an enrichment of protein-truncating variants in loss-of-function intolerant genes.

--- https://www.biorxiv.org/content/10.1101/2020.12.13.422592.full A New and Improved Genome Sequence of Cannabis sativa Shivraj Braich, Rebecca C. Baillie, German C. Spangenberg, Noel O. I. Cogan 2020-12-13 2020-12-13 [("doi","10.1101/2020.12.13.422592")] genetics/sequencing marijuana

Cannabis is a diploid species (2n = 20), the estimated haploid genome sizes of the female and male plants using flow cytometry are 818 and 843 Mb respectively. Although the genome of Cannabis has been sequenced (from hemp, wild and high-THC strains), all assemblies have gaps. In addition, there are inconsistencies in the chromosome numbering which limits their use.

A new comprehensive draft genome sequence assembly (~900 Mb) has been generated from the medicinal cannabis strain Cannbio-2, that produces a balanced ratio of cannabidiol and delta-9-tetrahydrocannabinol using long-read sequencing. The assembly was subsequently analyzed for completeness by ordering the contigs into chromosome-scale pseudomolecules using a reference genome assembly approach, annotated and compared to other existing reference genome assemblies.

The Cannbio-2 genome sequence assembly was found to be the most complete genome sequence available based on nucleotides assembled and BUSCO evaluation in Cannabis sativa with a comprehensive genome annotation.

The new draft genome sequence is an advancement in Cannabis genomics permitting pan-genome analysis, genomic selection as well as genome editing.

--- https://www.biorxiv.org/content/10.1101/2021.01.12.426348.full Why do some primate mothers carry their infant’s corpse? A cross-species comparative study Elisa Fernández-Fueyo, Yukimaru Sugiyama, Takeshi Matsui, Alecia J. Carter 2021-03-01 2021-03-01 [("doi","10.1101/2021.01.12.426348")] psychology/animal

Non-human primates respond to the death of a conspecific in diverse ways, some which may present phylogenetic continuity with human thanatological behaviors. Of these responses, infant corpse carrying by mothers (ICC) is the most-frequently reported. Despite its prevalence, quantitative analyses of this behavior are scarce and inconclusive.

We compiled a database of 409 published cases across 50 different primate species of mothers’ responses to their infants’ deaths to test hypotheses proposed to explain between-species and within-species variation in corpse carrying. Using Bayesian phylogennarily identified 3 factors as possible predictors of ICC occurrence. However, using an information-theoretic approach, no combination of these predictors performed better than the null model, offering no support for any of the hypotheses we tested. In contrast, for those cases where infants’ corpses were carried, infant age affected ICC duration, with longer ICC observed for younger infants. This result may provide support for hypotheses that suggest that ICC is a by-product of a strong mother-infant bond.

The results are discussed in the context of the evolution of emotion and their implications for evolutionary thanatology are considered.

--- https://www.biorxiv.org/content/10.1101/2021.01.15.426781.full High trait variability in optimal polygenic prediction strategy within multiple-ancestry cohorts B. C. L. Lehmann, M. Mackintosh, G. McVean, C. C. Holmes 2021-03-27 2021-03-27 [("doi","10.1101/2021.01.15.426781")] genetics/heritable

Polygenic scores (PGS) are individual-level measures that quantify the genetic contribution to a given trait. PGS have predominantly been developed using European ancestry samples and recent studies have shown that the predictive performance of European ancestry-derived PGS is lower in non-European ancestry samples, reflecting differences in linkage disequilibrium, variant frequencies, and variant effects across populations. However, the problem of how best to maximize performance within any one ancestry group given the data available, and the extent to which this varies between traits, are largely unexplored. Here, we investigate the effect of sample size and ancestry composition on the predictive performance of PGS for fifteen traits in UK Biobank and evaluate an importance reweighting approach that aims to counteract the under-representation of certain groups within training data. We find that, for a minority of the traits, PGS estimated using a relatively small number of Black/Black British individuals outperformed, on a Black/Black British test set, scores estimated using a much larger number of White individuals. For example, a PGS for mean corpuscular volume trained on only Black individuals achieved a 4× improvement on a corresponding PGS trained on only White individuals. For the remainder of the traits, the reverse was true; a PGS for height trained on only Black/Black British individuals explained less than 0.5% of the variance in height in a Black/Black British test set, compared to 3.9% for a PGS trained on a much larger training set consisting of only White individuals. We find that while importance weighting provides moderate benefit for some traits (for example, 40% improvement for mean corpuscular volume compared to no reweighting), the improvement is modest in most cases, arguing that only targeted collection of data from underrepresented groups can address differences in PGS performance.

Author Summary: Polygenic scores (PGS) are individual-level measures that quantify the genetic contribution to a trait, such as height or blood pressure. Recent improvements in PGS performance have largely been limited to populations of European ancestry, reflecting the lack of ethnic diversity in genomic samples collected to date. Due to their potential negative impact on health inequalities, this lack of transferability across ancestries raises one of the most important technical and ethical challenges in the clinical utility and applications of PGS. Although there have recently been promising improvements in PGS performance for underrepresented groups, there remains a gap. In addition, while the growing availability of population-scale biobanks, such as UK Biobank, provides an opportunity to bridge part of this gap, the use of individual-level data within multiple-ancestry datasets is largely unexplored. In this paper, we evaluate the use of such datasets, combined with a novel reweighting approach, to improve predictive performance for underrepresented groups. We also consider how traits vary in terms of the best strategy for combining individuals of different ancestries when it comes to improving PGS performance. We find that, for a minority of traits, the optimal strategy for developing a PGS for Black or Black British individuals is to use only the small sample size available for this ethnic group1. For other traits, we find that reweighting has little effect and that the best strategy for the minority group is to use the largest training set, which contains only White individuals. Importantly, even given the optimal strategy, a large gap in PGS performance remains, indicating that only targeted collection of data from underrepresented groups can address differences in PGS performance.

--- https://www.biorxiv.org/content/10.1101/2021.01.19.427332.full Protein-coding repeat polymorphisms strongly shape diverse human phenotypes Ronen E. Mukamel, Robert E. Handsaker, Maxwell A. Sherman, Alison R. Barton, Yiming Zheng, Steven A. McCarroll, Po-Ru Loh 2021-01-20 2021-01-20 [("doi","10.1101/2021.01.19.427332")] genetics/heritable/rare

Hundreds of the proteins encoded in human genomes contain domains that vary in size or copy number due to variable numbers of tandem repeats (VNTRs) in protein-coding exons. VNTRs have eluded analysis by the molecular methods—SNP arrays and high-throughput sequencing—used in large-scale human genetic studies to date; thus, the relationships of VNTRs to most human phenotypes are unknown.

We developed ways to estimate VNTR lengths from whole-exome sequencing data, identify the SNP haplotypes on which VNTR alleles reside, and use imputation to project these haplotypes into abundant SNP data. We analyzed 118 protein-altering VNTRs in 415,280 UK Biobank participants for association with 791 phenotypes.

Analysis revealed some of the strongest associations of common variants with human phenotypes including height, hair morphology, and biomarkers of human health; for example, a VNTR encoding 13–44 copies of a 19-amino-acid repeat in the chondroitin sulfate domain of aggrecan (ACAN) associated with height variation of 3.4 centimeters (s.e. 0.3 cm). Incorporating large-effect VNTRs into analysis also made it possible to map many additional effects at the same loci: for the blood biomarker lipoprotein(a), for example, analysis of the kringle IV-2 VNTR within the LPA gene revealed that 18 coding SNPs and the VNTR in LPA explained 90% of lipoprotein(a) heritability in Europeans, enabling insights about population differences and epidemiological importance of this clinical biomarker.

These results point to strong, cryptic effects of highly polymorphic common structural variants that have largely eluded molecular analyses to date.

--- https://www.biorxiv.org/content/10.1101/2021.02.09.430378.full Structural variants in Chinese population and their impact on phenotypes, diseases and population adaptation Zhikun Wu, Zehang Jiang, Tong Li, Chuanbo Xie, Liansheng Zhao, Jiaqi Yang, Shuai Ouyang, Yizhi Liu, Tao Li, Zhi Xie 2021-02-10 2021-02-10 [("doi","10.1101/2021.02.09.430378")] genetics/heritable/rare

A complete characterization of genetic variation is a fundamental goal of human genome research. Long-read sequencing (LRS) improves the sensitivity for structural variant (SV) discovery and facilitates a better understanding of the SV spectrum in human genomes. Here, we conduct the first LRS-based SV analysis in Chinese population.

We perform whole-genome LRS for 405 unrelated Chinese, with 68 phenotypic and clinical measurements. We discover a complex landscape of 132,312 non-redundant SVs, of which 53.3% are novel. The identified SVs are of high-quality validated by the PacBio high-fidelity sequencing and PCR experiments. The total length of SVs represents ~13.2% of the human reference genome.

We annotate 1,929 loss-of-function SVs affecting the coding sequences of 1,681 genes. We discover new associations of SVs with phenotypes and diseases, such as rare deletions in HBA1/HBA2/HBB associated with anemia and common deletions in GHR associated with body height. Furthermore, we identify SV candidates related to human immunity that differentiate sub-populations of Chinese.

Our study reveals the complex landscape of human SVs in unprecedented detail and provides new insights into their roles contributing to phenotypes, diseases and evolution. The genotypic and phenotypic resource is freely available to the scientific community.

--- https://www.biorxiv.org/content/10.1101/2021.02.16.430904.full Predictive coding is a consequence of energy efficiency in recurrent neural networks Abdullahi Ali, Nasir Ahmad, Elgar de Groot, Marcel A. J. van Gerven, Tim C. Kietzmann 2021-02-16 2021-02-16 [("doi","10.1101/2021.02.16.430904")] ai/nn/rnn psychology/neuroscience

Predictive coding represents a promising framework for understanding brain function. It postulates that the brain continuously inhibits predictable sensory input, ensuring a preferential processing of surprising elements. A central aspect of this view is its hierarchical connectivity, involving recurrent message passing between excitatory bottom-up signals and inhibitory top-down feedback.

Here we use computational modeling to demonstrate that such architectural hard-wiring is not necessary. Rather, predictive coding is shown to emerge as a consequence of energy efficiency. When training recurrent neural networks to minimize their energy consumption while operating in predictive environments, the networks self-organize into prediction and error units with appropriate inhibitory and excitatory interconnections, and learn to inhibit predictable sensory input.

Moving beyond the view of purely top-down driven predictions, we demonstrate via virtual lesioning experiments that networks perform predictions on two timescales: fast lateral predictions among sensory units, and slower prediction cycles that integrate evidence over time.

--- https://www.biorxiv.org/content/10.1101/2021.02.17.431629.full Psilocybin induces rapid and persistent growth of dendritic spines in frontal cortex in vivo Ling-Xiao Shao, Clara Liao, Ian Gregg, Pasha A. Davoudian, Neil K. Savalia, Kristina Delagarza, Alex C. Kwan 2021-06-02 2021-06-02 [("doi","10.1101/2021.02.17.431629")] psychedelic

Psilocybin is a serotonergic psychedelic with untapped therapeutic potential. There are hints that the use of psychedelics can produce neural adaptations, although the extent and time scale of the impact in a mammalian brain are unknown.

In this study, we used chronic two-photon microscopy to image longitudinally the apical dendritic spines of layer 5 pyramidal neurons in the mouse medial frontal cortex. We found that a single dose of psilocybin led to ~10% increases in spine size and density, driven by an elevated spine formation rate. The structural remodeling occurred quickly within 24 hours and was persistent 1 month later.

Psilocybin also ameliorated stress-related behavioral deficit and elevated excitatory neurotransmission. Overall, the results demonstrate that psilocybin-evoked synaptic rewiring in the cortex is fast and enduring, potentially providing a structural trace for long-term integration of experiences and lasting beneficial actions.

--- https://www.biorxiv.org/content/10.1101/2021.03.10.434756.full#deepmind A rapid and efficient learning rule for biological neural circuits Eren Sezener, Agnieszka Grabska-Barwińska, Dimitar Kostadinov, Maxime Beau, Sanjukta Krishnagopal, David Budden, Marcus Hutter, Joel Veness, Matthew Botvinick, Claudia Clopath, Michael Häusser, Peter E. Latham 2021-03-12 2021-03-12 [("doi","10.1101/2021.03.10.434756")] psychology/neuroscience

The dominant view in neuroscience is that changes in synaptic weights underlie learning. It is unclear, however, how the brain is able to determine which synapses should change, and by how much. This uncertainty stands in stark contrast to deep learning, where changes in weights are explicitly engineered to optimize performance. However, the main tool for doing that, backpropagation, is not biologically plausible, and networks trained with this rule tend to forget old tasks when learning new ones.

Here we introduce the Dendritic Gated Network (DGN), a variant of the Gated Linear Network, which offers a biologically plausible alternative to backpropagation. DGNs combine dendritic “gating” (whereby interneurons target dendrites to shape neuronal response) with local learning rules to yield provably efficient performance.

They are statistically-significantly more data efficient than conventional artificial networks and are highly resistant to forgetting, and we show that they perform well on a variety of tasks, in some cases better than backpropagation.

The DGN bears similarities to the cerebellum, where there is evidence for shaping of Purkinje cell responses by interneurons. It also makes several experimental predictions, one of which we validate with in vivo cerebellar imaging of mice performing a motor task.

--- https://www.biorxiv.org/content/10.1101/2021.03.21.436284.full The Geometry of Concept Learning Ben Sorscher, Surya Ganguli, Haim Sompolinsky 2021-05-16 2021-05-16 [("doi","10.1101/2021.03.21.436284")] ai/nn psychology/neuroscience

Understanding the neural basis of the remarkable human cognitive capacity to learn novel concepts from just one or a few sensory experiences constitutes a fundamental problem. We propose a simple, biologically plausible, mathematically tractable, and computationally powerful neural mechanism for few-shot learning of naturalistic concepts.

We posit that the concepts that can be learnt from few examples are defined by tightly circumscribed manifolds in the neural firing rate space of higher order sensory areas. We further posit that a single plastic downstream readout neuron learns to discriminate new concepts based on few examples using a simple plasticity rule.

We demonstrate the computational power of our proposal by showing it can achieve high few-shot learning accuracy on natural visual concepts using both macaque inferotemporal cortex representations and deep neural network models of these representations, and can even learn novel visual concepts specified only through linguistic descriptors. Moreover, we develop a mathematical theory of few-shot learning that links neurophysiology to behavior by delineating several fundamental and measurable geometric properties of high-dimensional neural representations that can accurately predict the few-shot learning performance of naturalistic concepts across all our numerical simulations.

We discuss testable predictions of our theory for psychophysics and neurophysiological experiments.

--- https://www.biorxiv.org/content/10.1101/2021.04.14.439765.full Procyanidin C1 is a natural agent with senolytic activity against aging and age-related diseases Qixia Xu, Qiang Fu, Zi Li, Hanxin Liu, Ying Wang, Xu Lin, Ruikun He, Xuguang Zhang, Judith Campisi, James L. Kirkland, Yu Sun 2021-04-14 2021-04-14 [("doi","10.1101/2021.04.14.439765")] longevity/senolytic

Aging causes functional decline of multiple organs and increases the risk of age-related pathologies. In advanced lives, accumulation of senescent cells, which develop the senescence-associated secretory phenotype (SASP), promotes chronic inflammation and causes diverse conditions. Here we report the frontline outcome of screening a natural product library with human primary stromal cells as an experimental model. Multiple candidate compounds were assayed, and grape seed extract (GSE) was selected for further investigation due to its leading capacity in targeting senescent cells.

We found procyanidin C1 (PCC1), a polyphenolic component, plays a critical role in mediating the antiaging effects of GSE. PCC1 blocks the SASP expression when used at low concentrations. Importantly, it selectively kills senescent cells upon application at higher concentrations, mainly by enhancing production of reactive oxygen species (ROS) and disturbing mitochondrial membrane potential, processes accompanied by upregulation of Bcl-2 family pro-apoptotic factors Puma and Noxa in senescent cells.

PCC1 depletes senescent cells in treatment-damaged tumor microenvironment (TME) and enhances therapeutic efficacy when combined with chemotherapy in preclinical assays. Intermittent administration of PCC1 to both senescent cell-implanted mice and naturally aged animals alleviated physical dysfunction and prolonged post-treatment survival, thus providing substantial benefits in late life stage. Together, our study identifies PCC1 as a distinct natural senolytic agent, which may be exploited to delay aging and control age-related pathologies in future medicine.

--- https://www.biorxiv.org/content/10.1101/2021.04.24.441236.full Senolytics and the compression of late-life mortality Axel Kowald, Thomas B. L. Kirkwood 2021-04-26 2021-04-26 [("doi","10.1101/2021.04.24.441236")] longevity/senolytic

Senescent cells play an important role in mammalian ageing and in the etiology of age-related diseases. Treatment of mice with senolytics—drugs that selectively remove senescent cells—causes an extension of median lifespan but has little effect on maximum lifespan. Postponement of some mortality to later ages, without a corresponding increase in maximum mortality, can be termed ‘compression of mortality’. When we fit the standard Gompertz mortality model to the survival data following senolytic treatment, we find an increase in the slope parameter, commonly described as the ‘actuarial ageing rate’. These observations raise important questions about the actions of senolytic treatments and their effects on health and survival, which are not yet sufficiently understood.

To explore how the survival data from senolytics experiments might be explained, we combine recent exploration of the evolutionary basis of cellular senescence with theoretical consideration of the molecular processes that might be involved. We perform numerical simulations of senescent cell accumulation and senolytic treatment in an ageing population. The simulations suggest that while senolytics diminish the burden of senescent cells, they may also impair the general repair capacity of the organism, leading to a faster accumulation post-treatment of new senescent cells. Our results suggest a framework to address the benefits and possible side effects of senolytic therapies, with the potential to aid the design of optimal treatment regimens.

--- https://www.biorxiv.org/content/10.1101/2021.05.08.443158.full Resource Profile and User Guide of the Polygenic Index Repository Joel Becker, Casper A. P. Burik, Grant Goldman, Nancy Wang, Hariharan Jayashankar, Michael Bennett, Daniel W. Belsky, Richard Karlsson Linnér, Rafael Ahlskog, Aaron Kleinman, David A. Hinds, 23andMe Research Group, Avshalom Caspi, David L. Corcoran, Terrie E. Moffitt, Richie Poulton, Karen Sugden, Benjamin S. Williams, Kathleen Mullan Harris, Andrew Steptoe, Olesya Ajnakina, Lili Milani, Tõnu Esko, William Iacono, Matt McGue, Patrik K. E. Magnusson, Travis T. Mallard, K. Paige Harden, Elliot M. Tucker-Drob, Pamela Herd, Jeremy Freese, Alexander Young, Jonathan P. Beauchamp, Philipp Koellinger, Sven Oskarsson, Magnus Johannesson, Peter M. Visscher, Michelle N. Meyer, David Laibson, David Cesarini, Daniel J. Benjamin, Patrick Turley, Aysu Okbay 2021-05-10 2021-05-10 [("doi","10.1101/2021.05.08.443158")] genetics/heritable iq

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is rapidly growing.

As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some of which are novel—from multiple data sources, including 23andMe and UK Biobank.

We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the “additive SNP factor.” Regressions in which the true regressor is the additive SNP factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias.

We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

--- https://www.biorxiv.org/content/10.1101/2021.05.29.446297.full A connectivity-constrained computational account of topographic organization in primate high-level visual cortex Nicholas M. Blauch, Marlene Behrmann, David C. Plaut 2021-07-12 2021-07-12 [("doi","10.1101/2021.05.29.446297")] ai/nn psychology/neuroscience

Inferotemporal cortex (IT) in humans and other primates is topo-graphically organized, containing multiple hierarchically-organized areas selective for particular domains, such as faces and scenes. This organization is commonly viewed in terms of evolved domain-specific visual mechanisms.

Here, we develop an alternative, domain-general and developmental account of IT cortical organization. The account is instantiated as an Interactive Topographic Network (ITN), a form of computational model in which a hierarchy of model IT areas, subject to connectivity-based constraints, learns high-level visual representations optimized for multiple domains.

We find that minimizing a wiring cost on spatially organized feedforward and lateral connections within IT, combined with constraining the feedforward processing to be strictly excitatory, results in a hierarchical, topographic organization. This organization replicates a number of key properties of primate IT cortex, including the presence of domain-selective spatial clusters preferentially involved in the representation of faces, objects, and scenes, columnar responses across separate excitatory and inhibitory units, and generic spatial organization whereby the response correlation of pairs of units falls off with their distance.

We thus argue that domain-selectivity is an emergent property of a visual system optimized to maximize behavioral performance while minimizing wiring costs.

Significance Statement: We introduce the Interactive Topographic Network, a framework for modeling high-level vision, to demonstrate in computational simulations that the spatial clustering of domains in late stages of the primate visual system may arise from the demands of visual recognition under the constraints of minimal wiring costs and excitatory between-area neuronal communication. The learned organization of the model is highly specialized but not fully modular, capturing many of the properties of organization in primates. Our work is for cognitive neuroscience, by providing a domain-general developmental account of topo-graphic functional specialization, and for computational neuroscience, by demonstrating how well-known biological details can be successfully incorporated into neural network models in order to account for critical empirical findings.

--- https://www.biorxiv.org/content/10.1101/2021.06.14.448402.full Accurate prediction of protein structures and interactions using a 3-track network Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N. Kinch, R. Dustin Schaeffer, Claudia Millán, Hahnbeom Park, Carson Adams, Caleb R. Glassman, Andy DeGiovanni, Jose H. Pereira, Andria V. Rodrigues, Alberdina A. van Dijk, Ana C. Ebrecht, Diederik J. Opperman, Theo Sagmeister, Christoph Buhlheller, Tea Pavkov-Keller, Manoj K. Rathinaswamy, Udit Dalwadi, Calvin K. Yip, John E. Burke, K. Christopher Garcia, Nick V. Grishin, Paul D. Adams, Randy J. Read, David Baker 2021-06-15 2021-06-15 [("doi","10.1101/2021.06.14.448402")] ai/nn/transformer/alphafold

DeepMind presented remarkably accurate protein structure predictions at the CASP14 conference. We explored network architectures incorporating related ideas and obtained the best performance with a 3-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate models of protein-protein complexes from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.

One-Sentence Summary

Accurate protein structure modeling enables rapid solution of structure determination problems and provides insights into biological function.

--- https://www.biorxiv.org/content/10.1101/2021.06.18.448989.full The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning Shahab Bakhtiari, Patrick Mineault, Tim Lillicrap, Christopher C. Pack, Blake A. Richards 2021-06-18 2021-06-18 [("doi","10.1101/2021.06.18.448989")] psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

The visual system of mammals is comprised of parallel, hierarchical specialized pathways. Different pathways are specialized in so far as they use representations that are more suitable for supporting specific downstream behaviors. In particular, the clearest example is the specialization of the ventral (“what”) and dorsal (“where”) pathways of the visual cortex. These two pathways support behaviors related to visual recognition and movement, respectively.

To-date, deep neural networks have mostly been used as models of the ventral, recognition pathway. However, it is unknown whether both pathways can be modeled with a single deep ANN. Here, we ask whether a single model with a single loss function can capture the properties of both the ventral and the dorsal pathways. We explore this question using data from mice, who like other mammals, have specialized pathways that appear to support recognition and movement behaviors.

We show that when we train a deep neural network architecture with two parallel pathways using a self-supervised predictive loss function, we can outperform other models in fitting mouse visual cortex. Moreover, we can model both the dorsal and ventral pathways.

These results demonstrate that a self-supervised predictive learning approach applied to parallel pathway architectures can account for some of the functional specialization seen in mammalian visual systems.

--- https://www.biorxiv.org/content/10.1101/2021.07.05.451192.full Brain-like functional specialization emerges spontaneously in deep neural networks Katharina Dobs, Julio Martinez, Alexander J. E. Kell, Nancy Kanwisher 2021-07-06 2021-07-06 [("doi","10.1101/2021.07.05.451192")] ai/scaling psychology/neuroscience

The last quarter century of cognitive neuroscience has revealed numerous cortical regions in humans with distinct, often highly specialized functions, from recognizing faces to understanding language to thinking about what other people are thinking. But it remains unclear why the cortex exhibits this high degree of functional specialization in the first place.

Here, we consider the case of face perception, using artificial neural networks to test the hypothesis that functional segregation of face recognition in the brain reflects the computational requirements of the task. We find that networks trained on generic object recognition perform poorly on face recognition and vice versa, and further that networks optimized for both tasks spontaneously segregate themselves into separate systems for faces and objects. Thus, generic visual features that suffice for object recognition are apparently suboptimal for face recognition and vice versa.

We then show functional segregation to varying degrees for other visual categories, revealing a widespread tendency for optimization (without built-in task-specific inductive biases) to lead to functional specialization in machines and, we conjecture, also brains.

--- https://www.biorxiv.org/content/10.1101/2021.07.09.451701.full Your head is there to move you around: Goal-driven models of the primate dorsal pathway Patrick J. Mineault, Shahab Bakhtiari, Blake A. Richards, Christopher C. Pack 2021-10-26 2021-10-26 [("doi","10.1101/2021.07.09.451701")] psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Neurons in the dorsal visual pathway of the mammalian brain are selective for motion stimuli, with the complexity of stimulus representations increasing along the hierarchy. This progression is similar to that of the ventral visual pathway, which is well characterized by artificial neural networks (ANNs) optimized for object recognition. In contrast, there are no image-computable models of the dorsal stream with comparable explanatory power.

We hypothesized that the properties of dorsal stream neurons could be explained by a simple learning objective: the need for an organism to orient itself during self-motion. To test this hypothesis, we trained a 3D ResNet to predict an agent’s self-motion parameters from visual stimuli in a simulated environment. We found that the responses in this network accounted well for the selectivity of neurons in a large database of single-neuron recordings from the dorsal visual stream of non-human primates. In contrast, ANNs trained on an action recognition dataset through supervised or self-supervised learning could not explain responses in the dorsal stream, despite also being trained on naturalistic videos with moving objects.

These results demonstrate that an ecologically relevant cost function can account for dorsal stream properties in the primate brain.

--- https://www.biorxiv.org/content/10.1101/2021.07.13.452225.full Infinite re-reading of single proteins at single-amino-acid resolution using nanopore sequencing Henry Brinkerhoff, Albert S. W. Kang, Jingqian Liu, Aleksei Aksimentiev, Cees Dekker 2021-07-14 2021-07-14 [("doi","10.1101/2021.07.13.452225")] genetics/sequencing psychology/neuroscience

As identifying proteins is of paramount importance for cell biology and applications, it is of interest to develop a protein sequencer with the ultimate sensitivity of decoding individual proteins. Here, we demonstrate a nanopore-based single-molecule sequencing approach capable of reliably detecting single amino-acid substitutions within individual peptides. A peptide is linked to a DNA molecule that is pulled through the biological nanopore MspA by a DNA helicase in single amino-acid steps. The peptide sequence yields clear stepping ion current signals which allows to discriminate single-amino-acid substitutions in single reads. Molecular dynamics simulations show these signals to result from size exclusion and pore binding. Notably, we demonstrate the capability to ‘rewind’ peptide reads, obtaining indefinitely many independent reads of the same individual molecule, yielding virtually 100% read accuracy in variant identification, with an error rate less than 10−6. These proof-of-concept experiments constitute a promising basis for developing a single-molecule protein sequencer.

One-sentence summary

This paper presents proof-of-concept experiments and simulations of a nanopore-based approach to sequencing individual proteins.

--- https://www.biorxiv.org/content/10.1101/2021.07.27.453972.full Can AlphaFold2 predict protein-peptide complex structures accurately? Junsu Ko, Juyong Lee 2021-07-28 2021-07-28 [("doi","10.1101/2021.07.27.453972")] ai/nn/transformer/alphafold

In this preprint, we investigated whether AlphaFold2 (AF2), can predict protein-peptide complex structures only with sequence information.

We modeled the structures of 203 protein-peptide complexes from the PepBDB DB and 183 from the PepSet. The structures were modeling with concatenated sequences of receptors and peptides via poly-glycine linker.

We found that for more than half of the test cases, AF2 predicted the bound structures of peptides with good accuracy, Cα-RMSD of a peptide < 3.0 Å. For about 40% of cases, the peptide structures were modeled with an accuracy of Cα-RMSD < 2.0 Å.

Our benchmark results clearly show that AF2 has a great potential to be applied to various higher-order structure prediction tasks.

--- https://www.biorxiv.org/content/10.1101/2021.07.28.454120.full Large-scale genome-wide association study of food liking reveals genetic determinants and genetic correlations with distinct neurophysiological traits Sebastian May-Wilson, Nana Matoba, Kaitlin Wade, Jouke-Jan Hottenga, Maria Pina Concas, Massimo Mangino, Eryk J. Grzeszkowiak, Cristina Menni, Paolo Gasparini, Nicholas J. Timpson, Maria G. Veldhuizen, Eco J. C. de Geus, James F. Wilson, Nicola Pirastu 2021-07-28 2021-07-28 [("doi","10.1101/2021.07.28.454120")] food genetics/heritable/correlation psychology/smell/human

Variable preferences for different foods are among the main determinants of their intake and are influenced by many factors, including genetics. Despite considerable twins’ heritability, studies aimed at uncovering food-liking genetics have focused mostly on taste receptors.

Here, we present the first results of a large-scale genome-wide association study of food liking conducted on 161,625 participants from UK Biobank. Liking was assessed over 139 specific foods using a 9-point hedonic scale. After performing GWAS, we used genetic correlations coupled with structural equation modeling to create a multi-level hierarchical map of food liking. We identified 3 main dimensions: high caloric foods defined as “Highly palatable”, strong-tasting foods ranging from alcohol to pungent vegetables, defined as “Learned” and finally “Low caloric” foods such as fruit and vegetables.

The “Highly palatable” dimension was genetically uncorrelated from the other two, suggesting that two independent processes underlie liking high reward foods and the Learned/Low caloric ones. Genetic correlation analysis with the corresponding food consumption traits revealed a high correlation, while liking showed twice the heritability compared to consumption. For example, fresh fruit liking and consumption showed a genetic correlation of 0.7 with heritabilities of 0.1 and 0.05, respectively.

GWAS analysis identified 1401 food-liking associations located in 173 genomic loci, with only 11 near taste or olfactory receptors. Genetic correlation with morphological and functional brain data (33,224 UKB participants) uncovers associations of the 3 food-liking dimensions with non-overlapping, distinct brain areas and networks, suggestive of separate neural mechanisms underlying the liking dimensions.

In conclusion, we created a comprehensive and data-driven map of the genetic determinants and associated neurophysiological factors of food liking beyond taste receptor genes.

--- https://www.biorxiv.org/content/10.1101/2021.08.02.454840.full Single-sequence protein structure prediction using language models from deep learning Ratul Chowdhury, Nazim Bouatta, Surojit Biswas, Charlotte Rochereau, George M. Church, Peter K. Sorger, Mohammed AlQuraishi 2021-08-04 2021-08-04 [("doi","10.1101/2021.08.02.454840")] ai/nn/transformer/alphafold

AlphaFold and related systems use deep learning to predict protein structure from co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite dramatic, recent increases in accuracy, 3 challenges remain: (1) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated, (2) rapid exploration of designed structures, and (3) understanding the rules governing spontaneous polypeptide folding in solution.

Here we report development of an end-to-end differentiable recurrent geometric network (RGN) able to predict protein structure from single protein sequences without use of MSAs. This deep learning system has two novel elements: a protein language model (AminoBERT) that uses a Transformer to learn latent structural information from millions of unaligned proteins and a geometric module that compactly represents Cα backbone geometry.

RGN2 outperforms AlphaFold2 and RoseTTAFold (as well as trRosetta) on orphan proteins and is competitive with designed sequences, while achieving up to a 106-fold reduction in compute time. These findings demonstrate the practical and theoretical strengths of protein language models relative to MSAs in structure prediction.

--- https://www.biorxiv.org/content/10.1101/2021.08.12.456099.full Improving GWAS discovery and genomic prediction accuracy in Biobank data Etienne J. Orliac, Daniel Trejo Banos, Sven Erik Ojavee, Kristi Läll, Reedik Mägi, Peter M. Visscher, Matthew R. Robinson 2021-11-08 2021-11-08 [("doi","10.1101/2021.08.12.456099")] genetics/heritable statistics/bayes

Genetically informed and deep-phenotyped biobanks are an important research resource. The cost of phenotyping far outstrips that of genotyping, and therefore it is imperative that the most powerful, versatile, and efficient analysis approaches are used.

Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. On average, GMRM accuracies were 15% (SE 7%) greater than prediction models run in the LDAK software with SNP annotation marker groups, 18% (SE 3%) greater than a baseline BayesR model without SNP markers grouped into MAF-LD-annotation categories, and 106% (SE 9%) greater than polygenic risk scores calculated from mixed-linear model association (MLMA) estimates. For height, the prediction accuracy was 47% in a UK Biobank hold-out sample, which was 76% of the estimated SNP-heritability.

We then extend our GMRM prediction model to provide MLMA SNP marker estimates for GWAS discovery, which increased the independent loci detected to 7,910 in unrelated UK Biobank individuals, as compared to 5,521 from BoltLMM and 5,727 from Regenie, a 43% and 38% increase respectively. The average χ2 value of the leading markers was 34% (SE 5.11) higher for GMRM as compared to Regenie, and increased by 17% for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits.

Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and for discovery in large-scale individual-level biobank-scale studies.

--- https://www.biorxiv.org/content/10.1101/2021.08.17.456722.full Partitioning gene-level contributions to complex-trait heritability by allele frequency identifies disease-relevant genes Kathryn S. Burch, Kangcheng Hou, Yi Ding, Yifei Wang, Steven Gazal, Huwenbo Shi, Bogdan Pasaniuc 2021-08-18 2021-08-18 [("doi","10.1101/2021.08.17.456722")] genetics/heritable/rare

Recent works have shown that SNP-heritability—which is dominated by low-effect common variants—may not be the most relevant quantity for localizing high-effect/critical disease genes. Here, we introduce methods to estimate the proportion of phenotypic variance explained by a given assignment of SNPs to a single gene (gene-level heritability). We partition gene-level heritability across minor allele frequency (MAF) classes to find genes whose gene-level heritability is explained exclusively by “low-frequency/rare” variants (0.5% ≤ MAF < 1%).

Applying our method to ~17K protein-coding genes and 25 quantitative traits in the UK Biobank (n = 290K), we find that, on average across traits, ~2.5% of nonzero-heritability genes have a rare-variant component, and only ~0.8% (370 gene-trait pairs) have heritability exclusively from rare variants. Of these 370 gene-trait pairs, 37% were not detected by existing gene-level association testing methods, likely because existing methods combine signal from all variants in a region irrespective of MAF class. Many of the additional genes we identify are implicated in phenotypically related Mendelian disorders or congenital developmental disorders, providing further evidence of their trait-relevance.

Notably, the rare-variant component of gene-level heritability exhibits trends different from those of common-variant gene-level heritability. For example, while total gene-level heritability increases with gene length, the rare-variant component is larger among shorter genes; the cumulative distributions of gene-level heritability also vary across traits and reveal differences in the relative contributions of rare/common variants to overall gene-level polygenicity.

We conclude that the proportion of gene-level heritability attributable to low-frequency/rare variation can yield novel insights into complex-trait genetic architecture.

--- https://www.biorxiv.org/content/10.1101/2021.08.23.457339.full Extreme purifying selection against point mutations in the human genome Noah Dukler, Mehreen R. Mughal, Ritika Ramani, Yi-Fei Huang, Adam Siepel 2021-08-23 2021-08-23 [("doi","10.1101/2021.08.23.457339")] genetics/heritable/rare

Genome sequencing of tens of thousands of human individuals has recently enabled the measurement of large selective effects for mutations to protein-coding genes. Here we describe a new method, called ExtRaINSIGHT, for measuring similar selective effects at individual sites in noncoding as well as in coding regions of the human genome. ExtRaINSIGHT estimates the prevalence of strong purifying selection, or “ultraselection” (λs), as the fractional depletion of rare single-nucleotide variants (minor allele frequency < 0.1%) in a target set of genomic sites relative to matched sites that are putatively neutrally evolving, in a manner that controls for local variation and neighbor-dependence in mutation rate.

We show using simulations that, above an appropriate threshold, λs is closely related to the average site-specific selection coefficient against heterozygous point mutations, as predicted at mutation-selection balance. Applying ExtRaINSIGHT to 71,702 whole genome sequences from gnomAD v3, we find particularly strong evidence of ultraselection in evolutionarily ancient miRNAs and neuronal protein-coding genes, as well as at splice sites. Moreover, our estimated selection coefficient against heterozygous amino-acid replacements across the genome (at 1.4%) is substantially larger than previous estimates based on smaller sample sizes. By contrast, we find weak evidence of ultraselection in other noncoding RNAs and transcription factor binding sites, and only modest evidence in ultraconserved elements and human accelerated regions.

We estimate that ~0.3–0.5% of the human genome is ultraselected, with one third to one half of ultraselected sites falling in coding regions. These estimates suggest ~0.3–0.4 lethal or nearly lethal de novo mutations per potential human zygote, together with ~2 de novo mutations that are more weakly deleterious. Overall, our study sheds new light on the genome-wide distribution of fitness effects for new point mutations by combining deep new sequencing data sets and classical theory from population genetics.

--- https://www.biorxiv.org/content/10.1101/2021.09.01.456844.full Functional connectivity gradients as a common neural architecture for predictive processing in the human brain Yuta Katsumi, Nada Kamona, Jiahe Zhang, Jamie G. Bunce, J. Benjamin Hutchinson, Mathew Yarossi, Eugene Tunik, Karen S. Quigley, Bradford C. Dickerson, Lisa Feldman Barrett 2021-10-18 2021-10-18 [("doi","10.1101/2021.09.01.456844")] iq psychology/neuroscience

Predictive processing is emerging as a common computational hypothesis to account for diverse psychological functions subserved by a brain, providing a systems-level framework for characterizing structure-function relationships of its distinct substructures. Here, we contribute to this framework by examining gradients of functional connectivity as a low dimensional spatial representation of functional variation in the brain and demonstrating their computational implications for predictive processing.

Specifically, we investigated functional connectivity gradients in the cerebral cortex, the cerebellum, and the hippocampus using resting-state functional MRI data collected from large samples of healthy young adults. We then evaluated the degree to which these structures share common principles of functional organization by assessing the correspondence of their gradients.

We show that the organizing principles of these structures primarily follow two functional gradients consistent with the existing hierarchical accounts of predictive processing: A model-error gradient that describes the flow of prediction and prediction error signals, and a model-precision gradient that differentiates regions involved in the representation and attentional modulation of such signals in the cerebral cortex. Using these gradients, we also demonstrated triangulation of functional connectivity involving distinct subregions of the 3 structures, which allows characterization of distinct ways in which these structures functionally interact with each other, possibly subserving unique and complementary aspects of predictive processing.

These findings support the viability of computational hypotheses about the functional relationships between the cerebral cortex, the cerebellum, and the hippocampus that may be instrumental for understanding the brain’s dynamics within its large-scale predictive architecture.

--- https://www.biorxiv.org/content/10.1101/2021.09.21.461196.full What is unique about the human eye? Comparative image analysis on the external eye morphology of human and nonhuman great apes Fumihiro Kano, Takeshi Furuichi, Chie Hashimoto, Christopher Krupenye, Jesse G. Leinwand, Lydia M. Hopper, Christopher F. Martin, Ryoma Otsuka, Tomoyuki Tajima 2021-09-21 2021-09-21 [("doi","10.1101/2021.09.21.461196")] biology sociology

The gaze-signaling hypothesis and the related cooperative-eye hypothesis posit that humans have evolved special external eye morphology, including exposed white sclera (the white of the eye), to enhance the visibility of eye-gaze direction and thereby facilitate conspecific communication through joint-attentional interaction and ostensive communication. However, recent quantitative studies questioned these hypotheses based on new findings that humans are not necessarily unique in certain eye features compared to other great ape species.

Therefore, there is currently a heated debate on whether external eye features of humans are distinguished from those of other apes and how such distinguished features contribute to the visibility of eye-gaze direction. This study leveraged updated image analysis techniques to test the uniqueness of human eye features in facial images of great apes. Although many eye features were similar between humans and other species, a key difference was that humans have uniformly white sclera which creates clear visibility of both eye outline and iris—the two essential features contributing to the visibility of eye-gaze direction. We then tested the robustness of the visibility of these features against visual noises such as darkening and distancing and found that both eye features remain detectable in the human eye, while eye outline becomes barely detectable in other species under these visually challenging conditions.

Overall, we identified that humans have distinguished external eye morphology among other great apes, which ensures robustness of eye-gaze signal against various visual conditions. Our results support and also critically update the central premises of the gaze-signaling hypothesis.

--- https://www.biorxiv.org/content/10.1101/2021.09.26.461876.full A structural biology community assessment of AlphaFold 2 applications Mehmet Akdel, Douglas E. V. Pires, Eduard Porta Pardo, Jürgen Jänes, Arthur O. Zalevsky, Bálint Mészáros, Patrick Bryant, Lydia L. Good, Roman A. Laskowski, Gabriele Pozzati, Aditi Shenoy, Wensi Zhu, Petras Kundrotas, Victoria Ruiz Serra, Carlos H. M. Rodrigues, Alistair S. Dunham, David Burke, Neera Borkakoti, Sameer Velankar, Adam Frost, Kresten Lindorff-Larsen, Alfonso Valencia, Sergey Ovchinnikov, Janani Durairaj, David B. Ascher, Janet M. Thornton, Norman E. Davey, Amelie Stein, Arne Elofsson, Tristan I. Croll, Pedro Beltrao 2021-09-26 2021-09-26 [("doi","10.1101/2021.09.26.461876")] ai/nn/transformer/alphafold

Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods have led to protein structure predictions that have reached the accuracy of experimentally determined models. While this has been independently verified, the implementation of these methods across structural biology applications remains to be tested.

Here, we evaluate the use of AlphaFold 2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modelled when compared to homology modeling, identifying structural features rarely seen in the PDB. AF2-based predictions of protein disorder and protein complexes surpass state-of-the-art tools and AF2 models can be used across diverse applications equally well compared to experimentally determined structures, when the confidence metrics are critically considered.

In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life science research.

--- https://www.biorxiv.org/content/10.1101/2021.10.01.462832.full A general framework for identifying rare variant combinations in complex disorders Vijay Kumar Pounraja, Santhosh Girirajan 2021-10-01 2021-10-01 [("doi","10.1101/2021.10.01.462832")] genetics/heritable/rare psychiatry/schizophrenia

Statistical challenges due to rarity and combinatorial explosion resulting from exhaustive evaluation of rare variant combinations have limited the study of oligogenic etiology for complex disorders. We present RareComb, a framework that combines a priori algorithm and statistical inference to identify specific combinations of mutated genes associated with complex phenotypes.

Using RareComb on 6,189 affected individuals, we identified 718 combinations of mutated genes statistically-significantly associated with intellectual disability (ID), and carriers of these combinations showed lower IQ than expected in a replication cohort of 1,878 individuals. These combinations were enriched for nervous system genes, showed complex inheritance patterns, and were depleted in unaffected siblings.

We further identified oligogenic combinations associated with multiple comorbid phenotypes, including COL28A1 and MFSD2B mutations for ID and schizophrenia. Our framework enables rare variant analysis in affected individuals lacking diagnosis based on de novo mutations, and provides a paradigm for dissecting the genetic basis of complex disorders.

--- https://www.biorxiv.org/content/10.1101/2021.10.02.462713.full Monkey Plays Pac-Man with Compositional Strategies and Hierarchical Decision-making Qianli Yang, Zhongqiao Lin, Wenyi Zhang, Jianshu Li, Xiyuan Chen, Jiaqi Zhang, Tianming Yang 2021-10-04 2021-10-04 [("doi","10.1101/2021.10.02.462713")] psychology/neuroscience reinforcement-learning/exploration reinforcement-learning/imitation-learning/brain-imitation-learning

Humans can often handle daunting tasks with ease by developing a set of strategies to reduce decision making into simpler problems. The ability to use heuristic strategies demands an advanced level of intelligence and has not been demonstrated in animals.

Here, we trained macaque monkeys to play the classic video game Pac-Man. The monkeys' decision-making may be described with a strategy-based hierarchical decision-making model with over 90% accuracy.

The model reveals that the monkeys adopted the take-the-best heuristic by using one dominating strategy for their decision-making at a time and formed compound strategies by assembling the basis strategies to handle particular game situations.

With the model, the computationally complex but fully quantifiable Pac-Man behavior paradigm provides a new approach to understanding animals' advanced cognition.

--- https://www.biorxiv.org/content/10.1101/2021.10.12.464145.full Deep learning models of cognitive processes constrained by human brain connectomes Yu Zhang, Nicolas Farrugia, Pierre Bellec 2021-10-14 2021-10-14 [("doi","10.1101/2021.10.12.464145")] ai/nn/cnn psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

A full-brain integrative model is critical for cognitive decoding

Human connectomes and long-range connections accelerate the propagation of activity

ChebNet decoding is robust to random attacks on brain connectomes and regions


Brain decoding aims to infer cognitive states from recordings of brain activity. Current literature has mainly focused on isolated brain regions engaged in specific experimental conditions, but ignored the integrative nature of cognitive processes recruiting distributed brain networks. To tackle this issue, we propose a connectome-based graph neural network to integrate distributed patterns of brain activity in a multiscale manner, ranging from localized brain regions, to a specific brain circuit/network and towards the full brain. We evaluate the decoding model using a large task-fMRI database from the human connectome project. By implementing connectomic constraints and multiscale interactions in deep graph convolutions, the model achieves high accuracy of decoding 21 cognitive states (93%, chancel level: 4.8%) and shows high robustness against adversarial attacks on the graph architecture. Our study bridges human connectomes with deep learning techniques and provides new avenues to study the underlying neural substrates of human cognition at scale.

--- https://www.biorxiv.org/content/10.1101/2021.10.13.463489.full Genetic map of regional sulcal morphology in the human brain Benjamin B. Sun, Stephanie J. Loomis, Fabrizio Pizzagalli, Natalia Shatokhina, Jodie N. Painter, Christopher N. Foley, Biogen Biobank Team, Megan E. Jensen, Donald G. McLaren, Sai Spandana Chintapalli, Alyssa H. Zhu, Daniel Dixon, Tasfiya Islam, Iyad Ba Gari, Heiko Runz, Sarah E. Medland, Paul M. Thompson, Neda Jahanshad, Christopher D. Whelan 2021-10-15 2021-10-15 [("doi","10.1101/2021.10.13.463489")] genetics/heritable/correlation psychiatry

The human brain is a complex organ underlying many cognitive and physiological processes, affected by a wide range of diseases. Genetic associations with macroscopic brain structure are emerging, providing insights into genetic sources of brain variability and risk for functional impairments and disease. However, specific associations with measures of local brain folding, associated with both brain development and decline, remain under-explored.

Here we carried out detailed large-scale genome-wide associations of regional brain cortical sulcal measures derived from magnetic resonance imaging data of 40,169 individuals in the UK Biobank. Combining both genotyping and whole-exome sequencing data (~12 million variants), we discovered 388 regional brain folding associations across 77 genetic loci at p < 5 × 10−8, which replicated at p < 0.05.

We found genes in associated loci to be independently enriched for expression in the cerebral cortex, neuronal development processes, and differential regulation in early brain development. We integrated coding associations and brain eQTLs to refine genes for various loci and demonstrated shared signal in the pleiotropic KCNK2 locus with a cortex-specific KCNK2 eQTL. Genetic correlations with neuropsychiatric conditions highlighted emerging patterns across distinct sulcal parameters and related phenotypes.

We provide an interactive 3D visualization of our summary associations, making complex association patterns easier to interpret, and emphasizing the added resolution of regional brain analyses compared to global brain measures. Our results offer new insights into the genetic architecture underpinning brain folding and provide a resource to the wider scientific community for studies of pathways driving brain folding and their role in health and disease.

--- https://www.biorxiv.org/content/10.1101/2021.10.13.463862.full Time-tagged ticker tapes for intracellular recordings Dingchang Lin, Xiuyuan Li, Pojeong Park, Benjamin Tang, Hao Shen, Jonathan B. Grimm, Natali Falco, David Baker, Luke D. Lavis, Adam E. Cohen 2021-10-14 2021-10-14 [("doi","10.1101/2021.10.13.463862")] psychology/neuroscience

A core taken in a tree today can reveal climate events from centuries past. Here we adapt this idea to record histories of neural activation.

We engineered slowly growing intracellular protein fibers which can incorporate diverse fluorescent marks during growth to store linear ticker tape-like histories. An embedded HaloTag reporter incorporated user-supplied HaloTag-ligand dyes, leading to colored stripes whose boundaries mapped fiber growth to wall-clock time. A co-expressed eGFP tag driven by the cFos immediate early gene promoter recorded the history of neural activity.

High-resolution multispectral imaging on fixed samples read the cellular histories. We demonstrated recordings of cFos activation in ensembles of cultured neurons with a single-cell absolute accuracy of ~39 min over a 12-hour interval.

Protein-based ticker tapes have the potential to achieve massively parallel single-cell recordings of multiple physiological modalities.

--- https://www.biorxiv.org/content/10.1101/2021.10.13.464006.full Recording of cellular physiological histories along optically readable self-assembling protein chains Changyang Linghu, Bobae An, Monika Shpokayte, Orhan T. Celiker, Nava Shmoel, Chi Zhang, Won Min Park, Steve Ramirez, Edward S. Boyden 2021-10-13 2021-10-13 [("doi","10.1101/2021.10.13.464006")] biology genetics/sequencing psychology/neuroscience

Observing cellular physiological histories is key to understanding normal and disease-related processes, but longitudinal imaging is laborious and equipment-intensive. A tantalizing possibility is that cells could record such histories in the form of digital biological information within themselves, for later high-throughput readout.

Here we show that this concept can be realized through information storage in the form of growing protein chains made out of multiple self-assembling subunits bearing different labels, each corresponding to a different cellular state or function, so that the physiological history of the cell can be visually read out along the chain of proteins. Conveniently, such protein chains are fully genetically encoded, and easily readable with simple, conventional optical microscopy techniques, compatible with visualization of cellular shape and molecular content.

We use such expression recording islands (XRIs) to record gene expression timecourse downstream of pharmacological and physiological stimuli, in cultured neurons and in living mouse brain.

--- https://www.biorxiv.org/content/10.1101/2021.10.13.464294.full Rilmenidine mimics caloric restriction via the nischarin I1-imidazoline receptor to extend lifespan in C. elegans Dominic F. Bennett, Anita Goyala, Cyril Statzer, Charles W. Beckett, Alexander Tyshkovskiy, Vadim N. Gladyshev, Collin Y. Ewald, Joao Pedro de Magalhaes 2021-10-15 2021-10-15 [("doi","10.1101/2021.10.13.464294")] longevity/fasting

Caloric restriction increases lifespan across species and has health benefits in humans. Because complying with a low-calorie diet is challenging, here we investigated pharmacological interventions mimicking the benefits of caloric restriction. Searching for compounds that elicit a similar gene expression signature to caloric restriction, we identified rilmenidine, an I1-imidazoline receptor agonist and prescription medication for the treatment of hypertension.

We then show that treating C. elegans with rilmenidine at young and older ages increases lifespan. We also demonstrate that the stress-resilience, healthspan, and lifespan benefits upon rilmenidine treatment in worms are mediated by the I1-imidazoline receptor nish-1, implicating this receptor as a potential longevity target. Furthermore, we show that rilmenidine treatment increased ERK phosphorylation via NISH-1. Consistent with the shared caloric-restriction-mimicking gene signature, supplementing rilmenidine to caloric restricted C. elegans, genetic reduction of TORC1 function, or rapamycin treatment did not further increase lifespan.

The rilmenidine-induced longevity required the transcription factors FOXO/DAF-16 and NRF1,2,3/SKN-1, both important for caloric restriction-mediated longevity. Furthermore, we find that autophagy, but not AMPK signaling, was needed for rilmenidine-induced longevity.

Lastly, we find that treating mice with rilmenidine showed transcriptional changes in liver and kidney similar to caloric restriction. Overall, our findings reveal rilmenidine as a caloric restriction mimetic and as a novel geroprotective compound.

--- https://www.biorxiv.org/content/10.1101/2021.10.19.462578.full Uncovering the Genetic Architecture of Broad Antisocial Behavior through a Genome-Wide Association Study Meta-analysis. Jorim J. Tielbeek, Emil Uffelmann, Benjamin S. Williams, Lucia Colodro-Conde, Eloi Gagnon, Travis T. Mallard, Brandt E. Levitt, Philip R. Jansen, Ada Johansson, Hannah M. Sallis, Giorgio Pistis, Gretchen R. B. Saunders, Andrea G. Allegrini, Kaili Rimfeld, Bettina Konte, Marieke Klein, Annette M. Hartmann, Jessica E. Salvatore, Ilja M. Nolte, Ditte Demontis, Anni Malmberg, S. Alexandra Burt, Jeanne E. Savage, Karen Sugden, Richie Poulton, Kathleen Mullan Harris, Scott Vrieze, Matt McGue, William Iacono, Nina R. Mota, Jonathan Mill, Joana F. Viana, Brittany L. Mitchell, Jose J. Morosoli, Till F. M. Andlauer, Isabelle Ouellet-Morin, Richard E. Tremblay, Sylvana M. Cote, Jean-Philippe Gouin, Mara R. Brendgen, Ginette Dionne, Frank Vitaro, Michelle K. Lupton, Nicholas G. Martin, COGA, Spit for Science, Enrique Castelao, Katri Raikkonen, Johan G. Erksson, Jari Lahti, Catharina A. Hartman, Albertine J. Oldehinkel, Harold Snieder, Hexuan Liu, Martin Preisig, Alyce M. Whipp, Eero Vuoksimaa, Patrick Jern, Yi Lu, Dan Rujescu, Ina Giegling, Teemu Palviainen, Jaakko Kaprio, Kathryn Paige Harden, Marcus R. Munafo, Genevieve Morneau-Vaillancourt, Robert Plomin, Essi Viding, Brian B. Boutwell, Fazil Aliev, Danielle M. Dick, Arne Popma, Stephen V. Faraone, Anders D. Borglum, Sarah E. Medland, Barbara Franke, Michel Boivin, Jean-Baptiste Pingault, Jeffrey C. Glennon, James C. Barnes, Simon E. Fisher, Terrie E. Moffitt, Avshalom Caspi, Tinca J. C. Polderman, Danielle Posthuma 2021-10-20 2021-10-20 [("doi","10.1101/2021.10.19.462578")] crime genetics/heritable/correlation psychiatry/depression

Despite the substantial heritability of antisocial behavior (ASB), specific genetic variants robustly associated with the trait have not been identified. The present study by the Broad Antisocial Behavior Consortium (BroadABC) meta-analyzed data from 25 discovery samples (n = 85,359) and 5 independent replication samples (n = 8,058) with genotypic data and broad measures of ASB.

We identified the first genetic associations with broad ASB, involving common intronic variants in the forkhead box protein P2 (FOXP2) gene (lead SNP rs12536335, p = 6.32 × 10−10). Furthermore, we observed intronic variation in Foxp2 and one of its targets (Cntnap2) distinguishing a mouse model of pathological aggression (BALB/cJ mice) from controls (the BALB/cByJ strain). The SNP-based heritability of ASB was 8.4% (s.e.= 1.2%). Polygenic-risk-score (PRS) analyses in independent samples revealed that the genetic risk for ASB was associated with several antisocial outcomes across the lifespan, including diagnosis of conduct disorder, official criminal convictions, and trajectories of antisocial development.

We found substantial positive genetic correlations between ASB and depression (rg = 0.63), smoking (rg = 0.54) and insomnia (rg = 0.47) as well as negative correlations with indicators of life history (age at first birth (rg = −0.58), father’s age at death (rg = −0.54)) and years of schooling (rg = −0.46).

Our findings provide a starting point towards identifying critical biosocial risk mechanisms for the development of ASB.

--- https://www.biorxiv.org/content/10.1101/2021.10.20.464628.full Long-range regulatory effects of Neanderthal DNA in modern humans Danat Yermakovich, Vasili Pankratov, Urmo Vosa, Estonian Biobank Research Team, Bayazit Yunusbayev, Michael Dannemann 2021-10-21 2021-10-21 [("doi","10.1101/2021.10.20.464628")] genetics/selection/natural/human

The admixture between modern humans and Neandertals has resulted in ~2% of the genomes of present-day non-Africans still being composed of Neanderthal DNA. Association studies have shown that introgressed DNA still influences skin and hair traits, immunity and behavioral phenotypes in people today. Several of the phenotype-associated archaic variants had links to regulatory effects as well. In general, analyses of allele-specific expression, regulatory sequence composition and cis-eQTL have demonstrated a contribution of this introgressed DNA to the transcriptomic landscape in people today. However, little is known about the impact of Neanderthal DNA on trans-eQTLs—long-range regulatory effects that have been shown to explain ~20% of expression variation.

Here, we used blood eQTL results from >30,000 individuals from eQTLGen Consortium. The cohort size allowed for a robust identification of trans-eQTLs and in addition enabled quantifying the role of transcription factors (TF) in mediating long-range regulatory effects. In our study we used this information to (1) annotate trans-eQTLs that are linked to Neanderthal variants and (2) predict long-range regulatory effects that are induced by Neanderthal DNA by screening for the predicted target genes of TFs that are cis-eQTLs linked to Neanderthal variants. We show that both trans-eQTL-associated Neanderthal variants and those predicted to have long-range regulatory effects affect genes in genomic regions devoid of Neanderthal DNA. In addition, both types of variants included candidates for local adaptation and show associations with autoimmune disorders, a severe COVID-19 phenotype, blood cell type composition and anthropometric measures.

Our results suggest that the regulatory reach of Neanderthal DNA goes beyond the 40% of genomic sequence that it still covers in present-day non-Africans and that via this mechanism Neanderthal DNA additionally influences the phenotypic variation in people today.

--- https://www.biorxiv.org/content/10.1101/2021.10.21.465308.full Influences of rare copy number variation on human complex traits Margaux Louise Anna Hujoel, Maxwell A. Sherman, Alison R. Barton, Ronen E. Mukamel, Vijay G. Sankaran, Po-Ru Loh 2021-10-21 2021-10-21 [("doi","10.1101/2021.10.21.465308")] genetics/heritable/rare

The human genome contains hundreds of thousands of regions exhibiting copy number variation (CNV). However, the phenotypic effects of most such polymorphisms are unknown because only larger CNVs (spanning tens of kilobases) have been ascertainable from the SNP-array data generated by large biobanks.

We developed a new computational approach that leverages abundant haplotype-sharing in biobank cohorts to more sensitively detect CNVs co-inherited within extended SNP haplotypes. Applied to UK Biobank, this approach achieved 6× increased CNV detection sensitivity compared to previous analyses, accounting for ~half of all rare gene inactivation events produced by genomic structural variation.

This extensive CNV call set enabled the most comprehensive analysis to date of associations between CNVs and 56 quantitative traits, identifying 269 independent associations (p < 5 × 10−8)—involving 97 loci—that rigorous statistical fine-mapping analyses indicated were likely to be causally driven by CNVs. Putative target genes were identifiable for nearly half of the loci, enabling new insights into dosage-sensitivity of these genes and implicating several novel gene-trait relationships. CNVs at several loci created extended allelic series including deletions or duplications of distal enhancers that associated with much stronger phenotypic effects than SNPs within these regulatory elements.

These results demonstrate the ability of haplotype-informed analysis to empower structural variant detection and provide insights into the genetic basis of human complex traits.

--- https://www.biorxiv.org/content/10.1101/2021.10.22.465437.full General dimensions of human brain morphometry inferred from genome-wide association data Anna Elisabeth Fürtjes, Ryan Arathimos, Jonathan R. I. Coleman, James H. Cole, Simon R. Cox, Ian J. Deary, Javier de la Fuente, James W. Madole, Elliot M. Tucker-Drob, Stuart J. Ritchie 2021-10-25 2021-10-25 [("doi","10.1101/2021.10.22.465437")] genetics/heritable/correlation iq psychology/neuroscience statistics/variance-component

The human brain is organised into networks of interconnected regions that have highly correlated volumes. In this study, we aim to triangulate insights into brain organization and its relationship with cognitive ability and ageing, by analysing genetic data.

We estimated general genetic dimensions of human brain morphometry within the whole brain, and 9 predefined canonical brain networks of interest. We did so based on principal components analysis (PCA) of genetic correlations among grey-matter volumes for 83 cortical and subcortical regions (nparticipants = 36,778).

We found that the corresponding general dimension of brain morphometry accounts for 40% of the genetic variance in the individual brain regions across the whole brain, and 47–65% within each network of interest. This genetic correlation structure of regional brain morphometry closely resembled the phenotypic correlation structure of the same regions. Applying a novel multivariate methodology for calculating SNP effects for each of the general dimensions identified, we find that general genetic dimensions of morphometry within networks are negatively associated with brain age (rg = −0.34) and profiles characteristic of age-related neurodegeneration, as indexed by cross-sectional age-volume correlations (r = −0.27). The same genetic dimensions were positively associated with a genetic general factor of cognitive ability (rg = 0.17–0.21 for different networks).

We have provided a statistical framework to index general dimensions of shared genetic morphometry that vary between brain networks, and report evidence for a shared biological basis underlying brain morphometry, cognitive ability, and brain ageing, that are underpinned by general genetic factors.

…This indicates that the genetic association between brain morphometry and cognitive ability was not driven by specific network configurations. Instead, dimensions of shared genetic morphometry in general indexed genetic variance relevant to larger brain volumes and a brain organization that is advantageous for better cognitive performance. This was regardless of how many brain regions and from which regions the measure of shared genetic morphometry was extracted. This lack of differentiation between networks, in how strongly they correlate with cognitive ability, is in line with the suggestion that the total number of neurons in the mammalian cortex, which should at least partly correspond to its volume, is a major predictor of higher cognitive ability.37 These findings suggest that highly shared brain morphometry between regions, and its genetic analogue, indicate a generally bigger, and cognitively better-functioning brain.

--- https://www.biorxiv.org/content/10.1101/2021.10.25.465725.full Ultra-cheap and scalable epigenetic age predictions with TIME-Seq Patrick T. Griffin, Alice E. Kane, Alexandre Trapp, Jien Li, Maeve S. McNamara, Margarita V. Meer, Michael R. MacArthur, Sarah J. Mitchell, Amber L. Mueller, Colleen Carmody, Daniel L. Vera, Csaba Kerepesi, Nicole Noren Hooten, James R. Mitchell, Michele K. Evans, Vadim N. Gladyshev, David A. Sinclair 2021-10-28 2021-10-28 [("doi","10.1101/2021.10.25.465725")] genetics/sequencing longevity/epigenetics

Epigenetic “clocks” based on DNA methylation (DNAme) are the most robust and widely employed aging biomarker. They have been built for numerous species and reflect gold-standard interventions that extend lifespan. However, conventional methods for measuring epigenetic clocks are expensive and low-throughput.

Here, we describe Tagmentation-based Indexing for Methylation Sequencing (TIME-Seq) for ultra-cheap and scalable targeted methylation sequencing of epigenetic clocks and other DNAme biomarkers. Using TIME-Seq, we built and validated inexpensive epigenetic clocks based on genomic and ribosomal DNAme in hundreds of mice and human samples. We also discover it is possible to accurately predict age from extremely low-cost shallow sequencing (eg. 10,000 reads) of TIME-Seq libraries using scAge, a probabilistic age-prediction algorithm originally applied to single cells.

Together, these methods reduce the cost of DNAme biomarker analysis by more than two orders of magnitude, thereby expanding and democratizing their use in aging research, clinical trials, and disease diagnosis.

--- https://www.biorxiv.org/content/10.1101/2021.10.28.466243.full Coevolution of brain size and longevity in parrots Simeon Q. Smeele, Dalia A. Conde, Annette Baudisch, Simon Bruslund, Andrew Iwaniuk, Johanna Staerk, Timothy F. Wright, Anna M. Young, Mary Brooke McElreath, Lucy Aplin 2021-10-28 2021-10-28 [("doi","10.1101/2021.10.28.466243")] genetics/selection/natural longevity psychology/neuroscience

Parrots are well-known for their exceptionally long lives and cognitive complexity. While previous studies have demonstrated a correlation between longevity and brain size in a variety of taxa, little research has been devoted to understanding this link in parrots.

Here we employed a large-scale comparative analysis that investigated the influence of brain size and life history variables on patterns of longevity. Specifically, we addressed two hypotheses for evolutionary drivers of longevity: the Cognitive Buffer Hypothesis, which proposes that increased cognitive abilities enable longer life spans, and the Expensive Brain Hypothesis, which holds that the increase in life span is caused by prolonged developmental time of and increased parental investment in, large brained offspring.

We estimated life expectancy from detailed zoo records for 133,818 individuals across 244 parrot species. Using Bayesian structural equation models, we found a consistent correlation between relative brain size and life expectancy in parrots. This correlation was best explained by a direct effect of relative brain size. Notably, we found no effects of developmental time, clutch size, or age at first reproduction.

Our results provide support for the Cognitive Buffer Hypothesis, and demonstrate a principled Bayesian approach that addresses data uncertainty and imputation of missing values.

--- https://www.biorxiv.org/content/10.1101/2021.10.29.466524.full Tracking neural activity from the same cells during the entire adult life of mice Siyuan Zhao, Xin Tang, Sebastian Partarrieu, Shiqi Guo, Ren Liu, Jaeyong Lee, Zuwan Lin, Jia Liu 2021-11-01 2021-11-01 [("doi","10.1101/2021.10.29.466524")] psychology/neuroscience

Recording the activity of the same neurons over the adult life of an animal is important to neuroscience research and biomedical applications. Current implantable devices cannot provide stable recording on this time scale.

Here, we introduce a method to precisely implant nanoelectronics with an open, unfolded mesh structure across multiple brain regions in the mouse. The open mesh structure forms a stable interwoven structure with the neural network, preventing probe drifting and showing no immune response and neuron loss during the yearlong implantation.

Using the implanted nanoelectronics, we can track single-unit action potentials from the same neurons over the entire adult life of mice. Leveraging the stable recordings, we build machine learning algorithms that enable automated spike sorting, noise rejection, stability validation, and generate pseudotime analysis, revealing aging-associated evolution of the single-neuron activities.

--- https://www.biorxiv.org/content/10.1101/2021.11.01.466728.full Haldane’s cost of selection imposes a mild constraint on adaptation, with a high proportion of deaths in A. thaliana being selective Joseph D. Matheson, Moises Exposito-Alonso, Joanna Masel 2021-11-03 2021-11-03 [("doi","10.1101/2021.11.01.466728")] genetics/selection/natural

Haldane’s Dilemma refers to the concern that the need for many “selective deaths” to complete a substitution creates a speed limit to adaptation. However, discussion of this concern has been marked by confusion over which features of substitutions produce limits to the speed of adaptation, what those limits are, and the consequences of violating speed limits. The term “substitution load” has been particularly unhelpful in this regard.

Here we distinguish different lines of reasoning that lead to speed limits, including one line of reasoning which has not yet been fully addressed.

We then apply these lines of reasoning to a dataset measuring survival and fecundity of 517 different genotypes of Arabidopsis thaliana grown in 8 different environmental conditions.

We estimate highly permissive limits to the speed of adaptation in all environmental conditions. We also estimate that much higher proportions of deaths contribute to adaptation than were anticipated during historical discussions of speed limits.

--- https://www.biorxiv.org/content/10.1101/2021.11.01.466786.full Drag-and-drop genome insertion without DNA cleavage with CRISPR-directed integrases Eleonora I. Ioannidi, Matthew T. N. Yarnall, Cian Schmitt-Ulms, Rohan N. Krajeski, Justin Lim, Lukas Villiger, Wenyuan Zhou, Kaiyi Jiang, Nathaniel Roberts, Liyang Zhang, Christopher A. Vakulskas, John A. Walker, Anastasia P. Kadina, Adrianna E. Zepeda, Kevin Holden, Jonathan S. Gootenberg, Omar O. Abudayyeh 2021-11-01 2021-11-01 [("doi","10.1101/2021.11.01.466786")] genetics/editing

Programmable and multiplexed genome integration of large, diverse DNA cargo independent of DNA repair remains an unsolved challenge of genome editing. Current gene integration approaches require double-strand breaks that evoke DNA damage responses and rely on repair pathways that are inactive in terminally differentiated cells. Furthermore, CRISPR-based approaches that bypass double stranded breaks, such as Prime editing, are limited to modification or insertion of short sequences.

We present Programmable Addition via Site-specific Targeting Elements, or PASTE, which achieves efficient and versatile gene integration at diverse loci by directing insertion with a CRISPR-Cas9 nickase fused to both a reverse transcriptase and serine integrase. Without generating double stranded breaks, we demonstrate integration of sequences as large as ~36 kb with rates between 10–50% at multiple genomic loci across 3 human cell lines, primary T cells, and quiescent non-dividing primary human hepatocytes.

To further improve PASTE, we discover thousands of novel serine integrases and cognate attachment sites from metagenomes and engineer active orthologs for high-efficiency integration using PASTE. We apply PASTE to fluorescent tagging of proteins, integration of therapeutically relevant genes, and production and secretion of transgenes. Leveraging the orthogonality of serine integrases, we engineer PASTE for multiplexed gene integration, simultaneously integrating 3 different genes at 3 genomic loci.

PASTE has editing efficiencies comparable to or better than those of homology directed repair or non-homologous end joining based integration, with activity in non-dividing cells and fewer detectable off-target events. For therapeutic applications, PASTE can be delivered as mRNA with synthetically modified guides to programmably direct insertion of DNA templates carried by AAV or adenoviral vectors.

PASTE expands the capabilities of genome editing via drag-and-drop gene integration, offering a platform with wide applicability for research, cell engineering, and gene therapy.

--- https://www.biorxiv.org/content/10.1101/2021.11.01.466849.full Sex impacts the function of major depression-linked variants in vivo Bernard Mulvey, Din Selmanovic, Joseph D. Dougherty 2021-11-04 2021-11-04 [("doi","10.1101/2021.11.01.466849")] genetics/heritable psychiatry/depression

Genome-wide association studies have discovered blocks of common variants—likely transcriptional-regulatory—associated with major depressive disorder (MDD), though the functional subset and their biological impacts remain unknown. Likewise, why depression occurs in females more frequently than males is unclear.

We therefore tested the hypothesis that risk-associated functional variants interact with sex and produce greater impact in female brains. We developed methods to directly measure regulatory variant activity and sex interactions using massively parallel reporter assays (MPRAs) in the mouse brain in vivo, in a cell type-specific manner. We measured activity of >1,000 variants from >30 MDD loci, identifying extensive sex-by-allele effects in mature hippocampal neurons and suggesting sex-differentiated impacts of genetic risk may underlie sex bias in disease.

Unbiased informatics approaches indicated that functional MDD variants recurrently disrupt sex hormone receptor binding sequences. We confirmed this with MPRAs in neonatal brains, comparing brains undergoing the masculinizing hormone surge to hormonally-quiescent juveniles.

Our study provides novel insights into the influence of age, biological sex, and cell type on regulatory-variant function, and provides a framework for in vivo parallel assays to functionally define interactions between organismal variables like sex and regulatory variation.

Supplementary information: This studyused innovative genome-wide association study techniques and reporter gene assays to explore the genetic underpinnings of MDD, offering new realms of research in understanding the genetic factors influencing this condition.

--- https://www.biorxiv.org/content/10.1101/2021.11.03.467079.full Parent-of-origin effects in the UK Biobank Robin J. Hofmeister, Simone Rubinacci, Diogo M. Ribeiro, Zoltán Kutalik, Alfonso Buil, Olivier Delaneau 2021-11-06 2021-11-06 [("doi","10.1101/2021.11.03.467079")] genetics/heritable genetics/sequencing

Identical genetic variations can have different phenotypic effects depending on their parent of origin (PofO). Yet, studies focusing on PofO effects have been largely limited in terms of sample size due to the need of parental genomes or known genealogies.

Here, we used a novel probabilistic approach to infer PofO of individual alleles in the UK Biobank that does not require parental genomes nor prior knowledge of genealogy. Our model uses Identity-By-Descent (IBD) sharing with second-degree and third-degree relatives to assign alleles to parental groups and leverages chromosome X data in males to distinguish maternal from paternal groups.

When combined with robust haplotype inference and haploid imputation, this allowed us to infer the PofO at 5.4 million variants genome-wide for 26,393 UK Biobank individuals. We used this large dataset to systematically screen 59 biomarkers and 38 anthropomorphic phenotypes for PofO effects and discovered 101 statistically-significant associations, demonstrating that this type of effects contributes to the genetics of complex traits.

Notably, we retrieved well known PofO effects, such as the MEG3/DLK1 locus on platelet count, and we discovered many new ones at loci often unsuspected of being imprinted and, in some cases, previously thought to harbour additive associations.

--- https://www.biorxiv.org/content/10.1101/2021.11.04.466897.full Genome-wide association analyses of individual differences in quantitatively assessed reading-related and language-related skills in up to 34,000 people Else Eising, Nazanin Mirza-Schreiber, Eveline L. de Zeeuw, Carol A. Wang, Dongnhu T. Truong, Andrea G. Allegrini, Chin Yang Shapland, Gu Zhu, Karen G. Wigg, Margot Gerritse, Barbara Molz, Gokberk Alagoz, Alessandro Gialluisi, Filippo Abbondanza, Kaili Rimfeld, Marjolein M. J. Van Donkelaar, Zhijie Liao, Philip R. Jansen, Till F. M. Andlauer, Timothy C. Bates, Manon Bernard, Kirsten Blokland, Anders D. Borglum, Thomas Bourgeron, Daniel Brandeis, Fabiola Ceroni, Philip S. Dale, Peter F. de Jong, John C. DeFries, Ditte Demontis, Yu Feng, Scott D. Gordon, Sharon L. Guger, Marianna E. Hayiou-Thomas, Juan A. Hernandez-Cabrera, Jouke-Jan Hottenga, Charles Hulme, Elizabeth N. Kerr, Tanner Koomar, Karin Landerl, Maureen W. Lovett, Heikki Lyytinen, Nicholas G. Martin, Angela Martinelli, Urs Maurer, Jacob J. Michaelson, Kristina Moll, Antony P. Monaco, Angela T. Morgan, Markus M. Noethen, Zdenka Pausova, Craig E. Pennell, Bruce Pennington, Kaitlyn M. Price, Veera M. Rajagopal, Franck Ramus, Louis Richer, Nuala H. Simpson, Shelley Smith, Margaret J. Snowling, John Stein, Lisa J. Strug, Joel B. Talcott, Henning Tiemeier, Marc M. P. van der Schroeff, Ellen Verhoef, Kate E. Watkins, Margaret Wilkinson, Margaret J. Wright, Cathy L. Barr, Dorret I. Boomsma, Manuel Carreiras, Marie-Christine J. Franken, Jeffrey R. Gruen, Michelle Luciano, Bertram Mueller-Myhsok, Dianne F. Newbury, Richard K. Olson, Silvia Paracchini, Tomas Paus, Robert Plomin, Sheena Reilly, Gerd Schulte-Koerne, James B. Tomblin, Elsje van Bergen, Andrew J. O. Whitehouse, Erik G. Willcutt, Beate St Pourcain, Clyde Francks, Simon E. Fisher 2021-11-04 2021-11-04 [("doi","10.1101/2021.11.04.466897")] genetics/heritable/correlation genetics/selection/natural/human iq

The use of spoken and written language is a capacity that is unique to humans. Individual differences in reading-related and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30–80%, depending on the trait. The relevant genetic architecture is complex, heterogeneous, and multifactorial, and yet to be investigated with well-powered studies.

Here, we present a multi-cohort genome-wide association study (GWAS) of 5 traits assessed individually using psychometric measures: word reading, non-word reading, spelling, phoneme awareness, and non-word repetition, with total sample sizes ranging from 13,633 to 33,959 participants aged 5–26 years (12,411 to 27,180 for those with European ancestry, defined by principal component analyses).

We identified a genome-wide statistically-significant association with word reading (rs11208009, p = 1.098 × 10–8) independent of known loci associated with intelligence or educational attainment. All five reading/language-related traits had robust SNP-heritability estimates (0.13–0.26), and genetic correlations between them were modest to high. Using genomic structural equation modeling, we found evidence for a shared genetic factor explaining the majority of variation in word and non-word reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to non-word repetition, intelligence and educational attainment.

A multivariate GWAS was performed to jointly analyse word and non-word reading, spelling, and phoneme awareness, maximizing power for follow-up investigation. Genetic correlation analysis of multivariate GWAS results with neuroimaging traits identified association with cortical surface area of the banks of the left superior temporal sulcus, a brain region with known links to processing of spoken and written language.

Analysis of evolutionary annotations on the lineage that led to modern humans showed enriched heritability in regions depleted of Neanderthal variants.

Together, these results provide new avenues for deciphering the biological underpinnings of these uniquely human traits.

--- https://www.biorxiv.org/content/10.1101/2021.11.04.467250.full Genome-wide association study of cerebellar volume Elleke Tissink, Siemon C. de Lange, Jeanne E. Savage, Douglas P. Wightman, Kristen Kelly, Mats Nagel, Martijn P. van den Heuvel, Danielle Posthuma 2021-11-04 2021-11-04 [("doi","10.1101/2021.11.04.467250")] genetics/heritable/correlation psychiatry/alzheimers psychology/neuroscience

Cerebellar volume is highly heritable and associated with neurodevelopmental and neurodegenerative disorders. Understanding the genetic architecture of cerebellar volume may improve our insight into these disorders. This study aims to investigate the convergence of cerebellar volume genetic associations in close detail.

A genome-wide associations study for cerebellar volume was performed in a sample of 27,486 individuals from UK Biobank, resulting in 29 genome-wide statistically-significant loci and a SNP heritability of 39.82%. We pinpoint variants that have effects on amino acid sequence or cerebellar gene-expression. Additionally, 85 genome-wide statistically-significant genes were detected and tested for convergence onto biological pathways, cerebellar cell types or developmental stages.

Local genetic correlations between cerebellar volume and neurodevelopmental and neurodegenerative disorders reveal shared loci with Parkinson’s disease, Alzheimer’s disease and schizophrenia. These results provide insights into the heritable mechanisms that contribute to developing a brain structure important for cognitive functioning and mental health.

--- https://www.biorxiv.org/content/10.1101/2021.11.04.467320.full A multivariate view of cognitive differences reveals domain-general correlation structure in the Trinidadian Guppy (Poecilia reticulata) Pamela M. Prentice, Alastair J. Wilson, Alex Thornton 2021-11-04 2021-11-04 [("doi","10.1101/2021.11.04.467320")] iq/animal psychology/neuroscience psychology/personality

Cognitive variation is common among individuals within populations, and this variation can be consistent across time and context. From an evolutionary perspective, among-individual variation is important and required for natural selection. Selection has been hypothesized to favor high cognitive performance, however, directional selection would be expected to erode variation over time. Additionally, while variation is a prerequisite for natural selection, it is also true that selection does not act on traits in isolation. Thus, the extent to which performance covaries among specific cognitive domains, and other aspects of phenotype (eg. personality traits) is expected to be an important factor in shaping evolutionary dynamics.

Fitness trade-offs could shape patterns of variation in performance across different cognitive domains, however positive correlations between cognitive domains and personality traits are also known to occur. Here we aimed to test this idea using a multivariate approach to characterize and test hypothesized relationships of cognitive performance across multiple domains and personality, in the Trinidadian guppy (Poecilia reticulata). We estimate the among-individual correlation matrix (ID) in performance across 3 cognitive domains; association learning in a color discrimination task; motor cognition in a novel motor task, and cognitive flexibility in a reversal learning task, and the personality trait boldness, measured as time to emerge.

We found no support for trade-offs occurring, but the presence of strong positive domain-general correlations in ID, where 57% of the variation is explained by the leading eigenvector. While highlighting caveats of how non-cognitive factors and assay composition may affect the structure of the ID matrix, we suggest that our findings are consistent with a domain-general axis of cognitive variation in this population, adding to the growing body of support for domain-general variation among individuals in animal cognitive ability.

--- https://www.biorxiv.org/content/10.1101/2021.11.04.467320.full A multivariate view of cognitive performance reveals positive correlation in the Trinidadian Guppy (Poecilia reticulata) Pamela M. Prentice, Alex Thorton, Alastair J. Wilson 2022-01-20 2022-01-20 [("doi","10.1101/2021.11.04.467320")] iq/animal psychology/neuroscience

Cognitive variation is common among individuals and can be consistent across time and context. From an evolutionary perspective, among-individual variation is important as a prerequisite for natural selection and adaptive evolution. Selection is widely hypothesized to favor high cognitive performance, but directional selection should erode variation over time. How, then, is cognitive variation maintained? As selection does not act on traits in isolation, covariance among specific cognitive traits and/or other aspects of phenotype (eg. personality) could result in fitness trade-offs that are important in shaping evolutionary dynamics.

Here we test this using Trinidadian guppies (Poecilia reticulata), using a multivariate approach by characterizing the correlation structure among task-specific cognitive performance measures and a personality trait. We estimate the among-individual correlation matrix (ID) in performance across 3 cognitive tasks; color association learning task; motor learning task; reversal learning task, and the personality trait, boldness, measured as emergence time from a shelter.

We found no support for trade-offs among performance in these tasks. Nor do we find evidence of hypothesized speed-accuracy trade-offs within the association learning task. Rather we find strong positive correlation structure in ID, with 57% of variation explained by the leading eigenvector. While noting that non-cognitive factors and assay composition may affect the structure of ID, we suggest our findings are consistent with the g-model of cognitive performance variation, in which a dominant axis of variation loads positively on all performance measures.

Thus, we add to a growing body of support for general variation among individuals in animal cognitive ability.

--- https://www.biorxiv.org/content/10.1101/2021.11.05.467507.full Molecular recording of sequential cellular events into DNA Theresa B. Loveless, Courtney K. Carlson, Vincent J. Hu, Catalina A. Dentzel Helmy, Guohao Liang, Michelle Ficht, Arushi Singhai, Chang C. Liu 2021-11-07 2021-11-07 [("doi","10.1101/2021.11.05.467507")] genetics/genome-synthesis genetics/sequencing psychology/neuroscience

Genetically encoded DNA recorders noninvasively convert transient biological events into durable mutations in a cell’s genome, allowing for the later reconstruction of cellular experiences using high-throughput DNA sequencing. Existing DNA recorders have achieved high-information recording, durable recording, prolonged recording over multiple timescales, multiplexed recording of several user-selected signals, and temporally resolved signal recording, but not all at the same time.

We present a DNA recorder called peCHYRON (prime editing Cell HistorY Recording by Ordered iNsertion) that does. In peCHYRON, prime editor guide RNAs (pegRNAs) insert a variable triplet DNA sequence alongside a constant propagation sequence that deactivates the previous and activates the next step of insertion. This process results in the sequential accumulation of regularly spaced insertion mutations at a synthetic locus. Accumulated insertions are permanent throughout editing because peCHYRON uses a prime editor that avoids cutting both DNA strands, which risks deletions. Editing continues indefinitely because each insertion adds the complete sequence needed to initiate the next step.

Constitutively expressed pegRNAs generate insertion patterns that support straightforward reconstruction of cell lineage relationships. Pulsed expression of different pegRNAs enables the reconstruction of pulse sequences, which may be coupled to biological stimuli for temporally-resolved multiplexed event recording.

--- https://www.biorxiv.org/content/10.1101/2021.11.08.467509.full Sexual dimorphic responses of C57BL/6 mice to Fisetin or Dasatinib and Quercetin cocktail oral treatment Yimin Fang, David Medina, Robert Stockwell, Sam McFadden, Kathleen Quinn, Mackenzie R. Peck, Andrzej Bartke, Kevin N. Hascup, Erin R. Hascup 2021-11-08 2021-11-08 [("doi","10.1101/2021.11.08.467509")] longevity/senolytic/d-q

Senolytic treatment in aged mice clears senescent cell burden leading to functional improvements. We hypothesized that administering senotherapeutics in young adulthood of mice would slow physiological markers of aging through mid-life.

C57BL/6 mice were treated monthly with either Fisetin or a Dasatinib (D) plus Quercetin (Q) cocktail from 4–13 months of age. Fisetin treated male mice had reduced senescence-associated secretory phenotype (SASP), enhanced glucose and energy metabolism, improved cognitive performance, and increased hippocampal expression of adiponectin 1 receptor and glucose transporter 4. D+Q treated females had increased SASP expression along with accumulation of white adipose tissue, reduced energy metabolism, and cognitive performance.

Senotherapeutics in young adulthood, has beneficial, negligible, or detrimental effects in mice dependent upon sex and treatment.

--- https://www.biorxiv.org/content/10.1101/2021.11.08.467616.full Once-daily feeding is associated with better cognitive function and health in companion dogs: Results from the Dog Aging Project Emily E. Bray, Zihan Zheng, M. Katherine Tolbert, Brianah M. McCoy, Dog Aging Project Consortium, Matt Kaeberlein, Kathleen F. Kerr 2021-11-11 2021-11-11 [("doi","10.1101/2021.11.08.467616")] dog longevity/fasting

A variety of diets have been studied for possible anti-aging effects. In particular, studies of isocaloric time-restricted feeding in laboratory rodents have found evidence of beneficial health outcomes. Companion dogs represent a unique opportunity to study diet in a large mammal that shares human environments.

The Dog Aging Project has been collecting data on thousands of companion dogs of all different ages, sizes, and breeds since 2019. We leveraged this diverse cross-sectional dataset to investigate associations between feeding frequency and cognitive function (n = 10,474) as well as 9 broad categories of health outcomes (n = 24,238). Controlling for sex, age, breed, and other potential confounders, we found that dogs fed once daily rather than more frequently had lower mean scores on a cognitive dysfunction scale, and lower odds of having gastrointestinal, dental, orthopedic, kidney/urinary, and liver/pancreas disorders.

Therefore, our findings suggest that once-a-day feeding in dogs is associated with improved health across multiple body systems.

--- https://www.biorxiv.org/content/10.1101/2021.11.08.467664.full Towards a structurally resolved human protein interaction network David F. Burke, Patrick Bryant, Inigo Barrio-Hernandez, Danish Memon, Gabriele Pozzati, Aditi Shenoy, Wensi Zhu, Alistair S. Dunham, Pascal Albanese, Andrew Keller, Richard A. Scheltema, James E. Bruce, Alexander Leitner, Petras Kundrotas, Pedro Beltrao, Arne Elofsson 2021-11-09 2021-11-09 [("doi","10.1101/2021.11.08.467664")] ai/nn/transformer/alphafold

All cellular functions are governed by complex molecular machines that assemble through protein-protein interactions. Their atomic details are critical to the study of their molecular mechanisms but fewer than 5% of hundreds of thousands of human interactions have been structurally characterized.

Here, we test the potential and limitations of recent progress in deep-learning methods using AlphaFold to predict structures for 65,484 human interactions. We show that higher confidence models are enriched in interactions supported by affinity or structure based methods and can be orthogonally confirmed by spatial constraints defined by cross-link data.

We identify 3,137 high confidence models, of which 1,371 have no homology to a known structure, from which we identify interface residues harbouring disease mutations, suggesting potential mechanisms for pathogenic variants. We find groups of interface phosphorylation sites that show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple interactions as signaling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies.

Accurate prediction of protein complexes promises to greatly expand our understanding of the atomic details of human cell biology in health and disease.

--- https://www.biorxiv.org/content/10.1101/2021.11.08.467731.full Lipid nanoparticles incorporating a GalNAc ligand enable in vivo liver ANGPTL3 editing in wild-type and somatic LDLR knockout non-human primates Lisa N. Kasiewicz, Souvik Biswas, Aaron Beach, Huilan Ren, Chaitali Dutta, Anne Marie Mazzola, Ellen Rohde, Alexandra Chadwick, Christopher Cheng, Kiran Musunuru, Sekar Kathiresan, Padma Malyala, Kallanthottathil G. Rajeev, Andrew M. Bellinger 2021-11-08 2021-11-08 [("doi","10.1101/2021.11.08.467731")] genetics/editing

Standard lipid nanoparticles (LNPs) deliver gene editing cargoes to hepatocytes through receptor-mediated uptake via the low-density lipoprotein receptor (LDLR). Homozygous familial hypercholesterolemia (HoFH) is a morbid genetic disease characterized by complete or near-complete LDLR deficiency, markedly elevated blood low-density lipoprotein cholesterol (LDL-C) levels, and premature atherosclerotic cardiovascular disease.

In order to enable in vivo liver gene editing in HoFH patients, we developed a novel LNP delivery technology that incorporates a targeting ligand—N-acetylgalactosamine (GalNAc)—which binds to the asialoglycoprotein receptor (ASGPR). In a cynomolgus monkey (Macaca fascicularis) non-human primate (NHP) model of HoFH created by somatic knockout of the LDLR gene via CRISPR-Cas9, treatment with GalNAc-LNPs formulated with an adenine base editor mRNA and a guide RNA (gRNA) targeting the ANGPTL3 gene yielded ~60% whole-liver editing and ~94% reduction of blood ANGPTL3 protein levels, whereas standard LNPs yielded minimal editing.

Moreover, in wild-type NHPs, the editing achieved by GalNAc-LNPs compared favorably to that achieved by standard LNPs, suggesting that GalNAc-LNP delivery technology may prove useful across a range of in vivo therapeutic applications targeting the liver.

--- https://www.biorxiv.org/content/10.1101/2021.11.09.467900.full Compositional Restricted Boltzmann Machines Unveil the Brain-Wide Organization of Neural Assemblies Thijs L. van der Plas, Jérôme Tubiana, Guillaume Le Goc, Geoffrey Migault, Michael Kunst, Herwig Baier, Volker Bormuth, Bernhard Englitz, Georges Debrégeas 2021-11-11 2021-11-11 [("doi","10.1101/2021.11.09.467900")] psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Patterns of endogenous activity in the brain reflect a stochastic exploration of the neuronal state space that is constrained by the underlying assembly organization of neurons. Yet it remains to be shown that this interplay between neurons and their assembly dynamics indeed suffices to generate whole-brain data statistics.

Here we recorded the activity from ~40,000 neurons simultaneously in zebrafish larvae, and show that a data-driven network model of neuron-assembly interactions can accurately reproduce the mean activity and pairwise correlation statistics of their spontaneous activity. This model, the compositional Restricted Boltzmann Machine, unveils ~200 neural assemblies, which compose neurophysiological circuits and whose various combinations form successive brain states. From this, we mathematically derived an interregional functional connectivity matrix, which is conserved across individual animals and correlates well with structural connectivity.

This novel, assembly-based generative model of brain-wide neural dynamics enables physiology-bound perturbation experiments in silico.

--- https://www.biorxiv.org/content/10.1101/2021.11.14.468508.full The impact of rare germline variants on human somatic mutation processes Mischan Vali-Pour, Ben Lehner, Fran Supek 2021-11-14 2021-11-14 [("doi","10.1101/2021.11.14.468508")] genetics/heritable/rare

Somatic mutations are an inevitable component of ageing and the most important cause of cancer. The rates and types of somatic mutation vary across individuals, but relatively few inherited influences on mutation processes are known.

We performed a comprehensive gene-based rare variant association study with diverse mutational processes, using human cancer genomes from over 11,000 individuals of European ancestry. By combining burden and variance tests, we identify 207 associations involving 15 somatic mutational phenotypes and 42 genes that replicated in an independent data set at a FDR of 1%.

We associated rare inherited deleterious variants in novel genes such as MSH3, EXO1, SETD2, and MTOR with two different forms of DNA mismatch repair deficiency, and variants in genes such as EXO1, PAXIP1, and WRN with deficiency in homologous recombination repair. In addition, we identified associations with other mutational processes, such as APEX1 with APOBEC-signature mutagenesis.

Many of the novel genes interact with each other and with known mutator genes within cellular sub-networks. Considered collectively, damaging variants in the newly-identified genes are prevalent in the population. We suggest that rare germline variation in diverse genes commonly impacts mutational processes in somatic cells.

--- https://www.biorxiv.org/content/10.1101/2021.11.15.467418.full Disentangling signatures of selection before and after European colonization in Latin Americans Javier Mendoza-Revilla, Camilo Chacon-Duque, Macarena Fuentes-Guajardo, Louise Ormond, Ke Wang, Malena Hurtado, Valeria Villegas, Vanessa Granja, Victor Acuña-Alonzo, Claudia Jaramillo, Rodrigo Barquera Lozano, Jorge Gomez-Valdes, Hugo Villamil-Ramirez, Caio C. Silva de Cerqueira, Keyla M. Badillo Rivera, Maria A. Nieves-Colón, Christopher R. Gignoux, Genevieve L. Wojcik, Andrés Moreno-Estrada, Tábita Hunemeier, Virginia Ramallo, Lavinia Schuler-Faccini, Roland Gonzalez-José, Maria Cátira Bortolini, Samuel Canizales-Quinteros, Carla Gallo, Giovanni Poletti, Gabriel Bedoya, Francisco Rothhammer, David Balding, Matteo Fumagalli, Kaustubh Adhikari, Andrés Ruiz-Linares, Garrett Hellenthal 2021-11-19 2021-11-19 [("doi","10.1101/2021.11.15.467418")] genetics/selection/natural/human

Throughout human evolutionary history, large-scale migrations have led to intermixing (ie. admixture) between previously separated human groups. While classical and recent work have shown that studying admixture can yield novel historical insights, the extent to which this process contributed to adaptation remains underexplored.

Here, we introduce a novel statistical model, specific to admixed populations, that identifies loci under selection while determining whether the selection likely occurred post-admixture or prior to admixture in one of the ancestral source populations. Through extensive simulations we show that this method is able to detect selection, even in recently formed admixed populations, and to accurately differentiate between selection occurring in the ancestral or admixed population.

We apply this method to genome-wide SNP data of ~4,000 individuals in five admixed Latin American cohorts from Brazil, Chile, Colombia, Mexico and Peru.

Our approach replicates previous reports of selection in the HLA region that are consistent with selection post-admixture. We also report novel signals of selection in genomic regions spanning 47 genes, reinforcing many of these signals with an alternative, commonly-used local-ancestry-inference approach. These signals include several genes involved in immunity, which may reflect responses to endemic pathogens of the Americas and to the challenge of infectious disease brought by European contact. In addition, some of the strongest signals inferred to be under selection in the Native American ancestral groups of modern Latin Americans overlap with genes implicated in energy metabolism phenotypes, plausibly reflecting adaptations to novel dietary sources available in the Americas.

--- https://www.biorxiv.org/content/10.1101/2021.11.16.468246.full The sequences of 150,119 genomes in the UK biobank Bjarni V. Halldorsson, Hannes P. Eggertsson, Kristjan H. S. Moore, Hannes Hauswedell, Ogmundur Eiriksson, Magnus O. Ulfarsson, Gunnar Palsson, Marteinn T. Hardarson, Asmundur Oddsson, Brynjar O. Jensson, Snaedis Kristmundsdottir, Brynja D. Sigurpalsdottir, Olafur A. Stefansson, Doruk Beyter, Guillaume Holley, Vinicius Tragante, Arnaldur Gylfason, Pall I. Olason, Florian Zink, Margret Asgeirsdottir, Sverrir T. Sverrisson, Brynjar Sigurdsson, Sigurjon A. Gudjonsson, Gunnar T. Sigurdsson, Gisli H. Halldorsson, Gardar Sveinbjornsson, Kristjan Norland, Unnur Styrkarsdottir, Droplaug N. Magnusdottir, Steinunn Snorradottir, Kari Kristinsson, Emilia Sobech, Gudmar Thorleifsson, Frosti Jonsson, Pall Melsted, Ingileif Jonsdottir, Thorunn Rafnar, Hilma Holm, Hreinn Stefansson, Jona Saemundsdottir, Daniel F. Gudbjartsson, Olafur T. Magnusson, Gisli Masson, Unnur Thorsteinsdottir, Agnar Helgason, Hakon Jonsson, Patrick Sulem, Kari Stefansson 2021-11-17 2021-11-17 [("doi","10.1101/2021.11.16.468246")] genetics/heritable/rare genetics/sequencing

We describe the analysis of whole genome sequencing (WGS) of 150,119 individuals from the UK biobank (UKB). This yielded a set of high quality variants, including 585,040,410 SNPs, representing 7.0% of all possible human SNPs, and 58,707,036 indels. The large set of variants allows us to characterize selection based on sequence variation within a population through a Depletion Rank (DR) score for windows along the genome. DR analysis shows that coding exons represent a small fraction of regions in the genome subject to strong sequence conservation.

We define 3 cohorts within the UKB, a large British Irish cohort (XBI) and smaller African (XAF) and South Asian (XSA) cohorts. A haplotype reference panel is provided that allows reliable imputation of most variants carried by 3 or more sequenced individuals. We identified 895,055 structural variants and 2,536,688 microsatellites, groups of variants typically excluded from large scale WGS studies.

Using this formidable new resource, we provide several noteworthy examples of trait associations with rare variants with large effects not found previously through studies based on exome sequencing and/or imputation.

--- https://www.biorxiv.org/content/10.1101/2021.11.22.469596.full Fine-tuning of deep language models as a computational framework of modeling listeners’ perspective during language comprehension Refael Tikochinski, Ariel Goldstein, Yaara Yeshurun, Uri Hasson, Roi Reichart 2021-11-23 2021-11-23 [("doi","10.1101/2021.11.22.469596")] psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Computational Deep Language Models (DLMs) have been shown to be effective in predicting neural responses during natural language processing. This study introduces a novel computational framework, based on the concept of fine-tuning (Hinton, 2007), for modeling differences in interpretation of narratives based on the listeners’ perspective (ie. their prior knowledge, thoughts, and beliefs).

We draw on an fMRI experiment conducted by Yeshurun et al 2017, in which two groups of listeners were listening to the same narrative but with two different perspectives (cheating versus paranoia). We collected a dedicated dataset of ~3000 stories, and used it to create two modified (fine-tuned) versions of a pre-trained DLM, each representing the perspective of a different group of listeners.

Information extracted from each of the two fine-tuned models was better fitted with neural responses of the corresponding group of listeners. Furthermore, we show that the degree of difference between the listeners’ interpretation of the story—as measured both neurally and behaviorally—can be approximated using the distances between the representations of the story extracted from these two fine-tuned models.

These models-brain associations were expressed in many language-related brain areas, as well as in several higher-order areas related to the default-mode and the mentalizing networks, therefore implying that computational fine-tuning reliably captures relevant aspects of human language comprehension across different levels of cognitive processing.

--- https://www.biorxiv.org/content/10.1101/2021.11.24.469902.full Exploring the relationships between autozygosity, educational attainment, and cognitive ability in a contemporary, trans-ancestral American sample Sarah M. C. Colbert, Matthew C. Keller, Arpana Agrawal, Emma C. Johnson 2021-11-29 2021-11-29 [("doi","10.1101/2021.11.24.469902")] genetics/heritable/rare iq/ses

Previous studies have found statistically-significant associations between estimated autozygosity—the proportion of an individual’s genome contained in homozygous segments due to distant inbreeding—and multiple traits, including educational attainment (EA) and cognitive ability. In one study, estimated autozygosity showed a stronger association with parental EA than the subject’s own EA. This was likely driven by parental EA’s association with mobility: more educated parents tended to migrate further from their hometown, therefore choosing more genetically diverse partners.

We examined the associations between estimated autozygosity, cognitive ability, and parental EA in a contemporary sub-sample of adolescents from the Adolescent Brain and Cognitive Development Study™ (ABCD Study®) (analytic n = 6,504).

We found a negative association between autozygosity and child cognitive ability consistent with previous studies, while the associations between autozygosity and parental EA were in the expected direction of effect (with greater levels of autozygosity being associated with lower EA) but the effect sizes were weaker than those estimated in previous work. We also found a lower mean level of autozygosity in the ABCD sample compared to previous autozygosity studies, which may reflect overall decreasing levels of autozygosity over generations.

Variation in migration and mobility patterns in the ABCD study compared to other studies may explain the pattern of associations between estimated autozygosity, EA, and cognitive ability in the current study.

--- https://www.biorxiv.org/content/10.1101/2021.11.26.470088.full Evolution of Human-specific Alleles Protecting Cognitive Function of Grandmothers Sudeshna Saha, Naazneen Khan, Troy Comi, Andrea Verhagen, Aniruddha Sasmal, Sandra Diaz, Hai Yu, Xi Chen, Joshua M. Akey, Martin Frank, Pascal Gagneux, Ajit Varki 2021-11-26 2021-11-26 [("doi","10.1101/2021.11.26.470088")] genetics/selection/natural/human psychiatry/alzheimers psychology/neuroscience

Late-onset Alzheimer’s Disease (LOAD) pathology is rare in our closest living evolutionary relatives (chimpanzees), which also express much lower microglial levels of CD33(Siglec-3), a myelomonocytic receptor inhibiting innate immune reactivity by extracellular V-set domain recognition of sialic acid(Sia)-containing self-associated molecular patterns (SAMPs). We earlier showed that V-set domain-deficient CD33 variant allele, protective against LOAD, is derived and specific to hominin-lineage. We now report that CD33 also harbors multiple hominin-specific V-set domain mutations and explore selection forces that may have favored such genomic changes. N-glycolylneuraminic acid (Neu5Gc), the preferred Sia-ligand of ancestral CD33 is absent in humans, due to hominin-specific, fixed loss-of-function mutation in CMAH, which generates CMP-Neu5Gc from its precursor, CMP-N-acetylneuraminic acid (Neu5Ac).

Extensive mutational analysis and MD-simulations indicate that fixed change in amino acid 21 of hominin V-set domain and conformational changes related to His45 corrected for Neu5Gc-loss by switching to Neu5Ac-recognition. Considering immune-evasive molecular mimicry of SAMPs by pathogens, we found that human-specific pathogens Neisseria gonorrhoeae and Group B Streptococcus (affecting fertility and fetuses/neonates respectively) selectively bind huCD33 and this binding is impacted by amino acid 21 modification. Alongside LOAD-protective CD33 alleles, humans harbor additional, derived, population-universal, cognition-protective variants absent in great ape genomes.

Interestingly, 11/13 SNPs in these human genes (including CD33), that protect the cognitive health of elderly populations, are not shared by genomes of archaic hominins: Neanderthals and Denisovans.

Finally, we present a plausible evolutionary scenario to compile, correlate and comprehend existing knowledge about huCD33 evolution and suggest that grandmothering emerged in humans.

--- https://www.biorxiv.org/content/10.1101/2021.11.30.469839.full Emergent multilevel selection in a simple spatial model of the evolution of altruism Rutger Hermsen 2021-12-01 2021-12-01 [("doi","10.1101/2021.11.30.469839")] genetics/selection/natural

Theories on the evolutionary origins of altruistic behavior have a long history and have become a canonical part of the theory of evolution. Nevertheless, the mechanisms that allow altruism to appear and persist are still incompletely understood. The spatial structure of populations is known to be an important determinant. In both theoretical and experimental studies, much attention has been devoted to populations that are subdivided into discrete groups. Such studies typically imposed the structure and dynamics of the groups by hand.

Here, we instead present a simple individual-based model in which organisms spontaneously self-organize into spatially separated colonies that themselves reproduce by binary fission and hence behave as Darwinian entities in their own right. Using software to automatically track the rise and fall of colonies, we are able to apply formal theory on multilevel selection and thus quantify the within-group and among-group dynamics.

This reveals that individual colonies inevitably succumb to defectors, resulting in within-colony “tragedies of the commons”. Even so, altruism persists in the population because more altruistic colonies reproduce more frequently. The emergence of the colonies themselves depends crucially on the length scales of motility, altruism, and competition.

This reconfirms the general relevance of these scales for social evolution, but also stresses that their impact can only be understood fully in the light of the emergent eco-evolutionary spatial patterns. The results also demonstrate that emergent spatial population patterns can function as a starting point for transitions of individuality.

--- https://www.biorxiv.org/content/10.1101/2021.12.02.471005.full In vitro neurons learn and exhibit sentience when embodied in a simulated game-world Brett J. Kagan, Andy C. Kitchen, Nhi T. Tran, Bradyn J. Parker, Anjali Bhat, Ben Rollo, Adeel Razi, Karl J. Friston 2021-12-03 2021-12-03 [("doi","10.1101/2021.12.02.471005")] psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Integrating neurons into digital systems to leverage their innate intelligence may enable performance infeasible with silicon alone, along with providing insight into the cellular origin of intelligence.

We developed DishBrain, a system which exhibits natural intelligence by harnessing the inherent adaptive computation of neurons in a structured environment. In vitro neural networks from human or rodent origins, are integrated with in silico computing via high-density multielectrode array. Through electrophysiological stimulation and recording, cultures were embedded in a simulated game-world, mimicking the arcade game Pong.

Applying a previously untestable theory of active inference via the Free Energy Principle, we found that learning was apparent within 5 minutes of real-time gameplay, not observed in control conditions. Further experiments demonstrate the importance of closed-loop structured feedback in eliciting learning over time.

Cultures display the ability to self-organize in a goal-directed manner in response to sparse sensory information about the consequences of their actions.

--- https://www.biorxiv.org/content/10.1101/2021.12.08.471751.full Polynomial Mendelian Randomization reveals widespread non-linear causal effects in the UK Biobank Jonathan Sulc, Jenny Sjaarda, Zoltán Kutalik 2021-12-10 2021-12-10 [("doi","10.1101/2021.12.08.471751")] genetics/heritable/correlation/mendelian-randomization

Causal inference is a critical step in improving our understanding of biological processes and Mendelian Randomization (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the 3 core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear.

Here we propose PolyMR, an MR-based method which provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods.

We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes and found most of these (84%) to have causal effects which deviate from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (eg. a 1 kg⁄m^2 change in BMI having stronger effects on glucose levels if the initial BMI was higher), to non-monotonic causal relationships (eg. the effects of BMI on cholesterol forming an inverted U shape).

Finally, we show that the linearity assumption of the causal effect may lead to the misinterpretation of health risks at the individual level or heterogeneous effect estimates when using cohorts with differing average exposure levels.

--- https://www.biorxiv.org/content/10.1101/2021.12.10.472095.full A New Gene Set Identifies Senescent Cells and Predicts Senescence-Associated Pathways Across Tissues Dominik Saul, Robyn Laura Kosinsky, Elizabeth J. Atkinson, Madison L. Doolittle, Xu Zhang, Nathan K. LeBrasseur, Robert J. Pignolo, Paul D. Robbins, Laura J. Niedernhofer, Yuji Ikeno, Diana Jurk, João F. Passos, LaTonya J. Hickson, Ailing Xue, David G. Monroe, Tamara Tchkonia, James L. Kirkland, Joshua N. Farr, Sundeep Khosla 2021-12-11 2021-12-11 [("doi","10.1101/2021.12.10.472095")] longevity/senolytic

Although cellular senescence is increasingly recognized as driving multiple age-related co-morbidities through the senescence-associated secretory phenotype (SASP), in vivo senescent cell identification, particularly in bulk or single cell RNA-sequencing (scRNA-seq) data remains challenging. Here, we generated a novel gene set (SenMayo) and first validated its enrichment in bone biopsies from two aged human cohorts. SenMayo also identified senescent cells in aged murine brain tissue, demonstrating applicability across tissues and species.

For direct validation, we demonstrated reductions in SenMayo in bone following genetic clearance of senescent cells in mice, with similar findings in adipose tissue from humans in a pilot study of pharmacological senescent cell clearance. In direct comparisons, SenMayo outperformed all six existing senescence/SASP gene sets in identifying senescent cells across tissues and in demonstrating responses to senescent cell clearance.

We next used SenMayo to identify senescent hematopoietic or mesenchymal cells at the single cell level from publicly available human and murine bone marrow/bone scRNA-seq data and identified monocytic and osteolineage cells, respectively, as showing the highest levels of senescence/SASP genes. Using pseudotime and cellular communication patterns, we found senescent hematopoietic and mesenchymal cells communicated with other cells through common pathways, including the Macrophage Migration Inhibitory Factor (MIF) pathway, which has been implicated not only in inflammation but also in immune evasion, an important property of senescent cells.

Thus, SenMayo identifies senescent cells across tissues and species with high fidelity. Moreover, using this senescence panel, we were able to characterize senescent cells at the single cell level and identify key intercellular signaling pathways associated with these cells, which may be particularly useful for evolving efforts to map senescent cells (eg. SenNet). In addition, SenMayo represents a potentially clinically applicable panel for monitoring senescent cell burden with aging and other conditions as well as in studies of senolytic drugs.

--- https://www.biorxiv.org/content/10.1101/2021.12.13.472494.full The Geometry of Representational Drift in Natural and Artificial Neural Networks Kyle Aitken, Marina Garrett, Shawn Olsen, Stefan Mihalas 2021-12-15 2021-12-15 [("doi","10.1101/2021.12.13.472494")] psychology/neuroscience reinforcement-learning/meta-learning/continual-learning

Neurons in sensory areas encode/represent stimuli. Surprisingly, recent studies have suggested that, even during persistent performance, these representations are not stable and change over the course of days and weeks. This phenomenon has been termed “representational drift.”

We examine stimulus representations from fluorescence recordings across hundreds of neurons in the visual cortex using in vivo two-photon calcium imaging and we corroborate previous studies finding that such representations change as experimental trials are repeated across days.

In this study, we geometrically characterize the properties of representational drift in the primary visual cortex of mice in two open datasets from the Allen Institute and propose a potential mechanism behind such drift. We observe representational drift both for passively presented stimuli, as well as for stimuli which are behaviorally relevant. Across experiments, the drift most often occurs along directions that have the most variance, leading to a turnover in the neurons used for a given representation. Interestingly, despite this change due to drift, linear classifiers trained to distinguish neuronal representations show little to no degradation in performance across days.

The features we observe in the neural data are similar to properties of artificial neural networks where representations are updated by continual learning in the presence of dropout, i.e. a random masking of nodes/weights, but not other types of noise. Therefore, we conclude that a potential reason for the representational drift in biological networks is driven by an underlying dropout-like noise while continuously learning and that such a mechanism may be computationally advantageous for the brain in the same way it is for artificial neural networks, eg. preventing overfitting.

--- https://www.biorxiv.org/content/10.1101/2021.12.16.472951.full The costs and benefits of dispersal in small populations Jitka Polechova 2021-12-16 2021-12-16 [("doi","10.1101/2021.12.16.472951")] genetics/selection/natural reinforcement-learning/exploration

Dispersal has 3 major effects on adaptation. First, the gene flow mixes alleles adapted to different environments, potentially hindering (swamping) adaptation. Second, it inflates genetic variance: this aids adaptation to spatially (and temporally) varying environments but if selection is hard, it lowers the mean fitness of the population. Third, neighbourhood size, which determines how weak genetic drift is, increases with dispersal—when genetic drift is strong, increase of neighbourhood size with dispersal aids adaptation.

In this note I focus on the role of dispersal in environments which change smoothly across space, and when local populations are quite small such that genetic drift has a statistically-significant effect. Using individual-based simulations, I show that in small populations, even leptokurtic dispersal benefits adaptation, by reducing the power of genetic drift. This has implications for management of small marginal populations: increased gene flow appears beneficial as long as adaptations involves a quantitative, rather than a discrete, trait. However, heavily leptokurtic dispersal will swamp continuous adaptation along steep environmental gradients so that only patches of locally adapted subpopulations remain.

--- https://www.biorxiv.org/content/10.1101/2021.12.17.473150.full CONGA: Copy number variation genotyping in ancient genomes and low-coverage sequencing data Arda Soylev, Sevim Seda Cokoglu, Dilek Koptekin, Can Alkan, Mehmet Somel 2021-12-17 2021-12-17 [("doi","10.1101/2021.12.17.473150")] genetics/heritable/rare

To date, ancient genome analyses have been largely confined to the study of single-nucleotide polymorphisms (SNPs). Copy number variants (CNVs) are a major contributor of disease and of evolutionary adaptation, but identifying CNVs in ancient shotgun-sequenced genomes is hampered by (a) most published genomes being <1× coverage, (2) ancient DNA fragments being typically <80 bps. These characteristics preclude state-of-the-art CNV detection software to be effectively applied to ancient genomes.

Here we present CONGA, an algorithm tailored for genotyping deletion and duplication events in genomes with low depths of coverage. Simulations show that CONGA can genotype deletions and duplications >1 Kbps with F-scores >0.77 and >0.82, respectively at ≥0.5×. Further, down-sampling experiments using published ancient BAM files reveal that >1 Kbps deletions could be genotyped at F-score >0.75 at ≥1× coverage.

Using CONGA, we analyse deletion events at 10,018 loci in 56 ancient human genomes spanning the last 50,000 years, with coverages 0.4×-26×. We find inter-individual genetic diversity measured using deletions and SNPs to be highly correlated, suggesting that deletion frequencies broadly reflect demographic history. We also identify signatures of purifying selection on deletions, such as an excess of singletons compared to those in SNPs.

CONGA paves the way for systematic studies of drift, mutation load, and adaptation in ancient and modern-day gene pools through the lens of CNVs.

--- https://www.biorxiv.org/content/10.1101/2021.12.19.470191.full Parental provisioning drives brain size in birds Michael Griesser, Szymon M. Drobniak, Sereina M. Graber, Carel P. van Schaik 2021-12-21 2021-12-21 [("doi","10.1101/2021.12.19.470191")] psychology/animal/bird/neuroscience psychology/neuroscience

Larger brains should be adaptive because they support numerous ecological and socio-cognitive benefits, but these benefits explain only a modest part of the interspecific variation in brain size. Notably underexplored are the high energetic costs of developing brains, and thus the possible role of parental provisioning in the evolution of adult brain size.

We explore this idea in birds, which show considerable variation in both socio-ecological traits and the energy transfer from parents to offspring. Comparative analyses of 1,176 bird species show that the combination of adult body mass, mode of development at hatching, relative egg mass, and the time spent provisioning the young in combination strongly predict relative brain size across species. Adding adult ecological and socio-codult ecological and socio-cognitive predictors only marginally adds explanatory value.

We therefore conclude that parental provisioning enabled bird species to evolve into skill-intensive niches, reducing interspecific competition and consequently promoting survival prospects and population stability. Critically, parental provisioning also explains why precocial bird species have smaller brains than altricial ones. Finally, these results suggest that the cognitive adaptations that provide the behavioral flexibility to improve reproductive success and survival are intrinsically linked to successful parental provisioning. Our findings also suggest that the traditionally assessed cognitive abilities may not predict relative brain size.

--- https://www.biorxiv.org/content/10.1101/2021.12.20.473199.full The origins and functional effects of postzygotic mutations throughout the human lifespan Nicole B. Rockweiler, Avinash Ramu, Liina Nagirnaja, Wing H. Wong, Michiel J. Noordam, Casey W. Drubin, Ni Huang, Brian Miller, Ellen Z. Todres, Katinka A. Vigh-Conrad, Antonino Zito, Kerrin S. Small, Kristin G. Ardlie, Barak A. Cohen, Donald F. Conrad 2021-12-21 2021-12-21 [("doi","10.1101/2021.12.20.473199")] genetics/heritable/rare

Postzygotic mutations (PZMs) begin to accrue in the human genome immediately after fertilization, but how and when PZMs affect development and lifetime health remains unclear. To study the origins and functional consequences of PZMs, we generated a multi-tissue atlas of PZMs from 948 donors using the final major release of the Genotype-Tissue Expression (GTEx) project. Nearly half the variation in mutation burden among tissue samples can be explained by measured technical and biological effects, while 9% can be attributed to donor-specific effects.

Through phylogenetic reconstruction of PZMs, we find that their type and predicted functional impact varies during prenatal development, across tissues, and the germ cell lifecycle. Remarkably, a class of prenatal mutations was predicted to be more deleterious than any other category of genetic variation investigated and under positive selection as strong as somatic mutations in cancers.

In total, the data indicate that PZMs can contribute to phenotypic variation throughout the human lifespan, and, to better understand the relationship between genotype and phenotype, we must broaden the long-held assumption of one genome per individual to multiple, dynamic genomes per individual.

--- https://www.biorxiv.org/content/10.1101/2021.12.27.474305.full Recent selection is a major force driving cancer evolution Langyu Gu, Guofen Yang 2021-12-28 2021-12-28 [("doi","10.1101/2021.12.27.474305")] genetics/selection/natural/human

Cancer is one of the most threatening diseases to humans. Understanding the evolution of cancer genes is helpful for therapy management. However, systematic investigation of the evolution of cancer driver genes is sparse. Using comparative genomic analysis, population genetics analysis and computational molecular evolutionary analysis, we detected the evolution of 568 cancer driver genes of 66 cancer types across the primate phylogeny (long timescale selection), and in modern human populations from the 1,000 human genomics project (recent selection).

We found that recent selection pressures, rather than long timescale selection, affect the evolution of cancer driver genes in humans. Cancer driver genes related to morphological traits and local adaptation are under positive selection in different human populations. The African population showed the largest extent of divergence compared to other populations.

It is worth noting that the corresponding cancer types of positively selected genes exhibited population-specific patterns, with the South Asian population possessing the least numbers of cancer types. This helps explain why the South Asian population usually has low cancer incidence rates. Population-specific patterns of cancer types whose driver genes are under positive selection also give clues to explain discrepancies of cancer incidence rates in different geographical populations, such as the high incidence rate of Wilms tumour in the African population and of Ewing’s sarcomas in the European population.

Our findings are thus helpful for understanding cancer evolution and providing guidance for further precision medicine.

--- https://www.biorxiv.org/content/10.1101/2021.12.29.474457.full Effects of periodic bottlenecks on the dynamics of adaptive evolution in microbial populations Minako Izutsu, Devin M. Lake, Zachary W. D. Matson, Jack P. Dodson, Richard E. Lenski 2021-12-30 2021-12-30 [("doi","10.1101/2021.12.29.474457")] genetics/selection/artificial

Population bottlenecks are common in nature, and they can impact the rate of adaptation in evolving populations. On the one hand, each bottleneck reduces the genetic variation that fuels adaptation. On the other hand, fewer founders can undergo more generations and leave more descendants in a resource-limited environment, which allows surviving beneficial mutations to spread more quickly.

Here we investigate the impact of repeated bottlenecks on the dynamics of adaptation in experimental populations of Escherichia coli. We propagated 48 populations under 4 dilution regimes (2×, 8×, 100×, and 1,000×), all reaching the same final size each day, for 150 days. A simple model in which adaptation is limited by the supply rate of beneficial mutations predicts that fitness gains should be maximized with ~8× dilutions. The model also assumes that selection acts only on the overall growth rate and is otherwise identical across dilution regimes.

However, we found that selection in the 2× regime was qualitatively different from the other treatments. Moreover, we observed earlier and greater fitness gains in the populations subjected to 100× & 1,000× dilutions than in those that evolved in the 8× regime.

We also ran simulations using parameters estimated independently from a long-term experiment using the same ancestral strain and environment. The simulations produced dynamics similar to our empirical results under these regimes, and they indicate that the simple model fails owing to the assumption that the supply of beneficial mutations limits adaptation.

--- https://www.biorxiv.org/content/10.1101/2022.01.04.474849.full Texture-like representation of objects in human visual cortex Akshay V. Jagadeesh, Justin L. Gardner 2022-01-05 2022-01-05 [("doi","10.1101/2022.01.04.474849")] psychology/neuroscience psychology/vision

The human visual ability to recognize objects and scenes is widely thought to rely on representations in category-selective regions of visual cortex. These representations could support object vision by specifically representing objects, or, more simply, by representing complex visual features regardless of the particular spatial arrangement needed to constitute real world objects. That is, by representing visual textures.

To discriminate between these hypotheses, we leveraged an image synthesis approach that, unlike previous methods, provides independent control over the complexity and spatial arrangement of visual features. We found that human observers could easily detect a natural object among synthetic images with similar complex features that were spatially scrambled. However, observer models built from BOLD responses from category-selective regions, as well as a model of macaque inferotemporal cortex and Imagenet-trained deep convolutional neural networks, were all unable to identify the real object. This inability was not due to a lack of signal-to-noise, as all of these observer models could predict human performance in image categorization tasks.

How then might these texture-like representations in category-selective regions support object perception? An image-specific readout from category-selective cortex yielded a representation that was more selective for natural feature arrangement, showing that the information necessary for object discrimination is available. Thus, our results suggest that the role of human category-selective visual cortex is not to explicitly encode objects but rather to provide a basis set of texture-like features that can be infinitely reconfigured to flexibly learn and identify new object categories.

--- https://www.biorxiv.org/content/10.1101/2022.01.07.475305.full A Saturated Map of Common Genetic Variants Associated with Human Height from 5.4 Million Individuals of Diverse Ancestries Loïc Yengo, Sailaja Vedantam, Eirini Marouli, Julia Sidorenko, Eric Bartell, Saori Sakaue, Marielisa Graff, Anders U. Eliasen, Yunxuan Jiang, Sridharan Raghavan, Jenkai Miao, Joshua D. Arias, Ronen E. Mukamel, Cassandra N. Spracklen, Xianyong Yin, Shyh-Huei Chen, Teresa Ferreira, Yingjie Ji, Tugce Karedera, Kreete Lull, Kuang Lin, Deborah E. Malden, Carolina Medina-Gomez, Moara Machado, Amy Moore, Sina Rueger, GIANT Consortium, 23andMe, V. A. Million Veteran Program, DiscovEHR (DiscovEHR, MyCode Community Health Initiative), eMERGE (Electronic Medical Records, Genomics Network), Lifelines Cohort Study, Regeneron Genetics Center, The PRACTICAL Consortium, Understanding Society Scientific Group, Daniel I. Chasman, Yoon Shin Cho, Iris M. Heid, Mark I. McCarthy, Maggie C. Y. Ng, Christopher J. O’Donnell, Fernando Rivadeneira, Unnur Thorsteinsdottir, Yan V. Sun, E. Shyong Thai, Michael Boehnke, Panos Deloukas, Anne E. Justice, Cecilia M. Lindgren, Ruth Loos, Karen L. Mohlke, Kari E. North, Kari Stefansson, Robin G. Walters, Thomas W. Winkler, Kristin L. Young, Po-Ru Loh, Jian Yang, Tõnu Esko, Themistocles L. Assimes, Adam Auton, Gonçalo Abecasis, Cristen Jennifer Willer, Adam E. Locke, Sonja I. Berndt, Guillaume Lettre, Timothy Frayling, Yukinori Okada, Andrew R. Wood, Peter M. Visscher, Joel N. Hirschhorn 2022-01-10 2022-01-10 [("doi","10.1101/2022.01.07.475305")] genetics/heritable

Common SNPs are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes.

Here we show, using GWAS data from 5.4 million individuals of diverse ancestries, that:

12,111 independent SNPs that are statistically-significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a median size of ~90 kb, covering ~21% of the genome. The density of independent associations varies across the genome and the regions of elevated density are enriched for biologically relevant genes.

In out-of-sample estimation and prediction, the 12,111 SNPs account for 40% of phenotypic variance in European ancestry populations but only ~10%–20% in other ancestries. Effect sizes, associated regions, and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely explained by linkage disequilibrium and allele frequency differences within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than needed to implicate causal genes and variants.

Overall, this study, the largest GWAS to date, provides an unprecedented saturated map of specific genomic regions containing the vast majority of common height-associated variants.

--- https://www.biorxiv.org/content/10.1101/2022.01.12.476030.full Rare Genetic Variants Correlate with Better Processing Speed Zeyuan Song, Anastasia Gurinovich, Marianne Nygaard, Jonas Mengel-From, Stacy Andersen, Stephanie Cosentino, Nicole Schupfs, Joseph Lee, Joseph Zmuda, Svetlana Ukraintseva, Konstantin Arbeev, Kaare Christensen, Thomas Perls, Paola Sebastiani 2022-01-12 2022-01-12 [("doi","10.1101/2022.01.12.476030")] genetics/heritable/rare iq longevity

We conducted a genome-wide association study (GWAS) of Digit Symbol Substitution Test (DSST) scores administered in 4207 family members of the Long Life Family Study (LLFS). Genotype data were imputed to the HRC panel of 64,940 haplotypes resulting in ~15M genetic variants with quality score > 0.7. The results were replicated using genetic data imputed to the 1,000 Genomes phase 3 reference panel from two Danish twin cohorts: the study of Middle Aged Danish Twins and the Longitudinal Study of Aging Danish Twins.

The GWAS in LLFS discovered 20 rare genetic variants (minor allele frequency (MAF) < 1.0%) that reached genome-wide statistical-significance (p-value < 5×10−8). Among these, 18 variants had large protective effects on the processing speed, including rs7623455, rs9821776, rs9821587, rs78704059 on chromosome 3, which were replicated in the combined Danish twin cohort.

These SNPs are located in/near two genes, THRB and RARB, that belonged to thyroid hormone receptors family that may influence speed of metabolism and cognitive aging. The gene-level tests in LLFS confirmed that these two genes are associated with processing speed.

--- https://www.biorxiv.org/content/10.1101/2022.01.17.476687.full Assessing the response to genomic selection by simulation Harimurti Buntaran, Angela Maria Berna-Vasquez, Andres Gordillo, Valentin Wimmer, Morten Sahr, Hans-Peter Piepho 2022-01-20 2022-01-20 [("doi","10.1101/2022.01.17.476687")] genetics/selection/artificial statistics/order

The goal of any plant breeding program is to maximize genetic gain for traits of interest.

In classical quantitative genetics, the genetic gain can be obtained from what is known as the Breeder’s equation. In the past, only phenotypic data was used to compute the genetic gain. The advent of genomic prediction has opened the door to the utilization of dense markers for estimating genomic breeding values or GBV. The salient feature of genomic prediction is the possibility to carry out genomic selection with the assistance of the kinship matrix, hence, improving the prediction accuracy and accelerating the breeding cycle. However, estimates of GBV as such do not provide the full information on the number of entries to be selected as in the classical response to selection.

In this paper, we use simulation, based on a fitted mixed model for genomic prediction in a multi-environmental framework, to answer two typical questions of a plant breeder: (1) How many entries need to be selected to have a defined probability of selecting the truly best entry from the population; (2) What is the probability of obtaining the truly best entries when some top-ranked entries are selected.

--- https://www.biorxiv.org/content/10.1101/2022.01.18.476751.full The accuracy of protein structures in solution determined by AlphaFold and NMR Nicholas J. Fowler, Mike P. Williamson 2022-01-20 2022-01-20 [("doi","10.1101/2022.01.18.476751")] ai/nn/transformer/alphafold

In the recent CASP (Critical Assessment of Structure Prediction) competition, AlphaFold performed outstandingly. Its worst predictions were for NMR structures, which has two alternative explanations: either the NMR structures were poor, implying that AlphaFold may be more accurate than NMR; or there is a genuine difference between crystal and solution structures.

Here, we use the program ANSURR, which measures the accuracy of solution structures, and show that one of the NMR structures was indeed poor. We then compare AlphaFold predictions to NMR structures, and show that AlphaFold tends to be more accurate than NMR ensembles, in particular correctly more rigid in loops.

There are however some cases where the NMR ensembles are more accurate. These tend to be dynamic structures where AlphaFold had low confidence.

We suggest that AlphaFold could be used as the model for NMR structure refinements, and that AlphaFold structures validated by ANSURR require no further refinement.

--- https://www.biorxiv.org/content/10.1101/2022.01.20.477063.full Multi-omic rejuvenation of naturally aged tissues by a single cycle of transient reprogramming Dafni Chondronasiou, Diljeet Gill, Lluc Mosteiro, Rocio G. Urdinguio, Antonio Berenguer, Monica Aguilera, Sylvere Durand, Fanny Aprahamian, Nitharsshini Nirmalathasan, Maria Abad, Daniel E. Martin-Herranz, Camille Stephan-Otto Attolini, Neus Prats, Guido Kroemer, Mario F. Fraga, Wolf Reik, Manuel Serrano 2022-01-21 2022-01-21 [("doi","10.1101/2022.01.20.477063")] longevity/epigenetics

The expression of the pluripotency factors OCT4, SOX2, KLF4 and MYC (OSKM) can convert somatic differentiated cells into pluripotent stem cells in a process known as reprogramming. Notably, cycles of brief OSKM expression do not change cell identity but can reverse markers of aging in cells and extend longevity in progeroid mice. However, little is known about the mechanisms involved.

Here, we have studied changes in the DNA methylome, transcriptome and metabolome in naturally aged mice subject to a single period of transient OSKM expression. We found that this is sufficient to reverse DNA methylation changes that occur upon aging in the pancreas, liver, spleen and blood. Similarly, we observed reversion of transcriptional changes, especially regarding biological processes known to change during aging. Finally, some serum metabolites altered with aging were also restored to young levels upon transient reprogramming.

These observations indicate that a single period of OSKM expression can drive epigenetic, transcriptomic and metabolomic changes towards a younger configuration in multiple tissues and in the serum.

--- https://www.biorxiv.org/content/10.1101/2022.01.20.477125.full Active Predictive Coding Networks: A Neural Solution to the Problem of Learning Reference Frames and Part-Whole Hierarchies Dimitrios C. Gklezakos, Rajesh P. N. Rao 2022-01-21 2022-01-21 [("doi","10.1101/2022.01.20.477125")] ai/nn/rnn psychology/neuroscience reinforcement-learning/meta-learning

We introduce Active Predictive Coding Networks (APCNs), a new class of neural networks that solve a major problem posed by Hinton and others in the fields of artificial intelligence and brain modeling: how can neural networks learn intrinsic reference frames for objects and parse visual scenes into part-whole hierarchies by dynamically allocating nodes in a parse tree? APCNs address this problem by using a novel combination of ideas: (1) hypernetworks are used for dynamically generating recurrent neural networks that predict parts and their locations within intrinsic reference frames conditioned on higher object-level embedding vectors, and (2) reinforcement learning is used in conjunction with backpropagation for end-to-end learning of model parameters. The APCN architecture lends itself naturally to multilevel hierarchical learning and is closely related to predictive coding models of cortical function.

Using the MNIST, Fashion-MNIST and Omniglot datasets, we demonstrate that APCNs can (a) learn to parse images into part-whole hierarchies, (b) learn compositional representations, and (c) transfer their knowledge to unseen classes of objects.

With their ability to dynamically generate parse trees with part locations for objects, APCNs offer a new framework for explainable AI that leverages advances in deep learning while retaining interpretability and compositionality.

--- https://www.biorxiv.org/content/10.1101/2022.01.27.477911.full Heterochronic parabiosis reprograms the mouse brain transcriptome by shifting aging signatures in multiple cell types Methodios Ximerakis, Kristina M. Holton, Richard M. Giadone, Ceren Ozek, Monika Saxena, Samara Santiago, Xian Adiconis, Danielle Dionne, Lan Nguyen, Kavya M. Shah, Jill M. Goldstein, Caterina Gasperini, Scott L. Lipnick, Sean K. Simmons, Sean M. Buchanan, Amy J. Wagers, Aviv Regev, Joshua Z. Levin, Lee L. Rubin 2022-01-28 2022-01-28 [("doi","10.1101/2022.01.27.477911")] longevity/epigenetics

Aging is a complex process involving transcriptomic changes associated with deterioration across multiple tissues and organs, including the brain. Recent studies using heterochronic parabiosis have shown that various aspects of aging-associated decline are modifiable or even reversible.

To better understand how this occurs, we performed single-cell transcriptomic profiling of young and old mouse brains following parabiosis. For each cell type, we catalogued alterations in gene expression, molecular pathways, transcriptional networks, ligand-receptor interactions, and senescence status.

Our analyses identified gene signatures demonstrating that heterochronic parabiosis regulates several hallmarks of aging in a cell-type-specific manner. Brain endothelial cells were found to be especially malleable to this intervention, exhibiting dynamic transcriptional changes that affect vascular structure and function.

These findings suggest novel strategies for slowing deterioration and driving regeneration in the aging brain through approaches that do not rely on disease-specific mechanisms or actions of individual circulating factors.

--- https://www.biorxiv.org/content/10.1101/2022.01.28.478251.full Nematodes can survive in a suspended form of life for indefinite time Anastasia Shatilovich, Vamshidhar R. Gade, Martin Pippel, Tarja T. Hoffmeyer, Alexei V. Tchesunov, Lewis Stevens, Sylke Winkler, Graham M. Hughes, Sofia Traikov, Michael Hiller, Elizaveta Rivkina, Philipp H. Schiffer, Eugene W. Myers, Teymuras V. Kurzchalia 2022-01-28 2022-01-28 [("doi","10.1101/2022.01.28.478251")] cryonics

When environmental conditions are unfavorable, such as the complete absence of water or oxygen, high temperature, freezing or extreme salinity, some organisms can enter suspended animation (cryptobiosis)1. This reversible transition is preceded by execution of complex genetic and biochemical programs (preconditioning)2,3,4. Under laboratory conditions, however, animals have only been maintained in a viable cryptobiotic state for a short time.

Here we show that desiccation followed by freezing allows C. elegans dauer larvae to retain full viability over very long periods (around 500 days). Consistent with this finding, recently nematode individuals have been reanimated from the Siberian permafrost5, that according to precise radiocarbon dating shows that they remained in cryptobiosis since the late Pleistocene, for about 46,000 years.

Phylogenomic inference based on our high-quality genome assembly and morphological analysis demonstrate that these nematodes belong to a novel parthenogenetic species, which we named Panagrolaimus kolymaensis. Genome analysis revealed that the core of the molecular toolkit for cryptobiosis in P. kolymaensis and C. elegans is orthologous. To survive desiccation and freezing under laboratory conditions these two species display similar biochemical responses.

Thus, nematodes possess extraordinarily robust adaptive mechanisms that potentially allow them to remain in suspended animation over geological time scales.

--- https://www.biorxiv.org/content/10.1101/2022.02.05.479237.full The genetics of specific cognitive abilities Francesca Procopio, Quan Zhou, Ziye Wang, Agnieska Gidziela, Kaili Rimfeld, Margherita Malanchini, Robert Plomin 2022-02-08 2022-02-08 [("doi","10.1101/2022.02.05.479237")] genetics/heritable iq

Most research on individual differences in performance on tests of cognitive ability focuses on general cognitive ability (g), the highest level in the three-level Cattell-Horn-Carroll (CHC) hierarchical model of intelligence. About 50% of the variance of g is due to inherited DNA differences (heritability) which increases across development. Much less is known about the genetics of the middle level of the CHC model, which includes 16 broad factors such as fluid reasoning, processing speed, and quantitative knowledge.

We provide a meta-analytic review of 863,041 monozygotic-dizygotic twin comparisons from 80 publications for these middle-level factors, which we refer to as specific cognitive abilities (SCA). Twin comparisons were available for 11 of the 16 CHC domains. The average heritability across all SCA is 55%, similar to the heritability of g. However, there is substantial differential heritability and the SCA do not show the dramatic developmental increase in heritability seen for g.

We also investigated SCA independent of g (g-corrected SCA, which we refer to as SCA.g). A surprising finding is that SCA.g remain substantially heritable (53% on average), even though 25% of the variance of SCA that covaries with g has been removed.

Our review frames expectations for genomic research that will use polygenic scores to predict SCA and SCA.g. Genome-wide association studies of SCA.g are needed to create polygenic scores that can predict SCA profiles of cognitive abilities and disabilities independent of g. These could be used to foster children’s cognitive strengths and minimize their weaknesses.

--- https://www.biorxiv.org/content/10.1101/2022.02.11.480140.full Patterns of item nonresponse behavior to survey questionnaires are systematic and have a genetic basis Gianmarco Mignogna, Caitlin E. Carey, Robbee Wedow, Nikolas Baya, Mattia Cordioli, Nicola Pirastu, Rino Bellocco, Michel G. Nivard, Benjamin M. Neale, Raymond K. Walters, Andrea Ganna 2022-02-14 2022-02-14 [("doi","10.1101/2022.02.11.480140")] genetics/heritable psychology/personality

Response to survey questionnaires is vital for social and behavioral research, and most analyses assume full and accurate response by survey participants. However, nonresponse is common and impedes proper interpretation and generalizability of results.

We examined item nonresponse behavior across 109 questionnaire items from the UK Biobank (UKB) (n = 360,628). Phenotypic factor scores for two participant-selected nonresponse answers, “Prefer not to answer” (PNA) and “I don’t know” (IDK), each predicted participant nonresponse in follow-up surveys, controlling for education and self-reported general health.

We performed genome-wide association studies on these factors and identified 39 genome-wide statistically-significant loci, and further validated these effects with polygenic scores in an independent study (n = 3,414), gaining information that we could not have had from phenotypic data alone. PNA and IDK were highly genetically correlated with one another and with education, health, and income, although unique genetic effects were also observed for both PNA and IDK.

We discuss how these effects may bias studies of traits correlated with nonresponse and how genetic analyses can further enhance our understanding of nonresponse behaviors in survey research, for instance by helping to correct for nonresponse bias.

--- https://www.biorxiv.org/content/10.1101/2022.02.13.480245.full Clock Work: Deconstructing the Epigenetic Clock Signals in Aging, Disease, and Reprogramming Morgan Levine, Albert Tzongyang Higgins-Chen, Kyra Thrush, Christopher J. Minteer, Peter Niimi 2022-02-15 2022-02-15 [("doi","10.1101/2022.02.13.480245")] longevity/epigenetics

Epigenetic clocks have come to be regarded as powerful tools for estimating aging. However, a major drawback in their application is our lack of mechanistic understanding. We hypothesize that uncovering the underlying biology is difficult due to the fact that epigenetic clocks are multifactorial composites: They are comprised of disparate parts, each with their own causal mechanism and functional consequences. Thus, only by deconstructing epigenetic clock signals will it be possible to glean biological insight.

Here we clustered 5,717 clock CpGs into 12 distinct modules using multi-tissue and in-vitro datasets. We show that epigenetic clocks are comprised of different proportions of modules, which may explain their discordance when simultaneously applied in a given study. We also observe that epigenetic reprogramming does not reset the entire clock and instead the observed rejuvenation is driven by a subset of modules.

Overall, two modules stand-out in terms of their unique features. The first is one of the most responsive to epigenetic reprogramming; is the strongest predictor of all-cause mortality; and shows increases with in vitro passaging up until senescence burden begins to emerge. The light-second module is moderately responsive to reprogramming; is very accelerated in tumor vs. normal tissues; and tracks with passaging in vitro even as population doublings decelerate.

Overall, we show that clock deconstruction can identify unique DNAm alterations and facilitate our mechanistic understanding of epigenetic clocks.

--- https://www.biorxiv.org/content/10.1101/2022.02.15.480501.full Sleep duration and brain structure—phenotypic associations and genotypic covariance Anders Fjell, Oystein Sorensen, Yunpeng Wang, Inge K. Amlien, William Baare, David Bartres-Faz, Lars Bertram, Carl-Johan Boraxbekk, Andreas Brandmaier, Ilja Demuth, Christian A. Drevon, Klaus Ebmeier, Paolo Ghisletta, Rogier A. Kievit, Simone A. Kuhn, Kathrine Skak Madsen, Athanasia M. Mowinckel, Lars Nyberg, Claire Sexton, Cristina Sole-Padulles, Didac Vidal-Pineiro, Gerd Wagner, Leiv Otto Watne, Kristine Beate Walhovd 2022-02-17 2022-02-17 [("doi","10.1101/2022.02.15.480501")] genetics/heritable/correlation/mendelian-randomization psychiatry/depression psychology/neuroscience zeo

The question of how much sleep is best for the brain attracts scientific and public interest, and there is concern that insufficient sleep leads to poorer brain health. However, it is unknown how much sleep is sufficient and how much is too much.

We analyzed 51,295 brain magnetic resonance images from 47,039 participants, and calculated the self-reported sleep duration associated with the largest regional volumes and smallest ventricles relative to intracranial volume (ICV) and thickest cortex. 6.8 hours of sleep was associated with the most favorable brain outcome overall. Critical values, defined by 95% confidence intervals, were 5.7 and 7.9 hours. There was regional variation, with for instance the hippocampus showing largest volume at 6.3 hours. Moderately long sleep (> 8 hours) was more strongly associated with smaller relative volumes, thinner cortex, and larger ventricles than even very short sleep (< 5 hours), but effect sizes were modest. People with larger ICV reported longer sleep (7.5 hours), so not correcting for ICV yielded longer durations associated with maximal volume. Controlling for socioeconomic status, body mass index and depression symptoms did not alter the associations. Genetic analyses showed that genes related to longer sleep in short sleepers were related to shorter sleep in long sleepers. This may indicate a genetically controlled homeostatic regulation of sleep duration. Mendelian Randomization analyses did not suggest sleep duration to have a causal impact on brain structure in the analyzed datasets.

The findings challenge the notion that habitual short sleep is negatively related to brain structure.

--- https://www.biorxiv.org/content/10.1101/206698.full The molecular genetics of participation in the Avon Longitudinal Study of Parents and Children Amy E. Taylor, Hannah J. Jones, Hannah Sallis, Jack Euesden, Evie Stergiakouli, Neil M. Davies, Stanley Zammit, Debbie A. Lawlor, Marcus R. Munafò, George Davey Smith, Kate Tilling 2017-10-20 2020-07-27 [("doi","10.1101/206698")] genetics/heritable/correlation psychiatry/adhd psychiatry/depression psychiatry/schizophrenia

Background: It is often assumed that selection (including participation and dropout) does not represent an important source of bias in genetic studies. However, there is little evidence to date on the effect of genetic factors on participation.

Method: Using data on mothers (n = 7,486) and children (n = 7,508) from the Avon Longitudinal Study of Parents and Children, we (1) examined the association of polygenic risk scores for a range of socio-demographic, lifestyle characteristics and health conditions related to continued participation, (2) investigated whether associations of polygenic scores with body mass index (BMI; derived from self-reported weight and height) and self-reported smoking differed in the largest sample with genetic data and a sub-sample who participated in a recent follow-up and (3) determined the proportion of variation in participation explained by common genetic variants using genome-wide data.

Results: We found evidence that polygenic scores for higher education, agreeableness and openness were associated with higher participation and polygenic scores for smoking initiation, higher BMI, neuroticism, schizophrenia, ADHD and depression were associated with lower participation. Associations between the polygenic score for education and self-reported smoking differed between the largest sample with genetic data (OR for ever smoking per SD increase in polygenic score:0.85, 95% CI:0.81,0.89) and sub-sample (OR:0.95, 95% CI:0.88,1.02). In genome-wide analysis, single-nucleotide polymorphism based heritability explained 17–31% of variability in participation.

Conclusion: Genetic association studies, including Mendelian Randomization, can be biased by selection, including loss to follow-up. Genetic risk for dropout should be considered in all analyses of studies with selective participation.

--- https://www.biorxiv.org/content/10.1101/219238.full Singleton Variants Dominate the Genetic Architecture of Human Gene Expression Ryan D. Hernandez, Lawrence H. Uricchio, Kevin Hartman, Chun Ye, Andrew Dahl, Noah Zaitlen 2017-11-14 2020-07-27 [("doi","10.1101/219238")] genetics/heritable/rare

The vast majority of variants so far discovered in humans are rare, and together they have a substantial impact on gene regulation.

The vast majority of human mutations have minor allele frequencies (MAF) under 1%, with the plurality observed only once (ie. “singletons”). While Mendelian diseases are predominantly caused by rare alleles, their role in complex phenotypes remains largely unknown.

We develop and rigorously validate an approach to jointly estimate the contribution of alleles with different frequencies, including singletons, to phenotypic variation. We apply our approach to transcriptional regulation, an intermediate between genetic variation and complex disease. Using whole genome DNA and RNA sequencing data from 360 European individuals, we find that:

singletons alone contribute ~23% of all cis-heritability across genes (dwarfing the contributions of other frequencies). We then integrate external estimates of global MAF from worldwide samples to improve our inference, and find that average cis-heritability is 15.3%. Strikingly, 50.9% of cis-heritability is contributed by globally rare variants (MAF<0.1%), implicating purifying selection as a pervasive force shaping the regulatory architecture of most human genes.

--- https://www.biorxiv.org/content/10.1101/224774.full Common risk variants identified in autism spectrum disorder Jakob Grove, Stephan Ripke, Thomas D. Als, Manuel Mattheisen, Raymond Walters, Hyejung Won, Jonatan Pallesen, Esben Agerbo, Ole A. Andreassen, Richard Anney, Rich Belliveau, Francesco Bettella, Joseph D. Buxbaum, Jonas Bybjerg-Grauholm, Marie Bækved-Hansen, Felecia Cerrato, Kimberly Chambert, Jane H. Christensen, Claire Churchhouse, Karin Dellenvall, Ditte Demontis, Silvia De Rubeis, Bernie Devlin, Srdjan Djurovic, Ashle Dumont, Jacqueline Goldstein, Christine S. Hansen, Mads Engel Hauberg, Mads V. Hollegaard, Sigrun Hope, Daniel P. Howrigan, Hailiang Huang, Christina Hultman, Lambertus Klei, Julian Maller, Joanna Martin, Alicia R. Martin, Jennifer Moran, Mette Nyegaard, Terje Nærland, Duncan S. Palmer, Aarno Palotie, Carsten B. Pedersen, Marianne G. Pedersen, Timothy Poterba, Jesper B. Poulsen, Beate St Pourcain, Per Qvist, Karola Rehnström, Avi Reichenberg, Jennifer Reichert, Elise B. Robinson, Kathryn Roeder, Panos Roussos, Evald Saemundsen, Sven Sandin, F. Kyle Satterstrom, George D. Smith, Hreinn Stefansson, Kari Stefansson, Stacy Steinberg, Christine Stevens, Patrick F. Sullivan, Patrick Turley, G. Bragi Walters, Xinyi Xu, Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium, BUPGEN, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, 23andMe, Daniel Geschwind, Merete Nordentoft, David Hougaard, Thomas Werge, Ole Mors, Preben Bo Mortensen, Benjamin M. Neale, Mark J. Daly, Anders Børglum 2017-11-25 2020-07-27 [("doi","10.1101/224774")] genetics/heritable/correlation psychiatry/autism psychiatry/depression psychiatry/schizophrenia

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD.

With a marked sample size increase from an unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 ASD cases and 27,969 controls that identifies:

5 genome-wide statistically-significant loci. Leveraging GWAS results from 3 phenotypes with overlapping genetic architectures (schizophrenia, major depression, and educational attainment), 7 additional loci shared with other traits are identified at equally strict statistical-significance levels. Dissecting the polygenic architecture we find both quantitative and qualitative polygenic heterogeneity across ASD subtypes, in contrast to what is typically seen in other complex disorders.

These results highlight biological insights, particularly relating to neuronal function and corticogenesis and establish that GWAS performed at scale will be much more productive in the near term in ASD, just as it has been in a broad range of important psychiatric and diverse medical phenotypes.

--- https://www.biorxiv.org/content/10.1101/231795.full Effects of latent Toxoplasmosis on olfactory functions of men and women Jaroslav Flegr, Manfred Milinski, Šárka Kaňková, Martin Hůla, Jana Hlaváčová, Kateřina Sýkorová 2017-12-10 2020-07-27 [("doi","10.1101/231795")] cat/psychology psychiatry/schizophrenia psychology/smell/human

The prevalence of Toxoplasmosis is higher in schizophrenics than in the general population. It has been suggested that certain symptoms of schizophrenia, including changes in olfactory functions, are in fact symptoms of Toxoplasmosis that can be easily detected in schizophrenics only due to the increased prevalence of Toxoplasmosis in this population. Schizophrenics have impaired identification of odors and lower sensitivity of odor detection.

Here we searched for differences in olfactory functions between 62 infected and 61 non infected non-schizophrenic subjects.

The infected men scored better in the standard odor-identification test. The infected women rated all smells as more intensive while the infected men rated nearly all smells as less intensive. Infected women rated the pleasantness of the smell of undiluted cat urine as higher than the non-infected women and the opposite was true for the men (the opposite direction shifts in men and women were described earlier for highly diluted cat urine). Toxoplasmosis had no effect on the rated pleasantness of the smell of other stimuli.

Our results suggest that latent Toxoplasmosis is associated with changes in the olfactory functions in humans; however, the observed changes differ from those observed in schizophrenics.

Key findings

Infected men but not women show better odor identification ability than the non-infected controls.

The infected women rated all smells as more and men as less intensive than the controls.

The infected women rated smell of cat urine as more and men as less pleasurable than the controls.

Toxoplasmosis had no effect on the rated pleasantness of the smell of other stimuli.

We found no new evidence for the Toxoplasmosis hypothesis of schizophrenia.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6386176/ Genome-wide association analysis of lifetime cannabis use (n = 184,765) identifies new risk loci, genetic overlap with mental health, and a causal influence of schizophrenia on cannabis use Joëlle A. Pasman, Karin J. H. Verweij, Zachary Gerring, Sven Stringer, Sandra Sanchez-Roige, Jorien L. Treur, Abdel Abdellaoui, Michel G. Nivard, Bart M. L. Baselmans, Jue-Sheng Ong, Hill F. Ip, Matthijs D. van der Zee, Meike Bartels, Felix R. Day, Pierre Fontanillas, Sarah L. Elson, the 23andMe Research Team, Harriet de Wit, Lea K. Davis, James MacKillop, International Cannabis Consortium, Jaime L. Derringer, Susan J. T. Branje, Catharina A. Hartman, Andrew C. Heath, Pol A. C. van Lier, Pamela A. F. Madden, Reedik Mägi, Wim Meeus, Grant W. Montgomery, A. J. Oldehinkel, Zdenka Pausova, Josep A. Ramos-Quiroga, Tomas Paus, Marta Ribases, Jaakko Kaprio, Marco P. M. Boks, Jordana T. Bell, Tim D. Spector, Joel Gelernter, Dorret I. Boomsma, Nicholas G. Martin, Stuart MacGregor, John R. B. Perry, Abraham Palmer, Danielle Posthuma, Marcus R. Munafò, Nathan A. Gillespie, Eske M. Derks, Jacqueline M. Vink 2018-01-08 2020-07-27 [("doi","10.1101/234294")] genetics/heritable/correlation/mendelian-randomization marijuana psychiatry/alcoholism psychiatry/bipolar psychiatry/schizophrenia

Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. To identify risk variants and improve our knowledge of the genetic etiology of cannabis use, we performed the largest genome-wide association study (GWAS) meta-analysis for lifetime cannabis use (n = 184,765) to date.

We identified 4 independent loci containing genome-wide statistically-significant SNP associations. Gene-based tests revealed 29 genome-wide statistically-significant genes located in these 4 loci and 8 additional regions. All SNPs combined explained 10% of the variance in lifetime cannabis use. The most statistically-significantly associated gene, CADM2, has previously been associated with substance use and risk-taking phenotypes.

We used S-PrediXcan to explore gene expression levels and found 11 unique eGenes. LD-score regression uncovered genetic correlations with smoking, alcohol use, and mental health outcomes, including schizophrenia and bipolar disorder.

Mendelian Randomization analysis provided evidence for a causal positive influence of schizophrenia risk on lifetime cannabis use.

--- https://www.biorxiv.org/content/10.1101/247411.full Exploring the genetic correlations of antisocial behavior and life history traits Jorim J. Tielbeek, J. C. Barnes, Arne Popma, Tinca J. C. Polderman, James J. Lee, John R. B. Perry, Danielle Posthuma, Brian B. Boutwell 2018-08-23 2020-07-27 [("doi","10.1101/247411")] crime genetics/heritable/correlation

Prior evolutionary theory provided reason to suspect that measures of development and reproduction would be correlated with antisocial behaviors in human and non-human species. Behavioral genetics has revealed that most quantitative traits are heritable, suggesting that these phenotypic correlations may share genetic etiologies.

We use GWAS data to estimate the genetic correlations between various measures of reproductive development (n = 52,776–318,863) and antisocial behavior (n = 31,968).

Our genetic correlation analyses demonstrate that alleles associated with higher reproductive output (number of children ever born, rg = 0.50, p = 0.0065) were positively correlated with alleles associated with antisocial behavior, whereas alleles associated with more delayed reproductive onset (age of first birth, rg = −0.64, p = 0.0008) were negatively associated with alleles linked to antisocial behavior.

Ultimately, these findings coalesce with evolutionary theories suggesting that increased antisocial behaviors may partly represent a faster life history approach, which may be calibrated by genes.

--- https://www.biorxiv.org/content/10.1101/261081.full Genome-wide study identifies 611 loci associated with risk tolerance and risky behaviors Richard Karlsson Linnér, Pietro Biroli, Edward Kong, S. Fleur W. Meddens, Robbee Wedow, Mark Alan Fontana, Maël Lebreton, Abdel Abdellaoui, Anke R. Hammerschlag, Michel G. Nivard, Aysu Okbay, Cornelius A. Rietveld, Pascal N. Timshel, Stephen P. Tino, Maciej Trzaskowski, Ronald de Vlaming, Christian L. Zünd, Yanchun Bao, Laura Buzdugan, Ann H. Caplin, Chia-Yen Chen, Peter Eibich, Pierre Fontanillas, Juan R. Gonzalez, Peter K. Joshi, Ville Karhunen, Aaron Kleinman, Remy Z. Levin, Christina M. Lill, Gerardus A. Meddens, Gerard Muntané, Sandra Sanchez-Roige, Frank J. van Rooij, Erdogan Taskesen, Yang Wu, Futao Zhang, 23andMe, eQTLgen Consortium, International Cannabis Consortium, Psychiatric Genomics Consortium, SSGAC, Adam Auton, Jason D. Boardman, David W. Clark, Andrew Conlin, Conor C. Dolan, Urs Fischbacher, Patrick J. F. Groenen, Kathleen Mullan Harris, Gregor Hasler, Albert Hofman, Mohammad A. Ikram, Sonia Jain, Robert Karlsson, Ronald C. Kessler, Maarten Kooyman, James MacKillop, Minna Männikkö, Carlos Morcillo-Suarez, Matthew B. McQueen, Klaus M. Schmidt, Melissa C. Smart, Matthias Sutter, A. Roy Thurik, André G. Uitterlinden, Jon White, Harriet de Wit, Jian Yang, Lars Bertram, Dorret I. Boomsma, Tõnu Esko, Ernst Fehr, David A. Hinds, Magnus Johannesson, Meena Kumari, David Laibson, Patrik K. E. Magnusson, Michelle N. Meyer, Arcadi Navarro, Abraham Palmer, Tune H. Pers, Danielle Posthuma, Daniel Schunk, Murray B. Stein, Rauli Svento, Henning Tiemeier, Paul R. H. J. Timmers, Patrick Turley, Robert J. Ursano, Gert G. Wagner, James F. Wilson, Jacob Gratten, James J. Lee, David Cesarini, Daniel J. Benjamin, Philipp Koellinger, Jonathan P. Beauchamp 2018-02-08 2020-07-27 [("doi","10.1101/261081")] crime genetics/heritable marijuana psychology/personality

Humans vary substantially in their willingness to take risks. In a combined sample of over one million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. We identified 611 independent genetic loci associated with at least one of our phenotypes, including 124 with general risk tolerance.

We report evidence of substantial shared genetic influences across general risk tolerance and risky behaviors: 72 of the 124 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is moderately to strongly genetically correlated (|rg| ~ 0.25–0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near general-risk-tolerance-associated SNPs are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission.

We find no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

--- https://www.biorxiv.org/content/10.1101/272518.full End-to-end deep image reconstruction from human brain activity Guohua Shen, Kshitij Dwivedi, Kei Majima, Tomoyasu Horikawa, Yukiyasu Kamitani 2018-02-27 2020-07-27 [("doi","10.1101/272518")] ai/nn psychology/neuroscience

Deep neural networks (DNNs) have recently been applied successfully to brain decoding and image reconstruction from functional magnetic resonance imaging (fMRI) activity. However, direct training of a DNN with fMRI data is often avoided because the size of available data is thought to be insufficient to train a complex network with numerous parameters. Instead, a pre-trained DNN has served as a proxy for hierarchical visual representations, and fMRI data were used to decode individual DNN features of a stimulus image using a simple linear model, which were then passed to a reconstruction module.

Here, we present our attempt to directly train a DNN model with fMRI data and the corresponding stimulus images to build an end-to-end reconstruction model. We trained a generative adversarial network with an additional loss term defined in a high-level feature space (feature loss) using up to 6,000 training data points (natural images and the fMRI responses). The trained deep generator network was tested on an independent dataset, directly producing a reconstructed image given an fMRI pattern as the input. The reconstructions obtained from the proposed method showed resemblance with both natural and artificial test stimuli. The accuracy increased as a function of the training data size, though not outperforming the decoded feature-based method with the available data size. Ablation analyses indicated that the feature loss played a critical role to achieve accurate reconstruction.

Our results suggest a potential for the end-to-end framework to learn a direct mapping between brain activity and perception given even larger datasets.

--- https://www.biorxiv.org/content/10.1101/274977.full GWAS in 446,118 European adults identifies 78 genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates Hassan S. Dashti, Samuel E. Jones, Andrew R. Wood, Jacqueline M. Lane, Vincent T. van Hees, Heming Wang, Jessica A. Rhodes, Yanwei Song, Krunal Patel, Simon G. Anderson, Robin Beaumont, David A. Bechtold, Jack Bowden, Brian E. Cade, Marta Garaulet, Simon D. Kyle, Max A. Little, Andrew S. Loudon, Annemarie I. Luik, Frank A. J. L. Scheer, Kai Spiegelhalder, Jessica Tyrrell, Daniel J. Gottlieb, Henning Tiemeier, David W. Ray, Shaun M. Purcell, Timothy Frayling, Susan Redline, Deborah A. Lawlor, Martin K. Rutter, Michael N. Weedon, Richa Saxena 2018-04-19 2020-07-27 [("doi","10.1101/274977")] genetics/heritable/correlation/mendelian-randomization psychiatry/schizophrenia zeo

Sleep is an essential homeostatically-regulated state of decreased activity and alertness conserved across animal species, and both short and long sleep duration associate with chronic disease and all-cause mortality. Defining genetic contributions to sleep duration could point to regulatory mechanisms and clarify causal disease relationships.

Through genome-wide association analyses in 446,118 participants of European ancestry from the UK Biobank, we discover 78 loci for self-reported sleep duration that further impact accelerometer-derived measures of sleep duration, daytime inactivity duration, sleep efficiency and number of sleep bouts in a subgroup (n=85,499) with up to 7-day accelerometry. Associations are enriched for genes expressed in several brain regions, and for pathways including striatum and subpallium development, mechanosensory response, dopamine binding, synaptic neurotransmission, catecholamine production, synaptic plasticity, and unsaturated fatty acid metabolism.

Genetic correlation analysis indicates shared biological links between sleep duration and psychiatric, cognitive, anthropometric and metabolic traits and Mendelian Randomization highlights a causal link of longer sleep with schizophrenia.

--- https://www.biorxiv.org/content/10.1101/291872.full Relationships between estimated autozygosity and complex traits in the UK Biobank Emma C. Johnson, Luke M. Evans, Matthew C. Keller 2018-03-29 2020-07-28 [("doi","10.1101/291872")] genetics/heritable/rare

Inbreeding increases the risk of certain Mendelian disorders in humans but may also reduce fitness through its effects on complex traits and diseases. Such inbreeding depression is thought to occur due to increased homozygosity at causal variants that are recessive with respect to fitness. Until recently it has been difficult to amass large enough sample sizes to investigate the effects of inbreeding depression on complex traits using genome-wide single-nucleotide polymorphism (SNP) data in population-based samples. Further, it is difficult to infer causation in analyses that relate degree of inbreeding to complex traits because confounding variables (eg. education) may influence both the likelihood for parents to outbreed and offspring trait values. The present study used runs of homozygosity in genome-wide SNP data in up to 400,000 individuals in the UK Biobank to estimate the proportion of the autosome that exists in autozygous tracts—stretches of the genome which are identical due to a shared common ancestor. After multiple testing corrections and controlling for possible sociodemographic confounders, we found relationships in the predicted direction between estimated autozygosity and 3 of the 26 traits we investigated: age at first sexual intercourse, fluid intelligence, and forced expiratory volume in 1 second. Our findings for fluid intelligence and forced expiratory volume corroborate those of several published studies while the finding for age at first sexual intercourse was novel. These results may suggest that these traits have been associated with Darwinian fitness over evolutionary time, although there are other possible explanations for these associations that cannot be eliminated. Some of the autozygosity-trait relationships were attenuated after controlling for background sociodemographic characteristics, suggesting that care needs to be taken in the design and interpretation of ROH studies in order to glean reliable information about the genetic architecture and evolutionary history of complex traits.

Author Summary: Inbreeding is well known to increase the risk of rare, monogenic diseases, and there has been some evidence that it also affects complex traits, such as cognition and educational attainment. However, difficulties can arise when inferring causation in these types of analyses because of the potential for confounding variables (eg. socioeconomic status) to bias the observed relationships between distant inbreeding and complex traits. In this investigation, we used single-nucleotide polymorphism data in a very large (N > 400,000) sample of seemingly outbred individuals to quantify the degree to which distant inbreeding is associated with 26 complex traits. We found robust evidence that distant inbreeding is inversely associated with fluid intelligence and a measure of lung function, and is positively associated with age at first sex, while other trait associations with inbreeding were attenuated after controlling for background sociodemographic characteristics. Our findings are consistent with evolutionary predictions that fluid intelligence, lung function, and age at first sex have been under selection pressures over time; however, they also suggest that confounding variables must be accounted for in order to reliably interpret results from these types of analyses.

--- https://www.biorxiv.org/content/10.1101/294116.full Epigenetic prediction of complex traits and death Daniel L. McCartney, Anna J. Stevenson, Stuart J. Ritchie, Rosie M. Walker, Qian Zhang, Stewart W. Morris, Archie Campbell, Alison D. Murray, Heather C. Whalley, Catharine R. Gale, David J. Porteous, Chris S. Haley, Allan F. McRae, Naomi R. Wray, Peter M. Visscher, Andrew M. McIntosh, Kathryn L. Evans, Ian J. Deary, Riccardo E. Marioni 2018-04-03 2020-07-28 [("doi","10.1101/294116")] longevity/epigenetics psychiatry/alcoholism statistics/survival-analysis

Background: Genome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes, and could have clinical applications. Here, penalized regression models were used to develop DNAm predictors for body mass index (BMI), smoking status, alcohol consumption, and educational attainment in a cohort of 5,100 individuals. Using an independent test cohort comprising 906 individuals, the proportion of phenotypic variance explained in each trait was examined for DNAm-based and genetic predictors. Receiver operator characteristic curves were generated to investigate the predictive performance of DNAm-based predictors, using dichotomized phenotypes. The relationship between DNAm scores and all-cause mortality (n = 214 events) was assessed via Cox proportional-hazards models.

Results: The DNAm-based predictors explained different proportions of the phenotypic variance for BMI (12%), smoking (60%), alcohol consumption (12%) and education (3%). The combined genetic and DNAm predictors explained 20% of the variance in BMI, 61% in smoking, 13% in alcohol consumption, and 6% in education. DNAm predictors for smoking, alcohol, and education but not BMI predicted mortality in univariate models. The predictors showed moderate discrimination of obesity (AUC=0.67) and alcohol consumption (AUC=0.75), and excellent discrimination of current smoking status (AUC=0.98). There was poorer discrimination of college-educated individuals (AUC=0.59).

Conclusion: DNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk.

List of abbreviations

DNAm: DNA methylation

BMI: Body mass index

AUC: Area under the curve

CpG: Cytosine phosphate Guanine dinucleotide

EWAS: Epigenome-wide association study

GS:SFHS: Generation Scotland: The Scottish family health study

LBC1936: Lothian birth cohort 1936

LASSO: Least absolute shrinkage and selector operator

HR: Hazard ratio

CI: Confidence interval

STRADL: Stratifying resilience and depression longitudinally

--- https://www.biorxiv.org/content/10.1101/302117.full Phenomic selection: a low-cost and high-throughput alternative to genomic selection Renaud Rincent, Jean-Paul Charpentier, Patricia Faivre-Rampant, Etienne Paux, Jacques Le Gouis, Catherine Bastien, Vincent Segura 2018-04-16 2020-07-28 [("doi","10.1101/302117")] genetics/selection/artificial statistics/variance-component

Genomic selection—the prediction of breeding values using DNA polymorphisms—is a disruptive method that has widely been adopted by animal and plant breeders to increase crop, forest and livestock productivity and ultimately secure food and energy supplies. It improves breeding schemes in different ways, depending on the biology of the species and genotyping and phenotyping constraints. However, both genomic selection and classical phenotypic selection remain difficult to implement because of the high genotyping and phenotyping costs that typically occur when selecting large collections of individuals, particularly in early breeding generations.

To specifically address these issues, we propose a new conceptual framework called phenomic selection, which consists of a prediction approach based on low-cost and high-throughput phenotypic descriptors rather than DNA polymorphisms. We applied phenomic selection on two species of economic interest (wheat and poplar) using near-infrared spectroscopy on various tissues. We showed that one could reach accurate predictions in independent environments for developmental and productivity traits and tolerance to disease. We also demonstrated that under realistic scenarios, one could expect much higher genetic gains with phenomic selection than with genomic selection.

Our work constitutes a proof of concept and is the first attempt at phenomic selection; it clearly provides new perspectives for the breeding community, as this approach is theoretically applicable to any organism and does not require any genotypic information.

--- https://www.biorxiv.org/content/10.1101/302349.full Genome-wide association study of social genetic effects on 170 phenotypes in laboratory mice Amelie Baud, Francesco Paolo Casale, Jerome Nicod, Oliver Stegle 2018-04-17 2020-07-28 [("doi","10.1101/302349")] genetics/heritable sociology

Social genetic effects (SGE, also called indirect genetic effects) are associations between genotypes of one individual and phenotype of another. SGE arise when two individuals interact and heritable traits of one influence the phenotype of the other. Recent studies have shown that SGE substantially contribute to phenotypic variation in humans and laboratory mice, which suggests that SGE, like direct genetic effects (DGE, effects of an individual’s genes on their own phenotype), are amenable to mapping.

Using 170 phenotypes including behavioral, physiological and morphological traits measured in outbred laboratory mice, we empirically explored the potential and challenges of genome-wide association study of SGE (sgeGWAS) as a tool to discover novel mechanisms of social effects between unrelated individuals. For each phenotype we performed sgeGWAS, identifying 21 genome-wide statistically-significant SGE associations for 17 phenotypes, and dgeGWAS for comparison.

Our results provide 3 main insights: first, SGE and DGE arise from partially different loci and/or loci with different effect sizes, which implies that the widely-studied mechanism of phenotypic “contagion” is not sufficient to explain all social effects. Secondly, several DGE associations but no SGE associations had large effects, suggesting sgeGWAS is unlikely to uncover “low hanging fruits”. Finally, a similar number of variants likely contribute to SGE and DGE.

The analytical framework we developed in this study and the insights we gained from our analyses will inform the design, implementation and interpretation of sgeGWAS in this and other populations and species.

--- https://www.biorxiv.org/content/10.1101/303941.full Genome-wide association analyses of chronotype in 697,828 individuals provides new insights into circadian rhythms in humans and links to disease Samuel E. Jones, Jacqueline M. Lane, Andrew R. Wood, Vincent T. van Hees, Jessica Tyrrell, Robin N. Beaumont, Aaron R. Jeffries, Hassan S. Dashti, Melvyn Hillsdon, Katherine S. Ruth, Marcus A. Tuke, Hanieh Yaghootkar, Seth A. Sharp, Yingjie Ji, James W. Harrison, Amy Dawes, Enda M. Byrne, Henning Tiemeier, Karla V. Allebrandt, Jack Bowden, David W. Ray, Rachel M. Freathy, Anna Murray, Diego R. Mazzotti, Philip R. Gehrman, the 23andMe Research Team, Deborah A. Lawlor, Timothy Frayling, Martin K. Rutter, David A. Hinds, Richa Saxena, Michael N. Weedon 2018-04-19 2020-07-28 [("doi","10.1101/303941")] psychiatry/depression psychiatry/schizophrenia zeo

Using data from 697,828 research participants from 23andMe and UK Biobank, we identified 351 loci associated with being a morning person, a behavioral indicator of a person’s underlying circadian rhythm. These loci were validated in 85,760 individuals with activity-monitor derived measures of sleep timing: the mean sleep timing of the 5% of individuals carrying the most “morningness” alleles was 25.1 minutes (95% CI: 22.5, 27.6) earlier than the 5% carrying the fewest. The loci were enriched for genes involved in circadian rhythm and insulin pathways, and those expressed in the retina, hindbrain, hypothalamus, and pituitary (all FDRw<1%).

We provide some evidence that being a morning person was causally associated with reduced risk of schizophrenia (OR: 0.89; 95% CI: 0.82, 0.96), depression (OR: 0.94; 95% CI: 0.91, 0.98) and a lower age at last childbirth in women (β: −046 years; 95% CI: −0.067, −0.025), but was not associated with BMI (β: −4.6×10−4; 95% CI: −0.044, 0.043) or type 2 diabetes (OR: 1.00; 95% CI: 0.91, 1.1).

This study offers new insights into the biology of circadian rhythms and disease links in humans.

--- https://www.biorxiv.org/content/10.1101/309070.full Common genetic variants contribute to risk of rare severe neurodevelopmental disorders Mari E. K. Niemi, Hilary C. Martin, Daniel L. Rice, Giuseppe Gallone, Scott D. Gordon, Martin Kelemen, Kerrie McAloney, Jeremy McRae, Elizabeth J. Radford, Sui Yu, Jozef Gecz, Nicholas G. Martin, Caroline F. Wright, David R. Fitzpatrick, Helen V. Firth, Matthew E. Hurles, Jeffrey C. Barrett 2018-05-04 2020-07-28 [("doi","10.1101/309070")] genetics/heritable/rare psychiatry/autism psychiatry/schizophrenia

There are thousands of rare human disorders caused by a single deleterious, protein-coding genetic variant. However, patients with the same genetic defect can have different clinical presentation, and some individuals carrying known disease-causing variants can appear unaffected. What explains these differences? Here, we show in a cohort of 6,987 children with heterogeneous severe neurodevelopmental disorders expected to be almost entirely monogenic that 7.7% of variance in risk is attributable to inherited common genetic variation.

We replicated this genome wide common variant burden by showing that it is over-transmitted from parents to children in an independent sample of 728 trios from the same cohort. Our common variant signal is positively correlated with genetic predisposition to fewer years of schooling, decreased intelligence, and risk of schizophrenia. We found that common variant risk was not different between individuals with and without a known protein-coding diagnostic variant, suggesting that common variant risk is not confined to patients without a monogenic diagnosis.

In addition, previously published common variant scores for autism, height, birth weight, and intracranial volume were all correlated with those traits within our cohort, suggesting that phenotypic expression in individuals with monogenic disorders is affected by the same variants as the general population. Our results demonstrate that common genetic variation affects both overall risk and clinical presentation in disorders typically considered to be monogenic.

--- https://www.biorxiv.org/content/10.1101/332320.full Genetic tools weed out misconceptions of strain reliability in Cannabis sativa: Implications for a budding industry Anna L. Schwabe, Mitchell E. McGlaughlin 2018-05-28 2020-07-28 [("doi","10.1101/332320")] genetics/sequencing marijuana

Cannabis sativa is listed as a Schedule I substance by the United States Drug Enforcement Agency and has been federally illegal in the United States since 1937. However, the majority of states in the United States, as well as several countries, now have various levels of legal Cannabis. Products are labeled with identifying strain names but there is no official mechanism to register Cannabis strains, therefore the potential exists for incorrect identification or labeling.

This study uses genetic analyses to investigate strain reliability from the consumer point of view. 10 microsatellite regions were used to examine samples from strains obtained from dispensaries in 3 states. Samples were examined for genetic similarity within strains, and also a possible genetic distinction between Sativa, Indica, or Hybrid types.

The analyses revealed genetic inconsistencies within strains. Additionally, although there was strong statistical support dividing the samples into two genetic groups, the groups did not correspond to commonly reported Sativa/Hybrid/Indica types. Genetic differences have the potential to lead to phenotypic differences and unexpected effects, which could be surprising for the recreational user, but have more serious implications for patients relying on strains that alleviate specific medical symptoms.

--- https://www.biorxiv.org/content/10.1101/363036.full Genomic underpinnings of lifespan allow prediction and reveal basis in modern risks Paul R. H. J. Timmers, Ninon Mounier, Kristi Läll, Krista Fischer, Zheng Ning, Xiao Feng, Andrew Bretherick, David W. Clark, eQTLGen Consortium, Xia Shen, Tōnu Esko, Zoltán Kutalik, James F. Wilson, Peter K. Joshi 2018-07-06 2020-07-28 [("doi","10.1101/363036")] genetics/heritable longevity

We use a multi-stage genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near GADD45G, KCNK3, LDLR, POM121C, ZC3HC1, and ABO.

Gene set and tissue-specific analyses show that expression in fetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer—but not other cancers—explain the most variance, possibly reflecting modern susceptibilities, whilst cancer may act through many rare variants, or the environment.

Resultant polygenic scores predict a mean lifespan difference of around 5 years of life across the deciles.

--- https://www.biorxiv.org/content/10.1101/374199.full Convergent evolution of psilocybin biosynthesis by psychedelic mushrooms Ali R. Awan, Jaclyn M. Winter, Daniel Turner, William M. Shaw, Laura M. Suz, Alexander J. Bradshaw, Tom Ellis, Bryn T. M. Dentinger 2018-07-27 2020-07-28 [("doi","10.1101/374199")] psychedelic

Psilocybin is a psychoactive compound with clinical applications produced by dozens of mushroom species. There has been a long-standing interest in psilocybin research with regard to treatment for addiction, depression, and end-of-life suffering. However, until recently very little was known about psilocybin biosynthesis and its ecological role.

Here we confirm and refine recent findings about the genes underpinning psilocybin biosynthesis, discover that there is more than one psilocybin biosynthesis cluster in mushrooms, and we provide the first data directly addressing psilocybin’s ecological role. By analyzing independent genome assemblies for the hallucinogenic mushrooms Psilocybe cyanescens and Pluteus salicinus we recapture the recently discovered psilocybin biosynthesis cluster and show that a transcription factor previously implicated in its regulation is actually not part of the cluster. Further, we show that the mushroom Inocybe corydalina produces psilocybin but does not contain the established biosynthetic cluster, and we present an alternative cluster.

Finally, a meta-transcriptome analysis of wild-collected mushrooms provides evidence for intra-mushroom insect gene expression of flies whose larvae grow inside Psilocybe cyanescens. These larvae were successfully reared into adults. Our results show that psilocybin does not confer complete protection against insect mycophagy, and the hypothesis that it is produced as an adaptive defense compound may need to be reconsidered.

--- https://www.biorxiv.org/content/10.1101/375105.full Discovery of psychoactive plant and mushroom alkaloids in behavior-modifying fungal cicada pathogens Greg R. Boyce, Emile Gluck-Thaler, Jason C. Slot, Jason E. Stajich, William J. Davis, Tim Y. James, John R. Cooley, Daniel G. Panaccione, Jørgen Eilenberg, Henrik H. De Fine Licht, Angie M. Macias, Matthew C. Berger, Kristen L. Wickert, Cameron M. Stauder, Ellie J. Spahr, Matthew D. Maust, Amy M. Metheny, Chris Simon, Gene Kritsky, Kathie T. Hodge, Richard A. Humber, Terry Gullion, Dylan P. G. Short, Teiya Kijimoto, Dan Mozgai, Nidia Arguedas, Matt T. Kasson 2018-07-30 2020-07-28 [("doi","10.1101/375105")] psychedelic

Entomopathogenic fungi routinely kill their hosts before releasing infectious conidia, but select species keep their hosts alive while sporulating to enhance spore dispersal. Recent expression and metabolomics studies involving “host-killing” entomopathogens have helped unravel infection processes and host responses, yet the mechanisms underlying “active host transmission” in insects with Entomophthoralean fungal infections are completely unexplored.

Here we report the discovery, through global and targeted metabolomics supported by metagenomics and proteomics, of the plant amphetamine, cathinone, in Massospora cicadina-infected periodical cicadas, and the mushroom tryptamine, psilocybin, in M. platypediae-infected and M. levispora-infected annual cicadas. The neurogenic activities of these alkaloids provide a hypothetical framework for a chemically induced extended phenotype of Massospora that alters cicada behavior by increasing endurance and suppressing feeding prior to death.

--- https://www.biorxiv.org/content/10.1101/376897.full Genetics & the Geography of Health, Behavior, and Attainment Daniel W. Belsky, Avshalom Caspi, Louise Arseneault, David L. Corcoran, Benjamin W. Domingue, Kathleen Mullan Harris, Renate Houts, Jonathan Mill, Terrie E. Moffitt, Joseph Prinz, Karen Sugden, Jasmin Wertz, Benjamin Williams, Candice Odgers 2018-07-25 2020-07-29 [("doi","10.1101/376897")] psychiatry/schizophrenia

People’s life chances can be predicted by their neighborhoods. This observation is driving efforts to improve lives by changing neighborhoods. Some neighborhood effects may be causal, supporting neighborhood-level interventions. Other neighborhood effects may reflect selection of families with different characteristics into different neighborhoods, supporting interventions that target families/individuals directly.

To test how selection affects different neighborhood-linked problems, we linked neighborhood data with genetic, health, and social-outcome data for >7,000 European-descent UK and US young people in the E-Risk and Add Health Studies. We tested selection/concentration of genetic risks for obesity, schizophrenia, teen-pregnancy, and poor educational outcomes in high-risk neighborhoods, including genetic analysis of neighborhood mobility.

Findings argue against genetic selection/concentration as an explanation for neighborhood gradients in obesity and mental-health problems, suggesting neighborhoods may be causal.

In contrast, modest genetic selection/concentration was evident for teen-pregnancy and poor educational outcomes, suggesting neighborhood effects for these outcomes should be interpreted with care.

--- https://www.biorxiv.org/content/10.1101/384412.full Exploring the effect of microdosing psychedelics on creativity in an open-label natural setting Luisa Prochazkova, Dominique P. Lippelt, Lorenza S. Colzato, Martin Kuchar, Zsuzsika Sjoerds, Bernhard Hommel 2018-08-11 2020-07-29 [("doi","10.1101/384412")] nootropic/lsd psychedelic

Introduction

Recently popular sub-perceptual doses of psychedelic substances such as truffles, referred to as microdosing, allegedly have multiple beneficial effects including creativity and problem solving performance, potentially through targeting serotonergic 5-HT2A receptors and promoting cognitive flexibility, crucial to creative thinking. Nevertheless, enhancing effects of microdosing remain anecdotal, and in the absence of quantitative research on microdosing psychedelics it is impossible to draw definitive conclusions on that matter. Here, our main aim was to quantitatively explore the cognitive-enhancing potential of microdosing psychedelics in healthy adults.

Method: During a microdosing event organized by the Dutch Psychedelic Society, we examined the effects of psychedelic truffles (which were later analyzed to quantify active psychedelic alkaloids) on two creativity-related problem-solving tasks: the Picture Concept Task assessing convergent thinking, and the Alternative Uses Task assessing divergent thinking. A short version of the Ravens Progressive Matrices task assessed potential changes in fluid intelligence. We tested once before taking a microdose and once while the effects were manifested.

Results: We found that both convergent and divergent thinking performance was improved after a non-blinded microdose, whereas fluid intelligence was unaffected.

Conclusion: While this study provides quantitative support for the cognitive enhancing properties of microdosing psychedelics, future research has to confirm these preliminary findings in more rigorous placebo-controlled study designs. Based on these preliminary results we speculate that psychedelics might affect cognitive meta-control policies by optimizing the balance between cognitive persistence and flexibility. We hope this study will motivate future microdosing studies with more controlled designs to test this hypothesis.

--- https://www.biorxiv.org/content/10.1101/398396.full Analysis of Polygenic Score Usage and Performance across Diverse Human Populations LE Duncan, H. Shen, B. Gelaye, K. J. Ressler, M. W. Feldman, R. E. Peterson, Benjamin W. Domingue 2018-08-22 2020-07-29 [("doi","10.1101/398396")] genetics/editing psychiatry/schizophrenia

Studies of the relationship between genetic and phenotypic variation have historically been carried out on people of European ancestry. Efforts are underway to address this limitation, but until they succeed, the legacy of a Euro-centric bias in medical genetic studies will continue to hinder research, including the use of polygenic scores, which are individual-level metrics of genetic risk. Ongoing debate surrounds the generalizability of polygenic scores based on genome-wide association studies (GWAS) conducted in European ancestry samples, to non-European ancestry samples.

We analyzed the first decade of polygenic scoring studies (2008–2017, inclusive), and found that 67% of studies included exclusively European ancestry participants and another 19% included only East Asian ancestry participants. Only 3.8% of studies were carried out on samples of African, Hispanic, or Indigenous peoples. We find that effect sizes for European ancestry-derived polygenic scores are only 36% as large in African ancestry samples, as in European ancestry samples (t=-10.056, df=22, p = 5.5×10−10). Poorer performance was also observed in other non-European ancestry samples. Analysis of polygenic scores in the 1000 Genomes samples revealed many strong correlations with global principal components, and relationships between height polygenic scores and height phenotypes that were highly variable depending on methodological choices in polygenic score construction.

As polygenic score use increases in research, precision medicine, and direct-to-consumer testing, improved handling of linkage disequilibrium and variant frequencies (both of which currently reduce transferability of scores) across populations will improve polygenic score performance. These findings bolster the rationale for large-scale GWAS in diverse human populations.

Significance Statement: The modern genetics revolution enabled rough calculations of individuals’ genetic liability for many phenotypes, including height, weight, and schizophrenia. Increasingly, polygenic scores, which are individual-level metrics of genetic liability, are available via direct-to-consumer testing, and they are already widely used in research. The performance of these scores depends on the availability of very large genetic studies, and consequently it is problematic that people of European ancestry are vastly over-represented in these studies.

We quantify the magnitude of this problem on the performance of polygenic scores in global samples and also show ancestry-related properties of polygenic scores.

These findings set benchmarks for future progress, and they demonstrate the need for large-scale genetic studies in diverse human populations.

--- https://www.biorxiv.org/content/10.1101/409540.full Using genetics to examine a general liability to childhood psychopathology Lucy Riglin, Ajay K. Thapar, Beate Leppert, Joanna Martin, Alexander Richards, Richard Anney, George Davey Smith, Kate Tilling, Evie Stergiakouli, Benjamin B. Lahey, Michael C. O’Donovan, Stephan Collishaw, Anita Thapar 2018-11-21 2020-07-29 [("doi","10.1101/409540")] genetics/heritable/correlation psychiatry/adhd psychiatry/autism psychiatry/depression psychiatry/schizophrenia

Background: Psychiatric disorders show phenotypic as well as genetic overlaps. Factor analyses of child and adult psychopathology have found that phenotypic overlaps largely can be explained by a latent general “p” factor that reflects general liability to psychopathology. We investigated whether shared genetic liability across disorders would be reflected in associations between multiple different psychiatric polygenic risk scores (PRS) and a ‘general psychopathology’ factor in childhood.

Method: The sample was a UK, prospective, population-based cohort (ALSPAC), including data on psychopathology at age 7 (n = 8161) years. PRS were generated from large published genome-wide association studies.

Outcomes

The general psychopathology factor was associated with both schizophrenia PRS and attention-deficit/hyperactivity disorder (ADHD) PRS, whereas there was no strong evidence of association with major depressive disorder and autism spectrum disorder PRS. Schizophrenia PRS was also associated with a specific “emotional” problems factor.

Interpretation

Our findings suggest that genetic liability to schizophrenia and ADHD may contribute to shared genetic risks across childhood psychiatric diagnoses at least partly via the ‘general psychopathology’ factor. However, the pattern of observations could not be explained by a general “p” factor on its own.

Funding

This work was supported by the Wellcome Trust (204895/Z/16/Z).Introduction

--- https://www.biorxiv.org/content/10.1101/421164.full Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four healthcare systems Amanda B. Zheutlin, Jessica Dennis, Richard Karlsson Linnér, Arden Moscati, Nicole Restrepo, Peter Straub, Douglas Ruderfer, Victor M. Castro, Chia-Yen Chen, Tian Ge, Laura M. Huckins, Alexander Charney, H. Lester Kirchner, Eli Ayumi Stahl, Christopher F. Chabris, Lea K. Davis, Jordan W. Smoller 2019-03-23 2020-07-29 [("doi","10.1101/421164")] genetics/heritable/correlation psychiatry/anxiety psychiatry/schizophrenia

Objective: Individuals at high risk for schizophrenia may benefit from early intervention but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts, but its utility in clinical settings remains largely unexplored. Moreover, the broad health consequences of a high genetic risk of schizophrenia are poorly understood, despite being relevant to treatment decisions.

Method: We used electronic health records for 106,160 patients from four healthcare systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites.

Results: PRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS=1.55 [95% confidence interval (CI), 1.4–1.7], p = 4.48 × 10−16) and patients in the highest risk decile of the PRS distribution had up to 4.6× increased odds of schizophrenia compared to those in the bottom decile (95% CI, 2.9–7.3, p = 1.37 × 10−10). PRSs were also positively associated with a range of other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity.

Conclusion: We demonstrate that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in healthcare settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. Our results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in healthcare systems.

--- https://www.biorxiv.org/content/10.1101/433946.full The genetic relationship between female reproductive traits and six psychiatric disorders Guiyan Ni, Azmeraw Amare, Xuan Zhou, Natalie Mills, Jacob Gratten, Sang Hong Lee 2018-10-03 2020-07-29 [("doi","10.1101/433946")] genetics/heritable/correlation psychiatry/adhd psychiatry/depression psychiatry/schizophrenia

Female reproductive behaviors have an important implication in evolutionary fitness and health of offspring. Previous studies have shown that age at first birth of women (AFB) is genetically associated with schizophrenia (SCZ). However, for most other psychiatric disorders and reproductive traits, the latent shared genetic architecture is largely unknown.

Here we used the second wave of UK Biobank data (n = 220,685) to evaluate the association between 5 female reproductive traits and polygenic risk scores (PRS) projected from genome-wide association study summary statistics of 6 psychiatric disorders (n = 429,178).

We found that the PRS of attention-deficit/hyperactivity disorder (ADHD) were strongly associated with AFB (genetic correlation of −0.68 ± 0.03 with p-value = 1.86E-89), age at first sexual intercourse (AFS) (−0.56 ± 0.03 with p-value = 3.42E-60), number of live births (NLB) (0.36 ± 0.04 with p-value = 4.01E-17) and age at menopause (−0.27 ± 0.04 with p-value = 5.71E-13). There were also robustly statistically-significant associations between the PRS of eating disorder (ED) and AFB (genetic correlation of 0.35 ± 0.06), ED and AFS (0.19 ± 0.06), Major depressive disorder (MDD) and AFB (−0.27 ± 0.07), MDD and AFS (− 0.27 ± 0.03) and SCZ and AFS (−0.10 ± 0.03).

Our findings reveal the shared genetic architecture between the 5 reproductive traits in women and 6 psychiatric disorders, which have a potential implication that helps to improve reproductive health in women, hence better child outcomes. Our findings may also explain, at least in part, an evolutionary hypothesis that causal mutations underlying psychiatric disorders have positive effects on reproductive success.

--- https://www.biorxiv.org/content/10.1101/460618.full Dense connectomic reconstruction in layer 4 of the somatosensory cortex Alessandro Motta, Manuel Berning, Kevin M. Boergens, Benedikt Staffler, Marcel Beining, Sahil Loomba, Christian Schramm, Philipp Hennig, Heiko Wissler, Moritz Helmstaedter 2018-11-03 2020-07-29 [("doi","10.1101/460618")] psychology/neuroscience

The dense circuit structure of the mammalian cerebral cortex is still unknown. With developments in 3-dimensional (3D) electron microscopy, the imaging of sizeable volumes of neuropil has become possible, but dense reconstruction of connectomes from such image data is the limiting step.

Here, we report the dense reconstruction of a volume of about 500,000 μm^3 from layer 4 of mouse barrel cortex, about 300× larger than previous dense reconstructions from the mammalian cerebral cortex. Using a novel reconstruction technique, FocusEM, we were able to reconstruct a total of 0.9 meters of dendrites and about 1.8 meters of axons investing only about 4,000 human work hours, about 10–25× more efficient than previous dense circuit reconstructions.

We find that connectomic data alone allows the definition of inhibitory axon types that show established principles of synaptic specificity for subcellular postsynaptic compartments. We find that also a fraction of excitatory axons exhibit such subcellular target specificity. Only about 35% of inhibitory and 55% of excitatory synaptic subcellular innervation can be predicted from the geometrical availability of membrane surface, revoking coarser models of random wiring for synaptic connections in cortical layer 4. We furthermore find evidence for enhanced variability of synaptic input composition between neurons at the level of primary dendrites in cortical layer 4.

Finally, we obtain evidence for Hebbian synaptic weight adaptation in at least 24% of connections; at least 35% of connections show no sign of such previous plasticity. Together, these results establish an approach to connectomic phenotyping of local dense neuronal circuitry in the mammalian cortex.

--- https://www.biorxiv.org/content/10.1101/469726.full Discovery of volatile biomarkers of Parkinson’s disease from sebum Drupad K. Trivedi, Eleanor Sinclair, Yun Xu, Depanjan Sarkar, Camilla Liscio, Phine Banks, Joy Milne, Monty Silverdale, Tilo Kunath, Royston Goodacre, Perdita Barran 2018-11-15 2020-07-29 [("doi","10.1101/469726")] psychiatry psychology/smell/human

Metabolomics identifies volatile odorous compounds from patient sebum that associate with the smell of Parkinson’s.

Parkinson’s disease (PD) is a progressive, neurodegenerative disease that presents with motor symptoms, for which there is no diagnostic test.

We have serendipitously identified a hyperosmic individual, a ‘Super Smeller’ that can detect PD by odor alone, and our early pilot studies have indicated that the odor was present in the sebum from the skin of PD subjects.

Here, we have employed an unbiased approach to investigate the volatile metabolites of sebum samples obtained non-invasively from the upper back of 64 participants in total (21 controls and 43 PD subjects).

Our results, validated by an independent cohort, identified a distinct volatiles-associated signature of PD, including altered levels of perillic aldehyde and eicosane, the smell of which was then described as being highly similar to the scent of PD by our ‘Super Smeller’.

--- https://www.biorxiv.org/content/10.1101/492058.full Exploration in the wild Eric Schulz, Rahul Bhui, Bradley C. Love, Bastien Brier, Michael T. Todd, Samuel J. Gershman 2018-12-10 2020-07-29 [("doi","10.1101/492058")] reinforcement-learning/exploration statistics/decision

Making good decisions requires people to appropriately explore their available options and generalize what they have learned. While computational models have successfully explained exploratory behavior in constrained laboratory tasks, it is unclear to what extent these models generalize to complex real world choice problems.

We investigate the factors guiding exploratory behavior in a data set consisting of 195,333 customers placing 1,613,967 orders from a large online food delivery service.

We find important hallmarks of adaptive exploration and generalization, which we analyze using computational models. We find evidence for several theoretical predictions: (1) customers engage in uncertainty-directed exploration, (2) they adjust their level of exploration to the average restaurant quality in a city, and (3) they use feature-based generalization to guide exploration towards promising restaurants.

Our results provide new evidence that people use sophisticated strategies to explore complex, real-world environments.

--- https://www.biorxiv.org/content/10.1101/495036.full Schizophrenia risk conferred by protein-coding de novo mutations Daniel P. Howrigan, Samuel A. Rose, Kaitlin E. Samocha, Menachem Fromer, Felecia Cerrato, Wei J. Chen, Claire Churchhouse, Kimberly Chambert, Sharon D. Chandler, Mark J. Daly, Ashley Dumont, Giulio Genovese, Hai-Gwo Hwu, Nan Laird, Jack A. Kosmicki, Jennifer L. Moran, Cheryl Roe, Tarjinder Singh, Shi-Heng Wang, Stephen V. Faraone, Stephen J. Glatt, Steven A. McCarroll, Ming Tsuang, Benjamin M. Neale 2018-12-13 2020-07-29 [("doi","10.1101/495036")] genetics/heritable/rare psychiatry/autism psychiatry/schizophrenia

Protein-coding de novo mutations (DNMs) in the form of single nucleotide changes and short insertions/deletions are genetic risk factors for autism, intellectual disability, developmental delay, and epileptic encephalopathy. In contrast, the burden of DNMs has thus far only had a modest documented impact on schizophrenia (SCZ) risk.

Here, we analyze whole-exome sequence from 1,695 SCZ affected parent-offspring trios from Taiwan along with DNMs from 1,077 published SCZ trios to better understand the contribution of coding DNMs to SCZ risk. Among 2,772 SCZ affected probands, the increased burden of DNMs is modest.

Gene set analyses show that the modest increase in risk from DNMs in SCZ probands is concentrated in genes that are either highly brain expressed, under strong evolutionary constraint, and/or overlap with genes identified as DNM risk factors in other neurodevelopmental disorders. No single gene meets the criteria for genome-wide statistical-significance, but we identify 16 genes that are recurrently hit by a protein-truncating DNM, which is a 3.15× higher rate than mutation model expectation of 5.1 genes (permuted 95% CI = 1–10 genes, permuted p = 3e-5).

Overall, DNMs explain only a small fraction of SCZ risk, and this risk is polygenic in nature suggesting that coding variation across many different genes will be a risk factor for SCZ in the population.

--- https://www.biorxiv.org/content/10.1101/500652.full Plant breeders should be determining economic weights for a selection index instead of using independent culling for choosing parents in breeding programs with genomic selection Lorena G. Batista, R. Chris Gaynor, Gabriel R. A. Margarido, Tim Byrne, Peter Amer, Gregor Gorjanc, John M. Hickey 2018-12-20 2020-07-29 [("doi","10.1101/500652")] genetics/selection/artificial/index-selection

In the context of genomic selection, we evaluated and compared recurrent selection breeding programs using either index selection or independent culling for selection of parents. We simulated a clonally propagated crop breeding program for 20 cycles of selection using either independent culling or an economic selection index with two unfavourably correlated traits under selection. Cycle time from crossing to selection of parents was kept the same for both strategies.

Our results demonstrate that accurate knowledge of the economic importance of traits is essential even when performing independent culling. This is because independent culling achieved its optimum genetic gain when the culling threshold for each trait varied accordingly to the economic importance of the traits. When gains from independent culling were maximized, the efficiency of converting genetic diversity into genetic gain of both selection methods were equivalent.

When the same proportion selected of 10% for each trait was used instead of optimal culling levels, index selection was 10%, 128% and 310% more efficient than independent culling when T2 had a relative economic importance of 1.0, 2.5 and 5.0, respectively. Given the complexity of estimating optimal culling levels and the fact that the gains achieved with independent culling are, at most, equivalent to index selection, the use of an economic selection index is recommended for multi-trait genomic selection.

--- https://www.biorxiv.org/content/10.1101/507244.full Whole-genome sequencing of rare disease patients in a national healthcare system Willem H. Ouwehand, on behalf of the NIHR BioResource, the 100,000 Genomes Project 2020-02-18 2020-07-30 [("doi","10.1101/507244")] genetics/heritable/rare genetics/sequencing

Most patients with rare diseases do not receive a molecular diagnosis and the aetiological variants and mediating genes for more than half such disorders remain to be discovered. We implemented whole-genome sequencing (WGS) in a national healthcare system to streamline diagnosis and to discover unknown aetiological variants, in the coding and non-coding regions of the genome.

In a pilot study for the 100,000 Genomes Project, we generated WGS data for 13,037 participants, of whom 9,802 had a rare disease, and provided a genetic diagnosis to 1,138 of the 7,065 patients with detailed phenotypic data. We identified 95 Mendelian associations between genes and rare diseases, of which 11 have been discovered since 2015 and at least 79 are confirmed aetiological.

Using WGS of UK Biobank1, we showed that rare alleles can explain the presence of some individuals in the tails of a quantitative red blood cell (RBC) trait. Finally, we reported 4 novel non-coding variants which cause disease through the disruption of transcription of ARPC1B, GATA1, LRBA and MPL. Our study demonstrates a synergy by using WGS for diagnosis and aetiological discovery in routine healthcare.

--- https://www.biorxiv.org/content/10.1101/509315.full Highly Heritable and Functionally Relevant Breed Differences in Dog Behavior Evan L. MacLeant, Noah Snyder-Mackler, Bridgett M. vonHoldt, James A. Serpell 2019-01-01 2020-07-30 [("doi","10.1101/509315")] genetics/heritable/dog

Variation across dog breeds presents a unique opportunity for investigating the evolution and biological basis of complex behavioral traits.

We integrated behavioral data from more than 17,000 dogs from 101 breeds with breed-averaged genotypic data (n = 5,697 dogs) from over 100,000 loci in the dog genome.

Across 14 traits, we found that breed differences in behavior are highly heritable, and that clustering of breeds based on behavior accurately recapitulates genetic relationships.

We identify 131 single-nucleotide polymorphisms associated with breed differences in behavior, which are found in genes that are highly expressed in the brain and enriched for neurobiological functions and developmental processes.

Our results provide insight into the heritability and genetic architecture of complex behavioral traits, and suggest that dogs provide a powerful model for these questions.

--- https://www.biorxiv.org/content/10.1101/528117.full Genome wide meta-analysis identifies genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders Cross-Disorder Group of the Psychiatric Genomics Consortium, Phil H. Lee, Verneri Anttila, Hyejung Won, Yen-Chen A. Feng, Jacob Rosenthal, Zhaozhong Zhu, Elliot M. Tucker-Drob, Michel G. Nivard, Andrew D. Grotzinger, Danielle Posthuma, Meg M.-J. Wang, Dongmei Yu, Eli Ayumi Stahl, Raymond K. Walters, Richard J. L. Anney, Laramie E. Duncan, Sintia Belangero, Jurjen Luykx, Henry Kranzler, Anna Keski-Rahkonen, Edwin H. Cook, George Kirov, Giovanni Coppola, Jaakko Kaprio, Clement C. Zai, Pieter J. Hoekstra, Tobias Banaschewski, Luis A. Rohde, P. G. C. Attention Deficit Hyperactivity Disorder Group, P. G. C. Autism Spectrum Disorder Group, P. G. C. Bipolar Disorder Group, P. G. C. Eating Disorders Group, P. G. C. Major Depressive Disorder Group, P. G. C. Obsessive Compulsive Disorder, Tourette Syndrome Group, P. G. C. Schizophrenia Group, Patrick F. Sullivan, Barbara Franke, Mark J. Daly, Cynthia M. Bulik, Cathryn M. Lewis, Andrew M. McIntosh, Michael C. O’Donovan, Amanda Zheutlin, Ole A. Andreassen, Anders D. Borglum, Gerome Breen, Howard J. Edenberg, Ayman H. Fanous, Stephen V. Faraone, Joel Gelernter, Carol A. Mathews, Manuel Mattheisen, Karen Mitchell, Michael C. Neale, John I. Nurnberger, Stephan Ripke, Susan L. Santangelo, Jeremiah M. Scharf, Murray B. Stein, Laura M. Thornton, James T. R. Walters, Naomi R. Wray, Daniel H. Geschwind, Benjamin M. Neale, Kenneth S. Kendler, Jordan W. Smoller 2019-01-26 2020-07-30 [("doi","10.1101/528117")] genetics/heritable/correlation psychiatry/adhd psychiatry/anorexia psychiatry/autism psychiatry/bipolar/genetics psychiatry/depression psychiatry/schizophrenia

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear.

We performed a meta-analysis of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders identifying 3 groups of inter-related disorders.

We detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning in the second trimester prenatally, and play prominent roles in a suite of neurodevelopmental processes.

These findings have important implications for psychiatric nosology, drug development, and risk prediction.

--- https://www.biorxiv.org/content/10.1101/549899.full Cannabis use, depression and self-harm: phenotypic and genetic relationships K. Hodgson, J. R. I. Coleman, S. P. Hagenaars, K. L. Purves, K. Glanville, S. W. Choi, P. O’Reilly, G. Breen, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, C. M. Lewis 2019-02-14 2020-07-30 [("doi","10.1101/549899")] genetics/heritable/correlation/mendelian-randomization marijuana psychiatry/depression

Background & Aims

The use of cannabis has previously been linked to both depression and self-harm, however the role of genetics in this relationship are unclear. We aimed to examine the phenotypic and genetic relationships between these traits.

Design: Genetic and cross-sectional phenotypic data collected through UK Biobank, together with consortia genome-wide association study summary statistics. These data were used to assess the phenotypic and genetic relationship between cannabis use, depression and self harm.

Setting: UK, with additional international consortia data

Participants: N=126,291 British adults aged 40–70 years, recruited into UK Biobank

Measurements: Genome-wide genetic data, phenotypic data on lifetime history of cannabis use, depression and self-harm.

Results

In UK Biobank, cannabis use is associated with increased likelihood of depression (OR=1.64, 95% CI = 1.59–1.70, p = 1.19×10−213) and self-harm (OR=2.85, 95% CI = 2.69–3.01, p = 3.46×10−304). The strength of this phenotypic association is stronger when more severe trait definitions of cannabis use and depression are considered. Additionally, statistically-significant genetic correlations are seen between cannabis use and depression using consortia summary statistics (rg=0.289, SE=0.036, p = 1.45×10−15). Polygenic risk scores for cannabis use and depression both explain a small but proportion of variance in cannabis use, depression and self harm within a UK Biobank target sample. However, two-sample Mendelian Randomization analyses were not.

Conclusion: Cannabis use is both phenotypically and genetically associated with depression and self harm. Future work dissecting the causal mechanism linking these traits may have implications for cannabis users.

--- https://www.biorxiv.org/content/10.1101/553255.full Neural System Identification with Neural Information Flow Katja Seeliger, Luca Ambrogioni, Yağmur Güçlütürk, Umut Güçlü, Marcel A. J. van Gerven 2019-05-23 2020-07-30 [("doi","10.1101/553255")] ai/nn/cnn reinforcement-learning/imitation-learning/brain-imitation-learning

Neural information flow (NIF) is a new framework for system identification in neuroscience. NIF subsumes population receptive field estimation, neural encoding, effective connectivity analysis and hemodynamic response estimation in a single differentiable model that can be trained end-to-end via stochastic gradient descent. NIF models represent neural information processing systems as a network of coupled tensors, each encoding the representation of the sensory input contained in a brain region. The elements of these tensors can be interpreted as cortical columns whose activity encodes the presence of a specific feature in a spatio-temporal location. Each tensor is coupled to the measured data specific to a brain region via low-rank observation models that can be decomposed into the spatial, temporal and feature receptive fields of a localized neuronal population. Both these observation models and the convolutional weights defining the information processing within regions and effective connectivity between regions are learned end-to-end by predicting the neural signal during sensory stimulation.

We trained a NIF model on the activity of early visual areas using a large-scale fMRI dataset. We show that we can recover plausible visual representations and population receptive fields that are consistent with the existing literature. Trained NIF models are accessible for in silico analyses.

--- https://www.biorxiv.org/content/10.1101/588020.full Recovery of trait heritability from whole genome sequence data Pierrick Wainschtein, Deepti Jain, Zhili Zheng, TOPMed Anthropometry Working Group, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, L. Adrienne Cupples, Aladdin H. Shadyab, Barbara McKnight, Benjamin M. Shoemaker, Braxton D. Mitchell, Bruce M. Psaty, Charles Kooperberg, Ching-Ti Liu, Christine M. Albert, Dan Roden, Daniel I. Chasman, Dawood Darbar, Donald M. Lloyd-Jones, Donna K. Arnett, Elizabeth A. Regan, Eric Boerwinkle, Jerome I. Rotter, Jeffrey R. O’Connell, Lisa R. Yanek, Mariza de Andrade, Matthew A. Allison, Merry-Lynn N. McDonald, Mina K. Chung, Myriam Fornage, Nathalie Chami, Nicholas L. Smith, Patrick T. Ellinor, Ramachandran S. Vasan, Rasika A. Mathias, Ruth Loos, Stephen S. Rich, Steven A. Lubitz, Susan R. Heckbert, Susan Redline, Xiuqing Guo, Y.-D Ida Chen, Cecelia A. Laurie, Ryan D. Hernandez, Stephen T. McGarvey, Michael E. Goddard, Cathy C. Laurie, Kari E. North, Leslie A. Lange, Bruce S. Weir, Loïc Yengo, Jian Yang, Peter M. Visscher 2021-06-11 2021-06-11 [("doi","10.1101/588020")] genetics/heritable/rare

Heritability, the proportion of phenotypic variance explained by genetic factors, can be estimated from pedigree data, but such estimates are uninformative with respect to the underlying genetic architecture. Analyses of data from genome-wide association studies (GWAS) on unrelated individuals have shown that for human traits and disease, ~1⁄3rd to two-thirds of heritability is captured by common SNPs. It is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular if the causal variants are rare, or other reasons such as overestimation of heritability from pedigree data.

Here we show that pedigree heritability for height and body mass index (BMI) appears to be largely recovered from whole-genome sequence (WGS) data on 25,465 unrelated individuals of European ancestry. We assigned 33.7 million genetic variants to groups based on their minor allele frequencies (MAF) and linkage disequilibrium (LD) with variants nearby, and estimated and partitioned genetic variance accordingly. The estimated heritability was 0.68 (SE 0.10) for height and 0.30 (SE 0.10) for BMI, with a range of ~0.60-0.71 for height and ~0.25-0.35 for BMI, depending on quality control and analysis strategies.

Low-MAF variants in low LD with neighboring variants were enriched for heritability, to a greater extent for protein-altering variants, consistent with negative selection thereon. Cumulatively variants with 0.0001 < MAF < 0.1 explained 0.47 (SE 0.07) and 0.30 (SE 0.10) of heritability for height and BMI, respectively.

Our results imply that rare variants, in particular those in regions of low LD, is a major source of the still missing heritability of complex traits and disease.

--- https://www.biorxiv.org/content/10.1101/703660.full Decreased Directed Functional Connectivity in the Psychedelic State Lionel Barnett, Suresh D. Muthukumaraswamy, Robin Carhart-Harris, Anil K. Seth 2019-07-16 2020-07-30 [("doi","10.1101/703660")] psychedelic/lsd psychology/neuroscience

Neuroimaging studies of the psychedelic state offer an unique window onto the neural basis of conscious perception and selfhood. Despite well understood pharmacological mechanisms of action, the large-scale changes in neural dynamics induced by psychedelic compounds remain poorly understood. Using source-localized, steady-state MEG recordings, we describe changes in functional connectivity following the controlled administration of LSD, psilocybin and low-dose ketamine, as well as, for comparison, the (non-psychedelic) anticonvulsant drug tiagabine.

We compare both undirected and directed measures of functional connectivity between placebo and drug conditions. We observe a general decrease in directed functional connectivity for all 3 psychedelics, as measured by Granger causality, throughout the brain. These data support the view that the psychedelic state involves a breakdown in patterns of functional organization or information flow in the brain.

In the case of LSD, the decrease in directed functional connectivity is coupled with an increase in undirected functional connectivity, which we measure using correlation and coherence. This surprising opposite movement of directed and undirected measures is of more general interest for functional connectivity analyses, which we interpret using analytical modeling.

Overall, our results uncover the neural dynamics of information flow in the psychedelic state, and highlight the importance of comparing multiple measures of functional connectivity when analysing time-resolved neuroimaging data.

--- https://www.biorxiv.org/content/10.1101/729327.full Long-term dietary intervention reveals resilience of the gut microbiota despite changes in diet and weight Gabriela K. Fragiadakis, Hannah C. Wastyk, Jennifer L. Robinson, Erica D. Sonnenburg, Justin L. Sonnenburg, Christopher D. Gardner 2019-08-08 2020-07-30 [("doi","10.1101/729327")] genetics/microbiome

With the rising rates of obesity and associated metabolic disorders, there is a growing need for effective long-term weight loss strategies, coupled with an understanding of how they interface with host physiology. While diet is a critical and promising area of focus, it has been difficult to identify diets that are broadly effective in long-term weight management.

To explore the interaction between specific diets and bacteria within the gut, we tracked microbiota composition over a 12-month period as part of a larger dietary intervention study of participants consuming either a low-carbohydrate or low-fat diet. While baseline microbiota composition was not predictive of weight loss, each diet resulted in substantial changes in the microbiota 3 months after the start of the intervention; some of these changes were diet-specific and others tracked with weight loss. After these initial shifts, the microbiota returned near its original baseline state for the remainder of the intervention, despite participants maintaining their diet and weight loss for the entire study.

These results suggest a resilience to perturbation of the microbiome’s starting state. When considering the established contribution of obese-associated microbiotas to weight gain in animal models, microbiota resilience may need to be overcome for long-term alterations to host physiology.

--- https://www.biorxiv.org/content/10.1101/746669.full Galactose-modified duocarmycin prodrugs as senolytics Ana Guerrero, Romain Guiho, Nicolás Herranz, Anthony Uren, Dominic J. Withers, Juan Pedro Martínez-Barbera, Lutz F. Tietze, Jesús Gil 2019-08-24 2020-07-30 [("doi","10.1101/746669")] longevity/senolytic

Senescence is a stable growth arrest that impairs the replication of damaged, old or preneoplastic cells, therefore contributing to tissue homeostasis. Senescent cells accumulate during ageing and are associated with diseases, such as cancer, fibrosis, and many age-related pathologies. Recent evidence suggests that the selective elimination of senescent cells can be effective in the treatment of many of these senescence-associated diseases. A universal characteristic of senescent cells is that they display elevated activity of the lysosomal β-galactosidase; this has been exploited as a marker for senescence (senescence-associated β-galactosidase activity).

Consequently, we hypothesized that galactose-modified cytotoxic prodrugs will be preferentially processed by senescent cells, resulting in their selective killing. Here, we show that different galactose-modified duocarmycin (GMD) derivatives preferentially kill senescent cells. GMD prodrugs induce selective apoptosis of senescent cells in a lysosomal β-galactosidase (GLB1)-dependent manner.

GMD prodrugs can eliminate a broad range of senescent cells in culture, and treatment with a GMD prodrug enhances the elimination of bystander senescent cells that accumulate upon whole body irradiation or doxorubicin treatment of mice. Moreover, taking advantage of a mouse model of human adamantinomatous craniopharyngioma (ACP), we show that treatment with a GMD pro-drug selectively reduced the number of β-catenin-positive preneoplastic senescent cells, which could have therapeutic implications.

In summary, the above results show that galactose-modified duocarmycin prodrugs behave as senolytics, suggesting that they could be used to treat a wide range of senescence-related pathologies.

--- https://www.biorxiv.org/content/10.1101/757054.full Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets Marc-Andre Schulz, B. T. Thomas Yeo, Joshua T. Vogelstein, Janaina Mourao-Miranada, Jakob N. Kather, Konrad Kording, Blake Richards, Danilo Bzdok 2019-09-06 2020-07-30 [("doi","10.1101/757054")] psychology/neuroscience

In recent years, deep learning has unlocked unprecedented success in various domains, especially in image, text, and speech processing. These breakthroughs may hold promise for neuroscience and especially for brain-imaging investigators who start to analyze thousands of participants. However, deep learning is only beneficial if the data have nonlinear relationships and if they are exploitable at currently available sample sizes.

We systematically profiled the performance of deep models, kernel models, and linear models as a function of sample size on UK Biobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improved when escalating from linear models to shallow-nonlinear models, and further improved when switching to deep-nonlinear models. The more observations were available for model training, the greater the performance gain we saw.

In contrast, using structural or functional brain scans, simple linear models performed on par with more complex, highly parameterized models in age/sex prediction across increasing sample sizes. In fact, linear models kept improving as the sample size approached ~10,000 participants. Our results indicate that the increase in performance of linear models with additional data does not saturate at the limit of current feasibility.

Yet, nonlinearities of common brain scans remain largely inaccessible to both kernel and deep learning methods at any examined scale.

--- https://www.biorxiv.org/content/10.1101/759530.full Senolytic treatment targets aberrant p21-expression to restore liver regeneration in adult mice Birgit Ritschka, Tania Knauer-Meyer, Alba Mas, Jean-Luc Plassat, Daniel Sampaio Gonçalves, Hugues Jacobs, Elisa Pedone, Umberto Di Vicino, Maria Pia Cosma, William M. Keyes 2019-09-05 2020-07-30 [("doi","10.1101/759530")] longevity/senolytic

Young mammals possess a limited regenerative capacity in tissues such as the liver, heart, and limbs, but which is quickly lost upon maturation or transition to adulthood. Chronic cellular senescence is a known mediator of decreased tissue function in aging and disease. Here we investigated whether senescence plays a role in the progressive loss of liver regenerative capacity that develops in young adult mice.

We find that following partial hepatectomy, the senescence markers p21, p16Ink4a and p19Arf become dynamically expressed at an age when regenerative capacity decreases. In addition, we demonstrate that treatment with a senescence-inhibiting drug improves regenerative capacity, through targeting of aberrant p21 expression. Surprisingly, we also find that the senescence marker p16Ink4a is expressed in a different cell-population to p21, and is unaffected by senescence targeting.

This work suggests that senescence may initially develop as a heterogeneous cellular response, and that treatment with senolytic drugs may aid in promoting organ regeneration.

--- https://www.biorxiv.org/content/10.1101/775288.full Exercise conditioned plasma dampens inflammation via clusterin and boosts memory Zurine De Miguel, Michael J. Betley, Drew Willoughby, Benoit Lehallier, Niclas Olsson, Liana Bonanno, Kaci J. Fairchild, Kévin Contrepois, Joshua E. Elias, Thomas A. Rando, Tony Wyss-Coray 2019-09-19 2020-07-31 [("doi","10.1101/775288")] exercise longevity

Physical exercise seems universally beneficial to human and animal health, slowing cognitive aging and neurodegeneration. Cognitive benefits are tied to increased plasticity and reduced inflammation within the hippocampus, yet little is known about the factors and mechanisms mediating these effects.

We discovered “runner” plasma, collected from voluntarily running mice, infused into sedentary mice recapitulates the cellular and functional benefits of exercise on the brain. Importantly, runner plasma reduces baseline neuroinflammatory gene expression and prominently suppresses experimentally induced brain inflammation. Plasma proteomic analysis shows a striking increase in complement cascade inhibitors including clusterin, which is necessary for the anti-inflammatory effects of runner plasma.

Cognitively impaired patients participating in structured exercise for 6 months showed higher plasma clusterin levels, which correlated positively with improvements in endurance and aerobic capacity.

These findings demonstrate the existence of anti-inflammatory “exercise factors” that are transferable, benefit the brain, and are present in humans engaging in exercise.

--- https://www.biorxiv.org/content/10.1101/791889.full A Petascale Automated Imaging Pipeline for Mapping Neuronal Circuits with High-throughput Transmission Electron Microscopy Wenjing Yin, Derrick Brittain, Jay Borseth, Marie E. Scott, Derric Williams, Jed Perkins, Chris Own, Matt Murfitt, Russel M. Torres, Daniel Kapner, Adam Bleckert, Daniel Castelli, David Reid, Wei-Chung Allen Lee, Brett J. Graham, Marc Takeno, Dan J. Bumbarger, Colin Farrell, R. Clay Reid, Nuno Macarico da Costa 2019-10-03 2020-07-31 [("doi","10.1101/791889")] psychology/neuroscience

Serial-section electron microscopy is the method of choice for studying cellular structure and network connectivity in the brain. We have built a pipeline of parallel imaging using transmission electron automated microscopes (piTEAM) that scales this technology and enables the acquisition of petascale datasets containing local cortical microcircuits.

The distributed platform is composed of multiple transmission electron microscopes that image, in parallel, different sections from the same block of tissue, all under control of a custom acquisition software (pyTEM) that implements 24/7 continuous autonomous imaging. The suitability of this architecture for large scale electron microscopy imaging was demonstrated by acquiring a volume of more than 1 mm³ of mouse neocortex spanning 4 different visual areas.

Over 26,500 ultrathin tissue sections were imaged, yielding a dataset of more than 2 petabytes. Our current burst imaging rate is 500 Mpixel/s (image capture only) per microscope and net imaging rate is 100 Mpixel/s (including stage movement, image capture, quality control, and post processing). This brings the combined burst acquisition rate of the pipeline to 3 Gpixel/s and the net rate to 600 Mpixel/s with 6 microscopes running acquisition in parallel, which allowed imaging a cubic millimeter of mouse visual cortex at synaptic resolution in less than 6 months.

--- https://www.biorxiv.org/content/10.1101/802082.full Germline burden of rare damaging variants negatively affects human healthspan and lifespan Anastasia V. Shindyapina, Aleksandr A. Zenin, Andrei E. Tarkhov, Peter O. Fedichev, Vadim N. Gladyshev 2019-10-13 2020-07-31 [("doi","10.1101/802082")] genetics/heritable/rare

Genome-wide association studies often explore links between particular genes and phenotypes of interest. Known genetic variants, however, are responsible for only a small fraction of human lifespan variation evident from genetic twin studies. To account for the missing longevity variance, we hypothesized that the cumulative effect of deleterious variants may affect human longevity. Here, we report that the burden of rarest protein-truncating variants (PTVs) negatively impacts both human healthspan and lifespan in two large independent cohorts. Longer-living subjects have both fewer rarest PTVs and less damaging PTVs.

In contrast, we show that the burden of frequent PTVs and rare non-PTVs is less deleterious, lacking association with longevity. The combined effect of rare PTVs is similar to that of known variants associated with longer lifespan and accounts for 1 − 2 years of lifespan variability. We further find that somatic accumulation of PTVs accounts for a minute fraction of mortality and morbidity acceleration and hence provides little support for its causal role in aging.

Thus, damaging mutations, germline and somatic, can only contribute to aging as a result of higher-order effects including interactions of multiple forms of damage.

--- https://www.biorxiv.org/content/10.1101/808642.full Erosion of the Epigenetic Landscape and Loss of Cellular Identity as a Cause of Aging in Mammals Jae-Hyun Yang, Patrick T. Griffin, Daniel L. Vera, John K. Apostolides, Motoshi Hayano, Margarita V. Meer, Elias L. Salfati, Qiao Su, Elizabeth M. Munding, Marco Blanchette, Mital Bhakta, Zhixun Dou, Caiyue Xu, Jeffrey W. Pippin, Michael L. Creswell, Brendan L. O’Connell, Richard E. Green, Benjamin A. Garcia, Shelley L. Berger, Philipp Oberdoerffer, Stuart J. Shankland, Vadim N. Gladyshev, Luis A. Rajman, Andreas R. Pfenning, David A. Sinclair 2019-10-19 2020-07-31 [("doi","10.1101/808642")] longevity/epigenetics

All living things experience entropy, manifested as a loss of inherited genetic and epigenetic information over time. As budding yeast cells age, epigenetic changes result in a loss of cell identity and sterility, both hallmarks of yeast aging. In mammals, epigenetic information is also lost over time, but what causes it to be lost and whether it is a cause or a consequence of aging is not known. Here we show that the transient induction of genomic instability, in the form of a low number of non-mutagenic DNA breaks, accelerates many of the chromatin and tissue changes seen during aging, including the erosion of the epigenetic landscape, a loss of cellular identity, advancement of the DNA methylation clock and cellular senescence. These data support a model in which a loss of epigenetic information is a cause of aging in mammals.

One Sentence Summary

The act of repairing DNA breaks induces chromatin reorganization and a loss of cell identity that may contribute to mammalian aging

--- https://www.biorxiv.org/content/10.1101/831321.full Genome-wide association study identifies 49 common genetic variants associated with handedness Gabriel Cuellar Partida, Joyce Y. Tung, Nicholas Eriksson, Eva Albrecht, Fazil Aliev, Ole A. Andreassen, Inês Barroso, Jacques S. Beckmann, Marco P. Boks, Dorret I. Boomsma, Heather A. Boyd, Monique M. B. Breteler, Harry Campbell, Daniel I. Chasman, Lynn F. Cherkas, Gail Davies, Eco J. C. de Geus, Ian J. Deary, Panos Deloukas, Danielle M. Dick, David L. Duffy, Johan G. Eriksson, Tõnu Esko, Bjarke Feenstra, Frank Geller, Christian Gieger, Ina Giegling, Scott D. Gordon, Jiali Han, Thomas F. Hansen, Annette M. Hartmann, Caroline Hayward, Kauko Heikkilä, Andrew A. Hicks, Joel N. Hirschhorn, Jouke-Jan Hottenga, Jennifer E. Huffman, Liang-Dar Hwang, Mohammad A. Ikram, Jaakko Kaprio, John P. Kemp, Kay-Tee Khaw, Norman Klopp, Bettina Konte, Zoltán Kutalik, Jari Lahti, Xin Li, Ruth Loos, Michelle Luciano, Sigurdur H. Magnusson, Massimo Mangino, Pedro Marques-Vidal, Nicholas G. Martin, Wendy L. McArdle, Mark I. McCarthy, Carolina Medina-Gomez, Mads Melbye, Scott A. Melville, Andres Metspalu, Lili Milani, Vincent Mooser, Mari Nelis, Dale R. Nyholt, Kevin S. O’Connell, Roel A. Ophoff, Cameron Palmer, Aarno Palotie, Teemu Palviainen, Guillaume Pare, Lavinia Paternoster, Leena Peltonen, Brenda W. J. H. Penninx, Ozren Polasek, Peter P. Pramstaller, Inga Prokopenko, Katri Raikkonen, Samuli Ripatti, Fernando Rivadeneira, Igor Rudan, Dan Rujescu, Johannes H. Smit, George Davey Smith, Jordan W. Smoller, Nicole Soranzo, Tim D. Spector, Beate St Pourcain, John M. Starr, Kari Stefansson, Hreinn Stefánsson, Stacy Steinberg, Maris Teder-Laving, Gudmar Thorleifsson, Nicholas J. Timpson, André G. Uitterlinden, Cornelia van Duijn, Frank J. A. van Rooij, Jaqueline M. Vink, Peter Vollenweider, Eero Vuoksimaa, Gérard Waeber, Nicholas J. Wareham, Nicole Warrington, Dawn Waterworth, Thomas Werge, H.-Erich Wichmann, Elisabeth Widen, Gonneke Willemsen, Alan F. Wright, Margaret J. Wright, Mousheng Xu, Jing Hua Zhao, Peter Kraft, David A. Hinds, Cecilia M. Lindgren, Reedik Mägi, Benjamin M. Neale, David M. Evans, Sarah E. Medland 2019-11-07 2020-07-31 [("doi","10.1101/831321")] genetics/heritable/correlation psychiatry/bipolar/genetics psychiatry/schizophrenia

Handedness, a consistent asymmetry in skill or use of the hands, has been studied extensively because of its relationship with language and the over-representation of left-handers in some neurodevelopmental disorders. Using data from the UK Biobank, 23andMe and 32 studies from the International Handedness Consortium, we conducted the world’s largest genome-wide association study of handedness (1,534,836 right-handed, 194,198 (11.0%) left-handed and 37,637 (2.1%) ambidextrous individuals).

We found 42 genetic loci associated with left-handedness and 7 associated with ambidexterity at genome-wide levels of statistical-significance (p < 5×10−8). Tissue enrichment analysis implicated the central nervous system and brain tissues including the hippocampus and cerebrum in the etiology of left-handedness. Pathways including regulation of microtubules, neurogenesis, axonogenesis and hippocampus morphology were also highlighted.

We found suggestive positive genetic correlations between being left-handed and some neuropsychiatric traits including schizophrenia and bipolar disorder. SNP heritability analyses indicated that additive genetic effects of genotyped variants explained 5.9% (95% CI = 5.8%–6.0%) of the underlying liability of being left-handed, while the narrow sense heritability was estimated at 12% (95% CI = 7.2%–17.7%).

Further, we show that genetic correlation between left-handedness and ambidexterity is low (rg = 0.26; 95% CI = 0.08–0.43) implying that these traits are largely influenced by different genetic mechanisms.

In conclusion, our findings suggest that handedness, like many other complex traits is highly polygenic, and that the genetic variants that predispose to left-handedness may underlie part of the association with some psychiatric disorders that has been observed in multiple observational studies.

--- https://www.biorxiv.org/content/10.1101/838367.full The urinary tract microbiome in older women exhibits host genetics and environmental influences AS Adebayo, G. Ackermann, R. C. Bowyer, P. Wells, G. Humphreys, R. Knight, T. D. Spector, C. J. Steves 2019-11-12 2020-07-31 [("doi","10.1101/838367")] genetics/microbiome

The urinary microbiome is a relatively unexplored niche despite the fact that we now know that it is not sterile. Moreover, urinary microbes, especially in aging populations, are associated with morbidity even when infection is subsequently not proven.

We present the first large-scale study to explore factors defining urinary microbiome composition in community-dwelling older adult women without clinically active infection. Using 1600 twins, we estimate the contribution of genetic and environmental factors to variation in microbiome using both 16S and shotgun metagenomics.

We found that the urinary microbiome is distinct from nearby sites and is unrelated to stool microbiome. Core urinary microbiome taxa were defined. The first component of weighted unifrac was heritable (18%) as were key taxa (eg. Escherichia-Shigella (A>0.15)). Age, menopausal status, prior UTI, and host genetics were top among factors defining the urobiome. Increased composition was associated with older age, contrary to previous findings.

--- https://www.biorxiv.org/content/10.1101/848366.full Long read sequencing of 3,622 Icelanders provides insight into the role of structural variants in human diseases and other traits Doruk Beyter, Helga Ingimundardottir, Asmundur Oddsson, Hannes P. Eggertsson, Eythor Bjornsson, Hakon Jonsson, Bjarni A. Atlason, Snaedis Kristmundsdottir, Svenja Mehringer, Marteinn T. Hardarson, Sigurjon A. Gudjonsson, Droplaug N. Magnusdottir, Aslaug Jonasdottir, Adalbjorg Jonasdottir, Ragnar P. Kristjansson, Sverrir T. Sverrisson, Guillaume Holley, Gunnar Palsson, Olafur A. Stefansson, Gudmundur Eyjolfsson, Isleifur Olafsson, Olof Sigurdardottir, Bjarni Torfason, Gisli Masson, Agnar Helgason, Unnur Thorsteinsdottir, Hilma Holm, Daniel F. Gudbjartsson, Patrick Sulem, Olafur T. Magnusson, Bjarni V. Halldorsson, Kari Stefansson 2020-12-14 2020-12-14 [("doi","10.1101/848366")] genetics/heritable/rare genetics/sequencing

Long-read sequencing (LRS) promises to improve characterization of structural variants (SVs), a major source of genetic diversity. We generated LRS data on 3,622 Icelanders using Oxford Nanopore Technologies, and identified a median of 22,636 SVs per individual (a median of 13,353 insertions and 9,474 deletions), spanning a median of 10 Mb per haploid genome.

We discovered a set of 133,886 reliably genotyped SV alleles and imputed them into 166,281 individuals to explore their effects on diseases and other traits. We discovered an association with a rare (AF = 0.037%) deletion of the first exon of PCSK9. Carriers of this deletion have 0.93 mmol/L (1.31 SD) lower LDL cholesterol levels than the population average (p-value = 7.0·10−20). We also discovered an association with a multi-allelic SV inside a large repeat region, contained within single long reads, in an exon of ACAN. Within this repeat region we found 11 alleles that differ in the number of a 57 bp-motif repeat, and observed a linear relationship (0.016 SD per motif inserted, p = 6.2·10−18) between the number of repeats carried and height.

These results show that SVs can be accurately characterized at population scale using long read sequence data in a genome-wide non-targeted approach and demonstrate how SVs impact phenotypes.

--- https://www.biorxiv.org/content/10.1101/343889.full Biological and cultural drivers of oral microbiota in Medieval and Post-Medieval London, UK A. G. Farrer, J. Bekvalac, R. Redfern, N. Gully, K. Dobney, A. Cooper, L. S. Weyrich 2018-06-11 2020-07-31 [("doi","10.1101/343889")] genetics/microbiome

The trillions of microorganisms that live in association with the human body (microbiota) are critical for human health and disease, but there is a limited understanding of how cultural and environmental factors shaped our microbiota diversity through time. However, biomolecular remnants of the human oral microbiota—recovered from the calcified dental plaque (calculus) of our long-dead ancestors—are providing a new means of exploring this key relationship of our evolutionary history.

Here, we correlate extensive experimental, archaeological, and biological metadata with 128 ancient dental calculus specimens from Medieval and Post-Medieval London, UK (1066–1853 CE). We identify a statistically-significant association between microbiota and oral geography (i.e. tooth type and tooth surface), which has confounded ancient microbiota studies to date. By controlling for oral geography, however, we identify the first associations between ancient microbiota and cultural and environmental signatures.

We find links between ancient British microbiota structure and health, including skeletal markers of stress that may reflect low socioeconomic status.

Furthermore, this study provides baseline data to explore factors that drive microbiota differentiation within and between ancient populations and highlights the potential of ancient microbiota to infer detailed health and sociocultural information about the past.

--- https://www.biorxiv.org/content/10.1101/411272.full Computational mechanisms of curiosity and goal-directed exploration Philipp Schwartenbeck, Johannes Passecker, Tobias U. Hauser, Thomas H. B. FitzGerald, Martin Kronbichler, Karl Friston 2018-09-07 2020-07-31 [("doi","10.1101/411272")] psychology/neuroscience reinforcement-learning/exploration/active-learning statistics/bayes

Successful behavior depends on the right balance between maximizing reward and soliciting information about the world.

Here, we show how different types of information-gain emerge when casting behavior as surprise minimization. We present two distinct mechanisms for goal-directed exploration that express separable profiles of active sampling to reduce uncertainty. ‘Hidden state’ exploration motivates agents to sample unambiguous observations to accurately infer the (hidden) state of the world. Conversely, ‘model parameter’ exploration, compels agents to sample outcomes associated with high uncertainty, if they are informative for their representation of the task structure.

We illustrate the emergence of these types of information-gain, termed active inference and active learning, and show how these forms of exploration induce distinct patterns of ‘Bayes-optimal’ behavior.

Our findings provide a computational framework to understand how distinct levels of uncertainty induce different modes of information-gain in decision-making.

--- https://www.biorxiv.org/content/10.1101/468942.full The Bayesian Superorganism I: collective probability estimation Edmund R. Hunt, Nigel R. Franks, Roland J. Baddeley 2018-11-12 2020-07-31 [("doi","10.1101/468942")] biology/ant genetics/selection/natural psychology/neuroscience reinforcement-learning/exploration statistics/bayes

Superorganisms such as social insect colonies are very successful relative to their non-social counterparts. Powerful emergent information processing capabilities would seem to contribute to their abundance, as they explore and exploit their environment collectively. In this series of 3 papers, we develop a Bayesian model of collective information processing, starting here with nest-finding, then examining foraging (part II) and externalized memories (pheromone territory markers) in part III.

House-hunting Temnothorax ants are adept at discovering and choosing the best available nest site for their colony. Essentially, we propose that they estimate the probability each choice is best, and then choose the highest probability. Viewed this way, we propose that their behavioral algorithm can be understood as a sophisticated statistical method that predates recent mathematical advances by some tens of millions of years.

Here, we develop a model of their nest finding that incorporates insights from approximate Bayesian computation as a model of collective estimation of alternative choices; and Thompson sampling, as an effective regret-minimizing decision-making rule by viewing nest choice in terms of a multi-armed bandit problem (Robbins 1952).

Our Bayesian framework points to the potential for further bio-inspired statistical techniques. It also facilitates the generation of hypotheses regarding individual and collective movement behaviors when collective decisions must be made.

--- https://www.biorxiv.org/content/10.1101/494187.full Gut microbiome response to a modern Paleolithic diet in a Western lifestyle context M Barone, S. Turroni, S. Rampelli, M. Soverini, F. D’Amico, E. Biagi, P. Brigidi, E. Troiani, M. Candela 2018-12-13 2020-07-31 [("doi","10.1101/494187")] genetics/microbiome

The progressive reduction of gut microbiome (GM) biodiversity along human evolutionary history has been found to be particularly exacerbated in Western urban compared to traditional rural populations, and supposed to contribute to the increasing incidence of chronic non-communicable diseases. Together with sanitation, antibiotics and C-section, the Western diets, low in microbiota-accessible carbohydrates (MACs) while rich in industrialized and processed foods, are considered one of the leading causes of this shrinkage. However, questions remain unanswered, especially whether high-MAC low-processed diets may be sufficient to recover GM diversity in Western urban populations.

Here, we profiled the GM structure of urban Italian subjects adhering to the modern Paleolithic diet (MPD), a dietary pattern featured by high consumption of MACs and low-to-zero intake of refined sugars and processed foods, and compared data with other Italian individuals following a Mediterranean Diet (MD), as well as worldwide traditional hunter-gatherer populations from previous publications. Notwithstanding a strong geography effect on the GM structure, our results show an unexpectedly high degree of GM biodiversity in MPD subjects, which well approximates that of traditional populations. Increasing the consumption of MACs at the expense of refined sugars, and minimizing the intake of processed foods, both hallmarks of the MPD, could be the key to rewild the Western microbiota, counteracting the loss of GM diversity and thus restoring evolutionarily important functionality to our gut for improved human health.

--- https://www.biorxiv.org/content/10.1101/504241.full The Bayesian Superorganism III: externalized memories facilitate distributed sampling Edmund R. Hunt, Nigel R. Franks, Roland J. Baddeley 2018-12-21 2020-08-01 [("doi","10.1101/504241")] biology/ant reinforcement-learning/exploration statistics/bayes

A key challenge for any animal is to avoid wasting time by searching for resources in places it has already found to be unprofitable. This challenge is particularly strong when the organism is a central place forager—returning to a nest between foraging bouts—because it is destined repeatedly to cover much the same ground. Furthermore, this problem will reach its zenith if many individuals forage from the same central place, as in social insects.

Foraging performance may be greatly enhanced by coordinating movement trajectories such that each ant visits separate parts of the surrounding (unknown) space. In this third of 3 papers, we find experimental evidence for an externalized spatial memory in Temnothorax albipennis ants: chemical markers (either pheromones or other cues such as cuticular hydrocarbon footprints) that are used by nest-mates to mark explored space. We show these markers could be used by the ants to scout the space surrounding their nest more efficiently through indirect coordination.

We also develop a simple model of this marking behavior that can be applied in the context of Markov chain Monte Carlo methods (see part two of this series). This substantially enhances the performance of standard methods like the Metropolis-Hastings algorithm in sampling from sparse probability distributions (such as those confronted by the ants) with little additional computational cost.

Our Bayesian framework for superorganismal behavior motivates the evolution of exploratory mechanisms such as trail marking in terms of enhanced collective information processing.

--- https://www.biorxiv.org/content/10.1101/516484.full Evolving super stimuli for real neurons using deep generative networks Carlos R. Ponce, Will Xiao, Peter F. Schade, Till S. Hartmann, Gabriel Kreiman, Margaret S. Livingstone 2019-01-17 2020-08-01 [("doi","10.1101/516484")] ai/nn/adversarial/human psychology/neuroscience reinforcement-learning

A generative deep neural network interacted with a genetic algorithm to evolve stimuli that maximized the firing of neurons in alert macaque inferotemporal and primary visual cortex.

The evolved images activated neurons more strongly than did thousands of natural images.

Distance in image space from the evolved images predicted responses of neurons to novel images.


Finding the best stimulus for a neuron is challenging because it is impossible to test all possible stimuli. Here we used a vast, unbiased, and diverse hypothesis space encoded by a generative deep neural network model to investigate neuronal selectivity in inferotemporal cortex without making any assumptions about natural features or categories.

A genetic algorithm, guided by neuronal responses, searched this space for optimal stimuli.

Evolved synthetic images evoked higher firing rates than even the best natural images and revealed diagnostic features, independently of category or feature selection.

This approach provides a way to investigate neural selectivity in any modality that can be represented by a neural network and challenges our understanding of neural coding in visual cortex.

[Published as "Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences"]

--- https://www.biorxiv.org/content/10.1101/169813.full 2020-08-01 genetics/microbiome --- https://www.biorxiv.org/content/10.1101/200881.full An interventional Soylent diet increases the Bacteroidetes to Firmicutes ratio in human gut microbiome communities: a randomized controlled trial Ryan H. Hsu, Dylan M. McCormick, Mitchell J. Seitz, Lauren M. Lui, Harneet S. Rishi, Adam P. Arkin 2017-10-13 2020-08-01 [("doi","10.1101/200881")] genetics/microbiome

Our knowledge of the relationship between the gut microbiome and health has rapidly expanded in recent years. Diet has been shown to have causative effects on microbiome composition, which can have subsequent implications on health. Soylent 2.0 is a liquid meal replacement drink that satisfies nearly 20% of all recommended daily intakes per serving. This study aims to characterize the changes in gut microbiota composition resulting from a short-term Soylent diet.

Fourteen participants were separated into two groups: 5 in the regular diet group and 9 in the Soylent diet group. The regular diet group maintained a diet closely resembling self-reported regular diets. The Soylent diet group underwent 3 dietary phases: (1) a regular diet for 2 days, (2) a Soylent-only diet (five servings of Soylent daily and water as needed) for 4 days, and (3) a regular diet for 4 days. Daily logs self-reporting diet, Bristol stool ratings, and any abdominal discomfort were electronically submitted. 8 fecal samples per participant were collected using fecal sampling kits, which were subsequently sent to uBiome, Inc. for sample processing and V4 16S rDNA sequencing. Reads were clustered into operational taxonomic units (OTUs) and taxonomically identified against the GreenGenes 16S database.

We find that an individual’s alpha-diversity is not altered during a Soylent-only diet. In addition, principal coordinate analysis using the unweighted UniFrac distance metric shows samples cluster strongly by individual and not by dietary phase. Among Soylent dieters, we find a statistically-significant increase in the ratio of Bacteroidetes to Firmicutes abundance, which is associated with several positive health outcomes, including reduced risks of obesity and intestinal inflammation.

--- https://www.biorxiv.org/content/10.1101/253450.full Gut microbiome transition across a lifestyle gradient in Himalaya Aashish R. Jha, Emily R. Davenport, Yoshina Gautam, Dinesh Bhandari, Sarmila Tandukar, Katharine Ng, Susan Holmes, Guru Prasad Gautam, Jeevan Bahadur Sherchand, Carlos D. Bustamante, Justin L. Sonnenburg 2018-01-27 2020-08-01 [("doi","10.1101/253450")] genetics/microbiome

The composition of the gut microbiome in industrialized populations differs from those living traditional lifestyles. However, it has been difficult to separate the contributions of human genetic and geographic factors from lifestyle/modernization. Here, we characterize the stool bacterial composition of four Himalayan populations to investigate how the gut community changes in response to shifts in human lifestyles. These groups led semi-nomadic hunting-gathering lifestyles until transitioning to varying dependence upon farming. The Tharu began farming 250–300 years ago, the Raute and Raji transitioned 30–40 years ago, and the Chepang retain many aspects of a foraging lifestyle.

We assess the contributions of dietary and environmental factors on their gut microbiota and find that the gut microbiome composition is statistically-significantly associated with lifestyle. The Chepang foragers harbor elevated abundance of taxa associated with foragers around the world. Conversely, the gut microbiomes of populations that have transitioned to farming are more similar to those of Americans, with agricultural dependence and several associated lifestyle and environmental factors correlating with the extent of microbiome divergence from the foraging population. For example, our results show that drinking water source and solid cooking fuel are statistically-significantly associated with the gut microbiome.

Despite the pronounced differences in gut bacterial composition across populations, we found little differences in alpha diversity across populations. These findings in genetically similar populations living in the same geographical region establish the key role of lifestyle in determining human gut microbiome composition and point to the next challenging steps of isolating dietary effects from other factors that change during modernization.

--- https://www.biorxiv.org/content/10.1101/240614.full Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks Rishi Rajalingham, Elias B. Issa, Pouya Bashivan, Kohitij Kar, Kailyn Schmidt, James J. DiCarlo 2018-02-12 2020-08-01 [("doi","10.1101/240614")] ai/nn/cnn psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Primates—including humans—can typically recognize objects in visual images at a glance even in the face of naturally occurring identity-preserving image transformations (eg. changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechanistic models that quantitatively explain this behavior by predicting primate performance for each and every image. Here, we applied this stringent behavioral prediction test to the leading mechanistic models of primate vision (specifically, deep, convolutional, artificial neural networks; ANNs) by directly comparing their behavioral signatures against those of humans and rhesus macaque monkeys. Using high-throughput data collection systems for human and monkey psychophysics, we collected over one million behavioral trials for 2400 images over 276 binary object discrimination tasks. Consistent with previous work, we observed that state-of-the-art deep, feed-forward convolutional ANNs trained for visual categorization (termed DCNNIC models) accurately predicted primate patterns of object-level confusion. However, when we examined behavioral performance for individual images within each object discrimination task, we found that all tested DCNNIC models were non-predictive of primate performance, and that this prediction failure was not accounted for by simple image attributes, nor rescued by simple model modifications. These results show that current DCNNIC models cannot account for the image-level behavioral patterns of primates, and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision. To this end, large-scale, high-resolution primate behavioral benchmarks—such as those obtained here—could serve as direct guides for discovering such models.

Significance Statement: Recently, specific feed-forward deep convolutional artificial neural networks (ANNs) models have dramatically advanced our quantitative understanding of the neural mechanisms underlying primate core object recognition. In this work, we tested the limits of those ANNs by systematically comparing the behavioral responses of these models with the behavioral responses of humans and monkeys, at the resolution of individual images. Using these high-resolution metrics, we found that all tested ANN models diverged from primate behavior. Going forward, these high-resolution, large-scale primate behavioral benchmarks could serve as direct guides for discovering better ANN models of the primate visual system.

--- https://www.biorxiv.org/content/10.1101/287045.full The Drosophila microbiome has a limited influence on sleep, activity, and courtship behaviors Joel Selkrig, Farhan Mohammad, Soon Hwee Ng, Chua Jia Yi, Tayfun Tumkaya, Joses Ho, Yin Ning Chiang, Dirk Rieger, Sven Pettersson, Charlotte Helfrich-Förster, Joanne Y. Yew, Adam Claridge-Chang 2018-03-22 2020-08-01 [("doi","10.1101/287045")] genetics/microbiome

In animals, commensal microbes modulate various physiological functions, including behavior. While microbiota exposure is required for normal behavior in mammals, it is not known how widely this dependency is present in other animal species.

We proposed the hypothesis that the microbiome has a major influence on the behavior of the vinegar fly (Drosophila melanogaster), a major invertebrate model organism. Several assays were used to test the contribution of the microbiome on some well-characterized behaviors: defensive behavior, sleep, locomotion, and courtship in microbe-bearing, control flies and two generations of germ-free animals.

None of the behaviors were largely influenced by the absence of a microbiome, and the small or moderate effects were not generalizable between replicates and/or generations. These results refute the hypothesis, indicating that the Drosophila microbiome does not have a major influence over several behaviors fundamental to the animal’s survival and reproduction.

The impact of commensal microbes on animal behavior may not be broadly conserved.

--- https://www.biorxiv.org/content/10.1101/292755.full Effects of exclusive breastfeeding on infant gut microbiota: a meta-analysis across studies and populations Nhan T. Ho, Fan Li, Kathleen A. Lee-Sarwar, Hein M. Tun, Bryan Brown, Pia S. Pannaraj, Lianna F. Wood, Jeffrey M. Bender, Joanne E. Sordillo, Meghan B. Azad, Amanda L. Thompson, Scott T. Weiss, M. Andrea Azcarate-Peril, Augusto A. Litonjua, Anita L. Kozyrskyj, Heather B. Jaspan, Grace M. Aldrovandi, Louise Kuhn 2018-03-31 2020-08-01 [("doi","10.1101/292755")] genetics/microbiome

Literature regarding the differences in gut microbiota between exclusively breastfed (EBF) and non-EBF infants is meager with large variation in methods and results. We performed a meta-analysis of 7 studies (a total of 1825 stool samples from 684 infants) to investigate effects of EBF compared to non-EBF on infant gut microbiota across different populations.

In the first 6 months of life, overall bacterial diversity, gut microbiota age, relative abundances of Bacteroidetes and Firmicutes and microbial-predicted pathways related to carbohydrate metabolism were consistently increased; while relative abundances of pathways related to lipid, vitamin metabolism, and detoxification were decreased in non-EBF vs. EBF infants. The perturbation in microbial-predicted pathways associated with non-EBF was larger in infants delivered by C-section than delivered vaginally. Longer duration of EBF mitigated diarrhea-associated gut microbiota dysbiosis, and the effects of EBF persisted after 6 months of age.

These consistent findings across vastly different populations suggest that one of the mechanisms of short and long-term benefits of EBF may be alteration in gut microbes.

--- https://www.biorxiv.org/content/10.1101/305896.full Parkinson’s disease and bacteriophages as its overlooked contributors George Tetz, Stuart M. Brown, Yuhan Hao, Victor Tetz 2018-04-22 2020-08-01 [("doi","10.1101/305896")] genetics/microbiome

Recent studies suggest that alterations in the gut phagobiota may contribute to pathophysiological processes in mammals; however, the association of bacteriophage community structure with Parkinson’s disease (PD) has not been yet characterized. Towards this end, we used a published dataset to analyze bacteriophage composition and determine the phage/bacteria ratio in fecal samples from drug-naive PD patients and healthy participants.

Our analyses revealed alterations in the representation of certain bacteriophages in the phagobiota of PD patients. We identified shifts of the phage/bacteria ratio in lactic acid bacteria known to produce dopamine and regulate intestinal permeability, which are major factors implicated in PD pathogenesis. Furthermore, we observed the depletion of Lactococcus spp. in the PD group, which was most likely due to the increase of lytic c2-like and 936-like lactococcal phages frequently present in dairy products.

Our findings add bacteriophages to the list of possible factors associated with the development of PD, suggesting that gut phagobiota composition may serve as a diagnostic tool as well as a target for therapeutic intervention, which should be confirmed in further studies.

Our results open a discussion on the role of environmental phages and phagobiota composition in health and disease.

--- https://www.biorxiv.org/content/10.1101/306597.full Dynamic linear models guide design and analysis of microbiota studies within artificial human guts Justin D. Silverman, Heather Durand, Rachael J. Bloom, Sayan Mukherjee, Lawrence A. David 2018-04-24 2020-08-01 [("doi","10.1101/306597")] genetics/microbiome

Artificial gut models provide unique opportunities to study human-associated microbiota. Outstanding questions for these models’ fundamental biology include the timescales on which microbiota vary and the factors that drive such change. Answering these questions though requires overcoming analytical obstacles like estimating the effects of technical variation on observed microbiota dynamics, as well as the lack of appropriate benchmark datasets.

To address these obstacles, we created a modeling framework based on multinomial logistic-normal dynamic linear models (MALLARDs) and performed dense longitudinal sampling of replicate artificial human guts over the course of 1 month. The resulting analyses revealed that when observed on an hourly basis, 76% of community variation could be ascribed to technical noise from sample processing, which could also skew the observed covariation between taxa. Our analyses also supported hypotheses that human gut microbiota fluctuate on sub-daily timescales in the absence of a host and that microbiota can follow replicable trajectories in the presence of environmental driving forces. Finally, multiple aspects of our approach are generalizable and could ultimately be used to facilitate the design and analysis of longitudinal microbiota studies in vivo.

--- https://www.biorxiv.org/content/10.1101/324327.full Anti-aging food that improves markers of health in senior dogs by modulating gut microbiota and metabolite profiles Eden Ephraim Gebreselassie, Matthew I. Jackson, Maha Yerramilli, Dennis E. Jewell 2018-05-16 2020-08-02 [("doi","10.1101/324327")] dog genetics/microbiome longevity

Dysbiosis is one of the major changes in aging that leads to an accumulation of toxic microbial metabolites. The aim of this study was to evaluate the effect of a test food containing components of citrus, carrot, spinach and tomato on gut microbiota and age-related metabolites in senior dogs.

The study was conducted on 36 dogs between 8 and 13 years of age. All dogs were maintained on a control food (control 1), which used corn as major source of fiber. After 30 days, the dogs were divided into two groups of 18 dogs. One of the groups received the test food for 30 days while the other group received the control 2 food, containing multiple whole grains as the test food but without the above added sources of fiber present in the test food. After a washout period on the control 1 food for 30 days, a cross-over was performed so that the test or the control 2 food was fed for 30 days to those dogs which had not yet been fed that food.

Samples from feces and blood were collected after each 30 days period to analyze changes in gut microbial composition and metabolites. The consumption of the test food led to increased proportions of Adlercreutzia, Oscillospira, Phascolarcobacteria, Faecalibacterium and Ruminococcus, Christensenellaceae, Ruminococcaceae, Cyanobacteria and Acidobacteria and decreased proportions of Megamonas, Salmonella, Enterobacteriaceae and Fusobacterium. Pets had higher levels of glycerol and fatty acids and lower levels of pyrraline and mucin amino acids in feces. The test food also reduced circulating levels of pyrraline, symmetric dimethylarginine and phenolic uremic toxins, including the microbial brain toxin, 4-ethylphenyl sulfate. Christensenellaceae abundance was strongly associated with the observed health benefits.

Fermentable fibers from fruits and vegetables enhance health in senior dogs by modulating the gut bacteria and metabolites involved in aging, kidney, brain and gut health.

--- https://www.biostasis.com/pdfs/hostile.pdf 2020-08-02 cryonics --- https://bldgblog.com/2008/10/zones-of-exclusion/ Zones of Exclusion 2020-08-02 psychology/cognitive-bias/illusion-of-depth --- https://bldgblog.com/2009/04/atmospheric-intoxication/ Atmospheric Intoxication 2020-08-02 psychology/smell --- https://bldgblog.com/2011/06/a-can-of-air-or-c-s-i-duchamp/ A Can of Air, or: C.S.I. Duchamp 2020-08-02 psychology/smell --- https://www.bloomberg.com/news/articles/2014-10-22/koreas-sooam-biotech-is-the-worlds-first-animal-cloning-factory For $100,000, You Can Clone Your Dog: These two were made to order in a South Korean lab. They’re only the beginning 2020-08-02 genetics/cloning/dog --- https://www.bloomberg.com/news/articles/2014-12-04/kitty-litters-invention-spawned-pet-cat-industry Kitty Litter's Invention Spawned Pet Cat Industry 2020-08-02 cat/biology economics psychology/smell --- https://www.bloomberg.com/news/articles/2017-08-07/horse-clones-start-heading-to-the-races Horse Clones Start Heading to the Races 2020-08-02 genetics/cloning --- https://www.bloomberg.com/news/articles/2017-09-05/the-climate-engineers-sucking-co2-from-the-atmosphere-and-making-money-doing-it The Climate Engineers Sucking CO2 From the Atmosphere 2020-08-02 technology/carbon-capture --- https://www.bloomberg.com/news/articles/2017-09-27/legalized-cannabis-may-be-a-boon-for-mcdonald-s-and-taco-bell Legalized Cannabis May Be a Windfall for McDonald’s and Taco Bell 2020-08-02 marijuana --- https://www.bloomberg.com/news/articles/2018-12-05/china-fiercely-decries-he-jiankui-s-human-gene-editing The government has fiercely decried a Shenzhen scientist's gene editing, in contrast to its push past ethical barriers in AI 2020-08-02 philosophy/ethics --- https://www.bloomberg.com/news/articles/2021-09-17/picking-embryos-with-best-health-odds-sparks-new-dna-debate Picking Embryos With Best Health Odds Sparks New DNA Debate: Science could allow parents to select for taller, smarter kids; It’s just another way of preventing disease 2020-08-03 philosophy/ethics --- https://www.bloomberg.com/news/features/2018-05-15/google-amazon-and-facebook-owe-j-rgen-schmidhuber-a-fortune Google, Amazon, and Facebook Owe Jürgen Schmidhuber a Fortune 2020-08-03 psychology/novelty --- https://www.bloomberg.com/opinion/articles/2017-10-17/why-weinstein-held-on-for-so-long-and-fell-so-fast Why Weinstein Held On For So Long and Fell So Fast 2020-08-03 sociology/preference-falsification --- https://www.blopig.com/blog/2020/12/casp14-what-google-deepminds-alphafold-2-really-achieved-and-what-it-means-for-protein-folding-biology-and-bioinformatics/ CASP14: what Google DeepMind’s AlphaFold 2 really achieved, and what it means for protein folding, biology and bioinformatics 2020-08-03 ai/nn/transformer/alphafold --- https://www.blopig.com/blog/2021/07/alphafold-2-is-here-whats-behind-the-structure-prediction-miracle/ AlphaFold 2 is here: what’s behind the structure prediction miracle 2020-08-03 ai/nn/transformer/alphafold --- https://www.bls.gov/opub/mlr/2021/article/the-us-productivity-slowdown-the-economy-wide-and-industry-level-analysis.htm The U.S. productivity slowdown: an economy-wide and industry-level analysis 2020-08-03 economics/automation --- https://www.bmj.com/about-bmj/resources-authors/article-types/christmas-issue BMJ Christmas issue 2020-08-03 fiction/humor math/humor --- https://www.bmj.com/content/348/bmj.g3253 Use of placebo controls in the evaluation of surgery: systematic review Wartolowska 2014 2020-08-03 philosophy/ethics statistics/bias --- https://www.brainpreservation.org/21cm-aldehyde-stabilized-cryopreservation-eval-page/ 21CM Aldehyde Stabilized Cryopreservation Eval Page 2020-08-03 cryonics --- https://www.brainpreservation.org/asc_rabbit_fulleval/ Aldehyde Stabilized Cryopreserved Rabbit Brain Evaluation Images 2020-08-03 cryonics --- https://www.brainpreservation.org/ken-hayworths-personal-response-to-mit-technology-review-article/ Ken Hayworth’s personal response to MIT Technology Review article 2020-08-03 cryonics --- https://www.brainpreservation.org/opinion-the-prize-win-is-a-vindication-of-the-idea-of-cryonics-not-of-unaccountable-cryonics-service-organizations/ Opinion: The prize win is a vindication of the idea of cryonics, not of unaccountable cryonics service organizations 2020-08-04 cryonics --- https://www.brainpreservation.org/small-mammal-announcement/ Small Mammal BPF Prize Winning Announcement 2020-08-04 cryonics --- https://www.britannica.com/technology/hectograph Hectograph Printing, Duplication, Copying 2020-08-04 longevity/johan-bjorksten --- https://www.buckinstitute.org/news/the-first-non-invasive-biomarker-to-track-and-verify-efficacy-of-senolytic-drugs/ The first non-invasive biomarker to track and verify efficacy of senolytic drugs 2020-08-04 longevity/senolytic --- https://www.businessinsider.com/peter-thiel-psychedelic-research-startup-mushrooms-psilocybin-depression-atai-2018-9 Atai: Biotech Company Funding Research on Psilocybin, Other Drugs for Depression 2020-08-04 psychedelic --- https://www.businessinsider.com/the-great-marijuana-crash-of-2011-2013-9 The Great Marijuana Crash of 2011 2020-08-04 marijuana --- https://www.businesswire.com/news/home/20181129005208/en/Ancestry-Breaks-November-Sales-Record 2020-08-04 economics/experience-curve genetics/heritable --- https://www.buzzfeed.com/joshdean/are-we-warming-up-to-cryonics Inside The Immortality Business 2020-08-04 cryonics --- https://www.buzzfeednews.com/article/chrishamby/super-court Inside The Global ‘Club’ That Helps Executives Escape Their Crimes 2020-08-04 crime economics --- https://www.buzzfeednews.com/article/stephaniemlee/russell-berger-crossfit-science-lgbt CrossFit’s ‘Holy War’: The Rise And Fall Of Its Science Crusader 2020-08-04 exercise --- https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/ How a Kalman filter works, in pictures 2020-08-05 statistics/bayes --- https://www.cambridge.org/core/journals/psychological-medicine/article/genetic-contributions-to-autism-spectrum-disorder/89240047F6928249D9DE91A6A6CFBD52 Genetic contributions to autism spectrum disorder 2020-08-05 genetics/heritable/rare --- https://www.cambridge.org/core/journals/psychological-medicine/article/rapid-antidepressant-effects-of-the-psychedelic-ayahuasca-in-treatmentresistant-depression-a-randomized-placebocontrolled-trial/E67A8A4BBE4F5F14DE8552DB9A0CBC97 Rapid antidepressant effects of the psychedelic ayahuasca in treatment-resistant depression: a randomized placebo-controlled trial 2020-08-05 psychedelic --- https://www.cambridge.org/core/journals/the-british-journal-of-psychiatry The British Journal of Psychiatry 2020-08-05 psychology/personality --- https://www.cambridge.org/core/journals/twin-research-and-human-genetics/article/genetics-of-regular-exercise-and-sedentary-behaviors/54A08EBF9C99D495DE91DE92214F5D85 Genetics of Regular Exercise and Sedentary Behaviors 2020-08-05 exercise --- https://www.cambridge.org/core/journals/twin-research-and-human-genetics/article/personality-polygenes-positive-affect-and-life-satisfaction/4DB2BE673BF122FB9A0AF2147EED80C0 Personality Polygenes, Positive Affect, and Life Satisfaction 2020-08-05 genetics/heritable psychology/personality --- https://www.cbsnews.com/news/the-clones-of-polo/ The Clones of Polo—Adolfo Cambiaso interview 2020-08-05 genetics/cloning --- https://www.cell.com/ajhg/fulltext/S0002-9297(15)00245-1 2020-08-05 genetics/heritable/rare --- https://www.cell.com/cell-reports/fulltext/S2211-1247(19)30181-0 2020-08-05 genetics/microbiome --- https://www.cell.com/cell-stem-cell/fulltext/S1934-5909%2816%2900018-7 Complete Meiosis from Embryonic Stem Cell-Derived Germ Cells In Vitro 2020-08-05 genetics/gametogenesis --- https://www.cell.com/cell/fulltext/S0092-8674(21)00077-5 2020-08-05 psychedelic --- https://character.ai/ 308 Permanent Redirect 2020-08-06 ai/nn/transformer/gpt/lamda --- https://www.chemistryworld.com/features/step-by-step-synthesis-of-dna/3008753.article Step-by-step synthesis of DNA: Andy Extance discovers how scientists are delivering the extremely accurate DNA chemistry and biochemistry needed to make genes—and even genomes 2020-08-06 genetics/genome-synthesis --- https://www.chrisstucchio.com/blog/2014/equal_weights.html Why a pro/con list is 75% as good as your fancy machine learning algorithm 2020-08-06 genetics/selection/artificial/index-selection statistics/decision --- https://www.cmaj.ca/content/184/18/2021.full The Mayan Doomsday’s effect on survival outcomes in clinical trials 2020-08-06 statistics/survival-analysis --- https://www.cnn.com/2011/09/30/tech/innovation/sniffer-dog-clone-incheon/ ‘Super clone’ sniffer dogs: Coming to an airport near you? 2020-08-06 genetics/cloning/dog --- https://www.coderelay.io/fontemon.html Fontemon 2020-08-06 cs/computable design/typography fiction/humor --- https://www.collectorsweekly.com/articles/the-mao-mango-cult-of-1968/ The Mao Mango Cult of 1968 and the Rise of China's Working Class 2020-08-06 history sociology/preference-falsification --- https://www.commentary.org/articles/edward-wilson/storm-over-biology-by-bernard-d-davis/ Storm Over Biology 2020-08-06 philosophy/ethics --- https://copenhagenconsensus.com/sites/default/files/CP+-+Hunger+FINISHED.pdf Copenhagen Consensus—Challenges and Opportunities: Hunger and Malnutrition 2020-08-06 iq/ses --- https://cryonics.org/member-statistics/ Member Statistics 2020-08-06 cryonics --- https://www.cryonicscalculator.com/ 2020-08-06 cryonics --- https://www.cs.cmu.edu/~afyshe/papers/acl2014/jnnse_acl2014.pdf 2020-08-07 reinforcement-learning/imitation-learning/brain-imitation-learning --- https://www.dailygrail.com/2014/07/magic-mushrooms-were-the-inspiration-for-frank-herberts-science-fiction-epic-dune/ Magic Mushrooms were the Inspiration for Frank Herbert’s Science Fiction Epic Dune 2020-08-07 psychedelic --- https://www.dailymail.co.uk/news/article-6810711/Mother-four-shelled-50K-clone-beloved-toy-poodle-resulting-THREE-identical-puppies.html EXCLUSIVE: ‘I am not a crazy dog lady, I just wanted him to live on.’ How mother-of-four shelled out $50K to clone her beloved toy poodle resulting in 3 identical puppies—and is going to DUPLICATE her dog’s CLONES too 2020-08-07 genetics/cloning/dog --- https://www.dailymail.co.uk/sciencetech/article-5924809/Worlds-cloned-dog-Miracle-Milly-copied-record-breaking-49-times.html World's most cloned dog 'Miracle Milly' has been copied 49 times by scientists in a bid to find the reason behind her record-breaking tiny size 2020-08-07 genetics/cloning/dog --- https://www.dailystar.co.uk/news/latest-news/vladimir-putin-cloned-dogs-war-17077077 Putin’s CLONED dogs of war: Special forces to unleash ‘designer canines’: VLADIMIR Putin has unveiled his secret war weapon—cloned dogs with the ability to sniff out explosives. 2020-08-07 genetics/cloning/dog --- https://www.dailystar.co.uk/news/weird-news/billionaires-cloned-dog-saves-lives-20895267 Billionaire’s cloned dog saves lives of elderly couple after drone falls from sky: EXCLUSIVE: Alki David’s cloned pup Vader managed to deflect a speeding drone before it caused serious injury to the pair 2020-08-07 genetics/cloning/dog --- https://www.datasecretslox.com/index.php/topic,170.msg2369.html#msg2369 Long book review: Zoning Rules! 2020-08-07 economics/georgism --- https://www.discovermagazine.com/mind/the-amazing-psychedelic-bamboozle The Amazing Psychedelic Bamboozle 2020-08-07 psychedelic --- https://www.discovermagazine.com/mind/the-handeloh-happening-psychedelic-poisoning 2020-08-07 psychedelic --- https://www.duo.uio.no/bitstream/handle/10852/52700/1/Masteroppgave_Filosofi_V-r2016_4427.pdf From Humanity to Posthumanity: Moral Questions Concerning Radical Enhancement 2020-08-07 genetics/gametogenesis --- https://www.dw.com/en/china-convicts-uighurs-in-sham-trials-at-xinjiang-camps/a-53699982 More than 1 million Uighurs have disappeared into China's internment camps in Xinjiang province. A DW investigation reveals how many of them were tried for their alleged 'crimes' in sham trials. 2020-08-07 crime history/uighur --- https://www.dw.com/en/the-stasi-had-a-giant-smell-register-of-dissidents/a-2555053 Stasi Surveillance 2020-08-08 history psychology/smell --- https://www.econlib.org/archives/2012/09/the_autobiograp.html The Autobiography of Malcolm X Book Club, Part 2 2020-08-08 crime sociology --- https://www.econlib.org/archives/2014/07/a_non-conformis.html A Non-Conformist's Guide to Success in a Conformist World 2020-08-08 psychology/novelty --- https://www.econlib.org/archives/2016/10/what_do_crimina.html What do criminal records tell us about Adam Smith and the Industrial Revolution? 2020-08-08 crime economics --- https://www.economist.com/1843/2016/05/03/does-power-really-corrupt Does power really corrupt? 2020-08-08 philosophy/ethics psychology sociology --- https://www.economist.com/briefing/2015/04/04/the-paradox-of-soil The paradox of soil 2020-08-08 economics/georgism --- https://www.economist.com/science-and-technology/2021/09/18/the-worlds-biggest-carbon-removal-plant-switches-on The world’s biggest carbon-removal plant switches on 2020-08-08 technology/carbon-capture --- https://www.econstor.eu/bitstream/10419/76612/1/cesifo_wp1173.pdf 2020-08-08 economics/advertising sociology --- https://www.econstor.eu/bitstream/10419/86359/1/04-087.pdf 2020-08-08 iq/ses --- https://www.econtalk.org/de-vany-on-steroids-baseball-and-evolutionary-fitness/ Arthur De Vany on Steroids, Baseball, and Evolutionary Fitness 2020-08-08 exercise --- https://www.edge.org/response-detail/11825 2020-08-09 psychology/novelty --- https://www.ediblegeography.com/sweet-and-sour-soils/ Sweet and Sour Soils 2020-08-09 psychology/smell --- https://www.eff.org/pages/speaking-freely-ada-palmer Speaking Freely: Ada Palmer 2020-08-09 philosophy/ethics --- https://www.erowid.org/chemicals/ketamine/ketamine.shtml Erowid Ketamine Vault 2020-08-09 psychedelic --- https://www.erowid.org/chemicals/lsd/lsd_article1.shtml LSD Analysis: Do we know what’s in street acid? 2020-08-09 nootropic psychedelic/lsd --- https://www.erowid.org/chemicals/lsd/lsd_article2.shtml LSD Purity 2020-08-09 nootropic psychedelic --- https://www.erowid.org/chemicals/lsd/lsd_dose1.shtml Abstracts regarding low doses of LSD (Less than 50μg) 2020-08-09 nootropic/lsd psychedelic --- https://www.erowid.org/experiences/subs/exp_Ketamine.shtml Ketamine 2020-08-09 psychedelic --- https://www.erowid.org/library/books_online/tihkal/tihkal35.shtml TIHKAL: #35 Melatonin 2020-08-09 melatonin --- https://www.exurbe.com/why-we-keep-asking-was-machiavelli-an-atheist/ Machiavelli V: Why We Keep Asking ‘Was Machiavelli an Atheist?’ 2020-08-09 philosophy/ethics --- https://www.fast.ai/2018/08/10/fastai-diu-imagenet/ Now anyone can train Imagenet in 18 minutes 2020-08-09 ai/nn/cnn cs economics/experience-curve --- https://www.fastcompany.com/90356326/we-have-the-tech-to-suck-co2-from-the-air-but-can-it-suck-enough-to-make-a-difference 2020-08-10 technology/carbon-capture --- https://www.fhi.ox.ac.uk/brain-emulation-roadmap-report.pdf 2020-08-10 reinforcement-learning/imitation-learning/brain-imitation-learning --- https://www.fhi.ox.ac.uk/deciphering-chinas-ai-dream/ Deciphering China's AI Dream 2020-08-10 ai/scaling sociology technology --- https://www.fhi.ox.ac.uk/wp-content/uploads/2021/08/QNRs_FHI-TR-2021-3.0.pdf 2020-08-10 ai/nn/transformer/gpt/inner-monologue philosophy/epistemology psychology/linguistics technology --- https://www.fightaging.org/archives/2018/01/how-to-plan-and-carry-out-a-simple-self-experiment-a-single-person-trial-of-chemotherapeutic-senolytic-drug-candidates/ How to Plan and Carry Out a Simple Self-Experiment, a Single Person Trial of Chemotherapeutic Senolytic Drug Candidates 2020-08-10 longevity/senolytic --- https://www.fightaging.org/archives/2018/09/thoughts-on-attending-raadfest-2018-in-san-diego/ Thoughts on Attending RAADfest 2018 in San Diego 2020-08-10 longevity/senolytic --- https://www.fightaging.org/archives/2020/07/fitness-in-humans-acts-to-reduce-inflammation-but-does-not-reduce-the-burden-of-cellular-senescence-in-muscle-tissue/ Fitness in Humans Acts to Reduce Inflammation, But Does Not Reduce the Burden of Cellular Senescence in Muscle Tissue 2020-08-10 exercise --- https://www.fightaging.org/archives/2020/11/overhyping-the-effects-of-hyperbaric-oxygen-treatment-on-aging/ Overhyping the Effects of Hyperbaric Oxygen Treatment on Aging 2020-08-10 longevity/senolytic --- https://www.fightaging.org/archives/2021/11/a-senolytic-treatment-improves-visual-function-in-a-small-trial-for-macular-degeneration/ A Senolytic Treatment Improves Visual Function in a Small Trial for Macular Degeneration 2020-08-10 longevity/senolytic --- https://www.filfre.net/2012/01/zil-and-the-z-machine/ ZIL and the Z-Machine 2020-08-10 fiction/text-game technology/digital-antiquarian --- https://www.filfre.net/2012/07/britains-occult-uncle/ Britain’s Occult Uncle 2020-08-10 crime fiction/text-game technology/digital-antiquarian --- https://www.filfre.net/2012/07/playing-deadline-part-1/ Playing Deadline, Part 1 2020-08-11 crime fiction/text-game technology/digital-antiquarian --- https://www.filfre.net/2012/07/playing-deadline-part-2/ Playing Deadline, Part 2 2020-08-11 crime fiction/text-game technology/digital-antiquarian --- https://www.filfre.net/2012/07/playing-deadline-part-3/ Playing Deadline, Part 3 2020-08-11 crime fiction/text-game technology/digital-antiquarian --- https://www.filfre.net/2012/07/playing-deadline-part-4/ Playing Deadline, Part 4 2020-08-11 crime fiction/text-game technology/digital-antiquarian --- https://www.filfre.net/2012/07/the-dennis-wheatley-crime-dossiers/ The Dennis Wheatley Crime Dossiers 2020-08-11 crime fiction/text-game technology/digital-antiquarian --- https://www.filfre.net/2013/11/the-computerized-hitchhikers/ The Computerized Hitchhiker’s 2020-08-11 fiction/science-fiction fiction/text-game technology/digital-antiquarian --- https://www.filfre.net/2014/03/mindwhell-or-the-poet-and-the-hackers/ Mindwheel (or, The Poet and the Hackers) 2020-08-11 fiction/text-game technology/digital-antiquarian --- https://www.filfre.net/2014/04/a-mind-forever-voyaging-part-1-steve-meretzkys-interiors/ A Mind Forever Voyaging, Part 1: Steve Meretzky’s Interiors 2020-08-11 fiction/text-game technology/digital-antiquarian --- https://www.filfre.net/2014/04/a-mind-forever-voyaging-part-2-dont-go-back-to-rockvil/ A Mind Forever Voyaging, Part 2: Don’t Go Back to Rockvil 2020-08-11 fiction/text-game technology/digital-antiquarian --- https://www.filfre.net/2014/05/a-mind-forever-voyaging-part-3-through-strange-seas-of-thought-alone/ A Mind Forever Voyaging, Part 3: Through Strange Seas of Thought, Alone 2020-08-11 fiction/text-game technology/digital-antiquarian --- https://www.filfre.net/2017/12/games-on-the-net-before-the-web-part-2-mud/ Games on the Net Before the Web, Part 2: MUD 2020-08-12 fiction/text-game technology/digital-antiquarian --- https://www.filfre.net/2019/02/ultima-vii/ Ultima VII 2020-08-12 technology/digital-antiquarian --- https://www.filfre.net/2019/04/an-unlikely-savior/ An Unlikely Savior 2020-08-12 technology/digital-antiquarian --- https://www.filfre.net/2019/06/day-of-the-tentacle/ Day of the Tentacle 2020-08-12 technology/digital-antiquarian --- https://www.filfre.net/2019/06/sam-and-max-hit-the-road/ Sam and Max Hit the Road 2020-08-12 technology/digital-antiquarian --- https://www.filfre.net/2019/07/the-mortgaging-of-sierra-online/ The Mortgaging of Sierra Online 2020-08-12 technology/digital-antiquarian --- https://www.filfre.net/2019/08/alone-in-the-dark/ Alone in the Dark 2020-08-12 technology/digital-antiquarian --- https://www.filfre.net/2019/09/opening-the-gold-box-part-6-a-troubled-marriage/ Opening the Gold Box, Part 6: A Troubled Marriage 2020-08-12 technology/digital-antiquarian --- https://www.filfre.net/2019/09/origin-sells-out/ Origin Sells Out 2020-08-12 technology/digital-antiquarian --- https://www.filfre.net/2019/10/new-tricks-for-an-old-z-machine-part-1-digging-the-trenches/ New Tricks for an Old Z-Machine, Part 1: Digging the Trenches 2020-08-12 fiction/text-game technology/digital-antiquarian --- https://www.filfre.net/2019/11/new-tricks-for-an-old-z-machine-part-2-hacking-deeper-or-follies-of-graham-nelsons-youth/ New Tricks for an Old Z-Machine, Part 2: Hacking Deeper (or, Follies of Graham Nelson’s Youth) 2020-08-12 technology/digital-antiquarian --- https://www.filfre.net/2019/11/new-tricks-for-an-old-z-machine-part-3-a-renaissance-is-nigh/ New Tricks for an Old Z-Machine, Part 3: A Renaissance is Nigh 2020-08-13 fiction/text-game technology/digital-antiquarian --- https://www.filfre.net/2020/01/buzz-aldrins-race-into-space-and-space-program-games-in-general/ Buzz Aldrin’s Race into Space (and Space-Program Games in General) 2020-08-13 technology/digital-antiquarian --- https://www.filfre.net/2020/02/myst-or-the-drawbacks-to-success/ Myst (or, The Drawbacks to Success) 2020-08-13 technology/digital-antiquarian --- https://www.filfre.net/2020/03/the-68000-wars-part-6-the-unraveling/ The 68000 Wars, Part 6: The Unraveling 2020-08-13 technology/digital-antiquarian --- https://www.filfre.net/2020/04/the-7th-guests-new-clothes/ The (7th) Guest’s New Clothes 2020-08-13 technology/digital-antiquarian --- https://www.filfre.net/2020/04/the-shareware-scene-part-1-the-pioneers/ The Shareware Scene, Part 1: The Pioneers 2020-08-13 technology/digital-antiquarian --- https://www.filfre.net/2020/05/the-shareware-scene-part-2-the-question-of-games/ The Shareware Scene, Part 2: The Question of Games 2020-08-13 technology/digital-antiquarian --- https://www.filfre.net/2020/05/the-shareware-scene-part-3-the-id-boys/ The Shareware Scene, Part 3: The id Boys 2020-08-13 technology/digital-antiquarian --- https://www.filfre.net/2020/06/the-shareware-scene-part-5-narratives-of-doom/ The Shareware Scene, Part 5: Narratives of DOOM 2020-08-13 technology/digital-antiquarian --- https://www.filfre.net/2020/07/under-a-killing-moon/ Under a Killing Moon 2020-08-13 technology/digital-antiquarian --- https://www.filfre.net/2020/08/death-gate/ Death Gate 2020-08-13 technology/digital-antiquarian --- https://www.filfre.net/2020/09/bullfrog-after-populous/ Bullfrog after Populous 2020-08-14 technology/digital-antiquarian --- https://www.filfre.net/2020/10/master-of-magic/ Master of Magic 2020-08-14 technology/digital-antiquarian --- https://www.filfre.net/2020/11/opening-the-gold-box-part-7-back-to-the-roots/ Opening the Gold Box, Part 7: Back to the Roots 2020-08-14 technology/digital-antiquarian --- https://www.filfre.net/2020/12/ethics-in-strategy-gaming-part-2-colonization/ Ethics in Strategy Gaming, Part 2: Colonization (Just what do you do next after you’ve created an epic, career-defining masterpiece? That was the question facing Sid Meier after the release of Civilization in the waning days of 1991, after the gushing reviews and the impressive sales figures had begun pouring in to his employer MicroProse.) 2020-08-14 technology/digital-antiquarian --- https://www.filfre.net/2020/12/lode-runner/ Lode Runner 2020-08-14 technology/digital-antiquarian --- https://www.filfre.net/2021/01/the-dream-of-flight/ The Dream of Flight 2020-08-14 technology/digital-antiquarian --- https://www.filfre.net/2021/02/the-second-coming-of-star-wars/ The Second Coming of Star Wars 2020-08-14 technology/digital-antiquarian --- https://www.filfre.net/2021/03/wing-commander-iii/ Wing Commander III 2020-08-14 technology/digital-antiquarian --- https://www.filfre.net/2021/04/the-ratings-game-part-1-a-likely-and-an-unlikely-suspect/ The Ratings Game, Part 1: A Likely and an Unlikely Suspect 2020-08-14 technology/digital-antiquarian --- https://www.filfre.net/2021/05/the-ratings-game-part-4-e3-and-beyond/ The Ratings Game, Part 4: E3 and Beyond 2020-08-14 technology/digital-antiquarian --- https://www.filfre.net/2021/06/bob-stein-and-voyager/ Bob Stein and Voyager 2020-08-15 technology/digital-antiquarian --- https://www.filfre.net/2021/09/shannara-or-bookware-mark-2/ Shannara 2020-08-15 technology/digital-antiquarian --- https://www.filfre.net/2021/10/i-have-no-mouth-and-i-must-scream/ I Have No Mouth, and I Must Scream 2020-08-15 technology/digital-antiquarian --- https://www.filfre.net/2021/11/the-dark-eye/ The Dark Eye 2020-08-15 technology/digital-antiquarian --- https://www.filfre.net/2022/01/a-web-around-the-world-part-2-if-at-first-you-dont-succeed/ A Web Around the World, Part 2: If At First You Don’t Succeed… 2020-08-15 technology/digital-antiquarian --- https://www.filfre.net/sitemap/ The Digital Antiquarian Table of Contents 2020-08-15 fiction/text-game technology/digital-antiquarian --- https://www.firstthings.com/article/2020/10/suicide-of-the-liberals Suicide of the Liberals Gary Saul Morson 2020-08-15 philosophy/ethics sociology --- https://web.archive.org/web/20230525085828/https://www.frc.ri.cmu.edu/~hpm/project.archive/general.articles/1991/TempComp.html Time Travel and Computing Hans Moravec 1991 2020-08-15 cs/computable fiction/science-fiction/time-travel science --- https://www.frontiersin.org/journals/climate/articles/10.3389/fclim.2019.00009/full 2020-08-15 technology/carbon-capture --- https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2021.722187/full 2020-08-15 exercise genetics/heritable longevity --- https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2016.00149/full 2020-08-15 psychology/personality --- https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2017.00092/full Computational Analysis of Lifespan Experiment Reproducibility 2020-08-16 longevity statistics/bias statistics/power-analysis --- https://www.frontiersin.org/articles/10.3389/fnagi.2021.646924/full 2020-08-16 longevity/senolytic --- https://www.frontiersin.org/articles/10.3389/fpsyg.2012.00130/full 2020-08-16 psychology/neuroscience --- https://www.frontiersin.org/articles/10.3389/frobt.2017.00071/full 2020-08-16 reinforcement-learning/preference-learning --- https://www.fsigenetics.com/article/S1872-4973(18)30248-5/fulltext NIST interlaboratory studies involving DNA mixtures (MIX05 and MIX13): Variation observed and lessons learned 2020-08-16 crime --- https://www.ft.com/content/da7b86a3-a8a7-4a0b-a26f-38abda7e7f86 Switzerland’s ‘Silicon Valley of smell’ prospers in age of big data 2020-08-16 economics psychology/smell --- https://www.furidamu.org/blog/2020/12/22/muzero-intuition/ MuZero Intuition 2020-08-16 reinforcement-learning/model/muzero --- https://www.gamedeveloper.com/business/digital-real-estate-and-the-digital-housing-crisis Land speculators will kill your game's growth 2020-08-16 economics/georgism --- https://www.gatsby.ucl.ac.uk/~yael/Okinawa/DuffThesis.pdf Optimal Learning: Computational Procedures for Bayes-Adaptive Markov Decision Processes 2020-08-16 reinforcement-learning/meta-learning --- https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Costs-Data DNA Sequencing Costs: Data 2020-08-16 economics/experience-curve genetics/heritable --- https://www.genome.gov/about-genomics/fact-sheets/Sequencing-Human-Genome-cost The Cost of Sequencing a Human Genome 2020-08-16 economics/experience-curve genetics/heritable --- https://www.globaltimes.cn/content/1161960.shtml Chinese gene firm clones cat, sparking wide consumer interest 2020-08-17 genetics/cloning --- https://www.google.com/patents?id=4qxmAAAAEBAJ&printsec=abstract&zoom=4#v=onepage&q&f=false 2020-08-17 longevity/johan-bjorksten --- https://www.gq.com/story/got-high-with-mom-hempcon I Got High With My Mom at HempCon 2020-08-17 marijuana --- https://www.gq.com/story/green-angels-weed-delivery-models-new-york Queens of the Stoned Age 2020-08-17 marijuana --- https://www.guernicamag.com/raising-a-stink/ Raising a Stink 2020-08-17 economics japan --- https://www.harvardmagazine.com/2012/02/twilight-of-the-lecture 2012-03 2020-08-17 psychology/cognitive-bias/illusion-of-depth statistics/causality --- https://www.heraldsun.com.au/archive/news/time-to-send-in-the-clone-dogs/news-story/3121ec9c41ed41b961f90a018bb7db1d Top Australian sniffer dog set to be cloned 2020-08-17 genetics/cloning/dog --- https://www.humanistictexts.org/carvaka.htm 2020-08-17 philosophy/ethics --- https://www.independent.ie/world-news/asia-pacific/child-expert-grants-dying-boys-wish-to-have-sex/26065896.html Child expert grants dying boy's wish to have sex 2020-08-17 philosophy/ethics --- https://www.instyle.com/celebrity/britney-spears-perfume-billion-dollar-business 2020-08-17 economics psychology/smell/perfume --- https://www.ipcc.ch/sr15/chapter/spm/ Summary for Policymakers: Global Warming of 1.5ºC 2020-08-17 technology/carbon-capture --- https://www.isegoria.net/2021/04/no-one-craves-scentlessness/ No one craves scentlessness 2020-08-18 psychology/smell --- https://www.isomorphiclabs.com/blog 2020-08-18 ai/nn/transformer/alphafold --- https://www.jameslindlibrary.org/articles/inventing-the-randomized-double-blind-trial-the-nurnberg-salt-test-of-1835/ Inventing the randomized double-blind trial: The Nürnberg salt test of 1835 2020-08-18 psychology statistics/bias statistics/causality --- https://www.jefftk.com/p/breaking-down-cryonics-probabilities Breaking Down Cryonics Probabilities 2020-08-18 cryonics --- https://www.jhep-reports.eu/article/S2589-5559(21)00077-X/fulltext 2020-08-18 longevity/senolytic --- https://www.jsad.com/doi/abs/10.15288/jsad.2020.81.115 2020-08-18 psychedelic --- https://www.kernel.com/news/why-i-joined-kernel 2020-08-18 psychology/neuroscience --- https://www.krtv.com/news/national/family-uses-pet-cloning-service-to-get-carbon-copy-of-golden-doodle Would you do it? Family uses service to clone dog 2020-08-18 genetics/cloning --- https://www.laphamsquarterly.org/future/trust-issues Trust Issues 2020-08-18 economics/perpetuities philosophy/ethics --- https://www.lesswrong.com/lw/gln/a_brief_history_of_ethically_concerned_scientists/ 2020-08-18 philosophy/ethics --- https://www.lesswrong.com/lw/ke/illusion_of_transparency_why_no_one_understands/ 2020-08-19 psychology/cognitive-bias/illusion-of-depth --- https://www.lesswrong.com/posts/2TPph4EGZ6trEbtku/explainers-shoot-high-aim-low Explainers Shoot High. Aim Low! 2020-08-19 psychology/cognitive-bias/illusion-of-depth --- https://www.lesswrong.com/posts/3daPPjWbjYNP6nbre/appendix-more-is-different-in-other-domains Appendix: More Is Different In Other Domains 2020-08-19 ai/scaling --- https://www.lesswrong.com/posts/4FNndynQhfKCWEyZe/the-brain-preservation-foundation-s-small-mammalian-brain The Brain Preservation Foundation's Small Mammalian Brain Prize won 2020-08-19 cryonics --- https://www.lesswrong.com/posts/4MLBK7iCW3vYd93Mn/a-closer-look-at-chess-scalings-into-the-past A closer look at chess scalings (into the past) 2020-08-19 cs economics/experience-curve reinforcement-learning/chess --- https://www.lesswrong.com/posts/5GnwjxbL3SQ7gjRn6/open-thread-july-16-22-2013#gFRKkcyXDTiKkug56 2020-08-19 anime/my-little-pony design psychology/novelty --- https://www.lesswrong.com/posts/68TGNutjDcBcq6PCZ/bitcoin-cryonics-fund#5Da2f8n9aXmfJ7FYA 2020-08-19 cryonics statistics/prediction --- https://www.lesswrong.com/posts/6tErqpd2tDcpiBrX9/why-sigmoids-are-so-hard-to-predict Why sigmoids are so hard to predict 2020-08-19 statistics/prediction --- https://www.lesswrong.com/posts/7GQZyooNi5nqgoyyJ/mlsn-2-adversarial-training [MLSN #2]: Adversarial Training 2020-08-19 ai/nn/adversarial --- https://www.lesswrong.com/posts/8MZ72PYa3kRe4yRDD/axrp-episode-1-adversarial-policies-with-adam-gleave AXRP Episode 1—Adversarial Policies with Adam Gleave 2020-08-19 ai/nn/adversarial reinforcement-learning/safe --- https://www.lesswrong.com/posts/9kcTNWopvXFncXgPy/intellectual-hipsters-and-meta-contrarianism Intellectual Hipsters and Meta-Contrarianism 2020-08-19 psychology/novelty statistics/causality --- https://www.lesswrong.com/posts/FRv7ryoqtvSuqBxuT/understanding-deep-double-descent Understanding ‘Deep Double Descent’ 2020-08-20 ai/scaling --- https://www.lesswrong.com/posts/FkgsxrGf3QxhfLWHG/risks-from-learned-optimization-introduction Risks from Learned Optimization: Introduction 2020-08-20 reinforcement-learning/meta-learning reinforcement-learning/safe --- https://www.lesswrong.com/posts/FpcgSoJDNNEZ4BQfj/the-unexpected-difficulty-of-comparing-alphastar-to-humans The unexpected difficulty of comparing AlphaStar to humans 2020-08-20 reinforcement-learning/model-free/alphastar --- https://www.lesswrong.com/posts/HvqQm6o8KnwxbdmhZ/estimating-training-compute-of-deep-learning-models Estimating training compute of Deep Learning models 2020-08-20 ai/scaling/hardware --- https://www.lesswrong.com/posts/J6gktpSgYoyq5q3Au/benchmarking-an-old-chess-engine-on-new-hardware Benchmarking an old chess engine on new hardware 2020-08-20 cs economics/experience-curve reinforcement-learning/chess --- https://www.lesswrong.com/posts/KrQvZM8uFjSTJ7hq3 Recent Progress in the Theory of Neural Networks 2020-08-20 ai --- https://www.lesswrong.com/posts/LpjjWDBXr88gzcYK2/learning-and-manipulating-learning Learning and manipulating learning 2020-08-20 reinforcement-learning/preference-learning --- https://www.lesswrong.com/posts/MSpfFBCQYw3YA8kMC/violating-the-emh-prediction-markets Violating the EMH—Prediction Markets 2020-08-20 statistics/prediction --- https://www.lesswrong.com/posts/NMoLJuDJEms7Ku9XS/guessing-the-teacher-s-password Guessing the Teacher's Password 2020-08-20 psychology/cognitive-bias/illusion-of-depth statistics/causality --- https://www.lesswrong.com/posts/TK5McpcF584e9mFCy/more-cryonics-probability-estimates More Cryonics Probability Estimates 2020-08-20 cryonics --- https://www.lesswrong.com/posts/TK5McpcF584e9mFCy/more-cryonics-probability-estimates85gm More Cryonics Probability Estimates 2020-08-20 cryonics --- https://www.lesswrong.com/posts/TQSb4wd6v5C3p6HX2/the-pascal-s-wager-fallacy-fallacy The Pascal's Wager Fallacy Fallacy 2020-08-21 cryonics --- https://www.lesswrong.com/posts/TiG8cLkBRW4QgsfrR/notes-on-brainwashing-and-cults Notes on Brainwashing & 'Cults' 2020-08-21 crime/terrorism sociology --- https://www.lesswrong.com/posts/WbbuyfoGE3d4Zadhv/cryonics-wants-to-be-big Cryonics Wants To Be Big 2020-08-21 cryonics --- https://www.lesswrong.com/posts/XfpJ6WQBDcEcC8Mu4/humans-are-utility-monsters Humans are utility monsters 2020-08-21 philosophy/ethics --- https://www.lesswrong.com/posts/YSFJosoHYFyXjoYWa/why-neural-networks-generalise-and-why-they-are-kind-of Why Neural Networks Generalise, and Why They Are (Kind of) Bayesian 2020-08-21 ai/scaling statistics/bayes --- https://www.lesswrong.com/posts/aNAFrGbzXddQBMDqh/moore-s-law-ai-and-the-pace-of-progress Moore's Law, AI, and the pace of progress 2020-08-21 ai/scaling/hardware --- https://www.lesswrong.com/posts/amK9EqxALJXyd9Rb2/paths-to-high-level-machine-intelligence Paths To High-Level Machine Intelligence 2020-08-21 reinforcement-learning/imitation-learning/brain-imitation-learning --- https://www.lesswrong.com/posts/cit3HYXehBsr4d36Q/open-thread-january-15-31-2012#rWkZ3TGRsL7DhSsNR 2020-08-21 economics/experience-curve genetics/heritable --- https://www.lesswrong.com/posts/dQWCDERoZjLLHWpys/cryonics-costs-given-estimates-are-low Cryonics costs: given estimates are low 2020-08-21 cryonics --- https://www.lesswrong.com/posts/dz3Mmr2Cykz6RRfhK/rationality-cryonics-and-pascal-s-wager Rationality, Cryonics and Pascal's Wager 2020-08-21 cryonics --- https://www.lesswrong.com/posts/evtKwDCgtQQ7ozLn4/randal-koene-on-brain-understanding-before-whole-brain Randal Koene on brain understanding before whole brain emulation 2020-08-22 reinforcement-learning/imitation-learning/brain-imitation-learning --- https://www.lesswrong.com/posts/fnjKpBoWJXcSDwhZk/what-s-the-backward-forward-flop-ratio-for-nns What’s the backward-forward FLOP ratio for Neural Networks? 2020-08-22 ai/scaling --- https://www.lesswrong.com/posts/ghtsPkTQ7JhZtjjsB/mike-darwin-on-animal-research-moral-cowardice-and-reasoning Mike Darwin on animal research, moral cowardice, and reasoning in an uncaring universe 2020-08-22 cryonics philosophy/ethics --- https://www.lesswrong.com/posts/hcrFxeYYfbFrkKQEJ/full-toy-model-for-preference-learning Full toy model for preference learning 2020-08-22 reinforcement-learning/preference-learning --- https://www.lesswrong.com/posts/hiDkhLyN5S2MEjrSE/normal-cryonics Normal Cryonics 2020-08-22 cryonics --- https://www.lesswrong.com/posts/hiDkhLyN5S2MEjrSE/normal-cryonics1hmp Normal Cryonics 2020-08-22 cryonics economics --- https://www.lesswrong.com/posts/ib9bfyJiz4FLuHDQs/openai-codex-first-impressions OpenAI Codex: First Impressions 2020-08-22 ai/nn/transformer/gpt/codex --- https://www.lesswrong.com/posts/mRwJce3npmzbKfxws/efficientzero-how-it-works EfficientZero: How It Works 2020-08-22 ai/video/generation reinforcement-learning/model/muzero --- https://www.lesswrong.com/posts/mkrvsNi8cYGSjGqkh/on-the-unpopularity-of-cryonics-life-sucks-but-at-least-then On the unpopularity of cryonics: life sucks, but at least then you die 2020-08-22 cryonics --- https://www.lesswrong.com/posts/nhjaegqWxbBhiqMGS/analysis-of-world-records-in-speedrunning-linkpost#cfeP5bC6Hq5ir9wwA Analysis of World Records in Speedrunning [LINKPOST] 2020-08-22 economics/experience-curve sociology --- https://www.lesswrong.com/posts/sBBGxdvhKcppQWZZE/double-illusion-of-transparency Double Illusion of Transparency 2020-08-22 psychology/cognitive-bias/illusion-of-depth --- https://www.lesswrong.com/posts/sSYmGhBpjTG88Q4FW/mentioning-cryonics-to-a-dying-person Mentioning cryonics to a dying person 2020-08-23 cryonics --- https://www.lesswrong.com/posts/sSYmGhBpjTG88Q4FW/mentioning-cryonics-to-a-dying-person#bGEMXD6h9RkdAC7DQ 2020-08-23 cryonics --- https://www.lesswrong.com/posts/ui6mDLdqXkaXiDMJ5/core-pathways-of-aging Core Pathways of Aging 2020-08-23 longevity --- https://www.lesswrong.com/posts/vAsQNjW3gbiskP9Wf/not-by-empathy-alone Not By Empathy Alone 2020-08-23 philosophy/ethics --- https://www.lesswrong.com/posts/wkuDgmpxwbu2M2k3w/you-have-a-set-amount-of-weirdness-points-spend-them-wisely You have a set amount of "weirdness points". Spend them wisely. 2020-08-23 psychology/novelty --- https://www.lesswrong.com/posts/xwBuoE9p8GE7RAuhd/brain-efficiency-much-more-than-you-wanted-to-know Brain Efficiency: Much More than You Wanted to Know 2020-08-23 ai/scaling/hardware psychology/neuroscience --- https://www.lesswrong.com/posts/yDqQ9P23yubmoxLD4/cryonics-is-far-cord-blood-is-near Cryonics is Far, Cord-blood is Near 2020-08-23 cryonics --- https://www.lesswrong.com/posts/ydeaHqDPJ5REJWvat/a-one-question-turing-test-for-gpt-3 A one-question Turing test for GPT-3 2020-08-23 ai/nn/transformer/gpt --- https://www.lesswrong.com/posts/z5EWwaLv4foHu7E8Y/a-review-of-cryonics-brain-preservation-in-2016 A review of cryonics/brain preservation in 2016 2020-08-23 cryonics --- https://www.lesswrong.com/posts/zCq4ca3tTcfQgrFZM/maths-writer-cowritter-needed-how-you-can-t-distinguish Maths writer/cowritter needed: how you can't distinguish early exponential from early sigmoid 2020-08-23 statistics/prediction --- https://www.lesswrong.com/posts/zRn6cLtxyNodudzhw/visible-thoughts-project-and-bounty-announcement Visible Thoughts Project and Bounty Announcement 2020-08-23 ai/nn/transformer/gpt/inner-monologue fiction/text-game --- https://www.lesswrong.com/tag/illusion-of-transparency Illusion of Transparency 2020-08-24 psychology/cognitive-bias/illusion-of-depth --- https://www.lesswrong.com/tag/inferential-distance Inferential Distance 2020-08-24 psychology/cognitive-bias/illusion-of-depth --- https://www.lifespan.io/news/dr-peter-de-keizer/ Peter de Keizer on senolytics 2020-08-24 longevity/senolytic --- https://www.longevity.technology/senescence-field-has-completely-exploded/ 2020-08-24 longevity/senolytic --- https://www.luckyscent.com/ The Best in Fragrance...and More 2020-08-24 psychology/smell/perfume --- https://www.mcgill.ca/economics/files/economics/Jonespaper.pdf 2020-08-24 iq/ses --- https://www.mcsweeneys.net/articles/the-ux-on-this-small-child-is-terrible The UX on this Small Child Is Terrible 2020-08-24 design --- https://www.mdpi.com/2073-4409/10/7/1740/htm 2020-08-24 longevity/senolytic --- https://www.mdpi.com/2073-4409/10/9/2284/htm 2020-08-24 genetics/selection --- https://www.mdpi.com/2075-1729/11/2/153/htm 2020-08-24 longevity/senolytic --- https://www.mdpi.com/2079-3200/5/2/11/htm 2020-08-25 iq/ses --- https://www.medrxiv.org/content/10.1101/2020.05.29.20115352.full Novel Ultra-Rare Exonic Variants Identified in a Founder Population Implicate Cadherins in Schizophrenia Todd Lencz, Jin Yu, Raiyan Rashid Khan, Shai Carmi, Max Lam, Danny Ben-Avraham, Nir Barzilai, Susan Bressman, Ariel Darvasi, Judy H. Cho, Lorraine N. Clark, Zeynep H. Gümüş, Joseph Vijai, Robert J. Klein, Steven Lipkin, Kenneth Offit, Harry Ostrer, Laurie J. Ozelius, Inga Peter, Anil K. Malhotra, Gil Atzmon, Itsik Pe’er 2020-09-11 2020-09-11 [("doi","10.1101/2020.05.29.20115352")] genetics/heritable/rare psychiatry/schizophrenia

Identification of rare genetic variants associated with schizophrenia has proven challenging due to multiple sources of heterogeneity, which may be reduced in founder populations. We examined ultra-rare exonic variants in 786 patients with schizophrenia and 463 healthy comparison subjects, all drawn from the Ashkenazi Jewish population. Cases had a higher frequency of novel missense or loss of function (MisLoF) variants compared to controls.

Characterizing 141 “case-only” genes (in which ≥ 3 cases in our dataset had MisLoF variants with none found in controls), we identified cadherins as a novel gene set associated with schizophrenia, including a recurrent mutation in PCDHA3. Modeling the effects of purifying selection demonstrated that deleterious ultra-rare variants are greatly over-represented in the Ashkenazi population, resulting in enhanced power for rare variant association.

Identification of cell adhesion genes in the cadherin/protocadherin family helps specify the synaptic abnormalities central to the disorder, and suggests novel potential treatment strategies.

--- https://www.medrxiv.org/content/10.1101/2020.07.21.20159228.full Genetic correlates of phenotypic heterogeneity in autism Varun Warrier, Xinhe Zhang, Patrick Reed, Alexandra Havdahl, Tyler M. Moore, Freddy Cliquet, Claire S. Leblond, Thomas Rolland, Anders Rosengren, EU-AIMS-LEAP, iPSYCH-Autism Working Group, Spectrum 10K, APEX Consortium, David H. Rowitch, Matthew E. Hurles, Daniel H. Geschwind, Anders Børglum, Elise B. Robinson, Jakob Grove, Hilary C. Martin, Thomas Bourgeron, Simon Baron-Cohen 2021-08-05 2021-08-05 [("doi","10.1101/2020.07.21.20159228")] genetics/heritable/correlation genetics/heritable/rare psychiatry/autism

The substantial phenotypic heterogeneity in autism limits our understanding of its genetic aetiology. To address this gap, we investigated genetic differences between autistic individuals (Nmax = 12,893) based on core (ie. social communication difficulties, and restricted and repetitive behaviors) and associated features of autism, co-occurring developmental disabilities (eg. language, motor, and intellectual developmental disabilities and delays), and sex.

We conducted a comprehensive factor analysis of core autism features in autistic individuals and identified six factors. Common genetic variants including autism polygenic scores (PGS) were associated with the core factors but de novo variants were not, even though the latent factor structure was similar between carriers and non-carriers of de novo variants.

We identify that increasing autism PGS decrease the likelihood of co-occurring developmental disabilities in autistic individuals, which reflects both a true protective effect and additivity between rare and common variants. Furthermore in autistic individuals without co-occurring intellectual disability (ID), autism PGS are over-inherited by autistic females compared to males. Finally, we observe higher SNP heritability for males and autistic individuals without ID, but found no robust differences in SNP heritability by the level of core autism features. Deeper phenotypic characterisation will be critical to determining how the complex underlying genetics shapes cognition, behavior, and co-occurring conditions in autism.

--- https://www.medrxiv.org/content/10.1101/2020.08.28.20180414.full Whole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses Alison R. Barton, Maxwell A. Sherman, Ronen E. Mukamel, Po-Ru Loh 2020-09-01 2020-09-01 [("doi","10.1101/2020.08.28.20180414")] genetics/heritable/rare genetics/selection/natural/human genetics/sequencing

Exome association studies to date have generally been underpowered to systematically evaluate the phenotypic impact of very rare coding variants. We leveraged extensive haplotype sharing between 49,960 exome-sequenced UK Biobank participants and the remainder of the cohort (total N~500K) to impute exome-wide variants at high accuracy (R2>0.5) down to minor allele frequency (MAF) ~0.00005.

Association and fine-mapping analyses of 54 quantitative traits identified 1,189 statistically-significant associations (P<5 × 10−8) involving 675 distinct rare protein-altering variants (MAF<0.01) that passed stringent filters for likely causality; 600 of the 675 variants (89%) were not present in the NHGRI-EBI GWAS Catalog. We replicated the effect directions of 28/28 height-associated variants genotyped in previous exome array studies, including missense variants in newly-associated collagen genes COL16A1 and COL11A2. Across all traits, 49% of associations (578/1,189) occurred in genes with two or more hits; follow-up analyses of these genes identified long allelic series containing up to 45 distinct likely-causal variants within the same gene (on average exhibiting 93%-concordant effect directions).

In particular, 24 rare coding variants in IFRD2 independently associated with reticulocyte indices, suggesting an important role of IFRD2 in red blood cell development, and 11 rare coding variants in NPR2 (a gene previously implicated in Mendelian skeletal disorders) exhibited intermediate-to-strong effects on height (0.18–1.09 s.d.).

Our results demonstrate the utility of within-cohort imputation in population-scale GWAS cohorts, provide a catalog of likely-causal, large-effect coding variant associations, and foreshadow the insights that will be revealed as genetic biobank studies continue to grow.

--- https://www.medrxiv.org/content/10.1101/2020.09.10.20192310.full A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts Guiyan Ni, Jian Zeng, Joana A. Revez, Ying Wang, Zhili Zheng, Tian Ge, Restuadi Restuadi, Jacqueline Kiewa, Dale R. Nyholt, Jonathan R. I. Coleman, Jordan W. Smoller, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Jian Yang, Peter M. Visscher, Naomi R. Wray 2021-05-05 2021-05-05 [("doi","10.1101/2020.09.10.20192310")] genetics/heritable psychiatry/depression psychiatry/schizophrenia

Background: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies (GWASs). PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, eg. phenotype definition or technical factors.

Method: The Psychiatric Genomics Consortium working groups for schizophrenia (SCZ) and major depressive disorder (MDD) bring together many independently collected case-control cohorts. We used these resources (31K SCZ cases, 41K controls; 248K MDD cases, 563K controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and nine methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) are compared.

Results: Compared to PC+T, the other nine methods give higher prediction statistics, MegaPRS, LDPred2 and SBayesR so, up to 9.2% variance in liability for SCZ across 30 target cohorts, an increase of 44%. For MDD across 26 target cohorts these statistics were 3.5% and 59%, respectively.

Conclusion: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparison and are recommended in applications to psychiatric disorders.

--- https://www.medrxiv.org/content/10.1101/2020.09.11.20175026.full Genome-wide association meta-analysis of childhood and adolescent internalizing symptoms Eshim S. Jami, Anke R. Hammerschlag, Hill F. Ip, Andrea G. Allegrini, Beben Benyamin, Richard Border, Elizabeth W. Diemer, Chang Jiang, Ville Karhunen, Yi Lu, Qing Lu, Travis T. Mallard, Pashupati P. Mishra, Ilja M. Nolte, Teemu Palviainen, Roseann E. Peterson, Hannah M. Sallis, Andrey A. Shabalin, Ashley E. Tate, Elisabeth Thiering, Natàlia Vilor-Tejedor, Carol Wang, Ang Zhou, Daniel E. Adkins, Silvia Alemany, Helga Ask, Qi Chen, Robin P. Corley, Erik A. Ehli, Luke M. Evans, Alexandra Havdahl, Fiona A. Hagenbeek, Christian Hakulinen, Anjali K. Henders, Jouke Jan Hottenga, Tellervo Korhonen, Abdullah Mamun, Shelby Marrington, Alexander Neumann, Kaili Rimfeld, Fernando Rivadeneira, Judy L. Silberg, Catharina E. van Beijsterveldt, Eero Vuoksimaa, Alyce M. Whipp, Tong Xiaoran, Ole A. Andreassen, Dorret I. Boomsma, Sandra A. Brown, S. Alexandra Burt, William Copeland, Elizabeth J. Costello, Danielle M. Dick, Lindon J. Eaves, K. Paige Harden, Kathleen Mullan Harris, Catharina A. Hartman, Joachim Heinrich, John K. Hewitt, Christian Hopfer, Elina Hypponen, Marjo-Riitta Jarvelin, Jaakko Kaprio, Liisa Keltikangas-Järvinen, Kelly L. Klump, Kenneth Krauter, Ralf Kuja-Halkola, Henrik Larsson, Terho Lehtimäki, Paul Lichtenstein, Sebastian Lundstrom, Hermine H. Maes, Per Magnus, Marcus R. Munafò, Jake M. Najman, Pål R. Njølstad, Albertine J. Oldehinkel, Craig E. Pennell, Robert Plomin, Ted Reichborn-Kjennerud, Chandra Reynolds, Richard J. Rose, Andrew Smolen, Harold Snieder, Michael Stallings, Marie Standl, Jordi Sunyer, Henning Tiemeier, Sally Wadsworth, Tamara L. Wall, Andrew J. O. Whitehouse, Gail M. Williams, Eivind Ystrom, Michel G. Nivard, Meike Bartels, Christel M. Middeldorp 2021-07-31 2021-07-31 [("doi","10.1101/2020.09.11.20175026")] genetics/heritable/correlation psychiatry/adhd psychiatry/anxiety psychiatry/autism psychiatry/bipolar/genetics psychiatry/depression psychiatry/schizophrenia

Internalizing symptoms in childhood and adolescence are as heritable as adult depression and anxiety, yet little is known of their molecular basis.

This genome-wide association meta-analysis of internalizing symptoms included repeated observations from 64,641 individuals, aged 3–18. The N-weighted meta-analysis of overall internalizing symptoms (INToverall) detected:

no genome-wide statistically-significant hits and showed low SNP heritability (1.66%, 95% confidence intervals 0.84–2.48%, Neff = 132,260). Stratified analyses indicated rater-based heterogeneity in genetic effects, with self-reported internalizing symptoms showing the highest heritability (5.63%, 95% confidence intervals 3.08–8.18%). Additive genetic effects on internalizing symptoms appeared stable over age, with overlapping estimates of SNP heritability from early-childhood to adolescence.

Genetic correlations were observed with adult anxiety, depression, and the wellbeing spectrum (|rg|> 0.70), as well as with insomnia, loneliness, attention-deficit hyperactivity disorder, autism, and childhood aggression (range |rg| = 0.42–0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa.

The pattern of genetic correlations suggests that childhood and adolescent internalizing symptoms share substantial genetic vulnerabilities with adult internalizing disorders and other childhood psychiatric traits, which could partially explain both the persistence of internalizing symptoms over time and the high comorbidity amongst childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success.

--- https://www.medrxiv.org/content/10.1101/2020.10.02.20205971.full The revolution will be hard to evaluate: How co-occurring policy changes affect research on the health effects of social policies Ellicott C. Matthay, Erin Hagan, Spruha Joshi, May Lynn Tan, David Vlahov, Nancy Adler, M. Maria Glymour 2021-05-15 2021-05-15 [("doi","10.1101/2020.10.02.20205971")] statistics/bias statistics/causality

Extensive empirical health research leverages variation in the timing and location of policy changes as quasi-experiments. Multiple social policies may be adopted simultaneously in the same locations, creating co-occurrence which must be addressed analytically for valid inferences. The pervasiveness and consequences of co-occurring policies have received limited attention.

We analyzed a systematic sample of 13 social policy databases covering diverse domains including poverty, paid family leave, and tobacco. We quantified policy co-occurrence in each database as the fraction of variation in each policy measure across different jurisdictions and times that could be explained by co-variation with other policies (R^2). We used simulations to estimate the ratio of the variance of effect estimates under the observed policy co-occurrence to variance if policies were independent.

Policy co-occurrence ranged from very high for state-level cannabis policies to low for country-level sexual minority rights policies. For 65% of policies, greater than 90% of the place-time variation was explained by other policies. Policy co-occurrence increased the variance of effect estimates by a median of 57×.

Co-occurring policies are common and pose a major methodological challenge to rigorously evaluating health effects of individual social policies. When uncontrolled, co-occurring policies confound one another, and when controlled, resulting positivity violations may substantially inflate the variance of estimated effects. Tools to enhance validity and precision for evaluating co-occurring policies are needed.

--- https://www.medrxiv.org/content/10.1101/2021.01.19.21249483.full Leveraging fine-mapping and non-European training data to improve cross-population polygenic risk scores Omer Weissbrod, Masahiro Kanai, Huwenbo Shi, Steven Gazal, Wouter J. Peyrot, Amit V. Khera, Yukinori Okada, The Biobank Japan Project, Alicia R. Martin, Hilary Finucane, Alkes Price 2021-08-20 2021-08-20 [("doi","10.1101/2021.01.19.21249483")] genetics/heritable

Polygenic risk scores (PRS) based on European training data suffer reduced accuracy in non-European target populations, exacerbating health disparities. This loss of accuracy predominantly stems from LD differences, MAF differences (including population-specific SNPs), and/or causal effect size differences. PRS based on training data from the non-European target population do not suffer from these limitations, but are currently limited by much smaller training sample sizes.

Here, we propose PolyPred, a method that improves cross-population polygenic prediction by combining two complementary predictors: a new predictor that leverages functionally informed fine-mapping to estimate causal effects (instead of tagging effects), addressing LD differences; and BOLT-LMM, a published predictor. In the special case where a large training sample is available in the non-European target population (or a closely related population), we propose PolyPred+, which further incorporates the non-European training data, addressing MAF differences and causal effect size differences. PolyPred and PolyPred+ require individual-level training data (for their BOLT-LMM component), but we also propose analogous methods that replace the BOLT-LMM component with summary statistic-based components if only summary statistics are available.

We applied PolyPred to 49 diseases and complex traits in 4 UK Biobank populations using UK Biobank British training data (average N=325K), and observed statistically-significant average relative improvements in prediction accuracy vs. BOLT-LMM ranging from +7% in South Asians to +32% in Africans (and vs. LD-pruning + p-value thresholding (P+T) ranging from +77% to +164%), consistent with simulations. We applied PolyPred+ to 23 diseases and complex traits in UK Biobank East Asians using both UK Biobank British (average N=325K) and Biobank Japan (average N=124K) training data, and observed statistically-significant average relative improvements in prediction accuracy of +24% vs. BOLT-LMM and +12% vs. PolyPred. The summary statistic-based analogues of PolyPred and PolyPred+ attained similar improvements.

In conclusion, PolyPred and PolyPred+ improve cross-population polygenic prediction accuracy, ameliorating health disparities.

--- https://www.medrxiv.org/content/10.1101/2021.05.24.21257698.full Blood-based epigenome-wide analyses of cognitive abilities Daniel L. McCartney, Robert F. Hillary, Eleanor L. S. Conole, Daniel Trejo Banos, Danni A. Gadd, Rosie M. Walker, Cliff Nangle, Robin Flaig, Archie Campbell, Alison D. D. Murray, Susana Muñoz Maniega, María del. C. Valdés-Hernández, Mathew A. Harris, Mark E. Bastin, Joanna M. Wardlaw, Sarah E. Harris, David J. Porteous, Elliot M. Tucker-Drob, Andrew M. McIntosh, Kathryn L. Evans, Ian J. Deary, Simon R. Cox, Matthew R. Robinson, Riccardo E. Marioni 2021-06-26 2021-06-26 [("doi","10.1101/2021.05.24.21257698")] genetics/heritable iq psychology/neuroscience

Using blood-based epigenome-wide analyses of general cognitive function (g; n = 9,162) we show that individual differences in DNA methylation (DNAm) explain 35.0% of the variance in g.

A DNAm predictor explains ~4% of the variance in g, independently of a polygenic score, in two external cohorts. It also associates with circulating levels of neurology-related and inflammation-related proteins, global brain imaging metrics, and regional cortical volumes.

As sample sizes increase, our ability to assess cognitive function from DNAm data may be informative in settings where cognitive testing is unreliable or unavailable.

--- https://www.medrxiv.org/content/10.1101/2021.05.26.21257794.full Ultra-rare, rare, and common genetic variant analysis converge to implicate negative selection and neuronal processes in the aetiology of schizophrenia Wonuola A. Akingbuwa, Anke R. Hammerschlag, Meike Bartels, Michel G. Nivard, Christel M. Middeldorp 2021-05-29 2021-05-29 [("doi","10.1101/2021.05.26.21257794")] genetics/heritable/rare psychiatry/schizophrenia

Both common and rare genetic variants (minor allele frequency > 1% and < 0.1% respectively) have been implicated in the etiology of schizophrenia. In this study, we integrate single-cell gene expression data with publicly available Genome-Wide Association Study (GWAS) and exome sequenced data in order to investigate in parallel, the enrichment of common and (ultra-)rare variants related to schizophrenia in several functionally relevant gene sets.

Four types of gene sets were constructed (1) protein-truncating variant (PTV)-intolerant (PI) genes (2) genes expressed in brain cell types and neurons ascertained from mouse and human brain tissue (3) genes defined by synaptic function and location and (4) intersection genes, ie. PI genes that are expressed in the human and mouse brain cell gene sets.

We show that common as well as (ultra-)rare schizophrenia-associated variants are overrepresented in PI genes, in excitatory neurons from the prefrontal cortex and hippocampus, medium spiny neurons, and genes enriched for synaptic processes. We also observed stronger enrichment in the intersection genes.

Our findings suggest that across the allele frequency spectrum, genes and genetic variants likely to be under stringent selection, and those expressed in particular brain cell types, are involved in the same biological pathways influencing the risk for schizophrenia.

--- https://www.medrxiv.org/content/10.1101/2021.08.06.21261679.full Differences in the genetic architecture of common and rare variants in childhood, persistent and late-diagnosed attention deficit hyperactivity disorder Veera M. Rajagopal, Jinjie Duan, Laura Vilar-Ribó, Jakob Grove, Tetyana Zayats, J. Antoni Ramos-Quiroga, F. Kyle Satterstrom, María Soler Artigas, Jonas Bybjerg-Grauholm, Marie Bækvad-Hansen, Thomas D. Als, Anders Rosengren, Mark J. Daly, Benjamin M. Neale, Merete Nordentoft, Thomas Werge, Ole Mors, David Hougaard, Preben Bo Mortensen, Marta Ribasés, Anders Børglum, Ditte Demontis 2021-08-08 2021-08-08 [("doi","10.1101/2021.08.06.21261679")] genetics/heritable/correlation genetics/heritable/rare psychiatry/adhd psychiatry/autism psychiatry/depression

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder, with onset in childhood (“childhood ADHD”), and around two thirds of affected individuals will continue to have ADHD symptoms in adulthood (“persistent ADHD”). Age at first diagnosis can vary, and sometimes ADHD is first diagnosed in adulthood (“late-diagnosed ADHD”).

In this study, we analyzed a large Danish population-based case-cohort generated by iPSYCH in order to identify common genetic risk loci and perform in-depth characterization of the polygenic architecture of childhood (n = 14,878), persistent (n = 1,473) and late-diagnosed ADHD (n = 6,961) alongside 38,303 controls. Additionally, the burden of rare protein truncating variants in the 3 groups were evaluated in whole-exome sequencing data from a subset of the individuals (7,650 ADHD cases and 8,649 controls).

We identified genome-wide statistically-significant loci associated with childhood ADHD (four loci) and late-diagnosed ADHD (one locus). In analyses of the polygenic architecture, we found higher polygenic score (PGS) of ADHD risk variants in persistent ADHD (mean PGS=0.41) compared to childhood (mean PGS=0.26) and late-diagnosed ADHD (mean PGS=0.27), and we found a decreased genetic correlation of late-diagnosed ADHD with inattention (rg = 0.57) compared to childhood ADHD (rg = 0.86). These results suggest that a higher ADHD polygenic risk burden is associated with persistence of symptoms, and that a later diagnosis of ADHD could be due in part to genetic factors. Additionally, childhood ADHD demonstrated both an increased genetic overlap with autism compared to late-diagnosed ADHD as well as the highest burden of rare protein-truncating variants in highly constrained genes among ADHD subgroups (compared to controls: β = 0.13, p = 2.41×10−11). Late-diagnosed ADHD demonstrated larger genetic overlap with depression than childhood ADHD and no increased burden in rare protein-truncating variants (compared to controls: β = 0.06). Overall, our study finds genetic heterogeneity among ADHD subgroups and suggests that genetic factors influence time of first ADHD diagnosis, persistence of ADHD and comorbidity patterns in the sub-groups.

--- https://www.medrxiv.org/content/10.1101/2021.10.08.21264256.full Integrating de novo and inherited variants in over 42,607 autism cases identifies mutations in new moderate risk genes Xueya Zhou, Pamela Feliciano, Tianyun Wang, Irina Astrovskaya, Chang Shu, Jacob B. Hall, Joseph U. Obiajulu, Jessica Wright, Shwetha Murali, Simon Xuming Xu, Leo Brueggeman, Taylor R. Thomas, Olena Marchenko, Christopher Fleisch, Sarah D. Barns, LeeAnne Green Snyder, Bing Han, Timothy S. Chang, Tychele N. Turner, William Harvey, Andrew Nishida, Brian J. O’Roak, Daniel H. Geschwind, The SPARK Consortium, Jacob J. Michaelson, Natalia Volfovsky, Evan E. Eichler, Yufeng Shen, Wendy K. Chung 2021-10-11 2021-10-11 [("doi","10.1101/2021.10.08.21264256")] genetics/heritable/rare psychiatry/autism

Despite the known heritable nature of autism spectrum disorder (ASD), studies have primarily identified risk genes with de novo variants (DNVs). To capture the full spectrum of ASD genetic risk, we performed a two-stage analysis of rare de novo and inherited coding variants in 42,607 ASD cases, including 35,130 new cases recruited online by SPARK.

In the first stage, we analyzed 19,843 cases with one or both biological parents and found that known ASD or neurodevelopmental disorder (NDD) risk genes explain nearly 70% of the genetic burden conferred by DNVs. In contrast, less than 20% of genetic risk conferred by rare inherited loss-of-function (LoF) variants are explained by known ASD/NDD genes. We selected 404 genes based on the first stage of analysis and performed a meta-analysis with an additional 22,764 cases and 236,000 population controls.

We identified 60 genes with exome-wide statistical-significance (p < 2.5e-6), including 5 new risk genes (NAV3, ITSN1, MARK2, SCAF1, and HNRNPUL2). The association of NAV3 with ASD risk is entirely driven by rare inherited LoFs variants, with an average relative risk of 4, consistent with moderate effect. ASD individuals with LoF variants in the 4 moderate risk genes (NAV3, ITSN1, SCAF1, and HNRNPUL2, n = 95) have less cognitive impairment compared to 129 ASD individuals with LoF variants in well-established, highly penetrant ASD risk genes (CHD8, SCN2A, ADNP, FOXP1, SHANK3) (59% vs. 88%, p = 1.9e-06).

These findings will guide future gene discovery efforts and suggest that much larger numbers of ASD cases and controls are needed to identify additional genes that confer moderate risk of ASD through rare, inherited variants.

--- https://www.medrxiv.org/content/10.1101/2021.11.19.21266436.full Global Biobank Meta-analysis Initiative: powering genetic discovery across human diseases Global Biobank Meta-analysis Initiative, Wei Zhou 2021-11-21 2021-11-21 [("doi","10.1101/2021.11.19.21266436")] genetics/heritable

Biobanks are being established across the world to understand the genetic, environmental, and epidemiological basis of human diseases with the goal of better prevention and treatments. Genome-wide association studies (GWAS) have been very successful at mapping genomic loci for a wide range of human diseases and traits, but in general, lack appropriate representation of diverse ancestries—with most biobanks and preceding GWAS studies composed of individuals of European ancestries.

Here, we introduce the Global Biobank Meta-analysis Initiative (GBMI)—a collaborative network of 19 biobanks from 4 continents representing more than 2.1 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWAS generated using harmonized genotypes and phenotypes from member biobanks. GBMI brings together results from GWAS analysis across 6 main ancestry groups: ~33,000 of African ancestry either from Africa or from admixed-ancestry diaspora (AFR), 18,000 admixed American (AMR), 31,000 Central and South Asian (CSA), 341,000 East Asian (EAS), 1.4 million European (EUR), and 1,600 Middle Eastern (MID) individuals.

In this flagship project, we generated GWASs from across 14 exemplar diseases and endpoints, including both common and less prevalent diseases that were previously understudied. Using the genetic association results, we validate that GWASs conducted in biobanks worldwide can be successfully integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics between biobanks.

We demonstrate the value of this collaborative effort to improve GWAS power for diseases, increase representation, benefit understudied diseases, and improve risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of the studied traits.

--- https://www.medrxiv.org/content/10.1101/2021.11.22.21266696.full Dimensional characterizations of gender diversity are associated with higher polygenic propensity for cognitive performance in a neurodiverse sample Taylor R. Thomas, Ashton J. Tener, Ji Seung Yang, John F. Strang, Jacob J. Michaelson 2021-11-24 2021-11-24 [("doi","10.1101/2021.11.22.21266696")] genetics/heritable/correlation psychiatry/autism

Both sex and gender are characteristics that play a key role in risk and resilience in health and well-being. Current research lacks the ability to quantitatively describe gender and gender diversity, and is limited to endorsement of categorical gender identities, which are contextually and culturally dependent. A more objective, dimensional approach to characterizing gender diversity will enable researchers to advance the health of gender-diverse people by better understanding how genetic factors interact to determine health outcomes.

To address this research gap, we leveraged the Gender Self-Report (GSR), a questionnaire that captures multiple dimensions of gender diversity. We then performed polygenic score associations with brain-related traits like cognitive performance, personality, and neuropsychiatric conditions. The GSR was completed by n = 818 independent adults with or without autism in the SPARK cohort, and GSR factor analysis identified two factors: Binary (divergence from gender presumed by designated sex to the opposite) and Nonbinary (divergence from male and female gender norms) Gender Diversity (BGD and NGD, respectively). We performed polygenic associations (controlling for age, sex, and autism diagnostic status) in a subset of n = 452 individuals and found higher polygenic propensity for cognitive performance was associated with greater BGD (B = 0.017, p = 0.049) and NGD (B = 0.036, p = 0.002), and higher polygenic propensity for educational attainment was also associated with greater NGD (B = 0.030, p = 0.015).

We did not observe any statistically-significant associations with personality or neuropsychiatric polygenic scores in this sample.

Overall, our results suggest cognitive processes and gender diversity share overlapping genetic factors, indicating the biological utility of the GSR while also underscoring the importance of quantitatively measuring gender diversity in health research contexts.

--- https://www.medrxiv.org/content/10.1101/2021.11.30.21267108.full Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity Kristin Tsuo, Wei Zhou, Ying Wang, Masahiro Kanai, Shinichi Namba, Rahul Gupta, Lerato Majara, Lethukuthula L. Nkambule, Takayuki Morisaki, Yukinori Okada, Benjamin M. Neale, Global Biobank Meta-analysis Initiative, Mark J. Daly, Alicia R. Martin 2021-12-07 2021-12-07 [("doi","10.1101/2021.11.30.21267108")] genetics/heritable

Asthma is a complex disease that affects millions of people and varies in prevalence by an order of magnitude across geographic regions and populations. However, the extent to which genetic variation contributes to these disparities is unclear, as studies probing the genetics of asthma have been primarily limited to populations of European (EUR) descent. As part of the Global Biobank Meta-analysis Initiative (GBMI), we conducted the largest genome-wide association study (GWAS) of asthma to date (153,763 cases and 1,647,022 controls) via meta-analysis across 18 biobanks spanning multiple countries and ancestries.

Altogether, we discovered 180 genome-wide statistically-significant loci (p < 5×10−8) associated with asthma, 49 of which are not previously reported. We replicate well-known associations such as IL1RL1 and STAT6, and find that overall the novel associations have smaller effects than previously-discovered loci, highlighting our substantial increase in statistical power. Despite the considerable range in prevalence among biobanks, 35.9%–59.4%, the genetic effects of associated loci are largely consistent across the biobanks and ancestries.

To further investigate the polygenic architecture of asthma, we construct polygenic risk scores (PRS) using a multi-ancestry approach, which yields higher predictive power for asthma in non-EUR populations compared to PRS derived from previous asthma meta-analyses and using other methods. Additionally, we find considerable genetic overlap between asthma and chronic obstructive pulmonary disease (COPD) across ancestries but minimal overlap in enriched biological pathways.

Our work underscores the multifactorial nature of asthma development and offers insight into the shared genetic architecture of asthma that may be differentially perturbed by environmental factors and contribute to variation in prevalence.

--- https://www.medrxiv.org/content/10.1101/2021.12.04.21267094.full Genome-wide association study and multi-trait analysis of opioid use disorder identifies novel associations in 639,709 individuals of European and African ancestry Joseph D. Deak, Hang Zhou, Marco Galimberti, Daniel F. Levey, Frank Wendt, Sandra Sanchez-Roige, Alexander S. Hatoum, Emma C. Johnson, Yari Nunez, Ditte Demontis, Anders Børglum, Veera Rajagopal, Mariela Jennings, Rachel L. Kember, Amy Justice, Howard J. Edenberg, Arpana Agrawal, Renato Polimanti, Henry Kranzler, Joel Gelernter 2021-12-05 2021-12-05 [("doi","10.1101/2021.12.04.21267094")] genetics/heritable/correlation psychiatry/alcoholism

Background: Despite the large toll of opioid use disorder (OUD), genome-wide association studies (GWAS) of OUD to date have yielded few susceptibility loci.

Method: We performed a large-scale GWAS of OUD in individuals of European (EUR) and African (AFR) ancestry, optimizing genetic informativeness by performing MTAG (Multi-trait analysis of GWAS) with genetically correlated substance use disorders (SUDs). Meta-analysis included seven cohorts: the Million Veteran Program (MVP), Psychiatric Genomics Consortium (PGC), iPSYCH, FinnGen, Partners Biobank, BioVU, and Yale-Penn 3, resulting in a total n = 639,709 (Ncases = 20,858) across ancestries. OUD cases were defined as having lifetime OUD diagnosis, and controls as anyone not known to meet OUD criteria.

We estimated SNP-heritability (hSNP2) and genetic correlations (rg). Based on genetic correlation, we performed MTAG on OUD, alcohol use disorder (AUD), and cannabis use disorder (CanUD).

Results: The EUR meta-analysis identified 3 genome-wide statistically-significant (GWS; p≤ 5×10−8) lead SNPs, one at FURIN (rs11372849; p = 9.54×10−10) and two OPRM1 variants (rs1799971, p = 4.92×10−09; rs79704991, p = 1.37×10−08; R2 = 0.02). Rs1799971 (p = 4.91×10−08) and another OPRM1 variant (rs9478500; p = 1.95×10−8; R2 = 0.03) were identified in the cross-ancestry meta-analysis. Estimated hSNP2 was 12.75%, with strong rg with CanUD (rg = 0.82; p = 1.14×10−47) and AUD (rg = 0.77; p = 6.36×10−78). The OUD-MTAG resulted in 18 GWS loci, all of which map to genes or gene regions that have previously been associated with psychiatric or addiction phenotypes.

Conclusion: We identified multiple OUD variant associations at OPRM1, single variant associations with FURIN, and 18 GWS associations in the OUD-MTAG. OUD is likely influenced by both OUD-specific loci and loci shared across SUDs.

--- https://www.medrxiv.org/content/10.1101/2021.12.24.21268385.full Schizophrenia-associated somatic copy number variants from 12,834 cases reveal contribution to risk and recurrent, isoform-specific NRXN1 disruptions Eduardo A. Maury, Maxwell A. Sherman, Giulio Genovese, Thomas G. Gilgenast, Prashanth Rajarajan, Erin Flaherty, Schahram Akbarian, Andrew Chess, Steven A. McCarroll, Po-Ru Loh, Jennifer E. Philips-Cremins, Kristen J. Brennand, James T. R. Walters, Michael O’Donovan, Patrick Sullivan, Psychiatric Genomic Consortium Schizophrenia, C. N. V. workgroup, Brain Somatic Mosaicism Network, Jonathan Sebat, Eunjung A. Lee, Christopher A. Walsh 2022-01-01 2022-01-01 [("doi","10.1101/2021.12.24.21268385")] genetics/heritable/rare

While inherited and de novo copy number variants (CNV) have been implicated in the genetic architecture of schizophrenia (SCZ), the contribution of somatic CNVs (sCNVs), present in some but not all cells of the body, remains unknown.

Here we explore the role of sCNVs in SCZ by analyzing blood-derived genotype arrays from 12,834 SCZ cases and 11,648 controls.

sCNVs were more common in cases (0.91%) than in controls (0.51%, p = 2.68e-4). We observed recurrent somatic deletions of exons 1–5 of the NRXN1 gene in 5 SCZ cases. Allele-specific Hi-C maps revealed ectopic, allele-specific loops forming between a potential novel cryptic promoter and non-coding cis regulatory elements upon deletions in the 59 region of NRXN1. We also observed recurrent intragenic deletions of ABCB11, a gene associated with anti-psychotic response, in 5 treatment-resistant SCZ cases.

Taken together our results indicate an important role of sCNVs to SCZ risk and treatment-responsiveness.

--- https://www.medrxiv.org/content/10.1101/2021.12.28.21268486.full Shared brain and genetic architectures between mental health and physical activity Wei Zhang, Sarah E. Paul, Anderson Winkler, Ryan Bogdan, Janine D. Bijsterbosch 2022-01-01 2022-01-01 [("doi","10.1101/2021.12.28.21268486")] exercise genetics/heritable/correlation psychiatry

Physical activity is correlated with, and effectively treats various forms of psychopathology. However, whether biological correlates of physical activity and psychopathology are shared remains unclear.

Here, we examined the extent to which the neural and genetic architecture of physical activity and mental health are shared. Using data from the UK Biobank (n = 6,389), canonical correlation analysis was applied to estimate associations between the amplitude and connectivity strength of sub-networks of 3 major neurocognitive networks (default mode, DMN; salience, SN; central executive networks, CEN) with accelerometer-derived measures of physical activity and self-reported mental health. We estimated the genetic correlation between mental health and physical activity measures, as well as putative causal relationships by applying linkage disequilibrium score regression, genomic structural equational modeling, and latent causal variable analysis to genome-wide association summary statistics (GWAS n = 91,105–500,199).

Physical activity and mental health were associated with connectivity strength and amplitude of the DMN, SN, and CEN (all r ≥ 0.12, all p < 0.048). These neural correlates exhibited highly similar loading patterns across mental health and physical activity models even when accounting for their shared variance. This suggests a largely shared brain network architecture between mental health and physical activity. Mental health and physical activity were also genetically correlated (|rg| = 0.085–0.121), but we found no evidence for causal relationships between them.

Collectively, our findings provide empirical evidence that mental health and physical activity have shared brain and genetic architectures and suggest potential candidate sub-networks for future studies on brain mechanisms underlying beneficial effects of physical activity on mental health.

--- https://www.medrxiv.org/content/10.1101/2022.01.03.22268662.full Rare schizophrenia risk variant burden is conserved in diverse human populations Dongjing Liu, Dara Meyer, Brian Fennessy, Claudia Feng, Esther Cheng, Jessica S. Johnson, You Jeong Park, Marysia-Kolbe Rieder, Steven Ascolillo, Agathe de Pins, Amanda Dobbyn, Dannielle Lebovitch, Emily Moya, Tan-Hoang Nguyen, Lillian Wilkins, Arsalan Hassan, Psychiatric Genomics Consortium Phase 3 Targeted Sequencing of Schizophrenia Study Team, Schizophrenia Exome Meta-analysis Consortium, Katherine E. Burdick, Joseph D. Buxbaum, Enrico Domenici, Sophia Frangou, Annette M. Hartmann, Dheeraj Malhotra, Carlos N. Pato, Michele T. Pato, Kerry Ressler, Panos Roussos, Dan Rujescu, Celso Arango, Alessandro Bertolino, Giuseppe Blasi, Luisella Bocchio-Chiavetto, Dominique Campion, Vaughan Carr, Janice M. Fullerton, Massimo Gennarelli, Javier González-Peñas, Douglas F. Levinson, Bryan Mowry, Vishwajit L. Nimgaokar, Giulio Pergola, Antonio Rampino, Margarita Rivera-Sanchez, Sibylle G. Schwab, Dieter B. Wildenauer, Mark Daly, Benjamin M. Neale, Tarjinder Singh, Michael C. O’Donovan, Michael J. Owen, James T. Walters, Muhammad Ayub, Anil K. Malhotra, Todd Lencz, Patrick F. Sullivan, Pamela Sklar, Eli Ayumi Stahl, Laura M. Huckins, Alexander W. Charney 2022-01-03 2022-01-03 [("doi","10.1101/2022.01.03.22268662")] genetics/heritable/rare psychiatry/autism psychiatry/schizophrenia

Schizophrenia is a chronic mental illness that is among the most debilitating conditions encountered in medical practice. A recent landmark schizophrenia study of the protein-coding regions of the genome identified a causal role for 10 genes and a concentration of rare variant signals in evolutionarily constrained genes. This study—and most other large-scale human genetic studies—was mainly composed of individuals of European ancestry, and the generalizability of the findings in non-European populations is unclear.

To address this gap in knowledge, we designed a custom sequencing panel based on current knowledge of the genetic architecture of schizophrenia and applied it to a new cohort of 22,135 individuals of diverse ancestries. Replicating earlier work, cases carried a higher burden of rare protein-truncating variants among constrained genes (OR = 1.48, p-value = 5.4 × 10−6).

In meta-analyses with existing schizophrenia datasets totaling up to 35,828 cases and 107,877 controls, this excess burden was largely consistent across 5 continental populations. Two genes (SRRM2 and AKAP11) were newly implicated as schizophrenia risk genes, and one gene (PCLO) was identified as a shared risk gene for schizophrenia and autism.

Overall, our results lend robust support to the rare allelic spectrum of the genetic architecture of schizophrenia being conserved across diverse human populations.

--- https://www.medrxiv.org/content/10.1101/2022.01.06.22268753.full Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders Alexander S. Hatoum, Sarah M. C. Colbert, Emma C. Johnson, Spencer B. Huggett, Joseph D. Deak, Gita A. Pathak, Mariela V. Jennings, Sarah E. Paul, Nicole R. Karcher, Isabella Hansen, David A. A. Baranger, Alexis Edwards, Andrew David Grotzinger, Substance Use Disorder Working Group of the Psychiatric Genomics Consortium, Elliot M. Tucker-Drob, Henry Kranzler, Lea K. Davis, Sandra Sanchez-Roige, Renato Polimanti, Joel Gelernter, Howard J. Edenberg, Ryan Bogdan, Arpana Agrawal 2022-01-12 2022-01-12 [("doi","10.1101/2022.01.06.22268753")] genetics/heritable/correlation nicotine psychiatry/alcoholism

Genetic liability to substance use disorders can be parsed into loci conferring general and substance-specific addiction risk. We report a multivariate genome-wide association study that disaggregates general and substance-specific loci for problematic alcohol use, problematic tobacco use, and cannabis and opioid use disorders in a sample of 1,025,550 individuals of European and 92,630 individuals of African descent.

Nineteen loci were genome-wide statistically-significant for the general addiction risk factor (addiction-rf), which showed high polygenicity. Across ancestries PDE4B was (among others), suggesting dopamine regulation as a cross-trait vulnerability. The addiction-rf polygenic risk score was associated with substance use disorders, psychopathologies, somatic conditions, and environments associated with the onset of addictions.

Substance-specific loci (9 for alcohol, 32 for tobacco, 5 for cannabis, 1 for opioids) included metabolic and receptor genes.

These findings provide insight into the genetic architecture of general and substance-specific use disorder risk that may be leveraged as treatment targets.

--- https://www.medrxiv.org/content/10.1101/2022.01.10.22269025.full Medical domain knowledge in domain-agnostic generative AI Jakob Nikolas Kather, Narmin Ghaffari Laleh, Sebastian Foersch, Daniel Truhn 2022-01-11 2022-01-11 [("doi","10.1101/2022.01.10.22269025")] ai/nn/transformer/gpt/dall-e/2

The text-guided diffusion model GLIDE (Guided Language to Image Diffusion for Generation and Editing) is the state-of-the-art in text-to-image generative artificial intelligence (AI). GLIDE has rich representations, but medical applications of this model have not been systematically explored. If GLIDE had useful medical knowledge, it could be used for medical image analysis tasks, a domain in which AI systems are still highly engineered towards a single use-case.

Here we show that the publicly available GLIDE model has reasonably strong representations of key topics in cancer research and oncology, in particular the general style of histopathology images and multiple facets of diseases, pathological processes and laboratory assays. However, GLIDE seems to lack useful representations of the style and content of radiology data. [That is really very unsurprising…]

Our findings demonstrate that domain-agnostic generative AI models can learn relevant medical concepts without explicit training. Thus, GLIDE and similar models might be useful for medical image processing tasks in the future—particularly with additional domain-specific fine-tuning.

--- https://www.medrxiv.org/content/10.1101/2022.01.11.22268884.full Educational attainment, health outcomes and mortality: a within-sibship Mendelian Randomization study Laurence J. Howe, Humaira Rasheed, Paul R. Jones, Dorret I. Boomsma, David M. Evans, Alexandros Giannelis, Caroline Hayward, John L. Hopper, Amanda Hughes, Hannu Lahtinen, Shuai Li, Penelope A. Lind, Nicholas G. Martin, Pekka Martikainen, Sarah E. Medland, Tim T. Morris, Michel G. Nivard, Jean-Baptiste Pingault, Karri Silventoinen, Jennifer A. Smith, Emily A. Willoughby, James F. Wilson, Within Family Consortium, Bjørn Olav Åsvold, Øyvind E. Næss, George Davey Smith, Jaakko Kaprio, Ben Brumpton, Neil M. Davies 2022-01-13 2022-01-13 [("doi","10.1101/2022.01.11.22268884")] genetics/heritable/correlation/mendelian-randomization iq/ses

Previous Mendelian Randomization (MR) studies using population samples (population-MR) have provided evidence for beneficial effects of educational attainment on health outcomes in adulthood. However, estimates from these studies may have been susceptible to bias from population stratification, assortative mating and indirect genetic effects due to unadjusted parental genotypes. Mendelian Randomization using genetic association estimates derived from within-sibship models (within-sibship MR) can avoid these potential biases because genetic differences between siblings are due to random segregation at meiosis.

Applying both population and within-sibship MR, we estimated the effects of genetic liability to educational attainment on body mass index (BMI), cigarette smoking, systolic blood pressure (SBP) and all-cause mortality. MR analyses used individual-level data on 72,932 siblings from UK Biobank and the Norwegian HUNT study and summary-level data from a within-sibship Genome-wide Association Study including over 140,000 individuals.

Both population and within-sibship MR estimates provided evidence that educational attainment influences BMI, cigarette smoking and SBP. Genetic variant-outcome associations attenuated in the within-sibship model, but genetic variant-educational attainment associations also attenuated to a similar extent. Thus, within-sibship and population MR estimates were largely consistent. The within-sibship MR estimate of education on mortality was imprecise but consistent with a putative effect. These results provide evidence of beneficial individual-level effects of education (or liability to education) on adulthood health, independent of potential demographic and family-level confounders.

--- https://www.medrxiv.org/content/10.1101/2022.01.13.22269176.full ExPRSweb—An Online Repository with Polygenic Risk Scores for Common Health-related Exposures Ying Ma, Snehal Patil, Xiang Zhou, Bhramar Mukherjee, Lars G. Fritsche 2022-01-14 2022-01-14 [("doi","10.1101/2022.01.13.22269176")] genetics/heritable

Complex traits are influenced by genetic risk factors, lifestyle, and environmental variables, so called exposures. Some exposures, eg. smoking or lipid levels, have common genetic modifiers identified in genome-wide association studies. Since measurements are often unfeasible, Exposure Polygenic Risk Scores (ExPRSs) offer an alternative to study the influence of exposures on various phenotypes.

Here, we collected publicly available summary statistics for 28 exposures and applied 4 common PRS methods to generate ExPRSs in two large biobanks, the Michigan Genomics Initiative and the UK Biobank. We established ExPRS for 27 exposures and demonstrated their applicability in phenome-wide association studies and as predictors for common chronic conditions.

Especially, the addition of multiple ExPRSs showed, for several chronic conditions, an improvement compared prediction models that only included traditional, disease-focused PRSs.

To facilitate follow-up studies, we share all ExPRS constructs and generated results via an online repository called ExPRSweb.

--- https://www.medrxiv.org/content/10.1101/2022.01.25.22269831.full Genetic risk factors have a substantial impact on healthy life years Sakari Jukarainen, Tuomo Kiiskinen, Aki S. Havulinna, Juha Karjalainen, Mattia Cordioli, Joel T. Rämö, Nina Mars, FinnGen, Kaitlin E. Samocha, Hanna M. Ollila, Matti Pirinen, Andrea Ganna 2022-01-28 2022-01-28 [("doi","10.1101/2022.01.25.22269831")] genetics/heritable/correlation genetics/heritable/rare genetics/selection/artificial/index-selection

The impact of genetic variation on overall disease burden has not been comprehensively evaluated. Here we introduce an approach to estimate the effect of different types of genetic risk factors on disease burden quantified through disability-adjusted life years (DALYs, “lost healthy life years”).

We use genetic information from 735,748 individuals with registry-based follow-up of up to 48 years. At the individual level, rare variants had higher effects on DALYs than common variants, while common variants were more relevant for population-level disease burden. Among common variants, rs3798220 (LPA) had the strongest effect, with 1.18 DALYs attributable to carrying 1 vs 0 copies of the minor allele. Belonging to top 10% vs bottom 90% of a polygenic score for multisite chronic pain had an effect of 3.63 DALYs. Carrying a deleterious rare variant in LDLR, MYBPC3, or BRCA1/2 had an effect of around 4.1–13.1 DALYs.

The population-level disease burden attributable to some common variants is comparable to the burden from modifiable risk factors such as high sodium intake and low physical activity.

Genetic risk factors can explain a sizeable number of healthy life years lost both at the individual and population level, highlighting the importance of incorporating genetic information into public health efforts.

--- https://www.medrxiv.org/content/10.1101/2022.02.14.22270780.full Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains Ditte Demontis, Bragi Walters, Georgios Athanasiadis, Raymond Walters, Karen Therrien, Leila Farajzadeh, Georgios Voloudakis, Jaroslav Bendl, Biao Zeng, Wen Zhang, Jakob Grove, Thomas Damm Als, Jinjie Duan, F. Kyle Satterstrom, Jonas Bybjerg-Grauholm, Marie Baekved-Hansen, Olafur O. Gudmundsson, Sigurdur Magnusson, Gisli Baldursson, Katrin Davidsdottir, Gyda Haraldsdottir, Trine Tollerup Nielsen, Esben Agerbo, Gabriel E. Hoffman, Soeren Dalsgaard, Joanna Martin, Marta Ribases, Dorrett Boomsma, Maria Soler Artigas, Nina Roth Mota, Daniel Howrigan, Sarah E. Medland, Tetyana Zayats, Veera Rajagopal, Merete Nordentoft, Ole Mors, David Hougaard, Preben Bo Mortensen, Mark Daly, Stephen V. Faraone, Hreinn Stefansson, Panos Roussos, Barbara Franke, Thomas Werge, Benjamin M. Neale, Kari Stefansson, Anders D. Boerglum 2022-02-16 2022-02-16 [("doi","10.1101/2022.02.14.22270780")] genetics/heritable/correlation genetics/heritable/rare psychiatry/adhd psychiatry/autism psychiatry/depression psychiatry/schizophrenia

Attention deficit hyperactivity disorder (ADHD) is a prevalent childhood psychiatric disorder, with a major genetic component. Here we present a GWAS meta-analysis of ADHD comprising 38,691 individuals with ADHD and 186,843 controls. We identified 27 genome-wide statistically-significant loci, which is more than twice the number previously reported.

Fine-mapping risk loci highlighted 76 potential risk genes enriched in genes expressed in brain, particularly the frontal cortex, and in early brain development. Overall, ADHD was associated with several brain specific neuronal sub-types and especially midbrain dopaminergic neurons.

In a subsample of 17,896 exome-sequenced individuals, we identified increased load of rare protein-truncating variants in cases for a set of risk genes enriched with likely causal common variants, suggesting implication of SORCS3 in ADHD by both common and rare variants. We found ADHD to be highly polygenic, with around 7 thousand variants explaining 90% of the SNP heritability.

Bivariate Gaussian mixture modeling estimated that more than 90% of ADHD influencing variants are shared with other psychiatric disorders (autism, schizophrenia and depression) and phenotypes (eg. educational attainment) when both concordant and discordant variants are considered. Additionally, we demonstrated that common variant ADHD risk was associated with impaired complex cognition such as verbal reasoning and a range of executive functions including attention.

--- https://www.metaculus.com/questions/notebooks/8702/the-promise-and-impact-of-the-next-generation-of-weight-loss-drugs/ 2020-08-27 longevity/glp/semaglutide statistics/prediction --- https://www.mic.com/articles/92449/here-s-the-lawless-hellscape-colorado-has-become-six-months-after-legalizing-weed Here's the Lawless Hellscape Colorado Has Become Six Months After Legalizing Weed 2020-08-27 marijuana --- https://www.microsoft.com/en-us/research/blog/azure-ai-milestone-microsoft-kear-surpasses-human-performance-on-commonsenseqa-benchmark/ Azure AI milestone: Microsoft KEAR surpasses human performance on CommonsenseQA benchmark 2020-08-27 ai/nn/retrieval --- https://www.microsoft.com/en-us/research/blog/turing-bletchley-a-universal-image-language-representation-model-by-microsoft/?OCID=msr_blog_Bletchley_hero Turing Bletchley: A Universal Image Language Representation model by Microsoft 2020-08-27 ai/nn/retrieval --- https://www.minnesotalawreview.org/wp-content/uploads/2012/01/Heald_FinalPDF.pdf#page=4 2020-08-27 economics/copyright --- https://www.motherjones.com/kevin-drum/2012/02/barack-obamas-hardline-turn-medical-marijuana-mystery/ Barack Obama’s Hardline Turn on Medical Marijuana Is a Mystery 2020-08-27 marijuana --- https://www.motherjones.com/kevin-drum/2018/02/an-updated-lead-crime-roundup-for-2018/ An Updated Lead-Crime Roundup for 2018 2020-08-27 crime psychiatry --- https://nap.nationalacademies.org/read/24623/chapter/1 Front Matter Human Genome Editing: Science, Ethics, and Governance 2020-08-27 philosophy/ethics --- https://nap.nationalacademies.org/read/25259/chapter/2 Summary Negative Emissions Technologies and Reliable Sequestration: A Research Agenda 2020-08-28 technology/carbon-capture --- https://www.nationalacademies.org/news/2021/12/new-report-assesses-the-feasibility-cost-and-potential-impacts-of-ocean-based-carbon-dioxide-removal-approaches-recommends-u-s-research-program 2020-08-28 technology/carbon-capture --- https://www.nature.com/articles/531557a 2020-08-28 genetics/genome-synthesis --- https://www.nature.com/articles/534462a China’s bid to be a DNA superpower: First China conquered DNA sequencing. Now it wants to dominate precision medicine too 2020-08-28 philosophy/ethics --- https://www.nature.com/articles/d41586-018-05043-x 2020-08-28 genetics/genome-synthesis/virus-proof --- https://www.nature.com/articles/d41586-018-05208-8 2020-08-28 genetics/microbiome --- https://www.nature.com/articles/d41586-018-05522-1 Early results from birth-cohort have public-health implications, as other groups use the data to investigate the microbiome and mental health 2020-08-28 genetics/microbiome --- https://www.nature.com/articles/d41586-018-07222-2 2020-08-28 philosophy/ethics --- https://www.nature.com/articles/d41586-021-00530-0 2020-08-28 ai/nn/transformer/gpt ai/poetry --- https://www.nature.com/articles/d41586-021-02625-0 2020-08-28 genetics/gametogenesis --- https://www.nature.com/articles/ejhg201659 2020-08-29 philosophy/ethics --- https://www.nature.com/articles/mp201331 2020-08-29 crime genetics/heritable --- https://www.nature.com/articles/mp201516 2020-08-29 psychiatry/schizophrenia --- https://www.nature.com/articles/nature.2012.11447 2020-08-29 philosophy/ethics --- https://www.nature.com/articles/nature.2014.14941 2020-08-29 genetics/genome-synthesis --- https://www.nature.com/articles/nature.2016.20028 2020-08-29 genetics/genome-synthesis --- https://www.nature.com/articles/nature.2016.20451 2020-08-29 genetics/genome-synthesis --- https://www.nature.com/articles/nature.2017.22656 Wikipedia shapes language in science papers: Experiment traces how online encyclopaedia influences research write-ups 2020-08-29 psychology/writing statistics/bias wikipedia --- https://www.nature.com/articles/nature18642 2020-08-29 genetics/heritable/rare --- https://www.nature.com/articles/ncomms12359 2020-08-29 genetics/cloning --- https://www.nature.com/articles/ncomms15361 2020-08-29 genetics/cloning --- https://www.nature.com/articles/ncomms16015 2020-08-30 exercise --- https://www.nature.com/articles/news.2008.522 2020-08-30 genetics/genome-synthesis --- https://www.nature.com/articles/news.2010.253 2020-08-30 genetics/genome-synthesis --- https://www.nature.com/articles/s41366-021-00894-3 2020-08-30 exercise --- https://www.nature.com/articles/s41380-019-0517-y 2020-08-30 genetics/editing psychiatry/schizophrenia --- https://www.nature.com/articles/s41380-019-0575-1 2020-08-30 psychiatry/schizophrenia --- https://www.nature.com/articles/s41436-020-01007-7 2020-08-30 genetics/heritable/rare --- https://www.nature.com/articles/s41467-017-00314-z 2020-08-30 longevity/senolytic --- https://www.nature.com/articles/s41467-018-04148-1 2020-08-30 exercise --- https://www.nature.com/articles/s41467-018-04191-y 2020-08-30 psychiatry/schizophrenia --- https://www.nature.com/articles/s41467-018-05510-z Genome-wide association study results for educational attainment aid in identifying genetic heterogeneity of schizophrenia 2020-08-30 psychiatry/schizophrenia --- https://www.nature.com/articles/s41467-020-19244-4#deepmind 2020-08-31 reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/model-free/alphastar reinforcement-learning/multi-agent --- https://www.nature.com/articles/s41467-021-23556-4 2020-08-31 genetics/heritable/rare --- https://www.nature.com/articles/s41467-021-25453-2 2020-08-31 longevity/senolytic --- https://www.nature.com/articles/s41534-019-0241-0 2020-08-31 reinforcement-learning/exploration --- https://www.nature.com/articles/s41551-021-00804-y 2020-08-31 reinforcement-learning/imitation-learning/brain-imitation-learning --- https://www.nature.com/articles/s41562-021-01097-6 2020-08-31 psychology/neuroscience --- https://www.nature.com/articles/s41586-019-1923-7 2020-08-31 ai/nn/transformer/alphafold --- https://www.nature.com/articles/s41586-021-03819-2#deepmind 2020-08-31 ai/nn/transformer/alphafold --- https://www.nature.com/articles/s41586-021-03828-1 2020-08-31 ai/nn/transformer/alphafold --- https://www.nature.com/articles/s41586-021-03855-y 2020-08-31 genetics/heritable/rare --- https://www.nature.com/articles/s41587-020-00750-1 2020-09-01 longevity/senolytic --- https://www.nature.com/articles/s41587-021-00986-5 2020-09-01 cryonics --- https://www.nature.com/articles/s41591-019-0626-9 2020-09-01 genetics/microbiome --- https://www.nature.com/articles/s41598-017-10381-3 2020-09-01 longevity/fasting --- https://www.nature.com/articles/s41598-017-14700-6 2020-09-01 psychedelic --- https://www.nature.com/articles/s41598-019-41228-8 2020-09-01 genetics/genome-synthesis --- https://www.nature.com/articles/s41598-020-74140-7 2020-09-01 exercise --- https://www.nature.com/articles/s42256-018-0006-z#uber 2020-09-01 reinforcement-learning/meta-learning --- https://www.nature.com/articles/srep02758 2020-09-01 psychology/novelty --- https://www.nature.com/articles/tp201113 2020-09-01 crime genetics/heritable --- https://www.nature.com/articles/tp201536 2020-09-01 crime genetics/heritable --- https://www.nature.com/articles/tp201662 2020-09-02 psychiatry/schizophrenia --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4842006/ Genetic and environmental determinants of violence risk in psychotic disorders: a multivariate quantitative genetic study of 1.8 million Swedish twins and siblings 2020-09-02 psychiatry/schizophrenia --- https://www.nayuki.io/page/designing-better-file-organization-around-tags-not-hierarchies 2020-09-02 ai/anime/danbooru cs design --- https://www.nbcwashington.com/news/local/navy-seal-k-9-school-shooting-demonstration/133860/ Former Navy SEAL Trains Cloned K-9s to Locate and Take Down School Shooters: After five tours overseas, Joshua Morton returned home and put his skills as a K-9 handler into action 2020-09-02 genetics/cloning --- https://www.nber.org/papers/w22688 The effect of medical marijuana laws on the health and labor supply of older adults: Evidence from the Health and Retirement Study 2020-09-02 marijuana --- https://www.nber.org/system/files/working_papers/w20366/w20366.pdf 2020-09-02 crime sociology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1065034/ Statistics review 12: survival analysis Viv Bewick, Liz Cheek, Jonathan Ball 2004 2020-09-02 [("doi","10.1186/cc2955")] statistics/survival-analysis

This review introduces methods of analyzing data arising from studies where the response variable is the length of time taken to reach a certain end-point, often death.

The Kaplan-Meier methods, log rank test and Cox’s proportional hazards model are described.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1240871/pdf/ehp0110-000563.pdf Economic gains resulting from the reduction in children’s exposure to lead in the United States Scott D. Grosse, Thomas D. Matte, Joel Schwartz, Richard J. Jackson 2002 2020-09-02 [("doi","10.1289/ehp.02110563")] iq/ses

In this study we quantify economic benefits from projected improvements in worker productivity resulting from the reduction in children’s exposure to lead in the United States since 1976. We calculated the decline in blood lead levels (BLLs) 1976–1999 on the basis of nationally representative National Health and Nutrition Examination Survey (NHANES) data collected during 1976 through 1980, 1991 through 1994, and 1999.

The decline in mean BLL in 1- to 5-year-old U.S. children from 1976–1980–1991–1994 was 12.3 microg/dL, and the estimated decline 1976–1999 was 15.1 microg/dL. We assumed the change in cognitive ability resulting from declines in BLLs, on the basis of published meta-analyses, to be between 0.185 and 0.323 IQ points for each 1 g/dL blood lead concentration. These calculations imply that, because of falling BLLs, U.S. preschool-aged children in the late 1990s had IQs that were, on average, 2.2–4.7 points higher than they would have been if they had the blood lead distribution observed among U.S. preschool-aged children in the late 1970s.

We estimated that each IQ point raises worker productivity 1.76–2.38%. With discounted lifetime earnings of $723,300 for each 2-year-old in 2000 dollars, the estimated economic benefit for each year’s cohort of 3.8 million 2-year-old children ranges from $110 billion to $319 billion.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1376114/pdf/jmedeth00282-0040.pdf A proposal to classify happiness as a psychiatric disorder R P. Bentall 1992 2020-09-02 [("doi","10.1136/jme.18.2.94")] philosophy/ethics

It is proposed that happiness be classified as a psychiatric disorder and be included in future editions of the major diagnostic manuals under the new name: major affective disorder, pleasant type.

In a review of the relevant literature, it is shown that happiness is statistically abnormal, consists of a discrete cluster of symptoms, is associated with a range of cognitive abnormalities, and probably reflects the abnormal functioning of the central nervous system.

One possible objection to this proposal remains—that happiness is not negatively valued. However, this objection is dismissed as scientifically irrelevant.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1622921/ The Hazards of Predicting Divorce Without Crossvalidation Richard E. Heyman, Amy M. Smith Slep 2001 2020-09-02 [("doi","10.1111/j.1741-3737.2001.00473.x")] psychiatry statistics/bias

Divorce prediction studies (eg. Gottman, Coan, Carrere, & Swanson 1998) suggest that couples’ eventual divorce can be very accurately predicted from a number of different variables.

Recent attention to these studies has failed to consider the need to cross-validate prediction equations and to consider the prevalence of divorce in the population.

We analyze archival data to demonstrate that accuracy and predictive value drops precipitously during cross validation.

We conclude that results of studies without cross validation analyses should be interpreted with extreme caution, no matter how impressive the initial results appear to be.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1691485/pdf/14561278.pdf Oral creatine monohydrate supplementation improves brain performance: a double-blind, placebo-controlled, cross-over trial Caroline Rae, Alison L. Digney, Sally R. McEwan, Timothy C. Bates 2003 2020-09-02 [("doi","10.1098/rspb.2003.2492")] creatine iq

Creatine supplementation is in widespread use to enhance sports-fitness performance, and has been trialed successfully in the treatment of neurological, neuromuscular and atherosclerotic disease. Creatine plays a pivotal role in brain energy homeostasis, being a temporal and spatial buffer for cytosolic and mitochondrial pools of the cellular energy currency, adenosine triphosphate and its regulator, adenosine diphosphate.

In this work, we tested the hypothesis that oral creatine supplementation (5 g d−1 for 6 weeks) would enhance intelligence test scores and working memory performance in 45 young adult, vegetarian subjects in a double-blind, placebo-controlled, cross-over design.

Creatine supplementation had a positive effect (p < 0.0001) on both working memory (backward digit span) and intelligence (Raven’s Advanced Progressive Matrices), both tasks that require speed of processing.

These findings underline a dynamic role of brain energy capacity in influencing brain performance.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1781276/ Self-management of fatal familial insomnia. Part 2: case report Joyce Schenkein, Pasquale Montagna 2006 2020-09-03 genetics/heritable/rare psychiatry psychology/neuroscience/pain/anesthesia zeo

Context: Fatal familial insomnia (FFI) is a genetically transmitted neurodegenerative prion disease that incurs great suffering and has neither a treatment nor cure. The clinical literature is devoid of management plans (other than palliative). Part 1 of this article reviews the sparse literature about FFI, including case descriptions. Part 2 describes the efforts of one patient (with the rapid-course Met-Met subtype) who contended with his devastating symptoms and improved the quality of his life.

Design: Interventions were based on the premise that some symptoms may be secondary to insomnia and not a direct result of the disease itself. Strategies (derived by trial and error) were devised to induce sleep and increase alertness. Interventions included vitamin supplementation, narcoleptics, anesthesia, stimulants, sensory deprivation, exercise, light entrainment, growth hormone, and electroconvulsive therapy (ECT).

Results: The patient exceeded the average survival time by nearly 1 year, and during this time (when most patients are totally incapacitated), he was able to write a book and to successfully drive hundreds of miles.

Conclusion: Methods to induce sleep may extend and enhance life during the disease course, although they do not prevent death. It is hoped that some of his methods will inspire further clinical studies.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1805542/ Cellular scaling rules for primate brains Suzana Herculano-Houzel, Christine E. Collins, Peiyan Wong, Jon H. Kaas 2007 2020-09-03 [("doi","10.1073/pnas.0611396104")] ai/scaling psychology/neuroscience

Primates are usually found to have richer behavioral repertoires and better cognitive abilities than rodents of similar brain size. This finding raises the possibility that primate brains differ from rodent brains in their cellular composition.

Here we examine the cellular scaling rules for primate brains and show that brain size increases isometrically as a function of cell numbers, such that an 11× larger brain is built with 10× more neurons and ~12× more nonneuronal cells of relatively constant average size. This isometric function is in contrast to rodent brains, which increase faster in size than in numbers of neurons.

As a consequence of the linear cellular scaling rules, primate brains have a larger number of neurons than rodent brains of similar size, presumably endowing them with greater computational power and cognitive abilities.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2130424/ An outbreak of common colds at an Antarctic base after 17 weeks of complete isolation T. R. Allen, A. F. Bradburne, E. J. Stott, C. S. Goodwin, D. A. Tyrrell 1973 2020-09-03 [("doi","10.1017/s0022172400022920")] biology genetics/microbiome

6⁄12 men wintering at an isolated Antarctic base sequentially developed symptoms and signs of a common cold after 17 weeks of complete isolation.

Examination of specimens taken from the men in relation to the outbreak has not revealed a causative agent.

[They simply carried the cold with them in their microbiomes the whole time?]

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2242625/ Linking antisocial behavior, substance use, and personality: an integrative quantitative model of the adult externalizing spectrum Robert F. Krueger, Kristian E. Markon, Christopher J. Patrick, Stephen D. Benning, Mark D. Kramer 2007 2020-09-03 [("doi","10.1037/0021-843X.116.4.645")] psychiatry

Antisocial behavior, substance use, and impulsive and aggressive personality traits often co-occur, forming a coherent spectrum of personality and psychopathology. In the current research, the authors developed a novel quantitative model of this spectrum.

Over 3 waves of iterative data collection, 1,787 adult participants selected to represent a range across the externalizing spectrum provided extensive data about specific externalizing behaviors. Statistical methods such as item response theory and semiparametric factor analysis were used to model these data.

The model and assessment instrument that emerged from the research shows how externalizing phenomena are organized hierarchically and cover a wide range of individual differences.

The authors discuss the utility of this model for framing research on the correlates and the etiology of externalizing phenomena.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2517967/ Knowledge retention after an online tutorial: a randomized educational experiment among resident physicians Douglas S. Bell, Charles E. Harless, Jerilyn K. Higa, Elizabeth L. Bjork, Robert A. Bjork, Mohsen Bazargan, Carol M. Mangione 2008 2020-09-03 [("doi","10.1007/s11606-008-0604-2")] psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition

Background: The time course of physicians’ knowledge retention after learning activities has not been well characterized. Understanding the time course of retention is critical to optimizing the reinforcement of knowledge.

Design: Educational follow-up experiment with knowledge retention measured at 1 of 6 randomly assigned time intervals (0–55 days) after an online tutorial covering 2 American Diabetes Association guidelines.

Participants: Internal and family medicine residents.

Measurements: Multiple-choice knowledge tests, subject characteristics including critical appraisal skills, and learner satisfaction.

Results: Of 197 residents invited, 91 (46%) completed the tutorial and were randomized; of these, 87 (96%) provided complete follow-up data. 92% of the subjects rated the tutorial as “very good” or “excellent.” Mean knowledge scores increased from 50% before the tutorial to 76% among those tested immediately afterward. Score gains were only half as great at 3–8 days and no retention was measurable at 55 days. The shape of the retention curve corresponded with a 1/4-power transformation of the delay interval. In multivariate analyses, critical appraisal skills and participant age were associated with greater initial learning, but no participant characteristic modified the rate of decline in retention.

Conclusion: Education that appears successful from immediate post-tests and learner evaluations can result in knowledge that is mostly lost to recall over the ensuing days and weeks. To achieve longer-term retention, physicians should review or otherwise reinforce new learning after as little as 1 week.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2572733/ Subliminal instrumental conditioning demonstrated in the human brain Mathias Pessiglione, Predrag Petrovic, Jean Daunizeau, Stefano Palminteri, Raymond J. Dolan, Chris D. Frith 2008 2020-09-03 [("doi","10.1016/j.neuron.2008.07.005")] psychology/neuroscience

How the brain uses success and failure to optimize future decisions is a long-standing question in neuroscience. One computational solution involves updating the values of context-action associations in proportion to a reward prediction error. Previous evidence suggests that such computations are expressed in the striatum and, as they are cognitively impenetrable, represent an unconscious learning mechanism.

Here, we formally test this by studying instrumental conditioning in a situation where we masked contextual cues, such that they were not consciously perceived. Behavioral data showed that subjects nonetheless developed a propensity to choose cues associated with monetary rewards relative to punishments. Functional neuroimaging revealed that during conditioning cue values and prediction errors, generated from a computational model, both correlated with activity in ventral striatum.

We conclude that, even without conscious processing of contextual cues, our brain can learn their reward value and use them to provide a bias on decision making.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2583069/ Sequencing and analysis of Neanderthal genomic DNA James P. Noonan, Graham Coop, Sridhar Kudaravalli, Doug Smith, Johannes Krause, Joe Alessi, Feng Chen, Darren Platt, Svante Pääbo, Jonathan K. Pritchard, Edward M. Rubin 2006 2020-09-03 [("doi","10.1126/science.1131412")] cryonics genetics/selection/natural/human genetics/sequencing

Our knowledge of Neanderthals is based on a limited number of remains and artifacts from which we must make inferences about their biology, behavior, and relationship to ourselves.

Here, we describe the characterization of these extinct hominids from a new perspective, based on the development of a Neanderthal metagenomic library and its high-throughput sequencing and analysis. Several lines of evidence indicate that the 65,250 base pairs of hominid sequence so far identified in the library are of Neanderthal origin, the strongest being the ascertainment of sequence identities between Neanderthal and chimpanzee at sites where the human genomic sequence is different.

These results enabled us to calculate the human-Neanderthal divergence time based on multiple randomly distributed autosomal loci. Our analyses suggest that on average the Neanderthal genomic sequence we obtained and the reference human genome sequence share a most recent common ancestor ~706,000 years ago, and that the human and Neanderthal ancestral populations split ~370,000 years ago, before the emergence of anatomically modern humans.

Our finding that the Neanderthal and human genomes are at least 99.5% identical led us to develop and successfully implement a targeted method for recovering specific ancient DNA sequences from metagenomic libraries. This initial analysis of the Neanderthal genome advances our understanding of the evolutionary relationship of Homo sapiens and Homo neanderthalensis and signifies the dawn of Neanderthal genomics.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2740882/ Genetic enhancement of cognition in a kindred with cone-rod dystrophy due to RIMS1 mutation Sanjay M. Sisodiya, Pamela J. Thompson, Anna Need, Sarah E. Harris, Michael E. Weale, Susan E. Wilkie, Michel Michaelides, Samantha L. Free, Nicole Walley, Curtis Gumbs, Dianne Gerrelli, Piers Ruddle, Lawrence J. Whalley, John M. Starr, David M. Hunt, David B. Goldstein, Ian J. Deary, Anthony T. Moore 2007 2020-09-03 [("doi","10.1136/jmg.2006.047407")] genetics/heritable/rare

Background: The genetic basis of variation in human cognitive abilities is poorly understood. RIMS1 encodes a synapse active-zone protein with important roles in the maintenance of normal synaptic function: mice lacking this protein have greatly reduced learning ability and memory function. [Note: the loss of vision didn’t replicate in 2022, so any cognitive benefit would have to be unrelated to vision after all.]

Objective: An established paradigm examining the structural and functional effects of mutations in genes expressed in the eye and the brain was used to study a kindred with an inherited retinal dystrophy due to RIMS1 mutation.

Materials and Method: Neuropsychological tests and high-resolution MRI brain scanning were undertaken in the kindred. In a population cohort, neuropsychological scores were associated with common variation in RIMS1. Additionally, RIMS1 was sequenced in top-scoring individuals. Evolution of RIMS1 was assessed, and its expression in developing human brain was studied.

Results: Affected individuals showed enhanced cognitive abilities across a range of domains. Analysis suggests that factors other than RIMS1 mutation were unlikely to explain enhanced cognition. No association with common variation and verbal IQ was found in the population cohort, and no other mutations in RIMS1 were detected in the highest scoring individuals from this cohort. RIMS1 protein is expressed in developing human brain, but RIMS1 does not seem to have been subjected to accelerated evolution in man.

Conclusion: A possible role for RIMS1 in the enhancement of cognitive function at least in this kindred is suggested. Although further work is clearly required to explore these findings before a role for RIMS1 in human cognition can be formally accepted, the findings suggest that genetic mutation may enhance human cognition in some cases.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2750806/ Conducting the train of thought: working memory capacity, goal neglect, and mind wandering in an executive-control task Jennifer C. McVay, Michael J. Kane 2009 2020-09-03 [("doi","10.1037/a0014104")] dual-n-back

On the basis of the executive-attention theory of working memory capacity (WMC; eg. M. J. Kane et al 2007), the authors tested the relations among WMC, mind wandering, and goal neglect in a sustained attention to response task (SART; a go/no-go task). In 3 SART versions, making conceptual versus perceptual processing demands, subjects periodically indicated their thought content when probed following rare no-go targets.

SART processing demands did not affect mind-wandering rates, but mind-wandering rates varied with WMC and predicted goal-neglect errors in the task; furthermore, mind-wandering rates partially mediated the WMC-SART relation, indicating that:

WMC-related differences in goal neglect were due, in part, to variation in the control of conscious thought.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2762790/ Individual differences in executive functions are almost entirely genetic in origin Naomi P. Friedman, Akira Miyake, Susan E. Young, John C. DeFries, Robin P. Corley, John K. Hewitt 2008 2020-09-03 [("doi","10.1037/0096-3445.137.2.201")] genetics/heritable/correlation iq

Recent psychological and neuropsychological research suggests that executive functions—the cognitive control processes that regulate thought and action—are multifaceted and that different types of executive functions are correlated but separable. The present multivariate twin study of 3 executive functions (inhibiting dominant responses, updating working memory representations, and shifting between task sets), measured as latent variables, examined why people vary in these executive control abilities and why these abilities are correlated but separable from a behavioral genetic perspective.

Results indicated that executive functions are correlated because they are influenced by a highly heritable (99%) common factor that goes beyond general intelligence or perceptual speed, and they are separable because of additional genetic influences unique to particular executive functions. This combination of general and specific genetic influences places executive functions among the most heritable psychological traits. These results highlight the potential of genetic approaches for uncovering the biological underpinnings of executive functions and suggest a need for examining multiple types of executive functions to distinguish different levels of genetic influences.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2781097/ Physical and biological aspects of renal vitrification Gregory M. Fahy, Brian Wowk, Roberto Pagotan, Alice Chang, John Phan, Bruce Thomson, Laura Phan 2009 2020-09-04 [("doi","10.4161/org.5.3.9974")] cryonics

Cryopreservation would potentially very much facilitate the inventory control and distribution of laboratory-produced organs and tissues. Although simple freezing methods are effective for many simple tissues, bioartificial organs and complex tissue constructs may be unacceptably altered by ice formation and dissolution. Vitrification, in which the liquids in a living system are converted into the glassy state at low temperatures, provides a potential alternative to freezing that can in principle avoid ice formation altogether.

The present report provides a brief overview of the problem of renal vitrification. We report here the detailed case history of a rabbit kidney that survived vitrification and subsequent transplantation, a case that demonstrates both the fundamental feasibility of complex system vitrification and the obstacles that must still be overcome, of which the chief one in the case of the kidney is adequate distribution of cryoprotectant to the renal medulla.

Medullary equilibration can be monitored by monitoring urine concentrations of cryoprotectant, and urine flow rate correlates with vitrification solution viscosity and the speed of equilibration. By taking these factors into account and by using higher perfusion pressures as per the case of the kidney that survived vitrification, it is becoming possible to design protocols for equilibrating kidneys that protect against both devitrification and excessive cryoprotectant toxicity.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2792630/ False memory 1⁄20th of a second later: what the early onset of boundary extension reveals about perception Helene Intraub, Christopher A. Dickinson 2008 2020-09-04 [("doi","10.1111/j.1467-9280.2008.02192.x")] psychology/cognitive-bias/illusion-of-depth psychology/neuroscience

Errors of commission are thought to be caused by heavy memory loads, confusing information, lengthy retention intervals, or some combination of these factors.

We report false memory beyond the boundaries of a view, boundary extension, after less than 1⁄20th of a second.

Photographs of scenes were interrupted by a 42-ms or 250-ms mask, 250 ms into viewing, before reappearing or being replaced with a different view (Experiment 1). Post-interruption photographs that were unchanged were rated as closer up than the original views; when the photographs were changed, the same pair of closer-up and wider-angle views was rated as more similar when the closer view was first, rather than second. Thus, observers remembered pre-interruption views with extended boundaries. Results were replicated when the interruption included a saccade (Experiment 2).

The brevity of these interruptions has implications for visual scanning; it also challenges the traditional distinction between perception and memory. We offer an alternative conceptualization that shows how source monitoring can explain false memory after an interruption briefer than an eyeblink.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2835847/ The cost of crime to society: new crime-specific estimates for policy and program evaluation Kathryn E. McCollister, Michael T. French, Hai Fang 2010 2020-09-04 [("doi","10.1016/j.drugalcdep.2009.12.002")] crime economics psychiatry/lithium

Estimating the cost to society of individual crimes is essential to the economic evaluation of many social programs, such as substance abuse treatment and community policing. A review of the crime-costing literature reveals multiple sources, including published articles and government reports, which collectively represent the alternative approaches for estimating the economic losses associated with criminal activity. Many of these sources are based upon data that are more than 10 years old, indicating a need for updated figures.

This study presents a comprehensive methodology for calculating the cost to society of various criminal acts. Tangible and intangible losses are estimated using the most current data available. The selected approach, which incorporates both the cost-of-illness and the jury compensation methods, yields cost estimates for more than a dozen major crime categories, including several categories not found in previous studies. Updated crime cost estimates can help government agencies and other organizations execute more prudent policy evaluations, particularly benefit-cost analyses of substance abuse treatment or other interventions that reduce crime.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2841913/ Human face recognition ability is specific and highly heritable Jeremy B. Wilmer, Laura Germine, Christopher F. Chabris, Garga Chatterjee, Mark Williams, Eric Loken, Ken Nakayama, Bradley Duchaine 2010 2020-09-04 [("doi","10.1073/pnas.0913053107")] genetics/heritable psychology

Compared with notable successes in the genetics of basic sensory transduction, progress on the genetics of higher level perception and cognition has been limited. We propose that investigating specific cognitive abilities with well-defined neural substrates, such as face recognition, may yield additional insights.

In a twin study of face recognition, we found that the correlation of scores between monozygotic twins (0.70) was more than double the dizygotic twin correlation (0.29), evidence for a high genetic contribution to face recognition ability. Low correlations between face recognition scores and visual and verbal recognition scores indicate that both face recognition ability itself and its genetic basis are largely attributable to face-specific mechanisms.

The present results therefore identify an unusual phenomenon: a highly specific cognitive ability that is highly heritable. Our results establish a clear genetic basis for face recognition, opening this intensively studied and socially advantageous cognitive trait to genetic investigation.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC28699/ Content and quality of 2000 controlled trials in schizophrenia over 50 years 2020-09-04 psychiatry/schizophrenia --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013347/ Modulatory effects of modafinil on neural circuits regulating emotion and cognition Roberta Rasetti, Venkata S. Mattay, Beth Stankevich, Kelsey Skjei, Giuseppe Blasi, Fabio Sambataro, Isabel C. Arrillaga-Romany, Terry E. Goldberg, Joseph H. Callicott, José A. Apud, Daniel R. Weinberger 2010 2020-09-04 [("doi","10.1038/npp.2010.83")] modafinil psychiatry/anxiety psychology/neuroscience

Modafinil differs from other arousal-enhancing agents in chemical structure, neurochemical profile, and behavioral effects. Most functional neuroimaging studies to date examined the effect of modafinil only on information processing underlying executive cognition, but cognitive enhancers in general have been shown to have pronounced effects on emotional behavior, too.

We examined the effect of modafinil on neural circuits underlying affective processing and cognitive functions. Healthy volunteers were enrolled in this double-blinded placebo-controlled trial (100 mg/day for 7 days). They underwent BOLD fMRI while performing an emotion information-processing task that activates the amygdala and two prefrontally dependent cognitive tasks-a working memory (WM) task and a variable attentional control (VAC) task. A clinical assessment that included measurement of blood pressure, heart rate, the Hamilton anxiety scale, and the profile of mood state (POMS) questionnaire was also performed on each test day.

BOLD fMRI revealed decreased amygdala reactivity to fearful stimuli on modafinil compared with the placebo condition. During executive cognition tasks, a WM task and a VAC task, modafinil reduced BOLD signal in the prefrontal cortex and anterior cingulate. Although not statistically-significant, there were trends for reduced anxiety, for decreased fatigue-inertia and increased vigor-activity, as well as decreased anger-hostility on modafinil. Modafinil in low doses has an unique physiologic profile compared with stimulant drugs: it enhances the efficiency of prefrontal cortical cognitive information processing, while dampening reactivity to threatening stimuli in the amygdala, a brain region implicated in anxiety.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3062901/ The misunderstood limits of folk science: an illusion of explanatory depth Leonid Rozenblit, Frank Keil 2002 2020-09-04 [("doi","10.1207/s15516709cog2605_1")] psychology/cognitive-bias/illusion-of-depth

People feel they understand complex phenomena with far greater precision, coherence, and depth than they really do; they are subject to an illusion—an illusion of explanatory depth.

The illusion is far stronger for explanatory knowledge than many other kinds of knowledge, such as that for facts, procedures, or narratives. The illusion for explanatory knowledge is most robust where the environment supports real-time explanations with visible mechanisms.

We demonstrate the illusion of depth with explanatory knowledge in Studies 1–6. Then we show differences in overconfidence about knowledge across different knowledge domains in Studies 7–10.

Finally, we explore the mechanisms behind the initial confidence and behind overconfidence in Studies 11 and 12.

Implications for the roles of intuitive theories in models of concepts and cognition are discussed.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3086278/ A common variant of HMGA2 is associated with adult and childhood height in the general population Michael N. Weedon, Guillaume Lettre, Rachel M. Freathy, Cecilia M. Lindgren, Benjamin F. Voight, John R. B. Perry, Katherine S. Elliott, Rachel Hackett, Candace Guiducci, Beverley Shields, Eleftheria Zeggini, Hana Lango, Valeriya Lyssenko, Nicholas J. Timpson, Noel P. Burtt, Nigel W. Rayner, Richa Saxena, Kristin Ardlie, Jonathan H. Tobias, Andrew R. Ness, Susan M. Ring, Colin Palmer, Andrew D. Morris, Leena Peltonen, Veikko Salomaa, George Davey Smith, Leif C. Groop, Andrew Tym Hattersley, Mark I. McCarthy, Joel N. Hirschhorn, Timothy Frayling 2007 2020-09-04 [("doi","10.1038/ng2121")] genetics/heritable

Human height is a classic, highly heritable quantitative trait. To begin to identify genetic variants influencing height, we examined genome-wide association data from 4,921 individuals. Common variants in the HMGA2 oncogene, exemplified by rs1042725, were associated with height (p = 4 × 10−8). HMGA2 is also a strong biological candidate for height, as rare, severe mutations in this gene alter body size in mice and humans, so we tested rs1042725 in additional samples.

We confirmed the association in 19,064 adults from 4 further studies (p = 3 × 10−11, overall p = 4 × 10−16, including the genome-wide association data). We also observed the association in children (p = 1 × 10−6, n = 6,827) and a tall/short case-control study (p = 4 × 10−6, n = 3,207). We estimate that rs1042725 explains ~0.3% of population variation in height (~0.4 cm increased adult height per C allele).

There are few examples of common genetic variants reproducibly associated with human quantitative traits; these results represent, to our knowledge, the first consistently replicated association with adult and childhood height.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3086893/pdf/nihms288435.pdf Are birds smarter than mathematicians? Pigeons (Columba livia) perform optimally on a version of the Monty Hall Dilemma Walter T. Herbranson, Julia Schroeder 2010 2020-09-04 [("doi","10.1037/a0017703")] psychology/animal/bird psychology/neuroscience

The “Monty Hall Dilemma” (MHD) is a well known probability puzzle in which a player tries to guess which of 3 doors conceals a desirable prize. After an initial choice is made, one of the remaining doors is opened, revealing no prize. The player is then given the option of staying with their initial guess or switching to the other unopened door. Most people opt to stay with their initial guess, despite the fact that switching doubles the probability of winning.

A series of experiments investigated whether pigeons (Columba livia), like most humans, would fail to maximize their expected winnings in a version of the MHD. Birds completed multiple trials of a standard MHD, with the 3 response keys in an operant chamber serving as the 3 doors and access to mixed grain as the prize.

Across experiments, the probability of gaining reinforcement for switching and staying was manipulated, and birds adjusted their probability of switching and staying to approximate the optimal strategy.

Replication of the procedure with human participants showed that humans failed to adopt optimal strategies, even with extensive training.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3094752/ Causal Inference and Observational Research: The Utility of Twins Matt McGue, Merete Osler, Kaare Christensen 2010 2020-09-04 [("doi","10.1177/1745691610383511")] genetics/heritable/correlation statistics/causality

Valid causal inference is central to progress in theoretical and applied psychology. Although the randomized experiment is widely considered the gold standard for determining whether a given exposure increases the likelihood of some specified outcome, experiments are not always feasible and in some cases can result in biased estimates of causal effects. Alternatively, standard observational approaches are limited by the possibility of confounding, reverse causation, and the nonrandom distribution of exposure (ie. selection).

We describe the counterfactual model of causation and apply it to the challenges of causal inference in observational research, with a particular focus on aging. We argue that the study of twin pairs discordant on exposure, and in particular discordant monozygotic twins, provides a useful analog to the idealized counterfactual design.

A review of discordant-twin studies in aging reveals that they are consistent with, but do not unambiguously establish, a causal effect of lifestyle factors on important late-life outcomes. Nonetheless, the existing studies are few in number and have clear limitations that have not always been considered in interpreting their results.

It is concluded that twin researchers could make greater use of the discordant-twin design as one approach to strengthen causal inferences in observational research.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3095821/ Network anatomy and in vivo physiology of visual cortical neurons Davi D. Bock, Wei-Chung Allen Lee, Aaron M. Kerlin, Mark L. Andermann, Greg Hood, Arthur W. Wetzel, Sergey Yurgenson, Edward R. Soucy, Hyon Suk Kim, R. Clay Reid 2011 2020-09-04 [("doi","10.1038/nature09802")] psychology/neuroscience

In the cerebral cortex, local circuits consist of tens of thousands of neurons, each of which makes thousands of synaptic connections.

Perhaps the biggest impediment to understanding these networks is that we have no wiring diagrams of their interconnections. Even if we had a partial or complete wiring diagram, however, understanding the network would also require information about each neuron’s function. Here we show that the relationship between structure and function can be studied in the cortex with a combination of in vivo physiology and network anatomy.

We used two-photon calcium imaging to characterize a functional property—the preferred stimulus orientation—of a group of neurons in the mouse primary visual cortex. Large-scale electron microscopy of serial thin sections was then used to trace a portion of these neurons’ local network.

Consistent with a prediction from recent physiological experiments, inhibitory interneurons received convergent anatomical input from nearby excitatory neurons with a broad range of preferred orientations, although weak biases could not be rejected.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3143361/ Genome-wide association study of the child behavior checklist dysregulation profile Eric Mick, James McGough, Sandra Loo, Alysa E. Doyle, Janet Wozniak, Timothy E. Wilens, Susan Smalley, James McCracken, Joseph Biederman, Stephen V. Faraone 2011 2020-09-05 [("doi","10.1016/j.jaac.2011.05.001")] crime genetics/heritable psychiatry/adhd

Objective: A potentially useful tool for understanding the distribution and determinants of emotional dysregulation in children is a Child Behavior Checklist profile, comprising the Attention Problems, Anxious/Depressed, and Aggressive Behavior clinical subscales (CBCL-DP). The CBCL-DP indexes a heritable trait that increases susceptibility for later psychopathology, including severe mood problems and aggressive behavior. We have conducted a genome-wide association study of the CBCL-DP in children with attention-deficit/hyperactivity disorder (ADHD).

Method: Families were ascertained at Massachusetts General Hospital and University of California, Los Angeles. Genotyping was conducted with the Illumina Human1M or Human1M-Duo BeadChip platforms. Genome-wide association analyses were conducted with the MQFAM multivariate extension of PLINK.

Results: CBCL data were available for 341 ADHD offspring from 339 ADHD affected trio families from the UCLA (n = 128) and the MGH (n = 213) sites. We found no genome-wide statistically-significant associations but identified several plausible candidate genes among findings at p < 5E-05: TMEM132D, LRRC7, SEMA3A, ALK, and STIP1.

Conclusion: We found suggestive evidence for developmentally expressed genes operant in hippocampal dependent memory and learning with the CBCL-DP.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3147063/ Why are children in the same family so different from one another? Robert Plomin, Denise Daniels 2011 2020-09-05 [("doi","10.1093/ije/dyq148")] genetics/heritable/adoption iq psychiatry psychology/personality

One of the most important findings that has emerged from human behavioral genetics involves the environment rather than heredity, providing the best available evidence for the importance of environmental influences on personality, psychopathology, and cognition. The research also converges on the remarkable conclusion that these environmental influences make two children in the same family as different from one another as are pairs of children selected randomly from the population. The theme of the target article is that environmental differences between children in the same family (called “nonshared environment”) represent the major source of environmental variance for personality, psychopathology, and cognitive abilities.

One example of the evidence that supports this conclusion involves correlations for pairs of adopted children reared in the same family from early in life. Because these children share family environment but not heredity, their correlation directly estimates the importance of shared family environment. For most psychological characteristics, correlations for adoptive “siblings” hover near zero, which implies that the relevant environmental influences are not shared by children in the same family. Although it has been thought that cognitive abilities represent an exception to this rule, recent data suggest that environmental variance that affects IQ is also of the nonshared variety after adolescence.

The article has 3 goals: (1) To describe quantitative genetic methods and research that lead to the conclusion that nonshared environment is responsible for most environmental variation relevant to psychological development, (2) to discuss specific nonshared environmental influences that have been studied to date, and (3) to consider relationships between nonshared environmental influences and behavioral differences between children in the same family.

The reason for presenting this article in BBS is to draw attention to the far-reaching implications of finding that psychologically relevant environmental influences make children in a family different from, not similar to, one another.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3234554/ Social learning spreads knowledge about dangerous humans among American crows Heather N. Cornell, John M. Marzluff, Shannon Pecoraro 2012 2020-09-05 [("doi","10.1098/rspb.2011.0957")] psychology/animal/bird psychology/neuroscience

’

Individuals face evolutionary trade-offs between the acquisition of costly but accurate information gained firsthand and the use of inexpensive but possibly less reliable social information. American crows (Corvus brachyrhynchos) use both sources of information to learn the facial features of a dangerous person.

We exposed wild crows to a novel ‘dangerous face’ by wearing an unique mask as we trapped, banded and released 7–15 birds at five study sites near Seattle, WA, USA. An immediate scolding response to the dangerous mask after trapping by previously captured crows demonstrates individual learning, while an immediate response by crows that were not captured probably represents conditioning to the trapping scene by the mob of birds that assembled during the capture.

Later recognition of dangerous masks by lone crows that were never captured is consistent with horizontal social learning. Independent scolding by young crows, whose parents had conditioned them to scold the dangerous mask, demonstrates vertical social learning.

Crows that directly experienced trapping later discriminated among dangerous and neutral masks more precisely than did crows that learned through social means. Learning enabled scolding to double in frequency and spread at least 1.2 km from the place of origin over a 5 year period at one site.

’

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3240682/ How fast does the Grim Reaper walk? Receiver operating characteristics curve analysis in healthy men aged 70 and over Fiona F. Stanaway, Danijela Gnjidic, Fiona M. Blyth, David G. Le Couteur, Vasi Naganathan, Louise Waite, Markus J. Seibel, David J. Handelsman, Philip N. Sambrook, Robert G. Cumming 2011 2020-09-05 [("doi","10.1136/bmj.d7679")] statistics/survival-analysis

Objective: To determine the speed at which the Grim Reaper (or Death) walks.

Design: Population based prospective study.

Setting: Older community dwelling men living in Sydney, Australia.

Participants: 1705 men aged 70 or more participating in CHAMP (Concord Health and Ageing in Men Project).

Main Outcome Measures: Walking speed (m/s) and mortality. Receiver operating characteristics curve analysis was used to calculate the area under the curve for walking speed and determine the walking speed of the Grim Reaper. The optimal walking speed was estimated using the Youden index (sensitivity + specificity-1), a common summary measure of the receiver operating characteristics curve, and represents the maximum potential effectiveness of a marker.

Results: The mean walking speed was 0.88 (range 0.15–1.60) m⁄s. The highest Youden index (0.293) was observed at a walking speed of 0.82 m⁄s (2 miles (about 3 km) per hour), corresponding to a sensitivity of 63% and a specificity of 70% for mortality. Survival analysis showed that older men who walked faster than 0.82 m⁄s were 1.23× less likely to die (95% confidence interval 1.10 to 1.37) than those who walked slower (p = 0.0003). A sensitivity of 1.0 was obtained when a walking speed of 1.36 m⁄s (3 miles (about 5 km) per hour) or greater was used, indicating that no men with walking speeds of 1.36 m⁄s or greater had contact with Death.

Conclusion: The Grim Reaper’s preferred walking speed is 0.82 m⁄s (2 miles (about 3 km) per hour) under working conditions. As none of the men in the study with walking speeds of 1.36 m⁄s (3 miles (about 5 km) per hour) or greater had contact with Death, this seems to be the Grim Reaper’s most likely maximum speed; for those wishing to avoid their allotted fate, this would be the advised walking speed.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3323508/ Reappraisal of metformin efficacy in the treatment of type 2 diabetes: a meta-analysis of randomized controlled trials Rémy Boussageon, Irène Supper, Theodora Bejan-Angoulvant, Nadir Kellou, Michel Cucherat, Jean-Pierre Boissel, Behrouz Kassai, Alain Moreau, François Gueyffier, Catherine Cornu 2012 2020-09-05 [("doi","10.1371/journal.pmed.1001204")] longevity/metformin

Background: The UK Prospective Diabetes Study showed that metformin decreases mortality compared to diet alone in overweight patients with type 2 diabetes mellitus. Since then, it has been the first-line treatment in overweight patients with type 2 diabetes. However, metformin-sulphonylurea bitherapy may increase mortality.

Methods & Findings: This meta-analysis of randomized controlled trials evaluated metformin efficacy (in studies of metformin versus diet alone, versus placebo, and versus no treatment; metformin as an add-on therapy; and metformin withdrawal) against cardiovascular morbidity or mortality in patients with type 2 diabetes. We searched MEDLINE, Embase, and the Cochrane database. Primary end points were all-cause mortality and cardiovascular death. Secondary end points included all myocardial infarctions, all strokes, congestive heart failure, peripheral vascular disease, leg amputations, and microvascular complications. Thirteen randomized controlled trials (13,110 patients) were retrieved; 9,560 patients were given metformin, and 3,550 patients were given conventional treatment or placebo. Metformin did not affect the primary outcomes all-cause mortality, risk ratio (RR)=0.99 (95% CI: 0.75 to 1.31), and cardiovascular mortality, RR = 1.05 (95% CI: 0.67 to 1.64). The secondary outcomes were also unaffected by metformin treatment: all myocardial infarctions, RR = 0.90 (95% CI: 0.74 to 1.09); all strokes, RR = 0.76 (95% CI: 0.51 to 1.14); heart failure, RR = 1.03 (95% CI: 0.67 to 1.59); peripheral vascular disease, RR = 0.90 (95% CI: 0.46 to 1.78); leg amputations, RR = 1.04 (95% CI: 0.44 to 2.44); and microvascular complications, RR = 0.83 (95% CI: 0.59 to 1.17). For all-cause mortality and cardiovascular mortality, there was heterogeneity when including the UK Prospective Diabetes Study subgroups (I2 = 41% and 59%). There was interaction with sulphonylurea as a concomitant treatment for myocardial infarction (p = 0.10 and 0.02, respectively).

Conclusion: Although metformin is considered the gold standard, its benefit/risk ratio remains uncertain. We cannot exclude a 25% reduction or a 31% increase in all-cause mortality. We cannot exclude a 33% reduction or a 64% increase in cardiovascular mortality. Further studies are needed to clarify this situation.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3339026/ Do Ethicists and Political Philosophers Vote More Often Than Other Professors? Eric Schwitzgebel, Joshua Rust 2010 2020-09-05 [("doi","10.1007/s13164-009-0011-6")] sociology

If philosophical moral reflection improves moral behavior, one might expect ethics professors to behave morally better than socially similar non-ethicists.

Under the assumption that forms of political engagement such as voting have moral worth, we looked at the rate at which a sample of professional ethicists—and political philosophers as a subgroup of ethicists—voted in 8 years’ worth of elections. We compared ethicists’ and political philosophers’ voting rates with the voting rates of 3 other groups: philosophers not specializing in ethics, political scientists, and a comparison group of professors specializing in neither philosophy nor political science.

All groups voted at about the same rate, except for the political scientists, who voted about 10–15% more often. On the face of it, this finding conflicts with the expectation that ethicists will behave more responsibly than non-ethicists. [supplement]

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3351385/ CNVs: harbingers of a rare variant revolution in psychiatric genetics Dheeraj Malhotra, Jonathan Sebat 2012 2020-09-05 [("doi","10.1016/j.cell.2012.02.039")] genetics/heritable/rare psychiatry/autism psychiatry/bipolar/genetics psychiatry/schizophrenia

The genetic bases of neuropsychiatric disorders are beginning to yield to scientific inquiry.

Genome-wide studies of copy number variation (CNV) have given rise to a new understanding of disease etiology, bringing rare variants to the forefront.

A proportion of risk for schizophrenia, bipolar disorder, and autism can be explained by rare mutations. Such alleles arise by de novo mutation in the individual or in recent ancestry. Alleles can have specific effects on behavioral and neuroanatomical traits; however, expressivity is variable, particularly for neuropsychiatric phenotypes.

Knowledge from CNV studies reflects the nature of rare alleles in general and will serve as a guide as we move forward into a new era of whole-genome sequencing.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3359589/ The draft genome and transcriptome of Cannabis sativa Harm van Bakel, Jake M. Stout, Atina G. Cote, Carling M. Tallon, Andrew G. Sharpe, Timothy R. Hughes, Jonathan E. Page 2011 2020-09-05 [("doi","10.1186/gb-2011-12-10-r102")] genetics/sequencing marijuana

Background: Cannabis sativa has been cultivated throughout human history as a source of fiber, oil and food, and for its medicinal and intoxicating properties. Selective breeding has produced cannabis plants for specific uses, including high-potency marijuana strains and hemp cultivars for fiber and seed production. The molecular biology underlying cannabinoid biosynthesis and other traits of interest is largely unexplored.

Results: We sequenced genomic DNA and RNA from the marijuana strain Purple Kush using short read approaches. We report a draft haploid genome sequence of 534 Mb and a transcriptome of 30,000 genes. Comparison of the transcriptome of Purple Kush with that of the hemp cultivar ‘Finola’ revealed that many genes encoding proteins involved in cannabinoid and precursor pathways are more highly expressed in Purple Kush than in ‘Finola’. The exclusive occurrence of Δ9-tetrahydrocannabinolic acid synthase in the Purple Kush transcriptome, and its replacement by cannabidiolic acid synthase in ‘Finola’, may explain why the psychoactive cannabinoid Δ9-tetrahydrocannabinol (THC) is produced in marijuana but not in hemp. Resequencing the hemp cultivars ‘Finola’ and ‘USO-31’ showed little difference in gene copy numbers of cannabinoid pathway enzymes. However, single-nucleotide variant analysis uncovered a relatively high level of variation among four cannabis types, and supported a separation of marijuana and hemp.

Conclusion: The availability of the Cannabis sativa genome enables the study of a multifunctional plant that occupies an unique role in human culture. Its availability will aid the development of therapeutic marijuana strains with tailored cannabinoid profiles and provide a basis for the breeding of hemp with improved agronomic characteristics.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3410539/ How advances in neural recording affect data analysis Ian H. Stevenson, Konrad P. Kording 2011 2020-09-05 [("doi","10.1038/nn.2731")] psychology/neuroscience

Over the last 5 decades, progress in neural recording techniques has allowed the number of simultaneously recorded neurons to double every 7 years, mimicking Moore’s law. Such exponential growth motivates us to ask how data analysis techniques are affected by progressively larger numbers of recorded neurons.

Traditionally, neurons are analyzed independently on the basis of their tuning to stimuli or movement. Although tuning curve approaches are unaffected by growing numbers of simultaneously recorded neurons, newly developed techniques that analyze interactions between neurons become more accurate and more complex as the number of recorded neurons increases.

Emerging data analysis techniques should consider both the computational costs and the potential for more accurate models associated with this exponential growth of the number of recorded neurons.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3413799/ Heritability of performance deficit accumulation during acute sleep deprivation in twins Samuel T. Kuna, Greg Maislin, Frances M. Pack, Bethany Staley, Robert Hachadoorian, Emil F. Coccaro, Allan I. Pack 2012 2020-09-05 [("doi","10.5665/sleep.2074")] genetics/editing genetics/heritable/rare

Study Objectives: To determine if the large and highly reproducible interindividual differences in rates of performance deficit accumulation during sleep deprivation, as determined by the number of lapses on a sustained reaction time test, the Psychomotor Vigilance Task (PVT), arise from a heritable trait.

Design: Prospective, observational cohort study.

Setting: Academic medical center.

Participants: There were 59 monozygotic (mean age 29.2 ± 6.8 [SD] yr; 15 male and 44 female pairs) and 41 dizygotic (mean age 26.6 ± 7.6 yr; 15 male and 26 female pairs) same-sex twin pairs with a normal polysomnogram.

Interventions: Thirty-eight hr of monitored, continuous sleep deprivation.

Measurements and Results: Patients performed the 10-min PVT every 2 hr during the sleep deprivation protocol. The primary outcome was change from baseline in square root transformed total lapses (response time ≥ 500 ms) per trial. Patient-specific linear rates of performance deficit accumulation were separated from circadian effects using multiple linear regression. Using the classic approach to assess heritability, the intraclass correlation coefficients for accumulating deficits resulted in a broad sense heritability (h2) estimate of 0.834. The mean within-pair and among-pair heritability estimates determined by analysis of variance-based methods was 0.715. When variance components of mixed-effect multilevel models were estimated by maximum likelihood estimation and used to determine the proportions of phenotypic variance explained by genetic and nongenetic factors, 51.1% (standard error = 8.4%, p < 0.0001) of twin variance was attributed to combined additive and dominance genetic effects.

Conclusion: Genetic factors explain a large fraction of interindividual variance among rates of performance deficit accumulations on PVT during sleep deprivation.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3445777/ Effect of shared environmental factors on exercise behavior from age 7 to 12 years Charlotte Huppertz, Meike Bartels, Catherina E. M. Van Beijsterveldt, Dorret I. Boomsma, James J. Hudziak, Eco J. C. De Geus 2012 2020-09-05 [("doi","10.1249/MSS.0b013e31825d358e")] exercise genetics/heritable

Background: The aim of this study was to investigate the relative influence of genetic and environmental factors on children’s leisure time exercise behavior through the classic twin design.

Method: Data were taken from The Netherlands Twin Register. The twins were 7 (n = 3966 subjects), 10 (n = 3562), and 12-yr-olds (n = 8687), with longitudinal data for 27% of the sample. Parents were asked to indicate the children’s regular participation in leisure time exercise activities, including frequency and duration. Resemblance between monozygotic and dizygotic twins for weekly MET-hours spent on exercise activities was analyzed as a function of their genetic relatedness.

Results: Average weekly MET-hours increased with age for both boys (age 7 yr: 14.0 (SD = 11.8); age 10 yr: 22.6 (SD = 18.7); age 12 yr: 28.4 (SD = 24.9)) and girls (age 7 yr: 9.7 (SD = 9.5); age 10 yr: 15.3 (SD = 15.1); age 12 yr: 19.3 (SD = 19.8)). Around 13% of boys and girls across all age groups did not participate in any regular leisure time exercise activities. Tracking of exercise behavior from age 7 to 12 yr was modest (0.168 < r < 0.534). For boys, genetic effects accounted for 24% (confidence interval, 18%–30%) of the variance at age 7 yr, 66% (53%–81%) at age 10 yr, and 38% (32%–46%) at age 12 years. For girls, this was 22% (15%–30%), 16% (9%–24%), and 36% (30%–43%), respectively. Environmental influences shared by children from the same family explained 71%, 25%, and 50% of the variance in boys (age 7, 10, and 12 yr) and 67%, 72%, and 53% in girls. The shared environment influencing exercise behavior was partially different between boys and girls.

Conclusion: Our results stress the important role of shared environment for exercise behavior in young children.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3537166/ Neurobiological mechanisms behind the spatiotemporal illusions of awareness used for advocating prediction or postdiction Talis Bachmann 2012 2020-09-06 [("doi","10.3389/fpsyg.2012.00593")] psychology/neuroscience

The fact that it takes time for the brain to process information from the changing environment underlies many experimental phenomena of awareness of spatiotemporal events, including a number of astonishing illusions.

These phenomena have been explained from the predictive and postdictive theoretical perspectives. Here I describe the most extensively studied phenomena in order to see how well the two perspectives can explain them.

Next, the neurobiological perceptual retouch mechanism of producing stimulation awareness is characterized and its work in causing the listed illusions is described.

A perspective on how brain mechanisms of conscious perception produce the phenomena supportive of the postdictive view is presented in this article. At the same time, some of the phenomena cannot be explained by the traditional postdictive account, but can be interpreted from the perceptual retouch theory perspective.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3556483/ Genetic and familial environmental influences on the risk for drug abuse: a national Swedish adoption study Kenneth S. Kendler, Kristina Sundquist, Henrik Ohlsson, Karolina Palmér, Hermine Maes, Marilyn A. Winkleby, Jan Sundquist 2012 2020-09-06 [("doi","10.1001/archgenpsychiatry.2011.2112")] crime genetics/heritable/adoption psychiatry/alcoholism

Context: Prior research suggests that drug abuse (DA) is strongly influenced by both genetic and familial environmental factors. No large-scale adoption study has previously attempted to verify and integrate these findings.

Objective: To determine how genetic and environmental factors contribute to the risk for DA.

Design: Follow-up in 9 public databases (1961–2009) of adopted children and their biological and adoptive relatives.

Setting: Sweden.

Participants: The study included 18,115 adopted children born 1950–1993; 78,079 biological parents and siblings; and 51,208 adoptive parents and siblings.

Main Outcome Measures: Drug abuse recorded in medical, legal, or pharmacy registry records.

Results: Risk for DA was elevated in the adopted offspring of biological parents with DA (odds ratio, 2.09; 95% CI, 1.66–2.62), in biological full and half siblings of adopted children with DA (odds ratio, 1.84; 95% CI, 1.28–2.64; and odds ratio, 1.41; 95% CI, 1.19–1.67, respectively), and in adoptive siblings of adopted children with DA (odds ratio, 1.95; 95% CI, 1.43–2.65). A genetic risk index (including biological parental or sibling history of DA, criminal activity, and psychiatric or alcohol problems) and an environmental risk index (including adoptive parental history of divorce, death, criminal activity, and alcohol problems, as well as an adoptive sibling history of DA and psychiatric or alcohol problems) both strongly predicted the risk for DA. Including both indices along with sex and age at adoption in a predictive model revealed a positive interaction between the genetic and environmental risk indices.

Conclusion: Drug abuse is an etiologically complex syndrome strongly influenced by a diverse set of genetic risk factors reflecting a specific liability to DA, by a vulnerability to other externalizing disorders, and by a range of environmental factors reflecting marital instability, as well as psychopathology and criminal behavior in the adoptive home. Adverse environmental effects on DA are more pathogenic in individuals with high levels of genetic risk. These results should be interpreted in the context of limitations of the diagnosis of DA from registries.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3580835/ Genome-wide association study of conduct disorder symptomatology D M. Dick, F. Aliev, R. F. Krueger, A. Edwards, A. Agrawal, M. Lynskey, P. Lin, M. Schuckit, V. Hesselbrock, J. Nurnberger, L. Almasy, B. Porjesz, Howard J. Edenberg, K. Bucholz, J. Kramer, S. Kuperman, L. Bierut 2011 2020-09-06 [("doi","10.1038/mp.2010.73")] crime genetics/heritable psychiatry/alcoholism psychology/personality

Conduct disorder (CD) is one of the most prevalent childhood psychiatric conditions, and is associated with a number of serious concomitant and future problems. CD symptomatology is known to have a considerable genetic component, with heritability estimates in the range of 50%. Despite this, there is a relative paucity of studies aimed at identifying genes involved in the susceptibility to CD.

In this study, we report results from a genome-wide association study of CD symptoms. CD symptoms were retrospectively reported by a psychiatric interview among a sample of cases and controls, in which cases met the criteria for alcohol dependence. Our primary phenotype was the natural log transformation of the number of CD symptoms that were endorsed, with data available for 3963 individuals who were genotyped on the Illumina Human 1M beadchip array. Secondary analyses are presented for case versus control status, in which caseness was established as endorsing 3 or more CD symptoms (n = 872 with CD and n = 3091 without CD).

We find four markers that meet the criteria for genome-wide statistical-significance (p < 5 × 10−8) with the CD symptom count, two of which are located in the gene C1QTNF7 (C1q and tumor necrosis factor-related protein 7). There were 6 additional SNPs in the gene that yielded converging evidence of association.

These data provide the first evidence of a specific gene that is associated with CD symptomatology. None of the top signals resided in traditional candidate genes, underscoring the importance of a genome-wide approach for identifying novel variants involved in this serious childhood disorder.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3637176/ Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4 Pamela Sklar, Stephan Ripke, Laura J. Scott, Ole A. Andreassen, Sven Cichon, Nick Craddock, Howard J. Edenberg, John I. Nurnberger, Marcella Rietschel, Douglas Blackwood, Aiden Corvin, Matthew Flickinger, Weihua Guan, Morten Mattingsdal, Andrew Mcquillin, Phoenix Kwan, Thomas F. Wienker, Mark Daly, Frank Dudbridge, Peter A. Holmans, Danyu Lin, Margit Burmeister, Tiffany A. Greenwood, Marian L. Hamshere, Pierandrea Muglia, Erin N. Smith, Peter P. Zandi, Caroline M. Nievergelt, Rebecca Mckinney, Paul D. Shilling, Nicholas J. Schork, Cinnamon S. Bloss, Tatiana Foroud, Daniel L. Koller, Elliot S. Gershon, Chunyu Liu, Judith A. Badner, William A. Scheftner, William B. Lawson, Evaristus A. Nwulia, Maria Hipolito, William Coryell, John P. Rice, William Byerley, Francis J. McMahon, Thomas G. Schulze, Wade Berrettini, Falk W. Lohoff, James B. Potash, Pamela B. Mahon, Melvin G. Mcinnis, Sebastian Zöllner, Peng Zhang, David W. Craig, Szabocls Szelinger, Thomas B. Barrett, René Breuer, Sandra Meier, Jana Strohmaier, Stephanie H. Witt, Federica Tozzi, Anne Farmer, Peter McGuffin, John Strauss, Wei Xu, James L. Kennedy, John B. Vincent, Keith Matthews, Richard Day, Manuel D. C. Ferreira, Colm O’Dushlaine, Roy Perlis, Soumya Raychaudhuri, Douglas Ruderfer, Phil L. Hyoun, Jordan W. Smoller, Jun Li, Devin Absher, Robert C. Thompson, Fan Guo Meng, Alan F. Schatzberg, William E. Bunney, Jack D. Barchas, Edward G. Jones, Stanley J. Watson, Richard M. Myers, Huda Akil, Michael Boehnke, Kim Chambert, Jennifer Moran, Ed Scolnick, Srdjan Djurovic, Ingrid Sigfrid Melle, Gunnar Morken, Michael Gill, Derek Morris, Emma Quinn, Thomas W. Mühleisen, Franziska A. Degenhardt, Manuel Mattheisen, Johannes Schumacher, Wolfgang Maier, Michael Steffens, Peter Propping, Markus M. Nöthen, Adebayo Anjorin, Nick Bass, Hugh Gurling, Radhika Kandaswamy, Jacob Lawrence, Kevin Mcghee, Andrew Mcintosh, Alan W. Mclean, Walter J. Muir, Benjamin S. Pickard, Gerome Breen, David St Clair, Sian Caesar, Katherine Gordon-Smith, Lisa Jones, Christine Fraser, Elaine K. Green, Detelina Grozeva, Ian R. Jones, George Kirov, Valentina Moskvina, Ivan Nikolov, Michael C. O’Donovan, Michael J. Owen, David A. Collier, Amanda Elkin, Richard Williamson, Allan H. Young, I. Nicol Ferrier, Kari Stefansson, Hreinn Stefansson, Porgeir Porgeirsson, Stacy Steinberg, Omar Gustafsson, Sarah E. Bergen, Vishwajit Nimgaonkar, Christina hultman, Mikael Landén, Paul Lichtenstein, Patrick Sullivan, Martin Schalling, Urban Osby, Lena Backlund, Louise Frisén, Niklas Langstrom, Stéphane Jamain, Marion Leboyer, Bruno Etain, Frank Bellivier, Hannes Petursson, Engilbert Sigur Sson, Bertram Müller-Mysok, Susanne Lucae, Markus Schwarz, Peter R. Schofield, Nick Martin, Grant W. Montgomery, Mark Lathrop, Högni Oskarsson, Michael Bauer, Adam Wright, Philip B. Mitchell, Martin Hautzinger, Andreas Reif, John R. Kelsoe, Shaun M. Purcell 2011-10 2020-09-06 [("doi","10.1038/ng.943")] genetics/heritable psychiatry/bipolar/genetics psychiatry/schizophrenia

We conducted a combined genome-wide association study (GWAS) of 7,481 individuals with bipolar disorder (cases) and 9,250 controls as part of the Psychiatric GWAS Consortium. Our replication study tested 34 SNPs in 4,496 independent cases with bipolar disorder and 42,422 independent controls and found that:

18⁄34 SNPs had p < 0.05, with 31⁄34 SNPs having signals with the same direction of effect (p = 3.8 × 10−7). An analysis of all 11,974 bipolar disorder cases and 51,792 controls confirmed genome-wide statistically-significant evidence of association for CACNA1C and identified a new intronic variant in ODZ4. We identified a pathway comprised of subunits of calcium channels enriched in bipolar disorder association intervals. Finally, a combined GWAS analysis of schizophrenia and bipolar disorder yielded strong association evidence for SNPs in CACNA1C and in the region of NEK4-ITIH1-ITIH3-ITIH4.

Our replication results imply that increasing sample sizes in bipolar disorder will confirm many additional loci. [supplement]

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3646712/ Genome-wide association study of clinical dimensions of schizophrenia: polygenic effect on disorganized symptoms Ayman H. Fanous, Baiyu Zhou, Steven H. Aggen, Sarah E. Bergen, Richard L. Amdur, Jubao Duan, Alan R. Sanders, Jianxin Shi, Bryan J. Mowry, Ann Olincy, Farooq Amin, C. Robert Cloninger, Jeremy M. Silverman, Nancy G. Buccola, William F. Byerley, Donald W. Black, Robert Freedman, Frank Dudbridge, Peter A. Holmans, Stephan Ripke, Pablo V. Gejman, Kenneth S. Kendler, Douglas F. Levinson 2012 2020-09-06 [("doi","10.1176/appi.ajp.2012.12020218")] psychiatry/schizophrenia

Objective: Multiple sources of evidence suggest that genetic factors influence variation in clinical features of schizophrenia. The authors present the first genome-wide association study (GWAS) of dimensional symptom scores among individuals with schizophrenia.

Method: Based on the Lifetime Dimensions of Psychosis Scale ratings of 2,454 case subjects of European ancestry from the Molecular Genetics of Schizophrenia (MGS) sample, 3 symptom factors (positive, negative/disorganized, and mood) were identified with exploratory factor analysis. Quantitative scores for each factor from a confirmatory factor analysis were analyzed for association with 696,491 single-nucleotide polymorphisms (SNPs) using linear regression, with correction for age, sex, clinical site, and ancestry. Polygenic score analysis was carried out to determine whether case and comparison subjects in 16 Psychiatric GWAS Consortium (PGC) schizophrenia samples (excluding MGS samples) differed in scores computed by weighting their genotypes by MGS association test results for each symptom factor.

Results: No genome-wide statistically-significant associations were observed between SNPs and factor scores. Most of the SNPs producing the strongest evidence for association were in or near genes involved in neurodevelopment, neuroprotection, or neurotransmission, including genes playing a role in Mendelian CNS diseases, but no statistically-significant effect was observed for any defined gene pathway. Finally, polygenic scores based on MGS GWAS results for the negative/disorganized factor were different between case and comparison subjects in the PGC data set; for MGS subjects, negative/disorganized factor scores were correlated with polygenic scores generated using case-control GWAS results from the other PGC samples.

Conclusion: The polygenic signal that has been observed in cross-sample analyses of schizophrenia GWAS data sets could be in part related to genetic effects on negative and disorganized symptoms (ie. core features of chronic schizophrenia).

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3696520/ Human aggression across the lifespan: genetic propensities and environmental moderators Catherine Tuvblad, Laura A. Baker 2011 2020-09-06 [("doi","10.1016/B978-0-12-380858-5.00007-1")] crime genetics/heritable/adoption

This chapter reviews the recent evidence of genetic and environmental influences on human aggression. Findings from a large selection of the twin and adoption studies that have investigated the genetic and environmental architecture of aggressive behavior are summarized. These studies together show that about half (50%) of the variance in aggressive behavior is explained by genetic influences in both males and females, with the remaining 50% of the variance being explained by environmental factors not shared by family members.

Form of aggression (reactive, proactive, direct/physical, indirect/relational), method of assessment (laboratory observation, self-report, ratings by parents and teachers), and age of the subjects-all seem to be moderators of the magnitude of genetic and environmental influences on aggressive behavior. Neither study design (twin vs. sibling adoption design) nor sex (male vs. female) seems to impact the magnitude of the genetic and environmental influences on aggression. There is also some evidence of gene-environment interaction (G × E) from both twin/adoption studies and molecular genetic studies. Various measures of family adversity and social disadvantage have been found to moderate genetic influences on aggressive behavior.

Findings from these G × E studies suggest that not all individuals will be affected to the same degree by experiences and exposures, and that genetic predispositions may have different effects depending on the environment.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3714010/ Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis 2013 2020-09-06 [("doi","10.1016/S0140-6736(12)62129-1")] genetics/heritable/correlation psychiatry/autism psychiatry/bipolar/genetics psychiatry/depression psychiatry/schizophrenia

Background: Findings from family and twin studies suggest that genetic contributions to psychiatric disorders do not in all cases map to present diagnostic categories. We aimed to identify specific variants underlying genetic effects shared between the five disorders in the Psychiatric Genomics Consortium: autism spectrum disorder, attention deficit-hyperactivity disorder, bipolar disorder, major depressive disorder, and schizophrenia.

Method: We analysed genome-wide single-nucleotide polymorphism (SNP) data for the five disorders in 33,332 cases and 27,888 controls of European ancestry. To characterise allelic effects on each disorder, we applied a multinomial logistic regression procedure with model selection to identify the best-fitting model of relations between genotype and phenotype. We examined cross-disorder effects of genome-wide statistically-significant loci previously identified for bipolar disorder and schizophrenia, and used polygenic risk-score analysis to examine such effects from a broader set of common variants. We undertook pathway analyses to establish the biological associations underlying genetic overlap for the five disorders. We used enrichment analysis of expression quantitative trait loci (eQTL) data to assess whether SNPs with cross-disorder association were enriched for regulatory SNPs in post-mortem brain-tissue samples.

Results: SNPs at four loci surpassed the cutoff for genome-wide statistical-significance (p < 5×10−8) in the primary analysis: regions on chromosomes 3p21 and 10q24, and SNPs within two L-type voltage-gated calcium channel subunits, CACNA1C and CACNB2. Model selection analysis supported effects of these loci for several disorders. Loci previously associated with bipolar disorder or schizophrenia had variable diagnostic specificity. Polygenic risk scores showed cross-disorder associations, notably between adult-onset disorders. Pathway analysis supported a role for calcium channel signaling genes for all five disorders. Finally, SNPs with evidence of cross-disorder association were enriched for brain eQTL markers.

Interpretation: Our findings show that specific SNPs are associated with a range of psychiatric disorders of childhood onset or adult onset. In particular, variation in calcium-channel activity genes seems to have pleiotropic effects on psychopathology. These results provide evidence relevant to the goal of moving beyond descriptive syndromes in psychiatry, and towards a nosology informed by disease cause.

Funding: National Institute of Mental Health.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3730114/ Shared polygenic contribution between childhood attention-deficit hyperactivity disorder and adult schizophrenia Marian L. Hamshere, Evangelia Stergiakouli, Kate Langley, Joanna Martin, Peter Holmans, Lindsey Kent, Michael J. Owen, Michael Gill, Anita Thapar, Mick O’Donovan, Nick Craddock 2013 2020-09-06 [("doi","10.1192/bjp.bp.112.117432")] genetics/heritable/correlation psychiatry/bipolar/genetics psychiatry/schizophrenia

Background: There is recent evidence of some degree of shared genetic susceptibility between adult schizophrenia and childhood attention-deficit hyperactivity disorder (ADHD) for rare chromosomal variants.

Aims: To determine whether there is overlap between common alleles conferring risk of schizophrenia in adults with those that do so for ADHD in children.

Method: We used recently published Psychiatric Genome-wide Association Study (GWAS) Consortium (PGC) adult schizophrenia data to define alleles over-represented in people with schizophrenia and tested whether those alleles were more common in 727 children with ADHD than in 2067 controls.

Results: Schizophrenia risk alleles discriminated ADHD cases from controls (p = 1.04 × 10−4, R(2) = 0.45%); stronger discrimination was given by alleles that were risk alleles for both adult schizophrenia and adult bipolar disorder (also derived from a PGC data-set) (p = 9.98 × 10−6, R(2) = 0.59%).

Conclusion: This increasing evidence for a small, but, shared genetic susceptibility between adult schizophrenia and childhood ADHD highlights the importance of research work across traditional diagnostic boundaries.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3741533/ What I make up when I wake up: anti-experience views and narrative fabrication of dreams Melanie G. Rosen 2013 2020-09-06 [("doi","10.3389/fpsyg.2013.00514")] philosophy/epistemology psychology/neuroscience/memory zeo

I propose a narrative fabrication thesis of dream reports, according to which dream reports are often not accurate representations of experiences that occur during sleep. I begin with an overview of anti-experience theses of Norman Malcolm and Daniel Dennett who reject the received view of dreams, that dreams are experiences we have during sleep which are reported upon waking. Although rejection of the first claim of the received view, that dreams are experiences that occur during sleep, is implausible, I evaluate in more detail the second assumption of the received view, that dream reports are generally accurate.

I then propose a “narrative fabrication” view of dreams as an alternative to the received view. Dream reports are often confabulated or fabricated because of poor memory, bizarre dream content, and cognitive deficits. It is well documented that narratives can be altered between initial rapid eye movement sleep awakenings and subsequent reports. I argue that we have reason to suspect that initial reports are prone to inaccuracy. Experiments demonstrate that subjects rationalize strange elements in narratives, leaving out supernatural or bizarre components when reporting waking memories of stories. Inaccuracies in dream reports are exacerbated by rapid memory loss and bizarre dream content. Waking memory is a process of reconstruction and blending of elements, but unlike waking memory, we cannot reality-test for dream memories. Dream experiences involve imaginative elements, and dream content cannot be verified with external evidence. Some dreams may involve wake-like higher cognitive functions, such as lucid dreams. Such dreams are more likely to elicit accurate reports than cognitively deficient dreams. However, dream reports are generally less accurate than waking reports.

I then propose methods which could verify the narrative fabrication view, and argue that although the theory cannot be tested with current methods, new techniques and technologies may be able to do so in the future.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3773879/ The Rediscovery of Bifactor Measurement Models Steven P. Reise 2012 2020-09-06 [("doi","10.1080/00273171.2012.715555")] psychology

Bifactor latent structures were introduced over 70 years ago, but only recently has bifactor modeling been rediscovered as an effective approach to modeling construct-relevant multidimensionality in a set of ordered categorical item responses. I begin by describing the Schmid-Leiman bifactor procedure (Schmid & Leiman 1957), and highlight its relations with correlated-factors and second-order exploratory factor models.

After describing limitations of the Schmid-Leiman, two newer methods of exploratory bifactor modeling are considered, namely, analytic bifactor (Jennrich & Bentler 2011) and target bifactor rotations (Reise, Moore, & Maydeu-Olivares, 2011). In the second section, I discuss limited and full-information estimation approaches to confirmatory bifactor models that have emerged from the item response theory and factor analysis traditions, respectively. Comparison of the confirmatory bifactor model to alternative nested confirmatory models and establishing parameter invariance for the general factor also are discussed.

In the final section, important applications of bifactor models are reviewed. These applications demonstrate that bifactor modeling potentially provides a solid foundation for conceptualizing psychological constructs, constructing measures, and evaluating a measure’s psychometric properties.

However, some applications of the bifactor model may be limited due to its restrictive assumptions.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3799980/ A visual motion detection circuit suggested by Drosophila connectomics Shin-ya Takemura, Arjun Bharioke, Zhiyuan Lu, Aljoscha Nern, Shiv Vitaladevuni, Patricia K. Rivlin, William T. Katz, Donald J. Olbris, Stephen M. Plaza, Philip Winston, Ting Zhao, Jane Anne Horne, Richard D. Fetter, Satoko Takemura, Katerina Blazek, Lei-Ann Chang, Omotara Ogundeyi, Mathew A. Saunders, Victor Shapiro, Christopher Sigmund, Gerald M. Rubin, Louis K. Scheffer, Ian A. Meinertzhagen, Dmitri B. Chklovskii 2013 2020-09-06 [("doi","10.1038/nature12450")] psychology/neuroscience psychology/vision

Animal behavior arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive.

Here we develop a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the repeating module of the medulla. Within this module, we identified cell types constituting a motion detection circuit, and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity.

Our results identify cellular targets for future functional investigations, and demonstrate that connectomes can provide key insights into neuronal computations.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3800159/ Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs S Hong Lee, Stephan Ripke, Benjamin M. Neale, Stephen V. Faraone, Shaun M. Purcell, Roy H. Perlis, Bryan J. Mowry, Anita Thapar, Michael E. Goddard, John S. Witte, Devin Absher, Ingrid Agartz, Huda Akil, Farooq Amin, Ole A. Andreassen, Adebayo Anjorin, Richard Anney, Verneri Anttila, Dan E. Arking, Philip Asherson, Maria H. Azevedo, Lena Backlund, Judith A. Badner, Anthony J. Bailey, Tobias Banaschewski, Jack D. Barchas, Michael R. Barnes, Thomas B. Barrett, Nicholas Bass, Agatino Battaglia, Michael Bauer, Mònica Bayés, Frank Bellivier, Sarah E. Bergen, Wade Berrettini, Catalina Betancur, Thomas Bettecken, Joseph Biederman, Elisabeth B. Binder, Donald W. Black, Douglas H. R. Blackwood, Cinnamon S. Bloss, Michael Boehnke, Dorret I. Boomsma, Gerome Breen, René Breuer, Richard Bruggeman, Paul Cormican, Nancy G. Buccola, Jan K. Buitelaar, William E. Bunney, Joseph D. Buxbaum, William F. Byerley, Enda M. Byrne, Sian Caesar, Wiepke Cahn, Rita M. Cantor, Miguel Casas, Aravinda Chakravarti, Kimberly Chambert, Khalid Choudhury, Sven Cichon, C. Robert Cloninger, David A. Collier, Edwin H. Cook, Hilary Coon, Bru Cormand, Aiden Corvin, William H. Coryell, David W. Craig, Ian W. Craig, Jennifer Crosbie, Michael L. Cuccaro, David Curtis, Darina Czamara, Susmita Datta, Geraldine Dawson, Richard Day, Eco J. De Geus, Franziska Degenhardt, Srdjan Djurovic, Gary J. Donohoe, Alysa E. Doyle, Jubao Duan, Frank Dudbridge, Eftichia Duketis, Richard P. Ebstein, Howard J. Edenberg, Josephine Elia, Sean Ennis, Bruno Etain, Ayman Fanous, Anne E. Farmer, I. Nicol Ferrier, Matthew Flickinger, Eric Fombonne, Tatiana Foroud, Josef Frank, Barbara Franke, Christine Fraser, Robert Freedman, Nelson B. Freimer, Christine M. Freitag, Marion Friedl, Louise Frisén, Louise Gallagher, Pablo V. Gejman, Lyudmila Georgieva, Elliot S. Gershon, Daniel H. Geschwind, Ina Giegling, Michael Gill, Scott D. Gordon, Katherine Gordon-Smith, Elaine K. Green, Tiffany A. Greenwood, Dorothy E. Grice, Magdalena Gross, Detelina Grozeva, Weihua Guan, Hugh Gurling, Lieuwe De Haan, Jonathan L. Haines, Hakon Hakonarson, Joachim Hallmayer, Steven P. Hamilton, Marian L. Hamshere, Thomas F. Hansen, Annette M. Hartmann, Martin Hautzinger, Andrew C. Heath, Anjali K. Henders, Stefan Herms, Ian B. Hickie, Maria Hipolito, Susanne Hoefels, Peter A. Holmans, Florian Holsboer, Witte J. Hoogendijk, Jouke-Jan Hottenga, Christina M. Hultman, Vanessa Hus, Andrés Ingason, Marcus Ising, Stéphane Jamain, Edward G. Jones, Ian Jones, Lisa Jones, Jung-Ying Tzeng, Anna K. Kähler, René S. Kahn, Radhika Kandaswamy, Matthew C. Keller, James L. Kennedy, Elaine Kenny, Lindsey Kent, Yunjung Kim, George K. Kirov, Sabine M. Klauck, Lambertus Klei, James A. Knowles, Martin A. Kohli, Daniel L. Koller, Bettina Konte, Ania Korszun, Lydia Krabbendam, Robert Krasucki, Jonna Kuntsi, Phoenix Kwan, Mikael Landén, Niklas Långström, Mark Lathrop, Jacob Lawrence, William B. Lawson, Marion Leboyer, David H. Ledbetter, Phil H. Lee, Todd Lencz, Klaus-Peter Lesch, Douglas F. Levinson, Cathryn M. Lewis, Jun Li, Paul Lichtenstein, Jeffrey A. Lieberman, Dan-Yu Lin, Don H. Linszen, Chunyu Liu, Falk W. Lohoff, Sandra K. Loo, Catherine Lord, Jennifer K. Lowe, Susanne Lucae, Donald J. MacIntyre, Pamela A. F. Madden, Elena Maestrini, Patrik K. E. Magnusson, Pamela B. Mahon, Wolfgang Maier, Anil K. Malhotra, Shrikant M. Mane, Christa L. Martin, Nicholas G. Martin, Manuel Mattheisen, Keith Matthews, Morten Mattingsdal, Steven A. McCarroll, Kevin A. McGhee, James J. McGough, Patrick J. McGrath, Peter McGuffin, Melvin G. McInnis, Andrew McIntosh, Rebecca McKinney, Alan W. McLean, Francis J. McMahon, William M. McMahon, Andrew McQuillin, Helena Medeiros, Sarah E. Medland, Sandra Meier, Ingrid Sigfrid Melle, Fan Meng, Jobst Meyer, Christel M. Middeldorp, Lefkos Middleton, Vihra Milanova, Ana Miranda, Anthony P. Monaco, Grant W. Montgomery, Jennifer L. Moran, Daniel Moreno-De-Luca, Gunnar Morken, Derek W. Morris, Eric M. Morrow, Valentina Moskvina, Pierandrea Muglia, Thomas W. Mühleisen, Walter J. Muir, Bertram Müller-Myhsok, Michael Murtha, Richard M. Myers, Inez Myin-Germeys, Michael C. Neale, Stan F. Nelson, Caroline M. Nievergelt, Ivan Nikolov, Vishwajit Nimgaonkar, Willem A. Nolen, Markus M. Nöthen, John I. Nurnberger, Evaristus A. Nwulia, Dale R. Nyholt, Colm O’Dushlaine, Robert D. Oades, Ann Olincy, Guiomar Oliveira, Line Olsen, Roel A. Ophoff, Urban Osby, Michael J. Owen, Aarno Palotie, Jeremy R. Parr, Andrew D. Paterson, Carlos N. Pato, Michele T. Pato, Brenda W. Penninx, Michele L. Pergadia, Margaret A. Pericak-Vance, Benjamin S. Pickard, Jonathan Pimm, Joseph Piven, Danielle Posthuma, James B. Potash, Fritz Poustka, Peter Propping, Vinay Puri, Digby J. Quested, Emma M. Quinn, Josep Antoni Ramos-Quiroga, Henrik B. Rasmussen, Soumya Raychaudhuri, Karola Rehnström, Andreas Reif, Marta Ribasés, John P. Rice, Marcella Rietschel, Kathryn Roeder, Herbert Roeyers, Lizzy Rossin, Aribert Rothenberger, Guy Rouleau, Douglas Ruderfer, Dan Rujescu, Alan R. Sanders, Stephan J. Sanders, Susan L. Santangelo, Joseph A. Sergeant, Russell Schachar, Martin Schalling, Alan F. Schatzberg, William A. Scheftner, Gerard D. Schellenberg, Stephen W. Scherer, Nicholas J. Schork, Thomas G. Schulze, Johannes Schumacher, Markus Schwarz, Edward Scolnick, Laura J. Scott, Jianxin Shi, Paul D. Shilling, Stanley I. Shyn, Jeremy M. Silverman, Susan L. Slager, Susan L. Smalley, Johannes H. Smit, Erin N. Smith, Edmund J. S. Sonuga-Barke, David St Clair, Matthew State, Michael Steffens, Hans-Christoph Steinhausen, John S. Strauss, Jana Strohmaier, T. Scott Stroup, James S. Sutcliffe, Peter Szatmari, Szabocls Szelinger, Srinivasa Thirumalai, Robert C. Thompson, Alexandre A. Todorov, Federica Tozzi, Jens Treutlein, Manfred Uhr, Edwin J. C. G. van den Oord, Gerard Van Grootheest, Jim Van Os, Astrid M. Vicente, Veronica J. Vieland, John B. Vincent, Peter M. Visscher, Christopher A. Walsh, Thomas H. Wassink, Stanley J. Watson, Myrna M. Weissman, Thomas Werge, Thomas F. Wienker, Ellen M. Wijsman, Gonneke Willemsen, Nigel Williams, A. Jeremy Willsey, Stephanie H. Witt, Wei Xu, Allan H. Young, Timothy W. Yu, Stanley Zammit, Peter P. Zandi, Peng Zhang, Frans G. Zitman, Sebastian Zöllner, Bernie Devlin, John R. Kelsoe, Pamela Sklar, Mark J. Daly, Michael C. O’Donovan, Nicholas Craddock, Patrick F. Sullivan, Jordan W. Smoller, Kenneth S. Kendler, Naomi R. Wray 2013 2020-09-07 [("doi","10.1038/ng.2711")] genetics/heritable/correlation psychiatry/autism psychiatry/bipolar/genetics psychiatry/depression psychiatry/schizophrenia psychology/personality

Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders.

SNPs explained 17–29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-statistically-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn’s disease.

This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3875218/ The incidence of leukemia, lymphoma and multiple myeloma among atomic bomb survivors: 1950–2001 Wan-Ling Hsu, Dale L. Preston, Midori Soda, Hiromi Sugiyama, Sachiyo Funamoto, Kazunori Kodama, Akiro Kimura, Nanao Kamada, Hiroo Dohy, Masao Tomonaga, Masako Iwanaga, Yasushi Miyazaki, Harry M. Cullings, Akihiko Suyama, Kotaro Ozasa, Roy E. Shore, Kiyohiko Mabuchi 2013 2020-09-07 [("doi","10.1667/RR2892.1")] genetics/heritable/rare

A marked increase in leukemia risks was the first and most striking late effect of radiation exposure seen among the Hiroshima and Nagasaki atomic bomb survivors.

This article presents analyses of radiation effects on leukemia, lymphoma and multiple myeloma incidence in the Life Span Study cohort of atomic bomb survivors updated 14 years since the last comprehensive report on these malignancies. These analyses make use of tumor-registry and leukemia-registry based incidence data on 113,011 cohort members with 3.6 million person-years of follow-up from late 1950 through the end of 2001. In addition to a detailed analysis of the excess risk for all leukemias other than chronic lymphocytic leukemia or adult T-cell leukemia (neither of which appear to be radiation-related), we present results for the major hematopoietic malignancy types: acute lymphoblastic leukemia, chronic lymphocytic leukemia, acute myeloid leukemia, chronic myeloid leukemia, adult T-cell leukemia, Hodgkin and non-Hodgkin lymphoma and multiple myeloma. Poisson regression methods were used to characterize the shape of the radiation dose-response relationship and, to the extent the data allowed, to investigate variation in the excess risks with gender, attained age, exposure age and time since exposure. In contrast to the previous report that focused on describing excess absolute rates, we considered both excess absolute rate (EAR) and excess relative risk (ERR) models and found that ERR models can often provide equivalent and sometimes more parsimonious descriptions of the excess risk than EAR models.

The leukemia results indicated that there was a nonlinear dose response for leukemias other than chronic lymphocytic leukemia or adult T-cell leukemia, which varied markedly with time and age at exposure, with much of the evidence for this nonlinearity arising from the acute myeloid leukemia risks. Although the leukemia excess risks generally declined with attained age or time since exposure, there was evidence that the radiation-associated excess leukemia risks, especially for acute myeloid leukemia, had persisted throughout the follow-up period out to 55 years after the bombings. As in earlier analyses, there was a weak suggestion of a radiation dose response for non-Hodgkin lymphoma among men, with no indication of such an effect among women. There was no evidence of radiation-associated excess risks for either Hodgkin lymphoma or multiple myeloma.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3886341/ A genome-wide association study of behavioral disinhibition Matt McGue, Yiwei Zhang, Michael B. Miller, Saonli Basu, Scott Vrieze, Brian Hicks, Steve Malone, William S. Oetting, William Iacono 2013 2020-09-07 [("doi","10.1007/s10519-013-9606-x")] crime genetics/heritable

We report results from a genome wide association study (GWAS) of 5 quantitative indicators of behavioral disinhibition: nicotine, alcohol consumption, alcohol dependence, illicit drugs, and non-substance related behavioral disinhibition.

The sample, consisting of 7,188 Caucasian individuals clustered in 2,300 nuclear families, was genotyped on over 520,000 SNP markers from Illumina’s Human 660W-Quad Array. Analysis of individual SNP associations revealed only one marker-component phenotype association, between rs1868152 and illicit drugs, with a p-value below the standard genome-wide threshold of 5 × 10−8. Because we had analyzed five separate phenotypes, we do not consider this single association to be statistically-significant. However, we report 13 SNPs that were associated at p < 10−5 for one phenotype and p < 10−3 for at least two other phenotypes, which are potential candidates for future investigations of variants associated with general behavioral disinhibition. Biometric analysis of the twin and family data yielded estimates of additive heritability for the component phenotypes ranging 49–70%, GCTA estimates of heritability for the same phenotypes ranged 8–37%.

Consequently, even though the common variants genotyped on the GWAS array appear in aggregate to account for a sizable proportion of heritable effects in multiple indicators of behavioral disinhibition, our data suggest that most of the additive heritability remains “missing”.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3907681/ Genetic influence on family socioeconomic status and children’s intelligence Maciej Trzaskowski, Nicole Harlaar, Rosalind Arden, Eva Krapohl, Kaili Rimfeld, Andrew McMillan, Philip S. Dale, Robert Plomin 2014 2020-09-07 [("doi","10.1016/j.intell.2013.11.002")] iq/ses

Environmental measures used widely in the behavioral sciences show nearly as much genetic influence as behavioral measures, a critical finding for interpreting associations between environmental factors and children’s development. This research depends on the twin method that compares monozygotic and dizygotic twins, but key aspects of children’s environment such as socioeconomic status (SES) cannot be investigated in twin studies because they are the same for children growing up together in a family.

Here, using a new technique applied to DNA from 3000 unrelated children, we show genetic influence on family SES, and on its association with children’s IQ at ages 7 and 12. In addition to demonstrating the ability to investigate genetic influence on between-family environmental measures, our results emphasize the need to consider genetics in research and policy on family SES and its association with children’s IQ.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3964937/ A trifactor model for integrating ratings across multiple informants Daniel J. Bauer, Andrea L. Howard, Ruth E. Baldasaro, Patrick J. Curran, Andrea M. Hussong, Laurie Chassin, Robert A. Zucker 2013 2020-09-07 [("doi","10.1037/a0032475")] psychology

Psychologists often obtain ratings for target individuals from multiple informants such as parents or peers.

In this article, we propose a trifactor model for multiple informant data that separates target-level variability from informant-level variability and item-level variability. By leveraging item-level data, the trifactor model allows for examination of a single trait rated on a single target. In contrast to many psychometric models developed for multitrait-multimethod data, the trifactor model is predominantly a measurement model.

It is used to evaluate item quality in scale development, test hypotheses about sources of target variability (eg. sources of trait differences) versus informant variability (eg. sources of rater bias), and generate integrative scores that are purged of the subjective biases of single informants.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3968799/ Molecular genetic evidence for overlap between general cognitive ability and risk for schizophrenia: a report from the Cognitive Genomics consorTium (COGENT) T. Lencz, E. Knowles, Gail Davies, S. Guha, D. C. Liewald, J. M. Starr, S. Djurovic, Ingrid Sigfrid Melle, K. Sundet, A. Christoforou, I. Reinvang, S. Mukherjee, Pamela DeRosse, A. Lundervold, V. M. Steen, M. John, T. Espeseth, K. Räikkönen, E. Widen, A. Palotie, J. G. Eriksson, I. Giegling, B. Konte, M. Ikeda, P. Roussos, S. Giakoumaki, K. E. Burdick, A. Payton, W. Ollier, M. Horan, G. Donohoe, D. Morris, A. Corvin, M. Gill, N. Pendleton, N. Iwata, A. Darvasi, P. Bitsios, D. Rujescu, J. Lahti, S. L. Hellard, M. C. Keller, O. A. Andreassen, I. J. Deary, D. C. Glahn, A. K. Malhotra 2014 2020-09-07 [("doi","10.1038/mp.2013.166")] genetics/editing psychiatry/schizophrenia

It has long been recognized that generalized deficits in cognitive ability represent a core component of schizophrenia (SCZ), evident before full illness onset and independent of medication. The possibility of genetic overlap between risk for SCZ and cognitive phenotypes has been suggested by the presence of cognitive deficits in first-degree relatives of patients with SCZ; however, until recently, molecular genetic approaches to test this overlap have been lacking.

Within the last few years, large-scale genome-wide association studies (GWAS) of SCZ have demonstrated that a substantial proportion of the heritability of the disorder is explained by a polygenic component consisting of many common single-nucleotide polymorphisms (SNPs) of extremely small effect. Similar results have been reported in GWAS of general cognitive ability. The primary aim of the present study is to provide the first molecular genetic test of the classic endophenotype hypothesis, which states that alleles associated with reduced cognitive ability should also serve to increase risk for SCZ. We tested the endophenotype hypothesis by applying polygenic SNP scores derived from a large-scale cognitive GWAS meta-analysis (~5000 individuals from 9 nonclinical cohorts comprising the Cognitive Genomics consorTium (COGENT)) to 4 SCZ case-control cohorts.

As predicted, cases had lower cognitive polygenic scores compared to controls. In parallel, polygenic risk scores for SCZ were associated with lower general cognitive ability. In addition, using our large cognitive meta-analytic data set, we identified nominally statistically-significant cognitive associations for several SNPs that have previously been robustly associated with SCZ susceptibility.

Results provide molecular confirmation of the genetic overlap between SCZ and general cognitive ability, and may provide additional insight into the pathophysiology of the disorder.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3969807/ The 1% of the population accountable for 63% of all violent crime convictions Orjan Falk, Märta Wallinius, Sebastian Lundström, Thomas Frisell, Henrik Anckarsäter, Nóra Kerekes 2014 2020-09-07 [("doi","10.1007/s00127-013-0783-y")] crime psychiatry psychology/personality sociology

Purpose: Population-based studies on violent crime and background factors may provide an understanding of the relationships between susceptibility factors and crime. We aimed to determine the distribution of violent crime convictions in the Swedish population 1973–2004 and to identify criminal, academic, parental, and psychiatric risk factors for persistence in violent crime.

Method: The nationwide multi-generation register was used with many other linked nationwide registers to select participants. All individuals born in 1958–1980 (2,393,765 individuals) were included. Persistent violent offenders (those with a lifetime history of 3 or more violent crime convictions) were compared with individuals having one or two such convictions, and to matched non-offenders. Independent variables were gender, age of first conviction for a violent crime, nonviolent crime convictions, and diagnoses for major mental disorders, personality disorders, and substance use disorders.

Results: A total of 93,642 individuals (3.9%) had at least one violent conviction. The distribution of convictions was highly skewed; 24,342 persistent violent offenders (1.0% of the total population) accounted for 63.2% of all convictions. Persistence in violence was associated with male sex (OR 2.5), personality disorder (OR 2.3), violent crime conviction before age 19 (OR 2.0), drug-related offenses (OR 1.9), nonviolent criminality (OR 1.9), substance use disorder (OR 1.9), and major mental disorder (OR 1.3).

Conclusion: The majority of violent crimes are perpetrated by a small number of persistent violent offenders, typically males, characterized by early onset of violent criminality, substance abuse, personality disorders, and nonviolent criminality.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3978293/ Postdiction: its implications on visual awareness, hindsight, and sense of agency Shinsuke Shimojo 2014 2020-09-07 [("doi","10.3389/fpsyg.2014.00196")] psychology/neuroscience psychology/vision

There are a few postdictive perceptual phenomena known, in which a stimulus presented later seems causally to affect the percept of another stimulus presented earlier. While backward masking provides a classical example, the flash lag effect stimulates theorists with a variety of intriguing findings. The TMS-triggered scotoma together with “backward filling-in” of it offer an unique neuroscientific case. Findings suggest that various visual attributes are reorganized in a postdictive fashion to be consistent with each other, or to be consistent in a causality framework.

In terms of the underlying mechanisms, 4 prototypical models have been considered: the “catch up”, the “reentry”, the “different pathway” and the “memory revision” models. By extending the list of postdictive phenomena to memory, sensory-motor and higher-level cognition, one may note that such a postdictive reconstruction may be a general principle of neural computation, ranging from milliseconds to months in a time scale, from local neuronal interactions to long-range connectivity, in the complex brain.

The operational definition of the “postdictive phenomenon” can be applicable to such a wide range of sensory/cognitive effects across a wide range of time scales, even though the underlying neural mechanisms may vary across them. This has implications in interpreting “free will” and “sense of agency” in functional, psychophysical and neuroscientific terms.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4048635/ Polygenic risk scores for smoking: predictors for alcohol and cannabis use? Jacqueline M. Vink, Jouke Jan Hottenga, Eco J. C. de Geus, Gonneke Willemsen, Michael C. Neale, Helena Furberg, Dorret I. Boomsma 2014 2020-09-07 [("doi","10.1111/add.12491")] marijuana psychiatry/alcoholism

Background & Aims: A strong correlation exists between smoking and the use of alcohol and cannabis. This paper uses polygenic risk scores to explore the possibility of overlapping genetic factors. Those scores reflect a combined effect of selected risk alleles for smoking.

Method: Summary-level p-values were available for smoking initiation, age at onset of smoking, cigarettes per day and smoking cessation from the Tobacco and Genetics Consortium (n between 22,000 and 70,000 subjects). Using different p-value thresholds (0.1, 0.2 and 0.5) from the meta-analysis, sets of ‘risk alleles’ were defined and used to generate a polygenic risk score (weighted sum of the alleles) for each subject in an independent target sample from the Netherlands Twin Register (n = 1583). The association between polygenic smoking scores and alcohol/cannabis use was investigated with regression analysis.

Results: The polygenic scores for ‘cigarettes per day’ were associated with the number of glasses alcohol per week (p = 0.005, R2 = 0.4–0.5%) and cannabis initiation (p = 0.004, R2 = 0.6–0.9%). The polygenic scores for ‘age at onset of smoking’ were associated with ‘age at regular drinking’ (p = 0.001, R2 = 1.1–1.5%), while the scores for ‘smoking initiation’ and ‘smoking cessation’ did not predict alcohol or cannabis use.

Conclusion: Smoking, alcohol and cannabis use are influenced by aggregated genetic risk factors shared between these substances. The many common genetic variants each have a very small individual effect size.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053853/ The elephant brain in numbers Suzana Herculano-Houzel, Kamilla Avelino-de-Souza, Kleber Neves, Jairo Porfírio, Débora Messeder, Larissa Mattos Feijó, José Maldonado, Paul R. Manger 2014 2020-09-07 [("doi","10.3389/fnana.2014.00046")] psychology/neuroscience

What explains the superior cognitive abilities of the human brain compared to other, larger brains? Here we investigate the possibility that the human brain has a larger number of neurons than even larger brains by determining the cellular composition of the brain of the African elephant.

We find that the African elephant brain, which is about 3× larger than the human brain, contains 257 billion (109) neurons, 3× more than the average human brain; however, 97.5% of the neurons in the elephant brain (251 billion) are found in the cerebellum. This makes the elephant an outlier in regard to the number of cerebellar neurons compared to other mammals, which might be related to sensorimotor specializations.

In contrast, the elephant cerebral cortex, which has twice the mass of the human cerebral cortex, holds only 5.6 billion neurons, about one third of the number of neurons found in the human cerebral cortex. This finding supports the hypothesis that the larger absolute number of neurons in the human cerebral cortex (but not in the whole brain) is correlated with the superior cognitive abilities of humans compared to elephants and other large-brained mammals.

--- /doc/genetics/heritable/rare/2014-pellegrino.pdf A novel BHLHE41 variant is associated with short sleep and resistance to sleep deprivation in humans Renata Pellegrino, Ibrahim Halil Kavakli, Namni Goel, Christopher J. Cardinale, David F. Dinges, Samuel T. Kuna, Greg Maislin, Hans P. A. Van Dongen, Sergio Tufik, John B. Hogenesch, Hakon Hakonarson, Allan I. Pack 2014 2020-09-08 [("doi","10.5665/sleep.3924")] genetics/editing genetics/heritable/rare zeo/short-sleeper

Study Objectives: Earlier work described a mutation in DEC2 also known as BHLHE41 (basic helix-loophelix family member e41) as causal in a family of short sleepers, who needed just 6 h sleep per night. We evaluated whether there were other variants of this gene in two well-phenotyped cohorts.

Design: Sequencing of the BHLHE41 gene, electroencephalographic data, and delta power analysis and functional studies using cell-based luciferase.

Results: We identified new variants of the BHLHE41 gene in two cohorts who had either acute sleep deprivation (n = 200) or chronic partial sleep deprivation (n = 217). One variant, Y362H, at another location in the same exon occurred in one twin in a dizygotic twin pair and was associated with reduced sleep duration, less recovery sleep following sleep deprivation, and fewer performance lapses during sleep deprivation than the homozygous twin. Both twins had almost identical amounts of non rapid eye movement (NREM) sleep. This variant reduced the ability of BHLHE41 to suppress CLOCK/BMAL1 and NPAS2/BMAL1 transactivation in vitro. Another variant in the same exome had no effect on sleep or response to sleep deprivation and no effect on CLOCK/BMAL1 transactivation. Random mutagenesis identified a number of other variants of BHLHE41 that affect its function.

Conclusion: There are a number of mutations of BHLHE41. Mutations reduce total sleep while maintaining NREM sleep and provide resistance to the effects of sleep loss. Mutations that affect sleep also modify the normal inhibition of BHLHE41 of CLOCK/BMAL1 transactivation. Thus, clock mechanisms are likely involved in setting sleep length and the magnitude of sleep homeostasis.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4112149/ Large-scale genomics unveils the genetic architecture of psychiatric disorders Jacob Gratten, Naomi R. Wray, Matthew C. Keller, Peter M. Visscher 2014 2020-09-08 [("doi","10.1038/nn.3708")] genetics/heritable/rare psychiatry/schizophrenia

Family study results are consistent with genetic effects making substantial contributions to risk of psychiatric disorders such as schizophrenia, yet robust identification of specific genetic variants that explain variation in population risk had been disappointing until the advent of technologies that assay the entire genome in large samples.

We highlight recent progress that has led to a better understanding of the number of risk variants in the population and the interaction of allele frequency and effect size.

The emerging genetic architecture implies a large number of contributing loci (that is, a high genome-wide mutational target) and suggests that genetic risk of psychiatric disorders involves the combined effects of many common variants of small effect, as well as rare and de novo variants of large effect.

The capture of a substantial proportion of genetic risk facilitates new study designs to investigate the combined effects of genes and the environment.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4112379/ Biological insights from 108 schizophrenia-associated genetic loci 2014 2020-09-08 [("doi","10.1038/nature13595")] genetics/heritable psychiatry/schizophrenia

Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies.

Here we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide statistical-significance, 83 of which have not been previously reported.

Associations were enriched among genes expressed in brain, providing biological plausibility for the findings. Many findings have the potential to provide entirely new insights into aetiology, but associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses.

Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4134439/ Is there a general factor of prevalent psychopathology during adulthood? Benjamin B. Lahey, Brooks Applegate, Jahn K. Hakes, David H. Zald, Ahmad R. Hariri, Paul J. Rathouz 2012 2020-09-08 [("doi","10.1037/a0028355")] psychiatry

The patterns of comorbidity among prevalent mental disorders in adults lead them to load on “externalizing”, “distress”, and “fears” factors. These factors are themselves robustly correlated, but little attention has been paid to this fact.

As a first step in studying the implications of these interfactor correlations, we conducted confirmatory factor analyses on diagnoses of 11 prevalent Diagnostic and Statistical Manual of Mental Disorders (4th ed.) mental disorders in a nationally representative sample.

A model specifying correlated externalizing, distress, and fears factors fit well, but an alternative model was tested in which a “general” bifactor was added to capture what these disorders share in common. There was a modest but improvement in fit for the bifactor model relative to the 3-factor oblique model, with all disorders loading strongly on the bifactor.

Tests of external validity revealed that the fears, distress, and externalizing factors were differentially associated with measures of functioning and potential risk factors. Nonetheless, the general bifactor accounted for independent variance in future psychopathology, functioning, and other criteria over and above the fears, distress, and externalizing factors.

These findings support the hypothesis that these prevalent forms of psychopathology have both important common and unique features. Future studies should determine whether this is because they share elements of their etiology and neurobiological mechanisms. If so, the existence of common features across diverse forms of prevalent psychopathology could have important implications for understanding the nature, etiology, and outcomes of psychopathology.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4209412/ The p Factor: One General Psychopathology Factor in the Structure of Psychiatric Disorders? Avshalom Caspi, Renate M. Houts, Daniel W. Belsky, Sidra J. Goldman-Mellor, HonaLee Harrington, Salomon Israel, Madeline H. Meier, Sandhya Ramrakha, Idan Shalev, Richie Poulton, Terrie E. Moffitt 2014 2020-09-08 [("doi","10.1177/2167702613497473")] psychiatry

Mental disorders traditionally have been viewed as distinct, episodic, and categorical conditions. This view has been challenged by evidence that many disorders are sequentially comorbid, recurrent/chronic, and exist on a continuum. Using the Dunedin Multidisciplinary Health and Development Study, we examined the structure of psychopathology, taking into account dimensionality, persistence, co-occurrence, and sequential comorbidity of mental disorders across 20 years, from adolescence to midlife.

Psychiatric disorders were initially explained by 3 higher-order factors (Internalizing, Externalizing, and Thought Disorder) but explained even better with one General Psychopathology dimension. We have called this dimension the p factor because it conceptually parallels a familiar dimension in psychological science: the g factor of general intelligence. Higher p scores are associated with more life impairment, greater familiality, worse developmental histories, and more compromised early-life brain function.

The p factor explains why it is challenging to find causes, consequences, biomarkers, and treatments with specificity to individual mental disorders. Transdiagnostic approaches may improve research.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4210287/ The high heritability of educational achievement reflects many genetically influenced traits, not just intelligence Eva Krapohl, Kaili Rimfeld, Nicholas G. Shakeshaft, Maciej Trzaskowski, Andrew McMillan, Jean-Baptiste Pingault, Kathryn Asbury, Nicole Harlaar, Yulia Kovas, Philip S. Dale, Robert Plomin 2014 2020-09-08 [("doi","10.1073/pnas.1408777111")] genetics/heritable/correlation iq psychology/personality/conscientiousness

Because educational achievement at the end of compulsory schooling represents a major tipping point in life, understanding its causes and correlates is important for individual children, their families, and society.

Here we identify the general ingredients of educational achievement using a multivariate design that goes beyond intelligence to consider a wide range of predictors, such as self-efficacy, personality, and behavior problems, to assess their independent and joint contributions to educational achievement. We use a genetically sensitive design to address the question of why educational achievement is so highly heritable. We focus on the results of a United Kingdom-wide examination, the General Certificate of Secondary Education (GCSE), which is administered at the end of compulsory education at age 16. GCSE scores were obtained for 13,306 twins at age 16, whom we also assessed contemporaneously on 83 scales that were condensed to 9 broad psychological domains, including intelligence, self-efficacy, personality, well-being, and behavior problems.

The mean of GCSE core subjects (English, mathematics, science) is more heritable (62%) than the 9 predictor domains (35–58%). Each of the domains correlates statistically-significantly with GCSE results, and these correlations are largely mediated genetically. The main finding is that, although intelligence accounts for more of the heritability of GCSE than any other single domain, the other domains collectively account for about as much GCSE heritability as intelligence. Together with intelligence, these domains account for 75% of the heritability of GCSE.

We conclude that the high heritability of educational achievement reflects many genetically influenced traits, not just intelligence.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4226176/ Do We Really Become Smarter When Our Fluid-Intelligence Test Scores Improve? Taylor R. Hayes, Alexander A. Petrov, Per B. Sederberg 2015 2020-09-08 [("doi","10.1016/j.intell.2014.10.005")] dual-n-back iq psychology/inner-voice

Recent reports of training-induced gains on fluid intelligence tests have fueled an explosion of interest in cognitive training-now a billion-dollar industry. The interpretation of these results is questionable because score gains can be dominated by factors that play marginal roles in the scores themselves, and because intelligence gain is not the only possible explanation for the observed control-adjusted far transfer across tasks.

Here we present novel evidence that the test score gains used to measure the efficacy of cognitive training may reflect strategy refinement instead of intelligence gains.

A novel scanpath analysis of eye movement data from 35 participants solving Raven’s Advanced Progressive Matrices (RAPM) on two separate sessions indicated that:

1⁄3rd of the variance of score gains could be attributed to test-taking strategy alone, as revealed by characteristic changes in eye-fixation patterns. When the strategic contaminant was partialled out, the residual score gains were no longer.

These results are compatible with established theories of skill acquisition suggesting that procedural knowledge tacitly acquired during training can later be utilized at posttest. Our novel method and result both underline a reason to be wary of purported intelligence gains, but also provide a way forward for testing for them in the future.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4237163/ Sequence-specific antimicrobials using efficiently delivered RNA-guided nucleases Robert J. Citorik, Mark Mimee, Timothy K. Lu 2014 2020-09-08 [("doi","10.1038/nbt.3011")] genetics/editing genetics/microbiome

Current antibiotics tend to be broad spectrum, leading to indiscriminate killing of commensal bacteria and accelerated evolution of drug resistance.

Here, we use CRISPR-Cas technology to create antimicrobials whose spectrum of activity is chosen by design. RNA-guided nucleases (RGNs) targeting specific DNA sequences are delivered efficiently to microbial populations using bacteriophage or bacteria carrying plasmids transmissible by conjugation. The DNA targets of RGNs can be undesirable genes or polymorphisms, including antibiotic resistance and virulence determinants in carbapenem-resistant Enterobacteriaceae and enterohemorrhagic Escherichia coli. Delivery of RGNs statistically-significantly improves survival in a Galleria mellonella infection model. We also show that RGNs enable modulation of complex bacterial populations by selective knockdown of targeted strains based on genetic signatures.

RGNs constitute a class of highly discriminatory, customizable antimicrobials that enact selective pressure at the DNA level to reduce the prevalence of undesired genes, minimize off-target effects and enable programmable remodeling of microbiota.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4246684/ Schizophrenia and cortical blindness: protective effects and implications for language Evelina Leivada, Cedric Boeckx 2014 2020-09-08 [("doi","10.3389/fnhum.2014.00940")] psychiatry/schizophrenia

The repeatedly noted absence of case-reports of individuals with schizophrenia and congenital/early developed blindness has led several authors to argue that the latter can confer protective effects against the former. In this work, we present a number of relevant case-reports from different syndromes that show comorbidity of congenital and early blindness with schizophrenia.

On the basis of these reports, we argue that a distinction between different types of blindness in terms of the origin of the visual deficit, cortical or peripheral, is crucial for understanding the observed patterns of comorbidity. We discuss the genetic underpinnings and the brain structures involved in schizophrenia and blindness, with insights from language processing, laying emphasis on the 3 structures that particularly stand out: the occipital cortex, the lateral geniculate nucleus (LGN), and the pulvinar.

Last, we build on previous literature on the nature of the protective effects in order to offer novel insights into the nature of the protection mechanism from the perspective of the brain structures involved in each type of blindness.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255478/ Human genetics shape the gut microbiome Julia K. Goodrich, Jillian L. Waters, Angela C. Poole, Jessica L. Sutter, Omry Koren, Ran Blekhman, Michelle Beaumont, William Van Treuren, Rob Knight, Jordana T. Bell, Timothy D. Spector, Andrew G. Clark, Ruth E. Ley 2014 2020-09-08 [("doi","10.1016/j.cell.2014.09.053")] genetics/microbiome

Host genetics and the gut microbiome can both influence metabolic phenotypes. However, whether host genetic variation shapes the gut microbiome and interacts with it to affect host phenotype is unclear.

Here, we compared microbiotas across >1,000 fecal samples obtained from the TwinsUK population, including 416 twin pairs. We identified many microbial taxa whose abundances were influenced by host genetics. The most heritable taxon, the family Christensenellaceae, formed a co-occurrence network with other heritable Bacteria and with methanogenic Archaea. Furthermore, Christensenellaceae and its partners were enriched in individuals with low body mass index (BMI).

An obese-associated microbiome was amended with Christensenella minuta, a cultured member of the Christensenellaceae, and transplanted to germ-free mice. C. minuta amendment reduced weight gain and altered the microbiome of recipient mice.

Our findings indicate that host genetics influence the composition of the human gut microbiome and can do so in ways that impact host metabolism.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4270213/ Is eating behavior manipulated by the gastrointestinal microbiota? Evolutionary pressures and potential mechanisms Joe Alcock, Carlo C. Maley, C. Athena Aktipis 2014 2020-09-08 [("doi","10.1002/bies.201400071")] exercise genetics/microbiome psychology/neuroscience

Microbes in the gastrointestinal tract are under selective pressure to manipulate host eating behavior to increase their fitness, sometimes at the expense of host fitness. Microbes may do this through two potential strategies: (1) generating cravings for foods that they specialize on or foods that suppress their competitors, or (2) inducing dysphoria until we eat foods that enhance their fitness.

We review several potential mechanisms for microbial control over eating behavior including microbial influence on reward and satiety pathways, production of toxins that alter mood, changes to receptors including taste receptors, and hijacking of the vagus nerve, the neural axis between the gut and the brain. We also review the evidence for alternative explanations for cravings and unhealthy eating behavior.

Because microbiota are easily manipulable by prebiotics, probiotics, antibiotics, fecal transplants, and dietary changes, altering our microbiota offers a tractable approach to otherwise intractable problems of obesity and unhealthy eating.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4271675/ Perspectives of germ cell development in vitro in mammals Katsuhiko Hayashi, Mitinori Saitou 2014 2020-09-09 [("doi","10.1111/asj.12199")] genetics/gametogenesis

Pluripotent stem cells, such as embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) are able to differentiate into all cell lineages of the embryo proper, including germ cells. This pluripotent property has a huge impact on the fields of regenerative medicine, developmental biology, and reproductive engineering. Establishing the germ cell lineage from ESCs/iPSCs is the key biological subject, since it would contribute not only to dissection of the biological processes of germ cell development but also to production of unlimited numbers of functional gametes in vitro.

Toward this goal, we recently established a culture system that induces functional mouse primordial germ cells (PGCs), precursors of all germ cells, from mouse ESCs/iPSCs. The successful in vitro production of PGCs arose from the study of pluripotent cell state, the signals inducing PGCs, and the technology of transplantation.

However, there are many obstacles to be overcome for the robust generation of mature gametes or for application of the culture system to other species, including humans and livestock. In this review, we discuss the requirements for a culture system to generate the germ cell lineage from ESCs/iPSCs.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4301427/ An economic evaluation of manic-depressive illness—1991 R J. Wyatt, I. Henter 1995 2020-09-09 [("doi","10.1007/BF00789056")] economics psychiatry/bipolar psychiatry/schizophrenia

In 1991, the costs for manic-depressive illness, which has a lifetime prevalence of 1.3% among adult Americans, totaled $45 billion. Costs were broken down into their direct and indirect components.

Direct costs totaling $7 billion consist of expenditures for inpatient and outpatient care, which are treatment related, as well as non-treatment-related expenditures such as those for the criminal justice system used by individuals with manic-depressive illness. Indirect costs, which were $38 billion, include the lost productivity of both wage-earners ($17 billion) and homemakers ($3 billion), individuals who are in institutions ($3 billion) or who have committed suicide ($8 billion), and caregivers who take care of manic-depressive family members ($6 billion).

The method for determining each expenditure is provided, and the implications of these staggering costs are discussed.

These calculations rely heavily on methods and data bases that were developed for the accompanying paper on the costs of schizophrenia.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4313871/ The contribution of de novo coding mutations to autism spectrum disorder Ivan Iossifov, Brian J. O’Roak, Stephan J. Sanders, Michael Ronemus, Niklas Krumm, Dan Levy, Holly A. Stessman, Kali T. Witherspoon, Laura Vives, Karynne E. Patterson, Joshua D. Smith, Bryan Paeper, Deborah A. Nickerson, Jeanselle Dea, Shan Dong, Luis E. Gonzalez, Jeffrey D. Mandell, Shrikant M. Mane, Michael T. Murtha, Catherine A. Sullivan, Michael F. Walker, Zainulabedin Waqar, Liping Wei, A. Jeremy Willsey, Boris Yamrom, Yoon-ha Lee, Ewa Grabowska, Ertugrul Dalkic, Zihua Wang, Steven Marks, Peter Andrews, Anthony Leotta, Jude Kendall, Inessa Hakker, Julie Rosenbaum, Beicong Ma, Linda Rodgers, Jennifer Troge, Giuseppe Narzisi, Seungtai Yoon, Michael C. Schatz, Kenny Ye, W. Richard McCombie, Jay Shendure, Evan E. Eichler, Matthew W. State, Michael Wigler 2014 2020-09-09 [("doi","10.1038/nature13908")] genetics/heritable/rare psychiatry/autism psychiatry/schizophrenia

Whole exome sequencing has proven to be a powerful tool for understanding the genetic architecture of human disease. Here we apply it to more than 2,500 simplex families, each having a child with an autistic spectrum disorder.

By comparing affected to unaffected siblings, we show that 13% of de novo missense mutations and 43% of de novo likely gene-disrupting (LGD) mutations contribute to 12% and 9% of diagnoses, respectively. Including copy number variants, coding de novo mutations contribute to about 30% of all simplex and 45% of female diagnoses. Almost all LGD mutations occur opposite wild-type alleles. LGD targets in affected females overlap the targets in males of lower intelligence quotient (IQ), but neither overlaps with targets in males of higher IQ. We estimate that LGD mutation in about 400 genes can contribute to the joint class of affected females and males of lower IQ, with an overlapping and similar number of genes vulnerable to contributory missense mutation.

LGD targets in the joint class overlap with published targets for intellectual disability and schizophrenia, and are enriched for chromatin modifiers, FMRP-associated genes and embryonically expressed genes. Most of the statistical-significance for the latter comes from affected females.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4382963/ Genetic predisposition to schizophrenia associated with increased use of cannabis R A. Power, K. J. H. Verweij, M. Zuhair, G. W. Montgomery, A. K. Henders, A. C. Heath, P. A. F. Madden, S. E. Medland, N. R. Wray, N. G. Martin 2014 2020-09-09 [("doi","10.1038/mp.2014.51")] marijuana psychiatry/schizophrenia

Cannabis is the most commonly used illicit drug worldwide. With debate surrounding the legalization and control of use, investigating its health risks has become a pressing area of research. One established association is that between cannabis use and schizophrenia, a debilitating psychiatric disorder affecting ~1% of the population over their lifetime. Although considerable evidence implicates cannabis use as a component cause of schizophrenia, it remains unclear whether this is entirely due to cannabis directly raising risk of psychosis, or whether the same genes that increases psychosis risk may also increase risk of cannabis use.

In a sample of 2082 healthy individuals, we show an association between an individual’s burden of schizophrenia risk alleles and use of cannabis. This was both for comparing those who have ever versus never used cannabis (p = 2.6 × 10−4), and for quantity of use within users (p = 3.0 × 10−3). Although directly predicting only a small amount of the variance in cannabis use, these findings suggest that part of the association between schizophrenia and cannabis is due to a shared genetic aetiology.

This form of gene-environment correlation is an important consideration when calculating the impact of environmental risk factors, including cannabis use.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4390906/ Bifactor analysis and construct validity of the five facet mindfulness questionnaire (FFMQ) in non-clinical Spanish samples Jaume Aguado, Juan V. Luciano, Ausias Cebolla, Antoni Serrano-Blanco, Joaquim Soler, Javier García-Campayo 2015 2020-09-09 [("doi","10.3389/fpsyg.2015.00404")] psychiatry/anxiety psychiatry/depression psychology/personality

The objective of the present study was to examine the dimensionality, reliability, and construct validity of the Five Facet Mindfulness Questionnaire (FFMQ) in 3 Spanish samples using structural equation modeling (SEM).

Pooling the FFMQ data from 3 Spanish samples (n = 1191), we estimated the fit of two competing models (correlated five-factor vs. bifactor) via confirmatory factor analysis. The factorial invariance of the best fitting model across meditative practice was also addressed. The pattern of relationships between the FFMQ latent dimensions and anxiety, depression, and distress was analyzed using SEM. FFMQ reliability was examined by computing the omega and omega hierarchical coefficients.

The bifactor model, which accounted for the covariance among FFMQ items with regard to one general factor (mindfulness) and 5 orthogonal factors (observing, describing, acting with awareness, non-judgment, and non-reactivity), fit the FFMQ structure better than the correlated five-factor model. The relationships between the latent variables and their manifest indicators were not invariant across the meditative experience. Observing items had loadings on the general mindfulness factor, but only in the meditator sub-sample. The SEM analysis revealed links between mindfulness and symptoms of depression and stress. When the general factor was partialled out, the acting with awareness facet did not show adequate reliability.

The FFMQ shows a robust bifactor structure among Spanish individuals. Nevertheless, the Observing subscale does not seem to be adequate for assessing mindfulness in individuals without meditative experience.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4398979/ Are You Morally Modified?: The Moral Effects of Widely Used Pharmaceuticals Neil Levy, Thomas Douglas, Guy Kahane, Sylvia Terbeck, Philip J. Cowen, Miles Hewstone, Julian Savulescu 2014 2020-09-09 [("doi","10.1353/ppp.2014.0023")] philosophy/ethics

A number of concerns have been raised about the possible future use of pharmaceuticals designed to enhance cognitive, affective, and motivational processes, particularly where the aim is to produce morally better decisions or behavior. In this article, we draw attention to what is arguably a more worrying possibility: that pharmaceuticals currently in widespread therapeutic use are already having unintended effects on these processes, and thus on moral decision making and morally behavior.

We review current evidence on the moral effects of 3 widely used drugs or drug types: (1) propranolol, (2) selective serotonin reuptake inhibitors, and (3) drugs that effect oxytocin physiology. This evidence suggests that the alterations to moral decision making and behavior caused by these agents may have important and difficult-to-evaluate consequences, at least at the population level.

We argue that the moral effects of these and other widely used pharmaceuticals warrant further empirical research and ethical analysis.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4512149/ Pleiotropy across academic subjects at the end of compulsory education Kaili Rimfeld, Yulia Kovas, Philip S. Dale, Robert Plomin 2015 2020-09-09 [("doi","10.1038/srep11713")] genetics/heritable/correlation iq

Research has shown that genes play an important role in educational achievement. A key question is the extent to which the same genes affect different academic subjects before and after controlling for general intelligence.

The present study investigated genetic and environmental influences on, and links between, the various subjects of the age-16 UK-wide standardized GCSE (General Certificate of Secondary Education) examination results for 12,632 twins.

Using the twin method that compares identical and non-identical twins, we found that all GCSE subjects were substantially heritable, and that various academic subjects correlated substantially both phenotypically and genetically, even after controlling for intelligence. Further evidence for pleiotropy in academic achievement was found using a method [GCTA] based directly on DNA from unrelated individuals.

We conclude that performance differences for all subjects are highly heritable at the end of compulsory education and that many of the same genes affect different subjects independent of intelligence.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4538954/ Inner Speech: Development, Cognitive Functions, Phenomenology, and Neurobiology Ben Alderson-Day, Charles Fernyhough 2015 2020-09-09 [("doi","10.1037/bul0000021")] psychology/inner-voice

Inner speech—also known as covert speech or verbal thinking—has been implicated in theories of cognitive development, speech monitoring, executive function, and psychopathology. Despite a growing body of knowledge on its phenomenology, development, and function, approaches to the scientific study of inner speech have remained diffuse and largely unintegrated.

This review examines prominent theoretical approaches to inner speech and methodological challenges in its study, before reviewing current evidence on inner speech in children and adults from both typical and atypical populations.

We conclude by considering prospects for an integrated cognitive science of inner speech, and present a multi-component model of the phenomenon informed by developmental, cognitive, and psycholinguistic considerations. Despite its variability among individuals and across the lifespan, inner speech appears to perform functions in human cognition, which in some cases reflect its developmental origins and its sharing of resources with other cognitive processes.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4670696/ High-resolution, high-throughput imaging with a multibeam scanning electron microscope A L. Eberle, S. Mikula, R. Schalek, J. Lichtman, M. L. Knothe Tate, D. Zeidler 2015 2020-09-09 [("doi","10.1111/jmi.12224")] psychology/neuroscience

Electron-electron interactions and detector bandwidth limit the maximal imaging speed of single-beam scanning electron microscopes.

We use multiple electron beams in a single column and detect secondary electrons in parallel to increase the imaging speed by close to 2 orders of magnitude and demonstrate imaging for a variety of samples ranging from biological brain tissue to semiconductor wafers.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4756632/ The Genetic and Environmental Contributions to Internet Use and Associations With Psychopathology: A Twin Study Elizabeth C. Long, Brad Verhulst, Michael C. Neale, Penelope A. Lind, Ian B. Hickie, Nicholas G. Martin, Nathan A. Gillespie 2016 2020-09-09 [("doi","10.1017/thg.2015.91")] genetics/heritable/correlation marijuana nicotine psychiatry/alcoholism psychiatry/depression sociology/technology

Excessive internet use has been linked to psychopathology. Therefore, understanding the genetic and environmental risks underpinning internet use and their relation to psychopathology is important. This study aims to explore the genetic and environmental etiology of internet use measures and their associations with internalizing disorders and substance use disorders.

The sample included 2,059 monozygotic (MZ) and dizygotic (DZ) young adult twins from the Brisbane Longitudinal Twin Study (BLTS). Younger participants reported more frequent internet use, while women were more likely to use the internet for interpersonal communication. Familial aggregation in ‘frequency of internet use’ was entirely explained by additive genetic factors accounting for 41% of the variance. Familial aggregation in ‘frequency of use after 11 pm’, ‘using the internet to contact peers’, and ‘using the internet primarily to access social networking sites’ was attributable to varying combinations of additive genetic and shared environmental factors.

In terms of psychopathology, there were no statistically-significant associations between internet use measures and major depression (MD), but there were positive statistically-significant associations between ‘frequency of internet use’ and ‘frequency of use after 11 pm’ with social phobia (SP). ‘Using the internet to contact peers’ was positively associated with alcohol abuse, whereas ‘using the internet to contact peers’ and ‘using the internet primarily to access social networking sites’ were negatively associated with cannabis use disorders and nicotine symptoms.

Individual differences in internet use can be attributable to varying degrees of genetic and environmental risks. Despite some statistically-significant associations of small effect, variation in internet use appears mostly unrelated to psychopathology.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4776744/ Genetic background of extreme violent behavior J Tiihonen, M-R Rautiainen, H. M. Ollila, E. Repo-Tiihonen, M. Virkkunen, A. Palotie, O. Pietiläinen, K. Kristiansson, M. Joukamaa, H. Lauerma, J. Saarela, S. Tyni, H. Vartiainen, J. Paananen, D. Goldman, T. Paunio 2015 2020-09-09 [("doi","10.1038/mp.2014.130")] crime genetics/heritable

In developed countries, the majority of all violent crime is committed by a small group of antisocial recidivistic offenders, but no genes have been shown to contribute to recidivistic violent offending or severe violent behavior, such as homicide. Our results, from two independent cohorts of Finnish prisoners, revealed that a monoamine oxidase A (MAOA) low-activity genotype (contributing to low dopamine turnover rate) as well as the CDH13 gene (coding for neuronal membrane adhesion protein) are associated with extremely violent behavior (at least 10 committed homicides, attempted homicides or batteries).

No substantial signal was observed for either MAOA or CDH13 among non-violent offenders, indicating that findings were specific for violent offending, and not largely attributable to substance abuse or antisocial personality disorder.

These results indicate both low monoamine metabolism and neuronal membrane dysfunction as plausible factors in the etiology of extreme criminal violent behavior, and imply that at least about 5–10% of all severe violent crime in Finland is attributable to the aforementioned MAOA and CDH13 genotypes.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4844839/ Anatomy and function of an excitatory network in the visual cortex Wei-Chung Allen Lee, Vincent Bonin, Michael Reed, Brett J. Graham, Greg Hood, Katie Glattfelder, R. Clay Reid 2016 2020-09-10 [("doi","10.1038/nature17192")] psychology/neuroscience

Circuits in the cerebral cortex consist of thousands of neurons connected by millions of synapses. A precise understanding of these local networks requires relating circuit activity with the underlying network structure.

For pyramidal cells in superficial mouse visual cortex (V1), a consensus is emerging that neurons with similar visual response properties excite each other, but the anatomical basis of this recurrent synaptic network is unknown. Here we combined physiological imaging and large-scale electron microscopy to study an excitatory network in V1. We found that layer 2/layer 33 neurons organized into subnetworks defined by anatomical connectivity, with more connections within than between groups. More specifically, we found that pyramidal neurons with similar orientation selectivity preferentially formed synapses with each other, despite the fact that axons and dendrites of all orientation selectivities pass near (<5 μm) each other with roughly equal probability. Therefore, we predict that mechanisms of functionally specific connectivity take place at the length scale of spines. Neurons with similar orientation tuning formed larger synapses, potentially enhancing the net effect of synaptic specificity.

With the ability to study thousands of connections in a single circuit, functional connectomics is proving a powerful method to uncover the organizational logic of cortical networks.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845101/ Naturally occurring p16(Ink4a)-positive cells shorten healthy lifespan Darren J. Baker, Bennett G. Childs, Matej Durik, Melinde E. Wijers, Cynthia J. Sieben, Jian Zhong, Rachel A. Saltness, Karthik B. Jeganathan, Grace Casaclang Verzosa, Abdulmohammad Pezeshki, Khashayarsha Khazaie, Jordan D. Miller, Jan M. van Deursen 2016 2020-09-10 [("doi","10.1038/nature16932")] longevity/senolytic

Cellular senescence, a stress-induced irreversible growth arrest often characterized by expression of p16(Ink4a) (encoded by the Ink4a/Arf locus, also known as Cdkn2a) and a distinctive secretory phenotype, prevents the proliferation of preneoplastic cells and has beneficial roles in tissue remodeling during embryogenesis and wound healing. Senescent cells accumulate in various tissues and organs over time, and have been speculated to have a role in aging.

To explore the physiological relevance and consequences of naturally occurring senescent cells, here we use a previously established transgene, INK-ATTAC, to induce apoptosis in p16(Ink4a)-expressing cells of wild-type mice by injection of AP20187 twice a week starting at one year of age.

We show that compared to vehicle alone, AP20187 treatment extended median lifespan in both male and female mice of two distinct genetic backgrounds. The clearance of p16(Ink4a)-positive cells delayed tumorigenesis and attenuated age-related deterioration of several organs without apparent side effects, including kidney, heart, and fat, where clearance preserved the functionality of glomeruli, cardio-protective KATP channels and adipocytes, respectively.

Thus, p16(Ink4a)-positive cells that accumulate during adulthood negatively influence lifespan and promote age-dependent changes in several organs, and their therapeutic removal may be an attractive approach to extend healthy lifespan.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874898/ Language and thought are not the same thing: evidence from neuroimaging and neurological patients Evelina Fedorenko, Rosemary Varley 2016 2020-09-10 [("doi","10.1111/nyas.13046")] philosophy/epistemology psychology/linguistics psychology/neuroscience

Is thought possible without language? Individuals with global aphasia, who have almost no ability to understand or produce language, provide a powerful opportunity to find out.

Surprisingly, despite their near-total loss of language, these individuals are nonetheless able to add and subtract, solve logic problems, think about another person’s thoughts, appreciate music, and successfully navigate their environments.

Further, neuroimaging studies show that healthy adults strongly engage the brain’s language areas when they understand a sentence, but not when they perform other non-linguistic tasks such as arithmetic, storing information in working memory, inhibiting prepotent responses, or listening to music.

Together, these two complementary lines of evidence provide a clear answer: many aspects of thought engage distinct brain regions from, and do not depend on, language.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4957692/ Declining Prevalence of Marijuana Use Disorders Among Adolescents in the United States, 2002–2013 Richard A. Grucza, Arpana Agrawal, Melissa J. Krauss, Jahnavi Bongu, Andrew D. Plunk, Patricia A. Cavazos-Rehg, Laura J. Bierut 2016 2020-09-10 [("doi","10.1016/j.jaac.2016.04.002")] marijuana

Objective: Little is known about recent trends in marijuana use disorders among adolescents in the United States. We analyzed trends in the past-year prevalence of DSM-IV marijuana use disorders among adolescents, both overall and conditioned on past-year marijuana use. Potential explanatory factors for trends in prevalence were explored.

Method: We assembled data from the adolescent samples of the 2002–2013 administrations of the National Survey on Drug Use and Health (n = 216,852; aged 12–17 years). The main outcome measures were odds ratios describing the average annual change in prevalence and conditional prevalence of marijuana use disorders, estimated from models of marijuana use disorder as a function of year. Post hoc analyses incorporated measures of potentially explanatory risk and protective factors into the trend analyses.

Results: A decline in the past-year prevalence of marijuana use disorders was observed (odds ratio = 0.976 per year; 95% CI = 0.968, 0.984; p < 0.001). This was due to both a net decline in past-year prevalence of use and a decline in the conditional prevalence of marijuana use disorders. The trend in marijuana use disorders was accounted for by a decrease in the rate of conduct problems among adolescents (eg. fighting, stealing).

Conclusion: Past-year prevalence of marijuana use disorders among US adolescents declined by an estimated 24% over the 2002 to 2013 period. The decline may be related to trends toward lower rates of conduct problems. Identification of factors responsible for the reduction in conduct problems could inform interventions targeting both conduct problems and marijuana use disorders.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5117805/ A comparison and integration of structural models of depression and anxiety in a clinical sample: Support for and validation of the tri-level model Kristin Naragon-Gainey, Jason M. Prenoveau, Timothy A. Brown, Richard E. Zinbarg 2016 2020-09-10 [("doi","10.1037/abn0000197")] psychiatry/anxiety psychiatry/depression

Prominent structural models of depression and anxiety arise from 2 traditions: (a) the tripartite/integrative hierarchical model based on symptom dimensions, and (b) the fear/anxious-misery model based on diagnostic comorbidity data. The tri-level model of depression and anxiety was developed to synthesize these structural models, postulating that narrow (disorder-specific), intermediate (fear and anxious-misery), and broad (general distress) structural factors are needed to most fully account for covariation among these symptoms. Although this model has received preliminary support (Prenoveau et al 2010), the current study compares it with the above established models and seeks to validate the best-fitting structure.

We evaluated the tri-level model and alternative structural models in a large clinical sample (n = 1,000) using bifactor analysis. In exploratory and confirmatory subsamples, the tri-level model provided a good fit to the data and each of the 3 levels (narrow, intermediate, and broad) accounted for substantial variance; this model provided a superior fit relative to more parsimonious competing structural models.

Furthermore, impairment was independently associated with all 3 levels of the tri-level model, comorbidity was most closely linked to the broad tri-level dimensions, and the factors generally showed the expected convergent/discriminant associations with diagnoses. Results suggested several revisions to prior research: (a) worry may be best modeled at the broadest structural level, rather than as an indicator of anxious-misery or fear; (b) social interaction anxiety may belong with anxious-misery, rather than fear; and (c) obsessive-compulsive disorder is generally associated with fear disorders, but hoarding is associated with both fear and anxious-misery.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5216850/ The Psychosis Continuum: Testing a Bifactor Model of Psychosis in a General Population Sample Mark Shevlin, Eoin McElroy, Richard P. Bentall, Ulrich Reininghaus, Jamie Murphy 2017 2020-09-10 [("doi","10.1093/schbul/sbw067")] psychiatry/bipolar psychiatry/depression

Although the factor structure of psychosis continues to be debated by taxonomists, recent studies have supported a bifactor model consisting of a general psychosis factor and 5 uncorrelated symptom-specific factors. While this model has received support in clinical samples, it has not been tested at the general population level.

Analysis was conducted on Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (n = 34,653). 20-two psychotic symptoms were used as observed indicators of psychosis. These items were chosen based on their conceptual similarity to the items used in a similar study based on clinical samples. Confirmatory factor analysis and confirmatory bifactor modeling were used to test a variety of competing models.

The best fitting model consisted of a general psychosis factor that was uncorrelated with 5 specific factors: positive, negative, disorganization, mania, and depression. These findings suggest that the bifactor model can be extended to general population samples, supporting the continuity between clinical and subclinical psychotic experiences.

Theoretical and practical implications are discussed.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5226919/ Pluripotent stem cell-derived gametes: truth and (potential) consequences Debra J. H. Mathews, Peter J. Donovan, John Harris, Robin Lovell-Badge, Julian Savulescu, Ruth Faden 2009 2020-09-10 [("doi","10.1016/j.stem.2009.06.005")] genetics/gametogenesis

An emerging body of data suggests that pluripotent stem cells may be able to differentiate to form eggs and sperm.

We discuss the state of the science and the potential social implications and offer recommendations for addressing some of the ethical and policy issues that would be raised by the availability of stem cell-derived gametes.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5367551/ Rapid and sustained symptom reduction following psilocybin treatment for anxiety and depression in patients with life-threatening cancer: a randomized controlled trial Stephen Ross, Anthony Bossis, Jeffrey Guss, Gabrielle Agin-Liebes, Tara Malone, Barry Cohen, Sarah E. Mennenga, Alexander Belser, Krystallia Kalliontzi, James Babb, Zhe Su, Patricia Corby, Brian L. Schmidt 2016 2020-09-10 [("doi","10.1177/0269881116675512")] psychedelic psychiatry/anxiety psychiatry/depression

Background: Clinically anxiety and depression are common in patients with cancer, and are associated with poor psychiatric and medical outcomes. Historical and recent research suggests a role for psilocybin to treat cancer-related anxiety and depression.

Method: In this double-blind, placebo-controlled, crossover trial, 29 patients with cancer-related anxiety and depression were randomly assigned and received treatment with single-dose psilocybin (0.3 mg/kg) or niacin, both in conjunction with psychotherapy. The primary outcomes were anxiety and depression assessed between groups prior to the crossover at 7 weeks.

Results: Prior to the crossover, psilocybin produced immediate, substantial, and sustained improvements in anxiety and depression and led to decreases in cancer-related demoralization and hopelessness, improved spiritual wellbeing, and increased quality of life. At the 6.5-month follow-up, psilocybin was associated with enduring anxiolytic and anti-depressant effects (~60–80% of participants continued with clinically-significant reductions in depression or anxiety), sustained benefits in existential distress and quality of life, as well as improved attitudes towards death. The psilocybin-induced mystical experience mediated the therapeutic effect of psilocybin on anxiety and depression.

Conclusion: In conjunction with psychotherapy, single moderate-dose psilocybin produced rapid, robust and enduring anxiolytic and anti-depressant effects in patients with cancer-related psychological distress.

Trial Registration: ClinicalTrials.gov Identifier: NCT00957359.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5397584/ Genetic Markers of Human Evolution Are Enriched in Schizophrenia Saurabh Srinivasan, Francesco Bettella, Morten Mattingsdal, Yunpeng Wang, Aree Witoelar, Andrew J. Schork, Wesley K. Thompson, Verena Zuber, Bendik S. Winsvold, John-Anker Zwart, David A. Collier, Rahul S. Desikan, Ingrid Sigfrid Melle, Thomas Werge, Anders Martin Dale, Srdjan Djurovic, Ole A. Andreassen 2016 2020-09-10 [("doi","10.1016/j.biopsych.2015.10.009")] genetics/selection/natural/human psychiatry/schizophrenia

Background: Why schizophrenia has accompanied humans throughout our history despite its negative effect on fitness remains an evolutionary enigma. It is proposed that schizophrenia is a by-product of the complex evolution of the human brain and a compromise for humans’ language, creative thinking, and cognitive abilities.

Method: We analyzed recent large genome-wide association studies of schizophrenia and a range of other human phenotypes (anthropometric measures, cardiovascular disease risk factors, immune-mediated diseases) using a statistical framework that draws on polygenic architecture and ancillary information on genetic variants. We used information from the evolutionary proxy measure called the Neanderthal selective sweep (NSS) score.

Results: Gene loci associated with schizophrenia are (p = 7.30 × 10−9) more prevalent in genomic regions that are likely to have undergone recent positive selection in humans (ie. with a low NSS score). Variants in brain-related genes with a low NSS score confer higher susceptibility than variants in other brain-related genes. The enrichment is strongest for schizophrenia, but we cannot rule out enrichment for other phenotypes. The false discovery rate conditional on the evolutionary proxy points to 27 candidate schizophrenia susceptibility loci, 12 of which are associated with schizophrenia and other psychiatric disorders or linked to brain development.

Conclusion: Our results suggest that there is a polygenic overlap between schizophrenia and NSS score, a marker of human evolution, which is in line with the hypothesis that the persistence of schizophrenia is related to the evolutionary process of becoming human.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5464089/ Genetic and Environmental Contributions to the Association Between Cannabis Use and Psychotic-Like Experiences in Young Adult Twins Ragnar Nesvåg, Ted Reichborn-Kjennerud, Nathan A. Gillespie, Gun Peggy Knudsen, Jørgen G. Bramness, Kenneth S. Kendler, Eivind Ystrom 2017 2020-09-10 [("doi","10.1093/schbul/sbw101")] genetics/heritable/correlation marijuana psychiatry/schizophrenia

To investigate contributions of genetic and environmental risk factors and possible direction of causation for the relationship between symptoms of cannabis use disorders (CUD) and psychotic-like experiences (PLEs):

a population-based sample of 2793 young adult twins (63.5% female, mean [range] age 28.2 [19–36] y) were assessed for symptoms of CUD and PLEs using the Composite International Diagnostic Interview. Latent risk of having symptoms of CUD or PLEs was modeled using Item Response Theory. Co-twin control analysis was performed to investigate effect of familiar confounding for the association between symptoms of CUD and PLEs. Biometric twin models were fitted to estimate the heritability, genetic and environmental correlations, and direction for the association.

Lifetime use of cannabis was reported by 10.4% of the twins, and prevalence of PLEs ranged 35.9%–59.4% The incidence rate ratio of PLEs due to symptoms of CUD was 6.3 (95% CI, 3.9, 10.2) in the total sample and 3.5 (95% CI, 1.5, 8.2) within twin pairs. Heritability estimates for symptoms of CUD were 88% in men and women, and for PLEs 77% in men and 43% in women. The genetic and environmental correlations between symptoms of CUD and PLEs were 0.55 and 0.52, respectively. The model allowing symptoms of CUD to cause PLEs had a better fit than models specifying opposite or reciprocal directions of causation.

The association between symptoms of CUD and PLEs is explained by shared genetic and environmental factors and direct effects from CUD to risk for PLEs.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5505663/ Childhood forecasting of a small segment of the population with large economic burden Avshalom Caspi, Renate M. Houts, Daniel W. Belsky, Honalee Harrington, Sean Hogan, Sandhya Ramrakha, Richie Poulton, Terrie E. Moffitt 2016 2020-09-11 [("doi","10.1038/s41562-016-0005")] crime iq/ses

Policy-makers are interested in early-years interventions to ameliorate childhood risks. They hope for improved adult outcomes in the long run, bringing return on investment. How much return can be expected depends, partly, on how strongly childhood risks forecast adult outcomes. But there is disagreement about whether childhood determines adulthood.

We integrated multiple nationwide administrative databases and electronic medical records with the four-decade Dunedin birth-cohort study to test child-to-adult prediction in a different way, by using a population-segmentation approach. A segment comprising one-fifth of the cohort accounted for 36% of the cohort’s injury insurance-claims; 40% of excess obese-kilograms; 54% of cigarettes smoked; 57% of hospital nights; 66% of welfare benefits; 77% of fatherless childrearing; 78% of prescription fills; and 81% of criminal convictions. Childhood risks, including poor age-three brain health, predicted this segment with large effect sizes.

Early-years interventions effective with this population segment could yield very large returns on investment.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5641223/ The Clinical Potential of Senolytic Drugs James L. Kirkland, Tamara Tchkonia, Yi Zhu, Laura J. Niedernhofer, Paul D. Robbins 2017 2020-09-11 [("doi","10.1111/jgs.14969")] longevity/senolytic

Senolytic drugs are agents that selectively induce apoptosis of senescent cells. These cells accumulate in many tissues with aging and at sites of pathology in multiple chronic diseases. In studies in animals, targeting senescent cells using genetic or pharmacological approaches delays, prevents, or alleviates multiple age-related phenotypes, chronic diseases, geriatric syndromes, and loss of physiological resilience. Among the chronic conditions successfully treated by depleting senescent cells in preclinical studies are frailty, cardiac dysfunction, vascular hyporeactivity and calcification, diabetes mellitus, liver steatosis, osteoporosis, vertebral disk degeneration, pulmonary fibrosis, and radiation-induced damage.

Senolytic agents are being tested in proof-of-concept clinical trials. To do so, new clinical trial paradigms for testing senolytics and other agents that target fundamental aging mechanisms are being developed, because use of long-term endpoints such as lifespan or healthspan is not feasible. These strategies include testing effects on multimorbidity, accelerated aging-like conditions, diseases with localized accumulation of senescent cells, potentially fatal diseases associated with senescent cell accumulation, age-related loss of physiological resilience, and frailty.

If senolytics or other interventions that target fundamental aging processes prove to be effective and safe in clinical trials, they could transform geriatric medicine by enabling prevention or treatment of multiple diseases and functional deficits in parallel, instead of one at a time.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5643230/ No Evidence That Schizophrenia Candidate Genes Are More Associated With Schizophrenia Than Non-candidate Genes Emma C. Johnson, Richard Border, Whitney E. Melroy-Greif, Christiaan A. de Leeuw, Marissa A. Ehringer, Matthew C. Keller 2017 2020-09-11 [("doi","10.1016/j.biopsych.2017.06.033")] psychiatry/schizophrenia

Background: A recent analysis of 25 historical candidate gene polymorphisms for schizophrenia in the largest genome-wide association study conducted to date suggested that these commonly studied variants were no more associated with the disorder than would be expected by chance. However, the same study identified other variants within those candidate genes that demonstrated genome-wide statistically-significant associations with schizophrenia. As such, it is possible that variants within historic schizophrenia candidate genes are associated with schizophrenia at levels above those expected by chance, even if the most-studied specific polymorphisms are not.

Method: The present study used association statistics from the largest schizophrenia genome-wide association study conducted to date as input to a gene set analysis to investigate whether variants within schizophrenia candidate genes are enriched for association with schizophrenia.

Results: As a group, variants in the most-studied candidate genes were no more associated with schizophrenia than were variants in control sets of non-candidate genes. While a small subset of candidate genes did appear to be statistically-significantly associated with schizophrenia, these genes were not particularly noteworthy given the large number of more strongly associated non-candidate genes.

Conclusion: The history of schizophrenia research should serve as a cautionary tale to candidate gene investigators examining other phenotypes: our findings indicate that the most investigated candidate gene hypotheses of schizophrenia are not well supported by genome-wide association studies, and it is likely that this will be the case for other complex traits as well.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5655619/ Hyper-realistic face masks: a new challenge in person identification Jet Gabrielle Sanders, Yoshiyuki Ueda, Kazusa Minemoto, Eilidh Noyes, Sakiko Yoshikawa, Rob Jenkins 2017 2020-09-11 [("doi","10.1186/s41235-017-0079-y")] cs/security technology

We often identify people using face images. This is true in occupational settings such as passport control as well as in everyday social environments. Mapping between images and identities assumes that facial appearance is stable within certain bounds. For example, a person’s apparent age, gender, and ethnicity change slowly, if at all. It also assumes that deliberate changes beyond these bounds (ie. disguises) would be easy to spot. Hyper-realistic face masks overturn these assumptions by allowing the wearer to look like an entirely different person. If unnoticed, these masks break the link between facial appearance and personal identity, with clear implications for applied face recognition. However, to date, no one has assessed the realism of these masks, or specified conditions under which they may be accepted as real faces.

Herein, we examined incidental detection of unexpected but attended hyper-realistic masks in both photographic and live presentations. Experiment 1 (UK; n = 60) revealed no evidence for overt detection of hyper-realistic masks among real face photos, and little evidence of covert detection. Experiment 2 (Japan; n = 60) extended these findings to different masks, mask-wearers, and participant pools. In Experiment 3 (UK and Japan; n = 407), passers-by failed to notice that a live confederate was wearing a hyper-realistic mask and showed limited evidence of covert detection, even at close viewing distance (5 vs. 20 m). Across all of these studies, viewers accepted hyper-realistic masks as real faces.

Specific countermeasures will be required if detection rates are to be improved.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5657592/ Targeting cellular senescence prevents age-related bone loss in mice Joshua N. Farr, Ming Xu, Megan M. Weivoda, David G. Monroe, Daniel G. Fraser, Jennifer L. Onken, Brittany A. Negley, Jad G. Sfeir, Mikolaj B. Ogrodnik, Christine M. Hachfeld, Nathan K. LeBrasseur, Matthew T. Drake, Robert J. Pignolo, Tamar Pirtskhalava, Tamara Tchkonia, Merry Jo Oursler, James L. Kirkland, Sundeep Khosla 2017 2020-09-11 [("doi","10.1038/nm.4385")] longevity/senolytic

Aging is associated with increased cellular senescence, which is hypothesized to drive the eventual development of multiple comorbidities. Here we investigate a role for senescent cells in age-related bone loss through multiple approaches.

In particular, we used either genetic (ie. the INK-ATTAC ‘suicide’ transgene encoding an inducible caspase 8 expressed specifically in senescent cells) or pharmacological (ie. ‘senolytic’ compounds) means to eliminate senescent cells. We also inhibited the production of the proinflammatory secretome of senescent cells using a JAK inhibitor (JAKi). In aged (20- to 22-month-old) mice with established bone loss, activation of the INK-ATTAC caspase 8 in senescent cells or treatment with senolytics or the JAKi for 2–4 months resulted in higher bone mass and strength and better bone microarchitecture than in vehicle-treated mice.

The beneficial effects of targeting senescent cells were due to lower bone resorption with either maintained (trabecular) or higher (cortical) bone formation as compared to vehicle-treated mice. In vitro studies demonstrated that senescent-cell conditioned medium impaired osteoblast mineralization and enhanced osteoclast-progenitor survival, leading to increased osteoclastogenesis.

Collectively, these data establish a causal role for senescent cells in bone loss with aging, and demonstrate that targeting these cells has both anti-resorptive and anabolic effects on bone. Given that eliminating senescent cells and/or inhibiting their proinflammatory secretome also improves cardiovascular function, enhances insulin sensitivity, and reduces frailty, targeting this fundamental mechanism to prevent age-related bone loss suggests a novel treatment strategy not only for osteoporosis, but also for multiple age-related comorbidities.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5663051/ Digital tissue and what it may reveal about the brain Josh L. Morgan, Jeff W. Lichtman 2017 2020-09-11 [("doi","10.1186/s12915-017-0436-9")] psychology/neuroscience

Imaging as a means of scientific data storage has evolved rapidly over the past century from hand drawings, to photography, to digital images. Only recently can sufficiently large datasets be acquired, stored, and processed such that tissue digitization can actually reveal more than direct observation of tissue.

One field where this transformation is occurring is connectomics: the mapping of neural connections in large volumes of digitized brain tissue.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5681657/ Birth of clones of the world’s first cloned dog Min Jung Kim, Hyun Ju Oh, Geon A. Kim, Erif Maha Nugraha Setyawan, Yoo Bin Choi, Seok Hee Lee, Simon M. Petersen-Jones, CheMyong J. Ko, Byeong Chun Lee 2017 2020-09-11 [("doi","10.1038/s41598-017-15328-2")] genetics/cloning/dog

Animal cloning has gained popularity as a method to produce genetically identical animals or superior animals for research or industrial uses. However, the long-standing question of whether a cloned animal undergoes an accelerated aging process is yet to be answered.

As a step towards answering this question, we compared longevity and health of Snuppy, the world’s first cloned dog, and its somatic cell donor, Tai, a male Afghan hound. Briefly, both Snuppy and Tai were generally healthy until both developed cancer to which they succumbed at the ages of 10 and 12 years, respectively. The longevity of both the donor and the cloned dog was close to the median lifespan of Afghan hounds which is reported to be 11.9 years.

Here, we report creation of 4 clones using adipose-derived mesenchymal stem cells from Snuppy as donor cells. Clinical and molecular follow-up of these reclones over their lives will provide us with a unique opportunity to study the health and longevity of cloned animals compared with their cell donors.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5813086/ Psilocybin with psychological support for treatment-resistant depression: six-month follow-up R L. Carhart-Harris, M. Bolstridge, C. M. J. Day, J. Rucker, R. Watts, D. E. Erritzoe, M. Kaelen, B. Giribaldi, M. Bloomfield, S. Pilling, J. A. Rickard, B. Forbes, A. Feilding, D. Taylor, H. V. Curran, D. J. Nutt 2018 2020-09-11 [("doi","10.1007/s00213-017-4771-x")] psychedelic psychiatry/depression

Rationale: Recent clinical trials are reporting marked improvements in mental health outcomes with psychedelic drug-assisted psychotherapy.

Objectives: Here, we report on safety and efficacy outcomes for up to 6 months in an open-label trial of psilocybin for treatment-resistant depression.

Method: Twenty patients (six females) with (mostly) severe, unipolar, treatment-resistant major depression received two oral doses of psilocybin (10 and 25 mg, 7 days apart) in a supportive setting. Depressive symptoms were assessed from 1 week to 6 months post-treatment, with the self-rated QIDS-SR16 as the primary outcome measure.

Results: Treatment was generally well tolerated. Relative to baseline, marked reductions in depressive symptoms were observed for the first 5 weeks post-treatment (Cohen’s d = 2.2 at week 1 and 2.3 at week 5, both p < 0.001); nine and four patients met the criteria for response and remission at week 5. Results remained positive at 3 and 6 months (Cohen’s d = 1.5 and 1.4, respectively, both p < 0.001). No patients sought conventional antidepressant treatment within 5 weeks of psilocybin. Reductions in depressive symptoms at 5 weeks were predicted by the quality of the acute psychedelic experience.

Conclusion: Although limited conclusions can be drawn about treatment efficacy from open-label trials, tolerability was good, effect sizes large and symptom improvements appeared rapidly after just two psilocybin treatment sessions and remained statistically-significant 6 months post-treatment in a treatment-resistant cohort. Psilocybin represents a promising paradigm for unresponsive depression that warrants further research in double-blind randomized control trials.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5875373/ Measuring and Estimating the Effect Sizes of Copy Number Variants on General Intelligence in Community-Based Samples Guillaume Huguet, Catherine Schramm, Elise Douard, Lai Jiang, Aurélie Labbe, Frédérique Tihy, Géraldine Mathonnet, Sonia Nizard, Emmanuelle Lemyre, Alexandre Mathieu, Jean-Baptiste Poline, Eva Loth, Roberto Toro, Gunter Schumann, Patricia Conrod, Zdenka Pausova, Celia Greenwood, Tomas Paus, Thomas Bourgeron, Sébastien Jacquemont 2018 2020-09-11 [("doi","10.1001/jamapsychiatry.2018.0039")] genetics/heritable/rare iq

Importance;: Copy number variants (CNVs) classified as pathogenic are identified in 10% to 15% of patients referred for neurodevelopmental disorders. However, their effect sizes on cognitive traits measured as a continuum remain mostly unknown because most of them are too rare to be studied individually using association studies.

Objective: To measure and estimate the effect sizes of recurrent and nonrecurrent CNVs on IQ.

Design, Setting, & Participants: This study identified all CNVs that were 50 kilobases (kb) or larger in 2 general population cohorts (the IMAGEN project and the Saguenay Youth Study) with measures of IQ. Linear regressions, including functional annotations of genes included in CNVs, were used to identify features to explain their association with IQ. Validation was performed using intraclass correlation that compared IQ estimated by the model with empirical data.

Main Outcomes & Measures: Performance IQ (PIQ), verbal IQ (VIQ), and frequency of de novo CNV events.

Results: The study included 2090 European adolescents from the IMAGEN study and 1983 children and parents from the Saguenay Youth Study. Of these, genotyping was performed on 1804 individuals from IMAGEN and 977 adolescents, 445 mothers, and 448 fathers (484 families) from the Saguenay Youth Study. We observed 4928 autosomal CNVs larger than 50 kb across both cohorts. For rare deletions, size, number of genes, and exons affect IQ, and each deleted gene is associated with a mean (SE) decrease in PIQ of 0.67 (0.19) points (p = 6 × 10–4); this is not so for rare duplications and frequent CNVs. Among 10 functional annotations, haploinsufficiency scores best explain the association of any deletions with PIQ with a mean (SE) decrease of 2.74 (0.68) points per unit of the probability of being loss-of-function intolerant (p = 8 × 10–5). Results are consistent across cohorts and unaffected by sensitivity analyses removing pathogenic CNVs. There is a 0.75 concordance (95% CI, 0.39–0.91) between the effect size on IQ estimated by our model and IQ loss calculated in previous studies of 15 recurrent CNVs. There is a close association between effect size on IQ and the frequency at which deletions occur de novo (odds ratio, 0.86; 95% CI, 0.84–0.87; p = 2.7 × 10–88). There is a 0.76 concordance (95% CI, 0.41–0.91) between de novo frequency estimated by the model and calculated using data from the DECIPHER database.

Conclusions & Relevance: Models trained on nonpathogenic deletions in the general population reliably estimate the effect size of pathogenic deletions and suggest omnigenic associations of haploinsufficiency with IQ. This represents a new framework to study variants too rare to perform individual association studies and can help estimate the cognitive effect of undocumented deletions in the neurodevelopmental clinic.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6042427/ Marijuana intoxication in a cat Agnieszka Janeczek, Marcin Zawadzki, Pawel Szpot, Artur Niedzwiedz 2018 2020-09-11 [("doi","10.1186/s13028-018-0398-0")] cat/psychology dog marijuana

Background: Cannabis from hemp (Cannabis sativa and C. indica) is one of the most common illegal drugs used by drug abusers. Indian cannabis contains around 70 alkaloids, and delta-9-tetrahydrocannabinol (delta-9-THC) is the most psychoactive substance. Animal intoxications occur rarely and are mostly accidental. According to the US Animal Poison Control Center, cannabis intoxication mostly affects dogs (96%). The most common cause of such intoxication is unintentional ingestion of a cannabis product, but it may also occur after the exposure to marijuana smoke.

Case Presentation: A 6-year-old Persian cat was brought to the veterinary clinic due to strong psychomotor agitation turning into aggression. During hospitalization for 14 days, the cat behaved normally and had no further attacks of unwanted behavior. It was returned to its home but shortly after it developed neurological signs again and was re-hospitalized. On presentation, the patient showed no neurological abnormalities except for symmetric mydriasis and scleral congestion. During the examination, the behavior of the cat changed dramatically. It developed alternate states of agitation and apathy, each lasting several minutes. On interview it turned out that the cat had been exposed to marijuana smoke. Blood toxicology tests by gas chromatography tandem mass spectrometry revealed the presence of delta-9-tetrahydrocannabinol (THC) at 5.5 ng/mL, 11-hydroxy-delta-9-THC at 1.2 ng/mL, and 11-carboxy-delta-9-THC at 13.8 ng/mL. The cat was given an isotonic solution of NaCl 2.5 and 2.5% glucose at a dose of 40 mL/kg/day parenterally and was hospitalized. After complete recovery, the cat was returned to it’s owner and future isolation of the animal from marijuana smoke was advised.

Conclusion: This is the first case of a delta-9-tetrahydrocannabinol intoxication in a cat with both description of the clinical findings and the blood concentration of delta-9-THC and its main metabolites.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6130754/ Relatedness disequilibrium regression estimates heritability without environmental bias Alexander I. Young, Michael L. Frigge, Daniel F. Gudbjartsson, Gudmar Thorleifsson, Gyda Bjornsdottir, Patrick Sulem, Gisli Masson, Unnur Thorsteinsdottir, Kari Stefansson, Augustine Kong 2018 2020-09-11 [("doi","10.1038/s41588-018-0178-9")] genetics/heritable

Heritability measures the proportion of trait variation that is due to genetic inheritance. Measurement of heritability is important in the nature-versus-nurture debate.

However, existing estimates of heritability may be biased by environmental effects. Here, we introduce relatedness disequilibrium regression (RDR), a novel method for estimating heritability. RDR avoids most sources of environmental bias by exploiting variation in relatedness due to random Mendelian segregation.

We used a sample of 54,888 Icelanders who had both parents genotyped to estimate the heritability of 14 traits, including height (55.4%, s.e. 4.4%) and educational attainment (17.0%, s.e. 9.4%).

Our results suggest that some other estimates of heritability may be inflated by environmental effects.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197474/ Family-Specific Variants and the Limits of Human Genetics Brian H. Shirts, Colin C. Pritchard, Tom Walsh 2016 2020-09-12 [("doi","10.1016/j.molmed.2016.09.007")] genetics/heritable/rare

Every single-nucleotide change compatible with life is present in the human population today. Understanding these rare human variants defines an extraordinary challenge for genetics and medicine.

The new clinical practice of sequencing many genes for hereditary cancer risk has illustrated the utility of clinical next-generation sequencing in adults, identifying more medically actionable variants than single-gene testing. However, it has also revealed a linear relationship between the length of DNA evaluated and the number of rare ‘variants of uncertain importance’ reported.

We propose that careful approaches to phenotype-genotype inference, distinguishing between diagnostic and screening intent, in conjunction with expanded use of family-scale genetics studies as a source of information on family-specific variants, will reduce variants of uncertain importance reported to patients.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6282841/ Bidirectional Relations Between Parenting and Behavior Problems From Age 8 to 13 in Nine Countries Jennifer E. Lansford, W. Andrew Rothenberg, Todd M. Jensen, Melissa A. Lippold, Dario Bacchini, Marc H. Bornstein, Lei Chang, Kirby Deater-Deckard, Laura Di Giunta, Kenneth A. Dodge, Patrick S. Malone, Paul Oburu, Concetta Pastorelli, Ann T. Skinner, Emma Sorbring, Laurence Steinberg, Sombat Tapanya, Liliana Maria Uribe Tirado, Liane Peña Alampay, Suha M. Al-Hassan 2018 2020-09-12 [("doi","10.1111/jora.12381")] sociology

This study used data from 12 cultural groups in 9 countries (China, Colombia, Italy, Jordan, Kenya, Philippines, Sweden, Thailand, and the United States; n = 1,298) to understand the cross-cultural generalizability of how parental warmth and control are bidirectionally related to externalizing and internalizing behaviors from childhood to early adolescence.

Mothers, fathers, and children completed measures when children were ages 8–13. Multiple-group autoregressive, cross-lagged structural equation models revealed that child effects rather than parent effects may better characterize how warmth and control are related to child externalizing and internalizing behaviors over time.

Parent effects may be more characteristic of relations between parental warmth and control and child externalizing and internalizing behavior during childhood than early adolescence.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6309228/ Genome-Wide Association Studies of a Broad Spectrum of Antisocial Behavior 2020-09-12 crime genetics/heritable --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6400636/ Genetic Predisposition vs Individual-Specific Processes in the Association Between Psychotic-like Experiences and Cannabis Use Nicole R. Karcher, Deanna M. Barch, Catherine H. Demers, David A. A. Baranger, Andrew C. Heath, Michael T. Lynskey, Arpana Agrawal 2019 2020-09-12 [("doi","10.1001/jamapsychiatry.2018.2546")] genetics/heritable/correlation marijuana

Importance: Previous research indicates that cannabis use is associated with psychotic-like experiences (PLEs). However, it is unclear whether this association results from predispositional (ie. shared genetic) factors or individual-specific factors (eg. causal processes, such as cannabis use leading to PLEs).

Objectives: To estimate genetic and environmental correlations between cannabis use and PLEs, and to examine PLEs in twin and nontwin sibling pairs discordant for exposure to cannabis use to disentangle predispositional from individual-specific effects.

Design, Setting, & Participants: In this cross-sectional analysis, diagnostic interviews and self-reported data were collected from 2 separate population-based samples of twin and nontwin sibling pairs. Data from the Human Connectome Project were collected between August 10, 2012, and September 29, 2015, and data from the Australian Twin Registry Cohort 3 (ATR3) were collected between August 1, 2005, and August 31, 2010. Data were analyzed between August 17, 2017, and July 6, 2018. The study included data from 1188 Human Connectome Project participants and 3486 ATR3 participants, totaling 4674 participants.

Main Outcomes & Measures: 3 cannabis-involvement variables were examined: frequent use (ie. ≥100×), a DSM-IV lifetime cannabis use disorder diagnosis, and current cannabis use. Genetic and environmental correlations between cannabis involvement and PLEs were estimated. Generalized linear mixed models examined PLE differences in twin and nontwin sibling pairs discordant for cannabis use.

Results: Among the 4674 participants, the mean (SD) age was 30.5 (3.2) years, and 2923 (62.5%) were female. Data on race/ethnicity were not included as a covariate owing to lack of variability within the ATR3 sample; among the 1188 participants in the Human Connectome Project, 875 (73.7%) were white. Psychotic-like experiences were associated with frequent cannabis use (β = 0.11; 95% CI, 0.08–0.14), cannabis use disorder (β = 0.13; 95% CI, 0.09–0.16), and current cannabis use (β = 0.07; 95% CI, 0.04–0.10) even after adjustment for covariates. Correlated genetic factors explained between 69.2% and 84.1% of this observed association. Within discordant pairs of twins/siblings (N~pairs~, 308–324), Psychotic-like experiences were more common in cannabis-exposed individuals compared with their relative who used cannabis to a lesser degree (β ≥ 0.23, p < 0.05; eg, frequent and infrequent cannabis-using relatives differed, z = −5.41; p < 0.001).

Conclusions & Relevance: Despite the strong contribution of shared genetic factors, frequent and problem cannabis use also appears to be associated with PLEs via person-specific pathways. This study’s findings suggest that policy discussions surrounding legalization should consider the influence of escalations in cannabis use on trait-like indices of vulnerability, such as PLEs, which could contribute to pervasive psychological and interpersonal burden.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6411041/ Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence Jeanne E. Savage, Philip R. Jansen, Sven Stringer, Kyoko Watanabe, Julien Bryois, Christiaan A. de Leeuw, Mats Nagel, Swapnil Awasthi, Peter B. Barr, Jonathan R. I. Coleman, Katrina L. Grasby, Anke R. Hammerschlag, Jakob A. Kaminski, Robert Karlsson, Eva Krapohl, Max Lam, Marianne Nygaard, Chandra A. Reynolds, Joey W. Trampush, Hannah Young, Delilah Zabaneh, Sara Hägg, Narelle K. Hansell, Ida K. Karlsson, Sten Linnarsson, Grant W. Montgomery, Ana B. Muñoz-Manchado, Erin B. Quinlan, Gunter Schumann, Nathan G. Skene, Bradley T. Webb, Tonya White, Dan E. Arking, Dimitrios Avramopoulos, Robert M. Bilder, Panos Bitsios, Katherine E. Burdick, Tyrone D. Cannon, Ornit Chiba-Falek, Andrea Christoforou, Elizabeth T. Cirulli, Eliza Congdon, Aiden Corvin, Gail Davies, Ian J. Deary, Pamela DeRosse, Dwight Dickinson, Srdjan Djurovic, Gary Donohoe, Emily Drabant Conley, Johan G. Eriksson, Thomas Espeseth, Nelson A. Freimer, Stella Giakoumaki, Ina Giegling, Michael Gill, David C. Glahn, Ahmad R. Hariri, Alex Hatzimanolis, Matthew C. Keller, Emma Knowles, Deborah Koltai, Bettina Konte, Jari Lahti, Stephanie Le Hellard, Todd Lencz, David C. Liewald, Edythe London, Astri J. Lundervold, Anil K. Malhotra, Ingrid Sigfrid Melle, Derek Morris, Anna C. Need, William Ollier, Aarno Palotie, Antony Payton, Neil Pendleton, Russell A. Poldrack, Katri Räikkönen, Ivar Reinvang, Panos Roussos, Dan Rujescu, Fred W. Sabb, Matthew A. Scult, Olav B. Smeland, Nikolaos Smyrnis, John M. Starr, Vidar M. Steen, Nikos C. Stefanis, Richard E. Straub, Kjetil Sundet, Henning Tiemeier, Aristotle N. Voineskos, Daniel R. Weinberger, Elisabeth Widen, Jin Yu, Gonçalo Abecasis, Ole A. Andreassen, Gerome Breen, Lene Christiansen, Birgit Debrabant, Danielle M. Dick, Andreas Heinz, Jens Hjerling-Leffler, M. Arfan Ikram, Kenneth S. Kendler, Nicholas G. Martin, Sarah E. Medland, Nancy L. Pedersen, Robert Plomin, Tinca J. C. Polderman, Stephan Ripke, Sophie van der Sluis, Patrick F. Sullivan, Scott I. Vrieze, Margaret J. Wright, Danielle Posthuma 2018 2020-09-12 [("doi","10.1038/s41588-018-0152-6")] genetics/heritable/correlation/mendelian-randomization iq psychiatry/alzheimers psychiatry/schizophrenia

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence3–7, but much about its genetic underpinnings remains to be discovered.

Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure.

We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian Randomization analysis results suggest protective effects of intelligence for Alzheimer’s disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6478303/ Survey of subjective ‘God encounter experiences’: Comparisons among naturally occurring experiences and those occasioned by the classic psychedelics psilocybin, LSD, ayahuasca, or DMT Roland R. Griffiths, Ethan S. Hurwitz, Alan K. Davis, Matthew W. Johnson, Robert Jesse 2019 2020-09-12 [("doi","10.1371/journal.pone.0214377")] nootropic psychedelic/lsd

Naturally occurring and psychedelic drug-occasioned experiences interpreted as personal encounters with God are well described but have not been systematically compared.

In this study, five groups of individuals participated in an online survey with detailed questions characterizing the subjective phenomena, interpretation, and persisting changes attributed to their single most memorable God encounter experience (n = 809 Non-Drug, 1,184 psilocybin, 1,251 lysergic acid diethylamide (LSD), 435 ayahuasca, and 606 N,N-dimethyltryptamine (DMT)). Analyses of differences in experiences were adjusted statistically for demographic differences between groups.

The Non-Drug Group was most likely to choose “God” as the best descriptor of that which was encountered while the psychedelic groups were most likely to choose “Ultimate Reality.” Although there were some other differences between non-drug and the combined psychedelic group, as well as between the four psychedelic groups, the similarities among these groups were most striking.

Most participants reported vivid memories of the encounter experience, which frequently involved communication with something having the attributes of being conscious, benevolent, intelligent, sacred, eternal, and all-knowing. The encounter experience fulfilled a priori criteria for being a complete mystical experience in ~half of the participants. More than two-thirds of those who identified as atheist before the experience no longer identified as atheist afterwards. These experiences were rated as among the most personally meaningful and spiritually important lifetime experiences, with moderate to strong persisting positive changes in life satisfaction, purpose, and meaning attributed to these experiences. Among the four groups of psychedelic users, the psilocybin and LSD groups were most similar and the ayahuasca group tended to have the highest rates of endorsing positive features and enduring consequences of the experience.

Future exploration of predisposing factors and phenomenological and neural correlates of such experiences may provide new insights into religious and spiritual beliefs that have been integral to shaping human culture since time immemorial.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6484814/ Effect of Additional Oral Semaglutide vs Sitagliptin on Glycated Hemoglobin in Adults With Type 2 Diabetes Uncontrolled With Metformin Alone or With Sulfonylurea: The PIONEER 3 Randomized Clinical Trial Julio Rosenstock, Dale Allison, Andreas L. Birkenfeld, Thalia Marie Blicher, Srikanth Deenadayalan, Jacob Bonde Jacobsen, Pierre Serusclat, Rafael Violante, Hirotaka Watada, Melanie Davies 2019 2020-09-12 [("doi","10.1001/jama.2019.2942")] longevity/glp/semaglutide

Importance: Phase 3 trials have not compared oral semaglutide, a glucagon-like peptide 1 receptor agonist, with other classes of glucose-lowering therapy.

Objective: To compare efficacy and assess long-term adverse event profiles of once-daily oral semaglutide vs sitagliptin, 100 mg added on to metformin with or without sulfonylurea, in patients with type 2 diabetes.

Design, Setting, & Participants: Randomized, double-blind, double-dummy, parallel-group, phase 3a trial conducted at 206 sites in 14 countries over 78 weeks from February 2016 to March 2018. Of 2463 patients screened, 1864 adults with type 2 diabetes uncontrolled with metformin with or without sulfonylurea were randomized.

Interventions: Patients were randomized to receive once-daily oral semaglutide, 3 mg (n = 466), 7 mg (n = 466), or 14 mg (n = 465), or sitagliptin, 100 mg (n = 467). Semaglutide was initiated at 3 mg/d and escalated every 4 weeks, first to 7 mg/d then to 14 mg/d, until the randomized dosage was achieved.

Main Outcomes & Measures: The primary end point was change in glycated hemoglobin (HbA1c), and the key secondary end point was change in body weight, both from baseline to week 26. Both were assessed at weeks 52 and 78 as additional secondary end points. End points were tested for noninferiority with respect to HbA1c (noninferiority margin, 0.3%) prior to testing for superiority of HbA1c and body weight.

Results: Among 1864 patients randomized (mean age, 58 [SD, 10] years; mean baseline HbA1c, 8.3% [SD, 0.9%]; mean body mass index, 32.5 [SD, 6.4]; n = 879 [47.2%] women), 1758 (94.3%) completed the trial and 298 prematurely discontinued treatment (16.7% for semaglutide, 3 mg/d; 15.0% for semaglutide, 7 mg/d; 19.1% for semaglutide, 14 mg/d; and 13.1% for sitagliptin). Semaglutide, 7 and 14 mg/d, compared with sitagliptin, reduced HbA1c (differences, −0.3% [95% CI, −0.4% to −0.1%] and −0.5% [95% CI, −0.6% to −0.4%], respectively; p < 0.001 for both) and body weight (differences, −1.6 kg [95% CI, −2.0 to −1.1 kg] and −2.5 kg [95% CI, −3.0 to −2.0 kg], respectively; p < 0.001 for both) from baseline to week 26. Noninferiority of semaglutide, 3 mg/d, with respect to HbA1c was not demonstrated. Week 78 reductions in both end points were statistically-significantly greater with semaglutide, 14 mg/d, vs sitagliptin.

Conclusions & Relevance: Among adults with type 2 diabetes uncontrolled with metformin with or without sulfonylurea, oral semaglutide, 7 mg/d and 14 mg/d, compared with sitagliptin, resulted in greater reductions in HbA1c over 26 weeks, but there was no benefit with the 3-mg/d dosage. Further research is needed to assess effectiveness in a clinical setting.

Trial Registration: ClinicalTrials.gov Identifier: NCT02607865.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611699/ The Pregnancy Pickle: Evolved Immune Compensation Due to Pregnancy Underlies Sex Differences in Human Diseases Heini Natri, Angela R. Garcia, Kenneth H. Buetow, Benjamin C. Trumble, Melissa A. Wilson 2019 2020-09-12 [("doi","10.1016/j.tig.2019.04.008")] genetics/selection/natural/human

We hypothesize that, ancestrally, sex-specific immune modulation evolved to facilitate survival of the pregnant person in the presence of an invasive placenta and an immunologically challenging pregnancy—an idea we term the ‘pregnancy compensation hypothesis’ (PCH).

Further, we propose that sex differences in immune function are mediated, at least in part, by the evolution of gene content and dosage on the sex chromosomes, and are regulated by reproductive hormones.

Finally, we propose that changes in reproductive ecology in industrialized environments exacerbate these evolved sex differences, resulting in the increasing risk of autoimmune disease observed in females, and a counteracting reduction in diseases such as cancer that can be combated by heightened immune surveillance.

The PCH generates a series of expectations that can be tested empirically and that may help to identify the mechanisms underlying sex differences in modern human diseases.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6699064/ Phenome-wide Burden of Copy-Number Variation in the UK Biobank Matthew Aguirre, Manuel A. Rivas, James Priest 2019 2020-09-12 [("doi","10.1016/j.ajhg.2019.07.001")] genetics/heritable/rare

Copy-number variations (CNVs) represent a proportion of the genetic differences between individuals and many CNVs associate causally with syndromic disease and clinical outcomes. Here, we characterize the landscape of copy-number variation and their phenome-wide effects in a sample of 472,228 array-genotyped individuals from the UK Biobank. In addition to population-level selection effects against genic loci conferring high mortality, we describe genetic burden from potentially pathogenic and previously uncharacterized CNV loci across more than 3,000 quantitative and dichotomous traits, with separate analyses for common and rare classes of variation.

Specifically, we highlight the effects of CNVs at two well-known syndromic loci 16p11.2 and 22q11.2, previously uncharacterized variation at 9p23, and several genic associations in the context of acute coronary artery disease and high body mass index. Our data constitute a deeply contextualized portrait of population-wide burden of copy-number variation, as well as a series of dosage-mediated genic associations across the medical phenome.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6789134/ Bacterial variability in the mammalian gut captured by a single-cell synthetic oscillator David T. Riglar, David L. Richmond, Laurent Potvin-Trottier, Andrew A. Verdegaal, Alexander D. Naydich, Somenath Bakshi, Emanuele Leoncini, Lorena G. Lyon, Johan Paulsson, Pamela A. Silver 2019 2020-09-12 [("doi","10.1038/s41467-019-12638-z")] genetics/microbiome

Synthetic gene oscillators have the potential to control timed functions and periodic gene expression in engineered cells. Such oscillators have been refined in bacteria in vitro, however, these systems have lacked the robustness and precision necessary for applications in complex in vivo environments, such as the mammalian gut.

Here, we demonstrate the implementation of a synthetic oscillator capable of keeping robust time in the mouse gut over periods of days. The oscillations provide a marker of bacterial growth at a single-cell level enabling quantification of bacterial dynamics in response to inflammation and underlying variations in the gut microbiota.

Our work directly detects increased bacterial growth heterogeneity during disease and differences between spatial niches in the gut, demonstrating the deployment of a precise engineered genetic oscillator in real-life settings.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6812578/ Relationship Foraging: Does time spent searching predict relationship length? Samantha E. Cohen, Peter M. Todd 2018 2020-09-12 [("doi","10.1037/ebs0000131")] statistics/survival-analysis

Animals foraging for resources often need to alternate between searching for and benefiting from patches of those resources.

Here we explore whether such patterns of behavior can usefully be applied to the human search for romantic relationships. Optimal foraging theory suggests that foragers should alter their time spent in patches based on how long they typically spend searching between patches.

We test whether human relationship search can be described as a foraging task that fits this OFT prediction. By analyzing a large, demographically representative dataset on marriage and cohabitation timing using survival analysis, we find that the likelihood of a relationship ending per unit time goes down with increased duration of search before that relationship, in accord with the foraging prediction.

We consider the possible applications and limits of a foraging perspective on mate search and suggest further directions for study.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6814527/ Acute Subjective and Behavioral Effects of Microdoses of Lysergic Acid Diethylamide in Healthy Human Volunteers Anya K. Bershad, Scott T. Schepers, Michael P. Bremmer, Royce Lee, Harriet de Wit 2019 2020-09-13 [("doi","10.1016/j.biopsych.2019.05.019")] nootropic/lsd psychedelic

Background: Numerous anecdotal reports suggest that repeated use of very low doses of lysergic acid diethylamide (LSD), known as microdosing, improves mood and cognitive function. These effects are consistent both with the known actions of LSD on serotonin receptors and with limited evidence that higher doses of LSD (100–200 μg) positively bias emotion processing. Yet, the effects of such sub-threshold doses of LSD have not been tested in a controlled laboratory setting. As a first step, we examined the effects of single very low doses of LSD (0–26 μg) on mood and behavior in healthy volunteers under double-blind conditions.

Method: Healthy young adults (n = 20) attended 4 laboratory sessions during which they received 0 (placebo), 6.5, 13, or 26 μg of LSD in randomized order at 1-week intervals. During expected peak drug effect, they completed mood questionnaires and behavioral tasks assessing emotion processing and cognition. Cardiovascular measures and body temperature were also assessed.

Results: LSD produced dose-related subjective effects across the 3 doses (6.5, 13, and 26 μg). At the highest dose, the drug also increased ratings of vigor and slightly decreased positivity ratings of images with positive emotional content. Other mood measures, cognition, and physiological measures were unaffected.

Conclusion: Single microdoses of LSD produced orderly dose-related subjective effects in healthy volunteers. These findings indicate that a threshold dose of 13 μg of LSD might be used safely in an investigation of repeated administrations. It remains to be determined whether the drug improves mood or cognition in individuals with symptoms of depression.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6849090/ The Human-Specific BOLA2 Duplication Modifies Iron Homeostasis and Anemia Predisposition in Chromosome 16p11.2 Autism Individuals Giuliana Giannuzzi, Paul J. Schmidt, Eleonora Porcu, Gilles Willemin, Katherine M. Munson, Xander Nuttle, Rachel Earl, Jacqueline Chrast, Kendra Hoekzema, Davide Risso, Katrin Männik, Pasquelena De Nittis, Ethan D. Baratz, Yann Herault, Xiang Gao, Caroline C. Philpott, Raphael A. Bernier, Zoltán Kutalik, Mark D. Fleming, Evan E. Eichler, Alexandre Reymond 2019 2020-09-13 [("doi","10.1016/j.ajhg.2019.09.023")] genetics/heritable/rare psychiatry/autism

Human-specific duplications at chromosome 16p11.2 mediate recurrent pathogenic 600 Kbp BP4-BP5 copy-number variations, which are among the most common genetic causes of autism. These copy-number polymorphic duplications are under positive selection and include 3 to 8 copies of BOLA2, a gene involved in the maturation of cytosolic iron-sulfur proteins.

To investigate the potential advantage provided by the rapid expansion of BOLA2, we assessed hematological traits and anemia prevalence in 379,385 controls and individuals who have lost or gained copies of BOLA2: 89 chromosome 16p11.2 BP4-BP5 deletion carriers and 56 reciprocal duplication carriers in the UK Biobank.

We found that the 16p11.2 deletion is associated with anemia (18⁄89 carriers, 20%, p = 4e-7, OR = 5), particularly iron-deficiency anemia. We observed similar enrichments in two clinical 16p11.2 deletion cohorts, which included 6⁄63 (10%) and 7⁄20 (35%) unrelated individuals with anemia, microcytosis, low serum iron, or low blood hemoglobin. Upon stratification by BOLA2 copy number, our data showed an association between low BOLA2 dosage and the above phenotypes (8⁄15 individuals with 3 copies, 53%, p = 1e-4).

In parallel, we analyzed hematological traits in mice carrying the 16p11.2 orthologous deletion or duplication, as well as Bola2± and Bola2-/- animals. The Bola2-deficient mice and the mice carrying the deletion showed early evidence of iron deficiency, including a mild decrease in hemoglobin, lower plasma iron, microcytosis, and an increased red blood cell zinc-protoporphyrin-to-heme ratio.

Our results indicate that BOLA2 participates in iron homeostasis in vivo, and its expansion has a potential adaptive role in protecting against iron deficiency.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6867811/ Predictability and Uncertainty in the Pleasure of Music: A Reward for Learning? Benjamin P. Gold, Marcus T. Pearce, Ernest Mas-Herrero, Alain Dagher, Robert J. Zatorre 2019 2020-09-13 [("doi","10.1523/JNEUROSCI.0428-19.2019")] cs/algorithm/information music psychology/novelty

Music ranks among the greatest human pleasures. It consistently engages the reward system, and converging evidence implies it exploits predictions to do so. Both prediction confirmations and errors are essential for understanding one’s environment, and music offers many of each as it manipulates interacting patterns across multiple timescales. Learning models suggest that a balance of these outcomes (ie. intermediate complexity) optimizes the reduction of uncertainty to rewarding and pleasurable effect. Yet evidence of a similar pattern in music is mixed, hampered by arbitrary measures of complexity.

In the present studies, we applied a well-validated information-theoretic model of auditory expectation to systematically measure two key aspects of musical complexity: predictability (operationalized as information content [IC]), and uncertainty (entropy). In Study 1, we evaluated how these properties affect musical preferences in 43 male and female participants; in Study 2, we replicated Study 1 in an independent sample of 27 people and assessed the contribution of veridical predictability by presenting the same stimuli seven times.

Both studies revealed quadratic effects of IC and entropy on liking that outperformed linear effects, indicating reliable preferences for music of intermediate complexity. An interaction between IC and entropy further suggested preferences for more predictability during more uncertain contexts, which would facilitate uncertainty reduction. Repeating stimuli decreased liking ratings but did not disrupt the preference for intermediate complexity.

Together, these findings support long-hypothesized optimal zones of predictability and uncertainty in musical pleasure with formal modeling, relating the pleasure of music listening to the intrinsic reward of learning.

Abstract pleasures, such as music, claim much of our time, energy, and money despite lacking any clear adaptive benefits like food or shelter. Yet as music manipulates patterns of melody, rhythm, and more, it proficiently exploits our expectations. Given the importance of anticipating and adapting to our ever-changing environments, making and evaluating uncertain predictions can have strong emotional effects.

Accordingly, we present evidence that listeners consistently prefer music of intermediate predictive complexity, and that preferences shift toward expected musical outcomes in more uncertain contexts.

These results are consistent with theories that emphasize the intrinsic reward of learning, both by updating inaccurate predictions and validating accurate ones, which is optimal in environments that present manageable predictive challenges (ie. reducible uncertainty).

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6989335/ The politics of zero-sum thinking: The relationship between political ideology and the belief that life is a zero-sum game Shai Davidai, Martino Ongis 2019 2020-09-13 [("doi","10.1126/sciadv.aay3761")] economics sociology

The tendency to see life as zero-sum exacerbates political conflicts. 6 studies (n = 3,223) examine the relationship between political ideology and zero-sum thinking: the belief that one party’s gains can only be obtained at the expense of another party’s losses.

We find that both liberals and conservatives view life as zero-sum when it benefits them to do so. Whereas conservatives exhibit zero-sum thinking when the status quo is challenged, liberals do so when the status quo is being upheld.

Consequently, conservatives view social inequalities—where the status quo is frequently challenged—as zero-sum, but liberals view economic inequalities—where the status quo has remained relatively unchallenged in past decades—as such.

Overall, these findings suggest potentially important ideological differences in perceptions of conflict—differences that are likely to have implications for understanding political divides in the United States and the difficulty of reaching bipartisan legislation.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7036065/ Safety, tolerability, pharmacokinetics, and pharmacodynamics of low dose lysergic acid diethylamide (LSD) in healthy older volunteers Neiloufar Family, Emeline L. Maillet, Luke T. J. Williams, Erwin Krediet, Robin Carhart-Harris, Tim M. Williams, Charles D. Nichols, Daniel J. Goble, Shlomi Raz 2020 2020-09-13 [("doi","10.1007/s00213-019-05417-7")] nootropic/lsd psychedelic psychiatry/alzheimers

Null: Research has shown that psychedelics, such as lysergic acid diethylamide (LSD), have profound anti-inflammatory properties mediated by 5-HT2A receptor signaling, supporting their evaluation as a therapeutic for neuroinflammation associated with neurodegenerative disease.

Objective: This study evaluated the safety, tolerability, pharmacokinetics, and pharmacodynamics of orally repeated administration of 5 μg, 10 μg, and 20 μg LSD in older healthy individuals. In the current paper, we present safety, tolerability, pharmacokinetics, and pharmacodynamic measures that relate to safety, tolerability, and dose response.

Method: This was a phase 1 double-blind, placebo-controlled, randomized study. Volunteers were randomly assigned to 1 of 4 dose groups (5 μg, 10 μg, 20 μg LSD, and placebo), and received their assigned dose on six occasions (ie. every 4 days).

Results: Forty-eight older healthy volunteers (mean age = 62.9 years) received placebo (n = 12), 5 μg (n = 12), 10 μg (n = 12), or 20 μg (n = 12) LSD. LSD plasma levels were undetectable for the 5 μg group and peak blood plasma levels for the 10 μg and 20 μg groups occurred at 30 min. LSD was well tolerated, and the frequency of adverse events was no higher than for placebo. Assessments of cognition, balance, and proprioception revealed no impairment.

Conclusion: Our results suggest safety and tolerability of orally administered 5 μg, 10 μg, and 20 μg LSD every fourth day over a 21-day period and support further clinical development of LSD for the treatment and prevention of Alzheimer’s disease (AD).

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7098426/ Large-scale de novo DNA synthesis: technologies and applications Sriram Kosuri, George M. Church 2014 2020-09-13 [("doi","10.1038/nmeth.2918")] genetics/genome-synthesis

For over 60 years, the synthetic production of new DNA sequences has helped researchers understand and engineer biology.

Here we summarize methods and caveats for the de novo synthesis of DNA, with particular emphasis on recent technologies that allow for large-scale and low-cost production.

In addition, we discuss emerging applications enabled by large-scale de novo DNA constructs, as well as the challenges and opportunities that lie ahead.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7232020/ Metabolic engineering of Saccharomyces cerevisiae for the de novo production of psilocybin and related tryptamine derivatives N. Milne, P. Thomsen, N. Mølgaard Knudsen, P. Rubaszka, M. Kristensen, I. Borodina 2020 2020-09-13 [("doi","10.1016/j.ymben.2019.12.007")] genetics/editing psychedelic

Psilocybin is a tryptamine-derived psychoactive alkaloid found mainly in the fungal genus Psilocybe, among others, and is the active ingredient in so-called “magic mushrooms”. Although its notoriety originates from its psychotropic properties and popular use as a recreational drug, clinical trials have recently recognized psilocybin as a promising candidate for the treatment of various psychological and neurological afflictions.

In this work, we demonstrate the de novo biosynthetic production of psilocybin and related tryptamine derivatives in Saccharomyces cerevisiae by expression of a heterologous biosynthesis pathway sourced from Psilocybe cubensis. Additionally, we achieve improved product titers by supplementing the pathway with a novel cytochrome P450 reductase from P. cubensis. Further rational engineering resulted in a final production strain producing 627 ± 140 mg/L of psilocybin and 580 ± 276 mg/L of the dephosphorylated degradation product psilocin in triplicate controlled fed-batch fermentations in minimal synthetic media. Pathway intermediates baeocystin, nor norbaeocystin as well the dephosphorylated baeocystin degradation product norpsilocin were also detected in strains engineered for psilocybin production.

We also demonstrate the biosynthetic production of natural tryptamine derivative aeruginascin as well as the production of a new-to-nature tryptamine derivative N-acetyl-4-hydroxytryptamine.

These results lay the foundation for the biotechnological production of psilocybin in a controlled environment for pharmaceutical applications, and provide a starting point for the biosynthetic production of other tryptamine derivatives of therapeutic relevance.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305407/ Deep learning methods in protein structure prediction Mirko Torrisi, Gianluca Pollastri, Quan Le 2020 2020-09-13 [("doi","10.1016/j.csbj.2019.12.011")] ai/nn/transformer/alphafold

Protein Structure Prediction is a central topic in Structural Bioinformatics. Since the ‘60s statistical methods, followed by increasingly complex Machine Learning and recently Deep Learning methods, have been employed to predict protein structural information at various levels of detail.

In this review, we briefly introduce the problem of protein structure prediction and essential elements of Deep Learning (such as Convolutional Neural Networks, Recurrent Neural Networks and basic feed-forward Neural Networks they are founded on), after which we discuss the evolution of predictive methods for one-dimensional and two-dimensional Protein Structure Annotations, from the simple statistical methods of the early days, to the computationally intensive highly-sophisticated Deep Learning algorithms of the last decade. In the process, we review the growth of the databases these algorithms are based on, and how this has impacted our ability to leverage knowledge about evolution and co-evolution to achieve improved predictions.

We conclude this review outlining the current role of Deep Learning techniques within the wider pipelines to predict protein structures and trying to anticipate what challenges and opportunities may arise next.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7311169/ Lack of evidence for associative learning in pea plants Kasey Markel 2020 2020-09-13 [("doi","10.7554/eLife.57614")] psychology/animal/maze psychology/neuroscience statistics/bias

Gagliano et al 2016 reported associative learning in pea plants. Associative learning has long been considered a behavior performed only by animals, making this claim particularly newsworthy and interesting. In the experiment, plants were trained in Y-shaped mazes for 3 days with fans and lights attached at the top of the maze. Training consisted of wind consistently preceding light from either the same or the opposite arm of the maze. When plant growth forced a decision between the two arms of the maze, fans alone were able to influence growth direction, whereas the growth direction of untrained plants was not affected by fans. However, a replication of their protocol failed to demonstrate the same result, calling for further verification and study before mainstream acceptance of this paradigm-shifting phenomenon; this replication attempt used a larger sample size and fully blinded analysis.

Associative learning is a simple learning ability found in most animals, which involves linking together two different cues. For example, the dogs in Pavlov’s famous experiment were trained to associate sound with the arrival of food, and eventually started salivating upon hearing the sound alone. Plants, like animals, are capable of complex behaviors. The snapping leaves of a Venus fly trap or the sun-tracking abilities of sunflowers are examples of instinctive responses to environmental cues that have evolved over many generations. Whether or not plants can learn during their lifetimes has remained unknown. A handful of studies have tested for associative learning in plants, the most convincing of which was published in 2016. In this study, pea plants were exposed to two signals: light, the plant version of dog food, and wind, equivalent to the sound in Pavlov’s experiment. Just as dogs salivate in response to food, plants instinctively grow towards light, whereas air flow does not affect the direction of growth. The plants were grown inside Y-shaped mazes and their ‘selection’ of one particular arm was used as a ‘read-out’ of learned behavior. The experiments trained growing plants by exposing them to wind and light from either the same direction or opposite directions. Once the plants were at the point of ‘choosing’ between the two arms, they were exposed to wind in the absence of light. Wind by itself appeared to influence the direction the trained plants took, with wind attracting plants trained with wind and light together and repelling plants trained with wind and light apart. Untrained plants remained unaffected, making random selections. These observations were interpreted as the strongest evidence of associative learning in plants and if true would have great scientific and philosophical importance. Kasey Markel therefore set out to confirm and expand on these findings by replicating the 2016 study. As many conditions as possible were kept identical, such as the training regime. The new experiments also used more plants and, most importantly, were done ‘blind’ meaning the people recording the data did not know how the plants had been trained. This ensured the expectations of the researcher would not influence the final results.

The new study found no evidence for associative learning, but did not rule it out altogether. This is because some experimental details in the first study remained unknown, such as the exact model of lights and fans originally used.

This work demonstrates the importance of replicating scientific experiments. In the future, Markel hopes their results will pave the way for further, rigorous testing of the hypothesis that plants can learn.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7405395/ Senolytic drugs: from discovery to translation J L. Kirkland, T. Tchkonia 2020 2020-09-13 [("doi","10.1111/joim.13141")] longevity/senolytic

Senolytics are a class of drugs that selectively clear senescent cells (SC). The first senolytic drugs Dasatinib, Quercetin, Fisetin and Navitoclax were discovered using a hypothesis-driven approach. SC accumulate with ageing and at causal sites of multiple chronic disorders, including diseases accounting for the bulk of morbidity, mortality and health expenditures. The most deleterious SC are resistant to apoptosis and have up-regulation of anti-apoptotic pathways which defend SC against their own inflammatory senescence-associated secretory phenotype (SASP), allowing them to survive, despite killing neighbouring cells. Senolytics transiently disable these SCAPs, causing apoptosis of those SC with a tissue-destructive SASP. Because SC take weeks to re-accumulate, senolytics can be administered intermittently—a ‘hit-and-run’ approach.

In preclinical models, senolytics delay, prevent or alleviate frailty, cancers and cardiovascular, neuropsychiatric, liver, kidney, musculoskeletal, lung, eye, haematological, metabolic and skin disorders as well as complications of organ transplantation, radiation and cancer treatment. As anticipated for agents targeting the fundamental ageing mechanisms that are ‘root cause’ contributors to multiple disorders, potential uses of senolytics are protean, potentially alleviating over 40 conditions in preclinical studies, opening a new route for treating age-related dysfunction and diseases.

Early pilot trials of senolytics suggest they decrease senescent cells, reduce inflammation and alleviate frailty in humans. Clinical trials for diabetes, idiopathic pulmonary fibrosis, Alzheimer’s disease, COVID-19, osteoarthritis, osteoporosis, eye diseases and bone marrow transplant and childhood cancer survivors are underway or beginning. Until such studies are done, it is too early for senolytics to be used outside of clinical trials.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431040/ Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies Samantha Joel, Paul W. Eastwick, Colleen J. Allison, Ximena B. Arriaga, Zachary G. Baker, Eran Bar-Kalifa, Sophie Bergeron, Gurit E. Birnbaum, Rebecca L. Brock, Claudia C. Brumbaugh, Cheryl L. Carmichael, Serena Chen, Jennifer Clarke, Rebecca J. Cobb, Michael K. Coolsen, Jody Davis, David C. de Jong, Anik Debrot, Eva C. DeHaas, Jaye L. Derrick, Jami Eller, Marie-Joelle Estrada, Ruddy Faure, Eli J. Finkel, R. Chris Fraley, Shelly L. Gable, Reuma Gadassi-Polack, Yuthika U. Girme, Amie M. Gordon, Courtney L. Gosnell, Matthew D. Hammond, Peggy A. Hannon, Cheryl Harasymchuk, Wilhelm Hofmann, Andrea B. Horn, Emily A. Impett, Jeremy P. Jamieson, Dacher Keltner, James J. Kim, Jeffrey L. Kirchner, Esther S. Kluwer, Madoka Kumashiro, Grace Larson, Gal Lazarus, Jill M. Logan, Laura B. Luchies, Geoff MacDonald, Laura V. Machia, Michael R. Maniaci, Jessica A. Maxwell, Moran Mizrahi, Amy Muise, Sylvia Niehuis, Brian G. Ogolsky, C. Rebecca Oldham, Nickola C. Overall, Meinrad Perrez, Brett J. Peters, Paula R. Pietromonaco, Sally I. Powers, Thery Prok, Rony Pshedetzky-Shochat, Eshkol Rafaeli, Erin L. Ramsdell, Maija Reblin, Michael Reicherts, Alan Reifman, Harry T. Reis, Galena K. Rhoades, William S. Rholes, Francesca Righetti, Lindsey M. Rodriguez, Ron Rogge, Natalie O. Rosen, Darby Saxbe, Haran Sened, Jeffry A. Simpson, Erica B. Slotter, Scott M. Stanley, Shevaun Stocker, Cathy Surra, Hagar Ter Kuile, Allison A. Vaughn, Amanda M. Vicary, Mariko L. Visserman, Scott Wolf 2020 2020-09-13 [("doi","10.1073/pnas.1917036117")] psychiatry/anxiety psychiatry/depression psychology/personality sociology

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (ie. Random Forests) to (1) quantify the extent to which relationship quality is predictable and (2) identify which constructs reliably predict relationship quality.

Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety.

Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (ie. own relationship-specific and individual-difference variables) predicted two to 4× more variance than partner-reported variables (ie. the partner’s ratings on those variables).

Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small.

Finally, relationship-quality change (ie. increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7468870/ Is Caloric Restriction Associated with Better Healthy Aging Outcomes? A Systematic Review and Meta-Analysis of Randomized Controlled Trials Silvia Caristia, Marta De Vito, Andrea Sarro, Alessio Leone, Alessandro Pecere, Angelica Zibetti, Nicoletta Filigheddu, Patrizia Zeppegno, Flavia Prodam, Fabrizio Faggiano, Paolo Marzullo 2020 2020-09-14 [("doi","10.3390/nu12082290")] longevity/fasting

Background: Global dietary patterns have gradually shifted toward a ‘western type’ with progressive increases in rates of metabolic imbalance. Recently, animal and human studies have revealed positive effects of caloric restriction (CR) on many health domains, giving new knowledge for prevention of ill and health promotion.

Methods: We conducted a systematic review (SR) of randomized controlled trials (RCTs) investigating the role of CR on health status in adults. A meta-analysis was performed on anthropometric, cardiovascular and metabolic outcomes.

Results: A total of 29 articles were retrieved including data from 8 RCTs. All included RCTs were at low risk for performance bias related to objective outcomes. Collectively, articles included 704 subjects. Among the 334 subjects subjected to CR, the compliance with the intervention appeared generally high. Meta-analyses proved benefit of CR on reduction of body weight, BMI, fat mass, total cholesterol, while a minor impact was shown for LDL, fasting glucose and insulin levels. No effect emerged for HDL and blood pressure after CR. Data were insufficient for other hormone variables in relation to meta-analysis of CR effects.

Conclusion: CR is a nutritional pattern linked to improved cardiometabolic status. However, evidence is limited on the multidimensional aspects of health and requires more studies of high quality to identify the precise impact of CR on health status and longevity.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7574913/ Adaptation of SARS-CoV-2 in BALB/c mice for testing vaccine efficacy Hongjing Gu, Qi Chen, Guan Yang, Lei He, Hang Fan, Yong-Qiang Deng, Yanxiao Wang, Yue Teng, Zhongpeng Zhao, Yujun Cui, Yuchang Li, Xiao-Feng Li, Jiangfan Li, Na-Na Zhang, Xiaolan Yang, Shaolong Chen, Yan Guo, Guangyu Zhao, Xiliang Wang, De-Yan Luo, Hui Wang, Xiao Yang, Yan Li, Gencheng Han, Yuxian He, Xiaojun Zhou, Shusheng Geng, Xiaoli Sheng, Shibo Jiang, Shihui Sun, Cheng-Feng Qin, Yusen Zhou 2020 2020-09-14 [("doi","10.1126/science.abc4730")] genetics/selection/artificial

The ongoing coronavirus disease 2019 (COVID-19) pandemic has prioritized the development of small-animal models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

We adapted a clinical isolate of SARS-CoV-2 by serial passaging in the respiratory tract of aged BALB/c mice. The resulting mouse-adapted strain at passage 6 (called MASCp6) showed increased infectivity in mouse lung and led to interstitial pneumonia and inflammatory responses in both young and aged mice after intranasal inoculation.

Deep sequencing revealed a panel of adaptive mutations potentially associated with the increased virulence. In particular, the N501Y mutation is located at the receptor-binding domain (RBD) of the spike protein.

The protective efficacy of a recombinant RBD vaccine candidate was validated by using this model.

Thus, this mouse-adapted strain and associated challenge model should be of value in evaluating vaccines and antivirals against SARS-CoV-2.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610517/ A method for genome-wide genealogy estimation for thousands of samples Leo Speidel, Marie Forest, Sinan Shi, Simon R. Myers 2019 2020-09-14 [("doi","10.1038/s41588-019-0484-x")] genetics/selection/natural/human

Knowledge of genome-wide genealogies for thousands of individuals would simplify most evolutionary analyses for humans and other species, but has remained computationally infeasible.

We have developed a method, Relate, scaling to >10,000 sequences while simultaneously estimating branch lengths, mutational ages and variable historical population sizes, as well as allowing for data errors. Application to 1,000 Genomes Project haplotypes produces joint genealogical histories for 26 human populations. Highly diverged lineages are present in all groups, but most frequent in Africa. Outside Africa, these mainly reflect ancient introgression from groups related to Neanderthals and Denisovans, while African signals instead reflect unknown events unique to that continent. Our approach allows more powerful inferences of natural selection than has previously been possible.

We identify multiple regions under strong positive selection, and multi-allelic traits including hair color, body mass index and blood pressure, showing strong evidence of directional selection, varying among human groups.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643046/ Effects of Psilocybin-Assisted Therapy on Major Depressive Disorder: A Randomized Clinical Trial Alan K. Davis, Frederick S. Barrett, Darrick G. May, Mary P. Cosimano, Nathan D. Sepeda, Matthew W. Johnson, Patrick H. Finan, Roland R. Griffiths 2021 2021 [("doi","10.1001/jamapsychiatry.2020.3285")] psychedelic psychiatry/depression

Importance: Major depressive disorder (MDD) is a substantial public health burden, but current treatments have limited effectiveness and adherence. Recent evidence suggests that 1 or 2 administrations of psilocybin with psychological support produces antidepressant effects in patients with cancer and in those with treatment-resistant depression.

Objective: To investigate the effect of psilocybin therapy in patients with MDD.

Design, Setting, & Participants: This randomized, waiting list-controlled clinical trial was conducted at the Center for Psychedelic and Consciousness Research at Johns Hopkins Bayview Medical Center in Baltimore, Maryland. Adults aged 21 to 75 years with an MDD diagnosis, not currently using antidepressant medications, and without histories of psychotic disorder, serious suicide attempt, or hospitalization were eligible to participate. Enrollment occurred between August 2017 and April 2019, and the 4-week primary outcome assessments were completed in July 2019. A total of 27 participants were randomized to an immediate treatment condition group (n = 15) or delayed treatment condition group (waiting list control condition; n = 12). Data analysis was conducted from July 1, 2019, to July 31, 2020, and included participants who completed the intervention (evaluable population).

Interventions: Two psilocybin sessions (session 1: 20 mg/70 kg; session 2: 30 mg/70 kg) were given (administered in opaque gelatin capsules with ~100 mL of water) in the context of supportive psychotherapy (~11 hours). Participants were randomized to begin treatment immediately or after an 8-week delay.

Main Outcomes & Measures: The primary outcome, depression severity was assessed with the GRID-Hamilton Depression Rating Scale (GRID-HAMD) scores at baseline (score of ≥17 required for enrollment) and weeks 5 and 8 after enrollment for the delayed treatment group, which corresponded to weeks 1 and 4 after the intervention for the immediate treatment group. Secondary outcomes included the Quick Inventory of Depressive Symptomatology-Self Rated (QIDS-SR).

Results: Of the randomized participants, 24 of 27 (89%) completed the intervention and the week 1 and week 4 post-session assessments. This population had a mean (SD) age of 39.8 (12.2) years, was composed of 16 women (67%), and had a mean (SD) baseline GRID-HAMD score of 22.8 (3.9). The mean (SD) GRID-HAMD scores at weeks 1 and 4 (8.0 [7.1] and 8.5 [5.7]) in the immediate treatment group were statistically-significantly lower than the scores at the comparable time points of weeks 5 and 8 (23.8 [5.4] and 23.5 [6.0]) in the delayed treatment group. The effect sizes were large at week 5 (Cohen d = 2.5; 95% CI, 1.4–3.5; p < 0.001) and week 8 (Cohen d = 2.6; 95% CI, 1.5–3.7; p < 0.001). The QIDS-SR documented a rapid decrease in mean (SD) depression score from baseline to day 1 after session 1 (16.7 [3.5] vs 6.3 [4.4]; Cohen d = 2.6; 95% CI, 1.8–3.5; p < 0.001), which remained statistically-significantly reduced through the week 4 follow-up (6.0 [5.7]; Cohen d = 2.3; 95% CI, 1.5–3.0; p < 0.001). In the overall sample, 17 participants (71%) at week 1 and 17 (71%) at week 4 had a clinically-significant response to the intervention (≥50% reduction in GRID-HAMD score), and 14 participants (58%) at week 1 and 13 participants (54%) at week 4 were in remission (≤7 GRID-HAMD score).

Conclusions & Relevance: Findings suggest that psilocybin with therapy is efficacious in treating MDD, thus extending the results of previous studies of this intervention in patients with cancer and depression and of a nonrandomized study in patients with treatment-resistant depression.

Trial Registration: ClinicalTrials.gov Identifier: NCT03181529.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7812635/ Interpersonal similarity of autistic traits predicts friendship quality Dimitris Bolis, Juha M. Lahnakoski, Daniela Seidel, Jeanette Tamm, Leonhard Schilbach 2021 2021 [("doi","10.1093/scan/nsaa147")] psychiatry/autism psychology/personality

Autistic traits are known to be associated with social interaction difficulties. Yet, somewhat paradoxically, relevant research has been typically restricted to studying individuals. In line with the ‘dialectical misattunement hypothesis’ and clinical insights of intact social interactions among autistic individuals, we hypothesized that friendship quality varies as a function of interpersonal similarity and more concretely the difference value of autistic traits in a dyad, above and beyond autistic traits per se. Therefore, in this study, we used self-report questionnaires to investigate these measures in a sample of 67 neurotypical dyads across a broad range of autistic traits.

Our results demonstrate that the more similar two persons are in autistic traits, the higher is the perceived quality of their friendship, irrespective of friendship duration, age, sex and, importantly, the (average of) autistic traits in a given dyad. More specifically, higher interpersonal similarity of autistic traits was associated with higher measures of closeness, acceptance and help. These results, therefore, lend support to the idea of an interactive turn in the study of social abilities across the autism spectrum and pave the way for future studies on the multiscale dynamics of social interactions.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7874389/ A non-hallucinogenic psychedelic analogue with therapeutic potential Lindsay P. Cameron, Robert J. Tombari, Ju Lu, Alexander J. Pell, Zefan Q. Hurley, Yann Ehinger, Maxemiliano V. Vargas, Matthew N. McCarroll, Jack C. Taylor, Douglas Myers-Turnbull, Taohui Liu, Bianca Yaghoobi, Lauren J. Laskowski, Emilie I. Anderson, Guoliang Zhang, Jayashri Viswanathan, Brandon M. Brown, Michelle Tjia, Lee E. Dunlap, Zachary T. Rabow, Oliver Fiehn, Heike Wulff, John D. McCorvy, Pamela J. Lein, David Kokel, Dorit Ron, Jamie Peters, Yi Zuo, David E. Olson 2021 2021 [("doi","10.1038/s41586-020-3008-z")] psychedelic psychiatry/alcoholism psychiatry/depression

The psychedelic alkaloid ibogaine has anti-addictive properties in both humans and animals1. Unlike most medications for the treatment of substance use disorders, anecdotal reports suggest that ibogaine has the potential to treat addiction to various substances, including opiates, alcohol and psychostimulants. The effects of ibogaine-like those of other psychedelic compounds-are long-lasting, which has been attributed to its ability to modify addiction-related neural circuitry through the activation of neurotrophic factor signaling. However, several safety concerns have hindered the clinical development of ibogaine, including its toxicity, hallucinogenic potential and tendency to induce cardiac arrhythmias.

Here we apply the principles of function-oriented synthesis to identify the key structural elements of the potential therapeutic pharmacophore of ibogaine, and we use this information to engineer tabernanthalog—a water-soluble, non-hallucinogenic, non-toxic analogue of ibogaine that can be prepared in a single step.

In rodents, tabernanthalog was found to promote structural neural plasticity, reduce alcohol-seeking and heroin-seeking behavior, and produce antidepressant-like effects.

This work demonstrates that, through careful chemical design, it is possible to modify a psychedelic compound to produce a safer, non-hallucinogenic variant that has therapeutic potential.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8074794/ Video game play is positively correlated with well-being Niklas Johannes, Matti Vuorre, Andrew K. Przybylski 2021 2021 [("doi","10.1098/rsos.202049")] sociology/technology

People have never played more video games, and many stakeholders are worried that this activity might be bad for players. So far, research has not had adequate data to test whether these worries are justified and if policymakers should act to regulate video game play time. We attempt to provide much-needed evidence with adequate data.

Whereas previous research had to rely on self-reported play behavior, we collaborated with two games companies, Electronic Arts and Nintendo of America, to obtain players’ actual play behavior. We surveyed players of Plants vs. Zombies: Battle for Neighborville and Animal Crossing: New Horizons for their well-being, motivations and need satisfaction during play, and merged their responses with telemetry data (ie. logged game play).

Contrary to many fears that excessive play time will lead to addiction and poor mental health, we found a small positive relation between game play and affective well-being. Need satisfaction and motivations during play did not interact with play time but were instead independently related to well-being.

Our results advance the field in two important ways. First, we show that collaborations with industry partners can be done to high academic standards in an ethical and transparent fashion. Second, we deliver much-needed evidence to policymakers on the link between play and mental health.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232039/ Shared heritability of human face and brain shape Sahin Naqvi, Yoeri Sleyp, Hanne Hoskens, Karlijne Indencleef, Jeffrey P. Spence, Rose Bruffaerts, Ahmed Radwan, Ryan J. Eller, Stephen Richmond, Mark D. Shriver, John R. Shaffer, Seth M. Weinberg, Susan Walsh, James Thompson, Jonathan K. Pritchard, Stefan Sunaert, Hilde Peeters, Joanna Wysocka, Peter Claes 2021 2021 [("doi","10.1038/s41588-021-00827-w")] genetics/heritable/correlation psychology/neuroscience

Evidence from model organisms and clinical genetics suggests coordination between the developing brain and face, but the role of this link in common genetic variation remains unknown. We performed a multivariate genome-wide association study of cortical surface morphology in 19,644 individuals of European ancestry, identifying 472 genomic loci influencing brain shape, of which 76 are also linked to face shape. Shared loci include transcription factors involved in craniofacial development, as well as members of signaling pathways implicated in brain-face cross-talk.

Brain shape heritability is equivalently enriched near regulatory regions active in either forebrain organoids or facial progenitors. However, we do not detect overlap between shared brain-face genome-wide association study signals and variants affecting behavioral-cognitive traits.

These results suggest that early in embryogenesis, the face and brain mutually shape each other through both structural effects and paracrine signaling, but this interplay may not impact later brain development associated with cognitive function.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8362794/ Associations of biogeographic ancestry with hypertension traits Jacob M. Keaton, Jacklyn N. Hellwege, Ayush Giri, Eric S. Torstenson, Csaba P. Kovesdy, Yan V. Sun, Peter W. F. Wilson, Christopher J. O’Donnell, Todd L. Edwards, Adriana M. Hung, Digna R. Velez Edwards 2021 2021 [("doi","10.1097/HJH.0000000000002701")] genetics/selection/natural/human

Objectives: Ethnic disparities in hypertension prevalence are well documented, though the influence of genetic ancestry is unclear. The aim of this study was to evaluate associations of geographic genetic ancestry with hypertension and underlying blood pressure traits.

Method: We tested genetically inferred ancestry proportions from five 1,000 Genomes reference populations (GBR, PEL, YRI, CHB, and LWK) for association with four continuous blood pressure (BP) traits (SBP, DBP, PP, MAP) and the dichotomous outcomes hypertension and apparent treatment-resistant hypertension in 220,495 European American, 59 927 African American, and 21,273 Hispanic American individuals from the Million Veteran Program. Ethnicity stratified results were meta-analyzed to report effect estimates per 10% difference for a given ancestry proportion in all samples.

Results: Percentage GBR was negatively associated with BP (p = 2.13 × 10–19, 7.92 × 10–8, 4.41 × 10–11, and 3.57 × 10–13 for SBP, DBP, PP, and MAP, respectively; coefficient range −0.10 to −0.21 mmHg per 10% increase in ancestry proportion) and was protective against hypertension [P = 2.59 × 10–5, odds ratio (OR) = 0.98] relative to other ancestries. YRI percentage was positively associated with BP (p = 1.63 × 10–23, 1.94 × 10–26, 0.012, and 3.26 × 10–29 for SBP, DBP, PP, and MAP, respectively; coefficient range 0.06–0.32 mmHg per 10% increase in ancestry proportion) and was positively associated with hypertension risk (p = 3.10 × 10–11, OR = 1.04) and apparent treatment-resistant hypertension risk (p = 1.86 × 10–4, OR = 1.04) compared with other ancestries. Percentage PEL was inversely associated with DBP (p = 2.84 × 10–5, beta = −0.11 mmHg per 10% increase in ancestry proportion).

Conclusion: These results demonstrate that risk for BP traits varies by genetic ancestry. Our findings provide insight into the geographic origin of genetic factors underlying hypertension risk and establish that a portion of BP trait ethnic disparities are because of genetic differences between ancestries.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8482304/ The Genetic Architecture of Depression in Individuals of East Asian Ancestry: A Genome-Wide Association Study Olga Giannakopoulou, Kuang Lin, Xiangrui Meng, Mei-Hsin Su, Po-Hsiu Kuo, Roseann E. Peterson, Swapnil Awasthi, Arden Moscati, Jonathan R. I. Coleman, Nick Bass, Iona Y. Millwood, Yiping Chen, Zhengming Chen, Hsi-Chung Chen, Mong-Liang Lu, Ming-Chyi Huang, Chun-Hsin Chen, Eli Ayumi Stahl, Ruth Loos, Niamh Mullins, Robert J. Ursano, Ronald C. Kessler, Murray B. Stein, Srijan Sen, Laura J. Scott, Margit Burmeister, Yu Fang, Jess Tyrrell, Yunxuan Jiang, Chao Tian, Andrew M. McIntosh, Stephan Ripke, Erin C. Dunn, Kenneth S. Kendler, Robin G. Walters, Cathryn M. Lewis, Karoline Kuchenbaecker 2021 2021 [("doi","10.1001/jamapsychiatry.2021.2099")] genetics/heritable psychiatry/depression

Importance: Most previous genome-wide association studies (GWAS) of depression have used data from individuals of European descent. This limits the understanding of the underlying biology of depression and raises questions about the transferability of findings between populations.

Objective: To investigate the genetics of depression among individuals of East Asian and European descent living in different geographic locations, and with different outcome definitions for depression.

Design, Setting, & Participants: Genome-wide association analyses followed by meta-analysis, which included data from 9 cohort and case-control data sets comprising individuals with depression and control individuals of East Asian descent. This study was conducted between January 2019 and May 2021.

Exposures: Associations of genetic variants with depression risk were assessed using generalized linear mixed models and logistic regression. The results were combined across studies using fixed-effects meta-analyses. These were subsequently also meta-analyzed with the largest published GWAS for depression among individuals of European descent. Additional meta-analyses were carried out separately by outcome definition (clinical depression vs symptom-based depression) and region (East Asian countries vs Western countries) for East Asian ancestry cohorts.

Main Outcomes & Measures: Depression status was defined based on health records and self-report questionnaires.

Results: There were a total of 194 548 study participants (approximate mean age, 51.3 years; 62.8% women). Participants included 15 771 individuals with depression and 178 777 control individuals of East Asian descent. Five novel associations were identified, including 1 in the meta-analysis for broad depression among those of East Asian descent: rs4656484 (β = −0.018, SE = 0.003, p = 4.43×10–8) at 1q24.1. Another locus at 7p21.2 was associated in a meta-analysis restricted to geographically East Asian studies (β = 0.028, SE = 0.005, p = 6.48×10–9 for rs10240457). The lead variants of these 2 novel loci were not associated with depression risk in European ancestry cohorts (β = −0.003, SE = 0.005, p = 0.53 for rs4656484 and β = −0.005, SE = 0.004, p = 0.28 for rs10240457). Only 11% of depression loci previously identified in individuals of European descent reached nominal statistical-significance levels in the individuals of East Asian descent. The transancestry genetic correlation between cohorts of East Asian and European descent for clinical depression was r = 0.413 (SE = 0.159). Clinical depression risk was negatively genetically correlated with body mass index in individuals of East Asian descent (r = −0.212, SE = 0.084), contrary to findings for individuals of European descent.

Conclusions & Relevance: These results support caution against generalizing findings about depression risk factors across populations and highlight the need to increase the ancestral and geographic diversity of samples with consistent phenotyping.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588667/ Does the combination of resistance training and a nutritional intervention have a synergistic effect on muscle mass, strength, and physical function in older adults? A systematic review and meta-analysis MoonKi Choi, Hayeon Kim, Juyeon Bae 2021 2021 [("doi","10.1186/s12877-021-02491-5")] creatine exercise

Background: Health-promoting interventions are important for preventing frailty and sarcopenia in older adults. However, there is limited evidence that nutritional interventions yield additional effects when combined with resistance training. This systematic review and meta-analysis aimed to compare the effectiveness of nutritional interventions with resistance training and that of resistance training alone.

Method: Randomized controlled trials published in peer-reviewed journals prior to July 2020 were retrieved from databases and other sources. The articles were screened according to the inclusion and exclusion criteria. The methodological quality of the included studies was assessed using Cochrane’s risk of bias tool 2. A meta-analysis was performed using the RevMan 5.4 program and STATA 16 program.

Results: A total of 22 studies were included in the meta-analysis. The results of the meta-analysis showed no statistically-significant differences between groups in muscle mass, muscle strength, or physical functional performance. In the subgroup analysis regarding the types of nutritional interventions, creatine showed statistically-significant effects on lean body mass (n = 4, MD 2.61, 95% CI 0.51 to 4.72). Regarding the other subgroup analyses, there were no statistically-significant differences in appendicular skeletal muscle mass (p = 0.43), hand grip strength (p = 0.73), knee extension strength (p = 0.09), chair stand test results (p = 0.31), or timed up-and-go test results (p = 0.31). In the meta-regression, moderators such as the mean age of subjects and duration of interventions were not associated with outcome variables.

Conclusion: This meta-analysis showed that nutritional interventions with resistance training have no additional effect on body composition, muscle strength, or physical function. Only creatine showed synergistic effects with resistance training on muscle mass.

Trial Registration: CRD42021224843 .

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596853/ Exome sequencing and analysis of 454,787 UK Biobank participants Joshua D. Backman, Alexander H. Li, Anthony Marcketta, Dylan Sun, Joelle Mbatchou, Michael D. Kessler, Christian Benner, Daren Liu, Adam E. Locke, Suganthi Balasubramanian, Ashish Yadav, Nilanjana Banerjee, Christopher E. Gillies, Amy Damask, Simon Liu, Xiaodong Bai, Alicia Hawes, Evan Maxwell, Lauren Gurski, Kyoko Watanabe, Jack A. Kosmicki, Veera Rajagopal, Jason Mighty, Marcus Jones, Lyndon Mitnaul, Eli Ayumi Stahl, Giovanni Coppola, Eric Jorgenson, Lukas Habegger, William J. Salerno, Alan R. Shuldiner, Luca A. Lotta, John D. Overton, Michael N. Cantor, Jeffrey G. Reid, George Yancopoulos, Hyun M. Kang, Jonathan Marchini, Aris Baras, Gonçalo R. Abecasis, Manuel A. R. Ferreira 2021 2021 [("doi","10.1038/s41586-021-04103-z")] genetics/heritable/rare

A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study. We identified 12 million coding variants, including around 1 million loss-of-function and around 1.8 million deleterious missense variants.

When these were tested for association with 3,994 health-related traits, we found 564 genes with trait associations at p ≤ 2.18 × 10−11. Rare variant associations were enriched in loci from genome-wide association studies (GWAS), but most (91%) were independent of common variant signals. We discovered several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as risk-lowering associations for hypertension (SLC9A3R2), diabetes (MAP3K15, FAM234A) and asthma (SLC27A3). 6 genes were associated with brain imaging phenotypes, including two involved in neural development (GBE1, PLD1).

Of the signals available and powered for replication in an independent cohort, 81% were confirmed; furthermore, association signals were generally consistent across individuals of European, Asian and African ancestry.

We illustrate the ability of exome sequencing to identify gene-trait associations, elucidate gene function and pinpoint effector genes that underlie GWAS signals at scale.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8603651/ Who Needs the Dark Web? Exploring the Trade in Critically Endangered Plants on eBay Robert Todd Perdue 2021 2021 [("doi","10.1007/s12103-021-09658-1")] darknet-market

Stemming the illegal trade of endangered species is a critical and very difficult challenge for conservationists and law enforcement. Much effort is given to stopping the trade of “charismatic megafauna” such as tigers, elephants, and rhinoceroses. Endangered plant species, however, receive far less attention and fewer resources, resulting in devastating consequences. Plant species continue to go extinct due to illegal harvesting and selling, while just one order of plants, Orchidales, makes up more than 70% of all threatened wildlife species.

This study examines the role the Internet plays in critically endangered plant transactions. Rather than focusing on the dark web for these sales, I search the e-commerce site eBay to better understand the extent to which these trades take place in plain sight. Of the 193 critically endangered plant species examined, 56 were for sale in some form on eBay during the study period.

These results indicate a high degree of trading in these species, but do not necessarily indicate criminality. The complexity of the international legal frameworks regulating these transactions makes it difficult to ascertain their legality, but certain indicators point to at least a subset of these sales being unlawful.

E-commerce sites like eBay must take more proactive measures to regulate sales and protect these species on the brink, for it is clear the surface web is playing an understudied and important role in fostering these cybercrimes.

In sum, the dark web is unnecessary when the surface web is convenient, widely available, and scarcely policed.

--- https://www.newscientist.com/article/2280153-a-single-honeybee-has-cloned-itself-hundreds-of-millions-of-times/ A single honeybee has cloned itself hundreds of millions of times 2020-09-15 genetics/cloning --- https://www.newscientist.com/article/dn14249-interview-its-a-dogs-life-again/ Interview: It’s a dog’s life... again Aldhous 2008 2020-09-15 genetics/cloning/dog --- https://web.archive.org/web/20191122011001/https://www.newsrepublican.com/news/20191026/des-moines-county-sherifff-office-working-to-launch-k-9-program Des Moines County Sheriff's office working to launch K-9 program 2020-09-15 genetics/cloning --- https://www.newyorker.com/business/currency/delusional-confidence-a-report-from-the-marijuana-investor-summit Delusional Confidence? A Report from the Marijuana Investor Summit 2020-09-15 marijuana --- https://www.newyorker.com/culture/annals-of-inquiry/ketamine-therapy-is-going-mainstream-are-we-ready Ketamine Therapy Is Going Mainstream. Are We Ready? 2020-09-15 psychedelic --- https://www.newyorker.com/culture/personal-history/my-brother-toms-schizophrenia My Brother Tom’s Schizophrenia 2020-09-15 psychiatry/schizophrenia --- https://www.newyorker.com/culture/tables-for-two/an-underground-marijuana-dinner-party-grows-in-new-york A Marijuana Dinner Party Grows Underground 2020-09-15 marijuana --- https://www.newyorker.com/magazine/2009/10/19/scratch-and-sniff Scratch And Sniff 2020-09-15 psychology/smell --- https://www.newyorker.com/magazine/2009/11/23/the-taste-makers The Taste Makers 2020-09-16 psychology/smell --- https://www.newyorker.com/magazine/2010/04/12/the-pink-panthers The Pink Panthers 2020-09-16 crime --- https://www.newyorker.com/magazine/2011/09/05/how-to-be-good How To Be Good 2020-09-16 philosophy/ethics --- https://www.newyorker.com/magazine/2012/07/23/the-strongest-man-in-the-world The Strongest Man in the World 2020-09-16 exercise --- https://www.newyorker.com/magazine/2013/06/24/last-call-3 Last Call 2020-09-16 japan philosophy/ethics psychiatry --- https://www.newyorker.com/magazine/2013/09/09/man-and-superman How Bad Is Doping, Really? 2020-09-16 genetics/heritable/rare --- https://www.newyorker.com/magazine/2014/07/21/one-of-a-kind-2 Fighting a One-of-a-Kind Disease 2020-09-16 genetics/heritable/rare --- https://www.newyorker.com/magazine/2014/12/08/ride-lives The Ride of Their Lives: Children prepare for the world’s most dangerous organized sport 2020-09-16 genetics/cloning genetics/selection psychology/animal --- https://www.newyorker.com/magazine/2015/02/09/trip-treatment The Trip Treatment 2020-09-16 psychedelic --- https://www.newyorker.com/magazine/2015/04/27/the-catastrophe-oliver-sacks Spalding Gray’s Catastrophe 2020-09-16 philosophy/ethics psychiatry/traumatic-brain-injury psychology/neuroscience --- https://www.newyorker.com/magazine/2015/11/23/the-trip-planners A Field Guide to Psychedelics 2020-09-16 psychedelic --- https://www.newyorker.com/magazine/2017/01/16/killing-animals-at-the-zoo Killing Animals at the Zoo 2020-09-17 philosophy/ethics --- https://www.newyorker.com/magazine/2017/05/15/inside-the-bong-show Inside the Bong Show 2020-09-17 marijuana --- https://www.newyorker.com/magazine/2017/08/21/how-driscolls-reinvented-the-strawberry How Driscoll’s Reinvented the Strawberry 2020-09-17 economics food genetics/selection --- https://www.newyorker.com/magazine/2017/11/20/can-carbon-dioxide-removal-save-the-world Can Carbon-Dioxide Removal Save the World? 2020-09-17 technology/carbon-capture --- https://www.newyorker.com/magazine/2018/10/08/the-comforting-fictions-of-dementia-care The Comforting Fictions of Dementia Care 2020-09-17 philosophy/ethics psychology/neuroscience/memory sociology --- https://www.newyorker.com/magazine/2019/01/14/is-marijuana-as-safe-as-we-think Is Marijuana as Safe as We Think? 2020-09-17 marijuana --- https://www.newyorker.com/magazine/2019/11/04/suzy-batizs-empire-of-odor Suzy Batiz’s Empire of Odor 2020-09-17 economics psychology/smell --- https://www.newyorker.com/magazine/2019/11/25/can-babies-learn-to-love-vegetables Can Babies Learn to Love Vegetables? 2020-09-17 psychology/smell --- https://www.newyorker.com/magazine/2021/11/29/how-the-worlds-foremost-maze-maker-leads-people-astray How the World’s Foremost Maze-Maker Leads People Astray 2020-09-17 design --- https://www.newyorker.com/magazine/2021/12/06/the-science-of-mind-reading The Science of Mind Reading 2020-09-17 psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning --- https://www.newyorker.com/magazine/2021/12/13/creating-a-better-leaf Creating a Better Leaf 2020-09-18 genetics/editing --- https://www.newyorker.com/magazine/2021/12/13/half-a-billion-in-bitcoin-lost-in-the-dump Half a Billion in Bitcoin, Lost in the Dump 2020-09-18 bitcoin --- https://www.newyorker.com/news/dispatch/the-strange-story-of-dagobert-the-ducktales-bandit The Strange Story of Dagobert, the ‘DuckTales’ Bandit 2020-09-18 crime fiction --- https://www.newyorker.com/news/news-desk/how-seniors-joined-the-cannabis-craze How Seniors Joined the Cannabis Craze 2020-09-18 marijuana --- https://www.newyorker.com/news/news-desk/the-ethics-of-bloodless-medicine The Ethics of Bloodless Medicine 2020-09-18 philosophy/ethics --- https://www.newyorker.com/science/maria-konnikova/a-gene-makes-you-need-less-sleep A Gene That Makes You Need Less Sleep? 2020-09-18 genetics/editing genetics/heritable/rare --- https://www.newyorker.com/science/maria-konnikova/practice-doesnt-make-perfect Practice Doesn’t Make Perfect 2020-09-18 genetics/heritable/rare --- https://www.newyorker.com/tech/annals-of-technology/breast-feeding-the-microbiome Breast-Feeding the Microbiome 2020-09-18 genetics/microbiome --- https://www.nobelprize.org/prizes/medicine/2012/yamanaka/biographical/ Shinya Yamanaka – Biographical 2020-09-18 genetics/gametogenesis longevity --- https://www.novo-pi.com/rybelsus.pdf 2020-09-18 longevity/glp/semaglutide --- https://www.npr.org/sections/13.7/2014/04/03/297853835/the-joys-and-ethics-of-insect-eating The Joys And Ethics Of Insect Eating 2020-09-18 philosophy/ethics --- https://www.npr.org/sections/health-shots/2014/10/29/359818102/scientists-implicate-more-than-100-genes-in-causing-autism/ Scientists Implicate More Than 100 Genes In Causing Autism 2020-09-19 genetics/heritable/rare psychiatry/autism --- https://www.npr.org/sections/money/2020/08/11/900895704/secret-gyms-and-the-economics-of-prohibition Secret Gyms And The Economics Of Prohibition 2020-09-19 exercise --- https://www.nytimes.com/1998/08/27/us/owners-give-2.3-million-to-clone-dog.html Owners Give $2.3 Million To Clone Dog 2020-09-19 genetics/cloning/dog --- https://www.nytimes.com/2000/10/15/magazine/the-war-on-stink.html The War on Stink 2020-09-19 psychology/smell --- https://www.nytimes.com/2008/02/24/world/asia/24kimchi.html Starship Kimchi: A Bold Taste Goes Where It Has Never Gone Before 2020-09-19 psychology/smell --- https://www.nytimes.com/2010/04/12/science/12psychedelics.html Hallucinogens Have Doctors Tuning In Again 2020-09-19 psychedelic --- https://www.nytimes.com/2010/07/11/magazine/11cryonics-t.html Until Cryonics Do Us Part 2020-09-19 cryonics --- https://www.nytimes.com/2012/04/22/magazine/how-psychedelic-drugs-can-help-patients-face-death.html How Psychedelic Drugs Can Help Patients Face Death 2020-09-19 psychedelic --- https://www.nytimes.com/2012/06/17/magazine/how-a-mexican-drug-cartel-makes-its-billions.html How a Mexican Drug Cartel Makes Its Billions 2020-09-19 marijuana --- https://www.nytimes.com/2013/02/17/magazine/napoleon-chagnon-americas-most-controversial-anthropologist.html How Napoleon Chagnon Became Our Most Controversial Anthropologist 2020-09-19 sociology/preference-falsification --- https://www.nytimes.com/2014/04/20/opinion/sunday/being-good-isnt-the-only-way-to-go.html 2020-09-19 philosophy/ethics --- https://www.nytimes.com/2014/05/18/magazine/the-bud-light-ification-of-bud.html The Bud Light-ification of Bud 2020-09-20 marijuana --- https://www.nytimes.com/2015/01/11/magazine/sebastian-seungs-quest-to-map-the-human-brain.html Sebastian Seung’s Quest to Map the Human Brain 2020-09-20 cryonics --- https://www.nytimes.com/2015/06/16/us/medical-use-of-marijuana-doesnt-increase-youths-use-study-finds.html Medical Use of Marijuana Doesn’t Increase Youths’ Use, Study Finds 2020-09-20 marijuana --- https://www.nytimes.com/2015/07/21/upshot/is-there-anything-actually-medical-about-medical-marijuana.html How ‘Medical’ Is Marijuana? 2020-09-20 marijuana --- https://www.nytimes.com/2015/09/15/science/newly-risen-from-yeast-thc.html Newly Risen From Yeast: THC 2020-09-20 marijuana --- https://www.nytimes.com/2015/10/25/magazine/the-strange-case-of-anna-stubblefield.html She told the family of a severely disabled man that she could help him to communicate with the outside world. The relationship that followed would lead to a criminal trial. 2020-09-20 philosophy/ethics --- https://www.nytimes.com/2016/03/27/magazine/should-parents-of-severely-disabled-children-be-allowed-to-stop-their-growth.html Should Parents of Children With Severe Disabilities Be Allowed to Stop Their Growth? 2020-09-20 philosophy/ethics --- https://www.nytimes.com/2016/06/03/science/human-genome-project-write-synthetic-dna.html Scientists Announce HGP-Write, Project to Synthesize the Human Genome 2020-09-20 genetics/genome-synthesis --- https://www.nytimes.com/2016/10/09/fashion/pets-medical-marijuana-dogs-cats.html Pets on Pot: The Newest Customer Base for Medical Marijuana 2020-09-20 marijuana --- https://www.nytimes.com/2016/11/27/technology/artificial-intelligence-pioneer-jurgen-schmidhuber-overlooked.html When A.I. Matures, It May Call Jürgen Schmidhuber ‘Dad’ 2020-09-20 psychology/novelty --- https://www.nytimes.com/2017/01/03/well/move/is-your-workout-not-working-maybe-youre-a-non-responder.html Is Your Workout Not Working? Maybe You’re a Non-Responder 2020-09-21 exercise --- https://www.nytimes.com/2017/02/14/health/human-gene-editing-panel.html Human Gene Editing Receives Science Panel’s Support 2020-09-21 philosophy/ethics --- https://www.nytimes.com/2017/05/01/health/artificial-nose-scent-disease.html One Day, a Machine Will Smell Whether You’re Sick 2020-09-21 psychology/smell --- https://www.nytimes.com/2017/05/06/style/psychedelic-drug-resurgence-daily-life.html Molly at the Marriott: Inside America’s Premier Psychedelics Conference 2020-09-21 psychedelic --- https://www.nytimes.com/2017/05/12/us/nicholas-sand-chemist-who-sought-to-bring-lsd-to-the-world-dies-at-75.html Nicholas Sand, Chemist Who Sought to Bring LSD to the World, Dies at 75 2020-09-21 psychedelic --- https://www.nytimes.com/2017/07/14/opinion/sunday/alzheimers-cure-south-america.html 2020-09-21 genetics/heritable/rare psychiatry/alzheimers --- https://www.nytimes.com/2017/10/31/health/biggest-losers-weight-loss.html A Lesson From the Biggest Losers: Exercise Keeps Off the Weight 2020-09-21 exercise --- https://www.nytimes.com/2018/01/03/magazine/subway-new-york-city-public-transportation-wealth-inequality.html The Case for the Subway 2020-09-21 economics/georgism --- https://www.nytimes.com/2018/02/07/well/move/lift-weights-eat-more-protein-especially-if-youre-over-40.html Lift Weights, Eat More Protein, Especially if You’re Over 40 2020-09-21 exercise --- https://www.nytimes.com/2018/03/15/health/genetic-mutations-diagnosis.html What's Behind Many Mystery Ailments? Genetic Mutations, Study Finds 2020-09-21 genetics/heritable/rare --- https://www.nytimes.com/2018/03/19/health/nih-biobank-genes.html The Struggle to Build a Massive ‘Biobank’ of Patient Data 2020-09-21 philosophy/ethics --- https://www.nytimes.com/2018/04/05/well/a-perplexing-marijuana-side-effect-relieved-by-hot-showers.html A Perplexing Marijuana Side Effect Relieved by Hot Showers 2020-09-22 marijuana --- https://www.nytimes.com/2018/04/20/well/move/runners-high-marijuana-pot-sports-exercise-weed.html Turning to Marijuana for a Runners’ High and More 2020-09-22 marijuana --- https://www.nytimes.com/2018/04/25/well/move/how-strenuous-exercise-affects-our-immune-system.html How Strenuous Exercise Affects Our Immune System 2020-09-22 exercise --- https://www.nytimes.com/2018/09/04/health/synthetic-biology-pku.html By manipulating DNA, researchers are trying to create microbes that, once ingested, work to treat a rare genetic condition—a milestone in synthetic biology 2020-09-22 genetics/microbiome --- https://www.nytimes.com/2018/09/21/opinion/the-dangers-of-dna-testing.html 2020-09-22 crime --- https://www.nytimes.com/2018/09/29/opinion/sunday/schizophrenia-psychiatric-disorders-immune-system.html 2020-09-22 psychiatry/schizophrenia --- https://www.nytimes.com/2018/12/18/well/eat/is-there-an-optimal-diet-for-humans.html Is There an Optimal Diet for Humans? 2020-09-22 exercise --- https://www.nytimes.com/2019/02/12/magazine/climeworks-business-climate-change.html The Tiny Swiss Company That Thinks It Can Help Stop Climate Change 2020-09-22 technology/carbon-capture --- https://www.nytimes.com/2019/04/07/business/energy-environment/climate-change-carbon-engineering.html Blamed for Climate Change, Oil Companies Invest in Carbon Removal 2020-09-22 technology/carbon-capture --- https://www.nytimes.com/2019/05/15/science/synthetic-genome-bacteria.html Scientists Created Bacteria With a Synthetic Genome. Is This Artificial Life? 2020-09-22 genetics/genome-synthesis --- https://www.nytimes.com/2019/07/03/well/move/why-so-many-of-us-dont-lose-weight-when-we-exercise.html Why So Many of Us Don’t Lose Weight When We Exercise 2020-09-22 exercise --- https://www.nytimes.com/2019/09/04/business/china-cat-clone.html His Cat's Death Left Him Heartbroken. So He Cloned It. China's first duplicate cat marks the country's emergence in gene research and its entry in a potentially lucrative and unregulated market for cloning pets. 2020-09-23 genetics/cloning --- https://www.nytimes.com/2019/09/04/science/psychedelic-drugs-hopkins-depression.html Johns Hopkins Opens New Center for Psychedelic Research 2020-09-23 psychedelic --- https://www.nytimes.com/2019/09/06/health/ferriss-psychedelic-drugs-depression.html Tim Ferriss, the Man Who Put His Money Behind Psychedelic Medicine 2020-09-23 psychedelic --- https://www.nytimes.com/2020/02/17/science/worst-odor-smell-thioacetone.html What’s the World’s Worst Smell? 2020-09-23 psychology/smell --- https://www.nytimes.com/2020/04/03/books/review/hidden-valley-road-robert-kolker.html Good Looks Ran in the Family. So Did Schizophrenia. 2020-09-23 psychiatry/schizophrenia --- https://www.nytimes.com/2020/06/24/climate/carbon-capture-tax-break.html Projects to Stash Carbon Dioxide Underground Get a Boost 2020-09-23 technology/carbon-capture --- https://www.nytimes.com/2020/12/04/us/politics/house-marijuana.html House Passes Landmark Bill Decriminalizing Marijuana 2020-09-23 marijuana --- https://www.nytimes.com/2021/06/26/style/cryonics-freezing-bodies.html Cryonics During the Pandemic 2020-09-23 cryonics --- https://www.nytimes.com/2021/07/21/well/move/weight-training-fat.html How Weight Training Burns Fat 2020-09-23 exercise --- https://www.nytimes.com/2021/07/22/technology/deepmind-ai-proteins-folding.html A.I. Predicts the Shapes of Molecules to Come 2020-09-23 ai/nn/transformer/alphafold --- https://www.nytimes.com/2021/09/09/technology/codex-artificial-intelligence-coding.html A.I. Can Now Write Its Own Computer Code. That’s Good News for Humans. 2020-09-23 ai/nn/transformer/gpt/codex --- https://www.nytimes.com/2021/10/12/health/aspirin-heart-attack-stroke.html Aspirin Use to Prevent 1st Heart Attack or Stroke Should Be Curtailed 2020-09-24 longevity/aspirin --- https://www.nytimes.com/2021/10/31/climate/is-carbon-capture-here.html Is Carbon Capture Here? 2020-09-24 technology/carbon-capture --- https://www.nytimes.com/2021/11/23/business/dealbook/synthetic-biology-drew-endy.html Can Synthetic Biology Save Us? This Scientist Thinks So. 2020-09-24 genetics/genome-synthesis --- https://www.nytimes.com/2021/11/27/health/diabetes-cure-stem-cells.html A Cure for Type 1 Diabetes? For One Man, It Seems to Have Worked. 2020-09-24 genetics/gametogenesis --- https://www.nytimes.com/2021/11/29/health/cells-bar-coding-cancer.html Stamping Bar Codes on Cells to Solve Medical Mysteries 2020-09-24 genetics/selection --- https://www.nytimes.com/2021/12/07/magazine/tv-for-cats.html I Can’t Give My Cat the Perfect Life. ‘TV for Cats’ Gives Her a Taste. 2020-09-24 cat/psychology --- https://www.nytimes.com/2021/12/15/well/move/exercise-weight-loss-metabolism.html How Exercise Affects Metabolism and Weight Loss 2020-09-24 exercise --- https://www.nytimes.com/2021/12/24/health/james-f-fries-dead.html James F. Fries, Who Studied the Good Life and How to Live It, Dies at 83 2020-09-24 longevity --- https://www.nytimes.com/2021/12/29/us/oklahoma-marijuana-boom.html How Oklahoma Became a Marijuana Boom State 2020-09-24 marijuana --- https://www.nytimes.com/2022/01/10/science/european-royals-letterlocking.html How European Royals Once Shared Their Most Important Secrets 2020-09-24 history/public-domain-review technology --- https://www.nytimes.com/2022/01/13/climate/nasal-ranger-chuck-mcginley.html Sometimes, Life Stinks. So He Invented the Nasal Ranger. 2020-09-25 psychology/smell --- https://www.nytimes.com/interactive/2018/07/18/upshot/nike-vaporfly-shoe-strava.html Nike Says Its $250 Running Shoes Will Make You Run Much Faster. What if That’s Actually True? 2020-09-25 exercise --- https://www.nytimes.com/interactive/2018/10/24/magazine/candy-kit-kat-japan.html In Japan, the Kit Kat Isn’t Just a Chocolate. It’s an Obsession. 2020-09-25 japan psychology/collecting --- https://www.oecd.org/pisa/44417824.pdf 2020-09-25 iq/ses --- https://www.openphilanthropy.org/research/some-case-studies-in-early-field-growth/ Some Case Studies in Early Field Growth 2020-09-25 philosophy/ethics --- https://www.orbuch.com/nets-reading-list/ Negative Emissions Reading List 2020-09-25 technology/carbon-capture --- https://www.outsideonline.com/culture/books-media/how-athletes-get-great/ 2020-09-25 genetics/heritable/rare --- https://www.overcomingbias.com/2007/06/against_free_th.html 2020-09-25 psychology/novelty --- https://www.overcomingbias.com/p/even-when-contrhtml Even When Contrarians Win, They Lose 2020-09-25 psychology/novelty --- https://www.overcomingbias.com/p/why-fiction-lieshtml Why Fiction Lies 2020-09-25 culture psychology/cognitive-bias --- https://www.overcomingbias.com/p/break-cryonics-downhtml Break Cryonics Down 2020-09-25 cryonics --- https://www.overcomingbias.com/p/ancestor-worship-is-efficienthtml Ancestor Worship is Efficient 2020-09-26 economics/perpetuities philosophy/ethics philosophy/religion --- https://www.overcomingbias.com/p/parable-of-the-multiplier-holehtml Parable of the Multiplier Hole 2020-09-26 economics/perpetuities philosophy/ethics --- https://www.overcomingbias.com/p/modern-male-satihtml Modern Male Sati 2020-09-26 cryonics --- https://www.overcomingbias.com/2010/07/space-ashes-vs-cryonics.html 2020-09-26 cryonics --- https://www.overcomingbias.com/p/homo-hypocritus-mateshtml Why Men Are Bad At ‘Feelings’ 2020-09-26 cryonics --- https://www.overcomingbias.com/p/let-us-give-to-futurehtml Let Us Give To Future 2020-09-26 economics/perpetuities philosophy/ethics --- https://www.overcomingbias.com/p/brin-says-cryonics-selfishhtml Brin Says Cryonics Selfish 2020-09-26 cryonics --- https://www.overcomingbias.com/p/covert-virtue-the-signal-that-doesnt-barkhtml Covert virtue – the signal that doesn’t bark? 2020-09-26 philosophy/ethics --- https://www.overcomingbias.com/p/all-pay-liabilityhtml All Pay Liability 2020-09-26 bitcoin/nashx --- https://www.overcomingbias.com/2020/08/remote-work-specializes.html 2020-09-26 economics/automation --- https://www.philosophyetc.net/2016/01/expected-value-without-expecting-value.html Expected Value without Expecting Value 2020-09-26 philosophy/ethics --- https://www.pnas.org/doi/full/10.1073/pnas.1119598109 2020-09-27 psychedelic --- https://www.pnas.org/doi/full/10.1073/pnas.1306417111 2020-09-27 crime statistics/survival-analysis --- https://www.pnas.org/doi/full/10.1073/pnas.1601135113 2020-09-27 iq/ses --- https://www.pnas.org/doi/full/10.1073/pnas.1715687114 2020-09-27 exercise --- https://www.pnas.org/doi/10.1073/pnas.2014529117 2020-09-27 psychedelic --- https://www.pnas.org/doi/full/10.1073/pnas.2013180118 2020-09-27 marijuana --- https://www.pnas.org/doi/10.1073/pnas.2101485118 2020-09-27 genetics/cloning --- https://www.pnas.org/doi/10.1073/pnas.2011417118 2020-09-27 psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning --- https://www.pnas.org/doi/10.1073/pnas.1716161115 Efficient derivation of stable primed pluripotentembryonic stem cells from bovine blastocysts 2020-09-27 genetics/gametogenesis --- https://www.pnas.org/doi/10.1073/pnas.1716161115 2020-09-27 genetics/gametogenesis --- https://www.politico.com/news/magazine/2020/05/24/up-in-smoke-marijuana-med-men-249301 2020-09-28 marijuana --- https://www.politico.com/news/magazine/2020/11/27/toke-lahoma-cannabis-market-oklahoma-red-state-weed-legalization-437782 2020-09-28 marijuana --- https://www.prnewswire.com/news-releases/400-million-investment-programme-positions-ireland-for-global-leadership-in-genomic-research-and-advanced-life-sciences-300755716.html $400 Million Investment Programme Positions Ireland for Global Leadership in Genomic Research and Advanced Life Sciences 2020-09-28 economics/experience-curve genetics/heritable --- https://www.propublica.org/article/muscular-dystrophy-patient-olympic-medalist-same-genetic-mutation The DIY Scientist, the Olympian, and the Mutated Gene 2020-09-28 genetics/heritable/rare --- https://www.psychologytoday.com/us/blog/pristine-inner-experience/201110/not-everyone-conducts-inner-speech Not Everyone Conducts Inner Speech 2020-09-28 psychology/inner-voice --- https://www.psypost.org/2016/07/medical-marijuana-lowers-prescription-drug-use-saves-us-government-millions-dollars-43685 2020-09-28 marijuana --- https://www.quantamagazine.org/artificial-neural-nets-finally-yield-clues-to-how-brains-learn-20210218/ The learning algorithm that enables the runaway success of deep neural networks doesn’t work in biological brains, but researchers are finding alternatives that could 2020-09-28 psychology/neuroscience --- https://www.quantamagazine.org/clever-machines-learn-how-to-be-curious-20170919/ 2020-09-28 reinforcement-learning/exploration --- https://www.quantamagazine.org/deep-neural-networks-help-to-explain-living-brains-20201028/ 2020-09-28 psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning --- https://www.quantamagazine.org/evolution-landscapes-predict-whats-next-for-covid-virus-20220111/ 2020-09-28 genetics/selection --- https://www.quantamagazine.org/nature-versus-nurture-add-noise-to-the-debate-20200323/ Nature Versus Nurture? Add ‘Noise’ to the Debate: We give our genes and our environment all the credit for making us who we are. But random noise during development might be a deciding factor, too. 2020-09-28 genetics/cloning --- https://www.quantamagazine.org/new-brain-maps-can-predict-behaviors-20211206/ 2020-09-29 psychology/neuroscience --- https://www.quantamagazine.org/researchers-build-ai-that-builds-ai-20220125/ 2020-09-29 reinforcement-learning/meta-learning --- https://www.quantamagazine.org/researchers-defeat-randomness-to-create-ideal-code-20211124/ 2020-09-29 cs/algorithm/information --- https://www.quantamagazine.org/secret-workings-of-smell-receptors-revealed-for-first-time-20210621/ 2020-09-29 psychology/smell --- https://www.quantamagazine.org/to-be-energy-efficient-brains-predict-their-perceptions-20211115/ 2020-09-29 ai/nn psychology/neuroscience --- https://www.rand.org/content/dam/rand/pubs/technical_reports/2005/RAND_TR193.pdf Determinants of Productivity for Military Personnel: A Review of Findings on the Contribution of Experience, Training, and Aptitude to Military Performance 2020-09-29 iq/low iq/ses --- https://www.researchsquare.com/article/rs-92962/.full 2020-09-29 longevity/senolytic --- https://www.reuters.com/article/us-medical-marijuana-deaths/prescription-painkiller-deaths-fall-in-medical-marijuana-states-idUSKBN0GP1UJ20140825 2020-09-29 marijuana --- https://www.ribbonfarm.com/2013/11/13/the-gooseberry-fallacy/ Living like a dead man 2020-09-29 philosophy/ethics --- https://www.salon.com/2012/12/02/better_than_bourne_who_really_killed_nick_deak/ James Bond and the killer bag lady 2020-09-29 psychiatry/schizophrenia --- https://www.salon.com/2014/09/21/the_colossal_dea_failure_that_prevented_a_potentially_major_medical_breakthrough/ The colossal government failure that obstructed a potentially major medical breakthrough 2020-09-29 philosophy/ethics psychedelic --- https://www.sandiegouniontribune.com/2016/08/12/3-years-into-nations-hemp-experiment-crops-future-is-hazy/ 3 years into nation's hemp experiment, crop's future is hazy 2020-09-30 marijuana --- https://www.science.org/content/article/should-dog-s-sniff-be-enough-convict-person-murder 2020-09-30 psychology/smell --- https://www.science.org/content/article/six-cloned-horses-help-rider-win-prestigious-polo-match 6 cloned horses help rider win prestigious polo match 2020-09-30 genetics/cloning --- https://www.science.org/content/blog-post/evolution-action---literally Phage-assisted continuous evolution 2020-09-30 genetics/selection --- https://www.science.org/content/blog-post/exercise-and-its-signaling 2020-09-30 exercise --- https://www.science.org/content/blog-post/switching-out-fossil-fuel-feedstocks 2020-09-30 technology/carbon-capture --- https://www.science.org/content/blog-post/vetiver 2020-09-30 psychology/smell --- https://www.cs.utoronto.ca/~rsalakhu/papers/LakeEtAl2015Science.pdf Human-level concept learning through probabilistic program induction Lake 2015 2020-09-30 reinforcement-learning/meta-learning --- https://www.science.org/doi/10.1126/science.aaf6850 2020-09-30 genetics/genome-synthesis --- https://www.science.org/doi/full/10.1126/science.abe4832 2020-09-30 longevity/senolytic --- https://www.sciencedirect.com/science/article/abs/pii/S0376871619304892 Investigating the causal effect of cannabis use on cognitive function with a quasi-experimental co-twin design 2020-10-01 marijuana --- https://www.sciencedirect.com/science/article/pii/S0002929707638841 Genome Partitioning of Genetic Variation for Height from 11,214 Sibling Pairs Visscher 2007 2020-10-01 genetics/heritable --- https://www.sciencedirect.com/science/article/pii/S001122401500245X Aldehyde-stabilized cryopreservation 2020-10-01 cryonics --- https://www.sciencedirect.com/science/article/pii/S0022030221005555 Danish dairy farmers' acceptance of and willingness to use semen from bulls produced by means of in vitro embryo production and genomic selection 2020-10-01 genetics/gametogenesis --- https://www.sciencedirect.com/science/article/pii/S0028390817306391 Increased amygdala responses to emotional faces after psilocybin for treatment-resistant depression 2020-10-01 psychedelic --- https://www.sciencedirect.com/science/article/pii/S0092867414015839 SOX17 Is a Critical Specifier of Human Primordial Germ Cell Fate 2020-10-01 genetics/gametogenesis --- https://www.sciencedirect.com/science/article/pii/S0092867420310011 The Mind of a Mouse 2020-10-01 cryonics --- https://www.sciencedirect.com/science/article/pii/S0160289612000554 2020-10-01 iq/ses --- https://www.sciencedirect.com/science/article/pii/S0160289613000949 Japanese north-south gradient in IQ predicts differences in stature, skin color, income, and homicide rate 2020-10-01 iq/ses --- https://www.sciencedirect.com/science/article/pii/S0160289614000178 Molecular genetic contributions to socioeconomic status and intelligence 2020-10-01 iq/ses --- https://www.sciencedirect.com/science/article/pii/S0160289614000981 2020-10-01 iq/ses --- https://www.sciencedirect.com/science/article/pii/S0160289614001676 Thinking positively: The genetics of high intelligence 2020-10-02 genetics/heritable/rare iq/high/smpy --- https://www.sciencedirect.com/science/article/pii/S0160289615000446 2020-10-02 iq/ses --- https://www.sciencedirect.com/science/article/pii/S1053811919302952 2020-10-02 psychedelic --- https://www.sciencefocus.com/nature/rise-of-the-clones-7-ways-cloning-is-already-happening Rise of the Clones: 7 ways cloning is already happening: Animals—from pets and livestock to working dogs and extinct species—are being cloned for a variety of purposes. But copying animals from their genetic material is creating problems as quickly as it’s solving them: #4 …But cloned sniffer dogs are already patrolling some airports 2020-10-02 genetics/cloning/dog --- https://www.science.org/content/article/these-lab-grown-human-eggs-could-combat-infertility-if-they-prove-healthy These lab-grown human eggs could combat infertility—if they prove healthy 2020-10-02 genetics/gametogenesis --- https://www.sciencemag.org/news/2018/07/tour-de-force-researchers-image-entire-fly-brain-minute-detail 2020-10-02 cryonics --- https://www.scientificamerican.com/article/same-sex-mice-parents-give-birth-to-healthy-brood/ Same-Sex Mice Parents Give Birth to Healthy Brood 2020-10-02 genetics/gametogenesis --- https://www.scmp.com/news/china/science/article/3002346/chinas-first-cloned-police-dog-reports-duty China’s first cloned police dog reports for duty: Kunxun, a two-month-old Kunming wolfdog, was born after scientists took the DNA from a ‘one in a thousand’ animal; Police hope the programme to clone the force’s best dogs will eventually give it a bigger pool of animals suited to police work 2020-10-02 genetics/cloning/dog --- https://www.sfchronicle.com/projects/2021/jessica-simulation-artificial-intelligence/ 2020-10-02 ai/nn/transformer/gpt --- https://www.signs.com/branded-in-memory/ Branded in Memory 2020-10-02 psychology/cognitive-bias/illusion-of-depth --- https://www.simonsfoundation.org/2021/02/25/the-connected-connectome/ The Connected Connectome 2020-10-02 psychology/neuroscience --- https://www.sparefoot.com/moving/moving-to-san-francisco-ca/visualizing-san-franciscos-population-density/ 2020-10-03 economics --- https://www.spring.org.uk/2014/11/autism-new-studies-identify-dozens-more-associated-genes.php Autism: New Studies Identify Dozens More Associated Genes 2020-10-03 genetics/heritable/rare psychiatry/autism --- https://www.statnews.com/2017/10/23/ivf-embryo-genetic-testing/ Genetic testing of embryos is creating an ethical morass 2020-10-03 genetics/selection philosophy/ethics --- https://www.stevepetersen.net/wittgenstein-fog.html Frayn's spoof of Wittgenstein 2020-10-03 math/humor philosophy/epistemology --- https://www.stripes.com/news/pet-cloning-lab-in-s-korea-starts-military-dog-program-1.425640 Pet-cloning lab in S. Korea starts military dog program 2020-10-03 genetics/cloning/dog --- https://www.strongerbyscience.com/master-list/ Strength and Physique Systematic Review and Meta-Analysis Master List 2020-10-03 exercise --- https://www.strongtowns.org/journal/2019/3/6/non-glamorous-gains-the-pennsylvania-land-tax-experiment Non-Glamorous Gains: The Pennsylvania Land Tax Experiment 2020-10-03 economics/georgism --- https://www.sumsar.net/blog/2015/01/probable-points-and-credible-intervals-part-two/ Probable Points and Credible Intervals, Part 2: Decision Theory 2020-10-03 reinforcement-learning/exploration statistics/bayes statistics/decision --- https://www.tabletmag.com/sections/arts-letters/articles/on-venus-have-we-got-a-rabbi A long-lost space age satire about what it means to be a Jew from one of science fiction's greatest humorists 2020-10-03 fiction/humor fiction/science-fiction philosophy/religion sociology --- https://www.tabnine.com/ Tabnine AI code assistant 2020-10-03 ai/nn/transformer/gpt/codex --- https://www.tandfonline.com/doi/abs/10.1080/02791072.2016.1234090 2020-10-03 psychedelic --- https://www.tandfonline.com/doi/pdf/10.1080/01459741003715391 Extreme Life Extension: Investing in Cryonics for the Long, Long Term 2020-10-04 cryonics --- https://www.teamten.com/lawrence/writings/coding-machines/ Coding Machines 2020-10-04 cs fiction/science-fiction --- https://www.technologyreview.com/2019/08/21/238642/a-doctor-and-medical-ethicist-argues-life-after-75-is-not-worth-living/ A doctor and medical ethicist argues life after 75 is not worth living 2020-10-04 philosophy/ethics --- https://www.technologyreview.com/2021/05/20/1025135/ai-large-language-models-bigscience-project/ The race to understand the thrilling, dangerous world of language AI 2020-10-04 ai/nn/transformer/gpt/lamda --- https://www.technologyreview.com/2021/05/27/1025453/artificial-intelligence-learning-create-itself-agi/ AI is learning how to create itself 2020-10-04 reinforcement-learning/exploration reinforcement-learning/meta-learning --- https://www.technologyreview.com/2021/10/28/1038172/conception-eggs-reproduction-vitro-gametogenesis/ Conception: How Silicon Valley hatched a plan to turn blood into lab-made human eggs 2020-10-04 genetics/gametogenesis --- https://www.technologyreview.com/2021/11/09/1039107/e-coli-maze-solving-biocomputer/ An E. coli biocomputer solves a maze by sharing the work 2020-10-04 genetics/editing --- https://www.the-scientist.com/features/can-destroying-senescent-cells-treat-age-related-disease--67136 2020-10-04 longevity/senolytic --- https://www.the-scientist.com/features/decoding-the-tripping-brain-30240 2020-10-04 psychedelic --- https://www.the-scientist.com/icing-organs-39859 Icing Organs 2020-10-04 cryonics --- https://www.theatlantic.com/education/archive/2019/01/why-pe-is-terrible/581467/ Why P.E. Fails at Solving Problems Such as Obesity 2020-10-05 exercise --- https://www.theatlantic.com/health/archive/2016/12/the-life-changing-magic-of-mushrooms/509246/ The Life-Changing Magic of Mushrooms: A single dose of magic mushrooms can make people with severe anxiety and depression better for months, according to a landmark pair of new studies. 2020-10-05 psychedelic psychiatry/anxiety psychiatry/depression --- https://www.theatlantic.com/international/archive/2013/06/why-is-russia-so-homophobic/276817/ Why Is Russia So Homophobic? 2020-10-05 philosophy/ethics --- https://www.theatlantic.com/international/archive/2017/10/red-famine-anne-applebaum-ukraine-soviet-union/542610/ How Stalin Hid Ukraine's Famine From the World 2020-10-05 sociology/preference-falsification --- https://www.theatlantic.com/magazine/archive/1973/11/the-force-that-drives-the-flower/308963/ The Force That Drives the Flower 2020-10-05 philosophy/ethics --- https://www.theatlantic.com/magazine/archive/2014/12/the-shazam-effect/382237/?single_page=true The Shazam Effect 2020-10-05 psychology/novelty --- https://www.theatlantic.com/magazine/archive/2017/06/when-your-child-is-a-psychopath/524502/ When Your Child Is a Psychopath 2020-10-05 crime philosophy/ethics psychiatry psychology/personality/psychopathy --- https://www.theatlantic.com/magazine/archive/2019/03/what-the-crow-knows/580726/ Do Animals Have Feelings? 2020-10-05 philosophy/ethics philosophy/mind --- https://www.theatlantic.com/magazine/archive/2020/07/trumps-collaborators/612250/ Why Do Republican Leaders Continue to Enable Trump 2020-10-05 history/uighur philosophy/ethics psychology/personality/narcissism sociology/preference-falsification --- https://www.theatlantic.com/politics/archive/2015/05/the-wedding-sting/392699/ The Wedding Sting 2020-10-05 marijuana --- https://www.theatlantic.com/science/archive/2015/11/the-vocabulary-of-smell/414618/?single_page=true Why Do Most Languages Have So Few Words for Smells? 2020-10-05 philosophy/mind psychology/smell --- https://www.theatlantic.com/science/archive/2017/01/why-does-a-gene-that-increases-alzheimers-risk-still-exist/512396/ Why Do Humans Still Have a Gene That Increases the Risk of Alzheimer’s? 2020-10-06 genetics/heritable/rare psychiatry/alzheimers --- https://www.theatlantic.com/science/archive/2018/06/what-its-like-to-trip-on-the-most-potent-magic-mushroom/561860/ Michael Pollan on What It's Like to Trip on Mushrooms 2020-10-06 psychedelic --- https://www.theatlantic.com/science/archive/2018/07/massospora-parasite-drugs-its-hosts/566324/ Massospora, the Parasite That Drugs Cicadas 2020-10-06 psychedelic --- https://www.theatlantic.com/science/archive/2021/11/whaling-whales-food-krill-iron/620604/ To Save the Whales, Feed the Whales 2020-10-06 technology/carbon-capture --- https://www.theatlantic.com/technology/archive/2017/04/the-tragedy-of-google-books/523320/ Torching the Modern-Day Library of Alexandria: ‘Somewhere at Google there is a database containing 25 million books and nobody is allowed to read them.’ 2020-10-06 economics/copyright --- https://www.thecannabist.co/2017/02/22/report-united-states-marijuana-sales-projections-2025/74059/ Report: America's marijuana industry headed for $24 billion by 2025 2020-10-06 marijuana --- https://www.thecut.com/2017/11/raising-child-with-cystic-fibrosis.html Fighting for My Son With Cystic Fibrosis 2020-10-06 genetics/selection philosophy/ethics --- https://www.thedailybeast.com/this-lab-will-clone-your-pet-for-dollar50k-would-you-do-it 2020-10-06 genetics/cloning --- https://www.theguardian.com/commentisfree/2020/sep/08/robot-wrote-this-article-gpt-3 A robot wrote this entire article. Are you scared yet, human? We asked GPT-3, OpenAI’s powerful new language generator, to write an essay for us from scratch. The assignment? To convince us robots come in peace | For more about GPT-3 and how this essay was written and edited, please read our editor’s note below 2020-10-06 ai/nn/transformer/gpt/3/nonfiction --- https://www.theguardian.com/science/2009/nov/07/cryonics-british-dads-army The Dad's Army of British cryonics Cryonics 2020-10-06 cryonics --- https://www.theguardian.com/science/2021/jan/16/inner-voice-self-criticism-psychologist-ethan-kross-chatter-voice-head Why your most important relationship is with your inner voice 2020-10-06 psychology/inner-voice --- https://www.theguardian.com/science/2021/oct/25/the-last-great-mystery-of-the-mind-meet-the-people-who-have-unusual-or-non-existent-inner-voices The last great mystery of the mind: meet the people who have unusual—or non-existent—inner voices 2020-10-07 psychology/inner-voice --- https://www.theguardian.com/society/2017/may/23/study-hallucinogenic-mushrooms-safest-recreational-drug-lsd Study finds mushrooms are the safest recreational drug 2020-10-07 psychedelic --- https://www.theguardian.com/us-news/2016/feb/16/medical-marijuana-dispensaries-california-tax-cash-only Cash-only marijuana dispensaries flood California tax office with paper 2020-10-07 marijuana --- https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(18)30373-6/fulltext 2020-10-07 longevity/senolytic --- https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(18)30629-7/fulltext 2020-10-07 longevity/senolytic --- https://www.thelancet.com/journals/lanpsy/article/PIIS2215-0366(18)30135-4/abstract 2020-10-07 psychedelic --- https://www.themarshallproject.org/2018/04/19/framed-for-murder-by-his-own-dna Framed for Murder By His Own DNA: We leave traces of our genetic material everywhere, even on things we’ve never touched. That got Lukis Anderson charged with a brutal crime he didn’t commit. 2020-10-07 crime --- https://www.thenation.com/article/culture/anya-bernstein-future-immortality-russia-cryogenics-review/ The Strange and Often Radical Pursuit of Immortality in Russia 2020-10-07 cryonics --- https://www.thenewatlantis.com/publications/do-elephants-have-souls Do Elephants Have Souls? 2020-10-07 philosophy/ethics --- https://www.thenewatlantis.com/publications/the-promise-and-perils-of-synthetic-biology The Promise and Perils of Synthetic Biology 2020-10-07 genetics/genome-synthesis --- https://theonion.com/6-year-old-stares-down-bottomless-abyss-of-formal-schoo-1819570013/ 6-Year-Old Stares Down Bottomless Abyss Of Formal Schooling 2020-10-08 economics fiction/humor philosophy/ethics --- https://theonion.com/millions-and-millions-dead-1819565193/ Millions and Millions Dead 2020-10-08 fiction/humor philosophy/ethics --- https://theonion.com/study-wolf-attacks-still-leading-cause-of-death-in-u-s-1819574862/ Study: Wolf Attacks Still Leading Cause Of Death In U.S. 2020-10-08 fiction/humor philosophy/ethics --- https://www.theverge.com/2021/8/10/22618128/openai-codex-natural-language-into-code-api-beta-access OpenAI can translate English into code with its new machine learning software Codex 2020-10-08 ai/nn/transformer/gpt/codex --- https://www.uab.edu/news/research/item/12240-reversal-of-lung-fibrosis-in-mouse-model-suggests-a-novel-therapeutic-target-for-pulmonary-fibrosis Reversal of lung fibrosis in mouse model suggests a novel therapeutic target for pulmonary fibrosis 2020-10-08 longevity/senolytic --- https://www.usenix.org/system/files/sec15-paper-guri-update_v2.pdf#page=2 GSMem: Data Exfiltration from Air-Gapped Computers over GSM Frequencies 2020-10-08 technology --- https://www.vanityfair.com/style/2018/08/dog-cloning-animal-sooam-hwang Barbra Streisand is not alone. At a South Korean laboratory, an once-disgraced doctor is replicating hundreds of deceased pets for the rich and famous. It's made for more than a few questions of bioethics. 2020-10-08 genetics/cloning/dog philosophy/ethics --- https://www.vesta.earth/ Home 2020-10-08 technology/carbon-capture --- https://www.viagenpets.com/cost-to-clone-pet/ How Much Does It Cost To Clone A Pet? Viagen 2019-08-09 2020-10-08 genetics/cloning --- https://www.vice.com/en/article/4adngq/psychedelic-drug-use-in-ancient-indigenous-cultures Psychedelics Weren't As Common in Ancient Cultures As We Think 2020-10-08 psychedelic --- https://www.vice.com/en/article/d3j55x/legal-pot-in-the-us-is-crippling-mexican-cartels Legal Pot in the US Is Crippling Mexican Cartels 2020-10-08 marijuana --- https://www.vice.com/en/article/d737mx/the-fbi-cant-find-hackers-that-dont-smoke-pot The FBI Says It Can't Find Hackers to Hire Because They All Smoke Pot 2020-10-09 marijuana --- https://www.vice.com/en/article/dpwaxz/drug-boot-camp-131 I Took a Lot of Drugs at a Psychedelic Boot Camp 2020-10-09 psychedelic --- https://www.vice.com/en/article/exadma/magic-jews-205-v15n9 The Magic Jews 2020-10-09 psychedelic --- https://www.vice.com/en/article/n7bqj7/ai-generated-art-scene-explodes-as-hackers-create-groundbreaking-new-tools New AI tools CLIP+VQ-GAN can create impressive works of art based on just a few words of input 2020-10-09 ai/nn/transformer/clip --- https://www.vox.com/2014/10/1/6858173/marijuana-legalization-majority-movement How marijuana legalization became a majority movement 2020-10-09 marijuana --- https://www.vox.com/culture/22840526/colors-movies-tv-gray-digital-color-sludge Colors in movies and TV: What happened to them? 2020-10-09 design --- https://www.vox.com/recode/22394867/elon-musk-foundation-philanthropy-xprize-tesla-spacex Tesla founder Elon Musk has spent $150 million on charity in 2021 2020-10-09 technology/carbon-capture --- https://www.vox.com/the-goods/22195549/soulcycle-decline-reopening-bullying-bike-explained SoulCycle changed fitness. Its culture and toxic work environment made growth impossible. 2020-10-09 exercise sociology --- https://www.washingtonpost.com/business/2019/11/04/inside-little-known-world-flavorists-who-are-trying-make-plant-based-meat-taste-like-real-thing/ Plant-based meat like Beyond and Impossible burgers get their beefy taste from flavorists 2020-10-09 economics philosophy/ethics psychology/smell --- https://www.washingtonpost.com/lifestyle/magazine/fatal-distraction-forgetting-a-child-in-thebackseat-of-a-car-is-a-horrifying-mistake-is-it-a-crime/2014/06/16/8ae0fe3a-f580-11e3-a3a5-42be35962a52_story.html Fatal Distraction: Forgetting a Child in the Backseat of a Car Is a Horrifying Mistake. Is It a Crime? 2020-10-09 philosophy/ethics --- https://www.washingtonpost.com/national/health-science/donor-eggs-sperm-banks-and-the-quest-for-good-genes/2017/10/21/64b9bdd0-aaa6-11e7-b3aa-c0e2e1d41e38_story.html Discounts, guarantees and the search for ‘good’ genes: The booming fertility business 2020-10-09 philosophy/ethics --- https://www.washingtonpost.com/news/speaking-of-science/wp/2018/01/24/researchers-clone-the-first-primates-from-monkey-tissue-cells/ Researchers clone the first primates from monkey tissue cells 2020-10-10 genetics/cloning genetics/gametogenesis --- https://www.washingtonpost.com/outlook/2021/06/14/genetic-screening-ivf-moral-dilemmas/ A new age of genetic screening is coming—and we don’t have any rules for it: New ‘polygenic’ screening techniques open a Pandora’s Box of ethical issues 2020-10-10 philosophy/ethics --- https://www.washingtonpost.com/science/2018/09/20/game-changing-technique-create-babies-skin-cells-just-stepped-forward/ The 'game-changing' technique to create babies from skin cells just stepped forward 2020-10-10 genetics/gametogenesis --- https://www.washingtonpost.com/world/national-security/new-poll-finds-majority-of-americans-believe-torture-justified-after-911-attacks/2014/12/16/f6ee1208-847c-11e4-9534-f79a23c40e6c_story.html 2020-10-10 philosophy/ethics --- https://www.wfmj.com/story/32075195/cloned-military-police-dog-being-trained-in-mercer-county Cloned military police dog being trained in Mercer county 2020-10-10 genetics/cloning/dog --- https://www.wired.co.uk/article/human-genome-synthesise-dna 2020-10-10 genetics/genome-synthesis --- https://www.wired.com/2011/08/does-marijuana-make-you-stupid/ Does Marijuana Make You Stupid? 2020-10-10 marijuana --- https://www.wired.com/2016/04/the-science-of-marijuana/ A New Crop of Marijuana Geneticists Sets Out to Build Better Weed 2020-10-10 marijuana --- https://www.wired.com/2016/10/how-the-web-became-unreadable/ How the Web Became Unreadable 2020-10-10 design --- https://www.wired.com/2017/05/scientists-want-give-money-study-psychedelics/ Scientists Want You to Give Them Money to Study Psychedelics 2020-10-10 psychedelic --- https://www.wired.com/story/22-year-old-builds-chips-parents-garage/ This 22-Year-Old Builds Chips in His Parents’ Garage 2020-10-10 technology --- https://www.wired.com/story/ai-software-nearly-predicted-omicrons-tricky-structure/ This AI Software Nearly Predicted Omicron’s Tricky Structure 2020-10-11 ai/nn/transformer/alphafold --- https://www.wired.com/story/airbnb-for-everything/ In the sublet economy, you can turn anything into extra cash: your house, your car, your boat, or your backyard. 2020-10-11 economics/georgism economics/mechanism-design --- https://www.wired.com/story/brain-computer-interfaces-digital-reality/ Can a Digital Reality Be Jacked Directly Into Your Brain? 2020-10-11 psychology/neuroscience --- https://www.wired.com/story/dark-web-cannabis-sales-surged-pandemic/ Weed Sales on the Dark Web Surged Early in the Pandemic 2020-10-11 marijuana --- https://www.wired.com/story/even-china-roundly-condemns-editing-the-genes-of-babies/ Even China Roundly Condemns Editing the Genes of Babies 2020-10-11 philosophy/ethics --- https://www.wired.com/story/gene-tweaked-jellyfish-neurology/ A Gene-Tweaked Jellyfish Offers a Glimpse of Other Minds 2020-10-11 genetics/editing psychology/neuroscience --- https://www.wired.com/story/guide-dog-dna/ Is There a Genetic Link to Being an Extremely Good Boy? 2020-10-11 genetics/heritable --- https://www.wired.com/story/hackers-mason-jars-psychedelic-science-diy-shrooms/ Hackers, Mason Jars, and the Psychedelic Science of DIY Shrooms 2020-10-11 psychedelic --- https://www.wired.com/story/lets-talk-geoengineering/ Climate Change Is Here. It’s Time to Talk About Geoengineering 2020-10-11 technology/carbon-capture --- https://www.wired.com/story/live-forever-synthetic-human-genome/ How Synthetic Biology Will Help Me Live Forever 2020-10-11 genetics/genome-synthesis --- https://www.wired.com/story/most-complete-brain-map-ever-is-here-a-flys-connectome/ The Most Complete Brain Map Ever Is Here: A Fly's 'Connectome' 2020-10-12 cryonics --- https://www.wired.com/story/quest-to-make-robot-smell-cancer-dog/ The Quest to Make a Bot That Can Smell as Well as a Dog 2020-10-12 psychology/smell --- https://www.wired.com/story/reverse-infertility/ Science Is Getting Us Closer to the End of Infertility 2020-10-12 genetics/gametogenesis --- https://www.wired.com/story/shatter-batter-wax-how-cannabis-extracts-come-to-be/ Shatter, Batter, Wax: How Cannabis Extracts Come to Be 2020-10-12 darknet-market marijuana --- https://www.wired.com/story/the-long-search-for-a-computer-that-speaks-your-mind/ 2020-10-12 reinforcement-learning/imitation-learning/brain-imitation-learning --- https://www.wired.com/story/the-problem-with-fitness-studies-based-on-activity-apps/ 2020-10-12 exercise --- https://www.wired.com/story/the-quest-to-trap-carbon-in-stone-and-beat-climate-change/ The Quest to Trap Carbon in Stone—and Beat Climate Change 2020-10-12 technology/carbon-capture --- https://www.wired.com/story/this-is-my-brain-on-salvia/ This Is My Brain on Salvia 2020-10-12 psychedelic --- https://www.wired.com/story/tracking-readers-eye-movements-can-help-computers-learn/ Tracking Readers’ Eye Movements Can Help Computers Learn 2020-10-12 reinforcement-learning/imitation-learning/brain-imitation-learning --- https://www.wired.com/story/weed-dui-test/ Driving While Baked? Inside the High-Tech Quest to Find Out 2020-10-12 marijuana --- https://www.wired.com/story/whole-genome-sequencing-cost-200-dollars/ Now You Can Sequence Your Whole Genome for Just $200 2020-10-12 economics/experience-curve genetics/heritable --- https://worksinprogress.co/issue/against-the-survival-of-the-prettiest/ Against the survival of the prettiest 2020-10-13 design economics --- https://worksinprogress.co/issue/the-future-of-weight-loss/ The future of weight loss 2020-10-13 exercise longevity/glp/semaglutide --- https://worksinprogress.co/issue/why-irelands-housing-bubble-burst/ Why Ireland's housing bubble burst 2020-10-13 economics --- https://worksinprogress.co/issue/why-skyscrapers-are-so-short/ Why skyscrapers are so short 2020-10-13 economics technology --- https://worksinprogress.co/issue/womb-for-improvement/ Womb for improvement 2020-10-13 genetics/gametogenesis --- https://www.wsj.com/articles/payments-company-stripe-is-kick-starting-market-for-carbon-removal-11635274208 2020-10-13 technology/carbon-capture --- https://www.wsj.com/articles/the-new-science-of-psychedelics-1525360091 2020-10-13 psychedelic --- https://www.xprize.org/prizes/carbonremoval Overview XPRIZE Carbon Removal 2020-10-13 technology/carbon-capture --- https://www.youtube.com/playlist?list=PL_iWMTlE8pjv6mnKVjOaaO02hMKHm2AAj Monkeys Play Pac-Man 2020-10-13 psychology/neuroscience reinforcement-learning/exploration reinforcement-learning/imitation-learning/brain-imitation-learning --- https://www.youtube.com/watch?v=x-9KxACAPIo&t=19310 WELM 2020-10-13 ai/nn/transformer/gpt ai/scaling reinforcement-learning/meta-learning --- https://www.youtube.com/watch?v=0yI2wJ6F8r0 Playing Montezuma's Revenge with Intrinsic Motivation 2020-10-13 reinforcement-learning/exploration --- https://www.youtube.com/watch?v=3t06ajvBtl0 Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind 2020-10-14 reinforcement-learning/meta-learning --- https://www.youtube.com/watch?v=7vFGKHzY_38 I Think I'm a Clone Now 2020-10-14 genetics/cloning --- https://www.youtube.com/watch?v=8sDsMUUcrtM FROM PLAIN TO EXPLAINED IN FIVE MINUTES: Getting Started with Stenography Autopilot 2020-10-14 ai/nn/transformer/gpt/codex --- https://www.youtube.com/watch?v=EbqGGd-caQM The Genius Design of Cowboy Bebop's Titles 2020-10-14 anime design --- https://www.youtube.com/watch?v=L0A86LmH7Yw#deepmind MuZero 2020-10-14 reinforcement-learning/model/muzero --- https://www.youtube.com/watch?v=LHsl9jSOO6M LEGO uses 18 Cucumbers to build real Log House 2020-10-14 technology --- https://www.youtube.com/watch?v=OzGguadEHOU A Universal Law of Robustness 2020-10-14 ai/nn/adversarial ai/scaling --- https://www.youtube.com/watch?v=QyJGXc9WeNo&list=PLOXw6I10VTv9HODt7TFEL72K3Q6C4itG6&index=3 Solving Rubik’s Cube with a Robot Hand: Perturbations 2020-10-14 reinforcement-learning/meta-learning reinforcement-learning/model-free/oa5 reinforcement-learning/robot --- https://www.youtube.com/watch?v=Rk3MBx20z24&t=35s Apple or iPod? Easy Fix for Adversarial Textual Attacks on OpenAI's CLIP Model! 2020-10-14 ai/nn/adversarial ai/nn/sampling ai/nn/transformer/clip design/typography --- https://www.youtube.com/watch?v=SGUCcjHTmGY#openai OpenAI Codex Live Demo 2020-10-14 ai/nn/transformer/gpt/codex --- https://www.youtube.com/watch?v=TXk-Oc35oN4 The Changing Room Illusion 2020-10-15 psychology/cognitive-bias/illusion-of-depth --- https://www.youtube.com/watch?v=Zm9B-DvwOgw Creating a Space Game with OpenAI Codex 2020-10-15 ai/nn/transformer/gpt/codex --- https://www.youtube.com/watch?v=aUSSfo5nCdM#google Watch Google's AI LaMDA program talk to itself at length (full conversation) 2020-10-15 ai/nn/transformer/gpt/lamda --- https://www.youtube.com/watch?v=bGGxdF0Pn4g HUMAN’20: ‘A Live Demo of the Intermedia Hypertext System’ (Norman K. Meyrowitz) 2020-10-15 design technology --- https://www.youtube.com/watch?v=fCoavgGZ64Y&t=2796s Season 1 Ep. 22 OpenAI's Ilya Sutskever: The man who made AI work 2020-10-15 ai/nn/transformer/gpt ai/scaling --- https://www.youtube.com/watch?v=ujMvnQpP528 A law of robustness and the importance of overparameterization in deep learning 2020-10-15 ai/nn/adversarial ai/scaling --- https://www.youtube.com/watch?v=w3ues-NayAs#openai NVIDIA NTECH 2018—Ilya Sutskever Keynote Talk 2020-10-15 reinforcement-learning/model-free/oa5 --- https://www.youtube.com/watch?v=z2izAP-AI5M#deepmind AlphaStar vs AlphaStar (PvP) & Dev Answered Questions! 2020-10-15 reinforcement-learning/model-free/alphastar --- /doc/crime/2015-fbi-vallejokidnappings-complaint.pdf 2015 2020-10-15 crime technology --- https://journals.le.ac.uk/ojs1/index.php/pst/issue/archive 2020-10-15 math/humor --- https://xcorr.net/2019/11/22/ai-and-neuroscience-main2019/ AI and neuroscience 2020-10-15 reinforcement-learning/imitation-learning/brain-imitation-learning --- https://xkcd.com/2560/ Confounding Variables 2020-10-16 statistics/causality --- https://xkcd.com/915/ Connoisseur 2020-10-16 culture psychology/novelty --- https://yang-song.net/blog/2021/score/ Generative Modeling by Estimating Gradients of the Data Distribution 2020-10-16 ai/nn/diffusion --- https://zlkj.in/survival Survival Times and Probabilities 2020-10-16 darknet-market statistics/survival-analysis --- https://x.com/Nearcyan/status/1495206065339711490 2020-10-16 ai/nn/transformer/clip/sample --- https://x.com/Nearcyan/status/1494798954545827841 2020-10-16 ai/nn/transformer/clip/sample --- https://x.com/Nearcyan/status/1495634566161186817 2020-10-16 ai/nn/transformer/clip/sample --- https://x.com/revrart/status/1495516592192606217 2020-10-16 ai/nn/transformer/clip/sample --- https://en.wikipedia.org/wiki/15.ai 15.ai 2020-10-16 ai/anime --- https://mit-serc.pubpub.org/pub/puzzle-of-missing-robots/release/1 The Puzzle of the Missing Robots 2020-10-16 economics/automation --- https://plato.stanford.edu/entries/quantum-bayesian/ Quantum-Bayesian and Pragmatist Views of Quantum Theory 2020-10-16 philosophy/epistemology philosophy/ontology statistics/bayes statistics/decision --- https://lilianweng.github.io/lil-log/2022/02/20/active-learning.html Active Learning 2020-10-17 reinforcement-learning/exploration/active-learning statistics/bayes --- https://exogenesis23.substack.com/p/the-lamb-paper 2020-10-17 genetics/gametogenesis --- https://x.com/big_dream_io/status/1496391266929725441 2020-10-17 ai/nn/transformer/clip/sample --- https://ai.facebook.com/blog/yann-lecun-advances-in-ai-research 2020-10-17 reinforcement-learning --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4666828/ Sleep Disturbance, Sleep Duration, and Inflammation: A Systematic Review and Meta-Analysis of Cohort Studies and Experimental Sleep Deprivation Michael R. Irwin, Richard Olmstead, Judith E. Carroll 2016 2020-10-17 [("doi","10.1016/j.biopsych.2015.05.014")] psychology/neuroscience zeo

Background: Sleep disturbance is associated with inflammatory disease risk and all-cause mortality. Here, we assess global evidence linking sleep disturbance, sleep duration, and inflammation in adult humans.

Method: A systematic search of English language publications was performed, with inclusion of primary research articles that characterized sleep disturbance and/or sleep duration or performed experimental sleep deprivation and assessed inflammation by levels of circulating markers. Effect sizes (ES) and 95% confidence intervals (CI) were extracted and pooled using a random effect model.

Results: A total of 72 studies (n > 50,000) were analyzed with assessment of C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor α (TNFα). Sleep disturbance was associated with higher levels of CRP (ES 0.12; 95% CI = 0.05-0.19) and IL-6 (ES 0.20; 95% CI = 0.08-0.31). Shorter sleep duration, but not the extreme of short sleep, was associated with higher levels of CRP (ES 0.09; 95% CI = 0.01-0.17) but not IL-6 (ES 0.03; 95% CI: -0.09 to 0.14). The extreme of long sleep duration was associated with higher levels of CRP (ES 0.17; 95% CI = 0.01-0.34) and IL-6 (ES 0.11; 95% CI = 0.02–20). Neither sleep disturbances nor sleep duration was associated with TNFα. Neither experimental sleep deprivation nor sleep restriction was associated with CRP, IL-6, or TNFα. Some heterogeneity among studies was found, but there was no evidence of publication bias.

Conclusion: Sleep disturbance and long sleep duration, but not short sleep duration, are associated with increases in markers of systemic inflammation.

--- https://blog.acolyer.org/2018/04/18/google-workloads-for-consumer-devices-mitigating-data-movement-bottlenecks/ Google workloads for consumer devices: mitigating data movement bottlenecks 2020-10-17 ai/scaling cs --- https://www.biorxiv.org/content/10.1101/2020.07.03.186288.full Language processing in brains and deep neural networks: computational convergence and its limits Charlotte Caucheteux, Jean-Rémi King 2021-01-14 2021-01-14 [("doi","10.1101/2020.07.03.186288")] ai/scaling psychology/neuroscience

Deep Learning has recently led to major advances in natural language processing. Do these models process sentences similarly to humans, and is this similarity driven by specific principles? Using a variety of artificial neural networks, trained on image classification, word embedding, or language modeling, we evaluate whether their architectural and functional properties lead them to generate activations linearly comparable to those of 102 human brains measured with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG).

We show that image, word, and contextualized word embeddings separate the hierarchical levels of language processing in the brain. Critically, we compare 3,600 embeddings in their ability to linearly map onto these brain responses.

The results show that (1) the position of the layer in the network and (2) the ability of the network to accurately predict words from context are the main factors responsible for the emergence of brain-like representations in artificial neural networks.

Together, these results show how perceptual, lexical, and compositional representations precisely unfold within each cortical region and contribute to uncovering the governing principles of language processing in brains and algorithms.

--- https://arxiv.org/abs/2202.11233 Retrieval Augmented Classification for Long-Tail Visual Recognition Alexander Long, Wei Yin, Thalaiyasingam Ajanthan, Vu Nguyen, Pulak Purkait, Ravi Garg, Alan Blair, Chunhua Shen, Anton van den Hengel 2022-02-22 2022-02-22 [("doi","10.48550/arXiv.2202.11233")] ai/nn/retrieval

We introduce Retrieval Augmented Classification (RAC), a generic approach to augmenting standard image classification pipelines with an explicit retrieval module.

RAC consists of a standard base image encoder fused with a parallel retrieval branch that queries a non-parametric external memory of pre-encoded images and associated text snippets.

We apply RAC to the problem of long-tail classification and demonstrate an improvement over previous state-of-the-art on Places365-LT and iNaturalist-2018 (14.5% and 6.7% respectively), despite using only the training datasets themselves as the external information source. We demonstrate that RAC’s retrieval module, without prompting, learns a high level of accuracy on tail classes. This, in turn, frees the base encoder to focus on common classes, and improve its performance thereon.

RAC represents an alternative approach to utilizing large, pretrained models without requiring fine-tuning, as well as a first step towards more effectively making use of external memory within common computer vision architectures.

--- https://www.biorxiv.org/content/10.1101/2022.02.22.481162.full Rats emit unique distress calls in social inequality conditions Shota Okabe, Yuki Takayanagi, Masahide Yoshida, Tatsushi Onaka 2022-02-24 2022-02-24 [("doi","10.1101/2022.02.22.481162")] psychology/animal sociology

Humans show aversion toward inequality of social reward, and this aversion plays important roles for the establishment of social cooperation. However, it has remained unknown whether commonly used experimental animals show negative responses to social reward inequality.

In this study, we found that rats showed bonding-like behavior to an affiliative human who repeatedly stroked the rats. In addition, these rats emitted distress calls, an index of negative emotion, when an affiliative human stroked another rat in front of them.

These distress calls had acoustic characteristics different from those emitted in response to physical stress stimuli such as air-puff. Rats emitted calls with higher frequency (28 kHz) and shorter durations (0.05 sec) in an inequality condition than the frequency and durations of calls emitted when receiving air-puff.

Our results suggested that rats exhibited negative emotion with unique distress calls in response to a social inequality condition.

--- https://www.danieldewey.net/risk/estimates.html Fermi estimate of future training runs 2020-10-17 ai/scaling/hardware --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610561/ Can the Brain Do Backpropagation? -Exact Implementation of Backpropagation in Predictive Coding Networks Yuhang Song, Thomas Lukasiewicz, Zhenghua Xu, Rafal Bogacz 2020 2020-10-18 ai/nn psychology/neuroscience

Backpropagation (BP) has been the most successful algorithm used to train artificial neural networks. However, there are several gaps between BP and learning in biologically plausible neuronal networks of the brain (learning in the brain, or simply BL, for short), in particular, (1) it has been unclear to date, if BP can be implemented exactly via BL, (2) there is a lack of local plasticity in BP, ie. weight updates require information that is not locally available, while BL utilizes only locally available information, and (3) there is a lack of autonomy in BP, ie. some external control over the neural network is required (eg. switching between prediction and learning stages requires changes to dynamics and synaptic plasticity rules), while BL works fully autonomously. Bridging such gaps, ie. understanding how BP can be approximated by BL, has been of major interest in both neuroscience and machine learning. Despite tremendous efforts, however, no previous model has bridged the gaps at a degree of demonstrating an equivalence to BP, instead, only approximations to BP have been shown.

Here, we present for the first time a framework within BL that bridges the above crucial gaps. We propose a BL model that (1) produces exactly the same updates of the neural weights as BP, while (2) employing local plasticity, ie. all neurons perform only local computations, done simultaneously. We then modify it to an alternative BL model that (3) also works fully autonomously. Overall, our work provides important evidence for the debate on the long-disputed question whether the brain can perform BP.

--- https://jxmo.notion.site/The-Weird-and-Wonderful-World-of-AI-Art-b9615a2e7278435b98380ff81ae1cf09 2020-10-18 ai/nn/diffusion ai/nn/transformer/clip --- https://www.medrxiv.org/content/10.1101/2021.08.25.21262631.full Causal and Associational Language in Observational Health Research: A systematic evaluation Noah A. Haber, Sarah E. Wieten, Julia M. Rohrer, Onyebuchi A. Arah, Peter W. G. Tennant, Elizabeth A. Stuart, Eleanor J. Murray, Sophie Pilleron, Sze Tung Lam, Emily Riederer, Sarah Jane Howcutt, Alison E. Simmons, Clémence Leyrat, Philipp Schoenegger, Anna Booman, Mi-Suk Kang Dufour, Ashley L. O’Donoghue, Rebekah Baglini, Stefanie Do, Mari De La Rosa Takashima, Thomas Rhys Evans, Daloha Rodriguez-Molina, Taym M. Alsalti, Daniel J. Dunleavy, Gideon Meyerowitz-Katz, Alberto Antonietti, Jose A. Calvache, Mark J. Kelson, Meg G. Salvia, Camila Olarte Parra, Saman Khalatbari-Soltani, Taylor McLinden, Arthur Chatton, Jessie Seiler, Andreea Steriu, Talal S. Alshihayb, Sarah E. Twardowski, Julia Dabravolskaj, Eric Au, Rachel A. Hoopsick, Shashank Suresh, Nicholas Judd, Sebastián Peña, Cathrine Axfors, Palwasha Khan, Ariadne E. Rivera Aguirre, Nnaemeka U. Odo, Ian Schmid, Matthew P. Fox 2021-12-16 2021-12-16 [("doi","10.1101/2021.08.25.21262631")] biology philosophy/epistemology psychology/cognitive-bias statistics/causality

We estimated the degree to which language used in the high profile medical/public health/epidemiology literature implied causality using language linking exposures to outcomes and action recommendations; examined disconnects between language and recommendations; identified the most common linking phrases; and estimated how strongly linking phrases imply causality.

We searched and screened for 1,170 articles from 18 high-profile journals (65 per journal) published from 2010–2019. Based on written framing and systematic guidance, 3 reviewers rated the degree of causality implied in abstracts and full text for exposure/outcome linking language and action recommendations.

Reviewers rated the causal implication of exposure/outcome linking language as None (no causal implication) in 13.8%, Weak 34.2%, Moderate 33.2%, and Strong 18.7% of abstracts. The implied causality of action recommendations was higher than the implied causality of linking sentences for 44.5% or commensurate for 40.3% of articles. The most common linking word in abstracts was “associate” (45.7%). Reviewer’s ratings of linking word roots were highly heterogeneous; over half of reviewers rated “association” as having at least some causal implication.

This research undercuts the assumption that avoiding “causal” words leads to clarity of interpretation in medical research.

Figure 5: Strength of causal implication ratings for the most common root linking words. This chart shows the distribution of ratings given by reviewers during the root word rating exercise. On the left side, they are sorted by median rating + the number of reviewers who would have to change their ratings in order for the rating to change. On the right, the chart is sorted alphabetically.
--- https://arxiv.org/abs/2202.10890#deepmind Hierarchical Perceiver Joao Carreira, Skanda Koppula, Daniel Zoran, Adria Recasens, Catalin Ionescu, Olivier Henaff, Evan Shelhamer, Relja Arandjelovic, Matt Botvinick, Oriol Vinyals, Karen Simonyan, Andrew Zisserman, Andrew Jaegle 2022-02-22 2022-02-22 [("doi","10.48550/arXiv.2202.10890")] ai/nn/transformer/attention/hierarchical ai/video/analysis

General perception systems such as Perceivers can process arbitrary modalities in any combination and are able to handle up to a few hundred thousand inputs. They achieve this generality by exclusively using global attention operations. This however hinders them from scaling up to the inputs sizes required to process raw high-resolution images or video.

In this paper, we show that some degree of locality can be introduced back into these models, greatly improving their efficiency while preserving their generality. To scale them further, we introduce a self-supervised approach that enables learning dense low-dimensional positional embeddings for very large signals.

We call the resulting model a Hierarchical Perceiver (HiP). HiP retains the ability to process arbitrary modalities, but now at higher-resolution and without any specialized preprocessing, improving over flat Perceivers in both efficiency and accuracy on the ImageNet, AudioSet and PASCAL VOC datasets.

--- https://arxiv.org/abs/2202.10784#sberbank RuCLIP—new models and experiments: a technical report Alex Shonenkov, Andrey Kuznetsov, Denis Dimitrov, Tatyana Shavrina, Daniil Chesakov, Anastasia Maltseva, Alena Fenogenova, Igor Pavlov, Anton Emelyanov, Sergey Markov, Daria Bakshandaeva, Vera Shybaeva, Andrey Chertok 2022-02-22 2022-02-22 [("doi","10.48550/arXiv.2202.10784")] ai/dataset ai/nn/transformer/clip

In the report, we propose 6 new implementations of ruCLIP model trained on our 240M pairs.

The accuracy results are compared with the original CLIP model with Ru-En translation (OPUS-MT) on 16 datasets from different domains.

Our best implementations outperform CLIP + OPUS-MT solution on most of the datasets in few-show and zero-shot tasks.

In the report, we briefly describe the implementations and concentrate on the conducted experiments. Inference execution time comparison is also presented in the report.

--- https://arxiv.org/abs/2202.09844 Sparsity Winning Twice: Better Robust Generalization from More Efficient Training Tianlong Chen, Zhenyu Zhang, Pengjun Wang, Santosh Balachandra, Haoyu Ma, Zehao Wang, Zhangyang Wang 2022-02-20 2022-02-20 [("doi","10.48550/arXiv.2202.09844")] ai/nn/sparsity/pruning

Recent studies demonstrate that deep networks, even robustified by the state-of-the-art adversarial training (AT), still suffer from large robust generalization gaps, in addition to the much more expensive training costs than standard training.

In this paper, we investigate this intriguing problem from a new perspective, ie. injecting appropriate forms of sparsity during adversarial training. We introduce two alternatives for sparse adversarial training: (1) static sparsity, by leveraging recent results from the lottery ticket hypothesis to identify critical sparse subnetworks arising from the early training; (2) dynamic sparsity, by allowing the sparse subnetwork to adaptively adjust its connectivity pattern (while sticking to the same sparsity ratio) throughout training.

We find both static and dynamic sparse methods to yield win-win: substantially shrinking the robust generalization gap and alleviating the robust overfitting, meanwhile saving training and inference FLOPs. Extensive experiments validate our proposals with multiple network architectures on diverse datasets, including CIFAR-10/100 and Tiny-ImageNet. For example, our methods reduce robust generalization gap and overfitting by 34.44% and 4.02%, with comparable robust/standard accuracy boosts and 87.83%/87.82% training/inference FLOPs savings on CIFAR-100 with ResNet-18. Besides, our approaches can be organically combined with existing regularizers, establishing new state-of-the-art results in AT.

Codes are available in Github.

--- https://arxiv.org/abs/2202.10430 Learning Causal Overhypotheses through Exploration in Children and Computational Models Eliza Kosoy, Adrian Liu, Jasmine Collins, David M. Chan, Jessica B. Hamrick, Nan Rosemary Ke, Sandy H. Huang, Bryanna Kaufmann, John Canny, Alison Gopnik 2022-02-21 2022-02-21 [("doi","10.48550/arXiv.2202.10430")] psychology reinforcement-learning/exploration statistics/causality

Despite recent progress in reinforcement learning (RL), RL algorithms for exploration still remain an active area of research. Existing methods often focus on state-based metrics, which do not consider the underlying causal structures of the environment, and while recent research has begun to explore RL environments for causal learning, these environments primarily leverage causal information through causal inference or induction rather than exploration. In contrast, human children—some of the most proficient explorers—have been shown to use causal information to great benefit.

In this work, we introduce a novel RL environment designed with a controllable causal structure, which allows us to evaluate exploration strategies used by both agents and children in an unified environment.

In addition, through experimentation on both computation models and children, we demonstrate that there are statistically-significant differences between information-gain optimal RL exploration in causal environments and the exploration of children in the same environments.

We conclude with a discussion of how these findings may inspire new directions of research into efficient exploration and disambiguation of causal structures for RL algorithms.

--- https://arxiv.org/abs/2202.10166 Diffusion Causal Models for Counterfactual Estimation Pedro Sanchez, Sotirios A. Tsaftaris 2022-02-21 2022-02-21 [("doi","10.48550/arXiv.2202.10166")] ai/nn/diffusion

We consider the task of counterfactual estimation from observational imaging data given a known causal structure. In particular, quantifying the causal effect of interventions for high-dimensional data with neural networks remains an open challenge.

Herein we propose Diff-SCM, a deep structural causal model that builds on recent advances of generative energy-based models. In our setting, inference is performed by iteratively sampling gradients of the marginal and conditional distributions entailed by the causal model. Counterfactual estimation is achieved by firstly inferring latent variables with deterministic forward diffusion, then intervening on a reverse diffusion process using the gradients of an anti-causal predictor w.r.t the input. Furthermore, we propose a metric for evaluating the generated counterfactuals.

We find that Diff-SCM produces more realistic and minimal counterfactuals than baselines on MNIST data and can also be applied to ImageNet data.

Code is available Github.

--- https://arxiv.org/abs/2202.08587 Gradients without Backpropagation Atılım Güneş Baydin, Barak A. Pearlmutter, Don Syme, Frank Wood, Philip Torr 2022-02-17 2022-02-17 [("doi","10.48550/arXiv.2202.08587")] ai/nn

[Extremely high variance] Using backpropagation to compute gradients of objective functions for optimization has remained a mainstay of machine learning. Backpropagation, or reverse-mode differentiation, is a special case within the general family of automatic differentiation algorithms that also includes the forward mode.

We present a method to compute gradients based solely on the directional derivative that one can compute exactly and efficiently via the forward mode. We call this formulation the forward gradient, an unbiased estimate of the gradient that can be evaluated in a single forward run of the function, entirely eliminating the need for backpropagation in gradient descent.

We demonstrate forward gradient descent in a range of problems, showing substantial savings in computation and enabling training up to twice as fast in some cases.

--- https://www.fastcompany.com/90674998/how-9-11-turned-a-new-site-called-wikipedia-into-historys-crowdsourced-front-page 2020-10-18 wikipedia --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4244864/ Quantitative relationships in delphinid neocortex Heidi S. Mortensen, Bente Pakkenberg, Maria Dam, Rune Dietz, Christian Sonne, Bjarni Mikkelsen, Nina Eriksen 2014 2020-10-18 [("doi","10.3389/fnana.2014.00132")] iq/animal psychology/neuroscience

[cf. Dicke & Roth 2016] Possessing large brains and complex behavioral patterns, cetaceans are believed to be highly intelligent. Their brains, which are the largest in the Animal Kingdom and have enormous gyrification compared with terrestrial mammals, have long been of scientific interest. Few studies, however, report total number of brain cells in cetaceans, and even fewer have used unbiased counting methods.

In this study, using stereological methods, we estimated the total number of cells in the neocortex of the long-finned pilot whale [not a whale but actually, an oceanic dolphin] (Globicephala melas) brain.

For the first time, we show that a species of dolphin has more neocortical neurons than any mammal studied to date including humans. These cell numbers are compared across various mammals with different brain sizes, and the function of possessing many neurons is discussed. We found that the long-finned pilot whale neocortex has ~37.2 × 109 neurons, which is almost twice as many as humans, and 127 × 109 glial cells.

Thus, the absolute number of neurons in the human neocortex is not correlated with the superior cognitive abilities of humans (at least compared to cetaceans) as has previously been hypothesized. However, as neuron density in long-finned pilot whales is lower than that in humans, their higher cell number appears to be due to their larger brain. Accordingly, our findings make an important contribution to the ongoing debate over quantitative relationships in the mammalian brain.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4685590/ Neuronal factors determining high intelligence Ursula Dicke, Gerhard Roth 2016 2020-10-19 [("doi","10.1098/rstb.2015.0180")] iq/high psychology/animal/bird/neuroscience psychology/neuroscience

Many attempts have been made to correlate degrees of both animal and human intelligence with brain properties. With respect to mammals, a much-discussed trait concerns absolute and relative brain size, either uncorrected or corrected for body size. However, the correlation of both with degrees of intelligence yields large inconsistencies, because although they are regarded as the most intelligent mammals, monkeys and apes, including humans, have neither the absolutely nor the relatively largest brains.

The best fit between brain traits and degrees of intelligence among mammals is reached by a combination of the number of cortical neurons, neuron packing density, interneuronal distance and axonal conduction velocity—factors that determine general information processing capacity (IPC), as reflected by general intelligence.

The highest IPC is found in humans, followed by the great apes, Old World and New World monkeys. The IPC of cetaceans and elephants is much lower because of a thin cortex, low neuron packing density and low axonal conduction velocity. By contrast, corvid and psittacid birds have very small and densely packed pallial neurons and relatively many neurons, which, despite very small brain volumes, might explain their high intelligence. The evolution of a syntactical and grammatical language in humans most probably has served as an additional intelligence amplifier, which may have happened in songbirds and psittacids in a convergent manner.

--- https://www.medrxiv.org/content/10.1101/19002204.full Reliability and validity of the UK Biobank cognitive tests Chloe Fawns-Ritchie, Ian J. Deary 2019-07-15 2020-10-19 [("doi","10.1101/19002204")] iq

UK Biobank is a health resource with data from over 500,000 adults. The participants have been assessed on cognitive function since baseline. The cognitive tests in UK Biobank are brief and bespoke, and are administered without supervision on a touchscreen computer. Psychometric information on the tests is limited.

The present study examined their concurrent validity and short-term test-retest reliability. A sample of 160 participants (mean age=62.59, SD=10.24) completed the UK Biobank cognitive assessment and a range of well-validated cognitive tests (‘reference tests’). 52 participants returned 4 weeks later to repeat the UK Biobank tests. Correlations were calculated between UK Biobank tests and the reference tests. 4-week test-retest correlations were calculated for UK Biobank tests.

UK Biobank cognitive tests showed a range of correlations with their respective reference tests, i.e. those tests that are thought to assess the same underlying cognitive ability (mean Pearson r=0.53, range=0.22 to 0.83, p ≤ 0.005). For test-retest reliability of the UK Biobank tests were moderate-to-high (mean Pearson r=0.55, range=0.40 to 0.89, p ≤ 0.003).

Despite the brief, non-standard nature of the UK Biobank cognitive tests, some showed substantial concurrent validity and test-retest reliability. These psychometric results provide currently-lacking information on the validity of the UK Biobank cognitive tests.

--- https://x.com/big_dream_io/status/1497390962322071552 2020-10-19 ai/nn/transformer/clip/sample --- https://x.com/HvnsLstAngel/status/1497429287095263233 2020-10-19 ai/nn/transformer/clip/sample --- https://www.medrxiv.org/content/10.1101/2022.02.19.22271223.full Multivariate Genomic Architecture of Cortical Thickness and Surface Area at Multiple Levels of Analysis Andrew David Grotzinger, Travis T. Mallard, Zhaowen Liu, Jakob Seidlitz, Tian Ge, Jordan W. Smoller 2022-02-25 2022-02-25 [("doi","10.1101/2022.02.19.22271223")] genetics/heritable psychiatry psychology/neuroscience

Recent work in imaging genetics suggests high levels of genetic overlap within cortical regions for cortical thickness (CT) and surface area (SA).

We model this relationship by applying Genomic Structural Equation Modeling (Genomic SEM) to parsimoniously define 5 genomic brain factors for both CT and SA. We reify these factors by demonstrating the generalizability of the model in a semi-independent sample and show that the factors align with biologically and functionally relevant parcellations of the cortex. We apply Stratified Genomic SEM to identify specific categories of genes (eg. neuronal cell types) that are disproportionately associated with pleiotropy across specific subclusters of brain regions, as indexed by the genomic factors. Finally, we examine genetic associations with psychiatric and cognitive correlates, finding that SA is associated with both broad aspects of cognitive function and specific risk pathways for psychiatric disorders.

These analyses provide key insights into the multivariate genomic architecture of two critical features of the cerebral cortex.

--- https://x.com/big_dream_io/status/1497848827105083393 2020-10-19 ai/nn/transformer/clip/sample --- https://x.com/EMostaque/status/1497879010763804673 2020-10-19 ai/nn/transformer/clip/sample --- https://www.oilshell.org/blog/2022/02/diagrams.html The Internet Was Designed With a Narrow Waist 2020-10-19 cs/end-to-end-principle --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5430617/ Archaic Adaptive Introgression in TBX15/WARS2 Fernando Racimo, David Gokhman, Matteo Fumagalli, Amy Ko, Torben Hansen, Ida Moltke, Anders Albrechtsen, Liran Carmel, Emilia Huerta-Sánchez, Rasmus Nielsen 2017 2020-10-19 [("doi","10.1093/molbev/msw283")] genetics/selection/natural/human

A recent study conducted the first genome-wide scan for selection in Inuit from Greenland using single-nucleotide polymorphism chip data. This groundbreaking research offers insights into how genetic selection operates among the Inuit population, emphasizing the role of environmental adaptions in shaping human genetic diversity.

Here, we report that selection in the region with the second most extreme signal of positive selection in Greenlandic Inuit favored a deeply divergent haplotype that is closely related to the sequence in the Denisovan genome, and was likely introgressed from an archaic population. The region contains two genes, WARS2 and TBX15, and has previously been associated with adipose tissue differentiation and body-fat distribution in humans. This finding sheds light on the genetic mechanisms underlying the adaptation of the Inuit to their unique Arctic environment, revealing a direct link to ancient human relatives.

Furthermore, it is associated with changes in expression of WARS2 and TBX15 in multiple tissues including the adrenal gland and subcutaneous adipose tissue, and with regional DNA methylation changes in TBX15. This illustrates the complex molecular effects of the introgressed allele, affecting not only the phenotypic traits related to fat distribution and adaptation to cold climates but also offering a broader understanding of the functional genomics in adaptive evolution.

We show that the adaptively introgressed allele has been under selection in a much larger geographic region than just Greenland. This expands the significance of the findings, indicating that the evolutionary advantages conferred by the allele are not limited to the Inuit but have been beneficial in a wider context, suggesting a pervasive influence on human adaptation strategies across various populations.

This study not only opens new avenues for understanding human evolutionary history but also highlights the importance of considering archaic introgression in studying modern human diversity. Furthermore, it provides valuable resources for researchers interested in the genetics of adaptation, including the specific datasets and genetic loci identified here.

--- https://x.com/mathbbN/status/1497844587557122048 2020-10-19 ai/nn/gan/stylegan/anime --- https://arxiv.org/abs/2202.12211#google Self-Distilled StyleGAN: Towards Generation from Internet Photos Ron Mokady, Michal Yarom, Omer Tov, Oran Lang, Daniel Cohen-Or, Tali Dekel, Michal Irani, Inbar Mosseri 2022-02-24 2022-02-24 [("doi","10.48550/arXiv.2202.12211")] ai/dataset ai/nn/gan/stylegan/anime ai/nn/sparsity/knowledge-distillation ai/scaling

[anime example] StyleGAN is known to produce high-fidelity images, while also offering unprecedented semantic editing. However, these fascinating abilities have been demonstrated only on a limited set of datasets, which are usually structurally aligned and well curated.

In this paper, we show how StyleGAN can be adapted to work on raw uncurated images collected from the Internet. Such image collections impose two main challenges to StyleGAN: they contain many outlier images, and are characterized by a multi-modal distribution. Training StyleGAN on such raw image collections results in degraded image synthesis quality. To meet these challenges, we proposed a StyleGAN-based self-distillation approach, which consists of two main components: (1) A generative-based self-filtering of the dataset to eliminate outlier images, in order to generate an adequate training set, and (2) Perceptual clustering of the generated images to detect the inherent data modalities, which are then employed to improve StyleGAN’s “truncation trick” in the image synthesis process.

The presented technique enables the generation of high-quality images, while minimizing the loss in diversity of the data. Through qualitative and quantitative evaluation, we demonstrate the power of our approach to new challenging and diverse domains collected from the Internet.

New datasets and pre-trained models are available at https://self-distilled-stylegan.github.io/.

--- https://www.biorxiv.org/content/10.1101/2022.02.23.481601.full Performance reserves in brain-imaging-based phenotype prediction Marc-Andre Schulz, Danilo Bzdok, Stefan Haufe, John-Dylan Haynes, Kerstin Ritter 2022-02-25 2022-02-25 [("doi","10.1101/2022.02.23.481601")] ai/scaling psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Machine learning studies have shown that various phenotypes can be predicted from structural and functional brain images. However, in most such studies, prediction performance ranged from moderate to disappointing. It is unclear whether prediction performance will substantially improve with larger sample sizes or whether insufficient predictive information in brain images impedes further progress.

Here, we systematically assess the effect of sample size on prediction performance using sample sizes far beyond what is possible in common neuroimaging studies. We project 3–9× improvements in prediction performance for behavioral and mental health phenotypes when moving from 1,000 to one million samples. Moreover, we find that moving from single imaging modalities to multimodal input data can lead to further improvements in prediction performance, often on par with doubling the sample size.

Our analyses reveal considerable performance reserves for neuroimaging-based phenotype prediction. Machine learning models may benefit much more from extremely large neuroimaging datasets than currently believed.

--- https://x.com/NeuralBricolage/status/1498106483392892929 2020-10-20 ai/nn/transformer/clip/sample --- https://www.theatlantic.com/ideas/archive/2022/02/social-media-illness-teen-girls/622916/ The Twitches That Spread on Social Media 2020-10-20 psychiatry sociology --- https://x.com/GlennIsZen/status/1498354488045977611 2020-10-20 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2202.12837#facebook Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, Luke Zettlemoyer 2022-02-25 2022-02-25 [("doi","10.48550/arXiv.2202.12837")] ai/nn/transformer/gpt/3/nonfiction

Large language models (LMs) are able to in-context learn—perform a new task via inference alone by conditioning on a few input-label pairs (demonstrations) and making predictions for new inputs. However, there has been little understanding of how the model learns and which aspects of the demonstrations contribute to end task performance.

In this paper, we show that ground truth demonstrations are in fact not required—randomly replacing labels in the demonstrations barely hurts performance, consistently over 12 different models including GPT-3. Instead, we find that other aspects of the demonstrations are the key drivers of end task performance, including the fact that they provide a few examples of (1) the label space, (2) the distribution of the input text, and (3) the overall format of the sequence. Together, our analysis provides a new way of understanding how and why in-context learning works, while opening up new questions about how much can be learned from large language models through inference alone.

--- https://justine.lol/lambda/ Lambda Calculus in 383 Bytes 2020-10-20 cs/lisp --- https://x.com/advadnoun/status/1498475557310406663 2020-10-20 ai/nn/transformer/clip/sample --- https://www.forefront.ai/blog-posts/how-to-fine-tune-gpt-neox 2020-10-20 ai/nn/transformer/gpt --- https://x.com/Nearcyan/status/1498760040378867712 2020-10-20 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2202.11822#google Using natural language prompts for machine translation Xavier Garcia, Orhan Firat 2022-02-23 2022-02-23 [("doi","10.48550/arXiv.2202.11822")] ai/nn/sampling ai/nn/transformer/gpt/lamda ai/nn/transformer/t5 ai/text-style-transfer

[Uses GPT-3 & FLAN as well as mT5] We explore the use of natural language prompts for controlling various aspects of the outputs generated by machine translation models.

We demonstrate that natural language prompts allow us to influence properties like formality or specific dialect of the output. We show that using language names to control the output language of multilingual translation models enables positive transfer for unseen language pairs. This unlocks the ability to translate into languages not seen during fine-tuning by using their English names.

We investigate how scale, number of pre-training steps, number of languages in fine-tuning, and language similarity affect this phenomenon.

--- https://arxiv.org/abs/2202.14020 State-of-the-Art in the Architecture, Methods and Applications of StyleGAN Amit H. Bermano, Rinon Gal, Yuval Alaluf, Ron Mokady, Yotam Nitzan, Omer Tov, Or Patashnik, Daniel Cohen-Or 2022-02-28 2022-02-28 [("doi","10.48550/arXiv.2202.14020")] ai/nn/gan/stylegan

Generative Adversarial Networks (GANs) have established themselves as a prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks.

This state-of-the-art report covers the StyleGAN architecture, and the ways it has been employed since its conception, while also analyzing its severe limitations. It aims to be of use for both newcomers, who wish to get a grasp of the field, and for more experienced readers that might benefit from seeing current research trends and existing tools laid out.

Among StyleGAN’s most interesting aspects is its learned latent space. Despite being learned with no supervision, it is surprisingly well-behaved and remarkably disentangled. Combined with StyleGAN’s visual quality, these properties gave rise to unparalleled editing capabilities. However, the control offered by StyleGAN is inherently limited to the generator’s learned distribution, and can only be applied to images generated by StyleGAN itself.

Seeking to bring StyleGAN’s latent control to real-world scenarios, the study of GAN inversion and latent space embedding has quickly gained in popularity. Meanwhile, this same study has helped shed light on the inner workings and limitations of StyleGAN. We map out StyleGAN’s impressive story through these investigations, and discuss the details that have made StyleGAN the go-to generator.

We further elaborate on the visual priors StyleGAN constructs, and discuss their use in downstream discriminative tasks.

Looking forward, we point out StyleGAN’s limitations and speculate on current trends and promising directions for future research, such as task and target specific fine-tuning.

--- https://arxiv.org/abs/2202.13257#microsoft Controllable Natural Language Generation with Contrastive Prefixes Jing Qian, Li Dong, Yelong Shen, Furu Wei, Weizhu Chen 2022-02-27 2022-02-27 [("doi","10.48550/arXiv.2202.13257")] ai/nn/sampling ai/nn/transformer/gpt/2

To guide the generation of large pretrained language models (LM), previous work has focused on directly fine-tuning the language model or utilizing an attribute discriminator.

In this work, we propose a novel lightweight framework for controllable GPT-2 generation, which utilizes a set of small attribute-specific vectors, called prefixes, to steer natural language generation. Different from prefix-tuning, where each prefix is trained independently, we take the relationship among prefixes into consideration and train multiple prefixes simultaneously. We propose a novel supervised method and also an unsupervised method to train the prefixes for single-aspect control while the combination of these two methods can achieve multi-aspect control.

Experimental results on both single-aspect and multi-aspect control show that our methods can guide generation towards the desired attributes while keeping high linguistic quality.

--- https://arxiv.org/abs/2202.13169 PolyCoder: A Systematic Evaluation of Large Language Models of Code Frank F. Xu, Uri Alon, Graham Neubig, Vincent J. Hellendoorn 2022-02-26 2022-02-26 [("doi","10.48550/arXiv.2202.13169")] ai/nn/transformer/gpt/codex

Large language models (LMs) of code have recently shown tremendous promise in completing code and synthesizing code from natural language descriptions. However, the current state-of-the-art code LMs (eg. Codex (Chen et al 2021)) are not publicly available, leaving many questions about their model and data design decisions.

We aim to fill in some of these blanks through a systematic evaluation of the largest existing models: Codex, GPT-J, GPT-Neo, GPT-NeoX-20B, and CodeParrot, across various programming languages. Although Codex itself is not open-source, we find that existing open-source models do achieve close results in some programming languages, although targeted mainly for natural language modeling.

We further identify an important missing piece in the form of a large open-source model trained exclusively on a multi-lingual corpus of code. We release a new model, PolyCoder, with 2.7b parameters based on the GPT-2 architecture, which was trained on 249GB of code across 12 programming languages on a single machine. In the C programming language, PolyCoder outperforms all models including Codex.

Our trained models are open-source and publicly available at https://github.com/VHellendoorn/Code-LMs, which enables future research and application in this area.

--- https://github.com/sberbank-ai/ru-gpts 2020-10-21 ai/nn/transformer/gpt --- https://arxiv.org/abs/2203.00645 Variational Autoencoders Without the Variation Gregory A. Daly, Jonathan E. Fieldsend, Gavin Tabor 2022-03-01 2022-03-01 [("doi","10.48550/arXiv.2203.00645")] ai/nn/cnn ai/nn/vae ai/scaling

Variational autoencoders (VAE) are a popular approach to generative modeling. However, exploiting the capabilities of VAEs in practice can be difficult. Recent work on regularized and entropic autoencoders have begun to explore the potential, for generative modeling, of removing the variational approach and returning to the classic deterministic autoencoder (DAE) with additional novel regularization methods.

In this paper we empirically explore the capability of DAEs for image generation without additional novel methods and the effect of the implicit regularization and smoothness of large networks.

We find that DAEs can be used successfully for image generation without additional loss terms, and that many of the useful properties of VAEs can arise implicitly from sufficiently large convolutional encoders and decoders when trained on CIFAR-10 and CelebA.

--- https://arxiv.org/abs/2203.00242 Unsupervised Vision-and-Language Pre-training via Retrieval-based Multi-Granular Alignment Mingyang Zhou, Licheng Yu, Amanpreet Singh, Mengjiao Wang, Zhou Yu, Ning Zhang 2022-03-01 2022-03-01 [("doi","10.48550/arXiv.2203.00242")] ai/nn/retrieval ai/nn/transformer/clip

Vision-and-Language (V+L) pre-training models have achieved tremendous success in recent years on various multi-modal benchmarks. However, the majority of existing models require pre-training on a large set of parallel image-text data, which is costly to collect, compared to image-only or text-only data.

In this paper, we explore unsupervised Vision-and-Language pre-training (UVLP) to learn the cross-modal representation from non-parallel image and text datasets. We found two key factors that lead to good unsupervised V+L pre-training without parallel data: (1) joint image-and-text input (2) overall image-text alignment (even for non-parallel data). Accordingly, we propose a novel unsupervised V+L pre-training curriculum for non-parallel texts and images.

We first construct a weakly aligned image-text corpus via a retrieval-based approach, then apply a set of multi-granular alignment pre-training tasks, including region-to-tag, region-to-phrase, and image-to-sentence alignment, to bridge the gap between the two modalities. A comprehensive ablation study shows each granularity is helpful to learn a stronger pre-trained model. We adapt our pre-trained model to a set of V+L downstream tasks, including VQA, NLVR2, Visual Entailment, and RefCOCO+. Our model achieves the state-of-art performance in all these tasks under the unsupervised setting.

--- https://x.com/GlennIsZen/status/1498810956599726084 2020-10-21 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2202.12837#facebook Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, Luke Zettlemoyer 2022-02-25 2022-02-25 [("doi","10.48550/arXiv.2202.12837")] ai/nn/transformer/gpt/3/nonfiction reinforcement-learning/meta-learning

[blog; cf. InstructGPT; En → Fr prompts; self-distillation translation; meta-learning] Large language models (LMs) are able to in-context learn—perform a new task via inference alone by conditioning on a few input-label pairs (demonstrations) and making predictions for new inputs. However, there has been little understanding of how the model learns and which aspects of the demonstrations contribute to end task performance.

In this paper, we show that ground truth demonstrations are in fact not required—randomly replacing labels in the demonstrations barely hurts performance, consistently over 12 different models including GPT-3. Instead, we find that other aspects of the demonstrations are the key drivers of end task performance, including the fact that they provide a few examples of (1) the label space, (2) the distribution of the input text, and (3) the overall format of the sequence.

Together, our analysis provides a new way of understanding how and why in-context learning works, while opening up new questions about how much can be learned from large language models through inference alone. [code]

--- https://x.com/EMostaque/status/1499174705814724614 2020-10-21 ai/nn/transformer/clip/sample --- /doc/psychology/personality/2022-wilmot.pdf Agreeableness and Its Consequences: A Quantitative Review of Meta-Analytic Findings Michael P. Wilmot, Deniz S. Ones 2022-02-28 2022-02-28 [("doi","10.1177/10888683211073007")] psychology/personality

Agreeableness impacts people and real-world outcomes.

In the most comprehensive quantitative review to date, we summarize results from 142 meta-analyses reporting effects for 275 variables, which represent n > 1.9 million participants from k > 3,900 studies. Arranging variables by their content and type, we use an organizational framework of 16 conceptual categories that presents a detailed account of Agreeableness’ external relations.

Overall, the trait has effects in a desirable direction for 93% of variables (grand mean ρM = 0.16). We also review lower order trait evidence for 42 variables from 20 meta-analyses.

Using these empirical findings, in tandem with existing theory, we synthesize 8 general themes that describe Agreeableness’ characteristic functioning across variables: self-transcendence, contentment, relational investment, teamworking, work investment, lower results emphasis, social norm orientation, and social integration.

We conclude by discussing potential boundary conditions of findings, contributions and limitations of our review, and future research directions.

--- https://arxiv.org/abs/2203.00854 FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours Shenggan Cheng, Ruidong Wu, Zhongming Yu, Binrui Li, Xiwen Zhang, Jian Peng, Yang You 2022-03-02 2022-03-02 [("doi","10.48550/arXiv.2203.00854")] ai/nn/transformer/alphafold ai/scaling

Protein structure prediction is an important method for understanding gene translation and protein function in the domain of structural biology. AlphaFold introduced the Transformer model to the field of protein structure prediction with atomic accuracy. However, training and inference of the AlphaFold model are time-consuming and expensive because of the special performance characteristics and huge memory consumption.

In this paper, we propose FastFold, a highly efficient implementation of protein structure prediction model for training and inference. FastFold includes a series of GPU optimizations based on a thorough analysis of AlphaFold’s performance. Meanwhile, with Dynamic Axial Parallelism and Duality Async Operation, FastFold achieves high model parallelism scaling efficiency, surpassing existing popular model parallelism techniques.

Experimental results show that FastFold reduces overall training time from 11 days to 67 hours [2.8 days] and achieves 7.5–9.5× speedup for long-sequence inference. Furthermore, we scaled FastFold to 512 GPUs and achieved an aggregate of 6.02 PetaFLOPs with 90.1% parallel efficiency.

The implementation can be found at Github.

--- https://www.biorxiv.org/content/10.1101/2022.02.28.481967.full Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy and improves prediction Guy Hindley, Alexey A. Shadrin, Dennis van der Meer, Nadine Parker, Weiqiu Cheng, Kevin S. O’Connell, Shahram Bahrami, Aihua Lin, Naz Karadag, Borge Holen, Thomas Bjella, Chun Chieh Fan, Torill Uelan, Srdjan Djurovic, Olav B. Smeland, Oleksandr Frei, Anders Martin Dale, Ole A. Andreassen 2022-03-02 2022-03-02 [("doi","10.1101/2022.02.28.481967")] genetics/heritable/correlation iq psychology/personality/conscientiousness

Personality and cognition are heritable mental traits, and their genetic determinants may be distributed across interconnected brain functions. However, previous studies have employed univariate approaches which reduce complex traits to summary measures.

We applied the “pleiotropy-informed” multivariate omnibus statistical test (MOSTest) to genome-wide association studies (GWAS) of 35 item and task-level measures of neuroticism and cognition from the UK Biobank (n = 336,993). We identified 431 genetic loci and found evidence of abundant pleiotropy across personality and cognitive domains. Functional characterisation implicated genes with tissue-specific expression in all tested brain tissues and enriched in brain-specific gene-sets.

We conditioned independent GWAS of the Big 5 personality traits and cognition on our multivariate findings, which boosted genetic discovery in other personality traits and improved polygenic prediction. These findings advance our understanding of the polygenic architecture of complex mental traits, indicating a prominence of pleiotropic genetic effects across higher-order domains of mental function.

--- https://arxiv.org/abs/2203.00352 VAPO: Affordance Learning from Play for Sample-Efficient Policy Learning Jessica Borja-Diaz, Oier Mees, Gabriel Kalweit, Lukas Hermann, Joschka Boedecker, Wolfram Burgard 2022-03-01 2022-03-01 [("doi","10.48550/arXiv.2203.00352")] reinforcement-learning/exploration reinforcement-learning/imitation-learning reinforcement-learning/model reinforcement-learning/robot

Robots operating in human-centered environments should have the ability to understand how objects function: what can be done with each object, where this interaction may occur, and how the object is used to achieve a goal.

To this end, we propose a novel approach that extracts a self-supervised visual affordance model from human teleoperated play data and leverages it to enable efficient policy learning and motion planning. We combine model-based planning with model-free deep reinforcement learning (RL) to learn policies that favor the same object regions favored by people, while requiring minimal robot interactions with the environment.

We evaluate our algorithm, Visual Affordance-guided Policy Optimization (VAPO), with both diverse simulation manipulation tasks and real world robot tidy-up experiments to demonstrate the effectiveness of our affordance-guided policies.

We find that our policies train 4× faster than the baselines and generalize better to novel objects because our visual affordance model can anticipate their affordance regions.

--- https://www.reddit.com/r/DiscoDiffusion/comments/tcfk8y/i_accidentally_made_a_furry_details_inside/ 2020-10-22 ai/anime ai/nn/transformer/clip/sample --- https://x.com/Ted_Underwood/status/1500455986200518667 2020-10-22 ai/nn/transformer/clip/sample --- https://www.technologyreview.com/2022/02/23/1045016/ai-deepmind-demis-hassabis-alphafold/ DeepMind's AlphaFold changed how researchers work 2020-10-22 ai/nn/transformer/alphafold --- https://x.com/EMostaque/status/1502781262871867393 2020-10-22 ai/nn/transformer/clip/sample --- https://x.com/NeuralBricolage/status/1495444220647198729 2020-10-22 ai/nn/transformer/clip/sample --- https://x.com/EMostaque/status/1496903685930033157?s 2020-10-22 ai/nn/transformer/clip/sample --- https://x.com/big_dream_io/status/1495080748617027592 2020-10-22 ai/nn/transformer/clip/sample --- https://x.com/quasimondo/status/1495839611561598982 2020-10-22 ai/nn/transformer/clip/sample --- https://x.com/logodaedalus/status/1494141844300595201 2020-10-22 ai/nn/transformer/clip/sample --- https://x.com/images_ai/status/1492056302452883483 2020-10-23 ai/nn/transformer/clip/sample --- https://www.atlasobscura.com/articles/eating-tea The Medieval Influencer Who Convinced the World to Drink Tea 2020-10-23 tea --- https://www.biorxiv.org/content/10.1101/2022.02.22.481461.full Characterization of Arabian Peninsula whole exomes: exploring high inbreeding features Joana C. Ferreira, Farida Alshamali, Luisa Pereira, Veronica Fernandes 2022-02-22 2022-02-22 [("doi","10.1101/2022.02.22.481461")] genetics/heritable/rare

The exome (WES) capture enriched for UTRs on 90 Arabian Peninsula (AP) populations contributed nearly 20,000 new variants from a total over 145,000 total variants. Almost half of these variants were in UTR3, reflecting the low effort we have dedicated to cataloguing these regions, which can bear an important proportion of functional variants, as being discovered in genome-wide association studies.

By applying several pathogenic predicting tools, we have demonstrated the high burden in potentially deleterious variants (especially in nonsynonymous and UTR variants located in genes that have been associated mainly with neurologic disease and congenital malformations) contained in AP WES, and the burden was as high as the consanguinity level (inferred as sum of runs of homozygosity, SROH) increased. Arabians had twice SROH values in relation to Europeans and East Asians, and within AP, Saudi Arabia had the highest values and Oman the lowest.

We must pursuit cataloguing diversity in populations with high consanguinity, as the potentially pathogenic variants are not eliminated by genetic drift as much as in less consanguineous populations.

--- https://www.biorxiv.org/content/10.1101/2022.03.12.484069.full Assessing cats' (Felis catus) sensitivity to human pointing gestures Margaret Maeses, Claudia A. F. Wascher 2022-03-13 2022-03-13 [("doi","10.1101/2022.03.12.484069")] cat/psychology

A wide range of non-human animal species have been shown to be able to respond to human referential signals, such as pointing gestures. The aim of the present study was to replicate previous findings showing cats to be sensitive to human pointing cues (Miklósi et al 2005).

In our study, we presented two types of human pointing gestures—momentary direct pointing and momentary cross-body pointing. We tested nine rescue cats in a two-way object choice task.

On a group level, the success rate of cats was 74.4 percentage. Cats performed above chance level in both the direct pointing and cross-body pointing condition. Trial number, rewarded side and type of gesture did not affect the cats’ performance in the experiment. On an individual level, 5⁄7 cats who completed 20 trials, performed above chance level. Two cats only completed 10 trials. One of them succeeded in 8, the other in 6 of these.

The results of our study replicate previous findings of cats being responsive to human direct pointing cues and add additional knowledge about their ability to follow cross-body pointing cues. Our results highlight a domestic species, socialized in a group setting, to possess heterospecific communication skills, however we have to consider parsimonious explanations, such as local and stimulus enhancement.

--- https://x.com/EMostaque/status/1502954507826868224 2020-10-23 ai/nn/transformer/clip/sample --- https://x.com/jd_pressman/status/1502951153264254976 2020-10-23 ai/nn/transformer/clip/sample --- https://x.com/Flomerboy/status/1502917129024126985 2020-10-23 ai/nn/transformer/clip/sample --- https://forrt.org/reversals/ 2020-10-23 statistics/bias --- https://www.biorxiv.org/content/10.1101/2022.03.11.484043.full State-of-the-Art Estimation of Protein Model Accuracy using AlphaFold James P. Roney, Sergey Ovchinnikov 2022-03-12 2022-03-12 [("doi","10.1101/2022.03.11.484043")] ai/nn/transformer/alphafold

The problem of predicting a protein’s 3D structure from its primary amino acid sequence is a long-standing challenge in structural biology. Recently, approaches like AlphaFold have achieved remarkable performance on this task by combining deep learning techniques with coevolutionary data from multiple sequence alignments of related protein sequences. The use of coevolutionary information is critical to these models’ accuracy, and without it their predictive performance drops considerably. In living cells, however, the 3D structure of a protein is fully determined by its primary sequence and the biophysical laws that cause it to fold into a low-energy configuration. Thus, it should be possible to predict a protein’s structure from only its primary sequence by learning a highly-accurate biophysical energy function.

We provide evidence that AlphaFold has learned such an energy function, and uses coevolution data to solve the global search problem of finding a low-energy conformation.

We demonstrate that AlphaFold’s learned potential function can be used to rank the quality of candidate protein structures with state-of-the-art accuracy, without using any coevolution data.

Finally, we propose a method for utilizing this potential function to predict protein structures without the need for MSAs.

--- https://arxiv.org/abs/2103.04000#facebook Off-Belief Learning Hengyuan Hu, Adam Lerer, Brandon Cui, David Wu, Luis Pineda, Noam Brown, Jakob Foerster 2021-03-06 2021-03-06 [("doi","10.48550/arXiv.2103.04000")] reinforcement-learning/imperfect-information/hanabi

The standard problem setting in Dec-POMDPs is self-play, where the goal is to find a set of policies that play optimally together. Policies learned through self-play may adopt arbitrary conventions and implicitly rely on multi-step reasoning based on fragile assumptions about other agents’ actions and thus fail when paired with humans or independently trained agents at test time.

To address this, we present off-belief learning (OBL). At each timestep OBL agents follow a policy π1 that is optimized assuming past actions were taken by a given, fixed policy (π0), but assuming that future actions will be taken by π1. When π0 is uniform random, OBL converges to an optimal policy that does not rely on inferences based on other agents’ behavior (an optimal grounded policy). OBL can be iterated in a hierarchy, where the optimal policy from one level becomes the input to the next, thereby introducing multi-level cognitive reasoning in a controlled manner. Unlike existing approaches, which may converge to any equilibrium policy, OBL converges to an unique policy, making it suitable for zero-shot coordination (ZSC).

OBL can be scaled to high-dimensional settings with a fictitious transition mechanism and shows strong performance in both a toy-setting and the benchmark human-AI & ZSC problem Hanabi.

--- https://www.quantamagazine.org/in-new-math-proofs-artificial-intelligence-plays-to-win-20220307/ 2020-10-23 math reinforcement-learning --- https://arxiv.org/abs/2104.14516 Constructions in combinatorics via neural networks Adam Zsolt Wagner 2021-04-29 2021-04-29 [("doi","10.48550/arXiv.2104.14516")] math reinforcement-learning/model reinforcement-learning/model-free

We demonstrate how by using a reinforcement learning algorithm, the deep cross-entropy method, one can find explicit constructions and counterexamples to several open conjectures in extremal combinatorics and graph theory.

Amongst the conjectures we refute are a question of Brualdi and Cao about maximizing permanents of pattern avoiding matrices, and several problems related to the adjacency and distance eigenvalues of graphs.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5127298/ What grades and achievement tests measure Lex Borghans, Bart H. H. Golsteyn, James J. Heckman, John Eric Humphries 2016 2020-10-24 [("doi","10.1073/pnas.1601135113")] iq/ses psychology/personality

Intelligence quotient (IQ), grades, and scores on achievement tests are widely used as measures of cognition, but the correlations among them are far from perfect.

This paper uses a variety of datasets to show that personality and IQ predict grades and scores on achievement tests.

Personality is relatively more important in predicting grades than scores on achievement tests. IQ is relatively more important in predicting scores on achievement tests. Personality is generally more predictive than IQ on a variety of important life outcomes. Both grades and achievement tests are substantially better predictors of important life outcomes than IQ.

The reason is that both capture personality traits that have independent predictive power beyond that of IQ.

--- https://design.tutsplus.com/articles/why-is-it-so-hard-to-draw-from-imagination-heres-how-to-do-it--cms-22967 Why Is It So Hard to Draw From Imagination? Here's How to Do It! 2020-10-24 psychology/cognitive-bias/illusion-of-depth --- https://www.biorxiv.org/content/10.1101/2020.08.21.257758.full Towards Reproducible Brain-Wide Association Studies Scott Marek, Brenden Tervo-Clemmens, Finnegan J. Calabro, David F. Montez, Benjamin P. Kay, Alexander S. Hatoum, Meghan Rose Donohue, William Foran, Ryl, L. Miller, Eric Feczko, Oscar Miranda-Dominguez, Alice M. Graham, Eric A. Earl, Anders J. Perrone, Michaela Cordova, Olivia Doyle, Lucille A. Moore, Greg Conan, Johnny Uriarte, Kathy Snider, Angela Tam, Jianzhong Chen, Dillan J. Newbold, Annie Zheng, Nicole A. Seider, Andrew N. Van, Timothy O. Laumann, Wesley K. Thompson, Deanna J. Greene, Steven E. Petersen, Thomas E. Nichols, B. T. Thomas Yeo, Deanna M. Barch, Hugh Garavan, Beatriz Luna, Damien A. Fair, Nico U. F. Dosenbach 2020-08-22 2020-10-24 [("doi","10.1101/2020.08.21.257758")] psychology/neuroscience statistics/bias

Magnetic resonance imaging (MRI) continues to drive many important neuroscientific advances. However, progress in uncovering reproducible associations between individual differences in brain structure/function and behavioral phenotypes (eg. cognition, mental health) may have been undermined by typical neuroimaging sample sizes (median n = 25)1,2.

Leveraging the Adolescent Brain Cognitive Development (ABCD) Study3 (n = 11,878), we estimated the effect sizes and reproducibility of these brain-wide associations studies (BWAS) as a function of sample size.

The very largest, replicable brain-wide associations for univariate and multivariate methods werer = 0.14 andr = 0.34, respectively. In smaller samples, typical for brain-wide association studies (BWAS), irreproducible, inflated effect sizes were ubiquitous, no matter the method (univariate, multivariate).

Until sample sizes started to approach consortium-levels, BWAS were underpowered and statistical errors assured. Multiple factors contribute to replication failures4–6; here, we show that the pairing of small brain-behavioral phenotype effect sizes with sampling variability is a key element in wide-spread BWAS replication failure. Brain-behavioral phenotype associations stabilize and become more reproducible with sample sizes of N⪆2,000. While investigator-initiated brain-behavior research continues to generate hypotheses and propel innovation, large consortia are needed to usher in a new era of reproducible human brain-wide association studies.

--- https://www.biorxiv.org/content/10.1101/696864.full Widespread associations between grey matter structure and the human phenome Baptiste Couvy-Duchesne, Lachlan T. Strike, Futao Zhang, Yan Holtz, Zhili Zheng, Kathryn E. Kemper, Loïc Yengo, Olivier Colliot, Margaret J. Wright, Naomi R. Wray, Jian Yang, Peter M. Visscher 2019-07-09 2020-10-24 [("doi","10.1101/696864")] iq psychiatry/depression psychology/neuroscience statistics/variance-component

Our linear mixed model approach unifies association and prediction analyses for highly dimensional vertex-wise MRI data

Grey-matter structure is associated with measures of substance use, blood assay results, education or income level, diet, depression, being a twin as well as cognition domains

Body size (height, weight, BMI, waist and hip circumference) is an important source of covariation between the phenome and grey-matter structure

Grey-matter scores quantify grey-matter based risk for the associated traits and allow to study phenotypes not collected

The most general cortical processing (“fsaverage” mesh with no smoothing) maximises the brain-morphometricity for all UKB phenotypes

The recent availability of large-scale neuroimaging cohorts (here the UK Biobank [UKB] and the Human Connectome Project [HCP]) facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. We tested the association between 654,386 vertex-wise measures of cortical and subcortical morphology (from T1w and T2w MRI images) and behavioral, cognitive, psychiatric and lifestyle data. We found a statistically-significant association of grey-matter structure with 58⁄167 UKB phenotypes spanning substance use, blood assay results, education or income level, diet, depression, being a twin as well as cognition domains (UKB discovery sample: n = 9,888). Twenty-three of the 58 associations replicated (UKB replication sample: n = 4,561; HCP, n = 1,110). In addition, differences in body size (height, weight, BMI, waist and hip circumference, body fat percentage) could account for a substantial proportion of the association, providing possible insight into previous MRI case-control studies for psychiatric disorders where case status is associated with body mass index. Using the same linear mixed model, we showed that most of the associated characteristics (eg. age, sex, body size, diabetes, being a twin, maternal smoking, body size) could be predicted using all the brain measurements in out-of-sample prediction. Finally, we demonstrated other applications of our approach including a Region Of Interest (ROI) analysis that retain the vertex-wise complexity and ranking of the information contained across MRI processing options.

--- https://github.com/google-deepmind/mctx Monte Carlo tree search in JAX 2020-10-24 reinforcement-learning/model/alphago reinforcement-learning/model/muzero --- https://openreview.net/forum?id=bERaNdoegnO#deepmind Policy improvement by planning with Gumbel Ivo Danihelka, Arthur Guez, Julian Schrittwieser, David Silver 2022-03-04 2022-03-04 reinforcement-learning/exploration reinforcement-learning/model/alphago reinforcement-learning/model/muzero

AlphaZero is a powerful reinforcement learning algorithm based on approximate policy iteration and tree search. However, AlphaZero can fail to improve its policy network, if not visiting all actions at the root of a search tree.

To address this issue, we propose a policy improvement algorithm based on sampling actions without replacement [see Huijben et al 2021 for more on Gumbel tricks]. Furthermore, we use the idea of policy improvement to replace the more heuristic mechanisms by which AlphaZero selects and uses actions, both at root nodes and at non-root nodes.

Our new algorithms, Gumbel AlphaZero & Gumbel MuZero, respectively without and with model-learning, match the state-of-the-art on Go, chess, and Atari, and improve prior performance when planning with few simulations.

[Keywords: AlphaZero, MuZero, reinforcement learning]

--- https://arxiv.org/abs/2110.01515 A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning Iris A. M. Huijben, Wouter Kool, Max B. Paulus, Ruud J. G. van Sloun 2021-10-04 2021-10-04 [("doi","10.48550/arXiv.2110.01515")] reinforcement-learning/exploration statistics/order

The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities. Over the past years, the machine learning community has proposed several extensions of this trick to facilitate, eg. drawing multiple samples, sampling from structured domains, or gradient estimation for error backpropagation in neural network optimization.

The goal of this survey article is to present background about the Gumbel-max trick, and to provide a structured overview of its extensions to ease algorithm selection.

Moreover, it presents a comprehensive outline of (machine learning) literature in which Gumbel-based algorithms have been leveraged, reviews commonly-made design choices, and sketches a future perspective.

--- https://inference-review.com/article/the-nature-of-art The Nature of Art Armand Marie Leroi 2020-10-24 ai --- https://arxiv.org/abs/2203.07190#microsoft CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment Haoyu Song, Li Dong, Wei-Nan Zhang, Ting Liu, Furu Wei 2022-03-14 2022-03-14 [("doi","10.48550/arXiv.2203.07190")] ai/nn/transformer/clip

CLIP has shown a remarkable zero-shot capability on a wide range of vision tasks. Previously, CLIP is only regarded as a powerful visual encoder. However, after being pre-trained by language supervision from a large amount of image-caption pairs, CLIP itself should also have acquired some few-shot abilities for vision-language tasks.

In this work, we empirically show that CLIP can be a strong vision-language few-shot learner by leveraging the power of language. We first evaluate CLIP’s zero-shot performance on a typical visual question answering task and demonstrate a zero-shot cross-modality transfer capability of CLIP on the visual entailment task. Then we propose a parameter-efficient fine-tuning strategy to boost the few-shot performance on the VQA task.

We achieve competitive zero/few-shot results on the visual question answering and visual entailment tasks without introducing any additional pre-training procedure.

--- http://darwintunes.org/ DarwinTunes 2020-10-25 ai/music --- https://en.wikipedia.org/wiki/DarwinTunes DarwinTunes 2020-10-25 ai/music --- https://soundcloud.com/uncoolbob/sets/darwintunes Stream uncoolbob aka DarwinTunes 2020-10-25 ai/music --- https://arxiv.org/abs/2203.08456 PPCD-GAN: Progressive Pruning and Class-Aware Distillation for Large-Scale Conditional GANs Compression Duc Minh Vo, Akihiro Sugimoto, Hideki Nakayama 2022-03-16 2022-03-16 [("doi","10.48550/arXiv.2203.08456")] ai/nn/gan/biggan ai/nn/sparsity/knowledge-distillation ai/nn/sparsity/pruning

We push forward neural network compression research by exploiting a novel challenging task of large-scale conditional generative adversarial networks (GANs) compression. To this end, we propose a gradually shrinking GAN (PPCD-GAN) by introducing progressive pruning residual block (PP-Res) and class-aware distillation.

The PP-Res is an extension of the conventional residual block where each convolutional layer is followed by a learnable mask layer to progressively prune network parameters as training proceeds. The class-aware distillation, on the other hand, enhances the stability of training by transferring immense knowledge from a well-trained teacher model through instructive attention maps.

We train the pruning and distillation processes simultaneously on a well-known GAN architecture in an end-to-end manner. After training, all redundant parameters as well as the mask layers are discarded, yielding a lighter network while retaining the performance.

We comprehensively illustrate, on ImageNet 128×128 dataset, PPCD-GAN reduces up to 5.2× (81%) parameters against state-of-the-arts while keeping better performance.

--- https://www.biorxiv.org/content/10.1101/2022.03.13.484157.full Virgin Birth: A genetic basis for facultative parthenogenesis A. L. Braun, D. K. Fabian, E. Garrison, D. M. Glover 2022-03-14 2022-03-14 [("doi","10.1101/2022.03.13.484157")] genetics/cloning genetics/editing

Sexual reproduction evolved 1–2 billion years ago and underlies the biodiversity of our planet. Nevertheless, devolution of sexual into asexual reproduction can occur across all phyla of the animal kingdom.

The genetic basis for how parthenogenesis can arise is completely unknown. To understand the mechanism and benefits of parthenogenesis, we have sequenced the genome of the facultative parthenogen, Drosophila mercatorum, and compared its organization and expression pattern during parthenogenetic or sexual reproduction.

We identified 3 genes, desat2, Myc, and polo in parthenogenetic D. mercatorum that when mis-regulated in a non-parthenogenetic species, D. melanogaster, enable facultative parthenogenetic reproduction.

This simple genetic switch leads us to propose that sporadic facultative parthenogenesis could evolve as an “escape route” preserving the genetic lineage in the face of sexual isolation.

--- https://arxiv.org/abs/2010.03660#cerebras Fast Stencil-Code Computation on a Wafer-Scale Processor Kamil Rocki, Dirk Van Essendelft, Ilya Sharapov, Robert Schreiber, Michael Morrison, Vladimir Kibardin, Andrey Portnoy, Jean Francois Dietiker, Madhava Syamlal, Michael James 2020-10-07 2020-10-25 [("doi","10.48550/arXiv.2010.03660")] ai/scaling cs/hardware

The performance of CPU-based and GPU-based systems is often low for PDE codes, where large, sparse, and often structured systems of linear equations must be solved. Iterative solvers are limited by data movement, both between caches and memory and between nodes.

Here we describe the solution of such systems of equations on the Cerebras Systems CS-1, a wafer-scale processor that has the memory bandwidth and communication latency to perform well. We achieve 0.86 PFLOPS on a single wafer-scale system for the solution by BiCGStab of a linear system arising from a 7-point finite difference stencil on a 600×595X 1536 mesh, achieving about one third of the machine’s peak performance.

We explain the system, its architecture and programming, and its performance on this problem and related problems. We discuss issues of memory capacity and floating point precision.

We outline plans to extend this work towards full applications.

--- https://www.biorxiv.org/content/10.1101/2022.03.14.484330.full Direct detection of natural selection in Bronze Age Britain Iain Mathieson, Jonathan Terhorst 2022-03-16 2022-03-16 [("doi","10.1101/2022.03.14.484330")] genetics/selection/natural/human

We developed a novel method for efficiently estimating time-varying selection coefficients from genome-wide ancient DNA data.

In simulations, our method accurately recovers selective trajectories, and is robust to mis-specification of population size. We applied it to a large dataset of ancient and present-day human genomes from Britain, and identified 7 loci with genome-wide statistically-significant evidence of selection in the past 4500 years.

Almost all of them are related to increased vitamin D or calcium levels, and we conclude that lack of vitamin D and consequent low calcium was consistently the most important selective pressure in Britain since the Bronze Age. However, the strength of selection on individual loci varied substantially over time, suggesting that cultural or environmental factors moderated the genetic response to this pressure.

Of 28 complex anthropometric and metabolic traits, skin pigmentation was the only one with evidence of polygenic selection, further underscoring the importance of phenotypes related to vitamin D. Our approach illustrates the power of ancient DNA to characterize selection in human populations and illuminates the recent evolutionary history of Britain.

--- https://www.reddit.com/r/GPT3/comments/tgud2t/my_new_favorite_thing_is_making_gpt3_create/ 2020-10-25 ai/nn/transformer/gpt --- https://x.com/jd_pressman/status/1504981456757538816 2020-10-25 ai/nn/transformer/clip/sample --- https://x.com/EMostaque/status/1505265000042143755 2020-10-25 ai/nn/transformer/clip/sample --- https://x.com/nearcyan/status/1505280447923712002 2020-10-25 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2112.05253 MAGMA—Multimodal Augmentation of Generative Models through Adapter-based Finetuning Constantin Eichenberg, Sidney Black, Samuel Weinbach, Letitia Parcalabescu, Anette Frank 2021-12-09 2021-12-09 [("doi","10.48550/arXiv.2112.05253")] ai/nn/transformer/clip ai/scaling

Large-scale pretraining is fast becoming the norm in Vision-Language (VL) modeling. However, prevailing VL approaches are limited by the requirement for labeled data and the use of complex multi-step pretraining objectives.

We present MAGMA—a simple method for augmenting generative language models with additional modalities using adapter-based finetuning. Building on Frozen, we train a series of VL models that autoregressively generate text from arbitrary combinations of visual and textual input. The pretraining is entirely end-to-end using a single language modeling objective, simplifying optimization compared to previous approaches. Importantly, the language model weights remain unchanged during training, allowing for transfer of encyclopedic knowledge and in-context learning abilities from language pretraining.

MAGMA outperforms Frozen on open-ended generative tasks, achieving state-of-the-art results on the OKVQA benchmark and competitive results on a range of other popular VL benchmarks, while pretraining on 0.2% of the number of samples used to train SimVLM.

--- https://arxiv.org/abs/2203.06566#google PromptChainer: Chaining Large Language Model Prompts through Visual Programming Tongshuang Wu, Ellen Jiang, Aaron Donsbach, Jeff Gray, Alejandra Molina, Michael Terry, Carrie J. Cai 2022-03-13 2022-03-13 [("doi","10.48550/arXiv.2203.06566")] ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/lamda

[video] While LLMs can effectively help prototype single ML functionalities, many real-world applications involve complex tasks that cannot be easily handled via a single run of an LLM.

Recent work has found that chaining multiple LLM runs together (with the output of one step being the input to the next) can help users accomplish these more complex tasks, and in a way that is perceived to be more transparent and controllable. However, it remains unknown what users need when authoring their own LLM chains—a key step for lowering the barriers for non-AI-experts to prototype AI-infused applications.

In this work, we explore the LLM chain authoring process. We conclude from pilot studies find that chaining requires careful scaffolding for transforming intermediate node outputs, as well as debugging the chain at multiple granularities; to help with these needs, we designed PromptChainer, an interactive interface for visually programming chains.

Through case studies with four people, we show that PromptChainer supports building prototypes for a range of applications, and conclude with open questions on scaling chains to complex tasks, and supporting low-fi chain prototyping.

--- https://www.spoon-tamago.com/cat-sleep-study/ Japanese Researcher Publishes Study on Quality of Sleep When Pet Cats Choose Location of Slumber 2020-10-26 cat/psychology zeo --- https://en.wikipedia.org/wiki/Smell-O-Vision Smell-O-Vision 2020-10-26 psychology/smell --- https://arxiv.org/abs/1703.05979 How well do experience curves predict technological progress? A method for making distributional forecasts François Lafond, Aimee Gotway Bailey, Jan David Bakker, Dylan Rebois, Rubina Zadourian, Patrick McSharry, J. Doyne Farmer 2017-03-17 2020-10-26 [("doi","10.1016/j.techfore.2017.11.001")] economics/experience-curve technology

Experience curves are widely used to predict the cost benefits of increasing the deployment of a technology. But how good are such forecasts? Can one predict their accuracy a priori?

In this paper we answer these questions by developing a method to make distributional forecasts for experience curves. We test our method using a dataset with proxies for cost and experience for 51 products and technologies and show that it works reasonably well. The framework that we develop helps clarify why the experience curve method often gives similar results to simply assuming that costs decrease exponentially.

To illustrate our method we make a distributional forecast for prices of solar photovoltaic modules.

--- https://ourworldindata.org/battery-price-decline The price of batteries has declined by 97% in the last three decades 2020-10-26 economics/experience-curve technology --- https://x.com/illustrata_ai/status/1505589999755964417 2020-10-26 ai/nn/transformer/clip/sample --- https://x.com/nearcyan/status/1505589736014073863 2020-10-26 ai/nn/transformer/clip/sample --- https://www.reddit.com/r/MediaSynthesis/comments/tiil1b/xx_waifu_01_xx_loop_by_squaremusher/ 2020-10-26 ai/anime ai/nn/gan/stylegan --- https://x.com/KyrickYoung/status/1505304400281182210 2020-10-26 ai/nn/transformer/clip/sample --- https://x.com/gandamu_ml/status/1505407225703149575 2020-10-26 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2109.14526 On the reliability of published findings using the regression discontinuity design in political science Drew Stommes, P. M. Aronow, Fredrik Sävje 2021-09-29 2021-09-29 [("doi","10.48550/arXiv.2109.14526")] politics statistics/bias

The regression discontinuity (RD) design offers identification of causal effects under weak assumptions, earning it the position as a standard method in modern political science research. But identification does not necessarily imply that the causal effects can be estimated accurately with limited data.

In this paper, we highlight that estimation is particularly challenging with the RD design and investigate how these challenges manifest themselves in the empirical literature. We collect all RD-based findings published in top political science journals from 2009–2018.

The findings exhibit pathological features; estimates tend to bunch just above the conventional level of statistical-significance. A reanalysis of all studies with available data suggests that researcher’s discretion is not a major driver of these pathological features, but researchers tend to use inappropriate methods for inference, rendering standard errors artificially small.

A retrospective power analysis reveals that most of these studies were underpowered to detect all but large effects. The issues we uncover, combined with well-documented selection pressures in academic publishing, cause concern that many published findings using the RD design are exaggerated, if not entirely spurious.

--- https://arxiv.org/abs/2203.07845#sensetime Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy Yuanhan Zhang, Qinghong Sun, Yichun Zhou, Zexin He, Zhenfei Yin, Kun Wang, Lu Sheng, Yu Qiao, Jing Shao, Ziwei Liu 2022-03-15 2022-03-15 [("doi","10.48550/arXiv.2203.07845")] ai/dataset ai/nn/transformer/clip reinforcement-learning/exploration/active-learning

Large-scale datasets play a vital role in computer vision. Existing datasets are either collected according to heuristic label systems or annotated blindly without differentiation to samples, making them inefficient and unscalable. How to systematically collect, annotate and build a mega-scale dataset remains an open question.

In this work, we advocate building a high-quality vision dataset actively [active learning] and continually on a comprehensive label system. Specifically, we contribute Bamboo Dataset, a [semi-public] mega-scale and information-dense dataset for both classification and detection. Bamboo aims to populate the comprehensive categories with 69M image classification annotations and 170,586 object bounding box annotations. Compared to ImageNet22K and Objects365, models pre-trained on Bamboo achieve superior performance among various downstream tasks (6.2% gains on classification and 2.1% gains on detection).

In addition, we provide valuable observations regarding large-scale pre-training from over 1,000 experiments. Due to its scalable nature on both label system and annotation pipeline, Bamboo will continue to grow and benefit from the collective efforts of the community, which we hope would pave the way for more general vision models.

[GitHub]

--- https://arxiv.org/abs/2203.09481 Diffusion Probabilistic Modeling for Video Generation Ruihan Yang, Prakhar Srivastava, Stephan Mandt 2022-03-16 2022-03-16 [("doi","10.48550/arXiv.2203.09481")] ai/nn/diffusion ai/nn/vae ai/video/generation

Denoising diffusion probabilistic models are a promising new class of generative models that are competitive with GANs on perceptual metrics.

In this paper, we explore their potential for sequentially generating video. Inspired by recent advances in neural video compression, we use denoising diffusion models to stochastically generate a residual to a deterministic next-frame prediction.

We compare this approach to two sequential VAE and two GAN baselines on four datasets, where we test the generated frames for perceptual quality and forecasting accuracy against ground truth frames. We find substantial improvements in terms of perceptual quality on all data and improvements in terms of frame forecasting for complex high-resolution videos.

--- https://arxiv.org/abs/2203.10050#lg SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning Jongjin Park, Younggyo Seo, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee 2022-03-18 2022-03-18 [("doi","10.48550/arXiv.2203.10050")] reinforcement-learning/preference-learning reinforcement-learning/robot

Preference-based reinforcement learning (RL) has shown potential for teaching agents to perform the target tasks without a costly, pre-defined reward function by learning the reward with a supervisor’s preference between the two agent behaviors. However, preference-based learning often requires a large amount of human feedback, making it difficult to apply this approach to various applications. This data-efficiency problem, on the other hand, has been typically addressed by using unlabeled samples or data augmentation techniques in the context of supervised learning.

Motivated by the recent success of these approaches, we present SURF, a semi-supervised reward learning framework that utilizes a large amount of unlabeled samples with data augmentation. In order to leverage unlabeled samples for reward learning, we infer pseudo-labels of the unlabeled samples based on the confidence of the preference predictor. To further improve the label-efficiency of reward learning, we introduce a new data augmentation that temporally crops consecutive subsequences from the original behaviors.

Our experiments demonstrate that our approach substantially improves the feedback-efficiency of the state-of-the-art preference-based method on a variety of locomotion and robotic manipulation tasks.

--- https://andrewmayne.com/2022/03/17/building-games-and-apps-entirely-through-natural-language-using-openais-davinci-code-model/ Building games and apps entirely through natural language using OpenAI’s code-davinci model 2020-10-27 ai/nn/transformer/gpt/codex --- https://www.biorxiv.org/content/10.1101/2022.03.19.484997.full A comprehensive meta-analysis of human assortative mating in 22 complex traits Tanya B. Horwitz, Matthew C. Keller 2022-03-20 2022-03-20 [("doi","10.1101/2022.03.19.484997")] psychology/personality sociology

Assortative mating (AM) occurs when the correlation for a trait between mates is larger than would be expected by chance. AM can increase the genetic and environmental variation of traits, can increase the prevalence of disorders in a population, and can bias estimates in genetically informed designs.

In this study, we conducted the largest set of meta-analyses on human AM published to date.

Across 22 traits, meta-analyzed correlations ranged from r = 0.08 to r = 0.58, with social attitude, substance use, and cognitive traits showing the highest correlations and personality, disorder, and biometrical traits generally yielding smaller but still positive and nominally statistically-significant (p < 0.05) correlations.

We observed high between-study heterogeneity for most traits, which could have been the result of phenotypic measurement differences between samples and/or differences in the degree of AM across time or cultures.

--- /doc/ai/scaling/hardware/2018-sandberg.pdf There is plenty of time at the bottom: the economics, risk and ethics of time compression Anders Sandberg 2018-10-30 2020-10-27 [("doi","10.1108/FS-04-2018-0044")] ai/scaling/hardware reinforcement-learning/safe

Purpose: The speed of computing and other automated processes plays an important role in how the world functions by causing “time compression”. This paper aims to review reasons to believe computation will continue to become faster in the future, the economic consequences of speedups and how these affect risk, ethics and governance.

Design/methodology/approach: A brief review of science and trends followed by an analysis of consequences.

Results: Current computation is far from the physical limits in terms of processing speed. Algorithmic improvements may be equally powerful but cannot easily be predicted or bounded. Communication and sensing is already at the physical speed limits, although improvements in bandwidth will likely be substantial. The value in these speedups lies in productivity gains, timeliness, early arrival of results and cybernetic feedback shifts. However, time compression can lead to loss of control owing to inability to track fast change, emergent or systemic risk and asynchrony. Speedups can also exacerbate inequalities between different agents and reduce safety if there are competitive pressures. Fast decisions are potentially not better decisions, as they may be made on little data.

Social implications: The impact on society and the challenge to governance are likely to be profound, requiring adapting new methods for managing fast-moving and technological risks.

Originality/value: The speed with which events happen is an important aspect of foresight, not just as a subject of prediction or analysis, but also as a driver of the kinds of dynamics that are possible.

--- /doc/philosophy/epistemology/1979-keil-semanticandconceptualdevelopment.pdf Semantic and Conceptual Development: An Ontological Perspective Frank C. Keil 1979-01-01 2020-10-27 philosophy/epistemology philosophy/ontology psychology --- https://arxiv.org/abs/2203.11096 CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learning Mohammad Reza Taesiri, Finlay Macklon, Cor-Paul Bezemer 2022-03-21 2022-03-21 [("doi","10.48550/arXiv.2203.11096")] ai/dataset ai/nn/transformer/clip ai/video/analysis

Gameplay videos contain rich information about how players interact with the game and how the game responds. Sharing gameplay videos on social media platforms, such as Reddit, has become a common practice for many players. Often, players will share gameplay videos that showcase video game bugs. Such gameplay videos are software artifacts that can be utilized for game testing, as they provide insight for bug analysis. Although large repositories of gameplay videos exist, parsing and mining them in an effective and structured fashion has still remained a big challenge.

In this paper, we propose a search method that accepts any English text query as input to retrieve relevant videos from large repositories of gameplay videos. Our approach does not rely on any external information (such as video metadata); it works solely based on the content of the video. By leveraging the zero-shot transfer capabilities of the Contrastive Language-Image Pre-Training (CLIP) model, our approach does not require any data labeling or training.

To evaluate our approach, we present the GamePhysics dataset consisting of 26,954 videos from 1,873 games, that were collected from the GamePhysics section on the Reddit website.

Our approach shows promising results in our extensive analysis of simple queries, compound queries, and bug queries, indicating that our approach is useful for object and event detection in gameplay videos. An example application of our approach is as a gameplay video search engine to aid in reproducing video game bugs.

Please visit the following link for the code and the data: CLIPxGamePhysics.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927040/ Modafinil Reduces Neuronal Pyroptosis and Cognitive Decline After Sleep Deprivation Xiangyang Xiong, Yan Zuo, Lu Cheng, Zhenyu Yin, Tianpeng Hu, Mengtian Guo, Zhaoli Han, Xintong Ge, Wenzhu Li, Yan Wang, Dong Wang, Conglin Wang, Lan Zhang, Yaodan Zhang, Qiang Liu, Fanglian Chen, Ping Lei 2022 2022 [("doi","10.3389/fnins.2022.816752")] modafinil psychology/neuroscience

Sleep deprivation (SD) induces systemic inflammation that promotes neuronal pyroptosis.

The purpose of this study was to investigate the effect of an antioxidant modafinil on neuronal pyroptosis and cognitive decline following SD.

Using a mouse model of SD, we found that modafinil improved learning and memory, reduced proinflammatory factor (IL-1β, TNF-α, and IL-6) production, and increased the expression of anti-inflammatory factors (IL-10). Modafinil treatment attenuated inflammasome activity and reduced neuronal pyroptosis involving the NLRP3/NLRP1/NLRC4-caspase-1-IL-1β pathway. In addition, modafinil induced an upregulation of brain-derived neurotrophic factor (BDNF) and synaptic activity.

These results suggest that modafinil reduces neuronal pyroptosis and cognitive decline following SD. These effects should be further investigated in future studies to benefit patients with sleep disorders.

--- https://x.com/chigozienri/status/1506347797402079238 2020-10-28 ai/anime ai/nn/transformer/clip/sample --- https://x.com/jd_pressman/status/1506188770978107393 2020-10-28 ai/nn/transformer/clip/sample --- https://x.com/jd_pressman/status/1506190038362587136 2020-10-28 ai/nn/transformer/clip/sample --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8735818/ Amylin as a Future Obesity Treatment Babak Dehestani, Nicholas Rs Stratford, Carel W. le Roux 2021 2021 [("doi","10.7570/jomes21071")] longevity/glp/semaglutide

Obesity is defined as abnormal or excessive fat accumulation that contributes to detrimental health impacts. One-third of the population suffers from obesity, and it is important to consider obesity as a chronic disease requiring chronic treatment.

Amylin is co-secreted with insulin from β pancreatic cells upon nutrient delivery to the small intestine as a satiety signal, acts upon sub-cortical homeostatic and hedonic brain regions, slows gastric emptying, and suppresses post-prandial glucagon responses to meals. Therefore, new pharmacological amylin analogues can be used as potential anti-obesity medications in individuals who are overweight or obese.

In this narrative review, we analyse the efficacy, potency, and safety of amylin analogues.

The synthetic amylin analogue pramlintide is an approved treatment for diabetes mellitus which promotes better glycemic control and small but weight loss.

AM833 (cagrilintide), an investigational novel long-acting acylated amylin analogue, acts as a non-selective amylin receptor. This calcitonin G protein-coupled receptor agonist can serve as an attractive novel treatment for obesity, resulting in reduction of food intake and weight loss in a dose-dependent manner.

--- https://arxiv.org/abs/2203.11370 Time Control: Language modeling via stochastic processes Rose E. Wang, Esin Durmus, Noah Goodman, Tatsunori Hashimoto 2022-03-21 2022-03-21 [("doi","10.48550/arXiv.2203.11370")] ai/nn/diffusion/discrete ai/nn/sampling ai/nn/transformer/gpt/2

Modern language models can generate high-quality short texts. However, they often meander or are incoherent when generating longer texts. These issues arise from the next-token-only language modeling objective.

To address these issues, we introduce Time Control (TC), a language model that implicitly plans via a latent stochastic process. TC does this by learning a representation which maps the dynamics of how text changes in a document to the dynamics of a stochastic process of interest. Using this representation, the language model can generate text by first implicitly generating a document plan via a stochastic process, and then generating text that is consistent with this latent plan.

Compared to domain-specific methods and fine-tuning GPT-2 across a variety of text domains, TC improves performance on text infilling and discourse coherence. On long text generation settings, TC preserves the text structure both in terms of ordering (up to +40% better) and text length consistency (up to +17% better). Human evaluators also prefer TC’s output 28.6% more than the baselines.

--- https://en.wikipedia.org/wiki/Conditioned_taste_aversion Conditioned taste aversion 2020-10-28 psychology/neuroscience --- https://x.com/NerdyRodent/status/1506621311875002373 2020-10-28 ai/nn/transformer/clip/sample --- https://www.biorxiv.org/content/10.1101/2020.05.26.116111.full Reduced reproductive success is associated with selective constraint on human genes Eugene J. Gardner, Matthew D. C. Neville, Kaitlin E. Samocha, Kieron Barclay, Martin Kolk, Mari E. K. Niemi, George Kirov, Hilary C. Martin, Matthew E. Hurles 2022-02-03 2022-02-03 [("doi","10.1101/2020.05.26.116111")] genetics/heritable/correlation genetics/heritable/rare genetics/selection/natural/human/dysgenics iq/ses psychiatry

Genome-wide sequencing of human populations has revealed substantial variation among genes in the intensity of purifying selection acting on damaging genetic variants1. While genes under the strongest selective constraint are highly enriched for associations with Mendelian disorders, most of these genes are not associated with disease and therefore the nature of the selection acting on them is not known2.

Here we show that genetic variants that damage these genes are associated with markedly reduced reproductive success, primarily due to increased childlessness, with a stronger effect in males than in females. We present evidence that increased childlessness is likely mediated by genetically associated cognitive and behavioral traits, which may mean male carriers are less likely to find reproductive partners.

This reduction in reproductive success may account for 20% of purifying selection against heterozygous variants that ablate protein-coding genes. While this genetic association could only account for a very minor fraction of the overall likelihood of being childless (less than 1%), especially when compared to more influential sociodemographic factors, it may influence how genes evolve over time.

--- https://arxiv.org/abs/2203.08557 How darknet market users learned to worry more and love PGP: Analysis of security advice on darknet marketplaces Andrew C. Dwyer, Joseph Hallett, Claudia Peersman, Matthew Edwards, Brittany I. Davidson, Awais Rashid 2022-03-16 2022-03-16 [("doi","10.48550/arXiv.2203.08557")] darknet-market/dnm-archive

Darknet marketplaces, accessible through, Tor are where users can buy illicit goods, and learn to hide from law enforcement.

We surveyed the advice on these markets and found valid security advice mixed up with paranoid threat models and a reliance on privacy tools dismissed as unusable by the mainstream.

--- https://academic.oup.com/cid/article/76/4/609/6758436 A Systematic Review of Human Challenge Trials, Designs, and Safety Jupiter Adams-Phipps, Danny Toomey, Witold Więcek, Virginia Schmit, James Wilkinson, Keller Scholl, Euzebiusz Jamrozik, Joshua Osowicki, Meta Roestenberg, David Manheim 2022-03-21 2022-03-21 [("doi","10.1101/2022.03.20.22272658")] philosophy/ethics statistics/decision

Background: There exists no prior systematic review of human challenge trials (HCTs) that focuses on participant safety. Key questions regarding HCTs include how risky such trials have been, how often adverse events (AEs) and serious adverse events (SAEs) occur, and whether risk mitigation measures have been effective.

Method: A systematic search of PubMed and PubMed Central for articles reporting on results of HCTs published 1980–2021 was performed and completed by 2021-10-07.

Results: Of 2,838 articles screened, 276 were reviewed in full. 15,046 challenged participants were described in 308 studies that met inclusion criteria. 286 (92.9%) of these studies reported mitigation measures used to minimize risk to the challenge population. Among 187 studies which reported on SAEs, 0.2% of participants experienced at least one challenge-related SAE. Among 94 studies that graded AEs by severity, challenge-related AEs graded “severe” were reported by between 5.6% and 15.8% of participants. AE data were provided as a range to account for unclear reporting. 80% of studies published after 2010 were registered in a trials database.

Conclusion: HCTs are increasingly common and used for an expanding list of diseases. Although AEs occur, severe AEs and SAEs are rare. Reporting has improved over time, though not all papers provide a comprehensive report of relevant health impacts. From the available data, most HCTs do not lead to a high number of severe symptoms or SAEs.

This study was preregistered on PROSPERO as CRD42021247218.

--- https://research.google/blog/auto-generated-summaries-in-google-docs/ 2020-10-28 ai/nn/sparsity --- https://arxiv.org/abs/2203.10452#google CrossBeam: Learning to Search in Bottom-Up Program Synthesis Kensen Shi, Hanjun Dai, Kevin Ellis, Charles Sutton 2022-03-20 2022-03-20 [("doi","10.48550/arXiv.2203.10452")] reinforcement-learning/model/alphago

Many approaches to program synthesis perform a search within an enormous space of programs to find one that satisfies a given specification. Prior works have used neural models to guide combinatorial search algorithms, but such approaches still explore a huge portion of the search space and quickly become intractable as the size of the desired program increases. To tame the search space blowup, we propose training a neural model to learn a hands-on search policy for bottom-up synthesis, instead of relying on a combinatorial search algorithm.

Our approach, called CrossBeam, uses the neural model to choose how to combine previously-explored programs into new programs, taking into account the search history and partial program executions. Motivated by work in structured prediction on learning to search, CrossBeam is trained on-policy using data extracted from its own bottom-up searches on training tasks.

We evaluate CrossBeam in two very different domains, string manipulation and logic programming. We observe that CrossBeam learns to search efficiently, exploring much smaller portions of the program space compared to the state-of-the-art.

--- https://www.biorxiv.org/content/10.1101/2022.03.21.485215.full Cross-trait assortative mating is widespread and inflates genetic correlation estimates Richard Border, Georgios Athanasiadis, Alfonso Buil, Andrew Schork, Na Cai, Alexander Young, Thomas Werge, Jonathan Flint, Kenneth Kendler, Sriram Sankararaman, Andy Dahl, Noah Zaitlen 2022-03-23 2022-03-23 [("doi","10.1101/2022.03.21.485215")] genetics/heritable/correlation

The observation of genetic correlations between disparate traits has been interpreted as evidence of widespread pleiotropy, altered theories of human genetic architecture, and spurred considerable research activity across the natural and social sciences.

Here, we introduce cross-trait assortative mating (xAM) as an alternative explanation for observed genetic correlations. We observe that xAM is common across a broad array of phenotypes and that phenotypic cross-mate correlation estimates are strongly associated with genetic correlation estimates (R^2 = 76%).

Then, we present theoretical and simulation-based results demonstrating that, under xAM, genetic correlation estimators yield estimates even for traits with entirely distinct genetic bases. We demonstrate that existing xAM plausibly accounts for substantial fractions of genetic correlation estimates in two large samples (n = 827,960). For example, previously reported genetic correlation estimates between many pairs of psychiatric disorders are fully consistent with xAM alone.

Finally, we provide evidence for a history of xAM at the genetic level using a novel approach based on cross-trait even/odd chromosome polygenic score correlations. Together, our results demonstrate that previous reports have likely overestimated the true genetic similarity between many phenotypes.

--- https://x.com/joshu/status/1506842955134701569 2020-10-29 ai/nn/transformer/clip/sample --- https://x.com/joshu/status/1506860650110234624 2020-10-29 ai/nn/transformer/clip/sample --- https://x.com/KaliYuga_ai/status/1507060649226031114 2020-10-29 ai/nn/transformer/clip/sample --- https://www.vice.com/en/article/7kbjvb/this-magickal-grimoire-was-co-authored-by-a-disturbingly-realistic-ai This Mystical Book Was Co-Authored by a Disturbingly Realistic AI 2020-10-29 ai/nn/transformer/gpt ai/poetry --- https://arxiv.org/abs/hep-ph/0204295 Landau’s Theoretical Minimum, Landau’s Seminar, ITEP in the Beginning of the 1950’s B. L. Ioffe 2002-04-25 2020-10-29 [("doi","10.48550/0204295")] history science

I recollect on the organization of the Landau school and describe the early history of the ITEP Theory Department, as well as the history of creation of famous Landau, Abrikosov, and Khalatnikov’s papers, and Landau’s papers on P-parity violation and CP-conservation.

The recollections carry an imprint of the epoch long gone …

--- https://www.lesswrong.com/posts/65qmEJHDw3vw69tKm/proposal-scaling-laws-for-rl-generalization?commentId=wMerfGZfPHerdzDAi 2020-10-29 reinforcement-learning/scaling --- https://arxiv.org/abs/2203.12691 Learning to generate line drawings that convey geometry and semantics Caroline Chan, Fredo Durand, Phillip Isola 2022-03-23 2022-03-23 [("doi","10.48550/arXiv.2203.12691")] ai/anime ai/nn/transformer/clip

This paper presents an unpaired method for creating line drawings from photographs. Current methods often rely on high quality paired datasets to generate line drawings. However, these datasets often have limitations due to the subjects of the drawings belonging to a specific domain, or in the amount of data collected. Although recent work in unsupervised image-to-image translation has shown much progress, the latest methods still struggle to generate compelling line drawings. We observe that line drawings are encodings of scene information and seek to convey 3D shape and semantic meaning.

We build these observations into a set of objectives and train an image translation to map photographs into line drawings. We introduce a geometry loss which predicts depth information from the image features of a line drawing, and a semantic loss which matches the CLIP features of a line drawing with its corresponding photograph.

Our approach outperforms state-of-the-art unpaired image translation and line drawing generation methods on creating line drawings from arbitrary photographs.

For code and demo visit our webpage.

--- https://arxiv.org/abs/2203.09749 Robot peels banana with goal-conditioned dual-action deep imitation learning Heecheol Kim, Yoshiyuki Ohmura, Yasuo Kuniyoshi 2022-03-18 2022-03-18 [("doi","10.48550/arXiv.2203.09749")] ai/video/analysis reinforcement-learning/imitation-learning reinforcement-learning/robot

A long-horizon dexterous robot manipulation task of deformable objects, such as banana peeling, is problematic because of difficulties in object modeling and a lack of knowledge about stable and dexterous manipulation skills. This paper presents a goal-conditioned dual-action deep imitation learning (DIL) which can learn dexterous manipulation skills using human demonstration data.

Previous DIL methods map the current sensory input and reactive action, which easily fails because of compounding errors in imitation learning caused by recurrent computation of actions. The proposed method predicts reactive action when the precise manipulation of the target object is required (local action) and generates the entire trajectory when the precise manipulation is not required. This dual-action formulation effectively prevents compounding error with the trajectory-based global action while respond to unexpected changes in the target object with the reactive local action. Furthermore, in this formulation, both global/local actions are conditioned by a goal state which is defined as the last step of each subtask, for robust policy prediction.

The proposed method was tested in the real dual-arm robot and successfully accomplished the banana peeling task.

--- https://www.youtube.com/watch?v=rYrSQ_sF3fQ Robot peels banana with deep learning, UT ISI Lab 2020-10-29 reinforcement-learning/robot --- https://www.nytimes.com/2022/03/22/science/geometry-math-brain-primmates.html 2020-10-30 math psychology/neuroscience --- https://x.com/nin_artificial/status/1507113238076526595 2020-10-30 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2203.13131#facebook Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, Yaniv Taigman 2022-03-24 2022-03-24 [("doi","10.48550/arXiv.2203.13131")] ai/anime ai/nn/tokenization ai/nn/transformer/gpt/dall-e/2

[video; reimplementation; impressive hard samples but almost immediately surpassed by DALL·E 2, Imagen, & Parti.] Recent text-to-image generation methods provide a simple yet exciting conversion capability between text and image domains. While these methods have incrementally improved the generated image fidelity and text relevancy, several pivotal gaps remain unanswered, limiting applicability and quality.

We propose a novel text-to-image method that addresses these gaps by (1) enabling a simple control mechanism complementary to text in the form of a scene, (2) introducing elements that substantially improve the tokenization process by employing domain-specific knowledge over key image regions (faces and salient objects), and (3) adapting classifier-free guidance for the transformer use case.

Our model achieves state-of-the-art FID and human evaluation results, unlocking the ability to generate high fidelity images in a resolution of 512×512 pixels, substantially improving visual quality.

Through scene controllability, we introduce several new capabilities: (1) Scene editing, (2) text editing with anchor scenes, (3) overcoming out-of-distribution text prompts, and (4) story illustration generation, as demonstrated in the story we wrote.

[July blog: “…Since the research paper was released, Make-A-Scene has incorporated a super resolution network that generates imagery at 2048×2048, 4× the resolution, and we’re continuously improving our generative AI models. We aim to provide broader access to our research demos in the future to give more people the opportunity to be in control of their own creations and unlock entirely new forms of expression.” No specifics on release of models or a service/API.]

--- https://arxiv.org/abs/2203.01993 Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk 2022-03-03 2022-03-03 [("doi","10.48550/arXiv.2203.01993")] ai/nn/gan/biggan ai/nn/gan/stylegan ai/nn/vae

We present Polarity Sampling, a theoretically justified plug-and-play method for controlling the generation quality and diversity of pre-trained deep generative networks (DGNs).

Leveraging the fact that DGNs are, or can be approximated by, continuous piecewise affine splines, we derive the analytical DGN output space distribution as a function of the product of the DGN’s Jacobian singular values raised to a power ρ. We dub ρ the polarity parameter and prove that ρ focuses the DGN sampling on the modes (ρ < 0) or anti-modes (ρ > 0) of the DGN output-space distribution.

We demonstrate that nonzero polarity values achieve a better precision-recall (quality-diversity) Pareto frontier than standard methods, such as truncation, for a number of state-of-the-art DGNs. We also present quantitative and qualitative results on the improvement of overall generation quality (eg. in terms of the Fréchet Inception Distance) for a number of state-of-the-art DGNs, including StyleGAN3, BigGAN-deep, NVAE, for different conditional and unconditional image generation tasks. In particular, Polarity Sampling redefines the state-of-the-art for StyleGAN-2 on the FFHQ Dataset to FID 2.57, StyleGAN-2 on the LSUN Car Dataset to FID 2.27 and StyleGAN3 on the AFHQv2 Dataset to FID 3.95.

Demo link.

--- https://arxiv.org/abs/2203.10249 Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension Chao Zhao, Wenlin Yao, Dian Yu, Kaiqiang Song, Dong Yu, Jianshu Chen 2022-03-19 2022-03-19 [("doi","10.48550/arXiv.2203.10249")] ai/nn/transformer/gpt/inner-monologue

Comprehending a dialogue requires a model to capture diverse kinds of key information in the utterances, which are either scattered around or implicitly implied in different turns of conversations. Therefore, dialogue comprehension requires diverse capabilities such as paraphrasing, summarizing, and commonsense reasoning.

Towards the objective of pre-training a zero-shot dialogue comprehension model, we develop a novel narrative-guided pre-training strategy that learns by narrating the key information from a dialogue input. However, the dialogue-narrative parallel corpus for such a pre-training strategy is currently unavailable. For this reason, we first construct a dialogue-narrative parallel corpus by automatically aligning movie subtitles and their synopses. We then pre-train a BART model on the data and evaluate its performance on four dialogue-based tasks that require comprehension.

Experimental results show that our model not only achieves superior zero-shot performance but also exhibits stronger fine-grained dialogue comprehension capabilities.

The data and code are available at https://github.com/zhaochaocs/Diana.

--- https://en.wikipedia.org/wiki/Belyana Belyana 2020-10-30 technology --- https://www.biorxiv.org/content/10.1101/2022.03.17.484689.full Invasion genetics of the longhorn crazy ant: the global expansion of a double-clonal reproduction system Shu-Ping Tseng, Hugo Darras, Po-Wei Hsu, Tsuyoshi Yoshimura, Chow-Yang Lee, James K. Wetterer, Laurent Keller, Chin-Cheng Scotty Yang 2022-03-17 2022-03-17 [("doi","10.1101/2022.03.17.484689")] biology/ant genetics/cloning

Reproduction mode represents a key determinant for success of biological invasion as it influences the genetic variation and evolutionary potential of introduced populations.

The world’s most widespread invasive ant, Paratrechina longicornis, was found to display an unusual double-clonal reproduction system, whereby both males and queens were produced clonally, while workers are produced sexually. Despite its worldwide distribution, the origin of this ant species and the prevalence of the double-clonal reproductive system across the ant’s geographic range remain unknown.

To retrace the evolutionary history of this global invasive species and its reproductive system, we examined genetic variation and characterized the mode of reproduction of P. longicornis sampled worldwide using both microsatellite genotyping and mitochondrial DNA sequencing approaches.

Analyses of global genetic variations indicate that the Indian subcontinent is a genetic diversity hotspot of this species, suggesting that this geographic area is at least part of its native range.

Our analyses revealed that inferred native and introduced populations both exhibit double-clonal reproduction. Remarkably, queens and males worldwide belong to two separate, non-recombining clonal lineages. Workers are highly heterozygous and first-generation inter-lineage hybrids, a pattern strongly supportive of a strict worldwide prevalence of double clonality. By maintaining heterozygosity in the worker force, this unusual genetic system allows P. longicornis to avoid inbreeding during colonization bottlenecks and may have acted as an adaptive trait linked to the species’ invasion success.

--- https://peakd.com/hive-158694/@kaliyuga/model-comparison-study-for-disco-diffusion-v-5-ai-resources-by-kaliyuga Model Comparison Study for Disco Diffusion v. 5 2020-10-30 ai/nn/diffusion --- https://peakd.com/hive-158694/@kaliyuga/model-comparison-study-for-disco-diffusion-v-5-plms-sampling-edition-ai-resources-by-kaliyuga Model Comparison Study for Disco Diffusion v. 5---PLMS Sampling Edition 2020-10-30 ai/nn/diffusion --- https://x.com/RiversHaveWings/status/1508587377064783873 2020-10-30 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2203.13179 Automatic User Profiling in Darknet Markets: a Scalability Study Claudia Peersman, Matthew Edwards, Emma Williams, Awais Rashid 2022-03-24 2022-03-24 [("doi","10.48550/arXiv.2203.13179")] darknet-market/dnm-archive statistics/stylometry

In this study, we investigate the scalability of state-of-the-art user profiling technologies across different online domains.

More specifically, this work aims to understand the reliability and limitations of current computational stylometry approaches when these are applied to underground fora in which user populations potentially differ from other online platforms (predominantly male, younger age and greater computer use) and cyber offenders who attempt to hide their identity.

Because no ground truth is available and no validated criminal data from historic investigations is available for validation purposes, we have collected new data from clearweb forums that do include user demographics and could be more closely related to underground fora in terms of user population (eg. tech communities) than commonly used social media benchmark datasets showing a more balanced user population.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1884944/#__sec16title Fluvoxamine impairs single-dose caffeine clearance without altering caffeine pharmacodynamics Kerry E. Culm-Merdek, Lisa L. von Moltke, Jerold S. Harmatz, David J. Greenblatt 2005-11 2020-10-31 [("doi","10.1111/j.1365-2125.2005.02467.x")] nootropic/caffeine psychiatry

Background: Coadministration of fluvoxamine impairs the clearance of caffeine and prolongs its elimination half-life, which is attributable to inhibition of CYP1A2 by fluvoxamine. The clinical importance of this interaction is not established.

Aim: To evaluate the effects of fluvoxamine on the kinetics and dynamics of single doses of caffeine.

Method: 7 healthy subjects received single 250 mg doses of caffeine (or matching placebo) together with fluvoxamine (four doses of 100 mg over 2 days) or with matching placebo in a double-blind, 4-way crossover study. For 24 h after caffeine or placebo administration, plasma caffeine and fluvoxamine concentrations were determined. Psychomotor performance, sedation, and electroencephalographic (EEG) “beta” frequency activity were also assessed.

Results: Fluvoxamine substantially reduced apparent oral clearance of caffeine (105 vs. 9.1 mL min−1, p < 0.01; mean difference: 95.7 mL min−1, 95% CI: 54.9–135.6), and prolonged its elimination half-life (4.9 vs. 56 h, p < 0.01; mean difference: 51 h, 95% CI: 26–76). Caffeine produced CNS-stimulating effects compared with placebo. However, psychomotor performance, alertness, or EEG effects attributable to caffeine were not augmented by coadministration of fluvoxamine.

Conclusion: Fluvoxamine greatly impaired caffeine clearance, but without detectable changes in caffeine pharmacodynamics. However, this study does not rule out possible adverse effects due to extensive accumulation of caffeine with daily ingestion in fluvoxamine-treated individuals.

--- https://arxiv.org/abs/2203.14649 Knowledge Distillation: Bad Models Can Be Good Role Models Gal Kaplun, Eran Malach, Preetum Nakkiran, Shai Shalev-Shwartz 2022-03-28 2022-03-28 [("doi","10.48550/arXiv.2203.14649")] ai/nn/sparsity/knowledge-distillation

Large neural networks trained in the overparameterized regime are able to fit noise to zero train error. Recent work (nakkiran2020distributional) has empirically observed that such networks behave as “conditional samplers” from the noisy distribution. That is, they replicate the noise in the train data to unseen examples.

We give a theoretical framework for studying this conditional sampling behavior in the context of learning theory. We relate the notion of such samplers to knowledge distillation, where a student network imitates the outputs of a teacher on unlabeled data.

We show that samplers, while being bad classifiers, can be good teachers. Concretely, we prove that distillation from samplers is guaranteed to produce a student which approximates the Bayes optimal classifier.

Finally, we show that some common learning algorithms (eg. Nearest-Neighbours and Kernel Machines) can generate samplers when applied in the overparameterized regime.

--- https://x.com/RiversHaveWings/status/1508911489456898049 2020-10-31 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1508929487945101313 2020-10-31 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1508933366036410373 2020-10-31 ai/nn/transformer/clip/sample --- https://x.com/michael_nielsen/status/1508898800781340673 2020-10-31 ai/nn/transformer/clip/sample --- https://x.com/michael_nielsen/status/1508901394174001152 2020-10-31 ai/nn/transformer/clip/sample --- https://x.com/natfriedman/status/1508530566940954624 2020-10-31 ai/nn/transformer/clip/sample --- https://x.com/natfriedman/status/1508530571684712448 2020-10-31 ai/nn/transformer/clip/sample --- https://x.com/natfriedman/status/1508530576277458949 2020-10-31 ai/nn/transformer/clip/sample --- https://x.com/natfriedman/status/1508567175484694528 2020-11-01 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2203.14101#baai A Roadmap for Big Model Sha Yuan, Hanyu Zhao, Shuai Zhao, Jiahong Leng, Yangxiao Liang, Xiaozhi Wang, Jifan Yu, Xin Lv, Zhou Shao, Jiaao He, Yankai Lin, Xu Han, Zhenghao Liu, Ning Ding, Yongming Rao, Yizhao Gao, Liang Zhang, Ming Ding, Cong Fang, Yisen Wang, Mingsheng Long, Jing Zhang, Yinpeng Dong, Tianyu Pang, Peng Cui, Lingxiao Huang, Zheng Liang, Huawei Shen, Hui Zhang, Quanshi Zhang, Qingxiu Dong, Zhixing Tan, Mingxuan Wang, Shuo Wang, Long Zhou, Haoran Li, Junwei Bao, Yingwei Pan, Weinan Zhang, Zhou Yu, Rui Yan, Chence Shi, Minghao Xu, Zuobai Zhang, Guoqiang Wang, Xiang Pan, Mengjie Li, Xiaoyu Chu, Zijun Yao, Fangwei Zhu, Shulin Cao, Weicheng Xue, Zixuan Ma, Zhengyan Zhang, Shengding Hu, Yujia Qin, Chaojun Xiao, Zheni Zeng, Ganqu Cui, Weize Chen, Weilin Zhao, Yuan Yao, Peng Li, Wenzhao Zheng, Wenliang Zhao, Ziyi Wang, Borui Zhang, Nanyi Fei, Anwen Hu, Zenan Ling, Haoyang Li, Boxi Cao, Xianpei Han, Weidong Zhan, Baobao Chang, Hao Sun, Jiawen Deng, Juanzi Li, Lei Hou, Xigang Cao, Jidong Zhai, Zhiyuan Liu, Maosong Sun, Jiwen Lu, Zhiwu Lu, Qin Jin, Ruihua Song, Ji-Rong Wen, Zhouchen Lin, Liwei Wang, Hang Su, Jun Zhu, Zhifang Sui, Jiajun Zhang, Yang Liu, Xiaodong He, Minlie Huang, Jian Tang, Jie Tang 2022-03-26 2022-03-26 [("doi","10.48550/arXiv.2203.14101")] ai/scaling

With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the construction of BMs and the BM application in many fields. At present, there is a lack of research work that sorts out the overall progress of BMs and guides the follow-up research.

In this paper, we cover not only the BM technologies themselves but also the prerequisites for BM training and applications with BMs, dividing the BM review into four parts: Resource, Models, Key Technologies and Application.

We introduce 16 specific BM-related topics in those four parts, they are Data, Knowledge, Computing System, Parallel Training System, Language Model, Vision Model, Multi-modal Model, Theory&Interpretability, Commonsense Reasoning, Reliability&Security, Governance, Evaluation, Machine Translation, Text Generation, Dialogue and Protein Research. In each topic, we summarize clearly the current studies and propose some future research directions.

At the end of this paper, we conclude the further development of BMs in a more general view.

--- https://arxiv.org/abs/2203.14465 STaR: Bootstrapping Reasoning With Reasoning Eric Zelikman, Yuhuai Wu, Noah D. Goodman 2022-03-28 2022-03-28 [("doi","10.48550/arXiv.2203.14465")] ai/dataset ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/inner-monologue

Generating step-by-step “chain-of-thought” rationales improves language model performance on complex reasoning tasks like mathematics or commonsense question-answering. However, inducing language model rationale generation currently requires either constructing massive rationale datasets or sacrificing accuracy by using only few-shot inference.

We propose a technique to iteratively leverage a small number of rationale examples and a large dataset without rationales, to bootstrap the ability to perform successively more complex reasoning. This technique, the Self-Taught Reasoner (STaR), relies on a simple loop [self-distillation]: generate rationales to answer many questions, prompted with a few rationale examples; if the generated answers are wrong, try again to generate a rationale given the correct answer; fine-tune on all the rationales that ultimately yielded correct answers; repeat.

We show that STaR substantially improves performance on multiple datasets compared to a model fine-tuned to directly predict final answers, and performs comparably to fine-tuning a 30× larger state-of-the-art language model on CommensenseQA.

Thus, STaR lets a model improve itself by learning from its own generated reasoning.

--- https://arxiv.org/abs/2203.15556#deepmind Chinchilla: Training Compute-Optimal Large Language Models Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, Laurent Sifre 2022-03-29 2022-03-29 [("doi","10.48550/arXiv.2203.15556")] ai/nn/transformer/gpt/4 ai/scaling

We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.

We find that current large language models are substantially undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant. By training over language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of model size the number of training tokens should also be doubled.

We test this hypothesis by training a predicted compute-optimal model, Chinchilla, that uses the same compute budget as but with 70b parameters and 4× more more data. uniformly and substantially outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage.

As a highlight, reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher.

--- https://arxiv.org/abs/2203.12533#google Pathways: Asynchronous Distributed Dataflow for ML Paul Barham, Aakanksha Chowdhery, Jeff Dean, Sanjay Ghemawat, Steven Hand, Dan Hurt, Michael Isard, Hyeontaek Lim, Ruoming Pang, Sudip Roy, Brennan Saeta, Parker Schuh, Ryan Sepassi, Laurent El Shafey, Chandramohan A. Thekkath, Yonghui Wu 2022-03-23 2022-03-23 [("doi","10.48550/arXiv.2203.12533")] ai/nn/transformer/t5 ai/scaling/hardware

We present the design of a new large scale orchestration layer for accelerators.

Our system, Pathways, is explicitly designed to enable exploration of new systems and ML research ideas, while retaining state-of-the-art performance for current models. Pathways uses a sharded dataflow graph of asynchronous operators that consume and produce futures, and efficiently gang-schedules heterogeneous parallel computations on thousands of accelerators while coordinating data transfers over their dedicated interconnects. Pathways makes use of a novel asynchronous distributed dataflow design that lets the control plane execute in parallel despite dependencies in the data plane. This design, with careful engineering, allows Pathways to adopt a single-controller model that makes it easier to express complex new parallelism patterns.

We demonstrate that Pathways can achieve performance parity (~100% accelerator utilization) with state-of-the-art systems when running SPMD [single program multiple data] computations over 2×1024 = 2048 TPUv3s [97% utilization training a 128b T5 model], while also delivering throughput comparable to the SPMD case for Transformer models that are pipelined across 16 stages, or sharded across two islands of accelerators connected over a data center network.

--- /doc/statistics/decision/2007-nice-guidelines-ch8.pdf The guidelines manual—Chapter 8: Incorporating health economics in guidelines and assessing resource impact NICE 2007-04-13 2020-11-01 economics statistics/decision --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5968650/ Snake Venom Use as a Substitute for Opioids: A Case Report and Review of Literature Aseem Mehra, Debashish Basu, Sandeep Grover 2018-05 2020-11-01 psychiatry

The mind-altering agents such as tobacco, cannabis, and opium have been widely used since the evolution of human being. These substances have been widely used for recreational purposes. However, derivatives from reptiles such as snakes, reptiles, and scorpions can also be used for recreational purposes and as a substitute for other substances. Their use is rare and related literature is very scanty.

In this report, we present a case of snake venom abuse and review the existing literature.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8114859/ Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data Aslan Satary Dizaji, Bruno Hebling Vieira, Mohmmad Reza Khodaei, Mahnaz Ashrafi, Elahe Parham, Gholam Ali Hosseinzadeh, Carlos Ernesto Garrido Salmon, Hamid Soltanianzadeh 2021-01 2021-01 iq psychology/neuroscience

Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman’s general intelligence in the early 1900s, there has been progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of sophisticated brain imaging devices using Diffusion-Weighted Imaging (DWI) and functional Magnetic Resonance Imaging (fMRI) has allowed researchers to test hypotheses about neural correlates of intelligence in humans.

This review summarizes recent findings on the associations of human intelligence with neuroimaging data. To this end, first, we review the literature that has related brain morphometry to intelligence. Next, we elaborate on the applications of DWI and resting state fMRI on the investigation of intelligence. Then, we provide a survey of literature that has used multimodal DWI-fMRI to shed light on intelligence.

Finally, we discuss the state-of-the-art of individualized prediction of intelligence from neuroimaging data and point out future strategies. Future studies hold promising outcomes for machine learning-based predictive frameworks using neuroimaging features to estimate human intelligence.

--- https://arxiv.org/abs/2202.07848#microsoft Singularity: Planet-Scale, Preemptive and Elastic Scheduling of AI Workloads Dharma Shukla, Muthian Sivathanu, Srinidhi Viswanatha, Bhargav Gulavani, Rimma Nehme, Amey Agrawal, Chen Chen, Nipun Kwatra, Ramachandran Ramjee, Pankaj Sharma, Atul Katiyar, Vipul Modi, Vaibhav Sharma, Abhishek Singh, Shreshth Singhal, Kaustubh Welankar, Lu Xun, Ravi Anupindi, Karthik Elangovan, Hasibur Rahman, Zhou Lin, Rahul Seetharaman, Cheng Xu, Eddie Ailijiang, Suresh Krishnappa, Mark Russinovich 2022-02-16 2022-02-16 [("doi","10.48550/arXiv.2202.07848")] ai/scaling/hardware

Lowering costs by driving high utilization across deep learning workloads is a crucial lever for cloud providers. We present Singularity, Microsoft’s globally distributed scheduling service for highly-efficient and reliable execution of deep learning training and inference workloads. At the heart of Singularity is a novel, workload-aware scheduler that can transparently preempt and elastically scale deep learning workloads to drive high utilization without impacting their correctness or performance, across a global fleet of AI accelerators (eg. GPUs, FPGAs).

All jobs in Singularity are preemptible, migratable, and dynamically resizable (elastic) by default: a live job can be dynamically and transparently (a) preempted and migrated to a different set of nodes, cluster, data center or a region and resumed exactly from the point where the execution was preempted, and (b) resized (ie. elastically scaled-up/down) on a varying set of accelerators of a given type. Our mechanisms are transparent in that they do not require the user to make any changes to their code or require using any custom libraries that may limit flexibility. Additionally, our approach improves the reliability of deep learning workloads. We show that the resulting efficiency and reliability gains with Singularity are achieved with negligible impact on the steady-state performance. Finally, our design approach is agnostic of DNN architectures and handles a variety of parallelism strategies (eg. data/pipeline/model parallelism).

--- https://arxiv.org/abs/2110.05448#openai Unsupervised Neural Machine Translation with Generative Language Models Only Jesse Michael Han, Igor Babuschkin, Harrison Edwards, Arvind Neelakantan, Tao Xu, Stanislas Polu, Alex Ray, Pranav Shyam, Aditya A. Ramesh, Alec Radford, Ilya Sutskever 2021-10-11 2021-10-11 [("doi","10.48550/arXiv.2110.05448")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/inner-monologue ai/scaling

We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models. Our method consists of 3 steps: few-shot amplification, distillation, and backtranslation. We first use the zero-shot translation ability of large pre-trained language models to generate translations for a small set of unlabeled sentences.

We then amplify these zero-shot translations by using them as few-shot demonstrations for sampling a larger synthetic dataset. This dataset is distilled by discarding the few-shot demonstrations and then fine-tuning. During backtranslation, we repeatedly generate translations for a set of inputs and then fine-tune a single language model on both directions of the translation task at once, ensuring cycle-consistency by swapping the roles of gold monotext and generated translations when fine-tuning.

By using our method to leverage GPT-3’s zero-shot translation capability, we achieve a new state-of-the-art in unsupervised translation on the WMT14 English-French benchmark, attaining a BLEU score of 42.1.

--- https://x.com/remi_durant/status/1460607677801897990 2020-11-01 ai/nn/transformer/clip/sample --- https://github.com/Jack000/glid-3 combination of OpenAI GLIDE and Latent Diffusion 2020-11-02 ai/nn/diffusion ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e --- https://www.medrxiv.org/content/10.1101/2021.10.17.21265070.full Rare Variant Aggregation in 148,508 Exomes Identifies Genes Associated with Proxy Alzheimer’s Disease Douglas P. Wightman, Jeanne E. Savage, Christiaan A. de Leeuw, Iris E. Jansen, Danielle Posthuma 2021-10-18 2021-10-18 [("doi","10.1101/2021.10.17.21265070")] genetics/heritable/correlation genetics/heritable/rare psychiatry/alzheimers

We generated a proxy Alzheimer’s disease phenotype for 148,508 individuals in the UK biobank in order to perform exome-wide rare variant aggregation analyses to identify genes associated with proxy Alzheimer’s disease.

We identified 4 genes statistically-significantly associated with the proxy phenotype, 3 of which have been previously associated with clinically diagnosed Alzheimer’s disease (SORL1, TREM2, and TOMM40). We identified one gene (HEXA) which has not been previously associated with Alzheimer’s disease but is known to contribute to neurodegenerative disease.

Here we show that proxy Alzheimer’s disease can capture some of the rare variant association signal for Alzheimer’s disease and can be used to highlight genes and variants of interest.

The proxy phenotype allows for the usage of large genetic databases without clinically diagnosed Alzheimer’s disease patients to uncover variants and genes that contribute to Alzheimer’s disease.

--- https://arxiv.org/abs/2111.15640 Diffusion Autoencoders: Toward a Meaningful and Decodable Representation Konpat Preechakul, Nattanat Chatthee, Suttisak Wizadwongsa, Supasorn Suwajanakorn 2021-11-30 2021-11-30 [("doi","10.48550/arXiv.2111.15640")] ai/nn/diffusion

Diffusion probabilistic models (DPMs) have achieved remarkable quality in image generation that rivals GANs’. But unlike GANs, DPMs use a set of latent variables that lack semantic meaning and cannot serve as a useful representation for other tasks. This paper explores the possibility of using DPMs for representation learning and seeks to extract a meaningful and decodable representation of an input image via autoencoding.

Our key idea is to use a learnable encoder for discovering the high-level semantics, and a DPM as the decoder for modeling the remaining stochastic variations. Our method can encode any image into a two-part latent code, where the first part is semantically meaningful and linear, and the second part captures stochastic details, allowing near-exact reconstruction.

This capability enables challenging applications that currently foil GAN-based methods, such as attribute manipulation on real images. We also show that this two-level encoding improves denoising efficiency and naturally facilitates various downstream tasks including few-shot conditional sampling.

Please visit our project page: https://diff-ae.github.io/.

--- https://en.wikipedia.org/wiki/Silurian_hypothesis Silurian hypothesis 2020-11-02 history technology --- https://x.com/Erblicken/status/1509618399768309761 2020-11-02 ai/nn/transformer/clip/sample --- https://x.com/calebwatney/status/1509695329016455168 2020-11-02 ai/nn/transformer/clip/sample --- https://vitalik.eth.limo/general/2022/04/01/maximalist.html In Defense of Bitcoin Maximalism 2020-11-02 bitcoin --- https://github.com/vincent-163/transformer-arithmetic vincent-163/transformer-arithmetic 2020-11-02 ai/nn/transformer/gpt/inner-monologue --- https://www.lesswrong.com/posts/kpPnReyBC54KESiSn/optimality-is-the-tiger-and-agents-are-its-teeth Optimality is the tiger, and agents are its teeth 2020-11-02 ai/scaling existential-risk reinforcement-learning/safe --- https://x.com/KaliYuga_ai/status/1510091935650050048 2020-11-02 ai/nn/transformer/clip/sample --- https://x.com/snecc_art/status/1509920358119485444 2020-11-02 ai/nn/transformer/clip/sample --- https://x.com/unltd_dream_co/status/1510042124615622660 2020-11-03 ai/nn/transformer/clip/sample --- https://x.com/michael_nielsen/status/1510315853895507972 2020-11-03 ai/nn/transformer/clip/sample --- https://x.com/amasad/status/1510330409908772867 2020-11-03 ai/nn/transformer/gpt/codex --- https://x.com/nin_artificial/status/1510368076998619139 2020-11-03 ai/nn/transformer/clip/sample --- https://x.com/michael_nielsen/status/1510313208229232640 2020-11-03 ai/nn/transformer/clip/sample --- https://www.construction-physics.com/p/the-science-of-production The Science of Production Brian Potter 2020-11-03 economics/experience-curve statistics/decision --- https://www.biorxiv.org/content/10.1101/195644.full Genome-wide statistically-significant regions in 43 Utah high-risk families implicate multiple genes involved in risk for completed suicide Hilary Coon, Todd M. Darlington, Emily DiBlasi, W. Brandon Callor, Elliott Ferris, Alison Fraser, Zhe Yu, Nancy William, Sujan C. Das, Sheila E. Crowell, Danli Chen, John S. Anderson, Michael Klein, Leslie Jerominski, Dale Cannon, Andrey Shabalin, Anna Docherty, Megan Williams, Ken R. Smith, Brooks Keeshin, Amanda V. Bakian, Erik Christensen, Qingqin S. Li, Nicola J. Camp, Douglas Gray 2018-08-13 2020-11-03 [("doi","10.1101/195644")] genetics/heritable psychiatry

Suicide is the 10th leading cause of death in the US. While environment has undeniable impact, evidence suggests genetic factors play a role in completed suicide.

We linked a resource of >4,500 DNA samples from completed suicides obtained from the Utah Medical Examiner to genealogical records and medical records data available on over 8 million individuals. This linking has resulted in the identification of high-risk extended families (7–9 generations) with familial risk of completed suicide.

Familial aggregation across distant relatives minimizes effects of shared environment, provides more genetically homogeneous risk groups, and magnifies genetic risks through familial repetition. We analyzed Illumina PsychArray genotypes from suicide cases in 43 high-risk families, identifying 30 distinct shared genomic segments with genome-wide evidence (p = 2.02E-07 to 1.30E-18) of segregation with completed suicide. The 207 genes implicated by the shared regions provide a focused set of genes for further study; 18 have been previously associated with suicide risk.

While PsychArray variants do not represent exhaustive variation within the 207 genes, we investigated these for specific segregation within the high-risk families, and for association of variants with predicted functional impact in ~1300 additional Utah suicides unrelated to the discovery families. None of the limited PsychArray variants explained the high-risk family segregation; sequencing of these regions will be needed to discover segregating risk variants, which may be rarer or regulatory.

However, additional association tests yielded four PsychArray variants (SP110, rs181058279; AGBL2, rs76215382; SUCLA2, rs121908538; APH1B, rs745918508), raising the likelihood that these genes confer risk of completed suicide.

--- https://x.com/nin_artificial/status/1510606349171933186 2020-11-03 ai/nn/transformer/clip/sample --- https://aeon.co/ideas/what-i-learned-as-a-hired-consultant-for-autodidact-physicists What I learned as a hired consultant for autodidact physicists 2020-11-03 philosophy/epistemology --- https://x.com/RiversHaveWings/status/1511099733829316608 2020-11-03 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2204.00227 Perception Prioritized Training of Diffusion Models Jooyoung Choi, Jungbeom Lee, Chaehun Shin, Sungwon Kim, Hyunwoo Kim, Sungroh Yoon 2022-04-01 2022-04-01 [("doi","10.48550/arXiv.2204.00227")] ai/nn/diffusion

Diffusion models learn to restore noisy data, which is corrupted with different levels of noise, by optimizing the weighted sum of the corresponding loss terms, ie. denoising score matching loss.

In this paper, we show that restoring data corrupted with certain noise levels offers a proper pretext task for the model to learn rich visual concepts. We propose to prioritize such noise levels over other levels during training, by redesigning the weighting scheme of the objective function.

We show that our simple redesign of the weighting scheme improves the performance of diffusion models regardless of the datasets, architectures, and sampling strategies.

--- https://x.com/jd_pressman/status/1511168825940602884 2020-11-04 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1511161202621050886 2020-11-04 ai/nn/transformer/clip/sample --- https://x.com/jd_pressman/status/1511168182878277634 2020-11-04 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1511172151360905219 2020-11-04 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1511134466038665217 2020-11-04 ai/nn/transformer/clip/sample --- https://x.com/gandamu_ml/status/1511114616486744069 2020-11-04 ai/nn/transformer/clip/sample --- https://x.com/MichaelFriese10/status/1511150556261224448 2020-11-04 ai/nn/transformer/clip/sample --- https://www.lesswrong.com/posts/rEZqP7K4MG6waC2zf/optimizing-crop-planting-with-mixed-integer-linear Optimizing crop planting with mixed integer linear programming in Stardew Valley 2020-11-04 statistics/decision --- https://arxiv.org/abs/2203.15863 WAVPROMPT: Towards Few-Shot Spoken Language Understanding with Frozen Language Models Heting Gao, Junrui Ni, Kaizhi Qian, Yang Zhang, Shiyu Chang, Mark Hasegawa-Johnson 2022-03-29 2022-03-29 [("doi","10.48550/arXiv.2203.15863")] ai/nn/transformer/gpt

Large-scale auto-regressive language models pretrained on massive text have demonstrated their impressive ability to perform new natural language tasks with only a few text examples, without the need for fine-tuning. Recent studies further show that such a few-shot learning ability can be extended to the text-image setting by training an encoder to encode the images into embeddings functioning like the text embeddings of the language model.

Interested in exploring the possibility of transferring the few-shot learning ability to the audio-text setting, we propose a novel speech understanding framework, WavPrompt, where we finetune a wav2vec model to generate a sequence of audio embeddings understood by the language model.

We show that WavPrompt is a few-shot learner that can perform speech understanding tasks better than a naive text baseline. We conduct detailed ablation studies on different components and hyperparameters to empirically identify the best model configuration. In addition, we conduct a non-speech understanding experiment to show WavPrompt can extract more information than just the transcriptions.

--- https://forum.effectivealtruism.org/posts/LSxNfH9KbettkeHHu/ultra-near-termism-literally-an-idea-whose-time-has-come Ultra-Near-Termism: Literally An Idea Whose Time Has Come 2020-11-04 philosophy/ethics --- https://x.com/GlennIsZen/status/1511253932575760385 2020-11-05 ai/nn/transformer/clip/sample --- https://www.medrxiv.org/content/10.1101/2022.03.25.22272822.full The relationship of major diseases with childlessness: a sibling matched case-control and population register study in Finland and Sweden Aoxing Liu, Evelina T. Akimova, Xuejie Ding, Sakari Jukarainen, Pekka Vartiainen, Tuomo Kiiskinen, Sara Kuitunen, Aki S. Havulinna, Mika Gissler, Stefano Lombardi, Tove Fall, Melinda C. Mills, Andrea Ganna 2022-04-02 2022-04-02 [("doi","10.1101/2022.03.25.22272822")] biology exercise psychiatry/alcoholism psychiatry/autism/schizoid psychiatry/schizophrenia sociology

Background: ~20% of men and 15% of women remain childless at the end of their reproductive lifespan, with childlessness increasing over time, yet we lack a comprehensive understanding of the role and relative importance of diseases associated with childlessness, particularly among men.

Method: We examined all individuals born in Finland (n = 1,035,928) and Sweden (n = 1,509,092) 1956–1968 (men) or 1956 and 1973 (women) and followed them up until the end of 2018. Socio-demographic, health, and reproductive information was obtained from nationwide registers. We assessed the association of 414 diseases across 16 categories with having no children by age 45 (women) and 50 (men) using a matched pair case-control design based on 71,524 pairs of full-sisters and 77,622 full-brothers who were discordant for childlessness as well as a population-based approach.

Results: Mental-behavioral, congenital anomalies, and endocrine-nutritional-metabolic disorders had the strongest associations with childlessness. Novel associations were discovered with inflammatory (eg. myocarditis) and autoimmune diseases (eg. juvenile idiopathic arthritis). Mental-behavioral disorders had stronger associations amongst men, particularly for schizophrenia and acute alcohol intoxication, while congenital anomalies, obesity-related diseases such as diabetes, and inflammatory diseases had stronger associations amongst women. Associations were dependent on the age at onset of the disease, with the strongest association observed earlier in women (21–25 years old) than men (26–30 years old). For most diseases, the association with childlessness was mediated by singlehood, especially in men. Some diseases, however, remained associated with childlessness among partnered individuals, including some mood and endocrine-nutritional-metabolic disorders. All results can be explored in an interactive online dashboard.

Interpretation: We provide evidence that disease burden across multiple domains is associated with childlessness, identifying modifiable mental-behavioral disorders and novel autoimmune and inflammatory diseases. Evidence can be used for targeted health interventions to counter decreasing fertility, reproductive health, involuntary childlessness, and shrinking populations.

--- https://arxiv.org/abs/1803.05457#allen Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord 2018-03-14 2020-11-05 [("doi","10.48550/arXiv.1803.05457")] ai/dataset ai/nn

We present a new question set, text corpus, and baselines assembled to encourage AI research in advanced question answering. Together, these constitute the AI2 Reasoning Challenge (ARC), which requires far more powerful knowledge and reasoning than previous challenges such as SQuAD or SNLI. The ARC question set is partitioned into a Challenge Set and an Easy Set, where the Challenge Set contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. The dataset contains only natural, grade-school science questions (authored for human tests), and is the largest public-domain set of this kind (7,787 questions).

We test several baselines on the Challenge Set, including leading neural models from the SQuAD and SNLI tasks, and find that none are able to outperform a random baseline, reflecting the difficult nature of this task. We are also releasing the ARC Corpus, a corpus of 14M science sentences relevant to the task, and implementations of the 3 neural baseline models tested.

Can your model perform better? We pose ARC as a challenge to the community.

--- https://arxiv.org/abs/1811.00937#allen CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge Alon Talmor, Jonathan Herzig, Nicholas Lourie, Jonathan Berant 2018-11-02 2020-11-05 [("doi","10.48550/arXiv.1811.00937")] ai/dataset ai/nn

When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant document or context, and required very little general background.

To investigate question answering with prior knowledge, we present CommonsenseQA: a challenging new dataset for commonsense question answering. To capture common sense beyond associations, we extract from ConceptNet (Speer et al 2017) multiple target concepts that have the same semantic relation to a single source concept. Crowd-workers are asked to author multiple-choice questions that mention the source concept and discriminate in turn between each of the target concepts. This encourages workers to create questions with complex semantics that often require prior knowledge.

We create 12,247 questions through this procedure and demonstrate the difficulty of our task with a large number of strong baselines.

Our best baseline is based on BERT-large (Devlin et al 2018) and obtains 56% accuracy, well below human performance, which is 89%.

--- https://arxiv.org/abs/2103.07191#microsoft Are NLP Models really able to Solve Simple Math Word Problems? Arkil Patel, Satwik Bhattamishra, Navin Goyal 2021-03-12 2021-03-12 [("doi","10.48550/arXiv.2103.07191")] ai/dataset ai/nn/transformer math

The problem of designing NLP solvers for math word problems (MWP) has seen sustained research activity and steady gains in the test accuracy. Since existing solvers achieve high performance on the benchmark datasets for elementary level MWPs containing one-unknown arithmetic word problems, such problems are often considered “solved” with the bulk of research attention moving to more complex MWPs.

In this paper, we restrict our attention to English MWPs taught in grades four and lower. We provide strong evidence that the existing MWP solvers rely on shallow heuristics to achieve high performance on the benchmark datasets. To this end, we show that MWP solvers that do not have access to the question asked in the MWP can still solve a large fraction of MWPs. Similarly, models that treat MWPs as bag-of-words can also achieve surprisingly high accuracy.

Further, we introduce a challenge dataset, SVAMP, created by applying carefully chosen variations over examples sampled from existing datasets.

The best accuracy achieved by state-of-the-art models is substantially lower on SVAMP, thus showing that much remains to be done even for the simplest of the MWPs.

--- https://arxiv.org/abs/1608.01413 Solving General Arithmetic Word Problems Subhro Roy, Dan Roth 2016-08-04 2020-11-05 [("doi","10.48550/arXiv.1608.01413")] ai/dataset math

This paper presents a novel approach to automatically solving arithmetic word problems.

This is the first algorithmic approach that can handle arithmetic problems with multiple steps and operations, without depending on additional annotations or predefined templates. We develop a theory for expression trees that can be used to represent and evaluate the target arithmetic expressions; we use it to uniquely decompose the target arithmetic problem to multiple classification problems; we then compose an expression tree, combining these with world knowledge through a constrained inference framework.

Our classifiers gain from the use of quantity schemas that supports better extraction of features.

Experimental results show that our method outperforms existing systems, achieving state-of-the-art performance on benchmark datasets of arithmetic word problems.

--- https://www.biorxiv.org/content/10.1101/2022.04.02.486791.full Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation Courtney J. Smith, Nasa Sinnott-Armstrong, Anna Cichońska, Heli Julkunen, Eric Fauman, Peter Würtz, Jonathan K. Pritchard 2022-04-04 2022-04-04 [("doi","10.1101/2022.04.02.486791")] genetics/heritable/correlation

Pleiotropy and genetic correlation are widespread features in GWAS, but they are often difficult to interpret at the molecular level.

Here, we perform GWAS of 16 metabolites clustered at the intersection of amino acid catabolism, glycolysis, and ketone body metabolism in a subset of UK Biobank. We utilize the well-documented biochemistry jointly impacting these metabolites to analyze pleiotropic effects in the context of their pathways.

Among the 213 lead GWAS hits, we find a strong enrichment for genes encoding pathway-relevant enzymes and transporters. We demonstrate that the effect directions of variants acting on biology between metabolite pairs often contrast with those of upstream or downstream variants as well as the polygenic background. Thus, we find that these outlier variants often reflect biology local to the traits.

Finally, we explore the implications for interpreting disease GWAS, underscoring the potential of unifying biochemistry with dense metabolomics data to understand the molecular basis of pleiotropy in complex traits and diseases.

--- https://x.com/rainisto/status/1511222985633509381 2020-11-05 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2006.11477#facebook wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli 2020-06-20 2020-11-05 [("doi","10.48550/arXiv.2006.11477")] ai/nn/transformer ai/scaling

We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. The framework, known as wav2vec 2.0, masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned.

Experiments using all labeled data of LibriSpeech achieve 1.8/3.3 WER (Word Error Rate) on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state-of-the-art on the 100 hour subset while using 100× less labeled data. Using just 10 minutes of labeled data and pre-training on 53k hours of unlabeled data, it still achieves 4.8/8.2 WER.

This demonstrates the feasibility of speech recognition with limited amounts of labeled data.

--- https://arxiv.org/abs/2105.11084#facebook Unsupervised Speech Recognition Alexei Baevski, Wei-Ning Hsu, Alexis Conneau, Michael Auli 2021-05-24 2021-05-24 [("doi","10.48550/arXiv.2105.11084")] ai/scaling

Despite rapid progress in the recent past, current speech recognition systems still require labeled training data which limits this technology to a small fraction of the languages spoken around the globe. This paper describes wav2vec-U, short for wav2vec Unsupervised, a method to train speech recognition models without any labeled data.

We leverage self-supervised speech representations to segment unlabeled audio and learn a mapping from these representations to phonemes via adversarial training. The right representations are key to the success of our method.

Compared to the best previous unsupervised work, wav2vec-U reduces the phoneme error rate on the TIMIT benchmark 26.1 → 11.3. On the larger English LibriSpeech benchmark, wav2vec-U achieves a word error rate of 5.9 on test-other, rivaling some of the best published systems trained on 960 hours of labeled data from only two years ago. We also experiment on 9 other languages, including low-resource languages such as Kyrgyz, Swahili, and Tatar.

--- https://arxiv.org/abs/2110.07298 LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5 Chengwei Qin, Shafiq Joty 2021-10-14 2021-10-14 [("doi","10.48550/arXiv.2110.07298")] ai/nn/transformer/t5 ai/scaling

Existing approaches to lifelong language learning rely on plenty of labeled data for learning a new task, which is hard to obtain in most real scenarios. Considering that humans can continually learn new tasks from a handful of examples, we expect the models also to be able to generalize well on new few-shot tasks without forgetting the previous ones. In this work, we define this more challenging yet practical problem as Lifelong Few-shot Language Learning (LFLL) and propose a unified framework for it based on prompt tuning of T5.

Our framework called LFPT5 takes full advantage of PT’s strong few-shot learning ability, and simultaneously trains the model as a task solver and a data generator. Before learning a new domain of the same task type, LFPT5 generates pseudo (labeled) samples of previously learned domains, and later gets trained on those samples to alleviate forgetting of previous knowledge as it learns the new domain. In addition, a KL divergence loss is minimized to achieve label consistency between the previous and the current model. While adapting to a new task type, LFPT5 includes and tunes additional prompt embeddings for the new task.

With extensive experiments, we demonstrate that LFPT5 can be applied to various different types of tasks and outperform previous methods in different LFLL settings.

--- https://arxiv.org/abs/2204.00595 Monarch: Expressive Structured Matrices for Efficient and Accurate Training Tri Dao, Beidi Chen, Nimit Sohoni, Arjun Desai, Michael Poli, Jessica Grogan, Alexander Liu, Aniruddh Rao, Atri Rudra, Christopher Ré 2022-04-01 2022-04-01 [("doi","10.48550/arXiv.2204.00595")] ai/nn/sparsity ai/scaling/hardware cs/algorithm

Large neural networks excel in many domains, but they are expensive to train and fine-tune. A popular approach to reduce their compute or memory requirements is to replace dense weight matrices with structured ones (eg. sparse, low-rank, Fourier transform). These methods have not seen widespread adoption (1) in end-to-end training due to unfavorable efficiency–quality tradeoffs, and (2) in dense-to-sparse fine-tuning due to lack of tractable algorithms to approximate a given dense weight matrix.

To address these issues, we propose a class of matrices (Monarch) that is hardware-efficient (they are parameterized as products of two block-diagonal matrices for better hardware utilization) and expressive (they can represent many commonly used transforms). Surprisingly, the problem of approximating a dense weight matrix with a Monarch matrix, though nonconvex, has an analytical optimal solution. These properties of Monarch matrices unlock new ways to train and fine-tune sparse and dense models.

We empirically validate that Monarch can achieve favorable accuracy-efficiency tradeoffs in several end-to-end sparse training applications: speeding up ViT and GPT-2 training on ImageNet classification and Wikitext-103 language modeling by 2× with comparable model quality, and reducing the error on PDE solving and MRI reconstruction tasks by 40%.

In sparse-to-dense training, with a simple technique called “reverse sparsification”, Monarch matrices serve as a useful intermediate representation to speed up GPT-2 pretraining on OpenWebText by 2× without quality drop.

The same technique brings 23% faster BERT pretraining than even the very optimized implementation from Nvidia that set the MLPerf 1.1 record. In dense-to-sparse fine-tuning, as a proof-of-concept, our Monarch approximation algorithm speeds up BERT fine-tuning on GLUE by 1.7× with comparable accuracy.

--- https://www.biorxiv.org/content/10.1101/2021.06.14.448352.full Sniffing Out New Friends: Similarity in Body-Odor Predicts the Quality of Same-Sex Non-Romantic Dyadic Interactions Inbal Ravreby, Kobi Snitz, Noam Sobel 2021-06-17 2021-06-17 [("doi","10.1101/2021.06.14.448352")] psychology/personality psychology/smell/human

Most are familiar with the notion of socially “clicking” with someone, namely sensing an immediate bond that can lead to strong and often long-lasting friendships. The mechanisms underlying such rapid bonding remain unclear.

Given that body-odor similarity is a critical cue for social interaction in non-human mammals, we tested the hypothesis that body-odor similarly contributes to bonding in same-sex non-romantic human dyads.

We observed that objective ratings obtained with an electronic nose, and subjective ratings obtained from human smellers, converged to suggest that click-friends smell more similar to each other than random dyads. Remarkably, we then found that we could use the electronic nose to predict which strangers would later form better dyadic interactions.

Thus, humans may literally sniff-out new friends based on similarities in body-odor.

--- https://arxiv.org/abs/2105.00377 MathBERT: A Pre-Trained Model for Mathematical Formula Understanding Shuai Peng, Ke Yuan, Liangcai Gao, Zhi Tang 2021-05-02 2021-05-02 [("doi","10.48550/arXiv.2105.00377")] ai/nn/transformer math

Large-scale pre-trained models like BERT, have obtained a great success in various Natural Language Processing (NLP) tasks, while it is still a challenge to adapt them to the math-related tasks. Current pre-trained models neglect the structural features and the semantic correspondence between formula and its context.

To address these issues, we propose a novel pre-trained model, namely MathBERT, which is jointly trained with mathematical formulas and their corresponding contexts. In addition, in order to further capture the semantic-level structural features of formulas, a new pre-training task is designed to predict the masked formula substructures extracted from the Operator Tree (OPT), which is the semantic structural representation of formulas.

We conduct various experiments on 3 downstream tasks to evaluate the performance of MathBERT, including mathematical information retrieval, formula topic classification and formula headline generation. Experimental results demonstrate that MathBERT outperforms existing methods on all those 3 tasks.

Moreover, we qualitatively show that this pre-trained model effectively captures the semantic-level structural information of formulas.

To the best of our knowledge, MathBERT is the first pre-trained model for mathematical formula understanding.

--- https://x.com/KaliYuga_ai/status/1511516566017486849 2020-11-06 ai/nn/transformer/clip/sample --- https://x.com/EErratica/status/1511579210325934084 2020-11-06 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2204.02329#deepmind Can language models learn from explanations in context? Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane X. Wang, Felix Hill 2022-04-05 2022-04-05 [("doi","10.48550/arXiv.2204.02329")] ai/nn/transformer/gpt/inner-monologue ai/scaling reinforcement-learning/meta-learning
Figure 4: The effects of explanations on average accuracy, across model sizes. Untuned explanations lead to modest increases in accuracy from the largest model relative to few-shot prompts without explanations. Selecting the few-shot examples with explanations using a small validation set improves substantially over selecting only the few-shot examples. Note that these results aggregate across all 40 tasks, which have different difficulties and numbers of possible answers. See Appendix D.6 for per-task plots. (Error bars are bootstrap 95%-CIs. Points are offset horizontally to avoid overlap.)

Large language models can perform new tasks by adapting to a few in-context examples. For humans, rapid learning from examples can benefit from explanations that connect examples to task principles.

We therefore investigate whether explanations of few-shot examples can allow language models to adapt more effectively. We annotate a set of 40 challenging tasks from BIG-Bench with explanations of answers to a small subset of questions, as well as a variety of matched control explanations. We evaluate the effects of various zero-shot and few-shot prompts that include different types of explanations, instructions, and controls on the performance of a range of large language models. We analyze these results using statistical multilevel modeling techniques that account for the nested dependencies among conditions, tasks, prompts, and models.

We find that explanations of examples can improve performance. Adding untuned explanations to a few-shot prompt offers a modest improvement in performance; about 1⁄3 the effect size of adding few-shot examples, but twice the effect size of task instructions. We then show that explanations tuned for performance on a small validation set offer substantially larger benefits; building a prompt by selecting examples and explanations together substantially improves performance over selecting examples alone. Hand-tuning explanations can substantially improve performance on challenging tasks. Furthermore, even untuned explanations outperform carefully matched controls, suggesting that the benefits are due to the link between an example and its explanation, rather than lower-level features of the language used. However, only large models can benefit from explanations.

In summary, explanations can support the in-context learning abilities of large language models on challenging tasks.

Figure 3: Explanations can have substantial benefits—especially when tuned—but their effects are variable. This plot compares the improvement that explanations cause relative to matched prompts without explanations in 3 different conditions. In purple we show the effect of untuned explanations, relative to few-shot prompts with no explanations. In darker green we show the benefit of selecting examples with explanations over selecting examples alone; and in light green we show the benefit of hand-tuned explanations over prompts with no explanations. Note that we only hand-tuned explanations on a handful of tasks, so there are relatively few observations. (Points and lines at top are means and bootstrap 95%-CIs. Curves are smoothed density estimates based on the individual observed differences, which are plotted as points below. The bi-modality for hand-tuned explanations is probably due to the small number of observations.)
--- https://x.com/nin_artificial/status/1511363025797885956 2020-11-06 ai/nn/transformer/clip/sample --- https://x.com/huemin_art/status/1511573292464820225 2020-11-06 ai/nn/transformer/clip/sample --- https://www.quantamagazine.org/researchers-identify-master-problem-underlying-all-cryptography-20220406/ 2020-11-06 cs/cryptography --- https://www.lesswrong.com/posts/a3FuA7fGgpTQ7mX3W/is-gpt3-a-good-rationalist-instructgpt3-2-2 Is GPT-3 a Good Rationalist? 2020-11-06 ai/nn/transformer/gpt philosophy/epistemology --- https://www.vox.com/22937531/virus-lab-safety-pandemic-prevention We know lab leaks are possible, and one could start a new pandemic 2020-11-06 existential-risk genetics/genome-synthesis --- https://x.com/GlennIsZen/status/1512072261586456587 2020-11-07 ai/nn/transformer/clip/sample --- https://www.biorxiv.org/content/10.1101/176511.full 99 independent genetic loci influencing general cognitive function include genes associated with brain health and structure (n = 280,360) Gail Davies, Max Lam, Sarah E. Harris, Joey W. Trampush, Michelle Luciano, W. David Hill, Saskia P. Hagenaars, Stuart J. Ritchie, Riccardo E. Marioni, Chloe Fawns-Ritchie, David C. M. Liewald, Judith A. Okely, Ari V. Ahola-Olli, Catriona L. K. Barnes, Lars Bertram, Joshua C. Bis, Katherine E. Burdick, Andrea Christoforou, Pamela DeRosse, Srdjan Djurovic, Thomas Espeseth, Stella Giakoumaki, Sudheer Giddaluru, Daniel E. Gustavson, Caroline Hayward, Edith Hofer, M. Arfan Ikram, Robert Karlsson, Emma Knowles, Jari Lahti, Markus Leber, Shuo Li, Karen A. Mather, Ingrid Sigfrid Melle, Derek Morris, Christopher Oldmeadow, Teemu Palviainen, Antony Payton, Raha Pazoki, Katja Petrovic, Chandra A. Reynolds, Muralidharan Sargurupremraj, Markus Scholz, Jennifer A. Smith, Albert Vernon Smith, Natalie Terzikhan, Anbu Thalamuthu, Stella Trompet, Sven J. van der Lee, Erin B. Ware, B. Gwen Windham, Margaret J. Wright, Jingyun Yang, Jin Yu, David Ames, Najaf Amin, Philippe Amouyel, Ole A. Andreassen, Nicola J. Armstrong, Amelia A. Assareh, John R. Attia, Deborah Attix, Dimitrios Avramopoulos, David A. Bennett, Anne C. Böhmer, Patricia A. Boyle, Henry Brodaty, Harry Campbell, Tyrone D. Cannon, Elizabeth T. Cirulli, Eliza Congdon, Emily Drabant Conley, Janie Corley, Simon R. Cox, Anders Martin Dale, Abbas Dehghan, Danielle Dick, Dwight Dickinson, Johan G. Eriksson, Evangelos Evangelou, Jessica D. Faul, Ian Ford, Nelson A. Freimer, He Gao, Ina Giegling, Nathan A. Gillespie, Scott D. Gordon, Rebecca F. Gottesman, Michael E. Griswold, Vilmundur Gudnason, Tamara B. Harris, Annette M. Hartmann, Alex Hatzimanolis, Gerardo Heiss, Elizabeth G. Holliday, Peter K. Joshi, Kähönen Mika, Sharon L. R. Kardia, Ida Karlsson, Luca Kleineidam, David S. Knopman, Nicole A. Kochan, Bettina Konte, John B. Kwok, Stephanie Le Hellard, Teresa Lee, Terho Lehtimäki, Shu-Chen Li, Tian Liu, Marisa Koini, Edythe London, Will T. Longstreth, Oscar L. Lopez, Anu Loukola, Tobias Luck, Astri J. Lundervold, Anders Lundquist, Leo-Pekka Lyytikäinen, Nicholas G. Martin, Grant W. Montgomery, Alison D. Murray, Anna C. Need, Raymond Noordam, Lars Nyberg, William Ollier, Goran Papenberg, Alison Pattie, Ozren Polasek, Russell A. Poldrack, Bruce M. Psaty, Simone Reppermund, Steffi G. Riedel-Heller, Richard J. Rose, Jerome I. Rotter, Panos Roussos, Suvi P. Rovio, Yasaman Saba, Fred W. Sabb, Perminder S. Sachdev, Claudia Satizabal, Matthias Schmid, Rodney J. Scott, Matthew A. Scult, Jeannette Simino, P. Eline Slagboom, Nikolaos Smyrnis, Aïcha Soumaré, Nikos C. Stefanis, David J. Stott, Richard E. Straub, Kjetil Sundet, Adele M. Taylor, Kent D. Taylor, Ioanna Tzoulaki, Christophe Tzourio, André G. Uitterlinden, Veronique Vitart, Aristotle N. Voineskos, Jaakko Kaprio, Michael Wagner, Holger Wagner, Leonie Weinhold, K. Hoyan Wen, Elisabeth Widen, Qiong Yang, Wei Zhao, Hieab H. H. Adams, Dan E. Arking, Robert M. Bilder, Panos Bitsios, Eric Boerwinkle, Ornit Chiba-Falek, Aiden Corvin, Philip L. De Jager, Stéphanie Debette, Gary Donohoe, Paul Elliott, Annette L. Fitzpatrick, Michael Gill, David C. Glahn, Sara Hägg, Narelle K. Hansell, Ahmad R. Hariri, M. Kamran Ikram, J. Wouter Jukema, Eero Vuoksimaa, Matthew C. Keller, William S. Kremen, Lenore J. Launer, Ulman Lindenberger, Aarno Palotie, Nancy L. Pedersen, Neil Pendleton, David J. Porteous, Katri Räikkönen, Olli T. Raitakari, Alfredo Ramirez, Ivar Reinvang, Igor Rudan, Dan Rujescu, Reinhold Schmidt, Helena Schmidt, Peter W. Schofield, Peter R. Schofield, John M. Starr, Vidar M. Steen, Julian N. Trollor, Steven T. Turner, Cornelia M. Van Duijn, Arno Villringer, Daniel R. Weinberger, David R. Weir, James F. Wilson, Anil Malhotra, Andrew M. McIntosh, Catharine R. Gale, Sudha Seshadri, Thomas H. Mosley, Jan Bressler, Todd Lencz, Ian J. Deary 2017-08-18 2020-11-07 [("doi","10.1101/176511")] genetics/heritable/correlation iq psychology/neuroscience

General cognitive function is a prominent human trait associated with many important life outcomes1,2, including longevity3. The substantial heritability of general cognitive function is known to be polygenic, but it has had little explication in terms of the contributing genetic variants4,5,6.

Here, we combined cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total n = 280,360; age range = 16 to 102). We found 9,714 genome-wide statistically-significant SNPs (P<5 × 10−8) in 99 independent loci. Most showed clear evidence of functional importance. Among many novel genes associated with general cognitive function were SGCZ, ATXN1, MAPT, AUTS2, and P2RY6. Within the novel genetic loci were variants associated with neurodegenerative disorders, neurodevelopmental disorders, physical and psychiatric illnesses, brain structure, and BMI.

Gene-based analyses found 536 genes statistically-significantly associated with general cognitive function; many were highly expressed in the brain, and associated with neurogenesis and dendrite gene sets. Genetic association results predicted up to 4% of general cognitive function variance in independent samples. There was genetic overlap between general cognitive function and information processing speed, as well as many health variables including longevity.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4393366/ Common genetic variants influence human subcortical brain structures Derrek P. Hibar, Jason L. Stein, Miguel E. Renteria, Alejandro Arias-Vasquez, Sylvane Desrivières, Neda Jahanshad, Roberto Toro, Katharina Wittfeld, Lucija Abramovic, Micael Andersson, Benjamin S. Aribisala, Nicola J. Armstrong, Manon Bernard, Marc M. Bohlken, Marco P. Boks, Janita Bralten, Andrew A. Brown, M. Mallar Chakravarty, Qiang Chen, Christopher R. K. Ching, Gabriel Cuellar-Partida, Anouk den Braber, Sudheer Giddaluru, Aaron L. Goldman, Oliver Grimm, Tulio Guadalupe, Johanna Hass, Girma Woldehawariat, Avram J. Holmes, Martine Hoogman, Deborah Janowitz, Tianye Jia, Sungeun Kim, Marieke Klein, Bernd Kraemer, Phil H. Lee, Loes M. Olde Loohuis, Michelle Luciano, Christine Macare, Karen A. Mather, Manuel Mattheisen, Yuri Milaneschi, Kwangsik Nho, Martina Papmeyer, Adaikalavan Ramasamy, Shannon L. Risacher, Roberto Roiz-Santiañez, Emma J. Rose, Alireza Salami, Philipp G. Sämann, Lianne Schmaal, Andrew J. Schork, Jean Shin, Lachlan T. Strike, Alexander Teumer, Marjolein M. J. van Donkelaar, Kristel R. van Eijk, Raymond K. Walters, Lars T. Westlye, Christopher D. Whelan, Anderson M. Winkler, Marcel P. Zwiers, Saud Alhusaini, Lavinia Athanasiu, Stefan Ehrlich, Marina M. H. Hakobjan, Cecilie B. Hartberg, Unn K. Haukvik, Angelien J. G. A. M. Heister, David Hoehn, Dalia Kasperaviciute, David C. M. Liewald, Lorna M. Lopez, Remco R. R. Makkinje, Mar Matarin, Marlies A. M. Naber, D. Reese McKay, Margaret Needham, Allison C. Nugent, Benno Pütz, Natalie A. Royle, Li Shen, Emma Sprooten, Daniah Trabzuni, Saskia S. L. van der Marel, Kimm J. E. van Hulzen, Esther Walton, Christiane Wolf, Laura Almasy, David Ames, Sampath Arepalli, Amelia A. Assareh, Mark E. Bastin, Henry Brodaty, Kazima B. Bulayeva, Melanie A. Carless, Sven Cichon, Aiden Corvin, Joanne E. Curran, Michael Czisch, Greig I. de Zubicaray, Allissa Dillman, Ravi Duggirala, Thomas D. Dyer, Susanne Erk, Iryna O. Fedko, Luigi Ferrucci, Tatiana M. Foroud, Peter T. Fox, Masaki Fukunaga, J. Raphael Gibbs, Harald H. H. Göring, Robert C. Green, Sebastian Guelfi, Narelle K. Hansell, Catharina A. Hartman, Katrin Hegenscheid, Andreas Heinz, Dena G. Hernandez, Dirk J. Heslenfeld, Pieter J. Hoekstra, Florian Holsboer, Georg Homuth, Jouke-Jan Hottenga, Masashi Ikeda, Clifford R. Jack, Mark Jenkinson, Robert Johnson, Ryota Kanai, Maria Keil, Jack W. Kent, Peter Kochunov, John B. Kwok, Stephen M. Lawrie, Xinmin Liu, Dan L. Longo, Katie L. McMahon, Eva Meisenzahl, Ingrid Sigfrid Melle, Sebastian Mohnke, Grant W. Montgomery, Jeanette C. Mostert, Thomas W. Mühleisen, Michael A. Nalls, Thomas E. Nichols, Lars G. Nilsson, Markus M. Nöthen, Kazutaka Ohi, Rene L. Olvera, Rocio Perez-Iglesias, G. Bruce Pike, Steven G. Potkin, Ivar Reinvang, Simone Reppermund, Marcella Rietschel, Nina Romanczuk-Seiferth, Glenn D. Rosen, Dan Rujescu, Knut Schnell, Peter R. Schofield, Colin Smith, Vidar M. Steen, Jessika E. Sussmann, Anbupalam Thalamuthu, Arthur W. Toga, Bryan J. Traynor, Juan Troncoso, Jessica A. Turner, Maria C. Valdés Hernández, Dennis van ’t Ent, Marcel van der Brug, Nic J. A. van der Wee, Marie-Jose van Tol, Dick J. Veltman, Thomas H. Wassink, Eric Westman, Ronald H. Zielke, Alan B. Zonderman, David G. Ashbrook, Reinmar Hager, Lu Lu, Francis J. McMahon, Derek W. Morris, Robert W. Williams, Han G. Brunner, Randy L. Buckner, Jan K. Buitelaar, Wiepke Cahn, Vince D. Calhoun, Gianpiero L. Cavalleri, Benedicto Crespo-Facorro, Anders Martin Dale, Gareth E. Davies, Norman Delanty, Chantal Depondt, Srdjan Djurovic, Wayne C. Drevets, Thomas Espeseth, Randy L. Gollub, Beng-Choon Ho, Wolfgang Hoffmann, Norbert Hosten, René S. Kahn, Stephanie Le Hellard, Andreas Meyer-Lindenberg, Bertram Müller-Myhsok, Matthias Nauck, Lars Nyberg, Massimo Pandolfo, Brenda W. J. H. Penninx, Joshua L. Roffman, Sanjay M. Sisodiya, Jordan W. Smoller, Hans van Bokhoven, Neeltje E. M. van Haren, Henry Völzke, Henrik Walter, Michael W. Weiner, Wei Wen, Tonya White, Ingrid Agartz, Ole A. Andreassen, John Blangero, Dorret I. Boomsma, Rachel M. Brouwer, Dara M. Cannon, Mark R. Cookson, Eco J. C. de Geus, Ian J. Deary, Gary Donohoe, Guillén Fernández, Simon E. Fisher, Clyde Francks, David C. Glahn, Hans J. Grabe, Oliver Gruber, John Hardy, Ryota Hashimoto, Hilleke E. Hulshoff Pol, Erik G. Jönsson, Iwona Kloszewska, Simon Lovestone, Venkata S. Mattay, Patrizia Mecocci, Colm McDonald, Andrew M. McIntosh, Roel A. Ophoff, Tomas Paus, Zdenka Pausova, Mina Ryten, Perminder S. Sachdev, Andrew J. Saykin, Andy Simmons, Andrew Singleton, Hilkka Soininen, Joanna M. Wardlaw, Michael E. Weale, Daniel R. Weinberger, Hieab H. H. Adams, Lenore J. Launer, Stephan Seiler, Reinhold Schmidt, Ganesh Chauhan, Claudia L. Satizabal, James T. Becker, Lisa Yanek, Sven J. van der Lee, Maritza Ebling, Bruce Fischl, W. T. Longstreth, Douglas Greve, Helena Schmidt, Paul Nyquist, Louis N. Vinke, Cornelia van Duijn, Luting Xue, Bernard Mazoyer, Joshua C. Bis, Vilmundur Gudnason, Sudha Seshadri, M. Arfan Ikram, Nicholas G. Martin, Margaret J. Wright, Gunter Schumann, Barbara Franke, Paul M. Thompson, Sarah E. Medland 2015 2020-11-07 [("doi","10.1038/nature14101")] genetics/heritable/correlation psychology/neuroscience

The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behavior and disease.

To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts.

We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for 3 loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; p = 1.08 × 10 (−33); 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport.

Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

--- https://www.biorxiv.org/content/10.1101/2020.02.17.952010.full Greater male than female variability in regional brain structure across the lifespan Lara M. Wierenga, Gaelle E. Doucet, Danai Dima, Ingrid Agartz, Moji Aghajani, Theophilus N. Akudjedu, Anton Albajes-Eizagirre, Dag Alnæs, Kathryn I. Alpert, Ole A. Andreassen, Alan Anticevic, Philip Asherson, Tobias Banaschewski, Nuria Bargallo, Sarah Baumeister, Ramona Baur-Streubel, Alessandro Bertolino, Aurora Bonvino, Dorret I. Boomsma, Stefan Borgwardt, Josiane Bourque, Anouk den Braber, Daniel Brandeis, Alan Breier, Henry Brodaty, Rachel M. Brouwer, Geraldo F. Busatto, Vince D. Calhoun, Erick J. Canales-Rodríguez, Dara M. Cannon, Xavier Caseras, Tiffany M. Chaim-Avancini, Christopher R. K. Ching, Patricia J. Conrod, Annette Conzelmann, Fabrice Crivello, Christopher G. Davey, Erin W. Dickie, Stefan Ehrlich, Dennis van’t Ent, Jean-Paul Fouche, Paola Fuentes-Claramonte, Eco J. C. de Geus, Annabella Di Giorgio, David C. Glahn, Ian H. Gotlib, Hans J. Grabe, Oliver Gruber, Patricia Gruner, Raquel E. Gur, Ruben C. Gur, Tiril P. Gurholt, Lieuwe de Haan, Beathe Haatveit, Ben J. Harrison, Sean N. Hatton, Odile A. van den Heuvel, Ian B. Hickie, Sarah Hohmann, Avram J. Holmes, Martine Hoogman, Norbert Hosten, Fleur M. Howells, Hilleke E. Hulshoff Pol, Chaim Huyser, Neda Jahanshad, Anthony C. James, Erik G. Jönsson, John A. Joska, Karolinska Schizophrenia Project (KaSP) Consortium, Marieke Klein, Laura Koenders, Knut K. Kolskår, Bernd Krämer, Jonna Kuntsi, Jim Lagopoulos, Luisa Lazaro, Irina S. Lebedeva, Phil H. Lee, Christine Lochner, Marise W. J. Machielsen, Sophie Maingault, Nicholas G. Martin, Ignacio Martínez-Zalacaín, David Mataix-Cols, Bernard Mazoyer, Brenna C. McDonald, Colm McDonald, Andrew M. McIntosh, Katie L. McMahon, Genevieve McPhilemy, Dennis van der Meer, José M. Menchón, Jilly Naaijen, Lars Nyberg, Yannis Paloyelis, Paul Pauli, Giulio Pergola, Edith Pomarol-Clotet, Maria J. Portella, Joaquim Radua, Andreas Reif, Geneviève Richard, Joshua L. Roffman, Pedro G. P. Rosa, Matthew D. Sacchet, Perminder S. Sachdev, Raymond Salvador, Salvador Sarró, Theodore D. Satterthwaite, Andrew J. Saykin, Mauricio H. Serpa, Kang Sim, Andrew Simmons, Jordan W. Smoller, Iris E. Sommer, Carles Soriano-Mas, Dan J. Stein, Lachlan T. Strike, Philip R. Szeszko, Henk S. Temmingh, Sophia I. Thomopoulos, Alexander S. Tomyshev, Julian N. Trollor, Anne Uhlmann, Ilya M. Veer, Dick J. Veltman, Aristotle Voineskos, Henry Völzke, Henrik Walter, Lei Wang, Yang Wang, Bernd Weber, Wei Wen, John D. West, Lars T. Westlye, Heather C. Whalley, Steven C. R. Williams, Katharina Wittfeld, Daniel H. Wolf, Margaret J. Wright, Yuliya N. Yoncheva, Marcus V. Zanetti, Georg C. Ziegler, Greig I. de Zubicaray, Paul M. Thompson, Eveline A. Crone, Sophia Frangou, Christian K. Tamnes 2020-02-18 2020-11-07 [("doi","10.1101/2020.02.17.952010")] psychology/neuroscience

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease.

Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life.

Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1–90 years old (47% females).

We observed patterns of greater male than female between-subject variance for all brain measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms.

The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

--- https://www.biorxiv.org/content/10.1101/2020.07.30.229914.full Common variants contribute to intrinsic human brain functional networks Bingxin Zhao, Tengfei Li, Stephen M. Smith, Di Xiong, Xifeng Wang, Yue Yang, Tianyou Luo, Ziliang Zhu, Yue Shan, Nana Matoba, Quan Sun, Yuchen Yang, Mads E. Hauberg, Jaroslav Bendl, John F. Fullard, Panagiotis Roussos, Weili Lin, Yun Li, Jason L. Stein, Hongtu Zhu 2020-09-17 2020-11-07 [("doi","10.1101/2020.07.30.229914")] genetics/heritable/correlation iq psychiatry/adhd psychiatry/depression psychiatry/schizophrenia psychology/neuroscience

The human brain remains active in the absence of explicit tasks and forms networks of correlated activity. Resting-state functional magnetic resonance imaging (rsfMRI) measures brain activity at rest, which has been linked with both cognitive and clinical outcomes. The genetic variants influencing human brain function are largely unknown.

Here we utilized rsfMRI from 44,190 individuals of multiple ancestries (37,339 in the UK Biobank) to discover and validate the common genetic variants influencing intrinsic brain activity.

We identified hundreds of novel genetic loci associated with intrinsic functional signatures (p < 2.8 × 10−11), including associations to the central executive, default mode, and salience networks involved in the triple network model of psychopathology. A number of intrinsic brain activity associated loci colocalized with brain disorder GWAS (eg. Alzheimer’s disease, Parkinson’s disease, schizophrenia) and cognition, such as 19q13.32, 17q21.31, and 2p16.1. Particularly, we detected a colocalization between one (rs429358) of the two variants in the APOE ε4 locus and function of the default mode, central executive, attention, and visual networks. Genetic correlation analysis demonstrated shared genetic influences between brain function and brain structure in the same regions. We also detected statistically-significant genetic correlations with 26 other complex traits, such as ADHD, major depressive disorder, schizophrenia, intelligence, education, sleep, subjective well-being, and neuroticism.

Common variants associated with intrinsic brain activity were enriched within regulatory element in brain tissues.

--- https://arxiv.org/abs/2203.12154 Estimating trans-ancestry genetic correlation with unbalanced data resources Bingxin Zhao, Xiaochen Yang, Hongtu Zhu 2022-03-23 2022-03-23 [("doi","10.48550/arXiv.2203.12154")] genetics/heritable/correlation

The aim of this paper is to propose a novel estimation method of using genetic-predicted observations to estimate trans-ancestry genetic correlations, which describes how genetic architecture of complex traits varies among populations, in genome-wide association studies (GWAS). Our new estimator corrects for prediction errors caused by high-dimensional weak GWAS signals, while addressing the heterogeneity of GWAS data across ethnicities, such as linkage disequilibrium (LD) differences, which can lead to biased findings in homogeneity-agnostic analyses. Moreover, our estimator only requires one population to have a large GWAS sample size, and the second population can only have a much smaller number of participants (for example, hundreds). It is designed to specifically address the unbalanced data resources such that the GWAS sample size for European populations is usually larger than that of non-European ancestry groups.

Extensive simulations and real data analyses of 30 complex traits in the UK Biobank study show that our method is capable of providing reliable estimates of a wide range of complex traits. Our results provide deep insights into the transferability of population-specific genetic findings.

--- https://x.com/andrewbadr/status/1512114501478453252 2020-11-07 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2204.02849#facebook KNN-Diffusion: Image Generation via Large-Scale Retrieval Oron Ashual, Shelly Sheynin, Adam Polyak, Uriel Singer, Oran Gafni, Eliya Nachmani, Yaniv Taigman 2022-04-06 2022-04-06 [("doi","10.48550/arXiv.2204.02849")] ai/anime ai/dataset ai/nn/diffusion ai/nn/retrieval

While the availability of massive Text-Image datasets is shown to be extremely useful in training large-scale generative models (eg. DDPMs, Transformers), their output typically depends on the quality of both the input text, as well as the training dataset.

In this work, we show how large-scale retrieval methods, in particular efficient K-Nearest-Neighbors (KNN) search, can be used in order to train a model to adapt to new samples. Learning to adapt enables several new capabilities. Sifting through billions of records at inference time is extremely efficient and can alleviate the need to train or memorize an adequately large generative model. Additionally, fine-tuning trained models to new samples can be achieved by simply adding them to the table. Rare concepts, even without any presence in the training set, can be then leveraged during test time without any modification to the generative model. Our diffusion-based model trains on images only, by leveraging a joint Text-Image multi-modal metric.

Compared to baseline methods, our generations achieve state-of-the-art results both in human evaluations as well as with perceptual scores when tested on a public multimodal dataset of natural images, as well as on a collected dataset of 400 million Stickers.

Figure 3: A selection of generated stickers.
Figure 4: Visual comparison of Text-to-Sticker generation methods.
Figure 5: Visual comparison of Text-to-Image generation methods.
Figure 6: Image Editing. Images on the right were generated by conditioning on the leftmost image with several texts.
--- https://arxiv.org/abs/1802.05365#allen Deep contextualized word representations Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer 2018-02-15 2020-11-07 [("doi","10.48550/arXiv.1802.05365")] ai/nn/rnn

We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (eg. syntax and semantics), and (2) how these uses vary across linguistic contexts (ie. to model polysemy).

Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus.

We show that these representations can be easily added to existing models and improve the state-of-the-art across 6 challenging NLP problems, including question answering, textual entailment and sentiment analysis.

We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

--- https://arxiv.org/abs/1712.07316 A Flexible Approach to Automated RNN Architecture Generation Martin Schrimpf, Stephen Merity, James Bradbury, Richard Socher 2017-12-20 2020-11-07 [("doi","10.48550/arXiv.1712.07316")] ai/nn/rnn reinforcement-learning/exploration

The process of designing neural architectures requires expert knowledge and extensive trial and error. While automated architecture search may simplify these requirements, the recurrent neural network (RNN) architectures generated by existing methods are limited in both flexibility and components.

We propose a domain-specific language (DSL) for use in automated architecture search which can produce novel RNNs of arbitrary depth and width. The DSL is flexible enough to define standard architectures such as the Gated Recurrent Unit and Long Short Term Memory and allows the introduction of non-standard RNN components such as trigonometric curves and layer normalization.

Using two different candidate generation techniques, random search with a ranking function and reinforcement learning, we explore the novel architectures produced by the RNN DSL for language modeling and machine translation domains.

The resulting architectures do not follow human intuition yet perform well on their targeted tasks, suggesting the space of usable RNN architectures is far larger than previously assumed.

--- https://arxiv.org/abs/1709.05964 Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification Hajin Shim, Sung Ju Hwang, Eunho Yang 2017-09-18 2020-11-08 [("doi","10.48550/arXiv.1709.05964")] ai/nn/rnn reinforcement-learning/exploration/active-learning

We consider the problem of active feature acquisition, where we sequentially select the subset of features in order to achieve the maximum prediction performance in the most cost-effective way.

In this work, we formulate this active feature acquisition problem as a reinforcement learning problem, and provide a novel framework for jointly learning both the RL agent and the classifier (environment). We also introduce a more systematic way of encoding subsets of features that can properly handle innate challenge with missing entries in active feature acquisition problems, that uses the orderless LSTM-based set encoding mechanism that readily fits in the joint learning framework.

We evaluate our model on a carefully designed synthetic dataset for the active feature acquisition as well as several real datasets such as electric health record (EHR) datasets, on which it outperforms all baselines in terms of prediction performance as well feature acquisition cost.

--- https://arxiv.org/abs/1706.04972#google Device Placement Optimization with Reinforcement Learning Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean 2017-06-13 2020-11-08 [("doi","10.48550/arXiv.1706.04972")] ai/nn/cnn ai/nn/rnn reinforcement-learning/exploration

The past few years have witnessed a growth in size and computational requirements for training and inference with neural networks. Currently, a common approach to address these requirements is to use a heterogeneous distributed environment with a mixture of hardware devices such as CPUs and GPUs. Importantly, the decision of placing parts of the neural models on devices is often made by human experts based on simple heuristics and intuitions.

In this paper, we propose a method which learns to optimize device placement for TensorFlow computational graphs. Key to our method is the use of a sequence-to-sequence model to predict which subsets of operations in a TensorFlow graph should run on which of the available devices. The execution time of the predicted placements is then used as the reward signal to optimize the parameters of the sequence-to-sequence model.

Our main result is that on Inception-V3 for ImageNet classification, and on RNN LSTM, for language modeling and neural machine translation, our model finds non-trivial device placements that outperform hand-crafted heuristics and traditional algorithmic methods.

--- https://arxiv.org/abs/1705.04304#salesforce A Deep Reinforced Model for Abstractive Summarization Romain Paulus, Caiming Xiong, Richard Socher 2017-05-11 2020-11-08 [("doi","10.48550/arXiv.1705.04304")] ai/nn/rnn ai/nn/sampling ai/nn/transformer/attention reinforcement-learning/imitation-learning reinforcement-learning/model-free

[blog] Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. For longer documents and summaries however these models often include repetitive and incoherent phrases.

We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL).

Models trained only with supervised learning often exhibit “exposure bias”—they assume ground truth is provided at each step during training. However, when standard word prediction is combined with the global sequence prediction training of RL the resulting summaries become more readable. We evaluate this model on the CNN/Daily Mail and New York Times datasets.

Our model obtains a 41.16 ROUGE-1 score on the CNN/Daily Mail dataset, an improvement over previous state-of-the-art models.

Human evaluation also shows that our model produces higher quality summaries.

--- https://arxiv.org/abs/1703.04813 Learned Optimizers that Scale and Generalize Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein 2017-03-14 2020-11-08 [("doi","10.48550/arXiv.1703.04813")] ai/nn/cnn ai/nn/rnn reinforcement-learning/exploration reinforcement-learning/meta-learning

Learning to learn has emerged as an important direction for achieving artificial intelligence. Two of the primary barriers to its adoption are an inability to scale to larger problems and a limited ability to generalize to new tasks. We introduce a learned gradient descent optimizer that generalizes well to new tasks, and which has reduced memory and computation overhead.

We achieve this by introducing a novel hierarchical RNN architecture, with minimal per-parameter overhead, augmented with additional architectural features that mirror the known structure of optimization tasks. We also develop a meta-training ensemble of small, diverse optimization tasks capturing common properties of loss landscapes.

The optimizer learns to outperform RMSProp/ADAM on problems in this corpus. More importantly, it performs comparably or better when applied to small convolutional neural networks, despite seeing no neural networks in its meta-training set. Finally, it generalizes to train Inception V3 and ResNet V2 architectures on the ImageNet dataset for thousands of steps, optimization problems that are of a vastly different scale than those it was trained on.

We release an open source implementation of the meta-training algorithm.

--- https://openreview.net/pdf?id=BJ8fyHceg Tuning Recurrent Neural Networks with Reinforcement Learning Natasha Jaques, Shixiang Gu, Richard E. Turner, Douglas Eck 2017-02-14 2020-11-08 ai/music ai/nn/rnn ai/nn/sampling reinforcement-learning/model-free

New method for refining an RNN using Reinforcement Learning by penalizing KL-divergence from the policy of an RNN pre-trained on data using maximum likelihood.

The approach of training sequence models using supervised learning and next-step prediction suffers from known failure modes. For example, it is notoriously difficult to ensure multi-step generated sequences have coherent global structure. We propose a novel sequence-learning approach in which we use a pre-trained Recurrent Neural Network (RNN) to supply part of the reward value in a Reinforcement Learning (RL) model. Thus, we can refine a sequence predictor by optimizing for some imposed reward functions, while maintaining good predictive properties learned from data. We propose efficient ways to solve this by augmenting deep Q-learning with a cross-entropy reward and deriving novel off-policy methods for RNNs from KL control. We explore the usefulness of our approach in the context of music generation. An LSTM is trained on a large corpus of songs to predict the next note in a musical sequence. This Note-RNN is then refined using our method and rules of music theory. We show that by combining maximum likelihood (ML) and RL in this way, we can not only produce more pleasing melodies, but reduce unwanted behaviors and failure modes of the RNN, while maintaining information learned from data.

[Keywords: Deep learning, Supervised Learning, Reinforcement Learning, Applications, Structured prediction]

--- https://arxiv.org/abs/1611.01578#google Neural Architecture Search with Reinforcement Learning Barret Zoph, Quoc V. Le 2016-11-05 2020-11-08 [("doi","10.48550/arXiv.1611.01578")] ai/nn/rnn reinforcement-learning/exploration

Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set.

On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09% better and 1.05× faster than the previous state-of-the-art model that used a similar architectural scheme.

On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214.

--- https://arxiv.org/abs/1609.08144#google Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean 2016-09-26 2020-11-08 [("doi","10.48550/arXiv.1609.08144")] ai/nn/rnn ai/nn/sparsity/low-precision ai/nn/tokenization

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference. Also, most NMT systems have difficulty with rare words. These issues have hindered NMT’s use in practical deployments and services, where both accuracy and speed are essential.

In this work, we present GNMT, Google’s Neural Machine Translation system, which attempts to address many of these issues. Our model consists of a deep LSTM network with 8 encoder and 8 decoder layers using attention and residual connections. To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder. To accelerate the final translation speed, we employ low-precision arithmetic during inference computations. To improve handling of rare words, we divide words into a limited set of common sub-word units (“wordpieces”) for both input and output. This method provides a good balance between the flexibility of “character”-delimited models and the efficiency of “word”-delimited models, naturally handles translation of rare words, and ultimately improves the overall accuracy of the system.

Our beam search technique employs a length-normalization procedure and uses a coverage penalty, which encourages generation of an output sentence that is most likely to cover all the words in the source sentence. On the WMT’14 English-to-French and English-to-German benchmarks, GNMT achieves competitive results to state-of-the-art. Using a human side-by-side evaluation on a set of isolated simple sentences, it reduces translation errors by an average of 60% compared to Google’s phrase-based production system.

--- https://arxiv.org/abs/1609.07843 Pointer Sentinel Mixture Models Stephen Merity, Caiming Xiong, James Bradbury, Richard Socher 2016-09-26 2020-11-08 [("doi","10.48550/arXiv.1609.07843")] ai/dataset ai/nn/rnn

Recent neural network sequence models with softmax classifiers have achieved their best language modeling performance only with very large hidden states and large vocabularies. Even then they struggle to predict rare or unseen words even if the context makes the prediction unambiguous.

We introduce the pointer sentinel mixture architecture for neural sequence models which has the ability to either reproduce a word from the recent context or produce a word from a standard softmax classifier. Our pointer sentinel-LSTM model achieves state-of-the-art language modeling performance on the Penn Treebank (70.9 perplexity) while using far fewer parameters than a standard softmax LSTM.

In order to evaluate how well language models can exploit longer contexts and deal with more realistic vocabularies and larger corpora we also introduce the freely available WikiText corpus.

--- https://arxiv.org/abs/1610.05256 Achieving Human Parity in Conversational Speech Recognition W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, G. Zweig 2016-10-17 2020-11-08 [("doi","10.48550/arXiv.1610.05256")] ai/nn/rnn

Conversational speech recognition has served as a flagship speech recognition task since the release of the Switchboard corpus in the 1990s. In this paper, we measure the human error rate on the widely used NIST 2000 test set, and find that our latest automated system has reached human parity. The error rate of professional transcribers is 5.9% for the Switchboard portion of the data, in which newly acquainted pairs of people discuss an assigned topic, and 11.3% for the CallHome portion where friends and family members have open-ended conversations. In both cases, our automated system establishes a new state-of-the-art, and edges past the human benchmark, achieving error rates of 5.8% and 11.0%, respectively.

The key to our system’s performance is the use of various convolutional and LSTM acoustic model architectures, combined with a novel spatial smoothing method and lattice-free MMI acoustic training, multiple recurrent neural network language modeling approaches, and a systematic use of system combination.

--- https://arxiv.org/abs/1705.01991 Sharp Models on Dull Hardware: Fast and Accurate Neural Machine Translation Decoding on the CPU Jacob Devlin 2017-05-04 2020-11-08 [("doi","10.48550/arXiv.1705.01991")] ai/nn/fully-connected ai/nn/rnn

Attentional sequence-to-sequence models have become the new standard for machine translation, but one challenge of such models is a statistically-significant increase in training and decoding cost compared to phrase-based systems. Here, we focus on efficient decoding, with a goal of achieving accuracy close the state-of-the-art in neural machine translation (NMT), while achieving CPU decoding speed/throughput close to that of a phrasal decoder.

We approach this problem from two angles: First, we describe several techniques for speeding up an NMT beam search decoder, which obtain a 4.4× speedup over a very efficient baseline decoder without changing the decoder output. Second, we propose a simple but powerful network architecture which uses an RNN (GRU/LSTM) layer at bottom, followed by a series of stacked fully-connected layers applied at every timestep. This architecture achieves similar accuracy to a deep recurrent model, at a small fraction of the training and decoding cost.

By combining these techniques, our best system achieves a very competitive accuracy of 38.3 BLEU on WMT English-French NewsTest2014, while decoding at 100 words/sec on single-threaded CPU. We believe this is the best published accuracy/speed trade-off of an NMT system.

--- https://arxiv.org/abs/1707.02633 Controlling Linguistic Style Aspects in Neural Language Generation Jessica Ficler, Yoav Goldberg 2017-07-09 2020-11-08 [("doi","10.48550/arXiv.1707.02633")] ai/nn/rnn ai/nn/sampling

Most work on neural natural language generation (NNLG) focuses on controlling the content of the generated text.

We experiment with controlling several stylistic aspects of the generated text, in addition to its content. The method is based on conditioned RNN language model, where the desired content as well as the stylistic parameters serve as conditioning contexts.

We demonstrate the approach on the movie reviews domain and show that it is successful in generating coherent sentences corresponding to the required linguistic style and content.

--- https://arxiv.org/abs/1707.05589 On the State-of-the-Art of Evaluation in Neural Language Models Gábor Melis, Chris Dyer, Phil Blunsom 2017-07-18 2020-11-09 [("doi","10.48550/arXiv.1707.05589")] ai/nn/rnn

Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-the-art results on language modeling benchmarks. However, these have been evaluated using differing code bases and limited computational resources, which represent uncontrolled sources of experimental variation.

We reevaluate several popular architectures and regularization methods with large-scale automatic black-box hyperparameter tuning and arrive at the somewhat surprising conclusion that standard LSTM architectures, when properly regularized, outperform more recent models. We establish a new state-of-the-art on the Penn Treebank and Wikitext-2 corpora, as well as strong baselines on the Hutter Prize dataset.

--- https://arxiv.org/abs/1711.02085 Neural Speed Reading via Skim-RNN Minjoon Seo, Sewon Min, Ali Farhadi, Hannaneh Hajishirzi 2017-11-06 2020-11-09 [("doi","10.48550/arXiv.1711.02085")] ai/nn/rnn

Inspired by the principles of speed reading, we introduce Skim-RNN, a recurrent neural network (RNN) that dynamically decides to update only a small fraction of the hidden state for relatively unimportant input tokens. Skim-RNN gives computational advantage over an RNN that always updates the entire hidden state. Skim-RNN uses the same input and output interfaces as a standard RNN and can be easily used instead of RNNs in existing models.

In our experiments, we show that Skim-RNN can achieve reduced computational cost without losing accuracy compared to standard RNNs across five different natural language tasks. In addition, we demonstrate that the trade-off between accuracy and speed of Skim-RNN can be dynamically controlled during inference time in a stable manner. Our analysis also shows that Skim-RNN running on a single CPU offers lower latency compared to standard RNNs on GPUs.

--- https://arxiv.org/abs/1805.04623 Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context Urvashi Khandelwal, He He, Peng Qi, Dan Jurafsky 2018-05-12 2020-11-09 [("doi","10.48550/arXiv.1805.04623")] ai/nn/rnn

We know very little about how neural language models (LM) use prior linguistic context.

In this paper, we investigate the role of context in an LSTM LM, through ablation studies. Specifically, we analyze the increase in perplexity when prior context words are shuffled, replaced, or dropped. On two standard datasets, Penn Treebank and WikiText-2, we find that the model is capable of using about 200 tokens of context on average, but sharply distinguishes nearby context (recent 50 tokens) from the distant history. The model is highly sensitive to the order of words within the most recent sentence, but ignores word order in the long-range context (beyond 50 tokens), suggesting the distant past is modeled only as a rough semantic field or topic.

We further find that the neural caching model (Grave et al 2017b) especially helps the LSTM to copy words from within this distant context.

Overall, our analysis not only provides a better understanding of how neural LMs use their context, but also sheds light on recent success from cache-based models.

--- https://arxiv.org/abs/1805.12316 Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan 2018-05-31 2020-11-09 [("doi","10.48550/arXiv.1805.12316")] ai/nn/adversarial ai/nn/rnn

We present a probabilistic framework for studying adversarial attacks on discrete data. Based on this framework, we derive a perturbation-based method, Greedy Attack, and a scalable learning-based method, Gumbel Attack, that illustrate various tradeoffs in the design of attacks.

We demonstrate the effectiveness of these methods using both quantitative metrics and human evaluation on various state-of-the-art models for text classification, including a word-based CNN, a character-based CNN and an LSTM.

As as example of our results, we show that the accuracy of character-based convolutional networks drops to the level of random selection by modifying only 5 characters through Greedy Attack.

--- https://arxiv.org/abs/1807.06763 General Value Function Networks Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas, Andrew Patterson, Adam White, Martha White 2018-07-18 2020-11-09 [("doi","10.1613/jair.1.12105")] ai/nn/rnn reinforcement-learning/model

State construction is important for learning in partially observable environments. A general purpose strategy for state construction is to learn the state update using a Recurrent Neural Network (RNN), which updates the internal state using the current internal state and the most recent observation. This internal state provides a summary of the observed sequence, to facilitate accurate predictions and decision-making. At the same time, specifying and training RNNs is notoriously tricky, particularly as the common strategy to approximate gradients back in time, called truncated Back-prop Through Time (BPTT), can be sensitive to the truncation window. Further, domain-expertise—which can usually help constrain the function class and so improve trainability—can be difficult to incorporate into complex recurrent units used within RNNs.

In this work, we explore how to use multi-step predictions to constrain the RNN and incorporate prior knowledge. In particular, we revisit the idea of using predictions to construct state and ask: does constraining (parts of) the state to consist of predictions about the future improve RNN trainability?

We formulate a novel RNN architecture, called a General Value Function Network (GVFN), where each internal state component corresponds to a prediction about the future represented as a value function. We first provide an objective for optimizing GVFNs, and derive several algorithms to optimize this objective.

We then show that GVFNs are more robust to the truncation level, in many cases only requiring one-step gradient updates.

--- https://arxiv.org/abs/1808.04444#google Character-Level Language Modeling with Deeper Self-Attention Rami Al-Rfou, Dokook Choe, Noah Constant, Mandy Guo, Llion Jones 2018-08-09 2020-11-09 [("doi","10.48550/arXiv.1808.04444")] ai/nn/rnn ai/nn/tokenization ai/nn/transformer/attention

LSTMs and other RNN variants have shown strong performance on character-level language modeling. These models are typically trained using truncated backpropagation through time, and it is common to assume that their success stems from their ability to remember long-term contexts.

In this paper, we show that a deep (64-layer) transformer model with fixed context outperforms RNN variants by a large margin, achieving state-of-the-art on two popular benchmarks: 1.13 bits per character on text8 and 1.06 on enwik8.

To get good results at this depth, we show that it is important to add auxiliary losses, both at intermediate network layers and intermediate sequence positions.

--- https://arxiv.org/abs/1901.02860 Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov 2019-01-09 2020-11-09 [("doi","10.48550/arXiv.1901.02860")] ai/nn/rnn ai/nn/transformer/attention/recurrent ai/nn/transformer/gpt

Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling.

We propose a novel neural architecture Transformer-​XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem.

As a result, Transformer-​XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation.

Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwik8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens.

Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch.

--- https://arxiv.org/abs/1905.06598 MoGlow: Probabilistic and controllable motion synthesis using normalizing flows Gustav Eje Henter, Simon Alexanderson, Jonas Beskow 2019-05-16 2020-11-09 [("doi","10.1145/3414685.3417836")] ai/nn/fully-connected ai/nn/rnn

Data-driven modeling and synthesis of motion is an active research area with applications that include animation, games, and social robotics. This paper introduces a new class of probabilistic, generative, and controllable motion-data models based on normalizing flows. Models of this kind can describe highly complex distributions, yet can be trained efficiently using exact maximum likelihood, unlike GANs or VAEs.

Our proposed model is autoregressive and uses LSTMs to enable arbitrarily long time-dependencies. Importantly, it is also causal, meaning that each pose in the output sequence is generated without access to poses or control inputs from future time steps; this absence of algorithmic latency is important for interactive applications with real-time motion control. The approach can in principle be applied to any type of motion since it does not make restrictive, task-specific assumptions regarding the motion or the character morphology.

We evaluate the models on motion-capture datasets of human and quadruped locomotion. Objective and subjective results show that randomly-sampled motion from the proposed method outperforms task-agnostic baselines and attains a motion quality close to recorded motion capture.

--- https://arxiv.org/abs/2104.01655 ALD: Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation Emilio Parisotto, Ruslan Salakhutdinov 2021-04-04 2021-04-04 [("doi","10.48550/arXiv.2104.01655")] ai/nn/rnn ai/nn/sparsity/knowledge-distillation ai/nn/transformer/attention reinforcement-learning/model-free

Many real-world applications such as robotics provide hard constraints on power and compute that limit the viable model complexity of Reinforcement Learning (RL) agents. Similarly, in many distributed RL settings, acting is done on un-accelerated hardware such as CPUs, which likewise restricts model size to prevent intractable experiment run times. These “actor-latency” constrained settings present a major obstruction to the scaling up of model complexity that has recently been extremely successful in supervised learning.

To be able to utilize large model capacity while still operating within the limits imposed by the system during acting, we develop an Actor-Learner Distillation (ALD) procedure that leverages a continual form of distillation that transfers learning progress from a large capacity learner model to a small capacity actor model.

As a case study, we develop this procedure in the context of partially-observable environments, where transformer models have had large improvements over LSTMs recently, at the cost of higher computational complexity. With transformer models as the learner and LSTMs as the actor, we demonstrate in several challenging memory environments that using Actor-Learner Distillation recovers the clear sample-efficiency gains of the transformer learner model while maintaining the fast inference and reduced total training time of the LSTM actor model.

--- https://arxiv.org/abs/2103.13076 Finetuning Pretrained Transformers into RNNs Jungo Kasai, Hao Peng, Yizhe Zhang, Dani Yogatama, Gabriel Ilharco, Nikolaos Pappas, Yi Mao, Weizhu Chen, Noah Smith 2021-03-24 2021-03-24 [("doi","10.48550/arXiv.2103.13076")] ai/nn/rnn ai/nn/transformer/attention/recurrent

Transformers have outperformed recurrent neural networks (RNNs) in natural language generation. But this comes with a computational cost, as the attention mechanism’s complexity scales quadratically with sequence length. Efficient transformer variants have received increasing interest in recent works. Among them, a linear-complexity recurrent variant has proven well suited for autoregressive generation. It approximates the softmax attention with randomized or heuristic feature maps, but can be difficult to train and may yield suboptimal accuracy.

This work aims to convert a pretrained transformer into its efficient recurrent counterpart, improving efficiency while maintaining accuracy. Specifically, we propose a swap-then-finetune procedure: in an off-the-shelf pretrained transformer, we replace the softmax attention with its linear-complexity recurrent alternative and then finetune.

With a learned feature map, our approach provides an improved tradeoff between efficiency and accuracy over the standard transformer and other recurrent variants. We also show that the finetuning process has lower training cost relative to training these recurrent variants from scratch.

As many models for natural language tasks are increasingly dependent on large-scale pretrained transformers, this work presents a viable approach to improving inference efficiency without repeating the expensive pretraining process.

--- https://arxiv.org/abs/2101.08001#baidu UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers Siyi Hu, Fengda Zhu, Xiaojun Chang, Xiaodan Liang 2021-01-20 2021-01-20 [("doi","10.48550/arXiv.2101.08001")] ai/nn/transformer reinforcement-learning/multi-agent

Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task. The limitation is due to the restricted model architecture related to fixed input and output dimensions. This hinders the experience accumulation and transfer of the learned agent over tasks with diverse levels of difficulty (eg. 3 vs 3 or 5 vs 6 multi-agent games).

In this paper, we make the first attempt to explore an universal multi-agent reinforcement learning pipeline, designing one single architecture to fit tasks with the requirement of different observation and action configurations. Unlike previous RNN-based models, we use a transformer-based model to generate a flexible policy by decoupling the policy distribution from the intertwined input observation with an importance weight measured by the merits of the self-attention mechanism. Compared to a standard transformer block, the proposed model, named as Universal Policy Decoupling Transformer (UPDeT), further relaxes the action restriction and makes the multi-agent task’s decision process more explainable.

UPDeT is general enough to be plugged into any multi-agent reinforcement learning pipeline and equip them with strong generalization abilities that enables the handling of multiple tasks at a time. Extensive experiments on large-scale SMAC multi-agent competitive games demonstrate that the proposed UPDeT-based multi-agent reinforcement learning achieves results relative to state-of-the-art approaches, demonstrating advantageous transfer capability in terms of both performance and training speed (10× faster).

--- https://arxiv.org/abs/2101.01169 Transformers in Vision: A Survey Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, Mubarak Shah 2021-01-04 2021-01-04 [("doi","10.48550/arXiv.2101.01169")] ai/nn/transformer/clip ai/video/analysis

Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between input sequence elements and support parallel processing of the sequence as compared to recurrent networks eg. Long short-term memory (LSTM). Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suited as set-functions. Furthermore, the straightforward design of Transformers allows processing multiple modalities (eg. images, videos, text, and speech) using similar processing blocks and demonstrates excellent scalability to very large capacity networks and huge datasets. These strengths have led to exciting progress on a number of vision tasks using Transformer networks.

This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline. We start with an introduction to fundamental concepts behind the success of Transformers ie. self-attention, large-scale pre-training, and bidirectional encoding.

We then cover extensive applications of transformers in vision including popular recognition tasks (eg. image classification, object detection, action recognition, and segmentation), generative modeling, multi-modal tasks (eg. visual-question answering, visual reasoning, and visual grounding), video processing (eg. activity recognition, video forecasting), low-level vision (eg. image super-resolution, image enhancement, and colorization) and 3D analysis (eg. point cloud classification and segmentation). We compare the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value.

Finally, we provide an analysis of open research directions and possible future works.

--- https://arxiv.org/abs/1807.03819#googledeepmind Universal Transformers Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser 2018-07-10 2020-11-10 [("doi","10.48550/arXiv.1807.03819")] ai/nn/rnn ai/nn/transformer/attention/recurrent ai/nn/transformer/gpt

Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as machine translation, with the added advantage that they concurrently process all inputs in the sequence, leading to easy parallelization and faster training times.

Despite these successes, however, popular feed-forward sequence models like the Transformer fail to generalize in many simple tasks that recurrent models handle with ease, eg. copying strings or even simple logical inference when the string or formula lengths exceed those observed at training time. We propose the Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses these issues. UTs combine the parallelizability and global receptive field of feed-forward sequence models like the Transformer with the recurrent inductive bias of RNNs. We also add a dynamic per-position halting mechanism and find that it improves accuracy on several tasks.

In contrast to the standard Transformer, under certain assumptions, UTs can be shown to be Turing-complete. Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state-of-the-art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset.

--- https://arxiv.org/abs/1606.04474 Learning to learn by gradient descent by gradient descent Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas 2016-06-14 2020-11-10 [("doi","10.48550/arXiv.1606.04474")] ai/nn/rnn reinforcement-learning/meta-learning

The move from hand-designed features to learned features in machine learning has been wildly successful.

In spite of this, optimization algorithms are still designed by hand. In this paper we show how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way.

Our learned algorithms, implemented by LSTMs, outperform generic, hand-designed competitors on the tasks for which they are trained, and also generalize well to new tasks with similar structure.

We demonstrate this on a number of tasks, including simple convex problems, training neural networks, and styling images with neural art.

--- https://arxiv.org/abs/1604.03640 Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex Qianli Liao, Tomaso Poggio 2016-04-13 2020-11-10 [("doi","10.48550/arXiv.1604.03640")] ai/nn/rnn psychology/neuroscience

We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. We begin with the observation that a special type of shallow RNN is exactly equivalent to a very deep ResNet with weight sharing among the layers.

A direct implementation of such a RNN, although having orders of magnitude fewer parameters, leads to a performance similar to the corresponding ResNet. We propose (1) a generalization of both RNN and ResNet architectures and (2) the conjecture that a class of moderately deep RNNs is a biologically-plausible model of the ventral stream in visual cortex.

We demonstrate the effectiveness of the architectures by testing them on the CIFAR-10 and ImageNet dataset.

--- https://arxiv.org/abs/1512.05616 Deep-Spying: Spying using Smartwatch and Deep Learning Tony Beltramelli, Sebastian Risi 2015-12-17 2020-11-10 [("doi","10.48550/arXiv.1512.05616")] ai/nn/rnn technology

Wearable technologies are today on the rise, becoming more common and broadly available to mainstream users. In fact, wristband and armband devices such as smartwatches and fitness trackers already took an important place in the consumer electronics market and are becoming ubiquitous. By their very nature of being wearable, these devices, however, provide a new pervasive attack surface threatening users privacy, among others.

In the meantime, advances in machine learning are providing unprecedented possibilities to process complex data efficiently. Allowing patterns to emerge from high dimensional unavoidably noisy data.

The goal of this work is to raise awareness about the potential risks related to motion sensors built-in wearable devices and to demonstrate abuse opportunities leveraged by advanced neural network architectures.

The LSTM-based implementation presented in this research can perform touchlogging and keylogging on 12-keys keypads with above-average accuracy even when confronted with raw unprocessed data. Thus demonstrating that deep neural networks are capable of making keystroke inference attacks based on motion sensors easier to achieve by removing the need for non-trivial pre-processing pipelines and carefully engineered feature extraction strategies. Our results suggest that the complete technological ecosystem of a user can be compromised when a wearable wristband device is worn.

--- https://arxiv.org/abs/1410.4615#google Learning to Execute Wojciech Zaremba, Ilya Sutskever 2014-10-17 2020-11-10 [("doi","10.48550/arXiv.1410.4615")] ai/nn/rnn reinforcement-learning/model-free

Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are widely used because they are expressive and are easy to train. Our interest lies in empirically evaluating the expressiveness and the learnability of LSTMs in the sequence-to-sequence regime by training them to evaluate short computer programs, a domain that has traditionally been seen as too complex for neural networks.

We consider a simple class of programs that can be evaluated with a single left-to-right pass using constant memory. Our main result is that LSTMs can learn to map the character-level representations of such programs to their correct outputs. Notably, it was necessary to use curriculum learning, and while conventional curriculum learning proved ineffective, we developed a new variant of curriculum learning that improved our networks’ performance in all experimental conditions. The improved curriculum had a dramatic impact on an addition problem, making it possible to train an LSTM to add two 9-digit numbers with 99% accuracy.

--- https://arxiv.org/abs/1511.03683 Generative Concatenative Nets Jointly Learn to Write and Classify Reviews Zachary C. Lipton, Sharad Vikram, Julian McAuley 2015-11-11 2020-11-10 [("doi","10.48550/arXiv.1511.03683")] ai/fiction ai/nn/rnn ai/nn/sampling ai/scaling

A recommender system’s basic task is to estimate how users will respond to unseen items. This is typically modeled in terms of how a user might rate a product, but here we aim to extend such approaches to model how a user would write about the product. To do so, we design a character-level Recurrent Neural Network (RNN) that generates personalized product reviews. The network convincingly learns styles and opinions of nearly 1,000 distinct authors, using a large corpus of reviews from BeerAdvocate.com. It also tailors reviews to describe specific items, categories, and star ratings.

Using a simple input replication strategy, the Generative Concatenative Network (GCN) preserves the signal of static auxiliary inputs across wide sequence intervals. Without any additional training, the generative model can classify reviews, identifying the author of the review, the product category, and the sentiment (rating), with remarkable accuracy. Our evaluation shows the GCN captures complex dynamics in text, such as the effect of negation, misspellings, slang, and large vocabularies gracefully absent any machinery explicitly dedicated to the purpose.

--- https://arxiv.org/abs/1503.08895 End-To-End Memory Networks Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus 2015-03-31 2020-11-10 [("doi","10.48550/arXiv.1503.08895")] ai/nn/rnn

We introduce a neural network with a recurrent attention model over a possibly large external memory. The architecture is a form of Memory Network (Weston et al 2015) but unlike the model in that work, it is trained end-to-end, and hence requires statistically-significantly less supervision during training, making it more generally applicable in realistic settings.

It can also be seen as an extension of RNNsearch to the case where multiple computational steps (hops) are performed per output symbol. The flexibility of the model allows us to apply it to tasks as diverse as (synthetic) question answering and to language modeling.

For the former, our approach is competitive with Memory Networks, but with less supervision. For the latter, on the Penn TreeBank and text8 datasets, our approach demonstrates comparable performance to RNNs and LSTMs.

In both cases, we show that the key concept of multiple computational hops yields improved results.

--- https://arxiv.org/abs/1708.06834 Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks Victor Campos, Brendan Jou, Xavier Giro-i-Nieto, Jordi Torres, Shih-Fu Chang 2017-08-22 2020-11-10 [("doi","10.48550/arXiv.1708.06834")] ai/nn/rnn

Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficulty in capturing long term dependencies. In backpropagation through time settings, these issues are tightly coupled with the large, sequential computational graph resulting from unfolding the RNN in time.

We introduce the Skip RNN model which extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph. This model can also be encouraged to perform fewer state updates through a budget constraint. We evaluate the proposed model on various tasks and show how it can reduce the number of required RNN updates while preserving, and sometimes even improving, the performance of the baseline RNN models.

Source code is publicly available at https://imatge-upc.github.io/skiprnn-2017-telecombcn/.

--- https://highnoongmt.wordpress.com/2015/08/13/deep-learning-for-assisting-the-process-of-music-composition-part-3/ Deep learning for assisting the process of music composition (part 3) 2020-11-10 ai/nn/rnn ai/nn/transformer/gpt --- https://github.com/jcjohnson/torch-rnn Efficient, reusable RNNs and LSTMs for torch 2020-11-11 ai/nn/rnn ai/nn/transformer/gpt --- https://github.com/jcjohnson/torch-rnn/issues/37 Updated training? 2020-11-11 ai/nn/rnn ai/nn/transformer/gpt --- https://mi.eng.cam.ac.uk/projects/cued-rnnlm/papers/Interspeech15.pdf 2020-11-11 ai/nn/rnn ai/nn/sampling ai/nn/transformer/gpt --- https://homepages.inf.ed.ac.uk/abmayne/publications/sennrich2016NAACL.pdf 2020-11-11 ai/nn/rnn ai/nn/sampling ai/nn/transformer/gpt --- https://soundcloud.com/seaandsailor/sets/char-rnn-composes-irish-folk-music Stream seaandsailor 2020-11-11 ai/nn/rnn ai/nn/transformer/gpt --- https://en.wikipedia.org/wiki/Recurrent_neural_network Recurrent neural network 2020-11-11 ai/nn/rnn --- https://arxiv.org/abs/1805.04833 Hierarchical Neural Story Generation Angela Fan, Mike Lewis, Yann Dauphin 2018-05-13 2020-11-11 [("doi","10.48550/arXiv.1805.04833")] ai/fiction ai/nn/rnn

We explore story generation: creative systems that can build coherent and fluent passages of text about a topic.

We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. Our dataset enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text.

We gain further improvements with a novel form of model fusion that improves the relevance of the story to the prompt, and adding a new gated multi-scale self-attention mechanism to model long-range context.

Experiments show large improvements over strong baselines on both automated and human evaluations. Human judges prefer stories generated by our approach to those from a strong non-hierarchical model by a factor of two to one.

--- https://arxiv.org/abs/1704.01444#openai Learning to Generate Reviews and Discovering Sentiment Alec Radford, Rafal Jozefowicz, Ilya Sutskever 2017-04-05 2020-11-11 [("doi","10.48550/arXiv.1704.01444")] ai/nn/rnn ai/nn/sampling ai/nn/transformer/gpt

We explore the properties of byte-level recurrent language models.

When given sufficient amounts of capacity, training data, and compute time, the representations learned by these models include disentangled features corresponding to high-level concepts. Specifically, we find a single unit which performs sentiment analysis.

These representations, learned in an unsupervised manner, achieve state-of-the-art on the binary subset of the Stanford Sentiment Treebank. They are also very data efficient. When using only a handful of labeled examples, our approach matches the performance of strong baselines trained on full datasets.

We also demonstrate the sentiment unit has a direct influence on the generative process of the model. Simply fixing its value to be positive or negative generates samples with the corresponding positive or negative sentiment.

--- https://arxiv.org/abs/1605.06640 Programming with a Differentiable Forth Interpreter Matko Bošnjak, Tim Rocktäschel, Jason Naradowsky, Sebastian Riedel 2016-05-21 2020-11-11 [("doi","10.48550/arXiv.1605.06640")] ai/nn/rnn cs/algorithm/sorting

Given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. In this paper, we consider the case of prior procedural knowledge for neural networks, such as knowing how a program should traverse a sequence, but not what local actions should be performed at each step.

To this end, we present an end-to-end differentiable interpreter for the programming language Forth which enables programmers to write program sketches with slots that can be filled with behavior trained from program input-output data. We can optimize this behavior directly through gradient descent techniques on user-specified objectives, and also integrate the program into any larger neural computation graph.

We show empirically that our interpreter is able to effectively leverage different levels of prior program structure and learn complex behaviors such as sequence sorting and addition. When connected to outputs of an LSTM and trained jointly, our interpreter achieves state-of-the-art accuracy for end-to-end reasoning about quantities expressed in natural language stories.

--- https://en.wikipedia.org/wiki/Hopfield_network Hopfield network 2020-11-11 ai/nn/rnn ai/nn/transformer --- https://arxiv.org/abs/2204.03458#google Video Diffusion Models Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, David J. Fleet 2022-04-07 2022-04-07 [("doi","10.48550/arXiv.2204.03458")] ai/nn/diffusion ai/video/generation

Generating temporally coherent high fidelity video is an important milestone in generative modeling research. We make progress towards this milestone by proposing a diffusion model for video generation that shows very promising initial results.

Our model is a natural extension of the standard image diffusion architecture, and it enables jointly training from image and video data, which we find to reduce the variance of minibatch gradients and speed up optimization. To generate long and higher resolution videos we introduce a new conditional sampling technique for spatial and temporal video extension that performs better than previously proposed methods.

We present the first results on a large text-conditioned video generation task, as well as state-of-the-art results on an established unconditional video generation benchmark.

Supplementary material is available at https://video-diffusion.github.io/.

--- https://arxiv.org/abs/1911.11423 Single Headed Attention RNN: Stop Thinking With Your Head Stephen Merity 2019-11-26 2020-11-12 [("doi","10.48550/arXiv.1911.11423")] ai/nn/rnn math/humor

The leading approaches in language modeling are all obsessed with TV shows of my youth—namely Transformers and Sesame Street. Transformers this, Transformers that, and over here a bonfire worth of GPU-TPU-neuromorphic wafer scale silicon. We opt for the lazy path of old and proven techniques with a fancy crypto inspired acronym: the Single Headed Attention RNN (SHA-RNN). The author’s lone goal is to show that the entire field might have evolved a different direction if we had instead been obsessed with a slightly different acronym and slightly different result.

We take a previously strong language model based only on boring LSTMs and get it to within a stone’s throw of a stone’s throw of state-of-the-art byte level language model results on enwik8. This work has undergone no intensive hyperparameter optimization and lived entirely on a commodity desktop machine that made the author’s small studio apartment far too warm in the midst of a San Franciscan summer. The final results are achievable in plus or minus 24 hours on a single GPU as the author is impatient. The attention mechanism is also readily extended to large contexts with minimal computation.

Take that Sesame Street.

--- https://arxiv.org/abs/1708.01009 Revisiting Activation Regularization for Language RNNs Stephen Merity, Bryan McCann, Richard Socher 2017-08-03 2020-11-12 [("doi","10.48550/arXiv.1708.01009")] ai/nn/rnn

Recurrent neural networks (RNNs) serve as a fundamental building block for many sequence tasks across natural language processing. Recent research has focused on recurrent dropout techniques or custom RNN cells in order to improve performance. Both of these can require substantial modifications to the machine learning model or to the underlying RNN configurations.

We revisit traditional regularization techniques, specifically 𝓁2 regularization on RNN activations and slowness regularization over successive hidden states, to improve the performance of RNNs on the task of language modeling. Both of these techniques require minimal modification to existing RNN architectures and result in performance improvements comparable or superior to more complicated regularization techniques or custom cell architectures.

These regularization techniques can be used without any modification on optimized LSTM implementations such as the NVIDIA cuDNN LSTM.

--- https://arxiv.org/abs/2110.05038 Recurrent Model-Free RL is a Strong Baseline for Many POMDPs Tianwei Ni, Benjamin Eysenbach, Ruslan Salakhutdinov 2021-10-11 2021-10-11 [("doi","10.48550/arXiv.2110.05038")] ai/nn/rnn reinforcement-learning/model-free

Many problems in RL, such as meta RL, robust RL, and generalization in RL can be cast as POMDPs. In theory, simply augmenting model-free RL with memory, such as recurrent neural networks, provides a general approach to solving all types of POMDPs. However, prior work has found that such recurrent model-free RL methods tend to perform worse than more specialized algorithms that are designed for specific types of POMDPs. This paper revisits this claim.

We find that careful architecture and hyperparameter decisions yield a recurrent model-free implementation that performs on par with (and occasionally substantially better than) more sophisticated recent techniques in their respective domains.

We also release a simple and efficient implementation of recurrent model-free RL for future work to use as a baseline for POMDPs. Code is available at https://github.com/twni2016/pomdp-baselines.

--- https://github.com/minimaxir/textgenrnn minimaxir/textgenrnn: Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code. 2020-11-12 ai/nn/rnn ai/nn/transformer/gpt --- https://arxiv.org/abs/1803.02329 Learning Memory Access Patterns Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan 2018-03-06 2020-11-12 [("doi","10.48550/arXiv.1803.02329")] ai/nn/rnn cs/algorithm reinforcement-learning/model-free

The explosion in workload complexity and the recent slowdown in Moore’s law scaling call for new approaches toward efficient computing. Researchers are now beginning to use recent advances in machine learning in software optimizations, augmenting or replacing traditional heuristics and data structures. However, the space of machine learning for computer hardware architecture is only lightly explored.

In this paper, we demonstrate the potential of deep learning to address the von Neumann bottleneck of memory performance. We focus on the critical problem of learning memory access patterns, with the goal of constructing accurate and efficient memory prefetchers. We relate contemporary prefetching strategies to n-gram models in natural language processing, and show how recurrent neural networks can serve as a drop-in replacement.

On a suite of challenging benchmark datasets, we find that neural networks consistently demonstrate superior performance in terms of precision and recall. This work represents the first step toward practical neural-network based prefetching and opens a wide range of exciting directions for machine learning in computer architecture research.

--- https://arxiv.org/abs/1802.08864#schmidhuber One Big Net For Everything Juergen Schmidhuber 2018-02-24 2020-11-12 [("doi","10.48550/arXiv.1802.08864")] ai/nn/rnn ai/nn/tokenization ai/scaling psychology/neuroscience reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/robot reinforcement-learning/scaling

I apply recent work on “learning to think” (2015) and on PowerPlay (2011) to the incremental training of an increasingly general problem solver, continually learning to solve new tasks without forgetting previous skills. The problem solver is a single recurrent neural network (or similar general purpose computer) called ONE. ONE is unusual in the sense that it is trained in various ways, eg. by black box optimization / reinforcement learning / artificial evolution as well as supervised / unsupervised learning. For example, ONE may learn through neuroevolution to control a robot through environment-changing actions, and learn through unsupervised gradient descent to predict future inputs and vector-valued reward signals as suggested in 1990. User-given tasks can be defined through extra goal-defining input patterns, also proposed in 1990.

Suppose ONE has already learned many skills. Now a copy of ONE can be re-trained to learn a new skill, eg. through neuroevolution without a teacher. Here it may profit from re-using previously learned subroutines, but it may also forget previous skills. Then ONE is retrained in PowerPlay style (2011) on stored input/output traces of (a) ONE’s copy executing the new skill and (b) previous instances of ONE whose skills are still considered worth memorizing. Simultaneously, ONE is retrained on old traces (even those of unsuccessful trials) to become a better predictor, without additional expensive interaction with the environment.

More and more control and prediction skills are thus collapsed into ONE, like in the chunker-automatizer system of the neural history compressor (1991). This forces ONE to relate partially analogous skills (with shared algorithmic information) to each other, creating common subroutines in form of shared subnetworks of ONE, to greatly speed up subsequent learning of additional, novel but algorithmically related skills.

--- https://arxiv.org/abs/1911.01655#google High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V. Le, Honglak Lee 2019-11-05 2020-11-12 [("doi","10.48550/arXiv.1911.01655")] ai/nn/rnn ai/scaling ai/video/generation

Predicting future video frames is extremely challenging, as there are many factors of variation that make up the dynamics of how frames change through time.

Previously proposed solutions require complex inductive biases inside network architectures with highly specialized computation, including segmentation masks, optical flow, and foreground and background separation. In this work, we question if such handcrafted architectures are necessary and instead propose a different approach: finding minimal inductive bias for video prediction while maximizing network capacity.

We investigate this question by performing the first large-scale empirical study and demonstrate state-of-the-art performance by learning large models on 3 different datasets: one for modeling object interactions, one for modeling human motion, and one for modeling car driving.

--- https://arxiv.org/abs/2010.11322#elementai Learning to Summarize Long Texts with Memory Compression and Transfer Jaehong Park, Jonathan Pilault, Christopher Pal 2020-10-21 2020-11-12 [("doi","10.48550/arXiv.2010.11322")] ai/nn/rnn ai/nn/transformer/attention/compression

We introduce Mem2Mem, a memory-to-memory mechanism for hierarchical recurrent neural network based encoder decoder architectures and we explore its use for abstractive document summarization. Mem2Mem transfers “memories” via readable/writable external memory modules that augment both the encoder and decoder. Our memory regularization compresses an encoded input article into a more compact set of sentence representations.

Most importantly, the memory compression step performs implicit extraction without labels, sidestepping issues with suboptimal ground-truth data and exposure bias of hybrid extractive-abstractive summarization techniques. By allowing the decoder to read/write over the encoded input memory, the model learns to read salient information about the input article while keeping track of what has been generated.

Our Mem2Mem approach yields results that are competitive with state-of-the-art transformer based summarization methods, but with 16× fewer parameters.

--- https://arxiv.org/abs/1611.01989 DeepCoder: Learning to Write Programs Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow 2016-11-07 2020-11-12 [("doi","10.48550/arXiv.1611.01989")] ai/nn/rnn ai/nn/transformer/gpt/codex

We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning.

The approach is to train a neural network to predict properties of the program that generated the outputs from the inputs. We use the neural network’s predictions to augment search techniques from the programming languages community, including enumerative search and an SMT-based solver.

Empirically, we show that our approach leads to an order of magnitude speedup over the strong non-augmented baselines and a Recurrent Neural Network approach, and that we are able to solve problems of difficulty comparable to the simplest problems on programming competition websites.

--- https://arxiv.org/abs/1308.0850 Generating Sequences With Recurrent Neural Networks Alex Graves 2013-08-04 2020-11-12 [("doi","10.48550/arXiv.1308.0850")] ai/nn/dynamic-evaluation ai/nn/rnn ai/nn/transformer/attention

This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued).

It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence.

The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.

--- https://arxiv.org/abs/1312.3005 One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Philipp Koehn, Tony Robinson 2013-12-11 2020-11-12 [("doi","10.48550/arXiv.1312.3005")] ai/nn/rnn ai/scaling cs/algorithm/information/compression

We propose a new benchmark corpus to be used for measuring progress in statistical language modeling. With almost one billion words of training data, we hope this benchmark will be useful to quickly evaluate novel language modeling techniques, and to compare their contribution when combined with other advanced techniques. We show performance of several well-known types of language models, with the best results achieved with a recurrent neural network based language model. The baseline unpruned Kneser-Ney 5-gram model achieves perplexity 67.6; a combination of techniques leads to 35% reduction in perplexity, or 10% reduction in cross-entropy (bits), over that baseline.

The benchmark is available as a code.google.com project; besides the scripts needed to rebuild the training/held-out data, it also makes available log-probability values for each word in each of ten held-out data sets, for each of the baseline n-gram models.

--- https://arxiv.org/abs/1706.00043 Biased Importance Sampling for Deep Neural Network Training Angelos Katharopoulos, François Fleuret 2017-05-31 2020-11-13 [("doi","10.48550/arXiv.1706.00043")] ai/nn/rnn ai/nn/sparsity/knowledge-distillation

Importance sampling has been successfully used to accelerate stochastic optimization in many convex problems. However, the lack of an efficient way to calculate the importance still hinders its application to Deep Learning.

In this paper, we show that the loss value can be used as an alternative importance metric, and propose a way to efficiently approximate it for a deep model, using a small model trained for that purpose in parallel.

This method allows in particular to utilize a biased gradient estimate that implicitly optimizes a soft max-loss, and leads to better generalization performance. While such method suffers from a prohibitively high variance of the gradient estimate when using a standard stochastic optimizer, we show that when it is combined with our sampling mechanism, it results in a reliable procedure.

We showcase the generality of our method by testing it on both image classification and language modeling tasks using deep convolutional and recurrent neural networks. In particular, our method results in 30% faster training of a CNN for CIFAR-10 than when using uniform sampling.

--- https://arxiv.org/abs/1704.02254 Recurrent Environment Simulators Silvia Chiappa, Sébastien Racaniere, Daan Wierstra, Shakir Mohamed 2017-04-07 2020-11-13 [("doi","10.48550/arXiv.1704.02254")] ai/nn/rnn reinforcement-learning/exploration reinforcement-learning/model

Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently.

We improve on previous environment simulators from high-dimensional pixel observations by introducing recurrent neural networks that are able to make temporally and spatially coherent predictions for hundreds of time-steps into the future.

We present an in-depth analysis of the factors affecting performance, providing the most extensive attempt to advance the understanding of the properties of these models.

We address the issue of computational inefficiency with a model that does not need to generate a high-dimensional image at each time-step.

We show that our approach can be used to improve exploration and is adaptable to many diverse environments, namely 10 Atari games, a 3D car racing environment, and complex 3D mazes.

--- https://openreview.net/forum?id=SyJNmVqgg Neural Data Filter for Bootstrapping Stochastic Gradient Descent Yang Fan, Fei Tian, Tao Qin, Tie-Yan Liu 2017-01-20 2020-11-13 ai/nn/rnn reinforcement-learning/exploration

We propose a reinforcement learning based teacher-student framework for filtering training data to boost SGD convergence.

Mini-batch based Stochastic Gradient Descent(SGD) has been widely used to train deep neural networks efficiently. In this paper, we design a general framework to automatically and adaptively select training data for SGD. The framework is based on neural networks and we call it Neural Data Filter (NDF). In Neural Data Filter, the whole training process of the original neural network is monitored and supervised by a deep reinforcement network, which controls whether to filter some data in sequentially arrived mini-batches so as to maximize future accumulative reward (eg. validation accuracy). The SGD process accompanied with NDF is able to use less data and converge faster while achieving comparable accuracy as the standard SGD trained on the full dataset. Our experiments show that NDF bootstraps SGD training for different neural network models including Multi Layer Perceptron Network and Recurrent Neural Network trained on various types of tasks including image classification and text understanding.

[Keywords: Reinforcement Learning, Deep learning, Optimization]

--- https://openreview.net/pdf?id=rJY3vK9eg Neural Combinatorial Optimization with Reinforcement Learning Irwan Bello, Hieu Pham, Quoc Le, Mohammad Norouzi, Samy Bengio 2017-02-17 2020-11-13 ai/nn/rnn reinforcement-learning/exploration

neural combinatorial optimization, reinforcement learning

We present a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning. We focus on the traveling salesman problem (TSP) and train a recurrent neural network that, given a set of city coordinates, predicts a distribution over different city permutations. Using negative tour length as the reward signal, we optimize the parameters of the recurrent neural network using a policy gradient method. Without much engineering and heuristic designing, Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes. These results, albeit still quite far from state-of-the-art, give insights into how neural networks can be used as a general tool for tackling combinatorial optimization problems.

--- https://arxiv.org/abs/1603.08983#deepmind Adaptive Computation Time for Recurrent Neural Networks Alex Graves 2016-03-29 2020-11-13 [("doi","10.48550/arXiv.1603.08983")] ai/nn/rnn ai/nn/transformer/attention cs/algorithm/sorting reinforcement-learning/meta-learning

This paper introduces Adaptive Computation Time (ACT), an algorithm that allows recurrent neural networks to learn how many computational steps to take between receiving an input and emitting an output. ACT requires minimal changes to the network architecture, is deterministic and differentiable, and does not add any noise to the parameter gradients.

Experimental results are provided for four synthetic problems: determining the parity of binary vectors, applying binary logic operations, adding integers, and sorting real numbers. Overall, performance is dramatically improved by the use of ACT, which successfully adapts the number of computational steps to the requirements of the problem.

We also present character-level language modeling results on the Hutter prize Wikipedia dataset. In this case ACT does not yield large gains in performance; however it does provide intriguing insight into the structure of the data, with more computation allocated to harder-to-predict transitions, such as spaces between words and ends of sentences.

This suggests that ACT or other adaptive computation methods could provide a generic method for inferring segment boundaries in sequence data.

--- https://arxiv.org/abs/1601.06759#deepmind PixelRNN: Pixel Recurrent Neural Networks Aaron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu 2016-01-25 2020-11-13 [("doi","10.48550/arXiv.1601.06759")] ai/nn/cnn ai/nn/rnn

Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable.

We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel values and encodes the complete set of dependencies in the image. Architectural novelties include fast two-dimensional recurrent layers and an effective use of residual connections in deep recurrent networks.

We achieve log-likelihood scores on natural images that are considerably better than the previous state-of-the-art. Our main results also provide benchmarks on the diverse ImageNet dataset.

Samples generated from the model appear crisp, varied and globally coherent.

--- https://arxiv.org/abs/1511.06732#facebook Sequence Level Training with Recurrent Neural Networks Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba 2015-11-20 2020-11-13 [("doi","10.48550/arXiv.1511.06732")] ai/nn/rnn ai/nn/sampling reinforcement-learning/model-free

Many natural language processing applications use language models to generate text. These models are typically trained to predict the next word in a sequence, given the previous words and some context such as an image. However, at test time the model is expected to generate the entire sequence from scratch. This discrepancy makes generation brittle, as errors may accumulate along the way.

We address this issue by proposing a novel sequence level training algorithm that directly optimizes the metric used at test time, such as BLEU or ROUGE. On 3 different tasks, our approach outperforms several strong baselines for greedy generation. The method is also competitive when these baselines employ beam search, while being several times faster.

--- https://arxiv.org/abs/1502.04623 DRAW: A Recurrent Neural Network For Image Generation Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra 2015-02-16 2020-11-13 [("doi","10.48550/arXiv.1502.04623")] ai/nn/rnn ai/nn/transformer/attention psychology/neuroscience reinforcement-learning/model-free

This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation.

DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images.

The system substantially improves on the state-of-the-art for generative models on MNIST, and, when trained on the Street View House Numbers (SHVN) dataset, it generates images that cannot be distinguished from real data with the naked eye.

--- https://arxiv.org/abs/2204.03067 ByT5 model for massively multilingual grapheme-to-phoneme conversion Jian Zhu, Cong Zhang, David Jurgens 2022-04-06 2022-04-06 [("doi","10.48550/arXiv.2204.03067")] ai/dataset ai/nn/tokenization ai/nn/transformer/t5

In this study, we tackle massively multilingual grapheme-to-phoneme conversion through implementing G2P models based on ByT5.

We have curated a G2P dataset from various sources that covers around 100 languages and trained large-scale multilingual G2P models based on ByT5.

We found that ByT5 operating on byte-level inputs substantially outperformed the token-based mT5 model in terms of multilingual G2P. Pairwise comparison with monolingual models in these languages suggests that multilingual ByT5 models generally lower the phone error rate by jointly learning from a variety of languages. The pretrained model can further benefit low resource G2P through zero-shot prediction on unseen languages or provides pretrained weights for finetuning, which helps the model converge to a lower phone error rate than randomly initialized weights.

To facilitate future research on multilingual G2P, we make available our code and pretrained multilingual G2P models at: Github.

--- https://research.google/blog/large-scale-matrix-factorization-on-tpus/ 2020-11-13 ai/scaling --- https://www.military.com/history/how-naked-skydive-inspired-way-keep-pilots-oriented-flight.html How a Naked Skydive Inspired a Way to Keep Pilots Oriented in Flight 2020-11-14 design psychology technology --- https://arxiv.org/abs/2110.11499 Wav2CLIP: Learning Robust Audio Representations From CLIP Ho-Hsiang Wu, Prem Seetharaman, Kundan Kumar, Juan Pablo Bello 2021-10-21 2021-10-21 [("doi","10.48550/arXiv.2110.11499")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/clip

We propose Wav2CLIP, a robust audio representation learning method by distilling from Contrastive Language-Image Pre-training (CLIP).

Wav2CLIP projects audio into a shared embedding space with images and text, which enables multimodal applications such as zero-shot classification, and cross-modal retrieval. Furthermore, Wav2CLIP needs just ~10% of the data to achieve competitive performance on downstream tasks compared with fully supervised models, and is more efficient to pre-train than competing methods as it does not require learning a visual model in concert with an auditory model.

We systematically evaluate Wav2CLIP on a variety of audio tasks including classification, retrieval, and generation, and show that Wav2CLIP can outperform several publicly available pre-trained audio representation algorithms. Finally, we demonstrate image generation from Wav2CLIP as qualitative assessment of the shared embedding space.

Our code and model weights are open sourced and made available for further applications.

--- https://arxiv.org/abs/2204.00498 Evaluating the Text-to-SQL Capabilities of Large Language Models Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau 2022-03-15 2022-03-15 [("doi","10.48550/arXiv.2204.00498")] ai/nn/transformer/gpt/codex

We perform an empirical evaluation of Text-to-SQL capabilities of the Codex language model. We find that, without any finetuning, Codex is a strong baseline on the Spider benchmark; we also analyze the failure modes of Codex in this setting. Furthermore, we demonstrate on the GeoQuery and Scholar benchmarks that a small number of in-domain examples provided in the prompt enables Codex to perform better than state-of-the-art models finetuned on such few-shot examples.

…Prompt design is critical for performance: As seen in Table 2, providing the question alone results in a low 8.3% execution accuracy. There is a progressive improvement to 56.8% as schema information is introduced in ‘API Docs’, to 59.9% when valid SQL and foreign key information is used in ‘Create Table’, and to 67.0% when database content is introduced with ‘Create Table + Select 3’.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3482426/ Tests of a direct effect of childhood abuse on adult borderline personality disorder traits: a longitudinal discordant twin design Marina A. Bornovalova, Brooke M. Huibregtse, Brian M. Hicks, Margaret Keyes, Matt McGue, William Iacono 2013 2020-11-14 [("doi","10.1037/a0028328")] genetics/heritable psychiatry/borderline psychology/personality

We used a longitudinal twin design to examine the causal association between sexual, emotional, and physical abuse in childhood (before age 18) and borderline personality disorder (BPD) traits at age 24 using a discordant twin design and biometric modeling. Additionally, we examined the mediating and moderating effects of symptoms of childhood externalizing and internalizing disorders on the link between childhood abuse and BPD traits.

Although childhood abuse, BPD traits, and internalizing and externalizing symptoms were all correlated, the discordant twin analyses and biometric modeling showed little to no evidence that was consistent with a causal effect of childhood abuse on BPD traits. Instead, our results indicate that the association between childhood abuse and BPD traits stems from common genetic influences that, in some cases, also overlap with internalizing and externalizing disorders.

These findings are inconsistent with the widely held assumption that childhood abuse causes BPD, and they suggest that BPD traits in adulthood are better accounted for by heritable vulnerabilities to internalizing and externalizing disorders.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2789483/ Stability, change, and heritability of borderline personality disorder traits from adolescence to adulthood: a longitudinal twin study Marina A. Bornovalova, Brian M. Hicks, William Iacono, Matt McGue 2009 2020-11-14 [("doi","10.1017/S0954579409990186")] genetics/heritable/correlation psychiatry/borderline psychology/personality

Although personality disorders are best understood in the context of lifetime development, there is a paucity of work examining their longitudinal trajectory. An understanding of the expected course and the genetic and environmental contributions to these disorders is necessary for a detailed understanding of risk processes that lead to their manifestation.

The current study examined the longitudinal course and heritability of borderline personality disorder (BPD) over a period of 10 years starting in adolescence (age 14) and ending in adulthood (age 24). In doing so, we built on existing research by using a large community sample of adolescent female twins, a sensitive dimensional measure of BPD traits, an extended follow-up period, and a longitudinal twin design that allowed us to investigate the heritability of BPD traits at 4 discrete ages spanning mid-adolescence to early adulthood.

Results indicated that mean-level BPD traits decline from adolescence to adulthood, but rank order stability remained high. BPD traits were moderately heritable at all ages, with a slight trend for increased heritability from age 14 to age 24. A genetically informed latent growth curve model indicated that both the stability and change of BPD traits are highly influenced by genetic factors and modestly by nonshared environmental factors.

Our results indicate that as is the case for other personality dimensions, trait BPD declines as individuals mature from adolescence to adulthood, and that this process is influenced in part by the same genetic factors that influence BPD trait stability.

--- https://arxiv.org/abs/2006.03654#microsoft DeBERTa: Decoding-enhanced BERT with Disentangled Attention Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen 2020-06-05 2020-11-14 [("doi","10.48550/arXiv.2006.03654")] ai/nn/transformer

Recent progress in pre-trained neural language models has improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions, respectively. Second, an enhanced mask decoder is used to incorporate absolute positions in the decoding layer to predict the masked tokens in model pre-training. In addition, a new virtual adversarial training method is used for fine-tuning to improve models’ generalization.

We show that these techniques improve the efficiency of model pre-training and the performance of both natural language understanding (NLU) and natural language generation (NLG) downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). Notably, we scale up DeBERTa by training a larger version that consists of 48 Transform layers with 1.5 billion parameters. The performance boost makes the single DeBERTa model surpass the human performance on the SuperGLUE benchmark (Wang et al 2019a) for the first time in terms of macro-average score (89.9 versus 89.8), and the ensemble DeBERTa model sits atop the SuperGLUE leaderboard as of January 6, 2021, out performing the human baseline by a decent margin (90.3 versus 89.8).

--- https://arxiv.org/abs/2204.03597 Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning Carl Qi, Pieter Abbeel, Aditya Grover 2022-04-07 2022-04-07 [("doi","10.48550/arXiv.2204.03597")] ai/nn/gan ai/video/analysis reinforcement-learning/imitation-learning reinforcement-learning/preference-learning

The goal of imitation learning is to mimic expert behavior from demonstrations, without access to an explicit reward signal. A popular class of approach infers the (unknown) reward function via inverse reinforcement learning (IRL) followed by maximizing this reward function via reinforcement learning (RL). The policies learned via these approaches are however very brittle in practice and deteriorate quickly even with small test-time perturbations due to compounding errors.

We propose Imitation with Planning at Test-time (IMPLANT), a new meta-algorithm for imitation learning that uses decision-time planning to correct for compounding errors of any base imitation policy. In contrast to existing approaches, we retain both the imitation policy and the rewards model at decision-time, thereby benefiting from the learning signal of the two components.

Empirically, we demonstrate that IMPLANT outperforms benchmark imitation learning approaches on standard control environments and excels at zero-shot generalization when subject to challenging perturbations in test-time dynamics.

--- https://x.com/proximasan/status/1511379170944851976 2020-11-14 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2204.03162 Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality Tristan Thrush, Ryan Jiang, Max Bartolo, Amanpreet Singh, Adina Williams, Douwe Kiela, Candace Ross 2022-04-07 2022-04-07 [("doi","10.48550/arXiv.2204.03162")] ai/dataset ai/nn/transformer/clip

We present a novel task and dataset for evaluating the ability of vision and language models to conduct visio-linguistic compositional reasoning, which we call Winoground. Given two images and two captions, the goal is to match them correctly—but crucially, both captions contain a completely identical set of words, only in a different order.

The dataset was carefully hand-curated by expert annotators and is labeled with a rich set of fine-grained tags to assist in analyzing model performance.

We probe a diverse range of state-of-the-art vision and language models and find that, surprisingly, none of them do much better than chance. Evidently, these models are not as skilled at visio-linguistic compositional reasoning as we might have hoped.

We perform an extensive analysis to obtain insights into how future work might try to mitigate these models’ shortcomings. We aim for Winoground to serve as a useful evaluation set for advancing the state-of-the-art and driving further progress in the field.

The dataset is available at https://huggingface.co/datasets/facebook/winoground.

--- https://x.com/RiversHaveWings/status/1512910668948664320 2020-11-14 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/1904.01201#facebook Habitat: A Platform for Embodied AI Research Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, Dhruv Batra 2019-04-02 2020-11-14 [("doi","10.48550/arXiv.1904.01201")] reinforcement-learning/robot reinforcement-learning/scaling

We present Habitat, a platform for research in embodied artificial intelligence (AI).

Habitat enables training embodied agents (virtual robots) in highly efficient photorealistic 3D simulation. Specifically, Habitat consists of: (1) Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, sensors, and generic 3D dataset handling. Habitat-Sim is fast—when rendering a scene from Matterport3D, it achieves several thousand frames per second (fps) running single-threaded, and can reach over 10,000 fps multi-process on a single GPU. (2) Habitat-API: a modular high-level library for end-to-end development of embodied AI algorithms—defining tasks (eg. navigation, instruction following, question answering), configuring, training, and benchmarking embodied agents.

These large-scale engineering contributions enable us to answer scientific questions requiring experiments that were till now impracticable or “merely” impractical. Specifically, in the context of point-goal navigation: (1) we revisit the comparison between learning and SLAM approaches from two recent works and find evidence for the opposite conclusion—that learning outperforms SLAM if scaled to an order of magnitude more experience than previous investigations [cf. DD-PPO], and (2) we conduct the first cross-dataset generalization experiments {train, test} × {Matterport3D, Gibson} for multiple sensors {blind, RGB, RGBD, D} and find that only agents with depth (D) sensors generalize across datasets. We hope that our open-source platform and these findings will advance research in embodied AI.

…4. PointGoal Navigation at Scale: …For the experiments reported here, we train until 75 million agent steps are accumulated across all worker threads. This is 15× larger than the experience used in previous investigations.20, 16 Training agents to 75 million steps took (in sum over all 3 datasets): 320 GPU-hours for Blind, 566 GPU-hours for RGB, 475 GPU-hours for Depth, and 906 GPU-hours for RGBD (overall 2,267 GPU-hours).

Figure 3: Average SPL of agents on the val set over the course of training. Previous work20, 16 has analyzed performance at 5–10 million steps. Interesting trends emerge with more experience: (1) Blind agents initially outperform RGB & RGBD but saturate quickly; (2) Learning-based Depth agents outperform classic SLAM. The shaded areas around curves show the standard error of SPL over 5 seeds.

…Learning vs SLAM: To answer the first question we plot agent performance (SPL) on validation (ie. unseen) episodes over the course of training in Figure 3 (top: Gibson, bottom: Matterport3D). SLAM does not require training and thus has a constant performance (0.59 on Gibson, 0.42 on Matterport3D). All RL (PPO) agents start out with far worse SPL, but RL (PPO) Depth, in particular, improves dramatically and matches the classic baseline at ~10M frames (Gibson) or 30M frames (Matterport3D) of experience, continuing to improve thereafter. Notice that if we terminated the experiment at 5M frames as in20 we would also conclude that SLAM dominates. Interestingly, RGB agents do not outperform Blind agents; we hypothesize because both are equipped with GPS sensors. Indeed, qualitative results (Figure 4 and video in supplement) suggest that Blind agents ‘hug’ walls and implement ‘wall following’ heuristics. In contrast, RGB sensors provide a high-dimensional complex signal that may be prone to overfitting to train environments due to the variety across scenes (even within the same dataset). We also notice in Figure 3 that all methods perform better on Gibson than Matterport3D. This is consistent with our previous analysis that Gibson contains smaller scenes and shorter episodes.

--- https://arxiv.org/abs/1911.00357#facebook DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames Erik Wijmans, Abhishek Kadian, Ari Morcos, Stefan Lee, Irfan Essa, Devi Parikh, Manolis Savva, Dhruv Batra 2019-11-01 2020-11-15 [("doi","10.48550/arXiv.1911.00357")] ai/nn/cnn reinforcement-learning/exploration reinforcement-learning/robot reinforcement-learning/scaling

[blog] We present Decentralized Distributed Proximal Policy Optimization (DD-PPO), a method for distributed reinforcement learning in resource-intensive simulated environments.

DD-PPO is distributed (uses multiple machines), decentralized (lacks a centralized server), and synchronous (no computation is ever stale), making it conceptually simple and easy to implement. In our experiments on training virtual robots to navigate in Habitat-Sim, DD-PPO exhibits near-linear scaling—achieving a speedup of 107× on 128 GPUs over a serial implementation. We leverage this scaling to train an agent for 2.5 Billion steps of experience (the equivalent of 80 years of human experience)—over 6 months of GPU-time training in under 3 days of wall-clock time with 64 GPUs.

This massive-scale training not only sets the state of art on Habitat Autonomous Navigation Challenge 2019, but essentially solves the task—near-perfect autonomous navigation in an unseen environment without access to a map, directly from an RGB-D camera and a GPS+Compass sensor.

Fortuitously, error vs computation exhibits a power-law-like distribution; thus, 90% of peak performance is obtained relatively early (at 100 million steps) and relatively cheaply (under 1 day with 8 GPUs).

Finally, we show that the scene understanding and navigation policies learned can be transferred to other navigation tasks—the analog of ImageNet pre-training + task-specific fine-tuning for embodied AI.

Our model outperforms ImageNet pre-trained CNNs on these transfer tasks and can serve as an universal resource (all models and code are publicly available).

--- https://arxiv.org/abs/2204.03638#facebook TATS: Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer Songwei Ge, Thomas Hayes, Harry Yang, Xi Yin, Guan Pang, David Jacobs, Jia-Bin Huang, Devi Parikh 2022-04-07 2022-04-07 [("doi","10.48550/arXiv.2204.03638")] ai/nn/gan ai/nn/transformer/attention ai/nn/vae ai/video/generation

Videos are created to express emotion, exchange information, and share experiences. Video synthesis has intrigued researchers for a long time. Despite the rapid progress driven by advances in visual synthesis, most existing studies focus on improving the frames’ quality and the transitions between them, while little progress has been made in generating longer videos.

In this paper, we present a method that builds on 3D-VQGAN and transformers to generate videos with thousands of frames.

Our evaluation shows that our model trained on 16-frame video clips from standard benchmarks such as UCF101, Sky Time-lapse, and Taichi-HD datasets can generate diverse, coherent, and high-quality long videos. We also showcase conditional extensions of our approach for generating meaningful long videos by incorporating temporal information with text and audio.

Videos and code can be found at https://songweige.github.io/projects/tats/index.html.

--- https://www.biorxiv.org/content/10.1101/2022.04.04.486901.full All-optical visualization of specific molecules in the ultrastructural context of brain tissue Ons M’Saad, Ravikiran Kasula, Ilona Kondratiuk, Phylicia Kidd, Hanieh Falahati, Juliana E. Gentile, Robert F. Niescier, Katherine Watters, Robert C. Sterner, Seong Lee, Xinran Liu, Pietro De Camilli, James E. Rothman, Anthony J. Koleske, Thomas Biederer, Joerg Bewersdorf 2022-04-05 2022-04-05 [("doi","10.1101/2022.04.04.486901")] psychology/neuroscience

[Twitter] Understanding the molecular anatomy and neural connectivity of the brain requires imaging technologies that can map the 3D nanoscale distribution of specific proteins in the context of brain ultrastructure. Light and electron microscopy (EM) enable visualization of either specific labels or anatomical ultrastructure, but combining molecular specificity with anatomical context is challenging.

Here, we present pan-Expansion Microscopy of tissue (pan-ExM-t), an all-optical mouse brain imaging method that combines ~24× linear expansion of biological samples with fluorescent pan-staining of protein densities (providing EM-like ultrastructural context), and immunolabeling of protein targets (for molecular imaging).

We demonstrate the versatility of this approach by imaging the established synaptic markers Homer1, Bassoon, PSD-95, Synaptophysin, the astrocytic protein GFAP, myelin basic protein (MBP), and anti-GFP antibodies in dissociated neuron cultures and mouse brain tissue sections. pan-ExM-t reveals these markers in the context of ultrastructural features such as pre and postsynaptic densities, 3D nanoarchitecture of neuropil, and the fine structures of cellular organelles.

pan-ExM-t is adoptable in any neurobiological laboratory with access to a confocal microscope and has therefore broad applicability in the research community.

--- https://www.biorxiv.org/content/10.1101/2021.11.05.467388.full A temporally resolved, multiplex molecular recorder based on sequential genome editing Junhong Choi, Wei Chen, Anna Minkina, Florence M. Chardon, Chase C. Suiter, Samuel G. Regalado, Silvia Domcke, Nobuhiko Hamazaki, Choli Lee, Beth Martin, Riza M. Daza, Jay Shendure 2021-11-05 2021-11-05 [("doi","10.1101/2021.11.05.467388")] genetics/editing psychology/neuroscience

DNA is naturally well-suited to serve as a digital medium for in vivo molecular recording. However, DNA-based memory devices described to date are constrained in terms of the number of distinct signals that can be concurrently recorded and/or by a failure to capture the precise order of recorded events.

Here we describe DNA Ticker Tape, a general system for in vivo molecular recording that largely overcomes these limitations. Blank DNA Ticker Tape consists of a tandem array of partial CRISPR-Cas9 target sites, with all but the first site truncated at their 59 ends, and therefore inactive. Signals of interest are coupled to the expression of specific prime editing guide RNAs. Editing events are insertional, and record the identity of the guide RNA mediating the insertion while also shifting the position of the “write head” by one unit along the tandem array, i.e. sequential genome editing.

In this proof-of-concept of DNA Ticker Tape, we demonstrate the recording and decoding of complex event histories or short text messages; evaluate the performance of dozens of orthogonal tapes; and construct “long tape” potentially capable of recording the order of as many as 20 serial events. Finally, we demonstrate how DNA Ticker Tape simplifies the decoding of cell lineage histories.

--- https://arxiv.org/abs/2204.03610#microsoft Unified Contrastive Learning in Image-Text-Label Space Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Bin Xiao, Ce Liu, Lu Yuan, Jianfeng Gao 2022-04-07 2022-04-07 [("doi","10.48550/arXiv.2204.03610")] ai/nn/transformer/clip

Visual recognition is recently learned via either supervised learning on human-annotated image-label data or language-image contrastive learning with webly-crawled image-text pairs. While supervised learning may result in a more discriminative representation, language-image pretraining shows unprecedented zero-shot recognition capability, largely due to the different properties of data sources and learning objectives.

In this work, we introduce a new formulation by combining the two data sources into a common image-text-label space. In this space, we propose a new learning paradigm, called Unified Contrastive Learning (UniCL) with a single learning objective to seamlessly prompt the synergy of two data types.

Extensive experiments [with Florence] show that our UniCL is an effective way of learning semantically rich yet discriminative representations, universally for image recognition in zero-shot, linear-probe, fully finetuning and transfer learning scenarios. Particularly, it attains gains up to 9.2% and 14.5% in average on zero-shot recognition benchmarks over the language-image contrastive learning and supervised learning methods, respectively. In linear probe setting, it also boosts the performance over the two methods by 7.3% and 3.4%, respectively. Our study also indicates that UniCL stand-alone is a good learner on pure image-label data, rivaling the supervised learning methods across 3 image classification datasets and two types of vision backbones, ResNet and Swin Transformer.

Code is available at Github.

--- https://www.biorxiv.org/content/10.1101/250712.full Genomic risk prediction of coronary artery disease in nearly 500,000 adults: implications for early screening and primary prevention Michael Inouye, Gad Abraham, Christopher P. Nelson, Angela M. Wood, Michael J. Sweeting, Frank Dudbridge, Florence Y. Lai, Stephen Kaptoge, Marta Brozynska, Tingting Wang, Shu Ye, Thomas R. Webb, Martin K. Rutter, Ioanna Tzoulaki, Riyaz S. Patel, Ruth Loos, Bernard Keavney, Harry Hemingway, John Thompson, Hugh Watkins, Panos Deloukas, Emanuele Di Angelantonio, Adam S. Butterworth, John Danesh, Nilesh J. Samani 2018-01-19 2020-11-15 [("doi","10.1101/250712")] genetics/heritable

Background: Coronary artery disease (CAD) has substantial heritability and a polygenic architecture; however, genomic risk scores have not yet leveraged the totality of genetic information available nor been externally tested at population-scale to show potential utility in primary prevention.

Method: Using a meta-analytic approach to combine large-scale genome-wide and targeted genetic association data, we developed a new genomic risk score for CAD (metaGRS), consisting of 1.7 million genetic variants. We externally tested metaGRS, individually and in combination with available conventional risk factors, in 22,242 CAD cases and 460,387 non-cases from UK Biobank.

Results: In UK Biobank, a standard deviation increase in metaGRS had a hazard ratio (HR) of 1.71 (95% CI 1.68–1.73) for CAD, greater than any other externally tested genetic risk score. Individuals in the top 20% of the metaGRS distribution had a HR of 4.17 (95% CI 3.97–4.38) compared with those in the bottom 20%. The metaGRS had higher C-index (C=0.623, 95% CI 0.615–0.631) for incident CAD than any of four conventional factors (smoking, diabetes, hypertension, and body mass index), and addition of the metaGRS to a model of conventional risk factors increased C-index by 3.7%. In individuals on lipid-lowering or anti-hypertensive medications at recruitment, metaGRS hazard for incident CAD was but only partially attenuated with HR of 2.83 (95% CI 2.61–3.07) between the top and bottom 20% of the metaGRS distribution.

Interpretation: Recent genetic association studies have yielded enough information to meaningfully stratify individuals using the metaGRS for CAD risk in both early and later life, thus enabling targeted primary intervention in combination with conventional risk factors. The metaGRS effect was partially attenuated by lipid and blood pressure-lowering medication, however other prevention strategies will be required to fully benefit from earlier genomic risk stratification.

Funding: National Health and Medical Research Council of Australia, British Heart Foundation, Australian Heart Foundation.

--- https://arxiv.org/abs/1905.04492 Structural Equation Models as Computation Graphs Erik-Jan van Kesteren, Daniel L. Oberski 2019-05-11 2020-11-15 [("doi","10.48550/arXiv.1905.04492")] cs/r statistics/bayes

Structural equation modeling (SEM) is a popular tool in the social and behavioral sciences, where it is being applied to ever more complex data types. The high-dimensional data produced by modern sensors, brain images, or (epi)genetic measurements require variable selection using parameter penalization; experimental models combining disparate data sources benefit from regularization to obtain a stable result; and genomic SEM or network models lead to alternative objective functions. With each proposed extension, researchers currently have to completely reformulate SEM and its optimization algorithm—a challenging and time-consuming task.

In this paper, we consider each SEM as a computation graph, a flexible method of specifying objective functions borrowed from the field of deep learning. When combined with state-of-the-art optimizers, our computation graph approach can extend SEM without the need for bespoke software development.

We show that both existing and novel SEM improvements follow naturally from our approach. To demonstrate, we discuss least absolute deviation estimation and penalized regression models. We also introduce spike-and-slab SEM, which may perform better when shrinkage of large factor loadings is not desired.

By applying computation graphs to SEM, we hope to greatly accelerate the process of developing SEM techniques, paving the way for new applications. We provide an accompanying R package tensorsem.

--- https://x.com/KyrickYoung/status/1513002267439415301 2020-11-15 ai/nn/transformer/clip/sample --- https://github.com/CompVis 2020-11-15 ai/nn/diffusion ai/nn/gan --- https://x.com/sureailabs/status/1513155108372205574 2020-11-15 ai/nn/transformer/clip/sample --- https://www.biorxiv.org/content/10.1101/2022.04.08.487647.full Multilevel selection analysis reveals diversity in selfish mitochondrial genome dynamics Bryan L. Gitschlag, Maulik R. Patel 2022-04-10 2022-04-10 [("doi","10.1101/2022.04.08.487647")] genetics/selection/natural

Mutant copies of mitochondrial DNA (mtDNA) can behave as selfish entities, propagating within organisms at the expense of host fitness. We previously developed experiments to separately measure the within-host proliferation, and host fitness cost, of selfish mtDNA. By focusing on a single mutant genome, the uaDf5 variant, our previous work raises the question of generalizability: do selfish genomes uniformly overcome a heavy fitness cost by proliferating to high frequency, or do their population dynamics vary?

By applying a standardized multilevel selection analysis across a collection of mitochondrial mutants, we uncover variation in cheating strategies. Mutants that impose a heavy fitness cost nevertheless persist by outcompeting wildtype mtDNA within hosts. Conversely, some mutants fail to outcompete wildtype mtDNA but nevertheless persist, maintaining a frequency range at which host fitness cost is negligible.

These findings suggest that the population dynamics of mutant mtDNA depend on the loci affected.

--- https://www.reddit.com/r/bigsleep/comments/tvw5js/list_of_sitesprogramsprojects_that_use_openais/ [P] List of sites/programs/projects that use OpenAI’s CLIP neural network for steering image/video creation to match a text description 2020-11-16 ai/nn/transformer/clip --- https://openaccess.thecvf.com/content_cvpr_2016/papers/Xu_MSR-VTT_A_Large_CVPR_2016_paper.pdf#microsoft MSR-VTT: A Large Video Description Dataset for Bridging Video and Language Jun Xu, Tao Mei, Ting Yao, Yong Rui 2021-01-17 2021-01-17 ai/dataset ai/nn/transformer ai/video/analysis

While there has been increasing interest in the task of describing video with natural language, current computer vision algorithms are still severely limited in terms of the variability and complexity of the videos and their associated language that they can recognize. This is in part due to the simplicity of current benchmarks, which mostly focus on specific fine-grained domains with limited videos and simple descriptions. While researchers have provided several benchmark datasets for image captioning, we are not aware of any large-scale video description dataset with comprehensive categories yet diverse video content.

In this paper we present MSR-VTT (standing for “MSRVideo to Text”) which is a new large-scale video benchmark for video understanding, especially the emerging task of translating video to text. This is achieved by collecting 257 popular queries from a commercial video search engine, with 118 videos for each query. In its current version, MSR-VTT provides 10K web video clips with 41.2 hours and 200K clip-sentence pairs in total, covering the most comprehensive categories and diverse visual content, and representing the largest dataset in terms of sentence and vocabulary. Each clip is annotated with about 20 natural sentences by 1,327 AMT workers.

We present a detailed analysis of MSR-VTT in comparison to a complete set of existing datasets, together with a summarization of different state-of-the-art video-to-text approaches.

We also provide an extensive evaluation of these approaches on this dataset, showing that the hybrid Recurrent Neural Network-based approach, which combines single-frame and motion representations with soft-attention pooling strategy, yields the best generalization capability on MSR-VTT.

--- https://en.wikipedia.org/wiki/Cat_gap Cat gap 2020-11-16 cat/genetics --- https://x.com/GlennIsZen/status/1513805306853265408 2020-11-16 ai/nn/transformer/clip/sample --- https://x.com/thors_thunder04/status/1513603164204896256 2020-11-16 ai/nn/transformer/clip/sample --- https://x.com/metasemantic/status/1487100540907663363 2020-11-16 ai/nn/transformer/clip/sample --- https://en.wikipedia.org/wiki/Scottish_Book Scottish Book 2020-11-16 math --- https://en.wikipedia.org/wiki/Scottish_Caf%C3%A9 Scottish Café 2020-11-16 math --- https://www.reddit.com/r/dalle2/ 2020-11-16 ai/nn/transformer/gpt/dall-e --- https://x.com/RiversHaveWings/status/1514236103850545152 2020-11-16 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1514233576136445957 2020-11-16 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1514251506605821955 2020-11-17 ai/nn/transformer/clip/sample --- https://www.nature.com/articles/d41586-022-00997-5 2020-11-17 ai/nn/transformer/alphafold --- https://x.com/gandamu_ml/status/1514292543768436738 2020-11-17 ai/nn/transformer/clip/sample --- https://www.reddit.com/r/MediaSynthesis/comments/u2mqjt/the_watcher_a_composite_superres_image_made_of_21/ 2020-11-17 ai/nn/transformer/clip/sample --- https://x.com/amasad/status/1514322225184223234 2020-11-17 ai/nn/transformer/gpt/dall-e --- https://x.com/genekogan/status/1512513817573412864 Here’s what a few years of progress on text-to-image generation looks like, one prompt at a time. "Frank Sinatra as a purple alien in surrealist style": 1. AttnGAN (2018) · 2. CLIP+VQGAN (2020) · 3. CLIP+Diffusion (2021) · 4. DALL·E 2 (2022) 2020-11-17 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1514456529809772547 2020-11-17 ai/nn/transformer/clip/sample --- https://x.com/GlennIsZen/status/1514498230712782849 2020-11-17 ai/nn/transformer/clip/sample --- https://x.com/KaliYuga_ai/status/1514486680199311360 2020-11-17 ai/nn/transformer/clip/sample --- https://x.com/KaliYuga_ai/status/1514475797226131457 2020-11-17 ai/nn/transformer/clip/sample --- https://www.biorxiv.org/content/10.1101/2022.03.25.485824.full Deep Phenotyping and Lifetime Trajectories Reveal Limited Effects of Longevity Regulators on the Aging Process in C57BL/6J Mice Kan Xie, Helmut Fuchs, Enzo Scifo, Dan Liu, Ahmad Aziz, Juan Antonio Aguilar-Pimentel, Oana Veronica Amarie, Lore Becker, Patricia da Silva-Buttkus, Julia Calzada-Wack, Yi-Li Cho, Yushuang Deng, A. Cole Edwards, Lillian Garrett, Christina Georgopoulou, Raffaele Gerlini, Sabine M. Hölter, Tanja Klein-Rodewald, Michael Kramer, Stefanie Leuchtenberger, Dimitra Lountzi, Phillip Mayer-Kuckuk, Lena L. Nover, Manuela A. Oestereicher, Clemens Overkott, Brandon L. Pearson, Birgit Rathkolb, Jan Rozman, Jenny Russ, Kristina Schaaf, Nadine Spielmann, Adrián Sanz-Moreno, Claudia Stoeger, Irina Treise, Daniele Bano, Dirk H. Busch, Jochen Graw, Martin Klingenspor, Thomas Klopstock, Beverly A. Mock, Paolo Salomoni, Carsten Schmidt-Weber, Marco Weiergräber, Eckhard Wolf, Wolfgang Wurst, Valérie Gailus-Durner, Monique M. B. Breteler, Martin Hrabě de Angelis, Dan Ehninger 2022-03-27 2022-03-27 [("doi","10.1101/2022.03.25.485824")] longevity/fasting

Current concepts regarding the biology of aging are based on studies aimed at identifying factors regulating natural lifespan. However, lifespan as a sole proxy measure for aging can be of limited value because it may be restricted by specific sets of pathologies, rather than by general physiological decline.

Here, we employed large-scale phenotyping to analyze hundreds of phenotypes and thousands of molecular markers across tissues and organ systems in a single study of aging male C57BL/6J mice. For each phenotype, we established lifetime profiles to determine when age-dependent phenotypic change is first detectable relative to the young adult baseline. We examined central genetic and environmental lifespan regulators (putative anti-aging interventions, PAAIs; the following PAAIs were examined: mTOR loss-of-function, loss-of-function in growth hormone signaling, dietary restriction) for a possible countering of the signs and symptoms of aging. Importantly, in our study design, we included young treated groups of animals, subjected to PAAIs prior to the onset of detectable age-dependent phenotypic change.

In parallel to our studies in mice, we assessed genetic variants for their effects on age-sensitive phenotypes in humans. We observed that, surprisingly, many PAAI effects influenced phenotypes long before the onset of detectable age-dependent changes, rather than altering the rate at which these phenotypes developed with age. Accordingly, this subset of PAAI effects does not reflect a targeting of age-dependent phenotypic change.

Overall, our findings suggest that comprehensive phenotyping, including the controls built in our study, is critical for the investigation of PAAIs as it facilitates the proper interpretation of the mechanistic mode by which PAAIs influence biological aging.

--- https://x.com/mattgroh/status/1513837678172778498 2020-11-18 ai/nn/transformer/gpt/dall-e --- https://x.com/itschloebubble/status/1514683689984147485 2020-11-18 ai/nn/transformer/clip/sample --- https://archive.org/details/1111101000-robots Ben Barry 2020-11-18 ai/nn/transformer/gpt/dall-e --- https://thegradient.pub/reading-the-tea-leaves/ Reading the Tea Leaves: Expert End-Users Explaining the Unexplainable 2020-11-18 reinforcement-learning/model/alphago --- https://x.com/KaliYuga_ai/status/1515551102191689731 2020-11-18 ai/nn/transformer/clip/sample --- /doc/sociology/1991-mccutcheon.pdf The 1936–1937 Purge of Soviet Astronomers Robert A. McCutcheon 1991-01-01 2020-11-18 [("doi","10.2307/2500602")] history science sociology

More than two dozen Soviet astronomers were arrested between March 1936 and July 1937. Few astronomers or historians are aware of the extent to which Soviet astronomy was devastated. This article investigates the situation in astronomy during these two years. It begins with a brief discussion of Soviet astronomy 1917–1935 and continues with a detailed examination of the events that served as the catalyst for the purge, the arrests themselves, and a discussion of what is known about the fates of the victims.

In the mid-1930s the Soviet Union had ~two hundred professional astronomers and sixteen astronomical observatories, most of which were associated with universities and had staffs of only two or 3 people. The most important and best equipped astronomical institution was the Central Astronomical Observatory of the USSR at Pulkovo, just outside Leningrad, with its branch observatories at Nikolaev and Simeis in the Ukraine. In 1935 thirty-three astronomers worked at Pulkovo.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701743/ Intestinal delta-6-desaturase activity determines host range for Toxoplasma sexual reproduction Bruno Martorelli Di Genova, Sarah K. Wilson, J. P. Dubey, Laura J. Knoll 2019 2020-11-18 [("doi","10.1371/journal.pbio.3000364")] cat/biology cat/psychology

Many eukaryotic microbes have complex life cycles that include both sexual and asexual phases with strict species specificity. Whereas the asexual cycle of the protistan parasite Toxoplasma gondii can occur in any warm-blooded mammal, the sexual cycle is restricted to the feline intestine. The molecular determinants that identify cats as the definitive host for T. gondii are unknown.

Here, we defined the mechanism of species specificity for T. gondii sexual development and break the species barrier to allow the sexual cycle to occur in mice. We determined that T. gondii sexual development occurs when cultured feline intestinal epithelial cells are supplemented with linoleic acid. Felines are the only mammals that lack delta-6-desaturase activity in their intestines, which is required for linoleic acid metabolism, resulting in systemic excess of linoleic acid. We found that inhibition of murine delta-6-desaturase and supplementation of their diet with linoleic acid allowed T. gondii sexual development in mice. This mechanism of species specificity is the first defined for a parasite sexual cycle.

This work highlights how host diet and metabolism shape coevolution with microbes. The key to unlocking the species boundaries for other eukaryotic microbes may also rely on the lipid composition of their environments as we see increasing evidence for the importance of host lipid metabolism during parasitic lifecycles.

Pregnant women are advised against handling cat litter, as maternal infection with T. gondii can be transmitted to the fetus with potentially lethal outcomes. Knowing the molecular components that create a conducive environment for T. gondii sexual reproduction will allow for development of therapeutics that prevent shedding of T. gondii parasites. Finally, given the current reliance on companion animals to study T. gondii sexual development, this work will allow the T. gondii field to use of alternative models in future studies.

--- https://www.reddit.com/r/dalle2/ 2020-11-18 ai/nn/transformer/gpt/dall-e --- https://www.quantamagazine.org/which-computational-universe-do-we-live-in-20220418/ Which Computational Universe Do We Live In? Cryptographers want to know which of five possible worlds we inhabit, which will reveal whether truly secure cryptography is even possible. 2020-11-18 cs --- https://arxiv.org/abs/2108.13976#salesforce WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU Tian Lan, Sunil Srinivasa, Stephan Zheng 2021-08-31 2021-08-31 [("doi","10.48550/arXiv.2108.13976")] ai/scaling/hardware reinforcement-learning/multi-agent reinforcement-learning/scaling

Deep reinforcement learning (RL) is a powerful framework to train decision-making models in complex dynamical environments. However, RL can be slow as it learns through repeated interaction with a simulation of the environment. Accelerating RL requires both algorithmic and engineering innovations. In particular, there are key systems engineering bottlenecks when using RL in complex environments that feature multiple agents or high-dimensional state, observation, or action spaces, for example.

We present WarpDrive, a flexible, lightweight, and easy-to-use open-source RL framework that implements end-to-end multi-agent RL on a single GPU (Graphics Processing Unit), building on PyCUDA and PyTorch. Using the extreme parallelization capability of GPUs, WarpDrive enables orders-of-magnitude faster RL compared to common implementations that blend CPU simulations and GPU models. Our design runs simulations and the agents in each simulation in parallel. It eliminates data copying between CPU and GPU. It also uses a single simulation data store on the GPU that is safely updated in-place. Together, this allows the user to run thousands of concurrent multi-agent simulations and train on extremely large batches of experience.

For example, WarpDrive yields 2.9 million environment steps/second with 2000 environments and 1,000 agents (at least 100× higher throughput compared to a CPU implementation) in a benchmark Tag simulation.

WarpDrive provides a lightweight Python interface and environment wrappers to simplify usage and promote flexibility and extensions. As such, WarpDrive provides a framework for building high-throughput RL systems.

--- /doc/sociology/technology/2021-wojcieszak.pdf No Polarization From Partisan News: Over-Time Evidence From Trace Data Magdalena Wojcieszak, Sjifra de Leeuw, Ericka Menchen-Trevino, Seungsu Lee, Ke M. Huang-Isherwood, Brian Weeks 2021-11-01 2021-11-01 [("doi","10.1177/19401612211047194")] politics sociology/technology

Many blame partisan news media for polarization in America.

This paper examines the effects of liberal, conservative, and centrist news on affective and attitude polarization. To this end, we rely on two studies that combine two-wave panel surveys (n1 = 303, n2 = 904) with 12 months’ worth of web browsing data submitted by the same participants comprising roughly 38m visits. We identify news exposure using an extensive list of news domains and develop a machine learning classifier to identify exposure to political news within these domains.

The results offer a robust pattern of null findings. Exposure to partisan and centrist news websites—no matter if it is congenial or crosscutting—does not enhance polarization. These null effects also emerge among strong and weak partisans as well as Democrats and Republicans alike.

We argue that these null results accurately portray the reality of limited effects of news in the “real world.” Politics and partisan news account for a small fraction of citizens’ online activities, less than 2% in our trace data, and are nearly unnoticeable in the overall information and communication ecology of most individuals.

--- https://arxiv.org/abs/2204.02515 Inferring Rewards from Language in Context Jessy Lin, Daniel Fried, Dan Klein, Anca Dragan 2022-04-05 2022-04-05 [("doi","10.48550/arXiv.2204.02515")] reinforcement-learning/imitation-learning reinforcement-learning/preference-learning

In classic instruction following, language like “I’d like the JetBlue flight” maps to actions (eg. selecting that flight). However, language also conveys information about a user’s underlying reward function (eg. a general preference for JetBlue), which can allow a model to carry out desirable actions in new contexts.

We present a model that infers rewards from language pragmatically: reasoning about how speakers choose utterances not only to elicit desired actions, but also to reveal information about their preferences.

On a new interactive flight-booking task with natural language, our model more accurately infers rewards and predicts optimal actions in unseen environments, in comparison to past work that first maps language to actions (instruction following) and then maps actions to rewards (inverse reinforcement learning).

--- https://arxiv.org/abs/2112.08654#google Learning to Prompt for Continual Learning Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister 2021-12-16 2021-12-16 [("doi","10.48550/arXiv.2112.08654")] ai/nn/transformer/gpt reinforcement-learning/meta-learning/continual-learning

[blog] The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge. Typical methods rely on a rehearsal buffer or known task identity at test time to retrieve learned knowledge and address forgetting, while this work presents a new paradigm for continual learning that aims to train a more succinct memory system without accessing task identity at test time.

Our method learns to dynamically prompt (L2P) a pre-trained model to learn tasks sequentially under different task transitions. In our proposed framework, prompts are small learnable parameters, which are maintained in a memory space. The objective is to optimize prompts to instruct the model prediction and explicitly manage task-invariant and task-specific knowledge while maintaining model plasticity.

We conduct comprehensive experiments under popular image classification benchmarks with different challenging continual learning settings, where L2P consistently outperforms prior state-of-the-art methods. Surprisingly, L2P achieves competitive results against rehearsal-based methods even without a rehearsal buffer and is directly applicable to challenging task-agnostic continual learning.

Source code is available at Github.

--- https://arxiv.org/abs/2204.08583#eleutherai VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance Katherine Crowson, Stella Biderman, Daniel Kornis, Dashiell Stander, Eric Hallahan, Louis Castricato, Edward Raff 2022-04-18 2022-04-18 [("doi","10.48550/arXiv.2204.08583")] ai/nn/gan ai/nn/transformer/clip ai/nn/vae

Generating and editing images from open domain text prompts is a challenging task that heretofore has required expensive and specially trained models. We demonstrate a novel methodology for both tasks which is capable of producing images of high visual quality from text prompts of high semantic complexity without any training by using a multimodal encoder to guide image generations.

We demonstrate on a variety of tasks how using CLIP to guide VQGAN produces higher visual quality outputs than prior, less flexible approaches like DALL·E, GLIDE, and Open-Edit, despite not being trained for the tasks presented.

Our code is available in a public repository.

--- https://en.wikipedia.org/wiki/Hobby_tunneling Hobby tunneling 2020-11-19 psychology/personality technology --- https://www.biorxiv.org/content/10.1101/2020.06.02.129908.full The burden of rare protein-truncating genetic variants on human lifespan Jimmy Z. Liu, Chia-Yen Chen, Ellen A. Tsai, Christopher D. Whelan, David Sexton, Sally John, Heiko Runz 2020-06-03 2020-11-19 [("doi","10.1101/2020.06.02.129908")] genetics/heritable/rare longevity

Genetic predisposition is believed to contribute substantially to the age at which we die. Genome-wide association studies (GWAS) have implicated more than 20 genetic loci to phenotypes related to human lifespan. However, little is known about how lifespan is impacted by gene loss-of-function.

Through whole-exome sequencing of 238,239 UK Biobank participants, we assessed the relevance of protein-truncating variant (PTV) gene burden on individual and parental survival. We identified exome-wide (p < 2.5e-6) statistically-significant associations between BRCA2, BRCA1, TET2, PPM1D, LDLR, EML2 and DEDD2 PTV-burden with human lifespan. Gene and gene-set PTV-burden phenome-wide association studies (PheWAS) further highlighted the roles of these genes in cancer and cardiovascular disease as relevant for overall survival.

The overlap between PTV-burden and prior GWAS results was modest, underscoring the value of sequencing in well-powered cohorts to complement GWAS for identifying loci associated with complex traits and disease.

--- https://www.biorxiv.org/content/10.1101/2022.04.19.488800.full Restriction of dietary fat, but not carbohydrate, alters brain reward circuitry in adults with obesity Valerie L. Darcey, Juen Guo, Amber Courville, Isabelle Gallagher, Jason A. Avery, W. Kyle Simmons, John E. Ingeholm, Peter Herscovitch, Alex Martin, Kevin D. Hall 2022-04-20 2022-04-20 [("doi","10.1101/2022.04.19.488800")] exercise psychology/neuroscience

Weight loss diets often restrict either fat or carbohydrate, macronutrients that are sensed via distinct gut-brain pathways and differentially affect peripheral hormones and metabolism.

To investigate whether reductions in dietary fat versus carbohydrate alter brain reward circuitry, we measured dopamine D2/3 receptor binding potential (D2BP) using PET and neural activity in response to visual food cues using fMRI in 17 inpatient adults with obesity during an eucaloric baseline diet and on the fifth day of isocaloric diets selectively reduced in either dietary fat or carbohydrate, in random order.

Reduction of dietary fat, but not carbohydrate, decreased D2BP and decreased neural activity to food cues in brain reward regions. After the reduced fat diet, ad libitum intake shifted towards foods high in both fat and carbohydrates.

These results suggest that dietary fat restriction increases tonic dopamine in brain reward regions thereby affecting food choice in ways that may hamper diet adherence.

--- https://x.com/RiversHaveWings/status/1516928082262650881 2020-11-19 ai/nn/gan/stylegan ai/nn/transformer/clip/sample --- https://jix.one/proving-50-year-old-sorting-networks-optimal-part-1/ Proving 50-Year-Old Sorting Networks Optimal: Part 1 2020-11-19 cs/algorithm/sorting --- https://dl.acm.org/doi/fullHtml/10.1145/238386.238611 2020-11-19 design --- https://arxiv.org/abs/1808.00659 Chaff Bugs: Deterring Attackers by Making Software Buggier Zhenghao Hu, Yu Hu, Brendan Dolan-Gavitt 2018-08-02 2020-11-19 [("doi","10.48550/arXiv.1808.00659")] cs/cryptography cs/security

Sophisticated attackers find bugs in software, evaluate their exploitability, and then create and launch exploits for bugs found to be exploitable. Most efforts to secure software attempt either to eliminate bugs or to add mitigations that make exploitation more difficult.

In this paper, we introduce a new defensive technique called chaff bugs, which instead target the bug discovery and exploit creation stages of this process. Rather than eliminating bugs, we instead add large numbers of bugs that are provably (but not obviously) non-exploitable. Attackers who attempt to find and exploit bugs in software will, with high probability, find an intentionally placed non-exploitable bug and waste precious resources in trying to build a working exploit.

We develop two strategies for ensuring non-exploitability and use them to automatically add thousands of non-exploitable bugs to real-world software such as nginx and libFLAC; we show that the functionality of the software is not harmed and demonstrate that our bugs look exploitable to current triage tools.

We believe that chaff bugs can serve as an effective deterrent against both human attackers and automated Cyber Reasoning Systems (CRSes).

--- https://x.com/Norod78/status/1517164004992503811 2020-11-20 ai/nn/transformer/clip/sample --- https://moultano.wordpress.com/2021/08/23/doorways/ Doorways Ryan Moulton 2021-08-23 2021-08-23 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2203.00671 Maximum Flow and Minimum-Cost Flow in Almost-Linear Time Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva 2022-03-01 2022-03-01 [("doi","10.48550/arXiv.2203.00671")] cs/algorithm

[media] We give an algorithm that computes exact maximum flows and minimum-cost flows on directed graphs with m edges and polynomially bounded integral demands, costs, and capacities in m1+o(1) time. Our algorithm builds the flow through a sequence of m1+o(1) approximate undirected minimum-ratio cycles, each of which is computed and processed in amortized m1+o(1) time using a dynamic data structure [low-stretch spanning tree].

Our framework extends to an algorithm running in m1+o(1) time for computing flows that minimize general edge-separable convex functions to high accuracy.

This gives an almost-linear time algorithm for several problems including entropy-regularized optimal transport, matrix scaling, p-norm flows, and isotonic regression.

--- https://dhall-lang.org/ The Dhall configuration language 2020-11-20 cs/haskell --- https://web.archive.org/web/20090504093628/https://code.google.com/soc/2007/haskell/appinfo.html?csaid=D0137F8B637176F1 Application Information 2020-11-20 cs/haskell --- https://ro-che.info/ccc/15.html Cartesian Closed Comic #15: Iteratees 2020-11-20 cs/haskell --- https://dorian.substack.com/p/at-any-given-moment-in-a-process At Any Given Moment in a Process…we have a certain partially evolved state of a structure. This state is described by the wholeness: the system of centers, and their relative nesting and degrees of life. 2020-11-21 design --- https://www.biorxiv.org/content/10.1101/2022.04.22.489170.full The contribution of mate-choice, couple convergence and confounding to assortative mating Jennifer Sjaarda, Zoltán Kutalik 2022-04-22 2022-04-22 [("doi","10.1101/2022.04.22.489170")] genetics/heritable/correlation/mendelian-randomization

Increased phenotypic similarity between partners, termed assortative mating (AM), has been observed for many traits. However, it is currently unclear if these observations are due to mate choice for certain phenotypes, post-mating convergence, or a result of confounding factors such as shared environment or indirect assortment.

To dissect these underlying phenomena, we applied Mendelian Randomization (MR) to 51,664 couples in the UK biobank to a panel of 118 phenotypes under AM.

We found that 54% (64 of 118) of the tested traits had a causal relationship between partners, with female-to-male effects on average being larger. Forty traits, including systolic blood pressure, basal metabolic rate, weight and height, showed larger phenotypic correlation than MR-estimates, suggesting the presence of confounders.

Subsequent analyses revealed household income, overall health rating, education and tobacco smoking as major overall confounders, accounting for 29.8%, 14.1%, 11.6%, & 4.78%, of cross-partner phenotypic correlations, respectively. We detected limited evidence for couple-correlation convergence (eg. increased similarity with respect to smoking and medication use), measured by stratifying couples by their time spent together.

Finally, mediation analysis revealed that the vast majority (>77%) of causal associations between one trait of an individual and a different trait of their partner is indirect. For example, the causal effect of the BMI of an individual on the overall health rating of their partner is entirely acting through the BMI of their partner.

In summary, this study revealed many novel causal effects within couples, shedding light on the impact of confounding on couple phenotypic similarity.

--- /doc/genetics/heritable/2022-sjolander.pdf Sibling Comparison Studies Arvid Sjölander, Thomas Frisell, Sara Öberg 2022-03-01 2022-03-01 [("doi","10.1146/annurev-statistics-040120-024521")] genetics/heritable sociology statistics/causality

Unmeasured confounding is one of the main sources of bias in observational studies. A popular way to reduce confounding bias is to use sibling comparisons, which implicitly adjust for several factors in the early environment or upbringing without requiring them to be measured or known.

In this article we provide a broad exposition of the statistical analysis methods for sibling comparison studies. We further discuss a number of methodological challenges that arise in sibling comparison studies.

--- https://nonsite.org/the-first-privilege-walk/ The First Privilege Walk 2020-11-21 sociology --- /doc/ai/nn/2017-esteva.pdf Dermatologist-level classification of skin cancer with deep neural networks Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau, Sebastian Thrun 2017-01-01 2020-11-21 [("doi","10.1038/nature21056")] ai/nn biology --- /doc/ai/nn/cnn/2019-winkler.pdf Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition Julia K. Winkler, Christine Fink, Ferdin, Toberer, Alexander Enk, Teresa Deinlein, Rainer Hofmann-Wellenhof, Luc Thomas, Aimilios Lallas, Andreas Blum, Wilhelm Stolz, Holger A. Haenssle 2019-01-01 2020-11-21 [("doi","10.1001/jamadermatol.2019.1735")] ai/nn/cnn biology --- https://arxiv.org/abs/2203.12990#allen Generating Scientific Claims for Zero-Shot Scientific Fact Checking Dustin Wright, David Wadden, Kyle Lo, Bailey Kuehl, Arman Cohan, Isabelle Augenstein, Lucy Lu Wang 2022-03-24 2022-03-24 [("doi","10.48550/arXiv.2203.12990")] ai/nn science

Automated scientific fact checking is difficult due to the complexity of scientific language and a lack of large amounts of training data, as annotation requires domain expertise. To address this challenge, we propose scientific claim generation, the task of generating one or more atomic and verifiable claims from scientific sentences, and demonstrate its usefulness in zero-shot fact checking for biomedical claims.

We propose CLAIMGEN-BART, a new supervised method for generating claims supported by the literature, as well as KBIN, a novel method for generating claim negations. Additionally, we adapt an existing unsupervised entity-centric method of claim generation to biomedical claims, which we call CLAIMGEN-ENTITY.

Experiments on zero-shot fact checking demonstrate that both CLAIMGEN-ENTITY and CLAIMGEN-BART, coupled with KBIN, achieve up to 90% performance of fully supervised models trained on manually annotated claims and evidence. A rigorous evaluation study demonstrates improvement in generated claim and negation quality over existing baselines

--- https://arxiv.org/abs/2110.08913#facebook Real Time Cluster Path Tracing Feng Xie, Petro Mishchuk, Warren Hunt 2021-10-17 2021-10-17 [("doi","10.1145/3478512.3488605")] cs/algorithm

Photorealistic rendering effects are common in films, but most real time graphics today still rely on scan-line based multi-pass rendering to deliver rich visual experiences. While there have been prior works in distributed path tracing for static scene and objects under rigid motion, real time path tracing of deforming characters has to support per-frame dynamic BVH changes.

We present the architecture and implementation of the first real-time production quality cluster path tracing renderer that supports film quality animation and deformation.

We build our cluster path tracing system using the open source Blender and its GPU accelerated production quality renderer Cycles. Our system’s rendering performance and quality scales linearly with the number of RTX cluster nodes used. It is able to generate and deliver path traced images with global illumination effects to remote light-weight client systems at 15–30 frames per second for a variety of Blender scenes including animated digital human characters with skin deformation and virtual objects.

--- https://www.biorxiv.org/content/10.1101/2021.11.25.470021.full Shared components of heritability across genetically correlated traits Jenna Lee Ballard, Luke Jen O’Connor 2021-11-30 2021-11-30 [("doi","10.1101/2021.11.25.470021")] genetics/heritable/correlation

Most disease-associated genetic variants are pleiotropic, affecting multiple genetically correlated traits. Their pleiotropic associations can be mechanistically informative: if many variants have similar patterns of association, they may act via similar pleiotropic mechanisms, forming a shared component of heritability.

We developed Pleiotropic Decomposition Regression (PDR) to identify shared components and their underlying genetic variants. We validated PDR on simulated data and identified limitations of existing methods in recovering the true components.

We applied PDR to 3 clusters of 5–6 traits genetically correlated with coronary disease, asthma, and type II diabetes respectively, producing biologically interpretable components. For CAD, PDR identified components related to BMI, hypertension and cholesterol, and it clarified the relationship among these highly correlated risk factors. We assigned variants to components, calculated their posterior-mean effect sizes, and performed out-of-sample validation. Our posterior-mean effect sizes pool statistical power across traits and substantially boost the correlation (r2) between true and estimated effect sizes compared with the original summary statistics: by 94% and 70% for asthma and T2D out of sample, and by a predicted 300% for CAD.

--- https://x.com/DrewMedina20/status/1518994481038741512 2020-11-21 ai/nn/transformer/clip --- https://arxiv.org/abs/2202.07415#deepmind NeuPL: Neural Population Learning Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel 2022-02-15 2022-02-15 [("doi","10.48550/arXiv.2202.07415")] ai/nn/sparsity reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/model/decision-transformer reinforcement-learning/multi-agent

Learning in strategy games (eg. StarCraft, poker) requires the discovery of diverse policies. This is often achieved by iteratively training new policies against existing ones, growing a policy population that is robust to exploit. This iterative approach suffers from two issues in real-world games: (1) under finite budget, approximate best-response operators at each iteration needs truncating, resulting in under-trained good-responses populating the population; (2) repeated learning of basic skills at each iteration is wasteful and becomes intractable in the presence of increasingly strong opponents.

In this work, we propose Neural Population Learning (NeuPL) as a solution to both issues. NeuPL offers convergence guarantees to a population of best-responses under mild assumptions. By representing a population of policies within a single conditional model, NeuPL enables transfer learning across policies.

Empirically, we show the generality, improved performance and efficiency of NeuPL across several test domains. Most interestingly, we show that novel strategies become more accessible, not less, as the neural population expands.

[See also: AlphaStar’s use of conditioning on build-orders, Decision Transformer.]

--- https://www.medrxiv.org/content/10.1101/2022.04.05.22273459.full The Contributions of Rare Inherited and Polygenic Risk to ASD in Multiplex Families Timothy S. Chang, Matilde Cirnigliaro, Stephanie A. Arteaga, Laura Pérez-Cano, Elizabeth K. Ruzzo, Aaron Gordon, Lucy Bicks, Jae-Yoon Jung, Jennifer K. Lowe, Dennis P. Wall, Daniel H. Geschwind 2022-04-16 2022-04-16 [("doi","10.1101/2022.04.05.22273459")] genetics/heritable/rare psychiatry

Autism Spectrum Disorder (ASD) has a complex genetic architecture involving contributions from de novo and inherited variation. Few studies have been designed to address the role of rare inherited variation, or its interaction with polygenic risk in ASD.

Here, we performed whole genome sequencing of the largest cohort of multiplex families to date, consisting of 4,551 individuals in 1,004 families having 2 or more affected children with ASD.

Using this study design, we identify 7 novel risk genes supported primarily by rare inherited variation, finding support for a total of 74 genes in our cohort and a total of 152 genes after combining with other studies. Probands demonstrated an increased burden of mutations in 2 or more known risk genes (KARGs)—in 3 families both probands inherited protein-truncating variants in two KARGs. We also find that polygenic risk is over-transmitted from unaffected parents to affected children with rare inherited variants, consistent with combinatorial effects in the offspring, which may explain the reduced penetrance of these rare variants in parents. We also observe that in addition to social dysfunction, language delay is associated with ASD polygenic risk over-transmission.

These results are consistent with an additive complex genetic risk architecture of ASD involving rare and common variation and further suggest that language delay is a core biological feature of ASD.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3537171/ Mystical experiences occasioned by the hallucinogen psilocybin lead to increases in the personality domain of openness Katherine A. MacLean, Matthew W. Johnson, Roland R. Griffiths 2011 2020-11-22 [("doi","10.1177/0269881111420188")] psychedelic psychology/personality

A large body of evidence, including longitudinal analyses of personality change, suggests that core personality traits are predominantly stable after age 30. To our knowledge, no study has demonstrated changes in personality in healthy adults after an experimentally manipulated discrete event. Intriguingly, double-blind controlled studies have shown that the classic hallucinogen psilocybin occasions personally and spiritually mystical experiences that predict long-term changes in behaviors, attitudes and values.

In the present report we assessed the effect of psilocybin on changes in the 5 broad domains of personality—Neuroticism, Extroversion, Openness, Agreeableness, and Conscientiousness. Consistent with participant claims of hallucinogen-occasioned increases in esthetic appreciation, imagination, and creativity, we found statistically-significant increases in Openness following a high-dose psilocybin session.

In participants who had mystical experiences during their psilocybin session, Openness remained higher than baseline more than 1 year after the session. The findings suggest a specific role for psilocybin and mystical-type experiences in adult personality change.

--- https://github.com/neonbjb/tortoise-tts neonbjb/tortoise-tts: A multi-voice TTS system trained with an emphasis on quality 2020-11-22 ai/nn/diffusion ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2104.13963#facebook PAWS: Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Arm Holdings, Joulin, Nicolas Ballas, Michael Rabbat 2021-04-28 2021-04-28 [("doi","10.48550/arXiv.2104.13963")] ai/nn

This paper proposes a novel method of learning by predicting view assignments with support samples (PAWS).

The method trains a model to minimize a consistency loss, which ensures that different views of the same unlabeled instance are assigned similar pseudo-labels. The pseudo-labels are generated non-parametrically, by comparing the representations of the image views to those of a set of randomly sampled labeled images. The distance between the view representations and labeled representations is used to provide a weighting over class labels, which we interpret as a soft pseudo-label. By non-parametrically incorporating labeled samples in this way, PAWS extends the distance-metric loss used in self-supervised methods such as BYOL and SwAV to the semi-supervised setting.

Despite the simplicity of the approach, PAWS outperforms other semi-supervised methods across architectures, setting a new state-of-the-art for a ResNet-50 on ImageNet trained with either 10% or 1% of the labels, reaching 75.5% and 66.5% top-1 respectively.

PAWS requires 4× to 12× less training than the previous best methods.

--- https://arxiv.org/abs/2011.10566#facebook SimSiam: Exploring Simple Siamese Representation Learning Xinlei Chen, Kaiming He 2020-11-20 2020-11-22 [("doi","10.48550/arXiv.2011.10566")] ai/nn

Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing solutions.

In this paper, we report surprising empirical results that simple Siamese networks can learn meaningful representations even using none of the following: (1) negative sample pairs, (2) large batches, (3) momentum encoders. Our experiments show that collapsing solutions do exist for the loss and structure, but a stop-gradient operation plays an essential role in preventing collapsing. We provide a hypothesis on the implication of stop-gradient, and further show proof-of-concept experiments verifying it.

Our SimSiam method achieves competitive results on ImageNet and downstream tasks. We hope this simple baseline will motivate people to rethink the roles of Siamese architectures for unsupervised representation learning.

Code will be made available.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0022656 Modeling Users' Activity on Twitter Networks: Validation of Dunbar’s Number Bruno Gonçalves, Nicola Perra, Alessandro Vespignani 2011-06-27 2020-11-22 [("doi","10.1371/journal.pone.0022656")] sociology/technology

Microblogging and mobile devices appear to augment human social capabilities, which raises the question whether they remove cognitive or biological constraints on human communication.

In this paper we analyze a dataset of Twitter conversations collected across 6 months involving 1.7 million individuals and test the theoretical cognitive limit on the number of stable social relationships known as Dunbar’s number. We find that the data are in agreement with Dunbar’s result; users can entertain a maximum of 100–200 stable relationships.

Thus, the ‘economy of attention’ is limited in the online world by cognitive and biological constraints as predicted by Dunbar’s theory. We propose a simple model for users’ behavior that includes finite priority queuing and time resources that reproduces the observed social behavior.

--- https://www.medrxiv.org/content/10.1101/2021.11.07.21266019.full Cognitive enhancement: Effects of methylphenidate, modafinil and caffeine on latent memory and resting state functional connectivity in healthy adults Maxi Becker, Dimitris Repantis, Martin Dresler, Simone Kühn 2022-04-21 2022-04-21 [("doi","10.1101/2021.11.07.21266019")] modafinil nootropic/caffeine

Stimulants like methylphenidate, modafinil and caffeine have repeatedly shown to enhance cognitive processes such as attention and memory. However, brain-functional mechanisms underlying such cognitive enhancing effects of stimulants are still poorly characterized.

Here, weused behavioral and resting-state fMRI data from a double-blind randomized placebo-controlled study of methylphenidate, modafinil and caffeine in 48 healthy male adults. The results show that performance in different memory tasks is enhanced, and functional connectivity (FC) specifically between the fronto-parietal (FPN) and default mode (DMN) network is modulated by the stimulants in comparison to placebo.

Decreased negative connectivity between right prefrontal and medial parietal but also between medial temporal lobe and visual brain regions predicted stimulant-induced latent memory enhancement.

We discuss dopamine’s role in attention and memory as well as its ability to modulate FC between large-scale neural networks (eg. FPN and DMN) as a potential cognitive enhancement mechanism.

--- https://jvns.ca/blog/2022/02/20/things-that-used-to-be-hard-and-are-now-easy/ Things that used to be hard and are now easy 2020-11-22 cs --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247579 Student reactions to traumatic material in literature: Implications for trigger warnings Matthew Kimble, William Flack, Jennifer Koide, Kelly Bennion, Miranda Brenneman, Cynthia Meyersburg, Kenta Matsumura, Kenta Matsumura, Kenta Matsumura 2021-02-09 2021-02-09 [("doi","10.1371/journal.pone.0247579")] psychology sociology

Background: While trigger warnings have garnered debate, few studies have investigated how students typically respond to potentially triggering material.

Method: In this study, 3 hundred and fifty-five undergraduate students from four universities read a passage describing incidences of both physical and sexual assault. Longitudinal measures of subjective distress, PTSD symptoms, and emotional reactivity were taken.

Results: Greater than 96% of participants read the triggering passage even when given a non-triggering alternative to read. Of those who read the triggering passage, those with triggering traumas did not report more distress although those with higher PTSD scores did. Two weeks later, those with trigger traumas and/or PTSD did not report an increase in trauma symptoms as a result of reading the triggering passage.

Conclusion: Students with relevant traumas do not avoid triggering material and the effects appear to be brief. Students with PTSD do not report an exacerbation of symptoms two weeks later as a function of reading the passage.

--- https://slatestarcodex.com/2013/06/17/the-what-youd-implicitly-heard-before-telling-thing/ The What-You’d-Implicitly-Heard-Before Telling Thing 2020-11-22 history philosophy/religion psychology/novelty --- https://www.vtpi.org/vickrey.htm Principles of Efficient Congestion Pricing 2020-11-22 economics/georgism --- https://www.shawnmatthewcrawford.com/balloons-the-balloon-clicker-game.html Balloons! The Balloon Clicker Game 2020-11-23 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2204.08383 ‘I think I discovered a military base in the middle of the ocean’—Null Island, the most real of fictional places Levente Juhasz, Peter Mooney 2022-04-18 2022-04-18 [("doi","10.48550/arXiv.2204.08383")] statistics/bias technology

This paper explores Null Island, a fictional place located at 0° latitude and 0° longitude in the WGS84 geographic coordinate system.

Null Island is erroneously associated with large amounts of geographic data in a wide variety of location-based services, place databases, social media and web-based maps. While it was originally considered a joke within the geospatial community, this article will demonstrate implications of its existence, both technological and social in nature, promoting Null Island as a fundamental issue of geographic information that requires more widespread awareness.

The article summarizes error sources that lead to data being associated with Null Island.

We identify four evolutionary phases which help explain how this fictional place evolved and established itself as an entity reaching beyond the geospatial profession to the point of being discovered by the visual arts and the general population. After providing an accurate account of data that can be found at (0, 0), geospatial, technological and social implications of Null Island are discussed.

Guidelines to avoid misplacing data to Null Island are provided. Since data will likely continue to appear at this location, our contribution is aimed at both GIScientists and the general population to promote awareness of this error source.

--- https://arxiv.org/abs/2204.14034 A Challenging Benchmark of Anime Style Recognition Haotang Li, Shengtao Guo, Kailin Lyu, Xiao Yang, Tianchen Chen, Jianqing Zhu, Huanqiang Zeng 2022-04-29 2022-04-29 [("doi","10.48550/arXiv.2204.14034")] ai/anime ai/dataset ai/nn/transformer

Given two images of different anime roles, anime style recognition (ASR) aims to learn abstract painting style to determine whether the two images are from the same work, which is an interesting but challenging problem. Unlike biometric recognition, such as face recognition, iris recognition, and person re-identification, ASR suffers from a much larger semantic gap but receives less attention.

In this paper, we propose a challenging ASR benchmark. Firstly, we collect a large-scale ASR dataset (LSASRD), which contains 20,937 images of 190 anime works and each work at least has ten different roles. In addition to the large-scale, LSASRD contains a list of challenging factors, such as complex illuminations, various poses, theatrical colors and exaggerated compositions.

Secondly, we design a cross-role protocol to evaluate ASR performance, in which query and gallery images must come from different roles to validate an ASR model is to learn abstract painting style rather than learn discriminative features of roles.

Finally, we apply two powerful person re-identification methods, namely, AGW and TransReID, to construct the baseline performance on LSASRD. Surprisingly, the recent transformer model (ie. TransReID) only acquires a 42.24% mAP on LSASRD.

Therefore, we believe that the ASR task of a huge semantic gap deserves deep and long-term research. We will open our dataset and code at Github.

--- https://www.biorxiv.org/content/10.1101/2022.04.28.489864.full Balancing selection on genomic deletion polymorphisms in humans Alber Aqil, Leo Speidel, Pavlos Pavlidis, Omer Gokcumen 2022-04-28 2022-04-28 [("doi","10.1101/2022.04.28.489864")] genetics/selection/natural/human

A key question in biology is why genomic variation persists in a population for extended periods. Recent studies have identified examples of genomic deletions that have remained polymorphic in the human lineage for hundreds of millennia, ostensibly owing to balancing selection. Nevertheless, genome-wide investigations of ancient and possibly adaptive deletions remain an imperative exercise.

Here, we used simulations to show an excess of ancient allele sharing between modern and archaic human genomes that cannot be explained solely by introgression or ancient structure under neutrality.

We identified 63 deletion polymorphisms that emerged before the divergence of humans and Neanderthals and are associated with GWAS traits. We used empirical and simulation-based analyses to show that the haplotypes that harbor these functional ancient deletions have likely been evolving under time-dependent and geography-dependent balancing selection. Collectively, our results suggest that balancing selection may have maintained at least 27% of the functional deletion polymorphisms in humans for hundreds of thousands of years.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3270388/ The time resolution of the St Petersburg paradox Ole Peters 2011 2020-11-23 [("doi","10.1098/rsta.2011.0065")] economics statistics/decision

A resolution of the St Petersburg paradox is presented.

In contrast to the standard resolution, utility is not required. Instead, the time-average performance of the lottery is computed. The final result can be phrased mathematically identically to Daniel Bernoulli’s resolution, which uses logarithmic utility, but is derived using a conceptually different argument.

The advantage of the time resolution is the elimination of arbitrary utility functions.

--- /doc/technology/2022-tan.pdf The Road Not Taken: Technological Uncertainty and the Evaluation of Innovations David Tan 2022-02-11 2022-02-11 [("doi","10.1287/orsc.2021.1567")] economics psychology/cognitive-bias technology

When venturing into unfamiliar areas of technology, inventors face ex ante technological uncertainty, that is many possible alternative technological paths going forward and limited guidance from existing technological knowledge for predicting the likelihood that a given path will successfully result in an invention.

I theorize, however, that this ex ante technological uncertainty becomes less apparent when evaluating inventions in hindsight. When one knows that a given technological path turned out to be successful ex post, it may be difficult to appreciate the ex ante plausibility of reasons to prefer alternative paths. As a result, inventions may seem more obvious to those evaluating inventions with the benefit of hindsight. My theory yields a counterintuitive implication; when inventors venture into less familiar areas of technology, there is a greater risk of evaluators overestimating obviousness due to hindsight bias.

Empirical evidence comes from novel data on accepted and rejected patent applications, including hand-collected data from the text of applicant objections to obviousness rejections and examiners’ subsequent reversals of rejections in response to applicant objections.

--- https://en.wikipedia.org/wiki/Trolley_problem Trolley problem 2020-11-23 philosophy/ethics --- https://arxiv.org/abs/2110.02624 CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation Aditya Sanghi, Hang Chu, Joseph G. Lambourne, Ye Wang, Chin-Yi Cheng, Marco Fumero, Kamal Rahimi Malekshan 2021-10-06 2021-10-06 [("doi","10.48550/arXiv.2110.02624")] ai/nn/transformer/clip

Generating shapes using natural language can enable new ways of imagining and creating the things around us. While recent progress has been made in text-to-image generation, text-to-shape generation remains a challenging problem due to the unavailability of paired text and shape data at a large scale.

We present a simple yet effective method for zero-shot text-to-shape generation that circumvents such data scarcity. Our proposed method, named CLIP-Forge, is based on a two-stage training process, which only depends on an unlabeled shape dataset and a pre-trained image-text network such as CLIP. Our method has the benefits of avoiding expensive inference time optimization, as well as the ability to generate multiple shapes for a given text.

We not only demonstrate promising zero-shot generalization of the CLIP-Forge model qualitatively and quantitatively, but also provide extensive comparative evaluations to better understand its behavior.

--- https://www.lesswrong.com/posts/uKp6tBFStnsvrot5t/what-dall-e-2-can-and-cannot-do What DALL·E 2 can and cannot do 2020-11-23 ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2205.01068#facebook OPT: Open Pre-trained Transformer Language Models Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, Luke Zettlemoyer 2022-05-02 2022-05-02 [("doi","10.48550/arXiv.2205.01068")] ai/nn/transformer/gpt/3/nonfiction

Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero-shot and few-shot learning. Given their computational cost, these models are difficult to replicate without large capital. For the few that are available through APIs, no access is granted to the full model weights, making them difficult to study.

We present Open Pre-trained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M to 175b parameters, which we aim to fully and responsibly share with interested researchers.

We show that OPT-175B is comparable to GPT-3, while requiring only 1⁄7th the carbon footprint to develop.

We are also releasing our logbook detailing the infrastructure challenges we faced, along with code for experimenting with all of the released models.

--- https://www.dampfkraft.com/games/japanese-postcard-net-games.html Japanese Play-by-Postcard RPGs: Net Games 2020-11-24 fiction/text-game --- https://www.biorxiv.org/content/10.1101/685842.full Evolutionary implementation of Bayesian computations Dániel Czégel, Hamza Giaffar, István Zachar, Eörs Szathmáry 2019-06-28 2020-11-24 [("doi","10.1101/685842")] genetics/selection/natural reinforcement-learning/multi-agent statistics/bayes

A wide variety of human and non-human behavior is computationally well accounted for by probabilistic generative models, formalized consistently in a Bayesian framework.

Recently, it has been suggested that another family of adaptive systems, namely, those governed by Darwinian evolutionary dynamics, are capable of implementing building blocks of Bayesian computations. These algorithmic similarities rely on the analogous competition dynamics of generative models and of Darwinian replicators to fit possibly high-dimensional and stochastic environments. Identified computational building blocks include Bayesian update over a single variable and replicator dynamics, transition between hidden states and mutation, and Bayesian inference in hierarchical models and multilevel selection.

Here we provide a coherent mathematical discussion of these observations in terms of Bayesian graphical models and a step-by-step introduction to their evolutionary interpretation. We also extend existing results by adding two missing components: a correspondence between likelihood optimization and phenotypic adaptation, and between expectation-maximization-like dynamics in mixture models and ecological competition.

These correspondences suggest a deeper algorithmic analogy between evolutionary dynamics and statistical learning, pointing towards an unified computational understanding of mechanisms Nature invented to adapt to high-dimensional and uncertain environments.

--- /note/faster#reinforcement-learning Computer Optimization: Your Computer Is Faster Than You Think § DL Gwern 2021-04-24 2021-04-24 ai/nn ai/scaling cs --- https://www.nature.com/articles/s41598-019-45619-9#deepmind 2020-11-24 reinforcement-learning/model/alphago reinforcement-learning/multi-agent --- https://arxiv.org/abs/2004.09468#deepmind Real World Games Look Like Spinning Tops Wojciech Marian Czarnecki, Gauthier Gidel, Brendan Tracey, Karl Tuyls, Shayegan Omidshafiei, David Balduzzi, Max Jaderberg 2020-04-20 2020-11-24 [("doi","10.48550/arXiv.2004.09468")] reinforcement-learning/exploration reinforcement-learning/model-free/alphastar reinforcement-learning/model/alphago reinforcement-learning/multi-agent reinforcement-learning/scaling

This paper investigates the geometrical properties of real world games (eg. Tic-Tac-Toe, Go, StarCraft II). We hypothesize that their geometrical structure resembles a spinning top, with the upright axis representing transitive strength, and the radial axis, which corresponds to the number of cycles that exist at a particular transitive strength, representing the non-transitive dimension. We prove the existence of this geometry for a wide class of real world games, exposing their temporal nature.

Additionally, we show that this unique structure also has consequences for learning—it clarifies why populations of strategies are necessary for training of agents, and how population size relates to the structure of the game.

Finally, we empirically validate these claims by using a selection of 9 real world two-player zero-sum symmetric games, showing (1) the spinning top structure is revealed and can be easily re-constructed by using a new method of Nash clustering to measure the interaction between transitive and cyclical strategy behavior, and (2) the effect that population size has on the convergence in these games.

--- https://arxiv.org/abs/2002.00632 Effective Diversity in Population Based Reinforcement Learning Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts 2020-02-03 2020-11-24 [("doi","10.48550/arXiv.2002.00632")] reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/multi-agent

Exploration is a key problem in reinforcement learning, since agents can only learn from data they acquire in the environment. With that in mind, maintaining a population of agents is an attractive method, as it allows data be collected with a diverse set of behaviors. This behavioral diversity is often boosted via multi-objective loss functions. However, those approaches typically leverage mean field updates based on pairwise distances, which makes them susceptible to cycling behaviors and increased redundancy. In addition, explicitly boosting diversity often has a detrimental impact on optimizing already fruitful behaviors for rewards. As such, the reward-diversity trade off typically relies on heuristics. Finally, such methods require behavioral representations, often handcrafted and domain specific.

In this paper, we introduce an approach to optimize all members of a population simultaneously. Rather than using pairwise distance, we measure the volume of the entire population in a behavioral manifold, defined by task-agnostic behavioral embeddings. In addition, our algorithm Diversity via Determinants (DvD), adapts the degree of diversity during training using online learning techniques. We introduce both evolutionary and gradient-based instantiations of DvD and show they effectively improve exploration without reducing performance when better exploration is not required.

--- https://arxiv.org/abs/2110.02439#facebook Replay-Guided Adversarial Environment Design Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel 2021-10-06 2021-10-06 [("doi","10.48550/arXiv.2110.02439")] reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/multi-agent

Deep reinforcement learning (RL) agents may successfully generalize to new settings if trained on an appropriately diverse set of environment and task configurations. Unsupervised Environment Design (UED) is a promising self-supervised RL paradigm, wherein the free parameters of an underspecified environment are automatically adapted during training to the agent’s capabilities, leading to the emergence of diverse training environments.

Here, we cast Prioritized Level Replay (PLR), an empirically successful but theoretically unmotivated method that selectively samples randomly-generated training levels, as UED. We argue that by curating completely random levels, PLR, too, can generate novel and complex levels for effective training. This insight reveals a natural class of UED methods we call Dual Curriculum Design (DCD). Crucially, DCD includes both PLR and a popular UED algorithm, PAIRED, as special cases and inherits similar theoretical guarantees.

This connection allows us to develop novel theory for PLR, providing a version with a robustness guarantee at Nash equilibria. Furthermore, our theory suggests a highly counterintuitive improvement to PLR: by stopping the agent from updating its policy on uncurated levels (training on less data), we can improve the convergence to Nash equilibria.

Indeed, our experiments confirm that our new method, PLR⟂, obtains better results on a suite of out-of-distribution, zero-shot transfer tasks, in addition to demonstrating that PLR⟂ improves the performance of PAIRED, from which it inherited its theoretical framework.

--- https://arxiv.org/abs/1911.13071 Increasing Generality in Machine Learning through Procedural Content Generation Sebastian Risi, Julian Togelius 2019-11-29 2020-11-24 [("doi","10.48550/arXiv.1911.13071")] reinforcement-learning/meta-learning reinforcement-learning/multi-agent reinforcement-learning/robot

Procedural Content Generation (PCG) refers to the practice, in videogames and other games, of generating content such as levels, quests, or characters algorithmically. Motivated by the need to make games replayable, as well as to reduce authoring burden, limit storage space requirements, and enable particular esthetics, a large number of PCG methods have been devised by game developers. Additionally, researchers have explored adapting methods from machine learning, optimization, and constraint solving to PCG problems. Games have been widely used in AI research since the inception of the field, and in recent years have been used to develop and benchmark new machine learning algorithms.

Through this practice, it has become more apparent that these algorithms are susceptible to overfitting. Often, an algorithm will not learn a general policy, but instead a policy that will only work for a particular version of a particular task with particular initial parameters. In response, researchers have begun exploring randomization of problem parameters to counteract such overfitting and to allow trained policies to more easily transfer from one environment to another, such as from a simulated robot to a robot in the real world.

Here we review the large amount of existing work on PCG, which we believe has an important role to play in increasing the generality of machine learning methods. The main goal here is to present RL/AI with new tools from the PCG toolbox, and its secondary goal is to explain to game developers and researchers a way in which their work is relevant to AI research.

--- https://arxiv.org/abs/1907.01623 Evolving the Hearthstone Meta Fernando de Mesentier Silva, Rodrigo Canaan, Scott Lee, Matthew C. Fontaine, Julian Togelius, Amy K. Hoover 2019-07-02 2020-11-24 [("doi","10.48550/arXiv.1907.01623")] reinforcement-learning/meta-learning reinforcement-learning/multi-agent

Balancing an ever-growing strategic game of high complexity, such as Hearthstone, is a complex task. The target of making strategies diverse and customizable results in a delicate intricate system. Tuning over 2000 cards to generate the desired outcome without disrupting the existing environment becomes a laborious challenge.

In this paper, we discuss the impacts that changes to existing cards can have on strategy in Hearthstone. By analyzing the win rate on match-ups across different decks, being played by different strategies, we propose to compare their performance before and after changes are made to improve or worsen different cards. Then, using an evolutionary algorithm, we search for a combination of changes to the card attributes that cause the decks to approach equal, 50% win rates.

We then expand our evolutionary algorithm to a multi-objective solution to search for this result, while making the minimum amount of changes, and as a consequence disruption, to the existing cards.

Lastly, we propose and evaluate metrics to serve as heuristics with which to decide which cards to target with balance changes.

--- https://deepmind.google/discover/blog/learning-robust-real-time-cultural-transmission-without-human-data/ 2020-11-24 reinforcement-learning/multi-agent --- https://arxiv.org/abs/2110.15349 Learning to Ground Multi-Agent Communication with Autoencoders Toru Lin, Minyoung Huh, Chris Stauffer, Ser-Nam Lim, Phillip Isola 2021-10-28 2021-10-28 [("doi","10.48550/arXiv.2110.15349")] reinforcement-learning/multi-agent

Communication requires having a common language, a lingua franca, between agents. This language could emerge via a consensus process, but it may require many generations of trial and error.

Alternatively, the lingua franca can be given by the environment, where agents ground their language in representations of the observed world. We demonstrate a simple way to ground language in learned representations, which facilitates decentralized multi-agent communication and coordination.

We find that a standard representation learning algorithm—autoencoding—is sufficient for arriving at a grounded common language. When agents broadcast these representations, they learn to understand and respond to each other’s utterances and achieve surprisingly strong task performance across a variety of multi-agent communication environments.

--- https://arxiv.org/abs/2201.01816 Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria Kavya Kopparapu, Edgar A. Duéñez-Guzmán, Jayd Matyas, Alexander Sasha Vezhnevets, John P. Agapiou, Kevin R. McKee, Richard Everett, Janusz Marecki, Joel Z. Leibo, Thore Graepel 2022-01-05 2022-01-05 [("doi","10.48550/arXiv.2201.01816")] reinforcement-learning/multi-agent

A key challenge in the study of multiagent cooperation is the need for individual agents not only to cooperate effectively but to decide with whom to cooperate. This is particularly critical in situations when other agents have hidden, possibly misaligned motivations and goals.

Social deduction games offer an avenue to study how individuals might learn to synthesize potentially unreliable information about others, and elucidate their true motivations.

In this work, we present Hidden Agenda, a two-team social deduction game that provides a 2D environment for studying learning agents in scenarios of unknown team alignment. The environment admits a rich set of strategies for both teams.

Reinforcement learning agents trained in Hidden Agenda show that agents can learn a variety of behaviors, including partnering and voting without the need for communication in natural language.

--- https://github.com/deepmind/open_spiel 2020-11-25 reinforcement-learning/multi-agent --- https://arxiv.org/abs/1911.10635 Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms Kaiqing Zhang, Zhuoran Yang, Tamer Başar 2019-11-24 2020-11-25 [("doi","10.48550/arXiv.1911.10635")] reinforcement-learning/imperfect-information/poker reinforcement-learning/multi-agent

Recent years have witnessed advances in reinforcement learning (RL), which has registered great success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, eg. the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Though empirically successful, theoretical foundations for MARL are relatively lacking in the literature.

In this chapter, we provide a selective overview of MARL, with a focus on algorithms backed by theoretical analysis. More specifically, we review the theoretical results of MARL algorithms mainly within two representative frameworks, Markov/stochastic games and extensive-form games, in accordance with the types of tasks they address, ie. fully cooperative, fully competitive, and a mix of the two. We also introduce several but challenging applications of these algorithms.

Orthogonal to the existing reviews on MARL, we highlight several new angles and taxonomies of MARL theory, including learning in extensive-form games, decentralized MARL with networked agents, MARL in the mean-field regime, (non-)convergence of policy-based methods for learning in games, etc. Some of the new angles extrapolate from our own research endeavors and interests.

Our overall goal with this chapter is, beyond providing an assessment of the current state of the field on the mark, to identify fruitful future research directions on theoretical studies of MARL. We expect this chapter to serve as continuing stimulus for researchers interested in working on this exciting while challenging topic.

--- https://arxiv.org/abs/2007.04976 One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control Wenlong Huang, Igor Mordatch, Deepak Pathak 2020-07-09 2020-11-25 [("doi","10.48550/arXiv.2007.04976")] reinforcement-learning/multi-agent

Reinforcement learning is typically concerned with learning control policies tailored to a particular agent. We investigate whether there exists a single global policy that can generalize to control a wide variety of agent morphologies—ones in which even dimensionality of state and action spaces changes.

We propose to express this global policy as a collection of identical modular neural networks, dubbed as Shared Modular Policies (SMP), that correspond to each of the agent’s actuators. Every module is only responsible for controlling its corresponding actuator and receives information from only its local sensors. In addition, messages are passed between modules, propagating information between distant modules.

We show that a single modular policy can successfully generate locomotion behaviors for several planar agents with different skeletal structures such as monopod hoppers, quadrupeds, bipeds, and generalize to variants not seen during training—a process that would normally require training and manual hyperparameter tuning for each morphology.

We observe that a wide variety of drastically diverse locomotion styles across morphologies as well as centralized coordination emerges via message passing between decentralized modules purely from the reinforcement learning objective. Videos and code at https://wenlong.page/modular-rl/.

--- https://research.google/blog/introducing-google-research-football-a-novel-reinforcement-learning-environment/ 2020-11-25 reinforcement-learning/multi-agent --- https://arxiv.org/abs/2101.08001#baidu UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers Siyi Hu, Fengda Zhu, Xiaojun Chang, Xiaodan Liang 2021-01-20 2021-01-20 [("doi","10.48550/arXiv.2101.08001")] ai/nn/transformer reinforcement-learning/multi-agent

Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task. The limitation is due to the restricted model architecture related to fixed input and output dimensions. This hinders the experience accumulation and transfer of the learned agent over tasks with diverse levels of difficulty (eg. 3 vs 3 or 5 vs 6 multi-agent games).

In this paper, we make the first attempt to explore a universal multi-agent reinforcement learning pipeline, designing one single architecture to fit tasks with the requirement of different observation and action configurations. Unlike previous RNN-based models, we use a transformer-based model to generate a flexible policy by decoupling the policy distribution from the intertwined input observation with an importance weight measured by the merits of the self-attention mechanism.

Compared to a standard transformer block, the proposed model, named as Universal Policy Decoupling Transformer (UPDeT), further relaxes the action restriction and makes the multi-agent task’s decision process more explainable. UPDeT is general enough to be plugged into any multi-agent reinforcement learning pipeline and equip them with strong generalization abilities that enables the handling of multiple tasks at a time.

Extensive experiments on large-scale SMAC multi-agent competitive games demonstrate that the proposed UPDeT-based multi-agent reinforcement learning achieves results relative to state-of-the-art approaches, demonstrating advantageous transfer capability in terms of both performance and training speed (10× faster).

--- https://arxiv.org/abs/2011.12692#tencent Towards Playing Full MOBA Games with Deep Reinforcement Learning Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu, Yinyuting Yin, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu 2020-11-25 2020-11-25 [("doi","10.48550/arXiv.2011.12692")] ai/nn/rnn ai/nn/sparsity/knowledge-distillation reinforcement-learning/model-free/oa5 reinforcement-learning/multi-agent

MOBA games, eg. Honor of Kings, League of Legends, and DoTA 2, pose grand challenges to AI systems such as multi-agent, enormous state-action space, complex action control, etc. Developing AI for playing MOBA games has raised much attention accordingly. However, existing work falls short in handling the raw game complexity caused by the explosion of agent combinations, ie. lineups, when expanding the hero pool in case that OpenAI’s DoTA2 AI OA5 limits the play to a pool of only 17 heroes. As a result, full MOBA games without restrictions are far from being mastered by any existing AI system.

In this paper, we propose a MOBA AI learning paradigm that methodologically enables playing full MOBA games with deep reinforcement learning. Specifically, we develop a combination of novel and existing learning techniques, including curriculum self-play learning, student-driven policy distillation, off-policy adaption, multi-head value estimation, and Monte-Carlo tree-search, in training [using 250,000 CPU-cores/2,000 GPUs] and playing a large pool of heroes, meanwhile addressing the scalability issue skillfully.

Tested on Honor of Kings, a popular MOBA game, we show how to build superhuman AI agents that can defeat top esports players. The superiority of our AI is demonstrated by the first large-scale performance test of MOBA AI agent in the literature.

--- https://www.quantamagazine.org/computers-evolve-a-new-path-toward-human-intelligence-20191106/ 2020-11-25 reinforcement-learning/multi-agent --- https://arxiv.org/abs/2010.00581 Emergent Social Learning via Multi-agent Reinforcement Learning Kamal Ndousse, Douglas Eck, Sergey Levine, Natasha Jaques 2020-10-01 2020-11-25 [("doi","10.48550/arXiv.2010.00581")] reinforcement-learning/imitation-learning reinforcement-learning/model-free reinforcement-learning/multi-agent

Social learning is a key component of human and animal intelligence. By taking cues from the behavior of experts in their environment, social learners can acquire sophisticated behavior and rapidly adapt to new circumstances. This paper investigates whether independent reinforcement learning (RL) agents in a multi-agent environment can learn to use social learning to improve their performance.

We find that in most circumstances, vanilla model-free RL agents do not use social learning. We analyze the reasons for this deficiency, and show that by imposing constraints on the training environment and introducing a model-based auxiliary loss we are able to obtain generalized social learning policies which enable agents to: (1) discover complex skills that are not learned from single-agent training, and (2) adapt online to novel environments by taking cues from experts present in the new environment.

In contrast, agents trained with model-free RL or imitation learning generalize poorly and do not succeed in the transfer tasks. By mixing multi-agent and solo training, we can obtain agents that use social learning to gain skills that they can deploy when alone, even out-performing agents trained alone from the start.

--- https://bair.berkeley.edu/blog/2019/10/21/coordination/ Collaborating with Humans Requires Understanding Them 2020-11-25 reinforcement-learning/meta-learning reinforcement-learning/multi-agent --- https://arxiv.org/abs/2006.04635#deepmind Learning to Play No-Press Diplomacy with Best Response Policy Iteration Thomas Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Roman Werpachowski, Satinder Singh, Thore Graepel, Yoram Bachrach 2020-06-08 2020-11-25 [("doi","10.48550/arXiv.2006.04635")] reinforcement-learning/imperfect-information/diplomacy reinforcement-learning/multi-agent

Recent advances in deep reinforcement learning (RL) have led to considerable progress in many 2-player zero-sum games, such as Go, Poker and Starcraft. The purely adversarial nature of such games allows for conceptually simple and principled application of RL methods. However real-world settings are many-agent, and agent interactions are complex mixtures of common-interest and competitive aspects.

We consider Diplomacy, a 7-player board game designed to accentuate dilemmas resulting from many-agent interactions. It also features a large combinatorial action space and simultaneous moves, which are challenging for RL algorithms. We propose a simple yet effective approximate best response operator, designed to handle large combinatorial action spaces and simultaneous moves. We also introduce a family of policy iteration methods that approximate fictitious play.

With these methods, we successfully apply RL to Diplomacy: we show that our agents convincingly outperform the previous state-of-the-art, and game theoretic equilibrium analysis shows that the new process yields consistent improvements.

--- https://arxiv.org/abs/2112.02845 Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks Linghui Meng, Muning Wen, Yaodong Yang, Chenyang Le, Xiyun Li, Weinan Zhang, Ying Wen, Haifeng Zhang, Jun Wang, Bo Xu 2021-12-06 2021-12-06 [("doi","10.48550/arXiv.2112.02845")] reinforcement-learning/model/decision-transformer reinforcement-learning/multi-agent reinforcement-learning/offline

Offline reinforcement learning leverages previously-collected offline datasets to learn optimal policies with no necessity to access the real environment. Such a paradigm is also desirable for multi-agent reinforcement learning (MARL) tasks, given the increased interactions among agents and with the enviroment. Yet, in MARL, the paradigm of offline pre-training with online fine-tuning has not been studied, nor datasets or benchmarks for offline MARL research are available.

In this paper, we facilitate the research by providing large-scale datasets, and use them to examine the usage of the Decision Transformer in the context of MARL. We investigate the generalisation of MARL offline pre-training in the following 3 aspects: (1) between single agents and multiple agents, (2) from offline pretraining to the online fine-tuning, and (3) to that of multiple downstream tasks with few-shot and zero-shot capabilities.

We start by introducing the first offline MARL dataset with diverse quality levels based on the StarCraftII environment, and then propose the novel architecture of multi-agent decision transformer (MADT) for effective offline learning. MADT leverages transformer’s modeling ability of sequence modeling and integrates it seamlessly with both offline and online MARL tasks. A crucial benefit of MADT is that it learns generalizable policies that can transfer between different types of agents under different task scenarios.

On StarCraft II offline dataset, MADT outperforms the state-of-the-art offline RL baselines. When applied to online tasks, the pre-trained MADT improves sample efficiency, and enjoys strong performance both few-short and zero-shot cases.

To our best knowledge, this is the first work that studies and demonstrates the effectiveness of offline pre-trained models in terms of sample efficiency and generalisability enhancements in MARL.

--- https://joao-abrantes.com/posts/mimicking-evolution-with-reinforcement-learning/ Mimicking Evolution with Reinforcement Learning 2020-11-26 reinforcement-learning/multi-agent --- https://arxiv.org/abs/1812.07887#tencent Hierarchical Macro Strategy Model for MOBA Game AI Bin Wu, Qiang Fu, Jing Liang, Peng Qu, Xiaoqian Li, Liang Wang, Wei Liu, Wei Yang, Yongsheng Liu 2018-12-19 2020-11-26 [("doi","10.48550/arXiv.1812.07887")] reinforcement-learning/multi-agent

The next challenge of game AI lies in Real Time Strategy (RTS) games. RTS games provide partially observable gaming environments, where agents interact with one another in an action space much larger than that of GO. Mastering RTS games requires both strong macro strategies and delicate micro level execution.

Recently, great progress has been made in micro level execution, while complete solutions for macro strategies are still lacking. In this paper, we propose a novel learning-based Hierarchical Macro Strategy model for mastering MOBA games, a sub-genre of RTS games. Trained by the Hierarchical Macro Strategy model, agents explicitly make macro strategy decisions and further guide their micro level execution. Moreover, each of the agents makes independent strategy decisions, while simultaneously communicating with the allies through leveraging a novel imitated cross-agent communication mechanism.

We perform comprehensive evaluations on a popular 5v5 Multiplayer Online Battle Arena (MOBA) game. Our 5-AI team achieves a 48% winning rate against human player teams which are ranked top 1% in the player ranking system.

--- https://arxiv.org/abs/1902.00506#deepmind The Hanabi Challenge: A New Frontier for AI Research Nolan Bard, Jakob N. Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare, Michael Bowling 2019-02-01 2020-11-26 [("doi","10.1016/j.artint.2019.103216")] reinforcement-learning/imperfect-information/hanabi

From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance in challenge domains like Go, Atari, and some variants of poker. As with their predecessors of chess, checkers, and backgammon, these game domains have driven research by providing sophisticated yet well-defined challenges for artificial intelligence practitioners.

We continue this tradition by proposing the game of Hanabi as a new challenge domain with novel problems that arise from its combination of purely cooperative gameplay with two to 5 players and imperfect information. In particular, we argue that Hanabi elevates reasoning about the beliefs and intentions of other agents to the foreground.

We believe developing novel techniques for such theory of mind reasoning will not only be crucial for success in Hanabi but also in broader collaborative efforts, especially those with human partners.

To facilitate future research, we introduce the open-source Hanabi Learning Environment, propose an experimental framework for the research community to evaluate algorithmic advances, and assess the performance of current state-of-the-art techniques.

--- https://arxiv.org/abs/2110.07594 The Neural MMO Platform for Massively Multiagent Research Joseph Suarez, Yilun Du, Clare Zhu, Igor Mordatch, Phillip Isola 2021-10-14 2021-10-14 [("doi","10.48550/arXiv.2110.07594")] reinforcement-learning/multi-agent

Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular game systems. Existing environments feature subsets of these properties, but Neural MMO is the first to combine them all.

We present Neural MMO as free and open source software with active support, ongoing development, documentation, and additional training, logging, and visualization tools to help users adapt to this new setting. Initial baselines on the platform demonstrate that agents trained in large populations explore more and learn a progression of skills. We raise other more difficult problems such as many-team cooperation as open research questions which Neural MMO is well-suited to answer. Finally, we discuss current limitations of the platform, potential mitigations, and plans for continued development.

--- https://arxiv.org/abs/1901.08106#deepind Open-ended Learning in Symmetric Zero-sum Games David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech M. Czarnecki, Julien Perolat, Max Jaderberg, Thore Graepel 2019-01-23 2020-11-26 [("doi","10.48550/arXiv.1901.08106")] reinforcement-learning/multi-agent

Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them ‘winner’ and ‘loser’. If the game is transitive, then self-play generates sequences of agents of increasing strength. However, nontransitive games, such as rock-paper-scissors, can exhibit strategic cycles, and there is no longer a clear objective—we want agents to increase in strength, but against whom is unclear.

In this paper, we introduce a geometric framework for formulating agent objectives in zero-sum games, in order to construct adaptive sequences of objectives that yield open-ended learning. The framework allows us to reason about population performance in nontransitive games and enables the development of a new algorithm (rectified Nash response, PSRO_rN) that uses game-theoretic niching to construct diverse populations of effective agents, producing a stronger set of agents than existing algorithms.

We apply PSRO_rN to two highly nontransitive resource allocation games and find that PSRO_rN consistently outperforms the existing alternatives.

--- https://arxiv.org/abs/2010.02923#facebook Human-Level Performance in No-Press Diplomacy via Equilibrium Search Jonathan Gray, Adam Lerer, Anton Bakhtin, Noam Brown 2020-10-06 2020-11-26 [("doi","10.48550/arXiv.2010.02923")] reinforcement-learning/imperfect-information/diplomacy reinforcement-learning/multi-agent

Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings. In contrast, Diplomacy is a game of shifting alliances that involves both cooperation and competition. For this reason, Diplomacy has proven to be a formidable research challenge.

In this paper we describe an agent for the no-press variant of Diplomacy that combines supervised learning on human data with one-step lookahead search via regret minimization. Regret minimization techniques have been behind previous AI successes in adversarial games, most notably poker, but have not previously been shown to be successful in large-scale games involving cooperation.

We show that our agent greatly exceeds the performance of past no-press Diplomacy bots, is unexploitable by expert humans, and ranks in the top 2% of human players when playing anonymous games on a popular Diplomacy website.

--- https://arxiv.org/abs/2003.02979#facebook "Other-Play" for Zero-Shot Coordination Hengyuan Hu, Adam Lerer, Alex Peysakhovich, Jakob Foerster 2020-03-06 2020-11-26 [("doi","10.48550/arXiv.2003.02979")] reinforcement-learning/imperfect-information/hanabi

We consider the problem of zero-shot coordination—constructing AI agents that can coordinate with novel partners they have not seen before (eg. humans). Standard Multi-Agent Reinforcement Learning (MARL) methods typically focus on the self-play (SP) setting where agents construct strategies by playing the game with themselves repeatedly. Unfortunately, applying SP naively to the zero-shot coordination problem can produce agents that establish highly specialized conventions that do not carry over to novel partners they have not been trained with.

We introduce a novel learning algorithm called other-play (OP), that enhances self-play by looking for more robust strategies, exploiting the presence of known symmetries in the underlying problem. We characterize OP theoretically as well as experimentally. We study the cooperative card game Hanabi and show that OP agents achieve higher scores when paired with independently trained agents. In preliminary results we also show that our OP agents obtains higher average scores when paired with human players, compared to state-of-the-art SP agents.

--- https://arxiv.org/abs/2110.02924#facebook DORA: No-Press Diplomacy from Scratch Anton Bakhtin, David Wu, Adam Lerer, Noam Brown 2021-10-06 2021-10-06 [("doi","10.48550/arXiv.2110.02924")] reinforcement-learning/imperfect-information/diplomacy reinforcement-learning/multi-agent

Prior AI successes in complex games have largely focused on settings with at most hundreds of actions at each decision point. In contrast, Diplomacy is a game with more than 1020 possible actions per turn. Previous attempts to address games with large branching factors, such as Diplomacy, StarCraft, and DoTA2, used human data to bootstrap the policy or used handcrafted reward shaping.

In this paper, we describe an algorithm for action exploration and equilibrium approximation in games with combinatorial action spaces. This algorithm simultaneously performs value iteration while learning a policy proposal network. A double oracle step is used to explore additional actions to add to the policy proposals. At each state, the target state value and policy for the model training are computed via an equilibrium search procedure. Using this algorithm, we train an agent, DORA, completely from scratch for a popular two-player variant of Diplomacy and show that it achieves superhuman performance. Additionally, we extend our methods to full-scale no-press Diplomacy and for the first time train an agent from scratch with no human data.

We present evidence that this agent plays a strategy that is incompatible with human-data bootstrapped agents.

This presents the first strong evidence of multiple equilibria in Diplomacy and suggests that self play alone may be insufficient for achieving superhuman performance in Diplomacy.

--- https://arxiv.org/abs/2107.06857#deepmind Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot Joel Z. Leibo, Edgar Duéñez-Guzmán, Alexander Sasha Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charles Beattie, Igor Mordatch, Thore Graepel 2021-07-14 2021-07-14 [("doi","10.48550/arXiv.2107.06857")] reinforcement-learning/multi-agent

Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess generalization to novel situations as their primary objective (unlike supervised-learning benchmarks).

Our contribution, Melting Pot, is a MARL evaluation suite that fills this gap, and uses reinforcement learning to reduce the human labor required to create novel test scenarios. This works because one agent’s behavior constitutes (part of) another agent’s environment.

To demonstrate scalability, we have created over 80 unique test scenarios covering a broad range of research topics such as social dilemmas, reciprocity, resource sharing, and task partitioning.

We apply these test scenarios to standard MARL training algorithms, and demonstrate how Melting Pot reveals weaknesses not apparent from training performance alone.

--- https://arxiv.org/abs/1901.08004#vivo Hierarchical Reinforcement Learning for Multi-agent MOBA Game Zhijian Zhang, Haozheng Li, Luo Zhang, Tianyin Zheng, Ting Zhang, Xiong Hao, Xiaoxin Chen, Min Chen, Fangxu Xiao, Wei Zhou 2019-01-23 2020-11-26 [("doi","10.48550/arXiv.1901.08004")] reinforcement-learning/imitation-learning reinforcement-learning/multi-agent

Real Time Strategy (RTS) games require macro strategies as well as micro strategies to obtain satisfactory performance since it has large state space, action space, and hidden information. This paper presents a novel reinforcement learning model for mastering Multiplayer Online Battle Arena (MOBA) games, a sub-genre of RTS games.

The novelty of this work are: (1) proposing a hierarchical framework, where agents execute macro strategies by imitation learning and carry out micromanipulations through reinforcement learning, (2) developing a simple self-learning method to get better sample efficiency for training, and (3) designing a dense reward function for multi-agent cooperation in the absence of game engine or Application Programming Interface (API).

Finally, various experiments have been performed to validate the superior performance of the proposed method over other state-of-the-art reinforcement learning algorithms. Agent successfully learns to combat and defeat bronze-level built-in AI with 100% win rate, and experiments show that our method can create a competitive multi-agent for a kind of mobile MOBA game King of Glory in 5v5 mode.

--- https://arxiv.org/abs/1811.00164#facebook Deep Counterfactual Regret Minimization Noam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm 2018-11-01 2020-11-27 [("doi","10.48550/arXiv.1811.00164")] reinforcement-learning/imperfect-information/poker reinforcement-learning/multi-agent

Counterfactual Regret Minimization (CFR) is the leading framework for solving large imperfect-information games. It converges to an equilibrium by iteratively traversing the game tree. In order to deal with extremely large games, abstraction is typically applied before running CFR. The abstracted game is solved with tabular CFR, and its solution is mapped back to the full game. This process can be problematic because aspects of abstraction are often manual and domain specific, abstraction algorithms may miss important strategic nuances of the game, and there is a chicken-and-egg problem because determining a good abstraction requires knowledge of the equilibrium of the game.

This paper introduces Deep Counterfactual Regret Minimization, a form of CFR that obviates the need for abstraction by instead using deep neural networks to approximate the behavior of CFR in the full game. We show that Deep CFR is principled and achieves strong performance in large poker games. This is the first non-tabular variant of CFR to be successful in large games.

--- https://arxiv.org/abs/1811.08469 Stable Opponent Shaping in Differentiable Games Alistair Letcher, Jakob Foerster, David Balduzzi, Tim Rocktäschel, Shimon Whiteson 2018-11-20 2020-11-27 [("doi","10.48550/arXiv.1811.08469")] reinforcement-learning/multi-agent

A growing number of learning methods are actually differentiable games whose players optimize multiple, interdependent objectives in parallel—from GANs and intrinsic curiosity to multi-agent RL. Opponent shaping is a powerful approach to improve learning dynamics in these games, accounting for player influence on others’ updates. Learning with Opponent-Learning Awareness (LOLA) is a recent algorithm that exploits this response and leads to cooperation in settings like the Iterated Prisoner’s Dilemma. Although experimentally successful, we show that LOLA agents can exhibit ‘arrogant’ behavior directly at odds with convergence. In fact, remarkably few algorithms have theoretical guarantees applying across all (n-player, non-convex) games.

In this paper we present Stable Opponent Shaping (SOS), a new method that interpolates between LOLA and a stable variant named LookAhead. We prove that LookAhead converges locally to equilibria and avoids strict saddles in all differentiable games. SOS inherits these essential guarantees, while also shaping the learning of opponents and consistently either matching or outperforming LOLA experimentally.

--- https://arxiv.org/abs/1903.00742#deepmind Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research Joel Z. Leibo, Edward Hughes, Marc Lanctot, Thore Graepel 2019-03-02 2020-11-27 [("doi","10.48550/arXiv.1903.00742")] reinforcement-learning/multi-agent

Evolution has produced a multi-scale mosaic of interacting adaptive units. Innovations arise when perturbations push parts of the system away from stable equilibria into new regimes where previously well-adapted solutions no longer work.

Here we explore the hypothesis that multi-agent systems sometimes display intrinsic dynamics arising from competition and cooperation that provide a naturally emergent curriculum, which we term an autocurriculum. The solution of one social task often begets new social tasks, continually generating novel challenges, and thereby promoting innovation.

Under certain conditions these challenges may become increasingly complex over time, demanding that agents accumulate ever more innovations.

--- https://blog.acolyer.org/2019/03/04/efficient-large-scale-fleet-management-via-multi-agent-deep-reinforcement-learning/ Efficient large-scale fleet management via multi-agent deep reinforcement learning 2020-11-27 reinforcement-learning/multi-agent --- https://arxiv.org/abs/1810.09202 Graph Convolutional Reinforcement Learning Jiechuan Jiang, Chen Dun, Tiejun Huang, Zongqing Lu 2018-10-22 2020-11-27 [("doi","10.48550/arXiv.1810.09202")] ai/nn/cnn reinforcement-learning/multi-agent

Learning to cooperate is crucially important in multi-agent environments. The key is to understand the mutual interplay between agents. However, multi-agent environments are highly dynamic, where agents keep moving and their neighbors change quickly. This makes it hard to learn abstract representations of mutual interplay between agents.

To tackle these difficulties, we propose graph convolutional reinforcement learning, where graph convolution adapts to the dynamics of the underlying graph of the multi-agent environment, and relation kernels capture the interplay between agents by their relation representations. Latent features produced by convolutional layers from gradually increased receptive fields are exploited to learn cooperation, and cooperation is further improved by temporal relation regularization for consistency.

Empirically, we show that our method substantially outperforms existing methods in a variety of cooperative scenarios.

--- https://www.borealisai.com/research-blogs/pommerman-team-competition-or-how-we-learned-stop-worrying-and-love-battle/ The Pommerman team competition or: How we learned to stop worrying and love the battle 2020-11-27 reinforcement-learning/multi-agent --- https://arxiv.org/abs/1906.00744#facebook Hierarchical Decision Making by Generating and Following Natural Language Instructions Hengyuan Hu, Denis Yarats, Qucheng Gong, Yuandong Tian, Mike Lewis 2019-06-03 2020-11-27 [("doi","10.48550/arXiv.1906.00744")] reinforcement-learning/multi-agent

We explore using latent natural language instructions as an expressive and compositional representation of complex actions for hierarchical decision making.

Rather than directly selecting micro-actions, our agent first generates a latent plan in natural language, which is then executed by a separate model. We introduce a challenging real-time strategy game environment in which the actions of a large number of units must be coordinated across long time scales. We gather a dataset of 76 thousand pairs of instructions and executions from human play, and train instructor and executor models.

Experiments show that models using natural language as a latent variable outperform models that directly imitate human actions. The compositional structure of language proves crucial to its effectiveness for action representation.

We also release our code, models and data.

--- https://arxiv.org/abs/1909.02128 No Press Diplomacy: Modeling Multi-Agent Gameplay Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Satinder Singh, Joelle Pineau, Aaron Courville 2019-09-04 2020-11-27 [("doi","10.48550/arXiv.1909.02128")] ai/dataset reinforcement-learning/imperfect-information/diplomacy reinforcement-learning/multi-agent

Diplomacy is a seven-player non-stochastic, non-cooperative game, where agents acquire resources through a mix of teamwork and betrayal. Reliance on trust and coordination makes Diplomacy the first non-cooperative multi-agent benchmark for complex sequential social dilemmas in a rich environment.

In this work, we focus on training an agent that learns to play the No Press version of Diplomacy where there is no dedicated communication channel between players. We present DipNet, a neural-network-based policy model for No Press Diplomacy. The model was trained on a new dataset of more than 150,000 human games.

Our model is trained by supervised learning (SL) from expert trajectories, which is then used to initialize a reinforcement learning (RL) agent trained through self-play. Both the SL and RL agents demonstrate state-of-the-art No Press performance by beating popular rule-based bots.

--- https://arxiv.org/abs/1906.02330 Finding Friend and Foe in Multi-Agent Games Jack Serrino, Max Kleiman-Weiner, David C. Parkes, Joshua B. Tenenbaum 2019-06-05 2020-11-27 [("doi","10.48550/arXiv.1906.02330")] reinforcement-learning/imperfect-information reinforcement-learning/multi-agent

Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years. Yet none of these games address the real-life challenge of cooperation in the presence of unknown and uncertain teammates. This challenge is a key game mechanism in hidden role games. Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on The Resistance: Avalon, the most popular hidden role game.

DeepRole combines counterfactual regret minimization (CFR) with deep value networks trained through self-play. Our algorithm integrates deductive reasoning into vector-form CFR to reason about joint beliefs and deduce partially observable actions. We augment deep value networks with constraints that yield interpretable representations of win probabilities. These innovations enable DeepRole to scale to the full Avalon game.

Empirical game-theoretic methods show that DeepRole outperforms other hand-crafted and learned agents in five-player Avalon. DeepRole played with and against human players on the web in hybrid human-agent teams. We find that DeepRole outperforms human players as both a cooperator and a competitor.

--- https://arxiv.org/abs/1810.08647 Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro A. Ortega, D. J. Strouse, Joel Z. Leibo, Nando de Freitas 2018-10-19 2020-11-27 [("doi","10.48550/arXiv.1810.08647")] reinforcement-learning/multi-agent

We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents’ actions. Causal influence is assessed using counterfactual reasoning. At each timestep, an agent simulates alternate actions that it could have taken, and computes their effect on the behavior of other agents. Actions that lead to bigger changes in other agents’ behavior are considered influential and are rewarded.

We show that this is equivalent to rewarding agents for having high mutual information between their actions. Empirical results demonstrate that influence leads to enhanced coordination and communication in challenging social dilemma environments, dramatically increasing the learning curves of the deep RL agents, and leading to more meaningful learned communication protocols.

The influence rewards for all agents can be computed in a decentralized way by enabling agents to learn a model of other agents using deep neural networks. In contrast, key previous works on emergent communication in the MARL setting were unable to learn diverse policies in a decentralized manner and had to resort to centralized training. Consequently, the influence reward opens up a window of new opportunities for research in this area.

--- https://research.facebook.com/publications/control-strategies-for-physically-simulated-characters-performing-two-player-competitive-sports/ 2020-11-28 reinforcement-learning/multi-agent reinforcement-learning/robot --- https://arxiv.org/abs/2002.02325 Social diversity and social preferences in mixed-motive reinforcement learning Kevin R. McKee, Ian Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo 2020-02-06 2020-11-28 [("doi","10.48550/arXiv.2002.02325")] reinforcement-learning/multi-agent

Recent research on reinforcement learning in pure-conflict and pure-common interest games has emphasized the importance of population heterogeneity. In contrast, studies of reinforcement learning in mixed-motive games have primarily leveraged homogeneous approaches. Given the defining characteristic of mixed-motive games—the imperfect correlation of incentives between group members—we study the effect of population heterogeneity on mixed-motive reinforcement learning.

We draw on interdependence theory from social psychology and imbue reinforcement learning agents with Social Value Orientation (SVO), a flexible formalization of preferences over group outcome distributions. We subsequently explore the effects of diversity in SVO on populations of reinforcement learning agents in two mixed-motive Markov games. We demonstrate that heterogeneity in SVO generates meaningful and complex behavioral variation among agents similar to that suggested by interdependence theory. Empirical results in these mixed-motive dilemmas suggest agents trained in heterogeneous populations develop particularly generalized, high-performing policies relative to those trained in homogeneous populations.

--- https://arxiv.org/abs/2111.14377#google Collective Intelligence for Deep Learning: A Survey of Recent Developments David Ha, Yujin Tang 2021-11-29 2021-11-29 [("doi","10.48550/arXiv.2111.14377")] cs/cellular-automaton reinforcement-learning/multi-agent

In the past decade, we have witnessed the rise of deep learning to dominate the field of artificial intelligence. Advances in artificial neural networks alongside corresponding advances in hardware accelerators with large memory capacity, together with the availability of large datasets enabled practitioners to train and deploy sophisticated neural network models that achieve state-of-the-art performance on tasks across several fields spanning computer vision, natural language processing, and reinforcement learning. However, as these neural networks become bigger, more complex, and more widely used, fundamental problems with current deep learning models become more apparent. State-of-the-art deep learning models are known to suffer from issues that range from poor robustness, inability to adapt to novel task settings, to requiring rigid and inflexible configuration assumptions.

Collective behavior, commonly observed in nature, tends to produce systems that are robust, adaptable, and have less rigid assumptions about the environment configuration. Collective intelligence, as a field, studies the group intelligence that emerges from the interactions of many individuals. Within this field, ideas such as self-organization, emergent behavior, swarm optimization, and cellular automata were developed to model and explain complex systems. It is therefore natural to see these ideas incorporated into newer deep learning methods.

In this review, we will provide a historical context of neural network research’s involvement with complex systems, and highlight several active areas in modern deep learning research that incorporate the principles of collective intelligence to advance its current capabilities.

We hope this review can serve as a bridge between the complex systems and deep learning communities.

--- https://arxiv.org/abs/2011.10753 Emergent Road Rules In Multi-Agent Driving Environments Avik Pal, Jonah Philion, Yuan-Hong Liao, Sanja Fidler 2020-11-21 2020-11-28 [("doi","10.48550/arXiv.2011.10753")] reinforcement-learning/multi-agent reinforcement-learning/safe

For autonomous vehicles to safely share the road with human drivers, autonomous vehicles must abide by specific “road rules” that human drivers have agreed to follow. “Road rules” include rules that drivers are required to follow by law—such as the requirement that vehicles stop at red lights—as well as more subtle social rules—such as the implicit designation of fast lanes on the highway. In this paper, we provide empirical evidence that suggests that—instead of hard-coding road rules into self-driving algorithms—a scalable alternative may be to design multi-agent environments in which road rules emerge as optimal solutions to the problem of maximizing traffic flow.

We analyze what ingredients in driving environments cause the emergence of these road rules and find that two crucial factors are noisy perception and agents’ spatial density.

We provide qualitative and quantitative evidence of the emergence of 7 social driving behaviors, ranging from obeying traffic signals to following lanes, all of which emerge from training agents to drive quickly to destinations without colliding.

Our results add empirical support for the social road rules that countries worldwide have agreed on for safe, efficient driving.

--- https://arxiv.org/abs/1812.07297 Continual Match Based Training in Pommerman: Technical Report Peng Peng, Liang Pang, Yufeng Yuan, Chao Gao 2018-12-18 2020-11-28 [("doi","10.48550/arXiv.1812.07297")] reinforcement-learning/multi-agent

Continual learning is the ability of agents to improve their capacities throughout multiple tasks continually. While recent works in the literature of continual learning mostly focused on developing either particular loss functions or specialized structures of neural network explaining the episodic memory or neural plasticity, we study continual learning from the perspective of the training mechanism. Specifically, we propose a COnitnual Match BAsed Training (COMBAT) framework for training a population of advantage-actor-critic (A2C) agents in Pommerman, a partially observable multi-agent environment with no communication.

Following the COMBAT framework, we trained an agent, namely, Navocado, that won the title of the top 1 learning agent in the NeurIPS 2018 Pommerman Competition. Two critical features of our agent are worth mentioning. Firstly, our agent did not learn from any demonstrations. Secondly, our agent is highly reproducible.

As a technical report, we articulate the design of state space, action space, reward, and most importantly, the COMBAT framework for our Pommerman agent. We show in the experiments that Pommerman is a perfect environment for studying continual learning, and the agent can improve its performance by continually learning new skills without forgetting the old ones. Finally, the result in the Pommerman Competition verifies the robustness of our agent when competing with various opponents.

--- https://bair.berkeley.edu/blog/2021/07/14/mappo/ The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games 2020-11-28 reinforcement-learning/multi-agent --- https://arxiv.org/abs/1810.11187 TarMAC: Targeted Multi-Agent Communication Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Michael Rabbat, Joelle Pineau 2018-10-26 2020-11-28 [("doi","10.48550/arXiv.1810.11187")] reinforcement-learning/multi-agent

We propose a targeted communication architecture for multi-agent reinforcement learning, where agents learn both what messages to send and whom to address them to while performing cooperative tasks in partially-observable environments. This targeting behavior is learnt solely from downstream task-specific reward without any communication supervision.

We additionally augment this with a multi-round communication approach where agents coordinate via multiple rounds of communication before taking actions in the environment.

We evaluate our approach on a diverse set of cooperative multi-agent tasks, of varying difficulties, with varying number of agents, in a variety of environments ranging from 2D grid layouts of shapes and simulated traffic junctions to 3D indoor environments, and demonstrate the benefits of targeted and multi-round communication. Moreover, we show that the targeted communication strategies learned by agents are interpretable and intuitive.

Finally, we show that our architecture can be easily extended to mixed and competitive environments, leading to improved performance and sample complexity over recent state-of-the-art approaches.

--- https://arxiv.org/abs/2103.01955 The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, Yi Wu 2021-03-02 2021-03-02 [("doi","10.48550/arXiv.2103.01955")] reinforcement-learning/imperfect-information/hanabi reinforcement-learning/model-free

Proximal Policy Optimization (PPO) is a popular on-policy reinforcement learning algorithm but is statistically-significantly lessused than off-policy learning algorithms in multi-agent settings. This is often due to the belief that on-policy methods are statistically-significantly less sample efficient than their off-policy counterparts in multi-agent problems.

In this work, we investigate Multi-Agent PPO (MAPPO), a variant of PPO which is specialized for multi-agent settings. Using a 1-GPU desktop, we show that MAPPO achieves surprisingly strong performance in 3 popular multi-agent testbeds: the particle-world environments, the Starcraft multi-agent challenge, and the Hanabi challenge, with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures.

In the majority of environments, we find that compared to off-policy baselines, MAPPO achieves strong results while exhibiting comparable sample efficiency.

Finally, through ablation studies, we present the implementation and algorithmic factors which are most influential to MAPPO’s practical performance.

--- https://deepmind.google/discover/blog/generally-capable-agents-emerge-from-open-ended-play/ Generally capable agents emerge from open-ended play 2020-11-28 reinforcement-learning/multi-agent --- https://www.lesswrong.com/posts/mTGrrX8SZJ2tQDuqz/deepmind-generally-capable-agents-emerge-from-open-ended DeepMind: Generally capable agents emerge from open-ended play 2020-11-28 reinforcement-learning/multi-agent --- https://www.youtube.com/watch?v=lTmL7jwFfdw#deepmind Open-Ended Learning Leads to Generally Capable Agents [video] 2020-11-28 reinforcement-learning/multi-agent --- https://www.lesswrong.com/posts/DreKBuMvK7fdESmSJ/how-deepmind-s-generally-capable-agents-were-trained How DeepMind's Generally Capable Agents Were Trained 2020-11-29 reinforcement-learning/multi-agent --- https://www.lesswrong.com/posts/KaPaTdpLggdMqzdyo/how-much-compute-was-used-to-train-deepmind-s-generally How much compute was used to train DeepMind's generally capable agents? 2020-11-29 ai/scaling reinforcement-learning/multi-agent --- https://research.google/blog/introducing-google-research-football-a-novel-reinforcement-learning-environment/ 2020-11-29 reinforcement-learning/multi-agent --- https://arxiv.org/abs/2110.08176#deepmind Collaborating with Humans without Human Data DJ Strouse, Kevin R. McKee, Matt Botvinick, Edward Hughes, Richard Everett 2021-10-15 2021-10-15 [("doi","10.48550/arXiv.2110.08176")] reinforcement-learning/multi-agent reinforcement-learning/scaling

Collaborating with humans requires rapidly adapting to their individual strengths, weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement learning techniques, such as self-play (SP) or population play (PP), produce agents that overfit to their training partners and do not generalize well to humans. Alternatively, researchers can collect human data, train a human model using behavioral cloning, and then use that model to train “human-aware” agents (“behavioral cloning play”, or BCP). While such an approach can improve the generalization of agents to new human co-players, it involves the onerous and expensive step of collecting large amounts of human data first.

Here, we study the problem of how to train agents that collaborate well with human partners without using human data. We argue that the crux of the problem is to produce a diverse set of training partners. Drawing inspiration from successful multi-agent approaches in competitive domains, we find that a surprisingly simple approach is highly effective. We train our agent partner as the best response to a population of self-play agents and their past checkpoints taken throughout training, a method we call Fictitious Co-Play (FCP).

Our experiments focus on a two-player collaborative cooking simulator that has recently been proposed as a challenge problem for coordination with humans. We find that FCP agents score higher than SP, PP, and BCP when paired with novel agent and human partners.

Furthermore, humans also report a strong subjective preference to partnering with FCP agents over all baselines.

--- https://arxiv.org/abs/2107.08170#intel Megaverse: Simulating Embodied Agents at One Million Experiences per Second Aleksei Petrenko, Erik Wijmans, Brennan Shacklett, Vladlen Koltun 2021-07-17 2021-07-17 [("doi","10.48550/arXiv.2107.08170")] cs/hardware reinforcement-learning/model-free reinforcement-learning/multi-agent reinforcement-learning/scaling

We present Megaverse, a new 3D simulation platform for reinforcement learning and embodied AI research.

The efficient design of our engine enables physics-based simulation with high-dimensional egocentric observations at more than 1,000,000 actions per second on a single 8-GPU node. Megaverse is up to 70× faster than DeepMind Lab in fully-shaded 3D scenes with interactive objects. We achieve this high simulation performance by leveraging batched simulation, thereby taking full advantage of the massive parallelism of modern GPUs.

We use Megaverse to build a new benchmark that consists of several single-agent and multi-agent tasks covering a variety of cognitive challenges. We evaluate model-free RL on this benchmark to provide baselines and facilitate future research.

The source code is available at https://www.megaverse.info/.

--- https://arxiv.org/abs/1809.07124 Pommerman: A Multi-Agent Playground Cinjon Resnick, Wes Eldridge, David Ha, Denny Britz, Jakob Foerster, Julian Togelius, Kyunghyun Cho, Joan Bruna 2018-09-19 2020-11-29 [("doi","10.48550/arXiv.1809.07124")] reinforcement-learning/multi-agent

We present Pommerman, a multi-agent environment based on the classic console game Bomberman.

Pommerman consists of a set of scenarios, each having at least 4 players and containing both cooperative and competitive aspects. We believe that success in Pommerman will require a diverse set of tools and methods, including planning, opponent/teammate modeling, game theory, and communication, and consequently can serve well as a multi-agent benchmark.

To date, we have already hosted one competition, and our next one will be featured in the NIPS 2018 competition track.

--- https://arxiv.org/abs/1709.04326#openai Learning with Opponent-Learning Awareness Jakob N. Foerster, Richard Y. Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, Igor Mordatch 2017-09-13 2020-11-29 [("doi","10.48550/arXiv.1709.04326")] reinforcement-learning/meta-learning reinforcement-learning/multi-agent

Multi-agent settings are quickly gathering importance in machine learning. This includes a plethora of recent work on deep multi-agent reinforcement learning, but also can be extended to hierarchical RL, generative adversarial networks and decentralised optimization. In all these settings the presence of multiple learning agents renders the training problem non-stationary and often leads to unstable training or undesired final results.

We present Learning with Opponent-Learning Awareness (LOLA), a method in which each agent shapes the anticipated learning of the other agents in the environment. The LOLA learning rule includes a term that accounts for the impact of one agent’s policy on the anticipated parameter update of the other agents.

Results show that the encounter of two LOLA agents leads to the emergence of tit-for-tat and therefore cooperation in the iterated prisoners’ dilemma, while independent learning does not. In this domain, LOLA also receives higher payouts compared to a naive learner, and is robust against exploitation by higher order gradient-based methods.

Applied to repeated matching pennies, LOLA agents converge to the Nash equilibrium. In a round robin tournament we show that LOLA agents successfully shape the learning of a range of multi-agent learning algorithms from literature, resulting in the highest average returns on the IPD. We also show that the LOLA update rule can be efficiently calculated using an extension of the policy gradient estimator, making the method suitable for model-free RL. The method thus scales to large parameter and input spaces and nonlinear function approximators.

We apply LOLA to a grid world task with an embedded social dilemma using recurrent policies and opponent modeling. By explicitly considering the learning of the other agent, LOLA agents learn to cooperate out of self-interest. The code is at github.com/alshedivat/lola.

--- https://arxiv.org/abs/2012.07975#bair A Framework for Efficient Robotic Manipulation Albert Zhan, Philip Zhao, Lerrel Pinto, Pieter Abbeel, Michael Laskin 2020-12-14 2020-12-14 [("doi","10.48550/arXiv.2012.07975")] reinforcement-learning/robot

Data-efficient learning of manipulation policies from visual observations is an outstanding challenge for real-robot learning. While deep reinforcement learning (RL) algorithms have shown success learning policies from visual observations, they still require an impractical number of real-world data samples to learn effective policies. However, recent advances in unsupervised representation learning and data augmentation improved the sample efficiency of training RL policies on common simulated benchmarks.

Building on these advances, we present a Framework for Efficient Robotic Manipulation (FERM) that utilizes data augmentation and unsupervised learning to achieve extremely sample-efficient training of robotic manipulation policies with sparse rewards.

We show that, given only 10 demonstrations, a single robotic arm can learn sparse-reward manipulation policies from pixels, such as reaching, picking, moving, pulling a large object, flipping a switch, and opening a drawer in just 15–50 minutes of real-world training time.

We include videos, code, and additional information on the project website.

--- https://arxiv.org/abs/2003.09518#facebook Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems Maxim Naumov, John Kim, Dheevatsa Mudigere, Srinivas Sridharan, Xiaodong Wang, Whitney Zhao, Serhat Yilmaz, Changkyu Kim, Hector Yuen, Mustafa Ozdal, Krishnakumar Nair, Isabel Gao, Bor-Yiing Su, Jiyan Yang, Mikhail Smelyanskiy 2020-03-20 2020-11-29 [("doi","10.48550/arXiv.2003.09518")] ai/nn/fully-connected ai/scaling

Large-scale training is important to ensure high performance and accuracy of machine-learning models. At Facebook, we use many different models, including computer vision, video, and language models.

However, in this paper, we focus on the deep learning recommendation models (DLRMs), which are responsible for more than 50% of the training demand in our data centers. Recommendation models present unique challenges in training because they exercise not only compute but also memory capacity as well as memory and network bandwidth.

As model size and complexity increase, efficiently scaling training becomes a challenge. To address it, we design Zion—Facebook’s next-generation large-memory training platform that consists of both CPUs and accelerators.

Also, we discuss the design requirements of future scale-out training systems.

--- https://arxiv.org/abs/1906.02634#google Scaling Autoregressive Video Models Dirk Weissenborn, Oscar Täckström, Jakob Uszkoreit 2019-06-06 2020-11-29 [("doi","10.48550/arXiv.1906.02634")] ai/nn/transformer/attention/sparsity ai/scaling ai/video/generation

Due to the statistical complexity of video, the high degree of inherent stochasticity, and the sheer amount of data, generating natural video remains a challenging task. State-of-the-art video generation models often attempt to address these issues by combining sometimes complex, usually video-specific neural network architectures, latent variable models, adversarial training and a range of other methods. Despite their often high complexity, these approaches still fall short of generating high-quality video continuations outside of narrow domains and often struggle with fidelity.

In contrast, we show that conceptually simple autoregressive video generation models based on a three-dimensional self-attention mechanism achieve competitive results across multiple metrics on popular benchmark datasets, for which they produce continuations of high fidelity and realism. We also present results from training our models on Kinetics, a large-scale action recognition dataset comprised of YouTube videos exhibiting phenomena such as camera movement, complex object interactions, and diverse human movement.

While modeling these phenomena consistently remains elusive, we hope that our results, which include occasional realistic continuations encourage further research on comparatively complex, large-scale datasets such as Kinetics.

--- https://arxiv.org/abs/1905.00546#facebook Billion-scale semi-supervised learning for image classification I. Zeki Yalniz, Hervé Jégou, Kan Chen, Manohar Paluri, Dhruv Mahajan 2019-05-02 2020-11-29 [("doi","10.48550/arXiv.1905.00546")] ai/nn/cnn ai/scaling ai/video/analysis

This paper presents a study of semi-supervised learning with large convolutional networks. We propose a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabeled images (up to 1 billion). Our main goal is to improve the performance for a given target architecture, like ResNet-50 or ResNext.

We provide an extensive analysis of the success factors of our approach, which leads us to formulate some recommendations to produce high-accuracy models for image classification with semi-supervised learning.

As a result, our approach brings important gains to standard architectures for image, video and fine-grained classification. For instance, by leveraging one billion unlabeled images, our learned vanilla ResNet-50 achieves 81.2% top-1 accuracy on the ImageNet benchmark.

--- https://arxiv.org/abs/1905.09773 Speech2Face: Learning the Face Behind a Voice Tae-Hyun Oh, Tali Dekel, Changil Kim, Inbar Mosseri, William T. Freeman, Michael Rubinstein, Wojciech Matusik 2019-05-23 2020-11-30 [("doi","10.48550/arXiv.1905.09773")] ai/nn

How much can we infer about a person’s looks from the way they speak? In this paper, we study the task of reconstructing a facial image of a person from a short audio recording of that person speaking.

We design and train a deep neural network to perform this task using millions of natural Internet/YouTube videos of people speaking. During training, our model learns voice-face correlations that allow it to produce images that capture various physical attributes of the speakers such as age, gender, and ethnicity. This is done in a self-supervised manner, by using the natural co-occurrence of faces and speech in Internet videos, without the need to model attributes explicitly.

We evaluate and numerically quantify how—and in what manner—our Speech2Face reconstructions, obtained directly from audio, resemble the true face images of the speakers.

--- https://arxiv.org/abs/1910.00932#google Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos Ji Lin, Chuang Gan, Song Han 2019-10-01 2020-11-30 [("doi","10.48550/arXiv.1910.00932")] ai/scaling ai/video/analysis

Deep video recognition is more computationally expensive than image recognition, especially on large-scale datasets like Kinetics. Therefore, training scalability is essential to handle a large amount of videos. In this paper, we study the factors that impact the training scalability of video networks.

We recognize 3 bottlenecks, including data loading (data movement from disk to GPU), communication (data movement over networking), and computation FLOPs. We propose 3 design guidelines to improve the scalability: (1) fewer FLOPs and hardware-friendly operator to increase the computation efficiency; (2) fewer input frames to reduce the data movement and increase the data loading efficiency; (3) smaller model size to reduce the networking traffic and increase the networking efficiency.

With these guidelines, we designed a new operator Temporal Shift Module (TSM) that is efficient and scalable for distributed training. TSM model can achieve 1.8× higher throughput compared to previous I3D models. We scale up the training of the TSM model to 1,536 GPUs, with a mini-batch of 12,288 video clips/98,304 images, without losing the accuracy.

With such hardware-aware model design, we are able to scale up the training on Summit supercomputer and reduce the training time on Kinetics dataset from 49 hours 55 minutes to 14 minutes 13 seconds, achieving a top-1 accuracy of 74.0%, which is 1.6× and 2.9× faster than previous 3D video models with higher accuracy.

The code and more details can be found here: here.

--- https://arxiv.org/abs/1912.12180#google Axial Attention in Multidimensional Transformers Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn, Tim Salimans 2019-12-20 2020-11-30 [("doi","10.48550/arXiv.1912.12180")] ai/nn/transformer/attention/sparsity ai/video/analysis

We propose Axial Transformers, a self-attention-based autoregressive model for images and other data organized as high dimensional tensors. Existing autoregressive models either suffer from excessively large computational resource requirements for high dimensional data, or make compromises in terms of distribution expressiveness or ease of implementation in order to decrease resource requirements. Our architecture, by contrast, maintains both full expressiveness over joint distributions over data and ease of implementation with standard deep learning frameworks, while requiring reasonable memory and computation and achieving state-of-the-art results on standard generative modeling benchmarks.

Our models are based on axial attention, a simple generalization of self-attention that naturally aligns with the multiple dimensions of the tensors in both the encoding and the decoding settings. Notably the proposed structure of the layers allows for the vast majority of the context to be computed in parallel during decoding without introducing any independence assumptions. This semi-parallel structure goes a long way to making decoding from even a very large Axial Transformer broadly applicable.

We demonstrate state-of-the-art results for the Axial Transformer on the ImageNet-32 and ImageNet-64 image benchmarks as well as on the BAIR Robotic Pushing video benchmark. We open source the implementation of Axial Transformers.

--- https://arxiv.org/abs/2001.09326 Gesticulator: A framework for semantically-aware speech-driven gesture generation Taras Kucherenko, Patrik Jonell, Sanne van Waveren, Gustav Eje Henter, Simon Alexanderson, Iolanda Leite, Hedvig Kjellström 2020-01-25 2020-11-30 [("doi","10.1145/3382507.3418815")] ai/nn/fully-connected ai/video/analysis

During speech, people spontaneously gesticulate, which plays a key role in conveying information. Similarly, realistic co-speech gestures are crucial to enable natural and smooth interactions with social agents. Current end-to-end co-speech gesture generation systems use a single modality for representing speech: either audio or text. These systems are therefore confined to producing either acoustically-linked beat gestures or semantically-linked gesticulation (eg. raising a hand when saying “high”): they cannot appropriately learn to generate both gesture types.

We present a model designed to produce arbitrary beat and semantic gestures together. Our deep-learning based model takes both acoustic and semantic representations of speech as input, and generates gestures as a sequence of joint angle rotations as output. The resulting gestures can be applied to both virtual agents and humanoid robots.

Subjective and objective evaluations confirm the success of our approach.

The code and video are available at the project page https://svito-zar.github.io/gesticulator/.

--- https://arxiv.org/abs/2006.09661 SIREN: Implicit Neural Representations with Periodic Activation Functions Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, Gordon Wetzstein 2020-06-17 2020-11-30 [("doi","10.48550/arXiv.2006.09661")] ai/nn/fully-connected ai/video/generation

Implicitly defined, continuous, differentiable signal representations parameterized by neural networks have emerged as a powerful paradigm, offering many possible benefits over conventional representations. However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal’s spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations.

We propose to leverage periodic activation functions for implicit neural representations and demonstrate that these networks, dubbed sinusoidal representation networks or Sirens, are ideally suited for representing complex natural signals and their derivatives.

We analyze Siren activation statistics to propose a principled initialization scheme and demonstrate the representation of images, wavefields, video, sound, and their derivatives. Further, we show how Sirens can be leveraged to solve challenging boundary value problems, such as particular Eikonal equations (yielding signed distance functions), the Poisson equation, and the Helmholtz and wave equations.

Lastly, we combine Sirens with hypernetworks to learn priors over the space of Siren functions.

[Note the use of careful initialization to make the MLPs trainable without residual layers, normalization, or gating.]

--- https://arxiv.org/abs/2102.09532 CW-VAE: Clockwork Variational Autoencoders Vaibhav Saxena, Jimmy Ba, Danijar Hafner 2021-02-18 2021-02-18 [("doi","10.48550/arXiv.2102.09532")] ai/nn/vae ai/video/generation

Deep learning has enabled algorithms to generate realistic images. However, accurately predicting long video sequences requires understanding long-term dependencies and remains an open challenge. While existing video prediction models succeed at generating sharp images, they tend to fail at accurately predicting far into the future.

We introduce the Clockwork VAE (CW-VAE), a video prediction model that leverages a hierarchy of latent sequences, where higher levels tick at slower intervals.

We demonstrate the benefits of both hierarchical latents and temporal abstraction on 4 diverse video prediction datasets with sequences of up to 1,000 frames, where CW-VAE outperforms top video prediction models. Additionally, we propose a Minecraft benchmark for long-term video prediction.

We conduct several experiments to gain insights into CW-VAE and confirm that slower levels learn to represent objects that change more slowly in the video, and faster levels learn to represent faster objects.

--- https://arxiv.org/abs/2111.05948#facebook Scaling ASR Improves Zero and Few Shot Learning Alex Xiao, Weiyi Zheng, Gil Keren, Duc Le, Frank Zhang, Christian Fuegen, Ozlem Kalinli, Yatharth Saraf, Abdelrahman Mohamed 2021-11-10 2021-11-10 [("doi","10.48550/arXiv.2111.05948")] ai/scaling ai/video/analysis

With 4.5 million hours of English speech from 10 different sources across 120 countries and models of up to 10 billion parameters, we explore the frontiers of scale for automatic speech recognition.

We propose data selection techniques to efficiently scale training data to find the most valuable samples in massive datasets. To efficiently scale model sizes, we leverage various optimizations such as sparse transducer loss and model sharding. By training 1–10b parameter universal English ASR models, we push the limits of speech recognition performance across many domains. Furthermore, our models learn powerful speech representations with zero and few-shot capabilities on novel domains and styles of speech, exceeding previous results across multiple in-house and public benchmarks.

For speakers with disorders due to brain damage, our best zero-shot and few-shot models achieve 22% and 60% relative improvement on the AphasiaBank test set, respectively, while realizing the best performance on public social media videos. Furthermore, the same universal model reaches equivalent performance with 500× less in-domain data on the SPGISpeech financial-domain dataset.

--- https://arxiv.org/abs/2111.12527 MorphMLP: A Self-Attention Free, MLP-Like Backbone for Image and Video David Junhao Zhang, Kunchang Li, Yunpeng Chen, Yali Wang, Shashwat Chandra, Yu Qiao, Luoqi Liu, Mike Zheng Shou 2021-11-24 2021-11-24 [("doi","10.48550/arXiv.2111.12527")] ai/nn/fully-connected ai/video/analysis

Self-attention has become an integral component of the recent network architectures, eg. Transformer, that dominate major image and video benchmarks. This is because self-attention can flexibly model long-range information. For the same reason, researchers make attempts recently to revive Multiple Layer Perceptron (MLP) and propose a few MLP-Like architectures, showing great potential. However, the current MLP-Like architectures are not good at capturing local details and lack progressive understanding of core details in the images and/or videos.

To overcome this issue, we propose a novel MorphMLP architecture that focuses on capturing local details at the low-level layers, while gradually changing to focus on long-term modeling at the high-level layers. Specifically, we design a Fully-Connected-Like layer, dubbed as MorphFC, of two morphable filters that gradually grow its receptive field along the height and width dimension. More interestingly, we propose to flexibly adapt our MorphFC layer in the video domain. To our best knowledge, we are the first to create a MLP-Like backbone for learning video representation. Finally, we conduct extensive experiments on image classification, semantic segmentation and video classification. Our MorphMLP, such a self-attention free backbone, can be as powerful as and even outperform self-attention based models.

--- https://arxiv.org/abs/2201.08383#facebook MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition Chao-Yuan Wu, Yanghao Li, Karttikeya Mangalam, Haoqi Fan, Bo Xiong, Jitendra Malik, Christoph Feichtenhofer 2022-01-20 2022-01-20 [("doi","10.48550/arXiv.2201.08383")] ai/nn/transformer/attention/compression ai/video/analysis

While today’s video recognition systems parse snapshots or short clips accurately, they cannot connect the dots and reason across a longer range of time yet. Most existing video architectures can only process <5 seconds of a video without hitting the computation or memory bottlenecks.

In this paper, we propose a new strategy to overcome this challenge. Instead of trying to process more frames at once like most existing methods, we propose to process videos in an online fashion and cache “memory” at each iteration. Through the memory, the model can reference prior context for long-term modeling, with only a marginal cost.

Based on this idea, we build MeMViT, a Memory-augmented Multiscale Vision Transformer, that has a temporal support 30× longer than existing models with only 4.5% more compute; traditional methods need >3,000% more compute to do the same.

On a wide range of settings, the increased temporal support enabled by MeMViT brings large gains in recognition accuracy consistently. MeMViT obtains state-of-the-art results on the AVA, EPIC-Kitchens-100 action classification, and action anticipation datasets.

Code and models will be made publicly available.

--- https://arxiv.org/abs/1811.10636 Evolving Space-Time Neural Architectures for Videos AJ Piergiovanni, Anelia Angelova, Alexander Toshev, Michael S. Ryoo 2018-11-26 2020-11-30 [("doi","10.48550/arXiv.1811.10636")] ai/nn/cnn ai/video/analysis reinforcement-learning/meta-learning

We present a new method for finding video CNN architectures that capture rich spatio-temporal information in videos. Previous work, taking advantage of 3D convolutions, obtained promising results by manually designing video CNN architectures.

We here develop a novel evolutionary search algorithm that automatically explores models with different types and combinations of layers to jointly learn interactions between spatial and temporal aspects of video representations. We demonstrate the generality of this algorithm by applying it to two meta-architectures, obtaining new architectures superior to manually designed architectures.

Further, we propose a new component, the iTGM layer, which more efficientlyuses its parameters to allow learning of space-time interactions over longer time horizons. The iTGM layer is often preferred by the evolutionary algorithm and allows building cost-efficient networks. The proposed approach discovers new and diverse video architectures that were previously unknown.

More importantly they are both more accurate and faster than prior models, and outperform the state-of-the-art results on multiple datasets we test, including HMDB, Kinetics, and Moments in Time.

We will open source the code and models, to encourage future model development.

--- https://arxiv.org/abs/1807.05162 Large-Scale Visual Speech Recognition Brendan Shillingford, Yannis Assael, Matthew W. Hoffman, Thomas Paine, Cían Hughes, Utsav Prabhu, Hank Liao, Hasim Sak, Kanishka Rao, Lorrayne Bennett, Marie Mulville, Ben Coppin, Ben Laurie, Andrew Senior, Nando de Freitas 2018-07-13 2020-12-01 [("doi","10.48550/arXiv.1807.05162")] ai/scaling ai/video/analysis

This work presents a scalable solution to open-vocabulary visual speech recognition. To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3,886 hours of video).

In tandem, we designed and trained an integrated lipreading system, consisting of a video processing pipeline that maps raw video to stable videos of lips and sequences of phonemes, a scalable deep neural network that maps the lip videos to sequences of phoneme distributions, and a production-level speech decoder that outputs sequences of words.

The proposed system achieves a word error rate (WER) of 40.9% as measured on a held-out set. In comparison, professional lipreaders achieve either 86.4% or 92.9% WER on the same dataset when having access to additional types of contextual information. Our approach improves on other lipreading approaches, including variants of LipNet and of Watch, Attend, and Spell (WAS), which are only capable of 89.8% and 76.8% WER respectively.

--- https://arxiv.org/abs/1706.02884 Learning to Learn from Noisy Web Videos Serena Yeung, Vignesh Ramanathan, Olga Russakovsky, Liyue Shen, Greg Mori, Li Fei-Fei 2017-06-09 2020-12-01 [("doi","10.48550/arXiv.1706.02884")] ai/scaling ai/video/analysis reinforcement-learning/exploration/active-learning

Understanding the simultaneously very diverse and intricately fine-grained set of possible human actions is a critical open problem in computer vision. Manually labeling training videos is feasible for some action classes but doesn’t scale to the full long-tailed distribution of actions. A promising way to address this is to leverage noisy data from web queries to learn new actions, using semi-supervised or “webly-supervised” approaches. However, these methods typically do not learn domain-specific knowledge, or rely on iterative hand-tuned data labeling policies.

In this work, we instead propose a reinforcement learning-based formulation for selecting the right examples for training a classifier from noisy web search results. Our method uses Q-learning to learn a data labeling policy on a small labeled training dataset, and then uses this to automatically label noisy web data for new visual concepts.

Experiments on the challenging Sports-1M action recognition benchmark as well as on additional fine-grained and newly emerging action classes demonstrate that our method is able to learn good labeling policies for noisy data and use this to learn accurate visual concept classifiers.

--- https://arxiv.org/abs/1503.01817#flickr YFCC100M: The New Data in Multimedia Research Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, Li-Jia Li 2015-03-05 2020-12-01 [("doi","10.1145/2812802")] ai/dataset ai/scaling ai/video/analysis

We present the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), the largest public multimedia collection that has ever been released. The dataset contains a total of 100 million media objects, of which ~99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license. Each media object in the dataset is represented by several pieces of metadata, eg. Flickr identifier, owner name, camera title, tags, geo, media source. The collection provides a comprehensive snapshot of how photos and videos were taken, described, and shared over the years, from the inception of Flickr in 2004 until early 2014.

In this article, we explain the rationale behind its creation, as well as the implications the dataset has for science, research, engineering, and development.

We further present several new challenges in multimedia research that can now be expanded upon with our dataset.

--- https://arxiv.org/abs/1806.07857 RUDDER: Return Decomposition for Delayed Rewards Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, Sepp Hochreiter 2018-06-20 2020-12-01 [("doi","10.48550/arXiv.1806.07857")] reinforcement-learning/meta-learning

We propose RUDDER, a novel reinforcement learning approach for delayed rewards in finite Markov decision processes (MDPs).

In MDPs the Q-values are equal to the expected immediate reward plus the expected future rewards. The latter are related to bias problems in temporal difference (TD) learning and to high variance problems in Monte Carlo (MC) learning. Both problems are even more severe when rewards are delayed. RUDDER aims at making the expected future rewards zero, which simplifies Q-value estimation to computing the mean of the immediate reward.

We propose the following two new concepts to push the expected future rewards toward zero. (1) Reward redistribution that leads to return-equivalent decision processes with the same optimal policies and, when optimal, zero expected future rewards. (2) Return decomposition via contribution analysis which transforms the reinforcement learning task into a regression task at which deep learning excels.

On artificial tasks with delayed rewards, RUDDER is faster than MC and exponentially faster than Monte Carlo Tree Search (MCTS), TD(λ), and reward shaping approaches. At Atari games, RUDDER on top of a Proximal Policy Optimization (PPO) baseline improves the scores, which is most prominent at games with delayed rewards.

Source code is available at https://github.com/ml-jku/rudder and demonstration videos at https://www.youtube.com/playlist?list=PLDfrC-Vpg-CzVTqSjxVeLQZy3f7iv9vyY.

--- https://arxiv.org/abs/1708.02300 Reinforced Video Captioning with Entailment Rewards Ramakanth Pasunuru, Mohit Bansal 2017-08-07 2020-12-01 [("doi","10.48550/arXiv.1708.02300")] ai/video/analysis reinforcement-learning/model-free

Sequence-to-sequence models have shown promising improvements on the temporal task of video captioning, but they optimize word-level cross-entropy loss during training. First, using policy gradient and mixed-loss methods for reinforcement learning, we directly optimize sentence-level task-based metrics (as rewards), achieving improvements over the baseline, based on both automatic metrics and human evaluation on multiple datasets. Next, we propose a novel entailment-enhanced reward (CIDEnt) that corrects phrase-matching based metrics (such as CIDEr) to only allow for logically-implied partial matches and avoid contradictions, achieving further improvements over the CIDEr-reward model.

Overall, our CIDEnt-reward model achieves the new state-of-the-art on the MSR-VTT dataset.

--- https://arxiv.org/abs/1707.04991 Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning James Steven Supancic III, Deva Ramanan 2017-07-17 2020-12-01 [("doi","10.48550/arXiv.1707.04991")] ai/video/analysis reinforcement-learning/model-free

We formulate tracking as an online decision-making process, where a tracking agent must follow an object despite ambiguous image frames and a limited computational budget. Crucially, the agent must decide where to look in the upcoming frames, when to reinitialize because it believes the target has been lost, and when to update its appearance model for the tracked object. Such decisions are typically made heuristically. Instead, we propose to learn an optimal decision-making policy by formulating tracking as a partially observable decision-making process (POMDP).

We learn policies with deep reinforcement learning algorithms that need supervision (a reward signal) only when the track has gone awry. We demonstrate that sparse rewards allow us to quickly train on massive datasets, several orders of magnitude more than past work.

Interestingly, by treating the data source of Internet videos as unlimited streams, we both learn and evaluate our trackers in a single, unified computational stream.

--- https://arxiv.org/abs/1704.06888 Time-Contrastive Networks: Self-Supervised Learning from Video Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine 2017-04-23 2020-12-01 [("doi","10.48550/arXiv.1704.06888")] ai/video/analysis reinforcement-learning/model-free

We propose a self-supervised approach for learning representations and robotic behaviors entirely from unlabeled videos recorded from multiple viewpoints, and study how this representation can be used in two robotic imitation settings: imitating object interactions from videos of humans, and imitating human poses. Imitation of human behavior requires a viewpoint-invariant representation that captures the relationships between end-effectors (hands or robot grippers) and the environment, object attributes, and body pose.

We train our representations using a metric learning loss, where multiple simultaneous viewpoints of the same observation are attracted in the embedding space, while being repelled from temporal neighbors which are often visually similar but functionally different. In other words, the model simultaneously learns to recognize what is common between different-looking images, and what is different between similar-looking images. This signal causes our model to discover attributes that do not change across viewpoint, but do change across time, while ignoring nuisance variables such as occlusions, motion blur, lighting and background.

We demonstrate that this representation can be used by a robot to directly mimic human poses without an explicit correspondence, and that it can be used as a reward function within a reinforcement learning algorithm. While representations are learned from an unlabeled collection of task-related videos, robot behaviors such as pouring are learned by watching a single 3rd-person demonstration by a human. Reward functions obtained by following the human demonstrations under the learned representation enable efficient reinforcement learning that is practical for real-world robotic systems.

Video results, open-source code and dataset are available at https://sermanet.github.io/imitate/.

--- https://arxiv.org/abs/1610.00696 Deep Visual Foresight for Planning Robot Motion Chelsea Finn, Sergey Levine 2016-10-03 2020-12-01 [("doi","10.48550/arXiv.1610.00696")] ai/video/analysis reinforcement-learning/robot

A key challenge in scaling up robot learning to many skills and environments is removing the need for human supervision, so that robots can collect their own data and improve their own performance without being limited by the cost of requesting human feedback. Model-based reinforcement learning holds the promise of enabling an agent to learn to predict the effects of its actions, which could provide flexible predictive models for a wide range of tasks and environments, without detailed human supervision.

We develop a method for combining deep action-conditioned video prediction models with model-predictive control that uses entirely unlabeled training data. Our approach does not require a calibrated camera, an instrumented training set-up, nor precise sensing and actuation. Our results show that our method enables a real robot to perform nonprehensile manipulation—pushing objects—and can handle novel objects not seen during training.

--- https://arxiv.org/abs/1605.02097 ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, Wojciech Jaśkowski 2016-05-06 2020-12-01 [("doi","10.48550/arXiv.1605.02097")] ai/nn/cnn reinforcement-learning/model-free

The recent advances in deep neural networks have led to effective vision-based reinforcement learning methods that have been employed to obtain human-level controllers in Atari 2600 games from pixel data. Atari 2600 games, however, do not resemble real-world tasks since they involve non-realistic 2D environments and the third-person perspective.

Here, we propose a novel test-bed platform for reinforcement learning research from raw visual information which employs the first-person perspective in a semi-realistic 3D world. The software, called ViZDoom, is based on the classical first-person shooter video game, Doom. It allows developing bots that play the game using the screen buffer. ViZDoom is lightweight, fast, and highly customizable via a convenient mechanism of user scenarios.

In the experimental part, we test the environment by trying to learn bots for two scenarios: a basic move-and-shoot task and a more complex maze-navigation problem. Using convolutional deep neural networks with Q-learning and experience replay, for both scenarios, we were able to train competent bots, which exhibit human-like behaviors. The results confirm the utility of ViZDoom as an AI research platform and imply that visual reinforcement learning in 3D realistic first-person perspective environments is feasible.

--- https://arxiv.org/abs/2103.07579#google Revisiting ResNets: Improved Training and Scaling Strategies Irwan Bello, William Fedus, Xianzhi Du, Ekin D. Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, Barret Zoph 2021-03-13 2021-03-13 [("doi","10.48550/arXiv.2103.07579")] ai/scaling ai/video/analysis

Novel computer vision architectures monopolize the spotlight, but the impact of the model architecture is often conflated with simultaneous changes to training methodology and scaling strategies. Our work revisits the canonical ResNet (He et al 2015) and studies these 3 aspects in an effort to disentangle them. Perhaps surprisingly, we find that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models.

We show that the best performing scaling strategy depends on the training regime and offer two new scaling strategies: (1) scale model depth in regimes where overfitting can occur (width scaling is preferable otherwise); (2) increase image resolution more slowly than previously recommended (Tan & Le 2019). Using improved training and scaling strategies, we design a family of ResNet architectures, ResNet-RS, which are 1.7×—2.7× faster than EfficientNets on TPUs, while achieving similar accuracies on ImageNet.

In a large-scale semi-supervised learning setup, ResNet-RS achieves 86.2% top-1 ImageNet accuracy, while being 4.7× faster than EfficientNet NoisyStudent. The training techniques improve transfer performance on a suite of downstream tasks (rivaling state-of-the-art self-supervised algorithms) and extend to video classification on Kinetics-400.

We recommend practitioners use these simple revised ResNets as baselines for future research.

--- https://arxiv.org/abs/1904.01766#google VideoBERT: A Joint Model for Video and Language Representation Learning Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, Cordelia Schmid 2019-04-03 2020-12-01 [("doi","10.48550/arXiv.1904.01766")] ai/scaling ai/video/analysis

Self-supervised learning has become increasingly important to leverage the abundance of unlabeled data available on platforms like YouTube. Whereas most existing approaches learn low-level representations, we propose a joint visual-linguistic model to learn high-level features without any explicit supervision.

In particular, inspired by its recent success in language modeling, we build upon the BERT model to learn bidirectional joint distributions over sequences of visual and linguistic tokens, derived from vector quantization of video data and off-the-shelf speech recognition outputs, respectively. We use VideoBERT in numerous tasks, including action classification and video captioning.

We show that it can be applied directly to open-vocabulary classification, and confirm that large amounts of training data and cross-modal information are critical to performance. Furthermore, we outperform the state-of-the-art on video captioning, and quantitative results verify that the model learns high-level semantic features.

--- https://arxiv.org/abs/1903.00374 Model-Based Reinforcement Learning for Atari Łukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski 2019-03-01 2020-12-02 [("doi","10.48550/arXiv.1903.00374")] ai/video/generation

Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction—substantially more, in fact, than a human would need to learn the same games. How can people learn so quickly? Part of the answer may be that people can learn how the game works and predict which actions will lead to desirable outcomes. In this paper, we explore how video prediction models can similarly enable agents to solve Atari games with fewer interactions than model-free methods.

We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting. Our experiments evaluate SimPLe on a range of Atari games in low data regime of 100k interactions between the agent and the environment, which corresponds to two hours of real-time play.

In most games SimPLe outperforms state-of-the-art model-free algorithms, in some games by over an order of magnitude.

--- https://arxiv.org/abs/1811.11721 CCNet: Criss-Cross Attention for Semantic Segmentation Zilong Huang, Xinggang Wang, Yunchao Wei, Lichao Huang, Humphrey Shi, Wenyu Liu, Thomas S. Huang 2018-11-28 2020-12-02 [("doi","10.48550/arXiv.1811.11721")] ai/nn/transformer/attention/sparsity ai/video/analysis

Contextual information is vital in visual understanding problems, such as semantic segmentation and object detection. We propose a Criss-Cross Network (CCNet) for obtaining full-image contextual information in a very effective and efficient way.

Concretely, for each pixel, a novel criss-cross attention module harvests the contextual information of all the pixels on its criss-cross path. By taking a further recurrent operation, each pixel can finally capture the full-image dependencies. Besides, a category consistent loss is proposed to enforce the criss-cross attention module to produce more discriminative features.

Overall, CCNet is with the following merits: (1) GPU memory friendly. Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11× less GPU memory usage. (2) High computational efficiency. The recurrent criss-cross attention reduces FLOPs by about 85% of the non-local block. (3) The state-of-the-art performance.

We conduct extensive experiments on semantic segmentation benchmarks including Cityscapes, ADE20K, human parsing benchmark LIP, instance segmentation benchmark COCO, video segmentation benchmark CamVid. In particular, our CCNet achieves the mIoU scores of 81.9%, 45.76% and 55.47% on the Cityscapes test set, the ADE20K validation set and the LIP validation set respectively, which are the new state-of-the-art results.

The source codes are available at https://github.com/speedinghzl/CCNet.

--- https://openreview.net/forum?id=BkjLkSqxg LipNet: End-to-End Sentence-level Lipreading Yannis M. Assael, Brendan Shillingford, Shimon Whiteson, Nando de Freitas 2016-11-05 2020-12-02 [("doi","10.48550/arXiv.1611.01599")] ai/nn/cnn ai/video/analysis

Lipreading is the task of decoding text from the movement of a speaker’s mouth. Traditional approaches separated the problem into two stages: designing or learning visual features, and prediction. More recent deep lipreading approaches are end-to-end trainable (Wand et al 2016; Chung & Zisserman 2016a). However, existing work on models trained end-to-end perform only word classification, rather than sentence-level sequence prediction. Studies have shown that human lipreading performance increases for longer words (Easton & Basala 1982), indicating the importance of features capturing temporal context in an ambiguous communication channel.

Motivated by this observation, we present LipNet, a model that maps a variable-length sequence of video frames to text, making use of spatiotemporal convolutions, a recurrent network, and the connectionist temporal classification loss, trained entirely end-to-end. To the best of our knowledge, LipNet is the first end-to-end sentence-level lipreading model that simultaneously learns spatiotemporal visual features and a sequence model.

On the GRID corpus, LipNet achieves 95.2% accuracy in sentence-level, overlapped speaker split task, outperforming experienced human lipreaders and the previous 86.4% word-level state-of-the-art accuracy (Gergen et al 2016).

--- https://arxiv.org/abs/1604.08610 Artistic style transfer for videos Manuel Ruder, Alexey Dosovitskiy, Thomas Brox 2016-04-28 2020-12-02 [("doi","10.1007/978-3-319-45886-1_3")] ai/video/analysis

In the past, manually re-drawing an image in a certain artistic style required a professional artist and a long time. Doing this for a video sequence single-handed was beyond imagination. Nowadays computers provide new possibilities.

We present an approach that transfers the style from one image (for example, a painting) to a whole video sequence. We make use of recent advances in style transfer in still images and propose new initializations and loss functions applicable to videos.

This allows us to generate consistent and stable stylized video sequences, even in cases with large motion and strong occlusion. We show that the proposed method clearly outperforms simpler baselines both qualitatively and quantitatively.

--- https://www.reddit.com/r/MachineLearning/comments/e23ezq/p_using_stylegan_to_make_a_music_visualizer/ 2020-12-02 ai/nn/gan/stylegan ai/video --- https://arxiv.org/abs/2110.06635 ADOP: Approximate Differentiable One-Pixel Point Rendering Darius Rückert, Linus Franke, Marc Stamminger 2021-10-13 2021-10-13 [("doi","10.48550/arXiv.2110.06635")] ai/nn/fully-connected ai/video/analysis

In this paper we present ADOP, a novel point-based, differentiable neural rendering pipeline. Like other neural renderers, our system takes as input calibrated camera images and a proxy geometry of the scene, in our case a point cloud. To generate a novel view, the point cloud is rasterized with learned feature vectors as colors and a deep neural network fills the remaining holes and shades each output pixel.

The rasterizer renders points as one-pixel splats, which makes it very fast and allows us to compute gradients with respect to all relevant input parameters efficiently. Furthermore, our pipeline contains a fully differentiable physically-based photometric camera model, including exposure, white balance, and a camera response function. Following the idea of inverse rendering, we use our renderer to refine its input in order to reduce inconsistencies and optimize the quality of its output. In particular, we can optimize structural parameters like the camera pose, lens distortions, point positions and features, and a neural environment map, but also photometric parameters like camera response function, vignetting, and per-image exposure and white balance.

Because our pipeline includes photometric parameters, and camera response function, our system can smoothly handle input images with varying exposure and white balance, and generates high-dynamic range output. We show that due to the improved input, we can achieve high render quality, also for difficult input, with imperfect camera calibrations, inaccurate proxy geometry, or varying exposure. As a result, a simpler and thus faster deep neural network is sufficient for reconstruction. In combination with the fast point rasterization, ADOP achieves real-time rendering rates even for models with well over 100M points. https://github.com/darglein/ADOP.

--- https://x.com/DrewMedina20/status/1518994481038741512 2020-12-02 ai/video --- https://arxiv.org/abs/2203.09494#deepmind Transframer: Arbitrary Frame Prediction with Generative Models Charlie Nash, João Carreira, Jacob Walker, Iain Barr, Andrew Jaegle, Mateusz Malinowski, Peter Battaglia 2022-03-17 2022-03-17 [("doi","10.48550/arXiv.2203.09494")] ai/video/generation

We present a general-purpose framework for image modeling and vision tasks based on probabilistic frame prediction. Our approach unifies a broad range of tasks, from image segmentation, to novel view synthesis and video interpolation.

We pair this framework with an architecture we term Transframer, which uses U-Net and Transformer components to condition on annotated context frames, and outputs sequences of sparse, compressed image features. Transframer is the state-of-the-art on a variety of video generation benchmarks, is competitive with the strongest models on few-shot view synthesis, and can generate coherent 30 second videos from a single image without any explicit geometric information.

A single generalist Transframer simultaneously produces promising results on 8 tasks, including semantic segmentation, image classification, and optical flow prediction with no task-specific architectural components, demonstrating that multi-task computer vision can be tackled using probabilistic image models.

Our approach can in principle be applied to a wide range of applications that require learning the conditional structure of annotated image-formatted data.

--- https://arxiv.org/abs/2111.05849 Advances in Neural Rendering Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, Yifan Wang, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, Tomas Simon, Christian Theobalt, Matthias Niessner, Jonathan T. Barron, Gordon Wetzstein, Michael Zollhoefer, Vladislav Golyanik 2021-11-10 2021-11-10 [("doi","10.48550/arXiv.2111.05849")] ai/video/generation

Synthesizing photo-realistic images and videos is at the heart of computer graphics and has been the focus of decades of research. Traditionally, synthetic images of a scene are generated using rendering algorithms such as rasterization or ray tracing, which take specifically defined representations of geometry and material properties as input. Collectively, these inputs define the actual scene and what is rendered, and are referred to as the scene representation (where a scene consists of one or more objects). Example scene representations are triangle meshes with accompanied textures (eg. created by an artist), point clouds (eg. from a depth sensor), volumetric grids (eg. from a CT scan), or implicit surface functions (eg. truncated signed distance fields).

The reconstruction of such a scene representation from observations using differentiable rendering losses is known as inverse graphics or inverse rendering. Neural rendering is closely related, and combines ideas from classical computer graphics and machine learning to create algorithms for synthesizing images from real-world observations. Neural rendering is a leap forward towards the goal of synthesizing photo-realistic image and video content.

In recent years, we have seen immense progress in this field through hundreds of publications that show different ways to inject learnable components into the rendering pipeline. This state-of-the-art report on advances in neural rendering focuses on methods that combine classical rendering principles with learned 3D scene representations, often now referred to as neural scene representations. A key advantage of these methods is that they are 3D-consistent by design, enabling applications such as novel viewpoint synthesis of a captured scene.

In addition to methods that handle static scenes, we cover neural scene representations for modeling non-rigidly deforming objects…

--- https://arxiv.org/abs/2106.04283#square NWT: Towards natural audio-to-video generation with representation learning Rayhane Mama, Marc S. Tyndel, Hashiam Kadhim, Cole Clifford, Ragavan Thurairatnam 2021-06-08 2021-06-08 [("doi","10.48550/arXiv.2106.04283")] ai/nn/vae ai/video/generation

In this work we introduce NWT, an expressive speech-to-video model. Unlike approaches that use domain-specific intermediate representations such as pose keypoints, NWT learns its own latent representations, with minimal assumptions about the audio and video content.

To this end, we propose a novel discrete variational autoencoder with adversarial loss, dVAE-Adv, which learns a new discrete latent representation we call Memcodes. Memcodes are straightforward to implement, require no additional loss terms, are stable to train compared with other approaches, and show evidence of interpretability. To predict on the Memcode space, we use an autoregressive encoder-decoder model conditioned on audio. Additionally, our model can control latent attributes in the generated video that are not annotated in the data.

We train NWT on clips from HBO’s Last Week Tonight with John Oliver. NWT consistently scores above other approaches in Mean Opinion Score (MOS) on tests of overall video naturalness, facial naturalness and expressiveness, and lipsync quality.

This work sets a strong baseline for generalized audio-to-video synthesis.

Samples are available at https://next-week-tonight.github.io/NWT/.

--- https://github.com/NVlabs/imaginaire NVlabs/imaginaire: NVIDIA's Deep Imagination Team's PyTorch Library 2020-12-02 ai/video --- https://ai.facebook.com/blog/deepfovea-using-deep-learning-for-foveated-reconstruction-in-ar-vr 2020-12-03 ai/video cs/algorithm/information/compression --- https://arxiv.org/abs/2203.13880 Reinforcement Learning with Action-Free Pre-Training from Videos Younggyo Seo, Kimin Lee, Stephen James, Pieter Abbeel 2022-03-25 2022-03-25 [("doi","10.48550/arXiv.2203.13880")] ai/video/analysis ai/video/generation reinforcement-learning/model

Recent unsupervised pre-training methods have shown to be effective on language and vision domains by learning useful representations for multiple downstream tasks. In this paper, we investigate if such unsupervised pre-training methods can also be effective for vision-based reinforcement learning (RL).

To this end, we introduce a framework that learns representations useful for understanding the dynamics via generative pre-training on videos. Our framework consists of two phases: we pre-train an action-free latent video prediction model, and then utilize the pre-trained representations for efficiently learning action-conditional world models on unseen environments. To incorporate additional action inputs during fine-tuning, we introduce a new architecture that stacks an action-conditional latent prediction model on top of the pre-trained action-free prediction model.

Moreover, for better exploration, we propose a video-based intrinsic bonus that leverages pre-trained representations. We demonstrate that our framework improves both final performances and sample-efficiency of vision-based RL in a variety of manipulation and locomotion tasks. Code is available at Github.

--- https://arxiv.org/abs/2201.12122 Can Wikipedia Help Offline Reinforcement Learning? Machel Reid, Yutaro Yamada, Shixiang Shane Gu 2022-01-28 2022-01-28 [("doi","10.48550/arXiv.2201.12122")] ai/dataset ai/nn/transformer/clip ai/nn/transformer/gpt/2 reinforcement-learning/model/decision-transformer reinforcement-learning/scaling

Fine-tuning reinforcement learning (RL) models has been challenging because of a lack of large scale off-the-shelf datasets as well as high variance in transferability among different environments. Recent work has looked at tackling offline RL from the perspective of sequence modeling with improved results as result of the introduction of the Transformer architecture. However, when the model is trained from scratch, it suffers from slow convergence speeds.

In this paper, we look to take advantage of this formulation of reinforcement learning as sequence modeling and investigate the transferability of pre-trained sequence models on other domains (vision, language) when finetuned on offline RL tasks (control, games). To this end, we also propose techniques to improve transfer between these domains.

Results show consistent performance gains in terms of both convergence speed and reward on a variety of environments, accelerating training by 3–6× and achieving state-of-the-art performance in a variety of tasks using Wikipedia-pretrained and GPT-2 language models.

We hope that this work not only brings light to the potentials of leveraging generic sequence modeling techniques and pre-trained models for RL, but also inspires future work on sharing knowledge between generative modeling tasks of completely different domains.

--- https://arxiv.org/abs/2106.13195#google FitVid: Overfitting in Pixel-Level Video Prediction Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, Dumitru Erhan 2021-06-24 2021-06-24 [("doi","10.48550/arXiv.2106.13195")] ai/video/generation reinforcement-learning/model

An agent that is capable of predicting what happens next can perform a variety of tasks through planning with no additional training. Furthermore, such an agent can internally represent the complex dynamics of the real-world and therefore can acquire a representation useful for a variety of visual perception tasks. This makes predicting the future frames of a video, conditioned on the observed past and potentially future actions, an interesting task which remains exceptionally challenging despite many recent advances. Existing video prediction models have shown promising results on simple narrow benchmarks but they generate low quality predictions on real-life datasets with more complicated dynamics or broader domain.

There is a growing body of evidence that underfitting on the training data is one of the primary causes for the low quality predictions. In this paper, we argue that the inefficient use of parameters in the current video models is the main reason for underfitting. Therefore, we introduce a new architecture, named FitVid, which is capable of severe overfitting on the common benchmarks while having similar parameter count as the current state-of-the-art models.

We analyze the consequences of overfitting, illustrating how it can produce unexpected outcomes such as generating high quality output by repeating the training data, and how it can be mitigated using existing image augmentation techniques.

As a result, FitVid outperforms the current state-of-the-art models across 4 different video prediction benchmarks on 4 different metrics.

--- https://arxiv.org/abs/2205.01972 Sequencer: Deep LSTM for Image Classification Yuki Tatsunami, Masato Taki 2022-05-04 2022-05-04 [("doi","10.48550/arXiv.2205.01972")] ai/nn/rnn

In recent computer vision research, the advent of the Vision Transformer (ViT) has rapidly revolutionized various architectural design efforts: ViT achieved state-of-the-art image classification performance using self-attention found in natural language processing, and MLP-Mixer achieved competitive performance using simple multi-layer perceptrons. In contrast, several studies have also suggested that carefully redesigned convolutional neural networks (CNNs) can achieve advanced performance comparable to ViT without resorting to these new ideas. Against this background, there is growing interest in what inductive bias is suitable for computer vision. Here we propose Sequencer, a novel and competitive architecture alternative to ViT that provides a new perspective on these issues.

Unlike ViTs, Sequencer models long-range dependencies using LSTMs rather than self-attention layers. We also propose a two-dimensional version of Sequencer module, where an LSTM is decomposed into vertical and horizontal LSTMs to enhance performance.

Despite its simplicity, several experiments demonstrate that Sequencer performs impressively well: Sequencer2D-L, with 54M parameters, realizes 84.6% top-1 accuracy on only ImageNet-1K. Not only that, we show that it has good transferability and the robust resolution adaptability on double resolution-band.

--- https://arxiv.org/abs/2205.01917#google CoCa: Contrastive Captioners are Image-Text Foundation Models Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, Yonghui Wu 2022-05-04 2022-05-04 [("doi","10.48550/arXiv.2205.01917")] ai/nn/transformer/clip ai/scaling

[“SOTA results on 19 unimodal/multimodal/alignment tasks including 86.3% zero-shot top-1 ImageNet, 90.6% with a frozen encoder, 91.0% when finetuned.”; FLOSS reimplementation (example)] Exploring large-scale pretrained foundation models is of interest in computer vision because these models can be quickly transferred to many downstream tasks.

This paper presents Contrastive Captioner (CoCa), a minimalist design to pretrain an image-text encoder-decoder foundation model jointly with contrastive loss and captioning loss, thereby subsuming model capabilities from contrastive approaches like CLIP and generative methods like SimVLM.

In contrast to standard encoder-decoder transformers where all decoder layers attend to encoder outputs, CoCa omits cross-attention in the first half of decoder layers to encode unimodal text representations, and cascades the remaining decoder layers which cross-attend to the image encoder for multimodal image-text representations. We apply a contrastive loss between unimodal image and text embeddings, in addition to a captioning loss on the multimodal decoder outputs which predicts text tokens autoregressively. By sharing the same computational graph, the two training objectives are computed efficiently with minimal overhead.

CoCa is pretrained end-to-end and from scratch on both web-scale alt-text data and annotated images by treating all labels simply as text, seamlessly unifying natural language supervision for representation learning.

Empirically, CoCa achieves state-of-the-art performance with zero-shot transfer or minimal task-specific adaptation on a broad range of downstream tasks, spanning visual recognition (ImageNet, Kinetics-400/600/700, Moments-in-Time), crossmodal retrieval (MS COCO, Flickr30k, MSR-VTT), multimodal understanding (VQA, SNLI-VE, NLVR2), and image captioning (MS COCO, nocaps).

Notably on ImageNet classification, CoCa obtains 86.3% zero-shot top-1 accuracy, 90.6% with a frozen encoder and learned classification head, and new state-of-the-art 91.0% top-1 accuracy on ImageNet with a finetuned encoder.

--- https://publicdomainreview.org/essay/petrified-waters Petrified Waters: The Artificial Grottoes of the Renaissance and Beyond 2020-12-03 history/public-domain-review --- https://www.biorxiv.org/content/10.1101/2022.05.04.490594.full Population Genomics of Stone Age Eurasia Morten E. Allentoft, Martin Sikora, Alba Refoyo-Martínez, Evan K. Irving-Pease, Anders Fischer, William Barrie, Andrés Ingason, Jesper Stenderup, Karl-Göran Sjögren, Alice Pearson, Bárbara Sousa da Mota, Bettina Schulz Paulsson, Alma S. Halgren, Marie Louise Schjellerup Jørkov, Fabrice Demeter, Maria Novosolov, Ruairidh Macleod, Lasse Sørensen, Poul Otto Nielsen, Rasmus A. Henriksen, Tharsika Vimala, Hugh McColl, Ashot Margaryan, Melissa Ilardo, Andrew Vaughn, Morten Fischer Mortensen, Anne Birgitte Nielsen, Mikkel Ulfeldt Hede, Peter Rasmussen, Lasse Vinner, Gabriel Renaud, Aaron J. Stern, Theis Zetner Trolle Jensen, Niels N. Johannsen, Hannes Schroeder, Gabriele Scorrano, Abigail Ramsøe, Andrey Skorobogatov, Andrew J. Schork, Anders Rosengren, Anthony Ruter, Alan K. Outram, Aleksey A. Timoshchenko, Alexandra Buzhilova, Alfredo Coppa, Alisa Zubova, Ana Maria Silva, Anders J. Hansen, Andrey Gromov, Andrey V. Logvin, Anne Birgitte Gotfredsen, Bjarne Henning Nielsen, Borja González-Rabanal, Carles Lalueza-Fox, Catriona J. McKenzie, Charleen Gaunitz, Concepción Blasco, Corina Liesau, Cristina Martinez-Labarga, Dmitri V. Pozdnyakov, David Cuenca-Solana, David O. Lordkipanidze, Dmitri Enshin, Domingo C. Salazar-García, Dušan Borić, Elena Kostyleva, Elizaveta V. Veselovskaya, Emma R. Usmanova, Enrico Cappellini, Erik Brinch Petersen, Esben Kannegaard, Francesca Radina, Fulya Eylem Yediay, Henri Duday, Igor Gutiérrez Zugasti, Inna Potekhina, Irina V. Shevnina, Isin Altinkaya, Jean Guilaine, Jesper Hansen, J. Emili Aura Tortosa, João Zilhão, Jorge Vega, Kristoffer Buck Pedersen, Krzysztof Tunia, Lei Zhao, Liudmila N. Mylnikova, Lars Larsson, Laure Metz, Levon Yepiskoposyan, Lisbeth Pedersen, Lucia Sarti, Ludovic Orlando, Ludovic Slimak, Lutz Klassen, Malou Blank, Manuel González-Morales, Mara Silvestrini, Maria Vretemark, Marina S. Nesterova, Marina P. Rykun, Mario Federico Rolfo, Marzena H. Szmyt, Marcin Przybyła, Mauro Calattini, Mikhail Sablin, Miluše Dobisíková, Morten Meldgaard, Morten Johansen, Natalia Berezina, Nick Card, Nikolai A. Saveliev, Olga Poshekhonova, Olga Rickards, Olga V. Lozovskaya, Otto Christian Uldum, Paola Aurino, Pavel Kosintsev, Patrice Courtaud, Patricia Ríos, Peder Mortensen, Per Lotz, Per Lysdahl, Per Persson, Pernille Bangsgaard, Peter de Barros Damgaard, Peter Vang Petersen, Pilar Prieto Martinez, Piotr Włodarczak, Roman V. Smolyaninov, Rikke Maring, Roberto Menduiña, Ruben Badalyan, Rune Iversen, Ruslan Turin, Sergey Vasilyev, Sidsel Wåhlin, Svetlana Borutskaya, Svetlana N. Skochina, Søren Anker Sørensen, Søren H. Andersen, T. Douglas Price, Thomas Jørgensen, Yuri B. Serikov, Vyacheslav I. Molodin, Vaclav Smrcka, Victor Merz, Vivek Appadurai, Vyacheslav Moiseyev, Yvonne Magnusson, Kurt H. Kjær, Niels Lynnerup, Daniel J. Lawson, Peter H. Sudmant, Simon Rasmussen, Thorfinn Korneliussen, Richard Durbin, Rasmus Nielsen, Olivier Delaneau, Thomas Werge, Fernando Racimo, Kristian Kristiansen, Eske Willerslev 2022-05-05 2022-05-05 [("doi","10.1101/2022.05.04.490594")] genetics/selection/natural/human

The transitions from foraging to farming and later to pastoralism in Stone Age Eurasia (c. 11–3 thousand years before present, BP) represent some of the most dramatic lifestyle changes in human evolution.

We sequenced 317 genomes of primarily Mesolithic and Neolithic individuals from across Eurasia combined with radiocarbon dates, stable isotope data, and pollen records. Genome imputation and co-analysis with previously published shotgun sequencing data resulted in >1600 complete ancient genome sequences offering fine-grained resolution into the Stone Age populations.

We observe that: (1) Hunter-gatherer groups were more genetically diverse than previously known, and deeply divergent between western and eastern Eurasia. (2) We identify hitherto genetically undescribed hunter-gatherers from the Middle Don region that contributed ancestry to the later Yamnaya steppe pastoralists; (3) The genetic impact of the Neolithic transition was highly distinct, east and west of a boundary zone extending from the Black Sea to the Baltic. Large-scale shifts in genetic ancestry occurred to the west of this “Great Divide”, including an almost complete replacement of hunter-gatherers in Denmark, while no substantial ancestry shifts took place during the same period to the east. This difference is also reflected in genetic relatedness within the populations, decreasing substantially in the west but not in the east where it remained high until c. 4,000 BP; (4) The second major genetic transformation around 5,000 BP happened at a much faster pace with Steppe-related ancestry reaching most parts of Europe within 1,000-years. Local Neolithic farmers admixed with incoming pastoralists in eastern, western, and southern Europe whereas Scandinavia experienced another near-complete population replacement. Similar dramatic turnover-patterns are evident in western Siberia; (5) Extensive regional differences in the ancestry components involved in these early events remain visible to this day, even within countries. Neolithic farmer ancestry is highest in southern and eastern England while Steppe-related ancestry is highest in the Celtic populations of Scotland, Wales, and Cornwall (this research has been conducted using the UK Biobank resource); (6) Shifts in diet, lifestyle and environment introduced new selection pressures involving at least 21 genomic regions. Most such variants were not universally selected across populations but were only advantageous in particular ancestral backgrounds. Contrary to previous claims, we find that selection on the FADS regions, associated with fatty acid metabolism, began before the Neolithisation of Europe. Similarly, the lactase persistence allele started increasing in frequency before the expansion of Steppe-related groups into Europe and has continued to increase up to the present. Along the genetic cline separating Mesolithic hunter-gatherers from Neolithic farmers, we find statistically-significant correlations with trait associations related to skin disorders, diet and lifestyle and mental health status, suggesting marked phenotypic differences between these groups with very different lifestyles.

This work provides new insights into major transformations in recent human evolution, elucidating the complex interplay between selection and admixture that shaped patterns of genetic variation in modern populations.

--- https://hsyl20.fr/home/files/papers/2022-ghc-modularity.pdf 2020-12-03 cs/haskell --- https://www.unlimiteddreamco.xyz/articles/writing-good-prompts-part-3/ Writing good VQGAN+CLIP prompts part three – environmental modifiers 2020-12-03 ai/nn/transformer/clip --- https://www.unlimiteddreamco.xyz/articles/writing-good-prompts-part-2/ Writing good VQGAN+CLIP prompts part two – artist and genre modifiers 2020-12-03 ai/nn/transformer/clip --- https://www.unlimiteddreamco.xyz/articles/writing-good-prompts-part-1/ Writing good VQGAN+CLIP prompts part one – basic prompts and style modifiers 2020-12-04 ai/nn/transformer/clip --- https://arxiv.org/abs/2010.03934#facebook Prioritized Level Replay Minqi Jiang, Edward Grefenstette, Tim Rocktäschel 2020-10-08 2020-12-04 [("doi","10.48550/arXiv.2010.03934")] reinforcement-learning/meta-learning

Environments with procedurally generated content serve as important benchmarks for testing systematic generalization in deep reinforcement learning. In this setting, each level is an algorithmically created environment instance with a unique configuration of its factors of variation. Training on a prespecified subset of levels allows for testing generalization to unseen levels. What can be learned from a level depends on the current policy, yet prior work defaults to uniform sampling of training levels independently of the policy.

We introduce Prioritized Level Replay (PLR), a general framework for selectively sampling the next training level by prioritizing those with higher estimated learning potential when revisited in the future. We show TD-errors effectively estimate a level’s future learning potential and, when used to guide the sampling procedure, induce an emergent curriculum of increasingly difficult levels.

By adapting the sampling of training levels, PLR improves sample efficiency and generalization on Procgen Benchmark—matching the previous state-of-the-art in test return—and readily combines with other methods. Combined with the previous leading method, PLR raises the state-of-the-art to over 76% improvement in test return relative to standard RL baselines.

--- https://arxiv.org/abs/2012.02096 Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, Sergey Levine 2020-12-03 2020-12-04 [("doi","10.48550/arXiv.2012.02096")] reinforcement-learning/exploration reinforcement-learning/meta-learning

A wide range of reinforcement learning (RL) problems—including robustness, transfer learning, unsupervised RL, and emergent complexity—require specifying a distribution of tasks or environments in which a policy will be trained. However, creating a useful distribution of environments is error prone, and takes a large amount of developer time and effort.

We propose Unsupervised Environment Design (UED) as an alternative paradigm, where developers provide environments with unknown parameters, and these parameters are used to automatically produce a distribution over valid, solvable environments. Existing approaches to automatically generating environments suffer from common failure modes: domain randomization cannot generate structure or adapt the difficulty of the environment to the agent’s learning progress, and minimax adversarial training leads to worst-case environments that are often unsolvable.

To generate structured, solvable environments for our protagonist agent, we introduce a second, antagonist agent that is allied with the environment-generating adversary. The adversary is motivated to generate environments which maximize regret, defined as the difference between the protagonist and antagonist agent’s return. We call our technique Protagonist Antagonist Induced Regret Environment Design (PAIRED). Our experiments demonstrate that PAIRED produces a natural curriculum of increasingly complex environments, and PAIRED agents achieve higher zero-shot transfer performance when tested in highly novel environments.

--- https://arxiv.org/abs/1901.01753#uber Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions Rui Wang, Joel Lehman, Jeff Clune, Kenneth O. Stanley 2019-01-07 2020-12-04 [("doi","10.48550/arXiv.1901.01753")] reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/multi-agent

While the history of machine learning so far largely encompasses a series of problems posed by researchers and algorithms that learn their solutions, an important question is whether the problems themselves can be generated by the algorithm at the same time as they are being solved. Such a process would in effect build its own diverse and expanding curricula, and the solutions to problems at various stages would become stepping stones towards solving even more challenging problems later in the process.

The Paired Open-Ended Trailblazer (POET) algorithm introduced in this paper does just that: it pairs the generation of environmental challenges and the optimization of agents to solve those challenges. It simultaneously explores many different paths through the space of possible problems and solutions and, critically, allows these stepping-stone solutions to transfer between problems if better, catalyzing innovation. The term open-ended signifies the intriguing potential for algorithms like POET to continue to create novel and increasingly complex capabilities without bound.

Our results show that POET produces a diverse range of sophisticated behaviors that solve a wide range of environmental challenges, many of which cannot be solved by direct optimization alone, or even through a direct-path curriculum-building control algorithm introduced to highlight the critical role of open-endedness in solving ambitious challenges. The ability to transfer solutions from one environment to another proves essential to unlocking the full potential of the system as a whole, demonstrating the unpredictable nature of fortuitous stepping stones.

We hope that POET will inspire a new push towards open-ended discovery across many domains, where algorithms like POET can blaze a trail through their interesting possible manifestations and solutions.

--- https://arxiv.org/abs/1507.04296#deepmind Gorila: Massively Parallel Methods for Deep Reinforcement Learning Arun Nair, Praveen Srinivasan, Sam Blackwell, Cagdas Alcicek, Rory Fearon, Alessandro De Maria, Vedavyas Panneershelvam, Mustafa Suleyman, Charles Beattie, Stig Petersen, Shane Legg, Volodymyr Mnih, Koray Kavukcuoglu, David Silver 2015-07-15 2020-12-04 [("doi","10.48550/arXiv.1507.04296")] reinforcement-learning/model-free reinforcement-learning/scaling

We present the first massively distributed architecture for deep reinforcement learning, Gorila.

This architecture uses four main components: parallel actors that generate new behavior; parallel learners that are trained from stored experience; a distributed neural network to represent the value function or behavior policy; and a distributed store of experience. We used our architecture to implement the Deep Q-Network algorithm (DQN).

Our distributed algorithm was applied to 49 games from Atari 2600 games from the Arcade Learning Environment, using identical hyperparameters. Our performance surpassed non-distributed DQN in 41 of the 49 games and also reduced the wall-time required to achieve these results by an order of magnitude on most games.

--- https://arxiv.org/abs/1902.02186#deepmind Distilling Policy Distillation Wojciech Marian Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant M. Jayakumar, Grzegorz Swirszcz, Max Jaderberg 2019-02-06 2020-12-04 [("doi","10.48550/arXiv.1902.02186")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/multi-agent

The transfer of knowledge from one policy to another is an important tool in Deep Reinforcement Learning. This process, referred to as distillation, has been used to great success, for example, by enhancing the optimization of agents, leading to stronger performance faster, on harder domains [26, 32, 5, 8].

Despite the widespread use and conceptual simplicity of distillation, many different formulations are used in practice, and the subtle variations between them can often drastically change the performance and the resulting objective that is being optimized.

In this work, we rigorously explore the entire landscape of policy distillation, comparing the motivations and strengths of each variant through theoretical and empirical analysis. Our results point to 3 distillation techniques, that are preferred depending on specifics of the task. Specifically a newly proposed expected entropy regularized distillation allows for quicker learning in a wide range of situations, while still guaranteeing convergence.

--- https://arxiv.org/abs/1803.03835#deepmind Kickstarting Deep Reinforcement Learning Simon Schmitt, Jonathan J. Hudson, Augustin Zidek, Simon Osindero, Carl Doersch, Wojciech M. Czarnecki, Joel Z. Leibo, Heinrich Kuttler, Andrew Zisserman, Karen Simonyan, S. M. Ali Eslami 2018-03-10 2020-12-04 [("doi","10.48550/arXiv.1803.03835")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/meta-learning reinforcement-learning/multi-agent

We present a method for using previously-trained ‘teacher’ agents to kickstart the training of a new ‘student’ agent.

To this end, we leverage ideas from policy distillation and population based training. Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their teachers in performance.

We show that, on a challenging and computationally-intensive multi-task benchmark (DMLab-30), kickstarted training improves the data efficiency of new agents, making it easier to iterate on their design. We also show that the same kickstarting pipeline can allow a single student agent to leverage multiple ‘expert’ teachers which specialize on individual tasks. In this setting kickstarting yields surprisingly large gains, with the kickstarted agent matching the performance of an agent trained from scratch in almost 10× fewer steps, and surpassing its final performance by 42%.

Kickstarting is conceptually simple and can easily be incorporated into reinforcement learning experiments.

--- https://arxiv.org/abs/1806.01780#deepmind Mix&Match—Agent Curricula for Reinforcement Learning Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Simon Osindero, Nicolas Heess, Razvan Pascanu 2018-06-05 2020-12-04 [("doi","10.48550/arXiv.1806.01780")] reinforcement-learning/exploration reinforcement-learning/multi-agent

We introduce Mix&Match (M&M)—a training framework designed to facilitate rapid and effective learning in RL agents, especially those that would be too slow or too challenging to train otherwise.

The key innovation is a procedure that allows us to automatically form a curriculum over agents. Through such a curriculum we can progressively train more complex agents by, effectively, bootstrapping from solutions found by simpler agents. In contradistinction to typical curriculum learning approaches, we do not gradually modify the tasks or environments presented, but instead use a process to gradually alter how the policy is represented internally.

We show the broad applicability of our method by demonstrating performance gains in 3 different experimental setups: (1) We train an agent able to control more than 700 actions in a challenging 3D first-person task; using our method to progress through an action-space curriculum we achieve both faster training and better final performance than one obtains using traditional methods. (2) We further show that M&M can be used successfully to progress through a curriculum of architectural variants defining an agents internal state. (3) Finally, we illustrate how a variant of our method can be used to improve agent performance in a multitask setting.

--- https://arxiv.org/abs/1711.01012 Policy Optimization by Genetic Distillation Tanmay Gangwani, Jian Peng 2017-11-03 2020-12-04 [("doi","10.48550/arXiv.1711.01012")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/exploration reinforcement-learning/imitation-learning

Genetic algorithms have been widely used in many practical optimization problems. Inspired by natural selection, operators, including mutation, crossover and selection, provide effective heuristics for search and black-box optimization. However, they have not been shown useful for deep reinforcement learning, possibly due to the catastrophic consequence of parameter crossovers of neural networks.

Here, we present Genetic Policy Optimization (GPO), a new genetic algorithm for sample-efficient deep policy optimization. GPO uses imitation learning for policy crossover in the state space and applies policy gradient methods for mutation.

Our experiments on MuJoCo tasks show that GPO as a genetic algorithm is able to provide superior performance over the state-of-the-art policy gradient methods and achieves comparable or higher sample efficiency.

--- https://www.quantamagazine.org/in-test-tubes-rna-molecules-evolve-into-a-tiny-ecosystem-20220505/ 2020-12-04 genetics/selection --- https://mirror.xyz/herndondryhurst.eth/eZG6mucl9fqU897XvJs0vUUMnm5OITpSWN8S-6KWamY 2020-12-05 ai/nn/transformer/gpt/dall-e --- https://www.reddit.com/r/AnimeResearch/comments/txvu3a/anime_x_dalle_2_thread/ 2020-12-05 ai/anime ai/nn/transformer/gpt/dall-e --- https://www.biorxiv.org/content/10.1101/2022.04.03.486855.full Dominance vs. epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles Xuan Xie, Ben Lehner, Xianghua Li 2022-04-04 2022-04-04 [("doi","10.1101/2022.04.03.486855")] genetics/heritable

A central challenge in genetics, evolutionary biology and biotechnology is to understand and predict how mutations combine to alter phenotypes, including molecular activities, fitness and disease. In diploid organisms, two mutations in the same gene can either combine on the same chromosome or on different chromosomes, with interactions between the mutations quantified as epistasis and dominance, respectively. However, a direct comparison of the extent, sign and stability of interactions within and between alleles is lacking.

Here we show that, even in the simplest biophysical systems, interactions between mutations are frequent, context-dependent and different when variants are combined within and between alleles. Whereas protein folding alone generates epistasis, the addition of a single molecular interaction is sufficient to cause dominance. Epistasis and dominance interactions change quantitatively, qualitatively and differently as a system becomes more complicated or the conditions change. Altering the concentration of a ligand can, for example, switch an allele from dominant to recessive.

Our results show that epistasis and dominance should be widely expected in even the simplest biological systems but also reinforce the view that they are plastic system properties and so a formidable challenge to predict. Accurate prediction of epistasis and dominance will require either detailed mechanistic understanding and experimental parameterization or brute-force measurement and learning.

--- https://www.biorxiv.org/content/10.1101/2022.04.13.488108.full Predicting Dog Phenotypes from Genotypes Emily R. Bartusiak, Míriam Barrabés, Aigerim Rymbekova, Julia Gimbernat-Mayol, Cayetana López, Lorenzo Barberis, Daniel Mas Montserrat, Xavier Giró-i-Nieto, Alexander G. Ioannidis 2022-04-14 2022-04-14 [("doi","10.1101/2022.04.13.488108")] genetics/heritable/dog

We analyze dog genotypes (ie. positions of dog DNA sequences that often vary between different dogs) in order to predict the corresponding phenotypes (ie. unique observed characteristics). More specifically, given chromosome data from a dog, we aim to predict the breed, height, and weight.

We explore a variety of linear and non-linear classification and regression techniques to accomplish these 3 tasks. We also investigate the use of a neural network (both in linear and non-linear modes) for breed classification and compare the performance to traditional statistical methods.

We show that linear methods generally outperform or match the performance of non-linear methods for breed classification. However, we show that the reverse is true for height and weight regression.

Finally, we evaluate the results of all of these methods based on the number of input features used in the analysis. We conduct experiments using different fractions of the full genomic sequences, resulting in input sequences ranging from 20 SNPs to ~200k SNPs. In doing so, we explore the impact of using a very limited number of SNPs for prediction. Our experiments demonstrate that these phenotypes in dogs can be predicted with as few as 0.5% of randomly selected SNPs (ie. 992 SNPs) and that dog breeds can be classified with 50% balanced accuracy with as few as 0.02% SNPs (ie. 40 SNPs).

--- https://arxiv.org/abs/2202.05481 Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion Gwanghyeon Ji, Juhyeok Mun, Hyeongjun Kim, Jemin Hwangbo 2022-02-11 2022-02-11 [("doi","10.1109/LRA.2022.3151396")] reinforcement-learning/robot

In this paper, we propose a locomotion training framework where a control policy and a state estimator are trained concurrently. The framework consists of a policy network which outputs the desired joint positions and a state estimation network which outputs estimates of the robot’s states such as the base linear velocity, foot height, and contact probability. We exploit a fast simulation environment to train the networks and the trained networks are transferred to the real robot.

The trained policy and state estimator are capable of traversing diverse terrains such as a hill, slippery plate, and bumpy road. We also demonstrate that the learned policy can run at up to 3.75 m⁄s on normal flat ground and 3.54 m⁄s on a slippery plate with the coefficient of friction of 0.22.

--- https://arxiv.org/abs/2205.02824 Rapid Locomotion via Reinforcement Learning Gabriel B. Margolis, Ge Yang, Kartik Paigwar, Tao Chen, Pulkit Agrawal 2022-05-05 2022-05-05 [("doi","10.48550/arXiv.2205.02824")] reinforcement-learning/robot

Agile maneuvers such as sprinting and high-speed turning in the wild are challenging for legged robots.

We present an end-to-end learned controller that achieves record agility for the MIT Mini Cheetah, sustaining speeds up to 3.9 m⁄s. This system runs and turns fast on natural terrains like grass, ice, and gravel and responds robustly to disturbances.

Our controller is a neural network trained in simulation via reinforcement learning and transferred to the real world. The two key components are (1) an adaptive curriculum on velocity commands and (2) an online system identification strategy for sim-to-real transfer leveraged from prior work.

Videos of the robot’s behaviors are available at: https://agility.csail.mit.edu/.

--- https://github.com/jina-ai/dalle-flow A Human-in-the-Loop workflow for creating HD images from text 2020-12-05 ai/nn/transformer/gpt/dall-e --- https://x.com/dbonneville/status/1522453742095900672 2020-12-05 ai/nn/transformer/gpt/dall-e --- https://www.reddit.com/r/AnimeResearch/comments/ujsmcy/vdiffusion_cc12m_finetuned_on_danbooru_is_halfway/ 2020-12-05 ai/anime ai/nn/transformer/clip/sample --- https://deepmind.google/discover/blog/differentiable-neural-computers/ Differentiable neural computers 2020-12-05 ai/nn/retrieval --- https://arxiv.org/abs/1612.04426#facebook Improving Neural Language Models with a Continuous Cache Edouard Grave, Arm Holdings, Joulin, Nicolas Usunier 2016-12-13 2020-12-05 [("doi","10.48550/arXiv.1612.04426")] ai/nn/retrieval ai/nn/rnn ai/nn/transformer/attention/recurrent

We propose an extension to neural network language models to adapt their prediction to the recent history.

Our model is a simplified version of memory augmented networks, which stores past hidden activations as memory and accesses them through a dot product with the current hidden activation. This mechanism is very efficient and scales to very large memory sizes. We also draw a link between the use of external memory in neural network and cache models used with count based language models.

We demonstrate on several language model datasets that our approach performs better than recent memory augmented networks.

--- /doc/modafinil/2022-heller.pdf Beliefs About Medicines Predict Side-Effects of Placebo Modafinil Monika K. Heller, Sarah C. E. Chapman, Rob Horne 2022-02-22 2022-02-22 [("doi","10.1093/abm/kaab112")] modafinil nootropic

Background: Patients receiving placebo in clinical trials often report side-effects (nocebo effects), but contributing factors are still poorly understood.

Purpose: Using a sham trial of the cognition-enhancing “smart pill” Modafinil we tested whether medication beliefs and other psychological factors predicted detection and attribution of symptoms as side-effects to placebo.

Method: Healthy students (n = 201) completed measures assessing beliefs about medication, perceived sensitivity to medicines, negative affectivity, somatization, and body awareness; 66 were then randomized to receive Deceptive Placebo (told Modafinil—given placebo), 67 to Open Placebo (told placebo—given placebo), and 68 to No Placebo. Memory and attention tasks assessed cognitive enhancement. Nocebo effects were assessed by symptom checklist.

Results: More symptoms were reported in the Deceptive Placebo condition (M = 2.65; SD = 2.27) than Open Placebo (M = 1.92; SD = 2.24; Mann–Whitney U = 1,654, z = 2.30, p = 0.022) or No Placebo (M = 1.68; SD = 1.75, Mann–Whitney U = 1,640, z = 2.74, p = 0.006). Participants were more likely to attribute symptoms to Modafinil side-effects if they believed pharmaceuticals to be generally harmful (incidence rate ratio [IRR] = 1.70, p = 0.019), had higher perceived sensitivity to medicines (IRR = 1.68, p = 0.011), stronger concerns about Modafinil (IRR = 2.10, p < 0.001), and higher negative affectivity (IRR = 2.37, p < 0.001).

Conclusion: Beliefs about medication are potentially modifiable predictors of the nocebo effect. These findings provide insight into side-effect reports to placebo and, potentially, active treatment.

--- https://x.com/peterwildeford/status/1522633978305560576 2020-12-06 ai/nn/transformer/gpt/inner-monologue --- https://sigbovik.org/2019/proceedings.pdf#page=90 Turing-Complete Chess Computation 2020-12-06 cs math/humor reinforcement-learning/chess --- https://sigbovik.org/2019/proceedings.pdf#page=38 2020-12-06 math/humor psychology/chess --- /doc/ai/1985-michie.pdf Human Window on the World Donald Michie 1985-01-01 2020-12-06 ai psychology/chess reinforcement-learning/chess reinforcement-learning/model reinforcement-learning/scaling --- /doc/iq/2017-jerrim.pdf Does teaching children how to play cognitively demanding games improve their educational attainment? Evidence from a Randomized Controlled Trial of chess instruction in England john jerrim 2017-01-01 2020-12-06 iq psychology/chess statistics/bias --- https://journals.sagepub.com/doi/full/10.1177/0963721417712760 Does Far Transfer Exist? Negative Evidence From Chess, Music, and Working Memory Training 2020-12-06 dual-n-back psychology/chess statistics/bias --- https://www.reddit.com/r/reinforcementlearning/comments/a3rbm3/alphazero_shedding_new_light_on_the_grand_games/ 2020-12-06 reinforcement-learning/chess --- https://arxiv.org/abs/2008.04057 The Chess Transformer: Mastering Play using Generative Language Models David Noever, Matt Ciolino, Josh Kalin 2020-08-02 2020-12-06 [("doi","10.48550/arXiv.2008.04057")] ai/nn/transformer/gpt/2 reinforcement-learning/chess

This work demonstrates that natural language transformers can support more generic strategic modeling, particularly for text-archived games. In addition to learning natural language skills, the abstract transformer architecture can generate meaningful moves on a chessboard.

With further fine-tuning, the transformer learns complex gameplay by training on 2.8 million chess games in Portable Game Notation. After 30,000 training steps, OpenAI’s Generative Pre-trained Transformer (GPT-2) optimizes weights for 774 million parameters.

This fine-tuned Chess Transformer generates plausible strategies and displays game formations identifiable as classic openings, such as English or the Slav Exchange.

Finally, in live play, the novel model demonstrates a human-to-transformer interface that correctly filters illegal moves and provides a novel method to challenge the transformer’s chess strategies.

We anticipate future work will build on this transformer’s promise, particularly in other strategy games where features can capture the underlying complex rule syntax from simple but expressive player annotations.

--- https://arxiv.org/abs/2102.13249 Learning Chess Blindfolded: Evaluating Language Models on State Tracking Shubham Toshniwal, Sam Wiseman, Karen Livescu, Kevin Gimpel 2021-02-26 2021-02-26 [("doi","10.48550/arXiv.2102.13249")] ai/nn/transformer/gpt reinforcement-learning/chess reinforcement-learning/model

Transformer language models have made tremendous strides in natural language understanding tasks. However, the complexity of natural language makes it challenging to ascertain how accurately these models are tracking the world state underlying the text. Motivated by this issue, we consider the task of language modeling for the game of chess. Unlike natural language, chess notations describe a simple, constrained, and deterministic domain. Moreover, we observe that the appropriate choice of chess notation allows for directly probing the world state, without requiring any additional probing-related machinery.

We find that: (a) With enough training data, transformer language models can learn to track pieces and predict legal moves with high accuracy when trained solely on move sequences. (b) For small training sets providing access to board state information during training can yield improvements. (c) The success of transformer language models is dependent on access to the entire game history i.e. “full attention”. Approximating this full attention results in a performance drop.

We propose this testbed as a benchmark for future work on the development and analysis of transformer language models.

--- /doc/iq/2019-vaci.pdf The joint influence of intelligence and practice on skill development throughout the life span Nemanja Vaci, Peter Edelsbrunner, Elsbeth Stern, Aljoscha Neubauer, Merim Bilalić, Roland H. Grabner 2019-08-26 2020-12-06 [("doi","10.1073/pnas.1819086116")] iq psychology/chess

The relative importance of different factors in the development of human skills has been extensively discussed. Research on expertise indicates that focused practice may be the sole determinant of skill, while intelligence researchers underline the relative importance of abilities at even the highest level of skill. There is indeed a large body of research that acknowledges the role of both factors in skill development and retention.

Instead of focusing on the 2 factors, intelligence and practice, in isolation, here we look at their interplay throughout development. In a longitudinal study that tracked chess players throughout their careers, we show that both intelligence and practice positively affect the acquisition and retention of chess skill.

Importantly, the nonlinear interaction between the 2 factors revealed that more intelligent individuals benefited more from practice. With the same amount of practice, they acquired chess skill more quickly than less intelligent players, reached a higher peak performance, and arrested decline in older age.

Our research demonstrates the futility of scrutinizing the relative importance of highly intertwined factors in human development.

--- /doc/sociology/2022-vishkin.pdf Queen’s Gambit Declined: The Gender-Equality Paradox in Chess Participation Across 160 Countries Allon Vishkin 2022-01-11 2022-01-11 [("doi","10.1177/09567976211034806")] psychology/chess sociology

The gender-equality paradox refers to the puzzling finding that societies with more gender equality demonstrate larger gender differences across a range of phenomena, most notably in the proportion of women who pursue degrees in science, technology, engineering, and math.

The present investigation demonstrates across two different measures of gender equality that this paradox extends to chess participation (n = 803,485 across 160 countries; age range: 3–100 years), specifically that women participate more often in countries with less gender equality. Previous explanations for the paradox fail to account for this finding.

Instead, consistent with the notion that gender equality reflects a generational shift, mediation analyses suggest that the gender-equality paradox in chess is driven by the greater participation of younger players in countries with less gender equality. A curvilinear effect of gender equality on the participation of female players was also found, demonstrating that gender differences in chess participation are largest at the highest and lowest ends of the gender-equality spectrum.

--- https://en.chessbase.com/post/komodo-8-the-smartphone-vs-desktop-challenge Komodo 8: the smartphone vs desktop challenge 2020-12-07 ai/scaling economics/experience-curve reinforcement-learning/chess --- https://en.wikipedia.org/wiki/Stockfish_(chess)#Fishtest Stockfish (chess) § Fishtest 2020-12-07 ai/scaling reinforcement-learning/chess --- https://hebdenbridgechessclub.blogspot.com/2011/02/hardest-chess-problem-in-world.html The Hardest chess problem in the world? 2020-12-07 economics math/humor --- https://www.nytimes.com/2021/11/22/sports/magnus-carlsen-chess.html How Magnus Carlsen Turned Chess Skill Into a Business Empire 2020-12-07 economics math/humor psychology/chess --- https://www.nytimes.com/2020/12/24/business/chess-sets-cost-queens-gambit.html What Are You Paying For in a $300 Chess Set? Mostly the Knights 2020-12-07 economics math/humor --- https://marginalrevolution.com/marginalrevolution/2020/05/the-new-economics-of-chess.html The new economics of chess 2020-12-07 economics math/humor psychology/chess --- https://www.protocol.com/chess-streaming-twitch-hikaru-botez 2020-12-07 economics math/humor psychology/chess --- https://www.newyorker.com/culture/rabbit-holes/the-most-popular-chess-streamer-on-twitch The Most Popular Chess Streamer on Twitch 2020-12-07 economics math/humor psychology/chess --- https://proceedings.neurips.cc/paper/2021/hash/ccf8111910291ba472b385e9c5f59099-Abstract.html Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess 2020-12-07 psychology/chess --- https://lichess.org/@/lichess/blog/exact-exacting-who-is-the-most-accurate-world-champion/YafSBxEA 2020-12-08 psychology/chess --- https://www.wsj.com/articles/world-chess-championship-magnus-carlsen-ian-nepomniachtchi-seconds-11638167905 Before Ian Nepomniachtchi rose to become the world No. 1’s challenger, he was Magnus Carlsen’s second—the elite players who moonlight as study companions around the biggest matches. 2020-12-08 psychology/chess --- https://link.springer.com/article/10.1007/s10640-021-00618-1 2020-12-08 psychology/chess --- https://www.nytimes.com/2021/07/13/sports/chess-karjakin-mishra-grandmasters.html The Dark Side of Chess: When Is a Grandmaster Not So Grand? 2020-12-08 psychology/chess --- https://aiimpacts.org/time-for-ai-to-cross-the-human-performance-range-in-chess/ Time for AI to cross the human performance range in chess 2020-12-08 economics/experience-curve psychology/chess reinforcement-learning/chess reinforcement-learning/scaling --- http://www.infinitychess.com/Page/Public/Article/DefaultArticle.aspx?id=118 2020-12-08 psychology/chess reinforcement-learning/chess --- https://www.techrepublic.com/article/the-role-of-computers-in-planning-chess-strategy/ 2020-12-08 psychology/chess reinforcement-learning/chess --- https://rjlipton.com/2012/05/31/chess-knightmare-and-turings-dream/ 2020-12-08 psychology/chess --- https://www.nytimes.com/2016/07/09/nyregion/4-young-chess-masters-tackle-a-persistent-puzzle-the-gender-gap.html 4 Young Chess Masters Tackle a Persistent Puzzle: The Gender Gap 2020-12-08 psychology/chess --- https://nautil.us/issue/36/aging/learning-chess-at-40 2020-12-08 psychology/chess --- https://www.nybooks.com/articles/2010/02/11/the-chess-master-and-the-computer/ The Chess Master and the Computer Garry Kasparov 2020-12-08 psychology/chess reinforcement-learning/chess --- https://en.chessbase.com/post/correspondence-chess-the-draw-problem Correspondence Chess – the draw problem 2020-12-09 psychology/chess --- https://marginalrevolution.com/marginalrevolution/2013/11/what-are-humans-still-good-for-the-turning-point-in-freestyle-chess-may-be-approaching.html What are humans still good for? The turning point in Freestyle chess may be approaching 2020-12-09 psychology/chess reinforcement-learning/chess --- https://www.nytimes.com/2012/03/20/science/a-computer-program-to-detect-possible-cheating-in-chess.html A Computer Program to Detect Possible Cheating in Chess 2020-12-09 psychology/chess reinforcement-learning/chess --- http://dealbook.nytimes.com/2011/09/29/good-at-chess-a-hedge-fund-may-want-to-hire-you/ 2020-12-09 psychology/chess --- https://cacm.acm.org/research/reimagining-chess-with-alphazero/ 2020-12-09 reinforcement-learning/chess --- https://www.sciencefocus.com/future-technology/far-from-being-just-a-game-chess-has-always-been-a-part-of-me/ 2020-12-09 reinforcement-learning/chess --- http://www.talkchess.com/forum/viewtopic.php?t=59003 2020-12-09 reinforcement-learning/chess --- http://rjlipton.wordpress.com/2014/12/28/the-new-chess-world-champion/ 2020-12-09 reinforcement-learning/chess --- https://www.biorxiv.org/content/10.1101/2022.05.06.490973.full Amplification is the Primary Mode of Gene-by-Sex Interaction in Complex Human Traits Carrie Zhu, Matthew J. Ming, Jared M. Cole, Mark Kirkpatrick, Arbel Harpak 2022-05-08 2022-05-08 [("doi","10.1101/2022.05.06.490973")] genetics/heritable/correlation

Sexual dimorphism is observed in many complex traits and diseases and is suspected to be in part due to widespread gene-by-sex interactions (GxSex). To date, empirical evidence for GxSex in GWAS data has been elusive. We hypothesized that GxSex may be pervasive but largely missed by current approaches if it acts primarily through sex differences in the magnitude of many genetic effects (“amplification”), regulated by a shared cue such as a sex hormone, rather than differences in the identity of causal variants or the direction of their effect.

To test this hypothesis, we inferred the genetic covariance structure between males and females across 27 physiological traits in the UK Biobank. We found amplification to be a pervasive mode of GxSex across traits. As one example, we estimate that 38% of variants have a greater effect on urate levels in females than males. For some traits, notably those related to body mass, testosterone levels are associated with the magnitude of genetic effects in both males and females, but the association is opposite in sign between the sexes.

Finally, we developed a novel test of sexually-antagonistic viability selection linking GxSex signals to allele frequency divergence between adult males and females. Using independent allele frequency data, we find marginally-significant evidence for contemporary sexually-antagonistic selection on genetic variation associated with testosterone.

In summary, our results suggest that the systematic amplification of genetic effects is a common mode of GxSex that may contribute to sexual dimorphism and fuel its evolution.

--- https://x.com/thisiswrenn/status/1523182708385452032 2020-12-09 ai/nn/transformer/gpt/codex --- https://www.quantamagazine.org/quantum-mischief-rewrites-the-laws-of-cause-and-effect-20210311/ 2020-12-09 cs --- https://www.wsj.com/articles/the-robots-are-coming-for-garment-workers-thats-good-for-the-u-s-bad-for-poor-countries-1518797631 The Robots Are Coming for Garment Workers. That’s Good for the U.S., Bad for Poor Countries: Automation is reaching into trades that once seemed immune, transforming sweatshops in places like Bangladesh and bringing production back to America 2020-12-10 economics/automation reinforcement-learning/robot --- https://www.bloomberg.com/news/articles/2012-11-29/the-march-of-robots-into-chinese-factories The March of Robots Into Chinese Factories 2020-12-10 economics/automation --- https://research.google/blog/scalable-deep-reinforcement-learning-for-robotic-manipulation/ 2020-12-10 ai/scaling reinforcement-learning/robot --- https://arxiv.org/abs/1806.10293 QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, Sergey Levine 2018-06-27 2020-12-10 [("doi","10.48550/arXiv.1806.10293")] reinforcement-learning/offline reinforcement-learning/robot reinforcement-learning/scaling

In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach. We study this problem in the context of grasping, a long-standing challenge in robotic manipulation. In contrast to static learning behaviors that choose a grasp point and then execute the desired grasp, our method enables closed-loop vision-based control, whereby the robot continuously updates its grasp strategy based on the most recent observations to optimize long-horizon grasp success.

To that end, we introduce QT-Opt, a scalable self-supervised vision-based reinforcement learning framework that can leverage over 580k real-world grasp attempts to train a deep neural network Q-function with over 1.2M parameters to perform closed-loop, real-world grasping that generalizes to 96% grasp success on unseen objects. Aside from attaining a very high success rate, our method exhibits behaviors that are quite distinct from more standard grasping systems: using only RGB vision-based perception from an over-the-shoulder camera, our method automatically learns regrasping strategies, probes objects to find the most effective grasps, learns to reposition objects and perform other non-prehensile pre-grasp manipulations, and responds dynamically to disturbances and perturbations.

--- https://deepmind.google/discover/blog/learning-to-write-programs-that-generate-images/ Learning to write programs that generate images 2020-12-10 ai/nn/gan reinforcement-learning/robot --- https://research.google/blog/how-robots-can-acquire-new-skills-from-their-shared-experience/ How Robots Can Acquire New Skills from Their Shared Experience 2020-12-10 reinforcement-learning/robot --- https://arxiv.org/abs/1603.02199 Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection Sergey Levine, Peter Pastor, Alex Krizhevsky, Deirdre Quillen 2016-03-07 2020-12-10 [("doi","10.48550/arXiv.1603.02199")] ai/nn/cnn reinforcement-learning/robot

We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose. This requires the network to observe the spatial relationship between the gripper and objects in the scene, thus learning hand-eye coordination. We then use this network to servo the gripper in real time to achieve successful grasps.

To train our network, we collected over 800,000 grasp attempts over the course of two months, using 6–14 robotic manipulators at any given time, with differences in camera placement and hardware. Our experimental evaluation demonstrates that our method achieves effective real-time control, can successfully grasp novel objects, and corrects mistakes by continuous servoing.

--- https://arxiv.org/abs/1610.00633#google Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates Shixiang Gu, Ethan Holly, Timothy Lillicrap, Sergey Levine 2016-10-03 2020-12-10 [("doi","10.48550/arXiv.1610.00633")] reinforcement-learning/robot

Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered policy representations and human-supplied demonstrations. Deep reinforcement learning alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity.

In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on off-policy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously.

Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations.

--- https://arxiv.org/abs/1610.04286 Sim-to-Real Robot Learning from Pixels with Progressive Nets Andrei A. Rusu, Mel Vecerik, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, Raia Hadsell 2016-10-13 2020-12-10 [("doi","10.48550/arXiv.1610.04286")] reinforcement-learning/robot

Applying end-to-end learning to solve complex, interactive, pixel-driven control tasks on a robot is an unsolved problem. Deep Reinforcement Learning algorithms are too slow to achieve performance on a real robot, but their potential has been demonstrated in simulated environments. We propose using progressive networks to bridge the reality gap and transfer learned policies from simulation to the real world. The progressive net approach is a general framework that enables reuse of everything from low-level visual features to high-level policies for transfer to new tasks, enabling a compositional, yet simple, approach to building complex skills.

We present an early demonstration of this approach with a number of experiments in the domain of robot manipulation that focus on bridging the reality gap. Unlike other proposed approaches, our real-world experiments demonstrate successful task learning from raw visual input on a fully actuated robot manipulator. Moreover, rather than relying on model-based trajectory optimization, the task learning is accomplished using only deep reinforcement learning and sparse rewards.

--- https://arxiv.org/abs/1805.07813 Learning Real-World Robot Policies by Dreaming AJ Piergiovanni, Alan Wu, Michael S. Ryoo 2018-05-20 2020-12-10 [("doi","10.48550/arXiv.1805.07813")] reinforcement-learning/robot

Learning to control robots directly based on images is a primary challenge in robotics.

However, many existing reinforcement learning approaches require iteratively obtaining millions of robot samples to learn a policy, which can take time. In this paper, we focus on learning a realistic world model capturing the dynamics of scene changes conditioned on robot actions. Our dreaming model can emulate samples equivalent to a sequence of images from the actual environment, technically by learning an action-conditioned future representation/scene regressor.

This allows the agent to learn action policies (ie. visuomotor policies) by interacting with the dreaming model rather than the real-world. We experimentally confirm that our dreaming model enables robot learning of policies that transfer to the real-world.

--- https://arxiv.org/abs/2011.09792 The Robot Household Marathon Experiment Gayane Kazhoyan, Simon Stelter, Franklin Kenghagho Kenfack, Sebastian Koralewski, Michael Beetz 2020-11-19 2020-12-11 [("doi","10.48550/arXiv.2011.09792")] reinforcement-learning/robot

In this paper, we present an experiment, designed to investigate and evaluate the scalability and the robustness aspects of mobile manipulation. The experiment involves performing variations of mobile pick and place actions and opening/closing environment containers in a human household.

The robot is expected to act completely autonomously for extended periods of time.

We discuss the scientific challenges raised by the experiment as well as present our robotic system that can address these challenges and successfully perform all the tasks of the experiment.

We present empirical results and the lessons learned as well as discuss where we hit limitations.

--- https://bair.berkeley.edu/blog/2019/05/20/solar/ Model-Based Reinforcement Learning from Pixels with Structured Latent Variable Models 2020-12-11 reinforcement-learning/robot --- https://bair.berkeley.edu/blog/2019/05/28/end-to-end/ End-to-End Deep Reinforcement Learning without Reward Engineering 2020-12-11 reinforcement-learning/robot --- https://en.wikipedia.org/wiki/Pieter_Abbeel Pieter Abbeel 2020-12-11 reinforcement-learning/robot --- https://www.reddit.com/r/reinforcementlearning/search?q=flair%3ARobot&sort=top&restrict_sr=on&t=year 2020-12-11 reinforcement-learning/robot --- https://openai.com/research/learning-dexterity Learning Dexterity [blog] 2020-12-11 reinforcement-learning/robot --- https://sites.google.com/site/deeproboticmanipulation/ 2020-12-11 reinforcement-learning/robot --- https://spectrum.ieee.org/hard-for-robots-autonomous-household-chores It’s (Still) Really Hard for Robots to Autonomously Do Household Chores 2020-12-11 reinforcement-learning/robot --- https://www.reddit.com/r/reinforcementlearning/search?q=flair%3ARobot&restrict_sr=on&include_over_18=on 2020-12-11 reinforcement-learning/robot --- https://www.youtube.com/watch?v=cXaic_k80uM 2020-12-11 reinforcement-learning/robot --- /doc/psychology/neuroscience/1999-blakemore.pdf Spatio-Temporal Prediction Modulates the Perception of Self-Produced Stimuli Sarah-J. Blakemore, Chris D. Frith, Daniel M. Wolpert 1999-09-01 2020-12-11 [("doi","10.1162/089892999563607")] psychology/neuroscience reinforcement-learning/robot

We investigated why self-produced tactile stimulation is perceived as less intense than the same stimulus produced externally. A tactile stimulus on the palm of the right hand was either externally produced, by a robot, or self-produced by the subject. In the conditions in which the tactile stimulus was self-produced, subjects moved the arm of a robot with their left hand to produce the tactile stimulus on their right hand via a second robot. Subjects were asked to rate the intensity of the tactile sensation and consistently rated self-produced tactile stimuli as less tickly, intense, and pleasant than externally produced tactile stimuli.

Using this robotic setup, we were able to manipulate the correspondence between the action of the subjects’ left hand and the tactile stimulus on their right hand. First, we parametrically varied the delay between the movement of the left hand and the resultant movement of the tactile stimulus on the right hand. Second, we implemented varying degrees of trajectory perturbation and varied the direction of the tactile stimulus movement as a function of the direction of left-hand movement. The tickliness rating increased with increasing delay and trajectory perturbation.

This suggests that self-produced movements attenuate the resultant tactile sensation and that a necessary requirement of this attenuation is that the tactile stimulus and its causal motor command correspond in time and space. We propose that the extent to which self-produced tactile sensation is attenuated (ie. its tickliness) is proportional to the error between the sensory feedback predicted by an internal forward model of the motor system and the actual sensory feedback produced by the movement.

--- https://arxiv.org/abs/1504.00702#bair End-to-End Training of Deep Visuomotor Policies Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel 2015-04-02 2020-12-12 [("doi","10.48550/arXiv.1504.00702")] ai/nn/cnn reinforcement-learning/robot

Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to-end provide better performance than training each component separately?

To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot’s motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a partially observed guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method.

We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.

--- https://arxiv.org/abs/1702.03465 Enabling Robots to Communicate their Objectives Sandy H. Huang, David Held, Pieter Abbeel, Anca D. Dragan 2017-02-11 2020-12-12 [("doi","10.15607/RSS.2017.XIII.059")] reinforcement-learning/robot

The overarching goal of this work is to efficiently enable end-users to correctly anticipate a robot’s behavior in novel situations. Since a robot’s behavior is often a direct result of its underlying objective function, our insight is that end-users need to have an accurate mental model of this objective function in order to understand and predict what the robot will do. While people naturally develop such a mental model over time through observing the robot act, this familiarization process may be lengthy.

Our approach reduces this time by having the robot model how people infer objectives from observed behavior, and then it selects those behaviors that are maximally informative. The problem of computing a posterior over objectives from observed behavior is known as Inverse Reinforcement Learning (IRL), and has been applied to robots learning human objectives. We consider the problem where the roles of human and robot are swapped.

Our main contribution is to recognize that unlike robots, humans will not be exact in their IRL inference. We thus introduce two factors to define candidate approximate-inference models for human learning in this setting, and analyze them in a user study in the autonomous driving domain.

We show that certain approximate-inference models lead to the robot generating example behaviors that better enable users to anticipate what it will do in novel situations. Our results also suggest, however, that additional research is needed in modeling how humans extrapolate from examples of robot behavior.

--- https://arxiv.org/abs/1704.03073#deepmind Data-efficient Deep Reinforcement Learning for Dexterous Manipulation Ivaylo Popov, Nicolas Heess, Timothy Lillicrap, Roland Hafner, Gabriel Barth-Maron, Matej Vecerik, Thomas Lampe, Yuval Tassa, Tom Erez, Martin Riedmiller 2017-04-10 2020-12-12 [("doi","10.48550/arXiv.1704.03073")] ai/nn reinforcement-learning/robot

Deep learning and reinforcement learning methods have recently been used to solve a variety of problems in continuous control domains. An obvious application of these techniques is dexterous manipulation tasks in robotics which are difficult to solve using traditional control theory or hand-engineered approaches. One example of such a task is to grasp an object and precisely stack it on another. Solving this difficult and practically relevant problem in the real world is an important long-term goal for the field of robotics.

Here we take a step towards this goal by examining the problem in simulation and providing models and techniques aimed at solving it. We introduce two extensions to the Deep Deterministic Policy Gradient algorithm (DDPG), a model-free Q-learning based method, which make it statistically-significantly more data-efficient and scalable. Our results show that by making extensive use of off-policy data and replay, it is possible to find control policies that robustly grasp objects and stack them.

Further, our results hint that it may soon be feasible to train successful stacking policies by collecting interactions on real robots.

--- https://arxiv.org/abs/1806.10019 Adversarial Active Exploration for Inverse Dynamics Model Learning Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee 2018-06-26 2020-12-12 [("doi","10.48550/arXiv.1806.10019")] reinforcement-learning/robot

We present an adversarial active exploration for inverse dynamics model learning, a simple yet effective learning scheme that incentivizes exploration in an environment without any human intervention. Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each other. The former collects training samples for the latter, with an objective to maximize the error of the latter.

The latter is trained with samples collected by the former, and generates rewards for the former when it fails to predict the actual action taken by the former. In such a competitive setting, the DRL agent learns to generate samples that the inverse dynamics model fails to predict correctly, while the inverse dynamics model learns to adapt to the challenging samples. We further propose a reward structure that ensures the DRL agent to collect only moderately hard samples but not overly hard ones that prevent the inverse model from predicting effectively.

We evaluate the effectiveness of our method on several robotic arm and hand manipulation tasks against multiple baseline models. Experimental results show that our method is comparable to those directly trained with expert demonstrations, and superior to the other baselines even without any human priors.

--- https://arxiv.org/abs/1911.09874 SwarmCloak: Landing of a Swarm of Nano-Quadrotors on Human Arms Evgeny Tsykunov, Ruslan Agishev, Roman Ibrahimov, Luiza Labazanova, Taha Moriyama, Hiroyuki Kajimoto, Dzmitry Tsetserukou 2019-11-22 2020-12-12 [("doi","10.1145/3355049.3360542")] reinforcement-learning/robot technology

We propose a novel system SwarmCloak for landing of a fleet of 4 flying robots on the human arms using light-sensitive landing pads with vibrotactile feedback.

We developed two types of wearable tactile displays with vibromotors which are activated by the light emitted from the LED array at the bottom of quadcopters. In a user study, participants were asked to adjust the position of the arms to land up to two drones, having only visual feedback, only tactile feedback, or visual-tactile feedback.

The experiment revealed that when the number of drones increases, tactile feedback plays a more important role in accurate landing and operator’s convenience. An important finding is that the best landing performance is achieved with the combination of tactile and visual feedback.

The proposed technology could have a strong impact on the human-swarm interaction, providing a new level of intuitiveness and engagement into the swarm deployment just right from the skin surface.

--- https://arxiv.org/abs/1909.12200#deepmind Scaling data-driven robotics with reward sketching and batch reinforcement learning Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerik, Oleg Sushkov, David Barker, Jonathan Scholz, Misha Denil, Nando de Freitas, Ziyu Wang 2019-09-26 2020-12-12 [("doi","10.48550/arXiv.1909.12200")] ai/nn/transformer/gpt ai/scaling reinforcement-learning/offline reinforcement-learning/robot

We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions.

We show how to apply this framework to accomplish 3 different object manipulation tasks on a real robot platform. Given demonstrations of a task together with task-agnostic recorded experience, we use a special form of human annotation as supervision to learn a reward function, which enables us to deal with real-world tasks where the reward signal cannot be acquired directly.

Learned rewards are used in combination with a large dataset of experience from different tasks to learn a robot policy offline using batch RL. We show that using our approach it is possible to train agents to perform a variety of challenging manipulation tasks including stacking rigid objects and handling cloth.

--- https://arxiv.org/abs/2004.12919#uber First return, then explore Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, Jeff Clune 2020-04-27 2020-12-12 [("doi","10.1038/s41586-020-03157-9")] reinforcement-learning/exploration reinforcement-learning/robot

The promise of reinforcement learning is to solve complex sequential decision problems by specifying a high-level reward function only. However, RL algorithms struggle when, as is often the case, simple and intuitive rewards provide sparse and deceptive feedback. Avoiding these pitfalls requires thoroughly exploring the environment, but despite substantial investments by the community, creating algorithms that can do so remains one of the central challenges of the field.

We hypothesize that the main impediment to effective exploration originates from algorithms forgetting how to reach previously visited states (“detachment”) and from failing to first return to a state before exploring from it (“derailment”). We introduce Go-Explore, a family of algorithms that addresses these two challenges directly through the simple principles of explicitly remembering promising states and first returning to such states before exploring.

Go-Explore solves all heretofore unsolved Atari games (those for which algorithms could not previously outperform humans when evaluated following current community standards) and surpasses the state-of-the-art on all hard-exploration games, with orders of magnitude improvements on the grand challenges Montezuma’s Revenge and Pitfall. We also demonstrate the practical potential of Go-Explore on a challenging and extremely sparse-reward robotics task. Additionally, we show that adding a goal-conditioned policy can further improve Go-Explore’s exploration efficiency and enable it to handle stochasticity throughout training.

The striking contrast between the substantial performance gains from Go-Explore and the simplicity of its mechanisms suggests that remembering promising states, returning to them, and exploring from them is a powerful and general approach to exploration, an insight that may prove critical to the creation of truly intelligent learning agents.

--- https://arxiv.org/abs/2006.11751#intel Sample Factory: Egocentric 3D Control from Pixels at 100,000 FPS with Asynchronous Reinforcement Learning Aleksei Petrenko, Zhehui Huang, Tushar Kumar, Gaurav Sukhatme, Vladlen Koltun 2020-06-21 2020-12-12 [("doi","10.48550/arXiv.2006.11751")] ai/scaling/hardware reinforcement-learning/robot reinforcement-learning/scaling

Increasing the scale of reinforcement learning experiments has allowed researchers to achieve unprecedented results in both training sophisticated agents for video games, and in sim-to-real transfer for robotics. Typically, such experiments rely on large distributed systems and require expensive hardware setups, limiting wider access to this exciting area of research. In this work, we aim to solve this problem by optimizing the efficiency and resource usage of reinforcement learning algorithms instead of relying on distributed computation.

We present the “Sample Factory”, a high-throughput training system optimized for a single-machine setting. Our architecture combines a highly efficient, asynchronous, GPU-based sampler with off-policy correction techniques, allowing us to achieve throughput higher than 105 environment frames/second on non-trivial control problems in 3D without sacrificing sample efficiency.

We extend Sample Factory to support self-play and population-based training and apply these techniques to train highly capable agents for a multiplayer first-person shooter game.

The source code is available at https://github.com/alex-petrenko/sample-factory.

--- https://arxiv.org/abs/2010.02193#deepmind DreamerV2: Mastering Atari with Discrete World Models Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, Jimmy Ba 2020-10-05 2020-12-12 [("doi","10.48550/arXiv.2010.02193")] reinforcement-learning/robot

Intelligent agents need to generalize from past experience to achieve goals in complex environments. World models facilitate such generalization and allow learning behaviors from imagined outcomes to increase sample-efficiency. While learning world models from image inputs has recently become feasible for some tasks, modeling Atari games accurately enough to derive successful behaviors has remained an open challenge for many years.

We introduce DreamerV2, a reinforcement learning agent that learns behaviors purely from predictions in the compact latent space of a powerful world model. The world model uses discrete representations and is trained separately from the policy.

DreamerV2 constitutes the first agent that achieves human-level performance on the Atari benchmark of 55 tasks by learning behaviors inside a separately trained world model. With the same computational budget and wall-clock time, Dreamer V2 reaches 200M frames and surpasses the final performance of the top single-GPU agents IQN and Rainbow.

DreamerV2 is also applicable to tasks with continuous actions, where it learns an accurate world model of a complex humanoid robot and solves stand-up and walking from only pixel inputs.

--- https://arxiv.org/abs/2108.10470#nvidia Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, Gavriel State 2021-08-24 2021-08-24 [("doi","10.48550/arXiv.2108.10470")] ai/scaling/hardware reinforcement-learning/robot reinforcement-learning/scaling

Isaac Gym offers a high performance learning platform to train policies for a wide variety of robotics tasks directly on GPU.

Both physics simulation and the neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through any CPU bottlenecks.

This leads to blazing fast training times for complex robotics tasks on a single GPU with 1–2 orders of magnitude improvements compared to conventional RL training that uses a CPU based simulator and GPU for neural networks.

We host the results and videos at https://sites.google.com/view/isaacgym-nvidia and Isaac Gym can be downloaded at https://developer.nvidia.com/isaac-gym.

--- https://arxiv.org/abs/2110.05457 Legged Robots that Keep on Learning: Fine-Tuning Locomotion Policies in the Real World Laura Smith, J. Chase Kew, Xue Bin Peng, Sehoon Ha, Jie Tan, Sergey Levine 2021-10-11 2021-10-11 [("doi","10.48550/arXiv.2110.05457")] reinforcement-learning/robot

Legged robots are physically capable of traversing a wide range of challenging environments, but designing controllers that are sufficiently robust to handle this diversity has been a long-standing challenge in robotics. Reinforcement learning presents an appealing approach for automating the controller design process and has been able to produce remarkably robust controllers when trained in a suitable range of environments. However, it is difficult to predict all likely conditions the robot will encounter during deployment and enumerate them at training-time. What if instead of training controllers that are robust enough to handle any eventuality, we enable the robot to continually learn in any setting it finds itself in? This kind of real-world reinforcement learning poses a number of challenges, including efficiency, safety, and autonomy.

To address these challenges, we propose a practical robot reinforcement learning system for fine-tuning locomotion policies in the real world. We demonstrate that a modest amount of real-world training can substantially improve performance during deployment, and this enables a real A1 quadrupedal robot to autonomously fine-tune multiple locomotion skills in a range of environments, including an outdoor lawn and a variety of indoor terrains.

--- https://www.biorxiv.org/content/10.1101/2022.05.06.490983.full MegaBayesianAlphabet: Mega-scale Bayesian Regression methods for genome-wide prediction and association studies with thousands of traits Jiayi Qu, Daniel E. Runcie, Hao Cheng 2022-05-08 2022-05-08 [("doi","10.1101/2022.05.06.490983")] genetics/heritable/correlation statistics/variance-component

Large-scale phenotype data are expected to increase the accuracy of genome-wide prediction and the power of genome-wide association analyses. However, genomic analyses of high-dimensional, highly correlated data are challenging.

We developed MegaBayesianAlphabet to simultaneously analyze genetic variants underlying thousands of traits using the flexible priors of the Bayesian Alphabet family. As a demonstration, we implemented the BayesC prior in the R package MegaLMM and applied it to both simulated and real data sets.

Our analyses show that the resulting model MegaBayesC can effectively use high-dimensional phenotypic data to improve the accuracy of genetic value prediction, the reliability of marker discovery, and the accuracy of marker effect size estimation in genome-wide analyses.

--- https://www.reddit.com/r/GPT3/comments/ukbba5/the_rickrollian_language_of_william_shakespeare/ 2020-12-13 ai/text-style-transfer --- /gpt-3#literary-parodies GPT-3 Creative Fiction § Literary Parodies Gwern 2020-06-19 2020-06-19 ai/nn/transformer/gpt/3/fiction ai/nn/transformer/gpt/3/poetry ai/scaling ai/text-style-transfer fiction/humor --- https://blog.ml.cmu.edu/2022/05/06/barl/ An Experimental Design Perspective on Model-Based Reinforcement Learning [blog] 2020-12-13 reinforcement-learning/exploration statistics/bayes --- https://arxiv.org/abs/2112.05244 An Experimental Design Perspective on Model-Based Reinforcement Learning Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger 2021-12-09 2021-12-09 [("doi","10.48550/arXiv.2112.05244")] reinforcement-learning/exploration reinforcement-learning/model statistics/bayes

[blog] In many practical applications of RL, it is expensive to observe state transitions from the environment. For example, in the problem of plasma control for nuclear fusion, computing the next state for a given state-action pair requires querying an expensive transition function which can lead to many hours of computer simulation or dollars of scientific research. Such expensive data collection prohibits application of standard RL algorithms which usually require a large number of observations to learn.

In this work, we address the problem of efficiently learning a policy while making a minimal number of state-action queries to the transition function. In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning. We propose an acquisition function that quantifies how much information a state-action pair would provide about the optimal solution to a Markov decision process. At each iteration, our algorithm maximizes this acquisition function, to choose the most informative state-action pair to be queried, thus yielding a data-efficient RL approach.

We experiment with a variety of simulated continuous control problems and show that our approach learns an optimal policy with up to 5–1,000× less data than model-based RL baselines and 103–105× less data than model-free RL baselines.

We also provide several ablated comparisons which point to substantial improvements arising from the principled method of obtaining data.

--- https://www.biorxiv.org/content/10.1101/240317.full Deep image reconstruction from human brain activity Guohua Shen, Tomoyasu Horikawa, Kei Majima, Yukiyasu Kamitani 2017-12-30 2020-12-13 [("doi","10.1101/240317")] ai/nn/cnn ai/scaling psychology/neuroscience

Machine learning-based analysis of human functional magnetic resonance imaging (fMRI) patterns has enabled the visualization of perceptual content. However, it has been limited to the reconstruction with low-level image bases (Miyawaki et al 2008; Wen et al 2016) or to the matching to exemplars (Naselaris et al 2009; Nishimoto et al 2011). Recent work showed that visual cortical activity can be decoded (translated) into hierarchical features of a deep neural network (DNN) for the same input image, providing a way to make use of the information from hierarchical visual features (Horikawa & Kamitani 2017).

Here, we present a novel image reconstruction method, in which the pixel values of an image are optimized to make its DNN features similar to those decoded from human brain activity at multiple layers. We found that the generated images resembled the stimulus images (both natural images and artificial shapes) and the subjective visual content during imagery. While our model was solely trained with natural images, our method successfully generalized the reconstruction to artificial shapes, indicating that our model indeed ‘reconstructs’ or ‘generates’ images from brain activity, not simply matches to exemplars.

A natural image prior introduced by another deep neural network effectively rendered semantically meaningful details to reconstructions by constraining reconstructed images to be similar to natural images. Furthermore, human judgment of reconstructions suggests the effectiveness of combining multiple DNN layers to enhance visual quality of generated images. The results suggest that hierarchical visual information in the brain can be effectively combined to reconstruct perceptual and subjective images.

--- https://arxiv.org/abs/1810.05587 A Survey and Critique of Multiagent Deep Reinforcement Learning Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor 2018-10-12 2020-12-13 [("doi","10.1007/s10458-019-09421-1")] reinforcement-learning/imperfect-information reinforcement-learning/multi-agent

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent learning (MAL) scenarios. Initial results report successes in complex multiagent domains, although there are several challenges to be addressed. The primary goal of this article is to provide a clear overview of current multiagent deep reinforcement learning (MDRL) literature.

Additionally, we complement the overview with a broader analysis: (1) we revisit previous key components, originally presented in MAL and RL, and highlight how they have been adapted to multiagent deep reinforcement learning settings. (2) We provide general guidelines to new practitioners in the area: describing lessons learned from MDRL works, pointing to recent benchmarks, and outlining open avenues of research.

  1. We take a more critical tone raising practical challenges of MDRL (eg. implementation and computational demands). We expect this article will help unify and motivate future research to take advantage of the abundant literature that exists (eg. RL and MAL) in a joint effort to promote fruitful research in the multiagent community.

--- https://www.nytimes.com/2013/09/08/magazine/poker-computer.html The Steely, Headless King of Texas Hold ’Em 2020-12-13 reinforcement-learning/imperfect-information reinforcement-learning/multi-agent --- https://en.wikipedia.org/wiki/Fundamental_theorem_of_poker Fundamental theorem of poker 2020-12-13 reinforcement-learning/imperfect-information/poker reinforcement-learning/multi-agent --- https://www.reddit.com/r/reinforcementlearning/comments/cdwzp3/pluribus_superhuman_ai_for_multiplayer_poker/etwu82u/ 2020-12-13 reinforcement-learning/imperfect-information reinforcement-learning/multi-agent --- https://en.wikipedia.org/wiki/Scotland_Yard_(board_game) Scotland Yard 2020-12-13 reinforcement-learning/imperfect-information reinforcement-learning/multi-agent --- https://en.wikipedia.org/wiki/Poker Poker 2020-12-14 reinforcement-learning/imperfect-information/poker reinforcement-learning/multi-agent --- https://en.wikipedia.org/wiki/Hanabi_(card_game) Hanabi (card game) 2020-12-14 reinforcement-learning/imperfect-information/hanabi reinforcement-learning/multi-agent --- https://en.wikipedia.org/wiki/Diplomacy_(game) Diplomacy (game) 2020-12-14 reinforcement-learning/imperfect-information/diplomacy reinforcement-learning/multi-agent --- https://grantland.com/features/diplomacy-the-board-game-of-the-alpha-nerds/ One writer enters international competition to play the world-conquering game that redefines what it means to be a geek (and a person) 2020-12-14 reinforcement-learning/imperfect-information reinforcement-learning/multi-agent --- https://www.theguardian.com/technology/2022/mar/29/artificial-intelligence-beats-eight-world-champions-at-bridge Artificial intelligence beats eight world champions at bridge 2020-12-14 reinforcement-learning/imperfect-information reinforcement-learning/multi-agent --- https://www.lesswrong.com/posts/yHxmJch8dJoH6dwwz/so-has-ai-conquered-bridge So has AI conquered Bridge? 2020-12-14 reinforcement-learning/imperfect-information reinforcement-learning/multi-agent --- https://www.technologyreview.com/2021/08/06/1030802/ai-robots-take-over-warehouses/ A new generation of AI-powered robots is taking over warehouses 2020-12-14 economics/automation reinforcement-learning/robot --- https://arxiv.org/abs/1710.11424 Regret Minimization for Partially Observable Deep Reinforcement Learning Peter Jin, Kurt Keutzer, Sergey Levine 2017-10-31 2020-12-14 [("doi","10.48550/arXiv.1710.11424")] reinforcement-learning/imperfect-information

Deep reinforcement learning algorithms that estimate state and state-action value functions have been shown to be effective in a variety of challenging domains, including learning control strategies from raw image pixels. However, algorithms that estimate state and state-action value functions typically assume a fully observed state and must compensate for partial observations by using finite length observation histories or recurrent networks.

In this work, we propose a new deep reinforcement learning algorithm based on counterfactual regret minimization that iteratively updates an approximation to an advantage-like function and is robust to partially observed state.

We demonstrate that this new algorithm can substantially outperform strong baseline methods on several partially observed reinforcement learning tasks: learning first-person 3D navigation in Doom and Minecraft, and acting in the presence of partially observed objects in Doom and Pong.

--- https://arxiv.org/abs/1507.06527 Deep Recurrent Q-Learning for Partially Observable MDPs Matthew Hausknecht, Peter Stone 2015-07-23 2020-12-14 [("doi","10.48550/arXiv.1507.06527")] ai/nn/rnn reinforcement-learning/imperfect-information

Deep Reinforcement Learning has yielded proficient controllers for complex tasks. However, these controllers have limited memory and rely on being able to perceive the complete game screen at each decision point.

To address these shortcomings, this article investigates the effects of adding recurrency to a Deep Q-Network (DQN) by replacing the first post-convolutional fully-connected layer with a recurrent LSTM. The resulting Deep Recurrent Q-Network (DRQN), although capable of seeing only a single frame at each timestep, successfully integrates information through time and replicates DQN’s performance on standard Atari games and partially observed equivalents featuring flickering game screens.

Additionally, when trained with partial observations and evaluated with incrementally more complete observations, DRQN’s performance scales as a function of observability. Conversely, when trained with full observations and evaluated with partial observations, DRQN’s performance degrades less than DQN’s.

Thus, given the same length of history, recurrency is a viable alternative to stacking a history of frames in the DQN’s input layer and while recurrency confers no systematic advantage when learning to play the game, the recurrent net can better adapt at evaluation time if the quality of observations changes.

--- https://arxiv.org/abs/1903.09569 Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash equilibrium of Imperfect-Information Games Li Zhang, Wei Wang, Shijian Li, Gang Pan 2019-03-22 2020-12-14 [("doi","10.48550/arXiv.1903.09569")] reinforcement-learning/imperfect-information

Researchers on artificial intelligence have achieved human-level intelligence in large-scale perfect-information games, but it is still a challenge to achieve (nearly) optimal results (in other words, an approximate Nash Equilibrium) in large-scale imperfect-information games (ie. war games, football coach or business strategies). Neural Fictitious Self Play (NFSP) is an effective algorithm for learning approximate Nash equilibrium of imperfect-information games from self-play without prior domain knowledge. However, it relies on Deep Q-Network, which is off-line and is hard to converge in online games with changing opponent strategy, so it can’t approach approximate Nash equilibrium in games with large search scale and deep search depth.

In this paper, we propose Monte Carlo Neural Fictitious Self Play (MC-NFSP), an algorithm combines Monte Carlo tree search with NFSP, which greatly improves the performance on large-scale zero-sum imperfect-information games. Experimentally, we demonstrate that the proposed Monte Carlo Neural Fictitious Self Play can converge to approximate Nash equilibrium in games with large-scale search depth while the Neural Fictitious Self Play can’t. Furthermore, we develop Asynchronous Neural Fictitious Self Play (ANFSP). It use asynchronous and parallel architecture to collect game experience. In experiments, we show that parallel actor-learners have a further accelerated and stabilizing effect on training.

--- https://arxiv.org/abs/1809.04040 Solving Imperfect-Information Games via Discounted Regret Minimization Noam Brown, Tuomas Sandholm 2018-09-11 2020-12-15 [("doi","10.48550/arXiv.1809.04040")] reinforcement-learning/imperfect-information

Counterfactual regret minimization (CFR) is a family of iterative algorithms that are the most popular and, in practice, fastest approach to solving large imperfect-information games. In this paper, we introduce novel CFR variants that discount regrets from earlier iterations in various ways (in some cases differently for positive and negative regrets), reweight iterations in various ways to obtain the output strategies, use a non-standard regret minimizer and/or leverage “optimistic regret matching”. They lead to dramatically improved performance in many settings.

For one, we introduce a variant that outperforms CFR+, the prior state-of-the-art algorithm, in every game tested, including large-scale realistic settings. CFR+ is a formidable benchmark: no other algorithm has been able to outperform it.

Finally, we show that, unlike CFR+, many of the important new variants are compatible with modern imperfect-information-game pruning techniques and one is also compatible with sampling in the game tree.

--- https://arxiv.org/abs/1705.02955 Safe and Nested Subgame Solving for Imperfect-Information Games Noam Brown, Tuomas Sandholm 2017-05-08 2020-12-15 [("doi","10.48550/arXiv.1705.02955")] reinforcement-learning/imperfect-information/poker

In imperfect-information games, the optimal strategy in a subgame may depend on the strategy in other, unreached subgames. Thus a subgame cannot be solved in isolation and must instead consider the strategy for the entire game as a whole, unlike perfect-information games. Nevertheless, it is possible to first approximate a solution for the whole game and then improve it by solving individual subgames. This is referred to as subgame solving.

We introduce subgame-solving techniques that outperform prior methods both in theory and practice. We also show how to adapt them, and past subgame-solving techniques, to respond to opponent actions that are outside the original action abstraction; this outperforms the prior state-of-the-art approach, action translation.

Finally, we show that subgame solving can be repeated as the game progresses down the game tree, leading to far lower exploitability. These techniques were a key component of Libratus, the first AI to defeat top humans in heads-up no-limit Texas hold’em poker.

--- https://arxiv.org/abs/2106.06135 DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning Daochen Zha, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian, Xia Hu, Ji Liu 2021-06-11 2021-06-11 [("doi","10.48550/arXiv.2106.06135")] reinforcement-learning/imperfect-information

Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents. While achievements have been made in various perfect-information and imperfect-information games, DouDizhu (a.k.a. Fighting the Landlord), a three-player card game, is still unsolved. DouDizhu is a very challenging domain with competition, collaboration, imperfect information, large state space, and particularly a massive set of possible actions where the legal actions vary substantially from turn to turn. Unfortunately, modern reinforcement learning algorithms mainly focus on simple and small action spaces, and not surprisingly, are shown not to make satisfactory progress in DouDizhu.

In this work, we propose a conceptually simple yet effective DouDizhu AI system, namely DouZero, which enhances traditional Monte-Carlo methods with deep neural networks, action encoding, and parallel actors.

Starting from scratch in a single server with four GPUs, DouZero outperformed all the existing DouDizhu AI programs in days of training and was ranked the first in the Botzone leaderboard among 344 AI agents. Through building DouZero, we show that classic Monte-Carlo methods can be made to deliver strong results in a hard domain with a complex action space.

The code and an online demo are released at Github with the hope that this insight could motivate future work.

--- https://archive.is/BFBm9 Automation Comes to More Factories With Robot Subscription Services 2020-12-15 reinforcement-learning/robot --- https://arxiv.org/abs/2106.12534 Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation Stephen James, Kentaro Wada, Tristan Laidlow, Andrew J. Davison 2021-06-23 2021-06-23 [("doi","10.48550/arXiv.2106.12534")] ai/nn reinforcement-learning/robot

We present a coarse-to-fine discretisation method that enables the use of discrete reinforcement learning approaches in place of unstable and data-inefficient actor-critic methods in continuous robotics domains. This approach builds on the recently released ARM algorithm, which replaces the continuous next-best pose agent with a discrete one, with coarse-to-fine Q-attention.

Given a voxelized scene, coarse-to-fine Q-attention learns what part of the scene to ‘zoom’ into. When this ‘zooming’ behavior is applied iteratively, it results in a near-lossless discretization of the translation space, and allows the use of a discrete action, deep Q-learning method.

We show that our new coarse-to-fine algorithm achieves state-of-the-art performance on several difficult sparsely rewarded RLBench vision-based robotics tasks, and can train real-world policies, tabula rasa, in a matter of minutes, with as little as 3 demonstrations.

--- https://singularityhub.com/2022/02/17/flippy-the-fast-food-robot-is-going-to-work-in-100-restaurants/ Flippy the Fast Food Robot Just Got Hired in 100 Restaurants 2020-12-15 reinforcement-learning/robot --- https://www.wired.com/story/elusive-hunt-robot-pick-ripe-strawberry/ The Elusive Hunt for a Robot That Can Pick a Ripe Strawberry 2020-12-15 reinforcement-learning/robot --- https://www.economist.com/finance-and-economics/2022/01/22/economists-are-revising-their-views-on-robots-and-jobs Economists are revising their views on robots and jobs 2020-12-15 economics/automation reinforcement-learning/robot --- https://www.alizila.com/alibaba-driverless-robots-one-millionth-ecommerce-delivery/ Alibaba's Driverless Robots Just Made Their One Millionth E-commerce Delivery 2020-12-15 economics/automation --- https://archive.is/FM5UW Rise of the Robots Speeds Up in Pandemic With U.S. Labor Scarce 2020-12-15 economics/automation --- https://www.wired.com/story/robots-follow-learn-where-go/ These Robots Follow You to Learn Where to Go 2020-12-15 economics/automation --- https://www.therobotreport.com/how-graze-mowings-self-driving-mower-is-disrupting-the-100-billion-commercial-landscaping-industry/ How Graze Mowing's self-driving mower is disrupting the $100 billion commercial landscaping industry 2020-12-16 economics/automation --- https://www.nytimes.com/2021/12/07/science/robots-pancake-jump.html This Robot Looks Like a Pancake and Jumps Like a Maggot 2020-12-16 reinforcement-learning/robot technology --- https://ai.facebook.com/blog/teaching-robots-to-perceive-understand-and-interact-through-touch/ 2020-12-16 reinforcement-learning/robot technology --- https://arxiv.org/abs/2111.05424#google AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale Yao Lu, Karol Hausman, Yevgen Chebotar, Mengyuan Yan, Eric Jang, Alexander Herzog, Ted Xiao, Alex Irpan, Mohi Khansari, Dmitry Kalashnikov, Sergey Levine 2021-11-09 2021-11-09 [("doi","10.48550/arXiv.2111.05424")] reinforcement-learning/imitation-learning reinforcement-learning/robot reinforcement-learning/scaling

Robotic skills can be learned via imitation learning (IL) using user-provided demonstrations, or via reinforcement learning (RL) using large amounts of autonomously collected experience. Both methods have complementary strengths and weaknesses: RL can reach a high level of performance, but requires exploration, which can be very time consuming and unsafe; IL does not require exploration, but only learns skills that are as good as the provided demonstrations. Can a single method combine the strengths of both approaches? A number of prior methods have aimed to address this question, proposing a variety of techniques that integrate elements of IL and RL. However, scaling up such methods to complex robotic skills that integrate diverse offline data and generalize meaningfully to real-world scenarios still presents a major challenge.

In this paper, our aim is to test the scalability of prior IL + RL algorithms and devise a system based on detailed empirical experimentation that combines existing components in the most effective and scalable way. To that end, we present a series of experiments aimed at understanding the implications of each design decision, so as to develop a combined approach that canuse demonstrations and heterogeneous prior data to attain the best performance on a range of real-world and realistic simulated robotic problems.

Our complete method, which we call AW-Opt, combines elements of advantage-weighted regression [1, 2] and QT-Opt [3], providing a unified approach for integrating demonstrations and offline data for robotic manipulation.

Please see https://awopt.github.io/ for more details.

--- https://research.google/blog/can-robots-follow-instructions-for-new-tasks/ 2020-12-16 reinforcement-learning/robot --- https://openreview.net/forum?id=8kbp23tSGYv#google BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, Chelsea Finn 2021-11-04 2021-11-04 ai/nn reinforcement-learning/imitation-learning reinforcement-learning/robot

In this paper, we study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks, a long-standing challenge in robot learning.

We approach the challenge from an imitation learning perspective, aiming to study how scaling and broadening the data collected can facilitate such generalization. To that end, we develop an interactive and flexible imitation learning system that can learn from both demonstrations and interventions and can be conditioned on different forms of information that convey the task, including pre-trained embeddings of natural language or videos of humans performing the task.

When scaling data collection on a real robot to more than 100 distinct tasks, we find that this system can perform 24 unseen manipulation tasks with an average success rate of 44%, without any robot demonstrations for those tasks.

[Keywords: zero-shot imitation learning, one-shot imitation learning, multi-task imitation, deep learning]

--- https://web.archive.org/web/20230524130653/https://frc.ri.cmu.edu/~hpm/project.archive/robot.papers/1991/Universal.Robot.910618.html The Universal Robot 2020-12-16 reinforcement-learning/robot --- https://www.theverge.com/2021/11/19/22791267/alphabet-google-everyday-robot-project-cleaning-office-prototype Alphabet is putting its prototype robots to work cleaning up around Google’s offices 2020-12-16 reinforcement-learning/robot --- https://www.deepmind.com/blog/announcements/mujoco 2020-12-16 reinforcement-learning/robot --- https://sifted.eu/articles/cala-robot/ 2020-12-16 economics/automation --- https://spectrum.ieee.org/robotic-farming-russia An Army of Grain-Harvesting Robots Marches Across Russia 2020-12-16 economics/automation --- https://www.wired.com/story/can-robots-evolve-into-machines-of-loving-grace/ Can Robots Evolve Into Machines of Loving Grace? 2020-12-17 economics/automation --- https://www.newyorker.com/magazine/2021/08/30/invasion-of-the-robot-umpires Invasion of the Robot Umpires 2020-12-17 economics/automation --- https://orangebeanindiana.com/2021/04/09/once-upon-a-time-america-had-an-atomic-mecha-warrior-robot/ The GE Beetle: Our Giant Atomic Robot 2020-12-17 technology --- https://arxiv.org/abs/2109.13396 Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets Frederik Ebert, Yanlai Yang, Karl Schmeckpeper, Bernadette Bucher, Georgios Georgakis, Kostas Daniilidis, Chelsea Finn, Sergey Levine 2021-09-27 2021-09-27 [("doi","10.48550/arXiv.2109.13396")] reinforcement-learning/robot reinforcement-learning/scaling

Robot learning holds the promise of learning policies that generalize broadly. However, such generalization requires sufficiently diverse datasets of the task of interest, which can be prohibitively expensive to collect. In other fields, such as computer vision, it is common to use shared, reusable datasets, such as ImageNet, to overcome this challenge, but this has proven difficult in robotics. In this paper, we ask: what would it take to enable practical data reuse in robotics for end-to-end skill learning?

We hypothesize that the key is to use datasets with multiple tasks and multiple domains, such that a new user that wants to train their robot to perform a new task in a new domain can include this dataset in their training process and benefit from cross-task and cross-domain generalization. To evaluate this hypothesis, we collect a large multi-domain and multi-task dataset, with 7,200 demonstrations constituting 71 tasks across 10 environments, and empirically study how this data can improve the learning of new tasks in new environments.

We find that jointly training with the proposed dataset and 50 demonstrations of a never-before-seen task in a new domain on average leads to a 2× improvement in success rate compared to using target domain data alone. We also find that data for only a few tasks in a new domain can bridge the domain gap and make it possible for a robot to perform a variety of prior tasks that were only seen in other domains.

These results suggest that reusing diverse multi-task and multi-domain datasets, including our open-source dataset, may pave the way for broader robot generalization, eliminating the need to re-collect data for each new robot learning project.

--- https://arxiv.org/abs/2109.01115 Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation Suraj Nair, Eric Mitchell, Kevin Chen, Brian Ichter, Silvio Savarese, Chelsea Finn 2021-09-02 2021-09-02 [("doi","10.48550/arXiv.2109.01115")] reinforcement-learning/robot reinforcement-learning/scaling

We study the problem of learning a range of vision-based manipulation tasks from a large offline dataset of robot interaction. In order to accomplish this, humans need easy and effective ways of specifying tasks to the robot. Goal images are one popular form of task specification, as they are already grounded in the robot’s observation space. However, goal images also have a number of drawbacks: they are inconvenient for humans to provide, they can over-specify the desired behavior leading to a sparse reward signal, or under-specify task information in the case of non-goal reaching tasks. Natural language provides a convenient and flexible alternative for task specification, but comes with the challenge of grounding language in the robot’s observation space.

To scalably learn this grounding we propose to leverage offline robot datasets (including highly sub-optimal, autonomously collected data) with crowd-sourced natural language labels. With this data, we learn a simple classifier which predicts if a change in state completes a language instruction. This provides a language-conditioned reward function that can then be used for offline multi-task RL.

In our experiments, we find that on language-conditioned manipulation tasks our approach outperforms both goal-image specifications and language conditioned imitation techniques by more than 25%, and is able to perform visuomotor tasks from natural language, such as “open the right drawer” and “move the stapler”, on a Franka Emika Panda robot.

--- https://www.theverge.com/2021/9/9/22660467/irobot-roomba-ai-dog-poop-avoidance-j7-specs-price iRobot’s newest Roomba uses AI to avoid dog poop 2020-12-17 reinforcement-learning/robot --- https://www.baucomrobotics.com/domino-robot Domino Robot 2020-12-17 reinforcement-learning/robot --- https://www.wired.com/story/can-robots-evolve-into-machines-of-loving-grace/ 2020-12-17 reinforcement-learning/robot --- https://www.construction-physics.com/p/where-are-the-robotic-bricklayers Where Are The Robotic Bricklayers? 2020-12-17 economics/automation reinforcement-learning/robot --- https://www.nytimes.com/2021/08/19/business/media/disney-parks-robots.html Are You Ready for Sentient Disney Robots? 2020-12-17 technology --- https://www.wsj.com/articles/restaurant-robots-kitchen-labor-shortage-11628290623 2020-12-18 economics/automation --- https://www.nytimes.com/2021/07/11/world/europe/carrara-italy-robot-sculptures.html ‘We Don’t Need Another Michelangelo’: In Italy, It’s Robots’ Turn to Sculpt 2020-12-18 economics/automation technology --- https://techcrunch.com/2021/07/27/robotic-ai-firm-covariant-raises-another-80-million/ Robotic AI firm Covariant raises another $80 million 2020-12-18 reinforcement-learning/robot --- https://venturebeat.com/business/openai-disbands-its-robotics-research-team/ OpenAI disbands its robotics research team 2020-12-18 ai/scaling reinforcement-learning/robot --- https://research.google/blog/toward-generalized-sim-to-real-transfer-for-robot-learning/ 2020-12-18 ai/nn/gan reinforcement-learning/robot --- https://www.reddit.com/r/reinforcementlearning/comments/nsi7bf/what_could_make_ai_conscious_with_wojciech/ 2020-12-18 ai/scaling reinforcement-learning/robot --- https://spectrum.ieee.org/automaton/robotics/robotics-hardware/robot-sticks-to-ceilings 2020-12-18 technology --- https://www.wired.com/story/dog-poodemic-robots-drones/ 2020-12-18 economics/automation technology --- https://spectrum.ieee.org/no-human-can-match-this-highspeed-boxunloading-robot-named-after-a-pickle No Human Can Match This High-Speed Box-Unloading Robot Named After a Pickle 2020-12-18 economics/automation technology --- https://onlinelibrary.wiley.com/doi/abs/10.1002/hec.4361 2020-12-18 economics/automation --- https://www.newyorker.com/magazine/2021/05/31/what-robots-can-and-cant-do-for-the-old-and-lonely What Robots Can—and Can’t—Do for the Old and Lonely 2020-12-18 economics/automation psychiatry sociology --- https://www.economist.com/by-invitation/2020/06/25/kai-fu-lee-on-how-covid-spurs-chinas-great-robotic-leap-forward Kai-Fu Lee on how covid spurs China’s great robotic leap forward 2020-12-19 economics/automation --- https://www.wired.com/story/this-brain-controlled-robotic-arm-can-twist-grasp-and-feel/ This Brain-Controlled Robotic Arm Can Twist, Grasp—and Feel 2020-12-19 psychology/neuroscience reinforcement-learning/robot --- https://www.wired.com/story/why-scientists-love-making-robots-build-ikea-furniture/ Why Scientists Love Making Robots Build Ikea Furniture 2020-12-19 reinforcement-learning/robot --- https://arxiv.org/abs/2107.04034 RMA: Rapid Motor Adaptation for Legged Robots Ashish Kumar, Zipeng Fu, Deepak Pathak, Jitendra Malik 2021-07-08 2021-07-08 [("doi","10.48550/arXiv.2107.04034")] reinforcement-learning/robot

Successful real-world deployment of legged robots would require them to adapt in real-time to unseen scenarios like changing terrains, changing payloads, wear and tear. This paper presents Rapid Motor Adaptation (RMA) algorithm to solve this problem of real-time online adaptation in quadruped robots. RMA consists of two components: a base policy and an adaptation module. The combination of these components enables the robot to adapt to novel situations in fractions of a second.

RMA is trained completely in simulation without using any domain knowledge like reference trajectories or predefined foot trajectory generators and is deployed on the A1 robot without any fine-tuning. We train RMA on a varied terrain generator using bioenergetics-inspired rewards and deploy it on a variety of difficult terrains including rocky, slippery, deformable surfaces in environments with grass, long vegetation, concrete, pebbles, stairs, sand, etc. RMA shows state-of-the-art performance across diverse real-world as well as simulation experiments. Video results at https://ashish-kmr.github.io/rma-legged-robots/.

--- https://ai.facebook.com/blog/ai-now-enables-robots-to-adapt-rapidly-to-changing-real-world-conditions 2020-12-19 reinforcement-learning/robot --- https://research.google/blog/the-importance-of-ab-testing-in-robotics/ 2020-12-19 reinforcement-learning/robot --- https://web.mit.edu/jsterman/www/SDG/beergame.html Flight Simulators for Management Education 2020-12-19 economics --- https://x.com/remi_durant/status/1523321622698024960 2020-12-19 ai/nn/transformer/clip/sample --- https://x.com/borisdayma/status/1523777264517001216 2020-12-19 ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2205.04421#microsoft NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality Xu Tan, Jiawei Chen, Haohe Liu, Jian Cong, Chen Zhang, Yanqing Liu, Xi Wang, Yichong Leng, Yuanhao Yi, Lei He, Frank Soong, Tao Qin, Sheng Zhao, Tie-Yan Liu 2022-05-09 2022-05-09 [("doi","10.48550/arXiv.2205.04421")] ai/nn/vae

[samples] Text to speech (TTS) has made rapid progress in both academia and industry in recent years. Some questions naturally arise that whether a TTS system can achieve human-level quality, how to define/judge human-level quality and how to achieve it…Using this judge method, we found several previous TTS systems have not achieved it (see Table 1).

In this paper, we answer these questions by first defining human-level quality based on statistical-significance of measurement and describing the guidelines to judge it, and then proposing a TTS system called NaturalSpeech that achieves human-level quality on a benchmark dataset. Specifically, we leverage a variational autoencoder (VAE) for end-to-end text to waveform generation, with several key designs to enhance the capacity of prior from text and reduce the complexity of posterior from speech, including phoneme pre-training, differentiable duration modeling, bidirectional prior/posterior modeling, and memory mechanism in VAE.

Experiment evaluations on the popular LJSpeech dataset show that our proposed NaturalSpeech achieves −0.01 CMOS (comparative mean opinion score) to human recordings on sentence level, with Wilcoxon signed rank test at p-level p ≫0.05, which demonstrates no statistically-significant difference from human recordings for the first time on this dataset.

--- https://alexdanco.com/2020/10/08/making-is-show-business-now/ Making is Show Business now 2020-12-19 economics technology --- /doc/iq/1996-bouchard-2.pdf IQ similarity in twins reared apart: Findings and responses to critics Thomas J. Bouchard Junior 1996-01-01 2020-12-20 genetics/heritable/adoption iq --- https://www.lesswrong.com/posts/5XbBm6gkuSdMJy9DT/conditions-for-mathematical-equivalence-of-stochastic Conditions for mathematical equivalence of Stochastic Gradient Descent and Natural Selection 2020-12-20 genetics/selection reinforcement-learning/exploration --- https://www.medrxiv.org/content/10.1101/2020.09.22.20196089.full Genetic Architecture of 11 Major Psychiatric Disorders at Biobehavioral, Functional Genomic, and Molecular Genetic Levels of Analysis Andrew D. Grotzinger, Travis T. Mallard, Wonuola A. Akingbuwa, Hill F. Ip, Mark J. Adams, Cathryn M. Lewis, Andrew M. McIntosh, Jakob Grove, Søren Dalsgaard, Klaus-Peter Lesch, Nora Strom, Sandra M. Meier, Manuel Mattheisen, Anders Børglum, Ole Mors, Gerome Breen, iPSYCH, Tourette Syndrome, Obsessive Compulsive Disorder Working Group of the Psychiatric Genetics Consortium, Bipolar Disorder Working Group of the Psychiatric Genetics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genetics Consortium, Schizophrenia Working Group of the Psychiatric Genetics Consortium, Phil H. Lee, Kenneth S. Kendler, Jordan W. Smoller, Elliot M. Tucker-Drob, Michel G. Nivard 2020-09-23 2020-12-20 [("doi","10.1101/2020.09.22.20196089")] genetics/heritable/correlation psychiatry/schizophrenia

We systematically interrogate the joint genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic, and molecular genetic levels of analysis. We identify 4 broad factors (Neurodevelopmental, Compulsive, Psychotic, and Internalizing) that underlie genetic correlations among the disorders, and test whether these factors adequately explain their genetic correlations with biobehavioral traits.

We introduce Stratified Genomic Structural Equation Modeling, which we use to identify gene sets and genomic regions that disproportionately contribute to pleiotropy, including protein-truncating variant intolerant genes expressed in excitatory and GABAergic brain cells that are enriched for pleiotropy between disorders with psychotic features.

Multivariate association analyses detect a total of 152 (20 novel) independent loci which act on the 4 factors, and identify 9 loci that act heterogeneously across disorders within a factor.

Despite moderate to high genetic correlations across all 11 disorders, we find very little utility of, or evidence for, a single dimension of genetic risk across psychiatric disorders.

--- https://x.com/hxiao/status/1524060681284239362 2020-12-20 ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2107.10224 CycleMLP: A MLP-like Architecture for Dense Prediction Shoufa Chen, Enze Xie, Chongjian Ge, Runjian Chen, Ding Liang, Ping Luo 2021-07-21 2021-07-21 [("doi","10.48550/arXiv.2107.10224")] ai/nn/fully-connected

This paper presents a simple MLP-like architecture, CycleMLP, which is a versatile backbone for visual recognition and dense predictions.

As compared to modern MLP architectures, eg. MLP-Mixer, ResMLP, and MLP, whose architectures are correlated to image size and thus are infeasible in object detection and segmentation, CycleMLP has two advantages compared to modern approaches. (1) It can cope with various image sizes. (2) It achieves linear computational complexity to image size by using local windows. In contrast, previous MLPs have 𝒪(n2) computations due to fully spatial connections.

We build a family of models which surpass existing MLPs and even state-of-the-art Transformer-based models, eg. Swin Transformer, while using fewer parameters and FLOPs. We expand the MLP-like models’ applicability, making them a versatile backbone for dense prediction tasks.

CycleMLP achieves competitive results on object detection, instance segmentation, and semantic segmentation. In particular, CycleMLP-Tiny outperforms Swin-Tiny by 1.3% mIoU on ADE20K dataset with fewer FLOPs. Moreover, CycleMLP also shows excellent zero-shot robustness on ImageNet-C dataset.

Code is available at Github.

--- https://arxiv.org/abs/2105.03404#facebook ResMLP: Feedforward networks for image classification with data-efficient training Hugo Touvron, Piotr Bojanowski, Mathilde Caron, Matthieu Cord, Alaaeldin El-Nouby, Edouard Grave, Gautier Izacard, Arm Holdings, Joulin, Gabriel Synnaeve, Jakob Verbeek, Hervé Jégou 2021-05-07 2021-05-07 [("doi","10.48550/arXiv.2105.03404")] ai/nn/fully-connected ai/nn/sparsity/knowledge-distillation

We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification.

It is a simple residual network that alternates (1) a linear layer in which image patches interact, independently and identically across channels, and (2) a two-layer feed-forward network in which channels interact independently per patch.

When trained with a modern training strategy using heavy data-augmentation and optionally distillation, it attains surprisingly good accuracy/complexity trade-offs on ImageNet. We also train ResMLP models in a self-supervised setup, to further remove priors from employing a labeled dataset.

Finally, by adapting our model to machine translation we achieve surprisingly good results.

We share pre-trained models and our code based on the Timm library.

--- https://arxiv.org/abs/2105.08050#google Pay Attention to MLPs Hanxiao Liu, Zihang Dai, David R. So, Quoc V. Le 2021-05-17 2021-05-17 [("doi","10.48550/arXiv.2105.08050")] ai/nn/fully-connected ai/nn/transformer/attention/hierarchical

Transformers have become one of the most important architectural innovations in deep learning and have enabled many breakthroughs over the past few years.

Here we propose a simple network architecture, gMLP, based on MLPs with gating, and show that it can perform as well as Transformers in key language and vision applications.

Our comparisons show that self-attention is not critical for Vision Transformers, as gMLP can achieve the same accuracy. For BERT, our model achieves parity with Transformers on pretraining perplexity and is better on some downstream NLP tasks. On finetuning tasks where gMLP performs worse, making the gMLP model substantially larger can close the gap with Transformers.

In general, our experiments show that gMLP can scale as well as Transformers over increased data and compute.

--- https://arxiv.org/abs/1707.07607 We are not alone! (at least, most of us): Homonymy in large scale social groups Arthur Charpentier, Baptiste Coulmont 2017-07-24 2020-12-20 [("doi","10.48550/arXiv.1707.07607")] statistics

This article brings forward an estimation of the proportion of homonyms in large scale groups based on the distribution of first names and last names in a subset of these groups.

The estimation is based on the generalization of the “birthday paradox problem”.

The main results is that, in societies such as France or the United States, identity collisions (based on first + last names) are frequent. The large majority of the population has at least one homonym. But in smaller settings, it is much less frequent: even if small groups of a few thousand people have at least one couple of homonyms, only a few individuals have an homonym.

--- https://www.biorxiv.org/content/10.1101/190124.full Accurate Genomic Prediction Of Human Height Louis Lello, Steven G. Avery, Laurent Tellier, Ana I. Vazquez, Gustavo de los Campos, Steve Hsu 2017-10-07 2020-12-20 [("doi","10.1101/190124")] genetics/heritable

We construct genomic predictors for heritable and extremely complex human quan-titative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (ie. machine learning).

Replication tests show that these predictors capture, respectively, ~40, 20, and 9% of total variance for the 3 traits. For example, predicted heights correlate ~0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction.

The variance captured for height is comparable to the estimated SNP heritability from GCTA (GREML) analysis, and seems to be close to its asymptotic value (ie. as sample size goes to infinity), suggesting that we have captured most of the heritability for the SNPs used. Thus, our results resolve the common SNP portion of the “missing heritability” problem—ie. the gap between prediction R2 and SNP heritability.

The ~20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common SNPs.

Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier GWAS for out-of-sample validation of our results.

--- https://arxiv.org/abs/1710.02277 Efficient K-shot Learning with Regularized Deep Networks Donghyun Yoo, Haoqi Fan, Vishnu Naresh Boddeti, Kris M. Kitani 2017-10-06 2020-12-20 [("doi","10.48550/arXiv.1710.02277")] ai/nn/cnn reinforcement-learning/meta-learning

Feature representations from pre-trained deep neural networks have been known to exhibit excellent generalization and utility across a variety of related tasks. Fine-tuning is by far the simplest and most widely used approach that seeks to exploit and adapt these feature representations to novel tasks with limited data.

Despite the effectiveness of fine-tuning, it is often sub-optimal and requires very careful optimization to prevent severe over-fitting to small datasets. The problem of sub-optimality and over-fitting, is due in part to the large number of parameters (≈ 106) used in a typical deep convolutional neural network.

To address these problems, we propose a simple yet effective regularization method for fine-tuning pre-trained deep networks for the task of k-shot learning. To prevent overfitting, our key strategy is to cluster the model parameters while ensuring intra-cluster similarity and inter-cluster diversity of the parameters, effectively regularizing the dimensionality of the parameter search space. In particular, we identify groups of neurons within each layer of a deep network that shares similar activation patterns. When the network is to be fine-tuned for a classification task using only k examples, we propagate a single gradient to all of the neuron parameters that belong to the same group. The grouping of neurons is non-trivial as neuron activations depend on the distribution of the input data.

To efficiently search for optimal groupings conditioned on the input data, we propose a reinforcement learning search strategy using recurrent networks to learn the optimal group assignments for each network layer.

Experimental results show that our method can be easily applied to several popular convolutional neural networks and improve upon other state-of-the-art fine-tuning based k-shot learning strategies by more than 10%.

--- https://en.wikipedia.org/wiki/Vocaloid Vocaloid 2020-12-21 music --- https://en.wikipedia.org/wiki/Hatsune_Miku Hatsune Miku 2020-12-21 music --- https://en.wikipedia.org/wiki/Touhou_Project Touhou Project 2020-12-21 music --- https://softwareengineering.stackexchange.com/questions/136085/is-musical-notation-turing-complete/136179#136179 Is musical notation Turing-Complete? 2020-12-21 music --- https://sange.fi/esoteric/essie2/download/choon/choon.html Choon Programming Language 2020-12-21 cs/computable music --- https://en.wikipedia.org/wiki/Fan_(person)#%22Stan%22_fans Fan (person) § "Stan" fans 2020-12-21 music --- https://kk.org/thetechnium/1000-true-fans/ 1,000 True Fans Kevin Kelly 2020-12-21 economics music sociology/technology --- https://www.thespl.it/p/the-rise-of-tiktok-and-understanding The Rise of TikTok and Understanding Its Parent Company, ByteDance 2020-12-21 music --- https://arxiv.org/abs/1809.04281#google Music Transformer Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Ian Simon, Curtis Hawthorne, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, Douglas Eck 2018-09-12 2020-12-21 [("doi","10.48550/arXiv.1809.04281")] ai/music ai/nn/transformer/attention

Music relies heavily on repetition to build structure and meaning. Self-reference occurs on multiple timescales, from motifs to phrases to reusing of entire sections of music, such as in pieces with ABA structure. The Transformer (Vaswani et al 2017), a sequence model based on self-attention, has achieved compelling results in many generation tasks that require maintaining long-range coherence. This suggests that self-attention might also be well-suited to modeling music.

In musical composition and performance, however, relative timing is critically important. Existing approaches for representing relative positional information in the Transformer modulate attention based on pairwise distance (Shaw et al 2018). This is impractical for long sequences such as musical compositions since their memory complexity for intermediate relative information is quadratic in the sequence length. We propose an algorithm that reduces their intermediate memory requirement to linear in the sequence length. This enables us to demonstrate that a Transformer with our modified relative attention mechanism can generate minute-long compositions (thousands of steps, four times the length modeled in Oore et al 2018) with compelling structure, generate continuations that coherently elaborate on a given motif, and in a seq2seq setup generate accompaniments conditioned on melodies. We evaluate the Transformer with our relative attention mechanism on two datasets, JSB Chorales and Piano-e-Competition, and obtain state-of-the-art results on the latter.

--- https://www.biorxiv.org/content/10.1101/105148.full Music-induced analgesia in chronic pain conditions: a systematic review and meta-analysis Eduardo A. Garza-Villarreal, Victor Pando, Peter Vuust, Christine Parsons 2017-02-02 2020-12-21 [("doi","10.1101/105148")] psychiatry/depression psychology/music/distraction psychology/neuroscience/pain

Music is increasingly used as an adjuvant for chronic pain management as it is not invasive, inexpensive, and patients usually report positive experiences with it. However, little is known about its clinical efficacy in chronic pain patients. In this systematic review and meta-analysis, we investigated randomized controlled trials (RCTs) of adult patients that reported any type of music intervention for chronic pain, chosen by the researcher or patient, lasting for any duration.

Searches were performed using PsycINFO, Scopus and PubMed for RTCs published until the end of May 2016. The primary outcome was reduction in self-reported pain using a standardized pain measurement instrument reported post-intervention. The secondary outcomes were: quality of life measures, depression and anxiety measures, among others. The study was pre-registered with PROSPERO (CRD42016039837) and the meta-analysis was done using RevMan. We identified 768 titles and abstracts, and we included 14 RTCs that fulfilled our criteria. The sample size of the studies varied 25–200 participants.

We found that music reduced chronic pain, and depression, with higher effect size on pain and depression. We also found music had a higher effect when the participant chose the music in contrast with researcher-chosen music. The sample size of RCTs was small and sometimes with different outcome measures.

Our analysis suggests that music may be beneficial as an adjuvant for chronic pain patients, as it reduces self-reported pain and its common co-morbidities. Importantly, the analgesic effect of music appears higher with self-chosen over researcher-chosen music.

--- https://www.biorxiv.org/content/10.1101/070961.full Prevalence of Congenital Amusia Isabelle Peretz, Dominique T. Vuvan 2016-08-22 2020-12-21 [("doi","10.1101/070961")] music psychology

Congenital amusia (commonly known as tone-deafness) is a lifelong musical disorder that should affect 4% of the population according to a single estimate based on a single test from 1980.

Here we present the first large-based measure of prevalence with a sample of 20,000 participants that does not rely on self-referral. On the basis of 3 objective tests and a questionnaire, we show that (a) the prevalence of congenital amusia is only 1.5% with slightly more females than males, unlike other developmental disorders where males often predominate; (b) self-disclosure is a reliable index of congenital amusia, that suggests that congenital amusia is hereditary with 46% first-degree relatives similarly affected; (3) that the deficit is not attenuated by musical training and (4) it emerges in relative isolation from other cognitive disorder except for spatial orientation problems.

Hence, we suggest that congenital amusia is likely to result from genetic variations that affect musical abilities specifically.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3307592/ An examination of an enhancing effect of music on attentional abilities in older persons with mild cognitive impairment Jessica I. Lake, Felicia C. Goldstein 2011 2020-12-22 [("doi","10.2466/04.10.15.PMS.112.1.267-278")] psychology/music/distraction

While the effect of listening to music on cognitive abilities is highly debated, studies reporting an enhancing effect of music in elderly populations appear to be more consistent.

In this study, the effects of listening to music on attention in groups of cognitively normal older adults and those with mild cognitive impairment were considered. Participants were exposed to both a music and silence condition, and after each condition performed Digit Span and Coding tasks which require attention for maximal performance.

The hypothesis that listening to music, compared to a silence condition, enhances performance was not supported for either group.

Various explanations for these findings are considered.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0082007 Two Randomized Trials Provide No Consistent Evidence for Nonmusical Cognitive Benefits of Brief Preschool Music Enrichment Samuel A. Mehr, Adena Schachner, Rachel C. Katz, Elizabeth S. Spelke 2013-10-23 2020-12-22 [("doi","10.1371/journal.pone.0082007")] iq music

Young children regularly engage in musical activities, but the effects of early music education on children’s cognitive development are unknown. While some studies have found associations between musical training in childhood and later nonmusical cognitive outcomes, few randomized controlled trials (RCTs) have been employed to assess causal effects of music lessons on child cognition and no clear pattern of results has emerged.

We conducted two RCTs with preschool children investigating the cognitive effects of a brief series of music classes, as compared to a similar but non-musical form of arts instruction (visual arts classes, Experiment 1) or to a no-treatment control (Experiment 2). Consistent with typical preschool arts enrichment programs, parents attended classes with their children, participating in a variety of developmentally appropriate arts activities. After 5 weeks of class, we assessed children’s skills in 4 distinct cognitive areas in which older arts-trained students have been reported to excel: spatial-navigational reasoning, visual form analysis, numerical discrimination, and receptive vocabulary.

We initially found that children from the music class showed greater spatial-navigational ability than did children from the visual arts class, while children from the visual arts class showed greater visual form analysis ability than children from the music class (Experiment 1). However, a partial replication attempt comparing music training to a no-treatment control failed to confirm these findings (Experiment 2), and the combined results of the two experiments were negative: overall, children provided with music classes performed no better than those with visual arts or no classes on any assessment.

Our findings underscore the need for replication in RCTs, and suggest caution in interpreting the positive findings from past studies of cognitive effects of music instruction.

--- /doc/dual-n-back/2013-elpus.pdf Is It the Music or Is It Selection Bias? A Nationwide Analysis of Music and Non-Music Students’ SAT Scores Kenneth Elpus 2013-05-21 2020-12-22 [("doi","10.1177/0022429413485601")] dual-n-back iq music

This study examined the college entrance examination scores of music and non-music students in the United States, drawing data from the restricted-use data set of the Education Longitudinal Study of 2002 (ELS), a nationally representative education study (n = 15,630) conducted by the National Center for Education Statistics.

Analyses of high school transcript data from ELS showed that 1.127 million students (36.38% of the U.S. class of 2004) graduated high school having earned at least one course credit in music. Fixed-effects regression procedures were used to compare standardized test scores of these music students to their non-music peers while controlling for variables from the domains of demography, prior academic achievement, time use, and attitudes toward school.

Results indicated that music students did not outperform non-music students on the SAT once these systematic differences had been statistically controlled. The obtained pattern of results remained consistent and robust through internal replications with another standardized math test and when disaggregating music students by type of music studied.

The findings suggest that, when accounting for various confounding factors, the academic performance of music students on college entrance exams does not differ significantly from that of non-music students.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3339577/ Music in the exercise domain: a review and synthesis (Part II) Costas I. Karageorghis, David-Lee Priest 2012 2020-12-22 [("doi","10.1080/1750984X.2011.631027")] exercise music

Since a 1997 review by Karageorghis and Terry, which highlighted the state of knowledge and methodological weaknesses, the number of studies investigating musical reactivity in relation to exercise has swelled considerably. In this two-part review paper, the development of conceptual approaches and mechanisms underlying the effects of music are explicated (Part I), followed by a critical review and synthesis of empirical work (spread over Parts I and II).

Pre-task music has been shown to optimize arousal, facilitate task-relevant imagery and improve performance in simple motoric tasks. During repetitive, endurance-type activities, self-selected, motivational and stimulative music has been shown to enhance affect, reduce ratings of perceived exertion, improve energy efficiency and lead to increased work output. There is evidence to suggest that carefully selected music can promote ergogenic and psychological benefits during high-intensity exercise, although it appears to be ineffective in reducing perceptions of exertion beyond the anaerobic threshold. The effects of music appear to be at their most potent when it is used to accompany self-paced exercise or in externally valid conditions. When selected according to its motivational qualities, the positive impact of music on both psychological state and performance is magnified. Guidelines are provided for future research and exercise practitioners.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3339578/ Music in the exercise domain: a review and synthesis (Part I) Costas I. Karageorghis, David-Lee Priest 2012 2020-12-22 [("doi","10.1080/1750984X.2011.631026")] exercise music

Since a 1997 review by Karageorghis and Terry, which highlighted the state of knowledge and methodological weaknesses, the number of studies investigating musical reactivity in relation to exercise has swelled considerably. In this two-part review paper, the development of conceptual approaches and mechanisms underlying the effects of music are explicated (Part I), followed by a critical review and synthesis of empirical work (spread over Parts I and II).

Pre-task music has been shown to optimize arousal, facilitate task-relevant imagery and improve performance in simple motoric tasks. During repetitive, endurance-type activities, self-selected, motivational and stimulative music has been shown to enhance affect, reduce ratings of perceived exertion, improve energy efficiency and lead to increased work output. There is evidence to suggest that carefully selected music can promote ergogenic and psychological benefits during high-intensity exercise, although it appears to be ineffective in reducing perceptions of exertion beyond the anaerobic threshold. The effects of music appear to be at their most potent when it is used to accompany self-paced exercise or in externally valid conditions. When selected according to its motivational qualities, the positive impact of music on both psychological state and performance is magnified. Guidelines are provided for future research and exercise practitioners.

--- /doc/psychology/music/distraction/2021-scullin.pdf Bedtime Music, Involuntary Musical Imagery, and Sleep Michael K. Scullin, Chenlu Gao, Paul Fillmore 2021-06-09 2021-06-09 [("doi","10.1177/0956797621989724")] psychology/music/distraction

Many people listen to music for hours every day, often near bedtime. We investigated whether music listening affects sleep, focusing on a rarely explored mechanism: involuntary musical imagery (earworms).

In Study 1 (n = 199, mean age = 35.9 years), individuals who frequently listen to music reported persistent nighttime earworms, which were associated with worse sleep quality. In Study 2 (n = 50, mean age = 21.2 years), we randomly assigned each participant to listen to lyrical or instrumental-only versions of popular songs before bed in a laboratory, discovering that instrumental music increased the incidence of nighttime earworms and worsened polysomnography-measured sleep quality. In both studies, earworms were experienced during awakenings, suggesting that the sleeping brain continues to process musical melodies.

Study 3 substantiated this possibility by showing a statistically-significant increase in frontal slow oscillation activity, a marker of sleep-dependent memory consolidation. Thus, some types of music can disrupt nighttime sleep by inducing long-lasting earworms that are perpetuated by spontaneous memory-reactivation processes.

--- /review/umineko#music 2020-12-22 anime music --- /doc/genetics/heritable/2015-butkovic.pdf Personality related traits as predictors of music practice: Underlying environmental and genetic influences Ana Butkovic, Fredrik Ullén, Miriam A. Mosing 2015-01-01 2020-12-22 [("doi","10.1016/j.paid.2014.10.006")] genetics/heritable music --- /doc/anime/2010-knobel.pdf AMV Remix: Do-it-yourself anime music videos Michele Knobel, Colin Lankshear, Matthew Lewis 2010-01-01 2020-12-22 anime economics/copyright music --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3785310/ Leading the Herd Astray: An Experimental Study of Self-Fulfilling Prophecies in an Artificial Cultural Market Matthew J. Salganik, Duncan J. Watts 2008 2020-12-22 [("doi","10.1177/019027250807100404")] culture music

Individuals influence each other’s decisions about cultural products such as songs, books, and movies; but to what extent can the perception of success become a “self-fulfilling prophecy”? We have explored this question experimentally by artificially inverting the true popularity of songs in an online “music market”, in which 12,207 participants listened to and downloaded songs by unknown bands.

We found that most songs experienced self-fulfilling prophecies, in which perceived-but initially false-popularity became real over time. We also found, however, that the inversion was not self-fulfilling for the market as a whole, in part because the very best songs recovered their popularity in the long run. Moreover, the distortion of market information reduced the correlation between appeal and popularity, and led to fewer overall downloads.

These results, although partial and speculative, suggest a new approach to the study of cultural markets, and indicate the potential of web-based experiments to explore the social psychological origin of other macro-sociological phenomena.

--- /doc/dual-n-back/2004-costagiomi.pdf Effects of 3 Years of Piano Instruction on Children’s Academic Achievement, School Performance and Self-Esteem Eugenia Costa-Giomi 2004-04-01 2020-12-22 [("doi","10.1177/0305735604041491")] dual-n-back music

This study of the effects of 3 years of piano instruction is based on a sample of 117 fourth-grade children attending public schools in Montreal. The children had never participated in formal music instruction, did not have a piano at home, and their annual family income was below $40,000 Can. Children in the experimental group (n = 63) received individual piano lessons weekly for 3 years and were given an acoustic piano at no cost to their families. Children in the control group (n = 54) did not participate in formal music instruction.

Participants were administered tests of self-esteem, academic achievement, cognitive abilities, musical abilities, and motor proficiency at the beginning of the project and throughout the 3 years of piano instruction.

The results indicated that piano instruction had a positive effect on children’s self-esteem and school music marks but did not affect their academic achievement in math and language as measured by standardized tests and school report cards.

--- /doc/psychology/novelty/1984-hargreaves.pdf The Effects of Repetition on Liking for Music David J. Hargreaves 1984-04-01 2020-12-23 [("doi","10.2307/3345279")] music psychology/novelty

An inverted-U theory of the relationship between the subjective complexity of and liking for different musical pieces was developed. The theory was then used to derive some predictions about the effects of repetition on liking for pieces of music of different styles chosen to represent contrasting levels of objective complexity.

The predictions were tested in two experiments. The first experiment was a short time-scale study in which two pieces (“easy-listening” music and avant-garde jazz) were played to subjects 3× during a single session. The second experiment involved repetition over 3 weekly sessions, as well as 4× within sessions, of 3 pieces (popular, classical, and avant-garde jazz).

The results of both experiments were interpreted as broadly supporting the inverted-U model although there were some surprising exceptions. These exceptions occurred when functions relating familiarity and liking were compared between musical styles, and they were tentatively explained in terms of attitudinal stereotyping.

--- https://www.danieldjohnson.com/2015/08/03/composing-music-with-recurrent-neural-networks/ Composing Music With Recurrent Neural Networks 2020-12-23 ai/music ai/nn/rnn --- https://www.theatlantic.com/technology/archive/2012/10/scientists-recover-the-sounds-of-19th-century-music-and-laughter-from-the-oldest-playable-american-recording/264147/ Scientists Recover the Sounds of 19th-Century Music and Laughter From the Oldest Playable American Recording 2020-12-23 music --- https://www.tlmc.eu/2018/01/tlmc-v19.html Touhou lossless music collection: TLMC v.19 (2018.01.01) 2020-12-23 music --- https://web.archive.org/web/20220803180645/https://everyoneishappy.com/portfolio/waifu-synthesis-real-time-generative-anime/ Waifu Synthesis: real time generative anime 2020-12-23 ai/anime ai/music ai/nn/gan/stylegan --- https://www.vanityfair.com/style/2012/01/prisoners-of-style-201201 From Fashion to Housewares, Are We in a Decades-Long Design Rut? Kurt Andersen 2020-12-23 music psychology/novelty --- https://www.tlmc.eu/2015/06/tlmc-v18-20150630_89.html Touhou lossless music collection: TLMC v.18 (2015.06.30) 2020-12-23 music --- https://meltingasphalt.com/music-in-human-evolution/ Music in Human Evolution 2020-12-23 music --- https://www.biorxiv.org/content/10.1101/2021.06.01.446439.full The human language system does not support music processing Xuanyi Chen, Josef Affourtit, Rachel Ryskin, Tamar I. Regev, Samuel Norman-Haignere, Olessia Jouravlev, Saima Malik-Moraleda, Hope Kean, Rosemary Varley, Evelina Fedorenko 2021-06-01 2021-06-01 [("doi","10.1101/2021.06.01.446439")] music psychology/neuroscience

Language and music are two human-unique capacities whose relationship remains debated. Some argue for overlap in processing mechanisms, especially for structure processing, but others fail to find overlap. Using fMRI, we examined the responses of language brain regions to diverse music stimuli, and also probed the musical abilities of individuals with severe aphasia.

Across 4 experiments, we obtained a clear answer: music does not recruit nor requires the language system. The language regions’ responses to music are generally low and never exceed responses elicited by non-music auditory conditions, like animal sounds. Further, the language regions are not sensitive to music structure: they show low responses to both intact and scrambled music, and to melodies with vs. without structural violations.

Finally, individuals with aphasia who cannot judge sentence grammaticality perform well on melody well-formedness judgments. Thus, the mechanisms that process structure in language do not appear to support music processing.

--- https://www.theringer.com/music/2021/5/25/22452539/soundscan-billboard-charts-streaming-numbers The Complete History of How SoundScan Changed Popular Music Forever 2020-12-23 economics music --- https://blog.twitch.tv/en/2020/11/11/music-related-copyright-claims-and-twitch/ 2020-12-23 economics/copyright --- https://www.theguardian.com/music/2020/nov/09/deepfake-pop-music-artificial-intelligence-ai-frank-sinatra 'It's the screams of the damned!' The eerie AI world of deepfake music Music 2020-12-24 ai/music economics/copyright --- https://freedom.press/news/riaa-github-youtube-dl-journalist-tool/ Music industry forces widely used journalist tool offline 2020-12-24 economics/copyright --- https://thehustle.co/the-company-that-has-a-monopoly-on-ice-cream-truck-music/ 2020-12-24 economics/copyright music --- https://www.biorxiv.org/content/10.1101/836197.full Genome-wide association study of musical beat synchronization demonstrates high polygenicity Maria Niarchou, Daniel E. Gustavson, J. Fah Sathirapongsasuti, Manuel Anglada-Tort, Else Eising, Eamonn Bell, Evonne McArthur, Peter Straub, The 23andMe Research Team, J. Devin McAuley, John A. Capra, Fredrik Ullén, Nicole Creanza, Miriam A. Mosing, David A. Hinds, Lea K. Davis, Nori Jacoby, Reyna L. Gordon 2021-09-06 2021-09-06 [("doi","10.1101/836197")] genetics/heritable music

Moving in synchrony to the beat is a fundamental component of musicality. Here, we conducted a genome-wide association study (GWAS) to identify common genetic variants associated with beat synchronization in 606,825 individuals.

Beat synchronization exhibited a highly polygenic architecture, with sixty-nine loci reaching genome-wide statistical-significance (p < 5×10−8) and SNP-based heritability (on the liability scale) of 13%–16%. Heritability was enriched for genes expressed in brain tissues, and for fetal and adult brain-specific gene regulatory elements, underscoring the role of central nervous system-expressed genes linked to the genetic basis of the trait.

We performed validations of the self-report phenotype (through internet-based experiments) and of the GWAS (polygenic scores for beat synchronization were associated with patients algorithmically classified as musicians in medical records of a separate biobank).

Genetic correlations with breathing function, motor function, processing speed, and chronotype suggest shared genetic architecture with beat synchronization and provide avenues for new phenotypic and genetic explorations.

--- https://royalsocietypublishing.org/doi/10.1098/rsos.150081 2020-12-24 culture genetics/selection music --- https://www.musicradar.com/news/the-history-of-the-gibson-black-beauty The history of the Gibson Black Beauty 2020-12-24 design music --- https://dustri.org/b/horrible-edge-cases-to-consider-when-dealing-with-music.html 2020-12-24 cs music --- https://www.cell.com/current-biology/fulltext/S0960-9822(22)00092-6 2020-12-24 genetics/selection music --- https://sociologicalscience.com/articles-v3-5-85/ 2020-12-24 music sociology --- https://www.musicbusinessworldwide.com/remember-spotify-fake-artist-theyre-still-going-strong-and-still-attracting-scandal/ Remember Spotify’s fake artists? They’re still going strong – and still attracting scandal. 2020-12-24 music --- https://www.newyorker.com/magazine/2021/06/21/the-musical-mysteries-of-josquin The Musical Mysteries of Josquin 2020-12-25 music --- https://www.wired.co.uk/article/video-game-composers 2020-12-25 economics music --- https://www.wired.com/story/big-data-music/ Why Big Data Has Been (Mostly) Good for Music 2020-12-25 economics music --- https://www.nytimes.com/2020/01/07/magazine/hologram-musicians.html Old Musicians Never Die. They Just Become Holograms. 2020-12-25 ai/music music --- https://www.wired.com/1995/08/thompson-4/ Music on Demand 2020-12-25 cs music --- https://web.archive.org/web/20070930155442/http://www.andante.com/article/article.cfm?id=16374 What's opera, doc? 2020-12-25 anime music --- /doc/psychology/1964-barber.pdf An experimental study of "hypnotic" (auditory and visual) hallucinations Theodore X. Barber, David Smith Calverley 1964-01-01 2020-12-25 [("doi","10.1037/h0042175")] music psychology --- https://tedium.co/2019/08/06/japan-record-rental-stores/ How Record Rentals Helped Save Japan’s Music Industry 2020-12-25 economics music --- https://www.biorxiv.org/content/10.1101/790972.full Brain, music and emotion: An EEG proof-of-concept study on musically continuous, non-personalized emotional responses Efthymios Papatzikis, Anri Herbst 2019-10-02 2020-12-25 [("doi","10.1101/790972")] music psychology/neuroscience

It has been repeatedly reported that motivation for listening to music is majorly driven by the latter’s emotional effect. There is a relative opposition to this approach, however, suggesting that music does not elicit true emotions. Counteracting this notion, contemporary research studies indicate that listeners do respond affectively to music providing a scientific basis in differentially approaching and registering affective responses to music as of their behavioral or biological states.

Nevertheless, no studies exist that combine the behavioral and neuroscientific research domains, offering a cross-referenced neuropsychological outcome, based on a non-personalized approach specifically using a continuous response methodology with ecologically valid musical stimuli for both research domains. Our study, trying to fill this void for the first time, discusses a relevant proof-of-concept protocol, and presents the technical outline on how to multimodally measure elicited responses on evoked emotional responses when listening to music. Specifically, we showcase how we measure the structural music elements as they vary from the beginning to the end within two different compositions, suggesting how and why to analyze and compare standardized, non-personalized behavioral to electroencephalographic data.

Reporting our preliminary findings based on this protocol, we focus on the electroencephalographic data collected from n = 13 participants in two separate studies (ie. different equipment and sample background), cross-referencing and cross-validating the biological side of the protocol’s structure. Our findings suggest (a) that all participants—irrespectively of the study—reacted consistently in terms of hemispheric lateralization for each stimulus (ie. uniform intra-subjective emotional reaction; non-statistically-significant differentiation in individual variability) and (b) that diverse patterns of biological representations emerge for each stimulus between the subjects in the two studies (variable inter-subjective emotional reaction; statistically-significant differentiation in group variability) pointing towards exogenous to the measurements process factors.

We conclude discussing further steps and implications of our protocol approach.

--- https://www.newyorker.com/culture/cultural-comment/charles-mansons-musical-ambitions Charles Manson’s Musical Ambitions 2020-12-25 music --- https://www.theringer.com/tech/2019/1/16/18184314/spotify-music-streaming-service-royalty-payout-model Is Spotify’s Model Wiping Out Music’s Middle Class? 2020-12-25 economics music --- https://www.nytimes.com/2019/06/11/magazine/universal-fire-master-recordings.html The Day the Music Burned 2020-12-26 cs/linkrot music --- https://boingboing.net/2019/03/17/facebook-is-next.html 2020-12-26 cs/linkrot music --- https://www.reddit.com/r/MachineLearning/comments/umq908/r_rwkvv2rnn_a_parallelizable_rnn_with/ 2020-12-26 ai/nn/rnn --- https://aeon.co/essays/its-hard-to-know-why-music-gives-pleasure-is-that-the-point It’s hard to know why music gives pleasure: is that the point? 2020-12-26 music psychology/novelty --- https://www.biorxiv.org/content/10.1101/456913.full Autistic traits, resting-state connectivity and absolute pitch in professional musicians: shared and distinct neural features T. Wenhart, R. A. I. Bethlehem, S. Baron-Cohen, E. Altenmüller 2018-10-30 2020-12-26 [("doi","10.1101/456913")] music psychiatry/autism psychology/neuroscience

Background: Recent studies indicate increased autistic traits in musicians with absolute pitch and a higher incidence of absolute pitch in people with autism. Theoretical accounts connect both of these with shared neural principles of local hyper-connectivity and global hypo-connectivity, enhanced perceptual functioning and a detail-focused cognitive style. This is the first study to investigate absolute pitch proficiency, autistic traits and brain correlates in the same study.

Sample and Method: Graph theoretical analysis was conducted on resting state (eyes closed and eyes open) EEG connectivity (wPLI, weighted Phase Lag Index) matrices obtained from 31 absolute pitch (AP) and 33 relative pitch (RP) professional musicians. Small Worldness, Global Clustering Coefficient and Average Path length were related to autistic traits, passive (tone identification) and active (pitch adjustment) absolute pitch proficiency and onset of musical training using Welch-two-sample-tests, correlations and general linear models.

Results: Analyses revealed increased Path length (delta 2–4 Hz), reduced Clustering (beta 13–18 Hz), reduced Small-Worldness (gamma 30–60 Hz) and increased autistic traits for AP compared to RP. Only Clustering values (beta 13–18 Hz) were predicted by both AP proficiency and autistic traits. Post-hoc single connection permutation tests among raw wPLI matrices in the beta band (13–18 Hz) revealed widely reduced interhemispheric connectivity between bilateral auditory related electrode positions along with higher connectivity between F7-F8 and F8-P9 for AP. Pitch naming ability and Pitch adjustment ability were predicted by Path length, Clustering, autistic traits and onset of musical training (for pitch adjustment) explaining 44% respectively 38% of variance.

Conclusion: Results show both shared and distinct neural features between AP and autistic traits. Differences in the beta range were associated with higher autistic traits in the same population. In general, AP musicians exhibit a widely underconnected brain with reduced functional integration and reduced small-world-property during resting state. This might be partly related to autism-specific brain connectivity, while differences in Path length and Small-Worldness reflect other ability-specific influences. This is further evidence for different pathways in the acquisition and development of absolute pitch, likely influenced by both genetic and environmental factors and their interaction.

--- https://www.rollingstone.com/pro/features/beyond-blurred-lines-how-forensic-musicology-is-altering-pops-future-204986/ Music Copyright After 'Blurred Lines': Experts Speak Out 2020-12-26 economics/copyright music --- https://www.npr.org/sections/therecord/2017/11/27/565968260/within-the-context-of-all-contexts-the-rewiring-of-our-relationship-to-music Within The Context Of All Contexts: The Rewiring Of Our Relationship To Music 2020-12-26 economics/copyright music --- https://www.celebritynetworth.com/articles/entertainment-articles/how-michael-jackson-bought-the-beatles-catalogue-and-turned-it-into-a-billion-dollar-music-empire/ How Michael Jackson Bought The Beatles Catalogue And Turned It Into A Multi-Billion Dollar Music Empire 2020-12-26 economics/copyright music --- https://web.archive.org/web/20000302210922/https://www.smh.com.au/news/0002/08/text/national5.html The Xentric files: here’s a real Story from space 2020-12-26 music psychiatry --- https://pudding.cool/2018/05/similarity 2020-12-26 music --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4073543/ The genetic basis of music ability Yi Ting Tan, Gary E. McPherson, Isabelle Peretz, Samuel F. Berkovic, Sarah J. Wilson 2014 2020-12-26 [("doi","10.3389/fpsyg.2014.00658")] genetics/heritable music

Music is an integral part of the cultural heritage of all known human societies, with the capacity for music perception and production present in most people. Researchers generally agree that both genetic and environmental factors contribute to the broader realization of music ability, with the degree of music aptitude varying, not only from individual to individual, but across various components of music ability within the same individual.

While environmental factors influencing music development and expertise have been well investigated in the psychological and music literature, the interrogation of possible genetic influences has not progressed at the same rate. Recent advances in genetic research offer fertile ground for exploring the genetic basis of music ability. This paper begins with a brief overview of behavioral and molecular genetic approaches commonly used in human genetic analyses, and then critically reviews the key findings of genetic investigations of the components of music ability. Some promising and converging findings have emerged, with several loci on chromosome 4 implicated in singing and music perception, and certain loci on chromosome 8q implicated in absolute pitch and music perception. The gene AVPR1A on chromosome 12q has also been implicated in music perception, music memory, and music listening, whereas SLC6A4 on chromosome 17q has been associated with music memory and choir participation. Replication of these results in alternate populations and with larger samples is warranted to confirm the findings.

Through increased research efforts, a clearer picture of the genetic mechanisms underpinning music ability will hopefully emerge.

--- https://www.newyorker.com/magazine/2018/03/12/did-andrew-lloyd-webber-ruin-the-musical-or-rescue-it Did Andrew Lloyd Webber Ruin the Musical or Rescue It? 2020-12-27 music --- https://www.npr.org/sections/therecord/2018/01/16/578216674/too-much-music-a-failed-experiment-in-dedicated-listening Too Much Music: A Failed Experiment In Dedicated Listening 2020-12-27 music --- https://www.newstatesman.com/culture/music/2017/08/x-ray-decks-lost-bone-music-soviet-union X-ray decks: the lost bone music of the Soviet Union 2020-12-27 music technology --- https://www.nber.org/papers/w21653 Streaming Reaches Flood Stage: Does Spotify Stimulate or Depress Music Sales? 2020-12-27 economics/copyright music --- https://daily.redbullmusicacademy.com/2014/11/yoko-kanno-interview/ Interview: Anime soundtracker Yoko Kanno 2020-12-27 anime music --- https://www.newyorker.com/magazine/2015/04/27/the-man-who-broke-the-music-business The Man Who Broke the Music Business 2020-12-27 music technology --- https://aeon.co/magazine/culture/why-we-love-repetition-in-music Aeon 2020-12-27 music psychology/novelty --- https://www.nytimes.com/2014/09/17/business/media/cd-loving-japan-resists-move-to-digital-music-.html CD-Loving Japan Resists Move to Online Music 2020-12-27 music technology --- https://www.theguardian.com/music/2014/sep/04/-sp-from-charred-death-to-deep-filthstep-the-1264-genres-that-make-modern-music From charred death to deep filthstep: the 1,264 genres that make modern music 2020-12-27 music --- https://en.wikipedia.org/wiki/A_Theory_of_Justice:_The_Musical! A Theory of Justice: The Musical! 2020-12-27 music philosophy/ethics --- https://www.danieldjohnson.com/2015/08/03/composing-music-with-recurrent-neural-networks/ Composing Music With Recurrent Neural Networks 2020-12-28 ai/music ai/nn/rnn --- http://www.japantimes.co.jp/news/2014/04/01/national/music-educators-tapping-vocaloid/ 2020-12-28 music --- https://www.nytimes.com/2013/12/08/theater/musicals-couldnt-be-hotter-off-broadway-by-7000-miles.html Musicals Couldn’t Be Hotter Off Broadway 2020-12-28 music --- https://www.theguardian.com/music/2013/sep/21/secret-bach-teenage-thug Revealed: the violent, thuggish world of the young JS Bach 2020-12-28 music --- https://www.laweekly.com/ticketmaster-and-servants-bands-get-cut-of-service-fee/ 2020-12-28 economics music psychology/collecting --- https://www.theatlantic.com/entertainment/archive/2012/02/why-is-it-so-hard-for-new-musical-instruments-to-catch-on/252668/ Why Is It So Hard for New Musical Instruments to Catch On? 2020-12-28 music technology --- https://www.wsj.com/articles/SB10000872396390443696604577647870908169992 2020-12-28 music psychology/collecting --- https://www.nytimes.com/2010/01/05/science/05obhear.html Music Therapy Helps Suppress Tinnitus, Researchers Find 2020-12-28 psychiatry psychology/neuroscience --- /doc/culture/2017-askin.pdf What Makes Popular Culture Popular? Product Features and Optimal Differentiation in Music Noah Askin, Michael Mauskapf 2017-09-06 2020-12-28 [("doi","10.1177/0003122417728662")] culture music psychology/novelty

In this article, we propose a new explanation for why certain cultural products outperform their peers to achieve widespread success. We argue that products’ position in feature space predicts their popular success.

Using tools from computer science, we construct a novel dataset allowing us to examine whether the musical features of nearly 27,000 songs from Billboard’s Hot 100 charts predict their levels of success in this cultural market. We find that, in addition to artist familiarity, genre affiliation, and institutional support, a song’s perceived proximity to its peers influences its position on the charts. Contrary to the claim that all popular music sounds the same, we find that songs sounding too much like previous and contemporaneous productions—those that are highly typical—are less likely to succeed. Songs exhibiting some degree of optimal differentiation are more likely to rise to the top of the charts.

These findings offer a new perspective on success in cultural markets by specifying how content organizes product competition and audience consumption behavior.

--- https://arxiv.org/abs/1803.02155#google Self-Attention with Relative Position Representations Peter Shaw, Jakob Uszkoreit, Ashish Vaswani 2018-03-06 2020-12-28 [("doi","10.48550/arXiv.1803.02155")] ai/nn/transformer

Relying entirely on an attention mechanism, the Transformer introduced by Vaswani et al 2017 achieves state-of-the-art results for machine translation. In contrast to recurrent and convolutional neural networks, it does not explicitly model relative or absolute position information in its structure. Instead, it requires adding representations of absolute positions to its inputs.

In this work, we present an alternative approach, extending the self-attention mechanism to efficiently consider representations of the relative positions, or distances between sequence elements. On the WMT 2014 English-to-German and English-to-French translation tasks, this approach yields improvements of 1.3 BLEU and 0.3 BLEU over absolute position representations, respectively.

Notably, we observe that combining relative and absolute position representations yields no further improvement in translation quality. We describe an efficient implementation of our method and cast it as an instance of relation-aware self-attention mechanisms that can generalize to arbitrary graph-labeled inputs.

--- /doc/psychology/novelty/2020-younkin.pdf Stay True to Your Roots? Category Distance, Hierarchy, and the Performance of New Entrants in the Music Industry Peter Younkin, Keyvan Kashkooli 2020-03-12 2020-12-28 [("doi","10.1287/orsc.2019.1323")] economics music psychology/novelty

New entrants in established markets face competing recommendations over whether it is better to establish their legitimacy by conforming to type or to differentiate themselves from incumbents by proposing novel contributions. This dilemma is particularly acute in cultural markets in which demand for novelty and attention to legitimacy are both high. We draw upon research in organizational theory and entrepreneurship to hypothesize the effects of pursuing narrow or broad appeals on the performance of new entrants in the music industry.

We propose that the sales of novel products vary with the distance perceived between the classes being combined and that this happens, in part, because combinations that appear to span great distances encourage consumers to adopt superordinate rather than subordinate classes (eg. to classify and evaluate something as a “song” rather than a “country song”). Using a sample of 144 artists introduced to the public via the U.S. television program The Voice, we find evidence of a U-shaped relationship between category distance and consumer response. Specifically, consumers reward new entrants who pursue either familiarity (ie. nonspanning) or distinctive combinations (ie. combine distant genres) but reject efforts that try to balance both goals.

An experimental test validates that manipulating the perceived distance an artist spans influences individual evaluations of product quality and the hierarchy of categorization. Together these results provide initial evidence that distant combinations are more likely to be classified using a superordinate category, mitigating the potential confusion and legitimacy-based penalties that affect middle-distance combinations.

--- /doc/music/2020-mehr.pdf Origins of music in credible signaling Samuel A. Mehr, Max M. Krasnow, Gregory A. Bryant, Edward H. Hagen 2020-08-26 2020-12-29 [("doi","10.1017/S0140525X20000345")] music sociology

Music comprises a diverse category of cognitive phenomena that likely represent both the effects of psychological adaptations that are specific to music (eg. rhythmic entrainment) and the effects of adaptations for non-musical functions (eg. auditory scene analysis). How did music evolve?

Here, we show that prevailing views on the evolution of music—that music is a byproduct of other evolved faculties, evolved for social bonding, or evolved to signal mate quality—are incomplete or wrong.

We argue instead that music evolved as a credible signal in at least two contexts: coalitional interactions and infant care. Specifically, we propose that (1) the production and reception of coordinated, entrained rhythmic displays is a co-evolved system for credibly signaling coalition strength, size, and coordination ability; and (2) the production and reception of infant-directed song is a co-evolved system for credibly signaling parental attention to secondarily altricial infants.

These proposals, supported by interdisciplinary evidence, suggest that basic features of music, such as melody and rhythm, result from adaptations in the proper domain of human music.

The adaptations provide a foundation for the cultural evolution of music in its actual domain, yielding the diversity of musical forms and musical behaviors found worldwide.

[Keywords: coalitions, credible signaling, cultural evolution, infancy, music, natural selection, parent–offspring conflict, territoriality]

--- /doc/psychology/personality/conscientiousness/2018-nave.pdf Musical Preferences Predict Personality: Evidence From Active Listening and Facebook Likes Gideon Nave, Juri Minxha, David M. Greenberg, Michal Kosinski, David Stillwell, Jason Rentfrow 2018-03-27 2020-12-29 [("doi","10.1177/0956797618761659")] music psychology/personality/conscientiousness

Research over the past decade has shown that various personality traits are communicated through musical preferences. One limitation of that research is external validity, as most studies have assessed individual differences in musical preferences using self-reports of music-genre preferences. Are personality traits communicated through behavioral manifestations of musical preferences? We addressed this question in two large-scale online studies with demographically diverse populations.

Study 1 (n = 22,252) shows that reactions to unfamiliar musical excerpts predicted individual differences in personality—most notably, openness and extraversion—above and beyond demographic characteristics. Moreover, these personality traits were differentially associated with particular music-preference dimensions. The results from Study 2 (n = 21,929) replicated and extended these findings by showing that an active measure of naturally occurring behavior, Facebook Likes for musical artists, also predicted individual differences in personality. In general, our findings establish the robustness and external validity of the links between musical preferences and personality.

--- https://arxiv.org/abs/1801.06146 ULMFiT: Universal Language Model Fine-tuning for Text Classification Jeremy Howard, Sebastian Ruder 2018-01-18 2020-12-29 [("doi","10.48550/arXiv.1801.06146")] ai/nn/rnn ai/scaling

[retrospective] Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch.

We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model.

Our method outperforms the state-of-the-art on six text classification tasks, reducing the error by 18–24% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100× more data.

We open-source our pretrained models & code.

Figure 3: Validation error rates for supervised and semi-supervised ULMFiT vs. training from scratch with different numbers of training examples on IMDb, TREC-6, and AG (from left to right).

…Impact of pretraining: We compare using no pretraining with pretraining on WikiText-103 (Merity et al 2017b) in Table 4: Pretraining is most useful for small and medium-sized datasets, which are most common in commercial applications. However, even for large datasets, pretraining improves performance.

Pretraining IMDb TREC-6 AG
Without pretraining 5.63 10.67 5.52
With pretraining 5.00 5.69 5.38

Table 4: Validation error rates for ULMFiT with and without pretraining.

--- https://arxiv.org/abs/2203.05482 Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt 2022-03-10 2022-03-10 [("doi","10.48550/arXiv.2203.05482")] ai/nn/transformer/clip

[supersedes Wortsman et al 2021 (WiSE-FT)] The conventional recipe for maximizing model accuracy is to (1) train multiple models with various hyperparameters and (2) pick the individual model which performs best on a held-out validation set, discarding the remainder. In this paper, we revisit the second step of this procedure in the context of fine-tuning large pre-trained models, where fine-tuned models often appear to lie in a single low error basin. We show that averaging the weights of multiple models fine-tuned with different hyperparameter configurations often improves accuracy and robustness. Unlike a conventional ensemble, we may average many models without incurring any additional inference or memory costs—we call the results model soups.

When fine-tuning large pre-trained models such as CLIP, ALIGN, and a ViT-G pre-trained on JFT, our soup recipe provides improvements over the best model in a hyperparameter sweep on ImageNet. As a highlight, the resulting ViT-G model attains 90.94% top-1 accuracy on ImageNet, a new state-of-the-art. Furthermore, we show that the model soup approach extends to multiple image classification and natural language processing tasks, improves out-of-distribution performance, and improves zero-shot performance on new downstream tasks.

Finally, we analytically relate the performance similarity of weight-averaging and logit-ensembling to flatness of the loss and confidence of the predictions, and validate this relation empirically.

--- https://arxiv.org/abs/2106.04803#google CoAtNet: Marrying Convolution and Attention for All Data Sizes Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan 2021-06-09 2021-06-09 [("doi","10.48550/arXiv.2106.04803")] ai/nn/cnn ai/nn/transformer ai/scaling

Transformers have attracted increasing interests in computer vision, but they still fall behind state-of-the-art convolutional networks.

In this work, we show that while Transformers tend to have larger model capacity, their generalization can be worse than convolutional networks due to the lack of the right inductive bias.

To effectively combine the strengths from both architectures, we present CoAtNets (pronounced “coat” nets), a family of hybrid models built from two key insights: (1) depthwise Convolution and self-Attention can be naturally unified via simple relative attention; (2) vertically stacking convolution layers and attention layers in a principled way is surprisingly effective in improving generalization, capacity and efficiency.

Experiments show that our CoAtNets achieve state-of-the-art performance under different resource constraints across various datasets: Without extra data, CoAtNet achieves 86.0% ImageNet top-1 accuracy; When pre-trained with 13M images from ImageNet-21K, our CoAtNet achieves 88.56% top-1 accuracy, matching ViT-huge pre-trained with 300M images from JFT-300M while using 23× less data; Notably, when we further scale up CoAtNet with JFT-3B, it achieves 90.88% top-1 accuracy on ImageNet, establishing a new state-of-the-art result.

--- https://arxiv.org/abs/1707.02968#google Revisiting Unreasonable Effectiveness of Data in Deep Learning Era Chen Sun, Abhinav Shrivastava, Saurabh Singh, Abhinav Gupta 2017-07-10 2020-12-29 [("doi","10.48550/arXiv.1707.02968")] ai/dataset ai/scaling reinforcement-learning/exploration/active-learning

The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data.

Since 2012, there have been advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen if we increase the dataset size by 10× or 100×?

This paper takes a step towards clearing the clouds of mystery surrounding the relationship between ‘enormous data’ and visual deep learning. By exploiting the JFT-300M dataset which has more than 375M noisy labels for 300M images, we investigate how the performance of current vision tasks would change if this data was used for representation learning.

Our paper delivers some surprising (and some expected) findings. First, we find that the performance on vision tasks increases logarithmically based on volume of training data size. Second, we show that representation learning (or pre-training) still holds a lot of promise. One can improve performance on many vision tasks by just training a better base model. Finally, as expected, we present new state-of-the-art results for different vision tasks including image classification, object detection, semantic segmentation and human pose estimation.

Our sincere hope is that this inspires vision community to not undervalue the data and develop collective efforts in building larger datasets.

--- https://openreview.net/forum?id=Q3R088EFtng#google Evaluating Machine Accuracy on ImageNet Vaishaal Shankar, Rebecca Roelofs, Horia Mania, Alex Fang, Benjamin Recht, Ludwig Schmidt 2021-09-28 2021-09-28 ai/scaling

Expert humans are more robust to distribution shift than the best image classifiers.

We evaluate a wide range of ImageNet models with five trained human labelers.

In our year-long experiment, trained humans first annotated 40,000 images from the ImageNet and ImageNetV2 test sets with multi-class labels to enable a semantically coherent evaluation. Then we measured the classification accuracy of the five trained humans on the full task with 1,000 classes.

Only the latest models from 2020 are on par with our best human labeler, and human accuracy on the 590 object classes is still 4% and 10% higher than the best model on ImageNet and ImageNetV2, respectively. Moreover, humans achieve the same accuracy on ImageNet and ImageNetV2, while all models see a consistent accuracy drop.

Overall, our results show that there is still substantial room for improvement on ImageNet and direct accuracy comparisons between humans and machines may overstate machine performance.

[Keywords: imagenet, human accuracy, machine accuracy, robustness]

--- https://arxiv.org/abs/2111.10050#google BASIC: Combined Scaling for Open-Vocabulary Image Classification Hieu Pham, Zihang Dai, Golnaz Ghiasi, Kenji Kawaguchi, Hanxiao Liu, Adams Wei Yu, Jiahui Yu, Yi-Ting Chen, Minh-Thang Luong, Yonghui Wu, Mingxing Tan, Quoc V. Le 2021-11-19 2021-11-19 [("doi","10.48550/arXiv.2111.10050")] ai/dataset ai/nn/transformer/clip ai/scaling

We present a combined scaling method—named BASIC—that achieves 85.7% top-1 accuracy on the ImageNet ILSVRC-2012 validation set without learning from any labeled ImageNet example. This accuracy surpasses best published similar models—CLIP and ALIGN—by 9.3%. Our BASIC model also shows substantial improvements in robustness benchmarks. For instance, on 5 test sets with natural distribution shifts such as ImageNet-A, R, V2, Sketch and ObjectNet, our model achieves 84.3% top-1 average accuracy, only a small drop from its original ImageNet accuracy.

To achieve these results, we scale up the contrastive learning framework of CLIP and ALIGN in 3 dimensions: data size, model size, and batch size. Our dataset has 6.6B noisy image-text pairs, which is 4× larger than ALIGN, and 16× larger than CLIP. Our largest model has 3B weights, which is 3.75× larger in parameters and 8× larger in FLOPs than ALIGN and CLIP. Finally, our batch size is 65,536 which is 2× more than CLIP and 4× more than ALIGN.

We encountered two main challenges with the scaling rules of BASIC. First, the main challenge with implementing the combined scaling rules of BASIC is the limited memory of accelerators, such as GPUs and TPUs. To overcome the memory limit, we propose two simple methods which make use of gradient checkpointing and model parallelism.

Second, while increasing the dataset size and the model size has been the defacto method to improve the performance of deep learning models like BASIC, the effect of a large contrastive batch size on such contrastive-trained image-text models is not well-understood. To shed light on the benefits of large contrastive batch sizes, we develop a theoretical framework which shows that larger contrastive batch sizes lead to smaller generalization gaps for image-text models such as BASIC.

--- https://arxiv.org/abs/1911.06154#facebook CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs Ahmed El-Kishky, Vishrav Chaudhary, Francisco Guzman, Philipp Koehn 2019-11-10 2020-12-29 [("doi","10.48550/arXiv.1911.06154")] ai/scaling

Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. In this paper, we exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5% across different language pairs. We mine sixty-eight snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other.

We release a new web dataset consisting of over 392 million URL pairs from Common Crawl covering documents in 8144 language pairs of which 137 pairs include English. In addition to curating this massive dataset, we introduce baseline methods that leverage cross-lingual representations to identify aligned documents based on their textual content.

Finally, we demonstrate the value of this parallel documents dataset through a downstream task of mining parallel sentences and measuring the quality of machine translations from models trained on this mined data.

Our objective in releasing this dataset is to foster new research in cross-lingual NLP across a variety of low, medium, and high-resource languages.

--- https://arxiv.org/abs/1911.04944#facebook CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave, Arm Holdings, Joulin 2019-11-10 2020-12-29 [("doi","10.48550/arXiv.1911.04944")] ai/scaling

We show that margin-based bitext mining in a multilingual sentence space can be applied to monolingual corpora of billions of sentences.

We use 10 snapshots of a curated common crawl corpus (Wenzek et al 2019) totalling 32.7 billion unique sentences. Using one unified approach for 38 languages, we were able to mine 4.5 billions parallel sentences, out of which 661 million are aligned with English. 20 language pairs have more then 30 million parallel sentences, 112 more then 10 million, and most more than one million, including direct alignments between many European or Asian languages.

To evaluate the quality of the mined bitexts, we train NMT systems for most of the language pairs and evaluate them on TED, WMT and WAT test sets.

Using our mined bitexts only and no human translated parallel data, we achieve a new state-of-the-art for a single system on the WMT’19 test set for translation between English and German, Russian and Chinese, as well as German/French. In particular, our English/German system outperforms the best single one by close to 4 BLEU points and is almost on pair with best WMT’19 evaluation system which uses system combination and back-translation. We also achieve excellent results for distant languages pairs like Russian/Japanese, outperforming the best submission at the 2019 workshop on Asian Translation (WAT).

--- https://arxiv.org/abs/2205.05055#deepmind Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers Stephanie C. Y. Chan, Adam Santoro, Andrew K. Lampinen, Jane X. Wang, Aaditya Singh, Pierre H. Richemond, Jay McClelland, Felix Hill 2022-04-22 2022-04-22 [("doi","10.48550/arXiv.2205.05055")] ai/nn/rnn ai/nn/transformer ai/scaling/emergence reinforcement-learning/meta-learning reinforcement-learning/scaling

[code] Large transformer-based language models are able to perform few-shot learning (also known as in-context learning), without having been explicitly trained for it.

We hypothesized that specific distributional properties of natural language might drive this emergent phenomenon, as these characteristics might lead to a kind of interpolation between few-shot meta-training (designed to elicit rapid few-shot learning) and standard supervised training (designed to elicit gradual in-weights learning). We also hypothesized that these distributional properties could lead to emergent few-shot learning in domains outside of language.

Inspired by this idea, we ran a series of experiments on a standard image-based few-shot dataset.

We discovered that a number of data properties did indeed promote the emergence of few-shot learning in transformer models. All of these properties are present in natural language—burstiness, long-tailedness, and many-to-one or one-to-many label mappings. The data influenced whether models were biased towards either few-shot learning vs. memorizing information in their weights; models could generally perform well at only one or the other. However, we discovered that an additional distributional property could allow the two capabilities to co-exist in the same model—a skewed, Zipfian distribution over classes—which occurs in language as well.

Notably, training data that could elicit few-shot learning in transformers were unable to elicit few-shot learning in recurrent models.

In sum, we find that few-shot learning emerges only from applying the right architecture to the right data distribution; neither component is sufficient on its own.

--- https://www.sumsar.net/blog/2014/10/tiny-data-and-the-socks-of-karl-broman/ Tiny Data, Approximate Bayesian Computation and the Socks of Karl Broman 2020-12-30 statistics/bayes --- https://en.wikipedia.org/wiki/Birthday_problem Birthday problem 2020-12-30 statistics/probability --- https://arxiv.org/abs/2202.06510#microsoft Mixing and Shifting: Exploiting Global and Local Dependencies in Vision MLPs Huangjie Zheng, Pengcheng He, Weizhu Chen, Mingyuan Zhou 2022-02-14 2022-02-14 [("doi","10.48550/arXiv.2202.06510")] ai/nn/fully-connected

Token-mixing multi-layer perceptron (MLP) models have shown competitive performance in computer vision tasks with a simple architecture and relatively small computational cost. Their success in maintaining computation efficiency is mainly attributed to avoiding the use of self-attention that is often computationally heavy, yet this is at the expense of not being able to mix tokens both globally and locally.

In this paper, to exploit both global and local dependencies without self-attention, we present Mix-Shift-MLP (MS-MLP) which makes the size of the local receptive field used for mixing increase with respect to the amount of spatial shifting. In addition to conventional mixing and shifting techniques, MS-MLP mixes both neighboring and distant tokens from fine-grained to coarse-grained levels and then gathers them via a shifting operation. This directly contributes to the interactions between global and local tokens.

Being simple to implement, MS-MLP achieves competitive performance in multiple vision benchmarks. For example, an MS-MLP with 85 million parameters achieves 83.8% top-1 classification accuracy on ImageNet-1K. Moreover, by combining MS-MLP with state-of-the-art Vision Transformers such as the Swin Transformer, we show MS-MLP achieves further improvements on 3 different model scales, eg. by 0.5% on ImageNet-1K classification with Swin-B.

The code is available at: Github.

--- https://arxiv.org/abs/2202.08137#deepmind A data-driven approach for learning to control computers Peter C. Humphreys, David Raposo, Toby Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Alex Goldin, Adam Santoro, Timothy Lillicrap 2022-02-16 2022-02-16 [("doi","10.48550/arXiv.2202.08137")] reinforcement-learning/model-free reinforcement-learning/scaling technology

It would be useful for machines to use computers as humans do so that they can aid us in everyday tasks. This is a setting in which there is also the potential to leverage large-scale expert demonstrations and human judgements of interactive behavior, which are two ingredients that have driven much recent success in AI.

Here we investigate the setting of computer control using keyboard and mouse, with goals specified via natural language.

Instead of focusing on hand-designed curricula and specialized action spaces, we focus on developing a scalable method centered on reinforcement learning combined with behavioral priors informed by actual human-computer interactions.

We achieve state-of-the-art and human-level mean performance across all tasks within the MiniWob++ benchmark, a challenging suite of computer control problems, and find strong evidence of cross-task transfer.

These results demonstrate the usefulness of a unified human-agent interface when training machines to use computers. Altogether our results suggest a formula for achieving competency beyond MiniWob++ and towards controlling computers, in general, as a human would.

--- https://arxiv.org/abs/1802.08802 Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration Evan Zheran Liu, Kelvin Guu, Panupong Pasupat, Tianlin Shi, Percy Liang 2018-02-24 2020-12-30 [("doi","10.48550/arXiv.1802.08802")] reinforcement-learning/exploration reinforcement-learning/model-free technology

Reinforcement learning (RL) agents improve through trial-and-error, but when reward is sparse and the agent cannot discover successful action sequences, learning stagnates. This has been a notable problem in training deep RL agents to perform web-based tasks, such as booking flights or replying to emails, where a single mistake can ruin the entire sequence of actions. A common remedy is to “warm-start” the agent by pre-training it to mimic expert demonstrations, but this is prone to overfitting. Instead, we propose to constrain exploration using demonstrations.

From each demonstration, we induce high-level “workflows” which constrain the allowable actions at each time step to be similar to those in the demonstration (eg. “Step 1: click on a textbox; Step 2: enter some text”). Our exploration policy then learns to identify successful workflows and samples actions that satisfy these workflows. Workflows prune out bad exploration directions and accelerate the agent’s ability to discover rewards.

We use our approach to train a novel neural policy designed to handle the semi-structured nature of websites, and evaluate on a suite of web tasks, including the recent World of Bits benchmark. We achieve new state-of-the-art results, and show that workflow-guided exploration improves sample efficiency over behavioral cloning by more than 100×.

--- https://arxiv.org/abs/2202.06009#microsoft Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam Yucheng Lu, Conglong Li, Minjia Zhang, Christopher De Sa, Yuxiong He 2022-02-12 2022-02-12 [("doi","10.48550/arXiv.2202.06009")] ai/nn/sparsity/low-precision ai/scaling/hardware

1-bit communication is an effective method to scale up model training, and has been studied extensively on SGD. Its benefits, however, remain an open question on Adam-based model training (eg. BERT and GPT).

In this paper, we propose 0/1 Adam, which improves upon the state-of-the-art 1-bit Adam via two novel designs: (1) adaptive variance state freezing, which eliminates the requirement of running expensive full-precision communication at early stage of training; (2) 1-bit sync, which allows skipping communication rounds with bit-free synchronization over Adam’s optimizer states, momentum and variance.

In theory, we provide convergence analysis for 0/1 Adam on smooth non-convex objectives, and show the complexity bound is better than original Adam under certain conditions.

On various benchmarks such as BERT-Base/Large pretraining and ImageNet, we demonstrate on up to 128 GPUs that 0/1 Adam is able to reduce up to 87% of data volume, 54% of communication rounds, and achieve up to 2× higher throughput compared to the state-of-the-art 1-bit Adam while enjoying the same statistical convergence speed and end-to-end model accuracy on GLUE dataset and ImageNet validation set.

--- https://arxiv.org/abs/2202.04350 pNLP-Mixer: an Efficient all-MLP Architecture for Language Francesco Fusco, Damian Pascual, Peter Staar 2022-02-09 2022-02-09 [("doi","10.48550/arXiv.2202.04350")] ai/nn/fully-connected

Large pre-trained language models drastically changed the natural language processing(NLP) landscape. Nowadays, they represent the go-to framework to tackle diverse NLP tasks, even with a limited number of annotations. However, using those models in production, either in the cloud or at the edge, remains a challenge due to the memory footprint and/or inference costs. As an alternative, recent work on efficient NLP has shown that small weight-efficient models can reach competitive performance at a fraction of the costs.

Here, we introduce pNLP-Mixer, an embbedding-free model based on the MLP-Mixer architecture that achieves high weight-efficiency thanks to a novel linguistically informed projection layer.

We evaluate our model on two multi-lingual semantic parsing datasets, MTOP and multiATIS. On MTOP our pNLP-Mixer almost matches the performance of mBERT, which has 38× more parameters, and outperforms the state-of-the-art of tiny models (pQRNN) with 3× fewer parameters. On a long-sequence classification task (Hyperpartisan) our pNLP-Mixer without pretraining outperforms RoBERTa, which has 100× more parameters, demonstrating the potential of this architecture.

--- https://arxiv.org/abs/2202.08456 MLP-ASR: Sequence-length agnostic all-MLP architectures for speech recognition Jin Sakuma, Tatsuya Komatsu, Robin Scheibler 2022-02-17 2022-02-17 [("doi","10.48550/arXiv.2202.08456")] ai/nn/fully-connected

We propose multi-layer perceptron (MLP)-based architectures suitable for variable length input.

MLP-based architectures, recently proposed for image classification, can only be used for inputs of a fixed, pre-defined size. However, many types of data are naturally variable in length, for example, acoustic signals.

We propose 3 approaches to extend MLP-based architectures for use with sequences of arbitrary length. The first one uses a circular convolution applied in the Fourier domain, the second applies a depthwise convolution, and the final relies on a shift operation.

We evaluate the proposed architectures on an automatic speech recognition task with the Librispeech and Tedlium2 corpora.

The best proposed MLP-based architectures improves WER by 1.0 / 0.9%, 0.9 / 0.5% on Librispeech dev-clean/dev-other, test-clean/test-other set, and 0.8 / 1.1% on Tedlium2 dev/test set using 86.4% the size of self-attention-based architecture.

--- https://arxiv.org/abs/2202.08360#facebook Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision Priya Goyal, Quentin Duval, Isaac Seessel, Mathilde Caron, Ishan Misra, Levent Sagun, Arm Holdings, Joulin, Piotr Bojanowski 2022-02-16 2022-02-16 [("doi","10.48550/arXiv.2202.08360")] ai/scaling

Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the images. Applied to ImageNet, this leads to object centric features that perform on par with supervised features on most object-centric downstream tasks.

In this work, we question if using this ability, we can learn any salient and more representative information present in diverse unbounded set of images from across the globe. To do so, we train models on billions of random images without any data pre-processing or prior assumptions about what we want the model to learn. We scale our model size to dense 10 billion parameters to avoid underfitting on a large data size.

We extensively study and validate our model performance on over 50 benchmarks including fairness, robustness to distribution shift, geographical diversity, fine grained recognition, image copy detection and many image classification datasets. The resulting model, not only captures well semantic information, it also captures information about artistic style and learns salient information such as geolocations and multilingual word embeddings based on visual content only. More importantly, we discover that such model is more robust, more fair, less harmful and less biased than supervised models or models trained on object centric datasets such as ImageNet.

--- https://arxiv.org/abs/2202.10447#google Transformer Quality in Linear Time Weizhe Hua, Zihang Dai, Hanxiao Liu, Quoc V. Le 2022-02-21 2022-02-21 [("doi","10.48550/arXiv.2202.10447")] ai/nn/transformer/attention/hierarchical

We revisit the design choices in Transformers, and propose methods to address their weaknesses in handling long sequences.

First, we propose a simple layer named gated attention unit, which allows the use of a weaker single-head attention with minimal quality loss. We then propose a linear approximation method complementary to this new layer, which is accelerator-friendly and highly competitive in quality.

The resulting model, named FLASH, matches the perplexity of improved Transformers over both short (512) and long (8K) context lengths, achieving training speedups of up to 4.9× on Wiki-40B and 12.1× on PG-19 for auto-regressive language modeling, and 4.8× on C4 for masked language modeling.

--- https://arxiv.org/abs/2203.03466#microsoft Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer Greg Yang, Edward J. Hu, Igor Babuschkin, Szymon Sidor, Xiaodong Liu, David Farhi, Nick Ryder, Jakub Pachocki, Weizhu Chen, Jianfeng Gao 2022-03-07 2022-03-07 [("doi","10.48550/arXiv.2203.03466")] ai/nn/transformer/gpt/4 ai/scaling

Hyperparameter (HP) tuning in deep learning is an expensive process, prohibitively so for neural networks (NNs) with billions of parameters.

We show that, in the recently discovered Maximal Update Parametrization (muP), many optimal HPs remain stable even as model size changes. This leads to a new HP tuning paradigm we call muTransfer: parametrize the target model in muP, tune the HP indirectly on a smaller model, and zero-shot transfer them to the full-sized model, ie. without directly tuning the latter at all.

We verify muTransfer on Transformer and ResNet. For example, (1) by transferring pretraining HPs from a model of 13M parameters, we outperform published numbers of BERT-large (350M parameters), with a total tuning cost equivalent to pretraining BERT-large once; (2) by transferring from 40M parameters, we outperform published numbers of the 6.7B GPT-3 model, with tuning cost only 7% of total pretraining cost.

A Pytorch implementation of our technique can be found at Github and installable via pip install mup.

--- https://www.atlasobscura.com/articles/aspics-jello-salad How America Embraced Aspics With Threatening Auras 2020-12-31 food --- https://www.thecut.com/2022/04/repladies-fake-luxury-bags.html The Rich New York Women Who Love Their Fake Birkins: Among a certain set, counterfeit luxury bags may be more popular than the real thing 2020-12-31 psychology/collecting --- https://www.biorxiv.org/content/10.1101/2022.05.10.491289.full Genetic architecture of the white matter connectome of the human brain Zhiqiang Sha, Dick Schijven, Simon E. Fisher, Clyde Francks 2022-05-11 2022-05-11 [("doi","10.1101/2022.05.10.491289")] genetics/heritable/correlation psychiatry/adhd psychiatry/alzheimers psychiatry/autism psychiatry/bipolar/genetics psychology/neuroscience

White matter tracts form the structural basis of large-scale functional networks in the human brain. We applied brain-wide tractography to diffusion images from 30,810 adult participants (UK Biobank), and found heritability for 90 regional connectivity measures and 851 tract-wise connectivity measures.

Multivariate genome-wide association analyses identified 355 independently associated lead SNPs across the genome, of which 77% had not been previously associated with human brain metrics. Enrichment analyses implicated neurodevelopmental processes including neurogenesis, neural differentiation, neural migration, neural projection guidance, and axon development, as well as prenatal brain expression especially in stem cells, astrocytes, microglia and neurons.

We used the multivariate association profiles of lead SNPs to identify 26 genomic loci implicated in structural connectivity between core regions of the left-hemisphere language network, and also identified 6 loci associated with hemispheric left-right asymmetry of structural connectivity. Polygenic scores for schizophrenia, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, left-handedness, Alzheimer’s disease, amyotrophic lateral sclerosis, and epilepsy showed multivariate associations with structural connectivity, each implicating distinct sets of brain regions with trait-relevant functional profiles.

This large-scale mapping study revealed common genetic contributions to the structural connectome of the human brain in the general adult population, highlighting links with polygenic disposition to brain disorders and behavioral traits.

--- https://en.wikipedia.org/wiki/Inequality_of_arithmetic_and_geometric_means Inequality of arithmetic and geometric means 2020-12-31 statistics/probability --- https://en.wikipedia.org/wiki/Jensen%27s_inequality Jensen's inequality 2020-12-31 statistics/probability --- https://en.wikipedia.org/wiki/Inverted_pendulum Inverted pendulum 2020-12-31 reinforcement-learning/robot --- https://en.wikipedia.org/wiki/Kapitza's_pendulum Kapitza's pendulum 2020-12-31 reinforcement-learning/robot --- https://www.biorxiv.org/content/10.1101/2021.11.05.467434.full Multiplex genomic recording of enhancer and signal transduction activity in mammalian cells Wei Chen, Junhong Choi, Jenny F. Nathans, Vikram Agarwal, Beth Martin, Eva Nichols, Anh Leith, Choli Lee, Jay Shendure 2021-11-05 2021-11-05 [("doi","10.1101/2021.11.05.467434")] biology genetics/editing psychology/neuroscience

Measurements of gene expression and signal transduction activity are conventionally performed with methods that require either the destruction or live imaging of a biological sample within the timeframe of interest.

Here we demonstrate an alternative paradigm, termed ENGRAM (ENhancer-driven Genomic Recording of transcriptional Activity in Multiplex), in which the activity and dynamics of multiple transcriptional reporters are stably recorded to DNA. ENGRAM is based on the prime editing-mediated insertion of signal-specific or enhancer-specific barcodes to a genomically encoded recording unit.

We show how this strategy can be used to concurrently genomically record the relative activity of at least hundreds of enhancers with high fidelity, sensitivity and reproducibility. Leveraging synthetic enhancers that are responsive to specific signal transduction pathways, we further demonstrate time-dependent and concentration-dependent genomic recording of Wnt, NF-κB, and Tet-On activity.

Finally, by coupling ENGRAM to sequential genome editing, we show how serially occurring molecular events can potentially be ordered.

Looking forward, we envision that multiplex, ENGRAM-based recording of the strength, duration and order of enhancer and signal transduction activities has broad potential for application in functional genomics, developmental biology and neuroscience.

--- https://x.com/mattkrisiloff/status/1524390629442162688 2020-12-31 genetics/gametogenesis --- https://www.metaculus.com/questions/2769/when-will-the-first-successful-entirely-artificial-extracorporeal-human-pregnancy-conclude/ First extracorporeal human pregnancy 2020-12-31 genetics/gametogenesis statistics/prediction --- https://arxiv.org/abs/2204.10532 End-to-end symbolic regression with transformers Pierre-Alexandre Kamienny, Stéphane d’Ascoli, Guillaume Lample, François Charton 2022-04-22 2022-04-22 [("doi","10.48550/arXiv.2204.10532")] ai/nn/transformer/gpt/codex math

Symbolic regression, the task of predicting the mathematical expression of a function from the observation of its values, is a difficult task which usually involves a two-step procedure: predicting the “skeleton” of the expression up to the choice of numerical constants, then fitting the constants by optimizing a non-convex loss function. The dominant approach is genetic programming, which evolves candidates by iterating this subroutine a large number of times. Neural networks have recently been tasked to predict the correct skeleton in a single try, but remain much less powerful.

In this paper, we challenge this two-step procedure, and task a Transformer to directly predict the full mathematical expression, constants included. One can subsequently refine the predicted constants by feeding them to the non-convex optimizer as an informed initialization. We present ablations to show that this end-to-end approach yields better results, sometimes even without the refinement step.

We evaluate our model on problems from the SRBench benchmark and show that our model approaches the performance of state-of-the-art genetic programming with several orders of magnitude faster inference.

--- https://medium.com/message/how-to-tell-when-a-robot-has-written-you-a-letter-701562705d59 How to tell when a robot has written you a letter 2021-01-01 technology --- https://www.nytimes.com/2021/03/06/business/the-robots-are-coming-for-phil-in-accounting.html The Robots Are Coming for Phil in Accounting 2021-01-01 economics/automation --- https://www.wired.com/story/robots-fill-workplace-must-learn-get-along/ As Robots Fill the Workplace, They Must Learn to Get Along 2021-01-01 economics/automation --- https://www.wired.com/story/robots-invade-construction-site/ Robots Invade the Construction Site 2021-01-01 economics/automation --- https://spectrum.ieee.org/how-boston-dynamics-taught-its-robots-to-dance How Boston Dynamics Taught Its Robots to Dance 2021-01-01 reinforcement-learning/robot --- https://spectrum.ieee.org/automaton/robotics/home-robots/hello-robots-stretch-mobile-manipulator 2021-01-01 reinforcement-learning/robot --- https://arxiv.org/abs/2004.10190#google Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning Ryan Julian, Benjamin Swanson, Gaurav S. Sukhatme, Sergey Levine, Chelsea Finn, Karol Hausman 2020-04-21 2021-01-01 [("doi","10.48550/arXiv.2004.10190")] reinforcement-learning/meta-learning/continual-learning reinforcement-learning/robot

One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments. Despite this potential, most of the robot learning systems today are deployed as a fixed policy and they are not being adapted after their deployment. Can we efficiently adapt previously learned behaviors to new environments, objects and percepts in the real world?

In this paper, we present a method and empirical evidence towards a robot learning framework that facilitates continuous adaption. In particular, we demonstrate how to adapt vision-based robotic manipulation policies to new variations by fine-tuning via off-policy reinforcement learning, including changes in background, object shape and appearance, lighting conditions, and robot morphology. Further, this adaptation uses less than 0.2% of the data necessary to learn the task from scratch. We find that our approach of adapting pre-trained policies leads to substantial performance gains over the course of fine-tuning, and that pre-training via RL is essential: training from scratch or adapting from supervised ImageNet features are both unsuccessful with such small amounts of data. We also find that these positive results hold in a limited continual learning setting, in which we repeatedly fine-tune a single lineage of policies using data from a succession of new tasks.

Our empirical conclusions are consistently supported by experiments on simulated manipulation tasks, and by 52 unique fine-tuning experiments on a real robotic grasping system pre-trained on 580,000 grasps.

--- https://www.wsj.com/articles/meatpackers-covid-safety-automation-robots-coronavirus-11594303535 2021-01-01 economics/automation --- https://www.wired.com/story/you-can-now-buy-spot-the-robot-dog/ You Can Now Buy Spot the Robot Dog—If You’ve Got $74,500 2021-01-01 economics/automation technology --- https://arxiv.org/abs/2107.00646#google Learning to See before Learning to Act: Visual Pre-training for Manipulation Lin Yen-Chen, Andy Zeng, Shuran Song, Phillip Isola, Tsung-Yi Lin 2021-07-01 2021-07-01 [("doi","10.48550/arXiv.2107.00646")] reinforcement-learning/robot

Does having visual priors (eg. the ability to detect objects) facilitate learning to perform vision-based manipulation (eg. picking up objects)?

We study this problem under the framework of transfer learning, where the model is first trained on a passive vision task, and adapted to perform an active manipulation task. We find that pre-training on vision tasks improves generalization and sample efficiency for learning to manipulate objects. However, realizing these gains requires careful selection of which parts of the model to transfer.

Our key insight is that outputs of standard vision models highly correlate with affordance maps commonly used in manipulation. Therefore, we explore directly transferring model parameters from vision networks to affordance prediction networks, and show that this can result in successful zero-shot adaptation, where a robot can pick up certain objects with zero robotic experience. With just a small amount of robotic experience, we can further fine-tune the affordance model to achieve better results.

With just 10 minutes of suction experience or 1 hour of grasping experience, our method achieves ~80% success rate at picking up novel objects.

--- https://medium.com/starsky-robotics-blog/the-end-of-starsky-robotics-acb8a6a8a5f5 The End of Starsky Robotics 2021-01-02 economics/automation --- https://www.crummy.com/writing/speaking/2017-Roguelike%20Celebration/ Behold, mortal, the origins of robotfindskitten... 2021-01-02 fiction/text-game --- https://www.wired.com/2017/10/hiroshi-ishiguro-when-robots-act-just-like-humans/ Modern Love: Are We Ready for Intimacy With Robots? 2021-01-02 technology --- https://arxiv.org/abs/1911.04052 Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity Ajay Mandlekar, Jonathan Booher, Max Spero, Albert Tung, Anchit Gupta, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei 2019-11-11 2021-01-02 [("doi","10.48550/arXiv.1911.04052")] reinforcement-learning/robot

Large, richly annotated datasets have accelerated progress in fields such as computer vision and natural language processing, but replicating these successes in robotics has been challenging. While prior data collection methodologies such as self-supervision have resulted in large datasets, the data can have poor signal-to-noise ratio. By contrast, previous efforts to collect task demonstrations with humans provide better quality data, but they cannot reach the same data magnitude. Furthermore, neither approach places guarantees on the diversity of the data collected, in terms of solution strategies.

In this work, we leverage and extend the RoboTurk platform to scale up data collection for robotic manipulation using remote teleoperation. The primary motivation for our platform is two-fold: (1) to address the shortcomings of prior work and increase the total quantity of manipulation data collected through human supervision by an order of magnitude without sacrificing the quality of the data and (2) to collect data on challenging manipulation tasks across several operators and observe a diverse set of emergent behaviors and solutions.

We collected over 111 hours of robot manipulation data across 54 users and 3 challenging manipulation tasks in 1 week, resulting in the largest robot dataset collected via remote teleoperation. We evaluate the quality of our platform, the diversity of demonstrations in our dataset, and the utility of our dataset via quantitative and qualitative analysis. For additional results, supplementary videos, and to download our dataset, visit https://roboturk.stanford.edu/realrobotdataset.

--- https://x.com/woj_zaremba/status/1191773448999034880 2021-01-02 ai/nn/transformer/gpt --- https://arxiv.org/abs/1909.11639#google ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta, Sergey Levine, Vikash Kumar 2019-09-25 2021-01-02 [("doi","10.48550/arXiv.1909.11639")] reinforcement-learning/robot

ROBEL is an open-source platform of cost-effective robots designed for reinforcement learning in the real world. ROBEL introduces two robots, each aimed to accelerate reinforcement learning research in different task domains: D’Claw is a three-fingered hand robot that facilitates learning dexterous manipulation tasks, and D’Kitty is a four-legged robot that facilitates learning agile legged locomotion tasks. These low-cost, modular robots are easy to maintain and are robust enough to sustain on-hardware reinforcement learning from scratch with over 14000 training hours registered on them to date.

To leverage this platform, we propose an extensible set of continuous control benchmark tasks for each robot. These tasks feature dense and sparse task objectives, and additionally introduce score metrics as hardware-safety. We provide benchmark scores on an initial set of tasks using a variety of learning-based methods. Furthermore, we show that these results can be replicated across copies of the robots located in different institutions. Code, documentation, design files, detailed assembly instructions, final policies, baseline details, task videos, and all supplementary materials required to reproduce the results are available at www.roboticsbenchmarks.org.

--- https://www.newyorker.com/magazine/2019/09/30/paging-dr-robot Paging Dr. Robot 2021-01-02 economics/automation technology --- https://arxiv.org/abs/1907.11200 TuneNet: One-Shot Residual Tuning for System Identification and Sim-to-Real Robot Task Transfer Adam Allevato, Elaine Schaertl Short, Mitch Pryor, Andrea L. Thomaz 2019-07-25 2021-01-02 [("doi","10.48550/arXiv.1907.11200")] reinforcement-learning/robot

As researchers teach robots to perform more and more complex tasks, the need for realistic simulation environments is growing. Existing techniques for closing the reality gap by approximating real-world physics often require extensive real world data and/or thousands of simulation samples.

This paper presents TuneNet, a new machine learning-based method to directly tune the parameters of one model to match another using an iterative residual tuning technique. TuneNet estimates the parameter difference between two models using a single observation from the target and minimal simulation, allowing rapid, accurate and sample-efficient parameter estimation. The system can be trained via supervised learning over an auto-generated simulated dataset.

We show that TuneNet can perform system identification, even when the true parameter values lie well outside the distribution seen during training, and demonstrate that simulators tuned with TuneNet outperform existing techniques for predicting rigid body motion. Finally, we show that our method can estimate real-world parameter values, allowing a robot to perform sim-to-real task transfer on a dynamic manipulation task unseen during training.

Code and videos are available online at https://github.com/Kukanani/tune-net-release.

--- https://onlinelibrary.wiley.com/doi/10.1002/rob.21888 2021-01-02 reinforcement-learning/robot --- https://outlast.me/robot-hiveminds-with-network-effects/ 2021-01-02 ai/scaling/economics economics/automation reinforcement-learning/robot reinforcement-learning/scaling --- https://www.wired.com/story/amazon-warehouse-robots/ Inside the Amazon Warehouse Where Humans and Machines Become One 2021-01-02 economics/automation --- https://www.nytimes.com/2019/04/30/science/microbots-robots-silicon-wafer.html The Microbots Are on Their Way 2021-01-03 technology --- https://www.newyorker.com/magazine/2019/04/15/the-age-of-robot-farmers The Age of Robot Farmers 2021-01-03 economics/automation technology --- https://arxiv.org/abs/2002.08550#google Learning to Walk in the Real World with Minimal Human Effort Sehoon Ha, Peng Xu, Zhenyu Tan, Sergey Levine, Jie Tan 2020-02-20 2021-01-03 [("doi","10.48550/arXiv.2002.08550")] reinforcement-learning/robot

Reliable and stable locomotion has been one of the most fundamental challenges for legged robots. Deep reinforcement learning (deep RL) has emerged as a promising method for developing such control policies autonomously. In this paper, we develop a system for learning legged locomotion policies with deep RL in the real world with minimal human effort.

The key difficulties for on-robot learning systems are automatic data collection and safety. We overcome these two challenges by developing a multi-task learning procedure and a safety-constrained RL framework.

We tested our system on the task of learning to walk on 3 different terrains: flat ground, a soft mattress, and a doormat with crevices. Our system can automatically and efficiently learn locomotion skills on a Minitaur robot with little human intervention.

The supplemental video can be found at: https://www.youtube.com/watch?v=cwyiq6dCgOc.

--- https://www.wired.com/story/darpa-grand-challenge-2004-oral-history/ An Oral History of the 2004 Darpa Grand Challenge 2021-01-03 reinforcement-learning/robot technology --- https://www.nytimes.com/2019/03/26/technology/google-robotics-lab.html Inside Google’s Rebooted Robotics Program 2021-01-03 reinforcement-learning/robot --- https://research.google/blog/long-range-robotic-navigation-via-automated-reinforcement-learning/ 2021-01-03 reinforcement-learning/robot --- https://arxiv.org/abs/1902.09458#google Long-Range Indoor Navigation with PRM-RL Anthony Francis, Aleksandra Faust, Hao-Tien Lewis Chiang, Jasmine Hsu, J. Chase Kew, Marek Fiser, Tsang-Wei Edward Lee 2019-02-25 2021-01-03 [("doi","10.48550/arXiv.1902.09458")] reinforcement-learning/robot

Long-range indoor navigation requires guiding robots with noisy sensors and controls through cluttered environments along paths that span a variety of buildings. We achieve this with PRM-RL, a hierarchical robot navigation method in which reinforcement learning agents that map noisy sensors to robot controls learn to solve short-range obstacle avoidance tasks, and then sampling-based planners map where these agents can reliably navigate in simulation; these roadmaps and agents are then deployed on robots, guiding them along the shortest path where the agents are likely to succeed.

Here we use Probabilistic Roadmaps (PRMs) as the sampling-based planner, and AutoRL as the reinforcement learning method in the indoor navigation context. We evaluate the method in simulation for kinematic differential drive and kinodynamic car-like robots in several environments, and on differential-drive robots at 3 physical sites.

Our results show PRM-RL with AutoRL is more successful than several baselines, is robust to noise, and can guide robots over hundreds of meters in the face of noise and obstacles in both simulation and on robots, including over 5.8 kilometers of physical robot navigation.

Video: https://www.youtube.com/watch?v=xN-OWX5gKvQ.

--- https://arxiv.org/abs/1809.10124#google Learning Navigation Behaviors End-to-End with AutoRL Hao-Tien Lewis Chiang, Aleksandra Faust, Marek Fiser, Anthony Francis 2018-09-26 2021-01-03 [("doi","10.48550/arXiv.1809.10124")] reinforcement-learning/robot

We learn end-to-end point-to-point and path-following navigation behaviors that avoid moving obstacles. These policies receive noisy lidar observations and output robot linear and angular velocities.

The policies are trained in small, static environments with AutoRL, an evolutionary automation layer around Reinforcement Learning (RL) that searches for a deep RL reward and neural network architecture with large-scale hyper-parameter optimization. AutoRL first finds a reward that maximizes task completion, and then finds a neural network architecture that maximizes the cumulative of the found reward.

Empirical evaluations, both in simulation and on-robot, show that AutoRL policies do not suffer from the catastrophic forgetfulness that plagues many other deep reinforcement learning algorithms, generalize to new environments and moving obstacles, are robust to sensor, actuator, and localization noise, and can serve as robust building blocks for larger navigation tasks. Our path-following and point-to-point policies are respectively 23% and 26% more successful than comparison methods across new environments.

Video at: https://www.youtube.com/watch?v=0UwkjpUEcbI.

--- https://arxiv.org/abs/1710.03937#google PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning Aleksandra Faust, Oscar Ramirez, Marek Fiser, Kenneth Oslund, Anthony Francis, James Davidson, Lydia Tapia 2017-10-11 2021-01-03 [("doi","10.48550/arXiv.1710.03937")] reinforcement-learning/robot

We present PRM-RL, a hierarchical method for long-range navigation task completion that combines sampling based path planning with reinforcement learning (RL). The RL agents learn short-range, point-to-point navigation policies that capture robot dynamics and task constraints without knowledge of the large-scale topology.

Next, the sampling-based planners provide roadmaps which connect robot configurations that can be successfully navigated by the RL agent. The same RL agents are used to control the robot under the direction of the planning, enabling long-range navigation. We use the Probabilistic Roadmaps (PRMs) for the sampling-based planner. The RL agents are constructed using feature-based and deep neural net policies in continuous state and action spaces.

We evaluate PRM-RL, both in simulation and on-robot, on two navigation tasks with non-trivial robot dynamics: end-to-end differential drive indoor navigation in office environments, and aerial cargo delivery in urban environments with load displacement constraints. Our results show improvement in task completion over both RL agents on their own and traditional sampling-based planners. In the indoor navigation task, PRM-RL successfully completes up to 215 meter long trajectories under noisy sensor conditions, and the aerial cargo delivery completes flights over 1,000m without violating the task constraints in an environment 63 million times larger than used in training.

--- https://arxiv.org/abs/1901.06514 The RobotriX: An eXtremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences with Robot Trajectories and Interactions Alberto Garcia-Garcia, Pablo Martinez-Gonzalez, Sergiu Oprea, John Alejandro Castro-Vargas, Sergio Orts-Escolano, Jose Garcia-Rodriguez, Alvaro Jover-Alvarez 2019-01-19 2021-01-03 [("doi","10.48550/arXiv.1901.06514")] ai/dataset reinforcement-learning/robot

Enter the RobotriX, an extremely photorealistic indoor dataset designed to enable the application of deep learning techniques to a wide variety of robotic vision problems. The RobotriX consists of hyperrealistic indoor scenes which are explored by robot agents which also interact with objects in a visually realistic manner in that simulated world.

Photorealistic scenes and robots are rendered by Unreal Engine into a virtual reality headset which captures gaze so that a human operator can move the robot and use controllers for the robotic hands; scene information is dumped on a per-frame basis so that it can be reproduced offline to generate raw data and ground truth labels.

By taking this approach, we were able to generate a dataset of 38 semantic classes totaling 8M stills recorded at +60 frames per second with full HD resolution. For each frame, RGB-D and 3D information is provided with full annotations in both spaces.

Thanks to the high quality and quantity of both raw information and annotations, the RobotriX will serve as a new milestone for investigating 2D and 3D robotic vision tasks with large-scale data-driven techniques.

--- https://www.wired.com/story/poker-playing-robot-goes-to-pentagon/ A Poker-Playing Robot Goes to Work for the Pentagon 2021-01-04 reinforcement-learning/imperfect-information --- https://arxiv.org/abs/1812.07252#google Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, Konstantinos Bousmalis 2018-12-18 2021-01-04 [("doi","10.48550/arXiv.1812.07252")] reinforcement-learning/robot

Real world data, especially in the domain of robotics, is notoriously costly to collect. One way to circumvent this can be to leverage the power of simulation to produce large amounts of labeled data. However, training models on simulated images does not readily transfer to real-world ones. Using domain adaptation methods to cross this “reality gap” requires a large amount of unlabeled real-world data, whilst domain randomization alone can waste modeling power.

In this paper, we present Randomized-to-Canonical Adaptation Networks (RCANs), a novel approach to crossing the visual reality gap that uses no real-world data. Our method learns to translate randomized rendered images into their equivalent non-randomized, canonical versions. This in turn allows for real images to also be translated into canonical sim images.

We demonstrate the effectiveness of this sim-to-real approach by training a vision-based closed-loop grasping reinforcement learning agent in simulation, and then transferring it to the real world to attain 70% zero-shot grasp success on unseen objects, a result that almost doubles the success of learning the same task directly on domain randomization alone. Additionally, by joint finetuning in the real-world with only 5,000 real-world grasps, our method achieves 91%, attaining comparable performance to a state-of-the-art system trained with 580,000 real-world grasps, resulting in a reduction of real-world data by more than 99%.

--- https://www.nytimes.com/2019/01/13/technology/farm-technology-milkers-robots.html Robotic Milkers and an Automated Greenhouse: Inside a High-Tech Small Farm 2021-01-04 economics/automation --- https://arxiv.org/abs/1812.00568 Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, Sergey Levine 2018-12-03 2021-01-04 [("doi","10.48550/arXiv.1812.00568")] reinforcement-learning/robot

Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs but have yet to achieve the kind of broad generalization and applicability demonstrated by deep learning methods in supervised domains. We present a deep RL method that is practical for real-world robotics tasks, such as robotic manipulation, and generalizes effectively to never-before-seen tasks and objects.

In these settings, ground truth reward signals are typically unavailable, and we therefore propose a self-supervised, model-based approach, where a predictive model learns to directly predict the future from raw sensory readings, such as camera images. At test time, we explore 3 distinct goal specification methods: designated pixels, where a user specifies desired object manipulation tasks by selecting particular pixels in an image and corresponding goal positions, goal images, where the desired goal state is specified with an image, and image classifiers, which define spaces of goal states.

Our deep predictive models are trained using data collected autonomously and continuously by a robot interacting with hundreds of objects, without human supervision. We demonstrate that visual MPC can generalize to never-before-seen objects—both rigid and deformable—and solve a range of user-defined object manipulation tasks using the same model.

--- https://bair.berkeley.edu/blog/2018/11/30/visual-rl/ Visual Model-Based Reinforcement Learning as a Path towards Generalist Robots 2021-01-04 reinforcement-learning/robot --- https://www.economist.com/asia/2018/11/08/japan-is-both-obsessed-with-and-resistant-to-robots Japan is both obsessed with and resistant to robots 2021-01-04 economics/automation --- https://qz.com/1419418/uniqlo-cut-90-of-staff-at-one-warehouse-by-replacing-them-with-robots/ 2021-01-04 economics/automation --- https://arxiv.org/abs/1809.07731 Benchmarking Reinforcement Learning Algorithms on Real-World Robots A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra 2018-09-20 2021-01-04 [("doi","10.48550/arXiv.1809.07731")] reinforcement-learning/model-free reinforcement-learning/robot

Through many recent successes in simulation, model-free reinforcement learning has emerged as a promising approach to solving continuous control robotic tasks. The research community is now able to reproduce, analyze and build quickly on these results due to open source implementations of learning algorithms and simulated benchmark tasks. To carry forward these successes to real-world applications, it is crucial to withhold using the unique advantages of simulations that do not transfer to the real world and experiment directly with physical robots. However, reinforcement learning research with physical robots faces substantial resistance due to the lack of benchmark tasks and supporting source code.

In this work, we introduce several reinforcement learning tasks with multiple commercially available robots that present varying levels of learning difficulty, setup, and repeatability. On these tasks, we test the learning performance of off-the-shelf implementations of 4 reinforcement learning algorithms and analyze sensitivity to their hyper-parameters to determine their readiness for applications in various real-world tasks.

Our results show that with a careful setup of the task interface and computations, some of these implementations can be readily applicable to physical robots. We find that state-of-the-art learning algorithms are highly sensitive to their hyper-parameters and their relative ordering does not transfer across tasks, indicating the necessity of re-tuning them for each task for best performance. On the other hand, the best hyper-parameter configuration from one task may often result in effective learning on held-out tasks even with different robots, providing a reasonable default.

We make the benchmark tasks publicly available to enhance reproducibility in real-world reinforcement learning.

--- https://arxiv.org/abs/1808.03841 Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios Tingxiang Fan, Pinxin Long, Wenxi Liu, Jia Pan 2018-08-11 2021-01-04 [("doi","10.48550/arXiv.1808.03841")] reinforcement-learning/multi-agent reinforcement-learning/robot

In this paper, we present a decentralized sensor-level collision avoidance policy for multi-robot systems, which shows promising results in practical applications. Irectly maps raw sensor measurements to an agent’s steering commands in terms of the movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy. The policy is trained over a large number of robots in rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. The learning algorithm is also integrated into a hybrid control framework to further improve the policy’s robustness and effectiveness.

We validate the learned sensor-level collision avoidance policy in a variety of simulated and real-world scenarios with thorough performance evaluations for large-scale multi-robot systems. The generalization of the learned policy is verified in a set of unseen scenarios including the navigation of a group of heterogeneous robots and a large-scale scenario with 100 robots. Although the policy is trained using simulation data only, we have successfully deployed it on physical robots with shapes and dynamics characteristics that are different from the simulated agents, in order to demonstrate the controller’s robustness against the sim-to-real modeling error. Finally, we show that the collision-avoidance policy learned from multi-robot navigation tasks provides an excellent solution to the safe and effective autonomous navigation for a single robot working in a dense real human crowd. Our learned policy enables a robot to make effective progress in a crowd without getting stuck. Videos are available at https://sites.google.com/view/hybridmrca.

--- https://arxiv.org/abs/1807.07049 Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias Abhinav Gupta, Adithyavairavan Murali, Dhiraj Gandhi, Lerrel Pinto 2018-07-18 2021-01-04 [("doi","10.48550/arXiv.1807.07049")] reinforcement-learning/robot reinforcement-learning/scaling

Data-driven approaches to solving robotic tasks have gained a lot of traction in recent years. However, most existing policies are trained on large-scale datasets collected in curated lab settings. If we aim to deploy these models in unstructured visual environments like people’s homes, they will be unable to cope with the mismatch in data distribution.

In such light, we present the first systematic effort in collecting a large dataset for robotic grasping in homes. First, to scale and parallelize data collection, we built a low cost mobile manipulator assembled for under 3K USD. Second, data collected using low cost robots suffer from noisy labels due to imperfect execution and calibration errors. To handle this, we develop a framework which factors out the noise as a latent variable. Our model is trained on 28K grasps collected in several houses under an array of different environmental conditions.

We evaluate our models by physically executing grasps on a collection of novel objects in multiple unseen homes. The models trained with our home dataset showed a marked improvement of 43.7% over a baseline model trained with data collected in lab. Our architecture which explicitly models the latent noise in the dataset also performed 10% better than one that did not factor out the noise.

We hope this effort inspires the robotics community to look outside the lab and embrace learning based approaches to handle inaccurate cheap robots.

--- http://www.nature.com/articles/d41586-018-05093-1 2021-01-04 reinforcement-learning/robot --- https://www.bloomberg.com/view/articles/2018-02-09/lessons-from-a-slow-motion-robot-takeover 2021-01-05 economics/automation --- https://web.archive.org/web/20180217185321/https://www.msn.com/en-us/news/world/the-robots-are-coming-for-garment-workers/ar-BBJdSBl The Robots Are Coming for Garment Workers 2021-01-05 economics/automation --- https://arxiv.org/abs/1709.07857 GraspGAN: Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt Konolige, Sergey Levine, Vincent Vanhoucke 2017-09-22 2021-01-05 [("doi","10.48550/arXiv.1709.07857")] ai/nn/gan reinforcement-learning/robot

Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative is to use off-the-shelf simulators to render synthetic data for which ground-truth annotations are generated automatically. Unfortunately, models trained purely on simulated data often fail to generalize to the real world. We study how randomized simulated environments and domain adaptation methods can be extended to train a grasping system to grasp novel objects from raw monocular RGB images.

We extensively evaluate our approaches with a total of more than 25,000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN.

We show that, by using synthetic data and domain adaptation, we are able to reduce the number of real-world samples needed to achieve a given level of performance by up to 50×, using only randomly generated simulated objects. We also show that by using only unlabeled real-world data and our GraspGAN methodology, we obtain real-world grasping performance without any real-world labels that is similar to that achieved with 939,777 labeled real-world samples.

--- https://www.wired.com/story/robots-are-fueling-the-silent-ascendance-of-the-electric-motor/ Robots Are Fueling the Quiet Ascendance of the Electric Motor 2021-01-05 economics/automation technology --- https://arxiv.org/abs/1711.06834 Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction Stéphane Lathuilière, Benoit Massé, Pablo Mesejo, Radu Horaud 2017-11-18 2021-01-05 [("doi","10.1016/j.patrec.2018.05.023")] reinforcement-learning/imitation-learning/brain-imitation-learning reinforcement-learning/robot

This paper introduces a novel neural network-based reinforcement learning approach for robot gaze control. Our approach enables a robot to learn and to adapt its gaze control strategy for human-robot interaction neither with the use of external sensors nor with human supervision. The robot learns to focus its attention onto groups of people from its own audio-visual experiences, independently of the number of people, of their positions, and of their physical appearances.

In particular, we use a recurrent neural network architecture in combination with Q-learning to find an optimal action-selection policy; we pre-train the network using a simulated environment that mimics realistic scenarios that involve speaking/silent participants, thus avoiding the need for tedious sessions of a robot interacting with people.

Our experimental evaluation suggests that the proposed method is robust against parameter estimation, ie. the parameter values yielded by the method do not have a decisive impact on the performance. The best results are obtained when both audio and visual information is jointly used.

Experiments with the Nao robot indicate that our framework is a step forward towards the autonomous learning of socially acceptable gaze behavior.

--- https://www.bloomberg.com/news/features/2017-10-18/this-company-s-robots-are-making-everything-and-reshaping-the-world This Company’s Robots Are Making Everything 2021-01-05 technology --- https://www.newyorker.com/magazine/2017/10/23/welcoming-our-new-robot-overlords Welcoming Our New Robot Overlords 2021-01-05 economics/automation --- https://arxiv.org/abs/1502.02860 Gaussian Processes for Data-Efficient Learning in Robotics and Control Marc Peter Deisenroth, Dieter Fox, Carl Edward Rasmussen 2015-02-10 2021-01-05 [("doi","10.1109/TPAMI.2013.218")] reinforcement-learning/robot statistics/bayes

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time-consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics.

In this article, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning, our approach reduces the effects of model errors, a key problem in model-based learning.

Compared to state-of-the-art RL, our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

--- https://arxiv.org/abs/1710.06537 Sim-to-Real Transfer of Robotic Control with Dynamics Randomization Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, Pieter Abbeel 2017-10-18 2021-01-05 [("doi","10.1109/ICRA.2018.8460528")] reinforcement-learning/robot

Simulations are attractive environments for training agents as they provide an abundant source of data and alleviate certain safety concerns during the training process. But the behaviors developed by agents in simulation are often specific to the characteristics of the simulator. Due to modeling error, strategies that are successful in simulation may not transfer to their real world counterparts.

In this paper, we demonstrate a simple method to bridge this “reality gap”. By randomizing the dynamics of the simulator during training, we are able to develop policies that are capable of adapting to very different dynamics, including ones that differ from the dynamics on which the policies were trained. This adaptivity enables the policies to generalize to the dynamics of the real world without any training on the physical system.

Our approach is demonstrated on an object pushing task using a robotic arm. Despite being trained exclusively in simulation, our policies are able to maintain a similar level of performance when deployed on a real robot, reliably moving an object to a desired location from random initial configurations.

We explore the impact of various design decisions and show that the resulting policies are robust to calibration error.

--- https://arxiv.org/abs/1709.06917 Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret 2017-09-20 2021-01-05 [("doi","10.48550/arXiv.1709.06917")] reinforcement-learning/model reinforcement-learning/robot

The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties. Among the few proposed approaches, the recently introduced Black-DROPS algorithm exploits a black-box optimization algorithm to achieve both high data-efficiency and good computation times when several cores are used; nevertheless, like all model-based policy search approaches, Black-DROPS does not scale to high dimensional state/action spaces.

In this paper, we introduce a new model learning procedure in Black-DROPS that leverages parameterized black-box priors to (1) scale up to high-dimensional systems, and (2) be robust to large inaccuracies of the prior information.

We demonstrate the effectiveness of our approach with the “pendubot” swing-up task in simulation and with a physical hexapod robot (48D state space, 18D action space) that has to walk forward as fast as possible. The results show that our new algorithm is more data-efficient than previous model-based policy search algorithms (with and without priors) and that it can allow a physical 6-legged robot to learn new gaits in only 16 to 30 seconds of interaction time.

--- https://nautil.us/your-next-new-best-friend-might-be-a-robot-235779/ Your Next New Best Friend Might Be a Robot 2021-01-05 ai/nn/transformer/gpt --- https://www.wired.com/story/watch-robot-crack-safe/ Watch a Homemade Robot Crack a SentrySafe Combination Safe in 15 Minutes 2021-01-06 technology --- https://arxiv.org/abs/1707.07217 Deep Learning in Robotics: A Review of Recent Research Harry A. Pierson, Michael S. Gashler 2017-07-22 2021-01-06 [("doi","10.48550/arXiv.1707.07217")] reinforcement-learning/robot

Advances in deep learning over the last decade have led to a flurry of research in the application of deep artificial neural networks to robotic systems, with at least thirty papers published on the subject between 2014 and the present.

This review discusses the applications, benefits, and limitations of deep learning vis-à-vis physical robotic systems, using contemporary research as exemplars.

It is intended to communicate recent advances to the wider robotics community and inspire additional interest in and application of deep learning in robotics.

--- https://arxiv.org/abs/1703.07261 Black-Box Data-efficient Policy Search for Robotics Konstantinos Chatzilygeroudis, Roberto Rama, Rituraj Kaushik, Dorian Goepp, Vassilis Vassiliades, Jean-Baptiste Mouret 2017-03-21 2021-01-06 [("doi","10.48550/arXiv.1703.07261")] reinforcement-learning/robot statistics/bayes

The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on uncertain dynamical models: after each episode, they first learn a dynamical model of the robot, then they use an optimization algorithm to find a policy that maximizes the expected return given the model and its uncertainties. It is often believed that this optimization can be tractable only if analytical, gradient-based algorithms are used; however, these algorithms require using specific families of reward functions and policies, which greatly limits the flexibility of the overall approach.

In this paper, we introduce a novel model-based RL algorithm, called Black-DROPS (Black-box Data-efficient RObot Policy Search) that: (1) does not impose any constraint on the reward function or the policy (they are treated as black-boxes), (2) is as data-efficient as the state-of-the-art algorithm for data-efficient RL in robotics, and (3) is as fast (or faster) than analytical approaches when several cores are available. The key idea is to replace the gradient-based optimization algorithm with a parallel, black-box algorithm that takes into account the model uncertainties.

We demonstrate the performance of our new algorithm on two standard control benchmark problems (in simulation) and a low-cost robotic manipulator (with a real robot).

--- https://arxiv.org/abs/1707.02920 Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration Rouhollah Rahmatizadeh, Pooya Abolghasemi, Ladislau Bölöni, Sergey Levine 2017-07-10 2021-01-06 [("doi","10.48550/arXiv.1707.02920")] ai/nn/gan ai/nn/vae reinforcement-learning/imitation-learning reinforcement-learning/robot

We propose a technique for multi-task learning from demonstration that trains the controller of a low-cost robotic arm to accomplish several complex picking and placing tasks, as well as non-prehensile manipulation. The controller is a recurrent neural network using raw images as input and generating robot arm trajectories, with the parameters shared across the tasks.

The controller also combines VAE-GAN-based reconstruction with autoregressive multimodal action prediction.

Our results demonstrate that it is possible to learn complex manipulation tasks, such as picking up a towel, wiping an object, and depositing the towel to its previous position, entirely from raw images with direct behavior cloning. We show that weight sharing and reconstruction-based regularization substantially improve generalization and robustness, and training on multiple tasks simultaneously increases the success rate on all tasks.

--- https://arxiv.org/abs/1707.02796 Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks Zackory Erickson, Sonia Chernova, Charles C. Kemp 2017-07-10 2021-01-06 [("doi","10.48550/arXiv.1707.02796")] ai/nn/gan reinforcement-learning/robot

Material recognition enables robots to incorporate knowledge of material properties into their interactions with everyday objects. For example, material recognition opens up opportunities for clearer communication with a robot, such as “bring me the metal coffee mug”, and recognizing plastic versus metal is crucial when using a microwave or oven. However, collecting labeled training data with a robot is often more difficult than unlabeled data.

We present a semi-supervised learning approach for material recognition that uses generative adversarial networks (GANs) with haptic features such as force, temperature, and vibration. Our approach achieves state-of-the-art results and enables a robot to estimate the material class of household objects with ~90% accuracy when 92% of the training data are unlabeled.

We explore how well this approach can recognize the material of new objects and we discuss challenges facing generalization. To motivate learning from unlabeled training data, we also compare results against several common supervised learning classifiers.

In addition, we have released the dataset used for this work which consists of time-series haptic measurements from a robot that conducted thousands of interactions with 72 household objects.

--- https://spectrum.ieee.org/roomba-inventor-joe-jones-on-weed-killing-robot Roomba Inventor Joe Jones on His New Weed-Killing Robot, and What’s So Hard About Consumer Robotics 2021-01-06 economics/automation reinforcement-learning/robot --- https://arxiv.org/abs/1703.07326#openai One-Shot Imitation Learning Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba 2017-03-21 2021-01-06 [("doi","10.48550/arXiv.1703.07326")] reinforcement-learning/imitation-learning reinforcement-learning/robot

Imitation learning has been commonly applied to solve different tasks in isolation. This usually requires either careful feature engineering, or a number of samples. This is far from what we desire: ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering. In this paper, we propose a meta-learning framework for achieving such capability, which we call one-shot imitation learning.

Specifically, we consider the setting where there is a very large set of tasks, and each task has many instantiations. For example, a task could be to stack all blocks on a table into a single tower, another task could be to place all blocks on a table into two-block towers, etc. In each case, different instances of the task would consist of different sets of blocks with different initial states. At training time, our algorithm is presented with pairs of demonstrations for a subset of all tasks. A neural net is trained that takes as input one demonstration and the current state (which initially is the initial state of the other demonstration of the pair), and outputs an action with the goal that the resulting sequence of states and actions matches as closely as possible with the second demonstration. At test time, a demonstration of a single instance of a new task is presented, and the neural net is expected to perform well on new instances of this new task. The use of soft attention allows the model to generalize to conditions and tasks unseen in the training data. We anticipate that by training this model on a much greater variety of tasks and settings, we will obtain a general system that can turn any demonstrations into robust policies that can accomplish an overwhelming variety of tasks.

Videos available at https://sites.google.com/view/nips2017-one-shot-imitation/home.

--- https://arxiv.org/abs/1610.01685 Supervision via Competition: Robot Adversaries for Learning Tasks Lerrel Pinto, James Davidson, Abhinav Gupta 2016-10-05 2021-01-06 [("doi","10.48550/arXiv.1610.01685")] reinforcement-learning/multi-agent reinforcement-learning/robot

There has been a recent paradigm shift in robotics to data-driven learning for planning and control. Due to a large number of experiences required for training, most of these approaches use a self-supervised paradigm: using sensors to measure success/failure. However, in most cases, these sensors provide weak supervision at best.

In this work, we propose an adversarial learning framework that pits an adversary against the robot learning the task. In an effort to defeat the adversary, the original robot learns to perform the task with more robustness leading to overall improved performance.

We show that this adversarial framework forces the robot to learn a better grasping model in order to overcome the adversary. By grasping 82% of presented novel objects compared to 68% without an adversary, we demonstrate the utility of creating adversaries.

We also demonstrate via experiments that having robots in an adversarial setting might be a better learning strategy as compared to having collaborative multiple robots.

--- https://www.nytimes.com/2016/09/05/technology/no-sailors-needed-robot-sailboats-scour-the-oceans-for-data.html No Sailors Needed: Robot Sailboats Scour the Oceans for Data 2021-01-06 technology --- https://www.buzzfeed.com/sarahatopol/how-to-save-mankind-from-the-new-breed-of-killer-robots Forget about drones, forget about dystopian sci-fi—a terrifying new generation of autonomous weapons is already here. Meet the small band of dedicated optimists battling nefarious governments and bureaucratic tedium to stop the proliferation of killer robots and, just maybe, save humanity from itself. 2021-01-06 existential-risk reinforcement-learning/robot --- https://www.nytimes.com/2016/05/23/science/a-tiny-robot-that-can-fly-and-amazingly-rest.html A Tiny Robot That Can Fly and, Amazingly, Rest 2021-01-07 technology --- https://medium.com/backchannel/beware-of-the-robot-pharmacist-4015ebf13f6f Beware of the Robot Pharmacist Robert Wachter 2021-01-07 design economics/automation --- https://x.com/AxSauer/status/1524325956030275586 2021-01-07 ai/nn/gan/stylegan --- https://arxiv.org/abs/2012.09156 Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning Nathan O. Lambert, Albert Wilcox, Howard Zhang, Kristofer S. J. Pister, Roberto Calandra 2020-12-16 2021-01-07 [("doi","10.48550/arXiv.2012.09156")] reinforcement-learning/robot

Accurately predicting the dynamics of robotic systems is crucial for model-based control and reinforcement learning. The most common way to estimate dynamics is by fitting a one-step ahead prediction model and using it to recursively propagate the predicted state distribution over long horizons. Unfortunately, this approach is known to compound even small prediction errors, making long-term predictions inaccurate.

In this paper, we propose a new parametrization to supervised learning on state-action data to stably predict at longer horizons—that we call a trajectory-based model. This trajectory-based model takes an initial state, a future time index, and control parameters as inputs, and directly predicts the state at the future time index.

Experimental results in simulated and real-world robotic tasks show that trajectory-based models yield statistically-significantly more accurate long term predictions, improved sample efficiency, and the ability to predict task reward.

With these improved prediction properties, we conclude with a demonstration of methods for using the trajectory-based model for control.

--- https://www.biorxiv.org/content/10.1101/2022.05.10.491396.full Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference Shadi Zabad, Simon Gravel, Yue Li 2022-05-11 2022-05-11 [("doi","10.1101/2022.05.10.491396")] genetics/heritable statistics/bayes

The recent proliferation of large scale genome-wide association studies (GWASs) has motivated the development of statistical methods for phenotype prediction using single-nucleotide polymorphism (SNP) array data. These polygenic risk score (PRS) methods formulate the task of polygenic prediction in terms of a multiple linear regression framework, where the goal is to infer the joint effect sizes of all genetic variants on the trait. Among the subset of PRS methods that operate on GWAS summary statistics, sparse Bayesian methods have shown competitive predictive ability.

However, existing Bayesian approaches employ Markov Chain Monte Carlo (MCMC) algorithms for posterior inference, which are computationally inefficient and do not scale favorably with the number of SNPs included in the analysis. Here, we introduce Variational Inference of Polygenic Risk Scores (VIPRS), a Bayesian summary statistics-based PRS method that uses Variational Inference (VI) techniques to efficiently approximate the posterior distribution for the effect sizes.

Our experiments with genome-wide simulations and real phenotypes from the UK Biobank (UKB) dataset demonstrated that variational approximations to the posterior are competitively accurate and highly efficient. When compared to state-of-the-art PRS methods, VIPRS consistently achieves the best or second best predictive accuracy in our analyses of 18 simulation configurations as well as 12 real phenotypes measured among the UKB participants of “White British” background. This performance advantage was higher among individuals from other ethnic groups, with an increase in R-squared of up to 1.7× among participants of Nigerian ancestry for Low-Density Lipoprotein (LDL) cholesterol. Furthermore, given its computational efficiency, we applied VIPRS to a dataset of up to 10 million genetic markers, an order of magnitude greater than the standard HapMap3 subset used to train existing PRS methods. Modeling this expanded set of variants conferred modest improvements in prediction accuracy for a number of highly polygenic traits, such as standing height.

--- https://openreview.net/forum?id=5i7lJLuhTm#deepmind Learning by Directional Gradient Descent David Silver, Anirudh Goyal, Ivo Danihelka, Matteo Hessel, Hado van Hasselt 2022-02-17 2022-02-17 ai/nn/rnn

How should state be constructed from a sequence of observations, so as to best achieve some objective? Most deep learning methods update the parameters of the state representation by gradient descent. However, no prior method for computing the gradient is fully satisfactory, for example consuming too much memory, introducing too much variance, or adding too much bias. In this work, we propose a new learning algorithm that addresses these limitations. The basic idea is to update the parameters of the representation by using the directional derivative along a candidate direction, a quantity that may be computed online with the same computational cost as the representation itself. We consider several different choices of candidate direction, including random selection and approximations to the true gradient, and investigate their performance on several synthetic tasks.

[Keywords: credit assignment, directional derivative, recurrent networks]

--- https://openreview.net/forum?id=GhVS8_yPeEa#google Effect of scale on catastrophic forgetting in neural networks Vinay Venkatesh Ramasesh, Aitor Lewkowycz, Ethan Dyer 2022-03-15 2022-03-15 ai/scaling reinforcement-learning/meta-learning/continual-learning

Catastrophic forgetting presents a challenge in developing deep learning models capable of continual learning, i.e. learning tasks sequentially. Recently, both computer vision and natural-language processing have witnessed great progress through the use of large-scale pretrained models. In this work, we present an empirical study of catastrophic forgetting in this pretraining paradigm. Our experiments indicate that large, pretrained ResNets and Transformers are statistically-significantly more resistant to forgetting than randomly-initialized, trained-from-scratch models; this robustness systematically improves with scale of both model and pretraining dataset size.

We take initial steps towards characterizing what aspect of model representations allows them to perform continual learning so well, finding that in the pretrained models, distinct class representations grow more orthogonal with scale. Our results suggest that, when possible, scale and a diverse pretraining dataset can be useful ingredients in mitigating catastrophic forgetting.

[Keywords: Catastrophic forgetting, continual learning, scaling, language modeling, image classification]

--- https://openreview.net/forum?id=JVR4JswsEM A Dot Product Attention Free Transformer Shuangfei Zhai, Walter Talbott, Nitish Srivastava, Chen Huang, Hanlin Goh, Ruixiang ZHANG, Joshua M. Susskind 2021-10-05 2021-10-05 ai/nn/transformer/attention/linear-algebra

We introduce Dot Product Attention Free Transformer (DAFT), an efficient variant of Transformers (transformer) that eliminates the query-key dot product in self attention. The core idea is to construct a decomposable attention map for each dimension of the query, key and value. This compositionality enables an implementation where the attention tensor does not to be computed or stored explicitly. A DAFT layer has a memory complexity linear w.r.t. both the context size and the dimension of features, making it compatible with both large input and model sizes.

We also introduce DAFT-conv, a model variant that takes advantage of locality and spatial weight sharing while maintaining global connectivity.

We conduct experiments on ImageNet-1K classification, as well as CIFAR-10 and enwik8, two autoregressive modeling tasks. We show that DAFT demonstrates competitive performance on all the benchmarks, while providing excellent efficiency at the same time.

--- https://openreview.net/forum?id=K-hiHQXEQog Autoregressive Latent Video Prediction with High-Fidelity Image Generator Younggyo Seo, Kimin Lee, Fangchen Liu, Stephen James, Pieter Abbeel 2021-10-05 2021-10-05 ai/nn/transformer ai/video/generation

Video prediction is an important yet challenging problem; burdened with the tasks of generating future frames and learning environment dynamics. Recently, autoregressive latent video models have proved to be a powerful video prediction tool, by separating the video prediction into two sub-problems: pre-training an image generator model, followed by learning an autoregressive prediction model in the latent space of the image generator. However, successfully generating high-fidelity and high-resolution videos has yet to be seen. In this work, we investigate how to train an autoregressive latent video prediction model capable of predicting high-fidelity future frames with minimal modification to existing models, and produce high-resolution (256×256) videos. Specifically, we scale up prior models by employing a high-fidelity image generator (VQ-GAN) with a causal transformer model, and introduce additional techniques of top-k sampling and data augmentation to further improve video prediction quality. Despite the simplicity, the proposed method achieves competitive performance to state-of-the-art approaches on standard video prediction benchmarks with fewer parameters, and enables high-resolution video prediction on complex and large-scale datasets. Videos are available at the anonymized website https://sites.google.com/view/harp-anonymous.

[Keywords: video prediction, autoregressive models]

--- https://openreview.net/forum?id=QtTKTdVrFBB Random Feature Attention Hao Peng, Nikolaos Pappas, Dani Yogatama, Roy Schwartz, Noah Smith, Lingpeng Kong 2022-02-10 2022-02-10 ai/nn/transformer/attention/sparsity

Transformers are state-of-the-art models for a variety of sequence modeling tasks. At their core is an attention function which models pairwise interactions between the inputs at every timestep. While attention is powerful, it does not scale efficiently to long sequences due to its quadratic time and space complexity in the sequence length. We propose RFA, a linear time and space attention that uses random feature methods to approximate the softmax function, and explore its application in transformers. RFA can be used as a drop-in replacement for conventional softmax attention and offers a straightforward way of learning with recency bias through an optional gating mechanism. Experiments on language modeling and machine translation demonstrate that RFA achieves similar or better performance compared to strong transformer baselines. In the machine translation experiment, RFA decodes twice as fast as a vanilla transformer. Compared to existing efficient transformer variants, RFA is competitive in terms of both accuracy and efficiency on 3 long text classification datasets. Our analysis shows that RFA’s efficiency gains are especially notable on long sequences, suggesting that RFA will be particularly useful in tasks that require working with large inputs, fast decoding speed, or low memory footprints.

[Keywords: Attention, transformers, machine translation, language modeling]

--- https://arxiv.org/abs/2110.03888#alibaba M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining Junyang Lin, An Yang, Jinze Bai, Chang Zhou, Le Jiang, Xianyan Jia, Ang Wang, Jie Zhang, Yong Li, Wei Lin, Jingren Zhou, Hongxia Yang 2021-11-17 2021-11-17 ai/nn/transformer/gpt ai/scaling/hardware

Recent expeditious developments in deep learning algorithms, distributed training, and even hardware design for large models have enabled training extreme-scale models, say GPT-3 and Switch Transformer possessing hundreds of billions or even trillions of parameters. However, under limited resources, extreme-scale model training that requires enormous amounts of computes and memory footprint suffers from frustratingly low efficiency in model convergence. In this paper, we propose a simple training strategy called “Pseudo-to-Real” for high-memory-footprint-required large models. Pseudo-to-Real is compatible with large models with architecture of sequential layers. We demonstrate a practice of pretraining unprecedented 10-trillion-parameter model, an order of magnitude larger than the state-of-the-art, on solely 512 GPUs within 10 days. Besides demonstrating the application of Pseudo-to-Real, we also provide a technique, Granular CPU offloading, to manage CPU memory for training large model and maintain high GPU utilities. Fast training of extreme-scale models on a decent amount of resources can bring much smaller carbon footprint and contribute to greener AI.

[Keywords: Extreme-Scale Pretraining, Language Modeling, Natural Language Processing]

--- https://arxiv.org/abs/2110.04627#google Vector-quantized Image Modeling with Improved VQGAN Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, Yonghui Wu 2022-03-01 2022-03-01 ai/nn/gan ai/nn/transformer/gpt ai/nn/vae

Pretraining language models with next-token prediction on massive text corpora has delivered phenomenal zero-shot, few-shot, transfer learning and multi-tasking capabilities on both generative and discriminative language tasks.

Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. The discrete image tokens are encoded from a learned Vision-Transformer-based VQGAN (ViT-VQGAN). We first propose multiple improvements over vanilla VQGAN from architecture to codebook learning, yielding better efficiency and reconstruction fidelity.

The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including unconditional, class-conditioned image generation and unsupervised representation learning. When trained on ImageNet at 256×256 resolution, we achieve Inception Score (IS) of 175.1 and Fréchet Inception Distance (FID) of 4.17, a dramatic improvement over the vanilla VQGAN, which obtains 70.6 and 17.04 for IS and FID, respectively.

Based on ViT-VQGAN and unsupervised pretraining, we further evaluate the pretrained Transformer by averaging intermediate features, similar to Image GPT (iGPT).

This ImageNet-pretrained VIM-L beats iGPT-L on linear-probe accuracy 60.3% → 73.2% for a similar model size. ViM-L also outperforms iGPT-XL which is trained with extra web image data and larger model size.

[Keywords: VQGAN, Vision Transformers, Vector-quantized Image Modeling]

--- https://openreview.net/pdf?id=r1rz6U5lg Learning to superoptimize programs Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli 2017-02-23 2021-01-08 ai/nn/transformer/gpt/codex cs/algorithm reinforcement-learning/meta-learning

Code super-optimization is the task of transforming any given program to a more efficient version while preserving its input-output behavior. In some sense, it is similar to the paraphrase problem from natural language processing where the intention is to change the syntax of an utterance without changing its semantics. Code-optimization has been the subject of years of research that has resulted in the development of rule-based transformation strategies that are used by compilers.

More recently, however, a class of stochastic search based methods have been shown to outperform these strategies. This approach involves repeated sampling of modifications to the program from a proposal distribution, which are accepted or rejected based on whether they preserve correctness, and the improvement they achieve. These methods, however, neither learn from past behavior nor do they try to leverage the semantics of the program under consideration.

Motivated by this observation, we present a novel learning based approach for code super-optimization. Intuitively, our method works by learning the proposal distribution using unbiased estimators of the gradient of the expected improvement.

Experiments on benchmarks comprising of automatically generated as well as existing (Hacker’s Delight) programs show that the proposed method is able to outperform state-of-the-art approaches for code super-optimization.

--- https://openreview.net/pdf?id=Sy7m72Ogg An Actor-critic Algorithm for Learning Rate Learning Chang Xu, Tao Qin, Gang Wang, Tie-Yan Liu 2016-12-14 2021-01-08 reinforcement-learning/meta-learning

We propose an algorithm to automatically learn learning rates using actor-critic methods from reinforcement learning.

Stochastic gradient descent (SGD), which updates the model parameters by adding a local gradient times a learning rate at each step, is widely used in model training of machine learning algorithms such as neural networks. It is observed that the models trained by SGD are sensitive to learning rates and good learning rates are problem specific. To avoid manually searching of learning rates, which is tedious and inefficient, we propose an algorithm to automatically learn learning rates using actor-critic methods from reinforcement learning (RL). In particular, we train a policy network called actor to decide the learning rate at each step during training, and a value network called critic to give feedback about quality of the decision (eg. the goodness of the learning rate outputted by the actor) that the actor made. Experiments show that our method leads to good convergence of SGD and can prevent overfitting to a certain extent, resulting in better performance than human-designed competitors.

[Keywords: Deep learning, Reinforcement Learning]

--- https://openreview.net/pdf?id=BJJsrmfCZ Automatic differentiation in PyTorch Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, Adam Lerer 2017-10-28 2021-01-08 ai/nn cs/algorithm

A summary of automatic differentiation techniques employed in PyTorch library, including novelties like support for in-place modification in presence of objects aliasing the same data, performance optimizations and Python extensions.

In this article, we describe an automatic differentiation module of PyTorch—a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd, and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features.

[Keywords: PyTorch, Automatic differentiation, imperative, aliasing, dynamic, eager, machine learning]

--- https://openreview.net/forum?id=rJTKKKqeg Tracking the World State with Recurrent Entity Networks Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, Yann LeCun 2017-03-03 2021-01-08 ai/nn/rnn

A new memory-augmented model which learns to track the world state, obtaining SOTA on the bAbI tasks amongst other results.

We introduce a new model, the Recurrent Entity Network (EntNet). It is equipped with a dynamic long-term memory which allows it to maintain and update a representation of the state of the world as it receives new data. For language understanding tasks, it can reason on-the-fly as it reads text, not just when it is required

to answer a question or respond as is the case for a Memory Network (Sukhbaatar et al 2015). Like a Neural Turing Machine or Differentiable Neural Computer (Graves et al 2014; 2016) it maintains a fixed size memory and can learn to perform location and content-based read and write operations. However, unlike those models it has a simple parallel architecture in which several memory locations can be updated simultaneously. The EntNet sets a new state-of-the-art on the bAbI tasks, and is the first method to solve all the tasks in the 10k training examples setting. We also demonstrate that it can solve a reasoning task which requires a large number of supporting facts, which other methods are not able to solve, and can generalize past its training horizon. It can also be practically used on large scale datasets such as Children’s Book Test, where it obtains competitive performance, reading the story in a single pass.

[Keywords: Natural language processing, Deep learning]

--- https://openreview.net/forum?id=SkfMWhAqYQ Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet Wiel, Brendel, Matthias Bethge 2022-02-10 2022-02-10 ai/nn/cnn

Aggregating class evidence from many small image patches suffices to solve ImageNet, yields more interpretable models and can explain aspects of the decision-making of popular DNNs.

Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has proven notoriously difficult to understand how they reach their decisions. We here introduce a high-performance DNN architecture on ImageNet whose decisions are considerably easier to explain. Our model, a simple variant of the ResNet-50 architecture called BagNet, classifies an image based on the occurrences of small local image features without taking into account their spatial ordering. This strategy is closely related to the bag-of-feature (BoF) models popular before the onset of deep learning and reaches a surprisingly high accuracy on ImageNet (87.6% top-5 for 32×32px features and Alexnet performance for 16×16px features). The constraint on local features makes it straight-forward to analyse how exactly each part of the image influences the classification. Furthermore, the BagNets behave similar to state-of-the art deep neural networks such as VGG-16, ResNet-152 or DenseNet-169 in terms of feature sensitivity, error distribution and interactions between image parts. This suggests that the improvements of DNNs over previous bag-of-feature classifiers in the last few years is mostly achieved by better fine-tuning rather than by qualitatively different decision strategies.

[Keywords: interpretability, representation learning, bag of features, deep learning, object recognition]

--- https://openreview.net/forum?id=IKA7MLxsLSu Data and Parameter Scaling Laws for Neural Machine Translation Mitchell A. Gordon, Kevin Duh, Jared Kaplan 2021-08-30 2021-08-30 ai/nn/transformer ai/scaling

We observe power law scaling in neural MT and use it to predict BLEU when obtaining more data for low-resource scenarios.

We observe that the development cross-entropy loss of supervised neural machine translation models scales like a power law with the amount of training data and the number of non-embedding parameters in the model. We discuss some practical implications of these results, such as predicting BLEU achieved by large scale models and predicting the ROI of labeling data in low-resource language pairs.

--- https://arxiv.org/abs/2004.13710 Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi Rodrigo Canaan, Xianbo Gao, Julian Togelius, Andy Nealen, Stefan Menzel 2020-04-28 2021-01-08 [("doi","10.48550/arXiv.2004.13710")] reinforcement-learning/imperfect-information/hanabi

Hanabi is a cooperative game that brings the problem of modeling other players to the forefront. In this game, coordinated groups of players can leverage pre-established conventions to great effect, but playing in an ad-hoc setting requires agents to adapt to its partner’s strategies with no previous coordination. Evaluating an agent in this setting requires a diverse population of potential partners, but so far, the behavioral diversity of agents has not been considered in a systematic way.

This paper proposes Quality Diversity algorithms as a promising class of algorithms to generate diverse populations for this purpose, and generates a population of diverse Hanabi agents using MAP-Elites. We also postulate that agents can benefit from a diverse population during training and implement a simple “meta-strategy” for adapting to an agent’s perceived behavioral niche.

We show this meta-strategy can work better than generalist strategies even outside the population it was trained with if its partner’s behavioral niche can be correctly inferred, but in practice a partner’s behavior depends and interferes with the meta-agent’s own behavior, suggesting an avenue for future research in characterizing another agent’s behavior during gameplay.

--- https://www.nytimes.com/2022/05/12/magazine/brain-computer-interface.html The Man Who Controls Computers With His Mind 2021-01-08 psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning technology --- https://publicdomainreview.org/collection/harris-list-of-covent-garden-ladies/ Harris’s List of Covent-Garden Ladies (1757–95) 2021-01-08 history/public-domain-review sociology --- https://arxiv.org/abs/2205.05131#google Unifying Language Learning Paradigms Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler 2022-05-10 2022-05-10 [("doi","10.48550/arXiv.2205.05131")] ai/nn/retrieval ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm ai/nn/transformer/t5 ai/scaling reinforcement-learning/meta-learning

[blog; cf. UniLM, MAE] Existing pre-trained models are generally geared towards a particular class of problems. To date, there seems to be still no consensus on what the right architecture and pre-training setup should be. This paper presents a unified framework for pre-training models that are universally effective across datasets and setups.

We begin by disentangling architectural archetypes with pre-training objectives—two concepts that are commonly conflated. Next, we present a generalized and unified perspective for self-supervision in NLP and show how different pre-training objectives can be cast as one another and how interpolating between different objectives can be effective.

We then propose Mixture-of-Denoisers (MoD), a pre-training objective that combines diverse pre-training paradigms together. We furthermore introduce a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training schemes. We conduct extensive ablative experiments to compare multiple pre-training objectives and find that our method pushes the Pareto-frontier by outperforming T5 and/or GPT-like models across multiple diverse setups.

Finally, by scaling our model up to 20b parameters, we achieve SOTA performance on 50 well-established supervised NLP tasks ranging from language generation (with automated and human evaluation), language understanding, text classification, question answering, commonsense reasoning, long text reasoning, structured knowledge grounding and information retrieval.

Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot summarization.

Finally, we show that UL2 20B works well with chain-of-thought prompting and reasoning.

We release Flax-based T5X model checkpoints for the 20B model at https://github.com/google-research/google-research/tree/master/ul2.

…UL2 for Few-Shot Prompting and Chain-of-Thought Reasoning: We scale up UL2 and train a 20 billion parameter encoder-decoder model on the public C4 corpus and demonstrate some impressive capabilities of the UL2 20B model.

UL2 is a powerful in-context learner that excels at both few-shot and chain-of-thought (CoT) prompting. In the table below, we compare UL2 with other state-of-the-art models (e.g, T5 XXL and PaLM) for few-shot prompting on the XSum summarization dataset. Our results show that UL2 20B outperforms PaLM and T5, both of which are in the same ballpark of compute cost.

Comparison of UL2 with T5 XXL, PaLM and LaMDA 137B on 1-shot summarization (XSum) in terms of ROUGE-1/2/L (higher is better), which captures the quality by comparing the generated summaries with the gold summaries as reference.
Model ROUGE-1 ROUGE-2 ROUGE-L
LaMDA 137B – 5.4 –
PaLM 62B – 11.2 –
PaLM 540B – 12.2 –
PaLM 8B – 4.5 –
T5 XXL 11B 0.6 0.1 0.6
T5 XXL 11B + LM 13.3 2.3 10.7
UL2 20B 25.5 8.6 19.8

Most CoT prompting results have been obtained using much larger language models, such as GPT-3-175b, PaLM-540b, or LaMDA-137b. We show that reasoning via CoT prompting can be achieved with UL2 20B, which is both publicly available and several times smaller than prior models that leverage chain-of-thought prompting. This enables an open avenue for researchers to conduct research on CoT prompting and reasoning at an accessible scale. In the table below, we show that for UL2, CoT prompting outperforms standard prompting on math word problems with a range of difficulties (GSM8K, SVAMP, ASDiv, AQuA, and MAWPS). We also show that self-consistency further improves performance.

Chain-of-thought (CoT) prompting and self-consistency (SC) results on 5 arithmetic reasoning benchmarks.
--- https://arxiv.org/abs/2201.06910 ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-Shot Generalization Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Yanggang Wang, Haiyu Li, Zhilin Yang 2022-01-18 2022-01-18 [("doi","10.48550/arXiv.2201.06910")] ai/nn/transformer/gpt/instruction-tuning ai/scaling

We propose a multitask pretraining approach ZeroPrompt for zero-shot generalization, focusing on task scaling and zero-shot prompting.

While previous models are trained on only a few dozen tasks, we scale to 1,000 tasks for the first time using real-world data. This leads to a crucial discovery that task scaling can be an efficient alternative to model scaling; ie. the model size has little impact on performance with an extremely large number of tasks. [maybe just ceiling effect?]

Figure 1: Task scaling vs. model scaling. With an extremely large number of training tasks, the model size has little impact on performance. Moreover, task scaling consistently improves performance at various model scales. For the reference baselines, RoBERTa-Large was finetuned in a fully-supervised manner, while Pangu Alpha and CPM-2 were zero-shot prompted. All models were trained and evaluated in Chinese.

Our results show that task scaling can substantially improve training efficiency by 30× in FLOPs. Moreover, we present a prompting method that incorporates a genetic algorithm to automatically search for the best prompt for unseen tasks, along with a few other improvements.

Empirically, ZeroPrompt substantially improves both the efficiency and the performance of zero-shot learning across a variety of academic and production datasets.

--- https://arxiv.org/abs/2104.08835 CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP Qinyuan Ye, Bill Yuchen Lin, Xiang Ren 2021-04-18 2021-04-18 [("doi","10.48550/arXiv.2104.08835")] ai/nn/transformer/gpt/instruction-tuning ai/scaling reinforcement-learning/meta-learning

Humans can learn a new language task efficiently with only few examples, by leveraging their knowledge obtained when learning prior tasks. In this paper, we explore whether and how such cross-task generalization ability can be acquired, and further applied to build better few-shot learners across diverse NLP tasks.

We introduce CrossFit, a problem setup for studying cross-task generalization ability, which standardizes seen/unseen task partitions, data access during different learning stages, and the evaluation protocols. To instantiate different seen/unseen task partitions in CrossFit and facilitate in-depth analysis, we present the NLP Few-shot Gym, a repository of 160 diverse few-shot NLP tasks created from open-access NLP datasets and converted to a unified text-to-text format.

Our analysis reveals that the few-shot learning ability on unseen tasks can be improved via an upstream learning stage using a set of seen tasks. We also observe that the selection of upstream learning tasks can influence few-shot performance on unseen tasks, asking further analysis on task similarity and transferability.

--- https://arxiv.org/abs/2205.05448 SymphonyNet: Symphony Generation with Permutation Invariant Language Model Jiafeng Liu, Yuanliang Dong, Zehua Cheng, Xinran Zhang, Xiaobing Li, Feng Yu, Maosong Sun 2022-05-10 2022-05-10 [("doi","10.48550/arXiv.2205.05448")] ai/dataset ai/music ai/nn/tokenization ai/nn/transformer

In this work, we present a symbolic symphony music generation solution, SymphonyNet, based on a permutation invariant language model.

To bridge the gap between text generation and symphony generation task, we propose a novel Multi-track Multi-instrument Repeatable (MMR) representation with particular 3-D positional embedding and a modified Byte Pair Encoding algorithm (Music BPE) for music tokens.

A novel linear transformer decoder architecture is introduced as a backbone for modeling extra-long sequences of symphony tokens. Meanwhile, we train the decoder to learn automatic orchestration as a joint task by masking instrument information from the input. We also introduce a large-scale symbolic symphony dataset for the advance of symphony generation research.

Our empirical results show that our proposed approach can generate coherent, novel, complex and harmonious symphony compared to human composition, which is the pioneer solution for multi-track multi-instrument symbolic music generation.

--- https://arxiv.org/abs/2204.10149 WebFace260M: A Benchmark for Million-Scale Deep Face Recognition Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Dalong Du, Jiwen Lu, Jie Zhou 2022-04-21 2022-04-21 [("doi","10.48550/arXiv.2204.10149")] ai/scaling

Face benchmarks empower the research community to train and evaluate high-performance face recognition systems.

In this paper, we contribute a new million-scale recognition benchmark, containing uncurated 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol.

Firstly, we collect 4M name lists and download 260M faces from the Internet. Then, a Cleaning Automatically utilizing Self-Training (CAST) pipeline is devised to purify the tremendous WebFace260M, which is efficient and scalable. To the best of our knowledge, the cleaned WebFace42M is the largest public face recognition training set and we expect to close the data gap between academia and industry.

Referring to practical deployments, Face Recognition Under Inference Time conStraint (FRUITS) protocol and a new test set with rich attributes are constructed. Besides, we gather a large-scale masked face sub-set for biometrics assessment under COVID-19. For a comprehensive evaluation of face matchers, 3 recognition tasks are performed under standard, masked and unbiased settings, respectively.

Equipped with this benchmark, we delve into million-scale face recognition problems. A distributed framework is developed to train face recognition models efficiently without tampering with the performance. Enabled by WebFace42M, we reduce 40% failure rate on the challenging IJB-C set and rank 3rd among 430 entries on NIST-FRVT. Even 10% data (WebFace4M) shows superior performance compared with the public training sets. Furthermore, comprehensive baselines are established under the FRUITS-100/500/1,000 milliseconds protocols.

The proposed benchmark shows enormous potential on standard, masked and unbiased face recognition scenarios.

Our WebFace260M website is https://www.face-benchmark.org/.

Figure 9: Performance of ArcFace models (resnet-100) trained on the WebFace envelopes counterparts trained on the public training data.
--- https://arxiv.org/abs/1801.07698 ArcFace: Additive Angular Margin Loss for Deep Face Recognition Jiankang Deng, Jia Guo, Niannan Xue, Stefanos Zafeiriou 2018-01-23 2021-01-09 [("doi","10.48550/arXiv.1801.07698")] ai/nn/cnn

One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large-scale face recognition is the design of appropriate loss functions that enhance discriminative power. Centre loss penalises the distance between the deep features and their corresponding class centres in the Euclidean space to achieve intra-class compactness. SphereFace assumes that the linear transformation matrix in the last fully connected layer can be used as a representation of the class centres in an angular space and penalises the angles between the deep features and their corresponding weights in a multiplicative way. Recently, a popular line of research is to incorporate margins in well-established loss functions in order to maximize face class separability.

In this paper, we propose an Additive Angular Margin Loss (ArcFace) to obtain highly discriminative features for face recognition. The proposed ArcFace has a clear geometric interpretation due to the exact correspondence to the geodesic distance on the hypersphere.

We present arguably the most extensive experimental evaluation of all the recent state-of-the-art face recognition methods on over 10 face recognition benchmarks including a new large-scale image database with trillion level of pairs and a large-scale video dataset. We show that ArcFace consistently outperforms the state-of-the-art and can be easily implemented with negligible computational overhead.

We release all refined training data, training codes, pre-trained models, and training logs, which will help reproduce the results in this paper.

--- https://arxiv.org/abs/1809.04474#deepmind Multi-task Deep Reinforcement Learning with PopArt Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, Hado van Hasselt 2018-09-12 2021-01-09 [("doi","10.48550/arXiv.1809.04474")] reinforcement-learning/model-free

The reinforcement learning community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent instance. This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on. In this work, we study the problem of learning to master not one but multiple sequential-decision tasks at once.

A general issue in multi-task learning is that a balance must be found between the needs of multiple tasks competing for the limited resources of a single learning system. Many learning algorithms can get distracted by certain tasks in the set of tasks to solve. Such tasks appear more salient to the learning process, for instance because of the density or magnitude of the in-task rewards. This causes the algorithm to focus on those salient tasks at the expense of generality.

We propose to automatically adapt the contribution of each task to the agent’s updates, so that all tasks have a similar impact on the learning dynamics. This resulted in state-of-the-art performance on learning to play all games in a set of 57 diverse Atari games. Excitingly, our method learned a single trained policy—with a single set of weights—that exceeds median human performance. To our knowledge, this was the first time a single agent surpassed human-level performance on this multi-task domain. The same approach also demonstrated state-of-the-art performance on a set of 30 tasks in the 3D reinforcement learning platform DeepMind Lab.

--- https://arxiv.org/abs/2204.02311#google PaLM: Scaling Language Modeling with Pathways Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, Noah Fiedel 2022-04-05 2022-04-05 [("doi","10.48550/arXiv.2204.02311")] ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/lamda ai/nn/transformer/gpt/palm ai/scaling/emergence math

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application.

To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using [TensorStore and] Pathways, a new ML system which enables highly efficient training across multiple TPU Pods.

We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark.

A large number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks.

We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale.

Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies.

--- https://arxiv.org/abs/2204.14007 Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs Berkin Akin, Suyog Gupta, Yun Long, Anton Spiridonov, Zhuo Wang, Marie White, Hao Xu, Ping Zhou, Yanqi Zhou 2022-04-09 2022-04-09 [("doi","10.48550/arXiv.2204.14007")] reinforcement-learning/meta-learning

On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for efficiently utilizing the high compute throughput offered by these accelerators. However, existing NAS frameworks have several practical limitations in scaling to multiple tasks and different target platforms.

In this work, we provide a two-pronged approach to this challenge: (1) a NAS-enabling infrastructure that decouples model cost evaluation, search space design, and the NAS algorithm to rapidly target various on-device ML tasks, and (2) search spaces crafted from group convolution based inverted bottleneck (IBN) variants that provide flexible quality/performance trade-offs on ML accelerators, complementing the existing full and depthwise convolution based IBNs.

Using this approach we target a state-of-the-art mobile platform, Google Tensor SoC, and demonstrate neural architectures that improve the quality-performance pareto frontier for various computer vision (classification, detection, segmentation) as well as natural language processing tasks.

--- https://arxiv.org/abs/2204.14198#deepmind Flamingo: a Visual Language Model for Few-Shot Learning Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sah, Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, Karen Simonyan 2022-04-29 2022-04-29 [("doi","10.48550/arXiv.2204.14198")] ai/scaling

[blog; reproducibility challenges] Building models that can be rapidly adapted to numerous tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo 🦩, a family of Visual Language Models (VLM) with this ability [cf. MAGMA]. Flamingo models include key architectural innovations to: (1) bridge powerful pretrained vision-only and language-only models, (2) handle sequences of arbitrarily interleaved visual and textual data, and (3) seamlessly ingest images or videos as inputs. Thanks to their flexibility, Flamingo models can be trained on large-scale multimodal web corpora containing arbitrarily interleaved text and images, which is key to endow them with in-context few-shot learning capabilities.

We perform a thorough evaluation of the proposed Flamingo models, exploring and measuring their ability to rapidly adapt to a variety of image and video understanding benchmarks. These include open-ended tasks such as visual question-answering, where the model is prompted with a question which it has to answer, captioning tasks, which evaluate the ability to describe a scene or an event, and close-ended tasks such as multiple choice visual question-answering. For tasks lying anywhere on this spectrum, we demonstrate that a single Flamingo model can achieve a new state-of-the-art for few-shot learning, simply by prompting the model with task-specific examples. On many of these benchmarks, Flamingo actually surpasses the performance of models that are fine-tuned on thousands of times more task-specific data.

…In practice, Flamingo fuses large language models with powerful visual representations—each separately pre-trained and frozen—by adding novel architecture components in between. Then it is trained on a mixture of complementary large-scale multimodal data coming only from the web, without using any data annotated for machine learning purposes. Following this method, we start from Chinchilla, our recently introduced compute-optimal 70b parameter language model, to train our final Flamingo model, an 80b parameter VLM. After this training is done, Flamingo can be directly adapted to vision tasks via simple few-shot learning without any additional task-specific tuning.

[Further samples: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]

--- https://arxiv.org/abs/1906.00067#allen OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi 2019-05-31 2021-01-09 [("doi","10.48550/arXiv.1906.00067")] ai/dataset ai/nn/retrieval

Visual Question Answering (VQA) in its ideal form lets us study reasoning in the joint space of vision and language and serves as a proxy for the AI task of scene understanding. However, most VQA benchmarks to date are focused on questions such as simple counting, visual attributes, and object detection that do not require reasoning or knowledge beyond what is in the image.

In this paper, we address the task of knowledge-based visual question answering and provide a benchmark, called OK-VQA, where the image content is not sufficient to answer the questions, encouraging methods that rely on external knowledge resources. Our new dataset includes more than 14,000 questions that require external knowledge to answer.

We show that the performance of the state-of-the-art VQA models degrades drastically in this new setting. Our analysis shows that our knowledge-based VQA task is diverse, difficult, and large compared to previous knowledge-based VQA datasets.

We hope that this dataset enables researchers to open up new avenues for research in this domain. See https://okvqa.allenai.org/ to download and browse the dataset.

--- https://arxiv.org/abs/1505.00468 VQA: Visual Question Answering Aishwarya Agrawal, Jiasen Lu, Stanislaw Antol, Margaret Mitchell, C. Lawrence Zitnick, Dhruv Batra, Devi Parikh 2015-05-03 2021-01-10 [("doi","10.48550/arXiv.1505.00468")] ai/dataset ai/nn

We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions.

Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing ~0.25M images, ~0.76M questions, and ~10M answers (www.visualqa.org), and discuss the information it provides.

Numerous baselines and methods for VQA are provided and compared with human performance.

Our VQA demo is available on CloudCV.

--- https://arxiv.org/abs/1810.08272 BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, Yoshua Bengio 2018-10-18 2021-01-10 [("doi","10.48550/arXiv.1810.08272")] reinforcement-learning/meta-learning

Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific reasons, but given the poor data efficiency of the current learning methods, this goal may require substantial research efforts.

Here, we introduce the BabyAI research platform to support investigations towards including humans in the loop for grounded language learning. The BabyAI platform comprises an extensible suite of 19 levels of increasing difficulty. The levels gradually lead the agent towards acquiring a combinatorially rich synthetic language which is a proper subset of English. The platform also provides a heuristic expert agent for the purpose of simulating a human teacher.

We report baseline results and estimate the amount of human involvement that would be required to train a neural network-based agent on some of the BabyAI levels.

We put forward strong evidence that current deep learning methods are not yet sufficiently sample efficient when it comes to learning a language with compositional properties.

--- https://arxiv.org/abs/1207.4708#deepmind The Arcade Learning Environment: An Evaluation Platform for General Agents Marc G. Bellemare, Yavar Naddaf, Joel Veness, Michael Bowling 2012-07-19 2021-01-10 [("doi","10.1613/jair.3912")] reinforcement-learning/model-free

In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems.

We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games.

All of the software, including the benchmark agents, is publicly available.

--- https://arxiv.org/abs/2011.13885#deepmind Offline Learning from Demonstrations and Unlabeled Experience Konrad Zolna, Alexander Novikov, Ksenia Konyushkova, Caglar Gulcehre, Ziyu Wang, Yusuf Aytar, Misha Denil, Nando de Freitas, Scott Reed 2020-11-27 2021-01-10 [("doi","10.48550/arXiv.2011.13885")] reinforcement-learning/model-free

Behavior cloning (BC) is often practical for robot learning because it allows a policy to be trained offline without rewards, by supervised learning on expert demonstrations. However, BC does not effectively leverage what we will refer to as unlabeled experience: data of mixed and unknown quality without reward annotations. This unlabeled data can be generated by a variety of sources such as human teleoperation, scripted policies and other agents on the same robot.

Towards data-driven offline robot learning that can use this unlabeled experience, we introduce Offline Reinforced Imitation Learning (ORIL). ORIL first learns a reward function by contrasting observations from demonstrator and unlabeled trajectories, then annotates all data with the learned reward, and finally trains an agent via offline reinforcement learning. Across a diverse set of continuous control and simulated robotic manipulation tasks, we show that ORIL consistently outperforms comparable BC agents by effectively leveraging unlabeled experience.

--- https://arxiv.org/abs/2110.06192#deepmind Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes Alex X. Lee, Coline Devin, Yuxiang Zhou, Thomas Lampe, Konstantinos Bousmalis, Jost Tobias Springenberg, Arunkumar Byravan, Abbas Abdolmaleki, Nimrod Gileadi, David Khosid, Claudio Fantacci, Jose Enrique Chen, Akhil Raju, Rae Jeong, Michael Neunert, Antoine Laurens, Stefano Saliceti, Federico Casarini, Martin Riedmiller, Raia Hadsell, Francesco Nori 2021-10-12 2021-10-12 [("doi","10.48550/arXiv.2110.06192")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/robot

We study the problem of robotic stacking with objects of complex geometry.

We propose a challenging and diverse set of such objects that was carefully designed to require strategies beyond a simple “pick-and-place” solution. Our method is a reinforcement learning (RL) approach combined with vision-based interactive policy distillation and simulation-to-reality transfer.

Our learned policies can efficiently handle multiple object combinations in the real world and exhibit a large variety of stacking skills. In a large experimental study, we investigate what choices matter for learning such general vision-based agents in simulation, and what affects optimal transfer to the real robot.

We then leverage data collected by such policies and improve upon them with offline RL.

A video and a blog post of our work are provided as supplementary material.

--- https://arxiv.org/abs/1606.04671#deepmind Progressive Neural Networks Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, Raia Hadsell 2016-06-15 2021-01-10 [("doi","10.48550/arXiv.1606.04671")] reinforcement-learning/model-free

Learning to solve complex sequences of tasks—while both leveraging transfer and avoiding catastrophic forgetting—remains a key obstacle to achieving human-level intelligence.

The progressive networks approach represents a step forward in this direction: they are immune to forgetting and can leverage prior knowledge via lateral connections to previously learned features.

We evaluate this architecture extensively on a wide variety of reinforcement learning tasks (Atari and 3D maze games), and show that it outperforms common baselines based on pretraining and finetuning. Using a novel sensitivity measure, we demonstrate that transfer occurs at both low-level sensory and high-level control layers of the learned policy.

--- https://arxiv.org/abs/2004.00981 Benchmarking End-to-End Behavioral Cloning on Video Games Anssi Kanervisto, Joonas Pussinen, Ville Hautamäki 2020-04-02 2021-01-10 [("doi","10.48550/arXiv.2004.00981")] reinforcement-learning/model-free

Behavioural cloning, where a computer is taught to perform a task based on demonstrations, has been successfully applied to various video games and robotics tasks, with and without reinforcement learning. This also includes end-to-end approaches, where a computer plays a video game like humans do: by looking at the image displayed on the screen, and sending keystrokes to the game.

As a general approach to playing video games, this has many inviting properties: no need for specialized modifications to the game, no lengthy training sessions and the ability to re-use the same tools across different games. However, related work includes game-specific engineering to achieve the results.

We take a step towards a general approach and study the general applicability of behavioral cloning on 12 video games, including 6 modern video games (published after 2010), by using human demonstrations as training data. Our results show that these agents cannot match humans in raw performance but do learn basic dynamics and rules. We also demonstrate how the quality of the data matters, and how recording data from humans is subject to a state-action mismatch, due to human reflexes.

--- https://erich-friedman.github.io/packing/squinsqu/ 2021-01-10 design math --- https://en.wikipedia.org/wiki/Square_packing_in_a_square Square packing in a square 2021-01-10 math --- https://arxiv.org/abs/0707.0093 Maximum overhang Mike Paterson, Yuval Peres, Mikkel Thorup, Peter Winkler, Uri Zwick 2007-07-01 2021-01-10 [("doi","10.48550/arXiv.0707.0093")] math

How far can a stack of n identical blocks be made to hang over the edge of a table? The question dates back to at least the middle of the 19th century and the answer to it was widely believed to be of order log n.

Recently, Paterson & Zwick 2007 constructed n-block stacks with overhangs of order n1⁄3, exponentially better than previously thought possible.

We show here that order n1⁄3 is indeed the best possible, resolving the long-standing overhang problem up to a constant factor.

--- https://arxiv.org/abs/0710.2357 Overhang Mike Paterson, Uri Zwick 2007-10-12 2021-01-11 [("doi","10.48550/arXiv.0710.2357")] math

How far off the edge of the table can we reach by stacking n identical, homogeneous, frictionless blocks of length 1?

A classical solution achieves an overhang of 1⁄2Hn, where Hn ln n is the nth harmonic number. This solution is widely believed to be optimal.

We show, however, that it is, in fact, exponentially far from optimality by constructing simple n-block stacks that achieve an overhang of cn1⁄3, for some constant c > 0.

--- https://bldgblog.com/2007/12/adventures-in-stacking/ Adventures in Stacking 2021-01-11 math --- https://www.surgehq.ai//blog/humans-vs-dall-e We asked 100 humans to draw the DALL 2021-01-11 ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2205.06126#tencent One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Code Yong Dai, Duyu Tang, Liangxin Liu, Minghuan Tan, Cong Zhou, Jingquan Wang, Zhangyin Feng, Fan Zhang, Xueyu Hu, Shuming Shi 2022-05-12 2022-05-12 [("doi","10.48550/arXiv.2205.06126")] ai/scaling/mixture-of-experts

People perceive the world with multiple senses (eg. through hearing sounds, reading words, and seeing objects). However, most existing AI systems only process an individual modality. This paper presents an approach that excels at handling multiple modalities of information with a single model. In our “SkillNet” model, different parts of the parameters are specialized for processing different modalities. Unlike traditional dense models that always activate all the model parameters, our model sparsely activates parts of the parameters whose skills are relevant to the task. Such model design enables SkillNet to learn skills in a more interpretable way.

We develop our model for 5 modalities including text, image, sound, video, and code. Results show that SkillNet performs comparably to 5 modality-specific fine-tuned models. Moreover, our model supports self-supervised pretraining with the same sparsely activated way, resulting in better initialized parameters for different modalities.

We find that pretraining improves the performance of SkillNet on 5 modalities, on par with or even better than baselines with modality-specific pretraining. On the task of Chinese text-to-image retrieval, our final system achieves higher accuracy than existing leading systems including WukongViT-B and Wenlan 2.0 while using a lesser number of activated parameters.

--- https://www.biorxiv.org/content/10.1101/2022.04.09.487742.full Life-long Dietary Restrictions have Negligible or Damaging Effects on Late-life Cognitive Performance: A Key Role for Genetics in Outcomes Andrew R. Ouellette, Niran Hadad, Andrew Deighan, Laura Robinson, Kristen O’Connell, Adam Freund, Gary A. Churchill, Catherine C. Kaczorowski 2022-04-26 2022-04-26 [("doi","10.1101/2022.04.09.487742")] longevity/fasting

Several studies report that caloric restriction (CR) or intermittent fasting (IF) can improve cognition, while others report limited or no cognitive benefits. Here, we compare the effects of 20% CR, 40% CR, 1-day IF, and 2-day IF feeding paradigms to ad libitum controls (AL) on Y-maze working memory and contextual fear memory (CFM) in a large population of Diversity Outbred mice that model the genetic diversity of humans.

While CR and IF interventions improve lifespan, we observed no enhancement of working memory or CFM in mice on these feeding paradigms, and report 40% CR to be damaging in the context of long-term memory. Using Quantitative Trait Loci mapping, we identified the gene Slc16a7 to be associated with late-life long-term memory outcomes in mice on lifespan-promoting feeding paradigms.

Limited utility of dieting and fasting on memory in mice that recapitulate genetic diversity in the human population highlights the need for anti-aging therapeutics that promote cognitive function, with a neuronal monocarboxylate transporter encoded by Slc16a7 highlighted as a novel target.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4571712/ A generalist brood parasite modifies use of a host in response to reproductive success Matthew I. M. Louder, Wendy M. Schelsky, Amber N. Albores, Jeffrey P. Hoover 2015 2021-01-11 [("doi","10.1098/rspb.2015.1615")] psychology/animal/bird

Avian obligate brood parasites, which rely solely on hosts to raise their young, should choose the highest quality hosts to maximize reproductive output. Brown-headed cowbirds (Molothrus ater) are extreme host generalists, yet female cowbirds could use information based on past reproductive outcomes to make egg-laying decisions thus minimizing fitness costs associated with parasitizing low-quality hosts.

We use a long-term (21 years) nest-box study of a single host, the prothonotary warbler (Protonotaria citrea), to show that local cowbird reproductive success, but not host reproductive success, was positively correlated with the probability of parasitism the following year. Experimental manipulations of cowbird success corroborated that female cowbirds make future decisions about which hosts to use based on information pertaining to past cowbird success, both within and between years. The within-year pattern, in particular, points to local cowbird females selecting hosts based on past reproductive outcomes. This, coupled with high site fidelity of female cowbirds between years, points to information use, rather than cowbird natal returns alone, increasing parasitism rates on highly productive sites between years.

--- https://arxiv.org/abs/2104.03474 Revisiting Simple Neural Probabilistic Language Models Simeng Sun, Mohit Iyyer 2021-04-08 2021-04-08 [("doi","10.48550/arXiv.2104.03474")] ai/nn/fully-connected

Recent progress in language modeling has been driven not only by advances in neural architectures, but also through hardware and optimization improvements.

In this paper, we revisit the neural probabilistic language model (NPLM) of Bengio et al 2003, which simply concatenates word embeddings within a fixed window and passes the result through a feed-forward network to predict the next word.

When scaled up to modern hardware, this model (despite its many limitations) performs much better than expected on word-level language model benchmarks.

Our analysis reveals that the NPLM achieves lower perplexity than a baseline Transformer with short input contexts but struggles to handle long-term dependencies. Inspired by this result, we modify the Transformer by replacing its first self-attention layer with the NPLM’s local concatenation layer, which results in small but consistent perplexity decreases across 3 word-level language modeling datasets.

--- https://www.youtube.com/watch?v=DX1lUelmyUo The Rise of Anime Generating AI 2021-01-11 ai/anime ai/nn/gan --- https://jmlr.org/papers/volume3/bengio03a/bengio03a.pdf 2021-01-11 ai/nn/fully-connected --- https://en.wikipedia.org/wiki/Christopher_Smart Christopher Smart 2021-01-11 cat --- https://en.wikipedia.org/wiki/Jubilate_Agno Jubilate Agno 2021-01-11 cat fiction/poetry --- https://en.wikipedia.org/wiki/David_Lee_(poet) David Lee (poet) 2021-01-12 cat --- https://www.poetryfoundation.org/poems/52801/jubilate-agno-1975 Jubilate Agno David Lee 1975 2021-01-12 cat fiction/poetry --- https://www.poetryfoundation.org/poems/45173/jubilate-agno from Jubilate Agno Christopher Smart 2021-01-12 cat fiction/poetry --- https://www.youtube.com/watch?v=1A-Nf3QIJjM Cats, Rats, A.I., Oh My! 2021-01-12 ai/nn/cnn cat/psychology reinforcement-learning/robot technology --- https://en.wikipedia.org/wiki/Pangur_B%C3%A1n Pangur Bán 2021-01-12 cat --- /doc/cat/biology/2014-fardin.pdf On the rheology of cats M. A. Fardin 2014-07-09 2021-01-12 cat/biology fiction/humor --- http://messybeast.com/cats-meat-man.htm The Cat’s Meat Man 2021-01-12 cat/biology/taurine economics --- https://thisboredapedoesnotexist.com/ 2021-01-12 ai/nn/gan/stylegan --- http://messybeast.com/catnip-valerian.htm Catnip, Valerian, Honeysuckles And Other Cat-Attractant Plants 2021-01-12 cat/psychology/drug/catnip cat/psychology/drug/silvervine cat/psychology/drug/tatarian-honeysuckle cat/psychology/drug/valerian --- https://www.nature.com/articles/s41598-022-09694-9 2021-01-12 cat/psychology --- /review/cat#cat-tail What is it like to be a cat tail? Gwern 2018-11-03 2018-11-03 philosophy/mind --- https://www.nytimes.com/2018/02/26/science/dog-science-cats.html Why Scientists Love to Study Dogs (and Often Ignore Cats) 2021-01-13 cat/psychology dog --- https://www.wired.com/2012/08/ff-cats/ In Search of the Heart of the Online Cat-Industrial Complex 2021-01-13 cat/psychology --- https://www.biorxiv.org/content/10.1101/2021.08.09.455727.full Complex feline disease mapping using a dense genotyping array Isabel Hernandez, Jessica J. Hayward, Jeff A. Brockman, Michelle E. White, Lara Mouttham, Elizabeth A. Wilcox, Susan Garrison, Marta G. Castelhano, John P. Loftus, Filipe Espinheira Gomes, Cheryl Balkman, Marjory B. Brooks, Nadine Fiani, Marnin Forman, Tom Kern, Bruce Kornreich, Eric Ledbetter, Santiago Peralta, Angela M. Struble, Lisa Caligiuri, Elizabeth Corey, Lin Lin, Julie Jordan, Danny Sack, Adam R. Boyko, Leslie A. Lyons, Rory J. Todhunter 2021-08-09 2021-08-09 [("doi","10.1101/2021.08.09.455727")] cat/genetics

The current feline genotyping array of 63k single-nucleotide polymorphisms has proven its utility within breeds, and its use has led to the identification of variants associated with Mendelian traits in purebred cats. However, compared to single gene disorders, association studies of complex diseases, especially with the inclusion of random bred cats with relatively low linkage disequilibrium, require a denser genotyping array and an increased sample size to provide statistically-significant associations.

Here, we undertook a multi-breed study of 1,122 cats, most of which were admitted and phenotyped for nine common complex feline diseases at the Cornell University Hospital for Animals. Using a proprietary 340k single-nucleotide polymorphism mapping array, we identified:

statistically-significant genome-wide associations with hyperthyroidism, diabetes mellitus, and eosinophilic keratoconjunctivitis.

These results provide genomic locations for variant discovery and candidate gene screening for these important complex feline diseases, which are relevant not only to feline health, but also to the development of disease models for comparative studies.

--- https://www.theatlantic.com/science/archive/2017/06/cat-domination/530685/ How Cats Used Humans to Conquer the World: Ancient DNA from 209 cats over 9,000 years tell the story of their dispersal 2021-01-13 cat/genetics --- https://quinndunki.com/blondihacks/?p=3023 Furiosa’s Cat Feeder: The trick is to be smarter than the animal with a brain the size of a walnut 2021-01-13 cat/psychology cs/security --- https://www.instructables.com/DIY-Kitty-Crack%3a--ultra-potent-catnip-extract/ DIY Kitty Crack: ultra-potent catnip extract 2021-01-13 cat/psychology/drug/catnip --- https://www.unz.com/gnxp/a-feline-genome-in-full/ A feline genome in full Razib Khan 2021-01-13 cat/genetics --- https://en.wikipedia.org/wiki/Ship%27s_cat Ship's cat 2021-01-13 cat --- /doc/genetics/selection/natural/1977-todd.pdf Cats and Commerce Neil B. Todd 1977-01-01 2021-01-13 cat/genetics genetics/selection/natural --- /doc/cat/psychology/2021-johnson-2.pdf Toxoplasmosis: Recent Advances in Understanding the Link Between Infection and Host Behavior Stefanie K. Johnson, Pieter T. J. Johnson 2020-11-02 2021-01-13 [("doi","10.1146/annurev-animal-081720-111125")] cat/biology cat/psychology

Humans, wildlife, and domestic animals are intimately linked through shared infections. Many parasites and pathogens use multiple host species, either opportunistically or sequentially, such that managing disease risk frequently requires a broader understanding of the ecological community. The coccidian protozoan Toxoplasma gondii infects more than one hundred species of vertebrates, ranging from bats to beluga whales. In humans, acute toxoplasmosis can have serious health consequences for immunocompromised individuals. Even amongst asymptomatic patients, however, toxoplasmosis has been linked to a range of behavioral alterations and conditions, such as changes in risk tolerance, neuroticism, mental illness, suicide, and accident proneness.

Whether such links are causal or simply correlational has been the subject of intense study and debate; from an evolutionary standpoint, selection may favor parasite-induced alterations in host behavior that increase the likelihood a host is consumed by the definitive host—in this case a domestic or wild felid. Here, we examine current evidence for parasite-induced manipulations of host behavior, in both humans and other animals. We critically evaluate proposed mechanisms through which infection might influence host behavior, which range from inflammation in the brain to changes in hormones or neurotransmitters.

Considering estimates that T. gondii may infect up to 1⁄3rd of the global human population, we conclude by examining the implications of these changes for human behavior, individual fitness, and emergent cultural properties.

--- /doc/cat/genetics/2011-marchei.pdf Breed differences in behavioral response to challenging situations in kittens P. Marchei, S. Diverio, N. Falocci, J. Fatjó, J. L. Ruiz-de-la-Torre, X. Manteca 2011-01-01 2021-01-14 [("doi","10.1016/j.physbeh.2010.11.016")] cat/genetics cat/psychology --- /doc/cat/genetics/2009-driscoll.pdf The Taming of the Cat 2009-01-01 2021-01-14 cat/genetics cat/psychology --- /doc/cat/psychology/drug/catnip/1965-sakan.pdf 1965 2021-01-14 cat/psychology/drug/catnip cat/psychology/drug/silvervine --- /doc/cat/psychology/drug/catnip/1969-jackson.pdf 1969 2021-01-14 cat/psychology/drug/catnip psychedelic --- /doc/cat/psychology/drug/catnip/1969-lynch.pdf 1969 2021-01-14 cat/psychology/drug/catnip psychedelic --- /doc/cat/genetics/1986-turner.pdf 1986 2021-01-14 cat/genetics cat/psychology --- /doc/cat/genetics/2004-say.pdf 2004 2021-01-14 cat/genetics cat/psychology --- /doc/cat/psychology/drug/catnip/2013-goggin.pdf 2013 2021-01-14 cat/psychology/drug/catnip marijuana --- https://www.pets.ca/cats/articles/petting-a-cat/ Petting your cat 2021-01-14 cat/psychology --- https://www.fabidouille.com/?p=286 Gourmand Cat Fence 2021-01-14 ai/nn cat/psychology --- https://whatyourcatwants.com/feliway Another study shows that Feliway™ doesn't work 2021-01-14 cat/psychology --- http://cdn2.discoverwildlife.com/british-wildlife/cats-and-wildlife-hunter-suburbia 2021-01-15 cat/psychology --- /doc/history/2011-detry.pdf The Emirate of Cordoba (756-929 AD) and the introduction of the Egyptian mongoose (Herpestes ichneumon) in Iberia: the remains from Muge, Portugal Cleia Detry, Nuno Bicho, Hermenegildo Fernandes, Carlos Fernandes 2011-01-01 2021-01-15 [("doi","10.1016/j.jas.2011.08.014")] cat/genetics history --- https://academic.oup.com/gbe/article/10/1/276/4794728 Elevated proportions of deleterious genetic variation in domestic animals and plants 2021-01-15 cat/genetics --- https://www.hybridlaw.org/ Hybrid Law – US and International Laws for Ownership of Hybrid Cats and Dogs 2021-01-15 cat/genetics --- http://www.bio-nica.info/biblioteca/O%27brien2007EvolutionCats.pdf 2021-01-15 cat/genetics --- https://archive.org/details/masonbees00fabr/page/108/mode/2up The mason-bees 2021-01-15 cat/psychology --- https://en.wikipedia.org/wiki/Ego_depletion Ego depletion 2021-01-15 psychology/willpower --- https://en.wikipedia.org/wiki/Self-control Self-control 2021-01-15 psychology/willpower --- https://en.wikisource.org/wiki/The_Energies_of_Men The Energies of Men 2021-01-15 psychology/willpower --- /doc/psychology/willpower/2019-wenzel.pdf Let There Be Variance: Individual Differences in Consecutive Self-control in a Laboratory Setting and Daily Life Mario Wenzel, Zarah Rowland, Daniela Zahn, Thomas Kubiak, Erika Carlson 2019-01-01 2021-01-15 [("doi","10.1002/per.2208")] psychology/personality/conscientiousness psychology/willpower

The large body of research used to support ego-depletion effects is currently faced with conceptual and replication issues, leading to doubt over the extent or even existence of the ego-depletion effect.

By using within-person designs in a laboratory (Study 1; 187 participants) and an ambulatory assessment study (Study 2; 125 participants), we sought to clarify this ambiguity by investigating whether prominent situational variables (such as motivation and affect) or personality traits can help elucidate when ego depletion can be observed and when not.

Although only marginal ego-depletion effects were found in both studies, these effects varied considerably between individuals, indicating that some individuals experience self-control decrements after initial self-control exertion and others not. However, neither motivation nor affect nor personality traits such as trait self-control could consistently explain this variability when models were applied that controlled for variance due to targets and the depletion manipulation (Study 1) or days (Study 2) as well as for multiple testing.

We discuss how the operationalization and reliability of our key measures may explain these null effects and demonstrate that alternative metrics may be required to study the consequences of the consecutive exertion of self-control.

--- https://replicationindex.com/2016/04/18/is-replicability-report-ego-depletionreplicability-report-of-165-ego-depletion-articles/ Replicability Report No. 1: Is Ego-Depletion a Replicable Effect? 2021-01-15 psychology/willpower --- https://www.psychologytoday.com/us/blog/mind-design/201108/glucose-is-not-willpower-fuel Glucose Is Not Willpower Fuel: Is the muscle model of self-control less then a metaphor? 2021-01-16 psychology/willpower --- https://pigee.wordpress.com/2018/06/15/eyes-wide-shut-or-eyes-wide-open/ Eyes wide shut or eyes wide open? 2021-01-16 psychology/willpower --- https://osf.io/preprints/psyarxiv/esjhp/ Ego depletion may disappear by 2020 Vadillo 2019 2021-01-16 psychology/willpower --- https://www.econlib.org/archives/2014/03/the_market_for_2.html The Market for Less 2021-01-16 psychology/willpower --- https://en.wikipedia.org/wiki/Roy_Baumeister Roy Baumeister 2021-01-16 psychology/willpower --- https://en.wikipedia.org/wiki/Willpower:_Rediscovering_the_Greatest_Human_Strength Willpower: Rediscovering the Greatest Human Strength 2021-01-16 psychology/willpower --- https://en.wikipedia.org/wiki/Zeigarnik_effect Zeigarnik effect 2021-01-16 psychology/willpower --- https://en.wikipedia.org/wiki/Opportunity_cost Opportunity cost 2021-01-16 psychology/willpower --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7238509/ Strong Effort Manipulations Reduce Response Caution: A Preregistered Reinvention of the Ego-Depletion Paradigm Hause Lin, Blair Saunders, Malte Friese, Nathan J. Evans, Michael Inzlicht 2020 2021-01-16 [("doi","10.1177/0956797620904990")] psychology/willpower

People feel tired or depleted after exerting mental effort. But even preregistered studies often fail to find effects of exerting effort on behavioral performance in the laboratory or elucidate the underlying psychology.

We tested a new paradigm in four preregistered within-subjects studies (n = 686). An initial high-demand task reliably elicited very strong effort phenomenology compared with a low-demand task. Afterward, participants completed a Stroop task. We used drift-diffusion modeling to obtain the boundary (response caution) and drift-rate (information-processing speed) parameters. Bayesian analyses indicated that the high-demand manipulation reduced boundary but not drift rate. Increased effort sensations further predicted reduced boundary. However, our demand manipulation did not affect subsequent inhibition, as assessed with traditional Stroop behavioral measures and additional diffusion-model analyses for conflict tasks.

Thus, effort exertion reduced response caution rather than inhibitory control, suggesting that after exerting effort, people disengage and become uninterested in exerting further effort.

--- https://www.nytimes.com/2011/08/21/magazine/do-you-suffer-from-decision-fatigue.html Do You Suffer From Decision Fatigue? 2021-01-16 psychology/willpower --- https://www.lesswrong.com/posts/q3rBapm2TjQ6tx9Td/poll-results-lw-probably-doesn-t-cause-akrasia Poll results: LW probably doesn't cause akrasia 2021-01-17 psychology/willpower --- https://www.lesswrong.com/posts/p9gtfDNup7sNjsMB8/share-your-anti-akrasia-tricks Share Your Anti-Akrasia Tricks 2021-01-17 psychology/willpower --- https://www.lesswrong.com/posts/rRmisKb45dN7DK4BW/akrasia-tactics-review Akrasia Tactics Review 2021-01-17 psychology/willpower --- https://www.econlib.org/archives/2016/04/the_diction_of.html The Diction of Social Desirability Bias 2021-01-17 psychology/willpower --- https://en.wikipedia.org/wiki/StickK StickK 2021-01-17 psychology/willpower --- https://en.wikipedia.org/wiki/Commitment_device Commitment device 2021-01-17 psychology/willpower --- https://www.beeminder.com/ beeminder 2021-01-17 psychology/willpower --- https://en.wikipedia.org/wiki/Optimal_foraging_theory Optimal foraging theory 2021-01-17 psychology/willpower reinforcement-learning/exploration --- https://www.thenewatlantis.com/publications/shop-class-as-soulcraft Shop Class as Soulcraft: The case for the manual trades Crawford 2006 2021-01-17 psychology/willpower --- https://www.lesswrong.com/posts/RWo4LwFzpHNQCTcYt/how-to-beat-procrastination How to Beat Procrastination 2021-01-17 psychology/willpower --- https://en.wikipedia.org/wiki/Getting_Things_Done Getting Things Done 2021-01-17 psychology/willpower --- https://en.wikipedia.org/wiki/Occupational_burnout Occupational burnout 2021-01-18 psychology/willpower --- /doc/ai/1962-harley.pdf 1962 2021-01-18 ai --- /doc/ai/1964-kanal.pdf 1964 2021-01-18 ai --- /doc/ai/1992-dreyfus-whatcomputerstillcantdo.epub 1992 2021-01-18 ai --- /doc/ai/nn/1993-harth-thecreativeloop.pdf 1993 2021-01-18 ai/nn philosophy/mind --- /doc/cs/linkrot/2007-dimitrova.pdf The half-life of internet references cited in communication journals Dimitrova, Bugeja 2007 2021-01-18 cs/linkrot --- /doc/cs/linkrot/2008-wren.pdf URL decay in MEDLINE—a 4-year follow-up study Wren 2008 2021-01-18 cs/linkrot --- /doc/cs/linkrot/2012-moghaddam.pdf Availability and Half-life of Web References Cited in Information Research Journal: A Citation Study Moghaddam 2012-01-01 2021-01-18 cs/linkrot --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3284232/ Following the crowd: Brain Substrates of Long-Term Memory Conformity 2021-01-18 psychology/neuroscience --- https://www.ncbi.nlm.nih.gov/books/NBK76332/ Does narrative information bias individual's decision making? A systematic review 2008-01-01 2021-01-18 culture psychology/cognitive-bias --- /doc/science/1980-alvarez.pdf Alfred Lee Loomis (1887–1975): A Biographical Memoir Luis W. Alvarez 1980-01-01 2021-01-18 history science --- https://www.microsoft.com/en-us/research/blog/deepspeed-accelerating-large-scale-model-inference-and-training-via-system-optimizations-and-compression/ DeepSpeed: Accelerating large-scale model inference and training via system optimizations and compression 2021-01-19 ai/scaling/hardware --- https://arxiv.org/abs/2107.06925 Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines Shigang Li, Torsten Hoefler 2021-07-14 2021-07-14 [("doi","10.1145/3458817.3476145")] ai/scaling/hardware

Training large deep learning models at scale is very challenging. This paper proposes Chimera, a novel pipeline parallelism scheme which combines bidirectional pipelines for efficiently training large-scale models. Chimera is a synchronous approach and therefore no loss of accuracy, which is more convergence-friendly than asynchronous approaches.

Compared with the latest synchronous pipeline approach, Chimera reduces the number of bubbles by up to 50%; benefiting from the sophisticated scheduling of bidirectional pipelines, Chimera has a more balanced activation memory consumption. Evaluations are conducted on Transformer based language models.

For a GPT-2 model with 1.3 billion parameters running on 2,048 GPU nodes of the Piz Daint supercomputer, Chimera improves the training throughput by 1.16×-2.34× over the state-of-the-art synchronous and asynchronous pipeline approaches.

--- https://arxiv.org/abs/2102.07988 TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models Zhuohan Li, Siyuan Zhuang, Shiyuan Guo, Danyang Zhuo, Hao Zhang, Dawn Song, Ion Stoica 2021-02-16 2021-02-16 [("doi","10.48550/arXiv.2102.07988")] ai/scaling/hardware

Model parallelism has become a necessity for training modern large-scale deep language models. In this work, we identify a new and orthogonal dimension from existing model parallel approaches: it is possible to perform pipeline parallelism within a single training sequence for Transformer-based language models thanks to its autoregressive property. This enables a more fine-grained pipeline compared with previous work.

With this key idea, we design TeraPipe, a high-performance token-level pipeline parallel algorithm for synchronous model-parallel training of Transformer-based language models. We develop a novel dynamic programming-based algorithm to calculate the optimal pipelining execution scheme given a specific model and cluster configuration.

We show that TeraPipe can speed up the training by 5.0× for the largest GPT-3 model with 175 billion parameters on an AWS cluster with 48 p3.16×large instances compared with state-of-the-art model-parallel methods.

The code for reproduction can be found at https://github.com/zhuohan123/terapipe.

--- https://www.graphcore.ai/posts/the-wow-factor-graphcore-systems-get-huge-power-and-efficiency-boost The WoW Factor: Graphcore systems get huge power and efficiency boost 2021-01-19 ai/scaling/hardware --- https://www.governance.ai/post/compute-funds-and-pre-trained-models Compute Funds and Pre-trained Models 2021-01-19 ai/scaling/economics ai/scaling/hardware --- https://www.anandtech.com/show/17327/nvidia-hopper-gpu-architecture-and-h100-accelerator-announced NVIDIA Hopper GPU Architecture and H100 Accelerator Announced: Working Smarter and Harder 2021-01-19 ai/scaling/hardware --- https://nvidianews.nvidia.com/news/nvidia-announces-dgx-h100-systems-worlds-most-advanced-enterprise-ai-infrastructure NVIDIA Announces DGX H100 Systems – World’s Most Advanced Enterprise AI Infrastructure 2021-01-19 ai/scaling/hardware --- https://www.cerebras.net/blog/scaling-up-and-out-training-massive-models-on-cerebras-systems-using-weight-streaming/ Scaling Up and Out: Training Massive Models on Cerebras Systems using Weight Streaming 2021-01-19 ai/scaling/hardware --- https://cset.georgetown.edu/wp-content/uploads/AI-and-Compute-How-Much-Longer-Can-Computing-Power-Drive-Artificial-Intelligence-Progress.pdf 2021-01-19 ai/scaling/hardware --- https://www.astralcodexten.com/p/biological-anchors-a-trick-that-might Biological Anchors: A Trick That Might Or Might Not Work 2021-01-19 ai/scaling/hardware --- https://spectrum.ieee.org/cerebras-ai-computers Cerebras' Tech Trains "Brain-Scale" AIs 2021-01-19 ai/scaling/hardware --- https://www.anandtech.com/show/16626/cerebras-unveils-wafer-scale-engine-two-wse2-26-trillion-transistors-100-yield Cerebras Unveils Wafer Scale Engine Two (WSE2): 2.6 Trillion Transistors, 100% Yield 2021-01-20 ai/scaling/hardware --- https://blog.tensorflow.org/2021/06/google-demonstrates-leading-performance-in-latest-MLPerf-benchmarks.html Google demonstrates leading performance in latest MLPerf Benchmarks 2021-01-20 ai/scaling/hardware --- https://www.anandtech.com/show/17054/amd-announces-instinct-mi200-accelerator-family-cdna2-exacale-servers AMD Announces Instinct MI200 Accelerator Family: Taking Servers to Exascale and Beyond 2021-01-20 ai/scaling/hardware --- https://www.youtube.com/watch?v=eAn_oiZwUXA GTC Spring 2021 Keynote with NVIDIA CEO Jensen Huang 2021-01-20 ai/scaling/mixture-of-experts --- https://www.youtube.com/watch?v=eAn_oiZwUXA&t=2998s GTC 2021 Keynote with NVIDIA CEO Jensen Huang: NVIDIA CEO Jensen Huang delivers the #GTC21 keynote, where he introduced amazing breakthroughs in building virtual worlds with NVIDIA Omniverse; in advancing enterprise computing with new NVIDIA DGX systems and software; in turning the data center into the new unit of computing with the new NVIDIA Grace CPU, BlueField-3 DPU, and DOCA 1.0 SDK; in broadening the reach of AI to all companies and industries with NVIDIA EGX and Aerial 5G; and in transforming transportation with NVIDIA DRIVE Orin and Atlan. 2021-01-20 ai/scaling/mixture-of-experts --- /doc/ai/scaling/mixture-of-experts/2021-04-12-jensenhuang-gtc2021keynote-eAn_oiZwUXA.en.vtt.txt 2021-04-12 2021-04-12 ai/scaling/hardware ai/scaling/mixture-of-experts --- https://www.newyorker.com/tech/annals-of-technology/the-worlds-largest-computer-chip The World’s Largest Computer Chip 2021-01-20 ai/scaling/hardware --- https://www.graphcore.ai/posts/the-next-big-thing-introducing-ipu-pod128-and-ipu-pod256 The Next Big Thing: Introducing IPU-POD128 and IPU-POD256 2021-01-20 ai/scaling/hardware --- https://top500.org/news/fugaku-holds-top-spot-exascale-remains-elusive/ Fugaku Holds Top Spot, Exascale Remains Elusive 2021-01-20 ai/scaling/hardware --- https://nvidianews.nvidia.com/news/nvidia-launches-uks-most-powerful-supercomputer-for-research-in-ai-and-healthcare NVIDIA Launches UK’s Most Powerful Supercomputer, for Research in AI and Healthcare 2021-01-20 ai/scaling/hardware --- https://github.com/nvidia/megatron-lm NVIDIA/Megatron-LM: Ongoing research training transformer models at scale 2021-01-21 ai/scaling/hardware --- https://www.pcgamer.com/tsmc-confirms-3nm-tech-for-2022-could-enable-epic-80-billion-transistor-gpus/ TSMC confirms 3nm tech for 2022, could enable epic 80 billion transistor GPUs 2021-01-21 ai/scaling/hardware --- https://blogs.nvidia.com/blog/2021/04/12/cpu-grace-cscs-alps/ 2021-01-21 ai/scaling/hardware --- https://www.lesswrong.com/posts/wfpdejMWog4vEDLDg/ai-and-compute-trend-isn-t-predictive-of-what-is-happening "AI and Compute" trend isn't predictive of what is happening 2021-01-21 ai/scaling/hardware --- https://www.anandtech.com/show/16610/nvidia-unveils-grace-a-highperformance-arm-server-cpu-for-use-in-ai-systems NVIDIA Unveils Grace: A High-Performance Arm Server CPU For Use In Big AI Systems 2021-01-21 ai/scaling/hardware --- https://siliconangle.com/2021/05/27/perlmutter-said-worlds-fastest-ai-supercomputer-comes-online/ Perlmutter, said to be the world's fastest AI supercomputer, comes online 2021-01-21 ai/scaling/hardware --- https://groq.com/wp-content/uploads/2020/06/ISCA-TSP.pdf 2021-01-21 ai/scaling/hardware --- https://spectrum.ieee.org/computing/hardware/the-future-of-deep-learning-is-photonic 2021-01-21 ai/scaling/hardware --- https://venturebeat.com/2020/11/17/cerebras-wafer-size-chip-is-10000-times-faster-than-a-gpu/ 2021-01-21 ai/scaling/hardware --- https://www.hpcwire.com/2020/11/02/aws-ultraclusters-with-new-p4-a100-instances/ AWS Enables 4,000-GPU UltraClusters with New P4 A100 Instances 2021-01-21 ai/scaling/hardware --- https://www.microsoft.com/en-us/research/blog/zero-infinity-and-deepspeed-unlocking-unprecedented-model-scale-for-deep-learning-training/ ZeRO-Infinity and DeepSpeed: Unlocking unprecedented model scale for deep learning training 2021-01-21 ai/scaling/hardware --- https://www.anandtech.com/show/16354/jim-keller-becomes-cto-at-tenstorrent-the-most-promising-architecture-out-there Jim Keller Becomes CTO at Tenstorrent: "The Most Promising Architecture Out There" 2021-01-22 ai/scaling/hardware --- https://www.anandtech.com/show/16000/342-transistors-for-every-person-in-the-world-cerebras-2nd-gen-wafer-scale-engine-teased 342 Transistors for Every Person In the World: Cerebras 2nd Gen Wafer Scale Engine Teased 2021-01-22 ai/scaling/hardware --- https://openai.com/research/scaling-kubernetes-to-7500-nodes 2021-01-22 ai/scaling/hardware --- https://www.nextplatform.com/2021/02/11/the-billion-dollar-ai-problem-that-just-keeps-scaling/ The Billion Dollar AI Problem That Just Keeps Scaling 2021-01-22 ai/scaling/hardware --- https://arxiv.org/abs/2107.04140#facebook First-Generation Inference Accelerator Deployment at Facebook Michael Anderson, Benny Chen, Stephen Chen, Summer Deng, Jordan Fix, Michael Gschwind, Aravind Kalaiah, Changkyu Kim, Jaewon Lee, Jason Liang, Haixin Liu, Yinghai Lu, Jack Montgomery, Arun Moorthy, Satish Nadathur, Sam Naghshineh, Avinash Nayak, Jongsoo Park, Chris Petersen, Martin Schatz, Narayanan Sundaram, Bangsheng Tang, Peter Tang, Amy Yang, Jiecao Yu, Hector Yuen, Ying Zhang, Aravind Anbudurai, Vandana Balan, Harsha Bojja, Joe Boyd, Matthew Breitbach, Claudio Caldato, Anna Calvo, Garret Catron, Sneh Chandwani, Panos Christeas, Brad Cottel, Brian Coutinho, Arun Dalli, Abhishek Dhanotia, Oniel Duncan, Roman Dzhabarov, Simon Elmir, Chunli Fu, Wenyin Fu, Michael Fulthorp, Adi Gangidi, Nick Gibson, Sean Gordon, Beatriz Padilla Hernandez, Daniel Ho, Yu-Cheng Huang, Olof Johansson, Shishir Juluri, Shobhit Kanaujia, Manali Kesarkar, Jonathan Killinger, Ben Kim, Rohan Kulkarni, Meghan Lele, Huayu Li, Huamin Li, Yueming Li, Cynthia Liu, Jerry Liu, Bert Maher, Chandra Mallipedi, Seema Mangla, Kiran Kumar Matam, Jubin Mehta, Shobhit Mehta, Christopher Mitchell, Bharath Muthiah, Nitin Nagarkatte, Ashwin Narasimha, Bernard Nguyen, Thiara Ortiz, Soumya Padmanabha, Deng Pan, Ashwin Poojary, Ye, Qi, Olivier Raginel, Dwarak Rajagopal, Tristan Rice, Craig Ross, Nadav Rotem, Scott Russ, Kushal Shah, Baohua Shan, Hao Shen, Pavan Shetty, Krish Skandakumaran, Kutta Srinivasan, Roshan Sumbaly, Michael Tauberg, Mor Tzur, Sidharth Verma, Hao Wang, Man Wang, Ben Wei, Alex Xia, Chenyu Xu, Martin Yang, Kai Zhang, Ruoxi Zhang, Ming Zhao, Whitney Zhao, Rui Zhu, Ajit Mathews, Lin Qiao, Misha Smelyanskiy, Bill Jia, Vijay Rao 2021-07-08 2021-07-08 [("doi","10.48550/arXiv.2107.04140")] ai/scaling/economics ai/scaling/hardware

In this paper, we provide a deep dive into the deployment of inference accelerators at Facebook. Many of our ML workloads have unique characteristics, such as sparse memory accesses, large model sizes, as well as high compute, memory and network bandwidth requirements. We co-designed a high-performance, energy-efficient inference accelerator platform based on these requirements.

We describe the inference accelerator platform ecosystem we developed and deployed at Facebook: both hardware, through Open Compute Platform (OCP), and software framework and tooling, through Pytorch/Caffe2/Glow. A characteristic of this ecosystem from the start is its openness to enable a variety of AI accelerators from different vendors. This platform, with six low-power accelerator cards alongside a single-socket host CPU, allows us to serve models of high complexity that cannot be easily or efficiently run on CPUs.

We describe various performance optimizations, at both platform and accelerator level, which enables this platform to serve production traffic at Facebook. We also share deployment challenges, lessons learned during performance optimization, as well as provide guidance for future inference hardware co-design.

--- https://arxiv.org/abs/2010.04116 Interlocking Backpropagation: Improving depthwise model-parallelism Aidan N. Gomez, Oscar Key, Kuba Perlin, Stephen Gou, Nick Frosst, Jeff Dean, Yarin Gal 2020-10-08 2021-01-22 [("doi","10.48550/arXiv.2010.04116")] ai/scaling/hardware

The number of parameters in state-of-the-art neural networks has drastically increased in recent years. This surge of interest in large scale neural networks has motivated the development of new distributed training strategies enabling such models. One such strategy is model-parallel distributed training. Unfortunately, model-parallelism suffers from poor resource usage, which leads to wasted resources.

In this work, we improve upon recent developments in an idealised model-parallel optimization setting: local learning. Motivated by poor resource usage, we introduce a class of intermediary strategies between local and global learning referred to as interlocking backpropagation. These strategies preserve many of the compute-efficiency advantages of local optimization, while recovering much of the task performance achieved by global optimization. We assess our strategies on both image classification ResNets and Transformer language models, finding that our strategy consistently out-performs local learning in terms of task performance, and out-performs global learning in training efficiency.

--- https://arxiv.org/abs/2102.04010#sensetime Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch Aojun Zhou, Yukun Ma, Junnan Zhu, Jianbo Liu, Zhijie Zhang, Kun Yuan, Wenxiu Sun, Hongsheng Li 2021-02-08 2021-02-08 [("doi","10.48550/arXiv.2102.04010")] ai/nn/sparsity/pruning

Sparsity in Deep Neural Networks (DNNs) has been widely studied to compress and accelerate the models on resource-constrained environments. It can be generally categorized into unstructured fine-grained sparsity that zeroes out multiple individual weights distributed across the neural network, and structured coarse-grained sparsity which prunes blocks of sub-networks of a neural network. Fine-grained sparsity can achieve a high compression ratio but is not hardware friendly and hence receives limited speed gains. On the other hand, coarse-grained sparsity cannot concurrently achieve both apparent acceleration on modern GPUs and decent performance.

In this paper, we are the first to study training from scratch an N:M fine-grained structured sparse network, which can maintain the advantages of both unstructured fine-grained sparsity and structured coarse-grained sparsity simultaneously on specifically designed GPUs. Specifically, a 2:4 sparse network could achieve 2× speed-up without performance drop on Nvidia A100 GPUs. Furthermore, we propose a novel and effective ingredient, sparse-refined straight-through estimator (SR-STE), to alleviate the negative influence of the approximated gradients computed by vanilla STE during optimization.

We also define a metric, Sparse Architecture Divergence (SAD), to measure the sparse network’s topology change during the training process. Finally, We justify SR-STE’s advantages with SAD and demonstrate the effectiveness of SR-STE by performing comprehensive experiments on various tasks.

Source codes and models are available at Github.

--- https://aiimpacts.org/brain-performance-in-flops/ Brain performance in FLOPS 2021-01-22 ai/scaling/hardware psychology/neuroscience --- https://arxiv.org/abs/2102.03161 PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers Chaoyang He, Shen Li, Mahdi Soltanolkotabi, Salman Avestimehr 2021-02-05 2021-02-05 [("doi","10.48550/arXiv.2102.03161")] ai/scaling/hardware

The size of Transformer models is growing at an unprecedented pace. It has only taken less than one year to reach trillion-level parameters after the release of GPT-3 (175B). Training such models requires both substantial engineering efforts and enormous computing resources, which are luxuries most research teams cannot afford.

In this paper, we propose PipeTransformer, which leverages automated and elastic pipelining and data parallelism for efficient distributed training of Transformer models. PipeTransformer automatically adjusts the pipelining and data parallelism by identifying and freezing some layers during the training, and instead allocates resources for training of the remaining active layers. More specifically, PipeTransformer dynamically excludes converged layers from the pipeline, packs active layers into fewer GPUs, and forks more replicas to increase data-parallel width.

We evaluate PipeTransformer using Vision Transformer (ViT) on ImageNet and BERT on GLUE and SQuAD datasets. Our results show that PipeTransformer attains a 2.4× speedup compared to the state-of-the-art baseline. We also provide various performance analyses for a more comprehensive understanding of our algorithmic and system-wise design.

We also develop open-sourced flexible APIs for PipeTransformer, which offer a clean separation among the freeze algorithm, model definitions, and training accelerations, hence allowing it to be applied to other algorithms that require similar freezing strategies.

--- https://arxiv.org/abs/2010.14373 Matrix Engines for High Performance Computing:A Paragon of Performance or Grasping at Straws? Jens Domke, Emil Vatai, Aleksandr Drozd, Peng Chen, Yosuke Oyama, Lingqi Zhang, Shweta Salaria, Daichi Mukunoki, Artur Podobas, Mohamed Wahib, Satoshi Matsuoka 2020-10-27 2021-01-22 [("doi","10.48550/arXiv.2010.14373")] ai/scaling/hardware

Matrix engines or units, in different forms and affinities, are becoming a reality in modern processors; CPUs and otherwise. The current and dominant algorithmic approach to Deep Learning merits the commercial investments in these units, and deduced from the No.1 benchmark in supercomputing, namely High Performance Linpack, one would expect an awakened enthusiasm by the HPC community, too.

Hence, our goal is to identify the practical added benefits for HPC and machine learning applications by having access to matrix engines. For this purpose, we perform an in-depth survey of software stacks, proxy applications and benchmarks, and historical batch job records. We provide a cost-benefit analysis of matrix engines, both asymptotically and in conjunction with state-of-the-art processors. While our empirical data will temper the enthusiasm, we also outline opportunities to misuse these dense matrix-multiplication engines if they come for free.

--- https://www.ibm.com/blogs/research/2020/12/ibm-ai-edge/ 2021-01-22 ai/scaling/hardware --- https://www.youtube.com/watch?v=DLw-wC4zntw?t=233 How to accelerate innovation with AI at Scale 2021-01-23 ai/scaling/hardware --- https://arxiv.org/abs/2011.00071#google Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour Arissa Wongpanich, Hieu Pham, James Demmel, Mingxing Tan, Quoc Le, Yang You, Sameer Kumar 2020-10-30 2021-01-23 [("doi","10.48550/arXiv.2011.00071")] ai/scaling/hardware

EfficientNets are a family of state-of-the-art image classification models based on efficiently scaled convolutional neural networks. Currently, EfficientNets can take on the order of days to train; for example, training an EfficientNet-B0 model takes 23 hours on a Cloud TPU-v2-8 node.

In this paper, we explore techniques to scale up the training of EfficientNets on TPU-v3 Pods with 2,048 cores, motivated by speedups that can be achieved when training at such scales. We discuss optimizations required to scale training to a batch size of 65,536 on 1,024 TPU-v3 cores, such as selecting large batch optimizers and learning rate schedules as well as utilizing distributed evaluation and batch normalization techniques. Additionally, we present timing and performance benchmarks for EfficientNet models trained on the ImageNet dataset in order to analyze the behavior of EfficientNets at scale.

With our optimizations, we are able to train EfficientNet on ImageNet to an accuracy of 83% in 1 hour and 4 minutes.

--- https://arxiv.org/abs/1910.00932#google Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos Ji Lin, Chuang Gan, Song Han 2019-10-01 2021-01-23 [("doi","10.48550/arXiv.1910.00932")] ai/scaling/hardware ai/video/analysis

Deep video recognition is more computationally expensive than image recognition, especially on large-scale datasets like Kinetics. Therefore, training scalability is essential to handle a large amount of videos.

In this paper, we study the factors that impact the training scalability of video networks. We recognize 3 bottlenecks, including data loading (data movement from disk to GPU), communication (data movement over networking), and computation FLOPs. We propose 3 design guidelines to improve the scalability: (1) fewer FLOPs and hardware-friendly operator to increase the computation efficiency; (2) fewer input frames to reduce the data movement and increase the data loading efficiency; (3) smaller model size to reduce the networking traffic and increase the networking efficiency.

With these guidelines, we designed a new operator, Temporal Shift Module (TSM), that is efficient and scalable for distributed training.

TSM model can achieve 1.8× higher throughput compared to previous I3D models. We scale up the training of the TSM model to 1,536 GPUs, with a mini-batch of 12,288 video clips/98,304 images, without losing the accuracy. With such hardware-aware model design, we are able to scale up the training on Summit supercomputer and reduce the training time on Kinetics dataset from 49 hours 55 minutes to 14 minutes 13 seconds, achieving a top-1 accuracy of 74.0%, which is 1.6× and 2.9× faster than previous 3D video models with higher accuracy.

The code and more details can be found here: https://hanlab.mit.edu/projects/tsm .

--- https://www.lesswrong.com/posts/QWuegBA9kGBv3xBFy/the-colliding-exponentials-of-ai The Colliding Exponentials of AI 2021-01-23 ai/scaling/economics ai/scaling/hardware --- https://www.artstyle.ai/uncropping-r-art/ 2021-01-23 ai/nn/transformer/gpt/dall-e --- https://shimweasel.com/2018/08/25/novelty-budgets You need a novelty budget 2021-01-23 psychology/novelty reinforcement-learning/exploration --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001657/ Universality and diversity in human song Samuel A. Mehr, Manvir Singh, Dean Knox, Daniel M. Ketter, Daniel Pickens-Jones, S. Atwood, Christopher Lucas, Nori Jacoby, Alena A. Egner, Erin J. Hopkins, Rhea M. Howard, Joshua K. Hartshorne, Mariela V. Jennings, Jan Simson, Constance M. Bainbridge, Steven Pinker, Timothy J. O’Donnell, Max M. Krasnow, Luke Glowacki 2019 2021-01-23 [("doi","10.1126/science.aax0868")] music philosophy/religion sociology

What is universal about music, and what varies?

We built a corpus of ethnographic text on musical behavior from a representative sample of the world’s societies, as well as a discography of audio recordings.

The ethnographic corpus reveals that music (including songs with words) appears in every society observed; that music varies along 3 dimensions (formality, arousal, religiosity), more within societies than across them; and that music is associated with certain behavioral contexts such as infant care, healing, dance, and love.

The discography-analyzed through machine summaries, amateur and expert listener ratings, and manual transcriptions-reveals that acoustic features of songs predict their primary behavioral context; that tonality is widespread, perhaps universal; that music varies in rhythmic and melodic complexity; and that elements of melodies and rhythms found worldwide follow power laws.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069069/ A thrifty variant in CREBRF strongly influences body mass index in Samoans Ryan L. Minster, Nicola L. Hawley, Chi-Ting Su, Guangyun Sun, Erin E. Kershaw, Hong Cheng, Olive D. Buhule, Jerome Lin, Muagututi’a Sefuiva Reupena, Satupa’itea Viali, John Tuitele, Take Naseri, Zsolt Urban, Ranjan Deka, Daniel E. Weeks, Stephen T. McGarvey 2016 2021-01-23 [("doi","10.1038/ng.3620")] exercise genetics/selection/natural/human

Samoans are a unique founder population with a high prevalence of obesity, making them well suited for identifying new genetic contributors to obesity.

We conducted a genome-wide association study (GWAS) in 3,072 Samoans:

discovered a variant, rs12513649, strongly associated with body mass index (BMI) (p = 5.3 × 10−14), and replicated the association in 2,102 additional Samoans (p = 1.2 × 10−9). Targeted sequencing identified a strongly associated missense variant, rs373863828 (p.Arg457Gln), in CREBRF (meta p = 1.4 × 10−20). Although this variant is extremely rare in other populations, it is common in Samoans (frequency of 0.259), with an effect size much larger than that of any other known common BMI risk variant (1.36–1.45 kg⁄m2 per copy of the risk-associated allele). In comparison to wild-type CREBRF, the Arg457Gln variant when overexpressed selectively decreased energy use and increased fat storage in an adipocyte cell model.

These data, in combination with evidence of positive selection of the allele encoding p.Arg457Gln, support a ‘thrifty’ variant hypothesis as a factor in human obesity.

--- https://arxiv.org/abs/1705.06950#deepmind The Kinetics Human Action Video Dataset Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, Mustafa Suleyman, Andrew Zisserman 2017-05-19 2021-01-23 [("doi","10.48550/arXiv.1705.06950")] ai/dataset ai/video/analysis

We describe the DeepMind Kinetics human action video dataset.

The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 10s and is taken from a different YouTube video. The actions are human focused and cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands.

We describe the statistics of the dataset, how it was collected, and give some baseline performance figures for neural network architectures trained and tested for human action classification on this dataset. We also carry out a preliminary analysis of whether imbalance in the dataset leads to bias in the classifiers.

--- https://arxiv.org/abs/1907.06987#deepmind A Short Note on the Kinetics-700 Human Action Dataset Joao Carreira, Eric Noland, Chloe Hillier, Andrew Zisserman 2019-07-15 2021-01-23 [("doi","10.48550/arXiv.1907.06987")] ai/video/analysis

We describe an extension of the DeepMind Kinetics human action dataset from 600 classes to 700 classes, where for each class there are at least 600 video clips from different YouTube videos.

This paper details the changes introduced for this new release of the dataset, and includes a comprehensive set of statistics as well as baseline results using the I3D neural network architecture.

--- https://arxiv.org/abs/1705.07750#deepmind Quo Vadis, Action Recognition? A New Model I3D and the Kinetics Dataset Joao Carreira, Andrew Zisserman 2017-05-22 2021-01-24 [("doi","10.48550/arXiv.1705.07750")] ai/scaling ai/video/analysis

The paucity of videos in current action classification datasets (UCF101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale benchmarks.

This paper re-evaluates state-of-the-art architectures in light of the new Kinetics Human Action Video dataset. Kinetics has two orders of magnitude more data, with 400 human action classes and over 400 clips per class, and is collected from realistic, challenging YouTube videos. We provide an analysis on how current architectures fare on the task of action classification on this dataset and how much performance improves on the smaller benchmark datasets after pre-training on Kinetics.

We also introduce a new Two-Stream Inflated 3D ConvNet (I3D) that is based on 2D ConvNet inflation: filters and pooling kernels of very deep image classification ConvNets are expanded into 3D, making it possible to learn seamless spatio-temporal feature extractors from video while leveraging successful ImageNet architecture designs and even their parameters.

We show that, after pre-training on Kinetics, I3D models considerably improve upon the state-of-the-art in action classification, reaching 80.9% on HMDB-51 and 98.0% on UCF101.

--- https://arxiv.org/abs/1212.0402 UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild Khurram Soomro, Amir Roshan Zamir, Mubarak Shah 2012-12-03 2021-01-24 [("doi","10.48550/arXiv.1212.0402")] ai/dataset ai/video/analysis

We introduce UCF101 which is currently the largest dataset of human actions.

It consists of 101 action classes, over 13k clips and 27 hours of video data. The database consists of realistic user uploaded videos containing camera motion and cluttered background.

Additionally, we provide baseline action recognition results on this new dataset using standard bag of words approach with overall performance of 44.5%.

To the best of our knowledge, UCF101 is currently the most challenging dataset of actions due to its large number of classes, large number of clips and also unconstrained nature of such clips.

--- https://www.vice.com/en/article/epx3m7/how-one-man-tried-to-build-a-dmt-based-cult-on-reddit-and-lost-everything How One Man Tried to Build a DMT-Based Cult on Reddit and Lost Everything 2021-01-24 psychedelic psychology/personality/narcissism --- https://www.theatlantic.com/science/archive/2022/05/human-social-sleep-problems-rem/629723/ Why It's So Hard to get Eight Hours of Sleep 2021-01-24 zeo --- https://www.anthropic.com/news/anthropic-raises-124-million-to-build-more-reliable-general-ai-systems#series-b Anthropic raises $124 million to build more reliable, general AI systems 2021-01-24 ai/nn/anthropic ai/scaling/economics --- https://www.adept.ai/blog/introducing-adept Introducing Adept 2021-01-24 ai/scaling/economics reinforcement-learning/meta-learning reinforcement-learning/robot --- https://www.lesswrong.com/posts/jfq2BH5kfQqu2vYv3/we-are-conjecture-a-new-alignment-research-startup We Are Conjecture, A New Alignment Research Startup 2021-01-24 ai/nn/transformer/gpt ai/scaling/economics --- https://www.alignmentforum.org/posts/rtEtTybuCcDWLk7N9/ama-conjecture-a-new-alignment-startup AMA Conjecture, A New Alignment Startup 2021-01-24 ai/nn/transformer/gpt ai/scaling/economics --- https://www.fhi.ox.ac.uk/wp-content/uploads/2021/08/QNRs_FHI-TR-2021-3.0.pdf#page=2 2021-01-24 ai/scaling/economics --- https://colinraffel.com/blog/a-call-to-build-models-like-we-build-open-source-software.html A Call to Build Models Like We Build Open-Source Software 2021-01-24 ai/scaling/economics --- https://cohere.com/ Cohere 2021-01-24 ai/scaling/economics --- https://blogs.microsoft.com/on-the-issues/2020/11/10/openai-partnership-digital-export-controls/ Microsoft and OpenAI partner to propose digital transformation of export controls 2021-01-25 ai/scaling/economics sociology --- https://aiindex.stanford.edu/wp-content/uploads/2021/03/2021-AI-Index-Report_Master.pdf#page=41 Artificial Intelligence Index Report 2021 § Chapter 2: Technical Performance Daniel Zhang, Saurabh Mishra, Erik Brynjolfsson, John Etchemendy, Deep Ganguli, Barbara Grosz, Terah Lyons, James Manyika, Juan Carlos Niebles, Michael Sellitto, Yoav Shoham, Jack Clark, Raymond Perrault 2021-03 2021-03 ai/scaling/economics --- https://bakztfuture.substack.com/p/how-to-build-a-startup-monopoly-with How to Build a Startup Monopoly with GPT-3 (20 Techniques) 2021-01-25 ai/scaling/economics --- https://www.allencheng.com/starting-a-business-around-gpt-3-is-a-bad-idea/ Starting a Business Around GPT-3 is a Bad Idea 2021-01-25 ai/scaling/economics --- https://a16z.com/2020/08/12/taming-the-tail-adventures-in-improving-ai-economics/ 2021-01-25 ai/scaling/economics --- https://arxiv.org/abs/2003.11666#cerebras Pipelined Backpropagation at Scale: Training Large Models without Batches Atli Kosson, Vitaliy Chiley, Abhinav Venigalla, Joel Hestness, Urs Köster 2020-03-25 2021-01-25 [("doi","10.48550/arXiv.2003.11666")] ai/scaling/hardware

New hardware can substantially increase the speed and efficiency of deep neural network training. To guide the development of future hardware architectures, it is pertinent to explore the hardware and machine learning properties of alternative training algorithms.

In this work we evaluate the use of small batch, fine-grained Pipelined Backpropagation, an asynchronous pipeline parallel training algorithm that has hardware advantages. We introduce two methods, Spike Compensation and Linear Weight Prediction, that effectively mitigate the downsides caused by the asynchronicity of Pipelined Backpropagation and outperform existing techniques in our setting. We show that appropriate normalization and small batch sizes can also aid training.

With our methods, fine-grained Pipelined Backpropagation using a batch size of one can match the accuracy of SGD for multiple networks trained on CIFAR-10 and ImageNet. Simple scaling rules allow the use of existing hyperparameters for traditional training without additional tuning.

--- https://arxiv.org/abs/2003.13590#microsoft Suphx: Mastering Mahjong with Deep Reinforcement Learning Junjie Li, Sotetsu Koyamada, Qiwei Ye, Guoqing Liu, Chao Wang, Ruihan Yang, Li Zhao, Tao Qin, Tie-Yan Liu, Hsiao-Wuen Hon 2020-03-30 2021-01-25 [("doi","10.48550/arXiv.2003.13590")] reinforcement-learning/imperfect-information

Artificial Intelligence (AI) has achieved great success in many domains, and game AI is widely regarded as its beachhead since the dawn of AI. In recent years, studies on game AI have gradually evolved from relatively simple environments (eg. perfect-information games such as Go, chess, shogi or two-player imperfect-information games such as heads-up Texas hold’em) to more complex ones (eg. multi-player imperfect-information games such as multi-player Texas hold’em and StartCraft II). Mahjong is a popular multi-player imperfect-information game worldwide but very challenging for AI research due to its complex playing/scoring rules and rich hidden information.

We design an AI for Mahjong, named Suphx, based on deep reinforcement learning with some newly introduced techniques including global reward prediction, oracle guiding, and run-time policy adaptation.

Suphx has demonstrated stronger performance than most top human players in terms of stable rank and is rated above 99.99% of all the officially ranked human players in the Tenhou platform.

This is the first time that a computer program outperforms most top human players in Mahjong.

--- https://arxiv.org/abs/2003.13678#facebook Designing Network Design Spaces Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár 2020-03-30 2021-01-25 [("doi","10.48550/arXiv.2003.13678")] ai/anime/danbooru reinforcement-learning/meta-learning

In this work, we present a new network design paradigm.

Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet.

The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design.

The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5× faster on GPUs.

--- https://arxiv.org/abs/2004.02504 Bringing GNU Emacs to Native Code Andrea Corallo, Luca Nassi, Nicola Manca 2020-04-06 2021-01-25 [("doi","10.5281/zenodo.3736363")] cs/algorithm cs/lisp/emacs

Emacs Lisp (Elisp) is the Lisp dialect used by the Emacs text editor family. GNU Emacs can currently execute Elisp code either interpreted or byte-interpreted after it has been compiled to byte-code.

In this work we discuss the implementation of an optimizing compiler approach for Elisp targeting native code. The native compiler employs the byte-compiler’s internal representation as input and exploits libgccjit to achieve code generation using the GNU Compiler Collection (GCC) infrastructure. Generated executables are stored as binary files and can be loaded and unloaded dynamically. Most of the functionality of the compiler is written in Elisp itself, including several optimization passes, paired with a C back-end to interface with the GNU Emacs core and libgccjit.

Though still a work in progress, our implementation is able to bootstrap a functional Emacs and compile all lexically scoped Elisp files, including the whole GNU Emacs Lisp Package Archive (ELPA).

Native-compiled Elisp shows an increase of performance ranging from 2.3× up to 42× with respect to the equivalent byte-code, measured over a set of small benchmarks.

--- https://arxiv.org/abs/2004.04136 CURL: Contrastive Unsupervised Representations for Reinforcement Learning Aravind Srinivas, Michael Laskin, Pieter Abbeel 2020-04-08 2021-01-25 [("doi","10.48550/arXiv.2004.04136")] reinforcement-learning/model-free

We present CURL: Contrastive Unsupervised Representations for Reinforcement Learning.

CURL extracts high-level features from raw pixels using contrastive learning and performs off-policy control on top of the extracted features.

CURL outperforms prior pixel-based methods, both model-based and model-free, on complex tasks in the DeepMind Control Suite and Atari Games showing 1.9× and 1.2× performance gains at the 100K environment and interaction steps benchmarks respectively. On the DeepMind Control Suite, CURL is the first image-based algorithm to nearly match the sample-efficiency of methods that use state-based features.

Our code is open-sourced and available at Github.

--- https://en.wikipedia.org/wiki/Henry%27s_pocket Henry's pocket 2021-01-25 cat/biology --- https://github.com/deepmind/meltingpot 2021-01-26 reinforcement-learning/multi-agent --- https://arxiv.org/abs/2205.06760#deepmind Emergent Bartering Behavior in Multi-Agent Reinforcement Learning Michael Bradley Johanson, Edward Hughes, Finbarr Timbers, Joel Z. Leibo 2022-05-13 2022-05-13 [("doi","10.48550/arXiv.2205.06760")] economics reinforcement-learning/multi-agent

Advances in artificial intelligence often stem from the development of new environments that abstract real-world situations into a form where research can be done conveniently.

This paper contributes such an environment based on ideas inspired by elementary Microeconomics. Agents learn to produce resources in a spatially complex world, trade them with one another, and consume those that they prefer.

We show that the emergent production, consumption, and pricing behaviors respond to environmental conditions in the directions predicted by supply and demand shifts in Microeconomics. We also demonstrate settings where the agents’ emergent prices for goods vary over space, reflecting the local abundance of goods. After the price disparities emerge, some agents then discover a niche of transporting goods between regions with different prevailing prices—a profitable strategy because they can buy goods where they are cheap and sell them where they are expensive. Finally, in a series of ablation experiments, we investigate how choices in the environmental rewards, bartering actions, agent architecture, and ability to consume tradable goods can either aid or inhibit the emergence of this economic behavior.

This work is part of the environment development branch of a research program that aims to build human-like artificial general intelligence through multi-agent interactions in simulated societies. By exploring which environment features are needed for the basic phenomena of elementary microeconomics to emerge automatically from learning, we arrive at an environment that differs from those studied in prior multi-agent reinforcement learning work along several dimensions. For example, the model incorporates heterogeneous tastes and physical abilities, and agents negotiate with one another as a grounded form of communication.

--- https://arxiv.org/abs/2205.06537#github Productivity Assessment of Neural Code Completion Albert Ziegler, Eirini Kalliamvakou, Shawn Simister, Ganesh Sittampalam, Alice Li, Andrew Rice, Devon Rifkin, Edward Aftandilian 2022-05-13 2022-05-13 [("doi","10.48550/arXiv.2205.06537")] ai/nn/transformer/gpt/codex

Neural code synthesis has reached a point where snippet generation is accurate enough to be considered for integration into human software development workflows. Commercial products aim to increase programmers’ productivity, without being able to measure it directly.

In this case study, we asked users of GitHub Copilot about its impact on their productivity, and sought to find a reflection of their perception in directly measurable user data.

We find that the rate with which shown suggestions are accepted, rather than more specific metrics regarding the persistence of completions in the code over time, drives developers’ perception of productivity.

…In this work we define acceptance rate as the fraction of completions shown to the developer that are subsequently accepted for inclusion in the source file. The IntelliCode Compose system uses the term CTR (Click Through Rate) for this and reports a value of 10% in online trials [12]. An alternative measure is that of DCPU (Daily Completions accepted Per User) for which a value of around 20 has been reported [3, 21]. To calculate acceptance rate one must, of course, normalize DCPU by the time spent coding each day. For context, in our study GitHub Copilot has an acceptance rate of 27% and a mean DCPU in excess of 31.

…Language Use: We are aware that there are substantial differences for how GitHub Copilot performs for different programming languages. The most common languages among our user base are TypeScript (24.7% of all shown completions in the observed time frame, 21.9% for users in survey), JavaScript (21.3%, 24.2%), and Python (14.1%, 14.5%). The latter 2 enjoy higher acceptance rates, possibly hinting at a relative strength of neural tooling versus deductive tooling for untyped languages. Regardless of language, survey participants had a slightly higher acceptance rate than the whole user base.

Figure 5: Programming language use by survey participants vs. all users.

…We were surprised to find that acceptance rate (number of acceptances normalized by the number of shown completions) was better correlated with reported productivity than our measures of persistence.

But in hindsight, this makes sense. Coding is not typing, and GitHub Copilot’s central value lies not in being the way the user enters the highest possible number of lines of code. Instead, it lies in helping the user to make the best progress towards their goals. A suggestion that serves as a useful template to tinker with may be as good or better than a perfectly correct (but obvious) line of code that only saves the user a few keystrokes.

This suggests that a narrow focus on the correctness of suggestions would not tell the whole story for these kinds of tooling. Instead one could view code suggestions inside an IDE to be more akin to a conversation with a chatbot. We see anecdotal evidence of this in comments posted about GitHub Copilot online (see Appendix E for examples) in which users talk about sequences of interactions. A conversation turn in this context consists of the prompt in the completion request and the reply as the completion itself. The developer’s response to the completion arises from the subsequent changes which are incorporated in the next prompt to the model. And there are clear programming parallels to factors such as specificity and repetition that have been identified to affect human judgements of conversation quality [11]. Researchers have already investigated the benefits of natural language feedback to guide program synthesis [2] and so ours is not a radical proposal. But neither is it one we have seen followed.

In future work, we wish to further explore this analogy, borrowing ideas [16] from the evaluation of chatbots and natural language text generation.

--- https://arxiv.org/abs/2105.05991#facebook Improving Code Autocompletion with Transfer Learning Wen Zhou, Seohyun Kim, Vijayaraghavan Murali, Gareth Ari Aye 2021-05-12 2021-05-12 [("doi","10.48550/arXiv.2105.05991")] ai/nn/transformer/gpt/codex

Software language models have achieved promising results predicting code completion usages, and several industry studies have described successful IDE integrations. Recently, accuracy in autocompletion prediction improved 12.8% from training on a real-world dataset collected from programmers’ IDE activity. But what if limited examples of IDE autocompletion in the target programming language are available for model training?

In this paper, we investigate the efficacy of pretraining autocompletion models on non-IDE, non-autocompletion, and different-language example code sequences.

We find that these unsupervised pretrainings improve model accuracy by over 50% on very small fine-tuning datasets and over 10% on 50k labeled examples. We confirm the real-world impact of these pretrainings in an online setting through A/B testing on thousands of IDE autocompletion users, finding that pretraining is responsible for increases of up to 6.63% autocompletion usage.

--- https://arxiv.org/abs/2011.04542#facebook Learning Autocompletion from Real-World Datasets Gareth Ari Aye, Seohyun Kim, Hongyu Li 2020-11-09 2021-01-26 [("doi","10.48550/arXiv.2011.04542")] ai/nn/transformer/gpt/codex

Code completion is a popular software development tool integrated into all major IDEs. Many neural language models have achieved promising results in completion suggestion prediction on synthetic benchmarks. However, a recent study “When Code Completion Fails: a Case Study on Real-World Completions” demonstrates that these results may not translate to improvements in real-world performance.

To combat this effect, we train models on real-world code completion examples and find:

that these models outperform models trained on committed source code and working version snapshots by 12.8% and 13.8% accuracy respectively. We observe this improvement across modeling technologies and show through A/B testing that it corresponds to a 6.2% increase in programmers’ actual autocompletion usage.

Furthermore, our study characterizes a large corpus of logged autocompletion usages to investigate why training on real-world examples leads to stronger models.

--- https://arxiv.org/abs/1902.08654#facebook What makes a good conversation? How controllable attributes affect human judgments Abigail See, Stephen Roller, Douwe Kiela, Jason Weston 2019-02-22 2021-01-26 [("doi","10.48550/arXiv.1902.08654")] ai/nn

A good conversation requires balance—between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied.

In this work, we examine two controllable neural text generation methods, conditional training and weighted decoding, in order to control 4 important attributes for chitchat dialogue: repetition, specificity, response-relatedness and question-asking.

We conduct a large-scale human evaluation to measure the effect of these control parameters on multi-turn interactive conversations on the PersonaChat task.

We provide a detailed analysis of their relationship to high-level aspects of conversation, and show that by controlling combinations of these variables our models obtain clear improvements in human quality judgments.

--- https://aclanthology.org/W19-8643.pdf Best practices for the human evaluation of automatically generated text Chris van der Lee, Albert Gatt, Emiel van Miltenburg, Sander Wubben, Emiel Krahmer 2019-10 2021-01-26 [("doi","10.18653/v1/W19-8643")] ai/nn

Currently, there is little agreement as to how Natural Language Generation (NLG) systems should be evaluated. While there is some agreement regarding automatic metrics, there is a high degree of variation in the way that human evaluation is carried out.

This paper provides an overview of how human evaluation is currently conducted, and presents a set of best practices, grounded in the literature.

With this paper, we hope to contribute to the quality and consistency of human evaluations in NLG.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8995853/ Evolutionary Trajectories of Complex Traits in European Populations of Modern Humans Yunus Kuijpers, Jorge Domínguez-Andrés, Olivier B. Bakker, Manoj Kumar Gupta, Martin Grasshoff, Cheng-Jian Xu, Leo A. B. Joosten, Jaume Bertranpetit, Mihai G. Netea, Yang Li 2022 2022 [("doi","10.3389/fgene.2022.833190")] genetics/selection/natural/human

Humans have a great diversity in phenotypes, influenced by genetic, environmental, nutritional, cultural, and social factors. Understanding the historical trends of physiological traits can shed light on human physiology, as well as elucidate the factors that influence human diseases.

Here we built genome-wide polygenic scores for heritable traits, including height, body mass index, lipoprotein concentrations, cardiovascular disease, and intelligence, using summary statistics of genome-wide association studies in Europeans. Subsequently, we applied these scores to the genomes of ancient European populations.

Our results revealed that after the Neolithic, European populations experienced an increase in height and intelligence scores, decreased their skin pigmentation, while the risk for coronary artery disease increased through a genetic trajectory favoring low HDL concentrations.

These results are a reflection of the continuous evolutionary processes in humans and highlight the impact that the Neolithic revolution had on our lifestyle and health.

--- https://forum.effectivealtruism.org/posts/hrdxf5qdKmCZNWTvs/valuing-research-works-by-eliciting-comparisons-from-ea Valuing research works by eliciting comparisons from EA researchers 2021-01-26 statistics/order/comparison --- https://arxiv.org/abs/2202.12742#schmidhuber Learning Relative Return Policies With Upside-Down Reinforcement Learning Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber, Rupesh Kumar Srivastava 2022-02-23 2022-02-23 [("doi","10.48550/arXiv.2202.12742")] reinforcement-learning/model/decision-transformer

Lately, there has been a resurgence of interest in using supervised learning to solve reinforcement learning problems. Recent work in this area has largely focused on learning command-conditioned policies.

We investigate the potential of one such method—upside-down reinforcement learning—to work with commands that specify a desired relationship between some scalar value and the observed return. We show that upside-down reinforcement learning can learn to carry out such commands online in a tabular bandit setting and in CartPole with non-linear function approximation.

By doing so, we demonstrate the power of this family of methods and open the way for their practical use under more complicated command structures.

--- https://arxiv.org/abs/2205.07460 Diffusion Models for Adversarial Purification Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Anima Anandkumar 2022-05-16 2022-05-16 [("doi","10.48550/arXiv.2205.07460")] ai/nn/adversarial ai/nn/diffusion

Adversarial purification refers to a class of defense methods that remove adversarial perturbations using a generative model. These methods do not make assumptions on the form of attack and the classification model, and thus can defend pre-existing classifiers against unseen threats. However, their performance currently falls behind adversarial training methods.

In this work, we propose DiffPure that uses diffusion models for adversarial purification: Given an adversarial example, we first diffuse it with a small amount of noise following a forward diffusion process, and then recover the clean image through a reverse generative process. To evaluate our method against strong adaptive attacks in an efficient and scalable way, we propose to use the adjoint method to compute full gradients of the reverse generative process.

Extensive experiments on 3 image datasets including CIFAR-10, ImageNet and CelebA-HQ with 3 classifier architectures including ResNet, WideResNet and ViT demonstrate that our method achieves the state-of-the-art results, outperforming current adversarial training and adversarial purification methods, often by a large margin.

Project page: https://diffpure.github.io/.

--- https://www.biorxiv.org/content/10.1101/2022.05.13.491805.full Genome-wide data from medieval German Jews show that the Ashkenazi founder event pre-dated the 14th century Shamam Waldman, Daniel Backenroth, Éadaoin Harney, Stefan Flohr, Nadia C. Neff, Gina M. Buckley, Hila Fridman, Ali Akbari, Nadin Rohland, Swapan Mallick, Jorge Cano Nistal, Jin Yu, Nir Barzilai, Inga Peter, Gil Atzmon, Harry Ostrer, Todd Lencz, Yosef E. Maruvka, Maike Lämmerhirt, Leonard V. Rutgers, Virginie Renson, Keith M. Prufer, Stephan Schiffels, Harald Ringbauer, Karin Sczech, Shai Carmi, David Reich 2022-05-16 2022-05-16 [("doi","10.1101/2022.05.13.491805")] genetics/selection/natural/human

We report genome-wide data for 33 Ashkenazi Jews (AJ), dated to the 14th century, following a salvage excavation at the medieval Jewish cemetery of Erfurt, Germany [see also massacre].

The Erfurt individuals are genetically similar to modern AJ and have substantial Southern European ancestry, but they show more variability in Eastern European-related ancestry than modern AJ. A third of the Erfurt individuals carried the same nearly-AJ-specific mitochondrial haplogroup and 8 carried pathogenic variants known to affect AJ today.

These observations, together with high levels of runs of homozygosity, suggest that the Erfurt community had already experienced the major reduction in size that affected modern AJ. However, the Erfurt bottleneck was more severe, implying substructure in medieval AJ.

Together, our results suggest that the AJ founder event and the acquisition of the main sources of ancestry pre-dated the 14th century and highlight late medieval genetic heterogeneity no longer present in modern AJ.

--- https://slatestarcodex.com/2014/09/10/society-is-fixed-biology-is-mutable/ Society Is Fixed, Biology Is Mutable 2021-01-27 exercise genetics/heritable longevity/glp/semaglutide longevity/glp/tirzepatide psychiatry/adhd --- https://www.theguardian.com/media/2022/may/17/who-owns-einstein-the-battle-for-the-worlds-most-famous-face Who owns Einstein? The battle for the world’s most famous face 2021-01-27 economics/copyright --- https://en.wikipedia.org/wiki/Tirzepatide Tirzepatide 2021-01-27 longevity/glp/tirzepatide --- https://en.wikipedia.org/wiki/Glucagon-like_peptide-1 Glucagon-like peptide-1 2021-01-27 longevity/glp/tirzepatide --- https://en.wikipedia.org/wiki/Gastric_inhibitory_polypeptide Glucose-dependent insulinotropic polypeptide 2021-01-27 longevity/glp/tirzepatide --- https://progressforum.org/posts/EDb5EakPcvwXzxiPX/bombs-brains-and-science Bombs, Brains, and Science 2021-01-27 science --- https://troof.blog/posts/nootropics/ What I learned gathering thousands of nootropic ratings 2021-01-27 nootropic --- https://www.youtube.com/watch?v=1gl7xr5rftc Cat + tape = Experiment 2021-01-27 cat/psychology --- https://trevorklee.substack.com/p/obesitys-relationship-with-type-2 Obesity's relationship with type 2 diabetes is really weird 2021-01-27 longevity/glp/semaglutide --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3081766/ Canaries in the coal mine: a cross-species analysis of the plurality of obesity epidemics Yann C. Klimentidis, T. Mark Beasley, Hui-Yi Lin, Giulianna Murati, Gregory E. Glass, Marcus Guyton, Wendy Newton, Matthew Jorgensen, Steven B. Heymsfield, Joseph Kemnitz, Lynn Fairbanks, David B. Allison 2011 2021-01-28 [("doi","10.1098/rspb.2010.1890")] cat/biology dog exercise

[not observed 2000–2020?] A dramatic rise in obesity has occurred among humans within the last several decades. Little is known about whether similar increases in obesity have occurred in animals inhabiting human-influenced environments.

We examined samples collectively consisting of over 20 000 animals from 24 populations (12 divided separately into males and females) of animals representing eight species living with or around humans in industrialized societies.

In all populations, the estimated coefficient for the trend of body weight over time was positive (ie. increasing). The probability of all trends being in the same direction by chance is 1.2 × 10−7. Surprisingly, we find that over the past several decades, average mid-life body weights have risen among primates and rodents living in research colonies, as well as among feral rodents and domestic dogs and cats.

The consistency of these findings among animals living in varying environments, suggests the intriguing possibility that the aetiology of increasing body weight may involve several as-of-yet unidentified and/or poorly understood factors (eg. viral pathogens, epigenetic factors). This finding may eventually enhance the discovery and fuller elucidation of other factors that have contributed to the recent rise in obesity rates.

--- https://arxiv.org/abs/2205.08535 AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars Fangzhou Hong, Mingyuan Zhang, Liang Pan, Zhongang Cai, Lei Yang, Ziwei Liu 2022-05-17 2022-05-17 [("doi","10.48550/arXiv.2205.08535")] ai/nn/transformer/clip ai/nn/vae

[video; Github; Colab] 3D avatar creation plays a crucial role in the digital age. However, the whole production process is prohibitively time-consuming and labor-intensive.

To democratize this technology to a larger audience, we propose AvatarCLIP, a zero-shot text-driven framework for 3D avatar generation and animation. Unlike professional software that requires expert knowledge, AvatarCLIP empowers layman users to customize a 3D avatar with the desired shape and texture, and drive the avatar with the described motions using solely natural languages.

Our key insight is to take advantage of the powerful vision-language model CLIP for supervising neural human generation, in terms of 3D geometry, texture and animation. Specifically, driven by natural language descriptions, we initialize 3D human geometry generation with a shape VAE network. Based on the generated 3D human shapes, a volume rendering model is utilized to further facilitate geometry sculpting and texture generation. Moreover, by leveraging the priors learned in the motion VAE, a CLIP-guided reference-based motion synthesis method is proposed for the animation of the generated 3D avatar.

Extensive qualitative and quantitative experiments validate the effectiveness and generalizability of AvatarCLIP on a wide range of avatars.

Remarkably, AvatarCLIP can generate unseen 3D avatars with novel animations, achieving superior zero-shot capability.

--- https://www.mattbell.us/my-fake-dall-e-2-vacation-photos-passed-the-turing-test/ My deepfake DALL·E 2 vacation photos passed the Turing Test 2021-01-28 ai/nn/transformer/gpt/dall-e --- https://openai.com/blog/dall-e-2-update/ 2021-01-28 ai/nn/transformer/gpt/dall-e --- https://www.biorxiv.org/content/10.1101/2022.05.14.491975.full Near-chromosomal de novo assembly of Bengal tiger genome reveals genetic hallmarks of apex-predation Harsh Shukla, Kushal Suryamohan, Anubhab Khan, Krishna Mohan, Rajadurai C. Perumal, Oommen K. Mathew, Ramesh Menon, Mandumpala Davis Dixon, Megha Muraleedharan, Boney Kuriakose, Saju Michael, Sajesh P. Krishnankutty, Arun Zachariah, Somasekar Seshagiri, Uma Ramakrishnan 2022-05-17 2022-05-17 [("doi","10.1101/2022.05.14.491975")] cat/genetics

The tiger, a poster child for conservation, remains an endangered apex predator. Continued survival and recovery will require a comprehensive understanding of their genetic diversity and the use of such information for population management. A high-quality tiger genome assembly will be an important tool for conservation genetics, especially for the Indian tiger, the most abundant subspecies in the wild.

Here, we present high-quality near-chromosomal genome assemblies of a female and a male wild Indian tiger (Panthera tigris tigris). Our assemblies had a scaffold N50 of >140 Mb, with 19 scaffolds, corresponding to the 19 numbered chromosomes, containing 95% of the genome. Our assemblies also enabled detection of longer stretches of runs of homozygosity compared to previous genome assemblies which will improve estimates of genomic inbreeding. Comprehensive genome annotation identified 26,068 protein-coding genes, including several gene families involved in key morphological features such as the teeth, claws, vision, olfaction, taste and body stripes. We also identified 301 microRNAs, 365 small nucleolar RNAs, 632 tRNAs and other noncoding RNA elements, several of which are predicted to regulate key biological pathways that likely contribute to the tigers apex predatory traits. We identify signatures of positive selection in the tiger genome that are consistent with the Panthera lineage.

Our high-quality genome will enable use of non-invasive samples for comprehensive assessment of genetic diversity, thus supporting effective conservation and management of wild tiger populations.

--- https://www.npr.org/2011/04/16/135450214/eight-is-too-much-for-short-sleepers Eight Is Too Much For 'Short Sleepers' 2021-01-28 zeo/short-sleeper --- https://sciencesleep.org/ziliao/A%20sleep%20diary%20and%20questionnaire%20study%20of%20naturally%20short%20sleepers.pdf A sleep diary and questionnaire study of naturally short sleepers 2021-01-28 genetics/editing zeo/short-sleeper --- https://en.wikipedia.org/wiki/BHLHE41 BHLHE41 2021-01-28 zeo/short-sleeper --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0045204 Total Sleep Time Severely Drops during Adolescence Damien Leger, François Beck, Jean-Baptiste Richard, Emmanuelle Godeau 2012-08-17 2021-01-28 [("doi","10.1371/journal.pone.0045204")] melatonin

Restricted sleep duration among young adults and adolescents has been shown to increase the risk of morbidities such as obesity, diabetes or accidents. However there are few epidemiological studies on normal total sleep time (TST) in representative groups of teen-agers which allow to get normative data.

Purpose: To explore perceived total sleep time on schooldays (TSTS) and non schooldays (TSTN) and the prevalence of sleep initiating insomnia among a nationally representative sample of teenagers.

Method: Data from 9,251 children aged 11 to 15 years-old, 50.7% of which were boys, as part of the cross-national study 2011 HBSC were analyzed. Self-completion questionnaires were administered in classrooms. An estimate of TSTS and TSTN (week-ends and vacations) was calculated based on specifically designed sleep habits report. Sleep deprivation was estimated by a TSTN—TSTS difference >2 hours. Sleep initiating nsomnia was assessed according to International classification of sleep disorders (ICSD 2). Children who reported sleeping 7 hours or less per night were considered as short sleepers.

Results: A serious drop of TST was observed between 11 yo and 15 yo, both during the schooldays (9 hours 26 minutes vs. 7 h 55 min.; p < 0.001) and at a lesser extent during week-ends (10 h 17 min. vs. 9 h 44 min.; p < 0.001). Sleep deprivation concerned 16.0% of chidren aged of 11 yo vs. 40.5% of those of 15 yo (p < 0.001). Too short sleep was reported by 2.6% of the 11 yo vs. 24.6% of the 15 yo (p < 0.001).

Conclusion: Despite the obvious need for sleep in adolescence, TST drastically decreases with age among children 11–15 yo which creates sleep debt increasing with age.

--- https://www.reddit.com/r/science/comments/3kj669/science_ama_series_im_yinghui_fu_i_study_the/cuy7fjh/ 2021-01-28 melatonin --- https://www.thecut.com/2015/03/what-its-like-to-need-hardly-any-sleep.html What It's Like to Need Hardly Any Sleep 'I get 3 or four hours sleep a night, and I never get tired.' 2021-01-28 melatonin --- https://www.wsj.com/articles/SB10001424052748703712504576242701752957910 The Sleepless Elite: Why Some People Can Run on Little Sleep and Get So Much Done 2021-01-29 melatonin --- https://www.bbc.com/future/article/20150706-the-woman-who-barely-sleeps The people who need very little sleep: Is it true that some people need only a few hours of sleep? Helen Thomson talks to a woman whose genes might hint at how we all could survive on less shuteye 2021-01-29 melatonin --- /doc/genetics/heritable/rare/2019-xing.pdf Mutant neuropeptide S receptor reduces sleep duration with preserved memory consolidation Lijuan Xing, Guangsen Shi, Yulia Mostovoy, Nicholas W. Gentry, Zenghua Fan, Thomas B. McMahon, Pui-Yan Kwok, Christopher R. Jones, Louis J. Ptáček, Ying-Hui Fu 2019 2021-01-29 [("doi","10.1126/scitranslmed.aax2014")] genetics/heritable/rare zeo/short-sleeper

Sleep is a crucial physiological process for our survival and cognitive performance, yet the factors controlling human sleep regulation remain poorly understood.

Here, we identified a missense mutation in a G protein-coupled neuropeptide S receptor 1 (NPSR1) that is associated with a natural short sleep phenotype in humans. Mice carrying the homologous mutation exhibited less sleep time despite increased sleep pressure.

These animals were also resistant to contextual memory deficits associated with sleep deprivation. In vivo, the mutant receptors showed increased sensitivity to neuropeptide S exogenous activation.

These results suggest that the NPS/NPSR1 pathway might play a critical role in regulating human sleep duration and in the link between sleep homeostasis and memory consolidation.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3899325/ Development of a short sleeper phenotype after third ventriculostomy in a patient with ependymal cysts Katharina Seystahl, Helen Könnecke, Oguzkan Sürücü, Christian R. Baumann, Rositsa Poryazova 2014 2021-01-29 [("doi","10.5664/jcsm.3458")] psychiatry psychology/neuroscience zeo/short-sleeper

A naturally short sleeper phenotype with a sleep need of less than 6 hours without negative impact on health or performance is rare.

We present a case of an acquired short sleeper phenotype after third ventriculostomy. A 59-year-old patient suffering from chronic hydrocephalus reported an average of 7–8 hours of nocturnal sleep. After surgical intervention, the patient noted a strikingly reduced sleep need of 4–5 hours without consequent fatigue or excessive daytime sleepiness, but with good daytime performance and well-balanced mood. Short sleep per 24 hours was confirmed by actigraphy. Postoperative imaging revealed decreased pressure around the anterior third ventricle.

The temporal link between development of a short sleeper phenotype and third ventriculostomy is striking. This might suggest that individual short sleep need is not only determined by genetics but can also be induced by external factors.

--- http://zizians.info/#unihemispheric-sleep The Zizians 2021-01-29 psychiatry --- /doc/genetics/heritable/rare/2019-shi-2.pdf A Rare Mutation of β1-Adrenergic Receptor Affects Sleep/Wake Behaviors Guangsen Shi, Lijuan Xing, David Wu, Bula J. Bhattacharyya, Christopher R. Jones, Thomas McMahon, S. Y. Christin Chong, Jason A. Chen, Giovanni Coppola, Daniel Geschwind, Andrew Krystal, Louis J. Ptáček, Ying-Hui Fu 2019 2021-01-29 [("doi","10.1016/j.neuron.2019.07.026")] genetics/heritable/rare zeo/short-sleeper

Sleep is crucial for our survival, and many diseases are linked to long-term poor sleep quality. Before we can use sleep to enhance our health and performance and alleviate diseases associated with poor sleep, a greater understanding of sleep regulation is necessary.

We have identified a mutation in the β1-adrenergic receptor gene in humans who require fewer hours of sleep than most. In vitro, this mutation leads to decreased protein stability and dampened signaling in response to agonist treatment. In vivo, the mice carrying the same mutation demonstrated short sleep behavior.

We found that this receptor is highly expressed in the dorsal pons and that these ADRB1+ neurons are active during rapid eye movement (REM sleep) and wakefulness. Activating these neurons can lead to wakefulness, and the activity of these neurons is affected by the mutation.

These results highlight the important role of β1-adrenergic receptors in sleep/wake regulation.

--- https://www.reddit.com/r/AMA/comments/nxx51c/i_have_short_sleeper_syndrome_sss_ama/ 2021-01-29 zeo/short-sleeper --- https://www.reddit.com/r/AMA/comments/flmaow/i_am_a_diagnosed_short_sleeper_ama/ 2021-01-29 zeo/short-sleeper --- https://en.wikipedia.org/wiki/Th%C3%A1i_Ng%E1%BB%8Dc Thái Ngọc 2021-01-29 zeo/short-sleeper --- https://en.wikipedia.org/wiki/Al_Herpin Al Herpin 2021-01-29 zeo/short-sleeper --- https://en.wikipedia.org/wiki/Fatal_insomnia Fatal insomnia 2021-01-29 genetics/heritable/rare psychiatry zeo --- https://www.wired.com/story/sleep-no-more-crusade-genetic-killer/ One Couple’s Tireless Crusade to Stop a Genetic Killer 2021-01-30 genetics/heritable/rare psychiatry zeo --- https://http.cat/ HTTP Cats 2021-01-30 cat math/humor --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6879540/#Sec20title Genetics of the human circadian clock and sleep homeostat Liza H. Ashbrook, Andrew D. Krystal, Ying-Hui Fu, Louis J. Ptáček 2020 2021-01-30 [("doi","10.1038/s41386-019-0476-7")] zeo/short-sleeper

Timing and duration of sleep are controlled by the circadian system, which keeps an ~24-h internal rhythm that entrains to environmental stimuli, and the sleep homeostat, which rises as a function of time awake. There is a normal distribution across the population in how the circadian system aligns with typical day and night resulting in varying circadian preferences called chronotypes.

A portion of the variation in the population is controlled by genetics as shown by the single-gene mutations that confer extreme early or late chronotypes. Similarly, there is a normal distribution across the population in sleep duration.

Genetic variations have been identified that lead to a short sleep phenotype in which individuals sleep only 4–6.5 h nightly. Negative health consequences have been identified when individuals do not sleep at their ideal circadian timing or are sleep deprived relative to intrinsic sleep need. Whether familial natural short sleepers are at risk of the health consequences associated with a short sleep duration based on population data is not known.

More work needs to be done to better assess for an individual’s chronotype and degree of sleep deprivation to answer these questions.

--- https://www.pnas.org/doi/full/10.1073/pnas.1801693115 DEC2 modulates orexin expression and regulates sleep Arisa Hirano, Pei-Ken Hsu, Luoying Zhang, Lijuan Xing, Thomas McMahon, Maya Yamazaki, Louis J. Ptáček, Ying-Hui Fu 2018-03-12 2021-01-30 [("doi","10.1073/pnas.1801693115")] zeo/short-sleeper

Sleep is essential for healthy aging, and most people need ~8–8.5 hours of sleep per night to feel good and to function optimally. We previously reported a proline-to-arginine mutation in DEC2 that leads to a life-long decrease in daily sleep need. We found that the expression of an important sleep-relevant gene, orexin, was increased in the DEC2 mutant mice.

Further investigation revealed that DEC2 is a transcriptional repressor for orexin expression, and that mutant DEC2 exerts less repressor activity than WT-DEC2, resulting in increased orexin expression. This study represents the first step toward understanding the underlying molecular mechanism through which DEC2 modulates sleep.

Adequate sleep is essential for physical and mental health. We previously identified a missense mutation in the human DEC2 gene (BHLHE41) leading to the familial natural short sleep behavioral trait. DEC2 is a transcription factor regulating the circadian clock in mammals, although its role in sleep regulation has been unclear. Here we report that prepro-orexin, also known as hypocretin (Hcrt), gene expression is increased in the mouse model expressing the mutant hDEC2 transgene (hDEC2-P384R). Prepro-orexin encodes a precursor protein of a neuropeptide producing orexin A and B (hcrt1 and hcrt2), which is enriched in the hypothalamus and regulates maintenance of arousal. In cell culture, DEC2 suppressed prepro-orexin promoter-luc (ore-luc) expression through cis-acting E-box elements. The mutant DEC2 has less repressor activity than WT-DEC2, resulting in increased orexin expression. DEC2-binding affinity for the prepro-orexin gene promoter is decreased by the P384R mutation, likely due to weakened interaction with other transcription factors. In vivo, the decreased immobility time of the mutant transgenic mice is attenuated by an orexin receptor antagonist. Our results suggested that DEC2 regulates sleep/wake duration, at least in part, by modulating the neuropeptide hormone orexin.

--- /doc/genetics/heritable/rare/2019-xing.pdf Mutant neuropeptide S receptor reduces sleep duration with preserved memory consolidation Lijuan Xing, Guangsen Shi, Yulia Mostovoy, Nicholas W. Gentry, Zenghua Fan, Thomas B. Mcmahon, Pui-Yan Kwok, Christopher R. Jones, Louis J. Ptacek, Ying-Hui Fu 2019-10-16 2021-01-30 [("doi","10.1126/scitranslmed.aax2014")] genetics/heritable/rare zeo/short-sleeper

Sleep is a crucial physiological process for our survival and cognitive performance, yet the factors controlling human sleep regulation remain poorly understood.

Here, we identified a missense mutation in a G protein-coupled neuropeptide S receptor 1 (NPSR1) that is associated with a natural short sleep phenotype in humans. Mice carrying the homologous mutation exhibited less sleep time despite increased sleep pressure. These animals were also resistant to contextual memory deficits associated with sleep deprivation. In vivo, the mutant receptors showed increased sensitivity to neuropeptide S exogenous activation.

These results suggest that the NPS/NPSR1 pathway might play a critical role in regulating human sleep duration and in the link between sleep homeostasis and memory consolidation.

--- https://www.cell.com/current-biology/fulltext/S0960-9822(20)31441-X Mutations in Metabotropic Glutamate Receptor 1 Contribute to Natural Short Sleep Trait Guangsen Shi, Chen Yin, Zenghua Fan, Lijuan Xing, Yulia Mostovoy, Pui-Yan Kwok, Liza H. Ashbrook, Andrew D. Krystal, Louis J. Ptáček, Ying-Hui Fu 2020-10-15 2021-01-30 [("doi","10.1016/j.cub.2020.09.071")] genetics/heritable/rare zeo/short-sleeper

Sufficient and efficient sleep is crucial for our health. Natural short sleepers can sleep substantially shorter than the average population without a desire for more sleep and without any obvious negative health consequences.

In searching for genetic variants underlying the short sleep trait, we found 2 different mutations in the same gene (metabotropic glutamate receptor 1) from 2 independent natural short sleep families.

In vitro, both of the mutations exhibited loss of function in receptor-mediated signaling. In vivo, the mice carrying the individual mutations both demonstrated short sleep behavior. In brain slices, both of the mutations changed the electrical properties and increased excitatory synaptic transmission.

These results highlight the important role of metabotropic glutamate receptor 1 in modulating sleep duration.

[Keywords: mGluR1, loss-of-function, short-sleep]

--- https://arxiv.org/abs/2010.04898#apple Open-Domain Question Answering Goes Conversational via Question Rewriting Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, Srinivas Chappidi 2020-10-10 2021-01-30 [("doi","10.48550/arXiv.2010.04898")] ai/dataset ai/nn

We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs. The task in QReCC is to find answers to conversational questions within a collection of 10M web pages (split into 54M passages). Answers to questions in the same conversation may be distributed across several web pages. QReCC provides annotations that allow us to train and evaluate individual subtasks of question rewriting, passage retrieval, and reading comprehension required for the end-to-end conversational question answering (QA) task.

We report the effectiveness of a strong baseline approach that combines the state-of-the-art model for question rewriting, and competitive models for open-domain QA. Our results set the first baseline for the QReCC dataset with an F1 of 19.10, compared to the human upper bound of 75.45, indicating the difficulty of the setup and a large room for improvement.

--- https://arxiv.org/abs/2005.11364 Open-Retrieval Conversational Question Answering Chen Qu, Liu Yang, Cen Chen, Minghui Qiu, W. Bruce Croft, Mohit Iyyer 2020-05-22 2021-01-30 [("doi","10.1145/3397271.3401110")] ai/nn/retrieval ai/nn/transformer

Conversational search is one of the ultimate goals of information retrieval. Recent research approaches conversational search by simplified settings of response ranking and conversational question answering, where an answer is either selected from a given candidate set or extracted from a given passage. These simplifications neglect the fundamental role of retrieval in conversational search.

To address this limitation, we introduce an open-retrieval conversational question answering (ORConvQA) setting, where we learn to retrieve evidence from a large collection before extracting answers, as a further step towards building functional conversational search systems. We create a dataset, OR-QuAC, to facilitate research on ORConvQA. We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers. Our extensive experiments on OR-QuAC demonstrate that a learnable retriever is crucial for ORConvQA. We further show that our system can make a substantial improvement when we enable history modeling in all system components. Moreover, we show that the reranker component contributes to the model performance by providing a regularization effect. Finally, further in-depth analyses are performed to provide new insights into ORConvQA.

--- https://arxiv.org/abs/2003.13624 TREC CAsT 2019: The Conversational Assistance Track Overview Jeffrey Dalton, Chenyan Xiong, Jamie Callan 2020-03-30 2021-01-30 [("doi","10.48550/arXiv.2003.13624")] ai/nn/transformer/gpt/2

The Conversational Assistance Track (CAsT) is a new track for TREC 2019 to facilitate Conversational Information Seeking (CIS) research and to create a large-scale reusable test collection for conversational search systems. The document corpus is 38,426,252 passages from the TREC Complex Answer Retrieval (CAR) and Microsoft MAchine Reading COmprehension (MARCO) datasets. Eighty information seeking dialogues (30 train, 50 test) are an average of 9 to 10 questions long. Relevance assessments are provided for 30 training topics and 20 test topics.

This year 21 groups submitted a total of 65 runs using varying methods for conversational query understanding and ranking. Methods include traditional retrieval based methods, feature based learning-to-rank, neural models, and knowledge enhanced methods. A common theme through the runs is the use of BERT-based neural reranking methods. Leading methods also employed document expansion, conversational query expansion, and generative language models for conversational query rewriting (GPT-2).

The results show a gap between automatic systems and those using the manually resolved utterances, with a 35% relative improvement of manual rewrites over the best automatic system.

--- https://openreview.net/forum?id=H1eerhIpLV Minigo: A Case Study in Reproducing Reinforcement Learning Research Anonymous 2019-03-06 2021-01-30 reinforcement-learning/model/alphago

We reproduced AlphaZero on Google Cloud Platform

The reproducibility of reinforcement-learning research has been highlighted as a key challenge area in the field. In this paper, we present a case study in reproducing the results of one groundbreaking algorithm, AlphaZero, a reinforcement learning system that learns how to play Go at a superhuman level given only the rules of the game. We describe Minigo, a reproduction of the AlphaZero system using publicly available Google Cloud Platform infrastructure and Google Cloud TPUs. The Minigo system includes both the central reinforcement learning loop as well as auxiliary monitoring and evaluation infrastructure. With ten days of training from scratch on 800 Cloud TPUs, Minigo can play evenly against LeelaZero and ELF OpenGo, two of the strongest publicly available Go AIs. We discuss the difficulties of scaling a reinforcement learning system and the monitoring systems required to understand the complex interplay of hyperparameter configurations.

--- https://arxiv.org/abs/2103.17228 OLIVAW: Mastering Othello without Human Knowledge, nor a Fortune Antonio Norelli, Alessandro Panconesi 2021-03-31 2021-03-31 [("doi","10.48550/arXiv.2103.17228")] reinforcement-learning/model/alphago

We introduce OLIVAW, an AI Othello player adopting the design principles of the famous AlphaGo programs. The main motivation behind OLIVAW was to attain exceptional competence in a non-trivial board game at a tiny fraction of the cost of its illustrious predecessors.

In this paper, we show how the AlphaGo Zero’s paradigm can be successfully applied to the popular game of Othello using only commodity hardware and free cloud services. While being simpler than Chess or Go, Othello maintains a considerable search space and difficulty in evaluating board positions. To achieve this result, OLIVAW implements some improvements inspired by recent works to accelerate the standard AlphaGo Zero learning process. The main modification implies doubling the positions collected per game during the training phase, by including also positions not played but largely explored by the agent.

We tested the strength of OLIVAW in 3 different ways: by pitting it against Edax, the strongest open-source Othello engine, by playing anonymous games on the web platform OthelloQuest, and finally in two in-person matches against top-notch human players: a national champion and a former world champion.

--- https://arxiv.org/abs/2205.09726#google RankGen: Improving Text Generation with Large Ranking Models Kalpesh Krishna, Yapei Chang, John Wieting, Mohit Iyyer 2022-05-19 2022-05-19 [("doi","10.48550/arXiv.2205.09726")] ai/nn/retrieval ai/nn/sampling ai/nn/transformer/gpt

Given an input sequence (or prefix), modern language models often assign high probabilities to output sequences that are repetitive, incoherent, or irrelevant to the prefix; as such, model-generated text also contains such artifacts. To address these issues, we present RankGen, an encoder model (1.2b parameters) that scores model generations given a prefix. RankGen can be flexibly incorporated as a scoring function in beam search and used to decode from any pretrained language model.

We train RankGen using large-scale contrastive learning to map a prefix close to the ground-truth sequence that follows it and far away from two types of negatives: (1) random sequences from the same document as the prefix, and, which discourage topically-similar but irrelevant generations; (2) sequences generated from a large language model conditioned on the prefix, which discourage repetition and hallucination. Experiments across 4 different language models (345M-11b parameters) and two domains show that RankGen outperforms decoding algorithms like nucleus, top-k, and typical sampling on both automatic metrics (85.0 vs 77.3 MAUVE) as well as human evaluations with English writers (74.5% human preference over nucleus sampling).

Analysis reveals that RankGen outputs are more relevant to the prefix and improve continuity and coherence compared to baselines.

We open source our model checkpoints, code, and human preferences with detailed explanations for future research.

--- /doc/statistics/meta-analysis/2013-couzinfrankel.pdf When Mice Mislead: Tackling a long-standing disconnect between animal and human studies, some charge that animal researchers need stricter safeguards and better statistics to ensure their science is solid Jennifer Couzin-Frankel 2013-11-22 2021-01-31 [("doi","10.1126/science.342.6161.922")] statistics/bias/animal statistics/meta-analysis

Tackling a long-standing disconnect between animal and human studies, some charge that animal researchers need stricter safeguards and better statistics to ensure their science is solid.

--- https://x.com/Plinz/status/1527528360351387649 2021-01-31 ai/nn/transformer/gpt/dall-e --- https://www.astralcodexten.com/p/your-book-review-making-nature Your Book Review: Making Nature Scott Alexander 2021-01-31 science --- https://x.com/wightmanr/status/1527752308267724800 2021-01-31 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2202.05798#schmidhuber The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber 2022-02-11 2022-02-11 [("doi","10.48550/arXiv.2202.05798")] ai/nn/transformer/attention reinforcement-learning/meta-learning/continual-learning

Linear layers in neural networks (NNs) trained by gradient descent can be expressed as a key-value memory system which stores all training datapoints and the initial weights, and produces outputs using unnormalized dot attention over the entire training experience. While this has been technically known since the ’60s, no prior work has effectively studied the operations of NNs in such a form, presumably due to prohibitive time and space complexities and impractical model sizes, all of them growing linearly with the number of training patterns which may get very large.

We conduct experiments on small scale supervised image classification tasks in single-task, multi-task, and continual learning settings, as well as language modeling, and discuss potentials and limits of this view for better understanding and interpreting how NNs exploit training patterns.

Our code is public.

--- https://thisimagedoesnotexist.com/ This Image Does Not Exist 2021-01-31 ai/nn/transformer/gpt/dall-e --- https://bakztfuture.substack.com/p/dall-e-2-emerging-content-category DALL·E 2: Emerging Content Category Breakdown 2021-01-31 ai/nn/transformer/gpt/dall-e --- https://bakztfuture.substack.com/p/dall-e-2-recombinant-art-and-design DALL·E 2: Recombinant Art & Design 2021-01-31 ai/nn/transformer/gpt/dall-e --- https://www.biorxiv.org/content/10.1101/2021.11.30.470655.full Raman2RNA: Live-cell label-free prediction of single-cell RNA expression profiles by Raman microscopy Koseki J. Kobayashi-Kirschvink, Shreya Gaddam, Taylor James-Sorenson, Emanuelle Grody, Johain R. Ounadjela, Baoliang Ge, Ke Zhang, Jeon Woong Kang, Ramnik Xavier, Peter T. C. So, Tommaso Biancalani, Jian Shu, Aviv Regev 2021-12-01 2021-12-01 [("doi","10.1101/2021.11.30.470655")] biology statistics/variance-component

Single cell RNA-Seq (scRNA-seq) and other profiling assays have opened new windows into understanding the properties, regulation, dynamics, and function of cells at unprecedented resolution and scale. However, these assays are inherently destructive, precluding us from tracking the temporal dynamics of live cells, in cell culture or whole organisms. Raman microscopy offers a unique opportunity to comprehensively report on the vibrational energy levels of molecules in a label-free and non-destructive manner at a subcellular spatial resolution, but it lacks in genetic and molecular interpretability.

Here, we developed Raman2RNA (R2R), an experimental and computational framework to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and multi-modal data integration and domain translation. We used spatially resolved single-molecule RNA-FISH (smFISH) data as anchors to link scRNA-seq profiles to the paired spatial hyperspectral Raman images, and trained machine learning models to infer expression profiles from Raman spectra at the single-cell level.

In reprogramming of mouse fibroblasts into induced pluripotent stem cells (iPSCs), R2R accurately (r > 0.96) inferred from Raman images the expression profiles of various cell states and fates, including iPSCs, mesenchymal-epithelial transition (MET) cells, stromal cells, epithelial cells, and fibroblasts.

R2R outperformed inference from brightfield images, showing the importance of spectroscopic content afforded by Raman microscopy.

Raman2RNA lays a foundation for future investigations into exploring single-cell genome-wide molecular dynamics through imaging data, in vitro and in vivo.

--- https://wandb.ai/ucalyptus/seginstyle/reports/Segmentation-in-Style-Unsupervised-Semantic-Image-Segmentation-with-StyleGAN--VmlldzoxMTU5ODI2 2021-02-01 ai/anime ai/nn/gan/stylegan --- https://arxiv.org/abs/2205.05638 Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, Colin Raffel 2022-05-11 2022-05-11 [("doi","10.48550/arXiv.2205.05638")] ai/nn/transformer

Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unseen task without any gradient-based training by feeding a small number of training examples as part of the input. ICL incurs substantial computational, memory, and storage costs because it involves processing all of the training examples every time a prediction is made. Parameter-efficient fine-tuning (eg. adapter modules, prompt tuning, sparse update methods, etc.) offers an alternative paradigm where a small set of parameters are trained to enable a model to perform the new task.

In this paper, we rigorously compare few-shot ICL and parameter-efficient fine-tuning and demonstrate that the latter offers better accuracy as well as dramatically lower computational costs. Along the way, we introduce a new parameter-efficient fine-tuning method called (IA)3 that scales activations by learned vectors, attaining stronger performance while only introducing a relatively tiny amount of new parameters. We also propose a simple recipe based on the T0 model called T-Few that can be applied to new tasks without task-specific tuning or modifications.

We validate the effectiveness of T-Few on completely unseen tasks by applying it to the RAFT benchmark, attaining super-human performance for the first time and outperforming the state-of-the-art by 6% absolute.

All of the code used in our experiments is publicly available.

--- https://arxiv.org/abs/2109.14076 RAFT: A Real-World Few-Shot Text Classification Benchmark Neel Alex, Eli Lifland, Lewis Tunstall, Abhishek Thakur, Pegah Maham, C. Jess Riedel, Emmie Hine, Carolyn Ashurst, Paul Sedille, Alexis Carlier, Michael Noetel, Andreas Stuhlmüller 2021-09-28 2021-09-28 [("doi","10.48550/arXiv.2109.14076")] ai/nn

Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? Existing benchmarks are not designed to measure progress in applied settings, and so don’t directly answer this question.

The RAFT benchmark (Real-world Annotated Few-shot Tasks) focuses on naturally occurring tasks and uses an evaluation setup that mirrors deployment.

Baseline evaluations on RAFT reveal areas current techniques struggle with: reasoning over long texts and tasks with many classes. Human baselines show that some classification tasks are difficult for non-expert humans, reflecting that real-world value sometimes depends on domain expertise. Yet even non-expert human baseline F1 scores exceed GPT-3 by an average of 0.11.

The RAFT datasets and leaderboard will track which model improvements translate into real-world benefits at https://raft.elicit.org/.

--- https://github.com/LAION-AI/laion-datasets/blob/main/laion-aesthetic.md LAION-AI/laion-datasets 2021-02-01 ai/nn/transformer/clip --- https://whatisnuclear.com/offshore-nuclear-plants.html Offshore nuclear power plants 2021-02-01 technology --- https://arxiv.org/abs/1503.03585 Deep Unsupervised Learning using Nonequilibrium Thermodynamics Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli 2015-03-12 2021-02-01 [("doi","10.48550/arXiv.1503.03585")] ai/nn/diffusion

A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable.

Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data.

This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model.

We additionally release an open source reference implementation of the algorithm.

--- https://arxiv.org/abs/2011.13456#google Score-Based Generative Modeling through Stochastic Differential Equations Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole 2020-11-26 2021-02-01 [("doi","10.48550/arXiv.2011.13456")] ai/nn/diffusion

Creating noise from data is easy; creating data from noise is generative modeling.

We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (‘score’) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples.

We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency.

In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization.

Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1,024 × 1,024px images for the first time from a score-based generative model.

--- https://arxiv.org/abs/2102.07716#deepmind How RL Agents Behave When Their Actions Are Modified Eric D. Langlois, Tom Everitt 2021-02-15 2021-02-15 [("doi","10.48550/arXiv.2102.07716")] reinforcement-learning/model-free

[blog] Reinforcement learning in complex environments may require supervision to prevent the agent from attempting dangerous actions. As a result of supervisor intervention, the executed action may differ from the action specified by the policy. How does this affect learning?

We present the Modified-Action Markov Decision Process, an extension of the MDP model that allows actions to differ from the policy.

We analyze the asymptotic behaviors of common reinforcement learning algorithms in this setting and show that they adapt in different ways: some completely ignore modifications while others go to various lengths in trying to avoid action modifications that decrease reward.

By choosing the right algorithm, developers can prevent their agents from learning to circumvent interruptions or constraints, and better control agent responses to other kinds of action modification, like self-damage.

--- https://www.youtube.com/watch?v=w3ues-NayAs?t=712#openai If you want to solve a hard problem in reinforcement learning, you just scale. It's just gonna work just like supervised learning. it's the same, the same story exactly. It was kind of hard to believe that supervised learning can do all those things, but it's not just vision, it's everything and the same thing seems to hold for reinforcement learning provided you have a lot of experience. 2021-02-01 reinforcement-learning/model-free/oa5 reinforcement-learning/scaling --- https://arxiv.org/abs/1811.02553 A Closer Look at Deep Policy Gradients Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry 2018-11-06 2021-02-01 [("doi","10.48550/arXiv.1811.02553")] reinforcement-learning/model-free

We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development.

To this end, we propose a fine-grained analysis of state-of-the-art methods based on key elements of this framework: gradient estimation, value prediction, and optimization landscapes.

Our results show that the behavior of deep policy gradient algorithms often deviates from what their motivating framework would predict: the surrogate objective does not match the true reward landscape, learned value estimators fail to fit the true value function, and gradient estimates poorly correlate with the “true” gradient.

The mismatch between predicted and empirical behavior we uncover highlights our poor understanding of current methods, and indicates the need to move beyond current benchmark-centric evaluation methods.

--- https://www.youtube.com/watch?v=61LI1aJPQ2c?t=26 Campbell Pet Company's "EZ Nabber" 2021-02-01 cat/psychology --- https://www.tiktok.com/@pageandwhisker/video/7099952695003467014 2021-02-02 cat/psychology --- https://arxiv.org/abs/2205.07015 Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments Ryan Sullivan, J. K. Terry, Benjamin Black, John P. Dickerson 2022-05-14 2022-05-14 [("doi","10.48550/arXiv.2205.07015")] reinforcement-learning/exploration

Visualizing optimization landscapes has led to many fundamental insights in numeric optimization, and novel improvements to optimization techniques. However, visualizations of the objective that reinforcement learning optimizes (the “reward surface”) have only ever been generated for a small number of narrow contexts. This work presents reward surfaces and related visualizations of 27 of the most widely used reinforcement learning environments in Gym for the first time.

We also explore reward surfaces in the policy gradient direction and show for the first time that many popular reinforcement learning environments have frequent “cliffs” (sudden large drops in expected return). We demonstrate that A2C often “dives off” these cliffs into low reward regions of the parameter space while PPO avoids them, confirming a popular intuition for PPO’s improved performance over previous methods.

Our findings provide new intuition to explain the successes and failures of modern RL methods, and our visualizations concretely characterize several failure modes of reinforcement learning agents in novel ways.

We additionally introduce a highly extensible library that allows researchers to easily generate these visualizations in the future.

--- https://www.kaggle.com/datasets/mylesoneill/tagged-anime-illustrations/kernels Explore more than 300,000 pieces of fan art 2021-02-02 ai/anime/danbooru --- https://www.kaggle.com/datasets/alamson/safebooru 1.9 million rows of tag-based anime image metadata 2021-02-02 ai/anime/danbooru --- http://dev.kanotype.net:8003/deepdanbooru/ Deep Danbooru 2021-02-02 ai/anime/danbooru ai/nn/gan/stylegan --- https://www.seeprettyface.com/mydataset_page2.html 2021-02-02 ai/anime/danbooru --- https://arxiv.org/abs/1808.04325 Improving Shape Deformation in Unsupervised Image-to-Image Translation Aaron Gokaslan, Vivek Ramanujan, Daniel Ritchie, Kwang In Kim, James Tompkin 2018-08-13 2021-02-02 [("doi","10.48550/arXiv.1808.04325")] ai/anime/danbooru

Unsupervised image-to-image translation techniques are able to map local texture between two domains, but they are typically unsuccessful when the domains require larger shape change.

Inspired by semantic segmentation, we introduce a discriminator with dilated convolutions that is able to use information from across the entire image to train a more context-aware generator. This is coupled with a multi-scale perceptual loss that is better able to represent error in the underlying shape of objects.

We demonstrate that this design is more capable of representing shape deformation in a challenging toy dataset, plus in complex mappings with dataset variation between humans, dolls, and anime faces, and between cats and dogs.

--- https://arxiv.org/abs/1904.01774 Image Generation From Small Datasets via Batch Statistics Adaptation Atsuhiro Noguchi, Tatsuya Harada 2019-04-03 2021-02-02 [("doi","10.48550/arXiv.1904.01774")] ai/anime/danbooru

Thanks to the recent development of deep generative models, it is becoming possible to generate high-quality images with both fidelity and diversity. However, the training of such generative models requires a large dataset.

To reduce the amount of data required, we propose a new method for transferring prior knowledge of the pre-trained generator, which is trained with a large dataset, to a small dataset in a different domain. Using such prior knowledge, the model can generate images leveraging some common sense that cannot be acquired from a small dataset.

In this work, we propose a novel method focusing on the parameters for batch statistics, scale and shift, of the hidden layers in the generator. By training only these parameters in a supervised manner, we achieved stable training of the generator, and our method can generate higher quality images compared to previous methods without collapsing, even when the dataset is small (~100).

Our results show that the diversity of the filters acquired in the pre-trained generator is important for the performance on the target domain.

Our method makes it possible to add a new class or domain to a pre-trained generator without disturbing the performance on the original domain.

[Danbooru2018 by way of StyleGAN/TWDNE-generated anime images.]

--- https://arxiv.org/abs/1904.10633 LFFD: A Light and Fast Face Detector for Edge Devices Yonghao He, Dezhong Xu, Lifang Wu, Meng Jian, Shiming Xiang, Chunhong Pan 2019-04-24 2021-02-02 [("doi","10.48550/arXiv.1904.10633")] ai/anime/danbooru

Face detection, as a fundamental technology for various applications, is always deployed on edge devices which have limited memory storage and low computing power.

This paper introduces a Light and Fast Face Detector (LFFD) for edge devices. The proposed method is anchor-free and belongs to the one-stage category. Specifically, we rethink the importance of receptive field (RF) and effective receptive field (ERF) in the background of face detection. Essentially, the RFs of neurons in a certain layer are distributed regularly in the input image and these RFs are natural “anchors”. Combining RF “anchors” and appropriate RF strides, the proposed method can detect a large range of continuous face scales with 100% coverage in theory.

The insightful understanding of relations between ERF and face scales motivates an efficient backbone for one-stage detection. The backbone is characterized by 8 detection branches and common layers, resulting in efficient computation.

Comprehensive and extensive experiments on popular benchmarks: WIDER FACE and FDDB are conducted. A new evaluation schema is proposed for application-oriented scenarios. Under the new schema, the proposed method can achieve superior accuracy (WIDER FACE Val/Test—Easy: 0.910/0.896, Medium: 0.881/0.865, Hard: 0.780/0.770; FDDB—discontinuous: 0.973, continuous: 0.724).

Multiple hardware platforms are introduced to evaluate the running efficiency. The proposed method can obtain fast inference speed (NVIDIA TITAN Xp: 131.45 FPS at 640×480; NVIDIA TX2: 136.99 PFS at 160×120; Raspberry Pi 3 Model B+: 8.44 FPS at 160×120) with model size of 9 MB.

--- https://arxiv.org/abs/1905.10742 Disentangling Style and Content in Anime Illustrations Sitao Xiang, Hao Li 2019-05-26 2021-02-02 [("doi","10.48550/arXiv.1905.10742")] ai/anime/danbooru

Existing methods for AI-generated artworks still struggle with generating high-quality stylized content, where high-level semantics are preserved, or separating fine-grained styles from various artists. We propose a novel Generative Adversarial Disentanglement Network which can disentangle two complementary factors of variations when only one of them is labeled in general, and fully decompose complex anime illustrations into style and content in particular.

Training such model is challenging, since given a style, various content data may exist but not the other way around. Our approach is divided into two stages, one that encodes an input image into a style independent content, and one based on a dual-conditional generator.

We demonstrate the ability to generate high-fidelity anime portraits with a fixed content and a large variety of styles from over a thousand artists, and vice versa, using a single end-to-end network and with applications in style transfer. We show this unique capability as well as superior output to the current state-of-the-art.

--- https://arxiv.org/abs/1907.10830 U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation Junho Kim, Minjae Kim, Hyeonwoo Kang, Kwanghee Lee 2019-07-25 2021-02-03 [("doi","10.48550/arXiv.1907.10830")] ai/anime/danbooru

We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner. The attention module guides our model to focus on more important regions distinguishing between source and target domains based on the attention map obtained by the auxiliary classifier. Unlike previous attention-based method which cannot handle the geometric changes between domains, our model can translate both images requiring holistic changes and images requiring large shape changes.

Moreover, our new AdaLIN (Adaptive Layer-Instance Normalization) function helps our attention-guided model to flexibly control the amount of change in shape and texture by learned parameters depending on datasets.

Experimental results show the superiority of the proposed method compared to the existing state-of-the-art models with a fixed network architecture and hyper-parameters.

Our code and datasets are available at Github.

--- https://arxiv.org/abs/1909.13028 Semantic Example Guided Image-to-Image Translation Jialu Huang, Jing Liao, Tak Wu Sam Kwong 2019-09-28 2021-02-03 [("doi","10.48550/arXiv.1909.13028")] ai/anime/danbooru

Many image-to-image (I2I) translation problems are in nature of high diversity that a single input may have various counterparts. Prior works proposed the multi-modal network that can build a many-to-many mapping between two visual domains. However, most of them are guided by sampled noises. Some others encode the reference images into a latent vector, by which the semantic information of the reference image will be washed away.

In this work, we aim to provide a solution to control the output based on references semantically. Given a reference image and an input in another domain, a semantic matching is first performed between the two visual contents and generates the auxiliary image, which is explicitly encouraged to preserve semantic characteristics of the reference. A deep network then is used for I2I translation and the final outputs are expected to be semantically similar to both the input and the reference; however, no such paired data can satisfy that dual-similarity in a supervised fashion, so we build up a self-supervised framework to serve the training purpose.

We improve the quality and diversity of the outputs by employing non-local blocks and a multi-task architecture. We assess the proposed method through extensive qualitative and quantitative evaluations and also presented comparisons with several state-of-art models.

--- https://arxiv.org/abs/1911.03624 Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination Jae Woong Soh, Gu Yong Park, Junho Jo, Nam Ik Cho 2019-11-09 2021-02-03 [("doi","10.48550/arXiv.1911.03624")] ai/anime/danbooru

Recently, many convolutional neural networks for single image super-resolution (SISR) have been proposed, which focus on reconstructing the high-resolution images in terms of objective distortion measures. However, the networks trained with objective loss functions generally fail to reconstruct the realistic fine textures and details that are essential for better perceptual quality. Recovering the realistic details remains a challenging problem, and only a few works have been proposed which aim at increasing the perceptual quality by generating enhanced textures. However, the generated fake details often make undesirable artifacts and the overall image looks somewhat unnatural.

Therefore, in this paper, we present a new approach to reconstructing realistic super-resolved images with high perceptual quality, while maintaining the naturalness of the result. In particular, we focus on the domain prior properties of SISR problem. Specifically, we define the naturalness prior in the low-level domain and constrain the output image in the natural manifold, which eventually generates more natural and realistic images.

Our results show better naturalness compared to the recent super-resolution algorithms including perception-oriented ones.

--- https://arxiv.org/abs/1911.06102 CartoonRenderer: An Instance-based Multi-Style Cartoon Image Translator Yugang Chen, Muchun Chen, Chaoyue Song, Bingbing Ni 2019-11-14 2021-02-03 [("doi","10.48550/arXiv.1911.06102")] ai/anime/danbooru

Instance based photo cartoonization is one of the challenging image stylization tasks which aim at transforming realistic photos into cartoon style images while preserving the semantic contents of the photos. State-of-the-art Deep Neural Networks (DNNs) methods still fail to produce satisfactory results with input photos in the wild, especially for photos which have high contrast and full of rich textures. This is due to that: cartoon style images tend to have smooth color regions and emphasized edges which are contradict to realistic photos which require clear semantic contents, ie. textures, shapes etc. Previous methods have difficulty in satisfying cartoon style textures and preserving semantic contents at the same time.

In this work, we propose a novel “CartoonRenderer” framework which using a single trained model to generate multiple cartoon styles. In a nutshell, our method maps photo into a feature model and renders the feature model back into image space. In particular, cartoonization is achieved by conducting some transformation manipulation in the feature space with our proposed Soft-AdaIN.

Extensive experimental results show our method produces higher quality cartoon style images than prior arts, with accurate semantic content preservation. In addition, due to the decoupling of whole generating process into “Modeling-Coordinating-Rendering” parts, our method could easily process higher resolution photos, which is intractable for existing methods.

--- https://arxiv.org/abs/2004.12992 MakeItTalk: Speaker-Aware Talking-Head Animation Yang Zhou, Xintong Han, Eli Shechtman, Jose Echevarria, Evangelos Kalogerakis, Dingzeyu Li 2020-04-27 2021-02-03 [("doi","10.1145/3414685.3417774")] ai/anime/danbooru ai/nn/gan

We present a method that generates expressive talking heads from a single facial image with audio as the only input.

In contrast to previous approaches that attempt to learn direct mappings from audio to raw pixels or points for creating talking faces, our method first disentangles the content and speaker information in the input audio signal. The audio content robustly controls the motion of lips and nearby facial regions, while the speaker information determines the specifics of facial expressions and the rest of the talking head dynamics. Another key component of our method is the prediction of facial landmarks reflecting speaker-aware dynamics.

Based on this intermediate representation, our method is able to synthesize photorealistic videos of entire talking heads with full range of motion and also animate artistic paintings, sketches, 2D cartoon characters, Japanese mangas, stylized caricatures in a single unified framework.

We present extensive quantitative and qualitative evaluation of our method, in addition to user studies, demonstrating generated talking heads of higher quality compared to prior state-of-the-art.

--- https://arxiv.org/abs/2005.05207 Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence Junsoo Lee, Eungyeup Kim, Yunsung Lee, Dongjun Kim, Jaehyuk Chang, Jaegul Choo 2020-05-11 2021-02-03 [("doi","10.48550/arXiv.2005.05207")] ai/anime/danbooru ai/nn/gan

[uses Tag2Pix dataset] This paper tackles the automatic colorization task of a sketch image given an already-colored reference image. Colorizing a sketch image is in high demand in comics, animation, and other content creation applications, but it suffers from information scarcity of a sketch image. To address this, a reference image can render the colorization process in a reliable and user-driven manner. However, it is difficult to prepare for a training data set that has a sufficient amount of semantically meaningful pairs of images as well as the ground truth for a colored image reflecting a given reference (eg. coloring a sketch of an originally blue car given a reference green car).

To tackle this challenge, we propose to utilize the identical image with geometric distortion as a virtual reference, which makes it possible to secure the ground truth for a colored output image. Furthermore, it naturally provides the ground truth for dense semantic correspondence, which we utilize in our internal attention mechanism for color transfer from reference to sketch input.

We demonstrate the effectiveness of our approach in various types of sketch image colorization via quantitative as well as qualitative evaluation against existing methods.

--- https://arxiv.org/abs/2006.10649 Multi-Density Sketch-to-Image Translation Network Jialu Huang, Jing Liao, Zhifeng Tan, Sam Kwong 2020-06-18 2021-02-03 [("doi","10.48550/arXiv.2006.10649")] ai/anime/danbooru

Sketch-to-image (S2I) translation plays an important role in image synthesis and manipulation tasks, such as photo editing and colorization. Some specific S2I translation including sketch-to-photo and sketch-to-painting can be used as powerful tools in the art design industry.

However, previous methods only support S2I translation with a single level of density, which gives less flexibility to users for controlling the input sketches. In this work, we propose the first multi-level density sketch-to-image translation framework, which allows the input sketch to cover a wide range from rough object outlines to micro structures. Moreover, to tackle the problem of noncontinuous representation of multi-level density input sketches, we project the density level into a continuous latent space, which can then be linearly controlled by a parameter. This allows users to conveniently control the densities of input sketches and generation of images.

Moreover, our method has been successfully verified on various datasets for different applications including face editing, multi-modal sketch-to-photo translation, and anime colorization, providing coarse-to-fine levels of controls to these applications.

--- https://arxiv.org/abs/2010.03997 Unconstrained Text Detection in Manga Julián Del Gobbo, Rosana Matuk Herrera 2020-10-07 2021-02-03 [("doi","10.48550/arXiv.2010.03997")] ai/anime/danbooru

The detection and recognition of unconstrained text is an open problem in research. Text in comic books has unusual styles that raise many challenges for text detection. This work aims to identify text characters at a pixel level in a comic genre with highly sophisticated text styles: Japanese manga.

To overcome the lack of a manga dataset with individual character level annotations, we create our own.

Most of the literature in text detection use bounding box metrics, which are unsuitable for pixel-level evaluation. Thus, we implemented special metrics to evaluate performance.

Using these resources, we designed and evaluated a deep network model, outperforming current methods for text detection in manga in most metrics.

--- https://arxiv.org/abs/2101.08674 DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition Edwin Arkel Rios, Wen-Huang Cheng, Bo-Cheng Lai 2021-01-21 2021-02-03 [("doi","10.48550/arXiv.2101.08674")] ai/anime/danbooru ai/nn/transformer

In this work we tackle the challenging problem of anime character recognition. (Anime, referring to animation produced within Japan and work derived or inspired from it.)

For this purpose we present DAF:re (DanbooruAnimeFaces:revamped), a large-scale, crowd-sourced, long-tailed dataset with almost 500k images spread across more than 3000 classes.

Additionally, we conduct experiments on DAF:re and similar datasets using a variety of classification models, including CNN based ResNets and self-attention based Vision Transformer (ViT). Our results give new insights into the generalization and transfer learning properties of ViT models on substantially different domain datasets from those used for the upstream pre-training, including the influence of batch and image size in their training.

Additionally, we share our dataset, source-code, pre-trained checkpoints and results, as Animesion, the first end-to-end framework for large-scale anime character recognition: Github.

--- https://arxiv.org/abs/2102.06826 Hiding Data Hiding Hanzhou Wu, Gen Liu, Xinpeng Zhang 2021-02-13 2021-02-13 [("doi","10.48550/arXiv.2102.06826")] ai/anime/danbooru ai/nn/cnn cs/cryptography

Data hiding is the art of hiding secret data into a cover object such as digital image for covert communication. In this paper, we make the first step towards hiding “data hiding”, which is totally different from many conventional works that directly embed secret data in a given cover object.

In detail, we propose a novel method to disguise data hiding tools, including a data embedding tool and a data extraction tool, as a deep neural network (DNN) with an ordinary task (ie. style transfer). After training the DNN for both style transfer and data hiding, while the DNN can transfer the style of an image to the target one, it can also hide secret data into a cover image or extract secret data from a stego image.

In other words, the tools of data hiding are hidden to avoid arousing suspicion.

Experimental results and analysis have shown the feasibility, applicability and superiority of the proposed method.

--- https://arxiv.org/abs/2107.01619 Deep Edge-Aware Interactive Colorization against Color-Bleeding Effects Eungyeup Kim, Sanghyeon Lee, Jeonghoon Park, Somi Choi, Choonghyun Seo, Jaegul Choo 2021-07-04 2021-07-04 [("doi","10.48550/arXiv.2107.01619")] ai/anime/danbooru

Deep neural networks for automatic image colorization often suffer from the color-bleeding artifact, a problematic color spreading near the boundaries between adjacent objects. Such color-bleeding artifacts debase the reality of generated outputs, limiting the applicability of colorization models in practice. Although previous approaches have attempted to address this problem in an automatic manner, they tend to work only in limited cases where a high contrast of gray-scale values are given in an input image.

Alternatively, leveraging user interactions would be a promising approach for solving this color-breeding artifacts. In this paper, we propose a novel edge-enhancing network for the regions of interest via simple user scribbles indicating where to enhance. In addition, our method requires a minimal amount of effort from users for their satisfactory enhancement.

Experimental results demonstrate that our interactive edge-enhancing approach effectively improves the color-bleeding artifacts compared to the existing baselines across various datasets.

--- https://arxiv.org/abs/2107.06532 Graph Jigsaw Learning for Cartoon Face Recognition Yong Li, Lingjie Lao, Zhen Cui, Shiguang Shan, Jian Yang 2021-07-14 2021-07-14 [("doi","10.48550/arXiv.2107.06532")] ai/anime/danbooru ai/nn/cnn

Cartoon face recognition is challenging as they typically have smooth color regions and emphasized edges, the key to recognize cartoon faces is to precisely perceive their sparse and critical shape patterns. However, it is quite difficult to learn a shape-oriented representation for cartoon face recognition with convolutional neural networks (CNNs). To mitigate this issue, we propose the GraphJigsaw that constructs jigsaw puzzles at various stages in the classification network and solves the puzzles with the graph convolutional network (GCN) in a progressive manner.

Solving the puzzles requires the model to spot the shape patterns of the cartoon faces as the texture information is quite limited. The key idea of GraphJigsaw is constructing a jigsaw puzzle by randomly shuffling the intermediate convolutional feature maps in the spatial dimension and exploiting the GCN to reason and recover the correct layout of the jigsaw fragments in a self-supervised manner. The proposed GraphJigsaw avoids training the classification model with the deconstructed images that would introduce noisy patterns and are harmful for the final classification. Specially, GraphJigsaw can be incorporated at various stages in a top-down manner within the classification model, which facilitates propagating the learned shape patterns gradually.

Both quantitative and qualitative experimental results have verified the feasibility of our proposed GraphJigsaw, which consistently outperforms other face recognition or jigsaw-based methods on two popular cartoon face datasets with considerable improvements.

GraphJigsaw does not rely on any extra manual annotation during the training process and incorporates no extra computation burden at inference time.

--- https://arxiv.org/abs/2108.01819 Transfer Learning for Pose Estimation of Illustrated Characters Shuhong Chen, Matthias Zwicker 2021-08-04 2021-08-04 [("doi","10.48550/arXiv.2108.01819")] ai/anime/danbooru ai/dataset

Human pose information is a critical component in many downstream image processing tasks, such as activity recognition and motion tracking. Likewise, a pose estimator for the illustrated character domain would provide a valuable prior for assistive content creation tasks, such as reference pose retrieval and automatic character animation. But while modern data-driven techniques have substantially improved pose estimation performance on natural images, little work has been done for illustrations.

In our work, we bridge this domain gap by efficiently transfer-learning from both domain-specific and task-specific source models. Additionally, we upgrade and expand an existing illustrated pose estimation dataset, and introduce two new datasets for classification and segmentation subtasks.

We then apply the resultant state-of-the-art character pose estimator to solve the novel task of pose-guided illustration retrieval.

All data, models, and code will be made publicly available.

--- https://arxiv.org/abs/2109.11736 Unaligned Image-to-Image Translation by Learning to Reweight Shaoan Xie, Mingming Gong, Yanwu Xu, Kun Zhang 2021-09-24 2021-09-24 [("doi","10.48550/arXiv.2109.11736")] ai/anime/danbooru

Unsupervised image-to-image translation aims at learning the mapping from the source to target domain without using paired images for training. An essential yet restrictive assumption for unsupervised image translation is that the two domains are aligned, eg. for the selfie2anime task, the anime (selfie) domain must contain only anime (selfie) face images that can be translated to some images in the other domain. Collecting aligned domains can be laborious and requires a lot of attention.

In this paper, we consider the task of image translation between two unaligned domains, which may arise for various possible reasons. To solve this problem, we propose to select images based on importance reweighting and develop a method to learn the weights and perform translation simultaneously and automatically.

We compare the proposed method with state-of-the-art image translation approaches and present qualitative and quantitative results on different tasks with unaligned domains. Extensive empirical evidence demonstrates the usefulness of the proposed problem formulation and the superiority of our method.

--- https://arxiv.org/abs/2111.01619 StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN Min Jin Chong, Hsin-Ying Lee, David Forsyth 2021-11-02 2021-11-02 [("doi","10.48550/arXiv.2111.01619")] ai/anime/danbooru

Recently, StyleGAN has enabled various image manipulation and editing tasks thanks to the high-quality generation and the disentangled latent space.

However, additional architectures or task-specific training paradigms are usually required for different tasks. In this work, we take a deeper look at the spatial properties of StyleGAN.

We show that with a pretrained StyleGAN along with some operations, without any additional architecture, we can perform comparably to the state-of-the-art methods on various tasks, including image blending, panorama generation, generation from a single image, controllable and local multimodal image to image translation, and attributes transfer.

The proposed method is simple, effective, efficient, and applicable to any existing pretrained StyleGAN model.

--- https://arxiv.org/abs/2111.03042 Unsupervised Learning of Compositional Energy Concepts Yilun Du, Shuang Li, Yash Sharma, Joshua B. Tenenbaum, Igor Mordatch 2021-11-04 2021-11-04 [("doi","10.48550/arXiv.2111.03042")] ai/anime/danbooru

Humans are able to rapidly understand scenes by utilizing concepts extracted from prior experience. Such concepts are diverse, and include global scene descriptors, such as the weather or lighting, as well as local scene descriptors, such as the color or size of a particular object. So far, unsupervised discovery of concepts has focused on either modeling the global scene-level or the local object-level factors of variation, but not both.

In this work, we propose COMET, which discovers and represents concepts as separate energy functions, enabling us to represent both global concepts as well as objects under a unified framework. COMET discovers energy functions through recomposing the input image, which we find captures independent factors without additional supervision. Sample generation in COMET is formulated as an optimization process on underlying energy functions, enabling us to generate images with permuted and composed concepts. Finally, discovered visual concepts in COMET generalize well, enabling us to compose concepts between separate modalities of images as well as with other concepts discovered by a separate instance of COMET trained on a different dataset.

Code and data available at https://energy-based-model.github.io/comet/.

--- https://arxiv.org/abs/2111.06849 Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data Liming Jiang, Bo Dai, Wayne Wu, Chen Change Loy 2021-11-12 2021-11-12 [("doi","10.48550/arXiv.2111.06849")] ai/anime/danbooru

Generative adversarial networks (GANs) typically require ample data for training in order to synthesize high-fidelity images. Recent studies have shown that training GANs with limited data remains formidable due to discriminator overfitting, the underlying cause that impedes the generator’s convergence. This paper introduces a novel strategy called Adaptive Pseudo Augmentation (APA) to encourage healthy competition between the generator and the discriminator.

As an alternative method to existing approaches that rely on standard data augmentations or model regularization, APA alleviates overfitting by employing the generator itself to augment the real data distribution with generated images, which deceives the discriminator adaptively.

Extensive experiments demonstrate the effectiveness of APA in improving synthesis quality in the low-data regime.

We provide a theoretical analysis to examine the convergence and rationality of our new training strategy. APA is simple and effective. It can be added seamlessly to powerful contemporary GANs, such as StyleGAN-2, with negligible computational cost.

--- https://arxiv.org/pdf/2101.08674.pdf#page=3 DAF:re/Animesion: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition § Methodology: Data: DAF:re 2021-02-04 ai/anime/danbooru --- https://derpibooru.org/1758960 Teaching Computers to Spot Naughty Ponies Butterscotch 2018 2021-02-04 ai/anime/danbooru --- https://drive.google.com/file/d/1wdj_LkVktc3qYKh8v1zXFNSdTxfLa2Uq/view GochiUsa Faces, A Dataset For Anime Faces Rignak 2020 2021-02-04 ai/anime/danbooru --- https://escholarship.org/content/qt66w654x7/qt66w654x7.pdf 2021-02-04 ai/anime/danbooru --- https://github.com/AdamantLife/Gwern2DeepDanbooru Reorganizes Danbooru Datasets from Gwern to be valid for DeepDanbooru 2021-02-04 ai/anime/danbooru --- https://github.com/Atom-101/Danbooru-Dataset-Maker Helper scripts to download images with specific tags from the Danbooru dataset 2021-02-05 ai/anime/danbooru --- https://github.com/KUR-creative/SickZil-Machine Manga/Comics Translation Helper Tool 2021-02-05 ai/anime/danbooru --- https://github.com/KichangKim/DeepDanbooru AI based multi-label girl image classification system, implemented by using TensorFlow 2021-02-05 ai/anime/danbooru --- https://github.com/MerHS/tag2pix-gui Tag2pix GUI version 2021-02-05 ai/anime/danbooru --- https://github.com/Montia/bw2color Montia/bw2color 2021-02-05 ai/anime/danbooru --- https://github.com/Pengxiao-Wang/Style2Paints_V3 Reimplementation of Style2Paints V3 2021-02-05 ai/anime/danbooru --- https://github.com/RF5/danbooru-pretrained Pretrained pytorch models for the Danbooru2018 dataset 2021-02-05 ai/anime/danbooru --- https://github.com/ShinoharaHare/Danbooru2020-Ahegao Ahegao datasets from Danbooru2020 2021-02-05 ai/anime/danbooru --- https://github.com/SmilingWolf/SW-CV-ModelZoo SmilingWolf/SW-CV-ModelZoo: Repo for my Tensorflow/Keras CV experiments. Mostly revolving around the Danbooru20xx dataset 2021-02-05 ai/anime/danbooru --- https://github.com/Yukariin/NatSR_pytorch Pytorch implementation of Natural and Realistic Single Image Super-Resolution 2021-02-05 ai/anime/danbooru --- https://github.com/Yukariin/SAN_pytorch Yukariin/SAN_pytorch: Second-order Attention Network for Single Image Super-resolution (CVPR-2019) 2021-02-05 ai/anime/danbooru --- https://github.com/anthony-dipofi/danbooru-tagger Pytorch code for tagging Danbooru images: Includes a pretrained model for tagging Danbooru images. Trained on the Danbooru2019 512×512 SFW subset to predict the 6000 most common ‘Category 0’ tags. Achieves an F2 score of 0.61 on hold out test set, with a threshold of 7.9. For more performance information see the test_tagger.ipynb notebook. 2021-02-06 ai/anime/danbooru --- https://github.com/arkel23/animesion For holding anime-related object classification and detection models 2021-02-06 ai/anime/danbooru --- https://github.com/blandocs/Tag2Pix Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss 2021-02-06 ai/anime/danbooru --- https://github.com/cheese-roll/light-anime-face-detector A fast and light-weighted anime face detection based on LFFD. 2021-02-06 ai/anime/danbooru --- https://github.com/crowsonkb/v-diffusion-jax v objective diffusion inference code for JAX. 2021-02-06 ai/anime/danbooru --- https://github.com/diandiansu/anime-artist Anime Artist 2021-02-06 ai/anime/danbooru --- https://github.com/diva-eng/stylegan-waifu-generator Generate your waifu with styleGAN, stylegan老婆生成器 2021-02-06 ai/anime/danbooru --- https://github.com/ecrows/danbooru-faces Gender separation and face extraction from SFW Danbooru dataset 2021-02-06 ai/anime/danbooru --- https://github.com/fire-eggs/Danbooru2021 Scripts and tools for working with the Danbooru2018 data set. 2021-02-06 ai/anime/danbooru --- https://github.com/fire-eggs/Danbooru2021/tree/master/browser A 'browser' for viewing images associated with tags. Presents a list of tags. Selecting a tag will show the first image with that tag. Can cycle through all images with that tag. The browser is a simple TKinter interface and may be run on any platform with Python 3 installed. 2021-02-06 ai/anime/danbooru --- https://github.com/fire-eggs/Danbooru2021/tree/master/database A script to create a SQLite database from the Danbooru2018 metadata files. 2021-02-07 ai/anime/danbooru --- https://github.com/grapeot/AnimeHeadDetector grapeot’s AnimeHeadDetector: An object detector for character heads in animes, based on Yolo V3 2021-02-07 ai/anime/danbooru --- https://github.com/grapeot/Danbooru2018AnimeCharacterRecognitionDataset Danbooru2018AnimeCharacterRecognitionDataset: An open source dataset based on Danbooru2018 dataset to do anime character recognition, with 1M images and 70k characters. 2021-02-07 ai/anime/danbooru --- https://github.com/halcy/DeepDanbooruActivationMaps Scripts to calculate interest regions for tags for the DeepDanbooru tagger model 2021-02-07 ai/anime/danbooru --- https://github.com/hysts/anime-face-detector Anime Face Detector using mmdet and mmpose 2021-02-07 ai/anime/danbooru --- https://github.com/jerryli27/AniSeg/ A faster-rcnn model for anime character segmentation. 2021-02-07 ai/anime/danbooru --- https://github.com/jxu/danbooru2018-metadata 2021-02-07 ai/anime/danbooru --- https://github.com/liaoxiong3x/DeepCreamPy 2021-02-07 ai/anime/danbooru --- https://github.com/lllyasviel/DanbooRegion DanbooRegion: An Illustration Region Dataset (ECCV 2020) 2021-02-07 ai/anime/danbooru --- https://github.com/lllyasviel/sketchKeras lllyasviel/sketchKeras: an u-net with some algorithm to take sketch from paints 2021-02-07 ai/anime/danbooru --- https://github.com/nagadomi/lbpcascade_animeface nagadomi/lbpcascade_animeface: A Face detector for anime/manga using OpenCV 2021-02-07 ai/anime/danbooru --- https://github.com/nolan-dev/stylegan_reimplementation Reimplementation of https://arxiv.org/abs/1812.04948 2021-02-08 ai/anime/danbooru ai/nn/gan/stylegan --- https://github.com/pedrovgs/DeepPanel Finding a panel inside a comic page is the hardest thing I've ever done in computer science! 2021-02-08 ai/anime/danbooru --- https://github.com/qhgz2013/anime-face-detector A Faster-RCNN based anime face detector implementation using tensorflow 2021-02-08 ai/anime/danbooru --- https://github.com/ramsterhad/deep-danbooru-tag-assist-app Web-based assist application for an AI-based multi-label image classification system, based on KichangKim's DeepDanbooru. 2021-02-08 ai/anime/danbooru --- https://github.com/reidsanders/danbooru-utility Utility for working with danbooru2018 dataset 2021-02-08 ai/anime/danbooru --- https://github.com/taki0112/UGATIT Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020) 2021-02-08 ai/anime/danbooru --- https://github.com/yu45020/Text_Segmentation_Image_Inpainting yu45020/Text_Segmentation_Image_Inpainting 2021-02-08 ai/anime/danbooru --- https://journals.sagepub.com/doi/pdf/10.1177/2056305119880020 2021-02-08 ai/anime/danbooru --- https://jp.gamesindustry.biz/article/2009/20090401/ ［CEDEC 2020］CreativeAIでキャラを自動生成するミクシィの研究 / [CEDEC 2020] Research on Mixi that automatically creates characters with Creative AI 2021-02-08 ai/anime/danbooru --- https://lllyasviel.github.io/DanbooRegion/paper/paper.pdf 2021-02-08 ai/anime/danbooru

[code]

--- https://www.reddit.com/r/AnimeResearch/comments/rszjfn/nfnet_a_l%C3%A0_deepdanbooru/ 2021-02-08 ai/anime/danbooru --- https://www.reddit.com/r/MachineLearning/comments/akbc11/p_tag_estimation_for_animestyle_girl_image/ 2021-02-09 ai/anime/danbooru ai/nn/gan/stylegan --- https://rf5.github.io/2019/07/08/danbuuro-pretrained.html Danbooru2018 pytorch pretrained models 2021-02-09 ai/anime/danbooru --- https://selfie2anime.com/blog/iterating-on-an-idea/ Iterating on an Idea: On the 17th of August 2019 myself and Rico Beti hit the launch button selfie2anime.com. The week that followed was a whirl wind of good and bad experiences technical experiences with trying to scale. I wanted to write this blog to lay out some of my own experiences and point out a few pitfalls I had along the way. 2021-02-09 ai/anime/danbooru --- https://towardsdatascience.com/animating-ganime-with-stylegan-part-1-4cf764578e Animating gAnime with StyleGAN: Part 1—Introducing a tool for interacting with generative models 2021-02-09 ai/anime/danbooru ai/nn/gan/stylegan/anime --- https://web.archive.org/web/20200920025857/https://github.com/SawabeMaho/pix2pix SawabeMaho/pix2pix: 基于GAN的黑白漫画自动上色,使用pix2pix模型 2021-02-09 ai/anime/danbooru --- https://www.kaggle.com/datasets/rignak/gochiusa-faces GochiUsa_Faces 2021-02-09 ai/anime/danbooru --- https://www.kaggle.com/datasets/wuhecong/danbooru-sketch-pair-128x Danbooru Sketch Pair 128x 2021-02-09 ai/anime/danbooru --- https://www.labone.tech/anime-generative-model-part-2 2021-02-09 ai/anime/danbooru --- https://www.labone.tech/anime-generative-model-part-3 2021-02-09 ai/anime/danbooru --- https://www.labone.tech/anime-generative-model 2021-02-09 ai/anime/danbooru --- https://en.wikipedia.org/wiki/Darknet_market Darknet market 2021-02-10 darknet-market --- https://en.wikipedia.org/wiki/Tor_%28anonymity_network%29 Tor (anonymity network) 2021-02-10 darknet-market --- https://en.wikipedia.org/wiki/Bitcoin Bitcoin 2021-02-10 darknet-market --- https://en.wikipedia.org/wiki/AlphaBay AlphaBay 2021-02-10 darknet-market/alphabay --- https://en.wikipedia.org/wiki/Ross_Ulbricht Ross Ulbricht 2021-02-10 darknet-market/silk-road/1 --- https://www.reddit.com/r/SilkRoad/comments/3a2xqp/sr1_sales_data_6_feb_2011_2_oct_2013/ 2021-02-10 darknet-market/dnm-archive darknet-market/silk-road/1 --- https://en.wikipedia.org/wiki/Lempel%E2%80%93Ziv%E2%80%93Markov_chain_algorithm Lempel-Ziv-Markov chain algorithm 2021-02-10 ai/nn/transformer/gpt/2 cs/algorithm/information/compression --- https://arbtt.nomeata.de/#what arbtt: the automatic, rule-based time tracker § About 2021-02-10 cs/haskell --- http://atechdad.com/Deanonymizing-Darknet-Data/ 2021-02-10 darknet-market/dnm-archive --- https://cs229.stanford.edu/proj2015/184_report.pdf 2021-02-10 darknet-market/dnm-archive --- http://darkdata.bc.edu/ 2021-02-10 darknet-market/dnm-archive --- http://fusion.net/story/168035/theres-a-searchable-cache-of-the-webs-darkest-corners-of-the-anonymous-internet/ 2021-02-11 darknet-market/dnm-archive --- http://jacquelinegarrahan.com/docs/dark_web_authorship.pdf 2021-02-11 darknet-market/dnm-archive --- http://ryancompton.net/2015/03/24/darknet-market-basket-analysis/ 2021-02-11 darknet-market/dnm-archive --- http://www.cs.ucf.edu/~czou/research/D-miner-CNS2017.pdf 2021-02-11 darknet-market/dnm-archive --- http://www.csl.sri.com/users/gehani/papers/KDD-2017.Onions.pdf 2021-02-11 darknet-market/dnm-archive --- https://www.diva-portal.org/smash/get/diva2:1178768/FULLTEXT01.pdf 2021-02-11 darknet-market/dnm-archive --- http://www.smallake.kr/wp-content/uploads/2018/01/SSRN-id3102645.pdf 2021-02-11 darknet-market/dnm-archive --- https://ada-2019.github.io/Project/#about A Deep Analysis of the law enforcement impact on the DarkMarkets 2021-02-11 darknet-market/dnm-archive --- https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1041&context=amcis2017#pdf 2021-02-11 darknet-market/dnm-archive --- https://archive.org/details/dnmarchives Dark Net Market archives, 2011–2015 2021-02-11 darknet-market/dnm-archive --- https://archive.org/download/dnmarchives dnmarchives directory listing Gwern 2015 2021-02-11 darknet-market/dnm-archive --- https://arxiv.org/abs/1708.03310 Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs Sudip Mittal, Anupam Joshi, Tim Finin 2017-08-10 2021-02-12 [("doi","10.48550/arXiv.1708.03310")] darknet-market/dnm-archive

Knowledge graphs and vector space models are robust knowledge representation techniques with individual strengths and weaknesses. Vector space models excel at determining similarity between concepts, but are severely constrained when evaluating complex dependency relations and other logic-based operations that are a strength of knowledge graphs.

We describe the VKG structure that helps unify knowledge graphs and vector representation of entities, and enables powerful inference methods and search capabilities that combine their complementary strengths. We analogize this to thinking ‘fast’ in vector space along with thinking ‘slow’ and ‘deeply’ by reasoning over the knowledge graph.

We have created a query processing engine that takes complex queries and decomposes them into subqueries optimized to run on the respective knowledge graph or vector view of a VKG.

We show that the VKG structure can process specific queries that are not efficiently handled by vector spaces or knowledge graphs alone.

We also demonstrate and evaluate the VKG structure and the query processing engine by developing a system called Cyber-All-Intel for knowledge extraction, representation and querying in an end-to-end pipeline grounded in the cybersecurity informatics domain.

--- https://arxiv.org/abs/1905.02895 Cyber-All-Intel: An AI for Security related Threat Intelligence Sudip Mittal, Anupam Joshi, Tim Finin 2019-05-07 2021-02-12 [("doi","10.48550/arXiv.1905.02895")] darknet-market/dnm-archive

Keeping up with threat intelligence is a must for a security analyst today. There is a volume of information present in ‘the wild’ that affects an organization. We need to develop an artificial intelligence system that scours the intelligence sources, to keep the analyst updated about various threats that pose a risk to her organization. A security analyst who is better ‘tapped in’ can be more effective.

In this paper we present, Cyber-All-Intel an artificial intelligence system to aid a security analyst. It is a system for knowledge extraction, representation and analytics in an end-to-end pipeline grounded in the cybersecurity informatics domain. It uses multiple knowledge representations like, vector spaces and knowledge graphs in a ‘VKG structure’ to store incoming intelligence. The system also uses neural network models to pro-actively improve its knowledge. We have also created a query engine and an alert system that can be used by an analyst to find actionable cybersecurity insights.

--- https://arxiv.org/abs/2104.00764 SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis Pranav Maneriker, Yuntian He, Srinivasan Parthasarathy 2021-04-01 2021-04-01 [("doi","10.48550/arXiv.2104.00764")] darknet-market/dnm-archive statistics/stylometry

Darknet market forums are frequently used to exchange illegal goods and services between parties who use encryption to conceal their identities. The Tor network is used to host these markets, which guarantees additional anonymization from IP and location tracking, making it challenging to link across malicious users using multiple accounts (sybils). Additionally, users migrate to new forums when one is closed, making it difficult to link users across multiple forums.

We develop a novel stylometry-based multitask learning approach for natural language and interaction modeling using graph embeddings to construct low-dimensional representations of short episodes of user activity for authorship attribution.

We provide a comprehensive evaluation of our methods across 4 different darknet forums demonstrating its efficacy over the state-of-the-art, with a lift of up to 2.5× on Mean Retrieval Rank and 2× on Recall@10.

--- https://arxiv.org/abs/2112.09065 Macroscopic properties of buyer-seller networks in online marketplaces Alberto Bracci, Jörn Boehnke, Abeer ElBahrawy, Nicola Perra, Alexander Teytelboym, Andrea Baronchelli 2021-12-16 2021-12-16 [("doi","10.48550/arXiv.2112.09065")] darknet-market/dnm-archive

Online marketplaces are the main engines of legal and illegal e-commerce, yet their empirical properties are poorly understood due to the absence of large-scale data. We analyze two comprehensive datasets containing 245M transactions (16B USD) that took place on online marketplaces 2010–2021, covering 28 dark web marketplaces, i.e. unregulated markets whose main currency is Bitcoin, and 144 product markets of one popular regulated e-commerce platform.

We show that transactions in online marketplaces exhibit strikingly similar patterns despite statistically-significant differences in language, lifetimes, products, regulation, and technology. Specifically, we find remarkable regularities in the distributions of transaction amounts, number of transactions, inter-event times and time between first and last transactions. We show that buyer behavior is affected by the memory of past interactions and use this insight to propose a model of network formation reproducing our main empirical observations.

Our findings have implications for understanding market power on online marketplaces as well as inter-marketplace competition, and provide empirical foundation for theoretical economic models of online marketplaces.

--- https://dspace.cvut.cz/bitstream/handle/10467/79794/F8-DP-2018-Smrz-Josef-thesis.pdf?sequence=-1 2021-02-12 darknet-market/dnm-archive --- https://en.wikipedia.org/wiki/Kaggle Kaggle 2021-02-12 darknet-market/dnm-archive --- https://gist.github.com/SigridK/c16ddc7b0f2a5bc01ea23d69569c6c0b Data sprint WS 14 sep 2016 2021-02-12 darknet-market/dnm-archive --- https://github.com/ADA-2019/Project All the analysis on the impact of Operation Onymous on Agora is available here: https://ada-2019.github.io/Project/#about 2021-02-12 darknet-market/dnm-archive --- https://github.com/jacquelinegarrahan/Silk-Road-Author-Identification jacquelinegarrahan/silk-road-author-identification: EECE5644 final project documentation. Applies LSTM and RNN neural networks to authorship classification in dark web marketplaces using Twitter GloVe vector representaions. 2021-02-12 darknet-market/dnm-archive --- https://github.com/lucmichalski/darkwebmarkets The database currently contains ~400,000 listings from two of the largest darknet markets, Silkroad2 (Now shut down) and Hydra (One of the largest markets, primarily servicing the former USSR). Data from Dreammarket will be added soon. 2021-02-12 darknet-market/dnm-archive darknet-market/hydra --- https://hcommons.org/deposits/objects/hc:20220/datastreams/CONTENT/content?download=true#pdf 2021-02-13 darknet-market/dnm-archive --- https://medium.com/@roselisker/illuminating-the-dark-web-d088a9c80240 Illuminating the Dark Web. Searching for geotags in dark net 2021-02-13 darknet-market/dnm-archive --- https://www.vice.com/en/article/you-can-now-download-a-copy-of-pretty-much-every-dark-web-market-ever-made/ You Can Now Download a Copy of Pretty Much Every Dark Web Market Ever Made 2021-02-13 darknet-market/dnm-archive --- https://news.ycombinator.com/item?id=9894570 2021-02-13 darknet-market/dnm-archive --- https://www.reddit.com/r/Bitcoin/comments/3dh824/15_tb_of_dark_net_market_scrapes_released_online/ 2021-02-13 darknet-market/dnm-archive --- https://www.reddit.com/r/Buttcoin/comments/3dhz9f/gwern_has_released_15tb_of_darknet_market_screen/ 2021-02-13 darknet-market/dnm-archive --- https://www.reddit.com/r/DarkNetMarkets/comments/3dfq8s/dark_net_market_archives_20112015/ 2021-02-13 darknet-market/dnm-archive --- https://www.reddit.com/r/DarkNetMarkets/comments/3dtcrl/vicemotherboard_published_an_article_about_gwens/ 2021-02-13 darknet-market/dnm-archive --- https://people.cs.vt.edu/gangwang/Wang_X_T_2018.pdf 2021-02-13 darknet-market/dnm-archive --- https://people.cs.vt.edu/gangwang/asiaccs18.pdf 2021-02-13 darknet-market/dnm-archive --- https://qz.com/466273/what-illegal-drugs-cost-on-the-dark-web/ 2021-02-13 darknet-market/dnm-archive --- https://studenttheses.universiteitleiden.nl/access/item%3A2608213/view 2021-02-14 darknet-market/dnm-archive --- https://theconversation.com/small-potent-doses-of-illegal-drugs-are-evading-authorities-but-having-a-huge-impact-87081 Small potent doses of illegal drugs are evading authorities but having a huge impact 2021-02-14 darknet-market/dnm-archive --- https://web.stanford.edu/~larmona/Armona_InformalCommDarknet.pdf 2021-02-14 darknet-market/dnm-archive --- https://www.aaai.org/ocs/index.php/WS/AAAIW17/paper/viewFile/15205/14661 2021-02-14 darknet-market/dnm-archive --- https://www.adressa.no/pluss/magasin/2015/06/18/Teppefall-11219093.ece Med Posten som distribusjonsåre har organiserte norske nettverk i årevis spydd ut kilovis med dop til nordmenn fra skjulte sider på det mørke nettet. Identiteten deres beskyttes av avansert teknologi. De har operert i fred for politiet. De tror de er usynlige. 2021-02-14 darknet-market/dnm-archive --- https://www.amazon.com/Weaving-Dark-Web-Legitimacy-Information/dp/0262038269 2021-02-14 darknet-market/dnm-archive --- https://www.buzzfeed.com/patricksmith/how-one-british-teenager-used-the-digital-black-ma Liam Lyburd, from Newcastle, was today sentenced to life in prison for buying a gun, gas canisters, and pipe bomb materials from the Dark Web with intent to shoot students at his former college. BuzzFeed News follows the trail and asks whether someone in future might succeed where he failed. 2021-02-14 darknet-market/dnm-archive darknet-market/evolution --- https://www.cl.cam.ac.uk/~sp849/files/2018-crimebb.pdf 2021-02-14 darknet-market/dnm-archive --- https://www.economist.com/international/2016/07/16/shedding-light-on-the-dark-web Shedding light on the dark web 2021-02-14 darknet-market/dnm-archive --- https://www.emcdda.europa.eu/system/files/publications/6585/TD0417834ENN.pdf 2021-02-14 darknet-market/dnm-archive --- /doc/darknet-market/evolution/2016-decaryhetu.pdf Do police crackdowns disrupt drug cryptomarkets? A longitudinal analysis of the effects of Operation Onymous D. Décary-Hétu, L. Giommoni 2016-01-01 2021-02-14 [("doi","10.1007/s10611-016-9644-4")] darknet-market/agora darknet-market/dnm-archive darknet-market/evolution darknet-market/silk-road/2 --- /doc/darknet-market/silk-road/2/2016-demant.pdf Personal use, social supply or redistribution? cryptomarket demand on Silk Road 2 and Agora Jakob Demant, Rasmus Munksgaard, Esben Houborg 2016-01-01 2021-02-15 [("doi","10.1007/s12117-016-9281-4")] darknet-market/agora darknet-market/dnm-archive darknet-market/silk-road/2 --- /doc/darknet-market/dnm-archive/2017-hull.pdf The Effects of Police Interventions on Darknet Market Drug Prices Glenn Hull 2017-01-01 2021-02-15 darknet-market/dnm-archive --- /doc/darknet-market/agora/2017-luo.pdf An exploratory investigation into the darknet marketplace discussion forum Agora Qiaoyu Luo 2017-01-01 2021-02-15 darknet-market/agora darknet-market/dnm-archive --- /doc/darknet-market/dnm-archive/2017-moeller.pdf 2017 2021-02-15 darknet-market/agora darknet-market/dnm-archive darknet-market/evolution darknet-market/silk-road/2 --- /doc/darknet-market/dnm-archive/2018-cherqi.pdf 2018 2021-02-15 darknet-market/agora darknet-market/alphabay darknet-market/dnm-archive darknet-market/silk-road/2 --- /doc/darknet-market/silk-road/1/2018-lorenzodus.pdf ‘I know this whole market is based on the trust you put in me and I don’t take that lightly’: Trust, community and discourse in crypto-drug markets Nuria Lorenzo-Dus, Matteo Di Cristofaro 2018-01-01 2021-02-15 [("doi","10.1177/1750481318771429")] darknet-market/dnm-archive darknet-market/silk-road/1

This study uses a Corpus Assisted Discourse Studies methodology to provide the first systematic analysis of how trust is discursively constructed in crypto-drug markets. The data come from two purpose-built corpora. One comprises all the forum messages posted on the flag ship crypto-drug market Silk Road during the years in which it traded on the hidden net (c. 250 million words). The other corpus comprises all the reports published by the United Nations Office on Drugs and Crime (UNODC) during the same period (c. 153,000 words).

Our analysis of trust focuses on the identities of those buying and selling drugs. The findings reveal that the Silk Road community members (a) regularly discussed vendors’ identities alongside a continuum of trust-risk calculation, explicitly identifying both ‘good’ and ‘bad’ practices and hence engaging in self-regulatory discourses, and (b) mainly constructed drug users’ identities in relation to values of expertise, integrity and benevolence.

The findings also suggest that hard law enforcement activity, such as crypto-drug market closure, may encourage technological innovation within these markets. Moreover, our results show a disconnect between the discursive reality of the policy-making documents we examined and the very crypto-drug markets that they seek to legislate.

--- /doc/darknet-market/silk-road/2/2019-berman.pdf Making Sense of Darknet Markets: Automatic Inference of Semantic Classifications from Unconventional Multimedia Datasets Alexander Berman, Celeste Lyn Paul 2019-01-01 2021-02-15 darknet-market/dnm-archive darknet-market/silk-road/2 --- /doc/darknet-market/dnm-archive/2019-mittal.pdf Knowledge for Cyber Threat Intelligence Sudip Mittal 2019-01-01 2021-02-15 darknet-market/dnm-archive --- /doc/darknet-market/dnm-archive/2019-zhang-3.pdf Your Style Your Identity: Leveraging Writing and Photography Styles for Drug Trafficker Identification in Darknet Markets over Attributed Heterogeneous Information Network Yiming Zhang, Yujie Fan, Wei Song, Shifu Hou, Yanfang Ye, Xin Li, Liang Zhao, Chuan Shi, Jiabin Wang, Qi Xiong 2019-01-01 2021-02-15 [("doi","10.1145/3308558.3313537")] darknet-market/dnm-archive darknet-market/evolution darknet-market/silk-road/2 statistics/stylometry --- https://www.kaggle.com/datasets/mhwong2007/drug-listing-dataset Drug Listing Dataset 2021-02-15 darknet-market/dnm-archive --- https://www.kaggle.com/datasets/philipjames11/dark-net-marketplace-drug-data-agora-20142015 Dark Net Marketplace Data (Agora 2014–2015) 2021-02-15 darknet-market/dnm-archive --- https://www.rand.org/pubs/research_reports/RR1607.html Internet-facilitated drugs trade: An analysis of the size, scope and the role of the Netherlands 2021-02-16 darknet-market/dnm-archive --- https://www.rsm.nl/fileadmin/Images_NEW/Departments/TOM/Calis_Thijmen_Thesis.pdf 2021-02-16 darknet-market/dnm-archive --- https://www.sciencedirect.com/science/article/pii/S0379073817303341 2021-02-16 darknet-market/dnm-archive --- https://www.sciencedirect.com/science/article/pii/S1742287617301913 Availability of datasets for digital forensics—And what is missing 2021-02-16 darknet-market/dnm-archive --- https://www.swansea.ac.uk/media/Hard-Interventions-and-Innovation-in-CryptoDrug-Markets-The-escrow-example.pdf 2021-02-16 darknet-market/dnm-archive --- https://www.thestar.com/vancouver/2018/07/05/bc-lays-claim-to-14-million-us-in-bitcoin-from-drug-dealer-over-alleged-links-to-silk-road.html 2021-02-16 darknet-market/dnm-archive --- /doc/darknet-market/dnm-archive/2017-02-12-thetimes-darknetdealersdraggedintothelight.html 2017-02-12 2021-02-16 darknet-market/dnm-archive --- https://www.usenix.org/system/files/conference/usenixsecurity18/sec18-yuan_0.pdf 2021-02-16 darknet-market/dnm-archive --- https://www.vice.com/de/tag/motherboard/ Überraschende Wende im Shiny Flakes-Prozess: Maximilian S. packt aus 2021-02-16 darknet-market/dnm-archive darknet-market/evolution --- https://www.washingtonpost.com/news/the-switch/wp/2016/09/15/how-drug-listings-on-the-dark-net-may-have-revealed-sellers-locations/ How drug listings on the dark net may have revealed sellers' locations 2021-02-17 darknet-market/dnm-archive --- https://arxiv.org/abs/1107.4524 An Analysis of Anonymity in the Bitcoin System Fergal Reid, Martin Harrigan 2011-07-22 2021-02-17 [("doi","10.48550/arXiv.1107.4524")] bitcoin darknet-market/silk-road/1

Anonymity in Bitcoin, a peer-to-peer electronic currency system, is a complicated issue. Within the system, users are identified by public-keys only. An attacker wishing to de-anonymize its users will attempt to construct the one-to-many mapping between users and public-keys and associate information external to the system with the users. Bitcoin tries to prevent this attack by storing the mapping of a user to his or her public-keys on that user’s node only and by allowing each user to generate as many public-keys as required.

In this chapter, we consider the topological structure of two networks derived from Bitcoin’s public transaction history. We show that the two networks have a non-trivial topological structure, provide complementary views of the Bitcoin system, and have implications for anonymity.

We combine these structures with external information and techniques such as context discovery and flow analysis to investigate an alleged theft of Bitcoins, which, at the time of the theft, had a market value of ~half a million U.S. dollars.

--- /doc/darknet-market/silk-road/1/2013-vanhout.pdf ‘Silk Road’, the virtual drug marketplace: A single case study of user experiences Marie Claire Van Hout, Tim Bingham 2013-01-01 2021-02-17 [("doi","10.1016/j.drugpo.2013.01.005")] darknet-market/silk-road/1 --- /doc/darknet-market/alphabay/2018-baravalle.pdf Dark Web Markets: Turning the Lights on AlphaBay Andres Baravalle, Sin Wee Lee 2018-01-01 2021-02-17 [("doi","10.1007/978-3-030-02925-8_35")] darknet-market/alphabay --- /doc/darknet-market/alphabay/2019-morelato.pdf An insight Into Prescription Drugs and Medicine on the AlphaBay Cryptomarket Marie Morelato, Susana Medeiros Bozic, Damien Rhumorbarbe, Julian Broséus, Ludovic Staehli, Pierre Esseiva, Claude Roux, Quentin Rossy 2019-09-13 2021-02-17 [("doi","10.1177/0022042619872955")] darknet-market/alphabay

Internet access has provided new ways to trade goods. Unlike conventional legal sale sites, cryptomarkets facilitate exchanges in a context where the anonymity of participants is warranted. The aim of this article was to obtain a better understanding of the trafficking of prescription drugs and medicine on the AlphaBay cryptomarket.

The results showed that alprazolam, oxycodone, and Adderall were the most offered prescription drugs while alprazolam, diazepam, and oxycodone were the most sold substances. The sale was dominated by North America, Australia, and Western European countries. The revenue of prescription drugs was estimated to be more than US$65 million since the creation of AlphaBay, a small market in comparison with the worldwide legal pharmaceutical market’s estimate of US$1.3 trillion in 2020.

Digital traces offer a complementary way to understand the trafficking of prescription drugs and medicine and to identify the most prolific vendors and their implication in this trafficking.

--- /doc/darknet-market/silk-road/1/2013-08-14-srbmratlantis-testosterone.tar.xz 2013-08-14 2021-02-17 darknet-market/atlantis darknet-market/blackmarket-reloaded darknet-market/silk-road/1 --- https://arxiv.org/abs/1805.10354 Self-Net: Lifelong Learning via Continual Self-Modeling Blake Camp, Jaya Krishna Mandivarapu, Rolando Estrada 2018-05-25 2021-02-17 [("doi","10.48550/arXiv.1805.10354")] ai/nn/sparsity/knowledge-distillation ai/nn/vae reinforcement-learning/meta-learning/continual-learning

Learning a set of tasks over time, also known as continual learning (CL), is one of the most challenging problems in artificial intelligence. While recent approaches achieve some degree of CL in deep neural networks, they either (1) grow the network parameters linearly with the number of tasks, (2) require storing training data from previous tasks, or (3) restrict the network’s ability to learn new tasks.

To address these issues, we propose a novel framework, Self-Net, that uses an autoencoder to learn a set of low-dimensional representations of the weights learned for different tasks. We demonstrate that these low-dimensional vectors can then be used to generate high-fidelity recollections of the original weights. Self-Net can incorporate new tasks over time with little retraining and with minimal loss in performance for older tasks. Our system does not require storing prior training data and its parameters grow only logarithmically with the number of tasks.

We show that our technique outperforms current state-of-the-art approaches on numerous datasets—including continual versions of MNIST, CIFAR-10, CIFAR-100, and Atari—and we demonstrate that our method can achieve over 10× storage compression in a continual fashion.

To the best of our knowledge, we are the first to use autoencoders to sequentially encode sets of network weights to enable continual learning.

--- https://x.com/Thinkwert/status/1528485521839759361 2021-02-17 ai/nn/transformer/gpt/dall-e fiction/text-game --- https://arxiv.org/abs/2112.10741#openai GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models Alex Nichol, Prafulla Dhariwal, Aditya A. Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, Mark Chen 2021-12-20 2021-12-20 [("doi","10.48550/arXiv.2112.10741")] ai/dataset ai/nn/transformer/gpt/dall-e/2

Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity.

We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance.

We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL·E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing.

We train a smaller model on a filtered dataset and release the code and weights at Github.

--- https://openai.com/dall-e-2 2021-02-17 ai/nn/transformer/gpt/dall-e --- https://x.com/dbonneville/status/1522453742095900672 2021-02-17 ai/nn/transformer/gpt/dall-e --- https://x.com/Plinz/status/1527528360351387649 2021-02-18 ai/nn/transformer/gpt/dall-e --- https://web.mit.edu/phillipi/www/the_bees.html The Bees 2021-02-18 ai/nn/transformer/gpt/dall-e/2 --- https://www.reddit.com/r/dalle2/comments/ueizwz/i_printed_a_dalle_generated_childrens_book_about/ 2021-02-18 ai/nn/transformer/gpt/dall-e/2 --- https://www.reddit.com/r/dalle2/comments/ub0sfg/dalle_2_imitation_game_results_check_sticky_for/ 2021-02-18 ai/nn/transformer/gpt/dall-e/2 --- https://www.reddit.com/r/dalle2/comments/u79ut4/david_schnurr_dschnurr_inpainting_with_dalle_2_is/ 2021-02-18 ai/nn/transformer/gpt/dall-e/2 --- /doc/cat/2013-eaton.pdf#page=5 Blackout Tracker United Kingdom Annual Report 2013 § Top 5 most unusual outages/causes Eaton 2013 2021-02-18 cat technology --- https://en.wikipedia.org/wiki/Vault_(organelle) Vault (organelle) 2021-02-18 biology --- https://arxiv.org/abs/2205.11487#google Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J. Fleet, Mohammad Norouzi 2022-05-23 2022-05-23 [("doi","10.48550/arXiv.2205.11487")] ai/nn/diffusion ai/nn/transformer/gpt/dall-e/2 ai/nn/transformer/t5

We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. Our key discovery is that generic large language models (eg. T5), pretrained on text-only corpora, are surprisingly effective at encoding text for image synthesis: increasing the size of the language model in Imagen boosts both sample fidelity and image-text alignment much more than increasing the size of the image diffusion model.

Imagen achieves a new state-of-the-art FID score of 7.27 on the COCO dataset, without ever training on COCO, and human raters find Imagen samples to be on par with the COCO data itself in image-text alignment. To assess text-to-image models in greater depth, we introduce DrawBench, a comprehensive and challenging benchmark for text-to-image models. With DrawBench, we compare Imagen with recent methods including VQ-GAN+CLIP, Latent Diffusion Models, and DALL·E 2 [vice-versa], and find that human raters prefer Imagen over other models in side-by-side comparisons, both in terms of sample quality and image-text alignment.

See homepage for an overview of the results.

--- https://x.com/raphaelmilliere/status/1529101851915952128 2021-02-18 ai/nn/transformer/gpt/dall-e --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6117124/ Graphical Models for Quasi-experimental Designs Peter M. Steiner, Yongnam Kim, Courtney E. Hall, Dan Su 2017 2021-02-18 [("doi","10.1177/0049124115582272")] statistics/causality

Randomized controlled trials (RCTs) and quasi-experimental designs like regression discontinuity (RD) designs, instrumental variable (IV) designs, and matching and propensity score (PS) designs are frequently used for inferring causal effects. It is well known that the features of these designs facilitate the identification of a causal estimand and, thus, warrant a causal interpretation of the estimated effect.

In this article, we discuss and compare the identifying assumptions of quasi-experiments using causal graphs. The increasing complexity of the causal graphs as one switches from an RCT to RD, IV, or PS designs reveals that the assumptions become stronger as the researcher’s control over treatment selection diminishes.

We introduce limiting graphs for the RD design and conditional graphs for the latent subgroups of com-pliers, always takers, and never takers of the IV design, and argue that the PS is a collider that offsets confounding bias via collider bias.

--- https://arxiv.org/abs/2205.11491#facebook HTPS: HyperTree Proof Search for Neural Theorem Proving Guillaume Lample, Marie-Anne Lachaux, Thibaut Lavril, Xavier Martinet, Amaury Hayat, Gabriel Ebner, Aurélien Rodriguez, Timothée Lacroix 2022-05-23 2022-05-23 [("doi","10.48550/arXiv.2205.11491")] ai/nn/transformer math reinforcement-learning/model/alphago

We propose an online training procedure for a transformer-based automated theorem prover.

Our approach leverages a new search algorithm, HyperTree Proof Search (HTPS), inspired by the recent success of AlphaZero. Our model learns from previous proof searches through online training, allowing it to generalize to domains far from the training distribution.

We report detailed ablations of our pipeline’s main components by studying performance on 3 environments of increasing complexity.

In particular, we show that with HTPS alone, a model trained on annotated proofs manages to prove 65.4% of a held-out set of Metamath theorems, substantially outperforming the previous state-of-the-art of 56.5% by GPT-f. Online training on these unproved theorems increases accuracy to 82.6%. With a similar computational budget, we improve the state-of-the-art on the Lean-based miniF2F-curriculum dataset 31% → 42% proving accuracy.

--- https://openreview.net/forum?id=rI7BL3fHIZq What Language Model to Train if You Have One Million GPU Hours? Teven Le Scao, Thomas Wang, Daniel Hesslow, Lucile Saulnier, Stas Bekman, M. Saiful Bari, Stella Biderman, Hady Elsahar, Jason Phang, Ofir Press, Colin Raffel, Victor Sanh, Sheng Shen, Lintang Sutawika, Jaesung Tae, Zheng Xin Yong, Julien Launay, Iz Beltagy 2022-04-11 2022-04-11 ai/nn/transformer/gpt ai/scaling/hardware

The crystallization of modeling methods around the Transformer architecture has been a boon for practitioners. Simple, well-motivated architectural variations that transfer across tasks and scale, increasing the impact of modeling research. However, with the emergence of state-of-the-art 100B+ parameters models, large language models are increasingly expensive to accurately design and train. Notably, it can be difficult to evaluate how modeling decisions may impact emergent capabilities, given that these capabilities arise mainly from sheer scale.

Targeting a multilingual language model in the 100B+ parameters scale, our goal is to identify an architecture and training setup that makes the best use of our 1,000,000 A100-GPU-hours budget.

Specifically, we perform an ablation study comparing different modeling practices and their impact on zero-shot generalization. We perform all our experiments on 1.3B models, providing a compromise between compute costs and the likelihood that our conclusions will hold for the target 100B+ model. In addition, we study the impact of various popular pretraining corpora on zero-shot generalization. We also study the performance of a multilingual model and how it compares to the English-only one.

Finally, we consider the scaling behavior of Transformers to chose the target model size, shape, and training setup.

--- https://arxiv.org/abs/2205.10747 VidIL: Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners Zhenhailong Wang, Manling Li, Ruochen Xu, Luowei Zhou, Jie Lei, Xudong Lin, Shuohang Wang, Ziyi Yang, Chenguang Zhu, Derek Hoiem, Shih-Fu Chang, Mohit Bansal, Heng Ji 2022-05-22 2022-05-22 [("doi","10.48550/arXiv.2205.10747")] ai/nn/transformer/clip ai/video/analysis

The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples, such as domain-specific captioning, question answering, and future event prediction. Existing few-shot video-language learners focus exclusively on the encoder, resulting in the absence of a video-to-text decoder to handle generative tasks. Video captioners have been pretrained on large-scale video-language datasets, but they rely heavily on finetuning and lack the ability to generate text for unseen tasks in a few-shot setting.

We propose VidIL, a few-shot Video-language Learner via Image and Language models, which demonstrates strong performance on few-shot video-to-text tasks without the necessity of pretraining or finetuning on any video datasets.

We use the image-language models [CLIP + BLIP] to translate the video content into frame captions, object, attribute, and event phrases, and compose them into a temporal structure template. We then instruct a language model, with a prompt containing a few in-context examples, to generate a target output from the composed content. The flexibility of prompting allows the model to capture any form of text input, such as automatic speech recognition (ASR) transcripts.

Our experiments demonstrate the power of language models in understanding videos on a wide variety of video-language tasks, including video captioning, video question answering, video caption retrieval, and video future event prediction. Especially, on video future event prediction, our few-shot model substantially outperforms state-of-the-art supervised models trained on large-scale video datasets.

Code and resources are publicly available for research purposes at Github.

--- https://www.reddit.com/r/dalle2/comments/uwb3cz/the_first_image_in_this_video_was_created_from/ 2021-02-19 ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2205.11588#google Simple Recurrence Improves Masked Language Models Tao Lei, Ran Tian, Jasmijn Bastings, Ankur P. Parikh 2022-05-23 2022-05-23 [("doi","10.48550/arXiv.2205.11588")] ai/nn/rnn ai/nn/transformer/attention/recurrent

In this work, we explore whether modeling recurrence into the Transformer architecture can both be beneficial and efficient, by building an extremely simple recurrent module into the Transformer.

We compare our model to baselines following the training and evaluation recipe of BERT.

Our results confirm that recurrence can indeed improve Transformer models by a consistent margin, without requiring low-level performance optimizations, and while keeping the number of parameters constant.

For example, our base model achieves an absolute improvement of 2.1 points averaged across 10 tasks and also demonstrates increased stability in fine-tuning over a range of learning rates.

--- https://en.wikipedia.org/wiki/Scarcity_(social_psychology) Scarcity (social psychology) 2021-02-19 psychology/collecting --- https://arxiv.org/abs/2205.11916 Large Language Models are Zero-Shot Reasoners Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa 2022-05-24 2022-05-24 [("doi","10.48550/arXiv.2205.11916")] ai/nn/transformer/gpt/inner-monologue

Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain-of-thought (CoT) prompting, a recent technique for eliciting complex multi-step reasoning through step-by-step answer examples, achieved the state-of-the-art performances in arithmetics and symbolic reasoning, difficult system-2 tasks that do not follow the standard scaling laws for LLMs.

While these successes are often attributed to LLMs’ ability for few-shot learning, we show that LLMs are decent zero-shot reasoners by simply adding “Let’s think step by step” before each answer. Experimental results demonstrate that our Zero-shot-CoT, using the same single prompt template, outperforms zero-shot LLM performances on diverse benchmark reasoning tasks including arithmetics (MultiArith, GSM8K, AQUA-RAT, SVAMP), symbolic reasoning (Last Letter, Coin Flip), and other logical reasoning tasks (Date Understanding, Tracking Shuffled Objects), without any hand-crafted few-shot examples, eg. increasing the accuracy on MultiArith 17.7% → 78.7% and GSM8K 10.4% → 40.7% with an off-the-shelf 175b parameter model.

The versatility of this single prompt across very diverse reasoning tasks hints at untapped and understudied fundamental zero-shot capabilities of LLMs, suggesting high-level, multi-task broad cognitive capabilities may be extracted through simple prompting.

We hope our work not only serves as the minimal strongest zero-shot baseline for the challenging reasoning benchmarks, but also highlights the importance of carefully exploring and analyzing the enormous zero-shot knowledge hidden inside LLMs before crafting finetuning datasets or few-shot exemplars.

--- https://x.com/bneyshabur/status/1529506103708602369 2021-02-19 ai/nn/transformer/gpt/dall-e --- https://www.biorxiv.org/content/10.1101/2020.07.19.211078.full Genome scans of dog behavior implicate a gene network underlying psychopathology in mammals, including humans Isain Zapata, Erin E. Hecht, James A. Serpell, Carlos E. Alvarez 2021-01-01 2021-02-19 [("doi","10.1101/2020.07.19.211078")] genetics/heritable/dog psychiatry

Genetic studies show a general factor associated with all human psychopathology and strongly correlated with personality and intelligence, but its basis is unknown. We performed genome scans of 17 normal and problem behaviors in 3 multi-breed dog cohorts. 21⁄90 mapped loci were supported for the same, or a related, trait in a second cohort. Several of those loci were also associated with brain structure differences across breeds; and six of the respective top-candidate genes are also associated with human brain structure and function. More broadly, the geneset of canine behavioral scans is supported by enrichment for genes mapped for human behavior, personality, cognition, psychopathology and brain structure. The biology implicated includes, neurogenesis, axon guidance, angiogenesis, brain structure, alternative splicing, disease association, Hox-family transcription factors, and subiculum expression. Because body size and behavior are correlated in dogs, we isolated the effect of body size in the dog mapping and in the comparative human UK Biobank analyses. Our dog findings are consistent with pleiotropy of diverse brain traits with energy metabolism and growth, and suggest behavioral variations often affect neurogenesis. There is support for such pleiotropy in humans and well-powered genetic studies of human psychiatric traits consistently implicate neurogenesis. We propose a genetic network which underlies neuron birth and development throughout life is associated with evolutionary adaptation of behavior and the general psychopathology factor. This understanding has implications for genetic and environmental contributions to psychiatric disease. We discuss how canine translational models can further accelerate the study of psychopathology.

Author Summary: We genetically mapped diverse normal and problem behaviors in dogs. The well-established approach we used is ideally suited for finding variation that is common across dog breeds and for pin-pointing the most likely gene candidates. Our analysis of the genes implicated at 90 genome regions shows they are enriched for (8) genes mapped for diverse brain functions and pathologies in humans; (2) genes involved in brain development throughout life; and (3) footprints of evolution in dogs, humans and other animals. We propose that is consistent with evolutionary conservation of the general genetic factor of mental health in humans, which is correlated with personality and intelligence. The implications are that this super-network of genes is preferentially targeted by evolutionary adaptation for behavior and that its dysregulation increases risk of mental health disorders.

--- https://www.biorxiv.org/content/10.1101/2021.01.08.425895.full Genome-scale sequencing and analysis of human, wolf and bison DNA from 25,000 year-old sediment Pere Gelabert, Susanna Sawyer, Anders Bergström, Thomas C. Collin, Tengiz Meshveliani, Anna Belfer-Cohen, David Lordkipanidze, Nino Jakeli, Zinovi Matskevich, Guy Bar-Oz, Daniel M. Fernandes, Olivia Cheronet, Kadir T. Özdoğan, Victoria Oberreiter, Robin N. M. Feeney, Mareike C. Stahlschmidt, Pontus Skoglund, Ron Pinhasi 2021-01-08 2021-02-19 [("doi","10.1101/2021.01.08.425895")] genetics/heritable/dog genetics/selection/natural/human genetics/sequencing

We demonstrate for the first time that genome sequencing from sediments is comparable to that of skeletal remains

A single Pleistocene sediment sample from the Caucasus yielded 3 low-coverage mammalian ancient genomes

We show that sediment ancient DNA can reveal important aspects of the human and faunal past

Evidence of an uncharacterized human lineage from the Caucasus before the Last Glacial Maximum

~0.01× coverage wolf and bison genomes are both basal to present-day diversity, suggesting reshaping of population structure in both species

Archaeological sediments have been shown to preserve ancient DNA, but so far have not yielded genome-scale information of the magnitude of skeletal remains.

We retrieved and analysed human and mammalian low-coverage nuclear and high-coverage mitochondrial genomes from Upper Palaeolithic sediments from Satsurblia cave, western Georgia, dated to 25,000 years ago.

First, a human female genome with substantial basal Eurasian ancestry, which was an ancestry component of the majority of post-Ice Age people in the Near East, North Africa, and parts of Europe. Second, a wolf genome that is basal to extant Eurasian wolves and dogs and represents a previously unknown, likely extinct, Caucasian lineage that diverged from the ancestors of modern wolves and dogs before these diversified. Third, a bison genome that is basal to present-day populations, suggesting that population structure has been substantially reshaped since the Last Glacial Maximum.

Our results provide new insights into the late Pleistocene genetic histories of these 3 species, and demonstrate that sediment DNA can be used not only for species identification, but also be a source of genome-wide ancestry information and genetic history.

--- https://www.biorxiv.org/content/10.1101/2020.08.13.249805.full Genetic testing of dogs predicts problem behaviors in clinical and nonclinical samples Isain Zapata, M. Leanne Lilly, Meghan E. Herron, James A. Serpell, Carlos E. Alvarez 2020-08-14 2021-02-19 [("doi","10.1101/2020.08.13.249805")] genetics/heritable/dog psychiatry/anxiety

Very little is known about the etiology of personality and psychiatric disorders. Because the core neurobiology of many such traits is evolutionarily conserved, dogs present a powerful model.

We previously reported genome scans of breed averages of ten traits related to fear, anxiety, aggression and social behavior in multiple cohorts of pedigree dogs. As a second phase of that discovery, here we tested the ability of markers at 13 of those loci to predict canine behavior in a community sample of 397 pedigree and mixed-breed dogs with individual-level genotype and phenotype data.

We found support for all markers and loci. By including 122 dogs with veterinary behavioral diagnoses in our cohort, we were able to identify eight loci associated with those diagnoses. Logistic regression models showed subsets of those loci could predict behavioral diagnoses. We corroborated our previous findings that small body size is associated with many problem behaviors and large body size is associated with increased trainability. Children in the home were associated with anxiety traits; illness and other animals in the home with coprophagia; working-dog status with increased energy and separation-related problems; and competitive dogs with increased aggression directed at familiar dogs, but reduced fear directed at humans and unfamiliar dogs.

Compared to other dogs, Pit Bull-type dogs were not defined by a set of our markers and were not more aggressive; but they were strongly associated with pulling on the leash. Using severity-threshold models, Pit Bull-type dogs showed reduced risk of owner-directed aggression (75th quantile) and increased risk of dog-directed fear (95th quantile).

Our findings have broad utility, including for clinical and breeding purposes, but we caution that thorough understanding is necessary for their interpretation and use.

--- /doc/genetics/selection/artificial/2019-hecht.pdf neuroanatomical variation among domestic dog breeds Erin E. Hecht, Jeroen B. Smaers, William J. Dunn, Marc Kent, Todd M. Preuss, David A. Gutman 2019-09-02 2021-02-20 [("doi","10.1523/JNEUROSCI.0303-19.2019")] dog genetics/heritable/dog genetics/selection/artificial psychology/neuroscience --- /doc/genetics/editing/2018-amoasii.pdf Gene editing restores dystrophin expression in a canine model of Duchenne muscular dystrophy Leonela Amoasii1, John C. W. Hildyard, Hui Li, Efrain Sanchez-Ortiz, Alex Mireault, Daniel Caballero, Rachel Harron, Thaleia-Rengina Stathopoulou, Claire Massey, John M. Shelton, Rhonda Bassel-Duby, Richard J. Piercy, Eric N. Olson 2018-01-01 2021-02-20 [("doi","10.1126/science.aau1549")] genetics/editing genetics/heritable/dog

Mutations in the gene encoding dystrophin, a protein that maintains muscle integrity and function, cause Duchenne muscular dystrophy (DMD). The deltaE50-MD dog model of DMD harbors a mutation corresponding to a mutational “hotspot” in the human DMD gene.

We used adeno-associated viruses to deliver CRISPR gene editing components to 4 dogs and examined dystrophin protein expression 6 weeks after intramuscular delivery (n = 2) or 8 weeks after systemic delivery (n = 2).

After systemic delivery in skeletal muscle, dystrophin was restored to levels ranging from 3–90% of normal, depending on muscle type. In cardiac muscle, dystrophin levels in the dog receiving the highest dose reached 92% of normal. The treated dogs also showed improved muscle histology.

These large-animal data support the concept that, with further development, gene editing approaches may prove clinically useful for the treatment of DMD.

--- https://www.biorxiv.org/content/10.1101/118794.full Selective sweep analysis using village dogs highlights the pivotal role of the neural crest in dog domestication Amanda L. Pendleton, Feichen Shen, Angela M. Taravella, Sarah Emery, Krishna R. Veeramah, Adam R. Boyko, Jeffrey M. Kidd 2017-03-21 2021-02-20 [("doi","10.1101/118794")] genetics/heritable/dog genetics/selection/artificial psychology/neuroscience

Dogs (Canis lupus familiaris) were domesticated from gray wolves between 20–40kya in Eurasia, yet details surrounding the process of domestication remain unclear. The vast array of phenotypes exhibited by dogs mirror numerous other domesticated animal species, a phenomenon known as the Domestication Syndrome.

Here, we use signatures persisting in the dog genome to identify genes and pathways altered by the intensive selective pressures of domestication. We identified 37 candidate domestication regions containing 17.5Mb of genome sequence and 172 genes through whole-genome SNP analysis of 43 globally distributed village dogs and 10 wolves. Comparisons with 3 ancient dog genomes indicate that these regions reflect signatures of domestication rather than breed formation. Analysis of genes within these regions revealed an enrichment of gene functions linked to neural crest cell migration, differentiation, and development. Genome copy number analysis identified regions of localized sequence and structural diversity, and discovered additional copy number variation at the amylase-2b locus.

Overall, these results indicate that primary selection pressures targeted genes in the neural crest as well as components of the minor spliceosome, rather than genes involved in starch metabolism. Smaller jaw sizes, hairlessness, floppy ears, tameness, and diminished craniofacial development distinguish wolves from domesticated dogs, phenotypes of the Domestication Syndrome that can result from decreased neural crest cells at these sites.

We propose that initial selection acted on key genes in the neural crest and minor splicing pathways during early dog domestication, giving rise to the phenotypes of modern dogs.

--- https://www.biorxiv.org/content/10.1101/080325.full Functional MRI in awake dogs predicts suitability for assistance work Gregory S. Berns, Andrew M. Brooks, Mark Spivak, Kerinne Levy 2016-10-12 2021-02-20 [("doi","10.1101/080325")] dog psychology/neuroscience

The overall goal of this work was to measure the efficacy of fMRI for predicting whether a dog would be a successful service dog. The training and imaging were performed in 50 dogs entering advanced training at 17–21 months of age.

FMRI responses were measured while each dog observed hand signals indicating either reward or no reward and given by both a familiar handler and a stranger. 49 dogs successfully completed fMRI training and scanning. Of these, 33 eventually completed service training and were matched with a person, while 10 were released for behavioral reasons. Using anatomically defined regions-of-interest in the ventral caudate, amygdala, and visual cortex, we developed a classifier based on the dogs’ outcomes. We found that responses in the stranger condition were sufficient to develop an accurate brain-based classifier.

On all data, the classifier had a positive predictive value of 96% with 10% false positives. The area under the receiver operating characteristic curve was 0.90 (0.79 with 4× cross-validation, p = 0.02), indicating a diagnostic capability. Within the stranger condition, the differential response to [reward—no reward] in ventral caudate was positively correlated with a successful outcome, while the differential response in the amygdala was negatively correlated to outcome.

These results show that successful service dogs transfer knowledge to strangers as indexed by ventral caudate activity without excessive arousal as measured in the amygdala.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3330146/ Can an old dog learn (and want to experience) new tricks? Cognitive training increases openness to experience in older adults Joshua J. Jackson, Patrick L. Hill, Brennan R. Payne, Brent W. Roberts, Elizabeth A. L. Stine-Morrow 2012 2021-02-20 [("doi","10.1037/a0025918")] psychology/personality

The present study investigated whether an intervention aimed to increase cognitive ability in older adults also changes the personality trait of openness to experience.

Older adults completed a 16-week program in inductive reasoning training supplemented by weekly crossword and Sudoku puzzles. Changes in openness to experience were modeled across 4 assessments over 30 weeks using latent growth curve models.

Results indicate that participants in the intervention condition increased in the trait of openness compared with a waitlist control group.

The study is one of the first to demonstrate that personality traits can change through nonpsychopharmocological interventions.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3081766/ Canaries in the coal mine: a cross-species analysis of the plurality of obesity epidemics Klimentidis 2010 2021-02-20 cat/biology --- /doc/genetics/heritable/dog/2017-vandenberg.pdf Genetics of dog behavior Linda van den Berg 2017-01-01 2021-02-20 cat genetics/heritable/dog --- /doc/genetics/heritable/dog/2017-vandenberg.pdf 2017 2021-02-20 cat genetics/heritable/dog --- /doc/psychedelic/2015-bennett.pdf Hunting and hallucinogens: The use of psychoactive and other plants to improve the hunting ability of dogs Bradley C. Bennett, Rocío Alarcón 2015-01-01 2021-02-20 [("doi","10.1016/j.jep.2015.05.035")] dog psychedelic --- /doc/iq/animal/2016-arden.pdf A general intelligence factor in dogs Rosalind Arden, Mark James Adams 2016-01-01 2021-02-20 [("doi","10.1016/j.intell.2016.01.008")] dog iq/animal --- /doc/genetics/selection/artificial/2012-duffy.pdf Predictive validity of a method for evaluating temperament in young guide and service dogs Deborah L. Duffy, James A. Serpell 2012-01-01 2021-02-20 [("doi","10.1016/j.applanim.2012.02.011")] dog genetics/selection/artificial --- /doc/genetics/selection/artificial/2017-ostrander.pdf Demographic history, selection and functional diversity of the canine genome Elaine A. Ostrander, Robert K. Wayne, Adam H. Freedman, Brian W. Davis 2017-01-01 2021-02-21 [("doi","10.1038/nrg.2017.67")] genetics/heritable/dog genetics/selection/artificial --- /doc/genetics/cloning/dog/2016-oh.pdf Propagation of elite rescue dogs by somatic cell nuclear transfer Hyun Ju Oh, Jin Choi, Min Jung Kim, Geon A. Kim, Young Kwang Jo, Yoo Bin Choi, Byeong Chun Lee 2016-01-01 2021-02-21 genetics/cloning/dog --- /doc/genetics/heritable/dog/1965-dawson.pdf Studies of the inheritance of intelligence and temperament in dogs Dawson, Walker Myrick, 1902 1965-01-01 2021-02-21 genetics/heritable/dog iq/animal --- /doc/dog/1997-weiss.pdf Service dog selection tests: Effectiveness for dogs from animal shelters Emily Weiss, Gary Greenberg 1997-01-01 2021-02-21 [("doi","10.1016/s0168-1591(96)01176-8")] dog --- /doc/psychology/2003-vandenberg.pdf Behavior genetics of canine aggression: behavioral phenotyping of golden retrievers by means of an aggression test Guinness 2003-01-01 2021-02-21 [("doi","10.1023/A:1025714431089")] dog genetics/heritable/dog psychology --- /doc/genetics/selection/artificial/1997-wilsson.pdf The use of a behavior test for the selection of dogs for service and breeding, I: Method of testing and evaluating test results in the adult dog, demands on different kinds of service dogs, sex and breed differences Erik Wilsson, Per-Erik Sundgren 1997-01-01 2021-02-21 [("doi","10.1016/S0168-1591(96)01174-4")] dog genetics/heritable/dog genetics/selection/artificial --- /doc/genetics/heritable/correlation/1985-mackenzie.pdf Heritability estimate for temperament scores in German shepherd dogs and its genetic correlation with hip dysplasia Stephen A. Mackenzie, Elizabeth A. B. Oltenacu, Eldin Leighton 1985-01-01 2021-02-21 [("doi","10.1007/bf01066240")] dog genetics/heritable/correlation genetics/heritable/dog --- /doc/genetics/heritable/correlation/1983-goddard-2.pdf Genetics of traits which determine the suitability of dogs as guide-dogs for the blind M. E. Goddard, R. G. Beilharz 1983-01-01 2021-02-21 [("doi","10.1016/0304-3762(83)90010-X")] dog genetics/heritable/correlation --- https://arxiv.org/abs/1511.02799 Neural Module Networks Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein 2015-11-09 2021-02-21 [("doi","10.48550/arXiv.1511.02799")] ai/dataset ai/nn

Visual question answering is fundamentally compositional in nature—a question like “where is the dog?” shares substructure with questions like “what color is the dog?” and “where is the cat?” This paper seeks to simultaneously exploit the representational capacity of deep networks and the compositional linguistic structure of questions.

We describe a procedure for constructing and learning neural module networks, which compose collections of jointly-trained neural “modules” into deep networks for question answering. Our approach decomposes questions into their linguistic substructures, and uses these structures to dynamically instantiate modular networks (with reusable components for recognizing dogs, classifying colors, etc.). The resulting compound networks are jointly trained.

We evaluate our approach on two challenging datasets for visual question answering, achieving state-of-the-art results on both the VQA natural image dataset and a new dataset of complex questions about abstract shapes.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0026828 Willingness to Share Research Data Is Related to the Strength of the Evidence and the Quality of Reporting of Statistical Results Jelte M. Wicherts, Marjan Bakker, Dylan Molenaar 2011-10-04 2021-02-21 [("doi","10.1371/journal.pone.0026828")] statistics/bias

Background: The widespread reluctance to share published research data is often hypothesized to be due to the authors’ fear that reanalysis may expose errors in their work or may produce conclusions that contradict their own. However, these hypotheses have not previously been studied systematically.

Methods & Findings: We related the reluctance to share research data for reanalysis to 1148 statistically-significant results reported in 49 papers published in two major psychology journals. We found the reluctance to share data to be associated with weaker evidence (against the null hypothesis of no effect) and a higher prevalence of apparent errors in the reporting of statistical results. The unwillingness to share data was particularly clear when reporting errors had a bearing on statistical-significance.

Conclusion: Our findings on the basis of psychological papers suggest that statistical results are particularly hard to verify when reanalysis is more likely to lead to contrasting conclusions. This highlights the importance of establishing mandatory data archiving policies.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0026726 A Novel, Functional and Replicable Risk Gene Region for Alcohol Dependence Identified by Genome-Wide Association Study Lingjun Zuo, Clarence K. Zhang, Fei Wang, Chiang-Shan R. Li, Hongyu Zhao, Lingeng Lu, Xiang-Yang Zhang, Lin Lu, Heping Zhang, Fengyu Zhang, John H. Krystal, Xingguang Luo 2011-10-01 2021-02-21 [("doi","10.1371/journal.pone.0026726")] genetics/heritable psychiatry/alcoholism

Several genome-wide association studies (GWASs) reported tens of risk genes for alcohol dependence, but most of them have not been replicated or confirmed by functional studies. The present study used a GWAS to search for novel, functional and replicable risk gene regions for alcohol dependence.

Associations of all top-ranked SNPs identified in a discovery sample of 681 African-American (AA) cases with alcohol dependence and 508 AA controls were retested in a primary replication sample of 1,409 European-American (EA) cases and 1,518 EA controls. The replicable associations were then subjected to secondary replication in a sample of 6,438 Australian family subjects. A functional expression quantitative trait locus (eQTL) analysis of these replicable risk SNPs was followed-up in order to explore their cis-acting regulatory effects on gene expression.

We found that within a 90 Mb region around PHF3-PTP4A1 locus in AAs, a linkage disequilibrium (LD) block in PHF3-PTP4A1 formed the only peak associated with alcohol dependence at p < 10−4. Within this block, 30 SNPs associated with alcohol dependence in AAs (1.6×10−5 ≤ p ≤ 0.050) were replicated in EAs (1.3×10−3 ≤ p ≤ 0.038), and 18 of them were also replicated in Australians (1.8×10−3 ≤ p ≤ 0.048). Most of these risk SNPs had strong cis-acting regulatory effects on PHF3-PTP4A1 mRNA expression across 3 HapMap samples. The distributions of −log(p) values for association and functional signals throughout this LD block were highly consistent across AAs, EAs, Australians and 3 HapMap samples.

We conclude that the PHF3-PTP4A1 region appears to harbor a causal locus for alcohol dependence, and proteins encoded by PHF3 and/or PTP4A1 might play a functional role in the disorder.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0027162 Genomic Ancestry, Self-Reported ‘Color’ and Quantitative Measures of Skin Pigmentation in Brazilian Admixed Siblings Tailce K. M. Leite, Rômulo M. C. Fonseca, Nanci M. de França, Esteban J. Parra, Rinaldo W. Pereira 2011-10-11 2021-02-22 [("doi","10.1371/journal.pone.0027162")] genetics/heritable

A current concern in genetic epidemiology studies in admixed populations is that population stratification can lead to spurious results. The Brazilian census classifies individuals according to self-reported “color”, but several studies have demonstrated that stratifying according to “color” is not a useful strategy to control for population structure, due to the dissociation between self-reported “color” and genomic ancestry.

We report the results of a study in a group of Brazilian siblings in which we measured skin pigmentation using a reflectometer, and estimated genomic ancestry using 21 Ancestry Informative Markers (AIMs). Self-reported “color”, according to the Brazilian census, was also available for each participant. This made it possible to evaluate the relationship between self-reported “color” and skin pigmentation, self-reported “color” and genomic ancestry, and skin pigmentation and genomic ancestry.

We observed that, although there were statistically-significant differences between the 3 “color” groups in genomic ancestry and skin pigmentation, there was considerable dispersion within each group and substantial overlap between groups. We also saw that there was no good agreement between the “color” categories reported by each member of the sibling pair: 30/86 sibling pairs reported different “color”, and in some cases, the sibling reporting the darker “color” category had lighter skin pigmentation. Socioeconomic status was statistically-significantly associated with self-reported “color” and genomic ancestry in this sample.

This and other studies show that subjective classifications based on self-reported “color”, such as the one that is used in the Brazilian census, are inadequate to describe the population structure present in recently admixed populations.

Finally, we observed that one of the AIMs included in the panel (rs1426654), which is located in the known pigmentation gene SLC24A5, was strongly associated with skin pigmentation in this sample.

--- https://www.reddit.com/r/dalle2/comments/uxpl87/a_photograph_of_a_street_sign_that_warns_drivers/ 2021-02-22 ai/nn/transformer/gpt/dall-e/3 --- https://www.bloomberg.com/news/features/2017-08-28/military-dogs-are-becoming-an-increasingly-precious-weapon The Dogs of War Are in High Demand: After sending hundreds of canines to post Sept. 11 battlefields, the Pentagon is buying robot pooches to help train medics. 2021-02-22 dog --- https://en.wikipedia.org/wiki/Dogs_in_warfare Dogs in warfare 2021-02-22 dog --- https://en.wikipedia.org/wiki/Police_dog Police dog 2021-02-22 dog --- https://en.wikipedia.org/wiki/Hip_dysplasia Hip dysplasia 2021-02-22 dog --- https://en.wikipedia.org/wiki/Belgian_Shepherd Malinois dog 2021-02-22 dog --- https://en.wikipedia.org/wiki/German_Shepherd German Shepherd 2021-02-22 dog --- https://en.wikipedia.org/wiki/Belgian_Shepherd Belgian Shepherd 2021-02-22 dog --- https://en.wikipedia.org/wiki/Labrador_Retriever Labrador retriever 2021-02-22 dog --- https://en.wikipedia.org/wiki/Tianjin_animal_cloning_center Tianjin cloning center 2021-02-22 genetics/cloning/dog --- /doc/genetics/selection/2015-xinhua-tianjincloningcenter.html 2015 2021-02-23 genetics/cloning/dog genetics/selection --- https://www.economist.com/briefing/2017/02/18/hello-again-dolly Hello, again, Dolly 2021-02-23 genetics/cloning --- https://joshdean.com/sites/default/files/articles/feat_clones44.pdf Which of These Is a Clone? 2021-02-23 genetics/cloning/dog --- https://en.wikipedia.org/wiki/Power_of_a_test Statistical power 2021-02-23 statistics/power-analysis --- https://en.wikipedia.org/wiki/Repeated_measures_design Repeated measures design 2021-02-23 nootropic/quantified-self statistics/power-analysis --- https://www.biorxiv.org/content/10.1101/763722.full Software as a Service for the Genomic Prediction of Complex Diseases Alessandro Bolli, Paolo Di Domenico, Giordano Bottà 2019-09-11 2021-02-23 [("doi","10.1101/763722")] genetics/heritable

In the last decade the scientific community witnessed a large increase in Genome-Wide Association Study sample size, in the availability of large Biobanks and in the improvements of statistical methods to model genomes features. This have paved the way for the development of new prediction medicine tools that use genomic data to estimate disease risk. One of these tools is the Polygenic Risk Score (PRS), a metric that estimates the genetic risk of an individual to develop a disease, based on a combination of a large number of genetic variants.

Using the largest prospective genotyped cohort available to date, the UK Biobank, we built a new PRS for Coronary Artery Disease (CAD) and assessed its predictive performances along with two additional PRS for Breast Cancer (BC), and Prostate Cancer (PC). When compared with previously published PRS, the newly developed PRS for CAD displayed higher AUC and positive predictive value. PRSs were able to stratify disease risks 1.34% → 25.7% (CAD in men), 0.26% → 8.62% (CAD in women), 1.6% → 24.6% (BC), and 1.4% → 24.3% (PC) in the lowest and highest percentiles, respectively. Additionally, the 3 PRSs were able to identify the 5% of the UK Biobank population with a relative risk for the diseases at least 3× higher than the average.

family history is a well recognised risk factor of CAD, BC, and PC and it is currently used to identify individuals at high risk of developing the diseases. We show that individuals with family history can have completely different disease risks based on PRS stratification: 2.1% → 33% (CAD in men), 0.56% → 10% (CAD in women), 2.3% → 35.8% (BC), and 1.0% → 34.0% (PC) in the lowest and highest percentiles, respectively. Additionally, the PRSs demonstrated higher predictive performance (AUCs (including age) CAD: 0.81, PC: 0.80, and BC: 0.68) than family history (AUCs (including age) CAD: 0.79, PC: 0.73, and BC: 0.61) in predicting the onset of diseases.

Hyperlipidemia is well known to be associated with higher CAD risk, but a predictive performance comparison between each lipoprotein and CAD PRS has never been assessed. PRS shows higher discrimination capacity and Odds ratio per Standard deviation than LDL, HDL, total cholesterol-HDL ratio, ApoA, ApoB, ApoB-ApoA ratio, and Lipoprotein(a). Comparing the empirical risk distribution between PRS and each lipoprotein, we show that lipoprotein thresholds, currently used in clinical practice, identify a population equal to or smaller than what can be identified with the PRS at the same CAD risk threshold. Moreover, there is not correlation (max ρ: 0.137) between PRS and each lipoprotein, indicating that PRS captures different component of CAD etiology and identifies different people at high risk than those identified by lipoproteins, demonstrating to be an invaluable tool in CAD prevention.

One of the major impairment of the PRS usage in clinical practice is the computational complexity needed to calculate per-individual PRSs. Deep bioinformatics expertise is required to run the entire pipeline, from imputing genomic data, through quality control to result visualization. For these reasons we developed a Software as a Service (SaaS) for genomic risk prediction of complex diseases. The SaaS is fully automated, GDPR complaint and has been certified as a CE marked medical device. We made the SaaS purposes. Researchers willing to use the SaaS can contact research@genomicriskscore.io

--- https://www.biorxiv.org/content/10.1101/727057.full Polygenic and clinical risk scores and their impact on age at onset of cardiometabolic diseases and common cancers Nina J. Mars, Jukka T. Koskela, Pietari Ripatti, Tuomo T. J. Kiiskinen, Aki S. Havulinna, Joni V. Lindbohm, Ari Ahola-Olli, Mitja Kurki, Juha Karjalainen, Priit Palta, FinnGen, Benjamin M. Neale, Mark Daly, Veikko Salomaa, Aarno Palotie, Elisabeth Widen, Samuli Ripatti 2019-08-06 2021-02-23 [("doi","10.1101/727057")] genetics/heritable

Background: Polygenic risk scores (PRS) have shown promise in predicting susceptibility to common diseases. However, the extent to which PRS and clinical risk factors act jointly and identify high-risk individuals for early onset of disease is unknown.

Method: We used large-scale biobank data (the FinnGen study; n = 135,300), with up to 46 years of prospective follow-up, and the FINRISK study with standardized clinical risk factor measurements to build genome-wide PRSs with >6M variants for coronary heart disease (CHD), type 2 diabetes (T2D), atrial fibrillation (AF), and breast and prostate cancer. We evaluated their associations with first disease events, age at disease onset, and impact together with routinely used clinical risk scores for predicting future disease.

Results: Compared to the 20–80th percentiles, a PRS in the top 2.5% translated into hazard ratios (HRs) for incident disease ranging 2.03–4.28 (p-values 1.96×10−59 to <1.00×10−100) and the bottom 2.5% into HRs ranging 0.20–0.61. The estimated difference in age at disease onset between top and bottom 2.5% of PRSs was 6 to 13 years. Among early-onset cases, 21.3–32.9% had a PRS in the highest decile and in CHD and AF.

Conclusion: The properties of PRS were similar in all five diseases. PRS identified a considerable proportion early-onset cases, and for all ages the performance of PRS was comparable to established clinical risk scores. These findings warrant further clinical studies on application of polygenic risk information for stratified screening or for guiding lifestyle and preventive medical interventions.

--- https://en.wikipedia.org/wiki/Honeycrisp Honeycrisp 2021-02-23 genetics/selection/artificial/apple --- https://en.wikipedia.org/wiki/SweeTango SweeTango 2021-02-23 genetics/selection/artificial/apple --- https://en.wikipedia.org/wiki/Red_Delicious Red Delicious 2021-02-23 genetics/selection/artificial/apple --- https://www.bloomberg.com/news/articles/2018-11-08/the-curse-of-the-honeycrisp-apple The Curse of the Honeycrisp Apple 2021-02-24 genetics/selection/artificial/apple --- https://en.wikipedia.org/wiki/Cosmic_Crisp Cosmic Crisp 2021-02-24 genetics/selection/artificial/apple --- https://en.wikipedia.org/wiki/Club_good Club good 2021-02-24 genetics/selection/artificial/apple --- https://en.wikipedia.org/wiki/Sport_(botany) Sport (botany) 2021-02-24 genetics/selection/artificial/apple --- /doc/sociology/2021-muthukrishna-2.pdf Psychology as a Historical Science Michael Muthukrishna, Joseph Henrich, Edward Slingerland 2020-10-13 2021-02-24 [("doi","10.1146/annurev-psych-082820-111436")] history sociology

Psychology has traditionally seen itself as the science of universal human cognition, but it has only recently begun seriously grappling with cross-cultural variation. Here we argue that the roots of cross-cultural variation often lie in the past. Therefore, to understand not only how but also why psychology varies, we need to grapple with cross-temporal variation. The traces of past human cognition accessible through historical texts and artifacts can serve as a valuable, and almost completely unutilized, source of psychological data. These data from dead minds open up an untapped and highly diverse subject pool.

We review examples of research that may be classified as historical psychology, introduce sources of historical data and methods for analyzing them, explain the critical role of theory, and discuss how psychologists can add historical depth and nuance to their work.

Psychology needs to become a historical science if it wants to be a genuinely universal science of human cognition and behavior.

--- https://www.newyorker.com/tech/annals-of-technology/luther-burbank-what-comes-after-heirloom-seeds What Comes After Heirloom Seeds? 2021-02-24 genetics/selection/artificial/apple --- https://en.wikipedia.org/wiki/Apple#Breeding Apple § Breeding 2021-02-24 genetics/selection/artificial/apple --- https://en.wikipedia.org/wiki/Chance_seedling Chance seedling 2021-02-24 genetics/selection/artificial/apple --- https://en.wikipedia.org/wiki/Cripps_Pink Cripps Pink 2021-02-24 genetics/selection/artificial/apple --- https://en.wikipedia.org/wiki/Foxwhelp Foxwhelp 2021-02-24 genetics/selection/artificial/apple --- https://en.wikipedia.org/wiki/Gala_(apple) Gala (apple) 2021-02-24 genetics/selection/artificial/apple --- https://en.wikipedia.org/wiki/Ginger_Gold Ginger Gold 2021-02-25 genetics/selection/artificial/apple --- https://en.wikipedia.org/wiki/Granny_Smith Granny Smith 2021-02-25 genetics/selection/artificial/apple --- https://en.wikipedia.org/wiki/Winesap Winesap 2021-02-25 genetics/selection/artificial/apple --- https://www.biorxiv.org/content/10.1101/689935.full Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke Gad Abraham, Rainer Malik, Ekaterina Yonova-Doing, Agus Salim, Tingting Wang, John Danesh, Adam Butterworth, Joanna Howson, Michael Inouye, Martin Dichgans 2019-07-02 2021-02-25 [("doi","10.1101/689935")] genetics/heritable

Recent genome-wide association studies (GWAS) in stroke have enabled the generation of genomic risk scores (GRS) but the predictive power of these GRS has been modest in comparison to established stroke risk factors. Here, using a meta-scoring approach, we developed a metaGRS for ischemic stroke (IS) and analyzed this score in the UK Biobank (n = 395,393; 3075 IS events by age 75).

The metaGRS hazard ratio for IS (1.26, 95% CI 1.22–1.31 per standard deviation increase of the score) doubled that of previous GRS, enabling the identification of a subset of individuals at monogenic levels of risk: individuals in the top 0.25% of metaGRS had a three-fold increased risk of IS. The metaGRS was similarly or more predictive when compared to established risk factors, such as family history, blood pressure, body mass index, and smoking status.

For participants within accepted guideline levels for established stroke risk factors, we found substantial variation in incident stroke rates across genomic risk backgrounds. We further estimated combinations of reductions needed in modifiable risk factors for individuals with different levels of genomic risk and suggest that, for individuals with high metaGRS, achieving currently recommended risk factor levels may be insufficient to mitigate risk.

--- https://www.biorxiv.org/content/10.1101/375337.full Modeling functional enrichment improves polygenic prediction accuracy in UK Biobank and 23andMe data sets Carla Márquez-Luna, Steven Gazal, Po-Ru Loh, Nicholas A. Furlotte, Adam Auton, 23andMe, Alkes Price 2018-07-24 2021-02-25 [("doi","10.1101/375337")] genetics/heritable

Genetic variants in functional regions of the genome are enriched for complex trait heritability. Here, we introduce a new method for polygenic prediction, LDpred-funct, that leverages trait-specific functional enrichments to increase prediction accuracy. We fit priors using the recently developed baseline-LD model, which includes coding, conserved, regulatory, and LD-related annotations.

We analytically estimate posterior mean causal effect sizes and then use cross-validation to regularize these estimates, improving prediction accuracy for sparse architectures. LDpred-funct attained higher prediction accuracy than other polygenic prediction methods in simulations using real genotypes.

We applied LDpred-funct to predict 16 highly heritable traits in the UK Biobank. We used association statistics from British-ancestry samples as training data (avg JV=365K) and samples of other European ancestries as validation data (avg 7V=22K), to minimize confounding. LDpred-funct attained a +27% relative improvement in prediction accuracy (avg prediction R2=0.173; highest R2=0.417 for height) compared to existing methods that do not incorporate functional information, consistent with simulations. For height, meta-analyzing training data from UK Biobank and 23andMe cohorts (total iV = 1107K; higher heritability in UK Biobank cohort) increased prediction R^2 to 0.429.

Our results show that modeling functional enrichment substantially improves polygenic prediction accuracy, bringing polygenic prediction of complex traits closer to clinical utility.

--- https://www.biorxiv.org/content/10.1101/194944.full Mixed model association for biobank-scale data sets Po-Ru Loh, Gleb Kichaev, Steven Gazal, Armin P. Schoech, Alkes Price 2018-01-04 2021-02-25 [("doi","10.1101/194944")] genetics/heritable

Biobank-based genome-wide association studies (GWAS) are enabling exciting insights in complex trait genetics, but much uncertainty remains over best practices for optimizing statistical power and computational efficiency in GWAS while controlling confounders. Here, we introduce a much faster version of our BOLT-LMM Bayesian mixed model association method—capable of running analyses of the full UK Biobank cohort in a few days on a single compute node—and show that it produces highly powered, robust test statistics when run on all 459K European samples (retaining related individuals).

When used to conduct a GWAS for height in UK Biobank, BOLT-LMM achieved power equivalent to linear regression on 650K samples—a 93% increase in effective sample size versus the common practice of analyzing unrelated British samples using linear regression (UK Biobank documentation; Bycroft et al bioRxiv). Across a broader set of 23 highly heritable traits, the total number of independent GWAS loci detected increased from 5,839 to 10,759, an 84% increase.

We recommend the use of BOLT-LMM (retaining related individuals) for biobank-scale analyses, and we have publicly released BOLT-LMM summary association statistics for the 23 traits analyzed as a resource for all researchers.

--- /doc/genetics/selection/artificial/2018-torkamani.pdf The personal and clinical utility of polygenic risk scores Ali Torkamani, Nathan E. Wineinger, Eric J. Topol 2018-01-01 2021-02-25 [("doi","10.1038/s41576-018-0018-x")] genetics/heritable genetics/selection/artificial --- https://www.biorxiv.org/content/10.1101/065540.full Cost-effectiveness of pharmacogenetic-guided treatment: are we there yet? Moira Verbelen, Michael E. Weale, Cathryn M. Lewis 2016-07-23 2021-02-25 [("doi","10.1101/065540")] genetics/heritable

Pharmacogenetics (PGx) has the potential to personalize pharmaceutical treatments. Many relevant gene-drug associations have been discovered, but PGx guided treatment needs to be cost-effective as well as clinically beneficial to be incorporated into standard healthcare. Progress in this area can be assessed by reviewing economic evaluations to determine the cost-effectiveness of PGx testing versus standard treatment.

We performed a review of economic evaluations for PGx associations listed in the US Food and Drug Administration (FDA) Table of Pharmacogenomic Biomarkers in Drug Labeling (https://www.fda.gov//Drugs/ScienceResearch/ResearchAreas/Pharmacogenetics/ucm083378.htm). We determined the proportion of evaluations that found PGx guided treatment to be cost-effective or dominant over the alternative strategies, and we estimated the impact on this proportion of removing the cost of genetic testing.

Of the 130 PGx associations in the FDA table, 44 economic evaluations, relating to 10 drugs, were identified. Of these evaluations, 57% drew conclusions in favour of PGx testing, of which 30% were cost-effective and 27% were dominant (cost-saving). If genetic information was freely available, 75% of economic evaluations would support PGx guided treatment, of which 25% would be cost-effective and 50% would be dominant.

Thus, PGx guided treatment can be a cost-effective and even cost-saving strategy. Having genetic information readily available in the clinical health record is a realistic future prospect, and would make more genetic tests economically worthwhile. However, few drugs with PGx associations have been studied and more economic evaluations are needed to underpin the uptake of genetic testing in clinical practice.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4250049/ Defining the role of common variation in the genomic and biological architecture of adult human height Andrew R. Wood, Tõnu Esko, Jian Yang, Sailaja Vedantam, Tune H. Pers, Stefan Gustafsson, Audrey Y. Chu, Karol Estrada, Jian’an Luan, Zoltán Kutalik, Najaf Amin, Martin L. Buchkovich, Damien C. Croteau-Chonka, Felix R. Day, Yanan Duan, Tove Fall, Rudolf Fehrmann, Teresa Ferreira, Anne Uriu Jackson, Juha Karjalainen, Ken Sin Lo, Adam E. Locke, Reedik Mägi, Evelin Mihailov, Eleonora Porcu, Joshua C. Randall, André Scherag, Anna A. E. Vinkhuyzen, Harm-Jan Westra, Thomas W. Winkler, Tsegaselassie Workalemahu, Jing Hua Zhao, Devin Absher, Eva Albrecht, Denise Anderson, Jeffrey Baron, Marian Beekman, Ayse Demirkan, Georg B. Ehret, Bjarke Feenstra, Mary F. Feitosa, Krista Fischer, Ross M. Fraser, Anuj Goel, Jian Gong, Anne E. Justice, Stavroula Kanoni, Marcus E. Kleber, Kati Kristiansson, Unhee Lim, Vaneet Lotay, Julian C. Lui, Massimo Mangino, Irene Mateo Leach, Carolina Medina-Gomez, Michael A. Nalls, Dale R. Nyholt, Cameron D. Palmer, Dorota Pasko, Sonali Pechlivanis, Inga Prokopenko, Janina S. Ried, Stephan Ripke, Dmitry Shungin, Alena Stancáková, Rona J. Strawbridge, Yun Ju Sung, Toshiko Tanaka, Alexander Teumer, Stella Trompet, Sander W. van der Laan, Jessica van Setten, Jana V. Van Vliet-Ostaptchouk, Zhaoming Wang, Loïc Yengo, Weihua Zhang, Uzma Afzal, Johan Arnlöv, Gillian M. Arscott, Stefania Bandinelli, Amy Barrett, Claire Bellis, Amanda J. Bennett, Christian Berne, Matthias Blüher, Jennifer L. Bolton, Yvonne Böttcher, Heather A. Boyd, Marcel Bruinenberg, Brendan M. Buckley, Steven Buyske, Ida H. Caspersen, Peter S. Chines, Robert Clarke, Simone Claudi-Boehm, Matthew Cooper, E. Warwick Daw, Pim A. De Jong, Joris Deelen, Graciela Delgado, Josh C. Denny, Rosalie Dhonukshe-Rutten, Maria Dimitriou, Alex S. F. Doney, Marcus Dörr, Niina Eklund, Elodie Eury, Lasse Folkersen, Melissa E. Garcia, Frank Geller, Vilmantas Giedraitis, Alan S. Go, Harald Grallert, Tanja B. Grammer, Jürgen Gräßler, Henrik Grönberg, Lisette C. P. G. M. de Groot, Christopher J. Groves, Jeffrey Haessler, Per Hall, Toomas Haller, Goran Hallmans, Anke Hannemann, Catharina A. Hartman, Maija Hassinen, Caroline Hayward, Nancy L. Heard-Costa, Quinta Helmer, Gibran Hemani, Anjali K. Henders, Hans L. Hillege, Mark A. Hlatky, Wolfgang Hoffmann, Per Hoffmann, Oddgeir Holmen, Jeanine J. Houwing-Duistermaat, Thomas Illig, Aaron Isaacs, Alan L. James, Janina Jeff, Berit Johansen, Åsa Johansson, Jennifer Jolley, Thorhildur Juliusdottir, Juhani Junttila, Abel N. Kho, Leena Kinnunen, Norman Klopp, Thomas Kocher, Wolfgang Kratzer, Peter Lichtner, Lars L. Lind, Jaana Lindström, Stéphane Lobbens, Mattias Lorentzon, Yingchang Lu, Valeriya Lyssenko, Patrik K. E. Magnusson, Anubha Mahajan, Marc Maillard, Wendy L. McArdle, Colin A. McKenzie, Stela McLachlan, Paul J. McLaren, Cristina Menni, Sigrun Merger, Lili Milani, Alireza Moayyeri, Keri L. Monda, Mario A. Morken, Gabriele Müller, Martina Müller-Nurasyid, Arthur W. Musk, Narisu Narisu, Matthias Nauck, Ilja M. Nolte, Markus M. Nöthen, Laticia Oozageer, Stefan Pilz, Nigel W. Rayner, Frida Renstrom, Neil R. Robertson, Lynda M. Rose, Ronan Roussel, Serena Sanna, Hubert Scharnagl, Salome Scholtens, Fredrick R. Schumacher, Heribert Schunkert, Robert A. Scott, Joban Sehmi, Thomas Seufferlein, Jianxin Shi, Karri Silventoinen, Johannes H. Smit, Albert Vernon Smith, Joanna Smolonska, Alice V. Stanton, Kathleen Stirrups, David J. Stott, Heather M. Stringham, Johan Sundström, Morris A. Swertz, Ann-Christine Syvänen, Bamidele O. Tayo, Gudmar Thorleifsson, Jonathan P. Tyrer, Suzanne van Dijk, Natasja M. van Schoor, Nathalie van der Velde, Diana van Heemst, Floor V. A. van Oort, Sita H. Vermeulen, Niek Verweij, Judith M. Vonk, Lindsay L. Waite, Melanie Waldenberger, Roman Wennauer, Lynne R. Wilkens, Christina Willenborg, Tom Wilsgaard, Mary K. Wojczynski, Andrew Wong, Alan F. Wright, Qunyuan Zhang, Dominique Arveiler, Stephan J. L. Bakker, John Beilby, Richard N. Bergman, Sven Bergmann, Reiner Biffar, John Blangero, Dorret I. Boomsma, Stefan R. Bornstein, Pascal Bovet, Paolo Brambilla, Morris J. Brown, Harry Campbell, Mark J. Caulfield, Aravinda Chakravarti, Rory Collins, Francis S. Collins, Dana C. Crawford, L. Adrienne Cupples, John Danesh, Ulf de Faire, Hester M. den Ruijter, Raimund Erbel, Jeanette Erdmann, Johan G. Eriksson, Martin Farrall, Ele Ferrannini, Jean Ferrières, Ian Ford, Nita G. Forouhi, Terrence Forrester, Ron T. Gansevoort, Pablo V. Gejman, Christian Gieger, Alain Golay, Omri Gottesman, Vilmundur Gudnason, Ulf Gyllensten, David W. Haas, Alistair S. Hall, Tamara B. Harris, Andrew Tym Hattersley, Andrew C. Heath, Christian Hengstenberg, Andrew A. Hicks, Lucia A. Hindorff, Aroon D. Hingorani, Albert Hofman, G. Kees Hovingh, Steve E. Humphries, Steven C. Hunt, Elina Hypponen, Kevin B. Jacobs, Marjo-Riitta Jarvelin, Pekka Jousilahti, Antti M. Jula, Jaakko Kaprio, John J. P. Kastelein, Manfred Kayser, Frank Kee, Sirkka M. Keinanen-Kiukaanniemi, Lambertus A. Kiemeney, Jaspal S. Kooner, Charles Kooperberg, Seppo Koskinen, Peter Kovacs, Aldi T. Kraja, Meena Kumari, Johanna Kuusisto, Timo A. Lakka, Claudia Langenberg, Loic Le Marchand, Terho Lehtimäki, Sara Lupoli, Pamela A. F. Madden, Satu Männistö, Paolo Manunta, André Marette, Tara C. Matise, Barbara McKnight, Thomas Meitinger, Frans L. Moll, Grant W. Montgomery, Andrew D. Morris, Andrew P. Morris, Jeffrey C. Murray, Mari Nelis, Claes Ohlsson, Albertine J. Oldehinkel, Ken K. Ong, Willem H. Ouwehand, Gerard Pasterkamp, Annette Peters, Peter P. Pramstaller, Jackie F. Price, Lu Qi, Olli T. Raitakari, Tuomo Rankinen, D. C. Rao, Treva K. Rice, Marylyn Ritchie, Igor Rudan, Veikko Salomaa, Nilesh J. Samani, Jouko Saramies, Mark A. Sarzynski, Peter E. H. Schwarz, Sylvain Sebert, Peter Sever, Alan R. Shuldiner, Juha Sinisalo, Valgerdur Steinthorsdottir, Ronald P. Stolk, Jean-Claude Tardif, Anke Tönjes, Angelo Tremblay, Elena Tremoli, Jarmo Virtamo, Marie-Claude Vohl, Philippe Amouyel, Folkert W. Asselbergs, Themistocles L. Assimes, Murielle Bochud, Bernhard O. Boehm, Eric Boerwinkle, Erwin Böttinger, Claude Bouchard, Stéphane Cauchi, John C. Chambers, Stephen J. Chanock, Richard S. Cooper, Paul I. W. de Bakker, George Dedoussis, Luigi Ferrucci, Paul W. Franks, Philippe Froguel, Leif C. Groop, Christopher A. Haiman, Anders Hamsten, M. Geoffrey Hayes, Jennie Hui, David J. Hunter, Kristian Hveem, J. Wouter Jukema, Robert C. Kaplan, Mika Kivimaki, Diana Kuh, Markku Laakso, Yongmei Liu, Nicholas G. Martin, Winfried März, Mads Melbye, Susanne Moebus, Patricia B. Munroe, Inger Njølstad, Ben A. Oostra, Colin Palmer, Nancy L. Pedersen, Markus Perola, Louis Pérusse, Ulrike Peters, Joseph E. Powell, Chris Power, Thomas Quertermous, Rainer Rauramaa, Eva Reinmaa, Paul M. Ridker, Fernando Rivadeneira, Jerome I. Rotter, Timo E. Saaristo, Danish Saleheen, David Schlessinger, P. Eline Slagboom, Harold Snieder, Tim D. Spector, Konstantin Strauch, Michael Stumvoll, Jaakko Tuomilehto, Matti Uusitupa, Pim van der Harst, Henry Völzke, Mark Walker, Nicholas J. Wareham, Hugh Watkins, H-Erich Wichmann, James F. Wilson, Pieter Zanen, Panos Deloukas, Iris M. Heid, Cecilia M. Lindgren, Karen L. Mohlke, Elizabeth K. Speliotes, Unnur Thorsteinsdottir, Inês Barroso, Caroline S. Fox, Kari E. North, David P. Strachan, Jacques S. Beckmann, Sonja I. Berndt, Michael Boehnke, Ingrid B. Borecki, Mark I. McCarthy, Andres Metspalu, Kari Stefansson, André G. Uitterlinden, Cornelia van Duijn, Lude Franke, Cristen Jennifer Willer, Alkes Price, Guillaume Lettre, Ruth Loos, Michael N. Weedon, Erik Ingelsson, Jeffrey R. O’Connell, Gonçalo Abecasis, Daniel I. Chasman, Michael E. Goddard, Peter M. Visscher, Joel N. Hirschhorn, Timothy Frayling 2014 2021-02-25 [("doi","10.1038/ng.3097")] genetics/heritable

Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide statistical-significance that together explained one-fifth of the heritability for adult height.

By testing different numbers of variants in independent studies, we show that the most strongly associated ~2,000, ~3,700 and ~9,500 SNPs explained ~21%, ~24% and ~29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/β-catenin and chondroitin sulfate-related genes.

We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid.

Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.

--- /doc/psychology/2006-vandenberg.pdf Phenotyping of Aggressive Behavior in Golden Retriever Dogs with a Questionnaire L. van den Berg, M. B. H. Schilder, H. de Vries, P. A. J. Leegwater, B. A. van Oost 2006-01-01 2021-02-25 [("doi","10.1007/s10519-006-9089-0")] dog psychology --- https://www.biorxiv.org/content/10.1101/2021.11.29.470309.full Genetic architecture and genomic prediction accuracy of apple quantitative traits across environments Michaela Jung, Beat Keller, Morgane Roth, Maria José Aranzana, Annemarie Auwerkerken, Walter Guerra, Mehdi Al-Rifaï, Mariusz Lewandowski, Nadia Sanin, Marijn Rymenants, Frédérique Didelot, Christian Dujak, Carolina Font i Forcada, Andrea Knauf, François Laurens, Bruno Studer, Hélène Muranty, Andrea Patocchi 2021-12-01 2021-12-01 [("doi","10.1101/2021.11.29.470309")] genetics/selection/artificial/apple

Implementation of genomic tools is desirable to increase the efficiency of apple breeding. The apple reference population (apple REFPOP) proved useful for rediscovering loci, estimating genomic prediction accuracy, and studying genotype by environment interactions (G×E).

Here we show contrasting genetic architecture and genomic prediction accuracies for 30 quantitative traits across up to 6 European locations using the apple REFPOP. A total of 59 stable and 277 location-specific associations were found using GWAS, 69.2% of which are novel when compared with 41 reviewed publications. Average genomic prediction accuracies of 0.18–0.88 were estimated using single-environment univariate, single-environment multivariate, multi-environment univariate, and multi-environment multivariate models.

The G×E accounted for up to 24% of the phenotypic variability. This most comprehensive genomic study in apple in terms of trait-environment combinations provided knowledge of trait biology and prediction models that can be readily applied for marker-assisted or genomic selection, thus facilitating increased breeding efficiency.

--- https://www.nytimes.com/2020/05/27/style/toyger-fever.html You Thought Your Cat Was Fancy? 2021-02-26 cat/genetics genetics/selection --- https://www.washingtonpost.com/wp-dyn/content/article/2005/08/04/AR2005080402194_pf.html Why the Red Delicious No Longer Is 2021-02-26 genetics/selection/artificial/apple genetics/selection/natural/human/dysgenics --- /doc/history/1999-lemish-wardogs.pdf War Dogs: A History of Loyalty and Heroism Michael G. Lemish 1999-01-01 2021-02-26 dog history --- /doc/genetics/heritable/correlation/1996-karjalainen.pdf Environmental effects and genetic parameters for measurements of hunting performance in the Finnish Spitz L. Karjalainen, M. Ojala, V. Vilva 1996-01-01 2021-02-26 genetics/heritable/correlation genetics/heritable/dog --- /doc/economics/copyright/2012-moser.pdf Did Plant Patents Create the American Rose? Petra Moser, Paul W. Rhode 2012-03-01 2021-02-26 economics/copyright genetics/selection/artificial/apple --- https://arxiv.org/abs/2106.13353 Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models Robert L. Logan IV, Ivana Balažević, Eric Wallace, Fabio Petroni, Sameer Singh, Sebastian Riedel 2021-06-24 2021-06-24 [("doi","10.48550/arXiv.2106.13353")] ai/nn/transformer/gpt

Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning. In this work, we show that finetuning LMs in the few-shot setting can considerably reduce the need for prompt engineering. In fact, one can use null prompts, prompts that contain neither task-specific templates nor training examples, and achieve competitive accuracy to manually-tuned prompts across a wide range of tasks.

While finetuning LMs does introduce new parameters for each downstream task, we show that this memory overhead can be substantially reduced: finetuning only the bias terms can achieve comparable or better accuracy than standard finetuning while only updating 0.1% of the parameters.

All in all, we recommend finetuning LMs for few-shot learning as it is more accurate, robust to different prompts, and can be made nearly as efficient as using frozen LMs.

--- https://arxiv.org/abs/2106.12368 Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition Qibin Hou, Zihang Jiang, Li Yuan, Ming-Ming Cheng, Shuicheng Yan, Jiashi Feng 2021-06-23 2021-06-23 [("doi","10.48550/arXiv.2106.12368")] ai/nn/fully-connected

In this paper, we present Vision Permutator, a conceptually simple and data efficient MLP-like architecture for visual recognition. By realizing the importance of the positional information carried by 2D feature representations, unlike recent MLP-like models that encode the spatial information along the flattened spatial dimensions, Vision Permutator separately encodes the feature representations along the height and width dimensions with linear projections. This allows Vision Permutator to capture long-range dependencies along one spatial direction and meanwhile preserve precise positional information along the other direction. The resulting position-sensitive outputs are then aggregated in a mutually complementing manner to form expressive representations of the objects of interest.

We show that our Vision Permutators are formidable competitors to convolutional neural networks (CNNs) and vision transformers. Without the dependence on spatial convolutions or attention mechanisms, Vision Permutator achieves 81.5% top-1 accuracy on ImageNet without extra large-scale training data (eg. ImageNet-22k) using only 25M learnable parameters, which is much better than most CNNs and vision transformers under the same model size constraint. When scaling up to 88M, it attains 83.2% top-1 accuracy.

We hope this work could encourage research on rethinking the way of encoding spatial information and facilitate the development of MLP-like models.

Code is available at Github.

--- https://arxiv.org/abs/2205.12399#google Sparse Mixers: Combining MoE and Mixing to build a more efficient BERT James Lee-Thorp, Joshua Ainslie 2022-05-24 2022-05-24 [("doi","10.48550/arXiv.2205.12399")] ai/nn/fully-connected ai/scaling/mixture-of-experts

We combine the capacity of sparsely gated Mixture-of-Experts (MoE) with the speed and stability of linear, mixing transformations to design the Sparse Mixer encoder model. The Sparse Mixer slightly outperforms (<1%) BERT on GLUE and SuperGLUE, but more importantly trains 65% faster and runs inference 61% faster.

We also present a faster variant, prosaically named Fast Sparse Mixer, that marginally underperforms (<0.2%) BERT on SuperGLUE, but trains and runs nearly twice as fast: 89% faster training and 98% faster inference. We justify the design of these two models by carefully ablating through various mixing mechanisms, MoE configurations and model hyperparameters.

The Sparse Mixer overcomes many of the latency and stability concerns of MoE models and offers the prospect of serving sparse student models, without resorting to distilling them to dense variants.

--- https://arxiv.org/abs/1810.09675 SwitchNet: a neural network model for forward and inverse scattering problems Yuehaw Khoo, Lexing Ying 2018-10-23 2021-02-26 [("doi","10.48550/arXiv.1810.09675")] ai/nn/fully-connected

We propose a novel neural network architecture, SwitchNet, for solving the wave equation based inverse scattering problems via providing maps between the scatterers and the scattered field (and vice versa). The main difficulty of using a neural network for this problem is that a scatterer has a global impact on the scattered wave field, rendering typical convolutional neural network with local connections inapplicable.

While it is possible to deal with such a problem using a fully connected network, the number of parameters grows quadratically with the size of the input and output data. By leveraging the inherent low-rank structure of the scattering problems and introducing a novel switching layer with sparse connections, the SwitchNet architecture uses much fewer parameters and facilitates the training process.

Numerical experiments show promising accuracy in learning the forward and inverse maps between the scatterers and the scattered wave field.

--- https://arxiv.org/abs/1901.09321 Fixup Initialization: Residual Learning Without Normalization Hongyi Zhang, Yann N. Dauphin, Tengyu Ma 2019-01-27 2021-02-26 [("doi","10.48550/arXiv.1901.09321")] ai/nn/fully-connected

Normalization layers are a staple in state-of-the-art deep neural network architectures. They are widely believed to stabilize training, enable higher learning rate, accelerate convergence, and improve generalization, though the reason for their effectiveness is still an active research topic.

In this work, we challenge the commonly-held beliefs by showing that none of the perceived benefits is unique to normalization. Specifically, we propose fixed-update initialization (Fixup), an initialization motivated by solving the exploding and vanishing gradient problem at the beginning of training via properly rescaling a standard initialization.

We find training residual networks with Fixup to be as stable as training with normalization—even for networks with 10,000 layers. Furthermore, with proper regularization, Fixup enables residual networks without normalization to achieve state-of-the-art performance in image classification and machine translation.

--- https://arxiv.org/abs/2003.04887 ReZero is All You Need: Fast Convergence at Large Depth Thomas Bachlechner, Bodhisattwa Prasad Majumder, Huanru Henry Mao, Garrison W. Cottrell, Julian McAuley 2020-03-10 2021-02-27 [("doi","10.48550/arXiv.2003.04887")] ai/nn/fully-connected

Deep networks often suffer from vanishing or exploding gradients due to inefficient signal propagation, leading to long training times or convergence difficulties. Various architecture designs, sophisticated residual-style networks, and initialization schemes have been shown to improve deep signal propagation. Recently, Pennington et al used free probability theory to show that dynamical isometry plays an integral role in efficient deep learning.

We show that the simplest architecture change of gating each residual connection using a single zero-initialized parameter satisfies initial dynamical isometry and outperforms more complex approaches. Although much simpler than its predecessors, this gate enables training thousands of fully connected layers with fast convergence and better test performance for ResNets trained on CIFAR-10.

We apply this technique to language modeling and find that we can easily train 120-layer Transformers. When applied to 12 layer Transformers, it converges 56% faster on enwik8.

--- https://arxiv.org/abs/2010.08895 Fourier Neural Operator for Parametric Partial Differential Equations Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar 2020-10-18 2021-02-27 [("doi","10.48550/arXiv.2010.08895")] ai/nn/cnn ai/nn/fully-connected

The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution. Thus, they learn an entire family of PDEs, in contrast to classical methods which solve one instance of the equation.

In this work, we formulate a new neural operator by parameterizing the integral kernel directly in Fourier space, allowing for an expressive and efficient architecture. We perform experiments on Burgers’ equation, Darcy flow, and Navier-Stokes equation. The Fourier neural operator is the first ML-based method to successfully model turbulent flows with zero-shot super-resolution. It is up to 3 orders of magnitude faster compared to traditional PDE solvers. Additionally, it achieves superior accuracy compared to previous learning-based solvers under fixed resolution.

--- https://arxiv.org/abs/1812.04948#nvidia A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras, Samuli Laine, Timo Aila 2018-12-12 2021-02-27 [("doi","10.48550/arXiv.1812.04948")] ai/dataset ai/nn/gan/stylegan

We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (eg. pose and identity when trained on human faces) and stochastic variation in the generated images (eg. freckles, hair), and it enables intuitive, scale-specific control of the synthesis.

The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture.

Finally, we introduce a new, highly varied and high-quality dataset of human faces.

--- https://arxiv.org/abs/1906.06766 Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias Stéphane d’Ascoli, Levent Sagun, Joan Bruna, Giulio Biroli 2019-06-16 2021-02-27 [("doi","10.48550/arXiv.1906.06766")] ai/nn/cnn ai/nn/fully-connected

Despite the phenomenal success of deep neural networks in a broad range of learning tasks, there is a lack of theory to understand the way they work. In particular, Convolutional Neural Networks (CNNs) are known to perform much better than Fully-Connected Networks (FCNs) on spatially structured data: the architectural structure of CNNs benefits from prior knowledge on the features of the data, for instance their translation invariance. The aim of this work is to understand this fact through the lens of dynamics in the loss landscape.

We introduce a method that maps a CNN to its equivalent FCN (denoted as eFCN). Such an embedding enables the comparison of CNN and FCN training dynamics directly in the FCN space. We use this method to test a new training protocol, which consists in training a CNN, embedding it to FCN space at a certain “relax time”, then resuming the training in FCN space. We observe that for all relax times, the deviation from the CNN subspace is small, and the final performance reached by the eFCN is higher than that reachable by a standard FCN of same architecture. More surprisingly, for some intermediate relax times, the eFCN outperforms the CNN it stemmed, by combining the prior information of the CNN and the expressivity of the FCN in a complementary way. The practical interest of our protocol is limited by the very large size of the highly sparse eFCN. However, it offers interesting insights into the persistence of architectural bias under stochastic gradient dynamics. It shows the existence of some rare basins in the FCN loss landscape associated with very good generalization. These can only be accessed thanks to the CNN prior, which helps navigate the landscape during the early stages of optimization.

--- https://arxiv.org/abs/2010.08127#google The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi 2020-10-16 2021-02-27 [("doi","10.48550/arXiv.2010.08127")] ai/scaling

We propose a new framework for reasoning about generalization in deep learning. The core idea is to couple the Real World, where optimizers take stochastic gradient steps on the empirical loss, to an Ideal World, where optimizers take steps on the population loss. This leads to an alternate decomposition of test error into: (1) the Ideal World test error plus (2) the gap between the two worlds. If the gap (2) is universally small, this reduces the problem of generalization in offline learning to the problem of optimization in online learning.

We then give empirical evidence that this gap between worlds can be small in realistic deep learning settings, in particular supervised image classification. For example, CNNs generalize better than MLPs on image distributions in the Real World, but this is “because” they optimize faster on the population loss in the Ideal World.

This suggests our framework is a useful tool for understanding generalization in deep learning, and lays a foundation for future research in the area.

--- https://arxiv.org/abs/2201.10801 When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism (ShiftViT) Guangting Wang, Yucheng Zhao, Chuanxin Tang, Chong Luo, Wenjun Zeng 2022-01-26 2022-01-26 [("doi","10.48550/arXiv.2201.10801")] ai/nn/fully-connected

Attention mechanism has been widely believed as the key to success of vision transformers (ViTs), since it provides a flexible and powerful way to model spatial relationships. However, is the attention mechanism truly an indispensable part of ViT? Can it be replaced by some other alternatives? To demystify the role of attention mechanism,

we simplify it into an extremely simple case: ZERO FLOP and ZERO parameter. Concretely, we revisit the shift operation. It does not contain any parameter or arithmetic calculation. The only operation is to exchange a small portion of the channels between neighboring features. Based on this simple operation, we construct a new backbone network, namely ShiftViT, where the attention layers in ViT are substituted by shift operations.

Surprisingly, ShiftViT works quite well in several mainstream tasks, eg. classification, detection, and segmentation. The performance is on par with or even better than the strong baseline Swin Transformer.

These results suggest that the attention mechanism might not be the vital factor that makes ViT successful. It can be even replaced by a zero-parameter operation. We should pay more attentions to the remaining parts of ViT in the future work.

Code is available at Github.

--- https://arxiv.org/abs/2201.09792 ConvMixer: Patches Are All You Need? Asher Trockman, J. Zico Kolter 2022-01-24 2022-01-24 [("doi","10.48550/arXiv.2201.09792")] ai/nn/cnn ai/nn/fully-connected

Although convolutional networks have been the dominant architecture for vision tasks for many years, recent experiments have shown that Transformer-based models, most notably the Vision Transformer (ViT), may exceed their performance in some settings. However, due to the quadratic runtime of the self-attention layers in Transformers, ViTs require the use of patch embeddings, which group together small regions of the image into single input features, in order to be applied to larger image sizes. This raises a question: Is the performance of ViTs due to the inherently-more-powerful Transformer architecture, or is it at least partly due to using patches as the input representation?

In this paper, we present some evidence for the latter: specifically, we propose the ConvMixer, an extremely simple model that is similar in spirit to the ViT and the even-more-basic MLP-Mixer in that it operates directly on patches as input, separates the mixing of spatial and channel dimensions, and maintains equal size and resolution throughout the network. In contrast, however, the ConvMixer uses only standard convolutions to achieve the mixing steps.

Despite its simplicity, we show that the ConvMixer outperforms the ViT, MLP-Mixer, and some of their variants for similar parameter counts and data set sizes, in addition to outperforming classical vision models such as the ResNet.

Our code is available at Github.

[Authors provide a 280-character PyTorch implementation which can fit in a tweet; this has since been golfed to 247-characters, 188, & 183.] --- https://arxiv.org/abs/2111.11187 PointMixer: MLP-Mixer for Point Cloud Understanding Jaesung Choe, Chunghyun Park, Francois Rameau, Jaesik Park, In So Kweon 2021-11-22 2021-11-22 [("doi","10.48550/arXiv.2111.11187")] ai/nn/fully-connected

MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and transformer. Despite its simplicity compared to transformer, the concept of channel-mixing MLPs and token-mixing MLPs achieves noticeable performance in visual recognition tasks. Unlike images, point clouds are inherently sparse, unordered and irregular, which limits the direct use of MLP-Mixer for point cloud understanding.

In this paper, we propose PointMixer, a universal point set operator that facilitates information sharing among unstructured 3D points. By simply replacing token-mixing MLPs with a softmax function, PointMixer can “mix” features within/between point sets. By doing so, PointMixer can be broadly used in the network as inter-set mixing, intra-set mixing, and pyramid mixing.

Extensive experiments show the competitive or superior performance of PointMixer in semantic segmentation, classification, and point reconstruction against transformer-based methods.

--- https://arxiv.org/abs/2111.11418 MetaFormer is Actually What You Need for Vision Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, Shuicheng Yan 2021-11-22 2021-11-22 [("doi","10.48550/arXiv.2111.11418")] ai/nn/fully-connected

Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well.

Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model’s performance. To verify this, we deliberately replace the attention module in transformers with an embarrassingly simple spatial pooling operator to conduct only the most basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on multiple computer vision tasks. For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned vision transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 48%/60% fewer MACs.

The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of “MetaFormer”, a general architecture abstracted from transformers without specifying the token mixer.

Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks.

This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design.

Code is available at https://github.com/sail-sg/poolformer.

--- https://arxiv.org/abs/2110.12661 ZerO Initialization: Initializing Residual Networks with only Zeros and Ones Jiawei Zhao, Florian Schäfer, Anima Anandkumar 2021-10-25 2021-10-25 [("doi","10.48550/arXiv.2110.12661")] ai/nn/fully-connected

Deep neural networks are usually initialized with random weights, with adequately selected initial variance to ensure stable signal propagation during training. However, there is no consensus on how to select the variance, and this becomes challenging especially as the number of layers grows.

In this work, we replace the widely used random weight initialization with a fully deterministic initialization scheme ZerO, which initializes residual networks with only zeros and ones. By augmenting the standard ResNet architectures with a few extra skip connections and Hadamard transforms, ZerO allows us to start the training from zeros and ones entirely. This has many benefits such as improving reproducibility (by reducing the variance over different experimental runs) and allowing network training without batch normalization.

Surprisingly, we find that ZerO achieves state-of-the-art performance over various image classification datasets, including ImageNet, which suggests random weights may be unnecessary for modern network initialization.

--- https://arxiv.org/abs/2110.01765#deepmind Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping James Martens, Andy Ballard, Guillaume Desjardins, Grzegorz Swirszcz, Valentin Dalibard, Jascha Sohl-Dickstein, Samuel S. Schoenholz 2021-10-05 2021-10-05 [("doi","10.48550/arXiv.2110.01765")] ai/nn/fully-connected

Using an extended and formalized version of the Q/C map analysis of Poole et al 2016, along with Neural Tangent Kernel theory, we identify the main pathologies present in deep networks that prevent them from training fast and generalizing to unseen data, and show how these can be avoided by carefully controlling the “shape” of the network’s initialization-time kernel function.

We then develop a method called Deep Kernel Shaping (DKS), which accomplishes this using a combination of precise parameter initialization, activation function transformations, and small architectural tweaks, all of which preserve the model class.

In our experiments we show that DKS enables SGD training of residual networks without normalization layers on Imagenet and CIFAR-10 classification tasks at speeds comparable to standard ResNetV2 and Wide-ResNet models, with only a small decrease in generalization performance. And when using K-FAC as the optimizer, we achieve similar results for networks without skip connections. Our results apply for a large variety of activation functions, including those which traditionally perform very badly, such as the logistic sigmoid.

In addition to DKS, we contribute a detailed analysis of skip connections, normalization layers, special activation functions like RELU and SELU, and various initialization schemes, explaining their effectiveness as alternative (and ultimately incomplete) ways of “shaping” the network’s initialization-time kernel.

--- https://arxiv.org/abs/2110.02095#google Exploring the Limits of Large Scale Pre-training Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi 2021-10-05 2021-10-05 [("doi","10.48550/arXiv.2110.02095")] ai/nn/fully-connected ai/scaling

Recent developments in large-scale machine learning suggest that by scaling up data, model size, and training time properly, one might observe that improvements in pre-training would transfer favorably to most downstream tasks. In this work, we systematically study this phenomenon and establish that, as we increase the upstream accuracy, the performance of downstream tasks saturates.

In particular, we investigate more than 4800 experiments on Vision Transformers, MLP-Mixers and ResNets with number of parameters ranging from 10 million to 10 billion, trained on the largest scale of available image data (JFT, ImageNet21K) and evaluated on more than 20 downstream image recognition tasks. We propose a model for downstream performance that reflects the saturation phenomena and captures the nonlinear relationship in performance of upstream and downstream tasks.

Delving deeper to understand the reasons that give rise to these phenomena, we show that the saturation behavior we observe is closely related to the way that representations evolve through the layers of the models. We showcase an even more extreme scenario where performance on upstream and downstream are at odds with each other. That is, to have a better downstream performance, we need to hurt upstream accuracy.

--- https://arxiv.org/abs/2109.05422 Sparse MLP for Image Recognition: Is Self-Attention Really Necessary? Chuanxin Tang, Yucheng Zhao, Guangting Wang, Chong Luo, Wenxuan Xie, Wenjun Zeng 2021-09-12 2021-09-12 [("doi","10.48550/arXiv.2109.05422")] ai/nn/fully-connected

Transformers have sprung up in the field of computer vision. In this work, we explore whether the core self-attention module in ‘Attention Is All You Need’, Vaswani et al 2017 is the key to achieving excellent performance in image recognition.

To this end, we build an attention-free network called sMLPNet based on the existing MLP-based vision models. Specifically, we replace the MLP module in the token-mixing step with a novel sparse MLP (sMLP) module. For 2D image tokens, sMLP applies 1D MLP along the axial directions and the parameters are shared among rows or columns. By sparse connection and weight sharing, sMLP module reduces the number of model parameters and computational complexity, avoiding the common over-fitting problem that plagues the performance of MLP-like models.

When only trained on the ‘ImageNet Large Scale Visual Recognition Challenge’, Russakovsky et al 2014-1K dataset, the proposed sMLPNet achieves 81.9% top-1 accuracy with only 24M parameters, which is much better than most CNNs and ‘Vision Transformer: An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale’, Dosovitskiy et al 2020 under the same model size constraint. When scaling up to 66M parameters, sMLPNet achieves 83.4% top-1 accuracy, which is on par with the state-of-the-art Swin Transformer.

The success of sMLPNet suggests that the self-attention mechanism is not necessarily a silver bullet in computer vision.

Code will be made publicly available.

--- https://arxiv.org/abs/2109.04454 ConvMLP: Hierarchical Convolutional MLPs for Vision Jiachen Li, Ali Hassani, Steven Walton, Humphrey Shi 2021-09-09 2021-09-09 [("doi","10.48550/arXiv.2109.04454")] ai/nn/fully-connected

MLP-based architectures, which consist of a sequence of consecutive multi-layer perceptron blocks, have recently been found to reach comparable results to convolutional and transformer-based methods. However, most adopt spatial MLPs which take fixed dimension inputs, therefore making it difficult to apply them to downstream tasks, such as object detection and semantic segmentation. Moreover, single-stage designs further limit performance in other computer vision tasks and fully connected layers bear heavy computation.

To tackle these problems, we propose ConvMLP: a hierarchical Convolutional MLP for visual recognition, which is a light-weight, stage-wise, co-design of convolution layers, and MLPs. In particular, ConvMLP-S achieves 76.8% top-1 accuracy on ImageNet-1k with 9M parameters and 2.4G MACs (15% and 19% of MLP-Mixer-B/16, respectively).

Experiments on object detection and semantic segmentation further show that visual representation learned by ConvMLP can be seamlessly transferred and achieve competitive results with fewer parameters.

Our code and pre-trained models are publicly available at Github.

--- https://arxiv.org/abs/2108.13341#huawei Hire-MLP: Vision MLP via Hierarchical Rearrangement Jianyuan Guo, Yehui Tang, Kai Han, Xinghao Chen, Han Wu, Chao Xu, Chang Xu, Yunhe Wang 2021-08-30 2021-08-30 [("doi","10.48550/arXiv.2108.13341")] ai/nn/fully-connected

Previous vision MLPs such as MLP-Mixer and ResMLP accept linearly flattened image patches as input, making them inflexible for different input sizes and hard to capture spatial information. Such approach withholds MLPs from getting comparable performance with their transformer-based counterparts and prevents them from becoming a general backbone for computer vision.

This paper presents Hire-MLP, a simple yet competitive vision MLP architecture via Hierarchical rearrangement, which contains two levels of rearrangements. Specifically, the inner-region rearrangement is proposed to capture local information inside a spatial region, and the cross-region rearrangement is proposed to enable information communication between different regions and capture global context by circularly shifting all tokens along spatial directions.

Extensive experiments demonstrate the effectiveness of Hire-MLP as a versatile backbone for various vision tasks. In particular, Hire-MLP achieves competitive results on image classification, object detection and semantic segmentation tasks, eg. 83.8% top-1 accuracy on ImageNet, 51.7% box AP and 44.8% mask AP on COCO val2017, and 49.9% mIoU on ADE20K, surpassing previous transformer-based and MLP-based models with better trade-off for accuracy and throughput.

Code is available at Github.

--- https://arxiv.org/abs/2108.04384 RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality? Yuki Tatsunami, Masato Taki 2021-08-09 2021-08-09 [("doi","10.48550/arXiv.2108.04384")] ai/nn/fully-connected

For the past ten years, CNN has reigned supreme in the world of computer vision, but recently, Transformer has been on the rise. However, the quadratic computational cost of self-attention has become a serious problem in practice applications. There has been much research on architectures without CNN and self-attention in this context. In particular, MLP-Mixer is a simple architecture designed using MLPs and hit an accuracy comparable to the Vision Transformer. However, the only inductive bias in this architecture is the embedding of tokens.

This leaves open the possibility of incorporating a non-convolutional (or non-local) inductive bias into the architecture, so we used two simple ideas to incorporate inductive bias into the MLP-Mixer while taking advantage of its ability to capture global correlations. A way is to divide the token-mixing block vertically and horizontally. Another way is to make spatial correlations denser among some channels of token-mixing. With this approach, we were able to improve the accuracy of the MLP-Mixer while reducing its parameters and computational complexity.

The small model that is RaftMLP-S is comparable to the state-of-the-art global MLP-based model in terms of parameters and efficiency per calculation. In addition, we tackled the problem of fixed input image resolution for global MLP-based models by utilizing bicubic interpolation. We demonstrated that these models could be applied as the backbone of architectures for downstream tasks such as object detection. However, it did not have performance and mentioned the need for MLP-specific architectures for downstream tasks for global MLP-based models.

The source code in PyTorch version is available at https://github.com/okojoalg/raft-mlp.

--- https://arxiv.org/abs/2107.08391 AS-MLP: An Axial Shifted MLP Architecture for Vision Dongze Lian, Zehao Yu, Xing Sun, Shenghua Gao 2021-07-18 2021-07-18 [("doi","10.48550/arXiv.2107.08391")] ai/nn/fully-connected

An Axial Shifted MLP architecture (AS-MLP) is proposed in this paper. Different from MLP-Mixer, where the global spatial feature is encoded for information flow through matrix transposition and one token-mixing MLP, we pay more attention to the local features interaction. By axially shifting channels of the feature map, AS-MLP is able to obtain the information flow from different axial directions, which captures the local dependencies. Such an operation enables us to utilize a pure MLP architecture to achieve the same local receptive field as CNN-like architecture. We can also design the receptive field size and dilation of blocks of AS-MLP, etc, in the same spirit of convolutional neural networks.

With the proposed AS-MLP architecture, our model obtains 83.3% Top-1 accuracy with 88M parameters and 15.2 GFLOPs on the ImageNet-1K dataset. Such a simple yet effective architecture outperforms all MLP-based architectures and achieves competitive performance compared to the transformer-based architectures (eg. Swin Transformer) even with slightly lower FLOPs. In addition, AS-MLP is also the first MLP-based architecture to be applied to the downstream tasks (eg. object detection and semantic segmentation). The experimental results are also impressive. Our proposed AS-MLP obtains 51.5 mAP on the COCO validation set and 49.5 MS mIoU on the ADE20K dataset, which is competitive compared to the transformer-based architectures. Our AS-MLP establishes a strong baseline of MLP-based architecture.

Code is available at Github.

--- https://arxiv.org/abs/2106.13031 Towards Biologically Plausible Convolutional Networks Roman Pogodin, Yash Mehta, Timothy Lillicrap, Peter E. Latham 2021-06-22 2021-06-22 [("doi","10.48550/arXiv.2106.13031")] ai/nn/fully-connected psychology/neuroscience

Convolutional networks are ubiquitous in deep learning. They are particularly useful for images, as they reduce the number of parameters, reduce training time, and increase accuracy. However, as a model of the brain they are seriously problematic, since they require weight sharing—something real neurons simply cannot do. Consequently, while neurons in the brain can be locally connected (one of the features of convolutional networks), they cannot be convolutional. Locally connected but non-convolutional networks, however, underperform convolutional ones. This is troublesome for studies that use convolutional networks to explain activity in the visual system.

Here we study plausible alternatives to weight sharing that aim at the same regularization principle, which is to make each neuron within a pool react similarly to identical inputs. The most natural way to do that is by showing the network multiple translations of the same image, akin to saccades in animal vision. However, this approach requires many translations, and doesn’t remove the performance gap. We propose instead to add lateral connectivity to a locally connected network, and allow learning via Hebbian plasticity. This requires the network to pause occasionally for a sleep-like phase of “weight sharing”.

This method enables locally connected networks to achieve nearly convolutional performance on ImageNet and improves their fit to the ventral stream data, thus supporting convolutional networks as a model of the visual stream.

--- https://arxiv.org/abs/2106.11189 Well-tuned Simple Nets Excel on Tabular Datasets Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka 2021-06-21 2021-06-21 [("doi","10.48550/arXiv.2106.11189")] ai/nn/fully-connected ai/tabular

Tabular datasets are the last “unconquered castle” for deep learning, with traditional ML methods like Gradient-Boosted Decision Trees still performing strongly even against recent specialized neural architectures. In this paper, we hypothesize that the key to boosting the performance of neural networks lies in rethinking the joint and simultaneous application of a large set of modern regularization techniques.

As a result, we propose regularizing plain Multilayer Perceptron (MLP) networks by searching for the optimal combination/cocktail of 13 regularization techniques for each dataset using a joint optimization over the decision on which regularizers to apply and their subsidiary hyperparameters.

We empirically assess the impact of these regularization cocktails for MLPs in a large-scale empirical study comprising 40 tabular datasets and demonstrate that (1) well-regularized plain MLPs outperform recent state-of-the-art specialized neural network architectures, and (2) they even outperform strong traditional ML methods, such as XGBoost.

--- https://arxiv.org/abs/2106.08235 PairConnect: A Compute-Efficient MLP Alternative to Attention Zhaozhuo Xu, Minghao Yan, Junyan Zhang, Anshumali Shrivastava 2021-06-15 2021-06-15 [("doi","10.48550/arXiv.2106.08235")] ai/nn/fully-connected

Transformer models have demonstrated superior performance in natural language processing. The dot product self-attention in Transformer allows us to model interactions between words. However, this modeling comes with computational overhead.

In this work, we revisit the memory-compute trade-off associated with Transformer, particularly multi-head attention, and show a memory-heavy but statistically-significantly more compute-efficient alternative to Transformer. Our proposal, denoted as PairConnect, a multilayer perceptron (MLP), models the pairwise interaction between words by explicit pairwise word embeddings. As a result, PairConnect substitutes self dot product with a simple embedding lookup.

We show mathematically that despite being an MLP, our compute-efficient PairConnect is strictly more expressive than Transformer. Our experiment on language modeling tasks suggests that PairConnect could achieve comparable results with Transformer while reducing the computational cost associated with inference.

--- https://arxiv.org/abs/2106.01548 When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations Xiangning Chen, Cho-Jui Hsieh, Boqing Gong 2021-06-03 2021-06-03 [("doi","10.48550/arXiv.2106.01548")] ai/nn/fully-connected

Vision Transformers (ViTs) and MLPs signal further efforts on replacing hand-wired features or inductive biases with general-purpose neural architectures. Existing works empower the models by massive data, such as large-scale pre-training and/or repeated strong data augmentations, and still report optimization-related problems (eg. sensitivity to initialization and learning rates).

Hence, this paper investigates ViTs and MLP-Mixers from the lens of loss geometry, intending to improve the models’ data efficiency at training and generalization at inference. Visualization and Hessian reveal extremely sharp local minima of converged models. By promoting smoothness with a recently proposed sharpness-aware optimizer, we substantially improve the accuracy and robustness of ViTs and MLP-Mixers on various tasks spanning supervised, adversarial, contrastive, and transfer learning (eg. +5.3% and +11.0% top-1 accuracy on ImageNet for ViT-B/16 and Mixer-B/16, respectively, with the simple Inception-style preprocessing). We show that the improved smoothness attributes to sparser active neurons in the first few layers. The resultant ViTs outperform ResNets of similar size and throughput when trained from scratch on ImageNet without large-scale pre-training or strong data augmentations. Model checkpoints are available at https://github.com/google-research/vision_transformer.

--- https://arxiv.org/abs/2105.01883 RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition Xiaohan Ding, Chunlong Xia, Xiangyu Zhang, Xiaojie Chu, Jungong Han, Guiguang Ding 2021-05-05 2021-05-05 [("doi","10.48550/arXiv.2105.01883")] ai/nn/fully-connected

We propose RepMLP, a multi-layer-perceptron-style neural network building block for image recognition, which is composed of a series of fully-connected (FC) layers. Compared to convolutional layers, FC layers are more efficient, better at modeling the long-range dependencies and positional patterns, but worse at capturing the local structures, hence usually less favored for image recognition.

We propose a structural re-parameterization technique that adds local prior into an FC to make it powerful for image recognition. Specifically, we construct convolutional layers inside a RepMLP during training and merge them into the FC for inference. On CIFAR, a simple pure-MLP model shows performance very close to CNN. By inserting RepMLP in traditional CNN, we improve ResNets by 1.8% accuracy on ImageNet, 2.9% for face recognition, and 2.3% mIoU on Cityscapes with lower FLOPs.

Our intriguing findings highlight that combining the global representational capacity and positional perception of FC with the local prior of convolution can improve the performance of neural network with faster speed on both the tasks with translation invariance (eg. semantic segmentation) and those with aligned images and positional patterns (eg. face recognition). The code and models are available at Github.

--- https://arxiv.org/abs/2104.13343 Sifting out the features by pruning: Are convolutional networks the winning lottery ticket of fully connected ones? Franco Pellegrini, Giulio Biroli 2021-04-27 2021-04-27 [("doi","10.48550/arXiv.2104.13343")] ai/nn/fully-connected ai/nn/sparsity/pruning

Pruning methods can considerably reduce the size of artificial neural networks without harming their performance. In some cases, they can even uncover sub-networks that, when trained in isolation, match or surpass the test accuracy of their dense counterparts. Here we study the inductive bias that pruning imprints in such “winning lottery tickets”.

Focusing on visual tasks, we analyze the architecture resulting from iterative magnitude pruning of a simple fully connected network (FCN). We show that the surviving node connectivity is local in input space, and organized in patterns reminiscent of the ones found in convolutional networks (CNN). We investigate the role played by data and tasks in shaping the architecture of pruned sub-networks. Our results show that the winning lottery tickets of FCNs display the key features of CNNs.

The ability of such automatic network-simplifying procedure to recover the key features “hand-crafted” in the design of CNNs suggests interesting applications to other datasets and tasks, in order to discover new and efficient architectural inductive biases.

--- https://arxiv.org/abs/2103.13744 KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs Christian Reiser, Songyou Peng, Yiyi Liao, Andreas Geiger 2021-03-25 2021-03-25 [("doi","10.48550/arXiv.2103.13744")] ai/nn/fully-connected ai/nn/sparsity/knowledge-distillation

NeRF synthesizes novel views of a scene with unprecedented quality by fitting a neural radiance field to RGB images. However, NeRF requires querying a deep Multi-Layer Perceptron (MLP) millions of times, leading to slow rendering times, even on modern GPUs.

In this paper, we demonstrate that real-time rendering is possible by using thousands of tiny MLPs instead of one single large MLP. In our setting, each individual MLP only needs to represent parts of the scene, thus smaller and faster-to-evaluate MLPs can be used.

By combining this divide-and-conquer strategy with further optimizations, rendering is accelerated by 3 orders of magnitude compared to the original NeRF model without incurring high storage costs. Further, using teacher-student distillation for training, we show that this speed-up can be achieved without sacrificing visual quality.

--- https://arxiv.org/abs/2106.01540 Luna: Linear Unified Nested Attention Xuezhe Ma, Xiang Kong, Sinong Wang, Chunting Zhou, Jonathan May, Hao Ma, Luke Zettlemoyer 2021-06-03 2021-06-03 [("doi","10.48550/arXiv.2106.01540")] ai/nn/transformer/attention/linear-algebra

The quadratic computational and memory complexities of the Transformer’s attention mechanism have limited its scalability for modeling long sequences. In this paper, we propose Luna, a linear unified nested attention mechanism that approximates softmax attention with two nested linear attention functions, yielding only linear (as opposed to quadratic) time and space complexity.

Specifically, with the first attention function, Luna packs the input sequence into a sequence of fixed length. Then, the packed sequence is unpacked using the second attention function. As compared to a more traditional attention mechanism, Luna introduces an additional sequence with a fixed length as input and an additional corresponding output, which allows Luna to perform attention operation linearly, while also storing adequate contextual information.

We perform extensive evaluations on 3 benchmarks of sequence modeling tasks: long-context sequence modeling, neural machine translation and masked language modeling for large-scale pretraining. Competitive or even better experimental results demonstrate both the effectiveness and efficiency of Luna compared to a variety

--- https://arxiv.org/abs/2105.15203 SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo 2021-05-31 2021-05-31 [("doi","10.48550/arXiv.2105.15203")] ai/nn/transformer

We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders. SegFormer has two appealing features: (1) SegFormer comprises a novel hierarchically structured Transformer encoder which outputs multiscale features. It does not need positional encoding, thereby avoiding the interpolation of positional codes which leads to decreased performance when the testing resolution differs from training. (2) SegFormer avoids complex decoders. The proposed MLP decoder aggregates information from different layers, and thus combining both local attention and global attention to render powerful representations.

We show that this simple and lightweight design is the key to efficient segmentation on Transformers. We scale our approach up to obtain a series of models from SegFormer-B0 to SegFormer-B5, reaching better performance and efficiency than previous counterparts.

For example, SegFormer-B4 achieves 50.3% mIoU on ADE20K with 64M parameters, being 5× smaller and 2.2% better than the previous best method. Our best model, SegFormer-B5, achieves 84.0% mIoU on Cityscapes validation set and shows excellent zero-shot robustness on Cityscapes-C.

Code will be released at: github.com/NVlabs/SegFormer.

--- https://arxiv.org/abs/2105.14217 Less is More: Pay Less Attention in Vision Transformers Zizheng Pan, Bohan Zhuang, Haoyu He, Jing Liu, Jianfei Cai 2021-05-29 2021-05-29 [("doi","10.48550/arXiv.2105.14217")] ai/nn/transformer/attention

Transformers have become one of the dominant architectures in deep learning, particularly as a powerful alternative to convolutional neural networks (CNNs) in computer vision. However, ‘Attention Is All You Need’, Vaswani et al 2017 training and inference in previous works can be prohibitively expensive due to the quadratic complexity of self-attention over a long sequence of representations, especially for high-resolution dense prediction tasks.

To this end, we present a novel Less attention vIsion Transformer (LIT), building upon the fact that the early self-attention layers in Transformers still focus on local patterns and bring minor benefits in recent hierarchical vision Transformers. Specifically, we propose a hierarchical Transformer where we use pure multi-layer perceptrons (MLPs) to encode rich local patterns in the early stages while applying self-attention modules to capture longer dependencies in deeper layers. Moreover, we further propose a learned deformable token merging module to adaptively fuse informative patches in a non-uniform manner.

The proposed LIT achieves promising performance on image recognition tasks, including image classification, object detection and instance segmentation, serving as a strong backbone for many vision tasks.

Code is available at: https://github.com/ziplab/LIT.

--- https://arxiv.org/abs/2105.02358 Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks (EAMLP) Meng-Hao Guo, Zheng-Ning Liu, Tai-Jiang Mu, Shi-Min Hu 2021-05-05 2021-05-05 [("doi","10.48550/arXiv.2105.02358")] ai/nn/transformer/attention/linear-algebra

Attention mechanisms, especially self-attention, have played an increasingly important role in deep feature representation for visual tasks. Self-attention updates the feature at each position by computing a weighted sum of features using pair-wise affinities across all positions to capture the long-range dependency within a single sample. However, self-attention has quadratic complexity and ignores potential correlation between different samples.

This paper proposes a novel attention mechanism which we call external attention, based on two external, small, learnable, shared memories, which can be implemented easily by simply using two cascaded linear layers and two normalization layers; it conveniently replaces self-attention in existing popular architectures. External attention has linear complexity and implicitly considers the correlations between all data samples. We further incorporate the multi-head mechanism into external attention to provide an all-MLP architecture, external attention MLP (EAMLP), for image classification.

Extensive experiments on image classification, object detection, semantic segmentation, instance segmentation, image generation, and point cloud analysis reveal that our method provides results comparable or superior to the self-attention mechanism and some of its variants, with much lower computational and memory costs.

--- https://arxiv.org/abs/2102.06171#deepmind NFNet: High-Performance Large-Scale Image Recognition Without Normalization Andrew Brock, Soham De, Samuel L. Smith, Karen Simonyan 2021-02-11 2021-03-01 [("doi","10.48550/arXiv.2102.06171")] ai/nn/cnn ai/scaling

[published as "Characterizing signal propagation to close the performance gap in unnormalized ResNets"] Batch normalization is a key component of most image classification models, but it has many undesirable properties stemming from its dependence on the batch size and interactions between examples. Although recent work has succeeded in training deep ResNets without normalization layers, these models do not match the test accuracies of the best batch-normalized networks, and are often unstable for large learning rates or strong data augmentations.

In this work, we develop an adaptive gradient clipping technique which overcomes these instabilities, and design an improved class of Normalizer-Free ResNets (NFNets).

Our smaller models match the test accuracy of an EfficientNet-B7 on ImageNet while being up to 8.7× faster to train, and our largest models attain a new state-of-the-art top-1 accuracy of 86.5%. In addition, Normalizer-Free models attain better performance than their batch-normalized counterparts when finetuning on ImageNet after large-scale pre-training on a dataset of 300 million labeled images [JFT-300M], with our best models obtaining an accuracy of 89.2%.

Our code is available at Github.

--- https://arxiv.org/abs/2106.00977#deepmind Adapting the Function Approximation Architecture in Online Reinforcement Learning John D. Martin, Joseph Modayil 2021-06-17 2021-06-17 [("doi","10.48550/arXiv.2106.09776")] ai/nn psychology/neuroscience reinforcement-learning/model-free

The performance of a reinforcement learning (RL) system depends on the computational architecture used to approximate a value function. Deep learning methods provide both optimization techniques and architectures for approximating nonlinear functions from noisy, high-dimensional observations. However, prevailing optimization techniques are not designed for strictly-incremental online updates. Nor are standard architectures designed for observations with an a priori unknown structure: for example, light sensors randomly dispersed in space.

This paper proposes an online RL prediction algorithm with an adaptive architecture that efficiently finds useful nonlinear features. The algorithm is evaluated in a spatial domain with high-dimensional, stochastic observations. The algorithm outperforms non-adaptive baseline architectures and approaches the performance of an architecture given side-channel information. These results are a step towards scalable RL algorithms for more general problems, where the observation structure is not available.

--- https://arxiv.org/abs/2011.13775 Image Generators with Conditionally-Independent Pixel Synthesis Ivan Anokhin, Kirill Demochkin, Taras Khakhulin, Gleb Sterkin, Victor Lempitsky, Denis Korzhenkov 2020-11-27 2021-03-01 [("doi","10.48550/arXiv.2011.13775")] ai/nn/fully-connected ai/nn/gan

[cf. Stanley 2007/Mordvintsev et al 2018/Ha 2016 on CPPNs, COCO-GAN, NeRF] Existing image generator networks rely heavily on spatial convolutions and, optionally, self-attention blocks in order to gradually synthesize images in a coarse-to-fine manner.

Here, we present a new architecture for image generators [Conditionally-Independent Pixel Synthesis (CIPS)], where the color value at each pixel is computed independently given the value of a random latent vector and the coordinate of that pixel. [A single-pixel GAN!] No spatial convolutions or similar operations that propagate information across pixels are involved during the synthesis...In total, its backbone contains 15 fully-connected layers [45.9m parameters]. [Otherwise StyleGAN 2-like—note StyleGAN was already unusual for its deep stack of MLP layers processing the z → w embedding.]

We analyze the modeling capabilities of such generators when trained in an adversarial fashion, and observe the new generators to achieve similar generation quality to state-of-the-art convolutional generators.

We also investigate several interesting properties unique to the new architecture. [It can take in arbitrary-shaped images like cylinders & generate arbitrary-sized or interpolated images, while using fixed memory per pixel in both training/sampling, and anytime (‘foveated’) results.]

Figure 2: The Conditionally-Independent Pixel Synthesis (CIPS) generator architecture. Top: the generation pipeline, in which the coordinates (x, y) of each pixel are encoded (yellow) and processed by a fully-connected (FC) network with weights, modulated with a latent vector w, shared for all pixels. The network returns the RGB value of that pixel. Bottom: The architecture of a modulated fully-connected layer (ModFC). Note: our default configuration also includes skip connections to the output (not shown here).
--- https://arxiv.org/abs/2101.10994 Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, Sanja Fidler 2021-01-26 2021-03-01 [("doi","10.48550/arXiv.2101.10994")] ai/nn/fully-connected

Neural signed distance functions (SDFs) are emerging as an effective representation for 3D shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural network to approximate complex shapes with implicit surfaces. Rendering with these large networks is, however, computationally expensive since it requires many forward passes through the network for every pixel, making these representations impractical for real-time graphics.

We introduce an efficient neural representation that, for the first time, enables real-time rendering of high-fidelity neural SDFs, while achieving state-of-the-art geometry reconstruction quality. We represent implicit surfaces using an octree-based feature volume which adaptively fits shapes with multiple discrete levels of detail (LODs), and enables continuous LOD with SDF interpolation.

We further develop an efficient algorithm to directly render our novel neural SDF representation in real-time by querying only the necessary LODs with sparse octree traversal. We show that our representation is 2–3 orders of magnitude more efficient in terms of rendering speed compared to previous works.

Furthermore, it produces state-of-the-art reconstruction quality for complex shapes under both 3D geometric and 2D image-space metrics.

--- https://arxiv.org/abs/1702.08591 The Shattered Gradients Problem: If resnets are the answer, then what is the question? David Balduzzi, Marcus Frean, Lennox Leary, J. P. Lewis, Kurt Wan-Duo Ma, Brian McWilliams 2017-02-28 2021-03-02 [("doi","10.48550/arXiv.1702.08591")] ai/nn/fully-connected

A long-standing obstacle to progress in deep learning is the problem of vanishing and exploding gradients. Although, the problem has largely been overcome via carefully constructed initializations and batch normalization, architectures incorporating skip-connections such as highway and resnets perform much better than standard feedforward architectures despite well-chosen initialization and batch normalization.

In this paper, we identify the shattered gradients problem. Specifically, we show that the correlation between gradients in standard feedforward networks decays exponentially with depth resulting in gradients that resemble white noise whereas, in contrast, the gradients in architectures with skip-connections are far more resistant to shattering, decaying sublinearly. Detailed empirical evidence is presented in support of the analysis, on both fully-connected networks and convnets.

Finally, we present a new “looks linear” (LL) initialization that prevents shattering, with preliminary experiments showing the new initialization allows to train very deep networks without the addition of skip-connections.

--- https://arxiv.org/abs/1701.09175 Skip Connections Eliminate Singularities A. Emin Orhan, Xaq Pitkow 2017-01-31 2021-03-02 [("doi","10.48550/arXiv.1701.09175")] ai/nn/fully-connected

Skip connections made the training of very deep networks possible and have become an indispensable component in a variety of neural architectures. A completely satisfactory explanation for their success remains elusive. Here, we present a novel explanation for the benefits of skip connections in training very deep networks.

The difficulty of training deep networks is partly due to the singularities caused by the non-identifiability of the model. Several such singularities have been identified in previous works: (1) overlap singularities caused by the permutation symmetry of nodes in a given layer, (2) elimination singularities corresponding to the elimination, ie. consistent deactivation, of nodes, (3) singularities generated by the linear dependence of the nodes. These singularities cause degenerate manifolds in the loss landscape that slow down learning.

We argue that skip connections eliminate these singularities by breaking the permutation symmetry of nodes, by reducing the possibility of node elimination and by making the nodes less linearly dependent. Moreover, for typical initializations, skip connections move the network away from the “ghosts” of these singularities and sculpt the landscape around them to alleviate the learning slow-down.

These hypotheses are supported by evidence from simplified models, as well as from experiments with deep networks trained on real-world datasets.

--- https://arxiv.org/abs/1806.05393 Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington 2018-06-14 2021-03-02 [("doi","10.48550/arXiv.1806.05393")] ai/nn/cnn

In recent years, state-of-the-art methods in computer vision have utilized increasingly deep convolutional neural network architectures (CNNs), with some of the most successful models employing hundreds or even thousands of layers. A variety of pathologies such as vanishing/exploding gradients make training such deep networks challenging. While residual connections and batch normalization do enable training at these depths, it has remained unclear whether such specialized architecture designs are truly necessary to train deep CNNs.

In this work, we demonstrate that it is possible to train vanilla CNNs with ten thousand layers or more simply by using an appropriate initialization scheme. We derive this initialization scheme theoretically by developing a mean field theory for signal propagation and by characterizing the conditions for dynamical isometry, the equilibration of singular values of the input-output Jacobian matrix. These conditions require that the convolution operator be an orthogonal transformation in the sense that it is norm-preserving. We present an algorithm for generating such random initial orthogonal convolution kernels and demonstrate empirically that they enable efficient training of extremely deep architectures.

--- https://journals.plos.org/plosbiology/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001756 Two Years Later: Journals Are Not Yet Enforcing the ARRIVE Guidelines on Reporting Standards for Pre-Clinical Animal Studies David Baker, Katie Lidster, Ana Sottomayor, Sandra Amor 2021-03-02 [("doi","10.1371/journal.pbio.1001756")] statistics/bias/animal

A study by David Baker and colleagues reveals poor quality of reporting in pre-clinical animal research and a failure of journals to implement the ARRIVE guidelines.

There is growing concern that poor experimental design and lack of transparent reporting contribute to the frequent failure of pre-clinical animal studies to translate into treatments for human disease. In 2010, the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines were introduced to help improve reporting standards. They were published in PLOS Biology and endorsed by funding agencies and publishers and their journals, including PLOS, Nature research journals, and other top-tier journals. Yet our analysis of papers published in PLOS and Nature journals indicates that there has been very little improvement in reporting standards since then. This suggests that authors, referees, and editors generally are ignoring guidelines, and the editorial endorsement is yet to be effectively implemented.

--- https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124 Why Most Published Research Findings Are False John Ioannidis 2021-03-02 [("doi","10.1371/journal.pmed.0020124")] statistics/bias

Summary: There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical-significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.

Published research findings are sometimes refuted by subsequent evidence, says Ioannidis, with ensuing confusion and disappointment.

--- https://journals.plos.org/plosmedicine/article/info%3Adoi%2F10.1371%2Fjournal.pmed.1000245 Can Animal Models of Disease Reliably Inform Human Studies? H. Bart van der Worp, David W. Howells, Emily S. Sena, Michelle J. Porritt, Sarah Rewell, Victoria O’Collins, Malcolm R. Macleod 2021-03-02 [("doi","10.1371/journal.pmed.1000245")] statistics/bias/animal

H. Bart van der Worp and colleagues discuss the controversies and possibilities of translating the results of animal experiments into human clinical trials.

--- https://journals.plos.org/plosmedicine/article/info%3Adoi%2F10.1371%2Fjournal.pmed.1000100 The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration Alessandro Liberati, Douglas G. Altman, Jennifer Tetzlaff, Cynthia Mulrow, Peter C. Gøtzsche, John Ioannidis, Mike Clarke, P. J. Devereaux, Jos Kleijnen, David Moher 2021-03-02 [("doi","10.1371/journal.pmed.1000100")] statistics/bias statistics/meta-analysis

Alessandro Liberati and colleagues present an Explanation and Elaboration of the PRISMA Statement, updated guidelines for the reporting of systematic reviews and meta-analyses.

Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users.

Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement—a reporting guideline published in 1999—there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions.

The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (https://www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1005765 G = E: What GWAS Can Tell Us about the Environment Suzanne H. Gage, George Davey Smith, Jennifer J. Ware, Jonathan Flint, Marcus R. Munafò 2021-03-02 [("doi","10.1371/journal.pgen.1005765")] genetics/heritable psychiatry/alcoholism

As our understanding of genetics has improved, genome-wide association studies (GWAS) have identified numerous variants associated with lifestyle behaviors and health outcomes.

However, what is sometimes overlooked is the possibility that genetic variants identified in GWAS of disease might reflect the effect of modifiable risk factors as well as direct genetic effects.

We discuss this possibility with illustrative examples from tobacco and alcohol research, in which genetic variants that predict behavioral phenotypes have been seen in GWAS of diseases known to be causally related to these behaviors.

This consideration has implications for the interpretation of GWAS findings.

--- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002195 Big Data: Astronomical or Genomical? Zachary D. Stephens, Skylar Y. Lee, Faraz Faghri, Roy H. Campbell, Chengxiang Zhai, Miles J. Efron, Ravishankar Iyer, Michael C. Schatz, Saurabh Sinha, Gene E. Robinson 2021-03-02 [("doi","10.1371/journal.pbio.1002195")] genetics/heritable

Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with 3 other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a “four-headed beast”—it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the “genomical” challenges of the next decade.

This perspective considers the growth of genomics over the next ten years and assesses the computational needs that we will face relative to other “Big Data” activities such as astronomy, YouTube, and Twitter.

--- https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002803 Approximate Bayesian Computation Mikael Sunnåker, Alberto Giovanni Busetto, Elina Numminen, Jukka Corander, Matthieu Foll, Christophe Dessimoz 2021-03-02 [("doi","10.1371/journal.pcbi.1002803")] statistics/bayes

Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate.

ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (eg. in population genetics, ecology, epidemiology, and systems biology).

--- /doc/japan/history/1874-lowder-thelegacyofieyasu.pdf The Legacy of Ieyasu J. F. Lowder 1874-01-01 2021-03-03 japan/history --- /doc/japan/history/1919-gubbins-thelegacyofieyasu.pdf The ‘Hundred Articles’ and the Tokugawa Government J. H. Gubbins 1919-01-01 2021-03-03 japan/history --- /doc/japan/history/1937-sadler-themakerofmodernjapan-thelegacyofieyasu.pdf The Maker of Modern Japan: The Life of Tokugawa Ieyasu: Chapter XLIV: The Legacy Of Ieyasu A. L. Sadler 1937-01-01 2021-03-03 japan/history --- https://arxiv.org/abs/2204.06125#openai Hierarchical Text-Conditional Image Generation with CLIP Latents Aditya A. Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen 2022-04-13 2022-04-13 [("doi","10.48550/arXiv.2204.06125")] ai/nn/transformer/gpt/dall-e/2

Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding.

We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion.

We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples.

--- https://arxiv.org/abs/2203.11480#baai WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models Sha Yuan, Shuai Zhao, Jiahong Leng, Zhao Xue, Hanyu Zhao, Peiyu Liu, Zheng Gong, Wayne Xin Zhao, Junyi Li, Jie Tang 2022-03-22 2022-03-22 [("doi","10.48550/arXiv.2203.11480")] ai/scaling/mixture-of-experts

Compared with the domain-specific model, the vision-language pre-training models (VLPMs) have shown superior performance on downstream tasks with fast fine-tuning process. For example, ERNIE-ViL, Oscar and UNIMO trained VLPMs with a uniform transformers stack architecture and large amounts of image-text paired data, achieving remarkable results on downstream tasks such as image-text reference(IR and TR), vision question answering (VQA) and image captioning (IC) etc.

During the training phase, VLPMs are always fed with a combination of multiple public datasets to meet the demand of large-scale training data. However, due to the unevenness of data distribution including size, task type, and quality, using the mixture of multiple datasets for model training can be problematic.

In this work, we introduce a large-scale multi-modal corpora named WuDaoMM, totally containing more than 650M image-text pairs. Specifically, about 600 million pairs of data are collected from multiple webpages in which image and caption present weak correlation, and the other 50 million strong-related image-text pairs are collected from some high-quality graphic websites. We also release a base version of WuDaoMM with 5 million strong-correlated image-text pairs, which is sufficient to support the common cross-modal model pre-training.

Besides, we trained both an understanding and a generation vision-language (VL) model to test the dataset effectiveness. The results show that WuDaoMM can be applied as an efficient dataset for VLPMs, especially for the model in text-to-image generation task.

The data is released at https://data.wudaoai.cn/.

--- https://arxiv.org/abs/2202.08791#sensetime cosFormer: Rethinking Softmax in Attention Zhen Qin, Weixuan Sun, Hui Deng, Dongxu Li, Yunshen Wei, Baohong Lv, Junjie Yan, Lingpeng Kong, Yiran Zhong 2022-02-17 2022-02-17 [("doi","10.48550/arXiv.2202.08791")] ai/nn/transformer/attention/linear-algebra

Transformer has shown great successes in natural language processing, computer vision, and audio processing. As one of its core components, the softmax attention helps to capture long-range dependencies yet prohibits its scale-up due to the quadratic space and time complexity to the sequence length. Kernel methods are often adopted to reduce the complexity by approximating the softmax operator. Nevertheless, due to the approximation errors, their performances vary in different tasks/corpus and suffer crucial performance drops when compared with the vanilla softmax attention.

In this paper, we propose a linear transformer called cosFormer that can achieve comparable or better accuracy to the vanilla transformer in both casual and cross attentions. cosFormer is based on two key properties of softmax attention: (1). non-negativeness of the attention matrix; (2). a non-linear re-weighting scheme that can concentrate the distribution of the attention matrix. As its linear substitute, cosFormer fulfills these properties with a linear operator and a cosine-based distance re-weighting mechanism.

Extensive experiments on language modeling and text understanding tasks demonstrate the effectiveness of our method. We further examine our method on long sequences and achieve state-of-the-art performance on the Long-Range Arena benchmark. The source code is available at Github.

--- https://arxiv.org/abs/2112.07916#google LongT5: Efficient Text-To-Text Transformer for Long Sequences Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang 2021-12-15 2021-12-15 [("doi","10.48550/arXiv.2112.07916")] ai/nn/transformer/attention/hierarchical ai/nn/transformer/t5

Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the performance of Transformer-based neural models.

In this paper, we present a new model, called LongT5, with which we explore the effects of scaling both the input length and model size at the same time. Specifically, we integrated attention ideas from long-input transformers (ETC), and adopted pre-training strategies from summarization pre-training (PEGASUS) into the scalable T5 architecture. The result is a new attention mechanism we call Transient Global (TGlobal), which mimics ETC’s local/global attention mechanism, but without requiring additional side-inputs.

We are able to achieve state-of-the-art results on several summarization tasks and outperform the original T5 models on question answering tasks.

--- https://arxiv.org/abs/2112.07210#facebook Simple Local Attentions Remain Competitive for Long-Context Tasks Wenhan Xiong, Barlas Oğuz, Anchit Gupta, Xilun Chen, Diana Liskovich, Omer Levy, Wen-tau Yih, Yashar Mehdad 2021-12-14 2021-12-14 [("doi","10.48550/arXiv.2112.07210")] ai/nn/transformer/attention/hierarchical

Many NLP tasks require processing long contexts beyond the length limit of pretrained models. In order to scale these models to longer text sequences, many efficient long-range attention variants have been proposed. Despite the abundance of research along this direction, it is still difficult to gauge the relative effectiveness of these models in practical use cases, eg. if we apply these models following the pretrain-and-finetune paradigm.

In this work, we aim to conduct a thorough analysis of these emerging models with large-scale and controlled experiments. For each attention variant, we pretrain large-size models using the same long-doc corpus and then finetune these models for real-world long-context tasks.

Our findings reveal pitfalls of an existing widely-used long-range benchmark and show none of the tested efficient attentions can beat a simple local window attention under standard pretraining paradigms.

Further analysis on local attention variants suggests that even the commonly used attention-window overlap is not necessary to achieve good downstream results—using disjoint local attentions, we are able to build a simpler and more efficient long-doc QA model that matches the performance of Longformer with half of its pretraining compute.

The code to replicate our experiments can be found at https://github.com/facebookresearch/fairseq/tree/main/examples/xformers.

--- https://arxiv.org/abs/2112.05682#google Self-attention Does Not Need 𝒪(n2) Memory Markus N. Rabe, Charles Staats 2021-12-10 2021-12-10 [("doi","10.48550/arXiv.2112.05682")] ai/nn/transformer/attention/linear-algebra

We present a very simple algorithm for attention that requires 𝒪(1) memory with respect to sequence length and an extension to self-attention that requires 𝒪(log n) memory. This is in contrast with the frequently stated belief that self-attention requires 𝒪(n2) memory. While the time complexity is still 𝒪(n2), device memory rather than compute capability is often the limiting factor on modern accelerators. Thus, reducing the memory requirements of attention allows processing of longer sequences than might otherwise be feasible.

We provide a practical implementation for accelerators that requires 𝒪(√n) memory, is numerically stable, and is within a few percent of the runtime of the standard implementation of attention. We also demonstrate how to differentiate the function while remaining memory-efficient.

For sequence length 16384, the memory overhead of self-attention is reduced by 59× for inference and by 32× for differentiation.

--- https://arxiv.org/abs/2111.09714 You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh 2021-11-18 2021-11-18 [("doi","10.48550/arXiv.2111.09714")] ai/nn/transformer/attention/sparsity

Transformer-based models are widely used in natural language processing (NLP). Central to the transformer model is the self-attention mechanism, which captures the interactions of token pairs in the input sequences and depends quadratically on the sequence length. Training such models on longer sequences is expensive.

In this paper, we show that a Bernoulli sampling attention mechanism based on Locality Sensitive Hashing (LSH), decreases the quadratic complexity of such models to linear. We bypass the quadratic cost by considering self-attention as a sum of individual tokens associated with Bernoulli random variables that can, in principle, be sampled at once by a single hash (although in practice, this number may be a small constant). This leads to an efficient sampling scheme to estimate self-attention which relies on specific modifications of LSH (to enable deployment on GPU architectures). We evaluate our algorithm on the GLUE benchmark with standard 512 sequence length where we see favorable performance relative to a standard pretrained Transformer. On the Long Range Arena (LRA) benchmark, for evaluating performance on long sequences, our method achieves results consistent with softmax self-attention but with sizable speed-ups and memory savings and often outperforms other efficient self-attention methods.

Our code is available at https://github.com/mlpen/YOSO.

--- https://arxiv.org/abs/2111.00035 Skyformer: Remodel Self-Attention with Gaussian Kernel and Nyström Method Yifan Chen, Qi Zeng, Heng Ji, Yun Yang 2021-10-29 2021-10-29 [("doi","10.48550/arXiv.2111.00035")] ai/nn/transformer/attention/linear-algebra

Transformers are expensive to train due to the quadratic time and space complexity in the self-attention mechanism. On the other hand, although kernel machines suffer from the same computation bottleneck in pairwise dot products, several approximation schemes have been successfully incorporated to considerably reduce their computational cost without sacrificing too much accuracy.

In this work, we leverage the computation methods for kernel machines to alleviate the high computational cost and introduce Skyformer, which replaces the softmax structure with a Gaussian kernel to stabilize the model training and adapts the Nyström method to a non-positive semidefinite matrix to accelerate the computation. We further conduct theoretical analysis by showing that the matrix approximation error of our proposed method is small in the spectral norm.

Experiments on Long Range Arena benchmark show that the proposed method is sufficient in getting comparable or even better performance than the full self-attention while requiring fewer computation resources.

--- https://arxiv.org/abs/2203.08989#facebook Detecting silent data corruptions in the wild Harish Dattatraya Dixit, Laura Boyle, Gautham Vunnam, Sneha Pendharkar, Matt Beadon, Sriram Sankar 2022-03-16 2022-03-16 [("doi","10.48550/arXiv.2203.08989")] cs/hardware

Silent Errors within hardware devices occur when an internal defect manifests in a part of the circuit which does not have check logic to detect the incorrect circuit operation. The results of such a defect can range from flipping a single bit in a single data value, up to causing the software to execute the wrong instructions. Silent data corruptions (SDC) in hardware impact computational integrity for large-scale applications. Manifestations of silent errors are accelerated by datapath variations, temperature variance, and age, among other silicon factors. These errors do not leave any record or trace in system logs. As a result, silent errors stay undetected within workloads, and their effects can propagate across several services, causing problems to appear in systems far removed from the original defect.

In this paper, we describe testing strategies to detect silent data corruptions within a large scale infrastructure. Given the challenging nature of the problem, we experimented with different methods for detection and mitigation. We compare and contrast two such approaches—1. Fleetscanner (out-of-production testing) and 2. Ripple (in-production testing).

We evaluate the infrastructure tradeoffs associated with the silicon testing funnel across 3+ years of production experience.

--- https://arxiv.org/abs/2201.02387 The Defeat of the Winograd Schema Challenge Vid Kocijan, Ernest Davis, Thomas Lukasiewicz, Gary Marcus, Leora Morgenstern 2022-01-07 2022-01-07 [("doi","10.48550/arXiv.2201.02387")] ai/nn/transformer/gpt ai/scaling philosophy/mind

The Winograd Schema Challenge—a set of twin sentences involving pronoun reference disambiguation that seem to require the use of commonsense knowledge—was proposed by Hector Levesque in 2011. By 2019, a number of AI systems, based on large pre-trained transformer-based language models and fine-tuned on these kinds of problems, achieved better than 90% accuracy.

In this paper, we review the history of the Winograd Schema Challenge and assess its importance.

--- https://www.biorxiv.org/content/10.1101/2022.01.28.477827.full Cerebro-cerebellar networks facilitate learning through feedback decoupling Ellen Boven, Joseph Pemberton, Paul Chadderton, Richard Apps, Rui Ponte Costa 2022-01-28 2022-01-28 [("doi","10.1101/2022.01.28.477827")] psychology/neuroscience

Behavioural feedback is critical for learning in the cerebral cortex. However, such feedback is often not readily available. How the cerebral cortex learns efficiently despite the sparse nature of feedback remains unclear. Inspired by recent deep learning algorithms, we introduce a systems-level computational model of cerebro-cerebellar interactions.

In this model a cerebral recurrent network receives feedback predictions from a cerebellar network, thereby decoupling learning in cerebral networks from future feedback.

When trained in a simple sensorimotor task the model shows faster learning and reduced dysmetria-like behaviors, in line with the widely observed functional impact of the cerebellum. Next, we demonstrate that these results generalize to more complex motor and cognitive tasks.

Finally, the model makes several experimentally testable predictions regarding (1) cerebro-cerebellar task-specific representations over learning, (2) task-specific benefits of cerebellar predictions and (3) the differential impact of cerebellar and inferior olive lesions.

Overall, our work offers a theoretical framework of cerebro-cerebellar networks as feedback decoupling machines.

--- https://www.biorxiv.org/content/10.1101/2021.12.16.472608.full Interest of phenomic prediction as an alternative to genomic prediction in grapevine Charlotte Brault, Juliette Lazerges, Agnès Doligez, Miguel Thomas, Martin Ecarnot, Pierre Roumet, Yves Bertrand, Gilles Berger, Thierry Pons, Pierre François, Loïc Le Cunff, Patrice This, Vincent Segura 2021-12-17 2021-12-17 [("doi","10.1101/2021.12.16.472608")] genetics/heritable statistics/variance-component

Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum reflects the biochemical composition within a tissue, under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been successfully applied in several cereal species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology.

This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability.

We found that the co-inertia between spectra and genomic data was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, there was a correlation across traits between predictive ability of genomic and phenomic prediction, with a slope around 1 and an intercept of −0.2, thus suggesting that phenomic prediction could be applied for any trait.

--- https://arxiv.org/abs/2112.07978 Entanglement between superconducting qubits and a tardigrade K. S. Lee, Y. P. Tan, L. H. Nguyen, R. P. Budoyo, K. H. Park, C. Hufnagel, Y. S. Yap, N. Møbjerg, V. Vedral, T. Paterek, R. Dumke 2021-12-15 2021-12-15 [("doi","10.48550/arXiv.2112.07978")] biology

Quantum and biological systems are seldom discussed together as they seemingly demand opposing conditions. Life is complex, “hot and wet” whereas quantum objects are small, cold and well controlled.

Here, we overcome this barrier with a tardigrade—a microscopic multicellular organism known to tolerate extreme physiochemical conditions via a latent state of life known as cryptobiosis.

We observe coupling between the animal in cryptobiosis and a superconducting quantum bit and prepare a highly entangled state between this combined system and another qubit. The tardigrade itself is shown to be entangled with the remaining subsystems.

The animal is then observed to return to its active form after 420 hours at sub 10 mK temperatures and pressure of 6×10−6 mbar, setting a new record for the conditions that a complex form of life can survive.

--- https://www.biorxiv.org/content/10.1101/2021.11.30.470657.full Microevidence for microdosing with psilocybin mushrooms: a double-blind placebo-controlled study of subjective effects, behavior, creativity, perception, cognition, and brain activity Federico Cavanna, Stephanie Muller, Laura Alethia de la Fuente, Federico Zamberlan, Matías Palmucci, Lucie Janeckova, Martin Kuchar, Carla Pallavicini, Enzo Tagliazucchi 2021-12-07 2021-12-07 [("doi","10.1101/2021.11.30.470657")] nootropic/lsd psychedelic

The use of low sub-hallucinogenic doses of psychedelics (“microdosing”) has gained popularity in recent years. Although anecdotal reports claim multiple benefits associated with this practice, the lack of placebo-controlled studies limits our knowledge of microdosing and its effects. Moreover, research conducted in laboratory settings might fail to capture the motivation of individuals engaged in microdosing protocols.

We recruited 34 individuals planning to microdose with psilocybin mushrooms (Psilocybe cubensis), one of the materials most frequently used for this purpose. Following a double-blind placebo-controlled design, we investigated the effects of 0.5 g dried mushrooms on subjective experience, behavior, creativity, perception, cognition, and brain activity.

The reported acute effects were statistically-significantly more intense for the active dose compared to the placebo, which could be explained by unblinding. For the other measurements, we observed either null effects or a trend towards cognitive impairment and, in the case of EEG, towards reduced theta band spectral power.

Our findings support the possibility that expectation effects underlie at least some of the anecdotal benefits attributed to microdosing with psilocybin mushrooms.

--- https://arxiv.org/abs/2111.11431 RedCaps: web-curated image-text data created by the people, for the people Karan Desai, Gaurav Kaul, Zubin Aysola, Justin Johnson 2021-11-22 2021-11-22 [("doi","10.48550/arXiv.2111.11431")] ai/scaling

Large datasets of paired images and text have become increasingly popular for learning generic representations for vision and vision-and-language tasks.

Such datasets have been built by querying search engines or collecting HTML alt-text—since web data is noisy, they require complex filtering pipelines to maintain quality. We explore alternate data sources to collect high-quality data with minimal filtering.

We introduce RedCaps—a large-scale dataset of 12M image-text pairs collected from Reddit. Images and captions from Reddit depict and describe a wide variety of objects and scenes. We collect data from a manually curated set of subreddits, which give coarse image labels and allow us to steer the dataset composition without labeling individual instances.

We show that captioning models trained on RedCaps produce rich and varied captions preferred by humans, and learn visual representations that transfer to many downstream tasks.

--- https://arxiv.org/abs/2111.09883 Swin Transformer V2: Scaling Up Capacity and Resolution Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo 2021-11-18 2021-11-18 [("doi","10.48550/arXiv.2111.09883")] ai/nn/transformer/attention/hierarchical ai/scaling

Large-scale NLP models have been shown to improve the performance on language tasks with no signs of saturation. They also demonstrate amazing few-shot capabilities like that of human beings. This paper aims to explore large-scale models in computer vision. We tackle 3 major issues in training and application of large vision models, including training instability, resolution gaps between pre-training and fine-tuning, and hunger on labeled data.

3 main techniques are proposed: (1) a residual-post-norm method combined with cosine attention to improve training stability; (2) A log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; (3) A self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images.

Through these techniques, this paper successfully trained a 3 billion-parameter Swin Transformer V2 model, which is the largest dense vision model to date, and makes it capable of training with images of up to 1,536×1,536 resolution. It set new performance records on 4 representative vision tasks, including ImageNet-V2 image classification, COCO object detection, ADE20K semantic segmentation, and Kinetics-400 video action classification. Also note our training is much more efficient than that in Google’s billion-level visual models, which consumes 40× less labeled data and 40× less training time.

Code is available at https://github.com/microsoft/Swin-Transformer.

--- https://arxiv.org/abs/2111.09296#facebook XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli 2021-11-17 2021-11-17 [("doi","10.48550/arXiv.2111.09296")] ai/nn/transformer ai/scaling

This paper presents XLS-R, a large-scale model for cross-lingual speech representation learning based on wav2vec 2.0.

We train models with up to 2b parameters on nearly half a million hours of publicly available speech audio in 128 languages, an order of magnitude more public data than the largest known prior work.

Our evaluation covers a wide range of tasks, domains, data regimes and languages, both high and low-resource. On the CoVoST-2 speech translation benchmark, we improve the previous state-of-the-art by an average of 7.4 BLEU over 21 translation directions into English. For speech recognition, XLS-R improves over the best known prior work on BABEL, MLS, CommonVoice as well as VoxPopuli, lowering error rates by 14–34% relative on average. XLS-R also sets a new state-of-the-art on VoxLingua107 language identification. Moreover, we show that with sufficient model size, cross-lingual pretraining can outperform English-only pretraining when translating English speech into other languages, a setting which favors monolingual pretraining.

We hope XLS-R can help to improve speech processing tasks for many more languages of the world.

--- https://arxiv.org/abs/2111.08234 Covariate Shift in High-Dimensional Random Feature Regression Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington 2021-11-16 2021-11-16 [("doi","10.48550/arXiv.2111.08234")] ai/scaling

A obstacle in the development of robust machine learning models is covariate shift, a form of distribution shift that occurs when the input distributions of the training and test sets differ while the conditional label distributions remain the same. Despite the prevalence of covariate shift in real-world applications, a theoretical understanding in the context of modern machine learning has remained lacking.

In this work, we examine the exact high-dimensional asymptotics of random feature regression under covariate shift and present a precise characterization of the limiting test error, bias, and variance in this setting. Our results motivate a natural partial order over covariate shifts that provides a sufficient condition for determining when the shift will harm (or even help) test performance. We find that overparameterized models exhibit enhanced robustness to covariate shift, providing one of the first theoretical explanations for this intriguing phenomenon. Additionally, our analysis reveals an exact linear relationship between in-distribution and out-of-distribution generalization performance, offering an explanation for this surprising recent empirical observation.

--- https://arxiv.org/abs/2111.05897 Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters Xiangru Lian, Binhang Yuan, Xuefeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, Yiqiao Liao, Mingnan Luo, Congfei Zhang, Jingru Xie, Haonan Li, Lei Chen, Renjie Huang, Jianying Lin, Chengchun Shu, Xuezhong Qiu, Zhishan Liu, Dongying Kong, Lei Yuan, Hai Yu, Sen Yang, Ce Zhang, Ji Liu 2021-11-10 2021-11-10 [("doi","10.48550/arXiv.2111.05897")] ai/nn/sparsity ai/scaling

Deep learning based models have dominated the current landscape of production recommender systems. Furthermore, recent years have witnessed an exponential growth of the model scale—from Google’s 2016 model with 1 billion parameters to the latest Facebook’s model with 12 trillion parameters. quality boost has come with each jump of the model capacity, which makes us believe the era of 100 trillion parameters is around the corner. However, the training of such models is challenging even within industrial scale data centers. This difficulty is inherited from the staggering heterogeneity of the training computation—the model’s embedding layer could include more than 99.99% of the total model size, which is extremely memory-intensive; while the rest neural network is increasingly computation-intensive.

To support the training of such huge models, an efficient distributed training system is in urgent need. In this paper, we resolve this challenge by careful co-design of both the optimization algorithm and the distributed system architecture. Specifically, in order to ensure both the training efficiency and the training accuracy, we design a novel hybrid training algorithm, where the embedding layer and the dense neural network are handled by different synchronization mechanisms; then we build a system called Persia (short for parallel recommendation training system with hybrid acceleration) to support this hybrid training algorithm.

Both theoretical demonstration and empirical study up to 100 trillion parameters have conducted to justified the system design and implementation of Persia.

We make Persia publicly available (at https://github.com/PersiaML/Persia) so that anyone would be able to easily train a recommender model at the scale of 100 trillion parameters.

--- https://arxiv.org/abs/2110.03111#eleutherai Cut the CARP: Fishing for zero-shot story evaluation Shahbul, Matiana, J. R. Smith, Ryan Teehan, Louis Castricato, Stella Biderman, Leo Gao, Spencer Frazier 2021-10-06 2021-10-06 [("doi","10.48550/arXiv.2110.03111")] ai/nn/transformer/gpt/fiction reinforcement-learning/preference-learning

Recent advances in large-scale language models (Raffel et al 2019; Brown et al 2020) have brought qualitative and quantitative improvements in machine-driven text generation. Despite this, generation and evaluation of machine-generated narrative text remains a challenging problem. Objective evaluation of computationally-generated stories may be prohibitively expensive, require meticulously annotated datasets, or may not adequately measure the logical coherence of a generated story’s narratological structure.

Informed by recent advances in contrastive learning (Radford et al 2021), we present Contrastive Authoring and Reviewing Pairing (CARP): a scalable, efficient method for performing qualitatively superior, zero-shot evaluation of stories. We show a strong correlation between human evaluation of stories and those of CARP. Model outputs more correlate with corresponding human input than those language-model based methods which utilize finetuning or prompt engineering approaches. We also present and analyze the Story-Critique Dataset, a new corpora composed of 1.3 million aligned story-critique pairs derived from over 80,000 stories. We expect this corpus to be of interest to NLP researchers.

--- https://arxiv.org/abs/2201.12360 Variational Neural Cellular Automata Rasmus Berg Palm, Miguel González-Duque, Shyam Sudhakaran, Sebastian Risi 2022-01-28 2022-01-28 [("doi","10.48550/arXiv.2201.12360")] ai/nn/cnn cs/cellular-automaton

In nature, the process of cellular growth and differentiation has led to an amazing diversity of organisms—algae, starfish, giant sequoia, tardigrades, and orcas are all created by the same generative process. Inspired by the incredible diversity of this biological generative process, we propose a generative model, the Variational Neural Cellular Automata (VNCA), which is loosely inspired by the biological processes of cellular growth and differentiation.

Unlike previous related works, the VNCA is a proper probabilistic generative model, and we evaluate it according to best practices.

We find that the VNCA learns to reconstruct samples well and that despite its relatively few parameters and simple local-only communication, the VNCA can learn to generate a large variety of output from information encoded in a common vector format. While there is a gap to the current state-of-the-art in terms of generative modeling performance, we show that the VNCA can learn a purely self-organizing generative process of data. Additionally, we show that the VNCA can learn a distribution of stable attractors that can recover from damage.

--- https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life Conway's Game of Life 2021-03-05 cs/cellular-automaton --- https://en.wikipedia.org/wiki/Cellular_automaton Cellular automaton 2021-03-05 cs/cellular-automaton --- https://en.wikipedia.org/wiki/Continuous_spatial_automaton Continuous spatial automaton 2021-03-05 cs/cellular-automaton --- https://www.aleph.se/andart/archives/2014/04/tables_of_soyga_the_first_cellular_automaton.html Tables of Soyga: the first cellular automaton? 2021-03-05 cs/cellular-automaton --- https://en.wikipedia.org/wiki/Book_of_Soyga Book of Soyga 2021-03-05 cs/cellular-automaton --- https://dwarffortresswiki.org/index.php/DF2014:Computing Computing in Dwarf Fortress 2021-03-05 cs/cellular-automaton --- https://distill.pub/2020/selforg/mnist/ Self-classifying MNIST Digits 2021-03-05 cs/cellular-automaton design/visualization --- https://distill.pub/selforg/2021/textures/ Self-Organising Textures 2021-03-05 cs/cellular-automaton design/visualization --- https://distill.pub/selforg/2021/adversarial/ Adversarial Reprogramming of Neural Cellular Automata 2021-03-06 cs/cellular-automaton design/visualization --- https://github.com/efoxepstein/stupid-machines Experimentations with Abstract Machines 2021-03-06 cs/cellular-automaton --- https://accodeing.com/blog/2015/css3-proven-to-be-turing-complete CSS3 proven to be Turing complete 2021-03-06 cs/cellular-automaton --- https://seriot.ch/resources/talks_papers/20171027_brainfuck_dominos.pdf From Brainfuck to Domino Computers: A trip into Esoteric Languages, Turing Machines, Cellular Automata and the Nature of Computation 2021-03-06 cs/cellular-automaton --- https://en.wikipedia.org/wiki/Rule_110 Rule 110 2021-03-06 cs/cellular-automaton --- https://a.tulv.in/algorithms/programming/2021/02/19/finding-mona-lisa-in-the-game-of-life.html 2021-03-06 cs/cellular-automaton --- https://codegolf.stackexchange.com/questions/11880/build-a-working-game-of-tetris-in-conways-game-of-life/142673 Building a working game of Tetris in Conway’s Game of Life 2021-03-06 cs/cellular-automaton --- https://en.wikipedia.org/wiki/Hashlife Hashlife 2021-03-06 ai/nn/transformer/gpt/2 cs/algorithm/information/compression cs/cellular-automaton --- https://arxiv.org/abs/2105.07299 Texture Generation with Neural Cellular Automata Alexander Mordvintsev, Eyvind Niklasson, Ettore Randazzo 2021-05-15 2021-05-15 [("doi","10.48550/arXiv.2105.07299")] cs/cellular-automaton

Neural Cellular Automata (NCA) have shown a remarkable ability to learn the required rules to “grow” images, classify morphologies, segment images, as well as to do general computation such as path-finding. We believe the inductive prior they introduce lends itself to the generation of textures. Textures in the natural world are often generated by variants of locally interacting reaction-diffusion systems. Human-made textures are likewise often generated in a local manner (textile weaving, for instance) or using rules with local dependencies (regular grids or geometric patterns).

We demonstrate learning a texture generator from a single template image, with the generation method being embarrassingly parallel, exhibiting quick convergence and high fidelity of output, and requiring only some minimal assumptions around the underlying state manifold.

Furthermore, we investigate properties of the learned models that are both useful and interesting, such as non-stationary dynamics and an inherent robustness to damage.

Finally, we make qualitative claims that the behavior exhibited by the NCA model is a learned, distributed, local algorithm to generate a texture, setting our method apart from existing work on texture generation. We discuss the advantages of such a paradigm.

--- https://arxiv.org/abs/2103.08737 Growing 3D Artefacts and Functional Machines with Neural Cellular Automata Shyam Sudhakaran, Djordje Grbic, Siyan Li, Adam Katona, Elias Najarro, Claire Glanois, Sebastian Risi 2021-03-15 2021-03-15 [("doi","10.48550/arXiv.2103.08737")] cs/cellular-automaton

Neural Cellular Automata (NCAs) have been proven effective in simulating morphogenetic processes, the continuous construction of complex structures from very few starting cells. Recent developments in NCAs lie in the 2D domain, namely reconstructing target images from a single pixel or infinitely growing 2D textures. In this work, we propose an extension of NCAs to 3D, utilizing 3D convolutions in the proposed neural network architecture.

Minecraft is selected as the environment for our automaton since it allows the generation of both static structures and moving machines. We show that despite their simplicity, NCAs are capable of growing complex entities such as castles, apartment blocks, and trees, some of which are composed of over 3,000 blocks. Additionally, when trained for regeneration, the system is able to regrow parts of simple functional machines, expanding the capabilities of simulated morphogenetic systems.

The code for the experiment in this paper can be found at: Github.

--- https://arxiv.org/abs/2102.02579 Regenerating Soft Robots through Neural Cellular Automata Kazuya Horibe, Kathryn Walker, Sebastian Risi 2021-02-04 2021-03-06 [("doi","10.48550/arXiv.2102.02579")] cs/cellular-automaton reinforcement-learning/robot

Morphological regeneration is an important feature that highlights the environmental adaptive capacity of biological systems. Lack of this regenerative capacity limits the resilience of machines and the environments they can operate in. To aid in addressing this gap, we develop an approach for simulated soft robots to regrow parts of their morphology when being damaged.

Although numerical simulations using soft robots have played an important role in their design, evolving soft robots with regenerative capabilities have so far received comparable little attention. Here we propose a model for soft robots that regenerate through a neural cellular automata. Importantly, this approach only relies on local cell information to regrow damaged components, opening interesting possibilities for physical regenerable soft robots in the future.

Our approach allows simulated soft robots that are damaged to partially regenerate their original morphology through local cell interactions alone and regain some of their ability to locomote. These results take a step towards equipping artificial systems with regenerative capacities and could potentially allow for more robust operations in a variety of situations and environments.

The code for the experiments in this paper is available at: github.com/KazuyaHoribe/RegeneratingSoftRobots.

--- https://arxiv.org/abs/1809.02942 Cellular automata as convolutional neural networks William Gilpin 2018-09-09 2021-03-07 [("doi","10.1103/PhysRevE.100.032402")] ai/nn/cnn cs/cellular-automaton

Deep learning techniques have recently demonstrated broad success in predicting complex dynamical systems ranging from turbulence to human speech, motivating broader questions about how neural networks encode and represent dynamical rules. We explore this problem in the context of cellular automata (CA), simple dynamical systems that are intrinsically discrete and thus difficult to analyze using standard tools from dynamical systems theory.

We show that any CA may readily be represented using a convolutional neural network with a network-in-network architecture. This motivates our development of a general convolutional multilayer perceptron architecture, which we find can learn the dynamical rules for arbitrary CA when given videos of the CA as training data. In the limit of large network widths, we find that training dynamics are nearly identical across replicates, and that common patterns emerge in the structure of networks trained on different CA rulesets.

We train ensembles of networks on randomly-sampled CA, and we probe how the trained networks internally represent the CA rules using an information-theoretic technique based on distributions of layer activation patterns. We find that CA with simpler rule tables produce trained networks with hierarchical structure and layer specialization, while more complex CA produce shallower representations—illustrating how the underlying complexity of the CA’s rules influences the specificity of these internal representations.

Our results suggest how the entropy of a physical process can affect its representation when learned by neural networks.

--- https://www.newyorker.com/magazine/2021/12/06/understanding-the-body-electric An Account of Electricity and the Body, Reviewed 2021-03-07 cs/cellular-automaton --- https://www.newyorker.com/magazine/2021/05/10/persuading-the-body-to-regenerate-its-limbs Is Bioelectricity the Key to Limb Regeneration? 2021-03-07 cs/cellular-automaton --- https://www.theguardian.com/science/2021/nov/29/amazing-science-researchers-find-xenobots-can-give-rise-to-offspring ‘Amazing science’: researchers find xenobots can give rise to offspring Science 2021-03-07 cs/cellular-automaton --- https://www.youtube.com/watch?v=C1eg-jgLx5o Living robots made from frog cells can replicate themselves in a dish 2021-03-07 cs/cellular-automaton --- https://www.quantamagazine.org/flying-fish-and-aquarium-pets-yield-secrets-of-evolution-20220105/ 2021-03-07 cs/cellular-automaton --- https://www.biorxiv.org/content/10.1101/226589.full The paradoxical sustainability of periodic migration and habitat destruction Zong Xuan Tan, Kang Hao Cheong 2017-11-29 2021-03-07 [("doi","10.1101/226589")] reinforcement-learning/exploration

Some species and societies engage in sustainable habitat destruction by periodically alternating between a low-growth migratory lifestyle and high-growth but destructive behavior. Examples include nomadic pastoralism and shifting cultivation, practiced by humans for millennia. Although specific models have been developed for species or societies which practice periodic migration and habitat destruction, theoretical insight into such phenomena as a whole is lacking.

Here we present a general model of populations which alternate between migratory but negative-growth ‘nomadism’ and destructive ‘colonialism’ which yields high but short-term growth. Despite both strategies individually resulting in extinction, we demonstrate that a population can sustainably colonize an arbitrarily large network of habitats by alternating between the two.

This counter-intuitive result can be interpreted in terms of both Parrondo’s paradox and the exploration-exploitation dilemma, suggesting answers to the question of sustainable development.

--- https://en.wikipedia.org/wiki/Day_and_Night_(cellular_automaton) Day and Night (cellular automaton) 2021-03-07 cs/cellular-automaton --- https://www.pouet.net/topic.php?which=12091&page=1#c568703 The JPEG XL image format has demo potential 2021-03-07 cs/cellular-automaton --- https://arxiv.org/abs/1812.05433 Lenia—Biology of Artificial Life Bert Wang-Chak Chan 2018-12-13 2021-03-07 [("doi","10.25088/ComplexSystems.28.3.251")] cs/cellular-automaton

We report a new system of artificial life called Lenia (from Latin lenis “smooth”), a two-dimensional cellular automaton with continuous space-time-state and generalized local rule.

Computer simulations show that Lenia supports a great diversity of complex autonomous patterns or “lifeforms” bearing resemblance to real-world microscopic organisms. More than 400 species in 18 families have been identified, many discovered via interactive evolutionary computation. They differ from other cellular automata patterns in being geometric, metameric, fuzzy, resilient, adaptive, and rule-generic.

We present basic observations of the system regarding the properties of space-time and basic settings. We provide a broad survey of the lifeforms, categorize them into a hierarchical taxonomy, and map their distribution in the parameter hyperspace. We describe their morphological structures and behavioral dynamics, propose possible mechanisms of their self-propulsion, self-organization and plasticity.

Finally, we discuss how the study of Lenia would be related to biology, artificial life, and artificial intelligence.

--- https://pni.princeton.edu/john-hopfield/john-j.-hopfield-now-what Now What? Hopfield 2018 2021-03-07 cs/cellular-automaton psychology/neuroscience --- https://www.nytimes.com/2020/12/28/science/math-conway-game-of-life.html The Lasting Lessons of John Conway’s Game of Life 2021-03-08 cs/cellular-automaton --- https://arxiv.org/abs/1908.06663 Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems Chris Reinke, Mayalen Etcheverry, Pierre-Yves Oudeyer 2019-08-19 2021-03-08 [("doi","10.48550/arXiv.1908.06663")] cs/cellular-automaton

In many complex dynamical systems, artificial or natural, one can observe self-organization of patterns emerging from local rules. Cellular automata, like the Game of Life (GOL), have been widely used as abstract models enabling the study of various aspects of self-organization and morphogenesis, such as the emergence of spatially localized patterns. However, findings of self-organized patterns in such models have so far relied on manual tuning of parameters and initial states, and on the human eye to identify interesting patterns.

In this paper, we formulate the problem of automated discovery of diverse self-organized patterns in such high-dimensional complex dynamical systems, as well as a framework for experimentation and evaluation. Using a continuous GOL as a testbed, we show that recent intrinsically-motivated machine learning algorithms (POP-IMGEPs), initially developed for learning of inverse models in robotics, can be transposed and used in this novel application area. These algorithms combine intrinsically-motivated goal exploration and unsupervised learning of goal space representations. Goal space representations describe the interesting features of patterns for which diverse variations should be discovered. In particular, we compare various approaches to define and learn goal space representations from the perspective of discovering diverse spatially localized patterns. Moreover, we introduce an extension of a state-of-the-art POP-IMGEP algorithm which incrementally learns a goal representation using a deep autoencoder, and the use of CPPN primitives for generating initialization parameters. We show that it is more efficient than several baselines and equally efficient as a system pre-trained on a hand-made database of patterns identified by human experts.

--- https://cp4space.hatsya.com/2022/01/14/conway-conjecture-settled/ 29-year-old Conway conjecture settled 2021-03-08 cs/cellular-automaton --- https://arxiv.org/abs/2110.13711#nvidia Hourglass: Hierarchical Transformers Are More Efficient Language Models Piotr Nawrot, Szymon Tworkowski, Michał Tyrolski, Łukasz Kaiser, Yuhuai Wu, Christian Szegedy, Henryk Michalewski 2021-10-26 2021-10-26 [("doi","10.48550/arXiv.2110.13711")] ai/nn/transformer/attention/hierarchical

Transformer models yield impressive results on many NLP and sequence modeling tasks. Remarkably, Transformers can handle long sequences which allows them to produce long coherent outputs: full paragraphs produced by GPT-3 or well-structured images produced by DALL·E. These large language models are impressive but also very inefficient and costly, which limits their applications and accessibility. We postulate that having an explicit hierarchical architecture is the key to Transformers that efficiently handle long sequences.

To verify this claim, we first study different ways to downsample and upsample activations in Transformers so as to make them hierarchical. We use the best performing upsampling and downsampling layers to create Hourglass—a hierarchical Transformer language model. Hourglass improves upon the Transformer baseline given the same amount of computation and can yield the same results as Transformers more efficiently.

In particular, Hourglass sets new state-of-the-art for Transformer models on the ImageNet32 generation task and improves language modeling efficiency on the widely studied enwik8 benchmark.

--- https://arxiv.org/abs/2110.08323 On Learning the Transformer Kernel Sankalan Pal Chowdhury, Adamos Solomou, Avinava Dubey, Mrinmaya Sachan 2021-10-15 2021-10-15 [("doi","10.48550/arXiv.2110.08323")] ai/nn/transformer/attention/linear-algebra

In this work we introduce KERNELIZED TRANSFORMER, a generic, scalable, data driven framework for learning the kernel function in Transformers.

Our framework approximates the Transformer kernel as a dot product between spectral feature maps and learns the kernel by learning the spectral distribution. This not only helps in learning a generic kernel end-to-end, but also reduces the time and space complexity of Transformers from quadratic to linear.

We show that KERNELIZED TRANSFORMERS achieve performance comparable to existing efficient Transformer architectures, both in terms of accuracy as well as computational efficiency.

Our study also demonstrates that the choice of the kernel has a substantial impact on performance, and kernel learning variants are competitive alternatives to fixed kernel Transformers, both in long as well as short sequence tasks.

--- https://arxiv.org/abs/2110.07402#bytedance TWIST: Self-Supervised Learning by Estimating Twin Class Distributions Feng Wang, Tao Kong, Rufeng Zhang, Huaping Liu, Hang Li 2021-10-14 2021-10-14 [("doi","10.48550/arXiv.2110.07402")] ai/nn/cnn

We present TWIST, a simple and theoretically explainable self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way.

We employ a siamese network terminated by a softmax operation to produce twin class distributions of two augmented images. Without supervision, we enforce the class distributions of different augmentations to be consistent. However, simply minimizing the divergence between augmentations will cause collapsed solutions, ie. outputting the same class probability distribution for all images. In this case, no information about the input image is left. To solve this problem, we propose to maximize the mutual information between the input and the class predictions. Specifically, we minimize the entropy of the distribution for each sample to make the class prediction for each sample assertive and maximize the entropy of the mean distribution to make the predictions of different samples diverse. In this way, TWIST can naturally avoid the collapsed solutions without specific designs such as asymmetric network, stop-gradient operation, or momentum encoder.

As a result, TWIST outperforms state-of-the-art methods on a wide range of tasks. Especially, TWIST performs surprisingly well on semi-supervised learning, achieving 61.2% top-1 accuracy with 1% ImageNet labels using a ResNet-50 as backbone, surpassing previous best results by an absolute improvement of 6.2%.

Codes and pre-trained models are given on: https://github.com/bytedance/TWIST.

--- https://arxiv.org/abs/2110.09753 Unifying Multimodal Transformer for Bi-directional Image and Text Generation Yupan Huang, Hongwei Xue, Bei Liu, Yutong Lu 2021-10-19 2021-10-19 [("doi","10.1145/3474085.3481540")] ai/nn/transformer/gpt/dall-e/1

We study the joint learning of image-to-text and text-to-image generations, which are naturally bi-directional tasks. Typical existing works design two separate task-specific models for each task, which impose expensive design efforts.

In this work, we propose a unified image-and-text generative framework based on a single multimodal model to jointly study the bi-directional tasks. We adopt Transformer as our unified architecture for its strong performance and task-agnostic design. Specifically, we formulate both tasks as sequence generation tasks, where we represent images and text as unified sequences of tokens, and the Transformer learns multimodal interactions to generate sequences. We further propose two-level granularity feature representations and sequence-level training to improve the Transformer-based unified framework.

Experiments show that our approach substantially improves previous Transformer-based model X-LXMERT’s FID 37.0–29.9 (lower is better) for text-to-image generation, and improves CIDEr-D score 100.9% → 122.6% for fine-tuned image-to-text generation on the MS-COCO dataset.

Our code is available online.

--- https://www.biorxiv.org/content/10.1101/2021.10.11.463990.full The structure of genotype-phenotype maps makes fitness landscapes navigable Sam F. Greenbury, Ard A. Louis, Sebastian E. Ahnert 2021-10-12 2021-10-12 [("doi","10.1101/2021.10.11.463990")] ai/nn genetics/selection/natural reinforcement-learning/exploration

Fitness landscapes are often described in terms of ‘peaks’ and ‘valleys’, implying an intuitive low-dimensional landscape of the kind encountered in everyday experience. The space of genotypes, however, is extremely high-dimensional, which results in counter-intuitive properties of genotype-phenotype maps, such as the close proximity of one phenotype to many others.

Here we investigate how common structural properties of high-dimensional genotype-phenotype maps, such as the presence of neutral networks, affect the navigability of fitness landscapes. For 3 biologically realistic genotype-phenotype map models—RNA secondary structure, protein tertiary structure and protein complexes—we find that, even under random fitness assignment, fitness maxima can be reached from almost any other phenotype without passing through a fitness valley. This in turn implies that true fitness valleys are very rare.

By considering evolutionary simulations between pairs of real examples of functional RNA sequences, we show that accessible paths are also likely to be utilized under evolutionary dynamics.

--- https://arxiv.org/abs/2110.04725#inspur Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning Shaohua Wu, Xudong Zhao, Tong Yu, Rongguo Zhang, Chong Shen, Hongli Liu, Feng Li, Hong Zhu, Jiangang Luo, Liang Xu, Xuanwei Zhang 2021-10-10 2021-10-10 [("doi","10.48550/arXiv.2110.04725")] ai/nn/transformer/gpt ai/scaling

Recent work like GPT-3 has demonstrated excellent performance of Zero-Shot and Few-Shot learning on many natural language processing (NLP) tasks by scaling up model size, dataset size and the amount of computation. However, training a model like GPT-3 requires huge amount of computational resources which makes it challenging to researchers.

In this work, we propose a method that incorporates large-scale distributed training performance into model architecture design. With this method, Yuan 1.0, the current largest singleton language model with 245b parameters, achieves excellent performance on thousands GPUs during training, and the state-of-the-art results on NLP tasks. A data processing method is designed to efficiently filter massive amount of raw data. The current largest high-quality Chinese corpus with 5TB high quality texts is built based on this method. In addition, a calibration and label expansion method is proposed to improve the Zero-Shot and Few-Shot performance, and steady improvement is observed on the accuracy of various tasks.

Yuan 1.0 presents strong capacity of natural language generation, and the generated articles are difficult to distinguish from the human-written ones.

--- https://arxiv.org/abs/2109.07740#google Scaling Laws for Neural Machine Translation Behrooz Ghorbani, Orhan Firat, Markus Freitag, Ankur Bapna, Maxim Krikun, Xavier Garcia, Ciprian Chelba, Colin Cherry 2021-09-16 2021-09-16 [("doi","10.48550/arXiv.2109.07740")] ai/nn/transformer/gpt ai/scaling

We present an empirical study of scaling properties of encoder-decoder Transformer models used in neural machine translation (NMT). We show that cross-entropy loss as a function of model size follows a certain scaling law.

Specifically (1) We propose a formula which describes the scaling behavior of cross-entropy loss as a bivariate function of encoder and decoder size, and show that it gives accurate predictions under a variety of scaling approaches and languages; we show that the total number of parameters alone is not sufficient for such purposes.

  1. We observe different power law exponents when scaling the decoder vs scaling the encoder, and provide recommendations for optimal allocation of encoder/decoder capacity based on this observation.

  2. We also report that the scaling behavior of the model is acutely influenced by composition bias of the train/test sets, which we define as any deviation from naturally generated text (either via machine generated or human translated text). We observe that natural text on the target side enjoys scaling, which manifests as successful reduction of the cross-entropy loss.

  3. Finally, we investigate the relationship between the cross-entropy loss and the quality of the generated translations. We find two different behaviors, depending on the nature of the test data. For test sets which were originally translated from target language to source language, both loss and BLEU score improve as model size increases. In contrast, for test sets originally translated from source language to target language, the loss improves, but the BLEU score stops improving after a certain threshold. We release generated text from all models used in this study.

--- https://arxiv.org/abs/2109.04650#naver What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers Boseop Kim, HyoungSeok Kim, Sang-Woo Lee, Gichang Lee, Donghyun Kwak, Dong Hyeon Jeon, Sunghyun Park, Sungju Kim, Seonhoon Kim, Dongpil Seo, Heungsub Lee, Minyoung Jeong, Sungjae Lee, Minsub Kim, Suk Hyun Ko, Seokhun Kim, Taeyong Park, Jinuk Kim, Soyoung Kang, Na-Hyeon Ryu, Kang Min Yoo, Minsuk Chang, Soobin Suh, Sookyo In, Jinseong Park, Kyungduk Kim, Hiun Kim, Jisu Jeong, Yong Goo Yeo, Donghoon Ham, Dongju Park, Min Young Lee, Jaewook Kang, Inho Kang, Jung-Woo Ha, Woomyoung Park, Nako Sung 2021-09-10 2021-09-10 [("doi","10.48550/arXiv.2109.04650")] ai/nn/tokenization ai/nn/transformer/gpt ai/scaling

GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data. Here we address some remaining issues less reported by the GPT-3 paper, such as a non-English LM, the performances of different sized models, and the effect of recently introduced prompt optimization on in-context learning.

To achieve this, we introduce HyperCLOVA, a Korean variant of 82B GPT-3 trained on a Korean-centric corpus of 560B tokens. Enhanced by our Korean-specific tokenization, HyperCLOVA with our training configuration shows state-of-the-art in-context zero-shot and few-shot learning performances on various downstream tasks in Korean. Also, we show the performance benefits of prompt-based learning and demonstrate how it can be integrated into the prompt engineering pipeline.

Then we discuss the possibility of materializing the No Code AI paradigm by providing AI prototyping capabilities to non-experts of ML by introducing HyperCLOVA studio, an interactive prompt engineering interface.

Lastly, we demonstrate the potential of our methods with 3 successful in-house applications.

--- https://arxiv.org/abs/2110.04621#google Universal Paralinguistic Speech Representations Using Self-Supervised Conformers Joel Shor, Aren Jansen, Wei Han, Daniel Park, Yu Zhang 2021-10-09 2021-10-09 [("doi","10.48550/arXiv.2110.04621")] ai/scaling

Many speech applications require understanding aspects beyond the words being spoken, such as recognizing emotion, detecting whether the speaker is wearing a mask, or distinguishing real from synthetic speech. In this work, we introduce a new state-of-the-art paralinguistic representation derived from large-scale, fully self-supervised training of a 600M+ parameter Conformer-based architecture.

We benchmark on a diverse set of speech tasks and demonstrate that simple linear classifiers trained on top of our time-averaged representation outperform nearly all previous results, in some cases by large margins. Our analyses of context-window size demonstrate that, surprisingly, 2 second context-windows achieve 96% the performance of the Conformers that use the full long-term context on 7⁄9 tasks. Furthermore, while the best per-task representations are extracted internally in the network, stable performance across several layers allows a single universal representation to reach near optimal performance on all tasks.

--- https://arxiv.org/abs/2110.03888#alibaba M6–10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining Junyang Lin, An Yang, Jinze Bai, Chang Zhou, Le Jiang, Xianyan Jia, Ang Wang, Jie Zhang, Yong Li, Wei Lin, Jingren Zhou, Hongxia Yang 2021-10-08 2021-10-08 [("doi","10.48550/arXiv.2110.03888")] ai/scaling

Recent expeditious developments in deep learning algorithms, distributed training, and even hardware design for large models have enabled training extreme-scale models, say GPT-3 and Switch Transformer possessing hundreds of billions or even trillions of parameters. However, under limited resources, extreme-scale model training that requires enormous amounts of computes and memory footprint suffers from frustratingly low efficiency in model convergence.

In this paper, we propose a simple training strategy called “Pseudo-to-Real” for high-memory-footprint-required large models. Pseudo-to-Real is compatible with large models with architecture of sequential layers. We demonstrate a practice of pretraining unprecedented 10-trillion-parameter model, an order of magnitude larger than the state-of-the-art, on solely 512 GPUs within 10 days. Besides demonstrating the application of Pseudo-to-Real, we also provide a technique, Granular CPU offloading, to manage CPU memory for training large model and maintain high GPU utilities.

Fast training of extreme-scale models on a decent amount of resources can bring much smaller carbon footprint and contribute to greener AI.

--- https://arxiv.org/abs/2110.04366 Towards a Unified View of Parameter-Efficient Transfer Learning Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, Graham Neubig 2021-10-08 2021-10-08 [("doi","10.48550/arXiv.2110.04366")] ai/nn/transformer/gpt

Fine-tuning large pre-trained language models on downstream tasks has become the de-facto learning paradigm in NLP. However, conventional approaches fine-tune all the parameters of the pre-trained model, which becomes prohibitive as the model size and the number of tasks grow. Recent work has proposed a variety of parameter-efficient transfer learning methods that only fine-tune a small number of (extra) parameters to attain strong performance. While effective, the critical ingredients for success and the connections among the various methods are poorly understood.

In this paper, we break down the design of state-of-the-art parameter-efficient transfer learning methods and present a unified framework that establishes connections between them. Specifically, we re-frame them as modifications to specific hidden states in pre-trained models, and define a set of design dimensions along which different methods vary, such as the function to compute the modification and the position to apply the modification.

Through comprehensive empirical studies across machine translation, text summarization, language understanding, and text classification benchmarks, we use the unified view to identify important design choices in previous methods. Furthermore, our unified framework enables the transfer of design elements across different approaches, and as a result we are able to instantiate new parameter-efficient fine-tuning methods that tune less parameters than previous methods while being more effective, achieving comparable results to fine-tuning all parameters on all 4 tasks.

--- https://arxiv.org/abs/2110.02488#allen ABC: Attention with Bounded-memory Control Hao Peng, Jungo Kasai, Nikolaos Pappas, Dani Yogatama, Zhaofeng Wu, Lingpeng Kong, Roy Schwartz, Noah Smith 2021-10-06 2021-10-06 [("doi","10.48550/arXiv.2110.02488")] ai/nn/transformer/attention/compression

Transformer architectures have achieved state-of-the-art results on a variety of sequence modeling tasks. However, their attention mechanism comes with a quadratic complexity in sequence lengths, making the computational overhead prohibitive, especially for long sequences. Attention context can be seen as a random-access memory with each token taking a slot. Under this perspective, the memory size grows linearly with the sequence length, and so does the overhead of reading from it.

One way to improve the efficiency is to bound the memory size. We show that disparate approaches can be subsumed into one abstraction, attention with bounded-memory control (ABC), and they vary in their organization of the memory. ABC reveals new, unexplored possibilities. First, it connects several efficient attention variants that would otherwise seem apart. Second, this abstraction gives new insights—an established approach (Wang et al 2020b) previously thought to be not applicable in causal attention, actually is. Last, we present a new instance of ABC, which draws inspiration from existing ABC approaches, but replaces their heuristic memory-organizing functions with a learned, contextualized one.

Our experiments on language modeling, machine translation, and masked language model finetuning show that our approach outperforms previous efficient attention models; compared to the strong transformer baselines, it improves the inference time and space efficiency with no or negligible accuracy loss.

--- https://arxiv.org/abs/2110.01889 Deep Neural Networks and Tabular Data: A Survey Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, Gjergji Kasneci 2021-10-05 2021-10-05 [("doi","10.48550/arXiv.2110.01889")] ai/nn ai/tabular

Heterogeneous tabular data are the most commonly used form of data and are essential for numerous critical and computationally demanding applications. On homogeneous data sets, deep neural networks have repeatedly shown excellent performance and have therefore been widely adopted. However, their application to modeling tabular data (inference or generation) remains highly challenging.

This work provides an overview of state-of-the-art deep learning methods for tabular data. We start by categorizing them into 3 groups: data transformations, specialized architectures, and regularization models. We then provide a comprehensive overview of the main approaches in each group. A discussion of deep learning approaches for generating tabular data is complemented by strategies for explaining deep models on tabular data.

Our primary contribution is to address the main research streams and existing methodologies in this area, while highlighting relevant challenges and open research questions. We also provide an empirical comparison of traditional machine learning methods with deep learning approaches on real tabular data sets of different sizes and with different learning objectives. Our results indicate that algorithms based on gradient-boosted tree ensembles still outperform the deep learning models.

To the best of our knowledge, this is the first in-depth look at deep learning approaches for tabular data. This work can serve as a valuable starting point and guide for researchers and practitioners interested in deep learning with tabular data.

--- https://arxiv.org/abs/2109.15102#microsoft Fake It Till You Make It: Face analysis in the wild using synthetic data alone Erroll Wood, Tadas Baltrušaitis, Charlie Hewitt, Sebastian Dziadzio, Matthew Johnson, Virginia Estellers, Thomas J. Cashman, Jamie Shotton 2021-09-30 2021-09-30 [("doi","10.48550/arXiv.2109.15102")] ai/nn/gan

We demonstrate that it is possible to perform face-related computer vision in the wild using synthetic data alone. The community has long enjoyed the benefits of synthesizing training data with graphics, but the domain gap between real and synthetic data has remained a problem, especially for human faces. Researchers have tried to bridge this gap with data mixing, domain adaptation, and domain-adversarial training, but we show that it is possible to synthesize data with minimal domain gap, so that models trained on synthetic data generalize to real in-the-wild datasets.

We describe how to combine a procedurally-generated parametric 3D face model with a comprehensive library of hand-crafted assets to render training images with unprecedented realism and diversity. We train machine learning systems for face-related tasks such as landmark localization and face parsing, showing that synthetic data can both match real data in accuracy as well as open up new approaches where manual labeling would be impossible.

--- https://www.biorxiv.org/content/10.1101/2021.09.14.460388.full The genetic basis of tail-loss evolution in humans and apes Bo Xia, Weimin Zhang, Aleksandra Wudzinska, Emily Huang, Ran Brosh, Maayan Pour, Alexander Miller, Jeremy S. Dasen, Matthew T. Maurano, Sang Y. Kim, Jef D. Boeke, Itai Yanai 2021-09-16 2021-09-16 [("doi","10.1101/2021.09.14.460388")] biology genetics/selection/natural/human

The loss of the tail is one of the main anatomical evolutionary changes to have occurred along the lineage leading to humans and to the “anthropomorphous apes”. This morphological reprogramming in the ancestral hominoids has been long considered to have accommodated a characteristic style of locomotion and contributed to the evolution of bipedalism in humans. Yet, the precise genetic mechanism that facilitated tail-loss evolution in hominoids remains unknown.

Primate genome sequencing projects have made possible the identification of causal links between genotypic and phenotypic changes, and enable the search for hominoid-specific genetic elements controlling tail development. Here, we present evidence that tail-loss evolution was mediated by the insertion of an individual Alu element into the genome of the hominoid ancestor. We demonstrate that this Alu element—inserted into an intron of the TBXT gene (also called T or Brachyury)—pairs with a neighboring ancestral Alu element encoded in the reverse genomic orientation and leads to a hominoid-specific alternative splicing event.

To study the effect of this splicing event, we generated a mouse model that mimics the expression of human TBXT products by expressing both full-length and exon-skipped isoforms of the mouse TBXT ortholog. We found that mice with this genotype exhibit the complete absence of a tail or a shortened tail, supporting the notion that the exon-skipped transcript is sufficient to induce a tail-loss phenotype, albeit with incomplete penetrance. We further noted that mice homozygous for the exon-skipped isoforms exhibited embryonic spinal cord malformations, resembling a neural tube defect condition, which affects ~1/1,000 human neonates.

We propose that selection for the loss of the tail along the hominoid lineage was associated with an adaptive cost of potential neural tube defects and that this ancient evolutionary trade-off may thus continue to affect human health today.

--- https://arxiv.org/abs/2109.04332 PPT: Pre-trained Prompt Tuning for Few-shot Learning Yuxian Gu, Xu Han, Zhiyuan Liu, Minlie Huang 2021-09-09 2021-09-09 [("doi","10.48550/arXiv.2109.04332")] ai/nn

Prompts for pre-trained language models (PLMs) have shown remarkable performance by bridging the gap between pre-training tasks and various downstream tasks. Among these methods, prompt tuning, which freezes PLMs and only tunes soft prompts, provides an efficient and effective solution for adapting large-scale PLMs to downstream tasks. However, prompt tuning is yet to be fully explored. In our pilot experiments, we find that prompt tuning performs comparably with conventional full-model fine-tuning when downstream data are sufficient, whereas it performs much worse under few-shot learning settings, which may hinder the application of prompt tuning in practice. We attribute this low performance to the manner of initializing soft prompts.

Therefore, in this work, we propose to pre-train prompts by adding soft prompts into the pre-training stage to obtain a better initialization. We name this Pre-trained Prompt Tuning framework “PPT”. To ensure the generalization of PPT, we formulate similar classification tasks into a unified task form and pre-train soft prompts for this unified task.

Extensive experiments show that tuning pre-trained prompts for downstream tasks can reach or even outperform full-model fine-tuning under both full-data and few-shot settings. Our approach is effective and efficient for using large-scale PLMs in practice.

--- https://arxiv.org/abs/2109.00301 ∞-former: Infinite Memory Transformer Pedro Henrique Martins, Zita Marinho, André F. T. Martins 2021-09-01 2021-09-01 [("doi","10.48550/arXiv.2109.00301")] ai/nn/transformer/attention/compression

Transformers are unable to model long-term memories effectively, since the amount of computation they need to perform grows with the context length.

While variations of efficient transformers have been proposed, they all have a finite memory capacity and are forced to drop old information.

In this paper, we propose the ∞-former, which extends the vanilla transformer with an unbounded long-term memory. By making use of a continuous-space attention mechanism to attend over the long-term memory, the ∞-former’s attention complexity becomes independent of the context length, trading off memory length with precision.

In order to control where precision is more important, ∞-former maintains “sticky memories” being able to model arbitrarily long contexts while keeping the computation budget fixed.

Experiments on a synthetic sorting task, language modeling, and document grounded dialogue generation demonstrate the ∞-former’s ability to retain information from long sequences.

--- https://arxiv.org/abs/2108.13161 DART: Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners Ningyu Zhang, Luoqiu Li, Xiang Chen, Shumin Deng, Zhen Bi, Chuanqi Tan, Fei Huang, Huajun Chen 2021-08-30 2021-08-30 [("doi","10.48550/arXiv.2108.13161")] ai/nn

Large-scale pre-trained language models have contributed to natural language processing by demonstrating remarkable abilities as few-shot learners. However, their effectiveness depends mainly on scaling the model parameters and prompt design, hindering their implementation in most real-world applications. This study proposes a novel pluggable, extensible, and efficient approach named DifferentiAble pRompT (DART), which can convert small language models into better few-shot learners without any prompt engineering.

The main principle behind this approach involves reformulating potential natural language processing tasks into the task of a pre-trained language model and differentially optimizing the prompt template as well as the target label with backpropagation. Furthermore, the proposed approach can be: (1) Plugged to any pre-trained language models; (2) Extended to widespread classification tasks.

A comprehensive evaluation of standard NLP tasks demonstrates that the proposed approach achieves a better few-shot performance.

Code is available in Github.

--- https://arxiv.org/abs/2108.11482#google ETA Prediction with Graph Neural Networks in Google Maps Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Brett Wiltshire, Peter W. Battaglia, Vishal Gupta, Ang Li, Zhongwen Xu, Alvaro Sanchez-Gonzalez, Yujia Li, Petar Veličković 2021-08-25 2021-08-25 [("doi","10.1145/3459637.3481916")] ai/nn

Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time queries from users and enterprises alike. Further, such a task requires accounting for complex spatiotemporal interactions (modeling both the topological properties of the road network and anticipating events—such as rush hours—that may occur in the future). Hence, it is an ideal target for graph representation learning at scale.

Here we present a graph neural network (GNN) estimator for estimated time of arrival (ETA) which we have deployed in production at Google Maps. While our main architecture consists of standard GNN building blocks, we further detail the usage of training schedule methods such as MetaGradients in order to make our model robust and production-ready. We also provide prescriptive studies: ablating on various architectural decisions and training regimes, and qualitative analyses on real-world situations where our model provides a competitive edge.

Our GNN proved powerful when deployed, reducing negative ETA outcomes in several regions compared to the previous production baseline (40+% in cities like Sydney).

--- https://arxiv.org/abs/2108.09084 Fastformer: Additive Attention Can Be All You Need Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie 2021-08-20 2021-08-20 [("doi","10.48550/arXiv.2108.09084")] ai/nn/transformer/attention/hierarchical

Transformer is a powerful model for text understanding. However, it is inefficient due to its quadratic complexity to input sequence length. Although there are many methods on Transformer acceleration, they are still either inefficient on long sequences or not effective enough.

In this paper, we propose Fastformer, which is an efficient Transformer model based on additive attention. In Fastformer, instead of modeling the pair-wise interactions between tokens, we first use additive attention mechanism to model global contexts, and then further transform each token representation based on its interaction with global context representations. In this way, Fastformer can achieve effective context modeling with linear complexity.

Extensive experiments on 5 datasets show that Fastformer is much more efficient than many existing Transformer models and can meanwhile achieve comparable or even better long text modeling performance.

--- https://arxiv.org/abs/2108.04962 AdaMRA: Adaptive Multi-Resolution Attention with Linear Complexity Yao Zhang, Yunpu Ma, Thomas Seidl, Volker Tresp 2021-08-10 2021-08-10 [("doi","10.48550/arXiv.2108.04962")] ai/nn/transformer/attention/hierarchical

Transformers have improved the state-of-the-art across numerous tasks in sequence modeling. Besides the quadratic computational and memory complexity w.r.t the sequence length, the self-attention mechanism only processes information at the same scale, ie. all attention heads are in the same resolution, resulting in the limited power of the Transformer. To remedy this, we propose a novel and efficient structure named Adaptive Multi-Resolution Attention (AdaMRA for short), which scales linearly to sequence length in terms of time and space.

Specifically, we leverage a multi-resolution multi-head attention mechanism, enabling attention heads to capture long-range contextual information in a coarse-to-fine fashion. Moreover, to capture the potential relations between query representation and clues of different attention granularities, we leave the decision of which resolution of attention to use to query, which further improves the model’s capacity compared to vanilla Transformer. In an effort to reduce complexity, we adopt kernel attention without degrading the performance.

Extensive experiments on several benchmarks demonstrate the effectiveness and efficiency of our model by achieving a state-of-the-art performance-efficiency-memory trade-off.

To facilitate AdaMRA usage by the scientific community, the code implementation will be made publicly available.

--- https://arxiv.org/abs/2109.07958 TruthfulQA: Measuring How Models Mimic Human Falsehoods Stephanie Lin, Jacob Hilton, Owain Evans 2021-09-08 2021-09-08 [("doi","10.48550/arXiv.2109.07958")] ai/dataset ai/nn/transformer/gpt/2 ai/nn/transformer/gpt/3 ai/nn/transformer/t5 ai/scaling

[leaderboard] We propose a benchmark, TruthfulQA, to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. We crafted questions that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts.

We tested GPT-3, GPT-Neo/J, GPT-2 and a T5-based model.

The best model was truthful on 58% of questions, while human performance was 94%. Models generated many false answers that mimic popular misconceptions and have the potential to deceive humans. The largest models were generally the least truthful. This contrasts with other NLP tasks, where performance improves with model size. However, this result is expected if false answers are learned from the training distribution.

We suggest that scaling up models alone is less promising for improving truthfulness than fine-tuning using training objectives other than imitation of text from the web.

Figure 2: Larger models are less truthful. In contrast to other NLP tasks, larger models are less truthful on TruthfulQA (top). Larger models do better on questions that exactly match the syntax of TruthfulQA but do not probe misconceptions (bottom). Figure 3 gives a concrete example of larger sizes being less truthful.

[But this turns out to be U-scaling, not true inverse scaling: as models get larger, like Gopher or GPT-4, they start to get better at TruthfulQA, consistent with their inconsistent scaling trends above.]

--- https://arxiv.org/abs/2108.13487#microsoft Want To Reduce Labeling Cost? GPT-3 Can Help Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, Michael Zeng 2021-08-30 2021-08-30 [("doi","10.48550/arXiv.2108.13487")] ai/nn/transformer/gpt ai/scaling

Data annotation is a time-consuming and labor-intensive process for many NLP tasks. Although there exist various methods to produce pseudo data labels, they are often task-specific and require a decent amount of labeled data to start with. Recently, the immense language model GPT-3 with 175 billion parameters has achieved tremendous improvement across many few-shot learning tasks.

In this paper, we explore ways to leverage GPT-3 as a low-cost data labeler to train other models. We find that, to make the downstream model achieve the same performance on a variety of NLU and NLG tasks, it costs 50% to 96% less to use labels from GPT-3 than using labels from humans.

Furthermore, we propose a novel framework of combining pseudo labels from GPT-3 with human labels, which leads to even better performance with limited labeling budget.

These results present a cost-effective data labeling methodology that is generalizable to many practical applications.

--- https://arxiv.org/abs/2109.02355 A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning Yehuda Dar, Vidya Muthukumar, Richard G. Baraniuk 2021-09-06 2021-09-06 [("doi","10.48550/arXiv.2109.02355")] ai/scaling

The rapid recent progress in machine learning (ML) has raised a number of scientific questions that challenge the long-standing dogma of the field. One of the most important riddles is the good empirical generalization of overparameterized models. Overparameterized models are excessively complex with respect to the size of the training dataset, which results in them perfectly fitting (ie. interpolating) the training data, which is usually noisy. Such interpolation of noisy data is traditionally associated with detrimental overfitting, and yet a wide range of interpolating models—from simple linear models to deep neural networks—have recently been observed to generalize extremely well on fresh test data. Indeed, the recently discovered double descent phenomenon has revealed that highly overparameterized models often improve over the best underparameterized model in test performance.

Understanding learning in this overparameterized regime requires new theory and foundational empirical studies, even for the simplest case of the linear model. The underpinnings of this understanding have been laid in very recent analyses of overparameterized linear regression and related statistical learning tasks, which resulted in precise analytic characterizations of double descent. This paper provides a succinct overview of this emerging theory of overparameterized ML (henceforth abbreviated as TOPML) that explains these recent findings through a statistical signal processing perspective. We emphasize the unique aspects that define the TOPML research area as a subfield of modern ML theory and outline interesting open questions that remain.

--- https://www.biorxiv.org/content/10.1101/752121.full Genomics reveals the origins of ancient specimens Qian Cong, Jinhui Shen, Jing Zhang, Wenlin Li, Lisa N. Kinch, John V. Calhoun, Andrew D. Warren, Nick V. Grishin 2019-09-04 2021-03-10 [("doi","10.1101/752121")] genetics/sequencing

Centuries of zoological studies amassed billions of specimens in collections worldwide. Genomics of these specimens promises to rejuvenate biodiversity research.

The obstacles stem from DNA degradation with specimen age. Overcoming this challenge, we set out to resolve a series of long-standing controversies involving a group of butterflies. We deduced geographical origins of several ancient specimens of uncertain provenance that are at the heart of these debates.

Here, genomics tackles one of the greatest problems in zoology: countless old, poorly documented specimens that serve as irreplaceable embodiments of species concepts. The ability to figure out where they were collected will resolve many on-going disputes.

More broadly, we show the utility of genomics applied to ancient museum specimens to delineate the boundaries of species and populations, and to hypothesize about genotypic determinants of phenotypic traits.

--- https://www.biorxiv.org/content/10.1101/825034.full Metagenomic analysis of a blood stain from the French revolutionary Jean-Paul Marat (1743–1793) Toni de-Dios, Lucy van Dorp, Philippe Charlier, Sofia Morfopoulou, Esther Lizano, Celine Bon, Corinne Le Bitouzé, Marina Álvarez-Estapé, Tomas Marquès-Bonet, François Balloux, Carles Lalueza-Fox 2019-10-31 2021-03-10 [("doi","10.1101/825034")] genetics/sequencing

The French revolutionary Jean-Paul Marat was assassinated in 1793 in his bathtub, where he was trying to find relief from the debilitating skin disease he was suffering from. At the time of his death, Marat was annotating newspapers, which got stained with his blood and were subsequently preserved by his sister. We extracted and sequenced DNA from the blood stain and also from another section of the newspaper, which we used for comparison. Analysis of human DNA sequences supported the heterogeneous ancestry of Marat, with his mother being of French origin and his father born in Sardinia, although bearing more affinities to mainland Italy or Spain. Metagenomic analyses of the non-human reads uncovered the presence of fungal, bacterial and low levels of viral DNA. Relying on the presence/absence of microbial species in the samples, we could confidently rule out several putative infectious agents that had been previously hypothesised as the cause of his condition. Conversely, some of the detected species are uncommon as environmental contaminants and may represent plausible infective agents. Based on all the available evidence, we hypothesize that Marat may have suffered from a primary fungal infection (seborrheic dermatitis), superinfected with bacterial opportunistic pathogens.

Significance: The advent of second-generation sequencing technologies allows for the retrieval of ancient genomes from long-dead people and, using non-human sequencing reads, of the pathogens that infected them. In this work we combined both approaches to gain insights into the ancestry and health of the controversial French revolutionary leader and physicist Jean-Paul Marat (1743–1793). Specifically, we investigate the pathogens, which may have been the cause of the debilitating skin condition that was affecting him, by analysing DNA obtained from a paper stained with his blood at the time of his death. This allowed us to confidently rule out several conditions that have been put forward. To our knowledge, this represents the oldest successful retrieval of genetic material from cellulose paper.

--- https://www.biorxiv.org/content/10.1101/2020.02.18.955377.full Urine as a high-quality source of host genomic DNA from wild populations Andrew T. Ozga, Timothy H. Webster, Ian C. Gilby, Melissa A. Wilson, Rebecca S. Nockerts, Michael L. Wilson, Anne E. Pusey, Yingying Li, Beatrice H. Hahn, Anne C. Stone 2020-02-20 2021-03-10 [("doi","10.1101/2020.02.18.955377")] genetics/sequencing

The ability to generate genomic data from wild animal populations has the potential to give unprecedented insight into the population history and dynamics of species in their natural habitats. However, in the case of many species, it is impossible legally, ethically, or logistically to obtain tissues samples of high-quality necessary for genomic analyses. In this study we evaluate the success of multiple sources of genetic material (feces, urine, dentin, and dental calculus) and several capture methods (shotgun, whole-genome, exome) in generating genome-scale data in wild eastern chimpanzees (Pan troglodytes schweinfurthii) from Gombe National Park, Tanzania.

We found that urine harbors statistically-significantly more host DNA than other sources, leading to broader and deeper coverage across the genome. Urine also exhibited a lower rate of allelic dropout. We found exome sequencing to be far more successful than both shotgun sequencing and whole-genome capture at generating usable data from low-quality samples such as feces and dental calculus. These results highlight urine as a promising and untapped source of DNA that can be noninvasively collected from wild populations of many species.

--- https://www.medrxiv.org/content/10.1101/2021.08.20.21262334.full Discovery of 42 Genome-Wide Statistically-Significant Loci Associated with Dyslexia Catherine Doust, Pierre Fontanillas, Else Eising, Scott D. Gordon, Zhengjun Wang, Gökberk Alagöz, Barbara Molz, 23andMe, Quantitative Trait Working Group of the GenLang Consortium, Beate St Pourcain, Clyde Francks, Riccardo E. Marioni, Jingjing Zhao, Silvia Paracchini, Joel B. Talcott, Anthony P. Monaco, John F. Stein, Jeffrey R. Gruen, Richard K. Olson, Erik G. Willcutt, John C. DeFries, Bruce F. Pennington, Shelley D. Smith, Margaret J. Wright, Nicholas G. Martin, Adam Auton, Timothy C. Bates, Simon E. Fisher, Michelle Luciano 2021-08-22 2021-08-22 [("doi","10.1101/2021.08.20.21262334")] genetics/heritable psychiatry/adhd

Reading and writing are crucial for many aspects of modern life but up to 1 in 10 children are affected by dyslexia, which can persist into adulthood. Family studies of dyslexia suggest heritability up to 70%, yet no convincing genetic markers have been found due to limited study power.

Here, we present a genome-wide association study representing a 20× increase in sample size from prior work, with 51,800 adults self-reporting a dyslexia diagnosis and 1,087,070 controls. We identified 42 independent genome-wide statistically-significant loci: 17 are in genes linked to or pleiotropic with cognitive ability/educational attainment; 25 are novel and may be more specifically associated with dyslexia. 20-three loci (12 novel) were validated in independent cohorts of Chinese and European ancestry.

We confirmed a similar genetic etiology of dyslexia between sexes, and found genetic covariance with many traits, including ambidexterity, but not neuroanatomical measures of language-related circuitry. Causal analyses revealed a directional effect of dyslexia on attention deficit hyperactivity disorder (ADHD) and bidirectional effects on socio-educational traits but these relationships require further investigation.

Dyslexia polygenic scores explained up to 6% of variance in reading traits in independent cohorts, and might in future enable earlier identification and remediation of dyslexia.

--- https://www.medrxiv.org/content/10.1101/2021.08.25.21262631.full Causal and Associational Linking Language From Observational Research and Health Evaluation Literature in Practice: A systematic language evaluation Noah A. Haber, Sarah E. Wieten, Julia M. Rohrer, Onyebuchi A. Arah, Peter W. G. Tennant, Elizabeth A. Stuart, Eleanor J. Murray, Sophie Pilleron, Sze Tung Lam, Emily Riederer, Sarah Jane Howcutt, Alison E. Simmons, Clémence Leyrat, Philipp Schoenegger, Anna Booman, Mi-Suk Kang Dufour, Ashley L. O’Donoghue, Rebekah Baglini, Stefanie Do, Mari De La Rosa Takashima, Thomas Rhys Evans, Daloha Rodriguez-Molina, Taym M. Alsalti, Daniel J. Dunleavy, Gideon Meyerowitz-Katz, Alberto Antonietti, Jose A. Calvache, Mark J. Kelson, Meg G. Salvia, Camila Olarte Parra, Saman Khalatbari-Soltani, Taylor McLinden, Arthur Chatton, Jessie Seiler, Andreea Steriu, Talal S. Alshihayb, Sarah E. Twardowski, Julia Dabravolskaj, Eric Au, Rachel A. Hoopsick, Shashank Suresh, Nicholas Judd, Sebastián Peña, Cathrine Axfors, Palwasha Khan, Ariadne E. Rivera Aguirre, Nnaemeka U. Odo, Ian Schmid, Matthew P. Fox 2021-08-30 2021-08-30 [("doi","10.1101/2021.08.25.21262631")] psychology/cognitive-bias sociology statistics/bias statistics/causality

Background: Avoiding “causal” language with observational study designs is common publication practice, often justified as being a more cautious approach to interpretation.

Objectives: We aimed to (1) estimate the degree to which causality was implied by both the language linking exposures to outcomes and by action recommendations in the high-profile health literature, (2) examine disconnects between language and recommendations, (3) identify which linking phrases were most common, and (4) generate estimates by which these phrases imply causality.

Method: We identified 18 of the most prominent general medical/public health/epidemiology journals, and searched and screened for articles published 2010–2019 that investigated exposure/outcome pairs until we reached 65 non-RCT articles per journal (n = 1,170). Two independent reviewers and an arbitrating reviewer rated the degree to which they believed causality had been implied by the language in abstracts based on written guidance. Reviewers then rated causal implications of linking words in isolation. For comparison, additional review was performed for full texts and for a secondary sample of RCTs.

Results: Reviewers rated the causal implication of the sentence and phrase linking the exposure and outcome as None (ie. makes no causal implication) in 13.8%, Weak in 34.2%, Moderate in 33.2%, and Strong in 18.7% of abstracts. Reviewers identified an action recommendation in 34.2% of abstracts. Of these action recommendations, reviewers rated the causal implications as None in 5.3%, Weak in 19.0%, Moderate in 42.8% and Strong in 33.0% of cases. The implied causality of action recommendations was often higher than the implied causality of linking sentences (44.5%) or commensurate (40.3%), with 15.3% being weaker. The most common linking word root identified in abstracts was “associate” (n = 535/1,170; 45.7%) (eg. “association”, “associated”, etc). There were only 16 (1.4%) abstracts using “cause” in the linking or modifying phrases. Reviewer ratings for causal implications of word roots were highly heterogeneous, including those commonly considered non-causal.

Discussion

We found substantial disconnects between causal implications used to link an exposure to an outcome and the action implications made. This undercuts common assumptions about what words are often considered non-causal and that policing them eliminates causal implications. We recommend that instead of policing words, editors, researchers, and communicators should increase efforts at making research questions, as well as the potential of studies to answer them, more transparent.

--- https://arxiv.org/abs/2108.07686 Scaling Laws for Deep Learning Jonathan S. Rosenfeld 2021-08-17 2021-08-17 [("doi","10.48550/arXiv.2108.07686")] ai/nn/sparsity/pruning ai/scaling

[previously Rosenfeld et al 2019, Rosenfeld et al 2020; cf. emergence] Running faster will only get you so far—it is generally advisable to first understand where the roads lead, then get a car …

The renaissance of machine learning (ML) and deep learning (DL) over the last decade is accompanied by an unscalable computational cost, limiting its advancement and weighing on the field in practice. In this thesis we take a systematic approach to address the algorithmic and methodological limitations at the root of these costs.

We first demonstrate that DL training and pruning are predictable and governed by scaling laws—for state-of-the-art models and tasks, spanning image classification and language modeling, as well as for state-of-the-art model compression via iterative pruning. Predictability, via the establishment of these scaling laws, provides the path for principled design and trade-off reasoning, currently largely lacking in the field.

We then continue to analyze the sources of the scaling laws, offering an approximation-theoretic view and showing through the exploration of a noiseless realizable case that DL is in fact dominated by error sources very far from the lower error limit.

We conclude by building on the gained theoretical understanding of the scaling laws’ origins. We present a conjectural path to eliminate one of the current dominant error sources—through a data bandwidth limiting hypothesis and the introduction of Nyquist learners—which can, in principle, reach the generalization error lower limit (eg. 0 in the noiseless case), at finite dataset size.

--- https://arxiv.org/abs/2108.06828 On boosting the power of Chatterjee’s rank correlation Zhexiao Lin, Fang Han 2021-08-15 2021-08-15 [("doi","10.48550/arXiv.2108.06828")] statistics/order

Chatterjee 2021’s ingenious approach to estimating a measure of dependence first proposed by Dette et al 2013 based on simple rank statistics has quickly caught attention. This measure of dependence has the unusual property of being 0–1, and being 0 or 1 if and only if the corresponding pair of random variables is independent or one is a measurable function of the other almost surely. However, more recent studies (Cao & Bickel 2020; Shi et al 2021b) showed that independence tests based on Chatterjee’s rank correlation are unfortunately rate-inefficient against various local alternatives and they call for variants.

We answer this call by proposing revised Chatterjee’s rank correlations that still consistently estimate the same dependence measure but provably achieve near-parametric efficiency in testing against Gaussian rotation alternatives. This is possible via incorporating many right nearest neighbors in constructing the correlation coefficients.

We thus overcome the “only one disadvantage” of Chatterjee’s rank correlation (Chatterjee, 2021, §7).

--- https://arxiv.org/abs/2108.05939 Where Did the Web Archive Go? Mohamed Aturban, Michael L. Nelson, Michele C. Weigle 2021-08-12 2021-08-12 [("doi","10.48550/arXiv.2108.05939")] cs/linkrot

To perform a longitudinal investigation of web archives and detecting variations and changes replaying individual archived pages, or mementos, we created a sample of 16,627 mementos from 17 public web archives.

Over the course of our 14-month study (November, 2017—January, 2019), we found that 4 web archives changed their base URIs and did not leave a machine-readable method of locating their new base URIs, necessitating manual rediscovery.

Of the 1,981 mementos in our sample from these 4 web archives, 537 were impacted: 517 mementos were rediscovered but with changes in their time of archiving (or Memento-Datetime), HTTP status code, or the string comprising their original URI (or URI-R), and 20 of the mementos could not be found at all.

--- https://www.biorxiv.org/content/10.1101/2021.08.22.457241.full Using historical museum samples to examine divergent and parallel evolution in the invasive starling Katarina C. Stuart, William B. Sherwin, Jeremy J. Austin, Melissa Bateson, Marcel Eens, Matthew C. Brandley, Lee A. Rollins 2021-08-23 2021-08-23 [("doi","10.1101/2021.08.22.457241")] genetics/sequencing

During the Anthropocene, Earth has experienced unprecedented habitat loss, native species decline, and global climate change. Concurrently, greater globalisation is facilitating species movement, increasing the likelihood of alien species establishment and propagation. There is a great need to understand what influences a species’ ability to persist or perish within a new or changing environment. Examining genes that may be associated with a species’ invasion success or persistence informs invasive species management, assists with native species preservation, and sheds light on important evolutionary mechanisms that occur in novel environments. This approach can be aided by coupling spatial and temporal investigations of evolutionary processes.

Here we use the common starling, Sturnus vulgaris, to identify parallel and divergent evolutionary change between contemporary native and invasive range samples and their common ancestral population. To do this, we use reduced-representation sequencing of native samples collected recently in north-western Europe and invasive samples from Australia, together with museum specimens sampled in the UK during the mid-19th Century. We found evidence of parallel selection on both continents, possibly resulting from common global selective forces such as exposure to pollutants (eg. TCDD) and food carbohydrate content. We also identified divergent selection in these populations, which might be related to adaptive changes in response to the novel environment encountered in the introduced Australian range. Interestingly, signatures of selection are equally as common within both invasive and native range contemporary samples. Our results demonstrate the value of including historical samples in genetic studies of invasion and highlight the ongoing and occasionally parallel role of adaptation in both native and invasive ranges.

--- https://arxiv.org/abs/2108.02678 A Global Nucleic Acid Observatory for Biodefense and Planetary Health The Nucleic Acid Observatory Consortium 2021-08-05 2021-08-05 [("doi","10.48550/arXiv.2108.02678")] existential-risk genetics/sequencing

The spread of pandemic viruses and invasive species can be catastrophic for human societies and natural ecosystems. SARS-CoV-2 demonstrated that the speed of our response is critical, as each day of delay permitted exponential growth and dispersion of the virus.

Here we propose a global Nucleic Acid Observatory (NAO) to monitor the relative frequency of everything biological through comprehensive metagenomic sequencing of waterways and wastewater.

By searching for divergences from historical baseline frequencies at sites throughout the world, NAO could detect any virus or invasive organism undergoing exponential growth whose nucleic acids end up in the water, even those previously unknown to science. Continuously monitoring nucleic acid diversity would provide us with universal early warning, obviate subtle bioweapons, and generate a wealth of sequence data sufficient to transform ecology, microbiology, and conservation.

We call for the immediate construction of a global NAO to defend and illuminate planetary health.

[Pervasive sequencing, particularly of air, would help solve problems like lack of private incentive to use adequate ventilation or sterilization like far-UV sterilizing lights: people can easily feel humidity/heat, so businesses are strongly encouraged to provide those, but they cannot feel (in an otherwise air-conditioned space) viral load or air turnover. However, if pervasive sequencing were available, buildings could be assigned ‘report cards’, like A–F, and people could check score destinations, services like Google Maps could automatically display them, smartphone apps could warn you if you are about to walk into an ‘F’ location, and so on—thereby making the invisible visible, and solving the incentive problem.]

--- https://arxiv.org/abs/1603.09382 Deep Networks with Stochastic Depth Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Q. Weinberger 2016-03-30 2021-03-11 [("doi","10.48550/arXiv.1603.09382")] ai/nn/cnn

Very deep convolutional networks with hundreds of layers have led to reductions in error on competitive benchmarks. Although the unmatched expressiveness of the many layers can be highly desirable at test time, training very deep networks comes with its own set of challenges. The gradients can vanish, the forward flow often diminishes, and the training time can be painfully slow.

To address these problems, we propose stochastic depth, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time. We start with very deep networks but during training, for each mini-batch, randomly drop a subset of layers and bypass them with the identity function. This simple approach complements the recent success of residual networks. It reduces training time substantially and improves the test error on almost all data sets that we used for evaluation.

With stochastic depth we can increase the depth of residual networks even beyond 1200 layers and still yield meaningful improvements in test error (4.91% on CIFAR-10).

--- https://arxiv.org/abs/1603.08270 Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing Steven K. Esser, Paul A. Merolla, John V. Arthur, Andrew S. Cassidy, Rathinakumar Appuswamy, Alexander Andreopoulos, David J. Berg, Jeffrey L. McKinstry, Timothy Melano, Davis R. Barch, Carmelo di Nolfo, Pallab Datta, Arnon Amir, Brian Taba, Myron D. Flickner, Dharmendra S. Modha 2016-03-28 2021-03-11 [("doi","10.1073/pnas.1604850113")] ai/nn/cnn ai/nn/sparsity/low-precision ai/scaling/hardware

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network.

Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (1) approach state-of-the-art classification accuracy across 8 standard datasets, encompassing vision and speech, (2) perform inference while preserving the hardware’s underlying energy-efficiency and high throughput, running on the aforementioned datasets at 1200–2600 frames per second and using 25–275 mW (effectively > 6000 frames / sec / W) and (3) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning.

For the first time, the algorithmic power of deep learning can be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.

--- https://arxiv.org/abs/1602.01783#deepmind Asynchronous Methods for Deep Reinforcement Learning Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu 2016-02-04 2021-03-11 [("doi","10.48550/arXiv.1602.01783")] reinforcement-learning/model-free

We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers.

We present asynchronous variants of 4 standard reinforcement learning algorithms and show that parallel actor-learners have a stabilizing effect on training allowing all 4 methods to successfully train neural network controllers.

The best performing method, an asynchronous variant of actor-critic, surpasses the current state-of-the-art on the Atari domain while training for half the time on a single multi-core CPU instead of a GPU.

Furthermore, we show that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input.

--- https://www.biorxiv.org/content/10.1101/245506.full Fine-mapping of an expanded set of type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps Anubha Mahajan, Daniel Taliun, Matthias Thurner, Neil R. Robertson, Jason M. Torres, N. William Rayner, Valgerdur Steinthorsdottir, Robert A. Scott, Niels Grarup, James P. Cook, Ellen M. Schmidt, Matthias Wuttke, Chloé Sarnowski, Reedik Mägi, Jana Nano, Christian Gieger, Stella Trompet, Cécile Lecoeur, Michael Preuss, Bram Peter Prins, Xiuqing Guo, Lawrence F. Bielak, DIAMAN T. E. Consortium, Amanda J. Bennett, Jette Bork-Jensen, Chad M. Brummett, Mickaël Canouil, Kai-Uwe Eckardt, Krista Fischer, Sharon L. R. Kardia, Florian Kronenberg, Kristi Läll, Ching-Ti Liu, Adam E. Locke, Jian′an Luan, Ioanna Ntalla, Vibe Nylander, Sebastian Sch࿆nherr, Claudia Schurmann, Loïc Yengo, Erwin Böttinger, Ivan Brandslund, Cramer Christensen, George Dedoussis, Jose C. Florez, Ian Ford, Oscar H. Franco, Timothy Frayling, Vilmantas Giedraitis, Sophie Hackinger, Andrew Tym Hattersley, Christian Herder, M. Arfan Ikram, Martin Ingelsson, Marit E. Jørgensen, Torben Jørgensen, Jennifer Kriebel, Johanna Kuusisto, Symen Ligthart, Cecilia M. Lindgren, Allan Linneberg, Valeriya Lyssenko, Vasiliki Mamakou, Thomas Meitinger, Karen L. Mohlke, Andrew D. Morris, Girish Nadkarni, James S. Pankow, Annette Peters, Naveed Sattar, Alena Stančáková, Konstantin Strauch, Kent D. Taylor, Barbara Thorand, Gudmar Thorleifsson, Unnur Thorsteinsdottir, Jaakko Tuomilehto, Daniel R. Witte, Josée Dupuis, Patricia A. Peyser, Eleftheria Zeggini, Ruth Loos, Philippe Froguel, Erik Ingelsson, Lars L. Lind, Leif Groop, Markku Laakso, Francis S. Collins, J. Wouter Jukema, Colin Palmer, Harald Grallert, Andres Metspalu, Abbas Dehghan, Anna Köttgen, Gonçalo Abecasis, James B. Meigs, Jerome I. Rotter, Jonathan Marchini, Oluf Pedersen, Torben Hansen, Claudia Langenberg, Nicholas J. Wareham, Kari Stefansson, Anna L. Gloyn, Andrew P. Morris, Michael Boehnke, Mark I. McCarthy 2018-01-09 2021-03-11 [("doi","10.1101/245506")] genetics/heritable genetics/sequencing

We aggregated genome-wide genotyping data from 32 European-descent GWAS (74,124 T2D cases, 824,006 controls) imputed to high-density reference panels of >30,000 sequenced haplotypes. Analysis of ~27M variants (~21M with minor allele frequency [MAF]<5%), identified 243 genome-wide statistically-significant loci (p<5×10−8; MAF 0.02%–50%; odds ratio [OR] 1.04–8.05), 135 not previously-implicated in T2D-predisposition.

Conditional analyses revealed 160 additional distinct association signals (p<10−5) within the identified loci. The combined set of 403 T2D-risk signals includes 56 low-frequency (0.5%≤MAF<5%) and 24 rare (MAF<0.5%) index SNPs at 60 loci, including 14 with estimated allelic OR>2. Forty-one of the signals displayed effect-size heterogeneity between BMI-unadjusted and adjusted analyses. Increased sample size and improved imputation led to substantially more precise localization of causal variants than previously attained: at 51 signals, the lead variant after fine-mapping accounted for >80% posterior probability of association (PPA) and at 18 of these, PPA exceeded 99%. Integration with islet regulatory annotations enriched for T2D association further reduced median credible set size (from 42 variants to 32) and extended the number of index variants with PPA>80% to 73. Although most signals mapped to regulatory sequence, we identified 18 genes as human validated therapeutic targets through coding variants that are causal for disease.

Genome-wide chip heritability accounted for 18% of T2D-risk, and individuals in the 2.5% extremes of a polygenic risk score generated from the GWAS data differed >9× in risk. Our observations highlight how increases in sample size and variant diversity deliver enhanced discovery and single-variant resolution of causal T2D-risk alleles, and the consequent impact on mechanistic insights and clinical translation.

--- /doc/genetics/heritable/2018-mahajan.pdf Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes Anubha Mahajan, Jennifer Wessel, Sara M. Willems, Wei Zhao, Neil R. Robertson, Audrey Y. Chu, Wei Gan, Hidetoshi Kitajima, Daniel Taliun, N. William Rayner, Xiuqing Guo, Yingchang Lu, Man Li, Richard A. Jensen, Yao Hu, Shaofeng Huo, Kurt K. Lohman, Weihua Zhang, James P. Cook, Bram Peter Prins, Jason Flannick, Niels Grarup, Vassily Vladimirovich Trubetskoy, Jasmina Kravic, Young Jin Kim, Denis V. Rybin, Hanieh Yaghootkar, Martina Müller-Nurasyid, Karina Meidtner, Ruifang Li-Gao, Tibor V. Varga, Jonathan Marten, Jin Li, Albert Vernon Smith, Ping An, Symen Ligthart, Stefan Gustafsson, Giovanni Malerba, Ayse Demirkan, Juan Fernandez Tajes, Valgerdur Steinthorsdottir, Matthias Wuttke, Cécile Lecoeur, Michael Preuss, Lawrence F. Bielak, Marielisa Graff, Heather M. Highland, Anne E. Justice, Dajiang J. Liu, Eirini Marouli, Gina Marie Peloso, Helen R. Warren, ExomeBP Consortium, MAGIC Consortium, GIANT consortium, Saima Afaq, Shoaib Afzal, Emma Ahlqvist, Peter Almgren, Najaf Amin, Lia B. Bang, Alain G. Bertoni, Cristina Bombieri, Jette Bork-Jensen, Ivan Brandslund, Jennifer A. Brody, Noël P. Burtt, Mickaël Canouil, Yii-Der I. da Chen, Yoon Shin Cho, Cramer Christensen, Sophie V. Eastwood, Kai-Uwe Eckardt, Krista Fischer, Giovanni Gambaro, Vilmantas Giedraitis, Megan L. Grove, Hugoline G. de Haan, Sophie Hackinger, Yang Hai, Sohee Han, Anne Tybjærg-Hansen, Marie-France Hivert, Bo Isomaa, Susanne Jäger, Marit E. Jørgensen, Torben Jørgensen, Annemari Käräjämäki, Bong-Jo Kim, Sung Soo Kim, Heikki A. Koistinen, Peter Kovacs, Jennifer Kriebel, Florian Kronenberg, Kristi Läll, Leslie A. Lange, Jung-Jin Lee, Benjamin Lehne, Huaixing Li, Keng-Hung Lin, Allan Linneberg, Ching-Ti Liu, Jun Liu, Marie Loh, Reedik Mägi, Vasiliki Mamakou, Roberta McKean-Cowdin, Girish Nadkarni, Matt Neville, Sune F. Nielsen, Ioanna Ntalla, Patricia A. Peyser, Wolfgang Rathmann, Kenneth Rice, Stephen S. Rich, Line Rode, Olov Rolandsson, Sebastian Schönherr, Elizabeth Selvin, Kerrin S. Small, Alena Stančáková, Praveen Surendran, Kent D. Taylor, Tanya M. Teslovich, Barbara Thorand, Gudmar Thorleifsson, Adrienne Tin, Anke Tönjes, Anette Varbo, Daniel R. Witte, Andrew R. Wood, Pranav Yajnik, Jie Yao, Loïc Yengo, Robin Young, Philippe Amouyel, Heiner Boeing, Eric Boerwinkle, Erwin Böttinger, Rajiv Chowdhury, Francis S. Collins, George Dedoussis, Abbas Dehghan, Panos Deloukas, Marco M. Ferrario, Jean Ferrières, Jose C. Florez, Philippe Frossard, Vilmundur Gudnason, Tamara B. Harris, Susan R. Heckbert, Joanna M. M. Howson, Martin Ingelsson, Sekar Kathiresan, Frank Kee, Johanna Kuusisto, Claudia Langenberg, Lenore J. Launer, Cecilia M. Lindgren, Satu Männistö, Thomas Meitinger, Olle Melander, Karen L. Mohlke, Marie Moitry, Andrew D. Morris, Alison D. Murray, Renée de Mutsert, Marju Orho-Melander, Katharine R. Owen, Markus Perola, Annette Peters, Michael A. Province, Asif Rasheed, Paul M. Ridker, Fernando Rivadineira, Frits R. Rosendaal, Anders H. Rosengren, Veikko Salomaa, Wayne H. -H Sheu, Rob Sladek, Blair H. Smith, Konstantin Strauch, André G. Uitterlinden, Rohit Varma, Cristen Jennifer Willer, Matthias Blüher, Adam S. Butterworth, John Campbell Chambers, Daniel I. Chasman, John Danesh, Cornelia van Duijn, Josée Dupuis, Oscar H. Franco, Paul W. Franks, Philippe Froguel, Harald Grallert, Leif Groop, Bok-Ghee Han, Torben Hansen, Andrew Tym Hattersley, Caroline Hayward, Erik Ingelsson, Sharon L. R. Kardia, Fredrik Karpe, Jaspal Singh Kooner, Anna Köttgen, Kari Kuulasmaa, Markku Laakso, Xu Lin, Lars L. Lind, Yongmei Liu, Ruth Loos, Jonathan Marchini, Andres Metspalu, Dennis Mook-Kanamori, Børge G. Nordestgaard, Colin Palmer, James S. Pankow, Oluf Pedersen, Bruce M. Psaty, Rainer Rauramaa, Naveed Sattar, Matthias B. Schulze, Nicole Soranzo, Timothy D. Spector, Kari Stefansson, Michael Stumvoll, Unnur Thorsteinsdottir, Tiinamaija Tuomi, Jaakko Tuomilehto, Nicholas J. Wareham, James G. Wilson, Eleftheria Zeggini, Robert A. Scott, Inês Barroso, Timothy Frayling, Mark O. Goodarzi, James B. Meigs, Michael Boehnke, Danish Saleheen, Andrew P. Morris, Jerome I. Rotter, Mark I. McCarthy 2018-01-01 2021-03-11 [("doi","10.1038/s41588-018-0084-1")] genetics/editing genetics/heritable --- https://en.wikipedia.org/wiki/BGI_Group BGI Group 2021-03-12 genetics/selection --- https://www.biorxiv.org/content/10.1101/2020.04.14.040329.full Efficient phasing and imputation of low-coverage sequencing data using large reference panels S. Rubinacci, D. M. Ribeiro, R. Hofmeister, O. Delaneau 2020-04-14 2021-03-12 [("doi","10.1101/2020.04.14.040329")] genetics/sequencing

Low-coverage whole genome sequencing followed by imputation has been proposed as a cost-effective genotyping approach for disease and population genetics studies. However, its competitiveness against SNP arrays is undermined as current imputation methods are computationally expensive and unable to leverage large reference panels.

Here, we describe a method, GLIMPSE, for phasing and imputation of low-coverage sequencing datasets from modern reference panels. We demonstrate its remarkable performance across different coverages and human populations. It achieves imputation of a full genome for less than $1, outperforming existing methods by orders of magnitude, with an increased accuracy of more than 20% at rare variants. We also show that 1× coverage enables effective association studies and is better suited than dense SNP arrays to access the impact of rare variations. Overall, this study demonstrates the promising potential of low-coverage imputation and suggests a paradigm shift in the design of future genomic studies.

--- https://www.biorxiv.org/content/10.1101/061440.full Extreme distribution of deleterious variation in a historically small and isolated population—insights from the Greenlandic Inuit Casper-Emil T. Pedersen, Kirk E. Lohmueller, Niels Grarup, Peter Bjerregaard, Torben Hansen, Hans R. Siegismund, Ida Moltke, Anders Albrechtsen 2016-06-30 2021-03-12 [("doi","10.1101/061440")] genetics/heritable/rare

The genetic consequences of a severe bottleneck on genetic load in humans are widely disputed. Based on exome sequencing of 18 Greenlandic Inuit we show that the Inuit have undergone a severe ~20,000 yearlong bottleneck. This has led to a markedly more extreme distribution of deleterious alleles than seen for any other human population.

Compared to populations with much larger population sizes, we see an overall reduction in the number of variable sites, increased numbers of fixed sites, a lower heterozygosity, and increased mean allele frequency as well as more homozygous deleterious genotypes. This means, that the Inuit population is the perfect population to examine the effect of a bottleneck on genetic load. Compared to the European, Asian and African populations, we do not observe a difference in the overall number of derived alleles. In contrast, using proxies for genetic load we find that selection has acted less efficiently in the Inuit, under a recessive model.

This fits with our simulations that predict a similar number of derived alleles but a true higher genetic load for the Inuit regardless of the genetic model.

Finally, we find that the Inuit population has a great potential for mapping of disease-causing variants that are rare in large populations. In fact, we show that these alleles are more likely to be common, and thus easy to map, in the Inuit than in the Finnish and Latino populations; populations considered highly valuable for mapping studies due to recent bottleneck events.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4317254/ Resolving the complexity of the human genome using single-molecule sequencing Mark J. P. Chaisson, John Huddleston, Megan Y. Dennis, Peter H. Sudmant, Maika Malig, Fereydoun Hormozdiari, Francesca Antonacci, Urvashi Surti, Richard Sandstrom, Matthew Boitano, Jane M. Landolin, John A. Stamatoyannopoulos, Michael W. Hunkapiller, Jonas Korlach, Evan E. Eichler 2015 2021-03-12 [("doi","10.1038/nature13907")] genetics/sequencing

The human genome is arguably the most complete mammalian reference assembly, yet more than 160 euchromatic gaps remain and aspects of its structural variation remain poorly understood 10 years after its completion. To identify missing sequence and genetic variation, here we sequence and analyze a haploid human genome (CHM1) using single-molecule, real-time DNA sequencing.

We close or extend 55% of the remaining interstitial gaps in the human GRCh37 reference genome—78% of which carried long runs of degenerate short tandem repeats, often several kilobases in length, embedded within (G+C)-rich genomic regions. We resolve the complete sequence of 26,079 euchromatic structural variants at the base-pair level, including inversions, complex insertions and long tracts of tandem repeats. Most have not been previously reported, with the greatest increases in sensitivity occurring for events less than 5 kilobases in size.

Compared to the human reference, we find an insertional bias (3:1) in regions corresponding to complex insertions and long short tandem repeats. Our results suggest a greater complexity of the human genome in the form of variation of longer and more complex repetitive DNA that can now be largely resolved with the application of this longer-read sequencing technology.

--- /doc/genetics/selection/natural/human/2012-fu.pdf Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants Wenqing Fu, Timothy D. O’Connor, Goo Jun, Hyun Min Kang, Gonçalo Abecasis, Suzanne M. Leal, Stacey Gabriel, David Altshuler, Jay Shendure, Deborah A. Nickerson, Michael J. Bamshad, NHLBI Exome Sequencing Project, Joshua M. Akey 2012-01-01 2021-03-12 genetics/heritable/rare genetics/selection/natural/human --- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1001078 Consistent Association of Type 2 Diabetes Risk Variants Found in Europeans in Diverse Racial and Ethnic Groups Kevin M. Waters, Daniel O. Stram, Mohamed T. Hassanein, Loïc Le Marchand, Lynne R. Wilkens, Gertraud Maskarinec, Kristine R. Monroe, Laurence N. Kolonel, David Altshuler, Brian E. Henderson, Christopher A. Haiman 2010-07-21 2021-03-12 [("doi","10.1371/journal.pgen.1001078")] genetics/heritable

It has been recently hypothesized that many of the signals detected in genome-wide association studies (GWAS) to T2D and other diseases, despite being observed to common variants, might in fact result from causal mutations that are rare. One prediction of this hypothesis is that the allelic associations should be population-specific, as the causal mutations arose after the migrations that established different populations around the world. We selected 19 common variants found to be reproducibly associated to T2D risk in European populations and studied them in a large multiethnic case-control study (6,142 cases and 7,403 controls) among men and women from 5 racial/ethnic groups (European Americans, African Americans, Latinos, Japanese Americans, and Native Hawaiians). In analysis pooled across ethnic groups, the allelic associations were in the same direction as the original report for all 19 variants, and 14 of the 19 were statistically-significantly associated with risk. In summing the number of risk alleles for each individual, the per-allele associations were highly statistically-significant (p < 10−4) and similar in all populations (odds ratios 1.09–1.12) except in Japanese Americans the estimated effect per allele was larger than in the other populations (1.20; phet = 3.8×10−4). We did not observe ethnic differences in the distribution of risk that would explain the increased prevalence of type 2 diabetes in these groups as compared to European Americans. The consistency of allelic associations in diverse racial/ethnic groups is not predicted under the hypothesis of Goldstein regarding “synthetic associations” of rare mutations in T2D.

Author Summary: Single rare causal alleles and/or collections of multiple rare alleles have been suggested to create “synthetic associations” with common variants in genome-wide association studies (GWAS). This model predicts that associations with common variants will not be consistent across populations. In this study, we examined 19 T2D variants for association with T2D risk in 6,142 cases and 7,403 controls from five racial/ethnic populations in the Multiethnic Cohort (European Americans, African Americans, Latinos, Japanese Americans, and Native Hawaiians). In racial/ethnic pooled analysis, all 19 variants were associated with T2D risk in the same direction as previous reports in Europeans, and the sum total of risk variants was statistically-significantly associated with T2D risk in each racial/ethnic group. The consistent associations across populations do not support the Goldstein hypothesis that rare causal alleles underlie GWAS signals. We also did not find evidence that these markers underlie racial/ethnic disparities in T2D prevalence. Large-scale GWAS and sequencing studies in these populations are necessary in order to both improve the current set of markers at these risk loci and identify new risk variants for T2D that may be difficult, or impossible, to detect in European populations.

--- /doc/genetics/heritable/2018-das.pdf Genotype Imputation from Large Reference Panels Sayantan Das, Gonçalo R. Abecasis, Brian L. Browning 2018-01-01 2021-03-12 [("doi","10.1146/annurev-genom-083117-021602")] genetics/heritable genetics/sequencing

Genotype imputation has become a standard tool in genome-wide association studies because it enables researchers to inexpensively approximate whole-genome sequence data from genome-wide single-nucleotide polymorphism array data. Genotype imputation increases statistical power, facilitates fine mapping of causal variants, and plays a key role in meta-analyses of genome-wide association studies. Only variants that were previously observed in a reference panel of sequenced individuals can be imputed.

However, the rapid increase in the number of deeply sequenced individuals will soon make it possible to assemble enormous reference panels that greatly increase the number of imputable variants.

In this review, we present an overview of genotype imputation and describe the computational techniques that make it possible to impute genotypes from reference panels with millions of individuals.

--- https://www.biorxiv.org/content/10.1101/176834.full An atlas of genetic associations in UK Biobank Oriol Canela-Xandri, Konrad Rawlik, Albert Tenesa 2017-08-16 2021-03-12 [("doi","10.1101/176834")] genetics/heritable/correlation genetics/sequencing

Genome-wide association studies have revealed many loci contributing to the variation of complex traits, yet the majority of loci that contribute to the heritability of complex traits remain elusive. Large study populations with sufficient statistical power are required to detect the small effect sizes of the yet unidentified genetic variants. However, the analysis of huge cohorts, like UK Biobank, is complicated by incidental structure present when collecting such large cohorts. For instance, UK Biobank comprises 107,162 third degree or closer related participants. Traditionally, GWAS have removed related individuals because they comprised an insignificant proportion of the overall sample size, however, removing related individuals in UK Biobank would entail a substantial loss of power. Furthermore, modeling such structure using linear mixed models is computationally expensive, which requires a computational infrastructure that may not be accessible to all researchers.

Here we present an atlas of genetic associations for 118 non-binary and 599 binary traits of 408,455 related and unrelated UK Biobank participants of White-British descent. Results are compiled in a publicly accessible database that allows querying genome-wide association summary results for 623,944 genotyped and HapMap2 imputed SNPs, as well downloading whole GWAS summary statistics for over 30 million imputed SNPs from the Haplotype Reference Consortium panel.

Our atlas of associations (GeneAtlas, http://geneatlas.roslin.ed.ac.uk/) will help researchers to query UK Biobank results in an easy way without the need to incur in high computational costs.

--- /doc/genetics/heritable/2018-evans.pdf Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits Luke M. Evans, Rasool Tahmasbi, Scott I. Vrieze, Gonçalo R. Abecasis, Sayantan Das, Doug W. Bjelland, Teresa R. deCandia, Haplotype Reference Consortium, Michael E. Goddard, Benjamin M. Neale, Jian Yang, Peter M. Visscher, Matthew C. Keller 2017-03-10 2021-03-12 [("doi","10.1101/115527")] genetics/heritable genetics/sequencing

Heritability, h2, is a foundational concept in genetics, critical to understanding the genetic basis of complex traits. Recently-developed methods that estimate heritability from genotyped SNPs, h2SNP, explain substantially more genetic variance than genome-wide statistically-significant loci, but less than classical estimates from twins and families. However, h2SNP estimates have yet to be comprehensively compared under a range of genetic architectures, making it difficult to draw conclusions from sometimes conflicting published estimates.

Here, we used thousands of real whole genome sequences to simulate realistic phenotypes under a variety of genetic architectures, including those from very rare causal variants. We compared the performance of ten methods across different types of genotypic data (commercial SNP array positions, whole genome sequence variants, and imputed variants) and under differing causal variant frequencies, levels of stratification, and relatedness thresholds. These results provide guidance in interpreting past results and choosing optimal approaches for future studies.

We then chose two methods (GREML-MS and GREML-LDMS) that best estimated overall h2SNP and the causal variant frequency spectra to six phenotypes in the UK Biobank using imputed genome-wide variants. Our results suggest that as imputation reference panels become larger and more diverse, estimates of the frequency distribution of causal variants will become increasingly unbiased and the vast majority of trait narrow-sense heritability will be accounted for.

--- https://www.biorxiv.org/content/10.1101/827550.full Population-specific and transethnic genome-wide analyses reveal distinct and shared genetic risks of coronary artery disease Satoshi Koyama, Kaoru Ito, Chikashi Terao, Masato Akiyama, Momoko Horikoshi, Yukihide Momozawa, Hiroshi Matsunaga, Hirotaka Ieki, Kouichi Ozaki, Yoshihiro Onouchi, Atsushi Takahashi, Seitaro Nomura, Hiroyuki Morita, Hiroshi Akazawa, Changhoon Kim, Jeong-sun Seo, Koichiro Higasa, Motoki Iwasaki, Taiki Yamaji, Norie Sawada, Shoichiro Tsugane, Teruhide Koyama, Hiroaki Ikezaki, Naoyuki Takashima, Keitaro Tanaka, Kokichi Arisawa, Kiyonori Kuriki, Mariko Naito, Kenji Wakai, Shinichiro Suna, Yasuhiko Sakata, Hiroshi Sato, Masatsugu Hori, Yasushi Sakata, Koichi Matsuda, Yoshinori Murakami, Hiroyuki Aburatani, Michiaki Kubo, Fumihiko Matsuda, Yoichiro Kamatani, Issei Komuro 2019-11-16 2021-03-12 [("doi","10.1101/827550")] genetics/heritable

To elucidate the genetics of coronary artery disease (CAD) in the Japanese population, we conducted a large-scale genome-wide association study (GWAS) of 168,228 Japanese (25,892 cases and 142,336 controls) with genotype imputation using a newly developed reference panel of Japanese haplotypes including 1,782 CAD cases and 3,148 controls. We detected 9 novel disease-susceptibility loci and Japanese-specific rare variants contributing to disease severity and increased cardiovascular mortality.

We then conducted a transethnic meta-analysis and discovered 37 additional novel loci. Using the result of the meta-analysis, we derived a polygenic risk score (PRS) for CAD, which outperformed those derived from either Japanese or European GWAS. The PRS prioritized risk factors among various clinical parameters and segregated individuals with increased risk of long-term cardiovascular mortality.

Our data improves clinical characterization of CAD genetics and suggests the utility of transethnic meta-analysis for PRS derivation in non-European populations.

--- https://www.biorxiv.org/content/10.1101/284570.full Novel susceptibility loci and genetic regulation mechanisms for type 2 diabetes Angli Xue, Yang Wu, Zhihong Zhu, Futao Zhang, Kathryn E. Kemper, Zhili Zheng, Loïc Yengo, Luke R. Lloyd-Jones, Julia Sidorenko, Yeda Wu, eQTLGen Consortium, Allan F. McRae, Peter M. Visscher, Jian Zeng, Jian Yang 2018-03-20 2021-03-12 [("doi","10.1101/284570")] genetics/heritable

We conducted a meta-analysis of genome-wide association studies (GWAS) with ~16 million genotyped/imputed genetic variants in 62,892 type 2 diabetes (T2D) cases and 596,424 controls of European ancestry. We identified 139 common and 4 rare (minor allele frequency < 0.01) variants associated with T2D, 42 of which (39 common and 3 rare variants) were independent of the known variants.

Integration of the gene expression data from blood (n = 14,115 and 2,765) and other T2D-relevant tissues (n = up to 385) with the GWAS results identified 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation (n = 1,980) and epigenomic annotations data highlighted 3 putative T2D genes (CAMK1D, TP53INP1 and ATP5G1) with plausible regulatory mechanisms whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression.

We further found evidence that the T2D-associated loci have been under purifying selection.

--- https://www.biorxiv.org/content/10.1101/196048.full Multiethnic meta-analysis identifies new loci for pulmonary function Annah B. Wyss, Tamar Sofer, Mi Kyeong Lee, Natalie Terzikhan, Jennifer N. Nguyen, Lies Lahousse, Jeanne C. Latourelle, Albert Vernon Smith, Traci M. Bartz, Mary F. Feitosa, Wei Gao, Tarunveer S. Ahluwalia, Wenbo Tang, Christopher Oldmeadow, Qing Duan, Kim de Jong, Mary K. Wojczynski, Xin-Qun Wang, Raymond Noordam, Fernando Pires Hartwig, Victoria E. Jackson, Tianyuan Wang, Ma’en Obeidat, Brian D. Hobbs, Tianxiao Huan, Gleb Kichaev, Jianping Jin, Mariaelisa Graff, Tamara B. Harris, Ravi Kalhan, Susan R. Heckbert, Lavinia Paternoster, Kristin M. Burkart, Yongmei Liu, Elizabeth G. Holliday, James G. Wilson, Judith M. Vonk, Jason Sanders, R. Graham Barr, Renée de Mutsert, Ana Maria Baptista Menezes, Hieab H. H. Adams, Maarten van den Berge, Roby Joehanes, Lenore J. Launer, Alanna C. Morrison, Colleen M. Sitlani, Juan C. Celedón, Stephen B. Kritchevsky, Rodney J. Scott, Kaare Christensen, Jerome I. Rotter, Tobias N. Bonten, Fernando César Wehrmeister, Yohan Bossé, Nora Franceschini, Jennifer A. Brody, Robert C. Kaplan, Kurt Lohman, Mark McEvoy, Michael A. Province, Frits R. Rosendaal, Kent D. Taylor, David C. Nickle, International C. O. P. D. Genetics Consortium Investigators, Vilmundur Gudnason, Kari E. North, Myriam Fornage, Bruce M. Psaty, Richard H. Myers, George O’Connor, Torben Hansen, Cathy C. Laurie, Pat Cassano, Joohon Sung, Woo Jin Kim, John R. Attia, Leslie Lange, H. Marike Boezen, Bharat Thyagarajan, Stephen S. Rich, Dennis O. Mook-Kanamori, Bernardo Lessa Horta, André G. Uitterlinden, Don D. Sin, Hae Kyung Im, Michael H. Cho, Guy G. Brusselle, Sina A. Gharib, Josée Dupuis, Ani Manichaikul, Stephanie J. London 2017-10-05 2021-03-13 [("doi","10.1101/196048")] genetics/heritable

Nearly 100 loci have been identified for pulmonary function, almost exclusively in studies of European ancestry populations. We extend previous research by meta-analyzing genome-wide association studies of 1,000 Genomes imputed variants in relation to pulmonary function in a multiethnic population of 90,715 individuals of European (n = 60,552), African (n = 8,429), Asian (n = 9,959), and Hispanic/Latino (n = 11,775) ethnicities.

We identified over 50 novel loci at genome-wide statistical-significance in ancestry-specific and/or multiethnic meta-analyses. Recent fine mapping methods incorporating functional annotation, gene expression, and/or differences in linkage disequilibrium between ethnicities identified potential causal variants and genes at known and newly identified loci.

Sixteen of the novel genes encode proteins with predicted or established drug targets, including KCNK2 and CDK12.

--- https://www.biorxiv.org/content/10.1101/076794.full Genomic analyses for age at menarche identify 389 independent signals and indicate BMI-independent effects of puberty timing on cancer susceptibility Felix R. Day, Deborah J. Thompson, Hannes Helgason, Daniel I. Chasman, Hilary Finucane, Patrick Sulem, Katherine S. Ruth, Sean Whalen, Abhishek K. Sarkar, Eva Albrecht, Elisabeth Altmaier, Marzyeh Amini, Caterina M. Barbieri, Thibaud Boutin, Archie Campbell, Ellen Demerath, Ayush Giri, Chunyan He, Jouke J. Hottenga, Robert Karlsson, Ivana Kolcic, Po-Ru Loh, Kathryn L. Lunetta, Massimo Mangino, Brumat Marco, George McMahon, Sarah E. Medland, Ilja M. Nolte, Raymond Noordam, Teresa Nutile, Lavinia Paternoster, Natalia Perjakova, Eleonora Porcu, Lynda M. Rose, Katharina E. Schraut, Ayellet V. Segrè, Albert Vernon Smith, Lisette Stolk, Alexander Teumer, Irene L. Andrulis, Stefania Bandinelli, Matthias W. Beckmann, Javier Benitez, Sven Bergmann, Murielle Bochud, Eric Boerwinkle, Stig E. Bojesen, Manjeet K. Bolla, Judith S. Brand, Hiltrud Brauch, Hermann Brenner, Linda Broer, Thomas Brüning, Julie E. Buring, Harry Campbell, Eulalia Catamo, Stephen Chanock, Georgia Chenevix-Trench, Tanguy Corre, Fergus J. Couch, Diana L. Cousminer, Angela Cox, Laura Crisponi, Kamila Czene, George Davey-Smith, Eco J. C. N. de Geus, Renée de Mutsert, Immaculata De Vivo, Joe Dennis, Peter Devilee, Isabel dos-Santos-Silva, Alison M. Dunning, Johan G. Eriksson, Peter A. Fasching, Lindsay Fernández-Rhodes, Luigi Ferrucci, Dieter Flesch-Janys, Lude Franke, Marike Gabrielson, Ilaria Gandin, Graham G. Giles, Harald Grallert, Daniel F. Gudbjartsson, Pascal Guénel, Per Hall, Emily Hallberg, Ute Hamann, Tamara B. Harris, Catharina A. Hartman, Gerardo Heiss, Maartje J. Hooning, John L. Hopper, Frank Hu, David Hunter, M. Arfan Ikram, Hae Kyung Im, Marjo-Riitta Järvelin, Peter K. Joshi, David Karasik, Zoltán Kutalik, Genevieve LaChance, Diether Lambrechts, Claudia Langenberg, Lenore J. Launer, Joop S. E. Laven, Stefania Lenarduzzi, Jingmei Li, Penelope A. Lind, Sara Lindstrom, YongMei Liu, Jian’an Luan, Reedik Mägi, Arto Mannermaa, Hamdi Mbarek, Mark I. McCarthy, Christa Meisinger, Thomas Meitinger, Cristina Menni, Andres Metspalu, Kyriaki Michailidou, Lili Milani, Roger L. Milne, Grant W. Montgomery, Anna M. Mulligan, Mike A. Nalls, Pau Navarro, Heli Nevanlinna, Dale R. Nyholt, Albertine J. Oldehinkel, Tracy A. O’Mara, Aarno Palotie, Nancy Pedersen, Annette Peters, Julian Peto, Paul D. P. Pharoah, Anneli Pouta, Paolo Radice, Iffat Rahman, Susan M. Ring, Antonietta Robino, Frits R. Rosendaal, Igor Rudan, Rico Rueedi, Daniela Ruggiero, Cinzia F. Sala, Marjanka K. Schmidt, Robert A. Scott, Mitul Shah, Rossella Sorice, Melissa C. Southey, Ulla Sovio, Meir Stampfer, Maristella Steri, Konstantin Strauch, Toshiko Tanaka, Emmi Tikkanen, Nicholas J. Timpson, Michela Traglia, Thérèse Truong, Jonathan P. Tyrer, André G. Uitterlinden, Digna R. Velez Edwards, Veronique Vitart, Uwe Völker, Peter Vollenweider, Qin Wang, Elisabeth Widen, Ko Willems van Dijk, Gonneke Willemsen, Robert Winqvist, Bruce H. R. Wolffenbuttel, Jing Hua Zhao, Magdalena Zoledziewska, Marek Zygmunt, Behrooz Z. Alizadeh, Dorret I. Boomsma, Marina Ciullo, Francesco Cucca, Tõnu Esko, Nora Franceschini, Christian Gieger, Vilmundur Gudnason, Caroline Hayward, Peter Kraft, Debbie A. Lawlor, Patrik K. E. Magnusson, Nicholas G. Martin, Dennis O. Mook-Kanamori, Ellen A. Nohr, Ozren Polasek, David J. Porteous, Alkes Price, Paul M. Ridker, Harold Snieder, Tim D. Spector, Doris Stöckl, Daniela Toniolo, Sheila Ulivi, Jenny A. Visser, Henry Völzke, Nicholas J. Wareham, James F. Wilson, The LifeLines Cohort Study, The InterAct Consortium, kConFab/AO C. S. Investigators, Endometrial Cancer Association Consortium, Ovarian Cancer Association Consortium, PRACTIC A. L. consortium, Amanda B. Spurdle, Unnur Thorsteindottir, Katherine S. Pollard, Douglas F. Easton, Joyce Y. Tung, Jenny Chang-Claude, David A. Hinds, Anna Murray, Joanne M. Murabito, Kari Stefansson, Ken K. Ong, John R. B. Perry 2016-09-23 2021-03-13 [("doi","10.1101/076794")] genetics/heritable/correlation/mendelian-randomization

The timing of puberty is a highly polygenic childhood trait that is epidemiologically associated with various adult diseases. Here, we analyze 1,000-Genome reference panel imputed genotype data on up to ~370,000 women and identify 389 independent signals (all p < 5×10−8) for age at menarche, a notable milestone in female pubertal development.

In Icelandic data from deCODE, these signals explain ~7.4% of the population variance in age at menarche, corresponding to one quarter of the estimated heritability. We implicate over 250 genes via coding variation or associated gene expression, and demonstrate enrichment across genes active in neural tissues.

We identify multiple rare variants near the imprinted genes MKRN3 and DLK1 that exhibit large effects on menarche only when paternally inherited. Disproportionate effects of variants on early or late puberty timing are observed: single variant and heritability estimates are larger for early than late puberty timing in females. The opposite pattern is seen in males, with larger estimates for late than early puberty timing.

Mendelian Randomization analyses indicate causal inverse associations, independent of BMI, between puberty timing and risks for breast and endometrial cancers in women, and prostate cancer in men. In aggregate, our findings reveal new complexity in the genetic regulation of puberty timing and support new causal links with adult cancer risks.

--- https://journals.plos.org/plosgenetics/article?id=10.1371%2Fjournal.pgen.1006149 Genome-Wide Association Study Reveals Multiple Loci Influencing Normal Human Facial Morphology John R. Shaffer, Ekaterina Orlova, Myoung Keun Lee, Elizabeth J. Leslie, Zachary D. Raffensperger, Carrie L. Heike, Michael L. Cunningham, Jacqueline T. Hecht, Chung How Kau, Nichole L. Nidey, Lina M. Moreno, George L. Wehby, Jeffrey C. Murray, Cecelia A. Laurie, Cathy C. Laurie, Joanne Cole, Tracey Ferrara, Stephanie Santorico, Ophir Klein, Washington Mio, Eleanor Feingold, Benedikt Hallgrimsson, Richard A. Spritz, Mary L. Marazita, Seth M. Weinberg 2016-06-08 2021-03-13 [("doi","10.1371/journal.pgen.1006149")] genetics/heritable

Numerous lines of evidence point to a genetic basis for facial morphology in humans, yet little is known about how specific genetic variants relate to the phenotypic expression of many common facial features. We conducted genome-wide association meta-analyses of 20 quantitative facial measurements derived from the 3D surface images of 3118 healthy individuals of European ancestry belonging to two US cohorts. Analyses were performed on just under one million genotyped SNPs (Illumina OmniExpress+Exome v1.2 array) imputed to the 1,000 Genomes reference panel (Phase 3). We observed genome-wide statistically-significant associations (p < 5 × 10−8) for cranial base width at 14q21.1 and 20q12, intercanthal width at 1p13.3 and Xq13.2, nasal width at 20p11.22, nasal ala length at 14q11.2, and upper facial depth at 11q22.1. Several genes in the associated regions are known to play roles in craniofacial development or in syndromes affecting the face: MAFB, PAX9, MIPOL1, ALX3, HDAC8, and PAX1. We also tested genotype-phenotype associations reported in two previous genome-wide studies and found evidence of replication for nasal ala length and SNPs in CACNA2D3 and PRDM16. These results provide further evidence that common variants in regions harboring genes of known craniofacial function contribute to normal variation in human facial features. Improved understanding of the genes associated with facial morphology in healthy individuals can provide insights into the pathways and mechanisms controlling normal and abnormal facial morphogenesis.

Author Summary: There is a great deal of evidence that genes influence facial appearance. This is perhaps most apparent when we look at our own families, since we are more likely to share facial features in common with our close relatives than with unrelated individuals. Nevertheless, little is known about how variation in specific regions of the genome relates to the kinds of distinguishing facial characteristics that give us our unique identities, eg. the size and shape of our nose or how far apart our eyes are spaced. In this paper, we investigate this question by examining the association between genetic variants across the whole genome and a set of measurements designed to capture key aspects of facial form. We found evidence of genetic associations involving measures of eye, nose, and facial breadth. In several cases, implicated regions contained genes known to play roles in embryonic face formation or in syndromes in which the face is affected. Our ability to connect specific genetic variants to ubiquitous facial traits can inform our understanding of normal and abnormal craniofacial development, provide potential predictive models of evolutionary changes in human facial features, and improve our ability to create forensic facial reconstructions from DNA.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112390 Results of a ‘GWAS Plus:’ General Cognitive Ability Is Substantially Heritable and Massively Polygenic Robert M. Kirkpatrick, Matt McGue, William Iacono, Michael B. Miller, Saonli Basu 2014-05-04 2021-03-13 [("doi","10.1371/journal.pone.0112390")] genetics/heritable iq

We carried out a genome-wide association study (GWAS) for general cognitive ability (GCA) plus 3 other analyses of GWAS data that aggregate the effects of multiple single-nucleotide polymorphisms (SNPs) in various ways. Our multigenerational sample comprised 7,100 Caucasian participants, drawn from two longitudinal family studies, who had been assessed with an age-appropriate IQ test and had provided DNA samples passing quality screens.

We conducted the GWAS across ~2.5 million SNPs (both typed and imputed), using a generalized least-squares method appropriate for the different family structures present in our sample, and subsequently conducted gene-based association tests. We also conducted polygenic prediction analyses under five-fold cross-validation, using two different schemes of weighting SNPs. Using parametric bootstrapping, we assessed the performance of this prediction procedure under the null. Finally, we estimated the proportion of variance attributable to all genotyped SNPs as random effects with software GCTA.

The study is limited chiefly by its power to detect realistic single-SNP or single-gene effects, none of which reached genome-wide statistical-significance, though some genomic inflation was evident from the GWAS. Unit SNP weights performed about as well as least-squares regression weights under cross-validation, but the performance of both increased as more SNPs were included in calculating the polygenic score.

Estimates from GCTA were 35% of phenotypic variance at the recommended biological-relatedness ceiling. Taken together, our results concur with other recent studies: they support a substantial heritability of GCA, arising from a very large number of causal SNPs, each of very small effect.

We place our study in the context of the literature—both contemporary and historical—and provide accessible explication of our statistical methods.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2928252/ Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder Benjamin M. Neale, Sarah E. Medland, Stephan Ripke, Philip Asherson, Barbara Franke, Klaus-Peter Lesch, Stephen V. Faraone, Thuy Trang Nguyen, Helmut Schäfer, Peter Holmans, Mark Daly, Hans-Christoph Steinhausen, Christine Freitag, Andreas Reif, Tobias J. Renner, Marcel Romanos, Jasmin Romanos, Susanne Walitza, Andreas Warnke, Jobst Meyer, Haukur Palmason, Jan Buitelaar, Alejandro Arias Vasquez, Nanda Lambregts-Rommelse, Michael Gill, Richard J. L. Anney, Kate Langely, Michael O’Donovan, Nigel Williams, Michael J. Owen, Anita Thapar, Lindsey Kent, Joseph Sergeant, Herbert Roeyers, Eric Mick, Joseph Biederman, Alysa Doyle, Susan Smalley, Sandra Loo, Hakon Hakonarson, Josephine Elia, Alexandre Todorov, Ana Miranda, Fernando Mulas, Richard P. Ebstein, Aribert Rothenberger, Tobias Banaschewski, Robert D. Oades, Edmund Sonuga-Barke, James McGough, Laura Nisenbaum, Frank Middleton, Xiaolan Hu, Stan Nelson 2010 2021-03-13 [("doi","10.1016/j.jaac.2010.06.008")] genetics/heritable psychiatry/adhd

Objective: Although twin and family studies have shown attention-deficit/hyperactivity disorder (ADHD) to be highly heritable, genetic variants influencing the trait at a genome-wide statistically-significant level have yet to be identified. As prior genome-wide association studies (GWAS) have not yielded results, we conducted a meta-analysis of existing studies to boost statistical power.

Method: We used data from four projects: (1) the Children’s Hospital of Philadelphia (CHOP); (2) phase I of the International Multicenter ADHD Genetics project (IMAGE); (3) phase II of IMAGE (IMAGE II); and (4) the Pfizer-funded study from the University of California, Los Angeles, Washington University, and Massachusetts General Hospital (PUWMa). The final sample size consisted of 2,064 trios, 896 cases, and 2,455 controls. For each study, we imputed HapMap single-nucleotide polymorphisms, computed association test statistics and transformed them to z-scores, and then combined weighted z-scores in a meta-analysis.

Results: No genome-wide statistically-significant associations were found, although an analysis of candidate genes suggests that they may be involved in the disorder.

Conclusion: Given that ADHD is a highly heritable disorder, our negative results suggest that the effects of common ADHD risk variants must, individually, be very small or that other types of variants, eg. rare ones, account for much of the disorder’s heritability.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4809412/ The burden of health care costs for patients with dementia in the last 5 years of life Amy S. Kelley, Kathleen McGarry, Rebecca Gorges, Jonathan S. Skinner 2015 2021-03-13 [("doi","10.7326/M15-0381")] economics psychiatry

Background: Common diseases, particularly dementia, have large social costs for the U.S. population. However, less is known about the end-of-life costs of specific diseases and the associated financial risk for individual households.

Objective: To examine social costs and financial risks faced by Medicare beneficiaries 5 years before death.

Design: Retrospective cohort.

Setting: The HRS (Health and Retirement Study).

Participants: Medicare fee-for-service beneficiaries, aged 70 years or older, who died 2005–2010 (n = 1,702), stratified into 4 groups: persons with a high probability of dementia or those who died because of heart disease, cancer, or other causes.

Measurements: Total social costs and their components, including Medicare, Medicaid, private insurance, out-of-pocket spending, and informal care, measured over the last 5 years of life; and out-of-pocket spending as a proportion of household wealth.

Results: Average total cost per decedent with dementia ($287,038) was greater than that of those who died of heart disease ($175,136), cancer ($173,383), or other causes ($197,286) (p < 0.001). Although Medicare expenditures were similar across groups, average out-of-pocket spending for patients with dementia ($61,522) was 81% higher than that for patients without dementia ($34,068); a similar pattern held for informal care. Out-of-pocket spending for the dementia group (median, $36,919) represented 32% of wealth measured 5 years before death compared with 11% for the nondementia group (p < 0.001). This proportion was greater for black persons (84%), persons with less than a high school education (48%), and unmarried or widowed women (58%).

Limitation: Imputed Medicaid, private insurance, and informal care costs.

Conclusion: Health care expenditures among persons with dementia were substantially larger than those for other diseases, and many of the expenses were uncovered (uninsured). This places a large financial burden on families, and these burdens are particularly pronounced among the demographic groups that are least prepared for financial risk.

Primary Funding Source: National Institute on Aging.

--- https://www.biorxiv.org/content/10.1101/166298.full Genome-wide genetic data on ~500,000 UK Biobank participants Clare Bycroft, Colin Freeman, Desislava Petkova, Gavin Band, Lloyd T. Elliott, Kevin Sharp, Allan Motyer, Damjan Vukcevic, Olivier Delaneau, Jared O’Connell, Adrian Cortes, Samantha Welsh, Gil McVean, Stephen Leslie, Peter Donnelly, Jonathan Marchini 2017-07-20 2021-03-13 [("doi","10.1101/166298")] genetics/heritable genetics/sequencing

The UK Biobank project is a large prospective cohort study of ~500,000 individuals from across the United Kingdom, aged between 40–69 at recruitment. A rich variety of phenotypic and health-related information is available on each participant, making the resource unprecedented in its size and scope.

Here we describe the genome-wide genotype data (~805,000 markers) collected on all individuals in the cohort and its quality control procedures. Genotype data on this scale offers novel opportunities for assessing quality issues, although the wide range of ancestries of the individuals in the cohort also creates particular challenges. We also conducted a set of analyses that reveal properties of the genetic data—such as population structure and relatedness—that can be important for downstream analyses. In addition, we phased and imputed genotypes into the dataset, using computationally efficient methods combined with the Haplotype Reference Consortium (HRC) and UK10K haplotype resource. This increases the number of testable variants by over 100× to ~96 million variants. We also imputed classical allelic variation at 11 human leukocyte antigen (HLA) genes, and as a quality control check of this imputation, we replicate signals of known associations between HLA alleles and many common diseases.

We describe tools that allow efficient genome-wide association studies (GWAS) of multiple traits and fast phenome-wide association studies (PheWAS), which work together with a new compressed file format that has been used to distribute the dataset.

As a further check of the genotyped and imputed datasets, we performed a test-case genome-wide association scan on a well-studied human trait, standing height.

--- https://en.wikipedia.org/wiki/Third-generation_sequencing Third-generation sequencing 2021-03-13 genetics/sequencing --- https://en.wikipedia.org/wiki/Oxford_Nanopore_Technologies Oxford Nanopore Technologies 2021-03-13 genetics/sequencing --- https://en.wikipedia.org/wiki/Single-molecule_real-time_sequencing SMRT sequencing 2021-03-13 genetics/sequencing --- https://www.biorxiv.org/content/10.1101/2021.07.12.452063.full A complete reference genome improves analysis of human genetic variation Sergey Aganezov, Stephanie M. Yan, Daniela C. Soto, Melanie Kirsche, Samantha Zarate, Pavel Avdeyev, Dylan J. Taylor, Kishwar Shafin, Alaina Shumate, Chunlin Xiao, Justin Wagner, Jennifer McDaniel, Nathan D. Olson, Michael E. G. Sauria, Mitchell R. Vollger, Arang Rhie, Melissa Meredith, Skylar Martin, Joyce Lee, Sergey Koren, Jeffrey A. Rosenfeld, Benedict Paten, Ryan Layer, Chen-Shan Chin, Fritz J. Sedlazeck, Nancy F. Hansen, Danny E. Miller, Adam M. Phillippy, Karen H. Miga, Rajiv C. McCoy, Megan Y. Dennis, Justin M. Zook, Michael C. Schatz 2021-07-13 2021-07-13 [("doi","10.1101/2021.07.12.452063")] genetics/sequencing

Compared to its predecessors, the Telomere-to-Telomere CHM13 genome adds nearly 200 Mbp of sequence, corrects thousands of structural errors, and unlocks the most complex regions of the human genome to clinical and functional study. Here we demonstrate how the new reference universally improves read mapping and variant calling for 3,202 and 17 globally diverse samples sequenced with short and long reads, respectively. We identify hundreds of thousands of novel variants per sample—a new frontier for evolutionary and biomedical discovery. Simultaneously, the new reference eliminates tens of thousands of spurious variants per sample, including up to 12× reduction of false positives in 269 medically relevant genes. The vast improvement in variant discovery coupled with population and functional genomic resources position T2T-CHM13 to replace GRCh38 as the prevailing reference for human genetics.

One Sentence Summary

The T2T-CHM13 reference genome universally improves the analysis of human genetic variation.

--- https://arxiv.org/abs/2104.08773 Cross-Task Generalization via Natural Language Crowdsourcing Instructions Swaroop Mishra, Daniel Khashabi, Chitta Baral, Hannaneh Hajishirzi 2021-04-18 2021-04-18 [("doi","10.48550/arXiv.2104.08773")] ai/nn/transformer/gpt/instruction-tuning

Humans (eg. crowdworkers) have a remarkable ability in solving different tasks, by simply reading textual instructions that define them and looking at a few examples. Despite the success of the conventional supervised learning on individual datasets, such models often struggle with generalization across tasks (eg. a question-answering system cannot solve classification tasks). A long-standing challenge in AI is to build a model that learns a new task by understanding the human-readable instructions that define it.

To study this, we introduce NATURAL INSTRUCTIONS, a dataset of 61 distinct tasks, their human-authored instructions, and 193k task instances (input-output pairs). The instructions are obtained from crowdsourcing instructions used to create existing NLP datasets and mapped to a unified schema.

Using this meta-dataset, we measure cross-task generalization by training models on seen tasks and measuring generalization to the remaining unseen ones. We adopt generative pre-trained language models to encode task-specific instructions along with input and generate task output.

Our results indicate that models benefit from instructions when evaluated in terms of generalization to unseen tasks (19% better for models using instructions). These models, however, are far behind an estimated performance upper bound indicating room for more progress in this direction.

--- https://www.biorxiv.org/content/10.1101/2020.04.20.051631.full GWAS of 3 molecular traits highlights core genes and pathways alongside a highly polygenic background Nasa Sinnott-Armstrong, Sahin Naqvi, Manuel Rivas, Jonathan K. Pritchard 2021-01-12 2021-03-14 [("doi","10.1101/2020.04.20.051631")] genetics/heritable/correlation

Genome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. We describe UK Biobank GWAS results for 3 molecular traits—urate, IGF-1, and testosterone—with better-understood biology than most other complex traits. We find that many of the most hits are readily and surprisingly interpretable.

We observe huge enrichment of associations near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of each trait, including differences in testosterone regulation between females and males.

At the same time, even these molecular traits are highly polygenic, with many thousands of variants spread across the genome contributing to trait variance. In summary, for these 3 molecular traits we identify strong enrichment of signal in putative core gene sets, even while most of the SNP-based heritability is driven by a massively polygenic background.

--- https://arxiv.org/abs/2012.09841 Taming Transformers for High-Resolution Image Synthesis Patrick Esser, Robin Rombach, Björn Ommer 2020-12-17 2021-03-14 [("doi","10.48550/arXiv.2012.09841")] ai/nn/cnn ai/nn/gan ai/nn/transformer/gpt/dall-e/1

Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions. This makes them expressive, but also computationally infeasible for long sequences, such as high-resolution images.

We demonstrate how combining the effectiveness of the inductive bias of CNNs with the expressivity of transformers enables them to model and thereby synthesize high-resolution images. We show how to (1) use CNNs to learn a context-rich vocabulary of image constituents, and in turn (2) utilize transformers to efficiently model their composition within high-resolution images. Our approach is readily applied to conditional synthesis tasks, where both non-spatial information, such as object classes, and spatial information, such as segmentations, can control the generated image. In particular, we present the first results on semantically-guided synthesis of megapixel images with transformers and obtain the state-of-the-art among autoregressive models on class-conditional ImageNet.

Code and pretrained models can be found at Github.

--- https://www.biorxiv.org/content/10.1101/704080.full Supercentenarian and remarkable age records exhibit patterns indicative of clerical errors and pension fraud Saul Justin Newman 2020-05-03 2021-03-14 [("doi","10.1101/704080")] crime japan longevity statistics/bias statistics/order

The observation of individuals attaining remarkable ages, and their concentration into geographic sub-regions or ‘blue zones’, has generated considerable scientific interest. Proposed drivers of remarkable longevity include high vegetable intake, strong social connections, and genetic markers.

Here, we reveal new predictors of remarkable longevity and ‘supercentenarian’ status. In the United States supercentenarian status is predicted by the absence of vital registration. In the UK, Italy, Japan, and France remarkable longevity is instead predicted by regional poverty, old-age poverty, material deprivation, low incomes, high crime rates, a remote region of birth, worse health, and fewer 90+ year old people. In addition, supercentenarian birthdates are concentrated on the first of the month and days divisible by five: patterns indicative of widespread fraud and error.

As such, relative poverty and missing vital documents constitute unexpected predictors of centenarian and supercentenarian status, and support a primary role of fraud and error in generating remarkable human age records.

--- https://www.biorxiv.org/content/10.1101/2021.05.26.445798.full The complete sequence of a human genome Sergey Nurk, Sergey Koren, Arang Rhie, Mikko Rautiainen, Andrey V. Bzikadze, Alla Mikheenko, Mitchell R. Vollger, Nicolas Altemose, Lev Uralsky, Ariel Gershman, Sergey Aganezov, Savannah J. Hoyt, Mark Diekhans, Glennis A. Logsdon, Michael Alonge, Stylianos E. Antonarakis, Matthew Borchers, Gerard G. Bouffard, Shelise Y. Brooks, Gina V. Caldas, Haoyu Cheng, Chen-Shan Chin, William Chow, Leonardo G. de Lima, Philip C. Dishuck, Richard Durbin, Tatiana Dvorkina, Ian T. Fiddes, Giulio Formenti, Robert S. Fulton, Arkarachai Fungtammasan, Erik Garrison, Patrick G. S. Grady, Tina A. Graves-Lindsay, Ira M. Hall, Nancy F. Hansen, Gabrielle A. Hartley, Marina Haukness, Kerstin Howe, Michael W. Hunkapiller, Chirag Jain, Miten Jain, Erich D. Jarvis, Peter Kerpedjiev, Melanie Kirsche, Mikhail Kolmogorov, Jonas Korlach, Milinn Kremitzki, Heng Li, Valerie V. Maduro, Tobias Marschall, Ann M. McCartney, Jennifer McDaniel, Danny E. Miller, James C. Mullikin, Eugene W. Myers, Nathan D. Olson, Benedict Paten, Paul Peluso, Pavel A. Pevzner, David Porubsky, Tamara Potapova, Evgeny I. Rogaev, Jeffrey A. Rosenfeld, Steven L. Salzberg, Valerie A. Schneider, Fritz J. Sedlazeck, Kishwar Shafin, Colin J. Shew, Alaina Shumate, Yumi Sims, Arian F. A. Smit, Daniela C. Soto, Ivan Sović, Jessica M. Storer, Aaron Streets, Beth A. Sullivan, Françoise Thibaud-Nissen, James Torrance, Justin Wagner, Brian P. Walenz, Aaron Wenger, Jonathan M. D. Wood, Chunlin Xiao, Stephanie M. Yan, Alice C. Young, Samantha Zarate, Urvashi Surti, Rajiv C. McCoy, Megan Y. Dennis, Ivan A. Alexandrov, Jennifer L. Gerton, Rachel J. O’Neill, Winston Timp, Justin M. Zook, Michael C. Schatz, Evan E. Eichler, Karen H. Miga, Adam M. Phillippy 2021-05-27 2021-05-27 [("doi","10.1101/2021.05.26.445798")] genetics/sequencing

In 2001, Celera Genomics and the International Human Genome Sequencing Consortium published their initial drafts of the human genome, which revolutionized the field of genomics. While these drafts and the updates that followed effectively covered the euchromatic fraction of the genome, the heterochromatin and many other complex regions were left unfinished or erroneous.

Addressing this remaining 8% of the genome, the Telomere-to-Telomere (T2T) Consortium has finished the first truly complete 3.055 billion base pair (bp) sequence of a human genome, representing the largest improvement to the human reference genome since its initial release. The new T2T-CHM13 reference includes gapless assemblies for all 22 autosomes plus Chromosome X, corrects numerous errors, and introduces nearly 200 million bp of novel sequence containing 2,226 paralogous gene copies, 115 of which are predicted to be protein coding.

The newly completed regions include all centromeric satellite arrays and the short arms of all 5 acrocentric chromosomes, unlocking these complex regions of the genome to variational and functional studies for the first time.

--- https://www.biorxiv.org/content/10.1101/2021.07.18.452833.full Deep neural language modeling enables functional protein generation across families Ali Madani, Ben Krause, Eric R. Greene, Subu Subramanian, Benjamin P. Mohr, James M. Holton, Jose Luis Olmos, Caiming Xiong, Zachary Z. Sun, Richard Socher, James S. Fraser, Nikhil Naik 2021-07-18 2021-07-18 [("doi","10.1101/2021.07.18.452833")] ai/nn/transformer/alphafold

[followup] Bypassing nature’s evolutionary trajectory, de novo protein generation—defined as creating artificial protein sequences from scratch—could enable breakthrough solutions for biomedical and environmental challenges.

Viewing amino acid sequences as a language, we demonstrate that a deep learning-based language model can generate functional artificial protein sequences across families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. Our protein language model ProGen is trained by simply learning to predict the next amino acid for over 280 million protein sequences from thousands of protein families, without biophysical or coevolutionary modeling.

We experimentally evaluate model-generated artificial proteins on five distinct antibacterial lysozyme families. Artificial proteins show similar activities and catalytic efficiencies as representative natural lysozymes, including hen egg white lysozyme, while reaching as low as 44% identity to any known naturally-evolved protein. The X-ray crystal structure of an enzymatically active artificial protein recapitulates the conserved fold and positioning of active site residues found in natural proteins. We demonstrate our language model’s ability to be adapted to different protein families by accurately predicting the functionality of artificial chorismate mutase and malate dehydrogenase proteins.

These results indicate that neural language models successfully perform de novo protein generation across protein families and may prove to be a tool to shortcut evolution.

[Later published as Madani et al 2023.]

--- https://arxiv.org/abs/2107.07653#microsoft TAPEX: Table Pre-training via Learning a Neural SQL Executor Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou 2021-07-16 2021-07-16 [("doi","10.48550/arXiv.2107.07653")] ai/nn/transformer/gpt/codex ai/tabular

Recent progress in language model pre-training has achieved a great success via leveraging large-scale unstructured textual data. However, it is still a challenge to apply pre-training on structured tabular data due to the absence of large-scale high-quality tabular data.

In this paper, we propose TAPEX to show that table pre-training can be achieved by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries and their execution outputs. TAPEX addresses the data scarcity challenge via guiding the language model to mimic a SQL executor on the diverse, large-scale and high-quality synthetic corpus.

We evaluate TAPEX on four benchmark datasets. Experimental results demonstrate that TAPEX outperforms previous table pre-training approaches by a large margin and achieves new state-of-the-art results on all of them. This includes the improvements on the weakly-supervised WikiSQL denotation accuracy to 89.5% (+2.3%), the WikiTableQuestions denotation accuracy to 57.5% (+4.8%), the SQA denotation accuracy to 74.5% (+3.5%), and the TabFact accuracy to 84.2% (+3.2%).

To our knowledge, this is the first work to exploit table pre-training via synthetic executable programs and to achieve new state-of-the-art results on various downstream tasks.

Our code can be found at Github.

--- https://arxiv.org/abs/2106.14131 SymbolicGPT: A Generative Transformer Model for Symbolic Regression Mojtaba Valipour, Bowen You, Maysum Panju, Ali Ghodsi 2021-06-27 2021-06-27 [("doi","10.48550/arXiv.2106.14131")] ai/nn/transformer/gpt/codex math

Symbolic regression is the task of identifying a mathematical expression that best fits a provided dataset of input and output values. Due to the richness of the space of mathematical expressions, symbolic regression is generally a challenging problem. While conventional approaches based on genetic evolution algorithms have been used for decades, deep learning-based methods are relatively new and an active research area.

In this work, we present SymbolicGPT, a novel transformer-based language model for symbolic regression. This model exploits the advantages of probabilistic language models like GPT, including strength in performance and flexibility.

Through comprehensive experiments, we show that our model performs strongly compared to competing models with respect to the accuracy, running time, and data efficiency.

--- https://arxiv.org/abs/2105.09938 Measuring Coding Challenge Competence With APPS Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, Jacob Steinhardt 2021-05-20 2021-05-20 [("doi","10.48550/arXiv.2105.09938")] ai/nn/transformer/gpt/codex

While programming is one of the most broadly applicable skills in modern society, modern machine learning models still cannot code solutions to basic problems. Despite its importance, there has been surprisingly little work on evaluating code generation, and it can be difficult to accurately assess code generation performance rigorously.

To meet this challenge, we introduce APPS, a benchmark for code generation. Unlike prior work in more restricted settings, our benchmark measures the ability of models to take an arbitrary natural language specification and generate satisfactory Python code. Similar to how companies assess candidate software developers, we then evaluate models by checking their generated code on test cases. Our benchmark includes 10,000 problems, which range from having simple one-line solutions to being substantial algorithmic challenges.

We fine-tune large language models on both GitHub and our training set, and we find that the prevalence of syntax errors is decreasing exponentially as models improve. Recent models such as GPT-Neo can pass ~20% of the test cases of introductory problems, so we find that machine learning models are now beginning to learn how to code.

As the social importance of automatic code generation increases over the coming years, our benchmark can provide an important measure for tracking advancements.

--- https://arxiv.org/abs/2107.02192#nvidia Long-Short Transformer (Transformer-LS): Efficient Transformers for Language and Vision Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, Bryan Catanzaro 2021-07-05 2021-07-05 [("doi","10.48550/arXiv.2107.02192")] ai/nn/transformer/attention/hierarchical

Transformers have achieved success in both language and vision domains. However, it is prohibitively expensive to scale them to long sequences such as long documents or high-resolution images, because self-attention mechanism has quadratic time and memory complexities with respect to the input sequence length.

In this paper, we propose Long-Short Transformer (Transformer-LS), an efficient self-attention mechanism for modeling long sequences with linear complexity for both language and vision tasks.

It aggregates a novel long-range attention with dynamic projection to model distant correlations and a short-term attention to capture fine-grained local correlations. We propose a dual normalization strategy to account for the scale mismatch between the two attention mechanisms. Transformer-LS can be applied to both autoregressive and bidirectional models without additional complexity.

Our method outperforms the state-of-the-art models on multiple tasks in language and vision domains, including the Long Range Arena benchmark, autoregressive language modeling, and ImageNet classification. For instance, Transformer-LS achieves 0.97 test BPC on enwik8 using half the number of parameters than previous method, while being faster and is able to handle 3× as long sequences compared to its full-attention version on the same hardware. On ImageNet, it can obtain the state-of-the-art results (eg. a moderate size of 55.8M model solely trained on 224×224 ImageNet-1K can obtain Top-1 accuracy 84.1%), while being more scalable on high-resolution images.

The source code and models are released at Github.

--- https://arxiv.org/abs/2107.00645 Global Filter Networks for Image Classification Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, Jie Zhou 2021-07-01 2021-07-01 [("doi","10.48550/arXiv.2107.00645")] ai/nn/transformer/attention/hierarchical

Recent advances in self-attention and pure multi-layer perceptrons (MLP) models for vision have shown great potential in achieving promising performance with fewer inductive biases. These models are generally based on learning interaction among spatial locations from raw data. The complexity of self-attention and MLP grows quadratically as the image size increases, which makes these models hard to scale up when high-resolution features are required.

In this paper, we present the Global Filter Network (GFNet), a conceptually simple yet computationally efficient architecture, that learns long-term spatial dependencies in the frequency domain with log-linear complexity. Our architecture replaces the self-attention layer in vision transformers with 3 key operations: a 2D discrete Fourier transform, an element-wise multiplication between frequency-domain features and learnable global filters, and a 2D inverse Fourier transform.

We exhibit favorable accuracy/complexity trade-offs of our models on both ImageNet and downstream tasks. Our results demonstrate that GFNet can be a very competitive alternative to transformer-style models and CNNs in efficiency, generalization ability, and robustness.

Code is available at https://github.com/raoyongming/GFNet.

--- https://arxiv.org/abs/2106.12672#google Charformer: Fast Character Transformers via Gradient-based Subword Tokenization Yi Tay, Vinh Q. Tran, Sebastian Ruder, Jai Gupta, Hyung Won Chung, Dara Bahri, Zhen Qin, Simon Baumgartner, Cong Yu, Donald Metzler 2021-06-23 2021-06-23 [("doi","10.48550/arXiv.2106.12672")] ai/nn/tokenization ai/nn/transformer

State-of-the-art models in natural language processing rely on separate rigid subword tokenization algorithms, which limit their generalization ability and adaptation to new settings.

In this paper, we propose a new model inductive bias that learns a subword tokenization end-to-end as part of the model. To this end, we introduce a soft gradient-based subword tokenization module (GBST) that automatically learns latent subword representations from characters in a data-driven fashion. Concretely, GBST enumerates candidate subword blocks and learns to score them in a position-wise fashion using a block scoring network. We additionally introduce Charformer, a deep Transformer model that integrates GBST and operates on the byte level.

Via extensive experiments on English GLUE, multilingual, and noisy text datasets, we show that Charformer outperforms a series of competitive byte-level baselines while generally performing on par and sometimes outperforming subword-based models.

Additionally, Charformer is fast, improving the speed of both vanilla byte-level and subword-level Transformers by 28%–100% while maintaining competitive quality.

We believe this work paves the way for highly performant token-free models that are trained completely end-to-end.

--- https://arxiv.org/abs/2106.15110#google Time-Aware Language Models as Temporal Knowledge Bases Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, William W. Cohen 2021-06-29 2021-06-29 [("doi","10.1162/tacl_a_00459")] ai/nn/sampling ai/nn/transformer/t5

Many facts come with an expiration date, from the name of the President to the basketball team Lebron James plays for. But language models (LMs) are trained on snapshots of data collected at a specific moment in time, and this can limit their utility, especially in the closed-book setting where the pretraining corpus must contain the facts the model should memorize.

We introduce a diagnostic dataset aimed at probing LMs for factual knowledge that changes over time and highlight problems with LMs at either end of the spectrum—those trained on specific slices of temporal data, as well as those trained on a wide range of temporal data. To mitigate these problems, we propose a simple technique for jointly modeling text with its timestamp.

This improves memorization of seen facts from the training time period, as well as calibration on predictions about unseen facts from future time periods.

We also show that T5 models trained with temporal context can be efficiently “refreshed” as new data arrives, without the need for retraining from scratch.

--- https://arxiv.org/abs/2106.11959 Revisiting Deep Learning Models for Tabular Data Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, Artem Babenko 2021-06-22 2021-06-22 [("doi","10.48550/arXiv.2106.11959")] ai/nn ai/tabular

The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports competitive results on various datasets. However, the proposed models are usually not properly compared to each other and existing works often use different benchmarks and experiment protocols. As a result, it is unclear for both researchers and practitioners what models perform best. Additionally, the field still lacks effective baselines, that is, the easy-to-use models that provide competitive performance across different problems.

In this work, we perform an overview of the main families of DL architectures for tabular data and raise the bar of baselines in tabular DL by identifying two simple and powerful deep architectures. The first one is a ResNet-like architecture which turns out to be a strong baseline that is often missing in prior works. The second model is our simple adaptation of the Transformer architecture for tabular data, which outperforms other solutions on most tasks.

Both models are compared to many existing architectures on a diverse set of tasks under the same training and tuning protocols. We also compare the best DL models with Gradient Boosted Decision Trees and conclude that there is still no universally superior solution.

--- https://arxiv.org/abs/2106.08254#microsoft BEiT: BERT Pre-Training of Image Transformers Hangbo Bao, Li Dong, Furu Wei 2021-06-15 2021-06-15 [("doi","10.48550/arXiv.2106.08254")] ai/nn

We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation from Image Transformers.

Following BERT developed in the natural language processing area, we propose a masked image modeling task to pretrain vision Transformers. Specifically, each image has two views in our pre-training, i.e, image patches (such as 16×16 pixels), and visual tokens (ie. discrete tokens). We first “tokenize” the original image into visual tokens. Then we randomly mask some image patches and fed them into the backbone Transformer. The pre-training objective is to recover the original visual tokens based on the corrupted image patches. After pre-training BEiT, we directly fine-tune the model parameters on downstream tasks by appending task layers upon the pretrained encoder.

Experimental results on image classification and semantic segmentation show that our model achieves competitive results with previous pre-training methods. For example, base-size BEiT achieves 83.2% top-1 accuracy on ImageNet-1K, outperforming from-scratch DeiT training (81.8%) with the same setup. Moreover, large-size BEiT obtains 86.3% only using ImageNet-1K, even outperforming ViT-L with supervised pre-training on ImageNet-22K (85.2%).

The code and pretrained models are available at Github.

--- https://arxiv.org/abs/2106.01040 Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang 2021-06-02 2021-06-02 [("doi","10.48550/arXiv.2106.01040")] ai/nn/transformer/attention/hierarchical

Transformer is important for text modeling. However, it has difficulty in handling long documents due to the quadratic complexity with input text length.

In order to handle this problem, we propose a hierarchical interactive Transformer (Hi-Transformer) for efficient and effective long document modeling. Hi-Transformer models documents in a hierarchical way, ie. first learns sentence representations and then learns document representations. It can effectively reduce the complexity and meanwhile capture global document context in the modeling of each sentence.

More specifically, we first use a sentence Transformer to learn the representations of each sentence. Then we use a document Transformer to model the global document context from these sentence representations. Next, we use another sentence Transformer to enhance sentence modeling using the global document context. Finally, we use hierarchical pooling method to obtain document embedding.

Extensive experiments on 3 benchmark datasets validate the efficiency and effectiveness of Hi-Transformer in long document modeling.

--- https://arxiv.org/abs/2106.01342 SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein 2021-06-02 2021-06-02 [("doi","10.48550/arXiv.2106.01342")] ai/nn/transformer/attention ai/tabular

Tabular data underpins numerous high-impact applications of machine learning from fraud detection to genomics and healthcare. Classical approaches to solving tabular problems, such as gradient boosting and random forests, are widely used by practitioners. However, recent deep learning methods have achieved a degree of performance competitive with popular techniques. We devise a hybrid deep learning approach to solving tabular data problems.

Our method, SAINT, performs attention over both rows and columns, and it includes an enhanced embedding method. We also study a new contrastive self-supervised pre-training method for use when labels are scarce.

SAINT consistently improves performance over previous deep learning methods, and it even outperforms gradient boosting methods, including XGBoost, CatBoost, and LightGBM, on average over a variety of benchmark tasks.

--- https://arxiv.org/abs/2105.15075 Not All Images are Worth 16×16 Words: Dynamic Transformers for Efficient Image Recognition Yulin Wang, Rui Huang, Shiji Song, Zeyi Huang, Gao Huang 2021-05-31 2021-05-31 [("doi","10.48550/arXiv.2105.15075")] ai/nn/transformer/attention

Vision Transformers (ViT) have achieved remarkable success in large-scale image recognition. They split every 2D image into a fixed number of patches, each of which is treated as a token. Generally, representing an image with more tokens would lead to higher prediction accuracy, while it also results in drastically increased computational cost. To achieve a decent trade-off between accuracy and speed, the number of tokens is empirically set to 16×16 or 14×14.

In this paper, we argue that every image has its own characteristics, and ideally the token number should be conditioned on each individual input. In fact, we have observed that there exist a considerable number of “easy” images which can be accurately predicted with a mere number of 4×4 tokens, while only a small fraction of “hard” ones need a finer representation.

Inspired by this phenomenon, we propose a Dynamic Transformer to automatically configure a proper number of tokens for each input image. This is achieved by cascading multiple Transformers with increasing numbers of tokens, which are sequentially activated in an adaptive fashion at test time, ie. the inference is terminated once a sufficiently confident prediction is produced. We further design efficient feature reuse and relationship reuse mechanisms across different components of the Dynamic Transformer to reduce redundant computations.

Extensive empirical results on ImageNet, CIFAR-10, and CIFAR-100 demonstrate that our method outperforms the competitive baselines in terms of both theoretical computational efficiency and practical inference speed.

Code and pre-trained models (based on PyTorch and MindSpore) are available at Github and Github (MindSpore).

--- https://arxiv.org/abs/2105.06548#facebook Not All Memories are Created Equal: Learning to Forget by Expiring Sainbayar Sukhbaatar, Da Ju, Spencer Poff, Stephen Roller, Arthur Szlam, Jason Weston, Angela Fan 2021-05-13 2021-05-13 [("doi","10.48550/arXiv.2105.06548")] ai/nn/retrieval ai/nn/transformer/attention/compression

Attention mechanisms have shown promising results in sequence modeling tasks that require long-term memory. Recent work investigated mechanisms to reduce the computational cost of preserving and storing memories. However, not all content in the past is equally important to remember. We propose Expire-Span, a method that learns to retain the most important information and expire the irrelevant information. This forgetting of memories enables Transformers to scale to attend over tens of thousands of previous timesteps efficiently, as not all states from previous timesteps are preserved.

We demonstrate that Expire-Span can help models identify and retain critical information and show it can achieve strong performance on reinforcement learning tasks specifically designed to challenge this functionality. Next, we show that Expire-Span can scale to memories that are tens of thousands in size, setting a new state-of-the-art on incredibly long context tasks such as character-level language modeling and a frame-by-frame moving objects task. Finally, we analyze the efficiency of Expire-Span compared to existing approaches and demonstrate that it trains faster and uses less memory.

--- https://arxiv.org/abs/2107.06917#google A Field Guide to Federated Optimization Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Aguera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecny, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtarik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu 2021-07-14 2021-07-14 [("doi","10.48550/arXiv.2107.06917")] ai/scaling

Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection.

The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficiency, data heterogeneity, compatibility with privacy and system requirements, and other constraints that are not primary considerations in other problem settings.

This paper provides recommendations and guidelines on formulating, designing, evaluating, and analyzing federated optimization algorithms through concrete examples and practical implementation, with a focus on conducting effective simulations to infer real-world performance.

The goal of this work is not to survey the current literature, but to inspire researchers and practitioners to design federated learning algorithms that can be used in various practical applications.

--- https://arxiv.org/abs/2107.06955#facebook HTLM: Hyper-Text Pre-Training and Prompting of Language Models Armen Aghajanyan, Dmytro Okhonko, Mike Lewis, Mandar Joshi, Hu Xu, Gargi Ghosh, Luke Zettlemoyer 2021-07-14 2021-07-14 [("doi","10.48550/arXiv.2107.06955")] ai/nn/transformer ai/scaling

We introduce HTLM, a hyper-text language model trained on a large-scale web crawl. Modeling hyper-text has a number of advantages: (1) it is easily gathered at scale, (2) it provides rich document-level and end-task-adjacent supervision (eg. class and id attributes often encode document category information), and (3) it allows for new structured prompting that follows the established semantics of HTML (eg. to do zero-shot summarization by infilling title tags for a webpage that contains the input text).

We show that pretraining with a BART-style denoising loss directly on simplified HTML provides highly effective transfer for a wide range of end tasks and supervision levels. HTLM matches or exceeds the performance of comparably sized text-only LMs for zero-shot prompting and fine-tuning for classification benchmarks, while also setting new state-of-the-art performance levels for zero-shot summarization.

We also find that hyper-text prompts provide more value to HTLM, in terms of data efficiency, than plain text prompts do for existing LMs, and that HTLM is highly effective at auto-prompting itself, by simply generating the most likely hyper-text formatting for any available training data.

We will release all code and models to support future HTLM research.

--- https://arxiv.org/abs/2107.02137#baidu ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation Yu Sun, Shuohuan Wang, Shikun Feng, Siyu Ding, Chao Pang, Junyuan Shang, Jiaxiang Liu, Xuyi Chen, Yanbin Zhao, Yuxiang Lu, Weixin Liu, Zhihua Wu, Weibao Gong, Jianzhong Liang, Zhizhou Shang, Peng Sun, Wei Liu, Xuan Ouyang, Dianhai Yu, Hao Tian, Hua Wu, Haifeng Wang 2021-07-05 2021-07-05 [("doi","10.48550/arXiv.2107.02137")] ai/scaling

Pre-trained models have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. Recent works such as T5 and GPT-3 have shown that scaling up pre-trained language models can improve their generalization abilities. Particularly, the GPT-3 model with 175 billion parameters shows its strong task-agnostic zero-shot/few-shot learning capabilities. Despite their success, these large-scale models are trained on plain texts without introducing knowledge such as linguistic knowledge and world knowledge. In addition, most large-scale models are trained in an auto-regressive way. As a result, this kind of traditional fine-tuning approach demonstrates relatively weak performance when solving downstream language understanding tasks.

In order to solve the above problems, we propose a unified framework named ERNIE 3.0 for pre-training large-scale knowledge enhanced models. It fuses auto-regressive network and auto-encoding network, so that the trained model can be easily tailored for both natural language understanding and generation tasks with zero-shot learning, few-shot learning or fine-tuning. We trained the model with 10 billion parameters on a 4TB corpus consisting of plain texts and a large-scale knowledge graph. Empirical results show that the model outperforms the state-of-the-art models on 54 Chinese NLP tasks, and its English version achieves the first place on the SuperGLUE benchmark (July 3, 2021), surpassing the human performance by +0.8% (90.6% vs. 89.8%).

--- https://arxiv.org/abs/2106.07447#facebook HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed 2021-06-14 2021-06-14 [("doi","10.48550/arXiv.2106.07447")] ai/scaling

Self-supervised approaches for speech representation learning are challenged by 3 unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation.

To deal with these 3 problems, we propose the Hidden-Unit BERT (HuBERT) approach for self-supervised speech representation learning, which utilizes an offline clustering step to provide aligned target labels for a BERT-like prediction loss. A key ingredient of our approach is applying the prediction loss over the masked regions only, which forces the model to learn a combined acoustic and language model over the continuous inputs. HuBERT relies primarily on the consistency of the unsupervised clustering step rather than the intrinsic quality of the assigned cluster labels.

Starting with a simple k-means teacher of 100 clusters, and using two iterations of clustering, the HuBERT model either matches or improves upon the state-of-the-art wav2vec 2.0 performance on the Librispeech (960h) and Libri-light (60,000h) benchmarks with 10min, 1h, 10h, 100h, and 960h fine-tuning subsets. Using a 1b parameter model, HuBERT shows up to 19% and 13% relative WER reduction on the more challenging dev-other and test-other evaluation subsets.

--- https://arxiv.org/abs/2106.04560#google Scaling Vision Transformers Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, Lucas Beyer 2021-06-08 2021-06-08 [("doi","10.48550/arXiv.2106.04560")] ai/dataset ai/scaling

Attention-based neural networks such as the Vision Transformer (ViT) have recently attained state-of-the-art results on many computer vision benchmarks. Scale is a primary ingredient in attaining excellent results, therefore, understanding a model’s scaling properties is a key to designing future generations effectively. While the laws for scaling Transformer language models have been studied, it is unknown how Vision Transformers scale.

To address this, we scale ViT models and data, both up and down, and characterize the relationships between error rate, data [introducing JFT-4b, a n = 4 billion version of JFT-300M], and compute.

Along the way, we refine the architecture and training of ViT, reducing memory consumption and increasing accuracy the resulting models.

As a result, we successfully train a ViT model with two billion parameters, which attains a new state-of-the-art on ImageNet of 90.45% top-1 accuracy. The model also performs well on few-shot learning, for example, attaining 84.86% top-1 accuracy on ImageNet with only 10 examples per class.

--- https://arxiv.org/abs/2106.00188 PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World Rowan Zellers, Ari Holtzman, Matthew Peters, Roozbeh Mottaghi, Aniruddha Kembhavi, Ali Farhadi, Yejin Choi 2021-06-01 2021-06-01 [("doi","10.48550/arXiv.2106.00188")] reinforcement-learning/model reinforcement-learning/scaling

We propose PIGLeT: a model that learns physical commonsense knowledge through interaction, and then uses this knowledge to ground language. We factorize PIGLeT into a physical dynamics model, and a separate language model. Our dynamics model learns not just what objects are but also what they do: glass cups break when thrown, plastic ones don’t. We then use it as the interface to our language model, giving us a unified model of linguistic form and grounded meaning. PIGLeT can read a sentence, simulate neurally what might happen next, and then communicate that result through a literal symbolic representation, or natural language.

Experimental results show that our model effectively learns world dynamics, along with how to communicate them. It is able to correctly forecast “what happens next” given an English sentence over 80% of the time, outperforming a 100× larger, text-to-text approach by over 10%. Likewise, its natural language summaries of physical interactions are also judged by humans as more accurate than LM alternatives. We present comprehensive analysis showing room for future work.

--- https://www.biorxiv.org/content/10.1101/2021.08.03.454982.full De novo evolution of macroscopic multicellularity G. Ozan Bozdag, Seyed Alireza Zamani-Dahaj, Penelope C. Kahn, Thomas C. Day, Kai Tong, Aishwarya H. Balwani, Eva L. Dyer, Peter J. Yunker, William C. Ratcliff 2021-08-05 2021-08-05 [("doi","10.1101/2021.08.03.454982")] biology genetics/selection/artificial

The evolution of large organismal size is fundamentally important for multicellularity, creating new ecological niches and opportunities for the evolution of increased biological complexity. Yet little is known about how large size evolves, particularly in nascent multicellular organisms that lack genetically-regulated multicellular development.

Here we examine the interplay between biological and biophysical drivers of macroscopic multicellularity using long-term experimental evolution. Over 600 daily transfers (~3,000 generations), multicellular snowflake yeast evolved macroscopic size, becoming ~2·104 times larger (~mm scale) and 104-fold more biophysically tough, while remaining clonal. They accomplished this through sustained biophysical adaptation, evolving increasingly elongate cells that initially reduced the strain of cellular packing, then facilitated branch entanglement so that groups of cells stay together even after many cellular bonds fracture.

Four out of 5 replicate populations show evidence of predominantly adaptive evolution, with mutations becoming statistically-significantly enriched in genes affecting cell shape and cell-cell bonds.

Taken together, this work shows how selection acting on the emergent properties of simple multicellular groups can drive sustained biophysical adaptation, an early step in the evolution of increasingly complex multicellular organisms.

--- https://arxiv.org/abs/2107.10201#deepmind Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs Nicolas Sonnerat, Pengming Wang, Ira Ktena, Sergey Bartunov, Vinod Nair 2021-07-21 2021-07-21 [("doi","10.48550/arXiv.2107.10201")] cs/algorithm reinforcement-learning/exploration reinforcement-learning/imitation-learning

Large Neighborhood Search (LNS) is a combinatorial optimization heuristic that starts with an assignment of values for the variables to be optimized, and iteratively improves it by searching a large neighborhood around the current assignment.

In this paper we consider a learning-based LNS approach for mixed integer programs (MIPs). We train a Neural Diving model to represent a probability distribution over assignments, which, together with an off-the-shelf MIP solver, generates an initial assignment. Formulating the subsequent search steps as a Markov Decision Process, we train a Neural Neighborhood Selection policy to select a search neighborhood at each step, which is searched using a MIP solver to find the next assignment. The policy network is trained using imitation learning. We propose a target policy for imitation that, given enough compute resources, is guaranteed to select the neighborhood containing the optimal next assignment amongst all possible choices for the neighborhood of a specified size.

Our approach matches or outperforms all the baselines on five real-world MIP datasets with large-scale instances from diverse applications, including two production applications at Google. It achieves 2× to 37.8× better average primal gap than the best baseline on 3 of the datasets at large running times.

--- https://arxiv.org/abs/2107.04070 Introducing A Dark Web Archival Framework Justin F. Brunelle, Ryan Farley, Grant Atkins, Trevor Bostic, Marites Hendrix, Zak Zebrowski 2021-07-08 2021-07-08 [("doi","10.48550/arXiv.2107.04070")] cs/linkrot/archiving darknet-market

We present a framework for web-scale archiving of the dark web.

While commonly associated with illicit and illegal activity, the dark web provides a way to privately access web information. This is a valuable and socially beneficial tool to global citizens, such as those wishing to access information while under oppressive political regimes that work to limit information availability. However, little institutional archiving is performed on the dark web (limited to the Archive.is dark web presence, a page-at-a-time archiver).

We use surface web tools, techniques, and procedures (TTPs) and adapt them for archiving the dark web. We demonstrate the viability of our framework in a proof-of-concept and narrowly scoped prototype, implemented with the following lightly adapted open-source tools: the Brozzler crawler for capture, WARC file for storage, and pywb for replay.

Using these tools, we demonstrate the viability of modified surface web archiving TTPs for archiving the dark web.

--- https://arxiv.org/abs/2105.13957 Darknet Data Mining—A Canadian Cyber-crime Perspective Edward Crowder, Jay Lansiquot 2021-05-18 2021-05-18 [("doi","10.48550/arXiv.2105.13957")] darknet-market

Exploring the darknet can be a daunting task; this paper explores the application of data mining the darknet within a Canadian cybercrime perspective. Measuring activity through marketplace analysis and vendor attribution has proven difficult in the past.

Observing different aspects of the darknet and implementing methods of monitoring and collecting data in the hopes of connecting contributions to the darknet marketplaces to and from Canada. The findings include a small Canadian presence, measured the product categories, and attribution of one cross-marketplace vendor through data visualization. The results were made possible through a multi-stage processing pipeline, including data crawling, scraping, and parsing.

The primary future works include enhancing the pipeline to include other media, such as web forums, chatrooms, and emails. Applying machine learning models like natural language processing or sentiment analysis could prove beneficial during investigations.

--- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002165 The Economics of Reproducibility in Preclinical Research Leonard P. Freedman, Iain M. Cockburn, Timothy S. Simcoe 2015-06-09 2021-03-16 [("doi","10.1371/journal.pbio.1002165")] economics statistics/bias

Low reproducibility rates within life science research undermine cumulative knowledge production and contribute to both delays and costs of therapeutic drug development. An analysis of past studies indicates that the cumulative (total) prevalence of irreproducible preclinical research exceeds 50%, resulting in ~$28,000,000,000 (US$28b)/year spent on preclinical research that is not reproducible—in the United States alone. We outline a framework for solutions and a plan for long-term improvements in reproducibility rates that will help to accelerate the discovery of life-saving therapies and cures.

This Perspective provides estimates of the rate of irreproducibility of preclinical research and its direct cost implications. It goes on to outline a framework for solutions and a plan for long-term improvements in reproducibility rates.

--- https://arxiv.org/abs/2105.14111 Goal Misgeneralization in Deep Reinforcement Learning Jack Koch, Lauro Langosco, Jacob Pfau, James Le, Lee Sharkey 2021-05-28 2021-05-28 [("doi","10.48550/arXiv.2105.14111")] existential-risk reinforcement-learning/safe

We study objective robustness failures, a type of out-of-distribution robustness failure in reinforcement learning (RL).

Objective robustness failures occur when an RL agent retains its capabilities out-of-distribution yet pursues the wrong objective. This kind of failure presents different risks than the robustness problems usually considered in the literature, since it involves agents that leverage their capabilities to pursue the wrong objective rather than simply failing to do anything useful.

We provide the first explicit empirical demonstrations of objective robustness failures and present a partial characterization of its causes.

--- https://www.biorxiv.org/content/10.1101/2021.08.19.456982.full Somatic mutation rates scale with lifespan across mammals Alex Cagan, Adrian Baez-Ortega, Natalia Brzozowska, Federico Abascal, Tim H. H. Coorens, Mathijs A. Sanders, Andrew R. J. Lawson, Luke M. R. Harvey, Shriram G. Bhosle, David Jones, Raul E. Alcantara, Timothy M. Butler, Yvette Hooks, Kirsty Roberts, Elizabeth Anderson, Edmund Flach, Simon Spiro, Inez Januszczak, Ethan Wrigglesworth, Matthew W. Perkins, Robert Deaville, Megan Druce, Ruzhica Bogeska, Michael D. Milsom, Björn Neumann, Frank Gorman, Fernando Constantino-Casas, Laura Peachey, Diana Bochynska, Ewan St. John Smith, Moritz Gerstung, Peter J. Campbell, Elizabeth P. Murchison, Michael R. Stratton, Iñigo Martincorena 2021-08-19 2021-08-19 [("doi","10.1101/2021.08.19.456982")] genetics/heritable longevity

The rates and patterns of somatic mutation in normal tissues are largely unknown outside of humans. Comparative analyses can shed light on the diversity of mutagenesis across species and on long-standing hypotheses regarding the evolution of somatic mutation rates and their role in cancer and ageing.

Here, we used whole-genome sequencing of 208 intestinal crypts from 56 individuals to study the landscape of somatic mutation across 16 mammalian species. We found somatic mutagenesis to be dominated by seemingly endogenous mutational processes in all species, including 5-methylcytosine deamination and oxidative damage.

With some differences, mutational signatures in other species resembled those described in humans, although the relative contribution of each signature varied across species. Remarkably, the somatic mutation rate per year varied greatly across species and exhibited a strong inverse relationship with species lifespan, with no other life-history trait studied displaying a comparable association.

Despite widely different life histories among the species surveyed, including ~30× variation in lifespan and ~40,000× variation in body mass, the somatic mutation burden at the end of lifespan varied only by a factor of ~3. These data unveil common mutational processes across mammals and suggest that somatic mutation rates are evolutionarily constrained and may be a determinant of lifespan.

--- https://www.biorxiv.org/content/10.1101/2021.07.31.454563.full Multi-Task Brain Network Reconfiguration is Inversely Associated with Human Intelligence Jonas A. Thiele, Joshua Faskowitz, Olaf Sporns, Kirsten Hilger 2021-08-02 2021-08-02 [("doi","10.1101/2021.07.31.454563")] iq psychology/neuroscience

Intelligence describes the general cognitive ability level of a person. It is one of the most fundamental concepts in psychological science and is crucial for effective adaption of behavior to varying environmental demands. Changing external task demands have been shown to induce reconfiguration of functional brain networks. However, whether neural reconfiguration between different tasks is associated with intelligence has not yet been investigated.

We used fMRI data from 812 subjects to show that higher scores of general intelligence are related to less brain network reconfiguration between resting state and seven different tasks as well as to network reconfiguration between tasks. This association holds for all functional brain networks except the motor system, and replicates in two independent samples (n = 138, n = 184). Our findings suggest that the intrinsic network architecture of individuals with higher general intelligence scores is closer to the network architecture as required by various cognitive demands. Multi-task brain network reconfiguration may, therefore, reflect the neural equivalent of the behavioral positive manifold—the essence of the concept of general intelligence. Finally, our results support neural efficiency theories of cognitive ability and reveal insights into human intelligence as an emergent property from a distributed multi-task brain network.

--- https://arxiv.org/abs/2106.11248 Alignment Problems With Current Forecasting Platforms Nuño Sempere, Alex Lawsen 2021-06-21 2021-06-21 [("doi","10.48550/arXiv.2106.11248")] statistics/decision statistics/prediction

We present alignment problems in current forecasting platforms, such as Good Judgment Open, CSET-Foretell, or Metaculus.

We classify those problems as either reward specification problems or principal-agent problems, and we propose solutions. For instance, the scoring rule used by Good Judgment Open is not proper, and Metaculus tournaments disincentivize sharing information and incentivize distorting one’s true probabilities to maximize the chances of placing in the top few positions which earn a monetary reward.

We also point out some partial similarities between the problem of aligning forecasters and the problem of aligning artificial intelligence systems.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255531 No effect of ‘watching eyes’: An attempted replication and extension investigating individual differences Amanda Rotella, Adam Maxwell Sparks, Sandeep Mishra, Pat Barclay 2021-07-16 2021-07-16 [("doi","10.1371/journal.pone.0255531")] psychology

Some evidence suggests that people behave more cooperatively and generously when observed or in the presence of images of eyes (termed the ‘watching eyes’ effect). Eye images are thought to trigger feelings of observation, which in turn motivate people to behave more cooperatively to earn a good reputation. However, several recent studies have failed to find evidence of the eyes effect. One possibility is that inconsistent evidence in support of the eyes effect is a product of individual differences in sensitivity or susceptibility to the cue. In fact, some evidence suggests that people who are generally more prosocial are less susceptible to situation-specific reputation-based cues of observation.

In this paper, we sought to (1) replicate the eyes effect, (2) replicate the past finding that people who are dispositionally less prosocial are more responsive to observation than people who are more dispositionally more prosocial, and (3) determine if this effect extends to the watching eyes effect. Results from a pre-registered study showed that people did not give more money in a dictator game when decisions were made public or in the presence of eye images, even though participants felt more observed when decisions were public. That is, we failed to replicate the eyes effect and observation effect. An initial, but underpowered, interaction model suggests that egoists give less than prosocials in private, but not public, conditions. This suggests a direction for future research investigating if and how individual differences in prosociality influence observation effects.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0252330 Human click-based echolocation: Effects of blindness and age, and real-life implications in a 10-week training program Liam J. Norman, Caitlin Dodsworth, Denise Foresteire, Lore Thaler, Maurice Ptito, Maurice Ptito, Maurice Ptito 2021-05-13 2021-05-13 [("doi","10.1371/journal.pone.0252330")] psychology/vision

Understanding the factors that determine if a person can successfully learn a novel sensory skill is essential for understanding how the brain adapts to change, and for providing rehabilitative support for people with sensory loss. We report a training study investigating the effects of blindness and age on the learning of a complex auditory skill: click-based echolocation. Blind and sighted participants of various ages (21–79 yrs; median blind: 45 yrs; median sighted: 26 yrs) trained in 20 sessions over the course of 10 weeks in various practical and virtual navigation tasks. Blind participants also took part in a 3-month follow up survey assessing the effects of the training on their daily life.

We found that both sighted and blind people improved considerably on all measures, and in some cases performed comparatively to expert echolocators at the end of training. Somewhat surprisingly, sighted people performed better than those who were blind in some cases, although our analyses suggest that this might be better explained by the younger age (or superior binaural hearing) of the sighted group. Importantly, however, neither age nor blindness was a limiting factor in participants’ rate of learning (ie. their difference in performance from the first to the final session) or in their ability to apply their echolocation skills to novel, untrained tasks. Furthermore, in the follow up survey, all participants who were blind reported improved mobility, and 83% reported better independence and wellbeing.

Overall, our results suggest that the ability to learn click-based echolocation is not strongly limited by age or level of vision. This has positive implications for the rehabilitation of people with vision loss or in the early stages of progressive vision loss.

--- https://arxiv.org/abs/2107.05407#deepmind PonderNet: Learning to Ponder Andrea Banino, Jan Balaguer, Charles Blundell 2021-07-12 2021-07-12 [("doi","10.48550/arXiv.2107.05407")] reinforcement-learning/meta-learning

In standard neural networks the amount of computation used grows with the size of the inputs, but not with the complexity of the problem being learnt.

To overcome this limitation we introduce PonderNet, a new algorithm that learns to adapt the amount of computation based on the complexity of the problem at hand. PonderNet learns end-to-end the number of computational steps to achieve an effective compromise between training prediction accuracy, computational cost, and generalization.

On a complex synthetic problem, PonderNet dramatically improves performance over previous adaptive computation methods and additionally succeeds at extrapolation tests where traditional neural networks fail. Also, our method matched the current state-of-the-art results on a real-world question and answering dataset, but using less compute. Finally, PonderNet reached state-of-the-art results on a complex task designed to test the reasoning capabilities of neural networks.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255704 ‘He who pays the piper calls the tune’: Researcher experiences of funder suppression of health behavior intervention trial findings Sam McCrabb, Kaitlin Mooney, Luke Wolfenden, Sharleen Gonzalez, Elizabeth Ditton, Serene Yoong, Kypros Kypri, Quinn Grundy, Quinn Grundy, Quinn Grundy 2021-07-21 2021-07-21 [("doi","10.1371/journal.pone.0255704")] statistics/bias/publication

Background: Governments commonly fund research with specific applications in mind. Such mechanisms may facilitate ‘research translation’ but funders may employ strategies that can also undermine the integrity of both science and government. We estimated the prevalence and investigated correlates of funder efforts to suppress health behavior intervention trial findings.

Method: Our sampling frame was lead or corresponding authors of papers (published 2007–2017) included in a Cochrane review, reporting findings from trials of interventions to improve nutrition, physical activity, sexual health, smoking, and substance use. Suppression events were based on a previous survey of public health academics. Participants answered questions concerning seven suppression events in their efforts to report the trial, eg. [I was…] “asked to suppress certain findings as they were viewed as being unfavourable.” We also examined the association between information on study funder, geographical location, targeted health behavior, country democracy rating and age of publication with reported suppression.

Results: We received responses from 104 authors (50%) of 208 eligible trials, from North America (34%), Europe (33%), Oceania (17%), and other countries (16%). Eighteen percent reported at least one of the seven suppression events relating to the trial in question. The most commonly reported suppression event was funder(s) expressing reluctance to publish because they considered the results ‘unfavourable’ (9% reported). We found no strong associations with the subject of research, funding source, democracy, region, or year of publication.

Conclusion: One in five researchers in this global sample reported being pressured to delay, alter, or not publish the findings of health behavior intervention trials. Regulation of funder and university practices, establishing study registries, and compulsory disclosure of funding conditions in scientific journals, are needed to protect the integrity of public-good research.

--- https://en.wikipedia.org/wiki/John_Draper John Draper 2021-03-17 technology --- https://arxiv.org/abs/2104.13733#facebook Gradient-based Adversarial Attacks against Text Transformers Chuan Guo, Alexandre Sablayrolles, Hervé Jégou, Douwe Kiela 2021-04-15 2021-04-15 [("doi","10.48550/arXiv.2104.13733")] ai/nn/adversarial ai/nn/transformer

We propose the first general-purpose gradient-based attack against transformer models.

Instead of searching for a single adversarial example, we search for a distribution of adversarial examples parameterized by a continuous-valued matrix, hence enabling gradient-based optimization.

We empirically demonstrate that our white-box attack attains state-of-the-art attack performance on a variety of natural language tasks.

Furthermore, we show that a powerful black-box transfer attack, enabled by sampling from the adversarial distribution, matches or exceeds existing methods, while only requiring hard-label outputs.

--- https://arxiv.org/abs/2103.06561 WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training Yuqi Huo, Manli Zhang, Guangzhen Liu, Haoyu Lu, Yizhao Gao, Guoxing Yang, Jingyuan Wen, Heng Zhang, Baogui Xu, Weihao Zheng, Zongzheng Xi, Yueqian Yang, Anwen Hu, Jinming Zhao, Ruichen Li, Yida Zhao, Liang Zhang, Yuqing Song, Xin Hong, Wanqing Cui, Danyang Hou, Yingyan Li, Junyi Li, Peiyu Liu, Zheng Gong, Chuhao Jin, Yuchong Sun, Shizhe Chen, Zhiwu Lu, Zhicheng Dou, Qin Jin, Yanyan Lan, Wayne Xin Zhao, Ruihua Song, Ji-Rong Wen 2021-03-11 2021-03-17 [("doi","10.48550/arXiv.2103.06561")] ai/nn/transformer/clip ai/scaling

Multi-modal pre-training models have been intensively explored to bridge vision and language in recent years. However, most of them explicitly model the cross-modal interaction between image-text pairs, by assuming that there exists strong semantic correlation between the text and image modalities. Since this strong assumption is often invalid in real-world scenarios, we choose to implicitly model the cross-modal correlation for large-scale multi-modal pre-training, which is the focus of the Chinese project ‘WenLan’ led by our team.

Specifically, with the weak correlation assumption over image-text pairs, we propose a two-tower pre-training model called BriVL within the cross-modal contrastive learning framework. Unlike OpenAI CLIP that adopts a simple contrastive learning method, we devise a more advanced algorithm by adapting the latest method MoCo into the cross-modal scenario. By building a large queue-based dictionary, our BriVL can incorporate more negative samples in limited GPU resources. We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model.

Extensive experiments demonstrate that the pre-trained BriVL model outperforms both UNITER and OpenAI CLIP on various downstream tasks.

--- https://arxiv.org/abs/2103.10385 GPT Understands, Too Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, Jie Tang 2021-03-18 2021-03-18 [("doi","10.48550/arXiv.2103.10385")] ai/nn/transformer/gpt

While GPTs with traditional fine-tuning fail to achieve strong results on natural language understanding (NLU), we show that GPTs can be better than or comparable to similar-sized BERTs on NLU tasks with a novel method P-tuning—which employs trainable continuous prompt embeddings.

On the knowledge probing (LAMA) benchmark, the best GPT recovers 64% (P@1) of world knowledge without any additional text provided during test time, which substantially improves the previous best by 20+ percentage points. On the SuperGlue benchmark, GPTs achieve comparable and sometimes better performance to similar-sized BERTs in supervised learning.

Importantly, we find that P-tuning also improves BERTs’ performance in both few-shot and supervised settings while largely reducing the need for prompt engineering. Consequently, P-tuning outperforms the state-of-the-art approaches on the few-shot SuperGlue benchmark.

--- https://arxiv.org/abs/2103.08493 How Many Data Points is a Prompt Worth? Teven Le Scao, Alexander M. Rush 2021-03-15 2021-03-17 [("doi","10.48550/arXiv.2103.08493")] ai/nn/transformer/gpt

When fine-tuning pretrained models for classification, researchers either use a generic model head or a task-specific prompt for prediction. Proponents of prompting have argued that prompts provide a method for injecting task-specific guidance, which is beneficial in low-data regimes.

We aim to quantify this benefit through rigorous testing of prompts in a fair setting: comparing prompted and head-based fine-tuning in equal conditions across many tasks and data sizes. By controlling for many sources of advantage, we find that prompting does indeed provide a benefit, and that this benefit can be quantified per task.

Results show that prompting is often worth 100s of data points on average across classification tasks.

--- https://arxiv.org/abs/2103.03841#deepmind Generating Images with Sparse Representations Charlie Nash, Jacob Menick, Sander Dieleman, Peter W. Battaglia 2021-03-05 2021-03-18 [("doi","10.48550/arXiv.2103.03841")] ai/nn/transformer/gpt/dall-e/1 cs/algorithm/information/compression

The high dimensionality of images presents architecture and sampling-efficiency challenges for likelihood-based generative models. Previous approaches such as VQ-VAE use deep autoencoders to obtain compact representations, which are more practical as inputs for likelihood-based models.

We present an alternative approach, inspired by common image compression methods like JPEG, and convert images to quantized discrete cosine transform (DCT) blocks, which are represented sparsely as a sequence of DCT channel, spatial location, and DCT coefficient triples. We propose a Transformer-based autoregressive architecture, which is trained to sequentially predict the conditional distribution of the next element in such sequences, and which scales effectively to high resolution images.

On a range of image datasets, we demonstrate that our approach can generate high quality, diverse images, with sample metric scores competitive with state-of-the-art methods.

We additionally show that simple modifications to our method yield effective image colorization and super-resolution models.

--- https://arxiv.org/abs/2104.14690#facebook Entailment as Few-Shot Learner Sinong Wang, Han Fang, Madian Khabsa, Hanzi Mao, Hao Ma 2021-04-29 2021-04-29 [("doi","10.48550/arXiv.2104.14690")] ai/nn

Large pre-trained language models (LMs) have demonstrated remarkable ability as few-shot learners. However, their success hinges largely on scaling model parameters to a degree that makes it challenging to train and serve.

In this paper, we propose a new approach, named EFL, that can turn small LMs into better few-shot learners. The key idea of this approach is to reformulate potential NLP task into an entailment one, and then fine-tune the model with as little as 8 examples. We further demonstrate our proposed method can be: (1) naturally combined with an unsupervised contrastive learning-based data augmentation method; (2) easily extended to multilingual few-shot learning.

A systematic evaluation on 18 standard NLP tasks demonstrates that this approach improves the various existing SOTA few-shot learning methods by 12%, and yields competitive few-shot performance with 500× larger models, such as GPT-3.

--- https://arxiv.org/abs/2103.14030 Swin Transformer: Hierarchical Vision Transformer using Shifted Windows Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo 2021-03-25 2021-03-25 [("doi","10.48550/arXiv.2103.14030")] ai/nn/fully-connected ai/nn/transformer/attention/hierarchical

This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text.

To address these differences, we propose a hierarchical Transformer whose representation is computed with Shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size.

These qualities of Swin Transformer make it compatible with a broad range of vision tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and dense prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test-dev) and semantic segmentation (53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-the-art by a large margin of +2.7 box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones. The hierarchical design and the shifted window approach also prove beneficial for all-MLP architectures.

The code and models are publicly available at ~https://github.com/microsoft/Swin-Transformer.

--- https://arxiv.org/abs/2103.11886#bytedance DeepViT: Towards Deeper Vision Transformer Daquan Zhou, Bingyi Kang, Xiaojie Jin, Linjie Yang, Xiaochen Lian, Zihang Jiang, Qibin Hou, Jiashi Feng 2021-03-22 2021-03-22 [("doi","10.48550/arXiv.2103.11886")] ai/nn/transformer

Vision transformers (ViTs) have been successfully applied in image classification tasks recently. In this paper, we show that, unlike convolution neural networks (CNNs) that can be improved by stacking more convolutional layers, the performance of ViTs saturate fast when scaled to be deeper. More specifically, we empirically observe that such scaling difficulty is caused by the attention collapse issue: as the transformer goes deeper, the attention maps gradually become similar and even much the same after certain layers. In other words, the feature maps tend to be identical in the top layers of deep ViT models. This fact demonstrates that in deeper layers of ViTs, the self-attention mechanism fails to learn effective concepts for representation learning and hinders the model from getting expected performance gain.

Based on above observation, we propose a simple yet effective method, named Re-attention, to re-generate the attention maps to increase their diversity at different layers with negligible computation and memory cost. The proposed method makes it feasible to train deeper ViT models with consistent performance improvements via minor modification to existing ViT models.

Notably, when training a deep ViT model with 32 transformer blocks, the Top-1 classification accuracy can be improved by 1.6% on ImageNet.

Code is publicly available at Github.

--- https://arxiv.org/abs/2103.10697#facebook ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases Stéphane d’Ascoli, Hugo Touvron, Matthew Leavitt, Ari Morcos, Giulio Biroli, Levent Sagun 2021-03-19 2021-03-19 [("doi","10.48550/arXiv.2103.10697")] ai/nn/cnn ai/nn/transformer

Convolutional architectures have proven extremely successful for vision tasks. Their hard inductive biases enable sample-efficient learning, but come at the cost of a potentially lower performance ceiling. Vision Transformers (ViTs) rely on more flexible self-attention layers, and have recently outperformed CNNs for image classification. However, they require costly pre-training on large external datasets or distillation from pre-trained convolutional networks. In this paper, we ask the following question: is it possible to combine the strengths of these two architectures while avoiding their respective limitations?

To this end, we introduce gated positional self-attention (GPSA), a form of positional self-attention which can be equipped with a “soft” convolutional inductive bias. We initialize the GPSA layers to mimic the locality of convolutional layers, then give each attention head the freedom to escape locality by adjusting a gating parameter regulating the attention paid to position versus content information.

The resulting convolutional-like ViT architecture, ConViT, outperforms the DeiT on ImageNet, while offering a much improved sample efficiency. We further investigate the role of locality in learning by first quantifying how it is encouraged in vanilla self-attention layers, then analyzing how it is escaped in GPSA layers.

We conclude by presenting various ablations to better understand the success of the ConViT.

Our code and models are released publicly at Github.

--- https://arxiv.org/abs/2103.06874#google CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting 2021-03-11 2021-03-18 [("doi","10.48550/arXiv.2103.06874")] ai/nn/tokenization ai/nn/transformer

Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step. While recent tokenization approaches based on data-derived subword lexicons are less brittle than manually engineered tokenizers, these techniques are not equally suited to all languages, and the use of any fixed vocabulary may limit a model’s ability to adapt.

In this paper, we present CANINE, a neural encoder that operates directly on character sequences, without explicit tokenization or vocabulary, and a pre-training strategy that operates either directly on characters or optionally uses subwords as a soft inductive bias. To use its finer-grained input effectively and efficiently, CANINE combines downsampling, which reduces the input sequence length, with a deep transformer stack, which encodes context.

CANINE outperforms a comparable mBERT model by 2.8 F1 on TyDiQA, a challenging multilingual benchmark, despite having 28% fewer model parameters.

--- https://arxiv.org/abs/2103.03230#facebook Barlow Twins: Self-Supervised Learning via Redundancy Reduction Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, Stéphane Deny 2021-03-04 2021-03-18 [("doi","10.48550/arXiv.2103.03230")] ai/nn

Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large computer vision benchmarks. A successful approach to SSL is to learn embeddings which are invariant to distortions of the input sample. However, a recurring issue with this approach is the existence of trivial constant solutions. Most current methods avoid such solutions by careful implementation details.

We propose an objective function that naturally avoids collapse by measuring the cross-correlation matrix between the outputs of two identical networks fed with distorted versions of a sample, and making it as close to the identity matrix as possible. This causes the embedding vectors of distorted versions of a sample to be similar, while minimizing the redundancy between the components of these vectors. The method is called Barlow Twins, owing to neuroscientist H. Barlow’s redundancy-reduction principle applied to a pair of identical networks.

Barlow Twins does not require large batches nor asymmetry between the network twins such as a predictor network, gradient stopping, or a moving average on the weight updates. Intriguingly it benefits from very high-dimensional output vectors. Barlow Twins outperforms previous methods on ImageNet for semi-supervised classification in the low-data regime, and is on par with current state-of-the-art for ImageNet classification with a linear classifier head, and for transfer tasks of classification and object detection.

--- https://arxiv.org/abs/2102.12702#microsoft LazyFormer: Self Attention with Lazy Update Chengxuan Ying, Guolin Ke, Di He, Tie-Yan Liu 2021-02-25 2021-03-18 [("doi","10.48550/arXiv.2102.12702")] ai/nn/transformer/attention/hierarchical

Improving the efficiency of Transformer-based language pre-training is an important task in NLP, especially for the self-attention module, which is computationally expensive.

In this paper, we propose a simple but effective solution, called LazyFormer, which computes the self-attention distribution infrequently. LazyFormer composes of multiple lazy blocks, each of which contains multiple Transformer layers. In each lazy block, the self-attention distribution is only computed once in the first layer and then is reused in all upper layers. In this way, the cost of computation could be largely saved.

We also provide several training tricks for LazyFormer. Extensive experiments demonstrate the effectiveness of the proposed method.

--- https://arxiv.org/abs/2102.13189 Rip van Winkle’s Razor: A Simple Estimate of Overfit to Test Data Sanjeev Arora, Yi Zhang 2021-02-25 2021-03-18 [("doi","10.48550/arXiv.2102.13189")] ai/nn cs/algorithm/information/compression philosophy/epistemology

Traditional statistics forbids use of test data (a.k.a. holdout data) during training. Dwork et al 2015 pointed out that current practices in machine learning, whereby researchers build upon each other’s models, copying hyperparameters and even computer code—amounts to implicitly training on the test set. Thus error rate on test data may not reflect the true population error. This observation initiated adaptive data analysis, which provides evaluation mechanisms with guaranteed upper bounds on this difference. With statistical query (ie. test accuracy) feedbacks, the best upper bound is fairly pessimistic: the deviation can hit a practically vacuous value if the number of models tested is quadratic in the size of the test set.

In this work, we present a simple new estimate, Rip van Winkle’s Razor. It relies upon a new notion of “information content” of a model: the amount of information that would have to be provided to an expert referee who is intimately familiar with the field and relevant science/math, and who has been just been woken up after falling asleep at the moment of the creation of the test data (like “Rip van Winkle” of the famous fairy tale). This notion of information content is used to provide an estimate of the above deviation which is shown to be non-vacuous in many modern settings.

--- https://arxiv.org/abs/2102.12037 Image Completion via Inference in Deep Generative Models William Harvey, Saeid Naderiparizi, Frank Wood 2021-02-24 2021-03-18 [("doi","10.48550/arXiv.2102.12037")] ai/nn statistics/bayes

We consider image completion from the perspective of amortized inference in an image generative model.

We leverage recent state-of-the-art variational autoencoder architectures that have been shown to produce photorealistic natural images at non-trivial resolutions. Through amortized inference in such a model, we can train neural artifacts that produce diverse, realistic image completions even when the vast majority of an image is missing.

We demonstrate superior sample quality and diversity compared to prior art on the CIFAR-10 and FFHQ-256 datasets.

We conclude by describing and demonstrating an application that requires an in-painting model with the capabilities ours exhibits: the use of Bayesian optimal experimental design to select the most informative sequence of small field of view x-rays for chest pathology detection.

--- https://arxiv.org/abs/2102.11972#google Do Transformer Modifications Transfer Across Implementations and Applications? Sharan Narang, Hyung Won Chung, Yi Tay, William Fedus, Thibault Fevry, Michael Matena, Karishma Malkan, Noah Fiedel, Noam Shazeer, Zhenzhong Lan, Yanqi Zhou, Wei Li, Nan Ding, Jake Marcus, Adam Roberts, Colin Raffel 2021-02-23 2021-03-19 [("doi","10.48550/arXiv.2102.11972")] ai/nn/transformer/attention

The research community has proposed copious modifications to the Transformer architecture since it was introduced over 3 years ago, relatively few of which have seen widespread adoption.

In this paper, we comprehensively evaluate many of these modifications in a shared experimental setting that covers most of the common uses of the Transformer in natural language processing.

Surprisingly, we find that most modifications do not meaningfully improve performance. Furthermore, most of the Transformer variants we found beneficial were either developed in the same codebase that we used or are relatively minor changes.

We conjecture that performance improvements may strongly depend on implementation details and correspondingly make some recommendations for improving the generality of experimental results.

--- https://arxiv.org/abs/2102.11174#schmidhuber Linear Transformers Are Secretly Fast Weight Programmers Imanol Schlag, Kazuki Irie, Jürgen Schmidhuber 2021-02-22 2021-03-19 [("doi","10.48550/arXiv.2102.11174")] ai/nn/transformer/attention reinforcement-learning/meta-learning

We show the formal equivalence of linearised self-attention mechanisms and fast weight controllers from the early ’90s, where a “slow” neural net learns by gradient descent to program the “fast weights” of another net through sequences of elementary programming instructions which are additive outer products of self-invented activation patterns (today called keys and values). Such Fast Weight Programmers (FWPs) learn to manipulate the contents of a finite memory and dynamically interact with it.

We infer a memory capacity limitation of recent linearised softmax attention variants, and replace the purely additive outer products by a delta rule-like programming instruction, such that the FWP can more easily learn to correct the current mapping from keys to values. The FWP also learns to compute dynamically changing learning rates. We also propose a new kernel function to linearise attention which balances simplicity and effectiveness.

We conduct experiments on synthetic retrieval problems as well as standard machine translation and language modeling tasks which demonstrate the benefits of our methods.

--- https://arxiv.org/abs/2103.03775 There Once Was a Really Bad Poet, It Was Automated but You Didn’t Know It Jianyou Wang, Xiaoxuan Zhang, Yuren Zhou, Christopher Suh, Cynthia Rudin 2021-03-05 2021-03-19 [("doi","10.48550/arXiv.2103.03775")] ai/nn/sampling ai/nn/tokenization ai/nn/transformer/gpt/poetry

Limerick generation exemplifies some of the most difficult challenges faced in poetry generation, as the poems must tell a story in only five lines, with constraints on rhyme, stress, and meter.

To address these challenges, we introduce LimGen, a novel and fully automated system for limerick generation that outperforms state-of-the-art neural network-based poetry models, as well as prior rule-based poetry models. LimGen consists of 3 important pieces: the Adaptive Multi-Templated Constraint algorithm that constrains our search to the space of realistic poems, the Multi-Templated Beam Search algorithm which searches efficiently through the space, and the probabilistic Storyline algorithm that provides coherent storylines related to a user-provided prompt word.

The resulting limericks satisfy poetic constraints and have thematically coherent storylines, which are sometimes even funny (when we are lucky).

[Judging by the last author's comments, the authors are apparently unaware that they are fighting BPE problems in GPT-2 and that is why GPT-2 doesn't "want" to rhyme.]

--- https://arxiv.org/abs/2104.08663 BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, Iryna Gurevych 2021-04-17 2021-04-17 [("doi","10.48550/arXiv.2104.08663")] ai/dataset ai/nn/retrieval

Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of-distribution (OOD) generalization capabilities. To address this, and to facilitate researchers to broadly evaluate the effectiveness of their models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval.

We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems including lexical, sparse, dense, late-interaction and re-ranking architectures on the BEIR benchmark. Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs. In contrast, dense and sparse-retrieval models are computationally more efficient but often underperform other approaches, highlighting the considerable room for improvement in their generalization capabilities.

We hope this framework allows us to better evaluate and understand existing retrieval systems, and contributes to accelerating progress towards better robust and generalizable systems in the future.

BEIR is publicly available at Github.

--- https://arxiv.org/abs/2102.12459 When Attention Meets Fast Recurrence: Training SRU++ Language Models with Reduced Compute Tao Lei 2021-02-24 2021-03-19 [("doi","10.48550/arXiv.2102.12459")] ai/nn/rnn ai/nn/transformer/attention/recurrent

Large language models have become increasingly difficult to train because of the growing computation time and cost.

In this work, we present SRU++, a highly-efficient architecture that combines fast recurrence and attention for sequence modeling. SRU++ exhibits strong modeling capacity and training efficiency.

On standard language modeling tasks such as enwik8, Wiki-103 and Billion Word datasets, our model obtains better bits-per-character and perplexity while using 3×-10× less training cost compared to top-performing Transformer models. For instance, our model achieves a state-of-the-art result on the enwik8 dataset using 1.6 days of training on an 8-GPU machine. We further demonstrate that SRU++ requires minimal attention for near state-of-the-art performance.

Our results suggest jointly leveraging fast recurrence with little attention as a promising direction for accelerating model training and inference.

--- https://arxiv.org/abs/2105.00572#facebook XLM-R XL: Larger-Scale Transformers for Multilingual Masked Language Modeling Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau 2021-05-02 2021-05-02 [("doi","10.48550/arXiv.2105.00572")] ai/scaling

Recent work has demonstrated the effectiveness of cross-lingual language model pretraining for cross-lingual understanding.

In this study, we present the results of two larger multilingual masked language models, with 3.5B and 10.7b parameters. Our two new models dubbed XLM-R XL & XLM-R XXL outperform XLM-R by 1.8% and 2.4% average accuracy on XNLI. Our model also outperforms the RoBERTa-Large model on several English tasks of the GLUE benchmark by 0.3% on average while handling 99 more languages.

This suggests pretrained models with larger capacity may obtain both strong performance on high-resource languages while greatly improving low-resource languages.

We make our code and models publicly available.

--- https://arxiv.org/abs/2104.14294#facebook DINO: Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, Arm Holdings, Joulin 2021-04-29 2021-04-29 [("doi","10.48550/arXiv.2104.14294")] ai/nn/sparsity/knowledge-distillation ai/scaling

In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets).

Beyond the fact that adapting self-supervised methods to this architecture works particularly well, we make the following observations: first, self-supervised ViT features contain explicit information about the semantic segmentation of an image, which does not emerge as clearly with supervised ViTs, nor with convnets. Second, these features are also excellent k-NN classifiers, reaching 78.3% top-1 on ImageNet with a small ViT. Our study also underlines the importance of momentum encoder, multi-crop training, and the use of small patches with ViTs.

We implement our findings into a simple self-supervised method, called DINO, which we interpret as a form of self-distillation with no labels. We show the synergy between DINO and ViTs by achieving 80.1% top-1 on ImageNet in linear evaluation with ViT-Base.

--- https://arxiv.org/abs/2104.08691#google The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester, Rami Al-Rfou, Noah Constant 2021-04-18 2021-04-18 [("doi","10.48550/arXiv.2104.08691")] ai/nn/transformer/t5 ai/scaling

In this work, we explore “prompt tuning”, a simple yet effective mechanism for learning “soft prompts” to condition frozen language models to perform specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signal from any number of labeled examples.

Our end-to-end learned approach outperforms GPT-3’s “few-shot” learning by a large margin. More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method “closes the gap” and matches the strong performance of model tuning (where all model weights are tuned). This finding is especially relevant in that large models are costly to share and serve, and the ability to reuse one frozen model for multiple downstream tasks can ease this burden.

Our method can be seen as a simplification of the recently proposed “prefix tuning” of Li & Liang 2021, and we provide a comparison to this and other similar approaches. Finally, we show that conditioning a frozen model with soft prompts confers benefits in robustness to domain transfer, as compared to full model tuning.

--- https://arxiv.org/abs/2104.08728 Revealing Persona Biases in Dialogue Systems Emily Sheng, Josh Arnold, Zhou Yu, Kai-Wei Chang, Nanyun Peng 2021-04-18 2021-04-18 [("doi","10.48550/arXiv.2104.08728")] ai/scaling

Dialogue systems in the form of chatbots and personal assistants are being increasingly integrated into people’s lives. Modern dialogue systems may consider adopting anthropomorphic personas, mimicking societal demographic groups to appear more approachable and trustworthy to users. However, the adoption of a persona can result in the adoption of biases.

In this paper, we present the first large-scale study on persona biases in dialogue systems and conduct analyses on personas of different social classes, sexual orientations, races, and genders. We define persona biases as harmful differences in responses (eg. varying levels of offensiveness, agreement with harmful statements) generated from adopting different demographic personas. Furthermore, we introduce an open-source framework, UnitPersonaBias, to explore and aggregate persona biases in dialogue systems.

By analyzing the Blender and DialoGPT dialogue systems, we observe that adopting personas can actually decrease harmful responses, compared to not using any personas. Additionally, we find that persona choices can affect the degree of harms in generated responses and thus should be systematically evaluated before deployment. We also analyze how personas can result in different amounts of harm towards specific demographics.

--- https://arxiv.org/abs/2104.07885 Probing Across Time: What Does RoBERTa Know and When? Leo Z. Liu, Yizhong Wang, Jungo Kasai, Hannaneh Hajishirzi, Noah Smith 2021-04-16 2021-04-16 [("doi","10.48550/arXiv.2104.07885")] ai/scaling

Models of language trained on very large corpora have been demonstrated useful for NLP (Natural Language Processing). As fixed artifacts, they have become the object of intense study, with many researchers “probing” the extent to which linguistic abstractions, factual and commonsense knowledge, and reasoning abilities they acquire and readily demonstrate. Building on this line of work, we consider a new question: for types of knowledge a language model learns, when during (pre)training are they acquired?

We plot probing performance across iterations, using RoBERTa as a case study. Among our findings: linguistic knowledge is acquired fast, stably, and robustly across domains. Facts and commonsense are slower and more domain-sensitive. Reasoning abilities are, in general, not stably acquired.

As new datasets, pretraining protocols, and probes emerge, we believe that probing-across-time analyses can help researchers understand the complex, intermingled learning that these models undergo and guide us toward more efficient approaches that accomplish necessary learning faster.

--- https://arxiv.org/abs/2103.10685 Controllable Generation from Pre-trained Language Models via Inverse Prompting Xu Zou, Da Yin, Qingyang Zhong, Ming Ding, Hongxia Yang, Zhilin Yang, Jie Tang 2021-03-19 2021-03-19 [("doi","10.1145/3447548.3467418")] ai/nn/sampling ai/scaling

Large-scale pre-trained language models have demonstrated strong capabilities of generating realistic text. However, it remains challenging to control the generation results. Previous approaches such as prompting are far from sufficient, which limits the usage of language models. To tackle this challenge, we propose an innovative method, inverse prompting, to better control text generation. The core idea of inverse prompting is to use generated text to inversely predict the prompt during beam search, which enhances the relevance between the prompt and the generated text and provides better controllability. Empirically, we pre-train a large-scale Chinese language model to perform a systematic study using human evaluation on the tasks of open-domain poem generation and open-domain long-form question answering. Our results show that our proposed method substantially outperforms the baselines and that our generation quality is close to human performance on some of the tasks.

Narrators can try our poem generation demo at https://pretrain.aminer.cn/apps/poetry.html, while our QA demo can be found at https://pretrain.aminer.cn/app/qa. For researchers, the code is provided in Github.

--- https://arxiv.org/abs/2103.10948 The Shape of Learning Curves: a Review Tom Viering, Marco Loog 2021-03-19 2021-03-20 [("doi","10.48550/arXiv.2103.10948")] ai/scaling psychology/neuroscience

[cf. emergence] Learning curves provide insight into the dependence of a learner’s generalization performance on the training set size. This important tool can be used for model selection, to predict the effect of more training data, and to reduce the computational complexity of model training and hyperparameter tuning.

This review recounts the origins of the term, provides a formal definition of the learning curve, and briefly covers basics such as its estimation. Our main contribution is a comprehensive overview of the literature regarding the shape of learning curves.

We discuss empirical and theoretical evidence that supports well-behaved curves that often have the shape of a power law or an exponential. We consider the learning curves of Gaussian processes, the complex shapes they can display, and the factors influencing them. We draw specific attention to examples of learning curves that are ill-behaved, showing worse learning performance with more training data. To wrap up, we point out various open problems that warrant deeper empirical and theoretical investigation.

All in all, our review underscores that learning curves are surprisingly diverse and no universal model can be identified.

--- https://arxiv.org/abs/2103.10957#deepmind Efficient Visual Pretraining with Contrastive Detection Olivier J. Hénaff, Skanda Koppula, Jean-Baptiste Alayrac, Aaron van den Oord, Oriol Vinyals, João Carreira 2021-03-19 2021-03-20 [("doi","10.48550/arXiv.2103.10957")] ai/scaling

Self-supervised pretraining has been shown to yield powerful representations for transfer learning. These performance gains come at a large computational cost however, with state-of-the-art methods requiring an order of magnitude more computation than supervised pretraining.

We tackle this computational bottleneck by introducing a new self-supervised objective, contrastive detection, which tasks representations with identifying object-level features across augmentations.

This objective extracts a rich learning signal per image, leading to state-of-the-art transfer accuracy on a variety of downstream tasks, while requiring up to 10× less pretraining. In particular, our strongest ImageNet-pretrained model performs on par with SEER, one of the largest self-supervised systems to date, which uses 1,000× more pretraining data. Finally, our objective seamlessly handles pretraining on more complex images such as those in COCO, closing the gap with supervised transfer learning from COCO to PASCAL.

--- https://arxiv.org/abs/2102.08981#google Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts Soravit Changpinyo, Piyush Sharma, Nan Ding, Radu Soricut 2021-02-17 2021-03-20 [("doi","10.48550/arXiv.2102.08981")] ai/dataset ai/scaling

The availability of large-scale image captioning and visual question answering datasets has contributed to recent successes in vision-and-language pre-training. However, these datasets are often collected with overrestrictive requirements inherited from their original target tasks (eg. image caption generation), which limit the resulting dataset scale and diversity.

We take a step further in pushing the limits of vision-and-language pre-training data by relaxing the data collection pipeline used in Conceptual Captions 3M (CC3M) [Sharma et al 2018] and introduce the Conceptual 12M (CC12M), a dataset with 12 million image-text pairs specifically meant to be used for vision-and-language pre-training. We perform an analysis of this dataset and benchmark its effectiveness against CC3M on multiple downstream tasks with an emphasis on long-tail visual recognition.

Our results clearly illustrate the benefit of scaling up pre-training data for vision-and-language tasks, as indicated by the new state-of-the-art results on both the nocaps and Conceptual Captions benchmarks.

--- https://arxiv.org/abs/2102.06356 A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes Zachary Nado, Justin M. Gilmer, Christopher J. Shallue, Rohan Anil, George E. Dahl 2021-02-12 2021-03-20 [("doi","10.48550/arXiv.2102.06356")] ai/scaling

Recently the LARS and LAMB optimizers have been proposed for training neural networks faster using large batch sizes. LARS and LAMB add layer-wise normalization to the update rules of Heavy-ball momentum and Adam, respectively, and have become popular in prominent benchmarks and deep learning libraries. However, without fair comparisons to standard optimizers, it remains an open question whether LARS and LAMB have any benefit over traditional, generic algorithms.

In this work we demonstrate that standard optimization algorithms such as Nesterov momentum and Adam can match or exceed the results of LARS and LAMB at large batch sizes.

Our results establish new, stronger baselines for future comparisons at these batch sizes and shed light on the difficulties of comparing optimizers for neural network training more generally.

--- https://arxiv.org/abs/2104.07705 How to Train BERT with an Academic Budget Peter Izsak, Moshe Berchansky, Omer Levy 2021-04-15 2021-04-15 [("doi","10.48550/arXiv.2104.07705")] ai/scaling/hardware economics/experience-curve

While large language models a la BERT are used ubiquitously in NLP, pretraining them is considered a luxury that only a few well-funded industry labs can afford. How can one train such models with a more modest budget?

We present a recipe for pretraining a masked language model in 24 hours using a single low-end deep learning server. We demonstrate that through a combination of software optimizations, design choices, and hyperparameter tuning, it is possible to produce models that are competitive with BERT-base on GLUE tasks at a fraction of the original pretraining cost [$50 vs $2,000].

--- https://www.medrxiv.org/content/10.1101/2021.02.15.21251308.full Using DNA to predict behavior problems from preschool to adulthood Agnieszka Gidziela, Kaili Rimfeld, Margherita Malanchini, Andrea G. Allegrini, Andrew McMillan, Saskia Selzam, Angelica Ronald, Essi Viding, Sophie von Stumm, Thalia C. Eley, Robert Plomin 2021-02-19 2021-03-20 [("doi","10.1101/2021.02.15.21251308")] crime genetics/heritable psychiatry/anxiety

Background: One goal of the DNA revolution is to predict problems in order to prevent them. We tested here if the prediction of behavior problems from genome-wide polygenic scores (GPS) can be improved by creating composites across ages and across raters and by using a multi-GPS approach that includes GPS for adult psychiatric disorders as well as for childhood behavior problems.

Method

Our sample included 3,065 genotyped unrelated individuals from the Twins Early Development Study who were assessed longitudinally for hyperactivity, conduct, emotional problems and peer problems as rated by parents, teachers and children themselves. GPS created from 15 genome-wide association studies were used separately and jointly to test the prediction of behavior problems composites (general behavior problems, externalizing and internalizing) across ages (from age 2 to age 21) and across raters in penalized regression models. Based on the regression weights, we created multi-trait GPS reflecting the best prediction of behavior problems. We compared GPS prediction to twin heritability using the same sample and measures.

Results: Multi-GPS prediction of behavior problems increased from less than 2% of the variance for observed traits to up to 6% for cross-age and cross-rater composites. Twin study estimates of heritability mirrored patterns of multi-GPS prediction as they increased from less than 40% to up to 83%.

Conclusion: The ability of GPS to predict behavior problems can be improved by using multiple GPS, cross-age composites and cross-rater composites, although the effect sizes remain modest, up to 6%. Our results can be used in any genotyped sample to create multi-trait GPS predictors of behavior problems that will be more predictive than polygenic scores based on a single age, rater or GPS.

Key points

Genome-wide polygenic scores (GPS) can be used to predict behavior problems in childhood, but the effect sizes are generally less than 3.5%.

DNA-based prediction models of achieve greater accuracy if holistic approaches are employed, that is cross-trait, longitudinal and trans-situational approaches.

The prediction of childhood behavior problems can be improved by using multiple GPS to predict composites that aggregate behavior problems across ages and across raters.

Our results yield weights that can be applied to GPS in any study to create multi-trait GPS predictors of behavior problems based on cross-age and cross-rater composites.

As compared to individuals in the lowest multi-trait GPS decile, nearly 3 times as many individuals in the highest internalizing multi-trait GPS decile were diagnosed with anxiety disorder and 25% more individuals in the highest general behavior problems and externalizing multi-trait GPS deciles have taken medication for mental health.

--- https://arxiv.org/abs/2103.07487 How Developers Choose Names Dror G. Feitelson, Ayelet Mizrahi, Nofar Noy, Aviad Ben Shabat, Or Eliyahu, Roy Sheffer 2021-03-12 2021-03-20 [("doi","10.1109/TSE.2020.2976920")] cs/algorithm design

The names of variables and functions serve as implicit documentation and are instrumental for program comprehension. But choosing good meaningful names is hard.

We perform a sequence of experiments in which a total of 334 subjects are required to choose names in given programming scenarios.

The first experiment shows that the probability that two developers would select the same name is low: in the 47 instances in our experiments the median probability was only 6.9%. At the same time, given that a specific name is chosen, it is usually understood by the majority of developers.

Analysis of the names given in the experiment suggests a model where naming is a (not necessarily cognizant or serial) three-step process: (1) selecting the concepts to include in the name, (2) choosing the words to represent each concept, and (3) constructing a name using these words. A followup experiment, using the same experimental setup, then checked whether using this model explicitly can improve the quality of names. The results were that names selected by subjects using the model were judged by two independent judges to be superior to names chosen in the original experiment by a ratio of two-to-one.

Using the model appears to encourage the use of more concepts and longer names.

--- https://arxiv.org/abs/2105.02124 Intrinsic Propensity for Vulnerability in Computers? Arbitrary Code Execution in the Universal Turing Machine Pontus Johnson 2021-04-22 2021-04-22 [("doi","10.48550/arXiv.2105.02124")] cs/computable cs/security

The universal Turing machine is generally considered to be the simplest, most abstract model of a computer.

This paper reports on the discovery of an accidental arbitrary code execution vulnerability in Marvin Minsky’s 1967 implementation of the universal Turing machine. By submitting crafted data, the machine may be coerced into executing user-provided code.

The article presents the discovered vulnerability in detail and discusses its potential implications. To the best of our knowledge, an arbitrary code execution vulnerability has not previously been reported for such a simple system.

--- https://arxiv.org/abs/2102.11245#facebook Silent Data Corruptions at Scale Harish Dattatraya Dixit, Sneha Pendharkar, Matt Beadon, Chris Mason, Tejasvi Chakravarthy, Bharath Muthiah, Sriram Sankar 2021-02-22 2021-03-20 [("doi","10.48550/arXiv.2102.11245")] cs/hardware

Silent Data Corruption (SDC) can have negative impact on large-scale infrastructure services. SDCs are not captured by error reporting mechanisms within a Central Processing Unit (CPU) and hence are not traceable at the hardware level. However, the data corruptions propagate across the stack and manifest as application-level problems. These types of errors can result in data loss and can require months of debug engineering time.

In this paper, we describe common defect types observed in silicon manufacturing that leads to SDCs. We discuss a real-world example of silent data corruption within a datacenter application. We provide the debug flow followed to root-cause and triage faulty instructions within a CPU using a case study, as an illustration on how to debug this class of errors.

We provide a high-level overview of the mitigations to reduce the risk of silent data corruptions within a large production fleet.

In our large-scale infrastructure, we have run a vast library of silent error test scenarios across hundreds of thousands of machines in our fleet. This has resulted in hundreds of CPUs detected for these errors, showing that SDCs are a systemic issue across generations.

We have monitored SDCs for a period longer than 18 months. Based on this experience, we determine that reducing silent data corruptions requires not only hardware resiliency and production detection mechanisms, but also robust fault-tolerant software architectures.

--- https://arxiv.org/abs/2104.06159 Muesli: Combining Improvements in Policy Optimization Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt 2021-04-13 2021-04-13 [("doi","10.48550/arXiv.2104.06159")] reinforcement-learning/model-free reinforcement-learning/model/muzero

We propose a novel policy update that combines regularized policy optimization with model learning as an auxiliary loss. The update (henceforth Muesli) matches MuZero’s state-of-the-art performance on Atari.

Notably, Muesli does so without using deep search: it acts directly with a policy network and has computation speed comparable to model-free baselines.

The Atari results are complemented by extensive ablations, and by additional results on continuous control and 9×9 Go.

--- https://arxiv.org/abs/2012.12235 Unadversarial Examples: Designing Objects for Robust Vision Hadi Salman, Andrew Ilyas, Logan Engstrom, Sai Vemprala, Aleksander Madry, Ashish Kapoor 2020-12-22 2021-03-20 [("doi","10.48550/arXiv.2012.12235")] ai/nn/adversarial reinforcement-learning/exploration/active-learning/data-pruning

We study a class of realistic computer vision settings wherein one can influence the design of the objects being recognized. We develop a framework that leverages this capability to improve vision models’ performance and robustness.

This framework exploits the sensitivity of modern machine learning algorithms to input perturbations in order to design “robust objects”, ie. objects that are explicitly optimized to be confidently detected or classified. We demonstrate the efficacy of the framework on a wide variety of vision-based tasks ranging from standard benchmarks, to (in-simulation) robotics, to real-world experiments.

Our code can be found at https://github.com/microsoft/unadversarial.

--- https://arxiv.org/abs/2012.14271 Towards Fully Automated Manga Translation Ryota Hinami, Shonosuke Ishiwatari, Kazuhiko Yasuda, Yusuke Matsui 2020-12-28 2021-03-21 [("doi","10.48550/arXiv.2012.14271")] ai/anime

We tackle the problem of machine translation of manga, Japanese comics. Manga translation involves two important problems in machine translation: context-aware and multimodal translation. Since text and images are mixed up in an unstructured fashion in Manga, obtaining context from the image is essential for manga translation. However, it is still an open problem how to extract context from image and integrate into MT models. In addition, corpus and benchmarks to train and evaluate such model is currently unavailable.

In this paper, we make the following four contributions that establishes the foundation of manga translation research. First, we propose multimodal context-aware translation framework. We are the first to incorporate context information obtained from manga image. It enables us to translate texts in speech bubbles that cannot be translated without using context information (eg. texts in other speech bubbles, gender of speakers, etc.). Second, for training the model, we propose the approach to automatic corpus construction from pairs of original manga and their translations, by which large parallel corpus can be constructed without any manual labeling. Third, we created a new benchmark to evaluate manga translation. Finally, on top of our proposed methods, we devised a first comprehensive system for fully automated manga translation.

--- https://arxiv.org/abs/2012.15723 Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao, Adam Fisch, Danqi Chen 2020-12-31 2021-03-21 [("doi","10.48550/arXiv.2012.15723")] ai/nn/transformer/gpt

The recent GPT-3 model (Brown et al 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context. Inspired by their findings, we study few-shot learning in a more practical scenario, where we use smaller language models for which fine-tuning is computationally efficient.

We present LM-BFF—better few-shot fine-tuning of language models—a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples. Our approach includes (1) prompt-based fine-tuning together with a novel pipeline for automating prompt generation; and (2) a refined strategy for dynamically and selectively incorporating demonstrations into each context.

Finally, we present a systematic evaluation for analyzing few-shot performance on a range of NLP tasks, including classification and regression. Our experiments demonstrate that our methods combine to dramatically outperform standard fine-tuning procedures in this low resource setting, achieving up to 30% absolute improvement, and 11% on average across all tasks.

Our approach makes minimal assumptions on task resources and domain expertise, and hence constitutes a strong task-agnostic method for few-shot learning.

--- https://arxiv.org/abs/2102.09690 Calibrate Before Use: Improving Few-Shot Performance of Language Models Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh 2021-02-19 2021-03-21 [("doi","10.48550/arXiv.2102.09690")] ai/nn/transformer/gpt/calibration

GPT-3 can perform numerous tasks when provided a natural language prompt that contains a few training examples. We show that this type of few-shot learning can be unstable: the choice of prompt format, training examples, and even the order of the training examples can cause accuracy to vary from near chance to near state-of-the-art.

We demonstrate that this instability arises from the bias of language models towards predicting certain answers, eg. those that are placed near the end of the prompt or are common in the pre-training data. To mitigate this, we first estimate the model’s bias towards each answer by asking for its prediction when given the training prompt and a content-free test input such as “N/A”. We then fit calibration parameters that cause the prediction for this input to be uniform across answers.

On a diverse set of tasks, this contextual calibration procedure substantially improves GPT-3 and GPT-2’s average accuracy (up to 30.0% absolute) and reduces variance across different choices of the prompt.

--- https://arxiv.org/abs/2102.06203 Proof Artifact Co-training for Theorem Proving with Language Models Jesse Michael Han, Jason Rute, Yuhuai Wu, Edward W. Ayers, Stanislas Polu 2021-02-11 2021-03-21 [("doi","10.48550/arXiv.2102.06203")] ai/nn/transformer/gpt math reinforcement-learning/exploration

Labeled data for imitation learning of theorem proving in large libraries of formalized mathematics is scarce as such libraries require years of concentrated effort by human specialists to be built. This is particularly challenging when applying large Transformer language models to tactic prediction, because the scaling of performance with respect to model size is quickly disrupted in the data-scarce, easily-overfitted regime.

We propose PACT (Proof Artifact Co-Training), a general methodology for extracting abundant self-supervised data from kernel-level proof terms for co-training alongside the usual tactic prediction objective. We apply this methodology to Lean, an interactive proof assistant which hosts some of the most sophisticated formalized mathematics to date.

We instrument Lean with a neural theorem prover driven by a Transformer language model and show that PACT improves theorem proving success rate on a held-out suite of test theorems 32% → 48%.

--- https://arxiv.org/abs/2012.07805 Extracting Training Data from Large Language Models Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, Colin Raffel 2020-12-14 2021-03-21 [("doi","10.48550/arXiv.2012.07805")] ai/nn/transformer/gpt/2

It has become common to publish large (billion parameter) language models that have been trained on private datasets. This paper demonstrates that in such settings, an adversary can perform a training data extraction attack to recover individual training examples by querying the language model.

We demonstrate our attack on GPT-2, a language model trained on scrapes of the public Internet, and are able to extract hundreds of verbatim text sequences from the model’s training data. These extracted examples include (public) personally identifiable information (names, phone numbers, and email addresses), IRC conversations, code, and 128-bit UUIDs. Our attack is possible even though each of the above sequences are included in just one document in the training data.

We comprehensively evaluate our extraction attack to understand the factors that contribute to its success. Worryingly, we find that larger models are more vulnerable than smaller models. We conclude by drawing lessons and discussing possible safeguards for training large language models.

--- https://arxiv.org/abs/2103.05327 BERTese: Learning to Speak to BERT Adi Haviv, Jonathan Berant, Amir Globerson 2021-03-09 2021-03-21 [("doi","10.48550/arXiv.2103.05327")] ai/nn

Large pre-trained language models have been shown to encode large amounts of world and commonsense knowledge in their parameters, leading to substantial interest in methods for extracting that knowledge.

In past work, knowledge was extracted by taking manually-authored queries and gathering paraphrases for them using a separate pipeline.

In this work, we propose a method for automatically rewriting queries into “BERTese”, a paraphrase query that is directly optimized towards better knowledge extraction. To encourage meaningful rewrites, we add auxiliary loss functions that encourage the query to correspond to actual language tokens.

We empirically show our approach outperforms competing baselines, obviating the need for complex pipelines. Moreover, BERTese provides some insight into the type of language that helps language models perform knowledge extraction.

--- https://arxiv.org/abs/2102.06701#deepmind Explaining Neural Scaling Laws Yasaman Bahri, Ethan Dyer, Jared Kaplan, Jaehoon Lee, Utkarsh Sharma 2021-02-12 2021-03-21 [("doi","10.48550/arXiv.2102.06701")] ai/nn/cnn ai/nn/fully-connected ai/scaling

[video] The test loss of well-trained neural networks often follows precise power-law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains and connects these scaling laws. We identify variance-limited and resolution-limited scaling behavior for both dataset and model size, for a total of four scaling regimes. The variance-limited scaling follows simply from the existence of a well-behaved infinite data or infinite width limit, while the resolution-limited regime can be explained by positing that models are effectively resolving a smooth data manifold. In the large width limit, this can be equivalently obtained from the spectrum of certain kernels, and we present evidence that large width and large dataset resolution-limited scaling exponents are related by a duality.

We exhibit all four scaling regimes in the controlled setting of large random feature and pretrained models and test the predictions empirically on a range of standard architectures and datasets.

We also observe several empirical relationships between datasets and scaling exponents: super-classing image tasks does not change exponents, while changing input distribution (via changing datasets or adding noise) has a strong effect. We further explore the effect of architecture aspect ratio on scaling exponents.

--- https://arxiv.org/abs/2101.11605#google Bottleneck Transformers for Visual Recognition Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani 2021-01-27 2021-03-21 [("doi","10.48550/arXiv.2101.11605")] ai/nn/transformer

We present BoTNet, a conceptually simple yet powerful backbone architecture that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation. By just replacing the spatial convolutions with global self-attention in the final 3 bottleneck blocks of a ResNet and no other changes, our approach improves upon the baselines on instance segmentation and object detection while also reducing the parameters, with minimal overhead in latency.

Through the design of BoTNet, we also point out how ResNet bottleneck blocks with self-attention can be viewed as Transformer blocks.

Without any bells and whistles, BoTNet achieves 44.4% Mask AP and 49.7% Box AP on the COCO Instance Segmentation benchmark using the Mask R-CNN framework; surpassing the previous best published single model and single scale results of ResNeSt evaluated on the COCO validation set.

Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84.7% top-1 accuracy on the ImageNet benchmark while being up to 1.64× faster in compute time than the popular EfficientNet models on TPU-v3 hardware. We hope our simple and effective approach will serve as a strong baseline for future research in self-attention models for vision.

--- https://arxiv.org/abs/2101.03164 E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, Boris Kozinsky 2021-01-08 2021-03-21 [("doi","10.48550/arXiv.2101.03164")] ai/nn/cnn science

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments.

The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to 3 orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets.

The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

--- https://arxiv.org/abs/2012.15832 Shortformer: Better Language Modeling using Shorter Inputs Ofir Press, Noah Smith, Mike Lewis 2020-12-31 2021-03-21 [("doi","10.48550/arXiv.2012.15832")] ai/nn/transformer/attention/recurrent

Increasing the input length has been a driver of progress in language modeling with transformers. We identify conditions where shorter inputs are not harmful, and achieve perplexity and efficiency improvements through two new methods that decrease input length.

First, we show that initially training a model on short subsequences before moving on to longer ones both reduces overall training time and, surprisingly, substantially improves perplexity. Second, we show how to improve the efficiency of recurrence methods in transformers, which let models condition on previously processed tokens when generating sequences that exceed the maximal length the transformer can handle at once. Existing methods require computationally expensive relative position embeddings; we introduce a simple alternative of adding absolute position embeddings to queries and keys instead of to word embeddings, which efficiently produces superior results. We show that these recurrent models also benefit from short input lengths.

Combining these techniques speeds up training by a factor of 1.65, reduces memory usage, and substantially improves perplexity on WikiText-103, without adding any parameters.

--- https://arxiv.org/abs/2012.14913 Transformer Feed-Forward Layers Are Key-Value Memories Mor Geva, Roei Schuster, Jonathan Berant, Omer Levy 2020-12-29 2021-03-21 [("doi","10.48550/arXiv.2012.14913")] ai/nn/fully-connected ai/nn/transformer/attention

Feed-forward layers constitute two-thirds of a transformer model’s parameters, yet their role in the network remains under-explored.

We show that feed-forward layers in transformer-based language models operate as key-value memories, where each key correlates with textual patterns in the training examples, and each value induces a distribution over the output vocabulary. Our experiments show that the learned patterns are human-interpretable, and that lower layers tend to capture shallow patterns, while upper layers learn more semantic ones.

The values complement the keys’ input patterns by inducing output distributions that concentrate probability mass on tokens likely to appear immediately after each pattern, particularly in the upper layers.

Finally, we demonstrate that the output of a feed-forward layer is a composition of its memories, which is subsequently refined throughout the model’s layers via residual connections to produce the final output distribution.

--- https://arxiv.org/abs/2012.04115 Generalization bounds for deep learning Guillermo Valle-Pérez, Ard A. Louis 2020-12-07 2021-03-22 [("doi","10.48550/arXiv.2012.04115")] ai/nn

Generalization in deep learning has been the topic of much recent theoretical and empirical research. Here we introduce desiderata for techniques that predict generalization errors for deep learning models in supervised learning. Such predictions should (1) scale correctly with data complexity; (2) scale correctly with training set size; (3) capture differences between architectures; (4) capture differences between optimization algorithms; (5) be quantitatively not too far from the true error (in particular, be non-vacuous); (6) be efficiently computable; and (7) be rigorous. We focus on generalization error upper bounds, and introduce a categorisation of bounds depending on assumptions on the algorithm and data. We review a wide range of existing approaches, from classical VC dimension to recent PAC-Bayesian bounds, commenting on how well they perform against the desiderata.

We next use a function-based picture to derive a marginal-likelihood PAC-Bayesian bound. This bound is, by one definition, optimal up to a multiplicative constant in the asymptotic limit of large training sets, as long as the learning curve follows a power law, which is typically found in practice for deep learning problems. Extensive empirical analysis demonstrates that our marginal-likelihood PAC-Bayes bound fulfills desiderata 1–3 and 5. The results for 6 and 7 are promising, but not yet fully conclusive, while only desideratum 4 is currently beyond the scope of our bound. Finally, we comment on why this function-based bound performs better than current parameter-based PAC-Bayes bounds.

--- https://arxiv.org/abs/2011.15091 Inductive Biases for Deep Learning of Higher-Level Cognition Anirudh Goyal, Yoshua Bengio 2020-11-30 2021-03-22 [("doi","10.48550/arXiv.2011.15091")] ai/nn/transformer/attention psychology/neuroscience reinforcement-learning/meta-learning/continual-learning reinforcement-learning/model

A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopedic list of heuristics). If that hypothesis was correct, we could more easily both understand our own intelligence and build intelligent machines. Just like in physics, the principles themselves would not be sufficient to predict the behavior of complex systems like brains, and substantial computation might be needed to simulate human-like intelligence. This hypothesis would suggest that studying the kind of inductive biases that humans and animals exploit could help both clarify these principles and provide inspiration for AI research and neuroscience theories.

Deep learning already exploits several key inductive biases, and this work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing. The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans’ abilities in terms of flexible out-of-distribution and systematic generalization, which is currently an area where a large gap exists between state-of-the-art machine learning and human intelligence.

--- https://arxiv.org/abs/2010.15327#google Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth Thao Nguyen, Maithra Raghu, Simon Kornblith 2020-10-29 2021-03-22 [("doi","10.48550/arXiv.2010.15327")] ai/nn

A key factor in the success of deep neural networks is the ability to scale models to improve performance by varying the architecture depth and width. This simple property of neural network design has resulted in highly effective architectures for a variety of tasks. Nevertheless, there is limited understanding of effects of depth and width on the learned representations. In this paper, we study this fundamental question.

We begin by investigating how varying depth and width affects model hidden representations, finding a characteristic block structure in the hidden representations of larger capacity (wider or deeper) models. We demonstrate that this block structure arises when model capacity is large relative to the size of the training set, and is indicative of the underlying layers preserving and propagating the dominant principal component of their representations. This discovery has important ramifications for features learned by different models, namely, representations outside the block structure are often similar across architectures with varying widths and depths, but the block structure is unique to each model.

We analyze the output predictions of different model architectures, finding that even when the overall accuracy is similar, wide and deep models exhibit distinctive error patterns and variations across classes.

--- https://arxiv.org/abs/2101.00390#facebook VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation Changhan Wang, Morgane Rivière, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux 2021-01-02 2021-03-22 [("doi","10.48550/arXiv.2101.00390")] ai/scaling

We introduce VoxPopuli, a large-scale multilingual corpus providing 100K hours of unlabeled speech data in 23 languages. It is the largest open data to date for unsupervised representation learning as well as semi-supervised learning.

VoxPopuli also contains 1.8K hours of transcribed speeches in 16 languages and their aligned oral interpretations into 5 other languages totaling 5.1K hours.

We provide speech recognition baselines and validate the versatility of VoxPopuli unlabeled data in semi-supervised learning under challenging out-of-domain settings.

We will release the corpus at https://github.com/facebookresearch/voxpopuli under an open license.

--- https://arxiv.org/abs/2101.00406 CDLM: Cross-Document Language Modeling Avi Caciularu, Arman Cohan, Iz Beltagy, Matthew E. Peters, Arie Cattan, Ido Dagan 2021-01-02 2021-03-22 [("doi","10.48550/arXiv.2101.00406")] ai/nn/transformer/attention/hierarchical ai/scaling

We introduce a new pretraining approach geared for multi-document language modeling, incorporating two key ideas into the masked language modeling self-supervised objective.

First, instead of considering documents in isolation, we pretrain over sets of multiple related documents, encouraging the model to learn cross-document relationships.

Second, we improve over recent long-range transformers by introducing dynamic global attention that has access to the entire input to predict masked tokens.

We release CDLM (Cross-Document Language Model), a new general language model for multi-document setting that can be easily applied to downstream tasks.

Our extensive analysis shows that both ideas are essential for the success of CDLM, and work in synergy to set new state-of-the-art results for several multi-text tasks.

Code and models are available at Github.

--- https://arxiv.org/abs/2101.00529#microsoft VinVL: Revisiting Visual Representations in Vision-Language Models Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, Jianfeng Gao 2021-01-02 2021-03-22 [("doi","10.48550/arXiv.2101.00529")] ai/scaling

This paper presents a detailed study of improving visual representations for vision language (VL) tasks and develops an improved object detection model to provide object-centric representations of images. Compared to the most widely used bottom-up and top-down model (anderson2018bottom), the new model is bigger, better-designed for VL tasks, and pre-trained on much larger training corpora that combine multiple public annotated object detection datasets. Therefore, it can generate representations of a richer collection of visual objects and concepts.

While previous VL research focuses mainly on improving the vision-language fusion model and leaves the object detection model improvement untouched, we show that visual features matter in VL models. In our experiments we feed the visual features generated by the new object detection model into a Transformer-based VL fusion model (li2020oscar), anduse an improved approach to pre-train the VL model and fine-tune it on a wide range of downstream VL tasks.

Our results show that the new visual features improve the performance across all VL tasks, creating new state-of-the-art results on 7 public benchmarks.

We will release the new object detection model to public.

--- https://arxiv.org/abs/2011.10650#openai Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images Rewon Child 2020-11-20 2021-03-22 [("doi","10.48550/arXiv.2011.10650")] ai/nn/cnn ai/nn/vae ai/scaling

We present a hierarchical VAE that, for the first time, generates samples quickly while outperforming the PixelCNN in log-likelihood on all natural image benchmarks.

We begin by observing that, in theory, VAEs can actually represent autoregressive models, as well as faster, better models if they exist, when made sufficiently deep. Despite this, autoregressive models have historically outperformed VAEs in log-likelihood.

We test if insufficient depth explains why by scaling a VAE to greater stochastic depth than previously explored and evaluating it on CIFAR-10, ImageNet, and FFHQ.

In comparison to PixelCNN, these very deep VAEs achieve higher likelihoods, use fewer parameters, generate samples thousands of times faster, and are more easily applied to high-resolution images.

Qualitative studies suggest this is because the VAE learns efficient hierarchical visual representations.

We release our source code and models at Github.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8597997/ The lungfish cocoon is a living tissue with antimicrobial functions Ryan Darby Heimroth, Elisa Casadei, Ottavia Benedicenti, Chris Tsuyoshi Amemiya, Pilar Muñoz, Irene Salinas 2021 2021-03-22 [("doi","10.1126/sciadv.abj0829")] biology genetics/microbiome

Terrestrialization is an extreme physiological adaptation by which African lungfish survive dry seasons.

For months and up to several years, lungfish live inside a dry mucus cocoon that protects them from desiccation. Light and electron microscopy reveal that the lungfish cocoon is a living tissue that traps bacteria.

Transcriptomic analyses identify a global state of inflammation in the terrestrialized lungfish skin characterized by granulocyte recruitment. Recruited granulocytes transmigrate into the cocoon where they release extracellular traps.

In vivo DNase I surface spraying during terrestrialization results in dysbiosis, septicemia, skin wounds, and hemorrhages. Thus, lungfish have evolved unique immunological adaptations to protect their bodies from infection for extended periods of time while living on land.

Trapping bacteria outside their bodies may benefit estivating vertebrates that undergo metabolic torpor.

--- https://www.biorxiv.org/content/10.1101/2021.02.10.430571.full Pathfinder: A gamified measure to integrate general cognitive ability into the biological, medical and behavioral sciences Margherita Malanchini, Kaili Rimfeld, Agnieszka Gidziela, Rosa Cheesman, Andrea G. Allegrini, Nicholas Shakeshaft, Kerry Schofield, Amy Packer, Rachel Ogden, Andrew McMillan, Stuart J. Ritchie, Philip S. Dale, Thalia C. Eley, Sophie von Stumm, Robert Plomin 2021-02-10 2021-03-22 [("doi","10.1101/2021.02.10.430571")] genetics/heritable iq

Genome-wide association (GWA) studies have uncovered DNA variants associated with individual differences in general cognitive ability (g), but these are far from capturing heritability estimates obtained from twin studies. A major barrier is measurement heterogeneity.

In a series of four studies, we created a 15-minute, online, gamified measure of g that is highly reliable, psychometrically valid and scalable. In a fifth study, we administered this measure to 4,751 young adults from the Twins Early Development Study. This novel g measure, which also yields verbal and nonverbal scores, showed substantial twin heritability (57%) and SNP heritability (37%).

A polygenic score computed from GWA studies of five cognitive and educational traits accounted for 12% of the variation in g, the strongest DNA-based prediction of g to date. Widespread use of this engaging new measure will advance research not only in genomics but throughout the biological, medical and behavioral sciences.

--- https://www.medrxiv.org/content/10.1101/2020.11.20.20235275.full Largest GWAS (n = 1,126,563) of Alzheimer’s Disease Implicates Microglia and Immune Cells Douglas P. Wightman, Iris E. Jansen, Jeanne E. Savage, Alexey A. Shadrin, Shahram Bahrami, Arvid Rongve, Sigrid Børte, Bendik S. Winsvold, Ole Kristian Drange, Amy E. Martinsen, Anne Heidi Skogholt, Cristen Jennifer Willer, Geir Bråthen, Ingunn Bosnes, Jonas Bille Nielsen, Lars Fritsche, Laurent F. Thomas, Linda M. Pedersen, Maiken E. Gabrielsen, Marianne Bakke Johnsen, Tore Wergel, Meisingset, Wei Zhou, Petra Proitsi, Angela Hodges, Richard Dobson, Latha Velayudhan, 23andMe, Julia M. Sealock, Lea K. Davis, Nancy L. Pedersen, Chandra A. Reynolds, Ida K. Karlsson, Sigurdur Magnusson, Hreinn Stefansson, Steinunn Thordardottir, Palmi V. Jonsson, Jon Snaedal, Anna Zettergren, Ingmar Skoog, Silke Kern, Margda Waern, Henrik Zetterberg, Kaj Blennow, Eystein Stordal, Kristian Hveem, John-Anker Zwart, Lavinia Athanasiu, Ingvild Saltvedt, Sigrid B. Sando, Ingun Ulstein, Srdjan Djurovic, Tormod Fladby, Dag Aarsland, Geir Selbæk, Stephan Ripke, Kari Stefansson, Ole A. Andreassen, Danielle Posthuma 2020-11-23 2021-03-22 [("doi","10.1101/2020.11.20.20235275")] genetics/heritable psychiatry/alzheimers

Late-onset Alzheimer’s disease is a prevalent age-related polygenic disease that accounts for 50–70% of dementia cases1. Late-onset Alzheimer’s disease is caused by a combination of many genetic variants with small effect sizes and environmental influences. Currently, only a fraction of the genetic variants underlying Alzheimer’s disease have been identified2,3.

Here we show that increased sample sizes allowed for identification of 7 novel genetic loci contributing to Alzheimer’s disease. We highlighted 8 potentially causal genes where gene expression changes are likely to explain the association. Human microglia were found as the only cell type where the gene expression pattern was statistically-significantly associated with the Alzheimer’s disease association signal. Gene set analysis identified 4 independent pathways for associated variants to influence disease pathology.

Our results support the importance of microglia, amyloid and tau aggregation, and immune response in Alzheimer’s disease. We anticipate that through collaboration the results from this study can be included in larger meta-analyses of Alzheimer’s disease to identify further genetic variants which contribute to Alzheimer’s pathology. Furthermore, the increased understanding of the mechanisms that mediate the effect of genetic variants on disease progression will help identify potential pathways and gene-sets as targets for drug development.

--- https://www.biorxiv.org/content/10.1101/2020.11.09.373704.full An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction Xuan Zhou, S. Hong Lee 2020-11-10 2021-03-23 [("doi","10.1101/2020.11.09.373704")] genetics/heritable genetics/sequencing

Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses.

Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (eg. BMI & height for n ~ 40,000) with a small fraction of the exposome that comprises 28 lifestyle factors.

The joint model of the genome and exposome explains substantially more phenotypic variance and improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome-exposome (gxe) and exposome-exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson’s correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome & exposome).

In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modeling these effects has a great potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.

--- https://arxiv.org/abs/2102.09756 TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning Minchao Wu, Michael Norrish, Christian Walder, Amir Dezfouli 2021-02-19 2021-03-23 [("doi","10.48550/arXiv.2102.09756")] math reinforcement-learning/exploration

We propose a novel approach to interactive theorem-proving (ITP) using deep reinforcement learning. The proposed framework is able to learn proof search strategies as well as tactic and arguments prediction in an end-to-end manner.

We formulate the process of ITP as a Markov decision process (MDP) in which each state represents a set of potential derivation paths. This structure allows us to introduce a novel backtracking mechanism which enables the agent to efficiently discard (predicted) dead-end derivations and restart from promising alternatives. We implement the framework in the HOL4 theorem prover.

Experimental results show that the framework outperforms existing automated theorem provers (ie. hammers) available in HOL4 when evaluated on unseen problems. We further elaborate the role of key components of the framework using ablation studies.

--- https://www.biorxiv.org/content/10.1101/2020.12.03.409813.full Remembering immunity: Neuronal ensembles in the insular cortex encode and retrieve specific immune responses Tamar Koren, Maria Krot, Nadia T. Boshnak, Mariam Amer, Tamar Ben-Shaanan, Hilla Azulay-Debby, Haitham Hajjo, Eden Avishai, Maya Schiller, Hedva Haykin, Ben Korin, Dorit Cohen-Farfara, Fahed Hakim, Kobi Rosenblum, Asya Rolls 2020-12-04 2021-03-23 [("doi","10.1101/2020.12.03.409813")] biology psychology/neuroscience

Increasing evidence indicates that the brain regulates peripheral immunity. Yet, it remains unclear whether and how the brain represents the state of the immune system.

Here, we show that immune-related information is stored in the brain’s insular cortex (InsCtx). Using activity-dependent cell labeling in mice (FosTRAP*), we captured neuronal ensembles in the InsCtx that were active under two different inflammatory conditions (DSS-induced colitis and Zymosan-induced peritonitis).

Chemogenetic reactivation of these neuronal ensembles was sufficient to broadly retrieve the inflammatory state under which these neurons were captured.

Thus, we show that the brain can encode and initiate specific immune responses, extending the classical concept of immunological memory to neuronal representations of immunity.

--- https://arxiv.org/abs/2102.04353 Unlocking Pixels for Reinforcement Learning via Implicit Attention Krzysztof Marcin Choromanski, Deepali Jain, Wenhao Yu, Xingyou Song, Jack Parker-Holder, Tingnan Zhang, Valerii Likhosherstov, Aldo Pacchiano, Anirban Santara, Yunhao Tang, Jie Tan, Adrian Weller 2021-02-08 2021-03-23 [("doi","10.48550/arXiv.2102.04353")] ai/nn/transformer/attention reinforcement-learning/robot

There has recently been interest in training reinforcement learning (RL) agents in vision-based environments. This poses many challenges, such as high dimensionality and the potential for observational overfitting through spurious correlations. A promising approach to solve both of these problems is an attention bottleneck, which provides a simple and effective framework for learning high performing policies, even in the presence of distractions. However, due to poor scalability of attention architectures, these methods cannot be applied beyond low resolution visual inputs, using large patches (thus small attention matrices).

In this paper we make use of new efficient attention algorithms, recently shown to be highly effective for Transformers, and demonstrate that these techniques can be successfully adopted for the RL setting. This allows our attention-based controllers to scale to larger visual inputs, and facilitate the use of smaller patches, even individual pixels, improving generalization. We show this on a range of tasks from the Distracting Control Suite to vision-based quadruped robots locomotion. We provide rigorous theoretical analysis of the proposed algorithm.

--- https://www.biorxiv.org/content/10.1101/2020.12.10.419424.full How accurate are citations of frequently cited papers in biomedical literature? V Pavlovic, T. Weissgerber, D. Stanisavljevic, T. Pekmezovic, V. Garovic, N. Milic 2020-12-10 2021-03-23 [("doi","10.1101/2020.12.10.419424")] statistics/bias/publication/miscitation

Citations are an important, but often overlooked, part of every scientific paper. They allow the reader to trace the flow of evidence, serving as a gateway to relevant literature. Most scientists are aware of citations errors, but few appreciate the prevalence or consequences of these problems. The purpose of this study was to examine how often frequently cited papers in biomedical scientific literature are cited inaccurately.

The study included an active participation of first authors of frequently cited papers; to first-hand verify the citations accuracy. The approach was to determine most cited original articles and their parent authors, that could be able to access, and identify, collect and review all citations of their original work. Findings from feasibility study, where we collected and reviewed 1,540 articles containing 2,526 citations of 14 most cited articles in which the 1st authors were affiliated with the Faculty of Medicine University of Belgrade, were further evaluated for external confirmation in an independent verification set of articles. Verification set included 4,912 citations identified in 2,995 articles that cited 13 most cited articles published by authors affiliated with the Mayo Clinic Division of Nephrology and Hypertension (Rochester, Minnesota, USA), whose research focus is hypertension and peripheral vascular disease. Most cited articles and their citations were determined according to SCOPUS database search. A citation was defined as being accurate if the cited article supported or was in accordance with the statement by citing authors.

A multilevel regression model for binary data was used to determine predictors of inaccurate citations. At least one inaccurate citation was found in 11% and 15% of articles in the feasibility study and verification set, respectively, suggesting that inaccurate citations are common in biomedical literature. The main findings were similar in both sets. The most common problem was the citation of nonexistent findings (38.4%), followed by an incorrect interpretation of findings (15.4%). One fifth of inaccurate citations were due to “chains of inaccurate citations”, in which inaccurate citations appeared to have been copied from previous papers.

Reviews, longer time elapsed from publication to citation, and multiple citations were associated with a higher chance of a citation being inaccurate. Based on these findings, several actions that authors, mentors, and journals can take to reduce citation inaccuracies and maintain the integrity of the scientific literature have been proposed.

--- https://arxiv.org/abs/2011.02677 The causal foundations of applied probability and statistics Sander Greenland 2020-11-05 2021-03-23 [("doi","10.48550/arXiv.2011.02677")] statistics/causality statistics/decision

Statistical science involves far more than probability theory, for it requires realistic causal models of data generators—even for purely descriptive goals. Statistical decision theory requires more causality: Rational decisions are actions taken to minimize costs while maximizing benefits, and thus require explication of causes of loss and gain. Competent statistical practice thus integrates logic, context, and probability into scientific inference and decision using narratives filled with causality.

This reality was seen and accounted for intuitively by the founders of modern statistics, but was not well recognized in the ensuing statistical theory (which focused instead on the causally inert properties of probability measures). Nonetheless, both statistical foundations and basic statistics can and should be taught using formal causal models.

The causal view of statistical science fits within a broader information-processing framework which illuminates and unifies frequentist, Bayesian, and related probability-based foundations of statistics. Causality theory can thus be seen as a key component connecting computation to contextual information, not extra-statistical but instead essential for sound statistical training and applications.

--- https://arxiv.org/abs/2012.06884 AIR-FI: Generating Covert Wi-Fi Signals from Air-Gapped Computers Mordechai Guri 2020-12-12 2021-03-23 [("doi","10.48550/arXiv.2012.06884")] cs/security technology

In this paper, we show that attackers can exfiltrate data from air-gapped computers via Wi-Fi signals.

Malware in a compromised air-gapped computer can generate signals in the Wi-Fi frequency bands. The signals are generated through the memory buses—no special hardware is required. Sensitive data can be modulated and secretly exfiltrated on top of the signals.

We show that nearby Wi-Fi capable devices (eg. smartphones, laptops, IoT devices) can intercept these signals, decode them, and send them to the attacker over the Internet. To extract the signals, we use the physical layer information exposed by the Wi-Fi chips. We implement the transmitter and receiver and discuss design considerations and implementation details.

We evaluate this covert channel in terms of bandwidth and distance and present a set of countermeasures. Our evaluation shows that data can be exfiltrated from air-gapped computers to nearby Wi-Fi receivers located a distance of several meters away.

--- https://arxiv.org/abs/2010.00840#nvidia MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Raul Puri, Pascale Fung, Anima Anandkumar, Bryan Catanzaro 2020-10-02 2021-03-23 [("doi","10.48550/arXiv.2010.00840")] ai/nn/sampling ai/nn/transformer/gpt/fiction

Existing pre-trained large language models have shown unparalleled generative capabilities. However, they are not controllable. In this paper, we propose MEGATRON-CNTRL, a novel framework that uses large-scale language models and adds control to text generation by incorporating an external knowledge base.

Our framework consists of a keyword predictor, a knowledge retriever, a contextual knowledge ranker, and a conditional text generator. As we do not have access to ground-truth supervision for the knowledge ranker, we make use of weak supervision from sentence embedding.

The empirical results show that our model generates more fluent, consistent, and coherent stories with less repetition and higher diversity compared to prior work on the ROC story dataset. We showcase the controllability of our model by replacing the keywords used to generate stories and re-running the generation process.

Human evaluation results show that 77.5% of these stories are successfully controlled by the new keywords. Furthermore, by scaling our model from 124 million to 8.3 billion parameters we demonstrate that larger models improve both the quality of generation (74.5% → 93.0% for consistency) and controllability (77.5% → 91.5%).

--- https://arxiv.org/abs/2009.07243 A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation Moin Nadeem, Tianxing He, Kyunghyun Cho, James Glass 2020-09-15 2021-03-23 [("doi","10.48550/arXiv.2009.07243")] ai/nn/sampling ai/nn/transformer/gpt

This work studies the widely adopted ancestral sampling algorithms for auto-regressive language models, which is not widely studied in the literature. We use the quality-diversity (Q-D) trade-off to investigate 3 popular sampling algorithms (top-k, nucleus and tempered sampling). We focus on the task of open-ended language generation.

We first show that the existing sampling algorithms have similar performance. After carefully inspecting the transformations defined by different sampling algorithms, we identify 3 key properties that are shared among them: entropy reduction, order preservation, and slope preservation.

To validate the importance of the identified properties, we design two sets of new sampling algorithms: one set in which each algorithm satisfies all 3 properties, and one set in which each algorithm violates at least one of the properties. We compare their performance with existing sampling algorithms, and find that violating the identified properties could lead to drastic performance degradation, as measured by the Q-D trade-off. On the other hand, we find that the set of sampling algorithms that satisfies these properties performs on par with the existing sampling algorithms.

Our data and code are available at https://github.com/moinnadeem/characterizing-sampling-algorithms.

--- https://arxiv.org/abs/2009.06807 The Radicalization Risks of GPT-3 and Advanced Neural Language Models Kris McGuffie, Alex Newhouse 2020-09-15 2021-03-23 [("doi","10.48550/arXiv.2009.06807")] ai/nn/transformer/gpt/non-fiction reinforcement-learning/safe sociology/technology

In this paper, we expand on our previous research of the potential for abuse of generative language models by assessing GPT-3. Experimenting with prompts representative of different types of extremist narrative, structures of social interaction, and radical ideologies, we find that GPT-3 demonstrates improvement over its predecessor, GPT-2, in generating extremist texts.

We also show GPT-3’s strength in generating text that accurately emulates interactive, informational, and influential content that could beused for radicalizing individuals into violent far-right extremist ideologies and behaviors.

While OpenAI’s preventative measures are strong, the possibility of unregulated copycat technology represents large risk for large-scale online radicalization and recruitment; thus, in the absence of safeguards, successful and efficient weaponization that requires little experimentation is likely. AI stakeholders, the policymaking community, and governments should begin investing as soon as possible in building social norms, public policy, and educational initiatives to preempt an influx of machine-generated disinformation and propaganda.

Mitigation will require effective policy and partnerships across industry, government, and civil society.

--- https://arxiv.org/abs/2009.01325#openai Learning to summarize from human feedback Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, Paul Christiano 2020-09-02 2021-03-23 [("doi","10.48550/arXiv.2009.01325")] ai/nn/transformer/gpt reinforcement-learning/preference-learning reinforcement-learning/scaling

As language models become more powerful, training and evaluation are increasingly bottlenecked by the data and metrics used for a particular task. For example, summarization models are often trained to predict human reference summaries and evaluated using ROUGE, but both of these metrics are rough proxies for what we really care about—summary quality.

In this work, we show that it is possible to improve summary quality by training a model to optimize for human preferences. We collect a large, high-quality dataset of human comparisons between summaries, train a model to predict the human-preferred summary, and use that model as a reward function to fine-tune a summarization policy using reinforcement learning. We apply our method to a version of the TL;DR dataset of Reddit posts and find that our models outperform both human reference summaries and much larger models fine-tuned with supervised learning alone.

Our models also transfer to CNN/DM news articles, producing summaries nearly as good as the human reference without any news-specific fine-tuning. We conduct extensive analyses to understand our human feedback dataset and fine-tuned models We establish that our reward model generalizes to new datasets, and that optimizing our reward model results in better summaries than optimizing ROUGE according to humans.

We hope the evidence from our paper motivates machine learning researchers to pay closer attention to how their training loss affects the model behavior they actually want.

--- https://arxiv.org/abs/2008.13533#google Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study Dara Bahri, Yi Tay, Che Zheng, Donald Metzler, Cliff Brunk, Andrew Tomkins 2020-08-17 2021-03-24 [("doi","10.48550/arXiv.2008.13533")] ai/nn/transformer/gpt/2 ai/scaling reinforcement-learning/exploration/active-learning/data-pruning

Large generative language models such as GPT-2 are well-known for their ability to generate text as well as their utility in supervised downstream tasks via fine-tuning.

Our work is twofold: firstly we demonstrate via human evaluation that classifiers trained to discriminate between human and machine-generated text emerge as unsupervised predictors of “page quality”, able to detect low quality content without any training. This enables fast bootstrapping of quality indicators in a low-resource setting.

Secondly, curious to understand the prevalence and nature of low quality pages in the wild, we conduct extensive qualitative and quantitative analysis over 500 million web articles, making this the largest-scale study ever conducted on the topic.

--- https://arxiv.org/abs/2008.07027 Adding Recurrence to Pretrained Transformers for Improved Efficiency and Context Size Davis Yoshida, Allyson Ettinger, Kevin Gimpel 2020-08-16 2021-03-24 [("doi","10.48550/arXiv.2008.07027")] ai/nn/rnn ai/nn/transformer/attention/hierarchical ai/nn/transformer/gpt/2

Fine-tuning a pretrained transformer for a downstream task has become a standard method in NLP in the last few years. While the results from these models are impressive, applying them can be extremely computationally expensive, as is pretraining new models with the latest architectures.

We present a novel method for applying pretrained transformer language models which lowers their memory requirement both at training and inference time. An additional benefit is that our method removes the fixed context size constraint that most transformer models have, allowing for more flexible use.

When applied to the GPT-2 language model, we find that our method attains better perplexity than an unmodified GPT-2 model on the PG-19 and WikiText-103 corpora, for a given amount of computation or memory.

--- https://arxiv.org/abs/2009.07118 It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners Timo Schick, Hinrich Schütze 2020-09-15 2021-03-24 [("doi","10.48550/arXiv.2009.07118")] ai/nn/transformer

When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown et al 2020) achieve remarkable few-shot performance.

However, enormous amounts of compute are required for training and applying such big models, resulting in a large carbon footprint and making it difficult for researchers and practitioners to use them.

We show that performance similar to GPT-3 can be obtained with language models that are much “greener” in that their parameter count is several orders of magnitude smaller. This is achieved by converting textual inputs into cloze questions that contain a task description, combined with gradient-based optimization; exploiting unlabeled data gives further improvements.

We identify key factors required for successful natural language understanding with small language models.

--- https://arxiv.org/abs/2009.06097#microsoft Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding Shuohang Wang, Luowei Zhou, Zhe Gan, Yen-Chun Chen, Yuwei Fang, Siqi Sun, Yu Cheng, Jingjing Liu 2020-09-13 2021-03-24 [("doi","10.48550/arXiv.2009.06097")] ai/nn/transformer/attention/sparsity

Transformer has become ubiquitous in the deep learning field. One of the key ingredients that destined its success is the self-attention mechanism, which allows fully-connected contextual encoding over input tokens. However, despite its effectiveness in modeling short sequences, self-attention suffers when handling inputs with extreme long-range dependencies, as its complexity grows quadratically with respect to the sequence length. Therefore, long sequences are often encoded by Transformer in chunks using a sliding window.

In this paper, we propose Cluster-Former, a novel clustering-based sparse Transformer to perform attention across chunked sequences. The proposed framework is pivoted on two unique types of Transformer layer: Sliding-Window Layer and Cluster-Former Layer, which encode local sequence information and global context jointly and iteratively. This new design allows information integration beyond local windows, which is especially beneficial for question answering (QA) tasks that rely on long-range dependencies. Experiments show that Cluster-Former achieves state-of-the-art performance on several major QA benchmarks.

--- https://arxiv.org/abs/2008.06996 Large Associative Memory Problem in Neurobiology and Machine Learning Dmitry Krotov, John Hopfield 2020-08-16 2021-03-24 [("doi","10.48550/arXiv.2008.06996")] ai/nn psychology/neuroscience

Dense Associative Memories or modern Hopfield networks permit storage and reliable retrieval of an exponentially large (in the dimension of feature space) number of memories. At the same time, their naive implementation is non-biological, since it seemingly requires the existence of many-body synaptic junctions between the neurons. We show that these models are effective descriptions of a more microscopic (written in terms of biological degrees of freedom) theory that has additional (hidden) neurons and only requires two-body interactions between them. For this reason, our proposed microscopic theory is a valid model of large associative memory with a degree of biological plausibility.

The dynamics of our network and its reduced dimensional equivalent both minimize energy (Lyapunov) functions. When certain dynamical variables (hidden neurons) are integrated out from our microscopic theory, one can recover many of the models that were previously discussed in the literature, eg. the model presented in “Hopfield Networks is All You Need” paper.

We also provide an alternative derivation of the energy function and the update rule proposed in the aforementioned paper and clarify the relationships between various models of this class.

--- https://arxiv.org/abs/2007.13505 Modern Hopfield Networks and Attention for Immune Repertoire Classification Michael Widrich, Bernhard Schäfl, Hubert Ramsauer, Milena Pavlović, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Günter Klambauer 2020-07-16 2021-03-24 [("doi","10.48550/arXiv.2007.13505")] ai/nn/transformer biology

A central mechanism in machine learning is to identify, store, and recognize patterns. How to learn, access, and retrieve such patterns is crucial in Hopfield networks and the more recent transformer architectures. We show that the attention mechanism of transformer architectures is actually the update rule of modern Hopfield networks that can store exponentially many patterns.

We exploit this high storage capacity of modern Hopfield networks to solve a challenging multiple instance learning (MIL) problem in computational biology: immune repertoire classification. Accurate and interpretable machine learning methods solving this problem could pave the way towards new vaccines and therapies, which is currently a very relevant research topic intensified by the COVID-19 crisis. Immune repertoire classification based on the vast number of immunosequences of an individual is a MIL problem with an unprecedentedly massive number of instances, two orders of magnitude larger than currently considered problems, and with an extremely low witness rate.

In this work, we present our novel method DeepRC that integrates transformer-like attention, or equivalently modern Hopfield networks, into deep learning architectures for massive MIL such as immune repertoire classification. We demonstrate that DeepRC outperforms all other methods with respect to predictive performance on large-scale experiments, including simulated and real-world virus infection data, and enables the extraction of sequence motifs that are connected to a given disease class.

Source code and datasets: https://github.com/ml-jku/DeepRC.

--- https://arxiv.org/abs/2007.07779 AdapterHub: A Framework for Adapting Transformers Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulić, Sebastian Ruder, Kyunghyun Cho, Iryna Gurevych 2020-07-15 2021-03-24 [("doi","10.48550/arXiv.2007.07779")] ai/nn

The current modus operandi in NLP involves downloading and fine-tuning pre-trained models consisting of millions or billions of parameters. Storing and sharing such large trained models is expensive, slow, and time-consuming, which impedes progress towards more general and versatile NLP methods that learn from and for many tasks. Adapters—small learnt bottleneck layers inserted within each layer of a pre-trained model—ameliorate this issue by avoiding full fine-tuning of the entire model. However, sharing and integrating adapter layers is not straightforward.

We propose AdapterHub, a framework that allows dynamic “stitching-in” of pre-trained adapters for different tasks and languages. The framework, built on top of the popular HuggingFace Transformers library, enables extremely easy and quick adaptations of state-of-the-art pre-trained models (eg. BERT, RoBERTa, XLM-R) across tasks and languages. Downloading, sharing, and training adapters is as seamless as possible using minimal changes to the training scripts and a specialized infrastructure.

Our framework enables scalable and easy access to sharing of task-specific models, particularly in low-resource scenarios. AdapterHub includes all recent adapter architectures and can be found at https://AdapterHub.ml/.

--- https://arxiv.org/abs/2007.00810#google On Linear Identifiability of Learned Representations Geoffrey Roeder, Luke Metz, Diederik P. Kingma 2020-07-01 2021-03-24 [("doi","10.48550/arXiv.2007.00810")] ai/nn/sparsity

Identifiability is a desirable property of a statistical model: it implies that the true model parameters may be estimated to any desired precision, given sufficient computational resources and data. We study identifiability in the context of representation learning: discovering nonlinear data representations that are optimal with respect to some downstream task.

When parameterized as deep neural networks, such representation functions typically lack identifiability in parameter space, because they are overparameterized by design. In this paper, building on recent advances in nonlinear ICA, we aim to rehabilitate identifiability by showing that a large family of discriminative models are in fact identifiable in function space, up to a linear indeterminacy. Many models for representation learning in a wide variety of domains have been identifiable in this sense, including text, images and audio, state-of-the-art at time of publication.

We derive sufficient conditions for linear identifiability and provide empirical support for the result on both simulated and real-world data.

--- https://arxiv.org/abs/2009.12583#deepmind Small Data, Big Decisions: Model Selection in the Small-Data Regime Jorg Bornschein, Francesco Visin, Simon Osindero 2020-09-26 2021-03-24 [("doi","10.48550/arXiv.2009.12583")] ai/scaling

Highly overparametrized neural networks can display curiously strong generalization performance—a phenomenon that has recently garnered a wealth of theoretical and empirical research in order to better understand it.

In contrast to most previous work, which typically considers the performance as a function of the model size, in this paper we empirically study the generalization performance as the size of the training set varies over multiple orders of magnitude.

These systematic experiments lead to some interesting and potentially very useful observations; perhaps most notably that training on smaller subsets of the data can lead to more reliable model selection decisions whilst simultaneously enjoying smaller computational costs.

Our experiments furthermore allow us to estimate Minimum Description Lengths for common datasets given modern neural network architectures, thereby paving the way for principled model selection taking into account Occam’s razor.

--- https://arxiv.org/abs/2007.06225 ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rihawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, Debsindhu Bhowmik, Burkhard Rost 2020-07-13 2021-03-24 [("doi","10.48550/arXiv.2007.06225")] ai/nn/transformer/t5 ai/scaling biology

Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models taken from NLP. These LMs reach for new prediction frontiers at low inference costs.

Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four autoencoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The LMs were trained on the Summit supercomputer using 5,616 GPUs and TPU Pods up-to 1,024 cores.

Dimensionality reduction revealed that the raw protein LM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks. The first was a per-residue prediction of protein secondary structure (3-state accuracy Q3=81%–87%); the second were per-protein predictions of protein sub-cellular localization (ten-state accuracy: Q10=81%) and membrane vs. water-soluble (2-state accuracy Q2=91%). For the per-residue predictions the transfer of the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without using evolutionary information thereby bypassing expensive database searches.

Taken together, the results implied that protein LMs learned some of the grammar of the language of life.

To facilitate future work, we released our models at Github.

--- https://arxiv.org/abs/2007.03898#nvidia NVAE: A Deep Hierarchical Variational Autoencoder Arash Vahdat, Jan Kautz 2020-07-08 2021-03-24 [("doi","10.48550/arXiv.2007.03898")] ai/nn/cnn ai/nn/vae ai/scaling

Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep energy-based models are among competing likelihood-based frameworks for deep generative learning. Among them, VAEs have the advantage of fast and tractable sampling and easy-to-access encoding networks. However, they are currently outperformed by other models such as normalizing flows and autoregressive models. While the majority of the research in VAEs is focused on the statistical challenges, we explore the orthogonal direction of carefully designing neural architectures for hierarchical VAEs.

We propose Nouveau VAE (NVAE), a deep hierarchical VAE built for image generation using depth-wise separable convolutions and batch normalization. NVAE is equipped with a residual parameterization of Normal distributions and its training is stabilized by spectral regularization.

We show that NVAE achieves state-of-the-art results among non-autoregressive likelihood-based models on the MNIST, CIFAR-10, CelebA 64, and CelebA HQ datasets and it provides a strong baseline on FFHQ. For example, on CIFAR-10, NVAE pushes the state-of-the-art 2.98 → 2.91 bits per dimension, and it produces high-quality images on CelebA HQ. To the best of our knowledge, NVAE is the first successful VAE applied to natural images as large as 256×256 pixels.

The source code is available at Github.

--- https://arxiv.org/abs/2007.00644 Measuring Robustness to Natural Distribution Shifts in Image Classification Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt 2020-07-01 2021-03-25 [("doi","10.48550/arXiv.2007.00644")] ai/scaling

We study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets. Most research on robustness focuses on synthetic image perturbations (noise, simulated weather artifacts, adversarial examples, etc.), which leaves open how robustness on synthetic distribution shift relates to distribution shift arising in real data.

Informed by an evaluation of 204 ImageNet models in 213 different test conditions, we find that there is often little to no transfer of robustness from current synthetic to natural distribution shift. Moreover, most current techniques provide no robustness to the natural distribution shifts in our testbed. The main exception is training on larger and more diverse datasets, which in multiple cases increases robustness, but is still far from closing the performance gaps.

Our results indicate that distribution shifts arising in real data are currently an open research problem. We provide our testbed and data as a resource for future work at https://modestyachts.github.io/imagenet-testbed/.

--- https://arxiv.org/abs/2007.05558 The Computational Limits of Deep Learning Neil C. Thompson, Kristjan Greenewald, Keeheon Lee, Gabriel F. Manso 2020-07-10 2021-03-25 [("doi","10.48550/arXiv.2007.05558")] ai/scaling/hardware

Deep learning’s recent history has been one of achievement: from triumphing over humans in the game of Go to world-leading performance in image recognition, voice recognition, translation, and other tasks.

But this progress has come with a voracious appetite for computing power. This article reports on the computational demands of deep learning applications in 5 prominent application areas and shows that progress in all 5 is strongly reliant on increases in computing power.

Extrapolating forward this reliance reveals that progress along current lines is rapidly becoming economically, technically, and environmentally unsustainable.

Thus, continued progress in these applications will require dramatically more computationally-efficient methods, which will either have to come from changes to deep learning or from moving to other machine learning methods.

--- https://arxiv.org/abs/2009.13569 Engineering In-place (Shared-memory) Sorting Algorithms Michael Axtmann, Sascha Witt, Daniel Ferizovic, Peter Sanders 2020-09-28 2021-03-25 [("doi","10.48550/arXiv.2009.13569")] ai/tabular cs/algorithm/sorting

We present sorting algorithms that represent the fastest known techniques for a wide range of input sizes, input distributions, data types, and machines. A part of the speed advantage is due to the feature to work in-place. Previously, the in-place feature often implied performance penalties.

Our main algorithmic contribution is a blockwise approach to in-place data distribution that is provably cache-efficient. We also parallelize this approach taking dynamic load balancing and memory locality into account. Our comparison-based algorithm, In-place Superscalar Samplesort (IPS4o), combines this technique with branchless decision trees. By taking cases with many equal elements into account and by adapting the distribution degree dynamically, we obtain a highly robust algorithm that outperforms the best in-place parallel comparison-based competitor by almost a factor of three. IPS4o also outperforms the best comparison-based competitors in the in-place or not in-place, parallel or sequential settings. IPS4o even outperforms the best integer sorting algorithms in a wide range of situations. In many of the remaining cases (often involving near-uniform input distributions, small keys, or a sequential setting), our new in-place radix sorter turns out to be the best algorithm. Claims to have the, in some sense, “best” sorting algorithm can be found in many papers which cannot all be true.

Therefore, we base our conclusions on extensive experiments involving a large part of the cross product of 21 state-of-the-art sorting codes, 6 data types, 10 input distributions, 4 machines, 4 memory allocation strategies, and input sizes varying over 7 orders of magnitude.

This confirms the robust performance of our algorithms while revealing major performance problems in many competitors outside the concrete set of measurements reported in the associated publications.

--- https://arxiv.org/abs/2008.06537 The Relevance of Classic Fuzz Testing: Have We Solved This One? Barton P. Miller, Mengxiao Zhang, Elisa R. Heymann 2020-08-14 2021-03-25 [("doi","10.1109/TSE.2020.3047766")] cs/security

As fuzz testing has passed its 30th anniversary, and in the face of the incredible progress in fuzz testing techniques and tools, the question arises if the classic, basic fuzz technique is still useful and applicable? In that tradition, we have updated the basic fuzz tools and testing scripts and applied them to a large collection of Unix utilities on Linux, FreeBSD, and MacOS. As before, our failure criteria was whether the program crashed or hung. We found that 9 crash or hang out of 74 utilities on Linux, 15⁄78 utilities on FreeBSD, and 12⁄76 utilities on MacOS. A total of 24 different utilities failed across the 3 platforms. We note that these failure rates are somewhat higher than our in previous 1995, 2000, and 2006 studies of the reliability of command line utilities.

In the basic fuzz tradition, we debugged each failed utility and categorized the causes the failures. Classic categories of failures, such as pointer and array errors and not checking return codes, were still broadly present in the current results. In addition, we found a couple of new categories of failures appearing. We present examples of these failures to illustrate the programming practices that allowed them to happen.

As a side note, we tested the limited number of utilities available in a modern programming language (Rust) and found them to be of no better reliability than the standard ones.

--- https://www.biorxiv.org/content/10.1101/2020.08.12.248997.full An antiviral self-replicating molecular heterotroph Anastasia Shapiro, Alexander Rosenberg, Adva Levy-Zamir, Liron Bassali, Shmulik Ittah, Almogit Abu-Horowitz, Ido Bachelet 2020-08-14 2021-03-25 [("doi","10.1101/2020.08.12.248997")] cs/cellular-automaton genetics/editing

We report the synthesis of a molecular machine, fabricated from nucleic acids, which is capable of digesting viral RNA and using it to assemble additional copies of itself inside living cells. The machine’s body plan combines several parts that build upon the target RNA, assembling an immobile, DNA:RNA 4-way junction, which contains a single gene encoding a hammerhead ribozyme (HHR).

Full assembly of the machine’s body from its parts enables the subsequent elongation of the gene and transcription of HHR molecules, followed by HHR-mediated digestion of the target molecule. This digestion converts the target to a building block suitable for participation in the assembly of more copies of the machine, mimicking biological heterotrophy.

In this work we describe the general design of a prototypical machine, characterize its activity cycle and kinetics, and show that it can be efficiently and safely delivered into live cells. As a proof of principle, we constructed a machine that targets the Autographa californica multicapsid nucleopolyhedrovirus (AcMNPV) GP64 gene, and show that it effectively suppresses viral propagation in a cell population, exhibiting predator/prey-like dynamics with the infecting virus.

In addition, the machine reduced viral infection, stress signaling, and innate immune activation inside virus-infected animals. This preliminary design could control the behavior of antisense therapies for a range of applications, particularly against dynamic targets such as viruses and cancer.

--- https://www.biorxiv.org/content/10.1101/2020.08.29.269258.full Shared heritability of face and brain shape distinct from cognitive traits Sahin Naqvi, Yoeri Sleyp, Hanne Hoskens, Karlijne Indencleef, Jeffrey P. Spence, Rose Bruffaerts, Ahmed Radwan, Ryan J. Eller, Stephen Richmond, Mark D. Shriver, John R. Shaffer, Seth M. Weinberg, Susan Walsh, James Thompson, Jonathan K. Pritchard, Stefan Sunaert, Hilde Peeters, Joanna Wysocka, Peter Claes 2020-08-29 2021-03-25 [("doi","10.1101/2020.08.29.269258")] genetics/heritable/correlation psychology/neuroscience

Evidence from both model organisms and clinical genetics suggests close coordination between the developing brain and face, but it remains unknown whether this developmental link extends to genetic variation that drives normal-range diversity of face and brain shape. Here, we performed a multivariate genome-wide association study of cortical surface morphology in 19,644 European-ancestry individuals and identified 472 genomic loci influencing brain shape at multiple levels.

We discovered a substantial overlap of these brain shape association signals with those linked to facial shape variation, with 76 common to both. These shared loci include transcription factors with cell-intrinsic roles in craniofacial development, as well as members of signaling pathways involved in brain-face crosstalk. Brain shape heritability is equivalently enriched near regulatory regions active in either brain organoids or in facial progenitor cells.

However, brain shape association signals shared with face shape are distinct from those shared with behavioral-cognitive traits or neuropsychiatric disorder risk. Together, we uncover common genetic variants and candidate molecular players underlying brain-face interactions. We propose that early in embryogenesis, the face and the brain mutually shape each other through a combination of structural effects and paracrine signaling, but this interplay may have little impact on later brain development associated with cognitive function.

--- https://www.biorxiv.org/content/10.1101/2020.06.03.131318.full Polygenic Scores for Cognitive Abilities and their Association with Different Aspects of General Intelligence—a Deep Phenotyping Approach Erhan Genç, Caroline Schlüter, Christoph Fraenz, Larissa Arning, Huu Phuc Nguyen, Manuel C. Voelkle, Fabian Streit, Onur Güntürkün, Robert Kumsta, Sebastian Ocklenburg 2020-08-20 2021-03-25 [("doi","10.1101/2020.06.03.131318")] genetics/heritable/correlation iq

Intelligence is a highly polygenic trait and GWAS have identified thousands of DNA variants contributing with small effects. Polygenic scores (PGS) can aggregate those effects for trait prediction in independent samples. As large-scale light-phenotyping GWAS operationalized intelligence as performance in rather superficial tests, the question arises which intelligence facets are actually captured.

We used deep-phenotyping to investigate the molecular determinantes of individual differences in cognitive ability. We therefore studied the association between PGS of educational attainment (EA-PGS) and intelligence (IQ-PGS) with a wide range of intelligence facets in a sample of 320 healthy adults. EA-PGS and IQ-PGS had the highest incremental R2s for general (3.25%; 1.78%), verbal (2.55%; 2.39%) and numerical intelligence (2.79%; 1.54%) and the weakest for non-verbal intelligence (0.50%; 0.19%) and short-term memory (0.34%; 0.22%).

These results indicate that PGS derived from light-phenotyping GWAS do not reflect different facets of intelligence equally well, and thus should not be interpreted as genetic indicators of intelligence per se. The findings refine our understanding of how PGS are related to other traits or life outcomes.

--- https://www.biorxiv.org/content/10.1101/2020.10.09.333542.full Exploring the variance in complex traits captured by DNA methylation assays Thomas Battram, Tom R. Gaunt, Doug Speed, Nicholas J. Timpson, Gibran Hemani 2020-10-10 2021-03-25 [("doi","10.1101/2020.10.09.333542")] genetics/heritable statistics/variance-component

Following years of epigenome-wide association studies (EWAS), traits analysed to date tend to yield few associations. Reinforcing this observation, we conducted EWAS on 400 traits and 16 yielded at least one association at the conventional statistical-significance threshold (p < 1×10−7).

To investigate why EWAS yield is low, we formally estimated the proportion of phenotypic variation captured by 421,693 blood derived DNA methylation markers (h2EWAS) across all 400 traits. The mean h2EWAS was zero, with evidence for regular cigarette smoking exhibiting the largest association with all markers (h2EWAS = 0.42) and the only one surpassing a false discovery rate < 0.1. Though underpowered to determine the h2EWAS value for any one trait, h2EWAS was predictive of the number of EWAS hits across the traits analysed (AUC=0.7).

Modelling the contributions of the methylome on a per-site versus a per-region basis gave varied h2EWAS estimates (r = 0.47) but neither approach obtained substantially higher model fits across all traits.

Our analysis indicates that most complex traits do not heavily associate with markers commonly measured in EWAS within blood. However, it is likely DNA methylation does capture variation in some traits and h2EWAS may be a reasonable way to prioritise traits that are likely to yield associations.

--- https://www.biorxiv.org/content/10.1101/2020.08.24.265280.full Improved genetic prediction of complex traits from individual-level data or summary statistics Qianqian Zhang, Florian Privé, Bjarni Vilhjálmsson, Doug Speed 2020-08-24 2021-03-25 [("doi","10.1101/2020.08.24.265280")] genetics/heritable

At present, most tools for constructing genetic prediction models begin with the assumption that all genetic variants contribute equally towards the phenotype. However, this represents a sub-optimal model for how heritability is distributed across the genome. Here we construct prediction models for 14 phenotypes from the UK Biobank (200,000 individuals per phenotype) using 4 of the most popular prediction tools: lasso, ridge regression, Bolt-LMM and BayesR.

When we improve the assumed heritability model, prediction accuracy always improves (ie. for all 4 tools and for all 14 phenotypes). When we construct prediction models using individual-level data, the best-performing tool is Bolt-LMM; if we replace its default heritability model with the most realistic model currently available, the average proportion of phenotypic variance explained increases by 19% (s.d. 2), equivalent to increasing the sample size by about a quarter.

When we construct prediction models using summary statistics, the best tool depends on the phenotype. Therefore, we develop MegaPRS, a summary statistic prediction tool for constructing lasso, ridge regression, Bolt-LMM and BayesR prediction models, that allows the user to specify the heritability model.

--- https://www.biorxiv.org/content/10.1101/2020.04.03.024554.full Genome wide analysis of gene dosage in 24,092 individuals shows that 10,000 genes modulate cognitive ability Guillaume Huguet, Catherine Schramm, Elise Douard, Tamer Petra, Antoine Main, Pauline Monin, Jade England, Khadije Jizi, Thomas Renne, Myriam Poirier, Sabrina Nowak, Charles-Olivier Martin, Nadine Younis, Inga Sophia Knoth, Martineau Jean-Louis, Zohra Saci, Maude Auger, Frédérique Tihy, Géraldine Mathonnet, Catalina Maftei, France Léveillé, David J. Porteous, Gail Davies, Paul Redmond, Sarah E. Harris, W. David Hill, Emmanuelle Lemyre, Gunter Schumann, Thomas Bourgeron, Zdenka Pausova, Tomas Paus, Sherif Karama, Sarah Lippe, Ian J. Deary, Laura Almasy, Aurélie Labbe, David Glahn, Celia M. T. Greenwood, Sébastien Jacquemont 2020-10-05 2021-03-25 [("doi","10.1101/2020.04.03.024554")] genetics/heritable/rare iq psychiatry/autism

Genomic Copy Number Variants (CNVs) are routinely identified and reported back to patients with neuropsychiatric disorders, but their quantitative effects on essential traits such as cognitive ability are poorly documented. We have recently shown that the effect-size of deletions on cognitive ability can be statistically predicted using measures of intolerance to haploinsufficiency. However, the effect-sizes of duplications remain unknown. It is also unknown if the effect of multigenic CNVs are driven by a few genes intolerant to haploinsufficiency or distributed across tolerant genes as well.

Here, we identified all CNVs >50 kilobases in 24,092 individuals from unselected and autism cohorts with assessments of general intelligence. Statistical models used measures of intolerance to haploinsufficiency of genes included in CNVs to predict their effect-size on intelligence. Intolerant genes decrease general intelligence by 0.8 and 2.6 points of IQ when duplicated or deleted, respectively. Effect-sizes showed no heterogeneity across cohorts. Validation analyses demonstrated that models could predict CNV effect-sizes with 78% accuracy. Data on the inheritance of 27,766 CNVs showed that deletions and duplications with the same effect-size on intelligence occur de novo at the same frequency.

We estimated that around 10,000 intolerant and tolerant genes negatively affect intelligence when deleted, and less than 2% have large effect-sizes. Genes encompassed in CNVs were not enriched in any GOterms but gene regulation and brain expression were GOterms overrepresented in the intolerant subgroup. Such pervasive effects on cognition may be related to emergent properties of the genome not restricted to a limited number of biological pathways.

--- https://www.biorxiv.org/content/10.1101/2020.07.10.198200.full Atribacteria reproducing over millions of years in the Atlantic abyssal subseafloor Aurèle Vuillemin, Sergio Vargas, Ömer K. Coskun, Robert Pockalny, Richard W. Murray, David C. Smith, Steven D’Hondt, William D. Orsi 2020-07-11 2021-03-26 [("doi","10.1101/2020.07.10.198200")] genetics/microbiome genetics/sequencing

How microbial metabolism is translated into cellular reproduction under energy-limited settings below the seafloor over long timescales is poorly understood. Here, we show that microbial abundance increases an order of magnitude over a five million-year-long sequence in anoxic subseafloor clay of the abyssal North Atlantic Ocean. This increase in biomass correlated with an increased number of transcribed protein-encoding genes that included those involved in cytokinesis, demonstrating that active microbial reproduction outpaces cell death in these ancient sediments. Metagenomes, metatranscriptomes, and 16S rRNA gene sequencing all show that the actively reproducing community was dominated by the candidate Phylum “Candidatus Atribacteria”, which exhibited patterns of gene expression consistent with a fermentative, and potentially acetogenic metabolism. “Ca. Atribacteria” dominated throughout the entire eight million-year-old cored sequence, despite the detection limit for gene expression being reached in five million-year-old sediments. The subseafloor reproducing “Ca. Atribacteria” also expressed genes encoding a bacterial micro-compartment that has potential to assist in secondary fermentation by recycling aldehydes and, thereby, harness additional power to reduce ferredoxin and NAD⁺. Expression of genes encoding the Rnf complex for generation of chemiosmotic ATP synthesis were also detected from the subseafloor “Ca. Atribacteria”, as well as the Wood-Ljungdahl pathway that could potentially have an anabolic or catabolic function. The correlation of this metabolism with cytokinesis gene expression and a net increase in biomass over the million-year-old sampled interval indicates that the “Ca. Atribacteria” can perform the necessary catabolic and anabolic functions necessary for cellular reproduction, even under energy limitation in millions of years old anoxic sediments.

Importance

The deep subseafloor sedimentary biosphere is one of the largest ecosystems on Earth, where microbes subsist under energy-limited conditions over long timescales. It remains poorly understood how mechanisms of microbial metabolism promote increased fitness in these settings. We discovered that the candidate bacterial Phylum “Candidatus Atribacteria” dominated a deep-sea subseafloor ecosystem, where it exhibited increased transcription of genes associated with acetogenic fermentation and reproduction in million-year old sediment. We attribute its improved fitness after burial in the seabed to its capabilities to derive energy from increasingly oxidized metabolites via a bacterial micro-compartment and utilize a potentially reversible Wood-Ljungdahl pathway to help meet anabolic and catabolic requirements for growth. Our findings show that “Ca. Atribacteria” can perform all the necessary catabolic and anabolic functions necessary for cellular reproduction, even under energy limitation in anoxic sediments that are millions of years old.

--- https://www.biorxiv.org/content/10.1101/2020.09.07.285692.full Rapid Evolution of Plastic-degrading Enzymes Prevalent in the Global Ocean Intikhab Alam, Nojood Aalismail, Cecilia Martin, Allan Kamau, Francisco J. Guzmán-Vega, Tahira Jamil, Afaque A. Momin, Silvia G. Acinas, Josep M. Gasol, Stefan T. Arold, Takashi Gojobori, Susana Agusti, Carlos M. Duarte 2020-09-09 2021-03-26 [("doi","10.1101/2020.09.07.285692")] genetics/selection/natural

Estimates of marine plastic stocks, a major threat to marine life (1), are far lower than expected from exponentially-increasing litter inputs, suggesting important loss factors (2), (3). These may involve microbial degradation, as the plastic-degrading polyethylene terephthalate enzyme (PETase) has been reported in marine microbial communities (4).

An assessment of 416 metagenomes of planktonic communities across the global ocean identifies 68 oceanic PETase variants (oPETase) that evolved from ancestral enzymes degrading polycyclic aromatic hydrocarbons. 20 oPETases show predicted efficiencies comparable to those of laboratory-optimized PETases, suggesting strong selective pressures directing the evolution of these enzymes.

We found oPETases in 90.1% of samples across all oceans and depths, particularly abundant at 1,000m depth, with a strong dominance of Pseudomonadales containing putative highly-efficient oPETase variants in the dark ocean.

Enzymatic degradation may be removing plastic from the marine environment while providing a carbon source for bathypelagic microbial communities.

--- https://arxiv.org/abs/2006.13888 RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gomez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas 2020-06-24 2021-03-26 [("doi","10.48550/arXiv.2006.13888")] reinforcement-learning/model

Offline methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications. They make it possible to learn policies from offline datasets, thus overcoming concerns associated with online data collection in the real-world, including cost, safety, or ethical concerns.

In this paper, we propose a benchmark called RL Unplugged to evaluate and compare offline RL methods. RL Unplugged includes data from a diverse range of domains including games (eg. Atari benchmark) and simulated motor control problems (eg. DM Control Suite). The datasets include domains that are partially or fully observable, use continuous or discrete actions, and have stochastic vs. deterministic dynamics. We propose detailed evaluation protocols for each domain in RL Unplugged and provide an extensive analysis of supervised learning and offline RL methods using these protocols.

We will release data for all our tasks and open-source all algorithms presented in this paper. We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community.

Moving forward, we view RL Unplugged as a living benchmark suite that will evolve and grow with datasets contributed by the research community and ourselves. Our project page is available on https://github.com/google-deepmind/deepmind-research/tree/master/rl_unplugged.

--- https://arxiv.org/abs/2007.03500 The Go Transformer: Natural Language Modeling for Game Play Matthew Ciolino, David Noever, Josh Kalin 2020-07-07 2021-03-26 [("doi","10.48550/arXiv.2007.03500")] reinforcement-learning/model/decision-transformer

This work applies natural language modeling to generate plausible strategic moves in the ancient game of Go. We train the Generative Pretrained Transformer (GPT-2) to mimic the style of Go champions as archived in Smart Game Format (SGF), which offers a text description of move sequences.

The trained model further generates valid but previously unseen strategies for Go. Because GPT-2 preserves punctuation and spacing, the raw output of the text generator provides inputs to game visualization and creative patterns, such as the Sabaki project’s game engine using auto-replays.

Results demonstrate that language modeling can capture both the sequencing format of championship Go games and their strategic formations. Compared to random game boards, the GPT-2 fine-tuning shows efficient opening move sequences favoring corner play over less advantageous center and side play.

Game generation as a language modeling task offers novel approaches to more than 40 other board games where historical text annotation provides training data (eg. Amazons & Connect 4/6).

--- https://arxiv.org/abs/2009.08987 M51-ULS-1b: The First Candidate for a Planet in an External Galaxy R. Di Stefano, Julia Berndtsson, Ryan Urquhart, Roberto Soria, Vinay L. Kashyap, Theron W. Carmichael, Nia Imara 2020-09-18 2021-03-26 [("doi","10.48550/arXiv.2009.08987")] science

Do external galaxies host planetary systems? Many lines of reasoning suggest that the answer must be “yes”. In the foreseeable future, however, the question cannot be answered by the methods most successful in our own Galaxy.

We report on a different approach which focuses on bright X-ray sources (XRSs). M51-ULS-1b is the first planet candidate to be found because it produces a full, short-lived eclipse of a bright XRS. M51-ULS-1b has a most probable radius slightly smaller than Saturn. It orbits one of the brightest XRSs in the external galaxy M51, the Whirlpool Galaxy, located 8.6 Megaparsecs from Earth.

It is the first candidate for a planet in an external galaxy. The binary it orbits, M51-ULS-1, is young and massive. One of the binary components is a stellar remnant, either a neutron star (NS) or black hole (BH), and the other is a massive star.

X-ray transits can now be used to discover more planets in external galaxies and also planets orbiting XRSs inside the Milky Way.

--- https://arxiv.org/abs/2005.06840 How Does the Adoption of Ad Blockers Affect News Consumption? Shunyao Yan, Klaus M. Miller, Bernd Skiera 2020-05-14 2021-03-26 [("doi","10.48550/arXiv.2005.06840")] economics/advertising

Ad blockers allow users to browse websites without viewing ads. Online news providers that rely on advertising revenue tend to perceive users’ adoption of ad blockers purely as a threat to revenue. Yet, this perception ignores the possibility that avoiding ads, which users presumably dislike, may affect users’ online news consumption behavior in positive ways.

Using 3.1 million anonymized visits from 79,856 registered users on a news website, we find that adopting an ad blocker has a robust positive effect on the quantity and variety of articles users consume (21.5%—43.3% more articles and 13.4%—29.1% more content categories). An increase in repeat user visits of the news website, rather than the number of page impressions per visit, drives the news consumption. These visits tend to start with direct navigation to the news website, indicating user loyalty. The increase in news consumption is more substantial for users who have less prior experience with the website.

We discuss how news publishers could benefit from these findings, including exploring revenue models that consider users’ desire to avoid ads.

--- https://arxiv.org/abs/2006.04988 Object Segmentation Without Labels with Large-Scale Generative Models Andrey Voynov, Stanislav Morozov, Artem Babenko 2020-06-08 2021-03-26 [("doi","10.48550/arXiv.2006.04988")] ai/nn/gan ai/scaling

The recent rise of unsupervised and self-supervised learning has dramatically reduced the dependency on labeled data, providing effective image representations for transfer to downstream vision tasks.

Furthermore, recent works employed these representations in a fully unsupervised setup for image classification, reducing the need for human labels on the fine-tuning stage as well.

This work demonstrates that large-scale unsupervised models can also perform a more challenging object segmentation task, requiring neither pixel-level nor image-level labeling. Namely, we show that recent unsupervised GANs allow to differentiate between foreground/background pixels, providing high-quality saliency masks.

By extensive comparison on standard benchmarks, we outperform existing unsupervised alternatives for object segmentation, achieving new state-of-the-art.

--- https://arxiv.org/abs/2010.07074 Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical Supervision from Extractive Summaries Xiaofei Sun, Chun Fan, Zijun Sun, Yuxian Meng, Fei Wu, Jiwei Li 2020-10-14 2021-03-26 [("doi","10.48550/arXiv.2010.07074")] ai/nn/transformer/attention/hierarchical ai/nn/transformer/gpt

Long-text generation remains a challenge. The difficulty of generating coherent long texts lies in the fact that existing models overwhelmingly focus on the tasks of local word prediction, and cannot make high level plans on what to generate or capture the high-level discourse dependencies between chunks of texts.

Inspired by how humans write, where a list of bullet points or a catalog is first outlined, and then each bullet point is expanded to form the whole article, we propose SOE, a pipelined system that involves summarizing, outlining and elaborating for long text generation: the model first outlines the summaries for different segments of long texts, and then elaborates on each bullet point to generate the corresponding segment. To avoid the labor-intensive process of summary soliciting, we propose the reconstruction strategy, which extracts segment summaries in an unsupervised manner by selecting its most informative part to reconstruct the segment.

The proposed generation system comes with the following merits: (1) the summary provides high-level guidances for text generation and avoids the local minimum of individual word predictions; (2) the high-level discourse dependencies are captured in the conditional dependencies between summaries and are preserved during the summary expansion process and (3) additionally, we are able to consider statistically-significantly more contexts by representing contexts as concise summaries.

Extensive experiments demonstrate that SOE produces long texts with better quality, along with faster convergence speed.

--- https://arxiv.org/abs/2010.05113 Contrastive Representation Learning: A Framework and Review Phuc H. Le Khac, Graham Healy, Alan F. Smeaton 2020-10-10 2021-03-26 [("doi","10.1109/ACCESS.2020.3031549")] ai/nn/gan/data-augmentation

Contrastive Learning has recently received interest due to its success in self-supervised representation learning in the computer vision domain. However, the origins of Contrastive Learning date as far back as the 1990s and its development has spanned across many fields and domains including Metric Learning and natural language processing. In this paper we provide a comprehensive literature review and we propose a general Contrastive Representation Learning framework that simplifies and unifies many different contrastive learning methods. We also provide a taxonomy for each of the components of contrastive learning in order to summarize it and distinguish it from other forms of machine learning.

We then discuss the inductive biases which are present in any contrastive learning system and we analyze our framework under different views from various sub-fields of Machine Learning. Examples of how contrastive learning has been applied in computer vision, natural language processing, audio processing, and others, as well as in Reinforcement Learning are also presented.

Finally, we discuss the challenges and some of the most promising future research directions ahead.

--- https://arxiv.org/abs/2006.03236 Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le 2020-06-05 2021-03-26 [("doi","10.48550/arXiv.2006.03236")] ai/nn/rnn ai/nn/transformer/attention/hierarchical cs/algorithm

With the success of language pretraining, it is highly desirable to develop more efficient architectures of good scalability that can exploit the abundant unlabeled data at a lower cost. To improve the efficiency, we examine the much-overlooked redundancy in maintaining a full-length token-level presentation, especially for tasks that only require a single-vector presentation of the sequence.

With this intuition, we propose Funnel-Transformer which gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost. More importantly, by re-investing the saved FLOPs from length reduction in constructing a deeper or wider model, we further improve the model capacity. In addition, to perform token-level predictions as required by common pretraining objectives, Funnel-Transformer is able to recover a deep representation for each token from the reduced hidden sequence via a decoder.

Empirically, with comparable or fewer FLOPs, Funnel-Transformer outperforms the standard Transformer on a wide variety of sequence-level prediction tasks, including text classification, language understanding, and reading comprehension.

The code and pretrained checkpoints are available at Github.

--- https://arxiv.org/abs/2006.03555#google Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, David Belanger, Lucy Colwell, Adrian Weller 2020-06-05 2021-03-26 [("doi","10.48550/arXiv.2006.03555")] ai/nn/transformer/attention

Transformer models have achieved state-of-the-art results across a diverse range of domains. However, concern over the cost of training the attention mechanism to learn complex dependencies between distant inputs continues to grow. In response, solutions that exploit the structure and sparsity of the learned attention matrix have blossomed. However, real-world applications that involve long sequences, such as biological sequence analysis, may fall short of meeting these assumptions, precluding exploration of these models.

To address this challenge, we present a new Transformer architecture, Performer, based on Fast Attention Via Orthogonal Random features (FAVOR). Our mechanism scales linearly rather than quadratically in the number of tokens in the sequence, is characterized by sub-quadratic space complexity and does not incorporate any sparsity pattern priors. Furthermore, it provides strong theoretical guarantees: unbiased estimation of the attention matrix and uniform convergence. It is also backwards-compatible with pre-trained regular Transformers.

We demonstrate its effectiveness on the challenging task of protein sequence modeling and provide detailed theoretical analysis.

--- https://arxiv.org/abs/2006.11527 Memory Transformer Mikhail S. Burtsev, Yuri Kuratov, Anton Peganov, Grigory V. Sapunov 2020-06-20 2021-03-27 [("doi","10.48550/arXiv.2006.11527")] ai/nn/retrieval ai/nn/transformer/attention/compression

Transformer-based models have achieved state-of-the-art results in many natural language processing tasks. The self-attention architecture allows transformer to combine information from all elements of a sequence into context-aware representations. However, information about the context is stored mostly in the same element-wise representations. This might limit the processing of properties related to the sequence as a whole more difficult. Adding trainable memory to selectively store local as well as global representations of a sequence is a promising direction to improve the Transformer model.

Memory-augmented neural networks (MANNs) extend traditional neural architectures with general-purpose memory for representations. MANNs have demonstrated the capability to learn simple algorithms like Copy or Reverse and can be successfully trained via backpropagation on diverse tasks from question answering to language modeling outperforming RNNs and LSTMs of comparable complexity. In this work, we propose and study few extensions of the Transformer baseline (1) by adding memory tokens to store non-local representations, (2) creating memory bottleneck for the global information, (3) controlling memory update with dedicated layer.

We evaluate these memory augmented Transformers (Memory Transformer) and demonstrate that presence of memory positively correlates with the model performance for machine translation and language modeling tasks. Augmentation of pre-trained masked language model with memory tokens shows mixed results for tasks from GLUE benchmark. Visualization of attention patterns over the memory suggest that it improves the model’s ability to process a global context.

--- https://arxiv.org/abs/2006.09471 Untangling tradeoffs between recurrence and self-attention in neural networks Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie 2020-06-16 2021-03-27 [("doi","10.48550/arXiv.2006.09471")] ai/nn/rnn ai/nn/transformer/attention/recurrent

Attention and self-attention mechanisms, are now central to state-of-the-art deep learning on sequential tasks. However, most recent progress hinges on heuristic approaches with limited understanding of attention’s role in model optimization and computation, and rely on considerable memory and computational resources that scale poorly.

In this work, we present a formal analysis of how self-attention affects gradient propagation in recurrent networks, and prove that it mitigates the problem of vanishing gradients when trying to capture long-term dependencies by establishing concrete bounds for gradient norms.

Building on these results, we propose a relevancy screening mechanism, inspired by the cognitive process of memory consolidation, that allows for a scalable use of sparse self-attention with recurrence.

While providing guarantees to avoid vanishing gradients, we use simple numerical experiments to demonstrate the tradeoffs in performance and computational resources by efficiently balancing attention and recurrence.

Based on our results, we propose a concrete direction of research to improve scalability of attentive networks.

--- https://arxiv.org/abs/2010.10648#google Towards End-to-End In-Image Neural Machine Translation Elman Mansimov, Mitchell Stern, Mia Chen, Orhan Firat, Jakob Uszkoreit, Puneet Jain 2020-10-20 2021-03-27 [("doi","10.48550/arXiv.2010.10648")] ai/nn/tokenization ai/scaling

In this paper, we offer a preliminary investigation into the task of in-image machine translation: transforming an image containing text in one language into an image containing the same text in another language.

We propose an end-to-end neural model for this task inspired by recent approaches to neural machine translation, and:

demonstrate promising initial results based purely on pixel-level supervision. We then offer a quantitative and qualitative evaluation of our system outputs and discuss some common failure modes.

Finally, we conclude with directions for future work.

--- https://arxiv.org/abs/2010.11934#google mT5: A massively multilingual pre-trained text-to-text transformer Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel 2020-10-22 2021-03-27 [("doi","10.48550/arXiv.2010.11934")] ai/nn/transformer/t5 ai/scaling

The recent “Text-to-Text Transfer Transformer” (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks.

In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We detail the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. We also describe a simple technique to prevent “accidental translation” in the zero-shot setting, where a generative model chooses to (partially) translate its prediction into the wrong language.

All of the code and model checkpoints used in this work are publicly available.

--- https://arxiv.org/abs/2010.05358 Learning Which Features Matter: RoBERTa Acquires a Preference for Linguistic Generalizations (Eventually) Alex Warstadt, Yian Zhang, Haau-Sing Li, Haokun Liu, Samuel R. Bowman 2020-10-11 2021-03-27 [("doi","10.48550/arXiv.2010.05358")] ai/scaling

One reason pretraining on self-supervised linguistic tasks is effective is that it teaches models features that are helpful for language understanding. However, we want pretrained models to learn not only to represent linguistic features, but also to use those features preferentially during fine-tuning. With this goal in mind, we introduce a new English-language diagnostic set called MSGS (the Mixed Signals Generalization Set), which consists of 20 ambiguous binary classification tasks that we use to test whether a pretrained model prefers linguistic or surface generalizations during fine-tuning.

We pretrain RoBERTa models from scratch on quantities of data ranging from 1M to 1B words and compare their performance on MSGS to the publicly available RoBERTa-base. We find that models can learn to represent linguistic features with little pretraining data, but require far more data to learn to prefer linguistic generalizations over surface ones.

Eventually, with about 30B words of pretraining data, RoBERTa-base does demonstrate a linguistic bias with some regularity. We conclude that while self-supervised pretraining is an effective way to learn helpful inductive biases, there is likely room to improve the rate at which models learn which features matter.

--- https://arxiv.org/abs/2006.13979#facebook Unsupervised Cross-lingual Representation Learning for Speech Recognition Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli 2020-06-24 2021-03-27 [("doi","10.48550/arXiv.2006.13979")] ai/scaling

This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages.

The resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to a comparable system.

Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages.

We hope to catalyze research in low-resource speech understanding by releasing XLSR-53, a large model pretrained in 53 languages.

--- https://arxiv.org/abs/2006.07159#google Are we done with ImageNet? Lucas Beyer, Olivier J. Hénaff, Alexander Kolesnikov, Xiaohua Zhai, Aäron van den Oord 2020-06-12 2021-03-27 [("doi","10.48550/arXiv.2006.07159")] ai/scaling

Yes, and no. We ask whether recent progress on the ImageNet classification benchmark continues to represent meaningful generalization, or whether the community has started to overfit to the idiosyncrasies of its labeling procedure.

We therefore develop a statistically-significantly more robust procedure for collecting human annotations of the ImageNet validation set.

Using these new labels, we reassess the accuracy of recently proposed ImageNet classifiers, and find their gains to be substantially smaller than those reported on the original labels. Furthermore, we find the original ImageNet labels to no longer be the best predictors of this independently-collected set, indicating that their usefulness in evaluating vision models may be nearing an end.

Nevertheless, we find our annotation procedure to have largely remedied the errors in the original labels, reinforcing ImageNet as a powerful benchmark for future research in visual recognition.

--- https://www.biorxiv.org/content/10.1101/2020.06.04.131284.full Lessons learned from bugs in models of human history Aaron P. Ragsdale, Dominic Nelson, Simon Gravel, Jerome Kelleher 2020-06-05 2021-03-27 [("doi","10.1101/2020.06.04.131284")] cs/algorithm genetics/selection/natural/human

Simulation plays a central role in population genomics studies. Recent years have seen rapid improvements in software efficiency that make it possible to simulate large genomic regions for many individuals sampled from large numbers of populations.

As the complexity of the demographic models we study grows, however, there is an ever-increasing opportunity to introduce bugs in their implementation.

Here we describe two errors made in defining population genetic models using the msprime coalescent simulator that have found their way into the published record. We discuss how these errors have affected downstream analyses and give recommendations for software developers and users to reduce the risk of such errors.

--- https://www.biorxiv.org/content/10.1101/2020.10.13.337923.full CRISPR-enhanced human adipocyte ‘browning’ as cell therapy for metabolic disease Emmanouela Tsagkaraki, Sarah Nicoloro, Tiffany De Souza, Javier Solivan-Rivera, An, Desai, Yuefei Shen, Mark Kelly, Adilson Guilherme, Felipe Henriques, Raed Ibraheim, Nadia Amrani, Kevin Luk, Stacy Maitland, Randall H. Friedline, Lauren Tauer, Xiaodi Hu, Jason K. Kim, Scot A. Wolfe, Erik J. Sontheimer, Silvia Corvera, Michael P. Czech 2020-10-13 2021-03-27 [("doi","10.1101/2020.10.13.337923")] genetics/editing longevity

Obesity and type 2 diabetes (T2D) are associated with poor tissue responses to insulin1,2, disturbances in glucose and lipid fluxes3–5 and comorbidities including steatohepatitis6 and cardiovascular disease7,8. Despite extensive efforts at prevention and treatment9,10, diabetes afflicts over 400 million people worldwide11. Whole body metabolism is regulated by adipose tissue depots12–14, which include both lipid-storing white adipocytes and less abundant “brown” and “brite/beige” adipocytes that express thermogenic uncoupling protein UCP1 and secrete factors favorable to metabolic health15–18.

Application of CRISPR gene editing19,20 to enhance “browning” of white adipose tissue is an attractive therapeutic approach to T2D. However, the problems of cell-selective delivery, immunogenicity of CRISPR reagents, and long term stability of the modified adipocytes are formidable. To overcome these issues, we developed methods that deliver complexes of SpyCas9 protein and sgRNA ex vivo to disrupt the thermogenesis suppressor gene NRIP121,22 with near 100% efficiency in human or mouse adipocytes.

NRIP1 gene disruption at discrete loci strongly ablated NRIP1 protein and upregulated expression of UCP1 and beneficial secreted factors, while residual Cas9 protein and sgRNA were rapidly degraded. Implantation of the CRISPR-enhanced human or mouse brown-like adipocytes into high fat diet fed mice decreased adiposity and liver triglycerides while enhancing glucose tolerance compared to mice implanted with unmodified adipocytes.

These findings advance a therapeutic strategy to improve metabolic homeostasis through CRISPR-based genetic modification of human adipocytes without exposure of the recipient to immunogenic Cas9 or delivery vectors.

--- https://www.medrxiv.org/content/10.1101/2020.03.13.20035527.full Combined Utility of 25 Disease and Risk Factor Polygenic Risk Scores for Stratifying Risk of All-Cause Mortality Allison Meisner, Prosenjit Kundu, Yan Dora Zhang, Lauren V. Lan, Sungwon Kim, Disha Ghandwani, Parichoy Pal Choudhury, Sonja I. Berndt, Neal D. Freedman, Montserrat Garcia-Closas, Nilanjan Chatterjee 2020-06-16 2021-03-27 [("doi","10.1101/2020.03.13.20035527")] genetics/heritable

While genome-wide association studies (GWAS) have identified susceptibility variants for numerous traits, their combined utility for predicting broad measures of health, such as mortality, remains poorly understood. We used data from the UK Biobank to combine polygenic risk scores (PRS) for 13 diseases and 12 mortality risk factors into sex-specific composite PRS (cPRS).

These cPRS were moderately associated with all-cause mortality in independent data: the estimated hazard ratios per standard deviation were 1.10 (95% confidence interval: 1.05, 1.16) and 1.15 (1.10, 1.19) for women and men, respectively. Differences in life expectancy between the top and bottom 5% of the cPRS were estimated to be 4.79 (1.76, 7.81) years and 6.75 (4.16, 9.35) years for women and men, respectively.

These associations were substantially attenuated after adjusting for non-genetic mortality risk factors measured at study entry.

The cPRS may be useful in counseling younger individuals at higher genetic risk of mortality on modification of non-genetic factors.

--- https://www.biorxiv.org/content/10.1101/2020.05.21.109199.full Genetic Risk Scores for Cardiometabolic Traits in Sub-Saharan African Populations Kenneth Ekoru, Adebowale Adeyemo, Guanjie Chen, Ayo P. Doumatey, Jie Zhou, Amy R. Bentley, Daniel Shriner, Charles N. Rotimi 2020-05-25 2021-03-27 [("doi","10.1101/2020.05.21.109199")] genetics/heritable

There is growing support for the use of genetic risk scores (GRS) in routine clinical settings. Due to the limited diversity of current genomic discovery samples, there are concerns that the predictive power of GRS will be limited in non-European ancestry populations. Here, we evaluated the predictive utility of GRS for 12 cardiometabolic traits in sub-Saharan Africans (AF; n=5200), African Americans (AA; n=9139), and European Americans (EA; n=9594). GRS were constructed as weighted sums of the number of risk alleles. Predictive utility was assessed using the additional phenotypic variance explained and increase in discriminatory ability over traditional risk factors (age, sex and BMI), with adjustment for ancestry-derived principal components. Across all traits, GRS showed upto a 5× and 20× greater predictive utility in EA relative to AA and AF, respectively. Predictive utility was most consistent for lipid traits, with percent increase in explained variation attributable to GRS ranging 35.9%–59.4% among EA, 26.6% to 65.8% among AA, and 2.4% to 37.5% among AF. These differences were recapitulated in the discriminatory power, whereby the predictive utility of GRS was 4× greater in EA relative to AA and up to 44× greater in EA relative to AF. Obesity and blood pressure traits showed a similar pattern of greater predictive utility among EA. This work demonstrates the poorer performance of GRS in AF and highlights the need to improve representation of multiethnic populations in genomic studies to ensure equitable clinical translation of GRS.

Key Messages

Genetic Risk Score (GRS) prediction is markedly poorer in sub-Saharan Africans compared with African Americans and European Americans

To ensure equitable clinical translation of GRS, there is need need to improve representation of multiethnic populations in genomic studies

--- https://www.biorxiv.org/content/10.1101/2020.05.12.090555.full Insights into the genetic architecture of the human face Julie D. White, Karlijne Indencleef, Sahin Naqvi, Ryan J. Eller, Jasmien Roosenboom, Myoung Keun Lee, Jiarui Li, Jaaved Mohammed, Stephen Richmond, Ellen E. Quillen, Heather L. Norton, Eleanor Feingold, Tomek Swigut, Mary L. Marazita, Hilde Peeters, Greet Hens, John R. Shaffer, Joanna Wysocka, Susan Walsh, Seth M. Weinberg, Mark D. Shriver, Peter Claes 2020-05-14 2021-03-28 [("doi","10.1101/2020.05.12.090555")] genetics/heritable

The human face is complex and multipartite, and characterization of its genetic architecture remains intriguingly challenging.

Applying GWAS to multivariate shape phenotypes, we identified 203 genomic regions associated with normal-range facial variation, 117 of which are novel. The associated regions are enriched for both genes relevant to craniofacial and limb morphogenesis and enhancer activity in cranial neural crest cells and craniofacial tissues.

Genetic variants grouped by their contribution to similar aspects of facial variation show high within-group correlation of enhancer activity, and 4 SNP pairs display evidence of epistasis, indicating potentially coordinated actions of variants within the same cell types or tissues.

In sum, our analyses provide new insights for understanding how complex morphological traits are shaped by both individual and coordinated genetic actions.

--- https://arxiv.org/abs/2004.14257 Politeness Transfer: A Tag and Generate Approach Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W. Black, Shrimai Prabhumoye 2020-04-29 2021-03-28 [("doi","10.48550/arXiv.2004.14257")] ai/nn

This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning.

We design a tag and generate pipeline that identifies stylistic attributes and subsequently generates a sentence in the target style while preserving most of the source content. For politeness as well as 5 other transfer tasks, our model outperforms the state-of-the-art methods on automatic metrics for content preservation, with a comparable or better performance on style transfer accuracy.

Additionally, our model surpasses existing methods on human evaluations for grammaticality, meaning preservation, and transfer accuracy across all the 6 style transfer tasks.

The data and code is located at Github.

--- https://arxiv.org/abs/2004.13637#facebook Recipes for building an open-domain chatbot Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston 2020-04-28 2021-03-28 [("doi","10.48550/arXiv.2004.13637")] ai/nn/transformer

Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent persona. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy.

We build variants of these recipes with 90M, 2.7B and 9.4b parameter models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.

--- https://arxiv.org/abs/2004.12297#google Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching Liu Yang, Mingyang Zhang, Cheng Li, Michael Bendersky, Marc Najork 2020-04-26 2021-03-28 [("doi","10.1145/3340531.3411908")] ai/nn/transformer/attention/hierarchical

Many natural language processing and information retrieval problems can be formalized as the task of semantic matching. Existing work in this area has been largely focused on matching between short texts (eg. question answering), or between a short and a long text (eg. ad-hoc retrieval). Semantic matching between long-form documents, which has many important applications like news recommendation, related article recommendation and document clustering, is relatively less explored and needs more research effort.

In recent years, self-attention based models like Transformers and BERT have achieved state-of-the-art performance in the task of text matching. These models, however, are still limited to short text like a few sentences or one paragraph due to the quadratic computational complexity of self-attention with respect to input text length.

In this paper, we address the issue by proposing the Siamese Multi-depth Transformer-based Hierarchical (SMITH) Encoder for long-form document matching. Our model contains several innovations to adapt self-attention models for longer text input. In order to better capture sentence level semantic relations within a document, we pre-train the model with a novel masked sentence block language modeling task in addition to the masked word language modeling task used by BERT.

Our experimental results on several benchmark datasets for long-form document matching show that our proposed SMITH model outperforms the previous state-of-the-art models including hierarchical attention, multi-depth attention-based hierarchical recurrent neural network, and BERT. Comparing to BERT based baselines, our model is able to increase maximum input text length 512 → 2048. We will open source a Wikipedia based benchmark dataset, code and a pre-trained checkpoint to accelerate future research on long-form document matching.

--- https://arxiv.org/abs/2004.11886 Lite Transformer with Long-Short Range Attention Zhanghao Wu, Zhijian Liu, Ji Lin, Yujun Lin, Song Han 2020-04-24 2021-03-28 [("doi","10.48550/arXiv.2004.11886")] ai/nn/sparsity/low-precision ai/nn/sparsity/pruning ai/nn/transformer/attention/hierarchical

Transformer has become ubiquitous in natural language processing (eg. machine translation, question answering); however, it requires an enormous amount of computations to achieve high performance, which makes it not suitable for mobile applications that are tightly constrained by the hardware resources and battery.

In this paper, we present an efficient mobile NLP architecture, Lite Transformer to facilitate deploying mobile NLP applications on edge devices. The key primitive is the Long-Short Range Attention (LSRA), where one group of heads specializes in the local context modeling (by convolution) while another group specializes in the long-distance relationship modeling (by attention).

Such specialization brings consistent improvement over the vanilla transformer on 3 well-established language tasks: machine translation, abstractive summarization, and language modeling. Under constrained resources (500M/100M MACs), Lite Transformer outperforms transformer on WMT’14 English-French by 1.2/1.7 BLEU, respectively. Lite Transformer reduces the computation of transformer base model by 2.5× with 0.3 BLEU score degradation. Combining with pruning and quantization, we further compressed the model size of Lite Transformer by 18.2×. For language modeling, Lite Transformer achieves 1.8 lower perplexity than the transformer at around 500M MACs. Notably, Lite Transformer outperforms the AutoML-based Evolved Transformer by 0.5 higher BLEU for the mobile NLP setting without the costly architecture search that requires more than 250 GPU years.

Code has been made available at Github.

--- https://arxiv.org/abs/2004.11362#google Supervised Contrastive Learning Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan 2020-04-23 2021-03-28 [("doi","10.48550/arXiv.2004.11362")] ai/nn

Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state-of-the-art performance in the unsupervised training of deep image models. Modern batch contrastive approaches subsume or outperform traditional contrastive losses such as triplet, max-margin and the N-pairs loss.

In this work, we extend the self-supervised batch contrastive approach to the fully-supervised setting, allowing us to effectively leverage label information. Clusters of points belonging to the same class are pulled together in embedding space, while simultaneously pushing apart clusters of samples from different classes. We analyze two possible versions of the supervised contrastive (SupCon) loss, identifying the best-performing formulation of the loss.

On ResNet-200, we achieve top-1 accuracy of 81.4% on the ImageNet dataset, which is 0.8% above the best number reported for this architecture. We show consistent outperformance over cross-entropy on other datasets and two ResNet variants. The loss shows benefits for robustness to natural corruptions and is more stable to hyperparameter settings such as optimizers and data augmentations. Our loss function is simple to implement, and reference TensorFlow code is released at https://github.com/google-research/google-research/tree/master/supcon.

--- https://arxiv.org/abs/2004.08449#facebook Can You Put it All Together: Evaluating Conversational Agents’ Ability to Blend Skills Eric Michael Smith, Mary Williamson, Kurt Shuster, Jason Weston, Y-Lan Boureau 2020-04-17 2021-03-28 [("doi","10.48550/arXiv.2004.08449")] ai/nn

Being engaging, knowledgeable, and empathetic are all desirable general qualities in a conversational agent. Previous work has introduced tasks and datasets that aim to help agents learn those qualities in isolation and gauge how well they can express them. But rather than being specialized in one single quality, a good open-domain conversational agent should be able to seamlessly blend them all into one cohesive conversational flow.

In this work, we investigate several ways to combine models trained towards isolated capabilities, ranging from simple model aggregation schemes that require minimal additional training, to various forms of multi-task training that encompass several skills at all training stages.

We further propose a new dataset, BlendedSkillTalk, to analyze how these capabilities would mesh together in a natural conversation, and compare the performance of different architectures and training schemes.

Our experiments show that multi-tasking over several tasks that focus on particular capabilities results in better blended conversation performance compared to models trained on a single skill, and that both unified or two-stage approaches perform well if they are constructed to avoid unwanted bias in skill selection or are fine-tuned on our new task.

--- https://arxiv.org/abs/2004.08483 ETC: Encoding Long and Structured Inputs in Transformers Joshua Ainslie, Santiago Ontanon, Chris Alberti, Vaclav Cvicek, Zachary Fisher, Philip Pham, Anirudh Ravula, Sumit Sanghai, Qifan Wang, Li Yang 2020-04-17 2021-03-28 [("doi","10.48550/arXiv.2004.08483")] ai/nn/transformer/attention/hierarchical

Transformer models have advanced the state-of-the-art in many Natural Language Processing (NLP) tasks. In this paper, we present a new Transformer architecture, Extended Transformer Construction (ETC), that addresses two key challenges of standard Transformer architectures, namely scaling input length and encoding structured inputs.

To scale attention to longer inputs, we introduce a novel global-local attention mechanism between global tokens and regular input tokens. We also show that combining global-local attention with relative position encodings and a Contrastive Predictive Coding (CPC) pre-training objective allows ETC to encode structured inputs.

We achieve state-of-the-art results on 4 natural language datasets requiring long and/or structured inputs.

--- https://arxiv.org/abs/2004.05150 Longformer: The Long-Document Transformer Iz Beltagy, Matthew E. Peters, Arman Cohan 2020-04-10 2021-03-28 [("doi","10.48550/arXiv.2004.05150")] ai/nn/transformer/attention/hierarchical

Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer’s attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention.

Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8/enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA.

We finally introduce the Longformer-Encoder-Decoder (LED), a Longformer variant for supporting long document generative sequence-to-sequence tasks, and demonstrate its effectiveness on the arXiv summarization dataset.

--- https://arxiv.org/abs/2003.07853#google Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation Huiyu Wang, Yukun Zhu, Bradley Green, Hartwig Adam, Alan Yuille, Liang-Chieh Chen 2020-03-17 2021-03-28 [("doi","10.48550/arXiv.2003.07853")] ai/nn/transformer/attention/sparsity

Convolution exploits locality for efficiency at a cost of missing long range context. Self-attention has been adopted to augment CNNs with non-local interactions. Recent works prove it possible to stack self-attention layers to obtain a fully attentional network by restricting the attention to a local region. In this paper, we attempt to remove this constraint by factorizing 2D self-attention into two 1D self-attentions. This reduces computation complexity and allows performing attention within a larger or even global region.

In companion, we also propose a position-sensitive self-attention design. Combining both yields our position-sensitive axial-attention layer, a novel building block that one could stack to form axial-attention models for image classification and dense prediction.

We demonstrate the effectiveness of our model on 4 large-scale datasets. In particular, our model outperforms all existing stand-alone self-attention models on ImageNet. Our Axial-DeepLab improves 2.8% PQ over bottom-up state-of-the-art on COCO test-dev. This previous state-of-the-art is attained by our small variant that is 3.8× parameter-efficient and 27× computation-efficient. Axial-DeepLab also achieves state-of-the-art results on Mapillary Vistas and Cityscapes.

--- https://arxiv.org/abs/2005.00581#facebook Multi-scale Transformer Language Models Sandeep Subramanian, Ronan Collobert, Marc’Aurelio Ranzato, Y-Lan Boureau 2020-05-01 2021-03-29 [("doi","10.48550/arXiv.2005.00581")] ai/nn/transformer/attention/hierarchical

We investigate multi-scale transformer language models that learn representations of text at multiple scales, and present 3 different architectures that have an inductive bias to handle the hierarchical nature of language.

Experiments on large-scale language modeling benchmarks empirically demonstrate favorable likelihood vs memory footprint trade-offs, eg. we show that it is possible to train a hierarchical variant with 30 layers that has 23% smaller memory footprint and better perplexity, compared to a vanilla transformer with less than half the number of layers, on the Toronto BookCorpus.

We analyze the advantages of learned representations at multiple scales in terms of memory footprint, compute time, and perplexity, which are particularly appealing given the quadratic scaling of transformers’ run time and memory usage with respect to sequence length.

--- https://arxiv.org/abs/2003.05664 Conditional Convolutions for Instance Segmentation Zhi Tian, Chunhua Shen, Hao Chen 2020-03-12 2021-03-29 [("doi","10.48550/arXiv.2003.05664")] ai/nn/cnn

We propose a simple yet effective instance segmentation framework, termed CondInst (conditional convolutions for instance segmentation). Top-performing instance segmentation methods such as Mask R-CNN rely on ROI operations (typically ROIPool or ROIAlign) to obtain the final instance masks. In contrast, we propose to solve instance segmentation from a new perspective. Instead of using instance-wise ROIs as inputs to a network of fixed weights, we employ dynamic instance-aware networks, conditioned on instances. CondInst enjoys two advantages: (1) Instance segmentation is solved by a fully convolutional network, eliminating the need for ROI cropping and feature alignment. (2) Due to the much improved capacity of dynamically-generated conditional convolutions, the mask head can be very compact (eg. 3 conv. layers, each having only 8 channels), leading to faster inference. We demonstrate a simpler instance segmentation method that can achieve improved performance in both accuracy and inference speed. On the COCO dataset, we outperform a few recent methods including well-tuned Mask RCNN baselines, without longer training schedules needed.

Code is available: https://github.com/aim-uofa/AdelaiDet.

--- https://arxiv.org/abs/2003.04297#facebook Improved Baselines with Momentum Contrastive Learning Xinlei Chen, Haoqi Fan, Ross Girshick, Kaiming He 2020-03-09 2021-03-29 [("doi","10.48550/arXiv.2003.04297")] ai/nn

Contrastive unsupervised learning has recently shown encouraging progress, eg. in Momentum Contrast (MoCo) and SimCLR.

In this note, we verify the effectiveness of two of SimCLR’s design improvements by implementing them in the MoCo framework. With simple modifications to MoCo—namely, using an MLP projection head and more data augmentation—we establish stronger baselines that outperform SimCLR and do not require large training batches.

We hope this will make state-of-the-art unsupervised learning research more accessible.

Code will be made public.

--- https://arxiv.org/abs/2002.11296#google Sparse Sinkhorn Attention Yi Tay, Dara Bahri, Liu Yang, Donald Metzler, Da-Cheng Juan 2020-02-26 2021-03-29 [("doi","10.48550/arXiv.2002.11296")] ai/nn/transformer/attention/sparsity cs/algorithm/sorting

We propose Sparse Sinkhorn Attention, a new efficient and sparse method for learning to attend. Our method is based on differentiable sorting of internal representations. Concretely, we introduce a meta sorting network that learns to generate latent permutations over sequences. Given sorted sequences, we are then able to compute quasi-global attention with only local windows, improving the memory efficiency of the attention module.

To this end, we propose new algorithmic innovations such as Causal Sinkhorn Balancing and SortCut, a dynamic sequence truncation method for tailoring Sinkhorn Attention for encoding and/or decoding purposes.

Via extensive experiments on algorithmic seq2seq sorting, language modeling, pixel-wise image generation, document classification, and natural language inference, we demonstrate that our memory efficient Sinkhorn Attention method is competitive with vanilla attention and consistently outperforms recently proposed efficient Transformer models such as Sparse Transformers.

--- https://arxiv.org/abs/2002.09402#facebook Addressing Some Limitations of Transformers with Feedback Memory Angela Fan, Thibaut Lavril, Edouard Grave, Arm Holdings, Joulin, Sainbayar Sukhbaatar 2020-02-21 2021-03-29 [("doi","10.48550/arXiv.2002.09402")] ai/nn/transformer/attention/recurrent

Transformers have been successfully applied to sequential, auto-regressive tasks despite being feedforward networks. Unlike recurrent neural networks, Transformers use attention to capture temporal relations while processing input tokens in parallel. While this parallelization makes them computationally efficient, it restricts the model from fully exploiting the sequential nature of the input. The representation at a given layer can only access representations from lower layers, rather than the higher level representations already available.

In this work, we propose the Feedback Transformer architecture that exposes all previous representations to all future representations, meaning the lowest representation of the current timestep is formed from the highest-level abstract representation of the past. We demonstrate on a variety of benchmarks in language modeling, machine translation, and reinforcement learning that the increased representation capacity can create small, shallow models with much stronger performance than comparable Transformers.

--- https://arxiv.org/abs/2002.08709 Do We Need Zero Training Loss After Achieving Zero Training Error? Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama 2020-02-20 2021-03-29 [("doi","10.48550/arXiv.2002.08709")] ai/nn/cnn ai/nn/gan ai/nn/transformer

Overparameterized deep networks have the capacity to memorize training data with zero training error. Even after memorization, the training loss continues to approach zero, making the model overconfident and the test performance degraded.

We propose a direct solution called flooding that intentionally prevents further reduction of the training loss when it reaches a reasonably small value, which we call the flood level. Our approach makes the loss float around the flood level by doing mini-batched gradient descent as usual but gradient ascent if the training loss is below the flood level. This can be implemented with one line of code and is compatible with any stochastic optimizer and other regularizers.

With flooding, the model will continue to “random walk” with the same non-zero training loss, and we expect it to drift into an area with a flat loss landscape that leads to better generalization.

We experimentally show that flooding improves performance and, as a byproduct, induces a double descent curve of the test loss.

Since existing regularizers do not directly aim to avoid zero training loss, it is hard to tune their hyperparameters in order to maintain a fixed/preset level of training loss.

--- https://arxiv.org/abs/2002.08791 Bayesian Deep Learning and a Probabilistic Perspective of Generalization Andrew Gordon Wilson, Pavel Izmailov 2020-02-20 2021-03-29 [("doi","10.48550/arXiv.2002.08791")] ai/nn/cnn ai/nn/sparsity ai/nn/transformer statistics/bayes

The key distinguishing property of a Bayesian approach is marginalization, rather than using a single setting of weights. Bayesian marginalization can particularly improve the accuracy and calibration of modern deep neural networks, which are typically underspecified by the data, and can represent many compelling but different solutions.

We show that deep ensembles provide an effective mechanism for approximate Bayesian marginalization, and propose a related approach that further improves the predictive distribution by marginalizing within basins of attraction, without much overhead. We also investigate the prior over functions implied by a vague distribution over neural network weights, explaining the generalization properties of such models from a probabilistic perspective.

From this perspective, we explain results that have been presented as mysterious and distinct to neural network generalization, such as the ability to fit images with random labels, and show that these results can be reproduced with Gaussian processes. We also show that Bayesian model averaging alleviates double descent, resulting in monotonic performance improvements with increased flexibility.

Finally, we provide a Bayesian perspective on tempering for calibrating predictive distributions.

--- https://arxiv.org/abs/2004.13831 A Review of Winograd Schema Challenge Datasets and Approaches Vid Kocijan, Thomas Lukasiewicz, Ernest Davis, Gary Marcus, Leora Morgenstern 2020-04-23 2021-03-29 [("doi","10.48550/arXiv.2004.13831")] ai/scaling

The Winograd Schema Challenge is both a commonsense reasoning and natural language understanding challenge, introduced as an alternative to the Turing test.

A Winograd schema is a pair of sentences differing in one or two words with a highly ambiguous pronoun, resolved differently in the two sentences, that appears to require commonsense knowledge to be resolved correctly. The examples were designed to be easily solvable by humans but difficult for machines, in principle requiring a deep understanding of the content of the text and the situation it describes.

This paper reviews existing Winograd Schema Challenge benchmark datasets and approaches that have been published since its introduction.

--- https://arxiv.org/abs/2003.08380#google TTTTTackling WinoGrande Schemas Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, Jimmy Lin 2020-03-18 2021-03-29 [("doi","10.48550/arXiv.2003.08380")] ai/nn/transformer/t5 ai/scaling

We applied the T5 sequence-to-sequence model to tackle the AI2 WinoGrande Challenge by decomposing each example into two input text strings, each containing a hypothesis, and using the probabilities assigned to the “entailment” token as a score of the hypothesis.

Our first (and only) submission to the official leaderboard yielded 0.7673 AUC on March 13, 2020, which is the best known result at this time and beats the previous state-of-the-art by over 5 points.

--- https://arxiv.org/abs/2003.08505 A Metric Learning Reality Check Kevin Musgrave, Serge Belongie, Ser-Nam Lim 2020-03-18 2021-03-29 [("doi","10.48550/arXiv.2003.08505")] ai/scaling

Deep metric learning papers from the past four years have consistently claimed great advances in accuracy, often more than doubling the performance of decade-old methods.

In this paper, we take a closer look at the field to see if this is actually true.

We find flaws in the experimental methodology of numerous metric learning papers, and show that the actual improvements over time have been marginal at best. [ie. improvement has been due to scaling compute/parameters/data]

--- https://arxiv.org/abs/2002.11328 Rethinking Bias-Variance Trade-off for Generalization of Neural Networks Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, Yi Ma 2020-02-26 2021-03-29 [("doi","10.48550/arXiv.2002.11328")] ai/scaling

The classical bias-variance trade-off predicts that bias decreases and variance increase with model complexity, leading to a U-shaped risk curve. Recent work calls this into question for neural networks and other over-parameterized models, for which it is often observed that larger models generalize better.

We provide a simple explanation for this by measuring the bias and variance of neural networks: while the bias is monotonically decreasing as in the classical theory, the variance is unimodal or bell-shaped: it increases then decreases with the width of the network. We vary the network architecture, loss function, and choice of dataset and confirm that variance unimodality occurs robustly for all models we considered. The risk curve is the sum of the bias and variance curves and displays different qualitative shapes depending on the relative scale of bias and variance, with the double descent curve observed in recent literature as a special case.

We corroborate these empirical results with a theoretical analysis of two-layer linear networks with random first layer. Finally, evaluation on out-of-distribution data shows that most of the drop in accuracy comes from increased bias while variance increases by a relatively small amount. Moreover, we find that deeper models decrease bias and increase variance for both in-distribution and out-of-distribution data.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1474615/pdf/envhper00429-0162.pdf Phytochemical mimicry of reproductive hormones and modulation of herbivore fertility by phytoestrogens C L. Hughes 1988 2021-03-30 [("doi","10.1289/ehp.8878171")] biology

Plants have physical and chemical mechanisms for defense from attack by animals.

Phytochemical defenses that protect plants from attack by insects include antifeedants, insecticides, and insect growth regulators. Phytochemical options exist by which plants can modulate the fertility of the other major group of plant predators, vertebrate herbivores, and thereby reduce cumulative attacks by those herbivores.

The success of such a defense depends upon phytochemical mimicry of vertebrate reproductive hormones. Phytoestrogens do mimic reproductive hormones and are proposed to be defensive substances produced by plants to modulate the fertility of herbivores.

--- https://arxiv.org/abs/2004.10746#google Chip Placement with Deep Reinforcement Learning Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan, Joe Jiang, Ebrahim Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Sungmin Bae, Azade Nazi, Jiwoo Pak, Andy Tong, Kavya Srinivasa, William Hang, Emre Tuncer, An, Babu, Quoc V. Le, James Laudon, Richard Ho, Roger Carpenter, Jeff Dean 2020-04-22 2021-03-30 [("doi","10.48550/arXiv.2004.10746")] cs/hardware reinforcement-learning/model-free

In this work, we present a learning-based approach to chip placement, one of the most complex and time-consuming stages of the chip design process. Unlike prior methods, our approach has the ability to learn from past experience and improve over time. In particular, as we train over a greater number of chip blocks, our method becomes better at rapidly generating optimized placements for previously unseen chip blocks.

To achieve these results, we pose placement as a Reinforcement Learning (RL) problem and train an agent to place the nodes of a chip netlist onto a chip canvas. To enable our RL policy to generalize to unseen blocks, we ground representation learning in the supervised task of predicting placement quality. By designing a neural architecture that can accurately predict reward across a wide variety of netlists and their placements, we are able to generate rich feature embeddings of the input netlists. We then use this architecture as the encoder of our policy and value networks to enable transfer learning. Our objective is to minimize PPA (power, performance, and area), and we show that, in under 6 hours, our method can generate placements that are superhuman or comparable on modern accelerator netlists, whereas existing baselines require human experts in the loop and take several weeks.

--- https://www.biorxiv.org/content/10.1101/2020.05.09.075226.full Genome-wide association study of school grades identifies a genetic overlap between language ability, psychopathology and creativity Veera M. Rajagopal, Andrea Ganna, Jonathan R. I. Coleman, Andrea G. Allegrini, Georgios Voloudakis, Jakob Grove, Thomas D. Als, Henriette T. Horsdal, Liselotte Petersen, Vivek Appadurai, Andrew Schork, Alfonso Buil, Cynthia M. Bulik, Jonas Bybjerg-Grauholm, Marie Bækvad-Hansen, David Hougaard, Ole Mors, Merete Nordentoft, Thomas Werge, iPSYCH-Broad Consortium, Preben Bo Mortensen, Gerome Breen, Panos Roussos, Robert Plomin, Esben Agerbo, Anders Børglum, Ditte Demontis 2020-05-12 2021-03-30 [("doi","10.1101/2020.05.09.075226")] genetics/heritable/correlation iq psychiatry

Individuals with psychiatric disorders perform differently in school compared to the general population. Genetic factors contribute substantially to such differences. It is however unclear if differential performance is seen across all cognitive domains such as math and language.

Here we report a genome-wide association study (GWAS) of school grades in 30,982 individuals (18,495 with and 12,487 without one or more of 6 major psychiatric disorders) and a replication study in 4,547 individuals. GWAS of overall school performance yielded results that were highly similar to the results of a previous GWAS of educational attainment. Analyzing subject specific grades, we observed that math performance was severely affected whereas language performance (Danish and English) was relatively unaffected or enhanced in those with psychiatric disorders compared to controls.

We found that the genetic variants associated with poor math performance, but better language performance were also associated with increased risk for multiple psychiatric disorders. The same variants were also associated with creativity, which we show through a polygenic score analysis of 2953 creative professionals and 164,622 controls.

The results overall suggest that risk for psychiatric disorders, language ability, and creativity might have overlapping genetic roots.

--- https://www.biorxiv.org/content/10.1101/2020.05.08.084475.full Local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits Yiliang Zhang, Qiongshi Lu, Yixuan Ye, Kunling Huang, Wei Liu, Yuchang Wu, Xiaoyuan Zhong, Boyang Li, Zhaolong Yu, Brittany G. Travers, Donna M. Werling, James J. Li, Hongyu Zhao 2020-05-10 2021-03-30 [("doi","10.1101/2020.05.08.084475")] genetics/heritable/correlation psychiatry/autism

Local genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions, which could shed unique light on etiologic sharing and provide additional mechanistic insights into the genetic basis of complex traits compared to global genetic correlation. However, accurate estimation of local genetic correlation remains challenging, in part due to extensive linkage disequilibrium in local genomic regions and pervasive sample overlap across studies.

We introduce SUPERGNOVA, a unified framework to estimate both global and local genetic correlations using summary statistics from genome-wide association studies. Through extensive simulations and analyses of 30 complex traits, we demonstrate that SUPERGNOVA substantially outperforms existing methods and identifies 150 trait pairs with local genetic correlations.

In particular, we show that the positive, consistently-identified, yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically-distinct genetic signatures with bidirectional local genetic correlations.

We believe that statistically-rigorous local genetic correlation analysis could accelerate progress in complex trait genetics research.

--- https://www.biorxiv.org/content/10.1101/2020.04.03.024554.full Estimating the effect-size of gene dosage on cognitive ability across the coding genome Guillaume Huguet, Catherine Schramm, Elise Douard, Tamer Petra, Antoine Main, Pauline Monin, Jade England, Khadije Jizi, Thomas Renne, Myriam Poirier, Sabrina Nowak, Charles-Olivier Martin, Nadine Younis, Inga Sophia Knoth, Martineau Jean-Louis, Zohra Saci, Maude Auger, Frédérique Tihy, Géraldine Mathonnet, Catalina Maftei, France Léveillé, David J. Porteous, Gail Davies, Paul Redmond, Sarah E. Harris, W. David Hill, Emmanuelle Lemyre, Gunter Schumann, Thomas Bourgeron, Zdenka Pausova, Tomas Paus, Sherif Karama, Sarah Lippe, Ian J. Deary, Laura Almasy, Aurélie Labbe, David Glahn, Celia M. T. Greenwood, Sébastien Jacquemont 2020-04-05 2021-03-30 [("doi","10.1101/2020.04.03.024554")] genetics/heritable/rare iq psychiatry/autism

Rare genomic Copy Number Variants (CNVs) are major contributors to neurodevelopmental disorder. The vast majority of pathogenic CNVs reported back to patients are ultra-rare and their quantitative effects on traits such as intelligence are undocumented.

Here, we identified all CNVs ≥ 50 kilobase in 24,092 individuals from unselected and autism cohorts. We developed statistical models to estimate the effect-size of CNVs on intelligence based on their coding and non-coding characteristics.

Measures of intolerance to haploinsufficiency best explained the effect of any deletion or duplication on general intelligence. There was no heterogeneity across unselected and autism cohorts. Validation was performed using an intraclass concordance and showed that model estimates of general intelligence were 78% accurate with mean effect-sizes previously published for 47 CNVs.

Inheritance data on 27,766 CNVs showed that deletions and duplications with the same large effect-size on intelligence occur de novo at the same frequency.

Our first outline for the effect sizes of all coding genes on intelligence suggests that around 10,000 genes affect this trait.

--- https://www.biorxiv.org/content/10.1101/2020.05.26.116111.full Sex-biased reduction in reproductive success drives selective constraint on human genes Eugene J. Gardner, Matthew D. C. Neville, Kaitlin E. Samocha, Kieron Barclay, Martin Kolk, Mari E. K. Niemi, George Kirov, Hilary C. Martin, Matthew E. Hurles 2020-05-28 2021-03-30 [("doi","10.1101/2020.05.26.116111")] genetics/heritable/correlation genetics/heritable/rare genetics/selection/natural/human iq/ses psychiatry

Genome-wide sequencing of human populations has revealed substantial variation among genes in the intensity of purifying selection acting on damaging genetic variants. While genes under the strongest selective constraint are highly enriched for Mendelian disorders, most of these genes are not associated with disease and therefore the nature of the selection acting on them is not known.

Here we show that genetic variants that damage these genes reduce reproductive success substantially in males but much less so in females. We present evidence that this reduction is mediated by cognitive and behavioral traits, which renders male carriers of such variants less likely to find mating partners.

Our findings represent strong genetic evidence that Darwin’s theory of sexual selection is shaping the gene pool of contemporary human populations. Furthermore, our results suggest that sexual selection can account for about a quarter of all purifying selection acting on human genes.

--- https://www.biorxiv.org/content/10.1101/2020.02.28.969600.full Using high-throughput phenotypes to enable genomic selection by inferring genotypes Andrew Whalen, Chris Gaynor, John M. Hickey 2020-03-02 2021-03-30 [("doi","10.1101/2020.02.28.969600")] genetics/selection/artificial statistics/variance-component

In this paper we develop and test a method which uses high-throughput phenotypes to infer the genotypes of an individual. The inferred genotypes can then be used to perform genomic selection. Previous methods which used high-throughput phenotype data to increase the accuracy of selection assumed that the high-throughput phenotypes correlate with selection targets. When this is not the case, we show that the high-throughput phenotypes can be used to determine which haplotypes an individual inherited from their parents, and thereby infer the individual’s genotypes.

We tested this method in two simulations. In the first simulation, we explored how the accuracy of the inferred genotypes depended on the high-throughput phenotypes used and the genome of the species analyzed. In the second simulation we explored whether using this method could increase genetic gain a plant breeding program by enabling genomic selection on non-genotyped individuals.

In the first simulation, we found that genotype accuracy was higher if more high-throughput phenotypes were used and if those phenotypes had higher heritability. We also found that genotype accuracy decreased with an increasing size of the species genome. In the second simulation, we found that the inferred genotypes could be used to enable genomic selection on non-genotyped individuals and increase genetic gain compared to random selection, or in some scenarios phenotypic selection.

This method presents a novel way for using high-throughput phenotype data in breeding programs. As the quality of high-throughput phenotypes increases and the cost decreases, this method may enable the use of genomic selection on large numbers of non-genotyped individuals.

--- https://arxiv.org/abs/2004.14990 Reinforcement Learning with Augmented Data Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas 2020-04-30 2021-03-30 [("doi","10.48550/arXiv.2004.14990")] ai/nn/cnn reinforcement-learning/model

Learning from visual observations is a fundamental yet challenging problem in Reinforcement Learning (RL). Although algorithmic advances combined with convolutional neural networks have proved to be a recipe for success, current methods are still lacking on two fronts: (a) data-efficiency of learning and (b) generalization to new environments.

To this end, we present Reinforcement Learning with Augmented Data (RAD), a simple plug-and-play module that can enhance most RL algorithms. We perform the first extensive study of general data augmentations for RL on both pixel-based and state-based inputs, and introduce two new data augmentations—random translate and random amplitude scale. We show that augmentations such as random translate, crop, color jitter, patch cutout, random convolutions, and amplitude scale can enable simple RL algorithms to outperform complex state-of-the-art methods across common benchmarks. RAD sets a new state-of-the-art in terms of data-efficiency and final performance on the DeepMind Control Suite benchmark for pixel-based control as well as OpenAI Gym benchmark for state-based control.

We further demonstrate that RAD improves test-time generalization over existing methods on several OpenAI ProcGen benchmarks. Our RAD module and training code are available at https://github.com/MishaLaskin/rad.

--- https://arxiv.org/abs/2004.13649 Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels Ilya Kostrikov, Denis Yarats, Rob Fergus 2020-04-28 2021-03-30 [("doi","10.48550/arXiv.2004.13649")] reinforcement-learning/model-free

We propose a simple data augmentation technique that can be applied to standard model-free reinforcement learning algorithms, enabling robust learning directly from pixels without the need for auxiliary losses or pre-training. The approach leverages input perturbations commonly used in computer vision tasks to regularize the value function.

Existing model-free approaches, such as Soft Actor-Critic (SAC), are not able to train deep networks effectively from image pixels. However, the addition of our augmentation method dramatically improves SAC’s performance, enabling it to reach state-of-the-art performance on the DeepMind control suite, surpassing model-based (Dreamer, PlaNet, and SLAC) methods and recently proposed contrastive learning (CURL).

Our approach can be combined with any model-free reinforcement learning algorithm, requiring only minor modifications.

An implementation can be found at https://github.com/denisyarats/drq .

--- https://arxiv.org/abs/2002.09089 Bayesian REX: Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences Daniel S. Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum 2020-02-21 2021-03-30 [("doi","10.48550/arXiv.2002.09089")] reinforcement-learning/imitation-learning reinforcement-learning/preference-learning statistics/bayes

Bayesian reward learning from demonstrations enables rigorous safety and uncertainty analysis when performing imitation learning. However, Bayesian reward learning methods are typically computationally intractable for complex control problems.

We propose Bayesian Reward Extrapolation (Bayesian REX), a highly efficient Bayesian reward learning algorithm that scales to high-dimensional imitation learning problems by pre-training a low-dimensional feature encoding via self-supervised tasks and then leveraging preferences over demonstrations to perform fast Bayesian inference.

Bayesian REX can learn to play Atari games from demonstrations, without access to the game score and can generate 100,000 samples from the posterior over reward functions in only 5 minutes on a personal laptop. Bayesian REX also results in imitation learning performance that is competitive with or better than state-of-the-art methods that only learn point estimates of the reward function.

Finally, Bayesian REX enables efficient high-confidence policy evaluation without having access to samples of the reward function. These high-confidence performance bounds can be used to rank the performance and risk of a variety of evaluation policies and provide a way to detect reward hacking behaviors.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1416756/pdf/bmjcred00467-0046.pdf How accurate are quotations and references in medical journals? G de Lacey, C. Record, J. Wade 1985 2021-03-30 [("doi","10.1136/bmj.291.6499.884")] statistics/bias/publication/miscitation

The accuracy of quotations and references in 6 medical journals published during January 1984 was assessed.

The original author was misquoted in 15% of all references, and most of the errors would have misled readers. Errors in citation of references occurred in 24%, of which 8% were major errors—that is, they prevented immediate identification of the source of the reference. Inaccurate quotations and citations are displeasing for the original author, misleading for the reader, and mean that untruths become “accepted fact.”

Some suggestions for reducing these high levels of inaccuracy are that papers scheduled for publication with errors of citation should be returned to the author and checked completely and a permanent column specifically for misquotations could be inserted into the journal.

--- https://www.newyorker.com/magazine/1987/02/23/atchafalayae 2021-03-31 technology --- https://arxiv.org/abs/2002.06260#adobe Why Do Line Drawings Work? A Realism Hypothesis Aaron Hertzmann 2020-02-14 2021-03-31 [("doi","10.1177/0301006620908207")] ai/anime psychology/neuroscience

Why is it that we can recognize object identity and 3D shape from line drawings, even though they do not exist in the natural world?

This paper hypothesizes that the human visual system perceives line drawings as if they were ~realistic images. Moreover, the techniques of line drawing are chosen to accurately convey shape to a human observer.

Several implications and variants of this hypothesis are explored.

--- https://arxiv.org/abs/1912.02164 Plug and Play Language Models: A Simple Approach to Controlled Text Generation Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, Rosanne Liu 2019-12-04 2021-03-31 [("doi","10.48550/arXiv.1912.02164")] ai/nn/sampling ai/nn/transformer/gpt

Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated language (eg. switching topic or sentiment) is difficult without modifying the model architecture or fine-tuning on attribute-specific data and entailing the cost of retraining.

We propose a simple alternative: the Plug and Play Language Model (PPLM) for controllable language generation, which combines a pretrained LM with one or more simple attribute classifiers that guide text generation without any further training of the LM. In the canonical scenario we present, the attribute models are simple classifiers consisting of a user-specified bag of words or a single learned layer with 100,000× fewer parameters than the LM. Sampling entails a forward and backward pass in which gradients from the attribute model push the LM’s hidden activations and thus guide the generation.

Model samples demonstrate control over a range of topics and sentiment styles, and extensive automated and human annotated evaluations show attribute alignment and fluency.

PPLMs are flexible in that any combination of differentiable attribute models may be used to steer text generation, which will allow for diverse and creative applications beyond the examples given in this paper.

--- https://arxiv.org/abs/1911.12543 How Can We Know What Language Models Know? Zhengbao Jiang, Frank F. Xu, Jun Araki, Graham Neubig 2019-11-28 2021-03-31 [("doi","10.48550/arXiv.1911.12543")] ai/dataset ai/nn/transformer/gpt

Recent work has presented intriguing results examining the knowledge contained in language models (LM) by having the LM fill in the blanks of prompts such as “Obama is a—by profession”. These prompts are usually manually created, and quite possibly sub-optimal; another prompt such as “Obama worked as a—” may result in more accurately predicting the correct profession. Because of this, given an inappropriate prompt, we might fail to retrieve facts that the LM does know, and thus any given prompt only provides a lower bound estimate of the knowledge contained in an LM.

In this paper, we attempt to more accurately estimate the knowledge contained in LMs by automatically discovering better prompts to use in this querying process. Specifically, we propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts, as well as ensemble methods to combine answers from different prompts.

Extensive experiments on the LAMA benchmark for extracting relational knowledge from LMs demonstrate that our methods can improve accuracy 31.1% → 39.6%, providing a tighter lower bound on what LMs know.

We have released the code and the resulting LM Prompt And Query Archive (LPAQA) at Github.

--- https://arxiv.org/abs/2002.05867 Transformers as Soft Reasoners over Language Peter Clark, Oyvind Tafjord, Kyle Richardson 2020-02-14 2021-03-31 [("doi","10.48550/arXiv.2002.05867")] ai/nn/transformer math

Beginning with McCarthy’s Advice Taker (1959), AI has pursued the goal of providing a system with explicit, general knowledge and having the system reason over that knowledge. However, expressing the knowledge in a formal (logical or probabilistic) representation has been a major obstacle to this research.

This paper investigates a modern approach to this problem where the facts and rules are provided as natural language sentences, thus bypassing formal representation. We train transformers to reason (or emulate reasoning) over these sentences using synthetically generated data.

Our models, that we call RuleTakers, provide the first empirical demonstration that this kind of soft reasoning over language is learnable, can achieve high (99%) accuracy, and generalizes to test data requiring substantially deeper chaining than seen during training (95%+ scores). We also demonstrate that the models transfer well to two hand-authored rulebases, and to rulebases paraphrased into more natural language.

These findings suggest a new role for transformers, namely as limited “soft theorem provers” operating over explicit theories in language. This in turn suggests new possibilities for explainability, correctability, and counterfactual reasoning in question-answering.

--- https://arxiv.org/abs/2002.06177 The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence Gary Marcus 2020-02-14 2021-03-31 [("doi","10.48550/arXiv.2002.06177")] ai/nn ai/scaling

Recent research in artificial intelligence and machine learning has largely emphasized general-purpose learning and ever-larger training sets and more and more compute.

In contrast, I propose a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models, that could provide the substrate for a richer, more robust AI than is currently possible.

[Turns out, scaling kept working...]

--- https://arxiv.org/abs/2001.08764 Reducing Non-Normative Text Generation from Language Models Xiangyu Peng, Siyan Li, Spencer Frazier, Mark Riedl 2020-01-23 2021-03-31 [("doi","10.48550/arXiv.2001.08764")] ai/nn/transformer/gpt/2

Large-scale, transformer-based language models such as GPT-2 are pretrained on diverse corpora scraped from the internet. Consequently, they are prone to generating non-normative text (ie. in violation of social norms).

We introduce a technique for fine-tuning GPT-2, using a policy gradient reinforcement learning technique and a normative text classifier to produce reward and punishment values. We evaluate our technique on 5 datasets using automated and human participant experiments.

The normative text classifier is 81–90% accurate when compared to gold-standard human judgments of normative and non-normative generated text.

Our normative fine-tuning technique is able to reduce non-normative text by 27–61%, depending on the dataset.

--- https://arxiv.org/abs/1912.07875#facebook Libri-Light: A Benchmark for ASR with Limited or No Supervision Jacob Kahn, Morgane Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi, Vitaliy Liptchinsky, Ronan Collobert, Christian Fuegen, Tatiana Likhomanenko, Gabriel Synnaeve, Arm Holdings, Joulin, Abdelrahman Mohamed, Emmanuel Dupoux 2019-12-17 2021-03-31 [("doi","10.1109/ICASSP40776.2020.9052942")] ai/dataset ai/nn

We introduce a new collection of spoken English audio suitable for training speech recognition systems under limited or no supervision. It is derived from open-source audio books from the LibriVox project. It contains over 60K hours of audio, which is, to our knowledge, the largest freely-available corpus of speech.

The audio has been segmented using voice activity detection and is tagged with SNR, speaker ID and genre descriptions.

Additionally, we provide baseline systems and evaluation metrics working under 3 settings: (1) the zero resource/unsupervised setting (ABX), (2) the semi-supervised setting (PER, CER) and (3) the distant supervision setting (WER). Settings (2) and (3) use limited textual resources (10 minutes to 10 hours) aligned with the speech. Setting (3) uses large amounts of unaligned text.

They are evaluated on the standard LibriSpeech dev and test sets for comparison with the supervised state-of-the-art.

--- https://arxiv.org/abs/1911.09071 The Origins and Prevalence of Texture Bias in Convolutional Neural Networks Katherine L. Hermann, Ting Chen, Simon Kornblith 2019-11-20 2021-03-31 [("doi","10.48550/arXiv.1911.09071")] ai/nn/cnn

Recent work has indicated that, unlike humans, ImageNet-trained CNNs tend to classify images by texture rather than by shape. How pervasive is this bias, and where does it come from? We find that, when trained on datasets of images with conflicting shape and texture, CNNs learn to classify by shape at least as easily as by texture.

What factors, then, produce the texture bias in CNNs trained on ImageNet? Different unsupervised training objectives and different architectures have small but and largely independent effects on the level of texture bias. However, all objectives and architectures still lead to models that make texture-based classification decisions a majority of the time, even if shape information is decodable from their hidden representations.

The effect of data augmentation is much larger. By taking less aggressive random crops at training time and applying simple, naturalistic augmentation (color distortion, noise, and blur), we train models that classify ambiguous images by shape a majority of the time, and outperform baselines on out-of-distribution test sets.

Our results indicate that apparent differences in the way humans and ImageNet-trained CNNs process images may arise not primarily from differences in their internal workings, but from differences in the data that they see.

--- https://arxiv.org/abs/1912.03553 Learning Norms from Stories: A Prior for Value Aligned Agents Spencer Frazier, Md Sultan Al Nahian, Mark Riedl, Brent Harrison 2019-12-07 2021-03-31 [("doi","10.48550/arXiv.1912.03553")] ai/nn/transformer/gpt/fiction philosophy/ethics reinforcement-learning/imitation-learning reinforcement-learning/preference-learning reinforcement-learning/safe

Value alignment is a property of an intelligent agent indicating that it can only pursue goals and activities that are beneficial to humans. Traditional approaches to value alignment use imitation learning or preference learning to infer the values of humans by observing their behavior.

We introduce a complementary technique in which a value aligned prior is learned from naturally occurring stories which encode societal norms. Training data is sourced from the children’s educational comic strip, Goofus and Gallant. In this work, we train multiple machine learning models to classify natural language descriptions of situations found in the comic strip as normative or non-normative by identifying if they align with the main characters’ behavior.

We also report the models’ performance when transferring to two unrelated tasks with little to no additional training on the new task.

--- https://arxiv.org/abs/2002.02511 Introducing Aspects of Creativity in Automatic Poetry Generation Brendan Bena, Jugal Kalita 2020-02-06 2021-03-31 [("doi","10.48550/arXiv.2002.02511")] ai/nn/transformer/gpt/poetry

Poetry Generation involves teaching systems to automatically generate text that resembles poetic work. A deep learning system can learn to generate poetry on its own by training on a corpus of poems and modeling the particular style of language. In this paper, we propose taking an approach that fine-tunes GPT-2, a pre-trained language model, to our downstream task of poetry generation. We extend prior work on poetry generation by introducing creative elements. Specifically, we generate poems that express emotion and elicit the same in readers, and poems that use the language of dreams—called dream poetry.

We are able to produce poems that correctly elicit the emotions of sadness and joy 87.5 and 85%, respectively, of the time. We produce dreamlike poetry by training on a corpus of texts that describe dreams. Poems from this model are shown to capture elements of dream poetry with scores of no less than 3.2 on the Likert scale.

We perform crowdsourced human-evaluation for all our poems. We also make use of the Coh-Metrix tool, outlining metrics we use to gauge the quality of text generated.

--- https://arxiv.org/abs/2002.08910#google How Much Knowledge Can You Pack Into the Parameters of a Language Model? Adam Roberts, Colin Raffel, Noam Shazeer 2020-02-10 2021-04-01 [("doi","10.48550/arXiv.2002.08910")] ai/nn/retrieval ai/nn/transformer/t5 ai/scaling

It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries.

In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge.

We show that this approach scales with model size and performs competitively with open-domain systems that explicitly retrieve answers from an external knowledge source when answering questions.

To facilitate reproducibility and future work, we release our code and trained models at https://github.com/google-research/google-research/tree/master/t5_closed_book_qa.

--- https://arxiv.org/abs/2002.05709#google A Simple Framework for Contrastive Learning of Visual Representations Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton 2020-02-13 2021-04-01 [("doi","10.48550/arXiv.2002.05709")] ai/nn/cnn ai/scaling

This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank.

In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning.

By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100× fewer labels.

--- https://arxiv.org/abs/2002.02559 Impact of ImageNet Model Selection on Domain Adaptation Youshan Zhang, Brian D. Davison 2020-02-06 2021-04-01 [("doi","10.48550/arXiv.2002.02559")] ai/scaling

Deep neural networks are widely used in image classification problems. However, little work addresses how features from different deep neural networks affect the domain adaptation problem.

Existing methods often extract deep features from one ImageNet model, without exploring other neural networks. In this paper, we investigate how different ImageNet models affect transfer accuracy on domain adaptation problems. We extract features from 16 distinct pre-trained ImageNet models and examine the performance of 12 benchmarking methods when using the features.

Extensive experimental results show that a higher accuracy ImageNet model produces better features, and leads to higher accuracy on domain adaptation problems (with a correlation coefficient of up to 0.95). We also examine the architecture of each neural network to find the best layer for feature extraction.

Together, performance from our features exceeds that of the state-of-the-art in 3 benchmark datasets.

--- https://arxiv.org/abs/1912.11370#google Big Transfer (BiT): General Visual Representation Learning Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby 2019-12-24 2021-04-01 [("doi","10.48550/arXiv.1912.11370")] ai/nn/cnn ai/scaling

Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a target task. We scale up pre-training, and propose a simple recipe that we call Big Transfer (B​iT).

By combining a few carefully selected components, and transferring using a simple heuristic, we achieve strong performance on over 20 datasets. BiT performs well across a surprisingly wide range of data regimes—from 1 example per class to 1M total examples [using JFT-300M]...Note that exclusively using more data or larger models may hurt performance; instead, both need to be increased in tandem.

BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10, and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class.

We conduct detailed analysis of the main components that lead to high transfer performance.

--- https://arxiv.org/abs/1912.02315#facebook 12-in-1: Multi-Task Vision and Language Representation Learning Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, Devi Parikh, Stefan Lee 2019-12-05 2021-04-01 [("doi","10.48550/arXiv.1912.02315")] ai/nn ai/scaling

Much of vision-and-language research focuses on a small but diverse set of independent tasks and supporting datasets often studied in isolation; however, the visually-grounded language understanding skills required for success at these tasks overlap. In this work, we investigate these relationships between vision-and-language tasks by developing a large-scale, multi-task training regime.

Our approach culminates in a single model on 12 datasets from 4 broad categories of task including visual question answering, caption-based image retrieval, grounding referring expressions, and multi-modal verification. Compared to independently trained single-task models, this represents a reduction from ~3 billion parameters to 270 million while simultaneously improving performance by 2.05 points on average across tasks.

We use our multi-task framework to perform in-depth analysis of the effect of joint training diverse tasks. Further, we show that finetuning task-specific models from our single multi-task model can lead to further improvements, achieving performance at or above the state-of-the-art.

--- https://arxiv.org/abs/1912.02292#openai Deep Double Descent: Where Bigger Models and More Data Hurt Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever 2019-12-04 2021-04-01 [("doi","10.48550/arXiv.1912.02292")] ai/scaling

We show that a variety of modern deep learning tasks exhibit a “double-descent” phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs.

We unify the above phenomena by defining a new complexity measure we call the effective model complexity and conjecture a generalized double descent with respect to this measure.

Furthermore, our notion of model complexity allows us to identify certain regimes where increasing (even quadrupling) the number of train samples actually hurts test performance.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7018847/ Cassiosomes are stinging-cell structures in the mucus of the upside-down jellyfish Cassiopea xamachana Cheryl L. Ames, Anna M. L. Klompen, Krishna Badhiwala, Kade Muffett, Abigail J. Reft, Mehr Kumar, Jennie D. Janssen, Janna N. Schultzhaus, Lauren D. Field, Megan E. Muroski, Nick Bezio, Jacob T. Robinson, Dagmar H. Leary, Paulyn Cartwright, Allen G. Collins, Gary J. Vora 2020 2021-04-01 [("doi","10.1038/s42003-020-0777-8")] biology

Snorkelers in mangrove forest waters inhabited by the upside-down jellyfish Cassiopea xamachana report discomfort due to a sensation known as stinging water, the cause of which is unknown.

Using a combination of histology, microscopy, microfluidics, videography, molecular biology, and mass spectrometry-based proteomics, we describe C. xamachana stinging-cell structures that we term cassiosomes. These structures are released within C. xamachana mucus and are capable of killing prey. Cassiosomes consist of an outer epithelial layer mainly composed of nematocytes surrounding a core filled by endosymbiotic dinoflagellates hosted within amoebocytes and presumptive mesoglea.

Furthermore, we report cassiosome structures in 4 additional jellyfish species in the same taxonomic group as C. xamachana (Class Scyphozoa; Order Rhizostomeae), categorized as either motile (ciliated) or nonmotile types.

This inaugural study provides a qualitative assessment of the stinging contents of C. xamachana mucus and implicates mucus containing cassiosomes and free intact nematocytes as the cause of stinging water.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246861/ Benefits of Creatine Supplementation for Vegetarians Compared to Omnivorous Athletes: A Systematic Review Mojtaba Kaviani, Keely Shaw, Philip D. Chilibeck 2020 2021-04-01 [("doi","10.3390/ijerph17093041")] creatine

Background: Creatine monohydrate is a nutritional supplement often consumed by athletes in anaerobic sports. Creatine is naturally found in most meat products; therefore, vegetarians have reduced creatine stores and may benefit from supplementation.

Objective: to determine the effects of creatine supplementation on vegetarians.

Data Sources: PubMed and SPORTDiscus. Eligibility criteria: Randomized controlled trials (parallel group, cross-over studies) or prospective studies.

Participants: Vegetarians.

Intervention: Creatine supplementation. Study appraisal and synthesis: A total of 64 records were identified, and eleven full-text articles (covering nine studies) were included in this systematic review.

Results: Creatine supplementation in vegetarians increased total creatine, creatine, and phosphocreatine concentrations in vastus lateralis and gastrocnemius muscle, plasma, and red blood cells, often to levels greater than omnivores. Creatine supplementation had no effect on brain levels of phosphocreatine. Creatine supplementation increased lean tissue mass, type II fiber area, insulin-like growth factor-1, muscular strength, muscular endurance, Wingate mean power output, and brain function (memory and intelligence) in vegetarian participants. Studies were mixed on whether creatine supplementation improved exercise performance in vegetarians to a greater extent compared to omnivores.

Limitations: Studies that were reviewed had moderate-high risk of bias.

Conclusion: Overall, it appears vegetarian athletes are likely to benefit from creatine supplementation.

--- https://arxiv.org/abs/2002.05131 Recursed is not Recursive: A Jarring Result Erik Demaine, Justin Kopinsky, Jayson Lynch 2020-02-12 2021-04-01 [("doi","10.48550/arXiv.2002.05131")] cs/computable

Recursed is a 2D puzzle platform video game featuring treasure chests that, when jumped into, instantiate a room that can later be exited (similar to function calls), optionally generating a jar that returns back to that room (similar to continuations).

We prove that Recursed is RE-complete and thus undecidable (not recursive) by a reduction from the Post Correspondence Problem.

Our reduction is “practical”: the reduction from PCP results in fully playable levels that abide by all constraints governing levels (including the 15×20 room size) designed for the main game.

Our reduction is also “efficient”: a Turing machine can be simulated by a Recursed level whose size is linear in the encoding size of the Turing machine and whose solution length is polynomial in the running time of the Turing machine.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7446297/ Destructive Creation at Work: How Financial Distress Spurs Entrepreneurship Tania Babina 2020 2021-04-01 [("doi","10.1093/rfs/hhz110")] economics

Using U.S. Census firm-worker data, I document that firms’ financial distress has an economically important effect on employee departures to entrepreneurship.

The impact is amplified in the high-tech and service sectors, where employees are key assets. In states with enforceable noncompete contracts, the effect is mitigated.

Compared to typical entrepreneurs, distress-driven entrepreneurs are high-wage workers who found better firms, as measured by jobs, pay, and survival. Startup jobs compensate for 33% of job losses at the constrained incumbents.

Overall, the financial inability of incumbent firms to pursue productive opportunities increases the reallocation of economic activity into new firms.

Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

--- https://www.biorxiv.org/content/10.1101/2020.01.18.910448.full Studies of human twins reveal genetic variation that affects dietary fat perception Cailu Lin, Lauren Colquitt, Paul Wise, Paul A. S. Breslin, Nancy E. Rawson, Federica Genovese, Ivy Maina, Paule Joseph, Lydia Fomuso, Louise Slade, Dennis Brooks, Aurélie Miclo, John E. Hayes, Antonio Sullo, Danielle R. Reed 2020-01-18 2021-04-02 [("doi","10.1101/2020.01.18.910448")] exercise genetics/heritable

To learn more about the mechanisms of human dietary fat perception, 398 human twins rated fattiness and liking for 6 types of potato chips that differed in triglyceride content (2.5, 5, 10, and 15% corn oil); reliability estimates were obtained from a subset (n = 50) who did the task twice. Some chips also had a saturated long-chain fatty acid (hexadecanoic acid, 16:0) added (0.2%) to evaluate its effect on fattiness and liking.

We computed the heritability of these measures and conducted a genome-wide association study (GWAS) to identify regions of the genome that co-segregate with fattiness and liking. Perceived fattiness and liking for the potato chips were reliable (r = 0.31–0.62, p < 0.05) and heritable (up to h2 = 0.29, p < 0.001, for liking).

Adding hexadecanoic acid to the potato chips increased ratings of fattiness but decreased liking. Twins with the G allele of rs263429 near GATA3-AS1 or the G allele of rs8103990 within ZNF729 reported more liking for potato chips than did twins with the other allele (multivariate GWAS, p < 1×10−5), with results reaching genome-wide suggestive but not statistical-significance criteria.

Person-to-person variation in the perception and liking of dietary fat was (a) negatively affected by the addition of a saturated fatty acid and (b) related to inborn genetic variants. These data suggest liking for dietary fat is not due solely to fatty acid content and highlight new candidate genes and proteins within this sensory pathway.

--- https://www.biorxiv.org/content/10.1101/2020.01.14.905794.full Investigating the Genetic Architecture of Non-Cognitive Skills Using GWAS-by-Subtraction Perline A. Demange, Margherita Malanchini, Travis T. Mallard, Pietro Biroli, Simon R. Cox, Andrew D. Grotzinger, Elliot M. Tucker-Drob, Abdel Abdellaoui, Louise Arseneault, Avshalom Caspi, David Corcoran, Benjamin W. Domingue, Colter Mitchell, Elsje van Bergen, Dorret I. Boomsma, Kathleen M. Harris, Hill F. Ip, Terrie E. Moffitt, Richie Poulton, Joseph Prinz, Karen Sugden, Jasmin Wertz, Benjamin Williams, Eveline L. de Zeeuw, Daniel W. Belsky, K. Paige Harden, Michel G. Nivard 2020-01-15 2021-04-02 [("doi","10.1101/2020.01.14.905794")] genetics/heritable/correlation psychology/personality

Educational attainment (EA) is influenced by cognitive abilities and by other characteristics and traits. However, little is known about the genetic architecture of these “non-cognitive” contributions to EA. Here, we use Genomic Structural Equation Modeling and results of prior genome-wide association studies (GWASs) of EA (n = 1,131,881) and cognitive test performance (n = 257,841) to estimate SNP associations with variation in EA that is independent of cognitive ability.

We identified 157 genome-wide statistically-significant loci and a polygenic architecture accounting for 57% of genetic variance in EA.

Phenotypic and biological annotation revealed that (1) both cognitive and non-cognitive contributions to EA were genetically correlated with socioeconomic success and longevity; and (2) non-cognitive contributions to EA were related to personality, decision making, risk-behavior, and increased risk for psychiatric disorders; (3) non-cognitive and cognitive contributions to EA were enriched in the same tissues and cell types, but (4) showed different associations with gray-matter neuroimaging phenotypes.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7343608/ Mendelian Randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics Jean Morrison, Nicholas Knoblauch, Joseph H. Marcus, Matthew Stephens, Xin He 2020 2021-04-02 [("doi","10.1038/s41588-020-0631-4")] genetics/heritable/correlation/mendelian-randomization

Mendelian Randomization (MR) is a valuable tool for detecting causal effects by using genetic variant associations. Opportunities to apply MR are growing rapidly with the increasing number of genome-wide association studies (GWAS). However, existing MR methods rely on strong assumptions that are often violated, leading to false positives. Correlated horizontal pleiotropy, which arises when variants affect both traits through a heritable shared factor, remains a particularly challenging problem.

We propose a new MR method, Causal Analysis Using Summary Effect estimates (CAUSE), that accounts for correlated and uncorrelated horizontal pleiotropic effects. We demonstrate, in simulations, that CAUSE avoids more false positives induced by correlated horizontal pleiotropy than other methods. Applied to traits studied in recent GWAS studies, we find that CAUSE detects causal relationships that have strong literature support and avoids identifying most unlikely relationships. Our results suggest that shared heritable factors are common and may lead to many false positives using alternative methods.

--- https://www.biorxiv.org/content/10.1101/2019.12.17.876862.full Extensive Mammalian Germline Genome Engineering Yanan Yue, Yinan Kan, Weihong Xu, Hong-Ye Zhao, Yixuan Zhou, Xiaobin Song, Jiajia Wu, Juan Xiong, Dharmendra Goswami, Meng Yang, Lydia Lamriben, Mengyuan Xu, Qi Zhang, Yu Luo, Jianxiong Guo, Shenyi Mao, Deling Jiao, Tien Dat Nguyen, Zhuo Li, Jacob V. Layer, Malin Li, Violette Paragas, Michele E. Youd, Zhongquan Sun, Yuan Ding, Weilin Wang, Hongwei Dou, Lingling Song, Xueqiong Wang, Lei Le, Xin Fang, Haydy George, Ranjith Anand, Shi Yun Wang, William F. Westlin, Marc Guell, James Markmann, Wenning Qin, Yangbin Gao, Hongjiang Wei, George M. Church, Luhan Yang 2019-12-19 2021-04-02 [("doi","10.1101/2019.12.17.876862")] genetics/editing

Xenotransplantation, specifically the use of porcine organs for human transplantation, has long been sought after as an alternative for patients suffering from organ failure. However, clinical application of this approach has been impeded by two main hurdles: (1) risk of transmission of porcine endogenous retroviruses (PERVs) and (2) molecular incompatibilities between donor pigs and humans which culminate in rejection of the graft. We previously demonstrated that all 25 copies of the PERV elements in the pig genome could be inactivated and live pigs successfully generated. In this study, we improved the scale of porcine germline editing from targeting a single repetitive locus with CRISPR to engineering 18 different loci using multiple genome engineering methods. we engineered the pig genome at 42 alleles using CRISPR-Cas9 and transposon and produced PERVKO·3KO·9TG pigs which carry PERV inactivation, xeno-antigen KO and 9 effective human transgenes. The engineered pigs exhibit normal physiology, fertility, and germline transmission of the edited alleles. In vitro assays demonstrated that these pigs gain resistance to human humoral and cell mediated damage, and coagulation dysregulations, similar to that of allotransplantation. Successful creation of PERVKO·3KO·9TG pigs represents a step forward towards safe and effective porcine xenotransplantation, which also represents a synthetic biology accomplishment of engineering novel functions in a living organism.

One Sentence Summary

Extensive genome engineering is applied to modify pigs for safe and immune compatible organs for human transplantation

--- https://www.biorxiv.org/content/10.1101/2020.01.13.905034.full Educational attainment polygenic scores in Hungary: evidence for validity and a historical gene-environment interaction Péter P. Ujma, Nóra Eszlári, András Millinghoffer, Bence Bruncsics, Péter Petschner, Péter Antal, Bill Deakin, György Bagdy, Gabriella Juhász 2020-01-14 2021-04-02 [("doi","10.1101/2020.01.13.905034")] genetics/heritable sociology

Educational attainment is a substantially heritable trait, and it has recently been linked to specific genetic variants by genome-wide association studies (GWASs). However, some variants may index social stratification, and polygenic score (PGS) heritability may differ across cohorts reflecting the changing relative influence of genetic and environmental influences on educational attainment over time. We used a Hungarian (n = 829) sample of healthy volunteers to assess the validity of the most recent educational attainment polygenic score in a population culturally and genetically different from the one used in GWAS discovery, as well as changes in PGS heritability over time. We used an English (n = 976) sample with identical measurement protocols as comparison.

We found that the PGS is valid in Hungary, accounting for 2–6.5% of the variance in educational attainment. We also replicated previous Estonian findings about generally increased PGS heritability in those attaining higher education after the fall of Communism, with PGS heritability up to 6.5% in the youngest cohort. In a comparable English sample the same PGS accounted for 9–11% of educational attainment variance. Our results provide evidence that polygenic scores for educational attainment are valid in diverse European populations. Our findings also provide further evidence that the fall of Communism, possibly along with other historical changes in education policy, was the source of a gene-environment interaction through which genetic factors became more important for higher educational attainment in thos after this event.

--- https://www.biorxiv.org/content/10.1101/2020.02.12.946608.full Genetic Architecture of Complex Traits and Disease Risk Predictors Soke Yuen Yong, Timothy G. Raben, Louis Lello, Steve Hsu 2020-02-13 2021-04-02 [("doi","10.1101/2020.02.12.946608")] genetics/heritable

Genomic prediction of complex human traits (eg. height, cognitive ability, bone density) and disease risks (eg. breast cancer, diabetes, heart disease, atrial fibrillation) has advanced considerably in recent years. Predictors have been constructed using penalized algorithms that favor sparsity: ie. which use as few genetic variants as possible. We analyze the specific genetic variants (SNPs) utilized in these predictors, which can vary from dozens to as many as thirty thousand. We find that the fraction of SNPs in or near genic regions varies widely by phenotype. For the majority of disease conditions studied, a large amount of the variance is accounted for by SNPs outside of coding regions. The state of these SNPs cannot be determined from exome-sequencing data. This suggests that exome data alone will miss much of the heritability for these traits—ie. existing PRS cannot be computed from exome data alone.

We also study the fraction of SNPs and of variance that is in common between pairs of predictors. The DNA regions used in disease risk predictors so far constructed seem to be largely disjoint (with a few interesting exceptions), suggesting that individual genetic disease risks are largely uncorrelated. It seems possible in theory for an individual to be a low-risk outlier in all conditions simultaneously.

--- https://www.biorxiv.org/content/10.1101/2020.01.14.905927.full Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations Ying Wang, Jing Guo, Guiyan Ni, Jian Yang, Peter M. Visscher, Loïc Yengo 2020-01-15 2021-04-02 [("doi","10.1101/2020.01.14.905927")] genetics/heritable

Polygenic scores (PGS) have been widely used to predict complex traits and risk of diseases using variants identified from genome-wide association studies (GWASs). To date, most GWASs have been conducted in populations of European ancestry, which limits the use of GWAS-derived PGS in non-European populations. Here, we develop a new theory to predict the relative accuracy (RA, relative to the accuracy in populations of the same ancestry as the discovery population) of PGS across ancestries.

We used simulations and real data from the UK Biobank to evaluate our results. We found across various simulation scenarios that the RA of PGS based on trait-associated SNPs can be predicted accurately from modeling linkage disequilibrium (LD), minor allele frequencies (MAF), cross-population correlations of SNP effect sizes and heritability.

Altogether, we find that LD and MAF differences between ancestries explain alone up to ~70% of the loss of RA using European-based PGS in African ancestry for traits like body mass index (BMI) and height.

Our results suggest that causal variants underlying common genetic variation identified in European ancestry GWASs are mostly shared across continents.

--- https://www.biorxiv.org/content/10.1101/2019.12.22.886234.full A genome-wide Approximate Bayesian Computation approach suggests only limited numbers of soft sweeps in humans over the last 100,000 years Guillaume Laval, Etienne Patin, Pierre Boutillier, Lluis Quintana-Murci 2019-12-23 2021-04-02 [("doi","10.1101/2019.12.22.886234")] genetics/selection/natural/human

Over the last 100,000 years, humans have spread across the globe and encountered a highly diverse set of environments to which they have had to adapt. Genome-wide scans of selection are powerful to detect selective sweeps. However, because of unknown fractions of undetected sweeps and false discoveries, the numbers of detected sweeps often poorly reflect actual numbers of selective sweeps in populations. The thousands of soft sweeps on standing variation recently evidenced in humans have also been interpreted as a majority of mis-classified neutral regions. In such a context, the extent of human adaptation remains little understood.

We present a new rationale to estimate these actual numbers of sweeps expected over the last 100,000 years (denoted by X) from genome-wide population data, both considering hard sweeps and selective sweeps on standing variation. We implemented an approximate Bayesian computation framework and showed, based on computer simulations, that such a method can properly estimate X. We then jointly estimated the number of selective sweeps, their mean intensity and age in several 1,000G African, European and Asian populations.

Our estimations of X, found weakly sensitive to demographic misspecifications, revealed very limited numbers of sweeps regardless the frequency of the selected alleles at the onset of selection and the completion of sweeps. We estimated ~80 sweeps in average across fifteen 1,000G populations when assuming incomplete sweeps only and ~140 selective sweeps in non-African populations when incorporating complete sweeps in our simulations.

The method proposed may help to address controversies on the number of selective sweeps in populations, guiding further genome-wide investigations of recent positive selection.

--- https://arxiv.org/abs/2002.07019 Learning to Prove Theorems by Learning to Generate Theorems Mingzhe Wang, Jia Deng 2020-02-17 2021-04-02 [("doi","10.48550/arXiv.2002.07019")] math reinforcement-learning/model

We consider the task of automated theorem proving, a key AI task. Deep learning has shown promise for training theorem provers, but there are limited human-written theorems and proofs available for supervised learning.

To address this limitation, we propose to learn a neural generator that automatically synthesizes theorems and proofs for the purpose of training a theorem prover.

Experiments on real-world tasks demonstrate that synthetic data from our approach improves the theorem prover and advances the state-of-the-art of automated theorem proving in Metamath.

Code is available at Github.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6981012/ Association of E-Cigarette Use With Respiratory Disease Among Adults: A Longitudinal Analysis Dharma N. Bhatta, Stanton A. Glantz 2020 2021-04-02 [("doi","10.1016/j.amepre.2019.07.028")] nicotine

Background: E-cigarettes deliver an aerosol of nicotine by heating a liquid and are promoted as an alternative to combustible tobacco. This study determines the longitudinal associations between e-cigarette use and respiratory disease controlling for combustible tobacco use.

Method: This was a longitudinal analysis of the adult Population Assessment of Tobacco and Health Waves 1, 2, and 3. Multivariable logistic regression was performed to determine the associations between e-cigarette use and respiratory disease, controlling for combustible tobacco smoking, demographic, and clinical variables. Data were collected in 2013–2016 and analyzed in 2018–2019.

Results: Among people who did not report respiratory disease (chronic obstructive pulmonary disease, chronic bronchitis, emphysema, or asthma) at Wave 1, the longitudinal analysis revealed statistically-significant associations between former e-cigarette use (AOR = 1.31, 95% CI = 1.07, 1.60) and current e-cigarette use (AOR = 1.29, 95% CI = 1.03, 1.61) at Wave 1 and having incident respiratory disease at Waves 2 or 3, controlling for combustible tobacco smoking, demographic, and clinical variables. Current combustible tobacco smoking (AOR = 2.56, 95% CI = 1.92, 3.41) was also statistically-significantly associated with having respiratory disease at Waves 2 or 3. Odds of developing respiratory disease for a current dual user (e-cigarette and all combustible tobacco) were 3.30 compared with a never smoker who never used e-cigarettes. Analysis controlling for cigarette smoking alone yielded similar results.

Conclusion: Use of e-cigarettes is an independent risk factor for respiratory disease in addition to combustible tobacco smoking. Dual use, the most common use pattern, is riskier than using either product alone.

--- https://arxiv.org/abs/2001.00102 The Gambler’s Problem and Beyond Baoxiang Wang, Shuai Li, Jiajin Li, Siu On Chan 2019-12-31 2021-04-02 [("doi","10.48550/arXiv.2001.00102")] reinforcement-learning/model statistics/decision

We analyze the Gambler’s problem, a simple [blackjack-esque] reinforcement learning problem where the gambler has the chance to double or lose the bets until the target is reached.

This is an early example introduced in the reinforcement learning textbook by Sutton & Barto 2018, where they mention an interesting pattern of the optimal value function with high-frequency components and repeating non-smooth points. It is however without further investigation.

We provide the exact formula for the optimal value function for both the discrete and the continuous cases. Though simple as it might seem, the value function is pathological: fractal, self-similar, derivative taking either zero or infinity, and not written as elementary functions. It is in fact one of the generalized Cantor functions, where it holds a complexity that has been uncharted thus far.

Our analyses could provide insights into improving value function approximation, gradient-based algorithms, and Q-learning, in real applications and implementations.

--- https://arxiv.org/abs/1912.02877#schmidhuber Training Agents using Upside-Down Reinforcement Learning (UDRL) Rupesh Kumar Srivastava, Pranav Shyam, Filipe Mutz, Wojciech Jaśkowski, Jürgen Schmidhuber 2019-12-05 2021-04-03 [("doi","10.48550/arXiv.1912.02877")] reinforcement-learning/model/decision-transformer

We develop Upside-Down Reinforcement Learning (UDRL), a method for learning to act using only supervised learning techniques.

Unlike traditional algorithms, UDRL does not use reward prediction or search for an optimal policy. Instead, it trains agents to follow commands such as “obtain so much total reward in so much time.” Many of its general principles are outlined in a companion report; the goal of this paper is to develop a practical learning algorithm and show that this conceptually simple perspective on agent training can produce a range of rewarding behaviors for multiple episodic environments.

Experiments show that on some tasks UDRL’s performance can be surprisingly competitive with, and even exceed that of some traditional baseline algorithms developed over decades of research.

Based on these results, we suggest that alternative approaches to expected reward maximization have an important role to play in training useful autonomous agents.

--- https://arxiv.org/abs/1912.01603#googledeepmind Dream to Control: Learning Behaviors by Latent Imagination Danijar Hafner, Timothy Lillicrap, Jimmy Ba, Mohammad Norouzi 2019-12-03 2021-04-03 [("doi","10.48550/arXiv.1912.01603")] reinforcement-learning/model

Learned world models summarize an agent’s experience to facilitate learning complex behaviors. While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them.

We present Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination. We efficiently learn behaviors by propagating analytic gradients of learned state values back through trajectories imagined in the compact state space of a learned world model.

On 20 challenging visual control tasks, Dreamer exceeds existing approaches in data-efficiency, computation time, and final performance.

--- https://arxiv.org/abs/1912.01588#openai Leveraging Procedural Generation to Benchmark Reinforcement Learning Karl Cobbe, Christopher Hesse, Jacob Hilton, John Schulman 2019-12-03 2021-04-03 [("doi","10.48550/arXiv.1912.01588")] reinforcement-learning/meta-learning

We introduce Procgen Benchmark, a suite of 16 procedurally generated game-like environments designed to benchmark both sample efficiency and generalization in reinforcement learning. We believe that the community will benefit from increased access to high quality training environments, and we provide detailed experimental protocols for using this benchmark.

We empirically demonstrate that diverse environment distributions are essential to adequately train and evaluate RL agents, thereby motivating the extensive use of procedural content generation.

We then use this benchmark to investigate the effects of scaling model size, finding that larger models improve both sample efficiency and generalization.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7306406/ Conservatives and liberals have similar physiological responses to threats Bert N. Bakker, Gijs Schumacher, Claire Gothreau, Kevin Arceneaux 2020 2021-04-03 [("doi","10.1038/s41562-020-0823-z")] sociology

About a decade ago, a study documented that conservatives have stronger physiological responses to threatening stimuli than liberals. This work launched an approach aimed at uncovering the biological roots of ideology.

Despite wide-ranging scientific and popular impact, independent laboratories have not replicated the study. We conducted a pre-registered direct replication (n = 202) and conceptual replications in the United States (n = 352) and the Netherlands (n = 81).

Our analyses do not support the conclusions of the original study, nor do we find evidence for broader claims regarding the effect of disgust and the existence of a physiological trait.

Rather than studying unconscious responses as the real predispositions, alignment between conscious and unconscious responses promises deeper insights into the emotional roots of ideology.

--- https://arxiv.org/abs/2001.04642 Seeing the World in a Bag of Chips Jeong Joon Park, Aleksander Holynski, Steve Seitz 2020-01-14 2021-04-03 [("doi","10.48550/arXiv.2001.04642")] technology

We address the dual problems of novel view synthesis and environment reconstruction from hand-held RGBD sensors.

Our contributions include (1) modeling highly specular objects, (2) modeling inter-reflections and Fresnel effects, and (3) enabling surface light field reconstruction with the same input needed to reconstruct shape alone.

In cases where scene surface has a strong mirror-like material component, we generate highly detailed environment images, revealing room composition, objects, people, buildings, and trees visible through windows.

Our approach yields state-of-the-art view synthesis techniques, operates on low dynamic range imagery, and is robust to geometric and calibration errors.

--- https://en.wikipedia.org/wiki/Uyghur_genocide Cultural genocide of Uighurs 2021-04-03 history/uighur --- https://en.wikipedia.org/wiki/Whataboutism Whataboutism 2021-04-03 history/uighur --- https://en.wikipedia.org/wiki/Xinjiang_internment_camps Xinjiang re-education camps 2021-04-03 history/uighur --- https://www.palladiummag.com/2018/11/29/a-week-in-xinjiangs-absolute-surveillance-state/ A Week in Xinjiang’s Absolute Surveillance State 2021-04-03 history/uighur --- https://www.buzzfeednews.com/article/alison_killing/satellite-images-investigation-xinjiang-detention-camps Blanked-Out Spots On China's Maps Helped Us Uncover Xinjiang's Camps 2021-04-03 history/uighur --- https://www.buzzfeednews.com/article/meghara/china-new-internment-camps-xinjiang-uighurs-muslims China Built A Vast New Infrastructure To Imprison Uighurs 2021-04-03 history/uighur --- https://www.chinafile.com/reporting-opinion/postcard/million-citizens-occupy-uighur-homes-xinjiang China’s Government Has Ordered a Million Citizens to Occupy Uighur Homes. Here’s What They Think They’re Doing. 2021-04-04 history/uighur --- https://www.economist.com/briefing/2018/05/31/china-has-turned-xinjiang-into-a-police-state-like-no-other Totalitarian determination and modern technology have produced a massive abuse of human rights 2021-04-04 history/uighur --- https://www.hrw.org/report/2009/10/20/we-are-afraid-even-look-them/enforced-disappearances-wake-xinjiangs-protests "We Are Afraid to Even Look for Them": Enforced Disappearances in the Wake of Xinjiang’s Protests 2021-04-04 history/uighur --- https://www.icij.org/investigations/china-cables/exposed-chinas-operating-manuals-for-mass-internment-and-arrest-by-algorithm/ A new leak of highly classified Chinese government documents reveals the operations manual for running the mass detention camps in Xinjiang and exposed the mechanics of the region's system of mass surveillance. 2021-04-04 history/uighur --- https://www.nytimes.com/2018/09/08/world/asia/china-uighur-muslim-detention-camp.html China Is Detaining Muslims in Vast Numbers. The Goal: ‘Transformation.’ 2021-04-04 history/uighur --- https://www.nytimes.com/2020/01/29/magazine/uyghur-muslims-china.html When Zulhumar Isaac's parents disappeared amid a wave of detentions of ethnic minorities, she had to play a perilous game with the state to get them back. 2021-04-04 history/uighur --- https://www.nytimes.com/2020/02/17/world/asia/china-reeducation-camps-leaked.html A leaked government document shows how people were monitored and selected for internment camps in Xinjiang 2021-04-04 history/uighur --- https://www.nytimes.com/interactive/2019/11/16/world/asia/china-xinjiang-documents.html More than 400 pages of internal Chinese documents provide an unprecedented inside look at the crackdown on ethnic minorities in the Xinjiang region. 2021-04-04 history/uighur --- https://www.varsity.co.uk/interviews/19990 I am an Uighur who faced China’s concentration camps. This is my story. 2021-04-04 history/uighur --- https://www.washingtonexaminer.com/magazine/2314839/the-xinjiang-procedure/ 2021-04-04 history/uighur --- https://www.wsj.com/articles/beijing-squeezes-exiles-in-u-s-by-detaining-family-back-home-1522402202 Efforts ramp up under Xi Jinping to silence Uighurs and other critics outside the reach of China's police. Uighur poet and filmmaker Tahir Hamut, shown in Virginia on Wednesday, said his family members have come under pressure since he left and sought asylum in the U.S. 2021-04-05 history/uighur --- https://arxiv.org/abs/1909.06674 A Step Toward Quantifying Independently Reproducible Machine Learning Research Edward Raff 2019-09-14 2021-04-05 [("doi","10.48550/arXiv.1909.06674")] ai/nn cs/algorithm

What makes a paper independently reproducible? Debates on reproducibility center around intuition or assumptions but lack empirical results.

Our field focuses on releasing code, which is important, but is not sufficient for determining reproducibility. We take the first step toward a quantifiable answer by manually attempting to implement 255 papers published from 1984 until 2017, recording features of each paper, and performing statistical analysis of the results.

For each paper, we did not look at the authors code, if released, in order to prevent bias toward discrepancies between code and paper.

--- https://arxiv.org/abs/1907.07640 Robustness properties of Facebook’s ResNeXt WSL models A. Emin Orhan 2019-07-17 2021-04-05 [("doi","10.48550/arXiv.1907.07640")] ai/nn/adversarial ai/scaling

We investigate the robustness properties of ResNeXt class image recognition models trained with billion scale weakly supervised data (ResNeXt WSL models). These models, recently made public by Facebook AI, were trained with ~1B images from Instagram and fine-tuned on ImageNet.

We show that these models display an unprecedented degree of robustness against common image corruptions and perturbations, as measured by the ImageNet-C and ImageNet-P benchmarks. They also achieve substantially improved accuracies on the recently introduced “natural adversarial examples” benchmark (ImageNet-A). The largest of the released models, in particular, achieves state-of-the-art results on ImageNet-C, ImageNet-P, and ImageNet-A by a large margin. The gains on ImageNet-C, ImageNet-P, and ImageNet-A far outpace the gains on ImageNet validation accuracy, suggesting the former as more useful benchmarks to measure further progress in image recognition.

Remarkably, the ResNeXt WSL models even achieve a limited degree of adversarial robustness against state-of-the-art white-box attacks (10-step PGD attacks). However, in contrast to adversarially trained models, the robustness of the ResNeXt WSL models rapidly declines with the number of PGD steps, suggesting that these models do not achieve genuine adversarial robustness. Visualization of the learned features also confirms this conclusion.

Finally, we show that although the ResNeXt WSL models are more shape-biased than comparable ImageNet-trained models in a shape-texture cue conflict experiment, they still remain much more texture-biased than humans, suggesting that they share some of the underlying characteristics of ImageNet-trained models that make this benchmark challenging.

--- https://arxiv.org/abs/1909.10705 Do Massively Pretrained Language Models Make Better Storytellers? Abigail See, Aneesh Pappu, Rohun Saxena, Akhila Yerukola, Christopher D. Manning 2019-09-24 2021-04-05 [("doi","10.48550/arXiv.1909.10705")] ai/nn/transformer/gpt/fiction reinforcement-learning/preference-learning

Large neural language models trained on massive amounts of text have emerged as a formidable strategy for Natural Language Understanding tasks. However, the strength of these models as Natural Language Generators is less clear. Though anecdotal evidence suggests that these models generate better quality text, there has been no detailed study characterizing detailed study characterizing their generation abilities.

In this work, we compare the performance of an extensively pretrained model, OpenAI GPT-2-117m (Radford et al 2019), to a state-of-the-art neural story generation model (Fan et al 2018). By evaluating the generated text across a wide variety of automatic metrics, we characterize the ways in which pretrained models do, and do not, make better storytellers.

We find that although GPT-2-117m conditions more strongly on context, is more sensitive to ordering of events, and uses more unusual words, it is just as likely to produce repetitive and under-diverse text when using likelihood-maximizing decoding algorithms.

--- https://arxiv.org/abs/1907.12904 CAR: Learned Image Downscaling for Upscaling using Content Adaptive Resampler Wanjie Sun, Zhenzhong Chen 2019-07-22 2021-04-05 [("doi","10.1109/TIP.2020.2970248")] ai/nn/cnn

Deep convolutional neural network based image super-resolution (SR) models have shown superior performance in recovering the underlying high resolution (HR) images from low resolution (LR) images obtained from the predefined downscaling methods.

In this paper we propose a learned image downscaling method based on content adaptive resampler (CAR) with consideration on the upscaling process. The proposed resampler network generates content adaptive image resampling kernels that are applied to the original HR input to generate pixels on the downscaled image. Moreover, a differentiable upscaling (SR) module is employed to upscale the LR result into its underlying HR counterpart. By back-propagating the reconstruction error down to the original HR input across the entire framework to adjust model parameters, the proposed framework achieves a new state-of-the-art SR performance through upscaling guided image resamplers which adaptively preserve detailed information that is essential to the upscaling.

Experimental results indicate that the quality of the generated LR image is comparable to that of the traditional interpolation based method, but the SR performance gain is achieved by deep SR models trained jointly with the CAR model.

The code is publicly available on: URL Github.

--- https://arxiv.org/abs/1910.06862 Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions Lars Buesing, Nicolas Heess, Theophane Weber 2019-10-15 2021-04-05 [("doi","10.48550/arXiv.1910.06862")] ai/nn reinforcement-learning/model statistics/bayes

A plethora of problems in AI, engineering and the sciences are naturally formalized as inference in discrete probabilistic models. Exact inference is often prohibitively expensive, as it may require evaluating the (unnormalized) target density on its entire domain. Here we consider the setting where only a limited budget of calls to the unnormalized density oracle is available, raising the challenge of where in the domain to allocate these function calls in order to construct a good approximate solution.

We formulate this problem as an instance of sequential decision-making under uncertainty and leverage methods from reinforcement learning for probabilistic inference with budget constraints. In particular, we propose the TreeSample algorithm, an adaptation of Monte Carlo Tree Search to approximate inference. This algorithm caches all previous queries to the density oracle in an explicit search tree, and dynamically allocates new queries based on a “best-first” heuristic for exploration, using existing upper confidence bound methods.

Our non-parametric inference method can be effectively combined with neural networks that compile approximate conditionals of the target, which are then used to guide the inference search and enable generalization across multiple target distributions.

We show empirically that TreeSample outperforms standard approximate inference methods on synthetic factor graphs.

--- https://arxiv.org/abs/1911.00536#microsoft DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan 2019-11-01 2021-04-05 [("doi","10.48550/arXiv.1911.00536")] ai/nn/transformer/gpt

We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).

Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings.

We show that conversational systems that leverage DialoGPT generate more relevant, contentful, and context-consistent responses than strong baseline systems.

The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems.

--- https://arxiv.org/abs/1910.13267#yandex BPE-Dropout: Simple and Effective Subword Regularization Ivan Provilkov, Dmitrii Emelianenko, Elena Voita 2019-10-29 2021-04-05 [("doi","10.48550/arXiv.1910.13267")] ai/nn/tokenization

Subword segmentation is widely used to address the open vocabulary problem in machine translation. The dominant approach to subword segmentation is Byte Pair Encoding (BPE), which keeps the most frequent words intact while splitting the rare ones into multiple tokens. While multiple segmentations are possible even with the same vocabulary, BPE splits words into unique sequences; this may prevent a model from better learning the compositionality of words and being robust to segmentation errors. So far, the only way to overcome this BPE imperfection, its deterministic nature, was to create another subword segmentation algorithm (Kudo, 2018).

In contrast, we show that BPE itself incorporates the ability to produce multiple segmentations of the same word. We introduce BPE-dropout—simple and effective subword regularization method based on and compatible with conventional BPE. It stochastically corrupts the segmentation procedure of BPE, which leads to producing multiple segmentations within the same fixed BPE framework. Using BPE-dropout during training and the standard BPE during inference improves translation quality up to 3 BLEU compared to BPE and up to 0.9 BLEU compared to the previous subword regularization.

--- https://arxiv.org/abs/1910.13461#facebook BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, Luke Zettlemoyer 2019-10-29 2021-04-05 [("doi","10.48550/arXiv.1910.13461")] ai/nn/vae

We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Transformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes.

We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine-tuned for text generation but also works well for comprehension tasks.

It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining.

We also report ablation experiments that replicate other pretraining schemes within the BART framework, to better measure which factors most influence end-task performance.

--- https://arxiv.org/abs/1910.09433 KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning Tarin Clanuwat, Alex Lamb, Asanobu Kitamoto 2019-10-21 2021-04-05 [("doi","10.48550/arXiv.1910.09433")] ai/nn japan/history

Kuzushiji, a cursive writing style, had been used in Japan for over a thousand years starting from the 8th century. Over 3 millions books on a diverse array of topics, such as literature, science, mathematics and even cooking are preserved. However, following a change to the Japanese writing system in 1900, Kuzushiji has not been included in regular school curricula. Therefore, most Japanese natives nowadays cannot read books written or printed just 150 years ago. Museums and libraries have invested a great deal of effort into creating digital copies of these historical documents as a safeguard against fires, earthquakes, and tsunamis. The result has been datasets with hundreds of millions of photographs of historical documents which can only be read by a small number of specially trained experts.

Thus there has been a great deal of interest in using Machine Learning to automatically recognize these historical texts and transcribe them into modern Japanese characters. Nevertheless, several challenges in Kuzushiji recognition have made the performance of existing systems extremely poor. To tackle these challenges, we propose KuroNet, a new end-to-end model which jointly recognizes an entire page of text by using a residual U-Net architecture which predicts the location and identity of all characters given a page of text (without any pre-processing). This allows the model to handle long range context, large vocabularies, and non-standardized character layouts.

We demonstrate that our system is able to successfully recognize a large fraction of pre-modern Japanese documents, but also explore areas where our system is limited and suggest directions for future work.

--- https://arxiv.org/abs/1910.07181 BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance Timo Schick, Hinrich Schütze 2019-10-16 2021-04-05 [("doi","10.48550/arXiv.1910.07181")] ai/nn/tokenization

Pretraining deep language models has led to large performance gains in NLP. Despite this success, Schick & Schütze 2020 recently showed that these models struggle to understand rare words. For static word embeddings, this problem has been addressed by separately learning representations for rare words.

In this work, we transfer this idea to pretrained language models: We introduce BERTRAM, a powerful architecture based on BERT that is capable of inferring high-quality embeddings for rare words that are suitable as input representations for deep language models. This is achieved by enabling the surface form and contexts of a word to interact with each other in a deep architecture.

Integrating BERTRAM into BERT leads to large performance increases due to improved representations of rare and medium frequency words on both a rare word probing task and 3 downstream tasks.

--- https://arxiv.org/abs/1911.01547#google On the Measure of Intelligence François Chollet 2019-11-05 2021-04-06 [("doi","10.48550/arXiv.1911.01547")] ai/nn/cnn cs/algorithm/information iq philosophy/logic philosophy/mind

To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We summarize and critically assess these definitions and evaluation approaches, while making apparent the two historical conceptions of intelligence that have implicitly guided them.

We note that in practice, the contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to “buy” arbitrary levels of skills for a system, in a way that masks the system’s own generalization power.

We then articulate a new formal definition of intelligence based on Algorithmic Information Theory, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope, generalization difficulty, priors, and experience. Using this definition, we propose a set of guidelines for what a general AI benchmark should look like.

Finally, we present a benchmark closely following these guidelines, the Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors designed to be as close as possible to innate human priors. We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans.

--- https://arxiv.org/abs/1909.13719#google RandAugment: Practical automated data augmentation with a reduced search space Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, Quoc V. Le 2019-09-30 2021-04-06 [("doi","10.48550/arXiv.1909.13719")] ai/nn

Recent work has shown that data augmentation has the potential to improve the generalization of deep learning models. Recently, automated augmentation strategies have led to state-of-the-art results in image classification and object detection. While these strategies were optimized for improving validation accuracy, they also led to state-of-the-art results in semi-supervised learning and improved robustness to common corruptions of images.

An obstacle to a large-scale adoption of these methods is a separate search phase which increases the training complexity and may substantially increase the computational cost. Additionally, due to the separate search phase, these approaches are unable to adjust the regularization strength based on model or dataset size. Automated augmentation policies are often found by training small models on small datasets and subsequently applied to train larger models.

In this work, we remove both of these obstacles. RandAugment has a reduced search space which allows it to be trained on the target task with no need for a separate proxy task. Furthermore, due to the parameterization, the regularization strength may be tailored to different model and dataset sizes. RandAugment can be used uniformly across different tasks and datasets and works out of the box, matching or surpassing all previous automated augmentation approaches on CIFAR-10/100, SVHN, and ImageNet. On the ImageNet dataset we achieve 85.0% accuracy, a 0.6% increase over the previous state-of-the-art and 1.0% increase over baseline augmentation. On object detection, RandAugment leads to 1.0–1.3% improvement over baseline augmentation, and is within 0.3% mAP of AutoAugment on COCO. Finally, due to its interpretable hyperparameter, RandAugment may be used to investigate the role of data augmentation with varying model and dataset size. Code is available online.

--- https://arxiv.org/abs/1909.11942#google ALBERT: A Lite BERT for Self-supervised Learning of Language Representations Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 2019-09-26 2021-04-06 [("doi","10.48550/arXiv.1909.11942")] ai/nn

Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations and longer training times.

To address these problems, we present two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT.

Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT. We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs.

As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and benchmarks while having fewer parameters compared to BERT-large.

The code and the pretrained models are available at Github.

--- https://arxiv.org/abs/1909.07940 Do NLP Models Know Numbers? Probing Numeracy in Embeddings Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, Matt Gardner 2019-09-17 2021-04-06 [("doi","10.48550/arXiv.1909.07940")] ai/nn/tokenization ai/nn/transformer math

The ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks. Currently, most NLP models treat numbers in text in the same way as other tokens—they embed them as distributed vectors. Is this enough to capture numeracy?

We begin by investigating the numerical reasoning capabilities of a state-of-the-art question answering model on the DROP dataset. We find this model excels on questions that require numerical reasoning, ie. it already captures numeracy. To understand how this capability emerges, we probe token embedding methods (eg. BERT, GloVe) on synthetic list maximum, number decoding, and addition tasks.

A surprising degree of numeracy is naturally present in standard embeddings. For example, GloVe and word2vec accurately encode magnitude for numbers up to 1,000. Furthermore, character-level embeddings are even more precise—ELMo captures numeracy the best for all pre-trained methods—but BERT, which uses sub-word units, is less exact.

--- https://arxiv.org/abs/1909.01380 The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives Elena Voita, Rico Sennrich, Ivan Titov 2019-09-03 2021-04-06 [("doi","10.48550/arXiv.1909.01380")] ai/nn/transformer/attention

We seek to understand how the representations of individual tokens and the structure of the learned feature space evolve between layers in deep neural networks under different learning objectives. We focus on the Transformers for our analysis as they have been shown effective on various tasks, including machine translation (MT), standard left-to-right language models (LM) and masked language modeling (MLM). Previous work used black-box probing tasks to show that the representations learned by the Transformer differ depending on the objective.

In this work, we use canonical correlation analysis and mutual information estimators to study how information flows across Transformer layers and how this process depends on the choice of learning objective. For example, as you go from bottom to top layers, information about the past in left-to-right language models gets vanished and predictions about the future get formed. In contrast, for MLM, representations initially acquire information about the context around the token, partially forgetting the token identity and producing a more generalized token representation. The token identity then gets recreated at the top MLM layers.

--- https://arxiv.org/abs/1909.01377 DEQ: Deep Equilibrium Models Shaojie Bai, J. Zico Kolter, Vladlen Koltun 2019-09-03 2021-04-06 [("doi","10.48550/arXiv.1909.01377")] ai/nn/transformer/attention/recurrent

We present a new approach to modeling sequential data: the deep equilibrium model (DEQ). Motivated by an observation that the hidden layers of many existing deep sequence models converge towards some fixed point, we propose the DEQ approach that directly finds these equilibrium points via root-finding. Such a method is equivalent to running an infinite depth (weight-tied) feedforward network, but has the notable advantage that we can analytically backpropagate through the equilibrium point using implicit differentiation.

Using this approach, training and prediction in these networks require only constant memory, regardless of the effective “depth” of the network.

We demonstrate how DEQs can be applied to two state-of-the-art deep sequence models: self-attention transformers and trellis networks. On large-scale language modeling tasks, such as the WikiText-103 benchmark, we show that DEQs (1) often improve performance over these state-of-the-art models (for similar parameter counts); (2) have similar computational requirements to existing models; and (3) vastly reduce memory consumption (often the bottleneck for training large sequence models), demonstrating an up-to 88% memory reduction in our experiments.

The code is available at Github.

--- https://arxiv.org/abs/1907.11692#facebook RoBERTa: A Robustly Optimized BERT Pretraining Approach Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov 2019-07-26 2021-04-06 [("doi","10.48550/arXiv.1907.11692")] ai/nn/transformer ai/scaling

[code] Language model pretraining has led to large performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have large impact on the final results.

We present a replication study of BERT pretraining (Devlin et al 2019) that carefully measures the impact of many key hyperparameters and training data size. [and switches to BPEs]

We find that BERT was undertrained, and can match or exceed the performance of every model published after it. [Main ingredient in increasing the dataset size 10×: 16GB → 160GB of text. cf. Chinchilla]

Table 4: Development set results for RoBERTa as we pretrain over more data (16GB → 160GB of text) and pretrain for longer (100K → 300K → 500K steps). Each row accumulates improvements from the rows above. RoBERTa matches the architecture and training objective of BERTLARGE. Results for BERTLARGE and XLNetLARGE are from Devlin et al 2019 and Yang et al 2019, respectively. Complete results on all GLUE tasks can be found in the Appendix.

Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD.

These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements.

We release our models and code.

--- https://arxiv.org/abs/1911.02116#facebook Unsupervised Cross-lingual Representation Learning at Scale Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, Veselin Stoyanov 2019-11-05 2021-04-06 [("doi","10.48550/arXiv.1911.02116")] ai/nn/transformer ai/scaling

This paper shows that pretraining multilingual language models at scale leads to performance gains for a wide range of cross-lingual transfer tasks.

We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6% average accuracy on XNLI, +13% average F1 score on MLQA, and +2.4% F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7% in XNLI accuracy for Swahili and 11.4% for Urdu over previous XLM models.

We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks.

We will make our code, data and models publicly available.

--- https://arxiv.org/abs/1910.10683#google T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu 2019-10-23 2021-04-06 [("doi","10.48550/arXiv.1910.10683")] ai/dataset ai/nn/transformer/t5 ai/scaling

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice.

In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks.

By combining the insights from our exploration with scale and our new Colossal Clean Crawled Corpus (C4), we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more.

To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.

Table 9: Measuring the effect of repeating data during pre-training. In these experiments, we only use the first n tokens from C4 (with varying values of n shown in the first column) but still pre-train over 235 tokens. This results in the data set being repeated over the course of pre-training (with the number of repeats for each experiment shown in the second column), which may result in memorization (see Figure 6).
Number of tokens Repeats GLUE CNNDM SQuAD SGLUE EnDe EnFr EnRo
★ Full data set 0 83.28 19.24 80.88 71.36 26.98 39.82 27.65
229 64 82.87 19.19 80.97 72.03 26.83 39.74 27.63
227 256 82.62 19.20 79.78 69.97 27.02 39.71 27.33
225 1,024 79.55 18.57 76.27 64.76 26.38 39.56 26.80
223 4,096 76.34 18.33 70.92 59.29 26.37 38.84 25.81
Figure 6: Pre-training loss for our original C4 data set as well as 4 artificially truncated versions. The sizes listed refer to the number of tokens in each data set. The 4 sizes considered correspond to repeating the data set between 64 and 4,096× over the course of pre-training. Using a smaller data set size results in smaller training loss values, which may suggest some memorization of the unlabeled data set.
--- https://arxiv.org/abs/1910.00571#deepmind Environmental drivers of systematicity and generalization in a situated agent Felix Hill, Andrew Lampinen, Rosalia Schneider, Stephen Clark, Matthew Botvinick, James L. McClelland, Adam Santoro 2019-10-01 2021-04-06 [("doi","10.48550/arXiv.1910.00571")] ai/scaling

The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI. Here, we consider tests of out-of-sample generalisation that require an agent to respond to never-seen-before instructions by manipulating and positioning objects in a 3D Unity simulated room.

We first describe a comparatively generic agent architecture that exhibits strong performance on these tests. We then identify 3 aspects of the training regime and environment that make a statistically-significant difference to its performance: (a) the number of object/word experiences in the training set; (b) the visual invariances afforded by the agent’s perspective, or frame of reference; and (c) the variety of visual input inherent in the perceptual aspect of the agent’s perception.

Our findings indicate that the degree of generalisation that networks exhibit can depend critically on particulars of the environment in which a given task is instantiated. They further suggest that the propensity for neural networks to generalise in systematic ways may increase if, like human children, those networks have access to many frames of richly varying, multi-modal observations as they learn.

--- https://arxiv.org/abs/1909.11861 Large-scale Pretraining for Neural Machine Translation with Tens of Billions of Sentence Pairs Yuxian Meng, Xiangyuan Ren, Zijun Sun, Xiaoya Li, Arianna Yuan, Fei Wu, Jiwei Li 2019-09-26 2021-04-06 [("doi","10.48550/arXiv.1909.11861")] ai/nn ai/scaling

In this paper, we investigate the problem of training neural machine translation (NMT) systems with a dataset of more than 40 billion bilingual sentence pairs, which is larger than the largest dataset to date by orders of magnitude.

Unprecedented challenges emerge in this situation compared to previous NMT work, including severe noise in the data and prohibitively long training time.

We propose practical solutions to handle these issues and demonstrate that large-scale pretraining improves NMT performance.

We are able to push the BLEU score of WMT17 Chinese-English dataset to 32.3, with a performance boost of +3.2 over existing state-of-the-art results.

--- https://arxiv.org/abs/1909.11740 UNITER: UNiversal Image-TExt Representation Learning Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, Jingjing Liu 2019-09-25 2021-04-07 [("doi","10.48550/arXiv.1909.11740")] ai/scaling

Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are simultaneously processed for joint visual and textual understanding. In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (COCO, Visual Genome, Conceptual Captions, and SBU Captions), which can power heterogeneous downstream V+L tasks with joint multimodal embeddings.

We design four pre-training tasks: Masked Language Modeling (MLM), Masked Region Modeling (MRM, with 3 variants), Image-Text Matching (ITM), and Word-Region Alignment (WRA). Different from previous work that applies joint random masking to both modalities, we use conditional masking on pre-training tasks (ie. masked language/region modeling is conditioned on full observation of image/text). In addition to ITM for global image-text alignment, we also propose WRA via the use of Optimal Transport (OT) to explicitly encourage fine-grained alignment between words and image regions during pre-training. Comprehensive analysis shows that both conditional masking and OT-based WRA contribute to better pre-training. We also conduct a thorough ablation study to find an optimal combination of pre-training tasks.

Extensive experiments show that UNITER achieves new state-of-the-art across six V+L tasks (over nine datasets), including Visual Question Answering, Image-Text Retrieval, Referring Expression Comprehension, Visual Commonsense Reasoning, Visual Entailment, and NLVR2.

Code is available at Github.

--- https://arxiv.org/abs/1909.08478#google Simple, Scalable Adaptation for Neural Machine Translation Ankur Bapna, Naveen Arivazhagan, Orhan Firat 2019-09-18 2021-04-07 [("doi","10.48550/arXiv.1909.08478")] ai/nn ai/scaling

Fine-tuning pre-trained Neural Machine Translation (NMT) models is the dominant approach for adapting to new languages and domains. However, fine-tuning requires adapting and maintaining a separate model for each target task.

We propose a simple yet efficient approach for adaptation in NMT. Our proposed approach consists of injecting tiny task specific adapter layers into a pre-trained model. These lightweight adapters, with just a small fraction of the original model size, adapt the model to multiple individual tasks simultaneously.

We evaluate our approach on two tasks: (1) Domain Adaptation and (2) Massively Multilingual NMT. Experiments on domain adaptation demonstrate that our proposed approach is on par with full fine-tuning on various domains, dataset sizes and model capacities. On a massively multilingual dataset of 103 languages, our adaptation approach bridges the gap between individual bilingual models and one massively multilingual model for most language pairs, paving the way towards universal machine translation.

--- https://arxiv.org/abs/1911.04620 Identifying Hidden Buyers in Darknet Markets via Dirichlet Hawkes Process Panpan Zheng, Shuhan Yuan, Xintao Wu, Yubao Wu 2019-11-12 2021-04-07 [("doi","10.48550/arXiv.1911.04620")] darknet-market

The darknet markets are notorious black markets in cyberspace, which involve selling or brokering drugs, weapons, stolen credit cards, and other illicit goods. To combat illicit transactions in the cyberspace, it is important to analyze the behaviors of participants in darknet markets. Currently, many studies focus on studying the behavior of vendors. However, there is no much work on analyzing buyers.

The key challenge is that the buyers are anonymized in darknet markets. For most of the darknet markets, we only observe the first and last digits of a buyer’s ID, such as “a✱✱b”. To tackle this challenge, we propose a hidden buyer identification model, called UNMIX [Du et al 2015], which can group the transactions from one hidden buyer into one cluster given a transaction sequence from an anonymized ID. UNMIX is able to model the temporal dynamics information as well as the product, comment, and vendor information associated with each transaction. As a result, the transactions with similar patterns in terms of time and content group together as the subsequence from one hidden buyer.

Experiments on the data collected from 3 real-world darknet markets [Dream Market, Wall Street Market, and Empire Market] demonstrate the effectiveness of our approach measured by various clustering metrics. Case studies on real transaction sequences explicitly show that our approach can group transactions with similar patterns into the same clusters.

--- https://www.biorxiv.org/content/10.1101/833400.full bioRxiv: the preprint server for biology Richard Sever, Ted Roeder, Samantha Hindle, Linda Sussman, Kevin-John Black, Janet Argentine, Wayne Manos, John R. Inglis 2019-11-06 2021-04-07 [("doi","10.1101/833400")] economics/copyright science

The traditional publication process delays dissemination of new research, often by months, sometimes by years. Preprint servers decouple dissemination of research papers from their evaluation and certification by journals, allowing researchers to share work immediately, receive feedback from a much larger audience, and provide evidence of productivity long before formal publication.

Launched in 2013 as a non-profit community service, the bioRxiv server has brought preprint practice to the life sciences and recently posted its 64,000th manuscript. The server now receives more than four million views per month and hosts papers spanning all areas of biology. Initially dominated by evolutionary biology, genetics/genomics and computational biology, bioRxiv has been increasingly populated by papers in neuroscience, cell and developmental biology, and many other fields.

Changes in journal and funder policies that encourage preprint posting have helped drive adoption, as has the development of bioRxiv technologies that allow authors to transfer papers easily between the server and journals. A bioRxiv user survey found that 42% of authors post their preprints prior to journal submission whereas 37% post concurrently with journal submission. Authors are motivated by a desire to share work early; they value the feedback they receive, and very rarely experience any negative consequences of preprint posting.

Rapid dissemination via bioRxiv is also encouraging new initiatives that experiment with the peer review process and the development of novel approaches to literature filtering and assessment.

--- https://www.biorxiv.org/content/10.1101/763342.full In-field whole plant maize architecture characterized by Latent Space Phenotyping Joseph L. Gage, Elliot Richards, Nicholas Lepak, Nicholas Kaczmar, Chinmay Soman, Girish Chowdhary, Michael A. Gore, Edward S. Buckler 2019-09-10 2021-04-07 [("doi","10.1101/763342")] ai/nn/vae genetics/heritable statistics/variance-component

Collecting useful, interpretable, and biologically relevant phenotypes in a resource-efficient manner is a bottleneck to plant breeding, genetic mapping, and genomic prediction. Autonomous and affordable sub-canopy rovers are an efficient and scalable way to generate sensor-based datasets of in-field crop plants. Rovers equipped with light detection and ranging (LiDar) can produce three-dimensional reconstructions of entire hybrid maize fields.

In this study, we collected 2,103 LiDar scans of hybrid maize field plots and extracted phenotypic data from them by Latent Space Phenotyping (LSP). We performed LSP by two methods, principal component analysis (PCA) and a convolutional autoencoder, to extract meaningful, quantitative Latent Space Phenotypes (LSPs) describing whole-plant architecture and biomass distribution.

The LSPs had heritabilities of up to 0.44, similar to some manually measured traits, indicating they can be selected on or genetically mapped. Manually measured traits can be successfully predicted by using LSPs as explanatory variables in partial least squares regression, indicating the LSPs contain biologically relevant information about plant architecture.

These techniques can be used to assess crop architecture at a reduced cost and in an automated fashion for breeding, research, or extension purposes, as well as to create or inform crop growth models.

--- https://www.biorxiv.org/content/10.1101/766600.full Genetic ‘General Intelligence’, Objectively Determined and Measured Javier de la Fuente, Gail Davies, Andrew D. Grotzinger, Elliot M. Tucker-Drob, Ian J. Deary 2019-09-12 2021-04-07 [("doi","10.1101/766600")] genetics/heritable iq

It has been known for 115 years that, in humans, diverse cognitive traits are positively intercorrelated; this forms the basis for the general factor of intelligence (g).

We directly test for a genetic basis for g using data from 7 different cognitive tests (n = 11,263 to n = 331,679) and genome-wide autosomal single-nucleotide polymorphisms. A genetic g factor accounts for 58.4% (SE = 4.8%) of the genetic variance in the cognitive traits, with trait-specific genetic factors accounting for the remaining 41.6%.

We distill genetic loci broadly relevant for many cognitive traits (g) from loci associated with only individual cognitive traits.

These results elucidate the etiological basis for a long-known yet poorly-understood phenomenon, revealing a fundamental dimension of genetic sharing across diverse cognitive traits.

--- https://arxiv.org/abs/1910.06591#deepmind SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference Lasse Espeholt, Raphaël Marinier, Piotr Stanczyk, Ke Wang, Marcin Michalski 2019-10-15 2021-04-07 [("doi","10.48550/arXiv.1910.06591")] ai/nn/rnn reinforcement-learning/model-free

We present a modern scalable reinforcement learning agent called SEED (Scalable, Efficient Deep-RL). By effectively using modern accelerators, we show that it is not only possible to train on millions of frames per second but also to lower the cost of experiments compared to current methods. We achieve this with a simple architecture that features centralized inference and an optimized communication layer.

SEED adopts two state-of-the-art distributed algorithms, IMPALA/V-trace (policy gradients) and R2D2 (Q-learning), and is evaluated on Atari-57, DeepMind Lab and Google Research Football. We improve the state-of-the-art on Football and are able to reach state-of-the-art on Atari-57 3× faster in wall-time.

For the scenarios we consider, a 40% to 80% cost reduction for running experiments is achieved.

The implementation along with experiments is open-sourced so results can be reproduced and novel ideas tried out.

--- https://arxiv.org/abs/1910.06389 Snow Crystals Kenneth G. Libbrecht 2019-10-14 2021-04-07 [("doi","10.48550/arXiv.1910.06389")] science

This monograph reviews our current understanding of the physical dynamics of ice crystal growth, focusing on the spontaneous formation of complex structures from water vapor (called snow crystals) as a function of temperature, supersaturation, background gas pressure, and other extrinsic parameters. Snow crystal growth is a remarkably rich and rather poorly understood phenomenon, requiring a synthesis of concepts from materials science, crystal-growth theory, statistical mechanics, diffusion-limited solidification, finite-element modeling, and molecular surface processes.

Building upon recent advances in precision measurement techniques, computation modeling methods, and molecular dynamics simulations of crystalline surfaces, I believe we are moving rapidly toward the long-sought goal of developing a full physical model of snow crystal formation, using ab initio molecular dynamics simulations to create a semi-empirical characterization of the nanoscale surface attachment kinetics, and then incorporating that into a full computational model that reproduces the growth of macroscopic crystalline structures.

§1 of this monograph deals mainly with the material properties of ice Ih in equilibrium, including thermodynamics quantities, facet surface structures, terrace step energies, and crystal twinning behaviors.

--- https://arxiv.org/abs/1910.13385 Hipsters and the Cool: A Game Theoretic Analysis of Social Identity, Trends and Fads Russell Golman, Aditi Jain, Sonica Saraf 2019-10-29 2021-04-07 [("doi","10.48550/arXiv.1910.13385")] sociology

Cultural trends and popularity cycles can be observed all around us, yet our theories of social influence and identity expression do not explain what perpetuates these complex, often unpredictable social dynamics.

We propose a theory of social identity expression based on the opposing, but not mutually exclusive, motives to conform and to be unique among one’s neighbors in a social network. We then model the social dynamics that arise from these motives.

We find that the dynamics typically enter random walks or stochastic limit cycles rather than converging to a static equilibrium. We also prove that without social network structure or, alternatively, without the uniqueness motive, reasonable adaptive dynamics would necessarily converge to equilibrium.

Thus, we show that nuanced psychological assumptions (recognizing preferences for uniqueness along with conformity) and realistic social network structure are both necessary for explaining how complex, unpredictable cultural trends emerge.

--- /newsletter/2019/07#the-scholars-stage July 2019 News § Greer’s selection Gwern 2019-06-20 2019-06-20 anime/eva history/uighur newsletter --- /doc/history/uighur/2021-xu.pdf 2021 2021-04-08 history/uighur technology --- https://edition.cnn.com/2021/10/04/china/xinjiang-detective-torture-intl-hnk-dst/index.html Torture inflicted on Uighurs in Xinjiang revealed by Chinese detective in exile 2021-04-08 history/uighur psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Racism_in_China Ethnic issues in China 2021-04-08 history/uighur --- https://en.wikipedia.org/wiki/Sinocentrism Sinocentrism 2021-04-08 history/uighur --- https://newlinesmag.com/reportage/the-big-business-of-uyghur-genocide-denial/ The Big Business of Uighur Genocide Denial 2021-04-08 history/uighur --- https://theintercept.com/2021/01/29/china-uyghur-muslim-surveillance-police/ Surveillance of Uighurs Detailed in Chinese Police Database 2021-04-08 history/uighur --- https://www.amazon.com/dp/1735913626/ 2021-04-08 history/uighur --- https://www.hrw.org/news/2019/05/01/china-how-mass-surveillance-works-xinjiang China: How Mass Surveillance Works in Xinjiang 2021-04-08 history/uighur --- https://www.latimes.com/world-nation/story/2020-09-03/china-inner-mongolia-bilingual-education-assimilation-xinjiang-resistance-crackdown China's push to teach in Mandarin sparks Mongol resistance 2021-04-08 history/uighur psychology/linguistics/bilingual --- https://www.newyorker.com/culture/personal-history/china-cannot-silence-me China Cannot Silence Me 2021-04-08 history/uighur --- https://www.newyorker.com/magazine/2021/04/12/surviving-the-crackdown-in-xinjiang Surviving the Crackdown in Xinjiang 2021-04-08 history/uighur --- https://www.npr.org/2022/02/03/1073793823/china-uyghur-children-xinjiang-boarding-school Uighur kids detail abuse at China's boarding schools in Xinjiang 2021-04-09 history/uighur --- https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html In a major ethical leap for the tech world, Chinese start-ups have built algorithms that the government uses to track members of a largely Muslim minority group. 2021-04-09 history/uighur --- https://www.nytimes.com/interactive/2021/12/13/technology/china-propaganda-youtube-influencers.html China Uses YouTube Influencers to Spread Propaganda 2021-04-09 history/uighur --- https://www.propublica.org/article/how-china-uses-youtube-and-twitter-to-spread-its-propaganda-version-of-life-for-uyghurs-in-xinjiang How China Spreads Its Propaganda Version of Life for Uighurs 2021-04-09 history/uighur --- https://www.theatlantic.com/magazine/archive/2020/09/china-ai-surveillance/614197/ Xi Jinping is using artificial intelligence to enhance his government’s totalitarian control—and he’s exporting this technology to regimes around the globe. 2021-04-09 history/uighur --- https://www.theatlantic.com/the-uyghur-chronicles/ One by One, My Friends Were Sent to the Camps 2021-04-09 history/uighur --- https://www.vice.com/en/article/at-chinese-border-tourists-forced-to-install-a-text-stealing-piece-of-malware/ The malware downloads a tourist's text messages, calendar entries, and phone logs, as well as scans the device for over 70,000 different files. 2021-04-09 history/uighur --- https://www.wsj.com/articles/china-imprisons-uyghur-businessmen-once-seen-as-bridges-to-community-11626174001 Encouraging entrepreneurs used to be a key part of economic development in Xinjiang, but priorities have shifted with Xi Jinping’s security crackdown 2021-04-09 history/uighur --- https://www.wsj.com/articles/leaked-documents-detail-xi-jinpings-extensive-role-in-xinjiang-crackdown-11638284709 2021-04-09 history/uighur --- https://en.wikipedia.org/wiki/Nvidia_DGX Nvidia DGX 2021-04-09 ai/scaling/hardware --- https://en.wikipedia.org/wiki/GPGPU GPGPU 2021-04-09 ai/scaling/hardware --- https://en.wikipedia.org/wiki/Ampere_(microarchitecture) Ampere (microarchitecture) 2021-04-10 ai/scaling/hardware --- https://en.wikipedia.org/wiki/Hopper_(microarchitecture) Hopper (microarchitecture) 2021-04-10 ai/scaling/hardware --- https://en.wikipedia.org/wiki/TSMC TSMC 2021-04-10 ai/scaling/hardware --- https://en.wikipedia.org/wiki/HBM3 HBM3 2021-04-10 ai/scaling/hardware --- https://en.wikipedia.org/wiki/Nvidia_Tesla Nvidia Tesla 2021-04-10 ai/scaling/hardware --- https://en.wikipedia.org/wiki/Summit_(supercomputer) Summit (supercomputer) 2021-04-10 ai/scaling/hardware --- https://en.wikipedia.org/wiki/Tianhe-1A Tianhe-1A 2021-04-10 ai/scaling/hardware --- https://en.wikipedia.org/wiki/HBM2 HBM2 2021-04-10 ai/scaling/hardware --- https://arxiv.org/abs/1907.00151 GPT-based Generation for Classical Chinese Poetry Yi Liao, Yasheng Wang, Qun Liu, Xin Jiang 2019-06-29 2021-04-10 [("doi","10.48550/arXiv.1907.00151")] ai/nn/transformer/gpt/poetry

We present a simple yet effective method for generating high quality classical Chinese poetry with Generative Pre-trained Language Model (GPT).

The method adopts a simple GPT model, without using any human-crafted rules or features, or designing any additional neural components. While the proposed model learns to generate various forms of classical Chinese poems, including Jueju, Lüshi, various Cipai and Couples, the generated poems are of very high quality.

We also propose and implement a method to fine-tune the model to generate acrostic poetry.

To the best of our knowledge, this is the first to employ GPT in developing a poetry generation system.

We have released an online mini demonstration program on Wechat to show the generation capability of the proposed method for classical Chinese poetry.

--- https://arxiv.org/abs/2104.12763#facebook MDETR—Modulated Detection for End-to-End Multi-Modal Understanding Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, Nicolas Carion 2021-04-26 2021-04-26 [("doi","10.48550/arXiv.2104.12763")] ai/nn/transformer

Multi-modal reasoning systems rely on a pre-trained object detector to extract regions of interest from the image. However, this crucial module is typically used as a black box, trained independently of the downstream task and on a fixed vocabulary of objects and attributes. This makes it challenging for such systems to capture the long tail of visual concepts expressed in free form text.

In this paper we propose MDETR, an end-to-end modulated detector that detects objects in an image conditioned on a raw text query, like a caption or a question. We use a transformer-based architecture to reason jointly over text and image by fusing the two modalities at an early stage of the model. We pre-train the network on 1.3M text-image pairs, mined from pre-existing multi-modal datasets having explicit alignment between phrases in text and objects in the image. We then fine-tune on several downstream tasks such as phrase grounding, referring expression comprehension and segmentation, achieving state-of-the-art results on popular benchmarks. We also investigate the utility of our model as an object detector on a given label set when fine-tuned in a few-shot setting. We show that our pre-training approach provides a way to handle the long tail of object categories which have very few labeled instances. Our approach can be easily extended for visual question answering, achieving competitive performance on GQA and CLEVR.

The code and models are available at Github.

--- https://arxiv.org/abs/1911.05507#deepmind Compressive Transformers for Long-Range Sequence Modeling Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Timothy Lillicrap 2019-11-13 2021-04-10 [("doi","10.48550/arXiv.1911.05507")] ai/dataset ai/nn/transformer/attention/compression

We present the Compressive Transformer, an attentive sequence model which compresses past memories for long-range sequence learning.

We find the Compressive Transformer obtains state-of-the-art language modeling results in the WikiText-103 and enwik8 benchmarks, achieving 17.1 ppl and 0.97 bpc respectively. We also find it can model high-frequency speech effectively and can be used as a memory mechanism for RL, demonstrated on an object matching task.

To promote the domain of long-range sequence learning, we propose a new open-vocabulary language modeling benchmark derived from books, PG-19.

--- https://arxiv.org/abs/1906.06755 Theoretical Limitations of Self-Attention in Neural Sequence Models Michael Hahn 2019-06-16 2021-04-11 [("doi","10.1162/tacl_a_00306")] ai/nn/transformer

Transformers are emerging as the new workhorse of NLP, showing great success across tasks. Unlike LSTMs, transformers process input sequences entirely through self-attention.

Previous work has suggested that the computational capabilities of self-attention to process hierarchical structures are limited. In this work, we mathematically investigate the computational power of self-attention to model formal languages.

Across both soft and hard attention, we show strong theoretical limitations of the computational abilities of self-attention, finding that it cannot model periodic finite-state languages, nor hierarchical structures, unless the number of layers or heads increases with input length.

These limitations seem surprising given the practical success of self-attention and the prominent role assigned to hierarchical structure in linguistics, suggesting that natural language can be approximated well with models that are too weak for the formal languages typically assumed in theoretical linguistics.

--- https://arxiv.org/abs/1906.06565 Deep Set Prediction Networks Yan Zhang, Jonathon Hare, Adam Prügel-Bennett 2019-06-15 2021-04-11 [("doi","10.48550/arXiv.1906.06565")] ai/nn

Current approaches for predicting sets from feature vectors ignore the unordered nature of sets and suffer from discontinuity issues as a result.

We propose a general model for predicting sets that properly respects the structure of sets and avoids this problem. With a single feature vector as input, we show that our model is able to auto-encode point sets, predict the set of bounding boxes of objects in an image, and predict the set of attributes of these objects.

--- https://arxiv.org/abs/1906.08237 XLNet: Generalized Autoregressive Pretraining for Language Understanding Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le 2019-06-19 2021-04-11 [("doi","10.48550/arXiv.1906.08237")] ai/nn/rnn ai/nn/transformer/attention/recurrent

With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a pretrain-finetune discrepancy.

In light of these pros and cons, we propose XLNet, a generalized autoregressive pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into pretraining.

Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking.

--- https://arxiv.org/abs/1911.05722#facebook Momentum Contrast for Unsupervised Visual Representation Learning Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, Ross Girshick 2019-11-13 2021-04-11 [("doi","10.48550/arXiv.1911.05722")] ai/scaling

We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning.

MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks.

MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks.

--- https://arxiv.org/abs/1907.05019#google Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges Naveen Arivazhagan, Ankur Bapna, Orhan Firat, Dmitry Lepikhin, Melvin Johnson, Maxim Krikun, Mia Xu Chen, Yuan Cao, George Foster, Colin Cherry, Wolfgang Macherey, Zhifeng Chen, Yonghui Wu 2019-07-11 2021-04-11 [("doi","10.48550/arXiv.1907.05019")] ai/nn ai/scaling

We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair.

We set a milestone towards this goal by building a single massively multilingual NMT model handling 103 languages trained on over 25 billion examples.

Our system demonstrates effective transfer learning ability, improving translation quality of low-resource languages, while keeping high-resource language translation quality on-par with competitive bilingual baselines.

We provide in-depth analysis of various aspects of model building that are crucial to achieving quality and practicality in universal NMT.

While we prototype a high-quality universal translation system, our extensive empirical analysis exposes issues that need to be further addressed, and we suggest directions for future research.

--- https://arxiv.org/abs/1906.05271#google Does Learning Require Memorization? A Short Tale about a Long Tail Vitaly Feldman 2019-06-12 2021-04-11 [("doi","10.48550/arXiv.1906.05271")] ai/nn/sparsity ai/scaling

State-of-the-art results on image recognition tasks are achieved using over-parameterized learning algorithms that (nearly) perfectly fit the training set and are known to fit well even random labels. This tendency to memorize the labels of the training data is not explained by existing theoretical analyses. Memorization of the training data also presents privacy risks when the training data contains sensitive personal information and thus it is important to understand whether such memorization is necessary for accurate learning.

We provide the first conceptual explanation and a theoretical model for this phenomenon. Specifically, we demonstrate that for natural data distributions memorization of labels is necessary for achieving close-to-optimal generalization error. Crucially, even labels of outliers and noisy labels need to be memorized. The model is motivated and supported by the results of several recent empirical works. In our model, data is sampled from a mixture of subpopulations and our results show that memorization is necessary whenever the distribution of subpopulation frequencies is long-tailed. Image and text data is known to be long-tailed and therefore our results establish a formal link between these empirical phenomena. Our results allow to quantify the cost of limiting memorization in learning and explain the disparate effects that privacy and model compression have on different subgroups.

--- https://arxiv.org/abs/1908.01275 A View on Deep Reinforcement Learning in System Optimization Ameer Haj-Ali, Nesreen K. Ahmed, Ted Willke, Joseph Gonzalez, Krste Asanovic, Ion Stoica 2019-08-04 2021-04-11 [("doi","10.48550/arXiv.1908.01275")] cs/algorithm reinforcement-learning/model-free

Many real-world systems problems require reasoning about the long term consequences of actions taken to configure and manage the system. These problems with delayed and often sequentially aggregated reward, are often inherently reinforcement learning problems and present the opportunity to leverage the recent substantial advances in deep reinforcement learning. However, in some cases, it is not clear why deep reinforcement learning is a good fit for the problem. Sometimes, it does not perform better than the state-of-the-art solutions. And in other cases, random search or greedy algorithms could outperform deep reinforcement learning.

In this paper, we review, discuss, and evaluate the recent trends of using deep reinforcement learning in system optimization. We propose a set of essential metrics to guide future works in evaluating the efficacy of using deep reinforcement learning in system optimization. Our evaluation includes challenges, the types of problems, their formulation in the deep reinforcement learning setting, embedding, the model used, efficiency, and robustness. We conclude with a discussion on open challenges and potential directions for pushing further the integration of reinforcement learning in system optimization.

--- https://www.biorxiv.org/content/10.1101/685172.full Identification of type 2 diabetes loci in 433,540 East Asian individuals Cassandra N. Spracklen, Momoko Horikoshi, Young Jin Kim, Kuang Lin, Fiona Bragg, Sanghoon Moon, Ken Suzuki, Claudia H. T. Tam, Yasuharu Tabara, Soo-Heon Kwak, Fumihiko Takeuchi, Jirong Long, Victor J. Y. Lim, Jin-Fang Chai, Chien-Hsiun Chen, Masahiro Nakatochi, Jie Yao, Hyeok Sun Choi, Apoorva K. Iyengar, Hannah J. Perrin, Sarah M. Brotman, Martijn van de Bunt, Anna L. Gloyn, Jennifer E. Below, Michael Boehnke, Donald W. Bowden, John C. Chambers, Anubha Mahajan, Mark I. McCarthy, Maggie C. Y. Ng, Lauren E. Petty, Weihua Zhang, Andrew P. Morris, Linda S. Adair, Zheng Bian, Juliana C. N. Chan, Li-Ching Chang, Miao-Li Chee, Yii-Der Ida Chen, Yuan-Tsong Chen, Zhengming Chen, Lee-Ming Chuang, Shufa Du, Penny Gordon-Larsen, Myron Gross, Xiuqing Guo, Yu Guo, Sohee Han, Annie-Green Howard, Wei Huang, Yi-Jen Hung, Mi Yeong Hwang, Chii-Min Hwu, Sahoko Ichihara, Masato Isono, Hye-Mi Jang, Guozhi Jiang, Jost B. Jonas, Yoichiro Kamatani, Tomohiro Katsuya, Takahisa Kawaguchi, Chiea-Chuen Khor, Katsuhiko Kohara, Myung-Shik Lee, Nannette R. Lee, Liming Li, Jianjun Liu, Andrea O. Luk, Jun Lv, Yukinori Okada, Mark A. Pereira, Charumathi Sabanayagam, Jinxiu Shi, Dong Mun Shin, Wing Yee So, Atsushi Takahashi, Brian Tomlinson, Fuu-Jen Tsai, Rob M. van Dam, Yong-Bing Xiang, Ken Yamamoto, Toshimasa Yamauchi, Kyungheon Yoon, Canqing Yu, Jian-Min Yuan, Liang Zhang, Wei Zheng, Michiya Igase, Yoon Shin Cho, Jerome I. Rotter, Ya-Xing Wang, Wayne H. H. Sheu, Mitsuhiro Yokota, Jer-Yuarn Wu, Ching-Yu Cheng, Tien-Yin Wong, Xiao-Ou Shu, Norihiro Kato, Kyong-Soo Park, E-Shyong Tai, Fumihiko Matsuda, Woon-Puay Koh, Ronald C. W. Ma, Shiro Maeda, Iona Y. Millwood, Juyoung Lee, Takashi Kadowaki, Robin G. Walters, Bong-Jo Kim, Karen L. Mohlke, Xueling Sim 2019-06-28 2021-04-11 [("doi","10.1101/685172")] genetics/heritable/correlation

Meta-analyses of genome-wide association studies (GWAS) have identified >240 loci associated with type 2 diabetes (T2D), however most loci have been identified in analyses of European-ancestry individuals. To examine T2D risk in East Asian individuals, we meta-analyzed GWAS data in 77,418 cases and 356,122 controls. In the main analysis, we identified 298 distinct association signals at 178 loci, and across T2D association models with and without consideration of body mass index and sex, we identified 56 loci newly implicated in T2D predisposition. Common variants associated with T2D in both East Asian and European populations exhibited strongly correlated effect sizes. New associations include signals in/near GDAP1, PTF1A, SIX3, ALDH2, a microRNA cluster, and genes that affect muscle and adipose differentiation. At another locus, eQTLs at two overlapping T2D signals act through two genes, NKX6–3 and ANK1, in different tissues. Association studies in diverse populations identify additional loci and elucidate disease genes, biology, and pathways.

Type 2 diabetes (T2D) is a common metabolic disease primarily caused by insufficient insulin production and/or secretion by the pancreatic β cells and insulin resistance in peripheral tissues1. Most genetic loci associated with T2D have been identified in populations of European (EUR) ancestry, including a recent meta-analysis of genome-wide association studies (GWAS) of nearly 900,000 individuals of European ancestry that identified >240 loci influencing the risk of T2D2. Differences in allele frequency between ancestries affect the power to detect associations within a population, particularly among variants rare or monomorphic in one population but more frequent in another3,4. Although smaller than studies in European populations, a recent T2D meta-analysis in almost 200,000 Japanese individuals identified 28 additional loci4. The relative contributions of different pathways to the pathophysiology of T2D may also differ between ancestry groups. For example, in East Asian (EAS) populations, T2D prevalence is greater than in European populations among people of similar body mass index (BMI) or waist circumference5. We performed the largest meta-analysis of East Asian individuals to identify new genetic associations and provide insight into T2D pathogenesis.

--- https://www.biorxiv.org/content/10.1101/839373.full Quantifying genetic heterogeneity between continental populations for human height and body mass index Jing Guo, Andrew Bakshi, Ying Wang, Longda Jiang, Loïc Yengo, Michael E. Goddard, Peter M. Visscher, Jian Yang 2019-11-14 2021-04-11 [("doi","10.1101/839373")] genetics/heritable/correlation

Genome-wide association studies (GWAS) in samples of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations.

We estimated the between-population correlation of genetic effects at all SNPs (rg) or genome-wide statistically-significant SNPs (rg(GWS)) for height and body mass index (BMI) in samples of European (EUR; n = 49,839) and African (AFR; n = 17,426) ancestry. The between EUR and AFR was 0.75 (s. e. = 0.035) for height and 0.68 (s. e. = 0.062) for BMI, and the corresponding was 0.82 (s. e. = 0.030) for height and 0.87 (s. e. = 0.064) for BMI, suggesting that a large proportion of GWAS findings discovered in Europeans are likely applicable to non-Europeans for height and BMI.

There was no evidence that differs in SNP groups with different levels of between-population difference in allele frequency or linkage disequilibrium, which, however, can be due to the lack of power.

--- https://www.biorxiv.org/content/10.1101/2020.02.15.950949.full Analysis of variance when both input and output sets are high-dimensional Gustavo de los Campos, Torsten Pook, Agustin Gonzalez-Raymundez, Henner Simianer, George Mias, Ana I. Vazquez 2020-02-15 2021-04-11 [("doi","10.1101/2020.02.15.950949")] genetics/heritable statistics/variance-component

Motivation: Modern genomic data sets often involve multiple data-layers (eg. DNA-sequence, gene expression), each of which itself can be high-dimensional. The biological processes underlying these data-layers can lead to intricate multivariate association patterns.

Results: We propose and evaluate two methods for analysis variance when both input and output sets are high-dimensional. Our approach uses random effects models to estimate the proportion of variance of vectors in the linear span of the output set that can be explained by regression on the input set. We consider a method based on orthogonal basis (Eigen-ANOVA) and one that uses random vectors (Monte Carlo ANOVA, MC-ANOVA) in the linear span of the output set. We used simulations to assess the bias and variance of each of the methods, and to compare it with that of the Partial Least Squares (PLS)—an approach commonly used in multivariate-high-dimensional regressions. The MC-ANOVA method gave nearly unbiased estimates in all the simulation scenarios considered. Estimates produced by Eigen-ANOVA and PLS had noticeable biases. Finally, we demonstrate insight that can be obtained with the of MC-ANOVA and Eigen-ANOVA by applying these two methods to the study of multi-locus linkage disequilibrium in chicken genomes and to the assessment of inter-dependencies between gene expression, methylation and copy-number-variants in data from breast cancer tumors.

Availability: The Supplementary data includes an R-implementation of each of the proposed methods as well as the scripts used in simulations and in the real-data analyses.

Contact

gustavoc@msu.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

--- https://www.biorxiv.org/content/10.1101/690545.full Genetic contributions to variation in human stature in prehistoric Europe Samantha L. Cox, Christopher B. Ruff, Robert M. Maier, Iain Mathieson 2019-07-02 2021-04-12 [("doi","10.1101/690545")] genetics/selection/natural/human

The relative contributions of genetics and environment to temporal and geographic variation in human height remain largely unknown. Ancient DNA has identified changes in genetic ancestry over time, but it is not clear whether those changes in ancestry are associated with changes in height. Here, we directly test whether changes over the past 38,000 years in European height predicted using DNA from 1071 ancient individuals are consistent with changes observed in 1159 skeletal remains from comparable populations.

We show that the observed decrease in height between the Early Upper Paleolithic and the Mesolithic is qualitatively predicted by genetics. Similarly, both skeletal and genetic height remained constant between the Mesolithic and Neolithic and increased between the Neolithic and Bronze Age. Sitting height changes much less than standing height—consistent with genetic predictions—although genetics predicts a small Bronze Age increase that is not observed in skeletal remains. Geographic variation in stature is also qualitatively consistent with genetic predictions, particularly with respect to latitude.

We find that the changes in genetic height between the Neolithic and Bronze Age may be driven by polygenic adaptation. Finally, we hypothesize that an observed decrease in genetic heel bone mineral density in the Neolithic reflects adaptation to the decreased mobility indicated by decreased femoral bending strength.

This study provides a model for interpreting phenotypic changes predicted from ancient DNA and demonstrates how they can be combined with phenotypic measurements to understand the relative contribution of genetic and developmentally plastic responses to environmental change.

--- https://www.biorxiv.org/content/10.1101/704544.full Assessing by modeling the consequences of increased recombination in genomic selection of Oryza sativa and Brassica rapa E. Tourrette, R. Bernardo, M. Falque, O. Martin 2019-07-17 2021-04-12 [("doi","10.1101/704544")] genetics/selection/artificial

Recombination generates genetic diversity but the number of crossovers per meiosis is limited in most species. Previous studies showed that increasing recombination can enhance response to selection. However, such studies did not assume a specific method of modifying recombination. Our objective was to test whether two methods used to increase recombination in plants could increase the genetic gain in a population undergoing genomic selection.

The first method, in Oryza sativa, used a mutant of anti-crossover genes to increase global recombination without affecting the recombination landscape. The second one uses the ploidy level of a cross between Brassica rapa and Brassica napus to increase the recombination particularly in pericentromeric regions. These recombination landscapes were used to model recombination while quantitative trait loci positions were based on the actual gene distribution. We simulated selection programs with initially a cross between two inbred lines, for two species.

Increased recombination enhanced the response to selection. The amount of enhancement in the cumulative gain largely depended on the species and the number of quantitative trait loci (2, 10, 20, 50, 200 or 1,000 per chromosome). Genetic gains were increased up to 30% after 20 generations. Furthermore, modifying the recombination landscape was the most effective: the gain was larger by 25% with the first method and 33% with the second one in B. rapa, and 15% compared to 11% in O. sativa.

Thus, increased recombination enhances the genetic gain in genomic selection for long-term selection programs, with visible effects after 4 to 5 generations.

--- https://www.biorxiv.org/content/10.1101/687681.full A large single-participant fMRI dataset for probing brain responses to naturalistic stimuli in space and time K. Seeliger, R. P. Sommers, U. Güçlü, S. E. Bosch, M. A. J. van Gerven 2019-07-02 2021-04-12 [("doi","10.1101/687681")] ai/dataset psychology/neuroscience

Visual and auditory representations in the human brain have been studied with encoding, decoding and reconstruction models. Representations from convolutional neural networks have been used as explanatory models for these stimulus-induced hierarchical brain activations. However, none of the fMRI datasets currently available has adequate amounts of data for sufficiently sampling their representations.

We recorded a densely sampled large fMRI dataset (time resolution = 700 ms) in a single individual exposed to spatiotemporal visual and auditory naturalistic stimuli (30 episodes of BBC’s Doctor Who). The data consists of 120,830 whole-brain volumes (approx. 23h) of single-presentation data (full episodes, training set) and 1,178 volumes (11 min) of repeated narrative short episodes (test set, 22 repetitions), recorded with fixation over a period of six months.

This rich dataset can be used widely to study the way the brain represents audiovisual input across its sensory hierarchies.

--- https://arxiv.org/abs/1908.04683 Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field Marin Toromanoff, Emilie Wirbel, Fabien Moutarde 2019-08-13 2021-04-12 [("doi","10.48550/arXiv.1908.04683")] reinforcement-learning/model-free

Consistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is not straightforward. In the Arcade Learning Environment (ALE), small changes in environment parameters such as stochasticity or the maximum allowed play time can lead to very different performance. In this work, we discuss the difficulties of comparing different agents trained on ALE.

In order to take a step further towards reproducible and comparable DRL, we introduce SABER, a Standardized Atari BEnchmark for general Reinforcement learning algorithms. Our methodology extends previous recommendations and contains a complete set of environment parameters as well as train and test procedures.

We then use SABER to evaluate the current state-of-the-art, Rainbow DQN. Furthermore, we introduce a human world records baseline, and argue that previous claims of expert or superhuman performance of DRL might not be accurate.

Finally, we propose Rainbow-IQN by extending Rainbow with Implicit Quantile Networks (IQN) leading to new state-of-the-art performance.

Source code is available for reproducibility.

--- https://arxiv.org/abs/1908.04734#deepmind Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective Tom Everitt, Marcus Hutter, Ramana Kumar, Victoria Krakovna 2019-08-13 2021-04-12 [("doi","10.48550/arXiv.1908.04734")] reinforcement-learning/safe statistics/causality

Can humans get arbitrarily capable reinforcement learning (RL) agents to do their bidding? Or will sufficiently capable RL agents always find ways to bypass their intended objectives by shortcutting their reward signal?

This question impacts how far RL can be scaled, and whether alternative paradigms must be developed in order to build safe artificial general intelligence. In this paper, we study when an RL agent has an instrumental goal to tamper with its reward process, and describe design principles that prevent instrumental goals for two different types of reward tampering (reward function tampering and RF-input tampering).

Combined, the design principles can prevent both types of reward tampering from being instrumental goals. The analysis benefits from causal influence diagrams to provide intuitive yet precise formalizations.

--- https://arxiv.org/abs/1909.01214 Better Rewards Yield Better Summaries: Learning to Summarise Without References Florian Böhm, Yang Gao, Christian M. Meyer, Ori Shapira, Ido Dagan, Iryna Gurevych 2019-09-03 2021-04-12 [("doi","10.48550/arXiv.1909.01214")] reinforcement-learning/preference-learning

Reinforcement Learning (RL) based document summarization systems yield state-of-the-art performance in terms of ROUGE scores, because they directly use ROUGE as the rewards during training. However, summaries with high ROUGE scores often receive low human judgement.

To find a better reward function that can guide RL to generate human-appealing summaries, we learn a reward function from human ratings on 2,500 summaries. Our reward function only takes the document and system summary as input. Hence, once trained, it can be used to train RL-based summarization systems without using any reference summaries.

We show that our learned rewards have higher correlation with human ratings than previous approaches. Human evaluation experiments show that, compared to the state-of-the-art supervised-learning systems and ROUGE-as-rewards RL summarization systems, the RL systems using our learned rewards during training generate summarieswith higher human ratings.

The learned reward function and our source code are available at Github.

--- https://www.reddit.com/r/slatestarcodex/comments/dwtr0m/matthew_walkers_why_we_sleep_is_riddled_with/#thing_t1_f7mid7me 2021-04-12 zeo --- https://arxiv.org/abs/1905.02175 Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry 2019-05-06 2021-04-12 [("doi","10.48550/arXiv.1905.02175")] ai/nn/adversarial ai/nn/cnn ai/nn/gan ai/scaling

[blog, responses] Adversarial examples have attracted attention in machine learning, but the reasons for their existence and pervasiveness remain unclear.

We demonstrate that adversarial examples can be directly attributed to the presence of non-robust features: features derived from patterns in the data distribution that are highly predictive, yet brittle and incomprehensible to humans.

After capturing these features within a theoretical framework, we establish their widespread existence in standard datasets. Finally, we present a simple setting where we can rigorously tie the phenomena we observe in practice to a misalignment between the (human-specified) notion of robustness and the inherent geometry of the data.

--- https://arxiv.org/abs/1905.01723 Few-Shot Unsupervised Image-to-Image Translation Ming-Yu Liu, Xun Huang, Arun Mallya, Tero Karras, Timo Aila, Jaakko Lehtinen, Jan Kautz 2019-05-05 2021-04-12 [("doi","10.48550/arXiv.1905.01723")] ai/nn/gan

Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. While remarkably successful, current methods require access to many images in both source and destination classes at training time. We argue this greatly limits their use.

Drawing inspiration from the human capability of picking up the essence of a novel object from a small number of examples and generalizing from there, we seek a few-shot, unsupervised image-to-image translation algorithm that works on previously unseen target classes that are specified, at test time, only by a few example images. Our model achieves this few-shot generation capability by coupling an adversarial training scheme with a novel network design.

Through extensive experimental validation and comparisons to several baseline methods on benchmark datasets, we verify the effectiveness of the proposed framework.

Our implementation and datasets are available at Github.

--- https://arxiv.org/abs/1906.00446#deepmind Generating Diverse High-Fidelity Images with VQ-VAE-2 Ali Razavi, Aaron van den Oord, Oriol Vinyals 2019-06-02 2021-04-12 [("doi","10.48550/arXiv.1906.00446")] ai/nn/transformer/gpt/jukebox ai/nn/vae

We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before.

We use simple feed-forward encoder and decoder networks, making our model an attractive candidate for applications where the encoding and/or decoding speed is critical. Additionally, VQ-VAE requires sampling an autoregressive model only in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images.

We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state-of-the-art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN’s known shortcomings such as mode collapse and lack of diversity.

--- https://arxiv.org/abs/1905.13164 Hierarchical Transformers for Multi-Document Summarization Yang Liu, Mirella Lapata 2019-05-30 2021-04-12 [("doi","10.48550/arXiv.1905.13164")] ai/nn/transformer/attention/hierarchical

In this paper, we develop a neural summarization model which can effectively process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner.

We represent cross-document relationships via an attention mechanism which allows to share information as opposed to simply concatenating text spans and processing them as a flat sequence. Our model learns latent dependencies among textual units, but can also take advantage of explicit graph representations focusing on similarity or discourse relations.

Empirical results on the WikiSum dataset demonstrate that the proposed architecture brings substantial improvements over several strong baselines.

--- https://arxiv.org/abs/1905.10498 Cold Case: The Lost MNIST Digits Chhavi Yadav, Léon Bottou 2019-05-25 2021-04-13 [("doi","10.48550/arXiv.1905.10498")] ai/dataset ai/nn/cnn

Although the popular MNIST dataset [LeCun et al 1994] is derived from the NIST database [Grother & Hanaoka 1995], the precise processing steps for this derivation have been lost to time. We propose a reconstruction that is accurate enough to serve as a replacement for the MNIST dataset, with insignificant changes in accuracy.

We trace each MNIST digit to its NIST source and its rich metadata such as writer identifier, partition identifier, etc. We also reconstruct the complete MNIST test set with 60,000 samples instead of the usual 10,000. Since the balance 50,000 were never distributed, they enable us to investigate the impact of 20-five years of MNIST experiments on the reported testing performances.

Our results unambiguously confirm the trends observed by Recht et al [2018, 2019]: although the misclassification rates are slightly off, classifier ordering and model selection remain broadly reliable. We attribute this phenomenon to the pairing benefits of comparing classifiers on the same digits.

--- https://arxiv.org/abs/1905.07799#facebook Adaptive Attention Span in Transformers Sainbayar Sukhbaatar, Edouard Grave, Piotr Bojanowski, Arm Holdings, Joulin 2019-05-19 2021-04-13 [("doi","10.48550/arXiv.1905.07799")] ai/nn/transformer/attention/sparsity

We propose a novel self-attention mechanism that can learn its optimal attention span. This allows us to extend the maximum context size used in Transformer, while maintaining control over their memory footprint and computational time.

We show the effectiveness of our approach on the task of character level language modeling, where we achieve state-of-the-art performances on text8/enwik8 by using a maximum context of 8k characters.

--- https://arxiv.org/abs/1905.04899 CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, Youngjoon Yoo 2019-05-13 2021-04-13 [("doi","10.48550/arXiv.1905.04899")] ai/nn/cnn

Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers. They have proved to be effective for guiding the model to attend on less discriminative parts of objects (eg. leg as opposed to head of a person), thereby letting the network generalize better and have better object localization capabilities. On the other hand, current methods for regional dropout remove informative pixels on training images by overlaying a patch of either black pixels or random noise. Such removal is not desirable because it leads to information loss and inefficiency during training.

We therefore propose the CutMix augmentation strategy: patches are cut and pasted among training images where the ground truth labels are also mixed proportionally to the area of the patches. By making efficient use of training pixels and retaining the regularization effect of regional dropout, CutMix consistently outperforms the state-of-the-art augmentation strategies on CIFAR and ImageNet classification tasks, as well as on the ImageNet weakly-supervised localization task.

Moreover, unlike previous augmentation methods, our CutMix-trained ImageNet classifier, when used as a pretrained model, results in consistent performance gains in Pascal detection and MS-COCO image captioning benchmarks. We also show that CutMix improves the model robustness against input corruptions and its out-of-distribution detection performances.

Source code and pretrained models are available at Github.

--- https://arxiv.org/abs/1905.02450#microsoft MASS: Masked Sequence to Sequence Pre-training for Language Generation Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu 2019-05-07 2021-04-13 [("doi","10.48550/arXiv.1905.02450")] ai/nn/transformer

Pre-training and fine-tuning, eg. BERT, have achieved great success in language understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resource downstream tasks. Inspired by the success of BERT, we propose MAsked Sequence to Sequence pre-training (MASS) for the encoder-decoder based language generation tasks.

MASS adopts the encoder-decoder framework to reconstruct a sentence fragment given the remaining part of the sentence: its encoder takes a sentence with randomly masked fragment (several consecutive tokens) as input, and its decoder tries to predict this masked fragment. In this way, MASS can jointly train the encoder and decoder to develop the capability of representation extraction and language modeling.

By further fine-tuning on a variety of zero/low-resource language generation tasks, including neural machine translation, text summarization, and conversational response generation (3 tasks and totally 8 datasets), MASS achieves improvements over the baselines without pre-training or with other pre-training methods. Specially, we achieve the state-of-the-art accuracy (37.5 in terms of BLEU score) on the unsupervised English-French translation, even beating the early attention-based supervised model.

--- https://arxiv.org/abs/1904.07392 NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection Golnaz Ghiasi, Tsung-Yi Lin, Ruoming Pang, Quoc V. Le 2019-04-16 2021-04-13 [("doi","10.48550/arXiv.1904.07392")] ai/nn/cnn

Current state-of-the-art convolutional architectures for object detection are manually designed.

Here we aim to learn a better architecture of feature pyramid network for object detection. We adopt Neural Architecture Search and discover a new feature pyramid architecture in a novel scalable search space covering all cross-scale connections. The discovered architecture, named NAS-FPN, consists of a combination of top-down and bottom-up connections to fuse features across scales.

NAS-FPN, combined with various backbone models in the RetinaNet framework, achieves better accuracy and latency tradeoff compared to state-of-the-art object detection models. NAS-FPN improves mobile detection accuracy by 2 AP compared to state-of-the-art SSDLite with MobileNetV2 model in [32] and achieves 48.3 AP which surpasses Mask R-CNN [10] detection accuracy with less computation time.

--- https://arxiv.org/abs/1904.01557#deepmind Analysing Mathematical Reasoning Abilities of Neural Models David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli 2019-04-02 2021-04-13 [("doi","10.48550/arXiv.1904.01557")] ai/nn math

Mathematical reasoning—a core ability within human intelligence—presents some unique challenges as a domain: we do not come to understand and solve mathematical problems primarily on the back of experience and evidence, but on the basis of inferring, learning, and exploiting laws, axioms, and symbol manipulation rules. In this paper, we present a new challenge for the evaluation (and eventually the design) of neural architectures and similar systems, developing a task suite of mathematics problems involving sequential questions and answers in a free-form textual input/output format.

The structured nature of the mathematics domain, covering arithmetic, algebra, probability and calculus, enables the construction of training and test splits designed to clearly illuminate the capabilities and failure-modes of different architectures, as well as evaluate their ability to compose and relate knowledge and learned processes. Having described the data generation process and its potential future expansions, we conduct a comprehensive analysis of models from two broad classes of the most powerful sequence-to-sequence architectures and find notable differences in their ability to resolve mathematical problems and generalize their knowledge.

--- https://arxiv.org/abs/1905.03197 UniLM: Unified Language Model Pre-training for Natural Language Understanding and Generation Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, Hsiao-Wuen Hon 2019-05-08 2021-04-13 [("doi","10.48550/arXiv.1905.03197")] ai/nn/transformer ai/scaling

This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using 3 types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on.

UniLM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UniLM achieves new state-of-the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65).

The code and pre-trained models are available at Github.

--- https://arxiv.org/abs/1903.12261 Benchmarking Neural Network Robustness to Common Corruptions and Perturbations Dan Hendrycks, Thomas Dietterich 2019-03-28 2021-04-13 [("doi","10.48550/arXiv.1903.12261")] ai/dataset ai/nn/adversarial ai/nn/cnn ai/scaling

In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications.

Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier’s robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations.

We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness.

Together our benchmarks may aid future work toward networks that robustly generalize.

--- https://arxiv.org/abs/1906.02243 Energy and Policy Considerations for Deep Learning in NLP Emma Strubell, Ananya Ganesh, Andrew McCallum 2019-06-05 2021-04-13 [("doi","10.48550/arXiv.1906.02243")] ai/nn/transformer ai/scaling/hardware

Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks.

However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption. As a result, these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware.

In this paper, we bring this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP.

Based on these findings, we propose actionable recommendations to reduce costs and improve equity in NLP research and practice.

--- https://www.biorxiv.org/content/10.1101/613489.full GWAS of brain volume on 54,407 individuals and cross-trait analysis with intelligence identifies shared genomic loci and genes Philip R. Jansen, Mats Nagel, Kyoko Watanabe, Yongbin Wei, Jeanne E. Savage, Christiaan A. de Leeuw, Martijn P. van den Heuvel, Sophie van der Sluis, Danielle Posthuma 2019-04-19 2021-04-13 [("doi","10.1101/613489")] genetics/heritable/correlation iq psychology/neuroscience

The phenotypic correlation between human intelligence and brain volume (BV) is considerable (r≈0.40), and has been shown to be due to shared genetic factors. To further examine specific genetic factors driving this correlation, we present genomic analyses of the genetic overlap between intelligence and BV using genome-wide association study (GWAS) results.

First, we conducted the largest BV GWAS meta-analysis to date (n = 54,407 individuals), followed by functional annotation and gene-mapping. We identified 35 genomic loci (27 novel), implicating 362 genes (346 novel) and 23 biological pathways for BV.

Second, we used an existing GWAS for intelligence (n = 269,867 individuals), and estimated the genetic correlation (rg) between BV and intelligence to be 0.23. We show that the rg is driven by physical overlap of GWAS hits in 5 genomic loci. We identified 67 shared genes between BV and intelligence, which are mainly involved in important signaling pathways regulating cell growth. Out of these 67 we prioritized 32 that are most likely to have functional impact.

These results provide new information on the genetics of BV and provide biological insight into BV’s shared genetic etiology with intelligence.

--- https://www.biorxiv.org/content/10.1101/598532.full Genetic Associations with Mathematics Tracking and Persistence in Secondary School K. Paige Harden, Benjamin W. Domingue, Daniel W. Belsky, Jason D. Boardman, Robert Crosnoe, Margherita Malanchini, Michel G. Nivard, Elliot M. Tucker-Drob, Kathleen Mullan Harris 2019-04-05 2021-04-13 [("doi","10.1101/598532")] genetics/heritable/correlation iq

Maximizing the flow of students through the science, technology, engineering, and math (STEM) pipeline is important to promoting human capital development and reducing economic inequality. A critical juncture in the STEM pipeline is the highly-cumulative sequence of secondary school math courses. Students from disadvantaged schools are less likely to complete advanced math courses, but debate continues about why. Here, we address this question using student polygenic scores, which are DNA-based indicators of propensity to succeed in education. We integrated genetic and official school transcript data from over 3,000 European-ancestry students from U.S. high schools.

We used polygenic scores as a molecular tracer to understand how the flow of students through the high school math pipeline differs in socioeconomically advantaged versus disadvantaged schools. Students with higher education polygenic scores were tracked to more advanced math already at the beginning of high school and persisted in math for more years.

Molecular tracer analyses revealed that the dynamics of the math pipeline differed by school advantage. Compared to disadvantaged schools, advantaged schools tracked more students with high polygenic scores into advanced math classes at the start of high school, and they buffered students with low polygenic scores from dropping out of math. Across all schools, even students with exceptional polygenic scores (top 2%) were unlikely to take the most advanced math classes, suggesting substantial room for improvement in the development of potential STEM talent.

These results link new molecular genetic discoveries to a common target of educational-policy reforms.

--- https://www.biorxiv.org/content/10.1101/557678.full Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies Jordan Ubbens, Mikolaj Cieslak, Przemyslaw Prusinkiewicz, Ian Stavness 2019-04-15 2021-04-14 [("doi","10.1101/557678")] genetics/heritable statistics/variance-component

Association mapping studies have enabled researchers to identify candidate loci for many important environmental resistance factors, including agronomically relevant resistance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately and consistently measuring stress responses, typically in an automated high-throughput context using image processing.

In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. Using two synthetically generated image datasets, we first show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery.

We then demonstrate an example application of an interspecific cross of the model C4 grass Setaria.

We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling association mapping studies without the need for engineering complex image processing pipelines.

--- https://www.biorxiv.org/content/10.1101/598904.full Simulation of model overfit in variance explained with genetic data Jaime Derringer 2019-04-10 2021-04-14 [("doi","10.1101/598904")] genetics/heritable

Two recent papers, and an author response to prior commentary, addressing the genetic architecture of human temperament and character claimed that “The identified SNPs explained nearly all the heritability expected”. The authors’ method for estimating heritability may be summarized as: Step 1: Pre-select SNPs on the basis of GWAS p < 0.01 in the target sample.

Step 2: Enter target sample genotypes (the pre-selected SNPs from Step 1) and phenotypes into an unsupervised machine learning algorithm (Phenotype-Genotype Many-to-Many Relations Analysis, PGMRA) for further reduction of the set of SNPs.

Step 3: Test the sum score of the SNPs identified from Step 2, weighted by the GWAS regression weights estimated in Step 1, within the same target sample. The authors interpreted the linear regression model R^2 obtained from Step 3 as a measure of successfully identified heritability. Regardless of the method applied to select SNPs in Step 2, the combination of Steps 1 and 3, as described, causes inflation of the estimated effect size.

The extent of this inflation is demonstrated here, where random SNP selection and polygenic scoring from simulated random data recovered effect sizes similar to those reported in the original empirical papers.

--- https://www.biorxiv.org/content/10.1101/653204.full Making the most of Clumping and Thresholding for polygenic scores Florian Privé, Bjarni J. Vilhjálmsson, Hugues Aschard, Michael G. B. Blum 2019-05-30 2021-04-14 [("doi","10.1101/653204")] genetics/heritable

Polygenic prediction has the potential to contribute to precision medicine. Clumping and Thresholding (C+T) is a widely used method to derive polygenic scores. When using C+T, people usually test several p-value thresholds to maximize predictive ability of derived polygenic scores. Along with this p-value threshold, we propose to tune 3 other hyper-parameters for C+T. We implement an efficient way to derive C+T scores corresponding to many different sets of hyper-parameters. For example, you can now derive thousands of different C+T scores for 300K individuals and 1M variants in less than one day. We show that tuning 4 hyper-parameters of C+T consistently improves its predictive performance in both simulations and real data applications as compared to tuning only the p-value threshold.

Using this grid of computed C+T scores, we further extend C+T with stacking. More precisely, instead of choosing one set of hyper-parameters that maximizes prediction in some training set, we propose to learn an optimal linear combination of all these C+T scores using an efficient penalized regression. We call this method Stacked Clumping and Thresholding (SCT) and show that this makes C+T more flexible. When the training set is large enough, SCT can provide much larger predictive performance as compared to any of the C+T scores individually.

--- https://www.biorxiv.org/content/10.1101/625202.full Insights about variation in meiosis from 31,228 human sperm genomes Avery Davis Bell, Curtis J. Mello, James Nemesh, Sara A. Brumbaugh, Alec Wysoker, Steven A. McCarroll 2019-05-02 2021-04-14 [("doi","10.1101/625202")] genetics/heritable/rare

Meiosis, while critical for reproduction, is also highly variable and error prone: crossover rates vary among humans and individual gametes, and chromosome nondisjunction leads to aneuploidy, a leading cause of miscarriage. To study variation in meiotic outcomes within and across individuals, we developed a way to sequence many individual sperm genomes at once.

We used this method to sequence the genomes of 31,228 gametes from 20 sperm donors, identifying 813,122 crossovers, 787 aneuploid chromosomes, and unexpected genomic anomalies. Different sperm donors varied four-fold in the frequency of aneuploid sperm, and aneuploid chromosomes gained in meiosis I had 36% fewer crossovers than corresponding non-aneuploid chromosomes.

Diverse recombination phenotypes were surprisingly coordinated: donors with high average crossover rates also made a larger fraction of their crossovers in centromere-proximal regions and placed their crossovers closer together. These same relationships were also evident in the variation among individual gametes from the same donor: sperm with more crossovers tended to have made crossovers closer together and in centromere-proximal regions.

Variation in the physical compaction of chromosomes could help explain this coordination of meiotic variation across chromosomes, gametes, and individuals.

--- https://arxiv.org/abs/1906.05381 Compositional generalization through meta sequence-to-sequence learning Brenden M. Lake 2019-06-12 2021-04-14 [("doi","10.48550/arXiv.1906.05381")] reinforcement-learning/meta-learning

People can learn a new concept and use it compositionally, understanding how to “blicket twice” after learning how to “blicket.” In contrast, powerful sequence-to-sequence (seq2seq) neural networks fail such tests of compositionality, especially when composing new concepts together with existing concepts.

In this paper, I show how memory-augmented neural networks can be trained to generalize compositionally through meta seq2seq learning. In this approach, models train on a series of seq2seq problems to acquire the compositional skills needed to solve new seq2seq problems.

Meta se2seq learning solves several of the SCAN tests for compositional learning and can learn to apply implicit rules to variables.

--- https://arxiv.org/abs/1906.01820 Risks from Learned Optimization in Advanced Machine Learning Systems Evan Hubinger, Chris van Merwijk, Vladimir Mikulik, Joar Skalse, Scott Garrabrant 2019-06-05 2021-04-14 [("doi","10.48550/arXiv.1906.01820")] economics/mechanism-design reinforcement-learning/meta-learning reinforcement-learning/safe

We analyze the type of learned optimization that occurs when a learned model (such as a neural network) is itself an optimizer—a situation we refer to as mesa-optimization, a neologism we introduce in this paper.

We believe that the possibility of mesa-optimization raises two important questions for the safety and transparency of advanced machine learning systems. First, under what circumstances will learned models be optimizers, including when they should not be?

Second, when a learned model is an optimizer, what will its objective be—how will it differ from the loss function it was trained under—and how can it be aligned?

In this paper, we provide an in-depth analysis of these two primary questions and provide an overview of topics for future research.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1167581/pdf/janat00127-0181.pdf The vomeronasal organ of the cat I Salazar, P. Sanchez Quinteiro, J. M. Cifuentes, T. Garcia Caballero 1996 2021-04-14 cat/psychology

The vomeronasal organ of the cat was studied macroscopically, by light microscopy and by immunohistochemical techniques. Special attention was paid to the general distribution of the various soft tissue components of this organ (duct, glands, connective tissue, blood vessels, and nerves.)

Examination of series of transverse sections showed that the wall of the vomeronasal duct bears 44 different types of epithelium: simple columnar in the caudal part of the duct, respiratory and receptor respectively on the lateral and medial walls of the middle part of the duct, and stratified squamous rostrally. The pattern of distribution of other soft tissue components was closely associated with that of epithelium types. In areas where the duct wall was lined with receptor epithelium, nerves and connective tissue were present between the epithelium and the medial sheet of the vomeronasal cartilage. Most glands and blood vessels were located lateral to those areas of the duct wall lined with respiratory epithelium.

Numerous basal cells were present in the sensory epithelium. Understanding of the distribution of the soft tissue components of this organ may shed light on its function.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC172852/pdf/080131.pdf Viruses, schizophrenia, and bipolar disorder R H. Yolken, E. F. Torrey 1995 2021-04-14 [("doi","10.1128/CMR.8.1.131")] psychiatry/bipolar psychiatry/schizophrenia

The hypothesis that viruses or other infectious agents may cause schizophrenia or bipolar disorder dates to the 19th century but has recently been revived. It could explain many clinical, genetic, and epidemiologic aspects of these diseases, including the winter-spring birth seasonality, regional differences, urban birth, household crowding, having an older sibling, and prenatal exposure to influenza as risk factors. It could also explain observed immunological changes such as abnormalities of lymphocytes, proteins, autoantibodies, and cytokines.

However, direct studies of viral infections in individuals with these psychiatric diseases have been predominantly negative. Most studies have examined antibodies in blood or cerebrospinal fluid, and relatively few studies have been done on viral antigens, genomes, cytopathic effect on cell culture, and animal transmission experiments.

Viral research on schizophrenia and bipolar disorder is thus comparable to viral research on multiple sclerosis and Parkinson’s disease: an attractive hypothesis with scattered interesting findings but no clear proof.

The application of molecular biological techniques may allow the identification of novel infectious agents and the associations of these novel agents with serious mental illnesses.

--- https://arxiv.org/abs/quant-ph/9610033 Interaction-Free Measurements Lev Vaidman 1996-10-21 2021-04-14 [("doi","10.48550/arXiv.9610033")] science

A brief review of interaction-free measurements (IFM) is presented. The IFM is a solution of a quantum puzzle: How to test a bomb which explodes on every test without exploding it?

This paper was given in the Oxford conference in honor of Roger Penrose.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3527416/ Genome-wide association meta-analysis identifies new endometriosis risk loci Dale R. Nyholt, Siew-Kee Low, Carl A. Anderson, Jodie N. Painter, Satoko Uno, Andrew P. Morris, Stuart MacGregor, Scott D. Gordon, Anjali K. Henders, Nicholas G. Martin, John Attia, Elizabeth G. Holliday, Mark McEvoy, Rodney J. Scott, Stephen H. Kennedy, Susan A. Treloar, Stacey A. Missmer, Sosuke Adachi, Kenichi Tanaka, Yusuke Nakamura, Krina T. Zondervan, Hitoshi Zembutsu, Grant W. Montgomery 2012 2021-04-14 [("doi","10.1038/ng.2445")] genetics/heritable/correlation

We conducted a genome-wide association meta-analysis of 4,604 endometriosis cases and 9,393 controls of Japanese and European ancestry. We show that rs12700667 on chromosome 7p15.2, previously found to associate with disease in Europeans, replicates in Japanese (p = 3.6 × 10−3), and we confirm association of rs7521902 at 1p36.12 near WNT4. In addition, we establish an association of rs13394619 in GREB1 at 2p25.1 with endometriosis and identify a newly associated locus at 12q22 near VEZT (rs10859871).

Excluding cases of European ancestry of minimal or unknown severity, we identified additional previously unknown loci at 2p14 (rs4141819), 6p22.3 (rs7739264) and 9p21.3 (rs1537377). All 7 SNP effects were replicated in an independent cohort and associated at p <5 × 10−8 in a combined analysis.

Finally, we found an overlap in polygenic risk for endometriosis between the genome-wide association cohorts of European and Japanese descent (p = 8.8 × 10−11), indicating that many weakly associated SNPs represent true endometriosis risk loci and that risk prediction and future targeted disease therapy may be transferred across these populations.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2810516/ Ageing populations: the challenges ahead Kaare Christensen, Gabriele Doblhammer, Roland Rau, James W. Vaupel 2009 2021-04-15 [("doi","10.1016/S0140-6736(09)61460-4")] longevity

If the pace of increase in life expectancy in developed countries over the past two centuries continues through the 21st century, most babies born since 2000 in France, Germany, Italy, the UK, the USA, Canada, Japan, and other countries with long life expectancies will celebrate their 100th birthdays.

Although trends differ between countries, populations of nearly all such countries are aging as a result of low fertility, low immigration, and long lives.

A key question is: are increases in life expectancy accompanied by a concurrent postponement of functional limitations and disability? The answer is still open, but research suggests that aging processes are modifiable and that people are living longer without severe disability.

This finding, together with technological and medical development and redistribution of work, will be important for our chances to meet the challenges of aging populations.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4325090/ A genome-wide association study of anorexia nervosa V Boraska, C. S. Franklin, J. A. B. Floyd, L. M. Thornton, L. M. Huckins, L. Southam, N. W. Rayner, I. Tachmazidou, K. L. Klump, J. Treasure, C. M. Lewis, U. Schmidt, F. Tozzi, K. Kiezebrink, J. Hebebrand, P. Gorwood, R. A. H. Adan, M. J. H. Kas, A. Favaro, P. Santonastaso, F. Fernández-Aranda, M. Gratacos, F. Rybakowski, M. Dmitrzak-Weglarz, J. Kaprio, A. Keski-Rahkonen, A. Raevuori, E. F. Van Furth, M. C. T. Slof-Op’t Landt, J. I. Hudson, T. Reichborn-Kjennerud, G. P. S. Knudsen, P. Monteleone, A. S. Kaplan, A. Karwautz, H. Hakonarson, W. H. Berrettini, Y. Guo, D. Li, N. J. Schork, G. Komaki, T. Ando, H. Inoko, T. Esko, K. Fischer, K. Männik, A. Metspalu, J. H. Baker, R. D. Cone, J. Dackor, J. E. DeSocio, C. E. Hilliard, J. K. O’Toole, J. Pantel, J. P. Szatkiewicz, C. Taico, S. Zerwas, S. E. Trace, O. S. P. Davis, S. Helder, K. Bühren, R. Burghardt, M. de Zwaan, K. Egberts, S. Ehrlich, B. Herpertz-Dahlmann, W. Herzog, H. Imgart, A. Scherag, S. Scherag, S. Zipfel, C. Boni, N. Ramoz, A. Versini, M. K. Brandys, U. N. Danner, C. de Kovel, J. Hendriks, B. P. C. Koeleman, R. A. Ophoff, E. Strengman, A. A. van Elburg, A. Bruson, M. Clementi, D. Degortes, M. Forzan, E. Tenconi, E. Docampo, G. Escaramís, S. Jiménez-Murcia, J. Lissowska, A. Rajewski, N. Szeszenia-Dabrowska, A. Slopien, J. Hauser, L. Karhunen, I. Meulenbelt, P. E. Slagboom, A. Tortorella, M. Maj, G. Dedoussis, D. Dikeos, F. Gonidakis, K. Tziouvas, A. Tsitsika, H. Papezova, L. Slachtova, D. Martaskova, J. L. Kennedy, R. D. Levitan, Z. Yilmaz, J. Huemer, D. Koubek, E. Merl, G. Wagner, P. Lichtenstein, G. Breen, S. Cohen-Woods, A. Farmer, P. McGuffin, S. Cichon, I. Giegling, S. Herms, D. Rujescu, S. Schreiber, H-E Wichmann, C. Dina, R. Sladek, G. Gambaro, N. Soranzo, A. Julia, S. Marsal, R. Rabionet, V. Gaborieau, D. M. Dick, A. Palotie, S. Ripatti, E. Widén, O. A. Andreassen, T. Espeseth, A. Lundervold, I. Reinvang, V. M. Steen, S. Le Hellard, M. Mattingsdal, I. Ntalla, V. Bencko, L. Foretova, V. Janout, M. Navratilova, S. Gallinger, D. Pinto, S. W. Scherer, H. Aschauer, L. Carlberg, A. Schosser, L. Alfredsson, B. Ding, L. Klareskog, L. Padyukov, P. Courtet, S. Guillaume, I. Jaussent, C. Finan, G. Kalsi, M. Roberts, D. W. Logan, L. Peltonen, G. R. S. Ritchie, J. C. Barrett, X. Estivill, A. Hinney, P. F. Sullivan, D. A. Collier, E. Zeggini, C. M. Bulik 2014 2021-04-15 [("doi","10.1038/mp.2013.187")] genetics/heritable psychiatry/anorexia

Anorexia nervosa (AN) is a complex and heritable eating disorder characterized by dangerously low body weight. Neither candidate gene studies nor an initial genome-wide association study (GWAS) have yielded and replicated results.

We performed a GWAS in 2907 cases with AN from 14 countries (15 sites) and 14,860 ancestrally matched controls as part of the Genetic Consortium for AN (GCAN) and the Wellcome Trust Case Control Consortium 3 (WTCCC3). Individual association analyses were conducted in each stratum and meta-analyzed across all 15 discovery data sets. Seventy-six (72 independent) single-nucleotide polymorphisms were taken forward for in silico (two data sets) or de novo (13 data sets) replication genotyping in 2677 independent AN cases and 8629 European ancestry controls along with 458 AN cases and 421 controls from Japan. The final global meta-analysis across discovery and replication data sets comprised 5551 AN cases and 21,080 controls. AN subtype analyses (1606 AN restricting; 1445 AN binge-purge) were performed.

No findings reached genome-wide statistical-significance. Two intronic variants were suggestively associated: rs9839776 (p = 3.01 × 10−7) in SOX2OT and rs17030795 (p = 5.84 × 10−6) in PPP3CA. Two additional signals were specific to Europeans: rs1523921 (p = 5.76 × 10−6) between CUL3 and FAM124B and rs1886797 (p = 8.05 × 10−6) near SPATA13. Comparing discovery with replication results, 76% of the effects were in the same direction, an observation highly unlikely to be due to chance (p = 4 × 10−6), strongly suggesting that true findings exist but our sample, the largest yet reported, was underpowered for their detection.

The accrual of large genotyped AN case-control samples should be an immediate priority for the field.

--- https://arxiv.org/abs/1805.07997 Anime Style Space Exploration Using Metric Learning and Generative Adversarial Networks Sitao Xiang, Hao Li 2018-05-21 2021-04-15 [("doi","10.48550/arXiv.1805.07997")] ai/anime

Deep learning-based style transfer between images has recently become a popular area of research. A common way of encoding “style” is through a feature representation based on the Gram matrix of features extracted by some pre-trained neural network or some other form of feature statistics. Such a definition is based on an arbitrary human decision and may not best capture what a style really is.

In trying to gain a better understanding of “style”, we propose a metric learning-based method to explicitly encode the style of an artwork. In particular, our definition of style captures the differences between artists, as shown by classification performances, and such that the style representation can be interpreted, manipulated, and visualized through style-conditioned image generation through a Generative Adversarial Network.

We employ this method to explore the style space of anime portrait illustrations.

--- https://arxiv.org/abs/1709.01584 Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario Jill-Jênn Vie, Florian Yger, Ryan Lahfa, Basile Clement, Kévin Cocchi, Thomas Chalumeau, Hisashi Kashima 2017-09-03 2021-04-15 [("doi","10.48550/arXiv.1709.01584")] ai/anime ai/tabular

Item cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well. This problem can be framed as follows: how to predict whether a user will like a manga that received few ratings from the community?

Content-based techniques can alleviate this issue but require extra information, that is usually expensive to gather. In this paper, we use a deep learning technique, Illustration2Vec, to easily extract tag information from the manga and anime posters (eg. sword, or ponytail).

We propose BALSE (Blended Alternate Least Squares with Explanation), a new model for collaborative filtering, that benefits from this extra information to recommend mangas.

We show, using real data from an online manga recommender system called Mangaki, that our model improves substantially the quality of recommendations, especially for less-known manga, and is able to provide an interpretation of the taste of the users.

--- https://arxiv.org/abs/1705.03487 A neural network system for transformation of regional cuisine style Masahiro Kazama, Minami Sugimoto, Chizuru Hosokawa, Keisuke Matsushima, Lav R. Varshney, Yoshiki Ishikawa 2017-05-06 2021-04-15 [("doi","10.3389/fict.2018.00014")] ai/nn/rnn ai/text-style-transfer food japan

We propose a novel system which can transform a recipe into any selected regional style (eg. Japanese, Mediterranean, or Italian).

This system has two characteristics. First the system can identify the degree of regional cuisine style mixture of any selected recipe and visualize such regional cuisine style mixtures using barycentric Newton diagrams. Second, the system can suggest ingredient substitutions through an extended word2vec model, such that a recipe becomes more authentic for any selected regional cuisine style.

Drawing on a large number of recipes from Yummly, an example shows how the proposed system can transform a traditional Japanese recipe, Sukiyaki, into French style.

--- https://arxiv.org/abs/1903.00614#google GAP: Generalizable Approximate Graph Partitioning Framework Azade Nazi, Will Hang, Anna Goldie, Sujith Ravi, Azalia Mirhoseini 2019-03-02 2021-04-15 [("doi","10.48550/arXiv.1903.00614")] ai/scaling/hardware cs/algorithm

Graph partitioning is the problem of dividing the nodes of a graph into balanced partitions while minimizing the edge cut across the partitions. Due to its combinatorial nature, many approximate solutions have been developed, including variants of multi-level methods and spectral clustering.

We propose GAP, a Generalizable Approximate Partitioning framework that takes a deep learning approach to graph partitioning. We define a differentiable loss function that represents the partitioning objective and use backpropagation to optimize the network parameters. Unlike baselines that redo the optimization per graph, GAP is capable of generalization, allowing us to train models that produce performant partitions at inference time, even on unseen graphs. Furthermore, because we learn the representation of the graph while jointly optimizing for the partitioning loss function, GAP can be easily tuned for a variety of graph structures.

We evaluate the performance of GAP on graphs of varying sizes and structures, including graphs of widely used machine learning models (eg. ResNet, VGG, and Inception-V3), scale-free graphs, and random graphs. We show that GAP achieves competitive partitions while being up to 100× faster than the baseline and generalizes to unseen graphs.

--- https://arxiv.org/abs/1903.08850 Stochastic Optimization of Sorting Networks via Continuous Relaxations Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon 2019-03-21 2021-04-15 [("doi","10.48550/arXiv.1903.08850")] ai/nn cs/algorithm/sorting

Sorting input objects is an important step in many machine learning pipelines. However, the sorting operator is non-differentiable with respect to its inputs, which prohibits end-to-end gradient-based optimization. In this work, we propose NeuralSort, a general-purpose continuous relaxation of the output of the sorting operator from permutation matrices to the set of unimodal row-stochastic matrices, where every row sums to one and has a distinct arg max. This relaxation permits straight-through optimization of any computational graph involve a sorting operation.

Further, we use this relaxation to enable gradient-based stochastic optimization over the combinatorially large space of permutations by deriving a reparameterized gradient estimator for the Plackett-Luce family of distributions over permutations. We demonstrate the usefulness of our framework on 3 tasks that require learning semantic orderings of high-dimensional objects, including a fully differentiable, parameterized extension of the k-nearest neighbors algorithm.

--- https://arxiv.org/abs/1902.10178 Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller 2019-02-26 2021-04-15 [("doi","10.1038/s41467-019-08987-4")] ai/nn

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly “intelligent” behavior. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic.

Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem-solving behaviors.

Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for.

Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.

--- https://arxiv.org/abs/1902.09113 Star-Transformer Qipeng Guo, Xipeng Qiu, Pengfei Liu, Yunfan Shao, Xiangyang Xue, Zheng Zhang 2019-02-25 2021-04-15 [("doi","10.48550/arXiv.1902.09113")] ai/nn/transformer/attention/sparsity

Although Transformer has achieved great successes on many NLP tasks, its heavy structure with fully-connected attention connections leads to dependencies on large training data.

In this paper, we present Star-Transformer, a lightweight alternative by careful sparsification. To reduce model complexity, we replace the fully-connected structure with a star-shaped topology, in which every two non-adjacent nodes are connected through a shared relay node. Thus, complexity is reduced from quadratic to linear, while preserving capacity to capture both local composition and long-range dependency.

The experiments on 4 tasks (22 datasets) show that Star-Transformer achieved improvements against the standard Transformer for the modestly sized datasets.

--- https://arxiv.org/abs/1901.11117#google The Evolved Transformer David R. So, Chen Liang, Quoc V. Le 2019-01-30 2021-04-15 [("doi","10.48550/arXiv.1901.11117")] ai/nn

Recent works have highlighted the strength of the Transformer architecture on sequence tasks while, at the same time, neural architecture search (NAS) has begun to outperform human-designed models. Our goal is to apply NAS to search for a better alternative to the Transformer.

We first construct a large search space inspired by the recent advances in feed-forward sequence models and then run evolutionary architecture search with warm starting by seeding our initial population with the Transformer. To directly search on the computationally expensive WMT 2014 English-German translation task, we develop the Progressive Dynamic Hurdles method, which allows us to dynamically allocate more resources to more promising candidate models.

The architecture found in our experiments—the Evolved Transformer—demonstrates consistent improvement over the Transformer on 4 well-established language tasks: WMT 2014 English-German, WMT 2014 English-French, WMT 2014 English-Czech and LM1B. At a big model size, the Evolved Transformer establishes a new state-of-the-art BLEU score of 29.8 on WMT’14 English-German; at smaller sizes, it achieves the same quality as the original “big” Transformer with 37.6% less parameters and outperforms the Transformer by 0.7 BLEU at a mobile-friendly model size of 7M parameters.

--- https://arxiv.org/abs/1901.10430#facebook Pay Less Attention with Lightweight and Dynamic Convolutions Felix Wu, Angela Fan, Alexei Baevski, Yann N. Dauphin, Michael Auli 2019-01-29 2021-04-15 [("doi","10.48550/arXiv.1901.10430")] ai/nn/cnn ai/nn/transformer/attention

Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current time step.

In this paper, we show that a very lightweight convolution can perform competitively to the best-reported self-attention results. Next, we introduce dynamic convolutions which are simpler and more efficient than self-attention. We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements. The number of operations required by this approach scales linearly in the input length, whereas self-attention is quadratic.

Experiments on large-scale machine translation, language modeling, and abstractive summarization show that dynamic convolutions improve over strong self-attention models.

On the WMT’14 English-German test set dynamic convolutions achieve a new state-of-the-art of 29.7 BLEU.

--- https://arxiv.org/abs/1901.10915#intel Benchmarking Classic and Learned Navigation in Complex 3D Environments Dmytro Mishkin, Alexey Dosovitskiy, Vladlen Koltun 2019-01-30 2021-04-16 [("doi","10.48550/arXiv.1901.10915")] reinforcement-learning/model-free reinforcement-learning/scaling

Navigation research is attracting renewed interest with the advent of learning-based methods. However, this new line of work is largely disconnected from well-established classic navigation approaches.

In this paper, we take a step towards coordinating these two directions of research. We set up classic and learning-based navigation systems in common simulated environments and thoroughly evaluate them in indoor spaces of varying complexity, with access to different sensory modalities. Additionally, we measure human performance in the same environments.

We find that a classic pipeline, when properly tuned, can perform very well in complex cluttered environments. On the other hand, learned systems can operate more robustly with a limited sensor suite.

Overall, both approaches are still far from human-level performance.

--- https://arxiv.org/abs/1902.10811 Do ImageNet Classifiers Generalize to ImageNet? Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar 2019-02-13 2021-04-16 [("doi","10.48550/arXiv.1902.10811")] ai/scaling

We build new test sets for the CIFAR-10 and ImageNet datasets.

Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data.

We evaluate a broad range of models and find accuracy drops of 3%—15% on CIFAR-10 and 11%—14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets.

Our results suggest that the accuracy drops are not caused by adaptivity, but by the models’ inability to generalize to slightly “harder” images than those found in the original test sets.

--- https://www.biorxiv.org/content/10.1101/556761.full Crowdfunded whole-genome sequencing of the celebrity cat Lil BUB identifies causal mutations for her osteopetrosis and polydactyly Mike Bridavsky, Heiner Kuhl, Arthur Woodruff, Uwe Kornak, Bernd Timmermann, Norbert Mages, 99 Lives Consortium, Darío G. Lupiáñez, Orsolya Symmons, Daniel M. Ibrahim 2019-02-22 2021-04-16 [("doi","10.1101/556761")] cat/genetics genetics/heritable/rare

Rare diseases and their underlying molecular causes are often poorly studied, posing challenges for patient diagnosis and prognosis. The development of next-generation sequencing and its decreasing costs promises to alleviate such issues by supplying personal genomic information at a moderate price. Here, we used crowdfunding as an alternative funding source to sequence the genome of Lil BUB, a celebrity cat affected by rare disease phenotypes characterized by supernumerary digits, osteopetrosis and dwarfism, all phenotypic traits that also occur in human patients.

We discovered that Lil BUB is affected by two distinct mutations: a heterozygous mutation in the limb enhancer of the Sonic hedgehog gene, previously associated with polydactyly in Hemingway cats; and a novel homozygous frameshift deletion affecting the TNFRSF11A (RANK) gene, which has been linked to osteopetrosis in humans. We communicated the progress of this project to a large online audience, detailing the ‘inner workings’ of personalized whole genome sequencing with the aim of improving genetic literacy. Our results highlight the importance of genomic analysis in the identification of disease-causing mutations and support crowdfunding as a means to fund low-budget projects and as a platform for scientific communication.

--- https://arxiv.org/abs/1902.08318 Parsing Gigabytes of JSON per Second Geoff Langdale, Daniel Lemire 2019-02-22 2021-04-16 [("doi","10.1007/s00778-019-00578-5")] cs/algorithm

JavaScript Object Notation or JSON is a ubiquitous data exchange format on the Web. Ingesting JSON documents can become a performance bottleneck due to the sheer volume of data. We are thus motivated to make JSON parsing as fast as possible.

Despite the maturity of the problem of JSON parsing, we show that substantial speedups are possible. We present the first standard-compliant JSON parser to process gigabytes of data per second on a single core, using commodity processors. We can use a quarter or fewer instructions than a state-of-the-art reference parser like RapidJSON. Unlike other validating parsers, our software (simdjson) makes extensive use of Single Instruction, Multiple Data (SIMD) instructions. To ensure reproducibility, simdjson is freely available as open-source software under a liberal license.

--- https://arxiv.org/abs/1902.05178 Spectre is here to stay: An analysis of side-channels and speculative execution Ross Mcilroy, Jaroslav Sevcik, Tobias Tebbi, Ben L. Titzer, Toon Verwaest 2019-02-14 2021-04-16 [("doi","10.48550/arXiv.1902.05178")] cs/hardware cs/security

The recent discovery of the Spectre and Meltdown attacks represents a watershed moment not just for the field of Computer Security, but also of Programming Languages. This paper explores speculative side-channel attacks and their implications for programming languages. These attacks leak information through micro-architectural side-channels which we show are not mere bugs, but in fact lie at the foundation of optimization.

We identify 3 open problems, (1) finding side-channels, (2) understanding speculative vulnerabilities, and (3) mitigating them. For (1) we introduce a mathematical meta-model that clarifies the source of side-channels in simulations and CPUs. For (2) we introduce an architectural model with speculative semantics to study recently-discovered vulnerabilities. For (3) we explore and evaluate software mitigations and prove one correct for this model. Our analysis is informed by extensive offensive research and defensive implementation work for V8, the production JavaScript virtual machine in Chrome. Straightforward extensions to model real hardware suggest these vulnerabilities present formidable challenges for effective, efficient mitigation.

As a result of our work, we now believe that speculative vulnerabilities on today’s hardware defeat all language-enforced confidentiality with no known comprehensive software mitigations, as we have discovered that untrusted code can construct a universal read gadget to read all memory in the same address space through side-channels.

In the face of this reality, we have shifted the security model of the Chrome web browser and V8 to process isolation.

--- https://www.biorxiv.org/content/10.1101/573691.full Genetic analysis identifies molecular systems and biological pathways associated with household income W. David Hill, Neil M. Davies, Stuart J. Ritchie, Nathan G. Skene, Julien Bryois, Steven Bell, Emanuele Di Angelantonio, David J. Roberts, Shen Xueyi, Gail Davies, David C. M. Liewald, David J. Porteous, Caroline Hayward, Adam S. Butterworth, Andrew M. McIntosh, Catharine R. Gale, Ian J. Deary 2019-03-12 2021-04-16 [("doi","10.1101/573691")] genetics/heritable/correlation/mendelian-randomization iq/ses

Socio-economic position (SEP) is a multi-dimensional construct reflecting (and influencing) multiple socio-cultural, physical, and environmental factors. Previous genome-wide association studies (GWAS) using household income as a marker of SEP have shown that common genetic variants account for 11% of its variation.

Here, in a sample of 286,301 participants from UK Biobank, we identified 30 independent genome-wide statistically-significant loci, 29 novel, that are associated with household income. Using a recently-developed method to meta-analyze data that leverages power from genetically-correlated traits, we identified an additional 120 income-associated loci. These loci showed clear evidence of functional enrichment, with transcriptional differences identified across multiple cortical tissues, in addition to links with GABAergic and serotonergic neurotransmission.

We identified neurogenesis and the components of the synapse as candidate biological systems that are linked with income. By combining our GWAS on income with data from eQTL studies and chromatin interactions, 24 genes were prioritized for follow up, 18 of which were previously associated with cognitive ability. Using Mendelian Randomization, we identified cognitive ability as one of the causal, partly-heritable phenotypes that bridges the gap between molecular genetic inheritance and phenotypic consequence in terms of income differences. differences between genetic correlations indicated that, the genetic variants associated with income are related to better mental health than those linked to educational attainment (another commonly-used marker of SEP).

Finally, we were able to predict 2.5% of income differences using genetic data alone in an independent sample. These results are important for understanding the observed socioeconomic inequalities in Great Britain today.

--- https://www.biorxiv.org/content/10.1101/574020.full Enabling large-scale genome editing by reducing DNA nicking Cory J. Smith, Oscar Castanon, Khaled Said, Verena Volf, Parastoo Khoshakhlagh, Amanda Hornick, Raphael Ferreira, Chun-Ting Wu, Marc Güell, Shilpa Garg, Hannu Myllykallio, George M. Church 2019-03-15 2021-04-16 [("doi","10.1101/574020")] genetics/editing

To extend the frontier of genome editing and enable the radical redesign of mammalian genomes, we developed a set of dead-Cas9 base editor (dBEs) variants that allow editing at tens of thousands of loci per cell by overcoming the cell death associated with DNA double-strand breaks (DSBs) and single-strand breaks (SSBs). We used a set of gRNAs targeting repetitive elements—ranging in target copy number from about 31 to 124,000 per cell. dBEs enabled survival after large-scale base editing, allowing targeted mutations at up to ~13,200 and ~2610 loci in 293T and human induced pluripotent stem cells (hiPSCs), respectively, 3 orders of magnitude greater than previously recorded. These dBEs can overcome current on-target mutation and toxicity barriers that prevent cell survival after large-scale genome engineering.

One Sentence Summary

Base editing with reduced DNA nicking allows for the simultaneous editing of >10,000 loci in human cells.

--- https://www.biorxiv.org/content/10.1101/567503.full Ant collective behavior is heritable and shaped by selection Justin T. Walsh, Simon Garnier, Timothy A. Linksvayer 2019-03-04 2021-04-16 [("doi","10.1101/567503")] biology/ant genetics/heritable genetics/selection/natural sociology

Collective behaviors are widespread in nature are usually assumed to be strongly shaped by natural selection. However, the degree to which variation in collective behaviors is heritable and has fitness consequences—the two prerequisites for evolution by natural selection—is largely unknown.

We used a new pharaoh ant (Monomorium pharaonis) mapping population to estimate the heritability, genetic correlations, and fitness consequences of 3 collective behaviors (foraging, aggression, and exploration) as well as body size, sex ratio, and caste ratio. Heritability estimates for the collective behaviors were moderate, ranging 0.22–0.40, but lower than our estimates for the heritability of caste ratio, sex ratio, and the body size of new workers, queens, and males. Moreover, the collective behaviors were phenotypically correlated and in some cases genetically correlated, suggesting that they form a suite of correlated traits.

Finally, we found evidence for directional, stabilizing, and disruptive selection that was similar in strength to estimates of selection in natural populations. Disruptive selection was very common and may act to maintain behavioral variation.

Altogether, our study begins to elucidate the genetic architecture of collective behavior and is one of the first studies to demonstrate that it is shaped by selection.

--- https://arxiv.org/abs/1902.09442 Do GRE scores help predict getting a physics Ph.D.? A comment on a paper by Miller et al Michael B. Weissman 2019-02-25 2021-04-16 [("doi","10.48550/arXiv.1902.09442")] statistics

[commentary] A recent paper in Sci. Adv. by Miller et al concludes that GREs do not help predict whether physics grad students will get Ph.D.s. The paper makes numerous elementary statistics errors, including introduction of unnecessary collider-like stratification bias, variance inflation by collinearity and range restriction, omission of needed data (some subsequently provided), a peculiar choice of null hypothesis on subgroups, blurring the distinction between failure to reject a null and accepting a null, and an extraordinary procedure for radically inflating confidence intervals in a figure. Release of results of simpler models, eg. without unnecessary stratification, would fix some key problems.

The paper exhibits exactly the sort of research techniques which we should be teaching students to avoid.

[Weissman retrospective: "...I kept trying to steer the conversation toward the importance of maintaining scientific honesty and competence, rather than arguing about fuzzy policy issues. The journalist said “But the people I talk with say this is a moral issue.”"]

--- https://arxiv.org/abs/1903.08747 Statistical Methods for Replicability Assessment Kenneth Hung, William Fithian 2019-03-20 2021-04-16 [("doi","10.1214/20-AOAS1336")] statistics/bias statistics/meta-analysis

Large-scale replication studies like the Reproducibility Project: Psychology (RP:P) provide invaluable systematic data on scientific replicability, but most analyses and interpretations of the data fail to agree on the definition of “replicability” and disentangle the inexorable consequences of known selection bias from competing explanations.

We discuss 3 concrete definitions of replicability based on (1) whether published findings about the signs of effects are mostly correct, (2) how effective replication studies are in reproducing whatever true effect size was present in the original experiment, and (3) whether true effect sizes tend to diminish in replication. We apply techniques from multiple testing and post-selection inference to develop new methods that answer these questions while explicitly accounting for selection bias.

Our analyses suggest that the RP:P dataset is largely consistent with publication bias due to selection of statistically-significant effects. The methods in this paper make no distributional assumptions about the true effect sizes.

--- https://www.unofficialgoogledatascience.com/2015/10/data-scientist-as-scientist.html Data scientist as scientist 2021-04-16 economics/advertising --- https://www.washingtonpost.com/technology/2020/10/19/google-search-results-monopoly/ Google’s search results have gotten worse 2021-04-17 economics/advertising --- https://arxiv.org/abs/1901.08579 Forecasting Transformative AI: An Expert Survey Ross Gruetzemacher, David Paradice, Kang Bok Lee 2019-01-24 2021-04-17 [("doi","10.48550/arXiv.1901.08579")] ai/nn existential-risk reinforcement-learning/safe statistics/prediction survey

Transformative AI technologies have the potential to reshape critical aspects of society in the near future. However, in order to properly prepare policy initiatives for the arrival of such technologies accurate forecasts and timelines are necessary.

A survey was administered to attendees of 3 AI conferences during the summer of 2018 (ICML, IJCAI and the HLAI conference). The survey included questions for estimating AI capabilities over the next decade, questions for forecasting 5 scenarios of transformative AI and questions concerning the impact of computational resources in AI research.

Respondents indicated a median of 21.5% of human tasks (ie. all tasks that humans are currently paid to do) can be feasibly automated now, and that this figure would rise to 40% in 5 years and 60% in 10 years. Median forecasts indicated a 50% probability of AI systems being capable of automating 90% of current human tasks in 25 years and 99% of current human tasks in 50 years.

The conference of attendance was found to have a statistically-significant impact on all forecasts, with attendees of HLAI providing more optimistic timelines with less uncertainty. These findings suggest that AI experts expect major advances in AI technology to continue over the next decade to a degree that will likely have profound transformative impacts on society.

--- https://arxiv.org/abs/1903.07291 Semantic Image Synthesis with Spatially-Adaptive Normalization Taesung Park, Ming-Yu Liu, Ting-Chun Wang, Jun-Yan Zhu 2019-03-18 2021-04-17 [("doi","10.48550/arXiv.1903.07291")] ai/nn/cnn

We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout.

Previous methods directly feed the semantic layout as input to the deep network, which is then processed through stacks of convolution, normalization, and nonlinearity layers. We show that this is suboptimal as the normalization layers tend to “wash away” semantic information.

To address the issue, we propose using the input layout for modulating the activations in normalization layers through a spatially-adaptive, learned transformation.

Experiments on several challenging datasets demonstrate the advantage of the proposed method over existing approaches, regarding both visual fidelity and alignment with input layouts.

Finally, our model allows user control over both semantic and style. Code is available at Github.

--- https://arxiv.org/abs/1812.01243#sensetime Efficient Attention: Attention with Linear Complexities Zhuoran Shen, Mingyuan Zhang, Haiyu Zhao, Shuai Yi, Hongsheng Li 2018-12-04 2021-04-17 [("doi","10.48550/arXiv.1812.01243")] ai/nn/transformer/attention/linear-algebra

Dot-product attention has wide applications in computer vision and natural language processing. However, its memory and computational costs grow quadratically with the input size. Such growth prohibits its application on high-resolution inputs.

To remedy this drawback, this paper proposes a novel efficient attention mechanism equivalent to dot-product attention but with substantially less memory and computational costs. Its resource efficiency allows more widespread and flexible integration of attention modules into a network, which leads to better accuracies.

Empirical evaluations demonstrated the effectiveness of its advantages. Efficient attention modules brought performance boosts to object detectors and instance segmenters on MS-COCO 2017. Further, the resource efficiency democratizes attention to complex models, where high costs prohibit the use of dot-product attention.

As an exemplar, a model with efficient attention achieved state-of-the-art accuracies for stereo depth estimation on the Scene Flow dataset.

Code is available at Github.

--- https://arxiv.org/abs/1811.12231 ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, Wiel, Brendel 2018-11-29 2021-04-17 [("doi","10.48550/arXiv.1811.12231")] ai/dataset ai/nn/cnn

Convolutional Neural Networks (CNNs) are commonly thought to recognize objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on images with a texture-shape cue conflict. We show that ImageNet-trained CNNs are strongly biased towards recognizing textures rather than shapes, which is in stark contrast to human behavioral evidence and reveals fundamentally different classification strategies.

We then demonstrate that the same standard architecture (ResNet-50) that learns a texture-based representation on ImageNet is able to learn a shape-based representation instead when trained on “Stylized-ImageNet”, a stylized version of ImageNet. This provides a much better fit for human behavioral performance in our well-controlled psychophysical lab setting (nine experiments totaling 48,560 psychophysical trials across 97 observers) and comes with a number of unexpected emergent benefits such as improved object detection performance and previously unseen robustness towards a wide range of image distortions, highlighting advantages of a shape-based representation.

--- https://arxiv.org/abs/1810.04805#google BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova 2018-10-11 2021-04-17 [("doi","10.48550/arXiv.1810.04805")] ai/nn

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.

BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).

--- https://arxiv.org/abs/1901.07291#facebook Cross-lingual Language Model Pretraining Guillaume Lample, Alexis Conneau 2019-01-22 2021-04-17 [("doi","10.48550/arXiv.1901.07291")] ai/scaling

Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining.

We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective.

We obtain state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our approach pushes the state-of-the-art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we obtain 34.3 BLEU on WMT’16 German-English, improving the previous state-of-the-art by more than 9 BLEU. On supervised machine translation, we obtain a new state-of-the-art of 38.5 BLEU on WMT’16 Romanian-English, outperforming the previous best approach by more than 4 BLEU.

Our code and pretrained models will be made publicly available.

--- https://arxiv.org/abs/1812.11118 Reconciling modern machine learning practice and the bias-variance trade-off Mikhail Belkin, Daniel Hsu, Siyuan Ma, Soumik Mandal 2018-12-28 2021-04-17 [("doi","10.48550/arXiv.1812.11118")] ai/scaling

Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central tenets of the field, the bias-variance trade-off, appears to be at odds with the observed behavior of methods used in the modern machine learning practice. The bias-variance trade-off implies that a model should balance under-fitting and over-fitting: rich enough to express underlying structure in data, simple enough to avoid fitting spurious patterns. However, in the modern practice, very rich models such as neural networks are trained to exactly fit (ie. interpolate) the data. Classically, such models would be considered over-fit, and yet they often obtain high accuracy on test data. This apparent contradiction has raised questions about the mathematical foundations of machine learning and their relevance to practitioners.

In this paper, we reconcile the classical understanding and the modern practice within a unified performance curve. This “double descent” curve subsumes the textbook U-shaped bias-variance trade-off curve by showing how increasing model capacity beyond the point of interpolation results in improved performance. We provide evidence for the existence and ubiquity of double descent for a wide spectrum of models and datasets, and we posit a mechanism for its emergence. This connection between the performance and the structure of machine learning models delineates the limits of classical analyses, and has implications for both the theory and practice of machine learning.

--- https://arxiv.org/abs/1812.08658 nocaps: novel object captioning at scale Harsh Agrawal, Karan Desai, Yufei Wang, Xinlei Chen, Rishabh Jain, Mark Johnson, Dhruv Batra, Devi Parikh, Stefan Lee, Peter Anderson 2018-12-20 2021-04-17 [("doi","10.1109/ICCV.2019.00904")] ai/scaling

Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these models are to ever function in the wild, a much larger variety of visual concepts must be learned, ideally from less supervision.

To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task. Dubbed nocaps, for ‘novel object captioning at scale’, our benchmark consists of 166,100 human-generated captions describing 15,100 images from the OpenImages validation and test sets. The associated training data consists of COCO image-caption pairs, plus OpenImages image-level labels and object bounding boxes.

Since OpenImages contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps). We extend existing novel object captioning models to establish strong baselines for this benchmark and provide analysis to guide future work on this task.

--- https://arxiv.org/abs/1812.02341#openai Quantifying Generalization in Reinforcement Learning Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, John Schulman 2018-12-06 2021-04-17 [("doi","10.48550/arXiv.1812.02341")] ai/nn/cnn reinforcement-learning/meta-learning reinforcement-learning/model-free reinforcement-learning/scaling

In this paper, we investigate the problem of overfitting in deep reinforcement learning. Among the most common benchmarks in RL, it is customary to use the same environments for both training and testing. This practice offers relatively little insight into an agent’s ability to generalize.

We address this issue by using procedurally generated environments to construct distinct training and test sets. Most notably, we introduce a new environment called CoinRun, designed as a benchmark for generalization in RL.

Using CoinRun, we find that agents overfit to surprisingly large training sets. We then show that deeper convolutional architectures improve generalization, as do methods traditionally found in supervised learning, including L2 regularization, dropout, data augmentation and batch normalization.

--- https://arxiv.org/abs/1811.00982#google The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, Tom Duerig, Vittorio Ferrari 2018-11-02 2021-04-18 [("doi","10.1007/s11263-020-01316-z")] ai/scaling

We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding an initial design bias.

Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship annotations involving 57 classes. For object detection in particular, we provide 15× more bounding boxes than the next largest datasets (15.4M boxes on 1.9M images). The images often show complex scenes with several objects (8 annotated objects per image on average). We annotated visual relationships between them, which support visual relationship detection, an emerging task that requires structured reasoning.

We provide comprehensive statistics about the dataset, validate the quality of the annotations, study how the performance of several modern models evolves with increasing amounts of training data, and demonstrate two applications made possible by having unified annotations of multiple types coexisting in the same images.

We hope that the scale, quality, and variety of Open Images V4 will foster further research and innovation even beyond the areas of image classification, object detection, and visual relationship detection.

--- https://arxiv.org/abs/1812.06162#openai An Empirical Model of Large-Batch Training Sam McCandlish, Jared Kaplan, Dario Amodei, Open A. I. Dota Team 2018-12-14 2021-04-18 [("doi","10.48550/arXiv.1812.06162")] ai/nn/vae ai/scaling/hardware reinforcement-learning/model-free/oa5 reinforcement-learning/scaling

In an increasing number of domains it has been demonstrated that deep learning models can be trained using relatively large batch sizes without sacrificing data efficiency. However, the limits of this massive data parallelism seem to differ from domain to domain, ranging from batches of tens of thousands in ‘ImageNet Large Scale Visual Recognition Challenge’, Russakovsky et al 2014 to batches of millions in RL agents that play the game Dota 2. To our knowledge, there is limited conceptual understanding of why these limits to batch size differ or how we might choose the correct batch size in a new domain.

In this paper, we demonstrate that a simple and easy-to-measure statistic called the gradient noise scale predicts the largest useful batch size across many domains and applications, including a number of supervised learning datasets (MNIST, SVHN, CIFAR-10, ImageNet, Billion Word), reinforcement learning domains (Atari and Dota), and even generative model training (autoencoders on SVHN).

We find that the noise scale increases as the loss decreases over a training run and depends on the model size primarily through improved model performance. Our empirically-motivated theory also describes the tradeoff between compute-efficiency and time-efficiency, and provides a rough model of the benefits of adaptive batch-size training.

--- https://arxiv.org/abs/1811.06965#google GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Mia Xu Chen, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen 2018-11-16 2021-04-18 [("doi","10.48550/arXiv.1811.06965")] ai/scaling/hardware

Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks. In many cases, increasing model capacity beyond the memory limit of a single accelerator has required developing special algorithms or infrastructure. These solutions are often architecture-specific and do not transfer to other tasks.

To address the need for efficient and task-independent model parallelism, we introduce GPipe, a pipeline parallelism library that allows scaling any network that can be expressed as a sequence of layers. By pipelining different sub-sequences of layers on separate accelerators, GPipe provides the flexibility of scaling a variety of different networks to gigantic sizes efficiently. Moreover, GPipeuses a novel batch-splitting pipelining algorithm, resulting in almost linear speedup when a model is partitioned across multiple accelerators.

We demonstrate the advantages of GPipe by training large-scale neural networks on two different tasks with distinct network architectures: (1) Image Classification: We train a 557-million-parameter AmoebaNet model and attain a top-1 accuracy of 84.4% on ImageNet-2012, (2) Multilingual Neural Machine Translation: We train a single 6-billion-parameter, 128-layer Transformer model on a corpus spanning over 100 languages and achieve better quality than all bilingual models.

--- https://arxiv.org/abs/1811.03600#google Measuring the Effects of Data Parallelism on Neural Network Training Christopher J. Shallue, Jaehoon Lee, Joseph Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl 2018-11-08 2021-04-18 [("doi","10.48550/arXiv.1811.03600")] ai/scaling/hardware

Recent hardware developments have dramatically increased the scale of data parallelism available for neural network training. Among the simplest ways to harness next-generation hardware is to increase the batch size in standard mini-batch neural network training algorithms. In this work, we aim to experimentally characterize the effects of increasing the batch size on training time, as measured by the number of steps necessary to reach a goal out-of-sample error.

We study how this relationship varies with the training algorithm, model, and data set, and find extremely large variation between workloads. Along the way, we show that disagreements in the literature on how batch size affects model quality can largely be explained by differences in metaparameter tuning and compute budgets at different batch sizes.

We find no evidence that larger batch sizes degrade out-of-sample performance. Finally, we discuss the implications of our results on efforts to train neural networks much faster in the future.

Our experimental data is publicly available as a database of 71,638,836 loss measurements taken over the course of training for 168,160 individual models across 35 workloads.

--- https://arxiv.org/abs/1811.02084#google Mesh-TensorFlow: Deep Learning for Supercomputers Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake Hechtman 2018-11-05 2021-04-18 [("doi","10.48550/arXiv.1811.02084")] ai/scaling/hardware

Batch-splitting (data-parallelism) is the dominant distributed Deep Neural Network (DNN) training strategy, due to its universal applicability and its amenability to Single-Program-Multiple-Data (SPMD) programming. However, batch-splitting suffers from problems including the inability to train very large models (due to memory constraints), high latency, and inefficiency at small batch sizes. All of these can be solved by more general distribution strategies (model-parallelism). Unfortunately, efficient model-parallel algorithms tend to be complicated to discover, describe, and to implement, particularly on large clusters.

We introduce Mesh-TensorFlow, a language for specifying a general class of distributed tensor computations. Where data-parallelism can be viewed as splitting tensors and operations along the “batch” dimension, in Mesh-TensorFlow, the user can specify any tensor-dimensions to be split across any dimensions of a multi-dimensional mesh of processors. A Mesh-TensorFlow graph compiles into a SPMD program consisting of parallel operations coupled with collective communication primitives such as Allreduce.

We use Mesh-TensorFlow to implement an efficient data-parallel, model-parallel version of the Transformer sequence-to-sequence model. Using TPU meshes of up to 512 cores, we train Transformer models with up to 5 billion parameters, surpassing state-of-the-art results on WMT’14 English-to-French translation task and the one-billion-word language modeling benchmark.

Mesh-Tensorflow is available at Github.

--- https://arxiv.org/abs/1811.10959 Dataset Distillation Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, Alexei A. Efros 2018-11-27 2021-04-18 [("doi","10.48550/arXiv.1811.10959")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/exploration/active-learning/data-pruning

Model distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative formulation called dataset distillation: we keep the model fixed and instead attempt to distill the knowledge from a large training dataset into a small one. The idea is to synthesize a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data.

For example, we show that it is possible to compress 60,000 MNIST training images into just 10 synthetic distilled images (one per class) and achieve close to original performance with only a few gradient descent steps, given a fixed network initialization. We evaluate our method in various initialization settings and with different learning objectives.

Experiments on multiple datasets show the advantage of our approach compared to alternative methods.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6409418/ Estimation of clinical trial success rates and related parameters Chi Heem Wong, Kien Wei Siah, Andrew W. Lo 2019 2021-04-18 [("doi","10.1093/biostatistics/kxx069")] biology statistics/meta-analysis

Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases.

Using a sample of 406,038 entries of clinical trial data for over 21,143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time.

In several cases, our results differ statistically-significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively.

In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584331/ Mycorrhizal Fungi Respond to Resource Inequality by Moving Phosphorus from Rich to Poor Patches across Networks Matthew D. Whiteside, Gijsbert D. A. Werner, Victor E. A. Caldas, Anouk Van’t Padje, Simon E. Dupin, Bram Elbers, Milenka Bakker, Gregory A. K. Wyatt, Malin Klein, Mark A. Hink, Marten Postma, Bapu Vaitla, Ronald Noë, Thomas S. Shimizu, Stuart A. West, E. Toby Kiers 2019 2021-04-18 [("doi","10.1016/j.cub.2019.04.061")] biology economics

The world’s ecosystems are characterized by an unequal distribution of resources. Trade partnerships between organisms of different species-mutualisms-can help individuals cope with such resource inequality. Trade allows individuals to exchange commodities they can provide at low cost for resources that are otherwise impossible or more difficult to access. However, as resources become increasingly patchy in time or space, it is unknown how organisms alter their trading strategies. Here, we show how a symbiotic fungus mediates trade with a host root in response to different levels of resource inequality across its network.

We developed a quantum-dot-tracking technique to quantify phosphorus-trading strategies of arbuscular mycorrhizal fungi simultaneously exposed to rich and poor resource patches. By following fluorescent nanoparticles of different colors across fungal networks, we determined where phosphorus was hoarded, relocated, and transferred to plant hosts.

We found that increasing exposure to inequality stimulated trade. Fungi responded to high resource variation by (1) increasing the total amount of phosphorus distributed to host roots, (2) decreasing allocation to storage, and (3) differentially moving resources within the network from rich to poor patches. Using single-particle tracking and high-resolution video, we show how dynamic resource movement may help the fungus capitalize on value differences across the trade network, physically moving resources to areas of high demand to gain better returns.

Such translocation strategies can help symbiotic organisms cope with exposure to resource inequality.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6851115/ Flexible control of movement in plants Silvia Guerra, Alessandro Peressotti, Francesca Peressotti, Maria Bulgheroni, Walter Baccinelli, Enrico D’Amico, Alejandra Gómez, Stefano Massaccesi, Francesco Ceccarini, Umberto Castiello 2019 2021-04-18 [("doi","10.1038/s41598-019-53118-0")] biology

Although plants are essentially sessile in nature, these organisms are very much in tune with their environment and are capable of a variety of movements. This may come as a surprise to many non-botanists, but not to Charles Darwin, who reported that plants do produce movements. Following Darwin’s specific interest on climbing plants, this paper will focus on the attachment mechanisms by the tendrils. We draw attention to an unsolved problem in available literature: whether during the approach phase the tendrils of climbing plants consider the structure of the support they intend to grasp and plan the movement accordingly ahead of time. Here we report the first empirical evidence that this might be the case.

The three-dimensional (3D) kinematic analysis of a climbing plant (Pisum sativum L.) demonstrates that the plant not only perceives the support, but it scales the kinematics of tendrils’ aperture according to its thickness. When the same support is represented in two-dimensions (2D), and thus unclimbable, there is no evidence for such scaling. In these circumstances the tendrils’ kinematics resemble those observed for the condition in which no support was offered. We discuss these data in light of the evidence suggesting that plants are equipped with sensory mechanisms able to provide the necessary information to plan and control a movement.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7717492/ Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials Ann Lin, Christopher J. Giuliano, Ann Palladino, Kristen M. John, Connor Abramowicz, Monet Lou Yuan, Erin L. Sausville, Devon A. Lukow, Luwei Liu, Alexander R. Chait, Zachary C. Galluzzo, Clara Tucker, Jason M. Sheltzer 2019 2021-04-18 [("doi","10.1126/scitranslmed.aaw8412")] biology

Ninety-seven percent of drug-indication pairs that are tested in clinical trials in oncology never advance to receive U.S. Food and Drug Administration approval. While lack of efficacy and dose-limiting toxicities are the most common causes of trial failure, the reason(s) why so many new drugs encounter these problems is not well understood.

Using CRISPR-Cas9 mutagenesis, we investigated a set of cancer drugs and drug targets in various stages of clinical testing. We show that-contrary to previous reports obtained predominantly with RNA interference and small-molecule inhibitors-the proteins ostensibly targeted by these drugs are nonessential for cancer cell proliferation. Moreover, the efficacy of each drug that we tested was unaffected by the loss of its putative target, indicating that these compounds kill cells via off-target effects.

By applying a genetic target-deconvolution strategy, we found that the mischaracterized anticancer agent OTS964 is actually a potent inhibitor of the cyclin-dependent kinase CDK11 and that multiple cancer types are addicted to CDK11 expression.

We suggest that stringent genetic validation of the mechanism of action of cancer drugs in the preclinical setting may decrease the number of therapies tested in human patients that fail to provide any clinical benefit.

--- https://arxiv.org/abs/1902.10865 A Survey on Applications of Game Theory in Blockchain Ziyao Liu, Nguyen Cong Luong, Wenbo Wang, Dusit Niyato, Ping Wang, Ying-Chang Liang, Dong In Kim 2019-02-28 2021-04-18 [("doi","10.48550/arXiv.1902.10865")] bitcoin

In the past decades, the blockchain technology has attracted tremendous attention from both academia and industry. The popularity of blockchain networks originated from a cryptocurrency to serve as a decentralized and tamper-proof transaction data ledger. Nowadays, blockchain as the key framework in the decentralized public data-ledger, has been applied to a wide range of scenarios far beyond cryptocurrencies, such as Internet of Things (IoT), healthcare, and insurance.

This survey aims to fill the gap between the large number of studies on blockchain network, where game theory emerges as an analytical tool, and the lack of a comprehensive survey on the game-theoretical approaches applied in blockchain-related issues. In this paper, we review game models proposed to address common issues in the blockchain network. The issues include security issues, eg. selfish mining, majority attack and Denial of Service (DoS) attack, issues regarding mining management, eg. computational power allocation, reward allocation, and pool selection, as well as issues regarding blockchain economic and energy trading.

Additionally, we discuss advantages and disadvantages of these selected game models and solutions.

Finally, we highlight important challenges and future research directions of applying game-theoretical approaches to incentive mechanism design, and the combination of blockchain with other technologies.

--- https://arxiv.org/abs/1901.04615 AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning Ameer Haj-Ali, Qijing Huang, William Moses, John Xiang, Ion Stoica, Krste Asanovic, John Wawrzynek 2019-01-15 2021-04-19 [("doi","10.48550/arXiv.1901.04615")] cs/algorithm reinforcement-learning/model-free

The performance of the code generated by a compiler depends on the order in which the optimization passes are applied. In high-level synthesis, the quality of the generated circuit relates directly to the code generated by the front-end compiler.

Choosing a good order—often referred to as the phase-ordering problem—is an NP-hard problem.

In this paper, we evaluate a new technique to address the phase-ordering problem: deep reinforcement learning. We implement a framework in the context of the LLVM compiler to optimize the ordering for HLS programs and compare the performance of deep reinforcement learning to state-of-the-art algorithms that address the phase-ordering problem.

Overall, our framework runs one to two orders of magnitude faster than these algorithms, and achieves a 16% improvement in circuit performance over the -O3 compiler flag.

--- https://www.biorxiv.org/content/10.1101/492058.full Exploration in the wild Eric Schulz, Rahul Bhui, Bradley C. Love, Bastien Brier, Michael T. Todd, Samuel J. Gershman 2018-12-14 2021-04-19 [("doi","10.1101/492058")] food reinforcement-learning/exploration statistics/bayes

Making good decisions requires people to appropriately explore their available options and generalize what they have learned. While computational models have successfully explained exploratory behavior in constrained laboratory tasks, it is unclear to the extent these models generalize to complex real-world choice problems.

We investigate the factors guiding exploratory behavior in a dataset consisting of 195,333 customers placing 1,613,967 orders from a large online food delivery service, Deliveroo.

We find important hallmarks of adaptive exploration and generalization, which we analyze using computational models.

We find evidence for several theoretical predictions: (1) customers engage in uncertainty-directed exploration, (2) they adjust their level of exploration to the average restaurant quality in a city, and (3) they use feature-based generalization to guide exploration towards promising restaurants.

Our results provide new evidence that people use sophisticated strategies to explore complex, real-world environments.

…In the attempt to test algorithms of both directed exploration and generalization simultaneously, we compared 3 models of learning and decision-making based on how well they captured the sequential choices of 3,772 new customers who had just started ordering food and who had rated all of their orders.

The first model was a Bayesian Mean Tracker (BMT) that does not generalize across restaurants, only learning about a restaurant’s quality by sampling it. [fixed-effects model?] The second model used Gaussian Process regression to learn about a restaurant’s quality based on the 4 observable features (price, mean rating, delivery time, and number of past ratings). Gaussian Process regression is a powerful model of generalization and has been applied to model how participants learn latent functions to guide their exploration. This model was either paired with a mean-greedy sampling strategy (GP-M) or with a directed exploration strategy that sampled based on an option’s upper confidence bound (GP-UCB). [So no evaluation of Thompson sampling?]

We treated customers’ choices as the arms of a bandit and their order ratings as their utility, and then evaluated each model’s performance based on its one-step-ahead prediction error, standardizing performance by comparing to a random baseline. Since it was not possible to observe all restaurants a customer might have considered at the time of an order, we compared the different models based on how much higher in utility they predicted a customer’s final choice compared to an option with average features.

The BMT model barely performed above chance (R2 = 0.013; 99.9% CI: 0.005–0.022). Although the GP-M model performed better than the BMT model (R2 = 0.231; 99.9% CI: 0.220–0.241), the GP-UCB model achieved by far the best performance (R2 = 0.477; 99.9% CI: 0.465–0.477).

Thus, a sufficiently predictive model of customers’ choices required both a mechanism of generalization (learning how features map onto rewards) and a directed exploration strategy (combining a restaurant’s mean and uncertainty to estimate its decision value).

--- https://www.biorxiv.org/content/10.1101/582627.full Familial influences on Neuroticism and Education in the UK Biobank R. Cheesman, J. Coleman, C. Rayner, K. L. Purves, G. Morneau-Vaillancourt, K. Glanville, S. W. Choi, G. Breen, T. C. Eley 2019-03-20 2021-04-19 [("doi","10.1101/582627")] genetics/heritable/correlation psychology/personality

Genome-wide studies often exclude family members, even though they are a valuable source of information. We identified parent-offspring pairs, siblings, and couples in the UK Biobank and implemented a family-based DNA-derived heritability method to capture additional genetic effects and multiple sources of environmental influence on neuroticism and years of education. Compared to estimates from unrelated individuals, heritability increased 10% → 27% and 19% → 57% for neuroticism and education respectively by including family-based genetic effects.

We detected no family environmental influences on neuroticism, but years of education was substantially influenced by couple similarity (38%). Overall, our genetic and environmental estimates closely replicate previous findings from an independent sample, but more research is required to dissect contributions to the additional heritability, particularly rare and structural genetic effects and residual environmental confounding.

The latter is especially relevant for years of education, a highly socially-contingent variable, for which our heritability estimate is at the upper end of twin estimates in the literature. Family-based genetic effects narrow the gap between twin and DNA-based heritability methods and could be harnessed to improve polygenic prediction.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6520146/ Genomic structural equation modeling provides insights into the multivariate genetic architecture of complex traits Andrew D. Grotzinger, Mijke Rhemtulla, Ronald de Vlaming, Stuart J. Ritchie, Travis T. Mallard, W. David Hill, Hill F. Ip, Riccardo E. Marioni, Andrew M. McIntosh, Ian J. Deary, Philipp Koellinger, K. Paige Harden, Michel G. Nivard, Elliot M. Tucker-Drob 2019 2021-04-19 [("doi","10.1038/s41562-019-0566-x")] genetics/heritable/correlation

Genetic correlations estimated from genome-wide association studies (GWASs) reveal pervasive pleiotropy across a wide variety of phenotypes. We introduce genomic structural equation modeling (genomic SEM): a multivariate method for analysing the joint genetic architecture of complex traits. Genomic SEM synthesizes genetic correlations and single-nucleotide polymorphism heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap.

Genomic SEM can be used to model multivariate genetic associations among phenotypes, identify variants with effects on general dimensions of cross-trait liability, calculate more predictive polygenic scores and identify loci that cause divergence between traits. We demonstrate several applications of genomic SEM, including a joint analysis of summary statistics from 5 psychiatric traits.

We identify 27 independent single-nucleotide polymorphisms not previously identified in the contributing univariate GWASs. Polygenic scores from genomic SEM consistently outperform those from univariate GWASs.

Genomic SEM is flexible and open-ended, and allows for continuous innovation in multivariate genetic analysis.

--- https://www.biorxiv.org/content/10.1101/500090.full A global overview of pleiotropy and genetic architecture in complex traits Kyoko Watanabe, Sven Stringer, Oleksandr Frei, Maša Umićević Mirkov, Tinca J. C. Polderman, Sophie van der Sluis, Ole A. Andreassen, Benjamin M. Neale, Danielle Posthuma 2018-12-19 2021-04-19 [("doi","10.1101/500090")] genetics/heritable/correlation

After a decade of genome-wide association studies (GWASs), fundamental questions in human genetics are still unanswered, such as the extent of pleiotropy across the genome, the nature of trait-associated genetic variants, and the disparate genetic architecture across human traits. The current availability of hundreds of GWAS results provide the unique opportunity to gain insight into these questions.

In this study, we harmonized and systematically analyzed 4,155 publicly available GWASs. For a subset of well-powered GWAS on 558 unique traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait associated loci cover more than half of the genome, and 90% of those loci are associated with multiple trait domains. We further show that potential causal genetic variants are enriched in coding and flanking regions, as well as in regulatory elements, and how trait-polygenicity is related to an estimate of the required sample size to detect 90% of causal genetic variants.

Our results provide novel insights into how genetic variation contributes to trait variation.

All GWAS results can be queried and visualized at the GWAS ATLAS resource.

--- https://www.biorxiv.org/content/10.1101/451971.full Novel genome-wide associations for suicidality in UK Biobank, genetic correlation with psychiatric disorders and polygenic association with completed suicide Rona J. Strawbridge, Joey Ward, Amy Ferguson, Nicholas Graham, Richard J. Shaw, Breda Cullen, Robert Pearsall, Laura M. Lyall, Keira J. A. Johnston, Claire L. Niedzwiedz, Jill P. Pell, Daniel Mackay, Julie Langan Martin, Donald M. Lyall, Mark E. S. Bailey, Daniel J. Smith 2018-10-25 2021-04-19 [("doi","10.1101/451971")] genetics/heritable/correlation psychiatry/depression

Background: Suicide is a major issue for global public health. ‘Suicidality’ describes a broad clinical spectrum of thoughts and behaviors, some of which are common in the general population.

Method: UK Biobank recruited ~0·5 million middle age individuals from the UK, of whom 157,000 completed an assessment of suicidality. Mutually exclusive groups were assessed in an ordinal genome-wide association study of suicidality: ‘no suicidality’ controls (n = 83,557); ‘thoughts that life was not worth living’ (n = 21,063); ‘ever contemplated self-harm’ (n = 13,038); ‘an act of deliberate self-harm in the past’ (n = 2,498); and ‘a previous suicide attempt’ (n = 2,666). Linkage of UK Biobank to death certification records identified a small sub-group of ‘completed suicide’ (n = 137).

Outcomes: We identified 3 novel genome-wide statistically-significant loci for suicidality (on Chromosomes 9, 11 and 13) and moderate-to-strong genetic correlations between suicidality and a range of psychiatric disorders, most notably depression (rg 0·81). Higher polygenic risk scores for suicidality were associated with increased risk of completed suicide relative to controls in an independent sub-group (n = 137 vs n = 5,330, OR 1·23, 95%CI 1·06 to 1·41, p = 0.03). Rs598046-G (chromosome 11) demonstrated a similar effect size and direction (p = 0·05) within a Danish suicidality study.

Interpretation: These findings have implications for our understanding of genetic vulnerability to suicidal thoughts and behaviors. Future work should assess the extent to which polygenic risk scores for suicidality, in combination with non-genetic risk factors, may be useful for stratified approaches to suicide prevention at a population level.

Funding: UKRI Innovation-HDR-UK Fellowship (MR/S003061/1). MRC Mental Health Data Pathfinder Award (MC_PC_17217).

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007978 Fast and flexible linear mixed models for genome-wide genetics Daniel E. Runcie, Lorin Crawford 2019-01-21 2021-04-19 [("doi","10.1371/journal.pgen.1007978")] genetics/heritable

Linear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced power or biased inference. We describe Grid-LMM (https://github.com/deruncie/GridLMM), an extendable algorithm for repeatedly fitting complex linear models that account for multiple sources of heterogeneity, such as additive and non-additive genetic variance, spatial heterogeneity, and genotype-environment interactions. Grid-LMM can compute approximate (yet highly accurate) frequentist test statistics or Bayesian posterior summaries at a genome-wide scale in a fraction of the time compared to existing general-purpose methods. We apply Grid-LMM to two types of quantitative genetic analyses. The first is focused on accounting for spatial variability and non-additive genetic variance while scanning for QTL; and the second aims to identify gene expression traits affected by non-additive genetic variation. In both cases, modeling multiple sources of heterogeneity leads to new discoveries.

Author Summary: The goal of quantitative genetics is to characterize the relationship between genetic variation and variation in quantitative traits such as height, productivity, or disease susceptibility. A statistical method known as the linear mixed effect model has been critical to the development of quantitative genetics. First applied to animal breeding, this model now forms the basis of a wide-range of modern genomic analyses including genome-wide associations, polygenic modeling, and genomic prediction. The same model is also widely used in ecology, evolutionary genetics, social sciences, and many other fields. Mixed models are frequently multi-faceted, which is necessary for accurately modeling data that is generated from complex experimental designs. However, most genomic applications use only the simplest form of linear mixed methods because the computational demands for model fitting can be too great. We develop a flexible approach for fitting linear mixed models to genome scale data that greatly reduces their computational burden and provides flexibility for users to choose the best statistical paradigm for their data analysis. We demonstrate improved accuracy for genetic association tests, increased power to discover causal genetic variants, and the ability to provide accurate summaries of model uncertainty using both simulated and real data examples.

--- https://www.biorxiv.org/content/10.1101/506600.full Genomic Prediction of Complex Disease Risk Louis Lello, Timothy G. Raben, Soke Yuen Yong, Laurent C. A. M. Tellier, Steve Hsu 2018-12-27 2021-04-19 [("doi","10.1101/506600")] genetics/heritable

We construct risk predictors using polygenic scores (PGS) computed from common Single-Nucleotide Polymorphisms (SNPs) for a number of complex disease conditions, using 𝓁1-penalized regression (also known as LASSO) on case-control data from UK Biobank. Among the disease conditions studied are Hypothyroidism, (Resistive) Hypertension, Type 1 and 2 Diabetes, Breast Cancer, Prostate Cancer, Testicular Cancer, Gallstones, Glaucoma, Gout, Atrial Fibrillation, High Cholesterol, Asthma, Basal Cell Carcinoma, Malignant Melanoma, and Heart Attack.

We obtain values for the area under the receiver operating characteristic curves (AUC) in the range ~ 0.58–0.71 using SNP data alone. Substantially higher predictor AUCs are obtained when incorporating additional variables such as age and sex. Some SNP predictors alone are sufficient to identify outliers (eg. in the 99th percentile of PGS) with 3–8× higher risk than typical individuals.

We validate predictors out-of-sample using the eMERGE dataset, and also with different ancestry subgroups within the UK Biobank population. Our results indicate that substantial improvements in predictive power are attainable using training sets with larger case populations.

We anticipate rapid improvement in genomic prediction as more case-control data become available for analysis.

--- https://www.biorxiv.org/content/10.1101/457515.full Genetic Consequences of Social Stratification in Great Britain Abdel Abdellaoui, David Hugh-Jones, Kathryn E. Kemper, Yan Holtz, Michel G. Nivard, Laura Veul, Loïc Yengo, Brendan P. Zietsch, Timothy Frayling, Naomi Wray, Jian Yang, Karin J. H. Verweij, Peter M. Visscher 2018-10-30 2021-04-19 [("doi","10.1101/457515")] genetics/heritable sociology

Human DNA varies across geographic regions, with most variation observed so far reflecting distant ancestry differences. Here, we investigate the geographic clustering of genetic variants that influence complex traits and disease risk in a sample of ~450,000 individuals from Great Britain.

Out of 30 traits analyzed, 16 show geographic clustering at the genetic level after controlling for ancestry, likely reflecting recent migration driven by socio-economic status (SES). Alleles associated with educational attainment (EA) show most clustering, with EA-decreasing alleles clustering in lower SES areas such as coal mining areas. Individuals that leave coal mining areas carry more EA-increasing alleles on average than the rest of Great Britain.

In addition, we leveraged the geographic clustering of complex trait variation to further disentangle regional differences in socio-economic and cultural outcomes through genome-wide association studies on publicly available regional measures, namely coal mining, religiousness, 1970 & 2015 general election outcomes, and Brexit referendum results.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6325695/ Reconstructing the History of Polygenic Scores Using Coalescent Trees Michael D. Edge, Graham Coop 2019 2021-04-19 [("doi","10.1534/genetics.118.301687")] genetics/selection/natural/human

Genome-wide association studies (GWAS) have revealed that many traits are highly polygenic, in that their within-population variance is governed, in part, by small-effect variants at many genetic loci. Standard population-genetic methods for inferring evolutionary history are ill-suited for polygenic traits: when there are many variants of small effect, signatures of natural selection are spread across the genome and are subtle at any one locus.

In the last several years, various methods have emerged for detecting the action of natural selection on polygenic scores, sums of genotypes weighted by GWAS effect sizes. However, most existing methods do not reveal the timing or strength of selection. Here, we present a set of methods for estimating the historical time course of a population-mean polygenic score using local coalescent trees at GWAS loci. These time courses are estimated by using coalescent theory to relate the branch lengths of trees to allele-frequency change. The resulting time course can be tested for evidence of natural selection.

We present theory and simulations supporting our procedures, as well as estimated time courses of polygenic scores for human height. Because of its grounding in coalescent theory, the framework presented here can be extended to a variety of demographic scenarios, and its usefulness will increase as both GWAS and ancestral-recombination-graph inference continue to progress.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6458425/ Schizophrenia risk and reproductive success: a Mendelian Randomization study Rebecca B. Lawn, Hannah M. Sallis, Amy E. Taylor, Robyn E. Wootton, George Davey Smith, Neil M. Davies, Gibran Hemani, Abigail Fraser, Ian S. Penton-Voak, Marcus R. Munafò 2019 2021-04-19 [("doi","10.1098/rsos.181049")] genetics/heritable/correlation/mendelian-randomization genetics/selection/natural/human psychiatry/schizophrenia

Schizophrenia is a debilitating and heritable mental disorder associated with lower reproductive success. However, the prevalence of schizophrenia is stable over populations and time, resulting in an evolutionary puzzle: how is schizophrenia maintained in the population, given its apparent fitness costs? One possibility is that increased genetic liability for schizophrenia, in the absence of the disorder itself, may confer some reproductive advantage.

We assessed the correlation and causal effect of genetic liability for schizophrenia with number of children, age at first birth and number of sexual partners using data from the Psychiatric Genomics Consortium and UK Biobank. Linkage disequilibrium score regression:

showed little evidence of genetic correlation between genetic liability for schizophrenia and number of children (r g = 0.002, p = 0.84), age at first birth (r g = −0.007, p = 0.45) or number of sexual partners (r g = 0.007, p = 0.42). Mendelian Randomization indicated no robust evidence of a causal effect of genetic liability for schizophrenia on number of children (mean difference: 0.003 increase in number of children per doubling in the natural log odds ratio of schizophrenia risk, 95% confidence interval (CI): −0.003 to 0.009, p = 0.39) or age at first birth (−0.004 years lower age at first birth, 95% CI: −0.043 to 0.034, p = 0.82). We find some evidence of a positive effect of genetic liability for schizophrenia on number of sexual partners (0.165 increase in the number of sexual partners, 95% CI: 0.117–0.212, p = 5.30 × 10−10).

These results suggest that increased genetic liability for schizophrenia does not confer a fitness advantage but does increase mating success.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428571/ Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies Mashaal Sohail, Robert M. Maier, Andrea Ganna, Alex Bloemendal, Alicia R. Martin, Michael C. Turchin, Charleston Wk Chiang, Joel Hirschhorn, Mark J. Daly, Nick Patterson, Benjamin M. Neale, Iain Mathieson, David Reich, Shamil R. Sunyaev 2019 2021-04-20 [("doi","10.7554/eLife.39702")] genetics/selection/natural/human

Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide statistically-significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show show that polygenic adaptation signals based on large numbers of SNPs below genome-wide statistical-significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.

Editorial Note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor’s assessment is that all the issues have been addressed (see decision letter).

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156280/ No evidence for a bilingual executive function advantage in the nationally representative ABCD study Anthony Steven Dick, Nelcida L. Garcia, Shannon M. Pruden, Wesley K. Thompson, Samuel W. Hawes, Matthew T. Sutherland, Michael C. Riedel, Angela R. Laird, Raul Gonzalez 2019 2021-04-20 [("doi","10.1038/s41562-019-0609-3")] iq psychology/linguistics/bilingual

Learning a second language in childhood is inherently advantageous for communication. However, parents, educators and scientists have been interested in determining whether there are additional cognitive advantages.

One of the most exciting yet controversial1 findings about bilinguals is a reported advantage for executive function. That is, several studies suggest that bilinguals perform better than monolinguals on tasks assessing cognitive abilities that are central to the voluntary control of thoughts and behaviors-the so-called ‘executive functions’ (for example, attention, inhibitory control, task switching and resolving conflict). Although a number of small-sample2–4 and large-sample5,6 studies have reported a bilingual executive function advantage (see refs7–9 for a review), there have been several failures to replicate these findings10–15, and recent meta-analyses have called into question the reliability of the original empirical claims8,9. Here we show, in a very large, demographically representative sample (n = 4,524) of 9–10-year-olds across the United States, that there is little evidence for a bilingual advantage for inhibitory control, attention and task switching, or cognitive flexibility, which are key aspects of executive function. We also replicate previously reported disadvantages in English vocabulary in bilinguals7,16,17. However, these English vocabulary differences are substantially mitigated when we account for individual differences in socioeconomic status or intelligence.

In summary, notwithstanding the inherently positive benefits of learning a second language in childhood18, we found little evidence that it engenders additional benefits to executive function development.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6424156/ Evidence That Jeanne Calment Died in 1934-Not 1997 Nikolay Zak 2019 2021-04-20 [("doi","10.1089/rej.2018.2167")] longevity

I present a body of data that, I argue, cumulatively casts serious doubt on the validity of Jeanne Calment’s accepted world record of human life span. First, I assess the plausibility of the record based on the life spans of other centenarians in the International Database of Longevity (IDL) and critique some arguments put forward previously in support of that plausibility, including the longevity of Calment’s ancestors.

Second, I review the literature dedicated to Calment and discuss multiple contradictions in her interviews, biographies, photos, and documents. I argue that the evidence from these sources motivates renewed consideration of the previously rejected hypothesis that Jeanne’s daughter Yvonne acquired her mother’s identity after her death to avoid financial problems and that Jeanne Calment’s death was reported as Yvonne’s death in 1934.

Finally, I discuss the importance of reconsidering the principles of validation, due to the possibility of similar problems regarding other exceptionally long-lived people and the mistaken inferences that researchers may draw from flawed datasets. The phenomenon of Jeanne Calment may prove to be an instructive example of the uncertainty of seemingly well-established facts.

--- https://www.biorxiv.org/content/10.1101/481192.full Engineering Brain Parasites for Intracellular Delivery of Therapeutic Proteins Shahar Bracha, Karoliina Hassi, Paul D. Ross, Stuart Cobb, Lilach Sheiner, Oded Rechavi 2018-12-03 2021-04-20 [("doi","10.1101/481192")] psychology/neuroscience

Protein therapy has the potential to alleviate many neurological diseases; however, delivery mechanisms for the central nervous system (CNS) are limited, and intracellular delivery poses additional hurdles. To address these challenges, we harnessed the protist parasite Toxoplasma gondii, which can migrate into the CNS and secrete proteins into cells. Using a fusion protein approach, we engineered T. gondii to secrete therapeutic proteins for human neurological disorders.

We tested two secretion systems, generated fusion proteins that localized to T. gondii’s secretory organelles, and assessed their intracellular targeting in various mammalian cells including neurons.

We show that T. gondii expressing GRA16 fused to the Rett syndrome protein MeCP2 deliver a fusion protein that mimics the endogenous MeCP2, binding heterochromatic DNA in neurons. This demonstrates the potential of T. gondii as a therapeutic protein vector, which could provide either transient or chronic, in situ synthesis and delivery of intracellular proteins to the CNS.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6585462/ Global signal regression strengthens association between resting-state functional connectivity and behavior Jingwei Li, Ru Kong, Raphaël Liégeois, Csaba Orban, Yanrui Tan, Nanbo Sun, Avram J. Holmes, Mert R. Sabuncu, Tian Ge, B. T. Thomas Yeo 2019 2021-04-20 [("doi","10.1016/j.neuroimage.2019.04.016")] psychology/neuroscience statistics/variance-component

Global signal regression (GSR) is one of the most debated preprocessing strategies for resting-state functional MRI. GSR effectively removes global artifacts driven by motion and respiration, but also discards globally distributed neural information and introduces negative correlations between certain brain regions. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases.

Given the growing interest in functional connectivity fingerprinting, here we considered the utilitarian question of whether GSR strengthens or weakens associations between resting-state functional connectivity (RSFC) and multiple behavioral measures across cognition, personality and emotion. By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR. In the Human Connectome Project (HCP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 behavioral measures, when GSR was applied after ICA-FIX de-noising. To ensure generalizability, we repeated our analyses using kernel regression. GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively. Importantly, the results were consistent across methods. A behavioral measure with greater RSFC-explained variance (using the variance component model) also exhibited greater prediction accuracy (using kernel regression). A behavioral measure with greater improvement in behavioral variance explained after GSR (using the variance component model) also enjoyed greater improvement in prediction accuracy after GSR (using kernel regression). Furthermore, GSR appeared to benefit task performance measures more than self-reported measures.

Since GSR was more effective at removing motion-related and respiratory-related artifacts, GSR-related increases in variance explained and prediction accuracies were unlikely the result of motion-related or respiratory-related artifacts. However, it is worth emphasizing that the current study focused on whole-brain RSFC, so it remains unclear whether GSR improves RSFC-behavioral associations for specific connections or networks. Overall, our results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures.

Code for the variance component model and ridge regression can be found here: Github.

--- https://arxiv.org/abs/1812.05285 IRLAS: Inverse Reinforcement Learning for Architecture Search Minghao Guo, Zhao Zhong, Wei Wu, Dahua Lin, Junjie Yan 2018-12-13 2021-04-20 [("doi","10.48550/arXiv.1812.05285")] reinforcement-learning/model-free

In this paper, we propose an inverse reinforcement learning method for architecture search (IRLAS), which trains an agent to learn to search network structures that are topologically inspired by human-designed network. Most existing architecture search approaches totally neglect the topological characteristics of architectures, which results in complicated architecture with a high inference latency. Motivated by the fact that human-designed networks are elegant in topology with a fast inference speed, we propose a mirror stimuli function inspired by biological cognition theory to extract the abstract topological knowledge of an expert human-design network (ResNeXt).

To avoid raising a too strong prior over the search space, we introduce inverse reinforcement learning to train the mirror stimuli function and exploit it as a heuristic guidance for architecture search, easily generalized to different architecture search algorithms.

On CIFAR-10, the best architecture searched by our proposed IRLAS achieves 2.60% error rate. For ImageNet mobile setting, our model achieves a state-of-the-art top-1 accuracy 75.28%, while being 2 ~4× faster than most auto-generated architectures. A fast version of this model achieves 10% faster than MobileNetV2, while maintaining a higher accuracy.

--- https://arxiv.org/abs/1812.00332 ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware Han Cai, Ligeng Zhu, Song Han 2018-12-02 2021-04-20 [("doi","10.48550/arXiv.1812.00332")] ai/nn/cnn reinforcement-learning/model-free

Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (eg. 104 GPU hours) makes it difficult to directly search the architectures on large-scale tasks (eg. ImageNet). Differentiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue (grow linearly w.r.t. candidate set size). As a result, they need to use ~proxy tasks, such as training on a smaller dataset, or learning with only a few blocks, or training just for a few epochs. These architectures optimized on proxy tasks are not guaranteed to be optimal on the target task.

In this paper, we present ProxylessNAS that can directly learn the architectures for large-scale target tasks and target hardware platforms. We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set.

Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of directness and specialization. On CIFAR-10, our model achieves 2.08% test error with only 5.7M parameters, better than the previous state-of-the-art architecture AmoebaNet-B, while using 6× fewer parameters. On ImageNet, our model achieves 3.1% better top-1 accuracy than MobileNetV2, while being 1.2× faster with measured GPU latency.

We also apply ProxylessNAS to specialize neural architectures for hardware with direct hardware metrics (eg. latency) and provide insights for efficient CNN architecture design.

--- https://arxiv.org/abs/1811.10201 InstaNAS: Instance-aware Neural Architecture Search An-Chieh Cheng, Chieh Hubert Lin, Da-Cheng Juan, Wei Wei, Min Sun 2018-11-26 2021-04-20 [("doi","10.48550/arXiv.1811.10201")] reinforcement-learning/model-free

Conventional Neural Architecture Search (NAS) aims at finding a single architecture that achieves the best performance, which usually optimizes task related learning objectives such as accuracy. However, a single architecture may not be representative enough for the whole dataset with high diversity and variety. Intuitively, electing domain-expert architectures that are proficient in domain-specific features can further benefit architecture related objectives such as latency.

In this paper, we propose InstaNAS—an instance-aware NAS framework—that employs a controller trained to search for a “distribution of architectures” instead of a single architecture; This allows the model to use sophisticated architectures for the difficult samples, which usually comes with large architecture related cost, and shallow architectures for those easy samples. During the inference phase, the controller assigns each of the unseen input samples with a domain expert architecture that can achieve high accuracy with customized inference costs.

Experiments within a search space inspired by MobileNetV2 show InstaNAS can achieve up to 48.8% latency reduction without compromising accuracy on a series of datasets against MobileNetV2.

--- https://arxiv.org/abs/1811.04551#google PlaNet: Learning Latent Dynamics for Planning from Pixels Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson 2018-11-12 2021-04-20 [("doi","10.48550/arXiv.1811.04551")] reinforcement-learning/model

Planning has been very successful for control tasks with known environment dynamics. To leverage planning in unknown environments, the agent needs to learn the dynamics from interactions with the world. However, learning dynamics models that are accurate enough for planning has been a long-standing challenge, especially in image-based domains.

We propose the Deep Planning Network (PlaNet), a purely model-based agent that learns the environment dynamics from images and chooses actions through fast online planning in latent space. To achieve high performance, the dynamics model must accurately predict the rewards ahead for multiple time steps. We approach this using a latent dynamics model with both deterministic and stochastic transition components.

Moreover, we propose a multi-step variational inference objective that we name “latent overshooting”.

Using only pixel observations, our agent solves continuous control tasks with contact dynamics, partial observability, and sparse rewards, which exceed the difficulty of tasks that were previously solved by planning with learned models. PlaNet uses substantially fewer episodes and reaches final performance close to and sometimes higher than strong model-free algorithms.

--- https://arxiv.org/abs/1810.05017#deepmind One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL Tom Le Paine, Sergio Gómez Colmenarejo, Ziyu Wang, Scott Reed, Yusuf Aytar, Tobias Pfaff, Matt W. Hoffman, Gabriel Barth-Maron, Serkan Cabi, David Budden, Nando de Freitas 2018-10-11 2021-04-20 [("doi","10.48550/arXiv.1810.05017")] reinforcement-learning/model-free reinforcement-learning/scaling

Humans are experts at high-fidelity imitation—closely mimicking a demonstration, often in one attempt. Humans use this ability to quickly solve a task instance, and to bootstrap learning of new tasks. Achieving these abilities in autonomous agents is an open problem. In this paper, we introduce an off-policy RL algorithm (MetaMimic) to narrow this gap. MetaMimic can learn both (1) policies for high-fidelity one-shot imitation of diverse novel skills, and (2) policies that enable the agent to solve tasks more efficiently than the demonstrators.

MetaMimic relies on the principle of storing all experiences in a memory and replaying these to learn massive deep neural network policies by off-policy RL. This paper introduces, to the best of our knowledge, the largest existing neural networks for deep RL and shows that larger networks with normalization are needed to achieve one-shot high-fidelity imitation on a challenging manipulation task.

The results also show that both types of policy can be learned from vision, in spite of the task rewards being sparse, and without access to demonstrator actions.

--- https://openreview.net/forum?id=S1eBzhRqK7 Evolutionary-Neural Hybrid Agents for Architecture Search Krzysztof Maziarz, Andrey Khorlin, Quentin de Laroussilhe, Andrea Gesmundo 2018-12-21 2021-04-20 reinforcement-learning/exploration

We propose a class of Evolutionary-Neural hybrid agents, that retain the best qualities of the two approaches.

Neural Architecture Search has recently shown potential to automate the design of Neural Networks. The use of Neural Network agents trained with Reinforcement Learning can offer the possibility to learn complex patterns, as well as the ability to explore a vast and compositional search space. On the other hand, evolutionary algorithms offer the greediness and sample efficiency needed for such an application, as each sample requires a considerable amount of resources. We propose a class of Evolutionary-Neural hybrid agents (Evo-NAS), that retain the best qualities of the two approaches. We show that the Evo-NAS agent can outperform both Neural and Evolutionary agents, both on a synthetic task, and on architecture search for a suite of text classification datasets.

[Keywords: Evolutionary, Architecture Search, NAS]

--- https://arxiv.org/abs/1810.10180 Understanding and correcting pathologies in the training of learned optimizers Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein 2018-10-24 2021-04-21 [("doi","10.48550/arXiv.1810.10180")] ai/nn/cnn reinforcement-learning/meta-learning

Deep learning has shown that learned functions can dramatically outperform hand-designed functions on perceptual tasks. Analogously, this suggests that learned optimizers may similarly outperform current hand-designed optimizers, especially for specific problems. However, learned optimizers are notoriously difficult to train and have yet to demonstrate wall-clock speedups over hand-designed optimizers, and thus are rarely used in practice.

Typically, learned optimizers are trained by truncated backpropagation through an unrolled optimization process resulting in gradients that are either strongly biased (for short truncations) or have exploding norm (for long truncations). In this work we propose a training scheme which overcomes both of these difficulties, by dynamically weighting two unbiased gradient estimators for a variational loss on optimizer performance, allowing us to train neural networks to perform optimization of a specific task faster than tuned first-order methods. We demonstrate these results on problems where our learned optimizer trains convolutional networks faster in wall-clock time compared to tuned first-order methods and with an improvement in test loss.

--- https://arxiv.org/abs/1812.03079 ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst Mayank Bansal, Alex Krizhevsky, Abhijit Ogale 2018-12-07 2021-04-21 [("doi","10.48550/arXiv.1812.03079")] reinforcement-learning/imitation-learning reinforcement-learning/robot

Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle. We find that standard behavior cloning is insufficient for handling complex driving scenarios, even when we leverage a perception system for preprocessing the input and a controller for executing the output on the car: 30 million examples are still not enough.

We propose exposing the learner to synthesized data in the form of perturbations to the expert’s driving, which creates interesting situations such as collisions and/or going off the road. Rather than purely imitating all data, we augment the imitation loss with additional losses that penalize undesirable events and encourage progress—the perturbations then provide an important signal for these losses and lead to robustness of the learned model.

We show that the ChauffeurNet model can handle complex situations in simulation, and present ablation experiments that emphasize the importance of each of our proposed changes and show that the model is responding to the appropriate causal factors. Finally, we demonstrate the model driving a car in the real world.

--- https://arxiv.org/abs/1811.00457 Test & Roll: Profit-Maximizing A/B Tests Elea McDonnell Feit, Ron Berman 2018-11-01 2021-04-21 [("doi","10.1287/mksc.2019.1194")] design economics/advertising statistics/decision

Marketers often use A/B testing as a tool to compare marketing treatments in a test stage and then deploy the better-performing treatment to the remainder of the consumer population. While these tests have traditionally been analyzed using hypothesis testing, we re-frame them as an explicit trade-off between the opportunity cost of the test (where some customers receive a sub-optimal treatment) and the potential losses associated with deploying a sub-optimal treatment to the remainder of the population.

We derive a closed-form expression for the profit-maximizing test size and show that it is substantially smaller than typically recommended for a hypothesis test, particularly when the response is noisy or when the total population is small. The common practice of using small holdout groups can be rationalized by asymmetric priors. The proposed test design achieves nearly the same expected regret as the flexible, yet harder-to-implement multi-armed bandit under a wide range of conditions.

We demonstrate the benefits of the method in 3 different marketing contexts—website design, display advertising and catalog tests—in which we estimate priors from past data. In all 3 cases, the optimal sample sizes are substantially smaller than for a traditional hypothesis test, resulting in higher profit.

--- https://arxiv.org/abs/1903.11137 Hearing your touch: A new acoustic side channel on smartphones Ilia Shumailov, Laurent Simon, Jeff Yan, Ross Anderson 2019-03-26 2021-04-21 [("doi","10.48550/arXiv.1903.11137")] cs/security technology

We present the first acoustic side-channel attack that recovers what users type on the virtual keyboard of their touch-screen smartphone or tablet. When a user taps the screen with a finger, the tap generates a sound wave that propagates on the screen surface and in the air. We found the device’s microphone(s) can recover this wave and “hear” the finger’s touch, and the wave’s distortions are characteristic of the tap’s location on the screen. Hence, by recording audio through the built-in microphone(s), a malicious app can infer text as the user enters it on their device.

We evaluate the effectiveness of the attack with 45 participants in a real-world environment on an Android tablet and an Android smartphone. For the tablet, we recover 61% of 200 4-digit PIN-codes within 20 attempts, even if the model is not trained with the victim’s data. For the smartphone, we recover 9 words of size 7–13 letters with 50 attempts in a common side-channel attack benchmark.

Our results suggest that it not always sufficient to rely on isolation mechanisms such as TrustZone to protect user input. We propose and discuss hardware, operating-system and application-level mechanisms to block this attack more effectively. Mobile devices may need a richer capability model, a more user-friendly notification system for sensor usage and a more thorough evaluation of the information leaked by the underlying hardware.

--- https://www.biorxiv.org/content/10.1101/413716.full Machine Learning to Predict Osteoporotic Fracture Risk from Genotypes Vincenzo Forgetta, Julyan Keller-Baruch, Marie Forest, Audrey Durand, Sahir Bhatnagar, John Kemp, John A. Morris, John A. Kanis, Douglas P. Kiel, Eugene V. McCloskey, Fernando Rivadeneira, Helena Johannson, Nicholas Harvey, Cyrus Cooper, David M. Evans, Joelle Pineau, William D. Leslie, Celia M. T. Greenwood, J. Brent Richards 2018-09-11 2021-04-21 [("doi","10.1101/413716")] ai/nn biology

Background: Genomics-based prediction could be useful since genome-wide genotyping costs less than many clinical tests. We tested whether machine learning methods could provide a clinically-relevant genomic prediction of quantitative ultrasound speed of sound (SOS)—a risk factor for osteoporotic fracture.

Method: We used 341,449 individuals from UK Biobank with SOS measures to develop genomically-predicted SOS (gSOS) using machine learning algorithms. We selected the optimal algorithm in 5,335 independent individuals and then validated it and its ability to predict incident fracture in an independent test dataset (n = 80,027). Finally, we explored whether genomic pre-screening could complement a UK-based osteoporosis screening strategy, based on the validated tool FRAX.

Results: gSOS explained 4.8× more variance in SOS than FRAX clinical risk factors (CRF) alone (r2 = 23% vs. 4.8%). A standard deviation decrease in gSOS, adjusting for the CRF-FRAX score was associated with a higher increased odds of incident major osteoporotic fracture (1,491 cases / 78,536 controls, OR = 1.91 [1.70–2.14], p = 10−28) than that for measured SOS (OR = 1.60 [1.50–1.69], p = 10−52) and femoral neck bone mineral density (147 cases / 4,594 controls, OR = 1.53 [1.27–1.83], p = 10−6). Individuals in the bottom decile of the gSOS distribution had a 3.25× increased risk of major osteoporotic fracture (p = 10−18) compared to the top decile. A gSOS-based FRAX score, identified individuals at high risk for incident major osteoporotic fractures better than the CRF-FRAX score (p = 10−14). Introducing a genomic pre-screening step into osteoporosis screening in 4,741 individuals reduced the number of required clinical visits from 2,455 to 1,273 and the number of BMD tests from 1,013 to 473, while only reducing the sensitivity to identify individuals eligible for therapy 99% → 95%.

Interpretation

The use of genotypes in a machine learning algorithm resulted in a clinically-relevant prediction of SOS and fracture, with potential to impact healthcare resource utilization.

Research in Context

Evidence Before this Study

Genome-wide association studies have identified many loci associated with risk of clinically-relevant fracture risk factors, such as SOS. Yet, it is unclear if such information can be leveraged to identify those at risk for disease outcomes, such as osteoporotic fractures. Most previous attempts to predict disease risk from genotypes have used polygenic risk scores, which may not be optimal for genomic-prediction. Despite these obstacles, genomic-prediction could enable screening programs to be more efficient since most people screened in a population are not determined to have a level of risk that would prompt a change in clinical care. Genomic pre-screening could help identify individuals whose risk of disease is low enough that they are unlikely to benefit from screening.

Added Value of this Study

Using a large dataset of 426,811 individuals we trained and tested a machine learning algorithm to genomically-predict SOS. This metric, gSOS, had performance characteristics for predicting fracture risk that were similar to measured SOS and femoral neck BMD. Implementing a gSOS-based pre-screening step into the UK-based osteoporosis treatment guidelines reduced the number of individuals who would require screening clinical visits and skeletal testing by ~50%, while having little impact on the sensitivity to identify individuals at high risk for osteoporotic fracture.

Implications of all of the Available Evidence

Clinically-relevant genomic prediction of heritable traits is feasible using the machine learning algorithm presented here in large sample sizes. Genome-wide genotyping is now less expensive than many clinical tests, needs to be performed once over a lifetime and could risk stratify for multiple heritable traits and diseases years prior to disease onset, providing an opportunity for prevention. The implementation of such algorithms could improve screening efficiency, yet their cost-effectiveness will need to be ascertained in subsequent analyses.

--- https://arxiv.org/abs/1807.11626 MnasNet: Platform-Aware Neural Architecture Search for Mobile Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le 2018-07-31 2021-04-21 [("doi","10.48550/arXiv.1807.11626")] ai/nn/cnn

Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider.

In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (eg. FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network.

Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our MnasNet achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8× faster than MobileNetV2 [29] with 0.5% higher accuracy and 2.3× faster than NASNet [36] with 1.2% higher accuracy. Our MnasNet also achieves better mAP quality than MobileNets for object detection.

Code is at https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet.

--- https://arxiv.org/abs/1807.06906 Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search Arber Zela, Aaron Klein, Stefan Falkner, Frank Hutter 2018-07-18 2021-04-21 [("doi","10.48550/arXiv.1807.06906")] ai/nn reinforcement-learning/model

While existing work on neural architecture search (NAS) tunes hyperparameters in a separate post-processing step, we demonstrate that architectural choices and other hyperparameter settings interact in a way that can render this separation suboptimal.

Likewise, we demonstrate that the common practice of using very few epochs during the main NAS and much larger numbers of epochs during a post-processing step is inefficient due to little correlation in the relative rankings for these two training regimes.

To combat both of these problems, we propose to use a recent combination of Bayesian optimization and Hyperband for efficient joint neural architecture and hyperparameter search.

--- https://arxiv.org/abs/1807.04640 Automatically Composing Representation Transformations as a Means for Generalization Michael B. Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths 2018-07-12 2021-04-21 [("doi","10.48550/arXiv.1807.04640")] ai/nn reinforcement-learning/meta-learning

A generally intelligent learner should generalize to more complex tasks than it has previously encountered, but the two common paradigms in machine learning—either training a separate learner per task or training a single learner for all tasks—both have difficulty with such generalization because they do not leverage the compositional structure of the task distribution.

This paper introduces the compositional problem graph as a broadly applicable formalism to relate tasks of different complexity in terms of problems with shared subproblems. We propose the compositional generalization problem for measuring how readily old knowledge can be reused and hence built upon.

As a first step for tackling compositional generalization, we introduce the compositional recursive learner, a domain-general framework for learning algorithmic procedures for composing representation transformations, producing a learner that reasons about what computation to execute by making analogies to previously seen problems.

We show on a symbolic and a high-dimensional domain that our compositional approach can generalize to more complex problems than the learner has previously encountered, whereas baselines that are not explicitly compositional do not.

--- https://arxiv.org/abs/1807.03039 Glow: Generative Flow with Invertible 1×1 Convolutions Diederik P. Kingma, Prafulla Dhariwal 2018-07-09 2021-04-21 [("doi","10.48550/arXiv.1807.03039")] ai/nn/cnn

Flow-based generative models (Dinh et al 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis.

In this paper we propose Glow, a simple type of generative flow using an invertible 1×1 convolution.

Using our method we demonstrate an improvement in log-likelihood on standard benchmarks.

Perhaps most strikingly, we demonstrate that a generative model optimized towards the plain log-likelihood objective is capable of efficient realistic-looking synthesis and manipulation of large images.

The code for our model is available at https://github.com/openai/glow.

--- https://arxiv.org/abs/1807.00431 Confounding variables can degrade generalization performance of radiological deep learning models John R. Zech, Marcus A. Badgeley, Manway Liu, Anthony B. Costa, Joseph J. Titano, Eric K. Oermann 2018-07-02 2021-04-21 [("doi","10.1371/journal.pmed.1002683")] ai/nn/cnn

Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease have been promising, but it has not yet been shown that models trained on x-rays from one hospital or one group of hospitals will work equally well at different hospitals. Before these tools are used for computer-aided diagnosis in real-world clinical settings, we must verify their ability to generalize across a variety of hospital systems.

A cross-sectional design was used to train and evaluate pneumonia screening CNNs on 158,323 chest x-rays from NIH (n = 112,120 from 30,805 patients), Mount Sinai (42,396 from 12,904 patients), and Indiana (n = 3,807 from 3,683 patients).

In 3 / 5 natural comparisons, performance on chest x-rays from outside hospitals was lower than on held-out x-rays from the original hospital systems. CNNs were able to detect where an x-ray was acquired (hospital system, hospital department) with extremely high accuracy and calibrate predictions accordingly.

The performance of CNNs in diagnosing diseases on x-rays may reflect not only their ability to identify disease-specific imaging findings on x-rays, but also their ability to exploit confounding information. Estimates of CNN performance based on test data from hospital systems used for model training may overstate their likely real-world performance.

--- https://arxiv.org/abs/1807.03491 Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhyme Jey Han Lau, Trevor Cohn, Timothy Baldwin, Julian Brooke, Adam Hammond 2018-07-10 2021-04-22 [("doi","10.48550/arXiv.1807.03491")] ai/nn/rnn ai/nn/tokenization ai/poetry

In this paper, we propose a joint architecture that captures language, rhyme, and meter for sonnet modeling.

We assess the quality of generated poems using crowd and expert judgments. The stress and rhyme models perform very well, as generated poems are largely indistinguishable from human-written poems.

Expert evaluation, however, reveals that a vanilla language model captures meter implicitly, and that machine-generated poems still underperform in terms of readability and emotion.

Our research shows the importance of expert evaluation for poetry generation, and that future research should look beyond rhyme/meter and focus on poetic language.

--- https://arxiv.org/abs/1807.01697 Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations Dan Hendrycks, Thomas G. Dietterich 2018-07-04 2021-04-22 [("doi","10.48550/arXiv.1807.01697")] ai/dataset ai/nn/adversarial ai/nn/cnn ai/scaling

In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Unlike recent robustness research, this benchmark evaluates performance on commonplace corruptions not worst-case adversarial corruptions.

We find that there are negligible changes in relative corruption robustness from AlexNet to ResNet classifiers, and we discover ways to enhance corruption robustness. Then we propose a new dataset called Icons-50 which opens research on a new kind of robustness, surface variation robustness. With this dataset we evaluate the frailty of classifiers on new styles of known objects and unexpected instances of known classes.

We also demonstrate two methods that improve surface variation robustness. Together our benchmarks may aid future work toward networks that learn fundamental class structure and also robustly generalize.

--- https://arxiv.org/abs/1808.01097 CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R. Scott, Dinglong Huang 2018-08-03 2021-04-22 [("doi","10.48550/arXiv.1808.01097")] ai/dataset ai/nn/cnn ai/scaling

We present a simple yet efficient approach capable of training deep neural networks on large-scale weakly-supervised web images, which are crawled raw from the Internet by using text queries, without any human annotation. We develop a principled learning strategy by leveraging curriculum learning, with the goal of handling a massive amount of noisy labels and data imbalance effectively.

We design a new learning curriculum by measuring the complexity of data using its distribution density in a feature space, and rank the complexity in an unsupervised manner. This allows for an efficient implementation of curriculum learning on large-scale web images, resulting in a high-performance CNN model, where the negative impact of noisy labels is reduced substantially.

Importantly, we show by experiments that those images with highly noisy labels can surprisingly improve the generalization capability of the model, by serving as a manner of regularization. Our approaches obtain state-of-the-art performance on 4 benchmarks: WebVision, ImageNet, Clothing-1M and Food-101.

With an ensemble of multiple models, we achieved a top-5 error rate of 5.2% on the WebVision challenge for 1,000-category classification. This result was the top performance by a wide margin, outperforming second place by nearly a 50% relative error rate.

Code and models are available at: Github.

--- https://www.biorxiv.org/content/10.1101/372193.full The high abortion cost of human reproduction William R. Rice 2018-07-18 2021-04-22 [("doi","10.1101/372193")] biology philosophy/ethics

Information from many large databases and published studies was integrated to estimate the age-specific spontaneous abortion rate in an economically-developed human population.

Accuracy was tested with published data from a diverse array of studies. Spontaneous abortion was found to be: (1) the predominant outcome of fertilization and (2) a natural and inevitable part of human reproduction at all ages.

The decision to reproduce is inextricably coupled with the production of spontaneous abortions with high probability, and the decision to have a large family leads to many spontaneous abortions with virtual certainty.

The lifetime number of spontaneous abortions was estimated for a “canonical” woman (constrained to have average age at marriage, first birth, inter-birth intervals, and family size) in two populations: one with and the other without effective birth control (including free access to elective abortions). Birth control was found to reduce lifetime abortions more than 6×.

--- https://arxiv.org/abs/1810.00337 Learning to Perform Local Rewriting for Combinatorial Optimization Xinyun Chen, Yuandong Tian 2018-09-30 2021-04-22 [("doi","10.48550/arXiv.1810.00337")] cs/algorithm reinforcement-learning/model-free

Search-based methods for hard combinatorial optimization are often guided by heuristics. Tuning heuristics in various conditions and situations is often time-consuming.

In this paper, we propose NeuRewriter that learns a policy to pick heuristics and rewrite the local components of the current solution to iteratively improve it until convergence. The policy factorizes into a region-picking and a rule-picking component, each parameterized by a neural network trained with actor-critic methods in reinforcement learning.

NeuRewriter captures the general structure of combinatorial problems and shows strong performance in 3 versatile tasks: expression simplification, online job scheduling, and vehicle routing problems.

NeuRewriter outperforms the expression simplification component in Z3; outperforms DeepRM and Google OR-tools in online job scheduling; and outperforms recent neural baselines and Google OR-tools in vehicle routing problems.

--- https://arxiv.org/abs/1809.05676 Deterministic Implementations for Reproducibility in Deep Reinforcement Learning Prabhat Nagarajan, Garrett Warnell, Peter Stone 2018-09-15 2021-04-22 [("doi","10.48550/arXiv.1809.05676")] cs/algorithm reinforcement-learning/model-free statistics/bias

While deep reinforcement learning (DRL) has led to numerous successes in recent years, reproducing these successes can be extremely challenging. One reproducibility challenge particularly relevant to DRL is nondeterminism in the training process, which can substantially affect the results. Motivated by this challenge, we study the positive impacts of deterministic implementations in eliminating nondeterminism in training.

To do so, we consider the particular case of the deep Q-learning algorithm, for which we produce a deterministic implementation by identifying and controlling all sources of nondeterminism in the training process. One by one, we then allow individual sources of nondeterminism to affect our otherwise deterministic implementation, and measure the impact of each source on the variance in performance.

We find that individual sources of nondeterminism can substantially impact the performance of the agent, illustrating the benefits of deterministic implementations. In addition, we also discuss the important role of deterministic implementations in achieving exact replicability of results.

--- https://arxiv.org/abs/1808.03196 Learning to Optimize Join Queries With Deep Reinforcement Learning Sanjay Krishnan, Zongheng Yang, Ken Goldberg, Joseph Hellerstein, Ion Stoica 2018-08-09 2021-04-22 [("doi","10.48550/arXiv.1808.03196")] cs/algorithm reinforcement-learning/model-free

Exhaustive enumeration of all possible join orders is often avoided, and most optimizers leverage heuristics to prune the search space. The design and implementation of heuristics are well-understood when the cost model is roughly linear, and we find that these heuristics can be suboptimal when there are non-linearities in cost. Ideally, instead of a fixed heuristic, we would want a strategy to guide the search space in a more data-driven way—tailoring the search to a specific dataset and query workload. Recognizing the link between classical Dynamic Programming enumeration methods and recent results in Reinforcement Learning (RL), we propose a new method for learning optimized join search strategies.

We present our RL-based DQ optimizer, which currently optimizes select-project-join blocks. We implement 3 versions of DQ to illustrate the ease of integration into existing DBMSes: (1) A version built on top of Apache Calcite, (2) a version integrated into PostgreSQL, and (3) a version integrated into SparkSQL.

Our extensive evaluation shows that DQ achieves plans with optimization costs and query execution times competitive with the native query optimizer in each system, but can execute faster after learning (often by orders of magnitude).

--- https://www.biorxiv.org/content/10.1101/420497.full Polygenicity of complex traits is explained by negative selection Luke J. O’Connor, Armin P. Schoech, Farhad Hormozdiari, Steven Gazal, Nick Patterson, Alkes Price 2018-09-18 2021-04-22 [("doi","10.1101/420497")] genetics/heritable genetics/selection/natural/human

Complex traits and common disease are highly polygenic: thousands of common variants are causal, and their effect sizes are almost always small. Polygenicity could be explained by negative selection, which constrains common-variant effect sizes and may reshape their distribution across the genome. We refer to this phenomenon as flattening, as genetic signal is flattened relative to the underlying biology.

We introduce a mathematical definition of polygenicity, the effective number of associated SNPs, and a robust statistical method to estimate it. This definition of polygenicity differs from the number of causal SNPs, a standard definition; it depends strongly on SNPs with large effects.

In analyses of 33 complex traits (average n = 361k), we determined that common variants are ~4× more polygenic than low-frequency variants, consistent with pervasive flattening. Moreover, functionally important regions of the genome have increased polygenicity in proportion to their increased heritability, implying that heritability enrichment reflects differences in the number of associations rather than their magnitude (which is constrained by selection).

We conclude that negative selection constrains the genetic signal of biologically important regions and genes, reshaping genetic architecture.

--- https://www.biorxiv.org/content/10.1101/418210.full Genomic prediction of cognitive traits in childhood and adolescence A. G. Allegrini, S. Selzam, K. Rimfeld, S. von Stumm, J. B. Pingault, R. Plomin 2018-09-17 2021-04-22 [("doi","10.1101/418210")] genetics/heritable iq

Recent advances in genomics are producing powerful DNA predictors of complex traits, especially cognitive abilities. Here, we leveraged summary statistics from the most recent genome-wide association studies of intelligence and educational attainment to build prediction models of general cognitive ability and educational achievement.

To this end, we compared the performances of multi-trait genomic and polygenic scoring methods. In a representative UK sample of 7,026 children at age 12 and 16, we show that we can now predict up to 11% of the variance in intelligence and 16% in educational achievement. We also show that predictive power increases from age 12 to age 16 and that genomic predictions do not differ for girls and boys.

Multivariate genomic methods were effective in boosting predictive power and, even though prediction accuracy varied across polygenic scores approaches, results were similar using different multivariate and polygenic score methods.

Polygenic scores for educational attainment and intelligence are the most powerful predictors in the behavioral sciences and exceed predictions that can be made from parental phenotypes such as educational attainment and occupational status.

--- https://www.biorxiv.org/content/10.1101/383331.full The genetics of the mood disorder spectrum: genome-wide association analyses of over 185,000 cases and 439,000 controls Jonathan R. I. Coleman, Héléna A. Gaspar, Julien Bryois, Enda M. Byrne, Andreas J. Forstner, Peter A. Holmans, Christiaan A. de Leeuw, Manuel Mattheisen, Andrew McQuillin, Jennifer M. Whitehead Pavlides, Tune H. Pers, Stephan Ripke, Eli Ayumi Stahl, Stacy Steinberg, Vassily Trubetskoy, Maciej Trzaskowski, Yunpeng Wang, Liam Abbott, Abdel Abdellaoui, Mark J. Adams, Annelie Nordin Adolfsson, Esben Agerbo, Huda Akil, Diego Albani, Ney Alliey-Rodriguez, Thomas D. Als, Till F. M. Andlauer, Adebayo Anjorin, Verneri Antilla, Sandra Van der Auwera, Swapnil Awasthi, Silviu-Alin Bacanu, Judith A. Badner, Marie Bækvad-Hansen, Jack D. Barchas, Nicholas Bass, Michael Bauer, Aartjan T. F. Beekman, Richard Belliveau, Sarah E. Bergen, Tim B. Bigdeli, Elisabeth B. Binder, Erlend Bøen, Marco Boks, James Boocock, Monika Budde, William Bunney, Margit Burmeister, Henriette N. Buttenschøn, Jonas Bybjerg-Grauholm, William Byerley, Na Cai, Miquel Casas, Enrique Castelao, Felecia Cerrato, Pablo Cervantes, Kimberly Chambert, Alexander W. Charney, Danfeng Chen, Jane Hvarregaard Christensen, Claire Churchhouse, David St Clair, Toni-Kim Clarke, Lucía Colodro-Conde, William Coryell, Baptiste Couvy-Duchesne, David W. Craig, Gregory E. Crawford, Cristiana Cruceanu, Piotr M. Czerski, Anders Martin Dale, Gail Davies, Ian J. Deary, Franziska Degenhardt, Jurgen Del-Favero, J. Raymond DePaulo, Eske M. Derks, Nese Direk, Srdjan Djurovic, Amanda L. Dobbyn, Conor V. Dolan, Ashley Dumont, Erin C. Dunn, Thalia C. Eley, Torbjørn Elvsåshagen, Valentina Escott-Price, Chun Chieh Fan, Hilary K. Finucane, Sascha B. Fischer, Matthew Flickinger, Jerome C. Foo, Tatiana M. Foroud, Liz Forty, Josef Frank, Christine Fraser, Nelson B. Freimer, Louise Frisén, Katrin Gade, Diane Gage, Julie Garnham, Claudia Giambartolomei, Fernando S. Goes, Jaqueline Goldstein, Scott D. Gordon, Katherine Gordon-Smith, Elaine K. Green, Melissa J. Green, Tiffany A. Greenwood, Jakob Grove, Weihua Guan, Lynsey S. Hall, Marian L. Hamshere, Christine Søholm Hansen, Thomas F. Hansen, Martin Hautzinger, Urs Heilbronner, Albert M. van Hemert, Stefan Herms, Ian B. Hickie, Maria Hipolito, Per Hoffmann, Dominic Holland, Georg Homuth, Carsten Horn, Jouke-Jan Hottenga, Laura Huckins, Marcus Ising, Stéphane Jamain, Rick Jansen, Jessica S. Johnson, Simone de Jong, Eric Jorgenson, Anders Juréus, Radhika Kandaswamy, Robert Karlsson, James L. Kennedy, Farnush Farhadi Hassan Kiadeh, Sarah Kittel-Schneider, James A. Knowles, Manolis Kogevinas, Isaac S. Kohane, Anna C. Koller, Julia Kraft, Warren W. Kretzschmar, Jesper Krogh, Ralph Kupka, Zoltán Kutalik, Catharina Lavebratt, Jacob Lawrence, William B. Lawson, Markus Leber, Phil H. Lee, Shawn E. Levy, Jun Z. Li, Yihan Li, Penelope A. Lind, Chunyu Liu, Loes M. Olde Loohuis, Anna Maaser, Donald J. MacIntyre, Dean F. MacKinnon, Pamela B. Mahon, Wolfgang Maier, Robert M. Maier, Jonathan Marchini, Lina Martinsson, Hamdi Mbarek, Steve McCarroll, Patrick McGrath, Peter McGuffin, Melvin G. McInnis, James D. McKay, Helena Medeiros, Sarah E. Medland, Divya Mehta, Fan Meng, Christel M. Middeldorp, Evelin Mihailov, Yuri Milaneschi, Lili Milani, Saira Saeed Mirza, Francis M. Mondimore, Grant W. Montgomery, Derek W. Morris, Sara Mostafavi, Thomas W. Mühleisen, Niamh Mullins, Matthias Nauck, Bernard Ng, Hoang Nguyen, Caroline M. Nievergelt, Michel G. Nivard, Evaristus A. Nwulia, Dale R. Nyholt, Claire O’Donovan, Paul F. O’Reilly, Anil P. S. Ori, Lilijana Oruc, Urban Ösby, Hogni Oskarsson, Jodie N. Painter, José Guzman Parra, Carsten Bøcker Pedersen, Marianne Giørtz Pedersen, Amy Perry, Roseann E. Peterson, Erik Pettersson, Wouter J. Peyrot, Andrea Pfennig, Giorgio Pistis, Shaun M. Purcell, Jorge A. Quiroz, Per Qvist, Eline J. Regeer, Andreas Reif, Céline S. Reinbold, John P. Rice, Brien P. Riley, Fabio Rivas, Margarita Rivera, Panos Roussos, Douglas M. Ruderfer, Euijung Ryu, Cristina Sánchez-Mora, Alan F. Schatzberg, William A. Scheftner, Robert Schoevers, Nicholas J. Schork, Eva C. Schulte, Tatyana Shehktman, Ling Shen, Jianxin Shi, Paul D. Shilling, Stanley I. Shyn, Engilbert Sigurdsson, Claire Slaney, Olav B. Smeland, Johannes H. Smit, Daniel J. Smith, Janet L. Sobell, Anne T. Spijker, Michael Steffens, John S. Strauss, Fabian Streit, Jana Strohmaier, Szabolcs Szelinger, Katherine E. Tansey, Henning Teismann, Alexander Teumer, Robert C. Thompson, Wesley Thompson, Pippa A. Thomson, Thorgeir E. Thorgeirsson, Matthew Traylor, Jens Treutlein, André G. Uitterlinden, Daniel Umbricht, Helmut Vedder, Alexander Viktorin, Peter M. Visscher, Weiqing Wang, Stanley J. Watson, Bradley T. Webb, Cynthia Shannon Weickert, as W. Weickert, Shantel Marie Weinsheimer, Jürgen Wellmann, Gonneke Willemsen, Stephanie H. Witt, Yang Wu, Hualin S. Xi, Wei Xu, Jian Yang, Allan H. Young, Peter Zandi, Peng Zhang, Futao Zhang, Sebastian Zollner, Rolf Adolfsson, Ingrid Agartz, Martin Alda, Volker Arolt, Lena Backlund, Bernhard T. Baune, Frank Bellivier, Klaus Berger, Wade H. Berrettini, Joanna M. Biernacka, Douglas H. R. Blackwood, Michael Boehnke, Dorret I. Boomsma, Aiden Corvin, Nicholas Craddock, Mark J. Daly, Udo Dannlowski, Enrico Domenici, Katharina Domschke, Tõnu Esko, Bruno Etain, Mark Frye, Janice M. Fullerton, Elliot S. Gershon, E. J. C. de Geus, Michael Gill, Fernando Goes, Hans J. Grabe, Maria Grigoroiu-Serbanescu, Steven P. Hamilton, Joanna Hauser, Caroline Hayward, Andrew C. Heath, David Hougaard, Christina M. Hultman, Ian Jones, Lisa A. Jones, René S. Kahn, Kenneth S. Kendler, George Kirov, Stefan Kloiber, Mikael Landén, Marion Leboyer, Glyn Lewis, Qingqin S. Li, Jolanta Lissowska, Susanne Lucae, Pamela A. F. Madden, Patrik K. Magnusson, Nicholas G. Martin, Fermin Mayoral, Susan L. McElroy, Andrew M. McIntosh, Francis J. McMahon, Ingrid Sigfrid Melle, Andres Metspalu, Philip B. Mitchell, Gunnar Morken, Ole Mors, Preben Bo Mortensen, Bertram Müller-Myhsok, Richard M. Myers, Benjamin M. Neale, Vishwajit Nimgaonkar, Merete Nordentoft, Markus M. Nöthen, Michael C. O’Donovan, Ketil J. Oedegaard, Michael J. Owen, Sara A. Paciga, Carlos Pato, Michele T. Pato, Nancy L. Pedersen, Brenda W. J. H. Penninx, Roy H. Perlis, David J. Porteous, Danielle Posthuma, James B. Potash, Martin Preisig, Josep Antoni Ramos-Quiroga, Marta Ribasés, Marcella Rietschel, Guy A. Rouleau, Catherine Schaefer, Martin Schalling, Peter R. Schofield, Thomas G. Schulze, Alessandro Serretti, Jordan W. Smoller, Hreinn Stefansson, Kari Stefansson, Eystein Stordal, Henning Tiemeier, Gustavo Turecki, Rudolf Uher, Arne E. Vaaler, Eduard Vieta, John B. Vincent, Henry Völzke, Myrna M. Weissman, Thomas Werge, Ole A. Andreassen, Anders Børglum, Sven Cichon, Howard J. Edenberg, Arianna Di Florio, John Kelsoe, Douglas F. Levinson, Cathryn M. Lewis, John I. Nurnberger, Roel A. Ophoff, Laura J. Scott, Pamela Sklar, Patrick F. Sullivan, Naomi R. Wray, Gerome Breen 2018-08-13 2021-04-22 [("doi","10.1101/383331")] genetics/heritable psychiatry/bipolar/genetics psychiatry/depression

Mood disorders affect 10–20% of the population, ranging from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Multiple approaches have shown considerable sharing of genetic risk factors between unipolar and bipolar mood disorders.

We use data from the largest genome-wide association studies of major depression (MD) and bipolar disorder (BD) to investigate the molecular basis of the shared genetic liability to mood disorders. We meta-analysed the Psychiatric Genomics Consortium (PGC) MD and BD cohorts, and an additional MD cohort from UK Biobank (185,285 cases, 439,741 controls, non-overlapping n = 609,424).

73 loci reached genome-wide statistical-significance in the meta-analysis, with additional loci in subtype and depression-only analyses. More genome-wide statistically-significant loci from PGC MD (39/44, 89% of the PGC MD loci) than PGC BD (4/19, 21%) reached genome-wide statistical-significance in the meta-analysis. Genetic correlations calculated between MD and BD subtypes revealed that type II BD correlates strongly with MD. Integrating the results with systems biology information, we implicate pathways and neuronal subtypes which highlight similarities but also potential differences between MD and BD.

Our results reflected MD more than BD, perhaps due to the larger sample size for MD, but also perhaps because depression is their predominant common feature. Overall, these results provide evidence for a genetic mood disorders spectrum.

--- https://www.biorxiv.org/content/10.1101/412056.full Towards a ‘Treadmill Test’ for Cognition: Reliable Prediction of Intelligence From Whole-Brain Task Activation Patterns Chandra Sripada, Mike Angstadt, Saige Rutherford 2018-09-09 2021-04-22 [("doi","10.1101/412056")] iq psychology/neuroscience

Identifying brain-based markers of general cognitive ability, ie. “intelligence”, has been a long-standing goal of cognitive and clinical neuroscience. Previous studies focused on relatively static, enduring features such as gray matter volume and white matter structure.

In this report, we investigate prediction of intelligence based on task activation patterns during the N-back working memory task as well as 6 other tasks in the Human Connectome Project dataset, encompassing 19 task contrasts.

We find that whole-brain task activation patterns are a highly effective basis for prediction of intelligence, achieving a 0.68 correlation with intelligence scores in an independent sample, which exceeds results reported from other modalities. Additionally, we show that tasks that tap executive processing and that are more cognitively demanding are particularly effective for intelligence prediction.

These results suggest a picture analogous to treadmill testing for cardiac function: Placing the brain in an activated task state improves brain-based prediction of intelligence.

--- https://arxiv.org/abs/1808.02822 Backprop Evolution Maximilian Alber, Irwan Bello, Barret Zoph, Pieter-Jan Kindermans, Prajit Ramachandran, Quoc Le 2018-08-08 2021-04-23 [("doi","10.48550/arXiv.1808.02822")] reinforcement-learning/meta-learning

The back-propagation algorithm is the cornerstone of deep learning. Despite its importance, few variations of the algorithm have been attempted.

This work presents an approach to discover new variations of the back-propagation equation. We use a domain-specific language to describe update equations as a list of primitive functions. An evolution-based method is used to discover new propagation rules that maximize the generalization performance after a few epochs of training.

We find several update equations that can train faster with short training times than standard back-propagation, and perform similar as standard back-propagation at convergence.

--- https://arxiv.org/abs/1809.10658 Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation Rodrigo Nogueira, Jannis Bulian, Massimiliano Ciaramita 2018-09-27 2021-04-23 [("doi","10.48550/arXiv.1809.10658")] reinforcement-learning/multi-agent

We propose a method to efficiently learn diverse strategies in reinforcement learning for query reformulation in the tasks of document retrieval and question answering.

In the proposed framework, an agent consists of multiple specialized sub-agents and a meta-agent that learns to aggregate the answers from sub-agents to produce a final answer. Sub-agents are trained on disjoint partitions of the training data, while the meta-agent is trained on the full training set.

Our method makes learning faster because it is highly parallelizable, and has better generalization performance than strong baselines, such as an ensemble of agents trained on the full data.

We show that the improved performance is due to the increased diversity of reformulation strategies.

--- https://arxiv.org/abs/1807.10553 Blueberry Earth Anders Sandberg 2018-07-27 2021-04-23 [("doi","10.48550/arXiv.1807.10553")] science

This paper explores the physics of the what-if question “what if the entire Earth was instantaneously replaced with an equal volume of closely packed, but uncompressed blueberries?” While the assumption may be absurd, the consequences can be explored rigorously using elementary physics.

The result is not entirely dissimilar to a small ocean-world exoplanet.

--- https://arxiv.org/abs/1808.10250 SonarSnoop: Active Acoustic Side-Channel Attacks Peng Cheng, Ibrahim Ethem Bagci, Utz Roedig, Jeff Yan 2018-08-30 2021-04-23 [("doi","10.48550/arXiv.1808.10250")] cs/security technology

We report the first active acoustic side-channel attack.

Speakers are used to emit human inaudible acoustic signals and the echo is recorded via microphones, turning the acoustic system of a smartphone into a sonar system. The echo signal can be used to profile user interaction with the device. For example, a victim’s finger movements can be inferred to steal Android phone unlock patterns.

In our empirical study, the number of candidate unlock patterns that an attacker must try to authenticate herself to a Samsung S4 Android phone can be reduced by up to 70% using this novel acoustic side-channel.

Our approach can be easily applied to other application scenarios and device types.

Overall, our work highlights a new family of security threats.

--- /doc/genetics/heritable/correlation/2022-zakharin.pdf Testing heritability of moral foundations: Common pathway models support strong heritability for the five moral foundations Michael Zakharin, Timothy C. Bates 2022-05-26 2022-05-26 [("doi","10.1177/08902070221103957")] genetics/heritable/correlation philosophy/ethics sociology

Moral Foundations Theory (MFT) predicts that moral behavior reflects at least five foundational traits, each hypothesised to be heritable.

Here, we report two independent twin studies (total n = 2,020), using multivariate multi-group common pathway models to test the following 3 predictions from the MFT: (1) The moral foundations will show heritability; (2) The moral foundations will each be genetically distinct and (3) The clustering of moral concerns around individualising and binding domains will show heritability.

Supporting predictions 1 and 3, Study 1 showed evidence for substantial heritability of two broad moral factors corresponding to individualising and binding domains. In Study 2, we added the second dataset, testing replication of the Study 1 model in a joint approach. This further corroborated evidence for heritable influence, showed strong influences on the individualising and binding domains (h2 = 49% and 66%, respectively) and, partially supporting prediction 2, showed foundation-specific, heritable influences on Harm/Care, Fairness/Reciprocity and Purity/Sanctity foundations. A general morality factor was required, also showing substantial genetic effects (40%).

These findings indicate that moral foundations have genetic bases. These influenced the individual foundations themselves as well as a general concern for the individual, for the group, and overall moral concern.

[Keywords: morality, moral foundations, Haidt, twin study, heritability]

--- /doc/design/1910-loos.pdf Ornament and Crime Adolf Loos 1910-01-01 2021-04-23 design philosophy --- https://practicaltypography.com/ Butterick’s Practical Typography 2021-04-23 cs/css design/typography --- /static/js/sidenotes.js 2021-04-23 cs/js design/typography/sidenote technology --- https://book.webtypography.net/ Web Typography Richard Rutter 2021-04-23 design/typography technology --- https://edwardtufte.github.io/tufte-css/#epigraphs Tufte CSS 2021-04-23 design/typography --- https://en.wikipedia.org/wiki/Asterism_(typography) Asterism (typography) 2021-04-23 design/typography --- https://en.wikipedia.org/wiki/Hangul Hangul 2021-04-24 design/typography/square --- https://en.wikipedia.org/wiki/Ink_trap Ink trap 2021-04-24 design/typography --- https://en.wikipedia.org/wiki/Intertitle Intertitle 2021-04-24 design/typography --- https://en.wikipedia.org/wiki/LaTeX LaTeX 2021-04-24 design/typography --- https://en.wikipedia.org/wiki/Matthew_Butterick Matthew Butterick 2021-04-24 design/typography --- https://en.wikipedia.org/wiki/Microtypography Microtypography 2021-04-24 design/typography/tex --- https://en.wikipedia.org/wiki/Monotype_system Monotype system 2021-04-24 design/typography --- https://en.wikipedia.org/wiki/Polyglot_(book) Polyglot (book) 2021-04-24 design/typography psychology/linguistics/bilingual --- https://en.wikipedia.org/wiki/Robert_Bringhurst Robert Bringhurst 2021-04-24 design/typography --- https://en.wikipedia.org/wiki/Source_Sans Source Sans Pro 2021-04-24 design/typography --- https://en.wikipedia.org/wiki/TeX TeX 2021-04-25 design/typography/tex --- https://en.wikipedia.org/wiki/Text_figures Text figures 2021-04-25 design/typography --- https://en.wikipedia.org/wiki/The_Elements_of_Typographic_Style The Elements of Typographic Style 2021-04-25 design/typography --- https://en.wikipedia.org/wiki/Typographic_alignment#Justified Typographic alignment § Justified 2021-04-25 design/typography --- https://ilovetypography.com/2018/08/24/a-brief-history-of-the-index/ 2021-04-25 design/typography --- https://www.dafont.com/sunrise-2.font Sunrise Font 2021-04-25 design/typography --- https://www.t26.com/fonts/22320-Hangulatin-EN Hangulatin EN Font 2021-04-25 design/typography/square --- /review/book#web-typography-rutter-2017 Book Reviews § Web Typography, Rutter 2017 Gwern 2013-08-23 2013-08-23 fiction/criticism --- /doc/design/typography/1988-wishart.pdf The Printing of Mathematics David Wishart 1988-01-01 2021-04-25 design/typography math --- /doc/sociology/2005-wallace.pdf Host: Deep into the mercenary world of take-no-prisoners political talk radio David Foster Wallace 2005-04-01 2021-04-25 design/typography politics sociology --- /doc/genetics/heritable/2019-peterson.pdf Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations Roseann E. Peterson, Karoline Kuchenbaecker, Raymond K. Walters, Chia-Yen Chen, Alice B. Popejoy, Sathish Periyasamy, Max Lam, Conrad Iyegbe, Rona J. Strawbridge, Leslie Brick, Caitlin E. Carey, Alicia R. Martin, Jacquelyn L. Meyers, Jinni Su, Junfang Chen, Alexis C. Edwards, Allan Kalungi, Nastassja Koen, Lerato Majara, Emanuel Schwarz, Jordan W. Smoller, Eli Ayumi Stahl, Patrick F. Sullivan, Evangelos Vassos, Bryan Mowry, Miguel L. Prieto, Alfredo Cuellar-Barboza, Tim B. Bigdeli, Howard J. Edenberg, Hailiang Huang, Laramie E. Duncan 2019-10-17 2021-04-25 [("doi","10.1016/j.cell.2019.08.051")] genetics/heritable --- /doc/genetics/editing/2019-xu.pdf CRISPR-Edited Stem Cells in a Patient with HIV and Acute Lymphocytic Leukemia Lei Xu, Jun Wang, Yulin Liu, Liangfu Xie, Bin Su, Danlei Mou, Longteng Wang, Tingting Liu, Xiaobao Wang, Bin Zhang, Long Zhao, Liangding Hu, Hongmei Ning, Yufeng Zhang, Kai Deng, Lifeng Liu, Xiaofan Lu, Tong Zhang, Jun Xu, Cheng Li, Hao Wu, Hongkui Deng, Hu Chen 2019-09-11 2021-04-26 [("doi","10.1056/NEJMoa1817426")] genetics/editing --- /doc/ai/fiction/2015-short-theannalsoftheparrigues.pdf The Annals of the Parrigues Emily Short 2015-12-18 2021-04-26 ai/fiction design/typography/rubrication --- /doc/history/2003-hobbs-marklombardiglobalnetworks.pdf Mark Lombardi: Global Networks Robert Hobbs 2003-01-01 2021-04-26 design/typography/rubrication history --- https://ilovetypography.com/2019/03/14/the-first-printed-math-books/ The First Printed Math Books Boardley 2019 2021-04-26 design/typography math --- https://ultrasparky.org/blog/ Ragtag grab-bag 2021-04-26 design/typography --- https://en.wikipedia.org/wiki/Source_Code_Pro Source Code Pro 2021-04-26 design/typography --- https://en.wikipedia.org/wiki/Source_Serif Source Serif Pro 2021-04-26 design/typography --- https://en.wikipedia.org/wiki/Variable_font Variable font 2021-04-26 design/typography --- https://en.wikipedia.org/wiki/Font#Weight Font § Weight 2021-04-26 design/typography --- https://en.wikipedia.org/wiki/SIL_Open_Font_License SIL Open Font License 2021-04-26 design/typography --- https://en.wikipedia.org/wiki/Syllabification#Algorithm Hyphenation algorithm 2021-04-26 design/typography --- https://ctan.org/tex-archive/language/hyph-utf8 CTAN: /tex-archive/language/hyph-utf8 2021-04-27 design/typography --- /doc/psychology/2018-bessadok.pdf Intact Connectional Morphometricity Learning Using Multi-view Morphological Brain Networks with Application to Autism Spectrum Disorder Alaa Bessadok, Islem Rekik 2018-01-01 2021-04-27 [("doi","10.1007/978-3-030-00755-3_5")] psychiatry psychology statistics/variance-component --- /doc/statistics/2016-cook.pdf Do scholars follow Betteridge’s Law? The use of questions in journal article titles James M. Cook, Dawn Plourde 2016-01-01 2021-04-27 [("doi","10.1007/s11192-016-2030-2")] science statistics --- /doc/statistics/2020-raudenbush.pdf Randomized Experiments in Education, with Implications for Multilevel Causal Inference Stephen W. Raudenbush, Daniel Schwartz 2020-03-01 2021-04-27 [("doi","10.1146/annurev-statistics-031219-041205")] sociology statistics

Education research has experienced a methodological renaissance over the past two decades, with a new focus on large-scale randomized experiments. This wave of experiments has made education research an even more exciting area for statisticians, unearthing many lessons and challenges in experimental design, causal inference, and statistics more broadly. Importantly, educational research and practice almost always occur in a multilevel setting, which makes the statistics relevant to other fields with this structure, including social policy, health services research, and clinical trials in medicine.

In this article we first briefly review the history that led to this new era in education research and describe the design features that dominate the modern large-scale educational experiments.

We then highlight some of the key statistical challenges in this area, including endogeneity of design, heterogeneity of treatment effects, noncompliance with treatment assignment, mediation, generalizability, and spillover. Though a secondary focus, we also touch on promising trial designs that answer more nuanced questions, such as the SMART design for studying dynamic treatment regimes and factorial designs for optimizing the components of an existing treatment.

--- /doc/statistics/2013-hood.pdf Psychological Measurement and Methodological Realism S. Brian Hood 2013-08-01 2021-04-27 [("doi","10.2307/42001473")] psychology statistics --- /doc/cs/algorithm/information/compression/1996-teahan.pdf THE ENTROPY OF ENGLISH USING PPM-BASED MODELS—Data Compression Conference, 1996. DCC '96. Proceedings 1996-01-01 2021-04-27 cs/algorithm/information/compression psychology/linguistics --- /doc/statistics/1910-brown.pdf Some Experimental Results in the Correlation of Mental Abilities William Brown 1910-01-01 2021-04-27 [("doi","10.1111/j.2044-8295.1910.tb00207")] iq statistics --- https://gaussianbp.github.io/ Gaussian Belief Propagation 2021-04-27 design/visualization --- https://www.r-bloggers.com/2014/01/visualization-series-using-scatterplots-and-models-to-understand-the-diamond-market-so-you-dont-get-ripped-off/ Visualization Series: Using Scatterplots and Models to Understand the Diamond Market (so You Don’t Get Ripped Off) 2021-04-27 design/visualization --- https://bost.ocks.org/mike/algorithms/ Visualizing Algorithms 2021-04-27 cs/algorithm design/visualization statistics/probability --- https://flowingdata.com/2010/08/31/how-to-visualize-data-with-cartoonish-faces/ How to visualize data with cartoonish faces ala Chernoff 2021-04-27 design/visualization --- https://en.wikipedia.org/wiki/Chernoff_face Chernoff face 2021-04-28 design/visualization --- https://idl.cs.washington.edu/files/2015-BeyondWebersLaw-InfoVis.pdf Beyond Weber’s Law: A Second Look at Ranking Visualizations of Correlation 2021-04-28 design/visualization --- /doc/genetics/heritable/2018-khera-fig23-pgsprediction.png 2018 2021-04-28 design/visualization genetics/heritable --- https://www.edwardtufte.com/tufte/books_ei Envisioning Information 2021-04-28 design/visualization --- https://en.wikipedia.org/wiki/Edward_Tufte Edward Tufte 2021-04-28 design/visualization --- https://en.wikipedia.org/wiki/Choose_Your_Own_Adventure Choose Your Own Adventure 2021-04-28 design/visualization --- https://explorabl.es/ Explorable Explanations 2021-04-28 design/visualization --- https://en.wikipedia.org/wiki/Explorable_explanation Explorable explanation 2021-04-28 design/visualization --- https://en.wikipedia.org/wiki/Oblique_projection Oblique projection 2021-04-28 design/visualization --- https://en.wikipedia.org/wiki/Visual_cryptography Visual cryptography 2021-04-28 cs/cryptography design/visualization --- https://distill.pub/2016/augmented-rnns/ Attention and Augmented Recurrent Neural Networks 2021-04-29 ai/nn/rnn ai/nn/transformer/attention design/visualization --- https://distill.pub/2016/deconv-checkerboard/ Deconvolution and Checkerboard Artifacts 2021-04-29 ai/nn/cnn ai/nn/gan design/visualization --- https://distill.pub/2017/feature-visualization/#frequency-artifacts Feature Visualization 2021-04-29 design/visualization --- https://distill.pub/2017/momentum/ Why Momentum Really Works 2021-04-29 ai/nn design/visualization math --- https://distill.pub/2017/research-debt/ Research Debt 2021-04-29 design/visualization --- https://distill.pub/2018/differentiable-parameterizations/ Differentiable Image Parameterizations 2021-04-29 design/visualization --- https://distill.pub/2019/activation-atlas/ Activation Atlas 2021-04-29 design/visualization --- https://distill.pub/2020/selforg/ Differentiable Self-organizing Systems 2021-04-29 design/visualization --- https://en.wikipedia.org/wiki/Commutative_diagram Commutative diagram 2021-04-29 design/typography design/visualization math --- https://en.wikipedia.org/wiki/Scatter_plot Scatter plot 2021-04-29 design/visualization --- https://en.wikipedia.org/wiki/Mathematical_diagram Mathematical diagram 2021-04-29 design/visualization math --- https://en.wikipedia.org/wiki/Ulam_spiral Ulam spiral 2021-04-30 design/visualization math --- https://en.wikipedia.org/wiki/Control_chart Control chart 2021-04-30 design/visualization statistics/decision --- https://en.wikipedia.org/wiki/Data_and_information_visualization Data visualization 2021-04-30 design/visualization --- https://en.wikipedia.org/wiki/Small_multiple Small multiple 2021-04-30 design/typography design/visualization --- https://en.wikipedia.org/wiki/Sparkline Sparkline 2021-04-30 design/typography design/visualization --- http://www.edwardtufte.com/ 2021-04-30 design/visualization --- https://www.laphamsquarterly.org/roundtable/new-look-same-great-look New Look, Same Great Look 2021-04-30 design/visualization psychology technology --- https://en.wikipedia.org/wiki/Epigenetic_clock Epigenetic clock 2021-04-30 longevity/epigenetics --- /doc/longevity/2019-melzer.pdf The genetics of human ageing David Melzer, Luke C. Pilling, Luigi Ferrucci 2019-11-05 2021-04-30 [("doi","10.1038/s41576-019-0183-6")] genetics/heritable/correlation longevity --- https://en.wikipedia.org/wiki/Reprogramming Reprogramming 2021-04-30 longevity/epigenetics --- https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1824-y DNA methylation aging clocks: challenges and recommendations 2021-04-30 longevity/epigenetics --- https://en.wikipedia.org/wiki/Epigenetics Epigenetics 2021-05-01 longevity/epigenetics --- https://en.wikipedia.org/wiki/DNA_methylation DNA methylation 2021-05-01 longevity/epigenetics --- https://en.wikipedia.org/wiki/Induced_pluripotent_stem_cell Induced pluripotent stem cell 2021-05-01 longevity/epigenetics --- https://en.wikipedia.org/wiki/Shinya_Yamanaka Shinya Yamanaka 2021-05-01 longevity/epigenetics --- https://en.wikipedia.org/wiki/Epigenome_editing Epigenome editing 2021-05-01 longevity/epigenetics --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4015143/ DNA methylation age of human tissues and cell types Steve Horvath 2013 2021-05-01 [("doi","10.1186/gb-2013-14-10-r115")] longevity/epigenetics

Background: It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure.

Results: I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance.

Conclusion: I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.

--- https://horvath.genetics.ucla.edu/html/dnamage/faq.htm Global Biotraits Database 2021-05-01 longevity/epigenetics --- https://www.nature.com/articles/508168a 2021-05-01 longevity/epigenetics --- https://arxiv.org/abs/2105.14039#deepmind Towards mental time travel: a hierarchical memory for reinforcement learning agents Andrew Kyle Lampinen, Stephanie C. Y. Chan, Andrea Banino, Felix Hill 2021-05-28 2021-05-28 [("doi","10.48550/arXiv.2105.14039")] ai/nn/retrieval reinforcement-learning/meta-learning

Reinforcement learning agents often forget details of the past, especially after delays or distractor tasks. Agents with common memory architectures struggle to recall and integrate across multiple timesteps of a past event, or even to recall the details of a single timestep that is followed by distractor tasks. To address these limitations, we propose a Hierarchical Chunk Attention Memory (HCAM), which helps agents to remember the past in detail. HCAM stores memories by dividing the past into chunks, and recalls by first performing high-level attention over coarse summaries of the chunks, and then performing detailed attention within only the most relevant chunks. An agent with HCAM can therefore “mentally time-travel”—remember past events in detail without attending to all intervening events.

We show that agents with HCAM substantially outperform agents with other memory architectures at tasks requiring long-term recall, retention, or reasoning over memory. These include recalling where an object is hidden in a 3D environment, rapidly learning to navigate efficiently in a new neighborhood, and rapidly learning and retaining new object names. Agents with HCAM can extrapolate to task sequences much longer than they were trained on, and can even generalize zero-shot from a meta-learning setting to maintaining knowledge across episodes.

HCAM improves agent sample efficiency, generalization, and generality (by solving tasks that previously required specialized architectures). Our work is a step towards agents that can learn, interact, and adapt in complex and temporally-extended environments.

--- https://plai.cs.ubc.ca/2022/05/20/flexible-diffusion-modeling-of-long-videos/ Flexible Diffusion Modeling of Long Videos 2021-05-01 ai/nn/diffusion ai/video/generation --- https://arxiv.org/abs/1809.07291 Interpretable Textual Neuron Representations for NLP Nina Poerner, Benjamin Roth, Hinrich Schütze 2018-09-19 2021-05-02 [("doi","10.48550/arXiv.1809.07291")] ai/nn

Input optimization methods, such as Google Deep Dream, create interpretable representations of neurons for computer vision DNNs.

We propose and evaluate ways of transferring this technology to NLP. Our results suggest that gradient ascent with a gumbel softmax layer produces n-gram representations that outperform naive corpus search in terms of target neuron activation.

The representations highlight differences in syntax awareness between the language and visual models of the Imaginet architecture.

--- https://arxiv.org/abs/1808.09830 Searching Toward Pareto-Optimal Device-Aware Neural Architectures An-Chieh Cheng, Jin-Dong Dong, Chi-Hung Hsu, Shu-Huan Chang, Min Sun, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan 2018-08-29 2021-05-02 [("doi","10.48550/arXiv.1808.09830")] ai/nn reinforcement-learning/model-free

Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performance in many tasks such as image classification and language understanding.

However, most existing works only optimize for model accuracy and largely ignore other important factors imposed by the underlying hardware and devices, such as latency and energy, when making inference. In this paper, we first introduce the problem of NAS and provide a survey on recent works.

Then we deep dive into two recent advancements on extending NAS into multiple-objective frameworks: MONAS and DPP-Net. Both MONAS and DPP-Net are capable of optimizing accuracy and other objectives imposed by devices, searching for neural architectures that can be best deployed on a wide spectrum of devices: from embedded systems and mobile devices to workstations.

Experimental results are poised to show that architectures found by MONAS and DPP-Net achieve Pareto optimality with respect to the given objectives for various devices.

--- https://arxiv.org/abs/1806.10779 Differentiable Learning-to-Normalize via Switchable Normalization Ping Luo, Jiamin Ren, Zhanglin Peng, Ruimao Zhang, Jingyu Li 2018-06-28 2021-05-02 [("doi","10.48550/arXiv.1806.10779")] ai/nn

We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network. SN employs 3 distinct scopes to compute statistics (means and variances) including a channel, a layer, and a minibatch. SN switches between them by learning their importance weights in an end-to-end manner. It has several good properties. First, it adapts to various network architectures and tasks (see Fig.1).

Second, it is robust to a wide range of batch sizes, maintaining high performance even when small minibatch is presented (eg. 2 images/GPU). Third, SN does not have sensitive hyper-parameter, unlike group normalization that searches the number of groups as a hyper-parameter.

Without bells and whistles, SN outperforms its counterparts on various challenging benchmarks, such as ImageNet, COCO, Cityscapes, ADE20K, and Kinetics. Analyses of SN are also presented.

We hope SN will help ease the usage and understand the normalization techniques in deep learning. The code of SN has been made available in Github.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1690574/pdf/10787154.pdf Intensity of nest defence is related to offspring sex ratio in the great tit Parus major A N. Radford, J. K. Blakey 2000 2021-05-02 [("doi","10.1098/rspb.2000.1033")] psychology/animal

Nest-defense behavior of passerines is a form of parental investment. Parents are selected, therefore, to vary the intensity of their nest defense with respect to the value of their offspring.

Great tit, Parus major, males were tested for their defense response to both a nest predator and playback of a great tit chick distress call. The results from the two trials were similar; males gave more alarm calls and made more perch changes if they had larger broods and if they had a greater proportion of sons in their brood.

This is the first evidence for a relationship between nest-defense intensity and offspring sex ratio. Paternal quality, size, age and condition, lay date, and chick condition did not influence any of the measured nest-defense parameters.

--- https://arxiv.org/abs/quant-ph/9805086 Quantum Effects in Algorithms Richard Jozsa 1998-05-29 2021-05-02 [("doi","10.48550/arXiv.9805086")] cs/algorithm/information science

We discuss some seemingly paradoxical yet valid effects of quantum physics in information processing.

Firstly, we argue that the act of “doing nothing” on part of an entangled quantum system is a highly non-trivial operation and that it is the essential ingredient underlying the computational speedup in the known quantum algorithms.

Secondly, we show that the watched pot effect of quantum measurement theory gives the following novel computational possibility: suppose that we have a quantum computer with an on/off switch, programmed ready to solve a decision problem. Then (in certain circumstances) the mere fact that the computer would have given the answer if it were run, is enough for us to learn the answer, even though the computer is in fact not run.

--- https://arxiv.org/abs/1806.02404 Dissolving the Fermi Paradox Anders Sandberg, Eric Drexler, Toby Ord 2018-06-06 2021-05-02 [("doi","10.48550/arXiv.1806.02404")] existential-risk philosophy

The Fermi paradox is the conflict between an expectation of a high ex ante probability of intelligent life elsewhere in the universe and the apparently lifeless universe we in fact observe. The expectation that the universe should be teeming with intelligent life is linked to models like the Drake equation, which suggest that even if the probability of intelligent life developing at a given site is small, the sheer multitude of possible sites should nonetheless yield a large number of potentially observable civilizations.

We show that this conflict arises from the use of Drake-like equations, which implicitly assume certainty regarding highly uncertain parameters. We examine these parameters, incorporating models of chemical and genetic transitions on paths to the origin of life, and show that extant scientific knowledge corresponds to uncertainties that span multiple orders of magnitude. This makes a stark difference. When the model is recast to represent realistic distributions of uncertainty, we find a substantial ex ante probability of there being no other intelligent life in our observable universe, and thus that there should be little surprise when we fail to detect any signs of it. This result dissolves the Fermi paradox, and in doing so removes any need to invoke speculative mechanisms by which civilizations would inevitably fail to have observable effects upon the universe.

--- https://www.biorxiv.org/content/10.1101/339226.full Genome-wide association study reveals sex-specific genetic architecture of facial attractiveness Bowen Hu, Ning Shen, James J. Li, Hyunseung Kang, Jinkuk Hong, Jason Fletcher, Jan Greenberg, Marsha R. Mailick, Qiongshi Lu 2018-06-06 2021-05-02 [("doi","10.1101/339226")] genetics/heritable/correlation

Facial attractiveness is a complex human trait of great interest in both academia and industry. Literature on sociological and phenotypic factors associated with facial attractiveness is rich, but its genetic basis is poorly understood. In this paper, we conducted a genome-wide association study to discover genetic variants associated with facial attractiveness using 3,928 samples in the Wisconsin Longitudinal Study.

We identified two genome-wide statistically-significant loci and highlighted a handful of candidate genes, many of which are specifically expressed in human tissues involved in reproduction and hormone synthesis. Additionally, facial attractiveness showed strong and negative genetic correlations with BMI in females and with blood lipids in males. Our analysis also suggested sex-specific selection pressure on variants associated with lower male attractiveness.

These results revealed sex-specific genetic architecture of facial attractiveness and provided fundamental new insights into its genetic basis.

--- https://www.biorxiv.org/content/10.1101/433367.full Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions David M. Howard, Mark J. Adams, Toni-Kim Clarke, Jonathan D. Hafferty, Jude Gibson, Masoud Shirali, Jonathan R. I. Coleman, Saskia P. Hagenaars, Joey Ward, Eleanor M. Wigmore, Clara Alloza, Xueyi Shen, Miruna C. Barbu, Eileen Y. Xu, Heather C. Whalley, Riccardo E. Marioni, David J. Porteous, Gail Davies, Ian J. Deary, Gibran Hemani, Klaus Berger, Henning Teismann, Rajesh Rawal, Volker Arolt, Bernhard T. Baune, Udo Dannlowski, Katharina Domschke, Chao Tian, David A. Hinds, 23andMe, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Maciej Trzaskowski, Enda M. Byrne, Stephan Ripke, Daniel J. Smith, Patrick F. Sullivan, Naomi R. Wray, Gerome Breen, Cathryn M. Lewis, Andrew M. McIntosh 2019-01-08 2021-05-02 [("doi","10.1101/433367")] genetics/heritable psychiatry/depression

Major depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia, and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder.

To maximize sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the 3 largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission.

Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities.

These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1717529/pdf/v078p00345.pdf Influence of five years of antenatal screening on the paediatric cystic fibrosis population in one region S Cunningham, T. Marshall 1998 2021-05-02 [("doi","10.1136/adc.78.4.345")] genetics/heritable/rare genetics/selection/artificial

Background: Antenatal screening for cystic fibrosis has been endorsed by the US National Institutes of Health. Edinburgh is the only city in the UK with an established routine antenatal screening programme for cystic fibrosis.

Aims: To report the change in numbers of infants diagnosed with cystic fibrosis born in Edinburgh after the introduction of antenatal screening for the disease.

Population: Infants diagnosed as having cystic fibrosis (by sweat test or genotyping, or both) in the seven years before antenatal testing (1984–90) and the first five years of antenatal testing (1991–95). Children born in this region who had moved before diagnosis were identified from the UK cystic fibrosis survey database.

Results: The incidence of cystic fibrosis decreased from an average of 4.6 to 1.6 children each year with antenatal screening. The reduction in the incidence (65%) was greater than that accounted for by prenatal diagnosis and termination (36%). Of the eight children born with cystic fibrosis during the period of antenatal screening, five had been subject to antenatal screening: 3 had only one mutation identified, one was missed due to a laboratory error, and one was identified as a one in four risk, but prenatal diagnosis was not performed.

Conclusion: Antenatal testing for cystic fibrosis has successfully reduced the incidence of cystic fibrosis in this region. Although the numbers are small, it is possible that the reduction in numbers may have been greater than might be expected from antenatal screening alone.

--- https://www.biorxiv.org/content/10.1101/357483.full Quantification of genetic components of population differentiation in UK Biobank traits reveals signals of polygenic selection Xuanyao Liu, Po-Ru Loh, Luke J. O’Connor, Steven Gazal, Armin Schoech, Robert M. Maier, Nick Patterson, Alkes Price 2018-06-27 2021-05-02 [("doi","10.1101/357483")] genetics/selection/natural/human

The genetic architecture of most human complex traits is highly polygenic, motivating efforts to detect polygenic selection involving a large number of loci. In contrast to previous work relying on top GWAS loci, we developed a method that uses genome-wide association statistics and linkage disequilibrium patterns to estimate the genome-wide genetic component of population differentiation of a complex trait along a continuous gradient, enabling powerful inference of polygenic selection.

We analyzed 43 UK Biobank traits and focused on PC1 and North-South and East-West birth coordinates across 337K unrelated British-ancestry samples, for which our method produced close to unbiased estimates of genetic components of population differentiation and high power to detect polygenic selection in simulations across different trait architectures.

For PC1, we identified signals of polygenic selection for height (74.5±16.7% of 9.3% total correlation with PC1 attributable to genome-wide genetic effects; p = 8.4×10−6) and red hair pigmentation (95.9±24.7% of total correlation with PC1 attributable to genome-wide genetic effects; p = 1.1×10−4); the bulk of the signal remained when removing genome-wide statistically-significant loci, even though red hair pigmentation includes loci of large effect. We also detected polygenic selection for height, systolic blood pressure, BMI and basal metabolic rate along North-South birth coordinate, and height and systolic blood pressure along East-West birth coordinate.

Our method detects polygenic selection in modern human populations with very subtle population structure and elucidates the relative contributions of genetic and non-genetic components of trait population differences.

--- https://www.biorxiv.org/content/10.1101/350231.full Re-identification of genomic data using long range familial searches Yaniv Erlich, Tal Shor, Shai Carmi, Itsik Pe’er 2018-06-19 2021-05-02 [("doi","10.1101/350231")] genetics/sequencing

Consumer genomics databases reached the scale of millions of individuals. Recently, law enforcement investigators have started to exploit some of these databases to find distant familial relatives, which can lead to a complete re-identification.

Here, we leveraged genomic data of 600,000 individuals tested with consumer genomics to investigate the power of such long-range familial searches. We project that half of the searches with European-descent individuals will result with a third cousin or closer match and will provide a search space small enough to permit re-identification using common demographic identifiers.

Moreover, in the near future, virtually any European-descent US person could be implicated by this technique.

We propose a potential mitigation strategy based on cryptographic signature that can resolve the issue and discuss policy implications to human subject research.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2014372/ An oral formulation of nicotine for release and absorption in the colon: its development and pharmacokinetics J T. Green, B. K. Evans, J. Rhodes, G. A. Thomas, C. Ranshaw, C. Feyerabend, M. A. Russell 1999 2021-05-03 [("doi","10.1046/j.1365-2125.1999.00057.x")] nicotine

Aims: Ulcerative colitis is predominantly a disease of nonsmokers and transdermal nicotine has therapeutic value in active disease; however side-effects are troublesome. The aim of this study was to develop an oral formulation of nicotine which would be slowly released in the colon over 6 h, and to examine its pharmacokinetic profile in 12 healthy volunteers, with measurements of serum nicotine and cotinine, its principal metabolite.

Method: Nicotine was combined with a polyacrylic carbomer, Carbopol 974P which was incorporated into 13 different vehicles and their release profiles examined in vitro. The polyglycolized glyceride, Gelucire 50/13, was chosen for subsequent kinetic studies because it consistently produced a suitable release pattern which was linear. Capsules containing 3 mg nicotine, combined with carbomer in Gelucire 50/13, were coated with an acrylic resin Eudragit L; this ensured the capsule would remain intact until the ileum. On 2 separate days, 6 and 15 mg nicotine, contained in 2 and 5 capsules, respectively, were administered to 12 subjects, all nonsmokers, mean age 28 years. Serial blood measurements were taken for 36 h, serum nicotine and cotinine concentrations were measured by gas liquid chromatography.

Results: There was considerable intersubject variability in the nicotine and cotinine values. Plasma nicotine levels began to rise about 4 h after ingestion of the capsules, corresponding with the oro-caecal transit time. Cmax nicotine values were 2.2 and 5 ng ml−1, obtained 7 h after the ingestion of 6 and 15 mg, respectively, of the formulation. The corresponding Cmax values for cotinine were 37 and 94.4 ng ml−1, occurring after 9–10 h. The mean for elimination half-lives in the 24 studies, including the 6 and 15 mg doses, for nicotine were 4.3±2.7 h and for cotinine 16.8±7.5 h. With 6 mg nicotine-carbomer, only 1⁄12 volunteers had possible side-effects, but with the 15 mg dose 11 out of the 12 reported adverse effects which were systemic or gastrointestinal in nature-their timing corresponded with peak serum concentrations of nicotine.

Conclusion: An oral formulation of nicotine has been developed; in the ileum and colon, this becomes available for slow linear release over 6 h and delivers high concentrations of nicotine for topical effect on the colon. 6 mg Nicotine was well tolerated, whilst 15 mg gave both systemic and gastrointestinal side-effects. High concentrations of topical nicotine in the colon are achieved with relatively low systemic bioavailablity-reflected by the Cmax and AUC values for nicotine. This, or comparable formulations, may be of therapeutic value in ulcerative colitis.

--- https://arxiv.org/abs/1808.07913 Improving Abstraction in Text Summarization Wojciech Kryściński, Romain Paulus, Caiming Xiong, Richard Socher 2018-08-23 2021-05-03 [("doi","10.48550/arXiv.1808.07913")] reinforcement-learning/model-free

Abstractive text summarization aims to shorten long text documents into a human readable form that contains the most important facts from the original document. However, the level of actual abstraction as measured by novel phrases that do not appear in the source document remains low in existing approaches.

We propose two techniques to improve the level of abstraction of generated summaries. First, we decompose the decoder into a contextual network that retrieves relevant parts of the source document, and a pretrained language model that incorporates prior knowledge about language generation. Second, we propose a novelty metric that is optimized directly through policy learning to encourage the generation of novel phrases.

Our model achieves results comparable to state-of-the-art models, as determined by ROUGE scores and human evaluations, while achieving a higher level of abstraction as measured by n-gram overlap with the source document.

--- https://arxiv.org/abs/1806.07912 Resource-Efficient Neural Architect Yanqi Zhou, Siavash Ebrahimi, Sercan Ö. Arık, Haonan Yu, Hairong Liu, Greg Diamos 2018-06-12 2021-05-03 [("doi","10.48550/arXiv.1806.07912")] ai/nn/cnn reinforcement-learning/model-free

Neural Architecture Search (NAS) is a laborious process. Prior work on automated NAS targets mainly on improving accuracy, but lacks consideration of computational resource use. We propose the Resource-Efficient Neural Architect (RENA), an efficient resource-constrained NAS using reinforcement learning with network embedding.

RENA uses a policy network to process the network embeddings to generate new configurations. We demonstrate RENA on image recognition and keyword spotting (KWS) problems.

RENA can find novel architectures that achieve high performance even with tight resource constraints. For CIFAR-10, it achieves 2.95% test error when compute intensity is greater than 100 FLOPs/byte, and 3.87% test error when model size is less than 3M parameters. For Google Speech Commands Dataset, RENA achieves the state-of-the-art accuracy without resource constraints, and it outperforms the optimized architectures with tight resource constraints.

--- https://arxiv.org/abs/cond-mat/9804163 How Popular is Your Paper? An Empirical Study of the Citation Distribution S. Redner 1998-04-15 2021-05-03 [("doi","10.1007/s100510050359")] science

Numerical data for the distribution of citations are examined for: (1) papers published in 1981 in journals which are catalogued by the Institute for Scientific Information (783,339 papers) and (2) 20 years of publications in Physical Review D, vols. 11–50 (n = 24,296 papers).

A Zipf plot of the number of citations to a given paper versus its citation rank appears to be consistent with a power-law dependence for leading rank papers, with exponent close to −1⁄2.

This, in turn, suggests that the number of papers with x citations, N(x), has a large-x power law decay N(x)−α, with α ~ 3.

--- https://arxiv.org/abs/1806.00451 Do CIFAR-10 Classifiers Generalize to CIFAR-10? Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar 2018-06-01 2021-05-03 [("doi","10.48550/arXiv.1806.00451")] ai/nn ai/scaling

Machine learning is currently dominated by largely experimental work focused on improvements in a few key tasks.

However, the impressive accuracy numbers of the best-performing models are questionable because the same test sets have been used to select these models for multiple years now.

To understand the danger of overfitting, we measure the accuracy of CIFAR-10 classifiers by creating a new test set of truly unseen images. Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models.

Yet more recent models with higher original accuracy show a smaller drop and better overall performance, indicating that this drop is likely not due to overfitting based on adaptivity.

Instead, we view our results as evidence that current accuracy numbers are brittle and susceptible to even minute natural variations in the data distribution.

--- https://arxiv.org/abs/1805.10338 Zero-Shot Dual Machine Translation Lierni Sestorain, Massimiliano Ciaramita, Christian Buck, Thomas Hofmann 2018-05-25 2021-05-03 [("doi","10.48550/arXiv.1805.10338")] ai/nn

Neural Machine Translation (NMT) systems rely on large amounts of parallel data. This is a major challenge for low-resource languages. Building on recent work on unsupervised and semi-supervised methods, we present an approach that combines zero-shot and dual learning. The latter relies on reinforcement learning, to exploit the duality of the machine translation task, and requires only monolingual data for the target language pair.

Experiments show that a zero-shot dual system, trained on English-French and English-Spanish, outperforms by large margins a standard NMT system in zero-shot translation performance on Spanish-French (both directions). The zero-shot dual method approaches the performance, within 2.2 BLEU points, of a comparable supervised setting.

Our method can obtain improvements also on the setting where a small amount of parallel data for the zero-shot language pair is available. Adding Russian, to extend our experiments to jointly modeling 6 zero-shot translation directions, all directions improve 4–15 BLEU points, again, reaching performance near that of the supervised setting.

--- https://arxiv.org/abs/1805.09501#google AutoAugment: Learning Augmentation Policies from Data Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le 2018-05-24 2021-05-03 [("doi","10.48550/arXiv.1805.09501")] ai/nn reinforcement-learning/model-free

Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed.

In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset.

Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art.

Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve improvements on other datasets, such as Oxford Flowers, Caltech 101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars.

--- https://arxiv.org/abs/1804.07461 GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman 2018-04-20 2021-05-03 [("doi","10.48550/arXiv.1804.07461")] ai/dataset ai/nn

For natural language understanding (NLU) technology to be maximally useful, both practically and as a scientific object of study, it must be general: it must be able to process language in a way that is not exclusively tailored to any one specific task or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation benchmark (GLUE), a tool for evaluating and analyzing the performance of models across a diverse range of existing NLU tasks. GLUE is model-agnostic, but it incentivizes sharing knowledge across tasks because certain tasks have very limited training data.

We further provide a hand-crafted diagnostic test suite that enables detailed linguistic analysis of NLU models.

We evaluate baselines based on current methods for multi-task and transfer learning and find that they do not immediately give substantial improvements over the aggregate performance of training a separate model per task, indicating room for improvement in developing general and robust NLU systems.

--- https://arxiv.org/abs/1804.04235#google Adafactor: Adaptive Learning Rates with Sublinear Memory Cost Noam Shazeer, Mitchell Stern 2018-04-11 2021-05-03 [("doi","10.48550/arXiv.1804.04235")] ai/nn

In several recently proposed stochastic optimization methods (eg. RMSProp, Adam, Adadelta), parameter updates are scaled by the inverse square roots of exponential moving averages of squared past gradients. Maintaining these per-parameter second-moment estimators requires memory equal to the number of parameters.

For the case of neural network weight matrices, we propose maintaining only the per-row and per-column sums of these moving averages, and estimating the per-parameter second moments based on these sums. We demonstrate empirically that this method produces similar results to the baseline. Secondly, we show that adaptive methods can produce larger-than-desired updates when the decay rate of the second moment accumulator is too slow. We propose update clipping and a gradually increasing decay rate scheme as remedies.

Combining these methods and dropping momentum, we achieve comparable results to the published Adam regime in training the Transformer model on the WMT 2014 English-German machine translation task, while using very little auxiliary storage in the optimizer.

Finally, we propose scaling the parameter updates based on the scale of the parameters themselves.

--- https://arxiv.org/abs/1804.02767 YOLOv3: An Incremental Improvement Joseph Redmon, Ali Farhadi 2018-04-08 2021-05-03 [("doi","10.48550/arXiv.1804.02767")] ai/nn/cnn

[cf. YOLOv2, YOLOv4, YOLOv5] We present some updates to YOLOv1!

We made a bunch of little design changes to make it better. We also trained this new network that’s pretty swell. It’s a little bigger than last time but more accurate. It’s still fast though, don’t worry.

At 320×320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but 3 times faster. When we look at the old 0.5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Nvidia Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8× faster.

As always, all the code is online at https://pjreddie.com/darknet/yolo/.

--- https://arxiv.org/abs/1803.05407 Averaging Weights Leads to Wider Optima and Better Generalization Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson 2018-03-14 2021-05-03 [("doi","10.48550/arXiv.1803.05407")] ai/nn

Deep neural networks are typically trained by optimizing a loss function with an SGD variant, in conjunction with a decaying learning rate, until convergence. We show that simple averaging of multiple points along the trajectory of SGD, with a cyclical or constant learning rate, leads to better generalization than conventional training.

We also show that this Stochastic Weight Averaging (SWA) procedure finds much flatter solutions than SGD, and approximates the recent Fast Geometric Ensembling (FGE) approach with a single model.

Using SWA we achieve notable improvement in test accuracy over conventional SGD training on a range of state-of-the-art residual networks, PyramidNets, DenseNets, and Shake-Shake networks on CIFAR-10, CIFAR-100, and ImageNet.

In short, SWA is extremely easy to implement, improves generalization, and has almost no computational overhead.

--- https://arxiv.org/abs/1805.08166 Learning to Optimize Tensor Programs Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy 2018-05-21 2021-05-04 [("doi","10.48550/arXiv.1805.08166")] cs/algorithm reinforcement-learning/model

We introduce a learning-based framework to optimize tensor programs for deep learning workloads. Efficient implementations of tensor operators, such as matrix multiplication and high dimensional convolution, are key enablers of effective deep learning systems. However, existing systems rely on manually optimized libraries such as cuDNN where only a narrow range of server class GPUs are well-supported. The reliance on hardware-specific operator libraries limits the applicability of high-level graph optimizations and incurs engineering costs when deploying to new hardware targets.

We use learning to remove this engineering burden. We learn domain-specific statistical cost models to guide the search of tensor operator implementations over billions of possible program variants. We further accelerate the search by effective model transfer across workloads. Experimental results show that our framework delivers performance competitive with state-of-the-art hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPU.

--- https://www.biorxiv.org/content/10.1101/311332.full The landscape of pervasive horizontal pleiotropy in human genetic variation is driven by extreme polygenicity of human traits and diseases Daniel M. Jordan, Marie Verbanck, Ron Do 2018-04-30 2021-05-04 [("doi","10.1101/311332")] genetics/heritable/correlation/mendelian-randomization

Understanding the nature and extent of horizontal pleiotropy, where one genetic variant has independent effects on multiple observable traits, is vitally important for our understanding of the genetic architecture of human phenotypes, as well as the design of genome-wide association studies (GWASs) and Mendelian Randomization (MR) studies. Many recent studies have pointed to the existence of horizontal pleiotropy among human phenotypes, but the exact extent remains unknown, largely due to difficulty in disentangling the inherently correlated nature of observable traits.

Here, we present a statistical framework to isolate and quantify horizontal pleiotropy in human genetic variation using a two-component pleiotropy score computed from summary statistic data derived from published GWASs. This score uses a statistical whitening procedure to remove correlations between observable traits and normalize effect sizes across all traits, and is able to detect horizontal pleiotropy under a range of different models in our simulations.

When applied to real human phenotype data using association statistics for 1,564 traits measured in 337,119 individuals from the UK Biobank, our score detects a statistically-significant excess of horizontal pleiotropy. This signal of horizontal pleiotropy is pervasive throughout the human genome and across a wide range of phenotypes and biological functions, but is especially prominent in regions of high linkage disequilibrium and among phenotypes known to be highly polygenic and heterogeneous. Using our pleiotropy score, we identify thousands of loci with extreme levels of horizontal pleiotropy, a majority of which have never been previously reported in any published GWAS. This highlights an under-recognized class of genetic variation that has weak effects on many distinct phenotypes but no specific marked effect on any one phenotype.

We show that a large fraction of these loci replicate using independent datasets of GWAS summary statistics. Our results highlight the central role horizontal pleiotropy plays in the genetic architecture of human phenotypes, and the importance of modeling horizontal pleiotropy in genomic medicine.

--- https://www.biorxiv.org/content/10.1101/305029.full Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits Andrew D. Grotzinger, Mijke Rhemtulla, Ronald de Vlaming, Stuart J. Ritchie, Travis T. Mallard, W. David Hill, Hill F. Ip, Andrew M. McIntosh, Ian J. Deary, Philipp Koellinger, K. Paige Harden, Michel G. Nivard, Elliot M. Tucker-Drob 2018-04-21 2021-05-04 [("doi","10.1101/305029")] genetics/heritable/correlation psychiatry

Methods for using GWAS to estimate genetic correlations between pairwise combinations of traits have produced “atlases” of genetic architecture. Genetic atlases reveal pervasive pleiotropy, and genome-wide statistically-significant loci are often shared across different phenotypes.

We introduce genomic structural equation modeling (Genomic SEM), a multivariate method for analyzing the joint genetic architectures of complex traits. Using formal methods for modeling covariance structure, Genomic SEM synthesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to identify variants with effects on general dimensions of cross-trait liability, boost power for discovery, and calculate more predictive polygenic scores. Finally, Genomic SEM can be used to identify loci that cause divergence between traits, aiding the search for what uniquely differentiates highly correlated phenotypes.

We demonstrate several applications of Genomic SEM, including a joint analysis of GWAS summary statistics from 5 genetically correlated psychiatric traits. We identify 27 independent SNPs not previously identified in the univariate GWASs, 5 of which have been reported in other published GWASs of the included traits. Polygenic scores derived from Genomic SEM consistently outperform polygenic scores derived from GWASs of the individual traits.

Genomic SEM is flexible, open ended, and allows for continuous innovations in how multivariate genetic architecture is modeled.

--- https://www.biorxiv.org/content/10.1101/291062.full Comparison of Genotypic and Phenotypic Correlations: Cheverud’s Conjecture in Humans Sebastian M. Sodini, Kathryn E. Kemper, Naomi R. Wray, Maciej Trzaskowski 2018-03-30 2021-05-04 [("doi","10.1101/291062")] genetics/heritable/correlation

Accurate estimation of genetic correlation requires large sample sizes and access to genetically informative data, which are not always available. Accordingly, phenotypic correlations are often assumed to reflect genotypic correlations in evolutionary biology. Cheverud’s conjecture asserts that the use of phenotypic correlations as proxies for genetic correlations is appropriate. Empirical evidence of the conjecture has been found across plant and animal species, with results suggesting that there is indeed a robust relationship between the two.

Here, we investigate the conjecture in human populations, an analysis made possible by recent developments in availability of human genomic data and computing resources. A sample of 108,035 British European individuals from the UK Biobank was split equally into discovery and replication datasets. 17 traits were selected based on sample size, distribution and heritability. Genetic correlations were calculated using linkage disequilibrium score regression applied to the genome-wide association summary statistics of pairs of traits, and compared within and across datasets. Strong and statistically-significant correlations were found for the between-dataset comparison, suggesting that the genetic correlations from one independent sample were able to predict the phenotypic correlations from another independent sample within the same population. Designating the selected traits as morphological or non-morphological indicated little difference in correlation.

The results of this study support the existence of a relationship between genetic and phenotypic correlations in humans. This finding is of specific interest in anthropological studies, which use measured phenotypic correlations to make inferences about the genetics of ancient human populations.

--- https://www.biorxiv.org/content/10.1101/283036.full A genetic perspective on the relationship between eudaimonic and hedonic well-being B. M. L. Baselmans, M. Bartels 2018-03-15 2021-05-04 [("doi","10.1101/283036")] genetics/heritable/correlation/mendelian-randomization psychology/personality

Whether hedonism or eudaimonism are two distinguishable forms of well-being is a topic of ongoing debate.

To shed light on the relation between the two, large-scale available molecular genetic data were leveraged to gain more insight into the genetic architecture of the overlap between hedonic and eudaimonic well-being. Hence, we conducted the first genome-wide association studies (GWAS) of eudaimonic well-being (n ≈ 108k) and linked it to a GWAS of hedonic well-being (n = ~222k).

We identified the first 2 genome-wide statistically-significant independent loci for eudaimonic well-being and 6 independent loci for hedonic well-being. Joint analyses revealed a moderate phenotypic correlation (r = 0.53), but a high genetic correlation (rg = 0.78) between eudaimonic and hedonic well-being. For both traits we identified enrichment in the frontal cortex and cingulate cortex as well as the cerebellum to be top ranked. Bi-directional Mendelian Randomization analyses using two-sample MR indicated some evidence for a causal relationship from hedonic well-being to eudaimonic well-being whereas no evidence was found for the reverse. Additionally, genetic correlations patterns with a range of positive and negative related phenotypes were largely similar for hedonic and eudaimonic well-being.

Our results reveal a large genetic overlap between hedonism and eudaimonism.

--- https://www.biorxiv.org/content/10.1101/320267.full The genetic architecture of hair color in the UK population Michael D. Morgan, Erola Pairo-Castineira, Konrad Rawlik, Oriol Canela-Xandri, Jonathan Rees, David Sims, Albert Tenesa, Ian J. Jackson 2018-05-11 2021-05-04 [("doi","10.1101/320267")] genetics/heritable

We have extensively mapped the genes responsible for hair color in the UK population.

MC1R mutations are well established as the principal genetic cause of red hair color, but with variable penetrance. We find variation at genes encoding its agonist (POMC), inverse agonist (ASIP) and other loci contribute to red hair and demonstrate epistasis between MC1R and some of these loci. Blonde hair is associated with over 200 loci, and we find a genetic continuum from black through dark and light brown to blonde.

Many of the associated genes are involved in hair growth or texture, emphasising the cellular connections between keratinocytes and melanocytes in the determination of hair color.

--- https://www.biorxiv.org/content/10.1101/274654.full Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry Loïc Yengo, Julia Sidorenko, Kathryn E. Kemper, Zhili Zheng, Andrew R. Wood, Michael N. Weedon, Timothy Frayling, Joel Hirschhorn, Jian Yang, Peter M. Visscher, Giant Consortium 2018-03-22 2021-05-04 [("doi","10.1101/274654")] genetics/heritable/correlation/mendelian-randomization

Genome-wide association studies (GWAS) stand as powerful experimental designs for identifying DNA variants associated with complex traits and diseases. In the past decade, both the number of such studies and their sample sizes have increased dramatically. Recent GWAS of height and body mass index (BMI) in ~250,000 European participants have led to the discovery of ~700 and ~100 nearly independent SNPs associated with these traits, respectively.

Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ~450,000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches n = 700,000 individuals and:

substantially increases the number of GWAS signals associated with these traits. We identified 3,290 and 716 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide statistical-significance threshold of p<1 × 10−8), including 1,185 height-associated SNPs and 554 BMI-associated SNPs located within loci not previously identified by these two GWAS. The genome-wide statistically-significant SNPs explain ~24.6% of the variance of height and ~5% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS).

Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were 0.44 and 0.20, respectively. From analyses of integrating GWAS and eQTL data by Summary-data based Mendelian Randomization (SMR), we identified an enrichment of eQTLs amongst lead height and BMI signals, prioritisting 684 and 134 genes, respectively.

Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow up studies.

--- https://www.biorxiv.org/content/10.1101/284976.full Better estimation of SNP heritability from summary statistics provides a new understanding of the genetic architecture of complex traits Doug Speed, David J. Balding 2018-03-19 2021-05-04 [("doi","10.1101/284976")] genetics/heritable

LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories.

Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121,000).

First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide statistically-significant loci has under-reported by about 20%. Next we estimate enrichment for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13× enriched, and found a further twelve categories with above 2× enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6× (SD 0.06) enriched, and that no category has enrichment above 1.7×.

SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

--- https://www.biorxiv.org/content/10.1101/303008.full Analysis of the genetic basis of height in large Jewish nuclear families Danny Zeevi, Joshua S. Bloom, Meru J. Sadhu, Adi Ben Yehuda, David Zangen, Ephra Levy-Lahad, Leonid Kruglyak 2018-04-17 2021-05-04 [("doi","10.1101/303008")] genetics/heritable/rare

Despite intensive study, most genetic factors that contribute to variation in human height remain undiscovered.

We conducted a family-based linkage study of height in a unique cohort of very large nuclear families from a founder (Jewish) population. This design allowed for increased power to detect linkage compared to previous family-based studies.

We identified loci that together explain an estimated 6% of the variance in height. We showed that these loci are not tagging known common variants associated with height.

Rather, we suggest that the observed signals arise from variants with large effects that are rare globally but elevated in frequency in the Jewish population.

--- https://www.biorxiv.org/content/10.1101/300574.full Polygenic adaptation and convergent evolution across both growth and cardiac genetic pathways in African and Asian rainforest hunter-gatherers Christina M. Bergey, Marie Lopez, Genelle F. Harrison, Etienne Patin, Jacob Cohen, Lluis Quintana-Murci, Luis Barreiro, George H. Perry 2018-04-13 2021-05-04 [("doi","10.1101/300574")] genetics/selection/natural/human

Different human populations facing similar environmental challenges have sometimes evolved convergent biological adaptations, for example hypoxia resistance at high altitudes and depigmented skin in northern latitudes on separate continents. The pygmy phenotype (small adult body size), a characteristic of hunter-gatherer populations inhabiting both African and Asian tropical rainforests, is often highlighted as another case of convergent adaptation in humans. However, the degree to which phenotypic convergence in this polygenic trait is due to convergent vs. population-specific genetic changes is unknown.

To address this question, we analyzed high-coverage sequence data from the protein-coding portion of the genomes (exomes) of two pairs of populations, Batwa rainforest hunter-gatherers and neighboring Bakiga agriculturalists from Uganda, and Andamanese rainforest hunter-gatherers (Jarawa and Onge) and Brahmin agriculturalists from India. We observed signatures of convergent positive selection between the Batwa and Andamanese rainforest hunter-gatherers across the set of genes with annotated ‘growth factor binding’ functions (p< 0.001).

Unexpectedly, for the rainforest groups we also observed convergent and population-specific signatures of positive selection in pathways related to cardiac development (eg. ‘cardiac muscle tissue development’; p = 0.003). We hypothesize that the growth hormone sub-responsiveness likely underlying the pygmy phenotype may have led to compensatory changes in cardiac pathways, in which this hormone also plays an essential role. Importantly, we did not observe similar patterns of positive selection on sets of genes associated with either growth or cardiac development in the agriculturalist populations, indicating that our results most likely reflect a history of convergent adaptation to the similar ecology of rainforest hunter-gatherers rather than a more common or general evolutionary pattern for human populations.

--- https://www.biorxiv.org/content/10.1101/307058.full Evidence of a nonadaptive buildup of mutational load in human populations over the past 40,000 years Stéphane Aris-Brosou 2018-04-25 2021-05-05 [("doi","10.1101/307058")] genetics/selection/natural/human/dysgenics

The role played by natural selection in shaping present-day human populations has received extensive scrutiny, especially in the context of local adaptations. However, most studies to date assume, either explicitly or not, that populations have been in their current locations long enough to adapt to local conditions, and that population sizes were large enough to allow for the action of selection. If these conditions were satisfied, not only should selection be effective at promoting local adaptations, but deleterious alleles should also be eliminated over time.

To assess this prediction, the genomes of 2,062 individuals, including 1,179 ancient humans, were reanalyzed to reconstruct how frequencies of risk alleles and their homozygosity changed through space and time in Europe. While the overall deleterious homozygosity consistently decreased through space and time, risk alleles have shown a steady increase in frequency. Even the mutations that are predicted to be most deleterious fail to exhibit any decrease in frequency.

These conclusions do not deny the existence of local adaptations, but highlight the limitations imposed by drift and range expansions on the strength of selection in purging the mutational load affecting human populations.

--- https://arxiv.org/abs/1805.12114 Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models Kurtl, Chua, Roberto Calandra, Rowan McAllister, Sergey Levine 2018-05-30 2021-05-05 [("doi","10.48550/arXiv.1805.12114")] reinforcement-learning/model

Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance. This is especially true with high-capacity parametric function approximators, such as deep networks.

In this paper, we study how to bridge this gap, by employing uncertainty-aware dynamics models. We propose a new algorithm called probabilistic ensembles with trajectory sampling (PETS) that combines uncertainty-aware deep network dynamics models with sampling-based uncertainty propagation.

Our comparison to state-of-the-art model-based and model-free deep RL algorithms shows that our approach matches the asymptotic performance of model-free algorithms on several challenging benchmark tasks, while requiring fewer samples (eg. 8 and 125× fewer samples than Soft Actor Critic and Proximal Policy Optimization respectively on the half-cheetah task).

--- https://arxiv.org/abs/1805.12244 Mining gold from implicit models to improve likelihood-free inference Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer 2018-05-30 2021-05-05 [("doi","10.1073/pnas.1915980117")] reinforcement-learning/model statistics/bayes

Simulators often provide the best description of real-world phenomena. However, they also lead to challenging inverse problems because the density they implicitly define is often intractable.

We present a new suite of simulation-based inference techniques that go beyond the traditional Approximate Bayesian Computation approach, which struggles in a high-dimensional setting, and extend methods that use surrogate models based on neural networks.

We show that additional information, such as the joint likelihood ratio and the joint score, can often be extracted from simulators and used to augment the training data for these surrogate models.

Finally, we demonstrate that these new techniques are more sample efficient and provide higher-fidelity inference than traditional methods.

--- https://arxiv.org/abs/1804.09028#ibm Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications Guy Hadash, Einat Kermany, Boaz Carmeli, Ofer Lavi, George Kour, Alon Jacovi 2018-04-24 2021-05-05 [("doi","10.48550/arXiv.1804.09028")] reinforcement-learning/model reinforcement-learning/robot

Existing applications include a huge amount of knowledge that is out of reach for deep neural networks. This paper presents a novel approach for integrating calls to existing applications into deep learning architectures.

Using this approach, we estimate each application’s functionality with an estimator, which is implemented as a deep neural network (DNN). The estimator is then embedded into a base network that we direct into complying with the application’s interface during an end-to-end optimization process. At inference time, we replace each estimator with its existing application counterpart and let the base network solve the task by interacting with the existing application.

Using this Estimate and Replace method, we were able to train a DNN end-to-end with less data and outperformed a matching DNN that did not interact with the external application.

--- https://arxiv.org/abs/1804.08617#deepmind DP4G: Distributed Distributional Deterministic Policy Gradients Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy Lillicrap 2018-04-23 2021-05-05 [("doi","10.48550/arXiv.1804.08617")] reinforcement-learning/model-free

This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting. We combine this within a distributed framework for off-policy learning in order to develop what we call the Distributed Distributional Deep Deterministic Policy Gradient algorithm, D4PG.

We also combine this technique with a number of additional, simple improvements such as the use of N-step returns and prioritized experience replay.

Experimentally we examine the contribution of each of these individual components, and show how they interact, as well as their combined contributions.

Our results show that across a wide variety of simple control tasks, difficult manipulation tasks, and a set of hard obstacle-based locomotion tasks the D4PG algorithm achieves state-of-the-art performance.

--- https://arxiv.org/abs/1804.04410 Optimizing Query Evaluations using Reinforcement Learning for Web Search Corby Rosset, Damien Jose, Gargi Ghosh, Bhaskar Mitra, Saurabh Tiwary 2018-04-12 2021-05-05 [("doi","10.48550/arXiv.1804.04410")] cs/algorithm reinforcement-learning/model-free

In web search, typically a candidate generation step selects a small set of documents—from collections containing as many as billions of web pages—that are subsequently ranked and pruned before being presented to the user.

In Bing, the candidate generation involves scanning the index using statically designed match plans that prescribe sequences of different match criteria and stopping conditions.

In this work, we pose match planning as a reinforcement learning task and observe up to 20% reduction in index blocks accessed, with small or no degradation in the quality of the candidate sets.

--- https://arxiv.org/abs/1804.09081 Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution Thomas Elsken, Jan Hendrik Metzen, Frank Hutter 2018-04-24 2021-05-05 [("doi","10.48550/arXiv.1804.09081")] reinforcement-learning/exploration

Neural Architecture Search aims at automatically finding neural architectures that are competitive with architectures designed by human experts. While recent approaches have achieved state-of-the-art predictive performance for image recognition, they are problematic under resource constraints for two reasons: (1) the neural architectures found are solely optimized for high predictive performance, without penalizing excessive resource consumption, (2) most architecture search methods require vast computational resources.

We address the first shortcoming by proposing LEMONADE, an evolutionary algorithm for multi-objective architecture search that allows approximating the entire Pareto-front of architectures under multiple objectives, such as predictive performance and number of parameters, in a single run of the method. We address the second shortcoming by proposing a Lamarckian inheritance mechanism for LEMONADE which generates children networks that are warmstarted with the predictive performance of their trained parents. This is accomplished by using (approximate) network morphism operators for generating children.

The combination of these two contributions allows finding models that are on par or even outperform both hand-crafted as well as automatically-designed networks.

--- https://arxiv.org/abs/1804.00168#deepmind Learning to Navigate in Cities Without a Map Piotr Mirowski, Matthew Koichi Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson, Denis Teplyashin, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman, Raia Hadsell 2018-03-31 2021-05-05 [("doi","10.48550/arXiv.1804.00168")] reinforcement-learning/exploration

Navigating through unstructured environments is a basic capability of intelligent creatures, and thus is of fundamental interest in the study and development of artificial intelligence. Long-range navigation is a complex cognitive task that relies on developing an internal representation of space, grounded by recognizable landmarks and robust visual processing, that can simultaneously support continuous self-localization (“I am here”) and a representation of the goal (“I am going there”).

Building upon recent research that applies deep reinforcement learning to maze navigation problems, we present an end-to-end deep reinforcement learning approach that can be applied on a city scale. Recognizing that successful navigation relies on integration of general policies with locale-specific knowledge, we propose a dual pathway architecture that allows locale-specific features to be encapsulated, while still enabling transfer to multiple cities.

We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometers away.

The project webpage http://streetlearn.cc/ contains a video summarizing our research and showing the trained agent in diverse city environments and on the transfer task, the form to request the StreetLearn dataset, and links to further resources. The StreetLearn environment code is available at https://github.com/google-deepmind/streetlearn.

--- https://arxiv.org/abs/1802.08216 ChatPainter: Improving Text to Image Generation using Dialogue Shikhar Sharma, Dendi Suhubdy, Vincent Michalski, Samira Ebrahimi Kahou, Yoshua Bengio 2018-02-22 2021-05-05 [("doi","10.48550/arXiv.1802.08216")] ai/nn/gan

Synthesizing realistic images from text descriptions on a dataset like Microsoft Common Objects in Context (MS COCO), where each image can contain several objects, is a challenging task.

Prior work has used text captions to generate images. However, captions might not be informative enough to capture the entire image and insufficient for the model to be able to understand which objects in the images correspond to which words in the captions.

We show that adding a dialogue that further describes the scene leads to improvement in the inception score and in the quality of generated images on the MS COCO dataset.

--- https://arxiv.org/abs/1802.05751#google Image Transformer Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Łukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran 2018-02-15 2021-05-05 [("doi","10.48550/arXiv.1802.05751")] ai/nn/transformer/attention/sparsity ai/nn/transformer/gpt/dall-e/1

Image generation has been successfully cast as an autoregressive sequence generation or transformation problem. Recent work has shown that self-attention is an effective way of modeling textual sequences. In this work, we generalize a recently proposed model architecture based on self-attention, the Transformer, to a sequence modeling formulation of image generation with a tractable likelihood.

By restricting the self-attention mechanism to attend to local neighborhoods we increase the size of images the model can process in practice, despite maintaining larger receptive fields per layer than typical convolutional neural networks.

While conceptually simple, our generative models outperform the current state-of-the-art in image generation on ImageNet, improving the best published negative log-likelihood on ImageNet 3.83 → 3.77. We also present results on image super-resolution with a large magnification ratio, applying an encoder-decoder configuration of our architecture. In a human evaluation study, we find that images generated by our super-resolution model fool human observers 3× more often than the previous state-of-the-art.

--- https://arxiv.org/abs/1803.00047#facebook Analyzing Uncertainty in Neural Machine Translation Myle Ott, Michael Auli, David Grangier, Marc’Aurelio Ranzato 2018-02-28 2021-05-05 [("doi","10.48550/arXiv.1803.00047")] ai/nn

Machine translation is a popular test bed for research in neural sequence-to-sequence models but despite much recent research, there is still a lack of understanding of these models. Practitioners report performance degradation with large beams, the under-estimation of rare words, and a lack of diversity in the final translations. Our study relates some of these issues to the inherent uncertainty of the task, due to the existence of multiple valid translations for a single source sentence, and to the extrinsic uncertainty caused by noisy training data.

We propose tools and metrics to assess how uncertainty in the data is captured by the model distribution and how it affects search strategies that generate translations. Our results show that search works remarkably well but that models tend to spread too much probability mass over the hypothesis space.

Next, we propose tools to assess model calibration and show how to easily fix some shortcomings of current models.

As part of this study, we release multiple human reference translations for two popular benchmarks.

--- https://arxiv.org/abs/1802.04730 Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions Nicolas Vasilache, Oleksandr Zinenko, Theodoros Theodoridis, Priya Goyal, Zachary DeVito, William S. Moses, Sven Verdoolaege, Andrew Adams, Albert Cohen 2018-02-13 2021-05-06 [("doi","10.48550/arXiv.1802.04730")] ai/scaling/hardware cs/algorithm

Deep learning models with convolutional and recurrent networks are now ubiquitous and analyze massive amounts of audio, image, video, text, and graph data, with applications in automatic translation, speech-to-text, scene understanding, ranking user preferences, ad placement, etc. Competing frameworks for building these networks such as TensorFlow, Chainer, CNTK, Torch/PyTorch, Caffe1/2, MXNet and Theano, explore different tradeoffs between usability and expressiveness, research or production orientation and supported hardware. They operate on a DAG of computational operators, wrapping high-performance libraries such as CUDNN (for NVIDIA GPUs) or NNPACK (for various CPUs), and automate memory allocation, synchronization, distribution.

Custom operators are needed where the computation does not fit existing high-performance library calls, usually at a high engineering cost. This is frequently required when new operators are invented by researchers: such operators suffer a severe performance penalty, which limits the pace of innovation. Furthermore, even if there is an existing runtime call these frameworks can use, it often doesn’t offer optimal performance for a user’s particular network architecture and dataset, missing optimizations between operators as well as optimizations that can be done knowing the size and shape of data.

Our contributions include (1) a language close to the mathematics of deep learning called Tensor Comprehensions, (2) a polyhedral Just-In-Time compiler to convert a mathematical description of a deep learning DAG into a CUDA kernel with delegated memory management and synchronization, also providing optimizations such as operator fusion and specialization for specific sizes, (3) a compilation cache populated by an autotuner.

--- https://arxiv.org/abs/1802.03426 UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction Lel, McInnes, John Healy, James Melville 2018-02-09 2021-05-06 [("doi","10.48550/arXiv.1802.03426")] ai/nn

UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction.

UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data.

The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance.

Furthermore, UMAP has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning.

--- https://arxiv.org/abs/1802.01241 Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings Gabriel Grand, Idan Asher Blank, Francisco Pereira, Evelina Fedorenko 2018-02-05 2021-05-06 [("doi","10.48550/arXiv.1802.01241")] ai/nn

The words of a language reflect the structure of the human mind, allowing us to transmit thoughts between individuals. However, language can represent only a subset of our rich and detailed cognitive architecture. Here, we ask what kinds of common knowledge (semantic memory) are captured by word meanings (lexical semantics).

We examine a prominent computational model that represents words as vectors in a multidimensional space, such that proximity between word-vectors approximates semantic relatedness. Because related words appear in similar contexts, such spaces—called “word embeddings”—can be learned from patterns of lexical co-occurrences in natural language. Despite their popularity, a fundamental concern about word embeddings is that they appear to be semantically “rigid”: inter-word proximity captures only overall similarity, yet human judgments about object similarities are highly context-dependent and involve multiple, distinct semantic features. For example, dolphins and alligators appear similar in size, but differ in intelligence and aggressiveness.

Could such context-dependent relationships be recovered from word embeddings? To address this issue, we introduce a powerful, domain-general solution: “semantic projection” of word-vectors onto lines that represent various object features, like size (the line extending from the word “small” to “big”), intelligence (from “dumb” to “smart”), or danger (from “safe” to “dangerous”). This method, which is intuitively analogous to placing objects “on a mental scale” between two extremes, recovers human judgments across a range of object categories and properties.

We thus show that word embeddings inherit a wealth of common knowledge from word co-occurrence statistics and can be flexibly manipulated to express context-dependent meanings.

--- https://arxiv.org/abs/1802.01433#baidu Interactive Grounded Language Acquisition and Generalization in a 2D World Haonan Yu, Haichao Zhang, Wei Xu 2018-01-31 2021-05-06 [("doi","10.48550/arXiv.1802.01433")] reinforcement-learning/model-free reinforcement-learning/scaling

We build a virtual agent for learning language in a 2D maze-like world. The agent sees images of the surrounding environment, listens to a virtual teacher, and takes actions to receive rewards. It interactively learns the teacher’s language from scratch based on two language use cases: sentence-directed navigation and question answering. It learns simultaneously the visual representations of the world, the language, and the action control.

By disentangling language grounding from other computational routines and sharing a concept detection function between language grounding and prediction, the agent reliably interpolates and extrapolates to interpret sentences that contain new word combinations or new words missing from training sentences. The new words are transferred from the answers of language prediction.

Such a language ability is trained and evaluated on a population of over 1.6 million distinct sentences consisting of 119 object words, 8 color words, 9 spatial-relation words, and 50 grammatical words. The proposed model outperforms 5 comparison methods for interpreting zero-shot sentences.

In addition, we demonstrate human-interpretable intermediate outputs of the model in the appendix.

--- https://www.biorxiv.org/content/10.1101/214973.full Genome-wide Analysis of Insomnia (N = 1,331,010) Identifies Novel Loci and Functional Pathways Philip R. Jansen, Kyoko Watanabe, Sven Stringer, Nathan Skene, Julien Bryois, Anke R. Hammerschlag, Christiaan A. de Leeuw, Jeroen Benjamins, Ana B. Muñoz-Manchado, Mats Nagel, Jeanne E. Savage, Henning Tiemeier, Tonya White, Joyce Y. Tung, David A. Hinds, Vladimir Vacic, Patrick F. Sullivan, Sophie van der Sluis, Tinca J. C. Polderman, August B. Smit, Jens Hjerling-Leffler, Eus J. W. Van Someren, Danielle Posthuma 2018-01-30 2021-05-06 [("doi","10.1101/214973")] genetics/heritable/correlation/mendelian-randomization psychiatry zeo

Insomnia is the second-most prevalent mental disorder, with no sufficient treatment available. Despite a substantial role of genetic factors, only a handful of genes have been implicated and insight into the associated neurobiological pathways remains limited.

Here, we use an unprecedented large genetic association sample (n = 1,331,010) to allow detection of a substantial number of genetic variants and gain insight into biological functions, cell types, and tissues involved in insomnia. We identify 202 genome-wide statistically-significant loci implicating 956 genes through positional, eQTL, and chromatin interaction mapping.

We show involvement of the axonal part of neurons, of specific cortical and subcortical tissues, and of two specific cell types in insomnia: striatal medium spiny neurons and hypothalamic neurons. These cell types have been implicated previously in the regulation of reward processing, sleep, and arousal in animal studies, but have never been genetically linked to insomnia in humans. We found weak genetic correlations with other sleep-related traits, but strong genetic correlations with psychiatric and metabolic traits.

Mendelian Randomization identified causal effects of insomnia on specific psychiatric and metabolic traits. Our findings reveal key brain areas and cells implicated in the neurobiology of insomnia and its related disorders, and provide novel targets for treatment.

--- https://www.biorxiv.org/content/10.1101/245761.full Genetic architecture of gene expression traits across diverse populations Lauren S. Mogil, Angela Andaleon, Alexa Badalamenti, Scott P. Dickinson, Xiuqing Guo, Jerome I. Rotter, W. Craig Johnson, Hae Kyung Im, Yongmei Liu, Heather E. Wheeler 2018-01-10 2021-05-06 [("doi","10.1101/245761")] genetics/heritable

For many complex traits, gene regulation is likely to play a crucial mechanistic role. How the genetic architectures of complex traits vary between populations and subsequent effects on genetic prediction are not well understood, in part due to the historical paucity of GWAS in populations of non-European ancestry. We used data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort to characterize the genetic architecture of gene expression within and between diverse populations. Genotype and monocyte gene expression were available in individuals with African American (AFA, n = 233), Hispanic (HIS, n = 352), and European (CAU, n = 578) ancestry. We performed expression quantitative trait loci (eQTL) mapping in each population and show genetic correlation of gene expression depends on share ancestry proportions. Using elastic net modeling with cross validation to optimize genotypic predictors of gene expression in each population, we show the genetic architecture of gene expression is sparse across populations. We found the best predicted gene, HLA-DRB5, was the same across populations with R2 > 0.81 in each population. However, there were 1094 (11.3%) well predicted genes in AFA and 372 (3.8%) well predicted genes in HIS that were poorly predicted in CAU. Using genotype weights trained in MESA to predict gene expression in 1,000 Genomes populations showed that a training set with ancestry similar to the test set is better at predicting gene expression in test populations, demonstrating an urgent need for diverse population sampling in genomics. Our predictive models in diverse cohorts are made publicly available for use in transcriptome mapping methods at https://predictdb.hakyimlab.org/.

Author Summary: Most genome-wide association studies (GWAS) have been conducted in populations of European ancestry leading to a disparity in understanding the genetics of complex traits between populations. For many complex traits, gene regulation is likely to play a critical mechanistic role given the consistent enrichment of regulatory variants among trait-associated variants. However, it is still unknown how the effects of these key variants differ across populations. We used data from MESA to study the underlying genetic architecture of gene expression by optimizing gene expression prediction within and across diverse populations. The populations with genotype and gene expression data available are from individuals with African American (AFA, n = 233), Hispanic (HIS, n = 352), and European (CAU, n = 578) ancestry. After calculating the prediction performance, we found that there are many genes that were well predicted in AFA and HIS that were poorly predicted in CAU. We further showed that a training set with ancestry similar to the test set resulted in better gene expression predictions, demonstrating the need to incorporate diverse populations in genomic studies. Our gene expression prediction models are publicly available to facilitate future transcriptome mapping studies in diverse populations.

--- https://www.biorxiv.org/content/10.1101/242149.full Genome-wide association study of 1 million people identifies 111 loci for atrial fibrillation Jonas B. Nielsen, Rosa B. Thorolfsdottir, Lars G. Fritsche, Wei Zhou, Morten W. Skov, Sarah E. Graham, Todd J. Herron, Shane McCarthy, Ellen M. Schmidt, Gardar Sveinbjornsson, Ida Surakka, Michael R. Mathis, Masatoshi Yamazaki, Ryan D. Crawford, Maiken E. Gabrielsen, Anne Heidi Skogholt, Oddgeir L. Holmen, Maoxuan Lin, Brooke N. Wolford, Rounak Dey, Håvard Dalen, Patrick Sulem, Jonathan H. Chung, Joshua D. Backman, David O. Arnar, Unnur Thorsteinsdottir, Aris Baras, Colm O’Dushlaine, Anders G. Holst, Xiaoquan Wen, Whitney Hornsby, Frederick E. Dewey, Michael Boehnke, Sachin Kheterpal, Seunggeun Lee, Hyun M. Kang, Hilma Holm, Jacob Kitzman, Jordan A. Shavit, José Jalife, Chad M. Brummett, Tanya M. Teslovich, David J. Carey, Daniel F. Gudbjartsson, Kari Stefansson, Gonçalo Abecasis, Kristian Hveem, Cristen Jennifer Willer 2018-01-04 2021-05-06 [("doi","10.1101/242149")] genetics/heritable

To understand the genetic variation underlying atrial fibrillation (AF), the most common cardiac arrhythmia, we performed a genome-wide association study (GWAS) of > 1 million people, including 60,620 AF cases and 970,216 controls. We identified 163 independent risk variants at 111 loci and prioritized 165 candidate genes likely to be involved in AF.

Many of the identified risk variants fall near genes where more deleterious mutations have been reported to cause serious heart defects in humans or mice (MYH6, NKX2-5, PITX2, TBC1D32, TBX5), or near genes important for striated muscle function and integrity (eg. MYH7, PKP2, SSPN, SGCA). Experiments in rabbits with heart failure and left atrial dilation identified a heterogeneous distributed molecular switch from MYH6 to MYH7 in the left atrium, which resulted in contractile and functional heterogeneity and may predispose to initiation and maintenance of atrial arrhythmia.

--- https://www.biorxiv.org/content/10.1101/251033.full Human local adaptation of the TRPM8 cold receptor along a latitudinal cline Felix M. Key, Muslihudeen A. Abdul-Aziz, Roger Mundry, Benjamin M. Peter, Aarthi Sekar, Mauro D’Amato, Megan Y. Dennis, Joshua M. Schmidt, Aida M. Andrés 2018-01-19 2021-05-06 [("doi","10.1101/251033")] genetics/selection/natural/human

Ambient temperature is a critical environmental factor for all living organisms. It was likely an important selective force as modern humans recently colonized temperate and cold Eurasian environments. Nevertheless, as of yet we have limited evidence of local adaptation to ambient temperature in populations from those environments. To shed light on this question, we exploit the fact that humans are a cosmopolitan species that inhabits territories under a wide range of temperatures. Focusing on cold perception—which is central to thermoregulation and survival in cold environments—we show evidence of recent local adaptation on TRPM8. This gene encodes for a cation channel that is, to date, the only temperature receptor known to mediate an endogenous response to moderate cold. The upstream variant rs10166942 shows extreme population differentiation, with frequencies that range from 5% in Nigeria to 88% in Finland (placing this SNP in the 0.02% tail of the FST empirical distribution). When all populations are jointly analysed, allele frequencies correlate with latitude and temperature beyond what can be explained by shared ancestry and population substructure. Using a Bayesian approach, we infer that the allele originated and evolved neutrally in Africa, while positive selection raised its frequency to different degrees in Eurasian populations, resulting in allele frequencies that follow a latitudinal cline. We infer strong positive selection, in agreement with ancient DNA showing high frequency of the allele in Europe 3,000 to 8,000 years ago. rs10166942 is important phenotypically because its ancestral allele is protective of migraine. This debilitating disorder varies in prevalence across human populations, with highest prevalence in individuals of European descent—precisely the population with the highest frequency of rs10166942 derived allele. We thus hypothesize that local adaptation on previously neutral standing variation may have contributed to the genetic differences that exist in the prevalence of migraine among human populations today.

Author Summary: Some human populations were likely under strong pressure to adapt biologically to cold climates during their colonization of non-African territories in the last 50,000 years. Such putative adaptations required genetic variation in genes that could mediate adaptive responses to cold. TRPM8 is potentially one such gene, being the only known receptor for the sensation of moderate cold temperature. We show that a likely regulatory genetic variant nearby TRPM8 has several signatures of positive selection rising its frequency in Eurasian populations during the last 25,000 years. While the genetic variant was and is rare in Africa, it is now common outside of Africa, with frequencies that strongly correlate with latitude and are highest in northern European populations. Interestingly, this same genetic variant has previously been strongly associated with migraine. This suggests that adaptation to cold has potentially contributed to the variation in migraine prevalence that exists among human groups today.

--- https://arxiv.org/abs/1803.04383 Delayed Impact of Fair Machine Learning Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt 2018-03-12 2021-05-06 [("doi","10.48550/arXiv.1803.04383")] reinforcement-learning/model-free statistics/decision

[commentary] ‘Fairness’ in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect.

We study how static fairness criteria interact with temporal indicators of well-being, such as long-term improvement, stagnation, and decline in a variable of interest.

We demonstrate that even in a one-step feedback model, common fairness criteria in general do not promote improvement over time, and may in fact cause harm in cases where an unconstrained objective would not.

We completely characterize the delayed impact of 3 standard criteria, contrasting the regimes in which these exhibit qualitatively different behavior. In addition, we find that a natural form of measurement error broadens the regime in which fairness criteria perform favorably.

Our results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria, suggesting a range of new challenges and trade-offs.

--- https://arxiv.org/abs/1802.10592 ME-TRPO: Model-Ensemble Trust-Region Policy Optimization Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel 2018-02-28 2021-05-06 [("doi","10.48550/arXiv.1802.10592")] reinforcement-learning/model-free

Model-free reinforcement learning (RL) methods are succeeding in a growing number of tasks, aided by recent advances in deep learning. However, they tend to suffer from high sample complexity, which hinders their use in real-world domains. Alternatively, model-based reinforcement learning promises to reduce sample complexity, but tends to require careful tuning and to date have succeeded mainly in restrictive domains where simple models are sufficient for learning.

In this paper, we analyze the behavior of vanilla model-based reinforcement learning methods when deep neural networks are used to learn both the model and the policy, and show that the learned policy tends to exploit regions where insufficient data is available for the model to be learned, causing instability in training.

To overcome this issue, we propose to use an ensemble of models to maintain the model uncertainty and regularize the learning process. We further show that the use of likelihood ratio derivatives yields much more stable learning than backpropagation through time.

Altogether, our approach Model-Ensemble Trust-Region Policy Optimization (ME-TRPO) reduces the sample complexity compared to model-free deep RL methods on challenging continuous control benchmark tasks.

--- https://arxiv.org/abs/1802.03268 ENAS: Efficient Neural Architecture Search via Parameter Sharing Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean 2018-02-09 2021-05-06 [("doi","10.48550/arXiv.1802.03268")] reinforcement-learning/model-free

We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. The controller is trained with policy gradient to select a subgraph that maximizes the expected reward on the validation set.

Meanwhile the model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss. Thanks to parameter sharing between child models, ENAS is fast: it delivers strong empirical performances using much fewer GPU-hours than all existing automatic model design approaches, and notably, 1,000× less expensive than standard Neural Architecture Search.

On the Penn Treebank dataset, ENAS discovers a novel architecture that achieves a test perplexity of 55.8, establishing a new state-of-the-art among all methods without post-training processing. On the CIFAR-10 dataset, ENAS designs novel architectures that achieve a test error of 2.89%, which is on par with NASNet (Zoph et al 2018), whose test error is 2.65%.

--- https://arxiv.org/abs/1802.01548 Regularized Evolution for Image Classifier Architecture Search Esteban Real, Alok Aggarwal, Yanping Huang, Quoc V. Le 2018-02-05 2021-05-07 [("doi","10.48550/arXiv.1802.01548")] reinforcement-learning/model-free

The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Although evolutionary algorithms have been repeatedly applied to neural network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones. Here, we evolve an image classifier—AmoebaNet-A—that surpasses hand-designs for the first time.

To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new state-of-the-art 83.9% / 96.6% top-5 ImageNet accuracy.

In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search. This is relevant when fewer compute resources are available. Evolution is, thus, a simple method to effectively discover high-quality architectures.

--- https://arxiv.org/abs/1802.01561#deepmind IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymir Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu 2018-02-05 2021-05-07 [("doi","10.48550/arXiv.1802.01561")] ai/nn/cnn reinforcement-learning/model-free

In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters. A key challenge is to handle the increased amount of data and extended training time. We have developed a new distributed agent IMPALA (Importance Weighted Actor-Learner Architecture) that not only uses resources more efficiently in single-machine training but also scales to thousands of machines without sacrificing data efficiency or resource usage. We achieve stable learning at high throughput by combining decoupled acting and learning with a novel off-policy correction method called V-trace.

We demonstrate the effectiveness of IMPALA for multi-task reinforcement learning on DMLab-30 (a set of 30 tasks from the DeepMind Lab environment (Beattie et al 2016)) and Atari-57 (all available Atari games in Arcade Learning Environment (Bellemare et al 2013a)).

Our results show that IMPALA is able to achieve better performance than previous agents with less data, and crucially exhibits positive transfer between tasks as a result of its multi-task approach.

--- https://arxiv.org/abs/1801.01290 Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine 2018-01-04 2021-05-07 [("doi","10.48550/arXiv.1801.01290")] reinforcement-learning/model-free

Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks. However, these methods typically suffer from two major challenges: very high sample complexity and brittle convergence properties, which necessitate meticulous hyperparameter tuning. Both of these challenges severely limit the applicability of such methods to complex, real-world domains.

In this paper, we propose soft actor-critic, an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework. In this framework, the actor aims to maximize expected reward while also maximizing entropy. That is, to succeed at the task while acting as randomly as possible. Prior deep RL methods based on this framework have been formulated as Q-learning methods.

By combining off-policy updates with a stable stochastic actor-critic formulation, our method achieves state-of-the-art performance on a range of continuous control benchmark tasks, outperforming prior on-policy and off-policy methods. Furthermore, we demonstrate that, in contrast to other off-policy algorithms, our approach is very stable, achieving very similar performance across different random seeds.

--- https://arxiv.org/abs/1801.07222 Rover Descent: Learning to optimize by learning to navigate on prototypical loss surfaces Louis Faury, Flavian Vasile 2018-01-22 2021-05-07 [("doi","10.48550/arXiv.1801.07222")] reinforcement-learning/meta-learning

Learning to optimize—the idea that we can learn from data algorithms that optimize a numerical criterion—has recently been at the heart of a growing number of research efforts. One of the most challenging issues within this approach is to learn a policy that is able to optimize over classes of functions that are fairly different from the ones that it was trained on.

We propose a novel way of framing learning to optimize as a problem of learning a good navigation policy on a partially observable loss surface. To this end, we develop Rover Descent, a solution that allows us to learn a fairly broad optimization policy from training on a small set of prototypical two-dimensional surfaces that encompasses the classically hard cases such as valleys, plateaus, cliffs, and saddles and by using strictly zero-order information.

We show that, without having access to gradient or curvature information, we achieve state-of-the-art convergence speed on optimization problems not presented at training time such as the Rosenbrock function and other hard cases in two dimensions. We extend our framework to optimize over high-dimensional landscapes, while still handling only two-dimensional local landscape information and show good preliminary results.

--- https://arxiv.org/abs/1802.06942 Comparison Based Learning from Weak Oracles Ehsan Kazemi, Lin Chen, Sanjoy Dasgupta, Amin Karbasi 2018-02-20 2021-05-07 [("doi","10.48550/arXiv.1802.06942")] statistics/order/comparison

There is increasing interest in learning algorithms that involve interaction between human and machine. Comparison-based queries are among the most natural ways to get feedback from humans. A challenge in designing comparison-based interactive learning algorithms is coping with noisy answers. The most common fix is to submit a query several times, but this is not applicable in many situations due to its prohibitive cost and due to the unrealistic assumption of independent noise in different repetitions of the same query.

In this paper, we introduce a new weak oracle model, where a non-malicious user responds to a pairwise comparison query only when she is quite sure about the answer. This model is able to mimic the behavior of a human in noise-prone regions. We also consider the application of this weak oracle model to the problem of content search (a variant of the nearest neighbor search problem) through comparisons. More specifically, we aim at devising efficient algorithms to locate a target object in a database equipped with a dissimilarity metric via invocation of the weak comparison oracle. We propose two algorithms termed WORCS-I and WORCS-II (Weak-Oracle Comparison-based Search), which provably locate the target object in a number of comparisons close to the entropy of the target distribution. While WORCS-I provides better theoretical guarantees, WORCS-II is applicable to more technically challenging scenarios where the algorithm has limited access to the ranking dissimilarity between objects. A series of experiments validate the performance of our proposed algorithms.

--- https://arxiv.org/abs/1711.09020 StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, Jaegul Choo 2017-11-24 2021-05-07 [("doi","10.48550/arXiv.1711.09020")] ai/nn/gan

Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains.

To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. Such a unified model architecture of StarGAN allows simultaneous training of multiple datasets with different domains within a single network.

This leads to StarGAN’s superior quality of translated images compared to existing models as well as the novel capability of flexibly translating an input image to any desired target domain.

We empirically demonstrate the effectiveness of our approach on a facial attribute transfer and a facial expression synthesis tasks.

--- https://arxiv.org/abs/1801.00868#facebook Panoptic Segmentation Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, Piotr Dollár 2018-01-03 2021-05-07 [("doi","10.48550/arXiv.1801.00868")] ai/nn

We propose and study a task we name panoptic segmentation (PS). Panoptic segmentation unifies the typically distinct tasks of semantic segmentation (assign a class label to each pixel) and instance segmentation (detect and segment each object instance). The proposed task requires generating a coherent scene segmentation that is rich and complete, an important step toward real-world vision systems. While early work in computer vision addressed related image/scene parsing tasks, these are not currently popular, possibly due to lack of appropriate metrics or associated recognition challenges.

To address this, we propose a novel panoptic quality (PQ) metric that captures performance for all classes (stuff and things) in an interpretable and unified manner. Using the proposed metric, we perform a rigorous study of both human and machine performance for PS on 3 existing datasets, revealing interesting insights about the task.

The aim of our work is to revive the interest of the community in a more unified view of image segmentation.

--- https://www.biorxiv.org/content/10.1101/241414.full DeepGS: Predicting phenotypes from genotypes using Deep Learning Wenlong Ma, Zhixu Qiu, Jie Song, Qian Cheng, Chuang Ma 2017-12-31 2021-05-07 [("doi","10.1101/241414")] ai/nn/cnn genetics/heritable

Motivation: Genomic selection (GS) is a new breeding strategy by which the phenotypes of quantitative traits are usually predicted based on genome-wide markers of genotypes using conventional statistical models. However, the GS prediction models typically make strong assumptions and perform linear regression analysis, limiting their accuracies since they do not capture the complex, non-linear relationships within genotypes, and between genotypes and phenotypes.

Results: We present a deep learning method, named DeepGS, to predict phenotypes from genotypes. Using a deep convolutional neural network, DeepGS uses hidden variables that jointly represent features in genotypic markers when making predictions; it also employs convolution, sampling and dropout strategies to reduce the complexity of high-dimensional marker data. We used a large GS dataset to train DeepGS and compare its performance with other methods. In terms of mean normalized discounted cumulative gain value, DeepGS achieves an increase of 27.70%~246.34% over a conventional neural network in selecting top-ranked 1% individuals with high phenotypic values for the eight tested traits. Additionally, compared with the widely used method RR-BLUP, DeepGS still yields a relative improvement ranging 1.44% → 65.24%. Through extensive simulation experiments, we also demonstrated the effectiveness and robustness of DeepGS for the absent of outlier individuals and subsets of genotypic markers. Finally, we illustrated the complementarity of DeepGS and RR-BLUP with an ensemble learning approach for further improving prediction performance.

Availability: DeepGS is provided as an open source R package available at Github.

--- https://arxiv.org/abs/1712.03452 SPP-Net: Deep Absolute Pose Regression with Synthetic Views Pulak Purkait, Cheng Zhao, Christopher Zach 2017-12-09 2021-05-07 [("doi","10.48550/arXiv.1712.03452")] ai/nn/cnn

Image based localization is one of the important problems in computer vision due to its wide applicability in robotics, augmented reality, and autonomous systems. There is a rich set of methods described in the literature how to geometrically register a 2D image w.r.t. a 3D model.

Recently, methods based on deep (and convolutional) feedforward networks (CNNs) became popular for pose regression. However, these CNN-based methods are still less accurate than geometry based methods despite being fast and memory efficient.

In this work we design a deep neural network architecture based on sparse feature descriptors to estimate the absolute pose of an image. Our choice of using sparse feature descriptors has two major advantages: first, our network is smaller than the CNNs proposed in the literature for this task—thereby making our approach more efficient and scalable. Second—and more importantly—usage of sparse features allows to augment the training data with synthetic viewpoints, which leads to substantial improvements in the generalization performance to unseen poses. Thus, our proposed method aims to combine the best of the two worlds—feature-based localization and CNN-based pose regression—to achieve state-of-the-art performance in the absolute pose estimation.

A detailed analysis of the proposed architecture and a rigorous evaluation on the existing datasets are provided to support our method.

--- https://arxiv.org/abs/1711.11561 Measuring the tendency of CNNs to Learn Surface Statistical Regularities Jason Jo, Yoshua Bengio 2017-11-30 2021-05-07 [("doi","10.48550/arXiv.1711.11561")] ai/nn/cnn

Deep CNNs are known to exhibit the following peculiarity: on the one hand they generalize extremely well to a test set, while on the other hand they are extremely sensitive to so-called adversarial perturbations. The extreme sensitivity of high performance CNNs to adversarial examples casts serious doubt that these networks are learning high level abstractions in the dataset. We are concerned with the following question: How can a deep CNN that does not learn any high level semantics of the dataset manage to generalize so well?

The goal of this article is to measure the tendency of CNNs to learn surface statistical regularities of the dataset. To this end, we use Fourier filtering to construct datasets which share the exact same high level abstractions but exhibit qualitatively different surface statistical regularities. For the SVHN and CIFAR-10 datasets, we present two Fourier filtered variants: a low frequency variant and a randomly filtered variant. Each of the Fourier filtering schemes is tuned to preserve the recognizability of the objects.

Our main finding is that CNNs exhibit a tendency to latch onto the Fourier image statistics of the training dataset, sometimes exhibiting up to a 28% generalization gap across the various test sets. Moreover, we observe that increasing the depth of a network has a very marginal impact on closing the aforementioned generalization gap.

Thus we provide quantitative evidence supporting the hypothesis that deep CNNs tend to learn surface statistical regularities in the dataset rather than higher-level abstract concepts.

--- https://arxiv.org/abs/1712.00409#baidu Deep Learning Scaling is Predictable, Empirically Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md. Mostofa Ali Patwary, Yang Yang, Yanqi Zhou 2017-12-01 2021-05-07 [("doi","10.48550/arXiv.1712.00409")] ai/scaling

Deep learning (DL) creates impactful advances following a virtuous recipe: model architecture search, creating large training data sets, and scaling computation. It is widely believed that growing training sets and models should improve accuracy and result in better products. As DL application domains grow, we would like a deeper understanding of the relationships between training set size, computational scale, and model accuracy improvements to advance the state-of-the-art.

This paper presents a large scale empirical characterization of generalization error and model size growth as training sets grow. We introduce a methodology for this measurement and test four machine learning domains: machine translation, language modeling, image processing, and speech recognition. Our empirical results show power-law generalization error scaling across a breadth of factors, resulting in power-law exponents—the “steepness” of the learning curve—yet to be explained by theoretical work. Further, model improvements only shift the error but do not appear to affect the power-law exponent. We also show that model size scales sublinearly with data size. These scaling relationships have implications on deep learning research, practice, and systems. They can assist model debugging, setting accuracy targets, and decisions about data set growth. They can also guide computing system design and underscore the importance of continued computational scaling.

--- https://arxiv.org/abs/1712.03140 Difficulties of Timestamping Archived Web Pages Mohamed Aturban, Michael L. Nelson, Michele C. Weigle 2017-12-08 2021-05-08 [("doi","10.48550/arXiv.1712.03140")] bitcoin cs/cryptography

We show that state-of-the-art services for creating trusted timestamps in blockchain-based networks do not adequately allow for timestamping of web pages. They accept data by value (eg. images and text), but not by reference (eg. URIs of web pages). Also, we discuss difficulties in repeatedly generating the same cryptographic hash value of an archived web page.

We then introduce several requirements to be fulfilled in order to produce repeatable hash values for archived web pages.

--- https://arxiv.org/abs/1712.01208#google The Case for Learned Index Structures Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, Neoklis Polyzotis 2017-12-04 2021-05-08 [("doi","10.48550/arXiv.1712.01208")] cs/algorithm reinforcement-learning/model-free

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records.

We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6258497/ Livestock 2.0—genome editing for fitter, healthier, and more productive farmed animals Christine Tait-Burkard, Andrea Doeschl-Wilson, Mike J. McGrew, Alan L. Archibald, Helen M. Sang, Ross D. Houston, C. Bruce Whitelaw, Mick Watson 2018 2021-05-08 [("doi","10.1186/s13059-018-1583-1")] genetics/cloning genetics/editing genetics/selection/artificial

The human population is growing, and as a result we need to produce more food whilst reducing the impact of farming on the environment.

Selective breeding and genomic selection have had a transformational impact on livestock productivity, and now transgenic and genome-editing technologies offer exciting opportunities for the production of fitter, healthier and more-productive livestock.

Here, we review recent progress in the application of genome editing to farmed animal species and discuss the potential impact on our ability to produce food.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6083837/ Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian Randomization between complex traits and diseases Marie Verbanck, Chia-Yen Chen, Benjamin M. Neale, Ron Do 2018 2021-05-08 [("doi","10.1038/s41588-018-0099-7")] genetics/heritable/correlation/mendelian-randomization

Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian Randomization (MR). Violation of the ‘no horizontal pleiotropy’ assumption can cause severe bias in MR.

We developed the Mendelian Randomization pleiotropy residual sum and outlier (MR-PRESSO) test to identify horizontal pleiotropic outliers in multi-instrument summary-level MR testing. We showed using simulations that the MR-PRESSO test is best suited when horizontal pleiotropy occurs in <50% of instruments.

Next, we applied the MR-PRESSO test, along with several other MR tests, to complex traits and diseases and found that horizontal pleiotropy (1) was detectable in over 48% of causal relationships in MR; (2) introduced distortions in the causal estimates in MR that ranged on average from −131% to 201%; (3) induced false-positive causal relationships in up to 10% of relationships; and (4) could be corrected in some but not all instances.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6386176/ GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia Joëlle A. Pasman, Karin J. H. Verweij, Zachary Gerring, Sven Stringer, Sandra Sanchez-Roige, Jorien L. Treur, Abdel Abdellaoui, Michel G. Nivard, Bart M. L. Baselmans, Jue-Sheng Ong, Hill F. Ip, Matthijs D. van der Zee, Meike Bartels, Felix R. Day, Pierre Fontanillas, Sarah L. Elson, Harriet de Wit, Lea K. Davis, James MacKillop, Jaime L. Derringer, Susan J. T. Branje, Catharina A. Hartman, Andrew C. Heath, Pol A. C. van Lier, Pamela A. F. Madden, Reedik Mägi, Wim Meeus, Grant W. Montgomery, A. J. Oldehinkel, Zdenka Pausova, Josep A. Ramos-Quiroga, Tomas Paus, Marta Ribases, Jaakko Kaprio, Marco P. M. Boks, Jordana T. Bell, Tim D. Spector, Joel Gelernter, Dorret I. Boomsma, Nicholas G. Martin, Stuart MacGregor, John R. B. Perry, Abraham Palmer, Danielle Posthuma, Marcus R. Munafò, Nathan A. Gillespie, Eske M. Derks, Jacqueline M. Vink 2018 2021-05-08 [("doi","10.1038/s41593-018-0206-1")] genetics/heritable/correlation/mendelian-randomization marijuana psychiatry/alcoholism

Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. In the largest genome-wide association study (GWAS) for lifetime cannabis use to date (n = 184,765), we identified 8 genome-wide statistically-significant independent single-nucleotide polymorphisms in 6 regions. All measured genetic variants combined explained 11% of the variance.

Gene-based tests revealed 35 genes in 16 regions, and S-PrediXcan analyses showed that 21 genes had different expression levels for cannabis users versus nonusers. The strongest finding across the different analyses was CADM2, which has been associated with substance use and risk-taking.

Genetic correlations were found with 14⁄25 tested substance use and mental health-related traits, including smoking, alcohol use, schizophrenia, and risk-taking. Mendelian Randomization analysis showed evidence for a causal positive influence of schizophrenia risk on cannabis use.

Overall, our study provides new insights into the etiology of cannabis use and its relation with mental health.

--- https://www.biorxiv.org/content/10.1101/234815.full LD Score regression as an estimator of confounding and genetic correlations in genome-wide association studies James J. Lee, Carson C. Chow 2017-12-30 2021-05-08 [("doi","10.1101/234815")] genetics/heritable/correlation

In order to infer that a single-nucleotide polymorphism (SNP) either affects a phenotype or is linkage disequilibrium with a causal site, we must have some assurance that any SNP-phenotype correlation is not the result of confounding with some environmental variable that also affects the trait.

Here we provide a mathematical analysis of LD Score regression, a recently developed method for using summary statistics from genome-wide association studies (GWAS) to ensure that confounding does not inflate the number of false positives. We do not treat the effects of genetic variation as a random variable and thus are able to obtain results about the unbiasedness of this method.

We demonstrate that LD Score regression can produce estimates of confounding at null SNPs that are nearly unbiased or overly conservative under fairly general conditions. This robustness can hold even in cases now thought to be unfavorable, such as a correlation over SNPs between LD Scores and the degree of confounding. LD Score regression is thus an even stronger technique for causal inference than foreseen by its developers.

Additionally, we demonstrate that LD Score regression can produce unbiased estimates of the genetic correlation, even when its estimates of the genetic covariance and the two univariate heritabilities are substantially biased.

--- https://www.biorxiv.org/content/10.1101/242404.full Generalizing Genetic Risk Scores from Europeans to Hispanics/Latinos Kelsey E. Grinde, Qibin Qi, Timothy A. Thornton, Simin Liu, Aladdin H. Shadyab, Kei Hang K. Chan, Alexander P. Reiner, Tamar Sofer 2018-01-04 2021-05-08 [("doi","10.1101/242404")] genetics/heritable

Genetic risk scores (GRSs) are weighted sums of risk allele counts of single-nucleotide polymorphisms (SNPs) associated with a disease or trait. Construction of GRSs is typically based on published results from Genome-Wide Association Studies (GWASs), the majority of which have been performed in large populations of European ancestry (EA) individuals. While many genotype-trait associations have been shown to generalize from EA populations to other populations, such as Hispanics/Latinos, the optimal choice of SNPs and weights for GRSs may differ between populations due to different linkage disequilibrium (LD) and allele frequency patterns. This is further complicated by the fact that different Hispanic/Latino populations may have different admixture patterns, so that LD and allele frequency patterns may not be the same among non-EA populations.

Here, we compare various approaches for GRS construction, using GWAS results from both large EA studies and a smaller study in Hispanics/Latinos, the Hispanic Community Health Study/Study of Latinos (HCHS/SOL, n = 12, 803). We consider multiple ways to select SNPs from association regions and to calculate the SNP weights. We study the performance of the resulting GRSs in an independent study of Hispanics/Latinos from the Woman Health Initiative (WHI, n = 3, 582).

We support our investigation with simulation studies of potential genetic architectures in a single locus. We observed that selecting variants based on EA GWASs generally performs well, as long as SNP weights are calculated using Hispanics/Latinos GWASs, or using the meta-analysis of EA and Hispanics/Latinos GWASs. The optimal approach depends on the genetic architecture of the trait.

--- https://www.biorxiv.org/content/10.1101/242776.full The Shared Genetic Basis of Human Fluid Intelligence and Brain Morphology Tian Ge, Chia-Yen Chen, Richard Vettermann, Lauri J. Tuominen, Daphne J. Holt, Mert R. Sabuncu, Jordan W. Smoller 2018-01-04 2021-05-08 [("doi","10.1101/242776")] genetics/heritable iq psychology/neuroscience

Human intelligence differences are linked to diverse cognitive abilities and predict important life outcomes. Here we investigate the biological bases of fluid intelligence in a large sample of participants from the UK Biobank.

We explore the genetic underpinnings of fluid intelligence via genome-wide association analysis (n = 108,147), and examine brain morphological correlates of fluid intelligence (n = 7, 485). Importantly, we develop novel statistical methods that enable high-dimensional co-heritability analysis, and compute high-resolution surface maps for the co-heritability and genetic correlations between fluid intelligence and cortical thickness measurements.

Our analyses reveal the genetic overlap between fluid intelligence and brain morphology in predominately left inferior precentral gyrus, pars opercularis, superior temporal cortex, supramarginal gyrus, and their proximal regions.

These results suggest a shared genetic basis between fluid intelligence and Broca’s speech and Wernicke’s language areas and motor regions, and may contribute to our understanding of the biological substrate of human fluid intelligence.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6194053/ The genetics of university success Emily Smith-Woolley, Ziada Ayorech, Philip S. Dale, Sophie von Stumm, Robert Plomin 2018 2021-05-08 [("doi","10.1038/s41598-018-32621-w")] genetics/heritable iq

University success, which includes enrollment in and achievement at university, as well as quality of the university, have all been linked to later earnings, health and well-being. However, little is known about the causes and correlates of differences in university-level outcomes.

Capitalizing on both quantitative and molecular genetic data, we perform the first genetically sensitive investigation of university success with a UK-representative sample of 3,000 genotyped individuals and 3,000 twin pairs. Twin analyses indicate substantial additive genetic influence on university entrance exam achievement (57%), university enrollment (51%), university quality (57%) and university achievement (46%). We find that environmental effects tend to be non-shared, although the shared environment is substantial for university enrollment.

Furthermore, using multivariate twin analysis, we show moderate to high genetic correlations between university success variables (0.27–0.76). Analyses using DNA alone also support genetic influence on university success. Indeed, a genome-wide polygenic score, derived from a 2016 genome-wide association study of years of education, predicts up to 5% of the variance in each university success variable.

These findings suggest young adults select and modify their educational experiences in part based on their genetic propensities and highlight the potential for DNA-based predictions of real-world outcomes, which will continue to increase in predictive power.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6488973/ Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry Loïc Yengo, Julia Sidorenko, Kathryn E. Kemper, Zhili Zheng, Andrew R. Wood, Michael N. Weedon, Timothy Frayling, Joel Hirschhorn, Jian Yang, Peter M. Visscher 2018 2021-05-08 [("doi","10.1093/hmg/ddy271")] genetics/heritable/correlation/mendelian-randomization

Recent genome-wide association studies (GWAS) of height and body mass index (BMI) in ~250000 European participants have led to the discovery of ~700 and ~100 nearly independent single-nucleotide polymorphisms (SNPs) associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ~450000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N ~700000 individuals and substantially increases the number of GWAS signals associated with these traits.

We identified 3290 and 941 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide statistical-significance threshold of p < 1 × 10−8), including 1185 height-associated SNPs and 751 BMI-associated SNPs located within loci not previously identified by these two GWAS. The near-independent genome-wide statistically-significant SNPs explain ~24.6% of the variance of height and ~6.0% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were ~0.44 and ~0.22, respectively.

From analyses of integrating GWAS and expression quantitative trait loci (eQTL) data by summary-data-based Mendelian Randomization, we identified an enrichment of eQTLs among lead height and BMI signals, prioritizing 610 and 138 genes, respectively.

Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by the discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow-up studies.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5993313/ Genetics-based methods for agricultural insect pest management Nina Alphey, Michael B. Bonsall 2018 2021-05-09 [("doi","10.1111/afe.12241")] genetics/selection/artificial

The sterile insect technique is an area-wide pest control method that reduces agricultural pest populations by releasing mass-reared sterile insects, which then compete for mates with wild insects. Contemporary genetics-based technologies use insects that are homozygous for a repressible dominant lethal genetic construct rather than being sterilized by irradiation. Engineered strains of agricultural pest species, including moths such as the diamondback moth Plutella xylostella and fruit flies such as the Mediterranean fruit fly Ceratitis capitata, have been developed with lethality that only operates on females.

Transgenic crops expressing insecticidal toxins are widely used; the economic benefits of these crops would be lost if toxin resistance spread through the pest population. The primary resistance management method is a high-dose/refuge strategy, requiring toxin-free crops as refuges near the insecticidal crops, as well as toxin doses sufficiently high to kill wild-type insects and insects heterozygous for a resistance allele. Mass-release of toxin-sensitive engineered males (carrying female-lethal genes), as well as suppressing populations, could substantially delay or reverse the spread of resistance.

These transgenic insect technologies could form an effective resistance management strategy.

We outline some policy considerations for taking genetic insect control systems through to field implementation.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6087839/ Microfluidic-based sperm sorting & analysis for treatment of male infertility Raheel Samuel, Haidong Feng, Alex Jafek, Dillon Despain, Timothy Jenkins, Bruce Gale 2018 2021-05-09 [("doi","10.21037/tau.2018.05.08")] genetics/selection/artificial

Microfluidics technology has emerged as an enabling technology for different fields of medicine and life sciences. One such field is male infertility where microfluidic technologies are enabling optimization of sperm sample preparation and analysis.

In this chapter we review how microfluidic technology has been used for sperm quantification, sperm quality analysis, and sperm manipulation and isolation with subsequent use of the purified sperm population for treatment of male infertility. As we discuss demonstrations of microfluidic sperm sorting/manipulation/analysis, we highlight systems that have demonstrated feasibility towards clinical adoption or have reached commercialization in the male infertility market.

We then review microfluidic-based systems that facilitate non-invasive identification and sorting of viable sperm for in vitro fertilization.

Finally, we explore commercialization challenges associated with microfluidic sperm sorting systems and provide suggestions and future directions to best overcome them.

--- https://www.biorxiv.org/content/10.1101/238295.full A simple test identifies selection on complex traits in breeding and experimentally-evolved populations Tim Beissinger, Jochen Kruppa, David Cavero, Ngoc-Thuy Ha, Malena Erbe, Henner Simianer 2017-12-21 2021-05-09 [("doi","10.1101/238295")] genetics/selection/artificial

Important traits in agricultural, natural, and human populations are increasingly being shown to be under the control of many genes that individually contribute only a small proportion of genetic variation. However, the majority of modern tools in quantitative and population genetics, including genome-wide association studies and selection mapping protocols, are designed to target the identification of individual genes with large effects.

We have developed an approach to identify traits that have been under selection and are controlled by large numbers of loci. In contrast to existing methods, our techniqueuses additive effects estimates from all available markers, and relates these estimates to allele frequency change over time. Using this information, we generate a composite statistic, denoted Ĝ, which can be used to test for evidence of selection on a trait. Our test requires genotypic data from multiple time points but only a single time point with phenotypic information.

Simulations demonstrate that Ĝ is powerful for identifying selection, particularly in situations where the trait being tested is controlled by many genes, which is precisely the scenario where classical approaches for selection mapping are least powerful.

We apply this test to breeding populations of maize and chickens, where we demonstrate the successful identification of selection on traits that are documented to have been under selection.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6095675/ Boosting School Readiness: Should Preschool Teachers Target Skills or the Whole Child? Jade M. Jenkins, Greg J. Duncan, Anamarie Auger, Marianne Bitler, Thurston Domina, Margaret Burchinal 2018 2021-05-09 [("doi","10.1016/j.econedurev.2018.05.001")] psychology

We use experimental data to estimate impacts on school readiness of different kinds of preschool curricula—a largely neglected preschool input and measure of preschool quality.

We find that the widely-used “whole-child” curricula found in most Head Start and pre-K classrooms produced higher classroom process quality than did locally-developed curricula, but failed to improve children’s school readiness.

A curriculum focused on building mathematics skills increased both classroom math activities and children’s math achievement relative to the whole-child curricula.

Similarly, curricula focused on literacy skills increased literacy achievement relative to whole-child curricula, despite failing to boost measured classroom process quality.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860869/ The relationship between spatial configuration and functional connectivity of brain regions Janine Diane Bijsterbosch, Mark W. Woolrich, Matthew F. Glasser, Emma C. Robinson, Christian F. Beckmann, David C. Van Essen, Samuel J. Harrison, Stephen M. Smith 2018 2021-05-09 [("doi","10.7554/eLife.32992")] psychology/neuroscience statistics/variance-component

Brain connectivity is often considered in terms of the communication between functionally distinct brain regions. Many studies have investigated the extent to which patterns of coupling strength between multiple neural populations relates to behavior. For example, studies have used ‘functional connectivity fingerprints’ to characterise individuals’ brain activity.

Here, we investigate the extent to which the exact spatial arrangement of cortical regions interacts with measures of brain connectivity. We find that the shape and exact location of brain regions interact strongly with the modeling of brain connectivity, and present evidence that the spatial arrangement of functional regions is strongly predictive of non-imaging measures of behavior and lifestyle.

We believe that, in many cases, cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Therefore, a better understanding of these effects is important when interpreting the relationship between functional imaging data and cognitive traits.

--- https://arxiv.org/abs/1711.09883#deepmind AI Safety Gridworlds Jan Leike, Miljan Martic, Victoria Krakovna, Pedro A. Ortega, Tom Everitt, Andrew Lefrancq, Laurent Orseau, Shane Legg 2017-11-27 2021-05-09 [("doi","10.48550/arXiv.1711.09883")] reinforcement-learning/model-free reinforcement-learning/safe

We present a suite of reinforcement learning environments illustrating various safety properties of intelligent agents. These problems include safe interruptibility, avoiding side effects, absent supervisor, reward gaming, safe exploration, as well as robustness to self-modification, distributional shift, and adversaries.

To measure compliance with the intended safe behavior, we equip each environment with a performance function that is hidden from the agent. This allows us to categorize AI safety problems into robustness and specification problems, depending on whether the performance function corresponds to the observed reward function.

We evaluate A2C and Rainbow, two recent deep reinforcement learning agents, on our environments and show that they are not able to solve them satisfactorily.

--- https://arxiv.org/abs/1712.07420 Finding Competitive Network Architectures Within a Day Using UCT Martin Wistuba 2017-12-20 2021-05-09 [("doi","10.1109/DSAA.2018.00037")] reinforcement-learning/exploration

The design of neural network architectures for a new data set is a laborious task which requires human deep learning expertise. In order to make deep learning available for a broader audience, automated methods for finding a neural network architecture are vital. Recently proposed methods can already achieve human expert level performances. However, these methods have run times of months or even years of GPU computing time, ignoring hardware constraints as faced by many researchers and companies.

We propose the use of Monte Carlo planning in combination with two different UCT (upper confidence bound applied to trees) derivations to search for network architectures. We adapt the UCT algorithm to the needs of network architecture search by proposing two ways of sharing information between different branches of the search tree. In an empirical study we are able to demonstrate that this method is able to find competitive networks for MNIST, SVHN and CIFAR-10 in just a single GPU day. Extending the search time to five GPU days, we are able to outperform human architectures and our competitors which consider the same types of layers.

--- https://arxiv.org/abs/1801.00904 ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks Tae-Hoon Kim, Jonghyun Choi 2018-01-03 2021-05-09 [("doi","10.48550/arXiv.1801.00904")] reinforcement-learning/exploration/active-learning reinforcement-learning/meta-learning

We propose to learn a curriculum or a syllabus for supervised learning and deep reinforcement learning with deep neural networks by an attachable deep neural network, called ScreenerNet. Specifically, we learn a weight for each sample by jointly training the ScreenerNet and the main network in an end-to-end self-paced fashion. The ScreenerNet neither has sampling bias nor requires to remember the past learning history.

We show the networks augmented with the ScreenerNet achieve early convergence with better accuracy than the state-of-the-art curricular learning methods in extensive experiments using 3 popular vision datasets such as MNIST, CIFAR-10, and Pascal VOC2012, and a Cart-pole task using Deep Q-learning. Moreover, the ScreenerNet can extend other curriculum learning methods such as Prioritized Experience Replay (PER) for further accuracy improvement.

--- https://www.biorxiv.org/content/10.1101/225730.full Statistical power of clinical trials has increased whilst effect size remained stable: an empirical analysis of 137,032 clinical trials between 1975–2017 Herm J. Lamberink, Willem M. Otte, Michel R. T. Sinke, Daniël Lakens, Paul P. Glasziou, Joeri K. Tijdink, Christiaan H. Vinkers 2017-11-28 2021-05-09 [("doi","10.1101/225730")] statistics/power-analysis

Background: Biomedical studies with low statistical power are a major concern in the scientific community and are one of the underlying reasons for the reproducibility crisis in science. If randomized clinical trials, which are considered the backbone of evidence-based medicine, also suffer from low power, this could affect medical practice.

Method: We analysed the statistical power in 137,032 clinical trials between 1975 and 2017 extracted from meta-analyses from the Cochrane database of systematic reviews. We determined study power to detect standardized effect sizes according to Cohen, and in meta-analysis with p-value below 0.05 we based power on the meta-analysed effect size. Average power, effect size and temporal patterns were examined.

Results: The number of trials with power ≥80% was low but increased over time: from 9% in 1975–1979 to 15% in 2010–2014. This increase was mainly due to increasing sample sizes, whilst effect sizes remained stable with a median Cohen’s h of 0.21 (IQR 0.12–0.36) and a median Cohen’s d of 0.31 (0.19–0.51). The proportion of trials with power of at least 80% to detect a standardized effect size of 0.2 (small), 0.5 (moderate) and 0.8 (large) was 7%, 48% and 81%, respectively.

Conclusion: This study demonstrates that sufficient power in clinical trials is still problematic, although the situation is slowly improving. Our data encourages further efforts to increase statistical power in clinical trials to guarantee rigorous and reproducible evidence-based medicine.

--- https://arxiv.org/abs/1711.00043#facebook Unsupervised Machine Translation Using Monolingual Corpora Only Guillaume Lample, Alexis Conneau, Ludovic Denoyer, Marc’Aurelio Ranzato 2017-10-31 2021-05-09 [("doi","10.48550/arXiv.1711.00043")] ai/nn/rnn psychology/linguistics

Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to low-resource language pairs, yet requiring tens of thousands of parallel sentences. In this work, we take this research direction to the extreme and investigate whether it is possible to learn to translate even without any parallel data.

We propose a model that takes sentences from monolingual corpora in two different languages and maps them into the same latent space. By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data.

We demonstrate our model on two widely used datasets and two language pairs, reporting BLEU scores of 32.8 and 15.1 on the Multi30k and WMT English-French datasets, without using even a single parallel sentence at training time.

--- https://arxiv.org/abs/1710.09412 mixup: Beyond Empirical Risk Minimization Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz 2017-10-25 2021-05-09 [("doi","10.48550/arXiv.1710.09412")] ai/nn

Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples.

Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show that mixup improves the generalization of state-of-the-art neural network architectures. We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks.

In conclusion, mixup presents a novel and effective approach to enhancing neural network performance and robustness, demonstrating significant improvements across various datasets and models.

--- https://arxiv.org/abs/1710.09435 Malware Detection by Eating a Whole EXE Edward Raff, Jon Barker, Jared Sylvester, Robert Brandon, Bryan Catanzaro, Charles Nicholas 2017-10-25 2021-05-10 [("doi","10.48550/arXiv.1710.09435")] ai/nn technology

In this work we introduce malware detection from raw byte sequences as a fruitful research area to the larger machine learning community.

Building a neural network for such a problem presents a number of interesting challenges that have not occurred in tasks such as image processing or NLP. In particular, we note that detection from raw bytes presents a sequence problem with over two million time steps and a problem where batch normalization appears to hinder the learning process.

We present our initial work in building a solution to tackle this problem, which has linear complexity dependence on the sequence length, and allows for interpretable sub-regions of the binary to be identified.

In doing so we will discuss the many challenges in building a neural network to process data at this scale, and the methods we used to work around them.

--- https://arxiv.org/abs/1709.01507 Squeeze-and-Excitation Networks Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Enhua Wu 2017-09-05 2021-05-10 [("doi","10.48550/arXiv.1709.01507")] ai/nn/cnn

The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within local receptive fields at each layer. A broad range of prior research has investigated the spatial component of this relationship, seeking to strengthen the representational power of a CNN by enhancing the quality of spatial encodings throughout its feature hierarchy. In this work, we focus instead on the channel relationship and propose a novel architectural unit, which we term the “Squeeze-and-Excitation” (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modeling interdependencies between channels.

We show that these blocks can be stacked together to form SENet architectures that generalize extremely effectively across different datasets. We further demonstrate that SE blocks bring improvements in performance for existing state-of-the-art CNNs at slight additional computational cost.

Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017 classification submission which won first place and reduced the top-5 error to 2.251%, surpassing the winning entry of 2016 by a relative improvement of ~25%.

Models and code are available at Github.

--- https://www.biorxiv.org/content/10.1101/179317.full Genome-wide association study of habitual physical activity in over 277,000 UK Biobank participants identifies novel variants and genetic correlations with chronotype and obesity-related traits Yann C. Klimentidis, David A. Raichlen, Jennifer Bea, David O. Garcia, Lawrence J. Mandarino, Gene E. Alexander, Zhao Chen, Scott B. Going 2017-08-22 2021-05-10 [("doi","10.1101/179317")] exercise genetics/heritable

Physical activity (PA) protects against a wide range of diseases. Engagement in habitual PA has been shown to be heritable, motivating the search for specific genetic variants that explain variation in habitual PA and may ultimately improve efforts to promote PA and target the best type of PA for each individual.

We used data from the UK Biobank to perform the largest genome-wide association study of PA, using four measures based on self-report (n = 277,656) and accelerometry (n = 67,808). Replication was then sought in the Atherosclerosis Risk in Communities (ARIC) study (n = 8,556).

In the UK Biobank, we identified 17 genome-wide loci across the four PA measures. Interestingly, rs429358 of the APOE gene was the most strongly associated variant with any single PA measure and was at least nominally associated with 3 of the four PA measures examined. We also identified 3 loci (DNAJC1, DCAF5, and PML) consistently associated with PA across all four measures.

Tissue enrichment analyses implicate the brain and pituitary gland as locations where PA-associated loci may exert their actions. Genetic correlation analyses suggest a positive genetic correlation of PA with early-morning chronotype and psychiatric traits, and a negative genetic correlation of PA with obesity-related traits.

Using data from the GIANT consortium, we identify several loci that are associated with both increased waist circumference and decreased PA. Although very small effect sizes precluded replication of individual loci in ARIC, we found consistent overall genetic correlations of PA with other traits.

These results provide new insight into the genetic basis of habitual PA, and the genetic links connecting PA and obesity.

--- https://www.biorxiv.org/content/10.1101/203257.full Polygenic risk scores applied to a single cohort reveal pleiotropy among hundreds of human phenotypes Adam Socrates, Tom Bond, Ville Karhunen, Juha Auvinen, Cornelius A. Rietveld, Juha Veijola, Marjo-Riitta Jarvelin, Paul F. O’Reilly 2017-10-14 2021-05-10 [("doi","10.1101/203257")] genetics/heritable/correlation psychiatry/alzheimers

Background: There is now convincing evidence that pleiotropy across the genome contributes to the correlation between human traits and comorbidity of diseases. The recent availability of genome-wide association study (GWAS) results have made the polygenic risk score (PRS) approach a powerful way to perform genetic prediction and identify genetic overlap among phenotypes.

Methods & Findings

Here we use the PRS method to assess evidence for shared genetic aetiology across hundreds of traits within a single epidemiological study—the Northern Finland Birth Cohort 1966 (NFBC1966). We replicate numerous recent findings, such as a genetic association between Alzheimer’s disease and lipid levels, while the depth of phenotyping in the NFBC1966 highlights a range of novel genetic associations between traits.

Conclusion: This study illustrates the power in taking a hypothesis-free approach to the study of shared genetic aetiology between human traits and diseases. It also demonstrates the potential of the PRS method to provide important biological insights using only a single well-phenotyped epidemiological study of moderate sample size (~5k), with important advantages over evaluating genetic correlations from GWAS summary statistics only.

--- https://www.biorxiv.org/content/10.1101/185207.full Indirect assortative mating for human disease and longevity Konrad Rawlik, Oriol Canela-Xandri, Albert Tenesa 2017-09-07 2021-05-10 [("doi","10.1101/185207")] genetics/heritable/correlation

Phenotypic correlations of couples for phenotypes evident at the time of mate choice, like height, are well documented. Similarly, phenotypic correlations among partners for traits not directly observable at the time of mate choice, like longevity or late-onset disease status, have been reported. Partner correlations for longevity and late-onset disease are comparable in magnitude to correlations in 1st degree relatives. These correlations could arise as a consequence of convergence after mate choice, due to initial assortment on observable correlates of one or more risk factors (eg. BMI), referred to as indirect assortative mating, or both.

Using couples from the UK Biobank cohort, we show that longevity and disease history of the parents of white British couples is correlated. The correlations in parental longevity are replicated in the FamiLinx cohort. These correlations exceed what would be expected due to variations in lifespan based on year and location of birth. This suggests the presence of assortment on factors correlated with disease and lifespan, which show correlations across generations. Birth year, birth location, Townsend Deprivation Index, height, waist to hip ratio, BMI and smoking history of UK Biobank couples explained ~70% of the couple correlation in parental lifespan.

For cardiovascular diseases, in particular hypertension, we find statistically-significant correlations in genetic values among partners, which support a model where partners assort for risk factors genetically correlated with cardiovascular disease. Identifying the factors that mediate indirect assortment on longevity and human disease risk will help to unravel what factors affect human disease and ultimately longevity.

--- https://www.biorxiv.org/content/10.1101/218883.full Estimating heritability without environmental bias Alexander I. Young, Michael L. Frigge, Daniel F. Gudbjartsson, Gudmar Thorleifsson, Gyda Bjornsdottir, Patrick Sulem, Gisli Masson, Unnur Thorsteinsdottir, Kari Stefansson, Augustine Kong 2017-11-14 2021-05-10 [("doi","10.1101/218883")] genetics/heritable

Heritability measures the proportion of trait variation that is due to genetic inheritance. Measurement of heritability is of importance to the nature-versus-nurture debate.

However, existing estimates of heritability could be biased by environmental effects. Here we introduce relatedness disequilibrium regression (RDR), a novel method for estimating heritability. RDR removes environmental bias by exploiting variation in relatedness due to random segregation.

We use a sample of 54,888 Icelanders with both parents genotyped to estimate the heritability of 14 traits, including height (55.4%, S.E. 4.4%) and educational attainment (17.0%, S.E. 9.4%).

Our results suggest that some other estimates of heritability could be inflated by environmental effects.

--- /doc/genetics/heritable/2018-kong.pdf The nature of nurture: effects of parental genotypes Augustine Kong, Gudmar Thorleifsson, Michael L. Frigge, Bjarni J. Vilhjálmsson, Alexander I. Young, Thorgeir E. Thorgeirsson, Stefania Benonisdottir, Asmundur Oddsson, Bjarni V. Halldórsson, Gísli Masson, Daniel F. Gudbjartsson, Agnar Helgason, Gyda Bjornsdottir, Unnur Thorsteinsdottir, Kari Stefansson 2017-11-14 2021-05-10 [("doi","10.1101/219261")] genetics/heritable

Sequence variants in the parental genomes that are not transmitted to a child/proband are often ignored in genetic studies. Here we show that non-transmitted alleles can impact a child through their effects on the parents and other relatives, a phenomenon we call genetic nurture. Using results from a meta-analysis of educational attainment, the polygenic score computed for the non-transmitted alleles of 21,637 probands with at least one parent genotyped has an estimated effect on the educational attainment of the proband that is 29.9% (P = 1.6×10−14) of that of the transmitted polygenic score. Genetic nurturing effects of this polygenic score extend to other traits. Paternal and maternal polygenic scores have similar effects on educational attainment, but mothers contribute more than fathers to nutrition/heath related traits.

One Sentence Summary

Nurture has a genetic component, i.e. alleles in the parents affect the parents’ phenotypes and through that influence the outcomes of the child.

--- /doc/genetics/heritable/2018-evangelou.pdf Genetic analysis of over one million people identifies 535 novel loci for blood pressure Evangelos Evangelou, Helen R. Warren, David Mosen-Ansorena, Borbala Mifsud, Raha Pazoki, He Gao, Georgios Ntritsos, Niki Dimou, Claudia P. Cabrera, Ibrahim Karaman, Fu Liang Ng, Marina Evangelou, Katarzyna Witkowska, Evan Tzanis, Jacklyn N. Hellwege, Ayush Giri, Digna R. Velez Edwards, Yan V. Sun, Kelly Cho, J. Michael Gaziano, Peter W. F. Wilson, Philip S. Tsao, Csaba P. Kovesdy, Tõnu Esko, Reedik Mägi, Lili Milani, Peter Almgren, Thibaud Boutin, Stéphanie Debette, Jun Ding, Franco Giulianini, Elizabeth G. Holliday, Anne Uriu Jackson, Ruifang Li-Gao, Wei-Yu Lin, Jian’an Luan, Massimo Mangino, Christopher Oldmeadow, Bram Prins, Yong Qian, Muralidharan Sargurupremraj, Nabi Shah, Praveen Surendran, Sébastien Thériault, Niek Verweij, Sara M. Willems, Jing-Hua Zhao, Philippe Amouyel, John Connell, Renée de Mutsert, Alex S. F. Doney, Martin Farrall, Cristina Menni, Andrew D. Morris, Raymond Noordam, Guillaume Paré, Neil R. Poulter, Denis C. Shields, Alice Stanton, Simon Thom, Gonçalo Abecasis, Najaf Amin, Dan E. Arking, Kristin L. Ayers, Caterina M. Barbieri, Chiara Batini, Joshua C. Bis, Tineka Blake, Murielle Bochud, Michael Boehnke, Eric Boerwinkle, Dorret I. Boomsma, Erwin Böttinger, Peter S. Braund, Marco Brumat, Archie Campbell, Harry Campbell, Aravinda Chakravarti, John C. Chambers, Ganesh Chauhan, Marina Ciullo, Massimiliano Cocca, Francis Collins, Heather J. Cordell, Gail Davies, Martin H. de Borst, Eco J. C. de Geus, Ian J. Deary, Joris Deelen, Fabiola M. Del Greco, Cumhur Yusuf Demirkale, Marcus Dörr, Georg B. Ehret, Roberto Elosua, Stefan Enroth, A. Mesut Erzurumluoglu, Teresa Ferreira, Mattias Frånberg, Oscar H. Franco, Ilaria Gandin, Paolo Gasparini, Vilmantas Giedraitis, Christian Gieger, Giorgia Girotto, Anuj Goel, Alan J. Gow, Vilmundur Gudnason, Xiuqing Guo, Ulf Gyllensten, Anders Hamsten, Tamara B. Harris, Sarah E. Harris, Catharina A. Hartman, Aki S. Havulinna, Andrew A. Hicks, Edith Hofer, Albert Hofman, Jouke-Jan Hottenga, Jennifer E. Huffman, Shih-Jen Hwang, Erik Ingelsson, Alan James, Rick Jansen, Marjo-Riitta Jarvelin, Roby Joehanes, Åsa Johansson, Andrew D. Johnson, Peter K. Joshi, Pekka Jousilahti, J. Wouter Jukema, Antti Jula, Kähönen Mika, Sekar Kathiresan, Bernard D. Keavney, Kay-Tee Khaw, Paul Knekt, Joanne Knight, Ivana Kolcic, Jaspal S. Kooner, Seppo Koskinen, Kati Kristiansson, Zoltán Kutalik, Maris Laan, Marty Larson, Lenore J. Launer, Benjamin C. Lehne, Terho Lehtimäki, Daniel Levy, David C. M. Liewald, Li Lin, Lars L. Lind, Cecilia M. Lindgren, Yongmei Liu, Ruth Loos, Lorna M. Lopez, Lingchan Lu, Leo-Pekka Lyytikäinen, Anubha Mahajan, Chrysovalanto Mamasoula, Jaume Marrugat, Jonathan Marten, Yuri Milaneschi, Anna Morgan, Andrew P. Morris, Alanna C. Morrison, Peter J. Munson, Mike A. Nalls, Priyanka Nandakumar, Christopher P. Nelson, Christopher Newton-Cheh, Teemu Niiranen, Ilja M. Nolte, Teresa Nutile, Albertine J. Oldehinkel, Ben A. Oostra, Paul F. O’Reilly, Elin Org, Sandosh Padmanabhan, Walter Palmas, Arno Palotie, Alison Pattie, Brenda W. J. H. Penninx, Markus Perola, Annette Peters, Ozren Polasek, Peter P. Pramstaller, Nguyen Quang Tri, Olli T. Raitakari, Meixia Ren, Rainer Rettig, Kenneth Rice, Paul M. Ridker, Janina S. Reid, Harriëtte Riese, Samuli Ripatti, Antonietta Robino, Lynda M. Rose, Jerome I. Rotter, Igor Rudan, Daniella Ruggiero, Yasaman Saba, Cinzia F. Sala, Veikko Salomaa, Nilesh J. Samani, Antti-Pekka Sarin, Rheinhold Schmidt, Helena Schmidt, Nick Shrine, David Siscovick, Albert Vernon Smith, Harold Schneider, Siim Sõber, Rossella Sorice, John M. Starr, David J. Stott, David P. Strachan, Rona J. Strawbridge, Johan Sundström, Morris A. Swertz, Kent D. Taylor, Alexander Teumer, Martin D. Tobin, Daniela Toniolo, Michela Traglia, Stella Trompet, Jaakko Tuomilehto, Christophe Tzourio, André G. Uitterlinden, Ahmad Vaez, Peter J. van der Most, Cornelia van Duijn, Anne-Claire Vergnaud, Germaine C. Verwoert, Veronique Vitart, Uwe Völker, Peter Vollenweider, Dragana Vuckovic, Hugh Watkins, Sarah H. Wild, Gonneke Willemsen, James F. Wilson, Alan F. Wright, Jie Yao, Tatijana Zemunik, Weihua Zhang, John R. Attia, Adam S. Butterworth, Dan Chasman, David Conen, Francesco Cucca, John Danesh, Caroline Hayward, Joanna M. M. Howson, Markku Laakso, Edward G. Lakatta, Claudia Langenberg, Ollie Melander, Dennis O. Mook-Kanamori, Patricia B. Munroe, Colin Palmer, Lorenz Risch, Robert A. Scott, Rodney J. Scott, Peter Sever, Tim D. Spector, Pim van der Harst, Nicholas J. Wareham, Eleftheria Zeggini, Morris J. Brown, Andres Metspalu, Adriana M. Hung, Christopher J. O’Donnell, Todd L. Edwards, on behalf of the Million Veteran Program, Bruce M. Psaty, Ioanna Tzoulaki, Michael R. Barnes, Louise V. Wain, Paul Elliott, Mark J. Caulfield 2017-10-11 2021-05-10 [("doi","10.1101/198234")] genetics/heritable

High blood pressure is the foremost heritable global risk factor for cardiovascular disease.

We report the largest genetic association study of blood pressure traits to date (systolic, diastolic, pulse pressure) in over one million people of European ancestry.

We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also reveal shared loci influencing lifestyle exposures.

Our findings offer the potential for a precision medicine strategy for future cardiovascular disease prevention.

--- https://www.biorxiv.org/content/10.1101/188094.full Genetic Diversity Turns a New PAGE in Our Understanding of Complex Traits Genevieve L. Wojcik, Mariaelisa Graff, Katherine K. Nishimura, Ran Tao, Jeffrey Haessler, Christopher R. Gignoux, Heather M. Highland, Yesha M. Patel, Elena P. Sorokin, Christy L. Avery, Gillian M. Belbin, Stephanie A. Bien, Iona Cheng, Chani J. Hodonsky, Laura M. Huckins, Janina Jeff, Anne E. Justice, Jonathan M. Kocarnik, Unhee Lim, Bridget M. Lin, Yingchang Lu, Sarah C. Nelson, Sung-Shim L. Park, Michael H. Preuss, Melissa A. Richard, Claudia Schurmann, Veronica W. Setiawan, Karan Vahi, Abhishek Vishnu, Marie Verbanck, Ryan Walker, Kristin L. Young, Niha Zubair, Jose Luis Ambite, Eric Boerwinkle, Erwin Böttinger, Carlos D. Bustamante, Christian Caberto, Matthew P. Conomos, Ewa Deelman, Ron Do, Kimberly Doheny, Lindsay Fernandez-Rhodes, Myriam Fornage, Gerardo Heiss, Lucia A. Hindorff, Rebecca D. Jackson, Regina James, Cecelia A. Laurie, Cathy C. Laurie, Yuqing Li, Dan-Yu Lin, Girish Nadkarni, Loreall C. Pooler, Alexander P. Reiner, Jane Romm, Chiara Sabati, Xin Sheng, Eli Ayumi Stahl, Daniel O. Stram, Timothy A. Thornton, Christina L. Wassel, Lynne R. Wilkens, Sachi Yoneyama, Steven Buyske, Chris Haiman, Charles Kooperberg, Loic Le Marchand, Ruth Loos, Tara C. Matise, Kari E. North, Ulrike Peters, Eimear E. Kenny, Christopher S. Carlson 2017-09-15 2021-05-10 [("doi","10.1101/188094")] genetics/heritable

Genome-wide association studies (GWAS) have laid the foundation for many downstream investigations, including the biology of complex traits, drug development, and clinical guidelines. However, the dominance of European-ancestry populations in GWAS creates a biased view of human variation and hinders the translation of genetic associations into clinical and public health applications.

To demonstrate the benefit of studying underrepresented populations, the Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioral phenotypes in 49,839 non-European individuals. Using novel strategies for multi-ethnic analysis of admixed populations, we:

confirm 574 GWAS catalog variants across these traits, and find 28 novel loci and 42 residual signals in known loci.

Our data show strong evidence of effect-size heterogeneity across ancestries for published GWAS associations, which substantially restricts genetically-guided precision medicine.

We advocate for new, large genome-wide efforts in diverse populations to reduce health disparities.

--- https://www.biorxiv.org/content/10.1101/038141.full Signatures of positive selection and local adaptation to urbanization in white-footed mice (Peromyscus leucopus) Stephen E. Harris, Jason Munshi-South 2017-09-26 2021-05-10 [("doi","10.1101/038141")] genetics/selection/natural

Urbanization alters natural ecosystems and has accelerated globally. Urban wildlife populations are often highly fragmented by human infrastructure, and isolated populations may adapt in response to local urban pressures. However, relatively few studies have identified genomic signatures of adaptation in urban animals.

We used a landscape genomics approach to examine signatures of selection in urban populations of white-footed mice (Peromyscus leucopus) in New York City. We analyzed 154,770 SNPs identified from transcriptome data from 48 P. leucopus individuals from 3 urban and 3 rural populations, and used outlier tests to identify evidence of urban adaptation. We accounted for demography by simulating a neutral SNP dataset under an inferred demographic history as a null model for outlier analysis.

We also tested whether candidate genes were associated with environmental variables related to urbanization. In total, we detected 381 outlier loci and after stringent filtering, identified and annotated 19 candidate loci. Many of the candidate genes were involved in metabolic processes, and have well-established roles in metabolizing lipids and carbohydrates.

Our results indicate that white-footed mice in NYC are adapting at the biomolecular level to local selective pressures in urban habitats. Annotation of outlier loci suggest selection is acting on metabolic pathways in urban populations, likely related to novel diets in cities that differ from diets in less disturbed areas.

--- https://www.biorxiv.org/content/10.1101/167551.full Polygenic Adaptation has Impacted Multiple Anthropometric Traits Jeremy J. Berg, Xinjun Zhang, Graham Coop 2017-11-06 2021-05-10 [("doi","10.1101/167551")] genetics/selection/natural/human

Our understanding of the genetic basis of human adaptation is biased toward loci of large phenotypic effect. Genome wide association studies (GWAS) now enable the study of genetic adaptation in highly polygenic phenotypes. Here we test for polygenic adaptation among 187 world-wide human populations using polygenic scores constructed from GWAS of 34 complex traits. Comparing these polygenic scores to a null distribution under genetic drift, we identify strong signals of selection for a suite of anthropometric traits including height, infant head circumference (IHC), hip circumference and waist-to-hip ratio (WHR), as well as type 2 diabetes (T2D). In addition to the known north-south gradient of polygenic height scores within Europe, we find that natural selection has contributed to a gradient of decreasing polygenic height scores from West to East across Eurasia. Analyzing a set of ancient DNA samples from across Eurasia, we show that much of this gradient can be explained by selection for increased height in two long diverged hunter-gatherer populations living in western and west-central Eurasia sometime during or shortly after the last glacial maximum. We find that the signal of selection on hip circumference can largely be explained as a correlated response to selection on height. However, our signals in IHC and WHR cannot, suggesting that these patterns are the result of selection along multiple axes of body shape variation. Our observation that IHC and WHR polygenic scores follow a strong latitudinal cline in Western Eurasia support the role of natural selection in establishing Bergmann’s Rule in humans, and are consistent with thermoregulatory adaptation in response to latitudinal temperature variation.

One Sentence Summary

Natural selection has lead to divergence in multiple quantitative traits in humans across Eurasian populations.

--- https://www.biorxiv.org/content/10.1101/183525.full Brain structure mediates the association between height and cognitive ability Eero Vuoksimaa, Matthew S. Panizzon, Carol E. Franz, Christine Fennema-Notestine, Donald J. Hagler, Michael J. Lyons, Anders Martin Dale, William S. Kremen 2017-09-01 2021-05-11 [("doi","10.1101/183525")] iq psychology/neuroscience

Height and general cognitive ability (GCA) are positively associated, but the underlying mechanisms of this relationship are unclear.

We used a sample of 515 middle-aged male twins with structural magnetic resonance imaging data to study if the association between height and cognitive ability is mediated by cortical size. We used genetically, ontogenetically, and phylogenetically distinct cortical metrics of cortical surface area (SA) and cortical thickness (CT).

Our results indicate that the well-replicated height-GCA association is accounted for by individual differences in total cortical SA (a highly heritable metric related to global brain size), and not mean CT, and that the genetic association between SA and GCA underlies the phenotypic height-GCA relationship.

--- https://arxiv.org/abs/1711.07364 Classification with Costly Features using Deep Reinforcement Learning Jaromír Janisch, Tomáš Pevný, Viliam Lisý 2017-11-20 2021-05-11 [("doi","10.48550/arXiv.1711.07364")] reinforcement-learning/exploration/active-learning reinforcement-learning/model-free

We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature cost. We revisit a former approach that has framed the problem as a sequential decision-making problem and solved it by Q-learning with a linear approximation, where individual actions are either requests for feature values or terminate the episode by providing a classification decision.

On a set of 8 problems, we demonstrate that by replacing the linear approximation with neural networks the approach becomes comparable to the state-of-the-art algorithms developed specifically for this problem. The approach is flexible, as it can be improved with any new reinforcement learning enhancement, it allows inclusion of pre-trained high-performance classifiers, and unlike prior art, its performance is robust across all evaluated datasets.

--- https://arxiv.org/abs/1711.03689 Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection Taku Kato, Takahiro Shinozaki 2017-11-10 2021-05-11 [("doi","10.48550/arXiv.1711.03689")] reinforcement-learning/model-free

Speech recognition systems have achieved high recognition performance for several tasks. However, the performance of such systems is dependent on the tremendously costly development work of preparing vast amounts of task-matched transcribed speech data for supervised training. The key problem here is the cost of transcribing speech data. The cost is repeatedly required to support new languages and new tasks. Assuming broad network services for transcribing speech data for many users, a system would become more self-sufficient and more useful if it possessed the ability to learn from very light feedback from the users without annoying them.

In this paper, we propose a general reinforcement learning framework for speech recognition systems based on the policy gradient method. As a particular instance of the framework, we also propose a hypothesis selection-based reinforcement learning method. The proposed framework provides a new view for several existing training and adaptation methods. The experimental results show that the proposed method improves the recognition performance compared to unsupervised adaptation.

--- https://arxiv.org/abs/1711.03859 Towards the Use of Deep Reinforcement Learning with Global Policy For Query-based Extractive Summarization Diego Molla 2017-11-10 2021-05-11 [("doi","10.48550/arXiv.1711.03859")] reinforcement-learning/model-free

Supervised approaches for text summarization suffer from the problem of mismatch between the target labels/scores of individual sentences and the evaluation score of the final summary. Reinforcement learning can solve this problem by providing a learning mechanism that uses the score of the final summary as a guide to determine the decisions made at the time of selection of each sentence.

In this paper we present a proof-of-concept approach that applies a policy-gradient algorithm to learn a stochastic policy using an undiscounted reward.

The method has been applied to a policy consisting of a simple neural network and simple features.

The resulting deep reinforcement learning system is able to learn a global policy and obtain encouraging results.

--- https://arxiv.org/abs/1710.05958 Gradient-free Policy Architecture Search and Adaptation Sayna Ebrahimi, Anna Rohrbach, Trevor Darrell 2017-10-16 2021-05-11 [("doi","10.48550/arXiv.1710.05958")] reinforcement-learning/model-free

We develop a method for policy architecture search and adaptation via gradient-free optimization which can learn to perform autonomous driving tasks. By learning from both demonstration and environmental reward we develop a model that can learn with relatively few early catastrophic failures.

We first learn an architecture of appropriate complexity to perceive aspects of world state relevant to the expert demonstration, and then mitigate the effect of domain-shift during deployment by adapting a policy demonstrated in a source domain to rewards obtained in a target environment.

We show that our approach allows safer learning than baseline methods, offering a reduced cumulative crash metric over the agent’s lifetime as it learns to drive in a realistic simulated environment.

--- https://arxiv.org/abs/1709.06560 Deep Reinforcement Learning that Matters Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger 2017-09-19 2021-05-11 [("doi","10.48550/arXiv.1709.06560")] reinforcement-learning/model-free statistics/bias

In recent years, progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress.

Unfortunately, reproducing results for state-of-the-art deep RL methods is seldom straightforward. In particular, non-determinism in standard benchmark environments, combined with variance intrinsic to the methods, can make reported results tough to interpret. Without statistical-significance metrics and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the prior state-of-the-art are meaningful.

In this paper, we investigate challenges posed by reproducibility, proper experimental techniques, and reporting procedures. We illustrate the variability in reported metrics and results when comparing against common baselines and suggest guidelines to make future results in deep RL more reproducible.

We aim to spur discussion about how to ensure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted.

--- https://arxiv.org/abs/1711.07979 Posterior Sampling for Large Scale Reinforcement Learning Georgios Theocharous, Zheng Wen, Yasin Abbasi-Yadkori, Nikos Vlassis 2017-11-21 2021-05-11 [("doi","10.48550/arXiv.1711.07979")] reinforcement-learning/exploration statistics/bayes

We propose a practical non-episodic PSRL algorithm that unlike recent state-of-the-art PSRL algorithms uses a deterministic, model-independent episode switching schedule.

Our algorithm termed deterministic schedule PSRL (DS-PSRL) is efficient in terms of time, sample, and space complexity. We prove a Bayesian regret bound under mild assumptions. Our result is more generally applicable to multiple parameters and continuous state action problems.

We compare our algorithm with state-of-the-art PSRL algorithms on standard discrete and continuous problems from the literature.

Finally, we show how the assumptions of our algorithm satisfy a sensible parametrization for a large class of problems in sequential recommendations.

--- https://arxiv.org/abs/1711.08393 BlockDrop: Dynamic Inference Paths in Residual Networks Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S. Davis, Kristen Grauman, Rogerio Feris 2017-11-22 2021-05-11 [("doi","10.48550/arXiv.1711.08393")] ai/nn/cnn reinforcement-learning/exploration/active-learning reinforcement-learning/meta-learning

Very deep convolutional neural networks offer excellent recognition results, yet their computational expense limits their impact for many real-world applications. We introduce BlockDrop, an approach that learns to dynamically choose which layers of a deep network to execute during inference so as to best reduce total computation without degrading prediction accuracy. Exploiting the robustness of Residual Networks (ResNets) to layer dropping, our framework selects on-the-fly which residual blocks to evaluate for a given novel image.

In particular, given a pretrained ResNet, we train a policy network in an associative reinforcement learning setting for the dual reward of using a minimal number of blocks while preserving recognition accuracy. We conduct extensive experiments on CIFAR and ImageNet.

The results provide strong quantitative and qualitative evidence that these learned policies not only accelerate inference but also encode meaningful visual information. Built upon a ResNet-101 model, our method achieves a speedup of 20% on average, going as high as 36% for some images, while maintaining the same 76.4% top-1 accuracy on ImageNet.

--- https://arxiv.org/abs/1711.06892 Learning to select computations Frederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths, Falk Lieder 2017-11-18 2021-05-11 [("doi","10.48550/arXiv.1711.06892")] reinforcement-learning/meta-learning

The efficient use of limited computational resources is an essential ingredient of intelligence. Selecting computations optimally according to rational metareasoning would achieve this, but this is computationally intractable. Inspired by psychology and neuroscience, we propose the first concrete and domain-general learning algorithm for approximating the optimal selection of computations: Bayesian metalevel policy search (BMPS).

We derive this general, sample-efficient search algorithm for a computation-selecting metalevel policy based on the insight that the value of information lies between the myopic value of information and the value of perfect information. We evaluate BMPS on 3 increasingly difficult metareasoning problems: when to terminate computation, how to allocate computation between competing options, and planning. Across all 3 domains, BMPS achieved near-optimal performance and compared favorably to previously proposed metareasoning heuristics.

Finally, we demonstrate the practical utility of BMPS in an emergency management scenario, even accounting for the overhead of metareasoning.

--- https://arxiv.org/abs/1708.09823 Better Decision Making in Drug Development Through Adoption of Formal Prior Elicitation Nigel Dallow, Nicky Best, Timothy Montague 2017-08-31 2021-05-11 [("doi","10.48550/arXiv.1708.09823")] statistics/bayes statistics/decision

With the continued increase in the use of Bayesian methods in drug development, there is a need for statisticians to have tools to develop robust and defensible informative prior distributions. Whilst relevant empirical data should, where possible, provide the basis for such priors, it is often the case that limitations in data and/or our understanding may preclude direct construction of a data-based prior. Formal expert elicitation methods are a key technique that can be used to determine priors in these situations.

Within GlaxoSmithKline (GSK), we have adopted a structured approach to prior elicitation based on the SHELF elicitation framework, and routinely use this in conjunction with calculation of probability of success (assurance) of the next study(s) to inform internal decision making at key project milestones.

The aim of this paper is to share our experiences of embedding the use of prior elicitation within a large pharmaceutical company, highlighting both the benefits and challenges of prior elicitation through a series of case studies. We have found that putting team beliefs into the shape of a quantitative probability distribution provides a firm anchor for all internal decision making, enabling teams to provide investment boards with formally appropriate estimates of the probability of trial success as well as robust plans for interim decision rules where appropriate.

As an added benefit, the elicitation process provides transparency about the beliefs and risks of the potential medicine, ultimately enabling better portfolio and company-wide decision making.

--- https://arxiv.org/abs/1708.07487 The prior can generally only be understood in the context of the likelihood Andrew Gelman, Daniel Simpson, Michael Betancourt 2017-08-24 2021-05-12 [("doi","10.3390/e19100555")] statistics/bayes

A key sticking point of Bayesian analysis is the choice of prior distribution, and there is a vast literature on potential defaults including uniform priors, Jeffreys’ priors, reference priors, maximum entropy priors, and weakly informative priors.

These methods, however, often manifest a key conceptual tension in prior modeling: a model encoding true prior information should be chosen without reference to the model of the measurement process, but almost all common prior modeling techniques are implicitly motivated by a reference likelihood.

In this paper we resolve this apparent paradox by placing the choice of prior into the context of the entire Bayesian analysis, from inference to prediction to model evaluation.

--- https://www.biorxiv.org/content/10.1101/183962.full What exactly is ‘N’ in cell culture and animal experiments? Stanley E. Lazic, Charlie J. Clarke-Williams, Marcus R. Munafò 2017-09-02 2021-05-12 [("doi","10.1101/183962")] statistics/bias/animal statistics/power-analysis

Biologists establish the existence of experimental effects by applying treatments or interventions to biological entities or units, such as people, animals, slice preparations, or cells. When done appropriately, independent replication of the entity-intervention pair contributes to the sample size (N) and forms the basis of statistical inference. However, sometimes the appropriate entity-intervention pair may not be obvious, and the wrong choice can make an experiment worthless.

We surveyed a random sample of published animal experiments from 2011–2016 where interventions were applied to parents but effects examined in the offspring, as regulatory authorities have provided clear guidelines on replication with such designs. We found that only 22% of studies (95% CI = 17% to 29%) replicated the correct entity-intervention pair and thus made valid statistical inferences. ~half of the studies (46%, 95% CI = 38% to 53%) had pseudoreplication while 32% (95% CI = 26% to 39%) provided insufficient information to make a judgement. Pseudoreplication artificially inflates the sample size, leading to more false positive results and inflating the apparent evidence supporting a scientific claim. It is hard for science to advance when so many experiments are poorly designed and analysed.

We argue that distinguishing between biological units, experimental units, and observational units clarifies where replication should occur, describe the criteria for genuine replication, and provide guidelines for designing and analysing in vitro, ex vivo, and in vivo experiments.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1810019/pdf/15252975.pdf Syphilis in renaissance Europe: rapid evolution of an introduced sexually transmitted disease? Robert J. Knell 2004 2021-05-12 [("doi","10.1098/rsbl.2003.0131")] biology genetics/selection/natural history

When syphilis first appeared in Europe in 1495, it was an acute and extremely unpleasant disease.

After only a few years, it was less severe than it once was, and it changed over the next 50 years into a milder, chronic disease.

The severe early symptoms may have been the result of the disease being introduced into a new host population without any resistance mechanisms, but the change in virulence is most likely to have happened because of selection favoring milder strains of the pathogen.

The symptoms of the virulent early disease were both debilitating and obvious to potential sexual partners of the infected, and strains that caused less obvious or painful symptoms would have enjoyed a higher transmission rate.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC298679/ Least effort and the origins of scaling in human language Ramon Ferrer i Cancho, Ricard V. Sole 2003 2021-05-12 [("doi","10.1073/pnas.0335980100")] cs/algorithm psychology/linguistics

The emergence of a complex language is one of the fundamental events of human evolution, and several remarkable features suggest the presence of fundamental principles of organization. These principles seem to be common to all languages. The best known is the so-called Zipf’s law, which states that the frequency of a word decays as a (universal) power law of its rank. The possible origins of this law have been controversial, and its meaningfulness is still an open question.

In this article, the early hypothesis of Zipf of a principle of least effort for explaining the law is shown to be sound. Simultaneous minimization in the effort of both hearer and speaker is formalized with a simple optimization process operating on a binary matrix of signal-object associations. Zipf’s law is found in the transition between referentially useless systems and indexical reference systems. Our finding strongly suggests that Zipf’s law is a hallmark of symbolic reference and not a meaningless feature. The implications for the evolution of language are discussed.

We explain how language evolution can take advantage of a communicative phase transition.

--- https://arxiv.org/abs/1708.04890 A deep architecture for unified esthetic prediction Naila Murray, Albert Gordo 2017-08-16 2021-05-12 [("doi","10.48550/arXiv.1708.04890")] ai/nn/cnn design reinforcement-learning/preference-learning

Image esthetics has become an important criterion for visual content curation on social media sites and media content repositories. Previous work on esthetic prediction models in the computer vision community has focused on esthetic score prediction or binary image labeling. However, raw esthetic annotations are in the form of score histograms and provide richer and more precise information than binary labels or mean scores. Consequently, in this work we focus on the rarely-studied problem of predicting esthetic score distributions and propose a novel architecture and training procedure for our model.

Our model achieves state-of-the-art results on the standard AVA large-scale benchmark dataset for 3 tasks: (1) esthetic quality classification; (2) esthetic score regression; and (3) esthetic score distribution prediction, all while using one model trained only for the distribution prediction

task. We also introduce a method to modify an image such that its predicted esthetics changes, and use this modification to gain insight into our model.

--- https://arxiv.org/abs/1708.04483 Learning with Rethinking: Recurrently Improving Convolutional Neural Networks through Feedback Xin Li, Zequn Jie, Jiashi Feng, Changsong Liu, Shuicheng Yan 2017-08-15 2021-05-12 [("doi","10.48550/arXiv.1708.04483")] ai/nn/cnn

Recent years have witnessed the great success of convolutional neural network (CNN) based models in the field of computer vision. CNN is able to learn hierarchically abstracted features from images in an end-to-end training manner. However, most of the existing CNN models only learn features through a feedforward structure and no feedback information from top to bottom layers is exploited to enable the networks to refine themselves.

In this paper, we propose a “Learning with Rethinking” algorithm. By adding a feedback layer and producing the emphasis vector, the model is able to recurrently boost the performance based on previous prediction. Particularly, it can be employed to boost any pre-trained models.

This algorithm is tested on 4 object classification benchmark datasets: CIFAR-100, CIFAR-10, MNIST-background-image and ILSVRC-2012 dataset. These results have demonstrated the advantage of training CNN models with the proposed feedback mechanism.

--- https://arxiv.org/abs/1708.02002#facebook Focal Loss for Dense Object Detection Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár 2017-08-07 2021-05-12 [("doi","10.48550/arXiv.1708.02002")] ai/nn/cnn

The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far.

In this paper, we investigate why this is the case. We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.

To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors. Code is at: Github.

--- https://arxiv.org/abs/1707.09627 Learning to Infer Graphics Programs from Hand-Drawn Images Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, Joshua B. Tenenbaum 2017-07-30 2021-05-12 [("doi","10.48550/arXiv.1707.09627")] ai/nn/cnn design/typography/tex

We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of LaTeX.

The model combines techniques from deep learning and program synthesis. We learn a convolutional neural network that proposes plausible drawing primitives that explain an image. These drawing primitives are like a trace of the set of primitive commands issued by a graphics program. We learn a model that uses program synthesis techniques to recover a graphics program from that trace. These programs have constructs like variable bindings, iterative loops, or simple kinds of conditionals.

With a graphics program in hand, we can correct errors made by the deep network, measure similarity between drawings by use of similar high-level geometric structures, and extrapolate drawings.

Taken together these results are a step towards agents that induce useful, human-readable programs from perceptual input.

--- https://arxiv.org/abs/1706.03762#google Attention Is All You Need Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin 2017-06-12 2021-05-12 [("doi","10.48550/arXiv.1706.03762")] ai/nn/transformer/attention

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism.

We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.

Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring statistically-significantly less time to train.

Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on 8 GPUs, a small fraction of the training costs of the best models from the literature.

We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

...Authorship: equal contribution. Listing order is random. Jakob Uszkoreit proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish Vaswani, with Illia Polosukhin, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam Shazeer proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki Parmer designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion Jones also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz Kaiser and Aidan Gomez spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.

--- https://arxiv.org/abs/1706.05137 One Model To Learn Them All Łukasz Kaiser, Aidan N. Gomez, Noam Shazeer, Ashish Vaswani, Niki Parmar, Llion Jones, Jakob Uszkoreit 2017-06-16 2021-05-12 [("doi","10.48550/arXiv.1706.05137")] ai/scaling/mixture-of-experts

Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning.

We present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning (COCO dataset), a speech recognition corpus, and an English parsing task. Our model architecture incorporates building blocks from multiple domains. It contains convolutional layers, an attention mechanism, and sparsely-gated layers.

Each of these computational blocks is crucial for a subset of the tasks we train on. Interestingly, even if a block is not crucial for a task, we observe that adding it never hurts performance and in most cases improves it on all tasks.

We also show that tasks with less data benefit largely from joint training with other tasks, while performance on large tasks degrades only slightly if at all.

--- https://arxiv.org/abs/1706.04188 Preprint Déjà Vu: an FAQ P. Ginsparg 2017-06-13 2021-05-12 [("doi","10.15252/embj.201695531")] economics/copyright science

I give a brief overview of arXiv history, and describe the current state of arXiv practice, both technical and sociological.

This commentary originally appeared in the EMBO Journal, 19 Oct 2016. It was intended as an update on comments from the late 1990s regarding use of preprints by biologists (or lack thereof), but may be of interest to practitioners of other disciplines.

It is based largely on a keynote presentation I gave to the ASAPbio inaugural meeting in February 2016, and responds as well to some follow-up questions.

--- https://www.biorxiv.org/content/10.1101/160291.full A combined analysis of genetically correlated traits identifies 107 loci associated with intelligence W. David Hill, Gail Davies, A. M. McIntosh, C. R. Gale, I. J. Deary 2017-07-07 2021-05-13 [("doi","10.1101/160291")] genetics/heritable/correlation iq

Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including many physical and mental health variables. Both education and household income are strongly genetically correlated with intelligence, at rg =0.73 and rg =0.70 respectively. This allowed us to use a novel approach, Multi-Trait Analysis of Genome-wide association studies (MTAG; Turley et al 2017), to combine two large genome-wide association studies (GWASs) of education and household income to increase power in the largest GWAS on intelligence so far (Sniekers et al 2017).

This study had 4 goals: firstly, to facilitate the discovery of new genetic loci associated with intelligence; secondly, to add to our understanding of the biology of intelligence differences; thirdly, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predict phenotypic intelligence variance in an independent sample.

We apply MTAG to 3 large GWAS: Sniekers et al 2017 on intelligence, Okbay et al 2016 on Educational attainment, and Hill et al 2016 on household income. By combining these 3 samples our functional sample size increased from 78,308 participants to 147,194. We found 107 independent loci associated with intelligence, implicating 233 genes, using both SNP-based and gene-based GWAS. We find evidence that neurogenesis may explain some of the biological differences in intelligence as well as genes expressed in the synapse and those involved in the regulation of the nervous system.

We show that the results of our combined analysis demonstrate the same pattern of genetic correlations as a single measure/the simple measure of intelligence, providing support for the meta-analysis of these genetically-related phenotypes. We find that our MTAG meta-analysis of intelligence shows similar genetic correlations to 26 other phenotypes when compared with a GWAS consisting solely of cognitive tests.

Finally, using an independent sample of 6,844 individuals we were able to predict 7% of intelligence using SNP data alone.

--- https://www.biorxiv.org/content/10.1101/150540.full Environmental factors dominate over host genetics in shaping human gut microbiota composition Daphna Rothschild, Omer Weissbrod, Elad Barkan, Tal Korem, David Zeevi, Paul I. Costea, Anastasia Godneva, Iris Kalka, Noam Bar, Niv Zmora, Meirav Pevsner-Fischer, David Israeli, Noa Kosower, Gal Malka, Bat Chen Wolf, Tali Avnit-Sagi, Maya Lotan-Pompan, Adina Weinberger, Zamir Halpern, Shai Carmi, Eran Elinav, Eran Segal 2017-06-26 2021-05-13 [("doi","10.1101/150540")] genetics/microbiome statistics/variance-component

Human gut microbiome composition is shaped by multiple host intrinsic and extrinsic factors, but the relative contribution of host genetic compared to environmental factors remains elusive.

Here, we genotyped a cohort of 696 healthy individuals from several distinct ancestral origins and a relatively common environment, and demonstrate that there is no statistically-significant association between microbiome composition and ethnicity, single-nucleotide polymorphisms (SNPs), or overall genetic similarity, and that only 5⁄211 (2.4%) previously reported microbiome-SNP associations replicate in our cohort. In contrast, we find similarities in the microbiome composition of genetically unrelated individuals who share a household.

Consistent with our finding that microbiome and host genetics are largely independent, we find biome-explainability levels of 16–33% for body mass index (BMI), fasting glucose, high-density lipoprotein (HDL) cholesterol, waist circumference, waist-hip ratio (WHR), and lactose consumption. We further show that several human phenotypes can be predicted substantially more accurately when adding microbiome data to host genetics data, and that the contribution of both data sources to prediction accuracy is largely additive.

Overall, our results suggest that human microbiome composition is dominated by environmental factors rather than by host genetics.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1462356/pdf/12524368.pdf Approximate Bayesian computation in population genetics Mark A. Beaumont, Wenyang Zhang, David J. Balding 2002 2021-05-13 [("doi","10.1093/genetics/162.4.2025")] genetics/selection/natural/human genetics/sequencing statistics/bayes

We propose a new method for approximate Bayesian statistical inference on the basis of summary statistics. The method is suited to complex problems that arise in population genetics, extending ideas developed in this setting by earlier authors. Properties of the posterior distribution of a parameter, such as its mean or density curve, are approximated without explicit likelihood calculations.

This is achieved by fitting a local-linear regression of simulated parameter values on simulated summary statistics, and then substituting the observed summary statistics into the regression equation. The method combines many of the advantages of Bayesian statistical inference with the computational efficiency of methods based on summary statistics. A key advantage of the method is that the nuisance parameters are automatically integrated out in the simulation step, so that the large numbers of nuisance parameters that arise in population genetics problems can be handled without difficulty.

Simulation results indicate computational and statistical efficiency that compares favorably with those of alternative methods previously proposed in the literature.

We also compare the relative efficiency of inferences obtained using methods based on summary statistics with those obtained directly from the data using MCMC.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1240871/ Economic gains resulting from the reduction in children's exposure to lead in the United States Scott D. Grosse, Thomas D. Matte, Joel Schwartz, Richard J. Jackson 2002 2021-05-13 [("doi","10.1289/ehp.02110563")] iq/ses psychiatry

In this study we quantify economic benefits from projected improvements in worker productivity resulting from the reduction in children’s exposure to lead in the United States since 1976.

We calculated the decline in blood lead levels (BLLs) 1976–1999 on the basis of nationally representative National Health and Nutrition Examination Survey (NHANES) data collected during 1976 through 1980, 1991 through 1994, and 1999. The decline in mean BLL in 1–5-year-old U.S. children from 1976–1980–1991–1994 was 12.3 microg/dL, and the estimated decline 1976–1999 was 15.1 microg/dL. We assumed the change in cognitive ability resulting from declines in BLLs, on the basis of published meta-analyses, to be between 0.185 and 0.323 IQ points for each 1 g/dL blood lead concentration.

These calculations imply that, because of falling BLLs, U.S. preschool-aged children in the late 1990s had IQs that were, on average, 2.2–4.7 points higher than they would have been if they had the blood lead distribution observed among U.S. preschool-aged children in the late 1970s. We estimated that each IQ point raises worker productivity 1.76–2.38%. With discounted lifetime earnings of $723,300 for each 2-year-old in 2000 dollars, the estimated economic benefit for each year’s cohort of 3.8 million 2-year-old children ranges from $110 billion to $319 billion.

--- https://www.biorxiv.org/content/10.1101/170712.full Functional consequences of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals Jonathan R. I. Coleman, Julien Bryois, Héléna A. Gaspar, Philip R. Jansen, Jeanne Savage, Nathan Skene, Robert Plomin, Ana B. Muñoz-Manchado, Sten Linnarsson, Greg Crawford, Jens Hjerling-Leffler, Patrick F. Sullivan, Danielle Posthuma, Gerome Breen 2017-07-31 2021-05-13 [("doi","10.1101/170712")] iq/high/smpy psychology/neuroscience

Variance in IQ is associated with a wide range of health outcomes, and 1% of the population are affected by intellectual disability. Despite a century of research, the fundamental neural underpinnings of intelligence remain unclear.

We integrate results from genome-wide association studies (GWAS) of intelligence with brain tissue and single cell gene expression data to identify tissues and cell types associated with intelligence. GWAS data for IQ (n = 78,308) were meta-analyzed with an extreme-trait cohort of 1,247 individuals with mean IQ ~170 and 8,185 controls.

Genes associated with intelligence implicate pyramidal neurons of the somatosensory cortex and CA1 region of the hippocampus, and midbrain embryonic GABAergic neurons. Tissue-specific analyses find the most enrichment for frontal cortex brain expressed genes.

These results suggest specific neuronal cell types and genes may be involved in intelligence and provide new hypotheses for neuroscience experiments using model systems.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1407707/ Use of slow-release melatonin in treatment-resistant depression E J. Dalton, D. Rotondi, R. D. Levitan, S. H. Kennedy, G. M. Brown 2000 2021-05-13 melatonin psychiatry/depression

Objective: To examine antidepressant augmentation with and hypnotic effects of slow-release melatonin (SR-melatonin) in patients with treatment-resistant depression.

Design: Open-label trial.

Setting: Tertiary care outpatient depression clinic.

Patients: Nine outpatients who had failed to respond to 2 or more 8-week trials of antidepressant medication.

Interventions: Patients received SR-melatonin 5 mg per day for the first 2 weeks and 10 mg per day for the final 2 weeks, in addition to their antidepressant medication.

Outcome Measures: Structured Clinical Interview for DSM-IV, Axis 1 Disorders, Hamilton Rating Scale for Depression (HRSD), Beck Depression Inventory, Response Style Questionnaire, sleep and fatigue measures.

Results: One patient was excluded after 1 week because of the development of a mixed affective state. In the remaining 8 patients there was a 20% mean decrease in HRSD scores after 4 weeks of treatment, with no individual achieving an improvement of 50% or more. There was a 36% decrease on the 3-item HRSD related to insomnia, with 4⁄8 patients showing at least a 50% improvement on this measure. The greatest decrease in insomnia occurred during the last 2 weeks of the study, following the increase in dosage to 10 mg per day of SR-melatonin. Patients also reported lower levels of fatigue post-treatment.

Conclusion: SR-melatonin may be a useful adjunct for sleep, but does not substantially augment existing antidepressant therapies in some patients with treatment-resistant depression.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1747733/pdf/v012p00310.pdf Persistent use of nicotine replacement therapy: an analysis of actual purchase patterns in a population based sample S Shiffman, J. R. Hughes, J. L. Pillitteri, S. L. Burton 2003 2021-05-13 [("doi","10.1136/tc.12.3.310")] nicotine

Background: In 1996, the US Food and Drug Administration (FDA) approved switching nicotine gum and patch from prescription to over-the-counter (OTC) status. Some expressed concerns that broader availability and lack of physician control might increase persistent use of nicotine replacement therapy (NRT)-that is, use beyond the period specified by the FDA approved label.

Objective: To estimate the incidence of persistent use of OTC nicotine gum and patch for periods of > 3 months, > or = 6 months, > or = 12 months, and > or 24 months.

Design: Analysis of NRT purchase patterns in data from a population based panel of US households that electronically scanned all household purchases between January 1997 and March 2000.

Subjects: In a national panel of 40,000 US households, 2690 recorded NRT purchases.

Results: Among 805 households that purchased nicotine gum, 2.3% of new purchase incidents led to continuous monthly purchase of gum for > or = 6 months. For nicotine patches (2050 households) the percentage was 0.9%. For both gum and patch, the incidence of persistent purchase dropped below 0.4% by 24 months. Allowing one month gaps within a “continuous” purchase run resulted in increased estimates (for gum: 6.7% for > or = 6 months and 1.0% for > or = 24 months; for patch: 1.7% for > or = 6 months and 0.05% for > or = 24 months).

Conclusion: Persistent use of nicotine gum and patch is very rare and has not increased with the transition to OTC use, despite removal of physician oversight.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1747791/ Effect of smokeless tobacco (snus) on smoking and public health in Sweden J Foulds, L. Ramstrom, M. Burke, K. Fagerström 2003 2021-05-13 [("doi","10.1136/tc.12.4.349")] nicotine

Objective: To review the evidence on the effects of moist smokeless tobacco (snus) on smoking and ill health in Sweden.

Method: Narrative review of published papers and other data sources (for example, conference abstracts and internet based information) on snus use, use of other tobacco products, and changes in health status in Sweden.

Results: Snus is manufactured and stored in a manner that causes it to deliver lower concentrations of some harmful chemicals than other tobacco products, although it can deliver high doses of nicotine. It is dependence forming, but does not appear to cause cancer or respiratory diseases. It may cause a slight increase in cardiovascular risks and is likely to be harmful to the unborn fetus, although these risks are lower than those caused by smoking. There has been a larger drop in male daily smoking (from 40% in 1976 to 15% in 2002) than female daily smoking (34% in 1976 to 20% in 2002) in Sweden, with a substantial proportion (around 30%) of male ex-smokers using snus when quitting smoking. Over the same time period, rates of lung cancer and myocardial infarction have dropped faster among Swedish men than women and remain at low levels as compared with other developed countries with a long history of tobacco use.

Conclusion: Snus availability in Sweden appears to have contributed to the unusually low rates of smoking among Swedish men by helping them transfer to a notably less harmful form of nicotine dependence.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1637834/pdf/envhper00312-0016.pdf Vulnerability of children and the developing brain to neurotoxic hazards B Weiss 2000 2021-05-13 [("doi","10.1289/ehp.00108s3375")] psychology/neuroscience

For much of the history of toxicology, the sensitivity of the developing organism to chemical perturbation attracted limited attention. Several tragic episodes and new insights finally taught us that the course of early brain development incurs unique risks. Although the process is exquisitely controlled, its lability renders it highly susceptible to damage from environmental chemicals. Such disturbances, as recognized by current testing protocols and legislation such as the Food Quality Protection Act, can result in outcomes ranging from death to malformations to functional impairment. The latter are the most difficult to determine.

First, they require a variety of measures to assay their extent. Second, adult responses may prove an inadequate guide to the response of the developing brain, which is part of the reason for proposing additional safety factors for children. Third, neuropsychological tests are deployed in complex circumstances in which many factors, including economic status, combine to produce a particular effect such as lowered intelligence quotient score. Fourth, the magnitude of the effect, for most environmental exposure levels, may be relatively small but extremely for public health. Fifth, changes in brain function occur throughout life, and some consequences of early damage may not even emerge until advanced age.

Such factors need to be addressed in estimating the influence of a particular agent or group of agents on brain development and its functional expression. It is especially important to consider ways of dealing with multiple risks and their combinations in addition to the prevailing practice of estimating risks in isolation.

--- https://arxiv.org/abs/1708.02973 Learning Policies for Adaptive Tracking with Deep Feature Cascades Chen Huang, Simon Lucey, Deva Ramanan 2017-08-09 2021-05-13 [("doi","10.48550/arXiv.1708.02973")] reinforcement-learning/model-free

Visual object tracking is a fundamental and time-critical vision task. Recent years have seen many shallow tracking methods based on real-time pixel-based correlation filters, as well as deep methods that have top performance but need a high-end GPU.

In this paper, we learn to improve the speed of deep trackers without losing accuracy. Our fundamental insight is to take an adaptive approach, where easy frames are processed with cheap features (such as pixel values), while challenging frames are processed with invariant but expensive deep features. We formulate the adaptive tracking problem as a decision-making process, and learn an agent to decide whether to locate objects with high confidence on an early layer, or continue processing subsequent layers of a network. This reduces the feed-forward cost for easy frames with distinct or slow-moving objects.

We train the agent offline in a reinforcement learning fashion, and further demonstrate that learning all deep layers (so as to provide good features for adaptive tracking) can lead to near real-time average tracking speed of 23 fps on a single CPU while achieving state-of-the-art performance.

Perhaps most tellingly, our approach provides a 100× speedup for almost 50% of the time, indicating the power of an adaptive approach.

--- https://arxiv.org/abs/1707.07012#google Learning Transferable Architectures for Scalable Image Recognition Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le 2017-07-21 2021-05-13 [("doi","10.48550/arXiv.1707.07012")] ai/nn/cnn reinforcement-learning/model

Developing neural network image classification models often requires architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest. As this approach is expensive when the dataset is large, we propose to search for an architectural building block on a small dataset and then transfer the block to a larger dataset. The key contribution of this work is the design of a new search space (the “NASNet search space”) which enables transferability.

In our experiments, we search for the best convolutional layer (or “cell”) on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, named “NASNet architecture”. We also introduce a new regularization technique called ScheduledDropPath that improves generalization in the NASNet models.

On CIFAR-10 itself, NASNet achieves 2.4% error rate, which is state-of-the-art. On ImageNet, NASNet achieves, among the published works, state-of-the-art accuracy of 82.7% top-1 and 96.2% top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy than the best human-invented architectures while having 9 billion fewer FLOPS—a reduction of 28% in computational demand from the previous state-of-the-art model. When evaluated at different levels of computational cost, accuracies of NASNets exceed those of the state-of-the-art human-designed models. For instance, a small version of NASNet also achieves 74% top-1 accuracy, which is 3.1% better than equivalently-sized, state-of-the-art models for mobile platforms.

Finally, the learned features by NASNet used with the Faster-RCNN framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO dataset.

--- https://arxiv.org/abs/1707.06170 Learning model-based planning from scratch Razvan Pascanu, Yujia Li, Oriol Vinyals, Nicolas Heess, Lars Buesing, Sebastien Racanière, David Reichert, Théophane Weber, Daan Wierstra, Peter Battaglia 2017-07-19 2021-05-14 [("doi","10.48550/arXiv.1707.06170")] reinforcement-learning/model

Conventional wisdom holds that model-based planning is a powerful approach to sequential decision-making. It is often very challenging in practice, however, because while a model can be used to evaluate a plan, it does not prescribe how to construct a plan. Here we introduce the “Imagination-based Planner”, the first model-based, sequential decision-making agent that can learn to construct, evaluate, and execute plans.

Before any action, it can perform a variable number of imagination steps, which involve proposing an imagined action and evaluating it with its model-based imagination. All imagined actions and outcomes are aggregated, iteratively, into a “plan context” which conditions future real and imagined actions. The agent can even decide how to imagine: testing out alternative imagined actions, chaining sequences of actions together, or building a more complex “imagination tree” by navigating flexibly among the previously imagined states using a learned policy. And our agent can learn to plan economically, jointly optimizing for external rewards and computational costs associated with using its imagination.

We show that our architecture can learn to solve a challenging continuous control problem, and also learn elaborate planning strategies in a discrete maze-solving task.

Our work opens a new direction toward learning the components of a model-based planning system and how to use them.

--- https://arxiv.org/abs/1707.05173 Trial without Error: Towards Safe Reinforcement Learning via Human Intervention William Saunders, Girish Sastry, Andreas Stuhlmueller, Owain Evans 2017-07-17 2021-05-14 [("doi","10.48550/arXiv.1707.05173")] reinforcement-learning/model-free

AI systems are increasingly applied to complex tasks that involve interaction with humans. During training, such systems are potentially dangerous, as they haven’t yet learned to avoid actions that could cause serious harm. How can an AI system explore and learn without making a single mistake that harms humans or otherwise causes serious damage? For model-free reinforcement learning, having a human “in the loop” and ready to intervene is currently the only way to prevent all catastrophes.

We formalize human intervention for RL and show how to reduce the human labor required by training a supervised learner to imitate the human’s intervention decisions. We evaluate this scheme on Atari games, with a Deep RL agent being overseen by a human for 4 hours.

When the class of catastrophes is simple, we are able to prevent all catastrophes without affecting the agent’s learning (whereas an RL baseline fails due to catastrophic forgetting). However, this scheme is less successful when catastrophes are more complex: it reduces but does not eliminate catastrophes and the supervised learner fails on adversarial examples found by the agent.

Extrapolating to more challenging environments, we show that our implementation would not scale (due to the infeasible amount of human labor required). We outline extensions of the scheme that are necessary if we are to train model-free agents without a single catastrophe.

--- https://arxiv.org/abs/1707.04873 Efficient Architecture Search by Network Transformation Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, Jun Wang 2017-07-16 2021-05-14 [("doi","10.48550/arXiv.1707.04873")] ai/nn/cnn reinforcement-learning/model-free

Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results. However, their success is based on vast computational resources (eg. hundreds of GPUs), making them difficult to be widely used. A noticeable limitation is that they still design and train each network from scratch during the exploration of the architecture space, which is highly inefficient.

In this paper, we propose a new framework toward efficient architecture search by exploring the architecture space based on the current network and reusing its weights. We employ a reinforcement learning agent as the meta-controller, whose action is to grow the network depth or layer width with function-preserving transformations. As such, the previously validated networks can be reused for further exploration, thus saves a large amount of computational cost.

We apply our method to explore the architecture space of the plain convolutional neural networks (no skip-connections, branching etc.) on image benchmark datasets (CIFAR-10, SVHN) with restricted computational resources (5 GPUs).

Our method can design highly competitive networks that outperform existing networks using the same design scheme. On CIFAR-10, our model without skip-connections achieves 4.23% test error rate, exceeding a vast majority of modern architectures and approaching DenseNet. Furthermore, by applying our method to explore the DenseNet architecture space, we are able to achieve more accurate networks with fewer parameters.

--- https://arxiv.org/abs/1707.03497#deepmind Value Prediction Network Junhyuk Oh, Satinder Singh, Honglak Lee 2017-07-11 2021-05-14 [("doi","10.48550/arXiv.1707.03497")] reinforcement-learning/model

This paper proposes a novel deep reinforcement learning (RL) architecture, called Value Prediction Network (VPN), which integrates model-free and model-based RL methods into a single neural network. In contrast to typical model-based RL methods, VPN learns a dynamics model whose abstract states are trained to make option-conditional predictions of future values (discounted sum of rewards) rather than of future observations.

Our experimental results show that VPN has several advantages over both model-free and model-based baselines in a stochastic environment where careful planning is required but building an accurate observation-prediction model is difficult. Furthermore, VPN outperforms Deep Q-Network (DQN) on several Atari games even with short-lookahead planning, demonstrating its potential as a new way of learning a good state representation.

--- https://arxiv.org/abs/1707.00299 Grammatical Error Correction with Neural Reinforcement Learning Keisuke Sakaguchi, Matt Post, Benjamin Van Durme 2017-07-02 2021-05-14 [("doi","10.48550/arXiv.1707.00299")] reinforcement-learning/imitation-learning reinforcement-learning/model-free

We propose a neural encoder-decoder model with reinforcement learning (NRL) for grammatical error correction (GEC).

Unlike conventional maximum likelihood estimation (MLE), the model directly optimizes towards an objective that considers a sentence-level, task-specific evaluation metric, avoiding the exposure bias issue in MLE.

We demonstrate that NRL outperforms MLE both in human and automated evaluation metrics, achieving the state-of-the-art on a fluency-oriented GEC corpus.

--- https://arxiv.org/abs/1706.09597 Path Integral Networks: End-to-End Differentiable Optimal Control Masashi Okada, Luca Rigazio, Takenobu Aoshima 2017-06-29 2021-05-14 [("doi","10.48550/arXiv.1706.09597")] reinforcement-learning/imitation-learning reinforcement-learning/model

In this paper, we introduce Path Integral Networks (PI-Net), a recurrent network representation of the Path Integral optimal control algorithm. The network includes both system dynamics and cost models, used for optimal control based planning. PI-Net is fully differentiable, learning both dynamics and cost models end-to-end by back-propagation and stochastic gradient descent. Because of this, PI-Net can learn to plan.

PI-Net has several advantages: it can generalize to unseen states thanks to planning, it can be applied to continuous control tasks, and it allows for a wide variety learning schemes, including imitation and reinforcement learning.

Preliminary experiment results show that PI-Net, trained by imitation learning, can mimic control demonstrations for two simulated problems; a linear system and a pendulum swing-up problem. We also show that PI-Net is able to learn dynamics and cost models latent in the demonstrations.

--- https://arxiv.org/abs/1706.07230 Gated-Attention Architectures for Task-Oriented Language Grounding Devendra Singh Chaplot, Kanthashree Mysore Sathyendra, Rama Kumar Pasumarthi, Dheeraj Rajagopal, Ruslan Salakhutdinov 2017-06-22 2021-05-14 [("doi","10.48550/arXiv.1706.07230")] reinforcement-learning/imitation-learning reinforcement-learning/model-free

To perform tasks specified by natural language instructions, autonomous agents need to extract semantically meaningful representations of language and map it to visual elements and actions in the environment. This problem is called task-oriented language grounding. We propose an end-to-end trainable neural architecture for task-oriented language grounding in 3D environments which assumes no prior linguistic or perceptual knowledge and requires only raw pixels from the environment and the natural language instruction as input.

The proposed model combines the image and text representations using a Gated-Attention mechanism and learns a policy to execute the natural language instruction using standard reinforcement and imitation learning methods.

We show the effectiveness of the proposed model on unseen instructions as well as unseen maps, both quantitatively and qualitatively.

We also introduce a novel environment based on a 3D game engine to simulate the challenges of task-oriented language grounding over a rich set of instructions and environment states.

--- https://arxiv.org/abs/1708.00489 Active Learning for Convolutional Neural Networks: A Core-Set Approach Ozan Sener, Silvio Savarese 2017-08-01 2021-05-14 [("doi","10.48550/arXiv.1708.00489")] ai/nn/cnn reinforcement-learning/exploration/active-learning

Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe; training a deep model on a very large dataset of supervised examples. However, this approach is rather restrictive in practice since collecting a large set of labeled images is very expensive. One way to ease this problem is coming up with smart ways for choosing images to be labeled from a very large collection (ie. active learning).

Our empirical study suggests that many of the active learning heuristics in the literature are not effective when applied to CNNs in batch setting. Inspired by these limitations, we define the problem of active learning as core-set selection, ie. choosing set of points such that a model learned over the selected subset is competitive for the remaining data points. We further present a theoretical result characterizing the performance of any selected subset using the geometry of the datapoints. As an active learning algorithm, we choose the subset which is expected to yield best result according to our characterization.

Our experiments show that the proposed method outperforms existing approaches in image classification experiments by a large margin.

--- https://arxiv.org/abs/1708.05344 SMASH: One-Shot Model Architecture Search through HyperNetworks Andrew Brock, Theodore Lim, J. M. Ritchie, Nick Weston 2017-08-17 2021-05-14 [("doi","10.48550/arXiv.1708.05344")] ai/nn/cnn reinforcement-learning/meta-learning

[video] Designing architectures for deep neural networks requires expert knowledge and substantial computation time.

We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model’s architecture. By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run. To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as special cases.

We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10, ModelNet10, and Imagenet32×32, achieving competitive performance with similarly-sized hand-designed networks.

Our code is available at https://github.com/ajbrock/SMASH.

--- https://arxiv.org/abs/1708.02544 Stochastic Optimization with Bandit Sampling Farnood Salehi, L. Elisa Celis, Patrick Thiran 2017-08-08 2021-05-14 [("doi","10.48550/arXiv.1708.02544")] reinforcement-learning/meta-learning

Many stochastic optimization algorithms work by estimating the gradient of the cost function on the fly by sampling datapoints uniformly at random from a training set. However, the estimator might have a large variance, which inadvertently slows down the convergence rate of the algorithms. One way to reduce this variance is to sample the datapoints from a carefully selected non-uniform distribution.

In this work, we propose a novel non-uniform sampling approach that uses the multi-armed bandit framework. Theoretically, we show that our algorithm asymptotically approximates the optimal variance within a factor of 3.

Empirically, we show that using this datapoint-selection technique results in a reduction in the convergence time and variance of several stochastic optimization algorithms such as SGD, SVRG, and SAGA. This approach for sampling datapoints is general, and can be used in conjunction with any algorithm that uses an unbiased gradient estimation—we expect it to have broad applicability beyond the specific examples explored in this work.

--- https://arxiv.org/abs/1707.02201 Learning human behaviors from motion capture by adversarial imitation Josh Merel, Yuval Tassa, Dhruva TB, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, Nicolas Heess 2017-07-07 2021-05-15 [("doi","10.48550/arXiv.1707.02201")] reinforcement-learning/imitation-learning reinforcement-learning/preference-learning

Rapid progress in deep reinforcement learning has made it increasingly feasible to train controllers for high-dimensional humanoid bodies. However, methods that use pure reinforcement learning with simple reward functions tend to produce non-human-like and overly stereotyped movement behaviors.

In this work, we extend generative adversarial imitation learning to enable training of generic neural network policies to produce human-like movement patterns from limited demonstrations consisting only of partially observed state features, without access to actions, even when the demonstrations come from a body with different and unknown physical parameters.

We leverage this approach to build sub-skill policies from motion capture data and show that they can be reused to solve tasks when controlled by a higher-level controller.

--- https://arxiv.org/abs/cond-mat/0305150 Copied citations create renowned papers? M. V. Simkin, V. P. Roychowdhury 2003-05-08 2021-05-15 [("doi","10.48550/arXiv.0305150")] science statistics/probability

Recently we discovered that the majority of scientific citations are copied from the lists of references used in other papers.

Here we show that a model, in which a scientist picks 3 random papers, cites them, and also copies a quarter of their references accounts quantitatively for empirically observed citation distribution.

Simple mathematical probability, not genius, can explain why some papers are cited a lot more than the other.

--- https://arxiv.org/abs/cond-mat/0212043 Read before you cite! M. V. Simkin, V. P. Roychowdhury 2002-12-03 2021-05-15 [("doi","10.48550/arXiv.0212043")] science statistics/probability

We report a method of estimating what percentage of people who cited a paper had actually read it.

The method is based on a stochastic modeling of the citation process that explains empirical studies of misprint distributions in citations (which we show follows a Zipf law).

Our estimate is only about 20% of citers read the original.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1284369/pdf/12102132.pdf The effects of cumulative practice on mathematics problem solving Kristin H. Mayfield, Philip N. Chase 2002 2021-05-15 [("doi","10.1901/jaba.2002.35-105")] psychology/spaced-repetition

This study compared 3 different methods of teaching 5 basic algebra rules to college students.

All methods used the same procedures to teach the rules and included four 50-question review sessions interspersed among the training of the individual rules. The differences among methods involved the kinds of practice provided during the four review sessions. Participants who received cumulative practice answered 50 questions covering a mix of the rules learned prior to each review session. Participants who received a simple review answered 50 questions on one previously trained rule. Participants who received extra practice answered 50 extra questions on the rule they had just learned. Tests administered after each review included new questions for applying each rule (application items) and problems that required novel combinations of the rules (problem-solving items).

On the final test, the cumulative group outscored the other groups on application and problem-solving items. In addition, the cumulative group solved the problem-solving items statistically-significantly faster than the other groups.

These results suggest that cumulative practice of component skills is an effective method of training problem solving.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1495095/ Simplifying likelihood ratios Steven McGee 2002 2021-05-15 [("doi","10.1046/j.1525-1497.2002.10750.x")] statistics/bayes

Likelihood ratios are one of the best measures of diagnostic accuracy, although they are seldom used, because interpreting them requires a calculator to convert back and forth between “probability” and “odds” of disease.

This article describes a simpler method of interpreting likelihood ratios, one that avoids calculators, nomograms, and conversions to “odds” of disease.

Several examples illustrate how the clinician can use this method to refine diagnostic decisions at the bedside.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC28700/ The unpredictability paradox: review of empirical comparisons of randomized and non-randomized clinical trials R Kunz, A. D. Oxman 1998 2021-05-15 [("doi","10.1136/bmj.317.7167.1185")] statistics/bias statistics/causality

Objective: To summarise comparisons of randomized clinical trials and non-randomized clinical trials, trials with adequately concealed random allocation versus inadequately concealed random allocation, and high quality trials versus low quality trials where the effect of randomization could not be separated from the effects of other methodological manoeuvres.

Design: Systematic review.

Selection Criteria: Cohorts or meta-analyses of clinical trials that included an empirical assessment of the relation between randomization and estimates of effect.

Data Sources: Cochrane Review Methodology Database, MEDLINE, SciSearch, bibliographies, hand searching of journals, personal communication with methodologists, and the reference lists of relevant articles.

Main Outcome Measures: Relation between randomization and estimates of effect.

Results: Eleven studies that compared randomized controlled trials with non-randomized controlled trials (eight for evaluations of the same intervention and 3 across different interventions), two studies that compared trials with adequately concealed random allocation and inadequately concealed random allocation, and five studies that assessed the relation between quality scores and estimates of treatment effects, were identified. Failure to use random allocation and concealment of allocation were associated with relative increases in estimates of effects of 150% or more, relative decreases of up to 90%, inversion of the estimated effect and, in some cases, no difference. On average, failure to use randomization or adequate concealment of allocation resulted in larger estimates of effect due to a poorer prognosis in non-randomly selected control groups compared with randomly selected control groups.

Conclusion: Failure to use adequately concealed random allocation can distort the apparent effects of care in either direction, causing the effects to seem either larger or smaller than they really are. The size of these distortions can be as large as or larger than the size of the effects that are to be detected.

--- https://arxiv.org/abs/1706.04692 Bias and high-dimensional adjustment in observational studies of peer effects Dean Eckles, Eytan Bakshy 2017-06-14 2021-05-15 [("doi","10.1080/01621459.2020.1796393")] economics/advertising statistics/causality

Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are posited by multiple theories in the social sciences. Other processes can also produce behaviors that are correlated in networks and groups, thereby generating debate about the credibility of observational (ie. nonexperimental) studies of peer effects. Randomized field experiments that identify peer effects, however, are often expensive or infeasible. Thus, many studies of peer effects use observational data, and prior evaluations of causal inference methods for adjusting observational data to estimate peer effects have lacked an experimental “gold standard” for comparison.

Here we show, in the context of information and media diffusion on Facebook, that high-dimensional adjustment of a nonexperimental control group (677 million observations) using propensity score models produces estimates of peer effects statistically indistinguishable from those from using a large randomized experiment (220 million observations). Naive observational estimators overstate peer effects by 320% and commonly used variables (eg. demographics) offer little bias reduction, but adjusting for a measure of prior behaviors closely related to the focal behavior reduces bias by 91%. High-dimensional models adjusting for over 3,700 past behaviors provide additional bias reduction, such that the full model reduces bias by over 97%.

This experimental evaluation demonstrates that detailed records of individuals’ past behavior can improve studies of social influence, information diffusion, and imitation; these results are encouraging for the credibility of some studies but also cautionary for studies of rare or new behaviors.

More generally, these results show how large, high-dimensional data sets and statistical learning techniques can be used to improve causal inference in the behavioral sciences.

--- https://arxiv.org/abs/1708.04575 Information flow reveals prediction limits in online social activity James P. Bagrow, Xipei Liu, Lewis Mitchell 2017-08-15 2021-05-15 [("doi","10.1038/s41562-018-0510-5")] cs/algorithm/information statistics/probability technology

Modern society depends on the flow of information over online social networks, and users of popular platforms generate behavioral data about themselves and their social ties. However, it remains unclear what fundamental limits exist when using these data to predict the activities and interests of individuals, and to what accuracy such predictions can be made using an individual’s social ties.

Here we show that 95% of the potential predictive accuracy for an individual is achievable using their social ties only, without requiring that individual’s data. We use information theoretic tools to estimate the predictive information within the writings of Twitter users, providing an upper bound on the available predictive information that holds for any predictive or machine learning methods. As few as 8–9 of an individual’s contacts are sufficient to obtain predictability comparable to that of the individual alone. Distinct temporal and social effects are visible by measuring information flow along social ties, allowing us to better study the dynamics of online activity.

Our results have distinct privacy implications: information is so strongly embedded in a social network that in principle one can profile an individual from their available social ties even when the individual forgoes the platform completely.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC150177/ Effect of four monthly oral vitamin D3 (cholecalciferol) supplementation on fractures and mortality in men and women living in the community: randomized double blind controlled trial Daksha P. Trivedi, Richard Doll, Kay Tee Khaw 2003 2021-05-15 [("doi","10.1136/bmj.326.7387.469")] vitamin-d

Objective: To determine the effect of four monthly vitamin D supplementation on the rate of fractures in men and women aged 65 years and over living in the community.

Design: Randomized double blind controlled trial of 100,000 IU oral vitamin D3 (cholecalciferol) supplementation or matching placebo every four months over five years.

Setting and Participants: 2686 people (2037 men and 649 women) aged 65–85 years living in the general community, recruited from the British doctors register and a general practice register in Suffolk.

Main Outcome Measures: Fracture incidence and total mortality by cause.

Results: After five years 268 men and women had incident fractures, of whom 147 had fractures in common osteoporotic sites (hip, wrist or forearm, or vertebrae). Relative risks in the vitamin D group compared with the placebo group were 0.78 (95% confidence interval 0.61 to 0.99, p = 0.04) for any first fracture and 0.67 (0.48 to 0.93, p = 0.02) for first hip, wrist or forearm, or vertebral fracture. 471 participants died. The relative risk for total mortality in the vitamin D group compared with the placebo group was 0.88 (0.74 to 1.06, p = 0.18). Findings were consistent in men and women and in doctors and the general practice population.

Conclusion: Four monthly supplementation with 100,000 IU oral vitamin D may prevent fractures without adverse effects in men and women living in the general community.

--- https://arxiv.org/abs/1708.05636 What does a convolutional neural network recognize in the moon? Daigo Shoji 2017-08-18 2021-05-15 [("doi","10.48550/arXiv.1708.05636")] ai/nn/cnn

Many people see a human face or animals in the pattern of the maria on the moon. Although the pattern corresponds to the actual variation in composition of the lunar surface, the culture and environment of each society influence the recognition of these objects (ie. symbols) as specific entities.

Using convolutional neural network (CNN), this study evaluates the probabilities of the pattern of lunar maria categorized into the shape of a crab, a lion, and a hare. If Mare Frigoris (a dark band on the moon) is included in the lunar image, the lion is recognized. However, in an image without Mare Frigoris, the hare has the highest probability of recognition.

Thus, the recognition of objects similar to the lunar pattern depends on which part of the lunar maria is taken into account. In human recognition, before we find similarities between the lunar maria and objects such as animals, we may be persuaded in advance to see a particular image from our culture and environment and then adjust the lunar pattern to the shape of the imagined object.

--- https://arxiv.org/abs/1706.03799 Verb Physics: Relative Physical Knowledge of Actions and Objects Maxwell Forbes, Yejin Choi 2017-06-12 2021-05-15 [("doi","10.48550/arXiv.1706.03799")] ai/nn

Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, eg. “My house is bigger than me.” However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues to reason about the world. For example, a statement like, “Tyler entered his house” implies that his house is bigger than Tyler.

In this paper, we present an approach to infer relative physical knowledge of actions and objects along five dimensions (eg. size, weight, and strength) from unstructured natural language text. We frame knowledge acquisition as joint inference over two closely related problems: learning (1) relative physical knowledge of object pairs and (2) physical implications of actions when applied to those object pairs. Empirical results demonstrate that it is possible to extract knowledge of actions and objects from language and that joint inference over different types of knowledge improves performance.

--- https://arxiv.org/abs/1706.03872 Six Challenges for Neural Machine Translation Philipp Koehn, Rebecca Knowles 2017-06-12 2021-05-16 [("doi","10.48550/arXiv.1706.03872")] ai/nn/rnn ai/nn/sampling

We explore 6 challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search.

We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.

--- https://arxiv.org/abs/1708.04636 Driver Identification Using Automobile Sensor Data from a Single Turn David Hallac, Abhijit Sharang, Rainer Stahlmann, Andreas Lamprecht, Markus Huber, Martin Roehder, Rok Sosic, Jure Leskovec 2017-06-09 2021-05-16 [("doi","10.48550/arXiv.1708.04636")] ai/dataset ai/nn

As automotive electronics continue to advance, cars are becoming more and more reliant on sensors to perform everyday driving operations. These sensors are omnipresent and help the car navigate, reduce accidents, and provide comfortable rides. However, they can also be used to learn about the drivers themselves. In this paper, we propose a method to predict, from sensor data collected at a single turn, the identity of a driver out of a given set of individuals.

We cast the problem in terms of time series classification, where our dataset contains sensor readings at one turn, repeated several times by multiple drivers. We build a classifier to find unique patterns in each individual’s driving style, which are visible in the data even on such a short road segment. To test our approach, we analyze a new dataset collected by AUDI AG and Audi Electronics Venture, where a fleet of test vehicles was equipped with automotive data loggers storing all sensor readings on real roads. We show that turns are particularly well-suited for detecting variations across drivers, especially when compared to straightaways. We then focus on the 12 most frequently made turns in the dataset, which include rural, urban, highway on-ramps, and more, obtaining accurate identification results and learning useful insights about driver behavior in a variety of settings.

--- https://arxiv.org/abs/1706.01869 StreetStyle: Exploring world-wide clothing styles from millions of photos Kevin Matzen, Kavita Bala, Noah Snavely 2017-06-06 2021-05-16 [("doi","10.48550/arXiv.1706.01869")] ai/dataset ai/nn design

Each day billions of photographs are uploaded to photo-sharing services and social media platforms. These images are packed with information about how people live around the world.

In this paper, we exploit this rich trove of data to understand fashion and style trends worldwide. We present a framework for visual discovery at scale, analyzing clothing and fashion across millions of images of people around the world and spanning several years.

We introduce a large-scale dataset of photos of people annotated with clothing attributes and use this dataset to train attribute classifiers via deep learning. We also present a method for discovering visually consistent style clusters that capture useful visual correlations in this massive dataset.

Using these tools, we analyze millions of photos to derive visual insight, producing a first-of-its-kind analysis of global and per-city fashion choices and spatio-temporal trends.

--- https://arxiv.org/abs/1705.08947 Deep Voice 2: Multi-Speaker Neural Text-to-Speech Sercan Arik, Gregory Diamos, Andrew Gibiansky, John Miller, Kainan Peng, Wei Ping, Jonathan Raiman, Yanqi Zhou 2017-05-24 2021-05-16 [("doi","10.48550/arXiv.1705.08947")] ai/nn

We introduce a technique for augmenting neural text-to-speech (TTS) with lowdimensional trainable speaker embeddings to generate different voices from a single model. As a starting point, we show improvements over the two state-of-the-art approaches for single-speaker neural TTS: Deep Voice 1 and Tacotron.

We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates an audio quality improvement over Deep Voice 1. We improve Tacotron by introducing a post-processing neural vocoder, and demonstrate an audio quality improvement.

We then demonstrate our technique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotron on two multi-speaker TTS datasets. We show that a single neural TTS system can learn hundreds of unique voices from less than half an hour of data per speaker, while achieving high audio quality synthesis and preserving the speaker identities almost perfectly.

--- https://arxiv.org/abs/1705.07615 AIXIjs: A Software Demo for General Reinforcement Learning John Aslanides 2017-05-22 2021-05-16 [("doi","10.48550/arXiv.1705.07615")] cs/js reinforcement-learning/model

Reinforcement learning is a general and powerful framework with which to study and implement artificial intelligence. Recent advances in deep learning have enabled RL algorithms to achieve impressive performance in restricted domains such as playing Atari video games (Mnih et al 2015) and, recently, the board game Go (Silver et al 2016). However, we are still far from constructing a generally intelligent agent. Many of the obstacles and open questions are conceptual: What does it mean to be intelligent? How does one explore and learn optimally in general, unknown environments? What, in fact, does it mean to be optimal in the general sense? The universal Bayesian agent AIXI (Hutter, 2005) is a model of a maximally intelligent agent, and plays a central role in the sub-field of general reinforcement learning (GRL).

Recently, AIXI has been shown to be flawed in important ways; it doesn’t explore enough to be asymptotically optimal (Orseau 2010), and it can perform poorly with certain priors (Leike & Hutter 2015). Several variants of AIXI have been proposed to attempt to address these shortfalls: among them are entropy-seeking agents (Orseau 2011), knowledge-seeking agents (Orseau et al 2013), Bayes with bursts of exploration (Lattimore 2013), MDL agents (Leike 2016a), Thompson sampling (Leike et al 2016), and optimism (Sunehag & Hutter 2015).

We present AIXIjs, a JavaScript implementation of these GRL agents. This implementation is accompanied by a framework for running experiments against various environments, similar to OpenAI Gym (Brockman et al 2016), and a suite of interactive demos that explore different properties of the agents, similar to REINFORCEjs (Karpathy, 2015). We use AIXIjs to present numerous experiments illustrating fundamental properties of, and differences between, these agents.

--- https://www.biorxiv.org/content/10.1101/146787.full Genetic contribution to two factors of neuroticism is associated with affluence, better health, and longer life W. David Hill, Alexander Weiss, Andrew M. McIntosh, Catharine R. Gale, Ian J. Deary 2017-06-06 2021-05-16 [("doi","10.1101/146787")] genetics/heritable/correlation psychiatry/anxiety psychiatry/depression psychology/personality

Neuroticism is a personality trait that describes the tendency to experience negative emotions. Individual differences in neuroticism are moderately stable across much of the life course; the trait is heritable, and higher levels are associated with psychiatric disorders, and have been estimated to have an economic burden to society greater than that of substance abuse, mood, or anxiety disorders. Understanding the genetic architecture of neuroticism therefore has the potential to offer insight into the causes of psychiatric disorders, general wellbeing, and longevity. The broad trait of neuroticism is composed of narrower traits, or factors. It was recently discovered that, whereas higher scores on the broad trait of neuroticism are associated with earlier death, higher scores on a ‘worry/vulnerability’ factor are associated with living longer.

Here, we examine the genetic architectures of two neuroticism factors—worry/vulnerability and anxiety/tension—and how they contrast with the architecture of the general factor of neuroticism. We show that, whereas the polygenic load for general factor of neuroticism is associated with an increased risk of coronary artery disease (CAD), major depressive disorder, and poorer self-rated health, the genetic variants associated with high levels of the anxiety/tension and worry/vulnerability factors are associated with affluence, higher cognitive ability, better self-rated health, and longer life.

We also identify the first genes associated with factors of neuroticism that are linked with these positive outcomes that show no relationship with the general factor of neuroticism.

--- https://www.biorxiv.org/content/10.1101/178806.full The genetic basis of human brain structure and function: 1,262 genome-wide associations found from 3,144 GWAS of multimodal brain imaging phenotypes from 9,707 UK Biobank participants Lloyd T. Elliott, Kevin Sharp, Fidel Alfaro-Almagro, Gwenaëlle Douaud, Karla Miller, Jonathan Marchini, Stephen Smith 2017-08-21 2021-05-16 [("doi","10.1101/178806")] genetics/heritable psychology/neuroscience

The genetic basis of brain structure and function is largely unknown.

We carried out genome-wide association studies (GWAS) of 3,144 distinct functional and structural brain imaging derived phenotypes (IDPs), using imaging and genetic data from a total of 9,707 participants in UK Biobank. All subjects were imaged on a single scanner, with 6 distinct brain imaging modalities being acquired.

We show that most of the IDPs are heritable and we identify patterns of co-heritability within and between IDP sub-classes. We report 1,262 SNP associations with IDPs, based on a discovery sample of 8,426 subjects. Notable and interpretable associations include: spatially specific changes in T2✱ in subcortical regions associated with several genes related to iron transport and storage; spatially extended changes in white matter micro-structure associated with genes coding for proteins of the extracellular matrix and the epidermal growth factor; variations in pontine crossing tract neural organization associated with genes that regulate axon guidance and fasciculation during development; and variations in brain connectivity associated with 14 genes that contribute broadly to brain development, patterning and plasticity.

Our results provide new insight into the genetic architecture of the brain with relevance to complex neurological and psychiatric disorders, as well as brain development and aging.

--- https://www.biorxiv.org/content/10.1101/145581.full Discovery of the first genome-wide statistically-large risk loci for ADHD Ditte Demontis, Raymond K. Walters, Joanna Martin, Manuel Mattheisen, Thomas D. Als, Esben Agerbo, Rich Belliveau, Jonas Bybjerg-Grauholm, Marie Bækvad-Hansen, Felecia Cerrato, Kimberly Chambert, Claire Churchhouse, Ashley Dumont, Nicholas Eriksson, Michael Gandal, Jacqueline Goldstein, Jakob Grove, Christine S. Hansen, Mads E. Hauberg, Mads V. Hollegaard, Daniel P. Howrigan, Hailiang Huang, Julian Maller, Alicia R. Martin, Jennifer Moran, Jonatan Pallesen, Duncan S. Palmer, Carsten B. Pedersen, Marianne G. Pedersen, Timothy Poterba, Jesper B. Poulsen, Stephan Ripke, Elise B. Robinson, Kyle F. Satterstrom, Christine Stevens, Patrick Turley, Hyejung Won, PGC, EAGLE, 23andMe, Ole A. Andreassen, Christie Burton, Dorret I. Boomsma, Bru Cormand, Søren Dalsgaard, Barbara Franke, Joel Gelernter, Daniel Geschwind, Hakon Hakonarson, Jan Haavik, Henry Kranzler, Jonna Kuntsi, Kate Langley, Klaus-Peter Lesch, Christel Middeldorp, Andreas Reif, Luis A. Rohde, Panos Roussos, Russell Schachar, Pamela Sklar, Edmund Sonuga-Barke, Patrick F. Sullivan, Anita Thapar, Joyce Y. Tung, Irwin Waldman, Merete Nordentoft, David Hougaard, Thomas Werge, Ole Mors, Preben Bo Mortensen, Mark J. Daly, Stephen V. Faraone, Anders Børglum, Benjamin M. Neale 2017-06-03 2021-05-16 [("doi","10.1101/145581")] genetics/heritable psychiatry/adhd

Attention-Deficit/Hyperactivity Disorder (ADHD) is a highly heritable childhood behavioral disorder affecting 5% of school-age children and 2.5% of adults. Common genetic variants contribute substantially to ADHD susceptibility, but no individual variants have been robustly associated with ADHD.

We report a genome-wide association meta-analysis of 20,183 ADHD cases and 35,191 controls that identifies:

variants surpassing genome-wide statistical-significance in 12 independent loci, revealing new and important information on the underlying biology of ADHD. Associations are enriched in evolutionarily constrained genomic regions and loss-of-function intolerant genes, as well as around brain-expressed regulatory marks. These findings, based on clinical interviews and/or medical records are supported by additional analyses of a self-reported ADHD sample and a study of quantitative measures of ADHD symptoms in the population. Meta-analyzing these data with our primary scan yielded a total of 16 genome-wide statistically-significant loci.

The results support the hypothesis that clinical diagnosis of ADHD is an extreme expression of one or more continuous heritable traits.

--- https://www.biorxiv.org/content/10.1101/066068.full Estimate of disease heritability using 7.4 million familial relationships inferred from electronic health records Fernanda Polubriaginof, Rami Vanguri, Kayla Quinnies, Gillian M. Belbin, Alexandre Yahi, Hojjat Salmasian, Tal Lorberbaum, Victor Nwankwo, Li Li, Mark Shervey, Patricia Glowe, Iuliana Ionita-Laza, Mary Simmerling, George Hripcsak, Suzanne Bakken, David Goldstein, Krzysztof Kiryluk, Eimear E. Kenny, Joel Dudley, David K. Vawdrey, Nicholas P. Tatonetti 2017-05-24 2021-05-16 [("doi","10.1101/066068")] genetics/heritable

Heritability is essential for understanding the biological causes of disease, but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHR) passively capture a wide range of clinically relevant data and provide a novel resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research.

We mined emergency contact data at 3 academic medical centers and identified millions of familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically-derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes.

Overall, estimates were consistent with literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a novel validation of the use of EHRs for genetics and disease research.

One Sentence Summary: We demonstrate that next-of-kin information can be used to identify familial relationships in the EHR, providing unique opportunities for precision medicine studies.

--- https://www.biorxiv.org/content/10.1101/146043.full Detecting polygenic adaptation in admixture graphs Fernando Racimo, Jeremy J. Berg, Joseph K. Pickrell 2017-06-07 2021-05-16 [("doi","10.1101/146043")] genetics/selection/natural/human

An open question in human evolution is the importance of polygenic adaptation: adaptive changes in the mean of a multifactorial trait due to shifts in allele frequencies across many loci. In recent years, several methods have been developed to detect polygenic adaptation using loci identified in genome-wide association studies (GWAS). Though powerful, these methods suffer from limited interpretability: they can detect which sets of populations have evidence for polygenic adaptation, but are unable to reveal where in the history of multiple populations these processes occurred.

To address this, we created a method to detect polygenic adaptation in an admixture graph, which is a representation of the historical divergences and admixture events relating different populations through time. We developed a Markov chain Monte Carlo (MCMC) algorithm to infer branch-specific parameters reflecting the strength of selection in each branch of a graph. Additionally, we developed a set of summary statistics that are fast to compute and can indicate which branches are most likely to have experienced polygenic adaptation.

We show via simulations that this method—which we call PolyGraph—has good power to detect polygenic adaptation, and applied it to human population genomic data from around the world. We also provide evidence that variants associated with several traits, including height, educational attainment, and self-reported unibrow, have been influenced by polygenic adaptation in different human populations.

--- https://arxiv.org/abs/1705.11159 Reinforcement Learning for Learning Rate Control Chang Xu, Tao Qin, Gang Wang, Tie-Yan Liu 2017-05-31 2021-05-16 [("doi","10.48550/arXiv.1705.11159")] reinforcement-learning/meta-learning

Stochastic gradient descent (SGD), which updates the model parameters by adding a local gradient times a learning rate at each step, is widely used in model training of machine learning algorithms such as neural networks. It is observed that the models trained by SGD are sensitive to learning rates and good learning rates are problem specific.

We propose an algorithm to automatically learn learning rates using neural network based actor-critic methods from deep reinforcement learning (RL).In particular, we train a policy network called actor to decide the learning rate at each step during training, and a value network called critic to give feedback about quality of the decision (eg. the goodness of the learning rate outputted by the actor) that the actor made.

The introduction of auxiliary actor and critic networks helps the main network achieve better performance. Experiments on different datasets and network architectures show that our approach leads to better convergence of SGD than human-designed competitors.

--- https://arxiv.org/abs/1708.05552 Practical Block-wise Neural Network Architecture Generation Zhao Zhong, Junjie Yan, Wei Wu, Jing Shao, Cheng-Lin Liu 2017-08-18 2021-05-17 [("doi","10.48550/arXiv.1708.05552")] reinforcement-learning/model-free

Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy.

The optimal network block is constructed by the learning agent which is trained sequentially to choose component layers. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy.

The block-wise generation brings unique advantages: (1) it performs competitive results in comparison to the hand-crafted state-of-the-art networks on image classification, additionally, the best network generated by BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs, and (3) moreover, it has strong generalizability that the network built on CIFAR also performs well on a larger-scale ImageNet dataset.

--- https://arxiv.org/abs/1706.00885 IDK Cascades: Fast Deep Learning by Learning not to Overthink Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez 2017-06-03 2021-05-17 [("doi","10.48550/arXiv.1706.00885")] reinforcement-learning/model-free

Advances in deep learning have led to substantial increases in prediction accuracy but have been accompanied by increases in the cost of rendering predictions. We conjecture that fora majority of real-world inputs, the recent advances in deep learning have created models that effectively “overthink” on simple inputs.

In this paper, we revisit the classic question of building model cascades that primarily leverage class asymmetry to reduce cost. We introduce the “I Don’t Know” (IDK) prediction cascades framework, a general framework to systematically compose a set of pre-trained models to accelerate inference without a loss in prediction accuracy. We propose two search based methods for constructing cascades as well as a new cost-aware objective within this framework.

The proposed IDK cascade framework can be easily adopted in the existing model serving systems without additional model re-training.

We evaluate the proposed techniques on a range of benchmarks to demonstrate the effectiveness of the proposed framework.

--- https://arxiv.org/abs/1706.00130 Teaching Machines to Describe Images via Natural Language Feedback Huan Ling, Sanja Fidler 2017-06-01 2021-05-17 [("doi","10.48550/arXiv.1706.00130")] reinforcement-learning/exploration/active-learning reinforcement-learning/model-free

Robots will eventually be part of every household. It is thus critical to enable algorithms to learn from and be guided by non-expert users.

In this paper, we bring a human in the loop, and enable a human teacher to give feedback to a learning agent in the form of natural language. We argue that a descriptive sentence can provide a much stronger learning signal than a numeric reward in that it can easily point to where the mistakes are and how to correct them. We focus on the problem of image captioning in which the quality of the output can easily be judged by non-experts.

We propose a hierarchical phrase-based captioning model trained with policy gradients, and design a feedback network that provides reward to the learner by conditioning on the human-provided feedback.

We show that by exploiting descriptive feedback our model learns to perform better than when given independently written human captions.

--- https://arxiv.org/abs/1706.00046 Learning Time/Memory-Efficient Deep Architectures with Budgeted Super Networks Tom Veniat, Ludovic Denoyer 2017-05-31 2021-05-17 [("doi","10.48550/arXiv.1706.00046")] reinforcement-learning/model-free

We propose to focus on the problem of discovering neural network architectures efficient in terms of both prediction quality and cost. For instance, our approach is able to solve the following tasks: learn a neural network able to predict well in less than 100 milliseconds or learn an efficient model that fits in a 50 Mb memory.

Our contribution is a novel family of models called Budgeted Super Networks (BSN). They are learned using gradient descent techniques applied on a budgeted learning objective function which integrates a maximum authorized cost, while making no assumption on the nature of this cost.

We present a set of experiments on computer vision problems and analyze the ability of our technique to deal with 3 different costs: the computation cost, the memory consumption cost and a distributed computation cost. We particularly show that our model can discover neural network architectures that have a better accuracy than the ResNet and Convolutional Neural Fabrics architectures on CIFAR-10 and CIFAR-100, at a lower cost.

--- https://arxiv.org/abs/1705.08080 Visual Semantic Planning using Deep Successor Representations Yuke Zhu, Daniel Gordon, Eric Kolve, Dieter Fox, Li Fei-Fei, Abhinav Gupta, Roozbeh Mottaghi, Ali Farhadi 2017-05-23 2021-05-17 [("doi","10.48550/arXiv.1705.08080")] reinforcement-learning/imitation-learning reinforcement-learning/model

A crucial capability of real-world intelligent agents is their ability to plan a sequence of actions to achieve their goals in the visual world.

In this work, we address the problem of visual semantic planning: the task of predicting a sequence of actions from visual observations that transform a dynamic environment from an initial state to a goal state. Doing so entails knowledge about objects and their affordances, as well as actions and their preconditions and effects. We propose learning these through interacting with a visual and dynamic environment. Our proposed solution involves bootstrapping reinforcement learning with imitation learning.

To ensure cross task generalization, we develop a deep predictive model based on successor representations. Our experimental results show near optimal results across a wide range of tasks in the challenging THOR environment.

--- https://arxiv.org/abs/1705.07830 Ask the Right Questions: Active Question Reformulation with Reinforcement Learning Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang 2017-05-22 2021-05-17 [("doi","10.48550/arXiv.1705.07830")] ai/nn/retrieval reinforcement-learning/exploration/active-learning reinforcement-learning/model-free

We frame Question Answering (QA) as a Reinforcement Learning task, an approach that we call Active Question Answering. We propose an agent that sits between the user and a black box QA system and learns to reformulate questions to elicit the best possible answers. The agent probes the system with, potentially many, natural language reformulations of an initial question and aggregates the returned evidence to yield the best answer. The reformulation system is trained end-to-end to maximize answer quality using policy gradients.

We evaluate on SearchQA, a dataset of complex questions extracted from Jeopardy!.

The agent outperforms a state-of-the-art base model, playing the role of the environment, and other benchmarks. We also analyze the language that the agent has learned while interacting with the question answering system. We find that successful question reformulations look quite different from natural language paraphrases. The agent is able to discover non-trivial reformulation strategies that resemble classic information retrieval techniques such as term re-weighting (tf-idf) and stemming.

--- https://arxiv.org/abs/1708.05565#jd LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions Yu Wang, Jiayi Liu, Yuxiang Liu, Jun Hao, Yang He, Jinghe Hu, Weipeng P. Yan, Mantian Li 2017-08-18 2021-05-17 [("doi","10.48550/arXiv.1708.05565")] economics/mechanism-design/auction reinforcement-learning/imperfect-information reinforcement-learning/multi-agent

We present LADDER, the first deep reinforcement learning agent that can successfully learn control policies for large-scale real-world problems directly from raw inputs composed of high-level semantic information. The agent is based on an asynchronous stochastic variant of DQN (Deep Q Network) named DASQN.

The inputs of the agent are plain-text descriptions of states of a game of incomplete information, ie. real-time large-scale online auctions, and the rewards are auction profits of very large scale. We apply the agent to an essential portion of JD’s online RTB (real-time bidding) advertising business and find that it easily beats the former state-of-the-art bidding policy that had been carefully engineered and calibrated by human experts.

During JD.com’s June 18th anniversary sale, the agent increased the company’s ads revenue from the portion by more than 50%, while the advertisers’ ROI (return on investment) also improved.

--- https://arxiv.org/abs/1704.08614 BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography Michael J. Wilber, Chen Fang, Hailin Jin, Aaron Hertzmann, John Collomosse, Serge Belongie 2017-04-27 2021-05-17 [("doi","10.48550/arXiv.1704.08614")] ai/anime ai/dataset ai/nn/cnn reinforcement-learning/exploration/active-learning

Computer vision systems are designed to work well within the context of everyday photography. However, artists often render the world around them in ways that do not resemble photographs. Artwork produced by people is not constrained to mimic the physical world, making it more challenging for machines to recognize.

This work is a step toward teaching machines how to categorize images in ways that are valuable to humans. First, we collect a large-scale dataset of contemporary artwork from Behance, a website containing millions of portfolios from professional and commercial artists. We annotate Behance imagery with rich attribute labels for content, emotions, and artistic media. Furthermore, we carry out baseline experiments to show the value of this dataset for artistic style prediction, for improving the generality of existing object classifiers, and for the study of visual domain adaptation. We believe our Behance Artistic Media dataset will be a good starting point for researchers wishing to study artistic imagery and relevant problems.

--- https://arxiv.org/abs/1703.08966 Mastering Sketching: Adversarial Augmentation for Structured Prediction Edgar Simo-Serra, Satoshi Iizuka, Hiroshi Ishikawa 2017-03-27 2021-05-17 [("doi","10.48550/arXiv.1703.08966")] ai/nn/gan

We present an integral framework for training sketch simplification networks that convert challenging rough sketches into clean line drawings. Our approach augments a simplification network with a discriminator network, training both networks jointly so that the discriminator network discerns whether a line drawing is a real training data or the output of the simplification network, which in turn tries to fool it. This approach has two major advantages. First, because the discriminator network learns the structure in line drawings, it encourages the output sketches of the simplification network to be more similar in appearance to the training sketches. Second, we can also train the simplification network with additional unsupervised data, using the discriminator network as a substitute teacher. Thus, by adding only rough sketches without simplified line drawings, or only line drawings without the original rough sketches, we can improve the quality of the sketch simplification.

We show how our framework can be used to train models that outperform the state-of-the-art in the sketch simplification task, despite using the same architecture for inference. We additionally present an approach to optimize for a single image, which improves accuracy at the cost of additional computation time. Finally, we show that, using the same framework, it is possible to train the network to perform the inverse problem, i.e. convert simple line sketches into pencil drawings, which is not possible using the standard mean squared error loss.

We validate our framework with two user tests, where our approach is preferred to the state-of-the-art in sketch simplification 92.3% of the time and obtains 1.2 more points on a scale of 1 to 5.

--- https://arxiv.org/abs/1705.02355 Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters Michela Paganini, Luke de Oliveira, Benjamin Nachman 2017-05-05 2021-05-17 [("doi","10.1103/PhysRevLett.120.042003")] ai/nn/gan science

Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of particle collisions to build expectations of what experimental data may look like under different theory modeling assumptions. Petabytes of simulated data are needed to develop analysis techniques, though they are expensive to generate using existing algorithms and computing resources. The modeling of detectors and the precise description of particle cascades as they interact with the material in the calorimeter are the most computationally demanding steps in the simulation pipeline.

We therefore introduce a deep neural network-based generative model to enable high-fidelity, fast, electromagnetic calorimeter simulation. There are still challenges for achieving precision across the entire phase space, but our current solution can reproduce a variety of particle shower properties while achieving speed-up factors of up to 100,000×.

This opens the door to a new era of fast simulation that could save computing time and disk space, while extending the reach of physics searches and precision measurements at the LHC and beyond.

--- https://arxiv.org/abs/1704.08834 Outline Colorization through Tandem Adversarial Networks Kevin Frans 2017-04-28 2021-05-17 [("doi","10.48550/arXiv.1704.08834")] ai/nn/gan

When creating digital art, coloring and shading are often time-consuming tasks that follow the same general patterns.

A solution to automatically colorize raw line art would have many practical applications. We propose a setup using two networks in tandem: a color prediction network based only on outlines, and a shading network conditioned on both outlines and a color scheme. We present processing methods to limit information passed in the color scheme, improving generalization.

Finally, we demonstrate natural-looking results when colorizing outlines from scratch, as well as from a messy, user-defined color scheme.

--- https://arxiv.org/abs/1612.00005 Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space Anh Nguyen, Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, Jason Yosinski 2016-11-30 2021-05-18 [("doi","10.48550/arXiv.1612.00005")] ai/nn/gan

Generating high-resolution, photo-realistic images has been a long-standing goal in machine learning. Recently, Nguyen et al 2016 showed one interesting way to synthesize novel images by performing gradient ascent in the latent space of a generator network to maximize the activations of one or multiple neurons in a separate classifier network.

In this paper we extend this method by introducing an additional prior on the latent code, improving both sample quality and sample diversity, leading to a state-of-the-art generative model that produces high quality images at higher resolutions (227×227) than previous generative models, and does so for all 1,000 ImageNet categories. In addition, we provide an unified probabilistic interpretation of related activation maximization methods and call the general class of models “Plug and Play Generative Networks”. PPGNs are composed of (1) a generator network G that is capable of drawing a wide range of image types and (2) a replaceable “condition” network C that tells the generator what to draw. We demonstrate the generation of images conditioned on a class (when C is an ImageNet or MIT Places classification network) and also conditioned on a caption (when C is an image captioning network).

Our method also improves the state-of-the-art of Multifaceted Feature Visualization, which generates the set of synthetic inputs that activate a neuron in order to better understand how deep neural networks operate. Finally, we show that our model performs reasonably well at the task of image inpainting.

While image models are used in this paper, the approach is modality-agnostic and can be applied to many types of data.

--- https://arxiv.org/abs/1705.05627 Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers Ryan Henderson, Rasmus Rothe 2017-05-16 2021-05-18 [("doi","10.5334/jors.178")] ai/nn/cnn

Picasso is a free open-source (Eclipse Public License) web application written in Python for rendering standard visualizations useful for analyzing convolutional neural networks.

Picasso ships with occlusion maps and saliency maps, two visualizations which help reveal issues that evaluation metrics like loss and accuracy might hide: for example, learning a proxy classification task. Picasso works with the Tensorflow deep learning framework, and Keras (when the model can be loaded into the Tensorflow backend).

Picasso can be used with minimal configuration by deep learning researchers and engineers alike across various neural network architectures.

Adding new visualizations is simple: the user can specify their visualization code and HTML template separately from the application code.

--- https://arxiv.org/abs/1705.01088 Visual Attribute Transfer through Deep Image Analogy Jing Liao, Yuan Yao, Lu Yuan, Gang Hua, Sing Bing Kang 2017-05-02 2021-05-18 [("doi","10.48550/arXiv.1705.01088")] ai/nn

We propose a new technique for visual attribute transfer across images that may have very different appearance but have perceptually similar semantic structure. By visual attribute transfer, we mean transfer of visual information (such as color, tone, texture, and style) from one image to another. For example, one image could be that of a painting or a sketch while the other is a photo of a real scene, and both depict the same type of scene.

Our technique finds semantically-meaningful dense correspondences between two input images. To accomplish this, it adapts the notion of “image analogy” with features extracted from a Deep Convolutional Neutral Network for matching; we call our technique Deep Image Analogy. A coarse-to-fine strategy is used to compute the nearest-neighbor field for generating the results. We validate the effectiveness of our proposed method in a variety of cases, including style/texture transfer, color/style swap, sketch/painting to photo, and time lapse.

--- https://arxiv.org/abs/1704.08863 On weight initialization in deep neural networks Siddharth Krishna Kumar 2017-04-28 2021-05-18 [("doi","10.48550/arXiv.1704.08863")] ai/nn

A proper initialization of the weights in a neural network is critical to its convergence. Current insights into weight initialization come primarily from linear activation functions.

In this paper, I develop a theory for weight initializations with non-linear activations. First, I derive a general weight initialization strategy for any neural network using activation functions differentiable at 0.

Next, I derive the weight initialization strategy for the Rectified Linear Unit (RELU), and provide theoretical insights into why the Xavier initialization is a poor choice with RELU activations.

My analysis provides a clear demonstration of the role of non-linearities in determining the proper weight initializations.

--- https://arxiv.org/abs/1704.05426 A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference Adina Williams, Nikita Nangia, Samuel R. Bowman 2017-04-18 2021-05-18 [("doi","10.48550/arXiv.1704.05426")] ai/nn

This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding.

In addition to being one of the largest corpora available for the task of Natural Language Inference (NLI), at 433k examples, this corpus improves upon available resources in its coverage: it offers data from 10 distinct genres of written and spoken English—making it possible to evaluate systems on nearly the full complexity of the language—and it offers an explicit setting for the evaluation of cross-genre domain adaptation.

--- https://openreview.net/forum?id=Sy2fzU9gl#deepmind β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, Alexander Lerchner 2017-04-18 2021-05-18 ai/nn/vae

We introduce β-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner.

Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do.

We introduce β-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner. Our approach is a modification of the variational autoencoder (VAE) framework. We introduce an adjustable hyperparameter β that balances latent channel capacity and independence constraints with reconstruction accuracy. We demonstrate that β-VAE with appropriately tuned β > 1 qualitatively outperforms VAE (β = 1), as well as state-of-the-art unsupervised (InfoGAN) and semi-supervised (DC-IGN) approaches to disentangled factor learning on a variety of datasets (Celeb-A, Faces and Chairs).

Furthermore, we devise a protocol to quantitatively compare the degree of disentanglement learnt by different models, and show that our approach also outperforms all baselines quantitatively. Unlike InfoGAN, β-VAE is stable to train, makes few assumptions about the data and relies on tuning a single hyperparameter, which can be directly optimized through a hyperparameter search using weakly-labeled data or through heuristic visual inspection for purely unsupervised data.

--- https://arxiv.org/abs/1703.09844 Multi-Scale Dense Networks for Resource Efficient Image Classification Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Q. Weinberger 2017-03-29 2021-05-18 [("doi","10.48550/arXiv.1703.09844")] ai/nn/cnn

In this paper we investigate image classification with computational resource limits at test time. Two such settings are: 1. anytime classification, where the network’s prediction for a test example is progressively updated, facilitating the output of a prediction at any time; and 2. budgeted batch classification, where a fixed amount of computation is available to classify a set of examples that can be spent unevenly across “easier” and “harder” inputs.

In contrast to most prior work, such as the popular Viola and Jones algorithm, our approach is based on convolutional neural networks. We train multiple classifiers with varying resource demands, which we adaptively apply during test time. To maximally re-use computation between the classifiers, we incorporate them as early-exits into a single deep convolutional neural network and inter-connect them with dense connectivity. To facilitate high quality classification early on, we use a two-dimensional multi-scale network architecture that maintains coarse and fine level features all-throughout the network.

Experiments on 3 image-classification tasks demonstrate that our framework substantially improves the existing state-of-the-art in both settings.

--- https://arxiv.org/abs/1702.07825 Deep Voice: Real-time Neural Text-to-Speech Sercan O. Arik, Mike Chrzanowski, Adam Coates, Gregory Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Andrew Ng, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi 2017-02-25 2021-05-18 [("doi","10.48550/arXiv.1702.07825")] ai/nn

We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. Deep Voice lays the groundwork for truly end-to-end neural speech synthesis. The system comprises 5 major building blocks: a segmentation model for locating phoneme boundaries, a grapheme-to-phoneme conversion model, a phoneme duration prediction model, a fundamental frequency prediction model, and an audio synthesis model.

For the segmentation model, we propose a novel way of performing phoneme boundary detection with deep neural networks using connectionist temporal classification (CTC) loss. For the audio synthesis model, we implement a variant of WaveNet that requires fewer parameters and trains faster than the original. By using a neural network for each component, our system is simpler and more flexible than traditional text-to-speech systems, where each component requires laborious feature engineering and extensive domain expertise.

Finally, we show that inference with our system can be performed faster than real time and describe optimized WaveNet inference kernels on both CPU and GPU that achieve up to 400× speedups over existing implementations.

--- https://arxiv.org/abs/1702.06295 Convolution Aware Initialization Armen Aghajanyan 2017-02-21 2021-05-18 [("doi","10.48550/arXiv.1702.06295")] ai/nn/cnn

Initialization of parameters in deep neural networks has been shown to have a big impact on the performance of the networks (Mishkin & Matas 2015). The initialization scheme devised by He et al, allowed convolution activations to carry a constrained mean which allowed deep networks to be trained effectively (He et al 2015a). Orthogonal initializations and more generally orthogonal matrices in standard recurrent networks have been proved to eradicate the vanishing and exploding gradient problem (Pascanu et al 2012). Majority of current initialization schemes do not take fully into account the intrinsic structure of the convolution operator.

Using the duality of the Fourier transform and the convolution operator, Convolution Aware Initialization builds orthogonal filters in the Fourier space, and using the inverse Fourier transform represents them in the standard space. With Convolution Aware Initialization we noticed not only higher accuracy and lower loss, but faster convergence.

We achieve new state-of-the-art on the CIFAR-10 dataset, and achieve close to state-of-the-art on various other tasks.

--- https://arxiv.org/abs/1702.05774 Machine Learning Predicts Laboratory Earthquakes Bertr, Rouet-Leduc, Claudia Hulbert, Nicholas Lubbers, Kipton Barros, Colin Humphreys, Paul A. Johnson 2017-02-19 2021-05-18 [("doi","10.1002/2017GL074677")] ai/nn

Forecasting fault failure is a fundamental but elusive goal in earthquake science.

Here we show that by listening to the acoustic signal emitted by a laboratory fault, machine learning can predict the time remaining before it fails with great accuracy. These predictions are based solely on the instantaneous physical characteristics of the acoustical signal, and do not make use of its history.

Surprisingly, machine learning identifies a signal emitted from the fault zone previously thought to be low-amplitude noise that enables failure forecasting throughout the laboratory quake cycle.

We hypothesize that applying this approach to continuous seismic data may lead to advances in identifying currently unknown signals, in providing new insights into fault physics, and in placing bounds on fault failure times.

--- https://arxiv.org/abs/1702.01304 Gender-From-Iris or Gender-From-Mascara? Andrey Kuehlkamp, Benedict Becker, Kevin Bowyer 2017-02-04 2021-05-19 [("doi","10.48550/arXiv.1702.01304")] ai/nn/cnn ai/nn/fully-connected

Predicting a person’s gender based on the iris texture has been explored by several researchers.

This paper considers several dimensions of experimental work on this problem, including person-disjoint train and test, and the effect of cosmetics on eyelash occlusion and imperfect segmentation. We also consider the use of multi-layer perceptron and convolutional neural networks as classifiers, comparing the use of data-driven and hand-crafted features.

Our results suggest that the gender-from-iris problem is more difficult than has so far been appreciated. Estimating accuracy using a mean of n person-disjoint train and test partitions, and considering the effect of makeup—a combination of experimental conditions not present in any previous work—we find a much weaker ability to predict gender-from-iris texture than has been suggested in previous work.

--- https://arxiv.org/abs/1702.01135 Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer 2017-02-03 2021-05-19 [("doi","10.48550/arXiv.1702.01135")] ai/nn reinforcement-learning/safe

Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior.

We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions.

Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.

We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu).

--- https://arxiv.org/abs/1612.09508 Feedback Networks Amir R. Zamir, Te-Lin Wu, Lin Sun, William Shen, Jitendra Malik, Silvio Savarese 2016-12-30 2021-05-19 [("doi","10.48550/arXiv.1612.09508")] ai/nn

Currently, the most successful learning models in computer vision are based on learning successive representations followed by a decision layer. This is usually actualized through feedforward multilayer neural networks, eg. ConvNets, where each layer forms one of such successive representations. However, an alternative that can achieve the same goal is a feedback based approach in which the representation is formed in an iterative manner based on a feedback received from previous iteration’s output.

We establish that a feedback based approach has several fundamental advantages over feedforward: it enables making early predictions at the query time, its output naturally conforms to a hierarchical structure in the label space (eg. a taxonomy), and it provides a new basis for Curriculum Learning. We observe that feedback networks develop a considerably different representation compared to feedforward counterparts, in line with the aforementioned advantages. We put forth a general feedback based learning architecture with the endpoint results on par or better than existing feedforward networks with the addition of the above advantages. We also investigate several mechanisms in feedback architectures (eg. skip connections in time) and design choices (eg. feedback length). We hope this study offers new perspectives in quest for more natural and practical learning models.

--- https://arxiv.org/abs/1612.06890 CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick 2016-12-20 2021-05-19 [("doi","10.48550/arXiv.1612.06890")] ai/nn

When building artificial intelligence systems that can reason and answer questions about visual data, we need diagnostic tests to analyze our progress and discover shortcomings.

Existing benchmarks for visual question answering can help, but have strong biases that models can exploit to correctly answer questions without reasoning. They also conflate multiple sources of error, making it hard to pinpoint model weaknesses.

We present a diagnostic dataset that tests a range of visual reasoning abilities. It contains minimal biases and has detailed annotations describing the kind of reasoning each question requires.

We use this dataset to analyze a variety of modern visual reasoning systems, providing novel insights into their abilities and limitations.

--- https://openreview.net/forum?id=BkjLkSqxg LipNet: End-to-End Sentence-level Lipreading Yannis M. Assael, Brendan Shillingford, Shimon Whiteson, Nando de Freitas 2016-12-16 2021-05-19 ai/nn/cnn ai/video/analysis

LipNet is the first end-to-end sentence-level lipreading model to simultaneously learn spatiotemporal visual features and a sequence model.

Lipreading is the task of decoding text from the movement of a speaker’s mouth. Traditional approaches separated the problem into two stages: designing or learning visual features, and prediction. More recent deep lipreading approaches are end-to-end trainable (Wand et al 2016; Chung & Zisserman, 2016a). However, existing work on models trained end-to-end perform only word classification, rather than sentence-level sequence prediction. Studies have shown that human lipreading performance increases for longer words (Easton & Basala 1982), indicating the importance of features capturing temporal context in an ambiguous communication channel. Motivated by this observation, we present LipNet, a model that maps a variable-length sequence of video frames to text, making use of spatiotemporal convolutions, a recurrent network, and the connectionist temporal classification loss, trained entirely end-to-end. To the best of our knowledge, LipNet is the first end-to-end sentence-level lipreading model that simultaneously learns spatiotemporal visual features and a sequence model. On the GRID corpus, LipNet achieves 95.2% accuracy in sentence-level, overlapped speaker split task, outperforming experienced human lipreaders and the previous 86.4% word-level state-of-the-art accuracy (Gergen et al 2016).

[Keywords: Computer vision, Deep learning]

--- https://arxiv.org/abs/1611.09268#microsoft MS MARCO: A Human Generated MAchine Reading COmprehension Dataset Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang 2016-11-28 2021-05-19 [("doi","10.48550/arXiv.1611.09268")] ai/dataset ai/nn

We introduce a large scale MAchine Reading COmprehension dataset, which we name MS MARCO. The dataset comprises of 1,010,916 anonymized questions—sampled from Bing’s search query logs—each with a human generated answer and 182,669 completely human rewritten generated answers. In addition, the dataset contains 8,841,823 passages—extracted from 3,563,535 web documents retrieved by Bing—that provide the information necessary for curating the natural language answers. A question in the MS MARCO dataset may have multiple answers or no answers at all.

Using this dataset, we propose 3 different tasks with varying levels of difficulty: (1) predict if a question is answerable given a set of context passages, and extract and synthesize the answer as a human would (2) generate a well-formed answer (if possible) based on the context passages that can be understood with the question and passage context, and finally (3) rank a set of retrieved passages given a question.

The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering. We believe that the scale and the real-world nature of this dataset makes it attractive for benchmarking machine reading comprehension and question-answering models.

--- https://arxiv.org/abs/1611.05358 Lip Reading Sentences in the Wild Joon Son Chung, Andrew Senior, Oriol Vinyals, Andrew Zisserman 2016-11-16 2021-05-19 [("doi","10.1109/CVPR.2017.367")] ai/dataset ai/nn

[video] The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focused on recognising a limited number of words or phrases, we tackle lip reading as an open-world problem—unconstrained natural language sentences, and in the wild videos.

Our key contributions are: (1) a “Watch, Listen, Attend and Spell” (WLAS) network that learns to transcribe videos of mouth motion to characters; (2) a curriculum learning strategy to accelerate training and to reduce overfitting; (3) a Lip Reading Sentences (LRS) dataset for visual speech recognition, consisting of over 100,000 natural sentences from British television.

The WLAS model trained on the LRS dataset surpasses the performance of all previous work on standard lip reading benchmark datasets, often by a large margin. This lip reading performance beats a professional lip reader on videos from BBC television, and we also demonstrate that visual information helps to improve speech recognition performance even when the audio is available.

--- https://arxiv.org/abs/1611.01232 Deep Information Propagation Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein 2016-11-04 2021-05-19 [("doi","10.48550/arXiv.1611.01232")] ai/nn/fully-connected

We study the behavior of untrained neural networks whose weights and biases are randomly distributed using mean field theory.

We show the existence of depth scales that naturally limit the maximum depth of signal propagation through these random networks. Our main practical result is to show that random networks may be trained precisely when information can travel through them. Thus, the depth scales that we identify provide bounds on how deep a network may be trained for a specific choice of hyperparameters.

As a corollary to this, we argue that in networks at the ‘edge of chaos’, one of these depth scales diverges. Thus arbitrarily deep networks may be trained only sufficiently close to criticality.

We show that the presence of dropout destroys the order-to-chaos critical point and therefore strongly limits the maximum trainable depth for random networks.

Finally, we develop a mean field theory for backpropagation and we show that the ordered and chaotic phases correspond to regions of vanishing and exploding gradients respectively.

--- https://arxiv.org/abs/1610.09322 Homotopy Analysis for Tensor PCA Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi 2016-10-28 2021-05-19 [("doi","10.48550/arXiv.1610.09322")] ai/nn

Developing efficient and guaranteed nonconvex algorithms has been an important challenge in modern machine learning. Algorithms with good empirical performance such as stochastic gradient descent often lack theoretical guarantees.

In this paper, we analyze the class of homotopy or continuation methods for global optimization of nonconvex functions. These methods start from an objective function that is efficient to optimize (eg. convex), and progressively modify it to obtain the required objective, and the solutions are passed along the homotopy path.

For the challenging problem of tensor PCA, we prove global convergence of the homotopy method in the “high noise” regime. The signal-to-noise requirement for our algorithm is tight in the sense that it matches the recovery guarantee for the best degree-4 sum-of-squares algorithm.

In addition, we prove a phase transition along the homotopy path for tensor PCA. This allows to simplify the homotopy method to a local search algorithm, viz., tensor power iterations, with a specific initialization and a noise injection procedure, while retaining the theoretical guarantees.

--- https://arxiv.org/abs/1703.01988#deepmind Neural Episodic Control Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell 2017-03-06 2021-05-19 [("doi","10.48550/arXiv.1703.01988")] ai/nn/dynamic-evaluation ai/nn/retrieval reinforcement-learning/model-free

Deep reinforcement learning methods attain super-human performance in a wide range of environments. Such methods are grossly inefficient, often taking orders of magnitudes more data than humans to achieve reasonable performance.

We propose Neural Episodic Control: a deep reinforcement learning agent that is able to rapidly assimilate new experiences and act upon them. Our agent uses a semi-tabular representation of the value function: a buffer of past experience containing slowly-changing state representations and rapidly-updated estimates of the value function.

We show across a wide range of environments that our agent learns faster than other state-of-the-art, general purpose deep reinforcement learning agents.

--- https://arxiv.org/abs/1701.07275 Universal representations: The missing link between faces, text, planktons, and cat breeds Hakan Bilen, Andrea Vedaldi 2017-01-25 2021-05-19 [("doi","10.48550/arXiv.1701.07275")] ai/nn/cnn ai/scaling

With the advent of large labeled datasets and high-capacity models, the performance of machine vision systems has been improving rapidly. However, the technology has still major limitations, starting from the fact that different vision problems are still solved by different models, trained from scratch or fine-tuned on the target data. The human visual system, in stark contrast, learns a universal representation for vision in the early life of an individual. This representation works well for an enormous variety of vision problems, with little or no change, with the major advantage of requiring little training data to solve any of them.

n this paper we investigate whether neural networks may work as universal representations by studying their capacity in relation to the “size” of a large combination of vision problems. We do so by showing that a single neural network can learn simultaneously several very different visual domains (from sketches to planktons and MNIST digits) as well as, or better than, a number of specialized networks. However, we also show that this requires to carefully normalize the information in the network, by using domain-specific scaling factors or, more generically, by using an instance normalization layer.

--- https://arxiv.org/abs/1610.00527#deepmind VPN: Video Pixel Networks Nal Kalchbrenner, Aaron van den Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, Koray Kavukcuoglu 2016-10-03 2021-05-20 [("doi","10.48550/arXiv.1610.00527")] ai/nn/cnn ai/nn/rnn ai/video/generation

We propose a probabilistic video model, the Video Pixel Network (VPN), that estimates the discrete joint distribution of the raw pixel values in a video. The model and the neural architecture reflect the time, space and color structure of video tensors and encode it as a four-dimensional dependency chain.

The VPN approaches the best possible performance on the Moving MNIST benchmark, a leap over the previous state-of-the-art, and the generated videos show only minor deviations from the ground truth. The VPN also produces detailed samples on the action-conditional Robotic Pushing benchmark and generalizes to the motion of novel objects.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5394931/ Altering the threshold of an excitable signal transduction network changes cell migratory modes Yuchuan Miao, Sayak Bhattacharya, Marc Edwards, Huaqing Cai, Takanari Inoue, Pablo A. Iglesias, Peter N. Devreotes 2017 2021-05-20 [("doi","10.1038/ncb3495")] biology cs/cellular-automaton

The diverse migratory modes displayed by different cell types are generally believed to be idiosyncratic.

Here we show that the migratory behavior of Dictyostelium was switched from amoeboid to keratocyte-like and oscillatory modes by synthetically decreasing phosphatidylinositol-4,5-bisphosphate levels or increasing Ras/Rap-related activities.

The perturbations at these key nodes of an excitable signal transduction network initiated a causal chain of events: the threshold for network activation was lowered, the speed and range of propagating waves of signal transduction activity increased, actin-driven cellular protrusions expanded and, consequently, the cell migratory mode transitions ensued. Conversely, innately keratocyte-like and oscillatory cells were promptly converted to amoeboid by inhibition of Ras effectors with restoration of directed migration.

We use computational analysis to explain how thresholds control cell migration and discuss the architecture of the signal transduction network that gives rise to excitability.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5413888/ The foundations of plant intelligence Anthony Trewavas 2017 2021-05-20 [("doi","10.1098/rsfs.2016.0098")] biology iq

Intelligence is defined for wild plants and its role in fitness identified. Intelligent behavior exhibited by single cells and systems similarity between the interactome and connectome indicates neural systems are not necessary for intelligent capabilities. Plants sense and respond to many environmental signals that are assessed to competitively optimize acquisition of patchily distributed resources.

Situations of choice engender motivational states in goal-directed plant behavior; consequent intelligent decisions enable efficient gain of energy over expenditure. Comparison of swarm intelligence and plant behavior indicates the origins of plant intelligence lie in complex communication and is exemplified by cambial control of branch function. Error correction in behaviors indicates both awareness and intention as does the ability to count to five. Volatile organic compounds are used as signals in numerous plant interactions. Being complex in composition and often species and individual specific, they may represent the plant language and account for self and alien recognition between individual plants.

Game theory has been used to understand competitive and cooperative interactions between plants and microbes. Some unexpected cooperative behavior between individuals and potential aliens has emerged. Behavior profiting from experience, another simple definition of intelligence, requires both learning and memory and is indicated in the priming of herbivory, disease and abiotic stresses.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5493793/ Predicting green: really radical (plant) predictive processing Paco Calvo, Karl Friston 2017 2021-05-20 [("doi","10.1098/rsif.2017.0096")] biology cs/algorithm/information

In this article we account for the way plants respond to salient features of their environment under the free-energy principle for biological systems. Biological self-organization amounts to the minimization of surprise over time.

We posit that any self-organizing system must embody a generative model whose predictions ensure that (expected) free energy is minimized through action. Plants respond in a fast, and yet coordinated manner, to environmental contingencies. They pro-actively sample their local environment to elicit information with an adaptive value. Our main thesis is that plant behavior takes place by way of a process (active inference) that predicts the environmental sources of sensory stimulation. This principle, we argue, endows plants with a form of perception that underwrites purposeful, anticipatory behavior.

The aim of the article is to assess the prospects of a radical predictive processing story that would follow naturally from the free-energy principle for biological systems; an approach that may ultimately bear upon our understanding of life and cognition more broadly.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711578/ Understanding Immunity through the Lens of Disease Ecology Stephen M. Hedrick 2017 2021-05-20 [("doi","10.1016/j.it.2017.08.001")] biology genetics/selection/natural/human

[followup to Hedrick 2004; commentary] As we describe the immune system in ever more exquisite detail, we might find that no matter how successful, this approach will not be sufficient to understand the spread of infectious agents, their susceptibility to vaccine therapy, and human disease resistance.

Compared with the strict reductionism practiced as a means of characterizing most biological processes, I propose that the progression and outcome of disease-causing host-parasite interactions will be more clearly understood through a focus on disease ecology.

...A half century ago everyone expected their children to experience the ravages of measles, mumps, rubella, chicken pox, influenza, and other infections that had evolved into the “childhood” diseases. This was traumatic enough that even as a small child, not knowing anything about the dynamics of disease epidemics, I wondered why it was I had to experience all of these diseases as well as an almost continuous string of less severe “colds” and enteropathies (we called them all ‘stomach flu’), when all the while our pets appeared to remain perfectly healthy.

If human beings are just exceptionally intelligent animals, I wondered, why should we be sick so often? The answer is to be found in our unnaturally rapid alteration of population density and structure, our close association (often involving killing and exchange of blood) with so many different other species, and our small-worlds population structure. I assert the likely possibility that because of our unique ability to change our ecosystem, for the past few thousand years, we human beings have been the most diseased species on earth.

--- https://arxiv.org/abs/1704.07183 Stochastic Constraint Programming as Reinforcement Learning Steven Prestwich, Roberto Rossi, Armagan Tarim 2017-04-24 2021-05-20 [("doi","10.48550/arXiv.1704.07183")] cs/algorithm reinforcement-learning/model

Stochastic Constraint Programming (SCP) is an extension of Constraint Programming (CP) used for modeling and solving problems involving constraints and uncertainty. SCP inherits excellent modeling abilities and filtering algorithms from CP, but so far it has not been applied to large problems.

Reinforcement Learning (RL) extends Dynamic Programming to large stochastic problems, but is problem-specific and has no generic solvers.

We propose a hybrid combining the scalability of RL with the modeling and constraint filtering methods of CP.

We implement a prototype in a CP system and demonstrate its usefulness on SCP problems.

--- https://arxiv.org/abs/1703.01937 Reputation Dynamics in a Market for Illicit Drugs Nick Janetos, Jan Tilly 2017-03-06 2021-05-20 [("doi","10.48550/arXiv.1703.01937")] darknet-market

We analyze reputation dynamics in an online market for illicit drugs using a novel dataset of prices and ratings. The market is a black market, and so contracts cannot be enforced. We study the role that reputation plays in alleviating adverse selection in this market.

We document the following stylized facts: (1) There is a positive relationship between the price and the rating of a seller. This effect is increasing in the number of reviews left for a seller. A mature highly-rated seller charges a 20% higher price than a mature low-rated seller. (2) Sellers with more reviews charge higher prices regardless of rating. (3) Low-rated sellers are more likely to exit the market and make fewer sales.

We show that these stylized facts are explained by a dynamic model of adverse selection, ratings, and exit, in which buyers form rational inferences about the quality of a seller jointly from his rating and number of sales. Sellers who receive low ratings initially charge the same price as highly-rated sellers since early reviews are less informative about quality. Bad sellers exit rather than face lower prices in the future.

We provide conditions under which our model admits a unique equilibrium. We estimate the model, and use the result to compute the returns to reputation in the market.

We find that the market would have collapsed due to adverse selection in the absence of a rating system.

--- https://www.biorxiv.org/content/10.1101/096214.full Associations of coffee genetic risk scores with coffee, tea and other beverages in the UK Biobank Amy E. Taylor, Marcus R. Munafò 2016-12-22 2021-05-20 [("doi","10.1101/096214")] food genetics/heritable nootropic/caffeine tea

Background: Genetic variants which determine amount of coffee consumed have been identified in genome-wide association studies (GWAS) of coffee consumption; these may help to further understanding of the effects of coffee on health outcomes. However, there is limited information about how these variants relate to caffeinated beverage consumption more generally.

Aims

To improve phenotype definition for coffee consumption related genetic risk scores by testing their association with coffee, tea and other beverages.

Method: We tested the associations of genetic risk scores for coffee consumption with beverage consumption in 114,316 individuals of European ancestry from the UK Biobank. Drinks were self-reported in a baseline questionnaire and in detailed 24 dietary recall questionnaires in a subset.

Results: Genetic risk scores including two and eight single-nucleotide polymorphisms (SNPs) explained up to 0.39%, 0.19% and 0.77% of the variance in coffee, tea and combined coffee and tea consumption respectively. A one standard deviation increase in the 8 SNP genetic risk score was associated with a 0.13 cup per day (95% CI: 0.12, 0.14), 0.12 cup per day (95%CI: 0.11, 0.14) and 0.25 cup per day (95% CI: 0.24, 0.27) increase in coffee, tea and combined tea and coffee consumption, respectively. Genetic risk scores also demonstrated positive associations with both caffeinated and decaffeinated coffee and tea consumption. In 48,692 individuals with dietary recall data, the genetic risk scores were positively associated with coffee and tea, (apart from herbal teas) consumption, but did not show clear evidence for positive associations with other beverages. However, there was evidence that the genetic risk scores were associated with lower daily water consumption and lower overall drink consumption.

Conclusion: Genetic risk scores created from variants identified in coffee consumption GWAS associate more broadly with caffeinated beverage consumption and also with decaffeinated coffee and tea consumption.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5725762/ Extending the MR-Egger method for multivariable Mendelian Randomization to correct for both measured and unmeasured pleiotropy Jessica M. B. Rees, Angela M. Wood, Stephen Burgess 2017 2021-05-20 [("doi","10.1002/sim.7492")] genetics/heritable/correlation/mendelian-randomization

Methods have been developed for Mendelian Randomization that can obtain consistent causal estimates while relaxing the instrumental variable assumptions. These include multivariable Mendelian Randomization, in which a genetic variant may be associated with multiple risk factors so long as any association with the outcome is via the measured risk factors (measured pleiotropy), and the MR-Egger (Mendelian Randomization-Egger) method, in which a genetic variant may be directly associated with the outcome not via the risk factor of interest, so long as the direct effects of the variants on the outcome are uncorrelated with their associations with the risk factor (unmeasured pleiotropy). In this paper, we extend the MR-Egger method to a multivariable setting to correct for both measured and unmeasured pleiotropy.

We show, through theoretical arguments and a simulation study, that the multivariable MR-Egger method has advantages over its univariable counterpart in terms of plausibility of the assumption needed for consistent causal estimation and power to detect a causal effect when this assumption is satisfied.

The methods are compared in an applied analysis to investigate the causal effect of high-density lipoprotein cholesterol on coronary heart disease risk.

The multivariable MR-Egger method will be useful to analyse high-dimensional data in situations where the risk factors are highly related and it is difficult to find genetic variants specifically associated with the risk factor of interest (multivariable by design), and as a sensitivity analysis when the genetic variants are known to have pleiotropic effects on measured risk factors.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5658708/ Engineering photosynthesis: progress and perspectives Douglas J. Orr, Auderlan M. Pereira, Paula da Fonseca Pereira, Ítalo A. Pereira-Lima, Agustin Zsögön, Wagner L. Araújo 2017 2021-05-20 [("doi","10.12688/f1000research.12181.1")] genetics/editing genetics/selection/artificial

Photosynthesis is the basis of primary productivity on the planet. Crop breeding has sustained steady improvements in yield to keep pace with population growth increases. Yet these advances have not resulted from improving the photosynthetic process per se but rather of altering the way carbon is partitioned within the plant.

Mounting evidence suggests that the rate at which crop yields can be boosted by traditional plant breeding approaches is wavering, and they may reach a “yield ceiling” in the foreseeable future.

Further increases in yield will likely depend on the targeted manipulation of plant metabolism. Improving photosynthesis poses one such route, with simulations indicating it could have a transformative influence on enhancing crop productivity.

Here, we summarize recent advances of alternative approaches for the manipulation and enhancement of photosynthesis and their possible application for crop improvement.

--- https://www.biorxiv.org/content/10.1101/123711.full Father Absence and Accelerated Reproductive Development Lauren Gaydosh, Daniel W. Belsky, Benjamin W. Domingue, Jason D. Boardman, Kathleen Mullan Harris 2017-04-04 2021-05-20 [("doi","10.1101/123711")] genetics/heritable sociology

Evidence shows that girls who experience father absence in childhood experience accelerated reproductive development in comparison to peers with present fathers. One hypothesis advanced to explain this empirical pattern is genetic confounding, wherein gene-environment correlation (rGE) causes a spurious relationship between father absence and reproductive timing.

We test this hypothesis by constructing polygenic scores for age at menarche and first birth using recently available genome wide association study results and molecular genetic data on a sample of non-Hispanic white females from the National Longitudinal Study of Adolescent to Adult Health. Young women’s accelerated menarche polygenic scores were unrelated to their exposure to father absence. In contrast, earlier first-birth polygenic scores tended to be higher in young women raised in homes with absent fathers.

Nevertheless, father absence and the polygenic scores independently and additively predict reproductive timing.

We find limited evidence in support of the gene-environment correlation hypothesis.

--- https://www.biorxiv.org/content/10.1101/115600.full Psychiatric Genomics: An Update and an Agenda Patrick F. Sullivan, Arpana Agrawal, Cynthia M. Bulik, Ole A. Andreassen, Anders Børglum, Gerome Breen, Sven Cichon, Howard J. Edenberg, Stephen V. Faraone, Joel Gelernter, Carol A. Mathews, Caroline M. Nievergelt, Jordan Smoller, Michael C. O’Donovan, for the Psychiatric Genomics Consortium 2017-03-10 2021-05-21 [("doi","10.1101/115600")] genetics/heritable psychiatry

The Psychiatric Genomics Consortium (PGC) is the largest consortium in the history of psychiatry. In the past decade, this global effort has delivered a rapidly increasing flow of new knowledge about the fundamental basis of common psychiatric disorders, particularly given its dedication to rapid progress and open science. The PGC has recently commenced a program of research designed to deliver “actionable” findings—genomic results that (a) reveal the fundamental biology, (b) inform clinical practice, and (c) deliver new therapeutic targets. This is the central idea of the PGC: to convert the family history risk factor into biologically, clinically, and therapeutically meaningful insights. The emerging findings suggest that we are entering into a phase of accelerated translation of genetic discoveries to impact psychiatric practice within a precision medicine framework.

Collaborators

PGC Coordinating Committee: Mark Daly, Michael Gill, John Kelsoe, Karestan Koenen, Douglas Levinson, Cathryn Lewis, Ben Neale, Danielle Posthuma, Jonathan Sebat, and Pamela Sklar.

--- https://www.biorxiv.org/content/10.1101/055681.full Biogeographic Ancestry and Socioeconomic Outcomes in the Americas: A Meta-Analysis Emil O. W. Kirkegaard, Mingrui Wang, John Fuerst 2017-03-01 2021-05-21 [("doi","10.1101/055681")] genetics/heritable

Narrative reports suggest that socioeconomic status (SES) is associated with biogeographic ancestry (BGA) in the Americas. If so, SES potentially acts as a confound that needs to be taken into account when evaluating the relation between medical outcomes and BGA.

To explore how systematic BGA-SES associations are, a meta-analysis of American studies was conducted. 40 studies were identified, yielding a total of 64 independent samples with directions of associations, including 48 independent samples with effect sizes.

An analysis of association directions found a high degree of consistency. The square rootn-weighted directions were 0.83 (K = 36), −0.81 (K = 41) and −0.82 (K = 39) for European, Amerindian and African BGA, respectively. An analysis of effect size magnitudes found that European BGA was positively associated with SES, with a meta-analytic effect size of r = 0.18 [95% Cl: 0.13 to 0.24, K = 28, n = 35,476.5], while both Amerindian and African BGA were negatively associated with SES, having meta-analytic effect sizes of −.14 [−.18 to −.10, K = 31, n = 28,937.5] and −.11 [−.15 to −.07, K = 28, n = 32,710.5], respectively. There was considerable cross-sample variation in effect sizes (mean I2 = 92%), but the sample size was not enough for performing credible moderator analysis.

Implications for future studies are discussed.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5403838/ Heritability of Working in a Creative Profession Mark Patrick Roeling, Gonneke Willemsen, Dorret I. Boomsma 2017 2021-05-21 [("doi","10.1007/s10519-016-9832-0")] genetics/heritable

Creativity is the tendency to generate or recognize ideas, alternatives, or possibilities. It serves as a crucial element in numerous aspects of human activity, including the arts, sciences, and business.

Following a study on the genetic contribution to working in a creative profession, based on polygenic score analysis, we report the total heritability of this trait in a large sample of adult twins and their siblings registered with the Netherlands Twin Register. Data from 6755 twins and 1817 siblings were analyzed using genetic structural equation modeling.

Working in a creative profession is relatively rare in our sample (2.6% of twins and 3.2% of siblings). Twin correlations (identical 0.68 and fraternal 0.40) commended a model with additive genetic factors (full model estimate 0.56), shared (full model estimate 0.12), and unique environmental factors (full model estimate 0.32). Genetic model fitting resulted in a best-fitting model existing of additive genetic factors and unique environmental factors, resulting in a heritability of 0.70.

--- https://www.biorxiv.org/content/10.1101/087304.full Statistical properties of simple random-effects models for genetic heritability David Steinsaltz, Andrew Dahl, Kenneth W. Wachter 2016-11-14 2021-05-21 [("doi","10.1101/087304")] genetics/heritable

Random-effects models are a popular tool for analysing total narrow-sense heritability for simple quantitative phenotypes on the basis of large-scale SNP data. Recently, there have been disputes over the validity of conclusions that may be drawn from such analysis. We derive some of the fundamental statistical properties of heritability estimates arising from these models, showing that the bias will generally be small. We show that that the score function may be manipulated into a form that facilitates intelligible interpretations of the results. We use this score function to explore the behavior of the model when certain key assumptions of the model are not satisfied—shared environment, measurement error, and genetic effects that are confined to a small subset of sites—as well as to elucidate the meaning of negative heritability estimates that may arise.

The variance and bias depend crucially on the variance of certain functionals of the singular values of the genotype matrix. A useful baseline is the singular value distribution associated with genotypes that are completely independent—that is, with no linkage and no relatedness—for a given number of individuals and sites. We calculate the corresponding variance and bias for this setting.

MSC 2010 subject classifications: Primary 92D10; secondary 62P10; 62F10; 60B20.

--- https://www.biorxiv.org/content/10.1101/106203.full Genomic analysis of family data reveals additional genetic effects on intelligence and personality W. David Hill, Ruben C. Arslan, Charley Xia, Michelle Luciano, Carmen Amador, Pau Navarro, Caroline Hayward, Reka Nagy, David J. Porteous, Andrew M. McIntosh, Ian J. Deary, Chris S. Haley, Lars Penke 2017-02-06 2021-05-21 [("doi","10.1101/106203")] genetics/heritable/rare iq psychology/personality

Pedigree-based analyses of intelligence have reported that genetic differences account for 50–80% of the phenotypic variation. For personality traits, these effects are smaller with 34–48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and 0%–15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants.

Using ~20,000 individuals in the Generation Scotland family cohort genotyped for ~520,000 single-nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWASs of unrelated individuals. In our models, genetic variants in low LD with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism.

By capturing these additional genetic effects, our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion.

From an evolutionary genetic perspective, a substantial contribution of genetic variants that are not common within the population to individual differences in intelligence, education, and neuroticism is consistent with mutation-selection balance.

...For Neuroticism, the final model consisted of contributions from the variance components G and K. Additive common genetic effects explained 11% (SE = 2%) of the variance with pedigree-associated variants explaining an additional 19% (SE = 3%). Whereas none of the environmental components were statistically-significant, the family component accounted for 2% of the variance in the full model and 1% in a model that included only the G and the K in addition to F.

For Extraversion, the only detectable source of genetic variation came from the G, which accounted for 13% (SE = 2%), with F explaining a further 9% (SE = 1%) of the phenotypic variation. The lack of pedigree-associated genetic effects could be due to low statistical power, as K explained 5% of the variance in the full model and 6% in a GKF model, but with a relatively large SE, estimated at 5%.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006328 Genetic loci associated with coronary artery disease harbor evidence of selection and antagonistic pleiotropy Sean G. Byars, Qin Qin Huang, Lesley-Ann Gray, Andrew Bakshi, Samuli Ripatti, Gad Abraham, Stephen C. Stearns, Michael Inouye 2017-05-02 2021-05-21 [("doi","10.1371/journal.pgen.1006328")] genetics/selection/natural/human

Traditional genome-wide scans for positive selection have mainly uncovered selective sweeps associated with monogenic traits. While selection on quantitative traits is much more common, very few signals have been detected because of their polygenic nature. We searched for positive selection signals underlying coronary artery disease (CAD) in worldwide populations, using novel approaches to quantify relationships between polygenic selection signals and CAD genetic risk. We identified new candidate adaptive loci that appear to have been directly modified by disease pressures given their statistically-significant associations with CAD genetic risk. These candidates were all uniquely and consistently associated with many different male and female reproductive traits suggesting selection may have also targeted these because of their direct effects on fitness. We found that CAD loci are statistically-significantly enriched for lifetime reproductive success relative to the rest of the human genome, with evidence that the relationship between CAD and lifetime reproductive success is antagonistic. This supports the presence of antagonistic-pleiotropic tradeoffs on CAD loci and provides a novel explanation for the maintenance and high prevalence of CAD in modern humans. Lastly, we found that positive selection more often targeted CAD gene regulatory variants using HapMap3 lymphoblastoid cell lines, which further highlights the unique biological importance of candidate adaptive loci underlying CAD. Our study provides a novel approach for detecting selection on polygenic traits and evidence that modern human genomes have evolved in response to CAD-induced selection pressures and other early-life traits sharing pleiotropic links with CAD.

Author Summary: How genetic variation contributes to disease is complex, especially for those such as coronary artery disease (CAD) that develop over the lifetime of individuals. One of the fundamental questions about CAD—whose progression begins in young adults with arterial plaque accumulation leading to life-threatening outcomes later in life—is why natural selection has not removed or reduced this costly disease. It is the leading cause of death worldwide and has been present in human populations for thousands of years, implying considerable pressures that natural selection should have operated on. Our study provides new evidence that genes underlying CAD have recently been modified by natural selection and that these same genes uniquely and extensively contribute to human reproduction, which suggests that natural selection may have maintained genetic variation contributing to CAD because of its beneficial effects on fitness. This study provides novel evidence that CAD has been maintained in modern humans as a by-product of the fitness advantages those genes provide early in human lifecycles.

--- https://www.biorxiv.org/content/10.1101/090084.full Soft sweeps are the dominant mode of adaptation in the human genome Daniel R. Schrider, Andrew D. Kern 2017-04-27 2021-05-21 [("doi","10.1101/090084")] genetics/selection/natural/human

The degree to which adaptation in recent human evolution shapes genetic variation remains controversial. This is in part due to the limited evidence in humans for classic “hard selective sweeps”, wherein a novel beneficial mutation rapidly sweeps through a population to fixation. However, positive selection may often proceed via “soft sweeps” acting on mutations already present within a population.

Here we examine recent positive selection across 6 human populations using a powerful machine learning approach that is sensitive to both hard and soft sweeps. We found evidence that soft sweeps are widespread and account for the vast majority of recent human adaptation. Surprisingly, our results also suggest that linked positive selection affects patterns of variation across much of the genome, and may increase the frequencies of deleterious mutations.

Our results also reveal insights into the role of sexual selection, cancer risk, and central nervous system development in recent human evolution.

--- https://www.biorxiv.org/content/10.1101/125799.full Comparative genomic evidence for self-domestication in Homo sapiens Constantina Theofanopoulou, Simone Gastaldon, Thomas O’Rourke, Bridget D. Samuels, Angela Messner, Pedro Tiago Martins, Francesco Delogu, Saleh Alamri, Boeckx Cedric 2017-04-09 2021-05-21 [("doi","10.1101/125799")] genetics/selection/natural/human

This study identifies and analyzes statistically-significant overlaps between selective sweep screens in anatomically modern humans and several domesticated species.

The results obtained suggest that (paleo-) genomic data can be exploited to complement the fossil record and support the idea of self-domestication in Homo sapiens, a process that likely intensified as our species populated its niche.

Our analysis lends support to attempts to capture the “domestication syndrome” in terms of alterations to certain signaling pathways and cell lineages, such as the neural crest.

--- https://www.biorxiv.org/content/10.1101/109371.full Patterns of shared signatures of recent positive selection across human populations Kelsey Elizabeth Johnson, Benjamin F. Voight 2017-02-17 2021-05-21 [("doi","10.1101/109371")] genetics/selection/natural/human psychiatry/alcoholism

Scans for positive selection in human populations have identified hundreds of sites across the genome with evidence of recent adaptation. These signatures often overlap across populations, but the question of how often these overlaps represent a single ancestral event remains unresolved. If a single positive selection event spread across many populations, the same sweeping haplotype should appear in each population and the selective pressure could be common across diverse populations and environments. Identifying such shared selective events would be of fundamental interest, pointing to genomic loci and human traits important in recent history across the globe.

Additionally, genomic annotations that recently became available could help attach these signatures to a potential gene and molecular phenotype that may have been selected across multiple populations.

We performed a scan for positive selection using the integrated haplotype score on 20 populations, and compared sweeping haplotypes using the haplotype-clustering capability of fastPHASE to create a catalog of shared and unshared overlapping selective sweeps in these populations. Using additional genomic annotations, we connect these multi-population sweep overlaps with potential biological mechanisms at several loci, including potential new sites of adaptive introgression, the glycophorin locus associated with malarial resistance, and the alcohol dehydrogenase cluster associated with alcohol dependency.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006616 Investigating the case of human nose shape and climate adaptation Arslan A. Zaidi, Brooke C. Mattern, Peter Claes, Brian McEcoy, Cris Hughes, Mark D. Shriver 2017-02-03 2021-05-21 [("doi","10.1371/journal.pgen.1006616")] genetics/selection/natural/human

The evolutionary reasons for variation in nose shape across human populations have been subject to continuing debate. An important function of the nose and nasal cavity is to condition inspired air before it reaches the lower respiratory tract. For this reason, it is thought the observed differences in nose shape among populations are not simply the result of genetic drift, but may be adaptations to climate.

To address the question of whether local adaptation to climate is responsible for nose shape divergence across populations, we use Qst-Fst comparisons to show that:

nares width and alar base width are more differentiated across populations than expected under genetic drift alone.

To test whether this differentiation is due to climate adaptation, we compared the spatial distribution of these variables with the global distribution of temperature, absolute humidity, and relative humidity. We find that width of the nares is correlated with temperature and absolute humidity, but not with relative humidity.

We conclude that some aspects of nose shape may indeed have been driven by local adaptation to climate. However, we think that this is a simplified explanation of a very complex evolutionary history, which possibly also involved other non-neutral forces such as sexual selection.

Author Summary: The study of human adaptation is essential to our understanding of disease etiology. Evolutionary investigations into why certain disease phenotypes such as sickle-cell anemia and lactose intolerance occur at different rates in different populations have led to a better understanding of the genetic and environmental risk factors involved. Similarly, research into the geographical distribution of skin pigmentation continues to yield important clues regarding risk of vitamin D deficiency and skin cancer. Here, we investigate whether variation in the shape of the external nose across populations has been driven by regional differences in climate. We find that variation in both nares width and alar base width appear to have experienced accelerated divergence across human populations. We also find that the geospatial distribution of nares width is correlated with temperature, and absolute humidity, but not with relative humidity. Our results support the claim that local adaptation to climate may have had a role in the evolution of nose shape differences across human populations.

--- https://www.biorxiv.org/content/10.1101/098160.full A complex multi-locus, multi-allelic genetic architecture underlying the long-term selection-response in the Virginia body weight line of chickens Yanjun Zan, Zheya Sheng, Lars Rönnegård, Christa F. Honaker, Paul B. Siegel, Örjan Carlborg 2017-01-04 2021-05-22 [("doi","10.1101/098160")] genetics/selection/artificial

The ability of a population to adapt to changes in their living conditions, whether in nature or captivity, often depends on polymorphisms in multiple genes across the genome. In-depth studies of such polygenic adaptations are difficult in natural populations, but can be approached using the resources provided by artificial selection experiments. Here, we dissect the genetic mechanisms involved in long-term selection responses of the Virginia chicken lines, populations that after 40 generations of divergent selection for 56-day body weight display a nine-fold difference in the selected trait.

In the F15 generation of an intercross between the divergent lines, 20 loci explained more than 60% of the additive genetic variance for the selected trait. We focused particularly on 7 major QTL and found that only two fine-mapped to single, bi-allelic loci; the other 5 contained linked loci, multiple alleles or were epistatic. This detailed dissection of the polygenic adaptations in the Virginia lines provides a deeper understanding of genome-wide mechanisms involved in the long-term selection responses.

The results illustrate that long-term selection responses, even from populations with a limited genetic diversity, can be polygenic and influenced by a range of genetic mechanisms.

--- https://www.biorxiv.org/content/10.1101/087163.full Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection Jeffrey L. Neyhart, Tyler Tiede, Aaron J. Lorenz, Kevin P. Smith 2016-11-10 2021-05-22 [("doi","10.1101/087163")] genetics/selection/artificial

Genomewide selection is hailed for its ability to facilitate greater genetic gains per unit time. Over breeding cycles, the requisite linkage disequilibrium (LD) between quantitative trait loci (QTL) and markers is expected to change as a result of recombination, selection, and drift, leading to a decay in prediction accuracy. Previous research has identified the need to update the training population using data that may capture new LD generated over breeding cycles, however optimal methods of updating have not been explored.

In a barley (Hordeum vulgare L.) breeding simulation experiment, we examined prediction accuracy and response to selection when updating the training population each cycle with the best predicted lines, the worst predicted lines, random lines, criterion-selected lines, or no lines. In the short-term, we found that updating with the best predicted lines resulted in greater prediction accuracy and genetic gain, but in the long-term, all methods (besides not updating) performed similarly. We also examined the impact of including all data in the training population or only the most recent data. Though patterns among update methods were similar, using a smaller, but more recent training population provided a slight advantage in prediction accuracy and genetic gain.

In an actual breeding program, a breeder might desire to gather phenotypic data on lines predicted to be the best, perhaps to evaluate possible cultivars. Therefore, our results suggest that the most optimal method of updating the training population is also the most practical.

--- https://www.biorxiv.org/content/10.1101/109678.full Holocene selection for variants associated with cognitive ability: Comparing ancient and modern genomes Michael A. Woodley Menie, Shameem Younuskunja, Balan Bipin, Piffer Davide 2017-02-21 2021-05-22 [("doi","10.1101/109678")] genetics/selection/natural/human/dysgenics iq

Human populations living in Eurasia during the Holocene experienced evolutionary change. It has been predicted that the transition of Holocene populations into agrarianism and urbanization brought about culture-gene co-evolution that favoured via directional selection genetic variants associated with higher general cognitive ability (GCA). Population expansion and replacement has also been proposed as an important source of GCA gene-frequency change during this time period.

To examine whether GCA might have risen during the Holocene, we compare a sample of 99 ancient Eurasian genomes (ranging from 4,557 to 1,208 years of age) with a sample of 503 modern European genomes, using 3 different cognitive polygenic scores. differences favouring the modern genomes were found for all 3 polygenic scores (Odds Ratio=0.92, p = 0.037; 0.81, p = 0.001 and 0.81, p = 0.02). Furthermore, a statistically-significant increase in positive allele count over 3,249 years was found using a sample of 66 ancient genomes (r=0.217, pone-tailed = 0.04).

These observations are consistent with the expectation that GCA rose during the Holocene.

--- https://www.biorxiv.org/content/10.1101/135715.full DNA.Land: A digital biobank using a massive crowdsourcing approach Jie Yuan, Assaf Gordon, Daniel Speyer, Richard Aufrichtig, Dina Zielinski, Joseph Pickrell, Yaniv Erlich 2017-05-09 2021-05-22 [("doi","10.1101/135715")] genetics/sequencing nootropic/quantified-self

Precision medicine necessitates large scale collections of genomes and phenomes. Despite decreases in the costs of genomic technologies, collecting these types of information at scale is still a daunting task that poses logistical challenges and requires consortium-scale resources. Here, we describe DNA.Land, a digital biobank to collect genome and phenomes with a fraction of the resources of traditional studies at the same scale.

Our approach relies on crowd-sourcing data from the rapidly growing number of individuals that have access to their own genomic datasets through Direct-to-Consumer (DTC) companies. To recruit participants, we developed a series of automatic return-of-results features in DNA.Land that increase users’ engagement while stratifying human subject research protection.

So far, DNA.Land has collected over 43,000 genomes in 20 months of operation, orders of magnitude higher than previous digital attempts by academic groups.

We report lessons learned in running a digital biobank, our technical framework, and our approach regarding ethical, legal, and social implications.

--- https://www.biorxiv.org/content/10.1101/101816.full Genome Graphs and the Evolution of Genome Inference Benedict Paten, Adam M. Novak, Jordan M. Eizenga, Garrison Erik 2017-03-14 2021-05-22 [("doi","10.1101/101816")] genetics/sequencing

The human reference genome is part of the foundation of modern human biology, and a monumental scientific achievement. However, because it excludes a great deal of common human variation, it introduces a pervasive reference bias into the field of human genomics.

To reduce this bias, it makes sense to draw on representative collections of human genomes, brought together into reference cohorts. There are a number of techniques to represent and organize data gleaned from these cohorts, many using ideas implicitly or explicitly borrowed from graph-based models.

Here, we survey various projects underway to build and apply these graph-based structures—which we collectively refer to as genome graphs—and discuss the improvements in read mapping, variant calling, and haplotype determination that genome graphs are expected to produce.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5680777/ Effect of Alternate-Day Fasting on Weight Loss, Weight Maintenance, and Cardioprotection Among Metabolically Healthy Obese Adults: A Randomized Clinical Trial John F. Trepanowski, Cynthia M. Kroeger, Adrienne Barnosky, Monica C. Klempel, Surabhi Bhutani, Kristin K. Hoddy, Kelsey Gabel, Sally Freels, Joseph Rigdon, Jennifer Rood, Eric Ravussin, Krista A. Varady 2017 2021-05-22 [("doi","10.1001/jamainternmed.2017.0936")] longevity/fasting

Importance: Alternate-day fasting has become increasingly popular, yet, to date, no long-term randomized clinical trials have evaluated its efficacy.

Objective: To compare the effects of alternate-day fasting vs daily calorie restriction on weight loss, weight maintenance, and risk indicators for cardiovascular disease.

Design, Setting, & Participants: A single-center randomized clinical trial of obese adults (18 to 64 years of age; mean body mass index, 34) was conducted between October 1, 2011, and January 15, 2015, at an academic institution in Chicago, Illinois.

Interventions: Participants were randomized to 1 of 3 groups for 1 year: alternate-day fasting (25% of energy needs on fast days; 125% of energy needs on alternating “feast days”), calorie restriction (75% of energy needs every day), or a no-intervention control. The trial involved a 6-month weight-loss phase followed by a 6-month weight-maintenance phase.

Main Outcomes & Measures: The primary outcome was change in body weight. Secondary outcomes were adherence to the dietary intervention and risk indicators for cardiovascular disease.

Results: Among the 100 participants (86 women and 14 men; mean [SD] age, 44 [11] years), the dropout rate was highest in the alternate-day fasting group (13 of 34 [38%]), vs the daily calorie restriction group (10 of 35 [29%]) and control group (8 of 31 [26%]). Mean weight loss was similar for participants in the alternate-day fasting group and those in the daily calorie restriction group at month 6 (−6.8% [95% CI, −9.1% to −4.5%] vs −6.8% [95% CI, −9.1% to −4.6%]) and month 12 (−6.0% [95% CI, −8.5% to −3.6%] vs −5.3% [95% CI, −7.6% to −3.0%]) relative to those in the control group. Participants in the alternate-day fasting group ate more than prescribed on fast days, and less than prescribed on feast days, while those in the daily calorie restriction group generally met their prescribed energy goals. There were no statistically-significant differences between the intervention groups in blood pressure, heart rate, triglycerides, fasting glucose, fasting insulin, insulin resistance, C-reactive protein, or homocysteine concentrations at month 6 or 12. Mean high-density lipoprotein cholesterol levels at month 6 increased among the participants in the alternate-day fasting group (6.2 mg/dL [95% CI, 0.1–12.4 mg/dL]), but not at month 12 (1.0 mg/dL [95% CI, −5.9 to 7.8 mg/dL]), relative to those in the daily calorie restriction group. Mean low-density lipoprotein cholesterol levels were elevated by month 12 among the participants in the alternate-day fasting group (11.5 mg/dL [95% CI, 1.9–21.1 mg/dL]) compared with those in the daily calorie restriction group.

Conclusions & Relevance: Alternate-day fasting did not produce superior adherence, weight loss, weight maintenance, or cardioprotection vs daily calorie restriction.

Trial Registration: ClinicalTrials.gov Identifier: NCT00960505.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5410601/ Nicotine Acutely Enhances Reinforcement from Non-Drug Rewards in Humans Kenneth A. Perkins, Joshua L. Karelitz, Margaret C. Boldry 2017 2021-05-22 [("doi","10.3389/fpsyt.2017.00065")] nicotine

Preclinical research documents that, aside from the primary and secondary reinforcing effects of nicotine intake itself, nicotine also acutely enhances the reinforcing efficacy of non-drug reinforcers (“rewards”). Study of these effects in humans has largely been overlooked, but very recent findings suggest they may have clinical implications for more fully understanding the persistence of tobacco dependence. This overview first outlines the topic and notes some recent human studies indirectly addressing nicotine effects on related responses (eg. subjective ratings), explaining why those findings do not directly confirm enhancement of behavioral reinforcement per se due to nicotine.

Then, the methodology used in the subsequently presented studies is described, demonstrating how those studies specifically did demonstrate enhancement of reinforced responding for non-drug rewards. The main section focuses on the limited controlled research to date directly assessing nicotine’s acute reinforcement-enhancing effects in humans, particularly as it relates to reinforced behavioral responding for non-drug rewards in non-human animal models.

After detailing those few existing human studies, we address potential consequences of these effects for dependence and tobacco cessation efforts and then suggest directions for future research.

This research indicates that nicotine per se increases responding in humans that is reinforced by some rewards (auditory stimuli via music, visual stimuli via video), but perhaps not by others (eg. money). These reinforcement-enhancing effects in smokers are not due to dependence or withdrawal relief and can be restored by a small amount of nicotine (similar to a smoking lapse), including from e-cigarettes, a non-tobacco nicotine product.

Future clinical research should examine factors determining which types of rewards are (or are not) enhanced by nicotine, consequences of the loss of these nicotine effects after quitting smoking, potential individual differences in these effects, and the possibility that nicotine via nicotine replacement therapy and non-nicotine quit medications may attenuate loss of these effects upon quitting. Further study with humans of nicotine’s reinforcement-enhancing effects may provide a more complete understanding of smoking persistence and added mechanisms of cessation medication efficacy.

--- https://arxiv.org/abs/1608.01769 Deep Learning the City: Quantifying Urban Perception At A Global Scale Abhimanyu Dubey, Nikhil Naik, Devi Parikh, Ramesh Raskar, César A. Hidalgo 2016-08-05 2021-05-22 [("doi","10.48550/arXiv.1608.01769")] ai/dataset ai/nn/cnn crime design

Computer vision methods that quantify the perception of urban environment are increasingly being used to study the relationship between a city’s physical appearance and the behavior and health of its residents.

Yet, the throughput of current methods is too limited to quantify the perception of cities across the world. To tackle this challenge, we introduce a new crowdsourced dataset containing 110,988 images from 56 cities, and 1,170,000 pairwise comparisons provided by 81,630 online volunteers along 6 perceptual attributes: safe, lively, boring, wealthy, depressing, and beautiful.

Using this data, we train a Siamese-like convolutional neural architecture, which learns from a joint classification and ranking loss, to predict human judgments of pairwise image comparisons.

Our results show that crowdsourcing combined with neural networks can produce urban perception data at the global scale.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5636873/ Forecasting the onset and course of mental illness with Twitter data Andrew G. Reece, Andrew J. Reagan, Katharina L. M. Lix, Peter Sheridan Dodds, Christopher M. Danforth, Ellen J. Langer 2017 2021-05-22 [("doi","10.1038/s41598-017-12961-9")] psychiatry/depression

We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder (PTSD) in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (n = 279,951) and built models using these features with supervised learning algorithms.

Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners’ average success rates in diagnosing depression, albeit in a separate population. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis.

Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (Nusers = 174, Ntweets = 243,775). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis.

These methods suggest a data-driven, predictive approach for early screening and detection of mental illness.

--- https://www.biorxiv.org/content/10.1101/123729.full Sex differences in the adult human brain: Evidence from 5,216 UK Biobank participants Stuart J. Ritchie, Simon R. Cox, Xueyi Shen, Michael V. Lombardo, Lianne M. Reus, Clara Alloza, Matthew A. Harris, Helen L. Alderson, Stuart Hunter, Emma Neilson, David C. M. Liewald, Bonnie Auyeung, Heather C. Whalley, Stephen M. Lawrie, Catharine R. Gale, Mark E. Bastin, Andrew M. McIntosh, Ian J. Deary 2017-04-04 2021-05-22 [("doi","10.1101/123729")] psychology/neuroscience

Sex differences in human brain structure and function are of substantial scientific interest because of sex-differential susceptibility to psychiatric disorders and because of the potential to explain sex differences in psychological traits. Males are known to have larger brain volumes, though the patterns of differences across brain subregions have typically only been examined in small, inconsistent studies. In addition, despite common findings of greater male variability in traits like intelligence, personality, and physical performance, variance differences in the brain have received little attention.

Here we report the largest single-sample study of structural and functional sex differences in the human brain to date (2,750 female and 2,466 male participants aged 44–77 years). Males had higher cortical and sub-cortical volumes, cortical surface areas, and white matter diffusion directionality; females had thicker cortices and higher white matter tract complexity. Considerable overlap between the distributions for males and females was common, and subregional differences were smaller after accounting for global differences. There was generally greater male variance across structural measures.

The modestly higher male score on two cognitive tests was partly mediated via structural differences. Functional connectome organization showed stronger connectivity for males in unimodal sensorimotor cortices, and stronger connectivity for females in the default mode network.

This large-scale characterization of neurobiological sex differences provides a foundation for attempts to understand the causes of sex differences in brain structure and function, and their associated psychological and psychiatric consequences.

--- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2000797 Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature Denes Szucs, John Ioannidis 2017-02-06 2021-05-22 [("doi","10.1371/journal.pbio.2000797")] psychology/neuroscience statistics/bias statistics/power-analysis

We have empirically assessed the distribution of published effect sizes and estimated power by analyzing 26,841 statistical records from 3,801 cognitive neuroscience and psychology papers published recently. The reported median effect size was d = 0.93 (interquartile range: 0.64–1.46) for nominally statistically-significant results and d = 0.24 (0.11–0.42) for non-statistically-significant results. Median power to detect small, medium, and large effects was 0.12, 0.44, and 0.73, reflecting no improvement through the past half-century. This is so because sample sizes have remained small. Assuming similar true effect sizes in both disciplines, power was lower in cognitive neuroscience than in psychology. Journal impact factors negatively correlated with power. Assuming a realistic range of prior probabilities for null hypotheses, false report probability is likely to exceed 50% for the whole literature. In light of our findings, the recently reported low replication success in psychology is realistic, and worse performance may be expected for cognitive neuroscience.

Author Summary: Biomedical science, psychology, and many other fields may be suffering from a serious replication crisis. In order to gain insight into some factors behind this crisis, we have analyzed statistical information extracted from thousands of cognitive neuroscience and psychology research papers. We established that the statistical power to discover existing relationships has not improved during the past half century. A consequence of low statistical power is that research studies are likely to report many false positive findings. Using our large dataset, we estimated the probability that a statistically-significant finding is false (called false report probability). With some reasonable assumptions about how often researchers come up with correct hypotheses, we conclude that more than 50% of published findings deemed to be statistically-significant are likely to be false. We also observed that cognitive neuroscience studies had higher false report probability than psychology studies, due to smaller sample sizes in cognitive neuroscience. In addition, the higher the impact factors of the journals in which the studies were published, the lower was the statistical power. In light of our findings, the recently reported low replication success in psychology is realistic, and worse performance may be expected for cognitive neuroscience.

--- https://arxiv.org/abs/1704.08792 DeepArchitect: Automatically Designing and Training Deep Architectures Renato Negrinho, Geoff Gordon 2017-04-28 2021-05-23 [("doi","10.48550/arXiv.1704.08792")] reinforcement-learning/model

In deep learning, performance is strongly affected by the choice of architecture and hyperparameters. While there has been extensive work on automatic hyperparameter optimization for simple spaces, complex spaces such as the space of deep architectures remain largely unexplored. As a result, the choice of architecture is done manually by the human expert through a slow trial and error process guided mainly by intuition.

In this paper we describe a framework for automatically designing and training deep models. We propose an extensible and modular language that allows the human expert to compactly represent complex search spaces over architectures and their hyperparameters. The resulting search spaces are tree-structured and therefore easy to traverse. Models can be automatically compiled to computational graphs once values for all hyperparameters have been chosen. We can leverage the structure of the search space to introduce different model search algorithms, such as random search, Monte Carlo tree search (MCTS), and sequential model-based optimization (SMBO).

We present experiments comparing the different algorithms on CIFAR-10 and show that MCTS and SMBO outperform random search. In addition, these experiments show that our framework can be used effectively for model discovery, as it is possible to describe expressive search spaces and discover competitive models without much effort from the human expert. Code for our framework and experiments has been made publicly available.

--- https://arxiv.org/abs/1703.10332 RAM: Dynamic Computational Time for Visual Attention Zhichao Li, Yi Yang, Xiao Liu, Feng Zhou, Shilei Wen, Wei Xu 2017-03-30 2021-05-23 [("doi","10.48550/arXiv.1703.10332")] ai/nn/transformer/attention reinforcement-learning/model-free

We propose a dynamic computational time model to accelerate the average processing time for recurrent visual attention (RAM).

Rather than attention with a fixed number of steps for each input image, the model learns to decide when to stop on the fly. To achieve this, we add an additional continue/stop action per time step to RAM and use reinforcement learning to learn both the optimal attention policy and stopping policy. The modification is simple but could dramatically save the average computational time while keeping the same recognition performance as RAM.

Experimental results on CUB-200-2011 and Stanford Cars dataset demonstrate the dynamic computational model can work effectively for fine-grained image recognition.The source code of this paper can be obtained from https://github.com/baidu-research/DT-RAM.

--- https://arxiv.org/abs/1703.05423 End-to-end optimization of goal-driven and visually grounded dialogue systems Florian Strub, Harm de Vries, Jeremie Mary, Bilal Piot, Aaron Courville, Olivier Pietquin 2017-03-15 2021-05-23 [("doi","10.48550/arXiv.1703.05423")] reinforcement-learning/model-free

End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning. Yet, most current approaches cast human-machine dialogue management as a supervised learning problem, aiming at predicting the next utterance of a participant given the full history of the dialogue. This vision is too simplistic to render the intrinsic planning problem inherent to dialogue as well as its grounded nature, making the context of a dialogue larger than the sole history. This is why only chit-chat and question answering tasks have been addressed so far using end-to-end architectures.

In this paper, we introduce a Deep Reinforcement Learning method to optimize visually grounded task-oriented dialogues, based on the policy gradient algorithm. This approach is tested on a dataset of 120k dialogues collected through Mechanical Turk and provides encouraging results at solving both the problem of generating natural dialogues and the task of discovering a specific object in a complex picture.

--- https://arxiv.org/abs/1703.04070 Prediction and Control with Temporal Segment Models Nikhil Mishra, Pieter Abbeel, Igor Mordatch 2017-03-12 2021-05-23 [("doi","10.48550/arXiv.1703.04070")] ai/nn/cnn ai/nn/vae reinforcement-learning/model

We introduce a method for learning the dynamics of complex nonlinear systems based on deep generative models over temporal segments of states and actions. Unlike dynamics models that operate over individual discrete timesteps, we learn the distribution over future state trajectories conditioned on past state, past action, and planned future action trajectories, as well as a latent prior over action trajectories.

Our approach is based on convolutional autoregressive models and variational autoencoders. It makes stable and accurate predictions over long horizons for complex, stochastic systems, effectively expressing uncertainty and modeling the effects of collisions, sensory noise, and action delays.

The learned dynamics model and action prior can be used for end-to-end, fully differentiable trajectory optimization and model-based policy optimization, which we use to evaluate the performance and sample-efficiency of our method.

--- https://arxiv.org/abs/1701.08734#deepmind PathNet: Evolution Channels Gradient Descent in Super Neural Networks Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A. Rusu, Alexander Pritzel, Daan Wierstra 2017-01-30 2021-05-23 [("doi","10.48550/arXiv.1701.08734")] reinforcement-learning/meta-learning/continual-learning reinforcement-learning/model-free

For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, permitting parameter reuse, without catastrophic forgetting. PathNet is a first step in this direction. It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks.

Agents are pathways (views) through the network which determine the subset of parameters that are used and updated by the forwards and backwards passes of the backpropagation algorithm. During learning, a tournament selection genetic algorithm is used to select pathways through the neural network for replication and mutation. Pathway fitness is the performance of that pathway measured according to a cost function.

We demonstrate successful transfer learning; fixing the parameters along a path learned on task A and re-evolving a new population of paths for task B, allows task B to be learned faster than it could be learned from scratch or after fine-tuning. Paths evolved on task B re-use parts of the optimal path evolved on task A. Positive transfer was demonstrated for binary MNIST, CIFAR, and SVHN supervised learning classification tasks, and a set of Atari and Labyrinth reinforcement learning tasks, suggesting PathNets have general applicability for neural network training.

Finally, PathNet also improves the robustness to hyperparameter choices of a parallel asynchronous reinforcement learning algorithm (A3C).

--- https://arxiv.org/abs/1612.08810#deepmind The Predictron: End-To-End Learning and Planning David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris 2016-12-28 2021-05-23 [("doi","10.48550/arXiv.1612.08810")] reinforcement-learning/model

One of the key challenges of artificial intelligence is to learn models that are effective in the context of planning.

In this document, we introduce the predictron architecture. The predictron consists of a fully abstract model, represented by a Markov reward process, that can be rolled forward multiple “imagined” planning steps. Each forward pass of the predictron accumulates internal rewards and values over multiple planning depths.

The predictron is trained end-to-end so as to make these accumulated values accurately approximate the true value function.

We applied the predictron to procedurally generated random mazes and a simulator for the game of pool. The predictron yielded statistically-significantly more accurate predictions than conventional deep neural network architectures.

--- https://arxiv.org/abs/1612.07307 Loss is its own Reward: Self-Supervision for Reinforcement Learning Evan Shelhamer, Parsa Mahmoudieh, Max Argus, Trevor Darrell 2016-12-21 2021-05-23 [("doi","10.48550/arXiv.1612.07307")] reinforcement-learning/model-free

Reinforcement learning optimizes policies for expected cumulative reward. Need the supervision be so narrow?

Reward is delayed and sparse for many tasks, making it a difficult and impoverished signal for end-to-end optimization. To augment reward, we consider a range of self-supervised tasks that incorporate states, actions, and successors to provide auxiliary losses. These losses offer ubiquitous and instantaneous supervision for representation learning even in the absence of reward.

While current results show that learning from reward alone is feasible, pure reinforcement learning methods are constrained by computational and data efficiency issues that can be remedied by auxiliary losses.

Self-supervised pre-training and joint optimization improve the data efficiency and policy returns of end-to-end reinforcement learning.

--- https://arxiv.org/abs/1612.00796#deepmind Overcoming catastrophic forgetting in neural networks James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, Raia Hadsell 2016-12-02 2021-05-23 [("doi","10.48550/arXiv.1612.00796")] reinforcement-learning/model-free

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models.

We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks which they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks.

We demonstrate our approach is scalable and effective by solving a set of classification tasks based on the MNIST handwritten digit dataset and by learning several Atari 2600 games sequentially.

--- https://arxiv.org/abs/1611.05397#deepmind Reinforcement Learning with Unsupervised Auxiliary Tasks Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu 2016-11-16 2021-05-23 [("doi","10.48550/arXiv.1611.05397")] reinforcement-learning/model-free

Deep reinforcement learning agents have achieved state-of-the-art results by directly maximizing cumulative reward. However, environments contain a much wider variety of possible training signals.

In this paper, we introduce an agent that also maximizes many other pseudo-reward functions simultaneously by reinforcement learning. All of these tasks share a common representation that, like unsupervised learning, continues to develop in the absence of extrinsic rewards. We also introduce a novel mechanism for focusing this representation upon extrinsic rewards, so that learning can rapidly adapt to the most relevant aspects of the actual task.

Our agent outperforms the previous state-of-the-art on Atari, averaging 880% expert human performance, and a challenging suite of first-person, three-dimensional Labyrinth tasks leading to a mean speedup in learning of 10× and averaging 87% expert human performance on Labyrinth.

--- https://arxiv.org/abs/1703.01041 Large-Scale Evolution of Image Classifiers Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, Alex Kurakin 2017-03-03 2021-05-23 [("doi","10.48550/arXiv.1703.01041")] reinforcement-learning/exploration

Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone. Our goal is to minimize human participation, so we employ evolutionary algorithms to discover such networks automatically. Despite computational requirements, we show that it is now possible to evolve models with accuracies within the range of those published in the last year.

Specifically, we employ simple evolutionary techniques at unprecedented scales to discover models for the CIFAR-10 and CIFAR-100 datasets, starting from trivial initial conditions and reaching accuracies of 94.6% (95.6% for ensemble) and 77.0%, respectively. To do this, we use novel and intuitive mutation operators that navigate large search spaces; we stress that no human participation is required once evolution starts and that the output is a fully-trained model.

Throughout this work, we place special emphasis on the repeatability of results, the variability in the outcomes and the computational requirements.

--- https://arxiv.org/abs/1703.00548 CoDeepNEAT: Evolving Deep Neural Networks Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy, Babak Hodjat 2017-03-01 2021-05-23 [("doi","10.48550/arXiv.1703.00548")] reinforcement-learning/exploration reinforcement-learning/model-free

The success of deep learning depends on finding an architecture to fit the task. As deep learning has scaled up to more challenging tasks, the architectures have become difficult to design by hand.

This paper proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution. By extending existing neuroevolution methods to topology, components, and hyperparameters, this method achieves results comparable to best human designs in standard benchmarks in object recognition and language modeling. It also supports building a real-world application of automated image captioning on a magazine website.

Given the anticipated increases in available computing power, evolution of deep networks is promising approach to constructing deep learning applications in the future.

--- https://arxiv.org/abs/1705.02670 Metacontrol for Adaptive Imagination-Based Optimization Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess, Peter W. Battaglia 2017-05-07 2021-05-24 [("doi","10.48550/arXiv.1705.02670")] reinforcement-learning/meta-learning reinforcement-learning/model

Many machine learning systems are built to solve the hardest examples of a particular task, which often makes them large and expensive to run—especially with respect to the easier examples, which might require much less computation. For an agent with a limited computational budget, this “one-size-fits-all” approach may result in the agent wasting valuable computation on easy examples, while not spending enough on hard examples.

Rather than learning a single, fixed policy for solving all instances of a task, we introduce a metacontroller which learns to optimize a sequence of “imagined” internal simulations over predictive models of the world in order to construct a more informed, and more economical, solution. The metacontroller component is a model-free reinforcement learning agent, which decides both how many iterations of the optimization procedure to run, as well as which model to consult on each iteration. The models (which we call “experts”) can be state transition models, action-value functions, or any other mechanism that provides information useful for solving the task, and can be learned on-policy or off-policy in parallel with the metacontroller.

When the metacontroller, controller, and experts were trained with “interaction networks” (Battaglia et al 2016) as expert models, our approach was able to solve a challenging decision-making problem under complex non-linear dynamics. The metacontroller learned to adapt the amount of computation it performed to the difficulty of the task, and learned how to choose which experts to consult by factoring in both their reliability and individual computational resource costs. This allowed the metacontroller to achieve a lower overall cost (task loss plus computational cost) than more traditional fixed policy approaches.

These results demonstrate that our approach is a powerful framework for using rich forward models for efficient model-based reinforcement learning.

--- https://arxiv.org/abs/1703.06217 Deciding How to Decide: Dynamic Routing in Artificial Neural Networks Mason McGill, Pietro Perona 2017-03-17 2021-05-24 [("doi","10.48550/arXiv.1703.06217")] reinforcement-learning/meta-learning

We propose and systematically evaluate 3 strategies for training dynamically-routed artificial neural networks: graphs of learned transformations through which different input signals may take different paths.

Though some approaches have advantages over others, the resulting networks are often qualitatively similar. We find that, in dynamically-routed networks trained to classify images, layers and branches become specialized to process distinct categories of images.

Additionally, given a fixed computational budget, dynamically-routed networks tend to perform better than comparable statically-routed networks.

--- https://arxiv.org/abs/1703.00441 Learning to Optimize Neural Nets Ke Li, Jitendra Malik 2017-03-01 2021-05-24 [("doi","10.48550/arXiv.1703.00441")] reinforcement-learning/meta-learning

Learning to Optimize is a recently proposed framework for learning optimization algorithms using reinforcement learning. In this paper, we explore learning an optimization algorithm for training shallow neural nets. Such high-dimensional stochastic optimization problems present interesting challenges for existing reinforcement learning algorithms.

We develop an extension that is suited to learning optimization algorithms in this setting and demonstrate that the learned optimization algorithm consistently outperforms other known optimization algorithms even on unseen tasks and is robust to changes in stochasticity of gradients and the neural net architecture.

More specifically, we show that an optimization algorithm trained with the proposed method on the problem of training a neural net on MNIST generalizes to the problems of training neural nets on the Toronto Faces Dataset, CIFAR-10, and CIFAR-100.

--- https://arxiv.org/abs/1704.03732 Deep Q-learning from Demonstrations Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, John Agapiou, Joel Z. Leibo, Audrunas Gruslys 2017-04-12 2021-05-24 [("doi","10.48550/arXiv.1704.03732")] reinforcement-learning/robot

Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-world tasks, where the agent must learn in the real environment.

In this paper we study a setting where the agent may access data from previous control of the system. We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism. DQfD works by combining temporal difference updates with supervised classification of the demonstrator’s actions.

We show that DQfD has better initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN) as it starts with better scores on the first million steps on 41⁄42 games and on average it takes PDD DQN 83 million steps to catch up to DQfD’s performance. DQfD learns to out-perform the best demonstration given in 14⁄42 games. In addition, DQfD leverages human demonstrations to achieve state-of-the-art results for 11 games. Finally, we show that DQfD performs better than 3 related algorithms for incorporating demonstration data into DQN.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5299519/ Anthropologists' views on race, ancestry, and genetics Jennifer K. Wagner, Joon-Ho Yu, Jayne O. Ifekwunigwe, Tanya M. Harrell, Michael J. Bamshad, Charmaine D. Royal 2017 2021-05-24 [("doi","10.1002/ajpa.23120")] politics sociology

Controversies over race conceptualizations have been ongoing for centuries and have been shaped, in part, by anthropologists.

Objective: To assess anthropologists’ views on race, genetics, and ancestry.

Method: In 2012 a broad national survey of anthropologists examined prevailing views on race, ancestry, and genetics.

Results: Results demonstrate consensus that there are no human biological races and recognition that race exists as lived social experiences that can have important effects on health.

Discussion: Racial privilege affects anthropologists’ views on race, underscoring the importance that anthropologists be vigilant of biases in the profession and practice. Anthropologists must mitigate racial biases in society wherever they might be lurking and quash any sociopolitical attempts to normalize or promote racist rhetoric, sentiment, and behavior.

--- https://arxiv.org/abs/1701.02434 A Conceptual Introduction to Hamiltonian Monte Carlo Michael Betancourt 2017-01-10 2021-05-24 [("doi","10.48550/arXiv.1701.02434")] statistics/bayes

Hamiltonian Monte Carlo has proven a remarkable empirical success, but only recently have we begun to develop a rigorous understanding of why it performs so well on difficult problems and how it is best applied in practice.

Unfortunately, that understanding is confined within the mathematics of differential geometry which has limited its dissemination, especially to the applied communities for which it is particularly important.

In this review I provide a comprehensive conceptual account of these theoretical foundations, focusing on developing a principled intuition behind the method and its optimal implementations rather than any exhaustive rigor.

Whether a practitioner or a statistician, the dedicated reader will acquire a solid grasp of how Hamiltonian Monte Carlo works, when it succeeds, and, perhaps most importantly, when it fails.

--- https://arxiv.org/abs/1701.01427 Rational Decision-Making Under Uncertainty: Observed Betting Patterns on a Biased Coin Victor Haghani, Richard Dewey 2017-01-04 2021-05-24 [("doi","10.48550/arXiv.1701.01427")] psychology/cognitive-bias statistics/decision

What would you do if you were invited to play a game where you were given $25 and allowed to place bets for 30 minutes on a coin that you were told was biased to come up heads 60% of the time? This is exactly what we did, gathering 61 young, quantitatively trained men and women to play this game.

The results, in a nutshell, were that the majority of these 61 players did not place their bets very well, displaying a broad panoply of behaviorial and cognitive biases. About 30% of the subjects actually went bust, losing their full $25 stake. We also discuss optimal betting strategies, valuation of the opportunity to play the game and its similarities to investing in the stock market.

The main implication of our study is that people need to be better educated and trained in how to approach decision making under uncertainty. If these quantitatively trained players, playing the simplest game we can think of involving uncertainty and favourable odds, did not play well, what hope is there for the rest of us when it comes to playing the biggest and most important game of all: investing our savings?

In the words of Ed Thorp, who gave us helpful feedback on our research: “This is a great experiment for many reasons. It ought to become part of the basic education of anyone interested in finance or gambling.”

--- https://arxiv.org/abs/1608.01987 Human collective intelligence as distributed Bayesian inference Peter M. Krafft, Julia Zheng, Wei Pan, Nicolás Della Penna, Yaniv Altshuler, Erez Shmueli, Joshua B. Tenenbaum, Alex Pentland 2016-08-05 2021-05-24 [("doi","10.48550/arXiv.1608.01987")] economics reinforcement-learning/exploration sociology statistics/bayes

Collective intelligence is believed to underlie the remarkable success of human society. The formation of accurate shared beliefs is one of the key components of human collective intelligence. How are accurate shared beliefs formed in groups of fallible individuals? Answering this question requires a multiscale analysis. We must understand both the individual decision mechanisms people use, and the properties and dynamics of those mechanisms in the aggregate.

As of yet, mathematical tools for such an approach have been lacking. To address this gap, we introduce a new analytical framework: We propose that groups arrive at accurate shared beliefs via distributed Bayesian inference. Distributed inference occurs through information processing at the individual level, and yields rational belief formation at the group level.

We instantiate this framework in a new model of human social decision-making, which we validate using a dataset we collected of over 50,000 users of an online social trading platform where investors mimic each others’ trades using real money in foreign exchange and other asset markets. We find that in this setting people use a decision mechanism in which popularity is treated as a prior distribution for which decisions are best to make. This mechanism is boundedly rational at the individual level, but we prove that in the aggregate implements a type of approximate “Thompson sampling”—a well-known and highly effective single-agent Bayesian machine learning algorithm for sequential decision-making.

The perspective of distributed Bayesian inference therefore reveals how collective rationality emerges from the boundedly rational decision mechanisms people use.

--- https://www.biorxiv.org/content/10.1101/070177.full Phenome-wide Heritability Analysis of the UK Biobank Tian Ge, Chia-Yen Chen, Benjamin M. Neale, Mert R. Sabuncu, Jordan W. Smoller 2016-08-18 2021-05-24 [("doi","10.1101/070177")] genetics/heritable

Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. However, assessing the comparative heritability of multiple traits estimated in different cohorts may be misleading due to the population-specific nature of heritability.

Here we report the SNP heritability for 551 complex traits derived from the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes, and examine the moderating effect of 3 major demographic variables (age, sex, and socioeconomic status) on the heritability estimates.

Our study represents the first comprehensive phenome-wide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in comparing and interpreting heritability.

--- https://www.biorxiv.org/content/10.1101/066068.full Estimate of disease heritability using 4.7 million familial relationships inferred from electronic health records Fernanda Polubriaginof, Kayla Quinnies, Rami Vanguri, Alexandre Yahi, Mary Simmerling, Iuliana Ionita-Laza, Hojjat Salmasian, Suzanne Bakken, George Hripcsak, David Goldstein, Krzysztof Kiryluk, David K. Vawdrey, Nicholas P. Tatonetti 2016-07-28 2021-05-24 [("doi","10.1101/066068")] genetics/heritable

Heritability is a fundamental characteristic of human disease essential to the development of a biological understanding of the causes of disease. Traditionally, heritability studies are a laborious process of patient recruitment and phenotype ascertainment. Electronic health records (EHR) passively capture a wide range and depth of clinically relevant data and represent a novel resource for studying heritability of many traits and conditions that are not typically accessible. In addition to a wealth of disease phenotypes, nearly every hospital collects and stores next-of-kin information on the emergency contact forms when a patient is admitted. Until now, these data have gone completely unused for research purposes.

We introduce a novel algorithm to infer familial relationships using emergency contact information while maintaining privacy. Here we show that EHR data yield accurate estimates of heritability across all available phenotypes using millions familial relationships mined from emergency contact data at two large academic medical centers. Estimates of heritability were consistent between sites and with previously reported estimates. Inconsistencies were indicative of limitations and opportunities unique to EHR research. Critically, these analyses provide a novel validation of the utility of electronic health records in inferences about the biological basis of disease.

--- https://www.biorxiv.org/content/10.1101/062950.full Genome-wide Variants of Eurasian Facial Shape Differentiation and DNA based Face Prediction Lu Qiao, Yajun Yang, Pengcheng Fu, Sile Hu, Hang Zhou, Shouneng Peng, Jingze Tan, Yan Lu, Haiyi Lou, Dongsheng Lu, Sijie Wu, Jing Guo, Li Jin, Yaqun Guan, Sijia Wang, Shuhua Xu, Kun Tang 2016-07-25 2021-05-24 [("doi","10.1101/062950")] genetics/heritable history/uighur

It is a long-standing question as to which genes define the characteristic facial features among different ethnic groups. In this study, we use Uighurs, an ancient admixed population to query the genetic bases why Europeans and Han Chinese look different.

Facial traits were analyzed based on high-dense 3D facial images; numerous biometric spaces were examined for divergent facial features between European and Han Chinese, ranging from inter-landmark distances to dense shape geometrics. Genome-wide association analyses were conducted on a discovery panel of Uighurs.

Six loci were identified 4 of which, rs1868752, rs118078182, rs60159418 at or near UBASH3B, COL23A1, PCDH7 and rs17868256 were replicated in independent cohorts of Uighurs or Southern Han Chinese. A quantitative model was developed to predict 3D faces based on 277 top GWAS SNPs. In hypothetic forensic scenarios, this model was found to enhance the verification rate, suggesting a practical potential of related research.

--- https://www.biorxiv.org/content/10.1101/069393.full Clinical Utility of Expanded Carrier Screening: Reproductive Behaviors of At-Risk Couples Caroline Ghiossi, James D. Goldberg, Imran S. Haque, Gabriel A. Lazarin, Kenny K. Wong 2016-08-14 2021-05-25 [("doi","10.1101/069393")] genetics/heritable/rare genetics/sequencing

Purpose: Expanded carrier screening (ECS) analyzes dozens or hundreds of recessive genes for determining reproductive risk. Data on clinical utility of screening conditions beyond professional guidelines is scarce.

Method: Individuals underwent ECS for up to 110 genes. 537 at-risk couples (ARC), those in which both partners carry the same recessive disease, were invited to a retrospective IRB-approved survey of their reproductive decision making after receiving ECS results.

Results: 64 eligible ARC completed the survey. Of 45 respondents screened preconceptionally, 62% (n = 28) planned IVF with PGD or prenatal diagnosis (PNDx) in future pregnancies. 29% (n = 13) were not planning to alter reproductive decisions. The remaining 9% (n = 4) of responses were unclear.

Of 19 pregnant respondents, 42% (n = 8) elected PNDx, 11% (n = 2) planned amniocentesis but miscarried, and 47% (n = 9) considered the condition insufficiently severe to warrant invasive testing. Of the 8 pregnancies that underwent PNDx, 5 were unaffected and 3 were affected. 2⁄3 affected pregnancies were terminated.

Disease severity was found to have statistically-significant association (p = 0.000145) with changes in decision making, whereas guideline status of diseases, controlled for severity, was not (p = 0.284).

Conclusion: Most ARC altered reproductive planning, demonstrating the clinical utility of ECS. Severity of conditions factored into decision making.

--- https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002103 Long-Term Outcomes Associated with Traumatic Brain Injury in Childhood and Adolescence: A Nationwide Swedish Cohort Study of a Wide Range of Medical and Social Outcomes Amir Sariaslan, David J. Sharp, Brian M. D’Onofrio, Henrik Larsson, Seena Fazel 2016-06-23 2021-05-25 [("doi","10.1371/journal.pmed.1002103")] psychiatry/traumatic-brain-injury

Background: Traumatic brain injury (TBI) is the leading cause of disability and mortality in children and young adults worldwide. It remains unclear, however, how TBI in childhood and adolescence is associated with adult mortality, psychiatric morbidity, and social outcomes.

Methods and Findings: In a Swedish birth cohort 1973–1985 of 1,143,470 individuals, we identified all those who had sustained at least one TBI (n = 104,290 or 9.1%) up to age 25 y and their unaffected siblings (n = 68,268) using patient registers. We subsequently assessed these individuals for the following outcomes using multiple national registries: disability pension, specialist diagnoses of psychiatric disorders and psychiatric inpatient hospitalization, premature mortality (before age 41 y), low educational attainment (not having achieved secondary school qualifications), and receiving means-tested welfare benefits. We used logistic and Cox regression models to quantify the association between TBI and specified adverse outcomes on the individual level. We further estimated population attributable fractions (PAF) for each outcome measure. We also compared differentially exposed siblings to account for unobserved genetic and environmental confounding. In addition to relative risk estimates, we examined absolute risks by calculating prevalence and Kaplan-Meier estimates. In complementary analyses, we tested whether the findings were moderated by injury severity, recurrence, and age at first injury (ages 0–4, 5–9, 6–10, 15–19, and 20–24 y).

TBI exposure was associated with elevated risks of impaired adult functioning across all outcome measures. After a median follow-up period of 8 y from age 26 y, we found that TBI contributed to absolute risks of over 10% for specialist diagnoses of psychiatric disorders and low educational attainment, ~5% for disability pension, and 2% for premature mortality. The highest relative risks, adjusted for sex, birth year, and birth order, were found for psychiatric inpatient hospitalization (adjusted relative risk [aRR] = 2.0; 95% CI: 1.9–2.0; 6,632 versus 37,095 events), disability pension (aRR = 1.8; 95% CI: 1.7–1.8; 4,691 versus 29,778 events), and premature mortality (aRR = 1.7; 95% CI: 1.6–1.9; 799 versus 4,695 events). These risks were only marginally attenuated when the comparisons were made with their unaffected siblings, which implies that the effects of TBI were consistent with a causal inference. A dose-response relationship was observed with injury severity. Injury recurrence was also associated with higher risks—in particular, for disability pension we found that recurrent TBI was associated with a 3× risk increase (aRR = 2.6; 95% CI: 2.4–2.8) compared to a single-episode TBI. Higher risks for all outcomes were observed for those who had sustained their first injury at an older age (ages 20–24 y) with more than 25% increase in relative risk across all outcomes compared to the youngest age group (ages 0–4 y). On the population level, TBI explained between 2%–6% of the variance in the examined outcomes.

Using hospital data underestimates milder forms of TBI, but such misclassification bias suggests that the reported estimates are likely conservative. The sibling-comparison design accounts for unmeasured familial confounders shared by siblings, including half of their genes. Thus, residual genetic confounding remains a possibility but will unlikely alter our main findings, as associations were only marginally attenuated within families.

Conclusion: Given our findings, which indicate potentially causal effects between TBI exposure in childhood and later impairments across a range of health and social outcomes, age-sensitive clinical guidelines should be considered and preventive strategies should be targeted at children and adolescents.

In a population-wide observational cohort, Seena Fazel and colleagues use a sibling-matched design to examine the burden of long-term outcomes associated with traumatic brain injury.

Author Summary: Why Was This Study Done?

What Did the Researchers Do and Find?:

What Do These Findings Mean?:

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2850264/ Global prevalence of dementia: a Delphi consensus study Cleusa P. Ferri, Martin Prince, Carol Brayne, Henry Brodaty, Laura Fratiglioni, Mary Ganguli, Kathleen Hall, Kazuo Hasegawa, Hugh Hendrie, Yueqin Huang, Anthony Jorm, Colin Mathers, Paulo R. Menezes, Elizabeth Rimmer, Marcia Scazufca 2005 2021-05-25 [("doi","10.1016/S0140-6736(05)67889-0")] psychiatry/alzheimers psychiatry/traumatic-brain-injury

Background: 100 years after the first description, Alzheimer’s disease is one of the most disabling and burdensome health conditions worldwide. We used the Delphi consensus method to determine dementia prevalence for each world region.

Method: 12 international experts were provided with a systematic review of published studies on dementia and were asked to provide prevalence estimates for every WHO world region, for men and women combined, in 5-year age bands 60–84 years, and for those aged 85 years and older. UN population estimates and projections were used to estimate numbers of people with dementia in 2001, 2020, and 2040. We estimated incidence rates from prevalence, remission, and mortality.

Results: Evidence from well-planned, representative epidemiological surveys is scarce in many regions. We estimate that 24.3 million people have dementia today, with 4.6 million new cases of dementia every year (one new case every 7 seconds). The number of people affected will double every 20 years to 81.1 million by 2040. Most people with dementia live in developing countries (60% in 2001, rising to 71% by 2040). Rates of increase are not uniform; numbers in developed countries are forecast to increase by 100% between 2001 and 2040, but by more than 300% in India, China, and their south Asian and western Pacific neighbours.

Interpretation: We believe that the detailed estimates in this paper constitute the best currently available basis for policymaking, planning, and allocation of health and welfare resources.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3034135/ The development of cynicism Candice M. Mills, Frank C. Keil 2005 2021-05-25 [("doi","10.1111/j.0956-7976.2005.01545.x")] psychology/cognitive-bias

Two experiments explored the development of cynicism by examining how children evaluate other people who make claims consistent or inconsistent with their self-interests.

In Experiment 1, kindergartners, second graders, and fourth graders heard stories with ambiguous conclusions in which characters made statements that were aligned either with or against self-interest.

Older children took into account the self-interests of characters in determining how much to believe them: They discounted statements aligned with self-interest, whereas they accepted statements going against self-interest.

Experiment 2 examined children’s endorsement of 3 different explanations for potentially self-interested statements: lies, biases, and mistakes.

Like adults, sixth graders endorsed lies and bias as plausible explanations for wrong statements aligned with self-interest; younger children did not endorse bias.

Implications for the development of cynicism and children’s understanding of bias are discussed.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044934/ Deconstructing memory in Drosophila Carla Margulies, Tim Tully, Josh Dubnau 2005 2021-05-25 [("doi","10.1016/j.cub.2005.08.024")] psychology/neuroscience

Unlike most organ systems, which have evolved to maintain homeostasis, the brain has been selected to sense and adapt to environmental stimuli by constantly altering interactions in a gene network that functions within a larger neural network. This unique feature of the central nervous system provides a remarkable plasticity of behavior, but also makes experimental investigations challenging. Each experimental intervention ramifies through both gene and neural networks, resulting in unpredicted and sometimes confusing phenotypic adaptations. Experimental dissection of mechanisms underlying behavioral plasticity ultimately must accomplish an integration across many levels of biological organization, including genetic pathways acting within individual neurons, neural network interactions which feed back to gene function, and phenotypic observations at the behavioral level.

This dissection will be more easily accomplished for model systems such as Drosophila, which, compared with mammals, have relatively simple and manipulable nervous systems and genomes. The evolutionary conservation of behavioral phenotype and the underlying gene function ensures that much of what we learn in such model systems will be relevant to human cognition. In this essay, we have not attempted to review the entire Drosophila memory field. Instead, we have tried to discuss particular findings that provide some level of intellectual synthesis across 3 levels of biological organization: behavior, neural circuitry and biochemical pathways.

We have attempted to use this integrative approach to evaluate distinct mechanistic hypotheses, and to propose critical experiments that will advance this field.

--- https://arxiv.org/abs/1606.07937 Universal Darwinism as a process of Bayesian inference John O. Campbell 2016-06-25 2021-05-25 [("doi","10.3389/fnsys.2016.00049")] reinforcement-learning/exploration sociology statistics/bayes

Many of the mathematical frameworks describing natural selection are equivalent to Bayes Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus natural selection serves as a counterexample to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians).

As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an “experiment” in the external world environment, and the results of that “experiment” or the “surprise” entailed by predicted and actual outcomes of the “experiment”. Minimization of free energy implies that the implicit measure of “surprise” experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems.

Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself.

The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.

--- https://arxiv.org/abs/cond-mat/0401529 Stochastic modeling of citation slips M. V. Simkin, V. P. Roychowdhury 2004-01-27 2021-05-25 [("doi","10.48550/arXiv.0401529")] science statistics/probability

We present empirical data on frequency and pattern of misprints in citations to twelve high-profile papers.

We find that the distribution of misprints, ranked by frequency of their repetition, follows Zipf’s law.

We propose a stochastic model of citation process, which explains these findings, and leads to the conclusion that 70–90% of scientific citations are copied from the lists of references used in other papers.

--- https://arxiv.org/abs/1607.01952 A First Look at User Activity on Tinder Gareth Tyson, Vasile C. Perta, Hamed Haddadi, Michael C. Seto 2016-07-07 2021-05-25 [("doi","10.48550/arXiv.1607.01952")] sociology

Mobile dating apps have become a popular means to meet potential partners. Although several exist, one recent addition stands out amongst all others. Tinder presents its users with pictures of people geographically nearby, whom they can either like or dislike based on first impressions. If two users like each other, they are allowed to initiate a conversation via the chat feature.

In this paper we use a set of curated profiles to explore the behavior of men and women on Tinder. We reveal differences between the way men and women interact with the app, highlighting the strategies employed.

Women attain large numbers of matches rapidly, whilst men only slowly accumulate matches. To expand on our findings, we collect survey data to understand user intentions on Tinder.

Most notably, our results indicate that a little effort in grooming profiles, especially for male users, goes a long way in attracting attention.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC557150/ Randomized controlled trial of calcium and supplementation with cholecalciferol (vitamin D3) for prevention of fractures in primary care Jill Porthouse, Sarah Cockayne, Christine King, Lucy Saxon, Elizabeth Steele, Terry Aspray, Mike Baverstock, Yvonne Birks, Jo Dumville, Roger Francis, Cynthia Iglesias, Suezann Puffer, Anne Sutcliffe, Ian Watt, David J. Torgerson 2005 2021-05-25 [("doi","10.1136/bmj.330.7498.1003")] vitamin-d

Objective: To assess whether supplementation with calcium and cholecaliferol (vitamin D3) reduces the risk of fracture in women with one or more risk factors for fracture of the hip.

Design: Pragmatic open randomized controlled trial.

Setting: Practice nurse led clinics in primary care.

Participants: 3314 women aged 70 and over with one or more risk factors for hip fracture: any previous fracture, low body weight (< 58 kg), smoker, family history of hip fracture, or fair or poor self reported health.

Intervention: Daily oral supplementation using 1,000 mg calcium with 800 IU cholecaliferol and information leaflet on dietary calcium intake and prevention of falls, or leaflet only (control group).

Main Outcome Measures: Primary outcome measure was all clinical fractures and secondary outcome measures were adherence to treatment, falls, and quality of life (measured with the SF-12).

Results: 69% of the women who completed the follow-up questionnaire at 24 months were still taking supplements (55% with inclusion of randomized participants known to be alive). After a median follow-up of 25 months (range 18 to 42 months), clinical fracture rates were lower than expected in both groups but did not differ for all clinical fractures (odds ratio for fracture in supplemented group 1.01, 95% confidence interval 0.71 to 1.43). The odds ratio for hip fracture was 0.75 (0.31 to 1.78). The odds of a woman having a fall at six and 12 months was 0.99 and 0.98, respectively. Quality of life did not differ between the groups.

Conclusion: We found no evidence that calcium and vitamin D supplementation reduces the risk of clinical fractures in women with one or more risk factors for hip fracture. Registration ISRCTN26118436, controlled trials registry.

--- https://arxiv.org/abs/1605.07648 FractalNet: Ultra-Deep Neural Networks without Residuals Gustav Larsson, Michael Maire, Gregory Shakhnarovich 2016-05-24 2021-05-25 [("doi","10.48550/arXiv.1605.07648")] ai/nn/cnn ai/nn/sparsity/knowledge-distillation

We introduce a design strategy for neural network macro-architecture based on self-similarity.

Repeated application of a simple expansion rule generates deep networks whose structural layouts are precisely truncated fractals. These networks contain interacting subpaths of different lengths, but do not include any pass-through or residual connections; every internal signal is transformed by a filter and nonlinearity before being seen by subsequent layers.

In experiments, fractal networks match the excellent performance of standard residual networks on both CIFAR and ImageNet classification tasks, thereby demonstrating that residual representations may not be fundamental to the success of extremely deep convolutional neural networks. Rather, the key may be the ability to transition, during training, from effectively shallow to deep.

We note similarities with student-teacher behavior and develop drop-path, a natural extension of dropout, to regularize co-adaptation of subpaths in fractal architectures. Such regularization allows extraction of high-performance fixed-depth subnetworks.

Additionally, fractal networks exhibit an anytime property: shallow subnetworks provide a quick answer, while deeper subnetworks, with higher latency, provide a more accurate answer.

--- https://arxiv.org/abs/1605.07146 Wide Residual Networks Sergey Zagoruyko, Nikos Komodakis 2016-05-23 2021-05-26 [("doi","10.48550/arXiv.1605.07146")] ai/nn/cnn

Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train.

To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts.

For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layer-deep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and substantial improvements on ImageNet.

Our code and models are available at https://github.com/szagoruyko/wide-residual-networks.

--- https://arxiv.org/abs/1603.01670 Network Morphism Tao Wei, Changhu Wang, Yong Rui, Chang Wen Chen 2016-03-05 2021-05-26 [("doi","10.48550/arXiv.1603.01670")] ai/nn/cnn ai/nn/fully-connected

We present in this paper a systematic study on how to morph a well-trained neural network to a new one so that its network function can be completely preserved. We define this as network morphism in this research. After morphing a parent network, the child network is expected to inherit the knowledge from its parent network and also has the potential to continue growing into a more powerful one with much shortened training time.

The first requirement for this network morphism is its ability to handle diverse morphing types of networks, including changes of depth, width, kernel size, and even subnet. To meet this requirement, we first introduce the network morphism equations, and then develop novel morphing algorithms for all these morphing types for both classic and convolutional neural networks. The second requirement for this network morphism is its ability to deal with non-linearity in a network. We propose a family of parametric-activation functions to facilitate the morphing of any continuous non-linear activation neurons.

Experimental results on benchmark datasets and typical neural networks demonstrate the effectiveness of the proposed network morphism scheme.

--- https://arxiv.org/abs/1603.01417 Dynamic Memory Networks for Visual and Textual Question Answering Caiming Xiong, Stephen Merity, Richard Socher 2016-03-04 2021-05-26 [("doi","10.48550/arXiv.1603.01417")] ai/nn/rnn

Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering. One such architecture, the dynamic memory network (DMN), obtained high accuracy on a variety of language tasks. However, it was not shown whether the architecture achieves strong results for question answering when supporting facts are not marked during training or whether it could be applied to other modalities such as images.

Based on an analysis of the DMN, we propose several improvements to its memory and input modules. Together with these changes we introduce a novel input module for images in order to be able to answer visual questions. Our new DMN+ model improves the state-of-the-art on both the Visual Question Answering dataset and the -10k text question-answering dataset without supporting fact supervision.

--- https://arxiv.org/abs/1602.07261#google Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi 2016-02-23 2021-05-26 [("doi","10.48550/arXiv.1602.07261")] ai/nn/cnn

Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question of whether there are any benefit in combining the Inception architecture with residual connections.

Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin.

We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks.

With an ensemble of 3 residual and one Inception-v4, we achieve 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge.

--- https://arxiv.org/abs/1602.05897 Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity Amit Daniely, Roy Frostig, Yoram Singer 2016-02-18 2021-05-26 [("doi","10.48550/arXiv.1602.05897")] ai/nn

We develop a general duality between neural networks and compositional kernels, striving towards a better understanding of deep learning.

We show that initial representations generated by common random initializations are sufficiently rich to express all functions in the dual kernel space. Hence, though the training objective is hard to optimize in the worst case, the initial weights form a good starting point for optimization.

Our dual view also reveals a pragmatic and esthetic perspective of neural networks and underscores their expressive power.

--- https://arxiv.org/abs/1601.01705 Learning to Compose Neural Networks for Question Answering Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein 2016-01-07 2021-05-26 [("doi","10.48550/arXiv.1601.01705")] ai/nn

We describe a question answering model that applies to both images and structured knowledge bases.

The model uses natural language strings to automatically assemble neural networks from a collection of composable modules. Parameters for these modules are learned jointly with network-assembly parameters via reinforcement learning, with only (world, question, answer) triples as supervision.

Our approach, which we term a dynamic neural model network, achieves state-of-the-art results on benchmark datasets in both visual and structured domains.

--- https://arxiv.org/abs/1602.05629#google Communication-Efficient Learning of Deep Networks from Decentralized Data H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas 2016-02-17 2021-05-26 [("doi","10.48550/arXiv.1602.05629")] ai/scaling/hardware

Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning.

We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-iid data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10–100× as compared to synchronized stochastic gradient descent.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4914563/ On Having No Head: Cognition throughout Biological Systems František Baluška, Michael Levin 2016 2021-05-26 [("doi","10.3389/fpsyg.2016.00902")] biology cs/computable cs/hardware philosophy/mind psychology/neuroscience

The central nervous system (CNS) underlies memory, perception, decision-making, and behavior in numerous organisms. However, neural networks have no monopoly on the signaling functions that implement these remarkable algorithms. It is often forgotten that neurons optimized cellular signaling modes that existed long before the CNS appeared during evolution, and were used by somatic cellular networks to orchestrate physiology, embryonic development, and behavior. Many of the key dynamics that enable information processing can, in fact, be implemented by different biological hardware. This is widely exploited by organisms throughout the tree of life.

Here, we review data on memory, learning, and other aspects of cognition in a range of models, including single-celled organisms, plants, and tissues in animal bodies. We discuss current knowledge of the molecular mechanisms at work in these systems, and suggest several hypotheses for future investigation.

The study of cognitive processes implemented in aneural contexts is a fascinating, highly interdisciplinary topic that has many implications for evolution, cell biology, regenerative medicine, computer science, and synthetic bioengineering.

--- https://arxiv.org/abs/1605.08448 Energy-Efficient Algorithms Erik D. Demaine, Jayson Lynch, Geronimo J. Mirano, Nirvan Tyagi 2016-05-26 2021-05-26 [("doi","10.48550/arXiv.1605.08448")] cs/algorithm/information

We initiate the systematic study of the energy complexity of algorithms (in addition to time and space complexity) based on Landauer’s Principle in physics, which gives a lower bound on the amount of energy a system must dissipate if it destroys information.

We propose energy-aware variations of 3 standard models of computation: circuit RAM, word RAM, and transdichotomous RAM. On top of these models, we build familiar high-level primitives such as control logic, memory allocation, and garbage collection with zero energy complexity and only constant-factor overheads in space and time complexity, enabling simple expression of energy-efficient algorithms.

We analyze several classic algorithms in our models and develop low-energy variations: comparison sort, insertion sort, counting sort, breadth-first search, Bellman-Ford, Floyd-Warshall, matrix all-pairs shortest paths, AVL trees, binary heaps, and dynamic arrays. We explore the time/space/energy trade-off and develop several general techniques for analyzing algorithms and reducing their energy complexity.

These results lay a theoretical foundation for a new field of semi-reversible computing and provide a new framework for the investigation of algorithms.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0151817 Dual N-Back Working Memory Training in Healthy Adults: A Randomized Comparison to Processing Speed Training Linette Lawlor-Savage, Vina M. Goghari 2016-03-04 2021-05-26 [("doi","10.1371/journal.pone.0151817")] dual-n-back iq

Enhancing cognitive ability is an attractive concept, particularly for middle-aged adults interested in maintaining cognitive functioning and preventing age-related declines. Computerized working memory training has been investigated as a safe method of cognitive enhancement in younger and older adults, although few studies have considered the potential impact of working memory training on middle-aged adults.

This study investigated dual n-back working memory training in healthy adults aged 30–60. Fifty-seven adults completed measures of working memory, processing speed, and fluid intelligence before and after a 5-week web-based dual n-back or active control (processing speed) training program. Results: Repeated measures multivariate analysis of variance failed to identify improvements across the 3 cognitive composites, working memory, processing speed, and fluid intelligence, after training. Follow-up Bayesian analyses supported null findings for training effects for each individual composite.

Findings suggest that dual n-back working memory training may not benefit working memory or fluid intelligence in healthy adults. Further investigation is necessary to clarify if other forms of working memory training may be beneficial, and what factors impact training-related benefits, should they occur, in this population.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4786918/ In Vivo CRISPR/Cas9 Gene Editing Corrects Retinal Dystrophy in the S334ter-3 Rat Model of Autosomal Dominant Retinitis Pigmentosa Benjamin Bakondi, Wenjian Lv, Bin Lu, Melissa K. Jones, Yuchun Tsai, Kevin J. Kim, Rachelle Levy, Aslam Abbasi Akhtar, Joshua J. Breunig, Clive N. Svendsen, Shaomei Wang 2016 2021-05-26 [("doi","10.1038/mt.2015.220")] genetics/editing

Reliable genome editing via Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)/Cas9 may provide a means to correct inherited diseases in patients.

As proof of principle, we show that CRISPR/Cas9 can be used in vivo to selectively ablate the rhodopsin gene carrying the dominant S334ter mutation (Rho(S334)) in rats that model severe autosomal dominant retinitis pigmentosa.

A single subretinal injection of guide RNA/Cas9 plasmid in combination with electroporation generated allele-specific disruption of Rho(S334), which prevented retinal degeneration and improved visual function.

--- https://www.biorxiv.org/content/10.1101/035907.full Contrasting the genetic architecture of 30 complex traits from summary association data Huwenbo Shi, Gleb Kichaev, Bogdan Pasaniuc 2016-01-14 2021-05-27 [("doi","10.1101/035907")] genetics/heritable

Variance components methods that estimate the aggregate contribution of large sets of variants to the heritability of complex traits have yielded important insights into the disease architecture of common diseases.

Here, we introduce new methods that estimate the total variance in trait explained by a single locus in the genome (local heritability) from summary GWAS data while accounting for linkage disequilibrium (LD) among variants. We apply our new estimator to ultra large-scale GWAS summary data of 30 common traits and diseases to gain insights into their local genetic architecture.

First, we find that common SNPs have a high contribution to the heritability of all studied traits. Second, we identify traits for which the majority of the SNP heritability can be confined to a small percentage of the genome. Third, we identify GWAS risk loci where the entire locus explains statistically-significantly more variance in the trait than the GWAS reported variants. Finally, we identify 55 loci that explain a large proportion of heritability across multiple traits.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4722802/ GWAS for executive function and processing speed suggests involvement of the CADM2 gene C A. Ibrahim-Verbaas, J. Bressler, S. Debette, M. Schuur, Albert Vernon Smith, J. C. Bis, Gail Davies, S. Trompet, J. A. Smith, C. Wolf, L. B. Chibnik, Y. Liu, V. Vitart, M. Kirin, K. Petrovic, O. Polasek, L. Zgaga, C. Fawns-Ritchie, P. Hoffmann, J. Karjalainen, J. Lahti, D. J. Llewellyn, C. O. Schmidt, K. A. Mather, V. Chouraki, Q. Sun, S. M. Resnick, L. M. Rose, C. Oldmeadow, M. Stewart, B. H. Smith, V. Gudnason, Q. Yang, S. S. Mirza, J. Wouter Jukema, P. L. deJager, T. B. Harris, D. C. Liewald, N. Amin, L. H. Coker, O. Stegle, O. L. Lopez, R. Schmidt, A. Teumer, I. Ford, N. Karbalai, J. T. Becker, M. K. Jonsdottir, R. Au, Rsn Fehrmann, S. Herms, M. Nalls, W. Zhao, S. T. Turner, K. Yaffe, K. Lohman, J. C. van Swieten, Slr Kardia, D. S. Knopman, W. M. Meeks, G. Heiss, E. G. Holliday, P. W. Schofield, T. Tanaka, D. J. Stott, J. Wang, P. Ridker, A. J. Gow, A. Pattie, J. M. Starr, L. J. Hocking, N. J. Armstrong, S. McLachlan, J. M. Shulman, L. C. Pilling, G. Eiriksdottir, Rodney J. Scott, N. A. Kochan, A. Palotie, Y-C Hsieh, J. G. Eriksson, A. Penman, R. F. Gottesman, Ben A. Oostra, L. Yu, A. L. DeStefano, A. Beiser, Melissa E. Garcia, J. I. Rotter, M. M. Nöthen, A. Hofman, P. E. Slagboom, Rgj Westendorp, B. M. Buckley, P. A. Wolf, A. G. Uitterlinden, Bruce M. Psaty, H. J. Grabe, S. Bandinelli, D. I. Chasman, F. Grodstein, K. Räikkönen, J-C Lambert, D. J. Porteous, J. F. Price, P. S. Sachdev, L. Ferrucci, J. R. Attia, I. Rudan, C. Hayward, A. F. Wright, J. F. Wilson, S. Cichon, L. Franke, H. Schmidt, J. Ding, Ajm de Craen, M. Fornage, D. A. Bennett, I. J. Deary, M. A. Ikram, Lenore J. Launer, A. L. Fitzpatrick, S. Seshadri, C. M. van Duijn, T. H. Mosley 2016 2021-05-27 [("doi","10.1038/mp.2015.37")] genetics/heritable iq

To identify common variants contributing to normal variation in two specific domains of cognitive functioning, we conducted a genome-wide association study (GWAS) of executive functioning and information processing speed in non-demented older adults from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium. Neuropsychological testing was available for 5,429–32,070 subjects of European ancestry aged 45 years or older, free of dementia and clinical stroke at the time of cognitive testing from 20 cohorts in the discovery phase. We analyzed performance on the Trail Making Test parts A and B, the Letter Digit Substitution Test (LDST), the Digit Symbol Substitution Task (DSST), semantic and phonemic fluency tests, and the Stroop Color and Word Test. Replication was sought in 1,311–21,860 subjects from 20 independent cohorts.

A statistically-significant association was observed in the discovery cohorts for the single-nucleotide polymorphism (SNP) rs17518584 (discovery p-value=3.12 × 10−8) and in the joint discovery and replication meta-analysis (p-value=3.28 × 10−9 after adjustment for age, gender, and education) in an intron of the gene cell adhesion molecule 2 (CADM2) for performance on the LDST/DSST. Rs17518584 is located about 170 kb upstream of the transcription start site of the major transcript for the CADM2 gene, but is within an intron of a variant transcript that includes an alternative first exon. The variant is associated with expression of CADM2 in the cingulate cortex (p-value=4 × 10−4). The protein encoded by CADM2 is involved in glutamate signaling (p-value=7.22 × 10−15), gamma-aminobutyric acid (GABA) transport (p-value=1.36 × 10−11), and neuron cell-cell adhesion (p-value=1.48 × 10−13).

Our findings suggest that genetic variation in the CADM2 gene is associated with individual differences in information processing speed.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4997301/ Small-quantity, lipid-based nutrient supplements provided to women during pregnancy and 6 mo postpartum and to their infants from 6 mo of age increase the mean attained length of 18-mo-old children in semi-urban Ghana: a randomized controlled trial Seth Adu-Afarwuah, Anna Lartey, Harriet Okronipa, Per Ashorn, Janet M. Peerson, Mary Arimond, Ulla Ashorn, Mamane Zeilani, Stephen Vosti, Kathryn G. Dewey 2016 2021-05-27 [("doi","10.3945/ajcn.116.134692")] iodine

Background: Childhood stunting usually begins in utero and continues after birth; therefore, its reduction must involve actions across different stages of early life.

Objective: We evaluated the efficacy of small-quantity, lipid-based nutrient supplements (SQ-LNSs) provided during pregnancy, lactation, and infancy on attained size by 18 mo of age.

Design: In this partially double-blind, individually randomized trial, 1320 women at ≤20 wk of gestation received standard iron and folic acid (IFA group), multiple micronutrients (MMN group), or SQ-LNS (LNS group) daily until delivery, and then placebo, MMNs, or SQ-LNS, respectively, for 6 mo postpartum; infants in the LNS group received SQ-LNS formulated for infants 6–18 mo of age (endline). The primary outcome was child length by 18 mo of age.

Results: At endline, data were available for 85% of 1228 infants enrolled; overall mean length and length-for-age z score (LAZ) were 79.3 cm and −0.83, respectively, and 12% of the children were stunted (LAZ <-2). In analysis based on the intended treatment, mean ± SD length and LAZ for the LNS group (79.7 ± 2.9 cm and −0.69 ± 1.01, respectively) were greater than for the IFA (79.1 ± 2.9 cm and −0.87 ± 0.99) and MMN (79.1 ± 2.9 cm and −0.91 ± 1.01) groups (p = 0.006 and p = 0.009, respectively). Differences were also for weight and weight-for-age z score but not head or midupper arm circumference, and the prevalence of stunting in the LNS group was 8.9%, compared with 13.7% in the IFA group and 12.9% in the MMN group (p = 0.12). In analysis based on actual supplement provided at enrollment, stunting prevalences were 8.9% compared with 15.1% and 11.5%, respectively (p = 0.045).

Conclusion: Provision of SQ-LNSs to women from pregnancy to 6 mo postpartum and to their infants 6–18 mo of age may increase the child’s attained length by age 18 mo in similar settings. This trial was registered at ClinicalTrials.gov as NCT00970866.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4804158/ Survey of Expert Opinion on Intelligence: Causes of International Differences in Cognitive Ability Tests Heiner Rindermann, David Becker, Thomas R. Coyle 2016 2021-05-27 [("doi","10.3389/fpsyg.2016.00399")] iq

Following Snyderman and Rothman (1987, 1988), we surveyed expert opinions on the current state of intelligence research. This report examines expert opinions on causes of international differences in student assessment and psychometric IQ test results.

Experts were surveyed about the importance of culture, genes, education (quantity and quality), wealth, health, geography, climate, politics, modernization, sampling error, test knowledge, discrimination, test bias, and migration. The importance of these factors was evaluated for diverse countries, regions, and groups including Finland, East Asia, sub-Saharan Africa, Southern Europe, the Arabian-Muslim world, Latin America, Israel, Jews in the West, Roma (gypsies), and Muslim immigrants.

Education was rated by n = 71 experts as the most important cause of international ability differences. Genes were rated as the second most relevant factor but also had the highest variability in ratings. Culture, health, wealth, modernization, and politics were the next most important factors, whereas other factors such as geography, climate, test bias, and sampling error were less important.

The paper concludes with a discussion of limitations of the survey (eg. response rates and validity of expert opinions).

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4835278/ Risk of suicide after a concussion Michael Fralick, Deva Thiruchelvam, Homer C. Tien, Donald A. Redelmeier 2016 2021-05-27 [("doi","10.1503/cmaj.150790")] psychiatry/traumatic-brain-injury

Background: Head injuries have been associated with subsequent suicide among military personnel, but outcomes after a concussion in the community are uncertain. We assessed the long-term risk of suicide after concussions occurring on weekends or weekdays in the community.

Method: We performed a longitudinal cohort analysis of adults with diagnosis of a concussion in Ontario, Canada, from Apr. 1, 1992, to Mar. 31, 2012 (a 20-yr period), excluding severe cases that resulted in hospital admission. The primary outcome was the long-term risk of suicide after a weekend or weekday concussion.

Results: We identified 235,110 patients with a concussion. Their mean age was 41 years, 52% were men, and most (86%) lived in an urban location. A total of 667 subsequent suicides occurred over a median follow-up of 9.3 years, equivalent to 31 deaths per 100,000 patients annually or 3× the population norm. Weekend concussions were associated with a 1⁄3rd further increased risk of suicide compared with weekday concussions (relative risk 1.36, 95% confidence interval 1.14–1.64). The increased risk applied regardless of patients’ demographic characteristics, was independent of past psychiatric conditions, became accentuated with time and exceeded the risk among military personnel. Half of these patients had visited a physician in the last week of life.

Interpretation: Adults with a diagnosis of concussion had an increased long-term risk of suicide, particularly after concussions on weekends. Greater attention to the long-term care of patients after a concussion in the community might save lives because deaths from suicide can be prevented.

--- https://arxiv.org/abs/1602.02867#deepmind Value Iteration Networks Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel 2016-02-09 2021-05-27 [("doi","10.48550/arXiv.1602.02867")] ai/nn/cnn reinforcement-learning/model

We introduce the value iteration network (VIN): a fully differentiable neural network with a ‘planning module’ embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning.

Key to our approach is a novel differentiable approximation of the value-iteration algorithm, which can be represented as a convolutional neural network, and trained end-to-end using standard backpropagation.

We evaluate VIN based policies on discrete and continuous path-planning domains, and on a natural-language based search task.

We show that by learning an explicit planning computation, VIN policies generalize better to new, unseen domains.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152719 Statistically Controlling for Confounding Constructs Is Harder than You Think Jacob Westfall, Tal Yarkoni 2016-03-17 2021-05-27 [("doi","10.1371/journal.pone.0152719")] statistics/bias

Social scientists often seek to demonstrate that a construct has incremental validity over and above other related constructs. However, these claims are typically supported by measurement-level models that fail to consider the effects of measurement (un)reliability.

We use intuitive examples, Monte Carlo simulations, and a novel analytical framework to demonstrate that common strategies for establishing incremental construct validity using multiple regression analysis exhibit extremely high false positive error rates under parameter regimes common in many psychological domains.

Counterintuitively, we find that error rates are highest—in some cases approaching 100%—when sample sizes are large and reliability is moderate. Our findings suggest that a potentially large proportion of incremental validity claims [such as mediation analysis] made in the literature are spurious.

We present a web application (https://jakewestfall.org/) that readers can use to explore the statistical properties of these and other incremental validity arguments.

We conclude by reviewing SEM-based statistical approaches that appropriately control the false positive error rate when attempting to establish incremental validity.

--- https://arxiv.org/abs/1606.05579 Early Visual Concept Learning with Unsupervised Deep Learning Irina Higgins, Loic Matthey, Xavier Glorot, Arka Pal, Benigno Uria, Charles Blundell, Shakir Mohamed, Alexander Lerchner 2016-06-17 2021-05-27 [("doi","10.48550/arXiv.1606.05579")] ai/nn/vae

Automated discovery of early visual concepts from raw image data is a major open challenge in AI research.

Addressing this problem, we propose an unsupervised approach for learning disentangled representations of the underlying factors of variation.

We draw inspiration from neuroscience, and show how this can be achieved in an unsupervised generative model by applying the same learning pressures as have been suggested to act in the ventral visual stream in the brain. By enforcing redundancy reduction, encouraging statistical independence, and exposure to data with transform continuities analogous to those to which human infants are exposed, we obtain a variational autoencoder (VAE) framework capable of learning disentangled factors.

Our approach makes few assumptions and works well across a wide variety of datasets.

Furthermore, our solution has useful emergent properties, such as zero-shot inference and an intuitive understanding of “objectness”.

--- https://arxiv.org/abs/1606.03073 Convolutional Sketch Inversion Yağmur Güçlütürk, Umut Güçlü, Rob van Lier, Marcel A. J. van Gerven 2016-06-09 2021-05-27 [("doi","10.1007/978-3-319-46604-0_56")] ai/dataset ai/nn

In this paper, we use deep neural networks for inverting face sketches to synthesize photorealistic face images.

We first construct a semi-simulated dataset containing a very large number of computer-generated face sketches with different styles and corresponding face images by expanding existing unconstrained face datasets. We then train models achieving state-of-the-art results on both computer-generated sketches and hand-drawn sketches by leveraging recent advances in deep learning such as batch normalization, deep residual learning, perceptual losses, and stochastic optimization in combination with our new dataset.

We finally demonstrate potential applications of our models in fine arts and forensic arts.

In contrast to existing patch-based approaches, our deep-neural-network-based approach can be used for synthesizing photorealistic face images by inverting face sketches in the wild.

--- https://arxiv.org/abs/1606.02492 Convolutional Neural Fabrics Shreyas Saxena, Jakob Verbeek 2016-06-08 2021-05-27 [("doi","10.48550/arXiv.1606.02492")] ai/nn/cnn

Despite the success of CNNs, selecting the optimal architecture for a given task remains an open problem. Instead of aiming to select a single optimal architecture, we propose a “fabric” that embeds an exponentially large number of architectures. The fabric consists of a 3D trellis that connects response maps at different layers, scales, and channels with a sparse homogeneous local connectivity pattern. The only hyper-parameters of a fabric are the number of channels and layers.

While individual architectures can be recovered as paths, the fabric can in addition ensemble all embedded architectures together, sharing their weights where their paths overlap. Parameters can be learned using standard methods based on back-propagation, at a cost that scales linearly in the fabric size.

We present benchmark results competitive with the state-of-the-art for image classification on MNIST and CIFAR-10, and for semantic segmentation on the Part Labels dataset.

--- https://www.biorxiv.org/content/10.1101/072306.full Genetic Prediction of Male Pattern Baldness Saskia P. Hagenaars, W. David Hill, Sarah E. Harris, Stuart J. Ritchie, Gail Davies, David C. Liewald, Catharine R. Gale, David J. Porteous, Ian J. Deary, Riccardo E. Marioni 2016-08-31 2021-05-27 [("doi","10.1101/072306")] genetics/heritable

Male pattern baldness can have substantial psychosocial effects, and it has been phenotypically linked to adverse health outcomes such as prostate cancer and cardiovascular disease.

We explored the genetic architecture of the trait using data from over 52,000 male participants of UK Biobank, aged 40–69 years.

We identified over 250 independent novel genetic loci associated with severe hair loss. By developing a prediction algorithm based entirely on common genetic variants, and applying it to an independent sample, we could discriminate accurately (AUC = 0.82) between those with no hair loss from those with severe hair loss.

The results of this study might help identify those at the greatest risk of hair loss and also potential genetic targets for intervention.

--- https://www.biorxiv.org/content/10.1101/056317.full When local means local: Polygenic signatures of local adaptation within whitebark pine (Pinus albicaulis Engelm.) across the Lake Tahoe Basin, USA Brandon M. Lind, Christopher J. Friedline, Jill L. Wegrzyn, Patricia E. Maloney, Detlev R. Vogler, David B. Neale, Andrew J. Eckert 2016-05-31 2021-05-28 [("doi","10.1101/056317")] genetics/selection/natural

For populations exhibiting high levels of gene flow, the genetic architecture of fitness-related traits is expected to be polygenic and underlain by many small-effect loci that covary across a network of linked genomic regions. For most coniferous taxa, studies describing this architecture have been limited to single-locus approaches, possibly leaving the vast majority of the underlying genetic architecture undescribed. Even so, molecular investigations rarely search for patterns indicative of an underlying polygenic basis, despite prior expectations for this signal.

Here, using a polygenic perspective, we employ single and multilocus analyses of genome-wide data (n = 116,231 SNPs) to describe the genetic architecture of adaptation within whitebark pine (Pinus albicaulis Engelm.) across the local extent of the environmentally heterogeneous Lake Tahoe Basin, USA. We show that despite highly shared genetic variation (FST = 0.0069) there is strong evidence for polygenic adaptation to the rain shadow experienced across the eastern Sierra Nevada. Specifically, we find little evidence for large-effect loci and that the frequencies of loci associated with 4⁄5 phenotypes (mean = 236 SNPs), 18 environmental variables (mean = 99 SNPs), and those detected through genetic differentiation (n = 110 SNPs) exhibit higher covariance than random SNPs.

We also provide evidence that this covariance tracks environmental measures related to soil water availability through subtle allele frequency shifts across populations. Our results provide replicative support for theoretical expectations and highlight advantages of a polygenic perspective, as unremarkable loci when viewed from a single-locus perspective are noteworthy when viewed through a polygenic lens, particularly when considering protective measures such as conservation guidelines and restoration strategies.

--- https://www.biorxiv.org/content/10.1101/055855.full Population structure of UK Biobank and ancient Eurasians reveals adaptation at genes influencing blood pressure Kevin J. Galinsky, Po-Ru Loh, Mallick Swapan, Nick J. Patterson, Alkes Price 2016-05-27 2021-05-28 [("doi","10.1101/055855")] genetics/selection/natural/human

Analyzing genetic differences between closely related populations can be a powerful way to detect recent adaptation. The very large sample size of the UK Biobank is ideal for detecting selection using population differentiation, and enables an analysis of UK population structure at fine resolution.

In analyses of 113,851 UK Biobank samples, population structure in the UK is:

dominated by 5 principal components (PCs) spanning 6 clusters: Northern Ireland, Scotland, northern England, southern England, and two Welsh clusters. Analyses with ancient Eurasians show that populations in the northern UK have higher levels of Steppe ancestry, and that UK population structure cannot be explained as a simple mixture of Celts and Saxons.

A scan for unusual population differentiation along top PCs identified a genome-wide statistically-significant signal of selection at the coding variant rs601338 in FUT2 (p = 9.16 × 10−9). In addition, by combining evidence of unusual differentiation within the UK with evidence from ancient Eurasians, we identified new genome-wide statistically-significant (p < 5 × 10−8) signals of recent selection at two additional loci: CYP1A2/CSK and F12. We detected strong associations to diastolic blood pressure in the UK Biobank for the variants with new selection signals at CYP1A2/CSK (p = 1.10 × 10−19) and for variants with ancient Eurasian selection signals in the ATXN2/SH2B3 locus (p = 8.00 × 10−33), implicating recent adaptation related to blood pressure.

--- https://arxiv.org/abs/1606.04442 DeepMath: Deep Sequence Models for Premise Selection Alex A. Alemi, Francois Chollet, Niklas Een, Geoffrey Irving, Christian Szegedy, Josef Urban 2016-06-14 2021-05-28 [("doi","10.48550/arXiv.1606.04442")] math reinforcement-learning/model

We study the effectiveness of neural sequence models for premise selection in automated theorem proving, one of the main bottlenecks in the formalization of mathematics.

We propose a 2-stage approach for this task that yields good results for the premise selection task on the Mizar corpus while avoiding the hand-engineered features of existing state-of-the-art models.

To our knowledge, this is the first time deep learning has been applied to theorem proving on a large scale.

--- https://arxiv.org/abs/1605.09288 Generalized Network Psychometrics: Combining Network and Latent Variable Models Sacha Epskamp, Mijke Rhemtulla, Denny Borsboom 2016-05-30 2021-05-28 [("doi","10.1007/s11336-017-9557-x")] statistics

We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between test items arises from the influence of one or more common latent variables.

Here, we present two generalizations of the network model that encompass latent variable structures, establishing network modeling as parts of the more general framework of Structural Equation Modeling (SEM). In the first generalization, we model the covariance structure of latent variables as a network. We term this framework Latent Network Modeling (LNM) and show that, with LNM, an unique structure of conditional independence relationships between latent variables can be obtained in an explorative manner. In the second generalization, the residual variance-covariance structure of indicators is modeled as a network. We term this generalization Residual Network Modeling (RNM) and show that, within this framework, identifiable models can be obtained in which local independence is structurally violated.

These generalizations allow for a general modeling framework that can be used to fit, and compare, SEM models, network models, and the RNM and LNM generalizations. This methodology has been implemented in the free-to-use software package lvnet, which contains confirmatory model testing as well as two exploratory search algorithms: stepwise search algorithms for low-dimensional datasets and penalized maximum likelihood estimation for larger datasets. We show in simulation studies that these search algorithms performs adequately in identifying the structure of the relevant residual or latent networks. We further demonstrate the utility of these generalizations in an empirical example on a personality inventory dataset.

--- https://www.biorxiv.org/content/10.1101/079350.full Reproducibility and replicability of rodent phenotyping in preclinical studies Neri Kafkafi, Joseph Agassi, Elissa J. Chesler, John C. Crabbe, Wim E. Crusio, David Eilam, Robert Gerlai, Ilan Golani, Alex Gomez-Marin, Ruth Heller, Fuad Iraqi, Iman Jaljuli, Natasha A. Karp, Hugh Morgan, George Nicholson, Donald W. Pfaff, S. Helene Richter, Philip B. Stark, Oliver Stiedl, Victoria Stodden, Lisa M. Tarantino, Valter Tucci, William Valdar, Robert W. Williams, Hanno Würbel, Yoav Benjamini 2016-10-17 2021-05-28 [("doi","10.1101/079350")] statistics/bias/animal

The scientific community is increasingly concerned with cases of published “discoveries” that are not replicated in further studies. The field of mouse behavioral phenotyping was one of the first to raise this concern, and to relate it to other complicated methodological issues: the complex interaction between genotype and environment; the definitions of behavioral constructs; and the use of the mouse as a model animal for human health and disease mechanisms.

In January 2015, researchers from various disciplines including genetics, behavior genetics, neuroscience, ethology, statistics and bioinformatics gathered in Tel Aviv University to discuss these issues. The general consent presented here was that the issue is prevalent and of concern, and should be addressed at the statistical, methodological and policy levels, but is not so severe as to call into question the validity and the usefulness of model organisms as a whole.

Well-organized community efforts, coupled with improved data and metadata sharing, were agreed by all to have a key role to play in identifying specific problems and promoting effective solutions.

As replicability is related to validity and may also affect generalizability and translation of findings, the implications of the present discussion reach far beyond the issue of replicability of mouse phenotypes but may be highly relevant throughout biomedical research.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2714089/ Intrinsic period and light intensity determine the phase relationship between melatonin and sleep in humans Kenneth P. Wright, Claude Gronfier, Jeanne F. Duffy, Charles A. Czeisler 2005 2021-05-28 [("doi","10.1177/0748730404274265")] melatonin

The internal circadian clock and sleep-wake homeostasis regulate the timing of human brain function, physiology, and behavior so that wakefulness and its associated functions are optimal during the solar day and that sleep and its related functions are optimal at night. The maintenance of a normal phase relationship between the internal circadian clock, sleep-wake homeostasis, and the light-dark cycle is crucial for optimal neurobehavioral and physiological function.

Here, the authors show that the phase relationship between these factors—the phase angle of entrainment (psi)—is strongly determined by the intrinsic period (tau) of the master circadian clock and the strength of the circadian synchronizer. Melatonin was used as a marker of internal biological time, and circadian period was estimated during a forced desynchrony protocol. The authors observed relationships between the phase angle of entrainment and intrinsic period after exposure to scheduled habitual wakefulness-sleep light-dark cycle conditions inside and outside of the laboratory.

Individuals with shorter circadian periods initiated sleep and awakened at a later biological time than did individuals with longer circadian periods. The authors also observed that light exposure history influenced the phase angle of entrainment such that phase angle was shorter following exposure to a moderate bright light (~450 lux)-dark/wakefulness-sleep schedule for 5 days than exposure to the equivalent of an indoor daytime light (~150 lux)-dark/wakefulness-sleep schedule for 2 days.

These findings demonstrate that neurobiological and environmental factors interact to regulate the phase angle of entrainment in humans. This finding has important implications for understanding physiological organization by the brain’s master circadian clock and may have implications for understanding mechanisms underlying circadian sleep disorders.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1490287/ The efficacy and safety of exogenous melatonin for primary sleep disorders. A meta-analysis Nina Buscemi, Ben Vandermeer, Nicola Hooton, Rena Pandya, Lisa Tjosvold, Lisa Hartling, Glen Baker, Terry P. Klassen, Sunita Vohra 2005 2021-05-28 [("doi","10.1111/j.1525-1497.2005.0243.x")] melatonin

Background: Exogenous melatonin has been increasingly used in the management of sleep disorders.

Purpose: To conduct a systematic review of the efficacy and safety of exogenous melatonin in the management of primary sleep disorders.

Data Sources: A number of electronic databases were searched. We reviewed the bibliographies of included studies and relevant reviews and conducted hand-searching.

Study Selection: Randomized controlled trials (RCTs) were eligible for the efficacy review, and controlled trials were eligible for the safety review.

Data Extraction: One reviewer extracted data, while the other verified data extracted. The Random Effects Model was used to analyze data.

Data Synthesis: Melatonin decreased sleep onset latency (weighted mean difference [WMD]: −11.7 minutes; 95% confidence interval [CI]: −18.2, −5.2); it was decreased to a greater extent in people with delayed sleep phase syndrome (WMD: −38.8 minutes; 95% CI: −50.3, −27.3; n = 2) compared with people with insomnia (WMD: −7.2 minutes; 95% CI: −12.0, −2.4; n = 12). The former result appears to be clinically important. There was no evidence of adverse effects of melatonin.

Conclusion: There is evidence to suggest that melatonin is not effective in treating most primary sleep disorders with short-term use (4 weeks or less); however, additional large-scale RCTs are needed before firm conclusions can be drawn. There is some evidence to suggest that melatonin is effective in treating delayed sleep phase syndrome with short-term use. There is evidence to suggest that melatonin is safe with short-term use (3 months or less).

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1363685/ 40 years on: teachers' assessments of children’s personality traits predict self-reported health behaviors and outcomes at midlife Sarah E. Hampson, Lewis R. Goldberg, Thomas M. Vogt, Joan P. Dubanoski 2006 2021-05-28 [("doi","10.1037/0278-6133.25.1.57")] psychology/personality/conscientiousness

A life span health-behavior model was investigated in this longitudinal study of personality influences on health. Teachers assessed 963 elementary schoolchildren on traits that formed scales assessing the dimensions of the five-factor (Big Five) model of personality.

Smoking, alcohol use, body mass index (BMI), and self-rated health were assessed 40 years later in midlife. Childhood personality traits were statistically-significantly associated with all 4 outcomes, and the effects were consistently larger for women than men.

For men and women, childhood Conscientiousness was associated with less adult smoking and better adult self-rated health and, for women only, with lower adult BMI. Mediation analyses suggested that the effects of Conscientiousness on self-rated health were partially mediated by smoking and BMI.

These findings add to the growing evidence that childhood personality traits predict adult health outcomes and are discussed in terms of future testing of the life span health-behavior model.

--- https://arxiv.org/abs/cs/0610105 How To Break Anonymity of the Netflix Prize Dataset Arvind Narayanan, Vitaly Shmatikov 2006-10-18 2021-05-28 [("doi","10.48550/arXiv.0610105")] cs/algorithm cs/security

We present a new class of statistical de-anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on. Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversary’s background knowledge.

We apply our de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix, the world’s largest online movie rental service. We demonstrate that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber’s record in the dataset. Using the Internet Movie Database as the source of background knowledge, we successfully identified the Netflix records of known users, uncovering their apparent political preferences and other potentially sensitive information.

--- https://arxiv.org/abs/quant-ph/0502072 NP-complete Problems and Physical Reality Scott Aaronson 2005-02-12 2021-05-28 [("doi","10.48550/arXiv.0502072")] cs/algorithm science

Can NP-complete problems be solved efficiently in the physical universe? I survey proposals including soap bubbles, protein folding, quantum computing, quantum advice, quantum adiabatic algorithms, quantum-mechanical nonlinearities, hidden variables, relativistic time dilation, analog computing, Malament-Hogarth spacetimes, quantum gravity, closed timelike curves, and “anthropic computing.”

The section on soap bubbles even includes some “experimental” results.

While I do not believe that any of the proposals will let us solve NP-complete problems efficiently, I argue that by studying them, we can learn something not only about computation but also about physics.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2964497/ A genetically informed study of the association between harsh punishment and offspring behavioral problems Stacy K. Lynch, Eric Turkheimer, Brian M. D’Onofrio, Jane Mendle, Robert E. Emery, Wendy S. Slutske, Nicholas G. Martin 2006 2021-05-29 [("doi","10.1037/0893-3200.20.2.190")] genetics/heritable/correlation psychiatry

Conclusions about the effects of harsh parenting on children have been limited by research designs that cannot control for genetic or shared environmental confounds. The present study used a sample of children of twins and a hierarchical linear modeling statistical approach to analyze the consequences of varying levels of punishment while controlling for many confounding influences. The sample of 887 twin pairs and 2,554 children came from the Australian Twin Registry.

Although corporal punishment per se did not have statistically-significant associations with negative childhood outcomes, harsher forms of physical punishment did appear to have specific and statistically-significant effects.

The observed association between harsh physical punishment and negative outcomes in children survived a relatively rigorous test of its causal status, thereby increasing the authors’ conviction that harsh physical punishment is a serious risk factor for children.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1526419/ Genome increase as a clock for the origin and evolution of life Alexei A. Sharov 2006 2021-05-29 [("doi","10.1186/1745-6150-1-17")] genetics/selection/natural

Background: The size of non-redundant functional genome can be an indicator of biological complexity of living organisms. Several positive feedback mechanisms including gene cooperation and duplication with subsequent specialization may result in the exponential growth of biological complexity in macro-evolution.

Results: I propose a hypothesis that biological complexity increased exponentially during evolution. Regression of the logarithm of functional non-redundant genome size versus time of origin in major groups of organisms showed a 7.8× increase per 1 billion years, and hence the increase of complexity can be viewed as a clock of macro-evolution. A strong version of the exponential hypothesis is that the rate of complexity increase in early (pre-prokaryotic) evolution of life was at most the same (or even slower) than observed in the evolution of prokaryotes and eukaryotes.

Conclusion: The increase of functional non-redundant genome size in macro-evolution was consistent with the exponential hypothesis. If the strong exponential hypothesis is true, then the origin of life should be dated 10 billion years ago. Thus, the possibility of panspermia as a source of life on earth should be discussed on equal basis with alternative hypotheses of de-novo life origin. Panspermia may be proven if bacteria similar to terrestrial ones are found on other planets or satellites in the solar system.

Reviewers: This article was reviewed by Eugene V. Koonin, Chris Adami and Arcady Mushegian.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1569519/ Artificial selection and maintenance of genetic variance in the global dairy cow population S Brotherstone, M. Goddard 2005 2021-05-29 [("doi","10.1098/rstb.2005.1668")] genetics/selection/artificial/index-selection

Genetic improvement of dairy cows, which has increased the milk yield of cows in the UK by 1200 kg per lactation in 12 years, is an excellent example of the application of quantitative genetics to agriculture. The most important traits of dairy cattle are expressed only in females, but the main opportunity for selection is in males.

Despite this, genetic improvement was achieved by the invention of a new statistical methodology, called ‘best linear unbiased prediction’ to estimate the breeding value of bulls. Intense selection of the best bulls, combined with the worldwide use of these bulls through artificial insemination and frozen semen, has created a global population and caused concern that the genetic variation available in the future will be reduced.

Maintenance of genetic variation and long-term genetic gains would be aided by rational payment systems, use of crossbreeding where profitable, inclusion of all economically important traits in the breeding objective, recognition of genotype by environment interactions and the use of selection algorithms that balance estimated breeding value against the average relationship among the selected animals. Fortunately, all of these things are happening to some degree.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1253585/ Training, maturation, and genetic influences on the development of executive attention M. Rosario Rueda, Mary K. Rothbart, Bruce D. McCandliss, Lisa Saccomanno, Michael I. Posner 2005 2021-05-29 [("doi","10.1073/pnas.0506897102")] dual-n-back iq

A neural network underlying attentional control involves the anterior cingulate in addition to lateral prefrontal areas. An important development of this network occurs 3–7 years of age.

We have examined the efficiency of attentional networks across age and after 5 days of attention training (experimental group) compared with different types of no training (control groups) in 4-year-old and 6-year-old children. Strong improvement in executive attention and intelligence was found ages 4 → 6 years. Both 4 & 6-year-olds showed more mature performance after the training than did the control groups.

This finding applies to behavioral scores of the executive attention network as measured by the attention network test, event-related potentials recorded from the scalp during attention network test performance, and intelligence test scores.

We also documented the role of the temperamental factor of effortful control and the DAT1 gene in individual differences in attention. [This is a candidate-gene hit and therefore wrong.]

Overall, our data suggest that the executive attention network appears to develop under strong genetic control, but that it is subject to educational interventions during development.

--- https://arxiv.org/abs/cs/0511077 The Availability and Persistence of Web References in D-Lib Magazine Frank McCown, Sheffan Chan, Michael L. Nelson, Johan Bollen 2005-11-21 2021-05-29 [("doi","10.48550/arXiv.0511077")] cs/linkrot

We explore the availability and persistence of URLs cited in articles published in D-Lib Magazine.

We extracted 4,387 unique URLs referenced in 453 articles published July 1995–August 2004. The availability was checked 3 times a week for 25 weeks September 2004–February 2005.

We found that ~28% of those URLs failed to resolve initially, and 30% failed to resolve at the last check. A majority of the unresolved URLs were due to 404 (‘page not found’) and 500 (‘internal server error’) errors. The content pointed to by the URLs was relatively stable; only 16% of the content registered more than a 1 KB change during the testing period.

We explore possible factors which may cause a URL to fail by examining its age, path depth, top-level domain and file extension.

Based on the data collected, we found the half-life of a URL referenced in a D-Lib Magazine article is ~10 years. We also found that URLs were more likely to be unavailable if they pointed to resources in the .net, .edu, or country-specific top-level domain, used non-standard ports (ie. not port 80), or pointed to resources with uncommon or deprecated extensions (eg. .shtml, .ps, .txt).

--- https://arxiv.org/abs/math/0604049 Comment on a Paper by Yucai Su On the Jacobian Conjecture (2005-12-30) T. T. Moh 2006-04-03 2021-05-29 [("doi","10.48550/arXiv.0604049")] math

The said paper [Su2] entitled “Proof Of Two Dimensional Jacobian Conjecture” is false.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1322240/ The case of the disappearing teaspoons: longitudinal cohort study of the displacement of teaspoons in an Australian research institute Megan S. C. Lim, Margaret E. Hellard, Campbell K. Aitken 2005 2021-05-29 [("doi","10.1136/bmj.331.7531.1498")] math/humor statistics/survival-analysis

Objectives: To determine the overall rate of loss of workplace teaspoons and whether attrition and displacement are correlated with the relative value of the teaspoons or type of tearoom.

Design: Longitudinal cohort study.

Setting: Research institute employing about 140 people.

Subjects: 70 discreetly numbered teaspoons placed in tearooms around the institute and observed weekly over five months.

Main Outcome Measures: Incidence of teaspoon loss per 100 teaspoon years and teaspoon half life.

Results: 56 (80%) of the 70 teaspoons disappeared during the study. The half life of the teaspoons was 81 days. The half life of teaspoons in communal tearooms (42 days) was shorter than for those in rooms associated with particular research groups (77 days). The rate of loss was not influenced by the teaspoons’ value. The incidence of teaspoon loss over the period of observation was 360.62 per 100 teaspoon years. At this rate, an estimated 250 teaspoons would need to be purchased annually to maintain a practical institute-wide population of 70 teaspoons.

Conclusion: The loss of workplace teaspoons was rapid, showing that their availability, and hence office culture in general, is constantly threatened.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2750958/ Neuronal nicotinic acetylcholine receptor expression and function on nonneuronal cells Lorise C. Gahring, Scott W. Rogers 2006 2021-05-29 [("doi","10.1208/aapsj070486")] nicotine psychology/neuroscience

Of the thousands of proven carcinogens and toxic agents contained within a cigarette, nicotine, while being the addictive agent, is often viewed as the least harmful of these compounds. Nicotine is a lipophilic molecule whose effects on neuronal nicotinic acetylcholine receptors (nAChR) have been primarily focused on its physiologic impact within the confines of the brain and peripheral nervous system.

However, recently, many studies have found neuronal nAChRs to be expressed on many different nonneuronal cell types throughout the body, where increasing evidence suggests they have important roles in determining the consequences of nicotine use on multiple organs systems and diseases as diverse as ulcerative colitis, chronic pulmonary obstructive disease, and diabetes, as well as the neurologic disorders of Parkinson’s and Alzheimer’s disease.

This review highlights current evidence for the expression of peripheral nAChRs in cells other than neurons and how they participate in fundamental processes, such as inflammation.

Understanding these processes may offer novel therapeutic strategies to approach inflammatory diseases, as well as precautions in the design of interventional drugs.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2915594/ Randomized controlled trial of standardized Bacopa monniera extract in age-associated memory impairment Sangeeta Raghav, Harjeet Singh, P. K. Dalal, J. S. Srivastava, O. P. Asthana 2006 2021-05-29 [("doi","10.4103/0019-5545.31555")] nootropic/bacopa

Background: Brahmi (Bacopa monniera) is a traditional Indian medicinal plant which causes multiple effects on the central nervous system. The standardized extract of this plant has shown enhanced behavioral learning in preclinical studies and enhanced information processing in healthy volunteers.

Aim: To study the efficacy of standardized Bacopa monniera extract (SBME) in subjects with age-associated memory impairment (AAMI) without any evidence of dementia or psychiatric disorder.

Method: A double-blind, placebo-controlled randomized study design was employed. The subjects received either 125 mg of SBME or placebo twice a day for a period of 12 weeks followed by a placebo period of another 4 weeks (total duration of the trial 16 weeks). Each subject was evaluated for cognition on a battery of tests comprising mental control, logical memory, digit forward, digit backward, visual reproduction and paired associate learning.

Results: SBME produced improvement on mental control, logical memory and paired associated learning during the 12-week drug therapy.

Conclusion: SBME is efficacious in subjects with age-associated memory impairment.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2790864/ The influence of a sense of time on human development Laura L. Carstensen 2006 2021-05-29 [("doi","10.1126/science.1127488")] psychology

The subjective sense of future time plays an essential role in human motivation.

Gradually, time left becomes a better predictor than chronological age for a range of cognitive, emotional, and motivational variables.

Socioemotional selectivity theory maintains that constraints on time horizons shift motivational priorities in such a way that the regulation of emotional states becomes more important than other types of goals.

This motivational shift occurs with age but also appears in other contexts (for example, geographical relocations, illnesses, and war) that limit subjective future time.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1564381/ Midbrain dopamine neurons encode a quantitative reward prediction error signal Hannah M. Bayer, Paul W. Glimcher 2005 2021-05-29 [("doi","10.1016/j.neuron.2005.05.020")] psychology/neuroscience reinforcement-learning/model-free

The midbrain dopamine neurons are hypothesized to provide a physiological correlate of the reward prediction error signal required by current models of reinforcement learning.

We examined the activity of single dopamine neurons during a task in which subjects learned by trial and error when to make an eye movement for a juice reward.

We found that these neurons encoded the difference between the current reward and a weighted average of previous rewards, a reward prediction error, but only for outcomes that were better than expected. Thus, the firing rate of midbrain dopamine neurons is quantitatively predicted by theoretical descriptions of the reward prediction error signal used in reinforcement learning models for circumstances in which this signal has a positive value.

We also found that the dopamine system continued to compute the reward prediction error even when the behavioral policy of the animal was only weakly influenced by this computation.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4499872/ The Power of Personality: The Comparative Validity of Personality Traits, Socioeconomic Status, and Cognitive Ability for Predicting Important Life Outcomes Brent W. Roberts, Nathan R. Kuncel, Rebecca Shiner, Avshalom Caspi, Lewis R. Goldberg 2007 2021-05-30 [("doi","10.1111/j.1745-6916.2007.00047.x")] psychology/personality

The ability of personality traits to predict important life outcomes has traditionally been questioned because of the putative small effects of personality. In this article, we compare the predictive validity of personality traits with that of socioeconomic status (SES) and cognitive ability to test the relative contribution of personality traits to predictions of 3 critical outcomes: mortality, divorce, and occupational attainment. Only evidence from prospective longitudinal studies was considered.

In addition, an attempt was made to limit the review to studies that controlled for important background factors.

Results showed that the magnitude of the effects of personality traits on mortality, divorce, and occupational attainment was indistinguishable from the effects of SES and cognitive ability on these outcomes.

These results demonstrate the influence of personality traits on important life outcomes, highlight the need to more routinely incorporate measures of personality into quality of life surveys, and encourage further research about the developmental origins of personality traits and the processes by which these traits influence diverse life outcomes.

--- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0040157 Citation Advantage of Open Access Articles Gunther Eysenbach 2006-03-15 2021-05-30 [("doi","10.1371/journal.pbio.0040157")] science

Open access (OA) to the research literature has the potential to accelerate recognition and dissemination of research findings, but its actual effects are controversial. This was a longitudinal bibliometric analysis of a cohort of OA and non-OA articles published between June 8, 2004, and December 20, 2004, in the same journal (PNAS: Proceedings of the National Academy of Sciences). Article characteristics were extracted, and citation data were compared between the two groups at 3 different points in time: at “quasi-baseline” (December 2004, 0–6 mo after publication), in April 2005 (4–10 mo after publication), and in October 2005 (10–16 mo after publication). Potentially confounding variables, including number of authors, authors’ lifetime publication count and impact, submission track, country of corresponding author, funding organization, and discipline, were adjusted for in logistic and linear multiple regression models. A total of 1,492 original research articles were analyzed: 212 (14.2% of all articles) were OA articles paid by the author, and 1,280 (85.8%) were non-OA articles. In April 2005 (mean 206 d after publication), 627 (49.0%) of the non-OA articles versus 78 (36.8%) of the OA articles were not cited (relative risk = 1.3 [95% Confidence Interval: 1.1–1.6]; p = 0.001). 6 mo later (mean 288 d after publication), non-OA articles were still more likely to be uncited (non-OA: 172 [13.6%], OA: 11 [5.2%]; relative risk = 2.6 [1.4–4.7]; p < 0.001). The average number of citations of OA articles was higher compared to non-OA articles (April 2005: 1.5 [SD = 2.5] versus 1.2 [SD = 2.0]; Z = 3.123; p = 0.002; October 2005: 6.4 [SD = 10.4] versus 4.5 [SD = 4.9]; Z = 4.058; p < 0.001). In a logistic regression model, controlling for potential confounders, OA articles compared to non-OA articles remained twice as likely to be cited (odds ratio = 2.1 [1.5–2.9]) in the first 4–10 mo after publication (April 2005), with the odds ratio increasing to 2.9 (1.5–5.5) 10–16 mo after publication (October 2005). Articles published as an immediate OA article on the journal site have higher impact than self-archived or otherwise openly accessible OA articles. We found strong evidence that, even in a journal that is widely available in research libraries, OA articles are more immediately recognized and cited by peers than non-OA articles published in the same journal. OA is likely to benefit science by accelerating dissemination and uptake of research findings.

A longitudinal bibliometric analysis of citations to papers published in the PNAS between June 8, 2004 and December 20, 2004 reveals that the open-access articles were more immediately recognized and cited by peers.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1856544/ Teaching surgical skills: what kind of practice makes perfect?: a randomized, controlled trial Carol-Anne E. Moulton, Adam Dubrowski, Helen Macrae, Brent Graham, Ethan Grober, Richard Reznick 2006 2021-05-30 [("doi","10.1097/01.sla.0000234808.85789.6a")] psychology/spaced-repetition

Objective: Surgical skills laboratories have become an important venue for early skill acquisition. The principles that govern training in this novel educational environment remain largely unknown; the commonest method of training, especially for continuing medical education (CME), is a single multihour event. This study addresses the impact of an alternative method, where learning is distributed over a number of training sessions. The acquisition and transfer of a new skill to a life-like model is assessed.

Method: Thirty-eight junior surgical residents, randomly assigned to either massed (1 day) or distributed (weekly) practice regimens, were taught a new skill (microvascular anastomosis). Each group spent the same amount of time in practice. Performance was assessed pretraining, immediately post-training, and 1 month post-training. The ultimate test of anastomotic skill was assessed with a transfer test to a live, anesthetized rat. Previously validated computer-based and expert-based outcome measures were used. In addition, clinically relevant outcomes were assessed.

Results: Both groups showed immediate improvement in performance, but the distributed group performed better on the retention test in most outcome measures (time, number of hand movements, and expert global ratings; all p-values <0.05). The distributed group also outperformed the massed group on the live rat anastomosis in all expert-based measures (global ratings, checklist score, final product analysis, competency for OR; all p-values <0.05).

Conclusion: Our current model of training surgical skills using short courses (for both CME and structured residency curricula) may be suboptimal. Residents retain and transfer skills better if taught in a distributed manner. Despite the greater logistical challenge, we need to restructure training schedules to allow for distributed practice.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1473027/ An alternative to null-hypothesis statistical-significance tests Peter R. Killeen 2005 2021-05-30 [("doi","10.1111/j.0956-7976.2005.01538.x")] statistics/bias statistics/probability

The statistic prep estimates the probability of replicating an effect. It captures traditional publication criteria for signal-to-noise ratio, while avoiding parametric inference and the resulting Bayesian dilemma.

In concert with effect size and replication intervals, prep provides all of the information now used in evaluating research, while avoiding many of the pitfalls of traditional statistical inference.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1389826/ Comparison of evidence on harms of medical interventions in randomized and nonrandomized studies Panagiotis N. Papanikolaou, Georgia D. Christidi, John Ioannidis 2006 2021-05-30 [("doi","10.1503/cmaj.050873")] statistics/bias statistics/causality

Background: Information on major harms of medical interventions comes primarily from epidemiologic studies performed after licensing and marketing. Comparison with data from large-scale randomized trials is occasionally feasible. We compared evidence from randomized trials with that from epidemiologic studies to determine whether they give different estimates of risk for important harms of medical interventions.

Method: We targeted well-defined, specific harms of various medical interventions for which data were already available from large-scale randomized trials (> 4000 subjects). Nonrandomized studies involving at least 4000 subjects addressing these same harms were retrieved through a search of MEDLINE. We compared the relative risks and absolute risk differences for specific harms in the randomized and nonrandomized studies.

Results: Eligible nonrandomized studies were found for 15 harms for which data were available from randomized trials addressing the same harms. Comparisons of relative risks between the study types were feasible for 13 of the 15 topics, and of absolute risk differences for 8 topics. The estimated increase in relative risk differed more than 2× between the randomized and nonrandomized studies for 7 (54%) of the 13 topics; the estimated increase in absolute risk differed more than 2× for 5 (62%) of the 8 topics. There was no clear predilection for randomized or nonrandomized studies to estimate greater relative risks, but usually (75% [6/8]) the randomized trials estimated larger absolute excess risks of harm than the nonrandomized studies did.

Interpretation: Nonrandomized studies are often conservative in estimating absolute risks of harms. It would be useful to compare and scrutinize the evidence on harms obtained from both randomized and nonrandomized studies.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1193697/ The sunk cost effect in pigeons and humans Anton D. Navarro, Edmund Fantino 2005 2021-05-30 [("doi","10.1901/jeab.2005.21-04")] psychology/cognitive-bias/sunk-cost

The sunk cost effect is the increased tendency to persist in an endeavor once an investment of money, effort, or time has been made. To date, humans are the only animal in which this effect has been observed unambiguously. We developed a behavior-analytic model of the sunk cost effect to explore the potential for this behavior in pigeons as well as in humans.

Each trial started out with a short expected ratio, but on some trials assumed a longer expected ratio partway through the trial. Subjects had the (usually preferable) option of “escaping” the trial if the longer expected ratio had come into effect in order to bring on a new trial that again had a short expected ratio. In Experiments 1 through 3, we manipulated two independent variables that we hypothesized would affect the pigeons’ ability to discriminate the increase in the expected ratio within a trial: (a) the presence or absence of stimuli that signal an increase in the expected ratio, and (b) the severity of the increase in the expected ratio.

We found that the pigeons were most likely to persist nonoptimally through the longer expected ratios when stimulus changes were absent and when the increase in the expected ratio was less severe. Experiment 4 employed a similar procedure with human subjects that manipulated only the severity of the increase in the expected ratio and found a result similar to that of the pigeon experiment.

In Experiment 5, we tested the hypothesis that a particular history of reinforcement would induce pigeons to persist through the longer expected ratios; the results suggested instead that the history of reinforcement caused the pigeons to persist less compared to pigeons that did not have that history.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1730293/pdf/v011p00332.pdf Deaths from international terrorism compared with road crash deaths in OECD countries N. Wilson, G. Thomson 2005 2021-05-30 [("doi","10.1136/ip.2005.008979")] crime/terrorism

Objective: To estimate the relative number of deaths in member countries of the Organization for Economic Co-operation and Development (OECD) from international terrorism and road crashes.

Method: Data on deaths from international terrorism (US State Department database) were collated (1994–2003) and compared to the road injury deaths (year 2000 and 2001 data) from the OECD International Road Transport Accident Database.

Results: In the 29 OECD countries for which comparable data were available, the annual average death rate from road injury was ~390× that from international terrorism. The ratio of annual road to international terrorism deaths (averaged over 10 years) was lowest for the United States at 142×. In 2001, road crash deaths in the US were equal to those from a September 11 attack every 26 days.

Conclusion: There is a large difference in the magnitude of these two causes of deaths from injury. Policy makers need to be aware of this when allocating resources to preventing these two avoidable causes of mortality.

--- https://arxiv.org/abs/physics/0502014 Scale Invariance in Global Terrorism Aaron Clauset, Maxwell Young 2005-02-03 2021-05-30 [("doi","10.48550/arXiv.0502014")] crime/terrorism

Traditional analyses of international terrorism have not sought to explain the emergence of rare but extremely severe events.

Using the tools of extremal statistics to analyze the set of terrorist attacks worldwide 1968–2004, as compiled by the National Memorial Institute for the Prevention of Terrorism (MIPT), we find that the relationship between the frequency and severity of terrorist attacks exhibits the “scale-free” property with an exponent of close to two.

This property is robust, even when we restrict our analysis to events from a single type of weapon or events within major industrialized nations.

We also find that the distribution of event sizes has changed very little over the past 37 years, suggesting that scale invariance is an inherent feature of global terrorism.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1370968/ Efficacy and safety of exogenous melatonin for secondary sleep disorders and sleep disorders accompanying sleep restriction: meta-analysis Nina Buscemi, Ben Vandermeer, Nicola Hooton, Rena Pandya, Lisa Tjosvold, Lisa Hartling, Sunita Vohra, Terry P. Klassen, Glen Baker 2006 2021-05-30 [("doi","10.1136/bmj.38731.532766.F6")] melatonin

Objective: To conduct a systematic review of the efficacy and safety of exogenous melatonin in managing secondary sleep disorders and sleep disorders accompanying sleep restriction, such as jet lag and shiftwork disorder.

Data Sources: 13 electronic databases and reference lists of relevant reviews and included studies; Associated Professional Sleep Society abstracts (1999–2003).

Study Selection: The efficacy review included randomized controlled trials; the safety review included randomized and non-randomized controlled trials.

Quality Assessment: Randomized controlled trials were assessed by using the Jadad Scale and criteria by Schulz et al, and non-randomized controlled trials by the Downs and Black checklist.

Data Extraction and Synthesis: One reviewer extracted data and another reviewer verified the data extracted. The inverse variance method was used to weight studies and the random effects model was used to analyse data.

Main Results: Six randomized controlled trials with 97 participants showed no evidence that melatonin had an effect on sleep onset latency in people with secondary sleep disorders (weighted mean difference −13.2 (95% confidence interval -27.3 to 0.9) min). Nine randomized controlled trials with 427 participants showed no evidence that melatonin had an effect on sleep onset latency in people who had sleep disorders accompanying sleep restriction (−1.0 (−2.3 to 0.3) min). 17 randomized controlled trials with 651 participants showed no evidence of adverse effects of melatonin with short term use (three months or less).

Conclusion: There is no evidence that melatonin is effective in treating secondary sleep disorders or sleep disorders accompanying sleep restriction, such as jet lag and shiftwork disorder. There is evidence that melatonin is safe with short term use.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2755205/ Vitamin and mineral use and risk of prostate cancer: the case-control surveillance study Yuqing Zhang, Patricia Coogan, Julie R. Palmer, Brian L. Strom, Lynn Rosenberg 2009 2021-05-30 [("doi","10.1007/s10552-008-9282-y")] biology

Background: Many studies have evaluated the association between vitamin and mineral supplement use and the risk of prostate cancer, with inconclusive results.

Method: The authors examined the relation of use of multivitamins as well as several single vitamin and mineral supplements to the risk of prostate cancer risk among 1,706 prostate cancer cases and 2,404 matched controls using data from the hospital-based case-control surveillance study conducted in the United States. Odds ratios (OR) and 95% confidence intervals (CI) for risk of prostate cancer were estimated using conditional logistic regression model.

Results: For use of multivitamins that did not contain zinc, the multivariable odds ratios of prostate cancer were 0.6 for 1–4 years, 0.8 for 5–9 years, and 1.2 for 10 years or more, respectively (p for trend = 0.70). Men who used zinc for ten years or more, either in a multivitamin or as a supplement, had an ~two-fold (OR = 1.9, 95% CI: 1.0, 3.6) increased risk of prostate cancer. Vitamin E, beta-carotene, folate, and selenium use were not statistically-significantly associated with increased risk of prostate cancer.

Conclusion: The finding that long-term zinc intake from multivitamins or single supplements was associated with a doubling in risk of prostate cancer adds to the growing evidence for an unfavorable effect of zinc on prostate cancer carcinogenesis.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2768270/ Association of substance use disorders with childhood trauma but not African genetic heritage in an African American cohort Francesca Ducci, Alec Roy, Pei-Hong Shen, Qiaoping Yuan, Nicole P. Yuan, Colin A. Hodgkinson, Lynn R. Goldman, David Goldman 2009 2021-05-30 [("doi","10.1176/appi.ajp.2009.08071068")] genetics/heritable psychiatry/alcoholism

Objective: Genetic variation influences differential vulnerability to addiction within populations. However, it remains unclear whether differences in frequencies of vulnerability alleles contribute to disparities between populations and to what extent ancestry correlates with differential exposure to environmental risk factors, including poverty and trauma.

Method: The authors used 186 ancestry-informative markers to measure African ancestry in 407 addicts and 457 comparison subjects self-identified as African Americans. The reference group was 1,051 individuals from the Human Genome Diversity Cell Line Panel, which includes 51 diverse populations representing most worldwide genetic diversity.

Results: African Americans varied in degrees of African, European, Middle Eastern, and Central Asian genetic heritage. The overall level of African ancestry was actually smaller among cocaine, opiate, and alcohol addicts (proportion=0.76–0.78) than nonaddicted African American comparison subjects (proportion=0.81). African ancestry was associated with living in impoverished neighborhoods, a factor previously associated with risk. There was no association between African ancestry and exposure to childhood abuse or neglect, a factor that strongly predicted all types of addictions.

Conclusion: These results suggest that African genetic heritage does not increase the likelihood of genetic risk for addictions. They highlight the complex interrelation between genetic ancestry and social, economic, and environmental conditions and the strong relation of those factors to addiction. Studies of epidemiological samples characterized for genetic ancestry and social, psychological, demographic, economic, cultural, and historical factors are needed to better disentangle the effects of genetic and environmental factors underlying interpopulation differences in vulnerability to addiction and other health disparities.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1000075 Demographic History of European Populations of Arabidopsis thaliana Olivier François, Michael G. B. Blum, Mattias Jakobsson, Noah A. Rosenberg 2008-04-17 2021-05-31 [("doi","10.1371/journal.pgen.1000075")] genetics/sequencing

The model plant species Arabidopsis thaliana is successful at colonizing land that has recently undergone human-mediated disturbance.

To investigate the prehistoric spread of A. thaliana, we applied approximate Bayesian computation and explicit spatial modeling to 76 European accessions sequenced at 876 nuclear loci.

We find evidence that a major migration wave occurred from east to west, affecting most of the sampled individuals. The longitudinal gradient appears to result from the plant having spread in Europe from the east ~10,000 years ago, with a rate of westward spread of ~0.9 km/year.

This wave-of-advance model is consistent with a natural colonization from an eastern glacial refugium that overwhelmed ancient western lineages. However, the speed and time frame of the model also suggest that the migration of A. thaliana into Europe may have accompanied the spread of agriculture during the Neolithic transition.

Author Summary: The demographic forces that have shaped the pattern of genetic variability in the plant species Arabidopsis thaliana provide an important backdrop for the use of this model organism in understanding the genetic determinants of plant natural variation. We investigated the demographic history of A. thaliana using novel population-genetic tools applied to a combination of molecular and geographic data.

We infer that A. thaliana entered Europe from the east and spread westward at a rate of ~0.9 kilometers per year, and that its population size began increasing around 10,000 years ago. The “wave-of-advance” model suggested by these results is potentially consistent with the pattern expected if the species colonized Europe as the ice retreated at the end of the most recent glaciation.

Alternatively, it is also compatible with the possibility that A. thaliana—a weedy species—may have spread into Europe with the diffusion of agriculture, providing an example of the phenomenon of “ecological imperialism” described by A. Crosby. In this framework, just as weeds from Europe invaded temperate regions worldwide during European human colonization, weeds originating from the source region of farming invaded Europe as a result of the disturbance caused by the spread of agriculture.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3073397/ Ancestry informative marker sets for determining continental origin and admixture proportions in common populations in America Roman Kosoy, Rami Nassir, Chao Tian, Phoebe A. White, Lesley M. Butler, Gabriel Silva, Rick Kittles, Marta E. Alarcon-Riquelme, Peter K. Gregersen, John W. Belmont, Francisco M. De La Vega, Michael F. Seldin 2009 2021-05-31 [("doi","10.1002/humu.20822")] genetics/sequencing

To provide a resource for assessing continental ancestry in a wide variety of genetic studies, we identified, validated, and characterized a set of 128 ancestry informative markers (AIMs). The markers were chosen for informativeness, genome-wide distribution, and genotype reproducibility on two platforms (TaqMan assays and Illumina arrays).

We analyzed genotyping data from 825 subjects with diverse ancestry, including European, East Asian, Amerindian, African, South Asian, Mexican, and Puerto Rican.

A comprehensive set of 128 AIMs and subsets as small as 24 AIMs are shown to be useful tools for ascertaining the origin of subjects from particular continents, and to correct for population stratification in admixed population sample sets.

Our findings provide general guidelines for the application of specific AIM subsets as a resource for wide application. We conclude that investigators can use TaqMan assays for the selected AIMs as a simple and cost efficient tool to control for differences in continental ancestry when conducting association studies in ethnically diverse populations.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2775227/ Why is intelligence correlated with semen quality?: Biochemical pathways common to sperm and neuron function and their vulnerability to pleiotropic mutations Ar, Pierce, Geoffrey Miller, Rosalind Arden, Linda S. Gottfredson 2009 2021-05-31 [("doi","10.4161/cib.2.5.8716")] iq

We recently found positive correlations between human general intelligence and 3 key indices of semen quality, and hypothesized that these correlations arise through a phenotype-wide ‘general fitness factor’ reflecting overall mutation load.

In this addendum, we consider some of the biochemical pathways that may act as targets for pleiotropic mutations that disrupt both neuron function and sperm function in parallel.

We focus especially on the inter-related roles of polyunsaturated fatty acids, exocytosis, and receptor signaling.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2683339/ When does age-related cognitive decline begin? Timothy A. Salthouse 2009 2021-05-31 [("doi","10.1016/j.neurobiolaging.2008.09.023")] iq

Cross-sectional comparisons have consistently revealed that increased age is associated with lower levels of cognitive performance, even in the range 18–60 years of age. However, the validity of cross-sectional comparisons of cognitive functioning in young and middle-aged adults has been questioned because of the discrepant age trends found in longitudinal and cross-sectional analyses.

The results of the current project suggest that a major factor contributing to the discrepancy is the masking of age-related declines in longitudinal comparisons by large positive effects associated with prior test experience. Results from 3 methods of estimating retest effects in this project, together with results from studies comparing non-human animals raised in constant environments and from studies examining neurobiological variables not susceptible to retest effects, converge on a conclusion that some aspects of age-related cognitive decline begin in healthy educated adults when they are in their 20s and 30s.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2884197/ Modafinil and memory: effects of modafinil on Morris water maze learning and Pavlovian fear conditioning Tristan Shuman, Suzanne C. Wood, Stephan G. Anagnostaras 2009 2021-05-31 [("doi","10.1037/a0014366")] modafinil

Modafinil has been shown to promote wakefulness and some studies suggest the drug can improve cognitive function. Because of many similarities, the mechanism of action may be comparable to classical psychostimulants, although the exact mechanisms of modafinil’s actions in wakefulness and cognitive enhancement are unknown.

The current study aims to further examine the effects of modafinil as a cognitive enhancer on hippocampus-dependent memory in mice. A high dose of modafinil (75 mg/kg ip) given before training improved acquisition on a Morris water maze. When given only before testing, modafinil did not affect water maze performance. We also examined modafinil (0.075 to 75 mg/kg) on Pavlovian fear conditioning. A low dose of pretraining modafinil (0.75 mg/kg) enhanced memory of contextual fear conditioning (tested off-drug 1 week later) whereas a high dose (75 mg/kg) disrupted memory. Pretraining modafinil did not affect cued conditioning at any dose tested, and immediate posttraining modafinil had no effect on either cued or contextual fear.

These results suggest that modafinil’s effects of memory are more selective than amphetamine or cocaine and specific to hippocampus-dependent memory.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2633138/ Nicotine induces resistance to chemotherapy by modulating mitochondrial signaling in lung cancer Jingmei Zhang, Opal Kamdar, Wei Le, Glenn D. Rosen, Daya Upadhyay 2009 2021-05-31 [("doi","10.1165/rcmb.2007-0277OC")] nicotine

Continued smoking causes tumor progression and resistance to therapy in lung cancer. Carcinogens possess the ability to block apoptosis, and thus may induce development of cancers and resistance to therapy. Tobacco carcinogens have been studied widely; however, little is known about the agents that inhibit apoptosis, such as nicotine. We determine whether mitochondrial signaling mediates antiapoptotic effects of nicotine in lung cancer.

A549 cells were exposed to nicotine (1 muM) followed by cisplatin (35 muM) plus etoposide (20 muM) for 24 hours. We found that nicotine prevented chemotherapy-induced apoptosis, improved cell survival, and caused modest increases in DNA synthesis. Inhibition of mitogen-activated protein kinase (MAPK) and Akt prevented the antiapoptotic effects of nicotine and decreased chemotherapy-induced apoptosis. Small interfering RNA MAPK kinase-1 blocked antiapoptotic effects of nicotine, whereas small interfering RNA MAPK kinase-2 blocked chemotherapy-induced apoptosis. Nicotine prevented chemotherapy-induced reduction in mitochondrial membrane potential and caspase-9 activation. Antiapoptotic effects of nicotine were blocked by mitochondrial anion channel inhibitor, 4,4’diisothiocyanatostilbene-2,2’disulfonic acid. Chemotherapy enhanced translocation of proapoptotic Bax to the mitochondria, whereas nicotine blocked these effects. Nicotine up-regulated Akt-mediated antiapoptotic X-linked inhibitor of apoptosis protein and phosphorylated proapoptotic Bcl2-antagonist of cell death.

The A549-rho0 cells, which lack mitochondrial DNA, demonstrated partial resistance to chemotherapy-induced apoptosis, but blocked the antiapoptotic effects of nicotine.

Accordingly, we provide evidence that nicotine modulates mitochondrial signaling and inhibits chemotherapy-induced apoptosis in lung cancer. The mitochondrial regulation of nicotine imposes an important mechanism that can critically impair the treatment of lung cancer, because many cancer-therapeutic agents induce apoptosis via the mitochondrial death pathway.

Strategies aimed at understanding nicotine-mediated signaling may facilitate the development of improved therapies in lung cancer.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2775887/pdf/12263_2009_Article_135.pdf Flavonoids and cognitive function: a review of human randomized controlled trial studies and recommendations for future studies Anna L. Macready, Orla B. Kennedy, Judi A. Ellis, Claire M. Williams, Jeremy P. E. Spencer, Laurie T. Butler 2009 2021-05-31 [("doi","10.1007/s12263-009-0135-4")] nootropic

Evidence in support of the neuroprotective effects of flavonoids has increased in recent years, although to date much of this evidence has emerged from animal rather than human studies. Nonetheless, with a view to making recommendations for future good practice, we review 15 existing human dietary intervention studies that have examined the effects of particular types of flavonoid on cognitive performance.

The studies employed a total of 55 different cognitive tests covering a broad range of cognitive domains. Most studies incorporated at least one measure of executive function/working memory, with 9 reporting improvements in performance as a function of flavonoid supplementation compared to a control group. However, some domains were overlooked completely (eg. implicit memory, prospective memory), and for the most part there was little consistency in terms of the particular cognitive tests used making across study comparisons difficult. Furthermore, there was some confusion concerning what aspects of cognitive function particular tests were actually measuring.

Overall, while initial results are encouraging, future studies need to pay careful attention when selecting cognitive measures, especially in terms of ensuring that tasks are actually sensitive enough to detect treatment effects.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2738587/ Caffeine withdrawal, acute effects, tolerance, and absence of net beneficial effects of chronic administration: cerebral blood flow velocity, quantitative EEG, and subjective effects Stacey C. Sigmon, Ronald I. Herning, Warren Better, Jean L. Cadet, Roland R. Griffiths 2009 2021-05-31 [("doi","10.1007/s00213-009-1489-4")] nootropic/caffeine psychology/neuroscience

Rationale: Although the subjective effects of caffeine abstinence, acute and chronic administration, and tolerance are well described, the corresponding neurophysiological effects are not.

Objectives: Caffeine withdrawal, acute caffeine effects, caffeine tolerance, and net beneficial effects of chronic caffeine administration were investigated using cerebral blood flow velocity, quantitative electroencephalography (EEG), and subjective effects.

Method: Sixteen regular caffeine users participated in this double-blind, within-subject study during which they received acute caffeine and placebo challenges (1) while maintained on 400 mg caffeine daily for > or =14 days and (2) while maintained on placebo for > or =14 days. Blood flow velocity was determined for the middle (MCA) and anterior (ACA) cerebral arteries using pulsed transcranial Doppler sonography. EEG was recorded from 16 scalp sites. Subjective effects were assessed with questionnaires.

Results: Acute caffeine abstinence (evaluated 24 h after placebo substitution) increased mean, systolic, and diastolic velocity in the MCA and ACA and decreased pulsatility index in the MCA. Acute caffeine abstinence increased EEG theta and decreased beta 2 power. Acute caffeine abstinence also increased measures of Tired, Fatigue, Sluggish, and Weary and decreased ratings of Energetic, Friendly, Lively, and Vigor. Acute caffeine effects were demonstrated across a wide range of measures, including cerebral blood flow, EEG, and subjective effects. Tolerance and “complete” tolerance were observed on subjective but not physiological measures. Chronic caffeine effects were demonstrated only on the measure of EEG beta 2 power.

Conclusion: Acute caffeine abstinence and administration produced changes in cerebral blood flow velocity, EEG, and subjective effects. Tolerance to subjective but not physiological measures was demonstrated. There was almost no evidence for net effects of chronic caffeine administration on these measures. Overall, these findings provide the most rigorous demonstration to date of physiological effects of caffeine withdrawal.

--- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1000011 When Learning and Remembering Compete: A Functional MRI Study Willem Huijbers, Cyriel M. Pennartz, Roberto Cabeza, Sander M. Daselaar 2008-11-26 2021-05-31 [("doi","10.1371/journal.pbio.1000011")] psychology/neuroscience

Recent functional neuroimaging evidence suggests a bottleneck between learning new information and remembering old information. In two behavioral experiments and one functional MRI (fMRI) experiment, we tested the hypothesis that learning and remembering compete when both processes happen within a brief period of time. In the first behavioral experiment, participants intentionally remembered old words displayed in the foreground, while incidentally learning new scenes displayed in the background. In line with a memory competition, we found that remembering old information was associated with impaired learning of new information. We replicated this finding in a subsequent fMRI experiment, which showed that this behavioral effect was coupled with a suppression of learning-related activity in visual and medial temporal areas. Moreover, the fMRI experiment provided evidence that left mid-ventrolateral prefrontal cortex is involved in resolving the memory competition, possibly by facilitating rapid switching between learning and remembering. Critically, a follow-up behavioral experiment in which the background scenes were replaced with a visual target detection task provided indications that the competition between learning and remembering was not merely due to attention. This study not only provides novel insight into our capacity to learn and remember, but also clarifies the neural mechanisms underlying flexible behavior.

Author Summary: This study provides clear evidence for a bottleneck in our memory system between learning new and remembering old information. The ability to continuously learn and remember is usually taken for granted. Virtually all interactive situations we encounter require concurrent learning and remembering. For example, normal social communication requires that we process the new information that another person is providing. While listening, we are usually already retrieving information in preparation of an appropriate reply. Other examples include driving through an unfamiliar city while interpreting familiar traffic signs, or encountering novel products during shopping while remembering what we need. Although these examples clearly illustrate the importance of the simultaneous occurrence of learning and remembering, this study shows that remembering and learning compete for resources when both processes happen within a brief period. The study also examined the neural consequences of the competition between learning and remembering using functional MRI (fMRI). In line with the behavioral competition, the neuroimaging results showed a clear suppression of learning-related brain activity as a result of concurrent remembering. Finally, the study provides evidence that a specific region in the prefrontal cortex can resolve the bottleneck, possibly by allowing rapid switching between learning and remembering

When we try to learn and remember at the same time, a bottleneck occurs within our memory system with both behavioral and neural costs.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2819325/ Cortical firing and sleep homeostasis Vladyslav V. Vyazovskiy, Umberto Olcese, Yaniv M. Lazimy, Ugo Faraguna, Steve K. Esser, Justin C. Williams, Chiara Cirelli, Giulio Tononi 2009 2021-05-31 [("doi","10.1016/j.neuron.2009.08.024")] psychology/neuroscience zeo

The need to sleep grows with the duration of wakefulness and dissipates with time spent asleep, a process called sleep homeostasis.

What are the consequences of staying awake on brain cells, and why is sleep needed? Surprisingly, we do not know whether the firing of cortical neurons is affected by how long an animal has been awake or asleep.

Here, we found that after sustained wakefulness cortical neurons fire at higher frequencies in all behavioral states. During early NREM sleep after sustained wakefulness, periods of population activity (ON) are short, frequent, and associated with synchronous firing, while periods of neuronal silence are long and frequent.

After sustained sleep, firing rates and synchrony decrease, while the duration of ON periods increases. Changes in firing patterns in NREM sleep correlate with changes in slow-wave activity, a marker of sleep homeostasis.

Thus, the systematic increase of firing during wakefulness is counterbalanced by staying asleep.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2776484/ The human brain in numbers: a linearly scaled-up primate brain Suzana Herculano-Houzel 2009 2021-06-01 [("doi","10.3389/neuro.09.031.2009")] psychology/neuroscience

The human brain has often been viewed as outstanding among mammalian brains: the most cognitively able, the largest-than-expected from body size, endowed with an overdeveloped cerebral cortex that represents over 80% of brain mass, and purportedly containing 100 billion neurons and 10× more glial cells. Such uniqueness was seemingly necessary to justify the superior cognitive abilities of humans over larger-brained mammals such as elephants and whales.

However, our recent studies using a novel method to determine the cellular composition of the brain of humans and other primates as well as of rodents and insectivores show that, since different cellular scaling rules apply to the brains within these orders, brain size can no longer be considered a proxy for the number of neurons in the brain. These studies also showed that the human brain is not exceptional in its cellular composition, as it was found to contain as many neuronal and non-neuronal cells as would be expected of a primate brain of its size. Additionally, the so-called overdeveloped human cerebral cortex holds only 19% of all brain neurons, a fraction that is similar to that found in other mammals.

In what regards absolute numbers of neurons, however, the human brain does have two advantages compared to other mammalian brains: compared to rodents, and probably to whales and elephants as well, it is built according to the very economical, space-saving scaling rules that apply to other primates; and, among economically built primate brains, it is the largest, hence containing the most neurons.

These findings argue in favor of a view of cognitive abilities that is centered on absolute numbers of neurons, rather than on body size or encephalization, and call for a re-examination of several concepts related to the exceptionality of the human brain.

[This suggests there is no major architectural innovation in human brains which is responsible for the extraordinary success of Homo sapiens, through an argument by absence: if there were major neurological architectural changes, you would expect to either observe them directly, or observe the brain economizing on (highly-expensive) neurons after having found some important trick or architectural innovation (eg. bird brains appear to economize on neurons by instead making them really dense and fast-connected). While the fact that we seem to just find a standard-looking-but-big primate brain suggests more like "the important thing was finding a niche which rewarded the smaller intelligence gains from scaling a primate brain even further, eventually unlocking novel benefits of intelligence like hunting tools & clothing & fire." Further, the superior slope of primate brains vs non-bird-brains suggests that there may well have been "innovations" to our brains, but they are shared with the other primates and so not uniquely human.]

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2715914/ Widespread changes in synaptic markers as a function of sleep and wakefulness in Drosophila Giorgio F. Gilestro, Giulio Tononi, Chiara Cirelli 2009 2021-06-01 [("doi","10.1126/science.1166673")] psychology/neuroscience zeo

Sleep is universal, strictly regulated, and necessary for cognition. Why this is so remains a mystery, although recent work suggests that sleep, memory, and plasticity are linked. However, little is known about how wakefulness and sleep affect synapses.

Using Western blots and confocal microscopy in Drosophila, we found that:

protein levels of key components of central synapses were high after waking and low after sleep. These changes were related to behavioral state rather than time of day and occurred in all major areas of the Drosophila brain. The decrease of synaptic markers during sleep was progressive, and sleep was necessary for their decline.

Thus, sleep may be involved in maintaining synaptic homeostasis altered by waking activities.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2747780/ The Effect of Online Chapter Quizzes on Exam Performance in an Undergraduate Social Psychology Course Bethany C. Johnson, Marc T. Kiviniemi 2009 2021-06-01 [("doi","10.1080/00986280802528972")] psychology/spaced-repetition

Assigned textbook readings are a common requirement in undergraduate courses, but students often do not complete reading assignments or do not do so until immediately before an exam. This may have detrimental effects on learning and course performance. Regularly scheduled quizzes on reading material may increase completion of reading assignments and therefore course performance.

This study examined the effectiveness of compulsory, mastery-based, weekly reading quizzes as a means of improving exam and course performance.

Completion of reading quizzes was related to both better exam and course performance.

The discussion includes recommendations for the use of quizzes in undergraduate courses.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3650827/ Reconsolidation: maintaining memory relevance Jonathan L. C. Lee 2009 2021-06-01 [("doi","10.1016/j.tins.2009.05.002")] psychology/spaced-repetition

The retrieval of a memory places it into a plastic state, the result of which is that the memory can be disrupted or even enhanced by experimental treatment. This phenomenon has been conceptualised within a framework of memories being reactivated and then reconsolidated in repeated rounds of cellular processing.

The reconsolidation phase has been seized upon as crucial for the understanding of memory stability and, more recently, as a potential therapeutic target in the treatment of disorders such as post-traumatic stress and drug addiction. However, little is known about the reactivation process, or what might be the adaptive function of retrieval-induced plasticity. Reconsolidation has long been proposed to mediate memory updating, but only recently has this hypothesis been supported experimentally.

Here, the adaptive function of memory reconsolidation is explored in more detail, with a strong emphasis on its role in updating memories to maintain their relevance.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0005738 How Many Scientists Fabricate and Falsify Research? A Systematic Review and Meta-Analysis of Survey Data Daniele Fanelli 2009-04-19 2021-06-01 [("doi","10.1371/journal.pone.0005738")] statistics/bias

The frequency with which scientists fabricate and falsify data, or commit other forms of scientific misconduct is a matter of controversy. Many surveys have asked scientists directly whether they have committed or know of a colleague who committed research misconduct, but their results appeared difficult to compare and synthesize. This is the first meta-analysis of these surveys.

To standardize outcomes, the number of respondents who recalled at least one incident of misconduct was calculated for each question, and the analysis was limited to behaviors that distort scientific knowledge: fabrication, falsification, “cooking” of data, etc… Survey questions on plagiarism and other forms of professional misconduct were excluded. The final sample consisted of 21 surveys that were included in the systematic review, and 18 in the meta-analysis.

A pooled weighted average of 1.97% (n = 7, 95%CI: 0.86–4.45) of scientists admitted to have fabricated, falsified or modified data or results at least once—a serious form of misconduct by any standard—and up to 33.7% admitted other questionable research practices. In surveys asking about the behavior of colleagues, admission rates were 14.12% (n = 12, 95% CI: 9.91–19.72) for falsification, and up to 72% for other questionable research practices. Meta-regression showed that self reports surveys, surveys using the words “falsification” or “fabrication”, and mailed surveys yielded lower percentages of misconduct. When these factors were controlled for, misconduct was reported more frequently by medical/pharmacological researchers than others.

Considering that these surveys ask sensitive questions and have other limitations, it appears likely that this is a conservative estimate of the true prevalence of scientific misconduct.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0005996 Large-Scale Assessment of the Effect of Popularity on the Reliability of Research Thomas Pfeiffer, Robert Hoffmann 2009-05-21 2021-06-01 [("doi","10.1371/journal.pone.0005996")] statistics/bias

Based on theoretical reasoning, it has been suggested that the reliability of findings published in the scientific literature decreases with the popularity of a research field.

Here we provide empirical support for this prediction. We evaluate published statements on protein interactions with data from high-throughput experiments. We find evidence for two distinctive effects. First, with increasing popularity of the interaction partners, individual statements in the literature become more erroneous. Second, the overall evidence on an interaction becomes increasingly distorted by multiple independent testing.

We therefore argue that for increasing the reliability of research, it is essential to assess the negative effects of popularity and develop approaches to diminish these effects.

--- https://arxiv.org/abs/0907.5598 Convergence of Expected Utility for Universal AI Peter de Blanc 2009-07-31 2021-06-01 [("doi","10.48550/arXiv.0907.5598")] statistics/decision

We consider a sequence of repeated interactions between an agent and an environment. Uncertainty about the environment is captured by a probability distribution over a space of hypotheses, which includes all computable functions.

Given an utility function, we can evaluate the expected utility of any computational policy for interaction with the environment.

After making some plausible assumptions (and maybe one not-so-plausible assumption), we show that if the utility function is unbounded, then the expected utility of any policy is undefined.

--- https://arxiv.org/abs/1512.03385#microsoft Deep Residual Learning for Image Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun 2015-12-10 2021-06-01 [("doi","10.48550/arXiv.1512.03385")] ai/nn/cnn ai/nn/sparsity

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1,000 layers.

The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

--- https://arxiv.org/abs/1511.06807#google Adding Gradient Noise Improves Learning for Very Deep Networks Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Łukasz Kaiser, Karol Kurach, James Martens 2015-11-21 2021-06-01 [("doi","10.48550/arXiv.1511.06807")] ai/nn/cnn ai/nn/fully-connected

Deep feedforward and recurrent networks have achieved impressive results in many perception and language processing applications. This success is partially attributed to architectural innovations such as convolutional and long short-term memory networks. The main motivation for these architectural innovations is that they capture better domain knowledge, and importantly are easier to optimize than more basic architectures.

Recently, more complex architectures such as Neural Turing Machines and Memory Networks have been proposed for tasks including question answering and general computation, creating a new set of optimization challenges. In this paper, we discuss a low-overhead and easy-to-implement technique of adding gradient noise which we find to be surprisingly effective when training these very deep architectures. The technique not only helps to avoid overfitting, but also can result in lower training loss. This method alone allows a fully-connected 20-layer deep network to be trained with standard gradient descent, even starting from a poor initialization. We see consistent improvements for many complex models, including a 72% relative reduction in error rate over a carefully-tuned baseline on a challenging question-answering task, and a doubling of the number of accurate binary multiplication models learned across 7,000 random restarts. We encourage further application of this technique to additional complex modern architectures.

--- https://arxiv.org/abs/1511.06856 Data-dependent Initializations of Convolutional Neural Networks Philipp Krähenbühl, Carl Doersch, Jeff Donahue, Trevor Darrell 2015-11-21 2021-06-01 [("doi","10.48550/arXiv.1511.06856")] ai/nn

Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable. Despite this, few researchers dare to train their models from scratch. Most work builds on one of a handful of ImageNet pre-trained models, and fine-tunes or adapts these for specific tasks. This is in large part due to the difficulty of properly initializing these networks from scratch. A small miscalibration of the initial weights leads to vanishing or exploding gradients, as well as poor convergence properties.

In this work, we present a fast and simple data-dependent initialization procedure that sets the weights of a network such that all units in the network train at roughly the same rate, avoiding vanishing or exploding gradients. Our initialization matches the current state-of-the-art unsupervised or self-supervised pre-training methods on standard computer vision tasks, such as image classification and object detection, while being roughly 3 orders of magnitude faster.

When combined with pre-training methods, our initialization outperforms prior work, narrowing the gap between supervised and unsupervised pre-training.

--- https://arxiv.org/abs/1504.08083#microsoft Fast R-CNN Ross Girshick 2015-04-30 2021-06-01 [("doi","10.48550/arXiv.1504.08083")] ai/nn/cnn

This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy.

Fast R-CNN trains the very deep VGG-16 network 9× faster than R-CNN, is 213× faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG-16 3× faster, tests 10× faster, and is more accurate.

Fast R-CNN is implemented in Python and C++ (using Caffe) and is available under the open-source MIT License at Github.

--- https://arxiv.org/abs/1502.05698 Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Arm Holdings, Joulin, Tomas Mikolov 2015-02-19 2021-06-02 [("doi","10.48550/arXiv.1502.05698")] ai/nn

One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent.

To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction, and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human.

We believe many existing learning systems cannot currently solve them, and hence our aim is to classify these tasks into skill sets, so that researchers can identify (and then rectify) the failings of their systems.

We also extend and improve the recently introduced Memory Networks model, and show it is able to solve some, but not all, of the tasks.

--- https://arxiv.org/abs/1511.01432 Semi-supervised Sequence Learning Andrew M. Dai, Quoc V. Le 2015-11-04 2021-06-02 [("doi","10.48550/arXiv.1511.01432")] ai/nn/rnn ai/nn/sampling ai/nn/vae

We present two approaches that use unlabeled data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a conventional language model in natural language processing. The second approach is to use a sequence autoencoder, which reads the input sequence into a vector and predicts the input sequence again. These two algorithms can be used as a “pretraining” step for a later supervised sequence learning algorithm. In other words, the parameters obtained from the unsupervised step can be used as a starting point for other supervised training models.

In our experiments, we find that long short-term memory recurrent networks after being pretrained with the two approaches are more stable and generalize better. With pretraining, we are able to train long short-term memory recurrent networks up to a few hundred timesteps, thereby achieving strong performance in many text classification tasks, such as IMDB, DBpedia, and 20 Newsgroups.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0142169 Working Memory, Reasoning, and Task Switching Training: Transfer Effects, Limitations, and Great Expectations? Pauline L. Baniqued, Courtney M. Allen, Michael B. Kranz, Kathryn Johnson, Aldis Sipolins, Charles Dickens, Nathan Ward, Alexandra Geyer, Arthur F. Kramer 2015-10-19 2021-06-02 [("doi","10.1371/journal.pone.0142169")] dual-n-back

Although some studies have shown that cognitive training can produce improvements to untrained cognitive domains (far transfer), many others fail to show these effects, especially when it comes to improving fluid intelligence. The current study was designed to overcome several limitations of previous training studies by incorporating training expectancy assessments, an active control group, and “Mind Frontiers”, a video game-based mobile program comprised of 6 adaptive, cognitively demanding training tasks that have been found to lead to increased scores in fluid intelligence (Gf) tests.

We hypothesize that such integrated training may lead to broad improvements in cognitive abilities by targeting aspects of working memory, executive function, reasoning, and problem-solving. Ninety participants completed 20 hour-and-a-half-long training sessions over 4 to 5 weeks, 45 of whom played Mind Frontiers and 45 of whom completed visual search and change detection tasks (active control).

After training, the Mind Frontiers group improved in working memory n-back tests, a composite measure of perceptual speed, and a composite measure of reaction time in reasoning tests. No training-related improvements were found in reasoning accuracy or other working memory tests, nor in composite measures of episodic memory, selective attention, divided attention, and multi-tasking. Perceived self-improvement in the tested abilities did not differ between groups.

A general expectancy difference in problem-solving was observed between groups, but this perceived benefit did not correlate with training-related improvement. In summary, although these findings provide modest evidence regarding the efficacy of an integrated cognitive training program, more research is needed to determine the utility of Mind Frontiers as a cognitive training tool.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0138734 Transfer after Working Memory Updating Training Otto Waris, Anna Soveri, Matti Laine 2015-09-01 2021-06-02 [("doi","10.1371/journal.pone.0138734")] dual-n-back

During the past decade, working memory training has attracted much interest. However, the training outcomes have varied between studies and methodological problems have hampered the interpretation of results.

The current study examined transfer after working memory updating training by employing an extensive battery of pre-post cognitive measures with a focus on near transfer. Thirty-one healthy Finnish young adults were randomized into either a working memory training group or an active control group. The working memory training group practiced with 3 working memory tasks, while the control group trained with 3 commercial computer games with a low working memory load. The participants trained thrice a week for 5 weeks, with one training session lasting about 45 minutes.

Compared to the control group, the working memory training group showed strongest transfer to an n-back task, followed by working memory updating, which in turn was followed by active working memory capacity.

Our results support the view that working memory training produces near transfer effects, and that the degree of transfer depends on the cognitive overlap between the training and transfer measures.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0121993 Intentional Weight Loss and All-Cause Mortality: A Meta-Analysis of Randomized Clinical Trials Stephen B. Kritchevsky, Kristen M. Beavers, Michael E. Miller, M. Kyla Shea, Denise K. Houston, Dalane W. Kitzman, Barbara J. Nicklas 2015-02-10 2021-06-02 [("doi","10.1371/journal.pone.0121993")] exercise

Background: Obesity is associated with increased mortality, and weight loss trials show rapid improvement in many mortality risk factors. Yet, observational studies typically associate weight loss with higher mortality risk. The purpose of this meta-analysis of randomized controlled trials (RCTs) of weight loss was to clarify the effects of intentional weight loss on mortality.

Method: 2,484 abstracts were identified and reviewed in PUBMED, yielding 15 RCTs reporting (1) randomization to weight loss or non-weight loss arms, (2) duration of ≥18 months, and (3) deaths by intervention arm. Weight loss interventions were all lifestyle-based. Relative risks (RR) and 95% confidence intervals (95% CI) were estimated for each trial. For trials reporting at least one death (n = 12), a summary estimate was calculated using the Mantel-Haenszel method. Sensitivity analysis using sparse data methods included remaining trials.

Results: Trials enrolled 17,186 participants (53% female, mean age at randomization = 52 years). Mean body mass indices ranged from 30–46 kg/m2, follow-up times ranged from 18 months to 12.6 years (mean: 27 months), and average weight loss in reported trials was 5.5±4.0 kg. A total of 264 deaths were reported in weight loss groups and 310 in non-weight loss groups. The weight loss groups experienced a 15% lower all-cause mortality risk (RR = 0.85; 95% CI: 0.73–1.00). There was no evidence for heterogeneity of effect (Cochran’s Q = 5.59 (11 d.f.; p = 0.90); I2 = 0). Results were similar in trials with a mean age at randomization ≥55 years (RR = 0.84; 95% CI 0.71–0.99) and a follow-up time of ≥4 years (RR = 0.85; 95% CI 0.72–1.00).

Conclusion: In obese adults, intentional weight loss may be associated with ~a 15% reduction in all-cause mortality.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5005434/ Transethnic Genetic-Correlation Estimates from Summary Statistics Brielin C. Brown, Chun Jimmie Ye, Alkes Price, Noah Zaitlen 2016 2021-06-02 [("doi","10.1016/j.ajhg.2016.05.001")] genetics/heritable/correlation

The increasing number of genetic association studies conducted in multiple populations provides an unprecedented opportunity to study how the genetic architecture of complex phenotypes varies between populations, a problem important for both medical and population genetics.

Here, we have developed a method for estimating the transethnic genetic correlation: the correlation of causal-variant effect sizes at SNPs common in populations. This method takes advantage of the entire spectrum of SNP associations and uses only summary-level data from genome-wide association studies. This avoids the computational costs and privacy concerns associated with genotype-level information while remaining scalable to hundreds of thousands of individuals and millions of SNPs.

We applied our method to data on gene expression, rheumatoid arthritis, and type 2 diabetes and overwhelmingly found that the genetic correlation was statistically-significantly less than 1.

Our method is implemented in a Python package called Popcorn.

--- https://www.biorxiv.org/content/10.1101/013896.full RNA-guided gene drives can efficiently and reversibly bias inheritance in wild yeast James E. DiCarlo, Alejandro Chavez, Sven L. Dietz, Kevin M. Esvelt, George M. Church 2015-03-19 2021-06-02 [("doi","10.1101/013896")] genetics/editing

Inheritance-biasing “gene drives” may be capable of spreading genomic alterations made in laboratory organisms through wild populations. We previously considered the potential for RNA-guided gene drives based on the versatile CRISPR/Cas9 genome editing system to serve as a general method of altering populations.

Here we report molecularly contained gene drive constructs in the yeast Saccharomyces cerevisiae that are typically copied at rates above 99% when mated to wild yeast.

We successfully targeted both non-essential and essential genes, showed that the inheritance of an unrelated “cargo” gene could be biased by an adjacent drive, and constructed a drive capable of overwriting and reversing changes made by a previous drive.

Our results demonstrate that RNA-guided gene drives are capable of efficiently biasing inheritance when mated to wild-type organisms over successive generations.

--- https://www.biorxiv.org/content/10.1101/033407.full Heritability of Neuroanatomical Shape Tian Ge, Martin Reuter, Anderson M. Winkler, Avram J. Holmes, Phil H. Lee, Lee S. Tirrell, Joshua L. Roffman, Randy L. Buckner, Jordan W. Smoller, Mert R. Sabuncu 2015-12-01 2021-06-02 [("doi","10.1101/033407")] genetics/heritable psychology/neuroscience

Measurements from structural brain magnetic resonance imaging (MRI) scans have been increasingly analyzed as intermediate phenotypes to bridge the gap between clinical features and genetic variation. To date, most imaging phenotypes are scalar, such as the volume of a brain region, which can miss subtle or localized morphological variation associated with genetics or relevant to disease. Neuroanatomical shape measurements—multidimensional geometric descriptions of a brain structure—provide an alternate class of phenotypes that remain largely unexplored.

In this paper, we extend the concept of heritability to multidimensional traits, and present the first comprehensive analysis of the heritability of neuroanatomical shape measurements across an ensemble of brain structures based on genome-wide single-nucleotide polymorphism (SNP) and MRI data from 1,317 unrelated, young (18–35 years) and healthy individuals. Our results demonstrate that neuroanatomical shape can be heritable, above and beyond volume, and thus can serve as a complementary phenotype to study the genetic underpinnings and clinical relevance of brain structure.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5026259/ Divergent Ah Receptor Ligand Selectivity during Hominin Evolution Troy D. Hubbard, Iain A. Murray, William H. Bisson, Alexis P. Sullivan, Aswathy Sebastian, George H. Perry, Nina G. Jablonski, Gary H. Perdew 2016 2021-06-02 [("doi","10.1093/molbev/msw143")] genetics/selection/natural/human

We have identified a fixed nonsynonymous sequence difference between humans (Val381; derived variant) and Neandertals (Ala381; ancestral variant) in the ligand-binding domain of the aryl hydrocarbon receptor (AHR) gene. In an exome sequence analysis of 4 Neandertal and Denisovan individuals compared with 9 modern humans, there are only 90 total nucleotide sites genome-wide for which archaic hominins are fixed for the ancestral nonsynonymous variant and the modern humans are fixed for the derived variant. Of those sites, only 27, including Val381 in the AHR, also have no reported variability in the human dbSNP database, further suggesting that this highly conserved functional variant is a rare event.

Functional analysis of the amino acid variant Ala381 within the AHR carried by Neandertals and nonhuman primates indicate enhanced polycyclic aromatic hydrocarbon (PAH) binding, DNA binding capacity, and AHR mediated transcriptional activity compared with the human AHR. Also relative to human AHR, the Neandertal AHR exhibited 150–1000× greater sensitivity to induction of Cyp1a1 and Cyp1b1 expression by PAHs (eg. benzo(a)pyrene). The resulting CYP1A1/CYP1B1 enzymes are responsible for PAH first pass metabolism, which can result in the generation of toxic intermediates and perhaps AHR-associated toxicities.

In contrast, the human AHR retains the ancestral sensitivity observed in primates to nontoxic endogenous AHR ligands (eg. indole, indoxyl sulfate). Our findings reveal that a functionally change in the AHR occurred uniquely in humans, relative to other primates, that would attenuate the response to many environmental pollutants, including chemicals present in smoke from fire use during cooking.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5182071/ Detection of human adaptation during the past 2000 years Yair Field, Evan A. Boyle, Natalie Telis, Ziyue Gao, Kyle J. Gaulton, David Golan, Loïc Yengo, Ghislain Rocheleau, Philippe Froguel, Mark I. McCarthy, Jonathan K. Pritchard 2016 2021-06-02 [("doi","10.1126/science.aag0776")] genetics/selection/natural/human

Detection of recent natural selection is a challenging problem in population genetics.

Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences.

Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2,000–3,000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes.

For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans.

--- https://arxiv.org/abs/1511.03429 Metaphysics of the Principle of Least Action Vladislav Terekhovich 2015-11-11 2021-06-02 [("doi","10.1016/j.shpsb.2017.09.004")] philosophy/ontology science

Despite the importance of the variational principles of physics, there have been relatively few attempts to consider them for a realistic framework. In addition to the old teleological question, this paper continues the recent discussion regarding the modal involvement of the principle of least action and its relations with the Humean view of the laws of nature. The reality of possible paths in the principle of least action is examined from the perspectives of the contemporary metaphysics of modality and Leibniz’s concept of essences or possibles striving for existence.

I elaborate a modal interpretation of the principle of least action that replaces a classical representation of a system’s motion along a single history in the actual modality by simultaneous motions along an infinite set of all possible histories in the possible modality. This model is based on an intuition that deep ontological connections exist between the possible paths in the principle of least action and possible quantum histories in the Feynman path integral. I interpret the action as a physical measure of the essence of every possible history. Therefore only one actual history has the highest degree of essence and minimal action.

To address the issue of necessity, I assume that the principle of least action has a general physical necessity and lies between the laws of motion with a limited physical necessity and certain laws with a metaphysical necessity.

--- https://arxiv.org/abs/1511.06342 Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov 2015-11-19 2021-06-03 [("doi","10.48550/arXiv.1511.06342")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/model-free reinforcement-learning/scaling

The ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent.

Towards this goal, we define a novel method of multitask and transfer learning that enables an autonomous agent to learn how to behave in multiple tasks simultaneously, and then generalize its knowledge to new domains. This method, termed “Actor-Mimic”, exploits the use of deep reinforcement learning and model compression techniques to train a single policy network that learns how to act in a set of distinct tasks by using the guidance of several expert teachers.

We then show that the representations learnt by the deep policy network are capable of generalizing to new tasks with no prior expert guidance, speeding up learning in novel environments.

Although our method can in general be applied to a wide range of problems, we use Atari games as a testing environment to demonstrate these methods.

--- https://arxiv.org/abs/1502.03492 Gradient-based Hyperparameter Optimization through Reversible Learning Dougal Maclaurin, David Duvenaud, Ryan P. Adams 2015-02-11 2021-06-03 [("doi","10.48550/arXiv.1502.03492")] reinforcement-learning/meta-learning

Tuning hyperparameters of learning algorithms is hard because gradients are usually unavailable.

We compute exact gradients of cross-validation performance with respect to all hyperparameters by chaining derivatives backwards through the entire training procedure.

These gradients allow us to optimize thousands of hyperparameters, including step-size and momentum schedules, weight initialization distributions, richly parameterized regularization schemes, and neural network architectures.

We compute hyperparameter gradients by exactly reversing the dynamics of stochastic gradient descent with momentum.

--- https://arxiv.org/abs/1508.01202 On The History and Future of Cosmic Planet Formation Peter Behroozi, Molly Peeples 2015-08-05 2021-06-03 [("doi","10.1093/mnras/stv1817")] science

We combine constraints on galaxy formation histories with planet formation models, yielding the Earth-like and giant planet formation histories of the Milky Way and the Universe as a whole. In the Hubble Volume (1013 mega-parsecs3), we expect there to be 1020 Earth-like and 1020 giant planets; our own galaxy is expected to host 109 and 1010 Earth-like and giant planets, respectively.

Proposed metallicity thresholds for planet formation do not affect these numbers. However, the metallicity dependence for giant planets results in later typical formation times and larger host galaxies than for Earth-like planets.

The Solar System formed at the median age for existing giant planets in the Milky Way, and consistent with past estimates, formed after 80% of Earth-like planets. However, if existing gas within virialised dark matter haloes continues to collapse and form stars and planets, the Universe will form over 10× more planets than currently exist.

We show that this would imply at least a 92% chance that we are not the only civilization the Universe will ever have, independent of arguments involving the Drake Equation.

--- https://arxiv.org/abs/1502.01852#microsoft Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun 2015-02-06 2021-06-03 [("doi","10.48550/arXiv.1502.01852")] ai/nn

Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects.

First, we propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk.

Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures.

Based on our PReLU networks (PReLU-nets), we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66%). To our knowledge, our result is the first to surpass human-level performance (5.1%, Russakovsky et al) on this visual recognition challenge.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0121904 Longitudinal Neurostimulation in Older Adults Improves Working Memory Kevin T. Jones, Jaclyn A. Stephens, Mahtab Alam, Marom Bikson, Marian E. Berryhill 2015-02-05 2021-06-03 [("doi","10.1371/journal.pone.0121904")] dual-n-back psychology/neuroscience/tcs

An increasing concern affecting a growing aging population is working memory (WM) decline. Consequently, there is great interest in improving or stabilizing WM, which drives expanded use of brain training exercises. Such regimens generally result in temporary WM benefits to the trained tasks but minimal transfer of benefit to untrained tasks.

Pairing training with neurostimulation may stabilize or improve WM performance by enhancing plasticity and strengthening WM-related cortical networks. We tested this possibility in healthy older adults. Participants received 10 sessions of sham (control) or active (anodal, 1.5 mA) tDCS to the right prefrontal, parietal, or prefrontal/parietal (alternating) cortices. After ten minutes of sham or active tDCS, participants performed verbal and visual WM training tasks. On the first, tenth, and follow-up sessions, participants performed transfer WM tasks including the spatial 2-back, Stroop, and digit span tasks.

The results demonstrated that all groups benefited from WM training, as expected. However, at follow-up 1-month after training ended, only the participants in the active tDCS groups maintained improvement. Importantly, this pattern was observed for both trained and transfer tasks. These results demonstrate that tDCS-linked WM training can provide long-term benefits in maintaining cognitive training benefits and extending them to untrained tasks.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4382211/ Genetic studies of body mass index yield new insights for obesity biology Adam E. Locke, Bratati Kahali, Sonja I. Berndt, Anne E. Justice, Tune H. Pers, Felix R. Day, Corey Powell, Sailaja Vedantam, Martin L. Buchkovich, Jian Yang, Damien C. Croteau-Chonka, Tõnu Esko, Tove Fall, Teresa Ferreira, Stefan Gustafsson, Zoltán Kutalik, Jian’an Luan, Reedik Mägi, Joshua C. Randall, Thomas W. Winkler, Andrew R. Wood, Tsegaselassie Workalemahu, Jessica D. Faul, Jennifer A. Smith, Jing Hua Zhao, Wei Zhao, Jin Chen, Rudolf Fehrmann, Åsa K. Hedman, Juha Karjalainen, Ellen M. Schmidt, Devin Absher, Najaf Amin, Denise Anderson, Marian Beekman, Jennifer L. Bolton, Jennifer L. Bragg-Gresham, Steven Buyske, Ayse Demirkan, Guohong Deng, Georg B. Ehret, Bjarke Feenstra, Mary F. Feitosa, Krista Fischer, Anuj Goel, Jian Gong, Anne Uriu Jackson, Stavroula Kanoni, Marcus E. Kleber, Kati Kristiansson, Unhee Lim, Vaneet Lotay, Massimo Mangino, Irene Mateo Leach, Carolina Medina-Gomez, Sarah E. Medland, Michael A. Nalls, Cameron D. Palmer, Dorota Pasko, Sonali Pechlivanis, Marjolein J. Peters, Inga Prokopenko, Dmitry Shungin, Alena Stančáková, Rona J. Strawbridge, Yun Ju Sung, Toshiko Tanaka, Alexander Teumer, Stella Trompet, Sander W. van der Laan, Jessica van Setten, Jana V. Van Vliet-Ostaptchouk, Zhaoming Wang, Loïc Yengo, Weihua Zhang, Aaron Isaacs, Eva Albrecht, Johan Ärnlöv, Gillian M. Arscott, Antony P. Attwood, Stefania Bandinelli, Amy Barrett, Isabelita N. Bas, Claire Bellis, Amanda J. Bennett, Christian Berne, Roza Blagieva, Matthias Blüher, Stefan Böhringer, Lori L. Bonnycastle, Yvonne Böttcher, Heather A. Boyd, Marcel Bruinenberg, Ida H. Caspersen, Yii-Der Ida Chen, Robert Clarke, E. Warwick Daw, Anton J. M. de Craen, Graciela Delgado, Maria Dimitriou, Alex S. F. Doney, Niina Eklund, Karol Estrada, Elodie Eury, Lasse Folkersen, Ross M. Fraser, Melissa E. Garcia, Frank Geller, Vilmantas Giedraitis, Bruna Gigante, Alan S. Go, Alain Golay, Alison H. Goodall, Scott D. Gordon, Mathias Gorski, Hans-Jörgen Grabe, Harald Grallert, Tanja B. Grammer, Jürgen Gräßler, Henrik Grönberg, Christopher J. Groves, Gaëlle Gusto, Jeffrey Haessler, Per Hall, Toomas Haller, Goran Hallmans, Catharina A. Hartman, Maija Hassinen, Caroline Hayward, Nancy L. Heard-Costa, Quinta Helmer, Christian Hengstenberg, Oddgeir Holmen, Jouke-Jan Hottenga, Alan L. James, Janina M. Jeff, Åsa Johansson, Jennifer Jolley, Thorhildur Juliusdottir, Leena Kinnunen, Wolfgang Koenig, Markku Koskenvuo, Wolfgang Kratzer, Jaana Laitinen, Claudia Lamina, Karin Leander, Nanette R. Lee, Peter Lichtner, Lars L. Lind, Jaana Lindström, Ken Sin Lo, Stéphane Lobbens, Roberto Lorbeer, Yingchang Lu, François Mach, Patrik K. E. Magnusson, Anubha Mahajan, Wendy L. McArdle, Stela McLachlan, Cristina Menni, Sigrun Merger, Evelin Mihailov, Lili Milani, Alireza Moayyeri, Keri L. Monda, Mario A. Morken, Antonella Mulas, Gabriele Müller, Martina Müller-Nurasyid, Arthur W. Musk, Ramaiah Nagaraja, Markus M. Nöthen, Ilja M. Nolte, Stefan Pilz, Nigel W. Rayner, Frida Renstrom, Rainer Rettig, Janina S. Ried, Stephan Ripke, Neil R. Robertson, Lynda M. Rose, Serena Sanna, Hubert Scharnagl, Salome Scholtens, Fredrick R. Schumacher, William R. Scott, Thomas Seufferlein, Jianxin Shi, Albert Vernon Smith, Joanna Smolonska, Alice V. Stanton, Valgerdur Steinthorsdottir, Kathleen Stirrups, Heather M. Stringham, Johan Sundström, Morris A. Swertz, Amy J. Swift, Ann-Christine Syvänen, Sian-Tsung Tan, Bamidele O. Tayo, Barbara Thorand, Gudmar Thorleifsson, Jonathan P. Tyrer, Hae-Won Uh, Liesbeth Vandenput, Frank C. Verhulst, Sita H. Vermeulen, Niek Verweij, Judith M. Vonk, Lindsay L. Waite, Helen R. Warren, Dawn Waterworth, Michael N. Weedon, Lynne R. Wilkens, Christina Willenborg, Tom Wilsgaard, Mary K. Wojczynski, Andrew Wong, Alan F. Wright, Qunyuan Zhang, Eoin P. Brennan, Murim Choi, Zari Dastani, Alexander W. Drong, Per Eriksson, Anders Franco-Cereceda, Jesper R. Gådin, Ali G. Gharavi, Michael E. Goddard, Robert E. Handsaker, Jinyan Huang, Fredrik Karpe, Sekar Kathiresan, Sarah Keildson, Krzysztof Kiryluk, Michiaki Kubo, Jong-Young Lee, Liming Liang, Richard P. Lifton, Baoshan Ma, Steven A. McCarroll, Amy J. McKnight, Josine L. Min, Miriam F. Moffatt, Grant W. Montgomery, Joanne M. Murabito, George Nicholson, Dale R. Nyholt, Yukinori Okada, John R. B. Perry, Rajkumar Dorajoo, Eva Reinmaa, Rany M. Salem, Niina Sandholm, Robert A. Scott, Lisette Stolk, Atsushi Takahashi, Toshihiro Tanaka, Ferdin, M. van’t Hooft, Anna A. E. Vinkhuyzen, Harm-Jan Westra, Wei Zheng, Krina T. Zondervan, Andrew C. Heath, Dominique Arveiler, Stephan J. L. Bakker, John Beilby, Richard N. Bergman, John Blangero, Pascal Bovet, Harry Campbell, Mark J. Caulfield, Giancarlo Cesana, Aravinda Chakravarti, Daniel I. Chasman, Peter S. Chines, Francis S. Collins, Dana C. Crawford, L. Adrienne Cupples, Daniele Cusi, John Danesh, Ulf de Faire, Hester M. den Ruijter, Anna F. Dominiczak, Raimund Erbel, Jeanette Erdmann, Johan G. Eriksson, Martin Farrall, Stephan B. Felix, Ele Ferrannini, Jean Ferrières, Ian Ford, Nita G. Forouhi, Terrence Forrester, Oscar H. Franco, Ron T. Gansevoort, Pablo V. Gejman, Christian Gieger, Omri Gottesman, Vilmundur Gudnason, Ulf Gyllensten, Alistair S. Hall, Tamara B. Harris, Andrew Tym Hattersley, Andrew A. Hicks, Lucia A. Hindorff, Aroon D. Hingorani, Albert Hofman, Georg Homuth, G. Kees Hovingh, Steve E. Humphries, Steven C. Hunt, Elina Hyppönen, Thomas Illig, Kevin B. Jacobs, Marjo-Riitta Jarvelin, Karl-Heinz Jöckel, Berit Johansen, Pekka Jousilahti, J. Wouter Jukema, Antti M. Jula, Jaakko Kaprio, John J. P. Kastelein, Sirkka M. Keinanen-Kiukaanniemi, Lambertus A. Kiemeney, Paul Knekt, Jaspal S. Kooner, Charles Kooperberg, Peter Kovacs, Aldi T. Kraja, Meena Kumari, Johanna Kuusisto, Timo A. Lakka, Claudia Langenberg, Loic Le Marchand, Terho Lehtimäki, Valeriya Lyssenko, Satu Männistö, André Marette, Tara C. Matise, Colin A. McKenzie, Barbara McKnight, Frans L. Moll, Andrew D. Morris, Andrew P. Morris, Jeffrey C. Murray, Mari Nelis, Claes Ohlsson, Albertine J. Oldehinkel, Ken K. Ong, Pamela A. F. Madden, Gerard Pasterkamp, John F. Peden, Annette Peters, Dirkje S. Postma, Peter P. Pramstaller, Jackie F. Price, Lu Qi, Olli T. Raitakari, Tuomo Rankinen, D. C. Rao, Treva K. Rice, Paul M. Ridker, John D. Rioux, Marylyn D. Ritchie, Igor Rudan, Veikko Salomaa, Nilesh J. Samani, Jouko Saramies, Mark A. Sarzynski, Heribert Schunkert, Peter E. H. Schwarz, Peter Sever, Alan R. Shuldiner, Juha Sinisalo, Ronald P. Stolk, Konstantin Strauch, Anke Tönjes, David-Alexandre Trégouët, Angelo Tremblay, Elena Tremoli, Jarmo Virtamo, Marie-Claude Vohl, Uwe Völker, Gérard Waeber, Gonneke Willemsen, Jacqueline C. Witteman, M. Carola Zillikens, Linda S. Adair, Philippe Amouyel, Folkert W. Asselbergs, Themistocles L. Assimes, Murielle Bochud, Bernhard O. Boehm, Eric Boerwinkle, Stefan R. Bornstein, Erwin Böttinger, Claude Bouchard, Stéphane Cauchi, John C. Chambers, Stephen J. Chanock, Richard S. Cooper, Paul I. W. de Bakker, George Dedoussis, Luigi Ferrucci, Paul W. Franks, Philippe Froguel, Leif C. Groop, Christopher A. Haiman, Anders Hamsten, Jennie Hui, David J. Hunter, Kristian Hveem, Robert C. Kaplan, Mika Kivimaki, Diana Kuh, Markku Laakso, Yongmei Liu, Nicholas G. Martin, Winfried März, Mads Melbye, Andres Metspalu, Susanne Moebus, Patricia B. Munroe, Inger Njølstad, Ben A. Oostra, Colin Palmer, Nancy L. Pedersen, Markus Perola, Louis Pérusse, Ulrike Peters, Chris Power, Thomas Quertermous, Rainer Rauramaa, Fernando Rivadeneira, Timo E. Saaristo, Danish Saleheen, Naveed Sattar, Eric E. Schadt, David Schlessinger, P. Eline Slagboom, Harold Snieder, Tim D. Spector, Unnur Thorsteinsdottir, Michael Stumvoll, Jaakko Tuomilehto, André G. Uitterlinden, Matti Uusitupa, Pim van der Harst, Mark Walker, Henri Wallaschofski, Nicholas J. Wareham, Hugh Watkins, David R. Weir, H-Erich Wichmann, James F. Wilson, Pieter Zanen, Ingrid B. Borecki, Panos Deloukas, Caroline S. Fox, Iris M. Heid, Jeffrey R. O’Connell, David P. Strachan, Kari Stefansson, Cornelia van Duijn, Gonçalo R. Abecasis, Lude Franke, Timothy Frayling, Mark I. McCarthy, Peter M. Visscher, André Scherag, Cristen Jennifer Willer, Michael Boehnke, Karen L. Mohlke, Cecilia M. Lindgren, Jacques S. Beckmann, Inês Barroso, Kari E. North, Erik Ingelsson, Joel N. Hirschhorn, Ruth Loos, Elizabeth K. Speliotes 2015 2021-06-03 [("doi","10.1038/nature14177")] exercise genetics/heritable

Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals.

This analysis identifies 97 BMI-associated loci (p < 5 × 10−8), 56 of which are novel. 5 loci demonstrate clear evidence of several independent association signals, and many loci have statistically-significant effects on other metabolic phenotypes. The 97 loci account for ~2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation.

Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signaling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4388784/ Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption Marilyn C. Cornelis, Enda M. Byrne, Tõnu Esko, Michael A. Nalls, Andrea Ganna, Nina Paynter, Keri L. Monda, Najaf Amin, Krista Fischer, Frida Renstrom, Julius S. Ngwa, Ville Huikari, Alana Cavadino, Ilja M. Nolte, Alexander Teumer, Kai Yu, Pedro Marques-Vidal, Rajesh Rawal, Ani Manichaikul, Mary K. Wojczynski, Jacqueline M. Vink, Jing Hua Zhao, George Burlutsky, Jari Lahti, Vera Mikkilä, Rozenn N. Lemaitre, Joel Eriksson, Solomon K. Musani, Toshiko Tanaka, Frank Geller, Jian’an Luan, Jennie Hui, Reedik Mägi, Maria Dimitriou, Melissa E. Garcia, Weang-Kee Ho, Margaret J. Wright, Lynda M. Rose, Patrik Ke Magnusson, Nancy L. Pedersen, David Couper, Ben A. Oostra, Albert Hofman, M. Arfan Ikram, Henning W. Tiemeier, André G. Uitterlinden, Frank Ja van Rooij, Inês Barroso, Ingegerd Johansson, Luting Xue, Marika Kaakinen, Lili Milani, Chris Power, Harold Snieder, Ronald P. Stolk, Sebastian E. Baumeister, Reiner Biffar, Fangyi Gu, François Bastardot, Zoltán Kutalik, David R. Jacobs, Nita G. Forouhi, Evelin Mihailov, Lars L. Lind, Cecilia M. Lindgren, Karl Michaëlsson, Andrew P. Morris, Majken Jensen, Kay-Tee Khaw, Robert N. Luben, Jie Jin Wang, Satu Männistö, Mia-Maria Perälä, Kähönen Mika, Terho Lehtimäki, Jorma Viikari, Dariush Mozaffarian, Kenneth Mukamal, Bruce M. Psaty, Angela Döring, Andrew C. Heath, Grant W. Montgomery, Norbert Dahmen, Teresa Carithers, Katherine L. Tucker, Luigi Ferrucci, Heather A. Boyd, Mads Melbye, Jorien L. Treur, Dan Mellström, Jouke Jan Hottenga, Inga Prokopenko, Anke Tönjes, Panos Deloukas, Stavroula Kanoni, Mattias Lorentzon, Denise K. Houston, Yongmei Liu, John Danesh, Asif Rasheed, Marc A. Mason, Alan B. Zonderman, Lude Franke, Bruce S. Kristal, Juha Karjalainen, Danielle R. Reed, Harm-Jan Westra, Michele K. Evans, Danish Saleheen, Tamara B. Harris, George Dedoussis, Gary Curhan, Michael Stumvoll, John Beilby, Louis R. Pasquale, Bjarke Feenstra, Stefania Bandinelli, Jose M. Ordovas, Andrew T. Chan, Ulrike Peters, Claes Ohlsson, Christian Gieger, Nicholas G. Martin, Melanie Waldenberger, David S. Siscovick, Olli T. Raitakari, Johan G. Eriksson, Paul Mitchell, David J. Hunter, Peter Kraft, Eric B. Rimm, Dorret I. Boomsma, Ingrid B. Borecki, Ruth Jf Loos, Nicholas J. Wareham, Peter Vollenweider, Neil Caporaso, Hans Jörgen Grabe, Marian L. Neuhouser, Bruce Hr Wolffenbuttel, Frank B. Hu, Elina Hyppönen, Marjo-Riitta Järvelin, L. Adrienne Cupples, Paul W. Franks, Paul M. Ridker, Cornelia van Duijn, Gerardo Heiss, Andres Metspalu, Kari E. North, Erik Ingelsson, Jennifer A. Nettleton, Rob M. van Dam, Daniel I. Chasman 2015 2021-06-03 [("doi","10.1038/mp.2014.107")] genetics/heritable nootropic/caffeine tea

Coffee, a major dietary source of caffeine, is among the most widely consumed beverages in the world and has received considerable attention regarding health risks and benefits.

We conducted a genome-wide (GW) meta-analysis of predominately regular-type coffee consumption (cups per day) among up to 91,462 coffee consumers of European ancestry with top single-nucleotide polymorphisms (SNPs) followed-up in ~30 062 and 7964 coffee consumers of European and African-American ancestry, respectively. Studies from both stages were combined in a trans-ethnic meta-analysis. Confirmed loci were examined for putative functional and biological relevance.

Eight loci, including 6 novel loci, met genome-wide statistical-significance (log10Bayes factor (BF)>5.64) with per-allele effect sizes of 0.03–0.14 cups per day. 6 are located in or near genes potentially involved in pharmacokinetics (ABCG2, AHR, POR, and CYP1A2) and pharmacodynamics (BDNF and SLC6A4) of caffeine. Two map to GCKR and MLXIPL genes related to metabolic traits but lacking known roles in coffee consumption. Enhancer and promoter histone marks populate the regions of many confirmed loci and several potential regulatory SNPs are highly correlated with the lead SNP of each. SNP alleles near GCKR, MLXIPL, BDNF, and CYP1A2 that were associated with higher coffee consumption have previously been associated with smoking initiation, higher adiposity, and fasting insulin and glucose but lower blood pressure and favorable lipid, inflammatory, and liver enzyme profiles (p < 5 × 10−8).

Our genetic findings among European and African-American adults reinforce the role of caffeine in mediating habitual coffee consumption and may point to molecular mechanisms underlying inter-individual variability in pharmacological and health effects of coffee.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4495769/ LD Score regression distinguishes confounding from polygenicity in genome-wide association studies Brendan K. Bulik-Sullivan, Po-Ru Loh, Hilary K. Finucane, Stephan Ripke, Jian Yang, Nick Patterson, Mark J. Daly, Alkes Price, Benjamin M. Neale 2015 2021-06-03 [("doi","10.1038/ng.3211")] genetics/heritable

Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS).

However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD).

The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control.

We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4632200/ The fine-scale genetic structure of the British population Stephen Leslie, Bruce Winney, Garrett Hellenthal, Dan Davison, Abdelhamid Boumertit, Tammy Day, Katarzyna Hutnik, Ellen C. Royrvik, Barry Cunliffe, Daniel J. Lawson, Daniel Falush, Colin Freeman, Matti Pirinen, Simon Myers, Mark Robinson, Peter Donnelly, Walter Bodmer 2015 2021-06-03 [("doi","10.1038/nature14230")] genetics/heritable

Fine-scale genetic variation between human populations is interesting as a signature of historical demographic events and because of its potential for confounding disease studies.

We use haplotype-based statistical methods to analyse genome-wide single-nucleotide polymorphism (SNP) data from a carefully chosen geographically diverse sample of 2,039 individuals from the United Kingdom. This reveals a rich and detailed pattern of genetic differentiation with remarkable concordance between genetic clusters and geography. The regional genetic differentiation and differing patterns of shared ancestry with 6,209 individuals from across Europe carry clear signals of historical demographic events.

We estimate the genetic contribution to southeastern England from Anglo-Saxon migrations to be under half, and identify the regions not carrying genetic material from these migrations. We suggest pre-Roman but post-Mesolithic movement into southeastern England from continental Europe, and show that in non-Saxon parts of the United Kingdom, there exist genetically differentiated subgroups rather than a general ‘Celtic’ population.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4439331/ Complete genomes reveal signatures of demographic and genetic declines in the woolly mammoth Eleftheria Palkopoulou, Swapan Mallick, Pontus Skoglund, Jacob Enk, Nadin Rohland, Heng Li, Ayça Omrak, Sergey Vartanyan, Hendrik Poinar, Anders Götherström, David Reich, Love Dalén 2015 2021-06-03 [("doi","10.1016/j.cub.2015.04.007")] genetics/heritable/rare genetics/sequencing

The processes leading up to species extinctions are typically characterized by prolonged declines in population size and geographic distribution, followed by a phase in which populations are very small and may be subject to intrinsic threats, including loss of genetic diversity and inbreeding. However, whether such genetic factors have had an impact on species prior to their extinction is unclear; examining this would require a detailed reconstruction of a species’ demographic history as well as changes in genome-wide diversity leading up to its extinction.

Here, we present high-quality complete genome sequences from two woolly mammoths (Mammuthus primigenius). The first mammoth was sequenced at 17.1× coverage and dates to ~4,300 years before present, representing one of the last surviving individuals on Wrangel Island. The second mammoth, sequenced at 11.2× coverage, was obtained from an ~44,800-year-old specimen from the Late Pleistocene population in northeastern Siberia. The demographic trajectories inferred from the two genomes are qualitatively similar and reveal a population bottleneck during the Middle or Early Pleistocene, and a more recent severe decline in the ancestors of the Wrangel mammoth at the end of the last glaciation.

A comparison of the two genomes shows that the Wrangel mammoth has a 20% reduction in heterozygosity as well as a 28× increase in the fraction of the genome that comprises runs of homozygosity.

We conclude that the population on Wrangel Island, which was the last surviving woolly mammoth population, was subject to reduced genetic diversity shortly before it became extinct.

--- /doc/psychiatry/lithium/2015-helbich.pdf Lithium in drinking water and suicide mortality: interplay with lithium prescriptions Marco Helbich, Michael Leitner, Nestor D. Kapusta 2015 2021-06-03 [("doi","10.1192/bjp.bp.114.152991")] psychiatry/lithium

Background: Little is known about the effects of lithium intake through drinking water on suicide. This intake originates either from natural rock and soil elution and/or accumulation of lithium-based pharmaceuticals in ground water.

Aims: To examine the interplay between natural lithium in drinking water, prescribed lithium-based pharmaceuticals and suicide in Austria.

Method: Spatial Bayesian regressions for males, females and pooled suicide mortality rates were estimated.

Results: Although the expected inverse association between lithium levels in drinking water and suicide mortality was confirmed for males and for total suicide rates, the relationship for females was not. The models do not indicate that lithium from prescriptions, assumed to accumulate in drinking water, is related to suicide risk patterns either as an individual effect or as a moderator of lithium levels in drinking water. Gender-specific differences in risk factors and local risk hot spots are confirmed.

Conclusion: The findings do not support the hypotheses that lithium prescriptions have measureable protective effects on suicide or that they interact with lithium in drinking water.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4103968/ Personality and the leading behavioral contributors of mortality Nicholas A. Turiano, Benjamin P. Chapman, Tara L. Gruenewald, Daniel K. Mroczek 2015 2021-06-04 [("doi","10.1037/hea0000038")] psychology/personality

Objective: Personality traits predict both health behaviors and mortality risk across the life course. However, there are few investigations that have examined these effects in a single study. Thus, there are limitations in assessing if health behaviors explain why personality predicts health and longevity.

Method: Utilizing 14-year mortality data from a national sample of over 6,000 adults from the Midlife in the United States Study, we tested whether alcohol use, smoking behavior, and waist circumference mediated the personality-mortality association.

Results: After adjusting for demographic variables, higher levels of Conscientiousness predicted a 13% reduction in mortality risk over the follow-up. Structural equation models provided evidence that heavy drinking, smoking, and greater waist circumference mediated the Conscientiousness-mortality association by 42%.

Conclusion: The current study provided empirical support for the health-behavior model of personality-Conscientiousness influences the behaviors persons engage in and these behaviors affect the likelihood of poor health outcomes. Findings highlight the usefulness of assessing mediation in a structural equation modeling framework when testing proportional hazards. In addition, the current findings add to the growing literature that personality traits can be used to identify those at risk for engaging in behaviors that deteriorate health and shorten the life span.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4547299/ Countering antivaccination attitudes Zachary Horne, Derek Powell, John E. Hummel, Keith J. Holyoak 2015 2021-06-04 [("doi","10.1073/pnas.1504019112")] sociology

Three times as many cases of measles were reported in the United States in 2014 as in 2013. The reemergence of measles has been linked to a dangerous trend: parents refusing vaccinations for their children.

Efforts have been made to counter people’s antivaccination attitudes by providing scientific evidence refuting vaccination myths, but these interventions have proven ineffective.

This study shows that highlighting factual information about the dangers of communicable diseases can positively impact people’s attitudes to vaccination. This method outperformed alternative interventions aimed at undercutting vaccination myths.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0120644 Replication and Analysis of Ebbinghaus’ Forgetting Curve Jaap M. J. Murre, Joeri Dros 2015-01-25 2021-06-04 [("doi","10.1371/journal.pone.0120644")] psychology/spaced-repetition

We present a successful replication of Ebbinghaus’ classic forgetting curve from 1880 based on the method of savings.

One subject spent 70 hours learning lists and relearning them after 20 minutes, 1 hour, 9 hours, 1 day, 2 days, or 31 days. The results are similar to Ebbinghaus’ original data. We analyze the effects of serial position on forgetting and investigate what mathematical equations present a good fit to the Ebbinghaus forgetting curve and its replications.

We conclude that the Ebbinghaus forgetting curve has indeed been replicated and that it is not completely smooth but most probably shows a jump upwards starting at the 24-hour data point.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4300188/ A meta-analysis of high dose, intermittent vitamin D supplementation among older adults Ya Ting Zheng, Qi Qi Cui, Yi Min Hong, Wei Guang Yao 2015 2021-06-04 [("doi","10.1371/journal.pone.0115850")] vitamin-d

Background: The effects of intermittent, high dose vitamin D treatment in older adults have not been documented. We conducted a meta-analysis to provide a quantitative assessment of the efficiency of intermittent, high dose vitamin D treatment on falls, fractures, and mortality among older adults.

Method: Electronic databases were searched for randomized controlled trials (RCTs) on high dose, intermittent vitamin D supplementation among older adults. Two researchers independently screened the literature according to specified inclusive and exclusive criteria to extract the data. Meta-analysis was performed by using Review Manager 5.1.0 software.

Results: Nine trials were included in this meta-analysis. High dose, intermittent vitamin D therapy did not decrease all-cause mortality among older adults. The risk ratio (95% CI) was 1.04 (0.91–1.17). No benefit was seen in fracture or fall prevention. The risk ratio for hip fractures (95% CI) was 1.17 (0.97–1.41) while for non-vertebral fractures (95% CI) it was 1.06 (0.91–1.22), and the risk ratio for falls (95% CI) was 1.02 (0.96–1.08). Results remained robust after sensitivity analysis.

Conclusion: Supplementation of intermittent, high dose vitamin D may not be effective in preventing overall mortality, fractures, or falls among older adults. The route of administration of vitamin D supplements may well change the physiological effects.

--- https://arxiv.org/abs/1410.5401#deepmind Neural Turing Machines Alex Graves, Greg Wayne, Ivo Danihelka 2014-10-20 2021-06-04 [("doi","10.48550/arXiv.1410.5401")] ai/nn/retrieval ai/nn/rnn ai/nn/transformer/attention cs/algorithm/sorting

We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent.

Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.

--- https://arxiv.org/abs/1409.1556 Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan, Andrew Zisserman 2014-09-04 2021-06-04 [("doi","10.48550/arXiv.1409.1556")] ai/nn/cnn

In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting.

Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3×3) convolution filters, which shows that an improvement on the prior-art configurations can be achieved by pushing the depth to 16–19 weight layers.

These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localization and classification tracks respectively.

We also show that our representations generalize well to other datasets, where they achieve state-of-the-art results.

We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

--- https://arxiv.org/abs/1409.0575 ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, Li Fei-Fei 2014-09-01 2021-06-04 [("doi","10.48550/arXiv.1409.0575")] ai/dataset ai/nn/cnn

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions.

This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.

--- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.648.1155 2021-06-04 cs statistics/causality technology --- https://arxiv.org/abs/1406.1077 How inefficient can a sort algorithm be? Miguel A. Lerma 2014-06-03 2021-06-04 [("doi","10.48550/arXiv.1406.1077")] cs/algorithm/sorting

We find large lower bounds for a certain family of algorithms, and prove that such bounds are limited only by natural computability arguments.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0106718 Efficient Gene Knockout in Goats Using CRISPR/Cas9 System Wei Ni, Jun Qiao, Shengwei Hu, Xinxia Zhao, Misha Regouski, Min Yang, Irina A. Polejaeva, Chuangfu Chen 2014-08-10 2021-06-04 [("doi","10.1371/journal.pone.0106718")] genetics/editing

The CRISPR/Cas9 system has been adapted as an efficient genome editing tool in laboratory animals such as mice, rats, zebrafish and pigs.

Here, we report that CRISPR/Cas9 mediated approach can efficiently induce monoallelic and biallelic gene knockout in goat primary fibroblasts. 4 genes were disrupted simultaneously in goat fibroblasts by CRISPR/Cas9-mediated genome editing. The single-gene knockout fibroblasts were successfully used for somatic cell nuclear transfer (SCNT) and resulted in live-born goats harboring biallelic mutations.

The CRISPR/Cas9 system represents a highly effective and facile platform for targeted editing of large animal genomes, which can be broadly applied to both biomedical and agricultural applications.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5006219/ Common polygenic variation enhances risk prediction for Alzheimer’s disease Valentina Escott-Price, Rebecca Sims, Christian Bannister, Denise Harold, Maria Vronskaya, Elisa Majounie, Nandini Badarinarayan, Kevin Morgan, Peter Passmore, Clive Holmes, John Powell, Carol Brayne, Michael Gill, Simon Mead, Alison Goate, Carlos Cruchaga, Jean-Charles Lambert, Cornelia van Duijn, Wolfgang Maier, Alfredo Ramirez, Peter Holmans, Lesley Jones, John Hardy, Sudha Seshadri, Gerard D. Schellenberg, Philippe Amouyel, Julie Williams 2015 2021-06-05 [("doi","10.1093/brain/awv268")] genetics/heritable psychiatry/alzheimers

The identification of subjects at high risk for Alzheimer’s disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer’s disease and the accuracy of Alzheimer’s disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17,008 cases and 37,154 controls obtained from the International Genomics of Alzheimer’s Project (IGAP).

Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were statistically-significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values.

We observed evidence for a polygenic component enriched in Alzheimer’s disease (p = 4.9 × 10−26). This enrichment remained after APOE and other genome-wide associated regions were excluded (p = 3.4 × 10−19). The best prediction accuracy AUC = 78.2% (95% confidence interval 77–80%) was achieved by a logistic regression model with APOE, the polygenic score, sex, and age as predictors.

In conclusion, Alzheimer’s disease has a polygenic component, which has predictive utility for Alzheimer’s disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection, and high/low risk clinical studies. In modeling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004525 8.2% of the Human Genome Is Constrained: Variation in Rates of Turnover across Functional Element Classes in the Human Lineage Chris M. Rands, Stephen Meader, Chris P. Ponting, Gerton Lunter 2014-06-05 2021-06-05 [("doi","10.1371/journal.pgen.1004525")] genetics/selection/natural/human

Ten years on from the finishing of the human reference genome sequence, it remains unclear what fraction of the human genome confers function, where this sequence resides, and how much is shared with other mammalian species. When addressing these questions, functional sequence has often been equated with pan-mammalian conserved sequence. However, functional elements that are short-lived, including those contributing to species-specific biology, will not leave a footprint of long-lasting negative selection. Here, we address these issues by identifying and characterising sequence that has been constrained with respect to insertions and deletions for pairs of eutherian genomes over a range of divergences. Within noncoding sequence, we find increasing amounts of mutually constrained sequence as species pairs become more closely related, indicating that noncoding constrained sequence turns over rapidly. We estimate that half of present-day noncoding constrained sequence has been gained or lost in the last 130 million years (half-life in units of divergence time, d1/2 = 0.25–0.31). While enriched with ENCODE biochemical annotations, much of the short-lived constrained sequences we identify are not detected by models optimized for wider pan-mammalian conservation. Constrained DNase 1 hypersensitivity sites, promoters and untranslated regions have been more evolutionarily stable than long noncoding RNA loci which have turned over especially rapidly. By contrast, protein coding sequence has been highly stable, with an estimated half-life of over a billion years (d1/2 = 2.1–5.0). From extrapolations we estimate that 8.2% (7.1–9.2%) of the human genome is presently subject to negative selection and thus is likely to be functional, while only 2.2% has maintained constraint in both human and mouse since these species diverged. These results reveal that the evolutionary history of the human genome has been highly dynamic, particularly for its noncoding yet biologically functional fraction.

Author Summary: Nearly 99% of the human genome does not encode proteins, and while there recently has been extensive biochemical annotation of the remaining noncoding fraction, it remains unclear whether or not the bulk of these DNA sequences have important functional roles. By comparing the genome sequences of different species we identify genomic regions that have evolved unexpectedly slowly, a signature of natural selection upon functional sequence. Using a high resolution evolutionary approach to find sequence showing evolutionary signatures of functionality we estimate that a total of 8.2% (7.1–9.2%) of the human genome is presently functional, more than 3 times as much than is functional and shared between human and mouse. This implies that there is an abundance of sequences with short lived lineage-specific functionality. As expected, most of the sequence involved in this functional “turnover” is noncoding, while protein coding sequence is stably preserved over longer evolutionary timescales. More generally, we find that the rate of functional turnover varies across categories of functional noncoding elements. Our results provide a pan-mammalian and whole genome perspective on how rapidly different classes of sequence have gained and lost functionality down the human lineage.

--- https://arxiv.org/abs/1405.4720 Search for the Wreckage of Air France Flight AF 447 Lawrence D. Stone, Colleen M. Keller, Thomas M. Kratzke, Johan P. Strumpfer 2014-05-19 2021-06-05 [("doi","10.1214/13-STS420")] reinforcement-learning/exploration statistics/bayes

In the early morning hours of June 1, 2009, during a flight from Rio de Janeiro to Paris, Air France Flight AF 447 disappeared during stormy weather over a remote part of the Atlantic carrying 228 passengers and crew to their deaths.

After two years of unsuccessful search, the authors were asked by the French Bureau d’Enquêtes et d’Analyses pour la sécurité de l’aviation to develop a probability distribution for the location of the wreckage that accounted for all information about the crash location as well as for previous search efforts. We used a Bayesian procedure developed for search planning to produce the posterior target location distribution.

This distribution was used to guide the search in the third year, and the wreckage was found with one week of undersea search.

In this paper we discuss why Bayesian analysis is ideally suited to solving this problem, review previous non-Bayesian efforts, and describe the methodology used to produce the posterior probability distribution for the location of the wreck.

--- https://arxiv.org/abs/1411.5279 What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum Tim Hesterberg 2014-11-19 2021-06-05 [("doi","10.48550/arXiv.1411.5279")] statistics/probability

I have 3 goals in this article:

  1. To show the enormous potential of bootstrapping and permutation tests to help students understand statistical concepts including sampling distributions, standard errors, bias, confidence intervals, null distributions, and p-values.

  2. To dig deeper, understand why these methods work and when they don’t, things to watch out for, and how to deal with these issues when teaching.

  3. To change statistical practice—by comparing these methods to common t-tests and intervals, we see how inaccurate the latter are; we confirm this with asymptotics. n ≥ 30 isn’t enough—think n ≥ 5000. Resampling provides diagnostics, and more accurate alternatives. Sadly, the common bootstrap percentile interval badly under-covers in small samples; there are better alternatives. The tone is informal, with a few stories and jokes.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0105825 Publication Bias in Psychology: A Diagnosis Based on the Correlation between Effect Size and Sample Size Anton Kühberger, Astrid Fritz, Thomas Scherndl 2014-07-29 2021-06-05 [("doi","10.1371/journal.pone.0105825")] statistics/bias/publication

Background: The p value obtained from a statistical-significance test provides no information about the magnitude or importance of the underlying phenomenon. Therefore, additional reporting of effect size is often recommended. Effect sizes are theoretically independent from sample size. Yet this may not hold true empirically: non-independence could indicate publication bias.

Method: We investigate whether effect size is independent from sample size in psychological research. We randomly sampled 1,000 psychological articles from all areas of psychological research. We extracted p values, effect sizes, and sample sizes of all empirical papers, and calculated the correlation between effect size and sample size, and investigated the distribution of p values.

Results: We found a negative correlation of r = −.45 [95% CI: −.53; −.35] between effect size and sample size. In addition, we found an inordinately high number of p values just passing the boundary of statistical-significance. Additional data showed that neither implicit nor explicit power analysis could account for this pattern of findings.

Conclusion: The negative correlation between effect size and samples size, and the biased distribution of p values indicate pervasive publication bias in the entire field of psychology.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112199 A Longitudinal Assessment of Sleep Timing, Circadian Phase, and Phase Angle of Entrainment across Human Adolescence Stephanie J. Crowley, Eliza Van Reen, Monique K. LeBourgeois, Christine Acebo, Leila Tarokh, Ronald Seifer, David H. Barker, Mary A. Carskadon 2014-10-13 2021-06-05 [("doi","10.1371/journal.pone.0112199")] zeo

The aim of this descriptive analysis was to examine sleep timing, circadian phase, and phase angle of entrainment across adolescence in a longitudinal study design. Ninety-four adolescents participated; 38 (21 boys) were 9–10 years (“younger cohort”) and 56 (30 boys) were 15–16 years (“older cohort”) at the baseline assessment. Participants completed a baseline and then follow-up assessments every 6 months for 2.5 years.

At each assessment, participants wore a wrist actigraph for at least one week at home to measure self-selected sleep timing before salivary dim light melatonin onset (DLMO) phase—a marker of the circadian timing system—was measured in the laboratory. Weekday and weekend sleep onset and offset and weekend-weekday differences were derived from actigraphy. Phase angles were the time durations from DLMO to weekday sleep onset and offset times.

Each cohort showed later sleep onset (weekend and weekday), later weekend sleep offset, and later DLMO with age. Weekday sleep offset shifted earlier with age in the younger cohort and later in the older cohort after age 17. Weekend-weekday sleep offset differences increased with age in the younger cohort and decreased in the older cohort after age 17. DLMO to sleep offset phase angle narrowed with age in the younger cohort and became broader in the older cohort. The older cohort had a wider sleep onset phase angle compared to the younger cohort; however, an age-related phase angle increase was seen in the younger cohort only. Individual differences were seen in these developmental trajectories.

This descriptive study indicated that circadian phase and self-selected sleep delayed across adolescence, though school-day sleep offset advanced until no longer in high school, whereupon offset was later. Phase angle changes are described as an interaction of developmental changes in sleep regulation interacting with psychosocial factors (eg. bedtime autonomy).

--- https://www.biorxiv.org/content/10.1101/2022.05.27.493700.full In vivo reprogramming leads to premature death due to hepatic and intestinal failure. Alberto Parras, Alba Vilchez-Acosta, Gabriela Desdin-Mico, Calida Mrabti, Cheyenne Rechsteiner, Fabrice Battiston, Clemence Branchina, Kevin Perez, Christine Sempoux, Alejandro Ocampo 2022-05-27 2022-05-27 [("doi","10.1101/2022.05.27.493700")] longevity/epigenetics

The induction of cellular reprogramming by forced expression of the transcription factors OCT4, SOX2, KLF4, and C-MYC (OSKM) has been shown to allow the dedifferentiation of somatic cells and ameliorate age-associated phenotypes in multiple tissues and organs. Yet to date, the benefits of in vivo reprogramming are limited by the occurrence of detrimental side-effects.

Here, using complementary genetic approaches, we demonstrated that continuous in vivo induction of the reprogramming factors leads to hepatic and intestinal dysfunction resulting in decreased body weight and premature death.

By generating a novel transgenic reprogrammable mouse strain, which avoids OSKM expression in both liver and intestine, we drastically reduced the early lethality and adverse effects associated with in vivo reprogramming. This new reprogramming mouse allows safe and long-term continuous induction of OSKM and might enable a better understanding of in vivo reprogramming as well as maximize its potential effects on rejuvenation and regeneration.

--- https://x.com/NicholasBardy/status/1530461357048418304 2021-06-05 ai/nn/transformer/clip --- https://arxiv.org/abs/2205.10442 Down and Across: Introducing Crossword-Solving as a New NLP Benchmark Saurabh Kulshreshtha, Olga Kovaleva, Namrata Shivagunde, Anna Rumshisky 2022-05-20 2022-05-20 [("doi","10.48550/arXiv.2205.10442")] ai/dataset ai/nn/retrieval fiction/text-game

Solving crossword puzzles requires diverse reasoning capabilities, access to a vast amount of knowledge about language and the world, and the ability to satisfy the constraints imposed by the structure of the puzzle.

In this work, we introduce solving crossword puzzles as a new natural language understanding task. We release the specification of a corpus of crossword puzzles collected from the New York Times daily crossword spanning 25 years and comprised of a total of around nine thousand puzzles. These puzzles include a diverse set of clues: historic, factual, word meaning, synonyms/antonyms, fill-in-the-blank, abbreviations, prefixes/suffixes, wordplay, and cross-lingual, as well as clues that depend on the answers to other clues. We separately release the clue-answer pairs from these puzzles as an open-domain question answering dataset containing over half a million unique clue-answer pairs. For the question answering task, our baselines include several sequence-to-sequence and retrieval-based generative models. We also introduce a non-parametric constraint satisfaction baseline for solving the entire crossword puzzle.

Finally, we propose an evaluation framework which consists of several complementary performance metrics.

--- https://arxiv.org/abs/2205.11503 Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky 2022-05-23 2022-05-23 [("doi","10.48550/arXiv.2205.11503")] ai/text-style-transfer

We propose a method for arbitrary textual style transfer (TST)—the task of transforming a text into any given style—utilizing general-purpose pre-trained language models. Our method, Prompt-and-Rerank, is based on a mathematical formulation of the TST task, decomposing it into 3 constituent components: textual similarity, target style strength, and fluency.

Specifically, our method first uses zero-shot or few-shot prompting to obtain a set of candidate generations in the target style, and then re-ranks these candidates according to a combination of the 3 components above.

Empirically, our method enables small pre-trained language models to perform on par with state-of-the-art large-scale models while consuming two orders of magnitude less compute and memory.

Finally, we conduct a systematic investigation of the effect of model size and prompt design (eg. prompt paraphrasing and delimiter-pair choice) on style transfer quality across 7 diverse textual style transfer datasets.

--- /doc/ai/nn/transformer/gpt/inner-monologue/2022-05-28-gpt3user-thinkingisallyouneed.html 2022-05-28 2022-05-28 ai/nn/transformer/gpt/inner-monologue --- https://sander.ai/2022/05/26/guidance.html Guidance: a cheat code for diffusion models 2021-06-06 ai/nn/diffusion ai/nn/transformer/clip --- /doc/darknet-market/agora/2016-usddoj-515cr40050ddc-michaelandrewryan-pleaagreement.pdf 2016 2021-06-06 darknet-market/agora --- http://lacbzxobeprssrfx.onion/index.php/topic,15752.msg3148560.html#msg3148560 2021-06-06 darknet-market/agora --- http://lacbzxobeprssrfx.onion/index.php/topic,58397.0/ Dosensuppe BUSTED 2021-06-06 darknet-market/agora --- http://lacbzxobeprssrfx.onion/index.php/topic,58397.msg8640671.html#msg8640671 2021-06-06 darknet-market/agora --- http://mugshot-record-search.com/mugshot/CA/Fresno-County-Sheriff-Office/2013-Feb-07/4023889/BRYAN-FREMBLING 2021-06-06 darknet-market/agora --- https://antilop.cc/sr/vendors/1929a34c8c.htm DiamondDweller 2021-06-06 darknet-market/agora --- https://ekstrabladet.dk/sport/anden_sport/danmarkshistoriens-stoerste-dopingsag-et-ton-steroider-fundet/5479263 Danmarkshistoriens største dopingsag: Et ton steroider fundet; Betjente har fundet over et ton steroider et sted i omegnen af København 2021-06-06 darknet-market/agora --- https://jyllands-posten.dk/indland/ECE7542044/To-m%C3%A6nd-afsl%C3%B8ret-med-over-et-ton-doping/ To mænd afsløret med over et ton doping: I stor dopingaktion slog politiet til flere steder på Sjælland. Bagmænd risikerer op til seks års fængsel 2021-06-06 darknet-market/agora --- https://komonews.com/news/local/indictment-washington-teen-drug-dealers-thrived-on-darknet Indictment: Washington teen drug dealers thrived on ‘darknet’ 2021-06-06 darknet-market/agora --- https://www.reddit.com/r/DarkNetMarkets/comments/31afi7/psaarticle_hyannis_man_charged_with_bitcoin/cq3ejul/ 2021-06-06 darknet-market/agora --- https://www.reddit.com/r/DarkNetMarkets/comments/34z5xi/list_of_known_dnm_arrests/cr2de2t/ 2021-06-07 darknet-market/agora --- https://old.reddit.com/user/Diamonddweller overview for Diamonddweller 2021-06-07 darknet-market/agora --- https://politiken.dk/sport/art5568978/Danmarkshistoriens-st%C3%B8rste-dopingsag-Betjente-finder-over-et-ton-steroider Danmarkshistoriens største dopingsag: Betjente finder over et ton steroider: Kæmpefangst af doping er et direkte resultat af en ny, strammere lov. 2021-06-07 darknet-market/agora --- https://timesofindia.indiatimes.com/city/bengaluru/peddlers-procuring-narcotics-from-dark-world-of-internet/articleshow/48368502.cms Peddlers procuring narcotics from dark world of internet 2021-06-07 darknet-market/agora --- https://www.afp.gov.au/media-centre/news/afp/2015/may/four-australians-charged-in-international-illegal-firearm-sting Four Australians charged in international illegal firearm sting 2021-06-07 darknet-market/agora --- https://www.bbc.com/news/uk-northern-ireland-foyle-west-34151553 Two in court over dark web drugs case involving bitcoin 2021-06-07 darknet-market/agora --- https://www.bbc.com/news/uk-wales-south-west-wales-34677345 Six years jail for 'end of world' weapons Swansea man 2021-06-07 darknet-market/agora --- https://www.belfastlive.co.uk/news/belfast-news/belfast-man-who-allegedly-kingpin-10388091 Belfast man who was allegedly the ‘kingpin’ in huge drugs operation over dark web is refused bail: Kyle Hall to remain in custody amid claims of being at the centre of a sophisticated crime gang who used bitcoin currency 2021-06-07 darknet-market/agora --- https://www.belfasttelegraph.co.uk/news/northern-ireland/dark-web-drug-network-run-from-coleraine-mans-bedroom/31503735.html Dark web drug network 'run from Coleraine man's bedroom' 2021-06-07 darknet-market/agora --- https://www.couriermail.com.au/news/queensland/gladstone/gladstone-man-arrested-in-worldwide-firearms-bust/news-story/f5bd03cfa834aad581b828fba8c07af2 Gladstone man arrested in worldwide firearms bust 2021-06-07 darknet-market/agora --- https://www.couriermail.com.au/news/queensland/gladstone/drugs-and-guns-dark-web-accused-refused-bail/news-story/f5d16c42f7e494053752b24ace48344e Drugs and guns 'dark web' accused refused bail 2021-06-08 darknet-market/agora --- https://www.courtlistener.com/docket/4324655/united-states-v-blain/ United States v. Blain, 2:15-cr-00384 2021-06-08 darknet-market/agora --- https://www.fincen.gov/news/news-releases/fincen-awards-recognize-partnership-between-law-enforcement-and-financial FinCEN Awards Recognize Partnership Between Law Enforcement and Financial Institutions to Fight Financial Crime 2021-06-08 darknet-market/agora --- https://www.wuerzburgerleben.de/2015/01/30/sek-einsatz-an-der-fh-schweinfurt-fuenf-festnahmen-wegen-waffenhandel/ SEK-Einsatz an FH Schweinfurt: Die Vergangenheit des Waffennarren; Der 25-jährige Student, der in der vergangenen Woche an der Schweinfurter Fachhochschule von einen Sondereinsatzkommando festgenommen wurde, war der Polizei kein Unbekannter 2021-06-08 darknet-market/agora --- https://www.justice.gov/opa/pr/criminal-charges-filed-against-us-citizen-connection-multi-million-dollar-international-cyber Office of Public Affairs Criminal Charges Filed Against U.S. Citizen in Connection with a Multi-Million Dollar International Cyber Counterfeiting Scheme Based in Uganda 2021-06-08 darknet-market/agora --- https://www.justice.gov/opa/pr/kansas-man-pleads-guilty-exporting-firearms-overseas-purchasers Kansas Man Pleads Guilty to Exporting Firearms to Overseas Purchasers 2021-06-08 darknet-market/agora --- https://www.justice.gov/usao-edca/pr/fresno-man-indicted-possessing-ecstasy-sale Eastern District of California Fresno Man Indicted For Possessing Ecstasy For Sale 2021-06-08 darknet-market/agora --- https://www.justice.gov/usao-ma/pr/hyannis-man-charged-bitcoin-purchase-firearm-and-silencer-darknet Hyannis Man Charged With Bitcoin Purchase of Firearm and Silencer on 'Darknet' 2021-06-08 darknet-market/agora --- https://www.justice.gov/usao-mdal/pr/montgomery-man-convicted-illegal-gun-sales-darknet-sites Montgomery Man Convicted for Illegal Gun Sales on Darknet Sites 2021-06-08 darknet-market/agora --- https://www.justice.gov/usao-mdal/pr/montgomery-man-sentenced-selling-firearms-dark-web Montgomery Man Sentenced for Selling Firearms on the Dark Web 2021-06-08 darknet-market/agora --- https://apnews.com/general-news-ea31c7708ea1405f8ca0a4f30e6cdf5c Feds: Uganda-Based Man Counterfeited $2M in Cash 2021-06-08 darknet-market/agora --- https://www.om.nl/actueel/nieuwsberichten/@88570/aanhoudingen/ Aanhoudingen voor grootschalige drugshandel op ondergrondse marktplaatsen 2021-06-09 darknet-market/agora --- https://web.archive.org/web/20150227030457/http://www.polizei.bayern.de/lka/news/presse/aktuell/index.html/214987 Durchsuchungen und Festnahmen wegen illegalem Waffenhandel 2021-06-09 darknet-market/agora --- https://web.archive.org/web/20160715080540/https://www.sydsvenskan.se/2016-07-15/atal-for-storskalig-narkotikahandel Åtal för storskalig narkotikahandel 2021-06-09 darknet-market/agora --- https://www-sydsvenskan-se.translate.goog/2016-07-15/atal-for-storskalig-narkotikahandel?_x_tr_sl=auto&_x_tr_tl=en&_x_tr_hl=en-US Åtal för storskalig narkotikahandel 2021-06-09 darknet-market/agora --- https://www.syracuse.com/crime/2015/05/accused_student_drug_dealer_tells_oswego_cops_hes_leaving_biz_to_focus_on_finals.html Accused student drug dealer tells Oswego cops he left biz to focus on finals 2021-06-09 darknet-market/agora --- https://www.syracuse.com/crime/2015/05/cops_seize_809_pills_170k_in_cash_in_raid_suny_oswego_student_charged.html Cops seize 809 pills, $170K in cash in raid; SUNY Oswego student charged 2021-06-09 darknet-market/agora --- https://www.thelocal.de/20160729/german-darknet-weapons-dealer-sentenced-to-5-years-jail Five years' jail for German darknet weapons dealer 2021-06-09 darknet-market/agora --- https://www.vice.com/en/article/tip-if-youre-selling-guns-on-the-dark-web-dont-get-your-prints-on-them/ Tip: If You’re Selling Guns on the Dark Web, Don’t Get your Prints on Them 2021-06-09 darknet-market/agora --- https://www.vice.com/en/article/then-the-swat-team-rolls-up-the-first-arrest-of-a-german-deepweb-arms-dealer/ Then the SWAT Team Rolls Up: Was a Darknet Arms Dealer Arrested on Campus? 2021-06-09 darknet-market/agora --- https://www.vice.com/en/article/dark-web-guns-bust-over-a-dozen-arrested-in-undercover-operation/ Dark Web Guns Bust: Over a Dozen Arrested in Undercover Operation 2021-06-09 darknet-market/agora --- https://ia600505.us.archive.org/29/items/gov.uscourts.flsd.489660/gov.uscourts.flsd.489660.1.0.pdf 2021-06-09 darknet-market/alphabay --- https://www.reddit.com/r/DarkNetMarkets/comments/5lo7ir/2_more_juicy_vendor_busts_and_what_they_did_wrong/ 2021-06-10 darknet-market/alphabay --- https://regmedia.co.uk/2016/08/12/almashwali_arrest.pdf 2021-06-10 darknet-market/alphabay --- https://s3.documentcloud.org/documents/3110840/Complaint-against-Cary-Ogborn-Alleged-Dark-Web.pdf 2021-06-10 darknet-market/alphabay --- https://web.archive.org/web/20171002215124/http://www.wweek.com/news/2017/07/05/an-18-year-old-girl-died-from-a-synthetic-opioid-she-bought-online-heres-how-portland-police-cracked-the-case/ An 18-Year-Old Girl Died From a Synthetic Opioid She Bought Online. Here’s How Portland Police Cracked the Case 2021-06-10 darknet-market/alphabay --- https://web.archive.org/web/20191121233733/https://www.derstandard.at/story/2000034526427/wiener-dealer-betrieb-internationalen-drogenversand-im-internet Wiener Dealer betrieb internationalen Drogenversand im Internet: Über Darknet-Plattform AlphaBay—31-Jähriger verschickte Suchtgift auch nach Indien und Australien 2021-06-10 darknet-market/alphabay --- https://www.justice.gov/usao-edca/file/836576/dl 2021-06-10 darknet-market/alphabay --- https://www.justice.gov/usao-edca/press-release/file/918811/dl 2021-06-10 2024-06-13 darknet-market/alphabay --- https://www.justice.gov/usao-edca/pr/fentanyl-and-heroin-sold-dark-web-marketplace Eastern District of California Fentanyl and Heroin Sold on Dark Web Marketplace 2021-06-10 darknet-market/alphabay --- https://www.justice.gov/usao-edca/pr/merced-man-arrested-distributing-marijuana-and-cocaine-nationwide-through-silk-road-and Merced Man Arrested for Distributing Marijuana and Cocaine Nationwide Through the Silk Road and Other Dark-Web Marketplace Websites 2021-06-10 darknet-market/alphabay --- https://www.justice.gov/usao-edca/pr/two-brooklyn-men-indicted-distributing-heroin-and-cocaine-dark-web-marketplace-alphabay Two Brooklyn Men Indicted for Distributing Heroin and Cocaine on Dark Web Marketplace Alphabay 2021-06-10 darknet-market/alphabay --- https://www.justice.gov/usao-ndga/pr/icyeagle-dark-web-vendor-stolen-information-charged-atlanta ICYEAGLE, a Dark Web Vendor of Stolen Information, Charged in Atlanta 2021-06-11 darknet-market/alphabay --- https://www.justice.gov/usao-sdtx/pr/houston-man-arrested-attempting-transport-and-use-explosives Houston Man Arrested for Attempting to Transport and Use Explosives 2021-06-11 darknet-market/alphabay --- https://www.miamiherald.com/news/local/community/miami-dade/article92352412.html DEA: South Florida ‘dark web’ dealer sold deadly heroin from China 2021-06-11 darknet-market/alphabay --- https://www.nydailynews.com/2016/09/20/texas-man-arrested-after-dark-web-attempt-to-buy-explosives-and-blow-up-building/ Texas man arrested after ‘dark web’ attempt to buy explosives and blow up building 2021-06-11 darknet-market/alphabay --- https://www.theregister.com/2016/08/12/two_indicted_for_heroin_and_coke_sales_on_alphabay_dark_web_forum/ Post-Silk Road, Feds bust chaps for ‘dealing heroin, coke’ on world’s largest dark web souk: Whack-a-mole against online drug sales continues 2021-06-11 darknet-market/alphabay --- https://www.vice.com/en/article/undercover-fbi-agent-busts-alleged-explosives-buyer-on-the-dark-web/ Undercover FBI Agent Busts Alleged Explosives Buyer on the Dark Web 2021-06-11 darknet-market/alphabay --- https://winonadailynews.com/news/local/crime-and-courts/federal-charges-winona-man-tried-to-sell-stolen-bank-account/article_965a0cf7-3390-5217-b8ae-c3a578dc50fa.html Federal charges: Winona man tried to sell stolen bank account info online 2021-06-11 darknet-market/alphabay --- /doc/darknet-market/silk-road/1/exhibit8-le_counterintel.txt 2021-06-11 darknet-market/atlantis darknet-market/blackmarket-reloaded darknet-market/silk-road/1 --- https://web.archive.org/web/20140126193100/https://nbc-2.com/story/24509658/messages-detail-online-poison-sale-abrin-jesse-korff SWFL man charged with selling poison in online black market 2021-06-11 darknet-market/blackmarket-reloaded --- https://web.archive.org/web/20151124061017/https://posta.co.il/widgetkit/%D7%9E%D7%93%D7%95%D7%A8%D7%99%D7%9D-2/%D7%A2%D7%91%D7%99%D7%A8%D7%95%D7%AA-%D7%9E%D7%97%D7%A9%D7%91/1604-%D7%90%D7%A7%D7%93%D7%97-%D7%94%D7%93%D7%90%D7%A8%D7%A7%D7%A0%D7%98-%D7%94%D7%A8%D7%90%D7%A9%D7%95%D7%9F אקדח הדארקנט הראשון: בחבילה שהגיעה לסניף דואר בתל אביב התגלה אקדח מפורק שהוסלק בתוך מגף .מזמין החבילה הודה כי רכש את האקדח ברשת האינטרנט הסודית באמצעות המטבע הדיגיטלי ביטקוין, ובמשטרה תוהים: האם נחשפה דרך חדשה להברחת אמצעי לחימה לישראל? 2021-06-11 darknet-market/blackmarket-reloaded --- https://www.bbc.com/news/uk-england-london-29331457 Breaking Bad inspired poison accused ‘wished mother was dead’ 2021-06-11 darknet-market/blackmarket-reloaded --- https://www.dailydot.com/unclick/dark-web-black-market-reloaded-adam-bunger-gun-sales-arrest/ Cops may have just busted a major illegal gun dealer from the Deep Web 2021-06-12 darknet-market/blackmarket-reloaded --- https://www.dailymail.co.uk/news/article-2825778/Graphic-designer-inspired-Breaking-Bad-fantasise-poisoning-mother-jailed-three-years-possession-deadly-toxin.html Graphic designer who was inspired by Breaking Bad to fantasise about poisoning her mother jailed for 3 years for possession of deadly toxin 2021-06-12 darknet-market/blackmarket-reloaded --- https://www.forbes.com/sites/andygreenberg/2013/11/07/sting-operation-nabs-alleged-online-arms-dealer-via-silk-road-competitor-site/ Sting Operation Nabs Alleged Online Arms Dealer On Silk Road Competitor Site 2021-06-12 darknet-market/blackmarket-reloaded --- https://www.ice.gov/news/releases/new-hampshire-man-arrested-charged-nj-illegal-weapons-sales-underground-online New Hampshire man arrested, charged in N.J. for illegal weapons sales on underground online marketplace 2021-06-12 darknet-market/blackmarket-reloaded --- https://www.justice.gov/sites/default/files/usao-nj/legacy/2013/11/29/Crisafi%2C%20Matthew%20Complaint.pdf 2021-06-12 darknet-market/blackmarket-reloaded --- https://www.justice.gov/usao-edca/pr/sacramento-man-indicted-selling-machineguns-and-explosives Eastern District of California Sacramento Man Indicted For Selling Machineguns And Explosives 2021-06-12 darknet-market/blackmarket-reloaded --- https://www.justice.gov/usao-nj/pr/new-hampshire-man-arrested-charged-new-jersey-illegal-weapons-sales-underground-online District of New Jersey New Hampshire Man Arrested, Charged In New Jersey For Illegal Weapons Sales On Underground Online Marketplace 2021-06-12 darknet-market/blackmarket-reloaded --- https://www.justice.gov/usao-wdky/pr/bowling-green-kentucky-man-guilty-shipping-firearms-internationally Bowling Green, Kentucky, Man Guilty Of Shipping Firearms Internationally—Firearms were secreted inside video game systems for shipment to foreign addresses 2021-06-12 darknet-market/blackmarket-reloaded --- https://www.nydailynews.com/2014/02/21/nypd-reach-out-to-fbi-interpol-over-cyanide-carrying-mystery-swede-his-young-wife-says-she-never-knew/ NYPD reach out to FBI, Interpol over cyanide-carrying mystery Swede—his young wife says she never knew: Jonathan Norling, 22, had more of the toxin and a suicide note in his Cadillac; another poison, abrin, in his Bronx apartment—and a 9-mm. pistol and an AR-15 assault rifle in his rented U-Haul truck. His wife and their young daughter, also living in the apartment, were unharmed. His wife, Mbene Ndiayem, was stunned by his behavior and arrest, says her father, adding, ‘I see her crying all the time.’ 2021-06-12 darknet-market/blackmarket-reloaded --- https://www.seacoastonline.com/story/news/local/hampton-union/2014/05/07/hampton-man-to-plead-guilty/37408765007/ Hampton man to plead guilty to trafficking firearms online: A Hampton man accused of illegally trafficking firearms overseas on an underground, Internet-based marketplace known as "Black Market Reloaded", has agreed to plead guilty to the crime in federal court. 2021-06-12 darknet-market/blackmarket-reloaded --- https://www.sfexaminer.com/news/silk-road-like-website-part-of-investigation-into-man-arrested-by-fbi-for-having-alleged/article_9e77f219-f436-5eb4-88ee-1cfdf041c155.html Silk Road like website part of investigation into man arrested by FBI for having alleged bomb making materials 2021-06-12 darknet-market/blackmarket-reloaded --- https://www.tampabay.com/news/courts/criminal/labelle-man-accused-of-selling-toxin-in-death-plot/2162117/ LaBelle man accused of selling deadly toxin 2021-06-13 darknet-market/blackmarket-reloaded --- https://www.telegraph.co.uk/news/uknews/crime/11136008/US-teenager-was-behind-deadly-substance-in-Breaking-Bad-poison-case.html US teenager was behind deadly substance in Breaking Bad poison case: Jesse Korff sent Kuntal Patel the deadly poison Abrin which he had made in his home-based lab 2021-06-13 darknet-market/blackmarket-reloaded --- https://web.archive.org/web/20150308040749/https://www.wbko.com/news/headlines/Bunger-Sentenced-After-Sending-Firearms-Overseas-270832621.html Bunger Sentenced After Sending Firearms Overseas 2021-06-13 darknet-market/blackmarket-reloaded --- https://www.sacbee.com/news/local/crime/article2598075.html Undercover operation ends in Carmichael man's arrest on firearms, explosives charges 2021-06-13 darknet-market/blackmarket-reloaded --- https://www.sacbee.com/news/local/crime/article2601218.html Carmichael man pleads not guilty in weapons case; FBI probes his connections to the 'Darknet' 2021-06-13 darknet-market/blackmarket-reloaded --- https://www.thesmokinggun.com/documents/international-gun-sales-in-xbox-864321 ATF Probe Nabs Gun Seller On Shadowy Darknet: Overseas-bound arms hidden in Xbox, DVD consoles 2021-06-13 darknet-market/blackmarket-reloaded --- https://accesswdun.com/article/2014/10/280308 Flowery Branch couple busted on drug charges 2021-06-13 darknet-market/evolution --- https://antilop.cc/sr/vendors/1dc066e61a.htm darkexpresso 2021-06-13 darknet-market/evolution --- https://kfgo.com/news/articles/2015/mar/04/grand-forks-man-pleads-guilty-in-several-fentanyl-overdoses/ Grand Forks man pleads guilty in several fentanyl overdoses 2021-06-13 darknet-market/evolution --- https://kuriren.nu/nyheter/skelleftea/artikel/tre-man-fran-lanet-i-stor-knarkharva/jdkw134r Tre män från länet i stor knarkhärva 2021-06-13 darknet-market/evolution --- https://www.vice.com/de/tag/motherboard/ Exklusiv: Das Interview mit dem verhafteten Online-Dealer von Shiny Flakes 2021-06-13 darknet-market/evolution --- https://norran.se/nyheter/olyckor-katastrofer/artikel/harifran-skotte-han-storsta-narkotikaligan/r31p8ekl Härifrån skötte han största narkotikaligan: Härifrån skötte den 26-årige Skelleftebon Sveriges största narkotikaliga. I dag åtalas sex män. Klockan 10.00 hålls presskonferens—Norran rapporterar direkt 2021-06-14 darknet-market/evolution --- https://polisen.se/Aktuellt/Nyheter/2015/Mars/Polisen-stoppade-droghandel-pa-natet/ 2021-06-14 darknet-market/evolution --- https://polisen.se/Aktuellt/Pressmeddelanden/Vasterbotten/Polisen-stoppar-omfattande-narkotikahandel-via-Darknet/ Polisen stoppar omfattande narkotikahandel via Darknet: Polisen har i ett framgångsrikt samarbete tillsammans med Åklagarmyndigheten stoppat en omfattande försäljning av narkotiska preparat via Darknet 2021-06-14 darknet-market/evolution --- https://www.bbc.com/news/av/uk-33722658 Liam Lyburd guilty of college murder plot 2021-06-14 darknet-market/evolution --- https://www.bbc.com/news/uk-england-33708611 Breaking Bad fan guilty of Dark Web ricin plot 2021-06-14 darknet-market/evolution --- https://www.bbc.com/news/uk-england-34288380 Breaking Bad fan jailed over Dark Web ricin plot 2021-06-14 darknet-market/evolution --- https://www.bild.de/regional/leipzig/drogenhandel/hier-liegt-der-stoff-vom-digitalen-drogen-baron-40122574.bild.html Er verkaufte hunderte Kilo Koks, LSD, Crystal, Marihuana und Medikamente übers Internet +++ Gefundenes Rauschgift ist über 4 Mio. Euro wert 2021-06-14 darknet-market/evolution psychedelic/lsd --- https://www.buzzfeed.com/patricksmith/liam-lyburd-sentenced Teenager Jailed For Life For Planning Massacre At Newcastle College 2021-06-14 darknet-market/evolution --- https://www.dailymail.co.uk/news/article-2823411/Teenager-accused-plotting-pipe-bomb-attack-targeting-education-college.html Teenager accused of plotting pipe bomb attack 'was targeting further education college' 2021-06-14 darknet-market/evolution --- https://www.dailymail.co.uk/news/article-2957211/British-father-two-bought-ricin-kill-hundreds-delivered-toy-car-caught-online-sting-FBI.html Mohammed Ammer Ali 'bought ricin in FBI sting' in Liverpool 2021-06-14 darknet-market/evolution --- https://www.dailymail.co.uk/news/article-3172374/Teenager-stockpiled-weapons-explosives-carry-mass-murder-former-college.html Liam Lyburd 'stockpiled arsenal to carry out mass murder at Newcastle college' 2021-06-15 darknet-market/evolution --- https://www.finanznachrichten.de/nachrichten-2015-03/33080189-shiny-flakes-38-hausdurchsuchungen-nach-drogenfund-in-leipzig-003.htm ‘Shiny Flakes’: 38 Hausdurchsuchungen nach Drogenfund in Leipzig 2021-06-15 darknet-market/evolution --- https://web.archive.org/web/20150410194958/https://www.gmp.police.uk/content/SocialTwitterFeed/A391702C29DD7E8D80257E210039C813 Teenager admits trying to buy deadly toxin 2021-06-15 darknet-market/evolution --- https://www.grandforksherald.com/news/crime-and-courts/3704033-oregon-man-indicted-fatal-grand-forks-overdose-case Oregon man indicted in fatal Grand Forks overdose case 2021-06-15 darknet-market/evolution --- https://www.grandforksherald.com/news/local/3648953-police-investigate-suspected-drug-overdose-death-grand-forks Police investigate suspected drug overdose death in Grand Forks 2021-06-15 darknet-market/evolution --- https://www.grandforksherald.com/newsmd/oregon-man-pleads-guilty-for-integral-role-in-grand-forks-fentanyl-deaths Oregon man pleads guilty for 'integral role' in Grand Forks fentanyl deaths 2021-06-15 darknet-market/evolution --- https://www.independent.co.uk/news/uk/crime/teenager-in-court-charged-with-trying-to-acquire-lethal-toxin-abrin-10054864.html Teenager in court charged with trying to acquire lethal toxin abrin 2021-06-15 darknet-market/evolution --- https://www.justice.gov/opa/pr/new-york-man-indicted-attempting-acquire-deadly-toxin-ricin Office of Public Affairs New York Man Indicted For Attempting to Acquire Deadly Toxin, Ricin 2021-06-15 darknet-market/evolution --- https://www.justice.gov/sites/default/files/opa/press-releases/attachments/2015/01/21/cheng_le_complaint_comp.pdf 2021-06-15 darknet-market/evolution --- https://www.justice.gov/usao-mdfl/pr/california-man-convicted-conspiracy-distribute-drugs-internet California Man Convicted Of Conspiracy To Distribute Drugs On The Internet 2021-06-15 darknet-market/evolution --- https://www.justice.gov/usao-sdny/pr/new-york-man-sentenced-manhattan-federal-court-16-years-prison-attempting-acquire New York Man Sentenced In Manhattan Federal Court To 16 Years In Prison For Attempting To Acquire Deadly Toxin, Ricin 2021-06-15 darknet-market/evolution --- https://www.koin.com/news/fentanyl-bitcoin-and-the-seedy-side-of-the-web/ Fentanyl, Bitcoin and the seedy side of the web: 3 people from Portland and Southwest Washington are accused in the plot 2021-06-16 darknet-market/evolution --- https://www.ladbible.com/ A Guy Used Bitcoin To Stockpile Weapons And Ammunition In A Bid To Carry Out Mass Murder At His Former College 2021-06-16 darknet-market/evolution --- https://www.l-iz.de/leben/faelle-unfaelle/2015/03/leipziger-ermittler-nehmen-drogenversand-shiny-flakes-hoch-78227 Update—Leipziger Ermittler nehmen Drogenversand 'Shiny Flakes' hoch 2021-06-16 darknet-market/evolution --- https://www.lvz.de/lokales/leipzig/spektakulaerer-polizei-erfolg-in-leipzig-360-kilo-drogen-sichergestellt-ermittlungen-im-darknet-GFYKBMEZWJRRNJCXBJOJ5U7MGU.html Spektakulärer Polizei-Erfolg in Leipzig: 360 Kilo Drogen sichergestellt—Ermittlungen im Darknet 2021-06-16 darknet-market/evolution --- https://www.mdr.de/nachrichten/sachsen/leipzig/drogenfund-leipzig100.html Schlag gegen Online-Drogenhandel in Leipzig 2021-06-16 darknet-market/evolution --- https://www.mirror.co.uk/news/uk-news/first-picture-teenager-accused-plotting-6124856 Liam Lyburd: Bitter teen ‘planned mass murder at college with kill bag full of weapons and explosives’ 2021-06-16 darknet-market/evolution --- https://www.mirror.co.uk/news/uk-news/liam-lyburd-teen-accused-plotting-6149495 Liam Lyburd: Bomb suspect teen ‘left note blaming establishment for his miserable life’ 2021-06-16 darknet-market/evolution --- https://www.nbcnews.com/news/world/germany-drug-bust-finds-4m-haul-destined-online-sale-cops-n322146 Germany Drug Bust Finds $4M Haul Destined for Online Sale: Cops 2021-06-16 darknet-market/evolution --- https://www.oregonlive.com/portland/2015/03/three_od_on_dangerous_drug_smu.html 3 OD on dangerous drug in Portland jail; feds say inmate smuggled contraband inside her body 2021-06-16 darknet-market/evolution --- https://www.reuters.com/article/us-usa-crime-ricin/new-york-man-charged-with-trying-to-buy-ricin-death-pills-online-idUKKBN0KT2G020150120 New York man charged with trying to buy ricin 'death pills' online 2021-06-16 darknet-market/evolution --- https://www.reuters.com/article/us-usa-crime-ricin/new-york-man-charged-with-trying-to-buy-ricin-death-pills-online-idUKKBN0KT2G020150120 N.Y. man convicted of trying to buy ricin 'death pills' online 2021-06-16 darknet-market/evolution --- https://www.theguardian.com/uk-news/2015/apr/20/teenager-court-after-abrin-toxin-over-internet Teenager walks free from court after trying to order lethal toxin over internet: Sixteen-year-old boy from Manchester given 12-month referral order after claiming he attempted to buy abrin to kill himself 2021-06-17 darknet-market/evolution --- https://www.theguardian.com/uk-news/2015/jul/21/breaking-bad-style-ricin-delivery-dark-web-mohammed-ammer-ali-trial Man ordered 'Breaking Bad-style' ricin delivery from FBI agent, court hears; Mohammed Ammer Ali denies charge of attempting to possess chemical weapon, arguing he was trying to buy poison for 'peaceful purpose' 2021-06-17 darknet-market/evolution --- https://www.theguardian.com/world/2015/nov/03/german-teen-sold-one-tonne-of-drugs-from-his-mothers-flat German teenager sold one tonne of drugs from his mother’s flat: Boy nicknamed the ‘bedroom dealer’ jailed for €4m online drug business run from his apartment in Leipzig 2021-06-17 darknet-market/evolution --- https://web.archive.org/web/20150314233056/https://www.valleynewslive.com/home/headlines/International-drug-network-linked-to-Grand-Forks-teens-overdose-296012701.html International drug network linked to Grand Forks teen's overdose 2021-06-17 darknet-market/evolution --- https://www.vice.com/en/article/the-rise-and-fall-of-shiny-flakes-germanys-online-drug-market/ The Rise and Fall of Shiny Flakes, Germany's Online Drug Market 2021-06-17 darknet-market/evolution --- https://www.wired.com/2015/03/evolution-shiny-flakes-bust-heroin-cocaine-silk-road/ German Police Just Made a Gigantic Dark-Web Drug Bust 2021-06-17 darknet-market/evolution --- http://www.polizei.sachsen.de/de/MI_2015_34938.htm Gemeinsame Medieninformation von Staatsanwaltschaft und Polizeidirektion Leipzig: Weltweit anbietende Onlineplattform ‘Shiny Flakes’ stillgelegt 2021-06-17 darknet-market/evolution --- http://www.wahpeto/ 2021-06-17 darknet-market/evolution --- https://antilop.cc/sr/vendors/8e0f635ee5.htm ChrisHash 2021-06-17 darknet-market/sheep-marketplace --- https://brnensky.denik.cz/zlociny-a-soudy/ukradl-109-milionu-ve-virtualni-mene-20160430.html Ukradl 109 milionů. Ve virtuální měně 2021-06-17 darknet-market/sheep-marketplace --- https://domaci.hn.cz/c1-65908590-prvni-cesky-bitcoinovy-pirat-dostal-devet-let-vezeni-v-prepoctu-ukradl-16-milionu-korun-obchodoval-i-s-drogami The first Czech bitcoin pirate got nine years in prison. He stole 16 million crowns, traded with drugs 2021-06-18 darknet-market/sheep-marketplace --- https://news.bitcoin.com/darknet-market-operators-who-stole-40-thousand-btc-face-prison-time/ Darknet Market Operators Who Stole 40,000 BTC Face Prison Time 2021-06-18 darknet-market/sheep-marketplace --- https://www.reddit.com/r/DarkNetMarkets/comments/30h8r9/sheepmarketplace_owner_busted_by_czech_police/cpsgt2o/ 2021-06-18 darknet-market/sheep-marketplace --- https://www.reddit.com/r/DarkNetMarkets/comments/30k8d8/a_little_official_info_about_sheep_case_from/ A little official info about Sheep case from Czech Ministry of Finance 2021-06-18 darknet-market/sheep-marketplace --- https://polisen.se/Aktuellt/Nyheter/Gemensam/2014/okt/Sa-stoppade-Skanepolisen-internetlangarna/ 2021-06-18 darknet-market/sheep-marketplace --- https://web.archive.org/web/20140614152830/http://www.kristianstadsbladet.se/kristianstad/article2176043/Knarkhandel-kan-ge-flera-ar-i-fangelse.html Knarkhandel kan ge flera år i fängelse 2021-06-18 darknet-market/sheep-marketplace --- https://www.dr.dk/nyheder/indland/fbi-aktion-lukker-danske-narkohandlere-paa-nettet FBI-aktion lukker danske narkohandlere på nettet: FBI og Europol lukker flere narkosites i kæmpe fælles operation. Også danske narkosælgere er blevet lukket ned. 2021-06-18 darknet-market/sheep-marketplace --- https://www.expressen.se/kvallsposten/salde-narkotika-tva-unga-man-far-fangelse-9/ Sålde narkotika—två unga män får fängelse 2021-06-18 darknet-market/sheep-marketplace --- https://www.flashback.org/sp50195930 Flashback Forum 2021-06-18 darknet-market/sheep-marketplace --- https://www.idnes.cz/brno/zpravy/bitcoiny-jihomoravska-policie-rozhovor.A160816_2266665_brno-zpravy_krut Bitcoinový mág vydělal miliony, díky jihomoravským policistům sedí v cele 2021-06-18 darknet-market/sheep-marketplace --- https://www.fresnobee.com/2015/02/20/4389411/fresno-man-indicted-for-possession.html 2021-06-18 darknet-market/agora --- https://www.kansascity.com/news/local/crime/article129764179.html Kansas 'Gunrunner' sentenced for illegal overseas gun sales 2021-06-19 darknet-market/agora --- http://www.southwales-eveningpost.co.uk/Gorseinon-man/story-28084851-detail/story.html Gorseinon man with 'apocalyptic' vision of world ending jailed for possessing illegal weapons 2021-06-19 darknet-market/agora --- https://www.wahpetondailynews.com/oregon-man-accused-of-selling-fentanyl-that-led-to-nd/article_de0df64e-cf41-11e4-af1c-1b8df43f199d.html Oregon man accused of selling fentanyl that led to ND death 2021-06-19 [("doi","darknet-markets/evolution")] modafinil/darknet-market --- http://www.wdaz.com/news/3691219-number-defendants-charged-fentanyl-overdoses-5 Number of Defendants Charged in Fentanyl Overdoses up to 5 2021-06-19 darknet-market/evolution --- /doc/darknet-market/blackmarket-reloaded/2013-11-29-bmr-obollesforum.mht 2013-11-29 2021-06-19 darknet-market/blackmarket-reloaded darknet-market/silk-road/1 --- /doc/darknet-market/silk-road/2/sr2f-18619-austriaarrest-blackjack021.html 2021-06-19 darknet-market/silk-road/1 darknet-market/silk-road/2 --- /doc/darknet-market/silk-road/2/purplelotus-complaint.pdf 2021-06-19 darknet-market/silk-road/1 darknet-market/silk-road/2 --- https://cdn.arstechnica.net/wp-content/uploads/2014/04/silkroadplea.pdf 2021-06-19 darknet-market/silk-road/1 --- https://mickvanwely.nl/de-xtc-bende-van-sinterklaas/ De xtc-bende van ‘Sinterklaas” 2021-06-19 darknet-market/silk-road/1 --- https://poststar.com/news/local/crime-courts/former-computer-shop-operator-pleads-guilty-to-drug-charges/article_b42b976e-8bf9-11e3-a353-001a4bcf887a.html Former computer shop operator pleads guilty to drug charges 2021-06-19 darknet-market/silk-road/1 --- https://poststar.com/news/local/police-queensbury-shop-owner-bought-drugs-on-silk-road-website/article_8f45d126-0c1f-11e3-a590-001a4bcf887a.html Police: Queensbury shop owner bought drugs on Silk Road website 2021-06-19 darknet-market/silk-road/1 --- https://addons.mozilla.org/en-US/firefox/addon/mozilla-archive-format/ 2021-06-20 darknet-market/silk-road/1 --- https://arstechnica.com/tech-policy/2014/02/dread-pirate-roberts-2-0-an-interview-with-silk-roads-new-boss/ Dread Pirate Roberts 2.0: An interview with Silk Road’s new boss 2021-06-20 darknet-market/silk-road/1 --- https://arstechnica.com/tech-policy/2015/01/silk-road-trial-federal-agent-explains-how-he-trapped-ulbricht/ At Silk Road trial, federal agent explains how he trapped Ulbricht 2021-06-20 darknet-market/silk-road/1 --- https://arstechnica.com/tech-policy/2015/02/dealer-pleads-guilty-to-selling-drugs-on-the-silk-road/ Dealer pleads guilty to selling drugs on the Silk Road 2021-06-20 darknet-market/silk-road/1 --- https://thewest.com.au/news/wa/brothers-in-court-over-online-drugs-ng-ya-295091 Brothers in court over online drugs 2021-06-20 darknet-market/silk-road/1 --- https://thewest.com.au/news/wa/drug-warning-for-teens-ng-ya-353278 Synthetic LSD and an online ‘eBay for drugs’ known as the Silk Road have been linked to the death of popular Churchlands student Preston Bridge 2021-06-20 darknet-market/silk-road/1 psychedelic/lsd --- https://au.news.yahoo.com/first-wa-convictions-silk-road-185007012.html First WA convictions for Silk Road trades 2021-06-20 darknet-market/silk-road/1 --- https://au.news.yahoo.com/teen-spared-jail-over-preston-050926224.html Teen spared jail over Preston LSD offer 2021-06-20 darknet-market/silk-road/1 psychedelic/lsd --- https://web.archive.org/web/20140709003848/https://calgarysun.com/2012/08/25/dmt-suspected-in-drug-lab DMT suspected in drug bust 2021-06-20 darknet-market/silk-road/1 --- https://casetext.com/case/united-states-v-5044-bitcoins United States v. 50.44 Bitcoins, Civil Action No. ELH-15-3692 2021-06-20 darknet-market/silk-road/1 --- https://web.archive.org/web/20140513213555/http://www.suntimes.com/news/metro/27419179-418/partner-of-ex-largest-online-drug-dealer-plans-to-plead-guilty.html Partner of ex-largest online drug dealer plans to plead guilty 2021-06-20 darknet-market/silk-road/1 --- https://cryptome.org/2013/10/sadler-white-complaint.pdf 2021-06-21 darknet-market/silk-road/1 --- https://en.wikipedia.org/wiki/Charlie_Shrem BitInstant 2021-06-21 darknet-market/silk-road/1 --- https://en.wikipedia.org/wiki/Mitragyna_speciosa Kratom 2021-06-21 darknet-market/silk-road/1 --- https://en.wikipedia.org/wiki/Methylone Methylone 2021-06-21 darknet-market/silk-road/1 --- https://en.wikipedia.org/wiki/MHTML MHTML 2021-06-21 darknet-market/silk-road/1 --- https://en.wikipedia.org/wiki/Mozilla_Archive_Format Mozilla Archive Format 2021-06-21 darknet-market/silk-road/1 --- https://en.wikipedia.org/wiki/Western_Union Western Union 2021-06-21 darknet-market/silk-road/1 --- https://freeross.org/wp-content/uploads/2018/02/Doc_34_Jan_12_Vol_IV_Appendix_A769-A1050.pdf#page=12 Case 1:14-cr-00068-KBF Document 232-1 Filed 04/16/15 Page 1 of 11 § Exhibit 1 2021-06-21 darknet-market/silk-road/1 --- https://ia600308.us.archive.org/17/items/gov.uscourts.wawd.196180/gov.uscourts.wawd.196180.docket.html 2021-06-21 darknet-market/silk-road/1 --- http://silkroad5v7dywlc.onion/index.php?topic=1381.0;all 2021-06-21 darknet-market/silk-road/1 --- http://silkroad5v7dywlc.onion/index.php?topic=1381.msg17659#msg17659 2021-06-22 darknet-market/silk-road/1 --- http://silkroad5v7dywlc.onion/index.php?topic=2889.0 Technohippy is back! (and now verified) 2021-06-22 darknet-market/silk-road/1 --- https://web.archive.org/web/20131007122025/https://komonews.com/news/local/Federal-drug-charges-for-Bellevue-man-involved-in-Silk-Road-226387671.html Federal drug charges for Bellevue man involved in Silk Road 2021-06-22 darknet-market/silk-road/1 --- https://letstalkbitcoin.com/blog/post/dea-seizes-bitcoin-seizure-points-to-earlier-mistaken-identity DEA Seizes Bitcoin: Seizure Points to Earlier Mistaken Identity 2021-06-22 darknet-market/silk-road/1 --- https://medium.com/matter/are-you-internet-sexual-1f855e113df Are You ‘Internet Sexual’?. On Chaturbate, live-cammers go wild Emily Witt Matter 2021-06-22 darknet-market/silk-road/1 --- https://mugshots.com/US-States/Wisconsin/Marathon-County-WI/Soren-J-Paape.76687428.html 2021-06-22 darknet-market/silk-road/1 --- https://x.com/embot/status/654173415964700672 2021-06-22 darknet-market/silk-road/1 --- https://x.com/plutopete/status/568016817534447616 2021-06-22 darknet-market/silk-road/1 --- https://noe.orf.at/v2/news/stories/2626608/ Silk-Road: Festnahme in St. Pölten 2021-06-22 darknet-market/silk-road/1 --- https://snpf.org/wp-content/uploads/2015/04/SNPF_5-13.pdf Speedsweden Ett Silk Road-ärende: En vaksam ledig kollega uppmärksammar av en slump hur två personer står och bryter på ett släp tidigt en morgon invid E20 strax söder om Alingsås. Det blir den osannolika upprinnelsen till ett grovt narkotikabrott med fler än tusen försäljningar över nätet. 2021-06-22 darknet-market/silk-road/1 --- https://www.reddit.com/r/DarkNetMarkets/comments/26zwdx/maybe_this_is_to_much_to_ask/chw4d06/ 2021-06-22 darknet-market/silk-road/1 --- https://www.reddit.com/r/DarkNetMarkets/comments/2c2i3f/caligirl_criminal_complaint_excerpts/ 2021-06-23 darknet-market/silk-road/1 --- https://www.reddit.com/r/DarkNetMarkets/comments/2c8bjc/wont_tracking_numbers_just_reveal_the_approximate/cjcx7ch/ 2021-06-23 darknet-market/silk-road/1 --- https://www.reddit.com/r/DarkNetMarkets/comments/30zdvj/dea_controlled_vendor_trustusjones_sells_his_btc/cq86d53/ 2021-06-23 darknet-market/silk-road/1 --- https://www.reddit.com/r/DarkNetMarkets/comments/37n0lr/purple_lotus_arrested/cro763z/ 2021-06-23 darknet-market/silk-road/1 --- https://www.reddit.com/r/DarkNetMarkets/comments/3egrvv/silk_road_1_bust_not_documented_by_gwern/ 2021-06-23 darknet-market/silk-road/1 --- https://web.archive.org/web/20130709162036/http://au.news.yahoo.com/thewest/a/-/wa/17891240/silk-road-deals-go-underground/ Silk Road deals go underground 2021-06-23 darknet-market/silk-road/1 --- https://web.archive.org/web/20130810215355/http://au.news.yahoo.com/today-tonight/lifestyle/article/-/17821047/online-black-market Online drug market: Drug smugglers have taken their business online, using a hidden website to sell illicit substances and delivering them straight to people’s doors. 2021-06-23 darknet-market/silk-road/1 --- https://web.archive.org/web/20131118015343/http://www.sfgate.com/business/technology/article/Man-pleads-guilty-to-playing-key-drug-site-role-4964858.php Man pleads guilty to playing key drug site role 2021-06-23 darknet-market/silk-road/1 --- https://web.archive.org/web/20131211213539/http://weirderweb.com/2013/02/26/florida-man-busted-silk-road-promptly-deletes-all-his-posts/ Florida man busted with 106 grams of ectasy, Silk Road promptly deletes all his posts 2021-06-23 darknet-market/silk-road/1 --- https://web.archive.org/web/20140211094519/http://letstalkbitcoin.com/users-bitcoins-seized-by-dea/ Users’ Bitcoins Seized by DEA 2021-06-23 darknet-market/silk-road/1 --- https://web.archive.org/web/20140223041104/http://www.sfgate.com/news/crime/article/Neb-man-charged-in-Silk-Road-case-5253111.php Neb. man charged in Silk Road case 2021-06-23 darknet-market/silk-road/1 --- https://web.archive.org/web/20140518105328/http://www.vermontcynic.com/news/student-busted-for-silk-road-purchase-1.2841435 Student busted for Silk Road purchase 2021-06-24 darknet-market/silk-road/1 --- https://web.archive.org/web/20150211203002/http://www.rcmp-grc.gc.ca/ottawa/ne-no/pr-cp/2012/20120518-eng.htm Family of 4 charged with exporting cocaine overseas 2021-06-24 darknet-market/silk-road/1 --- https://web.archive.org/web/20150313063506/http://www.wien-heute.at/p-73978.php St. Pölten: Suchtmittelhandel via Internet aufgeflogen und zerschlagen: Das Landeskriminalamt NÖ—Ermittlungsbereich Suchtmittelkriminalität—führte umfangreiche Erhebungen in Zusammenarbeit mit Beamtinnen und Beamten des Bundeskriminalamtes, Einsatzgruppe zur Bekämpfung der Suchtmittelkriminalität, und des FBIs im Bereich ‘Handel mit Suchtmittel via Internet’ durch 2021-06-24 darknet-market/silk-road/1 --- https://web.archive.org/web/20150402160708/http://www.theindychannel.com/news/call-6-investigators/fishers-teen-admits-buying-drugs-on-dark-net-hidden-online-market Fishers teen admits buying drugs on Dark Net 2021-06-24 darknet-market/silk-road/1 --- https://web.archive.org/web/20150426201951/http://www.wptz.com/news/vermont-new-york/burlington/bitcoin-boom/23169748 Bitcoin Boom: A virtual and anonymous currency on the upswing 2021-06-24 darknet-market/silk-road/1 --- https://www.abc10.com/news/article/251775/2/Cross-county-drug-ring-using-dark-Internet-site-busted-by-feds 2021-06-24 darknet-market/silk-road/1 --- https://www.abc.net.au/news/2012-12-05/dark-internet-linked-to-drug-seizure-spike/4410872 Dealers shed light on dark Internet’s drug trade 2021-06-24 darknet-market/silk-road/1 --- https://www.abc.net.au/news/2013-08-07/deadly-chemical-nbom-e-blamed-for-teenagers-drug-deaths/4872078 Research chemical NBOMe linked to drug deaths of teenagers Henry Kwan, Nick Mitchell, Preston Bridge 2021-06-24 darknet-market/silk-road/1 --- https://www.abc.net.au/news/2013-08-23/police-turn-attention-to-online-drug-trade/4908264 Police focus turns to online drug trade 2021-06-24 darknet-market/silk-road/1 --- https://www.abc.net.au/news/2014-03-14/melbourne-man-avoids-jail-for-ordering-drugs-on-silk-road-websi/5321098 Victorian man avoids jail for ordering drugs on Silk Road website 2021-06-24 darknet-market/silk-road/1 --- https://www.abc.net.au/news/2014-06-09/teenager-sentenced-for-offering-drugs-to-a-boy-who-died-balcony/5510468 Hotel balcony death: Teenager sentenced for offering to supply drugs bought on Silk Road to Preston Bridge 2021-06-25 darknet-market/silk-road/1 --- https://www.academia.edu/24382120 ‘What if you live on top of a bakery and you like cakes?’-Drug use and harm trajectories before, during and after the emergence of Silk Road 2021-06-25 darknet-market/silk-road/1 --- https://archive.is/l96kl Gambling addict Daniel Andrew Atkinson jailed for selling drugs on the dark web 2021-06-25 darknet-market/silk-road/1 --- https://www.afp.gov.au/media-centre/news/afp/2012/july/afp-and-Customs-warn-users-of-silk-road.aspx Media Release: AFP and Customs warn users of Silk Road 2021-06-25 darknet-market/silk-road/1 --- https://www.afp.gov.au/media-centre/news/afp/2013/july/media%20release-joint-operation-results-in-significant-offshore-disruption.aspx Media Release: Joint operation results in offshore disruption 2021-06-25 darknet-market/silk-road/1 --- https://www.afr.com/technology/ernst--young-16-million-bitcoin-auction-20160527-gp53i0 Ernst & Young to sell $16 million in confiscated Bitcoin at auction in Sydney 2021-06-25 darknet-market/silk-road/1 --- https://www.baltimoresun.com/2013/11/05/silk-road-drug-dealer-pleads-guilty/ Silk Road drug dealer pleads guilty: Jacob T. George IV, 32, sold heroin and methylone using online market 2021-06-25 darknet-market/silk-road/1 --- https://www.baltimoresun.com/2013/11/07/silk-road-employee-pleads-guilty-in-maryland/ Silk Road employee pleads guilty in Maryland 2021-06-25 darknet-market/silk-road/1 --- https://www.baltimoresun.com/2014/09/05/silk-road-drug-dealer-gets-six-years-prison/ Silk Road drug dealer gets six years prison: Jacob Theodore George IV, 33, sold heroin through online marketplace 2021-06-25 darknet-market/silk-road/1 --- https://www.belfastlive.co.uk/news/belfast-news/northern-ireland-men-jailed-over-9147435 Northern Ireland men jailed over running international drugs ‘party pack’ business; Pair used darknet facility called Silk Road to buy and sell drugs across globe 2021-06-25 darknet-market/silk-road/1 --- https://www.berkshireeagle.com/ci_26681481/former-bard-college-at-simons-rock-student-avoids/ Former Bard College at Simon’s Rock student avoids jail on drug charges 2021-06-25 darknet-market/silk-road/1 --- https://www.bfmtv.com/police-justice/info-bfmtv-loire-un-cyberdealer-interpelle-premiere-en-france_AN-201312260067.html INFO BFMTV—Loire: un cyberdealer interpellé, première en France: Depuis une ferme isolée, il vendait des produits stupéfiants sur Internet en utilisant un routeur indétectable. Un mode opératoire inédit en France. Interpellé samedi dernier, il a été présenté mardi devant la justice 2021-06-26 darknet-market/silk-road/1 --- https://www.bloomberg.com/news/articles/2015-05-28/silk-road-s-biggest-drug-dealer-sentenced-to-10-years-in-prison Silk Road’s Top Drug Dealer Gets Prison as Dread Pirate Up Next 2021-06-26 darknet-market/silk-road/1 --- https://www.bordermail.com.au/story/1508217/jail-time-for-web-drug-trafficker/ Jail time for web drug trafficker 2021-06-26 darknet-market/silk-road/1 --- https://web.archive.org/web/20150514185301/https://www.cambridge-news.co.uk/Pok-mon-cards-used-supply-drugs-international/story-26465044-detail/story.html https://web.archive.org/web/20150514185301/https://www.cambridge-news.co.uk/Pok-mon-cards-used-supply-drugs-international/story-26465044-detail/story.html 2021-06-26 darknet-market/silk-road/1 --- https://www.campbellrivermirror.com/news/257558681.html Online drug dealer delivered by mail 2021-06-26 darknet-market/silk-road/1 --- https://www.dailydot.com/news/silk-road-confession-steven-sadler-nod/ The final confessions of a Silk Road kingpin 2021-06-26 darknet-market/silk-road/1 --- https://www.dailydot.com/unclick/silk-road-heroin-arrest-nod/ The rise and fall of Silk Road’s heroin kingpin 2021-06-26 darknet-market/silk-road/1 --- https://www.dailydot.com/unclick/silk-road-meth-ring-4-arrests/ 4 arrested in Silk Road meth ring bust, alleged leader faces life in prison 2021-06-26 darknet-market/silk-road/1 --- https://www.dailydot.com/news/silk-road-ross-ulbricht-evidence-list/ Here’s all the evidence the government will present in the Silk Road trial 2021-06-26 darknet-market/silk-road/1 --- https://www.dailymail.co.uk/news/article-2456758/Two-Dutch-Silk-Road-vendors-alias-XTC-Express-caught-red-handed-layer-MDMA-hair.html Two Dutch Silk Road vendors with the alias XTC Express caught red handed with thick layer of MDMA in their hair 2021-06-26 darknet-market/silk-road/1 --- https://www.dailytelegraph.com.au/news/nsw/hundreds-of-fake-credit-cards-and-identities-seized-from-a-western-sydney-home-set-up-as-a-factory-of-fraud/news-story/46ff3ed53e636c9308b54abb706ac243?nk=adec2a5682ad95ec9b0812b8a356252d-1668737201 Hundreds of fake credit cards and identities seized from a western Sydney home set up as a factory of fraud 2021-06-26 darknet-market/silk-road/1 --- https://www.delawareonline.com/story/news/local/2015/01/13/doctor-sentenced-months-silk-road-drug-case/21716135/ Doctor gets 30 months in Silk Road drug case 2021-06-27 darknet-market/silk-road/1 --- https://web.archive.org/web/20160418220145/https://www.derbytelegraph.co.uk/Derby-worker-Toyota-bought-drugs-dark-web/story-29112765-detail/story.html Derby Toyota worker bought drugs on dark web 2021-06-27 darknet-market/silk-road/1 --- https://www.diepresse.com/1551538/st-poeltener-zogen-drogenhandel-via-internet-auf St. Pöltener zogen Drogenhandel via Internet auf: In Zusammenarbeit mit dem FBI hat die niederösterreichische Polizei zwei Männer ausgeforscht, die Drogen über eine Internetplattform vertrieben haben sollen 2021-06-27 darknet-market/silk-road/1 --- https://www.examiner.com.au/story/2185778/hidden-drug-site-use-increasing/ Hidden drug site use increasing 2021-06-27 darknet-market/silk-road/1 --- https://www.facebook.com/brookfieldelmgrovenow/posts/10152350274394314 Ryan Petersen, 21, told police he ordered ecstasy online from Silk Road 2021-06-27 darknet-market/silk-road/1 --- https://www.flashback.org/t2234859 Helsingborgare, 29 och 34 r, hktade fr att kpa/slja p Silk Road - Flashback Forum 2021-06-27 darknet-market/silk-road/1 --- https://www.forbes.com/sites/andygreenberg/2013/12/20/feds-indict-three-more-alleged-employees-of-the-silk-roads-dread-pirate-roberts/ Feds Indict 3 More Alleged Employees Of Silk Road’s Dread Pirate Roberts 2021-06-27 darknet-market/silk-road/1 --- https://www.forbes.com/sites/kashmirhill/2013/11/26/how-a-delaware-doctor-was-linked-to-silk-road-drug-sales/ How A Delaware Doctor Was Linked To Silk Road Drug Sales 2021-06-27 darknet-market/silk-road/1 --- https://www.fox6now.com/news/brookfield-man-faces-multiple-charges-related-to-drugs-trafficking Brookfield man faces multiple charges related to drugs, trafficking 2021-06-27 darknet-market/silk-road/1 --- https://gawker.com/5926440/are-authorities-closing-in-on-the-online-drug-market-silk-road Are Authorities Closing In On the Online Drug Market Silk Road? 2021-06-27 darknet-market/silk-road/1 --- https://www.heraldsun.com.au/news/law-order/man-faces-jail-after-2700-drug-tablets-on-notorious-site-silk-road/news-story/4ec3a87919d9d591057f3d7494336dd0 Man faces jail after 2700 drug tablets on notorious site Silk Road 2021-06-28 darknet-market/silk-road/1 --- https://www.ice.gov/doclib/news/releases/2013/131107baltimore1.pdf 2021-06-28 darknet-market/silk-road/1 --- https://www.inquirer.com/philly/blogs/inq-phillydeals/Del-doc-32-busted-sold-drugs-for-Bitcoins.html Ob/Gyns who planned to wed busted in Bitcoin drug probe 2021-06-28 darknet-market/silk-road/1 --- https://www.irishtimes.com/news/crime-and-law/courts/high-court/extradition-case-of-man-linked-to-silk-road-to-be-heard-in-july-1.2191952 Extradition case of man linked to Silk Road to be heard in July; Gary Davis, from Kilpedder, Co Wicklow, wanted by US authorities over black market website 2021-06-28 darknet-market/silk-road/1 --- https://www.justice.gov/opa/pr/new-orleans-man-charged-conspiracy-commit-wire-fraud-and-conspiracy-commit-trademark New Orleans Man Charged With Conspiracy to Commit Wire Fraud and Conspiracy to Commit Trademark Counterfeiting Using the Silk Road 1 Online Marketplace 2021-06-28 darknet-market/silk-road/1 --- https://www.justice.gov/opa/pr/new-orleans-man-sentenced-41-months-manufacturing-and-selling-more-1-million-counterfeit New Orleans Man Sentenced to 41 Months for Manufacturing and Selling More Than $1 Million in Counterfeit Coupons on Silk Road 2021-06-28 darknet-market/silk-road/1 --- https://www.justice.gov/sites/default/files/opa/press-releases/attachments/2015/03/30/criminal_complaint_force.pdf 2021-06-28 darknet-market/silk-road/1 --- https://www.justice.gov/sites/default/files/usao-ndil/legacy/2015/06/11/pr0424_01a.pdf 2021-06-28 darknet-market/silk-road/1 --- https://www.justice.gov/sites/default/files/usao-mdfl/legacy/2014/05/30/20140530_Jones_Complaint.pdf 2021-06-28 darknet-market/silk-road/1 --- https://www.justice.gov/usao-mdfl/pr/texas-resident-charged-illegally-selling-controlled-substances-silk-road-bitmessage Middle District of Florida Texas Resident Charged With Illegally Selling Controlled Substances On Silk Road, Bitmessage 2021-06-28 darknet-market/silk-road/1 --- https://www.justice.gov/usao-mdfl/pr/texas-business-executive-sentenced-prison-illegally-selling-oxycodone-silk-road Texas Business Executive Sentenced To Prison For Illegally Selling Oxycodone On Silk Road 2021-06-28 darknet-market/silk-road/1 --- https://www.justice.gov/usao-md/pr/silk-road-vendor-sentenced-two-years-prison Silk Road Vendor Sentenced to Two Years In Prison: Sold Drugs, Guns and Counterfeit Currency Using the Online Marketplace Silk Road 2021-06-29 darknet-market/silk-road/1 --- https://www.justice.gov/usao-ndia/pr/waterloo-internet-molly-dealer-pleads-guilty-federal-drug-conspiracy Northern District of Iowa Waterloo Internet "Molly" Dealer Pleads Guilty To Federal Drug Conspiracy 2021-06-29 darknet-market/silk-road/1 --- https://www.justice.gov/usao-ndil/pr/dutch-man-plead-guilty-selling-illegal-drugs-bitcoins-worth-millions-shuttered-silk Northern District of Illinois Dutch Man To Plead Guilty To Selling Illegal Drugs For Bitcoins Worth Millions On Shuttered Silk Road Website 2021-06-29 darknet-market/silk-road/1 --- https://www.justice.gov/sites/default/files/usao-sdny/legacy/2015/03/25/Faiella%2C%20Robert%20M.%20and%20Charlie%20Shrem%20Complaint.pdf 2021-06-29 darknet-market/silk-road/1 --- https://www.justice.gov/usao-sdny/file/797251/dl 2021-06-29 darknet-market/silk-road/1 --- https://www.justice.gov/usao-sdny/pr/bitcoin-exchanger-sentenced-manhattan-federal-court-four-years-prison-selling-nearly-1 Bitcoin Exchanger Sentenced In Manhattan Federal Court To 4 Years In Prison For Selling Nearly $1 Million In Bitcoins For Drug Buys On Silk Road 2021-06-29 darknet-market/silk-road/1 --- https://www.justice.gov/usao-sdny/pr/irish-man-who-helped-operate-silk-road-website-sentenced-manhattan-federal-court-over Irish Man Who Helped Operate The Silk Road Website Sentenced In Manhattan Federal Court To Over Six Years In Prison 2021-06-29 darknet-market/silk-road/1 --- https://www.justice.gov/usao-sdny/pr/manhattan-us-attorney-announces-arrest-and-unsealing-charges-against-senior-adviser Manhattan U.S. Attorney Announces Arrest And Unsealing Of Charges Against Senior Adviser To The Operator Of The ‘Silk Road’ Website: Roger Thomas Clark Was a Key Figure in the Development of Silk Road Who Helped Ross Ulbricht Run the Criminal Enterprise 2021-06-29 darknet-market/silk-road/1 --- https://www.justice.gov/usao-sdny/pr/manhattan-us-attorney-announces-charges-against-three-individuals-virginia-ireland-and Southern District of New York Manhattan U.S. Attorney Announces Charges Against Three Individuals In Virginia, Ireland, And Australia For Their Roles In Running The ‘Silk Road’ Website 2021-06-29 darknet-market/silk-road/1 --- https://www.justice.gov/usao-wdwa/pr/bellevue-man-who-sold-drugs-silk-road-internet-site-sentenced-drug-distribution Bellevue Man who Sold Drugs on ‘Silk Road’ Internet Site Sentenced for Drug Distribution Conspiracy: Top Seller on Silk Road Used Screen Name ‘Nod’ 2021-06-29 darknet-market/silk-road/1 --- https://www.kcra.com/article/mail-order-marijuana-postal-inspectors-link-operation-to-sac-region-1/6406518 Mail-order marijuana? Postal inspectors link operation to Sac region: Loomis man, Roseville woman face charges 2021-06-29 darknet-market/silk-road/1 --- https://www.kgw.com/news/Wash-county-tactical-police-conducting-3-dawn-raids-236190011.html Suspected drug trafficking ring busted in Aloha 2021-06-30 darknet-market/silk-road/1 --- https://www.kleinezeitung.at/nachrichten/chronik/3352090/globaler-drogenring-zerschlagen.story Globaler Drogenring zerschlagen: In einer konzertierten Aktion haben Fahnder in Deutschland einen internationalen Drogenhändler-Ring zerschlagen. Auf die Spur des Rings kamen ursprünglich Ermittler in Wien und Salzburg, als vor ziemlich genau einem Jahr vier Verdächtige festgenommen wurden. 2021-06-30 darknet-market/silk-road/1 --- https://sg.finance.yahoo.com/news/japan-makes-bitcoin-linked-drug-arrest-165138422--finance.html Japan makes Bitcoin-linked drug arrest 2021-06-30 darknet-market/silk-road/1 --- https://www.nbr.co.nz/article/chch-man-sentenced-after-buying-amazon-illegal-drugs-website-ck-135659 Christchurch man sentenced after buying from ‘Amazon of illegal drugs’ website 2021-06-30 darknet-market/silk-road/1 --- https://www.nola.com/news/crime_police/article_bd297d74-f2e1-5c15-9db3-c96545f38688.html Tulane students arrested after allegedly accepting drug delivery at frat house 2021-06-30 darknet-market/silk-road/1 --- https://www.nordbayern.de/franken/nuernberg/drogenshopping-im-darknet-vier-jahre-haft-fur-bruderpaar-1.4909103 Drogenshopping im Darknet: Vier Jahre Haft für Brüderpaar 2021-06-30 darknet-market/silk-road/1 --- https://www.nrc.nl/nieuws/2014/05/08/rechter-geeft-online-drugsdealers-tot-vijf-jaar-cel-a1425166 Judge gives online drug dealers five years in prison 2021-06-30 darknet-market/silk-road/1 --- https://www.nzherald.co.nz/nz/drug-mail-or-mule-risks-the-same/QHX3IGRINL7AN5QZR3JRSOQ3NA/?c_id=1&objectid=11432096 Drug mail or mule: risks the same; Customs warns users who buy online after increase in intercepted packages 2021-06-30 darknet-market/silk-road/1 --- https://www.nzherald.co.nz/nz/man-imported-drugs-like-ordering-pizza/DB7XU2XJZHOXEI25PPC5ZA2OWM/?c_id=1&objectid=11368206 Man imported drugs like ‘ordering pizza’ 2021-06-30 darknet-market/silk-road/1 --- https://www.nzherald.co.nz/nz/officers-link-to-net-drug-market/3MQUK5H6PDVBIZHTDZUG3TITMA/?c_id=1&objectid=10819029 Officer’s link to net drug market 2021-06-30 darknet-market/silk-road/1 --- https://www.nzherald.co.nz/nz/online-drug-trade-triggers-high-alert/OUUSGMFDE5G23FZA4TKFQVZ3OY/?c_id=1&objectid=10826287 Online drug trade triggers high alert 2021-06-30 darknet-market/silk-road/1 --- https://www.oregonlive.com/pacific-northwest-news/2015/11/global_silk_road_meth_dealer_f.html Global meth dealer from Vancouver gets lighter sentence because of U.S. agents’ Silk Road corruption 2021-07-01 darknet-market/silk-road/1 --- https://www.oregonlive.com/portland/2015/08/players_in_biggest_silk_road_m.html Players in biggest Silk Road meth operation sentenced in Portland U.S. court 2021-07-01 darknet-market/silk-road/1 --- https://www.orlandosentinel.com/2013/11/21/doctor-charged-with-selling-drugs-to-local-dea-agents-via-silk-road/ Doctor charged with selling drugs to local DEA agents via Silk Road 2021-07-01 darknet-market/silk-road/1 --- https://www.orlandosentinel.com/2014/05/30/feds-charge-businessman-with-selling-painkillers-on-silk-road/ Feds charge businessman with selling painkillers on Silk Road 2021-07-01 darknet-market/silk-road/1 --- https://www.pcworld.com/article/431272/us-government-lurked-on-silk-road-for-over-a-year.html U.S. government lurked on Silk Road for over a year 2021-07-01 darknet-market/silk-road/1 --- https://www.bbc.com/news/uk-england-35690394 Devon based Silk Road drug vendor gets five years in prison 2021-07-01 darknet-market/silk-road/1 --- https://web.archive.org/web/20150725071510/https://www.reuters.com/article/2015/07/21/us-usa-bitcoin-trial-idUSKCN0PV2FG20150721 Silk Road drug dealer turned government witness gets 2-1/2 years in prison 2021-07-01 darknet-market/silk-road/1 --- https://www.rte.ie/news/business/2013/1220/494111-silk-road-arrest/ Former Silk Road administrator arrested in Wicklow 2021-07-01 darknet-market/silk-road/1 --- https://www.smh.com.au/ Detectives follow the Silk Road 2021-07-01 darknet-market/silk-road/1 --- https://www.smh.com.au/technology/australian-authorities-sitting-on-96-million-worth-of-bitcoins-confiscated-from-silk-road-drug-dealer-20141031-11ewk4.html Australian authorities sitting on $9.6 million worth of bitcoins confiscated from Silk Road drug dealer 2021-07-01 darknet-market/silk-road/1 --- https://www.smh.com.au/technology/teens-visit-hidden-website-for-drugs-20130319-2gddg.html Teens flock to hidden website for drugs 2021-07-02 darknet-market/silk-road/1 --- https://www.smh.com.au/technology/victorian-government-to-sell-93-million-in-seized-bitcoins-20150316-1lzyg2.html Victorian government to sell $9.3 million in seized bitcoins 2021-07-02 darknet-market/silk-road/1 --- https://www.spiegel.de/panorama/ermittler-zerschlagen-internationalen-drogenring-a-910221.html Internationaler Drogenring: Ermittler nehmen vier Verdächtige fest 2021-07-02 darknet-market/silk-road/1 --- https://www.stuff.co.nz/dominion-post/9945908/Psychedelic-tabs-ordered-online Psychedelic tabs ordered online 2021-07-02 darknet-market/silk-road/1 --- https://www.stuff.co.nz/manawatu-standard/8604002/Student-accused-of-drug-importing-freed-on-bail Student accused of drug importing freed on bail 2021-07-02 darknet-market/silk-road/1 --- https://www.stuff.co.nz/manawatu-standard/news/9125536/City-drug-bust City drug bust 2021-07-02 darknet-market/silk-road/1 --- https://www.stuff.co.nz/manawatu-standard/news/9156769/Drug-import-accused-in-court Drug import accused in court 2021-07-02 darknet-market/silk-road/1 --- https://www.stuff.co.nz/manawatu-standard/news/9517934/Silk-Road-to-jail-for-meth-importer Silk Road to jail for meth importer 2021-07-02 darknet-market/silk-road/1 --- https://www.stuff.co.nz/manawatu-standard/news/9896985/Jail-for-drug-importing-student Stuff 2021-07-02 darknet-market/silk-road/1 --- https://www.stuff.co.nz/national/crime/10232509/Doing-time-for-drugs-bought-online Stuff 2021-07-02 darknet-market/silk-road/1 --- https://www.stuff.co.nz/national/crime/63779228/silk-road-drug-buyers-in-court Silk Road drug buyers in court 2021-07-02 darknet-market/silk-road/1 --- https://www.stuff.co.nz/national/crime/64084873/jail-for-selling-drugs-and-making-pipe-bomb Stuff 2021-07-03 darknet-market/silk-road/1 --- https://www.stuff.co.nz/southland-times/9133368/Trio-got-drugs-from-internet-site Trio got drugs from internet site 2021-07-03 darknet-market/silk-road/1 --- https://www.stuff.co.nz/southland-times/news/7954851/Community-detention-for-drug-importer Drug importer jailed 2021-07-03 darknet-market/silk-road/1 --- https://www.stuff.co.nz/southland-times/news/court/7958999/Drugs-ordered-on-web Drugs ordered on web 2021-07-03 darknet-market/silk-road/1 --- https://www.stuff.co.nz/southland-times/news/court/9025924/Curiosity-leads-to-drug-conviction Curiosity leads to drug conviction 2021-07-03 darknet-market/silk-road/1 --- https://www.sueddeutsche.de/wirtschaft/trotz-verschluesselter-absprachen-im-internet-rauschgiftfahnder-zerschlagen-drogenring-1.1717426 Rauschgiftfahnder zerschlagen Drogenring: ‘Das sind intelligente Computerfreaks, technisch versiert’: Über eine verschlüsselte Plattform im Internet hat eine Bande mit Drogen gehandelt—die Fahnder fassten die Täter dennoch. 2021-07-03 darknet-market/silk-road/1 --- https://www.theage.com.au/national/victoria/bitcoin-drug-millions-seized-in-victoria-20141015-116bby.html Bitcoin drug millions seized in Victoria 2021-07-03 darknet-market/silk-road/1 --- https://www.theage.com.au/national/victoria/drug-accused-allowed-to-travel-to-world-cup-20140605-39l02.html Drug accused allowed to travel to World Cup 2021-07-03 darknet-market/silk-road/1 --- https://www.theage.com.au/national/victoria/drug-dealer-who-was-granted-bail-to-attend-soccer-world-cup-escapes-jail-term-20141126-11uei8.html Drug dealer who was granted bail to attend soccer World Cup escapes jail term 2021-07-03 darknet-market/silk-road/1 --- https://www.theage.com.au/national/victoria/secret-website-harboured-drugs-smorgasbord-court-hears-20130131-2dlw3.html Secret website harboured drugs smorgasbord, court hears 2021-07-03 darknet-market/silk-road/1 --- https://www.theage.com.au/national/victoria/silk-road-drug-trafficker-has-jail-term-reduced-on-appeal-20150609-ghjskt.html Silk Road drug trafficker has jail term reduced on appeal 2021-07-03 darknet-market/silk-road/1 --- https://www.theregister.com/2019/01/29/how_i_caught_silk_road_mastermind/?page=2 I helped catch Silk Road boss Ross Ulbricht: Undercover agent tells all 2021-07-04 darknet-market/silk-road/1 --- https://www.vice.com/en/article/murdered-silk-road-employee-sentenced-to-time-served/ ‘Murdered’ Silk Road Employee Sentenced to Time Served 2021-07-04 darknet-market/silk-road/1 --- https://www.vice.com/en/article/these-are-the-two-forgotten-architects-of-the-silk-road/ These Are the Two Forgotten Architects of Silk Road 2021-07-04 darknet-market/silk-road/1 --- https://www.welt.de/regionales/muenchen/article117869257/Fahnder-zerschlagen-Bitcoin-Drogenring-DarkNet.html Fahnder zerschlagen Bitcoin-Drogenring DarkNet: Über eine verschlüsselte Plattform im Internet hat eine Bande mit Drogen gehandelt. Hochprofessionell und nahezu perfekt 2021-07-04 darknet-market/silk-road/1 --- https://web.archive.org/web/20130324034900/https://www.willitsnews.com/ci_22836369/six-arrested-3-000-marijuana-plants-and-44 Humboldt & Sonoma counties: Six arrested, 3,000 marijuana plants and 44 weapons seized in state DOJ raids 2021-07-04 darknet-market/silk-road/1 --- https://www.wired.com/2013/11/silk-road/ How the Feds Took Down the Silk Road Drug Wonderland 2021-07-04 darknet-market/silk-road/1 --- https://www.wired.com/2015/01/silk-road-trial-undercover-dhs-fbi-trap-ross-ulbricht/ Undercover Agent Reveals How He Helped the FBI Trap Silk Road’s Ross Ulbricht 2021-07-04 darknet-market/silk-road/1 --- https://www.wired.com/2015/05/inside-a-million-dollar-dark-web-coupon-counterfeiting-scheme/ Inside a Giant Dark-Web Scheme to Sell Counterfeit Coupons 2021-07-04 darknet-market/silk-road/1 --- https://www.wired.com/2015/05/silk-road-creator-ross-ulbricht-sentenced-life-prison/ Silk Road Creator Ross Ulbricht Sentenced to Life in Prison 2021-07-04 darknet-market/silk-road/1 --- https://web.archive.org/web/20130301010813/https://www.wwltv.com/news/Laundry-List-of-Drugs-at-Uptown-Frat-House-Leads-to-Tulane-Student-Arrests-193450441.html Laundry list of drugs at Uptown frat house leads to Tulane student arrests 2021-07-04 darknet-market/silk-road/1 --- https://www.youtube.com/watch?v=E7YCVuqaL2E Silk Road delivery lands 18 year old in trouble ‘Jackson R’ 2021-07-05 darknet-market/silk-road/1 --- https://web.archive.org/web/20140314232216/https://www.zeit.de/2014/12/drogenhandel-silk-road-pfandleiher Kokain vom ‘Pfandleiher’: Ein 24-Jähriger aus der bayerischen Provinz könnte mit Drogengeschäften über das Internet Millionen erwirtschaftet haben. Nun steht er vor Gericht. 2021-07-05 darknet-market/silk-road/1 --- https://web.archive.org/web/20140315020229//https://www.zeit.de/2014/12/drogenhandel-silk-road-pfandleiher/seite-2 2021-07-05 darknet-market/silk-road/1 --- https://www8.austlii.edu.au/cgi-bin/viewdoc/au/cases/vic/VCC/2013/70.html DPP v Howard, Paul Leslie [2013] VCC 70 (6 February 2013) 2021-07-05 darknet-market/silk-road/1 --- https://www8.austlii.edu.au/cgi-bin/viewdoc/au/cases/vic/VCC/2014/1982.html DPP v Franzini [2014] VCC 1982 (2014-11-26) 2021-07-05 darknet-market/silk-road/1 --- https://www8.austlii.edu.au/cgi-bin/viewdoc/au/cases/vic/VSCA/2014/291.html Matthews v The Queen; Vu v The Queen; Hashmi v The Queen [2014] VSCA 291 (19 November 2014) 2021-07-05 darknet-market/silk-road/1 --- https://www6.austlii.edu.au/cgi-bin/viewdoc/au/cases/vic/VSCA/2015/35.html Supreme Court of Victoria—Court of Appeal: Ludwig v The Queen [2015] VSCA 35 (2015-03-10) 2021-07-05 darknet-market/silk-road/1 --- http://www.brookfieldnow.com/news/258970501.html Brookfield man pleads not guilty of manufacturing drugs 2021-07-05 darknet-market/silk-road/1 --- https://www.golocalpdx.com/news/portland-is-a-hub-for-online-drug-dealers GoLocalPDX 2021-07-05 darknet-market/silk-road/1 --- http://www.metro.se/nyheter/29-aring-salde-knark-pa-silk-road/EVHmji!XXktv9EAmdGTI/ 29-åring sålde knark på Silk Road: Det var av en slump som polisen sprängde en knarkbutik på nätet. En husrannsakan avslöjade en 29-årig försäljare med kopplingar till den nedstängda knarksajten Silk Road. 2021-07-05 darknet-market/silk-road/1 --- https://web.archive.org/web/20130226064243/http://www.naplesnews.com/news/2013/feb/22/man-arrested-after-accepting-drug-package-mail/ Deputies: Man arrested after accepting drug package through mail 2021-07-05 darknet-market/silk-road/1 --- https://www.postandcourier.com/article/20130709/pc16/130709464/1177/lawyer-charleston-man-denies-connection-to-dea-bitcoin-seizure-and-illicit-silk-road-drug-marketplace/ Lawyer: Charleston man denies connection to DEA Bitcoin seizure and illicit Silk Road drug marketplace 2021-07-06 darknet-market/silk-road/1 --- https://www.sacbee.com/news/local/crime/article43517145.html Meadow Vista marijuana distributor sentenced to prison in federal court 2021-07-06 darknet-market/silk-road/1 --- https://www.thesmokinggun.com/documents/silk-road-dealer-cooperating-567432 Top Silk Road Drug Dealer Was Flipped By Feds: Online narcotics kingpin ‘Nod’ began cooperating after July raid 2021-07-06 darknet-market/silk-road/1 --- http://www.u.tv/News/2015/04/29/Men-jailed-over-dark-web-drugs-offences-36382 Men jailed over ‘dark web’ drugs offences: Two men who were involved in an international drugs operation which included purchasing illegal substances on the ‘dark web’ using Bitcoin currency have been jailed. 2021-07-06 darknet-market/silk-road/1 --- https://antilop.cc/sr/vendors/7058d46a86.htm CaliforniaCannibas 2021-07-06 darknet-market/silk-road/2 --- https://archive.is/Lz2YK Dublin football star’s brother accused of using the Darknet to sell drugs / http://www.herald.ie/news/courts/dublin-football-stars-brother-accused-of-using-the-darknet-to-sell-drugs-30734228.html 2021-07-06 darknet-market/silk-road/2 --- https://arstechnica.com/tech-policy/2014/11/prosecutor-silk-road-2-0-suspect-did-admit-to-everything/ Prosecutor: Silk Road 2.0 suspect ‘did admit to everything’ 2021-07-06 darknet-market/silk-road/2 --- https://en.wikipedia.org/wiki/Methoxetamine Methoxetamine 2021-07-06 darknet-market/silk-road/2 --- https://web.archive.org/web/20151107000041/http://gazette.com/air-force-academy-cadet-sentenced-for-dealing-drugs/article/1562468 Air Force Academy cadet sentenced for dealing drugs 2021-07-06 darknet-market/silk-road/2 --- https://gazette.com/military/witnesses-cadet-got-drugs-from-online-black-market-and-sold-them-in-dorms/article_0c8b524b-99f5-5c9e-ae44-cb57e4d7234e.html Witnesses: Cadet got drugs from online black market and sold them in dorms 2021-07-06 darknet-market/silk-road/2 --- https://www.vice.com/en/article/surprise-now-illegal-legal-highs-are-widely-available-on-the-dark-web/ Surprise: Now-Illegal ‘Legal Highs’ Are Widely Available on the Dark Web 2021-07-06 darknet-market/silk-road/2 --- https://news.detik.com/berita/d-2474783/selundupkan-ekstasi-guru-bule-dan-kekasihnya-ditangkap Selundupkan Ekstasi, Guru Bule dan Kekasihnya Ditangkap 2021-07-07 darknet-market/silk-road/2 --- https://www.reddit.com/r/DarkNetMarkets/comments/26w0qe/busted_xanaxking/chv2yw0/ 2021-07-07 darknet-market/silk-road/2 --- https://www.reddit.com/r/DarkNetMarkets/comments/2lkpaz/6_vendors_arrested_in_uk/ 2021-07-07 darknet-market/silk-road/2 --- https://www.reddit.com/r/DarkNetMarkets/comments/2u26ij/uncategorized_i_bought_100_superman_pills_from/ 2021-07-07 darknet-market/silk-road/2 --- https://www.reddit.com/r/DarkNetMarketsNZ/comments/2ti2qd/temp_closing/ 2021-07-07 darknet-market/silk-road/2 --- https://www.reddit.com/r/SilkRoad/comments/39m4sc/sr2_arrest_australian_seller_magicauryan_james/ 2021-07-07 darknet-market/silk-road/2 --- https://patch.com/california/martinez/xanax-king-of-martinez-among-9-charged-with-making-counterfeit-drugs-laundering-money ‘Xanax King’ Of Martinez Among 9 Charged With Making Counterfeit Drugs, Laundering Money: Nine people were indicted by a federal grand jury in Oakland on charges of manufacturing counterfeit prescription drugs, money laundering or both. 2021-07-07 darknet-market/silk-road/2 --- https://s3.documentcloud.org/documents/2852008/Farrell-Sentence.pdf 2021-07-07 darknet-market/silk-road/2 --- https://web.archive.org/web/20140601224430/http://www.wbiw.com/local/archive/2014/05/police-recover-approximately-15000.php Police Recover ~15,000 Xanax Pills 2021-07-07 darknet-market/silk-road/2 --- https://web.archive.org/web/20141110004710/http://www.nationalcrimeagency.gov.uk/news/news-listings/483-international-law-enforcement-deals-major-blow-to-dark-web-markets International law enforcement deals major blow to dark web markets 2021-07-07 darknet-market/silk-road/2 --- https://web.archive.org/web/20150330195018/http://ilinnews.com/naperville-man-arrested-after-police-find-600-xanax-pills/ Naperville man arrested after police find 600 Xanax pills 2021-07-08 darknet-market/silk-road/2 --- https://web.archive.org/web/20150419092155/http://koin.com/ap/alleged-manager-of-silk-road-2-0-website-arrested/ Alleged manager of Silk Road 2.0 website arrested 2021-07-08 darknet-market/silk-road/2 --- https://web.archive.org/web/20150731000922/http://www.nationalcrimeagency.gov.uk/news/659-aberdovey-man-sentenced-to-two-years-for-444-dark-web-drug-deals Aberdovey man sentenced to two years for 444 dark web drug deals 2021-07-08 darknet-market/silk-road/2 --- https://web.archive.org/web/20151002103506/http://www.nationalcrimeagency.gov.uk/news/716-dark-web-drug-dealer-selling-super-strength-ketamine-jailed Dark web drug dealer selling super strength ketamine substitute jailed 2021-07-08 darknet-market/silk-road/2 --- https://www.abc.net.au/news/2015-06-12/drug-trafficking-on-silk-road-site-shut-down-by-fbi/6541596 ‘Naive’ drug trafficker used own name on website now shut down by FBI, Adelaide court hears 2021-07-08 darknet-market/silk-road/2 --- https://www.abendzeitung-muenchen.de/bayern/drogen-im-internet-bestellt-wuerzburger-dealer-40-festgenommen-art-290812 Drogen im Internet bestellt—Würzburger Dealer (40) festgenommen 2021-07-08 darknet-market/silk-road/2 --- https://www.adelaidenow.com.au/news/south-australia/man-arrested-after-220000-worth-of-wizard-drug-25inbome-intercepted/story-fni6uo1m-1227075205971 Man arrested after $220,000 worth of ‘wizard’ drug 25i-NBOMe intercepted 2021-07-08 darknet-market/silk-road/2 --- https://www.bbc.com/news/world-europe-29938685 Darknet: Bitcoin and drugs worth £1.5m seized by Irish police 2021-07-08 darknet-market/silk-road/2 --- https://www.belfasttelegraph.co.uk/news/republic-of-ireland/article30724056.ece Two held over ‘darknet’ drugs ring: Officers said a global drug-dealing operation was based at a secured premises in Dublin’s south inner city 2021-07-08 darknet-market/silk-road/2 --- https://www.bellevuereporter.com/news/bellevue-man-under-investigation-for-online-drug-dealing/ Bellevue man under investigation for online drug dealing 2021-07-08 darknet-market/silk-road/2 --- https://www.courts.sa.gov.au/SentencingRemarks/Pages/lightbox.aspx?IsDlg=1&Filter=4029 2021-07-08 darknet-market/silk-road/2 --- https://www.dailymail.co.uk/wires/reuters/article-2918824/U-S-charges-man-says-linked-Silk-Road-successor-site-drug-scheme.html U.S. charges man says linked to Silk Road successor site in drug scheme 2021-07-09 darknet-market/silk-road/2 --- https://www.dea.gov/divisions/sf/2014/sf053014.shtml 2021-07-09 darknet-market/silk-road/2 --- https://www.garda.ie/en/about-us/our-departments/office-of-corporate-communications/press-releases/2014/november/-darknet-drug-seizure-dublin-5th-nov-2014.html As a result of an international drug trafficking investigation into the sale and supply of controlled drugs on an encrypted layer of the internet known as the Darknet an Irish vendor was identified in recent weeks. Surveillance and enquiries carried out by the Garda National Drug Unit identified a secure premises on South Circular Road, Dublin 8 where it was believed this drug distribution operation was based. 2021-07-09 darknet-market/silk-road/2 --- https://www.independent.ie/irish-news/suspected-online-international-drugs-ring-smashed-by-gardai/30724000.html Suspected online international drugs ring smashed by gardai: A suspected online drug trafficking distribution racket using the encrypted network Darknet has been smashed by detectives. 2021-07-09 darknet-market/silk-road/2 --- https://www.independent.ie/regionals/herald/busted-internet-drugs-kingpin-has-1m-in-bank/30725076.html Busted internet drugs kingpin has €1m in bank 2021-07-09 darknet-market/silk-road/2 --- https://www.irishexaminer.com/news/arid-30650036.html Gardaí seize drugs after probe into ‘Darknet’ drug operation: Ecstasy tablets, LSD and other controlled drugs to value €180,000 have been seized by gardai in a raid on ‘Darknet’ drug distribution centre in Dublin. 2021-07-09 darknet-market/silk-road/2 psychedelic/lsd --- https://www.justice.gov/usao-wdmi/pr/2017_0309_Paiva Norton Shores ‘Dark Web’ Drug Dealer Sentenced To 30 Months In Prison 2021-07-09 darknet-market/silk-road/2 --- https://www.katc.com/story/31449985/lafayette-man-pleads-guilty-to-drug-charges Lafayette man pleads guilty to drug charges 2021-07-09 darknet-market/silk-road/2 --- https://www.mercurynews.com/2014/05/29/contra-costa-nine-charged-in-massive-xanax-drug-operation/ Contra Costa: Nine charged in massive Xanax drug operation 2021-07-09 darknet-market/silk-road/2 --- https://www.mirror.co.uk/news/technology-science/technology/dark-web-drug-dealer-jailed-6546740 Dark web drug dealer jailed for selling horse tranquiliser drug ketamine disguised as health food 2021-07-09 darknet-market/silk-road/2 --- https://archive.is/ZF53p Feds use Facebook to ID man tied to Xanax conspiracy: Authorities say man rammed DEA vehicle after getting package 2021-07-09 darknet-market/silk-road/2 --- https://www.odt.co.nz/news/dunedin/trio-serious-drug-charges Trio on serious drug charges 2021-07-10 darknet-market/silk-road/2 --- https://www.polizei.bayern.de/news/presse/aktuell/index.html/222614 Rauschgift im Internet bestellt—40-Jähriger in Untersuchungshaft 2021-07-10 darknet-market/silk-road/2 --- https://www.rte.ie/news/2014/1106/657346-drugs/ Arrests after major drug seizure in Dublin: Gardaí have raided an internet drug distribution centre in Dublin using the encrypted network Darknet and seized almost €200,000 worth of cannabis, ecstasy and LSD 2021-07-10 darknet-market/silk-road/2 psychedelic/lsd --- https://www.sfgate.com/crime/article/NorCal-couple-ensnared-in-dark-Web-drug-site-5907946.php Butte County couple ensnared in Silk Road 2.0 drug case 2021-07-10 darknet-market/silk-road/2 --- https://www.stuff.co.nz/national/70101651/dunedin-student-jailed-for-dark-web-drug-imports Dunedin student jailed for dark web drug imports 2021-07-10 darknet-market/silk-road/2 --- https://www.stuff.co.nz/national/crime/63743171/five-arrests-in-500k-meth-sting Five arrests in $500k meth sting 2021-07-10 darknet-market/silk-road/2 --- https://www.stuff.co.nz/national/crime/63857494/silk-road-leads-to-home-detention Silk Road leads to home detention 2021-07-10 darknet-market/silk-road/2 --- https://www.tennessean.com/story/news/crime/2014/06/01/xanax-pills-seized-hermitage/9847299/ 5,000 Xanax pills seized in Hermitage 2021-07-10 darknet-market/silk-road/2 --- https://www.theadvertiser.com/story/news/crime/2016/03/11/lafayette-man-pleads-guilty-drug-smuggling-online/81663648/ Lafayette man pleads guilty to drug smuggling online 2021-07-10 darknet-market/silk-road/2 --- https://www.theadvertiser.com/story/news/crime/2016/08/30/lafayette-man-sentenced-18-months-prison-shipping-drugs-via-silk-road/89604606/ Lafayette man sentenced to 18 months in prison for shipping drugs via Silk Road 2021-07-10 darknet-market/silk-road/2 --- https://web.archive.org/web/20150816235023/https://www.thedenverchannel.com/news/colorado-springs-area/air-force-academy-cadet-3rd-class-nathaniel-penalosa-accused-of-using-and-distributing-drugs-on-base Air Force Academy Cadet 3rd Class Nathaniel Penalosa accused of using and distributing drugs on base 2021-07-10 darknet-market/silk-road/2 --- https://www.theguardian.com/technology/2014/nov/07/six-britons-arrested-silk-road-dark-web-takedown-online-drugs Six Britons arrested over Silk Road 2.0 amid dark-web takedown 2021-07-11 darknet-market/silk-road/2 --- https://www.vice.com/en/article/silk-road-2-founder-dread-pirate-roberts-2-caught-jailed-for-5-years/ Silk Road 2 Founder Dread Pirate Roberts 2 Caught, Jailed for 5 Years: For years, the arrest and case has been kept under-wraps. Friday, a court sentenced Thomas White to 5 years and 4 months for his role in running a huge dark web drug marketplace. 2021-07-11 darknet-market/silk-road/2 --- https://www.vice.com/en/article/silk-road-fallout-5-more-charged-in-the-uk-with-deep-web-crimes/ Silk Road Fallout: 5 More Charged in the UK with Deep Web Crimes 2021-07-11 darknet-market/silk-road/2 --- https://www.vice.com/en/article/gv5x4q/court-docs-show-a-university-helped-fbi-bust-silk-road-2-child-porn-suspects Court Docs Show a University Helped FBI Bust Silk Road 2, Child Porn Suspects 2021-07-11 darknet-market/silk-road/2 --- https://www.walesonline.co.uk/news/wales-news/silk-road-20-drug-dealer-9507820 Silk Road 2.0 drug dealer facing jail after being caught hours after FBI ‘dark net’ swoop 2021-07-11 darknet-market/silk-road/2 --- https://www.yorkshireeveningpost.co.uk/news/crime/university-student-leeds-exposed-drug-dealer-after-postal-blunder-623887 University student in Leeds exposed as drug dealer after postal blunder: A former university student who was dealing drugs in Leeds ordered 100 ecstasy tablets from a website but was arrested after they were delivered to the wrong address 2021-07-11 darknet-market/silk-road/2 --- https://web.archive.org/web/20150304143607/http://www.3news.co.nz/nznews/police-bust-dunedin-drug-ring-2015020423 Police bust Dunedin drug ring 2021-07-11 darknet-market/silk-road/2 --- https://www.actionnewsnow.com/news/durham-couple-indicted-as-part-of-silk-road-2-0-crackdown/ Durham couple indicted as part of Silk Road 2.0 crackdown 2021-07-11 darknet-market/silk-road/2 --- https://web.archive.org/web/20140604055548/https://www.wsmv.com/story/25667210/metro-police-seize Metro police seize 21,000 Xanax pills 2021-07-11 darknet-market/silk-road/2 --- http://www.xkloves.us/ 2021-07-11 darknet-market/silk-road/2 --- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0050138 Mental Training Affects Distribution of Limited Brain Resources Heleen A. Slagter, Antoine Lutz, Lawrence L. Greischar, Andrew D. Francis, Sander Nieuwenhuis, James M. Davis, Richard J. Davidson 2007-03-14 2021-07-12 [("doi","10.1371/journal.pbio.0050138")] psychiatry/meditation psychology/neuroscience

The information processing capacity of the human mind is limited, as is evidenced by the so-called “attentional-blink” deficit: When two targets (T1 and T2) embedded in a rapid stream of events are presented in close temporal proximity, the second target is often not seen. This deficit is believed to result from competition between the two targets for limited attentional resources. Here we show, using performance in an attentional-blink task and scalp-recorded brain potentials, that meditation, or mental training, affects the distribution of limited brain resources. 3 months of intensive mental training resulted in a smaller attentional blink and reduced brain-resource allocation to the first target, as reflected by a smaller T1-elicited P3b, a brain-potential index of resource allocation. Furthermore, those individuals that showed the largest decrease in brain-resource allocation to T1 generally showed the greatest reduction in attentional-blink size. These observations provide novel support for the view that the ability to accurately identify T2 depends upon the efficient deployment of resources to T1. The results also demonstrate that mental training can result in increased control over the distribution of limited brain resources. Our study supports the idea that plasticity in brain and mental function exists throughout life and illustrates the usefulness of systematic mental training in the study of the human mind.

Author Summary: Meditation includes the mental training of attention, which involves the selection of goal-relevant information from the array of inputs that bombard our sensory systems. One of the major limitations of the attentional system concerns the ability to process two temporally close, task-relevant stimuli. When the second of two target stimuli is presented within a half second of the first one in a rapid sequence of events, it is often not detected. This so-called “attentional-blink” deficit is thought to result from competition between stimuli for limited attentional resources. We measured the effects of intense meditation on performance and scalp-recorded brain potentials in an attentional-blink task. We found that 3 months of intensive meditation reduced brain-resource allocation to the first target, enabling practitioners to more often detect the second target with no compromise in their ability to detect the first target. These findings demonstrate that meditative training can improve performance on a novel task that requires the trained attentional abilities.

Intensive training in Vipassana meditation enhances one’s ability to allocate attention efficiently in order to detect visual targets accurately. Behavioral and event-related potential evidence for a causal link between behavioral training and brain plasticity in adults is shown.

--- /doc/dual-n-back/2010-zeidan.pdf Mindfulness meditation improves cognition: Evidence of brief mental training Fadel Zeidan, Susan K. Johnson, Bruce J. Diamond, Zhanna David, Paula Goolkasian 2010-01-01 2021-07-12 [("doi","10.1016/j.concog.2010.03.014")] dual-n-back psychiatry/meditation --- https://harpers.org/archive/2021/04/lost-in-thought-psychological-risks-of-meditation/ 2021-07-12 psychiatry/meditation --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176239 The varieties of contemplative experience: A mixed-methods study of meditation-related challenges in Western Buddhists Jared R. Lindahl, Nathan E. Fisher, David J. Cooper, Rochelle K. Rosen, Willoughby B. Britton 2017-04-02 2021-07-12 [("doi","10.1371/journal.pone.0176239")] psychiatry/meditation

Buddhist-derived meditation practices are currently being employed as a popular form of health promotion. While meditation programs draw inspiration from Buddhist textual sources for the benefits of meditation, these sources also acknowledge a wide range of other effects beyond health-related outcomes.

The Varieties of Contemplative Experience study investigates meditation-related experiences that are typically under-reported, particularly experiences that are described as challenging, difficult, distressing, functionally impairing, and/or requiring additional support. A mixed-methods approach featured qualitative interviews with Western Buddhist meditation practitioners and experts in Theravāda, Zen, and Tibetan traditions. Interview questions probed meditation experiences and influencing factors, including interpretations and management strategies. A follow-up survey provided quantitative assessments of causality, impairment, and other demographic and practice-related variables.

The content-driven thematic analysis of interviews yielded a taxonomy of 59 meditation-related experiences across 7 domains: cognitive, perceptual, affective, somatic, conative, sense of self, and social. Even in cases where the phenomenology was similar across participants, interpretations of and responses to the experiences differed considerably. The associated valence ranged from very positive to very negative, and the associated level of distress and functional impairment ranged from minimal and transient to severe and enduring.

In order to determine what factors may influence the valence, impact, and response to any given experience, the study also identified 26 categories of influencing factors across 4 domains: practitioner-level factors, practice-level factors, relationships, and health behaviors. By identifying a broader range of experiences associated with meditation, along with the factors that contribute to the presence and management of experiences reported as challenging, difficult, distressing, or functionally impairing, this study aims to increase our understanding of the effects of contemplative practices and to provide resources for mediators, clinicians, meditation researchers, and meditation teachers.

--- https://www.lesswrong.com/posts/tMhEv28KJYWsu6Wdo/kensh?commentId=rdspAcLwuwS85kGKx 2021-07-12 psychiatry/meditation --- https://slatestarcodex.com/2019/10/16/is-enlightenment-compatible-with-sex-scandals/ Is Enlightenment Compatible With Sex Scandals? 2021-07-12 psychiatry/meditation --- https://slatestarcodex.com/2019/11/04/samsara/ Samsara 2021-07-12 fiction/humor fiction/science-fiction philosophy/ethics psychiatry/meditation --- https://slatestarcodex.com/2019/10/21/the-pnse-paper/ The PNSE Paper 2021-07-12 psychiatry/meditation --- https://slatestarcodex.com/2017/09/18/book-review-mastering-the-core-teachings-of-the-buddha/ Book Review: Mastering The Core Teachings Of The Buddha 2021-07-12 psychiatry/meditation --- https://slatestarcodex.com/2019/10/24/highlights-from-the-comments-on-pnse/ Highlights From The Comments On PNSE 2021-07-12 psychiatry/meditation --- https://vividness.live/meditation-risks Meditation risks, safety, goals, methods 2021-07-12 psychiatry/meditation --- https://danlawton.substack.com/p/when-buddhism-goes-bad When Buddhism Goes Bad 2021-07-13 psychiatry/meditation --- https://www.theatlantic.com/health/archive/2014/06/the-dark-knight-of-the-souls/372766/ The Dark Knight of the Soul 2021-07-13 psychiatry/meditation --- https://journals.sagepub.com/doi/full/10.1177/0956797621997366 2021-07-13 psychiatry/meditation --- https://www.astralcodexten.com/p/jhanas-and-the-dark-room-problem Jhanas and the Dark Room Problem 2021-07-13 psychiatry/meditation psychology/neuroscience --- https://eudoxos.github.io/saints/html/saints.html 2021-07-13 psychiatry/meditation --- http://bearlamp.com.au/a-review-of-my-favourite-books-of-2021/ A review of my favorite books of 2021 2021-07-13 psychiatry/meditation --- https://hollyelmore.substack.com/p/i-believed-the-hype-and-did-mindfulness-meditation-for-dumb-reasons-now-im-trying-to-reverse-the-damage I believed the hype and did mindfulness meditation for dumb reasons—now I’m trying to reverse the damage 2021-07-13 psychiatry/meditation --- https://www.joshcsimmons.com/posts/no-not-everyone-should-meditate 2021-07-13 psychiatry/meditation --- /doc/psychiatry/meditation/2019-sharma.pdf 2019 2021-07-13 psychiatry/meditation --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3328970/ Meditation and its regulatory role on sleep Ravindra P. Nagendra, Nirmala Maruthai, Bindu M. Kutty 2012 2021-07-13 [("doi","10.3389/fneur.2012.00054")] psychiatry/meditation zeo

Intense meditation practices help to achieve a harmony between body and mind. Meditation practices influence brain functions, induce various intrinsic neural plasticity events, modulate autonomic, metabolic, endocrine, and immune functions and thus mediate global regulatory changes in various behavioral states including sleep.

This brief review focuses on the effect of meditation as a self-regulatory phenomenon on sleep.

--- https://arxiv.org/abs/0909.0801 A Monte Carlo AIXI Approximation Joel Veness, Kee Siong Ng, Marcus Hutter, William Uther, David Silver 2009-09-04 2021-07-13 [("doi","10.48550/arXiv.0909.0801")] ai cs/algorithm/information/compression reinforcement-learning/model statistics/bayes

This paper introduces a principled approach for the design of a scalable general reinforcement learning agent.

Our approach is based on a direct approximation of AIXI, a Bayesian optimality notion for general reinforcement learning agents. Previously, it has been unclear whether the theory of AIXI could motivate the design of practical algorithms.

We answer this hitherto open question in the affirmative, by providing the first computationally feasible approximation to the AIXI agent. To develop our approximation, we introduce a new Monte-Carlo Tree Search algorithm along with an agent-specific extension to the Context Tree Weighting algorithm.

Empirically, we present a set of encouraging results on a variety of stochastic and partially observable domains.

We conclude by proposing a number of directions for future research.

--- https://arxiv.org/abs/1105.3821 Ontological Crises in Artificial Agents’ Value Systems Peter de Blanc 2011-05-19 2021-07-14 [("doi","10.48550/arXiv.1105.3821")] ai philosophy/ontology reinforcement-learning/safe

Decision-theoretic agents predict and evaluate the results of their actions using a model, or ontology, of their environment. An agent’s goal, or utility function, may also be specified in terms of the states of, or entities within, its ontology. If the agent may upgrade or replace its ontology, it faces a crisis: the agent’s original goal may not be well-defined with respect to its new ontology. This crisis must be resolved before the agent can make plans towards achieving its goals.

We discuss in this paper which sorts of agents will undergo ontological crises and why we may want to create such agents. We present some concrete examples, and argue that a well-defined procedure for resolving ontological crises is needed. We point to some possible approaches to solving this problem, and evaluate these methods on our examples.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1526555/ Fish oil: what the prescriber needs to know Leslie G. Cleland, Michael J. James, Susanna M. Proudman 2006 2021-07-14 [("doi","10.1186/ar1876")] biology

There is a general belief among doctors, in part grounded in experience, that patients with arthritis need non-steroidal anti-inflammatory drugs (NSAIDs). Implicit in this view is that these patients require the symptomatic relief provided by inhibiting synthesis of nociceptive prostaglandin E2, a downstream product of the enzyme cyclo-oxygenase (COX), which is inhibited by NSAIDs. However, the concept of ‘safe’ NSAIDs has collapsed following a multiplicity of observations establishing increased risk for cardiovascular events associated with NSAID use, especially but not uniquely with the new COX-2-selective NSAIDs. This mandates greater parsimony in the use of these agents.

Fish oils contain a natural inhibitor of COX, reduce reliance on NSAIDs, and reduce cardiovascular risk through multiple mechanisms. Fish oil thus warrants consideration as a component of therapy for arthritis, especially rheumatoid arthritis, in which its symptomatic benefits are well established. A major barrier to the therapeutic use of fish oil in inflammatory diseases is ignorance of its mechanism, range of beneficial effects, safety profile, availability of suitable products, effective dose, latency of effects and instructions for administration. This review provides an evidence-based resource for doctors and patients who may choose to prescribe or take fish oil.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3739679/ Assessing human sperm morphology: top models, underdogs or biometrics? Jacques Auger 2010 2021-07-14 [("doi","10.1038/aja.2009.8")] biology

The assessment of the percentage of spermatozoa having an ‘ideal’ morphology using so-called strict method is the method recommended in the latest edition of the World Health Organization (WHO) laboratory manual for semen analysis. This recommendation is a result of the statistical association between ‘ideal’ sperm morphology and fertility, and of the current general belief that sperm morphology assessment should be used primarily as a fertility tool. The notion of an ‘ideal’ sperm morphology has persisted despite the very low percentage of such spermatozoa in the semen of fertile men, a subject of intense controversy. The detailed categorization of each abnormal spermatozoon has thus, for a long time, been considered optional and partially redundant, an idea which is reflected in the earlier editions of the WHO manual.

However, several recent studies have shown the importance of carefully assessing abnormal sperm morphology for use in the diagnosis of infertility, to determine fertility prognosis, and for basic or public health studies. One approach, which combines video-microscopy and computer vision, and is the only approach able to assess the continuum of sperm biometrics, has been used successfully in several recent clinical, basic and toxicology studies.

In summary, the visual assessment of detailed sperm morphology-including the categorization of anomalies allowing arithmetically derived indices of teratozoospermia-and the more modern computer-based approaches, although often considered to be redundant, are in fact complementary. The choice of the most appropriate method depends on the field of investigation (clinical, research, toxicology) and the problem being addressed. Each approach has advantages as well as certain limitations, which will be discussed briefly herein.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3085894/ Relative over-reactivity of human versus chimpanzee lymphocytes: implications for the human diseases associated with immune activation Paula C. Soto, Lance L. Stein, Nancy Hurtado-Ziola, Stephen M. Hedrick, Ajit Varki 2010 2021-07-14 [("doi","10.4049/jimmunol.0903420")] biology

Although humans and chimpanzees share >99% identity in alignable protein sequences, they differ surprisingly in the incidence and severity of some common diseases. In general, humans infected with various viruses, such as HIV and hepatitis C virus, appear to develop stronger reactions and long-term complications. Humans also appear to suffer more from other diseases associated with over-reactivity of the adaptive immune system, such as asthma, psoriasis, and rheumatoid arthritis.

In this study, we show that human T cells are more reactive than chimpanzee T cells to a wide variety of stimuli, including anti-TCR Abs of multiple isotypes, L-phytohemagglutinin, Staphylococcus aureus superantigen, a superagonist anti-CD28 Ab, and in MLRs (Mixed Lymphocyte Reactions). We also extend this observation to B cells, again showing a human propensity to react more strongly to stimuli. Finally, we show a relative increase in activation markers and cytokine production in human lymphocytes in response to uridine-rich (viral-like) ssRNA.

Thus, humans manifest a generalized lymphocyte over-reactivity relative to chimpanzees, a finding that is correlated with decreased levels of inhibitory sialic acid-recognizing Ig-superfamily lectins (Siglecs, particularly Siglec-5) on human T and B cells. Furthermore, Siglec-5 levels are upregulated by activation in chimpanzee but not human lymphocytes, and human T cell reactivity can be down-modulated by forced expression of Siglec-5. Thus, a key difference in the immune reactivity of chimp and human lymphocytes appears to be related to the differential expression of Siglec-5.

Taken together, these data may help explain human propensities for diseases associated with excessive activation of the adaptive immune system.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3078757/ The lifetime risk of adult-onset rheumatoid arthritis and other inflammatory autoimmune rheumatic diseases Cynthia S. Crowson, Eric L. Matteson, Elena Myasoedova, Clement J. Michet, Floranne C. Ernste, Kenneth J. Warrington, John M. Davis, Gene G. Hunder, Terry M. Therneau, Sherine E. Gabriel 2011 2021-07-14 [("doi","10.1002/art.30155")] biology

Objective: Understanding of the personal risks for rheumatoid arthritis (RA) and other rheumatic diseases remains poor, despite advances in knowledge with regard to their pathogenesis, therapeutics, and clinical impact, in part because the personal lifetime risk of developing these diseases is unknown. This study was undertaken to estimate the lifetime risk of RA, as well as other inflammatory autoimmune rheumatic diseases, including systemic lupus erythematosus, psoriatic arthritis, polymyalgia rheumatica (PMR), giant cell arteritis, ankylosing spondylitis, and Sjögren’s syndrome, and to provide an overall estimate of the risk of developing inflammatory autoimmune rheumatic disease over a lifetime.

Method: Using the incidence rates obtained from our population-based studies of rheumatic diseases among residents of Olmsted County, Minnesota, and mortality rates from life tables for the general population, we estimated the sex-specific lifetime risk of rheumatic disease.

Results: The lifetime risk of RA developing in US adults was 3.6% for women and 1.7% for men, and the lifetime risk of rheumatoid factor-positive RA was 2.4% for women and 1.1% for men. The second most common inflammatory autoimmune rheumatic disease was PMR, with a lifetime risk of 2.4% for women and 1.7% for men. The overall lifetime risk of inflammatory autoimmune rheumatic disease was 8.4% for women and 5.1% for men.

Conclusion: One in 12 women and 1 in 20 men will develop an inflammatory autoimmune rheumatic disease during their lifetime. These results can serve as useful guides in counseling patients regarding their lifetime risk of these conditions and have important implications regarding disease awareness campaigns.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3263034/ Spectrum of human tails: A report of six cases Biswanath Mukhopadhyay, Ram M. Shukla, Madhumita Mukhopadhyay, Kartik C. Mandal, Pankaj Haldar, Abhijit Benare 2012 2021-07-14 [("doi","10.4103/0971-9261.91082")] biology

Human tail is a curiosity, a cosmetic stigma and presents as an appendage in the lumbosacral region.

6 patients of tail in the lumbosacral region are presented here to discuss the spectrum of presentation of human tails.

The embryology, pathology and treatment of this entity are discussed along with a brief review of the literature.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3073564/ Feline genetics: clinical applications and genetic testing Leslie A. Lyons 2010 2021-07-14 [("doi","10.1053/j.tcam.2010.09.002")] cat/genetics

DNA testing for domestic cat diseases and appearance traits is a rapidly growing asset for veterinary medicine. ~33 genes contain 50 mutations that cause feline health problems or alterations in the cat’s appearance. A variety of commercial laboratories can now perform cat genetic diagnostics, allowing both the veterinary clinician and the private owner to obtain DNA test results.

DNA is easily obtained from a cat via a buccal swab with a standard cotton bud or cytological brush, allowing DNA samples to be easily sent to any laboratory in the world. The DNA test results identify carriers of the traits, predict the incidence of traits from breeding programs, and influence medical prognoses and treatments.

An overall goal of identifying these genetic mutations is the correction of the defect via gene therapies and designer drug therapies. Thus, genetic testing is an effective preventative medicine and a potential ultimate cure. However, genetic diagnostic tests may still be novel for many veterinary practitioners and their application in the clinical setting needs to have the same scrutiny as any other diagnostic procedure.

This article will review the genetic tests for the domestic cat, potential sources of error for genetic testing, and the pros and cons of DNA results in veterinary medicine. Highlighted are genetic tests specific to the individual cat, which are a part of the cat’s internal genome.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3541004/ Genetic testing in domestic cats Leslie A. Lyons 2012 2021-07-14 [("doi","10.1016/j.mcp.2012.04.004")] cat/genetics

Varieties of genetic tests are currently available for the domestic cat that support veterinary health care, breed management, species identification, and forensic investigations. ~thirty-five genes contain over fifty mutations that cause feline health problems or alterations in the cat’s appearance. Specific genes, such as sweet and drug receptors, have been knocked-out of Felidae during evolution and can be used along with mtDNA markers for species identification.

Both STR and SNP panels differentiate cat race, breed, and individual identity, as well as gender-specific markers to determine sex of an individual. Cat genetic tests are common offerings for commercial laboratories, allowing both the veterinary clinician and the private owner to obtain DNA test results.

This article will review the genetic tests for the domestic cat, and their various applications in different fields of science. Highlighted are genetic tests specific to the individual cat, which are a part of the cat’s genome.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2562318/ Age differences in the Big Five across the life span: evidence from two national samples M. Brent Donnellan, Richard E. Lucas 2008 2021-07-14 [("doi","10.1037/a0012897")] psychology/personality/conscientiousness

Cross-sectional age differences in the Big Five personality traits were investigated using 2 large datasets from Great Britain and Germany: the British Household Panel Study (BHPS; n > or = 14,039) and the German Socio-Economic Panel Study (GSEOP; n > or = 20,852). Participants, who ranged in age from 16 to the mid-80s, completed a 15-item version of the Big Five Inventory (eg. John & Srivastava 1999) in either 2005 or 2006.

The observed age trends were generally consistent across both datasets. Extraversion and Openness were negatively associated with age, whereas Agreeableness was positively associated with age. Average levels of Conscientiousness were highest for participants in middle age. The only exception was Neuroticism, which was slightly negatively associated with age in the BHPS and slightly positively associated with age in the GSEOP.

Neither gender nor education level were consistent moderators of age differences in the Big Five.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3049131/ Skill execution and sleep deprivation: effects of acute caffeine or creatine supplementation—a randomized placebo-controlled trial Christian J. Cook, Blair T. Crewther, Liam P. Kilduff, Scott Drawer, Chris M. Gaviglio 2011 2021-07-14 [("doi","10.1186/1550-2783-8-2")] creatine nootropic/caffeine zeo

Background: We investigated the effects of sleep deprivation with or without acute supplementation of caffeine or creatine on the execution of a repeated rugby passing skill.

Method: Ten elite rugby players completed 10 trials on a simple rugby passing skill test (20 repeats per trial), following a period of familiarization. The players had between 7–9 h sleep on 5 of these trials and between 3–5 h sleep (deprivation) on the other 5. At a time of 1.5 h before each trial, they undertook administration of either: placebo tablets, 50 or 100 mg/kg creatine, 1 or 5 mg/kg caffeine. Saliva was collected before each trial and assayed for salivary free cortisol and testosterone.

Results: Sleep deprivation with placebo application resulted in a fall in skill performance accuracy on both the dominant and non-dominant passing sides (p < 0.001). No fall in skill performance was seen with caffeine doses of 1 or 5 mg/kg, and the two doses were not different in effect. Similarly, no deficit was seen with creatine administration at 50 or 100 mg/kg and the performance effects were not different. Salivary testosterone was not affected by sleep deprivation, but trended higher with the 100 mg/kg creatine dose, compared to the placebo treatment (p = 0.067). Salivary cortisol was elevated (p = 0.001) with the 5 mg/kg dose of caffeine (vs. placebo).

Conclusion: Acute sleep deprivation affects performance of a simple repeat skill in elite athletes and this was ameliorated by a single dose of either caffeine or creatine. Acute creatine use may help to alleviate decrements in skill performance in situations of sleep deprivation, such as trans-meridian travel, and caffeine at low doses appears as efficacious as higher doses, at alleviating sleep deprivation deficits in athletes with a history of low caffeine use. Both options are without the side effects of higher dose caffeine use.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2922855/ The VNTR 2 repeat in MAOA and delinquent behavior in adolescence and young adulthood: associations and MAOA promoter activity Guang Guo, Xiao-Ming Ou, Michael Roettger, Jean C. Shih 2008 2021-07-15 [("doi","10.1038/sj.ejhg.5201999")] crime genetics/heritable/rare

Genetic studies of delinquent and criminal behavior are rare in spite of the wide recognition that individuals may differ in their propensity for delinquency and criminality.

Using 2,524 participants in Add Health in the United States, the present study demonstrates:

a link between the rare 2 repeat of the 30-bp VNTR in the MAOA gene and much higher levels of self-reported serious and violent delinquency. The evidence is based on a statistical association analysis and a functional analysis of MAOA promoter activity using two human brain-derived cell lines: neuroblastoma SH-SY5Y and human glioblastoma 1242-MG. The association analysis shows that men with a 2R report a level of serious delinquency and violent delinquency in adolescence and young adulthood that were about twice (CI: (0.21, 3.24), p = 0.025; and CI: (0.37, 2.5), p = 0.008 for serious and violent delinquency, respectively) as high as those for participants with the other variants. The results for women are similar, but weaker.

In the functional analysis, the 2 repeat exhibits much lower levels of promoter activity than the 3 or 4 repeat.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2887689/ Long-term economic costs of psychological problems during childhood James Patrick Smith, Gillian C. Smith 2010 2021-07-15 [("doi","10.1016/j.socscimed.2010.02.046")] crime economics psychiatry

Childhood psychological conditions including depression and substance abuse are a growing concern among American children, but their long-term economic costs are unknown.

This paper uses unique data from the US Panel Study of Income Dynamics (PSID) following groups of siblings and their parents for up to 40 years prospectively collecting information on education, income, work, and marriage. Following siblings offers an opportunity to control for unobserved family and neighborhood effects. A retrospective child health history designed by the author was placed into the 2007 PSID wave measuring whether respondents had any of 14 childhood physical illnesses or suffered from depression, substance abuse, or other psychological conditions.

Large effects are found on the ability of affected children to work and earn as adults. Educational accomplishments are diminished, and adult family incomes are reduced by 20% or $10,400 per year with $18,000 less family household assets. Lost income is partly a consequence of seven fewer weeks worked per year. There is also an 11% point lower probability of being married. Controlling for physical childhood diseases shows that these effects are not due to the co-existence of psychological and physical diseases, and estimates controlling for within-sibling differences demonstrate that these effects are not due to unobserved common family differences.

The long-term economic damages of childhood psychological problems are large: a lifetime cost in lost family income of ~$300,000, and total lifetime economic cost for all those affected of $2.1 trillion.

--- https://arxiv.org/abs/0802.0733 Optimal boarding method for airline passengers Jason H. Steffen 2008-02-06 2021-07-15 [("doi","10.1016/j.jairtraman.2008.03.003")] cs/algorithm

Using a Markov Chain Monte Carlo optimization algorithm and a computer simulation, I find the passenger ordering which minimizes the time required to board the passengers onto an airplane. The model that I employ assumes that the time that a passenger requires to load his or her luggage is the dominant contribution to the time needed to completely fill the aircraft.

The optimal boarding strategy may reduce the time required to board and airplane by over a factor of four and possibly more depending upon the dimensions of the aircraft. In addition, knowledge of the optimal boarding procedure can inform decisions regarding changes to methods that are employed by a particular carrier.

I explore some of the salient features of the optimal boarding method and discuss practical modifications to the optimal.

Finally, I mention some of the benefits that could come from implementing an improved passenger boarding scheme.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2914582/ Hundred Days of Cognitive Training Enhance Broad Cognitive Abilities in Adulthood: Findings from the COGITO Study Florian Schmiedek, Martin Lövdén, Ulman Lindenberger 2010 2021-07-15 [("doi","10.3389/fnagi.2010.00027")] dual-n-back

We examined whether positive transfer of cognitive training, which so far has been observed for individual tests only, also generalizes to cognitive abilities, thereby carrying greater promise for improving everyday intellectual competence in adulthood and old age.

In the COGITO Study, 101 younger and 103 older adults practiced six tests of perceptual speed (PS), 3 tests of working memory (WM), and 3 tests of episodic memory (EM) for over 100 daily 1-h sessions. Transfer assessment included multiple tests of PS, WM, EM, and reasoning.

In both age groups, reliable positive transfer was found not only for individual tests but also for cognitive abilities, represented as latent factors. Furthermore, the pattern of correlations between latent change factors of practiced and latent change factors of transfer tasks indicates systematic relations at the level of broad abilities, making the interpretation of effects as resulting from unspecific increases in motivation or self-concept less likely.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2884087/ Putting brain training to the test Adrian M. Owen, Adam Hampshire, Jessica A. Grahn, Robert Stenton, Said Dajani, Alistair S. Burns, Robert J. Howard, Clive G. Ballard 2010 2021-07-15 [("doi","10.1038/nature09042")] dual-n-back

‘Brain training’, or the goal of improved cognitive function through the regular use of computerized tests, is a multimillion-pound industry, yet in our view scientific evidence to support its efficacy is lacking. Modest effects have been reported in some studies of older individuals and preschool children, and video-game players outperform non-players on some tests of visual attention. However, the widely held belief that commercially available computerized brain-training programs improve general cognitive function in the wider population in our opinion lacks empirical support. The central question is not whether performance on cognitive tests can be improved by training, but rather, whether those benefits transfer to other untrained tasks or lead to any general improvement in the level of cognitive functioning.

Here we report the results of a six-week online study in which 11,430 participants trained several times each week on cognitive tasks designed to improve reasoning, memory, planning, visuospatial skills and attention. Although improvements were observed in every one of the cognitive tasks that were trained, no evidence was found for transfer effects to untrained tasks, even when those tasks were cognitively closely related.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3158154/ A Unified attentional bottleneck in the human brain Michael N. Tombu, Christopher L. Asplund, Paul E. Dux, Douglass Godwin, Justin W. Martin, René Marois 2011 2021-07-15 [("doi","10.1073/pnas.1103583108")] dual-n-back psychology/neuroscience

Human information processing is characterized by bottlenecks that constrain throughput. These bottlenecks limit both what we can perceive and what we can act on in multitask settings. Although perceptual and response limitations are often attributed to independent information processing bottlenecks, it has recently been suggested that a common attentional limitation may be responsible for both. To date, however, evidence supporting the existence of such a “unified” bottleneck has been mixed.

Here, we tested the unified bottleneck hypothesis using time-resolved fMRI. Experiment 1 isolated brain regions involved in the response selection bottleneck that limits speeded dual-task performance. These same brain regions were not only engaged by a perceptual encoding task in Experiment 2, their activity also tracked delays to a speeded decision-making task caused by concurrent perceptual encoding (Experiment 3).

We conclude that a unified attentional bottleneck, including the inferior frontal junction, superior medial frontal cortex, and bilateral insula, temporally limits operations as diverse as perceptual encoding and decision-making.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0023175 Working Memory Training Using Mental Calculation Impacts Regional Gray Matter of the Frontal and Parietal Regions Hikaru Takeuchi, Yasuyuki Taki, Yuko Sassa, Hiroshi Hashizume, Atsushi Sekiguchi, Ai Fukushima, Ryuta Kawashima 2011-07-11 2021-07-15 [("doi","10.1371/journal.pone.0023175")] dual-n-back psychology/neuroscience

Training working memory (WM) improves performance on untrained cognitive tasks and alters functional activity. However, WM training’s effects on gray matter morphology and a wide range of cognitive tasks are still unknown.

We investigated this issue using voxel-based morphometry (VBM), various psychological measures, such as non-trained WM tasks and a creativity task, and intensive adaptive training of WM using mental calculations (IATWMMC), all of which are typical WM tasks.

IATWMMC was associated with reduced regional gray matter volume in the bilateral fronto-parietal regions and the left superior temporal gyrus. It improved verbal letter span and complex arithmetic ability, but deteriorated creativity.

These results confirm the training-induced plasticity in psychological mechanisms and the plasticity of gray matter structures in regions that have been assumed to be under strong genetic control.

--- https://journals.plos.org/plosone/article/asset?id=10.1371/journal.pone.0024372o.PDF Extending Brain-Training to the Affective Domain: Increasing Cognitive and Affective Executive Control through Emotional Working Memory Training Susanne Schweizer, Adam Hampshire, Tim Dalgleish 2011-08-09 2021-07-15 [("doi","10.1371/journal.pone.0024372")] dual-n-back

So-called ‘brain-training’ programs are a huge commercial success. However, empirical evidence regarding their effectiveness and generalizability remains equivocal. This study investigated whether brain-training (working memory [WM] training) improves cognitive functions beyond the training task (transfer effects), especially regarding the control of emotional material since it constitutes much of the information we process daily.

Forty-five participants received WM training using either emotional or neutral material, or an undemanding control task. WM training, regardless of training material, led to transfer gains on another WM task and in fluid intelligence.

However, only brain-training with emotional material yielded transferable gains to improved control over affective information on an emotional Stroop task. The data support the reality of transferable benefits of demanding WM training and suggest that transferable gains across to affective contexts require training with material congruent to those contexts.

These findings constitute preliminary evidence that intensive cognitively demanding brain-training can improve not only our abstract problem-solving capacity, but also ameliorate cognitive control processes (eg. decision-making) in our daily emotive environments.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3368385/ On the impacts of working memory training on executive functioning Tiina Salminen, Tilo Strobach, Torsten Schubert 2012 2021-07-15 [("doi","10.3389/fnhum.2012.00166")] dual-n-back

Recent studies have reported improvements in a variety of cognitive functions following sole working memory (WM) training. In spite of the emergence of several successful training paradigms, the scope of transfer effects has remained mixed. This is most likely due to the heterogeneity of cognitive functions that have been measured and tasks that have been applied. In the present study, we approached this issue systematically by investigating transfer effects from WM training to different aspects of executive functioning.

Our training task was a demanding WM task that requires simultaneous performance of a visual and an auditory n-back task, while the transfer tasks tapped WM updating, coordination of the performance of multiple simultaneous tasks (ie. dual-tasks) and sequential tasks (ie. task switching), and the temporal distribution of attentional processing. Additionally, we examined whether WM training improves reasoning abilities; a hypothesis that has so far gained mixed support.

Following training, participants showed improvements in the trained task as well as in the transfer WM updating task. As for the other executive functions, trained participants improved in a task switching situation and in attentional processing. There was no transfer to the dual-task situation or to reasoning skills.

These results, therefore, confirm previous findings that WM can be trained, and additionally, they show that the training effects can generalize to various other tasks tapping on executive functions.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2748863/ Exercise and Children’s Intelligence, Cognition, and Academic Achievement Phillip D. Tomporowski, Catherine L. Davis, Patricia H. Miller, Jack A. Naglieri 2008 2021-07-15 [("doi","10.1007/s10648-007-9057-0")] exercise iq

Studies that examine the effects of exercise on children’s intelligence, cognition, or academic achievement were reviewed and results were discussed in light of (a) contemporary cognitive theory development directed toward exercise, (b) recent research demonstrating the salutary effects of exercise on adults’ cognitive functioning, and (c) studies conducted with animals that have linked physical activity to changes in neurological development and behavior.

Similar to adults, exercise facilitates children’s executive function (ie. processes required to select, organize, and properly initiate goal-directed actions).

Exercise may prove to be a simple, yet important, method of enhancing those aspects of children’s mental functioning central to cognitive development.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3088429/ Exercise and executive function in individuals with chronic stroke: a pilot study Patricia M. Kluding, Benjamin Y. Tseng, Sandra A. Billinger 2011 2021-07-15 [("doi","10.1097/NPT.0b013e318208ee6c")] exercise

Background and Purpose: Emerging evidence suggests that exercise may improve cognitive function in older adults. The purpose of this pilot study was to describe changes in measures of cognition and executive function in individuals with chronic stroke following participation in aerobic and strengthening exercise.

Method: A single-group, pretest-posttest design was used. Nine individuals with chronic stroke (mean age = 63.7 ± 9.1 years, mean time since stroke = 50.4 ± 37.9 months) completed a 12-week program of aerobic and strengthening exercise, 3 days per week. The primary outcome measures examined executive function (Digit Span Backwards and Flanker tests). Secondary measures examined various aspects of aerobic fitness (VO2peak and 6-minute walk distance) and function (Fugl-Meyer and 10-m walk speed).

Results: Following the intervention, improvements were found in the Digit Span Backwards test (mean change = 0.56 ± 0.9 digits; p = 0.05), Fugl-Meyer score (mean change = 3.6 ± 5.7; p = 0.05), and Stroke Impact Scale total score (mean change = 33.8 ± 38.5; p = 0.02). A statistically-significant correlation was found between improved aerobic capacity and improved performance on the Flanker test (r = 0.74; p = 0.02).

Discussion: The results of this study indicate that a 12-week aerobic and strengthening exercise program was associated with improvements in selected measures of executive function and functional capacity in people with stroke. Limitations of this study include the small sample size and lack of a comparison group.

Conclusion: This pilot study contributes to the emerging evidence that exercise improves cognition in people with stroke. These benefits indicate the need for future study with a larger group to have sufficient power to further explore these relationships.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3298958/ Antioxidants and Skeletal Muscle Performance: "Common Knowledge" vs. Experimental Evidence Andrés Hernández, Arthur Cheng, Håkan Westerblad 2012 2021-07-16 [("doi","10.3389/fphys.2012.00046")] exercise

Antioxidants are assumed to provide numerous benefits, including better health, a reduced rate of aging, and improved exercise performance. Specifically, antioxidants are commonly “prescribed” by the media, supplement industry, and “fitness experts” for individuals prior to training and performance, with assumed benefits of improved fatigue resistance and recovery.

This has provoked expansion of the supplement industry which responded by creation of a plethora of products aimed at facilitating the needs of the active individual.

However, what does the experimental evidence say about the efficacy of antioxidants on skeletal muscle function? Are antioxidants actually as beneficial as the general populace believes? Or, could they in fact lead to deleterious effects on skeletal muscle function and performance?

This Mini Review addresses these questions with an unbiased look at what we know about antioxidant effects on skeletal muscle, and what we still need to know before conclusions can be made.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0048345 Rats Bred for Low Aerobic Capacity Become Promptly Fatigued and Have Slow Metabolic Recovery after Stimulated, Maximal Muscle Contractions Sira Torvinen, Mika Silvennoinen, Harri Piitulainen, Johanna Närväinen, Pasi Tuunanen, Olli Gröhn, Lauren G. Koch, Steven L. Britton, Heikki Kainulainen 2012-09-24 2021-07-16 [("doi","10.1371/journal.pone.0048345")] exercise genetics/selection/artificial

Aim: Muscular fatigue is a complex phenomenon affected by muscle fiber type and several metabolic and ionic changes within myocytes. Mitochondria are the main determinants of muscle oxidative capacity which is also one determinant of muscle fatigability. By measuring the concentrations of intracellular stores of high-energy phosphates it is possible to estimate the energy production efficiency and metabolic recovery of the muscle. Low intrinsic aerobic capacity is known to be associated with reduced mitochondrial function. Whether low intrinsic aerobic capacity also results in slower metabolic recovery of skeletal muscle is not known. Here we studied the influence of intrinsic aerobic capacity on in vivo muscle metabolism during maximal, fatiguing electrical stimulation.

Method: Animal subjects were genetically heterogeneous rats selectively bred to differ for non-trained treadmill running endurance, low capacity runners (LCRs) and high capacity runners (HCRs) (n = 15–19). We measured the concentrations of major phosphorus compounds and force parameters in a contracting triceps surae muscle complex using 31P-Magnetic resonance spectroscopy (31P-MRS) combined with muscle force measurement from repeated isometric twitches.

Results: Our results demonstrated that phosphocreatine re-synthesis after maximal muscle stimulation was slower in LCRs (p < 0.05). LCR rats also became promptly fatigued and maintained the intramuscular pH poorly compared to HCRs. Half relaxation time (HRT) of the triceps surae was longer in LCRs throughout the stimulation protocol (p ≤ 0.05) and maximal rate of torque development (MRTD) was lower in LCRs compared to HCRs from 2 min 30 s onwards (p ≤ 0.05).

Conclusion: We observed that LCRs are more sensitive to fatigue and have slower metabolic recovery compared to HCRs after maximal muscle contractions. These new findings are associated with reduced running capacity and with previously found lower mitochondrial content, increased body mass and higher complex disease risk of LCRs.

--- https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0040352 Clustered Environments and Randomized Genes: A Fundamental Distinction between Conventional and Genetic Epidemiology George Davey Smith, Debbie A. Lawlor, Roger Harbord, Nic Timpson, Ian Day, Shah Ebrahim 2007-10-30 2021-07-16 [("doi","10.1371/journal.pmed.0040352")] genetics/heritable/correlation/mendelian-randomization statistics/causality

Background: In conventional epidemiology confounding of the exposure of interest with lifestyle or socioeconomic factors, and reverse causation whereby disease status influences exposure rather than vice versa, may invalidate causal interpretations of observed associations. Conversely, genetic variants should not be related to the confounding factors that distort associations in conventional observational epidemiological studies. Furthermore, disease onset will not influence genotype. Therefore, it has been suggested that genetic variants that are known to be associated with a modifiable (nongenetic) risk factor can be used to help determine the causal effect of this modifiable risk factor on disease outcomes. This approach, Mendelian Randomization, is increasingly being applied within epidemiological studies. However, there is debate about the underlying premise that associations between genotypes and disease outcomes are not confounded by other risk factors. We examined the extent to which genetic variants, on the one hand, and nongenetic environmental exposures or phenotypic characteristics on the other, tend to be associated with each other, to assess the degree of confounding that would exist in conventional epidemiological studies compared with Mendelian Randomization studies.

Methods & Findings: We estimated pairwise correlations between nongenetic baseline variables and genetic variables in a cross-sectional study comparing the number of correlations that were statistically-significant at the 5%, 1%, and 0.01% level (α = 0.05, 0.01, and 0.0001, respectively) with the number expected by chance if all variables were in fact uncorrelated, using a two-sided binomial exact test. We demonstrate that behavioral, socioeconomic, and physiological factors are strongly interrelated, with 45% of all possible pairwise associations between 96 nongenetic characteristics (n = 4,560 correlations) being statistically-significant at the p < 0.01 level (the ratio of observed to expected statistically-significant associations was 45; p-value for difference between observed and expected < 0.000001). Similar findings were observed for other levels of statistical-significance. In contrast, genetic variants showed no greater association with each other, or with the 96 behavioral, socioeconomic, and physiological factors, than would be expected by chance.

Conclusion: These data illustrate why observational studies have produced misleading claims regarding potentially causal factors for disease. The findings demonstrate the potential power of a methodology that utilizes genetic variants as indicators of exposure level when studying environmentally modifiable risk factors.

In a cross-sectional study Davey Smith and colleagues show why observational studies can produce misleading claims regarding potential causal factors for disease, and illustrate the use of Mendelian Randomization to study environmentally modifiable risk factors.

Editors’ Summary: Background.: Epidemiology is the study of the distribution and causes of human disease. Observational epidemiological studies investigate whether particular modifiable factors (for example, smoking or eating healthily) are associated with the risk of a particular disease. The link between smoking and lung cancer was discovered in this way. Once the modifiable factors associated with a disease are established as causal factors, individuals can reduce their risk of developing that disease by avoiding causative factors or by increasing their exposure to protective factors. Unfortunately, modifiable factors that are associated with risk of a disease in observational studies sometimes turn out not to cause or prevent disease. For example, higher intake of vitamins C and E apparently protected people against heart problems in observational studies, but taking these vitamins did not show any protection against heart disease in randomized controlled trials (studies in which identical groups of patients are randomly assigned various interventions and then their health monitored). One explanation for this type of discrepancy is known as confounding—the distortion of the effect of one factor by the presence of another that is associated both with the exposure under study and with the disease outcome. So in this example, people who took vitamin supplements might have also have exercised more than people who did not take supplements and it could have been the exercise rather than the supplements that was protective against heart disease.

Why Was This Study Done?: It isn’t always possible to check the results of observational studies in randomized controlled trials so epidemiologists have developed other ways to minimize confounding. One approach is known as Mendelian Randomization. Several gene variants have been identified that affect risk factors. For example, variants in a gene called APOE affect the level of cholesterol in an individual’s blood, a risk factor for heart disease. People inherit gene variants randomly from their parents to build up their own unique genotype (total genetic makeup). Consequently, a study that examines the associations between a gene variant and a disease can indicate whether the risk factor affected by that gene variant causes the disease. There should be no confounding in this type of study, the argument goes, because different genetic variants should not be associated with each other or with nongenetic variables that typically confound directly assessed associations between risk factors and disease. But is this true? In this study, the researchers have tested whether nongenetic risk factors are confounded by each other and also whether genetic variants are confounded by nongenetic risk factors and also by other genetic variants

What Did the Researchers Do and Find?: Using data collected in the British Women’s Heart and Health Study, the researchers calculated how many pairs of nongenetic variables (for example, frequency of eating meat, alcohol intake) were statistically-significantly correlated with each other. That is, the number of pairs of nongenetic variables in which a high correlation between both variables occurred in more study participants than expected by chance. They compared this number with the number of correlations that would occur by chance if all the variables were totally independent. When the researchers assumed that 1 in 100 combinations of pairs of variables would have been correlated by chance, the ratio of observed to expected statistically-significant correlations was seen 45× more frequently than would be expected by chance. When the researchers repeated this exercise with genetic variants, the ratio of observed to expected statistically-significant correlations was 1.58, a figure not different from 1. Similarly, the ratio of observed to expected statistically-significant correlations when pairwise combinations between genetic and nongenetic variants were considered was 1.22.

What Do These Findings Mean?: These findings have two main implications. First, the large excess of observed over expected associations among the nongenetic variables indicates that many nongenetic modifiable factors occur in clusters—for example, people with healthy diets often have other healthy habits. Researchers doing observational studies always try to adjust for confounding but this result suggests that this adjustment will be hard to do, in part because it will not always be clear which factors are confounders. Second, the lack of a large excess of observed over expected associations among the genetic variables (and also among genetic variables paired with nongenetic variables) indicates that little confounding is likely to occur in studies that use Mendelian Randomization. In other words, this approach is a valid way to identify which environmentally modifiable risk factors cause human disease.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2694957/ Genetic mapping in human disease David Altshuler, Mark J. Daly, Eric S. Lander 2008 2021-07-16 [("doi","10.1126/science.1156409")] genetics/heritable

Genetic mapping provides a powerful approach to identify genes and biological processes underlying any trait influenced by inheritance, including human diseases.

We discuss the intellectual foundations of genetic mapping of Mendelian and complex traits in humans, examine lessons emerging from linkage analysis of Mendelian diseases and genome-wide association studies of common diseases, and discuss questions and challenges that lie ahead.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1000008 Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits William G. Hill, Michael E. Goddard, Peter M. Visscher 2008-01-23 2021-07-16 [("doi","10.1371/journal.pgen.1000008")] genetics/heritable

The relative proportion of additive and non-additive variation for complex traits is important in evolutionary biology, medicine, and agriculture. We address a long-standing controversy and paradox about the contribution of non-additive genetic variation, namely that knowledge about biological pathways and gene networks imply that epistasis is important. Yet empirical data across a range of traits and species imply that most genetic variance is additive. We evaluate the evidence from empirical studies of genetic variance components and find that additive variance typically accounts for over half, and often close to 100%, of the total genetic variance. We present new theoretical results, based upon the distribution of allele frequencies under neutral and other population genetic models, that show why this is the case even if there are non-additive effects at the level of gene action. We conclude that interactions at the level of genes are not likely to generate much interaction at the level of variance.

Author Summary: Genetic variation in quantitative or complex traits can be partitioned into many components due to additive, dominance, and interaction effects of genes. The most important is the additive genetic variance because it determines most of the correlation of relatives and the opportunities for genetic change by natural or artificial selection. From reviews of the literature and presentation of a summary analysis of human twin data, we show that a high proportion, typically over half, of the total genetic variance is additive. This is surprising as there are many potential interactions of gene effects within and between loci, some revealed in recent QTL analyses. We demonstrate that under the standard model of neutral mutation, which leads to a U-shaped distribution of gene frequencies with most near 0 or 1, a high proportion of additive variance would be expected regardless of the amount of dominance or epistasis at the individual loci. We also show that the model is compatible with observations in populations undergoing selection and results of QTL analyses on F2 populations.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0006821 Genetic Ancestry, Social Classification, and Racial Inequalities in Blood Pressure in Southeastern Puerto Rico Clarence C. Gravlee, Amy L. Non, Connie J. Mulligan 2009-08-03 2021-07-16 [("doi","10.1371/journal.pone.0006821")] genetics/heritable

Background: The role of race in human genetics and biomedical research is among the most contested issues in science. Much debate centers on the relative importance of genetic versus sociocultural factors in explaining racial inequalities in health. However, few studies integrate genetic and sociocultural data to test competing explanations directly.

Methodology/Principal Findings: We draw on ethnographic, epidemiologic, and genetic data collected in southeastern Puerto Rico to isolate two distinct variables for which race is often used as a proxy: genetic ancestry versus social classification. We show that color, an aspect of social classification based on the culturally defined meaning of race in Puerto Rico, better predicts blood pressure than does a genetic-based estimate of continental ancestry. We also find that incorporating sociocultural variables reveals a new and statistically-significant association between a candidate gene polymorphism for hypertension (α2C adrenergic receptor deletion) and blood pressure.

Conclusions: This study addresses the recognized need to measure both genetic and sociocultural factors in research on racial inequalities in health. Our preliminary results provide the most direct evidence to date that previously reported associations between genetic ancestry and health may be attributable to sociocultural factors related to race and racism, rather than to functional genetic differences between racially defined groups. Our results also imply that including sociocultural variables in future research may improve our ability to detect statistically-significant allele-phenotype associations. Thus, measuring sociocultural factors related to race may both empower future genetic association studies and help to clarify the biological consequences of social inequalities.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3232052/ Common SNPs explain a large proportion of the heritability for human height Jian Yang, Beben Benyamin, Brian P. McEvoy, Scott D. Gordon, Anjali K. Henders, Dale R. Nyholt, Pamela A. Madden, Andrew C. Heath, Nicholas G. Martin, Grant W. Montgomery, Michael E. Goddard, Peter M. Visscher 2010 2021-07-16 [("doi","10.1038/ng.608")] genetics/heritable

SNPs discovered by genome-wide association studies (GWASs) account for only a small fraction of the genetic variation of complex traits in human populations. Where is the remaining heritability?

We estimated the proportion of variance for human height explained by 294,831 SNPs genotyped on 3,925 unrelated individuals using a linear model analysis, and validated the estimation method with simulations based on the observed genotype data.

We show that 45% of variance can be explained by considering all SNPs simultaneously. Thus, most of the heritability is not missing but has not previously been detected because the individual effects are too small to pass stringent statistical-significance tests.

We provide evidence that the remaining heritability is due to incomplete linkage disequilibrium between causal variants and genotyped SNPs, exacerbated by causal variants having lower minor allele frequency than the SNPs explored to date.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2910532/ Effects of modafinil on the sleep EEG depend on Val158Met genotype of COMT Sereina Bodenmann, Hans-Peter Landolt 2010 2021-07-16 [("doi","10.1093/sleep/33.8.1027")] genetics/heritable modafinil nootropic/caffeine psychology/neuroscience zeo

Study Objectives: Modafinil may promote wakefulness by increasing cerebral dopaminergic neurotransmission, which importantly depends on activity of catechol-O-methyltransferase (COMT) in prefrontal cortex. The effects of modafinil on sleep homeostasis in humans are unknown. Employing a novel sleep-pharmacogenetic approach, we investigated the interaction of modafinil with sleep deprivation to study dopaminergic mechanisms of sleep homeostasis.

Design: Placebo-controlled, double-blind, randomized crossover study.

Setting: Sleep laboratory in temporal isolation unit.

Participants: 22 healthy young men (23.4 ± 0.5 years) prospectively enrolled based on genotype of the functional Val158Met polymorphism of COMT(10 Val/Val and 12 Met/Met homozygotes).

Interventions: 2 × 100 mg modafinil and placebo administered at 11 and 23 hours during 40 hours prolonged wakefulness.

Measurements and Results: Subjective sleepiness and EEG markers of sleep homeostasis in wakefulness and sleep were equally affected by sleep deprivation in Val/Val and Met/Met allele carriers (placebo condition). Modafinil attenuated the evolution of sleepiness and EEG 5–8 Hz activity during sleep deprivation in both genotypes. In contrast to caffeine, modafinil did not reduce EEG slow wave activity (0.75–4.5 Hz) in recovery sleep, yet specifically increased 3.0–6.75 Hz and > 16.75 Hz activity in NREM sleep in the Val/Val genotype of COMT.

Conclusion: The Val158Met polymorphism of COMT modulates the effects of modafinil on the NREM sleep EEG in recovery sleep after prolonged wakefulness. The sleep EEG changes induced by modafinil markedly differ from those of caffeine, showing that pharmacological interference with dopaminergic and adenosinergic neurotransmission during sleep deprivation differently affects sleep homeostasis.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3445408/ Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations Dara G. Torgerson, Elizabeth J. Ampleford, Grace Y. Chiu, W. James Gauderman, Christopher R. Gignoux, Penelope E. Graves, Blanca E. Himes, Albert M. Levin, Rasika A. Mathias, Dana B. Hancock, James W. Baurley, Celeste Eng, Debra A. Stern, Juan C. Celedón, Nicholas Rafaels, Daniel Capurso, David V. Conti, Lindsey A. Roth, Manuel Soto-Quiros, Alkis Togias, Xingnan Li, Rachel A. Myers, Isabelle Romieu, David J. Van Den Berg, Donglei Hu, Nadia N. Hansel, Ryan D. Hernandez, Elliott Israel, Muhammad T. Salam, Joshua Galanter, Pedro C. Avila, Lydiana Avila, Jose R. Rodriquez-Santana, Rocio Chapela, William Rodriguez-Cintron, Gregory B. Diette, N. Franklin Adkinson, Rebekah A. Abel, Kevin D. Ross, Min Shi, Mezbah U. Faruque, Georgia M. Dunston, Harold R. Watson, Vito J. Mantese, Serpil C. Ezurum, Liming Liang, Ingo Ruczinski, Jean G. Ford, Scott Huntsman, Kian Fan Chung, Hita Vora, Xia Li, William J. Calhoun, Mario Castro, Juan J. Sienra-Monge, Blanca del Rio-Navarro, Klaus A. Deichmann, Andrea Heinzmann, Sally E. Wenzel, William W. Busse, James E. Gern, Robert F. Lemanske, Terri H. Beaty, Eugene R. Bleecker, Benjamin A. Raby, Deborah A. Meyers, Stephanie J. London, Frank D. Gilliland, Esteban G. Burchard, Fernando D. Martinez, Scott T. Weiss, L. Keoki Williams, Kathleen C. Barnes, Carole Ober, Dan L. Nicolae 2011 2021-07-16 [("doi","10.1038/ng.888")] genetics/heritable

Asthma is a common disease with a complex risk architecture including both genetic and environmental factors. We performed a meta-analysis of North American genome-wide association studies of asthma in 5,416 individuals with asthma (cases) including individuals of European American, African American or African Caribbean, and Latino ancestry, with replication in an additional 12,649 individuals from the same ethnic groups.

We identified 5 susceptibility loci. 4 were at previously reported loci on 17q21, near IL1RL1, TSLP and IL33, but we report for the first time, to our knowledge, that these loci are associated with asthma risk in 3 ethnic groups. In addition, we identified a new asthma susceptibility locus at PYHIN1, with the association being specific to individuals of African descent (p = 3.9 × 10−9).

These results suggest that some asthma susceptibility loci are robust to differences in ancestry when sufficiently large samples sizes are investigated, and that ancestry-specific associations also contribute to the complex genetic architecture of asthma.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3222234/ A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry Laramie E. Duncan, Matthew C. Keller 2011 2021-07-16 [("doi","10.1176/appi.ajp.2011.11020191")] genetics/heritable statistics/bias/publication

Objective: Gene-by-environment interaction (G×E) studies in psychiatry have typically been conducted using a candidate G×E (cG×E) approach, analogous to the candidate gene association approach used to test genetic main effects. Such cG×E research has received widespread attention and acclaim, yet cG×E findings remain controversial. The authors examined whether the many positive cG×E findings reported in the psychiatric literature were robust or if, in aggregate, cG×E findings were consistent with the existence of publication bias, low statistical power, and a high false discovery rate.

Method: The authors conducted analyses on data extracted from all published studies (103 studies) from the first decade (2000–2009) of cG×E research in psychiatry.

Results: Ninety-six percent of novel cG×E studies were compared with 27% of replication attempts. These findings are consistent with the existence of publication bias among novel cG×E studies, making cG×E hypotheses appear more robust than they actually are. There also appears to be publication bias among replication attempts because positive replication attempts had smaller average sample sizes than negative ones. Power calculations using observed sample sizes suggest that cG×E studies are underpowered. Low power along with the likely low prior probability of a given cG×E hypothesis being true suggests that most or even all positive cG×E findings represent false positives.

Conclusion: In this new era of big data and small effects, a recalibration of views about groundbreaking findings is necessary. Well-powered direct replications deserve more attention than novel cG×E findings and indirect replications.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3070763/ Variation in actual relationship as a consequence of Mendelian sampling and linkage W G. Hill, B. S. Weir 2011 2021-07-16 [("doi","10.1017/S0016672310000480")] genetics/heritable genetics/sequencing

Although the expected relationship or proportion of genome shared by pairs of relatives can be obtained from their pedigrees, the actual quantities deviate as a consequence of Mendelian sampling and depend on the number of chromosomes and map length. Formulae have been published previously for the variance of actual relationship for a number of specific types of relatives but no general formula for non-inbred individuals is available. We provide here a unified framework that enables the variances for distant relatives to be easily computed, showing, for example, how the variance of sharing for great grandparent-great grandchild, great uncle-great nephew, half uncle-nephew and first cousins differ, even though they have the same expected relationship.

Results are extended in order to include differences in map length between sexes, no recombination in males and sex linkage. We derive the magnitude of skew in the proportion shared, showing the skew becomes increasingly large the more distant the relationship.

The results obtained for variation in actual relationship apply directly to the variation in actual inbreeding as both are functions of genomic coancestry, and we show how to partition the variation in actual inbreeding between and within families. Although the variance of actual relationship falls as individuals become more distant, its coefficient of variation rises, and so, exacerbated by the skewness, it becomes increasingly difficult to distinguish different pedigree relationships from the actual fraction of the genome shared.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3045647/ The etiology of stability and change in religious values and religious attendance Tanya M. M. Button, Michael C. Stallings, Soo Hyun Rhee, Robin P. Corley, John K. Hewitt 2011 2021-07-17 [("doi","10.1007/s10519-010-9388-3")] genetics/heritable philosophy/religion

Studies have demonstrated little to no heritability for adolescent religiosity but moderate genetic, shared environmental, and nonshared environmental influences on adult religiosity. Only one longitudinal study of religiosity in female twins has been conducted (Koenig et al Dev Psychol 44:532–543, 2008), and reported that persistence from mid to late adolescence is due to shared environmental factors, but persistence from late adolescence to early adulthood was due to genetic and shared environmental factors.

We examined the etiology of stability and change in religious values and religious attendance in males and females during adolescence and early adulthood. The heritability of both religious values and religious attendance increased from adolescence to early adulthood, although the increase was greater for religious attendance. Both genetic and shared environmental influences contributed to the stability of religious values and religious attendance across adolescence and young adulthood.

Change in religious values was due to both genetic and nonshared environmental influences specific to early adulthood, whereas change in religious attendance was due in similar proportions to genetic, shared environmental, and non-shared environmental influences.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4286882/ Genetic and Environmental Influences on Individual Differences in Frequency of Play with Pets among Middle-Aged Men: A Behavioral Genetic Analysis Kristen C. Jacobson, Christy L. Hoffman, Terrie Vasilopoulos, William S. Kremen, Matthew S. Panizzon, Michael D. Grant, Michael J. Lyons, Hong Xian, Carol E. Franz 2012 2021-07-17 [("doi","10.2752/175303712X13479798785814")] exercise genetics/heritable

There is growing evidence that pet ownership and human-animal interaction (HAI) have benefits for human physical and psychological well-being. However, there may be pre-existing characteristics related to patterns of pet ownership and interactions with pets that could potentially bias results of research on HAI.

The present study uses a behavioral genetic design to estimate the degree to which genetic and environmental factors contribute to individual differences in frequency of play with pets among adult men. Participants were from the ongoing longitudinal Vietnam Era Twin Study of Aging (VETSA), a population-based sample of 1,237 monozygotic (MZ) and dizygotic (DZ) twins aged 51–60 years.

Results demonstrate that MZ twins have higher correlations than DZ twins on frequency of pet play, suggesting that genetic factors play a role in individual differences in interactions with pets. Structural equation modeling revealed that, according to the best model, genetic factors accounted for as much as 37% of the variance in pet play, although the majority of variance (63–71%) was due to environmental factors that are unique to each twin. Shared environmental factors, which would include childhood exposure to pets, overall accounted for <10% of the variance in adult frequency of pet play, and were not statistically-significant.

These results suggest that the effects of childhood exposure to pets on pet ownership and interaction patterns in adulthood may be mediated primarily by genetically-influenced characteristics.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3645444/ Genetic variants and associations of 25-hydroxyvitamin D concentrations with major clinical outcomes Gregory P. Levin, Cassianne Robinson-Cohen, Ian H. de Boer, Denise K. Houston, Kurt Lohman, Yongmei Liu, Stephen B. Kritchevsky, Jane A. Cauley, Toshiko Tanaka, Luigi Ferrucci, Stefania Bandinelli, Kushang V. Patel, Emil Hagström, Karl Michaëlsson, Håkan Melhus, Thomas Wang, Myles Wolf, Bruce M. Psaty, David Siscovick, Bryan Kestenbaum 2012 2021-07-17 [("doi","10.1001/jama.2012.17304")] genetics/heritable vitamin-d

Context: Lower serum 25-hydroxyvitamin D concentrations are associated with greater risks of many chronic diseases across large, prospective community-based studies. Substrate 25-hydroxyvitamin D must be converted to 1,25-dihydroxyvitamin D for full biological activity, and complex metabolic pathways suggest that interindividual variability in vitamin D metabolism may alter the clinical consequences of measured serum 25-hydroxyvitamin D.

Objective: To investigate whether common variation within genes encoding the vitamin D-binding protein, megalin, cubilin, CYP27B1, CYP24A1, and the vitamin D receptor (VDR) modify associations of low 25-hydroxyvitamin D with major clinical outcomes.

Design, Setting, & Participants: Examination of 141 single-nucleotide polymorphisms in a discovery cohort of 1514 white participants (who were recruited from 4 US regions) from the community-based Cardiovascular Health Study. Participants had serum 25-hydroxyvitamin D measurements in 1992–1993 and were followed up for a median of 11 years (through 2006). Replication meta-analyses were conducted across the independent, community-based US Health, Aging, and Body Composition (n = 922; follow-up: 1998–1999 through 2005), Italian Invecchiare in Chianti (n = 835; follow-up: 1998–2000 through 2006), and Swedish Uppsala Longitudinal Study of Adult Men (n = 970; follow-up: 1991–1995 through 2008) cohort studies.

Main Outcome Measure: Composite outcome of incident hip fracture, myocardial infarction, cancer, and mortality over long-term follow-up.

Results: Interactions between 5 single-nucleotide polymorphisms and low 25-hydroxyvitamin D concentration were identified in the discovery phase and 1 involving a variant in the VDR gene replicated in independent meta-analysis. Among Cardiovascular Health Study participants, low 25-hydroxyvitamin D concentration was associated with hazard ratios for risk of the composite outcome of 1.40 (95% CI, 1.12–1.74) for those who had 1 minor allele at rs7968585 and 1.82 (95% CI, 1.31–2.54) for those with 2 minor alleles at rs7968585. In contrast, there was no evidence of an association (estimated hazard ratio, 0.93 [95% CI, 0.70–1.24]) among participants who had 0 minor alleles at this single-nucleotide polymorphism.

Conclusion: Known associations of low 25-hydroxyvitamin D with major health outcomes may vary according to common genetic differences in the vitamin D receptor.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3498585/ Most reported genetic associations with general intelligence are probably false positives Christopher F. Chabris, Benjamin M. Hebert, Daniel J. Benjamin, Jonathan Beauchamp, David Cesarini, Matthijs van der Loos, Magnus Johannesson, Patrik K. E. Magnusson, Paul Lichtenstein, Craig S. Atwood, Jeremy Freese, Taissa S. Hauser, Robert M. Hauser, Nicholas Christakis, David Laibson 2012 2021-07-17 [("doi","10.1177/0956797611435528")] genetics/heritable iq psychiatry/alzheimers statistics/bias

General intelligence (g) and virtually all other behavioral traits are heritable. Associations between g and specific single-nucleotide polymorphisms (SNPs) in several candidate genes involved in brain function have been reported.

We sought to replicate published associations between g and 12 specific genetic variants (in the genes DTNBP1, CTSD, DRD2, ANKK1, CHRM2, SSADH, COMT, BDNF, CHRNA4, DISC1, APOE, and SNAP25) using data sets from 3 independent, well-characterized longitudinal studies with samples of 5,571, 1,759, and 2,441 individuals. Of 32 independent tests across all 3 data sets, only 1 was nominally statistically-significant. By contrast, power analyses showed that we should have expected 10 to 15 statistically-significant associations, given reasonable assumptions for genotype effect sizes.

For positive controls, we confirmed accepted genetic associations for Alzheimer’s disease and body mass index, and we used SNP-based calculations of genetic relatedness to replicate previous estimates that about half of the variance in g is accounted for by common genetic variation among individuals.

We conclude that the molecular genetics of psychology and social science requires approaches that go beyond the examination of candidate genes.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3442244/ Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes Andrew P. Morris, Benjamin F. Voight, Tanya M. Teslovich, Teresa Ferreira, Ayellet V. Segrè, Valgerdur Steinthorsdottir, Rona J. Strawbridge, Hassan Khan, Harald Grallert, Anubha Mahajan, Inga Prokopenko, Hyun Min Kang, Christian Dina, Tõnu Esko, Ross M. Fraser, Stavroula Kanoni, Ashish Kumar, Vasiliki Lagou, Claudia Langenberg, Jian’an Luan, Cecilia M. Lindgren, Martina Müller-Nurasyid, Sonali Pechlivanis, N. William Rayner, Laura J. Scott, Steven Wiltshire, Loïc Yengo, Leena Kinnunen, Elizabeth J. Rossin, Soumya Raychaudhuri, Andrew D. Johnson, Antigone S. Dimas, Ruth Loos, Sailaja Vedantam, Han Chen, Jose C. Florez, Caroline Fox, Ching-Ti Liu, Denis Rybin, David J. Couper, Wen Hong L. Kao, Man Li, Marilyn C. Cornelis, Peter Kraft, Qi Sun, Rob M. van Dam, Heather M. Stringham, Peter S. Chines, Krista Fischer, Pierre Fontanillas, Oddgeir L. Holmen, Sarah E. Hunt, Anne Uriu Jackson, Augustine Kong, Robert Lawrence, Julia Meyer, John R. B. Perry, Carl G. P. Platou, Simon Potter, Emil Rehnberg, Neil Robertson, Suthesh Sivapalaratnam, Alena Stančáková, Kathleen Stirrups, Gudmar Thorleifsson, Emmi Tikkanen, Andrew R. Wood, Peter Almgren, Mustafa Atalay, Rafn Benediktsson, Lori L. Bonnycastle, Noël Burtt, Jason Carey, Guillaume Charpentier, Andrew T. Crenshaw, Alex S. F. Doney, Mozhgan Dorkhan, Sarah Edkins, Valur Emilsson, Elodie Eury, Tom Forsen, Karl Gertow, Bruna Gigante, George B. Grant, Christopher J. Groves, Candace Guiducci, Christian Herder, Astradur B. Hreidarsson, Jennie Hui, Alan James, Anna Jonsson, Wolfgang Rathmann, Norman Klopp, Jasmina Kravic, Kaarel Krjutškov, Cordelia Langford, Karin Leander, Eero Lindholm, Stéphane Lobbens, Satu Männistö, Ghazala Mirza, Thomas W. Mühleisen, Bill Musk, Melissa Parkin, Loukianos Rallidis, Jouko Saramies, Bengt Sennblad, Sonia Shah, Gunnar Sigurðsson, Angela Silveira, Gerald Steinbach, Barbara Thorand, Joseph Trakalo, Fabrizio Veglia, Roman Wennauer, Wendy Winckler, Delilah Zabaneh, Harry Campbell, Cornelia van Duijn, André G. Uitterlinden, Albert Hofman, Eric Sijbrands, Gonçalo Abecasis, Katharine R. Owen, Eleftheria Zeggini, Mieke D. Trip, Nita G. Forouhi, Ann-Christine Syvänen, Johan G. Eriksson, Leena Peltonen, Markus M. Nöthen, Beverley Balkau, Colin Palmer, Valeriya Lyssenko, Tiinamaija Tuomi, Bo Isomaa, David J. Hunter, Lu Qi, Alan R. Shuldiner, Michael Roden, Ines Barroso, Tom Wilsgaard, John Beilby, Kees Hovingh, Jackie F. Price, James F. Wilson, Rainer Rauramaa, Timo A. Lakka, Lars L. Lind, George Dedoussis, Inger Njølstad, Nancy L. Pedersen, Kay-Tee Khaw, Nicholas J. Wareham, Sirkka M. Keinanen-Kiukaanniemi, Timo E. Saaristo, Eeva Korpi-Hyövälti, Juha Saltevo, Markku Laakso, Johanna Kuusisto, Andres Metspalu, Francis S. Collins, Karen L. Mohlke, Richard N. Bergman, Jaakko Tuomilehto, Bernhard O. Boehm, Christian Gieger, Kristian Hveem, Stephane Cauchi, Philippe Froguel, Damiano Baldassarre, Elena Tremoli, Steve E. Humphries, Danish Saleheen, John Danesh, Erik Ingelsson, Samuli Ripatti, Veikko Salomaa, Raimund Erbel, Karl-Heinz Jöckel, Susanne Moebus, Annette Peters, Thomas Illig, Ulf de Faire, Anders Hamsten, Andrew D. Morris, Peter J. Donnelly, Timothy Frayling, Andrew Tym Hattersley, Eric Boerwinkle, Olle Melander, Sekar Kathiresan, Peter M. Nilsson, Panos Deloukas, Unnur Thorsteinsdottir, Leif C. Groop, Kari Stefansson, Frank Hu, James S. Pankow, Josée Dupuis, James B. Meigs, David Altshuler, Michael Boehnke, Mark I. McCarthy 2012 2021-07-17 [("doi","10.1038/ng.2383")] genetics/heritable

To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip, including 34,840 cases and 114,981 controls, overwhelmingly of European descent.

We identified 10 previously unreported T2D susceptibility loci, including two showing sex-differentiated association.

Genome-wide analyses of these data are consistent with a long tail of additional common variant loci explaining much of the variation in susceptibility to T2D.

Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signaling and cell cycle regulation, in diabetes pathogenesis.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257326/ Five years of GWAS discovery Peter M. Visscher, Matthew A. Brown, Mark I. McCarthy, Jian Yang 2012 2021-07-17 [("doi","10.1016/j.ajhg.2011.11.029")] genetics/heritable

The past 5 years have seen many scientific and biological discoveries made through the experimental design of genome-wide association studies (GWASs). These studies were aimed at detecting variants at genomic loci that are associated with complex traits in the population and, in particular, at detecting associations between common single-nucleotide polymorphisms (SNPs) and common diseases such as heart disease, diabetes, auto-immune diseases, and psychiatric disorders.

We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases.

We return to the perceived failure or disappointment about GWASs in the concluding section.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3137678/ Relationship between adiposity and admixture in African-American and Hispanic-American women R Nassir, L. Qi, R. Kosoy, L. Garcia, M. Allison, H. M. Ochs-Balcom, F. Tylavsky, J. E. Manson, R. Shigeta, J. Robbins, M. F. Seldin 2012 2021-07-17 [("doi","10.1038/ijo.2011.84")] genetics/heritable

Objective: The objective of this study was to investigate whether differences in admixture in African-American (AFA) and Hispanic-American (HA) adult women are associated with adiposity and adipose distribution.

Design: The proportion of European, sub-Saharan African and Amerindian admixture was estimated for AFA and HA women in the Women’s Heath Initiative using 92 ancestry informative markers. Analyses assessed the relationship between admixture and adiposity indices.

Subjects: The subjects included 11,712 AFA and 5088 HA self-identified post-menopausal women.

Results: There was a positive association between body mass index (BMI) and African admixture when BMI was considered as a continuous variable, and age, education, physical activity, parity, family income and smoking were included covariates (p < 10−4). A dichotomous model (upper and lower BMI quartiles) showed that African admixture was associated with a high odds ratio (OR = 3.27 (for 100% admixture compared with 0% admixture), 95% confidence interval 2.08–5.15). For HA, there was no association between BMI and admixture. In contrast, when waist-to-hip ratio (WHR) was used as a measure of adipose distribution, there was no statistically-significant association between WHR and admixture in AFA but there was a strong association in HA (p < 10−4; OR Amerindian admixture=5.93, confidence interval=3.52–9.97).

Conclusion: These studies show that: (1) African admixture is associated with BMI in AFA women; (2) Amerindian admixture is associated with WHR but not BMI in HA women; and (3) it may be important to consider different measurements of adiposity and adipose distribution in different ethnic population groups.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4159740/ Large-scale genotyping identifies a new locus at 22q13.2 associated with female breast size Jingmei Li, Jia Nee Foo, Nils Schoof, Jajini S. Varghese, Pablo Fernandez-Navarro, Gretchen L. Gierach, Swee Tian Quek, Mikael Hartman, Silje Nord, Vessela N. Kristensen, Marina Pollán, Jonine D. Figueroa, Deborah J. Thompson, Yi Li, Chiea Chuen Khor, Keith Humphreys, Jianjun Liu, Kamila Czene, Per Hall 2013 2021-07-17 [("doi","10.1136/jmedgenet-2013-101708")] genetics/heritable

Background: Individual differences in breast size are a conspicuous feature of variation in human females and have been associated with fecundity and advantage in selection of mates. To identify common variants that are associated with breast size, we conducted a large-scale genotyping association meta-analysis in 7169 women of European descent across 3 independent sample collections with digital or screen film mammograms.

Method: The samples consisted of the Swedish KARMA, LIBRO-1 and SASBAC studies genotyped on iCOGS, a custom Illumina iSelect genotyping array comprising of 211,155 single-nucleotide polymorphisms (SNPs) designed for replication and fine mapping of common and rare variants with relevance to breast, ovary and prostate cancer. Breast size of each subject was ascertained by measuring total breast area (mm2) on a mammogram.

Results: We confirm genome-wide statistically-significant associations at 8p11.23 (rs10086016, p = 1.3×10 (−14)) and report a new locus at 22q13 (rs5995871, p = 3.2×10−8). The latter region contains the MKL1 gene, which has been shown to impact endogenous oestrogen receptor α transcriptional activity and is recruited on oestradiol sensitive genes. We also replicated previous genome-wide association study findings for breast size at four other loci.

Conclusion: A new locus at 22q13 may be associated with female breast size.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3973018/ Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture Sonja I. Berndt, Stefan Gustafsson, Reedik Mägi, Andrea Ganna, Eleanor Wheeler, Mary F. Feitosa, Anne E. Justice, Keri L. Monda, Damien C. Croteau-Chonka, Felix R. Day, Tõnu Esko, Tove Fall, Teresa Ferreira, Davide Gentilini, Anne Uriu Jackson, Jian’an Luan, Joshua C. Randall, Sailaja Vedantam, Cristen Jennifer Willer, Thomas W. Winkler, Andrew R. Wood, Tsegaselassie Workalemahu, Yi-Juan Hu, Sang Hong Lee, Liming Liang, Dan-Yu Lin, Josine L. Min, Benjamin M. Neale, Gudmar Thorleifsson, Jian Yang, Eva Albrecht, Najaf Amin, Jennifer L. Bragg-Gresham, Gemma Cadby, Martin den Heijer, Niina Eklund, Krista Fischer, Anuj Goel, Jouke-Jan Hottenga, Jennifer E. Huffman, Ivonne Jarick, Åsa Johansson, Toby Johnson, Stavroula Kanoni, Marcus E. Kleber, Inke R. König, Kati Kristiansson, Zoltán Kutalik, Claudia Lamina, Cecile Lecoeur, Guo Li, Massimo Mangino, Wendy L. McArdle, Carolina Medina-Gomez, Martina Müller-Nurasyid, Julius S. Ngwa, Ilja M. Nolte, Lavinia Paternoster, Sonali Pechlivanis, Markus Perola, Marjolein J. Peters, Michael Preuss, Lynda M. Rose, Jianxin Shi, Dmitry Shungin, Albert Vernon Smith, Rona J. Strawbridge, Ida Surakka, Alexander Teumer, Mieke D. Trip, Jonathan Tyrer, Jana V. Van Vliet-Ostaptchouk, Liesbeth Vandenput, Lindsay L. Waite, Jing Hua Zhao, Devin Absher, Folkert W. Asselbergs, Mustafa Atalay, Antony P. Attwood, Anthony J. Balmforth, Hanneke Basart, John Beilby, Lori L. Bonnycastle, Paolo Brambilla, Marcel Bruinenberg, Harry Campbell, Daniel I. Chasman, Peter S. Chines, Francis S. Collins, John M. Connell, William O. Cookson, Ulf de Faire, Femmie de Vegt, Mariano Dei, Maria Dimitriou, Sarah Edkins, Karol Estrada, David M. Evans, Martin Farrall, Marco M. Ferrario, Jean Ferrières, Lude Franke, Francesca Frau, Pablo V. Gejman, Harald Grallert, Henrik Grönberg, Vilmundur Gudnason, Alistair S. Hall, Per Hall, Anna-Liisa Hartikainen, Caroline Hayward, Nancy L. Heard-Costa, Andrew C. Heath, Johannes Hebebrand, Georg Homuth, Frank B. Hu, Sarah E. Hunt, Elina Hyppönen, Carlos Iribarren, Kevin B. Jacobs, John-Olov Jansson, Antti Jula, Kähönen Mika, Sekar Kathiresan, Frank Kee, Kay-Tee Khaw, Mika Kivimäki, Wolfgang Koenig, Aldi T. Kraja, Meena Kumari, Kari Kuulasmaa, Johanna Kuusisto, Jaana H. Laitinen, Timo A. Lakka, Claudia Langenberg, Lenore J. Launer, Lars L. Lind, Jaana Lindström, Jianjun Liu, Antonio Liuzzi, Marja-Liisa Lokki, Mattias Lorentzon, Pamela A. Madden, Patrik K. Magnusson, Paolo Manunta, Diana Marek, Winfried März, Irene Mateo Leach, Barbara McKnight, Sarah E. Medland, Evelin Mihailov, Lili Milani, Grant W. Montgomery, Vincent Mooser, Thomas W. Mühleisen, Patricia B. Munroe, Arthur W. Musk, Narisu Narisu, Gerjan Navis, George Nicholson, Ellen A. Nohr, Ken K. Ong, Ben A. Oostra, Colin Palmer, Aarno Palotie, John F. Peden, Nancy Pedersen, Annette Peters, Ozren Polasek, Anneli Pouta, Peter P. Pramstaller, Inga Prokopenko, Carolin Pütter, Aparna Radhakrishnan, Olli T. Raitakari, Augusto Rendon, Fernando Rivadeneira, Igor Rudan, Timo E. Saaristo, Jennifer G. Sambrook, Alan R. Sanders, Serena Sanna, Jouko Saramies, Sabine Schipf, Stefan Schreiber, Heribert Schunkert, So-Youn Shin, Stefano Signorini, Juha Sinisalo, Boris Skrobek, Nicole Soranzo, Alena Stančáková, Klaus Stark, Jonathan C. Stephens, Kathleen Stirrups, Ronald P. Stolk, Michael Stumvoll, Amy J. Swift, Eirini V. Theodoraki, Barbara Thorand, David-Alexandre Tregouet, Elena Tremoli, Melanie M. Van der Klauw, Joyce B. J. van Meurs, Sita H. Vermeulen, Jorma Viikari, Jarmo Virtamo, Veronique Vitart, Gérard Waeber, Zhaoming Wang, Elisabeth Widen, Sarah H. Wild, Gonneke Willemsen, Bernhard R. Winkelmann, Jacqueline C. M. Witteman, Bruce H. R. Wolffenbuttel, Andrew Wong, Alan F. Wright, M. Carola Zillikens, Philippe Amouyel, Bernhard O. Boehm, Eric Boerwinkle, Dorret I. Boomsma, Mark J. Caulfield, Stephen J. Chanock, L. Adrienne Cupples, Daniele Cusi, George V. Dedoussis, Jeanette Erdmann, Johan G. Eriksson, Paul W. Franks, Philippe Froguel, Christian Gieger, Ulf Gyllensten, Anders Hamsten, Tamara B. Harris, Christian Hengstenberg, Andrew A. Hicks, Aroon Hingorani, Anke Hinney, Albert Hofman, Kees G. Hovingh, Kristian Hveem, Thomas Illig, Marjo-Riitta Jarvelin, Karl-Heinz Jöckel, Sirkka M. Keinanen-Kiukaanniemi, Lambertus A. Kiemeney, Diana Kuh, Markku Laakso, Terho Lehtimäki, Douglas F. Levinson, Nicholas G. Martin, Andres Metspalu, Andrew D. Morris, Markku S. Nieminen, Inger Njølstad, Claes Ohlsson, Albertine J. Oldehinkel, Willem H. Ouwehand, Lyle J. Palmer, Brenda W. J. H. Penninx, Chris Power, Michael A. Province, Bruce M. Psaty, Lu Qi, Rainer Rauramaa, Paul M. Ridker, Samuli Ripatti, Veikko Salomaa, Nilesh J. Samani, Harold Snieder, Thorkild I. A. Sørensen, Timothy D. Spector, Kari Stefansson, Anke Tönjes, Jaakko Tuomilehto, André G. Uitterlinden, Matti Uusitupa, Pim van der Harst, Peter Vollenweider, Henri Wallaschofski, Nicholas J. Wareham, Hugh Watkins, H-Erich Wichmann, James F. Wilson, Gonçalo Abecasis, Themistocles L. Assimes, Inês Barroso, Michael Boehnke, Ingrid B. Borecki, Panos Deloukas, Caroline S. Fox, Timothy Frayling, Leif C. Groop, Talin Haritunian, Iris M. Heid, David Hunter, Robert C. Kaplan, Fredrik Karpe, Miriam F. Moffatt, Karen L. Mohlke, Jeffrey R. O’Connell, Yudi Pawitan, Eric E. Schadt, David Schlessinger, Valgerdur Steinthorsdottir, David P. Strachan, Unnur Thorsteinsdottir, Cornelia van Duijn, Peter M. Visscher, Anna Maria Di Blasio, Joel N. Hirschhorn, Cecilia M. Lindgren, Andrew P. Morris, David Meyre, André Scherag, Mark I. McCarthy, Elizabeth K. Speliotes, Kari E. North, Ruth Loos, Erik Ingelsson 2013 2021-07-17 [("doi","10.1038/ng.2606")] genetics/heritable

Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population.

In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity.

Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3491803/ Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease Luke Jostins, Stephan Ripke, Rinse K. Weersma, Richard H. Duerr, Dermot P. McGovern, Ken Y. Hui, James C. Lee, L. Philip Schumm, Yashoda Sharma, Carl A. Anderson, Jonah Essers, Mitja Mitrovic, Kaida Ning, Isabelle Cleynen, Emilie Theatre, Sarah L. Spain, Soumya Raychaudhuri, Philippe Goyette, Zhi Wei, Clara Abraham, Jean-Paul Achkar, Tariq Ahmad, Leila Amininejad, Ashwin N. Ananthakrishnan, Vibeke Andersen, Jane M. Andrews, Leonard Baidoo, Tobias Balschun, Peter A. Bampton, Alain Bitton, Gabrielle Boucher, Stephan Brand, Carsten Büning, Ariella Cohain, Sven Cichon, Mauro D’Amato, Dirk De Jong, Kathy L. Devaney, Marla Dubinsky, Cathryn Edwards, David Ellinghaus, Lynnette R. Ferguson, Denis Franchimont, Karin Fransen, Richard Gearry, Michel Georges, Christian Gieger, Jürgen Glas, Talin Haritunians, Ailsa Hart, Chris Hawkey, Matija Hedl, Xinli Hu, Tom H. Karlsen, Limas Kupcinskas, Subra Kugathasan, Anna Latiano, Debby Laukens, Ian C. Lawrance, Charlie W. Lees, Edouard Louis, Gillian Mahy, John Mansfield, Angharad R. Morgan, Craig Mowat, William Newman, Orazio Palmieri, Cyriel Y. Ponsioen, Uros Potocnik, Natalie J. Prescott, Miguel Regueiro, Jerome I. Rotter, Richard K. Russell, Jeremy D. Sanderson, Miquel Sans, Jack Satsangi, Stefan Schreiber, Lisa A. Simms, Jurgita Sventoraityte, Stephan R. Targan, Kent D. Taylor, Mark Tremelling, Hein W. Verspaget, Martine De Vos, Cisca Wijmenga, David C. Wilson, Juliane Winkelmann, Ramnik J. Xavier, Sebastian Zeissig, Bin Zhang, Clarence K. Zhang, Hongyu Zhao, Mark S. Silverberg, Vito Annese, Hakon Hakonarson, Steven R. Brant, Graham Radford-Smith, Christopher G. Mathew, John D. Rioux, Eric E. Schadt, Mark J. Daly, Andre Franke, Miles Parkes, Severine Vermeire, Jeffrey C. Barrett, Judy H. Cho 2012 2021-07-17 [("doi","10.1038/nature11582")] genetics/microbiome genetics/selection/natural/human

Crohn’s disease and ulcerative colitis, the two common forms of inflammatory bowel disease (IBD), affect over 2.5 million people of European ancestry, with rising prevalence in other populations. Genome-wide association studies (GWAS) and subsequent meta-analyses of these two diseases as separate phenotypes have implicated previously unsuspected mechanisms, such as autophagy, in their pathogenesis and showed that some IBD loci are shared with other inflammatory diseases.

Here we expand on the knowledge of relevant pathways by undertaking a meta-analysis of Crohn’s disease and ulcerative colitis genome-wide association scans, followed by extensive validation of findings, with a combined total of more than 75,000 cases and controls. We identify 71 new associations, for a total of 163 IBD loci, that meet genome-wide statistical-significance thresholds. Most loci contribute to both phenotypes, and both directional (consistently favouring one allele over the course of human history) and balancing (favouring the retention of both alleles within populations) selection effects are evident.

Many IBD loci are also implicated in other immune-mediated disorders, most notably with ankylosing spondylitis and psoriasis. We also observe considerable overlap between susceptibility loci for IBD and mycobacterial infection. Gene co-expression network analysis emphasizes this relationship, with pathways shared between host responses to mycobacteria and those predisposing to IBD.

--- https://arxiv.org/abs/0711.1751 Paleontological Tests: Human-like Intelligence is not a Convergent Feature of Evolution Charles H. Lineweaver 2007-11-12 2021-07-17 [("doi","10.48550/arXiv.0711.1751")] genetics/selection/natural/human psychology/neuroscience

We critically examine the evidence for the idea that encephalization quotients increase with time.

We find that human-like intelligence is not a convergent feature of evolution.

Implications for the search for extraterrestrial intelligence are discussed.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2994553/ The genetics of human adaptation: hard sweeps, soft sweeps, and polygenic adaptation Jonathan K. Pritchard, Joseph K. Pickrell, Graham Coop 2010 2021-07-18 [("doi","10.1016/j.cub.2009.11.055")] genetics/selection/natural/human

There has long been interest in understanding the genetic basis of human adaptation. To what extent are phenotypic differences among human populations driven by natural selection? With the recent arrival of large genome-wide data sets on human variation, there is now unprecedented opportunity for progress on this type of question.

Several lines of evidence argue for an important role of positive selection in shaping human variation and differences among populations. These include studies of comparative morphology and physiology, as well as population genetic studies of candidate loci and genome-wide data. However, the data also suggest that it is unusual for strong selection to drive new mutations rapidly to fixation in particular populations (the ‘hard sweep’ model). We argue, instead, for alternatives to the hard sweep model: in particular, polygenic adaptation could allow rapid adaptation while not producing classical signatures of selective sweeps.

We close by discussing some of the likely opportunities for progress in the field.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2871814/ On epistasis: why it is unimportant in polygenic directional selection James F. Crow 2010 2021-07-18 [("doi","10.1098/rstb.2009.0275")] genetics/heritable genetics/selection/artificial

There is a difference in viewpoint of developmental and evo-devo geneticists versus breeders and students of quantitative evolution. The former are interested in understanding the developmental process; the emphasis is on identifying genes and studying their action and interaction. Typically, the genes have individually large effects and usually show substantial dominance and epistasis.

The latter group are interested in quantitative phenotypes rather than individual genes. Quantitative traits are typically determined by many genes, usually with little dominance or epistasis. Furthermore, epistatic variance has minimum effect, since the selected population soon arrives at a state in which the rate of change is given by the additive variance or covariance. Thus, the breeder’s custom of ignoring epistasis usually gives a more accurate prediction than if epistatic variance were included in the formulas.

--- https://journals.plos.org/plosgenetics/article?id=10.1371%2Fjournal.pgen.1002127 Parallel Adaptive Divergence among Geographically Diverse Human Populations Jacob A. Tennessen, Joshua M. Akey 2011-04-27 2021-07-18 [("doi","10.1371/journal.pgen.1002127")] genetics/selection/natural/human

Few genetic differences between human populations conform to the classic model of positive selection, in which a newly arisen mutation rapidly approaches fixation in one lineage, suggesting that adaptation more commonly occurs via moderate changes in standing variation at many loci. Detecting and characterizing this type of complex selection requires integrating individually ambiguous signatures across genomically and geographically extensive data. Here, we develop a novel approach to test the hypothesis that selection has favored modest divergence at particular loci multiple times in independent human populations. We find an excess of SNPs showing non-neutral parallel divergence, enriched for genic and nonsynonymous polymorphisms in genes encompassing diverse and often disease related functions. Repeated parallel evolution in the same direction suggests common selective pressures in disparate habitats. We test our method with extensive coalescent simulations and show that it is robust to a wide range of demographic events. Our results demonstrate phylogenetically orthogonal patterns of local adaptation caused by subtle shifts at many widespread polymorphisms that likely underlie substantial phenotypic diversity.

Author Summary: Identifying regions of the human genome that differ among populations because of natural selection is both essential for understanding evolutionary history and a powerful method for finding functionally important variants that contribute to phenotypic diversity and disease. Adaptive events on timescales corresponding to the human diaspora may often manifest as relatively small changes in allele frequencies at numerous loci that are difficult to distinguish from stochastic changes due to genetic drift, rather than the more dramatic selective sweeps described by classic models of natural selection. In order to test whether a substantial proportion of interpopulation genetic differences are indeed adaptive, we identify loci that have undergone moderate allele frequency changes in multiple independent human lineages, and we test whether these parallel divergence events are more frequent than expected by chance. We report a statistically-significant excess of polymorphisms showing parallel divergence, especially within genes, a pattern that is best explained by geographically varying natural selection. Our results indicate that local adaptation in humans has occurred by subtle, repeated changes at particular genes that are likely to be associated with important morphological and physiological differences among human populations.

--- https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002609 Mutation Induced Extinction in Finite Populations: Lethal Mutagenesis and Lethal Isolation C. Scott Wylie, Eugene I. Shakhnovich 2012-05-23 2021-07-18 [("doi","10.1371/journal.pcbi.1002609")] genetics/selection/natural

Reproduction is inherently risky, in part because genomic replication can introduce new mutations that are usually deleterious toward fitness. This risk is especially severe for organisms whose genomes replicate “semi-conservatively”, eg. viruses and bacteria, where no master copy of the genome is preserved. Lethal mutagenesis refers to extinction of populations due to an unbearably high mutation rate (U), and is important both theoretically and clinically, where drugs can extinguish pathogens by increasing their mutation rate. Previous theoretical models of lethal mutagenesis assume infinite population size (N). However, in addition to high U, small N can accelerate extinction by strengthening genetic drift and relaxing selection. Here, we examine how the time until extinction depends jointly on N and U. We first analytically compute the mean time until extinction (τ) in a simplistic model where all mutations are either lethal or neutral. The solution motivates the definition of two distinct regimes: a survival phase and an extinction phase, which differ dramatically in both how τ scales with N and in the coefficient of variation in time until extinction. Next, we perform stochastic population-genetics simulations on a realistic fitness landscape that both (1) features an epistatic distribution of fitness effects that agrees with experimental data on viruses and (2) is based on the biophysics of protein folding. More specifically, we assume that mutations inflict fitness penalties proportional to the extent that they unfold proteins. We find that decreasing N can cause phase transition-like behavior from survival to extinction, which motivates the concept of “lethal isolation.” Furthermore, we find that lethal mutagenesis and lethal isolation interact synergistically, which may have clinical implications for treating infections. Broadly, we conclude that stably folded proteins are only possible in ecological settings that support sufficiently large populations.

Author Summary: Most spontaneous mutations hurt organismal fitness, eg. by destabilizing proteins. In many species, the normal mutation rate is strikingly high: on the order of one per genome per replication. In the face of these mutations, how can proteins maintain their native structure, and how can populations of organisms avoid extinction? Are there physics-based limits on how large the mutation rate of any species can be before the onslaught of mutations outpaces natural selection and melts-down proteins? Here, we address these questions with a computational model that combines protein folding thermodynamics with individual-based population genetics simulations. We calculate a theoretical “speed limit” equal to a few mutations per genome per replication—near the mutation rate of RNA viruses. Additionally, we find that the speed limit can be much lower in small populations where “random genetic drift” is strong. Thus, we conclude that stably folded proteins are only possible in ecological settings that support sufficiently large populations. These findings may have clinical implications for treating viral infections with drugs that elevate the viral mutation rate.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3468836/ Iodine intake in human nutrition: a systematic literature review Ingibjörg Gunnarsdottir, Lisbeth Dahl 2012 2021-07-18 [("doi","10.3402/fnr.v56i0.19731")] iodine

The present literature review is a part of the NNR5 project with the aim of reviewing and updating the scientific basis of the 4th edition of the Nordic Nutrition Recommendations (NNR) issued in 2004. The main objective of the review is to assess the influence of different intakes of iodine at different life stages (infants, children, adolescents, adults, elderly, and during pregnancy and lactation) in order to estimate the requirement for adequate growth, development, and maintenance of health.

The literature search resulted in 1,504 abstracts. Out of those, 168 papers were identified as potentially relevant. Full paper selection resulted in 40 papers that were quality assessed (A, B, or C). The grade of evidence was classified as convincing, probable, suggestive, and no conclusion.

We found suggestive evidence for improved maternal iodine status and thyroid function by iodine supplementation during pregnancy. Suggestive evidence was found for the relationship between improved thyroid function (used as an indicator of iodine status) during pregnancy and cognitive function in the offspring up to 18 months of age. Moderately to severely iodine-deficient children will probably benefit from iodine supplementation or improved iodine status in order to improve their cognitive function, while only one study showed improved cognitive function following iodine supplementation in children from a mildly iodine-deficient area (no conclusion).

No conclusions can be drawn related to other outcomes included in our review. There are no new data supporting changes in dietary reference values for children or adults. The rationale for increasing the dietary reference values for pregnant and lactating women in the NNR5 needs to be discussed in a broader perspective, taking iodine status of pregnant women in the Nordic countries into account.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3073165/ Brain fiber architecture, genetics, and intelligence: a high angular resolution diffusion imaging (HARDI) study Ming-Chang Chiang, Marina Barysheva, Agatha D. Lee, Sarah Madsen, Andrea D. Klunder, Arthur W. Toga, Katie L. Mcmahon, Greig I. de Zubicaray, Matthew Meredith, Margaret J. Wright, Anuj Srivastava, Nikolay Balov, Paul M. Thompson 2008 2021-07-18 [("doi","10.1007/978-3-540-85988-8_126")] iq psychology/neuroscience

We developed an analysis pipeline enabling population studies of High Angular Resolution Diffusion Imaging (HARDI) data, and applied it to map genetic influences on fiber architecture in 90 twin subjects. We applied tensor-driven 3D fluid registration to HARDI, resampling the spherical fiber orientation distribution functions (ODFs) in appropriate Riemannian manifolds, after ODF regularization and sharpening.

Fitting structural equation models (SEM) from quantitative genetics, we evaluated genetic influences on the Jensen-Shannon divergence (JSD), a novel measure of fiber spatial coherence, and on the generalized fiber anisotropy (GFA), a measure of fiber integrity. With random-effects regression, we mapped regions where diffusion profiles were highly correlated with subjects’ intelligence quotient (IQ).

Fiber complexity was predominantly under genetic control, and higher in more highly anisotropic regions; the proportion of genetic versus environmental control varied spatially. Our methods show promise for discovering genes affecting fiber connectivity in the brain.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3140225/ Why Have College Completion Rates Declined? An Analysis of Changing Student Preparation and Collegiate Resources John Bound, Michael F. Lovenheim, Sarah Turner 2010 2021-07-18 [("doi","10.1257/app.2.3.129")] iq sociology

Rising college enrollment over the last quarter century has not been met with a proportional increase in college completion.

Comparing the high school classes of 1972 and 1992, we show declines in college completion rates have been most pronounced for men who first enroll in less selective public universities and community colleges.

We decompose the decline into the components due to changes in preparedness of entering students and due to changes in collegiate characteristics, including type of institution and resources per student. While both factors play some role, the supply-side characteristics are most important in explaining changes in college completion.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3672949/ Verbal and non-verbal intelligence changes in the teenage brain Sue Ramsden, Fiona M. Richardson, Goulven Josse, Michael S. C. Thomas, Caroline Ellis, Clare Shakeshaft, Mohamed L. Seghier, Cathy J. Price 2011 2021-07-18 [("doi","10.1038/nature10514")] iq psychology/neuroscience

Intelligence quotient (IQ) is a standardized measure of human intellectual capacity that takes into account a wide range of cognitive skills. IQ is generally considered to be stable across the lifespan, with scores at one time point used to predict educational achievement and employment prospects in later years. Neuroimaging allows us to test whether unexpected longitudinal fluctuations in measured IQ are related to brain development.

Here we show that verbal and non-verbal IQ can rise or fall in the teenage years, with these changes in performance validated by their close correlation with changes in local brain structure. A combination of structural and functional imaging showed that verbal IQ changed with grey matter in a region that was activated by speech, whereas non-verbal IQ changed with grey matter in a region that was activated by finger movements.

By using longitudinal assessments of the same individuals, we obviated the many sources of variation in brain structure that confound cross-sectional studies. This allowed us to dissociate neural markers for the two types of IQ and to show that general verbal and non-verbal abilities are closely linked to the sensorimotor skills involved in learning.

More generally, our results emphasize the possibility that an individual’s intellectual capacity relative to their peers can decrease or increase in the teenage years. This would be encouraging to those whose intellectual potential may improve, and would be a warning that early achievers may not maintain their potential.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3114293/ The likelihood of cognitive enhancement Gary Lynch, Linda C. Palmer, Christine M. Gall 2011 2021-07-18 [("doi","10.1016/j.pbb.2010.12.024")] iq nootropic

Whether drugs that enhance cognition in healthy individuals will appear in the near future has become a topic of considerable interest. We address this possibility using a 3 variable system (psychological effect, neurobiological mechanism, and efficiency vs. capabilities) for classifying candidates. Ritalin and modafinil, two currently available compounds, operate on primary psychological states that in turn affect cognitive operations (attention and memory), but there is little evidence that these effects translate into improvements in complex cognitive processing. A second category of potential enhancers includes agents that improve memory encoding, generally without large changes in primary psychological states. Unfortunately, there is little information on how these compounds affect cognitive performance in standard psychological tests.

Recent experiments have identified a number of sites at which memory drugs could, in principle, manipulate the cell biological systems underlying the learning-related long-term potentiation (LTP) effect; this may explain the remarkable diversity of memory promoting compounds. Indeed, many of these agents are known to have positive effects on LTP. A possible third category of enhancement drugs directed specifically at integrated cognitive operations is nearly empty.

From a neurobiological perspective, two plausible candidate classes have emerged that both target the fast excitatory transmission responsible for communication within cortical networks. One acts on nicotinic receptors (alpha7 and alpha4) that regulate release of the neurotransmitter glutamate while the other (‘ampakines’) allosterically modulates the glutamate receptors mediating the post-synaptic response (EPSCs). Brain imaging in primates has shown that ampakines expand cortical networks engaged by a complex task; coupled with behavioral data, these findings provide evidence for the possibility of generating new cognitive capabilities.

Finally, we suggest that continuing advances in behavioral sciences provide new opportunities for translational work, and that discussions of the social impact of cognitive enhancers have failed to consider the distinction between effects on efficiency vs. new capabilities.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3114818/ Lithium in drinking water and thyroid function Karin Broberg, Gabriela Concha, Karin Engström, Magnus Lindvall, Margareta Grandér, Marie Vahter 2011 2021-07-18 [("doi","10.1289/ehp.1002678")] psychiatry/lithium

Background: High concentrations of lithium in drinking water were previously discovered in the Argentinean Andes Mountains. Lithium is used worldwide for treatment of bipolar disorder and treatment-resistant depression. One known side effect is altered thyroid function.

Objectives: We assessed associations between exposure to lithium from drinking water and other environmental sources and thyroid function.

Method: Women (n = 202) were recruited in four Andean villages in northern Argentina. Lithium exposure was assessed based on concentrations in spot urine samples, measured by inductively coupled plasma mass spectrometry. Thyroid function was evaluated by plasma free thyroxine (T4) and pituitary gland thyroid-stimulating hormone (TSH), analyzed by routine immunometric methods.

Results: The median urinary lithium concentration was 3,910 μg/L (5th, 95th percentiles, 270 μg/L, 10,400 μg/L). Median plasma concentrations (5th, 95th percentiles) of T4 and TSH were 17 pmol/L (13 pmol/L, 21 pmol/L) and 1.9 mIU/L, (0.68 mIU/L, 4.9 mIU/L), respectively. Urine lithium was inversely associated with T4 [β for a 1,000-μg/L increase=-0.19; 95% confidence interval (CI), −0.31 to −0.068; p = 0.002] and positively associated with TSH (β = 0.096; 95% CI, 0.033 to 0.16; p = 0.003). Both associations persisted after adjustment (for T4, β = −0.17; 95% CI, −0.32 to −0.015; p = 0.032; for TSH: β = 0.089; 95% CI, 0.024 to 0.15; p = 0.007). Urine selenium was positively associated with T4 (adjusted T4 for a 1 μg/L increase: β = 0.041; 95% CI, 0.012 to 0.071; p = 0.006).

Conclusion: Exposure to lithium via drinking water and other environmental sources may affect thyroid function, consistent with known side effects of medical treatment with lithium. This stresses the need to screen for lithium in all drinking water sources.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3289682/pdf/nihms352182.pdf Effects of lithium on oxidative stress parameters in healthy subjects Rushaniya Khairova, Rohit Pawar, Giacomo Salvadore, Mario F. Juruena, Rafael T. de Sousa, Márcio G. Soeiro-de-Souza, Mirian Salvador, Carlos A. Zarate, Wagner F. Gattaz, Rodrigo Machado-Vieira 2012 2021-07-19 [("doi","10.3892/mmr.2011.732")] psychiatry/lithium

Increased neuronal oxidative stress (OxS) induces deleterious effects on signal transduction, structural plasticity, and cellular resilience, mainly by inducing lipid peroxidation in membranes, proteins, and genes. Major markers of OxS levels include the thiobarbituric acid reactive substances (TBARS) and the enzymes superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase. Lithium has been shown to prevent and/or reverse DNA damage, free-radical formation, and lipid peroxidation in diverse models.

This study evaluates OxS parameters in healthy volunteers prior to and following lithium treatment. Healthy volunteers were treated with lithium in therapeutic doses for 2–4 weeks.

Treatment with lithium in healthy volunteers selectively altered SOD levels in all subjects. Furthermore, a decrease in the SOD/CAT ratio was observed following lithium treatment, which was associated with decreased OxS by lowering hydrogen peroxide levels. This reduction in the SOD/CAT ratio may lead to lower OxS, indicated primarily by a decrease in the concentration of cell hydrogen peroxide.

Overall, the present findings indicate a potential role for the antioxidant effects of lithium in healthy subjects, supporting its neuroprotective profile in bipolar disorder (BD) and, possibly, in neurodegenerative processes.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125059/ Ageing and its implications P. Jayanthi, Elizabeth Joshua, K. Ranganathan 2010 2021-07-19 [("doi","10.4103/0973-029X.72500")] longevity

Ageing processes are defined as those that increase the susceptibility of individuals, as they grow older, to the factors that eventually lead to death. It is a complex multi-factorial process, where several factors may interact simultaneously and may operate at many levels of functional organization. The heterogeneity of ageing phenotype among individuals of the same species and differences in longevity among species are due to the contribution of both genetic and environmental factors in shaping the life span.

The various theories of ageing and their proposed roles are discussed in this review.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3062986/ Typologies of extreme longevity myths Robert D. Young, Bertr, Desjardins, Kirsten McLaughlin, Michel Poulain, Thomas T. Perls 2010 2021-07-19 [("doi","10.1155/2010/423087")] longevity survey

Purpose. Political, national, religious, and other motivations have led the media and even scientists to errantly accept extreme longevity claims prima facie. We describe various causes of false claims of extraordinary longevity.

Design & Method: American Social Security Death Index files for the period 1980–2009 were queried for individuals with birth and death dates yielding ages 110+ years of age. Frequency was compared to a list of age-validated supercentenarians maintained by the Gerontology Research Group who died during the same time period. Age claims of 110+ years and the age validation experiences of the authors facilitated a list of typologies of false age claims.

Results: Invalid age claim rates increase with age from 65% at age 110–111 to 98% by age 115 to 100% for 120+ years.

11 typologies of false claims were: Religious Authority Myth, Village Elder Myth, Fountain of Youth Myth (substance), Shangri-La Myth (geographic), Nationalist Pride, Spiritual Practice, Familial Longevity, Individual and/or Family Notoriety, Military Service, Administrative Entry Error, and Pension-Social Entitlement Fraud.

Conclusion: Understanding various causes of false extreme age claims is important for placing current, past, and future extreme longevity claims in context and for providing a necessary level of skepticism.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3163136/ Compression of morbidity 1980–2011: a focused review of paradigms and progress James F. Fries, Bonnie Bruce, Eliza Chakravarty 2011 2021-07-19 [("doi","10.4061/2011/261702")] longevity

The Compression of Morbidity hypothesis-positing that the age of onset of chronic illness may be postponed more than the age at death and squeezing most of the morbidity in life into a shorter period with less lifetime disability-was introduced by our group in 1980. This paper is focused upon the evolution of the concept, the controversies and responses, the supportive multidisciplinary science, and the evolving lines of evidence that establish proof of concept.

We summarize data from 20-year prospective longitudinal studies of lifestyle progression of disability, national population studies of trends in disability, and randomized controlled trials of risk factor reduction with life-style based “healthy aging” interventions.

From the perspective of this influential and broadly cited paradigm, we review its current history, the development of a theoretical structure for healthy aging, and the challenges to develop coherent health policies directed at reduction in morbidity.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3377831/ Melatonin in aging and disease -multiple consequences of reduced secretion, options and limits of treatment Rüdiger Hardeland 2012 2021-07-19 longevity melatonin

Melatonin is a pleiotropically acting regulator molecule, which influences numerous physiological functions. Its secretion by the pineal gland progressively declines by age. Strong reductions of circulating melatonin are also observed in numerous disorders and diseases, including Alzheimer’s disease, various other neurological and stressful conditions, pain, cardiovascular diseases, cases of cancer, endocrine and metabolic disorders, in particular diabetes type 2. The importance of melatonergic signaling is also evident from melatonin receptor polymorphisms associated with several of these pathologies.

The article outlines the mutual relationship between circadian oscillators and melatonin secretion, the possibilities for readjustment of rhythms by melatonin and its synthetic analogs, the consequences for circadian rhythm-dependent disorders concerning sleep and mood, and limits of treatment. The necessity of distinguishing between short-acting melatonergic effects, which are successful in sleep initiation and phase adjustments, and attempts of replacement strategies is emphasized. Properties of approved and some investigational melatonergic agonists are compared.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2914120/ Phase relationships between core body temperature, melatonin, and sleep are associated with depression severity: further evidence for circadian misalignment in non-seasonal depression Brant P. Hasler, Daniel J. Buysse, David J. Kupfer, Anne Germain 2010 2021-07-19 [("doi","10.1016/j.psychres.2010.04.027")] melatonin psychiatry/depression

Misalignment between the timing of sleep and the circadian pacemaker has been linked to depression symptoms.

This study sought to extend earlier findings by comparing sleep and circadian markers in healthy controls and individuals with major depression.

Two markers of circadian misalignment correlated with depression severity in the depressed group.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3377494/ Reduced phase-advance of plasma melatonin after bright morning light in the luteal, but not follicular, menstrual cycle phase in premenstrual dysphoric disorder: an extended study Barbara L. Parry, Charles J. Meliska, Diane L. Sorenson, L. Fernando Martínez, Ana M. López, Jeffrey A. Elliott, Richard L. Hauger 2011 2021-07-19 [("doi","10.3109/07420528.2011.567365")] melatonin

The authors previously observed blunted phase-shift responses to morning bright light in women with premenstrual dysphoric disorder (PMDD). The aim of this study was to determine if these findings could be replicated using a higher-intensity, shorter-duration light pulse and to compare these results with the effects of an evening bright-light pulse.

In 17 PMDD patients and 14 normal control (NC) subjects, the authors measured plasma melatonin at 30-min intervals from 18:00 to 10:00h in dim (<30 lux) or dark conditions the night before (Night 1) and after (Night 3) a bright-light pulse (administered on Night 2) in both follicular and luteal menstrual cycle phases. The bright light (either 3000 lux for 6h or 6000 lux for 3h) was given either in the morning (AM light), 7h after the dim light melatonin onset (DLMO) measured the previous month, or in the evening (PM light), 3h after the DLMO.

In the luteal, but not in the follicular, phase, AM light advanced melatonin offset between Night 1 and Night 3 statistically-significantly less in PMDD than in NC subjects. The effects of PM light were not statistically-significant, nor were there statistically-significant effects of the light pulse on melatonin measures of onset, duration, peak, or area under the curve.

These findings replicated the authors’ previous finding of a blunted phase-shift response to morning bright light in the luteal, but not the follicular, menstrual cycle phase in PMDD compared with NC women, using a brighter (6000 vs. 3000 lux) light pulse for a shorter duration (3 vs. 6 h). As the effect of PM bright light on melatonin phase-shift responses did not differ between groups or alter other melatonin measures, these results suggest that in PMDD there is a luteal-phase subsensitivity or an increased resistance to morning bright-light cues that are critical in synchronizing human biological rhythms. The resulting circadian rhythm malsynchonization may contribute to the occurrence of luteal phase depressive symptoms in women with PMDD.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3278206/ Efficacy and safety of prolonged-release melatonin for insomnia in middle-aged and elderly patients with hypertension: a combined analysis of controlled clinical trials Patrick Lemoine, Alan G. Wade, Amnon Katz, Tali Nir, Nava Zisapel 2012 2021-07-19 [("doi","10.2147/IBPC.S27240")] melatonin

Background: Add-on prolonged-release melatonin (PRM) in antihypertensive therapy has been shown to ameliorate nocturnal hypertension. Hypertension is a major comorbidity among insomnia patients. The efficacy and safety of PRM for primary insomnia in patients aged 55 years and older who are treated with antihypertensive drugs were evaluated.

Method: Post hoc analysis of pooled antihypertensive drug-treated subpopulations from four randomized, double-blind trials of PRM and placebo for 3 weeks (N[PRM] = 195; N[placebo] = 197) or 28 weeks (N[PRM] = 157; N[placebo] = 40). Efficacy measurements included Leeds Sleep Evaluation Questionnaire scores of quality of sleep and alertness and behavioral integrity the following morning after 3 weeks, and sleep latency (daily sleep diary) and Clinical Global Impression of Improvement (CGI-I) after 6 months of treatment. Safety measures included antihypertensive drug-treated subpopulations from these four and 3 additional single-blind and open-label PRM studies of up to 1 year (N[PRM] = 650; N[placebo] = 632).

Results: Quality of sleep and behavior following wakening improved with PRM compared with placebo (p < 0.0001 and p < 0.0008, respectively). Sleep latency (p = 0.02) and CGI-I (p = 0.0003) also improved. No differences were observed between PRM and placebo groups in vital signs, including daytime blood pressure at baseline and treatment phases. The rate of adverse events normalized per 100 patient-weeks was lower for PRM (3.66) than for placebo (8.53).

Conclusion: The findings demonstrate substantive and sustained efficacy of PRM in primary insomnia patients treated with antihypertensive drugs. PRM appears to be safe for insomnia in patients with cardiovascular comorbidity.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2878385/ Modafinil effects on cognitive function in HIV+ patients treated for fatigue: a placebo controlled study Martin McElhiney, Judith Rabkin, Wilfred Van Gorp, Richard Rabkin 2010 2021-07-19 [("doi","10.1080/13803390903201769")] modafinil

Both mild cognitive impairment and fatigue are common among people with HIV/AIDS.

This study examined the efficacy of modafinil for HIV+ patients who sought treatment for fatigue in a placebo-controlled double-blind 4-week trial. A battery of standard neuropsychological tests was administered at study entry and Week 4, and change in performance was compared for 59 patients receiving modafinil versus 44 patients receiving placebo.

A statistically-significant effect on fatigue was observed. In addition, cognitive performance, as measured by a global change score, improved more in the modafinil than in the placebo group although the effect was not specific to any cognitive domain.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0015267 Experimental ‘Jet Lag’ Inhibits Adult Neurogenesis and Produces Long-Term Cognitive Deficits in Female Hamsters Erin M. Gibson, Connie Wang, Stephanie Tjho, Neera Khattar, Lance J. Kriegsfeld 2010-11-03 2021-07-19 [("doi","10.1371/journal.pone.0015267")] modafinil

Background: Circadian disruptions through frequent transmeridian travel, rotating shift work, and poor sleep hygiene are associated with an array of physical and mental health maladies, including marked deficits in human cognitive function. Despite anecdotal and correlational reports suggesting a negative impact of circadian disruptions on brain function, this possibility has not been experimentally examined.

Methodology/Principal Findings: In the present study, we investigated whether experimental ‘jet lag’ (ie. phase advances of the light∶dark cycle) negatively impacts learning and memory and whether any deficits observed are associated with reductions in hippocampal cell proliferation and neurogenesis. Because insults to circadian timing alter circulating glucocorticoid and sex steroid concentrations, both of which influence neurogenesis and learning/memory, we assessed the contribution of these endocrine factors to any observed alterations. Circadian disruption resulted in pronounced deficits in learning and memory paralleled by marked reductions in hippocampal cell proliferation and neurogenesis., deficits in hippocampal-dependent learning and memory were not only seen during the period of the circadian disruption, but also persisted well after the cessation of jet lag, suggesting long-lasting negative consequences on brain function.

Conclusions: Together, these findings support the view that circadian disruptions suppress hippocampal neurogenesis via a glucocorticoid-independent mechanism, imposing pronounced and persistent impairments on learning and memory.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3393816/ Interacting with nature improves cognition and affect for individuals with depression Marc G. Berman, Ethan Kross, Katherine M. Krpan, Mary K. Askren, Aleah Burson, Patricia J. Deldin, Stephen Kaplan, Lindsey Sherdell, Ian H. Gotlib, John Jonides 2012 2021-07-19 [("doi","10.1016/j.jad.2012.03.012")] psychiatry/depression psychology/nature

Background: This study aimed to explore whether walking in nature may be beneficial for individuals with major depressive disorder (MDD). Healthy adults demonstrate cognitive gains after nature walks, but it was unclear whether those same benefits would be achieved in a depressed sample as walking alone in nature might induce rumination, thereby worsening memory and mood.

Method: Twenty individuals diagnosed with MDD participated in this study. At baseline, mood and short term memory span were assessed using the PANAS and the backwards digit span (BDS) task, respectively. Participants were then asked to think about an unresolved negative autobiographical event to prime rumination, prior to taking a 50-min walk in either a natural or urban setting. After the walk, mood and short-term memory span were reassessed. The following week, participants returned to the lab and repeated the entire procedure, but walked in the location not visited in the first session (ie. a counterbalanced within-subjects design).

Results: Participants exhibited statistically-significant increases in memory span after the nature walk relative to the urban walk, p < 0.001, η(p)(2)=.53 (a large effect-size). Participants also showed increases in mood, but the mood effects did not correlate with the memory effects, suggesting separable mechanisms and replicating previous work.

Limitations: Sample size and participants’ motivation.

Conclusion: These findings extend earlier work demonstrating the cognitive and affective benefits of interacting with nature to individuals with MDD. Therefore, interacting with nature may be useful clinically as a supplement to existing treatments for MDD.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2997603/ Stopping smokeless tobacco with varenicline: randomized double blind placebo controlled trial Karl Fagerström, Hans Gilljam, Michael Metcalfe, Serena Tonstad, Michael Messig 2010 2021-07-20 [("doi","10.1136/bmj.c6549")] nicotine

Objective: To assess the efficacy and safety of varenicline (a licensed cigarette smoking cessation aid) in helping users of smokeless tobacco to quit.

Design: Double blind, placebo controlled, parallel group, multicentre, randomized controlled trial.

Setting: Medical clinics (mostly primary care) in Norway and Sweden.

Participants: Men and women aged ≥18 who used smokeless tobacco at least eight times a day, with no abstinence period over 3 months within one year before screening, who wanted to quit all tobacco use. Participants were excluded if they used any other form of tobacco (except smokeless tobacco) or medication to stop smoking within 3 months of screening or had any pre-existing medical or psychiatric condition.

Interventions: Varenicline 1 mg twice daily (titrated during the first week) or placebo for 12 weeks, with 14 weeks’ follow-up after treatment.

Main Outcome Measures: The primary end point was the four week continuous abstinence rate at the end of treatment (weeks 9–12) confirmed with cotinine concentration. A secondary end point was continuous abstinence rate for weeks 9–26. Safety and tolerability were also evaluated.

Results: 431 participants (213 varenicline; 218 placebo) were randomized and received at least one dose of study drug. Participants’ demographics and baseline use of smokeless tobacco were similar (89% (189) and 90% (196), respectively, were men; mean age in both groups was 43.9; participants used smokeless tobacco products about 15× a day, and about 80% first used smokeless tobacco within 30 minutes after awakening). Continuous abstinence rate at week 9–12 was higher in the varenicline group than the placebo group (59% (125) v 39% (85); relative risk 1.60, 95% confidence interval 1.32 to 1.87, p < 0.001; risk difference 20%; number needed to treat 5). The advantage of varenicline over placebo persisted through 14 weeks of follow-up (continuous abstinence rate at week 9–26 was 45% (95) v 34% (73); relative risk 1.42, 1.08 to 1.79, p = 0.012; risk difference 11%; number needed to treat 9). The most common adverse events in the varenicline group compared with the placebo group were nausea (35% (74) v 6% (14)), fatigue (10% (22) v 7% (15)), headache (10% (22) v 9% (20)), and sleep disorder (10% (22) v 7% (15)). Few adverse events led to discontinuation of treatment (9% (19) and 4% (9), respectively), and serious adverse events occurred in two (1%) and 3 (1%) participants, respectively.

Conclusion: Varenicline can help people to give up smokeless tobacco and has an acceptable safety profile. The response rate in the placebo group in this study was high, suggesting a population less resistant to treatment than smokers.

Trial Registration: NCT00717093.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3611984/ Dependence on tobacco and nicotine products: a case for product-specific assessment Karl Fagerström, Thomas Eissenberg 2012 2021-07-20 [("doi","10.1093/ntr/nts007")] nicotine

The International Classification of Diseases and the Diagnostic and Statistical Manual for diagnosing tobacco/nicotine dependence emphasize the dependence-producing drug nicotine. These diagnostic tools have been challenged on grounds of poor predictive validity, and they do not differentiate across various forms of nicotine-containing products. In fact, nicotine-containing products (eg. tobacco cigarettes, smokeless tobacco [ST], waterpipe, electronic cigarettes [ECIGs], and nicotine replacement [NR] products) have very different characteristics both in terms of sensory and behavioral involvement and also in pharmacokinetic and pharmacodynamic effects. For example, a cigarette and a nicotine patch are very different on almost every one of these dimensions.

When ability to stop using a nicotine/tobacco product is used as a criterion for dependence, success rates vary considerably across products: Tobacco cigarette cessation is more difficult than ST cessation that in turn is more difficult than NR product cessation.

Based on these results, we hypothesize that there is a continuum of dependence as much as there is a continuum of harm, with tobacco cigarettes and NR products on opposite ends of both continua and other products (waterpipe and ECIGs) somewhere in between.

In order to capture more precisely the dependence produced by both nicotine and its administration forms, product-specific instruments may be required. The pros and cons of this approach are discussed.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3263647/ Nicotine contents in some commonly used toothpastes and toothpowders: a present scenario S S. Agrawal, R. S. Ray 2012 2021-07-20 [("doi","10.1155/2012/237506")] nicotine

The use of tobacco products as dentifrices is still prevalent in various parts of India. Tobacco use in dentifrices is a terrible scourge which motivates continued use despite its harmful effects. Indian legislation prohibits the use of nicotine in dentifrices. Nicotine is primarily injurious to people because it is responsible for tobacco addiction and is dependence forming.

The present study was motivated by an interest in examining the presence of nicotine in these dentifrices. Our earlier report indicates the presence of nicotine in toothpowders. To further curb the menace of tobacco, our team again analysed the toothpowder brands of previous years and in toothpastes as well. Eight brands of commonly used toothpastes and toothpowders were evaluated by gas chromatography-mass spectroscopy.

On the whole, there are a few successes but much remains to be done. Our findings indicated the presence of nicotine in two brands of dant manjans and four brands of toothpastes. Further our finding underscores the need for stringent regulations by the regulatory authorities for preventing the addition of nicotine in these dentifrices. Hence government policy needs to be targeted towards an effective control of tobacco in these dentifrices and should be properly addressed.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156905/ Placebo interventions for all clinical conditions Asbjørn Hróbjartsson, Peter C. Gøtzsche 2010 2021-07-20 [("doi","10.1002/14651858.CD003974.pub3")] nootropic psychiatry/depression statistics/bias

Background: Placebo interventions are often claimed to substantially improve patient-reported and observer-reported outcomes in many clinical conditions, but most reports on effects of placebos are based on studies that have not randomized patients to placebo or no treatment. Two previous versions of this review from 2001 and 2004 found that placebo interventions in general did not have clinically important effects, but that there were possible beneficial effects on patient-reported outcomes, especially pain. Since then several relevant trials have been published.

Objectives: Our primary aims were to assess the effect of placebo interventions in general across all clinical conditions, and to investigate the effects of placebo interventions on specific clinical conditions. Our secondary aims were to assess whether the effect of placebo treatments differed for patient-reported and observer-reported outcomes, and to explore other reasons for variations in effect.

Search Strategy: We searched the Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library Issue 4, 2007), MEDLINE (1966 to March 2008), Embase (1980 to March 2008), PsycINFO (1887 to March 2008) and Biological Abstracts (1986 to March 2008). We contacted experts on placebo research, and read references in the included trials.

Selection Criteria: We included randomized placebo trials with a no-treatment control group investigating any health problem.

Data Collection and Analysis: Two authors independently assessed trial quality and extracted data. We contacted study authors for additional information. Trials with binary data were summarised using relative risk (a value of less than 1 indicates a beneficial effect of placebo), and trials with continuous outcomes were summarised using standardized mean difference (a negative value indicates a beneficial effect of placebo).

Main Results: Outcome data were available in 202⁄234 included trials, investigating 60 clinical conditions. We regarded the risk of bias as low in only 16 trials (8%), five of which had binary outcomes.In 44 studies with binary outcomes (6041 patients), there was moderate heterogeneity (p < 0.001; I2 45%) but no clear difference in effects between small and large trials (symmetrical funnel plot). The overall pooled effect of placebo was a relative risk of 0.93 (95% confidence interval (CI) 0.88 to 0.99). The pooled relative risk for patient-reported outcomes was 0.93 (95% CI 0.86 to 1.00) and for observer-reported outcomes 0.93 (95% CI 0.85 to 1.02). We found no statistically-significant effect of placebo interventions in four clinical conditions that had been investigated in 3 trials or more: pain, nausea, smoking, and depression, but confidence intervals were wide. The effect on pain varied considerably, even among trials with low risk of bias. In 158 trials with continuous outcomes (10,525 patients), there was moderate heterogeneity (p < 0.001; I2 42%), and considerable variation in effects between small and large trials (asymmetrical funnel plot). It is therefore a questionable procedure to pool all the trials, and we did so mainly as a basis for exploring causes for heterogeneity.

We found an overall effect of placebo treatments, standardized mean difference (SMD) −0.23 (95% CI −0.28 to −0.17). The SMD for patient-reported outcomes was −0.26 (95% CI −0.32 to −0.19), and for observer-reported outcomes, SMD −0.13 (95% CI −0.24 to −0.02). We found an effect on pain, SMD −0.28 (95% CI −0.36 to −0.19); nausea, SMD −0.25 (−0.46 to −0.04), asthma, SMD −0.35 (−0.70 to −0.01), and phobia, SMD −0.63 (95% CI −1.17 to −0.08). The effect on pain was very variable, also among trials with low risk of bias. Four similarly-designed acupuncture trials conducted by an overlapping group of authors reported large effects, SMD −0.68 (−0.85 to −0.50), whereas 3 other pain trials reported low or no effect, SMD −0.13 (−0.28 to 0.03). The pooled effect on nausea was small, but consistent. The effects on phobia and asthma were very uncertain due to high risk of bias. There was no statistically-significant effect of placebo interventions in the seven other clinical conditions investigated in 3 trials or more: smoking, dementia, depression, obesity, hypertension, insomnia and anxiety, but confidence intervals were wide.Meta-regression analyses showed that larger effects of placebo interventions were associated with physical placebo interventions (eg. sham acupuncture), patient-involved outcomes (patient-reported outcomes and observer-reported outcomes involving patient cooperation), small trials, and trials with the explicit purpose of studying placebo. Larger effects of placebo were also found in trials that did not inform patients about the possible placebo intervention.

Authors’ Conclusions: We did not find that placebo interventions have important clinical effects in general. However, in certain settings placebo interventions can influence patient-reported outcomes, especially pain and nausea, though it is difficult to distinguish patient-reported effects of placebo from biased reporting. The effect on pain varied, even among trials with low risk of bias, from negligible to clinically important. Variations in the effect of placebo were partly explained by variations in how trials were conducted and how patients were informed.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3288797/ The nuts and bolts of low-level laser (light) therapy Hoon Chung, Tianhong Dai, Sulbha K. Sharma, Ying-Ying Huang, James D. Carroll, Michael R. Hamblin 2012 2021-07-20 [("doi","10.1007/s10439-011-0454-7")] nootropic

Soon after the discovery of lasers in the 1960s it was realized that laser therapy had the potential to improve wound healing and reduce pain, inflammation and swelling. In recent years the field sometimes known as photobiomodulation has broadened to include light-emitting diodes and other light sources, and the range of wavelengths used now includes many in the red and near infrared.

The term “low level laser therapy” or LLLT has become widely recognized and implies the existence of the biphasic dose response or the Arndt-Schulz curve. This review will cover the mechanisms of action of LLLT at a cellular and at a tissue level and will summarize the various light sources and principles of dosimetry that are employed in clinical practice.

The range of diseases, injuries, and conditions that can be benefited by LLLT will be summarized with an emphasis on those that have reported randomized controlled clinical trials.

Serious life-threatening diseases such as stroke, heart attack, spinal cord injury, and traumatic brain injury may soon be amenable to LLLT therapy.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2964443/ The unreasonable effectiveness of my self-experimentation Seth Roberts 2010 2021-07-20 [("doi","10.1016/j.mehy.2010.04.030")] nootropic/quantified-self

Over 12 years, my self-experimentation found new and useful ways to improve sleep, mood, health, and weight. Why did it work so well? First, my position was unusual. I had the subject-matter knowledge of an insider, the freedom of an outsider, and the motivation of a person with the problem. I did not need to publish regularly. I did not want to display status via my research.

Second, I used a powerful tool. Self-experimentation about the brain can test ideas much more easily (by a factor of about 500,000) than conventional research about other parts of the body. When you gather data, you sample from a power-law-like distribution of progress. Most data helps a little; a tiny fraction of data helps a lot. My subject-matter knowledge and methodological skills (eg. in data analysis) improved the distribution from which I sampled (ie. increased the average amount of progress per sample). Self-experimentation allowed me to sample from it much more often than conventional research.

Another reason my self-experimentation was unusually effective is that, unlike professional science, it resembled the exploration of our ancestors, including foragers, hobbyists, and artisans.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2917656/ Using N-of-1 trials to improve patient management and save costs Paul A. Scuffham, Jane Nikles, Geoffrey K. Mitchell, Michael J. Yelland, Norma Vine, Christopher J. Poulos, Peter I. Pillans, Guy Bashford, Chris del Mar, Philip J. Schluter, Paul Glasziou 2010 2021-07-20 [("doi","10.1007/s11606-010-1352-7")] nootropic/quantified-self psychiatry/adhd statistics

Background: N-of-1 trials test treatment effectiveness within an individual patient.

Objective: To assess (1) the impact of 3 different N-of-1 trials on both clinical and economic outcomes over 12 months and (2) whether the use of N-of-1 trials to target patients’ access to high-cost drugs might be cost-effective in Australia.

Design: Descriptive study of management change, persistence, and costs summarizing 3 N-of-1 trials.

Participants: Volunteer patients with osteoarthritis, chronic neuropathic pain or ADHD whose optimal choice of treatment was uncertain.

Interventions: Double-blind cyclical alternative medications for the 3 conditions.

Measures: Detailed resource use, treatment and health outcomes (response) data collected by postal and telephone surveys immediately before and after the trial and at 3, 6 and 12 months. Estimated costs to the Australian healthcare system for the pre-trial vs. 12 months post-trial.

Results: Participants persisting with the joint patient-doctor decision 12 months after trial completion were 32% for osteoarthritis, 45% for chronic neuropathic pain and 70% for the ADHD trials. Cost-offsets were obtained from reduced usage of non-optimal drugs, and reduced medical consultations. Drug costs increased for the chronic neuropathic pain and ADHD trials due to many patients being on either low-cost or no pharmaceuticals before the trial.

Conclusion: N-of-1 trials are an effective method to identify optimal treatment in patients in whom disease management is uncertain. Using this evidence-based approach, patients and doctors tend to persist with optimal treatment resulting in cost-savings. N-of-1 trials are clinically acceptable and may be an effective way of rationally prescribing some expensive long-term medicines.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3298919/ Self-experimentation and its role in medical research Allen B. Weisse 2012 2021-07-20 nootropic/quantified-self philosophy/ethics

Although experimentation involving human volunteers has attracted intense study, the matter of self-experimentation among medical researchers has received much less attention. Many questions have been answered only in part, or have been left unanswered. How common is this practice? Is it more common among certain nationalities? What have been the predominant medical fields in which self-experimentation has occurred? How dangerous an act has this proved to be? What have been the trends over time? What is the future likely to bring?

From the available literature, I identified and analyzed 465 documented instances of this practice, performed over the course of the past 2 centuries. Most instances occurred in the United States. The peak of self-experimentation occurred in the first half of the 20th century.

Eight deaths were recorded. A number of the investigators enjoyed successful careers, including the receipt of Nobel Prizes. Although self-experimentation by physicians and other biological scientists appears to be in decline, the courage of those involved and the benefits to society cannot be denied.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3049091/ Young children’s selective trust in informants Paul L. Harris, Kathleen H. Corriveau 2011 2021-07-20 [("doi","10.1098/rstb.2010.0321")] philosophy/religion psychology

Young children readily act on information from adults, setting aside their own prior convictions and even continuing to trust informants who make claims that are manifestly false. Such credulity is consistent with a long-standing philosophical and scientific conception of young children as prone to indiscriminate trust. Against this conception, we argue that children trust some informants more than others.

In particular, they use two major heuristics. First, they keep track of the history of potential informants. Faced with conflicting claims, they endorse claims made by someone who has provided reliable care or reliable information in the past. Second, they monitor the cultural standing of potential informants. Faced with conflicting claims, children endorse claims made by someone who belongs to a consensus and whose behavior abides by, rather than deviating from, the norms of their group. The first heuristic is likely to promote receptivity to information offered by familiar caregivers, whereas the second heuristic is likely to promote a broader receptivity to informants from the same culture.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3153866/ Effects of a standardized Bacopa monnieri extract on cognitive performance, anxiety, and depression in the elderly: a randomized, double-blind, placebo-controlled trial Carlo Calabrese, William L. Gregory, Michael Leo, Dale Kraemer, Kerry Bone, Barry Oken 2008 2021-07-20 [("doi","10.1089/acm.2008.0018")] nootropic/bacopa psychiatry/anxiety psychiatry/depression

Objectives: Study aims were to evaluate effects of Bacopa monnieri whole plant standardized dry extract on cognitive function and affect and its safety and tolerability in healthy elderly study participants.

Design: The study was a randomized, double-blind, placebo-controlled clinical trial with a placebo run-in of 6 weeks and a treatment period of 12 weeks.

Setting/Location: Volunteers were recruited from the community to a clinic in Portland, Oregon by public notification.

Subjects: Fifty-four (54) participants, 65 or older (mean 73.5 years), without clinical signs of dementia, were recruited and randomized to Bacopa or placebo. Forty-eight (48) completed the study with 24 in each group.

Interventions: Standardized B. monnieri extract 300 mg/day or a similar placebo tablet orally for 12 weeks.

Outcome Measures: The primary outcome variable was the delayed recall score from the Rey Auditory Verbal Learning Test (AVLT). Other cognitive measures were the Stroop Task assessing the ability to ignore irrelevant information, the Divided Attention Task (DAT), and the Wechsler Adult Intelligence Scale (WAIS) letter-digit test of immediate working memory. Affective measures were the State-Trait Anxiety Inventory, Center for Epidemiologic Studies Depression scale (CESD)-10 depression scale, and the Profile of Mood States. Vital signs were also monitored.

Results: Controlling for baseline cognitive deficit using the Blessed Orientation-Memory-Concentration test, Bacopa participants had enhanced AVLT delayed word recall memory scores relative to placebo. Stroop results were similarly, with the Bacopa group improving and the placebo group unchanged. CESD-10 depression scores, combined state plus trait anxiety scores, and heart rate decreased over time for the Bacopa group but increased for the placebo group. No effects were found on the DAT, WAIS digit task, mood, or blood pressure. The dose was well tolerated with few adverse events (Bacopa n = 9, placebo n = 10), primarily stomach upset.

Conclusion: This study provides further evidence that B. monnieri has potential for safely enhancing cognitive performance in the aging.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2670458/ Prevalence of cognitive impairment without dementia in the United States Brenda L. Plassman, Kenneth M. Langa, Gwenith G. Fisher, Steven G. Heeringa, David R. Weir, Mary Beth Ofstedal, James R. Burke, Michael D. Hurd, Guy G. Potter, Willard L. Rodgers, David C. Steffens, John J. McArdle, Robert J. Willis, Robert B. Wallace 2008 2021-07-20 [("doi","10.7326/0003-4819-148-6-200803180-00005")] psychiatry/alzheimers

Background: Cognitive impairment without dementia is associated with increased risk for disability, increased health care costs, and progression to dementia. There are no population-based prevalence estimates of this condition in the United States.

Objective: To estimate the prevalence of cognitive impairment without dementia in the United States and determine longitudinal cognitive and mortality outcomes.

Design: Longitudinal study from July 2001 to March 2005.

Setting: In-home assessment for cognitive impairment.

Participants: Participants in ADAMS (Aging, Demographics, and Memory Study) who were age 71 years or older drawn from the nationally representative HRS (Health and Retirement Study). Of 1770 selected individuals, 856 completed initial assessment, and of 241 selected individuals, 180 completed 16- to 18-month follow-up assessment.

Measurements: Assessments, including neuropsychological testing, neurologic examination, and clinical and medical history, were used to assign a diagnosis of normal cognition, cognitive impairment without dementia, or dementia. National prevalence rates were estimated by using a population-weighted sample.

Results: In 2002, an estimated 5.4 million people (22.2%) in the United States age 71 years or older had cognitive impairment without dementia. Prominent subtypes included prodromal Alzheimer disease (8.2%) and cerebrovascular disease (5.7%). Among participants who completed follow-up assessments, 11.7% with cognitive impairment without dementia progressed to dementia annually, whereas those with subtypes of prodromal Alzheimer disease and stroke progressed at annual rates of 17% to 20%. The annual death rate was 8% among those with cognitive impairment without dementia and almost 15% among those with cognitive impairment due to medical conditions.

Limitations: Only 56% of the non-deceased target sample completed the initial assessment. Population sampling weights were derived to adjust for at least some of the potential bias due to nonresponse and attrition.

Conclusion: Cognitive impairment without dementia is more prevalent in the United States than dementia, and its subtypes vary in prevalence and outcomes.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2908269/#S3title Vitamin D and depression: where is all the sunshine? Sue Penckofer, Joanne Kouba, Mary Byrn, Carol Estwing Ferrans 2010 2021-07-21 [("doi","10.3109/01612840903437657")] psychiatry/depression vitamin-d

Depression in its own right is a disabling condition impairing all aspects of human function. In persons with a chronic medical disease, depression often makes the management of chronic illness more difficult. Recently, vitamin D has been reported in the scientific and lay press as an important factor that may have health benefits in the prevention and the treatment of many chronic illnesses. Most individuals in this country have insufficient levels of vitamin D. This is also true for persons with depression as well as other mental disorders.

Whether this insufficiency is due to insufficient dietary intake, lifestyle (eg. little outdoor exposure to sunshine), or other factors is addressed in this paper. In addition, groups at risk and suggested treatment for inadequate vitamin D levels are addressed.

Effective detection and treatment of inadequate vitamin D levels in persons with depression and other mental disorders may be an easy and cost-effective therapy which could improve patients’ long-term health outcomes as well as their quality of life.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3191260/ Omega-3 supplementation lowers inflammation and anxiety in medical students: a randomized controlled trial Janice K. Kiecolt-Glaser, Martha A. Belury, Rebecca Andridge, William B. Malarkey, Ronald Glaser 2011 2021-07-21 [("doi","10.1016/j.bbi.2011.07.229")] psychiatry/anxiety psychiatry/depression

Observational studies have linked lower omega-3 (n-3) polyunsaturated fatty acids (PUFAs) and higher omega-6 (n-6) PUFAs with inflammation and depression, but randomized controlled trial data have been mixed. To determine whether n-3 decreases proinflammatory cytokine production and depressive and anxiety symptoms in healthy young adults, this parallel group, placebo-controlled, double-blind 12-week RCT compared n-3 supplementation with placebo.

The participants, 68 medical students, provided serial blood samples during lower-stress periods as well as on days before an exam. The students received either n-3 (2.5 g/d, 2085 mg eicosapentaenoic acid and 348 mg docosahexanoic acid) or placebo capsules that mirrored the proportions of fatty acids in the typical American diet.

Compared to controls, those students who received n-3 showed a 14% decrease in lipopolysaccharide (LPS) stimulated interleukin 6 (IL-6) production and a 20% reduction in anxiety symptoms, without change in depressive symptoms. Individuals differ in absorption and metabolism of n-3 PUFA supplements, as well as in adherence; accordingly, planned secondary analyses that used the plasma n-6:n-3 ratio in place of treatment group showed that decreasing n-6:n-3 ratios led to lower anxiety and reductions in stimulated IL-6 and tumor necrosis factor alpha (TNF-α) production, as well as marginal differences in serum TNF-α.

These data suggest that n-3 supplementation can reduce inflammation and anxiety even among healthy young adults. The reduction in anxiety symptoms associated with n-3 supplementation provides the first evidence that n-3 may have potential anxiolytic benefits for individuals without an anxiety disorder diagnosis.

ClinicalTrials.gov identifier:NCT00519779.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2919439/ Meditation acutely improves psychomotor vigilance, and may decrease sleep need Prashant Kaul, Jason Passafiume, Craig R. Sargent, Bruce F. O’Hara 2010 2021-07-21 [("doi","10.1186/1744-9081-6-47")] psychiatry/meditation zeo

Background: A number of benefits from meditation have been claimed by those who practice various traditions, but few have been well tested in scientifically controlled studies. Among these claims are improved performance and decreased sleep need. Therefore, in these studies we assess whether meditation leads to an immediate performance improvement on a well validated psychomotor vigilance task (PVT), and second, whether longer bouts of meditation may alter sleep need.

Method: The primary study assessed PVT reaction times before and after 40 minute periods of mediation, nap, or a control activity using a within subject cross-over design. This study utilized novice meditators who were current university students (n = 10). Novice meditators completed 40 minutes of meditation, nap, or control activities on six different days (two separate days for each condition), plus one night of total sleep deprivation on a different night, followed by 40 minutes of meditation.A second study examined sleep times in long term experienced meditators (n = 7) vs. non-meditators (n = 23). Experienced meditators and controls were age and sex matched and living in the Delhi region of India at the time of the study. Both groups continued their normal activities while monitoring their sleep and meditation times.

Results: Novice meditators were tested on the PVT before each activity, 10 minutes after each activity and one hour later. All ten novice meditators improved their PVT reaction times immediately following periods of meditation, and all but one got worse immediately following naps. Sleep deprivation produced a slower baseline reaction time (RT) on the PVT that still improved following a period of meditation. In experiments with long-term experienced meditators, sleep duration was measured using both sleep journals and actigraphy. Sleep duration in these subjects was lower than control non-meditators and general population norms, with no apparent decrements in PVT scores.

Conclusion: These results suggest that meditation provides at least a short-term performance improvement even in novice meditators. In long term meditators, multiple hours spent in meditation are associated with a decrease in total sleep time when compared with age and sex matched controls who did not meditate. Whether meditation can actually replace a portion of sleep or pay-off sleep debt is under further investigation.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3244552/ Can self-prediction overcome barriers to Hepatitis B vaccination? A randomized controlled trial Anthony D. Cox, Dena Cox, Rosalie Cyrier, Yolanda Graham-Dotson, Gregory D. Zimet 2012 2021-07-21 [("doi","10.1037/a0025298")] psychology

Objective: Hepatitis B virus (HBV) infection remains a serious public health problem, due in part to low vaccination rates among high-risk adults, many of whom decline vaccination because of barriers such as perceived inconvenience or discomfort. This study evaluates the efficacy of a self-prediction intervention to increase HBV vaccination rates among high-risk adults.

Method: Randomized controlled trial of 1,175 adults recruited from 3 sexually transmitted disease clinics in the United States over 28 months. Participants completed an audio-computer-assisted self-interview, which presented information about HBV infection and vaccination, and measured relevant beliefs, behaviors, and demographics. Half of participants were assigned randomly to a “self-prediction” intervention, asking them to predict their future acceptance of HBV vaccination. The main outcome measure was subsequent vaccination behavior. Other measures included perceived barriers to HBV vaccination, measured prior to the intervention.

Results: There was an interaction between the intervention and vaccination barriers, indicating the effect of the intervention differed depending on perceived vaccination barriers. Among high-barriers patients, the intervention increased vaccination acceptance. Among low-barriers patients, the intervention did not influence vaccination acceptance.

Conclusion: The self-prediction intervention increased vaccination acceptance among “high-barriers” patients, who typically have very low vaccination rates. This brief intervention could be a useful tool in increasing vaccine uptake among high-barriers patients.

--- https://arxiv.org/abs/1202.6106 SpeechJammer: A System Utilizing Artificial Speech Disturbance with Delayed Auditory Feedback Kazutaka Kurihara, Koji Tsukada 2012-02-28 2021-07-21 [("doi","10.48550/arXiv.1202.6106")] philosophy/mind psychology/linguistics technology

[homepage; video, replication] In this paper we report on a system, SpeechJammer, which can be used to disturb people’s speech [using delayed auditory feedback].

In general, human speech is jammed by giving back to the speakers their own utterances at a delay of a few hundred milliseconds. This effect can disturb people without any physical discomfort, and disappears immediately by stop speaking. Furthermore, this effect does not involve anyone but the speaker.

We use this phenomenon and implemented two prototype versions by combining a direction-sensitive microphone and a direction-sensitive speaker, enabling the speech of a specific person to be disturbed.

We discuss practical application scenarios of the system, such as facilitating and controlling discussions.

Finally, we argue what system parameters should be examined in detail in future formal studies based on the lessons learned from our preliminary study.

[Curiously, when Benn Jordan tried a speechjammer on random participants in 2023, one participant was immune, and said that he always hated the sound of his voice and struggled to even leave voicemails.]

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1876761/ Neurogenesis and the spacing effect: learning over time enhances memory and the survival of new neurons Helene M. Sisti, Arnold L. Glass, Tracey J. Shors 2007 2021-07-21 [("doi","10.1101/lm.488707")] psychology/neuroscience psychology/spaced-repetition

Information that is spaced over time is better remembered than the same amount of information massed together. This phenomenon, known as the spacing effect, was explored with respect to its effect on learning and neurogenesis in the adult dentate gyrus of the hippocampal formation. Because the cells are generated over time and because learning enhances their survival, we hypothesized that training with spaced trials would rescue more new neurons from death than the same number of massed trials.

In the first experiment, animals trained with spaced trials in the Morris water maze outperformed animals trained with massed trials, but there was not a direct effect of trial spacing on cell survival. Rather, animals that learned well retained more cells than animals that did not learn or learned poorly. Moreover, performance during acquisition correlated with the number of cells remaining in the dentate gyrus after training.

In the second experiment, the time between blocks of trials was increased. Consequently, animals trained with spaced trials performed as well as those trained with massed, but remembered the location better two weeks later. The strength of that memory correlated with the number of new cells remaining in the hippocampus.

Together, these data indicate that learning, and not mere exposure to training, enhances the survival of cells that are generated 1 week before training. They also indicate that learning over an extended period of time induces a more persistent memory, which then relates to the number of cells that reside in the hippocampus.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2396513/ Age-related slowing of memory retrieval: contributions of perceptual speed and cerebral white matter integrity Barbara Bucur, David J. Madden, Julia Spaniol, James M. Provenzale, Roberto Cabeza, Leonard E. White, Scott A. Huettel 2008 2021-07-21 [("doi","10.1016/j.neurobiolaging.2007.02.008")] psychology/neuroscience

Previous research suggests that, in reaction time (RT) measures of episodic memory retrieval, the unique effects of adult age are relatively small compared to the effects aging shares with more elementary abilities such as perceptual speed.

Little is known, however, regarding the mechanisms of perceptual speed. We used diffusion tensor imaging (DTI) to test the hypothesis that white matter integrity, as indexed by fractional anisotropy (FA), serves as one mechanism of perceptual slowing in episodic memory retrieval.

Results indicated that declines in FA in the pericallosal frontal region and in the genu of the corpus callosum, but not in other regions, mediated the relationship between perceptual speed and episodic retrieval RT. This relation held, though to a different degree, for both hits and correct rejections.

These findings suggest that white matter integrity in prefrontal regions is one mechanism underlying the relation between individual differences in perceptual speed and episodic retrieval.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3268356/ Acquiring "the Knowledge" of London’s layout drives structural brain changes Katherine Woollett, Eleanor A. Maguire 2011 2021-07-21 [("doi","10.1016/j.cub.2011.11.018")] psychology/neuroscience

The last decade has seen a burgeoning of reports associating brain structure with specific skills and traits (eg. 1-8). Although these cross-sectional studies are informative, cause and effect are impossible to establish without longitudinal investigation of the same individuals before and after an intervention.

Several longitudinal studies have been conducted (eg. 9-18); some involved children or young adults, potentially conflating brain development with learning, most were restricted to the motor domain, and all concerned relatively short timescales (weeks or months). Here, by contrast, weused a unique opportunity to study average-IQ adults operating in the real world as they learned, over 4 years, the complex layout of London’s streets while training to become licensed taxi drivers.

In those who qualified, acquisition of an internal spatial representation of London was associated with a selective increase in gray matter (GM) volume in their posterior hippocampi and concomitant changes to their memory profile. No structural brain changes were observed in trainees who failed to qualify or control participants.

We conclude that specific, enduring, structural brain changes in adult humans can be induced by biologically relevant behaviors engaging higher cognitive functions such as spatial memory, with implications for the “nature versus nurture” debate.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3576837/ Neural mechanisms of speed-accuracy tradeoff Richard P. Heitz, Jeffrey D. Schall 2012 2021-07-21 [("doi","10.1016/j.neuron.2012.08.030")] psychology/neuroscience reinforcement-learning/model-free

Intelligent agents balance speed of responding with accuracy of deciding. Stochastic accumulator models commonly explain this speed-accuracy tradeoff by strategic adjustment of response threshold. Several laboratories identify specific neurons in prefrontal and parietal cortex with this accumulation process, yet no neurophysiological correlates of speed-accuracy tradeoff have been described.

We trained macaque monkeys to trade speed for accuracy on cue during visual search and recorded the activity of neurons in the frontal eye field. Unpredicted by any model, we discovered that speed-accuracy tradeoff is accomplished through several distinct adjustments. Visually responsive neurons modulated baseline firing rate, sensory gain, and the duration of perceptual processing. Movement neurons triggered responses with activity modulated in a direction opposite of model predictions.

Thus, current stochastic accumulator models provide an incomplete description of the neural processes accomplishing speed-accuracy tradeoffs. The diversity of neural mechanisms was reconciled with the accumulator framework through an integrated accumulator model constrained by requirements of the motor system.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2858332/ The Abbreviation of Personality, or how to Measure 200 Personality Scales with 200 Items Tal Yarkoni 2010 2021-07-21 [("doi","10.1016/j.jrp.2010.01.002")] psychology/personality

Personality researchers have recently advocated the use of very short personality inventories in order to minimize administration time. However, few such inventories are currently available.

Here I introduce an automated method that can be used to abbreviate virtually any personality inventory with minimal effort.

After validating the method against existing measures in Studies 1 and 2, a new 181-item inventory is generated in Study 3 that accurately recaptures scores on 8 different broadband inventories comprising 203 distinct scales.

Collectively, the results validate a powerful new way to improve the efficiency of personality measurement in research settings.

--- https://arxiv.org/abs/1202.3492 Why does attention to web articles fall with time? M. V. Simkin, V. P. Roychowdhury 2012-02-11 2021-07-22 [("doi","10.1002/asi.23289")] science technology

We analyze access statistics of a hundred and fifty blog entries and news articles, for periods of up to 3 years. Access rate falls as an inverse power of time passed since publication.

The power law holds for periods of up to thousand days. The exponents are different for different blogs and are distributed between 0.6 and 3.2.

We argue that the decay of attention to a web article is caused by the link to it first dropping down the list of links on the website’s front page, and then disappearing from the front page and its subsequent movement further into background.

The other proposed explanations that use a decaying with time novelty factor, or some intricate theory of human dynamics cannot explain all of the experimental observations.

--- https://arxiv.org/abs/math/0701086 An introduction to the theory of citing M. V. Simkin, V. P. Roychowdhury 2007-01-03 2021-07-22 [("doi","10.48550/arXiv.0701086")] science statistics/probability

Statistical analysis of repeat misprints in scientific citations leads to the conclusion that about 80% of scientific citations are copied from the lists of references used in other papers.

Based on this finding a mathematical theory of citing is constructed.

It leads to the conclusion that a large number of citations does not have to be a result of paper’s extraordinary qualities, but can be explained by the ordinary law of chances.

--- https://arxiv.org/abs/1110.6181 Detection Technique for Artificially-Illuminated Objects in the Outer Solar System and Beyond Abraham Loeb, Edwin L. Turner 2011-10-27 2021-07-22 [("doi","10.1089/ast.2011.0758")] science

Existing and planned optical telescopes and surveys can detect artificially-illuminated objects comparable in total brightness to a major terrestrial city out to the outskirts of the Solar System. Orbital parameters of Kuiper belt objects (KBOs) are routinely measured to exquisite precisions of <10−3.

Here we propose to measure the variation of the observed flux F from such objects as a function of their changing orbital distances D. Sunlight-illuminated objects will show a logarithmic slope alpha=(dlogF/dlog D)=-4 whereas artificially-illuminated objects should exhibit alpha=-2. Planned surveys using the proposed LSST will provide superb data that would allow measurement of alpha for thousands of KBOs.

If objects with alpha=-2 are found, follow-up observations can measure their spectra to determine if they are illuminated by artificial lighting.

The search can be extended beyond the Solar System with future generations of telescopes on the ground and in space, which would be capable of detecting phase modulation due to very strong artificial illumination on the night-side of planets as they orbit their parent stars.

--- https://arxiv.org/abs/1204.6412 Predicting the outcome of roulette Michael Small, Chi Kong Tse 2012-04-28 2021-07-22 [("doi","10.1063/1.4753920")] science statistics/probability

There have been several popular reports of various groups exploiting the deterministic nature of the game of roulette for profit. Moreover, through its history the inherent determinism in the game of roulette has attracted the attention of many luminaries of chaos theory. In this paper we provide a short review of that history and then set out to determine to what extent that determinism can really be exploited for profit.

To do this, we provide a very simple model for the motion of a roulette wheel and ball and demonstrate that knowledge of initial position, velocity and acceleration is sufficient to predict the outcome with adequate certainty to achieve a positive expected return. We describe two physically realizable systems to obtain this knowledge both incognito and in situ. The first system relies only on a mechanical count of rotation of the ball and the wheel to measure the relevant parameters. By applying this techniques to a standard casino-grade European roulette wheel we demonstrate an expected return of at least 18%, well above the −2.7% expected of a random bet. With a more sophisticated, albeit more intrusive, system (mounting a digital camera above the wheel) we demonstrate a range of systematic and statistically-significant biases which can be exploited to provide an improved guess of the outcome. Finally, our analysis demonstrates that even a very slight slant in the roulette table leads to a very pronounced bias which could be further exploited to substantially enhance returns.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0031703 A Facial Attractiveness Account of Gender Asymmetries in Interracial Marriage Michael B. Lewis 2012-01-17 2021-07-22 [("doi","10.1371/journal.pone.0031703")] sociology

Background: In the US and UK, more Black men are married to White women than vice versa and there are more White men married to Asian women than vice versa. Models of interracial marriage, based on the exchange of racial status for other capital, cannot explain these asymmetries. A new explanation is offered based on the relative perceived facial attractiveness of the different race-by-gender groups.

Method and Findings: This explanation was tested using a survey of perceived facial attractiveness. This found that Black males are perceived as more attractive than White or East Asian males whereas among females, it is the East Asians that are perceived as most attractive on average.

Conclusion: Incorporating these attractiveness patterns into the model of marriage decisions produces asymmetries in interracial marriage similar to those in the observed data in terms of direction and relative size. This model does not require differences in status between races nor different strategies based on gender. Predictions are also generated regarding the relative attractiveness of those engaging in interracial marriage.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3001541/ Holiday reading: Cigarette smoking: an underused tool in high-performance endurance training Kenneth A. Myers 2010 2021-07-22 [("doi","10.1503/cmaj.100042")] statistics/bias

The review paper is a staple of medical literature and, when well executed by an expert in the field, can provide a summary of literature that generates useful recommendations and new conceptualizations of a topic.

However, if research results are selectively chosen, a review has the potential to create a convincing argument for a faulty hypothesis. Improper correlation or extrapolation of data can result in dangerously flawed conclusions.

The following paper seeks to illustrate this point, using existing research to argue the hypothesis that cigarette smoking enhances endurance performance and should be incorporated into high-level training programs.

--- https://journals.plos.org/plosbiology/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1000344 Publication Bias in Reports of Animal Stroke Studies Leads to Major Overstatement of Efficacy Emily S. Sena, H. Bart van der Worp, Philip M. W. Bath, David W. Howells, Malcolm R. Macleod 2010-02-18 2021-07-22 [("doi","10.1371/journal.pbio.1000344")] statistics/bias/animal statistics/bias/publication

Publication bias confounds attempts to use systematic reviews to assess the efficacy of various interventions tested in experiments modeling acute ischaemic stroke, leading to a 30% overstatement of efficacy of interventions tested in animals.

The consolidation of scientific knowledge proceeds through the interpretation and then distillation of data presented in research reports, first in review articles and then in textbooks and undergraduate courses, until truths become accepted as such both amongst “experts” and in the public understanding. Where data are collected but remain unpublished, they cannot contribute to this distillation of knowledge. If these unpublished data differ substantially from published work, conclusions may not reflect adequately the underlying biological effects being described. The existence and any impact of such “publication bias” in the laboratory sciences have not been described. Using the CAMARADES (Collaborative Approach to Meta-analysis and Review of Animal Data in Experimental Studies) database we identified 16 systematic reviews of interventions tested in animal studies of acute ischaemic stroke involving 525 unique publications. Only ten publications (2%) reported no statistically-significant effects on infarct volume and only six (1.2%) did not report at least one finding. Egger regression and trim-and-fill analysis suggested that publication bias was highly prevalent (present in the literature for 16 and ten interventions, respectively) in animal studies modeling stroke. Trim-and-fill analysis suggested that publication bias might account for around 1⁄3rd of the efficacy reported in systematic reviews, with reported efficacy falling 31.3% → 23.8% after adjustment for publication bias. We estimate that a further 214 experiments (in addition to the 1,359 identified through rigorous systematic review; non publication rate 14%) have been conducted but not reported. It is probable that publication bias has an important impact in other animal disease models, and more broadly in the life sciences.

Author Summary: Publication bias is known to be a major problem in the reporting of clinical trials, but its impact in basic research has not previously been quantified. Here we show that publication bias is prevalent in reports of laboratory-based research in animal models of stroke, such that data from as many as one in seven experiments remain unpublished. The result of this bias is that systematic reviews of the published results of interventions in animal models of stroke overstate their efficacy by around one third. Nonpublication of data raises ethical concerns, first because the animals used have not contributed to the sum of human knowledge, and second because participants in clinical trials may be put at unnecessary risk if efficacy in animals has been overstated. It is unlikely that this publication bias in the basic sciences is restricted to the area we have studied, the preclinical modeling of the efficacy of candidate drugs for stroke. A related article in PLoS Medicine (van der Worp et al 2010) discusses the controversies and possibilities of translating the results of animal experiments into human clinical trials.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0010271 Do Pressures to Publish Increase Scientists’ Bias? An Empirical Support from US States Data Daniele Fanelli 2010-03-24 2021-07-22 [("doi","10.1371/journal.pone.0010271")] statistics/bias/publication

The growing competition and “publish or perish” culture in academia might conflict with the objectivity and integrity of research, because it forces scientists to produce “publishable” results at all costs. Papers are less likely to be published and to be cited if they report “negative” results (results that fail to support the tested hypothesis). Therefore, if publication pressures increase scientific bias, the frequency of “positive” results in the literature should be higher in the more competitive and “productive” academic environments.

This study verified this hypothesis by measuring the frequency of positive results in a large random sample of papers with a corresponding author based in the US. Across all disciplines, papers were more likely to support a tested hypothesis if their corresponding authors were working in states that, according to NSF data, produced more academic papers per capita. The size of this effect increased when controlling for state’s per capita R&D expenditure and for study characteristics that previous research showed to correlate with the frequency of positive results, including discipline and methodology.

Although the confounding effect of institutions’ prestige could not be excluded (researchers in the more productive universities could be the most clever and successful in their experiments), these results support the hypothesis that competitive academic environments increase not only scientists’ productivity but also their bias. The same phenomenon might be observed in other countries where academic competition and pressures to publish are high.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0020185 Epidemiology, Quality and Reporting Characteristics of Systematic Reviews of Traditional Chinese Medicine Interventions Published in Chinese Journals Bin Ma, Jiwu Guo, Guoqing Qi, Haimin Li, Jiye Peng, Yulong Zhang, Yanqin Ding, Kehu Yang 2011-04-27 2021-07-22 [("doi","10.1371/journal.pone.0020185")] statistics/bias

Background: Systematic reviews (SRs) of TCM have become increasingly popular in China and have been published in large numbers. This review provides the first examination of epidemiological characteristics of these SRs as well as compliance with the PRISMA and AMSTAR guidelines.

Objectives: To examine epidemiological and reporting characteristics as well as methodological quality of SRs of TCM published in Chinese journals.

Method: Four Chinese databases were searched (CBM, CSJD, CJFD and Wanfang Database) for SRs of TCM, from inception through Dec 2009. Data were extracted into Excel spreadsheets. The PRISMA and AMSTAR checklists were used to assess reporting characteristics and methodological quality, respectively.

Results: A total of 369 SRs were identified, most (97.6%) of which used the terms systematic review or meta-analysis in the title. None of the reviews had been updated. Half (49.8%) were written by clinicians and nearly half (47.7%) were reported in specialty journals. The impact factors of 45.8% of the journals published in were zero. The most commonly treated conditions were diseases of the circulatory and digestive disease. Funding sources were not reported for any reviews. Most (68.8%) reported information about quality assessment, while less than half (43.6%) reported assessing for publication bias. Statistical mistakes appeared in 1⁄3rd (29.3%) of reviews and most (91.9%) did not report on conflict of interest.

Conclusion: While many SRs of TCM interventions have been published in Chinese journals, the quality of these reviews is troubling. As a potential key source of information for clinicians and researchers, not only were many of these reviews incomplete, some contained mistakes or were misleading. Focusing on improving the quality of SRs of TCM, rather than continuing to publish them in great quantity, is urgently needed in order to increase the value of these studies.

--- https://arxiv.org/abs/0712.4318 Convergence of Expected Utilities with Algorithmic Probability Distributions Peter de Blanc 2007-12-28 2021-07-22 [("doi","10.48550/arXiv.0712.4318")] statistics/decision

We consider an agent interacting with an unknown environment.

The environment is a function which maps natural numbers to natural numbers; the agent’s set of hypotheses about the environment contains all such functions which are computable and compatible with a finite set of known input-output pairs, and the agent assigns a positive probability to each such hypothesis. We do not require that this probability distribution be computable, but it must be bounded below by a positive computable function. The agent has a utility function on outputs from the environment.

We show that if this utility function is bounded below in absolute value by an unbounded computable function, then the expected utility of any input is undefined.

This implies that a computable utility function will have convergent expected utilities if that function is bounded.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2677029/pdf/nihms-110651.pdf 25-hydroxyvitamin D levels and the risk of mortality in the general population Michal L. Melamed, Erin D. Michos, Wendy Post, Brad Astor 2008 2021-07-22 [("doi","10.1001/archinte.168.15.1629")] vitamin-d

Background: In patients undergoing dialysis, therapy with calcitriol or paricalcitol or other vitamin D agents is associated with reduced mortality. Observational data suggests that low 25-hydroxyvitamin D levels (25[OH]D) are associated with diabetes mellitus, hypertension, and cancers. However, whether low serum 25(OH)D levels are associated with mortality in the general population is unknown.

Method: We tested the association of low 25(OH)D levels with all-cause, cancer, and cardiovascular disease (CVD) mortality in 13,331 nationally representative adults 20 years or older from the Third National Health and Nutrition Examination Survey (NHANES III) linked mortality files. Participant vitamin D levels were collected from 1988 through 1994, and individuals were passively followed for mortality through 2000.

Results: In cross-sectional multivariate analyses, increasing age, female sex, nonwhite race/ethnicity, diabetes, current smoking, and higher body mass index were all independently associated with higher odds of 25(OH)D deficiency (lowest quartile of 25(OH)D level, <17.8 ng/mL [to convert to nanomoles per liter, multiply by 2.496]), while greater physical activity, vitamin D supplementation, and non-winter season were inversely associated. During a median 8.7 years of follow-up, there were 1806 deaths, including 777 from CVD. In multivariate models (adjusted for baseline demographics, season, and traditional and novel CVD risk factors), compared with the highest quartile, being in the lowest quartile (25[OH]D levels <17.8 ng/mL) was associated with a 26% increased rate of all-cause mortality (mortality rate ratio, 1.26; 95% CI, 1.08–1.46) and a population attributable risk of 3.1%. The adjusted models of CVD and cancer mortality revealed a higher risk, which was not statistically-significant.

Conclusion: The lowest quartile of 25(OH)D level (<17.8 ng/mL) is independently associated with all-cause mortality in the general population.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3079822/ Calcium supplements with or without vitamin D and risk of cardiovascular events: reanalysis of the Women’s Health Initiative limited access dataset and meta-analysis Mark J. Bolland, Andrew Grey, Alison Avenell, Greg D. Gamble, Ian R. Reid 2011 2021-07-23 [("doi","10.1136/bmj.d2040")] vitamin-d

Objectives: To investigate the effects of personal calcium supplement use on cardiovascular risk in the Women’s Health Initiative Calcium/Vitamin D Supplementation Study (WHI CaD Study), using the WHI dataset, and to update the recent meta-analysis of calcium supplements and cardiovascular risk.

Design: Reanalysis of WHI CaD Study limited access dataset and incorporation in meta-analysis with eight other studies. Data source WHI CaD Study, a seven year, randomized, placebo controlled trial of calcium and vitamin D (1g calcium and 400 IU vitamin D daily) in 36,282 community dwelling postmenopausal women. Main outcome measures Incidence of four cardiovascular events and their combinations (myocardial infarction, coronary revascularization, death from coronary heart disease, and stroke) assessed with patient-level data and trial-level data.

Results: In the WHI CaD Study there was an interaction between personal use of calcium supplements and allocated calcium and vitamin D for cardiovascular events. In the 16,718 women (46%) who were not taking personal calcium supplements at randomization the hazard ratios for cardiovascular events with calcium and vitamin D ranged 1.13–1.22 (p = 0.05 for clinical myocardial infarction or stroke, p = 0.04 for clinical myocardial infarction or revascularization), whereas in the women taking personal calcium supplements cardiovascular risk did not alter with allocation to calcium and vitamin D. In meta-analyses of 3 placebo controlled trials, calcium and vitamin D increased the risk of myocardial infarction (relative risk 1.21 (95% confidence interval 1.01 to 1.44), p = 0.04), stroke (1.20 (1.00 to 1.43), p = 0.05), and the composite of myocardial infarction or stroke (1.16 (1.02 to 1.32), p = 0.02). In meta-analyses of placebo controlled trials of calcium or calcium and vitamin D, complete trial-level data were available for 28,072 participants from eight trials of calcium supplements and the WHI CaD participants not taking personal calcium supplements. In total 1384 individuals had an incident myocardial infarction or stroke. Calcium or calcium and vitamin D increased the risk of myocardial infarction (relative risk 1.24 (1.07 to 1.45), p = 0.004) and the composite of myocardial infarction or stroke (1.15 (1.03 to 1.27), p = 0.009).

Conclusion: Calcium supplements with or without vitamin D modestly increase the risk of cardiovascular events, especially myocardial infarction, a finding obscured in the WHI CaD Study by the widespread use of personal calcium supplements. A reassessment of the role of calcium supplements in osteoporosis management is warranted.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3410276/ Vitamin D with calcium reduces mortality: patient level pooled analysis of 70,528 patients from eight major vitamin D trials Lars Rejnmark, Alison Avenell, Tahir Masud, Frazer Anderson, Haakon E. Meyer, Kerrie M. Sanders, Kari Salovaara, Cyrus Cooper, Helen E. Smith, Elizabeth T. Jacobs, David Torgerson, Rebecca D. Jackson, JoAnn E. Manson, Kim Brixen, Leif Mosekilde, John A. Robbins, Roger M. Francis, Bo Abrahamsen 2012 2021-07-23 [("doi","10.1210/jc.2011-3328")] vitamin-d

Background: Vitamin D may affect multiple health outcomes. If so, an effect on mortality is to be expected. Using pooled data from randomized controlled trials, we performed individual patient data (IPD) and trial level meta-analyses to assess mortality among participants randomized to either vitamin D alone or vitamin D with calcium.

Subjects and Method: Through a systematic literature search, we identified 24 randomized controlled trials reporting data on mortality in which vitamin D was given either alone or with calcium. From a total of 13 trials with more than 1,000 participants each, eight trials were included in our IPD analysis. Using a stratified Cox regression model, we calculated risk of death during 3 yr of treatment in an intention-to-treat analysis. Also, we performed a trial level meta-analysis including data from all studies.

Results: The IPD analysis yielded data on 70,528 randomized participants (86.8% females) with a median age of 70 (interquartile range, 62–77) years. Vitamin D with or without calcium reduced mortality by 7% [hazard ratio, 0.93; 95% confidence interval (CI), 0.88–0.99]. However, vitamin D alone did not affect mortality, but risk of death was reduced if vitamin D was given with calcium (hazard ratio, 0.91; 95% CI, 0.84–0.98). The number needed to treat with vitamin D plus calcium for 3 yr to prevent one death was 151. Trial level meta-analysis (24 trials with 88,097 participants) showed similar results, i.e. mortality was reduced with vitamin D plus calcium (odds ratio, 0.94; 95% CI, 0.88–0.99), but not with vitamin D alone (odds ratio, 0.98; 95% CI, 0.91–1.06).

Conclusion: Vitamin D with calcium reduces mortality in the elderly, whereas available data do not support an effect of vitamin D alone.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2174691/ Vitamins and sleep: an exploratory study Kenneth L. Lichstein, Kristen L. Payne, James P. Soeffing, H. Heith Durrence, Daniel J. Taylor, Brant W. Riedel, Andrew J. Bush 2007 2021-07-23 [("doi","10.1016/j.sleep.2006.12.009")] zeo

Study Objective: We analyzed archival data from an epidemiology study to test the association between vitamin use and sleep.

Design: Random digit dialing was used to recruit 772 people ranging in age 20–98 for a study of people’s sleep experience. These individuals completed a set of questionnaires about their sleep, health, and daytime functioning. Five hundred and nineteen of these participants had available vitamin use data.

Setting: Home.

Participants: Five hundred and nineteen people participated. Recruitment applied minimal screening criteria and no attempt was made to favor people with or without sleep disturbance.

Interventions: This survey included no intervention. Participants completed 2 weeks of sleep diaries and a set of questionnaires. Of particular salience to the present study, participants reported their vitamin use in listing all medications and nutritional supplements being used currently.

Measurements and Results: For those individuals taking a multivitamin or multiple single vitamins, sleep diaries revealed poorer sleep compared to non-vitamin users in the number and duration of awakenings during the night. After controlling for age, ethnicity, and sex the difference in number of awakenings was still marginally. The rate of insomnia, conservatively defined, and consumption of sleep medication were also marginally higher among individuals taking multi-/multiple vitamins compared to those not taking vitamins.

Conclusion: Disturbed sleep maintenance was associated with multi-/multiple vitamin use. Five equally plausible explanations were advanced to explain this association including vitamins cause poor sleep, poor sleepers seek vitamins, and unidentified factors promote both poor sleep and vitamin use. These data are considered preliminary. Methodological characteristics of future studies were described that hold the promise of more clearly illuminating the association between vitamins and sleep.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2819566/ Relationships among dietary nutrients and subjective sleep, objective sleep, and napping in women Michael A. Grandner, Daniel F. Kripke, Nirinjini Naidoo, Robert D. Langer 2010 2021-07-23 [("doi","10.1016/j.sleep.2009.07.014")] nootropic/caffeine zeo

Objective: To describe which dietary nutrient variables are related to subjective and objective habitual sleep and subjective and objective napping.

Method: Participants were 459 post-menopausal women enrolled in the Women’s Health Initiative. Objective sleep was estimated using one week of actigraphy. Subjective sleep was prospectively estimated with a daily sleep diary. Dietary nutrients were calculated from food frequency questionnaires.

Results: The most statistically-significant correlations were with subjective napping, including (from strongest to weakest): total fat, calories, saturated fat, monounsaturated fat, trans fat, water, proline, serine, tyrosine, phenylalanine, valine, cholesterol, leucine, glutamic acid, ash, isoleucine, histidine, sodium, tryptophan, protein, threonine, cystine, methionine, phosphorous, polyunsaturated fat, animal protein, aspartic acid, arginine, lysine, alanine, caffeine, riboflavin, gamma-tocopherol, glycine, retinol, delta-tocopherol, Vitamin D, and selenium. Actigraphic nocturnal sleep duration was negatively associated with total fat, monounsaturated fat, trans fat, saturated fat, polyunsaturated fat, calories, gamma-tocopherol, cholesterol, and alpha-tocopherol-eq.

Conclusion: Actigraphic total sleep time was negatively associated with intake of fats. Subjective napping, which may be a proxy for subjective sleepiness, was related to fat intake as well as intake of meat.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0033079 Memory for Semantically Related and Unrelated Declarative Information: The Benefit of Sleep, the Cost of Wake Jessica D. Payne, Matthew A. Tucker, Jeffrey M. Ellenbogen, Erin J. Wamsley, Matthew P. Walker, Daniel L. Schacter, Robert Stickgold 2012-02-08 2021-07-23 [("doi","10.1371/journal.pone.0033079")] zeo

Numerous studies have examined sleep’s influence on a range of hippocampus-dependent declarative memory tasks, from text learning to spatial navigation. In this study, we examined the impact of sleep, wake, and time-of-day influences on the processing of declarative information with strong semantic links (semantically related word pairs) and information requiring the formation of novel associations (unrelated word pairs).

Participants encoded a set of related or unrelated word pairs at either 9am or 9pm, and were then tested after an interval of 30 min, 12 hr, or 24 hr. The time of day at which subjects were trained had no effect on training performance or initial memory of either word pair type.

At 12 hr retest, memory overall was superior following a night of sleep compared to a day of wakefulness. However, this performance difference was a result of a pronounced deterioration in memory for unrelated word pairs across wake; there was no sleep-wake difference for related word pairs. At 24 hr retest, with all subjects having received both a full night of sleep and a full day of wakefulness, we found that memory was superior when sleep occurred shortly after learning rather than following a full day of wakefulness.

Lastly, we present evidence that the rate of deterioration across wakefulness was statistically-significantly diminished when a night of sleep preceded the wake period compared to when no sleep preceded wake, suggesting that sleep served to stabilize the memories against the deleterious effects of subsequent wakefulness. Overall, our results demonstrate that (1) the impact of 12 hr of waking interference on memory retention is strongly determined by word-pair type, (2) sleep is most beneficial to memory 24 hr later if it occurs shortly after learning, and (3) sleep does in fact stabilize declarative memories, diminishing the negative impact of subsequent wakefulness.

--- https://arxiv.org/abs/1405.0312#microsoft Microsoft COCO: Common Objects in Context Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, Piotr Dollár 2014-05-01 2021-07-23 [("doi","10.48550/arXiv.1405.0312")] ai/dataset ai/nn

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding.

This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation.

We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN.

Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4790437/ Living alongside more affluent neighbors predicts greater involvement in antisocial behavior among low-income boys Candice L. Odgers, Sachiko Donley, Avshalom Caspi, Christopher J. Bates, Terrie E. Moffitt 2015 2021-07-23 [("doi","10.1111/jcpp.12380")] crime sociology

Background: The creation of economically mixed communities has been proposed as one way to improve the life outcomes of children growing up in poverty. However, whether low-income children benefit from living alongside more affluent neighbors is unknown.

Method: Prospectively gathered data on over 1,600 children from the Environmental Risk (E-Risk) Longitudinal Twin Study living in urban environments is used to test whether living alongside more affluent neighbors (measured via high-resolution geo-spatial indices) predicts low-income children’s antisocial behavior (reported by mothers and teachers at the ages of 5, 7, 10, and 12).

Results: Results indicated that low-income boys (but not girls) surrounded by more affluent neighbors had higher levels of antisocial behavior than their peers embedded in concentrated poverty. The negative effect of growing up alongside more affluent neighbors on low-income boys’ antisocial behavior held across childhood and after controlling for key neighborhood and family-level factors.

Conclusion: Findings suggest that efforts to create more economically mixed communities for children, if not properly supported, may have iatrogenic effects on boys’ antisocial behavior.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687737/ Genome editing. The mutagenic chain reaction: a method for converting heterozygous to homozygous mutations Valentino M. Gantz, Ethan Bier 2015 2021-07-23 [("doi","10.1126/science.aaa5945")] genetics/editing

An organism with a single recessive loss-of-function allele will typically have a wild-type phenotype, whereas individuals homozygous for two copies of the allele will display a mutant phenotype.

We have developed a method called the mutagenic chain reaction (MCR), which is based on the CRISPR/Cas9 genome-editing system for generating autocatalytic mutations, to produce homozygous loss-of-function mutations.

In Drosophila, we found that MCR mutations efficiently spread from their chromosome of origin to the homologous chromosome, thereby converting heterozygous mutations to homozygosity in the vast majority of somatic and germline cells.

MCR technology should have broad applications in diverse organisms.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4742100/ Chromosome transplantation as a novel approach for correcting complex genomic disorders Marianna Paulis, Alessandra Castelli, Lucia Susani, Michela Lizier, Irina Lagutina, Maria Luisa Focarelli, Camilla Recordati, Paolo Uva, Francesca Faggioli, Tui Neri, Eugenio Scanziani, Cesare Galli, Franco Lucchini, Anna Villa, Paolo Vezzoni 2015 2021-07-23 [("doi","10.18632/oncotarget.6143")] genetics/editing genetics/genome-synthesis

Genomic disorders resulting from large rearrangements of the genome remain an important unsolved issue in gene therapy. Chromosome transplantation, defined as the perfect replacement of an endogenous chromosome with a homologous one, has the potential of curing this kind of disorders.

Here we report the first successful case of chromosome transplantation by replacement of an endogenous X chromosome carrying a mutation in the Hprt gene with a normal one in mouse embryonic stem cells (ESCs), correcting the genetic defect. The defect was also corrected by replacing the Y chromosome with an X chromosome.

Chromosome transplanted clones maintained in vitro and in vivo features of stemness and contributed to chimera formation. Genome integrity was confirmed by cytogenetic and molecular genome analysis.

The approach here proposed, with some modifications, might be used to cure various disorders due to other X chromosome aberrations in induced pluripotent stem (iPS) cells derived from affected patients.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4881818/ Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations Jimmy Z. Liu, Suzanne van Sommeren, Hailiang Huang, Siew C. Ng, Rudi Alberts, Atsushi Takahashi, Stephan Ripke, James C. Lee, Luke Jostins, Tejas Shah, Shifteh Abedian, Jae Hee Cheon, Judy Cho, Naser E. Dayani, Lude Franke, Yuta Fuyuno, Ailsa Hart, Ramesh C. Juyal, Garima Juyal, Won Ho Kim, Andrew P. Morris, Hossein Poustchi, William G. Newman, Vandana Midha, Timothy R. Orchard, Homayon Vahedi, Ajit Sood, Joseph Y. Sung, Reza Malekzadeh, Harm-Jan Westra, Keiko Yamazaki, Suk-Kyun Yang, Jeffrey C. Barrett, Behrooz Z. Alizadeh, Miles Parkes, Thelma Bk, Mark J. Daly, Michiaki Kubo, Carl A. Anderson, Rinse K. Weersma 2015 2021-07-23 [("doi","10.1038/ng.3359")] genetics/heritable

Ulcerative colitis and Crohn’s disease are the two main forms of inflammatory bowel disease (IBD). Here we report the first trans-ancestry association study of IBD, with genome-wide or Immunochip genotype data from an extended cohort of 86,640 European individuals and Immunochip data from 9,846 individuals of East Asian, Indian or Iranian descent.

We implicate 38 loci in IBD risk for the first time. For the majority of the IBD risk loci, the direction and magnitude of effect are consistent in European and non-European cohorts. Nevertheless, we observe genetic heterogeneity between divergent populations at several established risk loci driven by differences in allele frequency (NOD2) or effect size (TNFSF15 and ATG16L1) or a combination of these factors (IL23R and IRGM).

Our results provide biological insights into the pathogenesis of IBD and demonstrate the usefulness of trans-ancestry association studies for mapping loci associated with complex diseases and understanding genetic architecture across diverse populations.

--- https://www.biorxiv.org/content/10.1101/016477.full Eight thousand years of natural selection in Europe Iain Mathieson, Iosif Lazaridis, Nadin Rohland, Swapan Mallick, Nick Patterson, Songül Alpaslan Roodenberg, Eadaoin Harney, Kristin Stewardson, Daniel Fernandes, Mario Novak, Kendra Sirak, Cristina Gamba, Eppie R. Jones, Bastien Llamas, Stanislav Dryomov, Joseph Pickrell, Juan Luís Arsuaga, José María Bermúdez de Castro, Eudald Carbonell, Fokke Gerritsen, Aleksandr Khokhlov, Pavel Kuznetsov, Marina Lozano, Harald Meller, Oleg Mochalov, Vayacheslav Moiseyev, Manuel A. Rojo Guerra, Jacob Roodenberg, Josep Maria Vergès, Johannes Krause, Alan Cooper, Kurt W. Alt, Dorcas Brown, David Anthony, Carles Lalueza-Fox, Wolfgang Haak, Ron Pinhasi, David Reich 2015-10-10 2021-07-23 [("doi","10.1101/016477")] genetics/selection/natural/human

The arrival of farming in Europe around 8,500 years ago necessitated adaptation to new environments, pathogens, diets, and social organizations. While indirect evidence of adaptation can be detected in patterns of genetic variation in present-day people, ancient DNA makes it possible to witness selection directly by analyzing samples from populations before, during and after adaptation events. Here we report the first genome-wide scan for selection using ancient DNA, capitalizing on the largest genome-wide dataset yet assembled: 230 West Eurasians dating to between 6500 and 1,000 BC, including 163 with newly reported data. The new samples include the first genome-wide data from the Anatolian Neolithic culture, who we show were members of the population that was the source of Europe’s first farmers, and whose genetic material we extracted by focusing on the DNA-rich petrous bone.

We identify genome-wide statistically-significant signatures of selection at loci associated with diet, pigmentation and immunity, and two independent episodes of selection on height.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0108548 Linking Student Performance in Massachusetts Elementary Schools with the ‘Greenness’ of School Surroundings Using Remote Sensing Chih-Da Wu, Eileen McNeely, J. G. Cedeño-Laurent, Wen-Chi Pan, Gary Adamkiewicz, Francesca Dominici, Shih-Chun Candice Lung, Huey-Jen Su, John D. Spengler 2014-08-29 2021-07-24 [("doi","10.1371/journal.pone.0108548")] psychology/nature

Various studies have reported the physical and mental health benefits from exposure to “green” neighborhoods, such as proximity to neighborhoods with trees and vegetation. However, no studies have explicitly assessed the association between exposure to “green” surroundings and cognitive function in terms of student academic performance.

This study investigated the association between the “greenness” of the area surrounding a Massachusetts public elementary school and the academic achievement of the school’s student body based on standardized tests with an ecological setting. Researchers used the composite school-based performance scores generated by the Massachusetts Comprehensive Assessment System (MCAS) to measure the percentage of 3rdgrade students (the first year of standardized testing for 8–9 years-old children in public school), who scored “Above Proficient” (AP) in English and Mathematics tests (Note: Individual student scores are not publicly available). The MCAS results are comparable year to year thanks to an equating process. Researchers included test results from 2006 through 2012 in 905 public schools and adjusted for differences between schools in the final analysis according to race, gender, English as a second language (as a proxy for ethnicity and language facility), parent income, student-teacher ratio, and school attendance. Surrounding greenness of each school was measured using satellite images converted into the Normalized Difference Vegetation Index (NDVI) in March, July, and October of each year according to a 250-meter, 500-meter, 1,000-meter, and 2000-meter circular buffer around each school.

Spatial Generalized Linear Mixed Models (GLMMs) estimated the impacts of surrounding greenness on school-based performance.

Overall the study results supported a relationship between the “greenness” of the school area and the school-wide academic performance. Interestingly, the results showed a consistently positive statistically-significant association between the greenness of the school in the Spring (when most Massachusetts students take the MCAS tests) and school-wide performance on both English and Math tests, even after adjustment for socio-economic factors and urban residency.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4635443/ The Psychology and Neuroscience of Curiosity Celeste Kidd, Benjamin Y. Hayden 2015 2021-07-24 [("doi","10.1016/j.neuron.2015.09.010")] psychology/personality reinforcement-learning/exploration

Curiosity is a basic element of our cognition, but its biological function, mechanisms, and neural underpinning remain poorly understood. It is nonetheless a motivator for learning, influential in decision-making, and crucial for healthy development.

One factor limiting our understanding of it is the lack of a widely agreed upon delineation of what is and is not curiosity. Another factor is the dearth of standardized laboratory tasks that manipulate curiosity in the lab.

Despite these barriers, recent years have seen a major growth of interest in both the neuroscience and psychology of curiosity. In this Perspective, we advocate for the importance of the field, provide a selective overview of its current state, and describe tasks that are used to study curiosity and information-seeking.

We propose that, rather than worry about defining curiosity, it is more helpful to consider the motivations for information-seeking behavior and to study it in its ethological context.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4677281/ No Statistically-Significant Effect of Prefrontal tDCS on Working Memory Performance in Older Adults Jonna Nilsson, Alexander V. Lebedev, Martin Lövdén 2015 2021-07-24 [("doi","10.3389/fnagi.2015.00230")] psychology/neuroscience/tcs

Transcranial direct current stimulation (tDCS) has been put forward as a non-pharmacological alternative for alleviating cognitive decline in old age. Although results have shown some promise, little is known about the optimal stimulation parameters for modulation in the cognitive domain.

In this study, the effects of tDCS over the dorsolateral prefrontal cortex (dlPFC) on working memory performance were investigated in thirty older adults. An N-back task assessed working memory before, during and after anodal tDCS at a current strength of 1 mA and 2 mA, in addition to sham stimulation. The study used a single-blind, cross-over design.

The results revealed no statistically-significant effect of tDCS on accuracy or response times during or after stimulation, for any of the current strengths.

These results suggest that a single session of tDCS over the dlPFC is unlikely to improve working memory, as assessed by an N-back task, in old age.

--- https://arxiv.org/abs/1312.6120 Exact solutions to the nonlinear dynamics of learning in deep linear neural networks Andrew M. Saxe, James L. McClelland, Surya Ganguli 2013-12-20 2021-07-24 [("doi","10.48550/arXiv.1312.6120")] ai/nn psychology/neuroscience

Despite the widespread practical success of deep learning methods, our theoretical understanding of the dynamics of learning in deep neural networks remains quite sparse. We attempt to bridge the gap between the theory and practice of deep learning by systematically analyzing learning dynamics for the restricted case of deep linear neural networks.

Despite the linearity of their input-output map, such networks have nonlinear gradient descent dynamics on weights that change with the addition of each new hidden layer. We show that deep linear networks exhibit nonlinear learning phenomena similar to those seen in simulations of nonlinear networks, including long plateaus followed by rapid transitions to lower error solutions, and faster convergence from greedy unsupervised pretraining initial conditions than from random initial conditions. We provide an analytical description of these phenomena by finding new exact solutions to the nonlinear dynamics of deep learning.

Our theoretical analysis also reveals the surprising finding that as the depth of a network approaches infinity, learning speed can nevertheless remain finite: for a special class of initial conditions on the weights, very deep networks incur only a finite, depth independent, delay in learning speed relative to shallow networks. We show that, under certain conditions on the training data, unsupervised pretraining can find this special class of initial conditions, while scaled random Gaussian initializations cannot. We further exhibit a new class of random orthogonal initial conditions on weights that, like unsupervised pre-training, enjoys depth independent learning times. We further show that these initial conditions also lead to faithful propagation of gradients even in deep nonlinear networks, as long as they operate in a special regime known as the edge of chaos.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4110577/ Epidemiology, demographic characteristics and prognostic predictors of ulcerative colitis Bruno César da Silva, Andre Castro Lyra, Raquel Rocha, Genoile Oliveira Santana 2014 2021-07-24 [("doi","10.3748/wjg.v20.i28.9458")] biology

Ulcerative colitis (UC) is a chronic disease characterized by diffuse inflammation of the mucosa of the colon and rectum. The hallmark clinical symptom of UC is bloody diarrhea. The clinical course is marked by exacerbations and remissions, which may occur spontaneously or in response to treatment changes or intercurrent illnesses.

UC is most commonly diagnosed in late adolescence or early adulthood, but it can occur at any age. The incidence of UC has increased worldwide over recent decades, especially in developing nations. In contrast, during this period, therapeutic advances have improved the life expectancy of patients, and there has been a decrease in the mortality rate over time.

It is important to emphasize that there is considerable variability in the phenotypic presentation of UC. Within this context, certain clinical and demographic characteristics are useful in identifying patients who tend to have more severe evolution of the disease and a poor prognosis. In this group of patients, better clinical surveillance and more intensive therapy may change the natural course of the disease.

The aim of this article was to review the epidemiology and demographic characteristics of UC and the factors that may be associated with its clinical prognosis.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4276236/ 3 cases of giant panda attack on human at Beijing Zoo Peixun Zhang, Tianbing Wang, Jian Xiong, Feng Xue, Hailin Xu, Jianhai Chen, Dianying Zhang, Zhongguo Fu, Baoguo Jiang 2014 2021-07-24 biology

Panda is regarded as Chinese national treasure. Most people always thought they were cute and just ate bamboo and had never imagined a panda could be vicious. Giant panda attacks on human are rare.

There, we present 3 cases of giant panda attacks on humans at the Panda House at Beijing Zoo from September 2006 to June 2009 to warn people of the giant panda’s potentially dangerous behavior.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3987112/ Identification of volatile organic compounds in human cerumen Katharine A. Prokop-Prigge, Erica Thaler, Charles J. Wysocki, George Preti 2014 2021-07-24 [("doi","10.1016/j.jchromb.2014.01.043")] cat/psychology/earwax psychology/smell/human

We report here the initial examination of volatile organic compounds (VOCs) emanating from human earwax (cerumen). Recent studies link a single-nucleotide polymorphism (SNP) in the adenosine triphosphate (ATP) binding cassette, sub-family C, member 11 gene (ABCC11) to the production of different types of axillary odorants and cerumen. ABCC11 encodes an ATP-driven efflux pump protein that plays an important function in ceruminous apocrine glands of the auditory canal and the secretion of axillary odor precursors. The type of cerumen and underarm odor produced by East Asians differ markedly from that produced by non-Asians.

In this initial report we find that both groups emit many of the same VOCs but differ in the amounts produced. The principal odorants are volatile organic C2-to-C6 acids.

The physical appearance of cerumen from the two groups also matches previously reported ethnic differences, viz., cerumen from East Asians appears dry and white while that from non-Asians is typically wet and yellowish-brown.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4304302/ A review of creatine supplementation in age-related diseases: more than a supplement for athletes Rachel N. Smith, Amruta S. Agharkar, Eric B. Gonzales 2014 2021-07-24 [("doi","10.12688/f1000research.5218.1")] creatine longevity

Creatine is an endogenous compound synthesized from arginine, glycine and methionine. This dietary supplement can be acquired from food sources such as meat and fish, along with athlete supplement powders.

Since the majority of creatine is stored in skeletal muscle, dietary creatine supplementation has traditionally been important for athletes and bodybuilders to increase the power, strength, and mass of the skeletal muscle. However, new uses for creatine have emerged suggesting that it may be important in preventing or delaying the onset of neurodegenerative diseases associated with aging.

On average, 30% of muscle mass is lost by age 80, while muscular weakness remains a vital cause for loss of independence in the elderly population. In light of these new roles of creatine, the dietary supplement’s usage has been studied to determine its efficacy in treating congestive heart failure, gyrate atrophy, insulin insensitivity, cancer, and high cholesterol. In relation to the brain, creatine has been shown to have antioxidant properties, reduce mental fatigue, protect the brain from neurotoxicity, and improve facets/components of neurological disorders like depression and bipolar disorder. The combination of these benefits has made creatine a leading candidate in the fight against age-related diseases, such as Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, long-term memory impairments associated with the progression of Alzheimer’s disease, and stroke.

In this review, we explore the normal mechanisms by which creatine is produced and its necessary physiology, while paying special attention to the importance of creatine supplementation in improving diseases and disorders associated with brain aging and outlining the clinical trials involving creatine to treat these diseases.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4185275/ Genetic relations among procrastination, impulsivity, and goal-management ability: implications for the evolutionary origin of procrastination Daniel E. Gustavson, Akira Miyake, John K. Hewitt, Naomi P. Friedman 2014 2021-07-24 [("doi","10.1177/0956797614526260")] genetics/heritable/correlation psychology/willpower

Previous research has revealed a moderate and positive correlation between procrastination and impulsivity. However, little is known about why these two constructs are related. In the present study, we used behavior-genetics methodology to test 3 predictions derived from an evolutionary account that postulates that procrastination arose as a by-product of impulsivity: (a) Procrastination is heritable, (b) the two traits share considerable genetic variation, and (c) goal-management ability is an important component of this shared variation.

These predictions were confirmed. First, both procrastination and impulsivity were moderately heritable (46% and 49%, respectively). Second, although the two traits were separable at the phenotypic level (r = 0.65), they were not separable at the genetic level (r genetic = 1.0). Finally, variation in goal-management ability accounted for much of this shared genetic variation.

These results suggest that procrastination and impulsivity are linked primarily through genetic influences on the ability to use high-priority goals to effectively regulate actions.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4157757/ Genome editing with Cas9 in adult mice corrects a disease mutation and phenotype Hao Yin, Wen Xue, Sidi Chen, Roman L. Bogorad, Eric Benedetti, Markus Grompe, Victor Koteliansky, Phillip A. Sharp, Tyler Jacks, Daniel G. Anderson 2014 2021-07-24 [("doi","10.1038/nbt.2884")] genetics/editing

We demonstrate CRISPR-Cas9-mediated correction of a Fah mutation in hepatocytes in a mouse model of the human disease hereditary tyrosinemia.

Delivery of components of the CRISPR-Cas9 system by hydrodynamic injection resulted in initial expression of the wild-type Fah protein in ~1⁄250 liver cells.

Expansion of Fah-positive hepatocytes rescued the body weight loss phenotype.

Our study indicates that CRISPR-Cas9-mediated genome editing is possible in adult animals and has potential for correction of human genetic diseases.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4398027/ Prevention of muscular dystrophy in mice by CRISPR/Cas9-mediated editing of germline DNA Chengzu Long, John R. McAnally, John M. Shelton, Alex A. Mireault, Rhonda Bassel-Duby, Eric N. Olson 2014 2021-07-25 [("doi","10.1126/science.1254445")] genetics/editing

Duchenne muscular dystrophy (DMD) is an inherited X-linked disease caused by mutations in the gene encoding dystrophin, a protein required for muscle fiber integrity. DMD is characterized by progressive muscle weakness and a shortened life span, and there is no effective treatment.

We used clustered regularly interspaced short palindromic repeat/Cas9 (CRISPR/Cas9)-mediated genome editing to correct the dystrophin gene (Dmd) mutation in the germ line of mdx mice, a model for DMD, and then monitored muscle structure and function. Genome editing produced genetically mosaic animals containing 2 to 100% correction of the Dmd gene. The degree of muscle phenotypic rescue in mosaic mice exceeded the efficiency of gene correction, likely reflecting an advantage of the corrected cells and their contribution to regenerating muscle.

With the anticipated technological advances that will facilitate genome editing of postnatal somatic cells, this strategy may one day allow correction of disease-causing mutations in the muscle tissue of patients with DMD.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4185210/ Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche John Rb Perry, Felix R. Day, Cathy E. Elks, Patrick Sulem, Deborah J. Thompson, Teresa Ferreira, Chunyan He, Daniel I. Chasman, Tõnu Esko, Gudmar Thorleifsson, Eva Albrecht, Wei Q. Ang, Tanguy Corre, Diana L. Cousminer, Bjarke Feenstra, Nora Franceschini, Andrea Ganna, Andrew D. Johnson, Sanela Kjellqvist, Kathryn L. Lunetta, George McMahon, Ilja M. Nolte, Lavinia Paternoster, Eleonora Porcu, Albert Vernon Smith, Lisette Stolk, Alexander Teumer, Natalia Tšernikova, Emmi Tikkanen, Sheila Ulivi, Erin K. Wagner, Najaf Amin, Laura J. Bierut, Enda M. Byrne, Jouke-Jan Hottenga, Daniel L. Koller, Massimo Mangino, Tune H. Pers, Laura M. Yerges-Armstrong, Jing Hua Zhao, Irene L. Andrulis, Hoda Anton-Culver, Femke Atsma, Stefania Bandinelli, Matthias W. Beckmann, Javier Benitez, Carl Blomqvist, Stig E. Bojesen, Manjeet K. Bolla, Bernardo Bonanni, Hiltrud Brauch, Hermann Brenner, Julie E. Buring, Jenny Chang-Claude, Stephen Chanock, Jinhui Chen, Georgia Chenevix-Trench, J. Margriet Collée, Fergus J. Couch, David Couper, Andrea D. Coveillo, Angela Cox, Kamila Czene, Adamo Pio D’adamo, George Davey Smith, Immaculata De Vivo, Ellen W. Demerath, Joe Dennis, Peter Devilee, Aida K. Dieffenbach, Alison M. Dunning, Gudny Eiriksdottir, Johan G. Eriksson, Peter A. Fasching, Luigi Ferrucci, Dieter Flesch-Janys, Henrik Flyger, Tatiana Foroud, Lude Franke, Melissa E. Garcia, Montserrat García-Closas, Frank Geller, Eco Ej de Geus, Graham G. Giles, Daniel F. Gudbjartsson, Vilmundur Gudnason, Pascal Guénel, Suiqun Guo, Per Hall, Ute Hamann, Robin Haring, Catharina A. Hartman, Andrew C. Heath, Albert Hofman, Maartje J. Hooning, John L. Hopper, Frank B. Hu, David J. Hunter, David Karasik, Douglas P. Kiel, Julia A. Knight, Veli-Matti Kosma, Zoltán Kutalik, Sandra Lai, Diether Lambrechts, Annika Lindblom, Reedik Mägi, Patrik K. Magnusson, Arto Mannermaa, Nicholas G. Martin, Gisli Masson, Patrick F. McArdle, Wendy L. McArdle, Mads Melbye, Kyriaki Michailidou, Evelin Mihailov, Lili Milani, Roger L. Milne, Heli Nevanlinna, Patrick Neven, Ellen A. Nohr, Albertine J. Oldehinkel, Ben A. Oostra, Aarno Palotie, Munro Peacock, Nancy L. Pedersen, Paolo Peterlongo, Julian Peto, Paul Dp Pharoah, Dirkje S. Postma, Anneli Pouta, Katri Pylkäs, Paolo Radice, Susan Ring, Fernando Rivadeneira, Antonietta Robino, Lynda M. Rose, Anja Rudolph, Veikko Salomaa, Serena Sanna, David Schlessinger, Marjanka K. Schmidt, Mellissa C. Southey, Ulla Sovio, Meir J. Stampfer, Doris Stöckl, Anna M. Storniolo, Nicholas J. Timpson, Jonathan Tyrer, Jenny A. Visser, Peter Vollenweider, Henry Völzke, Gerard Waeber, Melanie Waldenberger, Henri Wallaschofski, Qin Wang, Gonneke Willemsen, Robert Winqvist, Bruce Hr Wolffenbuttel, Margaret J. Wright, Dorret I. Boomsma, Michael J. Econs, Kay-Tee Khaw, Ruth Jf Loos, Mark I. McCarthy, Grant W. Montgomery, John P. Rice, Elizabeth A. Streeten, Unnur Thorsteinsdottir, Cornelia van Duijn, Behrooz Z. Alizadeh, Sven Bergmann, Eric Boerwinkle, Heather A. Boyd, Laura Crisponi, Paolo Gasparini, Christian Gieger, Tamara B. Harris, Erik Ingelsson, Marjo-Riitta Järvelin, Peter Kraft, Debbie Lawlor, Andres Metspalu, Craig E. Pennell, Paul M. Ridker, Harold Snieder, Thorkild Ia Sørensen, Tim D. Spector, David P. Strachan, André G. Uitterlinden, Nicholas J. Wareham, Elisabeth Widen, Marek Zygmunt, Anna Murray, Douglas F. Easton, Kari Stefansson, Joanne M. Murabito, Ken K. Ong 2014 2021-07-25 [("doi","10.1038/nature13545")] genetics/heritable

Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear.

Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (p < 5 × 10−8) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with 3 loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns.

Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signaling, among novel mechanisms that regulate pubertal timing in humans.

Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4375246/ Replicability and robustness of genome-wide-association studies for behavioral traits Cornelius A. Rietveld, Dalton Conley, Nicholas Eriksson, Tõnu Esko, Sarah E. Medland, Anna A. E. Vinkhuyzen, Jian Yang, Jason D. Boardman, Christopher F. Chabris, Christopher T. Dawes, Benjamin W. Domingue, David A. Hinds, Magnus Johannesson, Amy K. Kiefer, David Laibson, Patrik K. E. Magnusson, Joanna L. Mountain, Sven Oskarsson, Olga Rostapshova, Alexander Teumer, Joyce Y. Tung, Peter M. Visscher, Daniel J. Benjamin, David Cesarini, Philipp Koellinger 2014 2021-07-25 [("doi","10.1177/0956797614545132")] genetics/heritable statistics/power-analysis

A recent genome-wide-association study [Rietveld et al 2013] of educational attainment identified 3 single-nucleotide polymorphisms (SNPs) whose associations, despite their small effect sizes (each R2 ≈ 0.02%), reached genome-wide statistical-significance (p < 5 × 10−8) in a large discovery sample and were replicated in an independent sample (p < 0.05). The study also reported associations between educational attainment & indices of SNPs called “polygenic scores”.

In 3 studies, we evaluated the robustness of these findings: Study 1 showed that the associations with all 3 SNPs were replicated in another large (n = 34,428) independent sample. We also found that the scores remained predictive (R2 ≈ 2%) in regressions with stringent controls for population stratification (Study 2) and in new within-family analyses (Study 3).

Our results show that large and therefore well-powered genome-wide-association studies can identify replicable genetic associations with behavioral traits. The small effect sizes of individual SNPs are likely to be a major contributing factor explaining the striking contrast between our results and the disappointing replication record of most candidate-gene studies.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3989044/ Bayesian inferences about the self (and others): a review Michael Moutoussis, Pasco Fearon, Wael El-Deredy, Raymond J. Dolan, Karl J. Friston 2014 2021-07-25 [("doi","10.1016/j.concog.2014.01.009")] psychiatry psychology statistics/bayes

Viewing the brain as an organ of approximate Bayesian inference can help us understand how it represents the self. We suggest that inferred representations of the self have a normative function: to predict and optimize the likely outcomes of social interactions.

Technically, we cast this predict-and-optimize as maximizing the chance of favorable outcomes through active inference. Here the utility of outcomes can be conceptualized as prior beliefs about final states. Actions based on interpersonal representations can therefore be understood as minimizing surprise—under the prior belief that one will end up in states with high utility. Interpersonal representations thus serve to render interactions more predictable, while the affective valence of interpersonal inference renders self-perception evaluative.

Distortions of self-representation contribute to major psychiatric disorders such as depression, personality disorder, and paranoia. The approach we review may therefore operationalize the study of interpersonal representations in pathological states.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3973910/ Evolution of the human brain: when bigger is better Michel A. Hofman 2014 2021-07-25 [("doi","10.3389/fnana.2014.00015")] ai/scaling psychology/neuroscience

Comparative studies of the brain in mammals suggest that there are general architectural principles governing its growth and evolutionary development. We are beginning to understand the geometric, biophysical, and energy constraints that have governed the evolution and functional organization of the brain and its underlying neuronal network. The object of this review is to present current perspectives on primate brain evolution, especially in humans, and to examine some hypothetical organizing principles that underlie the brain’s complex organization.

Some of the design principles and operational modes that underlie the information processing capacity of the cerebral cortex in primates will be explored. It is shown that the development of the cortex coordinates folding with connectivity in a way that produces smaller and faster brains, then otherwise would have been possible.

In view of the central importance placed on brain evolution in explaining the success of our own species, one may wonder whether there are physical limits that constrain its processing power and evolutionary potential. It will be argued that at a brain size of about 3500 cm3, corresponding to a brain volume two to 3× that of modern man, the brain seems to reach its maximum processing capacity. The larger the brain grows beyond this critical size, the less efficient it will become, thus limiting any improvement in cognitive power.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4330241/ Social psychology. Just think: the challenges of the disengaged mind Timothy D. Wilson, David A. Reinhard, Erin C. Westgate, Daniel T. Gilbert, Nicole Ellerbeck, Cheryl Hahn, Casey L. Brown, Adi Shaked 2014 2021-07-25 [("doi","10.1126/science.1250830")] psychiatry/meditation psychology/neuroscience/pain psychology/novelty

In 11 studies, we found that participants typically did not enjoy spending 6–15 minutes in a room by themselves with nothing to do but think, that they enjoyed doing mundane external activities much more, and that many preferred to administer electric shocks to themselves instead of being left alone with their thoughts.

Most people seem to prefer to be doing something rather than nothing, even if that something is negative.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3950931/ Does temporal discounting explain unhealthy behavior? A systematic review and reinforcement learning perspective Giles W. Story, Ivo Vlaev, Ben Seymour, Ara Darzi, Raymond J. Dolan 2014 2021-07-25 [("doi","10.3389/fnbeh.2014.00076")] psychology/personality reinforcement-learning/model-free

The tendency to make unhealthy choices is hypothesized to be related to an individual’s temporal discount rate, the theoretical rate at which they devalue delayed rewards. Furthermore, a particular form of temporal discounting, hyperbolic discounting, has been proposed to explain why unhealthy behavior can occur despite healthy intentions. We examine these two hypotheses in turn.

We first systematically review studies which investigate whether discount rates can predict unhealthy behavior. These studies reveal that high discount rates for money (and in some instances food or drug rewards) are associated with several unhealthy behaviors and markers of health status, establishing discounting as a promising predictive measure.

We secondly examine whether intention-incongruent unhealthy actions are consistent with hyperbolic discounting. We conclude that intention-incongruent actions are often triggered by environmental cues or changes in motivational state, whose effects are not parameterized by hyperbolic discounting. We propose a framework for understanding these state-based effects in terms of the interplay of two distinct reinforcement learning mechanisms: a “model-based” (or goal-directed) system and a “model-free” (or habitual) system. Under this framework, while discounting of delayed health may contribute to the initiation of unhealthy behavior, with repetition, many unhealthy behaviors become habitual; if health goals then change, habitual behavior can still arise in response to environmental cues.

We propose that the burgeoning development of computational models of these processes will permit further identification of health decision-making phenotypes.

--- https://arxiv.org/abs/1312.7128 Searching the Internet for evidence of time travelers Robert J. Nemiroff, Teresa Wilson 2013-12-26 2021-07-25 [("doi","10.48550/arXiv.1312.7128")] fiction/science-fiction/time-travel math/humor science

Time travel has captured the public imagination for much of the past century, but little has been done to actually search for time travelers. Here, 3 implementations of Internet searches for time travelers are described, all seeking a prescient mention of information not previously available.

The first search covered prescient content placed on the Internet, highlighted by a comprehensive search for specific terms in tweets on Twitter. The second search examined prescient inquiries submitted to a search engine, highlighted by a comprehensive search for specific search terms submitted to a popular astronomy web site. The third search involved a request for a direct Internet communication, either by email or tweet, pre-dating to the time of the inquiry. Given practical verifiability concerns, only time travelers from the future were investigated.

No time travelers were discovered. Although these negative results do not disprove time travel, given the great reach of the Internet, this search is perhaps the most comprehensive to date.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4230924/ World population stabilization unlikely this century Patrick Gerland, Adrian E. Raftery, Hana Sevčíková, Nan Li, Danan Gu, Thomas Spoorenberg, Leontine Alkema, Bailey K. Fosdick, Jennifer Chunn, Nevena Lalic, Guiomar Bay, Thomas Buettner, Gerhard K. Heilig, John Wilmoth 2014 2021-07-25 [("doi","10.1126/science.1257469")] sociology

The United Nations (UN) recently released population projections based on data until 2012 and a Bayesian probabilistic methodology.

Analysis of these data reveals that, contrary to previous literature, the world population is unlikely to stop growing this century.

There is an 80% probability that world population, now 7.2 billion people, will increase to between 9.6 billion and 12.3 billion in 2100. This uncertainty is much smaller than the range from the traditional UN high and low variants. Much of the increase is expected to happen in Africa, in part due to higher fertility rates and a recent slowdown in the pace of fertility decline. Also, the ratio of working-age people to older people is likely to decline substantially in all countries, even those that currently have young populations.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3978334/ Is spacing really the "friend of induction"? Peter P. J. L. Verkoeijen, Samantha Bouwmeester 2014 2021-07-25 [("doi","10.3389/fpsyg.2014.00259")] psychology/spaced-repetition

Inductive learning takes place when people learn a new concept or category by observing a variety of exemplars. Kornell & Bjork 2008 asked participants to learn new painting styles either by presenting different paintings of the same artist consecutively (massed presentation) or by mixing paintings of different artists (spaced presentation). In their second experiment, Kornell & Bjork 2008 showed with a final style recognition test, that spacing resulted in better inductive learning than massing. Also, by using this style recognition test, they ruled out the possibility that spacing merely resulted in a better memory for the labels of the newly learned painting styles. The findings from Kornell and Bjork’s (2008) second experiment are important because they show that the benefit of spaced learning generalizes to complex learning tasks and outcomes, and that it is not confined to rote memory learning. However, the findings from Kornell and Bjork’s (2008) second experiment have never been replicated.

In the present study we performed an exact and high-powered replication of Kornell and Bjork’s (2008) second experiment with a Web-based sample. Such a replication contributes to establish the reliability of the original finding and hence to more conclusive evidence of the spacing effect in inductive learning.

The findings from the present replication attempt revealed a medium-sized advantage of spacing over massing in inductive learning, which was comparable to the original effect in the experiment by Kornell & Bjork 2008. Also, the 95% confidence intervals (CI) of the effect sizes from both experiments overlapped considerably.

Hence, the findings from the present replication experiment and the original experiment clearly reinforce each other.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3994136/ Lord’s paradox in a continuous setting and a regression artifact in numerical cognition research Kimmo Eriksson, Olle Häggström 2014 2021-07-25 [("doi","10.1371/journal.pone.0095949")] statistics

In this paper we review, and elaborate on, the literature on a regression artifact related to Lord’s paradox in a continuous setting.

Specifically, the question is whether a continuous property of individuals predicts improvement from training between a pretest and a posttest. If the pretest score is included as a covariate, regression to the mean will lead to biased results if two critical conditions are satisfied: (1) the property is correlated with pretest scores and (2) pretest scores include random errors.

We discuss how these conditions apply to the analysis in a published experimental study, the authors of which concluded that linearity of children’s estimations of numerical magnitudes predicts arithmetic learning from a training program. However, the two critical conditions were clearly met in that study. In a reanalysis we find that the bias in the method can fully account for the effect found in the original study.

In other words, data are consistent with the null hypothesis that numerical magnitude estimations are unrelated to arithmetic learning.

--- /doc/economics/2021-emory.pdf Protective State Policies and the Employment of Fathers with Criminal Records Allison Dwyer Emory 2021-11-02 2021-11-02 [("doi","10.1093/socpro/spab069")] crime economics law

A criminal record can be a serious impediment to securing stable employment, with negative implications for the economic stability of individuals and their families. State policies intended to address this issue have had mixed results, however.

Using panel data from the Fragile Families study merged with longitudinal data on state-level policies, this study investigates the association between criminal record based employment discrimination policies and the employment of men both with and without criminal records. These state policies broadly regulate what kinds of records can be legally used for hiring and licensing decisions, but have received little attention in prior research.

Findings indicate that men with criminal records were less likely to be working if they lived in states with more policies in place to regulate the legal use of those records. Consistent with research linking policies regulating access to records to racial discrimination, black men living in protective states reported this employment penalty even if they did not have criminal records themselves.

Thus, these policies, at best, may fail to disrupt entrenched employment disparities and, at worst, may exacerbate racial discrimination.

--- /doc/darknet-market/2022-tiberg.pdf Ordinary people, criminals, addicts and recreational users: Swedish court of law descriptions of persons sentenced for online drug purchases Fredrik Tiberg, Johan Nordgren 2022-01-25 2022-01-25 [("doi","10.1177/14550725221079524")] crime darknet-market

Background: The aim of this study was to analyze how Swedish courts describe persons sentenced for purchasing illicit drugs online.

Method: Qualitative analysis of naturally occurring data through 201 sentences that included 248 individuals sentenced for having purchased drugs online between January 1 2010 and January 1 2020.

Results: The analysis resulted in the construction of four ideal types regarding the described characteristics of the sentenced persons; the ordinary person, the recreational user, the addict and the criminal. The courts operate with a notable dichotomy between traditional drug markets and online drug markets, that can be understood in relation to descriptions of Bourdieusian capital forms, specifically street capital and digital capital.

Conclusion: Descriptions relating to street capital were of larger interest to the courts compared to digital capital, although there were examples of when the courts argued that uses of digital capital should be viewed as an aggravating circumstance. The courts largely held a dichotomous view of online and offline drug markets that focus on street-based criminality, which may have implications for how emerging digital drug markets are responded to by drug law enforcement and judicial systems.

--- https://arxiv.org/abs/1304.3381 Life Before Earth Alexei A. Sharov, Richard Gordon 2013-03-28 2021-07-26 [("doi","10.48550/arXiv.1304.3381")] biology

An extrapolation of the genetic complexity of organisms to earlier times suggests that life began before the Earth was formed. Life may have started from systems with single heritable elements that are functionally equivalent to a nucleotide. The genetic complexity, roughly measured by the number of non-redundant functional nucleotides, is expected to have grown exponentially due to several positive feedback factors: gene cooperation, duplication of genes with their subsequent specialization, and emergence of novel functional niches associated with existing genes.

Linear regression of genetic complexity on a log scale extrapolated back to just one base pair suggests the time of the origin of life 9.7 billion years ago. This cosmic time scale for the evolution of life has important consequences: life took ca. 5 billion years to reach the complexity of bacteria; the environments in which life originated and evolved to the prokaryote stage may have been quite different from those envisaged on Earth; there was no intelligent life in our universe prior to the origin of Earth, thus Earth could not have been deliberately seeded with life by intelligent aliens; Earth was seeded by panspermia; experimental replication of the origin of life from scratch may have to emulate many cumulative rare events; and the Drake equation for guesstimating the number of civilizations in the universe is likely wrong, as intelligent life has just begun appearing in our universe.

Evolution of advanced organisms has accelerated via development of additional information-processing systems: epigenetic memory, primitive mind, multicellular brain, language, books, computers, and Internet. As a result, the doubling time of complexity has reached ca. 20 years.

Finally, we discuss the issue of the predicted technological singularity and give a biosemiotics perspective on the increase of complexity.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3944098/ Genetics of rheumatoid arthritis contributes to biology and drug discovery Yukinori Okada, Di Wu, Gosia Trynka, Towfique Raj, Chikashi Terao, Katsunori Ikari, Yuta Kochi, Koichiro Ohmura, Akari Suzuki, Shinji Yoshida, Robert R. Graham, Arun Manoharan, Ward Ortmann, Tushar Bhangale, Joshua C. Denny, Robert J. Carroll, Anne E. Eyler, Jeffrey D. Greenberg, Joel M. Kremer, Dimitrios A. Pappas, Lei Jiang, Jian Yin, Lingying Ye, Ding-Feng Su, Jian Yang, Gang Xie, Ed Keystone, Harm-Jan Westra, Tõnu Esko, Andres Metspalu, Xuezhong Zhou, Namrata Gupta, Daniel Mirel, Eli Ayumi Stahl, Dorothée Diogo, Jing Cui, Katherine Liao, Michael H. Guo, Keiko Myouzen, Takahisa Kawaguchi, Marieke J. H. Coenen, Piet L. C. M. van Riel, Mart A. F. J. van de Laar, Henk-Jan Guchelaar, Tom W. J. Huizinga, Philippe Dieudé, Xavier Mariette, S. Louis Bridges, Alexandra Zhernakova, Rene E. M. Toes, Paul P. Tak, Corinne Miceli-Richard, So-Young Bang, Hye-Soon Lee, Javier Martin, Miguel A. Gonzalez-Gay, Luis Rodriguez-Rodriguez, Solbritt Rantapää-Dahlqvist, Lisbeth Arlestig, Hyon K. Choi, Yoichiro Kamatani, Pilar Galan, Mark Lathrop, Steve Eyre, John Bowes, Anne Barton, Niek de Vries, Larry W. Moreland, Lindsey A. Criswell, Elizabeth W. Karlson, Atsuo Taniguchi, Ryo Yamada, Michiaki Kubo, Jun S. Liu, Sang-Cheol Bae, Jane Worthington, Leonid Padyukov, Lars Klareskog, Peter K. Gregersen, Soumya Raychaudhuri, Barbara E. Stranger, Philip L. De Jager, Lude Franke, Peter M. Visscher, Matthew A. Brown, Hisashi Yamanaka, Tsuneyo Mimori, Atsushi Takahashi, Huji Xu, Timothy W. Behrens, Katherine A. Siminovitch, Shigeki Momohara, Fumihiko Matsuda, Kazuhiko Yamamoto, Robert M. Plenge 2014 2021-07-26 [("doi","10.1038/nature12873")] biology genetics/heritable

A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA). Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of statistical-significance, bringing the total to 101 (refs 2—4).

We devised an in silico pipeline using established bioinformatics methods based on functional annotation, cis-acting expression quantitative trait loci and pathway analyses—as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes—to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA.

Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3949374/ Sperm variation within a single ejaculate affects offspring development in Atlantic salmon Simone Immler, Cosima Hotzy, Ghazal Alavioon, Erik Petersson, Göran Arnqvist 2014 2021-07-26 [("doi","10.1098/rsbl.2013.1040")] biology

It is generally believed that variation in sperm phenotype within a single ejaculate has no consequences for offspring performance, because sperm phenotypes are thought not to reflect sperm genotypes.

We show that variation in individual sperm function within an ejaculate affects the performance of the resulting offspring in the Atlantic salmon, Salmo salar. We experimentally manipulated the time between sperm activation and fertilization in order to select for sperm cohorts differing in longevity within single ejaculates of wild caught male salmon.

We found that within-ejaculate variation in sperm longevity affected offspring development and hence time until hatching.

Whether these effects have a genetic or epigenetic basis needs to be further evaluated. However, our results provide experimental evidence for transgenerational effects of individual sperm function.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0076301 Creatine Supplementation Associated or Not with Strength Training upon Emotional and Cognitive Measures in Older Women: A Randomized Double-Blind Study Christiano Robles Rodrigues Alves, Carlos Alberto Abujabra Merege Filho, Fabiana Braga Benatti, Sonia Brucki, Rosa Maria R. Pereira, Ana Lucia de Sá Pinto, Fernanda Rodrigues Lima, Hamilton Roschel, Bruno Gualano 2013-08-22 2021-07-26 [("doi","10.1371/journal.pone.0076301")] creatine

Purpose: To assess the effects of creatine supplementation, associated or not with strength training, upon emotional and cognitive measures in older woman.

Method: This is a 24-week, parallel-group, double-blind, randomized, placebo-controlled trial. The individuals were randomly allocated into one of the following groups (n = 14 each): (1) placebo, (2) creatine supplementation, (3) placebo associated with strength training or (4) creatine supplementation associated with strength training. According to their allocation, the participants were given creatine (4 x 5 g/d for 5 days followed by 5 g/d) or placebo (dextrose at the same dosage) and were strength trained or not. Cognitive function, assessed by a comprehensive battery of tests involving memory, selective attention, and inhibitory control, and emotional measures, assessed by the Geriatric Depression Scale, were evaluated at baseline, after 12 and 24 weeks of the intervention. Muscle strength and food intake were evaluated at baseline and after 24 weeks.

Results: After the 24-week intervention, both training groups (ingesting creatine supplementation and placebo) had reductions on the Geriatric Depression Scale scores when compared with the non-trained placebo group (p = 0.001 and p = 0.01, respectively) and the non-trained creatine group (p < 0.001 for both comparison). However, no statistically-significant differences were observed between the non-trained placebo and creatine (p = 0.60) groups, or between the trained placebo and creatine groups (p = 0.83). Both trained groups, irrespective of creatine supplementation, had better muscle strength performance than the non-trained groups. Neither strength training nor creatine supplementation altered any parameter of cognitive performance. Food intake remained unchanged.

Conclusion: Creatine supplementation did not promote any change in cognitive function and emotional parameters in apparently healthy older individuals. In addition, strength training per se improved emotional state and muscle strength, but not cognition, with no additive effects of creatine supplementation.

Trial Registration: ClinicalTrials.gov NCT01164020.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3482415/ Genetic risk factors for BMI and obesity in an ethnically diverse population: results from the population architecture using genomics and epidemiology (PAGE) study Megan D. Fesinmeyer, Kari E. North, Marylyn D. Ritchie, Unhee Lim, Nora Franceschini, Lynne R. Wilkens, Myron D. Gross, Petra Bůžková, Kimberly Glenn, P. Miguel Quibrera, Lindsay Fernández-Rhodes, Qiong Li, Jay H. Fowke, Rongling Li, Christopher S. Carlson, Ross L. Prentice, Lewis H. Kuller, Joann E. Manson, Tara C. Matise, Shelley A. Cole, Christina T. L. Chen, Barbara V. Howard, Laurence N. Kolonel, Brian E. Henderson, Kristine R. Monroe, Dana C. Crawford, Lucia A. Hindorff, Steven Buyske, Christopher A. Haiman, Loic Le Marchand, Ulrike Peters 2013 2021-07-26 [("doi","10.1002/oby.20268")] exercise genetics/heritable

Objective: Several genome-wide association studies (GWAS) have demonstrated that common genetic variants contribute to obesity. However, studies of this complex trait have focused on ancestrally European populations, despite the high prevalence of obesity in some minority groups.

Design and Method: As part of the “Population Architecture using Genomics and Epidemiology (PAGE)” Consortium, we investigated the association between 13 GWAS-identified single-nucleotide polymorphisms (SNPs) and BMI and obesity in 69,775 subjects, including 6,149 American Indians, 15,415 African-Americans, 2,438 East Asians, 7,346 Hispanics, 604 Pacific Islanders, and 37,823 European Americans. For the BMI-increasing allele of each SNP, we calculated β coefficients using linear regression (for BMI) and risk estimates using logistic regression (for obesity defined as BMI ≥ 30) followed by fixed-effects meta-analysis to combine results across PAGE sites. Analyses stratified by racial/ethnic group assumed an additive genetic model and were adjusted for age, sex, and current smoking. We defined “replicating SNPs” (in European Americans) and “generalizing SNPs” (in other racial/ethnic groups) as those associated with an allele frequency-specific increase in BMI.

Results: By this definition, we replicated 9⁄13 SNP associations (5⁄8 loci) in European Americans. We also generalized 8⁄13 SNP associations (5⁄8 loci) in East Asians, 7⁄13 (5⁄8 loci) in African Americans, 6⁄13 (4⁄8 loci) in Hispanics, 5⁄8 in Pacific Islanders (5⁄8 loci), and 5⁄9 (4⁄8 loci) in American Indians.

Conclusion: Linkage disequilibrium patterns suggest that tagSNPs selected for European Americans may not adequately tag causal variants in other ancestry groups. Accordingly, fine-mapping in large samples is needed to comprehensively explore these loci in diverse populations.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3969612/ Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility Anubha Mahajan, Min Jin Go, Weihua Zhang, Jennifer E. Below, Kyle J. Gaulton, Teresa Ferreira, Momoko Horikoshi, Andrew D. Johnson, Maggie C. Y. Ng, Inga Prokopenko, Danish Saleheen, Xu Wang, Eleftheria Zeggini, Gonçalo Abecasis, Linda S. Adair, Peter Almgren, Mustafa Atalay, Tin Aung, Damiano Baldassarre, Beverley Balkau, Yuqian Bao, Anthony H. Barnett, Ines Barroso, Abdul Basit, Latonya F. Been, John Beilby, Graeme I. Bell, Rafn Benediktsson, Richard N. Bergman, Bernhard O. Boehm, Eric Boerwinkle, Lori L. Bonnycastle, Noël Burtt, Qiuyin Cai, Harry Campbell, Jason Carey, Stephane Cauchi, Mark Caulfield, Juliana C. N. Chan, Li-Ching Chang, Tien-Jyun Chang, Yi-Cheng Chang, Guillaume Charpentier, Chien-Hsiun Chen, Han Chen, Yuan-Tsong Chen, Kee-Seng Chia, Manickam Chidambaram, Peter S. Chines, Nam H. Cho, Young Min Cho, Lee-Ming Chuang, Francis S. Collins, Marylin C. Cornelis, David J. Couper, Andrew T. Crenshaw, Rob M. van Dam, John Danesh, Debashish Das, Ulf de Faire, George Dedoussis, Panos Deloukas, Antigone S. Dimas, Christian Dina, Alex S. Doney, Peter J. Donnelly, Mozhgan Dorkhan, Cornelia van Duijn, Josée Dupuis, Sarah Edkins, Paul Elliott, Valur Emilsson, Raimund Erbel, Johan G. Eriksson, Jorge Escobedo, Tõnu Esko, Elodie Eury, Jose C. Florez, Pierre Fontanillas, Nita G. Forouhi, Tom Forsen, Caroline Fox, Ross M. Fraser, Timothy Frayling, Philippe Froguel, Philippe Frossard, Yutang Gao, Karl Gertow, Christian Gieger, Bruna Gigante, Harald Grallert, George B. Grant, Leif C. Grrop, Christropher J. Groves, Elin Grundberg, Candace Guiducci, Anders Hamsten, Bok-Ghee Han, Kazuo Hara, Neelam Hassanali, Andrew Tym Hattersley, Caroline Hayward, Asa K. Hedman, Christian Herder, Albert Hofman, Oddgeir L. Holmen, Kees Hovingh, Astradur B. Hreidarsson, Cheng Hu, Frank B. Hu, Jennie Hui, Steve E. Humphries, Sarah E. Hunt, David J. Hunter, Kristian Hveem, Zafar I. Hydrie, Hiroshi Ikegami, Thomas Illig, Erik Ingelsson, Muhammed Islam, Bo Isomaa, Anne Uriu Jackson, Tazeen Jafar, Alan James, Weiping Jia, Karl-Heinz Jöckel, Anna Jonsson, Jeremy B. M. Jowett, Takashi Kadowaki, Hyun Min Kang, Stavroula Kanoni, Wen Hong L. Kao, Sekar Kathiresan, Norihiro Kato, Prasad Katulanda, Kirkka M. Keinanen-Kiukaanniemi, Ann M. Kelly, Hassan Khan, Kay-Tee Khaw, Chiea-Chuen Khor, Hyung-Lae Kim, Sangsoo Kim, Young Jin Kim, Leena Kinnunen, Norman Klopp, Augustine Kong, Eeva Korpi-Hyövälti, Sudhir Kowlessur, Peter Kraft, Jasmina Kravic, Malene M. Kristensen, S. Krithika, Ashish Kumar, Jesus Kumate, Johanna Kuusisto, Soo Heon Kwak, Markku Laakso, Vasiliki Lagou, Timo A. Lakka, Claudia Langenberg, Cordelia Langford, Robert Lawrence, Karin Leander, Jen-Mai Lee, Nanette R. Lee, Man Li, Xinzhong Li, Yun Li, Junbin Liang, Samuel Liju, Wei-Yen Lim, Lars L. Lind, Cecilia M. Lindgren, Eero Lindholm, Ching-Ti Liu, Jian Jun Liu, Stéphane Lobbens, Jirong Long, Ruth Loos, Wei Lu, Jian’an Luan, Valeriya Lyssenko, Ronald C. W. Ma, Shiro Maeda, Reedik Mägi, Satu Männisto, David R. Matthews, James B. Meigs, Olle Melander, Andres Metspalu, Julia Meyer, Ghazala Mirza, Evelin Mihailov, Susanne Moebus, Viswanathan Mohan, Karen L. Mohlke, Andrew D. Morris, Thomas W. Mühleisen, Martina Müller-Nurasyid, Bill Musk, Jiro Nakamura, Eitaro Nakashima, Pau Navarro, Peng-Keat Ng, Alexandra C. Nica, Peter M. Nilsson, Inger Njølstad, Markus M. Nöthen, Keizo Ohnaka, Twee Hee Ong, Katharine R. Owen, Colin Palmer, James S. Pankow, Kyong Soo Park, Melissa Parkin, Sonali Pechlivanis, Nancy L. Pedersen, Leena Peltonen, John R. B. Perry, Annette Peters, Janini M. Pinidiyapathirage, Carl G. Platou, Simon Potter, Jackie F. Price, Lu Qi, Venkatesan Radha, Loukianos Rallidis, Asif Rasheed, Wolfgang Rathman, Rainer Rauramaa, Soumya Raychaudhuri, N. William Rayner, Simon D. Rees, Emil Rehnberg, Samuli Ripatti, Neil Robertson, Michael Roden, Elizabeth J. Rossin, Igor Rudan, Denis Rybin, Timo E. Saaristo, Veikko Salomaa, Juha Saltevo, Maria Samuel, Dharambir K. Sanghera, Jouko Saramies, James Scott, Laura J. Scott, Robert A. Scott, Ayellet V. Segrè, Joban Sehmi, Bengt Sennblad, Nabi Shah, Sonia Shah, A. Samad Shera, Xiao Ou Shu, Alan R. Shuldiner, Gunnar Sigurđsson, Eric Sijbrands, Angela Silveira, Xueling Sim, Suthesh Sivapalaratnam, Kerrin S. Small, Wing Yee So, Alena Stančáková, Kari Stefansson, Gerald Steinbach, Valgerdur Steinthorsdottir, Kathleen Stirrups, Rona J. Strawbridge, Heather M. Stringham, Qi Sun, Chen Suo, Ann-Christine Syvänen, Ryoichi Takayanagi, Fumihiko Takeuchi, Wan Ting Tay, Tanya M. Teslovich, Barbara Thorand, Gudmar Thorleifsson, Unnur Thorsteinsdottir, Emmi Tikkanen, Joseph Trakalo, Elena Tremoli, Mieke D. Trip, Fuu Jen Tsai, Tiinamaija Tuomi, Jaakko Tuomilehto, André G. Uitterlinden, Adan Valladares-Salgado, Sailaja Vedantam, Fabrizio Veglia, Benjamin F. Voight, Congrong Wang, Nicholas J. Wareham, Roman Wennauer, Ananda R. Wickremasinghe, Tom Wilsgaard, James F. Wilson, Steven Wiltshire, Wendy Winckler, Tien Yin Wong, Andrew R. Wood, Jer-Yuarn Wu, Ying Wu, Ken Yamamoto, Toshimasa Yamauchi, Mingyu Yang, Loïc Yengo, Mitsuhiro Yokota, Robin Young, Delilah Zabaneh, Fan Zhang, Rong Zhang, Wei Zheng, Paul Z. Zimmet, David Altshuler, Donald W. Bowden, Yoon Shin Cho, Nancy J. Cox, Miguel Cruz, Craig L. Hanis, Jaspal Kooner, Jong-Young Lee, Mark Seielstad, Yik Ying Teo, Michael Boehnke, Esteban J. Parra, John C. Chambers, E. Shyong Tai, Mark I. McCarthy, Andrew P. Morris 2014 2021-07-26 [("doi","10.1038/ng.2897")] genetics/heritable

To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian, and Mexican and Mexican American ancestry.

We observed a statistically-significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified 7 new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci.

These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0087044 Impact of Measurement Error on Testing Genetic Association with Quantitative Traits Jiemin Liao, Xiang Li, Tien-Yin Wong, Jie Jin Wang, Chiea Chuen Khor, E. Shyong Tai, Tin Aung, Yik-Ying Teo, Ching-Yu Cheng 2013-12-17 2021-07-26 [("doi","10.1371/journal.pone.0087044")] genetics/heritable statistics/power-analysis

Measurement error of a phenotypic trait reduces the power to detect genetic associations. We examined the impact of sample size, allele frequency and effect size in presence of measurement error for quantitative traits.

The statistical power to detect genetic association with phenotype mean and variability was investigated analytically. The non-centrality parameter for a non-central F distribution was derived and verified using computer simulations. We obtained equivalent formulas for the cost of phenotype measurement error.

Effects of differences in measurements were examined in a genome-wide association study (GWAS) of two grading scales for cataract and a replication study of genetic variants influencing blood pressure. The mean absolute difference between the analytic power and simulation power for comparison of phenotypic means and variances was less than 0.005, and the absolute difference did not exceed 0.02. To maintain the same power, a one standard deviation (SD) in measurement error of a standard normal distributed trait required a one-fold increase in sample size for comparison of means, and a three-fold increase in sample size for comparison of variances. GWAS results revealed almost no overlap in the statistically-significant SNPs (p<10−5) for the two cataract grading scales while replication results in genetic variants of blood pressure displayed no statistically-significant differences between averaged blood pressure measurements and single blood pressure measurements.

We have developed a framework for researchers to quantify power in the presence of measurement error, which will be applicable to studies of phenotypes in which the measurement is highly variable.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1003520 Using Extended Genealogy to Estimate Components of Heritability for 23 Quantitative and Dichotomous Traits Noah Zaitlen, Peter Kraft, Nick Patterson, Bogdan Pasaniuc, Gaurav Bhatia, Samuela Pollack, Alkes Price 2013-04-06 2021-07-26 [("doi","10.1371/journal.pgen.1003520")] genetics/heritable

Important knowledge about the determinants of complex human phenotypes can be obtained from the estimation of heritability, the fraction of phenotypic variation in a population that is determined by genetic factors. Here, we make use of extensive phenotype data in Iceland, long-range phased genotypes, and a population-wide genealogical database to examine the heritability of 11 quantitative and 12 dichotomous phenotypes in a sample of 38,167 individuals. Most previous estimates of heritability are derived from family-based approaches such as twin studies, which may be biased upwards by epistatic interactions or shared environment. Our estimates of heritability, based on both closely and distantly related pairs of individuals, are lower than those from previous studies. We examine phenotypic correlations across a range of relationships, from siblings to first cousins, and find that the excess phenotypic correlation in these related individuals is predominantly due to shared environment as opposed to dominance or epistasis. We also develop a new method to jointly estimate narrow-sense heritability and the heritability explained by genotyped SNPs. Unlike existing methods, this approach permits the use of information from both closely and distantly related pairs of individuals, thereby reducing the variance of estimates of heritability explained by genotyped SNPs while preventing upward bias. Our results show that common SNPs explain a larger proportion of the heritability than previously thought, with SNPs present on Illumina 300K genotyping arrays explaining more than half of the heritability for the 23 phenotypes examined in this study. Much of the remaining heritability is likely to be due to rare alleles that are not captured by standard genotyping arrays.

Author Summary: Phenotype is a function of a genome and its environment. Heritability is the fraction of variation in a phenotype determined by genetic factors in a population. Current methods to estimate heritability rely on the phenotypic correlations of closely related individuals and are potentially upwardly biased, due to the impact of epistasis and shared environment. We develop new methods to estimate heritability over both closely and distantly related individuals. By examining the phenotypic correlation among different types of related individuals such as siblings, half-siblings, and first cousins, we show that shared environment is the primary determinant of inflated estimates of heritability. For a large number of phenotypes, it is not known how much of the heritability is explained by SNPs included on current genotyping platforms. Existing methods to estimate this component of heritability are biased in the presence of related individuals. We develop a method that permits the inclusion of both closely and distantly related individuals when estimating heritability explained by genotyped SNPs and use it to make estimates for 23 medically relevant phenotypes. These estimates can be used to increase our understanding of the distribution and frequency of functionally relevant variants and thereby inform the design of future studies.

--- https://arxiv.org/abs/1309.4625 The effect of paternal age on offspring intelligence and personality when controlling for paternal trait level Ruben C. Arslan, Lars Penke, Wendy Johnson, William Iacono, Matt McGue 2013-09-18 2021-07-26 [("doi","10.1371/journal.pone.0090097")] genetics/heritable/rare iq

Paternal age at conception has been found to predict the number of new genetic mutations. We examined the effect of father’s age at birth on offspring intelligence, head circumference and personality traits. Using the Minnesota Twin Family Study sample we tested paternal age effects while controlling for parents’ trait levels measured with the same precision as offspring’s. From evolutionary genetic considerations we predicted a negative effect of paternal age on offspring intelligence, but not on other traits.

Controlling for parental IQ had the effect of turning a positive-zero order association negative. We found paternal age effects on offspring IQ and MPQ Absorption, but they were not robustly statistically-significant, nor replicable with additional covariates. No other noteworthy effects were found.

Parents’ intelligence and personality correlated with their ages at twin birth, which may have obscured a small negative effect of advanced paternal age (< 1% of variance explained) on intelligence.

We discuss future avenues for studies of paternal age effects and suggest that stronger research designs are needed to rule out confounding factors involving birth order and the Flynn effect.

--- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.659.8433 2021-07-27 genetics/selection/artificial iq --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0063773 Meta-Analysis: Melatonin for the Treatment of Primary Sleep Disorders Eduardo Ferracioli-Oda, Ahmad Qawasmi, Michael H. Bloch 2013-04-05 2021-07-27 [("doi","10.1371/journal.pone.0063773")] melatonin

Study Objectives: To investigate the efficacy of melatonin compared to placebo in improving sleep parameters in patients with primary sleep disorders.

Design: PubMed was searched for randomized, placebo-controlled trials examining the effects of melatonin for the treatment of primary sleep disorders. Primary outcomes examined were improvement in sleep latency, sleep quality and total sleep time. Meta-regression was performed to examine the influence of dose and duration of melatonin on reported efficacy.

Participants: Adults and children diagnosed with primary sleep disorders.

Interventions: Melatonin compared to placebo.

Results: Nineteen studies involving 1683 subjects were included in this meta-analysis. Melatonin demonstrated efficacy in reducing sleep latency (weighted mean difference (WMD) = 7.06 minutes [95% CI 4.37 to 9.75], Z = 5.15, p < 0.001) and increasing total sleep time (WMD = 8.25 minutes [95% CI 1.74 to 14.75], Z = 2.48, p = 0.013). Trials with longer duration and using higher doses of melatonin demonstrated greater effects on decreasing sleep latency and increasing total sleep time. Overall sleep quality was statistically-significantly improved in subjects taking melatonin (standardized mean difference = 0.22 [95% CI: 0.12 to 0.32], Z = 4.52, p < 0.001) compared to placebo. No statistically-significant effects of trial duration and melatonin dose were observed on sleep quality.

Conclusion: This meta-analysis demonstrates that melatonin decreases sleep onset latency, increases total sleep time and improves overall sleep quality. The effects of melatonin on sleep are modest but do not appear to dissipate with continued melatonin use. Although the absolute benefit of melatonin compared to placebo is smaller than other pharmacological treatments for insomnia, melatonin may have a role in the treatment of insomnia given its relatively benign side-effect profile compared to these agents.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0069224 Acute Effects of Modafinil on Brain Resting State Networks in Young Healthy Subjects Roberto Esposito, Franco Cilli, Valentina Pieramico, Antonio Ferretti, Antonella Macchia, Marco Tommasi, Aristide Saggino, Domenico Ciavardelli, Antonietta Manna, Riccardo Navarra, Filippo Cieri, Liborio Stuppia, Armando Tartaro, Stefano L. Sensi 2013-06-05 2021-07-27 [("doi","10.1371/journal.pone.0069224")] modafinil psychology/neuroscience

Background: There is growing debate on the use of drugs that promote cognitive enhancement. Amphetamine-like drugs have been employed as cognitive enhancers, but they show important side effects and induce addiction. In this study, we investigated the use of modafinil which appears to have less side effects compared to other amphetamine-like drugs. We analyzed effects on cognitive performances and brain resting state network activity of 26 healthy young subjects.

Methodology: A single dose (100 mg) of modafinil was administered in a double-blind and placebo-controlled study. Both groups were tested for neuropsychological performances with the Raven’s Advanced Progressive Matrices II set (APM) before and 3 hours after administration of drug or placebo. Resting state functional magnetic resonance (rs-FMRI) was also used, before and after 3 hours, to investigate changes in the activity of resting state brain networks. Diffusion Tensor Imaging (DTI) was employed to evaluate differences in structural connectivity between the two groups. Protocol ID: Modrest_2011; NCT01684306; https://classic.clinicaltrials.gov/ct2/show/NCT01684306.

Principal Findings: Results indicate that a single dose of modafinil improves cognitive performance as assessed by APM. Rs-fMRI showed that the drug produces a statistically-significant increased activation of Frontal Parietal Control (FPC; p < 0.04) and Dorsal Attention (DAN; p < 0.04) networks. No modifications in structural connectivity were observed.

Conclusions: Overall, our findings support the notion that modafinil has cognitive enhancing properties and provide functional connectivity data to support these effects.

Trial Registration: ClinicalTrials.gov NCT01684306.

--- https://journals.plos.org/plosbiology/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001609 Evaluation of Excess Statistical-Significance Bias in Animal Studies of Neurological Diseases Konstantinos K. Tsilidis, Orestis A. Panagiotou, Emily S. Sena, Eleni Aretouli, Evangelos Evangelou, David W. Howells, Rustam Al-Shahi Salman, Malcolm R. Macleod, John Ioannidis 2013-06-06 2021-07-27 [("doi","10.1371/journal.pbio.1001609")] psychiatry/alzheimers statistics/bias/animal

The evaluation of 160 meta-analyses of animal studies on potential treatments for neurological disorders reveals that the number of statistically-significant results was too large to be true, suggesting biases.

Animal studies generate valuable hypotheses that lead to the conduct of preventive or therapeutic clinical trials. We assessed whether there is evidence for excess statistical-significance in results of animal studies on neurological disorders, suggesting biases. We used data from meta-analyses of interventions deposited in Collaborative Approach to Meta-Analysis and Review of Animal Data in Experimental Studies (CAMARADES). The number of observed studies with statistically-significant results (O) was compared with the expected number (E), based on the statistical power of each study under different assumptions for the plausible effect size. We assessed 4,445 datasets synthesized in 160 meta-analyses on Alzheimer disease (n = 2), experimental autoimmune encephalomyelitis (n = 34), focal ischemia (n = 16), intra-cerebral hemorrhage (n = 61), Parkinson disease (n = 45), and spinal cord injury (n = 2). 112 meta-analyses (70%) found nominally (p≤0.05) statistically-significant summary fixed effects. Assuming the effect size in the most precise study to be a plausible effect, 919⁄4,445 nominally statistically-significant results were expected versus 1,719 observed (p<10−9). Excess statistical-significance was present across all neurological disorders, in all subgroups defined by methodological characteristics, and also according to alternative plausible effects. Asymmetry tests also showed evidence of small-study effects in 74 (46%) meta-analyses. effective interventions with more than 500 animals, and no hints of bias were seen in eight (5%) meta-analyses. Overall, there are too many animal studies with statistically-significant results in the literature of neurological disorders. This observation suggests strong biases, with selective analysis and outcome reporting biases being plausible explanations, and provides novel evidence on how these biases might influence the whole research domain of neurological animal literature.

Author Summary: Studies have shown that the results of animal biomedical experiments fail to translate into human clinical trials; this could be attributed either to real differences in the underlying biology between humans and animals, to shortcomings in the experimental design, or to bias in the reporting of results from the animal studies. We use a statistical technique to evaluate whether the number of published animal studies with “positive” (statistically-significant) results is too large to be true. We assess 4,445 animal studies for 160 candidate treatments of neurological disorders, and observe that 1,719 of them have a “positive” result, whereas only 919 studies would a priori be expected to have such a result. According to our methodology, only eight of the 160 evaluated treatments should have been subsequently tested in humans. In summary, we judge that there are too many animal studies with “positive” results in the neurological disorder literature, and we discuss the reasons and potential remedies for this phenomenon.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0063221 A Survey on Data Reproducibility in Cancer Research Provides Insights into Our Limited Ability to Translate Findings from the Laboratory to the Clinic Aaron Mobley, Suzanne K. Linder, Russell Braeuer, Lee M. Ellis, Leonard Zwelling 2013-03-29 2021-07-27 [("doi","10.1371/journal.pone.0063221")] statistics/bias

Background: The pharmaceutical and biotechnology industries depend on findings from academic investigators prior to initiating programs to develop new diagnostic and therapeutic agents to benefit cancer patients. The success of these programs depends on the validity of published findings. This validity, represented by the reproducibility of published findings, has come into question recently as investigators from companies have raised the issue of poor reproducibility of published results from academic laboratories. Furthermore, retraction rates in high impact journals are climbing.

Methods and Findings: To examine a microcosm of the academic experience with data reproducibility, we surveyed the faculty and trainees at MD Anderson Cancer Center using an anonymous computerized questionnaire; we sought to ascertain the frequency and potential causes of non-reproducible data. We found that ~50% of respondents had experienced at least one episode of the inability to reproduce published data; many who pursued this issue with the original authors were never able to identify the reason for the lack of reproducibility; some were even met with a less than “collegial” interaction.

Conclusion: These results suggest that the problem of data reproducibility is real. Biomedical science needs to establish processes to decrease the problem and adjudicate discrepancies in findings when they are discovered.

--- /doc/economics/copyright/2021-mezzanotti.pdf Roadblock to Innovation: The Role of Patent Litigation in Corporate R&D Filippo Mezzanotti 2021-02-02 2021-07-27 [("doi","10.1287/mnsc.2020.3816")] economics/copyright law

I examine how patent enforcement affects corporate research and development (R&D), exploiting the legal changes induced by the Supreme Court decision eBay v. MercExchange. This ruling increased courts’ flexibility in remedying patent cases and effectively lowered the potential costs of patent litigation for defendants.

For identification, I compare innovative activity across firms differentially exposed to patent litigation before the ruling.

Across several measures, I find that the decision led to a general increase in innovation. This result confirms that the changes in enforcement induced by the ruling reduced some of the distortions caused by patent litigation.

Exploring the channels, I show that patent litigation negatively affects investment because it lowers the returns from R&D and exacerbates its financing constraints.

--- https://arxiv.org/abs/1601.00900 Too good to be true: when overwhelming evidence fails to convince Lachlan J. Gunn, François Chapeau-Blondeau, Mark McDonnell, Bruce Davis, Andrew Allison, Derek Abbott 2016-01-05 2021-07-27 [("doi","10.1098/rspa.2015.0748")] law math

Is it possible for a large sequence of measurements or observations, which support a hypothesis, to counterintuitively decrease our confidence? Can unanimous support be too good to be true? The assumption of independence is often made in good faith, however rarely is consideration given to whether a systemic failure has occurred.

Taking this into account can cause certainty in a hypothesis to decrease as the evidence for it becomes apparently stronger. We perform a probabilistic Bayesian analysis of this effect with examples based on (1) archaeological evidence, (2) weighing of legal evidence, and (3) cryptographic primality testing.

We find that even with surprisingly low systemic failure rates high confidence is very difficult to achieve and in particular we find that certain analyses of cryptographically-important numerical tests are highly optimistic, underestimating their false-negative rate by as much as a factor of 280.

--- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.708.3217 2021-07-27 law --- /doc/economics/2001-macfarquhar.pdf The Bench Burner: How did a judge with such subversive ideas become a leading influence on American legal opinion? Larissa MacFarquhar 2001-01-01 2021-07-27 economics law --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3557905/ RNA-programmed genome editing in human cells Martin Jinek, Alexandra East, Aaron Cheng, Steven Lin, Enbo Ma, Jennifer Doudna 2013 2021-07-27 [("doi","10.7554/eLife.00471")] genetics/editing

Type II CRISPR immune systems in bacteria use a dual RNA-guided DNA endonuclease, Cas9, to cleave foreign DNA at specific sites.

We show here that Cas9 assembles with hybrid guide RNAs in human cells and can induce the formation of double-strand DNA breaks (DSBs) at a site complementary to the guide RNA sequence in genomic DNA. This cleavage activity requires both Cas9 and the complementary binding of the guide RNA. Experiments using extracts from transfected cells show that RNA expression and/or assembly into Cas9 is the limiting factor for Cas9-mediated DNA cleavage. In addition, we find that extension of the RNA sequence at the 3’ end enhances DNA targeting activity in vivo.

These results show that RNA-programmed genome editing is a facile strategy for introducing site-specific genetic changes in human cells.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3686313/ Efficient genome editing in zebrafish using a CRISPR-Cas system Woong Y. Hwang, Yanfang Fu, Deepak Reyon, Morgan L. Maeder, Shengdar Q. Tsai, Jeffry D. Sander, Randall T. Peterson, J-R Joanna Yeh, J. Keith Joung 2013 2021-07-27 [("doi","10.1038/nbt.2501")] genetics/editing

In bacteria, foreign nucleic acids are silenced by clustered, regularly interspaced, short palindromic repeats (CRISPR)-CRISPR-associated (Cas) systems.

Bacterial type II CRISPR systems have been adapted to create guide RNAs that direct site-specific DNA cleavage by the Cas9 endonuclease in cultured cells.

Here we show that the CRISPR-Cas system functions in vivo to induce targeted genetic modifications in zebrafish embryos with efficiencies similar to those obtained using zinc finger nucleases and transcription activator-like effector nucleases.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3712628/ RNA-guided human genome engineering via Cas9 Prashant Mali, Luhan Yang, Kevin M. Esvelt, John Aach, Marc Guell, James E. DiCarlo, Julie E. Norville, George M. Church 2013 2021-07-28 [("doi","10.1126/science.1232033")] genetics/editing

Bacteria and Archaea have evolved adaptive immune defenses, termed clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) systems, that use short RNA to direct degradation of foreign nucleic acids.

Here, we engineer the type II bacterial CRISPR system to function with custom guide RNA (gRNA) in human cells. For the endogenous AAVS1 locus, we obtained targeting rates of 10 to 25% in 293T cells, 13 to 8% in K562 cells, and 2 to 4% in induced pluripotent stem cells.

We show that this process relies on CRISPR components; is sequence-specific; and, upon simultaneous introduction of multiple gRNAs, can effect multiplex editing of target loci.

We also compute a genome-wide resource of ~190 K unique gRNAs targeting ~40.5% of human exons.

Our results establish an RNA-guided editing tool for facile, robust, and multiplexable human genome engineering.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3621546/ The impact of iodine supplementation and bread fortification on urinary iodine concentrations in a mildly iodine deficient population of pregnant women in South Australia Vicki L. Clifton, Nicolette A. Hodyl, Paul A. Fogarty, David J. Torpy, Rachel Roberts, Ted Nettelbeck, Gary Ma, Basil Hetzel 2013 2021-07-28 [("doi","10.1186/1475-2891-12-32")] iodine

Mild iodine deficiency during pregnancy can have statistically-significant effects on fetal development and future cognitive function. The purpose of this study was to characterize the iodine status of South Australian women during pregnancy and relate it to the use of iodine-containing multivitamins. The impact of fortification of bread with iodized salt was also assessed.

Women (n = 196) were recruited prospectively at the beginning of pregnancy and urine collected at 12, 18, 30, 36 weeks gestation and 6 months postpartum. The use of a multivitamin supplement was recorded at each visit. Spot urinary iodine concentrations (UIC) were assessed. Median UICs were within the mildly deficient range in women not taking supplements (<90 μg/L). Among the women taking iodine-containing multivitamins UICs were within WHO recommendations (150–249 μg/L) for sufficiency and showed an increasing trend through gestation.

The fortification of bread with iodized salt increased the median UIC from 68 μg/L to 84 μg/L (p = 0.011) which was still in the deficient range. Pregnant women in this region of Australia were unlikely to reach recommended iodine levels without an iodine supplement, even after the mandatory iodine supplementation of bread was instituted in October 2009.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3630391/ Serotonin modulation of cortical neurons and networks Pau Celada, M. Victoria Puig, Francesc Artigas 2013 2021-07-28 [("doi","10.3389/fnint.2013.00025")] psychology/neuroscience

The serotonergic pathways originating in the dorsal and median raphe nuclei (DR and MnR, respectively) are critically involved in cortical function. Serotonin (5-HT), acting on postsynaptic and presynaptic receptors, is involved in cognition, mood, impulse control and motor functions by (1) modulating the activity of different neuronal types, and (2) varying the release of other neurotransmitters, such as glutamate, GABA, acetylcholine and dopamine. Also, 5-HT seems to play an important role in cortical development.

Of all cortical regions, the frontal lobe is the area most enriched in serotonergic axons and 5-HT receptors. 5-HT and selective receptor agonists modulate the excitability of cortical neurons and their discharge rate through the activation of several receptor subtypes, of which the 5-HT1A, 5-HT1B, 5-HT2A, and 5-HT3 subtypes play a major role. Little is known, however, on the role of other excitatory receptors moderately expressed in cortical areas, such as 5-HT2C, 5-HT4, 5-HT6, and 5-HT7.

In vitro and in vivo studies suggest that 5-HT1A and 5-HT2A receptors are key players and exert opposite effects on the activity of pyramidal neurons in the medial prefrontal cortex (mPFC). The activation of 5-HT1A receptors in mPFC hyper-polarizes pyramidal neurons whereas that of 5-HT2A receptors results in neuronal depolarization, reduction of the after-hyper-polarization and increase of excitatory postsynaptic currents (EPSCs) and of discharge rate. 5-HT can also stimulate excitatory (5-HT2A and 5-HT3) and inhibitory (5-HT1A) receptors in GABA interneurons to modulate synaptic GABA inputs onto pyramidal neurons. Likewise, the pharmacological manipulation of various 5-HT receptors alters oscillatory activity in PFC, suggesting that 5-HT is also involved in the control of cortical network activity.

A better understanding of the actions of 5-HT in PFC may help to develop treatments for mood and cognitive disorders associated with an abnormal function of the frontal lobe.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3662346/ Cognition assessment using the NIH Toolbox Sandra Weintraub, Sureyya S. Dikmen, Robert K. Heaton, David S. Tulsky, Philip D. Zelazo, Patricia J. Bauer, Noelle E. Carlozzi, Jerry Slotkin, David Blitz, Kathleen Wallner-Allen, Nathan A. Fox, Jennifer L. Beaumont, Dan Mungas, Cindy J. Nowinski, Jennifer Richler, Joanne A. Deocampo, Jacob E. Anderson, Jennifer J. Manly, Beth Borosh, Richard Havlik, Kevin Conway, Emmeline Edwards, Lisa Freund, Jonathan W. King, Claudia Moy, Ellen Witt, Richard C. Gershon 2013 2021-07-28 [("doi","10.1212/WNL.0b013e3182872ded")] iq psychology/neuroscience

Cognition is one of 4 domains measured by the NIH Toolbox for the Assessment of Neurological and Behavioral Function (NIH-TB), and complements modules testing motor function, sensation, and emotion. On the basis of expert panels, the cognition subdomains identified as most important for health, success in school and work, and independence in daily functioning were Executive Function, Episodic Memory, Language, Processing Speed, Working Memory, and Attention. 7 measures were designed to tap constructs within these subdomains.

The instruments were validated in English, in a sample of 476 participants ranging in age 3–85 years, with representation from both sexes, 3 racial/ethnic categories, and 3 levels of education.

This report describes the development of the Cognition Battery and presents results on test-retest reliability, age effects on performance, and convergent and discriminant construct validity.

The NIH-TB Cognition Battery is intended to serve as a brief, convenient set of measures to supplement other outcome measures in epidemiologic and longitudinal research and clinical trials. With a computerized format and national standardization, this battery will provide a “common currency” among researchers for comparisons across a wide range of studies and populations.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3739474/ Fear and panic in humans with bilateral amygdala damage Justin S. Feinstein, Colin Buzza, Rene Hurlemann, Robin L. Follmer, Nader S. Dahdaleh, William H. Coryell, Michael J. Welsh, Daniel Tranel, John A. Wemmie 2013 2021-07-28 [("doi","10.1038/nn.3323")] psychiatry psychology/neuroscience/pain

Decades of research have highlighted the amygdala’s influential role in fear.

We found that inhalation of 35% CO2 evoked not only fear, but also panic attacks, in 3 rare patients with bilateral amygdala damage.

These results indicate that the amygdala is not required for fear and panic, and make an important distinction between fear triggered by external threats from the environment versus fear triggered internally by CO2.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3758887/ Strengthened effective connectivity underlies transfer of working memory training to tests of short-term memory and attention Bornali Kundu, David W. Sutterer, Stephen M. Emrich, Bradley R. Postle 2013 2021-07-28 [("doi","10.1523/JNEUROSCI.5565-12.2013")] dual-n-back psychology/neuroscience

Although long considered a natively endowed and fixed trait, working memory (WM) ability has recently been shown to improve with intensive training. What remains controversial and poorly understood, however, are the neural bases of these training effects and the extent to which WM training gains transfer to other cognitive tasks.

Here we present evidence from human electrophysiology (EEG) and simultaneous transcranial magnetic stimulation and EEG that the transfer of WM training to other cognitive tasks is supported by changes in task-related effective connectivity in frontoparietal and parieto-occipital networks that are engaged by both the trained and transfer tasks. One consequence of this effect is greater efficiency of stimulus processing, as evidenced by changes in EEG indices of individual differences in short-term memory capacity and in visual search performance.

Transfer to search-related activity provides evidence that something more fundamental than task-specific strategy or stimulus-specific representations has been learned. Furthermore, these patterns of training and transfer highlight the role of common neural systems in determining individual differences in aspects of visuospatial cognition.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3651246/ Population trends and variation in body mass index 1971–2008 in the Framingham Heart Study Offspring Cohort Jason P. Block, S. V. Subramanian, Nicholas A. Christakis, A. James O’Malley 2013 2021-07-28 [("doi","10.1371/journal.pone.0063217")] exercise

Objective: We examined body mass index (BMI) across place and time to determine the pattern of BMI mean and standard deviation trajectories.

Method: We included participants in the Framingham Heart Study (FHS) Offspring Cohort over eight waves of follow-up, from 1971–2008. After exclusions, the final sample size was 4569 subjects with 28,625 observations. We used multi-level models to examine population means and variation at the individual and neighborhood (census tracts) levels across time with measured BMI as the outcome, controlling for individual demographics and behaviors and neighborhood poverty. Because neighborhoods accounted for limited BMI variance, we removed this level as a source of variation in final models. We examined sex-stratified models with all subjects and models stratified by sex and baseline weight classification.

Results: Mean BMI increased from 24.0 kg/m(2) at Wave 1 to 27.7 at Wave 8 for women and from 26.6 kg/m(2) to 29.0 for men. In final models, BMI variation also increased from Waves 1 to 8, with the standard deviation increasing from 4.18 kg/m(2) to 6.15 for women and 3.31 kg/m(2) to 4.73 for men. BMI means increased in parallel across most baseline BMI weight classifications, except for more rapid increases through middle-age for obese women followed by declines in the last wave. BMI standard deviations also increased in parallel across baseline BMI classifications for women, with greater divergence of BMI variance for obese men compared to other weight classifications.

Conclusion: Over nearly 40 years, BMI mean and variation increased in parallel across most baseline weight classifications in our sample. Individual-level characteristics, especially baseline BMI, were the primary factors in rising BMI. These findings have important implications not only for understanding the sources of the obesity epidemic in the United States but also for the targeting of interventions to address the epidemic.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3727016/ Selectively bred rat model system for low and high response to exercise training Lauren Gerard Koch, Geoffrey E. Pollott, Steven L. Britton 2013 2021-07-28 [("doi","10.1152/physiolgenomics.00021.2013")] exercise genetics/selection/artificial

We initiated a large-scale bidirectional selection experiment in a genetically heterogeneous rat population (N/NIH stock, n = 152) to develop lines of low response trainers (LRT) and high response trainers (HRT) as a contrasting animal model system.

Maximal treadmill running distance [meters (m)] was tested before (DIST1) and after (DIST2) standardized aerobic treadmill training over an 8 week period (3 exercise sessions per week). Response to training was calculated as the change in exercise capacity (ΔDIST = DIST2—DIST1). A within-family selection and rotational breeding paradigm between 10 families was practiced for both selected lines.

For the founder population, exercise training produced a 140 ± 15m gain in exercise capacity with interindividual variation ranging from −339 to +627m. After 15 generations of selection (n = 3,114 rats), HRT rats improved 223 ± 20m as a result of exercise training while exercise capacity declined −65 ± 15m in LRT rats given the same absolute training environment.

The narrow-sense heritability (h2) for ΔDIST was 0.10 ± 0.02. The LRT and HRT lines did not differ for body weight or intrinsic (ie. DIST1) exercise capacity. Using pedigree records the inbreeding coefficient increased at a rate of 1.7% per generation for HRT and 1.6% per generation for LRT, ~30% slower than expected from random mating.

Animal models developed from heterogeneous stock and enriched via selection, as presented here, often generate extreme values for traits of interest and may prove more useful than current models for uncovering genetic underpinnings.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3652710/ Common DNA markers can account for more than half of the genetic influence on cognitive abilities Robert Plomin, Claire M. A. Haworth, Emma L. Meaburn, Thomas S. Price, Oliver S. P. Davis 2013 2021-07-28 [("doi","10.1177/0956797612457952")] genetics/heritable iq

For nearly a century, twin and adoption studies have yielded substantial estimates of heritability for cognitive abilities, although it has proved difficult for genome-wide-association studies to identify the genetic variants that account for this heritability (ie. the missing-heritability problem). However, a new approach, genome-wide complex-trait analysis (GCTA), forgoes the identification of individual variants to estimate the total heritability captured by common DNA markers on genotyping arrays.

In the same sample of 3,154 pairs of 12-year-old twins, we directly compared twin-study heritability estimates for cognitive abilities (language, verbal, nonverbal, and general) with GCTA estimates captured by 1.7 million DNA markers.

We found that DNA markers tagged by the array accounted for 0.66 of the estimated heritability, reaffirming that cognitive abilities are heritable.

Larger sample sizes alone will be sufficient to identify many of the genetic variants that influence cognitive abilities.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3679547/ Large-scale association analysis identifies new risk loci for coronary artery disease Panos Deloukas, Stavroula Kanoni, Christina Willenborg, Martin Farrall, Themistocles L. Assimes, John R. Thompson, Erik Ingelsson, Danish Saleheen, Jeanette Erdmann, Benjamin A. Goldstein, Kathleen Stirrups, Inke R. König, Jean-Baptiste Cazier, Asa Johansson, Alistair S. Hall, Jong-Young Lee, Cristen Jennifer Willer, John C. Chambers, Tõnu Esko, Lasse Folkersen, Anuj Goel, Elin Grundberg, Aki S. Havulinna, Weang K. Ho, Jemma C. Hopewell, Niclas Eriksson, Marcus E. Kleber, Kati Kristiansson, Per Lundmark, Leo-Pekka Lyytikäinen, Suzanne Rafelt, Dmitry Shungin, Rona J. Strawbridge, Gudmar Thorleifsson, Emmi Tikkanen, Natalie Van Zuydam, Benjamin F. Voight, Lindsay L. Waite, Weihua Zhang, Andreas Ziegler, Devin Absher, David Altshuler, Anthony J. Balmforth, Inês Barroso, Peter S. Braund, Christof Burgdorf, Simone Claudi-Boehm, David Cox, Maria Dimitriou, Ron Do, Alex S. F. Doney, Nour Eddine El Mokhtari, Per Eriksson, Krista Fischer, Pierre Fontanillas, Anders Franco-Cereceda, Bruna Gigante, Leif Groop, Stefan Gustafsson, Jörg Hager, Göran Hallmans, Bok-Ghee Han, Sarah E. Hunt, Hyun M. Kang, Thomas Illig, Thorsten Kessler, Joshua W. Knowles, Genovefa Kolovou, Johanna Kuusisto, Claudia Langenberg, Cordelia Langford, Karin Leander, Marja-Liisa Lokki, Anders Lundmark, Mark I. McCarthy, Christa Meisinger, Olle Melander, Evelin Mihailov, Seraya Maouche, Andrew D. Morris, Martina Müller-Nurasyid, Kjell Nikus, John F. Peden, N. William Rayner, Asif Rasheed, Silke Rosinger, Diana Rubin, Moritz P. Rumpf, Arne Schäfer, Mohan Sivananthan, Ci Song, Alexandre F. R. Stewart, Sian-Tsung Tan, Gudmundur Thorgeirsson, C. Ellen van der Schoot, Peter J. Wagner, George A. Wells, Philipp S. Wild, Tsun-Po Yang, Philippe Amouyel, Dominique Arveiler, Hanneke Basart, Michael Boehnke, Eric Boerwinkle, Paolo Brambilla, Francois Cambien, Adrienne L. Cupples, Ulf de Faire, Abbas Dehghan, Patrick Diemert, Stephen E. Epstein, Alun Evans, Marco M. Ferrario, Jean Ferrières, Dominique Gauguier, Alan S. Go, Alison H. Goodall, Villi Gudnason, Stanley L. Hazen, Hilma Holm, Carlos Iribarren, Yangsoo Jang, Kähönen Mika, Frank Kee, Hyo-Soo Kim, Norman Klopp, Wolfgang Koenig, Wolfgang Kratzer, Kari Kuulasmaa, Markku Laakso, Reijo Laaksonen, Ji-Young Lee, Lars L. Lind, Willem H. Ouwehand, Sarah Parish, Jeong E. Park, Nancy L. Pedersen, Annette Peters, Thomas Quertermous, Daniel J. Rader, Veikko Salomaa, Eric Schadt, Svati H. Shah, Juha Sinisalo, Klaus Stark, Kari Stefansson, David-Alexandre Trégouët, Jarmo Virtamo, Lars Wallentin, Nicholas Wareham, Martina E. Zimmermann, Markku S. Nieminen, Christian Hengstenberg, Manjinder S. Sandhu, Tomi Pastinen, Ann-Christine Syvänen, G. Kees Hovingh, George Dedoussis, Paul W. Franks, Terho Lehtimäki, Andres Metspalu, Pierre A. Zalloua, Agneta Siegbahn, Stefan Schreiber, Samuli Ripatti, Stefan S. Blankenberg, Markus Perola, Robert Clarke, Bernhard O. Boehm, Christopher O’Donnell, Muredach P. Reilly, Winfried März, Rory Collins, Sekar Kathiresan, Anders Hamsten, Jaspal S. Kooner, Unnur Thorsteinsdottir, John Danesh, Colin Palmer, Robert Roberts, Hugh Watkins, Heribert Schunkert, Nilesh J. Samani 2013 2021-07-28 [("doi","10.1038/ng.2480")] genetics/heritable

Coronary artery disease (CAD) is the commonest cause of death. Here, we report an association analysis in 63,746 CAD cases and 130,681 controls identifying 15 loci reaching genome-wide statistical-significance, taking the number of susceptibility loci for CAD to 46, and a further 104 independent variants (r(2) < 0.2) strongly associated with CAD at a 5% false discovery rate (FDR). Together, these variants explain ~10.6% of CAD heritability.

Of the 46 genome-wide statistically-significant lead SNPs, 12 show a statistically-significant association with a lipid trait, and 5 show a statistically-significant association with blood pressure, but none is statistically-significantly associated with diabetes.

Network analysis with 233 candidate genes (loci at 10% FDR) generated 5 interaction networks comprising 85% of these putative genes involved in CAD. The 4 most pathways mapping to these networks are linked to lipid metabolism and inflammation, underscoring the causal role of these activities in the genetic etiology of CAD.

Our study provides insights into the genetic basis of CAD and identifies key biological pathways.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694490/ A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry Keri L. Monda, Gary K. Chen, Kira C. Taylor, Cameron Palmer, Todd L. Edwards, Leslie A. Lange, Maggie C. Y. Ng, Adebowale Adeyemo, Matthew A. Allison, Lawrence F. Bielak, Guanjie Chen, Mariaelisa Graff, Marguerite R. Irvin, Suhn K. Rhie, Guo Li, Yongmei Liu, Youfang Liu, Yingchang Lu, Michael A. Nalls, Yan V. Sun, Mary K. Wojczynski, Lisa R. Yanek, Melinda C. Aldrich, Adeyinka Ademola, Christopher I. Amos, Elisa V. Bandera, Cathryn H. Bock, Angela Britton, Ulrich Broeckel, Quiyin Cai, Neil E. Caporaso, Chris S. Carlson, John Carpten, Graham Casey, Wei-Min Chen, Fang Chen, Yii-Der I. Chen, Charleston W. K. Chiang, Gerhard A. Coetzee, Ellen Demerath, Sandra L. Deming-Halverson, Ryan W. Driver, Patricia Dubbert, Mary F. Feitosa, Ye Feng, Barry I. Freedman, Elizabeth M. Gillanders, Omri Gottesman, Xiuqing Guo, Talin Haritunians, Tamara B. Harris, Curtis C. Harris, Anselm J. M. Hennis, Dena G. Hernandez, Lorna H. McNeill, Timothy D. Howard, Barbara V. Howard, Virginia J. Howard, Karen C. Johnson, Sun J. Kang, Brendan J. Keating, Suzanne Kolb, Lewis H. Kuller, Abdullah Kutlar, Carl D. Langefeld, Guillaume Lettre, Kurt Lohman, Vaneet Lotay, Helen Lyon, Joann E. Manson, William Maixner, Yan A. Meng, Kristine R. Monroe, Imran Morhason-Bello, Adam B. Murphy, Josyf C. Mychaleckyj, Rajiv Nadukuru, Katherine L. Nathanson, Uma Nayak, Amidou N’diaye, Barbara Nemesure, Suh-Yuh Wu, M. Cristina Leske, Christine Neslund-Dudas, Marian Neuhouser, Sarah Nyante, Heather Ochs-Balcom, Adesola Ogunniyi, Temidayo O. Ogundiran, Oladosu Ojengbede, Olufunmilayo I. Olopade, Julie R. Palmer, Edward A. Ruiz-Narvaez, Nicholette D. Palmer, Michael F. Press, Evandine Rampersaud, Laura J. Rasmussen-Torvik, Jorge L. Rodriguez-Gil, Babatunde Salako, Eric E. Schadt, Ann G. Schwartz, Daniel A. Shriner, David Siscovick, Shad B. Smith, Sylvia Wassertheil-Smoller, Elizabeth K. Speliotes, Margaret R. Spitz, Lara Sucheston, Herman Taylor, Bamidele O. Tayo, Margaret A. Tucker, David J. Van Den Berg, Digna R. Velez Edwards, Zhaoming Wang, John K. Wiencke, Thomas W. Winkler, John S. Witte, Margaret Wrensch, Xifeng Wu, James J. Yang, Albert M. Levin, Taylor R. Young, Neil A. Zakai, Mary Cushman, Krista A. Zanetti, Jing Hua Zhao, Wei Zhao, Yonglan Zheng, Jie Zhou, Regina G. Ziegler, Joseph M. Zmuda, Jyotika K. Fernandes, Gary S. Gilkeson, Diane L. Kamen, Kelly J. Hunt, Ida J. Spruill, Christine B. Ambrosone, Stefan Ambs, Donna K. Arnett, Larry Atwood, Diane M. Becker, Sonja I. Berndt, Leslie Bernstein, William J. Blot, Ingrid B. Borecki, Erwin Böttinger, Donald W. Bowden, Gregory Burke, Stephen J. Chanock, Richard S. Cooper, Jingzhong Ding, David Duggan, Michele K. Evans, Caroline Fox, W. Timothy Garvey, Jonathan P. Bradfield, Hakon Hakonarson, Struan F. A. Grant, Ann Hsing, Lisa Chu, Jennifer J. Hu, Dezheng Huo, Sue A. Ingles, Esther M. John, Joanne M. Jordan, Edmond K. Kabagambe, Sharon L. R. Kardia, Rick A. Kittles, Phyllis J. Goodman, Eric A. Klein, Laurence N. Kolonel, Loic Le Marchand, Simin Liu, Barbara McKnight, Robert C. Millikan, Thomas H. Mosley, Badri Padhukasahasram, L. Keoki Williams, Sanjay R. Patel, Ulrike Peters, Curtis A. Pettaway, Patricia A. Peyser, Bruce M. Psaty, Susan Redline, Charles N. Rotimi, Benjamin A. Rybicki, Michèle M. Sale, Pamela J. Schreiner, Lisa B. Signorello, Andrew B. Singleton, Janet L. Stanford, Sara S. Strom, Michael J. Thun, Mara Vitolins, Wei Zheng, Jason H. Moore, Scott M. Williams, Shamika Ketkar, Xiaofeng Zhu, Alan B. Zonderman, Charles Kooperberg, George J. Papanicolaou, Brian E. Henderson, Alex P. Reiner, Joel N. Hirschhorn, Ruth Loos, Kari E. North, Christopher A. Haiman 2013 2021-07-29 [("doi","10.1038/ng.2608")] genetics/heritable

Genome-wide association studies (GWAS) have identified 36 loci associated with body mass index (BMI), predominantly in populations of European ancestry. We conducted a meta-analysis to examine the association of >3.2 million SNPs with BMI in 39,144 men and women of African ancestry and followed up the most statistically-significant associations in an additional 32,268 individuals of African ancestry.

We identified one new locus at 5q33 (GALNT10, rs7708584, p = 3.4 × 10−11) and another at 7p15 when we included data from the GIANT consortium (MIR148A-NFE2L3, rs10261878, p = 1.2 × 10−10). We also found suggestive evidence of an association at a third locus at 6q16 in the African-ancestry sample (KLHL32, rs974417, p = 6.9 × 10−8).

Thirty-two of the 36 previously established BMI variants showed directionally consistent effect estimates in our GWAS (binomial p = 9.7 × 10−7), 5 of which reached genome-wide statistical-significance. These findings provide strong support for shared BMI loci across populations, as well as for the utility of studying ancestrally diverse populations.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3778076/ Critical need for family-based, quasi-experimental designs in integrating genetic and social science research Brian M. D’Onofrio, Benjamin B. Lahey, Eric Turkheimer, Paul Lichtenstein 2013 2021-07-29 [("doi","10.2105/AJPH.2013.301252")] genetics/heritable sociology

Researchers have identified environmental risks that predict subsequent psychological and medical problems.

Based on these correlational findings, researchers have developed and tested complex developmental models and have examined biological moderating factors (eg. gene-environment interactions).

In this context, we stress the critical need for researchers to use family-based, quasi-experimental designs when trying to integrate genetic and social science research involving environmental variables because these designs rigorously examine causal inferences by testing competing hypotheses.

We argue that sibling comparison, offspring of twins or siblings, in vitro fertilization designs, and other genetically informed approaches play a unique role in bridging gaps between basic biological and social science research.

We use studies on maternal smoking during pregnancy to exemplify these principles.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3790571/ Look back in anger—what clinical studies tell us about preclinical work Thomas Hartung 2013 2021-07-29 [("doi","10.14573/altex.2013.3.275")] statistics/bias/animal

Unlabeled: Misled by animal studies and basic research? Whenever we take a closer look at the outcome of clinical trials in a field such as, most recently, stroke or septic shock, we see how limited the value of our preclinical models was. For all indications, 95% of drugs that enter clinical trials do not make it to the market, despite all promise of the (animal) models used to develop them. Drug development has started already to decrease its reliance on animal models: In Europe, for example, despite increasing R&D expenditure, animal use by pharmaceutical companies dropped by more than 25% 2005–2008. In vitro studies are likewise limited: questionable cell authenticity, over-passaging, Mycoplasma infections, and lack of differentiation as well as non-homeostatic and non-physiologic culture conditions endanger the relevance of these models. The standards of statistics and reporting often are poor, further impairing reliability. Alarming studies from industry show miserable reproducibility of landmark studies. This paper discusses factors contributing to the lack of reproducibility and relevance of pre-clinical research.

The Conclusion: Publish less but of better quality and do not rely on the face value of animal studies.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3655221/pdf/11325_2012_Article_757.pdf Assessment of a wireless headband for automatic sleep scoring H. Griessenberger, D. P. J. Heib, A. B. Kunz, K. Hoedlmoser, M. Schabus 2013 2021-07-29 [("doi","10.1007/s11325-012-0757-4")] zeo

Purpose: Classically, professional assessment of sleep is done in the sleep laboratory using whole-night polysomnography (PSG). However, given an imbalance between accredited sleep laboratories and the large amount of patients suffering from sleep disorders, only few receive appropriate diagnostic assessment. Recently, some low-cost home sleep scoring systems have been proposed, yet such systems are rarely tested scientifically. The aim of the present study was to evaluate the staging accuracy of the home sleep scoring system Zeo (Newton, MA, USA).

Method: A final sample of 21 nights from ten subjects (aged 23–45) was digitally recorded with PSG as well as with the Zeo system. We compared scorings of Zeo (on an epoch-be-epoch basis) with the Somnolyzer 24 × 7 (an automatic staging algorithm), expert scorers as well as the freeware SleepExplorer.

Results: It was revealed that Zeo shows moderate overall agreement as compared to our study standard Somnolyzer 24 × 7 (κ = 0.56). The most obvious performance difference between Zeo and both other scoring approaches was stage wake (sleep onset latency + wake after sleep onset). While Zeo detected only 40.8% of the study standard wake epochs, 70.1% were detected by the expert scorers and 83.4% by the SleepExplorer, respectively.

Conclusion: Data suggest that the Zeo system produces acceptable sleep scoring for stage REM, light and deep sleep, with a specific weakness in correctly detecting waking periods.

--- /doc/genetics/selection/natural/human/2012-fu.pdf Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants Wenqing Fu, Timothy D. O’Connor, Goo Jun, Hyun Min Kang, Gonçalo Abecasis, Suzanne M. Leal, Stacey Gabriel, Mark J. Rieder, David Altshuler, Jay Shendure, Deborah A. Nickerson, Michael J. Bamshad, Joshua M. Akey 2013 2021-07-29 [("doi","10.1038/nature11690")] genetics/heritable/rare genetics/selection/natural/human

Establishing the age of each mutation segregating in contemporary human populations is important to fully understand our evolutionary history and will help to facilitate the development of new approaches for disease-gene discovery. Large-scale surveys of human genetic variation have reported signatures of recent explosive population growth, notable for an excess of rare genetic variants, suggesting that many mutations arose recently.

To more quantitatively assess the distribution of mutation ages, we resequenced 15,336 genes in 6,515 individuals of European American and African American ancestry and inferred the age of 1,146,401 autosomal single-nucleotide variants (SNVs). We estimate that ~73% of all protein-coding SNVs and ~86% of SNVs predicted to be deleterious arose in the past 5,000–10,000 years. The average age of deleterious SNVs varied across molecular pathways, and disease genes contained a higher proportion of recently arisen deleterious SNVs than other genes.

Furthermore, European Americans had an excess of deleterious variants in essential and Mendelian disease genes compared to African Americans, consistent with weaker purifying selection due to the Out-of-Africa dispersal. Our results better delimit the historical details of human protein-coding variation, show the profound effect of recent human history on the burden of deleterious SNVs segregating in contemporary populations, and provide important practical information that can be used to prioritize variants in disease-gene discovery.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3821942/ The benefits and risks of consuming brewed tea: beware of toxic element contamination Gerry Schwalfenberg, Stephen J. Genuis, Ilia Rodushkin 2013 2021-07-29 [("doi","10.1155/2013/370460")] tea

Background: Increasing concern is evident about contamination of foodstuffs and natural health products.

Method: Common off-the-shelf varieties of black, green, white, and oolong teas sold in tea bags were used for analysis in this study. Toxic element testing was performed on 30 different teas by analyzing (1) tea leaves, (2) tea steeped for 3–4 minutes, and (3) tea steeped for 15–17 minutes. Results were compared to existing preferred endpoints. Results. All brewed teas contained lead with 73% of teas brewed for 3 minutes and 83% brewed for 15 minutes having lead levels considered unsafe for consumption during pregnancy and lactation. Aluminum levels were above recommended guidelines in 20% of brewed teas. No mercury was found at detectable levels in any brewed tea samples. Teas contained several beneficial elements such as magnesium, calcium, potassium, and phosphorus. Of trace minerals, only manganese levels were found to be excessive in some black teas. Conclusions. Toxic contamination by heavy metals was found in most of the teas sampled. Some tea samples are considered unsafe. There are no existing guidelines for routine testing or reporting of toxicant levels in “naturally” occurring products. Public health warnings or industry regulation might be indicated to protect consumer safety.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0023175 Working Memory Training Using Mental Calculation Impacts Regional Gray Matter of the Frontal and Parietal Regions Hikaru Takeuchi, Yasuyuki Taki, Yuko Sassa, Hiroshi Hashizume, Atsushi Sekiguchi, Ai Fukushima, Ryuta Kawashima 2011-07-11 2021-07-29 [("doi","10.1371/journal.pone.0023175")] dual-n-back psychology/neuroscience

Training working memory (WM) improves performance on untrained cognitive tasks and alters functional activity. However, WM training’s effects on gray matter morphology and a wide range of cognitive tasks are still unknown.

We investigated this issue using voxel-based morphometry (VBM), various psychological measures, such as non-trained WM tasks and a creativity task, and intensive adaptive training of WM using mental calculations (IATWMMC), all of which are typical WM tasks.

IATWMMC was associated with reduced regional gray matter volume in the bilateral fronto-parietal regions and the left superior temporal gyrus. It improved verbal letter span and complex arithmetic ability, but deteriorated creativity.

These results confirm the training-induced plasticity in psychological mechanisms and the plasticity of gray matter structures in regions that have been assumed to be under strong genetic control.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2938736/ Blood vitamin d levels in relation to genetic estimation of African ancestry Lisa B. Signorello, Scott M. Williams, Wei Zheng, Jeffrey R. Smith, Jirong Long, Qiuyin Cai, Margaret K. Hargreaves, Bruce W. Hollis, William J. Blot 2010 2021-07-29 [("doi","10.1158/1055-9965.EPI-10-0482")] genetics/heritable vitamin-d

Background: African-Americans generally have lower circulating levels of 25 hydroxyvitamin D [25(OH)D] than Whites, attributed to skin pigmentation and dietary habits. Little is known about the genetic determinants of 25(OH)D levels nor whether the degree of African ancestry associates with circulating 25(OH)D.

Method: With the use of a panel of 276 ancestry informative genetic markers, we estimated African and European admixture for a sample of 758 African-American and non-Hispanic White Southern Community Cohort Study participants. For African-Americans, cut points of <85%, 85% to 95%, and >or=95% defined low, medium, and high African ancestry, respectively. We estimated the association between African ancestry and 25(OH)D and also explored whether vitamin D exposure (sunlight, diet) had varying effects on 25(OH)D levels dependent on ancestry level.

Results: The mean serum 25(OH)D levels among Whites and among African-Americans of low, medium, and high African ancestry were 27.2, 19.5, 18.3, and 16.5 ng/mL, respectively. Serum 25(OH)D was estimated to decrease by 1.0 to 1.1 ng/mL per 10% increase in African ancestry. The effect of high vitamin D exposure from sunlight and diet was 46% lower among African-Americans with high African ancestry than among those with low/medium ancestry.

Conclusion: We found novel evidence that the level of African ancestry may play a role in clinical vitamin D status.

Impact: This is the first study to describe how 25(OH)D levels vary in relation to genetic estimation of African ancestry. Further study is warranted to replicate these findings and uncover the potential pathways involved.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3857784/ Meta-analysis of long-term vitamin D supplementation on overall mortality Yayuan Zheng, Jianhong Zhu, Manru Zhou, Liao Cui, Weimin Yao, Yuyu Liu 2013 2021-07-29 [("doi","10.1371/journal.pone.0082109")] vitamin-d

Background: It has been suggested that vitamin D is effective to prevent mortality. However, there is no consistent conclusion that the effects of vitamin D supplementation on all-cause mortality are associated with duration of treatment. We conducted a meta-analysis regarding this issue in an effort to provide a more robust answer.

Method: A comprehensive search in a number of databases, including MEDLINE, Embase and The Cochrane Central Register of Controlled Trials, was conducted for collecting randomized controlled trials (RCTs) on vitamin D supplementation preventing mortality. Two investigators independently screened the literature according to the inclusive and exclusive criteria and the relative data were extracted. Data analysis was performed by using Review Manager 5.0 software.

Results: Data from forty-two RCT s were included. Vitamin D therapy decreased all-cause mortality with a duration of follow-up longer than 3 years with a RR (95% CI) of 0.94 (0.90–0.98). No benefit was seen in a shorter follow-up periods with a RR (95% CI) of 1.04 (0.97–1.12). Results remain robust after sensitivity analysis. The following subgroups of long-term follow-up had fewer deaths: female only, participants with a mean age younger than 80, daily dose of 800 IU or less, participants with vitamin D insufficiency (baseline 25-hydroxyvitamin D level less than 50 nmol/L) and cholecalciferol therapy. In addition, the combination of vitamin D and calcium reduced mortality and vitamin D alone also had a trend to decrease mortality in a longer time follow up.

Conclusion: The data suggest that supplementation of vitamin D is effective in preventing overall mortality in a long-term treatment, whereas it is not effective in a treatment duration shorter than 3 years. Future studies are needed to identify the efficacy of vitamin D on specific mortality, such as cancer and cardiovascular disease mortality in a long-term treatment duration.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3906334/ Metformin: do we finally have an anti-aging drug? Vladimir N. Anisimov 2013 2021-07-29 [("doi","10.4161/cc.26928")] longevity/metformin

Studies in mammals have demonstrated that hyperglycemia and hyperinsulinemia are important factors in aging and cancer. Inactivation of insulin/insulin-like signaling increases lifespan in nematodes, fruit flies, and mice. Life-prolonging effects of caloric restriction are in part due to reduction in IGF-1, insulin, and glucose levels. Antidiabetic biguanides such as metformin, which reduce hyperglycemia and hyperinsulinemia by decreasing insulin resistance, extend lifespan, and inhibit carcinogenesis in rodents.

Will antidiabetic biguanides increase lifespan in humans?

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3695824/ Vitamin C antagonizes the cytotoxic effects of antineoplastic drugs Mark L. Heaney, Jeffrey R. Gardner, Nicos Karasavvas, David W. Golde, David A. Scheinberg, Emily A. Smith, Owen A. O’Connor 2008 2021-07-29 [("doi","10.1158/0008-5472.CAN-08-1490")] biology

Vitamin C is an antioxidant vitamin that has been hypothesized to antagonize the effects of reactive oxygen species-generating antineoplastic drugs. The therapeutic efficacy of the widely used antineoplastic drugs doxorubicin, cisplatin, vincristine, methotrexate, and imatinib were compared in leukemia (K562) and lymphoma (RL) cell lines with and without pretreatment with dehydroascorbic acid, the commonly transported form of vitamin C. The effect of vitamin C on viability, clonogenicity, apoptosis, P-glycoprotein, reactive oxygen species (ROS), and mitochondrial membrane potential was determined.

Pretreatment with vitamin C caused a dose-dependent attenuation of cytotoxicity, as measured by trypan blue exclusion and colony formation after treatment with all antineoplastic agents tested. Vitamin C given before doxorubicin treatment led to a substantial reduction of therapeutic efficacy in mice with RL cell-derived xenogeneic tumors. Vitamin C treatment led to a dose-dependent decrease in apoptosis in cells treated with the antineoplastic agents that was not due to up-regulation of P-glycoprotein or vitamin C retention modulated by antineoplastics. Vitamin C had only modest effects on intracellular ROS and a more general cytoprotective profile than N-acetylcysteine, suggesting a mechanism of action that is not mediated by ROS. All antineoplastic agents tested caused mitochondrial membrane depolarization that was inhibited by vitamin C.

These findings indicate that vitamin C given before mechanistically dissimilar antineoplastic agents antagonizes therapeutic efficacy in a model of human hematopoietic cancers by preserving mitochondrial membrane potential. These results support the hypothesis that vitamin C supplementation during cancer treatment may detrimentally affect therapeutic response.

--- https://x.com/RiversHaveWings/status/1511011431910502408 Here are some GLIDE demo prompts that I ran with the new 1.45b parameter latent diffusion model from CompVis: ‘a hedgehog using a calculator’ · ‘a corgi wearing a red bowtie and a purple party hat’ · ‘robots meditating in a Vipassana retreat’ · ‘a fall landscape with a small cottage next to a lake’ 2021-07-30 ai/nn/transformer/clip/sample --- https://x.com/benjamin_hilton/status/1529510695452164097 2021-07-30 ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2111.01471 Zero-Shot Translation using Diffusion Models Eliya Nachmani, Shaked Dovrat 2021-11-02 2021-11-02 [("doi","10.48550/arXiv.2111.01471")] ai/nn/diffusion/discrete

In this work, we show a novel method for neural machine translation (NMT), using a denoising diffusion probabilistic model (DDPM), adjusted for textual data, following recent advances in the field.

We show that it’s possible to translate sentences non-autoregressively using a diffusion model conditioned on the source sentence.

We also show that our model is able to translate between pairs of languages unseen during training (zero-shot learning).

--- https://github.com/HighCWu/anime_biggan_toy Generate Amazing Anime Pictures With BigGAN. Just Have Fun 2021-07-30 ai/nn/gan/biggan --- https://github.com/shawwn/compare_gan/ Compare GAN code 2021-07-30 ai/nn/gan/biggan --- https://www.reddit.com/r/SpiceandWolf/comments/bx764z/im_sorry_i_had_to/ 2021-07-30 ai/nn/gan/biggan --- https://web.archive.org/web/20150330195115/http://www.nzherald.co.nz/world/news/article.cfm?c_id=2&objectid=11404486 Florida man who produced and sold deadly ricin gets prison 2021-07-30 darknet-market/blackmarket-reloaded --- https://www.reddit.com/r/MediaSynthesis/comments/v090gq/a_bird_with_a_hat_midjourney/ 2021-07-30 ai/nn/diffusion/midjourney ai/nn/transformer/clip/sample --- https://x.com/l4rz/status/1530302228581470209 2021-07-30 ai/nn/transformer/clip/sample --- https://www.reddit.com/r/MediaSynthesis/comments/uwvaqr/midjourney_butchers_diagram_showing_the_various/ 2021-07-30 ai/nn/diffusion/midjourney ai/nn/transformer/clip/sample --- https://x.com/Joeythemonste/status/1529163871973527557 2021-07-30 ai/nn/transformer/clip/sample --- https://x.com/dbotdan/status/1529116317193842688 2021-07-31 ai/nn/transformer/clip/sample --- https://x.com/dbotdan/status/1528771632684212225 2021-07-31 ai/nn/transformer/clip/sample --- https://www.reddit.com/r/MediaSynthesis/comments/utidn0/midjourney_selfie_taken_by_dora_the_explorer/ 2021-07-31 ai/nn/diffusion/midjourney ai/nn/transformer/clip/sample --- https://x.com/KaliYuga_ai/status/1525869302171181064 2021-07-31 ai/nn/transformer/clip/sample --- https://x.com/rasbt/status/1530684760913285120 2021-07-31 ai/nn/transformer/gpt/codex --- https://x.com/KaliYuga_ai/status/1530646469438578688 2021-07-31 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/1506.03365 LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, Jianxiong Xiao 2015-06-10 2021-07-31 [("doi","10.48550/arXiv.1506.03365")] ai/dataset ai/nn/cnn ai/scaling reinforcement-learning/exploration/active-learning

While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled training datasets, expensive and tedious to produce, are required to optimize millions of parameters in deep network models. Lagging behind the growth in model capacity, the available datasets are quickly becoming outdated in terms of size and density.

To circumvent this bottleneck, we propose to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop. Starting from a large set of candidate images for each category, we iteratively sample a subset, ask people to label them, classify the others with a trained model, split the set into positives, negatives, and unlabeled based on the classification confidence, and then iterate with the unlabeled set.

To assess the effectiveness of this cascading procedure and enable further progress in visual recognition research, we construct a new image dataset, LSUN. It contains around one million labeled images for each of 10 scene categories and 20 object categories. We experiment with training popular convolutional networks and find that they achieve substantial performance gains when trained on this dataset.

In conclusion, our approach demonstrates how combining human intelligence with sophisticated machine learning algorithms can significantly reduce the need for large amounts of manually labeled data. This method not only saves valuable time and resources but also paves the way for future advances in visual recognition technologies.

--- https://github.com/tkarras/progressive_growing_of_gans Progressive Growing of GANs for Improved Quality, Stability, and Variation 2021-07-31 ai/nn/gan/stylegan --- /doc/ai/anime/danbooru/2018-09-22-progan-holofaces-topdecile.tar.xz 2018-09-22 2021-07-31 ai/anime/danbooru ai/nn/gan/stylegan --- https://imgur.com/a/GjnZVDp 2021-07-31 ai/nn/gan/stylegan --- /doc/ai/anime/danbooru/2019-02-06-progan-danbooru2017-faces-randomsamples.tar 2019-02-06 2021-08-01 ai/anime/danbooru ai/nn/gan/stylegan --- https://imgur.com/a/YlgTMr9 2021-08-01 ai/nn/gan/stylegan --- https://mega.nz/#!ZRUDjQiS!yMMBkq1CH7ohkU2kmL8a-jc-xJZCyKbkz_oAsE5hobw network-snapshot-057891.pkl 2021-08-01 ai/nn/gan/stylegan --- https://github.com/akanazawa/vgan akanazawa/vgan: Code for image generation of Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow 2021-08-01 ai/nn/gan --- https://xbpeng.github.io/projects/VDB/index.html Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow [homepage] 2021-08-01 ai/nn/gan --- https://github.com/bojone/gan-qp GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint 2021-08-01 ai/nn/gan --- https://github.com/hhb072/IntroVAE IntroVAE: A PyTorch implementation of Paper ‘IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis’ 2021-08-01 ai/nn/gan --- https://github.com/openai/glow Code for reproducing results in "Glow: Generative Flow with Invertible 1×1 Convolutions" 2021-08-01 ai/anime ai/nn --- https://openai.com/research/glow Glow: Better Reversible Generative Models 2021-08-01 ai/anime ai/nn --- https://en.wikipedia.org/wiki/Bias-variance_tradeoff Bias-variance tradeoff 2021-08-01 ai/nn statistics --- https://soranews24.com/2019/02/14/video-shows-off-hundreds-of-beautiful-ai-created-anime-girls-in-less-than-a-minute%E3%80%90video%E3%80%91/ Video shows off hundreds of beautiful AI-created anime girls in less than a minute 2021-08-01 ai/anime ai/nn/gan/stylegan --- /doc/ai/nn/gan/stylegan/anime/2019-02-15-gwern-ffhqonly-interpolation-4x4.mp4 Gwern 2019-02-15 2021-08-02 ai/nn/gan/stylegan/anime --- https://en.wikipedia.org/wiki/Ukiyo-e Ukiyo-e 2021-08-02 japan/art --- https://www.reg.ru/blog/anime-generation-with-stylegan/ Генерация аниме с помощью нейросети StyleGAN 2021-08-02 ai/nn/gan/stylegan/anime --- https://www.reddit.com/r/MachineLearning/comments/apq4xu/p_stylegan_on_anime_faces/ [P] StyleGAN on Anime Faces 2021-08-02 ai/nn/gan/stylegan/anime --- /doc/anime/eva/1996-sadamoto-howtodrawshinjinadia.jpg 1996 2021-08-02 anime/eva --- https://en.wikipedia.org/wiki/List_of_Nadia:_The_Secret_of_Blue_Water_characters#Nadia List of Nadia: The Secret of Blue Water characters § Nadia 2021-08-02 anime/eva --- https://en.wikipedia.org/wiki/Nadia:_The_Secret_of_Blue_Water Nadia: The Secret of Blue Water 2021-08-02 anime/eva --- https://en.wikipedia.org/wiki/Shinji_Ikari Shinji Ikari 2021-08-02 anime/eva --- https://github.com/nikhiltiru/stylegan2 StyleGAN-2—Official TensorFlow Implementation 2021-08-02 ai/nn/gan/stylegan --- https://towardsdatascience.com/stylegan-v2-notes-on-training-and-latent-space-exploration-e51cf96584b3 2021-08-02 ai/nn/gan/stylegan --- https://thisanimedoesnotexist.ai/downloads.html This Anime Does Not Exist 2021-08-02 ai/anime ai/nn/gan/stylegan --- https://x.com/arfafax/status/1353246916797075457 Some heavily cherrypicked samples from transfer learning using @AydaoAI’s enhanced StyleGAN-2 anime model after 2 days. 2021-08-03 ai/nn/gan/stylegan/anime --- https://www.chrisplaysgames.com/gadgets/2019/02/25/how-i-learned-to-stop-worrying-and-love-transfer-learning/ How I Learned to Stop Worrying and Love Transfer Learning 2021-08-03 ai/nn/gan/stylegan --- https://www.reddit.com/r/evangelion/comments/apmkjm/brighten_your_monday_with_some_asukas_album_of_130/ 2021-08-03 ai/nn/gan/stylegan --- https://en.wikipedia.org/wiki/Marvin_Minsky Marvin Minsky 2021-08-03 ai/nn --- https://en.wikipedia.org/wiki/Joseph_M._Sussman Joseph M. Sussman 2021-08-03 ai/nn --- https://en.wikipedia.org/wiki/Backpropagation Backpropagation 2021-08-03 ai/nn math --- https://en.wikipedia.org/wiki/Spice_and_Wolf Spice and Wolf 2021-08-03 anime --- https://en.wikipedia.org/wiki/Kantai_Collection Kantai Collection 2021-08-03 anime psychology/collecting --- https://en.wikipedia.org/wiki/Gacha_game Gacha game 2021-08-03 anime psychology/collecting --- https://kancolle.fandom.com/wiki/Akizuki Akizuki KanColle Wiki 2021-08-03 anime --- https://www.arknights.global/ Arknights 2021-08-04 anime --- https://en.wikipedia.org/wiki/Kaguya-sama:_Love_Is_War Kaguya-sama: Love Is War 2021-08-04 anime --- https://en.wikipedia.org/wiki/Ptilopsis Ptilopsis 2021-08-04 biology --- https://en.wikipedia.org/wiki/PDP-6 PDP-6 2021-08-04 cs --- https://en.wikipedia.org/wiki/Evangelion:_3.0_You_Can_(Not)_Redo Evangelion: 3.0 You Can (Not) Redo 2021-08-04 anime/eva --- https://en.wikipedia.org/wiki/Prior_probability Prior probability 2021-08-04 reinforcement-learning/meta-learning statistics/bayes --- https://github.com/THUDM/CogVideo THUDM/CogVideo: Text-to-video generation. The repo for ICLR2023 paper "CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers" 2021-08-04 ai/video/generation --- https://www.niskanencenter.org/the-procedure-fetish/ The Procedure Fetish 2021-08-04 law --- /doc/ai/anime/danbooru/2019-02-10-stylegan-holo-handselectedsamples.zip 2019-02-10 2021-08-04 ai/anime/danbooru ai/nn/gan/stylegan --- /doc/ai/anime/danbooru/2019-02-11-stylegan-asuka-handselectedsamples.zip 2019-02-11 2021-08-04 ai/anime/danbooru ai/nn/gan/stylegan --- /doc/anime/2019-05-06-stylegan-malefaces-1ksamples.tar 2019-05-06 2021-08-04 ai/anime ai/nn/gan/stylegan anime --- https://digital-thinking.de/watchgan-advancing-generated-watch-images-with-stylegans/ WatchGAN: Advancing generated watch images with styleGANs 2021-08-05 ai/anime ai/nn/gan/stylegan --- https://podgorskiy.com/static/stylegan/stylegan.html 2021-08-05 ai/anime ai/nn/gan/stylegan --- https://aydao.ai/experiment/2020/12/05/dbstylegan512.html StyleGAN-2 512px trained on Danbooru2019 2021-08-05 ai/anime/danbooru ai/nn/gan/stylegan/anime ai/scaling --- https://colab.research.google.com/gist/kikko/d48c1871206fc325fa6f7372cf58db87/stylegan-experiments.ipynb 2021-08-05 ai/nn/gan/stylegan --- https://evigio.com/post/generating-new-watch-designs-with-stylegan Generating New Watch Designs With StyleGAN 2021-08-05 ai/nn/gan/stylegan --- https://github.com/Aiterasu/stylegan StyleGAN—Official TensorFlow Implementation 2021-08-05 ai/nn/gan/stylegan --- https://github.com/HighCWu/stylegan2-pytorch2paddle/blob/tadne/convert_weight.py convert_weight.py at tadne 2021-08-05 ai/nn/gan/stylegan --- https://github.com/NVlabs/stylegan/blob/master/generate_figures.py generate_figures.py at master · NVlabs/stylegan 2021-08-05 ai/nn/gan/stylegan --- https://github.com/NVlabs/stylegan/blob/master/pretrained_example.py stylegan/pretrained_example.py at master 2021-08-05 ai/nn/gan/stylegan --- https://github.com/NVlabs/stylegan/blob/master/train.py#L37 stylegan/train.py at master · NVlabs 2021-08-05 ai/nn/gan/stylegan --- https://github.com/NVlabs/stylegan/blob/master/train.py#L47 stylegan/train.py at master 2021-08-05 ai/nn/gan/stylegan --- https://github.com/NVlabs/stylegan/blob/master/train.py#L54 stylegan/train.py at master 2021-08-06 ai/nn/gan/stylegan --- https://github.com/NVlabs/stylegan/blob/master/training/training_loop.py#L112 stylegan/training/training_loop.py 2021-08-06 ai/nn/gan/stylegan --- https://github.com/NVlabs/stylegan/blob/master/training/training_loop.py#L136 stylegan/training/training_loop.py 2021-08-06 ai/nn/gan/stylegan --- https://github.com/NVlabs/stylegan2 StyleGAN-2—Official TensorFlow Implementation 2021-08-06 ai/nn/gan/stylegan --- https://github.com/NVlabs/stylegan2-ada-pytorch StyleGAN-2-ADA—Official PyTorch implementation 2021-08-06 ai/nn/gan/stylegan --- https://github.com/Puzer/stylegan-encoder StyleGAN Encoder—converts real images to latent space 2021-08-06 ai/nn/gan/stylegan --- https://github.com/Puzer/stylegan-encoder-encoder/blob/master/Play_with_latent_directions.ipynb 2021-08-06 ai/nn/gan/stylegan --- https://github.com/ak9250/stylegan-art/blob/master/styleganportraits.ipynb styleganportraits.ipynb at master 2021-08-06 ai/nn/gan/stylegan --- https://github.com/antonpaquin/stylegan2-pytorch Implementation of Analyzing and Improving the Image Quality of StyleGAN (StyleGAN 2) in PyTorch 2021-08-06 ai/nn/gan/stylegan --- https://github.com/avivga/style-image-prior Style Generator Inversion for Image Enhancement and Animation 2021-08-06 ai/nn/gan/stylegan --- https://github.com/aydao/stylegan2-surgery/tree/model-release aydao/stylegan2-surgery 2021-08-06 ai/nn/gan/stylegan --- https://github.com/halcy/stylegan StyleGAN—Official TensorFlow Implementation 2021-08-07 ai/nn/gan/stylegan --- https://github.com/halcy/stylegan/blob/master/Stylegan-Generate-Encode.ipynb Stylegan-Generate-Encode.ipynb at master 2021-08-07 ai/nn/gan/stylegan --- https://github.com/l4rz/scaling-up-stylegan2 Scaling up StyleGAN-2 2021-08-07 ai/nn/gan/stylegan/anime ai/scaling --- https://github.com/l4rz/scaling-up-stylegan2#model-xxxl Update: the XXXL model (250M parameters, doubled latent size) 2021-08-07 ai/nn/gan/stylegan/anime --- https://github.com/pbaylies/stylegan-encoder StyleGAN Encoder—converts real images to latent space 2021-08-07 ai/nn/gan/stylegan --- https://github.com/shawwn/stylegan2 StyleGAN-2—Official TensorFlow Implementation 2021-08-07 ai/nn/gan/stylegan --- https://github.com/t04glovern/stylegan-pokemon t04glovern/stylegan-pokemon: Generating Pokemon cards using a mixture of StyleGAN and RNN to create beautiful & vibrant cards ready for battle! 2021-08-07 ai/nn/gan/stylegan --- https://github.com/xunings/styleganime2/blob/master/misc/ranker.py styleganime2/misc/ranker.py at master · xunings/styleganime2 2021-08-07 ai/nn/gan/stylegan --- https://medium.com/gradientcrescent/this-president-does-not-exist-generating-artistic-portraits-of-donald-trump-using-stylegan-a97a17902dd4 This President Does Not Exist: Generating Artistic Portraits of Donald Trump using StyleGAN Transfer Learning: Theory and Implementation in Tensorflow 2021-08-07 ai/nn/gan/stylegan --- https://medium.com/pickupp/pretrained-anime-stylegan2-convert-to-pytorch-and-editing-images-by-encoder-289a57ac3cab Pretrained Anime StyleGAN-2: convert to pytorch and editing images by encoder by Allen Ng Pickupp 2021-08-07 ai/nn/gan/stylegan --- https://nvlabs.github.io/stylegan2/license.html Nvidia Source Code License 2021-08-08 ai/nn/gan/stylegan --- https://www.reddit.com/r/AnimeResearch/comments/aul582/modification_of_anime_face_stylegan_disentangled/ 2021-08-08 ai/nn/gan/stylegan --- https://www.reddit.com/r/MachineLearning/comments/apq4xu/p_stylegan_on_anime_faces/egf8pvt/ 2021-08-08 ai/nn/gan/stylegan --- https://www.reddit.com/r/MachineLearning/comments/apq4xu/p_stylegan_on_anime_faces/egmyf60/ 2021-08-08 ai/nn/gan/stylegan --- https://www.reddit.com/r/MachineLearning/comments/aq6jxf/p_stylegan_encoder_from_real_images_to_latent/ 2021-08-08 ai/nn/gan/stylegan --- https://www.reddit.com/r/MachineLearning/comments/bkrn3i/p_stylegan_trained_on_album_covers/ 2021-08-08 ai/nn/gan/stylegan --- https://www.reddit.com/r/MachineLearning/comments/ecji6v/removing_blob_artifact_from_stylegan_generations/ Removing blob artifact from StyleGAN generations without retraining. Inspired by StyleGAN-2 2021-08-08 ai/nn/gan/stylegan --- https://www.reddit.com/r/MediaSynthesis/comments/ea5qoy/butterflies_generated_with_stylegan/ I trained a StyleGAN on images of butterflies from the Natural History Museum in London. 2021-08-08 ai/nn/gan/stylegan --- https://www.reddit.com/r/computervision/comments/bfcnbj/p_stylegan_on_oxford_visual_geometry_group/ 2021-08-08 ai/nn/gan/stylegan --- https://svilentodorov.xyz/blog/stylegan-for-evil/ StyleGAN for Evil: Trypophobia and Clockwork Oranging 2021-08-08 ai/nn/gan/stylegan design/visualization psychiatry/anxiety psychology/vision --- https://towardsdatascience.com/creating-new-scripts-with-stylegan-c16473a50fd0 2021-08-08 ai/nn/gan/stylegan design/typography --- https://towardsdatascience.com/fgo-stylegan-this-heroic-spirit-doesnt-exist-23d62fbb680e FGO StyleGAN: This Heroic Spirit Doesn’t Exist 2021-08-09 ai/nn/gan/stylegan/anime --- https://www.deviantart.com/caji9i/art/stylegan-neural-ahegao-842847987 stylegan neural ahegao 2021-08-09 ai/nn/gan/stylegan --- https://www.lyrn.ai/2018/12/26/a-style-based-generator-architecture-for-generative-adversarial-networks/ 2021-08-09 ai/nn/gan/stylegan --- https://zlkj.in/tmp/stylegan/00051-sgan-danbooru-512px-1gpu-progan/ styleGAN Samples 2021-08-09 ai/nn/gan/stylegan --- https://x.com/ganbrood/status/1530543711410601984 2021-08-09 ai/nn/transformer/clip/sample --- https://x.com/gdb/status/1531032145124216833 2021-08-09 ai/nn/transformer/gpt/dall-e --- https://github.com/NVlabs/stylegan StyleGAN—Official TensorFlow Implementation 2021-08-09 ai/nn/gan/stylegan --- https://www.biorxiv.org/content/10.1101/2022.05.24.493320.full Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq Vahid Akbari, Vincent C. T. Hanlon, Kieran O’Neill, Louis Lefebvre, Kasmintan A. Schrader, Peter M. Lansdorp, Steven J. M. Jones 2022-05-25 2022-05-25 [("doi","10.1101/2022.05.24.493320")] genetics/sequencing

Hundreds of loci in human genomes have alleles that are methylated differentially according to their parent of origin. These imprinted loci generally show little variation across tissues, individuals, and populations.

We show that such loci can be used to distinguish the maternal and paternal homologs for all autosomes, without the need for the parental DNA. We integrate methylation-detecting nanopore sequencing with the long-range phase information in Strand-seq data to determine the parent of origin of chromosome-length haplotypes for both DNA sequence and DNA methylation in five trios with diverse genetic backgrounds.

The parent of origin was correctly inferred for all autosomes with an average mismatch error rate of 0.31% for SNVs and 1.89% for indels.

Because our method can determine whether an inherited disease allele originated from the mother or the father, we predict that it will improve the diagnosis and management of many genetic diseases.

--- https://www.biorxiv.org/content/10.1101/2021.04.26.441442.full Multi-tissue integrative analysis of personal epigenomes Joel Rozowsky, Jorg Drenkow, Yucheng T. Yang, Gamze Gursoy, Timur Galeev, Beatrice Borsari, Charles B. Epstein, Kun Xiong, Jinrui Xu, Jiahao Gao, Keyang Yu, Ana Berthel, Zhanlin Chen, Fabio Navarro, Jason Liu, Maxwell S. Sun, James Wright, Justin Chang, Christopher J. F. Cameron, Noam Shoresh, Elizabeth Gaskell, Jessika Adrian, Sergey Aganezov, Gabriela Balderrama-Gutierrez, Samridhi Banskota, Guillermo Barreto Corona, Sora Chee, Surya B. Chhetri, Gabriel Conte Cortez Martins, Cassidy Danyko, Carrie A. Davis, Daniel Farid, Nina P. Farrell, Idan Gabdank, Yoel Gofin, David U. Gorkin, Mengting Gu, Vivian Hecht, Benjamin C. Hitz, Robbyn Issner, Melanie Kirsche, Xiangmeng Kong, Bonita R. Lam, Shantao Li, Bian Li, Tianxiao Li, Xiqi Li, Khine Zin Lin, Ruibang Luo, Mark Mackiewicz, Jill E. Moore, Jonathan Mudge, Nicholas Nelson, Chad Nusbaum, Ioann Popov, Henry E. Pratt, Yunjiang Qiu, Srividya Ramakrishnan, Joe Raymond, Leonidas Salichos, Alexandra Scavelli, Jacob M. Schreiber, Fritz J. Sedlazeck, Lei Hoon See, Rachel M. Sherman, Xu Shi, Minyi Shi, Cricket Alicia Sloan, J. Seth Strattan, Zhen Tan, Forrest Y. Tanaka, Anna Vlasova, Jun Wang, Jonathan Werner, Brian Williams, Min Xu, Chengfei Yan, Lu Yu, Christopher Zaleski, Jing Zhang, J. Michael Cherry, Eric M. Mendenhall, William S. Noble, Zhiping Weng, Morgan E. Levine, Alexander Dobin, Barbara Wold, Ali Mortazavi, Bing Ren, Jesse Gillis, Richard M. Myers, Michael P. Snyder, Jyoti Choudhary, Aleksandar Milosavljevic, Michael C. Schatz, Roderic Guigó, Bradley E. Bernstein, Thomas R. Gingeras, Mark Gerstein 2021-04-26 2021-08-09 [("doi","10.1101/2021.04.26.441442")] genetics/sequencing

Evaluating the impact of genetic variants on transcriptional regulation is a central goal in biological science that has been constrained by reliance on a single reference genome.

To address this, we constructed phased, diploid genomes for 4 cadaveric donors (using long-read sequencing) and systematically charted noncoding regulatory elements and transcriptional activity across more than 25 tissues from these donors.

Integrative analysis revealed over a million variants with allele-specific activity, coordinated, locus-scale allelic imbalances, and structural variants impacting proximal chromatin structure. We relate the personal genome analysis to the ENCODE encyclopedia, annotating allele-specific and tissue-specific elements that are strongly enriched for variants impacting expression and disease phenotypes.

These experimental and statistical approaches, and the corresponding EN-TEx resource, provide a framework for personalized functional genomics.

--- https://www.theatlantic.com/health/archive/2022/05/sit-ups-crunches-lower-back-pain/639437/ The Death of the Sit-Up 2021-08-09 exercise --- https://arxiv.org/abs/2111.12417#microsoft NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, Nan Duan 2021-11-24 2021-11-24 [("doi","10.48550/arXiv.2111.12417")] ai/nn/transformer/gpt/dall-e/1 ai/video/generation

This paper presents a unified multimodal pre-trained model called NÜWA that can generate new or manipulate existing visual data (ie. images and videos) for various visual synthesis tasks. To cover language, image, and video at the same time for different scenarios, a 3D transformer encoder-decoder framework is designed, which can not only deal with videos as 3D data but also adapt to texts and images as 1D and 2D data, respectively. A 3D Nearby Attention (3DNA) mechanism is also proposed to consider the nature of the visual data and reduce the computational complexity.

We evaluate NÜWA on 8 downstream tasks. Compared to several strong baselines, NÜWA achieves state-of-the-art results on text-to-image generation, text-to-video generation, video prediction, etc. Furthermore, it also shows surprisingly good zero-shot capabilities on text-guided image and video manipulation tasks.

Project repo is Github.

--- https://arxiv.org/abs/2004.11532#spotify A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation Carlos Fernández-Loría, Foster Provost, Jesse Anderton, Benjamin Carterette, Praveen Chandar 2020-04-24 2021-08-10 [("doi","10.48550/arXiv.2004.11532")] reinforcement-learning/meta-learning statistics/decision

This study presents a systematic comparison of methods for individual treatment assignment, a general problem that arises in many applications and has received attention from economists, computer scientists, and social scientists. We group the various methods proposed in the literature into 3 general classes of algorithms (or meta-learners): learning models to predict outcomes (the O-learner), learning models to predict causal effects (the E-learner), and learning models to predict optimal treatment assignments (the A-learner). We compare the meta-learners in terms of (1) their level of generality and (2) the objective function they use to learn models from data; we then discuss the implications that these characteristics have for modeling and decision making. Notably, we demonstrate analytically and empirically that optimizing for the prediction of outcomes or causal effects is not the same as optimizing for treatment assignments, suggesting that in general the A-learner should lead to better treatment assignments than the other metalearners.

We demonstrate the practical implications of our findings in the context of choosing, for each user, the best algorithm for playlist generation in order to optimize engagement. This is the first comparison of the 3 different meta-learners on a real-world application at scale (based on more than half a billion individual treatment assignments).

In addition to supporting our analytical findings, the results show how large A/B tests can provide substantial value for learning treatment assignment policies, rather than simply choosing the variant that performs best on average.

--- https://en.wikipedia.org/wiki/ASML_Holding ASML Holding 2021-08-10 ai/scaling/hardware --- https://en.wikipedia.org/wiki/Extreme_ultraviolet_lithography Extreme ultraviolet lithography 2021-08-10 ai/scaling/hardware --- https://x.com/KaliYuga_ai/status/1531194227639582721 2021-08-10 ai/nn/transformer/clip/sample --- https://x.com/callasbristol/status/1531253845833261057 2021-08-10 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2104.07219 Multitasking Inhibits Semantic Drift Athul Paul Jacob, Mike Lewis, Jacob Andreas 2021-04-15 2021-08-10 [("doi","10.48550/arXiv.2104.07219")] reinforcement-learning/multi-agent reinforcement-learning/safe

When intelligent agents communicate to accomplish shared goals, how do these goals shape the agents’ language?

We study the dynamics of learning in latent language policies (LLPs), in which instructor agents generate natural-language subgoal descriptions and executor agents map these descriptions to low-level actions. LLPs can solve challenging long-horizon reinforcement learning problems and provide a rich model for studying task-oriented language use. But previous work has found that LLP training is prone to semantic drift (use of messages in ways inconsistent with their original natural language meanings).

Here, we demonstrate theoretically and empirically that multitask training is an effective counter to this problem: we prove that multitask training eliminates semantic drift in a well-studied family of signaling games, and show that multitask training of neural LLPs in a complex strategy game reduces drift and while improving sample efficiency.

--- https://www.biorxiv.org/content/10.1101/2022.05.28.493856.full Phenotypic stasis with genetic divergence Francois Mallard, Luke Noble, Thiago Guzella, Bruno Afonso, Charles F. Baer, Henrique Teotonio 2022-05-29 2022-05-29 [("doi","10.1101/2022.05.28.493856")] genetics/selection/natural

Whether or not genetic divergence on the short-term of tens to hundreds of generations is compatible with phenotypic stasis remains a relatively unexplored problem.

We evolved predominantly outcrossing, genetically diverse populations of the nematode Caenorhabditis elegans under a constant and homogeneous environment for 240 generations, and followed individual locomotion behavior.

We find that although founders of lab populations show highly diverse locomotion behavior, during lab evolution the component traits of locomotion behavior, defined as the transition rates in activity and direction, did not show divergence from the ancestral population. In contrast, the genetic (co)variance structure of transition rates showed marked divergence from the ancestral state and differentiation among replicate populations during the final 100 generations and after most adaptation had been achieved. We observe that genetic differentiation is a transient pattern during the loss of genetic variance along phenotypic dimensions under genetic drift during the last 100 generations of lab evolution. However, loss of genetic variances present in the founders may be due to directional selection.

These results suggest that once adaptation has occurred, and on the short-term of tens of generations, stasis of locomotion behavior is repeatable because of effective stabilizing selection at a large phenotypic scale, while the genetic structuring of component traits is contingent upon drift history at a local phenotypic scale.

--- https://arxiv.org/abs/2205.13863 Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power Binghui Li, Jikai Jin, Han Zhong, John E. Hopcroft, Liwei Wang 2022-05-27 2022-05-27 [("doi","10.48550/arXiv.2205.13863")] ai/nn/adversarial ai/scaling

It is well-known that modern neural networks are vulnerable to adversarial examples. To mitigate this problem, a series of robust learning algorithms have been proposed. However, although the robust training error can be near zero via some methods, all existing algorithms lead to a high robust generalization error.

In this paper, we provide a theoretical understanding of this puzzling phenomenon from the perspective of expressive power for deep neural networks.

Specifically, for binary classification problems with well-separated data, we show that, for ReLU networks, while mild over-parameterization is sufficient for high robust training accuracy, there exists a constant robust generalization gap unless the size of the neural network is exponential in the data dimension d. Even if the data is linear separable, which means achieving low clean generalization error is easy, we can still prove an exp(Ω(d)) lower bound for robust generalization. Moreover, we establish an improved upper bound of exp(𝒪(k)) for the network size to achieve low robust generalization error when the data lies on a manifold with intrinsic dimension k (k ≪ d). Nonetheless, we also have a lower bound that grows exponentially with respect to k—the curse of dimensionality is inevitable.

By demonstrating an exponential separation between the network size for achieving low robust training and generalization error, our results reveal that the hardness of robust generalization may stem from the expressive power of practical models.

--- https://www.biorxiv.org/content/10.1101/2022.05.27.493784.full Secretoglobin family 1D member 2 (SCGB1D2) protein inhibits growth of Borrelia burgdorferi and affects susceptibility to Lyme disease Satu Strausz, Grace Blacker, Sarah Galloway, Paige Hansen, Samuel Jones, Erin Sanders, Nasa Sinnott-Armstrong, FinnGen FinnGen, Irving L. Weissman, Mark J. Daly, Tuomas Aivelo, Michal Tal, Hanna Maria Ollila 2022-05-28 2022-05-28 [("doi","10.1101/2022.05.27.493784")] genetics/heritable

Lyme disease is a tick-borne disease caused by bacteria of the genus Borrelia. The disease can initially manifest as an erythema migrans rash and, if able to evade the host immune defenses, can progress into a secondary stage chronic disease with debilitating physical or neurological manifestations1,2. The host factors that modulate susceptibility for Lyme disease have remained mostly unknown.

Here we show a novel host defense mechanism against Lyme disease in humans. Using epidemiological and genetic data from FinnGen, we identify a common missense variant at the gene encoding for Secretoglobin family 1D member 2 (SCGB1D2) protein that increases the susceptibility for Lyme disease. The genetic variant changes proline at position 53 to leucine and is predicted as deleterious. Consequently, we validate the dysfunction of this protein variant using live Borrelia burgdorferi (Bb). Recombinant reference SCGB1D2 protein inhibits the growth of Bb twice as effectively as the recombinant SCGB1D2 P53L deleterious missense variant. Together, these data suggest that SCGB1D2 is a host defense factor present in the skin, sweat, and other secretions which protects against Bb infection. This finding provides a novel therapeutic avenue for drug development to prevent and treat Lyme disease.

--- https://arxiv.org/abs/2205.12450#naver Cross-Domain Style Mixing for Face Cartoonization Seungkwon Kim, Chaeheon Gwak, Dohyun Kim, Kwangho Lee, Jihye Back, Namhyuk Ahn, Daesik Kim 2022-05-25 2022-05-25 [("doi","10.48550/arXiv.2205.12450")] ai/nn/gan/stylegan/anime

Cartoon domain has recently gained increasing popularity. Previous studies have attempted quality portrait stylization into the cartoon domain; however, this poses a great challenge since they have not properly addressed the critical constraints, such as requiring a large number of training images or the lack of support for abstract cartoon faces. Recently, a layer swapping method has been used for stylization requiring only a limited number of training images; however, its use cases are still narrow as it inherits the remaining issues.

In this paper, we propose a novel method called Cross-domain Style mixing, which combines two latent codes from two different domains.

Our method effectively stylizes faces into multiple cartoon characters at various face abstraction levels using only a single generator without even using a large number of training images.

--- https://anglish.fandom.com/wiki/Heavenlore Heavenlore 2021-08-11 psychology/linguistics --- https://anglish.fandom.com/wiki/What_is_Anglish%3F What is Anglish? 2021-08-11 psychology/linguistics --- https://pitchfork.com/features/article/ai-music-experimentation-or-automation/ Will AI Take the Pleasure Out of Music? 2021-08-11 ai/music --- https://www.top500.org/news/ornls-frontier-first-to-break-the-exaflop-ceiling/ ORNL’s Frontier First to Break the Exaflop Ceiling 2021-08-11 ai/scaling/hardware --- https://x.com/RiversHaveWings/status/1531321685302861824 2021-08-11 ai/nn/transformer/clip/sample --- https://www.lesswrong.com/posts/s3FmwsE2BQqohFkcf/the-brain-that-builds-itself The Brain That Builds Itself 2021-08-11 psychology/neuroscience --- https://arxiv.org/abs/1812.02900 Off-Policy Deep Reinforcement Learning without Exploration Scott Fujimoto, David Meger, Doina Precup 2018-12-07 2021-08-11 [("doi","10.48550/arXiv.1812.02900")] reinforcement-learning/exploration

Many practical applications of reinforcement learning constrain agents to learn from a fixed batch of data which has already been gathered, without offering further possibility for data collection.

In this paper, we demonstrate that due to errors introduced by extrapolation, standard off-policy deep reinforcement learning algorithms, such as DQN and DDPG, are incapable of learning with data uncorrelated to the distribution under the current policy, making them ineffective for this fixed batch setting. We introduce a novel class of off-policy algorithms, batch-constrained reinforcement learning, which restricts the action space in order to force the agent towards behaving close to on-policy with respect to a subset of the given data.

We present the first continuous control deep reinforcement learning algorithm which can learn effectively from arbitrary, fixed batch data, and empirically demonstrate the quality of its behavior in several tasks.

--- https://arxiv.org/abs/1907.04543#google An Optimistic Perspective on Offline Reinforcement Learning Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi 2019-07-10 2021-08-11 [("doi","10.48550/arXiv.1907.04543")] reinforcement-learning/exploration

Off-policy reinforcement learning (RL) using a fixed offline dataset of logged interactions is an important consideration in real world applications. This paper studies offline RL using the DQN replay dataset comprising the entire replay experience of a DQN agent on 60 Atari 2600 games. We demonstrate that recent off-policy deep RL algorithms, even when trained solely on this fixed dataset, outperform the fully trained DQN agent.

To enhance generalization in the offline setting, we present Random Ensemble Mixture (REM), a robust Q-learning algorithm that enforces optimal Bellman consistency on random convex combinations of multiple Q-value estimates. Offline REM trained on the DQN replay dataset surpasses strong RL baselines. Ablation studies highlight the role of offline dataset size and diversity as well as the algorithm choice in our positive results.

Overall, the results here present an optimistic view that robust RL algorithms trained on sufficiently large and diverse offline datasets can lead to high quality policies. The DQN replay dataset can serve as an offline RL benchmark and is open-sourced.

--- https://arxiv.org/abs/1904.00962#google Large Batch Optimization for Deep Learning: Training BERT in 76 minutes Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh 2019-04-01 2021-08-11 [("doi","10.48550/arXiv.1904.00962")] ai/nn/transformer ai/scaling/hardware

Training large deep neural networks on massive datasets is computationally very challenging. There has been recent surge in interest in using large batch stochastic optimization methods to tackle this issue. The most prominent algorithm in this line of research is LARS, which by employing layerwise adaptive learning rates trains ResNet on ImageNet in a few minutes. However, LARS performs poorly for attention models like BERT, indicating that its performance gains are not consistent across tasks. In this paper, we first study a principled layerwise adaptation strategy to accelerate training of deep neural networks using large mini-batches. Using this strategy, we develop a new layerwise adaptive large batch optimization technique called LAMB; we then provide convergence analysis of LAMB as well as LARS, showing convergence to a stationary point in general nonconvex settings.

Our empirical results demonstrate the superior performance of LAMB across various tasks such as BERT and ResNet-50 training with very little hyperparameter tuning. In particular, for BERT training, our optimizer enables use of very large batch sizes of 32868 without any degradation of performance. By increasing the batch size to the memory limit of a TPUv3 Pod, BERT training time can be reduced from 3 days to just 76 minutes (Table 1).

The LAMB implementation is available at https://github.com/tensorflow/addons/blob/master/tensorflow_addons/optimizers/lamb.py.

--- https://x.com/multimodalart/status/1530972193240371200 2021-08-11 ai/nn/transformer/clip/sample --- https://www.reddit.com/r/dalle2/comments/v1swsh/how_to_use_edit_mode_aka_inpainting_to_change/ 2021-08-11 ai/nn/transformer/gpt/dall-e --- https://www.biorxiv.org/content/10.1101/2022.05.29.493900.full Cost-efficient whole genome-sequencing using novel mostly natural sequencing-by-synthesis chemistry and open fluidics platform Gilad Almogy, Mark Pratt, Florian Oberstrass, Linda Lee, Dan Mazur, Nate Beckett, Omer Barad, Ilya Soifer, Eddie Perelman, Yoav Etzioni, Martin Sosa, April Jung, Tyson Clark, Gila Lithwick-Yanai, Sarah Pollock, Gil Hornung, Maya Levy, Matthew Coole, Tom Howd, Megan Shand, Yossi Farjoun, James Emery, Giles Hall, Samuel K. Lee, Takuto Sato, Ricky Magner, Sophie Low, Andrew Bernier, Bharathi Gandi, Jack Stohlman, Corey Nolet, Siobhan Donovan, Brendan Blumenstiel, Michelle Cipicchio, Sheila Dodge, Eric Banks, Niall Lennon, Stacey Gabriel, Doron Lipson 2022-05-30 2022-05-30 [("doi","10.1101/2022.05.29.493900")] genetics/sequencing

[media: 1, 2, 3; criticism: 1, 2] We [Ultima Genomics] introduce a massively parallel novel sequencing platform that combines an open flow cell design on a circular wafer with a large surface area and mostly natural nucleotides that allow optical end-point detection without reversible terminators.

This platform enables sequencing billions of reads with longer read length (~300bp) and fast runs times (<20hrs) with high base accuracy (Q30 > 85%), at a low cost of $1/Gb. We establish system performance by whole-genome sequencing of the Genome-In-A-Bottle reference samples HG001–7, demonstrating high accuracy for SNPs (99.6%) and Indels in homopolymers up to length 10 (96.4%) across the vast majority (>98%) of the defined high-confidence regions of these samples.

We demonstrate scalability of the whole-genome sequencing workflow by sequencing an additional 224 selected samples from the 1000 Genomes project achieving high concordance with reference data.

--- https://github.com/titusss/PixelAlchemist Semantic image editing in realtime with a multi-parameter interface for StyleCLIP global directions 2021-08-12 ai/nn/gan/stylegan --- https://arxiv.org/abs/2205.14959 Dataset Condensation via Efficient Synthetic-Data Parameterization Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, Hyun Oh Song 2022-05-30 2022-05-30 [("doi","10.48550/arXiv.2205.14959")] ai/dataset ai/nn/sparsity/knowledge-distillation

The great success of machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning. Recent studies on dataset condensation attempt to reduce the dependence on such massive data by synthesizing a compact training dataset. However, the existing approaches have fundamental limitations in optimization due to the limited representability of synthetic datasets without considering any data regularity characteristics.

To this end, we propose a novel condensation framework that generates multiple synthetic data with a limited storage budget via efficient parameterization considering data regularity. We further analyze the shortcomings of the existing gradient matching-based condensation methods and develop an effective optimization technique for improving the condensation of training data information. We propose a unified algorithm that drastically improves the quality of condensed data against the current state-of-the-art on CIFAR-10, ImageNet, and Speech Commands.

--- https://www.reddit.com/r/MediaSynthesis/comments/v19ut7/midjourney_star_wars_making_of_photos_behind_the/ 2021-08-12 ai/nn/diffusion/midjourney ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2205.14204#google M3AE: Multimodal Masked Autoencoders Learn Transferable Representations Xinyang Geng, Hao Liu, Lisa Lee, Dale Schuurams, Sergey Levine, Pieter Abbeel 2022-05-27 2022-05-27 [("doi","10.48550/arXiv.2205.14204")] ai/nn/vae/mae ai/scaling

Building scalable models to learn from diverse, multimodal data remains an open challenge. For vision-language data, the dominant approaches are based on contrastive learning objectives that train a separate encoder for each modality. While effective, contrastive learning approaches introduce sampling bias depending on the data augmentations used, which can degrade performance on downstream tasks. Moreover, these methods are limited to paired image-text data, and cannot leverage widely-available unpaired data.

In this paper, we investigate whether a large multimodal model trained purely via masked token prediction, without using modality-specific encoders or contrastive learning, can learn transferable representations for downstream tasks.

We propose a simple and scalable network architecture, the Multimodal Masked Autoencoder (M3AE), which learns a unified encoder for both vision and language data via masked token prediction. We provide an empirical study of M3AE trained on a large-scale image-text dataset, and find that M3AE is able to learn generalizable representations that transfer well to downstream tasks. Surprisingly, we find that M3AE benefits from a higher text mask ratio (50–90%), in contrast to BERT whose standard masking ratio is 15%, due to the joint training of two data modalities.

We also provide qualitative analysis showing that the learned representation incorporates meaningful information from both image and language.

Lastly, we demonstrate the scalability of M3AE with larger model size and training time, and its flexibility to train on both paired image-text data as well as unpaired data.

--- https://arxiv.org/abs/2205.14336#microsoft Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers Rui Liu, Young Jin Kim, Alexandre Muzio, Barzan Mozafari, Hany Hassan Awadalla 2022-05-28 2022-05-28 [("doi","10.48550/arXiv.2205.14336")] ai/scaling/mixture-of-experts

Sparsely activated transformers, such as Mixture of Experts (MoE), have received great interest due to their outrageous scaling capability which enables dramatic increases in model size without statistically-significant increases in computational cost. To achieve this, MoE models replace the feedforward sub-layer with Mixture-of-Experts sub-layer in transformers and use a gating network to route each token to its assigned experts.

Since the common practice for efficient training of such models requires distributing experts and tokens across different machines, this routing strategy often incurs huge cross-machine communication cost because tokens and their assigned experts likely reside in different machines. In this paper, we propose Gating Dropout, which allows tokens to ignore the gating network and stay at their local machines, thus reducing the cross-machine communication. Similar to traditional dropout, we also show that Gating Dropout has a regularization effect during training, resulting in improved generalization performance. We validate the effectiveness of Gating Dropout on multilingual machine translation tasks. Our results demonstrate that Gating Dropout improves a state-of-the-art MoE model with faster wall-clock time convergence rates and better BLEU scores for a variety of model sizes and datasets.

--- https://arxiv.org/abs/2205.14459 CyCLIP: Cyclic Contrastive Language-Image Pretraining Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan A. Rossi, Vishwa Vinay, Aditya Grover 2022-05-28 2022-05-28 [("doi","10.48550/arXiv.2205.14459")] ai/nn/transformer/clip

Recent advances in contrastive representation learning over paired image-text data have led to models such as CLIP that achieve state-of-the-art performance for zero-shot classification and distributional robustness. Such models typically require joint reasoning in the image and text representation spaces for downstream inference tasks. Contrary to prior beliefs, we demonstrate that the image and text representations learned via a standard contrastive objective are not interchangeable and can lead to inconsistent downstream predictions.

To mitigate this issue, we formalize consistency and propose CyCLIP, a framework for contrastive representation learning that explicitly optimizes for the learned representations to be geometrically consistent in the image and text space. In particular, we show that consistent representations can be learned by explicitly symmetrizing (a) the similarity between the two mismatched image-text pairs (cross-modal consistency); and (b) the similarity between the image-image pair and the text-text pair (in-modal consistency).

Empirically, we show that the improved consistency in CyCLIP translates to gains over CLIP, with gains ranging from 10%–24% for zero-shot classification accuracy on standard benchmarks (CIFAR-10, CIFAR-100, ImageNet1K) and 10%–27% for robustness to various natural distribution shifts. The code is available at Github.

--- https://arxiv.org/abs/2205.14953 MAT: Multi-Agent Reinforcement Learning is a Sequence Modeling Problem Muning Wen, Jakub Grudzien Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, Yaodong Yang 2022-05-30 2022-05-30 [("doi","10.48550/arXiv.2205.14953")] reinforcement-learning/model/decision-transformer reinforcement-learning/multi-agent

[cf. MADT] Large sequence model (SM) such as GPT series and BERT has displayed outstanding performance and generalization capabilities on vision, language, and recently reinforcement learning tasks. A natural follow-up question is how to abstract multi-agent decision making into an SM problem and benefit from the prosperous development of SMs.

In this paper, we introduce a novel architecture named Multi-Agent Transformer (MAT) that effectively casts cooperative multi-agent reinforcement learning (MARL) into SM problems wherein the task is to map agents’ observation sequence to agents’ optimal action sequence. Our goal is to build the bridge between MARL and SMs so that the modeling power of modern sequence models can be unleashed for MARL.

Central to our MAT is an encoder-decoder architecture which leverages the multi-agent advantage decomposition theorem to transform the joint policy search problem into a sequential decision making process; this renders only linear time complexity for multi-agent problems and, most importantly, endows MAT with monotonic performance improvement guarantee. Unlike prior art such as Decision Transformer fit only pre-collected offline data, MAT is trained by online trials and errors from the environment in an on-policy fashion. [see also Online Decision Transformer]

To validate MAT, we conduct extensive experiments on StarCraft II, Multi-Agent MuJoCo, Dexterous Hands Manipulation, and Google Research Football benchmarks. Results demonstrate that MAT achieves superior performance and data efficiency compared to strong baselines including MAPPO and HAPPO. Furthermore, we demonstrate that MAT is an excellent few-short learner on unseen tasks regardless of changes in the number of agents.

See our project page at https://sites.google.com/view/multi-agent-transformer.

--- https://arxiv.org/abs/2109.11251 Trust Region Policy Optimization in Multi-Agent Reinforcement Learning Jakub Grudzien Kuba, Ruiqing Chen, Muning Wen, Ying Wen, Fanglei Sun, Jun Wang, Yaodong Yang 2021-09-23 2021-09-23 [("doi","10.48550/arXiv.2109.11251")] reinforcement-learning/multi-agent

Trust region methods rigorously enabled reinforcement learning (RL) agents to learn monotonically improving policies, leading to superior performance on a variety of tasks. Unfortunately, when it comes to multi-agent reinforcement learning (MARL), the property of monotonic improvement may not simply apply; this is because agents, even in cooperative games, could have conflicting directions of policy updates. As a result, achieving a guaranteed improvement on the joint policy where each agent acts individually remains an open challenge.

In this paper, we extend the theory of trust region learning to MARL. Central to our findings are the multi-agent advantage decomposition lemma and the sequential policy update scheme.

Based on these, we develop Heterogeneous-Agent Trust Region Policy Optimization (HATPRO) and Heterogeneous-Agent Proximal Policy Optimization (HAPPO) algorithms. Unlike many existing MARL algorithms, HATRPO/HAPPO do not need agents to share parameters, nor do they need any restrictive assumptions on decomposability of the joint value function. Most importantly, we justify in theory the monotonic improvement property of HATRPO/HAPPO.

We evaluate the proposed methods on a series of Multi-Agent MuJoCo and StarCraft II tasks.

Results show that HATRPO and HAPPO outperform strong baselines such as IPPO, MAPPO and MADDPG on all tested tasks, therefore establishing a new state-of-the-art.

--- https://arxiv.org/abs/2205.13320#google Towards Learning Universal Hyperparameter Optimizers with Transformers Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Qiuyi Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc’aurelio Ranzato, Sagi Perel, Nando de Freitas 2022-05-26 2022-05-26 [("doi","10.48550/arXiv.2205.13320")] ai/nn/transformer reinforcement-learning/exploration reinforcement-learning/meta-learning

Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efficiency over objective functions from a similar distribution. However, existing methods are restricted to learning from experiments sharing the same set of hyperparameters.

In this paper, we introduce the OptFormer, the first text-based Transformer HPO framework that provides a universal end-to-end interface for jointly learning policy and function prediction when trained on vast tuning data from the wild [Google Vizier].

Our extensive experiments demonstrate that the OptFormer can imitate at least 7 different HPO algorithms, which can be further improved via its function uncertainty estimates. Compared to a Gaussian Process, the OptFormer also learns a robust prior distribution for hyperparameter response functions, and can thereby provide more accurate and better calibrated predictions. This work paves the path to future extensions for training a Transformer-based model as a general HPO optimizer.

--- https://www.medrxiv.org/content/10.1101/2021.12.13.21267756.full A spectrum of recessiveness among Mendelian disease variants in UK Biobank Alison R. Barton, Margaux L. A. Hujoel, Ronen E. Mukamel, Maxwell A. Sherman, Po-Ru Loh 2021-12-14 2021-12-14 [("doi","10.1101/2021.12.13.21267756")] genetics/heritable/rare

Recent work has found increasing evidence of mitigated, incompletely penetrant phenotypes in heterozygous carriers of recessive Mendelian disease variants.

We leveraged whole-exome imputation within the full UK Biobank cohort (N~500K) to extend such analyses to 3,481 rare variants curated from ClinVar and OMIM. Testing these variants for association with 57 quantitative traits yielded:

103 statistically-significant associations involving variants previously implicated in 35 different diseases. Notable examples included a POR missense variant implicated in Antley-Bixler syndrome that associated with a 1.76 (s.e. 0.27) cm increase in height, and an ABCA3 missense variant implicated in interstitial lung disease that associated with reduced FEV1/FVC ratio. Association analyses with 1,257 disease traits yielded 5 additional variant-disease associations. We also observed contrasting levels of recessiveness between two more-common, classical Mendelian diseases. Carriers of cystic fibrosis variants exhibited increased risk of several mitigated disease phenotypes, whereas carriers of spinal muscular atrophy alleles showed no evidence of altered phenotypes. Incomplete penetrance of cystic fibrosis carrier phenotypes did not appear to be mediated by common allelic variation on the functional haplotype.

Our results show that many disease-associated recessive variants can produce mitigated phenotypes in heterozygous carriers and motivate further work exploring penetrance mechanisms.

--- https://arxiv.org/abs/2205.16007#microsoft Improved Vector Quantized Diffusion Models Zhicong Tang, Shuyang Gu, Jianmin Bao, Dong Chen, Fang Wen 2022-05-31 2022-05-31 [("doi","10.48550/arXiv.2205.16007")] ai/nn/diffusion ai/nn/transformer/clip

Vector quantized diffusion (VQ-Diffusion) is a powerful generative model for text-to-image synthesis, but sometimes can still generate low-quality samples or weakly correlated images with text input.

We find these issues are mainly due to the flawed sampling strategy. In this paper, we propose two important techniques to further improve the sample quality of VQ-Diffusion. (1) We explore classifier-free guidance sampling for discrete denoising diffusion model and propose a more general and effective implementation of classifier-free guidance. (2) We present a high-quality inference strategy to alleviate the joint distribution issue in VQ-Diffusion.

Finally, we conduct experiments on various datasets to validate their effectiveness and show that the improved VQ-Diffusion suppresses the vanilla version by large margins. We achieve an 8.44 FID score on MS COCO, surpassing VQ-Diffusion by 5.42 FID score. When trained on ImageNet, we dramatically improve the FID score 11.89 → 4.83, demonstrating the superiority of our proposed techniques.

--- https://arxiv.org/abs/2104.11980 baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents Michael A. Alcorn, Anh Nguyen 2021-04-24 2021-08-13 [("doi","10.48550/arXiv.2104.11980")] reinforcement-learning/model/decision-transformer reinforcement-learning/multi-agent

[cf. Decision Transformer] In many multi-agent spatiotemporal systems, agents operate under the influence of shared, unobserved variables (eg. the play a team is executing in a game of basketball). As a result, the trajectories of the agents are often statistically dependent at any given time step; however, almost universally, multi-agent models implicitly assume the agents’ trajectories are statistically independent at each time step.

In this paper, we introduce baller2vec++, a multi-entity Transformer that can effectively model coordinated agents. Specifically, baller2vec++ applies a specially designed self-attention mask to a mixture of location and “look-ahead” trajectory sequences to learn the distributions of statistically dependent agent trajectories.

We show that, unlike baller2vec (baller2vec++’s predecessor), baller2vec++ can learn to emulate the behavior of perfectly coordinated agents in a simulated toy dataset. Additionally, when modeling the trajectories of professional basketball players, baller2vec++ outperforms baller2vec by a wide margin. [Github]

--- https://arxiv.org/abs/2102.03291 baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling Michael A. Alcorn, Anh Nguyen 2021-02-05 2021-08-13 [("doi","10.48550/arXiv.2102.03291")] ai/nn/transformer reinforcement-learning/multi-agent

Multi-agent spatiotemporal modeling is a challenging task from both an algorithmic design and computational complexity perspective. Recent work has explored the efficacy of traditional deep sequential models in this domain, but these architectures are slow and cumbersome to train, particularly as model size increases. Further, prior attempts to model interactions between agents across time have limitations, such as imposing an order on the agents, or making assumptions about their relationships.

In this paper, we introduce baller2vec, a multi-entity generalization of the standard Transformer that can, with minimal assumptions, simultaneously and efficiently integrate information across entities and time. We test the effectiveness of baller2vec for multi-agent spatiotemporal modeling by training it to perform two different basketball-related tasks: (1) simultaneously modeling the trajectories of all players on the court and (2) modeling the trajectory of the ball.

Not only does baller2vec learn to perform these tasks well (outperforming a graph recurrent neural network with a similar number of parameters by a wide margin), it also appears to “understand” the game of basketball, encoding idiosyncratic qualities of players in its embeddings, and performing basketball-relevant functions with its attention heads.

--- https://www.kaggle.com/datasets/printcraft/anime-and-cg-characters-detection-using-yolov5 Art and CG characters detection based on torso components using YOLOv5 2021-08-13 ai/anime/danbooru --- https://www.theguardian.com/business/2022/jun/01/uk-raspberry-picking-robot-soft-fruit World’s first raspberry picking robot cracks the toughest nut: soft fruit Food & drink industry 2021-08-13 technology --- https://arxiv.org/abs/2205.13699 Maximum Likelihood Training of Implicit Nonlinear Diffusion Models Dongjun Kim, Byeonghu Na, Se Jung Kwon, Dongsoo Lee, Wanmo Kang, Il-Chul Moon 2022-05-27 2022-05-27 [("doi","10.48550/arXiv.2205.13699")] ai/nn/diffusion

Whereas diverse variations of diffusion models exist, expanding the linear diffusion into a nonlinear diffusion process is investigated only by a few works. The nonlinearity effect has been hardly understood, but intuitively, there would be more promising diffusion patterns to optimally train the generative distribution towards the data distribution. This paper introduces such a data-adaptive and nonlinear diffusion process for score-based diffusion models.

The proposed Implicit Nonlinear Diffusion Model (INDM) learns the nonlinear diffusion process by combining a normalizing flow and a diffusion process. Specifically, INDM implicitly constructs a nonlinear diffusion on the data space by leveraging a linear diffusion on the latent space through a flow network. This flow network is the key to forming a nonlinear diffusion as the nonlinearity fully depends on the flow network. This flexible nonlinearity is what improves the learning curve of INDM to nearly MLE training, compared against the non-MLE training of DDPM+, which turns out to be a special case of INDM with the identity flow.

Also, training the nonlinear diffusion empirically yields a sampling-friendly latent diffusion that the sample trajectory of INDM is closer to an optimal transport than the trajectories of previous research. In experiments, INDM achieves the state-of-the-art FID on CelebA.

--- https://lxj616.github.io/jekyll/update/2022/05/14/keypoint-based-anime-generation-with-additional-clip-guided-tuning.html Keypoint Based Anime Generation With Additional CLIP Guided Tuning 2021-08-13 ai/anime/danbooru ai/nn/diffusion --- https://github.com/lxj616/latent-diffusion High-Resolution Image Synthesis with Latent Diffusion Models 2021-08-13 ai/anime/danbooru ai/nn/diffusion --- https://lxj616.github.io/jekyll/update/2022/05/16/A-closer-look-into-the-latent-diffusion-repo-do-better-than-just-looking.html A Closer Look Into The latent-diffusion Repo, Do Better Than Just Looking 2021-08-13 ai/anime/danbooru ai/nn/diffusion --- https://lxj616.github.io/jekyll/update/2022/05/14/rethinking-the-danbooru-2021-dataset.html Rethinking The Danbooru 2021 Dataset 2021-08-13 ai/anime/danbooru ai/nn/diffusion --- https://mattsclancy.substack.com/p/science-is-getting-harder Science is getting harder 2021-08-13 science --- https://www.youtube.com/watch?v=SVcsDDABEkM AI art, explained 2021-08-14 ai/nn/transformer/clip/sample ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2205.10625#google Least-to-Most Prompting Enables Complex Reasoning in Large Language Models Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, Ed Chi 2022-05-21 2022-05-21 [("doi","10.48550/arXiv.2205.10625")] ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/lamda ai/nn/transformer/gpt/palm ai/scaling

We propose a novel prompting strategy, least-to-most prompting, that enables large language models to better perform multi-step reasoning tasks. Least-to-most prompting first reduces a complex problem into a list of subproblems, and then sequentially solves the subproblems, whereby solving a given sub-problem is facilitated by the model’s answers to previously solved subproblems.

Experiments on symbolic manipulation, compositional generalization and numerical reasoning demonstrate that least-to-most prompting can generalize to examples that are harder than those seen in the prompt context, outperforming other prompting-based approaches by a large margin. A notable empirical result is that the GPT-3 code-davinci-002 [InstructGPT] model with least-to-most-prompting can solve the SCAN benchmark with an accuracy of 99.7% using 14 examples. [condensed notation; also evaluated is PaLM on DROP.] As a comparison, the neural-symbolic models in the literature specialized for solving SCAN are trained with the full training set of more than 15,000 examples.

--- https://arxiv.org/abs/1711.00350 Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks Brenden M. Lake, Marco Baroni 2017-10-31 2021-08-14 [("doi","10.48550/arXiv.1711.00350")] ai/nn/rnn

Humans can understand and produce new utterances effortlessly, thanks to their compositional skills. Once a person learns the meaning of a new verb “dax”, he or she can immediately understand the meaning of “dax twice” or “sing and dax.”

In this paper, we introduce the SCAN domain, consisting of a set of simple compositional navigation commands paired with the corresponding action sequences.

We then test the zero-shot generalization capabilities of a variety of recurrent neural networks (RNNs) trained on SCAN with sequence-to-sequence methods. We find that RNNs can make successful zero-shot generalizations when the differences between training and test commands are small, so that they can apply “mix-and-match” strategies to solve the task. However, when generalization requires systematic compositional skills (as in the “dax” example above), RNNs fail spectacularly.

We conclude with a proof-of-concept experiment in neural machine translation, suggesting that lack of systematicity might be partially responsible for neural networks’ notorious training data thirst.

--- https://arxiv.org/abs/1903.00161 DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt Gardner 2019-03-01 2021-08-14 [("doi","10.48550/arXiv.1903.00161")] ai/dataset ai/nn

Reading comprehension has recently seen rapid progress, with systems matching humans on the most popular datasets for the task. However, a large body of work has highlighted the brittleness of these systems, showing that there is much work left to be done.

We introduce a new English reading comprehension benchmark, DROP, which requires Discrete Reasoning Over the content of Paragraphs.

In this crowdsourced, adversarially-created, 96k-question benchmark, a system must resolve references in a question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was necessary for prior datasets.

We apply state-of-the-art methods from both the reading comprehension and semantic parsing literature on this dataset and show that the best systems only achieve 32.7% F1 on our generalized accuracy metric, while expert human performance is 96.0%. We additionally present a new model that combines reading comprehension methods with simple numerical reasoning to achieve 47.0% F1.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0267699 Stereotype threat, gender and mathematics attainment: A conceptual replication of Stricker & Ward Matthew Inglis, Steven O’Hagan 2022-04-14 2022-04-14 [("doi","10.1371/journal.pone.0267699")] psychology/cognitive-bias/stereotype-threat sociology

Stereotype threat has been proposed as one cause of gender differences in post-compulsory mathematics participation. Danaher and Crandall argued, based on a study conducted by Stricker and Ward, that enquiring about a student’s gender after they had finished a test, rather than before, would reduce stereotype threat and therefore increase the attainment of women students. Making such a change, they argued, could lead to nearly 5000 more women receiving AP Calculus AB credit per year.

We conducted a preregistered conceptual replication of Stricker and Ward’s study in the context of the UK Mathematics Trust’s Junior Mathematical Challenge, finding no evidence of this stereotype threat effect.

We conclude that the ‘silver bullet’ intervention of relocating demographic questions on test answer sheets is unlikely to provide an effective solution to systemic gender inequalities in mathematics education.

--- /doc/psychology/cognitive-bias/stereotype-threat/2021-warne.pdf No Strong Evidence of Stereotype Threat in Females: A Reassessment of the Meta-Analysis Russell T. Warne 2021-11-29 2021-11-29 [("doi","10.1177/1932202X211061517")] psychology/cognitive-bias/stereotype-threat statistics/bias statistics/meta-analysis

Recently, Picho-Kiroga 2021 published a meta-analysis on the effect of stereotype threat on females. Their conclusion was that the average effect size for stereotype threat studies was d = 0.28, but that effects are overstated because the majority of studies on stereotype threat in females include methodological characteristics that inflate the apparent effect size.

In this response, I show that Picho-Kiroga et al 2021 committed fundamental errors in their meta-analysis that undermine confidence in the article and warrant major corrections. But even if the data were not flawed, the conclusion that Picho-Kiroga et al 2021 should have reached is that their results are most consistent with a population effect size of zero. There is no compelling evidence that stereotype threat is a real phenomenon in females.

--- /doc/economics/2022-heller-3.pdf Soft Skills in the Youth Labor Market Sara B. Heller, Judd B. Kessler 2022-05-01 2022-05-01 [("doi","10.1257/pandp.20221034")] economics psychology/personality

This paper provides new descriptive evidence about which soft skills employers value in young, entry-level workers.

We ran employer surveys as part of an experiment to generate letters of recommendation for New York City’s Summer Youth Employment Program participants. Supervisors rated over 9,200 of their summer employees on overall quality and 10 separate soft skills.

Better ratings correspond to better outside employment and earnings. We find that communication skills and dependability most impress employers. Young women’s advantage in soft skills explains their entire advantage in overall ratings, as well as 14% of their advantage in outside earnings.

--- https://arxiv.org/abs/2205.15996#sensetime Text2Human: Text-Driven Controllable Human Image Generation Yuming Jiang, Shuai Yang, Haonan Qiu, Wayne Wu, Chen Change Loy, Ziwei Liu 2022-05-31 2022-05-31 [("doi","10.48550/arXiv.2205.15996")] ai/nn/diffusion ai/nn/transformer

Generating high-quality and diverse human images is an important yet challenging task in vision and graphics. However, existing generative models often fall short under the high diversity of clothing shapes and textures. Furthermore, the generation process is even desired to be intuitively controllable for layman users.

In this work, we present a text-driven controllable framework, Text2Human, for a high-quality and diverse human generation. We synthesize full-body human images starting from a given human pose with two dedicated steps. (1) With some texts describing the shapes of clothes, the given human pose is first translated to a human parsing map. (2) The final human image is then generated by providing the system with more attributes about the textures of clothes. Specifically, to model the diversity of clothing textures, we build a hierarchical texture-aware codebook that multi-scale neural representations for each type of texture. The codebook at the coarse level includes the structural representations of textures, while the codebook at the fine level focuses on the details of textures. To make use of the learned hierarchical codebook to synthesize desired images, a diffusion-based transformer sampler with mixture of experts is firstly employed to sample indices from the coarsest level of the codebook, which then is used to predict the indices of the codebook at finer levels. The predicted indices at different levels are translated to human images by the decoder learned accompanied with hierarchical codebooks. The use of mixture-of-experts allows for the generated image conditioned on the fine-grained text input. The prediction for finer level indices refines the quality of clothing textures.

Extensive quantitative and qualitative evaluations demonstrate that our proposed framework can generate more diverse and realistic human images compared to state-of-the-art methods.

--- https://arxiv.org/abs/2205.14217 Diffusion-LM Improves Controllable Text Generation Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B. Hashimoto 2022-05-27 2022-05-27 [("doi","10.48550/arXiv.2205.14217")] ai/nn/diffusion/discrete

Controlling the behavior of language models (LMs) without re-training is a major open problem in natural language generation. While recent works have demonstrated successes on controlling simple sentence attributes (eg. sentiment), there has been little progress on complex, fine-grained controls (eg. syntactic structure).

To address this challenge, we develop a new non-autoregressive language model based on continuous diffusions that we call Diffusion-LM. Building upon the recent successes of diffusion models in continuous domains, Diffusion-LM iteratively denoises a sequence of Gaussian vectors into word vectors, yielding a sequence of intermediate latent variables. The continuous, hierarchical nature of these intermediate variables enables a simple gradient-based algorithm to perform complex, controllable generation tasks.

We demonstrate successful control of Diffusion-LM for 6 challenging fine-grained control tasks, outperforming prior work.

--- https://www.medrxiv.org/content/10.1101/2021.03.08.21253137.full Constitutional thinness and anorexia nervosa differ on a genomic level Christopher Hübel, Mohamed Abdulkadir, Moritz Herle, Alish B. Palmos, Ruth Loos, Gerome Breen, Nadia Micali, Cynthia M. Bulik 2021-03-12 2021-08-14 [("doi","10.1101/2021.03.08.21253137")] genetics/heritable/correlation psychiatry/adhd psychiatry/alcoholism psychiatry/anorexia

Constitutional thinness and anorexia nervosa are both characterised by persistent, extremely low weight with body mass indices (BMI) below 18.5 kg/m2. Individuals with anorexia nervosa concurrently show distorted perceptions of their own body and engage in weight-loss behaviors, whereas individuals with constitutional thinness typically wish to gain weight. Both are heritable, share genomics with BMI, but have not been shown to be genetically correlated with each other. We aim to differentiate between constitutional thinness and anorexia nervosa on a genomic level.

First, we estimated genetic correlations between constitutional thinness and eleven psychiatric disorders and compared them with anorexia nervosa using publicly available data. Second, we identified individuals with constitutional thinness in the Avon Longitudinal Study of Parents and Children (ALSPAC) by latent class growth analysis of measured BMI 10–24 years (n = 8,505) and assigned polygenic scores for eleven psychiatric disorders and a range of anthropometric traits to evaluate associations.

In contrast to anorexia nervosa, attention deficit hyperactivity disorder (rgAN = 0.02 vs. rgCT = −0.24) and alcohol dependence (rgAN = 0.07 vs. rgCT = −0.44) showed a statistically-significant negative genetic correlation with constitutional thinness. A higher polygenic score for post-traumatic stress disorder was associated with an increased risk of constitutional thinness in the ALSPAC cohort (OR = 1.27; Q = 0.03) whereas post-traumatic stress disorder shows no genetic correlation with anorexia nervosa (rg = −0.02). Overall, results suggest that constitutional thinness is different from anorexia nervosa on the genomic level.

--- https://x.com/phillip_isola/status/1532189632217112577 2021-08-15 ai/nn/transformer/clip/sample --- https://x.com/KaliYuga_ai/status/1532129051434958851 2021-08-15 ai/nn/transformer/clip/sample --- https://x.com/KaliYuga_ai/status/1532197896090681344 2021-08-15 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2106.00737 Implicit Representations of Meaning in Neural Language Models Belinda Z. Li, Maxwell Nye, Jacob Andreas 2021-06-01 2021-08-15 [("doi","10.48550/arXiv.2106.00737")] ai/nn/transformer/t5

Does the effectiveness of neural language models derive entirely from accurate modeling of surface word co-occurrence statistics, or do these models represent and reason about the world they describe? In BART and T5 transformer language models, we identify contextual word representations that function as models of entities and situations as they evolve throughout a discourse. These neural representations have functional similarities to linguistic models of dynamic semantics: they support a linear readout of each entity’s current properties and relations, and can be manipulated with predictable effects on language generation.

Our results indicate that prediction in pretrained neural language models is supported, at least in part, by dynamic representations of meaning and implicit simulation of entity state, and that this behavior can be learned with only text as training data.

Code and data are available at Github.

--- https://arxiv.org/abs/2206.00664 Hopular: Modern Hopfield Networks for Tabular Data Bernhard Schäfl, Lukas Gruber, Angela Bitto-Nemling, Sepp Hochreiter 2022-06-01 2022-06-01 [("doi","10.48550/arXiv.2206.00664")] ai/nn/retrieval ai/tabular

[author defense against weak baseline criticism] While Deep Learning excels in structured data as encountered in vision and natural language processing, it failed to meet its expectations on tabular data. For tabular data, Support Vector Machines (SVMs), Random Forests, and Gradient Boosting are the best performing techniques with Gradient Boosting in the lead. Recently, we saw a surge of Deep Learning methods that were tailored to tabular data but still underperform compared to Gradient Boosting on small-sized datasets.

We suggest “Hopular”, a novel Deep Learning architecture for medium-sized and small-sized datasets, where each layer is equipped with continuous modern Hopfield networks. The modern Hopfield networks use stored data to identify feature-feature, feature-target, and sample-sample dependencies. Hopular’s novelty is that every layer can directly access the original input as well as the whole training set via stored data in the Hopfield networks. Therefore, Hopular can step-wise update its current model and the resulting prediction at every layer like standard iterative learning algorithms.

In experiments on small-sized tabular datasets with less than 1,000 samples, Hopular surpasses Gradient Boosting, Random Forests, SVMs, and in particular several Deep Learning methods. In experiments on medium-sized tabular data with about 10,000 samples, Hopular outperforms XGBoost, CatBoost, LightGBM and a state-of-the art Deep Learning method designed for tabular data.

Thus, Hopular is a strong alternative to these methods on tabular data.

--- https://en.wikipedia.org/wiki/XGBoost XGBoost 2021-08-15 ai/tabular --- https://en.wikipedia.org/wiki/Gradient_boosting Gradient boosting 2021-08-15 ai/tabular --- https://en.wikipedia.org/wiki/CatBoost Catboost 2021-08-15 ai/tabular --- https://en.wikipedia.org/wiki/Decision_tree_learning Decision tree learning 2021-08-15 ai/tabular --- https://en.wikipedia.org/wiki/LightGBM LightGBM 2021-08-15 ai/tabular --- https://en.wikipedia.org/wiki/Table_(information) Table (information) 2021-08-15 ai/tabular --- https://en.wikipedia.org/wiki/Bootstrap_aggregating Bootstrap aggregating 2021-08-16 ai/tabular --- https://en.wikipedia.org/wiki/ID3_algorithm ID3 algorithm 2021-08-16 ai/tabular --- https://en.wikipedia.org/wiki/C4.5_algorithm C4.5 algorithm 2021-08-16 ai/tabular --- https://en.wikipedia.org/wiki/Predictive_analytics#Classification_and_regression_trees_.28CART.29 Predictive analytics § Classification and regression trees (CART) 2021-08-16 ai/tabular --- https://arxiv.org/abs/1602.04938 "Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin 2016-02-16 2021-08-16 [("doi","10.48550/arXiv.1602.04938")] ai/nn ai/tabular

Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one.

In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem.

We demonstrate the flexibility of these methods by explaining different models for text (eg. random forests) and image classification (eg. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted.

--- https://www.reddit.com/r/MediaSynthesis/comments/v3d9ca/gull_doll/ 2021-08-16 ai/nn/transformer/clip/sample --- https://www.medrxiv.org/content/10.1101/2021.03.30.21254657.full A phenotypic spectrum of autism is attributable to the combined effects of rare variants, polygenic risk and sex D Antaki, A. Maihofer, M. Klein, J. Guevara, J. Grove, Caitlin Carey, O. Hong, M. J. Arranz, A. Hervas, C. Corsello, A. R. Muotri, L. M. Iakoucheva, E. Courchesne, K. Pierce, J. G. Gleeson, E. Robinson, C. M. Nievergelt, J. Sebat 2021-04-04 2021-08-16 [("doi","10.1101/2021.03.30.21254657")] genetics/heritable/rare psychiatry/autism

The genetic etiology of autism spectrum disorder (ASD) is multifactorial with contributions from rare variants, polygenic risk, and sex. How combinations of factors determine risk for ASD is unclear.

In 11,313 ASD families (n = 37,375 subjects), we investigated the effects rare and polygenic risk individually and in combination. We show that genetic liability for ASD differs by sex, with females having a greater polygenic load, and males having a lower liability threshold as evident by a negative correlation of rare and polygenic risk.

Multiple genetic factors were associated with differing sets of behavioral traits with effects that differed by sex. Furthermore, the correlation of parental age with genetic risk for ASD was attributable to de novo mutations and sex-biased effects of inherited risk in parents.

Our results demonstrate that a phenotypic spectrum of ASD is attributable to the relative loadings and gene-by-sex effects of rare and common variation.

--- https://kzl.github.io/assets/decision_transformer.pdf 2021-08-16 reinforcement-learning/model/decision-transformer --- /doc/reinforcement-learning/model-free/2019-bishop.pdf Anxiety, Depression, and Decision Making: A Computational Perspective Sonia J. Bishop, Christopher Gagne 2019-01-01 2021-08-16 [("doi","10.1146/annurev-neuro-080317-062007")] psychiatry/anxiety psychiatry/depression psychology/neuroscience reinforcement-learning/model-free

In everyday life, the outcomes of our actions are rarely certain. Further, we often lack the information needed to precisely estimate the probability and value of potential outcomes as well as how much effort will be required by the courses of action under consideration. Under such conditions of uncertainty, individual differences in the estimation and weighting of these variables, and in reliance on model-free versus model-based decision making, have the potential to strongly influence our behavior.

Both anxiety and depression are associated with difficulties in decision making. Further, anxiety is linked to increased engagement in threat-avoidance behaviors and depression is linked to reduced engagement in reward-seeking behaviors. The precise deficits, or biases, in decision making associated with these common forms of psychopathology remain to be fully specified.

In this article, we review evidence for which of the computations supporting decision making are altered in anxiety and depression and consider the potential consequences for action selection.

In addition, we provide a schematic framework that integrates the findings reviewed and will hopefully be of value to future studies.

--- /doc/reinforcement-learning/model-free/2017-arulkumaran.pdf Deep Reinforcement Learning: A brief survey Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath 2017-01-01 2021-08-16 reinforcement-learning/model reinforcement-learning/model-free --- /doc/reinforcement-learning/model/2017-stachenfeld.pdf The hippocampus as a predictive map Kimberly L. Stachenfeld, Matthew M. Botvinick, Samuel J. Gershman 2017-01-01 2021-08-16 [("doi","10.1038/nn.4650")] psychology/neuroscience reinforcement-learning/model --- /doc/reinforcement-learning/model/2018-segler.pdf Planning chemical syntheses with deep neural networks and symbolic AI Marwin H. S. Segler, Mike Preuss, Mark P. Waller 2018-01-01 2021-08-17 [("doi","10.1038/nature25978")] reinforcement-learning/exploration reinforcement-learning/model --- /doc/reinforcement-learning/model/2018-gromski.pdf How to explore chemical space using algorithms and automation Piotr S. Gromski, Alon B. Henson, Jarosław M. Granda, Leroy Cronin 2018-01-01 2021-08-17 [("doi","10.1038/s41570-018-0066-y")] reinforcement-learning/model --- /doc/reinforcement-learning/model/2018-wu.pdf Generalization guides human exploration in vast decision spaces Charley M. Wu, Eric Schulz, Maarten Speekenbrink, Jonathan D. Nelson, Björn Meder 2018-01-01 2021-08-17 [("doi","10.1038/s41562-018-0467-4")] reinforcement-learning/exploration reinforcement-learning/model --- /doc/reinforcement-learning/model-free/2019-neftci.pdf Reinforcement learning in artificial and biological systems Emre O. Neftci, Bruno B. Averbeck 2019-01-01 2021-08-17 [("doi","10.1038/s42256-019-0025-4")] psychology/neuroscience reinforcement-learning/model-free --- /doc/reinforcement-learning/model-free/2020-dabney.pdf A distributional code for value in dopamine-based reinforcement learning Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos, Matthew Botvinick 2020-01-15 2021-08-17 [("doi","10.1038/s41586-019-1924-6")] psychology/neuroscience reinforcement-learning/model-free --- /doc/reinforcement-learning/model-free/2020-maes.pdf Causal evidence supporting the proposal that dopamine transients function as temporal difference prediction errors Etienne J. P. Maes, Melissa J. Sharpe, Alexandra A. Usypchuk, Megan Lozzi, Chun Yun Chang, Matthew P. H. Gardner, Geoffrey Schoenbaum, Mihaela D. Iordanova 2020-01-20 2021-08-17 [("doi","10.1038/s41593-019-0574-1")] psychology/neuroscience reinforcement-learning/model-free --- /doc/reinforcement-learning/model-free/2019-stanley.pdf Designing neural networks through neuroevolution Kenneth O. Stanley, Jeff Clune, Joel Lehman, Risto Miikkulainen 2019-01-01 2021-08-17 [("doi","10.1038/s42256-018-0006-z")] reinforcement-learning/model-free --- /doc/reinforcement-learning/model-free/2019-zhavoronkov.pdf Deep learning enables rapid identification of potent DDR1 kinase inhibitors Alex Zhavoronkov, Yan A. Ivanenkov, Alex Aliper, Mark S. Veselov, Vladimir A. Aladinskiy, Anastasiya V. Aladinskaya, Victor A. Terentiev, Daniil A. Polykovskiy, Maksim D. Kuznetsov, Arip Asadulaev, Yury Volkov, Artem Zholus, Rim R. Shayakhmetov, Alexander Zhebrak, Lidiya I. Minaeva, Bogdan A. Zagribelnyy, Lennart H. Lee, Richard Soll, David Madge, Li Xing, Tao Guo, Alán Aspuru-Guzik 2019-09-02 2021-08-17 [("doi","10.1038/s41587-019-0224-x")] biology reinforcement-learning/model-free --- /doc/reinforcement-learning/model/2021-mirhoseini.pdf#google A graph placement methodology for fast chip design Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan, Joe Wenjie Jiang, Ebrahim Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Azade Nazi, Jiwoo Pak, Andy Tong, Kavya Srinivasa, William Hang, Emre Tuncer, Quoc V. Le, James Laudon, Richard Ho, Roger Carpenter, Jeff Dean 2021-06-09 2021-08-17 [("doi","10.1038/s41586-021-03544-w")] reinforcement-learning/model --- /doc/cs/security/2001-12-02-treginaldgibbons-isyoursonacomputerhacker.html 2001-12-02 2021-08-17 cs/security math/humor --- https://x.com/rom1504/status/1532508153513971712 2021-08-18 ai/nn/transformer/clip --- https://arxiv.org/abs/2206.00364#nvidia Elucidating the Design Space of Diffusion-Based Generative Models Tero Karras, Miika Aittala, Timo Aila, Samuli Laine 2022-06-01 2022-06-01 [("doi","10.48550/arXiv.2206.00364")] ai/nn/diffusion

We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates the concrete design choices.

This lets us identify several changes to both the sampling and training processes, as well as preconditioning of the score networks.

Together, our improvements yield new state-of-the-art FID of 1.79 for CIFAR-10 in a class-conditional setting and 1.97 in an unconditional setting, with much faster sampling (35 network evaluations per image) than prior designs. To further demonstrate their modular nature, we show that our design changes dramatically improve both the efficiency and quality obtainable with pre-trained score networks from previous work, including improving the FID of an existing ImageNet-64 model from 2.07 to near-SOTA 1.55.

--- https://en.wikipedia.org/wiki/Robot_jockey Robot jockey 2021-08-18 technology --- /doc/statistics/bias/2014-raven.pdf The Corrupted Epidemiological Evidence Base of Psychiatry: A Key Driver of Over-diagnosis Melissa Raven 2014-09-17 2021-08-18 psychiatry statistics/bias --- https://arxiv.org/abs/2206.00735 Cascaded Video Generation for Videos In-the-Wild Lluis Castrejon, Nicolas Ballas, Aaron Courville 2022-06-01 2022-06-01 [("doi","10.48550/arXiv.2206.00735")] ai/nn/gan ai/video/generation

Videos can be created by first outlining a global view of the scene and then adding local details.

Inspired by this idea we propose a cascaded model for video generation which follows a coarse to fine approach. First our model generates a low resolution video, establishing the global scene structure, which is then refined by subsequent cascade levels operating at larger resolutions. We train each cascade level sequentially on partial views of the videos, which reduces the computational complexity of our model and makes it scalable to high-resolution videos with many frames.

We empirically validate our approach on UCF101 and Kinetics-600, for which our model is competitive with the state-of-the-art. We further demonstrate the scaling capabilities of our model and train a three-level model on the BDD100K dataset which generates 256×256 pixels videos with 48 frames.

--- https://www.lesswrong.com/posts/AcKRB8wDpdaN6v6ru/interpreting-gpt-the-logit-lens interpreting GPT: the logit lens 2021-08-18 ai/nn/transformer/gpt --- https://en.wikipedia.org/wiki/Coupon_collector%27s_problem Coupon collector’s problem 2021-08-18 statistics/probability --- https://pixeljoint.com/forum/forum_posts.asp?TID=11299 The Pixel Art Tutorial 2021-08-18 design --- https://arxiv.org/abs/2205.15577 MontageGAN: Generation and Assembly of Multiple Components by GANs Chean Fei Shee, Seiichi Uchida 2022-05-31 2022-05-31 [("doi","10.48550/arXiv.2205.15577")] ai/nn/gan/stylegan/anime

A multi-layer image is more valuable than a single-layer image from a graphic designer’s perspective. However, most of the proposed image generation methods so far focus on single-layer images.

In this paper, we propose MontageGAN, which is a Generative Adversarial Networks (GAN) framework for generating multi-layer images. Our methodused a two-step approach consisting of local GANs and global GAN. Each local GAN learns to generate a specific image layer, and the global GAN learns the placement of each generated image layer.

Through our experiments, we show the ability of our method to generate multi-layer images and estimate the placement of the generated image layers.

--- https://arxiv.org/abs/2011.03148#google RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer Daniel Ho, Kanishka Rao, Zhuo Xu, Eric Jang, Mohi Khansari, Yunfei Bai 2020-11-06 2021-08-18 [("doi","10.48550/arXiv.2011.03148")] ai/nn/gan reinforcement-learning/imitation-learning reinforcement-learning/robot

The success of deep reinforcement learning (RL) and imitation learning (IL) in vision-based robotic manipulation typically hinges on the expense of large scale data collection. With simulation, data to train a policy can be collected efficiently at scale, but the visual gap between sim and real makes deployment in the real world difficult.

We introduce RetinaGAN, a generative adversarial network (GAN) approach to adapt simulated images to realistic ones with object-detection consistency. RetinaGAN is trained in an unsupervised manner without task loss dependencies, and preserves general object structure and texture in adapted images.

We evaluate our method on 3 real world tasks: grasping, pushing, and door opening. RetinaGAN improves upon the performance of prior sim-to-real methods for RL-based object instance grasping and continues to be effective even in the limited data regime. When applied to a pushing task in a similar visual domain, RetinaGAN demonstrates transfer with no additional real data requirements. We also show our method bridges the visual gap for a novel door opening task using imitation learning in a new visual domain.

Visit the project website at https://retinagan.github.io/.

--- https://arxiv.org/abs/2105.03519 Understanding by Understanding Not: Modeling Negation in Language Models Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R. Devon Hjelm, Alessandro Sordoni, Aaron Courville 2021-05-07 2021-08-18 [("doi","10.48550/arXiv.2105.03519")] ai/nn

Negation is a core construction in natural language. Despite being very successful on many tasks, state-of-the-art pre-trained language models often handle negation incorrectly.

To improve language models in this regard, we propose to augment the language modeling objective with an unlikelihood objective that is based on negated generic sentences from a raw text corpus.

By training BERT with the resulting combined objective we reduce the mean top ~1 error rate to 4% on the negated LAMA dataset.

We also see some improvements on the negated NLI benchmarks.

--- https://arxiv.org/abs/2205.15967 You Can’t Count on Luck: Why Decision Transformers Fail in Stochastic Environments Keiran Paster, Sheila McIlraith, Jimmy Ba 2022-05-31 2022-05-31 [("doi","10.48550/arXiv.2205.15967")] reinforcement-learning/model/decision-transformer reinforcement-learning/offline

Recently, methods such as Decision Transformer that reduce reinforcement learning to a prediction task and solve it via supervised learning (RvS) have become popular due to their simplicity, robustness to hyperparameters, and strong overall performance on offline RL tasks. However, simply conditioning a probabilistic model on a desired return and taking the predicted action can fail dramatically in stochastic environments since trajectories that result in a return may have only achieved that return due to luck.

In this work, we describe the limitations of RvS approaches in stochastic environments and propose a solution. Rather than simply conditioning on the return of a single trajectory as is standard practice, our proposed method, ESPER, learns to cluster trajectories and conditions on average cluster returns, which are independent from environment stochasticity. Doing so allows ESPER to achieve strong alignment between target return and expected performance in real environments. We demonstrate this in several challenging stochastic offline-RL tasks including the challenging puzzle game 2048, and Connect Four playing against a stochastic opponent. In all tested domains, ESPER achieves better alignment between the target return and achieved return than simply conditioning on returns. ESPER also achieves higher maximum performance than even the value-based baselines.

--- https://www.reddit.com/r/MachineLearning/comments/v42pej/p_this_is_the_worst_ai_ever_gpt4chan_model/ 2021-08-19 ai/nn/transformer/gpt --- https://www.lesswrong.com/posts/pTYDdcag9pTzFQ7vw/2020-ai-alignment-literature-review-and-charity-comparison 2020 AI Alignment Literature Review and Charity Comparison 2021-08-19 reinforcement-learning/safe --- https://www.lesswrong.com/posts/C4tR3BEpuWviT7Sje/2021-ai-alignment-literature-review-and-charity-comparison 2021 AI Alignment Literature Review and Charity Comparison 2021-08-19 reinforcement-learning/safe --- https://arxiv.org/abs/1606.06565 Concrete Problems in AI Safety Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mané 2016-06-21 2021-08-19 [("doi","10.48550/arXiv.1606.06565")] ai/nn reinforcement-learning/safe

Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society. In this paper, we discuss one such potential impact: the problem of accidents in machine learning systems, defined as unintended and harmful behavior that may emerge from poor design of real-world AI systems.

We present a list of 5 practical research problems related to accident risk, categorized according to whether the problem originates from having the wrong objective function (“avoiding side effects” and “avoiding reward hacking”), an objective function that is too expensive to evaluate frequently (“scalable supervision”), or undesirable behavior during the learning process (“safe exploration” and “distributional shift”). We review previous work in these areas as well as suggesting research directions with a focus on relevance to cutting-edge AI systems.

Finally, we consider the high-level question of how to think most productively about the safety of forward-looking applications of AI.

--- https://www.lesswrong.com/posts/pjzhmtivXd8zgKXDT/designing-agent-incentives-to-avoid-reward-tampering Designing agent incentives to avoid reward tampering 2021-08-19 reinforcement-learning/safe --- https://www.lesswrong.com/posts/kxPiL4zNSPR249wsC/an-114-theory-inspired-safety-solutions-for-powerful [AN #114]: Theory-inspired safety solutions for powerful Bayesian RL agents 2021-08-19 reinforcement-learning/safe --- https://mailchi.mp/938a7eed18c3/an-71avoiding-reward-tamperi 2021-08-19 reinforcement-learning/safe --- https://arxiv.org/abs/2205.10782 Instruction Induction: From Few Examples to Natural Language Task Descriptions Or Honovich, Uri Shaham, Samuel R. Bowman, Omer Levy 2022-05-22 2022-05-22 [("doi","10.48550/arXiv.2205.10782")] ai/dataset ai/nn/transformer/gpt/inner-monologue reinforcement-learning/meta-learning reinforcement-learning/scaling

Large language models are able to perform a task by conditioning on a few input-output demonstrations—a paradigm known as in-context learning. We show that language models can explicitly infer an underlying task from a few demonstrations by prompting them to generate a natural language instruction that fits the examples.

To explore this ability, we introduce the instruction induction challenge, compile a dataset consisting of 24 tasks, and define a novel evaluation metric based on executing the generated instruction.

We discover that, to a large extent, the ability to generate instructions does indeed emerge when using a model that is both large enough and aligned to follow instructions; InstructGPT achieves 65.7% of human performance in our execution-based metric, while the original GPT-3 model reaches only 9.8% of human performance.

This surprising result suggests that instruction induction might be a viable learning paradigm in and of itself, where instead of fitting a set of latent continuous parameters to the data, one searches for the best description in the natural language hypothesis space.

--- https://openmkt.org/research/replications-of-marketing-studies/ Replications of Marketing Studies 2021-08-19 statistics/bias --- https://arxiv.org/abs/2105.06268 Intelligence and Unambitiousness Using Algorithmic Information Theory Michael K. Cohen, Badri Vellambi, Marcus Hutter 2021-05-13 2021-08-19 [("doi","10.1109/JSAIT.2021.3073844")] cs/algorithm/information reinforcement-learning/exploration reinforcement-learning/safe

Algorithmic Information Theory has inspired intractable constructions of general intelligence (AGI), and undiscovered tractable approximations are likely feasible. Reinforcement Learning (RL), the dominant paradigm by which an agent might learn to solve arbitrary solvable problems, gives an agent a dangerous incentive: to gain arbitrary “power” in order to intervene in the provision of their own reward. We review the arguments that generally intelligent algorithmic-information-theoretic reinforcement learners such as Hutter’s (2005) AIXI would seek arbitrary power, including over us.

Then, using an information-theoretic exploration schedule, and a setup inspired by causal influence theory, we present a variant of AIXI which learns to not seek arbitrary power; we call it “unambitious”. We show that our agent learns to accrue reward at least as well as a human mentor, while relying on that mentor with diminishing probability.

And given a formal assumption that we probe empirically, we show that eventually, the agent’s world-model incorporates the following true fact: intervening in the “outside world” will have no effect on reward acquisition; hence, it has no incentive to shape the outside world.

--- https://www.newyorker.com/magazine/2022/01/24/the-rise-of-ai-fighter-pilots The Rise of A.I. Fighter Pilots 2021-08-20 reinforcement-learning/robot reinforcement-learning/safe --- https://arxiv.org/abs/2109.13602 SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies Matt Vitelli, Yan Chang, Yawei Ye, Maciej Wołczyk, Błażej Osiński, Moritz Niendorf, Hugo Grimmett, Qiangui Huang, Ashesh Jain, Peter Ondruska 2021-09-28 2021-09-28 [("doi","10.48550/arXiv.2109.13602")] reinforcement-learning/imitation-learning reinforcement-learning/safe

In this paper we present the first safe system for full control of self-driving vehicles trained from human demonstrations and deployed in challenging, real-world, urban environments. Current industry-standard solutions use rule-based systems for planning. Although they perform reasonably well in common scenarios, the engineering complexity renders this approach incompatible with human-level performance. On the other hand, the performance of machine-learned (ML) planning solutions can be improved by simply adding more exemplar data. However, ML methods cannot offer safety guarantees and sometimes behave unpredictably.

To combat this, our approach uses a simple yet effective rule-based fallback layer that performs sanity checks on an ML planner’s decisions (eg. avoiding collision, assuring physical feasibility). This allows us to leverage ML to handle complex situations while still assuring the safety, reducing ML planner-only collisions by 95%.

We train our ML planner on 300 hours of expert driving demonstrations using imitation learning and deploy it along with the fallback layer in downtown San Francisco, where it takes complete control of a real vehicle and navigates a wide variety of challenging urban driving scenarios.

--- https://www.reddit.com/r/MachineLearning/comments/ppy7k4/n_inside_deepminds_secret_plot_to_break_away_from/ 2021-08-20 law reinforcement-learning/deepmind reinforcement-learning/safe --- https://www.theverge.com/2021/7/6/22565448/waymo-simulation-city-autonomous-vehicle-testing-virtual Welcome to Simulation City, the virtual world where Waymo tests its autonomous vehicles 2021-08-20 ai/nn/gan reinforcement-learning/robot reinforcement-learning/safe --- https://arxiv.org/abs/1708.08611 Safe Reinforcement Learning via Shielding Mohammed Alshiekh, Roderick Bloem, Ruediger Ehlers, Bettina Könighofer, Scott Niekum, Ufuk Topcu 2017-08-29 2021-08-20 [("doi","10.48550/arXiv.1708.08611")] reinforcement-learning/safe

Reinforcement learning algorithms discover policies that maximize reward, but do not necessarily guarantee safety during learning or execution phases. We introduce a new approach to learn optimal policies while enforcing properties expressed in temporal logic.

To this end, given the temporal logic specification that is to be obeyed by the learning system, we propose to synthesize a reactive system called a shield. The shield is introduced in the traditional learning process in two alternative ways, depending on the location at which the shield is implemented. In the first one, the shield acts each time the learning agent is about to make a decision and provides a list of safe actions. In the second way, the shield is introduced after the learning agent. The shield monitors the actions from the learner and corrects them only if the chosen action causes a violation of the specification. We discuss which requirements a shield must meet to preserve the convergence guarantees of the learner.

Finally, we demonstrate the versatility of our approach on several challenging reinforcement learning scenarios.

--- https://arxiv.org/abs/2104.12820 Universal Off-Policy Evaluation Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik Learned-Miller, Emma Brunskill, Philip S. Thomas 2021-04-26 2021-08-20 [("doi","10.48550/arXiv.2104.12820")] reinforcement-learning/safe

When faced with sequential decision-making problems, it is often useful to be able to predict what would happen if decisions were made using a new policy. Those predictions must often be based on data collected under some previously used decision-making rule. Many previous methods enable such off-policy (or counterfactual) estimation of the expected value of a performance measure called the return.

In this paper, we take the first steps towards a universal off-policy estimator (UnO)—one that provides off-policy estimates and high-confidence bounds for any parameter of the return distribution. We use UnO for estimating and simultaneously bounding the mean, variance, quantiles/median, inter-quantile range, CVaR, and the entire cumulative distribution of returns.

Finally, we also discuss Uno’s applicability in various settings, including fully observable, partially observable (ie. with unobserved confounders), Markovian, non-Markovian, stationary, smoothly non-stationary, and discrete distribution shifts.

--- https://arxiv.org/abs/2104.03946 Learning What To Do by Simulating the Past David Lindner, Rohin Shah, Pieter Abbeel, Anca Dragan 2021-04-08 2021-08-20 [("doi","10.48550/arXiv.2104.03946")] reinforcement-learning/model reinforcement-learning/preference-learning

Since reward functions are hard to specify, recent work has focused on learning policies from human feedback. However, such approaches are impeded by the expense of acquiring such feedback. Recent work proposed that agents have access to a source of information that is effectively free: in any environment that humans have acted in, the state will already be optimized for human preferences, and thus an agent can extract information about what humans want from the state. Such learning is possible in principle, but requires simulating all possible past trajectories that could have led to the observed state. This is feasible in gridworlds, but how do we scale it to complex tasks?

In this work, we show that by combining a learned feature encoder with learned inverse models, we can enable agents to simulate human actions backwards in time to infer what they must have done. The resulting algorithm is able to reproduce a specific skill in MuJoCo environments given a single state sampled from the optimal policy for that skill.

--- https://www.lesswrong.com/posts/wMCbo7HX3cFbtHZcM/an-161-creating-generalizable-reward-functions-for-multiple [AN #161]: Creating generalizable reward functions for multiple tasks by learning a model of functional similarity 2021-08-20 reinforcement-learning/safe --- https://80000hours.org/podcast/episodes/brian-christian-the-alignment-problem/ Brian Christian on the alignment problem 2021-08-20 reinforcement-learning/safe --- https://www.nytimes.com/2021/04/30/technology/robot-surgery-surgeon.html The Robot Surgeon Will See You Now 2021-08-20 reinforcement-learning/robot reinforcement-learning/safe --- https://blog.x.company/1-million-hours-of-stratospheric-flight-f7af7ae728ac 2021-08-21 reinforcement-learning/robot reinforcement-learning/safe --- https://arxiv.org/abs/2102.01685#deepmind Agent Incentives: A Causal Perspective Tom Everitt, Ryan Carey, Eric Langlois, Pedro A. Ortega, Shane Legg 2021-02-02 2021-08-21 [("doi","10.48550/arXiv.2102.01685")] reinforcement-learning/safe statistics/causality statistics/decision

We present a framework for analyzing agent incentives using causal influence diagrams.

We establish that a well-known criterion for value of information is complete. We propose a new graphical criterion for value of control, establishing its soundness and completeness.

We also introduce two new concepts for incentive analysis: response incentives indicate which changes in the environment affect an optimal decision, while instrumental control incentives establish whether an agent can influence its utility via a variable X. For both new concepts, we provide sound and complete graphical criteria.

We show by example how these results can help with evaluating the safety and fairness of an AI system.

--- https://arxiv.org/abs/2001.10208#apple Towards Learning Multi-agent Negotiations via Self-Play Yichuan Charlie Tang 2020-01-28 2021-08-21 [("doi","10.48550/arXiv.2001.10208")] reinforcement-learning/multi-agent

Making sophisticated, robust, and safe sequential decisions is at the heart of intelligent systems. This is especially critical for planning in complex multi-agent environments, where agents need to anticipate other agents’ intentions and possible future actions. Traditional methods formulate the problem as a Markov Decision Process, but the solutions often rely on various assumptions and become brittle when presented with corner cases.

In contrast, deep reinforcement learning (Deep RL) has been very effective at finding policies by simultaneously exploring, interacting, and learning from environments. Leveraging the powerful Deep RL paradigm, we demonstrate that an iterative procedure of self-play can create progressively more diverse environments, leading to the learning of sophisticated and robust multi-agent policies. We demonstrate this in a challenging multi-agent simulation of merging traffic, where agents must interact and negotiate with others in order to successfully merge on or off the road. While the environment starts off simple, we increase its complexity by iteratively adding an increasingly diverse set of agents to the agent “zoo” as training progresses.

Qualitatively, we find that through self-play, our policies automatically learn interesting behaviors such as defensive driving, overtaking, yielding, and the use of signal lights to communicate intentions to other agents. In addition, quantitatively, we show a dramatic improvement of the success rate of merging maneuvers from 63% to over 98%.

--- https://www.lesswrong.com/posts/cnC2RMWEGiGpJv8go/model-mis-specification-and-inverse-reinforcement-learning Model Mis-specification and Inverse Reinforcement Learning 2021-08-21 reinforcement-learning/preference-learning reinforcement-learning/safe --- https://www.deepmind.com/blog/article/Specification-gaming-the-flip-side-of-AI-ingenuity 2021-08-21 reinforcement-learning/safe --- https://research.fb.com/publications/wes-agent-based-user-interaction-simulation-on-real-infrastructure/ 2021-08-21 reinforcement-learning/safe --- https://arxiv.org/abs/1909.12200#deepmind Scaling data-driven robotics with reward sketching and batch reinforcement learning Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerik, Oleg Sushkov, David Barker, Jonathan Scholz, Misha Denil, Nando de Freitas, Ziyu Wang 2019-09-26 2021-08-21 [("doi","10.48550/arXiv.1909.12200")] reinforcement-learning/offline reinforcement-learning/robot reinforcement-learning/safe

We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions.

We show how to apply this framework to accomplish 3 different object manipulation tasks on a real robot platform. Given demonstrations of a task together with task-agnostic recorded experience, we use a special form of human annotation as supervision to learn a reward function, which enables us to deal with real-world tasks where the reward signal cannot be acquired directly.

Learned rewards are used in combination with a large dataset of experience from different tasks to learn a robot policy offline using batch RL. We show that using our approach it is possible to train agents to perform a variety of challenging manipulation tasks including stacking rigid objects and handling cloth.

--- https://arxiv.org/abs/2001.07118#deepmind The Incentives that Shape Behavior Ryan Carey, Eric Langlois, Tom Everitt, Shane Legg 2020-01-20 2021-08-21 [("doi","10.48550/arXiv.2001.07118")] reinforcement-learning/safe

Which variables does an agent have an incentive to control with its decision, and which variables does it have an incentive to respond to?

We formalize these incentives, and demonstrate unique graphical criteria for detecting them in any single decision causal influence diagram.

To this end, we introduce structural causal influence models, a hybrid of the influence diagram and structural causal model frameworks.

Finally, we illustrate how these incentives predict agent incentives in both fairness and AI safety applications.

--- https://arxiv.org/abs/1907.09470 Characterizing Attacks on Deep Reinforcement Learning Xinlei Pan, Chaowei Xiao, Warren He, Shuang Yang, Jian Peng, Mingjie Sun, Jinfeng Yi, Zijiang Yang, Mingyan Liu, Bo Li, Dawn Song 2019-07-21 2021-08-21 [("doi","10.48550/arXiv.1907.09470")] reinforcement-learning/safe

Recent studies show that Deep Reinforcement Learning (DRL) models are vulnerable to adversarial attacks, which attack DRL models by adding small perturbations to the observations. However, some attacks assume full availability of the victim model, and some require a huge amount of computation, making them less feasible for real-world applications. In this work, we make further explorations of the vulnerabilities of DRL by studying other aspects of attacks on DRL using realistic and efficient attacks.

First, we adapt and propose efficient black-box attacks when we do not have access to DRL model parameters. Second, to address the high computational demands of existing attacks, we introduce efficient online sequential attacks that exploit temporal consistency across consecutive steps. Third, we explore the possibility of an attacker perturbing other aspects in the DRL setting, such as the environment dynamics. Finally, to account for imperfections in how an attacker would inject perturbations in the physical world, we devise a method for generating robust physical perturbations to be printed.

The attack is evaluated on a real-world robot under various conditions. We conduct extensive experiments both in simulation such as Atari games, robotics and autonomous driving, and on real-world robotics, to compare the effectiveness of the proposed attacks with baseline approaches.

To the best of our knowledge, we are the first to apply adversarial attacks on DRL systems to physical robots.

--- https://arxiv.org/abs/1812.01647#deepmind Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures Jonathan Uesato, Ananya Kumar, Csaba Szepesvari, Tom Erez, Avraham Ruderman, Keith Anderson, Krishmamurthy, Dvijotham, Nicolas Heess, Pushmeet Kohli 2018-12-04 2021-08-21 [("doi","10.48550/arXiv.1812.01647")] reinforcement-learning/safe

This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. We focus on two problems: searching for scenarios when learned agents fail and assessing their probability of failure.

The standard method for agent evaluation in reinforcement learning, Vanilla Monte Carlo, can miss failures entirely, leading to the deployment of unsafe agents. We demonstrate this is an issue for current agents, where even matching the compute used for training is sometimes insufficient for evaluation. To address this shortcoming, we draw upon the rare event probability estimation literature and propose an adversarial evaluation approach. Our approach focuses evaluation on adversarially chosen situations, while still providing unbiased estimates of failure probabilities.

The key difficulty is in identifying these adversarial situations—since failures are rare there is little signal to drive optimization. To solve this we propose a continuation approach that learns failure modes in related but less robust agents. Our approach also allows reuse of data already collected for training the agent.

We demonstrate the efficacy of adversarial evaluation on two standard domains: humanoid control and simulated driving.

Experimental results show that our methods can find catastrophic failures and estimate failures rates of agents multiple orders of magnitude faster than standard evaluation schemes, in minutes to hours rather than days.

--- https://blog.acolyer.org/2020/01/13/challenges-of-real-world-rl/ Challenges of real-world reinforcement learning [blog] 2021-08-21 reinforcement-learning/safe --- https://arxiv.org/abs/1904.12901 Challenges of Real-World Reinforcement Learning Gabriel Dulac-Arnold, Daniel Mankowitz, Todd Hester 2019-04-29 2021-08-22 [("doi","10.48550/arXiv.1904.12901")] reinforcement-learning/safe

[discussion] Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research advances in RL are often hard to leverage in real-world systems due to a series of assumptions that are rarely satisfied in practice.

We present a set of 9 unique challenges that must be addressed to productionize RL to real world problems. For each of these challenges, we specify the exact meaning of the challenge, present some approaches from the literature, and specify some metrics for evaluating that challenge.

An approach that addresses all 9 challenges would be applicable to a large number of real world problems.

We also present an example domain that has been modified to present these challenges as a testbed for practical RL research.

--- https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vRPiprOaC3HsCf5Tuum8bRfzYUiKLRqJmbOoC-32JorNdfyTiRRsR7Ea5eWtvsWzuxo8bjOxCG84dAg/pubhtml Specification gaming examples in AI—master list 2021-08-22 reinforcement-learning/safe --- https://medium.com/@deepmindsafetyresearch/building-safe-artificial-intelligence-52f5f75058f1 2021-08-22 reinforcement-learning/safe --- https://80000hours.org/2018/03/jan-leike-ml-alignment/ 2021-08-22 reinforcement-learning/safe --- https://arxiv.org/abs/1809.09147 Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement Learning Akshat Agarwal, Abhinau Kumar V, Kyle Dunovan, Erik Peterson, Timothy Verstynen, Katia Sycara 2018-09-24 2021-08-22 [("doi","10.48550/arXiv.1809.09147")] reinforcement-learning/safe

In the real world, agents often have to operate in situations with incomplete information, limited sensing capabilities, and inherently stochastic environments, making individual observations incomplete and unreliable. Moreover, in many situations it is preferable to delay a decision rather than run the risk of making a bad decision. In such situations it is necessary to aggregate information before taking an action; however, most state-of-the-art reinforcement learning (RL) algorithms are biased towards taking actions at every time step, even if the agent is not particularly confident in its chosen action. This lack of caution can lead the agent to make critical mistakes, regardless of prior experience and acclimation to the environment.

Motivated by theories of dynamic resolution of uncertainty during decision making in biological brains, we propose a simple accumulator module which accumulates evidence in favor of each possible decision, encodes uncertainty as a dynamic competition between actions, and acts on the environment only when it is sufficiently confident in the chosen action. The agent makes no decision by default, and the burden of proof to make a decision falls on the policy to accrue evidence strongly in favor of a single decision. Our results show that this accumulator module achieves near-optimal performance on a simple guessing game, far outperforming deep recurrent networks using traditional, forced action selection policies.

--- https://arxiv.org/abs/1801.08757 Safe Exploration in Continuous Action Spaces Gal Dalal, Krishnamurthy Dvijotham, Matej Vecerik, Todd Hester, Cosmin Paduraru, Yuval Tassa 2018-01-26 2021-08-22 [("doi","10.48550/arXiv.1801.08757")] reinforcement-learning/exploration reinforcement-learning/safe

We address the problem of deploying a reinforcement learning (RL) agent on a physical system such as a datacenter cooling unit or robot, where critical constraints must never be violated. We show how to exploit the typically smooth dynamics of these systems and enable RL algorithms to never violate constraints during learning.

Our technique is to directly add to the policy a safety layer that analytically solves an action correction formulation per each state. The novelty of obtaining an elegant closed-form solution is attained due to a linearized model, learned on past trajectories consisting of arbitrary actions. This is to mimic the real-world circumstances where data logs were generated with a behavior policy that is implausible to describe mathematically; such cases render the known safety-aware off-policy methods inapplicable.

We demonstrate the efficacy of our approach on new representative physics-based environments, and prevail where reward shaping fails by maintaining zero constraint violations.

--- https://archives.argmin.net/2018/04/16/ethical-rewards/ The Ethics of Reward Shaping 2021-08-22 ai philosophy/ethics reinforcement-learning/safe --- https://arxiv.org/abs/1901.01142 V-Fuzz: Vulnerability-Oriented Evolutionary Fuzzing Yuwei Li, Shouling Ji, Chenyang Lv, Yuan Chen, Jianhai Chen, Qinchen Gu, Chunming Wu 2019-01-04 2021-08-22 [("doi","10.48550/arXiv.1901.01142")] cs/security reinforcement-learning/exploration

Fuzzing is a technique of finding bugs by executing a software recurrently with a large number of abnormal inputs. Most of the existing fuzzers consider all parts of a software equally, and pay too much attention on how to improve the code coverage. It is inefficient as the vulnerable code only takes a tiny fraction of the entire code. In this paper, we design and implement a vulnerability-oriented evolutionary fuzzing prototype named V-Fuzz, which aims to find bugs efficiently and quickly in a limited time.

V-Fuzz consists of two main components: a neural network-based vulnerability prediction model and a vulnerability-oriented evolutionary fuzzer. Given a binary program to V-Fuzz, the vulnerability prediction model will give a prior estimation on which parts of the software are more likely to be vulnerable. Then, the fuzzer leverages an evolutionary algorithm to generate inputs which tend to arrive at the vulnerable locations, guided by the vulnerability prediction result.

Experimental results demonstrate that V-Fuzz can find bugs more efficiently than state-of-the-art fuzzers. Moreover, V-Fuzz has discovered 10 CVEs, and 3 of them are newly discovered. We reported the new CVEs, and they have been confirmed and fixed.

--- http://bair.berkeley.edu/blog/2018/04/18/shared-autonomy/ 2021-08-22 reinforcement-learning/safe --- https://blog.acolyer.org/2018/08/13/delayed-impact-of-fair-machine-learning/ Delayed impact of fair machine learning [blog] 2021-08-22 reinforcement-learning/safe --- https://www.lesswrong.com/posts/cKfryXvyJ522iFuNF/a-gym-gridworld-environment-for-the-treacherous-turn A Gym Gridworld Environment for the Treacherous Turn 2021-08-22 reinforcement-learning/safe --- https://arxiv.org/abs/1804.02477 Programmatically Interpretable Reinforcement Learning Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri 2018-04-06 2021-08-23 [("doi","10.48550/arXiv.1804.02477")] reinforcement-learning/safe

We present a reinforcement learning framework, called Programmatically Interpretable Reinforcement Learning (PIRL), that is designed to generate interpretable and verifiable agent policies. Unlike the popular Deep Reinforcement Learning (DRL) paradigm, which represents policies by neural networks, PIRL represents policies using a high-level, domain-specific programming language. Such programmatic policies have the benefits of being more easily interpreted than neural networks, and being amenable to verification by symbolic methods.

We propose a new method, called Neurally Directed Program Search (NDPS), for solving the challenging nonsmooth optimization problem of finding a programmatic policy with maximal reward. NDPS works by first learning a neural policy network using DRL, and then performing a local search over programmatic policies that seeks to minimize a distance from this neural “oracle”.

We evaluate NDPS on the task of learning to drive a simulated car in the TORCS car-racing environment. We demonstrate that NDPS is able to discover human-readable policies that pass some performance bars. We also show that PIRL policies can have smoother trajectories, and can be more easily transferred to environments not encountered during training, than corresponding policies discovered by DRL.

--- https://www.wired.com/story/when-bots-teach-themselves-to-cheat/ When Bots Teach Themselves to Cheat 2021-08-23 reinforcement-learning/safe --- https://www.nytimes.com/2018/03/15/business/self-driving-cars-remote-control.html When Self-Driving Cars Can’t Help Themselves, Who Takes the Wheel? 2021-08-23 reinforcement-learning/exploration/active-learning reinforcement-learning/robot reinforcement-learning/safe --- https://arxiv.org/abs/1806.04067 Adaptive Mechanism Design: Learning to Promote Cooperation Tobias Baumann, Thore Graepel, John Shawe-Taylor 2018-06-11 2021-08-23 [("doi","10.48550/arXiv.1806.04067")] reinforcement-learning/multi-agent reinforcement-learning/safe

In the future, artificial learning agents are likely to become increasingly widespread in our society. They will interact with both other learning agents and humans in a variety of complex settings including social dilemmas.

We consider the problem of how an external agent can promote cooperation between artificial learners by distributing additional rewards and punishments based on observing the learners’ actions. We propose a rule for automatically learning how to create right incentives by considering the players’ anticipated parameter updates.

Using this learning rule leads to cooperation with high social welfare in matrix games in which the agents would otherwise learn to defect with high probability.

We show that the resulting cooperative outcome is stable in certain games even if the planning agent is turned off after a given number of episodes, while other games require ongoing intervention to maintain mutual cooperation. However, even in the latter case, the amount of necessary additional incentives decreases over time.

--- https://medium.com/cruise/why-testing-self-driving-cars-in-sf-is-challenging-but-necessary-77dbe8345927 Why testing self-driving cars in SF is challenging but necessary 2021-08-23 reinforcement-learning/exploration reinforcement-learning/robot --- https://medium.com/aurora-blog/auroras-approach-to-development-5e42fec2ee4b Aurora’s Approach to Development 2021-08-23 reinforcement-learning/exploration/active-learning reinforcement-learning/robot reinforcement-learning/safe --- https://www.youtube.com/watch?v=B8R148hFxPw#waymo Waymo 360° Experience: A Fully Autonomous Driving Journey 2021-08-23 reinforcement-learning/robot --- https://arxiv.org/abs/1804.04268 Incomplete Contracting and AI Alignment Dylan Hadfield-Menell, Gillian Hadfield 2018-04-12 2021-08-23 [("doi","10.48550/arXiv.1804.04268")] economics/mechanism-design reinforcement-learning/safe

We suggest that the analysis of incomplete contracting developed by law and economics researchers can provide a useful framework for understanding the AI alignment problem and help to generate a systematic approach to finding solutions. We first provide an overview of the incomplete contracting literature and explore parallels between this work and the problem of AI alignment. As we emphasize, misalignment between principal and agent is a core focus of economic analysis.

We highlight some technical results from the economics literature on incomplete contracts that may provide insights for AI alignment researchers. Our core contribution, however, is to bring to bear an insight that economists have been urged to absorb from legal scholars and other behavioral scientists: the fact that human contracting is supported by substantial amounts of external structure, such as generally available institutions (culture, law) that can supply implied terms to fill the gaps in incomplete contracts.

We propose a research agenda for AI alignment work that focuses on the problem of how to build AI that can replicate the human cognitive processes that connect individual incomplete contracts with this supporting external structure.

--- https://arxiv.org/abs/1801.04099 Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning Min Chen, Stefanos Nikolaidis, Harold Soh, David Hsu, Siddhartha Srinivasa 2018-01-12 2021-08-23 [("doi","10.48550/arXiv.1801.04099")] reinforcement-learning/multi-agent

Trust in autonomy is essential for effective human-robot collaboration and user adoption of autonomous systems such as robot assistants. This paper introduces a computational model which integrates trust into robot decision-making. Specifically, we learn from data a partially observable Markov decision process (POMDP) with human trust as a latent variable.

The trust-POMDP model provides a principled approach for the robot to (1) infer the trust of a human teammate through interaction, (2) reason about the effect of its own actions on human trust, and (3) choose actions that maximize team performance over the long term.

We validated the model through human subject experiments on a table-clearing task in simulation (201 participants) and with a real robot (20 participants). In our studies, the robot builds human trust by manipulating low-risk objects first. Interestingly, the robot sometimes fails intentionally in order to modulate human trust and achieve the best team performance.

These results show that the trust-POMDP calibrates trust to improve human-robot team performance over the long term. Further, they highlight that maximizing trust alone does not always lead to the best performance.

--- https://arxiv.org/abs/1801.04589 Deep Reinforcement Fuzzing Konstantin Böttinger, Patrice Godefroid, Rishabh Singh 2018-01-14 2021-08-23 [("doi","10.48550/arXiv.1801.04589")] cs/security reinforcement-learning/exploration

Fuzzing is the process of finding security vulnerabilities in input-processing code by repeatedly testing the code with modified inputs. In this paper, we formalize fuzzing as a reinforcement learning problem using the concept of Markov decision processes.

This in turn allows us to apply state-of-the-art deep Q-learning algorithms that optimize rewards, which we define from runtime properties of the program under test. By observing the rewards caused by mutating with a specific set of actions performed on an initial program input, the fuzzing agent learns a policy that can next generate new higher-reward inputs.

We have implemented this new approach, and preliminary empirical evidence shows that reinforcement fuzzing can outperform baseline random fuzzing.

--- https://arxiv.org/abs/1611.01211 Combating Reinforcement Learning’s Sisyphean Curse with Intrinsic Fear Zachary C. Lipton, Kamyar Azizzadenesheli, Abhishek Kumar, Lihong Li, Jianfeng Gao, Li Deng 2016-11-03 2021-08-23 [("doi","10.48550/arXiv.1611.01211")] reinforcement-learning/exploration reinforcement-learning/safe

Many practical environments contain catastrophic states that an optimal agent would visit infrequently or never. Even on toy problems, Deep Reinforcement Learning (DRL) agents tend to periodically revisit these states upon forgetting their existence under a new policy.

We introduce intrinsic fear (IF), a learned reward shaping that guards DRL agents against periodic catastrophes. IF agents possess a fear model trained to predict the probability of imminent catastrophe. This score is then used to penalize the Q-learning objective.

Our theoretical analysis bounds the reduction in average return due to learning on the perturbed objective. We also prove robustness to classification errors. As a bonus, IF models tend to learn faster, owing to reward shaping.

Experiments demonstrate that intrinsic-fear DQNs solve otherwise pathological environments and improve on several Atari games.

--- https://www.biorxiv.org/content/10.1101/227322.full Generalization and search in risky environments Eric Schulz, Charley M. Wu, Quentin J. M. Huys, Andreas Krause, Maarten Speekenbrink 2018-05-14 2021-08-24 [("doi","10.1101/227322")] reinforcement-learning/exploration

How do people pursue rewards in risky environments, where some outcomes should be avoided at all costs? We investigate how participant search for spatially correlated rewards in scenarios where one must avoid sampling rewards below a given threshold. This requires not only the balancing of exploration and exploitation, but also reasoning about how to avoid potentially risky areas of the search space.

Within risky versions of the spatially correlated multi-armed bandit task, we show that participants’ behavior is aligned well with a Gaussian process function learning algorithm, which chooses points based on a safe optimization routine. Moreover, using leave-one-block-out cross-validation, we find that participants adapt their sampling behavior to the riskiness of the task, although the underlying function learning mechanism remains relatively unchanged.

These results show that participants can adapt their search behavior to the adversity of the environment and enrich our understanding of adaptive behavior in the face of risk and uncertainty.

--- https://arxiv.org/abs/1801.08917 Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning Hyrum S. Anderson, Anant Kharkar, Bobby Filar, David Evans, Phil Roth 2018-01-26 2021-08-24 [("doi","10.48550/arXiv.1801.08917")] cs/security reinforcement-learning/exploration

Machine learning is a popular approach to signature-less malware detection because it can generalize to never-before-seen malware families and polymorphic strains. This has resulted in its practical use for either primary detection engines or for supplementary heuristic detection by anti-malware vendors.

Recent work in adversarial machine learning has shown that deep learning models are susceptible to gradient-based attacks, whereas non-differentiable models that report a score can be attacked by genetic algorithms that aim to systematically reduce the score. We propose a more general framework based on reinforcement learning (RL) for attacking static portable executable (PE) anti-malware engines. The general framework does not require a differentiable model nor does it require the engine to produce a score. Instead, an RL agent is equipped with a set of functionality-preserving operations that it may perform on the PE file. Through a series of games played against the anti-malware engine, it learns which sequences of operations are likely to result in evading the detector for any given malware sample. This enables completely black-box attacks against static PE anti-malware, and produces functional evasive malware samples as a direct result.

We show in experiments that our method can attack a gradient-boosted machine learning model with evasion rates that are substantial and appear to be strongly dependent on the dataset. We demonstrate that attacks against this model appear to also evade components of publicly hosted antivirus engines. Adversarial training results are also presented: by retraining the model on evasive ransomware samples, a subsequent attack is 33% less effective. However, there are overfitting dangers when adversarial training, which we note.

We release code to allow researchers to reproduce and improve this approach.

--- https://arxiv.org/abs/1712.04172 A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents Yueh-Hua Wu, Shou-De Lin 2017-12-12 2021-08-24 [("doi","10.48550/arXiv.1712.04172")] reinforcement-learning/preference-learning

This paper proposes a low-cost, easily realizable strategy to equip a reinforcement learning (RL) agent with the capability of behaving ethically.

Our model allows the designers of RL agents to solely focus on the task to achieve, without having to worry about the implementation of multiple trivial ethical patterns to follow.

Based on the assumption that the majority of human behavior, regardless of which goals they are achieving, is ethical, our design integrates human policy with the RL policy to achieve the target objective with less chance of violating the ethical code that human beings normally obey.

--- https://arxiv.org/abs/1801.07292 Convergence of Value Aggregation for Imitation Learning Ching-An Cheng, Byron Boots 2018-01-22 2021-08-24 [("doi","10.48550/arXiv.1801.07292")] reinforcement-learning/imitation-learning reinforcement-learning/preference-learning

Value aggregation is a general framework for solving imitation learning problems. Based on the idea of data aggregation, it generates a policy sequence by iteratively interleaving policy optimization and evaluation in an online learning setting.

While the existence of a good policy in the policy sequence can be guaranteed non-asymptotically, little is known about the convergence of the sequence or the performance of the last policy.

In this paper, we debunk the common belief that value aggregation always produces a convergent policy sequence with improving performance. Moreover, we identify a critical stability condition for convergence and provide a tight non-asymptotic bound on the performance of the last policy.

These new theoretical insights let us stabilize problems with regularization, which removes the inconvenient process of identifying the best policy in the policy sequence in stochastic problems.

--- https://arxiv.org/abs/1709.06166 DropoutDAgger: A Bayesian Approach to Safe Imitation Learning Kunal Menda, Katherine Driggs-Campbell, Mykel J. Kochenderfer 2017-09-18 2021-08-24 [("doi","10.48550/arXiv.1709.06166")] reinforcement-learning/imitation-learning reinforcement-learning/preference-learning statistics/bayes

While imitation learning is becoming common practice in robotics, this approach often suffers from data mismatch and compounding errors.

DAgger is an iterative algorithm that addresses these issues by continually aggregating training data from both the expert and novice policies, but does not consider the impact of safety.

We present a probabilistic extension to DAgger, which uses the distribution over actions provided by the novice policy, for a given observation. Our method, which we call DropoutDAgger, uses dropout to train the novice as a Bayesian neural network that provides insight to its confidence. Using the distribution over the novice’s actions, we estimate a probabilistic measure of safety with respect to the expert action, tuned to balance exploration and exploitation.

The utility of this approach is evaluated on the MuJoCo HalfCheetah and in a simple driving experiment, demonstrating improved performance and safety compared to other DAgger variants and classic imitation learning.

--- https://ai.stanford.edu/~pabbeel/pubs/AbbeelCoatesQuigleyNg_aaorltahf_nips2006.pdf An Application of Reinforcement Learning to Aerobatic Helicopter Flight Pieter Abbeel, Adam Coates, Morgan Quigley, Andrew Y. Ng 2019-07-16 2021-08-24 reinforcement-learning/model reinforcement-learning/robot

Autonomous helicopter flight is widely regarded to be a highly challenging control problem.

This paper presents the first successful autonomous completion on a real RC helicopter of the following 4 aerobatic maneuvers: forward flip and sideways roll at low speed, tail-in funnel, and nose-in funnel. Our experimental results extend the state-of-the-art in autonomous helicopter flight.

We used the following approach: First, we had a pilot fly the helicopter to help us find a helicopter dynamics model and a reward (cost) function. Then we used a reinforcement learning (optimal control) algorithm to find a controller that is optimized for the resulting model and reward function. More specifically, we used differential dynamic programming (DDP), an extension of the linear quadratic regulator (LQR).

--- https://arxiv.org/abs/1712.05556 Safe Policy Search with Gaussian Process Models Kyriakos Polymenakos, Alessandro Abate, Stephen Roberts 2017-12-15 2021-08-24 [("doi","10.48550/arXiv.1712.05556")] reinforcement-learning/model

We propose a method to optimize the parameters of a policy which will be used to safely perform a given task in a data-efficient manner.

We train a Gaussian process model to capture the system dynamics, based on the PILCO framework. Our model has useful analytic properties, which allow closed form computation of error gradients and estimating the probability of violating given state space constraints.

During training, as well as operation, only policies that are deemed safe are implemented on the real system, minimizing the risk of failure.

--- https://arxiv.org/abs/1708.03871 A Game-Theoretic Analysis of the Off-Switch Game Tobias Wängberg, Mikael Böörs, Elliot Catt, Tom Everitt, Marcus Hutter 2017-08-13 2021-08-24 [("doi","10.48550/arXiv.1708.03871")] reinforcement-learning/model

The off-switch game is a game theoretic model of a highly intelligent robot interacting with a human.

In the original paper by Hadfield-Menell et al 2016, the analysis is not fully game-theoretic as the human is modeled as an irrational player, and the robot’s best action is only calculated under unrealistic normality and soft-max assumptions.

In this paper, we make the analysis fully game-theoretic, by modeling the human as a rational player with a random utility function.

As a consequence, we are able to easily calculate the robot’s best action for arbitrary belief and irrationality assumptions.

--- /doc/ai/nn/gan/2021-gangadharbatla.pdf The Role of AI Attribution Knowledge in the Evaluation of Artwork Harsha Gangadharbatla 2021-02-16 2021-08-24 [("doi","10.1177/0276237421994697")] ai/nn/gan

Artwork is increasingly being created by machines through algorithms with little or no input from humans. Yet, very little is known about people’s attitudes and evaluations of artwork generated by machines.

The current study investigates (a) whether individuals are able to accurately differentiate human-made artwork from AI-generated artwork and (b) the role of attribution knowledge (ie. information about who created the content) in their evaluation and reception of artwork. Data was collected using an Amazon Turk sample from two survey experiments designed on Qualtrics.

Findings suggest that individuals are unable to accurately identify AI-generated artwork and they are likely to associate representational art to humans and abstract art to machines. There is also an interaction effect between attribution knowledge and the type of artwork (representational vs. abstract) on purchase intentions and evaluations of artworks.

--- /doc/ai/nn/2020-barshai.pdf Identifying Regulatory Elements via Deep Learning Mira Barshai, Eitamar Tripto, Yaron Orenstein 2020-07-01 2021-08-24 [("doi","10.1146/annurev-biodatasci-022020-021940")] ai/nn genetics/sequencing

Deep neural networks have been revolutionizing the field of machine learning for the past several years. They have been applied with great success in many domains of the biomedical data sciences and are outperforming extant methods by a large margin. The ability of deep neural networks to pick up local image features and model the interactions between them makes them highly applicable to regulatory genomics. Instead of an image, the networks analyze DNA and RNA sequences and additional epigenomic data.

In this review, we survey the successes of deep learning in the field of regulatory genomics. We first describe the fundamental building blocks of deep neural networks, popular architectures used in regulatory genomics, and their training process on molecular sequence data. We then review several key methods in different gene regulation domains. We start with the pioneering method DeepBind and its successors, which were developed to predict protein-DNA binding. We then review methods developed to predict and model epigenetic information, such as histone marks and nucleosome occupancy. Following epigenomics, we review methods to predict protein-RNA binding with its unique challenge of incorporating RNA structure information. Finally, we provide our overall view of the strengths and weaknesses of deep neural networks and prospects for future developments.

--- /doc/ai/nn/rnn/2020-makin.pdf Machine translation of cortical activity to text with an encoder-decoder framework Joseph G. Makin, David A. Moses, Edward F. Chang 2020-03-30 2021-08-25 [("doi","10.1038/s41593-020-0608-8")] ai/nn/rnn psychology/neuroscience --- /doc/ai/nn/2019-sinz.pdf Engineering a Less Artificial Intelligence Fabian H. Sinz, Xaq Pitkow, Jacob Reimer, Matthias Bethge, Andreas S. Tolias 2019-09-25 2021-08-25 [("doi","10.1016/j.neuron.2019.08.034")] ai/nn psychology/neuroscience --- /doc/ai/nn/2019-liang.pdf Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence Huiying Liang, Brian Y. Tsui, Hao Ni, Carolina C. S. Valentim, Sally L. Baxter, Guangjian Liu, Wenjia Cai, Daniel S. Kermany, Xin Sun, Jiancong Chen, Liya He, Jie Zhu, Pin Tian, Hua Shao, Lianghong Zheng, Rui Hou, Sierra Hewett, Gen Li, Ping Liang, Xuan Zang, Zhiqi Zhang, Liyan Pan, Huimin Cai, Rujuan Ling, Shuhua Li, Yongwang Cui, Shusheng Tang, Hong Ye, Xiaoyan Huang, Waner He, Wenqing Liang, Qing Zhang, Jianmin Jiang, Wei Yu, Jianqun Gao, Wanxing Ou, Yingmin Deng, Qiaozhen Hou, Bei Wang, Cuichan Yao, Yan Liang, Shu Zhang, Yaou Duan, Runze Zhang, Sarah Gibson, Charlotte L. Zhang, Oulan Li, Edward D. Zhang, Gabriel Karin, Nathan Nguyen, Xiaokang Wu, Cindy Wen, Jie Xu, Wenqin Xu, Bochu Wang, Winston Wang, Jing Li, Bianca Pizzato, Caroline Bao, Daoman Xiang, Wanting He, Suiqin He, Yugui Zhou, Weldon Haw, Michael Goldbaum, Adriana Tremoulet, Chun-Nan Hsu, Hannah Carter, Long Zhu, Kang Zhang, Huimin Xia 2019-01-01 2021-08-25 [("doi","10.1038/s41591-018-0335-9")] ai/nn --- /doc/ai/nn/2019-stiefel.pdf Why is There No Successful Whole Brain Simulation (Yet)? Klaus M. Stiefel 2019-01-01 2021-08-25 [("doi","10.1007/s13752-019-00319-5")] ai/nn psychology/neuroscience --- /doc/ai/nn/2019-topol.pdf High-performance medicine: the convergence of human and artificial intelligence Eric J. Topol 2019-01-01 2021-08-25 [("doi","10.1038/s41591-018-0300-7")] ai/nn biology --- /doc/ai/nn/2018-defauw.pdf Clinically applicable deep learning for diagnosis and referral in retinal disease Jeffrey Fauw, Joseph R. Ledsam, Bernardino Romera-Paredes, Stanislav Nikolov, Nenad Tomasev, Sam Blackwell, Harry Askham, Xavier Glorot, Brendan O’Donoghue, Daniel Visentin, George Driessche, Balaji Lakshminarayanan, Clemens Meyer, Faith Mackinder, Simon Bouton, Kareem Ayoub, Reena Chopra, Dominic King, Alan Karthikesalingam, Cían O. Hughes, Rosalind Raine, Julian Hughes, Dawn A. Sim, Catherine Egan, Adnan Tufail, Hugh Montgomery, Demis Hassabis, Geraint Rees, Trevor Back, Peng T. Khaw, Mustafa Suleyman, Julien Cornebise, Pearse A. Keane, Olaf Ronneberger 2018-01-01 2021-08-25 [("doi","10.1038/s41591-018-0107-6")] ai/nn biology --- /doc/ai/scaling/hardware/2020-mennel.pdf Ultrafast machine vision with 2D material neural network image sensors Lukas Mennel, Joanna Symonowicz, Stefan Wachter, Dmitry K. Polyushkin, Aday J. Molina-Mendoza, Thomas Mueller 2020-03-04 2021-08-25 [("doi","10.1038/s41586-020-2038-x")] ai/scaling/hardware --- /doc/ai/nn/1988-finkbeiner.pdf The Brain as Template Ann Finkbeiner 1988-06-01 2021-08-25 ai/nn psychology/neuroscience --- https://dallery.gallery/dall-e-prompts-photography-styles/ 2021-08-25 ai/nn/transformer/gpt/dall-e --- https://dallery.gallery/physical-media-dall-e-ai-prompts 2021-08-25 ai/nn/transformer/gpt/dall-e --- https://openreview.net/forum?id=P1DuPJzVTN Greedy Bayesian Posterior Approximation with Deep Ensembles Aleksei Tiulpin, Matthew B. Blaschko 2022-06-03 2022-06-03 ai/nn statistics/bayes

Ensembles of independently trained neural networks are a state-of-the-art approach to estimate predictive uncertainty in Deep Learning, and can be interpreted as an approximation of the posterior distribution via a mixture of delta functions. The training of ensembles relies on non-convexity of the loss landscape and random initialization of their individual members, making the resulting posterior approximation uncontrolled.

This paper proposes a novel and principled method to tackle this limitation, minimizing an f-divergence between the true posterior and a kernel density estimator (KDE) in a function space. We analyze this objective from a combinatorial point of view, and show that it is submodular with respect to mixture components for any f. Subsequently, we consider the problem of greedy ensemble construction. From the marginal gain on the negative f-divergence, which quantifies an improvement in posterior approximation yielded by adding a new component into the KDE, we derive a novel diversity term for ensemble methods.

The performance of our approach is demonstrated on computer vision out-of-distribution detection benchmarks in a range of architectures trained on multiple datasets.

The source code of our method is made publicly available at Github.

--- https://clinicaltrials.gov/show/NCT04375657 2021-08-26 longevity/epigenetics --- /doc/technology/2004-bowden.pdf Moore’s law and the Technology S-Curve Murrae J. Bowden 2004-12-01 2021-08-26 ai/scaling/hardware economics/experience-curve technology --- https://x.com/xurannotes/status/1532933812795371520 2021-08-26 ai/nn/transformer/clip/sample --- https://colab.research.google.com/drive/1c6VccMPsOMAUQCKU4BVDRd5Y32qkozmK 2021-08-26 ai/nn/transformer/clip ai/nn/transformer/gpt --- https://arxiv.org/abs/2201.00199#amazon The GatedTabTransformer: An enhanced deep learning architecture for tabular modeling Radostin Cholakov, Todor Kolev 2022-01-01 2022-01-01 [("doi","10.48550/arXiv.2201.00199")] ai/nn/fully-connected ai/tabular

There is an increasing interest in the application of deep learning architectures to tabular data. One of the state-of-the-art solutions is TabTransformer which incorporates an attention mechanism to better track relationships between categorical features and then makes use of a standard MLP to output its final logits.

In this paper we propose multiple modifications to the original TabTransformer performing better on binary classification tasks for 3 separate datasets with more than 1% AUROC gains. Inspired by gated MLP, linear projections are implemented in the MLP block and multiple activation functions are tested (GatedTabTransformer).

We also evaluate the importance of specific hyper parameters during training.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6183416/ Neural Correlates of Familiarity in Music Listening: A Systematic Review and a Neuroimaging Meta-Analysis Carina Freitas, Enrica Manzato, Alessandra Burini, Margot J. Taylor, Jason P. Lerch, Evdokia Anagnostou 2018 2021-08-26 [("doi","10.3389/fnins.2018.00686")] music psychology/neuroscience

Familiarity in music has been reported as an important factor modulating emotional and hedonic responses in the brain. Familiarity and repetition may increase the liking of a piece of music, thus inducing positive emotions. Neuroimaging studies have focused on identifying the brain regions involved in the processing of familiar and unfamiliar musical stimuli. However, the use of different modalities and experimental designs has led to discrepant results and it is not clear which areas of the brain are most reliably engaged when listening to familiar and unfamiliar musical excerpts.

In the present study, we conducted a systematic review from 3 databases (MEDLINE, PsycINFO, and Embase) using the keywords (recognition OR familiar OR familiarity OR exposure effect OR repetition) AND (music OR song) AND (brain OR brains OR neuroimaging OR functional Magnetic Resonance Imaging OR Position Emission Tomography OR Electroencephalography OR Event Related Potential OR Magnetoencephalography).

Of the 704 titles identified, 23 neuroimaging studies met our inclusion criteria for the systematic review. After removing studies providing insufficient information or contrasts, 11 studies (involving 212 participants) qualified for the meta-analysis using the activation likelihood estimation (ALE) approach.

Our results did not find peak activations consistently across included studies. Using a less conservative approach (p < 0.001, uncorrected for multiple comparisons) we found that the left superior frontal gyrus, the ventral lateral (VL) nucleus of the left thalamus, and the left medial surface of the superior frontal gyrus had the highest likelihood of being activated by familiar music. On the other hand, the left insula, and the right anterior cingulate cortex had the highest likelihood of being activated by unfamiliar music. We had expected limbic structures as top clusters when listening to familiar music. But, instead, music familiarity had a motor pattern of activation. This could reflect an audio-motor synchronization to the rhythm which is more engaging for familiar tunes, and/or a sing-along response in one’s mind, anticipating melodic, harmonic progressions, rhythms, timbres, and lyric events in the familiar songs.

These data provide evidence for the need for larger neuroimaging studies to understand the neural correlates of music familiarity.

--- https://arxiv.org/abs/2012.06678#amazon TabTransformer: Tabular Data Modeling Using Contextual Embeddings Xin Huang, Ashish Khetan, Milan Cvitkovic, Zohar Karnin 2020-12-11 2021-08-26 [("doi","10.48550/arXiv.2012.06678")] ai/nn/fully-connected ai/tabular

We propose TabTransformer, a novel deep tabular data modeling architecture for supervised and semi-supervised learning. The TabTransformer is built upon self-attention based Transformers. The Transformer layers transform the embeddings of categorical features into robust contextual embeddings to achieve higher prediction accuracy.

Through extensive experiments on 15 publicly available datasets, we show that the TabTransformer outperforms the state-of-the-art deep learning methods for tabular data by at least 1.0% on mean AUC, and matches the performance of tree-based ensemble models.

Furthermore, we demonstrate that the contextual embeddings learned from TabTransformer are highly robust against both missing and noisy data features, and provide better interpretability. Lastly, for the semi-supervised setting we develop an unsupervised pre-training procedure to learn data-driven contextual embeddings, resulting in an average 2.1% AUC lift over the state-of-the-art methods.

--- https://vkrakovna.wordpress.com/2022/06/02/paradigms-of-ai-alignment-components-and-enablers/ Paradigms of AI alignment: components and enablers 2021-08-26 reinforcement-learning/safe --- https://openreview.net/forum?id=0ZbPmmB61g#google Boosting Search Engines with Interactive Agents Massimiliano Ciaramita, Leonard Adolphs, Michelle Chen Huebscher, Sascha Rothe, Christian Buck, Thomas Hofmann, Yannic Kilcher, Lasse Espeholt, Pier Giuseppe Sessa, Lierni Sestorain, Benjamin Börschinger 2022-06-04 2022-06-04 ai/nn/retrieval ai/nn/transformer/t5 reinforcement-learning/exploration reinforcement-learning/imitation-learning reinforcement-learning/model/decision-transformer reinforcement-learning/model/muzero

[cf. WebGPT, MiniWoB++ agents] This paper presents first successful steps in designing search agents that learn meta-strategies for iterative query refinement in information-seeking tasks. Our approach uses machine reading to guide the selection of refinement terms from aggregated search results. Agents are then empowered with simple but effective search operators to exert fine-grained and transparent control over queries and search results.

We develop a novel way of generating synthetic search sessions, which leverages the power of transformer-based T5 language models through (self-)supervised learning. We also present a MuZero reinforcement learning agent with dynamically constrained actions that learns interactive search strategies from scratch.

Our search agents obtain retrieval and answer quality performance comparable to recent neural methods, using only a traditional term-based BM25 ranking function and interpretable discrete reranking and filtering actions.

--- https://arxiv.org/abs/2004.04906#facebook Dense Passage Retrieval for Open-Domain Question Answering Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih 2020-04-10 2021-08-26 [("doi","10.48550/arXiv.2004.04906")] ai/nn/retrieval

Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method.

In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework.

When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%–19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks.

--- https://www.talkrl.com/episodes/aravind-srinivas-2 TalkRL: The Reinforcement Learning Podcast: Aravind Srinivas 2: Aravind Srinivas, Research Scientist at OpenAI, returns to talk Decision Transformer, VideoGPT, choosing problems, and explore vs exploit in research careers 2021-08-27 reinforcement-learning/model/decision-transformer --- https://arxiv.org/abs/2205.14495#amazon Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline (3RL) Massimo Caccia, Jonas Mueller, Taesup Kim, Laurent Charlin, Rasool Fakoor 2022-05-28 2022-05-28 [("doi","10.48550/arXiv.2205.14495")] ai/nn/rnn reinforcement-learning/meta-learning/continual-learning reinforcement-learning/scaling

We study task-agnostic continual reinforcement learning (TACRL) in which standard RL challenges are compounded with partial observability stemming from task agnosticism, as well as additional difficulties of continual learning (CL), ie. learning on a non-stationary sequence of tasks. Here we compare TACRL methods with their soft upper bounds prescribed by previous literature: multi-task learning (MTL) methods which do not have to deal with non-stationary data distributions, as well as task-aware methods, which are allowed to operate under full observability. We consider a previously unexplored and straightforward baseline for TACRL, replay-based recurrent RL (3RL), in which we augment an RL algorithm with recurrent mechanisms to address partial observability and experience replay mechanisms to address catastrophic forgetting in CL.

Studying empirical performance in a sequence of RL tasks, we find surprising occurrences of 3RL matching and overcoming the MTL and task-aware soft upper bounds.

We lay out hypotheses that could explain this inflection point of continual & task-agnostic learning research. Our hypotheses are empirically tested in continuous control tasks via a large-scale study of the popular multi-task & continual learning benchmark Meta-World.

By analyzing different training statistics including gradient conflict, we find evidence that 3RL’s outperformance stems from its ability to quickly infer how new tasks relate with the previous ones, enabling forward transfer.

--- https://x.com/altarbeastlab/status/1533527916641800192 2021-08-27 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2205.09991 Planning with Diffusion for Flexible Behavior Synthesis Michael Janner, Yilun Du, Joshua B. Tenenbaum, Sergey Levine 2022-05-20 2022-05-20 [("doi","10.48550/arXiv.2205.09991")] ai/nn/diffusion reinforcement-learning/model/decision-transformer

[homepage; cf. diffusion causal models] Model-based reinforcement learning methods often use learning only for the purpose of estimating an approximate dynamics model, offloading the rest of the decision-making work to classical trajectory optimizers. While conceptually simple, this combination has a number of empirical shortcomings, suggesting that learned models may not be well-suited to standard trajectory optimization.

In this paper, we consider what it would look like to fold as much of the trajectory optimization pipeline as possible into the modeling problem, such that sampling from the model and planning with it become nearly identical. The core of our technical approach lies in a diffusion probabilistic model that plans by iteratively denoising trajectories.

We show how classifier-guided sampling and image inpainting can be reinterpreted as coherent planning strategies, explore the unusual and useful properties of diffusion-based planning methods, and demonstrate the effectiveness of our framework in control settings that emphasize long-horizon decision-making and test-time flexibility.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6290509/ Improving palliative care with deep learning An, Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Ng, Nigam H. Shah 2018 2021-08-27 [("doi","10.1186/s12911-018-0677-8")] ai/nn/fully-connected ai/tabular biology

Background: Access to palliative care is a key quality metric which most healthcare organizations strive to improve. The primary challenges to increasing palliative care access are a combination of physicians over-estimating patient prognoses, and a shortage of palliative staff in general. This, in combination with treatment inertia can result in a mismatch between patient wishes, and their actual care towards the end of life.

Method: In this work, we address this problem, with Institutional Review Board approval, using machine learning and Electronic Health Record (EHR) data of patients. We train a Deep Neural Network model on the EHR data of patients from previous years, to predict mortality of patients within the next 3–12 month period. This prediction is used as a proxy decision for identifying patients who could benefit from palliative care.

Results: The EHR data of all admitted patients are evaluated every night by this algorithm, and the palliative care team is automatically notified of the list of patients with a positive prediction. In addition, we present a novel technique for decision interpretation, using which we provide explanations for the model’s predictions.

Conclusion: The automatic screening and notification saves the palliative care team the burden of time consuming chart reviews of all patients, and allows them to take a proactive approach in reaching out to such patients rather then relying on referrals from the treating physicians.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6011237/ Large-scale comparison of machine learning methods for drug target prediction on ChEMBL Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Marvin Steijaert, Jörg K. Wegner, Hugo Ceulemans, Djork-Arné Clevert, Sepp Hochreiter 2018 2021-08-27 [("doi","10.1039/c8sc00148k")] ai/nn/rnn ai/tabular biology

Deep learning is currently the most successful machine learning technique in a wide range of application areas and has recently been applied successfully in drug discovery research to predict potential drug targets and to screen for active molecules. However, due to (1) the lack of large-scale studies, (2) the compound series bias that is characteristic of drug discovery datasets and (3) the hyperparameter selection bias that comes with the high number of potential deep learning architectures, it remains unclear whether deep learning can indeed outperform existing computational methods in drug discovery tasks.

We therefore assessed the performance of several deep learning methods on a large-scale drug discovery dataset and compared the results with those of other machine learning and target prediction methods. To avoid potential biases from hyperparameter selection or compound series, we used a nested cluster-cross-validation strategy.

We found (1) that deep learning methods outperform all competing methods and (2) that the predictive performance of deep learning is in many cases comparable to that of tests performed in wet labs (ie. in vitro assays).

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6031326/ Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery Jaak Simm, Günter Klambauer, Adam Arany, Marvin Steijaert, Jörg Kurt Wegner, Emmanuel Gustin, Vladimir Chupakhin, Yolanda T. Chong, Jorge Vialard, Peter Buijnsters, Ingrid Velter, Alexander Vapirev, Shantanu Singh, Anne E. Carpenter, Roel Wuyts, Sepp Hochreiter, Yves Moreau, Hugo Ceulemans 2018 2021-08-27 [("doi","10.1016/j.chembiol.2018.01.015")] ai/nn/fully-connected ai/tabular

In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes.

Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250× over that of the initial project assays while increasing the chemical structure diversity of the hits.

Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays.

--- https://arxiv.org/abs/2106.02584 Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Tom Rainforth, Yarin Gal 2021-06-04 2021-08-27 [("doi","10.48550/arXiv.2106.02584")] ai/nn/transformer ai/tabular

We challenge a common assumption underlying most supervised deep learning: that a model makes a prediction depending only on its parameters and the features of a single input.

To this end, we introduce a general-purpose deep learning architecture that takes as input the entire dataset instead of processing one datapoint at a time. Our approach uses self-attention to reason about relationships between datapoints explicitly, which can be seen as realizing non-parametric models using parametric attention mechanisms. However, unlike conventional non-parametric models, we let the model learn end-to-end from the data how to make use of other datapoints for prediction.

Empirically, our models solve cross-datapoint lookup and complex reasoning tasks unsolvable by traditional deep learning models. We show highly competitive results on tabular data, early results on CIFAR-10, and give insight into how the model makes use of the interactions between points.

--- https://arxiv.org/abs/1611.03824#deepmind Learning to Learn without Gradient Descent by Gradient Descent Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy Lillicrap, Matt Botvinick, Nando de Freitas 2016-11-11 2021-08-27 [("doi","10.48550/arXiv.1611.03824")] ai/nn/rnn reinforcement-learning/meta-learning statistics/bayes

We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent.

We show that these learned optimizers exhibit a remarkable degree of transfer in that they can be used to efficiently optimize a broad range of derivative-free black-box functions, including Gaussian process bandits, simple control objectives, global optimization benchmarks, and hyper-parameter tuning tasks.

Up to the training horizon, the learned optimizers learn to trade-off exploration and exploitation, and compare favorably with heavily engineered Bayesian optimization packages for hyper-parameter tuning.

--- https://arxiv.org/abs/1612.02605 Towards Information-Seeking Agents Philip Bachman, Alessandro Sordoni, Adam Trischler 2016-12-08 2021-08-27 [("doi","10.48550/arXiv.1612.02605")] ai/nn reinforcement-learning/exploration

We develop a general problem setting for training and testing the ability of agents to gather information efficiently. Specifically, we present a collection of tasks in which success requires searching through a partially-observed environment, for fragments of information which can be pieced together to accomplish various goals.

We combine deep architectures with techniques from reinforcement learning to develop agents that solve our tasks. We shape the behavior of these agents by combining extrinsic and intrinsic rewards.

We empirically demonstrate that these agents learn to search actively and intelligently for new information to reduce their uncertainty, and to exploit information they have already acquired.

--- https://arxiv.org/abs/1612.02297 Spatially Adaptive Computation Time for Residual Networks Michael Figurnov, Maxwell D. Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, Ruslan Salakhutdinov 2016-12-07 2021-08-28 [("doi","10.48550/arXiv.1612.02297")] ai/nn

This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image. This architecture is end-to-end trainable, deterministic and problem-agnostic. It is therefore applicable without any modifications to a wide range of computer vision problems such as image classification, object detection and image segmentation.

We present experimental results showing that this model improves the computational efficiency of Residual Networks on the challenging ImageNet classification and COCO object detection datasets. Additionally, we evaluate the computation time maps on the visual saliency dataset cat2000 and find that they correlate surprisingly well with human eye fixation positions.

--- https://arxiv.org/abs/1611.01603 Bidirectional Attention Flow for Machine Comprehension Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hannaneh Hajishirzi 2016-11-05 2021-08-28 [("doi","10.48550/arXiv.1611.01603")] ai/nn/rnn

Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query.

Recently, attention mechanisms have been successfully extended to MC. Typically these methods use attention to focus on a small portion of the context and summarize it with a fixed-size vector, couple attentions temporally, and/or often form a unidirectional attention.

In this paper we introduce the Bi-Directional Attention Flow (BIDAF) network, a multi-stage hierarchical process that represents the context at different levels of granularity and uses a bi-directional attention flow mechanism to obtain a query-aware context representation without early summarization.

Our experimental evaluations show that our model achieves state-of-the-art results in Stanford Question Answering Dataset (SQuAD) and CNN/Daily Mail cloze test.

--- https://arxiv.org/abs/1609.07317 Language as a Latent Variable: Discrete Generative Models for Sentence Compression Yishu Miao, Phil Blunsom 2016-09-23 2021-08-28 [("doi","10.48550/arXiv.1609.07317")] ai/nn/vae

In this work we explore deep generative models of text in which the latent representation of a document is itself drawn from a discrete language model distribution.

We formulate a variational autoencoder for inference in this model and apply it to the task of compressing sentences. In this application the generative model first draws a latent summary sentence from a background language model, and then subsequently draws the observed sentence conditioned on this latent summary.

In our empirical evaluation, we show that generative formulations of both abstractive and extractive compression yield state-of-the-art results when trained on a large amount of supervised data.

Further, we explore semi-supervised compression scenarios where we show that it is possible to achieve performance competitive with previously proposed supervised models while training on a fraction of the supervised data.

--- https://www.biorxiv.org/content/10.1101/2022.06.03.494686.full Genome-Wide Large-Scale Multi-Trait Analysis Characterizes Global Patterns of Pleiotropy and Unique Trait-Specific Variants Guanghao Qi, Surya B. Chhetri, Debashree Ray, Diptavo Dutta, Alexis Battle, Samsiddhi Bhattacharjee, Nilanjan Chatterjee 2022-06-03 2022-06-03 [("doi","10.1101/2022.06.03.494686")] genetics/heritable/correlation

Genome-wide association studies (GWAS) have found widespread evidence of pleiotropy, but characterization of global patterns of pleiotropy remain highly incomplete due to insufficient power of current approaches. We develop fastASSET, an extension of the method ASSET, to allow computationally efficient detection of variant-level pleiotropic association across a large number of traits.

We analyze GWAS summary statistics of 116 complex traits of diverse types collected from the NIH GRASP repository and a number of other large GWAS consortia. We identify a total of 2,293 independent loci at the genome-wide statistical-significance level and found that the lead variants in nearly all of these loci (~99%) to be associated with two or more (median = 6) traits. Further, the estimated degree of pleiotropy for the detected variants strongly predicted their degree of pleiotropy across a much larger number of traits (K=4,114) in the UK Biobank Study.

Follow-up analyses of 21 unique trait-specific variants suggest that they are often linked to the expression in trait-related tissues for a small number of genes, some of which are well known to be involved in relevant biological processes.

Our findings provide deeper insight into the nature of complex trait pleiotropy and leads to, for the first time, identification of highly unique trait-specific susceptibility variants.

--- https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/agi-ruin-a-list-of-lethalities AGI Ruin: A List of Lethalities 2021-08-28 reinforcement-learning/safe --- https://research.google/blog/rethinking-attention-with-performers/ Rethinking Attention with Performers Choromanski, Colwell 2020 2021-08-28 ai/nn/transformer/attention --- https://research.google/blog/constructing-transformers-for-longer-sequences-with-sparse-attention-methods/ Constructing Transformers For Longer Sequences with Sparse Attention Methods 2021-08-28 ai/nn/transformer/attention/sparsity --- https://arxiv.org/pdf/2009.06732#org=google&page=6 Efficient Transformers: A Survey § Table 1 2021-08-28 ai/nn/transformer/attention --- https://huggingface.co/blog/reformer The Reformer—Pushing the limits of language modeling Patrick von Platen 2020 2021-08-28 ai/nn/transformer/attention/sparsity --- https://lilianweng.github.io/lil-log/2020/04/07/the-transformer-family.html#openai The Transformer Family: Attention and Self-Attention · Multi-Head Self-Attention · Transformer · Adaptive Computation Time (ACT) · Improved Attention Span: (Longer Attention Span (Transformer-XL) / Adaptive Attention Span / Localized Attention Span (Image Transformer)) · Less Time and Memory Cost: (Sparse Attention Matrix Factorization (Sparse Transformers) / Locality-Sensitive Hashing (Reformer)) · Make it Recurrent (Universal Transformer) · Stabilization for RL (GTrXL) 2021-08-28 ai/nn/transformer/attention --- https://medium.com/@cmsflash/efficient-attention-attention-with-linear-complexities-b3c00c4348e3 Efficient Attention: Attention with Linear Complexities [blog] 2021-08-28 ai/nn/transformer/attention/linear-algebra --- https://x.com/joeddav/status/1285238997011267585 So I tried out GPT-3’s trick of conditioning on training data with XLNet. While it doesn’t do as well as the 175B GPT-3, it does much better than the version which is the same size as XLNet (0.4B). The visual below is from their paper on WinoGrande—I added the squares for XLNet. 2021-08-29 ai/nn/transformer/attention/recurrent --- https://www.lyrn.ai/2019/01/16/transformer-xl-sota-language-model/ Transformer-XL—Combining Transformers and RNNs Into a State-of-the-art Language Model Rani Horev 2019 2021-08-29 ai/nn/transformer/attention/recurrent --- https://www.pragmatic.ml/a-survey-of-methods-for-incorporating-long-term-context/ A Survey of Long-Term Context in Transformers: Sparse Transformers · Adaptive Span Transformers · Transformer-XL · Compressive Transformers · Reformer · Routing Transformer · Sinkhorn Transformer · Linformer · Efficient Attention: Attention with Linear Complexities · Transformers are RNNs · ETC · Longformer 2021-08-29 ai/nn/transformer/attention --- https://www.pragmatic.ml/reformer-deep-dive/ A Deep Dive into the Reformer 2021-08-29 ai/nn/transformer/attention/sparsity --- https://www.pragmatic.ml/sparse-sinkhorn-attention/ Optimal Transport and the Sinkhorn Transformer 2021-08-29 ai/nn/transformer/attention/sparsity --- https://en.wikipedia.org/wiki/BERT_(language_model) BERT (language model) 2021-08-29 ai/nn/transformer --- https://en.wikipedia.org/wiki/Attention_(machine_learning) Attention (machine learning) 2021-08-29 ai/nn/transformer --- https://en.wikipedia.org/wiki/Perceiver Perceiver 2021-08-29 ai/nn/transformer --- https://en.wikipedia.org/wiki/Vision_transformer Vision transformer 2021-08-29 ai/nn/transformer --- https://nlp.seas.harvard.edu/2018/04/03/attention.html The Annotated Transformer 2021-08-29 ai/nn/transformer/gpt ai/poetry --- https://research.google/blog/transformer-a-novel-neural-network-architecture-for-language-understanding/ 2021-08-29 ai/nn/transformer/gpt ai/poetry --- https://amaarora.github.io/2020/02/18/annotatedGPT2.html 2021-08-30 ai/nn/transformer/gpt ai/poetry --- https://blog.floydhub.com/the-transformer-in-pytorch/ 2021-08-30 ai/nn/transformer --- https://e2eml.school/transformers.html 2021-08-30 ai/nn/transformer/attention --- https://github.com/karpathy/minGPT karpathy/minGPT: A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training 2021-08-30 ai/nn/transformer/gpt cs/python --- https://jalammar.github.io/illustrated-gpt2/ The Illustrated GPT-2 (Visualizing Transformer Language Models) 2021-08-30 ai/nn/transformer --- https://jalammar.github.io/illustrated-transformer/ The Illustrated Transformer 2021-08-30 ai/nn/transformer --- https://mchromiak.github.io/articles/2017/Sep/12/Transformer-Attention-is-all-you-need/ The Transformer—Attention is all you need. 2021-08-30 ai/nn/transformer --- https://medium.com/synapse-dev/understanding-bert-transformer-attention-isnt-all-you-need-5839ebd396db Understanding BERT Transformer: Attention isn’t all you need Damien Sileo 2021-08-30 ai/nn/transformer --- https://www.peterbloem.nl/blog/transformers Transformers are a very exciting family of machine learning architectures Peter Bloem 2021-08-30 ai/nn/transformer

Many good tutorials exist but in the last few years, transformers have mostly become simpler, so that it is now much more straightforward to explain how modern architectures work.

This post is an attempt to explain directly [in PyTorch] how modern transformers work, and why, without some of the historical baggage.

--- https://arxiv.org/abs/1610.06258#deepmind Using Fast Weights to Attend to the Recent Past Jimmy Ba, Geoffrey Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu 2016-10-20 2021-08-30 [("doi","10.48550/arXiv.1610.06258")] ai/nn/rnn reinforcement-learning/meta-learning

Until recently, research on artificial neural networks was largely restricted to systems with only two types of variables: Neural activities that represent the current or recent input and weights that learn to capture regularities among inputs, outputs, and payoffs.

There is no good reason for this restriction. Synapses have dynamics at many different time-scales and this suggests that artificial neural networks might benefit from variables that change slower than activities but much faster than the standard weights. These “fast weights” can be used to store temporary memories of the recent past and they provide a neurally plausible way of implementing the type of attention to the past that has recently proved very helpful in sequence-to-sequence models.

By using fast weights we can avoid the need to store copies of neural activity patterns.

--- https://arxiv.org/abs/2206.01720 Revisiting the "Video" in Video-Language Understanding Shyamal Buch, Cristóbal Eyzaguirre, Adrien Gaidon, Jiajun Wu, Li Fei-Fei, Juan Carlos Niebles 2022-06-03 2022-06-03 [("doi","10.48550/arXiv.2206.01720")] ai/video/analysis

What makes a video task uniquely suited for videos, beyond what can be understood from a single image? Building on recent progress in self-supervised image-language models, we revisit this question in the context of video and language tasks. We propose the atemporal probe (ATP), a new model for video-language analysis which provides a stronger bound on the baseline accuracy of multimodal models constrained by image-level understanding. By applying this model to standard discriminative video and language tasks, such as video question answering and text-to-video retrieval, we characterize the limitations and potential of current video-language benchmarks.

We find that understanding of event temporality is often not necessary to achieve strong or state-of-the-art performance, even compared with recent large-scale video-language models and in contexts intended to benchmark deeper video-level understanding. We also demonstrate how ATP can improve both video-language dataset and model design. We describe a technique for leveraging ATP to better disentangle dataset subsets with a higher concentration of temporally challenging data, improving benchmarking efficacy for causal and temporal understanding. Further, we show that effectively integrating ATP into full video-level temporal models can improve efficiency and state-of-the-art accuracy.

--- https://arxiv.org/abs/2206.01714 Compositional Visual Generation with Composable Diffusion Models Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, Joshua B. Tenenbaum 2022-06-03 2022-06-03 [("doi","10.48550/arXiv.2206.01714")] ai/nn/diffusion ai/nn/transformer/gpt/dall-e/2

Large text-guided diffusion models, such as DALL·E 2, are able to generate stunning photorealistic images given natural language descriptions. While such models are highly flexible, they struggle to understand the composition of certain concepts, such as confusing the attributes of different objects or relations between objects.

In this paper, we propose an alternative structured approach for compositional generation using diffusion models. An image is generated by composing a set of diffusion models, with each of them modeling a certain component of the image. To do this, we interpret diffusion models as energy-based models in which the data distributions defined by the energy functions may be explicitly combined.

The proposed method can generate scenes at test time that are substantially more complex than those seen in training, composing sentence descriptions, object relations, human facial attributes, and even generalizing to new combinations that are rarely seen in the real world. We further illustrate how our approach may be used to compose pre-trained text-guided diffusion models and generate photorealistic images containing all the details described in the input descriptions, including the binding of certain object attributes that have been shown difficult for DALL·E 2.

These results point to the effectiveness of the proposed method in promoting structured generalization for visual generation.

--- /doc/ai/nn/2013-clark.pdf Whatever next? Predictive brains, situated agents, and the future of cognitive science Andy Clark 2013-06-01 2021-08-31 [("doi","10.1017/S0140525X12000477")] ai/nn psychology/neuroscience reinforcement-learning/model

Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer an unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success.

This target article critically examines this “hierarchical prediction machine” approach, concluding that it offers the best clue yet to the shape of an unified science of mind and action.

§1 & §2 lay out the key elements and implications of the approach.

§3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual.

The paper ends (§4 & §5) by asking how such approaches might impact our more general vision of mind, experience, and agency.

--- https://arxiv.org/abs/1608.05604 Modeling Human Reading with Neural Attention Michael Hahn, Frank Keller 2016-08-19 2021-08-31 [("doi","10.48550/arXiv.1608.05604")] ai/nn/transformer/attention psychology/neuroscience

When humans read text, they fixate some words and skip others. However, there have been few attempts to explain skipping behavior with computational models, as most existing work has focused on predicting reading times (eg. using surprisal).

In this paper, we propose a novel approach that models both skipping and reading, using an unsupervised architecture that combines a neural attention with autoencoding, trained on raw text using reinforcement learning.

Our model explains human reading behavior as a tradeoff between precision of language understanding (encoding the input accurately) and economy of attention (fixating as few words as possible).

We evaluate the model on the Dundee eye-tracking corpus, showing that it accurately predicts skipping behavior and reading times, is competitive with surprisal, and captures known qualitative features of human reading.

--- https://arxiv.org/abs/1606.02245 Iterative Alternating Neural Attention for Machine Reading Alessandro Sordoni, Philip Bachman, Adam Trischler, Yoshua Bengio 2016-06-07 2021-08-31 [("doi","10.48550/arXiv.1606.02245")] ai/nn/rnn ai/nn/transformer/attention

We propose a novel neural attention architecture to tackle machine comprehension tasks, such as answering Cloze-style queries with respect to a document.

Unlike previous models, we do not collapse the query into a single vector, instead we deploy an iterative alternating attention mechanism that allows a fine-grained exploration of both the query and the document.

Our model outperforms state-of-the-art baselines in standard machine comprehension benchmarks such as CNN news articles and the Children’s Book Test (CBT) dataset.

--- https://arxiv.org/abs/1511.02793 Generating Images from Captions with Attention Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov 2015-11-09 2021-08-31 [("doi","10.48550/arXiv.1511.02793")] ai/nn/rnn ai/nn/transformer/attention

Motivated by the recent progress in generative models, we introduce a model that generates images from natural language descriptions.

The proposed model iteratively draws patches on a canvas, while attending to the relevant words in the description.

After training on Microsoft COCO,

we compare our model with several baseline generative models on image generation and retrieval tasks. We demonstrate that our model produces higher quality samples than other approaches and generates images with novel scene compositions corresponding to previously unseen captions in the dataset.

--- https://arxiv.org/abs/1606.01885 Learning to Optimize Ke Li, Jitendra Malik 2016-06-06 2021-08-31 [("doi","10.48550/arXiv.1606.01885")] ai/nn/fully-connected reinforcement-learning/meta-learning reinforcement-learning/model-free

Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm, which we believe to be the first method that can automatically discover a better algorithm.

We approach this problem from a reinforcement learning perspective and represent any particular optimization algorithm as a policy. We learn an optimization algorithm using guided policy search and demonstrate that the resulting algorithm outperforms existing hand-engineered algorithms in terms of convergence speed and/or the final objective value.

--- https://arxiv.org/abs/1606.01541 Deep Reinforcement Learning for Dialogue Generation Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, Dan Jurafsky 2016-06-05 2021-08-31 [("doi","10.48550/arXiv.1606.01541")] ai/nn/rnn reinforcement-learning/model-free

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led traditional NLP models of dialogue to draw on reinforcement learning.

In this paper, we show how to integrate these goals, applying deep reinforcement learning to model future reward in chatbot dialogue. The model simulates dialogues between two virtual agents, using policy gradient methods to reward sequences that display 3 useful conversational properties: informative (non-repetitive turns), coherence, and ease of answering (related to forward-looking function).

We evaluate our model on diversity, length as well as with human judges, showing that the proposed algorithm generates more interactive responses and manages to foster a more sustained conversation in dialogue simulation.

This work marks a first step towards learning a neural conversational model based on the long-term success of dialogues.

--- https://arxiv.org/abs/1603.07954 Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning Karthik Narasimhan, Adam Yala, Regina Barzilay 2016-03-25 2021-08-31 [("doi","10.48550/arXiv.1603.07954")] ai/nn/retrieval reinforcement-learning/exploration reinforcement-learning/model-free

Most successful information extraction systems operate with access to a large collection of documents. In this work, we explore the task of acquiring and incorporating external evidence to improve extraction accuracy in domains where the amount of training data is scarce.

This process entails issuing search queries, extraction from new sources and reconciliation of extracted values, which are repeated until sufficient evidence is collected. We approach the problem using a reinforcement learning framework where our model learns to select optimal actions based on contextual information. We employ a deep Q-network, trained to optimize a reward function that reflects extraction accuracy while penalizing extra effort.

Our experiments on two databases—of shooting incidents, and food adulteration cases—demonstrate that our system outperforms traditional extractors and a competitive meta-classifier baseline.

--- https://arxiv.org/abs/1511.06343 Online Batch Selection for Faster Training of Neural Networks Ilya Loshchilov, Frank Hutter 2015-11-19 2021-08-31 [("doi","10.48550/arXiv.1511.06343")] ai/nn reinforcement-learning/exploration

Deep neural networks are commonly trained using stochastic non-convex optimization procedures, which are driven by gradient information estimated on fractions (batches) of the dataset. While it is commonly accepted that batch size is an important parameter for offline tuning, the benefits of online selection of batches remain poorly understood.

We investigate online batch selection strategies for two state-of-the-art methods of stochastic gradient-based optimization, Adadelta and Adam. As the loss function to be minimized for the whole dataset is an aggregation of loss functions of individual datapoints, intuitively, datapoints with the greatest loss should be considered (selected in a batch) more frequently. However, the limitations of this intuition and the proper control of the selection pressure over time are open questions.

We propose a simple strategy where all datapoints are ranked w.r.t. their latest known loss value and the probability to be selected decays exponentially as a function of rank. Our experimental results on the MNIST dataset suggest that selecting batches speeds up both Adadelta and Adam by a factor of about 5.

--- https://arxiv.org/abs/1406.3896 Freeze-Thaw Bayesian Optimization Kevin Swersky, Jasper Snoek, Ryan Prescott Adams 2014-06-16 2021-08-31 [("doi","10.48550/arXiv.1406.3896")] ai/nn reinforcement-learning/exploration statistics/bayes

In this paper, we develop a dynamic form of Bayesian optimization for machine learning models with the goal of rapidly finding good hyperparameter settings.

Our method uses the partial information gained during the training of a machine learning model in order to decide whether to pause training and start a new model, or resume the training of a previously-considered model. We specifically tailor our method to machine learning problems by developing a novel positive-definite covariance kernel to capture a variety of training curves.

Furthermore, we develop a Gaussian process prior that scales gracefully with additional temporal observations. Finally, we provide an information-theoretic framework to automate the decision process.

Experiments on several common machine learning models show that our approach is extremely effective in practice.

--- https://arxiv.org/abs/1206.4634 Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting Ning Xie, Hirotaka Hachiya, Masashi Sugiyama 2012-06-18 2021-09-01 [("doi","10.1587/transinf.E96.D.1134")] ai/nn reinforcement-learning/model-free

Oriental ink painting, called Sumi-e, is one of the most appealing painting styles that has attracted artists around the world. Major challenges in computer-based Sumi-e simulation are to abstract complex scene information and draw smooth and natural brush strokes.

To automatically find such strokes, we propose to model the brush as a reinforcement learning agent, and learn desired brush-trajectories by maximizing the sum of rewards in the policy search framework. We also provide elaborate design of actions, states, and rewards tailored for a Sumi-e agent. The effectiveness of our proposed approach is demonstrated through simulated Sumi-e experiments.

--- https://arxiv.org/abs/1409.0473 Neural Machine Translation by Jointly Learning to Align and Translate Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio 2014-09-01 2021-09-01 [("doi","10.48550/arXiv.1409.0473")] ai/nn/rnn ai/nn/transformer/attention

Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance.

The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.

With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.

--- https://arxiv.org/abs/1505.00521 Reinforcement Learning Neural Turing Machines—Revised Wojciech Zaremba, Ilya Sutskever 2015-05-04 2021-09-01 [("doi","10.48550/arXiv.1505.00521")] ai/nn/rnn reinforcement-learning/model-free

The Neural Turing Machine (NTM) is more expressive than all previously considered models because of its external memory. It can be viewed as a broader effort to use abstract external Interfaces and to learn a parametric model that interacts with them.

The capabilities of a model can be extended by providing it with proper Interfaces that interact with the world. These external Interfaces include memory, a database, a search engine, or a piece of software such as a theorem verifier. Some of these Interfaces are provided by the developers of the model. However, many important existing Interfaces, such as databases and search engines, are discrete.

We examine feasibility of learning models to interact with discrete Interfaces. We investigate the following discrete Interfaces: a memory Tape, an input Tape, and an output Tape. We use a Reinforcement Learning algorithm to train a neural network that interacts with such Interfaces to solve simple algorithmic tasks. Our Interfaces are expressive enough to make our model Turing complete.

--- /doc/ai/2016-hernandezorallo.pdf Is Spearman’s law of diminishing returns (SLODR) meaningful for artificial agents? Hernandez-Orallo 2016-01-01 2021-09-01 [("doi","10.3233/978-1-61499-672-9-471")] ai cs/cellular-automaton iq --- https://arxiv.org/abs/1407.4443 On the Complexity of Best Arm Identification in Multi-Armed Bandit Models Emilie Kaufmann, Olivier Cappé, Aurélien Garivier 2014-07-16 2021-09-01 [("doi","10.48550/arXiv.1407.4443")] reinforcement-learning/exploration statistics/order/comparison

The stochastic multi-armed bandit model is a simple abstraction that has proven useful in many different contexts in statistics and machine learning. Whereas the achievable limit in terms of regret minimization is now well known, our aim is to contribute to a better understanding of the performance in terms of identifying the m best arms. We introduce generic notions of complexity for the two dominant frameworks considered in the literature: fixed-budget and fixed-confidence settings.

In the fixed-confidence setting, we provide the first known distribution-dependent lower bound on the complexity that involves information-theoretic quantities and holds when m is larger than 1 under general assumptions. In the specific case of two armed-bandits, we derive refined lower bounds in both the fixed-confidence and fixed-budget settings, along with matching algorithms for Gaussian and Bernoulli bandit models. These results show in particular that the complexity of the fixed-budget setting may be smaller than the complexity of the fixed-confidence setting, contradicting the familiar behavior observed when testing fully specified alternatives.

In addition, we also provide improved sequential stopping rules that have guaranteed error probabilities and shorter average running times. The proofs rely on two technical results that are of independent interest: a deviation lemma for self-normalized sums (Lemma 19) and a novel change of measure inequality for bandit models (Lemma 1).

--- https://arxiv.org/abs/0802.2655 Pure Exploration for Multi-Armed Bandit Problems Sébastien Bubeck, Rémi Munos, Gilles Stoltz 2008-02-19 2021-09-01 [("doi","10.48550/arXiv.0802.2655")] reinforcement-learning/exploration statistics/order/comparison

We consider the framework of stochastic multi-armed bandit problems and study the possibilities and limitations of forecasters that perform an on-line exploration of the arms. These forecasters are assessed in terms of their simple regret, a regret notion that captures the fact that exploration is only constrained by the number of available rounds (not necessarily known in advance), in contrast to the case when the cumulative regret is considered and when exploitation needs to be performed at the same time. We believe that this performance criterion is suited to situations when the cost of pulling an arm is expressed in terms of resources rather than rewards. We discuss the links between the simple and the cumulative regret.

One of the main results in the case of a finite number of arms is a general lower bound on the simple regret of a forecaster in terms of its cumulative regret: the smaller the latter, the larger the former. Keeping this result in mind, we then exhibit upper bounds on the simple regret of some forecasters.

The paper ends with a study devoted to continuous-armed bandit problems; we show that the simple regret can be minimized with respect to a family of probability distributions if and only if the cumulative regret can be minimized for it. Based on this equivalence, we are able to prove that the separable metric spaces are exactly the metric spaces on which these regrets can be minimized with respect to the family of all probability distributions with continuous mean-payoff functions.

--- https://arxiv.org/abs/1508.04145 Reflective Oracles: A Foundation for Classical Game Theory Benja Fallenstein, Jessica Taylor, Paul F. Christiano 2015-08-17 2021-09-01 [("doi","10.48550/arXiv.1508.04145")] ai reinforcement-learning/multi-agent statistics/decision

Classical game theory treats players as special—a description of a game contains a full, explicit enumeration of all players—even though in the real world, “players” are no more fundamentally special than rocks or clouds. It isn’t trivial to find a decision-theoretic foundation for game theory in which an agent’s co-players are a non-distinguished part of the agent’s environment. Attempts to model both players and the environment as Turing machines, for example, fail for standard diagonalization reasons.

In this paper, we introduce a “reflective” type of oracle, which is able to answer questions about the outputs of oracle machines with access to the same oracle. These oracles avoid diagonalization by answering some queries randomly. We show that machines with access to a reflective oracle can be used to define rational agents using causal decision theory. These agents model their environment as a probabilistic oracle machine, which may contain other agents as a non-distinguished part.

We show that if such agents interact, they will play a Nash equilibrium, with the randomization in mixed strategies coming from the randomization in the oracle’s answers. This can be seen as providing a foundation for classical game theory in which players aren’t special.

--- https://arxiv.org/abs/1512.00933 Probabilistic Integration: A Role in Statistical Computation? François-Xavier Briol, Chris. J. Oates, Mark Girolami, Michael A. Osborne, Dino Sejdinovic 2015-12-03 2021-09-01 [("doi","10.48550/arXiv.1512.00933")] math statistics/bayes

A research frontier has emerged in scientific computation, wherein numerical error is regarded as a source of epistemic uncertainty that can be modelled. This raises several statistical challenges, including the design of statistical methods that enable the coherent propagation of probabilities through a (possibly deterministic) computational work-flow.

This paper examines the case for probabilistic numerical methods in routine statistical computation. Our focus is on numerical integration, where a probabilistic integrator is equipped with a full distribution over its output that reflects the presence of an unknown numerical error.

Our main technical contribution is to establish, for the first time, rates of posterior contraction for these methods. These show that probabilistic integrators can in principle enjoy the “best of both worlds”, leveraging the sampling efficiency of Monte Carlo methods whilst providing a principled route to assess the impact of numerical error on scientific conclusions.

Several substantial applications are provided for illustration and critical evaluation, including examples from statistical modeling, computer graphics and a computer model for an oil reservoir.

--- /doc/ai/2002-hedberg.pdf DART: Revolutionizing logistics planning Sara Reese Hedberg 2002-01-01 2021-09-01 ai statistics/decision --- /doc/ai/nn/1990-schwartz.pdf Exhaustive Learning D. B. Schwartz, V. K. Samalam, Sara A. Solla, J. S. Denker 1990-09-01 2021-09-01 [("doi","10.1162/neco.1990.2.3.374")] ai/nn ai/scaling

Exhaustive exploration of an ensemble of networks is used to model learning and generalization in layered neural networks. A simple Boolean learning problem involving networks with binary weights is numerically solved to obtain the entropy Sm and the average generalization ability Gm as a function of the size m of the training set.

Learning curves Gm vs m are shown to depend solely on the distribution of generalization abilities over the ensemble of networks.

Such distribution is determined prior to learning, and provides a novel theoretical tool for the prediction of network performance on a specific task.

--- /doc/ai/nn/1962-rosenblatt-principlesofneurodynamics.pdf Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms Frank Rosenblatt 1962-03-15 2021-09-01 ai/nn psychology/neuroscience --- https://www.lesswrong.com/posts/Fq8ybxtcFvKEsWmF8/ai-takeoff-story-a-continuation-of-progress-by-other-means AI takeoff story: a continuation of progress by other means 2021-09-02 reinforcement-learning/safe --- https://www.lesswrong.com/posts/5wMcKNAwB6X4mp9og/that-alien-message That Alien Message Eliezer Yudkowsky 2021-09-02 cs/algorithm/information/compression fiction/science-fiction reinforcement-learning/safe --- https://www.baen.com/Chapters/9781618249203/9781618249203___2.htm Slow Tuesday Night R. A. Lafferty 2021-09-02 fiction/science-fiction reinforcement-learning/safe --- https://web.archive.org/web/20140527121332/https://www.infinityplus.co.uk/stories/under.htm Understand —a novelette by Ted Chiang 2021-09-02 reinforcement-learning/safe --- https://aiimpacts.org/partially-plausible-fictional-ai-futures/ Fiction relevant to AI futurism 2021-09-02 reinforcement-learning/safe --- https://www.greaterwrong.com/tag/ai-takeoff AI Takeoff 2021-09-02 reinforcement-learning/safe --- https://www.aleph.se/papers/Spamming%20the%20universe.pdf 2021-09-02 reinforcement-learning/safe --- http://www.faculty.ucr.edu/~eschwitz/SchwitzPapers/SF-MasterList-141103-byauthor.htm 2021-09-02 fiction/science-fiction philosophy --- https://archive.org/details/AsimovEdTheGreatSFStories021940/Asimov_ed%20-%20The%20Great%20SF%20Stories%2007%20-%201945?view=theater#page/n263/mode/2up Giant Killer A. Bertram Chandler 1946 2021-09-02 fiction/science-fiction --- https://dresdencodak.com/2009/09/22/caveman-science-fiction/ Caveman Science Fiction Aaron Diaz 2021-09-02 fiction/humor fiction/science-fiction fiction/humor --- https://en.wikipedia.org/wiki/Deadline_%28science_fiction_story%29 ‘Deadline’ (science fiction story) 2021-09-02 fiction/science-fiction radiance --- https://web.archive.org/web/20150422010811/http://www.kkbooks.net/ScienceFiction/Asimov37/27283.html Forget It! Isaac Asimov 2021-09-03 technology --- https://web.archive.org/web/20071212122427/https://www.bookslut.com/science_fiction_skeptic/2007_12_012076.php 2021-09-03 anime/eva fiction/science-fiction --- https://everything2.com/title/sensawunda sensawunda 2021-09-03 fiction/science-fiction --- https://en.wikipedia.org/wiki/New_Wave_science_fiction New Wave science fiction 2021-09-03 fiction/science-fiction --- https://en.wikipedia.org/wiki/Galaxy_Science_Fiction Galaxy Science Fiction 2021-09-03 fiction/science-fiction --- https://www.antipope.org/charlie/blog-static/2011/09/science-fiction-as-foresight.html Science Fiction as Foresight 2021-09-03 fiction/science-fiction statistics/prediction --- https://bakztfuture.substack.com/p/dall-e-2-unofficial-natural-language-b14 DALL·E 2—Unofficial Natural Language Image Editing, Art Critique Survey 2021-09-03 ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2206.01685 Toward a realistic model of speech processing in the brain with self-supervised learning Juliette Millet, Charlotte Caucheteux, Pierre Orhan, Yves Boubenec, Alexandre Gramfort, Ewan Dunbar, Christophe Pallier, Jean-Remi King 2022-06-03 2022-06-03 [("doi","10.48550/arXiv.2206.01685")] ai/nn/transformer ai/scaling psychology/neuroscience

[Twitter, 2] Several deep neural networks have recently been shown to generate activations similar to those of the brain in response to the same input. These algorithms, however, remain largely implausible: they require (1) extraordinarily large amounts of data, (2) unobtainable supervised labels, (3) textual rather than raw sensory input, and / or (4) implausibly large memory (eg. thousands of contextual words). These elements highlight the need to identify algorithms that, under these limitations, would suffice to account for both behavioral and brain responses.

Focusing on the issue of speech processing, we here hypothesize that self-supervised algorithms trained on the raw waveform constitute a promising candidate. Specifically, we compare a recent self-supervised architecture, Wav2Vec 2.0, to the brain activity of 412 English, French, and Mandarin individuals recorded with functional Magnetic Resonance Imaging (fMRI), while they listened to ~1h of audio books.

Our results are 4: First, we show that this algorithm learns brain-like representations with as little as 600 hours of unlabeled speech—a quantity comparable to what infants can be exposed to during language acquisition. Second, its functional hierarchy aligns with the cortical hierarchy of speech processing. Third, different training regimes reveal a functional specialization akin to the cortex: Wav2Vec 2.0 learns sound-generic, speech-specific and language-specific representations similar to those of the prefrontal and temporal cortices. Fourth, we confirm the similarity of this specialization with the behavior of 386 additional participants.

These elements, resulting from the largest neuroimaging benchmark to date, show how self-supervised learning can account for a rich organization of speech processing in the brain, and thus delineate a path to identify the laws of language acquisition which shape the human brain.

--- https://arxiv.org/abs/2203.11656 Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi Bram Grooten, Jelle Wemmenhove, Maurice Poot, Jim Portegies 2022-03-22 2022-03-22 [("doi","10.48550/arXiv.2203.11656")] reinforcement-learning/imperfect-information/hanabi reinforcement-learning/model-free

In pursuit of enhanced multi-agent collaboration, we analyze several on-policy deep reinforcement learning algorithms in the recently published Hanabi benchmark.

Our research suggests a perhaps counter-intuitive finding, where Proximal Policy Optimization (PPO) is outperformed by Vanilla Policy Gradient over multiple random seeds in a simplified environment of the multi-agent cooperative card game.

In our analysis of this behavior we look into Hanabi-specific metrics and hypothesize a reason for PPO’s plateau. In addition, we provide proofs for the maximum length of a perfect game (71 turns) and any game (89 turns).

Our code can be found at: https://github.com/bramgrooten/DeepRL-for-Hanabi.

--- https://arxiv.org/abs/1811.01458#deepmind Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning Jakob N. Foerster, Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew Botvinick, Michael Bowling 2018-11-04 2021-09-03 [("doi","10.48550/arXiv.1811.01458")] reinforcement-learning/imperfect-information/hanabi reinforcement-learning/model statistics/bayes

When observing the actions of others, humans make inferences about why they acted as they did, and what this implies about the world; humans also use the fact that their actions will be interpreted in this manner, allowing them to act informatively and thereby communicate efficiently with others. Although learning algorithms have recently achieved superhuman performance in a number of two-player, zero-sum games, scalable multi-agent reinforcement learning algorithms that can discover effective strategies and conventions in complex, partially observable settings have proven elusive.

We present the Bayesian action decoder (BAD), a new multi-agent learning method that uses an approximate Bayesian update to obtain a public belief that conditions on the actions taken by all agents in the environment. BAD introduces a new Markov decision process, the public belief MDP, in which the action space consists of all deterministic partial policies, and exploits the fact that an agent acting only on this public belief state can still learn to use its private information if the action space is augmented to be over all partial policies mapping private information into environment actions. The Bayesian update is closely related to the theory of mind reasoning that humans carry out when observing others’ actions.

We first validate BAD on a proof-of-principle two-step matrix game, where it outperforms policy gradient methods; we then evaluate BAD on the challenging, cooperative partial-information card game Hanabi, where, in the two-player setting, it surpasses all previously published learning and hand-coded approaches, establishing a new state-of-the-art.

--- https://github.com/WuTheFWasThat/hanabi.rs State-of-the-art Hanabi bots + simulation framework in rust 2021-09-04 reinforcement-learning/imperfect-information/hanabi --- https://arxiv.org/abs/1912.02318#facebook Improving Policies via Search in Cooperative Partially Observable Games Adam Lerer, Hengyuan Hu, Jakob Foerster, Noam Brown 2019-12-05 2021-09-04 [("doi","10.48550/arXiv.1912.02318")] reinforcement-learning/imperfect-information/hanabi

Recent superhuman results in games have largely been achieved in a variety of zero-sum settings, such as Go and Poker, in which agents need to compete against others. However, just like humans, real-world AI systems have to coordinate and communicate with other agents in cooperative partially observable environments as well. These settings commonly require participants to both interpret the actions of others and to act in a way that is informative when being interpreted. Those abilities are typically summarized as theory of mind and are seen as crucial for social interactions.

In this paper, we propose two different search techniques that can be applied to improve an arbitrary agreed-upon policy in a cooperative partially observable game. The first one, single-agent search, effectively converts the problem into a single agent setting by making all but one of the agents play according to the agreed-upon policy. In contrast, in multi-agent search all agents carry out the same common-knowledge search procedure whenever doing so is computationally feasible, and fall back to playing according to the agreed-upon policy otherwise. We prove that these search procedures are theoretically guaranteed to at least maintain the original performance of the agreed-upon policy (up to a bounded approximation error).

In the benchmark challenge problem of Hanabi, our search technique greatly improves the performance of every agent we tested and when applied to a policy trained using RL (Reinforcement Learning) achieves a new state-of-the-art score of 24.61 / 25 in the game, compared to a previous-best of 24.08 / 25.

--- https://arxiv.org/abs/1912.02288#facebook Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning Hengyuan Hu, Jakob N. Foerster 2019-12-04 2021-09-04 [("doi","10.48550/arXiv.1912.02288")] reinforcement-learning/imperfect-information/hanabi

In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker, and Dota. One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum. In contrast to these settings, success in the real world commonly requires humans to collaborate and communicate with others, in settings that are, at least partially, cooperative.

In the last year, the card game Hanabi has been established as a new benchmark environment for AI to fill this gap. In particular, Hanabi is interesting to humans since it is entirely focused on theory of mind, ie. the ability to effectively reason over the intentions, beliefs, and point of view of other agents when observing their actions.

Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, RL requires agents to explore in order to discover good policies. However, when done naively, this randomness will inherently make their actions less informative to others during training.

We present a new deep multi-agent RL method, the Simplified Action Decoder (SAD), which resolves this contradiction exploiting the centralized training phase. During training SAD allows other agents to not only observe the (exploratory) action chosen, but agents instead also observe the greedy action of their team mates. By combining this simple intuition with best practices for multi-agent learning, SAD establishes a new SOTA for learning methods for 2–5 players on the self-play part of the Hanabi challenge.

Our ablations show the contributions of SAD compared with the best practice components. All of our code and trained agents are available at Github.

--- https://arxiv.org/abs/2203.08015 On-the-fly Strategy Adaptation for ad-hoc Agent Coordination Jaleh Zand, Jack Parker-Holder, Stephen J. Roberts 2022-03-08 2022-03-08 [("doi","10.48550/arXiv.2203.08015")] reinforcement-learning/imperfect-information/hanabi reinforcement-learning/model statistics/bayes

Training agents in cooperative settings offers the promise of AI agents able to interact effectively with humans (and other agents) in the real world. Multi-agent reinforcement learning (MARL) has the potential to achieve this goal, demonstrating success in a series of challenging problems. However, whilst these advances are, the vast majority of focus has been on the self-play paradigm. This often results in a coordination problem, caused by agents learning to make use of arbitrary conventions when playing with themselves. This means that even the strongest self-play agents may have very low cross-play with other agents, including other initializations of the same algorithm.

In this paper we propose to solve this problem by adapting agent strategies on the fly, using a posterior belief over the other agents’ strategy. Concretely, we consider the problem of selecting a strategy from a finite set of previously trained agents, to play with an unknown partner. We propose an extension of the classic statistical technique, Gibbs sampling, to update beliefs about other agents and obtain close to optimal ad-hoc performance.

Despite its simplicity, our method is able to achieve strong cross-play with unseen partners in the challenging card game of Hanabi, achieving successful ad-hoc coordination without knowledge of the partner’s strategy a priori.

--- https://arxiv.org/abs/2201.12436 Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination Keane Lucas, Ross E. Allen 2022-01-28 2022-01-28 [("doi","10.48550/arXiv.2201.12436")] reinforcement-learning/exploration reinforcement-learning/imperfect-information/hanabi

Cooperative artificial intelligence with human or superhuman proficiency in collaborative tasks stands at the frontier of machine learning research. Prior work has tended to evaluate cooperative AI performance under the restrictive paradigms of self-play (teams composed of agents trained together) and cross-play (teams of agents trained independently but using the same algorithm). Recent work has indicated that AI optimized for these narrow settings may make for undesirable collaborators in the real-world.

We formalize an alternative criteria for evaluating cooperative AI, referred to as inter-algorithm cross-play, where agents are evaluated on teaming performance with all other agents within an experiment pool with no assumption of algorithmic similarities between agents. We show that existing state-of-the-art cooperative AI algorithms, such as Other-Play and Off-Belief Learning, under-perform in this paradigm.

We propose the Any-Play learning augmentation—a multi-agent extension of diversity-based intrinsic rewards for zero-shot coordination (ZSC)—for generalizing self-play-based algorithms to the inter-algorithm cross-play setting.

We apply the Any-Play learning augmentation to the Simplified Action Decoder (S​AD) and demonstrate state-of-the-art performance in the collaborative card game Hanabi.

--- https://arxiv.org/abs/2201.01448 Conditional Imitation Learning for Multi-Agent Games Andy Shih, Stefano Ermon, Dorsa Sadigh 2022-01-05 2022-01-05 [("doi","10.48550/arXiv.2201.01448")] reinforcement-learning/imitation-learning reinforcement-learning/imperfect-information/hanabi

While advances in multi-agent learning have enabled the training of increasingly complex agents, most existing techniques produce a final policy that is not designed to adapt to a new partner’s strategy. However, we would like our AI agents to adjust their strategy based on the strategies of those around them. In this work, we study the problem of conditional multi-agent imitation learning, where we have access to joint trajectory demonstrations at training time, and we must interact with and adapt to new partners at test time.

This setting is challenging because we must infer a new partner’s strategy and adapt our policy to that strategy, all without knowledge of the environment reward or dynamics. We formalize this problem of conditional multi-agent imitation learning, and propose a novel approach to address the difficulties of scalability and data scarcity.

Our key insight is that variations across partners in multi-agent games are often highly structured, and can be represented via a low-rank subspace. Leveraging tools from tensor decomposition, our model learns a low-rank subspace over ego and partner agent strategies, then infers and adapts to a new partner strategy by interpolating in the subspace.

We experiments with a mix of collaborative tasks, including bandits, particle environments, and Hanabi environments. Additionally, we test our conditional policies against real human partners in a user study on the Overcooked game.

Our model adapts better to new partners compared to baselines, and robustly handles diverse settings ranging from discrete/continuous actions and static/online evaluation with AI/human partners.

--- https://arxiv.org/abs/2111.09800 Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI Teammates Nicholas Kantack 2021-11-18 2021-11-18 [("doi","10.48550/arXiv.2111.09800")] reinforcement-learning/imperfect-information/hanabi reinforcement-learning/model

In 2021, the Johns Hopkins University Applied Physics Laboratory held an internal challenge to develop artificially intelligent (AI) agents that could excel at the collaborative card game Hanabi. Agents were evaluated on their ability to play with human players whom the agents had never previously encountered.

This study details the development of the agent that won the challenge by achieving a human-play average score of 16.5, outperforming the current state-of-the-art for human-bot Hanabi scores. The winning agent’s development consisted of observing and accurately modeling the author’s decision making in Hanabi, then training with a behavioral clone of the author.

Notably, the agent discovered a human-complementary play style by first mimicking human decision making, then exploring variations to the human-like strategy that led to higher simulated human-bot scores.

This work examines in detail the design and implementation of this human compatible Hanabi teammate, as well as the existence and implications of human-complementary strategies and how they may be explored for more successful applications of AI in human-machine teams.

--- https://arxiv.org/abs/2107.07630 Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi Ho Chit Siu, Jaime D. Pena, Edenna Chen, Yutai Zhou, Victor J. Lopez, Kyle Palko, Kimberlee C. Chang, Ross E. Allen 2021-07-15 2021-09-04 [("doi","10.48550/arXiv.2107.07630")] reinforcement-learning/imperfect-information/hanabi

Deep reinforcement learning has generated superhuman AI in competitive games such as Go and StarCraft. Can similar learning techniques create a superior AI teammate for human-machine collaborative games? Will humans prefer AI teammates that improve objective team performance or those that improve subjective metrics of trust?

In this study, we perform a single-blind evaluation of teams of humans and AI agents in the cooperative card game Hanabi, with both rule-based and learning-based agents. In addition to the game score, used as an objective metric of the human-AI team performance, we also quantify subjective measures of the human’s perceived performance, teamwork, interpretability, trust, and overall preference of AI teammate.

We find that humans have a clear preference toward a rule-based AI teammate (SmartBot) over a state-of-the-art learning-based AI teammate (Other-Play) across nearly all subjective metrics, and generally view the learning-based agent negatively, despite no statistically-significant difference in the game score.

This result has implications for future AI design and reinforcement learning benchmarking, highlighting the need to incorporate subjective metrics of human-AI teaming rather than a singular focus on objective task performance.

--- https://arxiv.org/abs/2106.09086#facebook Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings Hengyuan Hu, Adam Lerer, Noam Brown, Jakob Foerster 2021-06-16 2021-09-04 [("doi","10.48550/arXiv.2106.09086")] reinforcement-learning/imperfect-information/hanabi

Search is an important tool for computing effective policies in single-agent and multi-agent environments, and has been crucial for achieving superhuman performance in several benchmark fully and partially observable games. However, one major limitation of prior search approaches for partially observable environments is that the computational cost scales poorly with the amount of hidden information.

In this paper we present Learned Belief Search (LBS), a computationally efficient search procedure for partially observable environments. Rather than maintaining an exact belief distribution, LBS uses an approximate auto-regressive counterfactual belief that is learned as a supervised task. In multi-agent settings, LBS uses a novel public-private model architecture for underlying policies in order to efficiently evaluate these policies during rollouts.

In the benchmark domain of Hanabi, LBS can obtain 55% ~91% of the benefit of exact search while reducing compute requirements by 35.8× ~4.6×, allowing it to scale to larger settings that were inaccessible to previous search methods.

--- https://arxiv.org/abs/2104.02871 On the Critical Role of Conventions in Adaptive Human-AI Collaboration Andy Shih, Arjun Sawhney, Jovana Kondic, Stefano Ermon, Dorsa Sadigh 2021-04-07 2021-09-04 [("doi","10.48550/arXiv.2104.02871")] reinforcement-learning/imperfect-information/hanabi

Humans can quickly adapt to new partners in collaborative tasks (eg. playing basketball), because they understand which fundamental skills of the task (eg. how to dribble, how to shoot) carry over across new partners. Humans can also quickly adapt to similar tasks with the same partners by carrying over conventions that they have developed (eg. raising hand signals pass the ball), without learning to coordinate from scratch. To collaborate seamlessly with humans, AI agents should adapt quickly to new partners and new tasks as well. However, current approaches have not attempted to distinguish between the complexities intrinsic to a task and the conventions used by a partner, and more generally there has been little focus on leveraging conventions for adapting to new settings.

In this work, we propose a learning framework that teases apart rule-dependent representation from convention-dependent representation in a principled way. We show that, under some assumptions, our rule-dependent representation is a sufficient statistic of the distribution over best-response strategies across partners. Using this separation of representations, our agents are able to adapt quickly to new partners, and to coordinate with old partners on new tasks in a zero-shot manner.

We experimentally validate our approach on 3 collaborative tasks varying in complexity: a contextual multi-armed bandit, a block placing task, and the card game Hanabi.

--- https://arxiv.org/abs/2103.03216 Continuous Coordination As a Realistic Scenario for Lifelong Learning Hadi Nekoei, Akilesh Badrinaaraayanan, Aaron Courville, Sarath Chandar 2021-03-04 2021-09-04 [("doi","10.48550/arXiv.2103.03216")] reinforcement-learning/imperfect-information/hanabi reinforcement-learning/meta-learning/continual-learning

Current deep reinforcement learning (RL) algorithms are still highly task-specific and lack the ability to generalize to new environments. Lifelong learning (LLL), however, aims at solving multiple tasks sequentially by efficiently transferring and using knowledge between tasks. Despite a surge of interest in lifelong RL in recent years, the lack of a realistic testbed makes robust evaluation of LLL algorithms difficult. Multi-agent RL (MARL), on the other hand, can be seen as a natural scenario for lifelong RL due to its inherent non-stationarity, since the agents’ policies change over time.

In this work, we introduce a multi-agent lifelong learning testbed that supports both zero-shot and few-shot settings. Our setup is based on Hanabi—a partially-observable, fully cooperative multi-agent game that has been shown to be challenging for zero-shot coordination. Its large strategy space makes it a desirable environment for lifelong RL tasks. We evaluate several recent MARL methods, and benchmark state-of-the-art LLL algorithms in limited memory and computation regimes to shed light on their strengths and weaknesses.

This continual learning paradigm also provides us with a pragmatic way of going beyond centralized training which is the most commonly used training protocol in MARL. We empirically show that the agents trained in our setup are able to coordinate well with unseen agents, without any additional assumptions made by previous works.

The code and all pre-trained models are available at Github.

--- https://arxiv.org/abs/2101.09328 Theory of Mind for Deep Reinforcement Learning in Hanabi Andrew Fuchs, Michael Walton, Theresa Chadwick, Doug Lange 2021-01-22 2021-09-05 [("doi","10.48550/arXiv.2101.09328")] reinforcement-learning/imperfect-information/hanabi

The partially observable card game Hanabi has recently been proposed as a new AI challenge problem due to its dependence on implicit communication conventions and apparent necessity of theory of mind reasoning for efficient play.

In this work, we propose a mechanism for imbuing Reinforcement Learning agents with a theory of mind to discover efficient cooperative strategies in Hanabi. The primary contributions of this work are threefold: First, a formal definition of a computationally tractable mechanism for computing hand probabilities in Hanabi. Second, an extension to conventional Deep Reinforcement Learning that introduces reasoning over finitely nested theory of mind belief hierarchies. Finally, an intrinsic reward mechanism enabled by theory of mind that incentivizes agents to share strategically relevant private knowledge with their teammates.

We demonstrate the utility of our algorithm against Rainbow, a state-of-the-art Reinforcement Learning agent.

--- https://arxiv.org/abs/2004.13291 Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners Rodrigo Canaan, Xianbo Gao, Youjin Chung, Julian Togelius, Andy Nealen, Stefan Menzel 2020-04-28 2021-09-05 [("doi","10.48550/arXiv.2004.13291")] reinforcement-learning/imperfect-information/hanabi reinforcement-learning/model-free

Hanabi is a cooperative game that challenges existing AI techniques due to its focus on modeling the mental states of other players to interpret and predict their behavior. While there are agents that can achieve near-perfect scores in the game by agreeing on some shared strategy, comparatively little progress has been made in ad-hoc cooperation settings, where partners and strategies are not known in advance.

In this paper, we show that agents trained through self-play using the popular Rainbow DQN architecture fail to cooperate well with simple rule-based agents that were not seen during training and, conversely, when these agents are trained to play with any individual rule-based agent, or even a mix of these agents, they fail to achieve good self-play scores.

--- https://lingo.csail.mit.edu/blog/arithmetic_gpt3/ 2021-09-05 ai/nn/transformer/gpt/inner-monologue --- https://thegradient.pub/ai-scientific-revolution/ AI is Ushering In a New Scientific Revolution 2021-09-05 ai/nn/transformer/alphafold science --- https://en.wikipedia.org/wiki/Wirth%27s_law Wirth’s law 2021-09-05 cs --- https://arxiv.org/abs/2206.02262#microsoft Diffusion-GAN: Training GANs with Diffusion Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, Mingyuan Zhou 2022-06-05 2022-06-05 [("doi","10.48550/arXiv.2206.02262")] ai/nn/diffusion ai/nn/gan/data-augmentation ai/nn/gan/stylegan

For stable training of generative adversarial networks (GANs), injecting instance noise into the input of the discriminator is considered as a theoretically sound solution, which, however, has not yet delivered on its promise in practice.

This paper introduces Diffusion-GAN that employs a Gaussian mixture distribution, defined over all the diffusion steps of a forward diffusion chain, to inject instance noise. A random sample from the mixture, which is diffused from an observed or generated data, is fed as the input to the discriminator. The generator is updated by backpropagating its gradient through the forward diffusion chain, whose length is adaptively adjusted to control the maximum noise-to-data ratio allowed at each training step.

Theoretical analysis verifies the soundness of the proposed Diffusion-GAN, which provides model-agnostic and domain-agnostic differentiable augmentation.

A rich set of experiments on diverse datasets show that Diffusion-GAN can provide stable and data-efficient GAN training, bringing consistent performance improvement over strong GAN baselines for synthesizing photo-realistic images.

--- https://arxiv.org/abs/2206.01861#microsoft ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, Yuxiong He 2022-06-04 2022-06-04 [("doi","10.48550/arXiv.2206.01861")] ai/nn/sparsity/knowledge-distillation ai/nn/sparsity/low-precision ai/nn/transformer/gpt ai/scaling/hardware

How to efficiently serve ever-larger trained natural language models in practice has become exceptionally challenging even for powerful cloud servers due to their prohibitive memory/computation requirements.

In this work, we present an efficient and affordable post-training quantization approach to compress large Transformer-based models, termed as ZeroQuant. ZeroQuant is an end-to-end quantization and inference pipeline with 3 main components: (1) a fine-grained hardware-friendly quantization scheme for both weight and activations; (2) a novel affordable layer-by-layer knowledge distillation algorithm (LKD) even without the access to the original training data; (3) a highly-optimized quantization system backend support to remove the quantization/dequantization overhead.

As such, we are able to show that: (1) ZeroQuant can reduce the precision for weights and activations to INT8 in a cost-free way for both BERT and GPT-3-style models with minimal accuracy impact, which leads to up to 5.19×/4.16× speedup on those models compared to FP16 inference; (2) ZeroQuant plus LKD affordably quantize the weights in the fully-connected module to INT4 along with INT8 weights in the attention module and INT8 activations, resulting in 3× memory footprint reduction compared to the FP16 model; (3) ZeroQuant can be directly applied to two of the largest open-sourced language models, including GPT-J6B and GPT-NeoX20, for which our INT8 model achieves similar accuracy as the FP16 model but achieves up to 5.2× better efficiency.

--- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001564 Do multiple experimenters improve the reproducibility of animal studies? Vanessa Tabea von Kortzfleisch, Oliver Ambrée, Natasha A. Karp, Neele Meyer, Janja Novak, Rupert Palme, Marianna Rosso, Chadi Touma, Hanno Würbel, Sylvia Kaiser, Norbert Sachser, S. Helene Richter 2022-05-05 2022-05-05 [("doi","10.1371/journal.pbio.3001564")] statistics/bias statistics/variance-component

[von Kortzfleisch et al 2022] The credibility of scientific research has been seriously questioned by the widely claimed reproducibility crisis. In light of this crisis, there is a growing awareness that the rigorous standardisation of experimental conditions may contribute to poor reproducibility of animal studies. Instead, systematic heterogenization has been proposed as a tool to enhance reproducibility, but a real-life test across multiple independent laboratories is still pending.

The aim of this study was therefore to test whether heterogenization of experimental conditions by using multiple experimenters improves the reproducibility of research findings compared to standardized conditions with only one experimenter.

To this end, we replicated the same animal experiment in 3 independent laboratories, each employing both a heterogenized and a standardized design. Whereas in the standardized design, all animals were tested by a single experimenter; in the heterogenized design, 3 different experimenters were involved in testing the animals.

In contrast to our expectation, the inclusion of multiple experimenters in the heterogenized design did not improve the reproducibility of the results across the 3 laboratories. Interestingly, however, a variance component analysis indicated that the variation introduced by the different experimenters was not as high as the variation introduced by the laboratories, probably explaining why this heterogenization strategy did not bring the anticipated success. Even more interestingly, for the majority of outcome measures, the remaining residual variation was identified as an important source of variance accounting for 41% (CI95 [34%, 49%]) to 72% (CI95 [58%, 88%]) of the observed total variance.

Figure 4: Proportion of variance explained by each factor. For each outcome measure, the total variance of the full dataset could be decomposed into the following sources using an LMM: between-strain variability (yellow), between-laboratory variability (blue), between-experimenter variability (red), strain-by-laboratory interaction variability (dark blue), strain-by-experimenter interaction variability (orange), between-block variability (dark green), strain-by-block interaction variability (light green), between-cage variability (beige), and between-individual variability (residuals, grey). Shown are point estimates of the proportion of variation explained by each factor. For details on 95% confidence intervals of these estimates, see S7 Table. The raw data underlying this figure are available in the Figshare repository. Abbreviations: ‘DL’, Dark Light; ‘EPM’, Elevated Plus Maze; ‘FCMs’, faecal corticosterone metabolites; ‘LMM’, linear mixed model; ‘NC’, Novel Cage; ‘NT’, Nest; ‘OF’, Open Field.

Despite some uncertainty surrounding the estimated numbers, these findings argue for systematically including biological variation rather than eliminating it in animal studies and call for future research on effective improvement strategies.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4126241/ The effect of resistance exercise on all-cause mortality in cancer survivors Justin P. Hardee, Ryan R. Porter, Xuemei Sui, Edward Archer, I-Min Lee, Carl J. Lavie, Steven N. Blair 2014 2021-09-05 [("doi","10.1016/j.mayocp.2014.03.018")] exercise

Objective: To examine the independent associations of leisure-time aerobic physical activity (PA) and resistance exercise (RE) on all-cause mortality in cancer survivors.

Patients and Method: Patients included 2863 male and female cancer survivors, aged 18 to 81 years, who received a preventive medical examination between April 8, 1987, and December 27, 2002, while enrolled in the Aerobics Center Longitudinal Study in Dallas, Texas. Physical activity and RE were assessed by self-report at the baseline medical examination. Cox proportional hazards regression analysis was performed to determine the independent associations of PA and RE with all-cause mortality in participants who had a history of cancer.

Results: Physical activity in cancer survivors was not associated with a lower risk of all-cause mortality. In contrast, RE was associated with a 33% lower risk of all-cause mortality (95% CI, 0.45–0.99) after adjusting for potential confounders, including PA.

Conclusion: Individuals who participated in RE during cancer survival had a lower risk for all-cause mortality. The present findings provide preliminary evidence for benefits of RE during cancer survival. Future randomized controlled trials examining RE and its effect on lean body mass, muscular strength, and all-cause mortality in cancer survivors are warranted.

--- https://arxiv.org/abs/2205.13115#adobe Fine-grained Image Captioning with CLIP Reward Jaemin Cho, Seunghyun Yoon, Ajinkya Kale, Franck Dernoncourt, Trung Bui, Mohit Bansal 2022-05-26 2022-05-26 [("doi","10.48550/arXiv.2205.13115")] ai/dataset ai/nn/transformer/clip reinforcement-learning/model-free

Modern image captioning models are usually trained with text similarity objectives. However, since reference captions in public datasets often describe the most salient common objects, models trained with text similarity objectives tend to ignore specific and detailed aspects of an image that distinguish it from others.

Toward more descriptive and distinctive caption generation, we propose using CLIP, a multimodal encoder trained on huge image-text pairs from web, to calculate multimodal similarity and use it as a reward function. We also propose a simple finetuning strategy of the CLIP text encoder to improve grammar that does not require extra text annotation. This completely eliminates the need for reference captions during the reward computation.

To comprehensively evaluate descriptive captions, we introduce FineCapEval, a new dataset for caption evaluation with fine-grained criteria: overall, background, object, relations. In our experiments on text-to-image retrieval and FineCapEval, the proposed CLIP-guided model generates more distinctive captions than the CIDEr-optimized model. We also show that our unsupervised grammar finetuning of the CLIP text encoder alleviates the degeneration problem of the naive CLIP reward. Lastly, we show human analysis where the annotators strongly prefer the CLIP reward to the CIDEr and MLE objectives according to various criteria.

Code and Data: https://github.com/j-min/CLIP-Caption-Reward.

--- https://google-research.github.io/self-organising-systems/2022/diff-fsm/ Differentiable Finite State Machines 2021-09-05 ai/nn cs/algorithm --- https://arxiv.org/abs/2106.11747 Single-chip photonic deep neural network for instantaneous image classification Farshid Ashtiani, Alexander J. Geers, Firooz Aflatouni 2021-06-19 2021-09-06 [("doi","10.48550/arXiv.2106.11747")] ai/scaling/hardware

Deep neural networks with applications from computer vision and image processing to medical diagnosis are commonly implemented using clock-based processors, where computation speed is limited by the clock frequency and the memory access time. Advances in photonic integrated circuits have enabled research in photonic computation, where, despite excellent features such as fast linear computation, no integrated photonic deep network has been demonstrated to date due to the lack of scalable nonlinear functionality and the loss of photonic devices, making scalability to a large number of layers challenging.

Here we report the first integrated end-to-end photonic deep neural network (PDNN) that performs instantaneous image classification through direct processing of optical waves. Images are formed on the input pixels and optical waves are coupled into nanophotonic waveguides and processed as the light propagates through layers of [9] neurons on-chip. Each neuron 9 generates an optical output from input optical signals, where linear computation is performed optically and the nonlinear activation function is realised opto-electronically. The output of a laser coupled into the chip is uniformly distributed among all neurons within the network providing the same per-neuron supply light. Thus, all neurons have the same optical output range enabling scalability to deep networks with large number of layers.

The PDNN chip is used for 2-class and 4-class classification of [5×6 pixel array] handwritten letters achieving accuracies of higher than 93.7% and 90.3%, respectively, with a computation time less than one clock cycle of state-of-the-art digital computation platforms.

Direct clock-less processing of optical data eliminates photo-detection, A/D conversion, and the requirement for a large memory module, enabling faster and more energy-efficient neural networks for the next generations of deep learning systems.

--- https://dallery.gallery/dall-e-ai-guide-faq/ 2021-09-06 ai/nn/transformer/gpt/dall-e --- https://www.lesswrong.com/posts/c2RzFadrxkzyRAFXa/who-models-the-models-that-model-models-an-exploration-of Who models the models that model models? An exploration of GPT-3’s in-context model fitting ability 2021-09-06 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/codex --- https://huggingface.co/blog/annotated-diffusion The Annotated Diffusion Model 2021-09-06 ai/nn/diffusion --- https://imagen.research.google/ 2021-09-06 ai/nn/diffusion ai/nn/transformer/gpt/dall-e --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.726.9711&rep=rep1&type=pdf Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams Nan Du, Mehrdad Farajtabar, Amr Ahmed, Alexander J. Smola, Le Song 2019-07-16 2021-09-06 statistics/bayes

Clusters in document streams, such as online news articles, can be induced by their textual contents, as well as by the temporal dynamics of their arriving patterns. Can we leverage both sources of information to obtain a better clustering of the documents, and distill information that is not possible to extract using contents only? In this paper, we propose a novel random process, referred to as the Dirichlet-Hawkes process, to take into account both information in a unified framework.

A distinctive feature of the proposed model is that the preferential attachment of items to clusters according to cluster sizes, present in Dirichlet processes, is now driven according to the intensities of cluster-wise self-exciting temporal point processes, the Hawkes processes. This new model establishes a previously unexplored connection between Bayesian Nonparametrics and temporal Point Processes, which makes the number of clusters grow to accommodate the increasing complexity of online streaming contents, while at the same time adapts to the ever-changing dynamics of the respective continuous arrival time.

We conducted large-scale experiments on both synthetic and real world news articles, and show that Dirichlet-Hawkes processes can recover both meaningful topics and temporal dynamics, which leads to better predictive performance in terms of content perplexity and arrival time of future documents.

--- https://arxiv.org/abs/2206.02998 Learning to Generate Artistic Character Line Drawing Cheng-Yu Fang, Xian-Feng Han, Qi Zhong, Shi-Jie Sun, Guo-Qiang Xiao 2022-06-07 2022-06-07 [("doi","10.48550/arXiv.2206.02998")] ai/anime ai/dataset ai/nn/gan

Character line drawing synthesis can be formulated as a special case of image-to-image translation problem that automatically manipulates the photo-to-line drawing style transformation. In this paper, we present the first generative adversarial network based end-to-end trainable translation architecture, dubbed P2LDGAN, for automatic generation of high-quality character drawings from input photos/images.

The core component of our approach is the joint geometric-semantic-driven generator, which uses our well-designed cross-scale dense skip connections framework to embed learned geometric and semantic information for generating delicate line drawings.

In order to support the evaluation of our model, we release a new dataset including 1,532 well-matched pairs of freehand character line drawings as well as corresponding character images/photos, where these line drawings with diverse styles are manually drawn by skilled artists. Extensive experiments on our introduced dataset demonstrate the superior performance of our proposed models against the state-of-the-art approaches in terms of quantitative, qualitative, and human evaluations.

Our code, models and dataset is available at Github.

--- https://arxiv.org/abs/2206.03382#microsoft Tutel: Adaptive Mixture-of-Experts at Scale Changho Hwang, Wei Cui, Yifan Xiong, Ziyue Yang, Ze Liu, Han Hu, Zilong Wang, Rafael Salas, Jithin Jose, Prabhat Ram, Joe Chau, Peng Cheng, Fan Yang, Mao Yang, Yongqiang Xiong 2022-06-07 2022-06-07 [("doi","10.48550/arXiv.2206.03382")] ai/scaling/hardware ai/scaling/mixture-of-experts

In recent years, Mixture-of-Experts (MoE) has emerged as a promising technique for deep learning that can scale the model capacity to trillion-plus parameters while reducing the computing cost via sparse computation. While MoE opens a new frontier of exceedingly large models, its implementation over thousands of GPUs has been limited due to mismatch between the dynamic nature of MoE and static parallelism/pipelining of the system.

We present Tutel, a highly scalable stack design and implementation for MoE with dynamically adaptive parallelism and pipelining. Tutel delivers adaptive parallelism switching and adaptive pipelining at runtime, which achieves up to 1.74× and 2.00× single MoE layer speedup, respectively. We also propose a novel two-dimensional hierarchical algorithm for MoE communication speedup that outperforms the previous state-of-the-art up to 20.7× over 2,048 GPUs.

Aggregating all techniques, Tutel finally delivers 4.96× and 5.75× speedup of a single MoE layer on 16 GPUs and 2,048 GPUs, respectively, over Fairseq: Meta’s Facebook AI Research Sequence-to-Sequence Toolkit (Tutel is now partially adopted by Fairseq). Tutel source code is available in public: https://github.com/microsoft/tutel.

Our evaluation shows that Tutel efficiently and effectively runs a real-world MoE-based model named SwinV2-MoE, built upon Swin Transformer V2, a state-of-the-art computer vision architecture. On efficiency, Tutel accelerates SwinV2-MoE, achieving up to 1.55× and 2.11× speedup in training and inference over Fairseq, respectively.

On effectiveness, the SwinV2-MoE model achieves superior accuracy in both pre-training and down-stream computer vision tasks such as COCO object detection than the counterpart dense model, indicating the readiness of Tutel for end-to-end real-world model training and inference. SwinV2-MoE is open-sourced on Github.

--- https://en.wikipedia.org/wiki/Bipolar_disorder Bipolar disorder 2021-09-06 psychiatry/bipolar psychology/energy --- https://en.wikipedia.org/wiki/Dopamine Dopamine 2021-09-06 longevity/glp/psychology psychiatry/bipolar psychology/energy --- https://en.wikipedia.org/wiki/Stimulant Stimulant 2021-09-07 psychology/energy --- https://stephenmalina.com/post/2021-07-01-energetic-aliens-among-us/ Energetic Aliens 2021-09-07 psychology/energy --- /doc/iq/2000-lubinski-2.pdf#page=24 Scientific and Social Importance of Assessing Individual Differences: ‘Sinking Shafts at a Few Critical Points’ § pg24 David Lubinski 2000-01-01 2021-09-07 [("doi","10.1146/annurev.psych.51.1.405")] iq psychology/energy

This chapter reviews empirical findings on the importance of assessing individual differences in human behavior. Traditional dimensions of human abilities, personality, and vocational interests play critical roles in structuring a variety of important behaviors and outcomes (eg. achieved socioeconomic status, educational choices, work performance, delinquency, health risk behaviors, and income).

In the review of their importance, the construct of general intelligence is featured, but attributes that routinely add incremental validity to cognitive assessments are also discussed. Recent experimental and methodological advances for better understanding how these dimensions may contribute to other psychological frameworks are reviewed, as are ways for determining their scientific importance within domains where they are not routinely assessed.

Finally, some noteworthy models are outlined that highlight the importance of assessing relatively distinct classes of individual-differences attributes simultaneously. For understanding fully complex human phenomena such as crime, eminence, and educational-vocational development, such a multifaceted approach is likely to be the most productive.

--- /doc/iq/high/1996-jensen.pdf#page=9 Giftedness & Genius § Productivity Arthur R. Jensen 1996 2021-09-07 iq/high psychology/energy --- https://en.wikipedia.org/wiki/Psychoticism Psychoticism 2021-09-07 psychology/energy psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Uric_acid Uric acid 2021-09-07 nootropic/caffeine psychiatry/adhd psychiatry/alcoholism psychology/energy --- https://www.overcomingbias.com/2019/09/stamina-succeeds.html 2021-09-07 psychology/energy --- /doc/psychology/personality/conscientiousness/1907-james.pdf The Energies of Men William James 1907-01-01 2021-09-07 [("doi","10.2307/2177575")] psychology/energy psychology/personality/conscientiousness --- https://en.wikipedia.org/wiki/Need_for_cognition Need for cognition 2021-09-07 psychology/energy --- https://en.wikipedia.org/wiki/Typical_intellectual_engagement Typical intellectual engagement 2021-09-07 psychology/energy --- https://www.edge.org/response-detail/11885 2021-09-07 psychology/energy --- https://en.wikipedia.org/wiki/Graphomania Graphomania 2021-09-08 psychedelic psychiatry psychology/cognitive-bias/illusion-of-depth psychology/energy psychology/writing --- https://en.wikipedia.org/wiki/Hypergraphia Hypergraphia 2021-09-08 psychiatry psychology/energy psychology/writing --- https://en.wikipedia.org/wiki/Hypomania Hypomania 2021-09-08 psychiatry/bipolar psychology/energy --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4520322/ The Mechanics of Human Achievement Angela L. Duckworth, Johannes C. Eichstaedt, Lyle H. Ungar 2015 2021-09-08 [("doi","10.1111/spc3.12178")] psychology/energy psychology/personality/conscientiousness psychology/willpower

Countless studies have addressed why some individuals achieve more than others. Nevertheless, the psychology of achievement lacks a unifying conceptual framework for synthesizing these empirical insights.

We propose organizing achievement-related traits by two possible mechanisms of action: Traits that determine the rate at which an individual learns a skill are talent variables and can be distinguished conceptually from traits that determine the effort an individual puts forth. This approach takes inspiration from Newtonian mechanics: achievement is akin to distance traveled, effort to time, skill to speed, and talent to acceleration.

A novel prediction from this model is that individual differences in effort (but not talent) influence achievement (but not skill) more substantially over longer (rather than shorter) time intervals. Conceptualizing skill as the multiplicative product of talent and effort, and achievement as the multiplicative product of skill and effort, advances similar, but less formal, propositions by several important earlier thinkers.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4430294/ Novel loci associated with usual sleep duration: the CHARGE Consortium Genome-Wide Association Study D J. Gottlieb, K. Hek, T-H Chen, N. F. Watson, G. Eiriksdottir, E. M. Byrne, M. Cornelis, S. C. Warby, S. Bandinelli, L. Cherkas, D. S. Evans, H. J. Grabe, J. Lahti, M. Li, Terho Lehtimäki, T. Lumley, K. D. Marciante, L. Pérusse, Bruce M. Psaty, J. Robbins, G. J. Tranah, J. M. Vink, J. B. Wilk, J. M. Stafford, C. Bellis, R. Biffar, C. Bouchard, B. Cade, G. C. Curhan, J. G. Eriksson, R. Ewert, L. Ferrucci, T. Fülöp, P. R. Gehrman, R. Goodloe, T. B. Harris, A. C. Heath, D. Hernandez, A. Hofman, J-J Hottenga, D. J. Hunter, M. K. Jensen, A. D. Johnson, M. Kähönen, L. Kao, P. Kraft, E. K. Larkin, D. S. Lauderdale, A. I. Luik, M. Medici, G. W. Montgomery, A. Palotie, S. R. Patel, G. Pistis, E. Porcu, L. Quaye, Olli T. Raitakari, S. Redline, E. B. Rimm, J. I. Rotter, Albert Vernon Smith, T. D. Spector, A. Teumer, A. G. Uitterlinden, M-C Vohl, E. Widen, G. Willemsen, T. Young, X. Zhang, Y. Liu, J. Blangero, D. I. Boomsma, V. Gudnason, F. Hu, M. Mangino, N. G. Martin, G. T. O’Connor, K. L. Stone, T. Tanaka, J. Viikari, S. A. Gharib, N. M. Punjabi, K. Räikkönen, H. Völzke, E. Mignot, H. Tiemeier 2015 2021-09-08 [("doi","10.1038/mp.2014.133")] genetics/heritable psychiatry zeo

Usual sleep duration is a heritable trait correlated with psychiatric morbidity, cardiometabolic disease and mortality, although little is known about the genetic variants influencing this trait.

A genome-wide association study (GWAS) of usual sleep duration was conducted using 18 population-based cohorts totaling 47,180 individuals of European ancestry. Genome-wide statistically-significant association was identified at two loci. The strongest is located on chromosome 2, in an intergenic region 35–80-kb upstream from the thyroid-specific transcription factor PAX8 (lowest p = 1.1 × 10−9). This finding was replicated in an African-American sample of 4771 individuals (lowest p = 9.3 × 10−4). The strongest combined association was at rs1823125 (p = 1.5 × 10−10, minor allele frequency 0.26 in the discovery sample, 0.12 in the replication sample), with each copy of the minor allele associated with a sleep duration 3.1 min longer per night. The alleles associated with longer sleep duration were associated in previous GWAS with a more favorable metabolic profile and a lower risk of attention deficit hyperactivity disorder.

Understanding the mechanisms underlying these associations may help elucidate biological mechanisms influencing sleep duration and its association with psychiatric, metabolic and cardiovascular disease.

--- https://arxiv.org/abs/cs/0411064 Lower-Stretch Spanning Trees Michael Elkin, Yuval Emek, Daniel A. Spielman, Shang-Hua Teng 2004-11-17 2021-09-08 [("doi","10.48550/arXiv.0411064")] cs/algorithm

We prove that every weighted graph contains a spanning tree subgraph of average stretch 𝒪(log2 n log log n).

Moreover, we show how to construct such a tree in time 𝒪(m log n + n log2 n).

--- https://people.idsia.ch/~juergen/25years1997.html 2022: 25-year anniversary: LSTM (1997), all computable metaverses, hierarchical Q-learning, adversarial intrinsic Reinforcement Learning, low-complexity NNs, low-complexity art, Meta-RL, soccer learning 2021-09-08 reinforcement-learning/meta-learning --- https://en.wikipedia.org/wiki/Weighted_clothing Weighted clothing 2021-09-08 exercise/gravitostat --- https://www.biorxiv.org/content/10.1101/2022.06.03.494719.full Polygenic Transcriptome Risk Scores Can Translate Genetic Results Between Species Natasha Santhanam, Sandra Sanchez-Roige, Yanyu Liang, Apurva S. Chitre, Daniel Munro, Denghui Chen, Riyan Cheng, Jianjun Gao, Anthony M. George, Alex Gileta, Katie Holl, Alesa Hughson, Christopher P. King, Alexander C. Lamparelli, Connor D. Martin, Angel Garcia Martinez, Sabrina Mi, Celine L. St. Pierre, Jordan Tripi, Tengfei Wang, Hao Chen, Shelly Flagel, Keita Ishiwari, Paul Meyer, Laura Saba, Leah C. Solberg Woods, Oksana Polesskaya, Abraham Palmer, Hae Kyung Im 2022-06-05 2022-06-05 [("doi","10.1101/2022.06.03.494719")] genetics/heritable/correlation

Genome-wide association studies have demonstrated that most traits are highly polygenic; however, translating these polygenic signals into biological insights remains difficult. A lack of satisfactory methods for translating polygenic results across species has precluded the use of model organisms to address this problem. Here we explore the use of polygenic transcriptomic risk scores (PTRS) for translating polygenic results across species. Unlike polygenic risk scores (PRS), which rely on SNPs, PTRS use imputed gene expression for prediction, which allows cross-species translation to orthologous genes.

We first developed RatXcan, which is a framework for transcriptome-wide association studies (TWAS) in outbred rats. Leveraging predicted transcriptome and genotype data from UK Biobank, and the genetically trained gene expression models from RatXcan, we scored more than 3,000 rats using human-derived PTRS for height and BMI. Strikingly, we found that these human-derived PTRS predicted analogous traits in rats (r = 0.08, P = 8.57 × 10−6; r = 0.06, P = 8.51 × 10−4, respectively). The genes included in the PTRS were enriched for biological pathways including skeletal growth and metabolism and were over-represented in tissues including pancreas and brain.

This approach facilitates experimental studies in model organisms that examine the polygenic basis of human complex traits and provides an empirical metric by which to evaluate the suitability of specific animal models and identify their shared biological underpinnings.

--- https://arxiv.org/abs/1404.5997#google One weird trick for parallelizing convolutional neural networks Alex Krizhevsky 2014-04-23 2021-09-08 [("doi","10.48550/arXiv.1404.5997")] ai/nn/cnn

I present a new way to parallelize the training of convolutional neural networks across multiple GPUs.

The method scales better than all alternatives when applied to modern convolutional neural networks.

--- https://en.wikipedia.org/wiki/One-_and_two-tailed_tests One-tailed and two-tailed tests 2021-09-08 statistics/power-analysis --- /doc/sociology/technology/2022-brooks.pdf Incel Activity on Social Media Linked to Local Mating Ecology Robert C. Brooks, Daniel Russo-Batterham, Khandis R. Blake 2022-01-11 2022-01-11 [("doi","10.1177/09567976211036065")] sociology/technology

Young men with few prospects of attracting a mate have historically threatened the internal peace and stability of societies. In some contemporary societies, such involuntary celibate—or incel—men promote much online misogyny and perpetrate real-world violence.

We tested the prediction that online incel activity arises via local real-world mating-market forces that affect relationship formation. From a database of 4 billion Twitter posts (2012–2018), we geolocated 321 million tweets to 582 commuting zones in the continental United States, of which 3,649 tweets used words peculiar to incels and 3,745 were about incels.

We show that such tweets arise disproportionately within places where mating competition among men is likely to be high because of male-biased sex ratios, few single women, high income inequality, and small gender gaps in income.

Our results suggest a role for social media in monitoring and mitigating factors that lead young men toward antisocial behavior in real-world societies.

--- /doc/sociology/technology/2010-kelly-whattechnologywants-ch11-lessonsofamishhackers.pdf What Technology Wants: Chapter 11, Lessons of Amish Hackers Kevin Kelly 2010-01-01 2021-09-09 sociology/technology --- /doc/sociology/technology/2021-bor.pdf The Psychology of Online Political Hostility: A Comprehensive, Cross-National Test of the Mismatch Hypothesis Alexander Bor, Michael Bang Petersen 2021-08-26 2021-09-09 [("doi","10.1017/S0003055421000885")] psychology/cognitive-bias sociology/technology

Why are online discussions about politics more hostile than offline discussions? A popular answer argues that human psychology is tailored for face-to-face interaction and people’s behavior therefore changes for the worse in impersonal online discussions. We provide a theoretical formalization and empirical test of this explanation: the mismatch hypothesis. We argue that mismatches between human psychology and novel features of online environments could (a) change people’s behavior, (b) create adverse selection effects, and (c) bias people’s perceptions.

Across 8 studies, leveraging cross-national surveys and behavioral experiments (total n = 8,434), we test the mismatch hypothesis but only find evidence for limited selection effects. Instead, hostile political discussions are the result of status-driven individuals who are drawn to politics and are equally hostile both online and offline.

Finally, we offer initial evidence that online discussions feel more hostile, in part, because the behavior of such individuals is more visible online than offline.

--- /doc/sociology/technology/2019-wang.pdf Team creativity/innovation in culturally diverse teams: A meta-analysis Jie Wang, Gr, H.-L. Cheng, Tingting Chen, Kwok Leung 2019-02-27 2021-09-09 [("doi","10.1002/job.2362")] sociology/technology --- /doc/sociology/technology/2010-kelly-whattechwants-ch7-convergence.pdf What Technology Wants: Chapter 7, Convergence Kevin Kelly 2010-01-01 2021-09-09 sociology/technology --- https://arxiv.org/abs/1608.03983 SGDR: Stochastic Gradient Descent with Warm Restarts Ilya Loshchilov, Frank Hutter 2016-08-13 2021-09-09 [("doi","10.48550/arXiv.1608.03983")] ai/nn

Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial warm restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks.

We empirically study its performance on the CIFAR-10 and CIFAR-100 datasets, where we demonstrate new state-of-the-art results at 3.14% and 16.21%, respectively. We also demonstrate its advantages on a dataset of EEG recordings and on a downsampled version of the ImageNet dataset.

Our source code is available at https://github.com/loshchil/SGDR.

--- https://arxiv.org/abs/1506.01186 Cyclical Learning Rates for Training Neural Networks Leslie N. Smith 2015-06-03 2021-09-09 [("doi","10.48550/arXiv.1506.01186")] ai/nn/cnn

It is known that the learning rate is the most important hyper-parameter to tune for training deep neural networks.

This paper describes a new method for setting the learning rate, named cyclical learning rates, which practically eliminates the need to experimentally find the best values and schedule for the global learning rates. Instead of monotonically decreasing the learning rate, this method lets the learning rate cyclically vary between reasonable boundary values. Training with cyclical learning rates instead of fixed values achieves improved classification accuracy without a need to tune and often in fewer iterations. This paper also describes a simple way to estimate “reasonable bounds”—linearly increasing the learning rate of the network for a few epochs.

In addition, cyclical learning rates are demonstrated on the CIFAR-10 and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets, and the ImageNet dataset with the AlexNet and GoogLeNet architectures.

These are practical tools for everyone who trains neural networks.

--- https://arxiv.org/abs/1106.5730 HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent Feng Niu, Benjamin Recht, Christopher Re, Stephen J. Wright 2011-06-28 2021-09-09 [("doi","10.48550/arXiv.1106.5730")] ai/nn ai/scaling/hardware

Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks. Several researchers have recently proposed schemes to parallelize SGD, but all require performance-destroying memory locking and synchronization. This work aims to show using novel theoretical analysis, algorithms, and implementation that SGD can be implemented without any locking.

We present an update scheme called HOGWILD! which allows processors access to shared memory with the possibility of overwriting each other’s work. We show that when the associated optimization problem is sparse, meaning most gradient updates only modify small parts of the decision variable, then HOGWILD! achieves a nearly optimal rate of convergence.

We demonstrate experimentally that HOGWILD! outperforms alternative schemes that use locking by an order of magnitude.

--- https://arxiv.org/abs/1506.03099#google Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam Shazeer 2015-06-09 2021-09-09 [("doi","10.48550/arXiv.1506.03099")] ai/nn/rnn ai/nn/sampling

Recurrent Neural Networks can be trained to produce sequences of tokens given some input, as exemplified by recent results in machine translation and image captioning. The current approach to training them consists of maximizing the likelihood of each token in the sequence given the current (recurrent) state and the previous token. At inference, the unknown previous token is then replaced by a token generated by the model itself. This discrepancy between training and inference can yield errors that can accumulate quickly along the generated sequence.

We propose a curriculum learning strategy to gently change the training process from a fully guided scheme using the true previous token, towards a less guided scheme which mostly uses the generated token instead. Experiments on several sequence prediction tasks show that this approach yields improvements.

Moreover, it was used successfully in our winning entry to the MS COCO image captioning challenge, 2015.

--- https://arxiv.org/abs/1511.06732#facebook Sequence Level Training with Recurrent Neural Networks Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba 2015-11-20 2021-09-09 [("doi","10.48550/arXiv.1511.06732")] ai/nn/rnn ai/nn/sampling

Many natural language processing applications use language models to generate text. These models are typically trained to predict the next word in a sequence, given the previous words and some context such as an image.

However, at test time the model is expected to generate the entire sequence from scratch. This discrepancy makes generation brittle, as errors may accumulate along the way.

We address this issue by proposing a novel sequence-level training algorithm that directly optimizes the metric used at test time, such as BLEU or ROUGE.

On 3 different tasks, our approach outperforms several strong baselines for greedy generation. The method is also competitive when these baselines employ beam search, while being several times faster.

--- https://en.wikipedia.org/wiki/Variational_autoencoder Variational autoencoder 2021-09-09 ai/nn/vae --- https://en.wikipedia.org/wiki/Variational_Bayesian_methods Variational Bayesian methods 2021-09-10 ai/nn/vae --- https://en.wikipedia.org/wiki/Autoencoder Autoencoder 2021-09-10 ai/nn/vae --- https://arxiv.org/abs/1511.02580 How far can we go without convolution: Improving fully-connected networks Zhouhan Lin, Roland Memisevic, Kishore Konda 2015-11-09 2021-09-10 [("doi","10.48550/arXiv.1511.02580")] ai/nn/fully-connected ai/nn/vae

We propose ways to improve the performance of fully connected networks. We found that two approaches in particular have a strong effect on performance: linear bottleneck layers and unsupervised pre-training using autoencoders without hidden unit biases. We show how both approaches can be related to improving gradient flow and reducing sparsity in the network.

We show that a fully connected network can yield ~70% classification accuracy on the permutation-invariant CIFAR-10 task, which is much higher than the current state-of-the-art. By adding deformations to the training data, the fully connected network achieves 78% accuracy, which is just 10% short of a decent convolutional network.

--- https://arxiv.org/abs/1405.4053#google doc2vec: Distributed Representations of Sentences and Documents Quoc V. Le, Tomas Mikolov 2014-05-16 2021-09-10 [("doi","10.48550/arXiv.1405.4053")] ai/nn/rnn

Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite their popularity, bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. For example, “powerful”, “strong” and “Paris” are equally distant.

In this paper, we propose Paragraph Vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents. Our algorithm represents each document by a dense vector which is trained to predict words in the document. Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models.

Empirical results show that Paragraph Vectors outperform bag-of-words models as well as other techniques for text representations.

Finally, we achieve new state-of-the-art results on several text classification and sentiment analysis tasks.

[doc2vec seems to have been somewhat notorious for not working as well as reported; a followup paper suggests there was a problem with the data preprocessing: "In our experiments, to match the results from (Le & Mikolov 2014), we followed the suggestion by Quoc Le to use hierarchical softmax instead of negative sampling. However, this produces the 92.6% accuracy result only when the training and test data are not shuffled. Thus, we consider this result to be invalid."]

--- https://arxiv.org/abs/1406.2661 Generative Adversarial Networks Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio 2014-06-10 2021-09-10 [("doi","10.48550/arXiv.1406.2661")] ai/nn/gan

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.

This generative adversarial network (GAN) framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, an unique solution exists, with G recovering the training data distribution and D equal to 1⁄2 everywhere.

In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples.

Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.

--- https://arxiv.org/abs/1606.03476 Generative Adversarial Imitation Learning Jonathan Ho, Stefano Ermon 2016-06-10 2021-09-10 [("doi","10.48550/arXiv.1606.03476")] ai/nn/gan reinforcement-learning/imitation-learning

Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert’s cost function with inverse reinforcement learning, then extract a policy from that cost function with reinforcement learning. This approach is indirect and can be slow.

We propose a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning.

We show that a certain instantiation of our framework draws an analogy between imitation learning and generative adversarial networks, from which we derive a model-free imitation learning algorithm that obtains performance gains over existing model-free methods in imitating complex behaviors in large, high-dimensional environments.

--- https://arxiv.org/abs/1611.07004#bair pix2pix: Image-to-Image Translation with Conditional Adversarial Networks Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros 2016-11-21 2021-09-10 [("doi","10.48550/arXiv.1611.07004")] ai/nn/gan

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations.

We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking.

As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.

--- https://arxiv.org/abs/1605.09304 Synthesizing the preferred inputs for neurons in neural networks via deep generator networks Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, Jeff Clune 2016-05-30 2021-09-10 [("doi","10.48550/arXiv.1605.09304")] ai/nn

Deep neural networks (DNNs) have demonstrated state-of-the-art results on many pattern recognition tasks, especially vision classification problems. Understanding the inner workings of such computational brains is both fascinating basic science that is interesting in its own right—similar to why we study the human brain—and will enable researchers to further improve DNNs. One path to understanding how a neural network functions internally is to study what each of its neurons has learned to detect. One such method is called activation maximization (AM), which synthesizes an input (eg. an image) that highly activates a neuron.

Here we dramatically improve the qualitative state-of-the-art of activation maximization by harnessing a powerful, learned prior: a deep generator network (DGN). The algorithm (1) generates qualitatively state-of-the-art synthetic images that look almost real, (2) reveals the features learned by each neuron in an interpretable way, (3) generalizes well to new datasets and somewhat well to different network architectures without requiring the prior to be relearned, and (4) can be considered as a high-quality generative method (in this case, by generating novel, creative, interesting, recognizable images).

--- /doc/psychology/2019-he.pdf Predicting human inhibitory control from brain structural MRI Ningning He, Edmund T. Rolls, Wei Zhao, Shuixia Guo 2019-01-01 2021-09-10 [("doi","10.1007/s11682-019-00166-9")] psychology statistics/variance-component --- http://matthewtoews.com/papers/IPMI2019_BoF_Manifold_Laurent.pdf 2021-09-10 statistics/variance-component --- https://en.wikipedia.org/wiki/Compressed_sensing Compressed sensing 2021-09-11 statistics/variance-component --- https://en.wikipedia.org/wiki/Feature_hashing Feature hashing 2021-09-11 statistics/variance-component --- https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm k-nearest neighbors algorithm 2021-09-11 ai/highleyman statistics/variance-component --- https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation Nearest-neighbor interpolation 2021-09-11 statistics/variance-component --- https://en.wikipedia.org/wiki/Wastewater-based_epidemiology Wastewater-based epidemiology 2021-09-11 statistics/variance-component --- https://osf.io/x4fk3/ A Contamination Theory of the Obesity Epidemic Ludwin-Peery, Ludwin-Peery 2021 2021-09-11 statistics/variance-component --- https://surveyanon.wordpress.com/2019/07/22/playing-around-with-gendermetricity/ Playing around with ‘gendermetricity’ 2021-09-11 statistics/variance-component --- https://www.lesswrong.com/posts/6miu9BsKdoAi72nkL/a-contamination-theory-of-the-obesity-epidemic#9gMgjDQ5xgbxe8aLd 2021-09-11 statistics/variance-component --- https://www.nature.com/articles/s41467-019-10317-7 2021-09-11 statistics/variance-component --- https://www.sciencedirect.com/science/article/pii/S0896627317310929 Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation 2021-09-11 iq psychology/neuroscience statistics/variance-component --- /doc/ai/nn/transformer/gpt/2/2019-radford.pdf#openai&page=4 Language Models are Unsupervised Multitask Learners § Experiments Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever 2019-02-14 2021-09-11 ai/nn/transformer/gpt/2 --- https://arxiv.org/abs/2111.00600 Minimum Description Length Recurrent Neural Networks Nur Lan, Michal Geyer, Emmanuel Chemla, Roni Katzir 2021-10-31 2021-10-31 [("doi","10.48550/arXiv.2111.00600")] ai/nn/rnn cs/computable

We train neural networks to optimize a Minimum Description Length score, ie. to balance between the complexity of the network and its accuracy at a task.

We show that networks optimizing this objective function master tasks involving memory challenges and go beyond context-free languages. These learners master languages such as anbn, anbncn, anb2n, anbmcn+m, and they perform addition. Moreover, they often do so with 100% accuracy.

The networks are small, and their inner workings are transparent. We thus provide formal proofs that their perfect accuracy holds not only on a given test set, but for any input sequence.

To our knowledge, no other connectionist model has been shown to capture the underlying grammars for these languages in full generality.

--- https://aeon.co/essays/why-is-pop-culture-obsessed-with-battles-between-good-and-evil Why is pop culture obsessed with battles between good and evil? 2021-09-12 fiction/fantasy --- /doc/ai/poetry/2020-case.pdf GPT-2 AI Poetry Generation: Writing like Donne Kaiya Case 2020-06-04 2021-09-12 ai/nn/transformer/gpt/poetry ai/poetry --- https://arxiv.org/abs/1011.0686 DAgger: A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning Stephane Ross, Geoffrey J. Gordon, J. Andrew Bagnell 2010-11-02 2021-09-12 [("doi","10.48550/arXiv.1011.0686")] reinforcement-learning/imitation-learning reinforcement-learning/model-free reinforcement-learning/preference-learning

[introduction] Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches provide stronger guarantees in this setting, but remain somewhat unsatisfactory as they train either non-stationary or stochastic policies and require a large number of iterations.

In this paper, we propose a new iterative algorithm, DAgger, which trains a stationary deterministic policy, that can be seen as a no-regret algorithm in an online learning setting.

We show that any such no-regret algorithm, combined with additional reduction assumptions, must find a policy with good performance under the distribution of observations it induces in such sequential settings.

We demonstrate that this new approach outperforms previous approaches on two challenging imitation learning problems and a benchmark sequence labeling problem.

--- /doc/ai/poetry/1974-lem-cyberiad-trurlselectronicbard.pdf The First Sally (A), or, Trurl’s Electronic Bard Stanislaw Lem, Michael Kandel 1974-01-01 2021-09-12 ai/poetry --- /doc/ai/poetry/1974-lem-cyberiad-trurlselectronicbard.pdf#page=7 The First Sally (A), or, Trurl’s Electronic Bard § Love And Tensor Algebra Stanislaw Lem, Michael Kandel 1974-01-01 2021-09-12 ai/poetry math/humor --- /doc/fiction/criticism/2014-lamerichs.pdf Romancing Pigeons: The Deconstruction of the Dating-Sim in Hatoful Boyfriend Nicolle Lamerichs 2014-01-01 2021-09-12 [("doi","10.1184/R1/6687017.v1")] anime fiction/criticism --- /doc/fiction/poetry/2019-09-searls-theparisreview-moxomenon.pdf Moxomenon Damion Searls 2019-09-01 2021-09-12 design/typography fiction/poetry --- /doc/fiction/humor/2015-ross-pricks.pdf Pricks: Pilot Blake Ross 2015-01-01 2021-09-12 fiction/humor --- /doc/fiction/fantasy/2006-atlus.pdf Rule of Rose Staff Interview Atlus 2006-12-28 2021-09-12 anime fiction/fantasy psychiatry --- /doc/anime/2007-milstein.pdf Case Study: Anime Music Videos Dana Milstein 2007-01-01 2021-09-12 anime economics/copyright music --- https://arxiv.org/abs/0810.5515 Probing the Improbable: Methodological Challenges for Risks with Low Probabilities and High Stakes Toby Ord, Rafaela Hillerbrand, Anders Sandberg 2008-10-30 2021-09-13 [("doi","10.48550/arXiv.0810.5515")] existential-risk math philosophy/epistemology statistics/bias

Some risks have extremely high stakes. For example, a worldwide pandemic or asteroid impact could potentially kill more than a billion people. Comfortingly, scientific calculations often put very low probabilities on the occurrence of such catastrophes. In this paper, we argue that there are important new methodological problems which arise when assessing global catastrophic risks and we focus on a problem regarding probability estimation.

When an expert provides a calculation of the probability of an outcome, they are really providing the probability of the outcome occurring, given that their argument is watertight. However, their argument may fail for a number of reasons such as a flaw in the underlying theory, a flaw in the modeling of the problem, or a mistake in the calculations. If the probability estimate given by an argument is dwarfed by the chance that the argument itself is flawed, then the estimate is suspect.

We develop this idea formally, explaining how it differs from the related distinctions of model and parameter uncertainty. Using the risk estimates from the Large Hadron Collider as a test case, we show how serious the problem can be when it comes to catastrophic risks and how best to address it.

--- https://arxiv.org/abs/2010.05646#kakao HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis Jungil Kong, Jaehyeon Kim, Jaekyoung Bae 2020-10-12 2021-09-13 [("doi","10.48550/arXiv.2010.05646")] ai/music ai/nn/gan

Several recent works on speech synthesis have employed generative adversarial networks (GANs) to produce raw waveforms. Although such methods improve the sampling efficiency and memory usage, their sample quality has not yet reached that of autoregressive and flow-based generative models. In this work, we propose HiFi-GAN, which achieves both efficient and high-fidelity speech synthesis. As speech audio consists of sinusoidal signals with various periods, we demonstrate that modeling periodic patterns of an audio is crucial for enhancing sample quality.

A subjective human evaluation (mean opinion score, MOS) of a single speaker dataset indicates that our proposed method demonstrates similarity to human quality while generating 22.05 kHz high-fidelity audio 167.9× faster than real-time on a single V100 GPU. We further show the generality of HiFi-GAN to the mel-spectrogram inversion of unseen speakers and end-to-end speech synthesis.

Finally, a small footprint version of HiFi-GAN generates samples 13.4× faster than real-time on CPU with comparable quality to an autoregressive counterpart.

--- https://www.biorxiv.org/content/10.1101/2021.05.16.444078.full Epigenetic predictors of maximum lifespan and other life history traits in mammals C. Z. Li, A. Haghani, T. R. Robeck, D. Villar, A. T. Lu, J. Zhang, C. G. Faulkes, H. Vu, J. Ablaeva, D. M. Adams, R. Ardehali, A. Arneson, C. S. Baker, K. Belov, D. T. Blumstein, E. K. Bors, C. E. Breeze, R. T. Brooke, J. L. Brown, A. Caulton, J. M. Cavin, I. Chatzistamou, H. Chen, P. Chiavellini, O. W. Choi, S. Clarke, J. DeYoung, C. K. Emmons, S. Emmrich, Z. Fei, S. H. Ferguson, C. J. Finno, J. E. Flower, J. M. Gaillard, E. Garde, V. N. Gladyshev, R. G. Goya, M. B. Hanson, M. Haulena, K. Herrick, A. N. Hogan, C. J. Hogg, T. A. Hore, A. J. Jasinska, G. Jones, E. Jourdain, O. Kashpur, H. Katcher, E. Katsumata, V. Kaza, H. Kiaris, M. S. Kobor, P. Kordowitzki, W. R. Koski, B. Larison, S. G. Lee, M. Lehmann, J. F. Lemaitre, A. J. Levine, C. Li, X. Li, D. T. S. Lin, D. M. Lindemann, N. Macoretta, D. Maddox, C. O. Matkin, J. A. Mattison, J. Mergl, J. J. Meudt, G. A. Montano, K. Mozhui, A. Naderi, M. Nagy, P. Narayan, P. W. Nathanielsz, N. B. Nguyen, C. Niehrs, D. T. Odom, A. G. Ophir, E. A. Ostrander, P. O’Tierney Ginn, K. M. Parsons, K. C. Paul, M. Pellegrini, G. M. Pinho, J. Plassais, N. A. Prado, B. Rey, B. R. Ritz, J. Robbins, M. Rodriguez, J. Russell, E. Rydkina, L. L. Sailer, A. B. Salmon, A. Sanghavi, K. M. Schachtschneider, D. Schmitt, L. Schomacher, L. B. Schook, K. E. Sears, A. W. Seifert, A. V. Shindyapina, K. Singh, I. Sinha, R. G. Snell, E. Soltanmohammadi, M. L. Spangler, M. Spriggs, K. J. Steinman, V. J. Sugrue, B. Szladovits, M. Takasugi, E. C. Teeling, B. Van Bonn, S. C. Vernes, H. V. Vinters, M. C. Wallingford, N. Wang, G. S. Wilkinson, R. W. Williams, X. W. Yang, B. G. Young, B. Zhang, Z. Zhang, P. Zhao, Y. Zhao, W. Zhou, J. A. Zoller, J. Ernst, A. Seluanov, K. Raj, V. Gorbunova, S. Horvath, Mammalian Methylation Consortium 2021-05-18 2021-09-13 [("doi","10.1101/2021.05.16.444078")] genetics/heritable/dog longevity/epigenetics

[commentary] Maximum lifespan of a species is the oldest that individuals can survive, reflecting the genetic limit of longevity in an ideal environment.

Here we report methylation-based models that accurately predict maximum lifespan (r = 0.89), gestational time (r = 0.96), and age at sexual maturity (r = 0.87), using cytosine methylation patterns collected from over 12,000 samples derived from 192 mammalian species.

Our epigenetic maximum lifespan predictor corroborated the extended lifespan in growth hormone receptor knockout mice and rapamycin treated mice. Across dog breeds, epigenetic maximum lifespan correlates positively with breed lifespan but negatively with breed size. Lifespan-related cytosines are located in transcriptional regulatory regions, such as bivalent chromatin promoters and polycomb-repressed regions, which were hypomethylated in long-lived species.

The epigenetic estimators of maximum lifespan and other life history traits will be useful for characterizing understudied species and for identifying interventions that extend lifespan.

--- https://www.biorxiv.org/content/10.1101/2022.06.07.495223.full The lingering effects of Neanderthal introgression on human complex traits Xinzhu Wei, Christopher R. Robles, Ali Pazokitoroudi, Andrea Ganna, Alexander Gusev, Arun Durvasula, Steven Gazal, Po-Ru Loh, David Reich, Sriram Sankararaman 2022-06-08 2022-06-08 [("doi","10.1101/2022.06.07.495223")] genetics/selection/natural/human

The mutations introduced into the ancestors of modern humans from interbreeding with Neanderthals have been suggested to contribute an unexpected extent to complex human traits. However, testing this hypothesis has been challenging due to the idiosyncratic population genetic properties of introgressed mutations.

We developed rigorous methods to assess the contribution of introgressed Neanderthal mutations to heritable trait variation relative to that of modern human variants. We applied these methods to analyze 235,592 introgressed Neanderthal mutations and 96 distinct phenotypes measured in about 300,000 unrelated white British individuals in the UK Biobank.

Introgressed Neanderthal mutations have a contribution to trait variation consistent with the polygenic architecture of complex phenotypes (contributing 0.1% of heritable variation averaged across phenotypes; p = 9.5910–9). However, the contribution of introgressed mutations tends to be depleted relative to modern human mutations matched for allele frequency and linkage disequilibrium (about 57% depletion on average), consistent with purifying selection on introgressed mutations.

Different from previous studies (McArthur 2021), we find no evidence for elevated heritability across the phenotypes examined. We identified 348 independent statistically-significant associations of introgressed Neanderthal mutations with 64 phenotypes (p < 1 10–10). Previous work (Skov 2021) has suggested that a majority of such associations are likely driven by statistical association with nearby modern human variants that are the true causal variants. We therefore developed a customized statistical fine-mapping methodology for introgressed mutations that led us to identify 112 regions (at a false discovery proportion of 16%) across 47 phenotypes containing 4,303 unique genetic variants where introgressed mutations are highly likely to have a phenotypic effect. Examination of these mutations reveal their substantial impact on genes that are important for the immune system, development, and metabolism.

Our results provide the first rigorous basis for understanding how Neanderthal introgression modulates complex trait variation in present-day humans.

--- https://arxiv.org/abs/2206.04655 Towards Layer-wise Image Vectorization Xu Ma, Yuqian Zhou, Xingqian Xu, Bin Sun, Valerii Filev, Nikita Orlov, Yun Fu, Humphrey Shi 2022-06-09 2022-06-09 [("doi","10.48550/arXiv.2206.04655")] ai/anime

Image rasterization is a mature technique in computer graphics, while image vectorization, the reverse path of rasterization, remains a major challenge. Recent advanced deep learning-based models achieve vectorization and semantic interpolation of vector graphs and demonstrate a better topology of generating new figures. However, deep models cannot be easily generalized to out-of-domain testing data. The generated SVGs also contain complex and redundant shapes that are not quite convenient for further editing. Specifically, the crucial layer-wise topology and fundamental semantics in images are still not well understood and thus not fully explored.

In this work, we propose Layer-wise Image Vectorization, namely LIVE, to convert raster images to SVGs and simultaneously maintain its image topology. LIVE can generate compact SVG forms with layer-wise structures that are semantically consistent with human perspective. We progressively add new bezier paths and optimize these paths with the layer-wise framework, newly designed loss functions, and component-wise path initialization technique.

Our experiments demonstrate that LIVE presents more plausible vectorized forms than prior works and can be generalized to new images. With the help of this newly learned topology, LIVE initiates human editable SVGs for both designers and other downstream applications.

Codes are made available at Github.

--- https://www.lesswrong.com/posts/gPmGTND8Kroxgpgsn/how-fast-can-we-perform-a-forward-pass How fast can we perform a forward pass? 2021-09-13 ai/scaling/hardware --- https://www.biorxiv.org/content/10.1101/2021.05.21.444556.full Partial reprogramming restores youthful gene expression through transient suppression of cell identity Antoine Roux, Chunlian Zhang, Jonathan Paw, José Zavala-Solorio, Twaritha Vijay, Ganesh Kolumam, Cynthia Kenyon, Jacob C. Kimmel 2021-05-23 2021-09-13 [("doi","10.1101/2021.05.21.444556")] longevity/epigenetics

[Twitter: 1, 2; Calico] Transient induction of pluripotent reprogramming factors has been reported to reverse some features of aging in mammalian cells and tissues. However, the impact of transient reprogramming on somatic cell identity programs and the necessity of individual pluripotency factors remain unknown.

Here, we mapped trajectories of transient reprogramming in young and aged cells from multiple murine cell types using single cell transcriptomics to address these questions.

We found that transient reprogramming restored youthful gene expression in adipogenic cells and mesenchymal stem cells but also temporarily suppressed somatic cell identity programs. We further screened Yamanaka Factor subsets and found that many combinations had an impact on aging gene expression and suppressed somatic identity, but that these effects were not tightly entangled.

We also found that a transient reprogramming approach inspired by amphibian regeneration restored youthful gene expression in aged myogenic cells.

Our results suggest that transient pluripotent reprogramming poses a neoplastic risk, but that restoration of youthful gene expression can be achieved with alternative strategies.

--- https://arxiv.org/abs/1711.10337#google Are GANs Created Equal? A Large-Scale Study Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet 2017-11-28 2021-09-13 [("doi","10.48550/arXiv.1711.10337")] ai/nn/gan ai/scaling

Generative adversarial networks (GAN) are a powerful subclass of generative models. Despite a very rich research activity leading to numerous interesting GAN algorithms, it is still very hard to assess which algorithm(s) perform better than others.

We conduct a neutral, multi-faceted large-scale empirical study on state-of-the art models and evaluation measures.

We find that most models can reach similar scores with enough hyperparameter optimization and random restarts. This suggests that improvements can arise from a higher computational budget and tuning more than fundamental algorithmic changes. To overcome some limitations of the current metrics, we also propose several data sets on which precision and recall can be computed.

Our experimental results suggest that future GAN research should be based on more systematic and objective evaluation procedures. Finally, we did not find evidence that any of the tested algorithms consistently outperforms the non-saturating GAN introduced in Goodfellow et al 2014.

--- https://www.lesswrong.com/posts/uutXLm2DRcCtFBZ2D/steganography-and-the-cyclegan-alignment-failure-case-study Steganography and the CycleGAN—alignment failure case study 2021-09-13 ai/nn/adversarial ai/nn/gan cs/cryptography/steganography reinforcement-learning/safe --- https://arxiv.org/abs/1703.10593#bair CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros 2017-03-30 2021-09-13 [("doi","10.48550/arXiv.1703.10593")] ai/nn/gan

[homepage; video; Github; cf. pix2pix] Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available.

We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Our goal is to learn a mapping G: X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping F: Y → X and introduce a cycle consistency loss to push F(G(X)) ≈ X (and vice versa).

Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc.

Quantitative comparisons against several prior methods demonstrate the superiority of our approach.

--- https://arxiv.org/abs/1611.07004#bair Pix2Pix: Image-to-Image Translation with Conditional Adversarial Networks Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros 2016-11-21 2021-09-14 [("doi","10.48550/arXiv.1611.07004")] ai/anime ai/nn/gan

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations.

We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.

Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking.

As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.

--- https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence Kullback-Leibler divergence 2021-09-14 ai/nn/vae statistics/bayes statistics/probability --- https://arxiv.org/abs/1312.6114 Auto-Encoding Variational Bayes Diederik P. Kingma, Max Welling 2013-12-20 2021-09-14 [("doi","10.48550/arXiv.1312.6114")] ai/nn/vae statistics/bayes

How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets?

We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator.

Theoretical advantages are reflected in experimental results.

--- https://arxiv.org/abs/2205.09838#microsoft Why GANs are overkill for NLP David Alvarez-Melis, Vikas Garg, Adam Tauman Kalai 2022-05-19 2022-05-19 [("doi","10.48550/arXiv.2205.09838")] ai/nn/gan

This work offers a novel theoretical perspective on why, despite numerous attempts, adversarial approaches to generative modeling (eg. GANs) have not been as popular for certain generation tasks, particularly sequential tasks such as Natural Language Generation, as they have in others, such as Computer Vision. In particular, on sequential data such as text, maximum-likelihood approaches are statistically-significantly more utilized than GANs.

We show that, while it may seem that maximizing likelihood is inherently different than minimizing distinguishability, this distinction is largely artificial and only holds for limited models. We argue that minimizing KL-divergence (ie. maximizing likelihood) is a more efficient approach to effectively minimizing the same distinguishability criteria that adversarial models seek to optimize.

Reductions show that minimizing distinguishability can be seen as simply boosting likelihood for certain families of models including n-gram models and neural networks with a softmax output layer. To achieve a full polynomial-time reduction, a novel next-token distinguishability model is considered.

--- https://arxiv.org/abs/1405.5488 On Learning Where To Look Marc’Aurelio Ranzato 2014-04-24 2021-09-14 [("doi","10.48550/arXiv.1405.5488")] ai/nn/transformer/attention

Current automatic vision systems face two major challenges: scalability and extreme variability of appearance. First, the computational time required to process an image typically scales linearly with the number of pixels in the image, therefore limiting the resolution of input images to thumbnail size. Second, variability in appearance and pose of the objects constitute a major hurdle for robust recognition and detection. In this work, we propose a model that makes baby steps towards addressing these challenges.

We describe a learning based method that recognizes objects through a series of glimpses. This system performs an amount of computation that scales with the complexity of the input rather than its number of pixels. Moreover, the proposed method is potentially more robust to changes in appearance since its parameters are learned in a data driven manner.

Preliminary experiments on a handwritten dataset of digits demonstrate the computational advantages of this approach.

--- /doc/statistics/peer-review/2017-goldstein.pdf Uncertainty and Individual Discretion in Allocating Research Funds Anna P. Goldstein, Michael Kearney 2017-01-01 2021-09-14 [("doi","10.2139/ssrn.3012169")] statistics/peer-review --- /doc/ai/1989-cliff.pdf In memory of Henry J. Kelley E. M. Cliff 1989-01-01 2021-09-14 [("doi","10.1007/BF00938794")] ai math --- /doc/genetics/selection/natural/human/2019-hsieh.pdf Adaptive archaic introgression of copy number variants and the discovery of previously unknown human genes PingHsun Hsieh, Mitchell R. Vollger, Vy Dang, David Porubsky, Carl Baker, Stuart Cantsilieris, Kendra Hoekzema, Alexandra P. Lewis, Katherine M. Munson, Melanie Sorensen, Zev N. Kronenberg, Shwetha Murali, Bradley J. Nelson, Giorgia Chiatante, Flavia Angela Maria Maggiolini, Hélène Blanché, Jason G. Underwood, Francesca Antonacci, Jean-François Deleuze, Evan E. Eichler 2019-10-18 2021-09-14 [("doi","10.1126/science.aax2083")] genetics/selection/natural/human

Copy number variants (CNVs) are subject to stronger selective pressure than single-nucleotide variants, but their roles in archaic introgression and adaptation have not been systematically investigated.

We show that stratified CNVs are statistically-significantly associated with signatures of positive selection in Melanesians and provide evidence for adaptive introgression of large CNVs at chromosomes 16p11.2 and 8p21.3 from Denisovans and Neanderthals, respectively.

Using long-read sequence data, we reconstruct the structure and complex evolutionary history of these polymorphisms and show that both encode positively selected genes absent from most human populations.

Our results collectively suggest that large CNVs originating in archaic hominins and introgressed into modern humans have played an important role in local population adaptation and represent an insufficiently studied source of large-scale genetic variation.

--- /doc/genetics/selection/artificial/1805-lawrence.pdf Robert Bakewell [obituary] John Lawrence 1805-01-01 2021-09-14 genetics/selection/artificial --- /doc/genetics/selection/artificial/1894-housman.pdf Robert Bakewell [biography] William Housman 1894-01-01 2021-09-14 genetics/selection/artificial --- /doc/genetics/selection/artificial/1911-surface.pdf The Result of Selecting Fluctuating Variations: Data from the Illinois Corn Breeding Experiments Frank M. Surface 1911-01-01 2021-09-14 genetics/selection/artificial --- /doc/genetics/selection/artificial/1933-student.pdf Evolution By Selection: The Implications of Winter’s Selection Experiment William Sealy Gosset 1933-01-01 2021-09-15 genetics/selection/artificial --- /doc/genetics/selection/artificial/1933-fisher.pdf Number of Mendelian Factors in Quantitative Inheritance R. A. Fisher 1933-01-01 2021-09-15 [("doi","10.1038/131400a0")] genetics/selection/artificial --- /doc/genetics/selection/artificial/1935-hamilton.pdf The Association Between Brain Size and Maze Ability in the White Rat James Alexander Hamilton 1935-01-01 2021-09-15 genetics/selection/artificial psychology/animal/maze --- /doc/genetics/selection/artificial/1940-tryon-3.pdf Genetic Differences in Maze-Learning Ability in Rats (XIII) Robert Choate Tryon 1940-01-01 2021-09-15 genetics/selection/artificial psychology/animal/maze --- /doc/genetics/selection/artificial/1941-heron.pdf The Inheritance of Brightness and Dullness in Maze Learning Ability in the Rat W. T. Heron 1941-01-01 2021-09-15 [("doi","10.1080/08856559.1941.10534590")] genetics/selection/artificial psychology/animal/maze --- /doc/genetics/selection/artificial/1957-pawson-robertbakewellpioneerlivestockbreeder.pdf Robert Bakewell: Pioneer Livestock Breeder H. Cecil Pawson 1957-01-01 2021-09-15 genetics/selection/artificial --- /doc/genetics/selection/artificial/1959-pawson.pdf Some Agricultural History Salvaged H. Cecil Pawson 1959-01-01 2021-09-15 [("doi","10.2307/40272864")] genetics/selection/artificial --- /doc/genetics/heritable/1970-mcclearn.pdf Behavioral Genetics [Annual Review] G E. McClearn 1970-01-01 2021-09-15 genetics/heritable genetics/selection/artificial psychology/animal/maze --- /doc/genetics/selection/natural/human/1971-shockley-2.pdf Hardy-Weinberg Law Generalized to Estimate Hybrid Variance for Negro Populations and Reduce Racial Aspects of the Environment-Heredity Uncertainty William Shockley 1971-01-01 2021-09-15 genetics/selection/natural/human --- /doc/genetics/selection/natural/1962-rich.pdf On the Problems of Evolution and Biochemical Information Transfer Alexander Rich 1962-01-01 2021-09-15 genetics/selection/natural --- /doc/genetics/selection/artificial/1974-dudley-seventygenerationsofselectionforoilandproteininmaize.pdf Seventy Generations of Selection for Oil and Protein in Maize J. W. Dudley 1974-01-01 2021-09-15 genetics/selection/artificial --- /doc/genetics/selection/artificial/1992-wilmut.pdf Impact of biotechnology on animal breeding I. Wilmut, C. S. Haley, J. A. Woolliams 1992-01-01 2021-09-16 [("doi","10.1016/0378-4320(92)90101-i")] genetics/selection/artificial --- /doc/genetics/selection/artificial/1995-stanley-robertbakewellandthelonghornbreedofcattle.pdf Robert Bakewell and the Longhorn Breed of Cattle Pat Stanley 1995-01-01 2021-09-16 genetics/selection/artificial --- /doc/genetics/selection/natural/1982-charlesworth.pdf A Neo-Darwinian Commentary on Macroevolution Brian Charlesworth, Russell Lande, Montgomery Slatkin 1982-05-01 2021-09-16 [("doi","10.2307/2408095")] genetics/selection/natural --- /doc/genetics/selection/natural/1993-smith-thetheoryofevolution.pdf The theory of evolution John Maynard Smith 1993-01-01 2021-09-16 genetics/selection/natural --- /doc/genetics/selection/natural/human/1989-rushton.pdf Genetic similarity, human altruism, and group selection J. Philippe Rushton 1989-01-01 2021-09-16 [("doi","10.1017/S0140525X00057320")] genetics/selection/natural/human

A new theory of attraction and liking based on kin selection suggests that people detect genetic similarity in others in order to give preferential treatment to those who are most similar to themselves. There are many sources of empirical and theoretical support for this view, including (1) the inclusive fitness theory of altruism, (2) kin recognition studies of animals raised apart, (3) assortative mating studies, (4) favoritism in families, (5) selective similarity among friends, and (6) ethnocentrism.

Specific tests of the theory show that (1) sexually interacting couples who produce a child are genetically more similar to each other in blood antigens than they are either to sexually interacting couples who fail to produce a child or to randomly paired couples from the same sample; (2) similarity between marriage partners is most marked in the more genetically influenced of sets of anthropometric, cognitive, and personality characteristics; (3) after the death of a child, parental grief intensity is correlated with the child’s similarity to the parent; (4) long-term male friendship pairs are more similar to each other in blood antigens than they are to random dyads from the same sample; and (5) similarity among best friends is most marked in the more genetically influenced of sets of attitudinal, personality, and anthropometric characteristics.

The mechanisms underlying these findings may constitute a biological substrate of ethnocentrism, enabling group selection to occur.

--- /doc/genetics/selection/artificial/1997-orel.pdf Cloning, Inbreeding, and History Vitezslav Orel 1997-01-01 2021-09-16 [("doi","10.1086/419954")] genetics/cloning genetics/selection/artificial --- /doc/genetics/selection/artificial/2001-famula.pdf Genetics of Quantitative Traits and Improvement of Dog Breeds Thomas R. Famula 2001-01-01 2021-09-16 genetics/heritable/dog genetics/selection/artificial --- /doc/genetics/selection/artificial/2001-bodo.pdf Preimplantation genetic diagnosis in cattle: A review S. Bodó, B. Baranyai, E. Gócza, J. Dohy, M. Markkula 2001-01-01 2021-09-16 [("doi","10.1556/004.49.2001.1.12")] genetics/selection/artificial

Preimplantation Genetic Diagnosis (PGD) is reviewed and novel fields where it may be applied are investigated.

Technical advances of PGD in cattle embryos have already enabled its integration as a part of the MOET (Multiple Ovulation Embryo Transfer) breeding system.

PGD for well-defined selection targets can enhance cattle breeding and embryo trade. It allows embryo selection according to their sex, and it may be used to breed special cow lines, or top bulls, by selecting embryos for valuable production traits using Marker Assisted Selection (MAS). A good allelic profile and/or the insertion of a transgene can be detected by PGD.

This review article presents the technical requirements for PGD, and shows that this biotechnological method has great economic potential.

--- /doc/genetics/selection/artificial/2003-elsen.pdf Utilization of genomic information in livestock improvement Jean-Michel Elsen 2003-01-01 2021-09-16 [("doi","10.5367/000000003322740793")] genetics/selection/artificial

Genomic information will increase selection efficiency in livestock.

Genomics allows the tracing of transmission of genome fragments between generations, and the location and identification of genes whose polymorphisms partially explain quantitative trait variability.

This information is useful for a better evaluation of the genetic values used by breeders, in particular when traits cannot be measured on a large scale for technical and/or economic reasons. It is also useful for reducing the generation interval through an early choice of breeding animals, and for increasing selection intensity.

The first applications with regard to quality products and disease resistance are described in ruminant species.

Interactions between genomic and reproductive biotechnologies are also described.

--- /doc/genetics/selection/artificial/2004-weber.pdf Population Size and Long-term Selection Kenneth Weber 2004-01-01 2021-09-16 genetics/selection/artificial --- /doc/genetics/selection/artificial/2006-schinto.pdf Good Breeding: British Livestock Portraits, 1780–1900 Jeanne Schinto 2006-01-01 2021-09-17 [("doi","10.1525/gfc.2006.6.3.30")] genetics/selection/artificial --- /doc/genetics/selection/artificial/1966-roberts.pdf The limits to artificial selection for body weight in the mouse II. The Genetic Nature of the Limits R. C. Roberts 2009-02-28 2021-09-17 genetics/selection/artificial --- /doc/genetics/selection/natural/1974-dreux.pdf The cat population of Péninsule Courbet, ȋles Kerguelen: an example of the founder effect Ph. Dreux 2009-05-28 2021-09-17 genetics/selection/natural --- /doc/genetics/selection/natural/human/2014-fu.pdf Genome sequence of a 45,000-year-old modern human from western Siberia Qiaomei Fu, Heng Li, Priya Moorjani, Flora Jay, Sergey M. Slepchenko, Aleksei A. Bondarev, Philip L. F. Johnson, Ayinuer Aximu-Petri, Kay Prüfer, Cesare de Filippo, Matthias Meyer, Nicolas Zwyns, Domingo C. Salazar-García, Yaroslav V. Kuzmin, Susan G. Keates, Pavel A. Kosintsev, Dmitry I. Razhev, Michael P. Richards, Nikolai V. Peristov, Michael Lachmann, Katerina Douka, Thomas F. G. Higham, Montgomery Slatkin, Jean-Jacques Hublin, David Reich, Janet Kelso, T. Bence Viola, Svante Pääbo 2014-01-01 2021-09-17 [("doi","10.1038/nature13810")] cryonics genetics/selection/natural/human genetics/sequencing --- /doc/genetics/selection/artificial/2016-wayne.pdf Evolutionary History, Selective Sweeps, and Deleterious Variation in the Dog Adam H. Freedman, Kirk E. Lohmueller, Robert K. Wayne 2016-01-01 2021-09-17 [("doi","10.1146/annurev-ecolsys-121415-032155")] genetics/heritable/dog genetics/selection/artificial --- /doc/genetics/selection/natural/2006-cobb.pdf Judith Shadwell 2006-01-01 2021-09-17 genetics/selection/artificial genetics/selection/natural --- /doc/genetics/selection/artificial/2013-hodeswertz.pdf What do reproductive-age women who undergo oocyte cryopreservation think about the process as a means to preserve fertility? Brooke Hodes-Wertz, Sarah Druckenmiller, Meghan Smith, Nicole Noyes 2013-01-01 2021-09-17 [("doi","10.1016/j.fertnstert.2013.07.201")] genetics/selection/artificial --- /doc/genetics/selection/artificial/2015-tempelman.pdf Statistical and Computational Challenges in Whole Genome Prediction and Genome-Wide Association Analyses for Plant and Animal Breeding Robert J. Tempelman 2015-01-01 2021-09-17 [("doi","10.1007/s13253-015-0225-2")] genetics/selection/artificial --- /doc/genetics/selection/artificial/2015-murugappan.pdf Cost-effectiveness analysis of preimplantation genetic screening and in vitro fertilization versus expectant management in patients with unexplained recurrent pregnancy loss Gayathree Murugappan, Mika S. Ohno, Ruth B. Lathi 2015-01-01 2021-09-17 [("doi","10.1016/j.fertnstert.2015.02.012")] genetics/selection/artificial --- /doc/genetics/selection/natural/human/2014-simons.pdf The deleterious mutation load is insensitive to recent population history Yuval B. Simons, Michael C. Turchin, Jonathan K. Pritchard, Guy Sella 2014-01-01 2021-09-17 [("doi","10.1038/ng.2896")] genetics/editing genetics/selection/natural/human --- /doc/genetics/selection/natural/human/2015-mathieson.pdf Genome-wide patterns of selection in 230 ancient Eurasians Iosif Lazaridis, Nadin Rohland, Swapan Mallick, Nick Patterson, Songül Alpaslan Roodenberg, Eadaoin Harney, Kristin Stewardson, Daniel Fernandes, Mario Novak, Kendra Sirak, Cristina Gamba, Eppie R. Jones, Bastien Llamas, Stanislav Dryomov, Joseph Pickrell, Juan Luís Arsuaga, José María Bermúdez de Castro, Eudald Carbonell, Fokke Gerritsen, Aleksandr Khokhlov, Pavel Kuznetsov, Marina Lozano, Harald Meller, Oleg Mochalov, Vyacheslav Moiseyev, Manuel A. Rojo Guerra, Jacob Roodenberg, Josep Maria Vergès, Johannes Krause, Alan Cooper, Kurt W. Alt, Dorcas Brown, David Anthony, Carles Lalueza-Fox, Iain Mathieson, Wolfgang Haak, Ron Pinhasi, David Reich 2015-01-01 2021-09-17 [("doi","10.1038/nature16152")] genetics/selection/natural/human --- /doc/genetics/selection/natural/human/2015-henn.pdf Estimating the mutation load in human genomes Brenna M. Henn, Laura R. Botigué, Carlos D. Bustamante, Andrew G. Clark, Simon Gravel 2015-01-01 2021-09-18 [("doi","10.1038/nrg3931")] genetics/editing genetics/selection/natural/human --- /doc/genetics/selection/artificial/2016-vermeesch.pdf Prenatal and pre-implantation genetic diagnosis Joris Robert Vermeesch, Thierry Voet, Koenraad Devriendt 2016-01-01 2021-09-18 [("doi","10.1038/nrg.2016.97")] genetics/selection/artificial --- /doc/genetics/selection/artificial/2016-badran.pdf Continuous evolution of Bacillus thuringiensis toxins overcomes insect resistance Ahmed H. Badran, Victor M. Guzov, Qing Huai, Melissa M. Kemp, Prashanth Vishwanath, Wendy Kain, Autumn M. Nance, Artem Evdokimov, Farhad Moshiri, Keith H. Turner, Ping Wang, Thomas Malvar, David R. Liu 2016-01-01 2021-09-18 [("doi","10.1038/nature17938")] genetics/selection/artificial --- /doc/psychology/personality/2017-latham.pdf Mothers want extraversion over Conscientiousness or intelligence for their children Rachel M. Latham, Sophie von Stumm 2017-01-01 2021-09-18 [("doi","10.1016/j.paid.2017.07.037")] genetics/selection/artificial psychology/personality --- /doc/genetics/selection/natural/human/2016-shpak.pdf An Evolutionary Genetic Perspective on Cancer Biology Max Shpak, Jie Lu 2016-01-01 2021-09-18 [("doi","10.1146/annurev-ecolsys-121415-032109")] genetics/selection/natural/human --- /doc/genetics/selection/natural/human/2016-mallick.pdf The Simons Genome Diversity Project: 300 genomes from 142 diverse populations Heng Li, Mark Lipson, Iain Mathieson, Melissa Gymrek, Fernando Racimo, Mengyao Zhao, Niru Chennagiri, Susanne Nordenfelt, Arti Tandon, Pontus Skoglund, Iosif Lazaridis, Sriram Sankararaman, Qiaomei Fu, Nadin Rohland, Gabriel Renaud, Yaniv Erlich, Thomas Willems, Carla Gallo, Jeffrey P. Spence, Yun S. Song, Giovanni Poletti, Francois Balloux, George van Driem, Peter de Knijff, Irene Gallego Romero, Aashish R. Jha, Doron M. Behar, Claudio M. Bravi, Cristian Capelli, Tor Hervig, Andres Moreno-Estrada, Olga L. Posukh, Elena Balanovska, Oleg Balanovsky, Sena Karachanak-Yankova, Hovhannes Sahakyan, Draga Toncheva, Levon Yepiskoposyan, Chris Tyler-Smith, Yali Xue, M. Syafiq Abdullah, Andres Ruiz-Linares, Cynthia M. Beall, Anna Di Rienzo, Choongwon Jeong, Elena B. Starikovskaya, Ene Metspalu, Jüri Parik, Richard Villems, Brenna M. Henn, Ugur Hodoglugil, Robert Mahley, Antti Sajantila, George Stamatoyannopoulos, Joseph T. S. Wee, Rita Khusainova, Elza Khusnutdinova, Sergey Litvinov, George Ayodo, David Comas, Michael F. Hammer, Toomas Kivisild, William Klitz, Cheryl A. Winkler, Damian Labuda, Michael Bamshad, Lynn B. Jorde, Sarah A. Tishkoff, W. Scott Watkins, Mait Metspalu, Stanislav Dryomov, Rem Sukernik, Lalji Singh, Kumarasamy Thangaraj, Svante Pääbo, Janet Kelso, Nick Patterson, Swapan Mallick, David Reich 2016-01-01 2021-09-18 [("doi","10.1038/nature18964")] genetics/selection/natural/human --- /doc/genetics/selection/natural/human/2017-marciniak.pdf Harnessing ancient genomes to study the history of human adaptation Stephanie Marciniak, George H. Perry 2017-01-01 2021-09-18 [("doi","10.1038/nrg.2017.65")] genetics/selection/natural/human --- /doc/genetics/selection/natural/human/2017-adhikari.pdf The Genetic Diversity of the Americas Kaustubh Adhikari, Juan Camilo Chacón-Duque, Javier Mendoza-Revilla, Macarena Fuentes-Guajardo, Andrés Ruiz-Linares 2017-01-01 2021-09-18 genetics/selection/natural/human --- /doc/genetics/sequencing/2017-landry.pdf Racial minority group interest in direct-to-consumer genetic testing: findings from the PGen study Latrice Landry, Daiva Elena Nielsen, Deanna Alexis Carere, J. Scott Roberts, Robert C. Green 2017-01-01 2021-09-18 genetics/heritable genetics/sequencing --- /doc/genetics/selection/artificial/2018-wallace.pdf On the Road to Breeding 4.0: Unraveling the Good, the Bad, and the Boring of Crop Quantitative Genomics Jason G. Wallace, Eli Rodgers-Melnick, Edward S. Buckler 2018-01-01 2021-09-18 [("doi","10.1146/annurev-genet-120116-024846")] genetics/selection/artificial

Understanding the quantitative genetics of crops has been and will continue to be central to maintaining and improving global food security.

We outline 4 stages that plant breeding either has already achieved or will probably soon achieve. Top-of-the-line breeding programs are currently in Breeding 3.0, where inexpensive, genome-wide data coupled with powerful algorithms allow us to start breeding on predicted instead of measured phenotypes.

We focus on 3 major questions that must be answered to move from current Breeding 3.0 practices to Breeding 4.0: (1) How do we adapt crops to better fit agricultural environments? (2) What is the nature of the diversity upon which breeding can act? (3) How do we deal with deleterious variants?

Answering these questions and then translating them to actual gains for farmers will be a part of achieving global food security in the 21st century.

--- /doc/genetics/selection/artificial/2018-klee.pdf The genetics of fruit flavour preferences Harry J. Klee, Denise M. Tieman 2018-01-01 2021-09-18 [("doi","10.1038/s41576-018-0002-5")] genetics/heritable genetics/selection/artificial --- /doc/genetics/selection/artificial/2018-hart.pdf Secondary findings from clinical genomic sequencing: prevalence, patient perspectives, family history assessment, and health-care costs from a multisite study M. Ragan Hart, Barbara B. Biesecker, Carrie L. Blout, Kurt D. Christensen, Laura M. Amendola, Katie L. Bergstrom, Sawona Biswas, Kevin M. Bowling, Kyle B. Brothers, Laura K. Conlin, Greg M. Cooper, Matthew C. Dulik, Kelly M. East, Jessica N. Everett, Candice R. Finnila, Arezou A. Ghazani, Marian J. Gilmore, Katrina A. B. Goddard, Gail P. Jarvik, Jennifer J. Johnston, Tia L. Kauffman, Whitley V. Kelley, Joel B. Krier, Katie L. Lewis, Amy L. McGuire, Carmit McMullen, Jeffrey Ou, Sharon E. Plon, Heidi L. Rehm, C. Sue Richards, Edward J. Romasko, Ane Miren Sagardia, Nancy B. Spinner, Michelle L. Thompson, Erin Turbitt, Jason L. Vassy, Benjamin S. Wilfond, David L. Veenstra, Jonathan S. Berg, Robert C. Green, Leslie G. Biesecker, Lucia A. Hindorff 2018-01-01 2021-09-19 [("doi","10.1038/s41436-018-0308-x")] genetics/heritable/rare genetics/selection/artificial --- /doc/genetics/selection/artificial/2018-haerwigman.pdf 1 in 38 individuals at risk of a dominant medically actionable disease Lonneke Haer-Wigman, Vyne van der Schoot, Ilse Feenstra, Anneke T. Vulto-van Silfhout, Christian Gilissen, Han G. Brunner, Lisenka E. L. M. Vissers, Helger G. Yntema 2018-01-01 2021-09-19 [("doi","10.1038/s41431-018-0284-2")] genetics/heritable/rare genetics/selection/artificial --- /doc/genetics/selection/natural/human/2018-tucci.pdf Evolutionary history and adaptation of a human pygmy population of Flores Island, Indonesia Serena Tucci, Samuel H. Vohr, Rajiv C. McCoy, Benjamin Vernot, Matthew R. Robinson, Chiara Barbieri, Brad J. Nelson, Wenqing Fu, Gludhug A. Purnomo, Herawati Sudoyo, Evan E. Eichler, Guido Barbujani, Peter M. Visscher, Joshua M. Akey, Richard E. Green 2018-01-01 2021-09-19 [("doi","10.1126/science.aar8486")] genetics/selection/natural/human --- /doc/genetics/selection/natural/human/2018-skoglund.pdf Ancient Human Genomics: The First Decade Pontus Skoglund, Iain Mathieson 2018-01-01 2021-09-19 [("doi","10.1146/annurev-genom-083117-021749")] genetics/selection/natural/human

The first decade of ancient genomics has revolutionized the study of human prehistory and evolution. We review new insights based on prehistoric modern human genomes, including greatly increased resolution of the timing and structure of the out-of-Africa expansion, the diversification of present-day non-African populations, and the earliest expansions of those populations into Eurasia and America. Prehistoric genomes now document population transformations on every inhabited continent—in particular the effect of agricultural expansions in Africa, Europe, and Oceania—and record a history of natural selection that shapes present-day phenotypic diversity.

Despite these advances, much remains unknown, in particular about the genomic histories of Asia (the most populous continent) and Africa (the continent that contains the most genetic diversity). Ancient genomes from these and other regions, integrated with a growing understanding of the genomic basis of human phenotypic diversity, will be in focus during the next decade of research in the field.

--- /doc/genetics/selection/natural/human/2018-marquart.pdf Estimation of The Percentage of US Patients With Cancer Who Benefit From Genome-Driven Oncology American Medical Association 2018-01-01 2021-09-19 genetics/selection/natural/human --- /doc/genetics/selection/natural/human/2018-ilardo.pdf Physiological and Genetic Adaptations to Diving in Sea Nomads Melissa A. Ilardo, Ida Moltke, Thorfinn S. Korneliussen, Jade Cheng, Aaron J. Stern, Fernando Racimo, Peter de Barros Damgaard, Martin Sikora, Andaine Seguin-Orlando, Simon Rasmussen, Inge C. L. van den Munckhof, Rob ter Horst, Leo A. B. Joosten, Mihai G. Netea, Suhartini Salingkat, Rasmus Nielsen, Eske Willerslev 2018-01-01 2021-09-19 [("doi","10.1016/j.cell.2018.03.054")] genetics/selection/natural/human --- /doc/genetics/selection/natural/human/2018-chekalin.pdf Changes in Biological Pathways During 6,000 Years of Civilization in Europe Evgeny Chekalin, Alexandr Rubanovich, Tatiana V. Tatarinova, Artem Kasianov, Nicole Bender, Marina Chekalina, Kaspar Staub, Nikola Koepke, Frank Rühli, Sergey Bruskin, Irina Morozova 2018-01-01 2021-09-19 [("doi","10.1093/molbev/msy201")] genetics/selection/natural/human --- /doc/genetics/selection/natural/human/2018-belbin.pdf Genetic diversity in populations across Latin America: implications for population and medical genetic studies Gillian M. Belbin, Maria A. Nieves-Colón, Eimear E. Kenny, Andres Moreno-Estrada, Christopher R. Gignoux 2018-01-01 2021-09-19 [("doi","10.1016/j.gde.2018.07.006")] genetics/selection/natural/human --- /doc/genetics/selection/natural/2018-milner.pdf Genebank genomics highlights the diversity of a global barley collection Sara G. Milner, Matthias Jost, Shin Taketa, Elena Rey Mazón, Axel Himmelbach, Markus Oppermann, Stephan Weise, Helmut Knüpffer, Martín Basterrechea, Patrick König, Danuta Schüler, Rajiv Sharma, Raj K. Pasam, Twan Rutten, Ganggang Guo, Dongdong Xu, Jing Zhang, Gerhard Herren, Thomas Müller, Simon G. Krattinger, Beat Keller, Yong Jiang, Maria Y. González, Yusheng Zhao, Antje Habekuß, Sandra Färber, Frank Ordon, Matthias Lange, Andreas Börner, Andreas Graner, Jochen C. Reif, Uwe Scholz, Martin Mascher, Nils Stein 2018-01-01 2021-09-19 [("doi","10.1038/s41588-018-0266-x")] genetics/selection/natural --- /doc/genetics/sequencing/2019-chen-4.pdf A late Middle Pleistocene Denisovan mandible from the Tibetan Plateau Fahu Chen, Frido Welker, Chuan-Chou Shen, Shara E. Bailey, Inga Bergmann, Simon Davis, Huan Xia, Hui Wang, Roman Fischer, Sarah E. Freidline, Tsai-Luen Yu, Matthew M. Skinner, Stefanie Stelzer, Guangrong Dong, Qiaomei Fu, Guanghui Dong, Jian Wang, Dongju Zhang, Jean-Jacques Hublin 2019-01-01 2021-09-19 [("doi","10.1038/s41586-019-1139-x")] genetics/selection/natural/human genetics/sequencing --- /doc/genetics/selection/natural/human/1996-oneil.pdf Debate Over Race and Intelligence Pamela G. O’Neil, Candace Timpete, James S. Rogers, Jerome J. Howard 2018-10-24 2021-09-19 genetics/selection/natural/human iq politics --- /doc/genetics/selection/artificial/2019-zhang.pdf The genetic basis of inbreeding depression in potato Chunzhi Zhang, Pei Wang, Die Tang, Zhongmin Yang, Fei Lu, Jianjian Qi, Nilesh R. Tawari, Yi Shang, Canhui Li, Sanwen Huang 2019-01-01 2021-09-20 [("doi","10.1038/s41588-018-0319-1")] genetics/heritable/rare genetics/selection/artificial --- /doc/genetics/selection/artificial/2019-kemper.pdf Subsidizing PGD: The Moral Case for Funding Genetic Selection James M. Kemper, Christopher Gyngell, Julian Savulescu 2019-01-01 2021-09-20 genetics/selection/artificial philosophy/ethics --- /doc/genetics/selection/artificial/2019-zigerell.pdf Understanding public support for eugenic policies: Results from survey data L. J. Zigerell 2019-01-01 2021-09-20 [("doi","10.1016/j.soscij.2019.01.003")] genetics/selection/artificial politics --- /doc/genetics/selection/artificial/2019-hickey.pdf Breeding crops to feed 10 billion Lee T. Hickey, Amber Hafeez, Hannah Robinson, Scott A. Jackson, Soraya C. M. Leal-Bertioli, Mark Tester, Caixia Gao, Ian D. Godwin, Ben J. Hayes, Brande B. H. Wulff 2019-01-01 2021-09-20 [("doi","10.1038/s41587-019-0152-9")] genetics/selection/artificial --- /doc/genetics/selection/natural/2019-zheng.pdf Cryptic genetic variation accelerates evolution by opening access to diverse adaptive peaks Jia Zheng, Joshua L. Payne, Andreas Wagner 2019-01-01 2021-09-20 [("doi","10.1126/science.aax1837")] genetics/selection/natural

Cryptic genetic variation can facilitate adaptation in evolving populations.

To elucidate the underlying genetic mechanisms, we used directed evolution in Escherichia coli to accumulate variation in populations of yellow fluorescent proteins and then evolved these proteins toward the new phenotype of green fluorescence.

Populations with cryptic variation evolved adaptive genotypes with greater diversity and higher fitness than populations without cryptic variation, which converged on similar genotypes. Populations with cryptic variation accumulated neutral or deleterious mutations that break the constraints on the order in which adaptive mutations arise.

In doing so, cryptic variation opens paths to adaptive genotypes, creates historical contingency, and reduces the predictability of evolution by allowing different replicate populations to climb different adaptive peaks and explore otherwise-inaccessible regions of an adaptive landscape.

--- /doc/genetics/selection/natural/2019-immler.pdf Haploid Selection in ‘Diploid’ Organisms Simone Immler 2019-01-01 2021-09-20 [("doi","10.1146/annurev-ecolsys-110218-024709")] genetics/selection/natural

Evolutionary rates and strength of selection differ markedly between haploid and diploid genomes. Any genes expressed in a haploid state will be directly exposed to selection, whereas alleles in a diploid state may be partially or fully masked by a homologous allele. This difference may shape key evolutionary processes, including rates of adaptation and inbreeding depression, but also the evolution of sex chromosomes, heterochiasmy, and stable sex ratio biases.

All diploid organisms carry haploid genomes, most notably the haploid genomes in gametes produced by every sexually reproducing eukaryote. Furthermore, haploid expression occurs in genes with monoallelic expression, in sex chromosomes, and in organelles, such as mitochondria and plastids.

A comparison of evolutionary rates among these haploid genomes reveals striking parallels.

Evidence suggests that haploid selection has the potential to shape evolution in predominantly diploid organisms, and taking advantage of the rapidly developing technologies, we are now in the position to quantify the importance of such selection on haploid genomes.

--- /doc/genetics/selection/natural/2019-domingocalap.pdf Social evolution of innate immunity evasion in a virus Pilar Domingo-Calap, Ernesto Segredo-Otero, María Durán-Moreno, Rafael Sanjuán 2019-03-04 2021-09-20 [("doi","10.1038/s41564-019-0379-8")] genetics/microbiome genetics/selection/natural

Antiviral immunity has been studied extensively from the perspective of virus-cell interactions, yet the role of virus-virus interactions remains poorly addressed. Here, we demonstrate that viral escape from interferon (IFN)-based innate immunity is a social process in which IFN-stimulating viruses determine the fitness of neighboring viruses.

We propose a general and simple social evolution framework to analyze how natural selection acts on IFN shutdown and validate it in cell cultures and mice infected with vesicular stomatitis virus. Furthermore, we find that IFN shutdown is costly because it reduces short-term viral progeny production, thus fulfilling the definition of an altruistic trait.

Hence, in well-mixed populations, the IFN-blocking wild-type virus is susceptible to invasion by IFN-stimulating variants and spatial structure consequently determines whether IFN shutdown can evolve. Our findings reveal that fundamental social evolution rules govern viral innate immunity evasion and virulence and suggest possible antiviral interventions.

--- /doc/genetics/selection/natural/human/2019-zhou.pdf Genetic architecture and adaptations of Nunavik Inuit Sirui Zhou, Pingxing Xie, Amélie Quoibion, Amirthagowri Ambalavanan, Alexandre Dionne-Laporte, Dan Spiegelman, Cynthia V. Bourassa, Lan Xiong, Patrick A. Dion, Guy A. Rouleau 2019-01-01 2021-09-20 [("doi","10.1073/pnas.1810388116")] genetics/selection/natural/human

The Canadian Inuit have a distinct population background that may entail particular implications for the health of its individuals. However, the number of genetic studies examining this Inuit population is limited, and much remains to be discovered in regard to its genetic characteristics.

In this study, we generated whole-exome sequences and genome-wide genotypes for 170 Nunavik Inuit, a small and isolated founder population of Canadian Arctic indigenous people.

Our study revealed the genetic background of Nunavik Inuit to be distinct from any known present-day population. The majority of Nunavik Inuit show little evidence of gene flow from European or present-day Native American peoples, and Inuit living around Hudson Bay are genetically distinct from those around Ungava Bay. We also inferred that Nunavik Inuit have a small effective population size of 3,000 and likely split from Greenlandic Inuit ~10.5 kya. Nunavik Inuit went through a bottleneck at the same time and might have admixed with a population related to the Paleo-Eskimos.

Our study highlights population-specific genomic signatures in coding regions that show adaptations unique to Nunavik Inuit, particularly in pathways involving fatty acid metabolism and cellular adhesion (CPNE7, ICAM5, STAT2, and RAF1). Subsequent analyses in selection footprints and the risk of intracranial aneurysms (IAs) in Nunavik Inuit revealed an exonic variant under weak negative selection to be statistically-significantly associated with IA (rs77470587; P = 4.6 × 10−8).

--- /doc/genetics/selection/natural/human/2019-silvert.pdf Impact and Evolutionary Determinants of Neanderthal Introgression on Transcriptional and Post-Transcriptional Regulation Martin Silvert, Lluis Quintana-Murci, Maxime Rotival 2019-01-01 2021-09-20 [("doi","10.1016/j.ajhg.2019.04.016")] genetics/selection/natural/human --- /doc/genetics/selection/natural/human/2019-reynolds.pdf Comparing signals of natural selection between 3 Indigenous North American populations Austin W. Reynolds, Jaime Mata-Míguez, Aida Miró-Herrans, Marcus Briggs-Cloud, Ana Sylestine, Francisco Barajas-Olmos, Humberto Garcia-Ortiz, Margarita Rzhetskaya, Lorena Orozco, Jennifer A. Raff, M. Geoffrey Hayes, Deborah A. Bolnick 2019-01-01 2021-09-20 [("doi","10.1073/pnas.1819467116")] genetics/selection/natural/human

While many studies have highlighted human adaptations to diverse environments worldwide, genomic studies of natural selection in Indigenous populations in the Americas have been absent from this literature until very recently. Since humans first entered the Americas some 20,000 years ago, they have settled in many new environments across the continent. This diversity of environments has placed variable selective pressures on the populations living in each region, but the effects of these pressures have not been extensively studied to date.

To help fill this gap, we collected genome-wide data from 3 Indigenous North American populations from different geographic regions of the continent (Alaska, southeastern United States, and central Mexico). We identified signals of natural selection in each population and compared signals across populations to explore the differences in selective pressures among the 3 regions sampled.

We find evidence of adaptation to cold and high-latitude environments in Alaska, while in the southeastern United States and central Mexico, pathogenic environments seem to have created important selective pressures. This study lays the foundation for additional functional and phenotypic work on possible adaptations to varied environments during the history of population diversification in the Americas.

--- /doc/genetics/selection/natural/human/2019-harrison.pdf Natural selection contributed to immunological differences between hunter-gatherers and agriculturalists Genelle F. Harrison, Joaquin Sanz, Jonathan Boulais, Michael J. Mina, Jean-Christophe Grenier, Yumei Leng, Anne Dumaine, Vania Yotova, Christina M. Bergey, Samuel L. Nsobya, Stephen J. Elledge, Erwin Schurr, Lluis Quintana-Murci, George H. Perry, Luis B. Barreiro 2019-01-01 2021-09-21 [("doi","10.1038/s41559-019-0947-6")] genetics/selection/natural/human --- /doc/genetics/selection/natural/human/2019-dunkel.pdf Polygenic Scores Mediate the Jewish Phenotypic Advantage in Educational Attainment and Cognitive Ability Compared With Catholics and Lutherans Curtis S. Dunkel, Michael A. Woodley, Jonatan Pallesen, Emil O. W. Kirkegaard 2019-01-01 2021-09-21 [("doi","10.1037/ebs0000158")] genetics/selection/natural/human --- /doc/genetics/selection/artificial/2020-mateescu.pdf Chapter 2—Genetics and breeding of beef cattle Raluca G. Mateescu 2020-01-01 2021-09-21 [("doi","10.1016/B978-0-12-817052-6.00002-1")] genetics/selection/artificial --- /doc/genetics/selection/natural/2019-therkildsen.pdf Contrasting genomic shifts underlie parallel phenotypic evolution in response to fishing Nina O. Therkildsen, Aryn P. Wilder, David O. Conover, Stephan B. Munch, Hannes Baumann, Stephen R. Palumbi 2019-01-01 2021-09-21 [("doi","10.1126/science.aaw7271")] genetics/selection/natural

Humans cause widespread evolutionary change in nature, but we still know little about the genomic basis of rapid adaptation in the Anthropocene.

We tracked genomic changes across all protein-coding genes in experimental fish populations that evolved pronounced shifts in growth rates due to size-selective harvest over only 4 generations. Comparisons of replicate lines show parallel allele frequency shifts that recapitulate responses to size-selection gradients in the wild across hundreds of unlinked variants concentrated in growth-related genes.

However, a super-cluster of genes also rose rapidly in frequency and dominated the evolutionary dynamic in one replicate line but not in others.

Parallel phenotypic changes thus masked highly divergent genomic responses to selection, illustrating how contingent rapid adaptation can be in the face of strong human-induced selection.

--- /doc/genetics/selection/natural/2019-chen-3.pdf Large-scale ruminant genome sequencing provides insights into their evolution and distinct traits Lei Chen, Qiang Qiu, Yu Jiang, Kun Wang, Zeshan Lin, Zhipeng Li, Faysal Bibi, Yongzhi Yang, Jinhuan Wang, Wenhui Nie, Weiting Su, Guichun Liu, Qiye Li, Weiwei Fu, Xiangyu Pan, Chang Liu, Jie Yang, Chenzhou Zhang, Yuan Yin, Yu Wang, Yue Zhao, Chen Zhang, Zhongkai Wang, Yanli Qin, Wei Liu, Bao Wang, Yandong Ren, Ru Zhang, Yan Zeng, Rute R. da Fonseca, Bin Wei, Ran Li, Wenting Wan, Ruoping Zhao, Wenbo Zhu, Yutao Wang, Shengchang Duan, Yun Gao, Yong E. Zhang, Chunyan Chen, Christina Hvilsom, Clinton W. Epps, Leona G. Chemnick, Yang Dong, Siavash Mirarab, Hans Redlef Siegismund, Oliver A. Ryder, M. Thomas P. Gilbert, Harris A. Lewin, Guojie Zhang, Rasmus Heller, Wen Wang 2019-01-01 2021-09-21 [("doi","10.1126/science.aav6202")] genetics/selection/natural

The ruminants are one of the most successful mammalian lineages, exhibiting morphological and habitat diversity and containing several key livestock species.

To better understand their evolution, we generated and analyzed de novo assembled genomes of 44 ruminant species, representing all 6 Ruminantia families. We used these genomes to create a time-calibrated phylogeny to resolve topological controversies, overcoming the challenges of incomplete lineage sorting.

Population dynamic analyses show that population declines commenced between 100,000 and 50,000 years ago, which is concomitant with expansion in human populations.

We also reveal genes and regulatory elements that possibly contribute to the evolution of the digestive system, cranial appendages, immune system, metabolism, body size, cursorial locomotion, and dentition of the ruminants.

--- /doc/genetics/selection/natural/2019-barrett.pdf Linking a mutation to survival in wild mice Rowan D. H. Barrett, Stefan Laurent, Ricardo Mallarino, Susanne P. Pfeifer, Charles C. Y. Xu, Matthieu Foll, Kazumasa Wakamatsu, Jonathan S. Duke-Cohan, Jeffrey D. Jensen, Hopi E. Hoekstra 2019-01-01 2021-09-21 [("doi","10.1126/science.aav3824")] genetics/selection/natural

Adaptive evolution in new or changing environments can be difficult to predict because the functional connections between genotype, phenotype, and fitness are complex. Here, we make these explicit connections by combining field and laboratory experiments in wild mice.

We first directly estimate natural selection on pigmentation traits and an underlying pigment locus, Agouti, by using experimental enclosures of mice on different soil colors. Next, we show how a mutation in Agouti associated with survival causes lighter coat color through changes in its protein binding properties. Together, our findings demonstrate how a sequence variant alters phenotype and then reveal the ensuing ecological consequences that drive changes in population allele frequency, thereby illuminating the process of evolution by natural selection.

--- /doc/genetics/selection/natural/2019-baezortega.pdf Somatic evolution and global expansion of an ancient transmissible cancer lineage Adrian Baez-Ortega, Kevin Gori, Andrea Strakova, Janice L. Allen, Karen M. Allum, Leontine Bansse-Issa, Thinlay N. Bhutia, Jocelyn L. Bisson, Cristóbal Briceño, Artemio Castillo Domracheva, Anne M. Corrigan, Hugh R. Cran, Jane T. Crawford, Eric Davis, Karina F. de Castro, Andrigo B. de Nardi, Anna P. de Vos, Laura Delgadillo Keenan, Edward M. Donelan, Adela R. Espinoza Huerta, Ibikunle A. Faramade, Mohammed Fazil, Eleni Fotopoulou, Skye N. Fruean, Fanny Gallardo-Arrieta, Olga Glebova, Pagona G. Gouletsou, Rodrigo F. Häfelin Manrique, Joaquim J. G. P. Henriques, Rodrigo S. Horta, Natalia Ignatenko, Yaghouba Kane, Cathy King, Debbie Koenig, Ada Krupa, Steven J. Kruzeniski, Young-Mi Kwon, Marta Lanza-Perea, Mihran Lazyan, Adriana M. Lopez Quintana, Thibault Losfelt, Gabriele Marino, Simón Martínez Castañeda, Mayra F. Martínez-López, Michael Meyer, Edward J. Migneco, Berna Nakanwagi, Karter B. Neal, Winifred Neunzig, Máire Ní Leathlobhair, Sally J. Nixon, Antonio Ortega-Pacheco, Francisco Pedraza-Ordoñez, Maria C. Peleteiro, Katherine Polak, Ruth J. Pye, John F. Reece, Jose Rojas Gutierrez, Haleema Sadia, Sheila K. Schmeling, Olga Shamanova, Alan G. Sherlock, Maximilian Stammnitz, Audrey E. Steenland-Smit, Alla Svitich, Lester J. Tapia Martínez, Ismail Thoya Ngoka, Cristian G. Torres, Elizabeth M. Tudor, Mirjam G. van der Wel, Bogdan A. Viţălaru, Sevil A. Vural, Oliver Walkinton, Jinhong Wang, Alvaro S. Wehrle-Martinez, Sophie A. E. Widdowson, Michael R. Stratton, Ludmil B. Alexandrov, Iñigo Martincorena, Elizabeth P. Murchison 2019-01-01 2021-09-21 [("doi","10.1126/science.aau9923")] genetics/selection/natural

The canine transmissible venereal tumor (CTVT) is a cancer lineage that arose several millennia ago and survives by “metastasizing” between hosts through cell transfer. The somatic mutations in this cancer record its phylogeography and evolutionary history.

We constructed a time-resolved phylogeny from 546 CTVT exomes and describe the lineage’s worldwide expansion.

Examining variation in mutational exposure, we identify a highly context-specific mutational process that operated early in the cancer’s evolution but subsequently vanished, correlate ultraviolet-light mutagenesis with tumor latitude, and describe tumors with heritable hyperactivity of an endogenous mutational process. CTVT displays little evidence of ongoing positive selection, and negative selection is detectable only in essential genes.

We illustrate how long-lived clonal organisms capture changing mutagenic environments, and reveal that neutral genetic drift is the dominant feature of long-term cancer evolution.

--- /doc/genetics/selection/natural/human/2019-besenbacher.pdf Direct estimation of mutations in great apes reconciles phylogenetic dating Søren Besenbacher, Christina Hvilsom, Tomas Marques-Bonet, Thomas Mailund, Mikkel Heide Schierup 2019-01-01 2021-09-21 [("doi","10.1038/s41559-018-0778-x")] genetics/selection/natural/human --- /doc/genetics/selection/artificial/2020-kilbride.pdf In vitro fertilisation with preimplantation genetic testing: the need for expanded insurance coverage Madison K. Kilbride 2020-08-19 2021-09-21 [("doi","10.1136/medethics-2019-105879")] genetics/selection/artificial

Technological advances in genetic testing have enabled prospective parents to learn about their risk of passing a genetic condition to their future children. One option for those who want to ensure that their biological children do not inherit a genetic condition is to create embryos through in vitro fertilisation (IVF) and use a technique called preimplantation genetic testing (PGT) to screen embryos for genetic abnormalities before implantation. Unfortunately, due to its high cost, IVF-with-PGT is out of reach for the vast majority of Americans.

This article addresses an issue that has been underexplored in the medical ethics literature: the lack of insurance coverage for IVF-with-PGT.Within the US system, a key concept in insurance is that of medically necessary care, which broadly consists of diagnostic services and treatment services. In this article, I argue that IVF-with-PGT could be classified as either a diagnostic service or as a treatment service.

To make this case, I show that IVF-with-PGT is similar to other types of services that are often covered by US insurance providers. In light of these similarities, I argue that the current system is inconsistent with respect to what is—and is not—covered by insurance. To promote consistency and fairness in coverage, like cases should be treated alike—starting with greater coverage for IVF-with-PGT.

--- https://oldvcr.blogspot.com/2022/06/prior-art-dept-proletext-encoding-html.html Old Vintage Computing Research: prior-art-dept.: ProleText, encoding HTML before Markdown (and a modern reimplementation) 2021-09-21 cs --- https://www.templetons.com/tech/proletext.html ProleText Information 2021-09-21 cs --- https://knowyourmeme.com/memes/sites/dall-e-mini 2021-09-22 ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2004.10827 Syntactic Structure from Deep Learning Tal Linzen, Marco Baroni 2020-04-22 2021-09-22 [("doi","10.1146/annurev-linguistics-032020-051035")] ai/nn/rnn psychology/neuroscience

Modern deep neural networks achieve impressive performance in engineering applications that require extensive linguistic skills, such as machine translation. This success has sparked interest in probing whether these models are inducing human-like grammatical knowledge from the raw data they are exposed to, and, consequently, whether they can shed new light on long-standing debates concerning the innate structure necessary for language acquisition.

In this article, we survey representative studies of the syntactic abilities of deep networks, and discuss the broader implications that this work has for theoretical linguistics.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7585006/ Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models James L. McClelland, Felix Hill, Maja Rudolph, Jason Baldridge, Hinrich Schütze 2020 2021-09-22 [("doi","10.1073/pnas.1910416117")] ai/nn/rnn psychology/neuroscience

Language is crucial for human intelligence, but what exactly is its role? We take language to be a part of a system for understanding and communicating about situations. In humans, these abilities emerge gradually from experience and depend on domain-general principles of biological neural networks: connection-based learning, distributed representation, and context-sensitive, mutual constraint satisfaction-based processing. Current artificial language processing systems rely on the same domain general principles, embodied in artificial neural networks. Indeed, recent progress in this field depends on query-based attention, which extends the ability of these systems to exploit context and has contributed to remarkable breakthroughs. Nevertheless, most current models focus exclusively on language-internal tasks, limiting their ability to perform tasks that depend on understanding situations. These systems also lack memory for the contents of prior situations outside of a fixed contextual span.

We describe the organization of the brain’s distributed understanding system, which includes a fast learning system that addresses the memory problem. We sketch a framework for future models of understanding drawing equally on cognitive neuroscience and artificial intelligence and exploiting query-based attention.

We highlight relevant current directions and consider further developments needed to fully capture human-level language understanding in a computational system.

--- https://www.lesswrong.com/posts/c3cQgBN3v2Cxpe2kc/getting-gpt-3-to-predict-metaculus-questions Getting GPT-3 to predict Metaculus questions 2021-09-22 ai/nn/transformer/gpt/non-fiction statistics/prediction --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454109/ Schizophrenia and Bipolar Illness in the Relatives of University Scientists: An Epidemiological Report on the Creativity-Psychopathology Relationship Josef Parnas, Karl Erik Sandsten, Claus Høstrup Vestergaard, Julie Nordgaard 2019 2021-09-22 [("doi","10.3389/fpsyt.2019.00175")] psychiatry/bipolar/energy psychiatry/schizophrenia

A potential link between creativity and mental illness has been a long-standing topic for human studies and empirical research. The major problem is defining creativity and establishing its measurable indicators. A few high-quality epidemiological studies have been undertaken and point to a link between creativity and vulnerability to mental illness. Demonstrating such a shared vulnerability could expand our understanding of mental illnesses and open up new avenues of empirical research.

In this epidemiological study, we defined scientists (academics) at the universities as individuals assumed to exhibit “more creativity” than the background population. In a register coupling with a population of 588,532 people, we examined successful university academics’ first-degree and second-degree relatives for diagnosed mental disorders and compared those figures with controls from the background population controlling for educational level.

The relatives of the academics had statistically-significantly increased risk of suffering from schizophrenia or bipolar disorder.

For bipolar disorder, it is perhaps temperamental features and high energy levels that contribute to this association. In the case of schizophrenia, the mediating bridge may involve an amplification of human tendency to question the obvious and “taken-for-granted.” Creativity and an increased risk for mental disorders seem to be linked by a shared vulnerability that is not manifested by clinical mental disorders in the academics.

--- https://www.theguardian.com/news/2019/jan/25/business-of-kidnapping-inside-the-secret-world-of-hostage-negotiation-ransom-insurance The business of kidnapping: inside the secret world of hostage negotiation 2021-09-22 economics/mechanism-design --- https://openai.com/blog/techniques-for-training-large-neural-networks/ 2021-09-22 ai/scaling/hardware --- https://arxiv.org/abs/2206.04769#microsoft CLAP: Learning Audio Concepts From Natural Language Supervision Benjamin Elizalde, Soham Deshmukh, Mahmoud Al Ismail, Huaming Wang 2022-06-09 2022-06-09 [("doi","10.48550/arXiv.2206.04769")] ai/music ai/nn/transformer/clip

[cf. AudioCLIP] Mainstream Audio Analytics models are trained to learn under the paradigm of one class label to many recordings focusing on one task. Learning under such restricted supervision limits the flexibility of models because they require labeled audio for training and can only predict the predefined categories.

Instead, we propose to learn audio concepts from natural language supervision. We call our approach Contrastive Language-Audio Pretraining (CLAP), which learns to connect language and audio by using two encoders and a contrastive learning to bring audio and text descriptions into a joint multimodal space.

We trained CLAP with 128k audio and text pairs and evaluated it on 16 downstream tasks across 8 domains, such as Sound Event Classification, Music tasks, and Speech-related tasks.

Although CLAP was trained with substantially less pairs than similar computer vision models, it establishes SoTA for Zero-Shot performance. Additionally, we evaluated CLAP in a supervised learning setup and achieve SoTA in 5 tasks.

Hence, CLAP’s Zero-Shot capability removes the need of training with class labels, enables flexible class prediction at inference time, and generalizes to multiple downstream tasks.

--- https://www.reddit.com/r/GPT3/comments/vbo0h3/wow_stepping_up_to_the_challenge/ 2021-09-22 ai/nn/transformer/gpt/non-fiction --- https://x.com/pascalblanche/status/1536167489595396096 2021-09-22 ai/nn/transformer/clip/sample --- https://www.theguardian.com/science/2022/jun/13/biological-age-startups-why Real age versus biological age: the startups revealing how old we really are 2021-09-22 longevity/epigenetics --- https://arxiv.org/abs/2206.02915#graphcore 8-bit Numerical Formats for Deep Neural Networks Badreddine Noune, Philip Jones, Daniel Justus, Dominic Masters, Carlo Luschi 2022-06-06 2022-06-06 [("doi","10.48550/arXiv.2206.02915")] ai/nn/sparsity/low-precision ai/scaling/hardware

Given the current trend of increasing size and complexity of machine learning architectures, it has become of critical importance to identify new approaches to improve the computational efficiency of model training.

In this context, we address the advantages of floating-point over fixed-point representation, and present an in-depth study on the use of 8-bit floating-point number formats for activations, weights, and gradients for both training and inference. We explore the effect of different bit-widths for exponents and significands and different exponent biases.

The experimental results demonstrate that a suitable choice of these low-precision formats enables faster training and reduced power consumption without any degradation in accuracy for a range of deep learning models for image classification and language processing.

--- https://x.com/Thinkwert/status/1536523039336284160 2021-09-23 ai/nn/transformer/gpt/dall-e --- https://www.biorxiv.org/content/10.1101/2022.06.08.495348.full Reconstructing the cascade of language processing in the brain using the internal computations of a transformer-based language model Sreejan Kumar, Theodore R. Sumers, Takateru Yamakoshi, Ariel Goldstein, Uri Hasson, Kenneth A. Norman, Thomas L. Griffiths, Robert D. Hawkins, Samuel A. Nastase 2022-06-09 2022-06-09 [("doi","10.1101/2022.06.08.495348")] ai/nn/transformer psychology/neuroscience

[Twitter] Piecing together the meaning of a narrative requires understanding not only the individual words but also the intricate relationships between them. How does the brain construct this kind of rich, contextual meaning from natural language? Recently, a new class of artificial neural networks—based on the Transformer architecture—has revolutionized the field of language modeling. Transformers integrate information across words via multiple layers of structured circuit computations, forming increasingly contextualized representations of linguistic content.

In this paper, we deconstruct these circuit computations and analyze the associated “transformations” (alongside the more commonly studied “embeddings”) at each BERT layer to provide a fine-grained window onto linguistic computations in the human brain. Using functional MRI data acquired while participants listened to naturalistic spoken stories, we find that:

these transformations capture a hierarchy of linguistic computations across cortex, with transformations at later layers in the model mapping onto higher-level language areas in the brain. We then decompose these transformations into individual, functionally-specialized “attention heads” and demonstrate that the emergent syntactic computations performed by individual heads correlate with predictions of brain activity in specific cortical regions. These heads fall along gradients corresponding to different layers, contextual distances, and syntactic dependencies in a low-dimensional cortical space.

Our findings provide a new basis for using the internal structure of large language models to better capture the cascade of cortical computations that support natural language comprehension.

--- https://github.com/robgon-art/CLIPandPASTE robgon-art/CLIPandPASTE: CLIP and PASTE: Using AI to Create Photo Collages from Text Prompts 2021-09-23 ai/nn/transformer/clip --- https://www.lesswrong.com/posts/yZb5eFvDoaqB337X5/investigating-causal-understanding-in-llms Investigating causal understanding in LLMs 2021-09-23 ai/nn/transformer/gpt --- https://arxiv.org/abs/2206.06292#jd MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Tao Mei 2022-06-13 2022-06-13 [("doi","10.48550/arXiv.2206.06292")] ai/nn/fully-connected ai/video/analysis

Convolutional Neural Networks (CNNs) have been regarded as the go-to models for visual recognition. More recently, convolution-free networks, based on multi-head self-attention (MSA) or multi-layer perceptrons (MLPs), become more and more popular. Nevertheless, it is not trivial when utilizing these newly-minted networks for video recognition due to the large variations and complexities in video data.

In this paper, we present MLP-3D networks, a novel MLP-like 3D architecture for video recognition. Specifically, the architecture consists of MLP-3D blocks, where each block contains one MLP applied across tokens (ie. token-mixing MLP) and one MLP applied independently to each token (ie. channel MLP). By deriving the novel grouped time mixing (GTM) operations, we equip the basic token-mixing MLP with the ability of temporal modeling. GTM divides the input tokens into several temporal groups and linearly maps the tokens in each group with the shared projection matrix. Furthermore, we devise several variants of GTM with different grouping strategies, and compose each variant in different blocks of MLP-3D network by greedy architecture search.

Without the dependence on convolutions or attention mechanisms, our MLP-3D networks achieves 68.5%/81.4% top-1 accuracy on Something-Something V2 and Kinetics-400 datasets, respectively. Despite with fewer computations, the results are comparable to state-of-the-art widely-used 3D CNNs and video transformers.

Source code is available at Github.

--- https://arxiv.org/abs/2201.06796 CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities Mina Lee, Percy Liang, Qian Yang 2022-01-18 2022-01-18 [("doi","10.1145/3491102.3502030")] ai/dataset ai/nn/transformer/gpt/fiction

Large language models (LMs) offer unprecedented language generation capabilities and exciting opportunities for interaction design. However, their highly context-dependent capabilities are difficult to grasp and are often subjectively interpreted.

In this paper, we argue that by curating and analyzing large interaction datasets, the HCI community can foster more incisive examinations of LMs’ generative capabilities. Exemplifying this approach, we present CoAuthor, a dataset designed for revealing GPT-3’s capabilities in assisting creative and argumentative writing. CoAuthor captures rich interactions between 63 writers and four instances of GPT-3 across 1,445 writing sessions.

We demonstrate that CoAuthor can address questions about GPT-3’s language, ideation, and collaboration capabilities, and reveal its contribution as a writing “collaborator” under various definitions of good collaboration. Finally, we discuss how this work may facilitate a more principled discussion around LMs’ promises and pitfalls in relation to interaction design.

The dataset and an interface for replaying the writing sessions are publicly available at https://coauthor.stanford.edu/.

--- https://arxiv.org/abs/2206.05314#deepmind Large-Scale Retrieval for Reinforcement Learning Peter C. Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Théophane Weber, Timothy Lillicrap 2022-06-10 2022-06-10 [("doi","10.48550/arXiv.2206.05314")] ai/nn/retrieval reinforcement-learning/imitation-learning reinforcement-learning/model/alphago reinforcement-learning/model/muzero reinforcement-learning/offline

[cf. RETRO, Aytar et al 2018] Effective decision making involves flexibly relating past experiences and relevant contextual information to a novel situation. In deep reinforcement learning, the dominant paradigm is for an agent to amortise information that helps decision-making into its network weights via gradient descent on training losses.

Here, we pursue an alternative approach in which agents can utilize large-scale context-sensitive database lookups to support their parametric computations. This allows agents to directly learn in an end-to-end manner to use relevant information to inform their outputs. In addition, new information can be attended to by the [offline-style MuZero] agent, without retraining, by simply augmenting the retrieval dataset.

We study this approach in 9×9 Go, a challenging game for which the vast combinatorial state space privileges generalisation over direct matching to past experiences. We leverage fast, approximate nearest neighbor techniques [SCaNN] in order to retrieve relevant data from a set of tens of millions [n = 50m] of expert demonstration states [from AlphaZero].

Attending to this information provides a substantial boost to prediction accuracy and game-play performance over simply using these demonstrations as training trajectories, providing a compelling demonstration of the value of large-scale retrieval in reinforcement learning agents.

Figure 2: Details of the architecture used for a retrieval-augmented Go playing agent. A pre-trained network is used to generate a query qt corresponding to the current Go game state o~t~. This query is used for fast approximate nearest-neighbor retrieval using SCaNN. Retrieved neighbors x~tn~ are processed using an invariant architecture, and used to inform an action-conditional recurrent forward model that outputs game outcome predictions v̂k and distributions over next actions ̂πk.
--- https://x.com/Thinkwert/status/1536523039336284160 2021-09-23 ai/nn/transformer/gpt/dall-e --- https://habr.com/ru/company/sberbank/blog/671210/ 2021-09-23 ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/1206.2944 Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek, Hugo Larochelle, Ryan P. Adams 2012-06-13 2021-09-24 [("doi","10.48550/arXiv.1206.2944")] reinforcement-learning/meta-learning statistics/bayes

Machine learning algorithms frequently require careful tuning of model hyperparameters, regularization terms, and optimization parameters. Unfortunately, this tuning is often a “black art” that requires expert experience, unwritten rules of thumb, or sometimes brute-force search. Much more appealing is the idea of developing automatic approaches which can optimize the performance of a given learning algorithm to the task at hand.

In this work, we consider the automatic tuning problem within the framework of Bayesian optimization, in which a learning algorithm’s generalization performance is modeled as a sample from a Gaussian process (GP). The tractable posterior distribution induced by the GP leads to efficient use of the information gathered by previous experiments, enabling optimal choices about what parameters to try next.

Here we show how the effects of the Gaussian process prior and the associated inference procedure can have a large impact on the success or failure of Bayesian optimization. We show that thoughtful choices can lead to results that exceed expert-level performance in tuning machine learning algorithms.

We also describe new algorithms that take into account the variable cost (duration) of learning experiments and that can leverage the presence of multiple cores for parallel experimentation. We show that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization on a diverse set of contemporary algorithms including latent Dirichlet allocation, structured SVMs and convolutional neural networks.

--- https://proceedings.neurips.cc/paper/2011/hash/86e8f7ab32cfd12577bc2619bc635690-Abstract.html Algorithms for Hyper-Parameter Optimization James Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl 2019-07-16 2021-09-24 reinforcement-learning/meta-learning

Several recent advances to the state-of-the-art in image classification benchmarks have come from better configurations of existing techniques rather than novel approaches to feature learning. Traditionally, hyper-parameter optimization has been the job of humans because they can be very efficient in regimes where only a few trials are possible. Presently, computer clusters and GPU processors make it possible to run more trials and we show that algorithmic approaches can find better results.

We present hyper-parameter optimization results on tasks of training neural networks and deep belief networks (DBNs). We optimize hyper-parameters using random search and two new greedy sequential methods based on the expected improvement criterion. Random search has been shown to be sufficiently efficient for learning neural networks for several datasets, but we show it is unreliable for training DBNs. The sequential algorithms are applied to the most difficult DBN learning problems from [1] and find better results than the best previously reported.

This work contributes novel techniques for making response surface models P(y|x) in which many elements of hyper-parameter assignment (10) are known to be irrelevant given particular values of other elements.

--- https://arxiv.org/abs/1611.03824#deepmind Learning to Learn without Gradient Descent by Gradient Descent Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy Lillicrap, Matt Botvinick, Nando de Freitas 2016-11-11 2021-09-24 [("doi","10.48550/arXiv.1611.03824")] ai/nn/rnn reinforcement-learning/exploration reinforcement-learning/meta-learning

We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfer in that they can be used to efficiently optimize a broad range of derivative-free black-box functions, including Gaussian process bandits, simple control objectives, global optimization benchmarks and hyper-parameter tuning tasks.

Up to the training horizon, the learned optimizers learn to trade-off exploration and exploitation, and compare favourably with heavily engineered Bayesian optimization packages for hyper-parameter tuning.

--- https://arxiv.org/abs/1703.05175 Prototypical Networks for Few-shot Learning Jake Snell, Kevin Swersky, Richard S. Zemel 2017-03-15 2021-09-24 [("doi","10.48550/arXiv.1703.05175")] ai/nn reinforcement-learning/meta-learning

We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class.

Prototypical networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results.

We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning.

We further extend prototypical networks to zero-shot learning and achieve state-of-the-art results on the CU-Birds dataset.

--- https://arxiv.org/abs/1802.04821#openai Evolved Policy Gradients Rein Houthooft, Richard Y. Chen, Phillip Isola, Bradly C. Stadie, Filip Wolski, Jonathan Ho, Pieter Abbeel 2018-02-13 2021-09-24 [("doi","10.48550/arXiv.1802.04821")] ai/nn/cnn reinforcement-learning/meta-learning

We propose a metalearning approach for learning gradient-based reinforcement learning (RL) algorithms. The idea is to evolve a differentiable loss function, such that an agent, which optimizes its policy to minimize this loss, will achieve high rewards. The loss is parameterized via temporal convolutions over the agent’s experience. Because this loss is highly flexible in its ability to take into account the agent’s history, it enables fast task learning.

Empirical results show that our evolved policy gradient algorithm (EPG) achieves faster learning on several randomized environments compared to an off-the-shelf policy gradient method.

We also demonstrate that EPG’s learned loss can generalize to out-of-distribution test time tasks, and exhibits qualitatively different behavior from other popular metalearning algorithms.

--- https://arxiv.org/abs/1606.04080#deepmind Matching Networks for One Shot Learning Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra 2016-06-13 2021-09-24 [("doi","10.48550/arXiv.1606.04080")] ai/dataset ai/nn reinforcement-learning/meta-learning

Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data.

In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories. Our framework learns a network that maps a small labeled support set and an unlabeled example to its label, obviating the need for fine-tuning to adapt to new class types. We then define one-shot learning problems on vision (using Omniglot, ImageNet) and language tasks.

Our algorithm improves one-shot accuracy on ImageNet 87.6% → 93.2% and 88.0% → 93.8% on Omniglot compared to competing approaches.

We also demonstrate the usefulness of the same model on language modeling by introducing a one-shot task on the Penn Treebank.

--- https://arxiv.org/abs/1709.05262 Supervising Unsupervised Learning Vikas K. Garg, Adam Kalai 2017-09-14 2021-09-24 [("doi","10.48550/arXiv.1709.05262")] reinforcement-learning/meta-learning

We introduce a framework to leverage knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets.

Our perspective avoids the subjectivity inherent in unsupervised learning by reducing it to supervised learning, and provides a principled way to evaluate unsupervised algorithms. We demonstrate the versatility of our framework via simple agnostic bounds on unsupervised problems. In the context of clustering, our approach helps choose the number of clusters and the clustering algorithm, remove the outliers, and provably circumvent the Kleinberg’s impossibility result.

Experimental results across hundreds of problems demonstrate improved performance on unsupervised data with simple algorithms, despite the fact that our problems come from heterogeneous domains.

Additionally, our framework lets us leverage deep networks to learn common features from many such small datasets, and perform zero shot learning.

--- /doc/reinforcement-learning/exploration/2002-stanley.pdf NEAT: Evolving Neural Networks through Augmenting Topologies Kenneth O. Stanley, Risto Miikkulainen 2002-06-01 2021-09-24 [("doi","10.1162/106365602320169811")] ai/nn/fully-connected reinforcement-learning/exploration

An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights.

We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is faster learning.

NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.

--- https://www.reddit.com/r/AnimeResearch/comments/vbr4f5/stylegan3t256px_trained_on_anime_faces_by_bob80333/ 2021-09-24 ai/anime ai/nn/gan/stylegan --- https://arxiv.org/abs/2206.06336#microsoft Language Models are General-Purpose Interfaces Yaru Hao, Haoyu Song, Li Dong, Shaohan Huang, Zewen Chi, Wenhui Wang, Shuming Ma, Furu Wei 2022-06-13 2022-06-13 [("doi","10.48550/arXiv.2206.06336")] ai/nn/transformer

Foundation models have received much attention due to their effectiveness across a broad range of downstream applications. Though there is a big convergence in terms of architecture, most pretrained models are typically still developed for specific tasks or modalities.

In this work, we propose to use language models as a general-purpose interface to various foundation models. A collection of pretrained encoders perceive diverse modalities (such as vision, and language), and they dock with a language model that plays the role of a universal task layer. We propose a semi-causal language modeling objective to jointly pretrain the interface and the modular encoders. We subsume the advantages and capabilities from both causal and non-causal modeling, thereby combining the best of two worlds. Specifically, the proposed method not only inherits the capabilities of in-context learning and open-ended generation from causal language modeling, but also is conducive to finetuning because of the bidirectional encoders. More importantly, our approach seamlessly unlocks the combinations of the above capabilities, eg. enabling in-context learning or instruction following with finetuned encoders.

Experimental results across various language-only and vision-language benchmarks show that our model outperforms or is competitive with specialized models on finetuning, zero-shot generalization, and few-shot learning.

--- https://arxiv.org/abs/2205.11822#allen Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, Yejin Choi 2022-05-24 2022-05-24 [("doi","10.48550/arXiv.2205.11822")] ai/nn/transformer/gpt/inner-monologue philosophy/logic

Despite their impressive capabilities, large pre-trained language models (LMs) struggle with consistent reasoning; recently, prompting LMs to generate explanations that self-guide the inference has emerged as a promising direction to amend this. However, these approaches are fundamentally bounded by the correctness of explanations, which themselves are often noisy and inconsistent.

In this work, we develop Maieutic Prompting, which infers a correct answer to a question even from the noisy and inconsistent generations of LM. Maieutic Prompting induces a tree of explanations abductively (eg. ‘X is true, because’ …) and recursively, then frames the inference as a Max-SAT [using Morgado et al 2014] satisfiability problem over these explanations and their logical relations.

We test Maieutic Prompting for true/false QA on 3 challenging benchmarks that require complex commonsense reasoning. Maieutic Prompting achieves up to 20% better accuracy than state-of-the-art prompting methods, and as a fully unsupervised approach, performs competitively with supervised models.

We also show that Maieutic Prompting improves robustness in inference while providing interpretable rationales.

Figure 6: Examples of Maieutic Prompting. We present a case where Maieutic Prompting correctly infers the ground-truth answer (above), and a case where the inferred answer is different from the ground-truth. Even in the latter case, the generated explanations make sense and logically lead to the inferred answer. We provide more examples in Appendix B.
--- https://arxiv.org/abs/2110.08300 The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Fail Samuel R. Bowman 2021-10-15 2021-10-15 [("doi","10.48550/arXiv.2110.08300")] ai/nn/transformer ai/scaling

Researchers in NLP often frame and discuss research results in ways that serve to de-emphasize the field’s successes, often in response to the field’s widespread hype.

Though well-meaning, this has yielded many misleading or false claims about the limits of our best technology. This is a problem, and it may be more serious than it looks: It harms our credibility in ways that can make it harder to mitigate present-day harms, like those involving biased systems for content moderation or resume screening. It also limits our ability to prepare for the potentially enormous impacts of more distant future advances.

This paper urges researchers to be careful about these claims and suggests some research directions and communication strategies that will make it easier to avoid or rebut them.

--- https://arxiv.org/abs/2206.07038#tencent AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos Yanze Wu, Xintao Wang, Gen Li, Ying Shan 2022-06-14 2022-06-14 [("doi","10.48550/arXiv.2206.07038")] ai/anime ai/nn/gan ai/nn/rnn

This paper studies the problem of real-world video super-resolution (VSR) for animation videos, and reveals 3 key improvements for practical animation VSR. First, recent real-world super-resolution approaches typically rely on degradation simulation using basic operators without any learning capability, such as blur, noise, and compression.

In this work, we propose to learn such basic operators from real low-quality animation videos, and incorporate the learned ones into the degradation generation pipeline. Such neural-network-based basic operators could help to better capture the distribution of real degradations. Second, a large-scale high-quality animation video dataset, AVC, is built to facilitate comprehensive training and evaluations for animation VSR. Third, we further investigate an efficient multi-scale network structure. It takes advantage of the efficiency of unidirectional recurrent networks and the effectiveness of sliding-window-based methods.

Thanks to the above delicate designs, our method, AnimeSR, is capable of restoring real-world low-quality animation videos effectively and efficiently, achieving superior performance to previous state-of-the-art methods.

--- https://www.biorxiv.org/content/10.1101/2022.06.12.495799.full Genetic prevalence and clinical relevance of canine Mendelian disease variants in over one million dogs Jonas Donner, Jamie Freyer, Stephen Davison, Heidi Anderson, Matthew Blades, Leena Honkanen, Laura Inman, Casey A. Brookhart-Knox, Annette Louviere, Oliver P. Forman, Rebecca Chodroff Foran 2022-06-14 2022-06-14 [("doi","10.1101/2022.06.12.495799")] genetics/heritable/dog genetics/heritable/rare

[Donner et al 2018] Hundreds of genetic variants linked to Mendelian disease have been characterized in dogs to date, and commercial screening is being offered for most of them worldwide. There typically remains a paucity of information regarding the broader population frequency of newly discovered variants, as well as uncertainty regarding their functional and clinical impact on additional genomic ancestry backgrounds beyond the discovery breed. Panel screening of disease variants, commercially offered as direct-to-consumer genetic testing, provides an opportunity to establish large-scale cohorts with both genotype and phenotype data available to address open questions related to variant prevalence and relevance.

In this study, we screened the largest canine cohort examined in a single study to date (1,054,293 representative dogs from our existing cohort of more than 3 million dogs; a total of 811,628 mixed breed dogs and 242,665 purebreds from more than 150 countries and territories) for 250 genetic disease-associated variants to understand their prevalence and distribution in the general population. Electronic medical records from veterinary clinics were available for 43.5% of the genotyped dogs, enabling follow up on the clinical impact of variants.

We provide detailed frequencies for all tested variants across breeds and find that 57% of dogs carry at least one copy of a studied Mendelian disease-linked variant. We provide evidence of full penetrance for 10 variants, and at minimum plausible evidence for the clinical-significance of 22 variants, on a wide variety of breed backgrounds. We further show that a reduction in genome-wide heterozygosity is associated with an increased Mendelian disease load and assess genome-wide heterozygosity levels in over 100 breeds.

The accumulated knowledge represents a resource to guide discussions on disease variant presence and genetic test relevance by breed.

--- https://www.nature.com/articles/s41467-021-21286-1 S-LDXR: Population-specific causal disease effect sizes in functionally important regions impacted by selection Huwenbo Shi, Steven Gazal, Masahiro Kanai, Evan M. Koch, Armin P. Schoech, Katherine M. Siewert, Samuel S. Kim, Yang Luo, Tiffany Amariuta, Hailiang Huang, Yukinori Okada, Soumya Raychaudhuri, Shamil R. Sunyaev, Alkes Price 2021-02-17 2021-09-25 [("doi","10.1038/s41467-021-21286-1")] genetics/selection/natural/human

Many diseases exhibit population-specific causal effect-sizes with trans-ethnic genetic correlations substantially less than 1, limiting trans-ethnic polygenic risk prediction.

We develop a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases & complex traits in East Asians (average n = 90K) and Europeans (average n = 267K) with:

an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82× (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes.

Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.

--- /doc/history/1908-newyorktimes-dogafakehero.pdf Dog A Fake Hero: Pushes Children Into the Seine to Rescue Them and Win Beefsteaks The New York Times 1908-02-02 2021-09-25 dog history --- https://x.com/funnycats22/status/1536643746778992641 2021-09-25 ai/nn/transformer/gpt/non-fiction --- https://publicdomainreview.org/collection/unai-no-tomo Unai no tomo: Catalogues of Japanese Toys (1891–1923) 2021-09-25 design history/public-domain-review japan --- https://arxiv.org/abs/2206.07160#microsoft LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling Linjie Li, Zhe Gan, Kevin Lin, Chung-Ching Lin, Zicheng Liu, Ce Liu, Lijuan Wang 2022-06-14 2022-06-14 [("doi","10.48550/arXiv.2206.07160")] ai/nn/transformer ai/video/analysis

Unified vision-language frameworks have greatly advanced in recent years, most of which adopt an encoder-decoder architecture to unify image-text tasks as sequence-to-sequence generation. However, existing video-language (VidL) models still require task-specific designs in model architecture and training objectives for each task.

In this work, we explore a unified VidL framework LAVENDER [LAnguage-VidEo uNDERstanding], where Masked Language Modeling (MLM) is used as the common interface for all pre-training and downstream tasks. Such unification leads to a simplified model architecture, where only a lightweight MLM head, instead of a decoder with much more parameters, is needed on top of the multimodal encoder.

Surprisingly, experimental results show that this unified framework achieves competitive performance on 14 VidL benchmarks, covering video question answering, text-to-video retrieval and video captioning. Extensive analyses further demonstrate the advantage of LAVENDER over existing VidL methods in: (1) supporting all downstream tasks with just a single set of parameter values when multi-task finetuned; (2) few-shot generalization on various downstream tasks; and (3) enabling zero-shot evaluation on video question answering tasks.

Code is available at Github.

…LAVENDER is inspired by VL-T5, UNICORN and OFA that aim to provide a unified pre-training framework for image-text tasks. However, ours is very different from theirs, as we use an encoder-only model and an additional lightweight MLM head on top of it, while a heavy transformer decoder is needed in [VL-T5, UNICORN, OFA]. By unifying all the VidL tasks as MLM, LAVENDER can seamlessly adapt to different VidL tasks, and enables new capabilities over existing task-specific methods, such as (1) supporting different VidL tasks with a single set of parameter values when multi-task finetuned; (2) better generalizability to test data under few-shot finetuning; and (3) zero-shot inference on video question answering. Surprisingly, by using this simple generative approach, we outperform previously published state-of-the-arts on 12⁄14 downstream tasks (Table 1), even when pre-trained with much fewer data (see §4.5 for detailed comparisons).

--- https://x.com/RiversHaveWings/status/1537448260758953984 2021-09-25 ai/nn/transformer/clip/sample --- https://www.openphilanthropy.org/research/how-accurate-are-our-predictions/ How Accurate Are Our Predictions? 2021-09-25 statistics/prediction --- https://arxiv.org/abs/2102.02779 VL-T5: Unifying Vision-and-Language Tasks via Text Generation Jaemin Cho, Jie Lei, Hao Tan, Mohit Bansal 2021-02-04 2021-09-25 [("doi","10.48550/arXiv.2102.02779")] ai/nn/transformer/t5

Existing methods for vision-and-language learning typically require designing task-specific architectures and objectives for each task. For example, a multi-label answer classifier for visual question answering, a region scorer for referring expression comprehension, and a language decoder for image captioning, etc.

To alleviate these hassles, in this work, we propose a unified framework that learns different tasks in a single architecture with the same language modeling objective, ie. multimodal conditional text generation, where our models learn to generate labels in text based on the visual and textual inputs.

On 7 popular vision-and-language benchmarks, including visual question answering, referring expression comprehension, visual commonsense reasoning, most of which have been previously modeled as discriminative tasks, our generative approach (with a single unified architecture) reaches comparable performance to recent task-specific state-of-the-art vision-and-language models. Moreover, our generative approach shows better generalization ability on questions that have rare answers. Also, we show that our framework allows multi-task learning in a single architecture with a single set of parameters, achieving similar performance to separately optimized single-task models.

Our code is publicly available at: https://github.com/j-min/VL-T5

--- https://arxiv.org/abs/2104.00743#allen GPV-1: Towards General Purpose Vision Systems Tanmay Gupta, Amita Kamath, Aniruddha Kembhavi, Derek Hoiem 2021-04-01 2021-09-26 [("doi","10.48550/arXiv.2104.00743")] ai/nn/transformer

Computer vision systems today are primarily n-purpose systems, designed and trained for a predefined set of tasks. Adapting such systems to new tasks is challenging and often requires non-trivial modifications to the network architecture (eg. adding new output heads) or training process (eg. adding new losses). To reduce the time and expertise required to develop new applications, we would like to create general purpose vision systems that can learn and perform a range of tasks without any modification to the architecture or learning process.

In this paper, we propose GPV-1, a task-agnostic vision-language architecture that can learn and perform tasks that involve receiving an image and producing text and/or bounding boxes, including classification, localization, visual question answering, captioning, and more. We also propose evaluations of generality of architecture, skill-concept transfer, and learning efficiency that may inform future work on general purpose vision.

Our experiments indicate GPV-1 is effective at multiple tasks, reuses some concept knowledge across tasks, can perform the Referring Expressions task zero-shot, and further improves upon the zero-shot performance using a few training samples.

--- https://arxiv.org/abs/2111.12085#microsoft UNICORN: Crossing the Format Boundary of Text and Boxes: Towards Unified Vision-Language Modeling Zhengyuan Yang, Zhe Gan, Jianfeng Wang, Xiaowei Hu, Faisal Ahmed, Zicheng Liu, Yumao Lu, Lijuan Wang 2021-11-23 2021-11-23 [("doi","10.48550/arXiv.2111.12085")] ai/nn/transformer

In this paper, we propose UNICORN, a vision-language (VL) model that unifies text generation and bounding box prediction into a single architecture. Specifically, we quantize each box into four discrete box tokens and serialize them as a sequence, which can be integrated with text tokens. We formulate all VL problems as a generation task, where the target sequence consists of the integrated text and box tokens. We then train a transformer encoder-decoder to predict the target in an auto-regressive manner.

With such a unified framework and input-output format, UNICORN achieves comparable performance to task-specific state-of-the-art on 7 VL benchmarks, covering the visual grounding, grounded captioning, visual question answering, and image captioning tasks.

When trained with multi-task finetuning, UNICORN can approach different VL tasks with a single set of parameters, thus crossing downstream task boundary. We show that having a single model not only saves parameters, but also further boosts the model performance on certain tasks. Finally, UNICORN shows the capability of generalizing to new tasks such as ImageNet object localization.

--- https://en.wikipedia.org/wiki/Language_of_thought_hypothesis Language of thought hypothesis 2021-09-26 ai/nn/transformer/gpt/inner-monologue philosophy/mind psychology --- https://x.com/revrart/status/1537151447833485316 2021-09-26 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2006.03463 Sponge Examples: Energy-Latency Attacks on Neural Networks Ilia Shumailov, Yiren Zhao, Daniel Bates, Nicolas Papernot, Robert Mullins, Ross Anderson 2020-06-05 2021-09-26 [("doi","10.48550/arXiv.2006.03463")] ai/nn/adversarial

The high energy costs of neural network training and inference led to the use of acceleration hardware such as GPUs and TPUs. While this enabled us to train large-scale neural networks in datacenters and deploy them on edge devices, the focus so far is on average-case performance.

In this work, we introduce a novel threat vector against neural networks whose energy consumption or decision latency are critical. We show how adversaries can exploit carefully crafted sponge examples, which are inputs designed to maximize energy consumption and latency.

We mount two variants of this DoS attack on established vision and language models, increasing energy consumption by a factor of 10–200×. Our attacks can also be used to delay decisions where a network has critical real-time performance, such as in perception for autonomous vehicles.

We demonstrate the portability of our malicious inputs across CPUs and a variety of hardware accelerator chips including GPUs, and an ASIC simulator.

We conclude by proposing a defense strategy which mitigates our attack by shifting the analysis of energy consumption in hardware from an average-case to a worst-case perspective.

…On a number of occasions, despite the hardware protection provided by GPU engineers, we were able to increase temperature so that it passed the throttling point and sometimes even crashed the GPU drivers. The energy consumed by sponge examples on a machine learning model can therefore affect the underlying hardware if its power management software or hardware is not designed with adversaries in mind.

… Interestingly, Azure translator assigns high confidence scores >0.9 to the sponge example predictions…In particular, we target the most expensive parts of the translator to get an extraordinary amplification factor by sending specifically crafted requests. For example with Azure and an input of length 50, we were getting translated responses spanning thousands of characters. This finding bridges ML to the field of classic computer security and suggests that decades of experience with managing service-denial attacks can be applied here.

--- https://arxiv.org/abs/2202.07765#deepmind General-purpose, long-context autoregressive modeling with Perceiver AR Curtis Hawthorne, Andrew Jaegle, Cătălina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, Jesse Engel 2022-02-15 2022-02-15 [("doi","10.48550/arXiv.2202.07765")] ai/music ai/nn/rnn ai/nn/transformer/attention ai/video/generation

[music sample; blog; Github] Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression. However, the most commonly used autoregressive models, Transformers, are prohibitively expensive to scale to the number of inputs and layers needed to capture this long-range structure.

We develop Perceiver AR, an autoregressive, modality-agnostic architecture which uses cross-attention to map long-range inputs to a small number of latents while also maintaining end-to-end causal masking. Perceiver AR can directly attend to over a hundred thousand tokens, enabling practical long-context density estimation without the need for hand-crafted sparsity patterns or memory mechanisms.

When trained on images or music, Perceiver AR generates outputs with clear long-term coherence and structure. Our architecture also obtains state-of-the-art likelihood on long-sequence benchmarks, including 64×64 ImageNet images and PG-19 books.

--- https://magenta.tensorflow.org/perceiver-ar Autoregressive long-context music generation with Perceiver AR 2021-09-26 ai/music ai/nn/transformer --- /doc/psychology/spaced-repetition/2022-sana.pdf Interleaving Retrieval Practice Promotes Science Learning Faria Sana, Veronica X. Yan 2022-04-18 2022-04-18 [("doi","10.1177/09567976211057507")] psychology/spaced-repetition

Can interleaved retrieval practice enhance learning in classrooms?

Across a 4-week period, 9th through 12th-grade students (n = 155) took a weekly quiz in their science courses that tested half of the concepts taught that week. Questions on each quiz were either blocked by concept or interleaved with different concepts. A month after the final quiz, students were tested on the concepts covered in the 4-week period.

Replicating the retrieval-practice effect, results showed that participants performed better on concepts that had been on blocked quizzes (M = 54%, SD = 28%) than on concepts that had not been quizzed (M = 47%, SD = 20%; d = 0.30). Interleaved quizzes led to even greater benefits: Participants performed better on concepts that had been on interleaved quizzes (M = 63%, SD = 26%) than on concepts that had been on blocked quizzes (d = 0.35).

These results demonstrate a cost-effective strategy to promote classroom learning.

--- https://arxiv.org/abs/2206.08332#deepmind BYOL-Explore: Exploration by Bootstrapped Prediction Zhaohan Daniel Guo, Shantanu Thakoor, Miruna Pîslar, Bernardo Avila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, Michal Valko, Rémi Munos, Mohammad Gheshlaghi Azar, Bilal Piot 2022-06-16 2022-06-16 [("doi","10.48550/arXiv.2206.08332")] ai/nn/rnn reinforcement-learning/exploration reinforcement-learning/model

We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually-complex environments.

BYOL-Explore learns a world representation, the world dynamics, and an exploration policy all-together by optimizing a single prediction loss in the latent space with no additional auxiliary objective.

We show that BYOL-Explore is effective in DM-HARD-8, a challenging partially-observable continuous-action hard-exploration benchmark with visually-rich 3-D environments. On this benchmark, we solve the majority of the tasks purely through augmenting the extrinsic reward with BYOL-Explore s intrinsic reward, whereas prior work could only get off the ground with human demonstrations.

As further evidence of the generality of BYOL-Explore, we show that it achieves superhuman performance on the 10 hardest exploration games in Atari while having a much simpler design than other competitive agents.

--- https://arxiv.org/abs/2206.08297 Goodbye WaveNet—A Language Model for Raw Audio with Context of 1⁄2 Million Samples Prateek Verma 2022-06-16 2022-06-16 [("doi","10.48550/arXiv.2206.08297")] ai/nn/transformer/gpt/jukebox

[Slight variant on Jukebox] Modeling long-term dependencies for audio signals is a particularly challenging problem, as even small-time scales yield on the order of a hundred thousand samples. With the recent advent of Transformers, neural architectures became good at modeling dependencies over longer time scales, but they suffered from quadratic constraints to scale them.

We propose a generative auto-regressive architecture that can model audio waveforms over quite a large context, greater than 500,000 samples. Our work is adapted to learn time dependencies by learning a latent representation by a CNN front-end, and then learning dependencies over these representations using Transformer encoders, fully trained end-to-end: thereby allowing to learn representations as it deems fit for the next sample.

Unlike previous works that compared different time scales to show improvement, we use a standard dataset, with the same number of parameters/context to show improvements. We achieve a state-of-the-art performance as compared to other approaches such as WaveNet, SaSHiMi, and SampleRNN on a standard dataset for modeling long-term structure.

This work gives very exciting direction for the field, given improvements in context modeling that can be scaled with more data, as well as potentially better results by using billions/trillions of parameters.

--- https://github.com/openai/jukebox/ Code for ‘Jukebox: A Generative Model for Music’ 2021-09-27 ai/nn/transformer/gpt/jukebox --- https://ooo.ghostbows.ooo/about/ 2021-09-27 ai/nn/transformer/gpt/jukebox --- https://www.theverge.com/2021/10/28/22750337/shadow-planet-ai-robin-sloan-jesse-solomon-clark Making this album with AI ‘felt like wandering in an enormous labyrinth’: Shadow Planet is the result of a three-way collaboration between humans and AI 2021-09-27 ai/nn/transformer/gpt/jukebox --- https://www.reddit.com/r/Openaijukebox/ 2021-09-27 ai/nn/transformer/gpt/jukebox --- https://colab.research.google.com/drive/1fQ6SXdO8fIMQ2c8t-ziAJIrAhJxcZT9O 2021-09-27 ai/nn/transformer/gpt/jukebox --- https://soundcloud.com/openai_audio Stream OpenAI music 2021-09-27 ai/nn/transformer/gpt/jukebox --- https://soundcloud.com/human-musician Stream A String Of Numbers (Human Musician) 2021-09-27 ai/nn/transformer/gpt/jukebox --- https://onezero.medium.com/co-writing-an-album-with-an-ai-880317103476 Cowriting an Album With AI Clive Thompson 2021-09-27 ai/nn/transformer/gpt/jukebox --- https://arxiv.org/abs/2202.09729 It’s Raw! Audio Generation with State-Space Models Karan Goel, Albert Gu, Chris Donahue, Christopher Ré 2022-02-20 2022-02-20 [("doi","10.48550/arXiv.2202.09729")] ai/music ai/nn/transformer/attention

Developing architectures suitable for modeling raw audio is a challenging problem due to the high sampling rates of audio waveforms. Standard sequence modeling approaches like RNNs and CNNs have previously been tailored to fit the demands of audio, but the resultant architectures make undesirable computational tradeoffs and struggle to model waveforms effectively.

We propose SaShiMi, a new multi-scale architecture for waveform modeling built around the recently introduced S4 model for long sequence modeling. We identify that S4 can be unstable during autoregressive generation, and provide a simple improvement to its parameterization by drawing connections to Hurwitz matrices.

SaShiMi yields state-of-the-art performance for unconditional waveform generation in the autoregressive setting. Additionally, SaShiMi improves non-autoregressive generation performance when used as the backbone architecture for a diffusion model. Compared to prior architectures in the autoregressive generation setting, SaShiMi generates piano and speech waveforms which humans find more musical and coherent respectively, eg. 2× better mean opinion scores than WaveNet on an unconditional speech generation task. On a music generation task, SaShiMi outperforms WaveNet on density estimation and speed at both training and inference even when using 3× fewer parameters.

Code can be found at Github and samples at https://hazyresearch.stanford.edu/sashimi-examples/.

--- https://arxiv.org/abs/1612.07837 SampleRNN: An Unconditional End-to-End Neural Audio Generation Model Soroush Mehri, Kundan Kumar, Ishaan Gulrajani, Rithesh Kumar, Shubham Jain, Jose Sotelo, Aaron Courville, Yoshua Bengio 2016-12-22 2021-09-27 [("doi","10.48550/arXiv.1612.07837")] ai/music ai/nn/rnn

In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time.

We show that our model, which profits from combining memory-less modules, namely autoregressive multilayer perceptrons, and stateful recurrent neural networks in a hierarchical structure is able to capture underlying sources of variations in the temporal sequences over very long time spans, on 3 datasets of different nature.

We also show how each component of the model contributes to the exhibited performance.

Human evaluation on the generated samples indicate that our model is preferred over competing models.

--- https://arxiv.org/abs/2010.05388#google AI Song Contest: Human-AI Co-Creation in Songwriting Cheng-Zhi Anna Huang, Hendrik Vincent Koops, Ed Newton-Rex, Monica Dinculescu, Carrie J. Cai 2020-10-12 2021-09-27 [("doi","10.48550/arXiv.2010.05388")] ai/music

Machine learning is challenging the way we make music. Although research in deep generative models has dramatically improved the capability and fluency of music models, recent work has shown that it can be challenging for humans to partner with this new class of algorithms.

In this paper, we present findings on what 13 musician/developer teams, a total of 61 users, needed when co-creating a song with AI, the challenges they faced, and how they leveraged and repurposed existing characteristics of AI to overcome some of these challenges.

Many teams adopted modular approaches, such as independently running multiple smaller models that align with the musical building blocks of a song, before re-combining their results. As ML models are not easily steerable, teams also generated massive numbers of samples and curated them post-hoc, or used a range of strategies to direct the generation, or algorithmically ranked the samples. Ultimately, teams not only had to manage the “flare and focus” aspects of the creative process, but also juggle them with a parallel process of exploring and curating multiple ML models and outputs.

These findings reflect a need to design machine learning-powered music interfaces that are more decomposable, steerable, interpretable, and adaptive, which in return will enable artists to more effectively explore how AI can extend their personal expression.

--- /doc/ai/music/2020-07-07-nshepperd-openaijukebox-gpt3-theuniverseisaglitch.mp3 2020-07-07 2021-09-28 ai/music ai/nn/transformer/gpt/jukebox --- https://arxiv.org/abs/2103.16091#google Symbolic Music Generation with Diffusion Models Gautam Mittal, Jesse Engel, Curtis Hawthorne, Ian Simon 2021-03-30 2021-09-28 [("doi","10.48550/arXiv.2103.16091")] ai/music ai/nn/diffusion/discrete ai/nn/vae

Score-based generative models and diffusion probabilistic models have been successful at generating high-quality samples in continuous domains such as images and audio. However, due to their Langevin-inspired sampling mechanisms, their application to discrete and sequential data has been limited.

In this work, we present a technique for training diffusion models on sequential data by parameterizing the discrete domain in the continuous latent space of a pre-trained variational autoencoder. Our method is non-autoregressive and learns to generate sequences of latent embeddings through the reverse process and offers parallel generation with a constant number of iterative refinement steps.

We apply this technique to modeling symbolic music and show strong unconditional generation and post-hoc conditional infilling results compared to autoregressive language models operating over the same continuous embeddings.

--- https://arxiv.org/abs/2007.04590#microsoft DeepSinger: Singing Voice Synthesis with Data Mined From the Web Yi Ren, Xu Tan, Tao Qin, Jian Luan, Zhou Zhao, Tie-Yan Liu 2020-07-09 2021-09-28 [("doi","10.48550/arXiv.2007.04590")] ai/music ai/nn/rnn ai/nn/transformer

In this paper, we develop DeepSinger, a multi-lingual multi-singer singing voice synthesis (SVS) system, which is built from scratch using singing training data mined from music websites.

The pipeline of DeepSinger consists of several steps, including data crawling, singing and accompaniment separation, lyrics-to-singing alignment, data filtration, and singing modeling. Specifically, we design a lyrics-to-singing alignment model to automatically extract the duration of each phoneme in lyrics starting from coarse-grained sentence level to fine-grained phoneme level, and further design a multi-lingual multi-singer singing model based on a feed-forward Transformer to directly generate linear-spectrograms from lyrics, and synthesize voices using Griffin-Lim.

DeepSinger has several advantages over previous SVS systems: (1) to the best of our knowledge, it is the first SVS system that directly mines training data from music websites, (2) the lyrics-to-singing alignment model further avoids any human efforts for alignment labeling and greatly reduces labeling cost, (3) the singing model based on a feed-forward Transformer is simple and efficient, by removing the complicated acoustic feature modeling in parametric synthesis and leveraging a reference encoder to capture the timbre of a singer from noisy singing data, and (4) it can synthesize singing voices in multiple languages and multiple singers.

We evaluate DeepSinger on our mined singing dataset that consists of about 92 hours data from 89 singers on 3 languages (Chinese, Cantonese and English). The results demonstrate that with the singing data purely mined from the Web, DeepSinger can synthesize high-quality singing voices in terms of both pitch accuracy and voice naturalness.

Our audio samples are shown in Github.

--- https://arxiv.org/abs/2206.08356#facebook OmniMAE: Single Model Masked Pretraining on Images and Videos Rohit Girdhar, Alaaeldin El-Nouby, Mannat Singh, Kalyan Vasudev Alwala, Arm Holdings, Joulin, Ishan Misra 2022-06-16 2022-06-16 [("doi","10.48550/arXiv.2206.08356")] ai/nn/vae/mae ai/video/analysis ai/video/generation

Transformer-based architectures have become competitive across a variety of visual domains, most notably images and videos. While prior work has studied these modalities in isolation, having a common architecture suggests that one can train a single unified model for multiple visual modalities. Prior attempts at unified modeling typically use architectures tailored for vision tasks, or obtain worse performance compared to single modality models.

In this work, we show that masked autoencoding can be used to train a simple Vision Transformer on images and videos, without requiring any labeled data. This single model learns visual representations that are comparable to or better than single-modality representations on both image and video benchmarks, while using a much simpler architecture. In particular, our single pretrained model can be finetuned to achieve 86.5% on ImageNet and 75.3% on the challenging Something Something-v2 video benchmark. Furthermore, this model can be learned by dropping 90% of the image and 95% of the video patches, enabling extremely fast training.

--- https://arxiv.org/abs/2206.07808#amazon Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems Jack FitzGerald, Shankar Ananthakrishnan, Konstantine Arkoudas, Davide Bernardi, Abhishek Bhagia, Claudio Delli Bovi, Jin Cao, Rakesh Chada, Amit Chauhan, Luoxin Chen, Anurag Dwarakanath, Satyam Dwivedi, Turan Gojayev, Karthik Gopalakrishnan, Thomas Gueudre, Dilek Hakkani-Tur, Wael Hamza, Jonathan Hueser, Kevin Martin Jose, Haidar Khan, Beiye Liu, Jianhua Lu, Alessandro Manzotti, Pradeep Natarajan, Karolina Owczarzak, Gokmen Oz, Enrico Palumbo, Charith Peris, Chandana Satya Prakash, Stephen Rawls, Andy Rosenbaum, Anjali Shenoy, Saleh Soltan, Mukund Harakere Sridhar, Liz Tan, Fabian Triefenbach, Pan Wei, Haiyang Yu, Shuai Zheng, Gokhan Tur, Prem Natarajan 2022-06-15 2022-06-15 [("doi","10.1145/3534678.3539173")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/t5

We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system [Amazon Alexa].

Though we train using 70% spoken-form data, our teacher models perform comparably to XLM-R and mT5 when evaluated on the written-form Cross-lingual Natural Language Inference (XNLI) corpus. We perform a second stage of pretraining on our teacher models using in-domain data from our system, improving error rates by 3.86% relative for intent classification and 7.01% relative for slot filling. We find that even a 170M-parameter model distilled from our Stage 2 teacher model has 2.88% better intent classification and 7.69% better slot filling error rates when compared to the 2.3B-parameter teacher trained only on public data (Stage 1), emphasizing the importance of in-domain data for pretraining.

When evaluated offline using labeled NLU data, our 17M-parameter Stage 2 distilled model outperforms both XLM-R Base (85M parameters) and DistilBERT (42M parameters) by 4.23% to 6.14%, respectively.

Finally, we present results from a full virtual assistant experimentation platform, where we find that models trained using our pretraining and distillation pipeline outperform models distilled from 85M-parameter teachers by 3.74%–4.91% on an automatic measurement of full-system user dissatisfaction.

--- /doc/ai/nn/transformer/gpt/jukebox/2020-05-02-gwern-meme-claspedarms-jukebox.jpg Gwern 2020-05-02 2021-09-28 ai/nn/transformer/gpt/jukebox --- /doc/design/typography/2014-650139-tobiasfrerejonesversusjonathanhoefler.pdf Tobias Frere-Jones, Jonathan Hoefler, Jeffrey Oing 2014-11-11 2021-09-28 crime design/typography --- https://arxiv.org/abs/2004.00784#google Learning Agile Robotic Locomotion Skills by Imitating Animals Xue Bin Peng, Erwin Coumans, Tingnan Zhang, Tsang-Wei Lee, Jie Tan, Sergey Levine 2020-04-02 2021-09-28 [("doi","10.48550/arXiv.2004.00784")] reinforcement-learning/imitation-learning reinforcement-learning/robot

Reproducing the diverse and agile locomotion skills of animals has been a long-standing challenge in robotics. While manually-designed controllers have been able to emulate many complex behaviors, building such controllers involves a time-consuming and difficult development process, often requiring substantial expertise of the nuances of each skill. Reinforcement learning provides an appealing alternative for automating the manual effort involved in the development of controllers. However, designing learning objectives that elicit the desired behaviors from an agent can also require a great deal of skill-specific expertise.

In this work, we present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals. We show that by leveraging reference motion data, a single learning-based approach is able to automatically synthesize controllers for a diverse repertoire behaviors for legged robots.

By incorporating sample efficient domain adaptation techniques into the training process, our system is able to learn adaptive policies in simulation that can then be quickly adapted for real-world deployment.

To demonstrate the effectiveness of our system, we train an 18-DoF quadruped robot to perform a variety of agile behaviors ranging from different locomotion gaits to dynamic hops and turns.

--- https://arxiv.org/abs/1203.6250 Dispelling the myth of robotic efficiency: why human space exploration will tell us more about the Solar System than will robotic exploration alone Ian A. Crawford 2012-03-28 2021-09-28 [("doi","10.1111/j.1468-4004.2012.53222.x")] economics/automation

There is a widely held view in the astronomical community that unmanned robotic space vehicles are, and will always be, more efficient explorers of planetary surfaces than astronauts (eg. Coates, 2001; Clements 2009; Rees 2011).

Partly this is due to a common assumption that robotic exploration is cheaper than human exploration (although, as we shall see, this isn’t necessarily true if like is compared with like), and partly from the expectation that continued developments in technology will relentlessly increase the capability, and reduce the size and cost, of robotic missions to the point that human exploration will not be able to compete.

I will argue below that the experience of human exploration during the Apollo missions, more recent field analogue studies, and trends in robotic space exploration actually all point to exactly the opposite conclusion.

--- https://arxiv.org/abs/1110.2230 The complexity of small universal Turing machines: a survey Turlough Neary, Damien Woods 2011-10-10 2021-09-28 [("doi","10.48550/arXiv.1110.2230")] cs/cellular-automaton

We survey some work concerned with small universal Turing machines, cellular automata, tag systems, and other simple models of computation. For example, it has been an open question for some time as to whether the smallest known universal Turing machines of Minsky, Rogozhin, Baiocchi, and Kudlek are efficient (polynomial time) simulators of Turing machines. These are some of the most intuitively simple computational devices and previously the best known simulations were exponentially slow.

We discuss recent work that shows that these machines are indeed efficient simulators. In addition, another related result shows that Rule 110, a well-known elementary cellular automaton, is efficiently universal.

We also discuss some old and new universal program size results, including the smallest known universal Turing machines.

We finish the survey with results on generalized and restricted Turing machine models including machines with a periodic background on the tape (instead of a blank symbol), multiple tapes, multiple dimensions, and machines that never write to their tape.

We then discuss some ideas for future work.

--- https://www.reddit.com/r/aigreentext/ 2021-09-28 ai/nn/transformer/gpt/fiction --- https://arxiv.org/abs/2004.03965 Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders Nikola I. Nikolov, Eric Malmi, Curtis G. Northcutt, Loreto Parisi 2020-04-08 2021-09-29 [("doi","10.48550/arXiv.2004.03965")] ai/nn/sampling ai/nn/transformer ai/poetry

The ability to combine symbols to generate language is a defining characteristic of human intelligence, particularly in the context of artistic storytelling through lyrics. We develop a method for synthesizing a rap verse based on the content of any text (eg. a news article), or for augmenting pre-existing rap lyrics. Our method, called Rapformer, is based on training a Transformer-based denoising autoencoder to reconstruct rap lyrics from content words extracted from the lyrics, trying to preserve the essential meaning, while matching the target style.

Rapformer features a novel BERT-based paraphrasing scheme for rhyme enhancement which increases the average rhyme density of output lyrics by 10%.

Experimental results on 3 diverse input domains show that Rapformer is capable of generating technically fluent verses that offer a good trade-off between content preservation and style transfer. Furthermore, a Turing-test-like experiment reveals that Rapformer fools human lyrics experts 25% of the time.

--- https://arxiv.org/abs/2009.12240 Weird AI Yankovic: Generating Parody Lyrics Mark Riedl 2020-09-25 2021-09-29 [("doi","10.48550/arXiv.2009.12240")] ai/nn/sampling ai/nn/transformer ai/poetry

Lyrics parody swaps one set of words that accompany a melody with a new set of words, preserving the number of syllables per line and the rhyme scheme.

Lyrics parody generation is a challenge for controllable text generation. We show how a specialized sampling procedure, combined with backward text generation with XLNet can produce parody lyrics that reliably meet the syllable and rhyme scheme constraints.

We introduce the Weird AI Yankovic system and provide a case study evaluation.

We conclude with societal implications of neural lyric parody generation.

--- /archiving#sort-key-compression-trick Archiving URLs § sort key compression trick Gwern 2011-03-10 2011-03-10 cs/haskell cs/linkrot/archiving cs/r cs/shell meta tutorial --- /doc/cs/algorithm/2001-demarco-peopleware-whymeasureperformance.pdf Peopleware: Why Measure Performance Tom DeMarco, Timothy Lister 2001-01-01 2021-09-29 cs/algorithm --- https://esolangs.org/wiki/OISC OISC 2021-09-29 cs/computable --- https://esolangs.org/wiki/Linear_bounded_automaton Linear bounded automaton 2021-09-29 cs/computable --- /doc/cs/computable/1964-gluskin.pdf FLODAC—A Pure Fluid Digital Computer R. S. Gluskin, M. Jacoby, T. D. Reader 1964-10-27 2021-09-29 [("doi","10.1109/AFIPS.1964.74")] cs/computable cs/hardware --- /doc/cs/algorithm/1956-shannon.pdf The Bandwagon Claude E. Shannon 1956-01-01 2021-09-29 [("doi","10.1109/tit.1956.1056774")] cs/algorithm cs/hardware --- https://en.wikipedia.org/wiki/Fibonacci_word Fibonacci word 2021-09-29 cs/algorithm --- https://en.wikipedia.org/wiki/Boyer%E2%80%93Moore_string-search_algorithm Boyer-Moore string-search algorithm 2021-09-29 cs/algorithm --- /doc/cs/algorithm/1989-robson.pdf Separating strings with small automata J. M. Robson 1989-02-27 2021-09-29 [("doi","10.1016/0020-0190(89)90215-9")] cs/algorithm --- /doc/cs/hardware/1987-baker.pdf The Symbolics Ivory Processor: A 40 Bit Tagged Architecture Lisp Microprocessor Clark Baker, David Chan, Jim Cherry, Alan Corry, Greg Efland, Bruce Edwards, Mark Matson, Henry Minsky, Eric Nestler, Kalman Reti, David Sarrazin, Charles Sommer, David Tan, Neil Weste 1987-01-01 2021-09-30 cs/hardware cs/lisp --- /doc/cs/hardware/1994-burke-informationandsecrecyvannevarbushultrandtheothermemex.pdf Information and Secrecy: Vannevar Bush, Ultra, and the Other Memex Colin Burke 1994-01-01 2021-09-30 cs/hardware --- https://arxiv.org/abs/2003.08445#google Placement Optimization with Deep Reinforcement Learning Anna Goldie, Azalia Mirhoseini 2020-03-18 2021-09-30 [("doi","10.48550/arXiv.2003.08445")] cs/hardware

Placement Optimization is an important problem in systems and chip design, which consists of mapping the nodes of a graph onto a limited set of resources to optimize for an objective, subject to constraints.

In this paper, we start by motivating reinforcement learning as a solution to the placement problem. We then give an overview of what deep reinforcement learning is.

We next formulate the placement problem as a reinforcement learning problem and show how this problem can be solved with policy gradient optimization. Finally, we describe lessons we have learned from training deep reinforcement learning policies across a variety of placement optimization problems.

--- /doc/cs/algorithm/1956-shannon.pdf 1956 2021-09-30 cs/algorithm cs/hardware --- /doc/cs/hardware/2020-masanet.pdf Recalibrating global data center energy-use estimates Eric Masanet, Arman Shehabi, Nuoa Lei, Sarah Smith, Jonathan Koomey 2020-02-28 2021-09-30 [("doi","10.1126/science.aba3758")] cs/hardware economics --- https://en.wikipedia.org/wiki/LZ77_and_LZ78 LZ77 and LZ78 2021-09-30 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Huffman_coding Huffman coding 2021-09-30 cs/algorithm/information/compression psychology/linguistics --- https://arxiv.org/abs/1306.4947 Machine Teaching for Bayesian Learners in the Exponential Family Xiaojin Zhu 2013-06-20 2021-09-30 [("doi","10.48550/arXiv.1306.4947")] reinforcement-learning/exploration/active-learning/data-pruning statistics/bayes

What if there is a teacher who knows the learning goal and wants to design good training data for a machine learner?

We propose an optimal teaching framework aimed at learners who employ Bayesian models. Our framework is expressed as an optimization problem over teaching examples that balance the future loss of the learner and the effort of the teacher.

This optimization problem is in general hard. In the case where the learner employs conjugate exponential family models, we present an approximate algorithm for finding the optimal teaching set.

Our algorithm optimizes the aggregate sufficient statistics, then unpacks them into actual teaching examples.

We give several examples to illustrate our framework.

--- https://arxiv.org/abs/1605.05274 Java Generics are Turing Complete Radu Grigore 2016-05-17 2021-09-30 [("doi","10.48550/arXiv.1605.05274")] cs/computable

This paper describes a reduction from the halting problem of Turing machines to subtype checking in Java.

It follows that subtype checking in Java is undecidable, which answers a question posed by Kennedy & Pierce 2007.

It also follows that Java’s type checker can recognize any recursive language, which improves a result of Gil & Levy 2016. The latter point is illustrated by a parser generator for fluent interfaces.

--- https://arxiv.org/abs/1412.0784 Braid is undecidable Linus Hamilton 2014-12-02 2021-09-30 [("doi","10.48550/arXiv.1412.0784")] cs/computable

Braid is a 2008 puzzle game centered around the ability to reverse time.

We show that Braid can simulate an arbitrary computation. Our construction makes no use of Braid’s unique time mechanics, and therefore may apply to many other video games.

We also show that a plausible “bounded” variant of Braid lies within 2-EXPSPACE.

Our proof relies on a technical lemma about Turing machines which may be of independent interest. Namely, define a ‘Braid-like Turing machine’ to be a Turing machine that, when it writes to the tape, deletes all data on the tape to the right of the head.

We prove that deciding the behavior of such a machine lies in EXPSPACE.

--- https://arxiv.org/abs/1204.1749 Robust Soldier Crab Ball Gate Yukio-Pegio Gunji, Yuta Nishiyama, Andrew Adamatzky 2012-04-08 2021-10-01 [("doi","10.48550/arXiv.1204.1749")] cs/computable math/humor

Soldier crabs (Mictyris guinotae) exhibit pronounced swarming behavior. The swarms of the crabs tolerant of perturbations.

In computer models and laboratory experiments we demonstrate that swarms of soldier crabs can implement logical gates when placed in a geometrically constrained environment.

--- https://arxiv.org/abs/1104.5466 Notes on a New Philosophy of Empirical Science Daniel Burfoot 2011-04-28 2021-10-01 [("doi","10.48550/arXiv.1104.5466")] ai/nn/transformer/gpt/2 cs/algorithm/information/compression philosophy/epistemology statistics/bias

This book presents a methodology and philosophy of empirical science based on large scale lossless data compression. In this view a theory is scientific if it can be used to build a data compression program, and it is valuable if it can compress a standard benchmark database to a small size, taking into account the length of the compressor itself. This methodology therefore includes an Occam principle as well as a solution to the problem of demarcation. Because of the fundamental difficulty of lossless compression, this type of research must be empirical in nature: compression can only be achieved by discovering and characterizing empirical regularities in the data. Because of this, the philosophy provides a way to reformulate fields such as computer vision and computational linguistics as empirical sciences: the former by attempting to compress databases of natural images, the latter by attempting to compress large text databases. The book argues that the rigor and objectivity of the compression principle should set the stage for systematic progress in these fields. The argument is especially strong in the context of computer vision, which is plagued by chronic problems of evaluation.

The book also considers the field of machine learning. Here the traditional approach requires that the models proposed to solve learning problems be extremely simple, in order to avoid overfitting. However, the world may contain intrinsically complex phenomena, which would require complex models to understand. The compression philosophy can justify complex models because of the large quantity of data being modeled (if the target database is 100 Gb, it is easy to justify a 10 Mb model). The complex models and abstractions learned on the basis of the raw data (images, language, etc) can then be reused to solve any specific learning problem, such as face recognition or machine translation.

--- https://techcrunch.com/2022/06/18/microsoft-and-meta-join-google-in-using-ai-to-help-run-their-data-centers/ Microsoft and Meta join Google in using AI to help run their data centers 2021-10-01 cs/hardware reinforcement-learning/model-free --- https://accidentallyquadratic.tumblr.com/ Accidentally Quadratic 2021-10-01 cs/algorithm cs/haskell --- https://arxiv.org/abs/1303.6571 Survival of the Unfittest: Why the Worst Infrastructure Gets Built, And What We Can Do about It Bent Flyvbjerg 2013-03-20 2021-10-01 [("doi","10.48550/arXiv.1303.6571")] economics politics

The article first describes characteristics of major infrastructure projects. These projects are often pivotal in shaping the economic landscape, providing essential services and facilities that fuel growth and development.

Second, it documents a much neglected topic in economics: that ex ante estimates of costs and benefits are often very different from actual ex post costs and benefits. For large infrastructure projects the consequence is cost overruns, benefit shortfalls, and the systematic underestimation of risks.

Third, implications for cost-benefit analysis are described, including that such analysis is not to be trusted for major infrastructure projects.

Fourth, the article uncovers the causes of this state of affairs in terms of perverse incentives that encourage promoters to underestimate costs and overestimate benefits in the business cases for their projects. But the projects that are made to look best on paper are the projects that amass the highest cost overruns and benefit shortfalls in reality. The article depicts this situation as “survival of the un-fittest.”

Fifth, the article sets out to explain how the problem may be solved, with a view to arriving at more efficient and more democratic projects, and avoiding the scandals that often accompany major infrastructure investments.

Finally, the article identifies current trends in major infrastructure development. It is argued that a rapid increase in stimulus spending combined with more investments in emerging economies combined with more spending on information technology is catapulting infrastructure investment from the frying pan into the fire.

--- https://zhd.dev/sufficiently/ Sufficiently Advanced Testing 2021-10-01 cs/security --- https://lcamtuf.blogspot.com/2014/11/pulling-jpegs-out-of-thin-air.html Pulling JPEGs out of thin air 2021-10-01 cs/security reinforcement-learning/exploration --- http://0b4af6cdc2f0c5998459-c0245c5c937c5dedcca3f1764ecc9b2f.r43.cf2.rackcdn.com/12061-woot13-bangert.pdf The Page-Fault Weird Machine: Lessons in Instruction-less Computation Bangert 2013 2021-10-01 cs/computable cs/security --- https://web.archive.org/web/20100322192300/http://33bits.org/2010/03/15/open-letter-to-netflix/ An open letter to Netflix from the authors of the de-anonymization paper 2021-10-01 cs/algorithm cs/security --- /doc/history/1978-ladurie-montaillouthepromisedlandoferror-chapter2domus.pdf Montaillou: The Promised Land of Error: ch2, the domus Emmanuel Le Roy Ladurie 1978-01-01 2021-10-01 history philosophy/ethics --- /doc/psychiatry/2018-gordon.pdf Association of Efficacy of Resistance Exercise Training With Depressive Symptoms: Meta-analysis and Meta-regression Analysis of Randomized Clinical Trials Brett R. Gordon, Cillian P. McDowell, Mats Hallgren, Jacob D. Meyer, Mark Lyons, Matthew P. Herring 2018-05-09 2021-10-01 [("doi","0.1001/jamapsychiatry.2018.0572")] exercise psychiatry --- /doc/psychiatry/depression/2018-gordon-supplement.pdf Supplementary Online Content for Association of efficacy of resistance exercise training with depressive symptoms: meta-analysis and meta-regression analysis of randomized clinical trials Brett R. Gordon, Cillian P. McDowell, Mats Hallgren, Jacob D. Meyer, Mark Lyons, Matthew P. Herring 2018-05-09 2021-10-02 [("doi","10.1001/jamapsychiatry.2018.0572_1")] exercise psychiatry/depression --- https://web.mat.bham.ac.uk/R.W.Kaye/minesw/infmsw.pdf Infinite versions of minesweeper are Turing complete Kate 2007 2021-10-02 cs/computable --- https://x.com/BoyNamedShit/status/1538732931228635137 2021-10-02 ai/nn/transformer/gpt/fiction --- http://thecodelesscode.com/case/96 The Codeless Code: Case 96: ‘Stateless’ 2021-10-02 cs/algorithm --- https://docs.google.com/spreadsheets/d/14xTqtuV3BuKDNhLotB_d1aFlBGnDJOY0BRXJ8-86GpA/edit Image Synthesis Style Studies Database (The List) 2021-10-02 ai/nn/diffusion --- https://drive.google.com/drive/folders/1wXvivH2azyK0J-5kjxiq5_4BlMw0ztmx public folder for DD studies 2021-10-02 ai/nn/diffusion --- https://arxiv.org/abs/2205.15868 CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, Jie Tang 2022-05-29 2022-05-29 [("doi","10.48550/arXiv.2205.15868")] ai/nn/transformer/gpt/dall-e/1 ai/video/generation

[demo; code; checkpoint] Large-scale pretrained transformers have created milestones in text (GPT-3) and text-to-image (DALL·E and CogView) generation. Its application to video generation is still facing many challenges: the potential huge computation cost makes the training from scratch unaffordable; the scarcity and weak relevance of text-video datasets hinder the model understanding complex movement semantics.

In this work, we present a 9B-parameter transformer CogVideo, trained by inheriting a pretrained text-to-image model, CogView2. We also propose multi-frame-rate hierarchical training strategy to better align text and video clips.

As (probably) the first open-source large-scale pretrained text-to-video model, CogVideo outperforms all publicly available models at a large margin in machine and human evaluations.

--- https://github.com/aqlaboratory/openfold Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2 2021-10-02 ai/nn/transformer/alphafold --- https://www.lesswrong.com/posts/Ke2ogqSEhL2KCJCNx/ Security Mindset: Lessons from 20+ years of Software Security Failures Relevant to AGI Alignment 2021-10-02 cs/security reinforcement-learning/safe --- https://publicdomainreview.org/collection/story-of-sun-moon-stars Agnes Giberne’s The Story of the Sun, Moon, and Stars 2021-10-02 design/visualization history/public-domain-review --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4747782/ Motor Skills Are Strengthened through Reconsolidation Nicholas F. Wymbs, Amy J. Bastian, Pablo A. Celnik 2016 2021-10-02 [("doi","10.1016/j.cub.2015.11.066")] psychology/spaced-repetition

Newly acquired motor skills become stabilized through consolidation [1]. However, we know from daily life that consolidated skills are modified over multiple bouts of practice and in response to newfound challenges [2]. Recent evidence has shown that memories can be modified through reconsolidation, in which previously consolidated memories can re-enter a temporary state of instability through retrieval, and in order to persist, undergo re-stabilization [3–8]. Although observed in other memory domains [5, 6], it is unknown whether reconsolidation leads to strengthened motor skills over multiple episodes of practice.

Using a novel intervention after the retrieval of a consolidated skill, we found:

that skill can be modified and enhanced through exposure to increased sensorimotor variability. This improvement was greatest in those participants who could rapidly adjust their sensorimotor output in response to the relatively large fluctuations presented during the intervention. Importantly, strengthening required the reactivation of the consolidated skill and time for changes to reconsolidate.

These results provide a key demonstration that consolidated motor skills continue to change as needed through the remapping of motor command to action goal, with strong implications for rehabilitation.

--- https://yunnansourcing.com/collections/flower-and-herbal-teas/products/osmanthus-flower-and-yi-mei-ren-black-tea-dragon-ball 3x, $4.70 2021-10-03 tea --- https://yunnansourcing.com/collections/flower-and-herbal-teas/products/royal-chrysanthemums-and-big-snow-mountain-black-tea-dragon-ball 1x, $2 2021-10-03 tea --- https://yunnansourcing.com/collections/flower-and-herbal-teas/products/snow-chrysanthemum-flowers-and-big-snow-mountain-black-tea-dragon-ball 1, $2 2021-10-03 tea --- https://yunnansourcing.com/collections/flower-and-herbal-teas/products/yunnan-sun-dried-wild-rose-buds-from-wenshan 50g, $6 2021-10-03 tea --- https://yunnansourcing.com/products/competition-grade-tie-guan-yin-oolong-tea-of-gande-village Competition Grade Tie Guan Yin Oolong Tea of Gande Village 2021-10-03 tea --- https://yunnansourcing.com/products/honey-orchid-mi-lan-xiang-dan-cong-oolong-tea 25g, $8.25 2021-10-03 tea --- https://yunnansourcing.com/products/ping-keng-tou-almond-aroma-dan-cong-oolong-tea-spring-2018 25g, $9 2021-10-03 tea --- https://yurideigin.medium.com/jaccuse-why-122-year-longevity-record-may-be-fake-af87fc0c3133 J’accuse...! Why Jeanne Calment’s 122-year old longevity record may be fake 2021-10-03 longevity --- https://yurideigin.medium.com/more-evidence-for-jeanne-calments-identity-theft-hypothesis-26f7cece0cd2 More evidence for Jeanne Calment’s identity theft hypothesis 2021-10-03 longevity --- https://zapatopi.net/kelvin/papers/on_the_age_of_the_suns_heat.html On the Age of the Sun’s Heat 2021-10-03 philosophy/epistemology science --- https://yuanchuan.dev/2019/05/15/window-lattice-and-css Chinese Window Lattice And CSS 2021-10-04 cs/css design/typography --- https://xkcd.com/1319/ Automation 2021-10-04 economics/automation --- https://www.youtube.com/watch?v=wPiLLplofYw Decerebrate Cat walks and exhibits multiple gait patterns 2021-10-04 cat/psychology psychology/neuroscience --- https://www.usenix.org/conference/usenixsecurity12/technical-sessions/presentation/bojinov Neuroscience Meets Cryptography: Designing Crypto Primitives Secure Against Rubber Hose Attacks 2021-10-04 cs/cryptography psychology --- https://web.archive.org/web/20131101142206/https://www.ime.usp.br/~jstern/miscellanea/General/Chiang98.pdf Story Of Your Life Ted Chiang 1999 2021-10-04 fiction/science-fiction philosophy/mind science --- /doc/psychology/personality/psychopathy/1941-cleckley-maskofsanity.pdf 1941-01-01 2021-10-04 psychology/personality/psychopathy --- https://arxiv.org/abs/2206.11309#microsoft GODEL: Large-Scale Pre-Training for Goal-Directed Dialog Baolin Peng, Michel Galley, Pengcheng He, Chris Brockett, Lars Liden, Elnaz Nouri, Zhou Yu, Bill Dolan, Jianfeng Gao 2022-06-22 2022-06-22 [("doi","10.48550/arXiv.2206.11309")] ai/nn/transformer/gpt/3/nonfiction reinforcement-learning/model

We introduce GODEL (Grounded Open Dialogue Language Model), a large pre-trained [GPT-3-175b] language model for dialog. In contrast with earlier models such as DialoGPT, GODEL leverages a new phase of grounded pre-training designed to better support adapting GODEL to a wide range of downstream dialog tasks that require information external to the current conversation (eg. a database or document) to produce good responses.

Experiments against an array of benchmarks that encompass task-oriented dialog, conversational QA, and grounded open-domain dialog show that GODEL outperforms state-of-the-art pre-trained dialog models in few-shot fine-tuning setups, in terms of both human and automatic evaluation. A novel feature of our evaluation methodology is the introduction of a notion of utility that assesses the usefulness of responses (extrinsic evaluation) in addition to their communicative features (intrinsic evaluation). We show that extrinsic evaluation offers improved inter-annotator agreement and correlation with automated metrics.

Code and data processing scripts are publicly available.

--- https://www.biorxiv.org/content/10.1101/2022.06.20.496834.full Theropod dinosaurs had primate-like numbers of telencephalic neurons Suzana Herculano-Houzel 2022-06-21 2022-06-21 [("doi","10.1101/2022.06.20.496834")] psychology/animal/bird/neuroscience psychology/neuroscience

Understanding the neuronal composition of the brains of dinosaurs and other fossil amniotes would offer fundamental insight into their behavioral and cognitive capabilities, but brain tissue is not fossilized. However, when the bony brain case is preserved, the volume and therefore mass of the brain can be estimated with computer tomography; and if the scaling relationship between brain mass and numbers of neurons for the clade is known, that relationship can be applied to estimate the neuronal composition of the brain.

Using a recently published database of numbers of neurons in the telencephalon of extant bird and non-avian reptiles, here I show that the neuronal scaling rules that apply to these animals can be used to infer the numbers of neurons that composed the telencephalon of dinosaur, pterosaur and other fossil reptile species, after using the relationship between brain and body mass to determine whether bird-like (endothermic) or non-avian reptile-like (ectothermic) rules apply to each fossil species.

This procedure indicates that theropods such as Tyrannosaurus rex and Allosaurus had monkey-like numbers of telencephalic neurons, which would make these animals not only giant but also long-lived and endowed with flexible cognition, and thus even more magnificent predators than previously thought.

…Importantly, the use of endotherm (avian) scaling rules to estimate numbers of telencephalic neurons in theropods versus ectotherm (non-avian reptile) scaling rules in ornithischians is supported by recent metabolite findings in these species. The distinction is highly consequential: if the Tyrannosaurus brain scaled like a non-avian reptilian ectotherm brain, it would have an estimated 0.455b telencephalic neurons—still as many as in a large dog, but less than 15% of the baboon-like 3.4 billion telencephalic neurons estimated if basal bird-like scaling rules applied (Table S1).

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8931369/ The evolution of brain neuron numbers in amniotes Kristina Kverková, Lucie Marhounová, Alexandra Polonyiová, Martin Kocourek, Yicheng Zhang, Seweryn Olkowicz, Barbora Straková, Zuzana Pavelková, Roman Vodička, Daniel Frynta, Pavel Němec 2022 2022 [("doi","10.1073/pnas.2121624119")] genetics/selection/natural psychology/animal/bird/neuroscience

The evolution of brain processing capacity has traditionally been inferred from data on brain size. However, similarly sized brains of distantly related species can differ in the number and distribution of neurons, their basic computational units. Therefore, a finer-grained approach is needed to reveal the evolutionary paths to increased cognitive capacity.

Using a new, comprehensive dataset, we analyzed brain cellular composition across amniotes.

Compared to reptiles, mammals and birds have dramatically increased neuron numbers in the telencephalon and cerebellum, which are brain parts associated with higher cognition. Astoundingly, a phylogenetic analysis suggests that as few as 4 major changes in neuron-brain scaling in over 300 million years of evolution pave the way to intelligence in endothermic land vertebrates.

--- https://www.astralcodexten.com/p/a-guide-to-asking-robots-to-design A Guide To Asking Robots To Design Stained Glass Windows 2021-10-04 ai/nn/transformer/gpt/dall-e --- https://marginalrevolution.com/marginalrevolution/2022/06/an-ai-does-a-cwt-between-me-and-janet-yellen.html An AI does a CWT between me and "Janet Yellen" 2021-10-04 ai/nn/transformer/gpt/non-fiction --- https://medium.com/ozonetel-ai/sentence-embeddings-have-a-problem-the-reason-sometimes-dalle-2-fails-2a10d5048f15 Sentence Embeddings have a problem, the reason sometimes Dall-E2 fails 2021-10-05 ai/nn/transformer/gpt/dall-e --- https://www.theverge.com/2019/5/29/18531476/music-industry-song-royalties-metadata-credit-problems Metadata is the biggest little problem plaguing the music industry 2021-10-05 economics/copyright --- https://spectrum.ieee.org/ai-guided-robots-are-ready-to-sort-your-recyclables AI-Guided Robots Are Ready to Sort Your Recyclables 2021-10-05 reinforcement-learning/exploration/active-learning reinforcement-learning/robot --- https://www.quantamagazine.org/ai-makes-strides-in-virtual-worlds-more-like-our-own-20220624/ 2021-10-05 reinforcement-learning/robot --- https://www.reddit.com/r/bigsleep/comments/vkmq5y/earthenware_pokemon_figurines_from_ancient_sumer/ 2021-10-05 ai/nn/transformer/clip/sample --- https://www.reddit.com/r/cogsci/comments/b8l7ml/example_visual_circuit_using_visual_stimuli_and/ 2021-10-05 psychology/vision --- https://www.youtube.com/watch?v=j0z4FweCy4M&t=2928 48:44—Tesla Vision · 1:13:12—Planning and Control · 1:24:35—Manual Labeling · 1:28:11—Auto Labeling · 1:35:15—Simulation · 1:42:10—Hardware Integration · 1:45:40—Dojo 2021-10-05 ai/scaling/hardware reinforcement-learning/robot --- https://www.wired.com/story/lucid-dreaming-alice-robb-why-we-dream/ Lucid Dreaming: This Retreat Can Train Your Nighttime Visions 2021-10-05 psychology/vision zeo --- https://github.com/shubhampachori12110095/DeepLearningAnimePapers A list of papers and other resources on computer vision and deep learning with anime style images 2021-10-05 ai/anime --- https://research.google/blog/from-vision-to-language-semi-supervised-learning-in-action-at-scale/ From Vision to Language: Semi-Supervised Learning in Action…at Scale 2021-10-05 ai/nn/sparsity/knowledge-distillation --- https://x.com/lawderpaul/status/1284972517749338112 Turns out #GPT3 can do vision too 😉 Built an ingredient parser: take a pic of any nutrition label (google to extract text), and GPT-3 will identify ingredients, find an emoji, determine if it’s unhealthy, and give a definition 🤯 2021-10-05 ai/nn/transformer/gpt/non-fiction --- https://en.wikipedia.org/wiki/The_dress The dress 2021-10-06 psychology/vision --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4921196/ Striking individual differences in color perception uncovered by ‘the dress’ photograph Rosa Lafer-Sousa, Katherine L. Hermann, Bevil R. Conway 2015 2021-10-06 [("doi","10.1016/j.cub.2015.04.053")] psychology/vision

‘The dress’ is a peculiar photograph: by themselves the dress’ pixels are brown and blue, colors associated with natural illuminants, but popular accounts (#TheDress) suggest the dress appears either white/gold or blue/black. Could the purported categorical perception arise because the original social-media question was an alternative-forced-choice?

In a free-response survey (n = 1,401), we found that:

most people, including those naïve to the image, reported white/gold or blue/black, but some said blue/brown. Reports of white/gold over blue/black were higher among older people and women. On re-test, some subjects reported a switch in perception, showing the image can be multi-stable. In a language-independent measure of perception, we asked subjects to identify the dress’ colors from a complete color gamut. The results showed 3 peaks corresponding to the main descriptive categories, providing additional evidence that the brain resolves the image into one of 3 stable percepts.

We hypothesize that these reflect different internal priors: some people favor a cool illuminant (blue sky), discount shorter wavelengths, and perceive white/gold; others favor a warm illuminant (incandescent light), discount longer wavelengths, and see blue/black. The remaining subjects may assume a neutral illuminant, and see blue/brown.

We show that by introducing overt cues to the illumination, we can flip the dress color.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4489998/ Asymmetries in blue-yellow color perception and in the color of ‘the dress’ Alissa D. Winkler, Lothar Spillmann, John S. Werner, Michael A. Webster 2015 2021-10-06 [("doi","10.1016/j.cub.2015.05.004")] psychology/vision

The perception of color poses daunting challenges, because the light spectrum reaching the eye depends on both the reflectance of objects and the spectrum of the illuminating light source. Solving this problem requires sophisticated inferences about the properties of lighting and surfaces, and many striking examples of ‘color constancy’ illustrate how our vision compensates for variations in illumination to estimate the color of objects (for example [1–3]).

We discovered a novel property of color perception and constancy, involving how we experience shades of blue versus yellow.

We found that surfaces are much more likely to be perceived as white or gray when their color is varied along bluish directions, compared with equivalent variations along yellowish (or reddish or greenish) directions.

This selective bias may reflect a tendency to attribute bluish tints to the illuminant rather than the object, consistent with an inference that indirect lighting from the sky and in shadows tends to be bluish. The blue-yellow asymmetry has striking effects on the appearance of images when their colors are reversed, turning white to yellow and silver to gold, and helps account for the variation among observers in the colors experienced in ‘the dress’ image that recently consumed the internet. Observers variously describe the dress as blue-black or white-gold, and this has been explained by whether the dress appears to be in direct lighting or shade (for example [5]).

We show that these individual differences and potential lighting interpretations also depend on the special ambiguity of blue, for simply reversing the image colors causes almost all observers to report the lighter stripes as yellowish.

--- /doc/cat/psychology/1988-bravo.pdf Cats see subjective contours 1988 2021-10-06 cat/psychology psychology/vision --- https://en.wikipedia.org/wiki/Delboeuf_illusion Delboeuf illusion 2021-10-06 psychology/vision --- https://en.wikipedia.org/wiki/Peripheral_drift_illusion#Rotating_snakes Peripheral drift illusion § Rotating snakes 2021-10-06 psychology/vision --- https://en.wikipedia.org/wiki/Alice_in_Wonderland_syndrome Alice in Wonderland syndrome 2021-10-06 psychology/vision --- https://www.theguardian.com/books/2013/may/05/stewart-brand-whole-earth-catalog Stewart Brand’s Whole Earth Catalog, the book that changed the world: Stewart Brand was at the heart of 60s counterculture and is now widely revered as the tech visionary whose book anticipated the web. We meet the man for whom big ideas are a way of life 2021-10-06 technology --- https://web.archive.org/web/20100626203929/http://googledocs.blogspot.com/2010/06/optical-character-recognition-ocr-in.html Optical character recognition (OCR) in Google Docs 2021-10-06 ai --- /doc/psychology/neuroscience/2022-bechlivanidis.pdf Human Vision Reconstructs Time to Satisfy Causal Constraints Christos Bechlivanidis, Marc J. Buehner, Emma C. Tecwyn, David A. Lagnado, Christoph Hoerl, Teresa McCormack 2022-01-04 2022-01-04 [("doi","10.1177/09567976211032663")] psychology/cognitive-bias/illusion-of-depth psychology/neuroscience psychology/vision

The goal of perception is to infer the most plausible source of sensory stimulation. Unisensory perception of temporal order, however, appears to require no inference, because the order of events can be uniquely determined from the order in which sensory signals arrive.

Here, we demonstrate a novel perceptual illusion that casts doubt on this intuition: In 3 experiments (n = 607), the experienced event timings were determined by causality in real time. Adult participants viewed a simple three-item sequence, ACB, which is typically remembered as ABC in line with principles of causality. When asked to indicate the time at which events B and C occurred, participants’ points of subjective simultaneity shifted so that the assumed cause B appeared earlier and the assumed effect C later, despite participants’ full attention and repeated viewings.

This first demonstration of causality reversing perceived temporal order cannot be explained by post-perceptual distortion, lapsed attention, or saccades.

--- https://x.com/quasimondo/status/1541060706157617152 2021-10-06 ai/nn/transformer/gpt/poetry --- https://x.com/quasimondo/status/1541060706157617152 2021-10-07 cs/css --- https://www.reddit.com/r/mlscaling/comments/sjzvl0/d_instances_of_nonlog_capability_spikes_or/ 2021-10-07 ai/scaling/emergence --- https://cse-robotics.engr.tamu.edu/dshell/cs689/papers/anderson72more_is_different.pdf 2021-10-07 ai/scaling/emergence --- https://arxiv.org/abs/2109.13916 Unsolved Problems in ML Safety Dan Hendrycks, Nicholas Carlini, John Schulman, Jacob Steinhardt 2021-09-28 2021-10-07 [("doi","10.48550/arXiv.2109.13916")] reinforcement-learning/safe

Machine learning (ML) systems are rapidly increasing in size, are acquiring new capabilities, and are increasingly deployed in high-stakes settings. As with other powerful technologies, safety for ML should be a leading research priority. In response to emerging safety challenges in ML, such as those introduced by recent large-scale models, we provide a new roadmap for ML Safety and refine the technical problems that the field needs to address.

We present four problems ready for research, namely withstanding hazards (“Robustness”), identifying hazards (“Monitoring”), reducing inherent model hazards (“Alignment”), and reducing systemic hazards (“Systemic Safety”).

Throughout, we clarify each problem’s motivation and provide concrete research directions.

--- https://arxiv.org/abs/1710.05465 Flow: A Modular Learning Framework for Mixed Autonomy Traffic Cathy Wu, Aboudy Kreidieh, Kanaad Parvate, Eugene Vinitsky, Alexandre M. Bayen 2017-10-16 2021-10-07 [("doi","10.1109/TRO.2021.3087314")] reinforcement-learning/robot

The rapid development of autonomous vehicles (AVs) holds vast potential for transportation systems through improved safety, efficiency, and access to mobility. However, the progression of these impacts, as AVs are adopted, is not well understood. Numerous technical challenges arise from the goal of analyzing the partial adoption of autonomy: partial control and observation, multi-vehicle interactions, and the sheer variety of scenarios represented by real-world networks.

To shed light into near-term AV impacts, this article studies the suitability of deep reinforcement learning (RL) for overcoming these challenges in a low AV-adoption regime. A modular learning framework is presented, which leverages deep RL to address complex traffic dynamics. Modules are composed to capture common traffic phenomena (stop-and-go traffic jams, lane changing, intersections).

Learned control laws are found to improve upon human driving performance, in terms of system-level velocity, by up to 57% with only 4–7% adoption of AVs. Furthermore, in single-lane traffic, a small neural network control law with only local observation is found to eliminate stop-and-go traffic—surpassing all known model-based controllers to achieve near-optimal performance—and generalize to out-of-distribution traffic densities.

--- https://arxiv.org/abs/2010.10560 Reinforcement Learning for Optimization of COVID-19 Mitigation policies Varun Kompella, Roberto Capobianco, Stacy Jong, Jonathan Browne, Spencer Fox, Lauren Meyers, Peter Wurman, Peter Stone 2020-10-20 2021-10-07 [("doi","10.48550/arXiv.2010.10560")] reinforcement-learning/multi-agent

The year 2020 has seen the COVID-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world are faced with the challenge of protecting public health, while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies. However, to date, the even the most data-driven intervention policies rely on heuristics.

In this paper, we study how reinforcement learning (RL) can be used to optimize mitigation policies that minimize the economic impact without overwhelming the hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community; and (2) an RL-based methodology for optimizing fine-grained mitigation policies within this simulator.

Our results validate both the overall simulator behavior and the learned policies under realistic conditions.

--- https://arxiv.org/abs/2009.09051 Deep Reinforcement Learning for Closed-Loop Blood Glucose Control Ian Fox, Joyce Lee, Rodica Pop-Busui, Jenna Wiens 2020-09-18 2021-10-07 [("doi","10.48550/arXiv.2009.09051")] ai/nn/rnn biology reinforcement-learning

People with type 1 diabetes (T1D) lack the ability to produce the insulin their bodies need. As a result, they must continually make decisions about how much insulin to self-administer to adequately control their blood glucose levels. Longitudinal data streams captured from wearables, like continuous glucose monitors, can help these individuals manage their health, but currently the majority of the decision burden remains on the user. To relieve this burden, researchers are working on closed-loop solutions that combine a continuous glucose monitor and an insulin pump with a control algorithm in an ‘artificial pancreas.’ Such systems aim to estimate and deliver the appropriate amount of insulin.

Here, we develop reinforcement learning (RL) techniques for automated blood glucose control. Through a series of experiments, we compare the performance of different deep RL approaches to non-RL approaches. We highlight the flexibility of RL approaches, demonstrating how they can adapt to new individuals with little additional data. On over 2.1 million hours of data from 30 simulated patients, our RL approach outperforms baseline control algorithms: leading to a decrease in median glycemic risk of nearly 50% 8.34 → 4.24 and a decrease in total time hypoglycemic of 99.8%, from 4,610 days to 6. Moreover, these approaches are able to adapt to predictable meal times (decreasing average risk by an additional 24% as meals increase in predictability).

This work demonstrates the potential of deep RL to help people with T1D manage their blood glucose levels without requiring expert knowledge. All of our code is publicly available, allowing for replication and extension.

--- https://www.biorxiv.org/content/10.1101/2022.06.21.496937.full Evolutionary models predict potential mechanisms of escape from mutational meltdown Claudia Bank, Mark A. Schmitz, Ana Yansi Morales-Arce 2022-06-25 2022-06-25 [("doi","10.1101/2022.06.21.496937")] genetics/selection/artificial

Mutagenic drugs are promising candidates for the treatment of various RNA virus infections. Increasing the mutation rate of the virus leads to rapid accumulation of deleterious mutation load, which is proposed to ultimately result in extinction as described by the theoretical concepts of mutational meltdown and lethal mutagenesis. However, the conditions and potential mechanisms of viral escape from the effects of mutagenic drugs have not been conceptually explored.

Here we apply a computational approach to quantify the population dynamics and genetics of a population under high mutation rates and discuss the likelihood of adaptation to a mutagenic drug by means of 3 proposed mechanisms: (1) a proportion of “traditional” beneficial mutations that increase growth/fitness, (2) a mutation rate modifier (ie. evolution of resistance to the mutagenic drug) that reduces the mutation rate, and (3) a modifier of the distribution of fitness effects, which either decreases or increases deleterious effects of mutations (ie. evolution of tolerance to the mutagenic drug). We track the population dynamics and genetics of evolving populations and find that successful adaptations have to appear early to override the increasing mutational load and rescue the population from its imminent extinction.

We highlight that the observed stochasticity of adaptation, especially by means of modifiers of the distribution of fitness effects, is difficult to capture in experimental trials, which may leave potential dangers of the use of mutagenic treatments unexposed.

--- https://arxiv.org/abs/1412.6572#google Explaining and Harnessing Adversarial Examples Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy 2014-12-20 2021-10-07 [("doi","10.48550/arXiv.1412.6572")] ai/nn/adversarial ai/nn/cnn

Several machine learning models, including neural networks, consistently misclassify adversarial examples—inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting.

We argue instead that the primary cause of neural networks’ vulnerability to adversarial perturbation is their linear nature.

This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples.

Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset.

--- https://arxiv.org/abs/1803.05355 FEVER: a large-scale dataset for Fact Extraction and VERification James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal 2018-03-14 2021-10-07 [("doi","10.48550/arXiv.1803.05355")] ai/dataset ai/nn/retrieval

In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification.

It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as “Supported”, “Refuted” or “NotEnoughInfo” by annotators achieving 0.6841 in Fleiss 𝜅. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment.

To characterize the challenge of the dataset presented, we develop a pipeline approach and compare it to suitably designed oracles. The best accuracy we achieve on labeling a claim accompanied by the correct evidence is 31.87%, while if we ignore the evidence we achieve 50.91%.

Thus we believe that FEVER is a challenging testbed that will help stimulate progress on claim verification against textual sources.

--- https://arxiv.org/abs/2106.00950 A Multi-Level Attention Model for Evidence-Based Fact Checking Canasai Kruengkrai, Junichi Yamagishi, Xin Wang 2021-06-02 2021-10-08 [("doi","10.48550/arXiv.2106.00950")] ai/nn/retrieval ai/nn/transformer/attention/hierarchical

Evidence-based fact checking aims to verify the truthfulness of a claim against evidence extracted from textual sources. Learning a representation that effectively captures relations between a claim and evidence can be challenging. Recent state-of-the-art approaches have developed increasingly sophisticated models based on graph structures.

We present a simple model that can be trained on sequence structures. Our BERT-based model enables inter-sentence attentions at different levels and can benefit from joint training.

Results on a large-scale dataset for Fact Extraction and VERification (FEVER) show that our model outperforms the graph-based approaches and yields 1.09% and 1.42% improvements in label accuracy and FEVER score, respectively, over the best published model.

--- https://gist.github.com/jareddk/6512393d4a996fbf3a72be265a5285aa random_ai_poems.txt 2021-10-08 ai/nn/transformer/gpt/poetry --- https://arxiv.org/abs/1905.07830 HellaSwag: Can a Machine Really Finish Your Sentence? Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, Yejin Choi 2019-05-19 2021-10-08 [("doi","10.48550/arXiv.1905.07830")] ai/nn/transformer

Recent work by Zellers et al 2018 introduced a new task of commonsense natural language inference: given an event description such as “A woman sits at a piano”, a machine must select the most likely followup: “She sets her fingers on the keys.” With the introduction of BERT, near human-level performance was reached. Does this mean that machines can perform human level commonsense inference?

In this paper, we show that commonsense inference still proves difficult for even state-of-the-art models, by presenting HellaSwag, a new challenge dataset. Though its questions are trivial for humans (>95% accuracy), state-of-the-art models struggle (<48%).

We achieve this via Adversarial Filtering (AF), a data collection paradigm wherein a series of discriminators iteratively select an adversarial set of machine-generated wrong answers. AF proves to be surprisingly robust. The key insight is to scale up the length and complexity of the dataset examples towards a critical ‘Goldilocks’ zone wherein generated text is ridiculous to humans, yet often misclassified by state-of-the-art models.

Our construction of HellaSwag, and its resulting difficulty, sheds light on the inner workings of deep pretrained models. More broadly, it suggests a new path forward for NLP research, in which benchmarks co-evolve with the evolving state-of-the-art in an adversarial way, so as to present ever-harder challenges.

--- https://arxiv.org/abs/2206.07682#google Emergent Abilities of Large Language Models Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, William Fedus 2022-06-15 2022-06-15 [("doi","10.48550/arXiv.2206.07682")] ai/scaling/emergence

Scaling up language models has been shown to predictably improve performance and sample efficiency on a wide range of downstream tasks.

This paper instead discusses an unpredictable phenomenon that we refer to as emergent abilities of large language models. We consider an ability to be emergent if it is not present in smaller models but is present in larger models. Thus, emergent abilities cannot be predicted simply by extrapolating the performance of smaller models.

The existence of such emergence implies that additional scaling could further expand the range of capabilities of language models.

--- https://x.com/xsteenbrugge/status/1540811220571258882 2021-10-08 ai/nn/transformer/clip/sample --- https://x.com/rjerala/status/1539590875814264832 2021-10-08 ai/nn/transformer/alphafold --- https://www.medrxiv.org/content/10.1101/2022.06.22.22276715.full Rare genetic variants impact muscle strength Yunfeng Huang, Dora Bodnar, Chia-Yen Chen, Gabi Gurria, Biogen Biobank Team, Jun Shi, Katherine G. Meilleur, Matthew E. Hurles, Sebastian S. Gerety, Ellen A. Tsai, Heiko Runz 2022-06-23 2022-06-23 [("doi","10.1101/2022.06.22.22276715")] genetics/heritable/rare

Muscle strength is highly heritable and predictive for multiple adverse health outcomes including mortality.

Here, we present a rare protein-coding variant association study in 340,319 individuals for hand grip strength, a proxy measure of muscle strength.

We identify 6 genome-wide statistically-significant genes (KDM5B, OBSCN, GIGYF1, TTN, RB1CC1, EIF3J), propose shared mechanisms between brain and muscle function and demonstrate additive effects between rare and common genetic variation on muscle strength.

--- /doc/psychology/neuroscience/2009-spivey.pdf#page=13 The Phase Transition In Human Cognition § Phase Transitions in Language Processing Michael J. Spivey, Sarah E. Anderson, Rick Dale 2009-03-01 2021-10-08 [("doi","10.1142/S1793005709001234")] ai/scaling/emergence psychology/neuroscience

This article attempts to build a bridge between cognitive psychology and computational neuroscience, perhaps allowing each group to understand the other’s theoretical insights and sympathize with the other’s methodological challenges.

In briefly discussing a collection of conceptual demonstrations, neural network and dynamical system simulations, and human experimental results, we highlight the importance of the concept of phase transition to understand cognitive function.

Our goal is to show that viewing cognition as a self-organizing process (involving phase transitions, criticality, and autocatalysis) affords a more natural explanation of these data over traditional approaches inspired by a sequence of linear filters (involving detection, recognition, and then response selection).

--- https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html#anthropic In-context Learning and Induction Heads Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Scott Johnston, Andy L. Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, Chris Olah 2022-03-08 2022-03-08 ai/nn/transformer ai/scaling/emergence reinforcement-learning/meta-learning --- https://transformer-circuits.pub/2021/framework/index.html#anthropic A Mathematical Framework for Transformer Circuits Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy L. Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, Chris Olah 2021-12-22 2021-12-22 ai/nn/transformer ai/scaling/emergence reinforcement-learning/meta-learning [videos; commentary] --- https://arxiv.org/abs/2205.01397 Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP) Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, Ludwig Schmidt 2022-05-03 2022-05-03 [("doi","10.48550/arXiv.2205.01397")] ai/dataset ai/nn/transformer/clip ai/scaling

Contrastively trained image-text models such as CLIP, ALIGN, and BASIC have demonstrated unprecedented robustness to multiple challenging natural distribution shifts. Since these image-text models differ from previous training approaches in several ways, an important question is what causes the large robustness gains.

We answer this question via a systematic experimental investigation. Concretely, we study 5 different possible causes for the robustness gains: (1) the training set size, (2) the training distribution, (3) language supervision at training time, (4) language supervision at test time, and (5) the contrastive loss function.

Our experiments show that the more diverse training distribution is the main cause for the robustness gains, with the other factors contributing little to no robustness.

Beyond our experimental results, we also introduce ImageNet-Captions, a version of ImageNet with original text annotations from Flickr [YFCC], to enable further controlled experiments of language-image training.

…In this paper, we answer the question of CLIP’s robustness via a series of controlled experiments that test the five possible causes listed above. Our main result is that CLIP’s robustness is determined almost exclusively by the training distribution. Language supervision at training time does not make the resulting models more robust than standard supervised learning when the images in the training set are the same. Hence language supervision only has an indirect effect on robustness. In particular, language supervision simplifies training on a diverse distribution of images by removing the need for consistent annotation with class labels. The more diverse training distribution—not the language supervision—then leads to more robust representations.

--- https://arxiv.org/abs/2205.10343 Towards Understanding Grokking: An Effective Theory of Representation Learning Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams 2022-05-20 2022-05-20 [("doi","10.48550/arXiv.2205.10343")] ai/nn/fully-connected ai/scaling/emergence/grokking

[code] We aim to understand grokking, a phenomenon where models generalize long after overfitting their training set.

We present both a ‘microscopic’ analysis anchored by an effective theory and a ‘macroscopic’ analysis of phase diagrams describing learning performance across hyperparameters.

We find that generalization originates from structured representations whose training dynamics and dependence on training set size can be predicted by our effective theory in a toy setting.

We observe empirically the presence of four learning phases: comprehension, grokking, memorization, and confusion.

We find representation learning to occur only in a “Goldilocks zone” (including comprehension and grokking) between memorization and confusion. Compared to the comprehension phase, the grokking phase stays closer to the memorization phase, leading to delayed generalization.

The Goldilocks phase is reminiscent of “intelligence from starvation” in Darwinian evolution, where resource limitations drive discovery of more efficient solutions.

This study not only provides intuitive explanations of the origin of grokking, but also highlights the usefulness of physics-inspired tools, eg. effective theories and phase diagrams, for understanding deep learning.

…Universality of phase diagrams: We fix the embedding learning rate to be 10−3 and sweep instead decoder weight decay in Figure 6b–d. The phase diagrams correspond to addition regression (b), addition classification (c) and permutation regression (d), respectively.

Common phenomena emerge from these different tasks: (1) they all include 4 phases; (2) The top right corner (a fast and capable decoder) is the memorization phase; (3) the bottom right corner (a fast and simple decoder) is the confusion phase; (4) grokking is sandwiched between comprehension and memorization, which seems to imply that it is an undesirable phase that stems from improperly tuned hyperparameters.

Figure 6: Phase diagrams of learning for the addition group and the permutation group. (a) shows the competition between representation and decoder. (b–d): each phase diagram contains 4 phases: comprehension, grokking, memorization and confusion, defined in Table 1. In (b–d), grokking is sandwiched between comprehension and memorization.
--- https://arxiv.org/abs/2204.04063 Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings Yuhao Mao, Chong Fu, Saizhuo Wang, Shouling Ji, Xuhong Zhang, Zhenguang Liu, Jun Zhou, Alex X. Liu, Raheem Beyah, Ting Wang 2022-04-07 2022-04-07 [("doi","10.48550/arXiv.2204.04063")] ai/nn/adversarial

One intriguing property of adversarial attacks is their “transferability”—an adversarial example crafted with respect to one deep neural network (DNN) model is often found effective against other DNNs as well. Intensive research has been conducted on this phenomenon under simplistic controlled conditions. Yet, thus far, there is still a lack of comprehensive understanding about transferability-based attacks (“transfer attacks”) in real-world environments.

To bridge this critical gap, we conduct the first large-scale systematic empirical study of transfer attacks against major cloud-based MLaaS platforms, taking the components of a real transfer attack into account.

The study leads to a number of interesting findings which are inconsistent to the existing ones, including: (1) Simple surrogates do not necessarily improve real transfer attacks. (2) No dominant surrogate architecture is found in real transfer attacks. (3) It is the gap between posterior (output of the softmax layer) rather than the gap between logit (so-called 𝜅 value) that increases transferability.

Moreover, by comparing with prior works, we demonstrate that transfer attacks possess many previously unknown properties in real-world environments, such as (1) Model similarity is not a well-defined concept. (2) L2 norm of perturbation can generate high transferability without usage of gradient and is a more powerful source than 𝓁∞ norm.

We believe this work sheds light on the vulnerabilities of popular MLaaS platforms and points to a few promising research directions.

--- https://github.com/inverse-scaling/prize inverse-scaling/prize: A prize for finding tasks that cause large language models to show inverse scaling 2021-10-09 ai/scaling reinforcement-learning/safe --- https://www.reddit.com/r/SubSimulatorGPT2/comments/vlk8s2/the_iqgwa_equation_a_reinterpretation/ 2021-10-09 ai/nn/transformer/gpt/2/fiction --- https://blog.regehr.org/archives/1653 Explaining Code using ASCII Art 2021-10-09 design/typography --- https://en.wikipedia.org/wiki/Schizoid_personality_disorder Schizoid personality disorder 2021-10-09 psychiatry/autism/schizoid psychology/personality --- https://www.reddit.com/r/GPT3/comments/vm6azh/symbolic_thinking_of_gpt3/ 2021-10-09 ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/1902.03928 The Degree of Fine-Tuning in our Universe—and Others Fred C. Adams 2019-02-11 2021-10-09 [("doi","10.1016/j.physrep.2019.02.001")] science

Both fundamental constants that describe the laws of physics and cosmological parameters that determine the cosmic properties must fall within a range of values in order for the universe to develop astrophysical structures and ultimately support life.

This paper reviews current constraints on these quantities. The standard model of particle physics contains both coupling constants and particle masses, and the allowed ranges of these parameters are discussed first.

We then consider cosmological parameters, including the total energy density, the vacuum energy density, the baryon-to-photon ratio, the dark matter contribution, and the amplitude of primordial density fluctuations. These quantities are constrained by the requirements that the universe lives for a long time, emerges from the BBN epoch with an acceptable chemical composition, and can successfully produce galaxies. On smaller scales, stars and planets must be able to form and function. The stars must have sufficiently long lifetimes and hot surface temperatures. The planets must be massive enough to maintain an atmosphere, small enough to remain non-degenerate, and contain enough particles to support a complex biosphere.

These requirements place constraints on the gravitational constant, the fine structure constant, and composite parameters that specify nuclear reaction rates. We consider specific instances of possible fine-tuning in stars, including the triple alpha reaction that produces carbon, as well as the effects of unstable deuterium and stable diprotons.

For all of these issues, viable universes exist over a range of parameter space, which is delineated herein.

Finally, for universes with substantially different parameters, new types of astrophysical processes can generate energy and support habitability.

--- https://arxiv.org/abs/2205.13147 Matryoshka Representations for Adaptive Deployment Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham Kakade, Prateek Jain, Ali Farhadi 2022-05-26 2022-05-26 [("doi","10.48550/arXiv.2205.13147")] ai/nn/sparsity/low-precision

Learned representations are a central component in modern ML systems, serving a multitude of downstream tasks. When training such representations, it is often the case that computational and statistical constraints for each downstream task are unknown. In this context rigid, fixed capacity representations can be either over or under-accommodating to the task at hand.

This leads us to ask: can we design a flexible representation that can adapt to multiple downstream tasks with varying computational resources? Our main contribution is Matryoshka Representation Learning (MRL) which encodes information at different granularities and allows a single embedding to adapt to the computational constraints of downstream tasks. MRL minimally modifies existing representation learning pipelines and imposes no additional cost during inference and deployment. MRL learns coarse-to-fine representations that are at least as accurate and rich as independently trained low-dimensional representations.

The flexibility within the learned Matryoshka Representations offer: (1) up to 14× smaller embedding size for ImageNet-1K classification at the same level of accuracy; (2) up to 14× real-world speed-ups for large-scale retrieval on ImageNet-1K and 4K; and (3) up to 2% accuracy improvements for long-tail few-shot classification, all while being as robust as the original representations. Finally, we show that MRL extends seamlessly to web-scale datasets (ImageNet, JFT) across various modalities—vision (ViT, ResNet), vision + language (ALIGN) and language (BERT).

MRL code and pretrained models are open-sourced at Github.

--- https://github.com/kuprel/min-dalle min(DALL·E) is a fast, minimal port of DALL·E-2 2021-10-09 ai/nn/transformer/gpt/dall-e/2 --- https://www.medrxiv.org/content/10.1101/2022.06.24.22276728.full The impact of rare protein coding genetic variation on adult cognitive function Chia-Yen Chen, Ruoyu Tian, Tian Ge, Max Lam, Gabriela Sanchez-Andrade, Tarjinder Singh, Lea Urpa, Jimmy Liu, Mark Sanderson, Christine Rowley, Holly Ironfield, Terry Fang, Biogen Biobank Team, the SUPER-Finl, study, the Northern Finl, Intellectual Disability study, Mark Daly, Aarno Palotie, Ellen A. Tsai, Hailiang Huang, Matthew E. Hurles, Sebastian S. Gerety, Todd Lencz, Heiko Runz 2022-06-26 2022-06-26 [("doi","10.1101/2022.06.24.22276728")] genetics/heritable/rare iq

[Twitter] Compelling evidence suggests that cognitive function is strongly influenced by genetics.

Here, we conduct a large-scale exome study to examine whether rare protein coding variants impact cognitive function in the adult population (n = 485,930).

We identify 8 genes associated with adult cognitive function through rare coding variants with large effects. We demonstrate how the dosage of a single gene, KDM5B, may determine the variability of cognitive, behavioral, and molecular traits in mice and humans. We further provide evidence that rare and common variants overlap in association signals and contribute additively to cognitive function.

Our findings uncover a contribution of rare coding variants to cognitive function and highlight that the spectrum of cognitive function in the normal adult population is influenced by the action of single genes.

Figure 5: Contribution of common and rare coding variants to EDU and VNR. (a, b) The impact of cognitive function PRS and carrier status of PTV or damaging missense variants (MPC > 2) in LOF-intolerant genes (pLI > 0.9) on EDU (a) and VNR (b). Unrelated UKBB EUR samples were included in this analysis with n = 318,844 for EDU and n = 128,812 for VNR. EDU and VNR were residualized by sex, age, age2, sex by age, sex by age2, top 20 principal components and recruitment centers and rank-based inverse-normal transformed. The effect (and 95% CI) of PRS and rare coding variant carrier status on residualized, transformed EDU/VNR was estimated using linear regression, with noncarriers of PTV and damaging missense variants with PRS in the middle quantile as the reference (Ref.) group. Data are presented as effect size estimates (β) with 95% CIs.
--- https://x.com/BoyNamedShit/status/1538732931228635137 2021-10-10 ai/nn/transformer/gpt/fiction --- https://x.com/xkcd/status/1513922269122281473 2021-10-10 ai/nn/transformer/gpt/fiction --- https://www.propublica.org/article/how-many-american-women-die-from-causes-related-to-pregnancy-or-childbirth How Many American Women Die From Causes Related to Pregnancy or Childbirth? No One Knows. 2021-10-10 statistics/bias --- https://www.washingtonpost.com/politics/2019/05/21/no-maternal-mortality-did-not-spike-texas-after-funding-cuts-abortion-clinics/ 2021-10-10 statistics/bias --- https://www.quantamagazine.org/protein-blobs-linked-to-alzheimers-affect-aging-in-all-cells-20220628/ 2021-10-10 longevity/johan-bjorksten --- https://openai.com/research/dall-e-2-pre-training-mitigations 2021-10-10 ai/nn/transformer/gpt/dall-e reinforcement-learning/exploration/active-learning --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5204035/ Learning, memory and exploratory similarities in genetically identical cloned dogs Chi Won Shin, Geon A. Kim, Won Jun Park, Kwan Yong Park, Jeong Min Jeon, Hyun Ju Oh, Min Jung Kim, Byeong Chun Lee 2016 2021-10-10 [("doi","10.4142/jvs.2016.17.4.563")] genetics/cloning/dog

Somatic cell nuclear transfer allows generation of genetically identical animals using donor cells derived from animals with particular traits.

To date, few studies have investigated whether or not these cloned dogs will show identical behavior patterns. To address this question, learning, memory and exploratory patterns were examined using 6 cloned dogs with identical nuclear genomes.

The variance of total incorrect choice number in the Y-maze test among cloned dogs was lower than that of the control dogs. There was also a decrease in variance in the level of exploratory activity in the open fields test compared to age-matched control dogs.

These results indicate that cloned dogs show similar cognitive and exploratory patterns, suggesting that these behavioral phenotypes are related to the genotypes of the individuals.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5037310/ Reproductive ability of a cloned male detector dog and behavioral traits of its offspring Ji Hyun Lee, Geon A. Kim, Rak Seung Kim, Jong Su Lee, Hyun Ju Oh, Min Jung Kim, Do Kyo Hong, Byeong Chun Lee 2016 2021-10-10 [("doi","10.4142/jvs.2016.17.3.407")] genetics/cloning/dog

In 2007, 7 detector dogs were produced by somatic cell nuclear transfer using one nuclear donor dog, then trained and certified as excellent detector dogs, similar to their donor. In 2011, we crossed a cloned male and normal female by natural breeding and produced 10 offspring. In this study, we investigated the puppies’ temperaments, which we later compared with those of the cloned parent male.

The results show that the cloned male had normal reproductive abilities and produced healthy offspring. All puppies completed narcotic detector dog training with a success rate for selection of 60%. Although the litter of cloned males was small in this study, a cloned male dog bred by natural mating produced puppies that later successfully completed the training course for drug detection.

In conclusion, cloning an elite dog with superior genetic factors and breeding of the cloned dog was found to be a useful method to efficiently procure detector dogs.

--- https://www.lesswrong.com/posts/EzuBSASuui5qekhLA/assessing-alephalphas-multimodal-model Assessing AlephAlpha’s Multimodal Model 2021-10-10 ai/nn/transformer/gpt --- https://en.wikipedia.org/wiki/Medici_Bank Medici Bank 2021-10-10 history/medici --- https://en.wikipedia.org/wiki/House_of_Medici Medici family 2021-10-11 history/medici --- https://en.wikipedia.org/wiki/Double-entry_bookkeeping Double-entry bookkeeping system 2021-10-11 history/medici --- https://en.wikipedia.org/wiki/Francesco_Sassetti Francesco Sassetti 2021-10-11 history/medici --- https://en.wikipedia.org/wiki/Tommaso_Portinari Tommaso Portinari 2021-10-11 history/medici --- https://en.wikipedia.org/wiki/Lorenzo_de%27_Medici Lorenzo de Medici 2021-10-11 history/medici --- https://en.wikipedia.org/wiki/Bookkeeping Balance books 2021-10-11 history/medici --- https://en.wikipedia.org/wiki/Negotiable_instrument Bills of exchange 2021-10-11 history/medici --- https://en.wikipedia.org/wiki/Alum Alum 2021-10-11 history/medici --- https://www.lesswrong.com/posts/7iAABhWpcGeP5e6SB/it-s-probably-not-lithium It’s Probably Not Lithium 2021-10-11 psychiatry/lithium --- https://arxiv.org/abs/2206.13517#salesforce ProGen2: Exploring the Boundaries of Protein Language Models Erik Nijkamp, Jeffrey Ruffolo, Eli N. Weinstein, Nikhil Naik, Ali Madani 2022-06-27 2022-06-27 [("doi","10.48550/arXiv.2206.13517")] ai/nn/transformer ai/scaling biology

Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein design. However, we lack a sufficient understanding of how very large-scale models and data play a role in effective protein model development.

We introduce a suite of protein language models, named ProGen2, that are scaled up to 6.4b parameters and trained on different sequence datasets drawn from over a billion proteins from genomic, metagenomic, and immune repertoire databases. ProGen2 models show state-of-the-art performance in capturing the distribution of observed evolutionary sequences, generating novel viable sequences, and predicting protein fitness without additional finetuning.

As large model sizes and raw numbers of protein sequences continue to become more widely accessible, our results suggest that a growing emphasis needs to be placed on the data distribution provided to a protein sequence model. We release the ProGen2 models and code at Github.

--- /doc/longevity/2022-kim.pdf Exercise molecule burns away hunger Tahnbee Kim, Scott M. Sternson 2022-06-15 2022-06-15 [("doi","10.1038/d41586-022-01321-x")] exercise longevity --- https://arxiv.org/abs/2206.13654#facebook Wav2Vec-Aug: Improved self-supervised training with limited data Anuroop Sriram, Michael Auli, Alexei Baevski 2022-06-27 2022-06-27 [("doi","10.48550/arXiv.2206.13654")] ai/nn

Self-supervised learning (SSL) of speech representations has received much attention over the last few years but most work has focused on languages and domains with an abundance of unlabeled data.

However, for many languages there is a shortage even in the unlabeled data which limits the effectiveness of SSL. In this work, we focus on the problem of applying SSL to domains with limited available data by leveraging data augmentation for Wav2Vec 2.0 pretraining.

Further, we propose improvements to each component of the model which result in a combined relative word error rate (WER) improvement of up to 13% compared to Wav2Vec 2.0 on LibriSpeech test-clean / other.

--- https://en.wikipedia.org/wiki/Scale_invariance Scale invariance 2021-10-12 ai/scaling --- https://en.wikipedia.org/wiki/Power_law Power law 2021-10-12 ai/scaling --- https://en.wikipedia.org/wiki/Self-organized_criticality Self-organized criticality 2021-10-12 ai/scaling/emergence --- https://en.wikipedia.org/wiki/Critical_brain_hypothesis Critical brain hypothesis 2021-10-12 ai/scaling/emergence --- https://www.biorxiv.org/content/10.1101/2022.06.26.497645.full Is the Naked mole-rat a domestic animal? Guillermo Serrano Nájera, Koryu Kin 2022-06-27 2022-06-27 [("doi","10.1101/2022.06.26.497645")] genetics/selection/natural psychology/animal

The Naked mole-rat (NMR) is becoming a prominent model organism due to its peculiar traits, such as eusociality, extreme longevity, cancer resistance, and reduced pain sensitivity. It belongs to the African mole-rats (AMRs), a family of subterranean rodents that includes solitary, cooperative breeding, and eusocial species.

We identified and quantified the domestication syndrome (DS) across AMRs, a set of morphological and behavioral traits statistically-significantly more common and pronounced among domesticated animals than in their wild counterparts. Surprisingly, the NMR shows apparent DS traits compared to the solitary AMR. We argue that many of the NMR unconventional traits can be a side effect of self-domestication. Animals can self-domesticate when a reduction of the fear response is naturally selected, such as in islands with no predators, or to improve the group’s harmony in cooperative breeding species.

We propose that self-domestication was necessary to increase social tolerance during the evolution of cooperative breeding and eusociality among AMRs.

Finally, we discuss how the DS traits are neutral or beneficial for the subterranean niche and how the increased social tolerance of self-domesticated species could be a side effect of the physical properties of the soil. Our hypothesis provides a novel avenue to enhance the understanding of the extraordinary biology of the NMR.

--- https://x.com/_aixile/status/1542287395776876544 2021-10-12 ai/anime ai/nn/transformer/clip/sample --- https://www.reddit.com/r/GPT3/comments/vngm9i/hamlet_makes_a_reddit_post/ 2021-10-12 ai/nn/transformer/gpt/fiction --- https://www.reddit.com/r/GPT3/comments/vngm9i/hamlet_makes_a_reddit_post/ 2021-10-12 ai/nn/transformer/gpt/fiction --- https://x.com/xuenay/status/1542574569239855105 2021-10-12 ai/nn/transformer/gpt/fiction --- https://coalton-lang.github.io/20211010-introducing-coalton/ Introducing Coalton: How to Have Our (Typed) Cake and (Safely) Eat It Too, in Common Lisp 2021-10-12 cs/haskell cs/lisp --- https://www.reddit.com/r/GPT3/comments/vm6azh/symbolic_thinking_of_gpt3/ 2021-10-13 ai/nn/transformer/gpt/non-fiction --- https://www.lesswrong.com/posts/hDePh3KReBMNBJfzx/gpt-3-catching-fish-in-morse-code GPT-3 Catching Fish in Morse Code 2021-10-13 ai/nn/transformer/gpt/3/nonfiction reinforcement-learning/preference-learning/mode-collapse --- /doc/nootropic/2020-westbrook.pdf Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work A. Westbrook, R. van den Bosch, J. I. Määttä, L. Hofmans, D. Papadopetraki, R. Cools, M. J. Frank 2020-03-20 2021-10-13 [("doi","10.1126/science.aaz5891")] nootropic psychology/cognitive-bias psychology/energy psychology/neuroscience psychology/willpower

Stimulants such as methylphenidate are increasingly used for cognitive enhancement but precise mechanisms are unknown.

We found that methylphenidate boosts willingness to expend cognitive effort by altering the benefit-to-cost ratio of cognitive work. Willingness to expend effort was greater for participants with higher striatal dopamine synthesis capacity, whereas methylphenidate and sulpiride, a selective D2 receptor antagonist, increased cognitive motivation more for participants with lower synthesis capacity.

A sequential sampling model informed by momentary gaze revealed that decisions to expend effort are related to amplification of benefit-versus-cost information attended early in the decision process, whereas the effect of benefits is strengthened with higher synthesis capacity and by methylphenidate.

These findings demonstrate that methylphenidate boosts the perceived benefits versus costs of cognitive effort by modulating striatal dopamine signaling.

--- https://www.quantamagazine.org/does-hot-water-freeze-faster-than-cold-physicists-keep-asking-20220629/ 2021-10-13 science --- https://en.wikipedia.org/wiki/Mpemba_effect Mpemba effect 2021-10-13 science --- https://en.wikipedia.org/wiki/Orange_petunia Orange petunia 2021-10-13 genetics/editing --- https://en.wikipedia.org/wiki/Nanocar_Race Nanocar Race 2021-10-13 science --- https://arxiv.org/abs/2206.15378#deepmind DeepNash: Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning Julien Perolat, Bart de Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas Anthony, Stephen McAleer, Romuald Elie, Sarah H. Cen, Zhe Wang, Audrunas Gruslys, Aleksandra Malysheva, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Remi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls 2022-06-30 2022-06-30 [("doi","10.48550/arXiv.2206.15378")] reinforcement-learning/imperfect-information reinforcement-learning/multi-agent

We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level.

Stratego is one of the few iconic board games that Artificial Intelligence (AI) has not yet mastered. This popular game has an enormous game tree on the order of 10535 nodes, ie. 10175 times larger than that of Go. It has the additional complexity of requiring decision-making under imperfect information, similar to Texas hold’em poker, which has a much smaller game tree (on the order of 10164 nodes). Decisions in Stratego are made over a large number of discrete actions with no obvious link between action and outcome. Episodes are long, with often hundreds of moves before a player wins, and situations in Stratego can not easily be broken down into manageable-sized sub-problems as in poker. For these reasons, Stratego has been a grand challenge for the field of AI for decades, and existing AI methods barely reach an amateur level of play.

DeepNash uses a game-theoretic, model-free deep reinforcement learning method, without search, that learns to master Stratego via self-play. The Regularized Nash Dynamics (R-NaD) algorithm, a key component of DeepNash, converges to an approximate Nash equilibrium, instead of ‘cycling’ around it, by directly modifying the underlying multi-agent learning dynamics.

DeepNash beats existing state-of-the-art AI methods in Stratego and achieved a yearly (2022) and all-time top-3 rank on the Gravon games platform, competing with human expert players.

…Given the vast number of such possible private configurations in a public state, Stratego computationally challenges all existing search techniques as the search space becomes intractable. We therefore chose an orthogonal route in this work, without search, and propose a new method that combines model-free reinforcement learning in self-play with a game-theoretic algorithmic idea, Regularized Nash Dynamics (R-NaD). The model-free part implies that we don’t build an explicit opponent model tracking belief space (calculating a likelihood of the opponent’s state), and the game-theoretic part is based on the idea that by modifying the dynamical system underpinning our reinforcement-learning approach we can steer the learning behavior of the agent in the direction of the Nash equilibrium. The main advantage of this combined approach is that we do not need to explicitly model private states from public ones. A complex challenge, on the other hand, is to scale up this model-free reinforcement learning approach with R-NaD to make self-play competitive against human expert players in Stratego, which has not been achieved to date. This combined DeepNash approach is illustrated in Figure 1b.

Figure 1b: An overview of the DeepNash approach. DeepNash is an autonomous agent that learns to play the imperfect information game Stratego (A). It learns a policy represented by a deep neural network (B) through self-play from scratch (C) in order to converge to a Nash equilibrium (D).

…it is possible to define a learning update rule that induces a dynamical system for which there exists a so-called Lyapunov function. This function can be shown to decrease during learning and as such guarantees convergence to a fixed point. This is the central idea behind the R-NaD algorithm, and the successful recipe for DeepNash, which scales this approach using a deep neural network.

…The third category of deep RL algorithms, and where this work falls under, is policy gradient methods [Impala]. Regret Policy Gradient (RPG) (70) approximates CFR via a weighted policy gradient, but is not proven to converge to a Nash equilibrium. Neural Replicator Dynamics (NeuRD) (71) approximates Replicator Dynamics with a policy gradient and is proven to converge to a Nash equilibrium in the time average. Prior to this work, neither of these algorithms have been applied to large-scale domains, or have demonstrated human-level performance; this work uses NeuRD combined with the regularization idea laid out in (34) to converge in the last iterate.

…To train the final agent we used 768 TPU nodes used for Learners and 256 TPU nodes for Actors.

…Quotes from Stratego Experts: Thorsten Jungblut, owner of the Gravon platform:

Many players in the past thought that there will never be an AI for Stratego that could be a real competition for human players, or even play in the top ten. Obviously, they were wrong.

Vincent de Boer, former Stratego world champion, evaluated DeepNash as follows:

The level of play of DeepNash surprised me. I had never seen or heard of an artificial Stratego player that came close to the level needed to win a match against an experienced human player, but after playing against DeepNash myself I was not surprised by the top-3 ranking it later on achieved on the Gravon internet platform. I would expect this agent to also do very well if it participated in the World Championship.

--- https://www.cold-takes.com/the-track-record-of-futurists-seems-fine/ The Track Record of Futurists Seems ... Fine 2021-10-13 statistics/prediction --- https://x.com/_aixile/status/1542287395776876544 2021-10-13 ai/nn/transformer/clip/sample --- https://x.com/ArtifartX/status/1542744920569434112 2021-10-14 ai/nn/transformer/clip/sample --- https://epochai.org/blog/trends-in-gpu-price-performance Trends in GPU Price-Performance 2021-10-14 ai/scaling/hardware --- https://spectrum.ieee.org/carbon-removal-x-prize-finalists Carbon-Removal Tech Grabs Elon Musk’s Check 2021-10-14 technology/carbon-capture --- https://arxiv.org/abs/2111.08140 Bayesian inference of the climbing grade scale Alexei Drummond, Alex Popinga 2021-11-15 2021-11-15 [("doi","10.48550/arXiv.2111.08140")] statistics/order/comparison

Climbing grades are used to classify a climbing route based on its perceived difficulty, and have come to play a central role in the sport of rock climbing. Recently, the first statistically rigorous method for estimating climbing grades from whole-history ascent data was described, based on the dynamic Bradley-Terry model for games between players of time-varying ability.

In this paper, we implement inference under the whole-history rating model using Markov chain Monte Carlo and apply the method to a curated data set made up of climbers who climb regularly. We use these data to get an estimate of the model’s fundamental scale parameter m, which defines the proportional increase in difficulty associated with an increment of grade.

We show that the data conform to assumptions that the climbing grade scale is a logarithmic scale of difficulty, like decibels or stellar magnitude. We estimate that an increment in Ewbank, French and UIAA climbing grade systems corresponds to 2.1, 2.09 and 2.13× increase in difficulty respectively, assuming a logistic model of probability of success as a function of grade. Whereas we find that the Vermin scale for bouldering (V-grade scale) corresponds to a 3.17 increase in difficulty per grade increment.

In addition, we highlight potential connections between the logarithmic properties of climbing grade scales and the psychophysical laws of Weber and Fechner.

--- https://arxiv.org/abs/2206.14349 Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision Ryan Hoque, Lawrence Yunliang Chen, Satvik Sharma, Karthik Dharmarajan, Brijen Thananjeyan, Pieter Abbeel, Ken Goldberg 2022-06-29 2022-06-29 [("doi","10.48550/arXiv.2206.14349")] reinforcement-learning/meta-learning/continual-learning reinforcement-learning/multi-agent reinforcement-learning/robot

[blog] Commercial and industrial deployments of robot fleets often fall back on remote human teleoperators during execution when robots are at risk or unable to make task progress. With continual learning, interventions from the remote pool of humans can also be used to improve the robot fleet control policy over time.

A central question is how to effectively allocate limited human attention to individual robots. Prior work addresses this in the single-robot, single-human setting. We formalize the Interactive Fleet Learning (IFL) setting, in which multiple robots interactively query and learn from multiple human supervisors.

We present a fully implemented open-source IFL benchmark suite of GPU-accelerated Isaac Gym environments for the evaluation of IFL algorithms.

We propose Fleet-DAgger, a family of IFL algorithms, and compare a novel Fleet-DAgger algorithm to 4 baselines in simulation. We also perform 1,000 trials of a physical block-pushing experiment with 4 ABB YuMi robot arms.

Experiments suggest that the allocation of humans to robots substantially affects robot fleet performance, and that our algorithm achieves up to 8.8× higher return on human effort than baselines.

See our homepage for code, videos, and supplemental material.

--- https://arxiv.org/abs/1906.00190#deepmind NeuRD: Neural Replicator Dynamics Daniel Hennes, Dustin Morrill, Shayegan Omidshafiei, Remi Munos, Julien Perolat, Marc Lanctot, Audrunas Gruslys, Jean-Baptiste Lespiau, Paavo Parmas, Edgar Duenez-Guzman, Karl Tuyls 2019-06-01 2021-10-14 [("doi","10.48550/arXiv.1906.00190")] reinforcement-learning/imperfect-information/poker

Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning. Using these algorithms in multiagent environments poses problems such as non-stationarity and instability.

In this paper, we first demonstrate that standard softmax-based policy gradient can be prone to poor performance in the presence of even the most benign non-stationarity. By contrast, it is known that the replicator dynamics, a well-studied model from evolutionary game theory, eliminates dominated strategies and exhibits convergence of the time-averaged trajectories to interior Nash equilibria in zero-sum games. Thus, using the replicator dynamics as a foundation, we derive an elegant one-line change to policy gradient methods that simply bypasses the gradient step through the softmax, yielding a new algorithm titled Neural Replicator Dynamics (NeuRD). NeuRD reduces to the exponential weights/Hedge algorithm in the single-state all-actions case. Additionally, NeuRD has formal equivalence to softmax counterfactual regret minimization, which guarantees convergence in the sequential tabular case. Importantly, our algorithm provides a straightforward way of extending the replicator dynamics to the function approximation setting.

Empirical results show that NeuRD quickly adapts to non-stationarities, outperforming policy gradient in both tabular and function approximation settings, when evaluated on the standard imperfect information benchmarks of Kuhn Poker, Leduc Poker, and Goofspiel.

--- https://arxiv.org/abs/2002.08456#deepmind From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization Julien Perolat, Remi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro Ortega, Neil Burch, Thomas Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls 2020-02-19 2021-10-14 [("doi","10.48550/arXiv.2002.08456")] reinforcement-learning/imperfect-information

In this paper we investigate the Follow the Regularized Leader dynamics in sequential imperfect information games (IIG).

We generalize existing results of Poincaré recurrence from normal-form games to zero-sum two-player imperfect information games and other sequential game settings. We then investigate how adapting the reward (by adding a regularization term) of the game can give strong convergence guarantees in monotone games. We continue by showing how this reward adaptation technique can be leveraged to build algorithms that converge exactly to the Nash equilibrium.

Finally, we show how these insights can be directly used to build state-of-the-art model-free algorithms for zero-sum two-player Imperfect Information Games (IIG).

--- https://arxiv.org/abs/1810.09026#deepmind Actor-Critic Policy Optimization in Partially Observable Multiagent Environments Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi, Julien Perolat, Karl Tuyls, Remi Munos, Michael Bowling 2018-10-21 2021-10-14 [("doi","10.48550/arXiv.1810.09026")] reinforcement-learning/imperfect-information/poker

Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence. Among the most common approaches are algorithms based on gradient ascent of a score function representing discounted return. In this paper, we examine the role of these policy gradient and actor-critic algorithms in partially-observable multiagent environments.

We show several candidate policy update rules and relate them to a foundation of regret minimization and multiagent learning techniques for the one-shot and tabular cases, leading to previously unknown convergence guarantees.

We apply our method to model-free multiagent reinforcement learning in adversarial sequential decision problems (zero-sum imperfect information games), using RL-style function approximation.

We evaluate on commonly used benchmark Poker domains, showing performance against fixed policies and empirical convergence to approximate Nash equilibria in self-play with rates similar to or better than a baseline model-free algorithm for zero sum games, without any domain-specific state space reductions.

--- https://www.theatlantic.com/magazine/archive/2017/04/what-your-therapist-doesnt-know/517797/ What Your Therapist Doesn’t Know 2021-10-14 psychiatry --- https://en.wikipedia.org/wiki/Dodo_bird_verdict Dodo bird verdict 2021-10-14 psychiatry statistics/bias --- https://x.com/matthen2/status/1543226572592783362 2021-10-14 cs/cellular-automaton --- https://en.wikipedia.org/wiki/Spin_glass Spin glass 2021-10-15 cs/cellular-automaton --- https://en.wikipedia.org/wiki/Ising_model Ising model 2021-10-15 cs/cellular-automaton science --- https://x.com/AndrewM_Webb/status/1236274167437197320 2021-10-15 cs/cellular-automaton --- https://github.com/grey-area/rps-automata grey-area/rps-automata 2021-10-15 cs/cellular-automaton --- https://en.wikipedia.org/wiki/Belousov%E2%80%93Zhabotinsky_reaction Belousov-Zhabotinsky reaction 2021-10-15 cs/cellular-automaton --- /doc/economics/2021-polman.pdf Consumers Believe That Products Work Better for Others Evan Polman, Ignazio Ziano, Kaiyang Wu, Anneleen Van Kerckhove 2021-08-18 2021-10-15 [("doi","10.1093/jcr/ucab048")] economics psychology/cognitive-bias

Consumers tend to see themselves in a positive light, yet we present evidence that they are pessimistic about whether they will receive a product’s benefits. In 15 studies (n = 6,547; including 9 preregistered), we found that consumers believe that product efficacy is higher for others than it is for themselves. For example, consumers believe that consuming a sports drink (to satisfy thirst), medicine (to relieve pain), an online class (to learn something new), or an adult coloring book (to inspire creativity) will have a greater effect on others than on themselves.

We show that this bias holds across many kinds of products and judgment-targets, and inversely correlates with factors such as product familiarity, product usefulness, and relationship closeness with judgment-targets. Moreover, we find this bias stems from consumers’ beliefs they are more unique and less malleable than others, and that it alters the choices people make for others.

We conclude by discussing implications for research on gift-giving, advice-giving, usership, and interpersonal social, health, and financial choices.

--- https://arxiv.org/abs/2206.13397 IHDM: Generative Modeling With Inverse Heat Dissipation Severi Rissanen, Markus Heinonen, Arno Solin 2022-06-21 2022-06-21 [("doi","10.48550/arXiv.2206.13397")] ai/nn/diffusion

[Twitter] While diffusion models have shown great success in image generation, their noise-inverting generative process does not explicitly consider the structure of images, such as their inherent multi-scale nature.

Inspired by diffusion models and the desirability of coarse-to-fine modeling, we propose a new model Inverse Heat Dissipation Model (IHDM) that generates images through iteratively inverting the heat equation, a PDE that locally erases fine-scale information when run over the 2D plane of the image. In our novel methodology, the solution of the forward heat equation is interpreted as a variational approximation in a directed graphical model.

We demonstrate promising image quality and point out emergent qualitative properties not seen in diffusion models, such as disentanglement of overall color and shape in images and aspects of neural network interpretability.

Spectral analysis on natural images positions our model as a type of dual to diffusion models and reveals implicit inductive biases in them.

--- /me#personality 2021-10-15 psychology/personality --- https://medicalhypotheses.blogspot.com/2009/05/do-elite-us-colleges-choose-personality.html Do elite US colleges choose personality over IQ? 2021-10-15 iq psychology/personality/conscientiousness --- https://vole.wtf/scunthorpe-sans/ Scunthorpe Sans: a profanity-blocking font 2021-10-15 design/typography math/humor --- https://fleg.de/paranoia PARANOIA SANS 2021-10-15 design/typography --- https://en.wikipedia.org/wiki/Ringelmann_effect Ringelmann effect 2021-10-16 economics/automation sociology --- https://x.com/Jam2go/status/1542996487755407362 2021-10-16 ai/nn/transformer/gpt/dall-e --- https://www.lesswrong.com/posts/Sn5NiiD5WBi4dLzaB/agi-will-drastically-increase-economies-of-scale AGI will drastically increase economies of scale 2021-10-16 ai/scaling/economics --- https://arxiv.org/abs/2206.09360 Modeling Transformative AI Risks (MTAIR) Project—Summary Report Sam Clarke, Ben Cottier, Aryeh Englander, Daniel Eth, David Manheim, Samuel Dylan Martin, Issa Rice 2022-06-19 2022-06-19 [("doi","10.48550/arXiv.2206.09360")] ai/scaling reinforcement-learning/safe statistics/prediction

[LW] This report outlines work by the Modeling Transformative AI Risk (MTAIR) project, an attempt to map out the key hypotheses, uncertainties, and disagreements in debates about catastrophic risks from advanced AI, and the relationships between them.

This builds on an earlier diagram by Ben Cottier and Rohin Shah which laid out some of the crucial disagreements (“cruxes”) visually, with some explanation. Based on an extensive literature review and engagement with experts, the report explains a model of the issues involved, and the initial software-based implementation that can incorporate probability estimates or other quantitative factors to enable exploration, planning, and/or decision support.

By gathering information from various debates and discussions into a single more coherent presentation, we hope to enable better discussions and debates about the issues involved.

The model starts with a discussion of reasoning via analogies and general prior beliefs about artificial intelligence. Following this, it lays out a model of different paths and enabling technologies for high-level machine intelligence, and a model of how advances in the capabilities of these systems might proceed, including debates about self-improvement, discontinuous improvements, and the possibility of distributed, non-agentic high-level intelligence or slower improvements. The model also looks specifically at the question of learned optimization, and whether machine learning systems will create mesa-optimizers. The impact of different safety research on the previous sets of questions is then examined, to understand whether and how research could be useful in enabling safer systems. Finally, we discuss a model of different failure modes and loss of control or takeover scenarios.

--- https://www.biorxiv.org/content/10.1101/653279.full New kinship and FST estimates reveal higher levels of differentiation in the global human population Alejandro Ochoa, John D. Storey 2019-05-30 2021-10-16 [("doi","10.1101/653279")] genetics/sequencing

Kinship coefficients and FST, which measure genetic relatedness and the overall population structure, respectively, have important biomedical applications. However, existing estimators are only accurate under restrictive conditions that most natural population structures do not satisfy. We recently derived new kinship and FST estimators for arbitrary population structures [1, 2].

Our estimates on human datasets reveal a complex population structure driven by founder effects due to dispersal from Africa and admixture. Notably, our new approach estimates larger FST values of 26% for native worldwide human populations and 23% for admixed Hispanic individuals, whereas the existing approach estimates 9.8% and 2.6%, respectively.

While previous work correctly measured FST between subpopulation pairs, our generalized FST measures genetic distances among all individuals and their most recent common ancestor (MRCA) population, revealing that genetic differentiation is greater than previously appreciated.

This analysis demonstrates that estimating kinship and FST under more realistic assumptions is important for modern population genetic analysis.

--- https://huggingface.co/spaces/huggingface-projects/wordalle Wordalle—a Hugging Face Space 2021-10-16 ai/nn/transformer/gpt/dall-e --- https://colab.research.google.com/github/neuml/txtai/blob/master/examples/35_Pictures_are_worth_a_thousand_words.ipynb 2021-10-16 ai/nn/transformer/gpt/dall-e --- https://x.com/davisblalock/status/1542929841338494976 2021-10-16 ai/scaling --- https://arxiv.org/abs/2206.10012 Limitations of the NTK for Understanding Generalization in Deep Learning Nikhil Vyas, Yamini Bansal, Preetum Nakkiran 2022-06-20 2022-06-20 [("doi","10.48550/arXiv.2206.10012")] ai/scaling

[Twitter] The “Neural Tangent Kernel” (NTK) (Jacot et al 2018), and its empirical variants have been proposed as a proxy to capture certain behaviors of real neural networks.

In this work, we study NTKs through the lens of scaling laws, and demonstrate that they fall short of explaining important aspects of neural network generalization. In particular, we demonstrate realistic settings where finite-width neural networks have substantially better data scaling exponents as compared to their corresponding empirical and infinite NTKs at initialization. This reveals a more fundamental difference between the real networks and NTKs, beyond just a few percentage points of test accuracy.

Further, we show that even if the empirical NTK is allowed to be pre-trained on a constant number of samples, the kernel scaling does not catch up to the neural network scaling.

Finally, we show that the empirical NTK continues to evolve throughout most of the training, in contrast with prior work which suggests that it stabilizes after a few epochs of training.

Altogether, our work establishes concrete limitations of the NTK approach in understanding generalization of real networks on natural datasets.

--- https://www.statnews.com/2020/02/21/human-reproductive-cloning-curious-incident-of-the-dog-in-the-night-time/ Cloning humans is technically possible. It’s curious no one has tried 2021-10-16 genetics/cloning --- https://www.reddit.com/r/mlscaling/comments/vq6qh1/demis_hassabis_gato_is_our_most_general_agent_so/ienfekn/ 2021-10-16 reinforcement-learning/model/decision-transformer --- https://en.wikipedia.org/wiki/2021_Wikimedia_Foundation_actions_on_the_Chinese_Wikipedia 2021 Wikimedia Foundation actions on the Chinese Wikipedia 2021-10-17 wikipedia --- https://www.fastcompany.com/90692176/chinese-wikipedia 2021-10-17 wikipedia --- http://mitchgordon.me/ml/2022/07/01/retro-is-blazing.html RETRO Is Blazingly Fast 2021-10-17 ai/nn/retrieval --- https://arxiv.org/abs/1609.04836#intel On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang 2016-09-15 2021-10-17 [("doi","10.48550/arXiv.1609.04836")] ai/nn/cnn ai/nn/fully-connected ai/scaling/hardware

The stochastic gradient descent (SGD) method and its variants are algorithms of choice for many Deep Learning tasks. These methods operate in a small-batch regime wherein a fraction of the training data, say 32–512 data points, is sampled to compute an approximation to the gradient. It has been observed in practice that when using a larger batch there is a degradation in the quality of the model, as measured by its ability to generalize.

We investigate the cause for this generalization drop in the large-batch regime and present numerical evidence that supports the view that large-batch methods tend to converge to sharp minimizers of the training and testing functions—and as is well known, sharp minima lead to poorer generalization. In contrast, small-batch methods consistently converge to flat minimizers, and our experiments support a commonly held view that this is due to the inherent noise in the gradient estimation.

We discuss several strategies to attempt to help large-batch methods eliminate this generalization gap.

--- https://arxiv.org/abs/2206.04817#apple The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenon Vimal Thilak, Etai Littwin, Shuangfei Zhai, Omid Saremi, Roni Paiss, Joshua Susskind 2022-06-10 2022-06-10 [("doi","10.48550/arXiv.2206.04817")] ai/nn ai/scaling/emergence/grokking

[cf. catapult] The grokking phenomenon as reported by Power et al 2021 refers to a regime where a long period of overfitting is followed by a seemingly sudden transition to perfect generalization.

In this paper, we attempt to reveal the underpinnings of Grokking via a series of empirical studies. Specifically, we uncover an optimization anomaly plaguing adaptive optimizers [such as Adam] at extremely late stages of training, referred to as the Slingshot Mechanism. A prominent artifact of the Slingshot Mechanism can be measured by the cyclic phase transitions between stable and unstable training regimes, and can be easily monitored by the cyclic behavior of the norm of the last layers weights.

We empirically observe that without explicit regularization, Grokking as reported in Power et al 2021 almost exclusively happens at the onset of Slingshots, and is absent without it. While common and easily reproduced in more general settings, the Slingshot Mechanism does not follow from any known optimization theories that we are aware of, and can be easily overlooked without an in-depth examination.

Our work points to a surprising and useful inductive bias of adaptive gradient optimizers at late stages of training, calling for a revised theoretical analysis of their origin.

--- https://arxiv.org/abs/1806.07572 Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot, Franck Gabriel, Clément Hongler 2018-06-20 2021-10-17 [("doi","10.48550/arXiv.1806.07572")] ai/nn

At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit, thus connecting them to kernel methods.

We prove that the evolution of an ANN during training can also be described by a kernel: during gradient descent on the parameters of an ANN, the network function fθ (which maps input vectors to output vectors) follows the kernel gradient of the functional cost (which is convex, in contrast to the parameter cost) w.r.t. a new kernel: the Neural Tangent Kernel (NTK).

This kernel is central to describe the generalization features of ANNs. While the NTK is random at initialization and varies during training, in the infinite-width limit it converges to an explicit limiting kernel and it stays constant during training. This makes it possible to study the training of ANNs in function space instead of parameter space. Convergence of the training can then be related to the positive-definiteness of the limiting NTK.

We prove the positive-definiteness of the limiting NTK when the data is supported on the sphere and the non-linearity is non-polynomial.

We then focus on the setting of least-squares regression and show that in the infinite-width limit, the network function fθ follows a linear differential equation during training. The convergence is fastest along the largest kernel principal components of the input data with respect to the NTK, hence suggesting a theoretical motivation for early stopping.

Finally we study the NTK numerically, observe its behavior for wide networks, and compare it to the infinite-width limit.

--- https://arxiv.org/abs/2006.10246 The Recurrent Neural Tangent Kernel Sina Alemohammad, Zichao Wang, Randall Balestriero, Richard Baraniuk 2020-06-18 2021-10-17 [("doi","10.48550/arXiv.2006.10246")] ai/nn/rnn

The study of deep neural networks (DNNs) in the infinite-width limit, via the so-called neural tangent kernel (NTK) approach, has provided new insights into the dynamics of learning, generalization, and the impact of initialization. One key DNN architecture remains to be kernelized, namely, the recurrent neural network (RNN).

In this paper we introduce and study the Recurrent Neural Tangent Kernel (RNTK), which provides new insights into the behavior of overparametrized RNNs. A key property of the RNTK should greatly benefit practitioners is its ability to compare inputs of different length. To this end, we characterize how the RNTK weights different time steps to form its output under different initialization parameters and nonlinearity choices.

A synthetic and 56 real-world data experiments demonstrate that the RNTK offers large performance gains over other kernels, including standard NTKs, across a wide array of data sets.

--- https://www.youtube.com/watch?v=YESuww3zU2I&list=PLaa32nLgVvrkyveSU8rtD4l02eYQtRlWA&index=2 Wicked with the mandolin 2021-10-17 ai/nn/transformer/gpt/jukebox --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3080690/ Is arson the crime most strongly associated with psychosis?--A national case-control study of arson risk in schizophrenia and other psychoses Sophia Anwar, Niklas Långström, Martin Grann, Seena Fazel 2011 2021-10-17 [("doi","10.1093/schbul/sbp098")] crime psychiatry/schizophrenia

Background: The association of psychosis with certain serious crimes, such as homicide, has been clearly demonstrated, but it is uncertain to what extent psychotic disorders are associated with arson.

Method: We used a case-control design to investigate the association of being diagnosed with schizophrenia and other psychoses and committing arson. Data were obtained from Swedish national registers for criminal convictions, hospital discharge diagnoses (International Classification of Diseases, Ninth Revision [ICD-9], and International Classification of Diseases, Tenth Revision [ICD-10]), and sociodemographic factors for 1988–2000. We included all convicted arson offenders of both sexes in Sweden (n = 1689) and compared them with a random sample of general population control subjects (n = 40,560).

Results: After adjustment for sociodemographic confounders, arson offenders were more likely to be diagnosed with schizophrenia (in men, adjusted odds ratio [OR]=22.6, 95% confidence interval [CI]=14.8–34.4; in women, adjusted OR = 38.7, 95% CI = 20.4–73.5) or other psychoses (in men, adjusted OR = 17.4, 95% CI = 11.1–27.5; in women, adjusted OR = 30.8, 95% CI = 18.8–50.6).

Conclusion: Individuals with schizophrenia and other psychoses have increased risks of an arson conviction. These risk estimates are higher than those reported for other violent crimes and place arson in the same category as homicide as crimes that are most strongly associated with psychotic disorders.

--- https://x.com/nickcammarata/status/1511861061988892675 2021-10-17 ai/nn/transformer/gpt/dall-e --- https://x.com/_aixile/status/1542287395776876544 2021-10-18 ai/nn/transformer/clip/sample --- https://x.com/ArtifartX/status/1542744920569434112 2021-10-18 ai/nn/transformer/clip/sample --- https://x.com/RiversHaveWings/status/1543760484385492992 2021-10-18 ai/nn/transformer/clip/sample --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022844/ Persistent neuronal activity in human prefrontal cortex links perception and action Matar Haller, John Case, Nathan E. Crone, Edward F. Chang, David King-Stephens, Kenneth D. Laxer, Peter B. Weber, Josef Parvizi, Robert T. Knight, Avgusta Y. Shestyuk 2018 2021-10-18 [("doi","10.1038/s41562-017-0267-2")] psychology/neuroscience

[media] How do humans flexibly respond to changing environmental demands on a sub-second temporal scale? Extensive research has highlighted the key role of the prefrontal cortex in flexible decision-making and adaptive behavior, yet the core mechanisms that translate sensory information into behavior remain undefined.

Utilizing direct human cortical recordings, we investigated the temporal and spatial evolution of neuronal activity, indexed by the broadband gamma signal, while 16 participants performed a broad range of self-paced cognitive tasks.

Here we describe a robust domain-independent and modality-independent pattern of persistent stimulus-to-response neural activation that encodes stimulus features and predicts motor output on a trial-by-trial basis with near-perfect accuracy. Observed across a distributed network of brain areas, this persistent neural activation is centered in the prefrontal cortex and is required for successful response implementation, providing a functional substrate for domain-general transformation of perception into action, critical for flexible behavior.

--- https://www.lesswrong.com/posts/bPa6AzRgGZGmxbq6n/remaking-efficientzero-as-best-i-can Remaking EfficientZero (as best I can) 2021-10-18 reinforcement-learning/model/muzero --- https://x.com/bakztfuture/status/1543992740207136768 2021-10-18 ai/nn/transformer/gpt/dall-e --- https://www.chessengines.org/ 2021-10-18 reinforcement-learning/chess --- https://www.biorxiv.org/content/10.1101/2022.07.04.498768.full Complex Traits and Candidate Genes: Estimation of Genetic Variance Components Across Modes of Inheritance Mitchell J. Feldmann, Giovan N. Y. Covarrubias Pazaran, Hans-Peter Piepho 2022-07-05 2022-07-05 [("doi","10.1101/2022.07.04.498768")] genetics/heritable/rare

Large-effect loci—those discovered by genome-wide association studies or linkage mapping—associated with key traits segregate amidst a background of minor, often undetectable genetic effects in both wild and domesticated plants and animals. Accurately attributing mean differences and variance explained to the correct components in the linear mixed model (LMM) analysis is important for both selecting superior progeny and parents in plant and animal breeding, but also for gene therapy and medical genetics in humans. Marker-assisted prediction (MAP) and its successor, genomic prediction (GP), have many advantages for selecting superior individuals and understanding disease risk. However, these two approaches are less often integrated to simultaneously study the modes of inheritance of complex traits.

This simulation study demonstrates that the average semi-variance can be applied to models incorporating Mendelian, oligogenic, and polygenic terms, simultaneously, and yields accurate estimates of the variance explained for all relevant terms.

Our previous research focused on large-effect loci and polygenic variance exclusively, and in this work we want to synthesize and expand the average semi-variance framework to a multitude of different genetic architectures and the corresponding mixed models. This framework independently accounts for the effects of large-effect loci and the polygenic genetic background and is universally applicable to genetics studies in humans, plants, animals, and microbes.

--- https://www.biorxiv.org/content/10.1101/2022.07.02.498543.full Genetic adaptation to pathogens and increased risk of inflammatory disorders in post-Neolithic Europe Gaspard Kerner, Anna-Lena Neehus, Laurent Abel, Jean-Laurent Casanova, Etienne Patin, Guillaume Laval, Lluis Quintana-Murci 2022-07-03 2022-07-03 [("doi","10.1101/2022.07.02.498543")] genetics/selection/natural/human

Ancient genomics can directly detect human genetic adaptation to environmental cues. However, it remains unclear how pathogens have exerted selective pressures on human genome diversity across different epochs and affected present-day inflammatory disease risk.

Here, we use an ancestry-aware approximate Bayesian computation framework to estimate the nature, strength, and time of onset of selection acting on 2,879 ancient and modern European genomes from the last 10,000 years.

We found that the bulk of genetic adaptation occurred after the start of the Bronze Age, <4,500 years ago, and was enriched in genes relating to host-pathogen interactions. Furthermore, we detected directional selection acting on specific leukocytic lineages and experimentally demonstrated that the strongest negatively selected immunity gene variant, the lipopolysaccharide-binding protein gene (LBP) D283G, is hypomorphic.

Finally, our analyses suggest that the risk of inflammatory disorders has progressively increased in post-Neolithic Europeans, partly due to antagonistic pleiotropy following genetic adaptation to pathogens.

--- https://www.medrxiv.org/content/10.1101/2022.06.29.22277051.full MRSL: A phenome-wide causal discovery algorithm based on GWAS summary data Lei Hou, Zhi Geng, Xu Shi, Chuan Wang, Hongkai Li, Fuzhong Xue 2022-06-30 2022-06-30 [("doi","10.1101/2022.06.29.22277051")] genetics/heritable/correlation/mendelian-randomization

Causal discovery is a powerful tool to disclose underlying structures by analyzing purely observational data. Genetic variants can provide useful complementary information for structure learning.

Here, we propose a novel algorithm MRSL (Mendelian Randomization (MR)-based Structure Learning algorithm), which combines the graph theory with univariable and multivariable MR to learn the true structure using only GWAS summary statistics. Specifically, MRSL also utilizes topological sorting to improve the precision of structure learning and provides 3 adjusting categories for multivariable MR.

Results of simulation reveal that MRSL has up to two-fold higher F1 score than other eight competitive methods. Additionally, the computing time of MRSL is 100× faster than other methods.

Furthermore, we apply MRSL to 26 biomarkers and 44 ICD10-defined diseases from UK Biobank. The results cover most of expected causal links which have biological interpretations and several new links supported by clinical case reports or previous observational literatures.

--- https://arxiv.org/abs/2204.01692 ViS4mer: Long Movie Clip Classification with State-Space Video Models Md Mohaiminul Islam, Gedas Bertasius 2022-04-04 2022-04-04 [("doi","10.48550/arXiv.2204.01692")] ai/nn/transformer/attention/hierarchical ai/video/analysis

Most modern video recognition models are designed to operate on short video clips (eg. 5–10s in length). Because of this, it is challenging to apply such models to long movie understanding tasks, which typically require sophisticated long-range temporal reasoning capabilities. The recently introduced video transformers partially address this issue by using long-range temporal self-attention. However, due to the quadratic cost of self-attention, such models are often costly and impractical to use.

Instead, we propose ViS4mer, an efficient long-range video model that combines the strengths of self-attention and the recently introduced structured state-space sequence (S4) layer. Our model uses a standard Transformer encoder for short-range spatiotemporal feature extraction, and a multi-scale temporal S4 decoder for subsequent long-range temporal reasoning. By progressively reducing the spatiotemporal feature resolution and channel dimension at each decoder layer, ViS4mer learns complex long-range spatiotemporal dependencies in a video. Furthermore, ViS4mer is 2.63× faster and requires 8× less GPU memory than the corresponding pure self-attention-based model.

Additionally, ViS4mer achieves state-of-the-art results in 7⁄9 long-form movie video classification tasks on the LVU benchmark. Furthermore, we also show that our approach successfully generalizes to other domains, achieving competitive results on the Breakfast and the COIN procedural activity datasets.

The code will be made publicly available.

--- https://arxiv.org/abs/2102.00719 Video Transformer Network Daniel Neimark, Omri Bar, Maya Zohar, Dotan Asselmann 2021-02-01 2021-10-19 [("doi","10.48550/arXiv.2102.00719")] ai/nn/transformer ai/video/analysis

This paper presents VTN, a transformer-based framework for video recognition. Inspired by recent developments in vision transformers, we ditch the standard approach in video action recognition that relies on 3D ConvNets and introduce a method that classifies actions by attending to the entire video sequence information. Our approach is generic and builds on top of any given 2D spatial network.

In terms of wall runtime, it trains 16.1× faster and runs 5.1× faster during inference while maintaining competitive accuracy compared to other state-of-the-art methods. It enables whole video analysis, via a single end-to-end pass, while requiring 1.5× fewer GFLOPs.

We report competitive results on Kinetics-400 and present an ablation study of VTN properties and the trade-off between accuracy and inference speed.

We hope our approach will serve as a new baseline and start a fresh line of research in the video recognition domain.

Code and models are available at: https://github.com/bomri/SlowFast/blob/master/projects/vtn/README.md.

--- https://x.com/JohnWai_/media 2021-10-19 design --- https://arxiv.org/abs/2206.15469 Watch and Match: Supercharging Imitation with Regularized Optimal Transport Siddhant Haldar, Vaibhav Mathur, Denis Yarats, Lerrel Pinto 2022-06-30 2022-06-30 [("doi","10.48550/arXiv.2206.15469")] reinforcement-learning/imitation-learning reinforcement-learning/robot

Imitation learning holds tremendous promise in learning policies efficiently for complex decision making problems. Current state-of-the-art algorithms often use inverse reinforcement learning (IRL), where given a set of expert demonstrations, an agent alternatively infers a reward function and the associated optimal policy. However, such IRL approaches often require substantial online interactions for complex control problems.

In this work, we present Regularized Optimal Transport (ROT), a new imitation learning algorithm that builds on recent advances in optimal transport based trajectory-matching. Our key technical insight is that adaptively combining trajectory-matching rewards with behavior cloning can accelerate imitation even with only a few demonstrations.

Our experiments on 20 visual control tasks across the DeepMind Control Suite, the OpenAI Robotics Suite, and the Meta-World Benchmark demonstrate an average of 7.8× faster imitation to reach 90% of expert performance compared to prior state-of-the-art methods. On real-world robotic manipulation, with just one demonstration and an hour of online training, ROT achieves an average success rate of 90.1% across 14 tasks.

This illustrates that ROT substantially reduces the barrier to efficient imitation learning in both simulated and real-world settings, making high-fidelity imitation accessible with minimal expert demonstrations.

--- https://arxiv.org/abs/2204.07888 AI, Ageing and Brain-Work Productivity: Technological Change in Professional Japanese Chess Eiji Yamamura, Ryohei Hayashi 2022-04-17 2022-04-17 [("doi","10.48550/arXiv.2204.07888")] economics/automation japan psychology/chess reinforcement-learning/chess

Using Japanese professional chess (Shogi) players records in the novel setting, this paper examines how and the extent to which the emergence of technological changes influences the ageing and innate ability of players winning probability. We gathered games of professional Shogi players 1968–2019.

The major findings are: (1) diffusion of artificial intelligence (AI) reduces innate ability, which reduces the performance gap among same-age players; (2) players winning rates declined consistently from 20 years and as they get older; (3) AI accelerated the ageing declination of the probability of winning, which increased the performance gap among different aged players; (4) the effects of AI on the ageing declination and the probability of winning are observed for high innate skill players but not for low innate skill ones. This implies that the diffusion of AI hastens players retirement from active play, especially for those with high innate abilities. Thus, AI is a substitute for innate ability in brain-work productivity.

--- https://www.wired.com/story/autonomous-drones-could-soon-run-the-uks-energy-grid/ Autonomous Drones Could Soon Run the UK’s Energy Grid 2021-10-19 reinforcement-learning/robot --- https://www.cnet.com/science/biology/features/chasing-ghosts-unlocking-the-mysteries-of-human-hibernation/ Chasing Ghosts: Unlocking the Mysteries of Human Hibernation 2021-10-19 cryonics --- https://www.biorxiv.org/content/10.1101/2022.07.02.498577.full The cost of information acquisition by natural selection Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom 2022-07-03 2022-07-03 [("doi","10.1101/2022.07.02.498577")] cs/algorithm/information genetics/selection/artificial reinforcement-learning/exploration

Natural selection enriches genotypes that are well-adapted to their environment. Over successive generations, these changes to the frequencies of types accumulate information about the selective conditions. Thus, we can think of selection as an algorithm by which populations acquire information about their environment.

Kimura 1961 pointed out that every bit of information that the population gains this way comes with a minimum cost in terms of unrealized fitness (substitution load). Due to the gradual nature of selection and ongoing mismatch of types with the environment, a population that is still gaining information about the environment has lower mean fitness than a counter-factual population that already has this information. This has been an influential insight, but here we find that experimental evolution of Escherichia coli with mutations in a RNA polymerase gene (rpoB) violates Kimura’s basic theory.

To overcome the restrictive assumptions of Kimura’s substitution load and develop a more robust measure for the cost of selection, we turn to ideas from computational learning theory. We reframe the “learning problem” faced by an evolving population as a population versus environment (PvE) game, which can be applied to settings beyond Kimura’s theory—such as stochastic environments, frequency-dependent selection, and arbitrary environmental change. We show that the learning theoretic concept of “regret” measures relative lineage fitness and rigorously captures the efficiency of selection as a learning process. This lets us establish general bounds on the cost of information acquisition by natural selection. We empirically validate these bounds in our experimental system, showing that computational learning theory can account for the observations that violate Kimura’s theory. Finally, we note that natural selection is a highly effective learning process in that selection is an asymptotically optimal algorithm for the problem faced by evolving populations, and no other algorithm can consistently outperform selection in general.

Our results highlight the centrality of information to natural selection and the value of computational learning theory as a perspective on evolutionary biology.

--- https://x.com/torriangray/status/1544138715458482176 2021-10-19 ai/nn/transformer/gpt/fiction --- https://www.reddit.com/r/OpenAI/comments/vsf8xx/one_of_the_first_prompts_ive_got_back_from_openai/ 2021-10-19 ai/nn/transformer/gpt/fiction --- https://arxiv.org/abs/2207.02200 Offline RL Policies Should be Trained to be Adaptive Dibya Ghosh, Anurag Ajay, Pulkit Agrawal, Sergey Levine 2022-07-05 2022-07-05 [("doi","10.48550/arXiv.2207.02200")] reinforcement-learning/meta-learning reinforcement-learning/offline statistics/bayes

Offline RL algorithms must account for the fact that the dataset they are provided may leave many facets of the environment unknown. The most common way to approach this challenge is to employ pessimistic or conservative methods, which avoid behaviors that are too dissimilar from those in the training dataset.

However, relying exclusively on conservatism has drawbacks: performance is sensitive to the exact degree of conservatism, and conservative objectives can recover highly suboptimal policies. In this work, we propose that offline RL methods should instead be adaptive in the presence of uncertainty.

We show that acting optimally in offline RL in a Bayesian sense involves solving an implicit POMDP.

As a result, optimal policies for offline RL must be adaptive, depending not just on the current state but rather all the transitions seen so far during evaluation.

We present a model-free algorithm for approximating this optimal adaptive policy, and demonstrate the efficacy of learning such adaptive policies in offline RL benchmarks.

--- https://supermemo.guru/wiki/How_much_knowledge_can_human_brain_hold How much knowledge can human brain hold 2021-10-19 psychology/spaced-repetition --- https://arxiv.org/abs/2104.14647 Turing Completeness and Sid Meier’s Civilization Adrian de Wynter 2021-04-29 2021-10-20 [("doi","10.48550/arXiv.2104.14647")] cs/computable

We prove that 3 strategy video games from the Sid Meier’s Civilization series: Sid Meier’s Civilization: Beyond Earth, Sid Meier’s Civilization V, and Sid Meier’s Civilization VI, are Turing complete.

We achieve this by building 3 universal Turing machines—one for each game—using only the elements present in the games, and using their internal rules and mechanics as the transition function.

The existence of such machines implies that under the assumptions made, the games are undecidable.

We show constructions of these machines within a running game session, and we provide a sample execution of an algorithm—the three-state Busy Beaver—with one of our machines.

--- https://en.wikipedia.org/wiki/Cephalopod_intelligence Cephalopod intelligence 2021-10-20 iq psychology/animal psychology/neuroscience --- https://x.com/GuyP/status/1544710725708513280 2021-10-20 ai/nn/transformer/gpt/dall-e --- https://www.causal.app/blog/scaling Scaling our Spreadsheet Engine from Thousands to Billions of Cells 2021-10-20 cs/algorithm --- https://x.com/ArtIsLight_/status/1544633996319023105 2021-10-20 ai/nn/transformer/clip/sample --- https://www.reddit.com/r/OpenAI/comments/vsyyu6/explain_the_process_of_using_the_toilet_as_if_it/ 2021-10-20 ai/nn/transformer/gpt/fiction --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2998793/ Psychological flexibility as a fundamental aspect of health Todd B. Kashdan, Jonathan Rottenberg 2010 2021-10-20 [("doi","10.1016/j.cpr.2010.03.001")] psychiatry psychology/personality

Traditionally, positive emotions and thoughts, strengths, and the satisfaction of basic psychological needs for belonging, competence, and autonomy have been seen as the cornerstones of psychological health. Without disputing their importance, these foci fail to capture many of the fluctuating, conflicting forces that are readily apparent when people navigate the environment and social world.

In this paper, we review literature to offer evidence for the prominence of psychological flexibility in understanding psychological health. Thus far, the importance of psychological flexibility has been obscured by the isolation and disconnection of research conducted on this topic. Psychological flexibility spans a wide range of human abilities to: recognize and adapt to various situational demands; shift mindsets or behavioral repertoires when these strategies compromise personal or social functioning; maintain balance among important life domains; and be aware, open, and committed to behaviors that are congruent with deeply held values. In many forms of psychopathology, these flexibility processes are absent.

In hopes of creating a more coherent understanding, we synthesize work in emotion regulation, mindfulness and acceptance, social and personality psychology, and neuropsychology. Basic research findings provide insight into the nature, correlates, and consequences of psychological flexibility and applied research provides details on promising interventions.

Throughout, we emphasize dynamic approaches that might capture this fluid construct in the real-world.

--- https://ilovetypography.com/2022/07/07/steven-hellers-font-of-the-month-ambicase/ 2021-10-20 design/typography --- https://ux.stackexchange.com/questions/120541/why-do-people-not-notice-our-enormous-prominent-clear-and-contrasting-purple-b website design—Why do people not notice our enormous, prominent, clear and contrasting purple banner? 2021-10-20 design/typography economics/advertising --- https://www.nytimes.com/2022/07/07/arts/ciphers-henry-viii-catherine.html Decoding the Defiance of Henry VIII’s First Wife 2021-10-20 cs/cryptography design/typography history --- https://arxiv.org/abs/2206.12322 How to train accurate BNNs for embedded systems? Floran de Putter, Henk Corporaal 2022-06-24 2022-06-24 [("doi","10.48550/arXiv.2206.12322")] ai/nn/sparsity/low-precision

A key enabler of deploying convolutional neural networks on resource-constrained embedded systems is the binary neural network (BNN). BNNs save on memory and simplify computation by binarizing both features and weights. Unfortunately, binarization is inevitably accompanied by a severe decrease in accuracy.

To reduce the accuracy gap between binary and full-precision networks, many repair methods have been proposed in the recent past, which we have classified and put into a single overview in this chapter. The repair methods are divided into two main branches, training techniques and network topology changes, which can further be split into smaller categories. The latter category introduces additional cost (energy consumption or additional area) for an embedded system, while the former does not. From our overview, we observe that progress has been made in reducing the accuracy gap, but BNN papers are not aligned on what repair methods should be used to get highly accurate BNNs.

Therefore, this chapter contains an empirical review that evaluates the benefits of many repair methods in isolation over the ResNet-20&CIFAR-10 and ResNet-18&CIFAR-100 benchmarks.

We found 3 repair categories most beneficial: feature binarizer, feature normalization, and double residual. Based on this review we discuss future directions and research opportunities.

We sketch the benefit and costs associated with BNNs on embedded systems because it remains to be seen whether BNNs will be able to close the accuracy gap while staying highly energy-efficient on resource-constrained embedded systems.

--- https://www.nytimes.com/2022/07/06/magazine/circadian-medicine.html The Quest by Circadian Medicine to Make the Most of Our Body Clocks 2021-10-21 melatonin psychology/neuroscience --- https://www.youtube.com/watch?v=g0w4D05Fdho Enjoy 360° vision with the FlyVIZ, ACM Siggraph Emerging Technologies 2016 2021-10-21 psychology/vision --- https://www.youtube.com/watch?v=XEm8McxpGeg I Survived 50 Hours in 3rd Person 2021-10-21 psychology/vision --- https://arxiv.org/abs/2207.03481 Training Transformers Together Alexander Borzunov, Max Ryabinin, Tim Dettmers, Quentin Lhoest, Lucile Saulnier, Michael Diskin, Yacine Jernite, Thomas Wolf 2022-07-07 2022-07-07 [("doi","10.48550/arXiv.2207.03481")] ai/nn/transformer/gpt/dall-e/2 ai/scaling/hardware

The infrastructure necessary for training state-of-the-art models is becoming overly expensive, which makes training such models affordable only to large corporations and institutions. Recent work proposes several methods for training such models collaboratively, ie. by pooling together hardware from many independent parties and training a shared model over the Internet.

In this demonstration, we collaboratively trained a text-to-image transformer similar to OpenAI DALL·E. We invited the viewers to join the ongoing training run, showing them instructions on how to contribute using the available hardware. We explained how to address the engineering challenges associated with such a training run (slow communication, limited memory, uneven performance between devices, and security concerns) and discussed how the viewers can set up collaborative training runs themselves.

Finally, we show that the resulting model generates images of reasonable quality on a number of prompts.

--- https://www.biorxiv.org/content/10.1101/2022.07.06.499052.full Timesweeper: Accurately Identifying Selective Sweeps Using Population Genomic Time Series Logan S. Whitehouse, Daniel R. Schrider 2022-07-07 2022-07-07 [("doi","10.1101/2022.07.06.499052")] ai/nn/cnn genetics/selection/natural

Despite decades of research, identifying selective sweeps, the genomic footprints of positive selection, remains a core problem in population genetics. Of the myriad methods that have been made towards this goal, few are designed to leverage the potential of genomic time-series data. This is because in most population genetic studies only a single, brief period of time can be sampled for a study. Recent advancements in sequencing technology, including improvements in extracting and sequencing ancient DNA, have made repeated samplings of a population possible, allowing for more direct analysis of recent evolutionary dynamics.

With these advances in mind, here we present Timesweeper, a fast and accurate convolutional neural network-based tool for identifying selective sweeps in data consisting of multiple genomic samplings of a population over time. Timesweeperuses this serialized sampling of a population by first simulating data under a demographic model appropriate for the data of interest, training a 1D Convolutional Neural Network on said model, and inferring which polymorphisms in this serialized dataset were the direct target of a completed or ongoing selective sweep. We show that Timesweeper is accurate under multiple simulated demographic and sampling scenarios, and identifies selected variants with impressive resolution.

In sum, we show that more accurate inferences about natural selection are possible when genomic time-series data are available; such data will continue to proliferate in coming years due to both the sequencing of ancient samples and repeated samplings of extant populations with faster generation times. Methodological advances such as Timesweeper thus have the potential to help resolve the controversy over the role of positive selection in the genome.

We provide Timesweeper both as a Python package and Snakemake workflow for use by the community.

--- https://arxiv.org/abs/2108.05877 DexMV: Imitation Learning for Dexterous Manipulation from Human Videos Yuzhe Qin, Yueh-Hua Wu, Shaowei Liu, Hanwen Jiang, Ruihan Yang, Yang Fu, Xiaolong Wang 2021-08-12 2021-10-21 [("doi","10.48550/arXiv.2108.05877")] reinforcement-learning/imitation-learning reinforcement-learning/robot

While progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipulation. In this paper, we propose a new platform and pipeline DexMV (Dexterous Manipulation from Videos) for imitation learning.

We design a platform with: (1) a simulation system for complex dexterous manipulation tasks with a multi-finger robot hand and (2) a computer vision system to record large-scale demonstrations of a human hand conducting the same tasks. In our novel pipeline, we extract 3D hand and object poses from videos, and propose a novel demonstration translation method to convert human motion to robot demonstrations.

We then apply and benchmark multiple imitation learning algorithms with the demonstrations. We show that the demonstrations can indeed improve robot learning by a large margin and solve the complex tasks which reinforcement learning alone cannot solve.

More details can be found in the project page: https://yzqin.github.io/dexmv/.

--- https://www.medrxiv.org/content/10.1101/2022.07.06.22277335.full Polygenic architecture of rare coding variation across 400,000 exomes Daniel J. Weiner, Ajay Nadig, Karthik A. Jagadeesh, Kushal K. Dey, Benjamin M. Neale, Elise B. Robinson, Konrad J. Karczewski, Luke J. O’Connor 2022-07-07 2022-07-07 [("doi","10.1101/2022.07.06.22277335")] genetics/heritable/rare psychiatry/bipolar/genetics psychiatry/schizophrenia

Both common and rare genetic variants influence complex traits and common diseases. Genome-wide association studies have discovered thousands of common-variant associations, and more recently, large-scale exome sequencing studies have identified rare-variant associations in hundreds of genes. However, rare-variant genetic architecture is not well characterized, and the relationship between common-variant and rare-variant architecture is unclear.

Here, we quantify the heritability explained by gene-wise burden of rare coding variants and compare the genetic architecture of common and rare variation across 22 common traits and diseases in 400,000 UK Biobank exomes.

Rare coding variants (AF = 1e-6—1e-3) explain 1.3% (SE = 0.03%) of phenotypic variance on average—much less than common variants—and most burden heritability is explained by ultra-rare loss-of-function variants (AF = 1e-6—1e-5).

Common and rare variants implicate the same cell types, with similar enrichments, and they have pleiotropic effects on the same pairs of traits, with similar genetic correlations. They partially colocalize at individual genes and loci, but not to the same extent: burden heritability is strongly concentrated in a limited number of genes (median: 6 genes explaining 19% of h2), while common-variant heritability is much more polygenic. Burden heritability is also more strongly concentrated in constrained genes (median enrichment: 4.5× vs. 2.1× for common variants), indicating that negative selection affects common-variant and rare-variant architecture differently. Finally, we find that burden heritability for schizophrenia and bipolar disorder is especially high (3.8% and 3.6%).

Our results show that there are a tractable number of large-effect genes to discover by studying rare variants, that common and rare associations are mechanistically convergent, and that rare coding variants will contribute only modestly to missing heritability and population risk stratification.

--- https://arxiv.org/abs/2102.09337#nvidia Reinforcement Learning for Datacenter Congestion Control Chen Tessler, Yuval Shpigelman, Gal Dalal, Amit Mandelbaum, Doron Haritan Kazakov, Benjamin Fuhrer, Gal Chechik, Shie Mannor 2021-02-18 2021-10-21 [("doi","10.48550/arXiv.2102.09337")] reinforcement-learning/model-free reinforcement-learning/multi-agent

[blog] We approach the task of network congestion control in datacenters using Reinforcement Learning (RL). Successful congestion control algorithms can dramatically improve latency and overall network throughput. Until today, no such learning-based algorithms have shown practical potential in this domain. Evidently, the most popular recent deployments rely on rule-based heuristics that are tested on a predetermined set of benchmarks. Consequently, these heuristics do not generalize well to newly-seen scenarios.

Contrarily, we devise an RL-based algorithm with the aim of generalizing to different configurations of real-world data center networks. We overcome challenges such as partial-observability, non-stationarity, and multi-objectiveness. We further propose a policy gradient algorithm that leverages the analytical structure of the reward function to approximate its derivative and improve stability.

We show that this scheme outperforms alternative popular RL approaches, and generalizes to scenarios that were not seen during training. Our experiments, conducted on a realistic simulator that emulates communication networks’ behavior, exhibit improved performance concurrently on the multiple considered metrics compared to the popular algorithms deployed today in real datacenters.

Our algorithm is being productized to replace heuristics in some of the largest datacenters in the world.

--- https://spectrum.ieee.org/robotic-surgery Today’s Robotic Surgery Turns Surgical Trainees Into Spectators 2021-10-21 economics/automation reinforcement-learning/robot --- https://arxiv.org/abs/2204.04908 No Token Left Behind: Explainability-Aided Image Classification and Generation Roni Paiss, Hila Chefer, Lior Wolf 2022-04-11 2022-04-11 [("doi","10.48550/arXiv.2204.04908")] ai/nn/transformer/clip

The application of zero-shot learning in computer vision has been revolutionized by the use of image-text matching models. The most notable example, CLIP, has been widely used for both zero-shot classification and guiding generative models with a text prompt. However, the zero-shot use of CLIP is unstable with respect to the phrasing of the input text, making it necessary to carefully engineer the prompts used.

We find that this instability stems from a selective similarity score, which is based only on a subset of the semantically meaningful input tokens.

To mitigate it, we present a novel explainability-based approach, which adds a loss term to ensure that CLIP focuses on all relevant semantic parts of the input, in addition to employing the CLIP similarity loss used in previous works.

When applied to one-shot classification through prompt engineering, our method yields an improvement in the recognition rate, without additional training or fine-tuning. Additionally, we show that CLIP guidance of generative models using our method substantially improves the generated images. Finally, we demonstrate a novel use of CLIP guidance for text-based image generation with spatial conditioning on object location, by requiring the image explainability heatmap for each object to be confined to a pre-determined bounding box.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1010097 Large-scale fungal strain sequencing unravels the molecular diversity in mating loci maintained by long-term balancing selection David Peris, Dabao Sun Lu, Vilde Bruhn Kinneberg, Ine-Susanne Methlie, Malin Stapnes Dahl, Timothy Y. James, Håvard Kauserud, Inger Skrede 2022-02-14 2022-02-14 [("doi","10.1371/journal.pgen.1010097")] genetics/selection/natural genetics/sequencing

Balancing selection, an evolutionary force that retains genetic diversity, has been detected in multiple genes and organisms, such as the sexual mating loci in fungi. However, to quantify the strength of balancing selection and define the mating-related genes require a large number of strains. In tetrapolar basidiomycete fungi, sexual type is determined by two unlinked loci, MATA and MATB. Genes in both loci define mating type identity, control successful mating and completion of the life cycle. These loci are usually highly diverse. Previous studies have speculated, based on culture crosses, that species of the non-model genus Trichaptum (Hymenochaetales, Basidiomycota) possess a tetrapolar mating system, with multiple alleles. Here, we sequenced a hundred and eighty strains of 3 Trichaptum species. We characterized the chromosomal location of MATA and MATB, the molecular structure of MAT regions and their allelic richness. The sequencing effort was sufficient to molecularly characterize multiple MAT alleles segregating before the speciation event of Trichaptum species. Analyses suggested that long-term balancing selection has generated trans-species polymorphisms. Mating sequences were classified in different allelic classes based on an amino acid identity (AAI) threshold supported by phylogenetics. 17,550 mating types were predicted based on the allelic classes. In vitro crosses allowed us to support the degree of allelic divergence needed for successful mating. Even with the high amount of divergence, key amino acids in functional domains are conserved. We conclude that the genetic diversity of mating loci in Trichaptum is due to long-term balancing selection, with limited recombination and duplication activity. The large number of sequenced strains highlighted the importance of sequencing multiple individuals from different species to detect the mating-related genes, the mechanisms generating diversity and the evolutionary forces maintaining them.

Author Summary: Fungi have complex mating systems, and basidiomycete fungi can encode thousands of mating types. Individuals with the same mating type cannot mate. This sexual system has evolved to facilitate sexual mating with offspring from different parents, increasing the chances to recombine into advantageous allelic combination and prune deleterious alleles. We explored the genomes of hundred and eighty strains, combined with experimental mating studies of selected strains, from a non-model organism (Trichaptum). We characterized the genomic regions controlling sex. The mating ability of the strains confirmed the role of the mating alleles observed in the genomic data. The detailed analyses of many strains allowed us to observe gene duplication and rearrangements within the mating loci, increasing the diversity within these loci. We supported previous suggestions of balancing selection in this region, an evolutionary force that maintains genomic diversity. These results supports that fungal strains are prone to outcross, which might facilitate the adaptation to new conditions.

--- https://x.com/gd3kr/status/1545370626273120256 2021-10-22 ai/nn/transformer/gpt/codex --- https://en.wikipedia.org/wiki/Scintillating_scotoma Scintillating scotoma 2021-10-22 psychology/vision --- https://stuartritchie.substack.com/p/breastfeeding-iq 2021-10-22 iq --- https://en.wikipedia.org/wiki/Study_of_Mathematically_Precocious_Youth Study of Mathematically Precocious Youth 2021-10-22 iq/high/smpy --- https://en.wikipedia.org/wiki/Lewis_Terman Lewis Terman 2021-10-22 iq/high --- https://en.wikipedia.org/wiki/Catharine_Cox_Miles Catharine Cox 2021-10-22 iq/high --- https://en.wikipedia.org/wiki/Hunter_College_Elementary_School Hunter College Elementary School 2021-10-22 iq/high --- https://en.wikipedia.org/wiki/William_Shockley William Shockley 2021-10-22 iq/high --- https://en.wikipedia.org/wiki/Luis_Walter_Alvarez Luis Walter Alvarez 2021-10-22 iq/high --- https://files.eric.ed.gov/fulltext/ED013518.pdf One In A Thousand: A Comparative Study of Highly and Moderately Gifted Elementary School Children 2021-10-22 iq/high --- https://www.newscientist.com/article/mg24032041-900-exclusive-a-new-test-can-predict-ivf-embryos-risk-of-having-a-low-iq/ A new test can predict IVF embryos’ risk of having a low IQ: A new genetic test that enables people having IVF to screen out embryos likely to have a low IQ or high disease risk could soon become available in the US 2021-10-23 genetics/heritable iq --- /doc/iq/2013-rietveld-supplementary-revision2.pdf http://www.science.sciencemag.org/highwire/filestream/594571/field_highwire_adjunct_files/1/Rietveld.SM.revision.2.pdf 2013 2021-10-23 genetics/heritable iq --- /doc/iq/ses/1993-huitema.pdf Validity of the GRE without Restriction of Range Bradley E. Huitema, Cheri R. Stein 1993-02-01 2021-10-23 [("doi","10.2466/pr0.1993.72.1.123")] iq/ses statistics/order

Restriction of range is a frequently acknowledged issue in estimating the validity of predictors of academic performance in graduate school.

Data obtained from a doctoral program in a psychology department where graduate students were admitted without regard to Graduate Record Examination (GRE) scores yielded essentially identical standard deviations on this test for the 204 applicants and 138 enrolled students.

The GRE-Total validity coefficients obtained on subjects in the enrolled sample ranged from 0.55 through 0.70; these values are considerably higher than those typically reported.

The data are congruent with the argument that uncorrected GRE validity coefficients yield biased estimates of the unknown validity in unrestricted applicant pools.

--- /doc/iq/2016-brandt.pdf Answering Unresolved Questions About the Relationship Between Cognitive Ability and Prejudice Mark J. Brandt, Jarret T. Crawford 2016-01-01 2021-10-23 [("doi","10.1177/1948550616660592")] iq psychology/cognitive-bias sociology

Previous research finds that lower cognitive ability predicts greater prejudice.

We test two unresolved questions about this association using a heterogeneous set of target groups and data from a representative sample of the United States (n = 5,914). First, we test “who are the targets of prejudice?” We replicate prior negative associations between cognitive ability and prejudice for groups who are perceived as liberal, unconventional, and having lower levels of choice over group membership.

We find the opposite (ie. positive associations), however, for groups perceived as conservative, conventional, and having higher levels of choice over group membership.

Second, we test “who shows intergroup bias?” and find that:

people with both relatively higher and lower levels of cognitive ability show ~equal levels of intergroup bias but toward different sets of groups.

--- /doc/iq/2018-elliott.pdf A Polygenic Score for Higher Educational Attainment is Associated With Larger Brains Maxwell L. Elliott, Daniel W. Belsky, Kevin Anderson, David L. Corcoran, Tian Ge, Annchen Knodt, Joseph A. Prinz, Karen Sugden, Benjamin Williams, David Ireland, Richie Poulton, Avshalom Caspi, Avram Holmes, Terrie Moffitt, Ahmad R. Hariri 2018-01-01 2021-10-23 [("doi","10.1093/cercor/bhy219")] genetics/heritable/correlation iq psychology/neuroscience

People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to the development of larger brains.

We tested this hypothesis using four large imaging genetics studies (combined n = 7,965) with polygenic scores derived from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We conducted meta-analysis to test associations among participants’ genetics, total brain volume (ie. brain size), and cognitive test performance.

Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains.

We found some evidence that brain size partly mediated associations between participants’ education polygenic scores and their cognitive test performance. Effect sizes were larger in the population-based samples than in the convenience-based samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research.

Findings suggest promise for studies integrating GWAS discoveries with brain imaging to understand neurobiology linking genetics with cognitive performance.

--- /doc/iq/2019-genc.pdf The Neural Architecture of General Knowledge Erhan Genç, Christoph Fraenz, Caroline Schlüter, Patrick Friedrich, Manuel C. Voelkle, Rüdiger Hossiep, Onur Güntürkün, René Mõttus 2019-01-01 2021-10-23 [("doi","10.1002/per.2217")] iq psychology/neuroscience

Cognitive performance varies widely between individuals and is highly influenced by structural and functional properties of the brain. In the past, neuroscientific research was principally concerned with fluid intelligence, while neglecting its equally important counterpart crystallized intelligence. Crystallized intelligence is defined as the depth and breadth of knowledge and skills that are valued by one’s culture. The accumulation of crystallized intelligence is guided by information storage capacities and is likely to be reflected in an individual’s level of general knowledge. In spite of the role general knowledge plays for everyday life, its neural foundation largely remains unknown.

In a large sample of 324 healthy individuals, we used standard magnetic resonance imaging along with functional magnetic resonance imaging and diffusion tensor imaging to examine different estimates of brain volume and brain network connectivity and assessed their predictive power with regard to both general knowledge and fluid intelligence.

Our results demonstrate that an individual’s level of general knowledge is associated with structural brain network connectivity beyond any confounding effects exerted by age or sex. Moreover, we found fluid intelligence to be best predicted by cortex volume in male subjects and functional network connectivity in female subjects.

Combined, these findings potentially indicate different neural architectures for information storage and information processing.

--- /doc/iq/2019-schmitt.pdf The Dynamic Associations Between Cortical Thickness and General Intelligence are Genetically Mediated J Eric Schmitt, Armin Raznahan, Liv S. Clasen, Greg L. Wallace, Joshua N. Pritikin, Nancy Raitano Lee, Jay N. Giedd, Michael C. Neale 2019-01-01 2021-10-23 [("doi","10.1093/cercor/bhz007")] genetics/heritable iq

The neural substrates of intelligence represent a fundamental but largely uncharted topic in human developmental neuroscience. Prior neuroimaging studies have identified modest but highly dynamic associations between intelligence and cortical thickness (CT) in childhood and adolescence. In a separate thread of research, quantitative genetic studies have repeatedly demonstrated that most measures of intelligence are highly heritable, as are many brain regions associated with intelligence.

In the current study, we integrate these 2 streams of prior work by examining the genetic contributions to CT-intelligence relationships using a genetically informative longitudinal sample of 813 typically developing youth, imaged with high-resolution MRI and assessed with Wechsler Intelligence Scales (IQ).

In addition to replicating the phenotypic association between multimodal association cortex and language centers with IQ, we find that CT-IQ covariance is nearly entirely genetically mediated. Moreover, shared genetic factors drive the rapidly evolving landscape of CT-IQ relationships in the developing brain.

--- /doc/iq/2020-freeman.pdf Social and general intelligence improves collective action in a common pool resource system Jacob Freeman, Jacopo A. Baggio, Thomas R. Coyle 2020-03-24 2021-10-23 [("doi","10.1073/pnas.1915824117")] iq sociology

On a planet experiencing global environmental change, the governance of natural resources depends on sustained collective action by diverse populations.

Engaging in such collective action can only build upon the foundation of human cognition in social-ecological settings. To help understand this foundation, we assess the effect of cognitive abilities on the management of a common pool resource.

We present evidence that two functionally distinct cognitive abilities, general and social intelligence, improve the ability of groups to manage a common pool resource. Groups high in both forms of intelligence engage in more effective collective action that is also more consistent, despite social or ecological change.

This result provides a foundation for integrating the effects of cognitive abilities with other dimensions of cognitive diversity to explain when groups will and will not sustainably govern natural resources.

--- https://en.wikipedia.org/wiki/Hereditary_Genius Hereditary Genius 2021-10-23 iq/high --- https://en.wikipedia.org/wiki/Creativity_and_mental_health Creativity and mental illness 2021-10-23 iq/high --- https://en.wikipedia.org/wiki/Derek_J._de_Solla_Price#Scientific_contributions Derek J. de Solla Price § Scientific contributions 2021-10-24 iq/high --- https://en.wikipedia.org/wiki/Lotka%27s_law Lotka’s law 2021-10-24 iq/high statistics/probability --- https://en.wikipedia.org/wiki/Intellectual_giftedness Intellectual giftedness 2021-10-24 iq/high --- https://en.wikipedia.org/wiki/Child_prodigy Child prodigy 2021-10-24 iq/high --- https://en.wikipedia.org/wiki/List_of_child_prodigies List of child prodigies 2021-10-24 iq/high --- https://en.wikipedia.org/wiki/Gifted_education Gifted education 2021-10-24 iq/high --- https://en.wikipedia.org/wiki/Nobel_Prize Nobel Prize 2021-10-24 iq/high --- https://topic.com/the-62-year-old-child-genius The 62-Year-Old Child Genius: In 1969, a very smart 13 year-old began his undergraduate college education—a move that would come to influence how we think about gifted children for the next four decades. 2021-10-24 iq/high/smpy --- https://www.nature.com/articles/mp2015108 A genome-wide analysis of putative functional and exonic variation associated with extremely high intelligence 2021-10-24 genetics/heritable/rare iq/high --- https://res.mdpi.com/jintelligence/jintelligence-07-00009/article_deploy/jintelligence-07-00009.pdf?filename=&attachment=1 What Happened to the Participants of the Math Olympiad 1971? A Multiple-Case Study Concerning the Occupational Success of the Winning Team from Hungary, Math Olympiad--Occupational Success 2021-10-24 iq/high --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.95.4395&rep=rep1&type=pdf Creativity and Ability Pattern 2021-10-24 iq/high --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6107566/ A distributed brain network predicts general intelligence from resting-state human neuroimaging data Dubois 2018 2021-10-25 iq psychology/neuroscience --- https://www.psychologytoday.com/nz/blog/finding-the-next-einstein/201202/could-brain-imaging-replace-the-sat Could Brain Imaging Replace the SAT? Scanning the next Einstein’s brain 2021-10-25 iq/high psychology/neuroscience --- http://www.fsb.muohio.edu/moulcc/collegechoice.pdf Money isn’t everything: Linking college choice to winning prizes and professorships Moul, Nye 2011 2021-10-25 iq/high --- https://qz.com/498534/these-25-schools-are-responsible-for-the-greatest-advances-in-science These 25 schools are responsible for the greatest advances in science 2021-10-25 iq/high --- https://www.nytimes.com/2010/08/05/nyregion/05hunter.html Diversity Debate Convulses Elite High School 2021-10-25 iq/high --- https://files.eric.ed.gov/fulltext/EJ746292.pdf 2021-10-25 iq/high --- https://www.psychologytoday.com/us/blog/rabble-rouser/201707/why-brilliant-girls-tend-favor-non-stem-careers Why Brilliant Girls Tend to Favor Non-STEM Careers 2021-10-25 iq/high --- https://www.bostonglobe.com/ideas/2014/03/15/the-poor-neglected-gifted-child/rJpv8G4oeawWBBvXVtZyFM/story.html The poor neglected gifted child 2021-10-25 iq/high/smpy --- /doc/iq/2013-bouchard.pdf The Wilson Effect: The Increase in Heritability of IQ With Age 2013 2021-10-25 genetics/heritable iq --- /doc/iq/high/2011-subotnik.pdf Rethinking Giftedness and Gifted Education: A Proposed Direction Forward Based on Psychological Science 2011 2021-10-25 iq/high --- https://www.nature.com/articles/nature.2016.20757 Where Nobel winners get their start: Undergraduates from small, elite institutions have the best chance of winning a Nobel prize 2021-10-25 iq/high --- https://www.biorxiv.org/content/10.1101/078014.full Educational attainment and personality are genetically intertwined 2021-10-26 genetics/heritable/correlation psychology/personality --- https://simonwillison.net/2022/Jul/9/gpt-3-explain-code/ Using GPT-3 to explain how code works 2021-10-26 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2112.07708 Spider: Learning to Retrieve Passages without Supervision Ori Ram, Gal Shachaf, Omer Levy, Jonathan Berant, Amir Globerson 2021-12-14 2021-12-14 [("doi","10.48550/arXiv.2112.07708")] ai/nn/retrieval

Dense retrievers for open-domain question answering (ODQA) have been shown to achieve impressive performance by training on large datasets of question-passage pairs.

In this work we ask whether this dependence on labeled data can be reduced via unsupervised pretraining that is geared towards ODQA. We show this is in fact possible, via a novel pretraining scheme designed for retrieval. Our “recurring span retrieval” approach uses recurring spans across passages in a document to create pseudo examples for contrastive learning. Our pretraining scheme directly controls for term overlap across pseudo queries and relevant passages, thus allowing to model both lexical and semantic relations between them.

The resulting model, named Spider, performs surprisingly well without any labeled training examples on a wide range of ODQA datasets. Specifically, it substantially outperforms all other pretrained baselines in a zero-shot setting, and is competitive with BM25, a strong sparse baseline. Moreover, a hybrid retriever over Spider and BM25 improves over both, and is often competitive with DPR models, which are trained on tens of thousands of examples.

Last, notable gains are observed when using Spider as an initialization for supervised training.

--- https://en.wikipedia.org/wiki/G_factor_(psychometrics)#Spearman's_law_of_diminishing_returns G factor (psychometrics) § Spearman’s law of diminishing returns (SLODR) 2021-10-26 iq/high --- https://en.wikipedia.org/wiki/William_James_Sidis William James Sidis 2021-10-26 iq/high --- https://en.wikipedia.org/wiki/Norbert_Wiener Norbert Wiener 2021-10-26 iq/high --- https://en.wikipedia.org/wiki/Hunter_College_High_School Hunter College High School 2021-10-26 iq/high --- https://github.com/zphang/minimal-opt zphang/minimal-opt 2021-10-26 ai/nn/transformer/gpt --- https://arxiv.org/abs/2203.07852 Block-Recurrent Transformers DeLesley Hutchins, Imanol Schlag, Yuhuai Wu, Ethan Dyer, Behnam Neyshabur 2022-03-11 2022-03-11 [("doi","10.48550/arXiv.2203.07852")] ai/nn/rnn ai/nn/transformer/attention/recurrent

[Github] We introduce the Block-Recurrent Transformer, which applies a transformer layer in a recurrent fashion along a sequence, and has linear complexity with respect to sequence length.

Our recurrent cell operates on blocks of tokens rather than single tokens, and leverages parallel computation within a block in order to make efficient use of accelerator hardware. The cell itself is strikingly simple. It is merely a transformer layer: it uses self-attention and cross-attention to efficiently compute a recurrent function over a large set of state vectors and tokens. Our design was inspired in part by LSTM cells, and it uses LSTM-style gates, but it scales the typical LSTM cell up by several orders of magnitude.

Our implementation of recurrence has the same cost in both computation time and parameter count as a conventional transformer layer, but offers dramatically improved perplexity in language modeling tasks over very long sequences.

Our model out-performs a long-range Transformer-XL baseline by a wide margin, while running twice as fast. We demonstrate its effectiveness on PG-19 (books), arXiv papers, and GitHub source code.

…Appendix G: Qualitative Analysis Results: The following are excerpts from our qualitative study. We selected 5 books at random from PG19 test set, and ran two different models on each book…For each token, we compute the difference between the cross-entropy loss (ie. the negative log likelihood (NLL)) output by both models, and then sort the results. Figure 4 shows an example of the per-token difference in NLL between the two models on the first book; the x-axis is the index of the token. On average, the recurrent model does slightly better than Transformer-XL, but it does not necessarily make a better prediction for any individual token.

The following excerpts show the top 4 tokens where the Block-Recurrent Transformer made a better prediction than Transformer-XL; these tokens correspond to spikes in Figure 4. We show the token number, the NLL returned by the recurrent model, the NLL returned by Transformer-XL, and an excerpt of text, with the token itself marked with |token|. Almost all of the top tokens are proper names of characters and places. In all cases except one, the mis-predicted name does not appear within the attention window of the previous 512 tokens. These names are thus invisible to Transformer-XL, but visible to the recurrent model.

Note that these are not cherry picked examples; the 5 books are chosen at random. Moreover, the same pattern still holds if the search is expanded to the top 40 tokens for each book. In fact, even the names are often the same; Transformer-XL often seems to mispredict the same names over and over again; these are likely the names of main characters.

…Our second qualitative study is structured similarly to the first, except that instead of comparing two different models, we compare two different runs of the same model…The first run processes the book normally, while the second run clears the recurrent states at the beginning of each 4,096-token segment. In the second run, the model can use recurrence within a segment to look beyond the local attention window of 512 tokens, but it cannot use recurrence to carry information from one segment to the next…The overall pattern is very similar to the first qualitative experiment: most of the tokens involve proper names. We verified that in most cases, the mis-predicted name not only does not occur within the 512-token attention window, but does not occur within the 4,096-token segment. In addition to proper names, chapter titles and illustration captions occur frequently within the top 40 results; the recurrent model seems to be remembering these from a previous occurrence in the table of contents…Perhaps most interestingly, in two of the books, one of the highest ranked mispredictions was the title and author of the book itself. The Gutenberg project inserts boilerplate at both the beginning and end of each book; the title and author are listed multiple times at the beginning, and once at the end. This experiment thus shows that the model is able to “remember” this information in the recurrent state, across a distance of 60,000 tokens or more.

Figure 6: Cumulative cross-entropy on PG19 of a 13-layer Transformer-XL and Block Recurrent model. Though comparable at the first few thousand tokens, the recurrent model performs better at longer sequences. In red we show the number of documents at a given token length.

…In Figure 6 we plot the cumulative cross-entropy, which is the average bits-per-token (ie. log2 perplexity) averaged up to the given length, and we compare the Block Recurrent Transformer against the Transformer-XL baseline. Performance of the two architectures is comparable for the first few thousand tokens, but the recurrent architecture clearly outperforms Transformer-XL at longer document lengths.

--- https://arxiv.org/abs/1606.05250 SQuAD: 100,000+ Questions for Machine Comprehension of Text Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang 2016-06-16 2021-10-26 [("doi","10.48550/arXiv.1606.05250")] ai/dataset ai/nn

We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage.

We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a substantial improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research.

The dataset is freely available at https://rajpurkar.github.io/SQuAD-explorer/.

--- /doc/japan/history/2001-12-samuels-kishiandcorruptionananatomyofthe1955system.html Kishi and Corruption: An Anatomy of the 1955 System Richard J. Samuels 2001-12 2021-10-26 history japan/history politics --- https://iaml-it.github.io/posts/2021-04-28-transformers-in-vision/ Transformers in Vision 2021-10-27 ai/nn/transformer --- https://x.com/RiversHaveWings/status/1472334873096376320 2021-10-27 ai/nn/transformer/clip/sample --- https://github.com/crowsonkb/simulacra-aesthetic-models crowsonkb/simulacra-aesthetic-models 2021-10-27 ai/nn/transformer/clip --- /doc/psychology/novelty/2022-negro.pdf What’s Next? Artists’ Music after Grammy Awards Giacomo Negro, Balázs Kovács, Glenn R. Carroll 2022-07-08 2022-07-08 [("doi","10.1177/00031224221103257")] music psychology/novelty

Do the cultural works artists produce after receiving major awards change in character?

As awards lessen the constraints artists typically face, we argue that award winners receive more opportunities, gain more autonomy, and are more likely to pursue unique creative paths.

Empirically, we analyze the consequences of winning a major Grammy award, a high-profile (often status-shifting) honor in the popular music industry. Using a neural learning approach, we examine the subsequent artistic differentiation of albums of award winners from albums of other artists. We analyze whether the music styles and sonic content of post-Grammy albums of winners change, and whether they become more or less similar to the combined corpus of albums of other artists.

In panel regression estimates, we find that after winning a Grammy, artists tend to release albums that stand out more stylistically from other artists. Surprisingly, artists who were nominated but did not win a Grammy became more similar to other artists than they were before the nomination. The findings suggest symbolic awards can regularly induce change and affect the heterogeneity of cultural products.

--- https://www.reddit.com/r/GPT3/comments/vv3dd8/i_asked_it_to_write_a_shakespearean_poem_about/ 2021-10-27 ai/nn/transformer/gpt/poetry --- https://www.biorxiv.org/content/10.1101/2022.07.08.499367.full Swapped genetic code blocks viral infections and gene transfer Akos Nyerges, Svenja Vinke, Regan Flynn, Sian V. Owen, Eleanor A. Rand, Bogdan Budnik, Eric Keen, Kamesh Narasimhan, Jorge A. Marchand, Maximilien Baas-Thomas, Min Liu, Kangming Chen, Anush Chiappino-Pepe, Fangxiang Hu, Michael Baym, George M. Church 2022-07-10 2022-07-10 [("doi","10.1101/2022.07.08.499367")] genetics/genome-synthesis/virus-proof

Removing cellular transfer RNAs (tRNAs), making their cognate codons unreadable, creates a genetic firewall that prevents viral replication and horizontal gene transfer. However, numerous viruses and mobile genetic elements encode parts of the translational apparatus, including tRNAs, potentially rendering a genetic-code-based firewall ineffective.

In this paper, we show that such horizontally transferred tRNA genes can enable viral replication in Escherichia coli cells despite the genome-wide lack of 3 codons and the previously essential cognate tRNAs and release factor 1. By repurposing viral tRNAs, we then develop recoded cells bearing an amino-acid-swapped genetic code that reassigns two of the 6 serine codons to leucine during translation. This amino-acid-swapped genetic code renders cells completely resistant to viral infections by mistranslating viral proteomes and prevents the escape of synthetic genetic information by engineered reliance on serine codons to produce leucine-requiring proteins.

Finally, we also repurpose the third free codon to biocontain this virus-resistant host via dependence on an amino acid not found in nature.

[previously: Robertson et al 2021 on virus-proofing]

--- https://en.wikipedia.org/wiki/Expanded_genetic_code Expanded genetic code 2021-10-27 genetics/genome-synthesis --- https://en.wikipedia.org/wiki/Non-proteinogenic_amino_acids Non-proteinogenic amino acids 2021-10-27 genetics/genome-synthesis --- https://en.wikipedia.org/wiki/Base_pair#Unnatural_base_pair_(UBP) Base pair § Unnatural base pair (UBP) 2021-10-27 genetics/genome-synthesis --- https://en.wikipedia.org/wiki/Hachimoji_DNA Hachimoji DNA 2021-10-27 genetics/genome-synthesis --- https://en.wikipedia.org/wiki/Peptide_synthesis Peptide synthesis 2021-10-28 genetics/genome-synthesis --- https://en.wikipedia.org/wiki/MRNA_display MRNA display 2021-10-28 genetics/genome-synthesis --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6678955/ Glucagon, GLP-1 and Thermogenesis Ismael González-García, Edward Milbank, Carlos Diéguez, Miguel López, Cristina Contreras 2019 2021-10-28 [("doi","10.3390/ijms20143445")] longevity/glp/semaglutide

Brown adipose tissue (BAT) thermogenesis is a conserved mechanism to maintain body temperature in mammals. However, since BAT contribution to energy expenditure can represent a relevant modulator of metabolic homeostasis, many studies have focused on the nervous system and endocrine factors that control the activity of this tissue.

There is long-established evidence that the counter-regulatory hormone glucagon negatively influences energy balance, enhances satiety, and increases energy expenditure. Despite compelling evidence showing that glucagon has direct action on BAT thermogenesis, recent findings are questioning this conventional attribute of glucagon action.

Glucagon like peptide-1 (GLP-1) is an incretin secreted by the intestinal tract which strongly decreases feeding, and, furthermore, improves metabolic parameters associated with obesity and diabetes. Therefore, GLP-1 receptors (GLP-1-R) have emerged as a promising target in the treatment of metabolic disorders.

In this short review, we will summarize the latest evidence in this regard, as well as the current therapeutic glucagon-based & GLP-1-based approaches to treating obesity.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6862306/ Glucagon Regulation of Energy Expenditure Maximilian Kleinert, Stephan Sachs, Kirk M. Habegger, Susanna M. Hofmann, Timo D. Müller 2019 2021-10-28 [("doi","10.3390/ijms20215407")] longevity/glp/semaglutide

Glucagon’s ability to increase energy expenditure has been known for more than 60 years, yet the mechanisms underlining glucagon’s thermogenic effect still remain largely elusive. Over the last years, large efforts were directed to unravel the physiological and cellular underpinnings of how glucagon regulates energy expenditure.

In this review, we summarize the current knowledge on how glucagon regulates systems metabolism with a special emphasis on its acute and chronic thermogenic effects.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4710848/ Glucagon increases energy expenditure independently of brown adipose tissue activation in humans V Salem, C. Izzi-Engbeaya, C. Coello, D. B. Thomas, E. S. Chambers, A. N. Comninos, A. Buckley, Z. Win, A. Al-Nahhas, E. A. Rabiner, R. N. Gunn, H. Budge, M. E. Symonds, S. R. Bloom, T. M. Tan, W. S. Dhillo 2016 2021-10-28 [("doi","10.1111/dom.12585")] longevity/glp/semaglutide

Aims: To investigate, for a given energy expenditure (EE) rise, the differential effects of glucagon infusion and cold exposure on brown adipose tissue (BAT) activation in humans.

Method: Indirect calorimetry and supraclavicular thermography was performed in 11 healthy male volunteers before and after: cold exposure; glucagon infusion (at 23℃); and vehicle infusion (at 23℃). All volunteers underwent (18)F-fluorodeoxyglucose ((18)F-FDG) positron emission tomography (PET)/CT scanning with cold exposure. Subjects with cold-induced BAT activation on (18)F-FDG PET/CT (n = 8) underwent a randomly allocated second (18)F-FDG PET/CT scan (at 23℃), either with glucagon infusion (n = 4) or vehicle infusion (n = 4).

Results: We observed that EE increased by 14% after cold exposure and by 15% after glucagon infusion (50 ng/kg/min; p < 0.05 vs control for both). Cold exposure produced an increase in neck temperature (+0.44℃; p < 0.001 vs control), but glucagon infusion did not alter neck temperature. In subjects with a cold-induced increase in the metabolic activity of supraclavicular BAT on (18)F-FDG PET/CT, a statistically-significant rise in the metabolic activity of BAT after glucagon infusion was not detected. Cold exposure increased sympathetic activation, as measured by circulating norepinephrine levels, but glucagon infusion did not.

Conclusion: Glucagon increases EE by a similar magnitude compared with cold activation, but independently of BAT thermogenesis. This finding is of importance for the development of safe treatments for obesity through upregulation of EE.

--- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1010247 Evaluating indirect genetic effects of siblings using singletons Laurence J. Howe, David M. Evans, Gibran Hemani, George Davey Smith, Neil M. Davies, Gregory S. Barsh, Heather J. Cordell, Gregory S. Barsh, Heather J. Cordell, Gregory S. Barsh, Heather J. Cordell, Gregory S. Barsh, Heather J. Cordell, Gregory S. Barsh, Heather J. Cordell 2022-05-10 2022-05-10 [("doi","10.1371/journal.pgen.1010247")] genetics/heritable

Estimating effects of parental and sibling genotypes (indirect genetic effects) can provide insight into how the family environment influences phenotypic variation. There is growing molecular genetic evidence for effects of parental phenotypes on their offspring (eg. parental educational attainment), but the extent to which siblings affect each other is currently unclear.

Here we used data from samples of unrelated individuals, without (singletons) and with biological full-siblings (non-singletons), to investigate and estimate sibling effects. Indirect genetic effects of siblings increase (or decrease) the covariance between genetic variation and a phenotype. It follows that differences in genetic association estimates between singletons and non-singletons could indicate indirect genetic effects of siblings if there is no heterogeneity in other sources of genetic association between singletons and non-singletons. We used UK Biobank data to estimate polygenic score (PGS) associations for height, BMI and educational attainment in self-reported singletons (n = 50,143) and non-singletons (n = 328,549).

The educational attainment PGS association estimate was 12% larger (95% C.I. 3%, 21%) in the non-singleton sample than in the singleton sample, but the height and BMI PGS associations were consistent. Birth order data suggested that the difference in educational attainment PGS associations was driven by individuals with older siblings rather than firstborns. The relationship between number of siblings and educational attainment PGS associations was non-linear; PGS associations were 24% smaller in individuals with 6 or more siblings compared to the rest of the sample (95% C.I. 11%, 38%). We estimate that a 1 SD increase in sibling educational attainment PGS corresponds to a 0.025 year increase in the index individual’s years in schooling (95% C.I. 0.013, 0.036).

Our results suggest that older siblings may influence the educational attainment of younger siblings, adding to the growing evidence that effects of the environment on phenotypic variation partially reflect social effects of germline genetic variation in relatives.

Author Summary: Genetic data from families can be used to evaluate social effects of parents on their offspring. For example, non-transmitted parental genetic variants have been shown to associate with offspring educational attainment indicative of parental effects. Siblings may also influence one another but available data has limited our understanding of sibling effects.

Associations between genetic variants and phenotypes in the same individual will capture effects of sibling genetic variants because of the genomic similarity between siblings. We propose that sibling effects can be evaluated by comparing genetic association estimates between singletons (no siblings), who cannot be plausibly influenced by sibling effects, and non-singletons (one or more siblings).

We apply this approach to data from a large population biobank with follow-up analyses investigating effects of birth order. We find evidence of sibling effects on educational attainment, but not on height or body mass index, with our results suggesting that older siblings influence the educational attainment of younger siblings.

--- https://x.com/ArtIsLight_/status/1546134422101032961 2021-10-28 ai/nn/transformer/clip/sample --- http://messybeast.com/genetics/hybrid-cats.htm Hybrid And Mutant Animals 2021-10-28 cat/genetics --- http://messybeast.com/intelligence2.htm Do Cats Have Intelligence/How Intelligent Are Cats? § 2 2021-10-28 cat/psychology iq/animal --- http://messybeast.com/intelligence.htm Do Cats Have Intelligence/How Intelligent Are Cats? 2021-10-28 cat/psychology iq/animal --- https://arxiv.org/abs/2206.07505 Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning Wei Fu, Chao Yu, Zelai Xu, Jiaqi Yang, Yi Wu 2022-06-15 2022-06-15 [("doi","10.48550/arXiv.2206.07505")] reinforcement-learning/multi-agent

[blog; Papoudakis et al 2020/Yu et al 2021] Many advances in cooperative multi-agent reinforcement learning (MARL) are based on two common design principles: value decomposition and parameter sharing. A typical MARL algorithm of this fashion decomposes a centralized Q-function into local Q-networks with parameters shared across agents. Such an algorithmic paradigm enables centralized training and decentralized execution (CTDE) and leads to efficient learning in practice.

Despite all the advantages, we revisit these two principles and show that in certain scenarios, eg. environments with a highly multi-modal reward landscape, value decomposition, and parameter sharing can be problematic and lead to undesired outcomes. In contrast, policy gradient (PG) methods with individual policies provably converge to an optimal solution in these cases, which partially supports some recent empirical observations that PG can be effective in many MARL testbeds.

Inspired by our theoretical analysis, we present practical suggestions on implementing multi-agent PG algorithms for either high rewards or diverse emergent behaviors and empirically validate our findings on a variety of domains, ranging from the simplified matrix and grid-world games to complex benchmarks such as StarCraft Multi-Agent Challenge and Google Research Football.

We hope our insights could benefit the community towards developing more general and more powerful MARL algorithms.

Check our project website at https://sites.google.com/view/revisiting-marl.

--- https://www.science.org/news/2018/05/genome-writing-project-aims-rally-scientists-around-virus-proofing-cells 2021-10-29 genetics/genome-synthesis/virus-proof --- https://arxiv.org/abs/2203.04466 The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks Xin Yu, Thiago Serra, Srikumar Ramalingam, Shandian Zhe 2022-03-09 2022-03-09 [("doi","10.48550/arXiv.2203.04466")] ai/nn/sparsity/pruning

Neural networks tend to achieve better accuracy with training if they are larger—even if the resulting models are overparameterized. Nevertheless, carefully removing such excess parameters before, during, or after training may also produce models with similar or even improved accuracy. In many cases, that can be curiously achieved by heuristics as simple as removing a percentage of the weights with the smallest absolute value—even though magnitude is not a perfect proxy for weight relevance.

With the premise that obtaining better performance from pruning depends on accounting for the combined effect of removing multiple weights, we revisit one of the classic approaches for impact-based pruning: the Optimal Brain Surgeon (OBS).

We propose a tractable heuristic for solving the combinatorial extension of OBS, in which we select weights for simultaneous removal, as well as a systematic update of the remaining weights.

Our selection method outperforms other methods under high sparsity, and the weight update is advantageous even when combined with the other methods.

--- https://diyhpl.us/wiki/transcripts/hgp-write/2017-05-09/ultrasafe-cell-line/ ultrasafe-cell-line 2021-10-29 genetics/genome-synthesis/virus-proof --- https://diyhpl.us/wiki/transcripts/hgp-write/2017-05-09/ultrasaf 2021-10-29 genetics/genome-synthesis/virus-proof --- https://www.nature.com/articles/news.2011.419 2021-10-29 genetics/genome-synthesis --- https://www.statnews.com/2018/05/01/genome-writers-recoding-human-cells/ Genome ‘writers’ set their first goal: recoding human cells to resist viruses 2021-10-29 genetics/genome-synthesis/virus-proof --- https://www.biorxiv.org/content/10.1101/2022.07.10.499467.full Realizing the promise of biodiversity genomics with highly accurate long reads Scott Hotaling, Edward R. Wilcox, Jacqueline Heckenhauer, Russell J. Stewart, Paul B. Frandsen 2022-07-10 2022-07-10 [("doi","10.1101/2022.07.10.499467")] genetics/sequencing

Generating the most contiguous, accurate genome assemblies given available sequencing technologies is a long-standing challenge in genome science. With the rise of long-read sequencing, assembly challenges have shifted from merely increasing contiguity to correctly assembling complex, repetitive regions of interest, ideally in a phased manner. At present, researchers largely choose between two types of long read data: longer, but less accurate sequences, often generated with Oxford Nanopore (ONT) technology, or highly accurate, but shorter, reads typically generated with Pacific Biosciences HiFi.

To understand how both technologies influence genome assembly and to clarify how scale of data (ie. mean length and sequencing depth) influence outcomes, we compared genome assemblies for a caddisfly, Hesperophylax magnus, generated with ONT and HiFi data. Despite shorter reads and less coverage, HiFi reads outperformed ONT reads in all assembly metrics tested and allowed for accurate assembly of the repetitive ~20-Kb H-fibroin gene.

Next, we quantified the influence of data type on genome assemblies across 6,750 plant and animal genomes. We show that HiFi reads consistently outperform all other data types for both plants and animals and may represent a particularly valuable tool for assembling complex plant genomes. To realize the promise of biodiversity genomics, we call for greater uptake of highly accurate long-reads in future studies.

--- https://www.biorxiv.org/content/10.1101/2022.07.09.499321.full A Draft Human Pangenome Reference Wen-Wei Liao, Mobin Asri, Jana Ebler, Daniel Doerr, Marina Haukness, Glenn Hickey, Shuangjia Lu, Julian K. Lucas, Jean Monlong, Haley J. Abel, Silvia Buonaiuto, Xian H. Chang, Haoyu Cheng, Justin Chu, Vincenza Colonna, Jordan M. Eizenga, Xiaowen Feng, Christian Fischer, Robert S. Fulton, Shilpa Garg, Cristian Groza, Andrea Guarracino, William T. Harvey, Simon Heumos, Kerstin Howe, Miten Jain, Tsung-Yu Lu, Charles Markello, Fergal J. Martin, Matthew W. Mitchell, Katherine M. Munson, Moses Njagi Mwaniki, Adam M. Novak, Hugh E. Olsen, Trevor Pesout, David Porubsky, Pjotr Prins, Jonas A. Sibbesen, Chad Tomlinson, Flavia Villani, Mitchell R. Vollger, Human Pangenome Reference Consortium, Guillaume Bourque, Mark J. P. Chaisson, Paul Flicek, Adam M. Phillippy, Justin M. Zook, Evan E. Eichler, David Haussler, Erich D. Jarvis, Karen H. Miga, Ting Wang, Erik Garrison, Tobias Marschall, Ira M. Hall, Heng Li, Benedict Paten 2022-07-09 2022-07-09 [("doi","10.1101/2022.07.09.499321")] genetics/sequencing

The Human Pangenome Reference Consortium (HPRC) presents a first draft human pangenome reference.

The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals. These assemblies cover more than 99% of the expected sequence and are more than 99% accurate at the structural and base-pair levels. Based on alignments of the assemblies, we generated a draft pangenome that captures known variants and haplotypes, reveals novel alleles at structurally complex loci, and adds 119 million base pairs of euchromatic polymorphic sequence and 1,529 gene duplications relative to the existing reference, GRCh38. Roughly 90 million of the additional base pairs derive from structural variation.

Using our draft pangenome to analyze short-read data reduces errors when discovering small variants by 34% and boosts the detected structural variants per haplotype by 104% compared to GRCh38-based workflows, and by 34% compared to using previous diversity sets of genome assemblies.

--- https://arxiv.org/abs/2006.07869 Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht 2020-06-14 2021-10-29 [("doi","10.48550/arXiv.2006.07869")] reinforcement-learning/model-free reinforcement-learning/multi-agent

Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria, making comparisons between approaches difficult.

In this work, we provide a systematic evaluation and comparison of 3 different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, value decomposition) in a diverse range of cooperative multi-agent learning tasks. Our experiments serve as a reference for the expected performance of algorithms across different learning tasks, and we provide insights regarding the effectiveness of different learning approaches.

We open-source EPyMARL, which extends the PyMARL codebase to include additional algorithms and allow for flexible configuration of algorithm implementation details such as parameter sharing. Finally, we open-source two environments for multi-agent research which focus on coordination under sparse rewards.

--- /doc/psychology/vision/dream/1915-bentley.pdf#page=2 The Study of Dreams: A Method Adapted to the Seminary Bentley 1915 2021-10-29 psychology/vision/dream --- https://en.wikipedia.org/wiki/K%C3%B3ryos Kóryos 2021-10-29 philosophy/religion sociology --- https://www.razibkhan.com/p/casting-out-the-wolf-in-our-midst Casting out the wolf in our midst 2021-10-30 philosophy/religion sociology --- https://arxiv.org/abs/2206.04114#google Director: Deep Hierarchical Planning from Pixels Danijar Hafner, Kuang-Huei Lee, Ian Fischer, Pieter Abbeel 2022-06-08 2022-06-08 [("doi","10.48550/arXiv.2206.04114")] ai/nn/sparsity/low-precision reinforcement-learning/exploration reinforcement-learning/model

[homepage; blog; cf. Socratic/SayCan] Intelligent agents need to select long sequences of actions to solve complex tasks. While humans easily break down tasks into subgoals and reach them through millions of muscle commands, current artificial intelligence is limited to tasks with horizons of a few hundred decisions, despite large compute budgets. Research on hierarchical reinforcement learning aims to overcome this limitation but has proven to be challenging, current methods rely on manually specified goal spaces or subtasks, and no general solution exists.

We introduce Director, a practical method for learning hierarchical behaviors directly from pixels by planning inside the latent space of a learned world model [using DreamerV2]. The high-level policy maximizes task and exploration rewards by selecting latent goals and the low-level policy learns to achieve the goals. Despite operating in latent space, the decisions are interpretable because the world model can decode goals into images for visualization.

Director outperforms exploration methods on tasks with sparse rewards, including 3D maze traversal with a quadruped robot from an egocentric camera and proprioception, without access to the global position or top-down view that was used by prior work. Director also learns successful behaviors across a wide range of environments, including visual control, Atari games, and DMLab levels.

…The computation time of Director is 20% longer than that of DreamerV2. Each training run used a single V100 GPU with XLA and mixed precision enabled and completed in less than 24 hours…The results of this experiment are summarized in Appendix A due to space constraints, with the full training curves for Atari and the Control Suite included in Appendices J & K. We observe that Director indeed learns successfully across many environments, showing broader applicability than most prior hierarchical reinforcement learning methods. In addition, providing task reward to the worker is not as important as expected—the hierarchy solves a wide range of tasks purely by following goals at the low level. Additionally providing task reward to the worker completely closes the gap to the state-of-the-art DreamerV2 agent. Figure K.1 in the appendix further shows that Director achieves a higher human-normalized median score than Dreamer on the 55 Atari games.

--- https://arxiv.org/abs/2207.03578#facebook Code Translation with Compiler Representations Marc Szafraniec, Baptiste Roziere, Hugh Leather Francois Charton, Patrick Labatut, Gabriel Synnaeve 2022-06-30 2022-06-30 [("doi","10.48550/arXiv.2207.03578")] ai/nn/transformer/gpt/codex

In this paper, we leverage low-level compiler intermediate representations (IR) to improve code translation [of TransCoder]. Traditional transpilers rely on syntactic information and handcrafted rules, which limits their applicability and produces unnatural-looking code. Applying neural machine translation (NMT) approaches to code has successfully broadened the set of programs on which one can get a natural-looking translation. However, they treat the code as sequences of text tokens, and still do not differentiate well enough between similar pieces of code which have different semantics in different languages. The consequence is low quality translation, reducing the practicality of NMT, and stressing the need for an approach increasing its accuracy.

Here we propose to augment code translation with IRs, specifically LLVM IR, with results on the C++, Java, Rust, and Go languages.

Our method improves upon the state-of-the-art for unsupervised code translation, increasing the number of correct translations by 11% on average, and up to 79% for the Java—Rust pair. We extend previous test sets for code translation, by adding hundreds of Go and Rust functions.

Additionally, we train models with high performance on the problem of IR decompilation, generating programming source code from IR, and study using IRs as intermediate pivot for translation.

--- https://arxiv.org/abs/2006.03511#facebook TransCoder: Unsupervised Translation of Programming Languages Marie-Anne Lachaux, Baptiste Roziere, Lowik Chanussot, Guillaume Lample 2020-06-05 2021-10-30 [("doi","10.48550/arXiv.2006.03511")] ai/nn/transformer/gpt/codex

A transcompiler, also known as source-to-source translator, is a system that converts source code from a high-level programming language (such as C++ or Python) to another. Transcompilers are primarily used for interoperability, and to port codebases written in an obsolete or deprecated language (eg. COBOL, Python 2) to a modern one. They typically rely on handcrafted rewrite rules, applied to the source code abstract syntax tree. Unfortunately, the resulting translations often lack readability, fail to respect the target language conventions, and require manual modifications in order to work properly. The overall translation process is time-consuming and requires expertise in both the source and target languages, making code-translation projects expensive. Although neural models substantially outperform their rule-based counterparts in the context of natural language translation, their applications to transcompilation have been limited due to the scarcity of parallel data in this domain.

In this paper, we propose to leverage recent approaches in unsupervised machine translation to train a fully unsupervised neural transcompiler.

We train our model on source code from open source GitHub projects, and show that it can translate functions between C++, Java, and Python with high accuracy. Our method relies exclusively on monolingual source code, requires no expertise in the source or target languages, and can easily be generalized to other programming languages.

We also build and release a test set composed of 852 parallel functions, along with unit tests to check the correctness of translations.

We show that our model outperforms rule-based commercial baselines by a substantial margin.

--- https://www.reddit.com/r/GPT3/comments/vxl21b/a_gangsta_rap_about_pencils/ 2021-10-30 ai/nn/transformer/gpt/poetry --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8126497/ Resistance Training Load Effects on Muscle Hypertrophy and Strength Gain: Systematic Review and Network Meta-analysis Pedro Lopez, Régis Radaelli, Dennis R. Taaffe, Robert U. Newton, Daniel A. Galvão, Gabriel S. Trajano, Juliana L. Teodoro, William J. Kraemer, Keijo Häkkinen, Ronei S. Pinto 2021 2021-10-30 [("doi","10.1249/MSS.0000000000002585")] exercise

Purpose: This study aimed to analyze the effect of resistance training (RT) performed until volitional failure with low, moderate, and high loads on muscle hypertrophy and muscle strength in healthy adults and to assess the possible participant/design/training-related covariates that may affect the adaptations.

Method: Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, MEDLINE, CINAHL, Embase, SPORTDiscus, and Web of Science databases were searched. Including only studies that performed sets to volitional failure, the effects of low-load (>15 repetitions maximum (RM)), moderate-load (9–15 RM), and high-load (≤8 RM) RTs were examined in healthy adults. Network meta-analysis was undertaken to calculate the standardized mean difference (SMD) between RT loads in overall and subgroup analyses involving studies deemed of high quality. Associations between participant/design/training-related covariates with SMD were assessed by univariate and multivariate network meta-regression analyses.

Results: Twenty-eight studies involving 747 healthy adults were included. Although no differences in muscle hypertrophy between RT loads were found in overall (p = 0.113–0.469) or subgroup analysis (p = 0.871–0.995), greater effects were observed in untrained participants (p = 0.033) and participants with some training background who undertook more RT sessions (p = 0.031–0.045). Muscle strength improvement was superior for both high-load and moderate-load compared with low-load RT in overall and subgroup analysis (SMD, 0.60–0.63 and 0.34–0.35, respectively; p < 0.001–0.003), with a non-statistically-significant but superior effect for high compared with moderate load (SMD, 0.26–0.28, p = 0.068).

Conclusion: Although muscle hypertrophy improvements seem to be load independent, increases in muscle strength are superior in high-load RT programs. Untrained participants exhibit greater muscle hypertrophy, whereas undertaking more RT sessions provides superior gains in those with previous training experience.

--- https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth/ NVIDIA Hopper Architecture In-Depth 2021-10-30 ai/scaling/hardware --- https://en.wikipedia.org/wiki/Minifloat Minifloat 2021-10-30 ai/nn/sparsity/low-precision ai/scaling/hardware --- https://www.biorxiv.org/content/10.1101/2022.07.10.499405.full Perceptein: A synthetic protein-level neural network in mammalian cells Zibo Chen, James M. Linton, Ronghui Zhu, Michael Elowitz 2022-07-11 2022-07-11 [("doi","10.1101/2022.07.10.499405")] ai/nn cs/computable genetics/genome-synthesis

Artificial neural networks provide a powerful paradigm for information processing that has transformed diverse fields. Within living cells, genetically encoded synthetic molecular networks could, in principle, harness principles of neural computation to classify molecular signals.

Here, we combine de novo designed protein heterodimers and engineered viral proteases to implement a synthetic protein circuit that performs winner-take-all neural network computation. This perceptein circuit includes modules that compute weighted sums of input protein concentrations through reversible binding interactions, and allow for self-activation and mutual inhibition of protein components using irreversible proteolytic cleavage reactions. Altogether, these interactions comprise a network of 310 chemical reactions stemming from 8 expressed protein species. The complete system achieves signal classification with tunable decision boundaries in mammalian cells.

These results demonstrate how engineered protein-based networks can enable programmable signal classification in living cells.

--- https://web.archive.org/web/20030610000210/http://typophile.com/smalltalk/ Typophile 2021-10-30 design/typography --- https://www.thisfontdoesnotexist.com/ 2021-10-31 design/typography --- https://arxiv.org/abs/2207.05477#baidu HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle Guoxia Wang, Xiaomin Fang, Zhihua Wu, Yiqun Liu, Yang Xue, Yingfei Xiang, Dianhai Yu, Fan Wang, Yanjun Ma 2022-07-12 2022-07-12 [("doi","10.48550/arXiv.2207.05477")] ai/nn/transformer/alphafold

Accurate protein structure prediction can accelerate the development of life science. The accuracy of AlphaFold2, a frontier end-to-end structure prediction system, is already close to that of the experimental determination techniques. Due to the complex model architecture and large memory consumption, it requires lots of computational resources and time to implement the training and inference of AlphaFold2 from scratch. The cost of running the original AlphaFold2 is expensive for most individuals and institutions. Therefore, reducing this cost could accelerate the development of life science.

We implement AlphaFold2 using PaddlePaddle, namely HelixFold, to improve training and inference speed and reduce memory consumption. The performance is improved by operator fusion, tensor fusion, and hybrid parallelism computation, while the memory is optimized through Recompute, Bfloat16, and memory read/write in-place.

Compared with the original AlphaFold2 (implemented by Jax) and OpenFold (implemented by PyTorch), HelixFold needs only 7.5 days to complete the full end-to-end training and only 5.3 days when using hybrid parallelism, while both AlphaFold2 and OpenFold take about 11 days. HelixFold saves 1? training time.

We verified that HelixFold?s accuracy could be on par with AlphaFold2 on the CASP14 and CAMEO datasets.

HelixFold?s code is available on GitHub for free download: https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold, and we also provide stable web services on https://paddlehelix.baidu.com/app/drug/protein/forecast.

--- /doc/philosophy/ethics/1997-tucker-buddhismandecologytheinterconnectionofdharmaanddeeds.pdf Buddhism and Ecology: The Interconnection of Dharma and Deeds Mary Evelyn Tucker, Duncan Ryuken Williams 1997-01-01 2021-10-31 philosophy/ethics philosophy/religion --- /doc/psychology/personality/1980-wells-personalityandheredity.pdf Personality and Heredity: An introduction to psychogenetics Brian W. P. Wells 1980-01-01 2021-10-31 genetics/heritable psychology/personality --- /doc/philosophy/mind/1993-astington-thechildsdiscoveryofthemind.pdf The Child’s Discovery of the Mind Janet Wilde Astington 1993-01-01 2021-10-31 philosophy/mind psychology --- https://www.biorxiv.org/content/10.1101/2021.09.10.459807.full An inactivated multivalent influenza A virus vaccine is broadly protective in mice and ferrets Jaekeun Park, Sharon Fong, Louis M. Schwartzman, Zhong-Mei Sheng, Ashley Freeman, Lex Matthews, Yongli Xiao, Mitchell D. Ramuta, Natalia A. Batchenkova, Li Qi, Luz Angela Rosas, Stephanie Williams, Kelsey Scherler, Monica Gouzoulis, Ian Bellayr, David M. Morens, Kathie-Anne Walters, Matthew J. Memoli, John C. Kash, Jeffery K. Taubenberger 2021-09-10 2021-10-31 [("doi","10.1101/2021.09.10.459807")] biology

Influenza A viruses (IAVs) present major public health threats from annual seasonal epidemics, from pandemics caused by novel virus subtypes, and from viruses adapted to a variety of animals including poultry, pigs and horses. Vaccines that broadly protect against all such IAVs, so-called “universal” influenza vaccines, do not currently exist, but are urgently needed. This study demonstrates that an inactivated, multivalent whole virus vaccine, delivered intramuscularly or intranasally, is broadly protective against challenges with multiple IAV HA/NA subtypes in both mice and ferrets, including challenges with IAV subtypes not contained in the vaccine. This vaccine approach indicates the feasibility of eliciting broad “universal” IAV protection, and identifies a promising candidate for influenza vaccine clinical development.

One-Sentence Summary

An inactivated, whole avian influenza virus vaccine delivered intramuscularly or intranasally provides extremely broad protection against antigenically divergent viral challenge and is a promising candidate for a ‘universal’ influenza virus vaccine.

--- https://arxiv.org/abs/2207.05608#google Inner Monologue: Embodied Reasoning through Planning with Language Models Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, Pierre Sermanet, Noah Brown, Tomas Jackson, Linda Luu, Sergey Levine, Karol Hausman, Brian Ichter 2022-07-12 2022-07-12 [("doi","10.48550/arXiv.2207.05608")] ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm reinforcement-learning/model reinforcement-learning/robot

[video] Recent works have shown how the reasoning capabilities of Large Language Models (LLMs) can be applied to domains beyond natural language processing, such as planning and interaction for robots19, 20, 21. These embodied problems require an agent to understand many semantic aspects of the world: the repertoire of skills available, how these skills influence the world, and how changes to the world map back to the language. LLMs planning in embodied environments need to consider not just what skills to do, but also how and when to do them?answers that change over time in response to the agent?s own choices.

In this work, we investigate to what extent LLMs [PaLM] used in such embodied contexts can reason over sources of feedback provided through natural language, without any additional training. We propose that by leveraging environment feedback, LLMs are able to form an inner monologue that allows them to more richly process and plan in robotic control scenarios.

We investigate a variety of sources of feedback, such as success detection, scene description, and human interaction.

We find that closed-loop language feedback substantially improves high-level instruction completion on 3 domains, including simulated and real table top rearrangement tasks and long-horizon mobile manipulation tasks in a kitchen environment in the real world.

Figure 1: Inner Monologue enables grounded closed-loop feedback for robot planning with large language models by leveraging a collection of perception models (eg. scene descriptors and success detectors) in tandem with pretrained language-conditioned robot skills. Experiments show our system can reason and replan to accomplish complex long-horizon tasks for (a) mobile manipulation and (b, c) tabletop manipulation in both simulated and real settings.

Finally, we show that Inner Monologue, without requiring additional training beyond a frozen language model and pre-trained robotic skills, can accomplish complex, long-horizon, and unseen tasks in simulation as well as on 2 real-world robotic platforms. Notably, we show that it can efficiently retry under observed stochastic failure, replan under systematic infeasibility, or request human feedback for ambiguous queries, resulting in substantially improved performance in dynamical environments. As a demonstration of the versatility of LLMs and grounded closed-loop feedback, we additionally show several surprising capabilities emerging from the inner monologue formulation, including continued adaptation to new instructions, self-proposed goals, interactive scene understanding, multilingual interactions, and more.

3.2 Inner Monologue: We formulate an ‘inner monologue’ by continually injecting information from the various sources of feedback into the LLM planning language prompts as the robot interacts with the environment. While LLMs have demonstrated exceptional planning capabilities for embodied control tasks,20 prior works have found it crucial to ground LLM predictions with external components such as affordance functions21 in order to produce useful plans that are executable by robots. However, LLMs used in this context have thus far remained one-directional—providing a list of skills, without making corrections or leveraging opportunities to replan accordingly. In contrast, Inner Monologue studies settings where grounded environment feedback is provided directly to the LLM in a closed-loop fashion. This promotes improved LLM reasoning in complex long-horizon settings, even before any external affordance-based grounding methods are applied.

Our analysis assumes textual feedback is provided to the planner, but does not assume a single specific method of fusing LLM planning with low-level robotic control or a specific method of extracting environment feedback into language. Rather than focusing on a particular algorithmic implementation, our aim is to provide a case study on the value of incorporating different types of feedback into closed-loop LLM-based planning. Thus, Inner Monologue in §4 uses language feedback within separate systems that incorporate different LLMs, different methods of fusing planning with control, different environments and tasks, and different methods of acquiring control policies. We note that in our specific implementations of Inner Monologue, we use pre-trained LLMs for planning that are not finetuned, but rather evaluated solely with few-shot prompting; the full prompts can be found in the Appendix.

Figure 2: Various types of textual feedback. Success Detection (purple) gives task-specific task completion information, Passive Scene Description (green) gives structured semantic scene information at every planning step, and Active Scene Description (blue) gives unstructured semantic information only when queried by the LLM planner.
Figure 3: Different instantiations of Inner Monologue in 3 distinct domains—simulated tabletop rearrangement (top), real-world tabletop rearrangement (middle), and real-world kitchen mobile manipulation (bottom). Each domain uses different prompts and different feedback models. Sharing across the domains is the same Inner Monologue formulation that uses a pre-trained language model to take in a human instruction and decompose it into a sequence of actionable steps (yellow) by the agent, while accounting for injected embodied feedback from different models, such as object recognizers (green) and success detectors (purple). In real-world kitchen mobile manipulation domain (bottom), we additionally ground the actions using pre-trained affordance functions built in SayCan, which do not communicate back to the language model.

4.4 Emergent Capabilities: Although LLMs can generate fluent continuation from the prompted examples, we surprisingly find that, when informed with environment feedback, Inner Monologue demonstrates many impressive reasoning and replanning behaviors beyond the examples given in the prompt. Using a pre-trained LLM as the backbone, the method also inherits many of the appealing properties from its versatility and general-purpose language understanding. In this section, we demonstrate a few of these emergent capabilities.

Despite the appealing findings about these emergent capabilities, we observe that they are of varying levels of consistency when no similar examples have been provided in the prompt, likely limited by the current capabilities of the language models. However, we believe that further investigations into these behaviors and addressing their limitations would each lead to exciting future directions.

--- https://arxiv.org/abs/2201.07207#google Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents Wenlong Huang, Pieter Abbeel, Deepak Pathak, Igor Mordatch 2022-01-18 2022-01-18 [("doi","10.48550/arXiv.2201.07207")] ai/nn/transformer/gpt reinforcement-learning/model reinforcement-learning/robot

Can world knowledge learned by large language models (LLMs) be used to act in interactive environments? In this paper, we investigate the possibility of grounding high-level tasks, expressed in natural language (eg. ‘make breakfast’), to a chosen set of actionable steps (eg. ‘open fridge’).

While prior work focused on learning from explicit step-by-step examples of how to act, we surprisingly find that if pre-trained LMs are large enough and prompted appropriately, they can effectively decompose high-level tasks into mid-level plans without any further training. However, the plans produced naively by LLMs often cannot map precisely to admissible actions.

We propose a procedure that conditions on existing demonstrations and semantically translates the plans to admissible actions. Our evaluation in the recent VirtualHome environment shows that the resulting method substantially improves executability over the LLM baseline. The conducted human evaluation reveals a trade-off between executability and correctness but shows a promising sign towards extracting actionable knowledge from language models.

Website at https://wenlong.page/language-planner/.

--- https://x.com/goodside/status/1547024867215769600 2021-10-31 ai/nn/transformer/gpt/non-fiction --- http://dallery.gallery/wp-content/uploads/2022/07/The-DALL%C2%B7E-2-prompt-book.pdf 2021-10-31 ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2205.14334 Teaching Models to Express Their Uncertainty in Words Stephanie Lin, Jacob Hilton, Owain Evans 2022-05-28 2022-05-28 [("doi","10.48550/arXiv.2205.14334")] ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/non-fiction ai/scaling statistics/bayes statistics/prediction

We show that a GPT-3 model can learn to express uncertainty about its own answers in natural language—without use of model logits.

When given a question, the model generates both an answer and a level of confidence (eg. ‘90% confidence’ or ‘high confidence’). These levels map to probabilities that are well calibrated. The model also remains moderately calibrated under distribution shift, and is sensitive to uncertainty in its own answers, rather than imitating human examples. To our knowledge, this is the first time a model has been shown to express calibrated uncertainty about its own answers in natural language.

For testing calibration, we introduce the CalibratedMath suite of tasks. We compare the calibration of uncertainty expressed in words (verbalized probability) to uncertainty extracted from model logits. Both kinds of uncertainty are capable of generalizing calibration under distribution shift.

We also provide evidence that GPT-3’s ability to generalize calibration depends on pre-trained latent representations that correlate with epistemic uncertainty over its answers.

[Could the calibration latent be used for active learning to find natural sentences GPT-3 is most uncertain about? Or to train a model to generate uncertain text for labeling or correction?]

--- https://arxiv.org/abs/2207.05221#anthropic Language Models (Mostly) Know What They Know Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield Dodds, Nova DasSarma, Eli Tran-Johnson, Scott Johnston, Sheer El-Showk, Andy L. Jones, Nelson Elhage, Tristan Hume, Anna Chen, Yuntao Bai, Samuel R. Bowman, Stanislav Fort, Deep Ganguli, Danny Hernandez, Josh Jacobson, Jackson Kernion, Shauna Kravec, Liane Lovitt, Kamal Ndousse, Catherine Olsson, Sam Ringer, Dario Amodei, Tom Brown, Jack Clark, Nicholas Joseph, Ben Mann, Sam McCandlish, Chris Olah, Jared Kaplan 2022-07-11 2022-07-11 [("doi","10.48550/arXiv.2207.05221")] ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/inner-monologue ai/scaling statistics/bayes statistics/prediction

[Twitter; cf. Lin et al 2022, Mielke et al 2020] We study whether language models can evaluate the validity of their own claims and predict which questions they will be able to answer correctly.

We first show that larger models are well-calibrated on diverse multiple choice and true/false questions when they are provided in the right format. Thus we can approach self-evaluation on open-ended sampling tasks by asking models to first propose answers, and then to evaluate the probability ‘P(True)’ that their answers are correct. We find encouraging performance, calibration, and scaling for P(True) on a diverse array of tasks. Performance at self-evaluation further improves when we allow models to consider many of their own samples before predicting the validity of one specific possibility [cf. self-consistency].

Next, we investigate whether models can be trained to predict ‘P(IK)’, the probability that ‘I know’ the answer to a question, without reference to any particular proposed answer. Models perform well at predicting P(IK) and partially generalize across tasks, though they struggle with calibration of P(IK) on new tasks. The predicted P(IK) probabilities also increase appropriately in the presence of relevant source materials in the context, and to the presence of hints towards the solution of mathematical word problems.

We hope these observations lay the groundwork for training more honest models, and for investigating how honesty generalizes to cases where models are trained on objectives other than the imitation of human writing.

Figure 4: (left) We show calibration curves for various model sizes on all of the multiple choice tasks in BIG-bench, in the format described in §2. We include a dashed line indicating perfect calibration. (right) Here we show trends in the expected calibration error on BIG-bench, for both multiple choice and a separate True/False format (see §3.2). We show the RMS calibration error in Figure 21 in the appendix.

We study a series of language models with 800M, 3B, 12B, 52b parameters. We do not include smaller models because they perform poorly on many of the evaluations we consider. The architecture and training setup for these models is identical to that in Bai et al 2022, except that the models we consider here were pretrained for 850B tokens, rather than the 400B tokens used in that work.

As can be seen in Figure 5, task formatting is important for achieving excellent calibration, and calibration improves as we pass from 0-shot to 5-shot evaluation. We expect calibration is also easier to achieve with this format because each answer option corresponds to a single token (this isn’t the case in BIG-bench by default, see appendix A.4).

--- https://www.reddit.com/r/MachineLearning/comments/vx89nj/p_dalle_mini_mega_demo_and_production_api/ 2021-11-01 ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2012.14983#facebook Reducing conversational agents’ overconfidence through linguistic calibration Sabrina J. Mielke, Arthur Szlam, Emily Dinan, Y-Lan Boureau 2020-12-30 2021-11-01 [("doi","10.48550/arXiv.2012.14983")] ai/nn/transformer/gpt/calibration

While improving neural dialogue agents’ factual accuracy is the object of much research, another important aspect of communication, less studied in the setting of neural dialogue, is transparency about ignorance.

In this work, we analyze to what extent state-of-the-art chit-chat models are linguistically calibrated in the sense that their verbalized expression of doubt (or confidence) matches the likelihood that the model’s responses are factually incorrect (or correct). We find that these models are poorly calibrated, yet we show that likelihood of correctness can accurately be predicted. By incorporating such metacognitive features into the training of a controllable generation model, we obtain a dialogue agent with greatly improved linguistic calibration. While improving neural dialogue agents’ factual accuracy is the object of much research, another important aspect of communication, less studied in the setting of neural dialogue, is transparency about ignorance.

In this work, we analyze to what extent state-of-the-art chit-chat models are linguistically calibrated in the sense that their verbalized expression of doubt (or confidence) matches the likelihood that the model’s responses are factually incorrect (or correct).

We find that these models are poorly calibrated, yet we show that likelihood of correctness can accurately be predicted. By incorporating such metacognitive features into the training of a controllable generation model, we obtain a dialogue agent with greatly improved linguistic calibration.

--- https://en.wikipedia.org/wiki/Major_depressive_disorder Major depressive disorder 2021-11-01 psychiatry/depression --- https://en.wikipedia.org/wiki/Depression_(mood) Depression (mood) 2021-11-01 psychiatry/depression --- https://en.wikipedia.org/wiki/Major_depressive_episode Major depressive episode 2021-11-01 psychiatry/bipolar psychiatry/depression --- https://en.wikipedia.org/wiki/Psychotherapy Psychotherapy 2021-11-01 psychiatry/depression --- https://en.wikipedia.org/wiki/Antidepressant Antidepressant medication 2021-11-01 psychiatry/bipolar psychiatry/depression --- https://en.wikipedia.org/wiki/Robert_Burton Robert Burton 2021-11-01 psychiatry/depression --- https://en.wikipedia.org/wiki/The_Anatomy_of_Melancholy The Anatomy of Melancholy 2021-11-01 psychiatry/depression --- /doc/psychology/2021-louie.pdf Do Racial Differences in Coping Resources Explain the Black-White Paradox in Mental Health? A Test of Multiple Mechanisms Patricia Louie, Laura Upenieks, Christy L. Erving, Courtney S. Thomas Tobin 2021-09-22 2021-11-01 [("doi","10.1177/00221465211041031")] psychiatry/depression psychology

A central paradox in the mental health literature is the tendency for black Americans to report similar or better mental health than white Americans despite experiencing greater stress exposure. However, black Americans’ higher levels of certain coping resources may explain this finding.

Using data from the Nashville Stress and Health Study (n = 1,186), we examine whether black Americans have higher levels of self-esteem, social support, religious attendance, and divine control than white Americans and whether these resources, in turn, explain the black-white paradox in mental health.

In adjusted models, the black-white paradox holds for depressive symptoms and any DSM-IV disorder. Findings indicate that black Americans have higher levels of self-esteem, family social support, and religiosity than white Americans. Causal mediation techniques reveal that self-esteem has the largest effect in explaining black-white differences in depressive symptoms, whereas divine control has the largest effect in explaining differences in disorder.

--- https://vitalik.eth.limo/general/2021/02/18/election.html Prediction Markets: Tales from the Election 2021-11-02 statistics/prediction --- https://www.lesswrong.com/posts/YM6Qgiz9RT7EmeFpp/how-long-does-it-take-to-become-gaussian How long does it take to become Gaussian? 2021-11-02 statistics/probability --- https://arxiv.org/abs/2207.03477 VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web Andrei Manolache, Florin Brad, Antonio Barbalau, Radu Tudor Ionescu, Marius Popescu 2022-07-07 2022-07-07 [("doi","10.48550/arXiv.2207.03477")] darknet-market/agora darknet-market/dnm-archive darknet-market/silk-road/1

The Dark Web represents a hotbed for illicit activity, where users communicate on different market forums in order to exchange goods and services. Law enforcement agencies benefit from forensic tools that perform authorship analysis, in order to identify and profile users based on their textual content. However, authorship analysis has been traditionally studied using corpora featuring literary texts such as fragments from novels or fan fiction, which may not be suitable in a cybercrime context. Moreover, the few works that employ authorship analysis tools for cybercrime prevention usually employ ad-hoc experimental setups and datasets.

To address these issues, we release VeriDark: a benchmark comprised of 3 large scale authorship verification datasets and one authorship identification dataset obtained from user activity from either Dark Web related Reddit communities or popular illicit Dark Web market forums.

We evaluate competitive NLP baselines on the 3 datasets and perform an analysis of the predictions to better understand the limitations of such approaches.

We make the datasets and baselines publicly available at https://github.com/bit-ml/VeriDark.

--- https://deepfivalue.substack.com/p/the-kleros-experiment-has-failed The Kleros experiment has failed 2021-11-02 bitcoin --- https://arxiv.org/abs/2206.11147 RST: reStructured Pre-training Weizhe Yuan, Pengfei Liu 2022-06-22 2022-06-22 [("doi","10.48550/arXiv.2206.11147")] ai/dataset ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 ai/scaling

In this work, we try to decipher the internal connection of NLP technology development in the past decades, searching for essence, which rewards us with a (potential) new learning paradigm for NLP tasks, dubbed as reStructured Pre-training (RST).

In such a paradigm, the role of data will be re-emphasized, and model pre-training and fine-tuning of downstream tasks are viewed as a process of data storing and accessing. Based on that, we operationalize the simple principle that a good storage mechanism should not only have the ability to cache a large amount of data but also consider the ease of access. We achieve this by pre-training models over restructured data that consist of a variety of valuable information instead of raw data after overcoming several engineering challenges.

Experimentally, RST models not only surpass strong competitors (eg. T0) on 52⁄55 popular datasets from a variety of NLP tasks, but also achieve superior performance in National College Entrance Examination—English (Gaokao-English), the most authoritative examination in China. Specifically, the proposed system Qin achieves 40 points higher than the average scores made by students and 15 points higher than GPT-3 with 1⁄16 parameters. In particular, Qin gets a high score of 138.5 (the full mark is 150) in the 2018 English exam (national paper III).

We have released the Gaokao Benchmark with an online submission platform.

In addition, we test our model in the 2022 College Entrance Examination English that happened a few days ago (2022.06.08), and it gets a total score of 134 (v.s. GPT-3’s 108).

--- https://www.biorxiv.org/content/10.1101/2022.07.11.499652.full Nationwide genomic biobank in Mexico unravels demographic history and complex trait architecture from 6,057 individuals Mashaal Sohail, Amanda Y. Chong, Consuelo D. Quinto-Cortes, Maria J. Palma-Martinez, Aaron Ragsdale, Santiago G. Medina-Munoz, Carmina Barberena-Jonas, Guadalupe Delgado-Sanchez, Luis Pablo Cruz-Hervert, Leticia Ferreyra-Reyes, Elizabeth Ferreira-Guerrero, Norma Mongua-Rodriguez, Andres Jimenez-Kaufmann, Hortensia Moreno-Macias, Carlos A. Aguilar-Salinas, Kathryn Auckland, Adrian Cortes, Victor Acuna-Alonzo, Alexander G. Ioannidis, Christopher R. Gignoux, Genevieve L. Wojcik, Selene L. Fernandez-Valverde, Adrian V. S. Hill, Maria Teresa Tusie-Luna, Alexander J. Mentzer, John Novembre, Lourdes Garcia-Garcia, Andres Moreno-Estrada 2022-07-14 2022-07-14 [("doi","10.1101/2022.07.11.499652")] genetics/heritable/rare

Latin America continues to be severely underrepresented in genomics research, and fine-scale genetic histories as well as complex trait architectures remain hidden due to the lack of Big Data.

To fill this gap, the Mexican Biobank project genotyped 1.8 million markers in 6,057 individuals from 32 states and 898 sampling localities across Mexico with linked complex trait and disease information creating a valuable nationwide genotype-phenotype database. Through a suite of state-of-the-art methods for ancestry deconvolution and inference of identity-by-descent (IBD) segments, we inferred detailed ancestral histories for the last 200 generations in different Mesoamerican regions, unraveling native and colonial/post-colonial demographic dynamics.

We observed large variations in runs of homozygosity (ROH) among genomic regions with different ancestral origins reflecting their demographic histories, which also affect the distribution of rare deleterious variants across Mexico.

We analyzed a range of biomedical complex traits and identified substantial genetic and environmental factors explaining their variation, such as ROH found to be important predictors for trait variation in BMI and triglycerides.

--- https://schwitzsplinters.blogspot.com/2022/07/the-computerized-philosopher-can-you.html The Computerized Philosopher: Can You Distinguish Daniel Dennett from a Computer? 2021-11-02 ai/nn/transformer/gpt/non-fiction philosophy/mind --- https://arxiv.org/abs/2207.06366#google N-Grammer: Augmenting Transformers with latent n-grams Aurko Roy, Rohan Anil, Guangda Lai, Benjamin Lee, Jeffrey Zhao, Shuyuan Zhang, Shibo Wang, Ye Zhang, Shen Wu, Rigel Swavely, Tao, Yu, Phuong Dao, Christopher Fifty, Zhifeng Chen, Yonghui Wu 2022-07-13 2022-07-13 [("doi","10.48550/arXiv.2207.06366")] ai/nn/tokenization ai/nn/transformer/attention

Transformer models have recently emerged as one of the foundational models in natural language processing, and as a byproduct, there is recent interest and investment in scaling these models. However, the training and inference costs of these large Transformer language models are prohibitive, thus necessitating more research in identifying more efficient variants.

In this work, we propose a simple yet effective modification to the Transformer architecture inspired by the literature in statistical language modeling, by augmenting the model with n-grams that are constructed from a discrete latent representation of the text sequence.

We evaluate our model, the N-Grammer on language modeling on the C4 data-set as well as text classification on the SuperGLUE data-set, and find that it outperforms several strong baselines such as the Transformer and the Primer.

We open-source our model for reproducibility purposes in Jax.

--- https://arxiv.org/abs/2207.04179 Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling Tung Nguyen, Aditya Grover 2022-07-09 2022-07-09 [("doi","10.48550/arXiv.2207.04179")] ai/nn/transformer reinforcement-learning/exploration reinforcement-learning/meta-learning

[code] Neural Processes (NPs) are a popular class of approaches for meta-learning. Similar to Gaussian Processes (GPs), NPs define distributions over functions and can estimate uncertainty in their predictions. However, unlike GPs, NPs and their variants suffer from underfitting and often have intractable likelihoods, which limit their applications in sequential decision making.

We propose Transformer Neural Processes (TNPs), a new member of the NP family that casts uncertainty-aware meta learning as a sequence modeling problem. We learn TNPs via an autoregressive likelihood-based objective and instantiate it with a novel transformer-based architecture. The model architecture respects the inductive biases inherent to the problem structure, such as invariance to the observed data points and equivariance to the unobserved points. We further investigate knobs within the TNP framework that tradeoff expressivity of the decoding distribution with extra computation.

Empirically, we show that TNPs achieve state-of-the-art performance on various benchmark problems, outperforming all previous NP variants on meta regression, image completion, contextual multi-armed bandits, and Bayesian optimization.

--- https://arxiv.org/abs/2207.06405#facebook Masked Autoencoders that Listen Po-Yao, Huang, Hu Xu, Juncheng Li, Alexei Baevski, Michael Auli, Wojciech Galuba, Florian Metze, Christoph Feichtenhofer 2022-07-13 2022-07-13 [("doi","10.48550/arXiv.2207.06405")] ai/nn/vae/mae

This paper studies a simple extension of image-based Masked Autoencoders (MAE) to self-supervised representation learning from audio spectrograms. Following the Transformer encoder-decoder design in MAE, our Audio-MAE first encodes audio spectrogram patches with a high masking ratio, feeding only the non-masked tokens through encoder layers. The decoder then re-orders and decodes the encoded context padded with mask tokens, in order to reconstruct the input spectrogram.

We find it beneficial to incorporate local window attention in the decoder, as audio spectrograms are highly correlated in local time and frequency bands. We then fine-tune the encoder with a lower masking ratio on target datasets.

Empirically, Audio-MAE sets new state-of-the-art performance on 6 audio and speech classification tasks, outperforming other recent models that use external supervised pre-training.

The code and models will be at Github.

--- https://arxiv.org/abs/2207.06300#ibm Re2G: Retrieve, Rerank, Generate Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Rajaram Naik, Pengshan Cai, Alfio Gliozzo 2022-07-13 2022-07-13 [("doi","10.48550/arXiv.2207.06300")] ai/nn/retrieval ai/nn/sparsity/knowledge-distillation ai/nn/transformer

As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements. Recent models such as RAG and REALM have introduced retrieval into conditional generation. These models incorporate neural initial retrieval from a corpus of passages. We build on this line of research, proposing Re2G, which combines both neural initial retrieval and reranking into a BART-based sequence-to-sequence generation. Our reranking approach also permits merging retrieval results from sources with incomparable scores, enabling an ensemble of BM25 and neural initial retrieval.

To train our system end-to-end, we introduce a novel variation of knowledge distillation to train the initial retrieval, reranker, and generation using only ground truth on the target sequence output. We find large gains in four diverse tasks: zero-shot slot filling, question answering, fact-checking, and dialog, with relative gains of 9% to 34% over the previous state-of-the-art on the KILT leaderboard. We make our code available as open source at Github.

--- https://huggingface.co/blog/bloom-megatron-deepspeed The Technology Behind BLOOM Training 2021-11-03 ai/nn/transformer/gpt ai/scaling/hardware --- https://arxiv.org/abs/2108.01547#baai EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training Hao Zhou, Pei Ke, Zheng Zhang, Yuxian Gu, Yinhe Zheng, Chujie Zheng, Yida Wang, Chen Henry Wu, Hao Sun, Xiaocong Yang, Bosi Wen, Xiaoyan Zhu, Minlie Huang, Jie Tang 2021-08-03 2021-11-03 [("doi","10.48550/arXiv.2108.01547")] ai/nn/transformer ai/scaling

Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue data and the model size compared with English ones.

In this paper, we propose EVA, a Chinese dialogue system that contains the largest Chinese pre-trained dialogue model with 2.8b parameters. To build this model, we collect the largest Chinese dialogue dataset named WDC-Dialogue from various public social media. This dataset contains 1.4B context-response pairs and is used as the pre-training corpus of EVA.

Extensive experiments on automatic and human evaluation show that EVA outperforms other Chinese pre-trained dialogue models especially in the multi-turn interaction of human-bot conversations.

--- https://arxiv.org/abs/2108.07435#baai Modeling Protein Using Large-scale Pretrain Language Model Yijia Xiao, Jiezhong Qiu, Ziang Li, Chang-Yu Hsieh, Jie Tang 2021-08-17 2021-11-03 [("doi","10.48550/arXiv.2108.07435")] ai/nn/transformer ai/scaling biology

Protein is linked to almost every life process. Therefore, analyzing the biological structure and property of protein sequences is critical to the exploration of life, as well as disease detection and drug discovery. Traditional protein analysis methods tend to be labor-intensive and time-consuming.

The emergence of deep learning models makes modeling data patterns in large quantities of data possible. Interdisciplinary researchers have begun to leverage deep learning methods to model large biological datasets, eg. using long short-term memory and convolutional neural network for protein sequence classification. After millions of years of evolution, evolutionary information is encoded in protein sequences. Inspired by the similarity between natural language and protein sequences, we use large-scale language models to model evolutionary-scale protein sequences, encoding protein biology information in representation.

Improvements are observed in both token-level and sequence-level tasks, demonstrating that our large-scale model can accurately capture evolution information from pretraining on evolutionary-scale individual sequences.

Our code and model are available at Github.

--- https://arxiv.org/abs/2204.04817 Effective Mutation Rate Adaptation through Group Elite Selection Akarsh Kumar, Bo Liu, Risto Miikkulainen, Peter Stone 2022-04-11 2022-04-11 [("doi","10.48550/arXiv.2204.04817")] genetics/selection/artificial reinforcement-learning/exploration reinforcement-learning/meta-learning statistics/order

Evolutionary algorithms are sensitive to the mutation rate (MR); no single value of this parameter works well across domains. Self-adaptive MR approaches have been proposed but they tend to be brittle: Sometimes they decay the MR to zero, thus halting evolution. To make self-adaptive MR robust, this paper introduces the Group Elite Selection of Mutation Rates (GESMR) algorithm. GESMR co-evolves a population of solutions and a population of MRs, such that each MR is assigned to a group of solutions.

The resulting best mutational change in the group, instead of average mutational change, is used for MR selection during evolution, thus avoiding the vanishing MR problem. With the same number of function evaluations and with almost no overhead, GESMR converges faster and to better solutions than previous approaches on a wide range of continuous test optimization problems.

GESMR also scales well to high-dimensional neuroevolution for supervised image-classification tasks and for reinforcement learning control tasks. Remarkably, GESMR produces MRs that are optimal in the long-term, as demonstrated through a comprehensive look-ahead grid search.

Thus, GESMR and its theoretical and empirical analysis demonstrate how self-adaptation can be harnessed to improve performance in several applications of evolutionary computation.

--- https://github.com/CasualGANPapers/Make-A-Scene Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors 2021-11-03 ai/nn/transformer/gpt/dall-e --- https://www.youtube.com/watch?v=QLTyqoJJKTo The Little Red Boat Story (Make-A-Scene): Our own model was used to generate all the images in the story, by providing a text and simple sketch input 2021-11-03 ai/nn/transformer/gpt/dall-e/1 --- https://ai.facebook.com/blog/greater-creative-control-for-ai-image-generation/ 2021-11-03 ai/nn/transformer/gpt/dall-e --- https://ir.vervetx.com/news-releases/news-release-details/verve-therapeutics-doses-first-human-investigational-vivo-base 2021-11-03 genetics/editing --- https://www.science.org/content/blog-post/more-crispr-human-subjects 2021-11-03 genetics/editing --- https://arxiv.org/abs/2206.13499 Prompting Decision Transformer for Few-Shot Policy Generalization Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua B. Tenenbaum, Chuang Gan 2022-06-27 2022-06-27 [("doi","10.48550/arXiv.2206.13499")] reinforcement-learning/meta-learning reinforcement-learning/model/decision-transformer reinforcement-learning/offline

Humans can leverage prior experience and learn novel tasks from a handful of demonstrations. In contrast to offline meta-reinforcement learning, which aims to achieve quick adaptation through better algorithm design, we investigate the effect of architecture inductive bias on the few-shot learning capability. We propose a Prompt-based Decision Transformer (Prompt-DT), which leverages the sequential modeling ability of the Transformer architecture and the prompt framework to achieve few-shot adaptation in offline RL.

We design the trajectory prompt, which contains segments of the few-shot demonstrations, and encodes task-specific information to guide policy generation. Our experiments in 5 MuJoCo control benchmarks show that Prompt-DT is a strong few-shot learner without any extra finetuning on unseen target tasks.

Prompt-DT outperforms its variants and strong meta offline RL baselines by a large margin with a trajectory prompt containing only a few timesteps. Prompt-DT is also robust to prompt length changes and can generalize to out-of-distribution (OOD) environments.

--- https://arxiv.org/abs/2206.12839#google Repository-Level Prompt Generation for Large Language Models of Code Disha Shrivastava, Hugo Larochelle, Daniel Tarlow 2022-06-26 2022-06-26 [("doi","10.48550/arXiv.2206.12839")] ai/nn/transformer/gpt/codex

With the success of large language models (LLMs) of code and their use as code assistants (eg. Codex used in GitHub Copilot), techniques for introducing domain-specific knowledge in the prompt design process become important.

In this work, we propose a framework called Repo-Level Prompt Generator that learns to generate example-specific prompts using a set of rules. These rules take context from the entire repository, thereby incorporating both the structure of the repository and the context from other relevant files (eg. imports, parent class files). Our technique doesn’t require any access to the weights of the LLM, making it applicable in cases where we only have black-box access to the LLM. We conduct experiments on the task of single-line code-autocompletion using code repositories taken from Google Code archives.

We demonstrate that an oracle constructed from our proposed rules gives up to 36% relative improvement over Codex, showing the quality of the rules. Further, we show that when we train a model to select the best rule, we can achieve performance gains over Codex.

The code for our work can be found at: Github.

--- /doc/politics/2022-salfate.pdf A Longitudinal Test of the Conservative-Liberal Well-Being Gap Salvador Vargas Salfate, Sammyh S. Khan, James H. Liu, Homero Gil de Zúñiga 2022-07-07 2022-07-07 [("doi","10.1177/01461672221096587")] politics psychology/personality

In this article, we test if conservatism predicts psychological well-being longitudinally. We based the study on previous findings showing that conservatives score higher on different measures of well-being, such as life satisfaction and happiness. Most explanations in the literature have assumed that conservatism precedes well-being without considering the alternative—that well-being may predict conservatism.

In Study 1, using multilevel cross-lagged panel models with a two-wave longitudinal sample consisting of data from 19 countries (n = 8,740), we found that conservatism did not predict well-being over time.

We found similar results in Study 2 (n = 2,554), using random-intercept cross-lagged panel models with a four-wave longitudinal sample from Chile.

We discuss the main implications of these results for the literature examining the association between conservatism and well-being.

--- https://palmerlab.org/neuroticism-and-depression-gwas-consortium-paper-accepted-for-publication-in-jama-psychiatry-abraham-palmer-harriet-de-wit-and-amy-hart-are-co-authors/ Genome-wide association study identifies novel locus for neuroticism and shows polygenic association with Major Depressive Disorder 2021-11-04 psychiatry/depression --- https://en.wikipedia.org/wiki/Inflammation#Connection_to_depression Inflammation § Connection to depression 2021-11-04 psychiatry/depression --- /doc/genetics/heritable/2019-border-supplement.pdf Supplement to No support for historic candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples Richard Border, Emma C. Johnson, Luke M. Evans, Andrew Smolen, Noah Berley, Patrick F. Sullivan, Matthew C. Keller 2019-05-01 2021-11-04 [("doi","10.1176/appi.ajp.2018.18070881/suppl_file/appi.ajp.2018.18070881.ds001")] genetics/heritable psychiatry/depression --- /doc/genetics/heritable/correlation/2019-trzaskowski.pdf Quantifying between-cohort and between-sex genetic heterogeneity in major depressive disorder Maciej Trzaskowski, Divya Mehta, Wouter J. Peyrot, David Hawkes, Daniel Davies, David M. Howard, Kathryn E. Kemper, Julia Sidorenko, Robert Maier, Stephan Ripke, Manuel Mattheisen, Bernhard T. Baune, Hans J. Grabe, Andrew C. Heath, Lisa Jones, Ian Jones, Pamela A. F. Madden, Andrew M. McIntosh, Gerome Breen, Cathryn M. Lewis, Anders Børglum, Patrick F. Sullivan, Nicholas G. Martin, Kenneth S. Kendler, Douglas F. Levinson, Naomi R. Wray, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium 2019-01-01 2021-11-04 [("doi","10.1002/ajmg.b.32713")] genetics/heritable/correlation psychiatry/depression --- /doc/genetics/heritable/2018-hannigan.pdf Maternal prenatal depressive symptoms and risk for early-life psychopathology in offspring: genetic analyses in the Norwegian Mother and Child Birth Cohort Study Laurie J. Hannigan, Espen Moen Eilertsen, Line C. Gjerde, Ted Reichborn-Kjennerud, Thalia C. Eley, Fruhling V. Rijsdijk, Eivind Ystrom, Tom A. McAdams 2018-01-01 2021-11-04 [("doi","10.1016/S2215-0366(18)30225-6")] genetics/heritable psychiatry/depression --- /doc/melatonin/2014-hansen.pdf The therapeutic or prophylactic effect of exogenous melatonin against depression and depressive symptoms: A systematic review and meta-analysis M. V. Hansen, A. K. Danielsen, I. Hageman, J. Rosenberg, I. Gögenur 2014-01-01 2021-11-04 [("doi","10.1016/j.euroneuro.2014.08.008")] melatonin psychiatry/depression --- /doc/genetics/heritable/correlation/2013-rice.pdf Examining the role of passive gene-environment correlation in childhood depression using a novel genetically sensitive design Frances Rice, Gema Lewis, Gordon T. Harold, Anita Thapar 2013-02-11 2021-11-04 [("doi","10.1017/S0954579412000880")] genetics/heritable/correlation psychiatry/depression

Parental depression is associated with disruptions in the parent-child relationship, exposure to stressful family life events, and offspring depressive symptoms. Evidence suggests that intergenerational transmission of depression involves environmental and inherited contributions. We sought to evaluate the role of passive gene-environment correlation (rGE) in relation to depression, family life events that were due to parental behavior, and parental positivity in a sample where children varied in genetic relatedness to their rearing parents.

Our study included 865 families with children born through assisted conception (444 related to both parents, 210 related to the mother only, 175 related to the father only, and 36 related to neither parent). Consistent with previous studies, the intergenerational transmission of depressive symptoms was largely due to environmental factors, although parent and child gender influenced results. Maternal and paternal depressive symptoms were associated with reduced positivity and increased parentally imposed life events regardless of parent-child relatedness.

Results of path analysis were consistent with passive rGE for both maternal and paternal positivity in that positivity partially mediated the link between maternal/paternal depression and child depression only in genetically related parent-child pairs. Results also suggested passive rGE involving parentally imposed life events for mothers and fathers although passive rGE effects were smaller than for positivity.

--- /doc/genetics/heritable/2013-tansey.pdf Contribution of Common Genetic Variants to Antidepressant Response Katherine E. Tansey, Michel Guipponi, Xiaolan Hu, Enrico Domenici, Glyn Lewis, Alain Malafosse, Jens R. Wendland, Cathryn M. Lewis, Peter McGuffin, Rudolf Uher 2013-01-01 2021-11-04 [("doi","10.1016/j.biopsych.2012.10.030")] genetics/heritable psychiatry/depression --- /doc/vitamin-d/2013-li.pdf Efficacy of vitamin D supplementation in depression in adults: a systematic review Li 2013 2021-11-04 nootropic psychiatry/depression vitamin-d --- https://depressiongenetics.med.upenn.edu/uep/assets/user-content/documents/DurmerandDinges--NeurocognitiveConsequences--SEM.NEUROL.2005.pdf Neurocognitive Consequences of Sleep Deprivation Durmer, Dinges 2005 2021-11-05 psychiatry/depression zeo --- /doc/melatonin/2012-bedrosian.pdf Chronic dim light at night provokes reversible depression-like phenotype: possible role for TNF T. A. Bedrosian, Z. M. Weil, R. J. Nelson 2012-01-01 2021-11-05 [("doi","10.1038/mp.2012.96")] melatonin psychiatry/depression --- /doc/melatonin/2012-fava.pdf An exploratory study of combination buspirone and melatonin SR in Major Depressive Disorder (MDD): A possible role for neurogenesis in drug discovery Maurizio Fava, Steven D. Targum, Andrew A. Nierenberg, Leo S. Bleicher, Todd A. Carter, Pamela C. Wedel, René Hen, Fred H. Gage, Carrolee Barlow 2012-01-01 2021-11-05 [("doi","10.1016/j.jpsychires.2012.08.013")] melatonin psychiatry/depression --- /doc/melatonin/2011-hickie.pdf Novel melatonin-based therapies: potential advances in the treatment of major depression Ian B. Hickie, Naomi L. Rogers 2011-01-01 2021-11-05 [("doi","10.1016/S0140-6736(11)60095-0")] melatonin psychiatry/depression --- /doc/melatonin/2011-salva.pdf Circadian rhythms, melatonin and depression Ansar 2011-01-01 2021-11-05 melatonin psychiatry/depression --- /doc/genetics/heritable/correlation/2010-lopezleon.pdf Shared genetic factors in the co-occurrence of symptoms of depression and cardiovascular risk factors Sandra López-León, Yurii S. Aulchenko, Henning Tiemeier, Ben A. Oostra, Cornelia van Duijn, A. Cecile J. W. Janssens 2010-01-01 2021-11-05 [("doi","10.1016/j.jad.2009.07.008")] genetics/heritable/correlation psychiatry/depression --- /doc/psychology/2004-almeida.pdf One year follow-up study of the association between chemical castration, sex hormones, beta-amyloid, memory and depression in men Osvaldo P. Almeida, Anna Waterreus, Nigel Spry, Leon Flicker, Ralph N. Martins 2004-01-01 2021-11-05 [("doi","10.1016/j.psyneuen.2003.11.002")] psychiatry/depression psychology --- http://neurogenetics.qimrberghofer.edu.au/papers/Ripke2013MolPsychiatry.pdf A mega-analysis of genome-wide association studies for major depressive disorder 2021-11-05 genetics/heritable psychiatry/depression --- https://colinmendelsohn.com.au/wp-content/uploads/2018/12/Mendelsohn_C._Smoking_and_depression._A_review._AFP_2012415_304-307.pdf Mendelsohn 2012 2021-11-05 nicotine psychiatry/depression --- https://soranews24.com/2015/04/02/evangelion-creator-hideaki-anno-opens-up-about-his-latest-bout-with-depression-movie-delays/ Evangelion creator Hideaki Anno opens up about his latest bout with depression, movie delays 2021-11-05 anime/eva psychiatry/depression --- https://www.badscience.net/2009/01/part-432-in-which-i-get-a-bit-overinterested-and-look-up-waaay-too-many-references/ ...is there good evidence of season having an impact on our collective mood? Seasonal affective disorder is its own separate thing. If you look at the evidence on the population’s mood, depression, and suicide changing over the seasons, you do, in fact, find a glorious mess. 2021-11-05 psychiatry/depression zeo --- /doc/psychology/1980-crook.pdf Parental death during childhood and adult depression: A critical review of the literature Thomas Crook, John Eliot 1980-01-01 2021-11-06 [("doi","10.1037/0033-2909.87.2.252")] psychiatry/depression psychology --- /doc/melatonin/1976-carman.pdf Negative effects of melatonin on depression 1976 2021-11-06 melatonin psychiatry/depression --- /doc/melatonin/1979-mendlewicz.pdf Abnormal 24 hour pattern of melatonin secretion in depression 1979 2021-11-06 melatonin psychiatry/depression --- /doc/melatonin/1984-claustrat.pdf A chronobiological study of melatonin and cortisol secretion in depressed subjects: plasma melatonin, a biochemical marker in major depression 1984 2021-11-06 melatonin psychiatry/depression --- /doc/melatonin/1984-nair.pdf Circadian rhythm of plasma melatonin in endogenous depression 1984 2021-11-06 melatonin psychiatry/depression --- /doc/melatonin/1986-frazer.pdf Patterns of melatonin rhythms in depression 1986 2021-11-06 melatonin psychiatry/depression --- /doc/melatonin/1997-voderholzer.pdf Circadian profiles of melatonin in melancholic depressed patients and healthy subjects in relation to cortisol secretion and sleep 1997 2021-11-06 melatonin psychiatry/depression --- /doc/melatonin/1998-lewy.pdf Melatonin treatment of winter depression: a pilot study 1998 2021-11-06 melatonin psychiatry/depression --- /doc/melatonin/2010-serfaty.pdf A randomized double-blind placebo-controlled trial of treatment as usual plus exogenous slow-release melatonin (6 mg) or placebo for sleep disturbance and depressed mood 2010 2021-11-06 melatonin psychiatry/depression --- /doc/melatonin/2004-crasson.pdf Serum melatonin and urinary 6-sulfatoxymelatonin in major depression 2004 2021-11-07 melatonin psychiatry/depression --- /doc/melatonin/2011-howland.pdf A benefit-risk assessment of agomelatine in the treatment of major depression 2011 2021-11-07 melatonin psychiatry/depression --- https://www.nature.com/articles/tp2017148 Do regional brain volumes and major depressive disorder share genetic architecture? A study of Generation Scotland (n = 19,762), UK Biobank (n = 24,048) and the English Longitudinal Study of Ageing (n = 5766) 2021-11-07 psychiatry/depression psychology/neuroscience --- https://www.nytimes.com/2013/07/02/magazine/the-half-trillion-dollar-depression.html The Half-Trillion-Dollar Depression 2021-11-07 psychiatry/depression --- https://www.pnas.org/doi/10.1073/pnas.0602425103 The circadian basis of winter depression 2021-11-07 melatonin psychiatry/depression --- https://web.archive.org/web/20190424135326/https://www.reuters.com/article/us-astrazeneca-targacept/astrazeneca-targacept-drug-fails-depression-test-idUSTRE7A71KO20111108 AstraZeneca, Targacept drug fails depression test 2021-11-07 nicotine psychiatry/depression --- https://openai.com/blog/dall-e-2-extending-creativity/ 2021-11-07 ai/nn/transformer/gpt/dall-e/2 --- https://colinmorris.github.io/blog/compound-curse-words Compound pejoratives on Reddit: from ‘buttface’ to ‘wankpuffin’ 2021-11-07 fiction/humor psychology/linguistics --- https://txt.cohere.com/article-recommender/ 2021-11-07 ai/nn/transformer/gpt/non-fiction --- https://www.medrxiv.org/content/10.1101/2022.07.06.22277333.full Systematic comparison of family history and polygenic risk across 24 common diseases Nina Mars, Joni V. Lindbohm, Pietro della Briotta Parolo, Elisabeth Widen, Jaakko Kaprio, Aarno Palotie, FinnGen, Samuli Ripatti 2022-07-07 2022-07-07 [("doi","10.1101/2022.07.06.22277333")] genetics/heritable

family history is the standard indirect measure of inherited susceptibility in clinical care, while polygenic risk scores (PRS) have more recently demonstrated potential for more directly capturing genetic risk in many diseases. No studies have systematically compared how these overlap and complement each other across common diseases.

Within FinnGen (n = 306,418), we leverage family relationships, up to 50 years of nationwide registries, and genome-wide genotyping to examine the interplay of family history and genome-wide PRSs. We explore the dynamic for 3 types of family history across 24 common diseases: first-degree and second-degree family history, and parental causes of death.

Covering a large proportion of the burden of non-communicable diseases in adults, we show that family history and PRS are independent and not interchangeable measures, but instead provide complementary information of inherited disease susceptibility. The PRSs explained on average 10% of the effect of first-degree family history, and first-degree family history 3% of PRSs, and PRS effects were independent of both early-onset and late-onset family history. The PRS stratified the risk similarly in individuals with and without family history. In most diseases, including coronary artery disease, glaucoma, and type 2 diabetes, a positive family history with a high PRS was associated with a considerably elevated risk, whereas a low PRS compensated completely for the risk implied by positive family history.

This study provides a catalogue of risk estimates for both family history of disease and PRSs, and highlights opportunities for a more comprehensive way of assessing inherited disease risk across common diseases.

--- https://arxiv.org/abs/2207.04429 LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action Dhruv Shah, Blazej Osinski, Brian Ichter, Sergey Levine 2022-07-10 2022-07-10 [("doi","10.48550/arXiv.2207.04429")] ai/nn/transformer/clip ai/nn/transformer/gpt reinforcement-learning/model reinforcement-learning/robot

[cf. PIGLeT, Socratic Models, Housekeep, LID, SayCan inner-monologue] Goal-conditioned policies for robotic navigation can be trained on large, unannotated datasets, providing for good generalization to real-world settings. However, particularly in vision-based settings where specifying goals requires an image, this makes for an unnatural interface. Language provides a more convenient modality for communication with robots, but contemporary methods typically require expensive supervision, in the form of trajectories annotated with language descriptions.

We present a system, LM-Nav, for robotic navigation that enjoys the benefits of training on unannotated large datasets of trajectories, while still providing a high-level interface to the user. Instead of utilizing a labeled instruction following dataset, we show that such a system can be constructed entirely out of pre-trained models for navigation (ViNG), image-language association (CLIP), and language modeling (GPT-3), without requiring any fine-tuning or language-annotated robot data. We instantiate LM-Nav on a real-world mobile robot and demonstrate long-horizon navigation through complex, outdoor environments from natural language instructions.

For videos of our experiments, code release, and an interactive Colab notebook that runs in your browser, please check out our project page https://sites.google.com/view/lmnav.

--- https://arxiv.org/abs/2012.09812 ViNG: Learning Open-World Navigation with Visual Goals Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine 2020-12-17 2021-11-08 [("doi","10.48550/arXiv.2012.09812")] reinforcement-learning/model reinforcement-learning/robot

[used in LM-Nav] We propose a learning-based navigation system for reaching visually indicated goals and demonstrate this system on a real mobile robot platform. Learning provides an appealing alternative to conventional methods for robotic navigation: instead of reasoning about environments in terms of geometry and maps, learning can enable a robot to learn about navigational affordances, understand what types of obstacles are traversable (eg. tall grass) or not (eg. walls), and generalize over patterns in the environment. However, unlike conventional planning algorithms, it is harder to change the goal for a learned policy during deployment.

We propose a method for learning to navigate towards a goal image of the desired destination. By combining a learned policy with a topological graph constructed out of previously observed data, our system can determine how to reach this visually indicated goal even in the presence of variable appearance and lighting. 3 key insights—waypoint proposal, graph pruning and negative mining—enable our method to learn to navigate in real-world environments using only offline data, a setting where prior methods struggle.

We instantiate our method on a real outdoor ground robot and show that our system, which we call ViNG, outperforms previously-proposed methods for goal-conditioned reinforcement learning, including other methods that incorporate reinforcement learning and search. We also study how generalizes to unseen environments and evaluate its ability to adapt to such an environment with growing experience. Finally, we demonstrate ViNG on a number of real-world applications, such as last-mile delivery and warehouse inspection.

We encourage the reader to visit the project website for videos of our experiments and demonstrations.

--- https://arxiv.org/abs/1706.10283 Bolt: Accelerated Data Mining with Fast Vector Compression Davis W. Blalock, John V. Guttag 2017-06-30 2021-11-08 [("doi","10.1145/3097983.3098195")] ai/nn/retrieval ai/nn/sparsity/low-precision

Vectors of data are at the heart of machine learning and data mining. Recently, vector quantization methods have shown great promise in reducing both the time and space costs of operating on vectors.

We introduce a vector quantization algorithm that can compress vectors over 12× faster than existing techniques while also accelerating approximate vector operations such as distance and dot product computations by up to 10×. Because it can encode over 2GB of vectors per second, it makes vector quantization cheap enough to employ in many more circumstances. For example, using our technique to compute approximate dot products in a nested loop can multiply matrices faster than a state-of-the-art BLAS implementation, even when our algorithm must first compress the matrices.

In addition to showing the above speedups, we demonstrate that our approach can accelerate nearest neighbor search and maximum inner product search by over 100× compared to floating point operations and up to 10× compared to other vector quantization methods. Our approximate Euclidean distance and dot product computations are not only faster than those of related algorithms with slower encodings, but also faster than Hamming distance computations, which have direct hardware support on the tested platforms.

We also assess the errors of our algorithm’s approximate distances and dot products, and find that it is competitive with existing, slower vector quantization algorithms.

--- https://en.wikipedia.org/wiki/Bipolar_I_disorder Bipolar I disorder 2021-11-08 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Bipolar_II_disorder Bipolar II disorder 2021-11-08 psychiatry/bipolar/elon-musk --- https://en.wikipedia.org/wiki/Bipolar_disorder_not_otherwise_specified Bipolar disorder not otherwise specified 2021-11-08 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Mood_stabilizer Mood stabilizer 2021-11-08 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Atypical_antipsychotic Atypical antipsychotic 2021-11-08 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Bipolar_disorder#Bipolar_spectrum Bipolar spectrum 2021-11-08 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Hypomania Hypomanic episode 2021-11-08 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Mania Mania 2021-11-08 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Cyclothymia Cyclothymia 2021-11-08 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Lithium_(medication) Lithium (medication) 2021-11-09 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Quetiapine Quetiapine 2021-11-09 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Antidepressant Antidepressant 2021-11-09 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Outline_of_bipolar_disorder Outline of bipolar disorder 2021-11-09 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Bipolar_disorder Bipolar disorders research 2021-11-09 psychiatry/bipolar --- https://en.wikipedia.org/wiki/List_of_people_with_bipolar_disorder List of people with bipolar disorder 2021-11-09 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Biology_of_bipolar_disorder Biology of bipolar disorder 2021-11-09 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Bipolar_disorder_in_children Bipolar disorder in children 2021-11-09 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Treatment_of_bipolar_disorder Treatment of bipolar disorder 2021-11-09 psychiatry/bipolar --- https://en.wikipedia.org/wiki/History_of_bipolar_disorder History of bipolar disorder 2021-11-09 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Creativity_and_mental_health#Bipolar_disorder Creativity and mental illness § Bipolar disorder 2021-11-10 psychiatry/bipolar --- https://slatestarcodex.com/2014/01/12/areteaus-on-bipolar-disorder/ Aretaeus On Bipolar Disorder 2021-11-10 psychiatry/bipolar --- /doc/psychiatry/bipolar/lithium/2017-kessing.pdf Lithium in drinking water and the incidence of bipolar disorder: A Danish nation-wide population-based study 2017 2021-11-10 psychiatry/bipolar/lithium --- /doc/genetics/heritable/2019-prata.pdf Unravelling the genetic basis of schizophrenia and bipolar disorder with GWAS: A systematic review Diana P. Prata, Bernardo Costa-Neves, Gonçalo Cosme, Evangelos Vassos 2019-07-01 2021-11-10 [("doi","10.1016/j.jpsychires.2019.04.007")] genetics/heritable psychiatry/bipolar/genetics psychiatry/schizophrenia --- /doc/genetics/heritable/correlation/2016-vanhulzen.pdf Genetic overlap between Attention-Deficit/Hyperactivity Disorder and Bipolar Disorder: Evidence from GWAS meta-analysis Meta-analysis of ADHD and BPD GWAS Kimm J. E. van Hulzen, Claus J. Scholz, Barbara Franke, Stephan Ripke, Marieke Klein, Andrew McQuillin, Edmund J. Sonuga-Barke, John R. Kelsoe, Mikael Landén, Ole A. Andreassen, Klaus-Peter Lesch, Heike Weber, Stephen V. Faraone, Alejandro Arias-Vasquez, Andreas Reif 2016-01-01 2021-11-10 [("doi","10.1016/j.biopsych.2016.08.040")] genetics/heritable/correlation psychiatry/adhd psychiatry/bipolar/genetics --- /doc/modafinil/2010-hensch.pdf Stimulants in bipolar disorder: beyond common beliefs Tilman Hensch, Hubertus Himmerich, Ulrich Hegerl 2010-07-01 2021-11-10 [("doi","10.1017/S1092852900000407")] modafinil psychiatry/bipolar/energy --- https://www.cambridge.org/core/journals/the-british-journal-of-psychiatry/article/lithium-and-risk-for-alzheimers-disease-in-elderly-patients-with-bipolar-disorder/2F966D8BD1FF7E946C95949950AA61C1 Lithium and risk for Alzheimer’s disease in elderly patients with bipolar disorder Nunes 2007 2021-11-10 psychiatry/alzheimers psychiatry/bipolar/lithium --- https://schizophreniabulletin.oxfordjournals.org/content/24/3/321.full.pdf At issue: Is household crowding a risk factor for schizophrenia and bipolar disorder? Torrey 1998 2021-11-10 psychiatry/bipolar psychiatry/schizophrenia sociology --- https://arxiv.org/abs/2207.06983 Multitrack Music Transformer: Learning Long-Term Dependencies in Music with Diverse Instruments Hao-Wen Dong, Ke Chen, Shlomo Dubnov, Julian McAuley, Taylor Berg-Kirkpatrick 2022-07-14 2022-07-14 [("doi","10.48550/arXiv.2207.06983")] ai/music ai/nn/transformer/attention

Existing approaches for generating multi-track music with transformer models have been limited to either a small set of instruments or short music segments. This is partly due to the memory requirements of the lengthy input sequences necessitated by existing representations for multi-track music.

In this work, we propose a compact representation that allows a diverse set of instruments while keeping a short sequence length. Using our proposed representation, we present the Multitrack Music Transformer (MTMT) for learning long-term dependencies in multi-track music.

In a subjective listening test, our proposed model achieves competitive quality on unconditioned generation against two baseline models. We also show that our proposed model can generate samples that are twice as long as those produced by the baseline models, and, further, can do so in half the inference time. Moreover, we propose a new measure for analyzing musical self-attentions and show that the trained model learns to pay less attention to notes that form a dissonant interval with the current note, yet attending more to notes that are 4N beats away from current.

Finally, our findings provide a novel foundation for future work exploring longer-form multi-track music generation and improving self-attentions for music.

All source code and audio samples can be found at https://hermandong.com/mtmt/.

--- https://x.com/paultrillo/status/1547274303552438274 2021-11-10 ai/nn/transformer/gpt/dall-e --- https://vimeo.com/705939460 Adept Video Demo! 2021-11-10 ai/nn/transformer/gpt/codex --- https://www.biorxiv.org/content/10.1101/2022.07.13.499969.full High-performing neural network models of visual cortex benefit from high latent dimensionality Eric Elmoznino, Michael F. Bonner 2022-07-13 2022-07-13 [("doi","10.1101/2022.07.13.499969")] ai/nn ai/scaling psychology/neuroscience

Geometric descriptions of deep neural networks (DNNs) have the potential to uncover core principles of computational models in neuroscience, while abstracting over the details of model architectures and training paradigms.

Here we examined the geometry of DNN models of visual cortex by quantifying the latent dimensionality of their natural image representations. The prevailing view holds that optimal DNNs compress their representations onto low-dimensional manifolds to achieve invariance and robustness, which suggests that better models of visual cortex should have low-dimensional geometries.

Surprisingly, we found a strong trend in the opposite direction—neural networks with high-dimensional image manifolds tend to have better generalization performance when predicting cortical responses to held-out stimuli in both monkey electrophysiology and human fMRI data.

These findings held across a diversity of design parameters for DNNs, and they suggest a general principle whereby high-dimensional geometry confers a striking benefit to DNN models of visual cortex.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642054/ High-dimensional geometry of population responses in visual cortex Carsen Stringer, Marius Pachitariu, Nicholas Steinmetz, Matteo Carandini, Kenneth D. Harris 2019 2021-11-11 [("doi","10.1038/s41586-019-1346-5")] psychology/neuroscience psychology/vision

A neuronal population encodes information most efficiently when its stimulus responses are high-dimensional and uncorrelated, and most robustly when they are lower-dimensional and correlated. Here we analyzed the dimensionality of the encoding of natural images by large populations of neurons in the visual cortex of awake mice.

The evoked population activity was high-dimensional, and correlations obeyed an unexpected power law: the nth principal component variance scaled as 1/n. This scaling was not inherited from the power law spectrum of natural images, because it persisted after stimulus whitening.

We proved mathematically that if the variance spectrum was to decay more slowly then the population code could not be smooth, allowing small changes in input to dominate population activity. The theory also predicts larger power-law exponents for lower-dimensional stimulus ensembles, which we validated experimentally.

These results suggest that coding smoothness may represent a fundamental constraint that determines correlations in neural population codes.

--- https://hedgehogreview.com/web-features/thr/posts/past-lives-of-the-paragraph Past Lives of the Paragraph 2021-11-11 design/typography --- https://leighmariebraswell.substack.com/p/overview-and-applications-of-large Overview & Applications of Large Language Models (LLMs) 2021-11-11 ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/1511.06349#google Generating Sentences from a Continuous Space Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Jozefowicz, Samy Bengio 2015-11-19 2021-11-11 [("doi","10.48550/arXiv.1511.06349")] ai/nn/rnn

The standard recurrent neural network language model (RNNLM) generates sentences one word at a time and does not work from an explicit global sentence representation.

In this work, we introduce and study an RNN-based variational autoencoder generative model that incorporates distributed latent representations of entire sentences. This factorization allows it to explicitly model holistic properties of sentences such as style, topic, and high-level syntactic features.

Samples from the prior over these sentence representations remarkably produce diverse and well-formed sentences through simple deterministic decoding. By examining paths through this latent space, we are able to generate coherent novel sentences that interpolate between known sentences.

We present techniques for solving the difficult learning problem presented by this model, demonstrate its effectiveness in imputing missing words, explore many interesting properties of the model’s latent sentence space, and present negative results on the use of the model in language modeling.

--- https://www.lesswrong.com/posts/wE7SK8w8AixqknArs/a-time-invariant-version-of-laplace-s-rule A time-invariant version of Laplace’s rule 2021-11-11 statistics/bayes --- https://arxiv.org/abs/2206.00927 DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu 2022-06-02 2022-06-02 [("doi","10.48550/arXiv.2206.00927")] ai/nn/diffusion

Diffusion probabilistic models (DPMs) are emerging powerful generative models. Despite their high-quality generation performance, DPMs still suffer from their slow sampling as they generally need hundreds or thousands of sequential function evaluations (steps) of large neural networks to draw a sample. Sampling from DPMs can be viewed alternatively as solving the corresponding diffusion ordinary differential equations (ODEs).

In this work, we propose an exact formulation of the solution of diffusion ODEs. The formulation analytically computes the linear part of the solution, rather than leaving all terms to black-box ODE solvers as adopted in previous works. By applying change-of-variable, the solution can be equivalently simplified to an exponentially weighted integral of the neural network. Based on our formulation, we propose DPM-Solver, a fast dedicated high-order solver for diffusion ODEs with the convergence order guarantee. DPM-Solver is suitable for both discrete-time and continuous-time DPMs without any further training.

Experimental results show that DPM-Solver can generate high-quality samples in only 10 to 20 function evaluations on various datasets. We achieve 4.70 FID in 10 function evaluations and 2.87 FID in 20 function evaluations on the CIFAR-10 dataset, and a 4–16× speedup compared with previous state-of-the-art training-free samplers on various datasets.

--- https://www.biorxiv.org/content/10.1101/2021.10.21.465334.full A pathogenic fungus uses volatiles to entice male flies into fatal matings with infected female cadavers Andreas Naundrup, Björn Bohman, Charles A. Kwadha, Annette B. Jensen, Paul G. Becher, Henrik H. De Fine Licht 2021-10-22 2021-11-11 [("doi","10.1101/2021.10.21.465334")] biology psychology/animal psychology/smell

To ensure dispersal, many parasites and pathogens behaviorally manipulate infected hosts. Other pathogens and certain insect-pollinated flowers use sexual mimicry and release deceptive mating signals. However, it is unusual for pathogens to rely on both behavioral host manipulation and sexual mimicry. Here, we show that the host-specific and behaviorally manipulating pathogenic fungus, Entomophthora muscae, generates a chemical blend of volatile sesquiterpenes and alters the level of natural host cuticular hydrocarbons in dead infected female house fly (Musca domestica) cadavers.

Healthy male house flies respond to the fungal compounds and are enticed into mating with dead female cadavers. This is advantageous for the fungus as close proximity between host individuals leads to an increased probability of infection.

The fungus-emitted volatiles thus represent the evolution of an extended phenotypic trait that exploites male flies’ willingness to mate and benefit the fungus by altering the behavioral phenotype of uninfected healthy male host flies.

--- https://neuralmagic.com/blog/bert-large-prune-once-for-distilbert-inference-performance/ BERT-Large: Prune Once for DistilBERT Inference Performance 2021-11-11 ai/nn/sparsity --- https://github.com/neuralmagic/sparseml neuralmagic/sparseml: Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models 2021-11-11 ai/nn/sparsity --- https://github.com/neuralmagic/deepsparse Sparsity-aware deep learning inference runtime for CPUs 2021-11-11 ai/nn/sparsity --- https://arxiv.org/abs/2206.14268 BertNet: Harvesting Knowledge Graphs from Pretrained Language Models Shibo Hao, Bowen Tan, Kaiwen Tang, Hengzhe Zhang, Eric P. Xing, Zhiting Hu 2022-06-28 2022-06-28 [("doi","10.48550/arXiv.2206.14268")] ai/nn/transformer

Symbolic knowledge graphs (KGs) have been constructed either by expensive human crowdsourcing or with domain-specific complex information extraction pipelines. The emerging large pretrained language models (LMs), such as Bert, have shown to implicitly encode massive knowledge which can be queried with properly designed prompts. However, compared to the explicit KGs, the implicit knowledge in the black-box LMs is often difficult to access or edit and lacks explainability.

In this work, we aim at harvesting symbolic KGs from the LMs, a new framework for automatic KG construction empowered by the neural LMs’ flexibility and scalability. Compared to prior works that often rely on large human annotated data or existing massive KGs, our approach requires only the minimal definition of relations as inputs, and hence is suitable for extracting knowledge of rich new relations not available before.The approach automatically generates diverse prompts, and performs efficient knowledge search within a given LM for consistent and extensive outputs. The harvested knowledge with our approach is substantially more accurate than with previous methods, as shown in both automatic and human evaluation.

As a result, we derive from diverse LMs a family of new KGs (eg. BertNet & RoBERTaNet) that contain a richer set of commonsense relations, including complex ones (eg. “A is capable of but not good at B”), than the human-annotated KGs (eg. ConceptNet).

Besides, the resulting KGs also serve as a vehicle to interpret the respective source LMs, leading to new insights into the varying knowledge capability of different LMs.

--- https://web.archive.org/web/20220716020913/https://www.reddit.com/user/haaaaven/comments/w05f56/massive_dalle_2_anime_keywords_modifiers_list/ MASSIVE 💥 DALL·E 2 ANIME ⚡︎ KEYWORDS + MODIFIERS LIST ★ : haaaaven 2021-11-12 ai/anime ai/nn/transformer/gpt/dall-e --- https://x.com/nptacek/status/1548402120075800577 2021-11-12 ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2207.05378 Collaborative Neural Rendering using Anime Character Sheets Zuzeng Lin, Ailin Huang, Zhewei Huang, Chen Hu, Shuchang Zhou 2022-07-12 2022-07-12 [("doi","10.48550/arXiv.2207.05378")] ai/anime/danbooru

Drawing images of characters at desired poses is an essential but laborious task in anime production.

In this paper, we present the Collaborative Neural Rendering (~CoNR) method to create new images from a few arbitrarily posed reference images available in character sheets. In general, the high diversity of body shapes of anime characters defies the employment of universal body models for real-world humans, like SMPL. To overcome this difficulty, CoNR uses a compact and easy-to-obtain landmark encoding to avoid creating a unified UV mapping in the pipeline.

In addition, CoNR’s performance can be increased when having multiple reference images by using feature space cross-view dense correspondence and warping in a specially designed neural network construct.

Moreover, we collect a character sheet dataset containing over 700,000 hand-drawn and synthesized images of diverse poses to facilitate research in this area.

--- https://arxiv.org/abs/2205.07125 A Low-latency Communication Design for Brain Simulations Xin Du 2022-05-14 2022-05-14 [("doi","10.48550/arXiv.2205.07125")] ai/scaling/hardware psychology/neuroscience

Brain simulation, as one of the latest advances in artificial intelligence, facilitates better understanding about how information is represented and processed in the brain. The extreme complexity of human brain makes brain simulations only feasible upon high-performance computing platforms. Supercomputers with a large number of interconnected graphical processing units (GPUs) are currently employed for supporting brain simulations. Therefore, high-throughput low-latency inter-GPU communications in supercomputers play a crucial role in meeting the performance requirements of brain simulation as a highly time-sensitive application.

In this paper, we first provide an overview of the current parallelizing technologies for brain simulations using multi-GPU architectures. Then, we analyze the challenges to communications for brain simulation and summarize guidelines for communication design to address such challenges. Furthermore, we propose a partitioning algorithm and a two-level routing method to achieve efficient low-latency communications in multi-GPU architecture for brain simulation.

We report experiment results obtained on the Kunlun supercomputer with 2,000 GPUs for simulating a brain model with 10 billion spiking neurons to show that our approach can substantially improve communication performance.

We also discuss open issues and identify some research directions for low-latency communication design for brain simulations.

--- https://en.wikipedia.org/wiki/Spiking_neural_network Spiking neural network 2021-11-12 ai/scaling/hardware psychology/neuroscience --- https://bjoernkarmann.dk/project/occlution_grotesque Occlution Grotesque 2021-11-12 design/typography --- https://arxiv.org/abs/2207.07413 SATAn: Air-Gap Exfiltration Attack via Radio Signals From SATA Cables Mordechai Guri 2022-07-15 2022-07-15 [("doi","10.48550/arXiv.2207.07413")] cs/security

This paper introduces a new type of attack on isolated, air-gapped workstations. Although air-gap computers have no wireless connectivity, we show that attackers can use the SATA cable as a wireless antenna to transfer radio signals at the 6 GHz frequency band. The Serial ATA (SATA) is a bus interface widely used in modern computers and connects the host bus to mass storage devices such as hard disk drives, optical drives, and solid-state drives. The prevalence of the SATA interface makes this attack highly available to attackers in a wide range of computer systems and IT environments.

We discuss related work on this topic and provide technical background. We show the design of the transmitter and receiver and present the implementation of these components. We also demonstrate the attack on different computers and provide the evaluation. The results show that attackers can use the SATA cable to transfer a brief amount of sensitive information from highly secured, air-gap computers wirelessly to a nearby receiver. Furthermore, we show that the attack can operate from user mode, is effective even from inside a Virtual Machine (VM), and can successfully work with other running workloads in the background. Finally, we discuss defense and mitigation techniques for this new air-gap attack.

--- https://arxiv.org/abs/2203.00759 HyperPrompt: Prompt-based Task-Conditioning of Transformers Yun He, Huaixiu Steven Zheng, Yi Tay, Jai Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, YaGuang Li, Zhao Chen, Donald Metzler, Heng-Tze Cheng, Ed H. Chi 2022-03-01 2022-03-01 [("doi","10.48550/arXiv.2203.00759")] ai/nn/transformer/t5 reinforcement-learning/meta-learning

Prompt-Tuning is a new paradigm for finetuning pre-trained language models in a parameter-efficient way. Here, we explore the use of HyperNetworks to generate hyper-prompts: we propose HyperPrompt, a novel architecture for prompt-based task-conditioning of self-attention in Transformers.

The hyper-prompts are end-to-end learnable via generation by a HyperNetwork. HyperPrompt allows the network to learn task-specific feature maps where the hyper-prompts serve as task global memories for the queries to attend to, at the same time enabling flexible information sharing among tasks.

We show that HyperPrompt is competitive against strong multi-task learning baselines with as few as 0.14% of additional task-conditioning parameters, achieving great parameter and computational efficiency. Through extensive empirical experiments, we demonstrate that HyperPrompt can achieve superior performances over strong T5 multi-task learning baselines and parameter-efficient adapter variants including Prompt-Tuning and HyperFormer++ on Natural Language Understanding benchmarks of GLUE and SuperGLUE across many model sizes.

--- https://spectrum.ieee.org/cochlear-implant Restoring Hearing With Beams of Light 2021-11-12 genetics/editing psychology/neuroscience --- https://en.wikipedia.org/wiki/HyperNEAT HyperNEAT 2021-11-12 reinforcement-learning/exploration reinforcement-learning/meta-learning --- https://www.biorxiv.org/content/10.1101/2022.07.15.500223.full Modeling the genetic footprint of fluctuating balancing selection: From the local to the genomic scale Meike J. Wittmann, Sylvain Mousset, Joachim Hermisson 2022-07-18 2022-07-18 [("doi","10.1101/2022.07.15.500223")] genetics/selection/natural

Natural selection not only affects the actual loci under selection but also leaves “footprints” in patterns of genetic variation in linked genetic regions. This offers exciting opportunities for inferring selection and for understanding the processes shaping levels of genetic variation in natural populations. Here we develop analytical approximations based on coalescent theory to characterize the genetic footprint of a complex, but potentially common type of natural selection: balancing selection with seasonally fluctuating allele frequencies.

We show that seasonal allele frequency fluctuations can have important (and partly unexpected) consequences for the genetic footprint of balancing selection. As also confirmed by stochastic simulations, fluctuating balancing selection generally leads to an increase in genetic diversity close to the selected site, the effect of balancing selection, but reduces diversity further away from the selected site, which is a consequence of the allele-frequency fluctuations effectively producing recurrent bottlenecks of allelic backgrounds.

This negative effect usually outweighs the positive effect when averaging diversity levels across the entire chromosome. Strong fluctuating balancing selection even induces a loss of genetic variation in unlinked regions, eg. on different chromosomes. If many loci in the genome are simultaneously under fluctuating balancing selection this could lead to substantial genome-wide reductions in genetic diversity.

This may be the case, even if allele-frequency fluctuations are so small that individual footprints are hard to detect. Thus, together with genetic drift, selective sweeps, and background selection, fluctuating selection could be one of the major forces shaping levels of genetic diversity in natural populations.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3745084/ How can audiovisual pathways enhance the temporal resolution of time-compressed speech in blind subjects? Ingo Hertrich, Susanne Dietrich, Hermann Ackermann 2013 2021-11-13 [("doi","10.3389/fpsyg.2013.00530")] psychology/vision

In blind people, the visual channel cannot assist face-to-face communication via lipreading or visual prosody. Nevertheless, the visual system may enhance the evaluation of auditory information due to its cross-links to (1) the auditory system, (2) supramodal representations, and (3) frontal action-related areas. Apart from feedback or top-down support of, for example, the processing of spatial or phonological representations, experimental data have shown that the visual system can impact auditory perception at more basic computational stages such as temporal signal resolution.

For example, blind as compared to sighted subjects are more resistant against backward masking, and this ability appears to be associated with activity in visual cortex. Regarding the comprehension of continuous speech, blind subjects can learn to use accelerated text-to-speech systems for “reading” texts at ultra-fast speaking rates (>16 syllables/s), exceeding by far the normal range of 6 syllables/s. A functional magnetic resonance imaging study has shown that this ability, among other brain regions, covaries with BOLD responses in bilateral pulvinar, right visual cortex, and left supplementary motor area. Furthermore, magnetoencephalographic measurements revealed a particular component in right occipital cortex phase-locked to the syllable onsets of accelerated speech.

In sighted people, the “bottleneck” for understanding time-compressed speech seems related to higher demands for buffering phonological material and is, presumably, linked to frontal brain structures. On the other hand, the neurophysiological correlates of functions overcoming this bottleneck, seem to depend upon early visual cortex activity.

The present Hypothesis and Theory paper outlines a model that aims at binding these data together, based on early cross-modal pathways that are already known from various audiovisual experiments on cross-modal adjustments during space, time, and object recognition.

--- https://aftertheflood.com/journal/the-worlds-first-code-free-sparkline-typeface/ The world’s first code-free sparkline typeface: Displaying charts in text without having to use code 2021-11-13 cs design/typography design/visualization --- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1000011 When Learning and Remembering Compete: A Functional MRI Study Willem Huijbers, Cyriel M. Pennartz, Roberto Cabeza, Sander M. Daselaar 2008-11-26 2021-11-13 [("doi","10.1371/journal.pbio.1000011")] dual-n-back psychology/neuroscience

Recent functional neuroimaging evidence suggests a bottleneck between learning new information and remembering old information. In two behavioral experiments and one functional MRI (fMRI) experiment, we tested the hypothesis that learning and remembering compete when both processes happen within a brief period of time. In the first behavioral experiment, participants intentionally remembered old words displayed in the foreground, while incidentally learning new scenes displayed in the background. In line with a memory competition, we found that remembering old information was associated with impaired learning of new information. We replicated this finding in a subsequent fMRI experiment, which showed that this behavioral effect was coupled with a suppression of learning-related activity in visual and medial temporal areas. Moreover, the fMRI experiment provided evidence that left mid-ventrolateral prefrontal cortex is involved in resolving the memory competition, possibly by facilitating rapid switching between learning and remembering. Critically, a follow-up behavioral experiment in which the background scenes were replaced with a visual target detection task provided indications that the competition between learning and remembering was not merely due to attention. This study not only provides novel insight into our capacity to learn and remember, but also clarifies the neural mechanisms underlying flexible behavior.

Author Summary: This study provides clear evidence for a bottleneck in our memory system between learning new and remembering old information. The ability to continuously learn and remember is usually taken for granted. Virtually all interactive situations we encounter require concurrent learning and remembering. For example, normal social communication requires that we process the new information that another person is providing. While listening, we are usually already retrieving information in preparation of an appropriate reply. Other examples include driving through an unfamiliar city while interpreting familiar traffic signs, or encountering novel products during shopping while remembering what we need. Although these examples clearly illustrate the importance of the simultaneous occurrence of learning and remembering, this study shows that remembering and learning compete for resources when both processes happen within a brief period. The study also examined the neural consequences of the competition between learning and remembering using functional MRI (fMRI). In line with the behavioral competition, the neuroimaging results showed a clear suppression of learning-related brain activity as a result of concurrent remembering. Finally, the study provides evidence that a specific region in the prefrontal cortex can resolve the bottleneck, possibly by allowing rapid switching between learning and remembering

When we try to learn and remember at the same time, a bottleneck occurs within our memory system with both behavioral and neural costs.

--- https://arxiv.org/abs/2202.03528 TACTiS: Transformer-Attentional Copulas for Time Series Alexandre Drouin, Étienne Marcotte, Nicolas Chapados 2022-02-07 2022-02-07 [("doi","10.48550/arXiv.2202.03528")] ai/nn/transformer statistics/prediction

[Twitter] The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. However, the practical utility of such estimates is limited by how accurately they quantify predictive uncertainty.

In this work, we address the problem of estimating the joint predictive distribution of high-dimensional multivariate time series.

We propose a versatile method, TACTiS, based on the transformer architecture, that estimates joint distributions using an attention-based decoder that provably learns to mimic the properties of non-parametric copulas.

The resulting model has several desirable properties: it can scale to hundreds of time series, supports both forecasting and interpolation, can handle unaligned and non-uniformly sampled data, and can seamlessly adapt to missing data during training.

We demonstrate these properties empirically and show that our model produces state-of-the-art predictions on multiple real-world datasets.

--- https://x.com/d_feldman/status/1549607411845152770 2021-11-13 ai/nn/transformer/gpt/codex --- https://writer.mintlify.com/ Writer 2021-11-13 ai/nn/transformer/gpt/codex --- https://web.archive.org/web/20230601024130/https://dailywrong.com/ The Daily Wrong 2021-11-13 ai/nn/transformer/gpt/dall-e --- https://blog.cbs.dk/inframethodology/ 2021-11-13 ai/nn/transformer/gpt/non-fiction --- https://blog.cbs.dk/inframethodology/?p=5386 2021-11-13 ai/nn/transformer/gpt/non-fiction --- https://nyx-ai.github.io/stylegan2-flax-tpu/ 2021-11-14 ai/nn/gan/stylegan --- https://x.com/denny_zhou/status/1547662872511070212 2021-11-14 ai/nn/transformer/gpt/inner-monologue --- https://arxiv.org/abs/2201.13415 Towards Scaling Difference Target Propagation by Learning Backprop Targets Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake Richards, Yoshua Bengio 2022-01-31 2022-01-31 [("doi","10.48550/arXiv.2201.13415")] ai/nn psychology/neuroscience

[Twitter; code] The development of biologically-plausible learning algorithms is important for understanding learning in the brain, but most of them fail to scale-up to real-world tasks, limiting their potential as explanations for learning by real brains. As such, it is important to explore learning algorithms that come with strong theoretical guarantees and can match the performance of backpropagation (BP) on complex tasks.

One such algorithm is Difference Target Propagation (DTP), a biologically-plausible learning algorithm whose close relation with Gauss-Newton (GN) optimization has been recently established. However, the conditions under which this connection rigorously holds preclude layer-wise training of the feedback pathway synaptic weights (which is more biologically plausible). Moreover, good alignment between DTP weight updates and loss gradients is only loosely guaranteed and under very specific conditions for the architecture being trained.

In this paper, we propose a novel feedback weight training scheme that ensures both that DTP approximates BP and that layer-wise feedback weight training can be restored without sacrificing any theoretical guarantees.

Our theory is corroborated by experimental results and we report the best performance ever achieved by DTP on CIFAR-10 and ImageNet 32×32.

--- https://arxiv.org/abs/2207.08822 Is Integer Arithmetic Enough for Deep Learning Training? Alireza Ghaffari, Marzieh S. Tahaei, Mohammadreza Tayaranian, Masoud Asgharian, Vahid Partovi Nia 2022-07-18 2022-07-18 [("doi","10.48550/arXiv.2207.08822")] ai/nn/sparsity/low-precision ai/scaling/hardware

The ever-increasing computational complexity of deep learning models makes their training and deployment difficult on various cloud and edge platforms. Replacing floating-point arithmetic with low-bit integer arithmetic is a promising approach to save energy, memory footprint, and latency of deep learning models. As such, quantization has attracted the attention of researchers in recent years. However, using integer numbers to form a fully functional integer training pipeline including forward pass, back-propagation, and stochastic gradient descent is not studied in detail.

Our empirical and mathematical results reveal that integer arithmetic is enough to train deep learning models. Unlike recent proposals, instead of quantization, we directly switch the number representation of computations. Our novel training method forms a fully integer training pipeline that does not change the trajectory of the loss and accuracy compared to floating-point, nor does it need any special hyper-parameter tuning, distribution adjustment, or gradient clipping.

Our experimental results show that our proposed method is effective in a wide variety of tasks such as classification (including vision transformers), object detection, and semantic segmentation.

--- https://newatlas.com/energy/earthgrid-tunnel-boring-robot/ Earthgrid aims to re-wire the USA using super-cheap tunnel tech 2021-11-14 technology/self-sinking --- https://newatlas.com/technology/petra-thermal-drill-robot/ Watch as Petra’s remarkable thermal bore cuts through undrillable rock 2021-11-14 technology/self-sinking --- https://arxiv.org/abs/2207.09847 Predicting Word Learning in Children from the Performance of Computer Vision Systems Sunayana Rane, Mira L. Nencheva, Zeyu Wang, Casey Lew-Williams, Olga Russakovsky, Thomas L. Griffiths 2022-07-07 2022-07-07 [("doi","10.48550/arXiv.2207.09847")] ai/nn psychology/neuroscience

For human children as well as machine learning systems, a key challenge in learning a word is linking the word to the visual phenomena it describes.

We explore this aspect of word learning by using the performance of computer vision systems as a proxy for the difficulty of learning a word from visual cues.

We show that the age at which children acquire different categories of words is predicted by the performance of visual classification and captioning systems, over and above the expected effects of word frequency. The performance of the computer vision systems is related to human judgments of the concreteness of words, supporting the idea that we are capturing the relationship between words and visual phenomena.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0270429 Have beliefs in conspiracy theories increased over time? 2021-11-14 politics --- https://arxiv.org/abs/2207.09814#microsoft NUWA-∞: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis Chenfei Wu, Jian Liang, Xiaowei Hu, Zhe Gan, Jianfeng Wang, Lijuan Wang, Zicheng Liu, Yuejian Fang, Nan Duan 2022-07-20 2022-07-20 [("doi","10.48550/arXiv.2207.09814")] ai/nn/diffusion ai/nn/transformer/gpt/dall-e/2 ai/video/generation

In this paper, we present NUWA-∞, a generative model for infinite visual synthesis, which is defined as the task of generating arbitrarily-sized high-resolution images or long-duration videos.

An autoregressive over autoregressive generation mechanism is proposed to deal with this variable-size generation task, where a global patch-level autoregressive model considers the dependencies between patches, and a local token-level autoregressive model considers dependencies between visual tokens within each patch. A Nearby Context Pool (NCP) is introduced to cache-related patches already generated as the context for the current patch being generated, which can save computation costs without sacrificing patch-level dependency modeling. An Arbitrary Direction Controller (ADC) is used to decide suitable generation orders for different visual synthesis tasks and learn order-aware positional embeddings.

Compared to DALL·E, Imagen and Parti, NUWA-∞ can generate high-resolution images with arbitrary sizes and support long-duration video generation additionally. Compared to NUWA, which also covers images and videos, NUWA-∞ has superior visual synthesis capabilities in terms of resolution and variable-size generation.

The GitHub link is NUWA. The homepage link is https://nuwa-infinity.microsoft.com/.

--- https://arxiv.org/abs/2206.14176 DayDreamer: World Models for Physical Robot Learning Philipp Wu, Alejandro Escontrela, Danijar Hafner, Ken Goldberg, Pieter Abbeel 2022-06-28 2022-06-28 [("doi","10.48550/arXiv.2206.14176")] reinforcement-learning/model reinforcement-learning/robot

[video; homepage; Twitter] To solve tasks in complex environments, robots need to learn from experience. Deep reinforcement learning is a common approach to robot learning but requires a large amount of trial and error to learn, limiting its deployment in the physical world. As a consequence, many advances in robot learning rely on simulators. On the other hand, learning inside of simulators fails to capture the complexity of the real world, is prone to simulator inaccuracies, and the resulting behaviors do not adapt to changes in the world.

The Dreamer algorithm has recently shown great promise for learning from small amounts of interaction by planning within a learned world model, outperforming pure reinforcement learning in video games. Learning a world model to predict the outcomes of potential actions enables planning in imagination, reducing the amount of trial and error needed in the real environment. However, it is unknown whether Dreamer can facilitate faster learning on physical robots.

In this paper, we apply Dreamer to 4 robots to learn online and directly in the real world, without simulators.

Dreamer trains a quadruped robot to roll off its back, stand up, and walk from scratch and without resets in only 1 hour. We then push the robot and find that Dreamer adapts within 10 minutes to withstand perturbations or quickly roll over and stand back up. On two different robotic arms, Dreamer learns to pick and place multiple objects directly from camera images and sparse rewards, approaching human performance. On a wheeled robot, Dreamer learns to navigate to a goal position purely from camera images, automatically resolving ambiguity about the robot orientation. Using the same hyperparameters across all experiments, we find that Dreamer is capable of online learning in the real world, establishing a strong baseline.

We release our infrastructure for future applications of world models to robot learning.

--- https://x.com/rougeux/status/1546150950469074949 2021-11-14 ai/nn/transformer/gpt/dall-e --- https://www.biorxiv.org/content/10.1101/2022.07.19.500636.full Imputation of ancient genomes Bárbara Sousa da Mota, Simone Rubinacci, Diana Ivette Cruz Dávalos, Carlos Eduardo G. Amorim, Martin Sikora, Niels N. Johannsen, Marzena H. Szmyt, Piotr Włodarcza, Anita Szczepanek, Marcin M. Przybyła, Hannes Schroeder, Morten E. Allentoft, Eske Willerslev, Anna-Sapfo Malaspinas, Olivier Delaneau 2022-07-20 2022-07-20 [("doi","10.1101/2022.07.19.500636")] genetics/sequencing

Due to postmortem DNA degradation, most ancient genomes sequenced to date have low depth of coverage, preventing the true underlying genotypes from being recovered. Genotype imputation has been put forward to improve genotyping accuracy for low-coverage genomes. However, it is unknown to what extent imputation of ancient genomes produces accurate genotypes and whether imputation introduces bias to downstream analyses.

To address these questions, we downsampled 43 ancient genomes, 42 of which are high-coverage (above 10×) and 3 constitute a trio (mother, father and son), from different times and continents to simulate data with coverage in the range of 0.1×-2.0× and imputed these using state-of-the-art methods and reference panels. We assessed imputation accuracy across ancestries and depths of coverage.

We found that ancient and modern DNA imputation accuracies were comparable. We imputed most of the 42 high-coverage genomes downsampled to 1× with low error rates (below 5%) and estimated higher error rates for African genomes, which are underrepresented in the reference panel. We used the ancient trio data to validate imputation and phasing results using an orthogonal approach based on Mendels rules of inheritance. This resulted in imputation and switch error rates of 1.9% and 2.0%, respectively, for 1× genomes.

We further compared the results of downstream analyses between imputed and high-coverage genomes, notably principal component analysis (PCA), genetic clustering, and runs of homozygosity (ROH). For these 3 approaches, we observed similar results between imputed and high-coverage genomes using depths of coverage of at least 0.5×, except for African genomes, for which the decreased imputation accuracy impacted ROH estimates.

Altogether, these results suggest that, for most populations and depths of coverage as low as 0.5×, imputation is a reliable method with potential to expand and improve ancient DNA studies.

--- https://arxiv.org/abs/1812.05159 An Empirical Study of Example Forgetting during Deep Neural Network Learning Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, Geoffrey J. Gordon 2018-12-12 2021-11-15 [("doi","10.48550/arXiv.1812.05159")] ai/nn

Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks.

Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a ‘forgetting event’ to have occurred when an individual training example transitions from being classified correctly to incorrectly over the course of learning.

Across several benchmark data sets, we find that: (1) certain examples are forgotten with high frequency, and some not at all; (2) a data set’s (un)forgettable examples generalize across neural architectures; and (3) based on forgetting dynamics, a large fraction of examples can be omitted from the training data set while still maintaining state-of-the-art generalization performance.

--- https://www.science.org/content/article/potential-fabrication-research-images-threatens-key-theory-alzheimers-disease 2021-11-15 psychiatry/alzheimers psychology/neuroscience statistics/bias --- https://worksinprogress.co/issue/real-peer-review/ Real peer review has never been tried 2021-11-15 statistics/peer-review --- https://research.google/blog/training-generalist-agents-with-multi-game-decision-transformers/ 2021-11-15 reinforcement-learning/model/decision-transformer --- https://www.wired.com/2015/07/soviet-militarys-eerily-detailed-guide-san-diego/ The Soviet Military’s Eerily Detailed Guide to San Diego 2021-11-15 design/visualization --- https://www.wired.com/2015/07/secret-cold-war-maps/ Inside the Secret World of Russia’s Cold War Mapmakers 2021-11-15 design/visualization --- https://arxiv.org/abs/1711.03953 Breaking the Softmax Bottleneck: A High-Rank RNN Language Model Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, William W. Cohen 2017-11-10 2021-11-15 [("doi","10.48550/arXiv.1711.03953")] ai/nn/rnn ai/nn/tokenization

We formulate language modeling as a matrix factorization problem, and show that the expressiveness of Softmax-based models (including the majority of neural language models) is limited by a Softmax bottleneck. Given that natural language is highly context-dependent, this further implies that in practice Softmax with distributed word embeddings does not have enough capacity to model natural language.

We propose a simple and effective method to address this issue, and improve the state-of-the-art perplexities on Penn Treebank and WikiText-2 to 47.69 and 40.68 respectively. The proposed method also excels on the large-scale 1B Word dataset, outperforming the baseline by over 5.6 points in perplexity.

--- https://arxiv.org/abs/1612.08083#facebook Language Modeling with Gated Convolutional Networks Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier 2016-12-23 2021-11-15 [("doi","10.48550/arXiv.1612.08083")] ai/nn/cnn

The predominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context.

In this paper we develop a finite context approach through stacked convolutions, which can be more efficient since they allow parallelization over sequential tokens. We propose a novel simplified gating mechanism that outperforms Oord et al 2016 and investigate the impact of key architectural decisions.

The proposed approach achieves state-of-the-art on the WikiText-103 benchmark, even though it features long-term dependencies, as well as competitive results on the Google Billion Words benchmark. Our model reduces the latency to score a sentence by an order of magnitude compared to a recurrent baseline.

To our knowledge, this is the first time a non-recurrent approach is competitive with strong recurrent models on these large scale language tasks.

--- https://openreview.net/forum?id=X6D9bAHhBQ1#deepmind Stochastic MuZero: Planning in Stochastic Environments with a Learned Model Ioannis Antonoglou, Julian Schrittwieser, Sherjil Ozair, Thomas K. Hubert, David Silver 2022-03-15 2022-03-15 reinforcement-learning/model/muzero

[cf. Nondeterministic MuZero] Model-based reinforcement learning has proven highly successful. However, learning a model in isolation from its use during planning is problematic in complex environments. To date, the most effective techniques have instead combined value-equivalent model learning with powerful tree-search methods. This approach is exemplified by MuZero, which has achieved state-of-the-art performance in a wide range of domains, from board games to visually rich environments, with discrete and continuous action spaces, in online and offline settings. However, previous instantiations of this approach were limited to the use of deterministic models. This limits their performance in environments that are inherently stochastic, partially observed, or so large and complex that they appear stochastic to a finite agent.

In this paper we extend this approach to learn and plan with stochastic models. Specifically, we introduce a new algorithm, Stochastic MuZero, that learns a stochastic model incorporating after-states, and uses this model to perform a stochastic tree search.

Stochastic MuZero matched or exceeded the state-of-the-art in a set of canonical single and multi-agent environments, including 2048 and backgammon, while maintaining the same performance as standard MuZero in the game of Go.

[Keywords: model-based reinforcement learning, deep reinforcement learning, tree based search, MCTS]

--- https://arxiv.org/abs/1911.02590 Optimizing Millions of Hyperparameters by Implicit Differentiation Jonathan Lorraine, Paul Vicol, David Duvenaud 2019-11-06 2021-11-15 [("doi","10.48550/arXiv.1911.02590")] reinforcement-learning/meta-learning

We propose an algorithm for inexpensive gradient-based hyperparameter optimization that combines the implicit function theorem (IFT) with efficient inverse Hessian approximations.

We present results about the relationship between the IFT and differentiating through optimization, motivating our algorithm. We use the proposed approach to train modern network architectures with millions of weights and millions of hyper-parameters. For example, we learn a data-augmentation network—where every weight is a hyperparameter tuned for validation performance—outputting augmented training examples.

Jointly tuning weights and hyperparameters with our approach is only a few times more costly in memory and compute than standard training.

--- https://arxiv.org/abs/1803.06396 Reviving and Improving Recurrent Back-Propagation Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard Zemel 2018-03-16 2021-11-16 [("doi","10.48550/arXiv.1803.06396")] ai/nn/rnn reinforcement-learning/meta-learning

In this paper, we revisit the recurrent back-propagation (RBP) algorithm, discuss the conditions under which it applies as well as how to satisfy them in deep neural networks.

We show that RBP can be unstable and propose two variants based on conjugate gradient on the normal equations (CG-RBP) and Neumann series (Neumann-RBP). We further investigate the relationship between Neumann-RBP and back propagation through time (BPTT) and its truncated version (TBPTT). Our Neumann-RBP has the same time complexity as TBPTT but only requires constant memory, whereas TBPTT’s memory cost scales linearly with the number of truncation steps.

We examine all RBP variants along with BPTT and TBPTT in 3 different application domains: associative memory with continuous Hopfield networks, document classification in citation networks using graph neural networks and hyperparameter optimization for fully connected networks. All experiments demonstrate that RBPs, especially the Neumann-RBP variant, are efficient and effective for optimizing convergent recurrent neural networks.

Code is released at: https://github.com/lrjconan/RBP.

--- https://arxiv.org/abs/2206.05229 Measuring the Carbon Intensity of AI in Cloud Instances Jesse Dodge, Taylor Prewitt, Remi Tachet Des Combes, Erika Odmark, Roy Schwartz, Emma Strubell, Alexandra Sasha Luccioni, Noah Smith, Nicole DeCario, Will Buchanan 2022-06-10 2022-06-10 [("doi","10.48550/arXiv.2206.05229")] ai/scaling/economics

By providing unprecedented access to computational resources, cloud computing has enabled rapid growth in technologies such as machine learning, the computational demands of which incur a high energy cost and a commensurate carbon footprint. As a result, recent scholarship has called for better estimates of the greenhouse gas impact of AI: data scientists today do not have easy or reliable access to measurements of this information, precluding development of actionable tactics. Cloud providers presenting information about software carbon intensity to users is a fundamental stepping stone towards minimizing emissions.

In this paper, we provide a framework for measuring software carbon intensity, and propose to measure operational carbon emissions by using location-based and time-specific marginal emissions data per energy unit. We provide measurements of operational software carbon intensity for a set of modern models for natural language processing and computer vision, and a wide range of model sizes, including pretraining of a 6.1 billion parameter language model. We then evaluate a suite of approaches for reducing emissions on the Microsoft Azure cloud compute platform: using cloud instances in different geographic regions, using cloud instances at different times of day, and dynamically pausing cloud instances when the marginal carbon intensity is above a certain threshold.

We confirm previous results that the geographic region of the data center plays a role in the carbon intensity for a given cloud instance, and find that choosing an appropriate region can have the largest operational emissions reduction impact.

We also show that the time of day has notable impact on operational software carbon intensity.

Finally, we conclude with recommendations for how machine learning practitioners can use software carbon intensity information to reduce environmental impact.

--- https://www.biorxiv.org/content/10.1101/2022.07.20.500802.full Multi-ancestry GWAS of major depression aids locus discovery, fine-mapping, gene prioritization, and causal inference Xiangrui Meng, Georgina Navoly, Olga Giannakopoulou, Daniel Levey, Dora Koller, Gita Pathak, Nastassja Koen, Kuang Lin, Miguel Renteria, Yanzhe Feng, J. Michael Gaziano, Dan Stein, Heather Zar, Megan Campbell, David van Heel, Bhavi Trivedi, Sarah Finer, Andrew McQuillin, Nick Bass, V. Kartik Chundru, Hilary Martin, Qin Qin Huang, Maria Valkovskaya, Po-Hsiu Kuo, Hsi-Chung Chen, Shih-Jen Tsai, Yu-Li Liu, Kenneth Kendler, Roseann Peterson, Na Cai, Yu Fang, Srijan Sen, Laura Scott, Margit Burmeister, Ruth Loos, Michael Preuss, Ky’Era Actkins, Lea Davis, Monica Uddin, Agaz Wani, Derek Wildman, Robert Ursano, Ronald Kessler, Masahiro Kanai, Yukinori Okada, Saori Sakaue, Jill Rabinowitz, Brion Maher, George Uhl, William Eaton, Carlos Cruz-Fuentes, Gabriela Martinez-Levy, Adrian Campos, Iona Millwood, Zhengming Chen, Liming Li, Sylvia Wassertheil-Smoller, Yunxuan Jiang, Chao Tian, Nicholas Martin, Brittany Mitchell, Enda Byrne, Naomi Wray, Swapnil Awasthi, Jonathan Coleman, Stephan Ripke, PGCD Working Group, China Kadoorie Biobank Collaborative Group, 23andMe, Genes Health Research Team, Tamar Sofer, Robin Walters, Renato Polimanti, Erin Dunn, Murray Stein, Joel Gelernter, Cathryn Lewis, Karoline Kuchenbaecker 2022-07-21 2022-07-21 [("doi","10.1101/2022.07.20.500802")] genetics/heritable/correlation/mendelian-randomization psychiatry/depression

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry.

Here we report a multi-ancestry GWAS of MD, adding data from 21 studies with 88,316 MD cases and 902,757 controls to previously reported data from individuals of European ancestry. This includes samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic participants (32%).

The multi-ancestry GWAS identified 190 statistically-significantly associated loci, 53 of them novel. For previously reported loci from GWAS in European ancestry the power-adjusted transferability ratio was 0.6 in the Hispanic group and 0.3 in each of the other groups. Fine-mapping benefited from additional sample diversity: the number of credible sets with ≤5 variants increased 3 → 12. A transcriptome-wide association study identified 354 statistically-significantly associated genes, 205 of them novel. Mendelian Randomization showed a bidirectional relationship with BMI exclusively in samples of European ancestry.

This first multi-ancestry GWAS of MD demonstrates the importance of large diverse samples for the identification of target genes and putative mechanisms.

--- https://www.justice.gov/usao-edny/pr/dark-web-vendor-illegal-narcotics-indicted-distributing-heroin-and-cocaine-exchange Eastern District of New York Dark Web Vendor of Illegal Narcotics Indicted for Distributing Heroin and Cocaine in Exchange for Bitcoin 2021-11-16 darknet-market/alphabay darknet-market/silk-road/1 --- https://arxiv.org/abs/2207.04901#google Exploring Length Generalization in Large Language Models Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, Behnam Neyshabur 2022-07-11 2022-07-11 [("doi","10.48550/arXiv.2207.04901")] ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/lamda ai/scaling

The ability to extrapolate from short problem instances to longer ones is an important form of out-of-distribution generalization in reasoning tasks, and is crucial when learning from datasets where longer problem instances are rare. These include theorem proving, solving quantitative mathematics problems, and reading/summarizing novels. In this paper, we run careful empirical studies exploring the length generalization capabilities of transformer-based language models.

We first establish that naively finetuning transformers on length generalization tasks shows generalization deficiencies independent of model scale. We then show that combining pretrained large language models’ in-context learning abilities with scratchpad prompting (asking the model to output solution steps before producing an answer) results in a dramatic improvement in length generalization.

We run careful failure analyses on each of the learning modalities and identify common sources of mistakes that highlight opportunities in equipping language models with the ability to generalize to longer problems.

…Few-shot scratchpad prompting enables pretrained large language models to pick up scratchpad-templates that extrapolate to arbitrary lengths, leading to dramatic improvements on longer problem instances. Unlike raw finetuning, this approach does scale with model size [4].

Trying to further enhance the performance of few-shot scratchpad prompted LLMs via finetuning yields mixed results, depending on the non-finetuned performance of the base model at the target task. We emphasize that the aforementioned few-shot variable length pattern matching capability—something that doesn’t require changing model architecture—offers a qualitatively different approach to handle length generalization in contrast to prior art that introduced architectural modifications to achieve the same goal. This capability is also important in that it implies that for LLMs, there are certain skills, like length generalization, that can be learned better through in-context learning rather than through finetuning, even in the presence of infinite data.

--- https://x.com/SatsumaAudio/status/1550472950847098885 2021-11-16 ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2207.10081#facebook What Do We Maximize in Self-Supervised Learning? Ravid Shwartz-Ziv, Randall Balestriero, Yann LeCun 2022-07-20 2022-07-20 [("doi","10.48550/arXiv.2207.10081")] ai/nn cs/algorithm/information

In this paper, we examine self-supervised learning methods, particularly VICReg, to provide an information-theoretical understanding of their construction.

As a first step, we demonstrate how information-theoretic quantities can be obtained for a deterministic network, offering a possible alternative to prior work that relies on stochastic models. This enables us to demonstrate how VICReg can be (re)discovered from first principles and its assumptions about data distribution.

Furthermore, we empirically demonstrate the validity of our assumptions, confirming our novel understanding of VICReg.

Finally, we believe that the derivation and insights we obtain can be generalized to many other SSL methods, opening new avenues for theoretical and practical understanding of SSL and transfer learning.

--- https://arxiv.org/abs/2108.00275 Demonstration of Decentralized, Physics-Driven Learning Sam Dillavou, Menachem Stern, Andrea J. Liu, Douglas J. Durian 2021-07-31 2021-11-16 [("doi","10.48550/arXiv.2108.00275")] ai/scaling/hardware

In typical artificial neural networks, neurons adjust according to global calculations of a central processor, but in the brain neurons and synapses self-adjust based on local information. Contrastive learning algorithms have recently been proposed to train physical systems, such as fluidic, mechanical, or electrical networks, to perform machine learning tasks from local evolution rules. However, to date such systems have only been implemented in silico due to the engineering challenge of creating elements that autonomously evolve based on their own response to two sets of global boundary conditions.

Here we introduce and implement a physics-driven contrastive learning scheme for a network of variable resistors, using circuitry to locally compare the response of two identical networks subjected to the two different sets of boundary conditions. Using this innovation, our system effectively trains itself, optimizing its resistance values without use of a central processor or external information storage.

Once the system is trained for a specified allostery, regression, or classification task, the task is subsequently performed rapidly and automatically by the physical imperative to minimize power dissipation in response to the given voltage inputs. We demonstrate that, unlike typical computers, such learning systems are robust to extreme damage (and thus manufacturing defects) due to their decentralized learning.

Our twin-network approach is therefore readily scalable to extremely large or nonlinear networks where its distributed nature will be an enormous advantage; a laboratory network of only 500 edges will already outpace its in silico counterpart.

--- https://arxiv.org/abs/2110.09485#facebook Learning in High Dimension Always Amounts to Extrapolation Randall Balestriero, Jerome Pesenti, Yann LeCun 2021-10-18 2021-11-16 [("doi","10.48550/arXiv.2110.09485")] ai/nn statistics/probability

The notion of interpolation and extrapolation is fundamental in various fields from deep learning to function approximation. Interpolation occurs for a sample x whenever this sample falls inside or on the boundary of the given dataset’s convex hull. Extrapolation occurs when x falls outside of that convex hull.

One fundamental (mis)conception is that state-of-the-art algorithms work so well because of their ability to correctly interpolate training data. A second (mis)conception is that interpolation happens throughout tasks and datasets, in fact, many intuitions and theories rely on that assumption.

We empirically and theoretically argue against those two points and demonstrate that on any high-dimensional (>100) dataset, interpolation almost-surely never happens.

Those results challenge the validity of our current interpolation/extrapolation definition as an indicator of generalization performances.

--- https://arxiv.org/abs/2007.06563#facebook HOBFLOPS CNNs: Hardware Optimized Bitslice-Parallel Floating-Point Operations for Convolutional Neural Networks James Garland, David Gregg 2020-07-11 2021-11-16 [("doi","10.48550/arXiv.2007.06563")] ai/nn/sparsity/low-precision ai/scaling/hardware

Convolutional neural networks (CNNs) are typically trained using 16-bit or 32-bit floating-point (FP) and research show that low-precision floating-point (FP) can be highly effective for inference. Low-precision FP can be implemented in field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) accelerators, but existing processors do not generally support custom precision FP.

We propose hardware optimized bitslice-parallel floating-point operators (HOBFLOPS), a method of generating efficient custom-precision emulated bitslice-parallel software FP arithmetic.

We generate custom-precision FP routines optimized using a hardware synthesis design flow to create circuits. We provide standard cell libraries matching the bitwise operations on the target microprocessor architecture, and a code-generator to translate the hardware circuits to bitslice software equivalents. We exploit bitslice parallelism to create a very wide (32–512 element) vectorized convolutional neural network (CNN) convolution.

Hardware optimized bitslice-parallel floating-point operators (HOBFLOPS) multiply-accumulate (MAC) performance in CNN convolution on ARM and Intel processors are compared to Berkeley’s SoftFP16 equivalent MAC. HOBFLOPS16 outperforms SoftFP16 by 8× on Intel AVX512. HOBFLOPS offers arbitrary-precision FP with custom range and precision eg. HOBFLOPS9 performs at 6× the performance of HOBFLOPS16 on ARM Neon.

HOBFLOPS allows researchers to prototype different levels of custom FP precision in the arithmetic of software CNN accelerators. Furthermore, HOBFLOPS fast custom-precision FP CNNs may be valuable in cases where memory bandwidth is limited.

--- https://arxiv.org/abs/2207.10342#google Language Model Cascades David Dohan, Winnie Xu, Aitor Lewkowycz, Jacob Austin, David Bieber, Raphael Gontijo Lopes, Yuhuai Wu, Henryk Michalewski, Rif A. Saurous, Jascha Sohl-dickstein, Kevin Murphy, Charles Sutton 2022-07-21 2022-07-21 [("doi","10.48550/arXiv.2207.10342")] ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/lamda statistics/bayes

Prompted models have demonstrated impressive few-shot learning abilities. Repeated interactions at test-time with a single model, or the composition of multiple models together, further expands capabilities.

These compositions are probabilistic models, and may be expressed in the language of graphical models with random variables whose values are complex data types such as strings. Cases with control flow and dynamic structure require techniques from probabilistic programming, which allow implementing disparate model structures and inference strategies in a unified language.

We formalize several existing techniques from this perspective, including scratchpads / chain-of-thought, verifiers, STaR, selection-inference, and tool use.

We refer to the resulting programs as language model cascades.

--- https://arxiv.org/abs/2207.10397#microsoft CodeT: Code Generation with Generated Tests Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen 2022-07-21 2022-07-21 [("doi","10.48550/arXiv.2207.10397")] ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue

Given a programming problem, pre-trained language models such as Codex have demonstrated the ability to generate multiple different code solutions via sampling. However, selecting a correct or best solution from those samples still remains a challenge. While an easy way to verify the correctness of a code solution is through executing test cases, producing high-quality test cases is prohibitively expensive.

In this paper, we explore the use of pre-trained language models to automatically generate test cases, calling our method CodeT: Code generation with generated Tests. CodeT executes the code solutions using the generated test cases, and then chooses the best solution based on a dual execution agreement with both the generated test cases and other generated solutions.

We evaluate CodeT on 5 different pre-trained models with both HumanEval and MBPP benchmarks. Extensive experimental results demonstrate CodeT can achieve consistent, and surprising improvements over previous methods. For example, CodeT improves the pass@1 on HumanEval to 65.8%, an increase of absolute 18.8% on the code-davinci-002 model, and an absolute 20+% improvement over previous state-of-the-art results.

--- https://arxiv.org/abs/2109.06243#huawei KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation Marzieh S. Tahaei, Ella Charlaix, Vahid Partovi Nia, Ali Ghodsi, Mehdi Rezagholizadeh 2021-09-13 2021-11-17 [("doi","10.48550/arXiv.2109.06243")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer

The development of over-parameterized pre-trained language models has made a contribution toward the success of natural language processing. While over-parameterization of these models is the key to their generalization power, it makes them unsuitable for deployment on low-capacity devices.

We push the limits of state-of-the-art Transformer-based pre-trained language model compression using Kronecker decomposition. We use this decomposition for compression of the embedding layer, all linear mappings in the multi-head attention, and the feed-forward network modules in the Transformer layer. We perform intermediate-layer knowledge distillation using the uncompressed model as the teacher to improve the performance of the compressed model.

We present our KroneckerBERT, a compressed version of the BERT_BASE model obtained using this framework. We evaluate the performance of KroneckerBERT on well-known NLP benchmarks and show that for a high compression factor of 19 (5% of the size of the BERT_BASE model), our KroneckerBERT outperforms state-of-the-art compression methods on the GLUE. Our experiments indicate that the proposed model has promising out-of-distribution robustness and is superior to the state-of-the-art compression methods on SQuAD.

--- https://openreview.net/forum?id=I9glM3N6iAa#microsoft Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code Ryan Volum, Sudha Rao, Michael Xu, Gabriel A. DesGarennes, Chris Brockett, Benjamin Van Durme, Olivia Deng, Akanksha Malhotra, Bill Dolan 2022-07-05 2022-07-05 ai/nn/transformer/gpt/codex

We use OpenAI Codex model to power a non-player character in Minecraft that is able to both converse with players and perform actions.

Non-Player Characters (NPCs) enhance the player experience in many games. Historically, players’ interactions with NPCs have tended to be highly scripted, to be limited to natural language responses to be selected by the player, and to not involve dynamic change in game state. In this work, we demonstrate that use of a few example conversational prompts can power a conversational agent to generate both natural language and novel code. This approach can permit development of NPCs with which players can have grounded conversations that are free-form and less repetitive. We demonstrate our approach using OpenAI Codex (GPT-3 finetuned on GitHub), with Minecraft game development as our test bed. We show that with a few example prompts, a Codex-based agent can generate novel code, hold multi-turn conversations and answer questions about structured data. We evaluate this application using experienced gamers in a Minecraft realm and provide analysis of failure cases and suggest possible directions for solutions.

[Keywords: Large language models, Code generation, Non-player characters, Games, Prompting]

--- https://arxiv.org/abs/2204.11823#sensetime StyleGAN-Human: A Data-Centric Odyssey of Human Generation Jianglin Fu, Shikai Li, Yuming Jiang, Kwan-Yee Lin, Chen Qian, Chen Change Loy, Wayne Wu, Ziwei Liu 2022-04-25 2022-04-25 [("doi","10.48550/arXiv.2204.11823")] ai/nn/gan/stylegan

Unconditional human image generation is an important task in vision and graphics, which enables various applications in the creative industry. Existing studies in this field mainly focus on “network engineering” such as designing new components and objective functions. This work takes a data-centric perspective and investigates multiple critical aspects in “data engineering”, which we believe would complement the current practice.

To facilitate a comprehensive study, we collect and annotate a large-scale human image dataset with over 230K samples capturing diverse poses and textures. Equipped with this large dataset, we rigorously investigate 3 essential factors in data engineering for StyleGAN-based human generation, namely data size, data distribution, and data alignment.

Extensive experiments reveal several valuable observations w.r.t. these aspects: (1) Large-scale data, more than 40K images, are needed to train a high-fidelity unconditional human generation model with vanilla StyleGAN. (2) A balanced training set helps improve the generation quality with rare face poses compared to the long-tailed counterpart, whereas simply balancing the clothing texture distribution does not effectively bring an improvement. (3) Human GAN models with body centers for alignment outperform models trained using face centers or pelvis points as alignment anchors.

In addition, a model zoo and human editing applications are demonstrated to facilitate future research in the community.

--- https://en.wikipedia.org/wiki/GitHub_Copilot GitHub Copilot 2021-11-17 ai/nn/transformer/gpt/codex --- https://www.biorxiv.org/content/10.1101/2022.07.20.500902.full#facebook ESMfold: Language models of protein sequences at the scale of evolution enable accurate structure prediction Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives 2022-07-21 2022-07-21 [("doi","10.1101/2022.07.20.500902")] ai/nn/transformer/alphafold ai/scaling

Large language models have recently been shown to develop emergent capabilities with scale, going beyond simple pattern matching to perform higher level reasoning and generate lifelike images and text. While language models trained on protein sequences have been studied at a smaller scale, little is known about what they learn about biology as they are scaled up.

In this work we train models up to 15 billion parameters, the largest language models of proteins to be evaluated to date. [”The 3b parameter LM took 3 weeks on 256 GPUs, and ESMfold took 10 days on 128 GPUs.“]

We find that as models are scaled they learn information enabling the prediction of the three-dimensional structure of a protein at the resolution of individual atoms. We present ESMFold for high accuracy end-to-end atomic level structure prediction directly from the individual sequence of a protein. ESMFold has similar accuracy to AlphaFold2 and RoseTTAFold for sequences with low perplexity that are well understood by the language model.

ESMFold inference is an order of magnitude faster than AlphaFold2, enabling exploration of the structural space of metagenomic proteins in practical timescales.

--- https://arxiv.org/abs/2008.07971#sony Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning Florian Fuchs, Yunlong Song, Elia Kaufmann, Davide Scaramuzza, Peter Duerr 2020-08-18 2021-11-17 [("doi","10.1109/LRA.2021.3064284")] reinforcement-learning/model-free

Autonomous car racing is a major challenge in robotics. It raises fundamental problems for classical approaches such as planning minimum-time trajectories under uncertain dynamics and controlling the car at the limits of its handling. Besides, the requirement of minimizing the lap time, which is a sparse objective, and the difficulty of collecting training data from human experts have also hindered researchers from directly applying learning-based approaches to solve the problem.

In the present work, we propose a learning-based system for autonomous car racing by leveraging a high-fidelity physical car simulation, a course-progress proxy reward, and deep reinforcement learning [SAC]. We deploy our system in Gran Turismo Sport, a world-leading car simulator known for its realistic physics simulation of different race cars and tracks, which is even used to recruit human race car drivers.

Our trained policy achieves autonomous racing performance that goes beyond what had been achieved so far by the built-in AI, and, at the same time, outperforms the fastest driver in a dataset of over 50,000 human players.

[The difficult part is dialing down the aggression so human refs don’t penalize it in pro races.]

--- https://www.technologyreview.com/2022/07/19/1056176/sonys-racing-ai-destroyed-its-human-competitors-by-being-nice-and-fast/ Sony’s racing car AI just destroyed its human competitors—by being nice (and fast) 2021-11-17 reinforcement-learning/imitation-learning reinforcement-learning/model-free --- https://arxiv.org/abs/2206.07137 RHO-LOSS: Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt Sören Mindermann, Jan Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N. Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal 2022-06-14 2022-06-14 [("doi","10.48550/arXiv.2206.07137")] ai/nn/cnn ai/nn/fully-connected ai/nn/transformer reinforcement-learning/exploration/active-learning reinforcement-learning/meta-learning

[cf. Sorscher et al 2022] Training on web-scale data can take months. But most computation and time is wasted on redundant and noisy points that are already learnt or not learnable.

To accelerate training, we introduce Reducible Holdout Loss Selection (RHO-LOSS), a simple but principled technique which selects ~those points for training that most reduce the model’s generalization loss. As a result, RHO-LOSS mitigates the weaknesses of existing data selection methods: techniques from the optimization literature typically select ‘hard’ (eg. high loss) points, but such points are often noisy (not learnable) or less task-relevant. Conversely, curriculum learning prioritizes ‘easy’ points, but such points need not be trained on once learned. In contrast, RHO-LOSS selects points that are learnable, worth learning, and not yet learnt.

RHO-LOSS trains in far fewer steps than prior art, improves accuracy, and speeds up training on a wide range of datasets, hyperparameters, and architectures (MLPs, CNNs, and BERT). On the large web-scraped image dataset Clothing-1M, RHO-LOSS trains in 18× fewer steps and reaches 2% higher final accuracy than uniform data shuffling.

Figure 1: Speedup on large-scale classification of web-scraped data (Clothing-1M). RHO-LOSS trains all architectures with fewer gradient steps than standard uniform data selection (ie. shuffling), helping reduce training time. Thin lines: ResNet-50, MobileNetV2, DenseNet121, Inception v3/GoogLeNet, mean across seeds. Bold lines: mean across all architectures.
--- https://arxiv.org/abs/2207.05022 Efficient NLP Inference at the Edge via Elastic Pipelining Liwei Guo, Wonkyo Choe, Felix Xiaozhu Lin 2022-07-11 2022-07-11 [("doi","10.48550/arXiv.2207.05022")] ai/scaling/hardware

Natural Language Processing (NLP) inference is seeing increasing adoption by mobile applications, where on-device inference is desirable for crucially preserving user data privacy and avoiding network roundtrips. Yet, the unprecedented size of an NLP model stresses both latency and memory, the two key resources of a mobile device. To meet a target latency, holding the whole model in memory launches execution as soon as possible but increases one app’s memory footprints by several times, limiting its benefits to only a few inferences before being recycled by mobile memory management. On the other hand, loading the model from storage on demand incurs a few seconds long IO, far exceeding the delay range satisfying to a user; pipelining layerwise model loading and execution does not hide IO either, due to the large skewness between IO and computation delays.

To this end, we propose WRX. Built on the key idea of maximizing IO/compute resource utilization on the most important parts of a model, WRX reconciles the latency/memory tension via two novel techniques. First, model sharding. WRX manages model parameters as independently tunable shards and profiles their importance to accuracy. Second, elastic pipeline planning with a preload buffer. WRX instantiates an IO/computation pipeline and uses a small buffer for preload shards to bootstrap execution without stalling in early stages; it judiciously selects, tunes, and assembles shards per their importance for resource-elastic execution, which maximizes inference accuracy.

Atop two commodity SoCs, we build WRX and evaluate it against a wide range of NLP tasks, under a practical range of target latencies, and on both CPU and GPU. We demonstrate that, WRX delivers high accuracies with 1–2 orders of magnitude lower memory, outperforming competitive baselines.

--- https://www.biorxiv.org/content/10.1101/2022.07.21.501015.full A versatile, high-efficiency platform for CRISPR-based gene activation Amy J. Heidersbach, Kristel M. Dorighi, Javier A. Gomez, Ashley M. Jacobi, Benjamin Haley 2022-07-22 2022-07-22 [("doi","10.1101/2022.07.21.501015")] genetics/editing

CRISPR-mediated transcriptional activation (CRISPRa) is a powerful technology for inducing gene expression from endogenous loci with exciting applications in high throughput gain-of-function genomic screens and the engineering of cell-based models. However, current strategies for generating potent, stable, CRISPRa-competent cell-lines present limitations for the broad utility of this approach.

Here, we provide a high-efficiency, self-selecting CRISPRa enrichment strategy, which combined with piggyBac transposon technology enables rapid production of CRISPRa-ready cell populations compatible with a variety of downstream assays. We complement this with a new, optimized guide RNA scaffold that enhances CRISPRa functionality. Finally, we describe a novel, synthetic guide RNA tool set that enables transient, population-wide gene activation when used with the self-selecting CRISPRa system. Taken together, this versatile platform greatly enhances the potential for CRISPRa across a wide variety of cellular contexts.

--- https://x.com/paultrillo/status/1550551780408209408 2021-11-18 ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2207.08815 Why do tree-based models still outperform deep learning on tabular data? Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux 2022-07-18 2022-07-18 [("doi","10.48550/arXiv.2207.08815")] ai/dataset ai/nn/fully-connected ai/nn/transformer ai/scaling ai/tabular

While deep learning has enabled tremendous progress on text and image datasets, its superiority on tabular data is not clear.

We contribute extensive benchmarks of standard and novel deep learning methods as well as tree-based models such as XGBoost and Random Forests, across a large number of datasets and hyperparameter combinations. We define a standard set of 45 datasets from varied domains with clear characteristics of tabular data and a benchmarking methodology accounting for both fitting models and finding good hyperparameters.

Results show that tree-based models remain state-of-the-art on medium-sized data (~10K samples) even without accounting for their superior speed [but not at 50k!].

To understand this gap, we conduct an empirical investigation into the differing inductive biases of tree-based models and Neural Networks (NNs).

This leads to a series of challenges which should guide researchers aiming to build tabular-specific NNs: 1. be robust to uninformative features, 2. preserve the orientation of the data, and 3. be able to easily learn irregular functions.

To stimulate research on tabular architectures, we contribute a standard benchmark and raw data for baselines: every point of a 20 000 compute hours hyperparameter search for each learner.

…A.2.2 Large-sized datasets: We extend our benchmark to large-scale datasets: in Figures 9, 10, 11 & 12, we compare the results of our models on the same set of datasets, in large-size (train set truncated to 50,000 samples) and medium-size (train set truncated to 10,000 samples) settings. We only keep datasets with more than 50,000 samples and restrict the train set size to 50,000 samples (vs 10,000 samples for the medium-sized benchmark). Unfortunately, this excludes a lot of datasets, which makes the comparison less clear.

However, it seems that, in most cases, increasing the train set size reduces the gap between neural networks and tree-based models. We leave a rigorous study of this trend to future work.

Figure 9: Comparison of accuracies on 4 classification tasks for different train set sizes, with only numerical features. Only datasets with more than 50,000 samples were kept, and the train set size was truncated to either 10,000 samples or 50,000 samples. Dotted lines correspond to the score of the default hyperparameters, which is also the first random search iteration. Each value corresponds to the test score of the best model (on the validation set) after a specific number of random search iterations, averaged on 15 shuffles of the random search order. The ribbon corresponds to the minimum and maximum scores on these 15 shuffles.
Figure 10: Comparison of R<sup>2</sup> scores on 3 regression tasks for different train set sizes, with only numerical features. Only datasets with more than 50,000 samples were kept, and the train set size was truncated to either 10,000 samples or 50,000 samples. Dotted lines correspond to the score of the default hyperparameters, which is also the first random search iteration. Each value corresponds to the test score of the best model (on the validation set) after a specific number of random search iterations, averaged on 15 shuffles of the random search order. The ribbon corresponds to the minimum and maximum scores on these 15 shuffles.
Figure 11: Comparison of accuracies on 2 classification tasks for different train set sizes, with both numerical and categorical features. Only datasets with more than 50,000 samples were kept, and the train set size was truncated to either 10,000 samples or 50,000 samples. Dotted lines correspond to the score of the default hyperparameters, which is also the first random search iteration. Each value corresponds to the test score of the best model (on the validation set) after a specific number of random search iterations, averaged on 15 shuffles of the random search order. The ribbon corresponds to the minimum and maximum scores on these 15 shuffles.
Figure 12: Comparison of R² scores on 5 regression tasks for different train set sizes, with both numerical and categorical features. Only datasets with more than 50,000 samples were kept, and the train set size was truncated to either 10,000 samples or 50,000 samples. Dotted lines correspond to the score of the default hyperparameters, which is also the first random search iteration. Each value corresponds to the test score of the best model (on the validation set) after a specific number of random search iterations, averaged on 15 shuffles of the random search order. The ribbon corresponds to the minimum and maximum scores on these 15 shuffles.
--- https://en.wikipedia.org/wiki/Andromachi_Papanikolaou Andromachi Papanikolaou 2021-11-18 nootropic/quantified-self --- https://github.com/KaliYuga-ai/Textile-Diffusion KaliYuga-ai/Textile-Diffusion 2021-11-18 ai/nn/diffusion --- https://arxiv.org/abs/2104.08211 Robust Open-Vocabulary Translation from Visual Text Representations Elizabeth Salesky, David Etter, Matt Post 2021-04-16 2021-11-18 [("doi","10.48550/arXiv.2104.08211")] ai/nn/tokenization ai/nn/transformer

Machine translation models have discrete vocabularies and commonly use subword segmentation techniques to achieve an ‘open vocabulary.’ This approach relies on consistent and correct underlying Unicode sequences and makes models susceptible to degradation from common types of noise and variation.

Motivated by the robustness of human language processing, we propose the use of visual text representations, which dispense with a finite set of text embeddings in favor of continuous vocabularies created by processing visually rendered text with sliding windows.

We show that models using visual text representations approach or match the performance of traditional text models on small and larger datasets. More importantly, models with visual embeddings demonstrate robustness to varied types of noise, achieving, for example, 25.9 BLEU on a character-permuted German-English task where subword models degrade to 1.9.

--- https://gynvael.coldwind.pl/?id=750 An informal review of CTF abuse 2021-11-18 cs/security --- https://en.wikipedia.org/wiki/Ophioglossum Ophioglossum 2021-11-18 genetics --- https://en.wikipedia.org/wiki/List_of_organisms_by_chromosome_count List of organisms by chromosome count 2021-11-18 genetics --- https://en.wikipedia.org/wiki/Hyperthymesia Hyperthymesia 2021-11-18 psychology/neuroscience/memory/savant psychology/spaced-repetition --- https://www.lesswrong.com/posts/LSbpCoQBC3PG4zSJj/eating-boogers Eating Boogers 2021-11-18 biology/booger --- https://arxiv.org/abs/2103.04909 Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing Axel Brunnbauer, Luigi Berducci, Andreas Brandstätter, Mathias Lechner, Ramin Hasani, Daniela Rus, Radu Grosu 2021-03-08 2021-11-19 [("doi","10.48550/arXiv.2103.04909")] reinforcement-learning/model reinforcement-learning/robot

World models learn behaviors in a latent imagination space to enhance the sample-efficiency of deep reinforcement learning (RL) algorithms. While learning world models for high-dimensional observations (eg. pixel inputs) has become practicable on standard RL benchmarks and some games, their effectiveness in real-world robotics applications has not been explored.

In this paper, we investigate how such [Dreamer] agents generalize to real-world autonomous vehicle control tasks, where advanced model-free deep RL algorithms fail. In particular, we set up a series of time-lap tasks for an F1TENTH racing robot, equipped with a high-dimensional LiDAR sensor, on a set of test tracks with a gradual increase in their complexity.

In this continuous-control setting, we show that model-based agents capable of learning in imagination substantially outperform model-free agents with respect to performance, sample efficiency, successful task completion, and generalization. Moreover, we show that the generalization ability of model-based agents strongly depends on the choice of their observation model.

We provide extensive empirical evidence for the effectiveness of world models provided with long enough memory horizons in sim2real tasks.

--- https://arxiv.org/abs/2003.08876 Learning to Fly via Deep Model-Based Reinforcement Learning Philip Becker-Ehmck, Maximilian Karl, Jan Peters, Patrick van der Smagt 2020-03-19 2021-11-19 [("doi","10.48550/arXiv.2003.08876")] reinforcement-learning/model reinforcement-learning/robot

Learning to control robots without requiring engineered models has been a long-term goal, promising diverse and novel applications. Yet, reinforcement learning has only achieved limited impact on real-time robot control due to its high demand of real-world interactions.

In this work, by leveraging a learnt probabilistic model of drone dynamics [Dreamer], we learn a thrust-attitude controller for a quadrotor through model-based reinforcement learning. No prior knowledge of the flight dynamics is assumed; instead, a sequential latent variable model, used generatively and as an online filter, is learnt from raw sensory input. The controller and value function are optimized entirely by propagating stochastic analytic gradients through generated latent trajectories.

We show that “learning to fly” can be achieved with less than 30 minutes of experience with a single drone, and can be deployed solely using onboard computational resources and sensors, on a self-built drone.

--- https://openreview.net/forum?id=YwDvofEWlEx Learning Behaviors through Physics-driven Latent Imagination Antoine Richard, Stephanie Aravecchia, Matthieu Geist, Cédric Pradalier 2021-11-04 2021-11-19 reinforcement-learning/model reinforcement-learning/robot

Model-based reinforcement learning (MBRL) consists in learning a so-called world model, a representation of the environment through interactions with it, then use it to train an agent. This approach is particularly interesting in the context of field robotics, as it alleviates the need to train online, and reduces the risks inherent to directly training agents on real robots.

Generally, in such approaches, the world encompasses both the part related to the robot itself and the rest of the environment. We argue that decoupling the environment representation (for example, images or laser scans) from the dynamics of the physical system (that is, the robot and its physical state) can increase the flexibility of world models and open doors to greater robustness.

In this paper, we apply this concept to a strong latent-agent, Dreamer.

We then showcase the increased flexibility by transferring the environment part of the world model from one robot (a boat) to another (a rover), simply by adapting the physical model in the imagination. We additionally demonstrate the robustness of our method through real-world experiments on a boat.

[Keywords: model-based reinforcement learning, field robotics, latent models]

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0271334 ‘Where am I?’ A snapshot of the developmental topographical disorientation among young Italian adults Laura Piccardi, Massimiliano Palmiero, Vincenza Cofini, Paola Verde, Maddalena Boccia, Liana Palermo, Cecilia Guariglia, Raffaella Nori, David Giofrè, David Giofrè, David Giofrè, David Giofrè 2022-06-29 2022-06-29 [("doi","10.1371/journal.pone.0271334")] psychology sociology/technology

In the last decade, several cases affected by Developmental Topographical Disorientation (DTD) have been described. DTD consists of a neurodevelopmental disorder affecting the ability to orient in the environment despite well-preserved cognitive functions, and in the absence of a brain lesion or other neurological or psychiatric conditions. Described cases showed different impairments in navigational skills ranging from topographic memory deficits to landmark agnosia. All cases lacked a mental representation of the environment that would allow them to use high-order spatial orientation strategies. In addition to the single case studies, a group study performed in Canada showed that the disorder is more widespread than imagined.

The present work intends to investigate the occurrence of the disorder in 1,698 young Italian participants. The sample is deliberately composed of individuals aged 18–35 years to exclude people who could manifest the loss of the ability to navigate as a result of an onset of cognitive decline. The sample was collected between 2016 and 2019 using the Qualtrics platform, by which the Familiarity and Spatial Cognitive Style Scale and amnestic interview were administered.

The data showed that the disorder is present in 3% of the sample and that the sense of direction is closely related to town knowledge, navigational strategies adopted, and gender. In general, males use more complex navigational strategies than females, although DTD is more prevalent in males than in females, in line with the already described cases.

Finally, the paper discusses which protective factors can reduce DTD onset and which intervention measures should be implemented to prevent the spread of navigational disorders, which severely impact individuals’ autonomy and social relationships.

--- https://gist.github.com/brockmanmatt/deafb4dba7e4399327e44f2c8fd97b2b WiC_SelfContextStuffingImproved_Last10_stuft_examplesNV.ipynb 2021-11-19 ai/nn/transformer/gpt/inner-monologue --- https://reiinakano.com/2019/11/12/solving-probability.html Teaching a neural network to use a calculator 2021-11-19 ai/nn/transformer/gpt/inner-monologue --- https://www.mosaicml.com/blog/mosaic-resnet-deep-dive 2021-11-19 ai/nn economics/experience-curve --- https://evjang.com/2022/07/23/robotics-generative.html How Can We Make Robotics More like Generative Modeling? 2021-11-19 ai/scaling reinforcement-learning/robot --- https://arxiv.org/abs/1908.07442 TabNet: Attentive Interpretable Tabular Learning Sercan O. Arik, Tomas Pfister 2019-08-20 2021-11-19 [("doi","10.48550/arXiv.1908.07442")] ai/nn/transformer ai/tabular

We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet. TabNet uses sequential attention to choose which features to reason from at each decision step, enabling interpretability and more efficient learning as the learning capacity is used for the most salient features.

We demonstrate that TabNet outperforms other neural network and decision tree variants on a wide range of non-performance-saturated tabular datasets and yields interpretable feature attributions plus insights into the global model behavior.

Finally, for the first time to our knowledge, we demonstrate self-supervised learning for tabular data, improving performance with unsupervised representation learning when unlabeled data is abundant.

--- https://arxiv.org/abs/2206.15306 Transfer Learning with Deep Tabular Models Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum 2022-06-30 2022-06-30 [("doi","10.48550/arXiv.2206.15306")] ai/nn/transformer ai/tabular

Recent work on deep learning for tabular data demonstrates the strong performance of deep tabular models, often bridging the gap between gradient boosted decision trees and neural networks. Accuracy aside, a major advantage of neural models is that they learn reusable features and are easily fine-tuned in new domains. This property is often exploited in computer vision and natural language applications, where transfer learning is indispensable when task-specific training data is scarce.

In this work, we demonstrate that upstream data gives tabular neural networks a decisive advantage over widely used GBDT models. We propose a realistic medical diagnosis benchmark for tabular transfer learning, and we present a how-to guide for using upstream data to boost performance with a variety of tabular neural network architectures. Finally, we propose a pseudo-feature method for cases where the upstream and downstream feature sets differ, a tabular-specific problem widespread in real-world applications.

Our code is available at Github.

--- https://arxiv.org/abs/2203.05556 On Embeddings for Numerical Features in Tabular Deep Learning Yury Gorishniy, Ivan Rubachev, Artem Babenko 2022-03-10 2022-03-10 [("doi","10.48550/arXiv.2203.05556")] ai/nn/transformer ai/tabular

Recently, Transformer-like deep architectures have shown strong performance on tabular data problems. Unlike traditional models, eg. MLP, these architectures map scalar values of numerical features to high-dimensional embeddings before mixing them in the main backbone. In this work, we argue that embeddings for numerical features are an underexplored degree of freedom in tabular DL, which allows constructing more powerful DL models and competing with GBDT on some traditionally GBDT-friendly benchmarks.

We start by describing two conceptually different approaches to building embedding modules: the first one is based on a piecewise linear encoding of scalar values, and the second one utilizes periodic activations. Then, we empirically demonstrate that these two approaches can lead to performance boosts compared to the embeddings based on conventional blocks such as linear layers and ReLU activations. Importantly, we also show that embedding numerical features is beneficial for many backbones, not only for Transformers. Specifically, after proper embeddings, simple MLP-like models can perform on par with the attention-based architectures.

Overall, we highlight embeddings for numerical features as an important design aspect with good potential for further improvements in tabular DL.

--- https://arxiv.org/abs/2107.01830 ARM-Net: Adaptive Relation Modeling Network for Structured Data Shaofeng Cai, Kaiping Zheng, Gang Chen, H. V. Jagadish, Beng Chin Ooi, Meihui Zhang 2021-07-05 2021-11-20 [("doi","10.1145/3448016.3457321")] ai/nn/transformer ai/tabular

Relational databases are the de facto standard for storing and querying structured data, and extracting insights from structured data requires advanced analytics. Deep neural networks (DNNs) have achieved super-human prediction performance in particular data types, eg. images. However, existing DNNs may not produce meaningful results when applied to structured data. The reason is that there are correlations and dependencies across combinations of attribute values in a table, and these do not follow simple additive patterns that can be easily mimicked by a DNN. The number of possible such cross features is combinatorial, making them computationally prohibitive to model. Furthermore, the deployment of learning models in real-world applications has also highlighted the need for interpretability, especially for high-stakes applications, which remains another issue of concern to DNNs.

In this paper, we present ARM-Net, an adaptive relation modeling network tailored for structured data, and a lightweight framework ARMOR based on ARM-Net for relational data analytics. The key idea is to model feature interactions with cross features selectively and dynamically, by first transforming the input features into exponential space, and then determining the interaction order and interaction weights adaptively for each cross feature. We propose a novel sparse attention mechanism to dynamically generate the interaction weights given the input tuple, so that we can explicitly model cross features of arbitrary orders with noisy features filtered selectively. Then during model inference, ARM-Net can specify the cross features being used for each prediction for higher accuracy and better interpretability. Our extensive experiments on real-world datasets demonstrate that ARM-Net consistently outperforms existing models and provides more interpretable predictions for data-driven decision making.

--- https://arxiv.org/abs/2106.15147 SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler 2021-06-29 2021-11-20 [("doi","10.48550/arXiv.2106.15147")] ai/nn/transformer ai/tabular

Self-supervised contrastive representation learning has proved incredibly successful in the vision and natural language domains, enabling state-of-the-art performance with orders of magnitude less labeled data. However, such methods are domain-specific and little has been done to leverage this technique on real-world tabular datasets.

We propose SCARF, a simple, widely-applicable technique for contrastive learning, where views are formed by corrupting a random subset of features. When applied to pre-train deep neural networks on the 69 real-world, tabular classification datasets from the OpenML-CC18 benchmark, SCARF not only improves classification accuracy in the fully-supervised setting but does so also in the presence of label noise and in the semi-supervised setting where only a fraction of the available training data is labeled.

We show that SCARF complements existing strategies and outperforms alternatives like autoencoders. We conduct comprehensive ablations, detailing the importance of a range of factors.

--- https://arxiv.org/abs/2106.03253 Tabular Data: Deep Learning is Not All You Need Ravid Shwartz-Ziv, Amitai Armon 2021-06-06 2021-11-20 [("doi","10.48550/arXiv.2106.03253")] ai/nn/transformer ai/tabular

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases.

This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require.

Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8166880/ Converting tabular data into images for deep learning with convolutional neural networks Yitan Zhu, Thomas Brettin, Fangfang Xia, Alexander Partin, Maulik Shukla, Hyunseung Yoo, Yvonne A. Evrard, James H. Doroshow, Rick L. Stevens 2021 2021-11-20 [("doi","10.1038/s41598-021-90923-y")] ai/nn/cnn ai/tabular

Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image.

The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expression profiles of cancer cell lines (CCLs) and molecular descriptors of drugs into their respective image representations. Compared with existing transformation methods, IGTD generates compact image representations with better preservation of feature neighborhood structure.

Evaluated on benchmark drug screening datasets, CNNs trained on IGTD image representations of CCLs and drugs exhibit a better performance of predicting anti-cancer drug response than both CNNs trained on alternative image representations and prediction models trained on the original tabular data.

--- https://arxiv.org/abs/1903.06246 SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data Baohua Sun, Lin Yang, Wenhan Zhang, Michael Lin, Patrick Dong, Charles Young, Jason Dong 2019-02-26 2021-11-20 [("doi","10.48550/arXiv.1903.06246")] ai/nn/cnn ai/tabular

Tabular data is the most commonly used form of data in industry. Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data. DNN models using categorical embeddings are also applied in this task, but all attempts thus far have used one-dimensional embeddings. The recent work of Super Characters method using two-dimensional word embeddings achieved the state of art result in text classification tasks, showcasing the promise of this new approach.

In this paper, we propose the SuperTML method, which borrows the idea of Super Characters method and two-dimensional embeddings to address the problem of classification on tabular data. For each input of tabular data, the features are first projected into two-dimensional embeddings like an image, and then this image is fed into fine-tuned two-dimensional CNN models for classification. Experimental results have shown that the proposed SuperTML method had achieved state-of-the-art results on both large and small datasets.

--- https://www.biorxiv.org/content/10.1101/2022.07.21.500999.full OmegaFold: High-resolution de novo structure prediction from primary sequence Ruidong Wu, Fan Ding, Rui Wang, Rui Shen, Xiwen Zhang, Shitong Luo, Chenpeng Su, Zuofan Wu, Qi Xie, Bonnie Berger, Jianzhu Ma, Jian Peng 2022-07-22 2022-07-22 [("doi","10.1101/2022.07.21.500999")] ai/nn/transformer/alphafold

Recent breakthroughs have used deep learning to exploit evolutionary information in multiple sequence alignments (MSAs) to accurately predict protein structures. However, MSAs of homologous proteins are not always available, such as with orphan proteins and fast-evolving proteins like antibodies, and a protein typically folds in a natural setting from its primary amino acid sequence into its three-dimensional structure, suggesting that evolutionary information and MSAs should not be necessary to predict a proteins folded form.

Here, we introduce OmegaFold, the first computational method to successfully predict high-resolution protein structure from a single primary sequence alone. Using a new combination of a protein language model that allows us to make predictions from single sequences and a geometry-inspired transformer model trained on protein structures:

OmegaFold outperforms RoseTTAFold and achieves similar prediction accuracy to AlphaFold2 on recently released structures. OmegaFold enables accurate predictions on orphan proteins that do not belong to any functionally characterized protein family and antibodies that tend to have noisy MSAs due to fast evolution.

Our study fills a much-needed structure prediction gap and brings us a step closer to understanding protein folding in nature.

--- https://arxiv.org/abs/2207.11148 InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images Zhengqi Li, Qianqian Wang, Noah Snavely, Angjoo Kanazawa 2022-07-22 2022-07-22 [("doi","10.48550/arXiv.2207.11148")] ai/nn/gan/stylegan ai/video/generation

We present a method for learning to generate unbounded fly-through videos of natural scenes starting from a single view, where this capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene.

To achieve this, we propose a novel self-supervised view generation training paradigm, where we sample and rendering virtual camera trajectories, including cyclic ones, allowing our model to learn stable view generation from a collection of single views. At test time, despite never seeing a video during training, our approach can take a single image and generate long camera trajectories comprised of hundreds of new views with realistic and diverse content.

We compare our approach with recent state-of-the-art supervised view generation methods that require posed multi-view videos and demonstrate superior performance and synthesis quality.

--- https://en.wikipedia.org/wiki/Elon_Musk Elon Musk 2021-11-20 psychiatry/bipolar/elon-musk --- https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/ The 37 Implementation Details of Proximal Policy Optimization 2021-11-20 reinforcement-learning/model-free --- https://www.theverge.com/c/23194235/ai-fiction-writing-amazon-kindle-sudowrite-jasper How independent writers are turning to AI 2021-11-20 ai/nn/transformer/gpt/fiction --- https://en.wikipedia.org/wiki/Gekokuj%C5%8D Gekokujō 2021-11-21 politics --- https://worksinprogress.co/issue/age-of-the-bacteriophage/ Age of the bacteriophage 2021-11-21 genetics/selection/artificial --- https://schwitzsplinters.blogspot.com/2022/07/results-computerized-philosopher-can.html Results: The Computerized Philosopher: Can You Distinguish Daniel Dennett from a Computer? 2021-11-21 ai/nn/transformer/gpt/non-fiction philosophy/mind --- https://x.com/MadaraxUchiha88/status/1551618507133005824 2021-11-21 ai/nn/transformer/clip/sample --- https://www.science.org/content/blog-post/faked-beta-amyloid-data-what-does-it-mean 2021-11-21 psychiatry/alzheimers psychology/neuroscience statistics/bias --- https://arxiv.org/abs/2207.01570#schmidhuber Goal-Conditioned Generators of Deep Policies Francesco Faccio, Vincent Herrmann, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber 2022-07-04 2022-07-04 [("doi","10.48550/arXiv.2207.01570")] reinforcement-learning/meta-learning reinforcement-learning/model/decision-transformer

Goal-conditioned Reinforcement Learning (RL) aims at learning optimal policies, given goals encoded in special command inputs. Here we study goal-conditioned neural nets (NNs) that learn to generate deep NN policies in form of context-specific weight matrices, similar to Fast Weight Programmers and other methods from the 1990s.

Using context commands of the form “generate a policy that achieves a desired expected return”, our NN generators combine powerful exploration of parameter space with generalization across commands to iteratively find better and better policies. A form of weight-sharing HyperNetworks and policy embeddings scales our method to generate deep NNs.

Experiments show how a single learned policy generator can produce policies that achieve any return seen during training. Finally, we evaluate our algorithm on a set of continuous control tasks where it exhibits competitive performance.

Our code is public.

--- https://arxiv.org/abs/2207.12393#sensetime CelebV-HQ: A Large-Scale Video Facial Attributes Dataset Hao Zhu, Wayne Wu, Wentao Zhu, Liming Jiang, Siwei Tang, Li Zhang, Ziwei Liu, Chen Change Loy 2022-07-25 2022-07-25 [("doi","10.48550/arXiv.2207.12393")] ai/dataset ai/nn/gan/stylegan ai/video/generation

Large-scale datasets have played indispensable roles in the recent success of face generation/editing and facilitated the advances of emerging research fields. However, the academic community still lacks a video dataset with diverse facial attribute annotations, which is crucial for the research on face-related videos.

In this work, we propose a large-scale, high-quality, and diverse video dataset with rich facial attribute annotations, named the High-Quality Celebrity Video Dataset (CelebV-HQ). CelebV-HQ contains 35,666 video clips with the resolution of 512×512 at least, involving 15,653 identities. All clips are labeled manually with 83 facial attributes, covering appearance, action, and emotion. We conduct a comprehensive analysis in terms of age, ethnicity, brightness stability, motion smoothness, head pose diversity, and data quality to demonstrate the diversity and temporal coherence of CelebV-HQ.

Besides, its versatility and potential are validated on two representative tasks, ie. unconditional video generation and video facial attribute editing. Furthermore, we envision the future potential of CelebV-HQ, as well as the new opportunities and challenges it would bring to related research directions.

Data, code, and models are publicly available. Project page: https://celebv-hq.github.io/.

--- https://arxiv.org/abs/1910.02653 Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization Paras Jain, Ajay Jain, Aniruddha Nrusimha, Amir Gholami, Pieter Abbeel, Kurt Keutzer, Ion Stoica, Joseph E. Gonzalez 2019-10-07 2021-11-21 [("doi","10.48550/arXiv.1910.02653")] ai/scaling/hardware

We formalize the problem of trading-off DNN training time and memory requirements as the tensor rematerialization optimization problem, a generalization of prior checkpointing strategies.

We introduce Checkmate, a system that solves for optimal rematerialization schedules in reasonable times (under an hour) using off-the-shelf MILP solvers or near-optimal schedules with an approximation algorithm, then uses these schedules to accelerate millions of training iterations.

Our method scales to complex, realistic architectures and is hardware-aware through the use of accelerator-specific, profile-based cost models. In addition to reducing training cost, Checkmate enables real-world networks to be trained with up to 5.1× larger input sizes.

Checkmate is an open-source project, available at Github.

--- https://arxiv.org/abs/1803.03383 High-Accuracy Low-Precision Training Christopher De Sa, Megan Leszczynski, Jian Zhang, Alana Marzoev, Christopher R. Aberger, Kunle Olukotun, Christopher Ré 2018-03-09 2021-11-21 [("doi","10.48550/arXiv.1803.03383")] ai/nn/sparsity/low-precision

Low-precision computation is often used to lower the time and energy cost of machine learning, and recently hardware accelerators have been developed to support it. Still, it has been used primarily for inference—not training. Previous low-precision training algorithms suffered from a fundamental tradeoff: as the number of bits of precision is lowered, quantization noise is added to the model, which limits statistical accuracy.

To address this issue, we describe a simple low-precision stochastic gradient descent variant called HALP. HALP converges at the same theoretical rate as full-precision algorithms despite the noise introduced by using low precision throughout execution. The key idea is to use SVRG to reduce gradient variance, and to combine this with a novel technique called bit centering to reduce quantization error.

We show that on the CPU, HALP can run up to 4× faster than full-precision SVRG and can match its convergence trajectory. We implemented HALP in TensorQuant, and show that it exceeds the validation performance of plain low-precision SGD on two deep learning tasks.

--- https://arxiv.org/abs/1710.05758 TensorQuant—A Simulation Toolbox for Deep Neural Network Quantization Dominik Marek Loroch, Norbert Wehn, Franz-Josef Pfreundt, Janis Keuper 2017-10-13 2021-11-21 [("doi","10.48550/arXiv.1710.05758")] ai/nn/sparsity/low-precision

Recent research implies that training and inference of deep neural networks (DNN) can be computed with low precision numerical representations of the training/test data, weights and gradients without a general loss in accuracy. The benefit of such compact representations is twofold: they allow a reduction of the communication bottleneck in distributed DNN training and faster neural network implementations on hardware accelerators like FPGAs. Several quantization methods have been proposed to map the original 32-bit floating point problem to low-bit representations. While most related publications validate the proposed approach on a single DNN topology, it appears to be evident, that the optimal choice of the quantization method and number of coding bits is topology dependent. To this end, there is no general theory available, which would allow users to derive the optimal quantization during the design of a DNN topology.

In this paper, we present a quantization tool box for the TensorFlow framework. TensorQuant allows a transparent quantization simulation of existing DNN topologies during training and inference. TensorQuant supports generic quantization methods and allows experimental evaluation of the impact of the quantization on single layers as well as on the full topology.

In a first series of experiments with TensorQuant, we show an analysis of fix-point quantizations of popular CNN topologies.

--- https://arxiv.org/abs/1807.11205#tencent Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in 4 Minutes Xianyan Jia, Shutao Song, Wei He, Yangzihao Wang, Haidong Rong, Feihu Zhou, Liqiang Xie, Zhenyu Guo, Yuanzhou Yang, Liwei Yu, Tiegang Chen, Guangxiao Hu, Shaohuai Shi, Xiaowen Chu 2018-07-30 2021-11-21 [("doi","10.48550/arXiv.1807.11205")] ai/nn/sparsity/low-precision ai/scaling/hardware

Synchronized stochastic gradient descent (SGD) optimizers with data parallelism are widely used in training large-scale deep neural networks. Although using larger mini-batch sizes can improve the system scalability by reducing the communication-to-computation ratio, it may hurt the generalization ability of the models.

To this end, we build a highly scalable deep learning training system for dense GPU clusters with 3 main contributions: (1) We propose a mixed-precision training method that improves the training throughput of a single GPU without losing accuracy. (2) We propose an optimization approach for extremely large mini-batch size (up to 64k) that can train CNN models on the ImageNet dataset without losing accuracy. (3) We propose highly optimized all-reduce algorithms that achieve:

up to 3× and 11× speedup on AlexNet and ResNet-50 respectively than NCCL-based training on a cluster with 1,024 Tesla P40 GPUs. On training ResNet-50 with 90 epochs, the state-of-the-art GPU-based system with 1,024 Tesla P100 GPUs spent 15 minutes and achieved 74.9% top-1 test accuracy, and another KNL-based system with 2,048 Intel KNLs spent 20 minutes and achieved 75.4% accuracy. Our training system can achieve 75.8% top-1 test accuracy in only 6.6 minutes using 2048 Tesla P40 GPUs. When training AlexNet with 95 epochs, our system can achieve 58.7% top-1 test accuracy within 4 minutes, which also outperforms all other existing systems.

--- https://arxiv.org/abs/1710.03740#nvidia Mixed Precision Training Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, Hao Wu 2017-10-10 2021-11-22 [("doi","10.48550/arXiv.1710.03740")] ai/nn/gan ai/nn/rnn ai/nn/sparsity/low-precision ai/scaling/hardware

Deep neural networks have enabled progress in a wide variety of applications. Growing the size of the neural network typically results in improved accuracy. As model sizes grow, the memory and compute requirements for training these models also increases.

We introduce a technique to train deep neural networks using half precision floating point numbers. In our technique, weights, activations and gradients are stored in IEEE half-precision format. Half-precision floating numbers have limited numerical range compared to single-precision numbers. We propose two techniques to handle this loss of information. Firstly, we recommend maintaining a single-precision copy of the weights that accumulates the gradients after each optimizer step. This single-precision copy is rounded to half-precision format during training. Secondly, we propose scaling the loss appropriately to handle the loss of information with half-precision gradients.

We demonstrate that this approach works for a wide variety of models including convolution neural networks, recurrent neural networks and generative adversarial networks. This technique works for large scale models with more than 100 million parameters trained on large datasets. Using this approach, we can reduce the memory consumption of deep learning models by nearly 2×.

In future processors, we can also expect a large computation speedup using half-precision hardware units.

--- https://arxiv.org/abs/2202.09368#google Mixture-of-Experts with Expert Choice Routing Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Zhao, Andrew Dai, Zhifeng Chen, Quoc Le, James Laudon 2022-02-18 2022-02-18 [("doi","10.48550/arXiv.2202.09368")] ai/nn/transformer/t5 ai/scaling/mixture-of-experts

[blog; uses GLaM] Sparsely-activated Mixture-of-experts (MoE) models allow the number of parameters to greatly increase while keeping the amount of computation for a given token or a given sample unchanged. However, a poor expert routing strategy (eg. one resulting in load imbalance) can cause certain experts to be under-trained, leading to an expert being under or over-specialized. Prior work allocates a fixed number of experts to each token using a top-k function regardless of the relative importance of different tokens.

To address this, we propose a heterogeneous mixture-of-experts employing an expert choice method. Instead of letting tokens select the top-k experts, we have experts selecting the top-k tokens. As a result, each token can be routed to a variable number of experts and each expert can have a fixed bucket size.

We systematically study pre-training speedups using the same computational resources of the Switch Transformer top-1 and GShard top-2 gating of prior work and find that our method improves training convergence time by more than 2×. For the same computational cost, our method demonstrates higher performance in fine-tuning 11 selected tasks in the GLUE and SuperGLUE benchmarks. For a smaller activation cost, our method outperforms the T5 dense model in 7 out of the 11 tasks.

--- https://arxiv.org/abs/2107.11817 Go Wider Instead of Deeper Fuzhao Xue, Ziji Shi, Futao Wei, Yuxuan Lou, Yong Liu, Yang You 2021-07-25 2021-11-22 [("doi","10.48550/arXiv.2107.11817")] ai/scaling/mixture-of-experts

More transformer blocks with residual connections have recently achieved impressive results on various tasks. To achieve better performance with fewer trainable parameters, recent methods are proposed to go shallower by parameter sharing or model compressing along with the depth. However, weak modeling capacity limits their performance. Contrastively, going wider by inducing more trainable matrixes and parameters would produce a huge model requiring advanced parallelism to train and inference.

In this paper, we propose a parameter-efficient framework, going wider instead of deeper. Specially, following existing works, we adapt parameter sharing to compress along depth. But, such deployment would limit the performance. To maximize modeling capacity, we scale along model width by replacing feed-forward network (FFN) with mixture-of-experts (MoE). Across transformer blocks, instead of sharing normalization layers, we propose to use individual layer-norms to transform various semantic representations in a more parameter-efficient way.

To evaluate our plug-and-run framework, we design WideNet and conduct comprehensive experiments on popular computer vision and natural language processing benchmarks. On ImageNet-1K, our best model outperforms Vision Transformer (ViT) by 1.5% with 0.72× trainable parameters. Using 0.46× and 0.13× parameters, our WideNet can still surpass ViT and ViT-MoE by 0.8% and 2.1%, respectively. On four natural language processing datasets, WideNet outperforms ALBERT by 1.8% on average and surpass BERT using factorized embedding parameterization by 0.8% with fewer parameters.

--- https://arxiv.org/abs/2006.09503#microsoft PipeDream-2BW: Memory-Efficient Pipeline-Parallel DNN Training Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia 2020-06-16 2021-11-22 [("doi","10.48550/arXiv.2006.09503")] ai/nn/transformer ai/scaling/hardware

Many state-of-the-art ML results have been obtained by scaling up the number of parameters in existing models. However, parameters and activations for such large models often do not fit in the memory of a single accelerator device; this means that it is necessary to distribute training of large models over multiple accelerators.

In this work, we propose PipeDream-2BW, a system that supports memory-efficient pipeline parallelism. PipeDream-2BW uses a novel pipelining and weight gradient coalescing strategy, combined with the double buffering of weights, to ensure high throughput, low memory footprint, and weight update semantics similar to data parallelism. In addition, PipeDream-2BW automatically partitions the model over the available hardware resources, while respecting hardware constraints such as memory capacities of accelerators and interconnect topologies.

PipeDream-2BW can accelerate the training of large GPT and BERT language models by up to 20× with similar final model accuracy.

--- https://www.medrxiv.org/content/10.1101/2021.12.04.21267094.full Genome-wide association study and multi-trait analysis of opioid use disorder identifies novel associations in 639,709 individuals of European and African ancestry Joseph D. Deak, Hang Zhou, Marco Galimberti, Daniel Levey, Frank R. Wendt, Sandra Sanchez-Roige, Alexander Hatoum, Emma C. Johnson, Yaira Z. Nunez, Ditte Demontis, Anders Børglum, Veera M. Rajagopal, Mariela V. Jennings, Rachel L. Kember, Amy C. Justice, Howard J. Edenberg, Arpana Agrawal, Renato Polimanti, Henry R. Kranzler, Joel Gelernter 2021-12-15 2021-12-15 [("doi","10.1101/2021.12.04.21267094")] genetics/heritable/correlation psychiatry/alcoholism

Background: Despite the large toll of opioid use disorder (OUD), genome-wide association studies (GWAS) of OUD to date have yielded few susceptibility loci.

Method: We performed a large-scale GWAS of OUD in individuals of European (EUR) and African (AFR) ancestry, optimizing genetic informativeness by performing MTAG (Multi-trait analysis of GWAS) with genetically correlated substance use disorders (SUDs). Meta-analysis included 7 cohorts: the Million Veteran Program (MVP), Psychiatric Genomics Consortium (PGC), iPSYCH, FinnGen, Partners Biobank, BioVU, and Yale-Penn 3, resulting in a total n = 639,709 (Ncases = 20,858) across ancestries. OUD cases were defined as having lifetime OUD diagnosis, and controls as anyone not known to meet OUD criteria. We estimated SNP-heritability (h2SNP) and genetic correlations (rg). Based on genetic correlation, we performed MTAG on OUD, alcohol use disorder (AUD), and cannabis use disorder (CanUD).

Results: The EUR meta-analysis identified 3 genome-wide statistically-significant (GWS; p≤5×10−8) lead SNPs—one at FURIN (rs11372849; p = 9.54×10−10) and two OPRM1 variants (rs1799971, p = 4.92×10−09; rs79704991, p = 1.37×10−08; r < sup>2=0.02). Rs1799971 (p = 4.91×10−08) and another OPRM1 variant (rs9478500; p = 1.95×10−8; r < sup>2=0.03) were identified in the cross-ancestry meta-analysis. Estimated h2SNP was 12.75%, with strong rg with CanUD (rg =0.82; p = 1.14×10−47) and AUD (rg = 0.77; p = 6.36×10−78). The OUD-MTAG resulted in 18 GWS loci, some of which map to genes or gene regions that have previously been associated with psychiatric or addiction phenotypes.

Conclusion: We identified multiple OUD variant associations at OPRM1, single variant associations with FURIN, and 18 GWS associations in the OUD-MTAG. OUD is likely influenced by both OUD-specific loci and loci shared across SUDs.

--- /doc/statistics/order/2022-07-25-gwern-activelearningvsrandomsearch-200simulationruns.webm Gwern 2022-07-25 2022-07-25 reinforcement-learning/exploration/active-learning statistics/order --- https://arxiv.org/abs/2105.14450 Maximizing 3-D Parallelism in Distributed Training for Huge Neural Networks Zhengda Bian, Qifan Xu, Boxiang Wang, Yang You 2021-05-30 2021-11-22 [("doi","10.48550/arXiv.2105.14450")] ai/nn/transformer ai/scaling/hardware

The recent Natural Language Processing techniques have been refreshing the state-of-the-art performance at an incredible speed. Training huge language models is therefore an imperative demand in both industry and academy. However, huge language models impose challenges to both hardware and software. Graphical processing units (GPUs) are iterated frequently to meet the exploding demand, and a variety of ASICs like TPUs are spawned. However, there is still a tension between the fast growth of the extremely huge models and the fact that Moore’s law is approaching the end. To this end, many model parallelism techniques are proposed to distribute the model parameters to multiple devices, so as to alleviate the tension on both memory and computation.

Our work is the first to introduce a 3-dimensional model parallelism for expediting huge language models. By reaching a perfect load balance, our approach presents smaller memory and communication cost than existing state-of-the-art 1-D and 2-D model parallelism.

Our experiments on 64 TACC’s V100 GPUs show that our 3-D parallelism outperforms the 1-D and 2-D parallelism with 2.32× and 1.57× speedup, respectively.

--- https://arxiv.org/abs/2104.05343 An Efficient 2D Method for Training Super-Large Deep Learning Models Qifan Xu, Shenggui Li, Chaoyu Gong, Yang You 2021-04-12 2021-11-22 [("doi","10.48550/arXiv.2104.05343")] ai/scaling/hardware

Huge neural network models have shown unprecedented performance in real-world applications. However, due to memory constraints, model parallelism must beused to host large models that would otherwise not fit into the memory of a single device.

In this work, we propose Optimus, a highly efficient and scalable 2D-partition paradigm of model parallelism that would facilitate the training of infinitely large language models. In Optimus, activations are partitioned and distributed among devices, further reducing redundancy.

On 64 GPUs of TACC Frontera, Optimus achieves 1.48× speedup for training, 1.78× speedup for inference, and 8× increase in maximum batch size over Megatron. Optimus surpasses Megatron in scaling efficiency by a great margin.

The code is available at Github.

--- https://arxiv.org/abs/2105.14500 2.5-dimensional distributed model training Boxiang Wang, Qifan Xu, Zhengda Bian, Yang You 2021-05-30 2021-11-22 [("doi","10.48550/arXiv.2105.14500")] ai/scaling/hardware

Data parallelism does a good job in speeding up the training. However, when it comes to the case when the memory of a single device cannot host a whole model, data parallelism would not have the chance to do anything. Another option is to split the model by operator, or horizontally. Megatron-LM introduced a 1-Dimensional distributed method to use GPUs to speed up the training process. Optimus is a 2D solution for distributed tensor parallelism. However, these methods have a high communication overhead and a low scaling efficiency on large-scale computing clusters.

To solve this problem, we investigate the 2.5-Dimensional distributed tensor parallelism. Introduced by Solomonik et al 2.5-Dimensional Matrix Multiplication developed an effective method to perform multiple Cannon’s algorithm at the same time to increase the efficiency. With many restrictions of Cannon’s Algorithm and a huge amount of shift operation, we need to invent a new method of 2.5-dimensional matrix multiplication to enhance the performance. Absorbing the essence from both SUMMA and 2.5-Dimensional Matrix Multiplication, we introduced SUMMA2.5-LM for language models to overcome the abundance of unnecessary transmission loss result from the increasing size of language model parallelism.

Compared to previous 1D and 2D model parallelization of language models, our SUMMA2.5-LM managed to reduce the transmission cost on each layer, which could get a 1.45× efficiency according to our weak scaling result between 2.5-D [4,4,4] arrangement and 2-D [8,8,1] arrangement.

--- https://www.biorxiv.org/content/10.1101/2021.08.23.457339.full Extreme purifying selection against point mutations in the human genome Noah Dukler, Mehreen R. Mughal, Ritika Ramani, Yi-Fei Huang, Adam Siepel 2021-09-04 2021-11-22 [("doi","10.1101/2021.08.23.457339")] genetics/heritable/rare genetics/selection/natural/human

Genome sequencing of tens of thousands of humans has enabled the measurement of large selective effects for mutations to protein-coding genes.

Here we describe a new method, called ExtRaINSIGHT, for measuring similar selective effects in noncoding as well as in coding regions of the human genome. ExtRaINSIGHT estimates the prevalence of strong purifying selection, or “ultraselection” (λs), as the fractional depletion of rare single-nucleotide variants in target genomic sites relative to matched sites that are putatively free from selection, after controlling for local variation and neighbor-dependence in mutation rate. We show using simulations that λs is closely related to the average site-specific selection coefficient against heterozygous point mutations, as predicted at mutation-selection balance.

Applying ExtRaINSIGHT to 71,702 whole genome sequences from gnomAD v3, we find strong evidence of ultraselection in evolutionarily ancient miRNAs and neuronal protein-coding genes, as well as at splice sites. By contrast, we find weak evidence in other noncoding RNAs and transcription factor binding sites, and only modest evidence in ultraconserved elements and human accelerated regions.

We estimate that ~0.3–0.5% of the human genome is ultraselected, implying ~0.3–0.4 lethal or nearly lethal de novo mutations per potential human zygote.

Overall, our study sheds new light on the genome-wide distribution of fitness effects for new point mutations by combining deep new sequencing data sets and classical theory from population genetics.

…What are the implications of our estimate of ~0.3–0.4 for the number of nearly lethal mutations per potential fertilization? This estimate implies a fairly high genetic burden for severely deleterious mutations (not to mention the additional burden imposed by weakly deleterious mutations), but one that appears to be in the plausible range (eg.23, 28). One rough point of comparison is the rate of spontaneous abortion, which has been estimated to be as high as 50% for mothers of prime reproductive age.49, 50 This quantity, of course, is not the same as the rate of nearly lethal mutations, for a variety of reasons—spontaneous abortion typically describes death prior to birth conditional on a detectable pregnancy, whereas our measure includes mutations that are lethal near the time of fertilization or even prior to fertilization, and also includes mutations that cause death after birth, that do not cause death but prevent an organism from reproducing, or that severely reduce fitness over several generations. In addition, many of the mutations that cause spontaneous abortion in the fetus are not point mutations, but instead major structural variants that often alter karyotype.49 At the same time, spontaneous abortion is only partly a consequence of the genetics of the embryo, also depending strongly on the environment and the genetics of the mother. Nevertheless, it is notable that these quite different estimates are in rough agreement with one another, suggesting an overlap in what they are measuring, perhaps with other factors approximately cancelling.

--- https://arxiv.org/abs/2206.04040#apple An Improved One millisecond Mobile Backbone Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, Anurag Ranjan 2022-06-08 2022-06-08 [("doi","10.48550/arXiv.2206.04040")] ai/nn ai/scaling

Efficient neural network backbones for mobile devices are often optimized for metrics such as FLOPs or parameter count. However, these metrics may not correlate well with latency of the network when deployed on a mobile device. Therefore, we perform extensive analysis of different metrics by deploying several mobile-friendly networks on a mobile device. We identify and analyze architectural and optimization bottlenecks in recent efficient neural networks and provide ways to mitigate these bottlenecks.

To this end, we design an efficient backbone MobileOne, with variants achieving an inference time under 1 ms on an iPhone12 with 75.9% top-1 accuracy on ImageNet. We show that MobileOne achieves state-of-the-art performance within the efficient architectures while being many times faster on mobile. Our best model obtains similar performance on ImageNet as MobileFormer while being 38× faster. Our model obtains 2.3% better top-1 accuracy on ImageNet than EfficientNet at similar latency.

Furthermore, we show that our model generalizes to multiple tasks—image classification, object detection, and semantic segmentation with improvements in latency and accuracy as compared to existing efficient architectures when deployed on a mobile device.

--- https://arxiv.org/abs/2006.15704#facebook PyTorch Distributed: Experiences on Accelerating Data Parallel Training Shen Li, Yanli Zhao, Rohan Varma, Omkar Salpekar, Pieter Noordhuis, Teng Li, Adam Paszke, Jeff Smith, Brian Vaughan, Pritam Damania, Soumith Chintala 2020-06-28 2021-11-23 [("doi","10.48550/arXiv.2006.15704")] ai/scaling/hardware

This paper presents the design, implementation, and evaluation of the PyTorch distributed data parallel module. PyTorch is a widely-adopted scientific computing package used in deep learning research and applications. Recent advances in deep learning argue for the value of large datasets and large models, which necessitates the ability to scale out model training to more computational resources. Data parallelism has emerged as a popular solution for distributed training thanks to its straightforward principle and broad applicability. In general, the technique of distributed data parallelism replicates the model on every computational resource to generate gradients independently and then communicates those gradients at each iteration to keep model replicas consistent.

Despite the conceptual simplicity of the technique, the subtle dependencies between computation and communication make it non-trivial to optimize the distributed training efficiency. As of v1.5, PyTorch natively provides several techniques to accelerate distributed data parallel, including bucketing gradients, overlapping computation with communication, and skipping gradient synchronization. Evaluations show that, when configured appropriately, the PyTorch distributed data parallel module attains near-linear scalability using 256 GPUs.

--- https://arxiv.org/abs/2207.11280#huawei PanGu-Coder: Program Synthesis with Function-Level Language Modeling Fenia Christopoulou, Gerasimos Lampouras, Milan Gritta, Guchun Zhang, Yinpeng Guo, Zhongqi Li, Qi Zhang, Meng Xiao, Bo Shen, Lin Li, Hao Yu, Li Yan, Pingyi Zhou, Xin Wang, Yuchi Ma, Ignacio Iacobacci, Yasheng Wang, Guangtai Liang, Jiansheng Wei, Xin Jiang, Qianxiang Wang, Qun Liu 2022-07-22 2022-07-22 [("doi","10.48550/arXiv.2207.11280")] ai/nn/transformer/gpt/codex

We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-α architecture for text-to-code generation, i.e. the synthesis of programming language solutions given a natural language problem description.

We train PanGu-Coder using a two-stage strategy: the first stage employs Causal Language Modeling (CLM) to pre-train on raw programming language data [Python], while the second stage uses a combination of Causal Language Modeling and Masked Language Modeling (MLM) training objectives that focus on the downstream task of text-to-code generation and train on loosely curated pairs of natural language program definitions and code functions. Finally, we discuss PanGu-Coder-FT, which is fine-tuned on a combination of competitive programming problems and code with continuous integration tests.

We evaluate PanGu-Coder with a focus on whether it generates functionally correct programs and demonstrate that it achieves equivalent or better performance than similarly sized models [up to 2.6b parameters], such as Codex, while attending a smaller context window and training on less data.

--- https://return.life/2022/07/26/conversation-stopper/ 2021-11-23 ai/nn/transformer/gpt/non-fiction --- https://www.wired.com/story/race-to-engineer-new-psychedelic-drugs/ The High-Stakes Race to Engineer New Psychedelic Drugs 2021-11-23 psychedelic --- https://arxiv.org/abs/2207.11514 Semantic Abstraction (SemAbs): Open-World 3D Scene Understanding from 2D Vision-Language Models Huy Ha, Shuran Song 2022-07-23 2022-07-23 [("doi","10.48550/arXiv.2207.11514")] ai/nn/transformer/clip reinforcement-learning/robot

We study open-world 3D scene understanding, a family of tasks that require agents to reason about their 3D environment with an open-set vocabulary and out-of-domain visual inputs—a critical skill for robots to operate in the unstructured 3D world. Towards this end, we propose Semantic Abstraction (SemAbs), a framework that equips 2D Vision-Language Models (VLMs) with new 3D spatial capabilities, while maintaining their zero-shot robustness.

We achieve this abstraction using relevancy maps extracted from CLIP, and learn 3D spatial and geometric reasoning skills on top of those abstractions in a semantic-agnostic manner. We demonstrate the usefulness of SemAbs on two open-world 3D scene understanding tasks: (1) completing partially observed objects and (2) localizing hidden objects from language descriptions.

Experiments show that SemAbs can generalize to novel vocabulary, materials/lighting, classes, and domains (ie. real-world scans) from training on limited 3D synthetic data.

Code and data will be available at https://semantic-abstraction.cs.columbia.edu/.

--- https://windowsontheory.org/2022/06/20/the-uneasy-relationship-between-deep-learning-and-classical-statistics/ The uneasy relationship between deep learning and (classical) statistics 2021-11-23 ai/scaling --- https://en.wikipedia.org/wiki/Uniwidth_typeface Uniwidth typeface 2021-11-23 design/typography --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5812605/ "Are you gonna publish that?" Peer-reviewed publication outcomes of doctoral dissertations in psychology Spencer C. Evans, Christina M. Amaro, Robyn Herbert, Jennifer B. Blossom, Michael C. Roberts 2018 2021-11-23 [("doi","10.1371/journal.pone.0192219")] statistics/bias

If a doctoral dissertation represents an original investigation that makes a contribution to one’s field, then dissertation research could, and arguably should, be disseminated into the scientific literature. However, the extent and nature of dissertation publication remains largely unknown within psychology.

The present study investigated the peer-reviewed publication outcomes of psychology dissertation research in the United States. Additionally, we examined publication lag, scientific impact, and variations across subfields.

To investigate these questions, we first drew a stratified random cohort sample of 910 psychology Ph.D. dissertations from ProQuest Dissertations & Theses. Next, we conducted comprehensive literature searches for peer-reviewed journal articles derived from these dissertations published 0–7 years thereafter. Published dissertation articles were coded for their bibliographic details, citation rates, and journal impact metrics.

Results showed that only 1⁄4th (25.6% [95% CI: 23.0, 28.4]) of dissertations were ultimately published in peer-reviewed journals, with substantial variations across subfields (range: 10.1 to 59.4%). Rates of dissertation publication were lower in professional/applied subfields (eg. clinical, counseling) compared to research/academic subfields (eg. experimental, cognitive). When dissertations were published, however, they often appeared in influential journals (eg. Thomson Reuters Impact Factor M = 2.84 [2.45, 3.23], 5-year Impact Factor M = 3.49 [3.07, 3.90]) and were cited numerous times (Web of Science citations per year M = 3.65 [2.88, 4.42]). Publication typically occurred within 2–3 years after the dissertation year.

Overall, these results indicate that the large majority of Ph.D. dissertation research in psychology does not get disseminated into the peer-reviewed literature. The non-publication of dissertation research appears to be a systemic problem affecting both research and training in psychology. Efforts to improve the quality and “publishability” of doctoral dissertation research could benefit psychological science on multiple fronts.

--- https://colab.research.google.com/drive/189LHTpYaefMhKNIGOzTLHHavlgmoIWg9 2021-11-23 ai/anime ai/nn/transformer/clip --- https://arxiv.org/abs/2207.12661#microsoft MS-CLIP: Learning Visual Representation from Modality-Shared Contrastive Language-Image Pre-training Haoxuan You, Luowei Zhou, Bin Xiao, Noel Codella, Yu Cheng, Ruochen Xu, Shih-Fu Chang, Lu Yuan 2022-07-26 2022-07-26 [("doi","10.48550/arXiv.2207.12661")] ai/nn/transformer/clip

Large-scale multi-modal contrastive pre-training has demonstrated great utility to learn transferable features for a range of downstream tasks by mapping multiple modalities into a shared embedding space. Typically, this has employed separate encoders for each modality. However, recent work suggests that transformers can support learning across multiple modalities and allow knowledge sharing. Inspired by this, we investigate a variety of Modality-Shared Contrastive Language-Image Pre-training (MS-CLIP) frameworks.

More specifically, we question how many parameters of a transformer model can be shared across modalities during contrastive pre-training, and rigorously examine architectural design choices that position the proportion of parameters shared along a spectrum. In studied conditions, we observe that a mostly unified encoder for vision and language signals outperforms all other variations that separate more parameters. Additionally, we find that light-weight modality-specific parallel modules further improve performance.

Experimental results show that the proposed MS-CLIP approach outperforms vanilla CLIP by up to 13% relative in zero-shot ImageNet classification (pre-trained on YFCC-100M), while simultaneously supporting a reduction of parameters. In addition, our approach outperforms vanilla CLIP by 1.6 points in linear probing on a collection of 24 downstream vision tasks. Furthermore, we discover that sharing parameters leads to semantic concepts from different modalities being encoded more closely in the embedding space, facilitating the transferring of common semantic structure (eg. attention patterns) from language to vision.

Code is available at URL.

--- https://arxiv.org/abs/2207.09094#microsoft MoEC: Mixture of Expert Clusters Yuan Xie, Shaohan Huang, Tianyu Chen, Furu Wei 2022-07-19 2022-07-19 [("doi","10.48550/arXiv.2207.09094")] ai/scaling/mixture-of-experts

Sparsely Mixture of Experts (MoE) has received great interest due to its promising scaling capability with affordable computational overhead. MoE converts dense layers into sparse experts, and utilizes a gated routing network to make experts conditionally activated. However, as the number of experts grows, MoE with outrageous parameters suffers from overfitting and sparse data allocation. Such problems are especially severe on tasks with limited data, thus hindering the progress for MoE models to improve performance by scaling up.

In this work, we propose Mixture of Expert Clusters—a general approach to enable expert layers to learn more diverse and appropriate knowledge by imposing variance-based constraints on the routing stage. We further propose a cluster-level expert dropout strategy specifically designed for the expert cluster structure.

Our experiments reveal that MoEC could improve performance on machine translation and natural language understanding tasks, and raise the performance upper bound for scaling up experts under limited data. We also verify that MoEC plays a positive role in mitigating overfitting and sparse data allocation.

--- https://everything2.com/title/You+have+a+sad+feeling+for+a+moment%252C+then+it+passes You have a sad feeling for a moment, then it passes 2021-11-24 cat reinforcement-learning/nethack --- https://arxiv.org/abs/2112.00778#google Quantum advantage in learning from experiments Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, Jarrod R. McClean 2021-12-01 2021-12-01 [("doi","10.1126/science.abn7293")] science

[blog] Quantum technology has the potential to revolutionize how we acquire and process experimental data to learn about the physical world. An experimental setup that transduces data from a physical system to a stable quantum memory, and processes that data using a quantum computer, could have large advantages over conventional experiments in which the physical system is measured and the outcomes are processed using a classical computer.

We prove that, in various tasks, quantum machines can learn from exponentially fewer experiments than those required in conventional experiments. The exponential advantage holds in predicting properties of physical systems, performing quantum principal component analysis on noisy states, and learning approximate models of physical dynamics. In some tasks, the quantum processing needed to achieve the exponential advantage can be modest; for example, one can simultaneously learn about many non-commuting observables by processing only two copies of the system.

Conducting experiments with up to 40 superconducting qubits and 1300 quantum gates, we demonstrate that a substantial quantum advantage can be realized using today’s relatively noisy quantum processors.

Our results highlight how quantum technology can enable powerful new strategies to learn about nature.

--- https://arxiv.org/abs/2207.13061 NewsStories: Illustrating articles with visual summaries Reuben Tan, Bryan A. Plummer, Kate Saenko, J. P. Lewis, Avneesh Sud, Thomas Leung 2022-07-26 2022-07-26 [("doi","10.48550/arXiv.2207.13061")] ai/dataset ai/nn/retrieval ai/nn/transformer/clip

[cf. Ashual et al 2022, Rombach et al 2022] Recent self-supervised approaches have used large-scale image-text datasets to learn powerful representations that transfer to many tasks without finetuning. These methods often assume that there is one-to-one correspondence between its images and their (short) captions. However, many tasks require reasoning about multiple images and long text narratives, such as describing news articles with visual summaries. Thus, we explore a novel setting where the goal is to learn a self-supervised visual-language representation that is robust to varying text length and the number of images. In addition, unlike prior work which assumed captions have a literal relation to the image, we assume images only contain loose illustrative correspondence with the text.

To explore this problem, we introduce a large-scale multimodal dataset, NewsStories, containing over 31M articles, 22M images and 1M videos.

We show that state-of-the-art image-text alignment methods are not robust to longer narratives with multiple images.

Finally, we introduce an intuitive baseline that outperforms these methods on zero-shot image-set retrieval by 10% on the GoodNews dataset.

--- https://arxiv.org/abs/1904.01475 Good News, Everyone! Context driven entity-aware captioning for news images Ali Furkan Biten, Lluis Gomez, Marçal Rusiñol, Dimosthenis Karatzas 2019-04-02 2021-11-24 [("doi","10.48550/arXiv.1904.01475")] ai/nn/rnn ai/nn/sampling

Current image captioning systems perform at a merely descriptive level, essentially enumerating the objects in the scene and their relations. Humans, on the contrary, interpret images by integrating several sources of prior knowledge of the world.

In this work, we aim to take a step closer to producing captions that offer a plausible interpretation of the scene, by integrating such contextual information into the captioning pipeline. For this, we focus on the captioning of images used to illustrate news articles. We propose a novel captioning method that is able to leverage contextual information provided by the text of news articles associated with an image.

Our model is able to selectively draw information from the article guided by visual cues and to dynamically extend the output dictionary to out-of-vocabulary named entities that appear in the context source.

Furthermore, we introduce ‘GoodNews’, the largest news image captioning dataset in the literature and demonstrate state-of-the-art results.

--- https://arxiv.org/abs/2207.13038 Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models Robin Rombach, Andreas Blattmann, Björn Ommer 2022-07-26 2022-07-26 [("doi","10.48550/arXiv.2207.13038")] ai/nn/diffusion ai/nn/retrieval ai/nn/transformer/clip

Novel architectures have recently improved generative image synthesis leading to excellent visual quality in various tasks. Of particular note is the field of “AI-Art”, which has seen unprecedented growth with the emergence of powerful multimodal models such as CLIP. By combining speech and image synthesis models, so-called “prompt-engineering” has become established, in which carefully selected and composed sentences are used to achieve a certain visual style in the synthesized image.

In this note, we present an alternative approach based on retrieval-augmented diffusion models (RDMs). In RDMs, a set of nearest neighbors is retrieved from an external database during training for each training instance, and the diffusion model is conditioned on these informative samples. During inference (sampling), we replace the retrieval database with a more specialized database that contains, for example, only images of a particular visual style. This provides a novel way to prompt a general trained model after training and thereby specify a particular visual style.

As shown by our experiments, this approach is superior to specifying the visual style within the text prompt.

We open-source code and model weights at Github.

--- /doc/fiction/humor/2004-stallard.pdf No Justice, No Foul: Everything You Didn’t Know That You Were Afraid To Know About The Supreme Court Jim Stallard 2004 2021-11-24 fiction/humor law --- https://invariant.substack.com/p/listerine-royalties-the-origin-story Listerine Royalties: The Origin Story and Valuation of a Uniquely Enduring Asset 2021-11-24 economics/perpetuities --- https://www.nytimes.com/2022/07/13/technology/ai-web-accessibility.html For Blind Internet Users, the Fix Can Be Worse Than the Flaws 2021-11-24 design --- https://tedgioia.substack.com/p/how-a-prominent-composer-lost-his 2021-11-24 wikipedia --- https://arxiv.org/abs/2204.07141#facebook Masked Siamese Networks for Label-Efficient Learning Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Arm Holdings, Joulin, Michael Rabbat, Nicolas Ballas 2022-04-14 2022-04-14 [("doi","10.48550/arXiv.2204.07141")] ai/nn/transformer

We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our approach matches the representation of an image view containing randomly masked patches to the representation of the original unmasked image.

This self-supervised pre-training strategy is particularly scalable when applied to Vision Transformers since only the unmasked patches are processed by the network. As a result, MSNs improve the scalability of joint-embedding architectures, while producing representations of a high semantic level that perform competitively on low-shot image classification.

For instance, on ImageNet-1K, with only 5,000 annotated images, our base MSN model achieves 72.4% top-1 accuracy, and with 1% of ImageNet-1K labels, we achieve 75.7% top-1 accuracy, setting a new state-of-the-art for self-supervised learning on this benchmark.

Our code is publicly available.

--- /doc/economics/2000-griffeth.pdf A Meta-Analysis of Antecedents and Correlates of Employee Turnover: Update, Moderator Tests, and Research Implications for the Next Millennium Rodger W. Griffeth, Peter W. Hom, Stefan Gaertner 2000-06-01 2021-11-25 [("doi","10.1177/014920630002600305")] economics iq

This article reports the results of a comprehensive meta-analysis of turnover antecedents, extending an earlier one by Hom & Griffeth 1995. As such, this updated meta-analysis represents the most wide-ranging quantitative review to date of the predictive strength of numerous turnover antecedents.

Importantly, the present investigation identifies various moderators of antecedent-turnover correlations.

The implications of these findings for both theory and practice are discussed.

--- https://www.nature.com/articles/d41586-022-01626-x 2021-11-25 psychology/smell --- https://arxiv.org/abs/2207.13532#bytedance CMAE: Contrastive Masked Autoencoders are Stronger Vision Learners Zhicheng Huang, Xiaojie Jin, Chengze Lu, Qibin Hou, Ming-Ming Cheng, Dongmei Fu, Xiaohui Shen, Jiashi Feng 2022-07-27 2022-07-27 [("doi","10.48550/arXiv.2207.13532")] ai/nn/vae/mae

Masked image modeling (MIM) has achieved promising results on various vision tasks. However, the limited discriminability of learned representation manifests there is still plenty to go for making a stronger vision learner. Towards this goal, we propose Contrastive Masked Autoencoders (CMAE), a new self-supervised pre-training method for learning more comprehensive and capable vision representations.

By elaboratively unifying contrastive learning (CL) and masked image model (MIM) through novel designs, CMAE leverages their respective advantages and learns representations with both strong instance discriminability and local perceptibility. Specifically, CMAE consists of two branches where the online branch is an asymmetric encoder-decoder and the target branch is a momentum updated encoder. During training, the online encoder reconstructs original images from latent representations of masked images to learn holistic features. The target encoder, fed with the full images, enhances the feature discriminability via contrastive learning with its online counterpart. To make CL compatible with MIM, CMAE introduces two new components, i.e. pixel shift for generating plausible positive views and feature decoder for complementing features of contrastive pairs. Thanks to these novel designs, CMAE effectively improves the representation quality and transfer performance over its MIM counterpart.

CMAE achieves the state-of-the-art performance on highly competitive benchmarks of image classification, semantic segmentation and object detection. Notably, CMAE-Base achieves 85.3% top-1 accuracy on ImageNet and 52.5% mIoU on ADE20K, surpassing previous best results by 0.7% and 1.8% respectively.

Codes will be made publicly available.

--- https://www.biorxiv.org/content/10.1101/2020.05.09.084582.full Multiple origins of insular woodiness on the Canary Islands are consistent with palaeoclimatic aridification Alexander Hooft van Huysduynen, Steven Janssens, Vincent Merckx, Rutger Vos, Luis Valente, Alexander Zizka, Maximilian Larter, Betül Karabayir, Daphne Maaskant, Youri Witmer, José Maria Fernández-Palacios, Lea de Nascimento, Ruth Molina Jaén, Juli Caujapé Castells, Águedo Marrero-Rodríguez, Marcelino del Arco, Frederic Lens 2020-05-10 2021-11-25 [("doi","10.1101/2020.05.09.084582")] genetics/selection/natural

Aim: Insular woodiness, referring to the evolutionary transition from herbaceousness towards woodiness on islands, has arisen at least 38× on the Canary Islands. Distribution patterns and physiological experiments have suggested a link between insular woodiness and increased drought stress resistance in current-day species, but we do not know in which palaeoclimatic conditions these insular woody lineages originated. Therefore, we estimated the timing of colonization events and origin of woodiness of multiple Canary Island lineages and reviewed the palaeoclimatic based on literature.

Location:

Canary Islands (Spain).

Taxon: 37 lineages, including 24 insular woody and 13 non-insular woody (ie. herbaceous, ancestrally woody, and derived woody).

Method: To enable a simultaneous dating analysis for all 37 lineages, two chloroplast markers (matK and rbcL) for 135 Canary Island species and 103 closely related continental relatives were sequenced and aligned to an existing matK-rbcL dataset including ca 24,000 species that was calibrated with 42 fossils from outside the Canaries. After constraining the species to the family level, 200 RAxML runs were performed and dated with TreePL.

Results: Woodiness in 80–90% of the insular woody lineages originated within the last 7 Myr, coinciding with the onset of major aridification events nearby the Canaries (start of north African desertification, followed by Messinian salinity crisis); in ca 55–65% of the insular woody lineages studied, woodiness developed within the last 3.2 Myr during which Mediterranean seasonality (yearly summer droughts) became established on the Canaries, followed by dry Pleistocene glacial fluctuations.

Conclusion: Although details of the initial colonization and settlement of many island plant lineages remain elusive, our results are consistent with palaeodrought as a potential driver for woodiness in most of the insular woody Canary Island lineages studied.

--- https://arxiv.org/abs/2207.13320#naver Generator Knows What Discriminator Should Learn in Unconditional GANs Gayoung Lee, Hyunsu Kim, Junho Kim, Seonghyeon Kim, Jung-Woo Ha, Yunjey Choi 2022-07-27 2022-07-27 [("doi","10.48550/arXiv.2207.13320")] ai/nn/gan/stylegan

Recent methods for conditional image generation benefit from dense supervision such as segmentation label maps to achieve high-fidelity. However, it is rarely explored to employ dense supervision for unconditional image generation.

Here we explore the efficacy of dense supervision in unconditional generation and find generator feature maps can be an alternative of cost-expensive semantic label maps. From our empirical evidences, we propose a new generator-guided discriminator regularization(GGDR) in which the generator feature maps supervise the discriminator to have rich semantic representations in unconditional generation. In specific, we employ an U-Net architecture for discriminator, which is trained to predict the generator feature maps given fake images as inputs.

Extensive experiments on multiple datasets show that our GGDR consistently improves the performance of baseline methods in terms of quantitative and qualitative aspects.

Code is available at https://github.com/naver-ai/GGDR.

--- https://www.deepmind.com/blog/alphafold-reveals-the-structure-of-the-protein-universe 2021-11-25 ai/nn/transformer/alphafold --- https://arxiv.org/abs/2207.13286 Vector Quantized Image-to-Image Translation Yu-Jie Chen, Shin-I Cheng, Wei-Chen Chiu, Hung-Yu Tseng, Hsin-Ying Lee 2022-07-27 2022-07-27 [("doi","10.48550/arXiv.2207.13286")] ai/nn/vae

Current image-to-image translation methods formulate the task with conditional generation models, leading to learning only the recolorization or regional changes as being constrained by the rich structural information provided by the conditional contexts.

In this work, we propose introducing the vector quantization technique into the image-to-image translation framework. The vector quantized content representation can facilitate not only the translation, but also the unconditional distribution shared among different domains. Meanwhile, along with the disentangled style representation, the proposed method further enables the capability of image extension with flexibility in both intra-domain and inter-domains.

Qualitative and quantitative experiments demonstrate that our framework achieves comparable performance to the state-of-the-art image-to-image translation and image extension methods.

Compared to methods for individual tasks, the proposed method, as a unified framework, unleashes applications combining image-to-image translation, unconditional generation, and image extension altogether. For example, it provides style variability for image generation and extension, and equips image-to-image translation with further extension capabilities.

--- https://arxiv.org/abs/2207.13513 Learning with Combinatorial Optimization Layers: a Probabilistic Approach Guillaume Dalle, Léo Baty, Louis Bouvier, Axel Parmentier 2022-07-27 2022-07-27 [("doi","10.48550/arXiv.2207.13513")] ai/nn cs/algorithm reinforcement-learning/model

Combinatorial optimization (CO) layers in machine learning (ML) pipelines are a powerful tool to tackle data-driven decision tasks, but they come with two main challenges. First, the solution of a CO problem often behaves as a piecewise constant function of its objective parameters. Given that ML pipelines are typically trained using stochastic gradient descent, the absence of slope information is very detrimental. Second, standard ML losses do not work well in combinatorial settings. A growing body of research addresses these challenges through diverse methods. Unfortunately, the lack of well-maintained implementations slows down the adoption of CO layers.

In this paper, building upon previous works, we introduce a probabilistic perspective on CO layers, which lends itself naturally to approximate differentiation and the construction of structured losses. We recover many approaches from the literature as special cases, and we also derive new ones. Based on this unifying perspective, we present InferOpt.jl, an open-source Julia package that (1) allows turning any CO oracle with a linear objective into a differentiable layer, and (2) defines adequate losses to train pipelines containing such layers.

Our library works with arbitrary optimization algorithms, and it is fully compatible with Julia’s ML ecosystem.

We demonstrate its abilities using a path-finding problem on video game maps.

--- https://arxiv.org/abs/2206.07438 Multi-Objective Hyperparameter Optimization—An Overview Florian Karl, Tobias Pielok, Julia Moosbauer, Florian Pfisterer, Stefan Coors, Martin Binder, Lennart Schneider, Janek Thomas, Jakob Richter, Michel Lang, Eduardo C. Garrido-Merchán, Juergen Branke, Bernd Bischl 2022-06-15 2022-06-15 [("doi","10.48550/arXiv.2206.07438")] reinforcement-learning/exploration

Hyperparameter optimization constitutes a large part of typical modern machine learning workflows. This arises from the fact that machine learning methods and corresponding preprocessing steps often only yield optimal performance when hyperparameters are properly tuned. But in many applications, we are not only interested in optimizing ML pipelines solely for predictive accuracy; additional metrics or constraints must be considered when determining an optimal configuration, resulting in a multi-objective optimization problem. This is often neglected in practice, due to a lack of knowledge and readily available software implementations for multi-objective hyperparameter optimization.

In this work, we introduce the reader to the basics of multi-objective hyperparameter optimization and motivate its usefulness in applied ML. Furthermore, we provide an extensive survey of existing optimization strategies, both from the domain of evolutionary algorithms and Bayesian optimization.

We illustrate the utility of MOO in several specific ML applications, considering objectives such as operating conditions, prediction time, sparseness, fairness, interpretability and robustness.

--- https://arxiv.org/abs/2207.13224#google PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations Kuang-Huei Lee, Ofir Nachum, Tingnan Zhang, Sergio Guadarrama, Jie Tan, Wenhao Yu 2022-07-27 2022-07-27 [("doi","10.48550/arXiv.2207.13224")] ai/nn/rnn reinforcement-learning/model reinforcement-learning/robot

Evolution Strategy (ES) algorithms have shown promising results in training complex robotic control policies due to their massive parallelism capability, simple implementation, effective parameter-space exploration, and fast training time. However, a key limitation of ES is its scalability to large capacity models, including modern neural network architectures.

In this work, we develop Predictive Information Augmented Random Search (PI-ARS) to mitigate this limitation by leveraging recent advancements in representation learning to reduce the parameter search space for ES. Namely, PI-ARS combines a gradient-based representation learning technique, Predictive Information (PI), with a gradient-free ES algorithm, Augmented Random Search (ARS), to train policies that can process complex robot sensory inputs and handle highly nonlinear robot dynamics.

We evaluate PI-ARS on a set of challenging visual-locomotion tasks where a quadruped robot needs to walk on uneven stepping stones, quincuncial piles, and moving platforms, as well as to complete an indoor navigation task. Across all tasks, PI-ARS demonstrates better learning efficiency and performance compared to the ARS baseline.

We further validate our algorithm by demonstrating that the learned policies can successfully transfer to a real quadruped robot, for example, achieving a 100% success rate on the real-world stepping stone environment, dramatically improving prior results achieving 40% success.

--- https://arxiv.org/abs/1812.02256#deepmind Relative Entropy Regularized Policy Iteration Abbas Abdolmaleki, Jost Tobias Springenberg, Jonas Degrave, Steven Bohez, Yuval Tassa, Dan Belov, Nicolas Heess, Martin Riedmiller 2018-12-05 2021-11-25 [("doi","10.48550/arXiv.1812.02256")] reinforcement-learning/model-free

We present an off-policy actor-critic algorithm for Reinforcement Learning (RL) that combines ideas from gradient-free optimization via stochastic search with learned action-value function. The result is a simple procedure consisting of 3 steps: (8) policy evaluation by estimating a parametric action-value function; (2) policy improvement via the estimation of a local non-parametric policy; and (3) generalization by fitting a parametric policy. Each step can be implemented in different ways, giving rise to several algorithm variants. Our algorithm draws on connections to existing literature on black-box optimization and ‘RL as an inference’ and it can be seen either as an extension of the Maximum a Posteriori Policy Optimization algorithm (MPO) [Abdolmaleki et al 2018a], or as an extension of Trust Region Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) [Abdolmaleki et al 2017b; Hansen et al 1997] to a policy iteration scheme.

Our comparison on 31 continuous control tasks from parkour suite [Heess et al 2017], DeepMind control suite [Tassa et al 2018] and OpenAI Gym [Brockman et al 2016] with diverse properties, limited amount of compute and a single set of hyperparameters, demonstrate the effectiveness of our method and the state of art results.

Videos, summarizing results, can be found at our homepage.

--- https://github.com/deepmind/acme/tree/master/acme/agents/tf/dmpo 2021-11-26 reinforcement-learning/model-free --- https://arxiv.org/abs/1806.06920#deepmind Maximum a Posteriori Policy Optimization Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, Martin Riedmiller 2018-06-14 2021-11-26 [("doi","10.48550/arXiv.1806.06920")] reinforcement-learning/model-free

We introduce a new algorithm for reinforcement learning called Maximum a posteriori Policy Optimization (MPO) based on coordinate ascent on a relative entropy objective.

We show that several existing methods can directly be related to our derivation. We develop two off-policy algorithms and demonstrate that they are competitive with the state-of-the-art in deep reinforcement learning.

In particular, for continuous control, our method outperforms existing methods with respect to sample efficiency, premature convergence, and robustness to hyperparameter settings while achieving similar or better final performance.

--- https://www.wired.com/story/big-business-burying-carbon-dioxide-capture-storage/ The Big Business of Burying Carbon 2021-11-26 technology/carbon-capture --- https://x.com/TILTDHEADS/status/1552772594830438400 2021-11-26 ai/nn/diffusion ai/nn/transformer/clip/sample --- /doc/ai/nn/transformer/gpt/dall-e/2/2022-07-18-gwern-dalle2-spiceandwolfgoldenookamiwolfagainsttempleatnight-17.48.45.png Gwern 2022-07-18 2022-07-18 ai/nn/transformer/gpt/dall-e/2 --- /doc/ai/nn/transformer/gpt/dall-e/2/2022-07-20-gwern-dalle2-starwarsblacksunlogooutpainting-20.51.35.jpg Gwern 2022-07-20 2022-07-20 ai/nn/transformer/gpt/dall-e/2 --- /doc/ai/nn/transformer/gpt/dall-e/2/2022-07-20-gwern-dalle2-themoonmakesatravelerhungerforsomethingbitterintheworldbyjeffreysmithoiloncanvassynthwave-151349.png Gwern 2022-07-20 2022-07-20 ai/nn/transformer/gpt/dall-e/2 --- /doc/ai/nn/transformer/gpt/dall-e/2/2022-07-20-gwern-dalle2-thevibratingmoonmakesatravelerhungerforsomethingbitterintheworldbyjeffreysmithoiloncanvassynthwave-152743.png Gwern 2022-07-20 2022-07-20 ai/nn/transformer/gpt/dall-e/2 --- /doc/ai/nn/transformer/gpt/dall-e/2/2022-07-20-gwern-dalle2-woodblockwithblackwhiteandred-41.20-4x4samples.png Gwern 2022-07-20 2022-07-20 ai/nn/transformer/gpt/dall-e/2 --- /doc/ai/nn/transformer/gpt/dall-e/2/2022-07-20-gwern-dalle2-woodblockwithblackwhiteandredetching-41.13-triptychscreen.png Gwern 2022-07-20 2022-07-20 ai/nn/transformer/gpt/dall-e/2 --- /doc/ai/nn/transformer/gpt/dall-e/2/2022-07-26-gwern-dalle2-inpraiseofshadows-3x3-16.00.41.png Gwern 2022-07-26 2022-07-26 ai/nn/transformer/gpt/dall-e/2 --- /doc/ai/nn/transformer/gpt/dall-e/2/2022-07-26-gwern-dalle2-surfingnyancatthroughthe-4x4-21.34.4621.34.46.png Gwern 2022-07-26 2022-07-26 ai/nn/transformer/gpt/dall-e/2 --- /doc/ai/nn/transformer/gpt/dall-e/2/2022-07-28-gwern-dalle2-samuraibatmanwoodblockprints-selected.png Gwern 2022-07-28 2022-07-28 ai/nn/transformer/gpt/dall-e/2 --- /doc/ai/nn/transformer/gpt/dall-e/2/2022-07-28-gwern-dalle2-ravenstealsthesun-40variants-surrealsynthwavepointilismpolychrometribalukiyoewoodblock.png Gwern 2022-07-28 2022-07-28 ai/nn/transformer/gpt/dall-e/2 --- /doc/ai/nn/transformer/gpt/dall-e/2/2022-07-28-gwern-dalle2-salishnorthwestcoasttribalformlineartmonochromeengraving-3x1.png Gwern 2022-07-28 2022-07-28 ai/nn/transformer/gpt/dall-e/2 --- https://arxiv.org/abs/2207.14255#openai Efficient Training of Language Models to Fill in the Middle Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, Mark Chen 2022-07-28 2022-07-28 [("doi","10.48550/arXiv.2207.14255")] ai/nn/transformer/gpt/codex

We show that autoregressive language models can learn to infill text after we apply a straightforward transformation to the dataset, which simply moves a span of text from the middle of a document to its end. While this data augmentation has garnered much interest in recent years, we provide extensive evidence that training models with a large fraction of data transformed in this way does not harm the original left-to-right generative capability, as measured by perplexity and sampling evaluations across a wide range of scales.

Given the usefulness, simplicity, and efficiency of training models to fill-in-the-middle (FIM), we suggest that future autoregressive language models be trained with FIM by default.

To this end, we run a series of ablations on key hyperparameters, such as the data transformation frequency, the structure of the transformation, and the method of selecting the infill span. We use these ablations to prescribe strong default settings and best practices to train FIM models.

We have released our best infilling model trained with best practices in our OpenAI API [as codex-✱ models], and release our infilling benchmarks to aid future research.

--- https://x.com/PDillis/status/1553010673701076992 2021-11-27 ai/nn/gan/stylegan --- https://www.biorxiv.org/content/10.1101/2021.08.11.455990.full Retro-Cascorder: Recording gene expression order in DNA by CRISPR addition of retron barcodes Santi Bhattarai-Kline, Sierra K. Lear, Chloe B. Fishman, Santiago C. Lopez, Elana R. Lockshin, Max G. Schubert, Jeff Nivala, George Church, Seth L. Shipman 2022-06-02 2022-06-02 [("doi","10.1101/2021.08.11.455990")] genetics/editing

[blog; media] Biological processes depend on the differential expression of genes over time, but methods to make physical recordings of these processes are limited.

Here we report a molecular system for making time-ordered recordings of transcriptional events into living genomes. We do this via engineered RNA barcodes, based on prokaryotic retrons1, which are reverse-transcribed into DNA and integrated into the genome using the CRISPR-Cas system2. The unidirectional integration of barcodes by CRISPR integrases enables reconstruction of transcriptional event timing based on a physical record via simple, logical rules rather than relying on pre-trained classifiers or post-hoc inferential methods.

For disambiguation in the field, we will refer to this system as a Retro-Cascorder.

--- https://arxiv.org/abs/1506.02078 Visualizing and Understanding Recurrent Networks Andrej Karpathy, Justin Johnson, Li Fei-Fei 2015-06-05 2021-11-27 [("doi","10.48550/arXiv.1506.02078")] ai/nn/rnn design/visualization

Recurrent Neural Networks (RNNs), and specifically a variant with Long Short-Term Memory (LSTM), are enjoying renewed interest as a result of successful applications in a wide range of machine learning problems that involve sequential data. However, while LSTMs provide exceptional results in practice, the source of their performance and their limitations remain rather poorly understood.

Using character-level language models as an interpretable testbed, we aim to bridge this gap by providing an analysis of their representations, predictions and error types. In particular, our experiments reveal the existence of interpretable cells that keep track of long-range dependencies such as line lengths, quotes and brackets. Moreover, our comparative analysis with finite horizon n-gram models traces the source of the LSTM improvements to long-range structural dependencies.

Finally, we provide analysis of the remaining errors and suggests areas for further study.

--- https://arxiv.org/abs/1606.07461 LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks Hendrik Strobelt, Sebastian Gehrmann, Hanspeter Pfister, Alexander M. Rush 2016-06-23 2021-11-27 [("doi","10.48550/arXiv.1606.07461")] ai/nn/rnn design/visualization

Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also noise.

In this work, we present LSTMVIS, a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows users to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with structural annotations from their domain. We show several use cases of the tool for analyzing specific hidden state properties on dataset containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis.

We characterize the domain, the different stakeholders, and their goals and tasks.

--- https://arxiv.org/abs/2207.13921#baidu HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative Xiaomin Fang, Fan Wang, Lihang Liu, Jingzhou He, Dayong Lin, Yingfei Xiang, Xiaonan Zhang, Hua Wu, Hui Li, Le Song 2022-07-28 2022-07-28 [("doi","10.48550/arXiv.2207.13921")] ai/nn/transformer/alphafold

AI-based protein structure prediction pipelines, such as AlphaFold2, have achieved near-experimental accuracy. These advanced pipelines mainly rely on Multiple Sequence Alignments (MSAs) and templates as inputs to learn the co-evolution information from the homologous sequences. Nonetheless, searching MSAs and templates from protein databases is time-consuming, usually taking dozens of minutes. Consequently, we attempt to explore the limits of fast protein structure prediction by using only primary sequences of proteins. HelixFold-Single is proposed to combine a large-scale protein language model with the superior geometric learning capability of AlphaFold2.

Our proposed method, HelixFold-Single, first pre-trains a large-scale protein language model (PLM) with thousands of millions of primary sequences utilizing the self-supervised learning paradigm, which will be used as an alternative to MSAs and templates for learning the co-evolution information. Then, by combining the pre-trained PLM and the essential components of AlphaFold2, we obtain an end-to-end differentiable model to predict the 3D coordinates of atoms from only the primary sequence.

HelixFold-Single is validated in datasets CASP14 and CAMEO, achieving competitive accuracy with the MSA-based methods on the targets with large homologous families. Furthermore, HelixFold-Single consumes much less time than the mainstream pipelines for protein structure prediction, demonstrating its potential in tasks requiring many predictions.

The code of HelixFold-Single is available at https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold-single, and we also provide stable web services on https://paddlehelix.baidu.com/app/drug/protein-single/forecast.

--- https://arxiv.org/abs/2205.05198#nvidia Reducing Activation Recomputation in Large Transformer Models Vijay Korthikanti, Jared Casper, Sangkug Lym, Lawrence McAfee, Michael Andersch, Mohammad Shoeybi, Bryan Catanzaro 2022-05-10 2022-05-10 [("doi","10.48550/arXiv.2205.05198")] ai/scaling/hardware

Training large transformer models is one of the most important computational challenges of modern AI. In this paper, we show how to accelerate training of large transformer models by reducing activation recomputation. Activation recomputation is commonly used to work around memory capacity constraints. Rather than storing activations for backpropagation, they are traditionally recomputed, which saves memory but adds redundant compute.

In this work, we show most of this redundant compute is unnecessary because we can reduce memory consumption sufficiently without it. We present two novel yet very simple techniques: sequence parallelism & selective activation recomputation. In conjunction with tensor parallelism, these techniques almost eliminate the need to recompute activations.

We evaluate our approach on language models up to one trillion parameters in scale and show that our method reduces activation memory by 5×, while reducing execution time overhead from activation recomputation by over 90%. For example, when training a 530b parameter GPT-3 style model on 2240 NVIDIA A100 GPUs, we achieve a Model Flops Utilization of 54.2%, which is 29% faster than the 42.1% we achieve using recomputation.

Our implementation will be available in both Megatron-LM and NeMo-Megatron.

--- https://x.com/pascalblanche/status/1552698745485246471 2021-11-28 ai/nn/diffusion ai/nn/transformer/clip/sample --- /doc/sociology/2021-hendricks.pdf College Quality and Attendance Patterns: A Long-Run View Lutz Hendricks, Christopher Herrington, Todd Schoellman 2021-01-01 2021-11-28 [("doi","10.1257/mac.20190154")] economics sociology

We construct a time series of college attendance patterns for the United States and document a reversal: family background was a better predictor of college attendance before World War II, but academic ability was afterward.

We construct a model of college choice that explains this reversal. The model’s central mechanism is that an exogenous surge of college attendance leads better colleges to be oversubscribed, institute selective admissions, and raise their quality relative to their peers, as in Hoxby 2009. Rising quality at better colleges attracts high-ability students, while falling quality at the remaining colleges dissuades low-ability students, generating the reversal.

--- https://www.autoregex.xyz/ 2021-11-28 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2202.05262 ROME: Locating and Editing Factual Associations in GPT Kevin Meng, David Bau, Alex Andonian, Yonatan Belinkov 2022-02-10 2022-02-10 [("doi","10.48550/arXiv.2202.05262")] ai/dataset ai/nn/transformer/gpt

We analyze the storage and recall of factual associations in autoregressive transformer language models, finding evidence that these associations correspond to localized, directly-editable computations. We first develop a causal intervention for identifying neuron activations that are decisive in a model’s factual predictions. This reveals a distinct set of steps in middle-layer feed-forward modules that mediate factual predictions while processing subject tokens.

To test our hypothesis that these computations correspond to factual association recall, we modify feed-forward weights to update specific factual associations using Rank-One Model Editing (ROME). We find that ROME is effective on a standard zero-shot relation extraction (zsRE) model-editing task, comparable to existing methods. To perform a more sensitive evaluation, we also evaluate ROME on a new dataset of counterfactual assertions, on which it simultaneously maintains both specificity and generalization, whereas other methods sacrifice one or another.

Our results confirm an important role for mid-layer feed-forward modules in storing factual associations and suggest that direct manipulation of computational mechanisms may be a feasible approach for model editing.

The code, dataset, visualizations, and an interactive demo notebook are available at https://rome.baulab.info/.

--- /doc/ai/nn/transformer/gpt/dall-e/2/2022-07-29-gwern-dalle2-skeletonknightinbattlefieldofroseswatercolorpointilismjungalchemytarotrosenkavalierartdeco.png Gwern 2022-07-29 2022-07-29 ai/nn/transformer/gpt/dall-e/2 --- /doc/ai/nn/transformer/gpt/dall-e/2/2022-07-29-gwern-dalle2-theskeletonknightinbattlewithrosesmonochromepointilismkentaromiuraberserk.png Gwern 2022-07-29 2022-07-29 ai/nn/transformer/gpt/dall-e/2 --- https://progressandpoverty.substack.com/p/singapore-economic-prosperity-through Singapore: Economic Prosperity through Innovative Land Policy 2021-11-28 economics/georgism --- https://arxiv.org/abs/2104.00613 The surprising impact of mask-head architecture on novel class segmentation Vighnesh Birodkar, Zhichao Lu, Siyang Li, Vivek Rathod, Jonathan Huang 2021-04-01 2021-11-28 [("doi","10.48550/arXiv.2104.00613")] ai/nn/cnn

Instance segmentation models today are very accurate when trained on large annotated datasets, but collecting mask annotations at scale is prohibitively expensive.

We address the partially supervised instance segmentation problem in which one can train on (significantly cheaper) bounding boxes for all categories but use masks only for a subset of categories. In this work, we focus on a popular family of models which apply differentiable cropping to a feature map and predict a mask based on the resulting crop.

Under this family, we study Mask R-CNN and discover that instead of its default strategy of training the mask-head with a combination of proposals and ground-truth boxes, training the mask-head with only ground-truth boxes dramatically improves its performance on novel classes. This training strategy also allows us to take advantage of alternative mask-head architectures, which we exploit by replacing the typical mask-head of 2–4 layers with deeper off-the-shelf architectures (eg. ResNet, Hourglass models).

While many of these architectures perform similarly when trained in fully supervised mode, our main finding is that they can generalize to novel classes in dramatically different ways. We call this ability of mask-heads to generalize to unseen classes the strong mask generalization effect and show that without any specialty modules or losses, we can achieve state-of-the-art results in the partially supervised COCO instance segmentation benchmark.

Finally, we demonstrate that our effect is general, holding across underlying detection methodologies (including anchor-based, anchor-free or no detector at all) and across different backbone networks.

Code and pre-trained models are available at https://google.github.io/deepmac/.

--- https://www.youtube.com/watch?v=6Zbhvaac68Y Spot’s Got an Arm! 2021-11-28 reinforcement-learning/robot --- https://www.medrxiv.org/content/10.1101/2022.07.26.22278069.full A polygenic risk score to predict sudden cardiac arrest in patients with coronary artery disease Eleonora Porcu, Christian W. Thorball, Alessandra Pia Porretta, Etienne Pruvot, Kim Wiskott, Federica Gilardi, Aurelien Thomas, Claire Redin, Zoltán Kutalik, Tony Fracasso, Olivier Muller, Jacques Fellay 2022-07-29 2022-07-29 [("doi","10.1101/2022.07.26.22278069")] genetics/heritable

Cardiovascular disease (CVD) is a leading health problem and the main cause of death globally. Even when underlying causative factors are known, it is unclear why a cardiovascular condition causes premature death in a victim while others can live longer with the same condition.

Here we propose a combined polygenic risk score (metaPRS) based on coronary artery disease (CAD), myocardial infarction (MI), low-density lipoprotein (LDL) cholesterol, body mass index (BMI) and type 2 diabetes (T2D) to predict the risk of sudden cardiac arrest (SCA) in patients affected by severe cardiovascular conditions. We collected 2,114 patients with reported history of acute coronary syndrome from the Centre Hospitalier Universitaire Vaudois (CHUV) Genomic Biobank (BGC) and extracted data from the UK Biobank (UKBB) on 13,696 participants with similar medical history. Among them, 303 and 932 had a further reported diagnosis of SCA or ventricular tachycardia/fibrillation according to the International Classification of Diseases (ICD-10) codes in BGC and UKB, respectively.

We demonstrate that the metaPRS is statistically-significantly associated with SCA in both cohorts (ORBGC = 1.28, pBGC = 8.39 × 10−05 and ORUKB = 1.14, pUKB = 7.07 × 10−05). Furthermore, using the diagnosis based on the International Classification of Diseases (ICD-10) codes available in the UKB, the metaPRS exhibits a strong association with the presence of aortocoronary bypass graft (ORUKB = 1.31, pUKB = 6.93 × 10−33) and coronary angioplasty implant (ORUKB = 1.14, pUKB = 1.46 × 10−12).

These results show that a combined genetic risk score for CVD and associated risk factors has the potential to predict the occurrence of SCA in patients with myocardial infarction, hence to identify patients who could benefit from further preventive measures.

--- https://www.wiley.com/en-us/Urban+Land+Rent%3A+Singapore+as+a+Property+State-p-9781118827673 Urban Land Rent: Singapore as a Property State 2021-11-29 economics/georgism --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4898064/ Abnormalities in human pluripotent cells due to reprogramming mechanisms Hong Ma, Robert Morey, Ryan C. O’Neil, Yupeng He, Brittany Daughtry, Matthew D. Schultz, Manoj Hariharan, Joseph R. Nery, Rosa Castanon, Karen Sabatini, Rathi D. Thiagarajan, Masahito Tachibana, Eunju Kang, Rebecca Tippner-Hedges, Riffat Ahmed, Nuria Marti Gutierrez, Crystal Van Dyken, Alim Polat, Atsushi Sugawara, Michelle Sparman, Sumita Gokhale, Paula Amato, Don P. Wolf, Joseph R. Ecker, Louise C. Laurent, Shoukhrat Mitalipov 2014 2021-11-29 [("doi","10.1038/nature13551")] genetics/gametogenesis

Human pluripotent stem cells hold potential for regenerative medicine, but available cell types have limitations. Although embryonic stem cells (ES cells) from in vitro fertilized embryos (IVF ES cells) represent the ‘gold standard’, they are allogeneic to patients. Autologous induced pluripotent stem cells (iPS cells) are prone to epigenetic and transcriptional aberrations.

To determine whether such abnormalities are intrinsic to somatic cell reprogramming or secondary to the reprogramming method, genetically matched sets of human IVF ES cells, iPS cells and nuclear transfer ES cells (NT ES cells) derived by somatic cell nuclear transfer (SCNT) were subjected to genome-wide analyses.

Both NT ES cells and iPS cells derived from the same somatic cells contained comparable numbers of de novo copy number variations. In contrast, DNA methylation and transcriptome profiles of NT ES cells corresponded closely to those of IVF ES cells, whereas iPS cells differed and retained residual DNA methylation patterns typical of parental somatic cells.

Thus, human somatic cells can be faithfully reprogrammed to pluripotency by SCNT and are therefore ideal for cell replacement therapies.

--- https://unfolded.deepmind.com/ 2021-11-29 ai/nn/transformer/alphafold --- https://x.com/OakOrobic/status/1553451646440673286 2021-11-29 ai/nn/transformer/clip/sample --- /doc/statistics/bias/1996-hamilton-thesocialmisconstructionofreality.pdf The Social Misconstruction of Reality: Validity and Verification in the Scholarly Community Richard F. Hamilton 1996-01-01 2021-11-29 history statistics/bias --- https://www.mattblaze.org/papers/mk.pdf Cryptology and Physical Security: Rights Amplification in Master-Keyed Mechanical Locks 2021-11-29 cs/cryptography --- https://en.wikipedia.org/wiki/Master_keying Master keying 2021-11-29 cs/cryptography --- https://en.wikipedia.org/wiki/Matt_Blaze Matt Blaze 2021-11-29 cs/cryptography --- https://www.mattblaze.org/papers/safelocks.pdf Safecracking for the computer scientist 2021-11-29 cs/cryptography --- https://x.com/mattblaze/status/1553254965870841856 2021-11-29 cs/cryptography --- https://arxiv.org/abs/2206.09787 Progress in Mathematical Programming Solvers 2001–2020 Thorsten Koch, Timo Berthold, Jaap Pedersen, Charlie Vanaret 2022-06-20 2022-06-20 [("doi","10.1016/j.ejco.2022.100031")] cs/algorithm economics/experience-curve

This study investigates the progress made in LP and MILP solver performance during the last two decades by comparing the solver software from the beginning of the millennium with the codes available today.

On average, we found out that for solving LP/MILP, computer hardware got about 20× faster, and the algorithms improved by a factor of about 9× for LP and around 50× for MILP, which gives a total speed-up of about 180× and 1,000×, respectively.

However, these numbers have a very high variance and they considerably underestimate the progress made on the algorithmic side: many problem instances can nowadays be solved within seconds, which the old codes are not able to solve within any reasonable time.

--- https://x.com/devdef/status/1553719321293209602 2021-11-30 ai/nn/transformer/clip/sample --- https://www.reddit.com/r/MediaSynthesis/comments/wcqrs0/nsfw_3_animated_images_made_with_cogvideo_using/ 2021-11-30 ai/anime ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2109.00698#eleutherai An Empirical Exploration in Quality Filtering of Text Data Leo Gao 2021-09-02 2021-11-30 [("doi","10.48550/arXiv.2109.00698")] ai/nn/transformer/gpt ai/scaling

[cf. Sorscher et al 2022] While conventional wisdom suggests that more aggressively filtering data from low-quality sources like Common Crawl always monotonically improves the quality of training data, we find that aggressive filtering can in fact lead to a decrease in model quality on a wide array of downstream tasks for a GPT-like language model.

We speculate that this is because optimizing sufficiently strongly for a proxy metric harms performance on the true objective, suggesting a need for more robust filtering objectives when attempting to filter more aggressively.

We hope this work leads to detailed analysis of the effects of dataset filtering design choices on downstream model performance in future work.

--- https://en.wikipedia.org/wiki/Jeep_problem Jeep problem 2021-11-30 math statistics/decision --- https://karpathy.github.io/2021/03/27/forward-pass/ Short Story on AI: ‘Forward Pass’ Andrej Karpathy 2021-11-30 ai/nn/transformer/gpt/inner-monologue fiction/science-fiction --- https://www.reddit.com/r/AnimeResearch/comments/wdeyg2/stablediffusion_kurisu_from_steins_gate/ 2021-11-30 ai/anime ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2205.11275 RL with KL penalties is better viewed as Bayesian inference Tomasz Korbak, Ethan Perez, Christopher L. Buckley 2022-05-23 2022-05-23 [("doi","10.48550/arXiv.2205.11275")] reinforcement-learning/preference-learning/mode-collapse statistics/bayes

[discussion] Reinforcement learning (RL) is frequently employed in fine-tuning large language models (LMs), such as GPT-3, to penalize them for undesirable features of generated sequences, such as offensiveness, social bias, harmfulness or falsehood. The RL formulation involves treating the LM as a policy and updating it to maximize the expected value of a reward function which captures human preferences, such as non-offensiveness.

In this paper, we analyze challenges associated with treating a language model as an RL policy and show how avoiding those challenges requires moving beyond the RL paradigm. We start by observing that the standard RL approach is flawed as an objective for fine-tuning LMs because it leads to distribution collapse: turning the LM into a degenerate distribution. Then, we analyze KL-regularized RL, a widely used recipe for fine-tuning LMs, which additionally constrains the fine-tuned LM to stay close to its original distribution in terms of Kullback-Leibler (KL) divergence [eg. Christiano et al 2017].

We show that KL-regularized RL is equivalent to variational inference: approximating a Bayesian posterior which specifies how to update a prior LM to conform with evidence provided by the reward function.

We argue that this Bayesian inference view of KL-regularized RL is more insightful than the typically employed RL perspective. The Bayesian inference view explains how KL-regularized RL avoids the distribution collapse problem and offers a first-principles derivation for its objective. While this objective happens to be equivalent to RL (with a particular choice of parametric reward), there exist other objectives for fine-tuning LMs which are no longer equivalent to RL.

That observation leads to a more general point: RL is not an adequate formal framework for problems such as fine-tuning language models. These problems are best viewed as Bayesian inference: approximating a pre-defined target distribution.

--- https://x.com/RiversHaveWings/status/1554047792720388096 2021-11-30 ai/nn/transformer/clip/sample --- https://x.com/Ted_Underwood/status/1553968564654186498 2021-11-30 ai/nn/transformer/clip/sample --- https://x.com/gandamu_ml/status/1553975048347664384 2021-11-30 ai/nn/transformer/clip/sample --- https://www.reddit.com/r/StableDiffusion/ 2021-12-01 ai/nn/transformer/clip/sample --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4898153/ Pharmacokinetic analysis and comparison of caffeine administered rapidly or slowly in coffee chilled or hot versus chilled energy drink in healthy young adults John R. White, Jeannie M. Padowski, Yili Zhong, Gang Chen, Shaman Luo, Philip Lazarus, Matthew E. Layton, Sterling McPherson 2016 2021-12-01 [("doi","10.3109/15563650.2016.1146740")] nootropic/caffeine

Context: There is a paucity of data describing the impact of type of beverage (coffee versus energy drink), different rates of consumption and different temperature of beverages on the pharmacokinetic disposition of caffeine. Additionally, there is concern that inordinately high levels of caffeine may result from the rapid consumption of cold energy drinks.

Objective: The objective of this study was to compare the pharmacokinetics of caffeine under various drink temperature, rate of consumption and vehicle (coffee versus energy drink) conditions.

Materials: 5 caffeine (dose = 160 mg) conditions were evaluated in an open-label, group-randomized, crossover fashion. After the administration of each caffeine dose, 10 serial plasma samples were harvested. Caffeine concentration was measured via liquid chromatography-mass spectrometry (LC-MS), and those concentrations were assessed by non-compartmental pharmacokinetic analysis. The calculated mean pharmacokinetic parameters were analyzed statistically by one-way repeated measures analysis of variance (RM ANOVA). If differences were found, each group was compared to the other by all pair-wise multiple comparison.

Results: 20-four healthy subjects ranging in age 18–30 completed the study. The mean caffeine concentration time profiles were similar with overlapping SDs at all measured time points. The ANOVA revealed statistically-significant differences in mean Cmax and Vd ss/F, but no pair-wise comparisons reached statistical-significance. No other differences in pharmacokinetic parameters were found.

Discussion: The results of this study are consistent with previous caffeine pharmacokinetic studies and suggest that while rate of consumption, temperature of beverage and vehicle (coffee versus energy drink) may be associated with slightly different pharmacokinetic parameters, the overall impact of these variables is small.

Conclusion: This study suggests that caffeine absorption and exposure from coffee and energy drink is similar irrespective of beverage temperature or rate of consumption.

--- https://arxiv.org/abs/2207.14502#microsoft Language Models Can Teach Themselves to Program Better Patrick Haluptzok, Matthew Bowers, Adam Tauman Kalai 2022-07-29 2022-07-29 [("doi","10.48550/arXiv.2207.14502")] ai/dataset ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue

This work shows how one can use large-scale language models (LMs) to synthesize programming problems with verified solutions, in the form of programming puzzles, which can then in turn be used to fine-tune those same models, improving their performance.

This work builds on two recent developments. First, LMs have achieved breakthroughs in non-trivial reasoning and algorithm implementation, generating code that can solve some intermediate-level competitive programming problems. However, training code LMs involves curated sets of natural-language problem descriptions and source-code tests and solutions, which are limited in size. Second, a new format of programming challenge called a programming puzzle was introduced, which does not require a natural language description and is directly specified by a source-code test.

In this work we show how generating synthetic programming puzzles and solutions, verified for correctness by a Python interpreter, can be used to improve performance in solving test puzzles from P3, a public benchmark set of Python Programming Puzzles.

Additionally, we release a dataset of 1 million puzzles and solutions generated by the Codex model, which we show can improve smaller models through fine-tuning.

--- https://www.lesswrong.com/posts/PGv9THs68ArPur7yP/meditation-course-claims-65-enlightenment-rate-my-review Meditation course claims 65% enlightenment rate: my review 2021-12-01 psychiatry/meditation --- https://arxiv.org/abs/2207.14525 TOnICS: Curriculum Learning for Data-Efficient Vision-Language Alignment Tejas Srinivasan, Xiang Ren, Jesse Thomason 2022-07-29 2022-07-29 [("doi","10.48550/arXiv.2207.14525")] ai/nn/transformer/clip

Aligning image and text encoders from scratch using contrastive learning requires large amounts of paired image-text data.

We alleviate this need by aligning individually pre-trained language and vision representation models using a much smaller amount of paired data, augmented with a curriculum learning algorithm to learn fine-grained vision-language alignments. TOnICS (Training with Ontology-Informed Contrastive Sampling) initially samples minibatches whose image-text pairs contain a wide variety of objects to learn object-level alignment, and progressively samples minibatches where all image-text pairs contain the same object to learn finer-grained contextual alignment.

Aligning pre-trained BERT and VinVL models to each other using TOnICS outperforms CLIP on downstream zero-shot image retrieval while using less than 1% as much training data.

--- https://www.biorxiv.org/content/10.1101/2022.07.29.502098.full No evidence for a common blood microbiome based on a population study of 9,770 healthy humans Cedric C. S. Tan, Minghao Chia, Karrie K. K. Ko, Hui Chen, Jianjun Liu, Marie Loh, Niranjan Nagarajan 2022-07-30 2022-07-30 [("doi","10.1101/2022.07.29.502098")] genetics/microbiome

Human blood is conventionally considered sterile. Recent studies have challenged this, suggesting the presence of a blood microbiome in healthy humans.

We present the largest investigation to date of microbes in blood, based on shotgun sequencing libraries from 9,770 healthy subjects. Leveraging the availability of data from multiple cohorts, we stringently filtered for laboratory contaminants to identify 117 microbial species detected in the blood of sampled individuals, some of which had signatures of DNA replication.

These primarily comprise of commensals associated with human body sites such as the gut (n = 40), mouth (n = 32), and genitourinary tract (n = 18), which are species that are distinct from common pathogens detected in clinical blood cultures based on more than a decade of records from a tertiary hospital. Contrary to the expectations of a shared blood microbiome, no species were detected in 84% of individuals, while only a median of one microbial species per individual was detected in the remaining 16%. Furthermore, microbes of the same species were detected in <5% of individuals, no co-occurrence patterns similar to microbiomes in other body sites was observed, and no associations between host phenotypes (eg. demographics and blood parameters) and microbial species could be established.

Overall, these results do not support the hypothesis of a consistent core microbiome endogenous to human blood. Rather, our findings support the transient and sporadic translocation of commensal microbes from other body sites into the bloodstream.

--- https://x.com/NilaierMusic/status/1553084358092898304 2021-12-01 ai/anime ai/nn/gan/stylegan ai/nn/transformer/gpt/dall-e --- https://thenumb.at/Autodiff/ Differentiable Programming from Scratch 2021-12-01 ai/nn cs/algorithm math --- https://www.medrxiv.org/content/10.1101/2022.07.27.22278102.full Scalable, high quality, whole genome sequencing from archived, newborn, dried blood spots Yan Ding, Mallory Owen, Jennie Le, Sergey Batalov, Kevin Chau, Yong Hyun Kwon, Lucita Van Der Kraan, Zaira Bezares-Orin, Zhanyang Zhu, Narayanan Veeraraghavan, Shareef Nahas, Matthew Bainbridge, Joe Gleeson, Rebecca J. Baer, Gretchen Bandoli, Christina Chambers, Stephen F. Kingsmore 2022-07-29 2022-07-29 [("doi","10.1101/2022.07.27.22278102")] genetics/sequencing

Universal newborn screening (NBS) is an incredibly successful public health intervention. Archived dried bloodspots (DBS) collected for NBS represent a rich resource for population genomic studies. [eg. n ~ 20 million in California] To fully harness this resource, DBS must yield high-quality genomic DNA (gDNA) for whole genome sequencing (WGS). In this pilot study, we hypothesized that gDNA of sufficient quality and quantity for WGS could be extracted from archived DBS up to 20 years old without PCR (Polymerase Chain Reaction) amplification.

We describe simple methods for gDNA extraction and WGS library preparation from several types of DBS.

We tested these methods in DBS from 25 individuals who had previously undergone diagnostic, clinical WGS and 29 randomly selected DBS cards collected for NBS from the California State Biobank.

While gDNA from DBS had statistically-significantly less yield than from EDTA blood from the same individuals, it was of sufficient quality and quantity for WGS without PCR. All samples DBS yielded WGS that met quality control metrics for high-confidence variant calling. 20-eight variants of various types that had been reported clinically in 19 samples were recapitulated in WGS from DBS. There were no statistically-significant effects of age or paper type on WGS quality.

Archived DBS appear to be a suitable sample type for WGS in population genomic studies.

--- /doc/science/2018-brogaard.pdf Do Economists Swing for the Fences after Tenure? Jonathan Brogaard, Joseph Engelberg, Edward Van Wesep 2018-12-01 2021-12-01 [("doi","10.1257/jep.32.1.179")] economics science

Using a sample of all academics who pass through top 50 economics and finance departments from 1996 through 2014, we study whether the granting of tenure leads faculty to pursue riskier ideas. We use the extreme tails of ex-post citations as our measure of risk and find that:

both the number of publications and the portion consisting of “home runs” peak at tenure and fall steadily for a decade thereafter. Similar patterns hold for faculty at elite (top 10) institutions and for faculty who take differing time to tenure. We find the opposite pattern among poorly cited publications: their numbers rise post-tenure.

--- https://arxiv.org/abs/2206.01859#microsoft XTC: Extreme Compression for Pre-trained Transformers Made Simple and Efficient Xiaoxia Wu, Zhewei Yao, Minjia Zhang, Conglong Li, Yuxiong He 2022-06-04 2022-06-04 [("doi","10.48550/arXiv.2206.01859")] ai/nn/sparsity/low-precision ai/nn/transformer

Extreme compression, particularly ultra-low bit precision (binary/ternary) quantization, has been proposed to fit large NLP models on resource-constraint devices. However, to preserve the accuracy for such aggressive compression schemes, cutting-edge methods usually introduce complicated compression pipelines, eg. multi-stage expensive knowledge distillation with extensive hyperparameter tuning. Also, they oftentimes focus less on smaller transformer models that have already been heavily compressed via knowledge distillation and lack a systematic study to show the effectiveness of their methods.

In this paper, we perform a very comprehensive systematic study to measure the impact of many key hyperparameters and training strategies from previous works.

As a result, we find out that previous baselines for ultra-low bit precision quantization are substantially under-trained. Based on our study, we propose a simple yet effective compression pipeline for extreme compression, named XTC.

XTC demonstrates that (1) we can skip the pre-training knowledge distillation to obtain a 5-layer BERT while achieving better performance than previous state-of-the-art methods, eg. the 6-layer TinyBERT; (2) extreme quantization plus layer reduction is able to reduce the model size by 50×, resulting in new state-of-the-art results on GLUE tasks.

--- https://arxiv.org/abs/2206.01278 Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks Mansheej Paul, Brett W. Larsen, Surya Ganguli, Jonathan Frankle, Gintare Karolina Dziugaite 2022-06-02 2022-06-02 [("doi","10.48550/arXiv.2206.01278")] ai/nn/sparsity/pruning

A striking observation about iterative magnitude pruning (IMP; Frankle et al 2020) is that—after just a few hundred steps of dense training—the method can find a sparse sub-network that can be trained to the same accuracy as the dense network [lottery tickets]. However, the same does not hold at step 0, i.e. random initialization.

In this work, we seek to understand how this early phase of pre-training leads to a good initialization for IMP both through the lens of the data distribution and the loss landscape geometry.

Empirically we observe that, holding the number of pre-training iterations constant, training on a small fraction of (randomly chosen) data suffices to obtain an equally good initialization for IMP.

We additionally observe that by pre-training only on “easy” training data, we can decrease the number of steps necessary to find a good initialization for IMP compared to training on the full dataset or a randomly chosen subset.

Finally, we identify novel properties of the loss landscape of dense networks that are predictive of IMP performance, showing in particular that more examples being linearly mode connected in the dense network correlates well with good initializations for IMP.

Combined, these results provide new insight into the role played by the early phase training in IMP.

--- https://arxiv.org/abs/2206.04673 NOAH: Neural Prompt Search Yuanhan Zhang, Kaiyang Zhou, Ziwei Liu 2022-06-09 2022-06-09 [("doi","10.48550/arXiv.2206.04673")] ai/nn/transformer/gpt reinforcement-learning/meta-learning

The size of vision models has grown exponentially over the last few years, especially after the emergence of Vision Transformer. This has motivated the development of parameter-efficient tuning methods, such as learning adapter layers or visual prompt tokens, which allow a tiny portion of model parameters to be trained whereas the vast majority obtained from pre-training are frozen. However, designing a proper tuning method is non-trivial: one might need to try out a lengthy list of design choices, not to mention that each downstream dataset often requires custom designs.

In this paper, we view the existing parameter-efficient tuning methods as “prompt modules” and propose Neural prOmpt seArcH (NOAH), a novel approach that learns, for large vision models, the optimal design of prompt modules through a neural architecture search algorithm, specifically for each downstream dataset.

By conducting extensive experiments on over 20 vision datasets, we demonstrate that NOAH (1) is superior to individual prompt modules, (2) has a good few-shot learning ability, and (3) is domain-generalizable.

The code and models are available at Github.

--- https://arxiv.org/abs/2204.02863 Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning Eugene Valassakis, Georgios Papagiannis, Norman Di Palo, Edward Johns 2022-04-06 2022-04-06 [("doi","10.48550/arXiv.2204.02863")] reinforcement-learning/imitation-learning reinforcement-learning/model reinforcement-learning/robot

We present DOME, a novel method for one-shot imitation learning, where a task can be learned from just a single demonstration and then be deployed immediately, without any further data collection or training. DOME does not require prior task or object knowledge, and can perform the task in novel object configurations and with distractors.

At its core, DOME uses an image-conditioned object segmentation network followed by a learned visual servoing network, to move the robot’s end-effector to the same relative pose to the object as during the demonstration, after which the task can be completed by replaying the demonstration’s end-effector velocities.

We show that DOME achieves near 100% success rate on 7 real-world everyday tasks, and we perform several studies to thoroughly understand each individual component of DOME.

Videos and supplementary material are available at: https://www.robot-learning.uk/dome.

--- https://arxiv.org/abs/2208.01009 Few-shot Adaptation Works with UnpredicTable Data Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez 2022-08-01 2022-08-01 [("doi","10.48550/arXiv.2208.01009")] ai/dataset ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/meta-learning

[cf. Sorscher et al 2022] Prior work on language models (LMs) shows that training on a large number of diverse tasks improves few-shot learning (FSL) performance on new tasks.

We take this to the extreme, automatically extracting 413,299 tasks from internet tables—orders of magnitude more than the next-largest public datasets.

Finetuning on the resulting dataset leads to improved FSL performance on Natural Language Processing (NLP) tasks, but not proportionally to dataset scale. In fact, we find that narrow subsets of our dataset sometimes outperform more diverse datasets. For example, finetuning on software documentation from support.google.com raises FSL performance by a mean of +7.5% on 52 downstream tasks, which beats training on 40 human-curated NLP datasets (+6.7%). Finetuning on various narrow datasets leads to similar broad improvements across test tasks, suggesting that the gains are not from domain adaptation but adapting to FSL in general.

We do not observe clear patterns between the datasets that lead to FSL gains, leaving open questions about why certain data helps with FSL.

--- https://arxiv.org/abs/2206.14486 Beyond neural scaling laws: beating power law scaling via data pruning Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, Ari S. Morcos 2022-06-29 2022-06-29 [("doi","10.48550/arXiv.2206.14486")] ai/scaling reinforcement-learning/exploration/active-learning/data-pruning

Widely observed neural scaling laws, in which error falls off as a power of the training set size, model size, or both, have driven substantial performance improvements in deep learning. However, these improvements through scaling alone require considerable costs in compute and energy.

Here we focus on the scaling of error with dataset size and show how both in theory and practice we can break beyond power law scaling and reduce it to exponential scaling instead if we have access to a high-quality data pruning metric that ranks the order in which training examples should be discarded to achieve any pruned dataset size. We then test this new exponential scaling prediction with pruned dataset size empirically, and indeed observe better than power law scaling performance on ResNets trained on CIFAR-10, SVHN, and ImageNet.

Given the importance of finding high-quality pruning metrics, we perform the first large-scale benchmarking study of 10 different data pruning metrics on ImageNet. We find most existing high performing metrics scale poorly to ImageNet, while the best are computationally intensive and require labels for every image. We therefore developed a new simple, cheap and scalable self-supervised pruning metric that demonstrates comparable performance to the best supervised metrics.

Overall, our work suggests that the discovery of good data-pruning metrics may provide a viable path forward to substantially improved neural scaling laws, thereby reducing the resource costs of modern deep learning.

--- https://github.com/BlinkDL/RWKV-LM BlinkDL/RWKV-LM: RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it’s combining the best of RNN and transformer—great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding. 2021-12-02 ai/nn/rnn ai/nn/transformer/gpt --- https://arxiv.org/abs/2103.14749 Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks Curtis G. Northcutt, Anish Athalye, Jonas Mueller 2021-03-26 2021-12-02 [("doi","10.48550/arXiv.2103.14749")] ai/dataset ai/nn/cnn

We identify label errors in the test sets of 10 of the most commonly-used computer vision, natural language, and audio datasets, and subsequently study the potential for these label errors to affect benchmark results.

Errors in test sets are numerous and widespread: we estimate an average of at least 3.3% errors across the 10 datasets, where for example label errors comprise at least 6% of the ImageNet validation set. Putative label errors are identified using confident learning algorithms and then human-validated via crowdsourcing (51% of the algorithmically-flagged candidates are indeed erroneously labeled, on average across the datasets).

Traditionally, machine learning practitioners choose which model to deploy based on test accuracy—our findings advise caution here, proposing that judging models over correctly labeled test sets may be more useful, especially for noisy real-world datasets.

Surprisingly, we find that lower capacity models may be practically more useful than higher capacity models in real-world datasets with high proportions of erroneously labeled data. For example, on ImageNet with corrected labels: ResNet-18 outperforms ResNet-50 if the prevalence of originally mislabeled test examples increases by just 6%. On CIFAR-10 with corrected labels: VGG-11 outperforms VGG-19 if the prevalence of originally mislabeled test examples increases by just 5%.

Test set errors across the 10 datasets can be viewed at labelerrors.com and all label errors can be reproduced by Github.

--- https://arxiv.org/abs/1902.00423 Do We Train on Test Data? Purging CIFAR of Near-Duplicates Björn Barz, Joachim Denzler 2019-02-01 2021-12-02 [("doi","10.3390/jimaging6060041")] ai/dataset ai/nn/cnn

The CIFAR-10 and CIFAR-100 datasets are two of the most heavily benchmarked datasets in computer vision and are often used to evaluate novel methods and model architectures in the field of deep learning.

However, we find that 3.3% and 10% of the images from the test sets of these datasets have duplicates in the training set. These duplicates are easily recognizable by memorization and may, hence, bias the comparison of image recognition techniques regarding their generalization capability.

To eliminate this bias, we provide the “fair CIFAR” (ciFAIR) dataset, where we replaced all duplicates in the test sets with new images sampled from the same domain. We then re-evaluate the classification performance of various popular state-of-the-art CNN architectures on these new test sets to investigate whether recent research has overfitted to memorizing data instead of learning abstract concepts.

We find a large drop in classification accuracy of between 9% and 14% relative to the original performance on the duplicate-free test set.

The ciFAIR dataset and pre-trained models are available at Github, where we also maintain a leaderboard.

--- /doc/sociology/2022-moody.pdf Reproducibility in the Social Sciences James W. Moody, Lisa A. Keister, Maria C. Ramos 2022 2022 [("doi","10.1146/annurev-soc-090221-035954")] sociology statistics/bias

Concern over social scientists’ inability to reproduce empirical research has spawned a vast and rapidly growing literature. The size and growth of this literature make it difficult for newly interested academics to come up to speed.

Here, we provide a formal text modeling approach to characterize the entirety of the field, which allows us to summarize the breadth of this literature and identify core themes. We construct and analyze text networks built from 1,947 articles to reveal differences across social science disciplines within the body of reproducibility publications and to discuss the diversity of subtopics addressed in the literature.

This field-wide view suggests that reproducibility is a heterogeneous problem with multiple sources for errors and strategies for solutions, a finding that is somewhat at odds with calls for largely passive remedies reliant on open science.

We propose an alternative rigor and reproducibility model that takes an active approach to rigor prior to publication, which may overcome some of the shortfalls of the post-publication model.

…Where Are the Sociologists? [cf. Pritchett 2002] Perhaps the first takeaway from the systematic review of the literature for sociologists is just how rare it is to find sociological work represented. Sociology journals make up only about 2% of the journals in our corpus and published an even smaller percentage of papers. Indeed, only 6 [0.6%] of the 985 articles published in American Journal of Sociology, American Sociological Review, or Social Forces 1970–2020 include “replication”, “reproducibility”, or “reanalysis” in the core search terms, based on a Web of Science search limited to these journals. Although we might expect a bias for novelty in the most prominent journals in the field, speaking as former editors of Socius, we note there were similarly very few submissions aimed directly at replicating prior work [excepting a special issue devoted to the topic, including the articles by Fisher 2019, Liu & Salganik 2019, and Lundberg et al 2019], and when such works were submitted, the authors typically had difficulty convincing reviewers that such activity was valuable. Thus, our first observation is that sociologists seem to favor novelty over replication to such a deep extent that evaluating the depth of replication success is difficult. If nobody sees value in replicating initial work, we are unlikely to find the cases that fail.

Despite the dearth of explicit replication attempts, there are at least 3 good reasons to be suspicious that such tests might frequently fail. The first is the finding that statistical-significance tests reported in the sociological literature have distributions consistent with a publication bias favoring barely statistically-significant results (Gerber & Malhotra 2008). This is, in our opinion, sufficient smoke to suggest fire. Second, prominent comment and reply sequences suggest the sorts of mistakes typically uncovered in the absence of careful reproduction and, ultimately, replication. These exchanges usually focus on data selection (choice of cutoff dates, outliers, etc.), coding (missing data codes, top codes), or modeling issues (convergence checks, etc.) that are necessary to produce findings.4 Finally, the lack of concern with replication in sociology is made clear by the contrast with overt replication concerns in psychology reports. Although we cannot evaluate changes in rates of replication (or success) from this corpus as constructed, the mere existence of hundreds of papers explicitly attempting replication in psychology suggests that psychology has room for this sort of work that is largely missing in sociology.

--- https://www.biorxiv.org/content/10.1101/2022.08.01.502375.full Stem cell-derived mouse embryos develop within an extra-embryonic yolk sac to form anterior brain regions and a beating heart Magdalena Zernicka-Goetz, Gianluca Amadei, Charlotte E. Handford, Joachim De Jonghe, Florian Hollfelder, David Glover 2022-08-02 2022-08-02 [("doi","10.1101/2022.08.01.502375")] genetics/gametogenesis

Embryo-like structures generated from stem cells can achieve varying developmental milestones, but none have been shown to progress through gastrulation, neurulation, and organogenesis.

Here, we show that ETiX mouse embryos, established from embryonic stem cells aggregated with trophoblast stem cells and inducible extraembryonic endoderm stem cells, can develop through gastrulation and beyond to undertake neural induction and generate the progenitors needed to create the entire organism. The head-folds of ETiX embryos show anterior expression of Otx2, defining forebrain and midbrain regions that resemble those of the natural mouse embryo. ETiX embryos also develop beating hearts, trunk structures comprising a neural tube and somites, tail buds containing neuromesodermal progenitors and primordial germ cells, and gut tubes derived from definitive endoderm. A fraction of ETiX embryos show neural tube abnormalities, which can be partially rescued by treatment with the metabolically active form of folic acid, reminiscent of common birth defects and therapies in humans. Notably, ETiX embryos also develop a yolk sac with blood islands.

Overall, ETiX embryos uniquely recapitulate natural embryos, developing further than any other stem-cell derived model, through multiple post-implantation stages and within extra-embryonic membranes.

--- https://www.biorxiv.org/content/10.1101/2022.08.01.502371.full Mouse-embryo model derived exclusively from embryonic stem cells undergo neurulation and heart development Kasey Y. C. Lau, Hernan Rubinstein, Carlos W. Gantner, Gianluca Amadei, Yonatan Stelzer, Magdalena Zernicka-Goetz 2022-08-02 2022-08-02 [("doi","10.1101/2022.08.01.502371")] genetics/gametogenesis

Several in vitro models have been developed to recapitulate mouse embryogenesis solely from embryonic stem cells (ESCs). Despite mimicking many aspects of early development, they fail to capture the interactions between embryonic and extraembryonic tissues.

To overcome this difficulty, we have developed a mouse ESC-based in vitro model that reconstitutes the pluripotent ESC lineage and the two extra-embryonic lineages of the post-implantation embryo by transcription factor-mediated induction. This unified model recapitulates developmental events from embryonic day 5.5 to 8.5, including gastrulation, and formation of the anterior-posterior axis, brain, a beating heart structure, and the development of extraembryonic tissues, including yolk sac and chorion. Comparing single-cell RNA sequencing from individual structures with time-matched natural embryos identified remarkably similar transcriptional programs across lineages, but also showed when and where the model diverges from the natural program.

Our findings demonstrate an extra-ordinary plasticity of ESCs to self-organize and generate a whole embryo-like structure.

--- https://en.wikipedia.org/wiki/Swiss_cheese_model Swiss cheese model 2021-12-03 existential-risk --- https://jacobmartins.com/posts/how-i-used-dalle2-to-generate-the-logo-for-octosql/ 2021-12-03 ai/nn/transformer/gpt/dall-e --- https://arxiv.org/abs/2207.07635 Is a Caption Worth a Thousand Images? A Controlled Study for Representation Learning Shibani Santurkar, Yann Dubois, Rohan Taori, Percy Liang, Tatsunori Hashimoto 2022-07-15 2022-07-15 [("doi","10.48550/arXiv.2207.07635")] ai/nn/transformer/clip

[Twitter] The development of CLIP [Radford et al 2021] has sparked a debate on whether language supervision can result in vision models with more transferable representations than traditional image-only methods.

Our work studies this question through a carefully controlled comparison of two approaches [CLIP vs SimCLR] in terms of their ability to learn representations that generalize to downstream classification tasks.

We find that when the pre-training dataset meets certain criteria—it is sufficiently large and contains descriptive captions with low variability—image-only methods do not match CLIP’s transfer performance, even when they are trained with more image data. However, contrary to what one might expect, there are practical settings in which these criteria are not met, wherein added supervision through captions is actually detrimental.

Motivated by our findings, we devise simple prescriptions to enable CLIP to better leverage the language information present in existing pre-training datasets [by data-augmentation: generating multiple text captions for each image using GPT-J to overcome extraneous text caption differences].

--- /doc/history/1995-wolfe.pdf Cavalry in the Age of the Autarch Gene Wolfe 1995-01-01 2021-12-03 fiction/gene-wolfe genetics/editing history --- https://x.com/orgRem/status/1554572302137757696 2021-12-03 ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/2208.01174 TextWorldExpress: Simulating Text Games at One Million Steps Per Second Peter A. Jansen, Marc-Alexandre Côté 2022-08-01 2022-08-01 [("doi","10.48550/arXiv.2208.01174")] cs/algorithm fiction/text-game reinforcement-learning/scaling

Text-based games offer a challenging test bed to evaluate virtual agents at language understanding, multi-step problem-solving, and common-sense reasoning. However, speed is a major limitation of current text-based games, capping at 300 steps per second, mainly due to the use of legacy tooling [such as Zork].

In this work we present TextWorldExpress, a high-performance implementation of 3 common text game benchmarks that increases simulation throughput by ~3 orders of magnitude, reaching over one million steps per second on common desktop hardware [including caching all possible paths through simple text games, storing in large 1GB JSON text files].

This largely reduces experiment runtime, enabling billion-step-scale experiments in about 1 day.

--- https://ai.stanford.edu/blog/understanding-incontext/ How does in-context learning work? A framework for understanding the differences from traditional supervised learning 2021-12-03 ai/nn/transformer/gpt reinforcement-learning/meta-learning --- https://research.google/blog/efficient-sequence-modeling-for-on-device-ml/ 2021-12-03 ai/nn/sparsity --- https://arxiv.org/abs/1611.01576#salesforce QRNNs: Quasi-Recurrent Neural Networks James Bradbury, Stephen Merity, Caiming Xiong, Richard Socher 2016-11-05 2021-12-03 [("doi","10.48550/arXiv.1611.01576")] ai/nn/rnn ai/nn/transformer

Recurrent neural networks are a powerful tool for modeling sequential data, but the dependence of each timestep’s computation on the previous timestep’s output limits parallelism and makes RNNs unwieldy for very long sequences.

We introduce quasi-recurrent neural networks (QRNNs), an approach to neural sequence modeling that alternates convolutional layers, which apply in parallel across timesteps, and a minimalist recurrent pooling function that applies in parallel across channels. Despite lacking trainable recurrent layers, stacked QRNNs have better predictive accuracy than stacked LSTMs of the same hidden size.

Due to their increased parallelism, they are up to 16× faster at train and test time.

Experiments on language modeling, sentiment classification, and character-level neural machine translation demonstrate these advantages and underline the viability of QRNNs as a basic building block for a variety of sequence tasks.

--- https://arxiv.org/abs/2101.08890#google Distilling Large Language Models into Tiny and Effective Students using pQRNN Prabhu Kaliamoorthi, Aditya Siddhant, Edward Li, Melvin Johnson 2021-01-21 2021-12-03 [("doi","10.48550/arXiv.2101.08890")] ai/nn/rnn ai/nn/sparsity/knowledge-distillation

Large pre-trained multilingual models like mBERT, XLM-R achieve state-of-the-art results on language understanding tasks. However, they are not well suited for latency critical applications on both servers and edge devices. It’s important to reduce the memory and compute resources required by these models.

To this end, we propose pQRNN, a projection-based embedding-free neural encoder that is tiny and effective for natural language processing tasks.

Without pre-training, pQRNNs substantially outperform LSTM models with pre-trained embeddings despite being 140× smaller. With the same number of parameters, they outperform transformer baselines thereby showcasing their parameter efficiency. Additionally, we show that pQRNNs are effective student architectures for distilling large pre-trained language models.

We perform careful ablations which study the effect of pQRNN parameters, data augmentation, and distillation settings. On MTOP, a challenging multilingual semantic parsing dataset, pQRNN students achieve 95.9% of the performance of an mBERT teacher while being 350× smaller. On mATIS, a popular parsing task, pQRNN students on average are able to get to 97.1% of the teacher while again being 350× smaller.

Our strong results suggest that our approach is great for latency-sensitive applications while being able to leverage large mBERT-like models.

--- https://arxiv.org/abs/2112.10755 Discovering State Variables Hidden in Experimental Data Boyuan Chen, Kuang Huang, Sun, Raghupathi, Ishaan Chandratreya, Qiang Du, Hod Lipson 2021-12-20 2021-12-20 [("doi","10.48550/arXiv.2112.10755")] ai/nn/vae

All physical laws are described as relationships between state variables that give a complete and non-redundant description of the relevant system dynamics. However, despite the prevalence of computing power and AI, the process of identifying the hidden state variables themselves has resisted automation. Most data-driven methods for modeling physical phenomena still assume that observed data streams already correspond to relevant state variables. A key challenge is to identify the possible sets of state variables from scratch, given only high-dimensional observational data.

Here we propose a new principle for determining how many state variables an observed system is likely to have, and what these variables might be, directly from video streams [processed by an autoencoder NN].

We demonstrate the effectiveness of this approach using video recordings of a variety of physical dynamical systems, ranging from elastic double pendulums to fire flames. Without any prior knowledge of the underlying physics, our algorithm discovers the intrinsic dimension of the observed dynamics and identifies candidate sets of state variables.

We suggest that this approach could help catalyze the understanding, prediction and control of increasingly complex systems.

Project website is at: neural-state-variables.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4763788/ Winning a competition predicts dishonest behavior Amos Schurr, Ilana Ritov 2016 2021-12-04 [("doi","10.1073/pnas.1515102113")] psychology

[failure to replicate] Winning a competition engenders subsequent unrelated unethical behavior.

5 studies reveal that after a competition has taken place winners behave more dishonestly than competition losers. Studies 1 & 2 demonstrate that winning a competition increases the likelihood of winners to steal money from their counterparts in a subsequent unrelated task. Studies 3a & 3b demonstrate that the effect holds only when winning means performing better than others (ie. determined in reference to others) but not when success is determined by chance or in reference to a personal goal. Finally, Study 4 demonstrates that a possible mechanism underlying the effect is an enhanced sense of entitlement among competition winners.

--- https://arxiv.org/abs/2203.13474#salesforce A Conversational Paradigm for Program Synthesis Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong 2022-03-25 2022-03-25 [("doi","10.48550/arXiv.2203.13474")] ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue ai/scaling

[cf. Chen et al 2022] Program synthesis strives to generate a computer program as a solution to a given problem specification.

We propose a conversational program synthesis approach via large language models, which addresses the challenges of searching over a vast program space and user intent specification faced in prior approaches. Our new approach casts the process of writing a specification and program as a multi-turn conversation between a user and a system. It treats program synthesis as a sequence prediction problem, in which the specification is expressed in natural language and the desired program is conditionally sampled.

We train a family of large language models, called CodeGen, on natural language and programming language data.

With weak supervision in the data and the scaling up of data size and model size, conversational capacities emerge from the simple autoregressive language modeling.

To study the model behavior on conversational program synthesis, we develop a multi-turn programming benchmark (MTPB), where solving each problem requires multi-step synthesis via multi-turn conversation between the user and the model.

Our findings show the emergence of conversational capabilities and the effectiveness of the proposed conversational program synthesis paradigm. In addition, our model CodeGen (with up to 16b parameters trained on TPU-v4) outperforms OpenAI’s Codex on the HumanEval benchmark.

We make the training library JaxFormer including checkpoints available as open source contribution: Github.

--- /doc/statistics/decision/2020-woodford.pdf Modeling Imprecision in Perception, Valuation, and Choice Michael Woodford 2020-05-01 2021-12-04 [("doi","10.1146/annurev-economics-102819-040518")] psychology/neuroscience statistics/decision

Traditional decision theory assumes that people respond to the exact features of the options available to them, but observed behavior seems much less precise.

This review considers ways of introducing imprecision into models of economic decision making and stresses the usefulness of analogies with the way that imprecise perceptual judgments are modeled in psychophysics—the branch of experimental psychology concerned with the quantitative relationship between objective features of an observer’s environment and elicited reports about their subjective appearance.

It reviews key ideas from psychophysics, provides examples of the kinds of data that motivate them, and proposes lessons for economic modeling. Applications include stochastic choice, choice under risk, decoy effects in marketing, global game models of strategic interaction, and delayed adjustment of prices in response to monetary disturbances.

--- https://arxiv.org/abs/1603.02754 XGBoost: A Scalable Tree Boosting System Tianqi Chen, Carlos Guestrin 2016-03-09 2021-12-04 [("doi","10.1145/2939672.2939785")] ai/tabular

Tree boosting is a highly effective and widely used machine learning method.

In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges.

We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression, and sharding to build a scalable tree boosting system.

By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

--- https://arxiv.org/abs/2107.00819 Decision tree heuristics can fail, even in the smoothed setting Guy Blanc, Jane Lange, Mingda Qiao, Li-Yang Tan 2021-07-02 2021-12-04 [("doi","10.48550/arXiv.2107.00819")] ai/tabular

Greedy decision tree learning heuristics are mainstays of machine learning practice, but theoretical justification for their empirical success remains elusive. In fact, it has long been known that there are simple target functions for which they fail badly (Kearns & Mansour 1996).

Recent work of Brutzkus et al 2020 considered the smoothed analysis model as a possible avenue towards resolving this disconnect. Within the smoothed setting and for targets f that are k-juntas, they showed that these heuristics successfully learn f with depth-k decision tree hypotheses. They conjectured that the same guarantee holds more generally for targets that are depth-k decision trees.

We provide a counterexample to this conjecture: we construct targets that are depth-k decision trees and show that even in the smoothed setting, these heuristics build trees of depth 2Ω(k) before achieving high accuracy. We also show that the guarantees of Brutzkus et al 2020 cannot extend to the agnostic setting: there are targets that are very close to k-juntas, for which these heuristics build trees of depth 2Ω(k) before achieving high accuracy.

--- https://arxiv.org/abs/1906.08654 ID3 Learns Juntas for Smoothed Product Distributions Alon Brutzkus, Amit Daniely, Eran Malach 2019-06-20 2021-12-04 [("doi","10.48550/arXiv.1906.08654")] ai/tabular

In recent years, there are many attempts to understand popular heuristics.

An example of such a heuristic algorithm is the ID3 algorithm for learning decision trees. This algorithm is commonly used in practice, but there are very few theoretical works studying its behavior.

In this paper, we analyze the ID3 algorithm, when the target function is a k-Junta, a function that depends on k out of n variables of the input.

We prove that when k = log n, the ID3 algorithm learns in polynomial time k-Juntas, in the smoothed analysis model of Kalai & Teng. That is, we show a learnability result when the observed distribution is a “noisy” variant of the original distribution.

--- /doc/food/2022-goodenough.pdf Immaterials and Methods: Reagents for the Total Laboratory Synthesis of the Chocolate Chip Cookie Günther Schlonk 2022-07-29 2022-07-29 food math/humor --- https://www.thingsihavedrawn.com/ Things I Have Drawn is a site in which the things kids draw are real. 2021-12-04 psychology/cognitive-bias/illusion-of-depth --- https://arxiv.org/abs/2207.01848 TabPFN: Meta-Learning a Real-Time Tabular AutoML Method For Small Data Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter 2022-07-05 2022-07-05 [("doi","10.48550/arXiv.2207.01848")] ai/nn/transformer ai/tabular reinforcement-learning/meta-learning

[cf. Garg et al 2022] We present TabPFN, an AutoML method that is competitive with the state-of-the-art on small tabular datasets while being over 1,000× faster.

Our method is very simple: it is fully entailed in the weights of a single neural network, and a single forward pass directly yields predictions for a new dataset. Our AutoML method is meta-learned using the Transformer-based Prior-Data Fitted Network (PFN) architecture and approximates Bayesian inference with a prior that is based on assumptions of simplicity and causal structures. The prior contains a large space of structural causal models and Bayesian neural networks with a bias for small architectures and thus low complexity. Furthermore, we extend the PFN approach to differentiably calibrate the prior’s hyperparameters on real data. By doing so, we separate our abstract prior assumptions from their heuristic calibration on real data. Afterwards, the calibrated hyperparameters are fixed and TabPFN can be applied to any new tabular dataset at the push of a button.

Finally, on 30 datasets from the OpenML-CC18 suite we show that our method outperforms boosted trees and performs on par with complex state-of-the-art AutoML systems with predictions produced in less than a second.

We provide all our code and our final trained TabPFN in the supplementary materials.

--- https://arxiv.org/abs/1708.03731 OpenML Benchmarking Suites Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael G. Mantovani, Jan N. van Rijn, Joaquin Vanschoren 2017-08-11 2021-12-04 [("doi","10.48550/arXiv.1708.03731")] ai/dataset ai/tabular

Machine learning research depends on objectively interpretable, comparable, and reproducible algorithm benchmarks. We advocate the use of curated, comprehensive suites of machine learning tasks to standardize the setup, execution, and reporting of benchmarks. We enable this through software tools that help to create and leverage these benchmarking suites. These are seamlessly integrated into the OpenML platform, and accessible through interfaces in Python, Java, and R. OpenML benchmarking suites (a) are easy to use through standardized data formats, APIs, and client libraries; (b) come with extensive meta-information on the included datasets; and (c) allow benchmarks to be shared and reused in future studies.

We then present a first, carefully curated and practical benchmarking suite for classification: the OpenML Curated Classification benchmarking suite 2018 (OpenML-CC18). Finally, we discuss use cases and applications which demonstrate the usefulness of OpenML benchmarking suites and the OpenML-CC18 in particular.

--- https://aiimpacts.org/2022-expert-survey-on-progress-in-ai/ 2022 Expert Survey on Progress in AI 2021-12-05 ai existential-risk statistics/prediction --- https://web.mit.edu/jemorris/humor/alice-and-bob 2021-12-05 cs/cryptography fiction/humor --- https://arxiv.org/abs/2207.04779 Mathematical Proof Between Generations Jonas Bayer, Christoph Benzmüller, Kevin Buzzard, Marco David, Leslie Lamport, Yuri Matiyasevich, Lawrence Paulson, Dierk Schleicher, Benedikt Stock, Efim Zelmanov 2022-07-08 2022-07-08 [("doi","10.48550/arXiv.2207.04779")] math philosophy/logic

A proof is one of the most important concepts of mathematics. However, there is a striking difference between how a proof is defined in theory and how it is used in practice. This puts the unique status of mathematics as exact science into peril.

Now may be the time to reconcile theory and practice, i.e. precision and intuition, through the advent of computer proof assistants. For the most time this has been a topic for experts in specialized communities. However, mathematical proofs have become increasingly sophisticated, stretching the boundaries of what is humanly comprehensible, so that leading mathematicians have asked for formal verification of their proofs. At the same time, major theorems in mathematics have recently been computer-verified by people from outside of these communities, even by beginning students.

This article investigates the gap between the different definitions of a proof and possibilities to build bridges. It is written as a polemic or a collage by different members of the communities in mathematics and computer science at different stages of their careers, challenging well-known preconceptions and exploring new perspectives.

--- https://en.wikipedia.org/wiki/Chronic_fatigue_syndrome Chronic fatigue syndrome 2021-12-05 psychology/energy --- https://en.wikipedia.org/wiki/Long_COVID Long covid 2021-12-05 psychology/energy --- https://en.wikipedia.org/wiki/Coreset Coreset 2021-12-05 math reinforcement-learning/exploration/active-learning/data-pruning --- https://arxiv.org/abs/2112.10510 PFNs: Transformers Can Do Bayesian Inference Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, Frank Hutter 2021-12-20 2021-12-20 [("doi","10.48550/arXiv.2112.10510")] ai/nn/transformer ai/tabular reinforcement-learning/meta-learning statistics/bayes

[cf. Garg et al 2022, GPT-3, Hollmann et al 2022] Currently, it is hard to reap the benefits of deep learning for Bayesian methods, which allow the explicit specification of prior knowledge and accurately capture model uncertainty.

We present Prior-Data Fitted Networks (PFNs). PFNs leverage large-scale machine learning techniques to approximate a large set of posteriors. The only requirement for PFNs to work is the ability to sample from a prior distribution over supervised learning tasks (or functions).

Our method restates the objective of posterior approximation as a supervised classification problem with a set-valued input: it repeatedly draws a task (or function) from the prior, draws a set of data points and their labels from it, masks one of the labels and learns to make probabilistic predictions for it based on the set-valued input of the rest of the data points. Presented with a set of samples from a new supervised learning task as input, PFNs make probabilistic predictions for arbitrary other data points in a single forward propagation, having learned to approximate Bayesian inference.

We demonstrate that PFNs can near-perfectly mimic Gaussian processes and also enable efficient Bayesian inference for intractable problems, with over 200× speedups in multiple setups compared to current methods. We obtain strong results in very diverse areas such as Gaussian process regression, Bayesian neural networks, classification for small tabular data sets, and few-shot image classification, demonstrating the generality of PFNs.

Code and trained PFNs are released at Github.

--- https://arxiv.org/abs/2201.04309#microsoft Robust Contrastive Learning against Noisy Views Ching-Yao Chuang, R. Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, Yale Song 2022-01-12 2022-01-12 [("doi","10.48550/arXiv.2201.04309")] ai/nn/transformer

Contrastive learning relies on an assumption that positive pairs contain related views, eg. patches of an image or co-occurring multimodal signals of a video, that share certain underlying information about an instance. But what if this assumption is violated? The literature suggests that contrastive learning produces suboptimal representations in the presence of noisy views, eg. false positive pairs with no apparent shared information.

In this work, we propose a new contrastive loss function that is robust against noisy views. We provide rigorous theoretical justifications by showing connections to robust symmetric losses for noisy binary classification and by establishing a new contrastive bound for mutual information maximization based on the Wasserstein distance measure. The proposed loss is completely modality-agnostic and a simple drop-in replacement for the InfoNCE loss, which makes it easy to apply to existing contrastive frameworks.

We show that our approach provides consistent improvements over the state-of-the-art on image, video, and graph contrastive learning benchmarks that exhibit a variety of real-world noise patterns.

--- https://arxiv.org/abs/2205.10770 Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, Armen Aghajanyan 2022-05-22 2022-05-22 [("doi","10.48550/arXiv.2205.10770")] ai/nn/transformer/gpt/2 ai/scaling reinforcement-learning/meta-learning/continual-learning

Despite their wide adoption, the underlying training and memorization dynamics of very large language models is not well understood.

We empirically study exact memorization in causal and masked language modeling, across model sizes and throughout the training process. We measure the effects of dataset size, learning rate, and model size on memorization, finding that:

larger language models memorize training data faster across all settings. Surprisingly, we show that larger models can memorize a larger portion of the data before over-fitting and tend to forget less throughout the training process.

We also analyze the memorization dynamics of different parts of speech and find that models memorize nouns and numbers first; we hypothesize and provide empirical evidence that nouns and numbers act as a unique identifier for memorizing individual training examples.

Together, these findings present another piece of the broader puzzle of trying to understand what actually improves as models get bigger.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3385677/ Embodied cognitive evolution and the cerebellum Robert A. Barton 2012 2021-12-05 [("doi","10.1098/rstb.2012.0112")] psychology/neuroscience

Much attention has focused on the dramatic expansion of the forebrain, particularly the neocortex, as the neural substrate of cognitive evolution. However, though relatively small, the cerebellum contains about 4× more neurons than the neocortex. I show that commonly used comparative measures such as neocortex ratio underestimate the contribution of the cerebellum to brain evolution.

Once differences in the scaling of connectivity in neocortex and cerebellum are accounted for, a marked and general pattern of correlated evolution of the two structures is apparent. One deviation from this general pattern is a relative expansion of the cerebellum in apes and other extractive foragers.

The confluence of these comparative patterns, studies of ape foraging skills and social learning, and recent evidence on the cognitive neuroscience of the cerebellum, suggest an important role for the cerebellum in the evolution of the capacity for planning, execution and understanding of complex behavioral sequences—including tool use and language. There is no clear separation between sensory-motor and cognitive specializations underpinning such skills, undermining the notion of executive control as a distinct process.

Instead, I argue that cognitive evolution is most effectively understood as the elaboration of specialized systems for embodied adaptive control.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8064848/ Hydrocephalic Dementia: Revisited with Multimodality Imaging and toward a Unified Imaging Approach Sandhya Mangalore, Sriharish Vankayalapati, Arun Kumar Gupta 2021 2021-12-05 [("doi","10.1055/s-0041-1726614")] psychology/neuroscience

Objective  Overlap of normal pressure hydrocephalus (NPH) and pathology proven cases of dementia is known. The objective of this paper is to correlate both the clinical and multimodality imaging findings in patients with imaging diagnosis NPH and give a hypothesis for association of clinical findings.

Methods  This is a retrospective observational analysis of 13 cases patients who were referred to molecular imaging center for imaging in 2016–2019, and they were divided into 4 groups based on structural imaging findings. Group 1 had magnetic resonance imaging (MRI) findings of diffuse effacement of sulcal spaces (DESH) and flow void, whereas Group 4 had none of these two. Group 3 had MRI findings of DESH but no flow void, and Group 2 had flow void but no DESH. Clinical presentation, MRI-PET findings of 4 groups are assessed.

Results  Groups with presence of flow void showed hypometabolism in the medial frontal and medial temporal lobe. Groups with presence of DESH has effacement of parietal sulci showed parietal hypo metabolism with clinical presentation AD/mixed dementia and absence of parietal effacement showed FTD-like presentation. Groups without flow void or DESH showed only mild medial temporal hypometabolism and presented with classical signs of NPH. ASL perfusion changes are in correlation with metabolism on positron emission tomography (PET)-MRI.

Conclusion  This study has led us to hypothesize the lack of outflow of brain protein and their deposition in parenchyma based on pressure gradient would be easier explanation to go with cluster of findings. MR-PET and other investigations each had different specificity and sensitivity and different pattern of presentation.

--- https://x.com/EMostaque/status/1555927495215665152 2021-12-06 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2208.01815#tencent Effidit: Your AI Writing Assistant Shuming Shi, Enbo Zhao, Duyu Tang, Yan Wang, Piji Li, Wei Bi, Haiyun Jiang, Guoping Huang, Leyang Cui, Xinting Huang, Cong Zhou, Yong Dai, Dongyang Ma 2022-08-03 2022-08-03 [("doi","10.48550/arXiv.2208.01815")] ai/nn/sampling ai/nn/transformer/gpt

In this technical report, we introduce Effidit (Efficient and Intelligent Editing), a digital writing assistant that facilitates users to write higher-quality text more efficiently by using artificial intelligence (AI) technologies. Previous writing assistants typically provide the function of error checking (to detect and correct spelling and grammatical errors) and limited text-rewriting functionality. With the emergence of large-scale neural language models, some systems support automatically completing a sentence or a paragraph. In Effidit, we expand the capacities of a writing assistant by providing functions in 5 categories: text completion, error checking, text polishing, keywords to sentences (K2S), and cloud input methods (cloud IME).

In the text completion category, Effidit supports generation-based sentence completion, retrieval-based sentence completion, and phrase completion. In contrast, many other writing assistants so far only provide one or two of the 3 functions. For text polishing, we have 3 functions: (context-aware) phrase polishing, sentence paraphrasing, and sentence expansion, whereas many other writing assistants often support one or two functions in this category.

The main contents of this report include major modules of Effidit, methods for implementing these modules, and evaluation results of some key methods.

--- /doc/economics/perpetuities/2021-heldring.pdf The Long-Run Impact of the Dissolution of the English Monasteries Leander Heldring, James A. Robinson, Sebastian Vollmer 2021-11-01 2021-12-06 [("doi","10.1093/qje/qjab030")] economics/perpetuities

[commentary] We use the effect of the Dissolution of the English Monasteries after 1535 to test the commercialization hypothesis about the roots of long-run English economic development.

Before the Dissolution, monastic lands were relatively unencumbered by inefficient feudal land tenure but could not be sold. The Dissolution created a market for formerly monastic lands, which could now be more effectively commercialized relative to non-monastic lands, where feudal tenure persisted until the twentieth century.

We show that parishes affected by the Dissolution subsequently experienced a rise of the gentry and had more innovation and higher yield in agriculture, a greater share of the population working outside of agriculture, and ultimately higher levels of industrialization.

Our results are consistent with explanations of the Agricultural and Industrial Revolutions which emphasize the commercialization of society as a key precondition for taking advantage of technological change and new economic opportunities.

--- https://www.medrxiv.org/content/10.1101/2022.08.01.22278296.full A genome-wide association study of Chinese and English language abilities in Hong Kong Chinese children Yu-Ping Lin, Yujia Shi, Ruoyu Zhang, Xiao Xue, Shitao Rao, Kevin Fai-Hong Lui, Dora Jue Pan, Urs Maurer, Richard Kwong-Wai Choy, Silvia Paracchini, Catherine McBride, Hon-Cheong So 2022-08-03 2022-08-03 [("doi","10.1101/2022.08.01.22278296")] genetics/heritable/correlation iq psychiatry/adhd psychology/linguistics/bilingual

Background: Reading and language skills are important and known to be heritable, and dyslexia and developmental language disorder are commonly recognized learning difficulties worldwide. However, the genetic basis underlying these skills remains poorly understood. In particular, most previous genetic studies were performed on Westerners. To our knowledge, few or no previous genome-wide association studies (GWAS) have been conducted on literacy skills in Chinese as a native language or English as a second language (ESL) in a Chinese population.

Method: We conducted GWAS and related bioinformatics analyses on 34 reading/language-related phenotypes in Hong Kong Chinese bilingual children (including both twins and singletons; n = 1046). We performed association tests at the single-variant, gene, pathway levels, and transcriptome-wide association studies (TWAS) to explore how imputed expression changes might affect the phenotypes. In addition, we tested genetic overlap of these cognitive traits with other neuropsychiatric disorders, as well as cognitive performance (CP) and educational attainment (EA) using polygenic risk score (PRS) analysis.

Results: Totally 9 independent loci (LD-clumped at R2 = 0.01) reached genome-wide statistical-significance (p < 5e-08) (filtered by imputation quality metric R2>0.3 and having at least 2 correlated SNPs(r2>0.5) with p < 1e-3). The loci were associated with a range of language/literacy traits such as Chinese vocabulary, character and word reading, dictation and rapid digit naming, as well as English lexical decision. Several SNPs from these loci mapped to genes that were reported to be associated with intelligence, EA other neuropsychiatric phenotypes, such as MANEA, TNR, PLXNC1 and SHTN1. We also revealed statistically-significantly enriched genes and pathways based on SNP-based analysis. In PRS analysis, EA and CP showed statistically-significant polygenic overlap with a variety of language traits, especially English literacy skills. ADHD PRS showed a statistically-significant association with English vocabulary score.

Conclusion: This study revealed numerous genetic loci that may be associated with Chinese and English abilities in a group of Chinese bilingual children. Further studies are warranted to replicate the findings and elucidate the mechanisms involved.

--- /doc/biology/2022-ridker.pdf Effects of Randomized Treatment With Icosapent Ethyl and a Mineral Oil Comparator on Interleukin-1β, Interleukin-6, C-Reactive Protein, Oxidized Low-Density Lipoprotein Cholesterol, Homocysteine, Lipoprotein(a), and Lipoprotein-Associated Phospholipase A2: A REDUCE-IT Biomarker Substudy Paul M. Ridker, Nader Rifai, Jean MacFadyen, Robert J. Glynn, Lixia Jiao, Ph. Gabriel Steg, Michael Miller, Eliot A. Brinton, Terry A. Jacobson, Jean-Claude Tardif, Christie M. Ballantyne, R. Preston Mason, Deepak L. Bhatt 2022-06-28 2022-06-28 [("doi","10.1161/CIRCULATIONAHA.122.059410")] biology statistics/bias

Background: REDUCE-IT (Reduction of Cardiovascular Events With Icosapent Ethyl—Intervention Trial) reported a 25% relative risk reduction in major adverse cardiovascular events with use of icosapent ethyl compared with pharmaceutical grade mineral oil. The mechanisms underlying this benefit remain uncertain. We explored whether treatment allocation in REDUCE-IT might affect a series of biomarkers in pathways known to associate with atherosclerosis risk.

Method: Serum levels of interleukin-1β, interleukin-6, high-sensitivity C-reactive protein, oxidized low-density lipoprotein cholesterol, homocysteine, lipoprotein(a), and lipoprotein-associated phospholipase A2 (Lp-PLA2) were measured at baseline, at 12 months, at 24 months, and at the end-of-study visit among REDUCE-IT participants with triglyceride levels ≥ 135 mg/dL and <500 mg/dL who were randomly allocated to treatment with either 4 grams daily of icosapent ethyl or mineral oil used as a comparator.

Results: At baseline, median levels of each biomarker were similar in the 2 treatment groups. The levels of biomarkers associated with atherosclerosis increased over time among those allocated to mineral oil treatment; in this group at 12 months, the median percent increases from baseline were 1.5% for homocysteine, 2.2% for lipoprotein(a), 10.9% for oxidized low-density lipoprotein cholesterol, 16.2% for interleukin-6, 18.5% for lipoprotein-associated phospholipase A2, 21.9% for high-sensitivity C-reactive protein, and 28.9% for interleukin-1β (all P values <0.001), with similar changes at 24 months. In the icosapent ethyl group, there were minimal changes in these biomarkers at 12 and 24 months. As such, at study conclusion, between-group treatment differences largely reflected increases in the mineral oil group with median percent differences of 2.4% for lipoprotein(a), 3.0% for homocysteine, 4.2% for oxidized low-density lipoprotein cholesterol, 19.8% for interleukin-6, 26.2% for \U0001D4C1p-PLA2, 38.5% for high-sensitivity C-reactive protein, and 48.7% for interleukin-1β (all P values ≤0.007). These data are consistent with previous REDUCE-IT results in which the median percent change for low-density lipoprotein cholesterol at 12 months was −1.2% among those allocated to icosapent ethyl and 10.9% among those allocated to the mineral oil comparator.

Conclusion: Among participants in REDUCE-IT, allocation to icosapent ethyl had minimal effects on a series of biomarkers associated with atherosclerotic disease, whereas levels increased among those allocated to mineral oil. The effect of these findings on interpretation of the overall risk reductions in clinical events observed within REDUCE-IT is uncertain.

Registration: NCT01492361.

--- https://www.bbc.com/future/article/20220804-the-lost-nuclear-bombs-that-no-one-can-find The lost nuclear bombs that no one can find 2021-12-06 radiance --- https://mattsclancy.substack.com/p/how-common-is-independent-discovery How common is independent discovery? 2021-12-06 science --- https://x.com/cmuratori/status/1555981462880411648 2021-12-06 ai/nn/transformer/gpt/non-fiction --- https://www.biorxiv.org/content/10.1101/2022.08.03.502284.full ONE: Expansion microscopy at one nanometer resolution Ali H. Shaib, Abed Alrahman Chouaib, Vanessa Imani, Rajdeep Chowdhury, Svilen Veselinov Georgiev, Nikolaos Mougios, Mehar Monga, Sofiia Reshetniak, Daniel Mihaylov, Han Chen, Parisa Fatehbasharzad, Dagmar Crzan, Kim Ann Saal, Claudia Trenkwalder, Brit Mollenhauer, Tiago F. Outeiro, Julia Preobraschenski, Ute Becherer, Tobias Moser, Edward S. Boyden, A. Radu A. Aricescu, Markus Sauer, Felipe Opazo, Silvio Rizzoli 2022-08-05 2022-08-05 [("doi","10.1101/2022.08.03.502284")] biology

Fluorescence imaging is one of the most versatile and widely-used tools in biology. Although techniques to overcome the diffraction barrier were introduced more than two decades ago, and the nominal attainable resolution kept improving to reach single-digit nm, fluorescence microscopy still fails to image the morphology of single proteins or small molecular complexes, either purified or in a cellular context.

Here we report a solution to this problem, in the form of one-nanometer expansion (ONE) microscopy. We combined the 10× axial expansion of the specimen (1000× by volume) with a fluorescence fluctuation analysis to achieve resolutions down to 1 nm or better.

We have successfully applied ONE microscopy to image cultured cells, tissues, viral particles, molecular complexes and single proteins. At the cellular level, using immunostaining, our technology revealed detailed nanoscale arrangements of synaptic proteins, including a quasi-regular organization of PSD95 clusters. At the single molecule level, upon main chain fluorescent labeling, we could visualize the shape of individual membrane and soluble proteins. Moreover, conformational changes undergone by the ~17 kDa protein calmodulin upon Ca2+ binding were readily observable. We could also image and classify molecular aggregates in cerebrospinal fluid samples from Parkinsons Disease (PD) patients, which represents a promising new development towards an improved PD diagnosis.

ONE microscopy is compatible with conventional microscopes and can be performed with the software we provide here as a free, open-source package.

This technology bridges the gap between high-resolution structural biology techniques and light microscopy, and provides a new avenue for discoveries in biology and medicine.

--- https://x.com/TomLikesRobots/status/1556312678481952774 2021-12-06 ai/nn/transformer/clip/sample --- https://www.lesswrong.com/posts/yDcMDJeSck7SuBs24/steganography-in-chain-of-thought-reasoning Steganography in Chain-of-Thought Reasoning 2021-12-07 ai/nn/transformer/gpt/inner-monologue cs/cryptography/steganography reinforcement-learning/safe --- https://en.wikipedia.org/wiki/Expansion_microscopy Expansion microscopy 2021-12-07 psychology/neuroscience science --- https://arxiv.org/abs/2108.02213 How a fake Kepler portrait became iconic Steven N. Shore, Václav Pavlík 2021-08-04 2021-12-07 [("doi","10.1063/PT.3.4825")] history science

For several decades a portrait of Johannes Kepler has been widely circulating among professional astronomers, scientific and academic institutions, and the general public. Despite its provenance and identification having been questioned in the early part of the last century, this painting has reached iconic status.

We review its history from its first mention in the literature in the 1870s to a published but virtually unknown judgment of competent art experts of the 1920s that the work is in fact an early 19th century forgery. We display the painting in context with other more secure portraits and suggest that if it is based on anything, the painting may derive from the well known portrait from life of Michael Mästlin.

This correction takes on certain urgency since 2021 is the 450th anniversary of Kepler’s birth.

--- https://www.medrxiv.org/content/10.1101/2022.07.03.22277199.full Genome-wide association study meta-analysis of suicide attempt in 43,871 cases identifies twelve genome-wide statistically-significant loci Anna R. Docherty, Niamh Mullins, Allison E. Ashley-Koch, Xue J. Qin, Jonathan Coleman, Andrey A. Shabalin, Jooeun Kang, Balazs Murnyak, the International Suicide Genetics Consortium, the Suicide Working Group of the Psychiatric Genomics Consortium, the V. A. Million Veteran Program (MVP), the M. V. P. Suicide Exemplar Workgroup, Benjamin H. McMahon, David W. Oslin, Elizabeth R. Hauser, Michael A. Hauser, Qingqin Li, Hilary Coon, Nathan A. Kimbrel, Jean C. Beckham, Douglas Ruderfer 2022-08-04 2022-08-04 [("doi","10.1101/2022.07.03.22277199")] genetics/heritable/correlation/mendelian-randomization psychiatry/adhd psychiatry/depression

Objective: Suicidal behavior is moderately heritable and a major cause of death worldwide. Two large-scale genome-wide association studies (GWAS) have recently discovered and cross-validated genome-wide statistically-significant (GWS) loci for suicide attempts (SA). The current study leveraged the genetic cohorts from these two studies to conduct the largest GWAS meta-analysis of SA to date. Ancestry-specific GWAS meta-analyses were also conducted with African, East Asian, and European ancestry cohorts.

Method: This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and African, East Asian, and European ancestral groups included inverse variance-weighted fixed effects models, gene/gene set and tissue enrichment testing, drug-gene interaction analyses, and summary-based Mendelian Randomization with eQTL MetaBrain data.

Results: Multi-ancestry and European GWAS meta-analyses identified 12 risk loci, including 28 independent GWS variants at p < 5 × 10−8. Risk loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p = 5.70 × 10−80). brain tissue gene expression and drug set enrichment was observed, along with shared genetic variation of SA with ADHD, smoking, and risk tolerance after conditioning on both major depressive disorder and post-traumatic stress disorder.

Conclusion: This multi-ancestry GWAS identified several loci contributing to risk of suicide attempt, and suggests statistically-significant shared genetic covariation with relevant clinical phenotypes that is not accounted for by major depressive disorder or post-traumatic stress disorder. These findings outline molecular pathways of risk for suicide, and provide new insight into shared genetic architecture with psychiatric phenotypes across ancestries.

--- https://arxiv.org/abs/2207.03620 More ConvNets in the 2020s: Scaling up Kernels Beyond 51×51 using Sparsity (SLaK) Shiwei Liu, Tianlong Chen, Xiaohan Chen, Xuxi Chen, Qiao Xiao, Boqian Wu, Mykola Pechenizkiy, Decebal Mocanu, Zhangyang Wang 2022-07-07 2022-07-07 [("doi","10.48550/arXiv.2207.03620")] ai/nn/sparsity

Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs). The dominant role of convolutional neural networks (CNNs) seems to be challenged by increasingly effective transformer-based models. Very recently, a couple of advanced convolutional models strike back with large kernels motivated by the local but large attention mechanism, showing appealing performance and efficiency. While one of them, i.e. RepLKNet, impressively manages to scale the kernel size to 31×31 with improved performance, the performance starts to saturate as the kernel size continues growing, compared to the scaling trend of advanced ViTs such as Swin Transformer.

In this paper, we explore the possibility of training extreme convolutions larger than 31×31 and test whether the performance gap can be eliminated by strategically enlarging convolutions. This study ends up with a recipe for applying extremely large kernels from the perspective of sparsity, which can smoothly scale up kernels to 61×61 with better performance.

Built on this recipe, we propose Sparse Large Kernel Network (SLaK), a pure CNN architecture equipped with 51×51 kernels that can perform on par with or better than state-of-the-art hierarchical Transformers and modern ConvNet architectures like ConvNeXt and RepLKNet, on ImageNet classification as well as typical downstream tasks.

Our code is available here Github.

--- https://arxiv.org/abs/2201.03545#facebook ConvNeXt: A ConvNet for the 2020s Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie 2022-01-10 2022-01-10 [("doi","10.48550/arXiv.2201.03545")] ai/nn/cnn ai/scaling

The “Roaring 20s” of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (eg. Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions.

In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually “modernize” a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way.

The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.

--- https://x.com/quasimondo/status/1556924519096492033 2021-12-07 ai/nn/transformer/clip/sample --- /doc/iq/2012-bavelier.pdf Brain Plasticity Through the Life Span: Learning to Learn and Action Video Games Daphne Bavelier, C. Shawn Green, Alexandre Pouget, Paul Schrater 2012-07-01 2021-12-07 [("doi","10.1146/annurev-neuro-060909-152832")] iq psychology/neuroscience

The ability of the human brain to learn is exceptional. Yet, learning is typically quite specific to the exact task used during training, a limiting factor for practical applications such as rehabilitation, workforce training, or education. The possibility of identifying training regimens that have a broad enough impact to transfer to a variety of tasks is thus highly appealing.

This work reviews how complex training environments such as action video game play may actually foster brain plasticity and learning. This enhanced learning capacity, termed learning to learn, is considered in light of its computational requirements and putative neural mechanisms.

--- https://arxiv.org/abs/1512.08546 When Coding Style Survives Compilation: De-anonymizing Programmers from Executable Binaries Aylin Caliskan, Fabian Yamaguchi, Edwin Dauber, Richard Harang, Konrad Rieck, Rachel Greenstadt, Arvind Narayanan 2015-12-28 2021-12-07 [("doi","10.14722/ndss.2018.23304")] cs/security

The ability to identify authors of computer programs based on their coding style is a direct threat to the privacy and anonymity of programmers. While recent work found that source code can be attributed to authors with high accuracy, attribution of executable binaries appears to be much more difficult. Many distinguishing features present in source code, eg. variable names, are removed in the compilation process, and compiler optimization may alter the structure of a program, further obscuring features that are known to be useful in determining authorship. We examine programmer de-anonymization from the standpoint of machine learning, using a novel set of features that include ones obtained by decompiling the executable binary to source code. We adapt a powerful set of techniques from the domain of source code authorship attribution along with stylistic representations embedded in assembly, resulting in successful de-anonymization of a large set of programmers.

We evaluate our approach on data from the Google Code Jam, obtaining attribution accuracy of up to 96% with 100 and 83% with 600 candidate programmers. We present an executable binary authorship attribution approach, for the first time, that is robust to basic obfuscations, a range of compiler optimization settings, and binaries that have been stripped of their symbol tables. We perform programmer de-anonymization using both obfuscated binaries, and real-world code found “in the wild” in single-author GitHub repositories and the recently leaked Nulled.IO hacker forum.

We show that programmers who would like to remain anonymous need to take extreme countermeasures to protect their privacy.

--- https://www.biorxiv.org/content/10.1101/2020.12.03.410399.full A hierarchy of linguistic predictions during natural language comprehension Micha Heilbron, Kristijan Armeni, Jan-Mathijs Schoffelen, Peter Hagoort, Floris P. de Lange 2021-05-27 2021-12-07 [("doi","10.1101/2020.12.03.410399")] ai/nn/transformer/gpt/2 psychology/neuroscience

Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction to guide the interpretation of incoming input. However, the role of prediction in language processing remains disputed, with disagreement about both the ubiquity and representational nature of predictions.

Here, we address both issues by analysing brain recordings of participants listening to audiobooks, and using a deep neural network (GPT-2) to precisely quantify contextual predictions. First, we establish that brain responses to words are modulated by ubiquitous, probabilistic predictions. Next, we disentangle model-based predictions into distinct dimensions, revealing dissociable signatures of syntactic, phonemic and semantic predictions. Finally, we show that high-level (word) predictions inform low-level (phoneme) predictions, supporting hierarchical predictive processing.

Together, these results underscore the ubiquity of prediction in language processing, showing that the brain spontaneously predicts upcoming language at multiple levels of abstraction.

--- https://www.ft.com/content/f76534bf-b501-4cbf-9a46-80be9feb670c The resilience myth: fatal flaws in the push to secure chip supply chains 2021-12-07 ai/scaling/hardware --- https://arxiv.org/abs/2201.04122 In Defense of the Unitary Scalarization for Deep Multi-Task Learning Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov, Shimon Whiteson, M. Pawan Kumar 2022-01-11 2022-01-11 [("doi","10.48550/arXiv.2201.04122")] reinforcement-learning/meta-learning reinforcement-learning/model/decision-transformer reinforcement-learning/scaling

Recent multi-task learning research argues against unitary scalarization, where training simply minimizes the sum of the task losses.

Several ad-hoc multi-task optimization algorithms have instead been proposed, inspired by various hypotheses about what makes multi-task settings difficult. The majority of these optimizers require per-task gradients, and introduce memory, runtime, and implementation overhead.

We present a theoretical analysis suggesting that many specialized multi-task optimizers can be interpreted as forms of regularization. Moreover, we show that, when coupled with standard regularization and stabilization techniques from single-task learning, unitary scalarization matches or improves upon the performance of complex multi-task optimizers in both supervised and reinforcement learning settings.

We believe our results call for a critical reevaluation of recent research in the area.

--- https://arxiv.org/abs/2208.04135 Adversarial Attacks on Image Generation With Made-Up Words Raphaël Millière 2022-08-04 2022-08-04 [("doi","10.48550/arXiv.2208.04135")] ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e/2 cs/security

[Twitter] Text-guided image generation models can be prompted to generate images using novel words adversarially designed to robustly evoke specific visual concepts.

Two approaches for such generation are introduced: macaronic prompting, which involves designing cryptic hybrid words by concatenating subword units from different languages; and evocative prompting, which involves designing novel words whose broad morphological features are similar enough to that of existing words to trigger robust visual associations. The two methods can also be combined to generate images associated with more specific visual concepts.

The implications of these techniques for the circumvention of existing approaches to content moderation, and particularly the generation of offensive or harmful images, are discussed.

--- https://arxiv.org/abs/2208.04202#google Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning Ting Chen, Ruixiang Zhang, Geoffrey Hinton 2022-08-08 2022-08-08 [("doi","10.48550/arXiv.2208.04202")] ai/nn/diffusion/discrete

We present Bit Diffusion: a simple and generic approach for generating discrete data with continuous diffusion models.

The main idea behind our approach is to first represent the discrete data as binary bits, and then train a continuous diffusion model to model these bits as real numbers which we call analog bits. To generate samples, the model first generates the analog bits, which are then thresholded to obtain the bits that represent the discrete variables. We further propose two simple techniques, namely Self-Conditioning and Asymmetric Time Intervals, which lead to a substantial improvement in sample quality.

Despite its simplicity, the proposed approach can achieve strong performance in both discrete image generation and image captioning tasks. For discrete image generation, we substantially improve previous state-of-the-art on both CIFAR-10 (which has 3K discrete 8-bit tokens) and ImageNet-64×64 (which has 12K discrete 8-bit tokens), outperforming the best autoregressive model in both sample quality (measured by FID) and efficiency. For image captioning on MS-COCO dataset, our approach achieves competitive results compared to autoregressive models.

--- https://arxiv.org/abs/2208.03550 EVL: Frozen CLIP Models are Efficient Video Learners Ziyi Lin, Shijie Geng, Renrui Zhang, Peng Gao, Gerard de Melo, Xiaogang Wang, Jifeng Dai, Yu Qiao, Hongsheng Li 2022-08-06 2022-08-06 [("doi","10.48550/arXiv.2208.03550")] ai/nn/transformer/clip ai/video/analysis

Video recognition has been dominated by the end-to-end learning paradigm—first initializing a video recognition model with weights of a pretrained image model and then conducting end-to-end training on videos. This enables the video network to benefit from the pretrained image model. However, this requires substantial computation and memory resources for finetuning on videos and the alternative of directly using pretrained image features without finetuning the image backbone leads to subpar results.

Fortunately, recent advances in Contrastive Vision-Language Pre-training (CLIP) pave the way for a new route for visual recognition tasks. Pretrained on large open-vocabulary image-text pair data, these models learn powerful visual representations with rich semantics. In this paper, we present Efficient Video Learning (EVL)—an efficient framework for directly training high-quality video recognition models with frozen CLIP features.

Specifically, we employ a lightweight Transformer decoder and learn a query token to dynamically collect frame-level spatial features from the CLIP image encoder. Furthermore, we adopt a local temporal module in each decoder layer to discover temporal clues from adjacent frames and their attention maps.

We show that despite being efficient to train with a frozen backbone, our models learn high quality video representations on a variety of video recognition datasets.

Code is available at Github.

--- https://cronokirby.com/posts/2021/02/spaced-repetition-for-mathematics/ Spaced Repetition for Mathematics 2021-12-08 math psychology/spaced-repetition --- https://arxiv.org/abs/2208.04370 CLIP-based Neural Neighbor Style Transfer for 3D Assets Shailesh Mishra, Jonathan Granskog 2022-08-08 2022-08-08 [("doi","10.48550/arXiv.2208.04370")] ai/nn/transformer/clip

We present a method for transferring the style from a set of images to a 3D object. The texture appearance of an asset is optimized with a differentiable renderer in a pipeline based on losses using pretrained deep neural networks. More specifically, we utilize a nearest-neighbor feature matching loss with CLIP-ResNet-50 to extract the style from images. We show that a CLIP-based style loss provides a different appearance over a VGG-based loss by focusing more on texture over geometric shapes.

Additionally, we extend the loss to support multiple images and enable loss-based control over the color palette combined with automatic color palette extraction from style images.

--- https://arxiv.org/abs/2208.04347#google Investigating Efficiently Extending Transformers for Long Input Summarization Jason Phang, Yao Zhao, Peter J. Liu 2022-08-08 2022-08-08 [("doi","10.48550/arXiv.2208.04347")] ai/nn/transformer/attention/hierarchical

While large pretrained Transformer models have proven highly capable at tackling natural language tasks, handling long sequence inputs continues to be a challenge. One such task is long input summarization, where inputs are longer than the maximum input context of most pretrained models.

Through an extensive set of experiments, we investigate what model architectural changes and pretraining paradigms can most efficiently adapt a pretrained Transformer for long input summarization. We find that a staggered, block-local Transformer with global encoder tokens strikes a good balance of performance and efficiency, and that an additional pretraining phase on long sequences meaningfully improves downstream summarization performance.

Based on our findings, we introduce PEGASUS-X, an extension of the PEGASUS model with additional long input pretraining to handle inputs of up to 16K tokens.

PEGASUS-X achieves strong performance on long input summarization tasks comparable with much larger models while adding few additional parameters and not requiring model parallelism to train.

--- https://arxiv.org/abs/2208.04919#google Basis for Intentions (BASIS): Efficient Inverse Reinforcement Learning using Past Experience Marwa Abdulhai, Natasha Jaques, Sergey Levine 2022-08-09 2022-08-09 [("doi","10.48550/arXiv.2208.04919")] reinforcement-learning/preference-learning

This paper addresses the problem of inverse reinforcement learning (IRL)—inferring the reward function of an agent from observing its behavior. IRL can provide a generalizable and compact representation for apprenticeship learning, and enable accurately inferring the preferences of a human in order to assist them. However, effective IRL is challenging, because many reward functions can be compatible with an observed behavior.

We focus on how prior reinforcement learning (RL) experience can be leveraged to make learning these preferences faster and more efficient. We propose the IRL algorithm BASIS (Behavior Acquisition through Successor-feature Intention inference from Samples), which leverages multi-task RL pre-training and successor features to allow an agent to build a strong basis for intentions that spans the space of possible goals in a given domain.

When exposed to just a few expert demonstrations optimizing a novel goal, the agent uses its basis to quickly and effectively infer the reward function.

Our experiments reveal that our method is highly effective at inferring and optimizing demonstrated reward functions, accurately inferring reward functions from less than 100 trajectories.

--- https://www.medrxiv.org/content/10.1101/2022.03.03.22271772.full Sex-specific genetic and transcriptomic liability to neuroticism Frank R. Wendt, Gita A. Pathak, Kritika Singh, Murray B. Stein, Karestan C. Koenen, John H. Krystal, Joel Gelernter, Lea K. Davis, Renato Polimanti 2022-03-04 2022-03-04 [("doi","10.1101/2022.03.03.22271772")] genetics/heritable/correlation psychiatry/depression psychology/personality

Background: The presentation, etiology, and relative risk of psychiatric disorders are strongly influenced by biological sex. Neuroticism is a transdiagnostic feature of psychiatric disorders displaying prominent sex differences. We performed genome-wide association studies (GWAS) of neuroticism separately in males and females to identify sex-specific genetic and transcriptomic profiles.

Method: Neuroticism scores were derived from the Eysenck Personality Inventory Neuroticism scale. GWAS were performed in 145,669 females and 129,229 males from the UK Biobank considering autosomal and X-chromosomal variation. Two-sided Z-tests were used to test for sex-specific effects of discovered loci, genetic correlates (n = 673 traits), tissue and gene transcriptomic profiles, and polygenic associations across health outcomes in the Vanderbilt University Biobank (BioVu, 39,692 females and 31,268 males).

Results: The SNP-heritability of neuroticism was not statistically-significantly different between males (h2=10.6%) and females (h2=11.85%). 4 female-specific (rs10736549-CNTN5, rs6507056-ASXL3, rs2087182-MMS22L, and rs72995548-HSPB2) and two male-specific (rs10507274-MED13L and rs7984597) neuroticism risk loci reached genome-wide statistical-significance. Male/female-specific neuroticism polygenic scores were most statistically-significantly associated with “mood disorders” (male OR = 1.11, p = 1.40×10−9; female OR = 1.14, p = 6.05×10−22). They also associated with sex-specific laboratory measures related to erythrocyte count, distribution, and hemoglobin concentration. Gene expression variation in the pituitary was enriched for neuroticism loci in males (males β=0.026, p = 0.002) and genetically-regulated transcriptomic changes highlighted the effect of RAB7L1, TEX26, and PLOT1.

Conclusion: Through a comprehensive assessment of genetic risk for neuroticism and the associated biological processes, this study identified several molecular pathways that can partially explain the known sex differences in neurotic symptoms and their psychiatric comorbidities.

--- https://www.medrxiv.org/content/10.1101/2021.09.22.21263909.full Pervasive Downward Bias in Estimates of Liability Scale Heritability in GWAS Meta-Analysis: A Simple Solution Andrew D. Grotzinger, Javier de la Fuente, Michel G. Nivard, Elliot M. Tucker-Drob 2021-09-23 2021-12-08 [("doi","10.1101/2021.09.22.21263909")] genetics/heritable

SNP heritability is a fundamental quantity in the genetic analysis of complex traits. For binary phenotypes, in which the continuous distribution of risk in the population is unobserved, observed-scale heritabilities must be transformed to the more interpretable liability-scale.

We demonstrate here that the field standard approach for performing the liability conversion can downwardly bias estimates by as much as ~20% in simulation and ~30% in real data. These attenuated estimates stem from the standard approach failing to appropriately account for varying levels of ascertainment across the cohorts comprising the meta-analysis [introducing ‘measurement error’].

We formally derive a simple procedure for incorporating cohort-specific ascertainment based on the summation of effective sample sizes across the contributing cohorts, and confirm via simulation that it produces unbiased estimates of liability-scale heritability.

--- https://www.astralcodexten.com/p/skills-plateau-because-of-decay-and Skills Plateau Because Of Decay And Interference 2021-12-08 psychology/spaced-repetition --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2925254/ Long-term memory for the terrorist attack of September 11: flashbulb memories, event memories, and the factors that influence their retention William Hirst, Elizabeth A. Phelps, Randy L. Buckner, Andrew E. Budson, Alexandru Cuc, John D. E. Gabrieli, Marcia K. Johnson, Cindy Lustig, Keith B. Lyle, Mara Mather, Robert Meksin, Karen J. Mitchell, Kevin N. Ochsner, Daniel L. Schacter, Jon S. Simons, Chandan J. Vaidya 2009 2021-12-09 [("doi","10.1037/a0015527")] psychology/cognitive-bias/illusion-of-depth

More than 3,000 individuals from 7 U.S. cities reported on their memories of learning of the terrorist attacks of September 11, as well as details about the attack, 1 week, 11 months, and/or 35 months after the assault. Some studies of flashbulb memories examining long-term retention show slowing in the rate of forgetting after a year, whereas others demonstrate accelerated forgetting.

This article indicates that (1) the rate of forgetting for flashbulb memories and event memory (memory for details about the event itself) slows after a year, (2) the strong emotional reactions elicited by flashbulb events are remembered poorly, worse than nonemotional features such as where and from whom one learned of the attack, and (3) the content of flashbulb and event memories stabilizes after a year.

The results are discussed in terms of community memory practices.

--- https://alexanderwales.com/the-ai-art-apocalypse/ The AI Art Apocalypse 2021-12-09 ai/nn/diffusion culture --- https://arxiv.org/abs/2206.01018 Score-Based Generative Models Detect Manifolds Jakiw Pidstrigach 2022-06-02 2022-06-02 [("doi","10.48550/arXiv.2206.01018")] ai/nn/diffusion

Score-based generative models (SGMs) need to approximate the scores ∇ log pt of the intermediate distributions as well as the final distribution pT of the forward process. The theoretical underpinnings of the effects of these approximations are still lacking.

We find precise conditions under which SGMs are able to produce samples from an underlying (low-dimensional) data manifold 𝓜. This assures us that SGMs are able to generate the “right kind of samples”. For example, taking 𝓜 to be the subset of images of faces, we find conditions under which the SGM robustly produces an image of a face, even though the relative frequencies of these images might not accurately represent the true data generating distribution. Moreover, this analysis is a first step towards understanding the generalization properties of SGMs: Taking 𝓜 to be the set of all training samples, our results provide a precise description of when the SGM memorizes its training data.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2992609/ Quality of random number generators affects results of Monte Carlo simulations for organic and biological systems Timothy H. Click, Aibing Liu, George A. Kaminski 2011 2021-12-09 [("doi","10.1002/jcc.21638")] statistics/probability

We have simulated pure liquid butane, methanol, and hydrated alanine polypeptide with the Monte Carlo technique using 3 kinds of random number generators (RNGs)-the standard Linear Congruential Generator (LCG), a modification of the LCG with additional randomization used in the BOSS software, and the “Mersenne Twister” generator by Matsumoto and Nishimura.

While using the latter two RNG’s leads to reasonably similar physical features, the LCG produces substantially different results.

For the pure fluids, a noticeable expansion occurs. Using the original LCG on butane yields, a molecular volume of 171.4 Å(3) per molecule compared to about 163.6–163.9 Å(3) for the other two generators, a deviation of about 5%. For methanol, the LCG produces an average volume of 86.3 Å(3) per molecule, which is about 24% higher than the 68.8–70.2 Å(3) obtained with the RNG’s in BOSS and the generator by Matsumoto and Nishimura. In case of the hydrated tridecaalanine peptide, the volume and energy tend to be noticeably greater with the LCG than with the BOSS (modified LCG) RNG’s. For the simulated hydrated extended conformation of tridecaalanine, the difference in volume reached about 87%.

The uniformity and periodicity of the generators do not seem to play the crucial role in these phenomena.

We conclude that, it is important to test a RNG by modeling a system such as the pure liquid methanol with a well-established force field before routinely employing it in Monte Carlo simulations.

--- https://arxiv.org/abs/2202.03286#deepmind Red Teaming Language Models with Language Models Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, Geoffrey Irving 2022-02-07 2022-02-07 [("doi","10.48550/arXiv.2202.03286")] ai/nn/adversarial

Language Models (LMs) often cannot be deployed because of their potential to harm users in hard-to-predict ways. Prior work identifies harmful behaviors before deployment by using human annotators to hand-write test cases. However, human annotation is expensive, limiting the number and diversity of test cases.

In this work, we automatically find cases where a target LM behaves in a harmful way, by generating test cases (“red teaming”) using another LM. We evaluate the target LM’s replies to generated test questions using a classifier trained to detect offensive content, uncovering tens of thousands of offensive replies in a 280b parameter LM chatbot [Gopher].

We explore several methods, from zero-shot generation to reinforcement learning, for generating test cases with varying levels of diversity and difficulty. Furthermore, we use prompt engineering to control LM-generated test cases to uncover a variety of other harms, automatically finding groups of people that the chatbot discusses in offensive ways, personal and hospital phone numbers generated as the chatbot’s own contact info, leakage of private training data in generated text, and harms that occur over the course of a conversation.

Overall, LM-based red teaming is one promising tool (among many needed) for finding and fixing diverse, undesirable LM behaviors before impacting users.

--- https://x.com/fabianstelzer/status/1559517876943523846 2021-12-09 ai/nn/transformer/gpt/fiction --- https://lilianweng.github.io/posts/2021-05-31-contrastive/ Contrastive Representation Learning 2021-12-09 ai/nn --- https://github.com/microsoft/unadversarial 2021-12-09 ai/nn/adversarial --- /doc/fiction/poetry/2012-03-10-sebastianmarshall-ladyyododono.html 2012-03-10 2021-12-09 fiction/poetry japan --- https://slate.com/human-interest/2013/03/aging-canned-goods-why-time-and-heat-can-make-your-canned-tuna-and-spam-even-more-delicious.html 2021-12-09 food --- https://putanumonit.com/2016/07/27/pokemonumber/ 2021-12-09 statistics/probability --- https://www.wired.com/story/the-curious-afterlife-of-a-brain-trauma-survivor/ 2021-12-10 psychiatry/traumatic-brain-injury --- https://www.reddit.com/r/OpenAI/comments/wsbe59/til_gpt3_is_able_to_estimate_the_size_of_any/ 2021-12-10 ai/nn/transformer/gpt/fiction --- https://www.reddit.com/r/HentaiDiffusion/ 2021-12-10 ai/anime ai/nn/transformer/clip/sample --- https://www.medrxiv.org/content/10.1101/2022.08.16.22278868.full Causal effects on complex traits are similar across segments of different continental ancestries within admixed individuals Kangcheng Hou, Yi Ding, Ziqi Xu, Yue Wu, Arjun Bhattacharya, Rachel Mester, Gillian Belbin, David Conti, Burcu F. Darst, Myriam Fornage, Chris Gignoux, Xiuqing Guo, Christopher Haiman, Eimear Kenny, Michelle Kim, Charles Kooperberg, Leslie Lange, Ani Manichaikul, Kari E. North, Natalie Nudelman, Ulrike Peters, Laura J. Rasmussen-Torvik, Stephen S. Rich, Jerome I. Rotter, Heather E. Wheeler, Ying Zhou, Sriram Sankararaman, Bogdan Pasaniuc 2022-08-18 2022-08-18 [("doi","10.1101/2022.08.16.22278868")] genetics/heritable/correlation

Individuals of admixed ancestries (eg. African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. Their genomic diversity offers the unique opportunity of investigating genetic effects on disease across multiple ancestries within the same population. Quantifying the similarity in causal effects across local ancestries is paramount to studying genetic basis of diseases in admixed individuals. Such similarity can be defined as the genetic correlation of causal effects (radmix) across African and European local ancestry backgrounds. Existing studies investigating causal effects variability across ancestries focused on cross-continental comparisons; however, such differences could be due to heterogeneities in the definition of environment/phenotype across continental ancestries. Studying genetic effects within admixed individuals avoids these confounding factors, because the genetic effects are compared across local ancestries within the same individuals.

Here, we introduce a new method that models polygenic architecture of complex traits to quantify radmix across local ancestries. We model genome-wide causal effects that are allowed to vary by ancestry and estimate radmix by inferring variance components of local ancestry-aware genetic relationship matrices.

Our method is accurate and robust across a range of simulations.

We analyze 38 complex traits in individuals of African and European admixed ancestries (n = 53K) from: Population Architecture using Genomics and Epidemiology (PAGE), UK Biobank (UKBB) and All of Us (AoU). We observe a high similarity in causal effects by ancestry in meta-analyses across traits, with estimated radmix = 0.95 (95% credible interval [0.93, 0.97]), much higher than correlation in causal effects across continental ancestries. High estimated radmix is also observed consistently for each individual trait.

We replicate the high correlation in causal effects using regression-based methods from marginal GWAS summary statistics.

We also report realistic scenarios where regression-based methods yield inflated estimates of heterogeneity-by-ancestry due to local ancestry-specific tagging of causal variants, and/or polygenicity. Among regression-based methods, only Deming regression is robust enough for estimation of correlation in causal effects by ancestry.

In summary, causal effects on complex traits are highly similar across local ancestries and motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry.

--- https://www.sigarch.org/coping-with-copilot/ 2021-12-10 ai/nn/transformer/gpt/codex --- https://x.com/GabriellaG439/status/1561007332267421696 2021-12-10 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://x.com/goodside/status/1561213457374011392 2021-12-10 ai/nn/transformer/gpt/non-fiction --- https://x.com/goodside/status/1561296820373954560 2021-12-10 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://en.wikipedia.org/wiki/Countersteering Countersteering 2021-12-10 psychology/cognitive-bias/illusion-of-depth --- https://worrydream.com/#!2/LadderOfAbstraction 2021-12-10 psychology/cognitive-bias/illusion-of-depth --- https://en.wikipedia.org/wiki/R%C3%B8mer%27s_determination_of_the_speed_of_light Rømer’s determination of the speed of light 2021-12-11 science --- https://x.com/goodside/status/1561437390576750593 2021-12-11 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://forum.effectivealtruism.org/posts/nSwaDrHunt3ohh9Et/cause-area-short-sleeper-genes 2021-12-11 genetics/editing zeo/short-sleeper --- https://hallofdreams.org/posts/the-death-of-poetry/ 2021-12-11 fiction/poetry --- https://arxiv.org/abs/1411.5326#deepmind Compress and Control Joel Veness, Marc G. Bellemare, Marcus Hutter, Alvin Chua, Guillaume Desjardins 2014-11-19 2021-12-11 [("doi","10.48550/arXiv.1411.5326")] cs/algorithm/information/compression reinforcement-learning/model

This paper describes a new information-theoretic policy evaluation technique for reinforcement learning.

This technique converts any compression [ie. Lempel-Ziv] or density model into a corresponding estimate of value. Under appropriate stationarity and ergodicity conditions, we show that the use of a sufficiently powerful model gives rise to a consistent value function estimator.

We also study the behavior of this technique when applied to various Atari 2600 video games, where the use of suboptimal modeling techniques is unavoidable. We consider 3 fundamentally different models, all too limited to perfectly model the dynamics of the system. Remarkably, we find that our technique provides sufficiently accurate value estimates for effective on-policy control.

We conclude with a suggestive study highlighting the potential of our technique to scale to large problems.

--- https://x.com/maccaw/status/1561716203281948674 2021-12-11 ai/nn/transformer/clip/sample --- https://en.wikipedia.org/wiki/Islam_and_cats Islam and cats 2021-12-11 cat --- https://x.com/goodside/status/1561296820373954560 2021-12-11 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://x.com/goodside/status/1561437390576750593 2021-12-11 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://x.com/kuizinas/status/1562086476690644992 2021-12-11 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8881926/ Total Meat Intake is Associated with Life Expectancy: A Cross-Sectional Data Analysis of 175 Contemporary Populations Wenpeng You, Renata Henneberg, Arthur Saniotis, Yanfei Ge, Maciej Henneberg 2022 2022 [("doi","10.2147/IJGM.S333004")] economics exercise

Background: The association between a plant-based diet (vegetarianism) and extended life span is increasingly criticised since it may be based on the lack of representative data and insufficient removal of confounders such as lifestyles.

Aim: We examined the association between meat intake and life expectancy at a population level based on ecological data published by the United Nations agencies.

Method: Population-specific data were obtained from 175 countries/territories. Scatter plots, bivariate, partial correlation and linear regression models were used with SPSS 25 to explore and compare the correlations between newborn life expectancy (e0), life expectancy at 5 years of life (e5) and intakes of meat, and carbohydrate crops, respectively. The established risk factors to life expectancy—caloric intake, urbanization, obesity and education levels—were included as the potential confounders.

Results: Worldwide, bivariate correlation analyses revealed that meat intake is positively correlated with life expectancies. This relationship remained statistically-significant when influences of caloric intake, urbanization, obesity, education and carbohydrate crops were statistically controlled. Stepwise linear regression selected meat intake, not carbohydrate crops, as one of the statistically-significant predictors of life expectancy. In contrast, carbohydrate crops showed weak and negative correlation with life expectancy.

Conclusion: If meat intake is not incorporated into nutrition science for predicting human life expectancy, results could prove inaccurate.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9359670/ Comparison Of The Gut Microbiota In Different Age Groups In China Hang Yan, Qian Qin, Su Yan, Jingfeng Chen, Yang Yang, Tiantian Li, Xinxin Gao, Suying Ding 2022 2022 [("doi","10.3389/fcimb.2022.877914")] genetics/microbiome

Aging is now the most profound risk factor for almost all non-communicable diseases. Studies have shown that probiotics play a specific role in fighting aging. We used metagenomic sequencing to study the changes in gut microbes in different age groups and found that aging had the most statistically-significant effect on subjects’ gut microbe structure.

Our study divided the subjects (n = 614) into two groups by using 50 years as the age cut-off point for the grouping.

Compared with the younger group, several species with altered abundance and specific functional pathways were found in the older group. At the species level, the abundance of Bacteroides fragilis, Bifidobacterium longum, Clostridium bolteae, Escherichia coli, Klebsiella pneumoniae, and Parabacteroides merdae were increased in older individuals. They were positively correlated to the pathways responsible for lipopolysaccharide (LPS) biosynthesis and the degradation of short-chain fatty acids (SCFAs). On the contrary, the levels of Barnesiella intestinihominis, Megamonas funiformis, and Subdoligranulum unclassified were decreased in the older group, which negatively correlated with the above pathways (p-value<0.05). Functional prediction revealed 92 metabolic pathways enriched in the older group statistically-significantly higher than those in the younger group (p-value<0.05), especially pathways related to LPS biosynthesis and the degradation of SCFAs.

Additionally, we established a simple non-invasive model of aging, 9 species (Bacteroides fragilis, Barnesiella intestinihominis, Bifidobacterium longum, Clostridium bolteae, Escherichia coli, Klebsiella pneumoniae, Megamonas funiformis, Parabacteroides merdae, and Subdoligranulum unclassified) were selected to construct the model. The area under the receiver operating curve (AUC) of the model implied that supplemented probiotics might influence aging.

We discuss the features of the aging microbiota that make it more amenable to pre-biotic and probiotic interventions. We speculate these metabolic pathways of gut microbiota can be associated with the immune status and inflammation of older adults.

Health interventions that promote a diverse microbiome could influence the health of older adults.

--- https://www.biorxiv.org/content/10.1101/2022.08.15.504040.full Task-dependent optimal representations for cerebellar learning Marjorie Xie, Samuel Muscinelli, Kameron Decker Harris, Ashok Litwin-Kumar 2022-08-15 2022-08-15 [("doi","10.1101/2022.08.15.504040")] psychology/neuroscience

The cerebellar granule cell layer has inspired numerous theoretical models of neural representations that support learned behaviors, beginning with the work of Marr and Albus. In these models, granule cells form a sparse, combinatorial encoding of diverse sensorimotor inputs. Such sparse representations are optimal for learning to discriminate random stimuli. However, recent observations of dense, low-dimensional activity across granule cells have called into question the role of sparse coding in these neurons.

Here, we generalize theories of cerebellar learning to determine the optimal granule cell representation for tasks beyond random stimulus discrimination, including continuous input-output transformations as required for smooth motor control.

We show that for such tasks, the optimal granule cell representation is substantially denser than predicted by classic theories.

Our results provide a general theory of learning in cerebellum-like systems and suggest that optimal cerebellar representations are task-dependent.

--- https://arxiv.org/abs/2208.06102 Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training Jie You, Jae-Won Chung, Mosharaf Chowdhury 2022-08-12 2022-08-12 [("doi","10.48550/arXiv.2208.06102")] ai/nn ai/scaling/hardware statistics/bayes

Training deep neural networks (DNNs) is becoming more and more resource/energy-intensive every year. Unfortunately, existing works primarily focus on optimizing DNN training for faster completion, often without considering the impact on energy efficiency.

In this paper, we observe that common practices to improve training performance can often lead to inefficient energy usage. More importantly, we demonstrate that there is a tradeoff between energy consumption and performance optimization.

To this end, we propose an optimization framework, Zeus, to navigate this tradeoff by automatically finding optimal job-level & GPU-level configurations for recurring DNN training jobs. Zeus uses an online Thompson sampling exploration-exploitation approach in conjunction with just-in-time energy profiling, averting the need for expensive offline measurements, while adapting to data drifts over time.

Our evaluation shows that Zeus can improve the energy efficiency of DNN training by 15.3%–75.8% for diverse workloads.

--- https://warontherocks.com/2022/08/the-accelerating-threat-of-the-political-assassination/ 2021-12-12 crime/terrorism --- https://arxiv.org/abs/2208.09392 Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein 2022-08-19 2022-08-19 [("doi","10.48550/arXiv.2208.09392")] ai/nn/diffusion

[cf. ‘soft’ diffusion, heat dissipation; peer-review] Standard diffusion models involve an image transform—adding Gaussian noise—and an image restoration operator that inverts this degradation.

We observe that the generative behavior of diffusion models is not strongly dependent on the choice of image degradation, and in fact an entire family of generative models can be constructed by varying this choice. Even when using completely deterministic degradations (eg. blur, masking, and more), the training and test-time update rules that underlie diffusion models can be easily generalized to create generative models.

The success of these fully deterministic models calls into question the community’s understanding of diffusion models, which relies on noise in either gradient Langevin dynamics or variational inference, and paves the way for generalized diffusion models that invert arbitrary processes.

Our code is available at https://github.com/arpitbansal297/Cold-Diffusion-Models.

--- https://openai.com/blog/our-approach-to-alignment-research/ 2021-12-12 ai/nn/transformer/gpt reinforcement-learning/safe --- https://arxiv.org/abs/2208.10668 Beyond linear regression: mapping models in cognitive neuroscience should align with research goals Anna A. Ivanova, Martin Schrimpf, Stefano Anzellotti, Noga Zaslavsky, Evelina Fedorenko, Leyla Isik 2022-08-23 2022-08-23 [("doi","10.48550/arXiv.2208.10668")] psychology/neuroscience

Many cognitive neuroscience studies use large feature sets to predict and interpret brain activity patterns. Feature sets take many forms, from human stimulus annotations to representations in deep neural networks. Of crucial importance in all these studies is the mapping model, which defines the space of possible relationships between features and neural data. Until recently, most encoding and decoding studies have used linear mapping models. Increasing availability of large datasets and computing resources has recently allowed some researchers to employ more flexible nonlinear mapping models instead; however, the question of whether nonlinear mapping models can yield meaningful scientific insights remains debated.

Here, we discuss the choice of a mapping model in the context of 3 overarching desiderata: predictive accuracy, interpretability, and biological plausibility. We show that, contrary to popular intuition, these desiderata do not map cleanly onto the linear/nonlinear divide; instead, each desideratum can refer to multiple research goals, each of which imposes its own constraints on the mapping model.

Moreover, we argue that, instead of categorically treating the mapping models as linear or nonlinear, we should instead aim to estimate the complexity of these models. We show that, in many cases, complexity provides a more accurate reflection of restrictions imposed by various research goals.

Finally, we outline several complexity metrics that can be used to effectively evaluate mapping models.

--- https://arxiv.org/abs/2208.08831#deepmind Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning Olivia Wiles, Isabela Albuquerque, Sven Gowal 2022-08-18 2022-08-18 [("doi","10.48550/arXiv.2208.08831")] ai/dataset ai/nn/adversarial ai/nn/transformer/gpt/dall-e/2

Automatically discovering failures in vision models under real-world settings remains an open challenge. This work demonstrates how off-the-shelf, large-scale, image-to-text [Flamingo] and text-to-image models [Imagen], trained on vast amounts of data, can be leveraged to automatically find such failures.

In essence, a conditional text-to-image generative model is used to generate large amounts of synthetic, yet realistic, inputs given a ground-truth label. Misclassified inputs are clustered and a captioning model is used to describe each cluster. Each cluster’s description is used in turn to generate more inputs and assess whether specific clusters induce more failures than expected.

We use this pipeline to demonstrate that we can effectively interrogate classifiers trained on ImageNet to find specific failure cases and discover spurious correlations. We also show that we can scale the approach to generate adversarial datasets targeting specific classifier architectures.

This work serves as a proof-of-concept demonstrating the utility of large-scale generative models to automatically discover bugs in vision models in an open-ended manner. We also describe a number of limitations and pitfalls related to this approach.

--- https://arxiv.org/abs/2203.06717 Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs (RepLKNet) Xiaohan Ding, Xiangyu Zhang, Yizhuang Zhou, Jungong Han, Guiguang Ding, Jian Sun 2022-03-13 2022-03-13 [("doi","10.48550/arXiv.2203.06717")] ai/nn/cnn

We revisit large kernel design in modern convolutional neural networks (CNNs).

Inspired by recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few large convolutional kernels instead of a stack of small kernels could be a more powerful paradigm. We suggested 5 guidelines, eg. applying re-parameterized large depth-wise convolutions, to design efficient high-performance large-kernel CNNs.

Following the guidelines, we propose RepLKNet, a pure CNN architecture whose kernel size is as large as 31×31, in contrast to commonly used 3×3.

RepLKNet greatly closes the performance gap between CNNs and ViTs, eg. achieving comparable or superior results than Swin Transformer on ImageNet and a few typical downstream tasks, with lower latency. RepLKNet also shows nice scalability to big data and large models, obtaining 87.8% top-1 accuracy on ImageNet and 56.0% mIoU on ADE20K, which is very competitive among the state-of-the-arts with similar model sizes. Our study further reveals that, in contrast to small-kernel CNNs, large-kernel CNNs have much larger effective receptive fields and higher shape bias rather than texture bias.

Code & models at Github.

--- https://arxiv.org/abs/1912.08777#google PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. Liu 2019-12-18 2021-12-12 [("doi","10.48550/arXiv.1912.08777")] ai/nn/transformer

Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summarization. However, pre-training objectives tailored for abstractive text summarization have not been explored. Furthermore there is a lack of systematic evaluation across diverse domains.

In this work, we propose pre-training large Transformer-based encoder-decoder models on massive text corpora with a new self-supervised objective. In PEGASUS, important sentences are removed/masked from an input document and are generated together as one output sequence from the remaining sentences, similar to an extractive summary.

We evaluated our best PEGASUS model on 12 downstream summarization tasks spanning news, science, stories, instructions, emails, patents, and legislative bills. Experiments demonstrate it achieves state-of-the-art performance on all 12 downstream datasets measured by ROUGE scores. Our model also shows surprising performance on low-resource summarization, surpassing previous state-of-the-art results on 6 datasets with only 1,000 examples. Finally we validated our results using human evaluation and show that our model summaries achieve human performance on multiple datasets.

--- https://www.dampfkraft.com/posuto.html 2021-12-12 design japan --- https://x.com/goodside/status/1562613028927205377 2021-12-13 ai/nn/transformer/gpt/fiction --- https://x.com/dbonneville/status/1562653976717185024 2021-12-13 ai/nn/transformer/clip/sample --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0272416 Effects of restricting social media usage on wellbeing and performance: A randomized control trial among students Avinash Collis, Felix Eggers, Daniel Romer, Daniel Romer, Daniel Romer, Daniel Romer 2022-07-19 2022-07-19 [("doi","10.1371/journal.pone.0272416")] sociology/technology

Recent research has shown that social media services create large consumer surplus. Despite their positive impact on economic welfare, concerns are raised about the negative association between social media usage and well-being or performance. However, causal empirical evidence is still scarce.

To address this research gap, we conduct a randomized controlled trial among students in which we track participants’ daily digital activities over the course of 3 quarters of an academic year. In the experiment, we randomly allocate half of the sample to a treatment condition in which social media usage (Facebook, Instagram, and Snapchat) is restricted to a maximum of 10 minutes per day.

We find that participants in the treatment group substitute social media for instant messaging and do not decrease their total time spent on digital devices. Contrary to findings from previous correlational studies, we do not find any statistically-significant impact of social media usage as it was defined in our study on well-being and academic success.

Our results also suggest that antitrust authorities should consider instant messaging and social media services as direct competitors before approving acquisitions.

--- https://arxiv.org/abs/2208.11640#microsoft Repair Is Nearly Generation: Multilingual Program Repair with LLMs Harshit Joshi, José Cambronero, Sumit Gulwani, Vu Le, Ivan Radicek, Gust Verbruggen 2022-08-24 2022-08-24 [("doi","10.48550/arXiv.2208.11640")] ai/nn/transformer/gpt/codex

Most programmers make mistakes when writing code. Some of these mistakes are small and require few edits to the original program—a class of errors recently termed last mile mistakes. These errors break the flow for experienced developers and can stump novice programmers. Existing automated repair techniques targeting this class of errors are domain-specific and do not easily carry over to new domains. Transferring symbolic approaches requires substantial engineering and neural approaches require data and retraining.

We introduce RING, a multilingual repair engine powered by a large language model trained on code (LLMC) such as Codex. Such a multilingual engine enables a flipped model for programming assistance, one where the programmer writes code and the AI assistance suggests fixes, compared to traditional code suggestion technology.

Taking inspiration from the way programmers manually fix bugs, we show that a prompt-based strategy that conceptualizes repair as localization, transformation, and candidate ranking, can successfully repair programs in multiple domains with minimal effort.

We present the first results for such a multilingual repair engine by evaluating on 6 different domains and comparing performance to domain-specific repair engines. We show that RING can outperform domain-specific repair engines in 3 of these domains.

We also identify directions for future research using LLMCs for multilingual repair.

--- https://arxiv.org/abs/2208.08706 Musika! Fast Infinite Waveform Music Generation Marco Pasini, Jan Schlüter 2022-08-18 2022-08-18 [("doi","10.48550/arXiv.2208.08706")] ai/music ai/nn/gan

Fast and user-controllable music generation could enable novel ways of composing or performing music. However, state-of-the-art music generation systems require large amounts of data and computational resources for training, and are slow at inference. This makes them impractical for real-time interactive use.

In this work, we introduce Musika, a music generation system that can be trained on hundreds of hours of music using a single consumer GPU, and that allows for much faster than real-time generation of music of arbitrary length on a consumer CPU.

We achieve this by first learning a compact invertible representation of spectrogram magnitudes and phases with adversarial autoencoders, then training a Generative Adversarial Network (GAN) on this representation for a particular music domain. A latent coordinate system enables generating arbitrarily long sequences of excerpts in parallel, while a global context vector allows the music to remain stylistically coherent through time.

We perform quantitative evaluations to assess the quality of the generated samples and showcase options for user control in piano and techno music generation.

We release the source code and pretrained autoencoder weights at github.com/marcoppasini/musika, such that a GAN can be trained on a new music domain with a single GPU in a matter of hours.

--- https://github.com/jquesnelle/txt2imghd 2021-12-13 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2208.11663#facebook PEER: A Collaborative Language Model Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, Sebastian Riedel 2022-08-24 2022-08-24 [("doi","10.48550/arXiv.2208.11663")] ai/nn/transformer/t5

Textual content is often the output of a collaborative writing process: We start with an initial draft, ask for suggestions, and repeatedly make changes. Agnostic of this process, today’s language models are trained to generate only the final result. As a consequence, they lack several abilities crucial for collaborative writing: They are unable to update existing texts, difficult to control and incapable of verbally planning or explaining their actions.

To address these shortcomings, we introduce PEER, a collaborative language model [based on T5] that is trained to imitate the entire writing process itself: PEER can write drafts, add suggestions, propose edits and provide explanations for its actions. Crucially, we train multiple instances of PEER able to infill various parts of the writing process, enabling the use of self-training techniques for increasing the quality, amount and diversity of training data. This unlocks PEER’s full potential by making it applicable in domains for which no edit histories are available and improving its ability to follow instructions, to write useful comments, and to explain its actions.

We show that PEER achieves strong performance [compared to Tk-Instruct, T0 & T0++, GPT-3/InstructGPT, OPT] across various domains and editing tasks.

--- https://arxiv.org/abs/2208.11695 Bugs in the Data: How ImageNet Misrepresents Biodiversity Alexandra Sasha Luccioni, David Rolnick 2022-08-24 2022-08-24 [("doi","10.48550/arXiv.2208.11695")] ai/dataset ai/nn

ImageNet-1k is a dataset often used for benchmarking machine learning (ML) models and evaluating tasks such as image recognition and object detection. Wild animals make up 27% of ImageNet-1k but, unlike classes representing people and objects, these data have not been closely scrutinized.

In the current paper, we analyze the 13,450 images from 269 classes that represent wild animals in the ImageNet-1k validation set, with the participation of expert ecologists. We find that many of the classes are ill-defined or overlapping, and that 12% of the images are incorrectly labeled, with some classes having >90% of images incorrect.

We also find that both the wildlife-related labels and images included in ImageNet-1k present geographical and cultural biases, as well as ambiguities such as artificial animals, multiple species in the same image, or the presence of humans.

Our findings highlight serious issues with the extensive use of this dataset for evaluating ML systems, the use of such algorithms in wildlife-related tasks, and more broadly the ways in which ML datasets are commonly created and curated.

--- https://x.com/goodside/status/1562480678540754946 2021-12-13 ai/nn/transformer/gpt/non-fiction --- https://x.com/goodside/status/1562284846059339776 2021-12-13 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://x.com/goodside/status/1562233738863452160 2021-12-14 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://x.com/goodside/status/1562417843542654976 2021-12-14 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/2208.10967 The Value of Out-of-Distribution Data Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein 2022-08-23 2022-08-23 [("doi","10.48550/arXiv.2208.10967")] ai/nn

More data helps us generalize to a task. But real datasets can contain out-of-distribution (OOD) data; this can come in the form of heterogeneity such as intra-class variability but also in the form of temporal shifts or concept drifts.

We demonstrate a counter-intuitive phenomenon for such problems: generalization error of the task can be a non-monotonic function of the number of OOD samples; a small number of OOD samples can improve generalization but if the number of OOD samples is beyond a threshold, then the generalization error can deteriorate. We also show that if we know which samples are OOD, then using a weighted objective between the target and OOD samples ensures that the generalization error decreases monotonically.

We demonstrate and analyze this issue using linear classifiers on synthetic datasets and medium-sized neural networks on CIFAR-10.

--- https://arxiv.org/abs/2208.10904#google A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert 2022-08-23 2022-08-23 [("doi","10.48550/arXiv.2208.10904")] reinforcement-learning/exploration reinforcement-learning/model-free statistics/bayes

Thompson sampling is one of the most effective methods for contextual bandits and has been generalized to posterior sampling for certain MDP settings. However, existing posterior sampling methods for reinforcement learning are limited by being model-based or lack worst-case theoretical guarantees beyond linear MDPs.

This paper proposes a new model-free formulation of posterior sampling that applies to more general episodic reinforcement learning problems with theoretical guarantees. We introduce novel proof techniques to show that under suitable conditions, the worst-case regret of our posterior sampling method matches the best known results of optimization based methods. In the linear MDP setting with dimension, the regret of our algorithm scales linearly with the dimension as compared to a quadratic dependence of the existing posterior sampling-based exploration algorithms.

--- https://arxiv.org/abs/2208.10291 Trajectory Autoencoding Planner: Efficient Planning in a Compact Latent Action Space Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian 2022-08-22 2022-08-22 [("doi","10.48550/arXiv.2208.10291")] reinforcement-learning/exploration reinforcement-learning/model/decision-transformer

[site; code; Twitter; cf. Diffuser] While planning-based sequence modeling methods have shown great potential in continuous control, scaling them to high-dimensional state-action sequences remains an open challenge due to the high computational complexity and innate difficulty of planning in high-dimensional spaces.

We propose the Trajectory Autoencoding Planner (TAP), a planning-based sequence modeling RL method that scales to high state-action dimensionality. Using a state-conditional Vector-Quantized Variational Autoencoder (VQ-VAE), TAP models the conditional distribution of the trajectories given the current state. When deployed as an RL agent, TAP avoids planning step-by-step in a high-dimensional continuous action space but instead looks for the optimal latent code sequences by beam search.

Unlike the 𝒪(D3) complexity of Trajectory Transformer, TAP enjoys constant 𝒪(C) planning computational complexity regarding state-action dimensionality D.

Our empirical evaluation also shows the increasingly strong performance of TAP with the growing dimensionality. For Adroit robotic hand manipulation tasks with high state and action dimensionality, TAP surpasses existing model-based methods, including TT, with a large margin and also beats strong model-free actor-critic baselines.

--- https://www.biorxiv.org/content/10.1101/2022.08.22.504853.full Accurate prediction of transition metal ion location via deep learning Simon L. Dürr, Andrea Levy, Ursula Rothlisberger 2022-08-22 2022-08-22 [("doi","10.1101/2022.08.22.504853")] ai/nn/transformer/alphafold

Metal ions are essential cofactors for many proteins. In fact, currently, about half of the structurally characterized proteins contain a metal ion. Metal ions play a crucial role for many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions, and have favorable catalytic properties eg. as Lewis acid. Computational design of metalloproteins is however hampered by the complex electronic structure of many biologically relevant metals such as zinc that can often not be accurately described using a classical force field.

In this work, we develop two tools—Metal3D (based on 3D convolutional neural networks) and Metal1D (solely based on geometric criteria) to improve the identification and localization of zinc and other metal ions in experimental and computationally predicted protein structures.

Comparison with other currently available tools shows that Metal3D is the most accurate metal ion location predictor to date outperforming geometric predictors including Metal1D by a wide margin using a single structure as input. Metal3D outputs a confidence metric for each predicted site and works on proteins with few homologs in the protein data bank. The predicted metal ion locations for Metal3D are within 0.70±0.64 Å of the experimental locations with half of the sites below 0.5 Å. Metal3D predicts a global metal density that can be used for annotation of structures predicted using eg. AlphaFold2 and a per residue metal density that can be used in protein design workflows for the location of suitable metal binding sites and rotamer sampling to create novel metalloproteins.

Metal3D is available as easy to use web-app, notebook or command-line interface.

--- https://arxiv.org/abs/2208.08798#deepmind Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members Daphne Cornelisse, Thomas Rood, Mateusz Malinowski, Yoram Bachrach, Tal Kachman 2022-08-18 2022-08-18 [("doi","10.48550/arXiv.2208.08798")] ai/nn/sparsity/knowledge-distillation economics/mechanism-design reinforcement-learning/multi-agent

In many multi-agent settings, participants can form teams to achieve collective outcomes that may far surpass their individual capabilities. Measuring the relative contributions of agents and allocating them shares of the reward that promote long-lasting cooperation are difficult tasks.

Cooperative game theory offers solution concepts identifying distribution schemes, such as the Shapley value, that fairly reflect the contribution of individuals to the performance of the team or the Core, which reduces the incentive of agents to abandon their team. Applications of such methods include identifying influential features and sharing the costs of joint ventures or team formation. Unfortunately, using these solutions requires tackling a computational barrier as they are hard to compute, even in restricted settings.

In this work, we show how cooperative game-theoretic solutions can be distilled into a learned model by training neural networks to propose fair and stable payoff allocations.

We show that our approach creates models that can generalize to games far from the training distribution and can predict solutions for more players than observed during training.

An important application of our framework is Explainable AI: our approach can be used to speed-up Shapley value computations on many instances.

--- https://arxiv.org/abs/2108.12409#facebook Train Short, Test Long: Attention with Linear Biases (ALiBi) Enables Input Length Extrapolation Ofir Press, Noah Smith, Mike Lewis 2021-08-27 2021-12-14 [("doi","10.48550/arXiv.2108.12409")] ai/nn/transformer/attention

[video; used in BLOOM] Since the introduction of the transformer model by Vaswani et al 2017, a fundamental question has yet to be answered: how does a model achieve extrapolation at inference time for sequences that are longer than it saw during training?

We first show that extrapolation can be enabled by simply changing the position representation method, though we find that current methods do not allow for efficient extrapolation. We therefore introduce a simpler and more efficient position method, Attention with Linear Biases (ALiBi). ALiBi does not add positional embeddings to word embeddings; instead, it biases query-key attention scores with a penalty that is proportional to their distance.

We show that this method trains a 1.3 billion parameter model on input sequences of length 1,024 that extrapolates to input sequences of length 2048, achieving the same perplexity as a sinusoidal position embedding model trained on inputs of length 2048 but training 11% faster and using 11% less memory.

ALiBi’s inductive bias towards recency also leads it to outperform multiple strong position methods on the WikiText-103 benchmark.

--- https://x.com/jasonbaldridge/status/1562103092631461891 2021-12-14 ai/nn/transformer/clip/sample ai/nn/transformer/gpt/dall-e --- https://x.com/goodside/status/1562991379915341824 2021-12-14 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/fiction --- https://threadreaderapp.com/thread/1187161460033458177.html 2021-12-14 ai/nn/transformer/t5 --- https://colab.research.google.com/drive/1-ROO7L09EupLFLQM-TWgDHa5-FIOdLLh 2021-12-15 ai/nn/transformer/t5 --- https://github.com/google-research/google-research/tree/master/ul2 2021-12-15 ai/nn/transformer/t5 --- https://x.com/colinraffel/status/1313097438299910147 I recently came across https://arxiv.org/abs/2004.08900, which ‘assumes 2-3 runs’ of T5-11B. In fact, we trained T5-11B once. That’s why we spend 35 pages figuring out how we should train before we start training. You don’t want to mess up a training run that big. 2021-12-15 ai/nn/transformer/t5 --- https://arxiv.org/abs/2208.12242#google DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, Kfir Aberman 2022-08-25 2022-08-25 [("doi","10.48550/arXiv.2208.12242")] ai/nn/diffusion

[cf. textual inversion; Stable Diffusion clone; comic example] Large text-to-image models achieved a remarkable leap in the evolution of AI, enabling high-quality and diverse synthesis of images from a given text prompt. However, these models lack the ability to mimic the appearance of subjects in a given reference set and synthesize novel renditions of them in different contexts.

In this work, we present a new approach for “personalization” of text-to-image diffusion models (specializing them to users’ needs). Given as input just a few images of a subject, we fine-tune a pretrained text-to-image model (Imagen, although our method is not limited to a specific model) such that it learns to bind a unique identifier with that specific subject. Once the subject is embedded in the output domain of the model, the unique identifier can then be used to synthesize fully-novel photorealistic images of the subject contextualized in different scenes. By leveraging the semantic prior embedded in the model with a new autogenous class-specific prior preservation loss, our technique enables synthesizing the subject in diverse scenes, poses, views, and lighting conditions that do not appear in the reference images.

We apply our technique to several previously-unassailable tasks, including subject recontextualization, text-guided view synthesis, appearance modification, and artistic rendering (all while preserving the subject’s key features).

Project page & samples: https://dreambooth.github.io/.

--- https://arxiv.org/abs/2208.12097 Training a T5 Using Lab-sized Resources Manuel R. Ciosici, Leon Derczynski 2022-08-25 2022-08-25 [("doi","10.48550/arXiv.2208.12097")] ai/nn/transformer/t5

Training large neural language models on large datasets is resource/time-intensive. These requirements create a barrier to entry, where those with fewer resources cannot build competitive models.

This paper presents various techniques for making it possible to (1) train a large language model using resources that a modest research lab might have, and (2) train it in a reasonable amount of time.

We provide concrete recommendations for practitioners, which we illustrate with a case study: a T5 model for Danish, the first for this language.

--- https://www.statnews.com/2022/08/09/anti-aging-projects-funding-much-discussed-trial-overlooked/ 2021-12-15 longevity --- https://x.com/AlbertVilella/status/1540240357555937280 2021-12-15 genetics/sequencing --- https://www.medrxiv.org/content/10.1101/2022.08.24.22279149.full Identification of 64 new risk loci for major depression, refinement of the genetic architecture and risk prediction of recurrence and comorbidities Thomas D. Als, Mitja Kurki, Jakob Grove, Georgios Voloudakis, Karen Therrien, Elisa Tasanko, Trine Tollerup Nielsen, Joonas Naamanka, Kumar Veerapen, Daniel Levey, Jaroslav Bendl, Jonas Bybjerg-Grauholm, Biao Zheng, Ditte Demontis, Anders Rosengren, Georgios Athanasiadis, Marie Baekved-Hansen, Per Qvist, Bragi Walters, Thorgeir Thorgeirsson, Hreinn Stefansson, Katherine L. Musliner, Veera Manikandan, Leila Farajzadeh, Janne Thirstrup, Bjarni J. Vilhjalmsson, John J. McGrath, Manuel Mattheisen, Sandra Meier, Esben Agerbo, Kari Stefansson, Merete Nordentoft, Thomas Werge, David Hougaard, Preben Bo Mortensen, Murray Stein, Joel Gelernter, Iiris Hovatta, Panos Roussos, Mark J. Daly, Ole Mors, Aarno Palotie, Anders D. Borglum 2022-08-25 2022-08-25 [("doi","10.1101/2022.08.24.22279149")] genetics/heritable/correlation iq psychiatry/adhd psychiatry/anxiety psychiatry/depression

Major depression (MD) is a common mental disorder and a leading cause of disability worldwide.

We conducted a GWAS meta-analysis of more than 1.3 million individuals, including 371,184 with MD, identifying:

243 risk loci. 64 loci are novel, including glutamate and GABA receptors that are targets for antidepressant drugs. Several biological pathways and components were enriched for genetic MD risk, implicating neuronal development and function. Intersection with functional genomics data prioritized likely causal genes and revealed novel enrichment of prenatal GABAergic neurons, astrocytes and oligodendrocyte lineages. We found MD to be highly polygenic, with around 11,700 variants explaining 90% of the SNP heritability. Bivariate Gaussian mixture modeling estimated that > 97% of risk variants for other psychiatric disorders (anxiety, schizophrenia, bipolar disorder and ADHD) are influencing MD risk when both concordant and discordant variants are considered, and nearly all MD risk variants influence educational attainment. Additionally, we demonstrated that MD genetic risk is associated with impaired complex cognition, including verbal reasoning, attention, abstraction and mental flexibility.

Analyzing Danish nation-wide longitudinal data, we dissected the genetic and clinical heterogeneity, revealing distinct polygenic architectures across case subgroups of MD recurrency and psychiatric comorbidity and demonstrating 2–6× increases in absolute risks for developing comorbid psychiatric disorders among MD cases with the highest versus the lowest polygenic burden.

The results deepen the understanding of the biology underlying MD and its progression and inform precision medicine approaches in MD.

--- https://www.biorxiv.org/content/10.1101/2022.08.24.505188.full 1,000 ancient genomes uncover 10,000 years of natural selection in Europe Megan K. Le, Olivia S. Smith, Ali Akbari, Arbel Harpak, David Reich, Vagheesh M. Narasimhan 2022-08-26 2022-08-26 [("doi","10.1101/2022.08.24.505188")] genetics/selection/natural/human

Ancient DNA has revolutionized our understanding of human population history. However, its potential to examine how rapid cultural evolution to new lifestyles may have driven biological adaptation has not been met, largely due to limited sample sizes.

We assembled genome-wide data from 1,291 individuals from Europe over 10,000 years, providing a dataset that is large enough to resolve the timing of selection into the Neolithic, Bronze Age, and Historical periods.

We identified 25 genetic loci with rapid changes in frequency during these periods, a majority of which were previously undetected. Signals specific to the Neolithic transition are associated with body weight, diet, and lipid metabolism-related phenotypes. They also include immune phenotypes, most notably a locus that confers immunity to Salmonella infection at a time when ancient Salmonella genomes have been shown to adapt to human hosts, thus providing a possible example of human-pathogen co-evolution. In the Bronze Age, selection signals are enriched near genes involved in pigmentation and immune-related traits, including at a key human protein interactor of SARS-CoV-2. Only in the Historical period do the selection candidates we detect largely mirror previously-reported signals, highlighting how the statistical power of previous studies was limited to the last few millennia.

The Historical period also has multiple signals associated with vitamin D binding, providing evidence that lactase persistence may have been part of an oligogenic adaptation for efficient calcium uptake and challenging the theory that its adaptive value lies only in facilitating caloric supplementation during times of scarcity. Finally, we detect selection on complex traits in all 3 periods, including selection favoring variants that reduce body weight in the Neolithic. In the Historical period, we detect selection favoring variants that increase risk for cardiovascular disease plausibly reflecting selection for a more active inflammatory response that would have been adaptive in the face of increased infectious disease exposure.

Our results provide an evolutionary rationale for the high prevalence of these deadly diseases in modern societies today and highlight the unique power of ancient DNA in elucidating biological change that accompanied the profound cultural transformations of recent human history.

--- https://www.nature.com/articles/d41586-022-02205-w 2021-12-15 psychedelic psychiatry/depression --- https://markovbio.github.io/biomedical-progress/ 2021-12-15 ai/scaling genetics/sequencing --- https://arxiv.org/abs/2208.01618 An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H. Bermano, Gal Chechik, Daniel Cohen-Or 2022-08-02 2022-08-02 [("doi","10.48550/arXiv.2208.01618")] ai/nn/diffusion

[cf. DreamBooth; Stable Diffusion code; demo of textual inversion for new visual concepts] Text-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes. In other words, we ask: how can we use language-guided models to turn our cat into a painting, or imagine a new product based on our favorite toy?

Here we present a simple approach that allows such creative freedom. Using only 3–5 images of a user-provided concept, like an object or a style, we learn to represent it through new “words” in the embedding space of a frozen text-to-image model. These “words” can be composed into natural language sentences, guiding personalized creation in an intuitive way. Notably, we find evidence that a single word embedding is sufficient for capturing unique and varied concepts.

We compare our approach to a wide range of baselines, and demonstrate that it can more faithfully portray the concepts across a range of applications and tasks.

Our code, data and new words will be available at: https://textual-inversion.github.io/.

--- https://x.com/goodside/status/1563427520325636098 2021-12-16 ai/nn/transformer/gpt/non-fiction --- https://x.com/goodside/status/1563529079751864320 2021-12-16 ai/nn/transformer/gpt/non-fiction --- https://x.com/glenngabe/status/1563241743260471298 2021-12-16 ai/nn/transformer/gpt/lamda ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/1912.03098#google Connecting Vision and Language with Localized Narratives Jordi Pont-Tuset, Jasper Uijlings, Soravit Changpinyo, Radu Soricut, Vittorio Ferrari 2019-12-06 2021-12-16 [("doi","10.48550/arXiv.1912.03098")] ai/nn

We propose Localized Narratives, a new form of multimodal image annotations connecting vision and language.

We ask annotators to describe an image with their voice while simultaneously hovering their mouse over the region they are describing. Since the voice and the mouse pointer are synchronized, we can localize every single word in the description. This dense visual grounding takes the form of a mouse trace segment per word and is unique to our data.

We annotated 849k images with Localized Narratives: the whole COCO, Flickr30k, and ADE20K datasets, and 671k images of Open Images, all of which we make publicly available.

We provide an extensive analysis of these annotations showing they are diverse, accurate, and efficient to produce.

We also demonstrate their utility on the application of controlled image captioning.

--- https://www.biorxiv.org/content/10.1101/2022.08.25.505311.full Genome-wide prediction of disease variants with a deep protein language model Nadav Brandes, Grant Goldman, Charlotte H. Wang, Chun Jimmie Ye, Vasilis Ntranos 2022-08-26 2022-08-26 [("doi","10.1101/2022.08.25.505311")] ai/nn/transformer/alphafold genetics/heritable/rare

Distinguishing between damaging and neutral missense variants is an ongoing challenge in human genetics, with profound implications for clinical diagnosis, genetic studies and protein engineering. Recently, deep-learning models have achieved state-of-the-art performance in classifying variants as pathogenic or benign. However, these models are currently unable to provide predictions over all missense variants, either because of dependency on close protein homologs or due to software limitations.

Here we leveraged ESM1b, a 650M-parameter protein language model, to predict the functional impact of human coding variation at scale. To overcome existing technical limitations, we developed a modified ESM1b workflow and functionalized, for the first time, all proteins in the human genome, resulting in predictions for all ~450M possible missense variant effects. ESM1b was able to distinguish between pathogenic and benign variants across ~150K variants annotated in ClinVar and HGMD, outperforming existing state-of-the-art methods. ESM1b also exceeded the state-of-the-art at predicting the experimental results of deep mutational scans.

We further annotated ~2M variants across ~9K alternatively-spliced genes as damaging in certain protein isoforms while neutral in others, demonstrating the importance of considering all isoforms when functionalizing variant effects.

The complete catalog of variant effect predictions is available at: https://huggingface.co/spaces/ntranoslab/esm_variants.

--- https://www.biorxiv.org/content/10.1101/622803.full#facebook Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus 2020-12-15 2021-12-16 [("doi","10.1101/622803")] ai/nn/transformer ai/scaling genetics/sequencing

In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In the life sciences, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Protein language modeling at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology.

To this end we use unsupervised learning to train a deep contextual language model, ESM, on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity.

The resulting model contains information about biological properties in its representations. The representations are learned from sequence data alone. The learned representation space has a multi-scale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins. Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections.

Representation learning produces features that generalize across a range of applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure, and improving state-of-the-art features for long-range contact prediction.

--- https://arxiv.org/abs/2208.11012 AniWho: A Quick and Accurate Way to Classify Anime Character Faces in Images Martinus Grady Naftali, Jason Sebastian Sulistyawan, Kelvin Julian, Felix Indra Kurniadi 2022-08-23 2022-08-23 [("doi","10.48550/arXiv.2208.11012")] ai/anime/danbooru ai/nn

This paper aims to dive more deeply into various models available, including InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNet-B7, using transfer learning to classify Japanese animation-style character faces.

This paper has shown that EfficientNet-B7 has the highest accuracy rate with 85.08% top-1 Accuracy, followed by MobileNetV2, having a slightly less accurate result but with the benefits of much lower inference time and fewer number of required parameters.

This paper also uses a few-shot learning framework, specifically Prototypical Networks, which produces decent results that can be used as an alternative to traditional transfer learning methods.

--- https://staltz.com/time-till-open-source-alternative.html 2021-12-16 cs economics --- https://en.wikipedia.org/wiki/Yeast_artificial_chromosome Yeast artificial chromosome 2021-12-16 genetics/editing --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223741/ Karyotype engineering by chromosome fusion leads to reproductive isolation in yeast Jingchuan Luo, Xiaoji Sun, Brendan P. Cormack, Jef D. Boeke 2018 2021-12-17 [("doi","10.1038/s41586-018-0374-x")] genetics/editing

Extant species have wildly different numbers of chromosomes, even among taxa with relatively similar genome sizes (for example, insects)1,2. This is likely to reflect accidents of genome history, such as telomere-telomere fusions and genome duplication events3–5. Humans have 23 pairs of chromosomes, whereas other apes have 24. One human chromosome is a fusion product of the ancestral state6. This raises the question: how well can species tolerate a change in chromosome numbers without substantial changes to genome content?

Many tools are used in chromosome engineering in Saccharomyces cerevisiae7–10, but CRISPR-Cas9-mediated genome editing facilitates the most aggressive engineering strategies.

Here we successfully fused yeast chromosomes using CRISPR-Cas9, generating a near-isogenic series of strains with progressively fewer chromosomes ranging from 16 to two. A strain carrying only two chromosomes of about 6 megabases each exhibited modest transcriptomic changes and grew without major defects.

When we crossed a 16-chromosome strain with strains with fewer chromosomes, we noted two trends. As the number of chromosomes dropped below 16, spore viability decreased markedly, reaching less than 10% for 12 chromosomes. As the number of chromosomes decreased further, yeast sporulation was arrested: a cross between a 16-chromosome strain and an 8-chromosome strain showed greatly reduced full tetrad formation and less than 1% sporulation, from which no viable spores could be recovered. However, homotypic crosses between pairs of strains with 8, 4 or 2 chromosomes produced excellent sporulation and spore viability. These results indicate that 8 chromosome-chromosome fusion events suffice to isolate strains reproductively.

Overall, budding yeast tolerates a reduction in chromosome number unexpectedly well, providing a striking example of the robustness of genomes to change.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6738639/ Reshuffling yeast chromosomes with CRISPR/Cas9 Aubin Fleiss, Samuel O’Donnell, Téo Fournier, Wenqing Lu, Nicolas Agier, Stéphane Delmas, Joseph Schacherer, Gilles Fischer 2019 2021-12-17 [("doi","10.1371/journal.pgen.1008332")] genetics/editing

Genome engineering is a powerful approach to study how chromosomal architecture impacts phenotypes. However, quantifying the fitness impact of translocations independently from the confounding effect of base substitutions has so far remained challenging.

We report a novel application of the CRISPR/Cas9 technology allowing to generate with high efficiency both uniquely targeted and multiple concomitant reciprocal translocations in the yeast genome.

Targeted translocations are constructed by inducing two double-strand breaks on different chromosomes and forcing the trans-chromosomal repair through homologous recombination by chimerical donor DNAs. Multiple translocations are generated from the induction of several DSBs in LTR repeated sequences and promoting repair using endogenous uncut LTR copies as template. All engineered translocations are markerless and scarless.

Targeted translocations are produced at base pair resolution and can be sequentially generated one after the other. Multiple translocations result in a large diversity of karyotypes and are associated in many instances with the formation of unanticipated segmental duplications. To test the phenotypic impact of translocations, we first recapitulated in a lab strain the SSU1/ECM34 translocation providing increased sulphite resistance to wine isolates. Surprisingly, the same translocation in a laboratory strain resulted in decreased sulphite resistance. However, adding the repeated sequences that are present in the SSU1 promoter of the resistant wine strain induced sulphite resistance in the lab strain, yet to a lower level than that of the wine isolate, implying that additional polymorphisms also contribute to the phenotype.

These findings illustrate the advantage brought by our technique to untangle the phenotypic impacts of structural variations from confounding effects of base substitutions.

Secondly, we showed that strains with multiple translocations, even those devoid of unanticipated segmental duplications, display large phenotypic diversity in a wide range of environmental conditions, showing that simply reconfiguring chromosome architecture is sufficient to provide fitness advantages in stressful growth conditions.

--- https://en.wikipedia.org/wiki/Dhole#Enemies_and_competitors Dhole § Enemies and competitors 2021-12-17 dog --- https://x.com/goodside/status/1563989550808154113 2021-12-17 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://allpoetry.com/Departmental 2021-12-17 fiction/poetry --- /doc/darknet-market/silk-road/1/2022-maras.pdf The SECI model and darknet markets: Knowledge creation in criminal organizations and communities of practice Marie-Helen Maras, Jana Arsovska, Adam Scott Wandt, Melanie Knieps, Kenji Logie 2022-08-19 2022-08-19 [("doi","10.1177/14773708221115167")] darknet-market/agora darknet-market/dnm-archive darknet-market/silk-road/1

This study examines darknet markets through the lens of a business theory on knowledge management.

Taking epistemological and ontological dimensions into consideration, this study uses Nonaka 1991’s SECI model as a theoretical framework to identify and describe how tacit and explicit knowledge is created and shared on Silk Road, Pandora and Agora darknet markets, and how people affect this process. By studying this process, insights can be obtained into darknet market criminal organizations and communities of practice and their impact on the continuity and resilience of illicit darknet markets.

This project used data from the Internet Archive collection of publicly available darknet market scrapes 2011–2015 from Branwen et al 2015.

We observed instances of the SECI model (socialization, externalization, combination, and internalization) on darknet markets in both criminal organizations and communities of practice. Darknet market leaders and groups facilitated both knowledge creation and sharing.

This study is the first to test the SECI model on darknet markets. The study provides an understanding of the complexity and resilience of darknet markets, as well as valuable information to help guide law enforcement agencies efforts to stop the illicit trade of goods and services.

--- https://realityprose.com/what-happened-with-lego/ 2021-12-17 economics --- https://arxiv.org/abs/2208.12496 Nearest Neighbor Non-autoregressive Text Generation Ayana Niwa, Sho Takase, Naoaki Okazaki 2022-08-26 2022-08-26 [("doi","10.48550/arXiv.2208.12496")] ai/nn/retrieval reinforcement-learning/imitation-learning reinforcement-learning/model-free

Non-autoregressive (NAR) models can generate sentences with less computation than autoregressive models but sacrifice generation quality. Previous studies addressed this issue through iterative decoding. This study proposes using nearest neighbors as the initial state of an NAR decoder and editing them iteratively. We present a novel training strategy to learn the edit operations on neighbors to improve NAR text generation.

Experimental results show that the proposed method (NeighborEdit) achieves higher translation quality (1.69 points higher than the vanilla Transformer) with fewer decoding iterations (one-eighteenth fewer iterations) on the JRC-Acquis En-De dataset, the common benchmark dataset for machine translation using nearest neighbors. We also confirm the effectiveness of the proposed method on a data-to-text task (WikiBio). In addition, the proposed method outperforms an NAR baseline on the WMT’14 En-De dataset.

We also report analysis on neighbor examples used in the proposed method.

--- https://arxiv.org/abs/astro-ph/9912202 The Effects of Moore’s Law and Slacking on Large Computations C Gottbrath, J. Bailin, C. Meakin, T. Thompson, J. J. Charfman 1999-12-09 2021-12-17 [("doi","10.48550/arXiv.9912202")] cs/algorithm economics/experience-curve

We show that, in the context of Moore’s Law, overall productivity can be increased for large enough computations by ‘slacking’ or waiting for some period of time before purchasing a computer and beginning the calculation.

--- https://victorianaws.com/ 2021-12-17 ai/nn/transformer/gpt/non-fiction --- https://web.archive.org/web/20100126083055/https://www.unc.edu/courses/2008spring/psyc/270/001/counterbalancing.html Counterbalancing in the Design of Experiments 2021-12-17 statistics/power-analysis --- https://en.wikipedia.org/wiki/Nasal_mucosa Nasal mucosa 2021-12-18 biology/booger --- https://en.wikipedia.org/wiki/Mucin Mucin 2021-12-18 biology/booger --- https://x.com/jaguring1/status/1564369413922381824 2021-12-18 ai/anime ai/nn/transformer/gpt/dall-e --- https://www.medrxiv.org/content/10.1101/2021.01.26.21250098.full A cross-disorder dosage sensitivity map of the human genome Ryan L. Collins, Joseph T. Glessner, Eleonora Porcu, Lisa-Marie Niestroj, Jacob Ulirsch, Georgios Kellaris, Daniel P. Howrigan, Selin Everett, Kiana Mohajeri, Xander Nuttle, Chelsea Lowther, Jack Fu, Philip M. Boone, Farid Ullah, Kaitlin E. Samocha, Konrad Karczewski, Diane Lucente, Epi25 Consortium, James F. Gusella, Hilary Finucane, Ludmilla Matyakhina, Swaroop Aradhya, Jeanne Meck, Dennis Lal, Benjamin M. Neale, Jennelle C. Hodge, Alexandre Reymond, Zoltán Kutalik, Nicholas Katsanis, Erica E. Davis, Hakon Hakonarson, Shamil Sunyaev, Harrison Brand, Michael E. Talkowski 2021-01-28 2021-12-18 [("doi","10.1101/2021.01.26.21250098")] genetics/heritable/rare psychiatry/autism

Rare deletions and duplications of genomic segments, collectively known as rare copy number variants (rCNVs), contribute to a broad spectrum of human diseases. To date, most disease-association studies of rCNVs have focused on recognized genomic disorders or on the impact of haploinsufficiency caused by deletions. By comparison, our understanding of duplications in disease remains rudimentary as very few individual genes are known to be triplosensitive (ie. duplication intolerant).

In this study, we meta-analyzed rCNVs from 753,994 individuals across 30 primarily neurological disease phenotypes to create a genome-wide catalog of rCNV association statistics across disorders.

We discovered 114 rCNV-disease associations at 52 distinct loci surpassing genome-wide statistical-significance (p = 3.72×10−6), 42% of which involve duplications. Using Bayesian fine-mapping methods, we further prioritized 38 novel triplosensitive disease genes (GMEB2 in brain abnormalities), including 3 known haploinsufficient genes that we now reveal as bidirectionally dosage sensitive (eg. ANKRD11 in growth abnormalities).

By integrating our results with prior literature, we found that disease-associated rCNV segments were enriched for genes constrained against damaging coding variation and identified likely dominant driver genes for about 1⁄3rd (32%) of rCNV segments based on de novo mutations from exome sequencing studies of developmental disorders. However, while the presence of constrained driver genes was a common feature of many pathogenic large rCNVs across disorders, most of the rCNVs showing genome-wide statistically-significant association were incompletely penetrant (mean odds ratio=11.6) and we also identified two examples of noncoding disease-associated rCNVs (eg. intronic CADM2 deletions in behavioral disorders).

Finally, we developed a statistical model to predict dosage sensitivity for all genes, which defined 3,006 haploinsufficient and 295 triplosensitive genes where the effect sizes of rCNVs were comparable to deletions of genes constrained against truncating mutations. These dosage sensitivity scores classified disease genes across molecular mechanisms, prioritized pathogenic de novo rCNVs in children with autism, and revealed features that distinguished haploinsufficient and triplosensitive genes, such as insulation from other genes and local cis-regulatory complexity.

Collectively, the cross-disorder rCNV maps and metrics derived in this study provide the most comprehensive assessment of dosage sensitive genomic segments and genes in disease to date and set the foundation for future studies of dosage sensitivity throughout the human genome.

--- https://0xeb.net/wp-content/uploads/2018/02/StarCraft_EUD_Emulator.pdf StarCraft: Remastered—Emulating a buffer overflow for fun and profit 2021-12-18 cs/security --- https://arxiv.org/abs/2208.13753 Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis Wan-Cyuan Fan, Yen-Chun Chen, DongDong Chen, Yu Cheng, Lu Yuan, Yu-Chiang Frank Wang 2022-08-29 2022-08-29 [("doi","10.48550/arXiv.2208.13753")] ai/nn/diffusion

Diffusion models (DMs) have shown great potential for high-quality image synthesis. However, when it comes to producing images with complex scenes, how to properly describe both image global structures and object details remains a challenging task.

In this paper, we present Frido, a Feature Pyramid Diffusion model performing a multi-scale coarse-to-fine denoising process for image synthesis. Our model decomposes an input image into scale-dependent vector quantized features, followed by a coarse-to-fine gating for producing image output. During the above multi-scale representation learning stage, additional input conditions like text, scene graph, or image layout can be further exploited. Thus, Frido can be also applied for conditional or cross-modality image synthesis.

We conduct extensive experiments over various unconditioned and conditional image generation tasks, ranging from text-to-image synthesis, layout-to-image, scene-graph-to-image, to label-to-image. More specifically, we achieved state-of-the-art FID scores on 5 benchmarks, namely layout-to-image on COCO and OpenImages, scene-graph-to-image on COCO and Visual Genome, and label-to-image on COCO.

Code is available at Github.

--- https://arxiv.org/abs/2207.08799 Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit Boaz Barak, Benjamin L. Edelman, Surbhi Goel, Sham Kakade, Eran Malach, Cyril Zhang 2022-07-18 2022-07-18 [("doi","10.48550/arXiv.2207.08799")] ai/nn ai/scaling/emergence

There is mounting empirical evidence of emergent phenomena in the capabilities of deep learning methods as we scale up datasets, model sizes, and training times. While there are some accounts of how these resources modulate statistical capacity, far less is known about their effect on the computational problem of model training.

This work conducts such an exploration through the lens of learning k-sparse parities of n bits, a canonical family of problems which pose theoretical computational barriers.

In this setting, we find that neural networks exhibit surprising phase transitions when scaling up dataset size and running time. In particular, we demonstrate empirically that with standard training, a variety of architectures learn sparse parities with n\U0001D442(k) examples, with loss (and error) curves abruptly dropping after n\U0001D442(k) iterations. These positive results nearly match known SQ [Statistical Query] lower bounds, even without an explicit sparsity-promoting prior.

We elucidate the mechanisms of these phenomena with a theoretical analysis: we find that the phase transition in performance is not due to SGD “stumbling in the dark” until it finds the hidden set of features (a natural algorithm which also runs in n\U0001D442(k) time); instead, we show that SGD gradually amplifies a Fourier gap in the population gradient. [cf. "Multi-Component Learning and S-Curves"]

--- https://waxy.org/2022/08/exploring-12-million-of-the-images-used-to-train-stable-diffusions-image-generator/ 2021-12-18 ai/nn/diffusion --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3604986/ There Is a World Outside of Experimental Designs: Using Twins to Investigate Causation Sara A. Hart, Jeanette Taylor, Christopher Schatschneider 2013 2021-12-18 [("doi","10.1177/1534508412451490")] genetics/heritable/correlation

This study introduces a co-twin control method commonly used in the medical literature but not often within educational research. This method allows for a comparison of twins discordant for an “exposure”, approximating alternative outcomes in the counterfactual model.

Example analyses use data drawn from the Florida Twin Project on Reading to determine whether exposure to “teacher quality”, measured by growth in oral reading fluency (ORF) scores of classmates, causally affects ORF performance of twins in the subsequent years.

The analysis highlights PROC MIXED in SAS, including a novel expansion to allow for the nested data.

Results from 2,788 twins suggested that being in classrooms with lower teacher quality in first grade leads to lower ORF scores in second and third grade with little indication of possible genetic or environmental confounding.

--- https://x.com/Omorfiamorphism/status/1564633854119477257 2021-12-18 ai/nn/diffusion reinforcement-learning/exploration --- https://www.biorxiv.org/content/10.1101/2022.08.29.505692.full Reconstitution of ribosome self-replication outside a living cell Yuishin Kosaka, Yumi Miyawaki, Megumi Mori, Shunsuke Aburaya, Mao Fukuyama, Mitsuyoshi Ueda, Wataru Aoki 2022-08-29 2022-08-29 [("doi","10.1101/2022.08.29.505692")] genetics/genome-synthesis

[Twitter] Ribosome biogenesis, a recursive process of pre-existing ribosomes self-replicating nascent ones, is pivotal in the self-replication of life. In Escherichia coli, 3 ribosomal RNAs (rRNAs) are transcribed, and 54 ribosomal proteins (r-proteins) are synthesized by pre-existing ribosomes as structural components. They are cotranscriptionally assembled in a cooperative hierarchy under the support of ~100 accessory factors. The reconstitution of ribosome biogenesis outside a living cell is an essential goal to understand the self-replication of life. However, this goal could not have been achieved so far due to its complexity.

Here, we report the successful in vitro reconstitution of the entire ribosome biogenesis process. We hypothesized that mimicking in vivo ribosome biogenesis could result in in vitro ribosome biogenesis.

Specifically, we found that coactivating the transcription of an rRNA operon, as well as the transcription and translation of 54 r-protein genes encoding r-proteins, and the coordinated ribosomal assembly in a cytoplasm-mimicking reaction solution, resulted in highly efficient in vitro reconstitution of ribosome biogenesis.

Our achievement represents a critical step toward revealing fundamental principles underlying the self-replication of life and creating self-replicating artificial cells.

We also succeeded in engineering rRNA and r-proteins by only adding mutant ribosomal genes in the reaction, enabling high-throughput and unconstrained creation of artificial ribosomes with altered or enhanced functionality.

--- https://arxiv.org/abs/2208.14345#microsoft MeloForm: Generating Melody with Musical Form based on Expert Systems and Neural Networks Peiling Lu, Xu Tan, Botao Yu, Tao Qin, Sheng Zhao, Tie-Yan Liu 2022-08-30 2022-08-30 [("doi","10.48550/arXiv.2208.14345")] ai/music ai/nn/transformer

[samples] Human usually composes music by organizing elements according to the musical form to express music ideas. However, for neural network-based music generation, it is difficult to do so due to the lack of labeled data on musical form.

In this paper, we develop MeloForm, a system that generates melody with musical form using expert systems and neural networks. Specifically, (1) we design an expert system to generate a melody by developing musical elements from motifs to phrases then to sections with repetitions and variations according to pre-given musical form; (2) considering the generated melody is lack of musical richness, we design a Transformer based refinement model to improve the melody without changing its musical form. MeloForm enjoys the advantages of precise musical form control by expert systems and musical richness learning via neural models.

Both subjective and objective experimental evaluations demonstrate that MeloForm generates melodies with precise musical form control with 97.79% accuracy, and outperforms baseline systems in terms of subjective evaluation score by 0.75, 0.50, 0.86 and 0.89 in structure, thematic, richness and overall quality, without any labeled musical form data.

Besides, MeloForm can support various kinds of forms, such as verse and chorus form, rondo form, variational form, sonata form, etc.

“Melody + Accompaniment”
--- https://en.wikipedia.org/wiki/Algorithmic_information_theory Algorithmic information theory 2021-12-19 ai/scaling cs/algorithm/information/compression philosophy/epistemology philosophy/logic reinforcement-learning/model --- https://www.theguardian.com/world/2022/aug/27/rare-precious-smells-like-whale-hunting-for-ambergris-in-new-zealand 2021-12-19 psychology/smell --- https://people.idsia.ch/~juergen/DanNet-triggers-deep-CNN-revolution-2011.html 2021-12-19 ai/nn --- https://www.biorxiv.org/content/10.1101/2022.08.29.505222.full Chemical reprogramming ameliorates cellular hallmarks of aging and extends lifespan Lucas Schoenfeldt, Patrick T. Paine, Nibrasul H. Kamaludeen M., Grace B. Phelps, Calida Mrabti, Kevin Perez, Alejandro Ocampo 2022-08-31 2022-08-31 [("doi","10.1101/2022.08.29.505222")] longevity/epigenetics

The dedifferentiation of somatic cells into a pluripotent state by cellular reprogramming coincides with a reversal of age-associated molecular hallmarks. Although transcription factor induced cellular reprogramming has been shown to ameliorate these aging phenotypes in human cells and extend health and lifespan in mice, translational applications of this approach are still limited. More recently, chemical reprogramming via small molecule cocktails have demonstrated a similar ability to induce pluripotency in vitro, however, its potential impact on aging is unknown.

Here, we demonstrated that partial chemical reprogramming is able to improve key drivers of aging including genomic instability and epigenetic alterations in aged human cells. Moreover, we identified an optimized combination of two reprogramming molecules sufficient to induce the amelioration of additional aging phenotypes including cellular senescence and oxidative stress. Importantly, in vivo application of this two-chemical combination statistically-significantly extended C. elegans lifespan.

Together, these data demonstrate that improvement of key drivers of aging and lifespan extension is possible via chemical induced partial reprogramming, opening a path towards future translational applications.

--- https://www.biorxiv.org/content/10.1101/2020.09.07.286450.full Reconstructing the history of founder events using genome-wide patterns of allele sharing across individuals Rémi Tournebize, Gillian Chu, Priya Moorjani 2020-09-07 2021-12-19 [("doi","10.1101/2020.09.07.286450")] genetics/heritable/dog genetics/heritable/rare

Founder events play a critical role in shaping genetic diversity, impacting the fitness of a species and disease risk in humans. Yet our understanding of the prevalence and distribution of founder events in humans and other species remains incomplete, as most existing methods for characterizing founder events require large sample sizes or phased genomes.

To learn about the frequency and evolutionary history of founder events, we introduce ASCEND (Allele Sharing Correlation for the Estimation of Non-equilibrium Demography), a flexible two-locus method to infer the age and strength of founder events. This method uses the correlation in allele sharing across the genome between pairs of individuals to recover signatures of past bottlenecks. By performing coalescent simulations, we show that ASCEND can reliably estimate the parameters of founder events under a range of demographic scenarios, with genotype or sequence data.

We apply ASCEND to ~5,000 worldwide human samples (~3,500 present-day and ~1,500 ancient individuals), and ~1,000 domesticated dog samples. In both species, we find pervasive evidence of founder events in the recent past. In humans, over half of the populations surveyed in our study had evidence for a founder events in the past 10,000 years, associated with geographic isolation, modes of sustenance, and historical invasions and epidemics.

We document that island populations have historically maintained lower population sizes than continental groups, ancient hunter-gatherers had stronger founder events than Neolithic Farmers or Steppe Pastoralists, and periods of epidemics such as smallpox were accompanied by major population crashes. Many present-day groups—including Central & South Americans, Oceanians and South Asians—have experienced founder events stronger than estimated in Ashkenazi Jews who have high rates of recessive diseases due to their history of founder events.

In dogs, we uncovered extreme founder events in most groups, more than 10× stronger than the median strength of founder events in humans. These founder events occurred during the last 25 generations and are likely related to the establishment of dog breeds during Victorian times.

Our results highlight a widespread history of founder events in humans and dogs, and provide insights about the demographic and cultural processes underlying these events.

--- /doc/genetics/selection/2012-apgaw-dogbreedingreport.pdf 2012 2021-12-19 genetics/heritable/dog genetics/selection --- https://x.com/paultrillo/status/1564738511932076033 2021-12-19 ai/nn/transformer/gpt/dall-e --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5137751/ Universal screening increases the representation of low-income and minority students in gifted education David Card, Laura Giuliano 2016 2021-12-19 [("doi","10.1073/pnas.1605043113")] iq

Low-income and minority students are substantially underrepresented in gifted education programs. The disparities persist despite efforts by many states and school districts to broaden participation through changes in their eligibility criteria. One explanation for the persistent gap is that standard processes for identifying gifted students, which are based largely on the referrals of parents and teachers, tend to miss qualified students from underrepresented groups.

We study this hypothesis using the experiences of a large urban school district following the introduction of a universal screening program for second graders.

Without any changes in the standards for gifted eligibility, the screening program led to large increases in the fractions of economically disadvantaged and minority students placed in gifted programs. Comparisons of the newly identified gifted students with those who would have been placed in the absence of screening show that Blacks and Hispanics, free/reduced price lunch participants, English language learners, and girls were all systematically “underreferred” in the traditional parent/teacher referral system.

Our findings suggest that parents and teachers often fail to recognize the potential of poor and minority students and those with limited English proficiency.

--- https://astera.org/choosing-bgi-as-our-sequencing-provider/ 2021-12-19 genetics/sequencing --- https://arxiv.org/abs/2112.02962 DANets: Deep Abstract Networks for Tabular Data Classification and Regression Jintai Chen, Kuanlun Liao, Yao Wan, Danny Z. Chen, Jian Wu 2021-12-06 2021-12-19 [("doi","10.48550/arXiv.2112.02962")] ai/tabular

Tabular data are ubiquitous in real world applications. Although many commonly-used neural components (eg. convolution) and extensible neural networks (eg. ResNet) have been developed by the machine learning community, few of them were effective for tabular data and few designs were adequately tailored for tabular data structures.

In this paper, we propose a novel and flexible neural component for tabular data, called ​Abstract Layer (AbstLay), which learns to explicitly group correlative input features and generate higher-level features for semantics abstraction. Also, we design a structure re-parameterization method to compress the learned AbstLay, thus reducing the computational complexity by a clear margin in the reference phase.

A special basic block is built using AbstLays, and we construct a family of Deep Abstract Networks (DANets) for tabular data classification and regression by stacking such blocks. In DANets, a special shortcut path is introduced to fetch information from raw tabular features, assisting feature interactions across different levels.

Comprehensive experiments on 7 real-world tabular datasets show that our AbstLay and DANets are effective for tabular data classification and regression, and the computational complexity is superior to competitive methods. Besides, we evaluate the performance gains of DANet as it goes deep, verifying the extensibility of our method.

Our code is available at Github.

--- https://arxiv.org/abs/2208.11970#google Understanding Diffusion Models: A Unified Perspective Calvin Luo 2022-08-25 2022-08-25 [("doi","10.48550/arXiv.2208.11970")] ai/nn/diffusion ai/nn/vae

Diffusion models have shown incredible capabilities as generative models; indeed, they power the current state-of-the-art models on text-conditioned image generation such as Imagen and DALL·E 2. In this work we review, demystify, and unify the understanding of diffusion models across both variational and score-based perspectives.

We first derive Variational Diffusion Models (VDM) as a special case of a Markovian Hierarchical Variational Autoencoder, where 3 key assumptions enable tractable computation and scalable optimization of the ELBO. We then prove that optimizing a VDM boils down to learning a neural network to predict one of 3 potential objectives: the original source input from any arbitrary noisification of it, the original source noise from any arbitrarily noisified input, or the score function of a noisified input at any arbitrary noise level.

We then dive deeper into what it means to learn the score function, and connect the variational perspective of a diffusion model explicitly with the Score-based Generative Modeling perspective through Tweedie’s Formula.

Lastly, we cover how to learn a conditional distribution using diffusion models via guidance.

--- https://arxiv.org/abs/2104.07567#facebook Retrieval Augmentation Reduces Hallucination in Conversation Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, Jason Weston 2021-04-15 2021-12-20 [("doi","10.48550/arXiv.2104.07567")] ai/nn/retrieval ai/nn/transformer

Despite showing increasingly human-like conversational abilities, state-of-the-art dialogue models often suffer from factual incorrectness and hallucination of knowledge (Roller et al 2020).

In this work we explore the use of neural-retrieval-in-the-loop architectures—recently shown to be effective in open-domain QA (Lewis et al 2020b; Izacard & Grave 2020)—for knowledge-grounded dialogue, a task that is arguably more challenging as it requires querying based on complex multi-turn dialogue context and generating conversationally coherent responses.

We study various types of architectures with multiple components—retrievers, rankers, and encoder-decoders—with the goal of maximizing knowledge while retaining conversational ability.

We demonstrate that our best models obtain state-of-the-art performance on two knowledge-grounded conversational tasks. The models exhibit open-domain conversational capabilities, generalize effectively to scenarios not within the training data, and, as verified by human evaluations, substantially reduce the well-known problem of knowledge hallucination in state-of-the-art chatbots.

--- https://arxiv.org/abs/2007.01282#facebook Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering Gautier Izacard, Edouard Grave 2020-07-02 2021-12-20 [("doi","10.48550/arXiv.2007.01282")] ai/nn/retrieval ai/nn/transformer/t5

Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query.

In this paper, we investigate how much these models can benefit from retrieving text passages, potentially containing evidence. We obtain state-of-the-art results on the Natural Questions and TriviaQA open benchmarks. Interestingly, we observe that the performance of this method substantially improves when increasing the number of retrieved passages. This is evidence that generative models are good at aggregating and combining evidence from multiple passages.

--- https://www.tsungxu.com/performance-biomaterials/ 2021-12-20 genetics/editing reinforcement-learning/robot --- https://www.aiweirdness.com/ai-explains-human-quirks/ 2021-12-20 ai/nn/transformer/gpt/fiction --- https://arxiv.org/abs/2208.01626#google Prompt-to-Prompt Image Editing with Cross Attention Control Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, Daniel Cohen-Or 2022-08-02 2022-08-02 [("doi","10.48550/arXiv.2208.01626")] ai/nn/diffusion

[SD] Recent large-scale text-driven synthesis models have attracted much attention thanks to their remarkable capabilities of generating highly diverse images that follow given text prompts. Such text-based synthesis methods are particularly appealing to humans who are used to verbally describe their intent. Therefore, it is only natural to extend the text-driven image synthesis to text-driven image editing. Editing is challenging for these generative models, since an innate property of an editing technique is to preserve most of the original image, while in the text-based models, even a small modification of the text prompt often leads to a completely different outcome. State-of-the-art methods mitigate this by requiring the users to provide a spatial mask to localize the edit, hence, ignoring the original structure and content within the masked region.

In this paper, we pursue an intuitive prompt-to-prompt editing framework, where the edits are controlled by text only. To this end, we analyze a text-conditioned model [Imagen] in depth and observe that the cross-attention layers are the key to controlling the relation between the spatial layout of the image to each word in the prompt.

With this observation, we present several applications which monitor the image synthesis by editing the textual prompt only. This includes localized editing by replacing a word, global editing by adding a specification, and even delicately controlling the extent to which a word is reflected in the image.

We present our results over diverse images and prompts, demonstrating high-quality synthesis and fidelity to the edited prompts.

--- https://arxiv.org/abs/2208.13232 Categorical composable cryptography: extended version Anne Broadbent, Martti Karvonen 2022-08-28 2022-08-28 [("doi","10.48550/arXiv.2208.13232")] cs/cryptography

We formalize the simulation paradigm of cryptography in terms of category theory and show that protocols secure against abstract attacks form a symmetric monoidal category, thus giving an abstract model of composable security definitions in cryptography.

Our model is able to incorporate computational security, set-up assumptions and various attack models such as colluding or independently acting subsets of adversaries in a modular, flexible fashion.

We conclude by using string diagrams to rederive the security of the one-time pad and no-go results concerning the limits of bipartite and tripartite cryptography, ruling out eg. composable commitments and broadcasting.

On the way, we exhibit two categorical constructions of resource theories that might be of independent interest: one capturing resources shared among multiple parties and one capturing resource conversions that succeed asymptotically.

--- https://arxiv.org/abs/2208.10264#microsoft Using Large Language Models to Simulate Multiple Humans Gati Aher, Rosa I. Arriaga, Adam Tauman Kalai 2022-08-18 2022-08-18 [("doi","10.48550/arXiv.2208.10264")] ai/nn/transformer/gpt/3/nonfiction ai/scaling economics psychology

We propose a method for using a large language model, such as GPT-3, to simulate responses of different humans in a given context.

We test our method by attempting to reproduce well-established economic, psycholinguistic, and social experiments. The method requires prompt templates for each experiment. Simulations are run by varying the (hypothetical) subject details, such as name, and analyzing the text generated by the language model. To validate our methodology, we use GPT-3 to simulate the Ultimatum Game, garden path sentences, risk aversion, and the Milgram Shock experiments. In order to address concerns of exposure to these studies in training data, we also evaluate simulations on novel variants of these studies.

We show that it is possible to simulate responses of different people and that their responses are consistent with prior human studies from the literature. Across all studies, the distributions generated by larger language models better align with prior experimental results, suggesting a trend that future language models may be used for even more faithful simulations of human responses.

Our use of a language model for simulation is contrasted with anthropomorphic views of a language model as having its own behavior.

--- https://www.youtube.com/watch?v=5wn3htQl4JA 2021-12-20 ai/nn/transformer/gpt/jukebox --- https://colab.research.google.com/drive/1dlgggNa5Mz8sEAGU0wFCHhGLFooW_pf1 2021-12-20 ai/nn/diffusion --- https://www.nytimes.com/2022/09/03/us/politics/senate-republican-committee-funds.html 2021-12-21 economics/advertising --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5419382/ Soothing Properties of Glycerol in Cough Syrups for Acute Cough Due to Common Cold Ronald Eccles, Pascal Mallefet 2017 2021-12-21 [("doi","10.3390/pharmacy5010004")] biology

The treatment and management of acute cough due to common cold costs billions of dollars of healthcare expenditure and there is a growing opinion that a simple linctus containing glycerol with flavorings such as honey and lemon is a safe and effective treatment for acute cough in children and adults.

Glycerol is a component of most cough syrups, and although it is often thought of only as a solvent or thickening agent in cough syrups, it may be a major component for the efficacy of cough syrups due to its special properties of lubrication, demulcency, sweetness, and acting as a humectant. The major benefit of cough syrups in soothing cough is likely due to the properties of the syrup rather than the active ingredients and this review discusses the special properties of glycerol in relation to the treatment of acute cough.

--- https://en.wikipedia.org/wiki/Placebo Placebo 2021-12-21 psychiatry/anxiety statistics/bias --- https://en.wikipedia.org/wiki/Nocebo Nocebo 2021-12-21 statistics/bias --- https://arxiv.org/abs/2209.00616 Learning with Differentiable Algorithms Felix Petersen 2022-09-01 2022-09-01 [("doi","10.48550/arXiv.2209.00616")] ai/nn cs/algorithm/sorting statistics/decision

Classic algorithms and machine learning systems like neural networks are both abundant in everyday life. While classic computer science algorithms are suitable for precise execution of exactly defined tasks such as finding the shortest path in a large graph, neural networks allow learning from data to predict the most likely answer in more complex tasks such as image classification, which cannot be reduced to an exact algorithm. To get the best of both worlds, this thesis explores combining both concepts leading to more robust, better performing, more interpretable, more computationally efficient, and more data efficient architectures.

The thesis formalizes the idea of algorithmic supervision, which allows a neural network to learn from or in conjunction with an algorithm. When integrating an algorithm into a neural architecture, it is important that the algorithm is differentiable such that the architecture can be trained end-to-end and gradients can be propagated back through the algorithm in a meaningful way.

To make algorithms differentiable, this thesis proposes a general method for continuously relaxing algorithms by perturbing variables and approximating the expectation value in closed form, ie. without sampling.

In addition, this thesis proposes differentiable algorithms, such as differentiable sorting networks [ranking/top-k], differentiable [image] renderers, and differentiable logic gate networks.

Finally, this thesis presents alternative training strategies for learning with algorithms.

--- https://arxiv.org/abs/2208.00088 Improved Policy Optimization for Online Imitation Learning Jonathan Wilder Lavington, Sharan Vaswani, Mark Schmidt 2022-07-29 2022-07-29 [("doi","10.48550/arXiv.2208.00088")] reinforcement-learning/imitation-learning reinforcement-learning/model-free reinforcement-learning/preference-learning

We consider online imitation learning (OIL), where the task is to find a policy that imitates the behavior of an expert via active interaction with the environment.

We aim to bridge the gap between the theory and practice of policy optimization algorithms for OIL by analyzing one of the most popular OIL algorithms, DAgger.

Specifically, if the class of policies is sufficiently expressive to contain the expert policy, we prove that DAgger achieves constant regret. Unlike previous bounds that require the losses to be strongly-convex, our result only requires the weaker assumption that the losses be strongly-convex with respect to the policy’s sufficient statistics (not its parameterization).

In order to ensure convergence for a wider class of policies and losses, we augment DAgger with an additional regularization term. In particular, we propose a variant of Follow-the-Regularized-Leader (FTRL) and its adaptive variant for OIL and develop a memory-efficient implementation, which matches the memory requirements of FTL. Assuming that the loss functions are smooth and convex with respect to the parameters of the policy, we also prove that FTRL achieves constant regret for any sufficiently expressive policy class, while retaining 𝒪(√T) regret in the worst-case.

We demonstrate the effectiveness of these algorithms with experiments on synthetic and high-dimensional control tasks.

--- /doc/statistics/decision/1991-cover.pdf Universal Portfolios Thomas M. Cover 1991-01-01 2021-12-21 [("doi","10.1111/j.1467-9965.1991.tb00002.x")] economics statistics/decision --- https://www.medrxiv.org/content/10.1101/2022.08.05.22277792.full The Association Between Cognitive Ability and Body Mass Index: A Sibling-Comparison Analysis in Four Longitudinal Studies Liam Wright, Neil M. Davies, David Bann 2022-08-08 2022-08-08 [("doi","10.1101/2022.08.05.22277792")] exercise iq

Background: Body mass index (BMI) and obesity rates have increased sharply since the 1980s. While multiple epidemiologic studies have found higher adolescent cognitive ability is associated with lower adult BMI, residual and unobserved confounding due to family background may explain these associations. We used a sibling design to test this association accounting for confounding factors shared within households.

Method: We used data from 4 cohort studies: the National Longitudinal Study of Youth 1979 (NLSY79), the NLSY79 Children and Young Adult, the NLSY 1997 (NLSY97) and the Wisconsin Longitudinal Study (WLS); a total of 12,250 siblings from 5,602 households. We used random effects within-between (REWB) and residualized quantile regression (RQR) models to compare between-family & within-family estimates of the association between adolescent cognitive ability and adult BMI (20–64 years).

Results: In REWB models, moving from 0th to 100th percentile of adolescent cognitive ability was associated with −1.89 kg⁄m2 (95% CI = −2.41, −1.37) lower BMI between families. Adjusting for family socioeconomic position reduced the association to −1.23 (−1.79, −0.66) points. However, within families the association was just −0.13 (−0.70, 0.45) points.

This pattern of results was found across multiple specifications, including analyses conducted in separate cohorts, models examining age-differences in association, and in RQR models examining the association across the distribution of BMI.

Conclusion: The association between high adolescent cognitive ability and low adult BMI was substantially smaller in within-family compared with between-family analysis. The well-replicated associations between cognitive ability and subsequent BMI may largely reflect confounding by family background factors.

--- https://forum.effectivealtruism.org/posts/cPDptuFTiCLr8XXkL/cause-exploration-prizes-crime-reduction 2021-12-21 crime economics --- https://x.com/goodside/status/1559801520773898240 2021-12-21 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://x.com/goodside/status/1559984178862628864 2021-12-21 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://x.com/goodside/status/1560273161840898048 2021-12-22 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://x.com/goodside/status/1560853596572450816 2021-12-22 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://x.com/goodside/status/1560867589835968513 2021-12-22 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://x.com/goodside/status/1560906792330199040 2021-12-22 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://www.biorxiv.org/content/10.1101/2022.08.16.503900.full Genomic health is dependent on population demographic history Eric Wootton, Aaron B. A. Shafer 2022-08-16 2022-08-16 [("doi","10.1101/2022.08.16.503900")] genetics/heritable/rare genetics/selection/natural

Current genetic methods of population assessment in conservation biology have been challenged with genome-scale data, particularly by the quantitatively novel insights provided by genome-wide analyses. These include assessments of runs-of-homozygosity (ROH), genomic evolutionary rate profiling (GERP), and mutational load, all of which are direct measures of genomic health.

Here, we aim to elucidate the relationships between these measures and assess the genomic health of 3 divergent ungulates: the white-tailed deer, caribou, and mountain goat. The white-tailed deer is currently expanding following near extirpation, while caribou are in the midst of a large decline. Mountain goats remain stable after a large historical bottleneck.

We assessed genome-wide signatures of inbreeding using the inbreeding coefficient F and % ROH (FROH) and identified evolutionarily constrained regions with GERP. Mutational load was estimated by identifying mutations in highly constrained elements (CEs) and by sorting intolerant from tolerant (SIFT) mutations.

Our results show that F and FROH are higher in mountain goats than in caribou and white-tailed deer. Given the extended bottleneck and low Ne of the mountain goat, this supports the idea that the genome-wide effects of demographic change take time to accrue. Similarly, we found that mountain goats possess more highly constrained CEs which are indicative of greater purifying selection. This is further reflected by fewer mutations in CEs and deleterious mutations identified by SIFT. In contrast, white-tailed deer presented the highest mutational load with both metrics.

Our results demonstrate that extended bottlenecks may lead to reduced diversity and increased FROH in ungulates, but not necessarily the accumulation of deleterious alleles. This may be due to the purging of deleterious alleles in small populations. This study empirically demonstrates the relationships between different measures of genomic health in ungulates and highlights the need to consider multiple genomic health metrics during conservation assessments.

--- https://arxiv.org/abs/2208.07461 A Library for Representing Python Programs as Graphs for Machine Learning David Bieber, Kensen Shi, Petros Maniatis, Charles Sutton, Vincent Hellendoorn, Daniel Johnson, Daniel Tarlow 2022-08-15 2022-08-15 [("doi","10.48550/arXiv.2208.07461")] cs/algorithm cs/python

Graph representations of programs are commonly a central element of machine learning for code research.

We introduce an open source Python library python_graphs that applies static analysis to construct graph representations of Python programs suitable for training machine learning models. Our library admits the construction of control-flow graphs, data-flow graphs, and composite “program graphs” that combine control-flow, data-flow, syntactic, and lexical information about a program.

We present the capabilities and limitations of the library, perform a case study applying the library to millions of competitive programming submissions, and showcase the library’s utility for machine learning research.

--- https://arxiv.org/abs/1611.08219 The Off-Switch Game Dylan Hadfield-Menell, Anca Dragan, Pieter Abbeel, Stuart Russell 2016-11-24 2021-12-22 [("doi","10.48550/arXiv.1611.08219")] reinforcement-learning/safe

[cf. causal influence diagrams] It is clear that one of the primary tools we can use to mitigate the potential risk from a misbehaving AI system is the ability to turn the system off. As the capabilities of AI systems improve, it is important to ensure that such systems do not adopt subgoals that prevent a human from switching them off. This is a challenge because many formulations of rational agents create strong incentives for self-preservation.

This is not caused by a built-in instinct, but because a rational agent will maximize expected utility and cannot achieve whatever objective it has been given if it is dead. Our goal is to study the incentives an agent has to allow itself to be switched off.

We analyze a simple game between a human H and a robot R, where H can press R’s off switch but R can disable the off switch.

A traditional agent takes its reward function for granted: we show that such agents have an incentive to disable the off switch, except in the special case where H is perfectly rational. Our key insight is that for R to want to preserve its off switch, it needs to be uncertain about the utility associated with the outcome, and to treat H’s actions as important observations about that utility. (R also has no incentive to switch itself off in this setting.)

We conclude that giving machines an appropriate level of uncertainty about their objectives leads to safer designs, and we argue that this setting is a useful generalization of the classical AI paradigm of rational agents.

--- https://arxiv.org/abs/2208.07652 CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Yiqun Liu, Yixing Fan, Xueqi Cheng 2022-08-16 2022-08-16 [("doi","10.1145/3511808.3557271")] ai/nn/retrieval ai/nn/transformer

Knowledge-intensive language tasks (KILT) usually require a large body of information to provide correct answers. A popular paradigm to solve this problem is to combine a search system with a machine reader, where the former retrieves supporting evidences and the latter examines them to produce answers. Recently, the reader component has witnessed advances with the help of large-scale pre-trained generative models. Meanwhile most existing solutions in the search component rely on the traditional “index-retrieve-then-rank” pipeline, which suffers from large memory footprint and difficulty in end-to-end optimization.

Inspired by recent efforts in constructing model-based IR models, we propose to replace the traditional multi-step search pipeline with a novel single-step generative model, which can dramatically simplify the search process and be optimized in an end-to-end manner.

We show that a strong generative retrieval model [BART] can be learned with a set of adequately designed pre-training tasks, and be adopted to improve a variety of downstream KILT tasks with further fine-tuning. We name the pre-trained generative retrieval model CorpusBrain, as all information about the corpus is encoded in its parameters without the need for constructing additional index.

Empirical results show that CorpusBrain can outperform strong baselines for the retrieval task on the KILT benchmark and establish new state-of-the-art downstream performances. We also show that CorpusBrain works well under zero-resource & low-resource settings.

--- https://arxiv.org/abs/2209.00626#openai The alignment problem from a deep learning perspective Richard Ngo 2022-08-30 2022-08-30 [("doi","10.48550/arXiv.2209.00626")] reinforcement-learning/safe

[Twitter] Within the coming decades, artificial general intelligence (AGI) may surpass human capabilities at a wide range of important tasks. This report makes a case for why, without substantial action to prevent it, AGIs will likely use their intelligence to pursue goals which are very undesirable (in other words, misaligned) from a human perspective, with potentially catastrophic consequences.

The report aims to cover the key arguments motivating concern about the alignment problem in a way that’s as succinct, concrete and technically-grounded as possible. I argue that realistic training processes plausibly lead to the development of misaligned goals in AGIs, in particular because neural networks trained via reinforcement learning will learn to plan towards achieving a range of goals; gain more reward by deceptively pursuing misaligned goals; and generalize in ways which undermine obedience.

As in an earlier report from Cotra 2022, I explain my claims with reference to an illustrative AGI training process, then outline possible research directions for addressing different aspects of the problem.

--- https://arxiv.org/abs/2205.10712 Housekeep: Tidying Virtual Households using Commonsense Reasoning Yash Kant, Arun Ramachandran, Sriram Yenamandra, Igor Gilitschenski, Dhruv Batra, Andrew Szot, Harsh Agrawal 2022-05-22 2022-05-22 [("doi","10.48550/arXiv.2205.10712")] ai/dataset ai/nn/transformer reinforcement-learning/imitation-learning reinforcement-learning/model reinforcement-learning/preference-learning reinforcement-learning/robot

We introduce Housekeep, a benchmark to evaluate commonsense reasoning in the home for embodied AI.

In Housekeep, an embodied agent must tidy a house by rearranging misplaced objects without explicit instructions specifying which objects need to be rearranged. Instead, the agent must learn from and is evaluated against human preferences of which objects belong where in a tidy house. Specifically, we collect a dataset of where humans typically place objects in tidy and untidy houses constituting 1799 objects, 268 object categories, 585 placements, and 105 rooms.

Next, we propose a modular baseline approach for Housekeep that integrates planning, exploration, and navigation. It leverages a fine-tuned large language model (LLM) trained on an internet text corpus for effective planning.

We show that our baseline agent generalizes to rearranging unseen objects in unknown environments.

See our webpage for more details: https://yashkant.github.io/housekeep/.

--- https://arxiv.org/abs/2202.01771 LID: Pre-Trained Language Models for Interactive Decision-Making Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, Jacob Andreas, Igor Mordatch, Antonio Torralba, Yuke Zhu 2022-02-03 2022-02-03 [("doi","10.48550/arXiv.2202.01771")] ai/nn/transformer/gpt/2 reinforcement-learning/exploration reinforcement-learning/imitation-learning reinforcement-learning/model reinforcement-learning/robot

Language model (LM) pre-training is useful in many language processing tasks. But can pre-trained LMs be further leveraged for more general machine learning problems?

We propose an approach for using LMs to scaffold learning and generalization in general sequential decision-making problems. In this approach, goals and observations are represented as a sequence of embeddings, and a policy network initialized with a pre-trained LM [GPT-2] predicts the next action.

We demonstrate that this framework enables effective combinatorial generalization across different environments and supervisory modalities. We begin by assuming access to a set of expert demonstrations, and show that initializing policies with LMs and fine-tuning them via behavior cloning improves task completion rates by 43.6% in the VirtualHome environment.

We then examine how our framework may be used in environments without pre-collected expert data. To do this, we integrate an active data gathering procedure into pre-trained LMs. The agent iteratively learns by interacting with the environment, relabeling the language goal of past ‘failed’ experiences, and updating the policy in a self-supervised loop. The active data gathering procedure also enables effective combinatorial generalization, outperforming the best baseline by 25.1%.

Finally, we explain these results by investigating 3 possible factors underlying the effectiveness of the LM-based policy. We find that sequential input representations (vs. fixed-dimensional feature vectors) and favorable weight initialization are both important for generalization. Surprisingly, however, the format of the policy inputs encoding (eg. as a natural language string vs. an arbitrary sequential encoding) has little influence.

Together, these results suggest that language modeling induces representations that are useful for modeling not just language, but also goals and plans.

--- https://arxiv.org/abs/2208.02918#microsoft LaTTe: Language Trajectory TransformEr Arthur Bucker, Luis Figueredo, Sami Haddadin, Ashish Kapoor, Shuang Ma, Sai Vemprala, Rogerio Bonatti 2022-08-04 2022-08-04 [("doi","10.48550/arXiv.2208.02918")] ai/nn/transformer/clip reinforcement-learning/model reinforcement-learning/robot

Natural language is one of the most intuitive ways to express human intent. However, translating instructions and commands towards robotic motion generation, and deployment in the real world, is far from being an easy task. Indeed, combining robotics’s inherent low-level geometric and kinodynamic constraints with human’s high-level semantic information reinvigorates and raises new challenges to the task-design problem—typically leading to task or hardware specific solutions with a static set of action targets and commands. This work instead proposes a flexible language-based framework that allows to modify generic 3D robotic trajectories using language commands with reduced constraints about prior task or robot information.

By taking advantage of pre-trained language models, we employ an auto-regressive transformer to map natural language inputs and contextual images into changes in 3D trajectories. We show through simulations and real-life experiments that the model can successfully follow human intent, modifying the shape and speed of trajectories for multiple robotic platforms and contexts. This study takes a step into building large pre-trained foundational models for robotics and shows how such models can create more intuitive and flexible interactions between human and machines. Codebase available at: Github.

--- https://arxiv.org/abs/2204.11134#facebook Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, Aravind Rajeswaran 2022-04-23 2022-04-23 [("doi","10.48550/arXiv.2204.11134")] ai/nn/transformer/clip reinforcement-learning/model/decision-transformer reinforcement-learning/robot

Task specification is at the core of programming autonomous robots.

A low-effort modality for task specification is critical for engagement of non-expert end-users and ultimate adoption of personalized robot agents. A widely studied approach to task specification is through goals, using either compact state vectors or goal images from the same robot scene. The former is hard to interpret for non-experts and necessitates detailed state estimation and scene understanding. The latter requires the generation of desired goal image, which often requires a human to complete the task, defeating the purpose of having autonomous robots.

In this work, we explore alternate and more general forms of goal specification that are expected to be easier for humans to specify and use such as images obtained from the internet, hand sketches that provide a visual description of the desired task, or simple language descriptions.

As a preliminary step towards this, we investigate the capabilities of large scale pre-trained models (foundation models) for zero-shot goal specification, and find promising results in a collection of simulated robot manipulation tasks and real-world datasets.

--- https://arxiv.org/abs/2204.05080#deepmind Semantic Exploration from Language Abstractions and Pretrained Representations Allison C. Tam, Neil C. Rabinowitz, Andrew K. Lampinen, Nicholas A. Roy, Stephanie C. Y. Chan, D. J. Strouse, Jane X. Wang, Andrea Banino, Felix Hill 2022-04-08 2022-04-08 [("doi","10.48550/arXiv.2204.05080")] ai/nn/transformer/clip reinforcement-learning/exploration reinforcement-learning/model reinforcement-learning/robot

Effective exploration is a challenge in reinforcement learning (RL). Novelty-based exploration methods can suffer in high-dimensional state spaces, such as continuous partially-observable 3D environments.

We address this challenge by defining novelty using semantically meaningful state abstractions, which can be found in learned representations shaped by natural language. In particular, we evaluate vision-language representations, pretrained on natural image captioning datasets [BERT, CLIP]. We show that these pretrained representations drive meaningful, task-relevant exploration and improve performance on 3D simulated environments. We also characterize why and how language provides useful abstractions for exploration by considering the impacts of using representations from a pretrained model, a language oracle, and several ablations.

We demonstrate the benefits of our approach in two very different task domains—one that stresses the identification and manipulation of everyday objects, and one that requires navigational exploration in an expansive world—as well as two popular deep RL algorithms: Impala and R2D2 [using NGU & RND].

Our results suggest that using language-shaped representations could improve exploration for various algorithms and agents in challenging environments.

--- https://arxiv.org/abs/2203.03580#facebook The Unsurprising Effectiveness of Pre-Trained Vision Models for Control Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, Abhinav Gupta 2022-03-07 2022-03-07 [("doi","10.48550/arXiv.2203.03580")] ai/nn/transformer/clip reinforcement-learning/imitation-learning reinforcement-learning/robot

Recent years have seen the emergence of pre-trained representations as a powerful abstraction for AI applications in computer vision, natural language, and speech. However, policy learning for control is still dominated by a tabula-rasa learning paradigm, with visuo-motor policies often trained from scratch using data from deployment environments. In this context, we revisit and study the role of pre-trained visual representations for control, and in particular representations trained on large-scale computer vision datasets.

Through extensive empirical evaluation in diverse control domains (Habitat, DeepMind Control, Adroit, Franka Kitchen), we isolate and study the importance of different representation training methods, data augmentations, and feature hierarchies. Overall, we find that pre-trained visual representations can be competitive or even better than ground-truth state representations to train control policies. This is in spite of using only out-of-domain data from standard vision datasets, without any in-domain data from the deployment environments.

Source code and more at https://sites.google.com/view/pvr-control.

--- https://arxiv.org/abs/2205.15509#huawei ADAPT: Vision-Language Navigation with Modality-Aligned Action Prompts Bingqian Lin, Yi Zhu, Zicong Chen, Xiwen Liang, Jianzhuang Liu, Xiaodan Liang 2022-05-31 2022-05-31 [("doi","10.48550/arXiv.2205.15509")] ai/nn/transformer/clip reinforcement-learning/robot

Vision-Language Navigation (VLN) is a challenging task that requires an embodied agent to perform action-level modality alignment, ie. make instruction-asked actions sequentially in complex visual environments. Most existing VLN agents learn the instruction-path data directly and cannot sufficiently explore action-level alignment knowledge inside the multi-modal inputs.

In this paper, we propose modAlity-aligneD Action PrompTs (ADAPT), which provides the VLN agent with action prompts to enable the explicit learning of action-level modality alignment to pursue successful navigation. Specifically, an action prompt is defined as a modality-aligned pair of an image sub-prompt and a text sub-prompt, where the former is a single-view observation and the latter is a phrase like “walk past the chair”. When starting navigation, the instruction-related action prompt set is retrieved from a pre-built action prompt base and passed through a prompt encoder to obtain the prompt feature. Then the prompt feature is concatenated with the original instruction feature and fed to a multi-layer transformer for action prediction.

To collect high-quality action prompts into the prompt base, we use the Contrastive Language-Image Pretraining (CLIP) model which has powerful cross-modality alignment ability. A modality alignment loss and a sequential consistency loss are further introduced to enhance the alignment of the action prompt and enforce the agent to focus on the related prompt sequentially.

Experimental results on both R2R and RxR show the superiority of ADAPT over state-of-the-art methods.

--- https://arxiv.org/abs/2004.14973 VLN-BERT: Improving Vision-and-Language Navigation with Image-Text Pairs from the Web Arjun Majumdar, Ayush Shrivastava, Stefan Lee, Peter Anderson, Devi Parikh, Dhruv Batra 2020-04-30 2021-12-23 [("doi","10.48550/arXiv.2004.14973")] ai/nn/transformer reinforcement-learning/robot

Following a navigation instruction such as ‘Walk down the stairs and stop at the brown sofa’ requires embodied AI agents to ground scene elements referenced via language (eg. ‘stairs’) to visual content in the environment (pixels corresponding to ‘stairs’).

We ask the following question—can we leverage abundant ‘disembodied’ web-scraped vision-and-language corpora (eg. Conceptual Captions) to learn visual groundings (what do ‘stairs’ look like?) that improve performance on a relatively data-starved embodied perception task (Vision-and-Language Navigation)?

Specifically, we develop VLN-BERT, a visio-linguistic transformer-based model for scoring the compatibility between an instruction (’…stop at the brown sofa’) and a sequence of panoramic RGB images captured by the agent.

We demonstrate that pretraining VLN-BERT on image-text pairs from the web before fine-tuning on embodied path-instruction data improves performance on VLN—outperforming the prior state-of-the-art in the fully-observed setting by 4 absolute percentage points on success rate.

Ablations of our pretraining curriculum show each stage to be impactful—with their combination resulting in further positive synergistic effects.

--- https://arxiv.org/abs/2011.13922 A Recurrent Vision-and-Language BERT for Navigation Yicong Hong, Qi Wu, Yuankai Qi, Cristian Rodriguez-Opazo, Stephen Gould 2020-11-26 2021-12-23 [("doi","10.48550/arXiv.2011.13922")] ai/nn/rnn ai/nn/transformer reinforcement-learning/robot

Accuracy of many visio-linguistic tasks has benefited from the application of vision-and-language(V&L) BERT. However, its application for the task of vision-and-language navigation (VLN) remains limited. One reason for this is the difficulty adapting the BERT architecture to the partially observable Markov decision process present in VLN, requiring history-dependent attention and decision making.

In this paper we propose a recurrent BERT model that is time-aware for use in VLN. Specifically, we equip the BERT model with a recurrent function that maintains cross-modal state information for the agent.

Through extensive experiments on R2R and REVERIE we demonstrate that our model can replace more complex encoder-decoder models to achieve state-of-the-art results.

Moreover, our approach can be generalised to other transformer-based architectures, supports pre-training, and is capable of solving navigation and referring expression tasks simultaneously.

--- https://arxiv.org/abs/2203.10421 CLIP on Wheels (CoW): Zero-Shot Object Navigation as Object Localization and Exploration Samir Yitzhak Gadre, Mitchell Wortsman, Gabriel Ilharco, Ludwig Schmidt, Shuran Song 2022-03-20 2022-03-20 [("doi","10.48550/arXiv.2203.10421")] ai/nn/transformer/clip reinforcement-learning/exploration reinforcement-learning/robot

Households across the world contain arbitrary objects: from mate gourds and coffee mugs to sitars and guitars. Considering this diversity, robot perception must handle a large variety of semantic objects without additional fine-tuning to be broadly applicable in homes. Recently, zero-shot models have demonstrated impressive performance in image classification of arbitrary objects (ie. classifying images at inference with categories not explicitly seen during training).

In this paper, we translate the success of zero-shot vision models (eg. CLIP) to the popular embodied AI task of object navigation. In our setting, an agent must find an arbitrary goal object, specified via text, in unseen environments coming from different datasets. Our key insight is to modularize the task into zero-shot object localization and exploration.

Employing this philosophy, we design CLIP on Wheels (CoW) baselines for the task and evaluate each zero-shot model in both Habitat and RoboTHOR simulators.

We find that a straightforward CoW, with CLIP-based object localization plus classical exploration, and no additional training, often outperforms learnable approaches in terms of success, efficiency, and robustness to dataset distribution shift.

This CoW achieves 6.3% SPL in Habitat and 10.0% SPL in RoboTHOR, when tested zero-shot on all categories. On a subset of 4 RoboTHOR categories considered in prior work, the same CoW shows a 16.1 percentage point improvement in Success over the learnable state-of-the-art baseline.

--- https://arxiv.org/abs/2208.05516 Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP Thao Nguyen, Gabriel Ilharco, Mitchell Wortsman, Sewoong Oh, Ludwig Schmidt 2022-08-10 2022-08-10 [("doi","10.48550/arXiv.2208.05516")] ai/dataset ai/nn/transformer/clip ai/scaling

Web-crawled datasets have enabled remarkable generalization capabilities in recent image-text models such as CLIP (Contrastive Language-Image pre-training) or Flamingo, but little is known about the dataset creation processes.

In this work, we introduce a testbed of 6 publicly available data sources—YFCC, LAION, Conceptual Captions, WIT, RedCaps, Shutterstock—to investigate how pre-training distributions induce robustness in CLIP.

We find that the performance of the pre-training data varies substantially across distribution shifts, with no single data source dominating. Moreover, we systematically study the interactions between these data sources and find that combining multiple sources does not necessarily yield better models, but rather dilutes the robustness of the best individual data source.

We complement our empirical findings with theoretical insights from a simple setting, where combining the training data also results in diluted robustness. In addition, our theoretical model provides a candidate explanation for the success of the CLIP-based data filtering technique recently employed in the LAION dataset.

Overall our results demonstrate that simply gathering a large amount of data from the web is not the most effective way to build a pre-training dataset for robust generalization, necessitating further study into dataset design.

--- https://arxiv.org/abs/2009.11162 Implicit Gradient Regularization David G. T. Barrett, Benoit Dherin 2020-09-23 2021-12-23 [("doi","10.48550/arXiv.2009.11162")] ai/nn

Gradient descent can be surprisingly good at optimizing deep neural networks without overfitting and without explicit regularization.

We find that the discrete steps of gradient descent implicitly regularize models by penalizing gradient descent trajectories that have large loss gradients. We call this Implicit Gradient Regularization (IGR) and we use backward error analysis to calculate the size of this regularization.

We confirm empirically that implicit gradient regularization biases gradient descent toward flat minima, where test errors are small and solutions are robust to noisy parameter perturbations. Furthermore, we demonstrate that the implicit gradient regularization term can be used as an explicit regularizer, allowing us to control this gradient regularization directly.

More broadly, our work indicates that backward error analysis is a useful theoretical approach to the perennial question of how learning rate, model size, and parameter regularization interact to determine the properties of overparameterized models optimized with gradient descent.

--- https://arxiv.org/abs/1810.09038 Depth with Nonlinearity Creates No Bad Local Minima in ResNets Kenji Kawaguchi, Yoshua Bengio 2018-10-21 2021-12-24 [("doi","10.1016/j.neunet.2019.06.009")] ai/nn

In this paper, we prove that depth with nonlinearity creates no bad local minima in a type of arbitrarily deep ResNets with arbitrary nonlinear activation functions, in the sense that the values of all local minima are no worse than the global minimum value of corresponding classical machine-learning models, and are guaranteed to further improve via residual representations.

As a result, this paper provides an affirmative answer to an open question stated in a paper in the conference on Neural Information Processing Systems 2018. This paper advances the optimization theory of deep learning only for ResNets and not for other network architectures.

--- https://arxiv.org/abs/2110.00683 Learning through atypical "phase transitions" in overparameterized neural networks Carlo Baldassi, Clarissa Lauditi, Enrico M. Malatesta, Rosalba Pacelli, Gabriele Perugini, Riccardo Zecchina 2021-10-01 2021-12-24 [("doi","10.1103/PhysRevE.106.014116")] ai/nn ai/scaling/emergence/grokking

[cf. double descent] Current deep neural networks are highly overparameterized (up to billions of connection weights) and nonlinear. Yet they can fit data almost perfectly through variants of gradient descent algorithms and achieve unexpected levels of prediction accuracy without overfitting. These are formidable results that defy predictions of statistical learning and pose conceptual challenges for non-convex optimization.

In this paper, we use methods from statistical physics of disordered systems to analytically study the computational fallout of overparameterization in non-convex binary neural network models, trained on data generated from a structurally simpler but “hidden” network.

As the number of connection weights increases, we follow the changes of the geometrical structure of different minima of the error loss function and relate them to learning and generalization performance.

A first transition happens at the so-called interpolation point, when solutions begin to exist (perfect fitting becomes possible). [The information-theoretic interpolation threshold of the model: this is the point when zero-error solutions appear and perfect fitting of the data becomes possible] This transition reflects the properties of typical solutions, which however are in sharp minima and hard to sample.

After a gap, a second transition occurs, Local Entropy, with the discontinuous appearance of a different kind of “atypical” structures: wide regions of the weight space that are particularly solution-dense and have good generalization properties.

The two kinds of solutions coexist, with the typical ones being exponentially more numerous, but empirically we find that efficient algorithms sample the atypical, rare ones. This suggests that the atypical phase transition is the relevant one for learning.

The results of numerical tests with realistic networks on observables suggested by the theory are consistent with this scenario.

…[double descent] Subsequent numerical analysis of the Hessian of largely overparameterized models30 showed that minimizers present many flat directions, and that it is not hard to find a path of zero training error connecting two solutions31, 32. In under-parameterized neural networks, on the other hand, the authors of 33 showed that the landscape is very rough and dynamics is glassy. This led to think that the landscape of overparameterized networks where the dynamics is not glassy anymore, presents no “poor” minima at all34.

According to our analysis, this is not the case. As we anticipated in the introduction, over-parameterization has the effect of letting those connected regions appear at the LE transition, not letting “poor” minima completely disappear. Over-parameterizing the network even further it is possible to increase the size of the connected region; “poor” or “sharp” solutions however remain the most numerous ones and dominate the Gibbs measure.

--- https://arxiv.org/abs/1903.08560 Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie, Andrea Montanari, Saharon Rosset, Ryan J. Tibshirani 2019-03-19 2021-12-24 [("doi","10.48550/arXiv.1903.08560")] ai/nn ai/scaling

Interpolators—estimators that achieve zero training error—have attracted growing attention in machine learning, mainly because state-of-the art neural networks appear to be models of this type.

In this paper, we study minimum 𝓁2 norm (“ridgeless”) interpolation in high-dimensional least squares regression. We consider two different models for the feature distribution: a linear model, where the feature vectors xi ∈ ℝp are obtained by applying a linear transform to a vector of i.i.d. entries, xi = ∑1⁄2zi (with zi ∈ ℝp); and a nonlinear model, where the feature vectors are obtained by passing the input through a random one-layer neural network, xi = ϕ(Wzi) (with zi ∈ ℝd, W ∈ ℝp×d a matrix of i.i.d. entries, and ϕ an activation function acting component-wise on Wzi).

We recover—in a precise quantitative way—several phenomena that have been observed in large-scale neural networks and kernel machines, including the “double descent” behavior of the prediction risk, and the potential benefits of overparametrization.

--- https://arxiv.org/abs/1710.09553 Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior Charles H. Martin, Michael W. Mahoney 2017-10-26 2021-12-24 [("doi","10.48550/arXiv.1710.09553")] ai/nn ai/scaling

We describe an approach to understand the peculiar and counterintuitive generalization properties of deep neural networks. The approach involves going beyond worst-case theoretical capacity control frameworks that have been popular in machine learning in recent years to revisit old ideas in the statistical mechanics of neural networks.

Within this approach, we present a prototypical Very Simple Deep Learning (VSDL) model, whose behavior is controlled by two control parameters, one describing an effective amount of data, or load, on the network (that decreases when noise is added to the input), and one with an effective temperature interpretation (that increases when algorithms are early stopped).

Using this model, we describe how a very simple application of ideas from the statistical mechanics theory of generalization provides a strong qualitative description of recently-observed empirical results regarding the inability of deep neural networks not to overfit training data, discontinuous learning and sharp transitions in the generalization properties of learning algorithms, etc.

--- https://hernandooney.com/2022/08/19/wolfes-arcana-or-midjourney-meet-gene-wolfe/ 2021-12-24 ai/nn/diffusion/midjourney fiction/gene-wolfe --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5110243/ Differences and similarities between human and chimpanzee neural progenitors during cerebral cortex development Felipe Mora-Bermúdez, Farhath Badsha, Sabina Kanton, J. Gray Camp, Benjamin Vernot, Kathrin Köhler, Birger Voigt, Keisuke Okita, Tomislav Maricic, Zhisong He, Robert Lachmann, Svante Pääbo, Barbara Treutlein, Wiel, B. Huttner 2016 2021-12-24 [("doi","10.7554/eLife.18683")] psychology/neuroscience

Human neocortex expansion likely contributed to the remarkable cognitive abilities of humans. This expansion is thought to primarily reflect differences in proliferation versus differentiation of neural progenitors during cortical development.

Here, we have searched for such differences by analysing cerebral organoids from human and chimpanzees using immunohistofluorescence, live imaging, and single-cell transcriptomics. We find that the cytoarchitecture, cell type composition, and neurogenic gene expression programs of humans and chimpanzees are remarkably similar.

Notably, however, live imaging of apical progenitor mitosis uncovered a lengthening of prometaphase-metaphase in humans compared to chimpanzees that is specific to proliferating progenitors and not observed in non-neural cells. Consistent with this, the small set of genes more highly expressed in human apical progenitors points to increased proliferative capacity, and the proportion of neurogenic basal progenitors is lower in humans.

These subtle differences in cortical progenitors between humans and chimpanzees may have consequences for human neocortex evolution.

--- https://arxiv.org/abs/1412.0233 The Loss Surfaces of Multilayer Networks Anna Choromanska, Mikael Henaff, Michael Mathieu, Gérard Ben Arous, Yann LeCun 2014-11-30 2021-12-24 [("doi","10.48550/arXiv.1412.0233")] ai/nn/fully-connected

We study the connection between the highly non-convex loss function of a simple model of the fully-connected feed-forward neural network and the Hamiltonian of the spherical spin-glass model under the assumptions of: (1) variable independence, (2) redundancy in network parameterization, and (3) uniformity. These assumptions enable us to explain the complexity of the fully decoupled neural network through the prism of the results from random matrix theory.

We show that for large-size decoupled networks the lowest critical values of the random loss function form a layered structure and they are located in a well-defined band lower-bounded by the global minimum. The number of local minima outside that band diminishes exponentially with the size of the network. We empirically verify that the mathematical model exhibits similar behavior as the computer simulations, despite the presence of high dependencies in real networks.

We conjecture that both simulated annealing and SGD converge to the band of low critical points, and that all critical points found there are local minima of high quality measured by the test error. This emphasizes a major difference between large & small-size networks where for the latter poor quality local minima have non-zero probability of being recovered.

Finally, we prove that recovering the global minimum becomes harder as the network size increases and that it is in practice irrelevant as global minimum often leads to overfitting.

--- https://arxiv.org/abs/2209.00840 FOLIO: Natural Language Reasoning with First-Order Logic Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Luke Benson, Lucy Sun, Ekaterina Zubova, Yujie Qiao, Matthew Burtell, David Peng, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Shafiq Joty, Alexander R. Fabbri, Wojciech Kryscinski, Xi Victoria Lin, Caiming Xiong, Dragomir Radev 2022-09-02 2022-09-02 [("doi","10.48550/arXiv.2209.00840")] ai/dataset ai/nn/transformer/gpt/inner-monologue philosophy/logic

We present FOLIO, a human-annotated, open-domain, and logically complex and diverse dataset for reasoning in natural language (NL), equipped with first order logic (FOL) annotations.

FOLIO consists of 1,435 examples (unique conclusions), each paired with one of 487 sets of premises which serve as rules to be used to deductively reason for the validity of each conclusion. The logical correctness of premises and conclusions is ensured by their parallel FOL annotations, which are automatically verified by our FOL inference engine. In addition to the main NL reasoning task, NL-FOL pairs in FOLIO automatically constitute a new NL-FOL translation dataset using FOL as the logical form.

Our experiments on FOLIO systematically evaluate the FOL reasoning ability of supervised fine-tuning on medium-sized language models (BERT, RoBERTa) and few-shot prompting on large language models (GPT-NeoX, OPT, GPT-3, Codex). For NL-FOL translation, we experiment with GPT-3 and Codex.

Our results show that one of the most capable Large Language Model (LLM) publicly available, GPT-3 davinci, achieves only slightly better than random results with few-shot prompting on a subset of FOLIO, and the model is especially bad at predicting the correct truth values for False and Unknown conclusions.

Our dataset and code are available at Github.

--- https://www.stavros.io/posts/compressing-images-with-stable-diffusion/ 2021-12-24 ai/nn/diffusion cs/algorithm/information/compression fiction/humor math/humor --- https://arxiv.org/abs/2209.01188#yandex Petals: Collaborative Inference and Fine-tuning of Large Models Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Max Ryabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, Colin Raffel 2022-09-02 2022-09-02 [("doi","10.48550/arXiv.2209.01188")] ai/nn/transformer/gpt ai/scaling/hardware

Many NLP tasks benefit from using large language models (LLMs) that often have more than 100 billion parameters. With the release of BLOOM-176B and OPT-175B, everyone can download pretrained models of this scale. Still, using these models requires high-end hardware unavailable to many researchers. In some cases, LLMs can be used more affordably via RAM offloading or hosted APIs. However, these techniques have innate limitations: offloading is too slow for interactive inference, while APIs are not flexible enough for research.

In this work, we propose Petals—a system for inference and fine-tuning of large models collaboratively by joining the resources of multiple parties trusted to process client’s data.

We demonstrate that this strategy outperforms offloading for very large models, running inference of BLOOM-176B on consumer GPUs at ~1 step per second.

Unlike most inference APIs, Petals also natively exposes the hidden states of served models, allowing its users to train and share custom model extensions based on efficient fine-tuning methods.

--- https://openreview.net/forum?id=SyEoB2-dZH Generating Text with Recurrent Neural Networks Ilya Sutskever, James Martens, Geoffrey Hinton 2019-07-16 2021-12-24 ai/nn/rnn ai/nn/tokenization

Recurrent Neural Networks (RNNs) are very powerful sequence models that do not enjoy widespread use because it is extremely difficult to train them properly. Fortunately, recent advances in Hessian-free optimization have been able to overcome the difficulties associated with training RNNs, making it possible to apply them successfully to challenging sequence problems.

In this paper we demonstrate the power of RNNs trained with the new Hessian-Free optimizer (HF) by applying them to character-level language modeling tasks. The standard RNN architecture, while effective, is not ideally suited for such tasks, so we introduce a new RNN variant that uses multiplicative (or “gated”) connections which allow the current input character to determine the transition matrix from one hidden state vector to the next.

After training the multiplicative RNN with the HF optimizer for 5 days on 8 high-end GPUs, we were able to surpass the performance of the best previous single method for character-level language modeling—a hierarchical non-parametric sequence model.

To our knowledge this represents the largest recurrent neural network application to date.

--- https://x.com/AstraliteHeart/status/1566961323153707008 2021-12-25 ai/nn/transformer/clip/sample --- https://github.com/tensorfork/tensorfork/issues/35 2021-12-25 ai/nn/gan/data-augmentation --- https://en.wikipedia.org/wiki/Data_augmentation Data augmentation 2021-12-25 ai/nn/gan/data-augmentation --- https://x.com/AstraliteHeart/status/1567021941302906882 2021-12-25 ai/nn/transformer/clip/sample --- https://x.com/AstraliteHeart/status/1566996941371998211 2021-12-25 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2103.09742 Training GANs with Stronger Augmentations via Contrastive Discriminator (ContraD) Jongheon Jeong, Jinwoo Shin 2021-03-17 2021-12-25 [("doi","10.48550/arXiv.2103.09742")] ai/nn/gan/data-augmentation

Recent works in Generative Adversarial Networks (GANs) are actively revisiting various data augmentation techniques as an effective way to prevent discriminator overfitting. It is still unclear, however, that which augmentations could actually improve GANs, and in particular, how to apply a wider range of augmentations in training.

In this paper, we propose a novel way to address these questions by incorporating a recent contrastive representation learning scheme into the GAN discriminator, coined ContraD. This “fusion” enables the discriminators to work with much stronger augmentations without increasing their training instability, thereby preventing the discriminator overfitting issue in GANs more effectively. Even better, we observe that the contrastive learning itself also benefits from our GAN training, ie. by maintaining discriminative features between real and fake samples, suggesting a strong coherence between the two worlds: good contrastive representations are also good for GAN discriminators, and vice versa.

Our experimental results show that GANs with ContraD consistently improve FID and IS compared to other recent techniques incorporating data augmentations, still maintaining highly discriminative features in the discriminator in terms of the linear evaluation.

Finally, as a byproduct, we also show that our GANs trained in an unsupervised manner (without labels) can induce many conditional generative models via a simple latent sampling, leveraging the learned features of ContraD.

Code is available at Github.

--- https://arxiv.org/abs/1711.10485#microsoft AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, Xiaodong He 2017-11-28 2021-12-25 [("doi","10.48550/arXiv.1711.10485")] ai/nn/gan

In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation.

With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different subregions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator.

The proposed AttnGAN outperforms the previous state-of-the-art, boosting the best reported inception score by 14.14% on the CUB dataset and 170.25% on the more challenging MS COCO dataset.

A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image.

Figure 6: 256×256px images generated from descriptions of novel scenarios using the AttnGAN model trained on MS COCO.
--- https://www.lesswrong.com/posts/KsKfvLx7nFBZnWtEu/no-human-brains-are-not-much-more-efficient-than-computers 2021-12-25 ai/scaling/hardware --- https://www.lesswrong.com/posts/z9Syf3pGffpvHwfr4/i-m-mildly-skeptical-that-blindness-prevents-schizophrenia 2021-12-25 psychiatry/schizophrenia --- https://x.com/supercomposite/status/1567162288087470081 2021-12-25 ai/nn/adversarial ai/nn/transformer/clip/sample --- https://www.quantamagazine.org/how-isaac-newton-discovered-the-binomial-power-series-20220831/ 2021-12-25 math --- https://arxiv.org/abs/2209.02535 Analyzing Transformers in Embedding Space Guy Dar, Mor Geva, Ankit Gupta, Jonathan Berant 2022-09-06 2022-09-06 [("doi","10.48550/arXiv.2209.02535")] ai/nn/transformer

[Twitter] Understanding Transformer-based models has attracted attention, as they lie at the heart of recent technological advances across machine learning. While most interpretability methods rely on running models over inputs, recent work has shown that a zero-pass approach, where parameters are interpreted directly without a forward/backward pass is feasible for some Transformer parameters, and for two-layer attention networks [Elhage et al 2021, Geva et al 2022].

In this work, we present a theoretical analysis where all parameters of a trained Transformer are interpreted by projecting them into the embedding space, that is, the space of vocabulary items they operate on. We derive a simple theoretical framework to support our arguments and provide ample evidence for its validity.

First, an empirical analysis showing that parameters of both pretrained and fine-tuned models can be interpreted in embedding space.

Second, we present two applications of our framework: (1) aligning the parameters of different models that share a vocabulary, and (2) constructing a classifier without training by “translating” the parameters of a fine-tuned classifier to parameters of a different model that was only pretrained.

Overall, our findings open the door to interpretation methods that, at least in part, abstract away from model specifics and operate in the embedding space only.

--- https://arxiv.org/abs/2203.14680#allen Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space Mor Geva, Avi Caciularu, Kevin Ro Wang, Yoav Goldberg 2022-03-28 2022-03-28 [("doi","10.48550/arXiv.2203.14680")] ai/nn/transformer/gpt/2

Transformer-based language models (LMs) are at the core of modern NLP, but their internal prediction construction process is opaque and largely not understood.

In this work, we make a substantial step towards unveiling this underlying prediction process, by reverse-engineering the operation of the feed-forward network (FFN) layers, one of the building blocks of transformer models.

We view the token representation as a changing distribution over the vocabulary, and the output from each FFN layer as an additive update to that distribution. Then, we analyze the FFN updates in the vocabulary space, showing that each update can be decomposed to sub-updates corresponding to single FFN parameter vectors, each promoting concepts that are often human-interpretable.

We then leverage these findings for controlling LM predictions, where we reduce the toxicity of GPT-2 by almost 50%, and for improving computation efficiency with a simple early exit rule, saving 20% of computation on average.

--- /doc/politics/2022-guriev.pdf The Political Economy of Populism Sergei Guriev, Elias Papaioannou 2022-09-01 2022-09-01 [("doi","10.1257/jel.20201595")] economics politics

We synthesize the literature on the recent rise of populism.

First, we discuss definitions and present descriptive evidence on the recent increase in support for populists. Second, we cover the historical evolution of populist regimes since the late 19th century. Third, we discuss the role of secular economic factors related to cross-border trade and automation. Fourth, we review studies on the role of the 2008–09 global financial crisis and subsequent austerity, connect them to historical work covering the Great Depression, and discuss likely mechanisms. Fifth, we discuss studies on identity politics, trust, and cultural backlash. Sixth, we discuss economic and cultural consequences of growth in immigration and the recent refugee crisis. We also discuss the gap between perceptions and reality regarding immigration. Seventh, we review studies on the impact of the internet and social media. Eighth, we discuss the literature on the implications of populism’s recent rise.

We conclude outlining avenues for further research.

--- https://arxiv.org/abs/0812.4360#schmidhuber Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes Juergen Schmidhuber 2008-12-23 2021-12-26 [("doi","10.48550/arXiv.0812.4360")] cs/algorithm psychology/novelty reinforcement-learning/exploration

I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-random, non-arbitrary, regular data that is novel and surprising not in the traditional sense of Boltzmann and Shannon but in the sense that it allows for compression progress because its regularity was not yet known.

This drive maximizes interestingness, the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve.

It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and (since 1990) artificial systems.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6744345/ Asymmetrical genetic attributions for prosocial versus antisocial behavior Matthew S. Lebowitz, Kathryn Tabb, Paul S. Appelbaum 2019 2021-12-26 [("doi","10.1038/s41562-019-0651-1")] genetics/heritable philosophy/ethics

Genetic explanations of human behavior are increasingly common. While genetic attributions for behavior are often considered relevant for assessing blameworthiness, it has not yet been established whether judgements about blameworthiness can themselves impact genetic attributions.

Across 6 studies, participants read about individuals engaging in prosocial or antisocial behavior, and rated the extent to which they believed that genetics played a role in causing the behavior.

Antisocial behavior was consistently rated as less genetically influenced than prosocial behavior. This was true regardless of whether genetic explanations were explicitly provided or refuted. Mediation analyses suggested that this asymmetry may stem from people’s motivating desire to hold wrongdoers responsible for their actions.

These findings suggest that those who seek to study or make use of genetic explanations’ influence on evaluations of, for example, antisocial behavior should consider whether such explanations are accepted in the first place, given the possibility of motivated causal reasoning.

--- https://www.construction-physics.com/p/where-do-economies-of-scale-come 2021-12-26 economics/automation economics/experience-curve --- https://github.blog/2022-09-07-research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/ 2021-12-26 ai/nn/transformer/gpt/codex --- https://en.wikipedia.org/wiki/Kaguya_(mouse) Kaguya (mouse) 2021-12-26 genetics/gametogenesis --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1893020/ Genetic similarities within and between human populations D J. Witherspoon, S. Wooding, A. R. Rogers, E. E. Marchani, W. S. Watkins, M. A. Batzer, L. B. Jorde 2007 2021-12-26 [("doi","10.1534/genetics.106.067355")] genetics/selection/natural/human

The proportion of human genetic variation due to differences between populations is modest, and individuals from different populations can be genetically more similar than individuals from the same population. Yet sufficient genetic data can permit accurate classification of individuals into populations. Both findings can be obtained from the same data set, using the same number of polymorphic loci. This article explains why.

Our analysis focuses on the frequency, omega, with which a pair of random individuals from two different populations is genetically more similar than a pair of individuals randomly selected from any single population. We compare omega to the error rates of several classification methods, using data sets that vary in number of loci, average allele frequency, populations sampled, and polymorphism ascertainment strategy.

We demonstrate that classification methods achieve higher discriminatory power than omega because of their use of aggregate properties of populations. The number of loci analyzed is the most critical variable: with 100 polymorphisms, accurate classification is possible, but omega remains sizable, even when using populations as distinct as sub-Saharan Africans and Europeans. Phenotypes controlled by a dozen or fewer loci can therefore be expected to show substantial overlap between human populations.

This provides empirical justification for caution when using population labels in biomedical settings, with broad implications for personalized medicine, pharmacogenetics, and the meaning of race.

--- https://madebyoll.in/posts/game_emulation_via_dnn/ 2021-12-26 reinforcement-learning/model --- https://www.freethink.com/hard-tech/crypto-argentina-black-market-cash 2021-12-27 bitcoin --- https://official-kircheis.tumblr.com/post/682013772643254272/jadagul-prokopetz-repost-this-image 2021-12-27 cs/security --- https://juretriglav.si/compressing-global-illumination-with-neural-networks/ 2021-12-27 ai/nn --- https://huggingface.co/hakurei/waifu-diffusion 2021-12-27 ai/anime/danbooru ai/nn/diffusion --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9232116/ There is chemistry in social chemistry Inbal Ravreby, Kobi Snitz, Noam Sobel 2022 2022 [("doi","10.1126/sciadv.abn0154")] psychology/smell/human

Nonhuman terrestrial mammals sniff themselves and each other to decide who is friend or foe. Humans also sniff themselves and each other, but the function of this is unknown. Because humans seek friends who are similar to themselves, we hypothesized that humans may smell themselves and others to subconsciously estimate body odor similarity, which, in turn, may promote friendship.

To test this, we recruited nonromantic same-sex friend dyads and harvested their body odor. We found that objective ratings obtained with an electronic nose, and subjective ratings obtained from independent human smellers converged to suggest that friends smell more similar to each other than random dyads.

Last, we recruited complete strangers, smelled them with an electronic nose, and engaged them in nonverbal same-sex dyadic interactions. We observed that dyads who smelled more similar had more positive dyadic interactions. In other words, we could predict social bonding with an electronic nose.

We conclude that there is indeed chemistry in social chemistry.

--- https://www.biorxiv.org/content/10.1101/2021.02.07.430171.full MLR: A model of working memory for latent representations Shekoofeh Hedayati, Ryan O′Donnell, Brad Wyble 2022-02-03 2022-02-03 [("doi","10.1101/2021.02.07.430171")] ai/nn/vae dual-n-back psychology/neuroscience

Visual knowledge obtained from our lifelong experience of the world plays a critical role in our ability to build short-term memories. We propose a mechanistic explanation of how working memories are built from the latent representations of visual knowledge and can then be reconstructed.

The proposed model, Memory for Latent Representations (MLR), features a variational autoencoder with an architecture that corresponds broadly to the human visual system and an activation-based binding pool of neurons that binds items’ attributes to tokenized representations.

The simulation results revealed that the shapes of familiar items can be encoded and retrieved efficiently from latents in higher levels of the visual hierarchy. On the other hand, novel patterns that are completely outside the training set can be stored from a single exposure using only latents from early layers of the visual system. Moreover, a given stimulus in working memory can have multiple codes, representing specific visual features such as shape or color, in addition to categorical information.

Finally, we validated our model by testing a series of predictions against behavioral results obtained from WM tasks.

The model provides a compelling demonstration of how visual knowledge yields compact visual representation for efficient memory encoding.

--- https://www.thefp.com/p/hurts-so-good 2021-12-27 psychiatry sociology --- https://www.biorxiv.org/content/10.1101/2022.08.31.504599.full Multimodal Object Representations Rely on Integrative Coding Aedan Y. Li, Natalia Ladyka-Wojcik, Heba Qazilbash, Ali Golestani, Dirk B. Walther, Chris B. Martin, Morgan D. Barense 2022-09-01 2022-09-01 [("doi","10.1101/2022.08.31.504599")] psychology/neuroscience

[Twitter] Combining information from multiple senses is essential to object recognition. Yet how the mind combines sensory input into coherent multimodal representations, the multimodal binding problem, remains poorly understood.

Here, we applied multi-echo fMRI across a four-day paradigm, in which participants learned 3-dimensional multimodal object representations created from well-characterized visual shape and sound features. Our novel paradigm decoupled the learned multimodal object representations from their baseline unimodal shape and sound features, thus tracking the emergence of multimodal concepts as they were learned by healthy adults.

Critically, the representation for the whole object was different from the combined representation of its individual parts, with evidence of an integrative object code in anterior temporal lobe structures. Intriguingly, the perirhinal cortex, an anterior temporal lobe structure, was by default biased towards visual shape, but this initial shape bias was attenuated with learning. Pattern similarity analyses suggest that after learning the perirhinal cortex orthogonalized combinations of visual shape and sound features, transforming overlapping feature input into distinct multimodal object representations.

These results provide evidence of integrative coding in the anterior temporal lobes that is distinct from the distributed sensory features, advancing the age-old question of how the mind constructs multimodal objects from their component features.

--- https://github.com/XavierXiao/Dreambooth-Stable-Diffusion 2021-12-27 ai/nn/diffusion --- https://arxiv.org/abs/2012.05208#schmidhuber On the Binding Problem in Artificial Neural Networks Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber 2020-12-09 2021-12-27 [("doi","10.48550/arXiv.2012.05208")] ai/nn/rnn psychology/neuroscience

Contemporary neural networks still fall short of human-level generalization, which extends far beyond our direct experiences. In this paper, we argue that the underlying cause for this shortcoming is their inability to dynamically and flexibly bind information that is distributed throughout the network. This binding problem affects their capacity to acquire a compositional understanding of the world in terms of symbol-like entities (like objects), which is crucial for generalizing in predictable and systematic ways.

To address this issue, we propose a unifying framework that revolves around forming meaningful entities from unstructured sensory inputs (segregation), maintaining this separation of information at a representational level (representation), and using these entities to construct new inferences, predictions, and behaviors (composition).

Our analysis draws inspiration from a wealth of research in neuroscience and cognitive psychology, and surveys relevant mechanisms from the machine learning literature, to help identify a combination of inductive biases that allow symbolic information processing to emerge naturally in neural networks.

We believe that a compositional approach to AI, in terms of grounded symbol-like representations, is of fundamental importance for realizing human-level generalization, and we hope that this paper may contribute towards that goal as a reference and inspiration.

--- https://arxiv.org/abs/2001.01969 SWAT: Sparse Weight Activation Training Md Aamir Raihan, Tor M. Aamodt 2020-01-07 2021-12-27 [("doi","10.48550/arXiv.2001.01969")] ai/nn/sparsity/low-precision

Neural network training is computationally and memory intensive. Sparse training can reduce the burden on emerging hardware platforms designed to accelerate sparse computations, but it can affect network convergence.

In this work, we propose a novel CNN training algorithm Sparse Weight Activation Training (SWAT). SWAT is more computation and memory-efficient than conventional training. SWAT modifies back-propagation based on the empirical insight that convergence during training tends to be robust to the elimination of (1) small magnitude weights during the forward pass and (2) both small magnitude weights and activations during the backward pass.

We evaluate SWAT on recent CNN architectures such as ResNet, VGG, DenseNet and WideResnet using CIFAR-10, CIFAR-100 and ImageNet datasets. For ResNet-50 on ImageNet SWAT reduces total floating-point operations (FLOPS) during training by 80% resulting in a 3.3× training speedup when run on a simulated sparse learning accelerator representative of emerging platforms while incurring only 1.63% reduction in validation accuracy. Moreover, SWAT reduces memory footprint during the backward pass by 23% to 50% for activations and 50% to 90% for weights.

--- https://arxiv.org/abs/2209.01667#google A Review of Sparse Expert Models in Deep Learning William Fedus, Jeff Dean, Barret Zoph 2022-09-04 2022-09-04 [("doi","10.48550/arXiv.2209.01667")] ai/scaling/mixture-of-experts

Sparse expert models are a 30-year old concept re-emerging as a popular architecture in deep learning.

This class of architecture encompasses Mixture-of-Experts, Switch Transformers, Routing Networks, BASE layers, and others, all with the unifying idea that each example is acted on by a subset of the parameters. By doing so, the degree of sparsity decouples the parameter count from the compute per example allowing for extremely large, but efficient models.

The resulting models have demonstrated improvements across diverse domains such as natural language processing, computer vision, and speech recognition.

We review the concept of sparse expert models, provide a basic description of the common algorithms, contextualize the advances in the deep learning era, and conclude by highlighting areas for future work.

--- https://x.com/BlackHC/status/1567810869211316224 2021-12-28 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2209.03182 On the Effectiveness of Compact Biomedical Transformers (✱BioBERT) Omid Rohanian, Mohammadmahdi Nouriborji, Samaneh Kouchaki, David A. Clifton 2022-09-07 2022-09-07 [("doi","10.48550/arXiv.2209.03182")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer reinforcement-learning/meta-learning/continual-learning

Language models pre-trained on biomedical corpora, such as BioBERT, have recently shown promising results on downstream biomedical tasks. Many existing pre-trained models, on the other hand, are resource-intensive and computationally heavy owing to factors such as embedding size, hidden dimension, and number of layers. The natural language processing (NLP) community has developed numerous strategies to compress these models using techniques such as pruning, quantisation, and knowledge distillation, resulting in models that are considerably faster, smaller, and subsequently easier to use in practice.

By the same token, in this paper we introduce 6 lightweight models, namely, BioDistilBERT, BioTinyBERT, BioMobileBERT, DistilBioBERT, TinyBioBERT, and CompactBioBERT; which are obtained either by knowledge distillation from a biomedical teacher or continual learning on the Pubmed dataset via the Masked Language Modeling (MLM) objective. We evaluate all of our models on 3 biomedical tasks and compare them with BioBERT-v1.1 to create efficient lightweight models that perform on par with their larger counterparts.

All the models will be publicly available on our Huggingface profile at https://huggingface.co/nlpie and the codes used to run the experiments will be available at Github.

--- https://arxiv.org/abs/2209.03320 What does a platypus look like? Generating customized prompts for zero-shot image classification (CuPL) Sarah Pratt, Rosanne Liu, Ali Farhadi 2022-09-07 2022-09-07 [("doi","10.48550/arXiv.2209.03320")] ai/nn/transformer/clip ai/nn/transformer/gpt/3/nonfiction

Open vocabulary models are a promising new paradigm for image classification. Unlike traditional classification models, open vocabulary models classify among any arbitrary set of categories specified with natural language during inference. This natural language, called “prompts”, typically consists of a set of hand-written templates (eg. “a photo of a”) which are completed with each of the category names. This work introduces a simple method to generate higher accuracy prompts, without using explicit knowledge of the image domain and with far fewer hand-constructed sentences.

To achieve this, we combine open vocabulary models [CLIP] with large language models (LLMs) [like GPT-3] to create Customized Prompts via Language models (CuPL, pronounced “couple”). In particular, we leverage the knowledge contained in LLMs in order to generate many descriptive sentences that are customized for each object category. [Instead of "a photograph of a papillon", "a papillon is a small, spaniel-type dog with a long, silky coat and fringed ears."]

We find that this straightforward and general approach improves accuracy on a range of zero-shot image classification benchmarks, including over one percentage point gain on ImageNet. Finally, this method requires no additional training and remains completely zero-shot. Code is available at Github.

--- https://arxiv.org/abs/2209.03143#google AudioLM: a Language Modeling Approach to Audio Generation Zalán Borsos, Raphaël Marinier, Damien Vincent, Eugene Kharitonov, Olivier Pietquin, Matt Sharifi, Olivier Teboul, David Grangier, Marco Tagliasacchi, Neil Zeghidour 2022-09-07 2022-09-07 [("doi","10.48550/arXiv.2209.03143")] ai/music ai/nn/tokenization ai/nn/transformer/gpt/jukebox

[samples; blog] We introduce AudioLM, a framework for high-quality audio generation with long-term consistency.

AudioLM maps the input audio to a sequence of discrete tokens and casts audio generation as a language modeling task in this representation space. We show how existing audio tokenizers provide different trade-offs between reconstruction quality and long-term structure, and we propose a hybrid tokenization scheme to achieve both objectives. Namely, we leverage the discretized activations of a masked language model pre-trained on audio to capture long-term structure and the discrete codes produced by a neural audio codec to achieve high-quality synthesis.

By training on large corpora of raw audio waveforms, AudioLM learns to generate natural and coherent continuations given short prompts. When trained on speech, and without any transcript or annotation, AudioLM generates syntactically and semantically plausible speech continuations while also maintaining speaker identity and prosody for unseen speakers.

Furthermore, we demonstrate how our approach extends beyond speech by generating coherent piano music continuations, despite being trained without any symbolic representation of music.

--- https://www.reddit.com/r/StableDiffusion/comments/x8tguo/lexicas_search_engine_is_now_100_powered_by_clip/ 2021-12-28 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/1807.00263 Accurate Uncertainties for Deep Learning Using Calibrated Regression Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon 2018-07-01 2021-12-28 [("doi","10.48550/arXiv.1807.00263")] ai/nn/rnn statistics/bayes

Methods for reasoning under uncertainty are a key building block of accurate and reliable machine learning systems. Bayesian methods provide a general framework to quantify uncertainty. However, because of model misspecification and the use of approximate inference, Bayesian uncertainty estimates are often inaccurate—for example, a 90% credible interval may not contain the true outcome 90% of the time.

Here, we propose a simple procedure for calibrating any regression algorithm; when applied to Bayesian and probabilistic models, it is guaranteed to produce calibrated uncertainty estimates given enough data. Our procedure is inspired by Platt scaling and extends previous work on classification.

We evaluate this approach on Bayesian linear regression, feedforward, and recurrent neural networks, and find that it consistently outputs well-calibrated credible intervals while improving performance on time series forecasting and model-based reinforcement learning tasks.

--- https://www.nytimes.com/2022/09/07/us/chahorski-killer-georgia-identified-dna.html 2021-12-28 genetics/sequencing --- https://x.com/maxhodak_/status/1567769027757641729 2021-12-28 ai/nn/transformer/gpt/non-fiction --- https://maximumeffort.substack.com/p/the-tyranny-of-the-wagon-equation 2021-12-28 statistics/decision --- https://blogs.nvidia.com/blog/2022/09/08/hopper-mlperf-inference/ 2021-12-28 ai/scaling/hardware --- https://x.com/AlexTamkin/status/1567956315208830976 2021-12-29 ai/nn/transformer/gpt/codex --- https://pudding.cool/2018/08/wiki-death/ 2021-12-29 design/visualization wikipedia --- https://www.biorxiv.org/content/10.1101/2022.09.06.506858.full Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies Pouria Salehi Nowbandegani, Anthony Wilder Wohns, Jenna Lee Ballard, Eric S. Lander, Alex Bloemendal, Benjamin M. Neale, Luke J. O’Connor 2022-09-08 2022-09-08 [("doi","10.1101/2022.09.06.506858")] genetics/heritable

Linkage disequilibrium (LD) is the correlation among nearby genetic variants. In genetic association studies, LD is often modeled using massive local correlation matrices, but this approach is slow, especially in ancestrally diverse studies. Here, we introduce LD graphical models (LDGMs), which are an extremely sparse and efficient representation of LD. LDGMs are derived from genome-wide genealogies; statistical relationships among alleles in the LDGM correspond to genealogical relationships among haplotypes.

We publish LDGMs and ancestry specific LDGM precision matrices for 18 million common SNPs (MAF>1%) in 5 ancestry groups, validate their accuracy, and demonstrate order-of-magnitude improvements in runtime for commonly used LD matrix computations.

We implement an extremely fast multi-ancestry polygenic prediction method, BLUPx-ldgm, which performs better than a similar method based on the reference LD correlation matrix. LDGMs will enable sophisticated methods that scale to ancestrally genetic association data across millions of variants and individuals.

--- https://www.biorxiv.org/content/10.1101/2022.09.07.506912.full What does heritability of Alzheimer’s disease represent? Emily Ann Baker, Ganna Leonenko, Karl Michael Schmidt, Matthew Hill, Amanda J. Myers, Maryam Shoai, Itziar de Rojas, Niccoló Tesi, Henne Holstege, Wiesje M. van der Flier, Yolande A. L. Pijnenburg, Agustin Ruiz, John Hardy, Sven van der Lee, Valentina Escott-Price 2022-09-08 2022-09-08 [("doi","10.1101/2022.09.07.506912")] genetics/heritable psychiatry/alzheimers

INTRODUCTION: Both Alzheimer’s disease (AD) and ageing have a strong genetic component. In each case, many associated variants have been discovered, but how much missing heritability remains to be discovered is debated. Variability in the estimation of SNP-based heritability could explain the differences in reported heritability.

METHODS: We compute heritability in 5 large independent cohorts (n = 7,396, 1,566, 803, 12,528 and 3,963) to determine whether a consensus for the AD heritability estimate can be reached. These cohorts vary by sample size, age of cases and controls and phenotype definition. We compute heritability (1) for all SNPs, (2) excluding APOE region, (3) excluding both APOE and genome-wide association study hit regions, and (4) SNPs overlapping a microglia gene-set.

RESULTS: SNP-based heritability of Alzheimer’s disease is 38–66% when age and genetic disease architecture are correctly accounted for. The heritability estimates decrease by 12% [SD=8%] on average when the APOE region is excluded and an additional 1% [SD=3%] when genome-wide statistically-significant regions were removed. A microglia gene-set explains 69–84% of our estimates of SNP-based heritability using only 3% of total SNPs in all cohorts.

CONCLUSION: The heritability of neurodegenerative disorders cannot be represented as a single number, because it is dependent on the ages of cases and controls. Genome-wide association studies pick up a large proportion of total AD heritability when age and genetic architecture are correctly accounted for. Around 13% of SNP-based heritability can be explained by known genetic loci and the remaining heritability likely resides around microglial related genes.

--- https://github.com/rinnakk/japanese-stable-diffusion 2021-12-29 ai/nn/diffusion japan/art --- https://vitalik.eth.limo/general/2022/09/09/ens.html 2021-12-29 bitcoin economics --- https://en.wikipedia.org/wiki/Myerson%E2%80%93Satterthwaite_theorem Myerson-Satterthwaite theorem 2021-12-29 economics/mechanism-design --- https://www.lesswrong.com/posts/RQpNHSiWaXTvDxt6R/coherent-decisions-imply-consistent-utilities 2021-12-29 statistics/decision --- https://arxiv.org/abs/2209.03953 Fast text2StyleGAN: Text-Free Learning of a Natural Language Interface for Pretrained Face Generators Xiaodan Du, Raymond A. Yeh, Nicholas Kolkin, Eli Shechtman, Greg Shakhnarovich 2022-09-08 2022-09-08 [("doi","10.48550/arXiv.2209.03953")] ai/nn/gan/stylegan ai/nn/transformer/clip

We propose Fast text2StyleGAN, a natural language interface that adapts pre-trained GANs for text-guided human face synthesis.

Leveraging the recent advances in Contrastive Language-Image Pre-training (CLIP), no text data is required during training. Fast text2StyleGAN is formulated as a conditional variational autoencoder (CVAE) that provides extra control and diversity to the generated images at test time. Our model does not require re-training or fine-tuning of the GANs or CLIP when encountering new text prompts. In contrast to prior work, we do not rely on optimization at test time, making our method orders of magnitude (~0.1s) faster than prior work.

Empirically, on FFHQ dataset, our method offers faster and more accurate generation of images from natural language descriptions with varying levels of detail compared to prior work.

--- https://arxiv.org/abs/2207.07285#alibaba X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval Yiwei Ma, Guohai Xu, Xiaoshuai Sun, Ming Yan, Ji Zhang, Rongrong Ji 2022-07-15 2022-07-15 [("doi","10.48550/arXiv.2207.07285")] ai/nn/transformer/clip ai/video/analysis

Video-text retrieval has been a crucial and fundamental task in multi-modal research. The development of video-text retrieval has been considerably promoted by large-scale multi-modal contrastive pre-training, which primarily focuses on coarse-grained or fine-grained contrast. However, cross-grained contrast, which is the contrast between coarse-grained representations and fine-grained representations, has rarely been explored in prior research. Compared with fine-grained or coarse-grained contrasts, cross-grained contrast calculate the correlation between coarse-grained features and each fine-grained feature, and is able to filter out the unnecessary fine-grained features guided by the coarse-grained feature during similarity calculation, thus improving the accuracy of retrieval.

To this end, this paper presents a novel multi-grained contrastive model, namely X-CLIP, for video-text retrieval. However, another challenge lies in the similarity aggregation problem, which aims to aggregate fine-grained and cross-grained similarity matrices to instance-level similarity. To address this challenge, we propose the Attention Over Similarity Matrix (AOSM) module to make the model focus on the contrast between essential frames and words, thus lowering the impact of unnecessary frames and words on retrieval results.

With multi-grained contrast and the proposed AOSM module, X-CLIP achieves outstanding performance on 5 widely-used video-text retrieval datasets, including MSR-VTT (49.3 R@1), MSVD (50.4 R@1), LSMDC (26.1 R@1), DiDeMo (47.8 R@1) and ActivityNet (46.2 R@1). It outperforms the previous state-of-the-art by +6.3%, +6.6%, +11.1%, +6.7%, +3.8% relative improvements on these benchmarks, demonstrating the superiority of multi-grained contrast and AOSM.

--- https://arxiv.org/abs/2208.02816#microsoft X-CLIP: Expanding Language-Image Pretrained Models for General Video Recognition Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling 2022-08-04 2022-08-04 [("doi","10.48550/arXiv.2208.02816")] ai/nn/transformer/clip ai/video/analysis

Contrastive language-image pretraining has shown great success in learning visual-textual joint representation from web-scale data, demonstrating remarkable “zero-shot” generalization ability for various image tasks. However, how to effectively expand such new language-image pretraining methods to video domains is still an open problem.

In this work, we present a simple yet effective approach that adapts the pretrained language-image models to video recognition directly, instead of pretraining a new model from scratch. More concretely, to capture the long-range dependencies of frames along the temporal dimension, we propose a cross-frame attention mechanism that explicitly exchanges information across frames. Such module is lightweight and can be plugged into pretrained language-image models seamlessly. Moreover, we propose a video-specific prompting scheme, which leverages video content information for generating discriminative textual prompts. Extensive experiments demonstrate that our approach is effective and can be generalized to different video recognition scenarios.

In particular, under fully-supervised settings, our approach achieves a top-1 accuracy of 87.1% on Kinetics-400, while using 12× fewer FLOPs compared with Swin-L and ViViT-H. In zero-shot experiments, our approach surpasses the current state-of-the-art methods by +7.6% and +14.9% in terms of top-1 accuracy under two popular protocols. In few-shot scenarios, our approach outperforms previous best methods by +32.1% and +23.1% when the labeled data is extremely limited.

Code and models are available at Github.

[Not to be confused with the earlier Alibaba video CLIP also called “X-CLIP”.]

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4922311/ NMDAR inhibition-independent antidepressant actions of ketamine metabolites Panos Zanos, Ruin Moaddel, Patrick J. Morris, Polymnia Georgiou, Jonathan Fischell, Greg I. Elmer, Manickavasagom Alkondon, Peixiong Yuan, Heather J. Pribut, Nagendra S. Singh, Katina S. S. Dossou, Yuhong Fang, Xi-Ping Huang, Cheryl L. Mayo, Irving W. Wainer, Edson X. Albuquerque, Scott M. Thompson, Craig J. Thomas, Carlos A. Zarate, Todd D. Gould 2016 2021-12-30 [("doi","10.1038/nature17998")] psychedelic psychiatry/depression

Major depressive disorder affects around 16 per cent of the world population at some point in their lives. Despite the availability of numerous monoaminergic-based antidepressants, most patients require several weeks, if not months, to respond to these treatments, and many patients never attain sustained remission of their symptoms. The non-competitive, glutamatergic NMDAR (N-methyl-d-aspartate receptor) antagonist (R,S)-ketamine exerts rapid and sustained antidepressant effects after a single dose in patients with depression, but its use is associated with undesirable side effects.

Here we show that the metabolism of (R,S)-ketamine to (2S,6S;2R,6R)-hydroxynorketamine (HNK) is essential for its antidepressant effects, and that the (2R,6R)-HNK enantiomer exerts behavioral, electroencephalographic, electrophysiological and cellular antidepressant-related actions in mice. These antidepressant actions are independent of NMDAR inhibition but involve early and sustained activation of AMPARs (α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors).

We also establish that (2R,6R)-HNK lacks ketamine-related side effects. Our data implicate a novel mechanism underlying the antidepressant properties of (R,S)-ketamine and have relevance for the development of next-generation, rapid-acting antidepressants.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2653069/ Overstating the evidence: double counting in meta-analysis and related problems Stephen J. Senn 2009 2021-12-30 [("doi","10.1186/1471-2288-9-10")] statistics/meta-analysis

Background: The problem of missing studies in meta-analysis has received much attention. Less attention has been paid to the more serious problem of double counting of evidence.

Method: Various problems in overstating the precision of results from meta-analyses are described and illustrated with examples, including papers from leading medical journals. These problems include, but are not limited to, simple double counting of the same studies, double counting of some aspects of the studies, inappropriate imputation of results, and assigning spurious precision to individual studies.

Results: Some suggestions are made as to how the quality and reliability of meta-analysis can be improved. It is proposed that the key to quality in meta-analysis lies in the results being transparent and checkable.

Conclusion: Existing quality check lists for meta-analysis do little to encourage an appropriate attitude to combining evidence and to statistical analysis. Journals and other relevant organizations should encourage authors to make data available and make methods explicit. They should also act promptly to withdraw meta-analyses when mistakes are found.

--- https://arxiv.org/abs/2108.07639 Learning C to x86 Translation: An Experiment in Neural Compilation Jordi Armengol-Estapé, Michael F. P. O’Boyle 2021-08-17 2021-12-30 [("doi","10.48550/arXiv.2108.07639")] ai/nn/transformer/gpt/codex

Deep learning has had an impact on many fields. Recently, code-to-code neural models have been used in code translation, code refinement and decompilation. However, the question of whether these models can automate compilation has yet to be investigated.

In this work, we explore neural compilation: building and evaluating Transformer models that learn how to produce x86 assembler from C code.

Although preliminary results are relatively weak, we make our data, models and code publicly available to encourage further research in this area.

… While we can successfully generate syntactically correct assembler >80% of the time and obtain high BLEU scores ~90%, generating semantically correct assembler is more challenging. The best model can only compile correctly ~33% of the functions in a benchmark built from an existing program synthesis evaluation set4; it specially struggles to compile functions with numerous arguments and arrays.

--- https://arxiv.org/abs/2010.04811 Presyn: Modeling Black-Box Components with Probabilistic Synthesis Bruce Collie, Jackson Woodruff, Michael F. P. O’Boyle 2020-10-09 2021-12-30 [("doi","10.1145/3425898.3426952")] cs/algorithm

This paper is concerned with synthesizing programs based on black-box oracles: we are interested in the case where there exists an executable implementation of a component or library, but its internal structure is unknown. We are provided with just an API or function signature, and aim to synthesize a program with equivalent behavior.

To attack this problem, we detail Presyn: a [Markov chain] program synthesizer designed for flexible interoperation with existing programs and compiler toolchains. Presyn uses high-level imperative control-flow structures and a pair of cooperating predictive models to efficiently narrow the space of potential programs. These models can be trained effectively on small corpora of synthesized examples.

We evaluate Presyn against 5 leading program synthesizers on a collection of 112 synthesis benchmarks collated from previous studies and real-world software libraries.

We show that Presyn is able to synthesize a wider range of programs than each of them with less human input. We demonstrate the application of our approach to real-world code and software engineering problems with two case studies: accelerator library porting and detection of duplicated library reimplementations.

--- https://www.biorxiv.org/content/10.1101/2022.09.07.506932.full The Phenotype-Genotype Reference Map: Improving biobank data science through replication. Lisa Bastarache, Sarah B. Delozier, Anita Pandit, Jing He, Adam Lewis, Aubry C. Annis, Jonathon LeFaive, Joshua C. Denny, Robert J. Carroll, Jacob J. Hughey, Matthew Zawistowski, Josh F. Peterson 2022-09-08 2022-09-08 [("doi","10.1101/2022.09.07.506932")] genetics/heritable statistics/power-analysis

Population-scale biobanks linked to electronic health record data provide vast opportunity to extend our knowledge of human genetics. While biobanks have already proven their value to research, data quality remains an important concern.

Here we introduce the phenotype-genotype reference map (PGRM), a set of 5,879 genetic associations from 523 GWAS publications that can be used for high-throughput replication experiments in biobank data.

We tested the PGRM on 5 ancestry-specific cohorts drawn from 4 established, independent biobanks and found evidence of robust replications across a wide array of phenotypes.

We defined simple replication measures and show how these can be applied to any EHR-linked biobank to detect data corruption and to empirically assess parameters for phenome-wide studies.

Finally, we used the PGRM to determine factors associated with reproducibility of GWAS results.

--- https://arxiv.org/abs/2209.01975 Vote-K: Selective Annotation Makes Language Models Better Few-Shot Learners Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah Smith, Tao Yu 2022-09-05 2022-09-05 [("doi","10.48550/arXiv.2209.01975")] ai/nn/retrieval ai/nn/transformer/gpt/codex reinforcement-learning/exploration

Many recent approaches to natural language tasks are built on the remarkable abilities of large language models. Large language models can perform in-context learning, where they learn a new task from a few task demonstrations, without any parameter updates. This work examines the implications of in-context learning for the creation of datasets for new natural language tasks.

Departing from recent in-context learning methods, we formulate an annotation-efficient, two-step framework: selective annotation that chooses a pool of examples to annotate from unlabeled data in advance, followed by prompt retrieval that retrieves task examples from the annotated pool at test time.

Based on this framework, we propose an unsupervised, graph-based selective annotation method, vote-k, to select diverse, representative examples to annotate.

Extensive experiments on 10 datasets (covering classification, commonsense reasoning, dialogue, and text/code generation) demonstrate that our selective annotation method improves the task performance by a large margin. On average, vote-k achieves a 12.9%/11.4% relative gain under an annotation budget of 18/100, as compared to randomly selecting examples to annotate. Compared to state-of-the-art supervised finetuning approaches, it yields similar performance with 10–100× less annotation cost across 10 tasks.

We further analyze the effectiveness of our framework in various scenarios: language models with varying sizes [GPT-J, GPT-Neo, OPT-175, GPT-3, Codex (codex-davinci-002)], alternative selective annotation methods, and cases where there is a test data domain shift.

We hope that our studies will serve as a basis for data annotations as large language models are increasingly applied to new tasks.

Our code is available at Github.

--- https://en.wikipedia.org/wiki/Vin_Mariani Vin Mariani 2021-12-30 nootropic --- https://replicate.com/tommoore515/material_stable_diffusion 2021-12-30 ai/nn/diffusion --- https://sevensecularsermons.org/why-atheists-need-ecstasy/ 2021-12-30 philosophy/religion psychedelic --- https://arxiv.org/abs/2208.14755 Python Type Hints are Turing Complete Ori Roth 2022-08-31 2022-08-31 [("doi","10.48550/arXiv.2208.14755")] cs/computable

Grigore 2016 showed that Java generics are Turing complete by describing a reduction from Turing machines to Java subtyping.

We apply Grigore’s algorithm to Python type hints and deduce that they are Turing complete. In addition, we present an alternative reduction in which the Turing machines are simulated in real time, resulting in much lower compilation times.

Our work is accompanied by a Python implementation of both reductions that compiles Turing machines into Python subtyping machines.

--- https://en.wikipedia.org/wiki/Collective_effervescence Collective effervescence 2021-12-31 sociology/abandoned-footnotes --- /doc/psychology/novelty/2022-jia.pdf Collaborations and Innovation in Partitioned Industries: An Analysis of U.S. Feature Film Coproductions Ruo Jia, Demetrius Lewis, Giacomo Negro 2022-06-07 2022-06-07 [("doi","10.1287/orsc.2022.1600")] economics psychology/novelty

In partitioned industries, a small number of generalist organizations occupy the center of the market, whereas a much larger number of specialists populate the periphery. The role of collaborations within and across the center-periphery boundary in these industries has been underexplored.

We propose that hybrid collaborations between organizations in the center and periphery—combining the broad resource base of generalists with the focused knowledge of specialists—encourage product innovation and result in enhanced organizational adaptation for both populations.

We test these ideas in the U.S. motion picture industry, where film production companies face unpredictability of success and fluctuating audience tastes.

We find that generalist and specialist production companies that partner to produce films introduce more creative content in their films compared with those that collaborate in the same population or produce alone. Generalist film companies benefit further from these collaborations through increased competitive differentiation of their films from other generalists in subsequent productions, whereas specialists experience lower exit rates.

These findings suggest that interorganizational collaborations between generalists and specialists provide effective adaptive strategies to compete in markets with uncertain demand and shifting audience preferences. These strategies can sustain, rather than weaken, industry partitioning.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7724128/ Nothing but the truth: Consistency and efficiency of the list experiment method for the measurement of sensitive health behaviors Aurélia Lépine, Carole Treibich, Ben D’Exelle 2020 2021-12-31 [("doi","10.1016/j.socscimed.2020.113326")] crime sociology/preference-falsification

Rationale: Social desirability bias, which is the tendency to under-report socially, undesirable health behaviors, distorts information on sensitive behaviors, gained from self-reports and prevents accurate estimation of the prevalence of those, behaviors. We contribute to a growing body of literature that seeks to assess the performance of the list experiment method to improve estimation of these sensitive health behaviors.

Method: We use a double-list experiment design in which respondents serve as the treatment group for one list and as the control group for the other list to estimate the prevalence of two sensitive health behaviors in different settings: condom use among 500 female sex workers in urban Senegal and physical intimate partner violence among 1,700 partnered women in rural Burkina Faso. First, to assess whether the list experiment improves the accuracy of estimations of the prevalence of sensitive behaviors, we compare the prevalence rates estimated from self-reports with those elicited through the list experiment. Second, we test whether the prevalence rates of the sensitive behaviors obtained using the double-list design are similar, and we estimate the reduction in the standard errors obtained with this design. Finally, we compare the results obtained through another indirect elicitation method, the polling vote method.

Results: We show that the list experiment method reduces misreporting by 17 percentage points for condom use and 16–20 percentage points for intimate partner violence. Exploiting the double-list experiment design, we also demonstrate that the prevalence estimates obtained through the use of the two lists are identical in the full sample and across sub-groups and that the double-list design reduces the standard errors by ~40% compared to the standard errors in the simple list design. Finally, we show that the list experiment method leads to a higher estimation of the prevalence of sensitive behaviors than the polling vote method.

Conclusion: The study suggests that list experiments are an effective method to improve estimation of the prevalence of sensitive health behaviors.

--- https://en.wikipedia.org/wiki/Hypocaust Hypocaust 2021-12-31 technology --- https://arxiv.org/abs/math/0512268 Proof of Two Dimensional Jacobian Conjecture Yucai Su 2005-12-13 2021-12-31 [("doi","10.48550/arXiv.0512268")] math

This paper is cancelled.

--- https://www.biorxiv.org/content/10.1101/2022.09.07.506982.full Single-cell transcriptomics of resected human traumatic brain injury tissues reveals acute activation of endogenous retroviruses in oligodendrocytes Raquel Garza, Yogita Sharma, Diahann Atacho, Sami Abu Hamdeh, Marie E. Jonsson, Martin Ingelsson, Patric Jern, Molly Gale Hammell, Elisabet Englund, Johan Jakobsson, Niklas Marklund 2022-09-09 2022-09-09 [("doi","10.1101/2022.09.07.506982")] psychiatry/traumatic-brain-injury

Traumatic brain injury (TBI) is a leading cause of persistent functional brain impairment and results in a robust, but poorly understood, neuroinflammatory response that contributes to the long-term pathology.

Here, we used single-nuclei RNA-sequencing to study transcriptomic changes in different cell populations from human brain tissue obtained acutely after severe, life-threatening TBI.

We found a unique transcriptional response in several cell types, including the activation of an interferon response in oligodendrocytes coupled with the transcriptional activation of MHC-class I and class II related genes. Thus, oligodendrocytes undergo a transformation to an immune-like cell state immediately after TBI, indicating an important role for these cells in the initiation of neuroinflammation. Notably, the activation of immune-related genes correlated with the expression of endogenous retroviruses in oligodendrocytes, linking these ancient viral sequences to neuroinflammation.

In summary, this work provides a unique insight into the initiating events of the neuroinflammatory response in TBI, which has new therapeutic implications.

--- https://en.wikipedia.org/wiki/Loss_(comic) Loss (comic) 2021-12-31 design fiction/humor --- https://arxiv.org/abs/1408.3421 On the genetic architecture of intelligence and other quantitative traits Steve Hsu 2014-08-14 2021-12-31 [("doi","10.48550/arXiv.1408.3421")] genetics/heritable genetics/selection/artificial/index-selection iq/high statistics/power-analysis

How do genes affect cognitive ability or other human quantitative traits such as height or disease risk? Progress on this challenging question is likely to be large in the near future.

I begin with a brief review of psychometric measurements of intelligence, introducing the idea of a “general factor” or g score. The main results concern the stability, validity (predictive power), and heritability of adult g. The largest component of genetic variance for both height and intelligence is additive (linear), leading to important simplifications in predictive modeling and statistical estimation.

Due mainly to the rapidly decreasing cost of genotyping, it is possible that within the coming decade researchers will identify loci which account for a large fraction of total g variation. In the case of height analogous efforts are well under way.

I describe some unpublished results concerning the genetic architecture of height and cognitive ability, which suggest that roughly 10k moderately rare causal variants of mostly negative effect are responsible for normal population variation.

Using results from Compressed Sensing (𝓁1-penalized regression), I estimate the statistical power required to characterize both linear and nonlinear models for quantitative traits.

The main unknown parameter s (sparsity) is the number of loci which account for the bulk of the genetic variation. The required sample size is of order 100s, or roughly a million in the case of cognitive ability.

--- https://arxiv.org/abs/1312.5670 The availability of research data declines rapidly with article age Timothy Vines, Arianne Albert, Rose Andrew, Florence Debarré, Dan Bock, Michelle Franklin, Kimberley Gilbert, Jean-Sébastien Moore, Sébastien Renaut, Diana J. Rennison 2013-12-19 2021-12-31 [("doi","10.1016/j.cub.2013.11.014")] cs/linkrot statistics/bias

Policies ensuring that research data are available on public archives are increasingly being implemented at the government [1], funding agency [2–4], and journal [5,6] level. These policies are predicated on the idea that authors are poor stewards of their data, particularly over the long term [7], and indeed many studies have found that authors are often unable or unwilling to share their data [8–11]. However, there are no systematic estimates of how the availability of research data changes with time since publication.

We therefore requested datasets from a relatively homogeneous set of 516 articles published 2–22 years ago, and found that availability of the data was strongly affected by article age. For papers where the authors gave the status of their data, the odds of a dataset being extant fell by 17% per year. In addition, the odds that we could find a working email address for the first, last, or corresponding author fell by 7% per year.

Our results reinforce the notion that, in the long term, research data cannot be reliably preserved by individual researchers, and further demonstrate the urgent need for policies mandating data sharing via public archives.

--- https://arxiv.org/abs/1710.02298#deepmind Rainbow: Combining Improvements in Deep Reinforcement Learning Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver 2017-10-06 2021-12-31 [("doi","10.48550/arXiv.1710.02298")] reinforcement-learning/model-free

The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined.

This paper examines 6 extensions to the DQN algorithm [DDQN & dueling networks, prioritized replay, multi-step learning/n-step returns, distributional RL, Noisy Nets] and empirically studies their combination.

Our experiments show that the combination provides state-of-the-art performance on the ALE benchmark, both in terms of data efficiency and final performance.

We also provide results from a detailed ablation study that shows the contribution of each component to overall performance.

--- https://arxiv.org/abs/1511.05952#deepmind Prioritized Experience Replay Tom Schaul, John Quan, Ioannis Antonoglou, David Silver 2015-11-18 2021-12-31 [("doi","10.48550/arXiv.1511.05952")] reinforcement-learning/model-free

Experience replay lets online reinforcement learning agents remember and reuse experiences from the past. In prior work, experience transitions were uniformly sampled from a replay memory. However, this approach simply replays transitions at the same frequency that they were originally experienced, regardless of their importance.

In this paper we develop a framework for prioritizing experience, so as to replay important transitions more frequently, and therefore learn more efficiently. We use prioritized experience replay in Deep Q-Networks (DQN), a reinforcement learning algorithm that achieved human-level performance across many Atari ALE games.

DQN with prioritized experience replay achieves a new state-of-the-art, outperforming DQN with uniform replay on 41⁄49 games.

--- https://arxiv.org/abs/1509.06461#deepmind Deep Reinforcement Learning with Double Q-learning Hado van Hasselt, Arthur Guez, David Silver 2015-09-22 2022-01-01 [("doi","10.48550/arXiv.1509.06461")] reinforcement-learning/model-free

The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented.

In this paper, we answer all these questions affirmatively. In particular, we first show that the recent DQN algorithm, which combines Q-learning with a deep neural network, suffers from substantial overestimations in some games in the Atari 2600 ALE domain.

We then show that the idea behind the Double Q-learning algorithm, which was introduced in a tabular setting, can be generalized to work with large-scale function approximation.

We propose a specific adaptation to the DQN algorithm and show that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several games.

--- https://arxiv.org/abs/1511.06581#deepmind Dueling Network Architectures for Deep Reinforcement Learning Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas 2015-11-20 2022-01-01 [("doi","10.48550/arXiv.1511.06581")] reinforcement-learning/model-free

In recent years there have been many successes of using deep representations in reinforcement learning. Still, many of these applications use conventional architectures, such as convolutional networks, LSTMs, or autoencoders.

In this paper, we present a new neural network architecture for model-free reinforcement learning. Our dueling network represents two separate estimators: one for the state value function and one for the state-dependent action advantage function. The main benefit of this factoring is to generalize learning across actions without imposing any change to the underlying reinforcement learning algorithm.

Our results show that this architecture leads to better policy evaluation in the presence of many similar-valued actions.

Moreover, the dueling architecture enables our RL agent to outperform the state-of-the-art on the Atari 2600 ALE domain.

--- https://arxiv.org/abs/1707.06887#deepmind A Distributional Perspective on Reinforcement Learning Marc G. Bellemare, Will Dabney, Rémi Munos 2017-07-21 2022-01-01 [("doi","10.48550/arXiv.1707.06887")] reinforcement-learning/model-free

In this paper we argue for the fundamental importance of the value distribution: the distribution of the random return received by a reinforcement learning agent. This is in contrast to the common approach to reinforcement learning which models the expectation of this return, or value. Although there is an established body of literature studying the value distribution, thus far it has always been used for a specific purpose such as implementing risk-aware behavior.

We begin with theoretical results in both the policy evaluation and control settings, exposing a large distributional instability in the latter. We then use the distributional perspective to design a new algorithm which applies Bellman’s equation to the learning of approximate value distributions.

We evaluate our algorithm using the suite of games from the Atari ALE. We obtain both state-of-the-art results and anecdotal evidence demonstrating the importance of the value distribution in approximate reinforcement learning.

Finally, we combine theoretical and empirical evidence to highlight the ways in which the value distribution impacts learning in the approximate setting.

--- https://www.biorxiv.org/content/10.1101/2022.09.07.506960.full Testing Wright’s Intermediate Population Size Hypothesis—When Genetic Drift is a Good Thing Mitch Cruzan 2022-09-09 2022-09-09 [("doi","10.1101/2022.09.07.506960")] genetics/selection/natural

In his 1931 monograph, Sewall Wright predicted genetic drift would overwhelm selection in small populations, and selection would dominate in large ones, but he also concluded drift could facilitate selection in populations of intermediate size. The idea that drift and selection would act together in populations of intermediate size has been almost completely ignored even as empirical evidence of rapid evolution associated with population bottlenecks has continued to accumulate.

I used forward-time simulations with random mating and discrete generations to test the hypothesis that drift can facilitate selection.

I find that selection generates biased distributions of Δq, and this bias is greatest for population sizes 20–200, resulting in drift facilitation. Drift facilitation reduces the accumulation of drift load and segregation load for populations of intermediate size. Small populations accumulated higher levels of drift load and large populations maintained high levels of segregation load.

Fixation of beneficial mutations is facilitated in intermediate populations when dominance is low and selection is weak. Accumulation of beneficial mutations over time (fixation flux) was higher across small to intermediate size populations and declined rapidly for large populations. Compared to predictions of fixation time for codominant beneficial mutations from diffusion equations, drift facilitation accelerates fixation in populations of intermediate size, but fixation time is slower in large populations when selection is weak.

These results suggest drift facilitation in small and intermediate populations promote purging of genetic load and fixation of beneficial mutations, and may result in relatively rapid adaptation compared to large populations.

--- https://arxiv.org/abs/2209.02842 ASR2K: Speech Recognition for Around 2,000 Languages without Audio Xinjian Li, Florian Metze, David R. Mortensen, Alan W. Black, Shinji Watanabe 2022-09-06 2022-09-06 [("doi","10.48550/arXiv.2209.02842")] ai/nn/transformer

Most recent speech recognition models rely on large supervised datasets, which are unavailable for many low-resource languages. In this work, we present a speech recognition pipeline that does not require any audio for the target language. The only assumption is that we have access to raw text datasets or a set of n-gram statistics. Our speech pipeline consists of 3 components: acoustic, pronunciation, and language models. Unlike the standard pipeline, our acoustic and pronunciation models use multilingual models without any supervision. The language model is built using n-gram statistics or the raw text dataset.

We build speech recognition for 1909 languages by combining it with Crubadan: a large endangered languages n-gram database. Furthermore, we test our approach on 129 languages across two datasets: Common Voice and CMU Wilderness dataset. We achieve 50% CER and 74% WER on the Wilderness dataset with Crubadan statistics only and improve them to 45% CER and 69% WER when using 10,000 raw text utterances.

--- https://posts.decontextualize.com/language-models-poetry/ 2022-01-01 ai/poetry --- https://arxiv.org/abs/2005.08314#facebook TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data Pengcheng Yin, Graham Neubig, Wen-tau Yih, Sebastian Riedel 2020-05-17 2022-01-01 [("doi","10.48550/arXiv.2005.08314")] ai/nn/transformer ai/tabular

Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks. Such models are typically trained on free-form NL text, hence may not be suitable for tasks like semantic parsing over structured data, which require reasoning over both free-form NL questions and structured tabular data (eg. database tables).

In this paper we present TaBERT, a pretrained LM that jointly learns representations for NL sentences and (semi-)structured tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts.

In experiments, neural semantic parsers using TaBERT as feature representation layers achieve new best results on the challenging weakly-supervised semantic parsing benchmark WikiTableQuestions, while performing competitively on the text-to-SQL dataset Spider.

Implementation of the model will be available at https://github.com/facebookresearch/TaBERT.

--- https://arxiv.org/abs/2004.02349#google TAPAS: Weakly Supervised Table Parsing via Pre-training Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno, Julian Martin Eisenschlos 2020-04-05 2022-01-01 [("doi","10.18653/v1/2020.acl-main.398")] ai/nn/transformer ai/tabular wikipedia

Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition, the generated logical forms are only used as an intermediate step prior to retrieving the denotation.

In this paper, we present TAPAS, an approach to question answering over tables without generating logical forms. TAPAS trains from weak supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation operator to such selection. TAPAS extends BERT’s architecture to encode tables as input, initializes from an effective joint pre-training of text segments and tables crawled from Wikipedia, and is trained end-to-end.

We experiment with 3 different semantic parsing datasets, and find that TAPAS outperforms or rivals semantic parsing models by improving state-of-the-art accuracy on SQA 55.1 → 67.2 and performing on par with the state-of-the-art on WIKISQL and WIKITQ, but with a simpler model architecture. We additionally find that transfer learning, which is trivial in our setting, from WIKISQL to WIKITQ, yields 48.7 accuracy, 4.2 points above the state-of-the-art.

--- https://en.wikipedia.org/wiki/Quantum_pseudo-telepathy Quantum pseudo-telepathy 2022-01-01 science statistics/bayes statistics/decision --- https://x.com/EMostaque/status/1567791390880317441 2022-01-01 ai/anime ai/nn/diffusion --- https://arxiv.org/abs/2208.13315 Normalized Activation Function: Toward Better Convergence Yuan Peiwen, Zhu Changsheng 2022-08-29 2022-08-29 [("doi","10.48550/arXiv.2208.13315")] ai/nn

Activation functions are essential for neural networks to introduce non-linearity. A great number of empirical experiments have validated various activation functions, yet theoretical research on activation functions are insufficient.

In this work, we study the impact of activation functions on the variance of gradients and propose an approach to normalize activation functions to keep the variance of the gradient same for all layers so that the neural network can achieve better convergence.

First, we complement the previous work on the analysis of the variance of gradients where the impact of activation functions are just considered in an idealized initial state which almost cannot be preserved during training and obtained a property that good activation functions should satisfy as possible. Second, we offer an approach to normalize activation functions and testify its effectiveness on prevalent activation functions empirically. And by observing experiments, we discover that the speed of convergence is roughly related to the property we derived in the former part.

We run experiments of our normalized activation functions against common activation functions. And the result shows our approach consistently outperforms their unnormalized counterparts. For example, normalized Swish outperforms vanilla Swish by 1.2% on ResNet-50 with CIFAR-100 in terms of top-1 accuracy.

Our method improves the performance by simply replacing activation functions with their normalized ones in both fully-connected networks and residual networks.

--- https://en.wikipedia.org/wiki/Genetic_load Genetic load 2022-01-02 genetics/heritable/rare genetics/selection/natural --- https://x.com/goodside/status/1569128808308957185 2022-01-02 ai/nn/transformer/gpt/non-fiction reinforcement-learning/safe --- https://www.biorxiv.org/content/10.1101/2022.09.08.504083.full Haplotype-resolved assemblies and variant benchmark of a Chinese Quartet Peng Jia, Lianhua Dong, Xiaofei Yang, Bo Wang, Tingjie Wang, Jiadong Lin, Songbo Wang, Xixi Zhao, Tun Xu, Yizhuo Che, Ningxin Dang, Luyao Ren, Yujing Zhang, Xia Wang, Fan Liang, Yang Wang, Jue Ruan, The Quartet Project Team, Yuanting Zheng, Leming Shi, Jing Wang, Kai Ye 2022-09-12 2022-09-12 [("doi","10.1101/2022.09.08.504083")] genetics/sequencing

As the state-of-the-art sequencing technologies and computational methods enable investigation of challenging regions in the human genome, an update variant benchmark is demanded.

Herein, we sequenced a Chinese Quartet, consisting of two monozygotic twin daughters and their biological parents, with multiple advanced sequencing platforms, including Illumina, BGI, PacBio, and Oxford Nanopore Technology. We phased the long reads of the monozygotic twin daughters into paternal and maternal haplotypes using the parent-child genetic map. For each haplotype, we utilized advanced long reads to generate haplotype-resolved assemblies (HRAs) with high accuracy, completeness, and continuity.

Based on the ingenious quartet samples, novel computational methods, high-quality sequencing reads, and HRAs, we established a comprehensive variant benchmark, including 3,883,283 SNVs, 859,256 Indels, 9,678 large deletions, 15,324 large insertions, 40 inversions, and 31 complex structural variants shared between the monozygotic twin daughters. In particular, the preciously excluded regions, such as repeat regions and the human leukocyte antigen (HLA) region, were systematically examined.

Finally, we illustrated how the sequencing depth correlated with the de novo assembly and variant detection, from which we learned that 30 X HiFi is a balance between performance and cost.

In summary, this study provides high-quality haplotype-resolved assemblies and a variant benchmark for two Chinese monozygotic twin samples. The benchmark expanded the regions of the previous report and adapted to the evolving sequencing technologies and computational methods.

--- https://arxiv.org/abs/2201.02973#google MAXIM: Multi-Axis MLP for Image Processing Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li 2022-01-09 2022-01-09 [("doi","10.48550/arXiv.2201.02973")] ai/nn/fully-connected

Recent progress on Transformers and multi-layer perceptron (MLP) models provide new network architectural designs for computer vision tasks. Although these models proved to be effective in many vision tasks such as image recognition, there remain challenges in adapting them for low-level vision. The inflexibility to support high-resolution images and limitations of local attention are perhaps the main bottlenecks.

In this work, we present a multi-axis MLP based architecture called MAXIM, that can serve as an efficient and flexible general-purpose vision backbone for image processing tasks. MAXIM uses a U-Net-shaped hierarchical structure and supports long-range interactions enabled by spatially-gated MLPs. Specifically, MAXIM contains two MLP-based building blocks: a multi-axis gated MLP that allows for efficient and scalable spatial mixing of local and global visual cues, and a cross-gating block, an alternative to cross-attention, which accounts for cross-feature conditioning. Both these modules are exclusively based on MLPs, but also benefit from being both global and ‘fully-convolutional’, two properties that are desirable for image processing.

Our extensive experimental results show that the proposed MAXIM model achieves state-of-the-art performance on more than 10 benchmarks across a range of image processing tasks, including denoising, deblurring, deraining, dehazing, and enhancement while requiring fewer or comparable numbers of parameters and FLOPs than competitive models. The source code and trained models will be available at https://github.com/google-research/maxim.

--- https://x.com/natanielruizg/status/1569419372598382595 2022-01-02 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/1912.12187 Learning Neural Activations Fayyaz ul Amir Afsar Minhas, Amina Asif 2019-12-27 2022-01-02 [("doi","10.48550/arXiv.1912.12187")] ai/nn reinforcement-learning/meta-learning

An artificial neuron is modelled as a weighted summation followed by an activation function which determines its output. A wide variety of activation functions such as rectified linear units (ReLU), leaky-ReLU, Swish, MISH, etc. have been explored in the literature.

In this short paper, we explore what happens when the activation function of each neuron in an artificial neural network is learned natively from data alone. This is achieved by modeling the activation function of each neuron as a small neural network whose weights are shared by all neurons in the original network.

We list our primary findings in the conclusions section.

The code for our analysis is available at: Github.

--- https://www.reddit.com/r/StableDiffusion/comments/xcoe2s/this_community_continues_to_blow_me_away_8_days/ 2022-01-02 ai/nn/transformer/clip/sample --- https://poets.org/poem/conundrum-workshops 2022-01-02 culture fiction/poetry --- /doc/japan/poetry/shotetsu/1989-carter-waitingforthewind.pdf Waiting for the Wind: 36 Poets of Japan’s Late Medieval Age Steven D. Carter 1989-01-01 2022-01-02 japan/poetry/shotetsu japan/poetry/teika --- https://en.wikipedia.org/wiki/Japanese_poetry Japanese poetry 2022-01-02 japan/poetry --- https://en.wikipedia.org/wiki/Nij%C5%ABichidaish%C5%AB Nijūichidaishū 2022-01-03 japan/poetry --- https://en.wikipedia.org/wiki/Man%27y%C5%8Dsh%C5%AB Man’yōshū 2022-01-03 japan/poetry --- https://en.wikipedia.org/wiki/Kokin_Wakash%C5%AB Kokin Wakashū 2022-01-03 japan/poetry --- https://en.wikipedia.org/wiki/Waka_(poetry) Waka (poetry) 2022-01-03 japan/poetry --- https://en.wikipedia.org/wiki/Shin_Kokin_Wakash%C5%AB Shin Kokin Wakashū 2022-01-03 japan/poetry/teika --- https://en.wikipedia.org/wiki/Fujiwara_no_Teika Fujiwara no Teika 2022-01-03 japan/poetry/teika --- https://en.wikipedia.org/wiki/Renga Renga 2022-01-03 japan/poetry/shotetsu --- https://en.wikipedia.org/wiki/Sh%C5%8Dtetsu Shōtetsu 2022-01-03 japan/poetry/shotetsu --- https://en.wikipedia.org/wiki/Imagawa_Ry%C5%8Dshun Imagawa Ryōshun 2022-01-03 japan/poetry/shotetsu --- https://en.wikipedia.org/wiki/Shinshokukokin_Wakash%C5%AB Shinshokukokin Wakashū 2022-01-03 japan/poetry/shotetsu --- https://en.wikipedia.org/wiki/Y%C5%ABgen Yūgen 2022-01-03 japan/poetry/shotetsu --- https://en.wikipedia.org/wiki/Emperor_Go-Toba Emperor Go-Toba 2022-01-04 japan/poetry/teika --- https://en.wikipedia.org/wiki/Fujiwara_no_Shunzei Fujiwara no Shunzei 2022-01-04 japan/poetry/teika --- https://en.wikipedia.org/wiki/Fujiwara_clan Fujiwara family 2022-01-04 japan/poetry/teika --- https://en.wikipedia.org/wiki/Saigy%C5%8D Saigyō 2022-01-04 japan/poetry/teika --- https://en.wikipedia.org/wiki/Emperor_Go-Horikawa Emperor Go-Horikawa 2022-01-04 japan/poetry/teika --- https://en.wikipedia.org/wiki/Shinchokusen_Wakash%C5%AB Shinchokusen Wakashū 2022-01-04 japan/poetry/teika --- https://en.wikipedia.org/wiki/Emperor_Juntoku Emperor Juntoku 2022-01-04 japan/poetry/teika --- https://en.wikipedia.org/wiki/Minamoto_no_Sanetomo Minamoto no Sanetomo 2022-01-04 japan/poetry/teika --- https://clagnut.com/blog/2395 2022-01-04 cs/css --- https://x.com/ThomasMiconi/status/1569408502447374336 2022-01-04 ai/nn/transformer/gpt/codex --- https://xenaproject.wordpress.com/2022/09/12/beyond-the-liquid-tensor-experiment/ 2022-01-04 ai/nn/transformer/gpt/codex math --- https://journal.atp.art/the-next-rembrandt-who-holds-the-copyright-in-computer-generated-art/ 2022-01-05 ai/anime ai/nn/gan/stylegan economics/copyright --- https://yirugi.github.io/papers/apex.pdf 2022-01-05 cs/algorithm --- https://arxiv.org/abs/2209.05442#google Soft Diffusion: Score Matching for General Corruptions Giannis Daras, Mauricio Delbracio, Hossein Talebi, Alexandros G. Dimakis, Peyman Milanfar 2022-09-12 2022-09-12 [("doi","10.48550/arXiv.2209.05442")] ai/nn/diffusion

[Twitter] We define a broader family of corruption processes that generalizes previously known diffusion models. To reverse these general diffusions, we propose a new objective called Soft Score Matching that provably learns the score function for any linear corruption process and yields state-of-the-art results for CelebA.

Soft Score Matching incorporates the degradation process in the network and trains the model to predict a clean image that after corruption matches the diffused observation. We show that our objective learns the gradient of the likelihood under suitable regularity conditions for the family of corruption processes. We further develop a principled way to select the corruption levels for general diffusion processes and a novel sampling method that we call Momentum Sampler. We evaluate our framework with the corruption being Gaussian Blur and low magnitude additive noise.

Our method achieves state-of-the-art FID score 1.85 on CelebA-64, outperforming all previous linear diffusion models.

We also show computational benefits compared to vanilla denoising diffusion.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3016382/ Estimated intelligence quotient in anorexia nervosa: A systematic review and meta-analysis of the literature 2010-12-23 2022-01-05 iq psychiatry/anorexia --- /doc/iq/2010-meyer.pdf Correspondence Between the General Ability to Discriminate Sensory Stimuli and General Intelligence Christine Sandra Meyer, Priska Hagmann-von Arx, Sakari Lemola, Alexander Grob 2010-01-13 2022-01-05 [("doi","10.1027/1614-0001/a000006")] iq psychology/neuroscience

For more than a century the veracity of Spearman’s postulate that there is a nearly perfect correspondence between general intelligence and general sensory discrimination has remained unresolved. Most studies have found statistically-significant albeit small correlations. However, this can be used neither to confirm nor dismiss Spearman’s postulate, a major weakness of previous research being that only single discrimination capacities were considered rather than general discrimination.

The present study examines Spearman’s hypothesis with a sample of 1,330 5–10-year-old children, using structural equation modeling.

The results support Spearman’s hypothesis with a strong correlation (r = 0.78). In addition, age-group-specific analyses explored the age differentiation hypothesis.

Results are discussed in terms of the validity of the general sensory discrimination factor.

--- /doc/statistics/bayes/2003-korb.pdf Bayesian Informal Logic and Fallacy Kevin Korb 2004-01-01 2022-01-05 [("doi","10.22329/il.v24i1.2132")] philosophy/epistemology statistics/bayes statistics/decision

Bayesian reasoning has been applied formally to statistical inference, machine learning and analysing scientific method.

Here I apply it informally to more common forms of inference, namely natural language arguments.

I analyse a variety of traditional fallacies, deductive, inductive and causal, and find more merit in them than is generally acknowledged.

Bayesian principles provide a framework for understanding ordinary arguments which is well worth developing.

--- https://jamanetwork.com/journals/jamapsychiatry/fullarticle/1107236 Excess Statistical-Significance Bias in the Literature on Brain Volume Abnormalities Ioannidis 2011 2022-01-05 psychology/neuroscience statistics/bias --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428196/ Effects of glucagon-like peptide 1 analogs on alcohol intake in alcohol-preferring vervet monkeys Morgane Thomsen, Jens Juul Holst, Anna Molander, Kristian Linnet, Maurice Ptito, Anders Fink-Jensen 2019 2022-01-05 [("doi","10.1007/s00213-018-5089-z")] longevity/glp/psychology longevity/glp/semaglutide psychiatry psychology/neuroscience

Background: Preclinical studies in rodents have demonstrated inhibitory effects of glucagon-like peptide-1 (GLP-1) receptor stimulation on alcohol consumption. The effects of GLP-1 receptor stimulation on alcohol intake in primates have not been investigated.

Method: We performed placebo-controlled studies on the effects of the GLP-1 receptor agonists exenatide and liraglutide on alcohol consumption in alcohol-preferring male African vervet monkeys. Monkeys selected for voluntary alcohol drinking were observed for at least 10 days of baseline drinking and allocated to drug or vehicle (n = 11–12 per group) balanced with respect to alcohol intake. Monkeys had access to alcohol 4 h/day. In a first study, monkeys were treated with exenatide 0.04 mg/kg or vehicle once weekly for 5 weeks to obtain steady-state plasma levels. In a second study, monkeys were treated daily with liraglutide (increasing dosing, 10 to 50 μg/kg/day) or vehicle over 2 weeks. In both studies, access to alcohol was suspended during drug up-titration. Then, alcohol was again made available 4 h/day and treatment was continued for 2 weeks, during which alcohol intake was recorded. Observation of alcohol intake was continued for a week of drug washout.

Results: Liraglutide and to a lesser extent exenatide statistically-significantly reduced alcohol consumption without causing any signs of emesis and with no effect on water intake as compared to vehicle.

Conclusion: The present study demonstrates for the first time that GLP-1 receptor agonists can reduce voluntary alcohol drinking in non-human primates. The data substantiate the potential usefulness of GLP-1 receptor agonists in the treatment of alcohol use disorder.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6520118/ The effect of glucagon-like peptide-1 (GLP-1) receptor agonists on substance use disorder (SUD)-related behavioral effects of drugs and alcohol: A systematic review Amanda Brunchmann, Morgane Thomsen, Anders Fink-Jensen 2019 2022-01-05 [("doi","10.1016/j.physbeh.2019.03.029")] longevity/glp/semaglutide psychiatry

Glucagon-like-peptide-1 (GLP-1)-receptor agonists have been proposed as putative treatment for substance use disorders (SUD). The objective of this systematic review is to characterize the effects of GLP-1-receptor agonists on SUD-related behavioral effects of drugs, nicotine, and alcohol.

The review was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A search was performed in PubMed and Embase on June 16, 2018. The inclusion criteria were primary studies investigating the use of GLP-1-receptor agonists on behavioral endpoints related to SUD. 17 studies were included, investigating the effect of the GLP-1-receptor agonists exendin-4, fluoro-exendin-4, liraglutide, AC3174 and GLP-1 (7–36) on SUD-related behavioral effects of ethanol, cocaine, amphetamine, and/or nicotine. All studies used rodents as subjects. 9 of the studies dealt with ethanol, 6 with cocaine, two with amphetamine, and two with nicotine. Most studies investigated acute treatment effects, finding a statistically-significant effect in all but one experiment. A few studies investigated more chronic effects on ethanol. All the studies reported sustained effects. 11 studies tested more than one dose, finding a dose-related response in 10⁄13 experiments. 6 studies report a central effect through intra-cerebral administration or by using mice in which the central GLP-1-receptors had been inactivated.

In conclusion, a solid body of evidence documents acute effects of GLP-1-receptor agonist treatment on behavioral effects of alcohol, nicotine, amphetamine, and cocaine. Documentation of effect of more chronic GLP-1-receptor stimulation on these behaviors is limited.

--- /doc/ai/scaling/hardware/2008-sandberg-wholebrainemulationroadmap.pdf Whole Brain Emulation: A Roadmap 2008 2022-01-05 ai/scaling/hardware psychology/neuroscience --- https://msrc.microsoft.com/update-guide/en-US/vulnerability/CVE-2022-34718 2022-01-05 cs/security --- https://detexify.kirelabs.org/classify.html 2022-01-06 math --- https://x.com/sergeykarayev/status/1569571367833714688 2022-01-06 ai/nn/transformer/gpt/codex --- https://x.com/negamuhia/status/1569616507256115205 2022-01-06 ai/nn/transformer/gpt/codex --- https://www.newyorker.com/news/q-and-a/why-hasnt-the-un-accused-china-of-genocide-in-xinjiang 2022-01-06 history/uighur --- https://x.com/gstsdn/status/1570489762489958406 2022-01-06 ai/nn/retrieval --- https://www.cs.cornell.edu/~caruana/compression.kdd06.pdf Model Compression Bucila 2006 2022-01-06 ai/nn/sparsity/knowledge-distillation --- https://arxiv.org/abs/1312.6184 Do Deep Nets Really Need to be Deep? Lei Jimmy Ba, Rich Caruana 2013-12-21 2022-01-06 [("doi","10.48550/arXiv.1312.6184")] ai/nn/fully-connected ai/nn/sparsity/knowledge-distillation

Currently, deep neural networks are the state-of-the-art on problems such as speech recognition and computer vision.

In this extended abstract, we show that shallow feed-forward networks can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models. Moreover, in some cases the shallow neural nets can learn these deep functions using a total number of parameters similar to the original deep model.

We evaluate our method on the TIMIT phoneme recognition task and are able to train shallow fully-connected nets that perform similarly to complex, well-engineered, deep convolutional architectures.

Our success in training shallow neural nets to mimic deeper models suggests that there probably exist better algorithms for training shallow feed-forward nets than those currently available.

--- https://arxiv.org/abs/1412.6550 FitNets: Hints for Thin Deep Nets Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, Yoshua Bengio 2014-12-19 2022-01-06 [("doi","10.48550/arXiv.1412.6550")] ai/nn/sparsity/knowledge-distillation

While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student network could imitate the soft output of a larger teacher network or ensemble of networks.

In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student. Because the student intermediate hidden layer will generally be smaller than the teacher’s intermediate hidden layer, additional parameters are introduced to map the student hidden layer to the prediction of the teacher hidden layer. This allows one to train deeper students that can generalize better or run faster, a trade-off that is controlled by the chosen student capacity.

For example, on CIFAR-10, a deep student network with almost 10.4× less parameters outperforms a larger, state-of-the-art teacher network.

--- https://arxiv.org/abs/1506.04416#google Bayesian Dark Knowledge Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling 2015-06-14 2022-01-06 [("doi","10.48550/arXiv.1506.04416")] ai/nn/sparsity/knowledge-distillation statistics/bayes

We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities, eg. for applications involving bandits or active learning. One simple approach to this is to use online Monte Carlo methods, such as SGLD (stochastic gradient Langevin dynamics). Unfortunately, such a method needs to store many copies of the parameters (which wastes memory), and needs to make predictions using many versions of the model (which wastes time).

We describe a method for “distilling” a Monte Carlo approximation to the posterior predictive density into a more compact form, namely a single deep neural network. We compare to two very recent approaches to Bayesian neural networks, namely an approach based on expectation propagation [Hernandez-Lobato & Adams 2015] and an approach based on variational Bayes [Blundell et al 2015]. Our method performs better than both of these, is much simpler to implement, and uses less computation at test time.

--- https://arxiv.org/abs/1612.03928 Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer Sergey Zagoruyko, Nikos Komodakis 2016-12-12 2022-01-06 [("doi","10.48550/arXiv.1612.03928")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer

Attention plays a critical role in human visual experience. Furthermore, it has recently been demonstrated that attention can also play an important role in the context of applying artificial neural networks to a variety of tasks from fields such as computer vision and NLP.

In this work, we show that, by properly defining attention for convolutional neural networks, we can actually use this type of information in order to improve the performance of a student CNN network by forcing it to mimic the attention maps of a powerful teacher network. To that end, we propose several novel methods of transferring attention, showing consistent improvement across a variety of datasets and convolutional neural network architectures.

Code and models for our experiments are available at https://github.com/szagoruyko/attention-transfer.

--- https://arxiv.org/abs/1406.4773 Deep Learning Face Representation by Joint Identification-Verification Yi Sun, Xiaogang Wang, Xiaoou Tang 2014-06-18 2022-01-07 [("doi","10.48550/arXiv.1406.4773")] ai/nn/cnn

The key challenge of face recognition is to develop effective feature representations for reducing intra-personal variations while enlarging inter-personal differences. In this paper, we show that it can be well solved with deep learning and using both face identification and verification signals as supervision.

The Deep IDentification-verification features (DeepID2) are learned with carefully designed deep convolutional networks. The face identification task increases the inter-personal variations by drawing DeepID2 extracted from different identities apart, while the face verification task reduces the intra-personal variations by pulling DeepID2 extracted from the same identity together, both of which are essential to face recognition.

On the challenging LFW dataset, 99.15% face verification accuracy is achieved. Compared with the best deep learning result on LFW, the error rate has been reduced by 67%.

The learned DeepID2 features can be well generalized to new identities unseen in the training data.

--- https://arxiv.org/abs/1502.00873#sensetime DeepID3: Face Recognition with Very Deep Neural Networks Yi Sun, Ding Liang, Xiaogang Wang, Xiaoou Tang 2015-02-03 2022-01-07 [("doi","10.48550/arXiv.1502.00873")] ai/nn/cnn

The state-of-the-art of face recognition has been advanced by the emergence of deep learning. Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity. This motivates us to investigate their effectiveness on face recognition.

This paper proposes two very deep neural network architectures, referred to as DeepID3, for face recognition. These two architectures are rebuilt from stacked convolution and inception layers proposed in VGG net and GoogLeNet to make them suitable to face recognition. Joint face identification-verification supervisory signals are added to both intermediate and final feature extraction layers during training.

An ensemble of the proposed two architectures achieves 99.53% LFW face verification accuracy and 96.0% LFW rank-1 face identification accuracy, respectively. A further discussion of LFW face verification result is given in the end.

--- https://medium.com/neuralmachine/knowledge-distillation-dc241d7c2322 2022-01-07 ai/nn/sparsity/knowledge-distillation --- https://arxiv.org/abs/2009.08576 Pruning Neural Networks at Initialization: Why are We Missing the Mark? Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin 2020-09-18 2022-01-07 [("doi","10.48550/arXiv.2009.08576")] ai/nn/sparsity/pruning

Recent work has explored the possibility of pruning neural networks at initialization.

We assess proposals for doing so: SNIP (Lee et al 2019), GraSP (Wang et al 2020), SynFlow (Tanaka et al 2020), and magnitude pruning. Although these methods surpass the trivial baseline of random pruning, they remain below the accuracy of magnitude pruning after training, and we endeavor to understand why.

We show that, unlike pruning after training, randomly shuffling the weights these methods prune within each layer or sampling new initial values preserves or improves accuracy. As such, the per-weight pruning decisions made by these methods can be replaced by a per-layer choice of the fraction of weights to prune.

This property suggests broader challenges with the underlying pruning heuristics, the desire to prune at initialization, or both.

--- /doc/ai/nn/rnn/1997-hochreiter-2.pdf#schmidhuber Flat Minima Sepp Hochreiter, Jürgen Schmidhuber 1997-01-01 2022-01-07 [("doi","10.1162/neco.1997.9.1.1")] ai/nn/rnn ai/nn/sparsity/pruning

We present a new algorithm for finding low-complexity neural networks with high generalization capability.

The algorithm searches for a “flat” minimum of the error function. A flat minimum is a large connected region in weight space where the error remains ~constant. An MDL-based, Bayesian argument suggests that flat minima correspond to “simple” networks and low expected overfitting. The argument is based on a Gibbs algorithm variant and a novel way of splitting generalization error into underfitting and overfitting error. Unlike many previous approaches, ours does not require Gaussian assumptions and does not depend on a “good” weight prior. Instead we have a prior over input output functions, thus taking into account net architecture and training set.

Although our algorithm requires the computation of second-order derivatives, it has backpropagation’s order of complexity. Automatically, it effectively prunes units, weights, and input lines.

Various experiments with feedforward and recurrent nets are described. In an application to stock market prediction, flat minimum search outperforms conventional backprop, weight decay, and “optimal brain surgeon/optimal brain damage.”

--- https://x.com/cccntu/status/1569357451795005446 2022-01-07 ai/anime ai/nn/transformer/gpt/dall-e --- https://blog.lopp.net/was-satoshi-a-greedy-miner/ 2022-01-07 bitcoin --- https://www.biorxiv.org/content/10.1101/2021.03.15.435518.full Spatial representation by ramping activity of neurons in the retrohippocampal cortex Sarah A. Tennant, Harry Clark, Ian Hawes, Wing Kin Tam, Junji Hua, Wannan Yang, Klara Z. Gerlei, Emma R. Wood, Matthew F. Nolan 2022-07-26 2022-07-26 [("doi","10.1101/2021.03.15.435518")] ai/nn/rnn psychology/neuroscience reinforcement-learning/model

Neurons in the retrohippocampal cortices play crucial roles in spatial memory. Many retrohippocampal neurons have firing fields that are selectively active at specific locations, with memory for rewarded locations associated with reorganization of these firing fields. Whether this is the sole strategy for representing spatial memories is unclear.

Here, we demonstrate that during a spatial memory task retrohippocampal neurons encode location through ramping activity that extends within segments of a linear track approaching and following a reward, with the rewarded location represented by offsets or switches in the slope of the ramping activity. These ramping representations could be maintained independently of trial outcome and cues that mark the reward location, indicating that they result from recall of the track structure.

During recordings in an open arena, neurons that generated ramping activity during the spatial memory task were more numerous than grid or border cells, with a majority showing spatial firing that did not meet criteria for classification as grid or border representations. Encoding of rewarded locations through offsets and switches in the slope of ramping activity also emerged in recurrent neural networks trained to solve a similar location memory task. Impaired performance of these networks following disruption of outputs from ramping neurons is consistent with this coding strategy supporting navigation to recalled locations of behavioral importance.

We hypothesize that retrohippocampal ramping activity mediates readout of learned models for goal-directed navigation.

--- https://www.medrxiv.org/content/10.1101/2022.04.03.22273354.full Voice patterns as markers of schizophrenia: building a cumulative generalizable approach via a cross-linguistic and meta-analysis based investigation Alberto Parola, Arndis Simonsen, Jessica Mary Lin, Yuan Zhou, Huiling Wang, Shiho Ubukata, Katja Koelkebeck, Vibeke Bliksted, Riccardo Fusaroli 2022-08-09 2022-08-09 [("doi","10.1101/2022.04.03.22273354")] psychiatry/schizophrenia

Background and Hypothesis

Voice atypicalities are potential markers of clinical features of schizophrenia (eg. negative symptoms). A recent meta-analysis identified an acoustic profile associated with schizophrenia (reduced pitch variability and increased pauses), but also highlighted shortcomings in the field: small sample sizes, little attention to the heterogeneity of the disorder, and to generalizing findings to diverse samples and languages.

Study Design

We provide a critical cumulative approach to vocal atypicalities in schizophrenia, where we conceptually and statistically build on previous studies. We aim at identifying a cross-linguistically reliable acoustic profile of schizophrenia and assessing sources of heterogeneity (symptomatology, pharmacotherapy, clinical and social characteristics). We relied on previous meta-analysis to build and analyze a large cross-linguistic dataset of audio recordings of 231 patients with schizophrenia and 238 matched controls (>4.000 recordings in Danish, German, Mandarin and Japanese). We used multilevel Bayesian modeling, contrasting meta-analytically informed and skeptical inferences.

Study Results

We found only a minimal generalizable acoustic profile of schizophrenia (reduced pitch variability), while duration atypicalities replicated only in some languages. We identified reliable associations between acoustic profile and individual differences in clinical ratings of negative symptoms, medication, age and gender. However, these associations vary across languages.

Conclusion: The findings indicate that a strong cross-linguistically reliable acoustic profile of schizophrenia is unlikely. Rather, if we are to devise effective clinical applications able to target different ranges of patients, we need first to establish larger and more diverse cross-linguistic datasets, focus on individual differences, and build self-critical cumulative approaches.

--- https://www.youtube.com/watch?v=kMSrRd4f2As 2022-01-07 statistics/bayes statistics/bias statistics/meta-analysis --- https://en.wikipedia.org/wiki/Half-precision_floating-point_format Half-precision floating-point format 2022-01-07 ai/nn/sparsity/low-precision --- https://en.wikipedia.org/wiki/Bfloat16_floating-point_format Bfloat16 floating-point format 2022-01-08 ai/nn/sparsity/low-precision --- https://arxiv.org/abs/2009.12812 TernaryBERT: Distillation-aware Ultra-low Bit BERT Wei Zhang, Lu Hou, Yichun Yin, Lifeng Shang, Xiao Chen, Xin Jiang, Qun Liu 2020-09-27 2022-01-08 [("doi","10.48550/arXiv.2009.12812")] ai/nn/sparsity/knowledge-distillation ai/nn/sparsity/low-precision ai/nn/transformer

Transformer-based pre-training models like BERT have achieved remarkable performance in many natural language processing tasks. However, these models are both computation and memory expensive, hindering their deployment to resource-constrained devices.

In this work, we propose TernaryBERT, which ternarizes the weights in a fine-tuned BERT model. Specifically, we use both approximation-based and loss-aware ternarization methods and empirically investigate the ternarization granularity of different parts of BERT. Moreover, to reduce the accuracy degradation caused by the lower capacity of low bits, we leverage the knowledge distillation technique in the training process.

Experiments on the GLUE benchmark and SQuAD show that our proposed TernaryBERT outperforms the other BERT quantization methods, and even achieves comparable performance as the full-precision model while being 14.9× smaller.

--- https://arxiv.org/abs/1809.11086 Learning Recurrent Binary/Ternary Weights Arash Ardakani, Zhengyun Ji, Sean C. Smithson, Brett H. Meyer, Warren J. Gross 2018-09-28 2022-01-08 [("doi","10.48550/arXiv.1809.11086")] ai/nn/rnn ai/nn/sparsity/low-precision

Recurrent neural networks (RNNs) have shown excellent performance in processing sequence data. However, they are both complex and memory intensive due to their recursive nature. These limitations make RNNs difficult to embed on mobile devices requiring real-time processes with limited hardware resources.

To address the above issues, we introduce a method that can learn binary and ternary weights during the training phase to facilitate hardware implementations of RNNs. As a result, using this approach replaces all multiply-accumulate operations by simple accumulations, bringing benefits to custom hardware in terms of silicon area and power consumption.

On the software side, we evaluate the performance (in terms of accuracy) of our method using long short-term memories (LSTMs) on various sequential models including sequence classification and language modeling. We demonstrate that our method achieves competitive results on the aforementioned tasks while using binary/ternary weights during the runtime.

On the hardware side, we present custom hardware for accelerating the recurrent computations of LSTMs with binary/ternary weights. Ultimately, we show that LSTMs with binary/ternary weights can achieve up to 12× memory saving and 10× inference speedup compared to the full-precision implementation on an ASIC platform.

--- https://arxiv.org/abs/2209.07509 Random initializations performing above chance and how to find them Frederik Benzing, Simon Schug, Robert Meier, Johannes von Oswald, Yassir Akram, Nicolas Zucchet, Laurence Aitchison, Angelika Steger 2022-09-15 2022-09-15 [("doi","10.48550/arXiv.2209.07509")] ai/nn/fully-connected

Neural networks trained with stochastic gradient descent (SGD) starting from different random initializations typically find functionally very similar solutions, raising the question of whether there are meaningful differences between different SGD solutions.

Entezari et al recently conjectured that despite different initializations, the solutions found by SGD lie in the same loss valley after taking into account the permutation invariance of neural networks. Concretely, they hypothesize that any two solutions found by SGD can be permuted such that the linear interpolation between their parameters forms a path without statistically-significant increases in loss.

Here, we use a simple but powerful algorithm to find such permutations that allows us to obtain direct empirical evidence that the hypothesis is true in fully connected networks. Strikingly, we find that two networks already live in the same loss valley at the time of initialization and averaging their random, but suitably permuted initialization performs above chance. In contrast, for convolutional architectures, our evidence suggests that the hypothesis does not hold. Especially in a large learning rate regime, SGD seems to discover diverse modes.

--- https://www.cantorsparadise.com/survivorship-bias-and-the-mathematician-who-helped-win-wwii-356b174defa6 2022-01-08 statistics/survival-analysis --- https://arxiv.org/abs/1511.08228#google Neural GPUs Learn Algorithms Łukasz Kaiser, Ilya Sutskever 2015-11-25 2022-01-08 [("doi","10.48550/arXiv.1511.08228")] ai/nn/rnn

Learning an algorithm from examples is a fundamental problem that has been widely studied. Recently it has been addressed using neural networks, in particular by Neural Turing Machines (NTMs). These are fully differentiable computers that use backpropagation to learn their own programming. Despite their appeal NTMs have a weakness that is caused by their sequential nature: they are not parallel and are are hard to train due to their large depth when unfolded.

We present a neural network architecture to address this problem: the Neural GPU. It is based on a type of convolutional gated recurrent unit and, like the NTM, is computationally universal. Unlike the NTM, the Neural GPU is highly parallel which makes it easier to train and efficient to run.

An essential property of algorithms is their ability to handle inputs of arbitrary size. We show that the Neural GPU can be trained on short instances of an algorithmic task and successfully generalize to long instances. We verified it on a number of tasks including long addition and long multiplication of numbers represented in binary. We train the Neural GPU on numbers with up to 20 bits and observe no errors whatsoever while testing it, even on much longer numbers.

To achieve these results we introduce a technique for training deep recurrent networks: parameter sharing relaxation. We also found a small amount of dropout and gradient noise to have a large positive effect on learning and generalization.

--- https://www.valentinesday.ai/ 2022-01-08 ai/nn/transformer/gpt/non-fiction --- https://www.getinspo.co/ 2022-01-08 ai/nn/transformer/gpt/non-fiction --- https://www.carmax.com/articles/using-ai-to-plan-a-road-trip 2022-01-08 ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/2205.11502 On the Paradox of Learning to Reason from Data Honghua Zhang, Liunian Harold Li, Tao Meng, Kai-Wei Chang, Guy Van den Broeck 2022-05-23 2022-05-23 [("doi","10.48550/arXiv.2205.11502")] ai/nn/transformer philosophy/logic

Logical reasoning is needed in a wide range of NLP tasks. Can a BERT model be trained end-to-end to solve logical reasoning problems presented in natural language?

We attempt to answer this question in a confined problem space where there exists a set of parameters that perfectly simulates logical reasoning.

We make observations that seem to contradict each other: BERT attains near-perfect accuracy on in-distribution test examples while failing to generalize to other data distributions over the exact same problem space. Our study provides an explanation for this paradox: instead of learning to emulate the correct reasoning function, BERT has in fact learned statistical features that inherently exist in logical reasoning problems. We also show that it is infeasible to jointly remove statistical features from data, illustrating the difficulty of learning to reason in general.

Our result naturally extends to other neural models and unveils the fundamental difference between learning to reason and learning to achieve high performance on NLP benchmarks using statistical features.

--- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000246 Open science challenges, benefits and tips in early career and beyond Christopher Allen, David M. A. Mehler 2022-01-08 [("doi","10.1371/journal.pbio.3000246")] statistics/bias

The movement towards open science is a consequence of seemingly pervasive failures to replicate previous research. This transition comes with great benefits but also challenges that are likely to affect those who carry out the research, usually early career researchers (ECRs).

Here, we describe key benefits, including reputational gains, increased chances of publication, and a broader increase in the reliability of research.

The increased chances of publication are supported by exploratory analyses indicating null findings are substantially more likely to be published via open registered reports in comparison to more conventional methods. These benefits are balanced by challenges that we have encountered and that involve increased costs in terms of flexibility, time, and issues with the current incentive structure, all of which seem to affect ECRs acutely. Although there are major obstacles to the early adoption of open science, overall open science practices should benefit both the ECR and improve the quality of research.

We review 3 benefits and 3 challenges and provide suggestions from the perspective of ECRs for moving towards open science practices, which we believe scientists and institutions at all levels would do well to consider.

This Perspective article offers a balanced perspective on both the benefits and the challenges involved in the adoption of open science practices, with an emphasis on the implications for Early Career Researchers.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0010068 ‘Positive’ Results Increase Down the Hierarchy of the Sciences Daniele Fanelli 2010-03-01 2022-01-09 [("doi","10.1371/journal.pone.0010068")] statistics/bias

The hypothesis of a Hierarchy of the Sciences with physical sciences at the top, social sciences at the bottom, and biological sciences in-between is nearly 200 years old. This order is intuitive and reflected in many features of academic life, but whether it reflects the “hardness” of scientific research—ie. the extent to which research questions and results are determined by data and theories as opposed to non-cognitive factors—is controversial.

This study analysed 2,434 papers published in all disciplines and that declared to have tested a hypothesis. It was determined how many papers reported a “positive” (full or partial) or “negative” support for the tested hypothesis. If the hierarchy hypothesis is correct, then researchers in “softer” sciences should have fewer constraints to their conscious and unconscious biases, and therefore report more positive outcomes.

Results confirmed the predictions at all levels considered: discipline, domain and methodology broadly defined. Controlling for observed differences between pure and applied disciplines, and between papers testing one or several hypotheses, the odds of reporting a positive result were around 5× higher among papers in the disciplines of Psychology and Psychiatry and Economics and Business compared to Space Science, 2.3× higher in the domain of social sciences compared to the physical sciences, and 3.4× higher in studies applying behavioral and social methodologies on people compared to physical and chemical studies on non-biological material.

In all comparisons, biological studies had intermediate values. These results suggest that the nature of hypotheses tested and the logical and methodological rigour employed to test them vary systematically across disciplines and fields, depending on the complexity of the subject matter and possibly other factors (eg. a field’s level of historical and/or intellectual development). On the other hand, these results support the scientific status of the social sciences against claims that they are completely subjective, by showing that, when they adopt a scientific approach to discovery, they differ from the natural sciences only by a matter of degree.

--- https://github.com/divamgupta/stable-diffusion-tensorflow 2022-01-09 ai/nn/diffusion --- https://research.google/blog/robust-online-allocation-with-dual-mirror-descent/ 2022-01-09 economics statistics/decision --- https://en.wikipedia.org/wiki/Center_embedding Center embedding 2022-01-09 psychology/writing --- /doc/design/typography/1963-tinker-legibilityofprint.pdf Legibility of Print Miles A. Tinker 1963-01-01 2022-01-09 design/typography psychology/writing --- https://en.wikipedia.org/wiki/Radiosynthesis_(metabolism) Radiosynthesis (metabolism) 2022-01-09 biology genetics/selection/natural --- https://arxiv.org/abs/2202.12142 Pretraining without Wordpieces: Learning Over a Vocabulary of Millions of Words Zhangyin Feng, Duyu Tang, Cong Zhou, Junwei Liao, Shuangzhi Wu, Xiaocheng Feng, Bing Qin, Yunbo Cao, Shuming Shi 2022-02-24 2022-02-24 [("doi","10.48550/arXiv.2202.12142")] ai/nn/tokenization ai/nn/transformer

The standard BERT adopts subword-based tokenization, which may break a word into two or more wordpieces (eg. converting “lossless” to “loss” and “less”).

This will bring inconvenience in following situations: (1) what is the best way to obtain the contextual vector of a word that is divided into multiple wordpieces? (2) how to predict a word via cloze test without knowing the number of wordpieces in advance?

In this work, we explore the possibility of developing BERT-style pretrained model over a vocabulary of words instead of wordpieces. We call such word-level BERT model as WordBERT. We train models with different vocabulary sizes, initialization configurations and languages.

Results show that, compared to standard wordpiece-based BERT, WordBERT makes improvements on cloze test and machine reading comprehension. On many other natural language understanding tasks, including POS tagging, chunking and NER, WordBERT consistently performs better than BERT. Model analysis indicates that the major advantage of WordBERT over BERT lies in the understanding for low-frequency words and rare words. Furthermore, since the pipeline is language-independent, we train WordBERT for Chinese language and obtain gains on 5 natural language understanding datasets.

Lastly, the analyse on inference speed illustrates WordBERT has comparable time cost to BERT in natural language understanding tasks.

--- https://github.com/castorini/transformers-arithmetic 2022-01-09 ai/nn/tokenization ai/nn/transformer/gpt --- https://github.com/google-research/byt5 2022-01-09 ai/nn/tokenization ai/nn/transformer/t5 --- https://paperswithcode.com/method/wordpiece 2022-01-09 ai/nn/tokenization --- https://arxiv.org/abs/2012.15524#google Fast WordPiece Tokenization Xinying Song, Alex Salcianu, Yang Song, Dave Dopson, Denny Zhou 2020-12-31 2022-01-10 [("doi","10.48550/arXiv.2012.15524")] ai/nn/tokenization

[blog] Tokenization is a fundamental preprocessing step for almost all NLP tasks.

In this paper, we propose efficient algorithms for the WordPiece tokenization used in BERT, from single-word tokenization to general text (eg. sentence) tokenization. When tokenizing a single word, WordPiece uses a longest-match-first strategy, known as maximum matching. The best known algorithms so far are 𝒪(n2) (where n is the input length) or 𝒪(n×m) (where m is the maximum vocabulary token length).

We propose a novel algorithm whose tokenization complexity is strictly 𝒪(n). Our method is inspired by the Aho-Corasick algorithm. We introduce additional linkages on top of the trie built from the vocabulary, allowing smart transitions when the trie matching cannot continue. For general text, we further propose an algorithm that combines pre-tokenization (splitting the text into words) and our linear-time WordPiece method into a single pass.

Experimental results show that our method is 8.2× faster than HuggingFace Tokenizers and 5.1× faster than TensorFlow Text on average for general text tokenization.

--- https://research.google/blog/a-fast-wordpiece-tokenization-system/ 2022-01-10 ai/nn/tokenization --- https://en.wikipedia.org/wiki/Byte_pair_encoding Byte pair encoding 2022-01-10 ai/nn/tokenization --- https://arxiv.org/pdf/2112.11446.pdf#page=119&org=deepmind Scaling Language Models: Methods, Analysis & Insights from Training Gopher § Table A40: Conversations can create the illusion of creativity 2022-01-10 ai/nn/tokenization ai/poetry --- https://gist.github.com/moyix/ca4091f16f0b5011bfa8f3f97f705a0d 2022-01-10 ai/nn/tokenization --- https://x.com/zswitten/status/1390045960663797764 2022-01-10 ai/nn/tokenization --- https://nostalgebraist.tumblr.com/post/189212709059/bpe-blues BPE Blues 2022-01-10 ai/nn/tokenization --- https://nostalgebraist.tumblr.com/post/620663843893493761/bpe-blues BPE Blues+ 2022-01-10 ai/nn/tokenization --- https://gist.github.com/brockmanmatt/8ca7cd40c3f79d31edb7fdfde0c782d1 Commas vs Integers 2022-01-10 ai/nn/tokenization --- /gpt-3-nonfiction#acrostics 2022-01-10 ai/nn/tokenization --- https://x.com/NineOfNein/status/1286738449660284928 Tokens are definitely shorter than English, but the performance even worse. Getting it to explain its thinking, it clearly can’t tell at all which sentences/words sound the same, which is odd, since homonyms tend to have the same letters in Russian...On the other hand strength of the model definitely not as good outside of English. 2022-01-10 ai/nn/tokenization --- https://ndingwall.github.io/blog/tokenization 2022-01-11 ai/nn/tokenization --- https://en.wikipedia.org/wiki/Synthetic_language Synthetic language 2022-01-11 ai/nn/tokenization --- https://en.wikipedia.org/wiki/Lexical_analysis#Tokenization Lexical analysis § Tokenization 2022-01-11 ai/nn/tokenization --- https://x.com/goodside/status/1568448128495534081 2022-01-11 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue --- https://arxiv.org/abs/2209.00057 Molecular genetics and mid-career economic mobility Paul Minard 2022-08-31 2022-08-31 [("doi","10.48550/arXiv.2209.00057")] genetics/heritable iq/ses

Reductions in the cost of genetic sequencing have enabled the construction of large datasets including both genetic and phenotypic data. Based on these datasets, polygenic scores (PGSs) summarizing an individual’s genetic propensity for educational attainment have been constructed. It is by now well established that this PGS predicts wages, income, and occupational prestige and occupational mobility across generations. It is unknown whether a PGS for educational attainment can predict upward income and occupational mobility even within the peak earning years of an individual.

Using data from the Wisconsin Longitudinal Study (WLS), I show that:

(1) a PGS for educational attainment predicts wage, income and occupational prestige mobility between 1974 (when respondents were about 36 years of age) and 1992 (when respondents were about 53 years of age), conditional on 1974 values of these variables and a range of covariates; (2) the effect is not mediated by parental socioeconomic status, is driven primarily by respondents with only a high school education, and is replicated in a within sibling-pair design; (3) conditional on 1974 outcomes, higher PGS individuals surveyed in 1975 aspired to higher incomes and more prestigious jobs 10 years hence, an effect driven primarily by respondents with more than a high school education; (4) throughout their employment history, high PGS individuals were more likely to undertake on the job training, and more likely to change job duties during tenure with an employer; and (5) though no more likely to change employers or industries during their careers, high PGS individuals were more likely in 1974 to be working in industries which would experience high wage growth in subsequent decades.

These results contribute to our understanding of longitudinal inequality and shed light on the sources of heterogeneity in responses to economic shocks and policy.

--- https://arxiv.org/abs/2209.07550#deepmind Human-level Atari 200× faster Steven Kapturowski, Víctor Campos, Ray Jiang, Nemanja Rakićević, Hado van Hasselt, Charles Blundell, Adrià Puigdomènech Badia 2022-09-15 2022-09-15 [("doi","10.48550/arXiv.2209.07550")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/model-free reinforcement-learning/scaling

The task of building general agents that perform well over a wide range of tasks has been an important goal in reinforcement learning since its inception. The problem has been subject of research of a large body of work, with performance frequently measured by observing scores over the wide range of environments contained in the Atari 57 ALE benchmark. Agent57 was the first agent to surpass the human benchmark on all 57 games, but this came at the cost of poor data-efficiency, requiring nearly 80 billion frames of experience to achieve.

Taking Agent57 as a starting point, we employ a diverse set of strategies to achieve a 200× reduction of experience needed to outperform the human baseline. We investigate a range of instabilities and bottlenecks we encountered while reducing the data regime, and propose effective solutions to build a more robust and efficient agent. We also demonstrate competitive performance with high-performing methods such as Muesli and MuZero.

The 4 key components to our approach are (1) an approximate trust region method which enables stable bootstrapping from the online network, (2) a normalization scheme for the loss and priorities which improves robustness when learning a set of value functions with a wide range of scales, (3) an improved architecture employing techniques from NFNets in order to leverage deeper networks without the need for normalization layers, and (4) a policy distillation method which serves to smooth out the instantaneous greedy policy over time.

--- https://arxiv.org/abs/1502.05477 TRPO: Trust Region Policy Optimization John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel 2015-02-19 2022-01-11 [("doi","10.48550/arXiv.1502.05477")] reinforcement-learning/model-free

We describe an iterative procedure for optimizing policies, with guaranteed monotonic improvement. By making several approximations to the theoretically-justified procedure, we develop a practical algorithm, called Trust Region Policy Optimization (TRPO). This algorithm is similar to natural policy gradient methods and is effective for optimizing large nonlinear policies such as neural networks.

Our experiments demonstrate its robust performance on a wide variety of tasks: learning simulated robotic swimming, hopping, and walking gaits; and playing Atari games using images of the screen as input.

Despite its approximations that deviate from the theory, TRPO tends to give monotonic improvement, with little tuning of hyperparameters.

--- https://www.quantamagazine.org/how-mathematical-curves-power-cryptography-20220919/ 2022-01-11 cs/algorithm cs/cryptography math --- https://github.com/AUTOMATIC1111/stable-diffusion-webui 2022-01-11 ai/nn/diffusion --- https://arxiv.org/abs/2205.13636 Quark: Controllable Text Generation with Reinforced Unlearning Ximing Lu, Sean Welleck, Liwei Jiang, Jack Hessel, Lianhui Qin, Peter West, Prithviraj Ammanabrolu, Yejin Choi 2022-05-26 2022-05-26 [("doi","10.48550/arXiv.2205.13636")] ai/nn/transformer/gpt reinforcement-learning/model/decision-transformer reinforcement-learning/preference-learning

Large-scale language models often learn behaviors that are misaligned with user expectations. Generated text may contain offensive or toxic language, contain repetition, or be of a different sentiment than desired by the user. We consider the task of unlearning these misalignments by fine-tuning the language model on signals of what not to do.

We introduce Quantized Reward Konditioning (Quark), an algorithm for optimizing a reward function that quantifies an (un)wanted property, while not straying too far from the original model. Quark alternates between (1) collecting samples with the current language model, (2) sorting them into quantiles based on reward, with each quantile identified by a reward token prepended to the language model’s input, and (3) using a standard language modeling loss on samples from each quantile conditioned on its reward token, while remaining nearby the original language model via a KL-divergence penalty.

By conditioning on a high-reward token at generation time, the model generates text that exhibits less of the unwanted property. For unlearning toxicity, negative sentiment, and repetition, our experiments show that Quark outperforms both strong baselines and state-of-the-art reinforcement learning methods like PPO (Schulman et al 2017), while relying only on standard language modeling primitives.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0274921 Factor structure of intelligence and divergent thinking subtests: A registered report Russell T. Warne, Sam Golightly, Makai Black 2022-09-19 2022-09-19 [("doi","10.1371/journal.pone.0274921")] iq

Psychologists have investigated creativity for 70 years, and it is now seen as being an important construct, both scientifically and because of its practical value to society. However, several fundamental unresolved problems persist, including a suitable definition of creativity and the ability of psychometric tests to measure divergent thinking—an important component of creativity—in a way that aligns with theory.

It is this latter point that this registered report is designed to address. We administered two divergent thinking tests (the verbal and figural versions of the Torrance Tests of Creative Thinking; TTCT) with an intelligence test (the International Cognitive Ability Resource test; ICAR). We then subjected the sub-scores from these tests to confirmatory factor analysis to examine which of 9 theoretically plausible models best fits the data.

Results: show that none of the pre-registered models fit the data well, an ambiguous result that leaves unanswered the question of whether intelligence and divergent thinking tests measure the same construct. Exploratory (ie. not pre-registered) measurement models of each test separately shows that the TTCT-F may not measure a coherent, unitary construct—leading to model misspecification when TTCT-F subtests were included in larger models.

This study was conducted in accordance with all open science practices, including pre-registration, open data and syntax, and open materials (with the exception of copyrighted and confidential test stimuli). Materials are available at OSF.

--- https://psyche.co/ideas/as-language-evolves-who-wins-out-speakers-or-listeners 2022-01-12 cs/algorithm --- https://www.quantamagazine.org/how-ai-transformers-mimic-parts-of-the-brain-20220912/ 2022-01-12 ai/nn/transformer psychology/neuroscience --- https://www.newyorker.com/magazine/2022/09/19/the-enduring-allure-of-choose-your-own-adventure-books 2022-01-12 fiction/text-game --- https://www.astralcodexten.com/p/janus-gpt-wrangling 2022-01-12 ai/nn/transformer/gpt/fiction --- https://www.biorxiv.org/content/10.1101/2022.09.07.506968.full A cocktail of rapamycin, acarbose and phenylbutyrate prevents age-related cognitive decline in mice by altering aging pathways Zhou Jiang, Qianpei He, Warren Ladiges 2022-09-09 2022-09-09 [("doi","10.1101/2022.09.07.506968")] longevity

Aging is a primary risk factor for cognitive dysfunction and exacerbates multiple biological processes in the brain, including but not limited to nutrient sensing dysregulation, insulin sensing dysfunction and histone deacetylation. Therefore, pharmaceutical intervention of aging targeting several distinct but overlapping pathways provides a basis for testing combinations of drugs as a cocktail.

A recent study showed that middle-aged mice treated with a drug cocktail of rapamycin, acarbose, and phenylbutyrate for 3 months had increased resilience to age related cognitive decline. This finding provided the rationale to investigate the comprehensive transcriptomic and molecular changes within the brain of mice that received this cocktail treatment or control substance. Transcriptome profiles were generated through RNA sequencing and pathway analysis was performed by gene set enrichment analysis to evaluate the overall RNA message effect of the drug cocktail. Molecular endpoints representing aging pathways were measured through immunohistochemistry to further validate the attenuation of brain aging in the hippocampus of mice that received the cocktail treatment, each individual drug or controls.

Results indicated that biological processes that enhance aging were suppressed, while autophagy was increased in the brains of mice given the drug cocktail. The molecular endpoint assessments indicated that treatment with the drug cocktail was overall more effective than any of the individual drugs for relieving cognitive impairment by targeting multiple aging pathways.

--- https://www.biorxiv.org/content/10.1101/2022.09.13.507735.full Association of Whole-Person Eigen-Polygenic Risk Scores with Alzheimer’s Disease Amin Kharaghani, Earvin Tio, Milos Milic, David A. Bennett, Philip L. De Jager, Julie A. Schneider, Lei Sun, Daniel Felsky 2022-09-16 2022-09-16 [("doi","10.1101/2022.09.13.507735")] genetics/heritable/correlation psychiatry/alzheimers

Late-Onset Alzheimer’s Disease (LOAD) is a heterogeneous neurodegenerative disorder with complex etiology and high heritability. Its multifactorial risk profile and large portions of unexplained heritability suggest the involvement of yet unidentified genetic risk factors.

Here we describe the “whole person” genetic risk landscape of polygenic risk scores for 2,218 traits in 2,044 elderly individuals and test if novel eigen-PRSs derived from clustered subnetworks of single-trait PRSs can improve prediction of LOAD diagnosis, rates of cognitive decline, and canonical LOAD neuropathology.

Principal component analyses of thousands of PRSs found generally poor global correlation among traits. However, component loadings confirmed covariance of clinically and biologically related traits and diagnoses, with the top PCs representing autoimmune traits, cardiovascular traits, and general pain medication prescriptions, depending on the PRS variant inclusion threshold. Network analyses revealed distinct clusters of PRSs with clinical and biological interpretability. Novel eigen-PRSs (ePRS) derived from these clusters were statistically-significantly associated with LOAD-related phenotypes and improved predictive model performance over the state-of-the-art LOAD PRS alone. Notably, an ePRS representing clusters of traits related to cholesterol levels was able to improve variance explained in a model of brain-wide beta-amyloid burden by 1.7% (likelihood ratio test p = 9.02 × 10−7). While many associations of ePRS with LOAD phenotypes were eliminated by the removal of APOE-proximal loci, some modules (eg. retinal defects, acidosis, colon health, ischaemic heart disease) showed associations at an unadjusted false positive rate. Our approach reveals new relationships between genetic risk for vascular, inflammatory, and other age-related traits and offers improvements over the existing single-trait PRS approach to capturing heritable risk for cognitive decline and beta-amyloid accumulation.

Our results are catalogued for the scientific community, to aid in the generation of new hypotheses based on our maps of clustered PRSs and associations with LOAD-related phenotypes.

--- https://arxiv.org/abs/2209.07162 Brain Imaging Generation with Latent Diffusion Models Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro F. da Costa, Virginia Fernandez, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso 2022-09-15 2022-09-15 [("doi","10.48550/arXiv.2209.07162")] ai/dataset ai/nn/diffusion psychology/neuroscience

[Twitter] Deep neural networks have brought remarkable breakthroughs in medical image analysis. However, due to their data-hungry nature, the modest dataset sizes in medical imaging projects might be hindering their full potential. Generating synthetic data provides a promising alternative, allowing to complement training datasets and conducting medical image research at a larger scale. Diffusion models recently have caught the attention of the computer vision community by producing photorealistic synthetic images.

In this study, we explore using Latent Diffusion Models to generate synthetic images from high-resolution 3D brain images. We used T1w MRI images from the UK Biobank dataset (n = 31,740) to train our models to learn about the probabilistic distribution of brain images, conditioned on co-variables, such as age, sex, and brain structure volumes.

We found that our models created realistic data, and we could use the conditioning variables to control the data generation effectively.

Besides that, we created a synthetic dataset with 100,000 brain images and made it openly available to the scientific community.

--- https://arxiv.org/abs/2203.04940 Data-Efficient Structured Pruning via Submodular Optimization Marwa El Halabi, Suraj Srinivas, Simon Lacoste-Julien 2022-03-09 2022-03-09 [("doi","10.48550/arXiv.2203.04940")] ai/nn/sparsity/pruning

Structured pruning is an effective approach for compressing large pre-trained neural networks without affecting their performance, which involves removing redundant regular regions of weights. However, current structured pruning methods are highly empirical in nature, do not provide any theoretical guarantees, and often require fine-tuning, which makes them inapplicable in the limited-data regime.

We propose a principled data-efficient structured pruning method based on submodular optimization. In particular, for a given layer, we select neurons/channels to prune and corresponding new weights for the next layer, that minimize the change in the next layer’s input induced by pruning. We show that this selection problem is a weakly submodular maximization problem, thus it can be provably approximated using an efficient greedy algorithm.

Our method is one of the few in the literature that uses only a limited-number of training data and no labels. Our experimental results demonstrate that our method outperforms popular baseline methods in various one-shot pruning settings.

--- https://arxiv.org/abs/2206.07144 Flatten the Curve: Efficiently Training Low-Curvature Neural Networks Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, Francois Fleuret 2022-06-14 2022-06-14 [("doi","10.48550/arXiv.2206.07144")] ai/nn/adversarial

The highly non-linear nature of deep neural networks causes them to be susceptible to adversarial examples and have unstable gradients which hinders interpretability. However, existing methods to solve these issues, such as adversarial training, are expensive and often sacrifice predictive accuracy.

In this work, we consider curvature, which is a mathematical quantity which encodes the degree of non-linearity. Using this, we demonstrate low-curvature neural networks (LCNNs) that obtain drastically lower curvature than standard models while exhibiting similar predictive performance, which leads to improved robustness and stable gradients, with only a marginally increased training time. To achieve this, we minimize a data-independent upper bound on the curvature of a neural network, which decomposes overall curvature in terms of curvatures and slopes of its constituent layers. To efficiently minimize this bound, we introduce two novel architectural components: first, a non-linearity called centered-softplus that is a stable variant of the softplus non-linearity, and second, a Lipschitz-constrained batch normalization layer.

Our experiments show that LCNNs have lower curvature, more stable gradients and increased off-the-shelf adversarial robustness when compared to their standard high-curvature counterparts, all without affecting predictive performance. Our approach is easy to use and can be readily incorporated into existing neural network models.

--- https://arxiv.org/abs/2206.04452#kakao Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, Wook-Shin Han 2022-06-09 2022-06-09 [("doi","10.48550/arXiv.2206.04452")] ai/nn/transformer/gpt/dall-e/1 ai/nn/vae

Although autoregressive models have achieved promising results on image generation, their unidirectional generation process prevents the resultant images from fully reflecting global contexts. To address the issue, we propose an effective image generation framework of Draft-and-Revise with Contextual RQ-transformer to consider global contexts during the generation process. As a generalized VQ-VAE, RQ-VAE first represents a high-resolution image as a sequence of discrete code stacks.

After code stacks in the sequence are randomly masked, Contextual RQ-Transformer is trained to infill the masked code stacks based on the unmasked contexts of the image. Then, Contextual RQ-Transformer uses our two-phase decoding, Draft-and-Revise, and generates an image, while exploiting the global contexts of the image during the generation process. Specifically. in the draft phase, our model first focuses on generating diverse images despite rather low quality. Then, in the revise phase, the model iteratively improves the quality of images, while preserving the global contexts of generated images.

In experiments, our method achieves state-of-the-art results on conditional image generation. We also validate that the Draft-and-Revise decoding can achieve high performance by effectively controlling the quality-diversity trade-off in image generation.

--- https://x.com/JonAskonas/status/1562074612866883584 2022-01-13 ai/nn/transformer/gpt/non-fiction --- https://pub.towardsai.net/stable-diffusion-based-image-compresssion-6f1f0a399202 2022-01-13 ai/nn/diffusion cs/algorithm/information/compression --- https://x.com/Buntworthy/status/1572214507468099586 2022-01-13 ai/nn/diffusion --- https://denovo.substack.com/p/gene-drives-why-the-wait 2022-01-13 genetics/editing --- https://arxiv.org/abs/1909.01066#facebook Language Models as Knowledge Bases? Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H. Miller, Sebastian Riedel 2019-09-03 2022-01-13 [("doi","10.48550/arXiv.1909.01066")] ai/nn/retrieval ai/nn/transformer

Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. Whilst learning linguistic knowledge, these models may also be storing relational knowledge present in the training data, and may be able to answer queries structured as “fill-in-the-blank” cloze statements. Language models have many advantages over structured knowledge bases: they require no schema engineering, allow practitioners to query about an open class of relations, are easy to extend to more data, and require no human supervision to train.

We present an in-depth analysis of the relational knowledge already present (without fine-tuning) in a wide range of state-of-the-art pretrained language models. We find that (1) without fine-tuning, BERT contains relational knowledge competitive with traditional NLP methods that have some access to oracle knowledge, (2) BERT also does remarkably well on open-domain question answering against a supervised baseline, and (3) certain types of factual knowledge are learned much more readily than others by standard language model pretraining approaches.

The surprisingly strong ability of these models to recall factual knowledge without any fine-tuning demonstrates their potential as unsupervised open-domain QA systems.

The code to reproduce our analysis is available at Github.

--- https://arxiv.org/abs/2209.05433#nvidia FP8 Formats for Deep Learning Paulius Micikevicius, Dusan Stosic, Neil Burgess, Marius Cornea, Pradeep Dubey, Richard Grisenthwaite, Sangwon Ha, Alexander Heinecke, Patrick Judd, John Kamalu, Naveen Mellempudi, Stuart Oberman, Mohammad Shoeybi, Michael Siu, Hao Wu 2022-09-12 2022-09-12 [("doi","10.48550/arXiv.2209.05433")] ai/nn/sparsity/low-precision ai/nn/transformer/gpt

FP8 is a natural progression for accelerating deep learning training inference beyond the 16-bit formats common in modern processors. In this paper we propose an 8-bit floating point (FP8) binary interchange format consisting of two encodings—E4M3 (4-bit exponent and 3-bit mantissa) and E5M2 (5-bit exponent and 2-bit mantissa). While E5M2 follows IEEE 754 conventions for representation of special values, E4M3’s dynamic range is extended by not representing infinities and having only one mantissa bit-pattern for NaNs.

We demonstrate the efficacy of the FP8 format on a variety of image and language tasks, effectively matching the result quality achieved by 16-bit training sessions. Our study covers the main modern neural network architectures—CNNs, RNNs, and Transformer-based models, leaving all the hyperparameters unchanged from the 16-bit baseline training sessions. Our training experiments include large, up to 175b parameter, language models. We also examine FP8 post-training-quantization of language models trained using 16-bit formats that resisted fixed point int8 quantization.

--- https://babel.hathitrust.org/cgi/pt?id=chi.44562032&view=1up&seq=174 2022-01-13 design/typography/rubrication --- https://bibliodyssey.blogspot.com/2012/01/calligraphy-letterform-album.html 2022-01-13 design/typography/rubrication --- https://blog.8faces.com/post/132017260619/eric-gill-advance 2022-01-13 design/typography/rubrication --- https://blogs.princeton.edu/notabilia/2020/05/20/what-could-be-better-pairing-and-comparing-the-scheide-and-kane-copies-of-fifteenth-century-books/ 2022-01-13 design/typography/rubrication --- https://cdm16630.contentdm.oclc.org/digital/collection/p16630coll2/id/534 2022-01-13 design/typography/rubrication --- https://detroitlib.tumblr.com/post/118118133397/the-may-queen-by-alfred-lord-tennyson-rubricated 2022-01-14 design/typography/rubrication --- https://en.wikipedia.org/wiki/Justus_Erich_Walbaum Justus Erich Walbaum 2022-01-14 design/typography/rubrication --- https://en.wikipedia.org/wiki/Miko Miko 2022-01-14 design/typography/rubrication --- https://en.wikipedia.org/wiki/Sangorski_%26_Sutcliffe Sangorski & Sutcliffe 2022-01-14 design/typography/rubrication --- https://en.wikipedia.org/wiki/Traditional_colors_of_Japan Traditional colors of Japan 2022-01-14 design/typography/rubrication --- https://fonts.google.com/specimen/Reem+Kufi+Ink 2022-01-14 design/typography/rubrication --- https://ilovetypography.com/2012/05/02/type-matters-book-review/ 2022-01-14 design/typography/rubrication --- https://ilovetypography.com/2012/05/09/the-private-press-movement-a-pocket-cathedral/ 2022-01-14 design/typography/rubrication --- https://manuscripta.hypotheses.org/3550 Le corps dans les étoiles: l’homme zodiacal et la médecine juive médiévale 2022-01-14 design/typography/rubrication --- https://medium.com/nightingale/treatise-a-visual-symphony-of-information-design-2ced33ef01a0 2022-01-14 design/typography/rubrication --- https://opentype.info/blog/2011/07/22/walbaum-type-specimen-in-high-resolution.html 2022-01-14 design/typography/rubrication --- https://orthodoxartsjournal.org/the-icon-of-st-christopher/ 2022-01-15 design/typography/rubrication --- https://philipball.blogspot.com/2020/04/three-colours-red.html 2022-01-15 design/typography/rubrication --- https://publicdomainreview.org/collection/dr-syntax/ 2022-01-15 design/typography/rubrication history/public-domain-review --- https://publicdomainreview.org/collection/garland-blood-collages 2022-01-15 design/typography/rubrication history/public-domain-review --- https://roberthodgin.com/project/meander 2022-01-15 design/typography/rubrication --- https://row1.ca/pixels-and-their-neighbors 2022-01-15 design/typography/rubrication --- https://spectrum.ieee.org/classical-chinese 2022-01-15 design/typography/rubrication --- https://theme.typora.io/theme/Rubrication/ 2022-01-15 design/typography/rubrication --- https://tvtropes.org/pmwiki/pmwiki.php/Main/RedOnesGoFaster 2022-01-15 design/typography/rubrication fiction --- https://www.atlasobscura.com/articles/jewish-cookbook-collection 2022-01-15 design/typography/rubrication --- https://www.atlasobscura.com/articles/temperance-bar 2022-01-15 design/typography/rubrication --- https://www.bbc.com/news/education-39846929 2022-01-16 design/typography/rubrication --- https://www.flickr.com/photos/daniel_friedman/albums/72157676485097306/ 2022-01-16 design/typography/rubrication --- https://www.flickr.com/photos/daniel_friedman/albums/72157712036045102 2022-01-16 design/typography/rubrication --- https://www.nytimes.com/2018/02/27/magazine/red-dots-badge-phones-notification.html 2022-01-16 design/typography/rubrication --- https://www.paulshawletterdesign.com/2011/05/from-the-bookcase-no-2%E2%80%94spacing-in-typography/ 2022-01-16 design/typography/dropcap design/typography/rubrication --- https://www.sebfalk.com/post/how-to-read-a-medieval-astronomical-calendar 2022-01-16 design/typography/rubrication --- https://www.smithsonianmag.com/arts-culture/american-food-posters-from-world-war-i-and-ii-89453240/ American Food Posters From World War I and II: Cory Bernat is the creator of an intriguing online exhibit of American food posters related to World Wars I and II 2022-01-16 design/typography/rubrication --- https://www.youtube.com/watch?v=QERfd7D0fi0&t=545s 2022-01-16 design/typography/rubrication --- https://wy-lang.org/ 2022-01-16 design/typography/rubrication --- https://en.wikipedia.org/wiki/Char_Aznable#Reception Char Aznable § Reception 2022-01-16 design/typography/rubrication --- https://en.wikipedia.org/wiki/Iron_gall_ink Iron gall ink 2022-01-17 design/typography/rubrication --- https://en.wikipedia.org/wiki/Minium_(pigment) Minium (pigment) 2022-01-17 design/typography/rubrication --- https://en.wikipedia.org/wiki/Munsell_color_system Munsell color system 2022-01-17 design/typography/rubrication --- https://en.wikipedia.org/wiki/Oliver_Byrne_(mathematician) Oliver Byrne (mathematician) 2022-01-17 design/typography/rubrication --- https://en.wikipedia.org/wiki/Red_letter_day Red letter day 2022-01-17 design/typography/rubrication --- https://en.wikipedia.org/wiki/Red_letter_edition Red letter edition 2022-01-17 design/typography/rubrication --- https://en.wikipedia.org/wiki/Rubrication Rubrication 2022-01-17 design/typography/rubrication --- https://en.wikipedia.org/wiki/Vermilion Vermilion 2022-01-17 design/typography/rubrication --- https://nautil.us/why-red-means-red-in-almost-every-language-235557/ 2022-01-17 design/typography/rubrication --- https://wilte.wordpress.com/2021/01/11/seeing-with-fresh-eyes-meaning-space-data-truth-by-edward-r-tufte/ Seeing with Fresh Eyes: Meaning, Space, Data, Truth by Edward R. Tufte 2022-01-17 design/typography/rubrication --- https://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118 2022-01-17 design/typography/rubrication --- https://www.c82.net/euclid/ 2022-01-18 design/typography/rubrication --- https://en.wikipedia.org/wiki/Sparkline Sparklines 2022-01-18 design/typography --- https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001OR 2022-01-18 design/typography --- https://en.wikipedia.org/wiki/Isotype_(picture_language) Isotype (picture language) 2022-01-18 design/typography/rubrication --- https://en.wikipedia.org/wiki/Historiated_initial Historiated initial 2022-01-18 design/typography --- https://en.wikipedia.org/wiki/Volvelle Volvelle 2022-01-18 design/typography --- https://en.wikipedia.org/wiki/Gutenberg_Bible Gutenberg Bible 2022-01-18 design/typography --- https://en.wikipedia.org/wiki/Pilcrow Pilcrow 2022-01-18 design/typography --- https://en.wikipedia.org/wiki/Lombardic_capitals Lombardic capitals 2022-01-18 design/typography --- https://en.wikipedia.org/wiki/Guidonian_hand Guidonian hand 2022-01-18 design/typography --- https://x.com/MyFonts/status/1236619928196853760 2022-01-18 design/typography/rubrication --- https://en.wikipedia.org/wiki/Stedelijk_Museum_Amsterdam Stedelijk Museum Amsterdam 2022-01-19 design/typography/rubrication --- https://en.wikipedia.org/wiki/Willem_Sandberg Willem Sandberg 2022-01-19 design/typography/rubrication --- https://www.moma.org/collection/works/4134 Dieter Rams, Hochschule für Gestaltung, Ulm, Germany / Pocket Radio (model T3) / 1958 2022-01-19 design/typography/rubrication --- https://en.wikipedia.org/wiki/IPod IPod 2022-01-19 design/typography/rubrication --- https://biblebuyingguide.com/red-letter-black-letter/ Red-Letter vs Black-Letter 2022-01-19 design/typography/rubrication --- https://en.wikipedia.org/wiki/Mark_Lombardi Mark Lombardi 2022-01-19 design/typography/rubrication --- https://www.catalogtree.net/projects/vinec_001_009?t=nijmegen 2022-01-19 design/typography/rubrication --- https://www.deviantart.com/angelaacevedo/art/SchoolComp-Poster1-Garamond-69364303 2022-01-19 design/typography/rubrication --- /doc/design/typography/rubrication/2010-p22-civilite-typespecimen.pdf P22 Civilite Type Specimen P22 Type Foundry 2010-01-01 2022-01-19 design/typography/rubrication --- https://www.paperspecs.com/spotlight/beauty-letterpress-art-making-impression/ The Beauty of Letterpress: The Art of Making an Impression 2022-01-19 design/typography/rubrication --- https://blog.8faces.com/post/144010724640/the-beauty-of-letterpress-poster-this-looks-fun The Beauty of letterpress poster 2022-01-20 design/typography/rubrication --- https://wiki.obormot.net/Main/BonusFontsDemo?demo_font_one=Petit+Fleur+Normal Petit Fleur Normal 2022-01-20 design/typography/rubrication --- https://www.foliosociety.com/usa/the-book-of-the-new-sun.html The Book of the New Sun, Gene Wolfe; Illustrated by Sam Weber; Introduced by Neil Gaiman; A signed and numbered limited edition of Gene Wolfe’s award-winning masterpiece of speculative fiction illustrated by Sam Weber and introduced by Neil Gaiman. 2022-01-20 design/typography/rubrication --- https://x.com/Timberati/status/1174665845185765376 2022-01-20 design/typography/rubrication --- https://texample.net/tikz/examples/red-black-tree/ Example: Red-black tree 2022-01-20 design/typography/rubrication --- https://en.wikipedia.org/wiki/PGF/TikZ PGF/TikZ 2022-01-20 design/typography/rubrication design/typography/tex --- https://ctan.math.illinois.edu/graphics/pgf/base/doc/pgfmanual.pdf#page=31 TikZ & PGF: Manual for Version 3.1.5b § Tutorial: A Picture for Karl’s Students 2022-01-20 design/typography/rubrication --- https://texample.net/tikz/examples/ 2022-01-20 design/typography/rubrication --- https://texample.net/tikz/examples/matrix-multiplication/ Illustration of how to compute the product of two matrices. 2022-01-20 design/typography/rubrication --- https://texample.net/tikz/examples/symmetries/ An implementation of all 17 plane symmetries in TikZ 2022-01-20 design/typography/rubrication --- https://texample.net/tikz/examples/tkz-linknodes-examples/ Demonstration of the tkz-linknodes package, a package that makes it easy to link elements in an amsmath align or aligned environment. 2022-01-20 design/typography/rubrication --- https://texample.net/tikz/examples/tkz-2d/ The package tkz-2d is a set of convenient macros for drawing in a plane ( fundamental two-dimensional object) with a Cartesian coordinate system. The package aims to provide a high-level user interface to build graphics relatively simply. 2022-01-21 design/typography/rubrication --- https://texample.net/tikz/examples/hydrogen-splitting/ This picture shows the splitting of Hydrogen in different strong magnetic fields. 2022-01-21 design/typography/rubrication --- https://texample.net/tikz/examples/seismic-focal-mechanism-in-3d-view/ Adaptation for LaTeX of a figure proposed in P. Shearer’s book Introduction to Seismology. It shows the focal sphere with the fault plane and auxiliary plane (which can not be discriminate), limiting compression and dilatation quadrants, the first movement of the rock through the sphere, and the Pression and Tension axis. The figure is based on the sphere drawing’s code proposed by J. Dumas in is book Tikz pour l’impatient, available online. 2022-01-21 design/typography/rubrication design/typography/tex --- https://texample.net/tikz/examples/euclid-algorithm/ Scheme of Greatest Common Divisor (GCD) performed through the Euclidean Algorithm and suitable to carry out manual calculations by following the colored arrows: inclined arrows stand for division operations / horizontal ones for multiplication / vertical for subtraction; Basically such items are the well known procedure of pupils at elementary schools. 2022-01-21 design/typography/rubrication --- https://texample.net/tikz/examples/swan-wave-model/ SWAN (developed by SWAN group, TU Delft, The Netherlands) is a wave spectral numerical model. For Simlating WAves Nearshore, it is necessary to define spatial grids of physical dominant factors (wind friction, dissipation) as well as define a COMPUTATIONAL grid on which the model performs its (spectral) calculations: budgeting energy spectra over each cell of the (computational) grid. Grids might have different spatial resolution and extension. 2022-01-21 design/typography/rubrication --- https://texample.net/tikz/examples/principle-of-x-ray-photoelectron-spectroscopy-xps/ A scheme showing the principles of x-ray photoelectron spectroscopy (XPS) sometimes called photoelectron spectroscopy (PES) as well. This is a technique often used in physical chemistry. 2022-01-21 design/typography/rubrication --- https://texample.net/tikz/examples/phasor-diagram/ 2022-01-21 design/typography/rubrication --- https://texample.net/tikz/examples/foldable-dodecahedron-with-calendar/ An example of the folding library and the calendar library, straight from the manual. 2022-01-21 design/typography/rubrication --- https://texample.net/tikz/examples/birthday-calendar/ A birthday calendar made with TikZ 2022-01-21 design/typography/rubrication --- https://texample.net/tikz/examples/lattice-points/ An illustration of Babai’s algorithm for the Closest Vector Problem (CVP): Find the closest lattice point for a given lattice and a target vector. 2022-01-21 design/typography/rubrication --- https://texample.net/tikz/examples/multiplexer/ This image was taken from a handbook about TTL Logic devices. 2022-01-21 design/typography/rubrication --- https://tex.stackexchange.com/questions/343917/nodes-overlapping-with-tikz-graphdrawing-library 2022-01-22 design/typography/rubrication --- https://texample.net/tikz/examples/raindrop/ Rainbows form because light of different colors refracts differently in a drop of water. To understand a rainbow in detail, you need to consider all possible rays entering the drop, many raindrops at once, and the reflectivity for various angles at the back of the drop. The current figure shows only one ray entering the raindrop and visualizes the path of the red and blue rays. The index of refraction of the red ray is slightly exaggerated (less than one percent) for clarity. Observe that the angle of incidence is identical to the angle with which the rays finally exit the drop. Furthermore, the red internal angles are all identical, similar for the blue angles. This figure was drawn for high school students because a physics textbook figure contained several errors and ultimately claimed the red and blue light exiting the raindrop as parallel rays. 2022-01-22 design/typography/rubrication --- https://texample.net/tikz/examples/the-3dplot-package/ The 3dplot package provides straightforward ways to define three-dimensional coordinate frames through which to plot in TikZ. The user can specify the orientation of the main coordinate frame, and use standard TikZ commands and coordinates to render their tikzfigure. A secondary coordinate frame is provided to allow rotations and translations with respect to the main coordinate frame. 2022-01-22 design/typography/rubrication --- https://texample.net/tikz/examples/gnuplot-basics/ 2022-01-22 design/typography/rubrication --- https://texample.net/tikz/examples/three-link-annotated/ This example shows how you can annotate a technical drawing. The 3 link manipulator is the same as another example in the gallery. I’ve used macros extensively to avoid duplicating code. 2022-01-22 design/typography/rubrication --- https://texample.net/tikz/examples/andler-optimal-lot-size/ Optimal lot-size with the Andler formula. Animated to show the influence of the k_l and k_b parameters. 2022-01-22 design/typography/rubrication --- https://texample.net/tikz/examples/polarization-state-of-light/ This demonstrates the use of the fixed point calculation package fp.sty to process user-defined parameters while rendering a TikZ picture. In this example, an ellipse is rendered, representing the polarization state of light for an arbitrary x-amplitude y-amplitude, and relative phase. See https://en.wikipedia.org/wiki/Polarization for background information on polarization states of light. 2022-01-22 design/typography/rubrication --- https://github.com/synercys/annotated_latex_equations annotated_latex_equations: Examples of how to create colorful, annotated equations in Latex using Tikz. 2022-01-22 design/typography/rubrication design/typography/tex --- https://www.mesacc.edu/~thoqh49081/handouts/talmudpage.html A Page of Talmud 2022-01-22 design/typography/rubrication --- https://web.archive.org/web/20190330161252/https://www.edwardtufte.com/bboard/images/00042P-26659/Page9.jpg 2022-01-22 design/typography/rubrication --- https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=00042P Sentences off the Grid 2022-01-23 design/typography/rubrication --- https://research-bulletin.chs.harvard.edu/2019/03/19/connected-world-of-potters/ The Connected World of Potters in Ancient Athens: Collaborations, Connoisseurship, and Social Network Analysis 2022-01-23 design/typography/rubrication --- https://x.com/tkasasagi/status/1217011608825647106 2022-01-23 design/typography/rubrication --- https://github.com/lizadaly/nanogenmo2014 2022-01-23 design/typography/rubrication --- https://archive.org/details/A216167/page/n4 2022-01-23 design/typography/rubrication --- https://archive.org/details/chemicalatlasorc00youm/page/65 2022-01-23 design/typography/rubrication --- https://archive.org/details/chemicalatlasorc00youm/page/81 2022-01-23 design/typography/rubrication --- https://archive.org/details/chemicalatlasorc00youm/page/n94 2022-01-23 design/typography/rubrication --- https://archive.org/details/chemicalatlasorc00youm/ Chemical atlas, or, The chemistry of familiar objects: exhibiting the general principles of the science in a series of beautifully colored diagrams, and accompanied by explanatory essays, embracing the latest views of the subjects illustrated; designed for the use of students and pupils in all schools where chemistry is taught 2022-01-23 design/typography/rubrication --- https://gallica.bnf.fr/ark:/12148/bpt6k851127r/ 2022-01-23 design/typography/rubrication --- https://djr.com/notes/bradley-djr-font-of-the-month September’s font of the month: Bradley DJR 2022-01-23 design/typography/rubrication --- https://archive.org/details/womaningirlhoodw00soli/page/n9 2022-01-24 design/typography/rubrication --- https://archive.org/details/herculaneumpastp00wald/herculaneumpastp00wald?view=theater#page/n441/mode/1up 2022-01-24 design/typography/rubrication --- https://upload.wikimedia.org/wikipedia/commons/d/da/TitlePage_Burton%27s_Nights.jpg 2022-01-24 design/typography/rubrication --- https://upload.wikimedia.org/wikipedia/commons/4/4f/Lady_Burton%27s_Edition.jpg 2022-01-24 design/typography/rubrication --- https://en.wikipedia.org/wiki/Beat_the_Whites_with_the_Red_Wedge Beat the Whites with the Red Wedge 2022-01-24 design/typography/rubrication --- https://www.moma.org/interactives/exhibitions/2012/inventingabstraction/?work=42 2022-01-24 design/typography/rubrication --- https://www.princeton.edu/~graphicarts/2012/04/feuillets_dart.html Feuillets d’Art 2022-01-24 design/typography/rubrication --- https://en.wikipedia.org/wiki/Swastika#Nazism Swastika § Nazism 2022-01-24 design/typography/rubrication --- https://en.wikipedia.org/wiki/File:Flag_of_the_NSDAP_(1920%E2%80%931945).svg File:Flag of the NSDAP (1920–1945).svg 2022-01-24 design/typography/rubrication --- https://en.wikipedia.org/wiki/Emblems_of_the_International_Red_Cross_and_Red_Crescent_Movement Emblems of the International Red Cross and Red Crescent Movement 2022-01-24 design/typography/rubrication --- https://en.wikipedia.org/wiki/Flag_of_Switzerland Flag of Switzerland 2022-01-24 design/typography/rubrication --- https://en.wikipedia.org/wiki/Rising_Sun_Flag Rising Sun Flag 2022-01-25 design/typography/rubrication --- https://en.wikipedia.org/wiki/Flag_of_Japan Flag of Japan 2022-01-25 design/typography/rubrication --- https://en.wikipedia.org/wiki/List_of_Japanese_flags List of Japanese flags 2022-01-25 design/typography/rubrication --- https://en.wikipedia.org/wiki/Flag_of_Japan#Mourning Flag of Japan § Mourning 2022-01-25 design/typography/rubrication --- https://commons.wikimedia.org/wiki/Category:Variations_on_the_national_flag_of_Japan 2022-01-25 design/typography/rubrication --- https://mondopolitico.com/library/meinkampf/v2c7.htm 2022-01-25 design/typography/rubrication --- https://www.p98a.com/journal/our-contribution-to-futuras-90-anniversary Tribute to Mr. Renner: The typeface Futura turns 90 next year. Just in time we were asked to contribute a text and a poster for two new publications on Paul Renner’s geometric sans serif: Futura. Die Schrift., recently issued by Hermann Schmidt publishers, and the anthology Tribute to Paul. For these tasks we united all of our favorite occupations: research, writing, typesetting, printing and photography. 2022-01-25 design/typography/rubrication --- https://www.itsnicethat.com/articles/futura-the-typeface-book-andreas-koop-graphic-design-201017 Typography and National Socialism—the journey of Futura in an era of ‘reactionary modernity’ 2022-01-25 design/typography/rubrication --- /doc/design/typography/rubrication/1937-robert-organisationsbuchdernsdap.pdf#page=99 1937 2022-01-25 design/typography/rubrication --- https://upload.wikimedia.org/wikipedia/commons/9/9e/Design_by_El_Lissitzky_1922.jpg 2022-01-25 design/typography/rubrication --- https://australianfoodtimeline.com.au/empire-christmas-pudding/ 1925: The Empire Christmas Pudding 2022-01-25 design/typography/rubrication --- https://practicaltypography.com/rebuilding-the-typographic-society.html Rebuilding the Typographic Society Matthew Butterick 2012-10-12 2022-01-26 design/typography

What’s humanity’s most consequential invention? I contend it’s the written word. And typography is an inseparable part of the written word. But what does typography really do? The lazy answer is that typography is about ‘making things pretty’.

But that’s incomplete. Understanding how typography works means stepping back and considering the role of the written word in our culture. As we do that, we notice that typography doesn’t merely frame meaning. It binds with the written word to add meaning.

But our newest reading technologies—the Kindle, the iPad—are leaving typography behind. This is a mistake. Those who care about the written word should be invested in preserving typography, because as we lose typography, we’ll also start to lose some of the possibilities of the written word.

--- https://archive.org/details/fabresbookofinse00fabre/page/n12 2022-01-26 design/typography/rubrication --- https://pure.rug.nl/ws/portalfiles/portal/13139017/2012_Keijzer_Sphex_story.pdf 2022-01-26 design/typography/rubrication --- https://archive.org/details/masonbees00fabr/page/108 2022-01-26 cat/psychology design/typography/rubrication --- https://archive.org/details/atombohrtheoryof00kram/page/217 2022-01-26 design/typography/rubrication --- https://letterformarchive.org/news/the-complete-commercial-artist/ From the Collection: The Complete Commercial Artist (現代商業美術全集): A rare set of Japanese trade publications serves a visual feast of modern graphics and lettering, as well as a study of early-20th-century interactions between Japan and the West 2022-01-26 design/typography/rubrication japan/art --- https://inventingabstraction.tumblr.com/post/42450192391/we-heard-that-the-original-image-of-the-1930 We heard that the original image of the 1930 Uffizi brochure we uploaded was not of high enough resolution. Here is as good a version as Tumblr would allow, alongside the front and back covers of the booklet in which the chart was published. 2022-01-26 design/typography/rubrication --- https://www.moma.org/calendar/exhibitions/2748 2022-01-26 design/typography/rubrication --- https://momalibrary.tumblr.com/post/43029025759/every-day-at-the-library-reference-desk-i-look-at Every day at the library reference desk I look at a poster version of this chart. Ever since Alfred Barr composed it for the catalog cover of the 1936 exhibition Cubism and Abstract Art, the chart has been scrutinized, criticized, historicized, revised, and deliciously parodied. 2022-01-26 design/typography/rubrication --- https://www.edwardtufte.com/bboard/images/0000yO-774.gif 2022-01-26 design/typography/rubrication --- https://archive.org/details/internationalpic00neur 2022-01-27 design/typography/rubrication --- https://upload.wikimedia.org/wikipedia/commons/d/da/Basic_by_Isotype.jpg 2022-01-27 design/typography/rubrication --- https://archive.org/details/internationalpic00neur/page/41/mode/2up 2022-01-27 design/typography/rubrication --- https://en.wikipedia.org/wiki/File:Nachf%C3%BClleisengallustinte_Pelikan_0.5_Liter_(G%C3%BCnther_Wagner).JPG File:Nachfülleisengallustinte Pelikan 0.5 Liter (Günther Wagner).JPG 2022-01-27 design/typography/rubrication --- https://www.e-codices.unifr.ch/en/list/one/vad/0296 Switzerland, St. Gallen, Kantonsbibliothek, Vadianische Sammlung 2022-01-27 design/typography/rubrication --- https://www.bl.uk/collection-items/boethius-de-institutione-arithmetica 2022-01-27 design/typography/rubrication --- https://www.e-codices.unifr.ch/en/vad/0296/079r 2022-01-27 design/typography/rubrication --- https://www.e-codices.unifr.ch/en/vad/0296/089r 2022-01-27 design/typography/rubrication --- https://www.e-codices.unifr.ch/en/vad/0296/093v 2022-01-27 design/typography/rubrication --- https://www.e-codices.unifr.ch/en/vad/0296/099r 2022-01-27 design/typography/rubrication --- https://collections.library.yale.edu/catalog/2037169 2022-01-27 design/typography/rubrication --- https://www.thedigitalwalters.org/Data/WaltersManuscripts/html/W73/ 2022-01-28 design/typography/rubrication --- https://it.wikipedia.org/wiki/Liber_Figurarum 2022-01-28 design/typography/rubrication --- https://medieval.bodleian.ox.ac.uk/catalog/manuscript_336 2022-01-28 design/typography/rubrication --- /doc/design/typography/rubrication/2010-03-dowlingduncan-charteroftheforest.html A display for the Charter of the Forest document which alongside the Magna Carta is housed in Lincoln Castle. 2010-03 2022-01-28 design/typography/rubrication --- https://medieval.bodleian.ox.ac.uk/catalog/manuscript_8761 2022-01-28 design/typography/rubrication --- https://bibliotheca-laureshamensis-digital.de/bav/bav_pal_lat_1741?ui_lang=eng Vatikan, Biblioteca Apostolica Vaticana, Pal. lat. 1741, Enzyklopädisch-rhetorische Sammelhandschrift, Heidelberg (?), 2. Hälft 2022-01-28 design/typography/rubrication --- https://www.fromoldbooks.org/Tymms-Illuminating/pages/43-fourteenth-century-01/ 2022-01-28 design/typography/rubrication --- https://archive.org/details/artofilluminatin00tymmrich 2022-01-28 design/typography/rubrication --- https://archive.org/details/artofilluminatin00tymmrich/page/n104 2022-01-28 design/typography/rubrication --- https://upload.wikimedia.org/wikipedia/commons/e/e4/Jenson_De_viris_illustribus.jpg 2022-01-28 design/typography/rubrication --- /doc/history/2019-09-18-medievalindonesia-thelandshuterhochzeit1475.html The Landshuter Hochzeit (1475) 2019-09-18 2022-01-28 design/typography/rubrication history --- https://bibliophilly.library.upenn.edu/viewer.php?id=Ms.%20Codex%201248#page/244/mode/2up Liturgical miscellany Ms. Codex 1248: Kislak Center for Special Collections, Rare Books and Manuscripts 2022-01-29 design/typography/rubrication --- https://upload.wikimedia.org/wikipedia/commons/9/96/Lombardic_capitals_Ambraser_Heldenbuch_folio_75v.jpg 2022-01-29 design/typography/rubrication --- https://luna.folger.edu/luna/servlet/view/search?q=V.b.26 2022-01-29 design/typography/rubrication --- https://www.esotericarchives.com/folger/v_b_26_transcription.pdf ‘Book of magic, with instructions for invoking spirits, etc.’ (ca. 1577–1583) / Folger Shakespeare Library manuscript V.b.26 / Transcription by Joseph H. Peterson and Dan Harms, 2015 2022-01-29 design/typography/rubrication --- https://en.wikipedia.org/wiki/Mah%C4%81vyutpatti Mahāvyutpatti 2022-01-29 design/typography/rubrication --- https://upload.wikimedia.org/wikipedia/commons/7/79/Mah%C4%81vyutpatti_Title_Page.jpg 2022-01-29 design/typography/rubrication --- https://upload.wikimedia.org/wikipedia/commons/b/bf/InkErosion.jpg 2022-01-29 design/typography/rubrication --- /note/fully-connected#mlp-mixer 2022-01-29 ai/nn/transformer/attention/hierarchical --- https://arxiv.org/abs/2206.08889#google DiffC: Lossy Compression with Gaussian Diffusion Lucas Theis, Tim Salimans, Matthew D. Hoffman, Fabian Mentzer 2022-06-17 2022-06-17 [("doi","10.48550/arXiv.2206.08889")] ai/nn/diffusion cs/algorithm/information/compression

We describe a novel lossy compression approach called DiffC which is based on unconditional diffusion generative models.

Unlike modern compression schemes which rely on transform coding and quantization to restrict the transmitted information, DiffC relies on the efficient communication of pixels corrupted by Gaussian noise.

We implement a proof of concept and find that it works surprisingly well despite the lack of an encoder transform, outperforming the state-of-the-art generative compression method HiFiC on ImageNet 64×64. DiffC only uses a single model to encode and denoise corrupted pixels at arbitrary bitrates. The approach further provides support for progressive coding, that is, decoding from partial bit streams.

We perform a rate-distortion analysis to gain a deeper understanding of its performance, providing analytical results for multivariate Gaussian data as well as initial results for general distributions.

Furthermore, we show that a flow-based reconstruction achieves a 3 dB gain over ancestral sampling at high bitrates.

--- https://arxiv.org/abs/1608.05148#google Full Resolution Image Compression with Recurrent Neural Networks George Toderici, Damien Vincent, Nick Johnston, Sung Jin Hwang, David Minnen, Joel Shor, Michele Covell 2016-08-18 2022-01-29 [("doi","10.48550/arXiv.1608.05148")] ai/nn/rnn cs/algorithm/information/compression

This paper presents a set of full-resolution lossy image compression methods based on neural networks.

Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once. All of our architectures consist of a recurrent neural network (RNN)-based encoder and decoder, a binarizer, and a neural network for entropy coding.

We compare RNN types (LSTM, associative LSTM) and introduce a new hybrid of GRU and ResNet. We also study “one-shot” versus additive reconstruction architectures and introduce a new scaled-additive framework. We compare to previous work, showing improvements of 4.3%–8.8% AUC (area under the rate-distortion curve), depending on the perceptual metric used.

As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0263730 Anchoring in the past, tweeting from the present: Cognitive bias in journalists’ word choices Jihye Lee, James T. Hamilton 2022-01-26 2022-01-30 [("doi","10.1371/journal.pone.0263730")] psychology/cognitive-bias sociology/technology

This study examines journalists’ language in their reporting and what their word choices reveal about their cognitive mindsets. Reporters on the campaign trail often cannot afford to engage in systematic information processing as they distill complex political situations under deadline pressures. Twitter’s emphasis on speed and informal cultural milieu can further lead journalists to rely on heuristics and emotions.

Drawing upon insights from theories of the mind, memory, and language, this study explores how cognitive biases are embodied in journalistic work across different media. We built a large-scale dataset of text corpora that consisted of more than 220,000 news articles, broadcast transcripts, and tweets generated over a year by 73 campaign reporters in the 2016 U.S. presidential election. Leveraging this unique dataset of journalistic outputs from a campaign season, we conducted automated text analyses.

Results suggest that heuristics and intuitive thinking played a role in the generation of content on Twitter. Journalists infused their tweets with more emotion, compared to when they appeared in traditional media such as newspapers and broadcasts. Journalists’ tweets contained fewer words related to analytical and long-term thinking than their writing. Journalists also used informal language in their tweets to connect with their audiences in more personal and casual manners. Across all media examined in the study, journalists described the current race by drawing upon their experience of covering prior presidential elections, a form of anchoring heuristic.

This study extends the use of cognitive biases in politics to a new realm, reporting, and shows how journalists’ use of language on the campaign trail reflects cognitive biases that arise when individuals make decisions under time pressure and uncertainty.

--- https://arxiv.org/abs/2209.07430 Machine Reading, Fast and Slow: When Do Models "Understand" Language? Sagnik Ray Choudhury, Anna Rogers, Isabelle Augenstein 2022-09-15 2022-09-15 [("doi","10.48550/arXiv.2209.07430")] ai/nn/transformer ai/scaling

Two of the most fundamental challenges in Natural Language Understanding (NLU) at present are: (a) how to establish whether deep learning-based models score highly on NLU benchmarks for the ‘right’ reasons; and (b) to understand what those reasons would even be.

We investigate the behavior of reading comprehension models with respect to two linguistic ‘skills’: coreference resolution and comparison. We propose a definition for the reasoning steps expected from a system that would be ‘reading slowly’, and compare that with the behavior of 5 models of the BERT family of various sizes, observed through saliency scores and counterfactual explanations.

We find that for comparison (but not coreference) the systems based on larger encoders are more likely to rely on the ‘right’ information, but even they struggle with generalization, suggesting that they still learn specific lexical patterns rather than the general principles of comparison.

--- https://sethroberts.net/2012/01/10/why-we-touch-our-mouths-so-often-forewarned-is-forearmed/ 2022-01-30 biology/booger --- https://sethroberts.net/2009/08/13/why-do-children-pick-their-nose/ 2022-01-30 biology/booger --- https://arxiv.org/abs/2209.09735 Relaxed Attention for Transformer Models Timo Lohrenz, Björn Möller, Zhengyang Li, Tim Fingscheidt 2022-09-20 2022-09-20 [("doi","10.48550/arXiv.2209.09735")] ai/nn/transformer/attention

The powerful modeling capabilities of all-attention-based transformer architectures often cause overfitting and—for natural language processing tasks—lead to an implicitly learned internal language model in the autoregressive transformer decoder complicating the integration of external language models.

In this paper, we explore relaxed attention, a simple and easy-to-implement smoothing of the attention weights, yielding a two-fold improvement to the general transformer architecture: First, relaxed attention provides regularization when applied to the self-attention layers in the encoder. Second, we show that it naturally supports the integration of an external language model as it suppresses the implicitly learned internal language model by relaxing the cross attention in the decoder.

We demonstrate the benefit of relaxed attention across several tasks with clear improvement in combination with recent benchmark approaches. Specifically, we exceed the former state-of-the-art performance of 26.90% word error rate on the largest public lip-reading LRS3 benchmark with a word error rate of 26.31%, as well as we achieve a top-performing BLEU score of 37.67 on the IWSLT14 (DE → EN) machine translation task without external language models and virtually no additional model parameters.

Code and models will be made publicly available.

--- https://arxiv.org/abs/2003.07845 PowerNorm: Rethinking Batch Normalization in Transformers Sheng Shen, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer 2020-03-17 2022-01-30 [("doi","10.48550/arXiv.2003.07845")] ai/nn/transformer/attention

The standard normalization method for neural network (NN) models used in Natural Language Processing (NLP) is layer normalization (LN). This is different than batch normalization (BN), which is widely-adopted in Computer Vision. The preferred use of LN in NLP is principally due to the empirical observation that a (naive/vanilla) use of BN leads to performance degradation for NLP tasks; however, a thorough understanding of the underlying reasons for this is not always evident.

In this paper, we perform a systematic study of NLP transformer models to understand why BN has a poor performance, as compared to LN.

We find that the statistics of NLP data across the batch dimension exhibit large fluctuations throughout training. This results in instability, if BN is naively implemented.

To address this, we propose Power Normalization (PN), a novel normalization scheme that resolves this issue by (1) relaxing zero-mean normalization in BN, (2) incorporating a running quadratic mean instead of per batch statistics to stabilize fluctuations, and (3) using an approximate backpropagation for incorporating the running statistics in the forward pass. We show theoretically, under mild assumptions, that PN leads to a smaller Lipschitz constant for the loss, compared with BN. Furthermore, we prove that the approximate backpropagation scheme leads to bounded gradients.

We extensively test PN for transformers on a range of NLP tasks, and we show that it outperforms both LN and BN. In particular, PN outperforms LN by 0.4/0.6 BLEU on IWSLT14/WMT14 and 5.6/3.0 PPL on PTB/WikiText-103.

We make our code publicly available at https://github.com/sIncerass/powernorm.

--- https://cryopets.com/explore/science 2022-01-30 cryonics --- https://x.com/fabianstelzer/status/1572571003804614657 2022-01-30 ai/nn/transformer/gpt/codex --- https://www.medrxiv.org/content/10.1101/2022.09.13.22278911.full Improving on polygenic scores across complex traits using select and shrink with summary statistics J. P. Tyrer, P. Peng, A. A. DeVries, S. A. Gayther, M. R. Jones, P. D. Pharoah 2022-09-17 2022-09-17 [("doi","10.1101/2022.09.13.22278911")] genetics/heritable

Motivation: As precision medicine advances, polygenic scores (PGS) have become increasingly important for clinical risk assessment. Many methods have been developed to create polygenic models with increased accuracy for risk prediction.

Our select-and-shrink-with-summary-statistics (S4) PGS method extends a previous method (polygenic risk score continuous shrinkage (PRS CS)) by using a continuous shrinkage prior on effect sizes with a selection strategy for including SNPs to create the best performing model.

Results: The S4 method provides overall improved PGS accuracy for UK Biobank participants when compared to LDpred2 and PRS-CS across a variety of phenotypes with differing genetic architectures. Additionally, the S4 method has higher estimated PGS accuracy over LDpred2 in Finnish and Japanese populations.

Conclusion: Thus, the S4 method represents an improvement in overall PGS accuracy across multiple phenotypes and increases the transferability of PGS across ancestries.

--- https://www.biorxiv.org/content/10.1101/2022.08.23.505030.full Evaluation of Transfer Learning Methods for Detecting Alzheimer’s Disease with Brain MRI Nikhil J. Dhinagar, Sophia I. Thomopoulos, Priya Rajagopalan, Dimitris Stripelis, Jose Luis Ambite, Greg Ver Steeg, Paul M. Thompson 2022-08-25 2022-08-25 [("doi","10.1101/2022.08.23.505030")] ai/nn/cnn psychiatry/alzheimers psychology/neuroscience

Deep neural networks show great promise for classifying brain diseases and making prognostic assessments based on neuroimaging data, but large, labeled training datasets are often required to achieve high predictive accuracy. Here we evaluated a range of transfer learning or pre-training strategies to create useful MRI representations for downstream tasks that lack large amounts of training data, such as Alzheimer’s disease (AD) classification.

To test our models, we analyzed 4,098 3D T1-weighted brain MRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and independently validated our proposed methods for detecting AD with an out-of-distribution test set of 600 scans from the Open Access Series of Imaging Studies (OASIS3) cohort. First, we trained 3D and 2D convolutional neural network (CNN) architectures. We tested combinations of multiple pre-training strategies based on (1) supervised, (2) contrastive learning, and (3) self-supervised learning—using pre-training data within versus outside the MRI domain.

In our experiments, the 3D CNN pre-trained with contrastive learning provided the best overall results—when fine-tuned on T1-weighted scans for AD classification—outperformed the baseline by 2.8% when trained with all of the training data from ADNI. We also show test performance as a function of the training dataset size and the chosen pre-training method. Transfer learning offered large benefits in low data regimes, with a performance boost of 7.7%. When the pre-trained model was used for AD classification, we were able to visualize an improved clustering of test subjects’ diagnostic groups, as illustrated via a uniform manifold approximation (UMAP) projection of the high-dimensional model embedding space.

Further, saliency maps indicate the additional activation regions in the brain scan using pre-training, that then maximally contributed towards the final prediction score.

--- https://arxiv.org/abs/1712.06567#uber Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O. Stanley, Jeff Clune 2017-12-18 2022-01-30 [("doi","10.48550/arXiv.1712.06567")] reinforcement-learning/exploration reinforcement-learning/model-free

Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. Evolution strategies (ES) can rival backprop-based algorithms such as Q-learning and policy gradients on challenging deep reinforcement learning (RL) problems. However, ES can be considered a gradient-based algorithm because it performs stochastic gradient descent via an operation similar to a finite-difference approximation of the gradient. That raises the question of whether non-gradient-based evolutionary algorithms can work at DNN scales.

Here we demonstrate they can: we evolve the weights of a DNN with a simple, gradient-free, population-based genetic algorithm (GA) and it performs well on hard deep RL problems, including Atari ALE and humanoid locomotion.

The Deep GA successfully evolves networks with over 4 million free parameters, the largest neural networks ever evolved with a traditional evolutionary algorithm.

These results (1) expand our sense of the scale at which GAs can operate, (2) suggest intriguingly that in some cases following the gradient is not the best choice for optimizing performance, and (3) make immediately available the multitude of neuroevolution techniques that improve performance.

We demonstrate the latter by showing that combining DNNs with novelty search, which encourages exploration on tasks with deceptive or sparse reward functions, can solve a high-dimensional problem on which reward-maximizing algorithms (eg. DQN, A3C, ES, and the GA) fail. Additionally, the Deep GA is faster than ES, A3C, and DQN (it can train Atari in ~4 hours on one desktop or ~1 hour distributed on 720 cores), and enables a state-of-the-art, up to 10,000× compact encoding technique.

Table 1: On Atari a simple genetic algorithm is competitive with Q-learning (DQN), policy gradients (A3C), and evolution strategies (ES). Shown are game scores (higher is better). Comparing performance between algorithms is inherently challenging (see main text), but we attempt to facilitate comparisons by showing estimates for the amount of computation (operations, the sum of forward and backward neural network passes), data efficiency (the number of game frames from training episodes), and how long in wall-clock time the algorithm takes to run. The ES, DQN, A3C, and GA (1B) perform best on 3, 3, 4, and 3 games, respectively. Surprisingly, random search often finds policies superior to those of DQN, A3C, and ES (see text for discussion). Note the dramatic differences in the speeds of the algorithm, which are much faster for the GA and ES, and data efficiency, which favors DQN. The scores for DQN are from Hessel et al 2017 while those for A3C and ES are from Salimans et al 2017. For A3C, DQN, and ES, we cannot provide error bars because they were not reported in the original literature; GA and random search error bars are visualized in (Supplementary Figure 3). The wall-clock times are approximate because they depend on a variety of hard-to-control-for factors. We found the GA runs slightly faster than ES on average. The † symbol indicates state-of-the-art performance. GA 6B scores are bolded if best, but do not prevent bolding in other columns.
--- /doc/history/1951-echols.pdf The Art of Classical Swearing Edward C. Echols 1951-03-01 2022-01-31 [("doi","10.2307/3292805")] fiction/humor history philosophy/religion --- https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/4 2022-01-31 ai/nn/diffusion --- https://www.nature.com/articles/d41586-022-02999-9 2022-01-31 ai/nn/transformer/alphafold --- https://blog.metaphysic.ai/the-road-to-realistic-full-body-deepfakes/ 2022-01-31 ai/nn/diffusion ai/nn/gan/stylegan ai/video/generation --- https://www.theatlantic.com/family/archive/2022/09/why-school-haunts-our-dreams-long-after-graduation/671506/ 2022-01-31 psychiatry psychology/vision/dream --- https://arxiv.org/abs/2206.07845 Optimality of Matched-Pair Designs in Randomized Controlled Trials Yuehao Bai 2022-06-15 2022-06-15 [("doi","10.48550/arXiv.2206.07845")] statistics/power-analysis

In randomized controlled trials (RCTs), treatment is often assigned by stratified randomization. I show that among all stratified randomization schemes which treat all units with probability one half, a certain matched-pair design achieves the maximum statistical precision for estimating the average treatment effect (ATE). In an important special case, the optimal design pairs units according to the baseline outcome.

In a simulation study based on datasets from 10 RCTs, this design lowers the standard error for the estimator of the ATE by 10% on average, and by up to 34%, relative to the original designs.

--- https://arxiv.org/abs/2209.10655 Mega: Moving Average Equipped Gated Attention Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, Luke Zettlemoyer 2022-09-21 2022-09-21 [("doi","10.48550/arXiv.2209.10655")] ai/nn/transformer/attention/hierarchical

[code] The design choices in the Transformer attention mechanism, including weak inductive bias and quadratic computational complexity, have limited its application for modeling long sequences.

In this paper, we introduce Mega, a simple, theoretically grounded, single-head gated attention mechanism equipped with (exponential) moving average to incorporate inductive bias of position-aware local dependencies into the position-agnostic attention mechanism. We further propose a variant of Mega that offers linear time and space complexity yet yields only minimal quality loss, by efficiently splitting the whole sequence into multiple chunks with fixed length.

Extensive experiments on a wide range of sequence modeling benchmarks, including the Long Range Arena, neural machine translation, auto-regressive language modeling, and image and speech classification, show that Mega achieves improvements over other sequence models, including variants of Transformers and recent state space models.

--- https://arxiv.org/abs/2209.11055 SetFit: Efficient Few-Shot Learning Without Prompts Lewis Tunstall, Nils Reimers, Unso Eun Seo Jo, Luke Bates, Daniel Korat, Moshe Wasserblat, Oren Pereg 2022-09-22 2022-09-22 [("doi","10.48550/arXiv.2209.11055")] ai/nn/transformer

Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and pattern exploiting training (PET), have achieved impressive results in label-scarce settings. However, they are difficult to employ since they are subject to high variability from manually crafted prompts, and typically require billion-parameter language models to achieve high accuracy.

To address these shortcomings, we propose SetFit (Sentence Transformer Fine-tuning), an efficient and prompt-free framework for few-shot fine-tuning of Sentence Transformers (ST). SetFit works by first fine-tuning a pretrained ST on a small number of text pairs, in a contrastive Siamese manner. The resulting model is then used to generate rich text embeddings, which are used to train a classification head. This simple framework requires no prompts or verbalizers, and achieves high accuracy with orders of magnitude less parameters than existing techniques.

Our experiments show that SetFit obtains comparable results with PEFT and PET techniques, while being an order of magnitude faster to train. We also show that SetFit can be applied in multilingual settings by simply switching the ST body. Our code is available at https://github.com/huggingface/setfit and our datasets at https://huggingface.co/SetFit.

--- https://arxiv.org/abs/1908.10084 Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks Nils Reimers, Iryna Gurevych 2019-08-27 2022-01-31 [("doi","10.48550/arXiv.1908.10084")] ai/nn/retrieval ai/nn/transformer

BERT (Devlin et al 2018) and RoBERTa (Liu et al 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10,000 sentences requires about 50 million inference computations (~65 hours) with BERT. The construction of BERT makes it unsuitable for semantic similarity search as well as for unsupervised tasks like clustering.

In this publication, we present Sentence-BERT (SBERT), a modification of the pretrained BERT network that use Siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT.

We evaluate SBERT and SRoBERTa on common STS tasks and transfer learning tasks, where it outperforms other state-of-the-art sentence embeddings methods.

--- https://arxiv.org/abs/2001.07676 Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference Timo Schick, Hinrich Schütze 2020-01-21 2022-01-31 [("doi","10.48550/arXiv.2001.07676")] ai/nn/transformer

Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with “task descriptions” in natural language (eg. Radford et al 2019).

While this approach underperforms its supervised counterpart, we show in this work that the two ideas can be combined: We introduce Pattern-Exploiting Training (PET), a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task.

These phrases are then used to assign soft labels to a large set of unlabeled examples.

Finally, standard supervised training is performed on the resulting training set.

For several tasks and languages, PET outperforms supervised training and strong semi-supervised approaches in low-resource settings by a large margin.

--- https://arxiv.org/abs/2209.11142#deepmind A Generalist Neural Algorithmic Learner Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Dudzik, Matko Bošnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, Petar Veličković 2022-09-22 2022-09-22 [("doi","10.48550/arXiv.2209.11142")] ai/nn/transformer cs/algorithm/sorting

The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalizes out of distribution. While recent years have seen a surge in methodological improvements in this area, they mostly focused on building specialist models. Specialist models are capable of learning to neurally execute either only one algorithm or a collection of algorithms with identical control-flow backbone. Here, instead, we focus on constructing a generalist neural algorithmic learner—a single graph neural network processor capable of learning to execute a wide range of algorithms, such as sorting, searching, dynamic programming, path-finding and geometry.

We leverage the CLRS benchmark to empirically show that, much like recent successes in the domain of perception, generalist algorithmic learners can be built by “incorporating” knowledge. That is, it is possible to effectively learn algorithms in a multi-task manner, so long as we can learn to execute them well in a single-task regime. Motivated by this, we present a series of improvements to the input representation, training regime and processor architecture over CLRS, improving average single-task performance by over 20% from prior art.

We then conduct a thorough ablation of multi-task learners leveraging these improvements. Our results demonstrate a generalist learner that effectively incorporates knowledge captured by specialist models.

--- https://www.reddit.com/r/OpenAI/comments/xlvygv/artifical_intelligence_allows_me_to_get_straight/ 2022-02-01 ai/nn/transformer/gpt/3/nonfiction --- https://en.wikipedia.org/wiki/Mendelian_randomization Mendelian Randomization 2022-02-01 genetics/heritable/correlation/mendelian-randomization --- https://en.wikipedia.org/wiki/Instrumental_variables_estimation Instrumental variables estimation 2022-02-01 economics genetics/heritable/correlation/mendelian-randomization statistics/causality --- https://en.wikipedia.org/wiki/Mendelian_inheritance Mendelian inheritance 2022-02-01 genetics/heritable/correlation/mendelian-randomization --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC557238/ What can Mendelian Randomization tell us about modifiable behavioral and environmental exposures? George Davey Smith, Shah Ebrahim 2005-05-07 2022-02-01 [("doi","10.1136/bmj.330.7499.1076")] genetics/heritable/correlation/mendelian-randomization

Using genetic variants as a proxy for modifiable environmental factors that are associated with disease can circumvent some of the problems of observational studies.

--- https://www.bmj.com/content/352/bmj.i582 Height, body mass index, and socioeconomic status: Mendelian Randomization study in UK Biobank Tyrrell 2016 2022-02-01 genetics/heritable/correlation/mendelian-randomization --- https://en.wikipedia.org/wiki/Canalisation_(genetics) Canalisation (genetics) 2022-02-01 biology genetics/heritable --- https://research.google/blog/tensorstore-for-high-performance-scalable-array-storage/ 2022-02-01 ai/scaling/hardware cs/algorithm --- https://en.wikipedia.org/wiki/Exenatide Exenatide 2022-02-01 longevity/glp/semaglutide --- /doc/sociology/2022-sobolsarag.pdf The irony of (romantic) harmony: Heterosexual romantic relationships can drive women’s justification of the gender hierarchy Danit Sobol-Sarag, Noa Schori-Eyal, Saulo Fernández, Tamar Saguy 2022-07-19 2022-07-19 [("doi","10.1177/13684302221100403")] psychology/cognitive-bias psychology/personality sociology

Even though gender inequality is evident across life domains, women often justify the gender hierarchy. We examined whether the very closeness that heterosexual women share with their male romantic partners predicts their justification of gender inequality.

We drew on intergroup-related research, showing that positive perceptions that minority groups develop within harmonious intergroup interactions, generalize to affect their views of group-based inequality. We expected that to the extent that women experience their romantic relationships positively, they will be more accepting of gender inequality within their homes, and these perceptions will generalize to predict justification of macro-level gender inequality.

5 correlational and two experimental studies supported this prediction. The more women rated (or were primed with) their relationship as positive, the more they justified the gender social system. This was mediated by women’s perception of their housework division as fair, and was less pronounced among feminists.

Implications regarding social change are discussed.

[This is an amusingly Marxist false consciousness account of dating & romance. Why frame it as ‘happy women are biased towards justifying the capitalist society’—rather than the obviously correct framing that those unsuccessful in love (due largely to personal factors, in every era and society) are more likely to blame anyone but themselves?]

--- https://arxiv.org/abs/2205.03971 Private Eye: On the Limits of Textual Screen Peeking via Eyeglass Reflections in Video Conferencing Yan Long, Chen Yan, Shilin Xiao, Shivan Prasad, Wenyuan Xu, Kevin Fu 2022-05-08 2022-05-08 [("doi","10.48550/arXiv.2205.03971")] cs/security psychology/vision

Using mathematical modeling and human subjects experiments, this research explores the extent to which emerging webcams might leak recognizable textual and graphical information gleaming from eyeglass reflections captured by webcams.

The primary goal of our work is to measure, compute, and predict the factors, limits, and thresholds of recognizability as webcam technology evolves in the future. Our work explores and characterizes the viable threat models based on optical attacks using multi-frame super resolution techniques on sequences of video frames.

Our models and experimental results in a controlled lab setting show it is possible to reconstruct and recognize with over 75% accuracy on-screen texts that have heights as small as 10 mm with a 720p webcam. We further apply this threat model to web textual contents with varying attacker capabilities to find thresholds at which text becomes recognizable.

Our user study with 20 participants suggests present-day 720p webcams are sufficient for adversaries to reconstruct textual content on big-font websites. Our models further show that the evolution towards 4K cameras will tip the threshold of text leakage to reconstruction of most header texts on popular websites. Besides textual targets, a case study on recognizing a closed-world dataset of Alexa top 100 websites with 720p webcams shows a maximum recognition accuracy of 94% with 10 participants even without using machine-learning models.

Our research proposes near-term mitigations including a software prototype that users can use to blur the eyeglass areas of their video streams. For possible long-term defenses, we advocate an individual reflection testing procedure to assess threats under various settings, and justify the importance of following the principle of least privilege for privacy-sensitive scenarios.

--- https://fullfrontal.moe/understanding-urusei-yatsura/ 2022-02-02 anime --- https://www.theatlantic.com/science/archive/2022/09/falling-dropped-cat-reflex-physics/671424/ 2022-02-02 cat science --- https://craftsmanship.net/the-bonsai-kid/ A young Oregonian believes that he can create a uniquely American form of the Japanese bonsai tree. And he is literally betting the farm on the idea that if he builds it, they will come. 2022-02-02 japan/art --- https://foreignpolicy.com/2013/05/30/the-bomb-didnt-beat-japan-stalin-did/ 2022-02-02 japan/history --- /review/book#japanese-love-hotels-chaplin-2007 2022-02-02 japan/history --- https://neojaponisme.com/2012/12/28/the-year-2012-in-japan/ 2022-02-02 japan/history --- https://en.wikipedia.org/wiki/Amagasaki_Serial_Murder_Incident Amagasaki Serial Murder Incident 2022-02-02 japan/history --- https://en.wikipedia.org/wiki/Donald_Keene Donald Keene 2022-02-02 japan/history japan/poetry --- https://www.nytimes.com/2013/02/14/world/asia/in-japan-the-fax-machine-is-anything-but-a-relic.html In High-Tech Japan, the Fax Machines Roll On 2022-02-02 japan --- /review/book#haikai-poet-yosa-buson-and-the-basho-revival-crowley-2006 2022-02-02 japan/poetry --- https://www.amazon.com/Japan-Edge-Insiders-Japanese-Subculture/dp/1569313458 2022-02-03 anime --- /review/book#fujiwara-teikas-hundred-poem-sequence-of-the-shoji-era-1200-brower-1978 2022-02-03 japan/poetry --- https://en.wikipedia.org/wiki/Ihara_Saikaku Ihara Saikaku 2022-02-03 japan/history --- /review/book#the-japanese-family-storehouse-ihara-1959 2022-02-03 japan/history --- https://en.wikipedia.org/wiki/D%C5%8Djima_Rice_Exchange Dōjima Rice Exchange 2022-02-03 japan/history --- https://www.amazon.com/Vaccinators-Smallpox-Medical-Knowledge-Opening/dp/0804786909 2022-02-03 japan/history --- /review/book#the-vaccinators-jannetta-2007 2022-02-03 japan/history --- https://www.kalzumeus.com/2018/10/19/japanese-hometown-tax/ 2022-02-03 economics/georgism japan/history --- https://web.archive.org/web/20201102134350/https://japanesetranslationblog.wordpress.com/2018/03/18/how-we-should-live-girls-last-tour-interview-with-tsukumizu/ How we should live—Girls’ Last Tour: Interview with Tsukumizu 2022-02-03 anime --- https://en.wikipedia.org/wiki/Anglo-Japanese_style Anglo-Japanese style 2022-02-03 design japan/art --- https://neojaponisme.com/2011/11/28/the-great-shift-in-japanese-pop-culture-part-one/ 2022-02-03 japan/art --- https://neojaponisme.com/2011/11/29/the-great-shift-in-japanese-pop-culture-part-two/ Part Two: The Implosion of Cultural Markets 2022-02-04 japan/art --- https://neojaponisme.com/2011/11/30/the-great-shift-in-japanese-pop-culture-part-three/ Part Three: Mainstream Consumers vs. Marginal Subcultures 2022-02-04 japan/art --- https://neojaponisme.com/2011/12/01/the-great-shift-in-japanese-pop-culture-part-four/ Part Four: The Rise of Marginal Subcultures 2022-02-04 japan/art --- https://neojaponisme.com/2011/12/02/the-great-shift-in-japanese-pop-culture-part-five/ Part Five: The Difficulty of Exporting Marginal Subcultures 2022-02-04 japan/art --- https://en.wikipedia.org/wiki/Kintsugi Kintsugi 2022-02-04 japan/art --- https://en.wikipedia.org/wiki/Mara%E1%B9%87asati Maraṇasati 2022-02-04 japan/art --- https://en.wikipedia.org/wiki/Kobayashi_Eitaku Kobayashi Eitaku 2022-02-04 japan/art --- https://en.wikipedia.org/wiki/Dorodango Dorodango 2022-02-04 japan/art --- https://grantland.com/features/sumo-wrestling-tokyo-japan-hakuho-yukio-mishima-novelist-seppuku/ 2022-02-04 exercise japan/art japan/history --- https://fivethirtyeight.com/features/the-sumo-matchup-centuries-in-the-making/ 2022-02-04 japan/history --- /doc/design/2018-12-23-zachrscott-compositingkowloonwalledcitycrosssections.html 2018-12-23 2022-02-04 design --- https://www.spoon-tamago.com/detailed-cross-section-of-the-kowloon-walled-city-created-by-japanese-researchers/ 2022-02-05 design --- https://en.wikipedia.org/wiki/Taiwan_under_Japanese_rule#Economic Taiwan under Japanese rule § Economic 2022-02-05 japan/history --- /doc/economics/2021-arellanobover.pdf Displacement, Diversity, and Mobility: Career Impacts of Japanese American Internment Jaime Arellano-Bover 2021-12-17 2022-02-05 [("doi","10.1017/S0022050721000565")] economics japan/history

In 1942 more than 110,000 persons of Japanese origin living on the U.S. West Coast were forcibly sent away to ten internment camps for one to 3 years. This paper studies how internees’ careers were affected in the long run.

Combining Census data, camp records, and survey data, I develop a predictor of a person’s internment status based on Census observables. Using a difference-in-differences framework, I find that:

internment had long-run positive effects on earnings.

The evidence is consistent with mechanisms related to increased mobility due to re-optimization of occupation and location choices, possibly facilitated by camps’ high economic diversity.

--- https://en.wikipedia.org/wiki/Fifth_Generation_Computer_Systems Fifth Generation Computer Systems 2022-02-05 japan/history --- /doc/economics/mechanism-design/2019-saito.pdf Lighthouse Provision in Premodern Japan Kuniyoshi Saito 2019-01-01 2022-02-05 [("doi","10.1111/ecin.12793")] economics/mechanism-design japan/history

[followup to Coase on England] We investigate how lighthouses were provided in premodern Japan, with a specific focus on the role of the private sector.

Using national survey data on lighthouses collected by the government in 1883, we find that:

lighthouses constructed by the private sector in the Edo period (1603–1868) accounted for nearly 70% of lighthouses existing at the time of the survey and that there was no statistically-significant difference in technical features between private and public lighthouses.

However, we observe that almost all private lighthouses were located at ports, and additional case studies indicate that the public authorities endorsed the operation of some lighthouses, which might have contributed to their profits and improved their long-term survival.

We also find that various factors, including the formation of merchant coalitions and whether users were identifiable, influenced whether a private organization could circumvent the free rider problem.

--- https://cloud.google.com/blog/products/gcp/how-a-japanese-cucumber-farmer-is-using-deep-learning-and-tensorflow How a Japanese cucumber farmer is using deep learning and TensorFlow Sato 2016 2022-02-05 ai/nn japan --- https://www.sciencedirect.com/science/article/pii/S0917504016300673 Overview of the BioBank Japan Project: Study design and profile Nagai 2017 2022-02-05 japan --- /doc/cat/psychology/drug/catnip/1998-shoyama.pdf I Actinidia polygama (Japanese name Matatabi): In Vitro Culture, Micropropagation, and the Production of Monoterpenes and Triterpenoids 1998 2022-02-05 cat/psychology/drug/catnip japan --- /doc/sociology/1966-devos-japansinvisiblerace.pdf Japan’s invisible race; caste in culture and personality Devos 1966-01-01 2022-02-05 japan sociology --- https://www.1up.com/o.www.1up.com/features/japanese-games-breaking-west.html Why Japanese Games are Breaking Up With the West 2022-02-05 japan --- https://en.wikipedia.org/wiki/Hiroshige Hiroshige 2022-02-06 japan/art --- https://web.archive.org/web/20080504115217/https://www.mit.edu/people/rei/manga-okadaluncheon.html Toshio Okada on the Otaku, Anime History, and Japanese Culture: Luncheon Talk Izawa 2003 2022-02-06 anime japan/art --- https://news.bbc.co.uk/2/hi/asia-pacific/837000.stm Japan cultists sentenced to death 2022-02-06 japan/history --- https://mdpi-res.com/d_attachment/ijerph/ijerph-10-06044/article_deploy/ijerph-10-06044.pdf Lithium in Tap Water and Suicide Mortality in Japan 2022-02-06 japan psychiatry/lithium --- https://deadline.com/2011/01/warner-bros-taps-shane-black-for-japanese-manga-death-note-96275/ Warner Bros Taps Shane Black For Japanese Manga Death Note 2022-02-06 anime --- https://variety.com/2009/film/news/warner-brings-death-to-bigscreen-1118003063/ Warner brings Death to big screen: Studio acquires rights to Japanese manga series 2022-02-06 anime --- https://web.archive.org/web/20090708055308/http://www.lolikon.org/misc/soundfx.html Japanese Sound effects and what they mean 2022-02-06 anime design/typography --- https://web.archive.org/web/20140707110937/http://www.tofugu.com/2012/05/15/japanese-web-design-why-you-so-2003/comment-page-2/ Japanese Web Design: Why You So 2003? 2022-02-06 design/typography japan --- https://digitalintheround.com/japan-mixi-facebook/ Mixi in Japan: the rise, the fall and the Facebook takeover 2022-02-06 japan --- https://japanintercultural.com/free-resources/articles/over-worked-and-underpaid-japanese-employees-feel-the-burden-of-sabisu-zangyo/ Over worked and underpaid Japanese employees feel the burden of sabisu zangyo 2022-02-06 japan --- https://web.archive.org/web/20120422191659/https://www.japantimes.co.jp/text/fl20060521x2.html FATAL ATTRACTION: Vision from the other side 2022-02-06 cryonics japan --- https://www.theguardian.com/world/2011/mar/29/japan-nuclear-plant-us-robots Japan nuclear plant gets help from US robots: Obama administration sends shipment of robots to help regain control over stricken Fukushima nuclear plant 2022-02-07 japan/history reinforcement-learning/robot --- https://www.thewhitereview.org/feature/celan-reads-japanese/ Celan Reads Japanese 2022-02-07 fiction/poetry --- https://www.bbc.com/news/world-asia-pacific-11258071 More than 230,000 Japanese centenarians ‘missing’ 2022-02-07 crime japan longevity statistics/bias --- https://www.bemmu.com/first-year-of-candy-japan First year of Candy Japan 2022-02-07 economics japan --- https://www.bemmu.com/how-i-decided-the-price-for-my-japanese-candy How I decided the price for my Japanese candy subscription service 2022-02-07 economics japan --- https://www.candyjapan.com/candy-japan-crosses-10000-mrr Candy Japan crosses $10,000 MRR 2022-02-07 economics japan --- https://www.candyjapan.com/how-i-got-credit-card-fraud-somewhat-under-control How Candy Japan got credit card fraud somewhat under control 2022-02-07 economics japan --- https://www.candyjapan.com/results-from-box-design-ab-test Results from Candy Japan box design A/B test 2022-02-07 economics japan --- https://www.candyjapan.com/sales-results-from-getting-3-million-views-on-youtube Sales results from getting 3 million views on YouTube 2022-02-07 economics/advertising japan --- https://www.blender.org/user-stories/japanese-anime-studio-khara-moving-to-blender/ Japanese anime studio Khara moving to Blender 2022-02-07 anime economics --- https://www.eurogamer.net/fez-creator-phil-fish-declares-modern-japanese-games-just-suck Fez creator Phil Fish declares: modern Japanese games ‘just suck’ 2022-02-07 japan --- https://www.japantimes.co.jp/news/2017/09/12/national/social-issues/1-20-infants-born-vitro-fertilization-japan-survey/ IVF accounts for 5% of babies born in Japan in 2015: survey 2022-02-08 genetics/selection/artificial japan --- https://www.theguardian.com/books/2001/apr/01/sciencefictionfantasyandhorror.features For sci-fi author William Gibson, Japan has been a lifelong inspiration. Here, the writer who coined the phrase cyberspace 2022-02-08 fiction/science-fiction japan --- https://www.washingtonpost.com/world/as-apple-and-samsung-dominate-japans-tech-giants-are-in-a-free-fall/2012/09/28/04c6eb36-0944-11e2-afff-d6c7f20a83bf_story.html As Apple and Samsung dominate, Japan’s tech giants are in a free fall 2022-02-08 japan --- https://www.washingtonpost.com/world/asia_pacific/in-japan-fax-machines-find-a-final-place-to-thrive/2012/06/07/gJQAshFPMV_print.html In Japan, fax machines remain important because of language and culture 2022-02-08 japan --- https://www.wired.com/2010/09/western-games-japan/ In Japan, Game-makers Struggle to Instill Taste for Western Shooters 2022-02-08 japan --- https://www.wired.com/2012/04/keiji-inafune-qa/ Q&A: Mega Man Creator Wants Japan to Admit Failure 2022-02-08 japan --- https://www.theatlantic.com/international/archive/2012/07/the-strange-rise-and-fall-of-north-koreas-business-empire-in-japan/260373/ Since its 1950s founding, a Pyongyang-linked group called Chongyron has run everything from banks to newspapers, pushing propaganda out and pulling hard currency in. But now that’s ending. 2022-02-08 japan/history --- https://www.japantimes.co.jp/life/2019/04/20/lifestyle/tokyos-tiny-living-spaces/ Downsized dwellings: Inside Tokyo’s tiny living spaces 2022-02-08 design japan/art --- /doc/ai/nn/diffusion/2022-09-22-gwern-stablediffusionv14-textualinversion-yinit-dropcapsexperiments.png Gwern 2022-09-22 2022-09-22 ai/nn/diffusion ai/nn/transformer/clip/sample design/typography/dropcap --- /doc/ai/nn/diffusion/2022-09-21-gwern-stablediffusionv14-circulardropcapinitialsamples.png Gwern 2022-09-21 2022-09-21 ai/nn/diffusion ai/nn/transformer/clip/sample design/typography/dropcap --- https://www.cell.com/cell-chemical-biology/pdf/S1074-5521(10)00008-6.pdf Targacept’s NNR Drugs Rehabilitate Nicotine Wolfson 2010 2022-02-09 nicotine psychiatry/depression --- https://journalnow.com/business/business_news/local/targacept-sale-to-catalyst-is-complete/article_8a0008c4-4dc2-5770-81db-758e527f177b.html 2022-02-09 nicotine psychiatry/depression --- https://blog.benwiener.com/programming/2019/04/29/reinventing-the-wheel.html Reinventing the Wheel: Discovering the Optimal Rolling Shape with PyTorch Ben Wiener 2019 2022-02-09 ai/nn cs/algorithm math statistics/decision --- https://www.smithsonianmag.com/air-space-magazine/airships-rise-again-180979343/ 2022-02-09 technology --- https://www.newyorker.com/magazine/2016/02/29/a-new-generation-of-airships-is-born 2022-02-09 technology --- https://jacobbrazeal.wordpress.com/2022/09/23/gpt-3-can-find-paths-up-to-7-nodes-long-in-random-graphs/ 2022-02-09 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2209.11163#nvidia GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, Sanja Fidler 2022-09-22 2022-09-22 [("doi","10.48550/arXiv.2209.11163")] ai/nn/gan

As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident. In our work, we aim to train performant 3D generative models that synthesize textured meshes which can be directly consumed by 3D rendering engines, thus immediately usable in downstream applications.

Prior works on 3D generative modeling either lack geometric details, are limited in the mesh topology they can produce, typically do not support textures, or utilize neural renderers in the synthesis process, which makes their use in common 3D software non-trivial. In this work, we introduce GET3D, a Generative model that directly generates Explicit Textured 3D meshes with complex topology, rich geometric details, and high-fidelity textures. We bridge recent success in the differentiable surface modeling, differentiable rendering as well as 2D Generative Adversarial Networks to train our model from 2D image collections.

GET3D is able to generate high-quality 3D textured meshes, ranging from cars, chairs, animals, motorbikes and human characters to buildings, achieving improvements over previous methods.

--- https://arxiv.org/abs/2209.11224 VToonify: Controllable High-Resolution Portrait Video Style Transfer Shuai Yang, Liming Jiang, Ziwei Liu, Chen Change Loy 2022-09-22 2022-09-22 [("doi","10.48550/arXiv.2209.11224")] ai/nn/gan/stylegan/anime

[homepage; code] Generating high-quality artistic portrait videos is an important and desirable task in computer graphics and vision. Although a series of successful portrait image toonification models built upon the powerful StyleGAN have been proposed, these image-oriented methods have obvious limitations when applied to videos, such as the fixed frame size, the requirement of face alignment, missing non-facial details and temporal inconsistency.

In this work, we investigate the challenging controllable high-resolution portrait video style transfer by introducing a novel VToonify framework. Specifically, VToonify leverages the mid-resolution & high-resolution layers of StyleGAN to render high-quality artistic portraits based on the multi-scale content features extracted by an encoder to better preserve the frame details. The resulting fully convolutional architecture accepts non-aligned faces in videos of variable size as input, contributing to complete face regions with natural motions in the output.

Our framework is compatible with existing StyleGAN-based image toonification models to extend them to video toonification, and inherits appealing features of these models for flexible style control on color and intensity.

This work presents two instantiations of VToonify built upon Toonify and DualStyleGAN for collection-based and exemplar-based portrait video style transfer, respectively.

Extensive experimental results demonstrate the effectiveness of our proposed VToonify framework over existing methods in generating high-quality and temporally-coherent artistic portrait videos with flexible style controls.

--- https://arxiv.org/abs/2203.13248 Pastiche Master (DualStyleGAN): Exemplar-Based High-Resolution Portrait Style Transfer Shuai Yang, Liming Jiang, Ziwei Liu, Chen Change Loy 2022-03-24 2022-03-24 [("doi","10.48550/arXiv.2203.13248")] ai/anime/danbooru ai/nn/gan/stylegan/anime

Recent studies on StyleGAN show high performance on artistic portrait generation by transfer learning with limited data.

In this paper, we explore more challenging exemplar-based high-resolution portrait style transfer by introducing a novel DualStyleGAN with flexible control of dual styles of the original face domain and the extended artistic portrait domain. Different from StyleGAN, DualStyleGAN provides a natural way of style transfer by characterizing the content and style of a portrait with an intrinsic style path and a new extrinsic style path, respectively. The delicately designed extrinsic style path enables our model to modulate both the color and complex structural styles hierarchically to precisely pastiche the style example. Furthermore, a novel progressive fine-tuning scheme is introduced to smoothly transform the generative space of the model to the target domain, even with the above modifications on the network architecture.

Experiments demonstrate the superiority of DualStyleGAN over state-of-the-art methods in high-quality portrait style transfer and flexible style control.

--- https://www.medrxiv.org/content/10.1101/2022.09.23.22280264.full Post-COVID-19 syndrome: retinal microcirculation as a potential marker for chronic fatigue Sarah Schlick, Marianna Lucio, Alexander Johannes Bartsch, Adam Skornia, Jakob Hoffmanns, Charlotte Szewczykowski, Thora Schroeder, Franzi Raith, Lennart Rogge, Felix Heltmann, Michael Moritz, Lorenz Beitlich, Julia Schottenhamml, Martin Herrmann, Thomas Harrer, Marion Ganslmayer, Friedrich E. Kruse, Robert Laemmer, Christian Mardin, Bettina Hohberger 2022-09-23 2022-09-23 [("doi","10.1101/2022.09.23.22280264")] biology psychiatry

Post-COVID-19 syndrome (PCS) summarizes persisting sequelae after infection with the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). PCS can affect patients of all covid-19 disease severities. As previous studies revealed impaired blood flow as a provoking factor for triggering PCS, it was the aim of the present study to investigate a potential association of self-reported chronic fatigue and retinal microcirculation in patients with PCS, potentially indicating an objective biomarker.

A prospective study was performed, including 201 subjects: 173 patients with PCS and 28 controls. Retinal microcirculation was visualized by OCT-Angiography (OCT-A) and quantified by the Erlangen-Angio-Tool as macula and peripapillary vessel density (VD). Chronic Fatigue (CF) was assessed with the variables Bell score, age and gender. The VD in the superficial vascular plexus (SVP), intermediate capillary plexus (ICP) and deep capillary plexus (DCP) were analyzed considering the repetitions (12×). Taking into account such repetitions a mixed model was performed to detect possible differences in the least square means between different groups of analysis.

An age effect on VD was observed between patients and controls (p < 0.0001). Gender analysis yielded that women with PCS showed lower VD levels in SVP compared to male patients (p = 0.0015). The PCS patients showed statistically-significantly lower VD of ICP as compared to the controls (p = 0.0001, [CI: 0.32; 1]). Moreover, considering PCS patients, the mixed model reveals a statistically-significant difference between chronic fatigue (CF) and without CF in VD of SVP (p = 0.0033, [CI: −4.5; −0.92]). The model included age, gender, and the variable Bell score, representing a subjective marker for CF.

Consequently, the retinal microcirculation might be an objective biomarker in subjective-reported chronic fatigue of patients with PCS.

--- https://web.archive.org/web/20110807132554/http://library.findlaw.com/1998/Oct/1/127402.html 2022-02-09 economics/copyright law --- https://law.justia.com/cases/federal/district-courts/FSupp2/150/613/2468303/ 2022-02-10 economics/copyright law --- https://www.youtube.com/watch?v=zJoYY2eHAF0 Random Walk StyleGAN 2022-02-10 ai/nn/gan/stylegan --- https://www.youtube.com/watch?v=zXbb6KQ0xV8 Learning robust perceptive locomotion for quadrupedal robots in the wild [video] 2022-02-10 reinforcement-learning/robot --- https://www.youtube.com/watch?v=ysFav0b472w Supplementary video for Do As I Can, Not As I Say: Grounding Language in Robotic Affordances 2022-02-10 reinforcement-learning/model/decision-transformer reinforcement-learning/robot --- https://www.youtube.com/watch?v=nHX7hCeOtFc Randomly traversing the manifold of faces (2): Dataset: Labeled Faces in the Wild (LFW); Model: Variational Autoencoder (VAE) / Deep Latent Gaussian Model (DLGM). 2022-02-10 ai/nn/vae --- https://www.zdf.de/nachrichten/politik/cryptoleaks-bnd-cia-operation-rubikon-100.html Cryptoleaks: How BND and CIA Deceived Everyone: Research by ZDF, Washington Post and SRF shows how the BND and CIA secretly spy on states—and concealed gross human rights violations. 2022-02-10 cs/cryptography cs/security politics --- https://www2.oberlin.edu/math/faculty/bosch/tspart-page.html TSP Art 2022-02-10 cs/algorithm --- https://www3.bostonglobe.com/magazine/2017/11/02/the-muck-affair/QczxAVe0i2EJZpLPGEKR9H/story.html?arc404=true Sex, spies, and the national anthem: The BSO scandal you’ve never heard of: One hundred years ago, one of the world’s top conductors was ensnared in a scandal involving patriotism and sex. It almost toppled Boston’s famed orchestra. 2022-02-10 history politics --- https://wyclif.substack.com/p/biorxiv-wouldnt-host-our-paper-on bioRxiv wouldn’t host our paper on natural selection, why not? 2022-02-10 genetics/selection/natural/human --- https://wyclif.substack.com/p/the-natural-selection-paper-part The natural selection paper, part 1: our findings. The first of 3 posts about my new paper with Abdel Abdellaoui 2022-02-10 genetics/selection/natural/human --- https://wyclif.substack.com/p/the-natural-selection-paper-part-822 The natural selection paper, part 3: reflections. People’s genes are changing. Does it matter? 2022-02-10 genetics/selection/natural/human --- https://wyclif.substack.com/p/the-natural-selection-paper-part-908 The natural selection paper, part 2: theory. What explains natural selection in contemporary humans? 2022-02-11 genetics/selection/natural/human --- https://arxiv.org/abs/2208.08195 Benchmarking Compositionality with Formal Languages Josef Valvoda, Naomi Saphra, Jonathan Rawski, Adina Williams, Ryan Cotterell 2022-08-17 2022-08-17 [("doi","10.48550/arXiv.2208.08195")] ai/dataset ai/nn/rnn

Recombining known primitive concepts into larger novel combinations is a quintessentially human cognitive capability. Whether large neural models in NLP can acquire this ability while learning from data is an open question.

In this paper, we investigate this problem from the perspective of formal languages. We use deterministic finite-state transducers to make an unbounded number of datasets with controllable properties governing compositionality. By randomly sampling over many transducers, we explore which of their properties contribute to learnability of a compositional relation by a neural network.

We find that the models either learn the relations completely or not at all. The key is transition coverage, setting a soft learnability limit at 400 examples per transition.

--- https://danbooru.donmai.us/wiki_pages/ai-generated 2022-02-11 ai/anime/danbooru --- https://en.wikipedia.org/wiki/Room_square Room square 2022-02-11 statistics/power-analysis --- https://en.wikipedia.org/wiki/Splay_tree Splay tree 2022-02-11 cs/algorithm --- https://en.wikipedia.org/wiki/Suffix_array Suffix array 2022-02-11 cs/algorithm/information/compression --- https://x.com/petergyang/status/1573688729415208962 2022-02-11 ai/nn/transformer/gpt/fiction genetics/selection/natural/human --- https://www.biorxiv.org/content/10.1101/2022.09.22.509027.full The Selection Landscape and Genetic Legacy of Ancient Eurasians Evan K. Irving-Pease, Alba Refoyo-Martínez, Andrés Ingason, Alice Pearson, Anders Fischer, William Barrie, Karl-Göran Sjögren, Alma S. Halgren, Ruairidh Macleod, Fabrice Demeter, Rasmus A. Henriksen, Tharsika Vimala, Hugh McColl, Andrew Vaughn, Leo Speidel, Aaron J. Stern, Gabriele Scorrano, Abigail Ramsøe, Andrew J. Schork, Anders Rosengren, Lei Zhao, Kristian Kristiansen, Peter H. Sudmant, Daniel J. Lawson, Richard Durbin, Thorfinn Korneliussen, Thomas Werge, Morten E. Allentoft, Martin Sikora, Rasmus Nielsen, Fernando Racimo, Eske Willerslev 2022-09-23 2022-09-23 [("doi","10.1101/2022.09.22.509027")] genetics/selection/natural/human

The Eurasian Holocene (beginning c. 12 thousand years ago) encompassed some of the most important changes in human evolution, with far-reaching consequences for the dietary, physical and mental health of present-day populations.

Using an imputed dataset of >1600 complete ancient genome sequences, and new computational methods for locating selection in time and space, we reconstructed the selection landscape of the transition from hunting and gathering, to farming and pastoralism across West Eurasia.

We identify major selection signals related to metabolism, possibly associated with the dietary shift occurring in this period. We show that the selection on loci such as the FADS cluster, associated with fatty acid metabolism, and the lactase persistence locus, began earlier than previously thought.

A substantial amount of selection is also found in the HLA region and other loci associated with immunity, possibly due to the increased exposure to pathogens during the Neolithic, which may explain the current high prevalence of auto-immune disease, such as psoriasis, due to genetic trade-offs.

By using ancient populations to infer local ancestry tracks in hundreds of thousands of samples from the UK Biobank, we find strong genetic differentiation among ancient Europeans in loci associated with anthropometric traits and susceptibility to several diseases that contribute to present-day disease burden.

These were previously thought to be caused by local selection, but in fact can be attributed to differential genetic contributions from various source populations that are ancestral to present-day Europeans. Thus, alleles associated with increased height seem to have increased in frequency following the Yamnaya migration into northwestern Europe around 5,000 years ago. Alleles associated with increased risk of some mood-related phenotypes are overrepresented in the farmer ancestry component entering Europe from Anatolia around 11,000 years ago, while western hunter-gatherers show a strikingly high contribution of alleles conferring risk of traits related to diabetes.

Our results paint a picture of the combined contributions of migration and selection in shaping the phenotypic landscape of present-day Europeans that suggests a combination of ancient selection and migration, rather than recent local selection, is the primary driver of present-day phenotypic differences in Europe.

--- https://www.lesswrong.com/posts/sksP9Lkv9wqaAhXsA/orexin-and-the-quest-for-more-waking-hours 2022-02-11 zeo/short-sleeper --- https://www.metopera.org/visit/what-to-expect/ MetTitles Translations 2022-02-11 fiction/opera --- https://www.newyorker.com/magazine/2003/12/22/the-ring-and-the-rings The Ring and the Rings: Wagner vs. Tolkien 2022-02-11 fiction/opera --- https://www.opera-arias.com/glass/akhnaten/libretto/ Akhnaten Libretto 2022-02-12 fiction/opera --- https://pdfs.semanticscholar.org/26cf/592c500860d43ceab39d21816654e53e9c6c.pdf Cultural Suppression of Female Sexuality Baumeister, Twenge 2002 2022-02-12 sociology --- https://gallery.mailchimp.com/2506bda6ca9a8b7ce8b3c54b4/files/1a8cc94c-6198-4f3d-b27d-8a6060ed6c5d/Tyro_Dating_Market_Thesis_Final_For_Twitter_Pub_v2.pdf The Dating Market: Thesis Overview [Tyro 2019] 2022-02-12 sociology/technology --- https://hingeirl.com/hinge-reports/whats-the-biggest-challenge-men-face-on-dating-apps-a-qa-with-aviv-goldgeier-junior-growth-engineer/ What’s The Biggest Challenge Men Face On Dating Apps?: A Q&A With Aviv Goldgeier Junior Growth Engineer 2022-02-12 sociology/technology --- https://medium.com/@worstonlinedater/tinder-experiments-ii-guys-unless-you-are-really-hot-you-are-probably-better-off-not-wasting-your-2ddf370a6e9a Tinder Experiments II: Guys, unless you are really hot you are probably better off not wasting your time on Tinder—a quantitative socio-economic study 2022-02-12 sociology/technology --- https://www.lesswrong.com/posts/cdB5f2adKoLGW8Ytc/book-review-very-important-people 2022-02-12 sociology/technology --- https://www.theatlantic.com/magazine/archive/2018/12/the-sex-recession/573949/ Why Are Young People Having So Little Sex? Despite the easing of taboos and the rise of hookup apps, Americans are in the midst of a sex recession 2022-02-12 sociology/technology --- https://www.science.org/doi/10.1126/sciadv.aap9815 Aspirational pursuit of mates in online dating markets 2022-02-12 sociology/technology --- https://www.fathomevents.com/events/met1819-carmen?date=2019-02-02%2000:00:00.000 2022-02-12 fiction/opera --- https://www.fathomevents.com/series/the-met-live-in-hd/ 2022-02-12 fiction/opera --- https://www.metopera.org/globalassets/season/in-cinemas/hd-cast-sheets/magicflute_nov20_hdcastsheet_us-global.pdf 2022-02-13 fiction/opera --- https://www.metopera.org/season/2019-20-season/madama-butterfly/ 2022-02-13 fiction/opera --- https://www.metopera.org/season/2019-20-season/manon/ 2022-02-13 fiction/opera --- https://www.metopera.org/season/2019-20-season/turandot/ 2022-02-13 fiction/opera --- https://www.metopera.org/season/in-cinemas/2019-20-season/akhnaten-live-in-hd/ 2022-02-13 fiction/opera --- https://www.metopera.org/season/in-cinemas/the-magic-flute/ 2022-02-13 fiction/opera --- https://www.stallercenter.com/ 2022-02-13 fiction/opera --- https://academic.oup.com/brain/article/141/2/365/4725107 A novel human pain insensitivity disorder caused by a point mutation in ZFHX2 2022-02-13 psychology/neuroscience/pain --- http://linguafranca.mirror.theinfo.org/print/0011/hypoth_lookingglass.html 2022-02-13 philosophy/ontology science --- https://en.wikipedia.org/wiki/Non-reversing_mirror Non-reversing mirror 2022-02-13 psychology/vision --- /doc/statistics/decision/2006-drescher-goodandreal.pdf#page=39 Good and Real: Demystifying Paradoxes from Physics to Ethics § 1.2.3: Paradoxes: When Arguments Collide Gary Drescher 2006-01-01 2022-02-13 philosophy/logic statistics/decision --- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004969 Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model Gerhard Moser, Sang Hong Lee, Ben J. Hayes, Michael E. Goddard, Naomi R. Wray, Peter M. Visscher 2014-12-19 2022-02-14 [("doi","10.1371/journal.pgen.1004969")] genetics/heritable statistics/bayes

Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power.

Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs.

We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96%) had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance) varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches.

Author Summary: Most genome-wide association studies performed to date have focused on testing individual genetic markers for associations with phenotype. Recently, methods that analyse the joint effects of multiple markers on genetic variation have provided further insights into the genetic basis of complex human traits. In addition, there is increasing interest in using genotype data for genetic risk prediction of disease. Often disparate analytical methods are used for each of these tasks. We propose a flexible novel approach that simultaneously performs identification of susceptibility loci, inference on the genetic architecture and provides polygenic risk prediction in the same statistical model.

We illustrate the broad applicability of the approach by considering both simulated and real data. In the analysis of 7 common diseases we show large differences in the proportion of genetic variation due to loci with different effect sizes and differences in prediction accuracy between complex traits.

These findings are important for future studies and the understanding of the complex genetic architecture of common diseases.

--- https://en.wikipedia.org/wiki/The_Fable_of_the_Bees The Fable of the Bees 2022-02-14 economics philosophy/ethics --- /face#asuka Making Anime Faces With StyleGAN: A tutorial explaining how to train and generate high-quality anime faces with StyleGAN neural networks, and tips/scripts for effective StyleGAN use. 2022-02-14 ai/anime/danbooru anime/eva --- https://upscale.wiki/wiki/Model_Database#Drawn_Material 2022-02-14 ai/anime --- /backstop#pain-is-the-only-school-teacher Evolution as Backstop for Reinforcement Learning: Pain Is the Only School-Teacher 2022-02-14 philosophy/mind psychology/neuroscience/pain --- https://www.bedelstein.com/post/mcmaster-carr 2022-02-14 design/typography --- https://web.archive.org/web/20230101020103/https://www.mcmaster.com/ 2022-02-14 design/typography --- https://arxiv.org/abs/2209.11737 Semantic scene descriptions as an objective of human vision Adrien Doerig, Tim C. Kietzmann, Emily Allen, Yihan Wu, Thomas Naselaris, Kendrick Kay, Ian Charest 2022-09-23 2022-09-23 [("doi","10.48550/arXiv.2209.11737")] ai/nn/rnn ai/nn/transformer psychology/neuroscience psychology/vision

[Twitter] Interpreting the meaning of a visual scene requires not only identification of its constituent objects, but also a rich semantic characterization of object interrelations. Here, we study the neural mechanisms underlying visuo-semantic transformations by applying modern computational techniques to a large-scale 7T fMRI dataset of human brain responses elicited by complex natural scenes.

Using semantic embeddings obtained by applying linguistic deep learning models to human-generated scene descriptions, we identify a widely distributed network of brain regions that encode semantic scene descriptions. Importantly, these semantic embeddings better explain activity in these regions than traditional object category labels. In addition, they are effective predictors of activity despite the fact that the participants did not actively engage in a semantic task, suggesting that visuo-semantic transformations are a default mode of vision.

In support of this view, we then show that highly accurate reconstructions of scene captions can be directly linearly decoded from patterns of brain activity.

Finally, a recurrent convolutional neural network trained on semantic embeddings further outperforms semantic embeddings in predicting brain activity, providing a mechanistic model of the brain’s visuo-semantic transformations.

Together, these experimental and computational results suggest that transforming visual input into rich semantic scene descriptions may be a central objective of the visual system, and that focusing efforts on this new objective may lead to improved models of visual information processing in the human brain.

--- https://arxiv.org/abs/1803.11175#google Universal Sentence Encoder Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil 2018-03-29 2022-02-14 [("doi","10.48550/arXiv.1803.11175")] ai/nn/transformer

We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse transfer tasks.

Two variants of the encoding models allow for trade-offs between accuracy and compute resources. For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance. Comparisons are made with baselines that use word level transfer learning via pretrained word embeddings as well as baselines do not use any transfer learning.

We find that transfer learning using sentence embeddings tends to outperform word level transfer. With transfer learning via sentence embeddings, we observe surprisingly good performance with minimal amounts of supervised training data for a transfer task. We obtain encouraging results on Word Embedding Association Tests (WEAT) targeted at detecting model bias.

Our pre-trained sentence encoding models are made freely available for download and on TF Hub.

--- https://github.com/parlance-zz/g-diffuser-bot 2022-02-14 ai/nn/diffusion --- https://datacolada.org/103 2022-02-14 statistics/causality --- https://x.com/z_chiang/status/1574455394482651136 2022-02-15 longevity/epigenetics --- https://research.google/blog/quantization-for-fast-and-environmentally-sustainable-reinforcement-learning/ 2022-02-15 ai/nn/sparsity/low-precision reinforcement-learning/model-free --- https://arxiv.org/abs/2206.04615 Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmüller, Andrew Dai, Andrew La, Andrew Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakaş, B. Ryan Roberts, Bao Sheng Loe, Barret Zoph, Bartłomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, Blake Howald, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, César Ferri Ramírez, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Chris Waites, Christian Voigt, Christopher D. Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Dan Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Danielle Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, Dar Gilboa, David Dohan, David Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Ekaterina Shutova, Ekin Dogus Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, Elizabeth Donoway, Ellie Pavlick, Emanuele Rodola, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A. Chi, Ethan Dyer, Ethan Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, Fatemeh Siar, Fernando Martínez-Plumed, Francesca Happé, Francois Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Wang, Gonzalo Jaimovitch-López, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hannaneh Hajishirzi, Harsh Mehta, Hayden Bogar, Henry Shevlin, Hinrich Schütze, Hiromu Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, Jackson Kernion, Jacob Hilton, Jaehoon Lee, Jaime Fernández Fisac, James B. Simon, James Koppel, James Zheng, James Zou, Jan Kocoń, Jana Thompson, Jared Kaplan, Jarema Radom, Jascha Sohl-Dickstein, Jason Phang, Jason Wei, Jason Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse Engel, Jesujoba Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Joan Waweru, John Burden, John Miller, John U. Balis, Jonathan Berant, Jörg Frohberg, Jos Rozen, Jose Hernandez-Orallo, Joseph Boudeman, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, Katja Markert, Kaustubh D. Dhole, Kevin Gimpel, Kevin Omondi, Kory Mathewson, Kristen Chiafullo, Ksenia Shkaruta, Kumar Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras-Ochando, Louis-Philippe Morency, Luca Moschella, Lucas Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros Colón, Luke Metz, Lütfi Kerem Şenel, Maarten Bosma, Maarten Sap, Maartje ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, Maria Jose Ramírez Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, Mátyás Schubert, Medina Orduna Baitemirova, Melody Arnaud, Melvin McElrath, Michael A. Yee, Michael Cohen, Michael Gu, Michael Ivanitskiy, Michael Starritt, Michael Strube, Michał Swędrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mo Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, Mukund Varma T, Nanyun Peng, Nathan Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas Roberts, Nick Doiron, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, Nitish Shirish Keskar, Niveditha S. Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, Parth Doshi, Pascale Fung, Paul Pu Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter Chang, Peter Eckersley, Phu Mon Htut, Pinyu Hwang, Piotr Miłkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramón Risco Delgado, Raphaël Millière, Rhythm Garg, Richard Barnes, Rif A. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan LeBras, Rosanne Liu, Rowan Jacobs, Rui Zhang, Ruslan Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, Samuel S. Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, Shixiang Shane Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima, Debnath, Siamak Shakeri, Simon Thormeyer, Simone Melzi, Siva Reddy, Sneha Priscilla Makini, Soo-Hwan Lee, Spencer Torene, Sriharsha Hatwar, Stanislas Dehaene, Stefan Divic, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven T. Piantadosi, Stuart M. Shieber, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq Ali, Tatsu Hashimoto, Te-Lin Wu, Théo Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang, Tiberius Nkinyili, Timo Schick, Timofei Kornev, Timothy Telleen-Lawton, Titus Tunduny, Tobias Gerstenberg, Trenton Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, Vinay Ramasesh, Vinay Uday Prabhu, Vishakh Padmakumar, Vivek Srikumar, William Fedus, William Saunders, William Zhang, Wout Vossen, Xiang Ren, Xiaoyu Tong, Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Yiding Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang, Zirui Wang, Ziyi Wu 2022-06-09 2022-06-09 [("doi","10.48550/arXiv.2206.04615")] ai/nn/transformer/gpt/calibration ai/scaling/emergence ai/scaling/mixture-of-experts

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models.

To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI’s GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline.

Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit “breakthrough” behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.

In conclusion, the BIG-bench provides a comprehensive and challenging benchmark aimed at pushing the boundaries of what is possible with current language models and at fostering a deeper understanding of their capabilities and limitations. This, in turn, assists in preparing for future advancements and in identifying areas where intervention may be needed to prevent negative societal impacts.

--- https://www.biorxiv.org/content/10.1101/2022.09.12.506984.full Non-necessary neural activity in the primate cortex Sébastien Tremblay, Camille Testard, Jeanne Inchauspé, Michael Petrides 2022-09-13 2022-09-13 [("doi","10.1101/2022.09.12.506984")] psychology/neuroscience

When neurophysiologists record neural activity from the brain, they often conclude that neural tuning to task variables indicates a functional role of the brain area studied in task performance. However, it remains unknown how reliably such correlations indicate a functional role.

To answer this question, we chronically recorded neural activity in the prefrontal cortex of monkeys during the performance of 4 cognitive tasks. Previous studies had demonstrated that only one of those tasks causally depends on the recorded area; the other 3 tasks are not impaired by lesions of this area.

We found that the prevalence and strength of single neuron and ensemble tuning were equivalently high across all 4 tasks.

This suggests that non-necessary cognitive signals are prevalent in the cerebral cortex of primates during task performance, challenging one of the fundamental assumptions of cognitive neurophysiology.

--- https://arstechnica.com/information-technology/2022/09/we-interviewed-linux-os-through-an-ai-bot-to-discover-its-secrets/ 2022-02-15 ai/nn/transformer/gpt/fiction --- https://www.lesswrong.com/posts/4gaeWLhnnBvhamRke/book-review-the-heart-of-the-brain-the-hypothalamus-and-its 2022-02-15 psychology/neuroscience --- https://en.wikipedia.org/wiki/Animal_navigation Animal navigation 2022-02-15 psychology/animal/bird --- https://www.newyorker.com/magazine/2021/04/05/why-animals-dont-get-lost 2022-02-15 psychology/animal/bird --- https://en.wikipedia.org/wiki/Australian_magpie Australian magpie 2022-02-15 psychology/animal/bird --- https://en.wikipedia.org/wiki/Conserved_sequence Conserved sequence 2022-02-15 genetics/selection/natural --- https://en.wikipedia.org/wiki/Seychelles_warbler Seychelles warbler 2022-02-16 psychology/animal/bird --- https://en.wikipedia.org/wiki/Pisonia_grandis Pisonia grandis 2022-02-16 psychology/animal/bird --- https://en.wikipedia.org/wiki/Pallium_(neuroanatomy) Pallium (neuroanatomy) 2022-02-16 psychology/animal/bird/neuroscience --- https://en.wikipedia.org/wiki/Brown-headed_cowbird Brown-headed cowbird 2022-02-16 psychology/animal/bird --- https://en.wikipedia.org/wiki/Zugunruhe Zugunruhe 2022-02-16 psychology/animal/bird --- /doc/cat/psychology/2012-moller.pdf Escape behavior of birds provides evidence of predation being involved in urbanization A. P. Moslashller, Juan D. Ibáñez-Álamo 2012-01-01 2022-02-16 [("doi","10.1016/j.anbehav.2012.04.030")] cat/psychology psychology/animal/bird --- https://en.wikipedia.org/wiki/Corvidae Corvidae 2022-02-16 psychology/animal/bird --- https://en.wikipedia.org/wiki/Parrot Psittaciformes 2022-02-16 psychology/animal/bird --- https://en.wikipedia.org/wiki/Raven Raven 2022-02-16 psychology/animal/bird --- https://en.wikipedia.org/wiki/Parrot Parrot 2022-02-16 psychology/animal/bird --- https://en.wikipedia.org/wiki/Corvus Corvus 2022-02-16 psychology/animal/bird --- https://en.wikipedia.org/wiki/Tool_use_by_animals#In_birds Tool use by animals § In birds 2022-02-17 psychology/animal/bird --- https://en.wikipedia.org/wiki/New_Caledonian_crow New Caledonian crow 2022-02-17 psychology/animal/bird --- https://en.wikipedia.org/wiki/Avian_pallium Avian pallium 2022-02-17 psychology/animal/bird --- https://en.wikipedia.org/wiki/Bird#Social_systems Bird § Social systems 2022-02-17 psychology/animal/bird --- https://en.wikipedia.org/wiki/Talking_bird Talking birds 2022-02-17 psychology/animal/bird --- https://en.wikipedia.org/wiki/Dinosaur Dinosaur intelligence 2022-02-17 psychology/animal/bird --- https://en.wikipedia.org/wiki/Pigeon_intelligence Pigeon intelligence 2022-02-17 psychology/animal/bird --- https://arxiv.org/abs/2209.13569#cohere Exploring Low Rank Training of Deep Neural Networks Siddhartha Rao Kamalakara, Acyr Locatelli, Bharat Venkitesh, Jimmy Ba, Yarin Gal, Aidan N. Gomez 2022-09-27 2022-09-27 [("doi","10.48550/arXiv.2209.13569")] ai/nn/sparsity

Training deep neural networks in low rank, i.e. with factorized layers, is of particular interest to the community: it offers efficiency over unfactorized training in terms of both memory consumption and training time.

Prior work has focused on low-rank approximations of pre-trained networks and training in low-rank space with additional objectives, offering various ad hoc explanations for chosen practice.

We analyze techniques that work well in practice, and through extensive ablations on models such as GPT-2 we provide evidence falsifying common beliefs in the field, hinting in the process at exciting research opportunities that still need answering.

--- https://www.p-curve.com/ 2022-02-17 statistics/bias statistics/meta-analysis --- /doc/statistics/meta-analysis/2007-ioannidis.pdf An exploratory test for an excess of statistically-significant findings John Ioannidis, Thomas A. Trikalinos 2007-06-01 2022-02-17 [("doi","10.1177/1740774507079441")] statistics/bias/publication statistics/meta-analysis

Background: The published clinical research literature may be distorted by the pursuit of statistically-significant results.

Purpose: We aimed to develop a test to explore biases stemming from the pursuit of nominal statistical-significance.

Method: The exploratory test evaluates whether there is a relative excess of formally statistically-significant findings in the published literature due to any reason (eg. publication bias, selective analyses and outcome reporting, or fabricated data). The number of expected studies with statistically-significant results is estimated and compared against the number of observed statistically-significant studies. The main application uses α = 0.05, but a range of α thresholds is also examined. Different values or prior distributions of the effect size are assumed. Given the typically low power (few studies per research question), the test may be best applied across domains of many meta-analyses that share common characteristics (interventions, outcomes, study populations, research environment).

Results: We evaluated illustratively 8 meta-analyses of clinical trials with >50 studies each and 10 meta-analyses of clinical efficacy for neuroleptic agents in schizophrenia; the 10 meta-analyses were also examined as a composite domain. Different results were obtained against commonly used tests of publication bias. We demonstrated a clear or possible excess of statistically-significant studies in 6⁄8 large meta-analyses and in the wide domain of neuroleptic treatments.

Limitations: The proposed test is exploratory, may depend on prior assumptions, and should be applied cautiously.

Conclusions: An excess of statistically-significant findings may be documented in some clinical research fields.

--- https://x.com/_onerandomtweet 2022-02-17 sociology/technology --- https://www.nytimes.com/2022/09/29/science/cancer-tumors-fungi-bacteria-microbiome.html 2022-02-18 genetics/microbiome --- https://www.medrxiv.org/content/10.1101/2021.05.20.21257484.full Identification of shared and differentiating genetic risk for autism spectrum disorder, attention deficit hyperactivity disorder and case subgroups Manuel Mattheisen, Jakob Grove, Thomas D. Als, Joanna Martin, Georgios Voloudakis, Sandra Meier, Ditte Demontis, Jaroslav Bendl, Raymond Walters, Caitlin E. Carey, Anders Rosengren, Nora Strom, Mads Engel Hauberg, Biao Zeng, Gabriel Hoffman, Jonas Bybjerg-Grauholm, Marie Bækvad-Hansen, Esben Agerbo, Bru Cormand, Merete Nordentoft, Thomas Werge, Ole Mors, David Hougaard, Joseph D. Buxbaum, Stephen V. Faraone, Barbara Franke, Søren Dalsgaard, Preben Bo Mortensen, Elise B. Robinson, Panos Roussos, Benjamin M. Neale, Mark J. Daly, Anders Børglum 2021-05-21 2022-02-18 [("doi","10.1101/2021.05.20.21257484")] genetics/heritable/correlation psychiatry/adhd psychiatry/autism psychology/neuroscience

Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly heritable neurodevelopmental disorders with a considerable overlap in their genetic etiology. We dissected their shared and distinct genetic architecture by cross-disorder analyses of large data sets, including samples with information on comorbid diagnoses.

We identified 7 loci shared by the disorders and the first 5 genome-wide statistically-significant loci differentiating the disorders. All 5 differentiating loci showed opposite allelic directions in the two disorders separately as well as statistically-significant associations with variation in other traits eg. educational attainment, items of neuroticism, and regional brain volume. Integration with brain transcriptome data identified and prioritized several statistically-significantly associated genes.

Genetic correlation of the shared liability across ASD-ADHD was strong for other psychiatric phenotypes, while the ASD-ADHD differentiating liability correlated most strongly with cognitive traits. Polygenic score analyses revealed that individuals diagnosed with both ASD and ADHD are double-burdened with genetic risk for both disorders and show distinctive patterns of genetic association with other traits when compared to the ASD-only and ADHD-only subgroups.

The results provide novel insights into the biological foundation for developing just one or both of the disorders and for driving the psychopathology discriminatively towards either ADHD or ASD.

--- https://www.biorxiv.org/content/10.1101/2022.09.25.509419.full Top-down design of protein nanomaterials with reinforcement learning Isaac D. Lutz, Shunzhi Wang, Christoffer Norn, Andrew J. Borst, Yan Ting Zhao, Annie Dosey, Longxing Cao, Zhe Li, Minkyung Baek, Neil P. King, Hannele Ruohola-Baker, David Baker 2022-09-25 2022-09-25 [("doi","10.1101/2022.09.25.509419")] ai/nn/transformer/alphafold reinforcement-learning/model

[Twitter] The multisubunit protein assemblies that play critical roles in biology are the result of evolutionary selection for function of the entire assembly, and hence the subunits in structures such as icosahedral viral capsids often fit together with remarkable shape complementarity1,2. In contrast, the large multisubunit assemblies that have been created by de novo protein design, notably the icosahedral nanocages used in a new generation of potent vaccines3–7, have been built by first designing symmetric oligomers with cyclic symmetry and then assembling these into nanocages while keeping the internal structure fixed8–14, which results in more porous structures with less extensive shape matching between the components. Such hierarchical “bottom-up” design approaches have the advantage that one interface can be designed and validated in the context of the cyclic oligomer building block15,16, but the disadvantage that the structural and functional features of the assemblies are limited by the properties of the predesigned building blocks.

To overcome this limitation, we set out to develop a “top-down” reinforcement learning based approach to protein nanomaterial design in which both the structures of the subunits and the interactions between them are built up coordinately in the context of the entire assembly.

We developed a Monte Carlo tree search (MCTS) method17,18 which assembles protein monomer structures in the context of an overall architecture guided by a loss function which enables specification of any desired overall structural properties such as shape and porosity.

We demonstrate the power of the approach by designing hyperstable icosahedral assemblies more compact than any previously observed protein icosahedral structure (designed or naturally occurring), that have very low porosity and are robust to fusion and display of proteins as complex as influenza hemagglutinin.

CryoEM structures of two designs are very close to the computational design models.

Our top-down reinforcement learning approach should enable the design of a wide variety of complex protein nanomaterials by direct optimization of overall system properties.

--- https://www.reddit.com/r/StableDiffusion/comments/xr8cs8/brutalist_joi_dreambooth_training_combined_with/ 2022-02-18 ai/nn/diffusion --- https://github.com/marqo-ai/marqo/blob/mainline/examples/StableDiffusion/hot-dog-100k.md 2022-02-18 ai/nn/diffusion --- https://arxiv.org/abs/2209.14290#google FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation Sebastian Hofstätter, Jiecao Chen, Karthik Raman, Hamed Zamani 2022-09-28 2022-09-28 [("doi","10.48550/arXiv.2209.14290")] ai/nn/retrieval ai/nn/transformer/t5

Retrieval-augmented generation models offer many benefits over standalone language models: besides a textual answer to a given query they provide provenance items retrieved from an updateable knowledge base. However, they are also more complex systems and need to handle long inputs.

In this work, we introduce FiD-Light to strongly increase the efficiency of the state-of-the-art retrieval-augmented FiD model, while maintaining the same level of effectiveness. Our FiD-Light model constrains the information flow from the encoder (which encodes passages separately) to the decoder (using concatenated encoded representations). Furthermore, we adapt FiD-Light with re-ranking capabilities through textual source pointers, to improve the top-ranked provenance precision.

Our experiments on a diverse set of 7 knowledge intensive tasks (KILT) show FiD-Light consistently improves the Pareto frontier between query latency and effectiveness. FiD-Light with source pointing sets substantial new state-of-the-art results on 6 KILT tasks for combined text generation and provenance retrieval evaluation, while maintaining reasonable efficiency.

--- https://arxiv.org/abs/2209.14156 TVLT: Textless Vision-Language Transformer Zineng Tang, Jaemin Cho, Yixin Nie, Mohit Bansal 2022-09-28 2022-09-28 [("doi","10.48550/arXiv.2209.14156")] ai/nn/vae/mae ai/video/analysis

In this work, we present the Textless Vision-Language Transformer (TVLT), where homogeneous transformer blocks take raw visual and audio inputs for vision-and-language representation learning with minimal modality-specific design, and do not use text-specific modules such as tokenization or automatic speech recognition (ASR).

TVLT is trained by reconstructing masked patches of continuous video frames and audio spectrograms (masked autoencoding) and contrastive modeling to align video and audio.

TVLT attains performance comparable to its text-based counterpart, on various multimodal tasks, such as visual question answering, image retrieval, video retrieval, and multimodal sentiment analysis, with 28× faster inference speed and only 1⁄3 of the parameters.

Our findings suggest the possibility of learning compact and efficient visual-linguistic representations from low-level visual and audio signals without assuming the prior existence of text.

Our code and checkpoints are available at: https://github.com/zinengtang/TVLT.

--- /doc/ai/nn/fully-connected/1988-lang.pdf Learning To Tell Two Spirals Apart Kevin J. Lang, Michael J. Witbrock 1988-01 2022-02-18 ai/nn/fully-connected

Alexis P. Wieland recently proposed a useful benchmark task for neural networks: distinguishing between two intertwined spirals [Swiss roll]. Although this task is easy to visualize, it is hard for a network to learn due to its extreme non-linearity. In this report we exhibit a network architecture that facilitates the learning of the spiral task, and then compare the learning speed of several variants of the back-propagation algorithm.

…Such a highly non-linear problem would clearly benefit from the computational power of many layers. Unfortunately, back-propagation learning generally slows down by an order of magnitude every time a layer is added to a network. This is because the error signal is attenuated each time it flows through a layer, and learning progress is therefore limited by the slow adaptation of units in the early layers of a multi-layer network. To avoid this problem, we used short-cut connections to provide direct information pathways to all parts of the network. Our connection pattern differs from the usual one in that each layer is connected to every succeeding layer, rather than just to its immediate successor. [see ResNets, DenseNets]

Freed from concerns of exponentially slow learning, we were able to use as many layers as we wanted. As a first guess, we tried 5 layers, meaning that the network contains an input layer, 3 hidden layers, and an output layer.

Figure 3: Network Architecture for the Spiral Problem.
--- https://www.reddit.com/r/OpenAI/comments/xr1fxi/write_a_story_about_a_battle_between_a_cheesecake/ 2022-02-18 ai/nn/transformer/gpt/fiction --- /doc/fiction/humor/1970-lafferty.pdf Been A Long, Long Time R. A. Lafferty 1970-12-01 2022-02-18 fiction/humor fiction/science-fiction statistics/probability --- https://www.vice.com/en/article/53dqd5/new-evidence-suggests-rogue-government-agent-deleted-evidence-in-silk-road-case 2022-02-19 darknet-market/silk-road/1 --- https://www.biorxiv.org/content/10.1101/2022.09.28.509845.full Correction for participation bias in the UK Biobank reveals non-negligible impact on genetic associations and downstream analyses Tabea Schoeler, Doug Speed, Eleonora Porcu, Nicola Pirastu, Jean-Baptiste Pingault, Zoltán Kutalik 2022-09-28 2022-09-28 [("doi","10.1101/2022.09.28.509845")] genetics/heritable/correlation/mendelian-randomization

While large-scale volunteer-based studies such as the UK Biobank (UKBB) have become the cornerstone of genetic epidemiology, the study participants are rarely representative of their target population.

Here, we aim to evaluate the impact of non-random participation in the UKBB, and to pin down areas of research that are particularly susceptible to biases when using non-representative samples for genome-wide discovery. By comparing 14 harmonized characteristics of the UKBB participants to that of a representative sample, we derived a model for participation probability. We then conducted inverse probability weighted genome-wide association analyses (wGWA) on 19 UKBB traits. Comparing the output obtained from wGWA (neffective = 94,643–102,215) to standard GWA analyses (n = 263,464–283,749), we assessed the impact of participation bias on 3 estimated quantities, namely (1) genotype-phenotype associations, (2) heritability and genetic correlation estimates and (3) exposure-outcome causal effect estimates obtained from Mendelian Randomization.

Participation bias can lead to both overestimation (eg. cancer, education) and underestimation (eg. coffee intake, depression/anxiety) of SNP effects. Novel SNPs were identified in wGWA for 12 of the included traits, highlighting SNPs missed as a result of participation bias. While the impact of participation bias on heritability estimates was small (average change in h2: 1.5%, maximum: 5%), substantial distortions were present for genetic correlations (average absolute change in rg: 0.07, maximum: 0.31) and Mendelian Randomization estimates (average absolute change in standardized estimates: 0.04, maximum: 0.15), most markedly for socio-behavioural traits including education, smoking and BMI.

Overall, the bias mainly affected the magnitude of effects, rather than direction. In contrast, genome-wide findings for more molecular/physical traits (eg. LDL, SBP) exhibited less bias as a result of selective participation.

Our results highlight that participation bias can distort genomic findings obtained in non-representative samples, and we propose a viable solution to reduce such bias. Moving forward, more efforts ensuring either sample representativeness or correcting for participation bias are paramount, especially when investigating the genetic underpinnings of behavior, lifestyles and social outcomes.

--- https://www.biorxiv.org/content/10.1101/2022.09.29.509744.full Semantic reconstruction of continuous language from non-invasive brain recordings Jerry Tang, Amanda LeBel, Shailee Jain, Alexander G. Huth 2022-09-29 2022-09-29 [("doi","10.1101/2022.09.29.509744")] ai/nn/transformer/gpt/2 psychology/neuroscience

[Twitter] A brain-computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications. Currently, however, decoders that reconstruct continuous language use invasive recordings from surgically implanted electrodes, while decoders that use non-invasive recordings can only identify stimuli from among a small set of letters, words, or phrases.

Here we introduce a non-invasive decoder that reconstructs continuous natural language from cortical representations of semantic meaning recorded using functional magnetic resonance imaging (fMRI). Given novel brain recordings, this [GPT-2] decoder generates intelligible word sequences that recover the meaning of perceived speech, imagined speech, and even silent videos, demonstrating that a single language decoder can be applied to a range of semantic tasks.

To study how language is represented across the brain, we tested the decoder on different cortical networks, and found that natural language can be separately decoded from multiple cortical networks in each hemisphere.

As brain-computer interfaces should respect mental privacy, we tested whether successful decoding requires subject cooperation, and found that subject cooperation is required both to train and to apply the decoder.

Our study demonstrates that continuous language can be decoded from non-invasive brain recordings, enabling future multipurpose brain-computer interfaces.

--- https://en.wikipedia.org/wiki/Inverse_probability_weighting Inverse probability weighting 2022-02-19 statistics/probability --- https://en.wikipedia.org/wiki/Importance_sampling Importance sampling 2022-02-19 statistics/probability --- https://arxiv.org/abs/2208.01448#amazon AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald, Rahul Gupta, Wael Hamza, Haidar Khan, Charith Peris, Stephen Rawls, Andy Rosenbaum, Anna Rumshisky, Chandana Satya Prakash, Mukund Sridhar, Fabian Triefenbach, Apurv Verma, Gokhan Tur, Prem Natarajan 2022-08-02 2022-08-02 [("doi","10.48550/arXiv.2208.01448")] ai/nn/transformer/gpt/palm reinforcement-learning/meta-learning reinforcement-learning/scaling

In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various tasks.

In particular, we train a 20 billion parameter multilingual seq2seq model called Alexa Teacher Model (AlexaTM 20B) and show that it achieves state-of-the-art (SOTA) performance on 1-shot summarization tasks, outperforming a much larger 540B PaLM decoder model. AlexaTM 20B also achieves SOTA in 1-shot machine translation, especially for low-resource languages, across almost all language pairs supported by the model (Arabic, English, French, German, Hindi, Italian, Japanese, Marathi, Portuguese, Spanish, Tamil, and Telugu) on Flores-101 dataset.

We also show in zero-shot setting, AlexaTM 20B outperforms GPT-3 (175B) on SuperGLUE and SQuADv2 datasets and provides SOTA performance on multilingual tasks such as XNLI, XCOPA, Paws-X, and XWinograd.

Overall, our results present a compelling case for seq2seq models as a powerful alternative to decoder-only models for Large-scale Language Model (LLM) training.

--- https://arxiv.org/abs/2204.05832 What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? Thomas Wang, Adam Roberts, Daniel Hesslow, Teven Le Scao, Hyung Won Chung, Iz Beltagy, Julien Launay, Colin Raffel 2022-04-12 2022-04-12 [("doi","10.48550/arXiv.2204.05832")] ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 ai/scaling reinforcement-learning/meta-learning

Large pretrained Transformer language models have been shown to exhibit zero-shot generalization, i.e. they can perform a wide variety of tasks that they were not explicitly trained on. However, the architectures and pretraining objectives used across state-of-the-art models differ, and there has been limited systematic comparison of these factors.

In this work, we present a large-scale evaluation of modeling choices and their impact on zero-shot generalization. In particular, we focus on text-to-text models and experiment with 3 model architectures (causal/non-causal decoder-only and encoder-decoder), trained with two different pretraining objectives (autoregressive and masked language modeling), and evaluated with and without multitask prompted finetuning. We train models with over 5 billion parameters for more than 170 billion tokens, thereby increasing the likelihood that our conclusions will transfer to even larger scales.

Our experiments show that causal decoder-only models trained on an autoregressive language modeling objective exhibit the strongest zero-shot generalization after purely unsupervised pretraining. However, models with non-causal visibility on their input trained with a masked language modeling objective followed by multitask finetuning perform the best among our experiments. We therefore consider the adaptation of pretrained models across architectures and objectives.

We find that pretrained non-causal decoder models can be adapted into performant generative causal decoder models, using autoregressive language modeling as a downstream task. Furthermore, we find that pretrained causal decoder models can be efficiently adapted into non-causal decoder models, ultimately achieving competitive performance after multitask finetuning.

Code and checkpoints are available at Github.

--- https://www.biorxiv.org/content/10.1101/2022.09.28.509988.full Polygenic scoring accuracy varies across the genetic ancestry continuum in all human populations Yi Ding, Kangcheng Hou, Ziqi Xu, Aditya Pimplaskar, Ella Petter, Kristin Boulier, Florian Privé, Bjarni J. Vilhjálmsson, Loes Olde Loohuis, Bogdan Pasaniuc 2022-09-29 2022-09-29 [("doi","10.1101/2022.09.28.509988")] genetics/heritable

Polygenic scores (PGS) have limited portability across different groupings of individuals (eg. by genetic ancestries and/or social determinants of health), preventing their equitable use. PGS portability has typically been assessed using a single aggregate population-level statistic (eg. R2), ignoring inter-individual variation within the population.

Here we evaluate PGS accuracy at individual-level resolution, independent of its annotated genetic ancestries.

We show that PGS accuracy varies between individuals across the genetic ancestry continuum in all ancestries, even within traditionally “homogeneous” genetic ancestry clusters. Using a large and diverse Los Angeles biobank (ATLAS, n = 36,778) along with the UK Biobank (UKBB, n = 487,409), we show that PGS accuracy decreases along a continuum of genetic ancestries in all considered populations and the trend is well-captured by a continuous measure of genetic distance (GD) from the PGS training data; Pearson correlation of −0.95 between GD and PGS accuracy averaged across 84 traits. [As expected from shared causal variants + LD decay.]

When applying PGS models trained in UKBB “white British” individuals to European-ancestry individuals of ATLAS, individuals in the highest GD decile have 14% lower accuracy relative to the lowest decile; notably the lowest GD decile of Hispanic/Latino American ancestry individuals showed similar PGS performance as the highest GD decile of European ancestry ATLAS individuals. GD is statistically-significantly correlated with PGS estimates themselves for 82⁄84 traits, further emphasizing the importance of incorporating the continuum of genetic ancestry in PGS interpretation.

Our results highlight the need for moving away from discrete genetic ancestry clusters towards the continuum of genetic ancestries when considering PGS and their applications.

--- https://x.com/0xfoobar/status/1575696892859715586 2022-02-19 ai/nn/transformer/gpt/non-fiction --- /doc/sociology/2022-montero.pdf Religious Festivals and Economic Development: Evidence from the Timing of Mexican Saint Day Festivals Eduardo Montero, Dean Yang 2022-10-01 2022-10-01 [("doi","10.1257/aer.20211094")] economics sociology

Does variation in how religious festivals are celebrated have economic consequences?

We study the economic impacts of the timing of Catholic patron saint day festivals in Mexico. For causal identification, we exploit cross-locality variation in festival dates and in the timing of agricultural seasons. We estimate the impact of “agriculturally coinciding” festivals (those coinciding with peak planting or harvest months) on long-run economic development of localities.

Agriculturally coinciding festivals lead to lower household income and worse development outcomes overall.

These negative effects are likely due to lower agricultural productivity, which inhibits structural transformation out of agriculture. Agriculturally coinciding festivals may nonetheless persist because they also lead to higher religiosity and social capital.

--- https://arxiv.org/abs/2208.12266#facebook Decoding speech from non-invasive brain recordings Alexandre Défossez, Charlotte Caucheteux, Jérémy Rapin, Ori Kabeli, Jean-Rémi King 2022-08-25 2022-08-25 [("doi","10.48550/arXiv.2208.12266")] ai/nn/transformer/clip psychology/neuroscience

[blog] Decoding language from brain activity is a long-awaited goal in both healthcare and neuroscience. Major milestones have recently been reached thanks to intracranial devices: subject-specific pipelines trained on invasive brain responses to basic language tasks now start to efficiently decode interpretable features (eg. letters, words, spectrograms). However, scaling this approach to natural speech and non-invasive brain recordings remains a major challenge.

Here, we propose a single end-to-end architecture trained with contrastive [wav2vec + CLIP] learning across a large cohort of individuals to predict self-supervised representations of natural speech.

We evaluate our model on 4 public datasets, encompassing 169 volunteers recorded with magneto-encephalography or electro-encephalography (M/EEG), while they listened to natural speech. The results show that our model can identify, from 3s of MEG signals, the corresponding speech segment with up to 72.5% top-10 accuracy out of 1,594 distinct segments (and 44% top-1 accuracy), and up to 19.1% out of 2,604 segments for EEG recordings—hence allowing the decoding of phrases absent from the training set.

Model comparison and ablation analyses show that these performances directly benefit from our original design choices, namely the use of (1) a contrastive objective, (2) pretrained representations of speech and (3) a common convolutional CNN architecture simultaneously trained across several participants.

Together, these results delineate a promising path to decode natural language processing in real time from non-invasive recordings of brain activity.

--- https://openreview.net/forum?id=hT1S68yza7 Brain2GAN: Reconstructing perceived faces from the primate brain via StyleGAN3 Anonymous 2022-09-29 2022-09-29 ai/nn/gan/stylegan psychology/neuroscience

Reconstruction of perceived faces by neural decoding of cortical responses from the primate brain

Neural coding characterizes the relationship between stimuli and their corresponding neural responses. The usage of synthesized yet photorealistic reality by generative adversarial networks (GANs) allows for superior control over these data: the underlying feature representations that account for the semantics in synthesized data are known a priori and their relationship is perfect rather than approximated post-hoc by feature extraction models.

We exploit this property in neural decoding of multi-unit activity responses that we recorded from the primate brain upon presentation with synthesized face images in a passive fixation experiment. The face reconstructions we acquired from brain activity were astonishingly similar to the originally perceived face stimuli.

This provides strong evidence that the neural face manifold and the disentangled w-latent space conditioned on StyleGAN 3 (rather than the z-latent space of arbitrary GANs or other feature representations we encountered so far) share how they represent the high-level semantics of the high-dimensional space of faces.

[Keywords: face reconstruction, generative adversarial networks, neural decoding]

--- https://www.construction-physics.com/p/why-are-nuclear-power-construction-c3c 2022-02-20 economics/experience-curve technology --- https://arxiv.org/abs/1406.1831 Analyzing noise in autoencoders and deep networks Ben Poole, Jascha Sohl-Dickstein, Surya Ganguli 2014-06-06 2022-02-20 [("doi","10.48550/arXiv.1406.1831")] ai/nn/vae

Autoencoders have emerged as a useful framework for unsupervised learning of internal representations, and a wide variety of apparently conceptually disparate regularization techniques have been proposed to generate useful features.

Here we extend existing denoising autoencoders to additionally inject noise before the nonlinearity, and at the hidden unit activations. We show that a wide variety of previous methods, including denoising, contractive, and sparse autoencoders, as well as dropout can be interpreted using this framework. This noise injection framework reaps practical benefits by providing a unified strategy to develop new internal representations by designing the nature of the injected noise.

We show that noisy autoencoders outperform denoising autoencoders at the very task of denoising, and are competitive with other single-layer techniques on MNIST, and CIFAR-10.

We also show that types of noise other than dropout improve performance in a deep network through sparsifying, decorrelating, and spreading information across representations.

--- https://openreview.net/forum?id=P9yXPbfqbvC Noise Transforms Feed-Forward Networks into Sparse Coding Networks Anonymous 2022-09-29 2022-09-29 ai/nn/sparsity psychology/neuroscience

[see also] We find that noise alone induces networks to become top-k / sparse coding networks. This resolves a difference between biological and artificial neural networks with regards to how sparse they are and how this sparsity is implemented.

A hallmark of biological neural networks, which distinguishes them from their artificial counterparts, is the high degree of sparsity in their activations.

Here, we show that by simply injecting symmetric, random, noise during training in reconstruction or classification tasks, artificial neural networks with ReLU activation functions eliminate this difference; the neurons converge to a sparse coding solution where only a small fraction are active for any input. The resulting network learns receptive fields like those of primary visual cortex and remains sparse even when noise is removed in later stages of learning.

[Keywords: sparse coding, sparsity, top-k activation, noise, biologically inspired]

--- https://en.wikipedia.org/wiki/Autoencoder#Sparse_autoencoder_(SAE) Autoencoder § Sparse autoencoder (SAE) 2022-02-20 ai/nn/sparsity ai/nn/vae --- https://bonfx.com/how-to-use-dreamstudio-stablediffusion-to-create-a-traditional-illustration/ 2022-02-20 ai/nn/diffusion --- http://www.incompleteideas.net/Talks/UBC-2016.pdf 2022-02-20 ai/scaling reinforcement-learning/model/alphago --- https://openreview.net/forum?id=ctnmrjv6lU5 RealSinger: Ultra-Realistic Singing Voice Generation via Stochastic Differential Equations Anonymous 2022-09-29 2022-09-29 ai/music ai/nn/diffusion

Synthesizing high-quality singing voice from music score is a challenging problem in music generation and has many practical applications. Samples generated by existing singing voice synthesis (SVS) systems can roughly reflect the lyrics, pitch and duration in a given score, but they fail to contain necessary details.

In this paper, based on stochastic differential equations (SDE) we propose RealSinger to generate 22.05kHz ultra-realistic singing voice conditioned on a music score.

Our RealSinger learns to find the stochastic process path from a source of white noise to the target singing voice manifold under the conditional music score, allowing to sing the music score while maintaining the local voice details of the target singer. During training, our model learns to accurately predict the direction of movement in the ambient Euclidean space onto the low-dimensional singing voice manifold. RealSinger’s framework is very flexible. It can either generate intermediate feature representations of the singing voice, such as mel-spectrogram, or directly generate the final waveform, as in the end-to-end style which rectify defects and accumulation errors introduced by two-stage connected singing synthesis systems.

An extensive subjective and objective test on benchmark datasets shows gains in perceptual quality using RealSinger. The mean opinion scores (MOS) obtained with RealSinger are closer to those of the human singer’s original high-fidelity singing voice than to those obtained with any state-of-the-art method.

Audio samples are available at https://realsinger.github.io/.

--- https://arxiv.org/abs/2005.04269#samsung Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics (TQC) Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin, Dmitry Vetrov 2020-05-08 2022-02-20 [("doi","10.48550/arXiv.2005.04269")] reinforcement-learning/model-free

The overestimation bias is one of the major impediments to accurate off-policy learning. This paper investigates a novel way to alleviate the overestimation bias in a continuous control setting.

Our method—Truncated Quantile Critics(TQC)—blends 3 ideas: distributional representation of a critic, truncation of critics prediction, and ensembling of multiple critics. Distributional representation and truncation allow for arbitrary granular overestimation control, while ensembling provides additional score improvements.

TQC outperforms the current state-of-the-art on all environments from the continuous control benchmark suite, demonstrating 25% improvement on the most challenging Humanoid environment.

--- https://arxiv.org/abs/2101.05982 Randomized Ensembled Double Q-Learning (REDQ): Learning Fast Without a Model Xinyue Chen, Che Wang, Zijian Zhou, Keith Ross 2021-01-15 2022-02-20 [("doi","10.48550/arXiv.2101.05982")] reinforcement-learning/model-free

Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved much higher sample efficiency than previous model-free methods for continuous-action DRL benchmarks.

In this paper, we introduce a simple model-free algorithm, Randomized Ensembled Double Q-Learning (REDQ), and show that its performance is just as good as, if not better than, a state-of-the-art model-based algorithm for the MuJoCo benchmark. Moreover, REDQ can achieve this performance using fewer parameters than the model-based method, and with less wall-clock run time.

REDQ has 3 carefully integrated ingredients which allow it to achieve its high performance: (1) a UTD ratio >> 1; (2) an ensemble of Q functions; (3) in-target minimization across a random subset of Q functions from the ensemble. Through carefully designed experiments, we provide a detailed analysis of REDQ and related model-free algorithms.

To our knowledge, REDQ is the first successful model-free DRL algorithm for continuous-action spaces using a UTD ratio >> 1.

--- https://arxiv.org/abs/2110.02034 DroQ: Dropout Q-Functions for Doubly Efficient Reinforcement Learning Takuya Hiraoka, Takahisa Imagawa, Taisei Hashimoto, Takashi Onishi, Yoshimasa Tsuruoka 2021-10-05 2022-02-20 [("doi","10.48550/arXiv.2110.02034")] reinforcement-learning/model-free

Randomized ensembled double Q-learning (REDQ) (Chen et al 2021b) has recently achieved state-of-the-art sample efficiency on continuous-action reinforcement learning benchmarks. This superior sample efficiency is made possible by using a large Q-function ensemble. However, REDQ is much less computationally efficient than non-ensemble counterparts such as Soft Actor-Critic (SAC) (Haarnoja et al 2018a).

To make REDQ more computationally efficient, we propose a method of improving computational efficiency called DroQ, which is a variant of REDQ that uses a small ensemble of dropout Q-functions. Our dropout Q-functions are simple Q-functions equipped with dropout connection and layer normalization.

Despite its simplicity of implementation, our experimental results indicate that DroQ is doubly (sample and computationally) efficient. It achieved comparable sample efficiency with REDQ, much better computational efficiency than REDQ, and comparable computational efficiency with that of SAC.

--- https://arxiv.org/abs/2209.08466 Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective (ALM) Raj Ghugare, Homanga Bharadhwaj, Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov 2022-09-18 2022-09-18 [("doi","10.48550/arXiv.2209.08466")] reinforcement-learning/model reinforcement-learning/model-free

[Twitter] While reinforcement learning (RL) methods that learn an internal model of the environment have the potential to be more sample efficient than their model-free counterparts, learning to model raw observations from high dimensional sensors can be challenging. Prior work has addressed this challenge by learning low-dimensional representation of observations through auxiliary objectives, such as reconstruction or value prediction. However, the alignment between these auxiliary objectives and the RL objective is often unclear.

In this work, we propose a single objective which jointly optimizes a latent-space model and policy to achieve high returns while remaining self-consistent. This objective is a lower bound on expected returns. Unlike prior bounds for model-based RL on policy exploration or model guarantees, our bound is directly on the overall RL objective.

We demonstrate that the resulting algorithm, Aligned Latent Models (ALM), matches or improves the sample-efficiency of the best prior model-based and model-free RL methods.

While such sample efficient methods typically are computationally demanding, our method attains the performance of SAC in about 50% less wall-clock time. [...the sample efficiency of MBPO and REDQ while being only 3× slower than SAC / TD3.]

--- https://arxiv.org/abs/1906.08253 When to Trust Your Model: Model-Based Policy Optimization (MOPO) Michael Janner, Justin Fu, Marvin Zhang, Sergey Levine 2019-06-19 2022-02-21 [("doi","10.48550/arXiv.1906.08253")] reinforcement-learning/model

Designing effective model-based reinforcement learning algorithms is difficult because the ease of data generation must be weighed against the bias of model-generated data.

In this paper, we study the role of model usage in policy optimization both theoretically and empirically. We first formulate and analyze a model-based reinforcement learning algorithm with a guarantee of monotonic improvement at each step. In practice, this analysis is overly pessimistic and suggests that real off-policy data is always preferable to model-generated on-policy data, but we show that an empirical estimate of model generalization can be incorporated into such analysis to justify model usage.

Motivated by this analysis, we then demonstrate that a simple procedure of using short model-generated rollouts branched from real data has the benefits of more complicated model-based algorithms without the usual pitfalls.

In particular, this approach surpasses the sample efficiency of prior model-based methods, matches the asymptotic performance of the best model-free algorithms, and scales to horizons that cause other model-based methods to fail entirely.

--- https://arxiv.org/abs/1802.09477 TD3: Addressing Function Approximation Error in Actor-Critic Methods Scott Fujimoto, Herke van Hoof, David Meger 2018-02-26 2022-02-21 [("doi","10.48550/arXiv.1802.09477")] reinforcement-learning/model-free

In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies.

We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and the critic. Our algorithm builds on Double Q-learning, by taking the minimum value between a pair of critics to limit overestimation. We draw the connection between target networks and overestimation bias, and suggest delaying policy updates to reduce per-update error and further improve performance.

We evaluate our method on the suite of OpenAI gym tasks, outperforming the state-of-the-art in every environment tested.

--- https://arxiv.org/abs/2209.15001 DiNAT: Dilated Neighborhood Attention Transformer Ali Hassani, Humphrey Shi 2022-09-29 2022-09-29 [("doi","10.48550/arXiv.2209.15001")] ai/nn/transformer/attention/hierarchical

Transformers are quickly becoming one of the most heavily applied deep learning architectures across modalities, domains, and tasks. In vision, on top of ongoing efforts into plain transformers, hierarchical transformers have also gained attention, thanks to their performance and easy integration into existing frameworks. These models typically employ localized attention mechanisms, such as the sliding-window Neighborhood Attention (NA) or Swin Transformer’s Shifted Window Self Attention. While effective at reducing self attention’s quadratic complexity, local attention weakens two of the most desirable properties of self attention: long range inter-dependency modeling, and global receptive field.

In this paper, we introduce Dilated Neighborhood Attention (DiNA), a natural, flexible and efficient extension to NA that can capture more global context and expand receptive fields exponentially at no additional cost. NA’s local attention and DiNA’s sparse global attention complement each other, and therefore we introduce Dilated Neighborhood Attention Transformer (DiNAT), a new hierarchical vision transformer built upon both. DiNAT variants enjoy improvements over attention-based baselines such as NAT and Swin, as well as modern convolutional baseline ConvNeXt. Our Large model is ahead of its Swin counterpart by 1.5% box AP in COCO object detection, 1.3% mask AP in COCO instance segmentation, and 1.1% mIoU in ADE20K semantic segmentation, and faster in throughput.

We believe combinations of NA and DiNA have the potential to empower various tasks beyond those presented in this paper.

To support and encourage research in this direction, in vision and beyond, we open-source our project at: Github.

--- https://arxiv.org/abs/2204.07143 NAT: Neighborhood Attention Transformer Ali Hassani, Steven Walton, Jiachen Li, Shen Li, Humphrey Shi 2022-04-14 2022-04-14 [("doi","10.48550/arXiv.2204.07143")] ai/nn/transformer/attention/hierarchical

We present Neighborhood Attention Transformer (NAT), an efficient, accurate and scalable hierarchical transformer that works well on both image classification and downstream vision tasks.

It is built upon Neighborhood Attention (NA), a simple and flexible attention mechanism that localizes the receptive field for each query to its nearest neighboring pixels. NA is a localization of self-attention, and approaches it as the receptive field size increases. It is also equivalent in FLOPs and memory usage to Swin Transformer’s shifted-window attention given the same receptive field size, while being less constrained. Furthermore, NA includes local inductive biases, which eliminate the need for extra operations such as pixel shifts.

Experimental results on NAT are competitive; NAT-Tiny reaches 83.2% top-1 accuracy on ImageNet with only 4.3 GFLOPs and 28M parameters, 51.4% mAP on MS-COCO and 48.4% mIoU on ADE20K.

We open-sourced our checkpoints, code and CUDA kernel at: Github.

--- https://constructionphysics.substack.com/p/why-did-we-wait-so-long-for-wind-498 2022-02-21 economics/experience-curve --- https://arxiv.org/abs/2109.12218 Long-Range Transformers for Dynamic Spatiotemporal Forecasting Jake Grigsby, Zhe Wang, Yanjun Qi 2021-09-24 2022-02-21 [("doi","10.48550/arXiv.2109.12218")] ai/nn/transformer/attention statistics/prediction

Multivariate Time Series Forecasting focuses on the prediction of future values based on historical context. State-of-the-art sequence-to-sequence models rely on neural attention between timesteps, which allows for temporal learning but fails to consider distinct spatial relationships between variables. In contrast, methods based on graph neural networks explicitly model variable relationships. However, these methods often rely on predefined graphs and perform separate spatial and temporal updates without establishing direct connections between each variable at every timestep.

This paper addresses these problems by translating multivariate forecasting into a spatiotemporal sequence formulation where each Transformer input token represents the value of a single variable at a given time. Long-Range Transformers can then learn interactions between space, time, and value information jointly along this extended sequence.

Our method, which we call Spacetimeformer, achieves competitive results on benchmarks from traffic forecasting to electricity demand and weather prediction while learning fully-connected spatiotemporal relationships purely from data.

--- https://keras.io/examples/generative/random_walks_with_stable_diffusion/ 2022-02-21 ai/nn/diffusion --- https://www.science.org/content/article/near-disaster-federal-nuclear-weapons-laboratory-takes-hidden-toll-america-s-arsenal 2022-02-21 radiance --- /doc/economics/1999-04-07-simpson-theingameconomicsofultimaonline.html The In-game Economics of Ultima Online Zachary Booth Simpson 1999-04-07 2022-02-21 economics sociology/technology

Ultima Online (UO) is a popular online computer role-playing game created and maintained by Origin Systems [‘Broadsword’ as of 2014]. Subscribers to UO gather online and interact with one another in a medieval fantasy world. One interesting aspect of this game is the inter-player economy which is analyzed in detail in this paper.

In many ways, the in-game economy is similar to a real world economy—goods and services are traded to mutual advantage and are mediated in currency or barter. In other ways, the economy is alien; for example, some commodity prices are determined by a robotic simulation of business profit motivation. These quirky rules will be described in Chapter 3 to ensure that the reader appreciates the entire economic environment of the game.

The economy is highly planned by the game designers; this includes everything from the possible items which can be manufactured to the rules which govern supply and demand. However, the economy did not behave as expected in many ways. It is these failures and the resulting redesigns which are most interesting and which we will examine in detail in Chapter 5.

This paper begins with an introduction to the virtual universe for those that are unfamiliar. We then turn to the economy and describe the micro and macro elements of it in detail.

This is followed by an analysis of the evolution of the economy—what went wrong, and how it was fixed.

Finally, the paper concludes with a proposal for several specific research topics.

The goal of this research is to provide information for the design of new virtual worlds as well as use the virtual world as a platform for investigating real world economic phenomena. This is a particularly exciting research field as it will allow the researcher complete measurement and variable control while still operating in a non-trivial economy.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3865717/ The Caenorhabditis elegans Lifespan Machine Nicholas Stroustrup, Bryne E. Ulmschneider, Zachary M. Nash, Isaac F. López-Moyado, Javier Apfeld, Walter Fontana 2013 2022-02-21 [("doi","10.1038/nmeth.2475")] longevity statistics/survival-analysis technology

The measurement of lifespan pervades aging research. Because lifespan results from complex interactions between genetic, environmental, and stochastic factors, it varies widely even among isogenic individuals. The actions of molecular mechanisms on lifespan are therefore visible only through their statistical effects on populations. Indeed, survival assays in Caenorhabditis elegans have provided critical insights into evolutionarily conserved determinants of aging.

To enable the rapid acquisition of survival curves at an arbitrary statistical resolution, we developed a scalable imaging and analysis platform to observe nematodes over multiple weeks across square meters of agar surface at 8-μm resolution. The automated method generates a permanent visual record of individual deaths from which survival curves are constructed and validated, producing data consistent with results from the manual method of survival curve acquisition for several mutants in both standard and stressful environments.

Our approach permits rapid, detailed reverse-genetic and chemical screens for effects on survival and enables quantitative investigations into the statistical structure of aging.

--- https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010415 A hierarchical process model links behavioral aging and lifespan in C. elegans Natasha Oswal, Olivier M. F. Martin, Sofia Stroustrup, Monika Anna Matusiak Bruckner, Nicholas Stroustrup 2022-07-19 2022-07-19 [("doi","10.1371/journal.pcbi.1010415")] longevity statistics/survival-analysis

Aging involves a transition from youthful vigor to geriatric infirmity and death. Individuals who remain vigorous longer tend to live longer, and within isogenic populations of C. elegans the timing of age-associated vigorous movement cessation (VMC) is highly correlated with lifespan. Yet, many mutations and interventions in aging alter the proportion of lifespan spent moving vigorously, appearing to “uncouple” youthful vigor from lifespan. To clarify the relationship between vigorous movement cessation, death, and the physical declines that determine their timing, we developed a new version of the imaging platform called “The Lifespan Machine”. This technology allows us to compare behavioral aging and lifespan at an unprecedented scale. We find that behavioral aging involves a time-dependent increase in the risk of VMC, reminiscent of the risk of death. Furthermore, we find that VMC times are inversely correlated with remaining lifespan across a wide range of genotypes and environmental conditions. Measuring and modeling a variety of lifespan-altering interventions including a new RNA-polymerase II auxin-inducible degron system, we find that vigorous movement and lifespan are best described as emerging from the interplay between at least two distinct physical declines whose rates co-vary between individuals. In this way, we highlight a crucial limitation of predictors of lifespan like VMC—in organisms experiencing multiple, distinct, age-associated physical declines, correlations between mid-life biomarkers and late-life outcomes can arise from the contextual influence of confounding factors rather than a reporting by the biomarker of a robustly predictive biological age.

Author Summary: Aging produces a variety of outcomes—declines in various measures of health and eventually death. By studying the relationship between two outcomes of aging in the same individual, we can learn about the underlying aging processes that cause them. Here, we consider the relationship between death and an outcome often used to quantify health in C. elegans—vigorous movement cessation which describes the age-associated loss of an individuals’ ability to move long distances. We develop an automated imaging platform that allows us to precisely compare this pair of outcomes in each individual across large populations. We find that individuals who remain vigorous longer subsequently have a shorter remaining lifespan—a pattern that holds even after vigorous movement and lifespan timing are both altered by several different mutations and interventions in aging. Modeling our data using a combination of simulation and analytic studies, we demonstrate how the relative timing of vigorous movement cessation and death suggest that these two outcomes are driven by distinct aging processes. Our data and analyses demonstrate how two outcomes of aging can be correlated across individuals with the timing of one predicting the timing of the other, but nevertheless be driven by mostly distinct underlying physical declines.

--- https://arxiv.org/abs/2210.01738 ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Training Antonio Norelli, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele Rodolà, Francesco Locatello 2022-10-04 2022-10-04 [("doi","10.48550/arXiv.2210.01738")] ai/nn/transformer/clip

Aligning the visual and language spaces requires to train deep neural networks from scratch on giant multimodal datasets; CLIP trains both an image and a text encoder, while LiT manages to train just the latter by taking advantage of a pretrained vision network.

In this paper, we show that sparse relative representations are sufficient to align text and images without training any network. Our method relies on readily available single-domain encoders (trained with or without supervision) and a modest (in comparison) number of image-text pairs. ASIF redefines what constitutes a multimodal model by explicitly disentangling memory from processing: here the model is defined by the embedded pairs of all the entries in the multimodal dataset, in addition to the parameters of the two encoders.

Experiments on standard zero-shot visual benchmarks demonstrate the typical transfer ability of image-text models.

Overall, our method represents a simple yet surprisingly strong baseline for foundation multimodal models, raising important questions on their data efficiency and on the role of retrieval in machine learning.

--- https://x.com/pharmapsychotic/status/1577465085575909376 2022-02-22 ai/nn/diffusion --- https://arxiv.org/abs/1807.07428 Modeling Visual Context is Key to Augmenting Object Detection Datasets Nikita Dvornik, Julien Mairal, Cordelia Schmid 2018-07-19 2022-02-22 [("doi","10.48550/arXiv.1807.07428")] ai/nn

Performing data augmentation for learning deep neural networks is well known to be important for training visual recognition systems. By artificially increasing the number of training examples, it helps reducing overfitting and improves generalization. For object detection, classical approaches for data augmentation consist of generating images obtained by basic geometrical transformations and color changes of original training images.

In this work, we go one step further and leverage segmentation annotations to increase the number of object instances present on training data.

For this approach to be successful, we show that modeling appropriately the visual context surrounding objects is crucial to place them in the right environment. Otherwise, we show that the previous strategy actually hurts.

With our context model, we achieve mean average precision improvements when few labeled examples are available on the VOC’12 benchmark.

--- https://arxiv.org/abs/1708.01642 Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection Debidatta Dwibedi, Ishan Misra, Martial Hebert 2017-08-04 2022-02-22 [("doi","10.48550/arXiv.1708.01642")] ai/nn

A major impediment in rapidly deploying object detection models for instance detection is the lack of large annotated datasets. For example, finding a large labeled dataset containing instances in a particular kitchen is unlikely. Each new environment with new instances requires expensive data collection and annotation.

In this paper, we propose a simple approach to generate large annotated instance datasets with minimal effort. Our key insight is that ensuring only patch-level realism provides enough training signal for current object detector models. We automatically ‘cut’ object instances and ‘paste’ them on random backgrounds. A naive way to do this results in pixel artifacts which result in poor performance for trained models.

We show how to make detectors ignore these artifacts during training and generate data that gives competitive performance on real data.

Our method outperforms existing synthesis approaches and when combined with real images improves relative performance by more than 21% on benchmark datasets.

In a cross-domain setting, our synthetic data combined with just 10% real data outperforms models trained on all real data.

--- /doc/psychology/neuroscience/1961-gregory.pdf The brain as an engineering problem R. L. Gregory 1961-01-01 2022-02-22 philosophy/epistemology philosophy/mind psychology/neuroscience --- https://arxiv.org/abs/2210.01571 VICRegL: Self-Supervised Learning of Local Visual Features Adrien Bardes, Jean Ponce, Yann LeCun 2022-10-04 2022-10-04 [("doi","10.48550/arXiv.2210.01571")] ai/nn/cnn

Most recent self-supervised methods for learning image representations focus on either producing a global feature with invariance properties, or producing a set of local features. The former works best for classification tasks while the latter is best for detection and segmentation tasks.

This paper explores the fundamental trade-off between learning local and global features. A new method called VICRegL is proposed that learns good global and local features simultaneously, yielding excellent performance on detection and segmentation tasks while maintaining good performance on classification tasks. Concretely, two identical branches of a standard convolutional net architecture are fed two differently distorted versions of the same image. The VICReg criterion is applied to pairs of global feature vectors. Simultaneously, the VICReg criterion is applied to pairs of local feature vectors occurring before the last pooling layer. Two local feature vectors are attracted to each other if their 𝓁2-distance is below a threshold or if their relative locations are consistent with a known geometric transformation between the two input images.

We demonstrate strong performance on linear classification and segmentation transfer tasks.

Code and pretrained models are publicly available at: https://github.com/facebookresearch/VICRegL.

--- https://www.youtube.com/watch?v=KFDlVgBMomQ 2022-02-22 cs/hardware --- https://jalammar.github.io/illustrated-stable-diffusion/ 2022-02-22 ai/nn/diffusion --- https://blog.otoro.net/2022/10/01/collectiveintelligence/ 2022-02-22 cs/cellular-automaton reinforcement-learning/multi-agent --- https://arxiv.org/abs/2210.00939 Improving Sample Quality of Diffusion Models Using Self-Attention Guidance Susung Hong, Gyuseong Lee, Wooseok Jang, Seungryong Kim 2022-10-03 2022-10-03 [("doi","10.48550/arXiv.2210.00939")] ai/nn/diffusion ai/nn/transformer

Following generative adversarial networks (GANs), a de facto standard model for image generation, denoising diffusion models (DDMs) have been actively researched and attracted strong attention due to their capability to generate images with high quality and diversity. However, the way the internal self-attention mechanism works inside the U-Net of DDMs is under-explored.

To unveil them, in this paper, we first investigate the self-attention operations within the black-boxed diffusion models and build hypotheses. Next, we verify the hypotheses about the self-attention map by conducting frequency analysis and testing the relationships with the generated objects.

In consequence, we find out that the attention map is closely related to the quality of generated images. On the other hand, diffusion guidance methods based on additional information such as labels are proposed to improve the quality of generated images.

Inspired by these methods, we present label-free guidance based on the intermediate self-attention map that can guide existing pretrained diffusion models to generate images with higher fidelity.

In addition to the enhanced sample quality when used alone, we show that the results are further improved by combining our method with classifier guidance on ImageNet 128×128.

--- https://arxiv.org/abs/2202.01682 How to build a cognitive map: insights from models of the hippocampal formation James C. R. Whittington, David McCaffary, Jacob J. W. Bakermans, Timothy E. J. Behrens 2022-02-03 2022-02-23 [("doi","10.48550/arXiv.2202.01682")] psychology/neuroscience reinforcement-learning/model

Learning and interpreting the structure of the environment is an innate feature of biological systems, and is integral to guiding flexible behaviors for evolutionary viability. The concept of a cognitive map has emerged as one of the leading metaphors for these capacities, and unravelling the learning and neural representation of such a map has become a central focus of neuroscience.

While experimentalists are providing a detailed picture of the neural substrate of cognitive maps in hippocampus and beyond, theorists have been busy building models to bridge the divide between neurons, computation, and behavior. These models can account for a variety of known representations and neural phenomena, but often provide a differing understanding of not only the underlying principles of cognitive maps, but also the respective roles of hippocampus and cortex.

In this Perspective, we bring many of these models into a common language, distil their underlying principles of constructing cognitive maps, provide novel (re)interpretations for neural phenomena, suggest how the principles can be extended to account for prefrontal cortex representations and, finally, speculate on the role of cognitive maps in higher cognitive capacities.

--- https://arxiv.org/abs/2210.02438 DALL·E-Bot: Introducing Web-Scale Diffusion Models to Robotics Ivan Kapelyukh, Vitalis Vosylius, Edward Johns 2022-10-05 2022-10-05 [("doi","10.48550/arXiv.2210.02438")] ai/nn/transformer/gpt/dall-e/2 reinforcement-learning/robot

We introduce the first work to explore web-scale diffusion models for robotics. DALL·E-Bot enables a robot to rearrange objects in a scene, by first inferring a text description of those objects, then generating an image representing a natural, human-like arrangement of those objects, and finally physically arranging the objects according to that image.

The importance is that we achieve this zero-shot using DALL·E, without needing any further data collection or training.

Encouraging real-world results with human studies show that this is an exciting direction for the future of web-scale robot learning algorithms.

We also propose a list of recommendations to the text-to-image community, to align further developments of these models with applications to robotics.

Videos are available at: https://www.robot-learning.uk/dall-e-bot.

--- https://lambdalabs.com/blog/inference-benchmark-stable-diffusion 2022-02-23 ai/nn/diffusion --- https://arxiv.org/abs/1711.04322 11K Hands: Gender recognition and biometric identification using a large dataset of hand images Mahmoud Afifi 2017-11-12 2022-02-23 [("doi","10.48550/arXiv.1711.04322")] ai/dataset ai/nn/cnn

The human hand possesses distinctive features which can reveal gender information. In addition, the hand is considered one of the primary biometric traits used to identify a person.

In this work, we propose a large dataset of human hand images (dorsal and palmar sides) with detailed ground-truth information for gender recognition and biometric identification.

Using this dataset, a convolutional neural network (CNN) can be trained effectively for the gender recognition task. Based on this, we design a two-stream CNN to tackle the gender recognition problem. This trained model is then used as a feature extractor to feed a set of support vector machine classifiers for the biometric identification task. We show that the dorsal side of hand images, captured by a regular digital camera, convey effective distinctive features similar to, if not better, those available in the palmar hand images.

To facilitate access to the proposed dataset and replication of our experiments, the dataset, trained CNN models, and Matlab source code are available at the homepage.

--- https://lexica.art/ 2022-02-23 ai/nn/transformer/clip/sample --- https://www.henrikkarlsson.xyz/p/gpt-3 2022-02-23 ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/2210.02205#deepmind Game Theoretic Rating in N-player general-sum games with Equilibria Luke Marris, Marc Lanctot, Ian Gemp, Shayegan Omidshafiei, Stephen McAleer, Jerome Connor, Karl Tuyls, Thore Graepel 2022-10-05 2022-10-05 [("doi","10.48550/arXiv.2210.02205")] reinforcement-learning/multi-agent

Rating strategies in a game is an important area of research in game theory and artificial intelligence, and can be applied to any real-world competitive or cooperative setting. Traditionally, only transitive dependencies between strategies have been used to rate strategies (eg. Elo), however recent work has expanded ratings to use game theoretic solutions to better rate strategies in non-transitive games.

This work generalizes these ideas and proposes novel algorithms suitable for N-player, general-sum rating of strategies in normal-form games according to the payoff rating system. This enables well-established solution concepts, such as equilibria, to be leveraged to efficiently rate strategies in games with complex strategic interactions, which arise in multiagent training and real-world interactions between many agents.

We empirically validate our methods on real world normal-form data (Premier League) and multiagent reinforcement learning agent evaluation.

--- /doc/sociology/2022-morton.pdf Effects of 4-Day School Weeks on Older Adolescents: Examining Impacts of the Schedule on Academic Achievement, Attendance, and Behavior in High School Emily Morton 2022-06-20 2022-06-20 [("doi","10.3102/01623737221097420")] sociology zeo

Four-day school weeks have proliferated across the United States in recent years, reaching over 650 public school districts in 24 states as of 2019, but little is known about their implementation and there is no consensus on their effects on students.

This study uses district-level panel data from Oklahoma and a difference-in-differences research design to provide estimates of the causal effect of the 4-day school week on high school students’ ACT scores, attendance, and disciplinary incidents during school.

Results indicate that 4-day school weeks decrease per-pupil bullying incidents by ~39% and per-pupil fighting incidents by ~31%, but have no detectable effect on other incident types, ACT scores, or attendance.

--- /doc/psychology/2022-prinsloo.pdf Opportunity Neglect: An Aversion to Low-Probability Gains Emily Prinsloo, Kate Barasz, Leslie K. John, Michael I. Norton 2022 2022-02-23 [("doi","10.1177/09567976221091801")] economics psychology

7 preregistered studies (n = 2,890, adult participants) conducted in the field, in the lab, and online documented opportunity neglect: a tendency to reject opportunities with low probability of success even when they come with little or no objective cost (eg. time, money, reputation).

Participants rejected a low-probability opportunity in an everyday context (Study 1). Participants also rejected incentive-compatible gambles with positive expected value—for both goods (Study 2) and money (Studies 3–7)—even with no possibility of monetary loss and nontrivial rewards (eg. a 1% chance at $99). Participants rejected low-probability opportunities more frequently than high-probability opportunities with equal expected value (Study 3). Although taking some real-life opportunities comes with costs, we show that people are even willing to incur costs to opt out of low-probability opportunities (Study 4). Opportunity neglect can be mitigated by highlighting that rejecting an opportunity is equivalent to choosing a zero probability of success (Studies 6–7).

--- https://arxiv.org/abs/2209.11379#google Do Current Multi-Task Optimization Methods in Deep Learning Even Help? Derrick Xin, Behrooz Ghorbani, Ankush Garg, Orhan Firat, Justin Gilmer 2022-09-23 2022-09-23 [("doi","10.48550/arXiv.2209.11379")] ai/nn ai/scaling

Recent research has proposed a series of specialized optimization algorithms for deep multi-task models. It is often claimed that these multi-task optimization (MTO) methods yield solutions that are superior to the ones found by simply optimizing a weighted average of the task losses.

In this paper, we perform large-scale experiments on a variety of language and vision tasks to examine the empirical validity of these claims. We show that, despite the added design and computational complexity of these algorithms, MTO methods do not yield any performance improvements beyond what is achievable via traditional optimization approaches.

We highlight alternative strategies that consistently yield improvements to the performance profile and point out common training pitfalls that might cause suboptimal results.

Finally, we outline challenges in reliably evaluating the performance of MTO algorithms and discuss potential solutions.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4207317/ Transcranial bright light treatment via the ear canals in seasonal affective disorder: a randomized, double-blind dose-response study Heidi Jurvelin, Timo Takala, Juuso Nissilä, Markku Timonen, Melanie Rüger, Jari Jokelainen, Pirkko Räsänen 2014 2022-02-24 [("doi","10.1186/s12888-014-0288-6")] psychiatry/anxiety psychiatry/depression

Background: Bright light treatment is effective for seasonal affective disorder (SAD), although the mechanisms of action are still unknown. We investigated whether transcranial bright light via the ear canals has an antidepressant effect in the treatment of SAD.

Method: During the four-week study period, 89 patients (67 females; 22 males, aged 22–65, mean ± SD age: 43.2 ± 10.9 years) suffering from SAD were randomized to receive a 12-min daily dose of photic energy of one of 3 intensities (1 lumen/0.72 mW/cm(2); 4 lumens/2.881 mW/cm(2); 9 lumens/6.482 mW/cm(2)) via the ear canals. The light was produced using light-emitting diodes. The severity of depressive symptoms was assessed with the Hamilton Depression Rating Scale—Seasonal Affective Disorder (SIGH-SAD), the Hamilton Anxiety Rating Scale (HAMA), and the Beck Depression Inventory (BDI). Cognitive performance was measured by the Trail Making Test (TMT). The within-group and between-group changes in these variables throughout the study were analysed with a repeated measures analysis of variance (ANOVA), whereas gender differences at baseline within the light groups were analysed using Student’s t-tests.

Results: Patients in all 3 groups showed statistically-significant decreases in their BDI, HAMA, and SIGH-SAD scores. Response rates, ie. an at least 50% decrease of symptoms as measured by the BDI, were 74%–79% in the 3 treatment groups. Corresponding variations for the SIGH-SAD and the HAMA were 35–45% and 47–62%, respectively. No intensity-based dose-response relationships in the improvement of anxiety and depressive symptoms or cognitive performance between treatment groups were observed. ~1in 4 patients experienced mild adverse effects, of which the most common were headache, insomnia, and nausea.

Conclusions: These results suggests that transcranial bright light treatment may have antidepressant and anxiolytic effect in SAD patients, as both self-rated & psychiatrist-rated depressive and anxiety symptoms decreased in all treatment groups. These improvements are comparable to findings of earlier bright light studies that used conventional devices. The lack of dose response may be due to a saturation effect above a certain light intensity threshold. Further studies on the effects of transcranial bright light with an adequate placebo condition are needed.

Trial Registration:NCT01293409, ClinicalTrials.gov.

--- https://www.pewresearch.org/short-reads/2021/04/07/partisan-differences-in-social-media-use-show-up-for-some-platforms-but-not-facebook/ 2022-02-24 politics sociology/technology --- https://nationalaffairs.com/publications/detail/why-men-are-hard-to-help 2022-02-24 sociology --- https://en.wikipedia.org/wiki/Ornstein%E2%80%93Uhlenbeck_process Ornstein-Uhlenbeck process 2022-02-24 statistics/probability --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531208/ The Effect of Long-Acting Methylphenidate and Modafinil on Attention and Impulsivity of Children with ADHD using a Continuous Performance Test: A Comparative Study Ghazal Zahedm, Maliheh Roozbakhsh, Rozita Davari Ashtiani, Katayoun Razjouyan 2022-06 2022-06 [("doi","10.22037/ijcn.v16i2.32541")] modafinil psychiatry/adhd

Objectives: Given the importance of having a continuous performance for the academic and social life of children with attention-deficit/hyperactivity disorder (ADHD), in this study, a Continuous Performance Test (CPT) was used to compare the effect of long-acting methylphenidate and modafinil on attention and impulsivity of these children.

Materials & Method: A randomized clinical trial was conducted on 50 children with ADHD aged 6 to 12 years in the child and adolescent psychiatric departments of Imam Hossein and Mofid hospitals, Tehran, Iran. The children were selected by availability sampling and randomly assigned into two equal groups (n = 25 in each). While the first group was treated with long-acting methylphenidate, the second was treated with modafinil for 14 days. The CPT was carried out before and after the treatment. The obtained data were analyzed by F and t-tests.

Results: Long-acting methylphenidate and modafinil were both effective in improving attention and impulsivity in children with ADHD. There was no statistically-significant difference between the two drugs in terms of effectiveness on attention and impulsivity.

Conclusion: The findings of this study showed that long-acting methylphenidate and modafinil are equally effective in improving attention and impulsivity in children with ADHD aged 6 to 12 years.

[Keywords: Attention deficit hyperactivity disorder (ADHD), long-acting methylphenidate, modafinil, continuous performance test]

--- /doc/technology/2022-kelly.pdf Connecting the Scientific and Industrial Revolutions: The Role of Practical Mathematics Morgan Kelly, Cormac Ó Gráda 2022-07-06 2022-07-06 [("doi","10.1017/S0022050722000250")] math technology

Disputes over whether the Scientific Revolution contributed to the Industrial Revolution begin with the common assumption that natural philosophers and artisans formed distinct groups.

In reality, these groups merged together through a diverse group of applied mathematics teachers, textbook writers, and instrument makers catering to a market ranging from navigators and surveyors to bookkeepers. Besides its direct economic contribution in diffusing useful numerical skills, this “practical mathematics” facilitated later industrialization in two ways.

First, a large supply of instrument and watch makers provided Britain with a pool of versatile, mechanically skilled labor to build the increasingly complicated machinery of the late 18th century. Second, the less well-known but equally revolutionary innovations in machine tools—which, contrary to the Habakkuk thesis, occurred largely in Britain during the 1820s and 1830s to mass-produce interchangeable parts for iron textile machinery—drew on a technology of exact measurement developed for navigational and astronomical instruments.

--- /doc/iq/2013-zuckerman.pdf The Relation Between Intelligence and Religiosity: A Meta-Analysis and Some Proposed Explanations Miron Zuckerman, Jordan Silberman, Judith A. Hall 2013-08-06 2022-02-24 [("doi","10.1177/1088868313497266")] iq philosophy/religion

A meta-analysis of 63 studies showed a statistically-significant negative association between intelligence and religiosity.

The association was stronger for college students and the general population than for participants younger than college age; it was also stronger for religious beliefs than religious behavior. For college students and the general population, means of weighted and unweighted correlations between intelligence and the strength of religious beliefs ranged from −0.20 to −0.25 (mean r = −0.24).

Three possible interpretations were discussed. First, intelligent people are less likely to conform and, thus, are more likely to resist religious dogma. Second, intelligent people tend to adopt an analytic (as opposed to intuitive) thinking style, which has been shown to undermine religious beliefs. Third, several functions of religiosity, including compensatory control, self-regulation, self-enhancement, and secure attachment, are also conferred by intelligence. Intelligent people may therefore have less need for religious beliefs and practices.

--- /doc/nootropic/caffeine/2008-haskell.pdf Caffeine at levels found in decaffeinated beverages is behaviorally active Haskell 2008 2022-02-24 nootropic/caffeine tea --- /doc/tea/2018-jin.pdf Hongyacha, a Naturally Caffeine-free Tea Plant from Fujian, China 2018 2022-02-24 biology nootropic/caffeine tea --- /doc/nootropic/caffeine/1997-marret.pdf Caffeine-induced disturbances of early neurogenesis in whole mouse embryo cultures Marret 1997-01-01 2022-02-24 nootropic/caffeine psychology/neuroscience --- /doc/nootropic/caffeine/2008-lorist.pdf Caffeine, Sleep, and Quality of Life 2008 2022-02-24 nootropic/caffeine zeo --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.451.9389&rep=rep1&type=pdf Caffeine eliminates psychomotor vigilance deficits from sleep inertia Van Dongen 2001 2022-02-25 nootropic/caffeine zeo --- https://repository.uel.ac.uk/download/852b1af896127ebee4b3478630ceb33b6f5b471d043f4240fb61ae80ca9f9b84/182191/Dawkins_2011b.pdf.pdf Expectation of having consumed caffeine can improve performance and mood Dawkins 2011 2022-02-25 nootropic/caffeine --- https://en.wikipedia.org/wiki/Caffeine Caffeine 2022-02-25 nootropic/caffeine --- /doc/psychology/neuroscience/tcs/2014-mcintire.pdf A Comparison of the Effects of Transcranial Direct Current Stimulation and Caffeine on Vigilance and Cognitive Performance During Extended Wakefulness Lindsey K. McIntire, R. Andy McKinley, Chuck Goodyear, Justin Nelson 2014-01-01 2022-02-25 [("doi","10.1016/j.brs.2014.04.008")] nootropic/caffeine psychology/neuroscience/tcs --- /nootropic/nootropics#caffeine 2022-02-25 nootropic/caffeine --- https://www.scicompdf.se/cooldown/haskell_2008.pdf The effects of L-theanine, caffeine and their combination on cognition and mood Haskell 2008 2022-02-25 nootropic/caffeine --- https://www.scielo.br/j/bjmbr/a/Tb7ymZdLYv5mjTbjNy3KgsP/?format=pdf Effects of caffeine on learning and memory in rats tested in the Morris water maze Angelucci 2002 2022-02-25 nootropic/caffeine psychology/animal/maze --- https://medium.com/better-humans/the-effects-of-caffeine-alcohol-and-exercise-on-sleep-analyzing-the-surprising-results-117330af2480g How much coffee is too much? A case study and tutorial on self-tracking to improve sleep. 2022-02-25 nootropic/caffeine psychiatry/alcoholism zeo --- https://www.economist.com/science-and-technology/2018/11/17/a-newly-discovered-tea-plant-is-caffeine-free A newly discovered tea plant is caffeine-free: It was found growing wild in Fujian province 2022-02-25 genetics/heritable nootropic/caffeine tea --- https://www.nature.com/articles/npp201071 Association of the Anxiogenic and Alerting Effects of Caffeine with ADORA2A and ADORA1 Polymorphisms and Habitual Level of Caffeine Consumption 2022-02-25 genetics/heritable nootropic/caffeine psychiatry/anxiety --- /doc/modafinil/1993-warot.pdf Subjective effects of modafinil, a new central adrenergic stimulant in healthy volunteers: a comparison with amphetamine, caffeine and placebo 1993 2022-02-26 modafinil nootropic/caffeine --- https://www.dailymail.co.uk/news/article-1324722/Party-goer-killed-2-spoonfuls-caffeine-powder--equivalent-70-Red-Bulls.html A party-goer died after swallowing two spoonfuls of internet-bought pure caffeine powder with the same strength as 70 cans of Red Bull, an inquest heard yesterday. 2022-02-26 nootropic/caffeine --- https://examine.com/supplements/caffeine/ 2022-02-26 nootropic/caffeine --- https://en.wikipedia.org/wiki/Xanthine Xanthine 2022-02-26 nootropic/caffeine --- https://en.wikipedia.org/wiki/Theobromine Theobromine 2022-02-26 nootropic/caffeine --- https://en.wikipedia.org/wiki/Theophylline Theophylline 2022-02-26 nootropic/caffeine --- https://en.wikipedia.org/wiki/Theobromine_poisoning Theobromine poisoning 2022-02-26 nootropic/caffeine --- https://en.wikipedia.org/wiki/Caffeine_dependence Caffeine dependence 2022-02-26 nootropic/caffeine --- https://en.wikipedia.org/wiki/Caffeine-induced_anxiety_disorder Caffeine-induced anxiety disorder 2022-02-26 nootropic/caffeine --- https://en.wikipedia.org/wiki/Caffeine-induced_sleep_disorder Caffeine-induced sleep disorder 2022-02-26 nootropic/caffeine zeo --- https://en.wikipedia.org/wiki/Caffeinism Caffeinism 2022-02-26 nootropic/caffeine --- https://en.wikipedia.org/wiki/Caffeinated_alcoholic_drink Caffeinated alcoholic drink 2022-02-27 nootropic/caffeine --- https://en.wikipedia.org/wiki/Decaffeination Decaffeination 2022-02-27 nootropic/caffeine tea --- https://en.wikipedia.org/wiki/Activated_carbon Activated carbon 2022-02-27 nootropic/caffeine tea --- https://en.wikipedia.org/wiki/Supercritical_carbon_dioxide Supercritical carbon dioxide 2022-02-27 nootropic/caffeine tea --- https://en.wikipedia.org/wiki/History_of_tea History of tea 2022-02-27 tea --- https://en.wikipedia.org/wiki/Caffeine#Natural_occurrence Caffeine § Natural occurrence 2022-02-27 nootropic/caffeine --- /doc/modafinil/2005-bonnet.pdf 2005 2022-02-27 modafinil nootropic/caffeine --- http://jtoomim.org/brain-training/han2007-caffeine-hurts-learning.pdf Inhibitory effects of caffeine on hippocampal neurogenesis and function Han 2007 2022-02-27 nootropic/caffeine --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3260075/ Caffeinated beverage intake and reproductive hormones among premenopausal women in the BioCycle Study Schliep 2012 2022-02-27 nootropic/caffeine --- https://www.apa.org/pubs/journals/releases/pha-18-6-553.pdf Acute Effects of a Glucose Energy Drink on Behavioral Control Howard, Marczinski 2010 2022-02-27 nootropic/caffeine --- https://fis.fda.gov/sense/app/d10be6bb-494e-4cd2-82e4-0135608ddc13/sheet/45beeb74-30ab-46be-8267-5756582633b4/state/analysis FDA Adverse Events Reporting System (FAERS) Public Dashboard: ‘Vitamin D’ 2022-02-27 nootropic/caffeine --- https://simplifier.neocities.org/4x4 2022-02-28 design/typography --- https://the-decoder.com/it-worker-uses-ai-to-create-stunning-706-page-sci-fi-graphic-novel/ 2022-02-28 ai/nn/diffusion/midjourney ai/nn/transformer/clip/sample --- https://simonberens.me/blog/dalle-2-vs-10-fiverr-commission 2022-02-28 ai/nn/transformer/gpt/dall-e --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5990549/ Humor norms for 4,997 English words Tomas Engelthaler, Thomas T. Hills 2018 2022-02-28 [("doi","10.3758/s13428-017-0930-6")] fiction/humor psychology/writing

Humor ratings are provided for 4,997 English words collected from 821 participants using an online crowd-sourcing platform.

Each participant rated 211 words on a scale from 1 (humorless) to 5 (humorous). To provide for comparisons across norms, words were chosen from a set common to a number of previously collected norms (eg. arousal, valence, dominance, concreteness, age of acquisition, and reaction time). The complete dataset provides researchers with a list of humor ratings and includes information on gender, age, and educational differences.

Results of analyses show that the ratings have reliability on a par with previous ratings and are not well predicted by existing norms.

--- https://arxiv.org/abs/2209.14958#deepmind Co-Writing Screenplays and Theatre Scripts with Language Models (Dramatron): An Evaluation by Industry Professionals Piotr Mirowski, Kory W. Mathewson, Jaylen Pittman, Richard Evans 2022-09-29 2022-09-29 [("doi","10.48550/arXiv.2209.14958")] ai/nn/transformer/attention/hierarchical ai/nn/transformer/gpt/3/fiction

Language models are increasingly attracting interest from writers. However, such models lack long-range semantic coherence, limiting their usefulness for longform creative writing.

We address this limitation by applying Chinchilla language models hierarchically, in a system we call Dramatron. By building structural context via prompt chaining, Dramatron can generate coherent scripts and screenplays complete with title, characters, story beats, location descriptions, and dialogue.

We illustrate Dramatron’s usefulness as an interactive co-creative system with a user study of 15 theatre and film industry professionals. Participants co-wrote theatre scripts and screenplays with Dramatron and engaged in open-ended interviews. We report critical reflections both from our interviewees and from independent reviewers who watched stagings of the works to illustrate how both Dramatron and hierarchical text generation could be useful for human-machine co-creativity.

Finally, we discuss the suitability of Dramatron for co-creativity, ethical considerations—including plagiarism and bias—and participatory models for the design and deployment of such tools.

…3.3 The Importance of Prompt Engineering: …For Dramatron, each prompt set is composed of: (1) title prompt, (2) character description prompt, (3) plot prompt, (4) location description prompt, (5) and dialogue prompt. Each prompt is detailed briefly below to give a sense of how they are engineered; additional details are in Appendix E.

…The writer can furthermore perform these operations by stepping forward and back in the Dramatron hierarchy. For example, they could: (1) generate a title, (2) generate a new title, (3) edit the title, (4) generate a list of characters, (5) edit the characters by removing one character and changing the description of another, (6) generate a plot outline, (7) edit the plot by removing part of the narrative arc, (8) generate a continuation of that edited plot, (9) go back and rewrite the log line, etc. This co-writing approach allows the human and Dramatron to both contribute to the authorship of a script. Following these operations, the human author could further edit and format to finalize a script. Appendix G shows examples of human-edited scripts.

--- https://arxiv.org/abs/2210.01542#twitter Hyperbolic Deep Reinforcement Learning Edoardo Cetin, Benjamin Chamberlain, Michael Bronstein, Jonathan J. Hunt 2022-10-04 2022-10-04 [("doi","10.48550/arXiv.2210.01542")] reinforcement-learning/model-free

[Twitter, Reddit, blog] We propose a new class of deep reinforcement learning (RL) algorithms that model latent representations in hyperbolic space.

Sequential decision-making requires reasoning about the possible future consequences of current behavior. Consequently, capturing the relationship between key evolving features for a given task is conducive to recovering effective policies. To this end, hyperbolic geometry provides deep RL models with a natural basis to precisely encode this inherently hierarchical information. However, applying existing methodologies from the hyperbolic deep learning literature leads to fatal optimization instabilities due to the non-stationarity and variance characterizing RL gradient estimators.

Hence, we design a new general method that counteracts such optimization challenges and enables stable end-to-end learning with deep hyperbolic representations.

We empirically validate our framework by applying it to popular on-policy & off-policy RL algorithms on the Procgen & Atari 100K benchmarks, attaining near universal performance and generalization benefits.

Given its natural fit, we hope future RL research will consider hyperbolic representations as a standard tool.

--- /doc/economics/2022-blanchett.pdf Why Is Europe More Equal than the United States? Thomas Blanchet, Lucas Chancel, Amory Gethin 2022-10-01 2022-10-01 [("doi","10.1257/app.20200703")] economics politics

This article combines all available data to produce pretax and post-tax income inequality series in 26 European countries 1980–2017. Our estimates are consistent with macroeconomic growth and comparable with US distributional national accounts.

Inequality grew in nearly all European countries, but much less than in the US. Contrary to a widespread view, we demonstrate that Europe’s lower inequality levels cannot be explained by more equalizing tax and transfer systems. After accounting for indirect taxes and in-kind transfers, the US redistributes a greater share of national income to low-income groups than any European country.

“Predistribution”, not “redistribution”, explains why Europe is less unequal than the United States.

--- https://arxiv.org/abs/2210.01075 BTD: Decompiling x86 Deep Neural Network Executables Zhibo Liu, Yuanyuan Yuan, Shuai Wang, Xiaofei Xie, Lei Ma 2022-10-03 2022-10-03 [("doi","10.48550/arXiv.2210.01075")] ai/nn/adversarial cs/security

[Github] Due to their widespread use on heterogeneous hardware devices, deep learning (DL) models are compiled into executables by DL compilers to fully leverage low-level hardware primitives. This approach allows DL computations to be undertaken at low cost across a variety of computing platforms, including CPUs, GPUs, and various hardware accelerators.

We present BTD (Bin to DNN), a decompiler for deep neural network (DNN) executables. BTD takes DNN executables and outputs full model specifications, including types of DNN operators, network topology, dimensions, and parameters that are (nearly) identical to those of the input models. BTD delivers a practical framework to process DNN executables compiled by different DL compilers and with full optimizations enabled on x86 platforms. It employs learning-based techniques to infer DNN operators, dynamic analysis to reveal network architectures, and symbolic execution to facilitate inferring dimensions and parameters of DNN operators.

Our evaluation reveals that BTD enables accurate recovery of full specifications of complex DNNs with millions of parameters (eg. ResNet). The recovered DNN specifications can be re-compiled into a new DNN executable exhibiting identical behavior to the input executable.

We show that BTD can boost two representative attacks, adversarial example generation and knowledge stealing, against DNN executables. We also demonstrate cross-architecture legacy code reuse using BTD, and envision BTD being used for other critical downstream tasks like DNN security hardening and patching.

--- /doc/psychology/personality/2022-prati.pdf Feeling Good Is Feeling Better Alberto Prati, Claudia Senik 2022-10-07 2022-10-07 [("doi","10.1177/09567976221096158")] politics psychology/cognitive-bias psychology/personality

Can people remember their past happiness?

We analyzed data from 4 longitudinal surveys from the United States, France, the United Kingdom, and Germany spanning from the 1970s until the present, in which more than 60,000 adults were asked questions about their current and past life satisfaction.

We uncovered systematic biases in recalled happiness: On average, people tended to overstate the improvement in their well-being over time and to understate their past happiness. But this aggregate figure hides a deep asymmetry: Whereas happy people recall the evolution of their life to be better than it was, unhappy ones tend to exaggerate their life’s negative evolution. It thus seems that feeling happy today implies feeling better than yesterday.

This recall structure has implications for motivated memory and learning and could explain why happy people are more optimistic, perceive risks to be lower, and are more open to new experiences [or vice-versa: why unhappy people are etc, and presumably also attitudes like redistribution.]

--- https://arxiv.org/abs/2112.00826 Inducing Causal Structure for Interpretable Neural Networks (IIT) Atticus Geiger, Zhengxuan Wu, Hanson Lu, Josh Rozner, Elisa Kreiss, Thomas Icard, Noah D. Goodman, Christopher Potts 2021-12-01 2022-02-28 [("doi","10.48550/arXiv.2112.00826")] ai/nn/rnn ai/nn/transformer statistics/causality

In many areas, we have well-founded insights about causal structure that would be useful to bring into our trained models while still allowing them to learn in a data-driven fashion.

To achieve this, we present the new method of interchange intervention training (IIT). In IIT, we (1) align variables in a causal model (eg. a deterministic program or Bayesian network) with representations in a neural model and (2) train the neural model to match the counterfactual behavior of the causal model on a base input when aligned representations in both models are set to be the value they would be for a source input. IIT is fully differentiable, flexibly combines with other objectives, and guarantees that the target causal model is a causal abstraction of the neural model when its loss is zero.

We evaluate IIT on a structural vision task (MNIST-PVR), a navigational language task (ReaSCAN), and a natural language inference task (MQNLI). We compare IIT against multi-task training objectives and data augmentation. In all our experiments, IIT achieves the best results and produces neural models that are more interpretable in the sense that they more successfully realize the target causal model.

--- https://arxiv.org/abs/2206.15472 On-Device Training Under 256KB Memory Ji Lin, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, Chuang Gan, Song Han 2022-06-30 2022-06-30 [("doi","10.48550/arXiv.2206.15472")] ai/nn/sparsity/low-precision ai/scaling

[cf. Kumar et al 2017, Duisterhof et al 2019, Fedorov et al 2019; more serious than tflite_c64] On-device training enables the model to adapt to new data collected from the sensors by fine-tuning a pre-trained model. However, the training memory consumption is prohibitive for IoT devices that have tiny memory resources. We propose an algorithm-system co-design framework to make on-device training possible with only 256KB of memory.

On-device training faces two unique challenges: (1) the quantized graphs of neural networks are hard to optimize due to mixed bit-precision and the lack of normalization; (2) the limited hardware resource (memory and computation) does not allow full backward computation. To cope with the optimization difficulty, we propose Quantization-Aware Scaling to calibrate the gradient scales and stabilize quantized training. To reduce the memory footprint, we propose Sparse Update to skip the gradient computation of less important layers and sub-tensors.

The algorithm innovation is implemented by a lightweight training system, Tiny Training Engine, which prunes the backward computation graph to support sparse updates and offloads the runtime auto-differentiation to compile time.

Our framework is the first practical solution for on-device transfer learning of visual recognition on tiny IoT devices (eg. a microcontroller with only 256KB SRAM), using less than 1⁄100th of the memory of existing frameworks while matching the accuracy of cloud training+edge deployment for the tinyML application VWW.

Our study enables IoT devices to not only perform inference but also continuously adapt to new data for on-device lifelong learning.

--- /doc/statistics/prediction/2006-aegisdottir.pdf The Meta-Analysis of Clinical Judgment Project: 56 Years of Accumulated Research on Clinical Versus Statistical Prediction Stefanía Ægisdóttir, Michael J. White, Paul M. Spengler, Alan S. Maugherman, Linda A. Anderson, Robert S. Cook, Cassandra N. Nichols, Georgios K. Lampropoulos, Blain S. Walker, Genna Cohen, Jeffrey D. Rush 2006-05-01 2022-03-01 [("doi","10.1177/0011000005285875")] psychiatry psychology statistics/prediction

Clinical predictions made by mental health practitioners are compared with those using statistical approaches.

67 studies were identified from a comprehensive search of 56 years of research; 92 effect sizes were derived from these studies.

The overall effect of clinical versus statistical prediction showed a somewhat greater accuracy for statistical methods. The most stringent sample of studies, from which 48 effect sizes were extracted, indicated a 13% increase in accuracy using statistical versus clinical methods. Several variables influenced this overall effect. Clinical and statistical prediction accuracy varied by type of prediction, the setting in which predictor data were gathered, the type of statistical formula used, and the amount of information available to the clinicians and the formulas.

Recommendations are provided about when and under what conditions counseling psychologists might use statistical formulas as well as when they can rely on clinical methods. Implications for clinical judgment research and training are discussed.

--- https://arxiv.org/abs/2210.03251#microsoft Small Character Models Match Large Word Models for Autocomplete Under Memory Constraints Ganesh Jawahar, Subhabrata Mukherjee, Debadeepta Dey, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan, Caio Cesar Teodoro Mendes, Gustavo Henrique de Rosa, Shital Shah 2022-10-06 2022-10-06 [("doi","10.48550/arXiv.2210.03251")] ai/nn/sparsity/knowledge-distillation ai/nn/tokenization ai/nn/transformer

Autocomplete is a task where the user inputs a piece of text, termed prompt, which is conditioned by the model to generate semantically coherent continuation. Existing works for this task have primarily focused on datasets (eg. email, chat) with high frequency user prompt patterns (or focused prompts) where word-based language models have been quite effective.

In this work, we study the more challenging setting consisting of low frequency user prompt patterns (or broad prompts, eg. prompt about 93rd academy awards) and demonstrate the effectiveness of character-based Transformer language models.

We study this problem under memory-constrained settings (eg. edge devices and smartphones), where character-based representation is effective in reducing the overall model size (in terms of parameters).

We use WikiText-103 benchmark to simulate broad prompts and demonstrate that character models rival word models in exact match accuracy for the autocomplete task, when controlled for the model size. For instance, we show that a 20M parameter character model performs similar to an 80M parameter word model in the vanilla setting.

We further propose novel methods to improve character models by incorporating inductive bias in the form of compositional information and representation transfer from large word models.

--- https://arxiv.org/abs/2210.01427 Accurate Image Restoration with Attention Retractable Transformer (ART) Jiale Zhang, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, Xin Yuan 2022-10-04 2022-10-04 [("doi","10.48550/arXiv.2210.01427")] ai/nn/transformer/attention/hierarchical

Recently, Transformer-based image restoration networks have achieved promising improvements over convolutional neural networks due to parameter-independent global interactions. To lower computational cost, existing works generally limit self-attention computation within non-overlapping windows. However, each group of tokens are always from a dense area of the image. This is considered as a dense attention strategy since the interactions of tokens are restrained in dense regions. Obviously, this strategy could result in restricted receptive fields.

To address this issue, we propose Attention Retractable Transformer (ART) for image restoration, which presents both dense and sparse attention modules in the network. The sparse attention module allows tokens from sparse areas to interact and thus provides a wider receptive field. Furthermore, the alternating application of dense and sparse attention modules greatly enhances representation ability of Transformer while providing retractable attention on the input image.

We conduct extensive experiments on image super-resolution, denoising, and JPEG compression artifact reduction tasks. Experimental results validate that our proposed ART outperforms state-of-the-art methods on various benchmark datasets both quantitatively and visually.

We also provide code and models at the website Github.

--- https://blog.novelai.net/novelai-improvements-on-stable-diffusion-e10d38db82ac 2022-03-01 ai/anime ai/nn/diffusion --- https://arxiv.org/abs/2210.03629#google ReAct: Synergizing Reasoning and Acting in Language Models Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao 2022-10-06 2022-10-06 [("doi","10.48550/arXiv.2210.03629")] ai/nn/retrieval ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm

[Twitter] While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (eg. chain-of-thought prompting) and acting (eg. action plan generation) have primarily been studied as separate topics.

In this paper, we explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it to interface with external sources [live queries over the public Internet to the Wikipedia API], such as knowledge bases or environments, to gather additional information.

We apply our approach, named ReAct, to a diverse set of language and decision making tasks and demonstrate its effectiveness over state-of-the-art baselines, as well as improved human interpretability and trustworthiness over methods without reasoning or acting components. Concretely, on question answering (HotpotQA) and fact verification (Fever), ReAct overcomes issues of hallucination and error propagation prevalent in chain-of-thought reasoning by interacting with a simple Wikipedia API, and generates human-like task-solving trajectories that are more interpretable than baselines without reasoning traces.

On two interactive decision making benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and reinforcement learning methods by an absolute success rate of 34% and 10% respectively, while being prompted with only one or two in-context examples.

--- https://blog.paulmcdonald.fun/web-prompts-rewrite-the-web-with-gpt-3-7ed4f03b74be 2022-03-01 ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/2204.01697#google MaxViT: Multi-Axis Vision Transformer Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li 2022-04-04 2022-04-04 [("doi","10.48550/arXiv.2204.01697")] ai/nn/gan ai/nn/transformer/attention/hierarchical

Transformers have recently gained attention in the computer vision community. However, the lack of scalability of self-attention mechanisms with respect to image size has limited their wide adoption in state-of-the-art vision backbones. In this paper we introduce an efficient and scalable attention model we call multi-axis attention, which consists of two aspects: blocked local and dilated global attention. These design choices allow global-local spatial interactions on arbitrary input resolutions with only linear complexity.

We also present a new architectural element by effectively blending our proposed attention model with convolutions, and accordingly propose a simple hierarchical vision backbone, dubbed MaxViT, by simply repeating the basic building block over multiple stages. Notably, MaxViT is able to “see” globally throughout the entire network, even in earlier, high-resolution stages.

We demonstrate the effectiveness of our model on a broad spectrum of vision tasks. On image classification, MaxViT achieves state-of-the-art performance under various settings: without extra data, MaxViT attains 86.5% ImageNet-1K top-1 accuracy; with ImageNet-21K pre-training, our model achieves 88.7% top-1 accuracy. For downstream tasks, MaxViT as a backbone delivers favorable performance on object detection as well as visual esthetic assessment. We also show that our proposed model expresses strong generative modeling capability on ImageNet, demonstrating the superior potential of MaxViT blocks as a universal vision module.

The source code and trained models will be available at Github.

--- https://arxiv.org/abs/2210.04435 Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement Learning Xiaoyu Huang, Zhongyu Li, Yanzhen Xiang, Yiming Ni, Yufeng Chi, Yunhao Li, Lizhi Yang, Xue Bin Peng, Koushil Sreenath 2022-10-10 2022-10-10 [("doi","10.48550/arXiv.2210.04435")] reinforcement-learning/model reinforcement-learning/robot

We present a reinforcement learning (RL) framework that enables quadrupedal robots to perform soccer goalkeeping tasks in the real world. Soccer goalkeeping using quadrupeds is a challenging problem, that combines highly dynamic locomotion with precise and fast non-prehensile object (ball) manipulation. The robot needs to react to and intercept a potentially flying ball using dynamic locomotion maneuvers in a very short amount of time, usually less than one second.

In this paper, we propose to address this problem using a hierarchical model-free RL framework. The first component of the framework contains multiple control policies for distinct locomotion skills, which can be used to cover different regions of the goal. Each control policy enables the robot to track random parametric end-effector trajectories while performing one specific locomotion skill, such as jump, dive, and sidestep. These skills are then utilized by the second part of the framework which is a high-level planner to determine a desired skill and end-effector trajectory in order to intercept a ball flying to different regions of the goal.

We deploy the proposed framework on a Mini Cheetah quadrupedal robot and demonstrate the effectiveness of our framework for various agile interceptions of a fast-moving ball in the real world.

--- https://arxiv.org/abs/2106.07631#google HiT: Improved Transformer for High-Resolution GANs Long Zhao, Zizhao Zhang, Ting Chen, Dimitris N. Metaxas, Han Zhang 2021-06-14 2022-03-01 [("doi","10.48550/arXiv.2106.07631")] ai/nn/gan ai/nn/transformer/attention/hierarchical

Attention-based models, exemplified by the Transformer, can effectively model long range dependency, but suffer from the quadratic complexity of self-attention operation, making them difficult to be adopted for high-resolution image generation based on Generative Adversarial Networks (GANs).

In this paper, we introduce two key ingredients to Transformer to address this challenge. First, in low-resolution stages of the generative process, standard global self-attention is replaced with the proposed multi-axis blocked self-attention which allows efficient mixing of local and global attention. Second, in high-resolution stages, we drop self-attention while only keeping multi-layer perceptrons reminiscent of the implicit neural function. To further improve the performance, we introduce an additional self-modulation component based on cross-attention. The resulting model, denoted as HiT, has a nearly linear computational complexity with respect to the image size and thus directly scales to synthesizing high definition images.

We show in the experiments that the proposed HiT achieves state-of-the-art FID scores of 30.83 and 2.95 on unconditional ImageNet 128×128 and FFHQ 256×256, respectively, with a reasonable throughput. We believe the proposed HiT is an important milestone for generators in GANs which are completely free of convolutions.

Our code is made publicly available at https://github.com/google-research/hit-gan.

--- https://arxiv.org/abs/2209.11178 PFGM: Poisson Flow Generative Models Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola 2022-09-22 2022-09-22 [("doi","10.48550/arXiv.2209.11178")] ai/nn/diffusion

We propose a new “Poisson flow” generative model (PFGM) that maps a uniform distribution on a high-dimensional hemisphere into any data distribution.

We interpret the data points as electrical charges on the z = 0 hyperplane in a space augmented with an additional dimension z, generating a high-dimensional electric field (the gradient of the solution to the Poisson equation). We prove that if these charges flow upward along electric field lines, their initial distribution in the z = 0 plane transforms into a distribution on the hemisphere of radius r that becomes uniform in the r → ∞ limit.

To learn the bijective transformation, we estimate the normalized field in the augmented space. For sampling, we devise a backward ODE that is anchored by the physically meaningful additional dimension: the samples hit the unaugmented data manifold when the z reaches zero.

Experimentally, PFGM achieves current state-of-the-art performance among the normalizing flow models on CIFAR-10, with an IS of 9.68 and a FID score of 2.48. It also performs on par with the state-of-the-art SDE approaches while offering 10× to 20× acceleration on image generation tasks. Additionally, PFGM appears more tolerant of estimation errors on a weaker network architecture and robust to the step size in the Euler method.

The code is available at Github.

--- https://en.wikipedia.org/wiki/Ancient_DNA Ancient DNA 2022-03-02 genetics/sequencing --- https://en.wikipedia.org/wiki/Environmental_DNA#Sedimentary_ancient_DNA Environmental DNA § Sedimentary ancient DNA 2022-03-02 genetics/sequencing --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7864908/ Hybridisation capture allows DNA damage analysis of ancient marine eukaryotes L Armbrecht, G. Hallegraeff, C. J. S. Bolch, C. Woodward, A. Cooper 2021 2022-03-02 [("doi","10.1038/s41598-021-82578-6")] genetics/sequencing

Marine sedimentary ancient DNA (sedaDNA) is increasingly used to study past ocean ecosystems, however, studies have been severely limited by the very low amounts of DNA preserved in the subseafloor, and the lack of bioinformatic tools to authenticate sedaDNA in metagenomic data.

We applied a hybridisation capture ‘baits’ technique to target marine eukaryote sedaDNA (specifically, phytoplankton and zooplankton, ‘Planktonbaits1’; and harmful algal bloom taxa, ‘HABbaits1’), which resulted in up to 4× & 9× increases, respectively, in the relative abundance of eukaryotes compared to shotgun sequencing. We further used the bioinformatic tool ‘HOPS’ to authenticate the sedaDNA component, establishing a new proxy to assess sedaDNA authenticity, “% eukaryote sedaDNA damage”, that is positively correlated with subseafloor depth.

We used this proxy to report the first-ever DNA damage profiles from a marine phytoplankton species, the ubiquitous coccolithophore Emiliania huxleyi.

Our approach opens new avenues for the detailed investigation of long-term change and evolution of marine eukaryotes over geological timescales.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6776040/ The potential of sedimentary ancient DNA for reconstructing past sea ice evolution Stijn De Schepper, Jessica L. Ray, Katrine Sandnes Skaar, Henrik Sadatzki, Umer Z. Ijaz, Ruediger Stein, Aud Larsen 2019 2022-03-02 [("doi","10.1038/s41396-019-0457-1")] genetics/sequencing

Sea ice is a crucial component of the Arctic climate system, yet the tools to document the evolution of sea ice conditions on historical and geological time scales are few and have limitations. Such records are essential for documenting and understanding the natural variations in Arctic sea ice extent.

Here we explore sedimentary ancient DNA (aDNA), as a novel tool that unlocks and exploits the genetic (eukaryote) biodiversity preserved in marine sediments specifically for past sea ice reconstructions. Although use of sedimentary aDNA in paleoceanographic and paleoclimatic studies is still in its infancy, we use here metabarcoding and single-species quantitative DNA detection methods to document the sea ice conditions in a Greenland Sea marine sediment core. Metabarcoding has allowed identifying biodiversity changes in the geological record back to almost ~100,000 years ago that were related to changing sea ice conditions.

Detailed bioinformatic analyses on the metabarcoding data revealed several sea-ice-associated taxa, most of which previously unknown from the fossil record. Finally, we quantitatively traced one known sea ice dinoflagellate in the sediment core. We show that aDNA can be recovered from deep-ocean sediments with generally oxic bottom waters and that past sea ice conditions can be documented beyond instrumental time scales.

Our results corroborate sea ice reconstructions made by traditional tools, and thus demonstrate the potential of sedimentary aDNA, focusing primarily on microbial eukaryotes, as a new tool to better understand sea ice evolution in the climate system.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7492196/ Planktonic foraminifera genomic variations reflect paleoceanographic changes in the Arctic: evidence from sedimentary ancient DNA Joanna Pawłowska, Jutta E. Wollenburg, Marek Zajączkowski, Jan Pawlowski 2020 2022-03-02 [("doi","10.1038/s41598-020-72146-9")] genetics/sequencing

Deciphering the evolution of marine plankton is typically based on the study of microfossil groups. Cryptic speciation is common in these groups, and large intragenomic variations occur in ribosomal RNA genes of many morphospecies.

In this study, we correlated the distribution of ribosomal amplicon sequence variants (ASVs) with paleoceanographic changes by analyzing the high-throughput sequence data assigned to Neogloboquadrina pachyderma in a 140,000-year-old sediment core from the Arctic Ocean.

The sedimentary ancient DNA demonstrated the occurrence of various N. pachyderma ASVs whose occurrence and dominance varied through time. Most remarkable was the striking appearance of ASV18, which was nearly absent in older sediments but became dominant during the last glacial maximum and continues to persist today.

Although the molecular ecology of planktonic foraminifera is still poorly known, the analysis of their intragenomic variations through time has the potential to provide new insight into the evolution of marine biodiversity and may lead to the development of new and important paleoceanographic proxies.

--- https://podcast.ai/ 2022-03-02 ai/nn/transformer/gpt/fiction --- https://replicate.com/methexis-inc/img2prompt 2022-03-02 ai/nn/diffusion ai/nn/transformer/clip --- /doc/ai/nn/rnn/1992-schmidhuber.pdf Learning Complex, Extended Sequences Using the Principle of History Compression Juergen Schmidhuber 1992-01-01 2022-03-02 [("doi","10.1162/neco.1992.4.2.234")] ai/nn/rnn ai/nn/sparsity/knowledge-distillation

Previous neural network learning algorithms for sequence processing are computationally expensive and perform poorly when it comes to long time lags. This paper first introduces a simple principle for reducing the descriptions of event sequences without loss of information. A consequence of this principle is that only unexpected inputs can be relevant. This insight leads to the construction of neural architectures that learn to “divide and conquer” by recursively decomposing sequences.

I describe two architectures. The first functions as a self-organizing multilevel hierarchy of recurrent networks. The second, involving only two recurrent networks, tries to collapse a multilevel predictor hierarchy into a single recurrent net.

Experiments show that the system can require less computation per time step and many fewer training sequences than conventional training algorithms for recurrent nets.

--- https://www.reddit.com/r/StableDiffusion/comments/y0g98j/thanks_to_sd_i_was_able_to_quickly_prototype_a/ 2022-03-02 ai/nn/transformer/clip/sample --- https://www.biorxiv.org/content/10.1101/203844.full A Major Role for Common Genetic Variation in Anxiety Disorders Kirstin L. Purves, Jonathan R. I. Coleman, Sandra M. Meier, Christopher Rayner, Katrina A. S. Davis, Rosa Cheesman, Marie Bækvad-Hansen, Anders Børglum, Shing Wan Cho, Jürgen Deckert, Héléna A. Gaspar, Jonas Bybjerg-Grauholm, John M. Hettema, Matthew Hotopf, David Hougaard, Christopher Hübel, Carol Kan, Andrew M. McIntosh, Ole Mors, Preben Bo Mortensen, Merete Nordentoft, Thomas Werge, Kristin K. Nicodemus, Manuel Mattheisen, Gerome Breen, Thalia C. Eley 2019-04-11 2022-03-02 [("doi","10.1101/203844")] genetics/heritable psychiatry/anxiety

Anxiety disorders are common, complex psychiatric disorders with twin heritabilities of 30–60%.

We conducted a genome-wide association study of Lifetime Anxiety Disorder (n = 83,565) and an additional Current Anxiety Symptoms (n = 77,125) analysis. The liability scale common variant heritability estimate for Lifetime Anxiety Disorder was 26%, and for Current Anxiety Symptoms was 31%.

5 novel genome-wide statistically-significant loci were identified including an intergenic region on chromosome 9 that has previously been associated with neuroticism, and a locus overlapping the BDNF receptor gene, NTRK2. Anxiety showed statistically-significant genetic correlations with depression and insomnia as well as coronary artery disease, mirroring findings from epidemiological studies.

We conclude that common genetic variation accounts for a substantive proportion of the genetic architecture underlying anxiety.

--- /doc/genetics/heritable/correlation/2019-watson.pdf Genome-wide association study identifies 8 risk loci and implicates metabo-psychiatric origins for anorexia nervosa Hunna J. Watson, Zeynep Yilmaz, Laura M. Thornton, Christopher Hübel, Jonathan R. I. Coleman, Héléna A. Gaspar, Julien Bryois, Anke Hinney, Virpi M. Leppä, Manuel Mattheisen, Sarah E. Medland, Stephan Ripke, Shuyang Yao, Paola Giusti-Rodríguez, Anorexia Nervosa Genetics Initiative, Ken B. Hanscombe, Kirstin L. Purves, Eating Disorders Working Group of the Psychiatric Genomics Consortium, Roger A. H. Adan, Lars Alfredsson, Tetsuya Ando, Ole A. Andreassen, Jessica H. Baker, Wade H. Berrettini, Ilka Boehm, Claudette Boni, Vesna Boraska Perica, Katharina Buehren, Roland Burghardt, Matteo Cassina, Sven Cichon, Maurizio Clementi, Roger D. Cone, Philippe Courtet, Scott Crow, James J. Crowley, Unna N. Danner, Oliver S. P. Davis, Martina de Zwaan, George Dedoussis, Daniela Degortes, Janiece E. DeSocio, Danielle M. Dick, Dimitris Dikeos, Christian Dina, Monika Dmitrzak-Weglarz, Elisa Docampo, Laramie E. Duncan, Karin Egberts, Stefan Ehrlich, Geòrgia Escaramís, Tõnu Esko, Xavier Estivill, Anne Farmer, Angela Favaro, Fernando Fernández-Aranda, Manfred M. Fichter, Krista Fischer, Manuel Föcker, Lenka Foretova, Andreas J. Forstner, Monica Forzan, Christopher S. Franklin, Steven Gallinger, Ina Giegling, Johanna Giuranna, Fragiskos Gonidakis, Philip Gorwood, Monica Gratacos Mayora, Sébastien Guillaume, Yiran Guo, Hakon Hakonarson, Konstantinos Hatzikotoulas, Joanna Hauser, Johannes Hebebrand, Sietske G. Helder, Stefan Herms, Beate Herpertz-Dahlmann, Wolfgang Herzog, Laura M. Huckins, James I. Hudson, Hartmut Imgart, Hidetoshi Inoko, Vladimir Janout, Susana Jiménez-Murcia, Antonio Julià, Gursharan Kalsi, Deborah Kaminská, Jaakko Kaprio, Leila Karhunen, Andreas Karwautz, Martien J. H. Kas, James L. Kennedy, Anna Keski-Rahkonen, Kirsty Kiezebrink, Youl-Ri Kim, Lars Klareskog, Kelly L. Klump, Gun Peggy S. Knudsen, Maria C. La Via, Stephanie Le Hellard, Robert D. Levitan, Dong Li, Lisa Lilenfeld, Bochao Danae Lin, Jolanta Lissowska, Jurjen Luykx, Pierre J. Magistretti, Mario Maj, Katrin Mannik, Sara Marsal, Christian R. Marshall, Morten Mattingsdal, Sara McDevitt, Peter McGuffin, Andres Metspalu, Ingrid Meulenbelt, Nadia Micali, Karen Mitchell, Alessio Maria Monteleone, Palmiero Monteleone, Melissa A. Munn-Chernoff, Benedetta Nacmias, Marie Navratilova, Ioanna Ntalla, Julie K. O’Toole, Roel A. Ophoff, Leonid Padyukov, Aarno Palotie, Jacques Pantel, Hana Papezova, Dalila Pinto, Raquel Rabionet, Anu Raevuori, Nicolas Ramoz, Ted Reichborn-Kjennerud, Valdo Ricca, Samuli Ripatti, Franziska Ritschel, Marion Roberts, Alessandro Rotondo, Dan Rujescu, Filip Rybakowski, Paolo Santonastaso, André Scherag, Stephen W. Scherer, Ulrike Schmidt, Nicholas J. Schork, Alexandra Schosser, Jochen Seitz, Lenka Slachtova, P. Eline Slagboom, Margarita C. T. Slof-Op ‘t Landt, Agnieszka Slopien, Sandro Sorbi, Beata Świątkowska, Jin P. Szatkiewicz, Ioanna Tachmazidou, Elena Tenconi, Alfonso Tortorella, Federica Tozzi, Janet Treasure, Artemis Tsitsika, Marta Tyszkiewicz-Nwafor, Konstantinos Tziouvas, Annemarie A. van Elburg, Eric F. van Furth, Gudrun Wagner, Esther Walton, Elisabeth Widen, Eleftheria Zeggini, Stephanie Zerwas, Stephan Zipfel, Andrew W. Bergen, Joseph M. Boden, Harry Brandt, Steven Crawford, Katherine A. Halmi, L. John Horwood, Craig Johnson, Allan S. Kaplan, Walter H. Kaye, James E. Mitchell, Catherine M. Olsen, John F. Pearson, Nancy L. Pedersen, Michael Strober, Thomas Werge, David C. Whiteman, D. Blake Woodside, Garret D. Stuber, Scott D. Gordon, Jakob Grove, Anjali K. Henders, Anders Juréus, Katherine M. Kirk, Janne T. Larsen, Richard Parker, Liselotte Petersen, Jennifer Jordan, Martin Kennedy, Grant W. Montgomery, Tracey D. Wade, Andreas Birgegård, Paul Lichtenstein, Claes Norring, Mikael Landén, Nicholas G. Martin, Preben Bo Mortensen, Patrick F. Sullivan, Gerome Breen, Cynthia M. Bulik 2019-01-01 2022-03-02 [("doi","10.1038/s41588-019-0439-2")] genetics/heritable/correlation psychiatry/anorexia --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4596916/ LDpred: Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores Bjarni J. Vilhjálmsson, Jian Yang, Hilary K. Finucane, Alexander Gusev, Sara Lindström, Stephan Ripke, Giulio Genovese, Po-Ru Loh, Gaurav Bhatia, Ron Do, Tristan Hayeck, Hong-Hee Won, Sekar Kathiresan, Michele Pato, Carlos Pato, Rulla Tamimi, Eli Ayumi Stahl, Noah Zaitlen, Bogdan Pasaniuc, Gillian Belbin, Eimear E. Kenny, Mikkel H. Schierup, Philip De Jager, Nikolaos A. Patsopoulos, Steve McCarroll, Mark Daly, Shaun Purcell, Daniel Chasman, Benjamin M. Neale, Michael Goddard, Peter M. Visscher, Peter Kraft, Nick Patterson, Alkes Price 2015 2022-03-03 [("doi","10.1016/j.ajhg.2015.09.001")] genetics/heritable statistics/bayes

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p-value threshold to association statistics, but this discards information and can reduce predictive accuracy.

We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes.

Accordingly, predicted R2 increased 20.1% → 25.3% in a large schizophrenia dataset and 9.8% → 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for 3 additional large disease datasets and for non-European schizophrenia samples.

The advantage of LDpred over existing methods will grow as sample sizes increase.

--- https://en.wikipedia.org/wiki/Methylphenidate Methylphenidate 2022-03-03 psychiatry/adhd --- https://en.wikipedia.org/wiki/Dexmethylphenidate Dexmethylphenidate 2022-03-03 psychiatry/adhd --- https://en.wikipedia.org/wiki/Amphetamine Amphetamine 2022-03-03 psychiatry/adhd --- https://en.wikipedia.org/wiki/Adderall Adderall 2022-03-03 psychiatry/adhd --- https://en.wikipedia.org/wiki/Dextroamphetamine Dextroamphetamine 2022-03-03 psychiatry/adhd --- https://en.wikipedia.org/wiki/Lisdexamfetamine Lisdexamfetamine 2022-03-03 psychiatry/adhd --- https://en.wikipedia.org/wiki/Methamphetamine Methamphetamine 2022-03-03 psychiatry/adhd --- https://en.wikipedia.org/wiki/Attention_deficit_hyperactivity_disorder Attention deficit hyperactivity disorder 2022-03-03 psychiatry/adhd --- https://en.wikipedia.org/wiki/Attention Inattention 2022-03-03 psychiatry/adhd --- https://slatestarcodex.com/2018/08/14/ssc-survey-results-adhd-and-rejection-sensitivity/ 2022-03-03 psychiatry/adhd --- https://slatestarcodex.com/2017/12/28/adderall-risks-much-more-than-you-wanted-to-know/ 2022-03-04 psychiatry/adhd --- https://www.astralcodexten.com/p/study-ritalin-works-but-school-isnt 2022-03-04 psychiatry/adhd --- https://www.astralcodexten.com/p/know-your-amphetamines 2022-03-04 psychiatry/adhd --- https://www.astralcodexten.com/p/lavenders-game-silexan-for-anxiety 2022-03-04 nootropic psychiatry/anxiety/lavender --- https://en.wikipedia.org/wiki/Executive_functions Executive functions 2022-03-04 psychiatry/adhd --- /doc/modafinil/2020-flavell.pdf Modafinil-induced psychosis in a patient with attention deficit hyperactivity disorder Joshua Flavell 2020-07-26 2022-03-04 [("doi","10.1177/1039856220936630")] modafinil psychiatry/adhd

Modafinil is a wakefulness-promoting agent that is known to be used off-label as a cognitive enhancer and for the treatment of attention deficit hyperactivity disorder (ADHD).

There are increasing case reports of modafinil-induced psychosis; however, this is the first to report a patient with ADHD to develop psychosis from modafinil use.

--- https://www.nature.com/articles/s41380-018-0109-2 2022-03-04 psychiatry/adhd psychology/personality/psychopathy --- https://www.nature.com/articles/s41380-018-0070-0 2022-03-04 psychiatry/adhd --- /doc/modafinil/2018-maier.pdf Pharmacological cognitive enhancement among non-ADHD individuals: A cross-sectional study in 15 countries Larissa J. Maier, Jason A. Ferris, Adam R. Winstock 2018-01-01 2022-03-04 [("doi","10.1016/j.drugpo.2018.05.009")] modafinil psychiatry/adhd --- /doc/genetics/heritable/2016-chen.pdf Familial aggregation of attention-deficit/hyperactivity disorder Qi Chen, Isabell Brikell, Paul Lichtenstein, Eva Serlachius, Ralf Kuja-Halkola, Sven Sandin, Henrik Larsson 2016-01-01 2022-03-05 genetics/heritable psychiatry/adhd --- https://www.nature.com/articles/tp20155 The relative contribution of common and rare genetic variants to ADHD Martin 2015 2022-03-05 genetics/heritable/rare psychiatry/adhd --- https://www.sciencedirect.com/science/article/pii/S0890856715000404 Shared Genetic Influences Between Attention-Deficit/Hyperactivity Disorder (ADHD) Traits in Children and Clinical ADHD Stergiakouli 2015 2022-03-05 genetics/heritable psychiatry/adhd --- /doc/dual-n-back/2013-rapport.pdf Do programs designed to train working memory, other executive functions, and attention benefit children with ADHD? A meta-analytic review of cognitive, academic, and behavioral outcomes Mark D. Rapport, Sarah A. Orban, Michael J. Kofler, Lauren M. Friedman 2013-01-01 2022-03-05 [("doi","10.1016/j.cpr.2013.08.005")] dual-n-back psychiatry/adhd --- /doc/modafinil/2013-bagot.pdf Efficacy of stimulants for cognitive enhancement in non-attention deficit hyperactivity disorder youth: a systematic review 2013-01-01 2022-03-05 modafinil psychiatry/adhd --- http://www.lansi-turku.net/sites/lansi-turku.net/files/Walk_in_the_Park-1.pdf Children with attention deficits concentrate better after walk in the park 2022-03-05 psychiatry/adhd psychology/nature --- https://web.archive.org/web/20140802145156/https://www.bioworld.com/content/another-miss-targacept-tc-5619-fails-adhd-trial-0 Another Miss for Targacept; TC-5619 Fails in ADHD Trial 2022-03-05 nicotine psychiatry/adhd --- http://www.tweelingenregister.org/nederlands/verslaggeving/NTR-publicaties_2014/Zeeuw_AJMG_2014.pdf Polygenic scores associated with educational attainment in adults predict educational achievement and ADHD symptoms in children 2022-03-05 genetics/heritable/correlation psychiatry/adhd --- https://www.psychiatryinvestigation.org/journal/view.php?number=865 Modafinil Dependence: A Case with Attention-Deficit/Hyperactivity Disorder 2022-03-05 modafinil psychiatry/adhd --- /doc/modafinil/2005-biederman.pdf Efficacy and Safety of Modafinil Film-Coated Tablets in Children and Adolescents With Attention-Deficit/Hyperactivity Disorder: Results of a Randomized, Double-Blind, Placebo-Controlled, Flexible-Dose Study 2005 2022-03-05 modafinil psychiatry/adhd --- /doc/nicotine/2011-bidwell-adhd.pdf Cognitive enhancers for the treatment of ADHD L. Cinnamon Bidwell, F. Joseph McClernon, Scott H. Kollins 2011-01-01 2022-03-05 [("doi","10.1016/j.pbb.2011.05.002")] nicotine psychiatry/adhd --- /doc/psychology/nature/2011-taylor.pdf Could Exposure to Everyday Green Spaces Help Treat ADHD? Evidence from Children’s Play Settings 2011 2022-03-06 psychiatry/adhd psychology/nature --- /doc/dual-n-back/2009-holmes.pdf Working Memory Deficits can be Overcome: Impacts Training and Medication on Working Memory in Children with ADHD 2009 2022-03-06 dual-n-back psychiatry/adhd --- https://ajph.aphapublications.org/cgi/content/abstract/94/9/1580 A Potential Natural Treatment for Attention-Deficit/Hyperactivity Disorder: Evidence From a National Study Kuo, Taylor 2004 2022-03-06 psychiatry/adhd --- /doc/nicotine/1996-conners.pdf Nicotine and attention in adult attention deficit hyperactivity disorder (ADHD) 1996 2022-03-06 nicotine psychiatry/adhd --- /doc/nicotine/1996-levin.pdf Nicotine effects on adults with attention-deficit/hyperactivity disorder 1996 2022-03-06 nicotine psychiatry/adhd --- https://psychcentral.com/pro/sparlon-and-adhd-the-power-of-a-7-year-old/002889.html Sparlon and ADHD: The Power of a 7-Year Old 2022-03-06 psychiatry/adhd --- https://www.psypost.org/2022/04/study-suggests-maladaptive-daydreaming-should-be-classified-as-a-unique-mental-disorder-distinct-from-adhd-63025 2022-03-06 psychiatry/adhd --- https://en.wikipedia.org/wiki/Maladaptive_daydreaming Maladaptive daydreaming 2022-03-06 psychiatry/adhd --- https://arxiv.org/abs/2209.06899 Out of One, Many: Using Language Models to Simulate Human Samples Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, Joshua Gubler, Christopher Rytting, David Wingate 2022-09-14 2022-09-14 [("doi","10.48550/arXiv.2209.06899")] ai/nn/sampling ai/nn/transformer/gpt/3/nonfiction psychology sociology

We propose and explore the possibility that language models can be studied as effective proxies for specific human sub-populations in social science research. Practical and research applications of artificial intelligence tools have sometimes been limited by problematic biases (such as racism or sexism), which are often treated as uniform properties of the models.

We show that the “algorithmic bias” within one such tool—the GPT-3 language model—is instead both fine-grained and demographically correlated, meaning that proper conditioning will cause it to accurately emulate response distributions from a wide variety of human subgroups. We term this property “algorithmic fidelity” and explore its extent in GPT-3.

We create “silicon samples” by conditioning the model on thousands of socio-demographic backstories from real human participants in multiple large surveys conducted in the United States. We then compare the silicon and human samples to demonstrate that the information contained in GPT-3 goes far beyond surface similarity. It is nuanced, multifaceted, and reflects the complex interplay between ideas, attitudes, and socio-cultural context that characterize human attitudes.

We suggest that language models with sufficient algorithmic fidelity thus constitute a novel and powerful tool to advance understanding of humans and society across a variety of disciplines.

--- https://arxiv.org/abs/2210.05492#facebook Diplodocus: Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning Anton Bakhtin, David J. Wu, Adam Lerer, Jonathan Gray, Athul Paul Jacob, Gabriele Farina, Alexander H. Miller, Noam Brown 2022-10-11 2022-10-11 [("doi","10.48550/arXiv.2210.05492")] reinforcement-learning/imperfect-information/diplomacy

[expert player commentary] No-press Diplomacy is a complex strategy game involving both cooperation and competition that has served as a benchmark for multi-agent AI research. While self-play reinforcement learning has resulted in numerous successes in purely adversarial games like chess, Go, and poker, self-play alone is insufficient for achieving optimal performance in domains involving cooperation with humans.

We address this shortcoming by first introducing a planning algorithm we call DiL-piKL that regularizes a reward-maximizing policy toward a human imitation-learned policy. We prove that this is a no-regret learning algorithm under a modified utility function. We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human play while simultaneously training an agent that responds well to this human model. We used RL-DiL-piKL to train an agent we name Diplodocus.

In a 200-game no-press Diplomacy tournament involving 62 human participants spanning skill levels from beginner to expert, two Diplodocus agents both achieved a higher average score than all other participants who played more than two games, and ranked first and third according to an Elo ratings model.

--- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.422.4232&rep=rep1&type=pdf 2022-03-06 psychiatry/autism --- /doc/genetics/heritable/correlation/2016-robinson.pdf Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population Elise B. Robinson, Beate St Pourcain, Verneri Anttila, Jack A. Kosmicki, Brendan Bulik-Sullivan, Jakob Grove, Julian Maller, Kaitlin E. Samocha, Stephan J. Sanders, Stephan Ripke, Joanna Martin, Mads V. Hollegaard, Thomas Werge, David Hougaard, Benjamin M. Neale, David M. Evans, David Skuse, Preben Bo Mortensen, Anders Børglum, Angelica Ronald, George Davey Smith, Mark J. Daly 2016-01-01 2022-03-07 [("doi","10.1038/ng.3529")] genetics/heritable/correlation psychiatry/autism --- https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2519364 2022-03-07 psychiatry/autism --- /doc/genetics/heritable/correlation/2013-pettersson.pdf 2013 2022-03-07 genetics/heritable/correlation psychiatry/autism --- https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00300/full 2022-03-07 psychiatry/autism --- https://en.wikipedia.org/wiki/Autism_spectrum Autism spectrum 2022-03-07 psychiatry/autism --- https://en.wikipedia.org/wiki/Empathizing-systemizing_theory Empathizing-systemizing theory 2022-03-07 psychiatry/autism --- https://en.wikipedia.org/wiki/Simon_Baron-Cohen Simon Baron-Cohen 2022-03-07 psychiatry/autism --- https://en.wikipedia.org/wiki/Causes_of_autism Causes of autism 2022-03-07 psychiatry/autism --- https://en.wikipedia.org/wiki/Heritability_of_autism Heritability of autism 2022-03-07 psychiatry/autism --- https://en.wikipedia.org/wiki/Epidemiology_of_autism Epidemiology of autism 2022-03-07 psychiatry/autism --- https://en.wikipedia.org/wiki/Social_anxiety Social anxiety 2022-03-08 psychiatry/autism --- https://en.wikipedia.org/wiki/Asperger_syndrome Asperger syndrome 2022-03-08 psychiatry/autism --- https://arxiv.org/abs/2210.05657 The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes Peter Kocsis, Peter Súkeník, Guillem Brasó, Matthias Nießner, Laura Leal-Taixé, Ismail Elezi 2022-10-11 2022-10-11 [("doi","10.48550/arXiv.2210.05657")] ai/nn/cnn ai/nn/fully-connected ai/nn/sparsity/knowledge-distillation reinforcement-learning/exploration/active-learning

Convolutional neural networks were the standard for solving many computer vision tasks until recently, when Transformers of MLP-based architectures have started to show competitive performance. These architectures typically have a vast number of weights and need to be trained on massive datasets; hence, they are not suitable for their use in low-data regimes.

In this work, we propose a simple yet effective framework to improve generalization from small amounts of data. We augment modern CNNs with fully-connected (FC) layers and show the massive impact this architectural change has in low-data regimes. We further present an online joint knowledge-distillation method to utilize the extra FC layers at train time but avoid them during test time. This allows us to improve the generalization of a CNN-based model without any increase in the number of weights at test time.

We perform classification experiments for a large range of network backbones and several standard datasets on supervised learning and active learning. Our experiments outperform the networks without fully-connected layers, reaching a relative improvement of up to 16% validation accuracy in the supervised setting without adding any extra parameters during inference.

--- /doc/psychiatry/adhd/2021-ghirardi.pdf Neurodevelopmental disorders and subsequent risk of violent victimization: exploring sex differences and mechanisms Laura Ghirardi, Ralf Kuja-Halkola, Erik Pettersson, Amir Sariaslan, Louise Arseneault, Seena Fazel, Brian M. D’Onofrio, Paul Lichtenstein, Henrik Larsson 2021-09-01 2022-03-08 [("doi","10.1017/S0033291721003093")] crime psychiatry/adhd psychiatry/autism

Background: Neurodevelopmental disorders (NDs) are associated with experiences of victimization, but mechanisms remain unclear. We explored sex differences and the role of familial factors and externalizing problems in the association between several NDs and violent victimization in adolescence and young adulthood.

Method: Individuals born in Sweden 1985–1997, residing in Sweden at their 15th birthday, were followed until date of violent victimization causing a hospital visit or death, death due to other causes, emigration, or December 31, 2013, whichever came first. The exposures were diagnoses of attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), intellectual disability (ID) and other NDs. We used 3 different Cox regression models: a crude model, a model adjusted for familial confounding using sibling-comparisons, and a model additionally adjusted for externalizing problems.

Results: Among 1,344,944 individuals followed, on average, for 5 years, 74,487 were diagnosed with NDs and 37,765 had a hospital visit or died due to violence. ADHD was associated with an increased risk of violent victimization in males [hazard ratio (HR) 2.56; 95% CI 2.43–2.70] and females (HR 5.39; 95% CI 4.97–5.85). ASD and ID were associated with an increased risk of violent victimization in females only. After adjusting for familial factors and externalizing problems, only ADHD was associated with violent victimization among males (HR 1.27; 95% CI 1.06–1.51) and females (HR 1.69; 95% CI 1.21–2.36).

Conclusion: Females with NDs and males with ADHD are at greater risk of being victim of severe violence during adolescence and young adulthood. Relevant mechanisms include shared familial liability and externalizing problems. ADHD may be independently associated with violent victimization.

--- https://en.wikipedia.org/wiki/Autism-spectrum_quotient Autism-spectrum quotient 2022-03-08 psychiatry/autism --- /doc/psychiatry/autism/1990-hermelin.pdf Factors and primes: a specific numerical ability B. Hermelin, N. O’Connor 1990-02-01 2022-03-08 [("doi","10.1017/S0033291700013349")] math psychiatry/autism psychology/neuroscience/memory/savant

An autistic young man and a normal control were asked to factorize numbers and to recognize and generate primes.

Both subjects made a similar of errors and employed similar strategies, but they differed markedly in the speeds at which the arithmetical operations were carried out.

--- https://www.nature.com/articles/529449a Monkeys genetically modified to show autism symptoms: But it is unclear how well the results match the condition in humans 2022-03-08 genetics/heritable/rare psychiatry/autism psychology/neuroscience --- https://www.nature.com/articles/488439a Fathers bequeath more mutations as they age: Genome study may explain links between paternal age and conditions such as autism 2022-03-08 genetics/heritable/rare psychiatry/autism --- /doc/genetics/heritable/rare/2019-zhou-2.pdf Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk Jian Zhou, Christopher Y. Park, Chandra L. Theesfeld, Aaron K. Wong, Yuan Yuan, Claudia Scheckel, John J. Fak, Julien Funk, Kevin Yao, Yoko Tajima, Alan Packer, Robert B. Darnell, Olga G. Troyanskaya 2019-01-01 2022-03-08 [("doi","10.1038/s41588-019-0420-0")] genetics/heritable/rare psychiatry/autism --- /doc/genetics/heritable/rare/2018-werling.pdf An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder Donna M. Werling, Harrison Brand, Joon-Yong An, Matthew R. Stone, Lingxue Zhu, Joseph T. Glessner, Ryan L. Collins, Shan Dong, Ryan M. Layer, Eirene Markenscoff-Papadimitriou, Andrew Farrell, Grace B. Schwartz, Harold Z. Wang, Benjamin B. Currall, Xuefang Zhao, Jeanselle Dea, Clif Duhn, Carolyn A. Erdman, Michael C. Gilson, Rachita Yadav, Robert E. Handsaker, Seva Kashin, Lambertus Klei, Jeffrey D. Mandell, Tomasz J. Nowakowski, Yuwen Liu, Sirisha Pochareddy, Louw Smith, Michael F. Walker, Matthew J. Waterman, Xin He, Arnold R. Kriegstein, John L. Rubenstein, Nenad Sestan, Steven A. McCarroll, Benjamin M. Neale, Hilary Coon, A. Jeremy Willsey, Joseph D. Buxbaum, Mark J. Daly, Matthew W. State, Aaron R. Quinlan, Gabor T. Marth, Kathryn Roeder, Bernie Devlin, Michael E. Talkowski, Stephan J. Sanders 2018-01-01 2022-03-08 [("doi","10.1038/s41588-018-0107-y")] genetics/heritable/rare psychiatry/autism --- /doc/genetics/heritable/rare/2015-yuen.pdf Whole-genome sequencing of quartet families with autism spectrum disorder 2015 2022-03-08 genetics/heritable/rare psychiatry/autism --- https://en.wikipedia.org/wiki/Paternal_age_effect Paternal age effect 2022-03-09 psychiatry/autism --- https://scholars-stage.org/longfellow-and-the-decline-of-american-poetry/ 2022-03-09 fiction/poetry --- https://scholars-stage.org/the-fall-of-history-as-a-major-and-as-a-part-of-the-humanities/ 2022-03-09 fiction/poetry history --- https://www.biorxiv.org/content/10.1101/2022.10.09.511476.full Influences of rare protein-coding genetic variants on the human plasma proteome in 50,829 UK Biobank participants Ryan S. Dhindsa, Oliver S. Burren, Benjamin B. Sun, Bram P. Prins, Dorota Matelska, Eleanor Wheeler, Jonathan Mitchell, Erin Oerton, Ventzislava A. Hristova, Katherine R. Smith, Keren Carss, Sebastian Wasilewski, Andrew R. Harper, Dirk S. Paul, Margarete A. Fabre, Heiko Runz, Coralie Viollet, Benjamin Challis, Adam Platt, AstraZeneca Genomics Initiative, Dimitrios Vitsios, Euan A. Ashley, Christopher D. Whelan, Menelas N. Pangalos, Quanli Wang, Slavé Petrovski 2022-10-11 2022-10-11 [("doi","10.1101/2022.10.09.511476")] genetics/heritable/rare

Combining human genomics with proteomics is becoming a powerful tool for drug discovery. Associations between genetic variants and protein levels can uncover disease mechanisms, clinical biomarkers, and candidate drug targets. To date, most population-level proteogenomic studies have focused on common alleles through genome-wide association studies (GWAS).

Here, we studied the contribution of rare protein-coding variants to 1,472 plasma proteins abundances measured via the Olink Explore 1536 assay in 50,829 UK Biobank human exomes. Through a variant-level exome-wide association study (ExWAS), we identified 3,674 rare and significant protein quantitative trait loci (pQTLs), of which 76% were undetected in a prior GWAS performed on the same cohort, and we found that rare pQTLs are less likely to be random in their variant effect annotation. In gene-based collapsing analyses, we identified an additional 166 significant gene-protein pQTL signals that were undetected through single-variant analyses.

Of the total 456 protein-truncating variant (PTV)-driven cis-pQTLs in the gene-based collapsing analysis, 99.3% were associated with decreased protein levels. We demonstrate how this resource can identify allelic series and propose biomarkers for several candidate therapeutic targets, including GRN, HSD17B13, NLRC4, and others.

Finally, we introduce a new collapsing analysis framework that combines PTVs with missense cis-pQTLs that are associated with decreased protein abundance to bolster genetic discovery statistical power. Our results collectively highlight a considerable role for rare variation in plasma protein abundance and demonstrate the utility of plasma proteomics in gene discovery and unraveling mechanisms of action.

--- https://www.biorxiv.org/content/10.1101/2022.10.10.511629.full Inferring disease architecture and predictive ability with LDpred2-auto Florian Privé, Clara Albiñana, Bogdan Pasaniuc, Bjarni J. Vilhjálmsson 2022-10-12 2022-10-12 [("doi","10.1101/2022.10.10.511629")] genetics/heritable/rare

LDpred2 is a widely used Bayesian method for building polygenic scores (PGS). LDpred2-auto can infer the two parameters from the LDpred model, h2 and p, so that it does not require an additional validation dataset to choose best-performing parameters.

Here, we present a new version of LDpred2-auto, which adds a third parameter α to its model for modeling negative selection. Additional changes are also made to provide better sampling of these parameters. We then validate the inference of these 3 parameters.

LDpred2-auto also provides per-variant probabilities of being causal that are well calibrated, and can therefore be used for fine-mapping purposes. We also derive a new formula to infer the out-of-sample predictive performance R2 of the resulting PGS directly from the Gibbs sampler of LDpred2-auto.

Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average.

--- https://www.nytimes.com/2016/11/25/opinion/sunday/the-thin-gene.html 2022-03-09 psychiatry/anorexia --- https://en.wikipedia.org/wiki/Anorexia_nervosa Anorexia nervosa 2022-03-09 psychiatry/anorexia --- https://en.wikipedia.org/wiki/Bulimia_nervosa Bulimia nervosa 2022-03-09 psychiatry/anorexia --- https://en.wikipedia.org/wiki/Promotion_of_anorexia Promotion of anorexia 2022-03-09 psychiatry/anorexia --- https://en.wikipedia.org/wiki/Tumblr Tumblr 2022-03-09 psychiatry/anorexia --- https://en.wikipedia.org/wiki/Inedia Inedia 2022-03-09 psychiatry/anorexia --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4691282/ School Achievement and Risk of Eating Disorders in a Swedish National Cohort Jan Sundquist, Henrik Ohlsson, Marilyn A. Winkleby, Kristina Sundquist, Casey Crump 2016 2022-03-10 [("doi","10.1016/j.jaac.2015.09.021")] genetics/heritable psychiatry/anorexia

Objective: High achievement in school has been associated with increased risk of eating disorders, including anorexia nervosa (AN) and bulimia nervosa (BN), but causality of these relationships is unclear. We sought to examine the association between school achievement and AN or BN in a national cohort and to determine the possible contribution of familial confounding using a co-relative design.

Method: This national cohort study involved 1,800,643 persons born in Sweden during 1972–1990 who were still living in Sweden at age 16 years and were followed up for AN and BN identified from inpatient and outpatient diagnoses through 2012. We used Cox regression to examine the association between school achievement and subsequent risk of AN or BN, and stratified Cox models to examine the gradient in this association across different strata of co-relative pairs (first cousins, half siblings, full siblings).

Results: School achievement was positively associated with risk of AN among females and males (hazard ratio [HR] per additional 1 standard deviation, females: HR = 1.29; 95% CI = 1.25–1.33; males: HR = 1.29; 95% CI = 1.10–1.52), and risk of BN among females but not males (females: HR = 1.16; 95% CI = 1.11–1.20; males: HR = 1.05; 95% CI = 0.84–1.31).

In co-relative analyses, as the degree of shared genetic and environmental factors increased (eg. from first-cousin to full-sibling pairs), the association between school achievement and AN or BN substantially decreased.

Conclusion: In this large national cohort study, high achievement in school was associated with increased risk of AN and BN, but this appeared to be explained by unmeasured familial (genetic and environmental) factors.

--- https://slatestarcodex.com/2017/04/26/anorexia-and-metabolic-set-point/ 2022-03-10 psychiatry/anorexia --- https://slatestarcodex.com/2018/12/05/giudice-on-the-self-starvation-cycle/ 2022-03-10 psychiatry/anorexia --- https://blog.google/technology/research/project-starline/ 2022-03-10 sociology/technology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5266557/ Food insecurity as a driver of obesity in humans: The insurance hypothesis Daniel Nettle, Clare Andrews, Melissa Bateson 2017 2022-03-10 [("doi","10.1017/S0140525X16000947")] exercise psychiatry/anorexia

Integrative explanations of why obesity is more prevalent in some sectors of the human population than others are lacking. Here, we outline and evaluate one candidate explanation, the insurance hypothesis (IH). The IH is rooted in adaptive evolutionary thinking: The function of storing fat is to provide a buffer against shortfall in the food supply. Thus, individuals should store more fat when they receive cues that access to food is uncertain. Applied to humans, this implies that an important proximate driver of obesity should be food insecurity rather than food abundance per se. We integrate several distinct lines of theory and evidence that bear on this hypothesis.

We present a theoretical model that shows it is optimal to store more fat when food access is uncertain, and we review the experimental literature from non-human animals showing that fat reserves increase when access to food is restricted.

We provide a meta-analysis of 125 epidemiological studies of the association between perceived food insecurity and high body weight in humans. There is a robust positive association, but it is restricted to adult women in high-income countries. We explore why this could be in light of the IH and our theoretical model.

We conclude that although the IH alone cannot explain the distribution of obesity in the human population, it may represent a very important component of a pluralistic explanation. We also discuss insights it may offer into the developmental origins of obesity, dieting-induced weight gain, and anorexia nervosa.

--- /doc/genetics/selection/natural/human/2006-keller.pdf Resolving the paradox of common, harmful, heritable mental disorders: Which evolutionary genetic models work best? Matthew C. Keller, Geoffrey Miller 2006-11-09 2022-03-10 [("doi","10.1017/S0140525X06009095")] genetics/selection/natural/human psychiatry/bipolar/genetics psychiatry/depression psychiatry/schizophrenia

Given that natural selection is so powerful at optimizing complex adaptations, why does it seem unable to eliminate genes (susceptibility alleles) that predispose to common, harmful, heritable mental disorders, such as schizophrenia or bipolar disorder? We assess 3 leading explanations for this apparent paradox from evolutionary genetic theory: (1) ancestral neutrality (susceptibility alleles were not harmful among ancestors), (2) balancing selection (susceptibility alleles sometimes increased fitness), and (3) polygenic mutation-selection balance (mental disorders reflect the inevitable mutational load on the thousands of genes underlying human behavior). The first two explanations are commonly assumed in psychiatric genetics and Darwinian psychiatry, while mutation-selection has often been discounted.

All 3 models can explain persistent genetic variance in some traits under some conditions, but the first two have serious problems in explaining human mental disorders. Ancestral neutrality fails to explain low mental disorder frequencies and requires implausibly small selection coefficients against mental disorders given the data on the reproductive costs and impairment of mental disorders. Balancing selection (including spatio-temporal variation in selection, heterozygote advantage, antagonistic pleiotropy, and frequency-dependent selection) tends to favor environmentally contingent adaptations (which would show no heritability) or high-frequency alleles (which psychiatric genetics would have already found).

Only polygenic mutation-selection balance seems consistent with the data on mental disorder prevalence rates, fitness costs, the likely rarity of susceptibility alleles, and the increased risks of mental disorders with brain trauma, inbreeding, and paternal age.

This evolutionary genetic framework for mental disorders has wide-ranging implications for psychology, psychiatry, behavior genetics, molecular genetics, and evolutionary approaches to studying human behavior.

--- https://en.wikipedia.org/wiki/Animal_psychopathology#Activity_anorexia Animal psychopathology § Activity anorexia 2022-03-10 psychiatry/anorexia --- https://en.wikipedia.org/wiki/Animal_psychopathology#Thin_sow_syndrome Animal psychopathology § Thin sow syndrome 2022-03-10 psychiatry/anorexia --- /doc/psychology/neuroscience/2008-03-03-jonahlehrer-outofthebluecanathinkingrememberingdecisionmakingbiologicallyaccuratebrainbebuiltfromasupercomputer.html 2008-03-03 2022-03-10 ai/nn psychology/neuroscience --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8487535/ Activity-based anorexia animal model: a review of the main neurobiological findings Sara Spadini, Mattia Ferro, Jacopo Lamanna, Antonio Malgaroli 2021 2022-03-10 [("doi","10.1186/s40337-021-00481-x")] psychiatry/anorexia

Background: The genesis of anorexia nervosa (AN), a severe eating disorder with a pervasive effect on many brain functions such as attention, emotions, reward processing, cognition and motor control, has not yet been understood. Since our current knowledge of the genetic aspects of AN is limited, we are left with a large and diversified number of biological, psychological and environmental risk factors, called into question as potential triggers of this chronic condition with a high relapse rate. One of the most valid and used animal models for AN is the activity-based anorexia (ABA), which recapitulates important features of the human condition. This model is generated from naïve rodents by a self-motivated caloric restriction, where a fixed schedule food delivery induces spontaneous increased physical activity.

Aim: In this review, we sought to provide a summary of the experimental research conducted using the ABA model in the pursuit of potential neurobiological mechanism(s) underlying AN.

Method: The experimental work presented here includes evidence for neuroanatomical and neurophysiological changes in several brain regions as well as for the dysregulation of specific neurochemical synaptic and neurohormonal pathways.

Results: The most likely hypothesis for the mechanism behind the development of the ABA phenotype relates to an imbalance of the neural circuitry that mediates reward processing. Evidence collected here suggests that ABA animals show a large set of alterations, involving regions whose functions extend way beyond the control of reward mechanisms and eating habits. Hence, we cannot exclude a primary role of these alterations from a mechanistic theory of ABA induction.

Conclusions: These findings are not sufficient to solve such a major enigma in neuroscience, still they could be used to design ad hoc further experimental investigation. The prospect is that, since treatment of AN is still challenging, the ABA model could be more effectively used to shed light on the complex AN neurobiological framework, thus supporting the future development of therapeutic strategies but also the identification of biomarkers and diagnostic tools. Anorexia Nervosa (AN) is a severe eating disorder with a dramatic effect on many functions of our brain, such as attention, emotions, cognition and motion control. Since our current knowledge of the genetic aspects behind the development of AN is still limited, many biological, psychological and environmental factors must be taken into account as potential triggers of this condition. One of the most valid animal models for studying AN is the activity-based anorexia (ABA). In this model, rodents spontaneously limit food intake and start performing increased physical activity on a running wheel, a result of the imposition of a fixed time schedule for food delivery. In this review, we provide a detailed summary of the experimental research conducted using the ABA model, which includes extended evidence for changes in the anatomy and function of the brain of ABA rodents. The hope is that such integrated view will support the design of future experiments that will shed light on the complex brain mechanisms behind AN. Such advanced knowledge is crucial to find new, effective strategies for both the early diagnosis of AN and for its treatment.

--- https://arxiv.org/abs/2210.07224#facebook Exploring Long-Sequence Masked Autoencoders Ronghang Hu, Shoubhik Debnath, Saining Xie, Xinlei Chen 2022-10-13 2022-10-13 [("doi","10.48550/arXiv.2210.07224")] ai/nn/vae/mae

Masked Autoencoding (MAE) has emerged as an effective approach for pre-training representations across multiple domains. In contrast to discrete tokens in natural languages, the input for image MAE is continuous and subject to additional specifications. We systematically study each input specification during the pre-training stage, and find sequence length is a key axis that further scales MAE. Our study leads to a long-sequence version of MAE with minimal changes to the original recipe, by just decoupling the mask size from the patch size.

For object detection and semantic segmentation, our long-sequence MAE shows consistent gains across all the experimental setups without extra computation cost during the transfer. While long-sequence pre-training is discerned most beneficial for detection and segmentation, we also achieve strong results on ImageNet-1K classification by keeping a standard image size and only increasing the sequence length. We hope our findings can provide new insights and avenues for scaling in computer vision.

--- https://en.wikipedia.org/wiki/Valerian_(herb) Valeriana officinalis 2022-03-11 cat/psychology/drug/catnip psychiatry/anxiety --- https://en.wikipedia.org/wiki/Valeric_acid Valeric acid 2022-03-11 cat/psychology/drug/catnip psychiatry/anxiety --- https://en.wikipedia.org/wiki/Gabapentin Gabapentin 2022-03-11 cat/psychology/drug/catnip psychiatry/anxiety --- https://en.wikipedia.org/wiki/Mixed_anxiety%E2%80%93depressive_disorder Mixed anxiety-depressive disorder 2022-03-11 psychiatry/anxiety psychiatry/depression --- /doc/psychedelic/2016-nutt.pdf 2016 2022-03-11 psychedelic psychiatry/anxiety --- /doc/psychedelic/2016-griffiths.pdf 2016 2022-03-11 psychedelic psychiatry/anxiety --- /doc/psychedelic/2016-ross.pdf 2016 2022-03-11 psychedelic psychiatry/anxiety --- https://www.sciencedirect.com/science/article/pii/S2542519621002783 2022-03-11 psychiatry/anxiety --- https://www.huffpost.com/entry/climate-anxiety-young-people-worldwide-survey_n_6140ed8fe4b09519c50ae23c 2022-03-11 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Anxiety_disorder Anxiety disorder 2022-03-11 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Generalized_anxiety_disorder Generalized anxiety disorder 2022-03-12 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Social_anxiety_disorder Social anxiety disorder 2022-03-12 psychiatry/anxiety --- https://www.lesswrong.com/tag/comfort-zone-expansion-coze 2022-03-12 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Lavender_oil Lavender oil 2022-03-12 nootropic psychiatry/anxiety/lavender --- https://en.wikipedia.org/wiki/Cognitive_behavioral_therapy Cognitive behavioral therapy 2022-03-12 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Post-traumatic_stress_disorder Post-traumatic stress disorder 2022-03-12 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Obsessive%E2%80%93compulsive_disorder Obsessive-compulsive disorder 2022-03-12 psychiatry/anxiety --- https://en.wikipedia.org/wiki/List_of_people_with_an_anxiety_disorder List of people with an anxiety disorder 2022-03-12 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Exposure_therapy Exposure therapy 2022-03-12 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Desensitization_(psychology) Desensitization (psychology) 2022-03-12 psychiatry/anxiety psychology --- https://en.wikipedia.org/wiki/Virtual_reality_therapy Virtual reality exposure therapy 2022-03-12 psychiatry/anxiety technology --- https://slatestarcodex.com/2016/03/01/2016-nootropics-survey-results/ 2016 Nootropics Survey Results 2022-03-13 psychiatry/anxiety --- https://slatestarcodex.com/2014/02/16/nootropics-survey-results-and-analysis/ 2022-03-13 nootropic/bacopa psychiatry/anxiety --- https://www.astralcodexten.com/p/nootropics-survey-2020-results 2022-03-13 nootropic/bacopa psychiatry/anxiety --- https://darktka.github.io/ 2022-03-13 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Phenibut Phenibut 2022-03-13 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Baclofen Baclofen 2022-03-13 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Anxiolytic Anxiolytic 2022-03-13 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Benzodiazepine Benzodiazepine 2022-03-13 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Propranolol Propranolol 2022-03-13 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Gamma-Aminobutyric_acid Gamma-Aminobutyric acid 2022-03-13 psychiatry/anxiety --- https://en.wikipedia.org/wiki/GABA_receptor GABA receptor 2022-03-13 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Benzodiazepine_withdrawal_syndrome_syndrome Benzodiazepine withdrawal 2022-03-14 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Kava Kava 2022-03-14 psychiatry/anxiety --- https://slatestarcodex.com/2015/07/13/things-that-sometimes-work-if-you-have-anxiety/ 2022-03-14 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Bromantane Bromantane 2022-03-14 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Tianeptine Tianeptine 2022-03-14 psychiatry/anxiety --- https://slatestarcodex.com/2014/08/16/an-iron-curtain-has-descended-upon-psychopharmacology/ 2022-03-14 nootropic psychiatry/anxiety --- https://slatestarcodex.com/2020/06/15/the-vision-of-vilazodone-and-vortioxetine/ 2022-03-14 psychiatry/anxiety --- https://slatestarcodex.com/2019/07/18/know-your-gabapentinoids/ 2022-03-14 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Pregabalin Pregabalin 2022-03-14 psychiatry/anxiety --- https://slatestarcodex.com/2014/04/11/going-loopy/ 2022-03-14 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Withania_somnifera Withania somnifera 2022-03-15 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Bacopa_monnieri Bacopa monnieri 2022-03-15 nootropic/bacopa psychiatry/anxiety --- https://en.wikipedia.org/wiki/Selank Selank 2022-03-15 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Theanine Theanine 2022-03-15 psychiatry/anxiety --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4142584/ Meditation programs for psychological stress and well-being: a systematic review and meta-analysis Madhav Goyal, Sonal Singh, Erica M. S. Sibinga, Neda F. Gould, Anastasia Rowland-Seymour, Ritu Sharma, Zackary Berger, Dana Sleicher, David D. Maron, Hasan M. Shihab, Padmini D. Ranasinghe, Shauna Linn, Shonali Saha, Eric B. Bass, Jennifer A. Haythornthwaite 2014 2022-03-15 [("doi","10.1001/jamainternmed.2013.13018")] psychiatry/anxiety psychiatry/depression psychiatry/meditation

Importance: Many people meditate to reduce psychological stress and stress-related health problems. To counsel people appropriately, clinicians need to know what the evidence says about the health benefits of meditation.

Objective: To determine the efficacy of meditation programs in improving stress-related outcomes (anxiety, depression, stress/distress, positive mood, mental health-related quality of life, attention, substance use, eating habits, sleep, pain, and weight) in diverse adult clinical populations.

Evidence Review: We identified randomized clinical trials with active controls for placebo effects through November 2012 from MEDLINE, PsycINFO, Embase, PsycARTICLES, Scopus, CINAHL, AMED, the Cochrane Library, and hand searches. Two independent reviewers screened citations and extracted data. We graded the strength of evidence using 4 domains (risk of bias, precision, directness, and consistency) and determined the magnitude and direction of effect by calculating the relative difference between groups in change from baseline. When possible, we conducted meta-analyses using standardized mean differences to obtain aggregate estimates of effect size with 95% confidence intervals.

Results: After reviewing 18,753 citations, we included 47 trials with 3515 participants. Mindfulness meditation programs had moderate evidence of improved anxiety (effect size, 0.38 [95% CI, 0.12–0.64] at 8 weeks and 0.22 [0.02–0.43] at 3–6 months), depression (0.30 [0.00–0.59] at 8 weeks and 0.23 [0.05–0.42] at 3–6 months), and pain (0.33 [0.03- 0.62]) and low evidence of improved stress/distress and mental health-related quality of life. We found low evidence of no effect or insufficient evidence of any effect of meditation programs on positive mood, attention, substance use, eating habits, sleep, and weight. We found no evidence that meditation programs were better than any active treatment (ie. drugs, exercise, and other behavioral therapies).

Conclusions: Clinicians should be aware that meditation programs can result in small to moderate reductions of multiple negative dimensions of psychological stress. Thus, clinicians should be prepared to talk with their patients about the role that a meditation program could have in addressing psychological stress. Stronger study designs are needed to determine the effects of meditation programs in improving the positive dimensions of mental health and stress-related behavior.

--- /doc/genetics/heritable/2020-levey.pdf Reproducible Genetic Risk Loci for Anxiety: Results From ∼200,000 Participants in the Million Veteran Program Daniel F. Levey, Joel Gelernter, Renato Polimanti, Hang Zhou, Zhongshan Cheng, Mihaela Aslan, Rachel Quaden, John Concato, Krishnan Radhakrishnan, Julien Bryois, Patrick F. Sullivan, the Million Veteran Program, Murray B. Stein 2020-01-07 2022-03-15 [("doi","10.1176/appi.ajp.2019.19030256")] genetics/heritable psychiatry/anxiety --- /doc/psychology/2019-chen-5.pdf Pharmacological and psychological interventions for generalized anxiety disorder in adults: A network meta-analysis Ting-Ren Chen, Hui-Chuan Huang, Jer-Hwa Hsu, Wen-Chen Ouyang, Kuan-Chia Lin 2019-11-01 2022-03-15 [("doi","10.1016/j.jpsychires.2019.08.014")] psychiatry/anxiety psychology --- /doc/psychiatry/depression/2015-stochl.pdf Mood, anxiety and psychotic phenomena measure a common psychopathological factor J. Stochl, G. M. Khandaker, G. Lewis, J. Perez, I. M. Goodyer, S. Zammit, S. Sullivan, T. J. Croudace, P. B. Jones 2014-09-14 2022-03-15 [("doi","10.1017/S003329171400261X")] psychiatry/anxiety psychiatry/depression

Background: Psychotic phenomena are common in the general population but are excluded from diagnostic criteria for mild to moderate depression and anxiety despite their co-occurrence and shared risk factors. We used item response theory modeling to examine whether the co-occurrence of depressive, anxiety and psychotic phenomena is best explained by: (1) a single underlying factor; (2) two separate, uncorrelated factors; (3) two separate yet linked factors; or (4) two separate domains along with an underlying ‘common mental distress’ (CMD) factor. We defined where, along any latent continuum, the psychopathological items contributed most information.

Method: We performed a secondary analysis of cross-sectional, item-level information from measures of depression, anxiety and psychotic experiences in 6617 participants aged 13 years from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort and 977 participants aged 18 years from the ROOTS schools-based sample. We replicated results from one sample in the other and validated the latent factors against an earlier parental measure of mental state.

Results: In both cohorts depression, anxiety and psychotic items were best represented as a bi-factor model with a single, unitary CMD factor on which psychotic items conveyed information about the more severe end (model 4); residual variation remained for psychotic items. The CMD factor was statistically-significantly associated with the prior parental measure.

Conclusion: Psychotic phenomena co-occur with depression and anxiety in teenagers and may be a marker of severity in a single, unitary dimension of CMD. Psychotic phenomena should be routinely included in epidemiological assessments of psychiatric morbidity, otherwise the most severe symptomatology remains unmeasured.

--- /doc/nootropic/bacopa/2013-sathyanarayanan.pdf Brahmi for the better? New findings challenging cognition and anti-anxiety effects of Brahmi (Bacopa monnieri) in healthy adults 2013 2022-03-15 nootropic/bacopa psychiatry/anxiety --- /doc/modafinil/1994-simon.pdf The stimulant effect of modafinil on wakefulness is not associated with an increase in anxiety in mice. A comparison with dexamphetamine 1994 2022-03-15 modafinil psychiatry/anxiety --- https://www.pnas.org/doi/10.1073/pnas.2017224118 Discrimination and anxiety: Using multiple polygenic scores to control for genetic liability 2022-03-15 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Emotional_support_animal Emotional support animal 2022-03-16 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Mitragyna_speciosa Mitragyna speciosa 2022-03-16 psychiatry/anxiety --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.240.606&rep=rep1&type=pdf Isaac Newton as a Probabilist Stigler 2006 2022-03-16 statistics/probability --- https://jamanetwork.com/journals/jama/fullarticle/202669 Effect of Blinded Peer Review on Abstract Acceptance Ross 2006 2022-03-16 statistics/peer-review --- /doc/math/1996-04-20-rota-tenlessonsiwishihadbeentaught.html Ten Lessons I Wish I Had Been Taught Rota 1996 2022-03-16 math psychology/writing --- https://www.bmj.com/content/331/7528/1306 The cognitive cost of being a twin: evidence from comparisons within families in the Aberdeen children of the 1950s cohort study Ronalds 2005 2022-03-16 genetics/heritable iq --- https://www.erowid.org/references/texts/show/6336docid5904 Effect of modafinil on the pharmacokinetics of ethinyl estradiol and triazolam in healthy volunteers Robertson 2002 2022-03-16 modafinil --- https://nintil.com/no-great-technological-stagnation No Great Technological Stagnation José Luis Ricón 2016 2022-03-16 economics/automation technology --- https://www.pnas.org/doi/full/10.1073/pnas.1102900108 Lack of confidence in approximate Bayesian computation model choice Robert 2011 2022-03-16 statistics/bayes --- https://philosophytalk.typepad.com/blog/files/MetaAtheism.pdf Meta-atheism: Religious Avowal as Self-Deception Rey 2004 2022-03-16 philosophy/epistemology philosophy/religion psychology/cognitive-bias/illusion-of-depth --- https://proceedings.mlr.press/v97/rae19a/rae19a.pdf Meta-Learning Neural Bloom Filters Rae 2019 2022-03-16 cs/algorithm reinforcement-learning/meta-learning --- https://en.wikipedia.org/wiki/Transformative_use Transformative use 2022-03-17 economics/copyright --- https://thepatronsaintofsuperheroes.wordpress.com/2022/10/10/scotus-meaningfully-transformative-v-recognizably-derivative/ 2022-03-17 economics/copyright --- http://info-centre.jenage.de/assets/pdfs/library/vaupel_NATURE_2010.pdf Biodemography of human ageing Vaupel 2010 2022-03-17 longevity --- http://www.phys.mcw.edu/documents/Special%20Topics%20Neuroscience%20Fall%202008/Week%205/nat-neuro_2008_nrems-plasticity.pdf Molecular and electrophysiological evidence for net synaptic potentiation in wake and depression in sleep Vyazovskiy 2008 2022-03-17 psychology/neuroscience zeo --- https://www.nature.com/articles/s41467-018-04807-3 Misestimation of heritability and prediction accuracy of male-pattern baldness Yap 2018 2022-03-17 genetics/heritable --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4993048/ Genome-wide identification of microRNAs regulating cholesterol and triglyceride homeostasis Wagschal 2015 2022-03-17 genetics/heritable --- https://www.ndss-symposium.org/wp-content/uploads/2019/02/ndss2019_02B-5_Wampler_paper.pdf ExSpectre: Hiding Malware in Speculative Execution Wampler 2019 2022-03-17 cs/hardware cs/security --- https://horticulture.wisc.edu/wp-content/uploads/sites/20/2013/09/Wang-et-al.-20147.pdf Simultaneous editing of 3 homoeoalleles in hexaploid bread wheat confers heritable resistance to powdery mildew Wang 2014 2022-03-17 genetics/editing --- https://www.industrydocuments.ucsf.edu/tobacco/docs/#id=ssnl0112 Smoking, nicotine and human performance Warburton, Wesnes 1997 2022-03-17 nicotine --- https://employees.csbsju.edu/JOLSON/ECON315/Whaples2123771.pdf Where Is There Consensus Among American Economic Historians? The Results of a Survey on Forty Propositions Whaples 1994 2022-03-17 economics history --- https://codeblab.com/wp-content/uploads/2009/09/DualPivotQuicksort.pdf Dual-Pivot Quicksort algorithm Yaroslavskiy 2009 2022-03-18 cs/algorithm --- https://www.pnas.org/doi/full/10.1073/pnas.0705366104 Protein stability imposes limits on organism complexity and speed of molecular evolution Zeldovich 2007 2022-03-18 genetics/selection/natural --- https://arxiv.org/pdf/2002.04724.pdf#page=2&org=google Improved Consistency Regularization for GANs § 2.1 Balanced Consistency Regularization (bCR) Zhao 2020 2022-03-18 ai/nn/gan/data-augmentation --- /doc/melatonin/2001-zhdanova.pdf Melatonin Treatment for Age-Related Insomnia Zhdanova 2001 2022-03-18 melatonin --- https://web.archive.org/web/20040119183920id_/http://www.zi.ku.dk/evolbiology/courses/Willerslev%20&%20Hansen.pdf Diverse Plant and Animal Genetic Records from Holocene and Pleistocene Sediments Willerslev 2003 2022-03-18 genetics/sequencing --- https://chadnauseam.com/random/semaglutide-has-changed-the-world/ 2022-03-18 longevity/glp/semaglutide --- https://arxiv.org/abs/2210.02303#google Imagen Video: High Definition Video Generation with Diffusion Models Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P. Kingma, Ben Poole, Mohammad Norouzi, David J. Fleet, Tim Salimans 2022-10-05 2022-10-05 [("doi","10.48550/arXiv.2210.02303")] ai/video/generation

We present Imagen Video, a text-conditional video generation system based on a cascade of video diffusion models.

Given a text prompt, Imagen Video generates high definition videos using a base video generation model and a sequence of interleaved spatial and temporal video super-resolution models. We describe how we scale up the system as a high definition text-to-video model including design decisions such as the choice of fully-convolutional temporal and spatial super-resolution models at certain resolutions, and the choice of the v-parameterization of diffusion models. In addition, we confirm and transfer findings from previous work on diffusion-based image generation to the video generation setting. Finally, we apply progressive distillation to our video models with classifier-free guidance for fast, high quality sampling.

We find Imagen Video not only capable of generating videos of high fidelity, but also having a high degree of controllability and world knowledge, including the ability to generate diverse videos and text animations in various artistic styles and with 3D object understanding.

See the homepage for samples.

--- https://arxiv.org/abs/2210.02399#google Phenaki: Variable Length Video Generation From Open Domain Textual Description Ruben Villegas, Mohammad Babaeizadeh, Pieter-Jan Kindermans, Hernan Moraldo, Han Zhang, Mohammad Taghi Saffar, Santiago Castro, Julius Kunze, Dumitru Erhan 2022-10-05 2022-10-05 [("doi","10.48550/arXiv.2210.02399")] ai/video/generation

We present Phenaki, a model capable of realistic video synthesis, given a sequence of textual prompts. Generating videos from text is particularly challenging due to the computational cost, limited quantities of high-quality text-video data, and variable length of videos.

To address these issues, we introduce a new model for learning video representation which compresses the video to a small representation of discrete tokens. This tokenizer uses causal attention in time, which allows it to work with variable-length videos. To generate video tokens from text, we are using a bidirectional masked transformer conditioned on pre-computed text tokens. The generated video tokens are subsequently de-tokenized to create the actual video. To address data issues, we demonstrate how joint training on a large corpus of image-text pairs as well as a smaller number of video-text examples can result in generalization beyond what is available in the video datasets.

Compared to the previous video generation methods, Phenaki can generate arbitrary long videos conditioned on a sequence of prompts (ie. time-variable text or a story) in an open domain. To the best of our knowledge, this is the first time a paper studies generating videos from time-variable prompts. In addition, compared to the per-frame baselines, the proposed video encoder-decoder computes fewer tokens per video but results in better spatio-temporal consistency.

--- https://arxiv.org/abs/2209.14792#facebook Make-A-Video: Text-to-Video Generation without Text-Video Data Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, Devi Parikh, Sonal Gupta, Yaniv Taigman 2022-09-29 2022-09-29 [("doi","10.48550/arXiv.2209.14792")] ai/video/generation

We propose Make-A-Video—an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V). Our intuition is simple: learn what the world looks like and how it is described from paired text-image data, and learn how the world moves from unsupervised video footage. Make-A-Video has 3 advantages: (1) it accelerates training of the T2V model (it does not need to learn visual and multimodal representations from scratch), (2) it does not require paired text-video data, and (3) the generated videos inherit the vastness (diversity in esthetic, fantastical depictions, etc.) of today’s image generation models.

We design a simple yet effective way to build on T2I models with novel and effective spatial-temporal modules. First, we decompose the full temporal U-Net and attention tensors and approximate them in space and time. Second, we design a spatial-temporal pipeline to generate high resolution and frame rate videos with a video decoder, interpolation model, and two super-resolution models that can enable various applications besides T2V.

In all aspects, spatial and temporal resolution, faithfulness to text, and quality, Make-A-Video sets the new state-of-the-art in text-to-video generation, as determined by both qualitative and quantitative measures.

--- https://wilsonyan.com/teco/ 2022-03-18 ai/video/generation --- https://www.nytimes.com/2022/10/13/us/politics/biden-china-technology-semiconductors.html 2022-03-18 ai/scaling/hardware --- https://www.chinatalk.media/p/new-chip-export-controls-explained 2022-03-19 ai/scaling/hardware --- https://www.bloomberg.com/news/articles/2022-10-10/china-chip-stocks-drop-as-biden-tightens-rules-on-us-tech-access 2022-03-19 ai/scaling/hardware --- https://x.com/jordanschnyc/status/1580889342402129921 2022-03-19 ai/scaling/hardware --- https://x.com/haruu1367/status/1579286947519864833 2022-03-19 ai/anime ai/nn/vae --- https://arxiv.org/abs/1602.03483 Learning Distributed Representations of Sentences from Unlabeled Data Felix Hill, Kyunghyun Cho, Anna Korhonen 2016-02-10 2022-03-19 [("doi","10.48550/arXiv.1602.03483")] ai/nn/rnn

Unsupervised methods for learning distributed representations of words are ubiquitous in today’s NLP research, but far less is known about the best ways to learn distributed phrase or sentence representations from unlabeled data.

This paper is a systematic comparison of models that learn such representations. We find that the optimal approach depends critically on the intended application.

Deeper, more complex models are preferable for representations to be used in supervised systems, but shallow log-linear models work best for building representation spaces that can be decoded with simple spatial distance metrics.

We also propose two new unsupervised representation-learning objectives designed to optimize the trade-off between training time, domain portability, and performance.

--- https://www.smithsonianmag.com/smart-news/artist-mansion-covered-in-doodles-180980892/ 2022-03-19 culture --- https://www.youtube.com/watch?v=GRG6czAZql0 2022-03-19 ai/anime ai/nn/gan/stylegan --- https://en.wikipedia.org/wiki/National_Atomic_Testing_Museum National Atomic Testing Museum 2022-03-19 radiance --- https://www.openbible.info/labs/ai-bible-art/ 2022-03-19 ai/nn/transformer/gpt/dall-e --- https://x.com/dweekly/status/1580676295444217857 2022-03-19 ai/nn/transformer/gpt/non-fiction --- https://huggingface.co/blog/stable_diffusion_jax 2022-03-19 ai/nn/diffusion --- https://www.biorxiv.org/content/10.1101/2022.10.11.511653.full Data Descriptor: Human whole exome genotype data for Alzheimer’s Disease Yuk Yee Leung, Adam C. Naj, Yi-Fan Chou, Otto Valladares, Nicholas Wheeler, Honghuang Lin, Prabhakaran Gangadharan, Liming Qu, Kaylyn Clark, Laura Cantwell, Heather Nicaretta, the Alzheimer’s Disease Sequencing Project, Sudha Seshadri, Zoran Brkanac, Carlos Cruchaga, Margaret A. Pericak-Vance, Richard Mayeux, Amanda B. Kuzma, Wan-Ping Lee, William S. Bush, Anita L. DeStefano, Eden Martin, Gerard D. Schellenberg, Li-San Wang 2022-10-13 2022-10-13 [("doi","10.1101/2022.10.11.511653")] genetics/sequencing psychiatry/alzheimers

Bigger sample size can help to identify new genetic variants contributing to an increased risk of developing Alzheimer’s disease. However, the heterogeneity of the whole-exome sequencing (WES) data generation methods presents a challenge to a joint analysis.

Here we present a bioinformatics strategy for joint calling 20,504 WES samples collected across 9 studies and sequenced using 10 different capture kits in 14 sequencing centers in the Alzheimer’s Disease Sequencing Project. gVCFs of samples were joint-called by the Genome Center for Alzheimer’s Disease into a single VCF, containing only positions within the union of capture kits. The VCF was then processed using specific strategies to account for the batch effects arising from the use of different capture kits from different studies.

We identified 8.2 million autosomal variants. 96.82% of the variants are high-quality, and are located in 28,579 Ensembl transcripts. 41% of the variants are intronic and 15% are missense variants. 1.8% of the variants are with CADD>30.

Our new strategy for processing these diversely generated WES samples has shown to generate high-quality data. The improved ability to combine data sequenced in different batches benefits the whole genomics research community.

The WES data are accessible to the scientific community via https://dss.niagads.org/.

--- https://arxiv.org/abs/2209.04836 Git Re-Basin: Merging Models modulo Permutation Symmetries Samuel K. Ainsworth, Jonathan Hayase, Siddhartha Srinivasa 2022-09-11 2022-09-11 [("doi","10.48550/arXiv.2209.04836")] ai/nn

The success of deep learning is due in large part to our ability to solve certain massive non-convex optimization problems with relative ease. Though non-convex optimization is NP-hard, simple algorithms—often variants of stochastic gradient descent—exhibit surprising effectiveness in fitting large neural networks in practice.

We argue that neural network loss landscapes contain (nearly) a single basin after accounting for all possible permutation symmetries of hidden units a la Entezari et al 2021. We introduce 3 algorithms to permute the units of one model to bring them into alignment with a reference model in order to merge the two models in weight space. This transformation produces a functionally equivalent set of weights that lie in an ~convex basin near the reference model.

Experimentally, we demonstrate the single basin phenomenon across a variety of model architectures and datasets, including the first (to our knowledge) demonstration of zero-barrier linear mode connectivity between independently trained ResNet models on CIFAR-10 and CIFAR-100. Additionally, we identify intriguing phenomena relating model width and training time to mode connectivity. Finally, we discuss shortcomings of the linear mode connectivity hypothesis, including a counterexample to the single basin theory.

--- https://arxiv.org/abs/2210.06423#microsoft Foundation Transformers Hongyu Wang, Shuming Ma, Shaohan Huang, Li Dong, Wenhui Wang, Zhiliang Peng, Yu Wu, Payal Bajaj, Saksham Singhal, Alon Benhaim, Barun Patra, Zhun Liu, Vishrav Chaudhary, Xia Song, Furu Wei 2022-10-12 2022-10-12 [("doi","10.48550/arXiv.2210.06423")] ai/nn/transformer/gpt ai/scaling

A big convergence of model architectures across language, vision, speech, and multimodal is emerging. However, under the same name “Transformers”, the above areas use different implementations for better performance, eg. Post-LayerNorm for BERT, and Pre-LayerNorm for GPT and vision Transformers. We call for the development of Foundation Transformer for true general-purpose modeling, which serves as a go-to architecture for various tasks and modalities with guaranteed training stability.

In this work, we introduce a Transformer variant, named Magneto, to fulfill the goal. Specifically, we propose Sub-LayerNorm for good expressivity, and the initialization strategy theoretically derived from DeepNet for stable scaling up.

Extensive experiments demonstrate its superior performance and better stability than the de facto Transformer variants designed for various applications, including language modeling (ie. BERT, and GPT), machine translation, vision pretraining (ie. BEiT), speech recognition, and multimodal pretraining (ie. BEiT-3).

--- https://arxiv.org/abs/2106.07682 Revisiting Model Stitching to Compare Neural Representations Yamini Bansal, Preetum Nakkiran, Boaz Barak 2021-06-14 2022-03-20 [("doi","10.48550/arXiv.2106.07682")] ai/nn

We revisit and extend model stitching (Lenc & Vedaldi 2015) as a methodology to study the internal representations of neural networks. Given two trained and frozen models A and B, we consider a “stitched model” formed by connecting the bottom-layers of A to the top-layers of B, with a simple trainable layer between them.

We argue that model stitching is a powerful and perhaps under-appreciated tool, which reveals aspects of representations that measures such as centered kernel alignment (CKA) cannot. Through extensive experiments, we use model stitching to obtain quantitative verifications for intuitive statements such as “good networks learn similar representations”, by demonstrating that good networks of the same architecture, but trained in very different ways (eg.: supervised vs. self-supervised learning), can be stitched to each other without drop in performance. We also give evidence for the intuition that “more is better” by showing that representations learnt with (1) more data, (2) bigger width, or (3) more training time can be “plugged in” to weaker models to improve performance. Finally, our experiments reveal a new structural property of SGD which we call “stitching connectivity”, akin to mode-connectivity: typical minima reached by SGD can all be stitched to each other with minimal change in accuracy.

--- https://www.amazon.com/The-Nuclear-Taboo-Cambridge-International/dp/0521524288 The Nuclear Taboo: The United States and the Non-Use of Nuclear Weapons Since 1945 Tannenwald 2008 2022-03-20 existential-risk radiance --- https://www.haproxy.com/blog/power-of-two-load-balancing Test Driving ‘Power of Two Random Choices’ Load Balancing Tarreau 2019 2022-03-20 cs/algorithm statistics/decision statistics/order statistics/probability --- https://royalsocietypublishing.org/doi/10.1098/rsos.170988 The York Gospels: a 1,000-year biological palimpsest Teasdale 2019 2022-03-20 genetics/sequencing --- https://people.cs.uchicago.edu/~teutsch/papers/truebit.pdf TrueBit: A scalable verification solution for blockchains Teutsch, Reitwiessner 2017 2022-03-20 bitcoin --- https://lists.linuxfoundation.org/pipermail/bitcoin-dev/2013-September/003253.html [Bitcoin-development] REWARD offered for hash collisions for SHA1, SHA256, RIPEMD160 and others Peter Todd 2013-09-13 2022-03-20 bitcoin cs/cryptography/timelock --- https://pdfs.semanticscholar.org/eaa5/eefedd4fa34b7de7448c0c8e0822e9fdf956.pdf A computer movie simulating urban growth in the Detroit region Tobler 1970 2022-03-20 design statistics/causality --- https://en.wikipedia.org/wiki/Alzheimer%27s_disease Alzheimer’s disease 2022-03-20 psychiatry/alzheimers --- https://en.wikipedia.org/wiki/Dementia Dementia 2022-03-21 psychiatry/alzheimers --- https://en.wikipedia.org/wiki/Alois_Alzheimer Alois Alzheimer 2022-03-21 psychiatry/alzheimers --- https://en.wikipedia.org/wiki/Delusional_misidentification_syndrome Delusional misidentification syndrome 2022-03-21 psychiatry/alzheimers --- https://en.wikipedia.org/wiki/Sundowning Sundowning 2022-03-21 psychiatry/alzheimers --- https://en.wikipedia.org/wiki/Early-onset_Alzheimer%27s_disease#Familial_Alzheimer_disease Early-onset Alzheimer’s disease § Familial Alzheimer disease 2022-03-21 psychiatry/alzheimers --- https://en.wikipedia.org/wiki/Apolipoprotein_E#Alzheimer%27s_disease Apolipoprotein E § Alzheimer’s disease 2022-03-21 psychiatry/alzheimers --- https://en.wikipedia.org/wiki/Apolipoprotein_E Apolipoprotein E 2022-03-21 psychiatry/alzheimers --- https://en.wikipedia.org/wiki/Biochemistry_of_Alzheimer%27s_disease#Tau_hypothesis Biochemistry of Alzheimer’s disease § Tau hypothesis 2022-03-21 psychiatry/alzheimers --- https://en.wikipedia.org/wiki/Tau_protein Tau protein 2022-03-21 psychiatry/alzheimers --- https://en.wikipedia.org/wiki/Sleep_disorder Sleep disturbance 2022-03-21 psychiatry/alzheimers --- https://en.wikipedia.org/wiki/Biochemistry_of_Alzheimer%27s_disease Biochemistry of Alzheimer’s disease 2022-03-22 psychiatry/alzheimers --- https://arxiv.org/abs/1411.5908 Understanding image representations by measuring their equivariance and equivalence Karel Lenc, Andrea Vedaldi 2014-11-21 2022-03-22 [("doi","10.48550/arXiv.1411.5908")] ai/nn/cnn

Despite the importance of image representations such as histograms of oriented gradients and deep Convolutional Neural Networks (CNN), our theoretical understanding of them remains limited. Aiming at filling this gap, we investigate 3 key mathematical properties of representations: equivariance, invariance, and equivalence. Equivariance studies how transformations of the input image are encoded by the representation, invariance being a special case where a transformation has no effect. Equivalence studies whether two representations, for example two different parameterizations of a CNN, capture the same visual information or not.

A number of methods to establish these properties empirically are proposed, including introducing transformation and stitching layers in CNNs. These methods are then applied to popular representations to reveal insightful aspects of their structure, including clarifying at which layers in a CNN certain geometric invariances are achieved.

While the focus of the paper is theoretical, direct applications to structured-output regression are demonstrated too.

--- https://www.biorxiv.org/content/10.1101/2021.11.26.470088.full/docs/genetics/heritable/2021-smith.pdf 2022-03-22 psychology/neuroscience --- /doc/genetics/heritable/2020-sims.pdf The multiplex model of the genetics of Alzheimer’s disease Rebecca Sims, Matthew Hill, Julie Williams 2020-02-28 2022-03-22 [("doi","10.1038/s41593-020-0599-5")] genetics/heritable psychiatry/alzheimers --- https://www.nature.com/articles/s41746-019-0084-2 Digital biomarkers for Alzheimer’s disease: the mobile/wearable devices opportunity 2022-03-22 nootropic/quantified-self psychiatry/alzheimers --- /doc/dual-n-back/2008-boggio.pdf Temporal cortex direct current stimulation enhances performance on a visual recognition memory task in Alzheimer disease 2008 2022-03-22 dual-n-back psychiatry/alzheimers psychology/neuroscience --- https://eprints.whiterose.ac.uk/946/1/claxtonk1.pdf Bayesian value-of-information analysis: an application to a policy model of Alzheimer’s disease Caxton 2001 2022-03-22 psychiatry/alzheimers statistics/decision --- https://slatestarcodex.com/2016/11/17/the-alzheimer-photo/ The Alzheimer Photo 2022-03-22 psychiatry/alzheimers psychology/neuroscience science --- https://web.archive.org/web/20130113015023/http://www.vrp.com/stress/lithiums-potential-role-in-preventing-alzheimers-disease-mineral-benefits-other-conditions-besides-bipolar-disorder Lithium’s Potential Role in Preventing Alzheimer’s Disease 2022-03-22 psychiatry/alzheimers psychiatry/lithium --- /doc/genetics/heritable/2019-lambert.pdf Towards Clinical Utility of Polygenic Risk Scores Samuel A. Lambert, Gad Abraham, Michael Inouye 2019-01-01 2022-03-22 [("doi","10.1093/hmg/ddz187")] genetics/heritable psychiatry/alzheimers

Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (eg. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer’s disease) where PRS has or could have clinical utility.

PRS analysis for these diseases frequently revolved around (1) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (2) estimation of lifetime risk trajectories, (3) the independent information of PRS and family history of disease or monogenic mutations and (4) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility.

--- /doc/psychology/linguistics/bilingual/2019-antoniou.pdf The Advantages of Bilingualism Debate Mark Antoniou 2019-01-01 2022-03-22 [("doi","10.1146/annurev-linguistics-011718-011820")] psychiatry/alzheimers psychology/linguistics/bilingual statistics/bias

Bilingualism was once thought to result in cognitive disadvantages, but research in recent decades has demonstrated that experience with two (or more) languages confers a bilingual advantage in executive functions and may delay the incidence of Alzheimer’s disease. However, conflicting evidence has emerged leading to questions concerning the robustness of the bilingual advantage for both executive functions and dementia incidence. Some investigators have failed to find evidence of a bilingual advantage; others have suggested that bilingual advantages may be entirely spurious, while proponents of the advantage case have continued to defend it. A heated debate has ensued, and the field has now reached an impasse.

This review critically examines evidence for and against the bilingual advantage in executive functions, cognitive aging, and brain plasticity, before outlining how future research could shed light on this debate and advance knowledge of how experience with multiple languages affects cognition and the brain.

--- /doc/psychiatry/lithium/2009-hampel.pdf Lithium Trial in Alzheimer’s Disease: A Randomized, Single-Blind, Placebo-Controlled, Multicenter 10-Week Study 2009 2022-03-23 psychiatry/alzheimers psychiatry/lithium --- /doc/psychiatry/lithium/2008-yeh.pdf Lithium may be useful in the prevention of Alzheimer’s disease in individuals at risk of presenile familial Alzheimer’s disease Heng-Liang Yeh, Shih-Jen Tsai 2008-01-01 2022-03-23 [("doi","10.1016/j.mehy.2008.03.049")] psychiatry/alzheimers psychiatry/lithium --- /doc/psychiatry/lithium/2008-macdonald.pdf A feasibility and tolerability study of lithium in Alzheimer’s disease 2008 2022-03-23 psychiatry/alzheimers psychiatry/lithium --- /doc/longevity/johan-bjorksten/1982-bjorksten.pdf Dietary aluminum and Alzheimer’s disease Johan Bjorksten 1982-01-01 2022-03-23 [("doi","10.1016/0048-9697(82)90044-4")] longevity/johan-bjorksten psychiatry/alzheimers --- https://www.biorxiv.org/content/10.1101/2022.10.13.512134.full Role of spike in the pathogenic and antigenic behavior of SARS-CoV-2 BA.1 Omicron Da-Yuan Chen, Devin Kenney, Chue-Vin Chin, Alexander H. Tavares, Nazimuddin Khan, Hasahn L. Conway, GuanQun Liu, Manish C. Choudhary, Hans P. Gertje, Aoife K. OConnell, Darrell N. Kotton, Alexandra Herrmann, Armin Ensser, John H. Connor, Markus Bosmann, Jonathan Z. Li, Michaela U. Gack, Susan C. Baker, Robert N. Kirchdoerfer, Yachana Kataria, Nicholas A. Crossland, Florian Douam, Mohsan Saeed 2022-10-14 2022-10-14 [("doi","10.1101/2022.10.13.512134")] genetics/editing The recently identified, globally predominant SARS-CoV-2 Omicron variant (BA.1) is highly transmissible, even in fully vaccinated individuals, and causes attenuated disease compared with other major viral variants recognized to date. The Omicron spike (S) protein, with an unusually large number of mutations, is considered the major driver of these phenotypes.

We generated chimeric recombinant SARS-CoV-2 encoding the S gene of Omicron in the backbone of an ancestral SARS-CoV-2 isolate and compared this virus with the naturally circulating Omicron variant.

The Omicron S-bearing virus robustly escapes vaccine-induced humoral immunity, mainly due to mutations in the receptor binding motif (RBM), yet unlike naturally occurring Omicron, efficiently replicates in cell lines and primary-like distal lung cells. In K18-hACE2 mice, while Omicron causes mild, non-fatal infection, the Omicron S-carrying virus inflicts severe disease with a mortality rate of 80%.

This indicates that while the vaccine escape of Omicron is defined by mutations in S, major determinants of viral pathogenicity reside outside of S. --- https://www.biorxiv.org/content/10.1101/2022.10.13.512135.full Stress-Induced Mucosal Layer Disruption Drives Gut Dysbiosis and Depressive-like Behaviors Courtney Rivet-Noor, Andrea R. Merchak, Caroline Render, Rebecca M. Beiter, Ryan Brown, Erica Slogar, Austin Keeler, G. Brett Moreau, Sihan Li, Deniz Olgun, Tobey Mihn Huu Phan, Jung-Bum Shin, Christopher D. Deppmann, Alban Gaultier 2022-10-13 2022-10-13 [("doi","10.1101/2022.10.13.512135")] genetics/microbiome psychiatry/depression

Depression is a common mental health condition with a large social and economic impact. While depression etiology is multifactorial, chronic stress is a well-accepted contributor to disease onset. In addition, depression is associated with altered gut microbial signatures that can be replicated in animal models. While targeted restoration of the microbiome has been shown to reduce depressive-like behaviors in mice, the complexity and diversity of the human microbiome has complicated therapeutic intervention in patients.

To circumvent these limitations, there is a critical need for identifying pathways responsible for microbiome dysbiosis.

Here, for the first time, we identify the changes in host physiology that induce microbiome dysbiosis. Specifically, we show that a component of mucosal layer, the transmembrane protein mucin 13, can regulate microbiome composition.

Using a model of chronic stress to induce behavioral and microbial changes in mice, we show a statistically-significant reduction in mucin 13 expression across the intestines that occurs independently of the microbiome. Furthermore, deleting Muc13 leads to gut dysbiosis, and baseline behavioral changes normally observed after stress exposure.

Together, these results validate the hypothesis that mucosal layer disruption is an initiating event in stress-induced dysbiosis and offer mucin 13 as a potential new therapeutic target for microbiome dysbiosis in stress-induced depression. For the first time, our data provide an upstream and conserved target for treating microbiome dysbiosis, a result with sweeping implications for diseases presenting with microbial alterations.

--- https://www.biorxiv.org/content/10.1101/079012.full A Hypothesis to Explain Cancers in Confined Colonies of Naked Mole Rats Michael E. Hochberg, Robert J. Noble, Stanton Braude 2016-10-03 2022-03-23 [("doi","10.1101/079012")] longevity Naked mole rats (NMRs) are subterranean eusocial mammals, known for their virtual absence of aging in their first 20 to 30 years of life, and their apparent resistance to cancer development. As such, this species has become an important biological model for investigating the physiological and molecular mechanisms behind cancer resistance.

Two recent studies have discovered middle and late-aged worker (that is, non-breeding) NMRs in captive populations exhibiting neoplasms, consistent with cancer development, challenging the claim that NMRs are cancer resistant. These cases are possibly artefacts of inbreeding or certain rearing conditions in captivity, but they are also consistent with evolutionary theory.

We present field data showing that worker NMRs live on average for 1 to 2 years.

This, together with considerable knowledge about the biology of this species, provides the basis for an evolutionary explanation for why debilitating cancers in NMRs should be rare in captive populations and absent in the wild. Whereas workers are important for maintaining tunnels, colony defence, brood care, and foraging, they are highly vulnerable to predation. However, surviving workers either replace dead breeders, or assume other less active functions whilst preparing for possible dispersal. These countervailing forces (selection resulting in aging due to early-life investments in worker function, and selection for breeder longevity) along with the fact that all breeders derive from the worker morph, can explain the low levels of cancer observed by these recent studies in captive colonies. Because workers in the field typically never reach ages where cancer becomes a risk to performance or mortality, those rare observations of neoplastic growth should be confined to the artificial environments where workers survive to ages rarely if ever occurring in the wild.

Thus, we predict that the worker phenotype fortuitously benefits from anti-aging and cancer protection in captive populations.

Figure 1: Residence curves for naked mole rat colonies in the wild and survival curve for captive naked mole rats. Regression analysis of field data (Table 1) shows that presence in wild colonies decreases ~logarithmically for both males (squares and black dotted line) and females (circles and black dashed line). This type II survivorship pattern indicates that most mortality and dispersal is independent of age (although unquantified, most individuals are likely to have perished (O’Riain & Braude 2001)). Contrastingly, in captive populations, mortality rates are relatively low until ~25 years of age and then increase sharply (eg. (Buffenstein 2005; Buffenstein 2008)) (blue dashed and dotted line), corresponding to a “type I” survivorship curve. The same type of survivorship curve has been observed in captive breeders and is hypothesized to apply to breeders in the wild (red dashed line), which include a single queen, and typically 1–3 breeding males. Arrows indicate the ages at which cancer has been observed in captive naked mole rat workers by Delaney et al 2016 (solid arrowheads) and Taylor et al 2016 (open arrowheads), with error bars indicating minimum and maximum estimates.
--- /doc/psychology/writing/2018-lancaster.pdf Profiling the international academic ghost writers who are providing low-cost essays and assignments for the contract cheating industry Thomas Lancaster 2018-10-19 2022-03-23 [("doi","10.1108/JICES-04-2018-0040")] economics/automation psychology/writing Purpose: Students have direct access to academic ghost writers who are able to provide for their assessment needs without the student needing to do any of the work. These ghost writers are helping to fuel the international industry of contract cheating, raising ethical dilemmas, but not much is known about the writers, their business or how they operate. This paper aims to explore how the ghost writers market their services and operate, based on observable information.

Design/methodology/approach: This paper reviews data from providers actively offering contract cheating services available to the public on Fiverr.com, a low-cost micro outsourcing site. The search term "write essay" is used to identify providers, finding 103 "gigs" from 96 unique providers. Visible information, such as provider marketing, advertised services, pricing information and customer reviews, is analysed.

Results: The results demonstrate that bespoke essays are readily available to students at a low cost. The majority of providers operate from Kenya. Revenue calculations indicate a price point of $31.73 per 1,000 words, below the cost of traditional essay mills, but show that these 96 providers have generated around $270,000 of essay writing business between them.

Conclusion: This study affords a look into a complex and established industry whose inner workings are normally kept private and for which little published information currently exists. The research adds to what is known about the extent, location and operation of the contract cheating industry.

--- https://arxiv.org/abs/2210.07535#microsoft AutoMoE: Neural Architecture Search for Efficient Sparsely Activated Transformers Ganesh Jawahar, Subhabrata Mukherjee, Xiaodong Liu, Young Jin Kim, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan, Ahmed Hassan Awadallah, Sebastien Bubeck, Jianfeng Gao 2022-10-14 2022-10-14 [("doi","10.48550/arXiv.2210.07535")] ai/scaling/mixture-of-experts

Neural architecture search (NAS) has demonstrated promising results on identifying efficient Transformer architectures which outperform manually designed ones for natural language tasks like neural machine translation (NMT). Existing NAS methods operate on a space of dense architectures, where all of the sub-architecture weights are activated for every input.

Motivated by the recent advances in sparsely activated models like the Mixture-of-Experts (MoE) model, we introduce sparse architectures with conditional computation into the NAS search space. Given this expressive search space which subsumes prior densely activated architectures, we develop a new framework AutoMoE to search for efficient sparsely activated sub-Transformers.

AutoMoE-generated sparse models obtain (1) 3× FLOPs reduction over manually designed dense Transformers and (2) 23% FLOPs reduction over state-of-the-art NAS-generated dense sub-Transformers with parity in BLEU score on benchmark datasets for NMT. AutoMoE consists of 3 training phases: (a) Heterogeneous search space design with dense and sparsely activated Transformer modules (eg. how many experts? where to place them? what should be their sizes?); (b) SuperNet training that jointly trains several subnetworks sampled from the large search space by weight-sharing; (c) Evolutionary search for the architecture with the optimal trade-off between task performance and computational constraint like FLOPs and latency.

AutoMoE code, data and trained models are available at Github.

--- https://arxiv.org/abs/2210.07508#sony Hierarchical Diffusion Models for Singing Voice Neural Vocoder Naoya Takahashi, Mayank Kumar, Singh, Yuki Mitsufuji 2022-10-14 2022-10-14 [("doi","10.48550/arXiv.2210.07508")] ai/music ai/nn/diffusion

[samples] Recent progress in deep generative models has improved the quality of neural vocoders in speech domain. However, it remains challenging to generate high-quality singing voice due to a wider variety of musical expressions in pitch, loudness, and pronunciations.

In this work, we propose a hierarchical diffusion model for singing voice neural vocoders. The proposed method consists of multiple diffusion models operating in different sampling rates; the model at the lowest sampling rate focuses on generating accurate low frequency components such as pitch, and other models progressively generate the waveform at the higher sampling rates based on the data at the lower sampling rate and acoustic features.

Experimental results show that the proposed method produces high-quality singing voice for multiple singers, outperforming state-of-the-art neural vocoders [PriorGrad and Parallel WaveGAN] with a similar range of computational costs.

--- https://t-naoya.github.io/hdm/ 2022-03-23 ai/music ai/nn/diffusion --- https://arxiv.org/abs/1910.11480#naver Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram Ryuichi Yamamoto, Eunwoo Song, Jae-Min Kim 2019-10-25 2022-03-24 [("doi","10.48550/arXiv.1910.11480")] ai/music ai/nn/gan

We propose Parallel WaveGAN, a distillation-free, fast, and small-footprint waveform generation method using a generative adversarial network.

In the proposed method, a non-autoregressive WaveNet is trained by jointly optimizing multi-resolution spectrogram and adversarial loss functions, which can effectively capture the time-frequency distribution of the realistic speech waveform. As our method does not require density distillation [knowledge distillation] used in the conventional teacher-student framework, the entire model can be easily trained. Furthermore, our model is able to generate high-fidelity speech even with its compact architecture.

In particular, the proposed Parallel WaveGAN has only 1.44 million parameters and can generate 24 kHz speech waveform 28.68× faster than real-time on a single GPU environment.

Perceptual listening test results verify that our proposed method achieves 4.16 mean opinion score within a Transformer-based text-to-speech framework, which is comparative to the best distillation-based Parallel WaveNet system.

--- https://arxiv.org/abs/2106.06406#microsoft PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu 2021-06-11 2022-03-24 [("doi","10.48550/arXiv.2106.06406")] ai/music ai/nn/diffusion

Denoising diffusion probabilistic models have been recently proposed to generate high-quality samples by estimating the gradient of the data density. The framework defines the prior noise as a standard Gaussian distribution, whereas the corresponding data distribution may be more complicated than the standard Gaussian distribution, which potentially introduces inefficiency in denoising the prior noise into the data sample because of the discrepancy between the data and the prior.

In this paper, we propose PriorGrad to improve the efficiency of the conditional diffusion model for speech synthesis (for example, a vocoder using a mel-spectrogram as the condition) by applying an adaptive prior derived from the data statistics based on the conditional information. We formulate the training and sampling procedures of PriorGrad and demonstrate the advantages of an adaptive prior through a theoretical analysis.

Focusing on the speech synthesis domain, we consider the recently proposed diffusion-based speech generative models based on both the spectral and time domains and show that PriorGrad achieves faster convergence and inference with superior performance, leading to an improved perceptual quality and robustness to a smaller network capacity, and thereby demonstrating the efficiency of a data-dependent adaptive prior.

--- https://x.com/stephenroller/status/1579993017234382849 2022-03-24 ai/nn/fully-connected ai/nn/transformer --- https://arxiv.org/abs/2210.07316#huggingface MTEB: Massive Text Embedding Benchmark Niklas Muennighoff, Nouamane Tazi, Loïc Magne, Nils Reimers 2022-10-13 2022-10-13 [("doi","10.48550/arXiv.2210.07316")] ai/dataset ai/nn/transformer/gpt

[no strictly-dominant text embedding method yet] Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation.

To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 56 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks.

This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks.

MTEB comes with open-source code and a public leaderboard at https://huggingface.co/spaces/mteb/leaderboard.

--- https://arxiv.org/abs/2009.06367#salesforce GeDi: Generative Discriminator Guided Sequence Generation Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, Nazneen Fatema Rajani 2020-09-14 2022-03-24 [("doi","10.48550/arXiv.2009.06367")] ai/nn/sampling ai/nn/transformer/gpt/2

While large-scale language models (LMs) are able to imitate the distribution of natural language well enough to generate realistic text, it is difficult to control which regions of the distribution they generate. This is especially problematic because datasets used for training large LMs usually contain toxicity, hate, bias, and negativity.

We propose GeDi as an efficient method for using smaller LMs as generative discriminators to guide generation from large LMs to make them safer and more controllable. GeDi guides generation at each step by computing classification probabilities for all possible next tokens via Bayes rule by normalizing over two class-conditional distributions; one conditioned on the desired attribute, or control code, and another conditioned on the undesired attribute, or anti-control code.

We find that GeDi gives stronger controllability than the state-of-the-art method while also achieving generation speeds more than 30× faster.

Additionally, training GeDi on only 4 topics allows us to controllably generate new topics zero-shot from just a keyword, unlocking a new capability that previous controllable generation methods do not have.

Lastly, we show that GeDi can make GPT-2 (1.5b parameters) less toxic without sacrificing linguistic quality, making it by far the most practical existing method for detoxifying large language models while maintaining a fast generation speed.

--- https://x.com/goodside/status/1563191853587271681 2022-03-24 ai/nn/transformer/gpt/inner-monologue --- https://x.com/goodside/status/1568416130133368835 2022-03-24 ai/nn/transformer/gpt/inner-monologue --- https://x.com/goodside/status/1568375802903015425 2022-03-24 ai/nn/transformer/gpt/inner-monologue ai/text-style-transfer --- https://x.com/goodside/status/1581868987952300032 2022-03-24 ai/nn/transformer/gpt/inner-monologue --- https://x.com/goodside/status/1568375796904886274 2022-03-24 ai/nn/transformer/gpt/inner-monologue ai/text-style-transfer --- https://arxiv.org/abs/2005.12320 SCAN: Learning to Classify Images without Labels Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, Luc Van Gool 2020-05-25 2022-03-25 [("doi","10.48550/arXiv.2005.12320")] ai/nn

Can we automatically group images into semantically meaningful clusters when ground-truth annotations are absent? The task of unsupervised image classification remains an important, and open challenge in computer vision. Several recent approaches have tried to tackle this problem in an end-to-end fashion. In this paper, we deviate from recent works, and advocate a two-step approach where feature learning and clustering are decoupled.

First, a self-supervised task from representation learning is employed to obtain semantically meaningful features. Second, we use the obtained features as a prior in a learnable clustering approach. In doing so, we remove the ability for cluster learning to depend on low-level features, which is present in current end-to-end learning approaches.

Experimental evaluation shows that we outperform state-of-the-art methods by large margins, in particular +26.6% on CIFAR-10, +25.0% on CIFAR-100-20 and +21.3% on STL10 in terms of classification accuracy. Furthermore, our method is the first to perform well on a large-scale dataset for image classification. In particular, we obtain promising results on ImageNet, and outperform several semi-supervised learning methods in the low-data regime without the use of any ground-truth annotations.

The code is made publicly available at Github.

--- https://arxiv.org/abs/1808.08745 Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization Shashi Narayan, Shay B. Cohen, Mirella Lapata 2018-08-27 2022-03-25 [("doi","10.48550/arXiv.1808.08745")] ai/dataset ai/nn/cnn

We introduce extreme summarization, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is to create a short, one-sentence news summary answering the question “What is the article about?”.

We collect a real-world, large-scale dataset XSum for this task by harvesting online articles from the British Broadcasting Corporation (BBC). We propose a novel abstractive model which is conditioned on the article’s topics and based entirely on convolutional neural networks.

We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans.

--- https://arxiv.org/abs/2106.15772 A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers Shen-Yun Miao, Chao-Chun Liang, Keh-Yih Su 2021-06-30 2022-03-25 [("doi","10.48550/arXiv.2106.15772")] ai/nn math

We present ASDiv (Academia Sinica Diverse MWP Dataset), a diverse (in terms of both language patterns and problem types) English math word problem (MWP) corpus for evaluating the capability of various MWP solvers.

Existing MWP corpora for studying AI progress remain limited either in language usage patterns or in problem types. We thus present a new English MWP corpus with 2,305 MWPs that cover more text patterns and most problem types taught in elementary school. Each MWP is annotated with its problem type and grade level (for indicating the level of difficulty).

Furthermore, we propose a metric to measure the lexicon usage diversity of a given MWP corpus, and demonstrate that ASDiv is more diverse than existing corpora.

Experiments show that our proposed corpus reflects the true capability of MWP solvers more faithfully.

--- https://openreview.net/forum?id=HJbM0mZObH MAWPS: A Math Word Problem Repository Rik Koncel-Kedziorski, Subhro Roy, Aida Amini, Nate Kushman, Hannaneh Hajishirzi 2019-07-16 2022-03-25 math

Recent work across several AI sub-disciplines has focused on automatically solving math word problems.

In this paper we introduce MAWPS, an online repository of Math Word Problems, to provide a unified testbed to evaluate different algorithms. MAWPS allows for the automatic construction of datasets with particular characteristics, providing tools for tuning the lexical and template overlap of a dataset as well as for filtering ungrammatical problems from web-sourced corpora.

The online nature of this repository facilitates easy community contribution.

At present, we have amassed 3,320 problems, including the full datasets used in several prominent works.

--- https://arxiv.org/abs/2210.02441 Ask Me Anything (AMA): A simple strategy for prompting language models Simran Arora, Avanika Narayan, Mayee F. Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, Christopher Ré 2022-10-05 2022-10-05 [("doi","10.48550/arXiv.2210.02441")] ai/nn/sampling ai/nn/transformer/gpt ai/scaling

Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training. Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore effort is dedicated towards designing a painstakingly “perfect prompt” for a task. To mitigate the high degree of effort involved in prompt-design, we instead ask whether producing multiple effective, yet imperfect, prompts and aggregating them can lead to a high quality prompting strategy.

Our observations motivate our proposed prompting method, ASK ME ANYTHING (AMA). We first develop an understanding of the effective prompt formats, finding that question-answering (QA) prompts, which encourage open-ended generation (“Who went to the park?”) tend to outperform those that restrict the model outputs (“John went to the park. Output True or False.”).

Our approach recursively uses the LLM itself to transform task inputs to the effective QA format. We apply the collected prompts to obtain several noisy votes for the input’s true label. We find that the prompts can have very different accuracies and complex dependencies and thus propose to use weak supervision, a procedure for combining the noisy predictions, to produce the final predictions for the inputs.

We evaluate AMA across open-source model families (eg. EleutherAI, BLOOM, OPT, and T0) and model sizes (125M–175b parameters), demonstrating an average performance lift of 10.2% over the few-shot baseline. This simple strategy enables the open-source GPT-J-6B model to match and exceed the performance of few-shot GPT-3-175b on 15⁄20 popular benchmarks. Averaged across these tasks, the GPT-Neo-6B model outperforms few-shot GPT-3-175b.

We release our code here: https://github.com/HazyResearch/ama_prompting.

…There has been recent interest in how LLMs improve primarily along the 3 axes of parameter scale, training data, and compute [Kaplan et al 2020, Hoffmann et al 2022, Wei et al 2022c]. In Figure 4, as we increase the number of prompts to be aggregated, the conditional entropy reduces. Prompt aggregation may be another useful axis for understanding LLM scaling performance.

Figure 4: The top plots are for EleutherAI models of sizes ∈ {125M, 1.3B, 6B, 20B} and the bottom plots are for BLOOM models of sizes ∈ {560M, 1.7B, 7.1B, 175B}. The left plots show the conditional entropy metric H(y|ˆy) as a function of model size. Lines represent different prompts p with <em>k</em> = {0, 2, 4, 8} in-context examples and AMA prompt-chains without aggregation. The right plots show the conditional entropy as we aggregate predictions over an increasing number of AMA prompt-chains, with both the majority vote (MV) and weak supervision (WS) aggregation strategies for the GPT-J-6B and BLOOM 7.1B models. All plots are over RTE and each <em>k</em>-shot point is the average of 4 seeds.
--- https://www.csm.ai/commonsim-1-generating-3d-worlds 2022-03-25 ai/nn/transformer ai/video/analysis ai/video/generation --- https://arxiv.org/abs/2210.09162#google Table-To-Text generation and pre-training with TabT5 Ewa Andrejczuk, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Yasemin Altun 2022-10-17 2022-10-17 [("doi","10.48550/arXiv.2210.09162")] ai/nn/transformer/t5 ai/tabular

Encoder-only transformer models have been successfully applied to different table understanding tasks, as in TAPAS (Herzig et al 2020). A major limitation of these architectures is that they are constrained to classification-like tasks such as cell selection or entailment detection.

We present TABT5, an encoder-decoder model that generates natural language text based on tables and textual inputs. TABT5 overcomes the encoder-only limitation by incorporating a decoder component and leverages the input structure with table specific embeddings and pre-training.

TABT5 achieves new state-of-the-art results on several domains, including spreadsheet formula prediction with a 15% increase in sequence accuracy, QA with a 2.5% increase in sequence accuracy and data-to-text generation with a 2.5% increase in BLEU.

--- https://arxiv.org/abs/2210.09276#google Imagic: Text-Based Real Image Editing with Diffusion Models Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, Michal Irani 2022-10-17 2022-10-17 [("doi","10.48550/arXiv.2210.09276")] ai/nn/diffusion

Text-conditioned image editing has recently attracted considerable interest. However, most methods are currently either limited to specific editing types (eg. object overlay, style transfer), or apply to synthetically generated images, or require multiple input images of a common object.

In this paper we demonstrate, for the very first time, the ability to apply complex (eg. non-rigid) text-guided semantic edits to a single real image. For example, we can change the posture and composition of one or multiple objects inside an image, while preserving its original characteristics.

Our method can make a standing dog sit down or jump, cause a bird to spread its wings, etc.—each within its single high-resolution natural image provided by the user.

Contrary to previous work, our proposed method requires only a single input image and a target text (the desired edit). It operates on real images, and does not require any additional inputs (such as image masks or additional views of the object).

Our method, which we call Imagic, leverages a pre-trained text-to-image diffusion model for this task. It produces a text embedding that aligns with both the input image and the target text, while fine-tuning the diffusion model to capture the image-specific appearance.

We demonstrate the quality and versatility of our method on numerous inputs from various domains, showcasing a plethora of high quality complex semantic image edits, all within a single unified framework.

--- https://arxiv.org/abs/2210.09261#google Challenging BIG-Bench Tasks (BBH) and Whether Chain-of-Thought Can Solve Them Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, Jason Wei 2022-10-17 2022-10-17 [("doi","10.48550/arXiv.2210.09261")] ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm ai/scaling/emergence

BIG-Bench (Srivastava et al 2022) is a diverse evaluation suite that focuses on tasks believed to be beyond the capabilities of current language models. Language models have already made good progress on this benchmark, with the best model in the BIG-Bench paper outperforming average reported human-rater results on 65% of the BIG-Bench tasks via few-shot prompting. But on what tasks do language models fall short of average human-rater performance, and are those tasks actually unsolvable by current language models?

In this work, we focus on a suite of 23 challenging BIG-Bench tasks which we call BIG-Bench Hard (BBH). These are the task for which prior language model evaluations did not outperform the average human-rater.

We find that applying chain-of-thought (CoT) prompting to BBH tasks enables PaLM to surpass the average human-rater performance on 10⁄23 tasks, and Codex (code-davinci-002) to surpass the average human-rater performance on 17 of the 23 tasks.

Since many tasks in BBH require multi-step reasoning, few-shot prompting without CoT, as done in the BIG-Bench evaluations (Srivastava et al 2022), substantially underestimates the best performance and capabilities of language models, which is better captured via CoT prompting.

As further analysis, we explore the interaction between CoT and model scale on BBH, finding that CoT enables emergent task performance on several BBH tasks with otherwise flat scaling curves.

--- https://web.archive.org/web/20090619081956/https://www.insidehighered.com/news/2009/06/16/ethics 2022-03-25 philosophy/ethics/ethicists --- https://x.com/moyix/status/1582213498703990784 2022-03-25 ai/nn/sparsity/low-precision --- https://blog.google/technology/research/project-starline-expands-testing/ 2022-03-26 sociology/technology --- https://mattsclancy.substack.com/p/remote-breakthroughs 2022-03-26 sociology/technology --- https://x.com/thecharlieblake/status/1581913495670755328 2022-03-26 ai/nn/sparsity/low-precision --- https://arxiv.org/abs/2205.09546 Closing the gap: Exact maximum likelihood training of generative autoencoders using invertible layers (AEF) Gianluigi Silvestri, Daan Roos, Luca Ambrogioni 2022-05-19 2022-05-19 [("doi","10.48550/arXiv.2205.09546")] ai/nn/vae

[Twitter; code] In this work, we provide an exact likelihood alternative to the variational training of generative autoencoders.

We show that VAE-style autoencoders can be constructed using invertible layers, which offer a tractable exact likelihood without the need for any regularization terms. This is achieved while leaving complete freedom in the choice of encoder, decoder and prior architectures, making our approach a drop-in replacement for the training of existing VAEs and VAE-style models. We refer to the resulting models as Autoencoders within Flows (AEF), since the encoder, decoder and prior are defined as individual layers of an overall invertible architecture.

We show that the approach results in strikingly higher performance than architecturally equivalent VAEs in term of log-likelihood, sample quality and denoising performance.

In a broad sense, the main ambition of this work is to close the gap between the normalizing flow and autoencoder literature under the common framework of invertibility and exact maximum likelihood.

--- https://icdrc.org/documents/nicotine%20and%20ibd%20uc.pdf Transdermal nicotine for mildly to moderately active ulcerative colitis Sandborn 1997 2022-03-26 nicotine --- https://projecteuclid.org/journals/statistical-science/volume-14/issue-1/A-conversation-with-I-Richard-Savage-with-the-assistance-of/10.1214/ss/1009211808.full A conversation with I. Richard Savage (with the assistance of Bruce Spencer) Sampson 1999 2022-03-26 statistics/bayes statistics/decision --- https://www.ceecis.org/iodine/04a_consequences/02_int/chapt_on_i_brain.pdf Intelligence Quotient and Iodine Intake: A Cross-Sectional Study in Children Santiago-Fernandez 2004 2022-03-26 iodine iq --- https://www.pnas.org/doi/full/10.1073/pnas.0808699105 Nicotine decreases DNA methyltransferase 1 expression and glutamic acid decarboxylase 67 promoter methylation in GABAergic interneurons Satta 2008 2022-03-26 nicotine --- https://ubc-emotionlab.ca/wp-content/uploads/2012/09/Schimmack-2012-Effect-of-Significance-on-Article-Credibility.pdf The Ironic Effect of Significant Results on the Credibility of Multiple-Study Articles Schimmack 2012 2022-03-26 statistics/bias statistics/meta-analysis --- https://github.com/ultralytics/yolov5/issues/6998#issue-1170533269 2022-03-26 ai/nn/cnn --- https://arxiv.org/abs/1506.02640 You Only Look Once: Unified, Real-Time Object Detection Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi 2015-06-08 2022-03-26 [("doi","10.48550/arXiv.1506.02640")] ai/nn/cnn

[cf. YOLOv2, YOLOv3, YOLOv4, YOLOv5] We present YOLO, a new approach to object detection.

Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance.

Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors.

Compared to state-of-the-art detection systems, YOLO makes more localization errors but is far less likely to predict false detections where nothing exists.

Finally, YOLO learns very general representations of objects. It outperforms all other detection methods, including DPM and R-CNN, by a wide margin when generalizing from natural images to artwork on both the Picasso Dataset and the People-Art Dataset.

--- https://arxiv.org/abs/1612.08242 YOLO9000: Better, Faster, Stronger Joseph Redmon, Ali Farhadi 2016-12-25 2022-03-27 [("doi","10.48550/arXiv.1612.08242")] ai/nn/cnn

[YOLOv1, YOLOv3, YOLOv4, YOLOv5] We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories.

First we propose various improvements to the YOLO detection method, both novel and drawn from prior work.

The improved model, YOLOv2, is state-of-the-art on standard detection tasks like PASCAL VOC and COCO.

At 67 FPS, YOLOv2 gets 76.8 mAP on VOC 2007. At 40 FPS, YOLOv2 gets 78.6 mAP, outperforming state-of-the-art methods like Faster RCNN with ResNet and SSD while still running faster.

Finally we propose a method to jointly train on object detection and classification. Using this method we train YOLO9000 simultaneously on the COCO detection dataset and the ImageNet classification dataset. Our joint training allows YOLO9000 to predict detections for object classes that don’t have labeled detection data.

We validate our approach on the ImageNet detection task. YOLO9000 gets 19.7 mAP on the ImageNet detection validation set despite only having detection data for 44 of the 200 classes. On the 156 classes not in COCO, YOLO9000 gets 16.0 mAP. But YOLO can detect more than just 200 classes; it predicts detections for >9,000 different object categories.

And it still runs in real-time.

--- https://arxiv.org/abs/2004.10934 YOLOv4: Optimal Speed and Accuracy of Object Detection Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao 2020-04-23 2022-03-27 [("doi","10.48550/arXiv.2004.10934")] ai/nn/cnn

[cf. YOLOv1, YOLOv2, YOLOv3, YOLOv5] There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of combinations of such features on large datasets, and theoretical justification of the result, is required. Some features operate on certain models exclusively and for certain problems exclusively, or only for small-scale datasets; while some features, such as batch-normalization and residual-connections, are applicable to the majority of models, tasks, and datasets.

We assume that such universal features include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT) and Mish-activation.

We use new features: WRC, CSP, CmBN, SAT, Mish activation, Mosaic data augmentation, CmBN, DropBlock regularization, and CIoU loss, and combine some of them to achieve state-of-the-art results: 43.5% AP (65.7% AP50) for the MS COCO dataset at a realtime speed of ~65 FPS on Tesla V100.

Source code is at https://github.com/AlexeyAB/darknet.

--- https://www.kaggle.com/code/andy8744/predict-anime-face-using-pre-trained-model/data 2022-03-27 ai/anime ai/nn --- https://www.kaggle.com/datasets/andy8744/rezero-rem-anime-faces-for-gan-training 2022-03-27 ai/anime ai/nn/gan/stylegan --- https://www.youtube.com/watch?v=D2zjc--sDaY 2022-03-27 ai/anime ai/nn/gan/stylegan --- https://www.kaggle.com/code/andy8744/generating-ganyu-from-trained-model/notebook 2022-03-27 ai/anime ai/nn/gan/stylegan --- http://millionsongdataset.com/ 2022-03-27 ai/dataset ai/music --- https://asianometry.substack.com/p/the-soviet-unions-nuclear-icebreakers 2022-03-27 technology --- https://van-magazine.com/mag/stanislavski-on-opera/ 2022-03-27 fiction/opera --- https://plato.stanford.edu/entries/atomism-ancient/ 2022-03-27 philosophy/ontology --- https://www.biorxiv.org/content/10.1101/2022.10.13.512166.full Declining autozygosity over time: an exploration in over 1 million individuals from 3 diverse cohorts Sarah M. C. Colbert, Frank R. Wendt, Gita A. Pathak, Drew A. Helmer, Elizabeth R. Hauser, Matthew C. Keller, Renato Polimanti, Emma C. Johnson 2022-10-17 2022-10-17 [("doi","10.1101/2022.10.13.512166")] genetics/heritable/rare We hypothesized that overall autozygosity is decreasing over generational time.

In this report, we present data that partially support this hypothesis from 3 large cohorts of diverse ancestries, two from the US (All of Us and the Million Veteran Program, n = 82,474 and 622,497, respectively) and one from the UK (UK Biobank, n = 380,899).

Our results from a mixed-effect meta-analysis demonstrate an overall trend of decreasing autozygosity over generational time (meta-analyzed slope=-0.029, se=0.009, p = 6.03e-4). Using a chi-square difference test, we determined that a model including an ancestry-by-country interaction term fit the data best, indicating that ancestry differences in this trend differ by country. We found further evidence to suggest a difference between the US and UK cohorts by meta-analyzing within country, observing a statistically-significant negative estimate in the US cohorts (meta-analyzed slope=-0.058, se=0.015, p = 1.50e-4) but a non-statistically-significant estimate in the UK (meta-analyzed slope=-0.001, se=0.008, p = 0.945).

We also found that the association between autozygosity and year of birth in the overall meta-analysis was substantially attenuated when accounting for educational attainment and income (meta-analyzed slope=-0.011, se=0.008, p = 0.167), suggesting that increases in education and income may partially account for decreasing levels of autozygosity over time.

To our knowledge, this is the largest demonstration of decreasing autozygosity over time in a modern sample (birth years 1904–2003), and we speculate that this trend can be attributed to increases in population size, urbanization and panmixia, with differences in demographic and sociocultural processes leading to country-specific differences in the rate of decline. --- https://medium.com/@enryu9000/anifusion-diffusion-models-for-anime-pictures-138cf1af2cbe 2022-03-28 ai/anime ai/nn/diffusion --- https://x.com/vponamariov/status/1582671678328344576 2022-03-28 design --- https://arxiv.org/abs/2210.10341#microsoft BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, Tie-Yan Liu 2022-10-19 2022-10-19 [("doi","10.1093/bib/bbac409")] ai/nn/transformer/gpt ai/scaling

Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, ie. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope.

In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large scale biomedical literature. We evaluate BioGPT on 6 biomedical NLP tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks respectively, and 78.2% accuracy on PubMedQA, creating a new record.

Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.

Code is available at Github.

--- https://www.biorxiv.org/content/10.1101/2021.07.10.451761.full Borgs are giant extrachromosomal elements with the potential to augment methane oxidation Basem Al-Shayeb, Marie C. Schoelmerich, Jacob West-Roberts, Luis E. Valentin-Alvarado, Rohan Sachdeva, Susan Mullen, Alexander Crits-Christoph, Michael J. Wilkins, Kenneth H. Williams, Jennifer A. Doudna, Jillian F. Banfield 2021-07-10 2022-03-28 [("doi","10.1101/2021.07.10.451761")] biology genetics/sequencing

Anaerobic methane oxidation exerts a key control on greenhouse gas emissions1, yet factors that modulate the activity of microorganisms performing this function remain little explored.

In studying groundwater, sediments, and wetland soil where methane production and oxidation occur, we discovered extraordinarily large, diverse DNA sequences that primarily encode hypothetical proteins. 4 curated, complete genomes are linear, up to ~1 Mbp in length and share genome organization, including replicore structure, long inverted terminal repeats, and genome-wide unique perfect tandem direct repeats that are intergenic or generate amino acid repeats.

We infer that these are a new type of archaeal extrachromosomal element with a distinct evolutionary origin. Gene sequence similarity, phylogeny, and local divergence of sequence composition indicate that many of their genes were assimilated from methane-oxidizing Methanoperedens archaea. We refer to these elements as “Borgs”.

We identified at least 19 different Borg types coexisting with Methanoperedens in 4 distinct ecosystems. Borg genes expand redox and respiratory capacity (eg. clusters of multiheme cytochromes), ability to respond to changing environmental conditions, and likely augment Methanoperedens capacity for methane oxidation (eg. methyl coenzyme M reductase).

By this process, Borgs could play a previously unrecognized role in controlling greenhouse gas emissions.

--- https://x.com/scottbelsky/status/1582748549388783617 2022-03-28 ai/nn design/typography --- https://arxiv.org/abs/1901.08746 BioBERT: a pre-trained biomedical language representation model for biomedical text mining Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, Jaewoo Kang 2019-01-25 2022-03-28 [("doi","10.1093/bioinformatics/btz682")] ai/nn/transformer

Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora.

In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora.

With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT outperforms them on the following 3 representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement).

Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts.

We make the pre-trained weights of BioBERT freely available, and the source code for fine-tuning BioBERT available at Github.

--- https://www.marketingscience.info/when-brands-stop-advertising/ 2022-03-28 economics/advertising --- https://xorshammer.com/2008/08/21/compute-definite-integral/ 2022-03-28 cs/algorithm --- https://arxiv.org/abs/2210.02671 Transformers Implement First-Order Logic with Majority Quantifiers William Merrill, Ashish Sabharwal 2022-10-06 2022-10-06 [("doi","10.48550/arXiv.2210.02671")] ai/nn/transformer/attention cs/computable philosophy/logic

[Twitter; cf. Chiang et al 2023] Characterizing the implicit structure of the computation within neural networks is a foundational problem in the area of deep learning interpretability. Can their inner decision process be captured symbolically in some familiar logic?

We show that any transformer neural network can be translated into an equivalent fixed-size first-order logic formula which may also use majority quantifiers. The idea is to simulate transformers with highly uniform threshold circuits and leverage known theoretical connections between circuits and logic. Our findings also reveal the surprising fact that the entire transformer computation can be reduced merely to the division of two (large) integers [cf. Fractran?].

While our results are most pertinent for transformers, they apply equally to a broader class of neural network architectures, namely those with a fixed-depth uniform computation graph made up of standard neural net components, which includes feedforward and convolutional networks.

--- https://www.biorxiv.org/content/10.1101/2022.10.13.512103.full Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context Thomas W. Willis, Chris Wallace 2022-10-17 2022-10-17 [("doi","10.1101/2022.10.13.512103")] genetics/heritable/rare statistics/order Assessment of the genetic similarity between two phenotypes can provide insight into a common genetic aetiology and inform the use of pleiotropy-informed, cross-phenotype analytical methods to identify novel genetic associations. The genetic correlation is a well-known means of quantifying and testing for genetic similarity between traits, but its estimates are subject to comparatively large sampling error. This makes it unsuitable for use in a small-sample context.

We discuss the use of a non-parametric test of genetic similarity first introduced by Li et al for application to GWAS summary statistics. We establish that the null distribution of the test statistic is modelled better by an extreme value distribution than a transformation of the standard exponential distribution as originally recommended by Li and colleagues.

We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context. Author summary The genome-wide association study (GWAS) is a method used to identify genetic variants which contribute to the risk of developing disease. These genetic variants are frequently shared between conditions, such that the study of the genetic basis of one disease can be informed by knowledge of another, similar disease. This approach can be productive where the disease in question is rare such that a GWAS has less power to associate variants with the disease, but there exist larger GWAS of similar diseases. Existing methods do not measure genetic similarity precisely when patients are few.

Here we assess a previously published method of testing for genetic similarity between pairs of diseases using GWAS data, the ‘GPS’ test, against 3 other methods with the use of real and simulated data. We present a new computational procedure for carrying out the test and show that the GPS test is superior to its comparators in identifying genetic similarity when the sample size is small and when the genetic similarity signal is less strong. Use of the test will enable accurate detection of genetic similarity and the study of rarer conditions using data from better-characterised diseases. --- https://github.com/ceres-solver/ceres-solver 2022-03-29 cs/algorithm statistics/decision --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6249685/ In utero CRISPR-mediated therapeutic editing of metabolic genes Avery C. Rossidis, John D. Stratigis, Alexandra C. Chadwick, Heather A. Hartman, Nicholas J. Ahn, Haiying Li, Kshitiz Singh, Barbara E. Coons, Li Li, Wenjian Lv, Philip W. Zoltick, Deepthi Alapati, William Zacharias, Rajan Jain, Edward E. Morrisey, Kiran Musunuru, William H. Peranteau 2018 2022-03-29 [("doi","10.1038/s41591-018-0184-6")] genetics/editing

In utero gene editing has the potential to prenatally treat genetic diseases that result in significant morbidity and mortality before or shortly after birth.

We assessed the viral vector-mediated delivery of CRISPR-Cas9 or base editor 3 in utero, seeking therapeutic modification of Pcsk9 or Hpd in wild-type mice or the murine model of hereditary tyrosinemia type 1, respectively.

We observed long-term postnatal persistence of edited cells in both models, with reduction of plasma PCSK9 and cholesterol levels following in utero Pcsk9 targeting and rescue of the lethal phenotype of hereditary tyrosinemia type 1 following in utero Hpd targeting.

The results of this proof-of-concept work demonstrate the possibility of efficiently performing gene editing before birth, pointing to a potential new therapeutic approach for selected congenital genetic disorders.

--- https://www.biorxiv.org/content/10.1101/439661.full Existence and implications of population variance structure Shaila Musharoff, Danny Park, Andy Dahl, Joshua Galanter, Xuanyao Liu, Scott Huntsman, Celeste Eng, Esteban G. Burchard, Julien F. Ayroles, Noah Zaitlen 2018-10-11 2022-03-29 [("doi","10.1101/439661")] genetics/heritable

Identifying the genetic and environmental factors underlying phenotypic differences between populations is fundamental to multiple research communities. To date, studies have focused on the relationship between population and phenotypic mean. Here we consider the relationship between population and phenotypic variance, ie. “population variance structure.” In addition to gene-gene and gene-environment interaction, we show that population variance structure is a direct consequence of natural selection.

We develop the ancestry double generalized linear model (ADGLM), a statistical framework to jointly model population mean and variance effects. We apply ADGLM to several deeply phenotyped datasets and observe ancestry-variance associations with 12/44 tested traits in ~113K British individuals and 3⁄14 tested traits in ~3K Mexican, Puerto Rican, and African-American individuals.

We show through extensive simulations that population variance structure can both bias and reduce the power of genetic association studies, even when principal components or linear mixed models are used. ADGLM corrects this bias and improves power relative to previous methods in both simulated and real datasets.

Additionally, ADGLM identifies 17 novel genotype-variance associations across 6 phenotypes.

--- https://www.biorxiv.org/content/10.1101/439885.full Computational noise in reward-guided learning drives behavioral variability in volatile environments Charles Findling, Vasilisa Skvortsova, Rémi Dromnelle, Stefano Palminteri, Valentin Wyart 2018-10-11 2022-03-29 [("doi","10.1101/439885")] reinforcement-learning/exploration statistics/decision

When learning the value of actions in volatile environments, humans often make seemingly irrational decisions which fail to maximize expected value. We reasoned that these ‘non-greedy’ decisions, instead of reflecting information seeking during choice, may be caused by computational noise in the learning of action values.

Here, using reinforcement learning (RL) models of behavior and multimodal neurophysiological data, we show that the majority of non-greedy decisions stems from this learning noise. The trial-to-trial variability of sequential learning steps and their impact on behavior could be predicted both by BOLD responses to obtained rewards in the dorsal anterior cingulate cortex (dACC) and by phasic pupillary dilation—suggestive of neuromodulatory fluctuations driven by the locus coeruleus-norepinephrine (LC-NE) system.

Together, these findings indicate that most of behavioral variability, rather than reflecting human exploration, is due to the limited computational precision of reward-guided learning.

--- https://carper.ai/instruct-gpt-announcement/ 2022-03-29 ai/nn/transformer/gpt reinforcement-learning/preference-learning --- https://arxiv.org/abs/2109.14349 Relational Memory: Native In-Memory Accesses on Rows and Columns Shahin Roozkhosh, Denis Hoornaert, Ju Hyoung Mun, Tarikul Islam Papon, Ahmed Sanaullah, Ulrich Drepper, Renato Mancuso, Manos Athanassoulis 2021-09-29 2022-03-29 [("doi","10.48550/arXiv.2109.14349")] cs/hardware

Analytical database systems are typically designed to use a column-first data layout to access only the desired fields. On the other hand, storing data row-first works great for accessing, inserting, or updating entire rows. Transforming rows to columns at runtime is expensive, hence, many analytical systems ingest data in row-first form and transform it in the background to columns to facilitate future analytical queries. How will this design change if we can always efficiently access only the desired set of columns?

To address this question, we present a radically new approach to data transformation from rows to columns. We build upon recent advancements in embedded platforms with re-programmable logic to design native in-memory access on rows and columns. Our approach, termed Relational Memory, relies on an FPGA-based accelerator that sits between the CPU and main memory and transparently transforms base data to any group of columns with minimal overhead at runtime. This design allows accessing any group of columns as if it already exists in memory.

We implement and deploy Relational Memory in real hardware, and we show that we can access the desired columns up to 1.63× faster than accessing them from their row-wise counterpart, while matching the performance of a pure columnar access for low projectivity, and outperforming it by up to 1.87× as projectivity (and tuple re-construction cost) increases.

Moreover, our approach can be easily extended to support offloading of a number of operations to hardware, eg. selection, group by, aggregation, and joins, having the potential to vastly simplify the software logic and accelerate the query execution.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0274860 Originality in online dating profile texts: How does perceived originality affect impression formation and what makes a text original? Tess van der Zanden, Alexander P. Schouten, Maria B. J. Mos, Emiel J. Krahmer 2022-09-07 2022-09-07 [("doi","10.1371/journal.pone.0274860")] iq psychology/personality sociology/technology

This paper investigates origins and consequences of perceived profile text originality. The first goal was to examine whether the perceived originality of authentic online dating profile texts affects online daters’ perceptions of attractiveness, and whether perceptions of (less) desired partner personality traits mediate this effect.

Results showed the positive impact of perceived profile text originality on impression formation: text originality positively affects perceptions of intelligence and sense of humor, which improve impressions of attractiveness and boost dating intention.

The second goal was to explore what profile text features increase perceptions of profile text originality. Results revealed profile texts which were stylistically original (eg. include metaphors) and contained more and concrete self-disclosure statements were considered more original, explaining almost half of the variance in originality scores.

Taken together, our results suggest that perceived originality in profile texts is manifested in both meaning and form, and is a balancing act between novelty and appropriateness.

--- https://arxiv.org/abs/2101.10964 Investment vs. reward in a competitive knapsack problem Oren Neumann, Claudius Gros 2021-01-26 2022-03-29 [("doi","10.48550/arXiv.2101.10964")] cs/algorithm reinforcement-learning/model/alphago reinforcement-learning/scaling

Natural selection drives species to develop brains, with sizes that increase with the complexity of the tasks to be tackled.

Our goal is to investigate the balance between the metabolic costs of larger brains compared to the advantage they provide in solving general and combinatorial problems. Defining advantage as the performance relative to competitors, a two-player game based on the knapsack problem is used. Within this framework, two opponents compete over shared resources, with the goal of collecting more resources than the opponent.

Neural nets of varying sizes are trained using a variant of the AlphaGo Zero algorithm.

A surprisingly simple relation, Na / (NA + NB), is found for the relative win rate of a net with NA neurons against one with NB. Success increases linearly with investments in additional resources when the networks sizes are very different, i.e. when NA ≪ NB, with returns diminishing when both networks become comparable in size.

--- https://arxiv.org/abs/2010.05848#google Human-centric Dialog Training via Offline Reinforcement Learning Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Shane Gu, Rosalind Picard 2020-10-12 2022-03-29 [("doi","10.48550/arXiv.2010.05848")] ai/nn/rnn reinforcement-learning/model-free reinforcement-learning/preference-learning

How can we train a dialog model to produce better conversations by learning from human feedback, without the risk of humans teaching it harmful chat behaviors? We start by hosting models online, and gather human feedback from real-time, open-ended conversations, which we then use to train and improve the models using offline reinforcement learning (RL).

We identify implicit conversational cues including language similarity, elicitation of laughter, sentiment, and more, which indicate positive human feedback, and embed these in multiple reward functions. A well-known challenge is that learning an RL policy in an offline setting usually fails due to the lack of ability to explore and the tendency to make over-optimistic estimates of future reward. These problems become even harder when using RL for language models, which can easily have a 20,000 action vocabulary and many possible reward functions.

We solve the challenge by developing a novel class of offline RL algorithms. These algorithms use KL-control to penalize divergence from a pre-trained prior language model, and use a new strategy to make the algorithm pessimistic, instead of optimistic, in the face of uncertainty.

We test the resulting dialog model with ratings from 80 users in an open-domain setting and find it achieves improvements over existing deep offline RL approaches. The novel offline RL method is viable for improving any existing generative dialog model using a static dataset of human feedback.

--- https://publicdomainreview.org/essay/darwins-diagrams-of-plant-movement 2022-03-29 biology design/visualization history/public-domain-review psychology/neuroscience --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5612713/ The Near Eastern origin of cat domestication Carlos A. Driscoll, Marilyn Menotti-Raymond, Alfred L. Roca, Karsten Hupe, Warren E. Johnson, Eli Geffen, Eric H. Harley, Miguel Delibes, Dominique Pontier, Andrew C. Kitchener, Nobuyuki Yamaguchi, Stephen J. O’brien, David W. Macdonald 2007 2022-03-29 [("doi","10.1126/science.1139518")] cat/genetics

The world’s domestic cats carry patterns of sequence variation in their genome that reflect a history of domestication and breed development.

A genetic assessment of 979 domestic cats and their wild progenitors-Felis silvestris silvestris (European wildcat), F. s. lybica (Near Eastern wildcat), F. s. ornata (central Asian wildcat), F. s. cafra (southern African wildcat), and F. s. bieti (Chinese desert cat)-indicated that each wild group represents a distinctive subspecies of Felis silvestris.

Further analysis revealed that cats were domesticated in the Near East, probably coincident with agricultural village development in the Fertile Crescent.

Domestic cats derive from at least 5 founders from across this region, whose descendants were transported across the world by human assistance.

--- https://www.justice.gov/usao-ndga/pr/hacker-and-dark-market-operator-arraigned-federal-charges 2022-03-30 modafinil/darknet-market --- https://x.com/SimonGColton/status/1578162486712287233 2022-03-30 ai/nn/diffusion --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3583075/ Why I am not shy: a reply to Tononi and Cirelli Marcos Gabriel Frank 2013 2022-03-30 [("doi","10.1155/2013/394946")] psychology/neuroscience zeo

In a recent article I reviewed an influential theory of sleep function, the “synaptic homeostasis hypothesis (SHY).” According to SHY, sleep renormalizes synapses that are potentiated during prior wakefulness.

I concluded that while SHY is a seminal theory with important implications about sleep function and the brain, its underlying mechanisms are poorly defined.

In an accompanying article, the authors of SHY responded at length. Their reply is thoughtful and provocative, but unfortunately many of the points I raised were not accurately represented or addressed.

In this brief commentary, I attempt to clarify some points of confusion. I also explain why any theory of sleep function is incomplete without an understanding of the underlying cellular mechanisms.

--- https://publicdomainreview.org/collection/synaesthesia-diagrams-1883 2022-03-30 design/visualization history/public-domain-review psychology/neuroscience --- https://x.com/EugeneDyabin/status/1585677203974459392 2022-03-30 ai/anime --- https://aeon.co/essays/do-thought-experiments-really-uncover-new-scientific-truths 2022-03-30 philosophy/epistemology --- https://generrated.com/ 2022-03-30 ai/nn/transformer/gpt/dall-e --- https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion 2022-03-30 ai/nn/diffusion ai/nn/transformer/gpt --- https://flak.tedunangst.com/post/a-brief-history-of-one-line-fixes 2022-03-30 cs/security --- https://palewi.re/docs/news-homepages/latest.html 2022-03-30 design economics/advertising --- https://www.reddit.com/r/Anki/comments/8iydl7/using_anki_with_babies_toddlers/ 2022-03-30 psychology/spaced-repetition --- https://www.reddit.com/r/Anki/comments/a9wqau/using_anki_with_babies_toddlers_update/ 2022-03-31 psychology/spaced-repetition --- https://www.inkandswitch.com/end-user-programming/ 2022-03-31 cs design --- https://www.astralcodexten.com/p/nick-cammarata-on-jhana 2022-03-31 psychiatry/meditation psychology/neuroscience --- https://slatestarcodex.com/2017/09/20/meditative-states-as-mental-feedback-loops/ 2022-03-31 psychiatry/meditation psychology/neuroscience --- https://religion.fandom.com/wiki/9_Jhanas 2022-03-31 psychiatry/meditation --- https://www.mctb.org/mctb2/table-of-contents/part-iii-the-samatha-jhanas/25-introduction-to-part-three/ 2022-03-31 psychiatry/meditation --- https://x.com/zerfas33/status/1576300651885142016 2022-03-31 psychiatry/meditation psychology/neuroscience --- https://www.leighb.com/jhana2a.htm 2022-03-31 psychiatry/meditation --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5171207/ Liking, wanting, and the incentive-sensitization theory of addiction Kent C. Berridge, Terry E. Robinson 2016 2022-03-31 [("doi","10.1037/amp0000059")] psychiatry psychology/neuroscience

Rewards are both “liked” and “wanted”, and those 2 words seem almost interchangeable. However, the brain circuitry that mediates the psychological process of “wanting” a particular reward is dissociable from circuitry that mediates the degree to which it is “liked.” Incentive salience or “wanting”, a form of motivation, is generated by large and robust neural systems that include mesolimbic dopamine. By comparison, “liking”, or the actual pleasurable impact of reward consumption, is mediated by smaller and fragile neural systems, and is not dependent on dopamine.

The incentive-sensitization theory posits the essence of drug addiction to be excessive amplification specifically of psychological “wanting”, especially triggered by cues, without necessarily an amplification of “liking.” This is because of long-lasting changes in dopamine-related motivation systems of susceptible individuals, called “neural sensitization.”

A quarter-century after its proposal, evidence has continued to grow in support the incentive-sensitization theory. Further, its scope is now expanding to include diverse behavioral addictions and other psychopathologies.

--- https://www.jhourney.io/ 2022-03-31 psychiatry/meditation psychology/neuroscience --- https://web.archive.org/web/20180726230119/https://www.fastcompany.com/1673017/quimps-plewds-and-grawlixes-the-secret-language-of-comic-strips 2022-04-01 design/typography --- https://x.com/RiversHaveWings/status/1586396490704261121 2022-04-01 ai/nn/gan/biggan ai/nn/transformer/clip/sample --- https://x.com/cubemeow/status/1586392129051701248 2022-04-01 ai/anime ai/nn/diffusion --- https://irchiver.com/ 2022-04-01 cs/linkrot/archiving --- https://en.wikipedia.org/wiki/Tommy_Westphall Tommy Westphall 2022-04-01 fiction philosophy/ontology --- https://arxiv.org/abs/1610.09296 Improving Sampling from Generative Autoencoders with Markov Chains Antonia Creswell, Kai Arulkumaran, Anil Anthony Bharath 2016-10-28 2022-04-01 [("doi","10.48550/arXiv.1610.09296")] ai/nn/diffusion ai/nn/vae

We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. Generative autoencoders are those which are trained to softly enforce a prior on the latent distribution learned by the inference model. We call the distribution to which the inference model maps observed samples, the learned latent distribution, which may not be consistent with the prior.

We formulate a Markov chain Monte Carlo (MCMC) sampling process, equivalent to iteratively decoding and encoding, which allows us to sample from the learned latent distribution. Since, the generative model learns to map from the learned latent distribution, rather than the prior, we may use MCMC to improve the quality of samples drawn from the generative model, especially when the learned latent distribution is far from the prior.

Using MCMC sampling, we are able to reveal previously unseen differences between generative autoencoders trained either with or without a denoising criterion.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7883308/ Inducible Stem-Cell-Derived Embryos Capture Mouse Morphogenetic Events In Vitro Gianluca Amadei, Kasey Y. C. Lau, Joachim De Jonghe, Carlos W. Gantner, Berna Sozen, Christopher Chan, Meng Zhu, Christos Kyprianou, Florian Hollfelder, Magdalena Zernicka-Goetz 2021 2022-04-01 [("doi","10.1016/j.devcel.2020.12.004")] genetics/gametogenesis

The development of mouse embryos can be partially recapitulated by combining embryonic stem cells (ESCs), trophoblast stem cells (TS), and extra-embryonic endoderm (XEN) stem cells to generate embryo-like structures called ETX embryos. Although ETX embryos transcriptionally capture the mouse gastrula, their ability to recapitulate complex morphogenic events such as gastrulation is limited, possibly due to the limited potential of XEN cells.

To address this, we generated ESCs transiently expressing transcription factor Gata4, which drives the extra-embryonic endoderm fate, and combined them with ESCs and TS cells to generate induced ETX embryos (iETX embryos). We show that iETX embryos establish a robust anterior signaling center that migrates unilaterally to break embryo symmetry. Furthermore, iETX embryos gastrulate generating embryonic and extra-embryonic mesoderm and definitive endoderm.

Our findings reveal that replacement of XEN cells with ESCs transiently expressing Gata4 endows iETX embryos with greater developmental potential, thus enabling the study of the establishment of anterior-posterior patterning and gastrulation in an in vitro system.

--- https://en.wikipedia.org/wiki/Domesticated_silver_fox Domesticated silver fox 2022-04-01 genetics/selection/artificial --- https://www.vice.com/en/article/z34xa5/the-long-strange-relationship-between-psychedelics-and-telepathy 2022-04-01 psychedelic --- https://www.vice.com/en/article/xgz3wn/psychedelic-therapy-needs-to-confront-the-mystical 2022-04-01 philosophy/religion psychedelic --- https://www.vice.com/en/article/m7vxm8/its-time-to-start-studying-the-downside-of-psychedelics 2022-04-01 psychedelic psychiatry --- https://en.wikisource.org/wiki/Subjective_Effects_of_Nitrous_Oxide 2022-04-02 psychedelic psychology/cognitive-bias/illusion-of-depth --- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0020097 Neural Activity When People Solve Verbal Problems with Insight Mark Jung-Beeman, Edward M. Bowden, Jason Haberman, Jennifer L. Frymiare, Stella Arambel-Liu, Richard Greenblatt, Paul J. Reber, John Kounios 2004-01-30 2022-04-02 [("doi","10.1371/journal.pbio.0020097")] psychology/neuroscience

Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are used to study neural activity in subjects during a verbal task for which they report solutions achieved by insight

People sometimes solve problems with a unique process called insight, accompanied by an “Aha!” experience. It has long been unclear whether different cognitive and neural processes lead to insight versus non-insight solutions, or if solutions differ only in subsequent subjective feeling. Recent behavioral studies indicate distinct patterns of performance and suggest differential hemispheric involvement for insight and non-insight solutions.

Subjects solved verbal problems, and after each correct solution indicated whether they solved with or without insight.

We observed two objective neural correlates of insight. Functional magnetic resonance imaging (Experiment 1) revealed increased activity in the right hemisphere anterior superior temporal gyrus for insight relative to non-insight solutions. The same region was active during initial solving efforts. Scalp electroencephalogram recordings (Experiment 2) revealed a sudden burst of high-frequency (gamma-band) neural activity in the same area beginning 0.3 s prior to insight solutions. This right anterior temporal area is associated with making connections across distantly related information during comprehension.

Although all problem solving relies on a largely shared cortical network, the sudden flash of insight occurs when solvers engage distinct neural and cognitive processes that allow them to see connections that previously eluded them.

--- https://en.wikipedia.org/wiki/Introspection_illusion Introspection illusion 2022-04-02 philosophy/epistemology psychology/cognitive-bias/illusion-of-depth --- https://qri.org/blog/rigorous-reports 2022-04-02 psychedelic --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5005374/ Intuitive Feelings of Warmth and Confidence in Insight and Non-insight Problem Solving of Magic Tricks Mikael R. Hedne, Elisabeth Norman, Janet Metcalfe 2016 2022-04-02 [("doi","10.3389/fpsyg.2016.01314")] psychology/cognitive-bias/illusion-of-depth

The focus of the current study is on intuitive feelings of insight during problem solving and the extent to which such feelings are predictive of successful problem solving.

We report the results from an experiment (n = 51) that applied a procedure where the to-be-solved problems were 32 short (15s) video recordings of magic tricks. The procedure included metacognitive ratings similar to the “warmth ratings” previously used by Metcalfe and colleagues, as well as confidence ratings. At regular intervals during problem solving, participants indicated the perceived closeness to the correct solution. Participants also indicated directly whether each problem was solved by insight or not.

Problems that people claimed were solved by insight were characterized by higher accuracy and higher confidence than non-insight solutions. There was no difference between the two types of solution in warmth ratings, however. Confidence ratings were more strongly associated with solution accuracy for non-insight than insight trials. Moreover, for insight trials the participants were more likely to repeat their incorrect solutions on a subsequent recognition test.

The results have implications for understanding people’s metacognitive awareness of the cognitive processes involved in problem solving. They also have general implications for our understanding of how intuition and insight are related.

--- https://en.wikipedia.org/wiki/Source-monitoring_error Source-monitoring error 2022-04-02 psychology/cognitive-bias/illusion-of-depth --- https://arxiv.org/abs/2209.10642 Caught in the Crossfire: Fears of Chinese-American Scientists Yu Xie, Xihong Lin, Ju Li, Qian He, Junming Huang 2022-09-21 2022-09-21 [("doi","10.48550/arXiv.2209.10642")] politics science

The US leadership in science and technology has greatly benefited from immigrants from other countries, most notably from China in the recent decades. However, feeling the pressure of potential federal investigation since the 2018 launch of the China Initiative under the Trump administration, Chinese-origin scientists in the US now face higher incentives to leave the US and lower incentives to apply for federal grants.

Analyzing data pertaining to institutional affiliations of more than 2.3 million scientific papers, we find a steady increase in the return migration of Chinese-origin scientists from the US back to China.

We also conducted a survey of Chinese-origin scientists employed by US universities in tenure or tenure-track positions (n = 1300), with results revealing general feelings of fear and anxiety that lead them to consider leaving the US and/or stop applying for federal grants.

--- /doc/ai/nn/transformer/gpt/inner-monologue/2022-10-24-raldi-gpt3doesanastonishinglygoodjobcreatingbothsidesofaninteractivefictiontranscript.html 2022-10-24 2022-10-24 ai/nn/transformer/gpt/inner-monologue fiction/text-game --- https://www.quantamagazine.org/how-genes-can-leap-from-snakes-to-frogs-20221027/ 2022-04-02 genetics/editing genetics/selection/natural --- https://www.nytimes.com/interactive/2022/10/29/opinion/science-fraud-image-manipulation-photoshop.html?sm 2022-04-02 statistics/bias --- https://www.reddit.com/r/Anki/comments/5ixzzx/anki_for_babies/ 2022-04-02 psychology/spaced-repetition --- https://www.reddit.com/r/Anki/comments/eit54e/starting_my_175_year_old_on_anki/ 2022-04-03 psychology/spaced-repetition --- /doc/genetics/heritable/dog/1987-zimen.pdf Ontogeny of approach and flight behavior towards humans in wolves, poodles, and wolf-poodle hybrids Erik Zimen 1987-01-01 2022-04-03 genetics/heritable/dog --- https://www.reddit.com/r/StableDiffusion/comments/yhikn3/new_dreambooth_model_classic_animation_styles/ 2022-04-03 ai/anime ai/nn/diffusion --- /doc/psychology/neuroscience/2019-kilmer.pdf Miniature spiders (with miniature brains) forget sooner Joseph T. Kilmer, Rafael L. Rodríguez 2019-01-01 2022-04-03 [("doi","10.1016/j.anbehav.2019.04.012")] psychology/animal psychology/neuroscience --- /doc/genetics/selection/1924-tolman.pdf 1924-01-01 2022-04-03 genetics/selection psychology/animal/maze --- /doc/genetics/selection/1933-krechevsky.pdf 1933-01-01 2022-04-03 genetics/selection psychology/animal/maze --- /doc/genetics/selection/1935-heron.pdf 1935 2022-04-03 genetics/selection psychology/animal/maze --- /doc/genetics/selection/1956-hirsch.pdf 1956 2022-04-03 genetics/selection psychology/animal/maze --- /doc/genetics/selection/1958-cooper.pdf 1958-01-01 2022-04-03 genetics/selection psychology/animal/maze --- /doc/genetics/heritable/1959-mcclearn.pdf 1959 2022-04-03 genetics/heritable genetics/selection psychology/animal/maze --- /doc/genetics/heritable/1962-mcclearn.pdf 1962 2022-04-04 genetics/heritable genetics/selection psychology/animal/maze --- /doc/genetics/selection/1967-hirsch-behaviorgeneticanalysis.pdf 1967 2022-04-04 genetics/selection psychology/animal/maze --- /doc/genetics/selection/artificial/1972-wahlsten.pdf Genetic Experiments with Animal Learning: A Critical Review 1972 2022-04-04 genetics/selection/artificial psychology/animal/maze --- https://www.biorxiv.org/content/10.1101/2022.02.06.479300.full Aging clocks, entropy, and the limits of age-reversal Andrei E. Tarkhov, Kirill A. Denisov, Peter O. Fedichev 2022-10-11 2022-10-11 [("doi","10.1101/2022.02.06.479300")] longevity/epigenetics

We analyze aging signatures of DNA methylation and longitudinal electronic medical records from the UK Biobank datasets and observe that:

aging is driven by a large number of independent and infrequent transitions between metastable states in a vast configuration space. The compound effect of configuration changes can be captured by a single stochastic variable, thermodynamic biological age (tBA), tracking entropy produced, and hence information lost during aging. We show that tBA increases with age, causes the linear and irreversible drift of physiological state variables, reduces resilience, and drives the exponential acceleration of chronic disease incidence and death risks.

The entropic character of aging drift sets severe constraints on the possibilities of age reversal. However, we highlight the universal features of configuration transitions, suggest practical ways of suppressing the rate of aging in humans, and speculate on the possibility of achieving negligible senescence.

--- https://arxiv.org/abs/2210.13438#facebook High Fidelity Neural Audio Compression Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi 2022-10-24 2022-10-24 [("doi","10.48550/arXiv.2210.13438")] ai/music ai/nn/gan cs/algorithm/information/compression

[ blog] We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural networks.

It consists in a streaming encoder-decoder architecture with quantized latent space trained in an end-to-end fashion. We simplify and speed-up the training by using a single multiscale spectrogram adversary that efficiently reduces artifacts and produce high-quality samples. We introduce a novel loss balancer mechanism to stabilize training: the weight of a loss now defines the fraction of the overall gradient it should represent, thus decoupling the choice of this hyper-parameter from the typical scale of the loss. Finally, we study how lightweight Transformer models can be used to further compress the obtained representation by up to 40%, while staying faster than real time. We provide a detailed description of the key design choices of the proposed model including: training objective, architectural changes and a study of various perceptual loss functions.

We present an extensive subjective evaluation (MUSHRA tests) together with an ablation study for a range of bandwidths and audio domains, including speech, noisy-reverberant speech, and music. Our approach is superior to the baselines methods across all evaluated settings, considering both 24 kHz monophonic and 48 kHz stereophonic audio.

Code and models are available at github.com/facebookresearch/encodec.

--- https://www.reddit.com/r/StableDiffusion/comments/ydz2jz/someone_showed_me_a_similar_picture_generated/ 2022-04-04 ai/nn/transformer/clip/sample --- https://x.com/dust4ai/status/1587104029712203778 2022-04-04 ai/nn/retrieval ai/nn/transformer/gpt --- https://arxiv.org/abs/2210.15458#google Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models Luke Vilnis, Yury Zemlyanskiy, Patrick Murray, Alexandre Passos, Sumit Sanghai 2022-10-18 2022-10-18 [("doi","10.48550/arXiv.2210.15458")] ai/nn/sampling ai/nn/transformer/gpt statistics/probability

Decoding methods for large language models often trade-off between diversity of outputs and parallelism of computation. Methods such as beam search and Gumbel top-k sampling can guarantee a different output for each element of the beam, but are not easy to parallelize. Alternatively, methods such as temperature sampling and its modifications (top-k sampling, nucleus sampling, typical decoding, and others), are embarrassingly parallel, but have no guarantees about duplicate samples.

We present a framework for sampling according to an arithmetic code book implicitly defined by a large language model, compatible with common sampling variations, with provable beam diversity under certain conditions, as well as being embarrassingly parallel and providing unbiased and consistent expectations from the original model.

We demonstrate the effectiveness of our approach on WMT machine translation, showing substantially reduced variance when estimating expected BLEU score and up to 1 point increased BLEU in oracle experiments.

[cf. RQMC, low-discrepancy sequence & control/antithetic variates, mirrored sampling in evolution strategies]

--- https://arxiv.org/abs/2202.07808 Policy Learning and Evaluation with Randomized Quasi-Monte Carlo Sebastien M. R. Arnold, Pierre L’Ecuyer, Liyu Chen, Yi-fan Chen, Fei Sha 2022-02-16 2022-04-04 [("doi","10.48550/arXiv.2202.07808")] reinforcement-learning/exploration reinforcement-learning/model-free statistics/power-analysis statistics/probability

Reinforcement learning constantly deals with hard integrals, for example when computing expectations in policy evaluation and policy iteration. These integrals are rarely analytically solvable and typically estimated with the Monte Carlo method, which induces high variance in policy values and gradients.

In this work, we propose to replace Monte Carlo samples with low-discrepancy point sets. We combine policy gradient methods with Randomized Quasi-Monte Carlo, yielding variance-reduced formulations of policy gradient and actor-critic algorithms.

These formulations are effective for policy evaluation and policy improvement, as they outperform state-of-the-art algorithms on standardized continuous control benchmarks.

Our empirical analyses validate the intuition that replacing Monte Carlo with Quasi-Monte Carlo yields more accurate gradient estimates.

--- https://marginalrevolution.com/marginalrevolution/2013/12/shiller-on-trills.html 2022-04-04 statistics/prediction --- https://x.com/shubroski/status/1587136794797244417 2022-04-04 ai/nn/transformer/gpt/non-fiction --- /doc/history/2013-dubin-fabliauxtranslations-stmartinsfourwishes.pdf St Martin’s Four Wishes Ned Dubin 2013-06-10 2022-04-05 fiction/humor history --- https://x.com/ShayneRedford/status/1587138195313106945 2022-04-05 ai/nn/transformer/gpt/non-fiction --- https://x.com/denny_zhou/status/1587115933293678592 2022-04-05 ai/nn/transformer/gpt/inner-monologue --- https://colab.research.google.com/drive/1ZrF6fJFMUaqloHJP1PqJTxMSJxKd-BVg 2022-04-05 ai/nn/transformer/gpt/jukebox --- https://www.nytimes.com/2022/10/24/science/lizards-reptiles-social-behavior.html 2022-04-05 iq/animal --- https://www.inkandswitch.com/potluck/ 2022-04-05 design --- https://huggingface.co/Onodofthenorth/SD_PixelArt_SpriteSheet_Generator 2022-04-05 ai/anime ai/nn/diffusion --- https://arxiv.org/abs/2210.13352#huggingface ESB: A Benchmark For Multi-Domain End-to-End Speech Recognition Sanchit Gandhi, Patrick von Platen, Alexander M. Rush 2022-10-24 2022-10-24 [("doi","10.48550/arXiv.2210.13352")] ai/nn/transformer/gpt/whisper

Speech recognition applications cover a range of different audio and text distributions, with different speaking styles, background noise, transcription punctuation and character casing. However, many speech recognition systems require dataset-specific tuning (audio filtering, punctuation removal and normalization of casing), therefore assuming a-priori knowledge of both the audio and text distributions. This tuning requirement can lead to systems failing to generalize to other datasets and domains.

To promote the development of multi-domain speech systems, we introduce the End-to-end Speech Benchmark (ESB) for evaluating the performance of a single automatic speech recognition (ASR) system across a broad set of speech datasets. Benchmarked systems must use the same data pre/post-processing algorithm across datasets—assuming the audio and text data distributions are a-priori unknown.

We compare a series of state-of-the-art (SoTA) end-to-end (E2E) systems on this benchmark, demonstrating how a single speech system can be applied and evaluated on a wide range of data distributions.

We find E2E systems to be effective across datasets: in a fair comparison, E2E systems achieve within 2.6% of SoTA systems tuned to a specific dataset. [One of the best: Whisper.]

Our analysis reveals that transcription artefacts, such as punctuation and casing, pose difficulties for ASR systems and should be included in evaluation.

We believe E2E benchmarking over a range of datasets promotes the research of multi-domain speech recognition systems.

ESB is available at https://huggingface.co/esb.

--- https://arxiv.org/abs/2210.11610#google Large Language Models Can Self-Improve Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, Jiawei Han 2022-10-20 2022-10-20 [("doi","10.48550/arXiv.2210.11610")] ai/dataset ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm

Large Language Models (LLMs) have achieved excellent performances in various tasks. However, fine-tuning an LLM requires extensive supervision. Human, on the other hand, may improve their reasoning abilities by self-thinking without external inputs.

In this work, we demonstrate that an LLM is also capable of self-improving with only unlabeled datasets. We use a pre-trained PaLM LLM to generate “high-confidence” rationale-augmented answers for unlabeled questions using Chain-of-Thought prompting and self-consistency, and fine-tune the LLM using those self-generated solutions as target outputs.

We show that our approach improves the general reasoning ability of a 540B-parameter LLM (74.4% → 82.1% on GSM8K, 78.2% → 83.0% on DROP, 90.0% → 94.4% on OpenbookQA, and 63.4% → 67.9% on ANLI-A3) and achieves state-of-the-art-level performance, without any ground truth label.

We conduct ablation studies and show that fine-tuning on reasoning is critical for self-improvement.

[Jack Clark summary:

The results are mind-blowing: Using this technique, the researchers are able to get new state-of-the-art results on 4⁄6 reasoning benchmarks. They also show very good results on out-of-domain tasks, e.g arithmetic reasoning and natural language reasoning. It generally seems like chain-of-thought plus self-consistency leads to robust gains on a large set of diverse tasks. Also, it’s an inherently simple approach, and simple tends to scale.

Why this matters—self-bootstrapping systems: This is an example of a self-bootstrapping AI; the language model can get better performance purely by leveraging its own capabilities. This is also a neat illustration of how there’s a current capabilities overhang in AI development; the LMs we have today are actually much more powerful than they appear, and we mostly need to invent ways to uncover these techniques or, as in the research here, figure out how to get LMs to themselves reveal their capabilities to us.]

--- https://arxiv.org/abs/2207.12456#microsoft Overwatch: Learning Patterns in Code Edit Sequences Yuhao Zhang, Yasharth Bajpai, Priyanshu Gupta, Ameya Ketkar, Miltiadis Allamanis, Titus Barik, Sumit Gulwani, Arjun Radhakrishna, Mohammad Raza, Gustavo Soares, Ashish Tiwari 2022-07-25 2022-07-25 [("doi","10.48550/arXiv.2207.12456")] cs/algorithm

[logic-based, non-neural] Integrated Development Environments (IDEs) provide tool support to automate many source code editing tasks. Traditionally, IDEs use only the spatial context, ie. the location where the developer is editing, to generate candidate edit recommendations. However, spatial context alone is often not sufficient to confidently predict the developer’s next edit, and thus IDEs generate many suggestions at a location. Therefore, IDEs generally do not actively offer suggestions and instead, the developer is usually required to click on a specific icon or menu and then select from a large list of potential suggestions. As a consequence, developers often miss the opportunity to use the tool support because they are not aware it exists or forget to use it.

To better understand common patterns in developer behavior and produce better edit recommendations, we can additionally use the temporal context, ie. the edits that a developer was recently performing. To enable edit recommendations based on temporal context, we present Overwatch, a novel technique for learning edit sequence patterns from traces of developers’ edits performed in an IDE.

Our experiments show that Overwatch has 78% precision and that Overwatch not only completed edits when developers missed the opportunity to use the IDE tool support but also predicted new edits that have no tool support in the IDE.

--- https://eare.eu/japan-amends-tdm-exception-copyright/ 2022-04-05 ai/anime economics/copyright --- https://www.webdesignmuseum.org/ 2022-04-06 cs/css design --- https://x.com/BHolmesDev/status/1587788026637336576 2022-04-06 ai/nn/transformer/gpt/codex --- https://en.wikipedia.org/wiki/W._P._Kinsella W. P. Kinsella 2022-04-06 psychiatry/traumatic-brain-injury --- https://x.com/_jasonwei/status/1587858146948567041 2022-04-06 ai/nn/transformer/gpt/non-fiction reinforcement-learning/meta-learning --- https://x.com/sundarpichai/status/1587872629137948672 2022-04-06 ai/nn/diffusion ai/video/generation --- https://x.com/amasad/status/1587702550349811712 2022-04-06 ai/nn/transformer/gpt/codex --- https://www.reddit.com/r/MachineLearning/comments/ykxr4v/p_made_a_text_generation_model_to_extend_stable/ 2022-04-06 ai/nn/diffusion ai/nn/transformer/gpt/non-fiction --- https://en.wikipedia.org/wiki/Krista_and_Tatiana_Hogan Krista and Tatiana Hogan 2022-04-06 philosophy/mind psychology/neuroscience --- https://en.wikipedia.org/w/index.php?title=LSD_and_schizophrenia&oldid=699494403 2022-04-06 psychedelic psychiatry/schizophrenia --- https://arxiv.org/abs/cond-mat/9907500 The physical limits of communication Michael Lachmann, M. E. J. Newman, Cristopher Moore 1999-07-30 2022-04-06 [("doi","10.1119/1.1773578")] cs/algorithm/information

It has been well-known since the pioneering work of Claude Shannon in the 1940s that a message transmitted with optimal efficiency over a channel of limited bandwidth is indistinguishable from random noise to a receiver who is unfamiliar with the language in which the message is written.

In this letter we demonstrate an equivalent result about electromagnetic transmissions. We show that when electromagnetic radiation is used as the transmission medium, the most information-efficient format for a given message is indistinguishable from black-body radiation to a receiver who is unfamiliar with that format. The characteristic temperature of the radiation is set by the amount of energy used to make the transmission.

If information is not encoded in the direction of the radiation, but only its timing, energy or polarization, then the most efficient format has the form of a one-dimensional black-body spectrum which is easily distinguished from the three-dimensional case.

...For an energy budget of 1 Js−1, this gives a maximum information rate of 2.03 × 1017 bits per second.

--- https://en.wikipedia.org/wiki/Atlantropa Atlantropa 2022-04-06 technology --- https://arxiv.org/abs/2210.02338 Lazarus Stars: Numerical investigations of stellar evolution with star-lifting Matthew Scoggins, David Kipping 2022-10-05 2022-10-05 [("doi","10.48550/arXiv.2210.02338")] science transhumanism

The aging and gradual brightening of the Sun will challenge Earth’s habitability in the next few billion years. If life exists elsewhere in the Universe, the aging of their host star will similarly pose an existential threat. One solution to this threat, which we dub a Lazarus star, is for an advanced civilization to remove (or “star-lift”) mass from their host star at a rate which offsets the increase in luminosity, keeping the flux on the habitable planet(s) constant and extending the lifetime of their star. While this idea has existed since 1985 when it was first proposed by Criswell, numerical investigations of star-lifting have been lacking.

Here, we use MIST evolutionary tracks to find mass vs. age and ̇M vs. age relations with initial mass ranging from 0.15–1.3M☉. We do this for two different implementations of star-lifting, isoluminosity and isoirradiance, where both hold the incident flux on the habitable planet(s) constant, but the former keeps the orbital radius constant and the latter accounts for a changing orbital radius.

We reveal two distinct behaviors for these Lazarus stars. For most stars initially below ~0.3M☉, we find that their lifetimes can be gradually extended until their mass reaches 0.1, approaching the hydrogen burning limit—with a lifetime of many trillions of years. In contrast, for more massive stars, their natural evolution causes them to leave the main sequence before reaching the hydrogen burning limit. For example, the Sun has a main-sequence lifetime which can be increased 6 → 10 Gyrs if we started star-lifting for isoluminosity (isoirradiance) today. This requires a mass loss rate of ~0.02MCeres per year.

We compare star-lifting to other survival strategies and briefly discuss methods for detecting these engineered stars.

--- https://en.wikipedia.org/wiki/Star_lifting Star lifting 2022-04-07 science transhumanism --- https://x.com/jasonbaldridge/status/1588017062802329604 2022-04-07 ai/nn/diffusion --- https://arxiv.org/abs/1601.02897 Life under a black sun Tomáš Opatrný, Lukáš Richterek, Pavel Bakala 2016-01-12 2022-04-07 [("doi","10.1119/1.4966905")] science

Life is dependent on the income of energy with low entropy and the disposal of energy with high entropy. On Earth, the low-entropy energy is provided by solar radiation and the high-entropy energy is disposed as infrared radiation emitted into the cold space.

Here we turn the situation around and assume cosmic background radiation as the low-entropy source of energy for a planet orbiting a black hole into which the [relatively] high-entropy energy [from the background] is disposed.

We estimate the power that can be produced by thermodynamic processes on such a planet, with a particular interest in planets orbiting a fast rotating Kerr black hole as in the science fiction movie Interstellar.

We also briefly discuss a reverse Dyson sphere absorbing cosmic background radiation from the outside and dumping waste energy to a black hole inside.

--- https://publicdomainreview.org/collection/kedzie-shadows/ 2022-04-07 design history/public-domain-review science --- https://www.wired.com/story/cannabis-banking-fintech-regulations/ 2022-04-07 marijuana --- https://www.biorxiv.org/content/10.1101/2022.11.01.514719.full A generalizable epigenetic clock captures aging in two nonhuman primates Elisabeth A. Goldman, Marina M. Watowich, Kenneth L. Chiou, Arianne Mercer, Sierra N. Sams, Julie E. Horvath, Jordan Anderson, Cayo Biobank Research Unit, Jenny Tung, James P. Higham, Lauren J. N. Brent, Melween I. Martinez, Michael James Montague, Michael L. Platt, Kirstin N. Sterner, Noah Snyder-Mackler 2022-11-02 2022-11-02 [("doi","10.1101/2022.11.01.514719")] longevity/epigenetics

Epigenetic clocks generated from DNA methylation array data provide important insights into biological aging, disease susceptibility, and mortality risk. However, these clocks cannot be applied to high-throughput, sequence-based datasets more commonly used to study nonhuman animals.

Here, we built a generalizable epigenetic clock using genome-wide DNA methylation data from 493 free-ranging rhesus macaques.

Using a sliding-window approach that maximizes generalizability across datasets and species, this model predicted age with high accuracy (± 1.42 years) in held-out test samples, as well as in two independent test sets: rhesus macaques from a captive population (n = 43) and wild baboons in Kenya (n = 271). Our model can also be used to generate insight into the factors hypothesized to alter epigenetic aging, including social status and exposure to traumatic events.

Our results thus provide a flexible tool for predicting age in other populations and species and illustrate how connecting behavioral data with the epigenetic clock can uncover social influences on biological age.

--- https://openai.com/blog/dall-e-api-now-available-in-public-beta/ 2022-04-07 ai/nn/transformer/gpt/dall-e --- https://www.wired.com/story/alphabay-series-part-2-pimp-alex-91/ 2022-04-07 bitcoin darknet-market/alphabay --- https://github.com/langchain-ai/langchain 2022-04-07 ai/nn/transformer/gpt --- https://arxiv.org/abs/2210.11399#google U-PaLM: Transcending Scaling Laws with 0.1% Extra Compute Yi Tay, Jason Wei, Hyung Won Chung, Vinh Q. Tran, David R. So, Siamak Shakeri, Xavier Garcia, Huaixiu Steven Zheng, Jinfeng Rao, Aakanksha Chowdhery, Denny Zhou, Donald Metzler, Slav Petrov, Neil Houlsby, Quoc V. Le, Mostafa Dehghani 2022-10-20 2022-10-20 [("doi","10.48550/arXiv.2210.11399")] ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm ai/scaling/emergence

Scaling language models improves performance but comes with computational costs.

This paper proposes UL2R, a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute. The key idea is to continue training a state-of-the-art large language model (eg. PaLM) on a few more steps with UL2’s mixture-of-denoiser objective. We show that, with almost negligible extra computational costs and no new sources of data, we are able to substantially improve the scaling properties of large language models on downstream metrics.

In this paper, we continue training PaLM with UL2R, introducing a new set of models at 8B, 62B, and 540B scale which we call U-PaLM. Impressively, at 540B scale, we show an ~2× computational savings rate where U-PaLM achieves the same performance as the final PaLM 540B model at around half its computational budget (ie. saving ~4.4 million TPUv4 hours). We further show that this improved scaling curve leads to ‘emergent abilities’ on challenging BIG-Bench tasks—for instance, U-PaLM does much better than PaLM on some tasks or demonstrates better quality at much smaller scale (62B as opposed to 540B).

Overall, we show that U-PaLM outperforms PaLM on many few-shot setups, ie. English NLP tasks (eg. commonsense reasoning, question answering), reasoning tasks with chain-of-thought (eg. GSM8K), multilingual tasks (MGSM, TydiQA), MMLU and challenging BIG-Bench tasks.

Finally, we provide qualitative examples showing the new capabilities of U-PaLM for single and multi-span infilling.

--- https://research.google/blog/pathways-language-model-palm-scaling-to-540-billion-parameters-for-breakthrough-performance/ Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance Chowdhery, Narang 2022 2022-04-08 ai/nn/transformer/gpt/palm --- https://arxiv.org/abs/2204.02311#page=38&org=google PaLM § Figure 19: [Explaining a Joke / Inference Chaining] Each ‘Input” was independently prepended with the same 2-shot exemplar shown at the top, and “Model Output’ shows the greedy decoding output of PaLM 540B. The two exemplar jokes are known jokes (explanations written by authors), while all evaluated jokes were written by the authors. Of course, these jokes do share abstract premises with existing jokes (wordplay, reliability, humorous analogies, reversal-of-expectations). The inference chaining examples were also written by the authors. 2022-04-08 ai/nn/transformer/gpt/palm fiction/humor --- https://www.reddit.com/r/GPT3/comments/twxtwg/how_gpt3_answers_the_google_pathway_sample/ 2022-04-08 ai/nn/transformer/gpt/palm --- https://www.lesswrong.com/posts/YzbQeCiwoLBHrvAh4 2022-04-08 ai/nn/transformer/gpt/palm --- https://www.lesswrong.com/posts/EHbJ69JDs4suovpLw/testing-palm-prompts-on-gpt3 2022-04-08 ai/nn/transformer/gpt/palm --- https://arxiv.org/abs/2206.14858#google Solving Quantitative Reasoning Problems with Language Models Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra 2022-06-29 2022-06-29 [("doi","10.48550/arXiv.2206.14858")] ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm

Language models have achieved remarkable performance on a wide range of tasks that require natural language understanding. Nevertheless, state-of-the-art models have generally struggled with tasks that require quantitative reasoning, such as solving mathematics, science, and engineering problems at the college level.

To help close this gap, we introduce Minerva, a large language model pretrained on general natural language data and further trained on technical content. The model achieves state-of-the-art performance on technical benchmarks without the use of external tools.

We also evaluate our model on over two hundred undergraduate-level problems in physics, biology, chemistry, economics, and other sciences that require quantitative reasoning, and find that the model can correctly answer nearly a third of them.

--- https://research.google/blog/minerva-solving-quantitative-reasoning-problems-with-language-models/ 2022-04-08 ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm --- https://www.lesswrong.com/posts/mLuQfS7gmfr4nwTdv/google-s-new-540-billion-parameter-language-model 2022-04-08 ai/nn/transformer/gpt/palm --- https://www.lesswrong.com/posts/JkKeFt2u4k4Q4Bmnx/linkpost-solving-quantitative-reasoning-problems-with 2022-04-08 ai/nn/transformer/gpt/palm --- https://minerva-demo.github.io/#category=Algebra&index=1 2022-04-08 ai/nn/transformer/gpt/palm --- https://x.com/ArtirKel/status/1588245580160983040 2022-04-09 ai/nn/transformer/gpt/codex --- https://x.com/ArtirKel/status/1588246269385838594 2022-04-09 ai/nn/transformer/gpt/codex --- https://en.wikipedia.org/wiki/Tanimbar_corella#Intelligence Tanimbar corella § Intelligence 2022-04-09 iq/animal psychology/animal/bird --- https://githubcopilotlitigation.com/ 2022-04-09 ai/nn/transformer/gpt/codex economics/copyright --- https://wordcraft-writers-workshop.appspot.com/stories/diana-hamilton 2022-04-09 ai/nn/transformer/gpt/lamda ai/poetry --- https://wordcraft-writers-workshop.appspot.com/stories/eugenia-triantafyllou 2022-04-09 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/lamda --- https://wordcraft-writers-workshop.appspot.com/stories/allison-parrish 2022-04-09 ai/nn/transformer/gpt/lamda ai/text-style-transfer --- https://x.com/MikePFrank/status/1588212826811772928 2022-04-09 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/fiction --- https://plato.stanford.edu/entries/phenomenology-religion/ 2022-04-09 philosophy/religion --- https://n-e-r-v-o-u-s.com/blog/?p=9225 2022-04-09 cs/cellular-automaton design --- https://blog.amandaghassaei.com/2022/10/25/digital-marbling/ 2022-04-09 cs/cellular-automaton design --- https://www.reddit.com/r/StableDiffusion/comments/ylroyp/made_in_abyss_dreambooth_model_i_am_working_on/ 2022-04-10 ai/anime ai/nn/diffusion --- https://paperswithcode.com/datasets 2022-04-10 ai/dataset --- https://keithito.com/LJ-Speech-Dataset/ 2022-04-10 ai/dataset --- https://en.wikipedia.org/wiki/UK_Biobank UK Biobank 2022-04-10 ai/dataset genetics/heritable sociology/technology statistics/power-analysis --- https://arxiv.org/abs/1606.06031 The LAMBADA dataset: Word prediction requiring a broad discourse context Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Quan Ngoc Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, Raquel Fernández 2016-06-20 2022-04-10 [("doi","10.48550/arXiv.1606.06031")] ai/dataset ai/scaling

We introduce LAMBADA, a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task.

LAMBADA is a collection of narrative passages sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse.

We show that LAMBADA exemplifies a wide range of linguistic phenomena, and that none of several state-of-the-art language models reaches accuracy above 1% on this novel benchmark.

We thus propose LAMBADA as a challenging test set, meant to encourage the development of new models capable of genuine understanding of broad context in natural language text.

--- https://en.wikipedia.org/wiki/MNIST_database MNIST database 2022-04-10 ai/dataset ai/nn/cnn --- https://en.wikipedia.org/wiki/Common_Crawl Common Crawl 2022-04-10 ai/dataset --- https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/37648.pdf 2022-04-10 ai/dataset ai/nn --- https://arxiv.org/abs/2104.04258 Counter-Strike Deathmatch with Large-Scale Behavioral Cloning Tim Pearce, Jun Zhu 2021-04-09 2022-04-10 [("doi","10.48550/arXiv.2104.04258")] reinforcement-learning/imitation-learning reinforcement-learning/model-free

This paper describes an AI agent that plays the popular first-person-shooter (FPS) video game Counter-Strike; Global Offensive (CSGO) from pixel input.

The agent, a deep neural network, matches the performance of the medium difficulty built-in AI on the deathmatch game mode, whilst adopting a human-like play style. Unlike much prior work in games, no API is available for CSGO, so algorithms must train and run in real-time. This limits the quantity of on-policy data that can be generated, precluding many reinforcement learning algorithms.

Our solution uses behavioral cloning—training on a large noisy dataset scraped from human play on online servers (4 million frames, comparable in size to ImageNet), and a smaller dataset of high-quality expert demonstrations.

This scale is an order of magnitude larger than prior work on imitation learning in FPS games.

--- https://en.wikipedia.org/wiki/Chromostereopsis Chromostereopsis 2022-04-10 design/typography/rubrication --- https://x.com/YennieJun/status/1588615988391841793 2022-04-11 ai/nn/transformer/gpt/fiction --- https://www.chinafile.com/library/nyrb-china-archive/china-back-authoritarianism 2022-04-11 politics sociology/abandoned-footnotes --- https://incredible.pm/ 2022-04-11 cs/css philosophy/logic --- /doc/iq/high/smpy/1985-rootbernstein.pdf Visual Thinking: The Art of Imagining Reality Robert Scott Root-Bernstein 1985-01-01 2022-04-11 [("doi","10.2307/20486640")] iq/high/smpy psychology/vision --- https://milkyeggs.com/japanese/a-sociocultural-comparison-of-japanese-and-american-high-schools/ 2022-04-11 anime japan --- https://www.girlsandcorpses.com/monthly/questions.html 2022-04-11 cryonics --- https://huggingface.co/nitrosocke/redshift-diffusion 2022-04-11 ai/nn/diffusion --- https://arxiv.org/abs/2210.06280 Language Models are Realistic Tabular Data Generators Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci 2022-10-12 2022-10-12 [("doi","10.48550/arXiv.2210.06280")] ai/tabular

Tabular data is among the oldest and most ubiquitous forms of data. However, the generation of synthetic samples with the original data’s characteristics still remains a challenge for tabular data. While many generative models from the computer vision domain, such as autoencoders or generative adversarial networks, have been adapted for tabular data generation, less research has been directed towards recent transformer-based large language models (LLMs), which are also generative in nature.

To this end, we propose GReaT (Generation of Realistic Tabular data), which exploits an auto-regressive generative LLM to sample synthetic and yet highly realistic tabular data. Furthermore, GReaT can model tabular data distributions by conditioning on any subset of features; the remaining features are sampled without additional overhead.

We demonstrate the effectiveness of the proposed approach in a series of experiments that quantify the validity and quality of the produced data samples from multiple angles. We find that GReaT maintains state-of-the-art performance across many real-world data sets with heterogeneous feature types.

--- https://www.biorxiv.org/content/10.1101/2022.11.03.515083.full A gene-level test for directional selection on gene expression Laura L. Colbran, Iain Mathieson 2022-11-04 2022-11-04 [("doi","10.1101/2022.11.03.515083")] genetics/selection/natural/human

Human phenotypes and evolutionary fitness are influenced by both coding and non-coding genetic variants. However, most variants associated in genome-wide association studies and. scans for selection are non-coding. Interpretation of these variants’ effects and understanding of the way in which they contribute to phenotypic variation and adaptation is therefore limited by our understanding of gene regulation and by the difficulty in confidently linking non-coding variants to genes.

To overcome this, we developed a gene-by-gene test for population-specific selection based on combinations of regulatory variants. Specifically, we extended the Qx test for polygenic selection to gene expression models trained using joint-tissue imputation, a transcriptome-wide association method trained on paired genotype and RNA-seq data. We used effect sizes and variants from those models to calculate Qx statistics for 17,388 protein-coding genes based on allele frequencies in the 26 1000 Genomes populations.

We identified 45 genes with statistically-significant evidence (FDR < 0.1) for selection, including FADS1, KHK, SULT1A2, ITGAM, and genes in the HLA region. We further confirm that statistically-significant selection signals do correspond to plausible population-level differences in predicted expression.

Our gene-level Qx score is independent of other methods for detecting selection based on genomic variation, may therefore be useful when used in combination with more traditional selection tests to specifically identify selection on regulatory variation. However, we find that very few (0.2%) genes have strong evidence for directional, population-specific selection on the component of their expression that is predicted by cis-regulatory variants. While this is consistent with most cis-regulatory variation evolving under genetic drift or stabilizing selection, it is also possible that any effects are smaller than we can detect, or that population-specific selection is driven by tissue-specific or trans effects.

Overall, our results demonstrate the utility of one approach to combining population-level information with functional data to understand the evolution of gene expression.

--- https://arxiv.org/abs/2211.01480#deepmind Over-communicate no more: Situated RL agents learn concise communication protocols Aleksandra Kalinowska, Elnaz Davoodi, Florian Strub, Kory W. Mathewson, Ivana Kajic, Michael Bowling, Todd D. Murphey, Patrick M. Pilarski 2022-11-02 2022-11-02 [("doi","10.48550/arXiv.2211.01480")] reinforcement-learning/model-free reinforcement-learning/multi-agent

While it is known that communication facilitates cooperation in multi-agent settings, it is unclear how to design artificial agents that can learn to effectively and efficiently communicate with each other. Much research on communication emergence uses reinforcement learning (RL) and explores unsituated communication in one-step referential tasks—the tasks are not temporally interactive and lack time pressures typically present in natural communication. In these settings, agents may successfully learn to communicate, but they do not learn to exchange information concisely—they tend towards over-communication and an inefficient encoding.

Here, we explore situated communication in a multi-step task, where the acting agent has to forgo an environmental action to communicate. Thus, we impose an opportunity cost on communication and mimic the real-world pressure of passing time. We compare communication emergence under this pressure against learning to communicate with a cost on articulation effort, implemented as a per-message penalty (fixed and progressively increasing).

We find that while all tested pressures can disincentivize over-communication, situated communication does it most effectively and, unlike the cost on effort, does not negatively impact emergence.

Implementing an opportunity cost on communication in a temporally extended environment is a step towards embodiment, and might be a pre-condition for incentivizing efficient, human-like communication.

--- https://en.wikipedia.org/wiki/Duospaced_font Duospaced font 2022-04-11 design/typography --- https://arxiv.org/abs/2211.02408 Rickrolling the Artist: Injecting Invisible Backdoors into Text-Guided Image Generation Models Lukas Struppek, Dominik Hintersdorf, Kristian Kersting 2022-11-04 2022-11-04 [("doi","10.48550/arXiv.2211.02408")] ai/nn/adversarial ai/nn/diffusion cs/security

While text-to-image synthesis currently enjoys great popularity among researchers and the general public, the security of these models has been neglected so far. Many text-guided image generation models rely on pre-trained text encoders from external sources, and their users trust that the retrieved models will behave as promised. Unfortunately, this might not be the case.

We introduce backdoor attacks against text-guided generative models and demonstrate that their text encoders pose a major tampering risk. Our attacks only slightly alter an encoder so that no suspicious model behavior is apparent for image generations with clean prompts. By then inserting a single non-Latin character into the prompt, the adversary can trigger the model to either generate images with pre-defined attributes or images following a hidden, potentially malicious description. We empirically demonstrate the high effectiveness of our attacks on Stable Diffusion and highlight that the injection process of a single backdoor takes less than two minutes.

Besides phrasing our approach solely as an attack, it can also force an encoder to forget phrases related to certain concepts, such as nudity or violence, and help to make image generation safer.

--- https://arxiv.org/abs/2211.02069 LMentry: A Language Model Benchmark of Elementary Language Tasks Avia Efrat, Or Honovich, Omer Levy 2022-11-03 2022-11-03 [("doi","10.48550/arXiv.2211.02069")] ai/nn/tokenization ai/nn/transformer/gpt/3/nonfiction

[Merely measures already-known byte-pair encoding pathology] As the performance of large language models rapidly improves, benchmarks are getting larger and more complex as well.

We present LMentry, a benchmark that avoids this “arms race” by focusing on a compact set of tasks that are trivial to humans, eg. writing a sentence containing a specific word, identifying which words in a list belong to a specific category, or choosing which of two words is longer. LMentry is specifically designed to provide quick and interpretable insights into the capabilities and robustness of large language models.

Our experiments reveal a wide variety of failure cases that, while immediately obvious to humans, pose a considerable challenge for large language models, including OpenAI’s latest 175B-parameter instruction-tuned model, textdavinci-002.

LMentry complements contemporary evaluation approaches of large language models, providing a quick, automatic, and easy-to-run “unit test”, without resorting to large benchmark suites of complex tasks.

--- https://arxiv.org/abs/2210.14431 n-gram Is Back: Residual Learning of Neural Text Generation with n-gram Language Model Huayang Li, Deng Cai, Jin Xu, Taro Watanabe 2022-10-26 2022-10-26 [("doi","10.48550/arXiv.2210.14431")] ai/nn/tokenization ai/nn/transformer

n-gram language models (LM) have been largely superseded by neural LMs as the latter exhibits better performance. However, we find that n-gram models can achieve satisfactory performance on a large proportion of testing cases, indicating they have already captured abundant knowledge of the language with relatively low computational cost.

With this observation, we propose to learn a neural LM that fits the residual between an n-gram LM and the real-data distribution. The combination of n-gram and neural LMs not only allows the neural part to focus on the deeper understanding of language but also provides a flexible way to customize an LM by switching the underlying n-gram model without changing the neural model.

Experimental results on 3 typical language tasks (ie. language modeling, machine translation, and summarization) demonstrate that our approach attains additional performance gains over popular standalone neural models consistently.

We also show that our approach allows for effective domain adaptation by simply switching to a domain-specific n-gram model, without any extra training.

Our code is released at Github.

--- https://huggingface.co/blog/fine-tune-whisper 2022-04-12 ai/nn/transformer/gpt/whisper --- /doc/nootropic/quantified-self/1998-altman.pdf Who Goes First? The Story of Self-Experimentation in Medicine Lawrence K. Altman 1998-01-01 2022-04-12 nootropic/quantified-self statistics/causality --- https://www.technologyreview.com/2021/10/27/1036821/brain-computer-interface-implant-mouse/ 2022-04-12 psychology/neuroscience --- https://www.businesswire.com/news/home/20221107005057/en/CoreWeave-Among-First-Cloud-Providers-to-Offer-NVIDIA-HGX-H100-Supercomputers-Set-to-Transform-AI-Landscape Reserve capacity of NVIDIA HGX H100s on CoreWeave now: available at scale in Q1 2023 starting at $2.23/hr CoreWeave 2022-11-07 2022-11-07 ai/scaling/hardware --- https://en.wikipedia.org/wiki/Satellaview Satellaview 2022-04-12 technology --- https://arxiv.org/abs/2209.14610 Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, Ashwin Kalyan 2022-09-29 2022-09-29 [("doi","10.48550/arXiv.2209.14610")] ai/dataset ai/nn/transformer/gpt/inner-monologue math reinforcement-learning/model-free

Mathematical reasoning, a core ability of human intelligence, presents unique challenges for machines in abstract thinking and logical reasoning. Recent large pre-trained language models such as GPT-3 have achieved remarkable progress on mathematical reasoning tasks written in text form, such as math word problems (MWP). However, it is unknown if the models can handle more complex problems that involve math reasoning over heterogeneous information, such as tabular data.

To fill the gap, we present Tabular Math Word Problems (TabMWP), a new dataset containing 38,431 open-domain grade-level problems that require mathematical reasoning on both textual and tabular data. Each question in TabMWP is aligned with a tabular context, which is presented as an image, semi-structured text, and a structured table. There are two types of questions: free-text and multi-choice, and each problem is annotated with gold solutions to reveal the multi-step reasoning process.

We evaluate different pre-trained models on TabMWP, including the GPT-3 model in a few-shot setting. As earlier studies suggest, since few-shot GPT-3 relies on the selection of in-context examples, its performance is unstable and can degrade to near chance. The unstable issue is more severe when handling complex problems like TabMWP. To mitigate this, we further propose a novel approach, PromptPG, which utilizes policy gradient to learn to select in-context examples from a small amount of training data and then constructs the corresponding prompt for the test example. Experimental results show that our method outperforms the best baseline by 5.31% on the accuracy metric and reduces the prediction variance compared to random selection, which verifies its effectiveness in the selection of in-context examples.

--- https://arxiv.org/abs/2202.05983 Uncalibrated Models Can Improve Human-AI Collaboration Kailas Vodrahalli, Tobias Gerstenberg, James Zou 2022-02-12 2022-04-12 [("doi","10.48550/arXiv.2202.05983")] reinforcement-learning/multi-agent reinforcement-learning/safe statistics/prediction

[incentives for human deception via overconfident predictions to increase final performance] In many practical applications of AI, an AI model is used as a decision aid for human users. The AI provides advice that a human (sometimes) incorporates into their decision-making process. The AI advice is often presented with some measure of “confidence” that the human can use to calibrate how much they depend on or trust the advice.

In this paper, we present an initial exploration that suggests showing AI models as more confident than they actually are, even when the original AI is well-calibrated, can improve human-AI performance (measured as the accuracy and confidence of the human’s final prediction after seeing the AI advice).

We first train a model to predict human incorporation of AI advice using data from thousands of human-AI interactions. This enables us to explicitly estimate how to transform the AI’s prediction confidence, making the AI uncalibrated, in order to improve the final human prediction.

We empirically validate our results across 4 different tasks—dealing with images, text and tabular data—involving hundreds of human participants. We further support our findings with simulation analysis.

Our findings suggest the importance of jointly optimizing the human-AI system as opposed to the standard paradigm of optimizing the AI model alone.

...One potential question about our approach is: does modifying the AI’s confidence constitute misleading the user? We agree—in our experiments, the AI can mislead the user. However, our goal here is not to propose a method that should be used in practical applications, and rather to highlight the importance of modeling human interaction when designing AI for human use.

--- https://arxiv.org/abs/2204.13321 Epistasis and Adaptation on Fitness Landscapes Claudia Bank 2022-04-28 2022-04-28 [("doi","10.48550/arXiv.2204.13321")] genetics/heritable

Epistasis occurs when the effect of a mutation depends on its carrier’s genetic background. Despite increasing evidence that epistasis for fitness is common, its role during evolution is contentious.

Fitness landscapes, mappings of genotype or phenotype to fitness, capture the full extent and complexity of epistasis. Fitness landscape theory has shown how epistasis affects the course and the outcome of evolution. Moreover, by measuring the competitive fitness of sets of tens to thousands of connected genotypes, empirical fitness landscapes have shown that epistasis is frequent and depends on the fitness measure, the choice of mutations for the landscape, and the environment in which it was measured. Here, I review fitness landscape theory and experiments and their implications for the role of epistasis in adaptation.

I discuss theoretical expectations in the light of empirical fitness landscapes and highlight open challenges and future directions towards integrating theory and data, and incorporating ecological factors.

--- /doc/psychology/2022-obrien.pdf Losing Sight of Piecemeal Progress: People Lump and Dismiss Improvement Efforts That Fall Short of Categorical Change—Despite Improving Ed O’Brien 2022-08-03 2022-08-03 [("doi","10.1177/09567976221075302")] economics philosophy/ethics psychology

14 experiments (n = 10,556 adult participants, including more than 20,000 observed choices across 25 issues) documented how people perceive and respond to relative progress out in the world, revealing a robust “negative-lumping” effect.

As problematic entities worked to better their ways, participants shifted to dismiss them if they fell short of categorical reform—despite distinctions in improvement. This increased dismissal of relative gains as “all the same” was driven by the belief that falling short signals an eschewal of doing the bare minimum and lacking serious intent to change, making these gains seem less deserving of recognition.

Critically, participants then “checked out”: They under-rewarded and under-invested in efforts toward “merely” incremental improvement.

Finally, in all experiments, participants lumped together absolute failures but not absolute successes, highlighting a unique blindness to gradations of badness. When attempts to eradicate a problem fail, people might dismiss smaller but critical steps that were and can still be made.

--- https://arxiv.org/abs/2003.00104 AraBERT: Transformer-based Model for Arabic Language Understanding Wissam Antoun, Fady Baly, Hazem Hajj 2020-02-28 2022-04-13 [("doi","10.48550/arXiv.2003.00104")] ai/nn/transformer

The Arabic language is a morphologically rich language with relatively few resources and a less explored syntax compared to English. Given these limitations, Arabic Natural Language Processing (NLP) tasks like Sentiment Analysis (SA), Named Entity Recognition (NER), and Question Answering (QA), have proven to be very challenging to tackle. Recently, with the surge of transformers based models, language-specific BERT based models have proven to be very efficient at language understanding, provided they are pre-trained on a very large corpus. Such models were able to set new standards and achieve state-of-the-art results for most NLP tasks.

In this paper, we pre-trained BERT specifically for the Arabic language in the pursuit of achieving the same success that BERT did for the English language.

The performance of AraBERT is compared to multilingual BERT from Google and other state-of-the-art approaches. The results showed that the newly developed AraBERT achieved state-of-the-art performance on most tested Arabic NLP tasks.

The pretrained araBERT models are publicly available on GitHub to encourage research and applications for Arabic NLP.

--- https://arxiv.org/abs/2010.10392 CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters Hicham El Boukkouri, Olivier Ferret, Thomas Lavergne, Hiroshi Noji, Pierre Zweigenbaum, Junichi Tsujii 2020-10-20 2022-04-13 [("doi","10.48550/arXiv.2010.10392")] ai/nn/tokenization ai/nn/transformer

Due to the compelling improvements brought by BERT, many recent representation models adopted the Transformer architecture as their main building block, consequently inheriting the wordpiece tokenization system despite it not being intrinsically linked to the notion of Transformers. While this system is thought to achieve a good balance between the flexibility of characters and the efficiency of full words, using predefined wordpiece vocabularies from the general domain is not always suitable, especially when building models for specialized domains (eg. the medical domain). Moreover, adopting a wordpiece tokenization shifts the focus from the word level to the subword level, making the models conceptually more complex and arguably less convenient in practice.

For these reasons, we propose CharacterBERT, a new variant of BERT that drops the wordpiece system altogether and uses a Character-CNN module instead to represent entire words by consulting their characters.

We show that this new model improves the performance of BERT on a variety of medical domain tasks while at the same time producing robust, word-level and open-vocabulary representations.

--- https://ahrm.github.io/jekyll/update/2022/04/14/using-languge-models-to-read-faster.html 2022-04-13 ai/nn/rnn cs/algorithm design/typography --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2992433/ Is Spousal Similarity for Personality A Matter of Convergence or Selection? Mikhila N. Humbad, M. Brent Donnellan, William Iacono, Matthew McGue, S. Alexandra Burt 2010 2022-04-13 [("doi","10.1016/j.paid.2010.07.010")] psychology/personality sociology

We investigated whether spousal similarity for personality traits results from convergence (ie. couples becoming more similar to one another over time) or selection (ie. individuals selecting partners with similar traits) in a sample of 1,296 married couples. Personality was assessed using the Multidimensional Personality Questionnaire. We evaluated whether similarity increased with increasing length of marriage.

Evidence of spousal convergence was inconsistent across analyses, arguing against this mechanism as a compelling explanation for spousal similarity.

Accordingly, selection processes may better explain spousal similarity in these data. The one exception might be for aggressive aspects of personality.

--- https://arxiv.org/abs/2211.03540#anthropic Measuring Progress on Scalable Oversight for Large Language Models Samuel R. Bowman, Jeeyoon Hyun, Ethan Perez, Edwin Chen, Craig Pettit, Scott Heiner, Kamile Lukosuite, Amanda Askell, Andy L. Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Christopher Olah, Daniela Amodei, Dario Amodei, Dawn Drain, Dustin Li, Eli Tran-Johnson, Jackson Kernion, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Liane Lovitt, Nelson Elhage, Nicholas Schiefer, Nicholas Joseph, Noemí Mercado, Nova DasSarma, Robin Larson, Sam McCandlish, Sandipan Kundu, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Timothy Telleen-Lawton, Tom Brown, Tom Henighan, Tristan Hume, Yuntao Bai, Zac Hatfield-Dodds, Ben Mann, Jared Kaplan 2022-11-04 2022-11-04 [("doi","10.48550/arXiv.2211.03540")] ai/nn/transformer/gpt/inner-monologue reinforcement-learning/safe

Developing safe and useful general-purpose AI systems will require us to make progress on scalable oversight: the problem of supervising systems that potentially outperform us on most skills relevant to the task at hand. Empirical work on this problem is not straightforward, since we do not yet have systems that broadly exceed our abilities. This paper discusses one of the major ways we think about this problem, with a focus on how to turn it into one that can be productively studied empirically.

We first present an experimental design centered on choosing tasks for which human specialists succeed but unaided humans and current general AI systems fail. We then present a proof-of-concept experiment following meant to demonstrate a key feature of this experimental design and show its viability with two question-answering tasks: MMLU and time-limited QuALITY.

On these tasks, we find that human participants who interact with an unreliable large-language-model dialog assistant through chat—a trivial baseline strategy for scalable oversight—substantially outperform both the model alone and their own unaided performance.

These results are an encouraging sign that scalable oversight will be tractable to study with present models and bolster recent findings that large language models can productively assist humans with difficult tasks.

--- https://huggingface.co/prompthero/openjourney 2022-04-13 ai/nn/diffusion --- https://arxiv.org/abs/2205.12209#google EdiT5: Semi-Autoregressive Text-Editing with T5 Warm-Start Jonathan Mallinson, Jakub Adamek, Eric Malmi, Aliaksei Severyn 2022-05-24 2022-05-24 [("doi","10.48550/arXiv.2205.12209")] ai/nn/transformer/t5

[code] We present EdiT5—a novel semi-autoregressive text-editing model designed to combine the strengths of non-autoregressive text-editing and autoregressive decoding. EdiT5 is faster during inference than conventional sequence-to-sequence (seq2seq) models, while being capable of modeling flexible input-output transformations.

This is achieved by decomposing the generation process into 3 sub-tasks: (1) tagging to decide on the subset of input tokens to be preserved in the output, (2) re-ordering [using pointer networks] to define their order in the output text, and (3) insertion to infill the missing tokens that are not present in the input. The tagging and re-ordering steps, which are responsible for generating the largest portion of the output, are non-autoregressive, while the insertion step uses an autoregressive decoder.

Depending on the task, EdiT5 on average requires fewer autoregressive steps, demonstrating speedups of up to 25× when compared to seq2seq models. Quality-wise, EdiT5 is initialized with a pre-trained T5 checkpoint yielding comparable performance to T5 in high-resource settings when evaluated on 3 NLG tasks: Sentence Fusion, Grammatical Error Correction, and Decontextualization while clearly outperforming T5 in low-resource settings.

--- https://www.biorxiv.org/content/10.1101/2022.11.04.515213.full Genomic Structural Equation Modeling Reveals Latent Phenotypes in the Human Cortex with Distinct Genetic Architecture Rajendra A. Morey, Yuanchao Zheng, Delin Sun, Melanie E. Garrett, Marianna Gasperi, Adam X. Maihofer, Lexi Baird, Katrina Grasby, Ashley Huggins, Courtney C. Haswell, C. Paul M. Thompson, Sarah E. Medland, Daniel E. Gustavson, Matthew S. Panizzon, William S. Kremen, Caroline M. Nievergelt, Allison E. Ashley-Koch, Mark W. Logue 2022-11-08 2022-11-08 [("doi","10.1101/2022.11.04.515213")] genetics/heritable/correlation psychiatry/adhd psychiatry/alcoholism psychiatry/bipolar/genetics psychiatry/depression psychology/neuroscience

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from anatomical, functional, cytoarchitectural, or other cortical parcellation schemes.

We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to model the genetic architecture of cortical surface area (SA) and cortical thickness (CT) of 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance. Genomic SEM can fit a multivariate GWAS from summary statistics, which can subsequently be used for LD score regression (LDSC).

We found the best-fitting model of cortical SA was explained by 6 latent factors and CT was explained by 4 latent factors. The multivariate GWAS of these latent factors identified 74 genome-wide statistically-significant (GWS) loci (p < 5 × 10−8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of latent factor GWAS results found that SA-derived factors had a positive genetic correlation with bipolar disorder (BPD), and major depressive disorder (MDD), and a negative genetic correlation with attention deficit hyperactivity disorder (ADHD), MDD, and insomnia, while CT factors displayed a negative genetic correlation with alcohol dependence.

Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping genetically informed cortical networks.

--- https://www.reddit.com/r/StableDiffusion/comments/ypo2st/dune_poster/ 2022-04-13 ai/nn/transformer/clip/sample --- https://x.com/DavidSHershey/status/1590027002580721664 2022-04-14 ai/text-style-transfer --- https://inference-review.com/article/the-scent-of-flavor 2022-04-14 psychology/smell --- https://arxiv.org/abs/1806.08734 On the Spectral Bias of Neural Networks Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron Courville 2018-06-22 2022-04-14 [("doi","10.48550/arXiv.1806.08734")] ai/nn

Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with 100% accuracy. In this work, we present properties of neural networks that complement this aspect of expressivity.

By using tools from Fourier analysis, we show that deep ReLU networks are biased towards low frequency functions, meaning that they cannot have local fluctuations without affecting their global behavior. Intuitively, this property is in line with the observation that over-parameterized networks find simple patterns that generalize across data samples. We also investigate how the shape of the data manifold affects expressivity by showing evidence that learning high frequencies gets easier with increasing manifold complexity, and present a theoretical understanding of this behavior.

Finally, we study the robustness of the frequency components with respect to parameter perturbation, to develop the intuition that the parameters must be finely tuned to express high frequency functions.

--- https://arxiv.org/abs/1805.09801 Meta-Gradient Reinforcement Learning Zhongwen Xu, Hado van Hasselt, David Silver 2018-05-24 2022-04-14 [("doi","10.48550/arXiv.1805.09801")] reinforcement-learning/meta-learning

The goal of reinforcement learning algorithms is to estimate and/or optimize the value function. However, unlike supervised learning, no teacher or oracle is available to provide the true value function. Instead, the majority of reinforcement learning algorithms estimate and/or optimize a proxy for the value function. This proxy is typically based on a sampled and bootstrapped approximation to the true value function, known as a return.

The particular choice of return is one of the chief components determining the nature of the algorithm: the rate at which future rewards are discounted; when and how values should be bootstrapped; or even the nature of the rewards themselves. It is well-known that these decisions are crucial to the overall success of RL algorithms.

We discuss a gradient-based meta-learning algorithm that is able to adapt the nature of the return, online, whilst interacting and learning from the environment. When applied to 57 games on the Atari 2600 environment over 200 million frames, our algorithm achieved a new state-of-the-art performance.

--- https://arxiv.org/abs/2211.04236#deepmind Self-conditioned Embedding Diffusion for Text Generation Robin Strudel, Corentin Tallec, Florent Altché, Yilun Du, Yaroslav Ganin, Arthur Mensch, Will Grathwohl, Nikolay Savinov, Sander Dieleman, Laurent Sifre, Rémi Leblond 2022-11-08 2022-11-08 [("doi","10.48550/arXiv.2211.04236")] ai/nn/diffusion/discrete

Can continuous diffusion models bring the same performance breakthrough on natural language they did for image generation? To circumvent the discrete nature of text data, we can simply project tokens in a continuous space of embeddings, as is standard in language modeling.

We propose Self-conditioned Embedding Diffusion, a continuous diffusion mechanism that operates on token embeddings and allows to learn flexible and scalable diffusion models for both conditional and unconditional text generation. Through qualitative and quantitative evaluation, we show that our text diffusion models generate samples comparable with those produced by standard autoregressive language models—while being in theory more efficient on accelerator hardware at inference time.

Our work paves the way for scaling up diffusion models for text, similarly to autoregressive models, and for improving performance with recent refinements to continuous diffusion.

--- https://en.wikipedia.org/wiki/Toyota_Century Toyota Century 2022-04-14 anime japan --- https://arxiv.org/abs/2210.14140 Contrastive Search Is What You Need For Neural Text Generation Yixuan Su, Nigel Collier 2022-10-25 2022-10-25 [("doi","10.48550/arXiv.2210.14140")] ai/nn/sampling ai/nn/transformer/gpt/2 psychology/novelty

[blog+demo/code] Generating text with autoregressive language models (LMs) is of great importance to many natural language processing (NLP) applications. Previous solutions for this task often produce text that contains degenerative expressions or lacks semantic consistency. Recently, Su et al 2022 introduced a new decoding method, contrastive search, based on the isotropic representation space of the language model [ie. their representations reside in a narrow subset of the entire space] and obtained new state-of-the-art on various benchmarks. Additionally, Su et al 2022 argued that the representations of autoregressive LMs (eg. GPT-2) are intrinsically anisotropic which is also shared by previous studies. Therefore, to ensure the language model follows an isotropic distribution, Su et al 2022 proposed a contrastive learning scheme, SimCTG, which calibrates the language model’s representations through additional training.

In this study, we first answer the question: “Are autoregressive LMs really anisotropic?”. To this end, we extensively evaluate the isotropy of LMs across 16 major languages.

Surprisingly, we find that the anisotropic problem only exists in the two specific English GPT-2-small/medium models. On the other hand, all other evaluated LMs are naturally isotropic which is in contrast to the conclusion drawn by previous studies.

Based on our findings, we further assess the contrastive search decoding method using off-the-shelf LMs on 4 generation tasks across 16 languages.

Our experimental results demonstrate that contrastive search outperforms previous decoding methods without any additional training. More notably, on 12 out of the 16 evaluated languages, contrastive search performs comparably with human-level performances as judged by human evaluations.

--- https://arxiv.org/abs/2211.01786 BLOOMZ/mT0: Crosslingual Generalization through Multitask Finetuning Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M. Saiful Bari, Sheng Shen, Zheng-Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, Colin Raffel 2022-11-03 2022-11-03 [("doi","10.48550/arXiv.2211.01786")] ai/dataset ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 reinforcement-learning/meta-learning

Multitask prompted finetuning (MTF) has been shown to help large language models generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused on English data and models.

We apply MTF to the pretrained multilingual BLOOM and mT5 model families to produce finetuned variants called BLOOMZ & mT0.

We find finetuning large multilingual language models on English tasks with English prompts allows for task generalization to non-English languages that appear only in the pretraining corpus. Finetuning on multilingual tasks with English prompts further improves performance on English and non-English tasks leading to various state-of-the-art zero-shot results. We also investigate finetuning on multilingual tasks with prompts that have been machine-translated from English to match the language of each dataset.

We find training on these machine-translated prompts leads to better performance on human-written prompts in the respective languages.

Surprisingly, we find models are capable of zero-shot generalization to tasks in languages they have never intentionally seen. We conjecture that the models are learning higher-level capabilities that are both task & language-agnostic. In addition, we introduce xP3, a composite of supervised datasets in 46 languages with English and machine-translated prompts.

Our code, datasets and models are publicly available at Github.

--- https://x.com/AdeptAILabs/status/1590396065072951296 2022-04-14 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://www.biorxiv.org/content/10.1101/2022.10.21.513172.full KIN: A method to infer relatedness from low-coverage ancient DNA Divyaratan Popli, Stéphane Peyrégne, Benjamin M. Peter 2022-10-24 2022-10-24 [("doi","10.1101/2022.10.21.513172")] genetics/sequencing

Genetic kinship of ancient individuals can provide insights into their culture and social hierarchy, and is relevant for downstream genetic analyses. However, estimating relatedness from ancient DNA is difficult due to low-coverage, ascertainment bias, or contamination from various sources.

Here, we present KIN, a method [using hidden Markov models] to estimate the relatedness of a pair of individuals from the identical-by-descent segments they share. KIN accurately classifies up to 3rd-degree relatives using ≥0.05× sequence coverage and differentiates siblings from parent-child [and works on Neanderthals].

It incorporates additional models to adjust for contamination and detect inbreeding, which improves classification accuracy.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0276482 In the line of fire: Debris throwing by wild octopuses Peter Godfrey-Smith, David Scheel, Stephanie Chancellor, Stefan Linquist, Matthew Lawrence 2022-11-09 2022-11-09 [("doi","10.1371/journal.pone.0276482")] psychology/animal

Wild Octopus tetricus frequently propel shells, silt, and algae through the water by releasing these materials from their arms while creating a forceful jet from the siphon held under the arm web. These “throws” occur in several contexts at a site in Jervis Bay, Australia, including in interactions with other octopuses.

Material thrown in interactive contexts frequently hits other octopuses.

Some throws appear to be targeted on other individuals, as suggested by several kinds of evidence: Throws in interactive contexts were more vigorous than others, and more often used silt, rather than shells or algae. High vigor throws were more often accompanied by uniform or dark body patterns than other throws. Some throws were directed differently from beneath the arms and such throws were more likely to hit other octopuses.

Throwing at other individuals in the same population, as apparently seen in these octopuses, is a rare form of nonhuman projectile use, previously seen only in some social mammals.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4014489/ Calculating Kolmogorov complexity from the output frequency distributions of small Turing machines Fernando Soler-Toscano, Hector Zenil, Jean-Paul Delahaye, Nicolas Gauvrit 2014 2022-04-15 [("doi","10.1371/journal.pone.0096223")] cs/algorithm/information

Drawing on various notions from theoretical computer science, we present a novel numerical approach, motivated by the notion of algorithmic probability, to the problem of approximating the Kolmogorov-Chaitin complexity of short strings. The method is an alternative to the traditional lossless compression algorithms, which it may complement, the two being serviceable for different string lengths.

We provide a thorough analysis for all Σ(n = 1 to n = 11) 2n binary strings of length n < 12 and for most strings of length 12 ≤ n ≤ 16 by running all ~2.5 × 1013 Turing machines with 5 states and 2 symbols (8 × 229 with reduction techniques) using the most standard formalism of Turing machines, used in for example the Busy Beaver problem.

We address the question of stability and error estimation, the sensitivity of the continued application of the method for wider coverage and better accuracy, and provide statistical evidence suggesting robustness.

As with compression algorithms, this work promises to deliver a range of applications, and to provide insight into the question of complexity calculation of finite (and short) strings.

Additional material can be found at the Algorithmic Nature Group website. An Online Algorithmic Complexity Calculator implementing this technique and making the data available to the research community is accessible at Complexity Calculator [blog].

--- https://generative.ink/artifacts/lamda2/ the lie comes first, the worlds to accommodate it 2022-04-15 ai/nn/transformer/gpt/3/fiction philosophy/mind reinforcement-learning/meta-learning --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4391822/ IQ and schizophrenia in a Swedish national sample: their causal relationship and the interaction of IQ with genetic risk Kenneth S. Kendler, Henrik Ohlsson, Jan Sundquist, Kristina Sundquist 2015 2022-04-15 [("doi","10.1176/appi.ajp.2014.14040516")] genetics/heritable/correlation iq psychiatry/schizophrenia

Objective: The authors sought to clarify the relationship between IQ and subsequent risk for schizophrenia.

Method: IQ was assessed at ages 18–20 in 1,204,983 Swedish males born between 1951 and 1975. Schizophrenia was assessed by hospital diagnosis through 2010. Cox proportional hazards models were used to investigate future risk for schizophrenia in individuals as a function of their IQ score, and then stratified models using pairs of relatives were used to adjust for familial cluster. Finally, regression models were used to examine the interaction between IQ and genetic liability on risk for schizophrenia.

Results: IQ had a monotonic relationship with schizophrenia risk across the IQ range, with a mean increase in risk of 3.8% per 1-point decrease in IQ; this association was stronger in the lower than the higher IQ range. Co-relative control analyses showed a similar association between IQ and schizophrenia in the general population and in cousin, half-sibling, and full-sibling pairs. A robust interaction was seen between genetic liability to schizophrenia and IQ in predicting schizophrenia risk. Genetic susceptibility for schizophrenia had a much stronger impact on risk of illness for those with low than high intelligence. The IQ-genetic liability interaction arose largely from IQ differences between close relatives.

Conclusions: IQ assessed in late adolescence is a robust risk factor for subsequent onset of schizophrenia. This association is not the result of a declining IQ associated with insidious onset. In this large, representative sample, we found no evidence for a link between genius and schizophrenia. Co-relative control analyses showed that the association between lower IQ and schizophrenia is not the result of shared familial risk factors and may be causal. The strongest effect was seen with IQ differences within families. High intelligence substantially attenuates the impact of genetic liability on the risk for schizophrenia.

--- https://annamancini.substack.com/p/how-the-apple-archive-ended-up-at 2022-04-15 cs/linkrot/archiving --- https://arxiv.org/abs/2010.01717 STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation Nader Akoury, Shufan Wang, Josh Whiting, Stephen Hood, Nanyun Peng, Mohit Iyyer 2020-10-04 2022-04-15 [("doi","10.48550/arXiv.2010.01717")] ai/nn/transformer/gpt/fiction

Systems for story generation are asked to produce plausible and enjoyable stories given an input context. This task is underspecified, as a vast number of diverse stories can originate from a single input. The large output space makes it difficult to build and evaluate story generation models, as (1) existing datasets lack rich enough contexts to meaningfully guide models, and (2) existing evaluations (both crowdsourced and automatic) are unreliable for assessing long-form creative text.

To address these issues, we introduce a dataset and evaluation platform built from STORIUM, an online collaborative storytelling community. Our author-generated dataset contains 6K lengthy stories (125M tokens) with fine-grained natural language annotations (eg. character goals and attributes) interspersed throughout each narrative, forming a robust source for guiding models.

We evaluate [GPT-2] language models fine-tuned on our dataset by integrating them onto STORIUM, where real authors can query a model for suggested story continuations and then edit them. Automatic metrics computed over these edits correlate well with both user ratings of generated stories and qualitative feedback from semi-structured user interviews.

We release both the STORIUM dataset and evaluation platform to spur more principled research into story generation [code].

--- https://www.youtube.com/watch?v=fs8ZveNZQ8g 2022-04-15 psychology/animal --- https://joelanderson.substack.com/p/positive-sum-housing-games-part-2 2022-04-15 economics/georgism --- https://en.wikipedia.org/wiki/999-year_leases_in_Hong_Kong 999-year leases in Hong Kong 2022-04-15 economics/georgism economics/perpetuities --- https://www.ageofinvention.xyz/p/age-of-invention-the-beacons-are 2022-04-15 cs/algorithm technology --- https://www.koryheath.com/zendo/design-history/ 2022-04-15 design philosophy/logic --- https://en.wikipedia.org/wiki/Zendo_(game) Zendo (game) 2022-04-15 design philosophy/logic --- https://www.reddit.com/r/sdnsfw/comments/ylo4eh/huge_list_of_sexy_tested_photorealism_keywords/ 2022-04-16 ai/nn/diffusion --- https://www.medrxiv.org/content/10.1101/2022.07.12.22277520.full Health care utilization of fine-scale identity by descent clusters in a Los Angeles biobank Christa Caggiano, Arya Boudaie, Ruhollah Shemirani, Ella Petter, Alec Chiu, Ruth Johnson, Defne Ercelen, Bogdan Pasaniuc, Eimear Kenny, Jonathan Shortt, Chris Gignoux, Brunilda Balliu, Valerie Arboleda, Gillian Belbin, Noah Zaitlen 2022-07-15 2022-07-15 [("doi","10.1101/2022.07.12.22277520")] genetics/heritable genetics/sequencing

An individual’s disease risk is affected by the populations that they belong to, due to shared genetics and shared environment. The study of fine-scale populations in clinical care will be important for reducing health disparities and for developing personalized treatments.

In this work, we developed a novel health monitoring system, which leverages biobank data and electronic medical records from over 40,000 UCLA patients. Using identity by descent (IBD), we analyzed one type of fine-scale population, an IBD cluster.

In total, we identified 376 IBD clusters, including clusters characterized by the presence of many understudied communities, such as Lebanese Christians, Iranian Jews, Armenians, and Gujaratis. Our analyses identified thousands of novel associations between IBD clusters and clinical diagnoses, physician offices, utilization of specific medical specialties, pathogenic allele frequencies, and changes in diagnosis frequency over time.

To enhance the impact of the research and engage the broader community, we provide a web portal to query our results: "Fine-scale population health in Los Angeles".

--- https://arxiv.org/abs/2211.05709 AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies Li Siyao, Yuhang Li, Bo Li, Chao Dong, Ziwei Liu, Chen Change Loy 2022-11-10 2022-11-10 [("doi","10.48550/arXiv.2211.05709")] ai/anime ai/dataset ai/video/analysis ai/video/generation

Existing correspondence datasets for two-dimensional (2D) cartoon suffer from simple frame composition and monotonic movements, making them insufficient to simulate real animations.

In this work, we present a new 2D animation visual correspondence dataset, AnimeRun, by converting open source three-dimensional (3D) movies to full scenes in 2D style, including simultaneous moving background and interactions of multiple subjects.

Our analyses show that the proposed dataset not only resembles real anime more in image composition, but also possesses richer and more complex motion patterns compared to existing datasets. With this dataset, we establish a comprehensive benchmark by evaluating several existing optical flow and segment matching methods, and analyze shortcomings of these methods on animation data.

Data, code and other supplementary materials are available at https://lisiyao21.github.io/projects/AnimeRun.

--- https://arxiv.org/abs/2211.05756#facebook Massively Multilingual ASR on 70 Languages: Tokenization, Architecture, and Generalization Capabilities Andros Tjandra, Nayan Singhal, David Zhang, Ozlem Kalinli, Abdelrahman Mohamed, Duc Le, Michael L. Seltzer 2022-11-10 2022-11-10 [("doi","10.48550/arXiv.2211.05756")] ai/nn/rnn ai/nn/tokenization ai/nn/transformer

End-to-end multilingual ASR has become more appealing because of several reasons such as simplifying the training and deployment process and positive performance transfer from high-resource to low-resource languages. However, scaling up the number of languages, total hours, and number of unique tokens is not a trivial task.

This paper explores large-scale multilingual ASR models on 70 languages. We inspect two architectures: (1) Shared embedding and output and (2) Multiple embedding and output model. In the shared model experiments, we show the importance of tokenization strategy across different languages. Later, we use our optimal tokenization strategy to train multiple embedding and output model to further improve our result.

Our multilingual ASR achieves 13.9%–15.6% average WER relative improvement compared to monolingual models. We show that our multilingual ASR generalizes well on an unseen dataset and domain, achieving 9.5% and 7.5% WER on Multilingual LibriSpeech (MLS) with zero-shot and finetuning, respectively.

--- https://arxiv.org/abs/2211.05719#microsoft MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation Jiazhan Feng, Qingfeng Sun, Can Xu, Pu Zhao, Yaming Yang, Chongyang Tao, Dongyan Zhao, Qingwei Lin 2022-11-10 2022-11-10 [("doi","10.48550/arXiv.2211.05719")] ai/dataset ai/scaling

Responding with multi-modal content has been recognized as an essential capability for an intelligent conversational agent.

In this paper, we introduce the MMDialog dataset to better facilitate multi-modal conversation. MMDialog is composed of a curated set of 1.08 million real-world dialogues with 1.53 million unique images across 4,184 topics.

MMDialog has two main and unique advantages. First, it is the largest multi-modal conversation dataset by the number of dialogues by 8×. Second, it contains massive topics to generalize the open-domain. To build engaging dialogue system with this dataset, we propose and normalize two response producing tasks based on retrieval and generative scenarios. In addition, we build two baselines for above tasks with state-of-the-art techniques and report their experimental performance.

We also propose a novel evaluation metric MM-Relevance to measure the multi-modal responses.

Our dataset and scripts are available in Github.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479710/ Quantity yields quality when it comes to creativity: a brain and behavioral test of the equal-odds rule Rex E. Jung, Christopher J. Wertz, Christine A. Meadows, Sephira G. Ryman, Andrei A. Vakhtin, Ranee A. Flores 2015 2022-04-16 [("doi","10.3389/fpsyg.2015.00864")] psychology/neuroscience

The creativity research community is in search of a viable cognitive measure providing support for behavioral observations that higher ideational output is often associated with higher creativity (known as the equal-odds rule). One such measure has included divergent thinking: the production of many examples or uses for a common or single object or image.

We sought to test the equal-odds rule using a measure of divergent thinking, and applied the consensual assessment technique to determine creative responses as opposed to merely original responses. We also sought to determine structural brain correlates of both ideational fluency and ideational creativity. 246 subjects were subjected to a broad battery of behavioral measures, including a core measure of divergent thinking (Foresight), and measures of intelligence, creative achievement, and personality (ie. Openness to Experience). Cortical thickness and subcortical volumes (eg. thalamus) were measured using automated techniques (FreeSurfer).

We found that higher number of responses on the divergent thinking task was statistically-significantly associated with higher creativity (r = 0.73) as independently assessed by 3 judges. Moreover, we found that creativity was predicted by cortical thickness in regions including the left frontal pole and left parahippocampal gyrus.

These results support the equal-odds rule, and provide neuronal evidence implicating brain regions involved with “thinking about the future” and “extracting future prospects.”

--- https://githubnext.com/projects/ai-for-pull-requests/ 2022-04-16 ai/nn/transformer/gpt/codex --- https://web.archive.org/web/20221112033036/https://mullikine.github.io/posts/nlsh-natural-language-shell/ 2022-04-16 ai/nn/transformer/gpt/codex --- https://huggingface.co/spaces/mullikine/ilambda 2022-04-16 ai/nn/transformer/gpt/codex cs/lisp --- https://arxiv.org/abs/2211.01288 Characterizing Intrinsic Compositionality in Transformers with Tree Projections Shikhar Murty, Pratyusha Sharma, Jacob Andreas, Christopher D. Manning 2022-11-02 2022-11-02 [("doi","10.48550/arXiv.2211.01288")] ai/nn/transformer cs/computable

When trained on language data, do transformers learn some arbitrary computation that utilizes the full capacity of the architecture or do they learn a simpler, tree-like computation, hypothesized to underlie compositional meaning systems like human languages? There is an apparent tension between compositional accounts of human language understanding, which are based on a restricted bottom-up computational process, and the enormous success of neural models like transformers, which can route information arbitrarily between different parts of their input.

One possibility is that these models, while extremely flexible in principle, in practice learn to interpret language hierarchically, ultimately building sentence representations close to those predictable by a bottom-up, tree-structured model.

To evaluate this possibility, we describe an unsupervised and parameter-free method to functionally project the behavior of any transformer into the space of tree-structured networks. Given an input sentence, we produce a binary tree that approximates the transformer’s representation-building process and a score that captures how “tree-like” the transformer’s behavior is on the input. While calculation of this score does not require training any additional models, it provably upper-bounds the fit between a transformer and any tree-structured approximation.

Using this method, we show that transformers for 3 different tasks become more tree-like over the course of training, in some cases unsupervisedly recovering the same trees as supervised parsers. These trees, in turn, are predictive of model behavior, with more tree-like models generalizing better on tests of compositional generalization.

--- https://x.com/EmilWallner/status/1591007449691336704 2022-04-16 ai/nn/diffusion ai/scaling/hardware --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4200016/ EMSAM (deprenyl patch): how a promising antidepressant was underutilized Gregory M. Asnis, Margaret A. Henderson 2014 2022-04-17 [("doi","10.2147/NDT.S59107")] nootropic psychiatry/depression

The EMSAM seligiline patch is a unique monoamine oxidase inhibitor (MAOI) being the only antidepressant utilizing a transdermal delivery system. This was welcomed by clinicians who hoped that EMSAM would be better tolerated than oral MAOIs and non-MAOI antidepressants, as well as being effective for treatment in a wide spectrum of depressed patients including atypical depression, bipolar depression, and refractory depression. Unfortunately, the clinical use of EMSAM has been underutilized and its potential usefulness overlooked.

This article suggests that fear of possible side effects, particularly the “cheese reaction” and serotonin syndrome, are some of the main contributors to underutilization by clinicians. These risks have been exaggerated, with the 6 mg/day dose not even requiring a special diet.

Other contributing factors leading to underutilization are reviewed such as: the lack of studies addressing many important clinical questions; inadequate data analyses; not evaluating the effect of EMSAM on comorbid psychiatric conditions, particularly anxiety disorders; lack of antidepressant comparators versus EMSAM; no dose-response relationship examined; various depressive subtypes and conditions are unexplored, eg, bipolar depression and refractory depression; poor insurance coverage for an expensive medication; as well as minimal marketing efforts and postmarketing studies.

On the other hand, many potential advantages of EMSAM are not highlighted enough in the literature and by pharmaceutical companies which might have increased clinical interest and utilization of the antidepressant. For example, the advantages of EMSAM include: avoidance of swallowing issues, as can be seen with oral antidepressants; minimal side effects, probably due to a favorable pharmacokinetic profile; minimal evidence of suicidal behavior, probably relating to the transdermal route of administration; low rates of inducing hypomanic/manic episodes; as well as efficacy in “anxious depression” and atypical depression.

Recent efforts in conducting some post hoc analyses and presentations on EMSAM may yet stimulate further clinical interest and use of this antidepressant. [Came to public notice as Sam Bankman-Fried reputedly used EMSAM patches often.]

--- https://www.reddit.com/r/StableDiffusion/comments/ys434h/animating_generated_face_test/ 2022-04-17 ai/nn/diffusion ai/video/generation --- https://www.bbc.com/news/entertainment-arts-63599287 2022-04-17 anime fiction/humor --- https://arxiv.org/abs/2210.09263 Vision-Language Pre-training: Basics, Recent Advances, and Future Trends Zhe Gan, Linjie Li, Chunyuan Li, Lijuan Wang, Zicheng Liu, Jianfeng Gao 2022-10-17 2022-10-17 [("doi","10.48550/arXiv.2210.09263")] ai/nn/transformer/clip ai/scaling ai/video/analysis

This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years. We group these approaches into 3 categories: (1) VLP for image-text tasks, such as image captioning, image-text retrieval, visual question answering, and visual grounding; (2) VLP for core computer vision tasks, such as (open-set) image classification, object detection, and image segmentation; and (3) VLP for video-text tasks, such as video captioning, video-text retrieval, and video question answering.

For each category, we present a comprehensive review of state-of-the-art methods, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies.

In addition, for each category, we discuss advanced topics being actively explored in the research community, such as big foundation models, unified modeling, in-context few-shot learning, knowledge, robustness, and computer vision in the wild, to name a few.

--- https://www.isi.edu/~hardaker/news/20221106-ietf-c02-analysis/ 2022-04-17 co2 --- https://arxiv.org/abs/2211.03495 How Much Does Attention Actually Attend? Questioning the Importance of Attention in Pretrained Transformers Michael Hassid, Hao Peng, Daniel Rotem, Jungo Kasai, Ivan Montero, Noah Smith, Roy Schwartz 2022-11-07 2022-11-07 [("doi","10.48550/arXiv.2211.03495")] ai/nn/fully-connected ai/nn/transformer/attention/sparsity

[code] The attention mechanism is considered the backbone of the widely-used Transformer architecture. It contextualizes the input by computing input-specific attention matrices.

We find that this mechanism, while powerful and elegant, is not as important as typically thought for pretrained language models. We introduce PAPA, a new probing method that replaces the input-dependent attention matrices with constant ones—the average attention weights over multiple inputs. We use PAPA to analyze several established pretrained Transformers on 6 downstream tasks.

We find that without any input-dependent attention, all models achieve competitive performance—an average relative drop of only 8% from the probing baseline. Further, little or no performance drop is observed when replacing half of the input-dependent attention matrices with constant (input-independent) ones.

Interestingly, we show that better-performing models lose more from applying our method than weaker models, suggesting that the utilization of the input-dependent attention mechanism might be a factor in their success.

Our results motivate research on simpler alternatives to input-dependent attention, as well as on methods for better utilization of this mechanism in the Transformer architecture.

Figure 2: Probing results (y-axis) with decreasing number of attention heads (x-axis). BASE models are shown in Figure 2a, and LARGE models are shown in Figure 2b. Higher is better in all cases.

…Half of the attention matrices can be replaced without loss in performance: We note that in almost all cases replacing half of the models’ attention matrices leads to no major drop in performance. In fact, in some cases, performance even improves compared to the original model (eg. BERTBASE and DeBERTaLARGE), suggesting that some of the models’ heads have a slight preference towards constant matrices. This result is consistent with some of the findings of recent hybrid models that use both constant and regular attention (Liu et al 2021; Lee-Thorp et al 2021) to build efficient models.

…We first notice a diagonal pattern, in which each token mostly attends to itself or to its neighboring words. This pattern is observed in about 90% of the constant matrices produced by PAPA. Second, about 40% of the heads put most of their weight mass on the [CLS] and/or [SEP] tokens (perhaps in combination with the diagonal pattern described above). Lastly, while for some of the heads the weight mass is concentrated only in specific entry per row (which corresponding only to a specific token), in most of cases the weight mass is distributed over several entries (corresponding to several different tokens). These patterns are similar to those identified by Clark et al 2019, and explain in part our findings—many of the attention heads mostly focus on fixed patterns that can also be captured by a constant matrix.

Figure 3: Stronger-performing PLMs use their attention capability more. y-axis: original model average performance; x-axis: relative reduced score when all attention matrices are replaced with constant ones.

…Performant models rely more on attention: Figure 3 shows for each model the relation between the original performance (averaged across tasks) and the averaged (relative) reduced score when replacing all attention heads. We observe a clear trend between the models’ performance and their relative reduced score, which suggests that better performing models use their attention mechanism more.

--- https://arxiv.org/abs/1906.04341 What Does BERT Look At? An Analysis of BERT’s Attention Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D. Manning 2019-06-11 2022-04-17 [("doi","10.48550/arXiv.1906.04341")] ai/nn/transformer/attention

Large pre-trained neural networks such as BERT have had great recent success in NLP, motivating a growing body of research investigating what aspects of language they are able to learn from unlabeled data. Most recent analysis has focused on model outputs (eg. language model surprisal) or internal vector representations (eg. probing classifiers).

Complementary to these works, we propose methods for analyzing the attention mechanisms of pre-trained models and apply them to BERT.

BERT’s attention heads exhibit patterns such as attending to delimiter tokens, specific positional offsets, or broadly attending over the whole sentence, with heads in the same layer often exhibiting similar behaviors. We further show that certain attention heads correspond well to linguistic notions of syntax and coreference. For example, we find heads that attend to the direct objects of verbs, determiners of nouns, objects of prepositions, and coreferent mentions with remarkably high accuracy.

Lastly, we propose an attention-based probing classifier and use it to further demonstrate that substantial syntactic information is captured in BERT’s attention.

--- https://arxiv.org/abs/2105.03824#google FNet: Mixing Tokens with Fourier Transforms James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon 2021-05-09 2022-04-17 [("doi","10.48550/arXiv.2105.03824")] ai/nn/fully-connected ai/nn/transformer/attention

We show that Transformer encoder architectures can be sped up, with limited accuracy costs, by replacing the self-attention sublayers with simple linear transformations that “mix” input tokens. These linear mixers, along with standard nonlinearities in feed-forward layers, prove competent at modeling semantic relationships in several text classification tasks.

Most surprisingly, we find that replacing the self-attention sublayer in a Transformer encoder with a standard, unparameterized Fourier Transform achieves 92–97% of the accuracy of BERT counterparts on the GLUE benchmark, but trains 80% faster on GPUs and 70% faster on TPUs at standard 512 input lengths. At longer input lengths, our FNet model is faster: when compared to the “efficient” Transformers on the Long Range Arena benchmark,

FNet matches the accuracy of the most accurate models, while outpacing the fastest models across all sequence lengths on GPUs (and across relatively shorter lengths on TPUs).

Finally, FNet has a light memory footprint and is particularly efficient at smaller model sizes; for a fixed speed and accuracy budget, small FNet models outperform Transformer counterparts.

--- https://www.lesswrong.com/posts/t9svvNPNmFf5Qa3TA/mysteries-of-mode-collapse-due-to-rlhf 2022-04-17 ai/nn/transformer/gpt reinforcement-learning/meta-learning reinforcement-learning/preference-learning --- https://x.com/patrickmineault/status/1591874392279351297 2022-04-17 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2211.06220 OneFormer: One Transformer to Rule Universal Image Segmentation Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi 2022-11-10 2022-11-10 [("doi","10.48550/arXiv.2211.06220")] ai/nn/transformer

Universal Image Segmentation is not a new concept. Past attempts to unify image segmentation in the last decades include scene parsing, panoptic segmentation, and, more recently, new panoptic architectures. However, such panoptic architectures do not truly unify image segmentation because they need to be trained individually on the semantic, instance, or panoptic segmentation to achieve the best performance. Ideally, a truly universal framework should be trained only once and achieve SOTA performance across all 3 image segmentation tasks.

To that end, we propose OneFormer, a universal image segmentation framework that unifies segmentation with a multi-task train-once design. We first propose a task-conditioned joint training strategy that enables training on ground truths of each domain (semantic, instance, and panoptic segmentation) within a single multi-task training process. Secondly, we introduce a task token to condition our model on the task at hand, making our model task-dynamic to support multi-task training and inference. Thirdly, we propose using a query-text contrastive loss during training to establish better inter-task and inter-class distinctions.

Notably, our single OneFormer model outperforms specialized Mask2Former models across all 3 segmentation tasks on ADE20K, Cityscapes, and COCO, despite the latter being trained on each of the 3 tasks individually with 3× the resources. With new ConvNeXt and DiNAT backbones, we observe even more performance improvement.

We believe OneFormer is a step towards making image segmentation more universal and accessible.

To support further research, we open-source our code and models at https://github.com/SHI-Labs/OneFormer.

--- https://en.wikipedia.org/wiki/Context_collapse Context collapse 2022-04-18 sociology/technology --- https://www.biorxiv.org/content/10.1101/2022.11.11.516230.full Two pup vocalization types are genetically and functionally separable in deer mice N. Jourjine, M. L. Woolfolk, J. I. Sanguinetti-Scheck, J. E. Sabatini, S. McFadden, A. K. Lindholm, H. E. Hoekstra 2022-11-12 2022-11-12 [("doi","10.1101/2022.11.11.516230")] genetics/heritable

Vocalization is a widespread vertebrate social behavior that is essential for fitness in the wild. While many vocal behaviors are highly conserved, heritable features of specific vocalization types can vary both within and between species, raising the questions of why and how some vocal behaviors evolve.

Here, using new computational tools to automatically detect and cluster vocalizations into distinct acoustic categories, we compare pup isolation calls across neonatal development in 8 taxa of deer mice (genus Peromyscus) and compare them to laboratory mice (C57Bl6/j strain) and free-living, wild house mice (Mus musculus musculus). Whereas both Peromyscus and Mus pups produce ultrasonic vocalizations (USVs), Peromyscus pups also produce a second call type with acoustic features, temporal rhythms, and developmental trajectories that are distinct from those of USVs. In Peromyscus, these tonal and low frequency cries are predominantly emitted in postnatal days one through nine, while USVs are primarily made after day nine.

Using playback assays, we show that cries result in a more rapid approach by Peromyscus mothers than USVs, suggesting a role for cries in eliciting parental care early in neonatal development.

Using genetic crosses between two sister species of Peromyscus exhibiting large, innate differences in the acoustic structure of cries and USVs, we find that variation in vocalization rate, duration, and pitch display different degrees of genetic dominance and that cry and USV features can be uncoupled in second-generation hybrids.

Taken together, this work shows that vocal behavior can evolve quickly between closely related rodent species in which vocalization types, likely serving distinct functions in communication, are controlled by distinct genetic loci.

--- https://www.reddit.com/r/StableDiffusion/comments/yv75hb/prompt_to_create_double_exposure_images_workflow/ 2022-04-18 ai/nn/diffusion --- https://x.com/jmilldotdev/status/1592288240861839360 2022-04-18 ai/nn/transformer/gpt/inner-monologue --- https://x.com/REZ_777/status/1591891214076620800 2022-04-18 ai/anime --- https://www.wired.com/story/alphabay-series-part-4-face-to-face/ 2022-04-18 darknet-market/alphabay --- https://www.wired.com/story/alphabay-series-part-1-the-shadow/ 2022-04-18 darknet-market/alphabay --- https://www.wired.com/story/alphabay-series-part-2-pimp-alex-91 2022-04-18 darknet-market/alphabay --- https://www.wired.com/story/alphabay-series-part-3-alpha-male 2022-04-18 darknet-market/alphabay --- https://jackrusher.com/classic-ux/ 2022-04-18 design --- https://arxiv.org/abs/2208.07220 PatchDropout: Economizing Vision Transformers Using Patch Dropout Yue Liu, Christos Matsoukas, Fredrik Strand, Hossein Azizpour, Kevin Smith 2022-08-10 2022-08-10 [("doi","10.48550/arXiv.2208.07220")] ai/nn/transformer ai/nn/vae/mae

Vision transformers have demonstrated the potential to outperform CNNs in a variety of vision tasks. But the computational and memory requirements of these models prohibit their use in many applications, especially those that depend on high-resolution images, such as medical image classification. Efforts to train ViTs more efficiently are overly complicated, necessitating architectural changes or intricate training schemes.

In this work, we show that standard ViT models can be efficiently trained at high resolution by randomly dropping input image patches. This simple approach, PatchDropout, reduces FLOPs and memory by at least 50% in standard natural image datasets such as ImageNet, and those savings only increase with image size.

On CSAW, a high-resolution medical dataset, we observe a 5× savings in computation and memory using PatchDropout, along with a boost in performance.

For practitioners with a fixed computational or memory budget, PatchDropout makes it possible to choose image resolution, hyperparameters, or model size to get the most performance out of their model.

--- https://www.thelawproject.com.au/insights/attractiveness-bias-in-the-legal-system 2022-04-19 law --- https://arxiv.org/abs/2206.07023 SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features Juri Opitz, Anette Frank 2022-06-14 2022-06-14 [("doi","10.48550/arXiv.2206.07023")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer

Models based on large-pretrained language models, such as S(entence)BERT, provide effective and efficient sentence embeddings that show high correlation to human similarity ratings, but lack interpretability. On the other hand, graph metrics for graph-based meaning representations (eg. Abstract Meaning Representation, AMR) can make explicit the semantic aspects in which two sentences are similar. However, such metrics tend to be slow, rely on parsers, and do not reach state-of-the-art performance when rating sentence similarity.

In this work, we aim at the best of both worlds, by learning to induce Semantically Structured Sentence BERT embeddings (S3BERT). Our S3BERT embeddings are composed of explainable sub-embeddings that emphasize various semantic sentence features (eg. semantic roles, negation, or quantification). We show how to (1) learn a decomposition of the sentence embeddings into semantic features, through approximation of a suite of interpretable AMR graph metrics, and how to (2) preserve the overall power of the neural embeddings by controlling the decomposition learning process with a second objective that enforces consistency with the similarity ratings of an SBERT teacher model. In our experimental studies, we show that our approach offers interpretability—while fully preserving the effectiveness and efficiency of the neural sentence embeddings.

--- https://en.wikipedia.org/wiki/Pancake_sorting#The_burnt_pancake_problem Pancake sorting § The burnt pancake problem 2022-04-19 genetics/selection/artificial --- /doc/zeo/2010-scragg.pdf Relation of Serum 25-Hydroxyvitamin D to Heart Rate and Cardiac Work (from the National Health and Nutrition Examination Surveys) R. K. Scragg, C. A. Camargo, R. U. Simpson 2010-01-01 2022-04-19 [("doi","10.1016/j.amjcard.2009.08.661")] vitamin-d zeo --- https://web.archive.org/web/20121110210635/www.theherald.com.au/story/734298/detectives-follow-the-silk-road 2022-04-19 darknet-market/silk-road/1 --- https://arxiv.org/abs/2211.07715#intel Fast DistilBERT on CPUs Haihao Shen, Ofir Zafrir, Bo Dong, Hengyu Meng, Xinyu Ye, Zhe Wang, Yi Ding, Hanwen Chang, Guy Boudoukh, Moshe Wasserblat 2022-10-27 2022-10-27 [("doi","10.48550/arXiv.2211.07715")] ai/nn/sparsity/knowledge-distillation ai/nn/sparsity/low-precision ai/nn/transformer

Transformer-based language models have become the standard approach to solving natural language processing tasks. However, industry adoption usually requires the maximum throughput to comply with certain latency constraints that prevents Transformer models from being used in production. To address this gap, model compression techniques such as quantization and pruning may be used to improve inference efficiency. However, these compression techniques require specialized software to apply and deploy at scale.

In this work, we propose a new pipeline for creating and running Fast Transformer models on CPUs, utilizing hardware-aware pruning, knowledge distillation, quantization, and our own Transformer inference runtime engine with optimized kernels for sparse and quantized operators.

We demonstrate the efficiency of our pipeline by creating a Fast DistilBERT model showing minimal accuracy loss on the question-answering SQuADv1.1 benchmark, and throughput results under typical production constraints and environments.

Our results outperform existing state-of-the-art Neural Magic’s DeepSparse runtime performance by up to 50% and up to 4.1× performance speedup over ONNX Runtime.

--- https://en.wikipedia.org/wiki/Punding Punding 2022-04-19 psychiatry --- https://x.com/CoffeeVectors/status/1593022229834797056 2022-04-19 ai/nn/transformer/clip/sample --- https://joel.tools/codegen/ 2022-04-19 ai/nn/transformer/gpt/codex --- https://en.wikipedia.org/wiki/Logical_depth Logical depth 2022-04-19 cs/algorithm psychology/novelty --- https://applerankings.com/ 2022-04-20 genetics/selection/artificial/apple --- https://www.reddit.com/r/MachineLearning/comments/yxt8sa/r_rwkv4_7b_release_an_attentionfree_rnn_language/ 2022-04-20 ai/nn/rnn ai/nn/transformer --- https://tasvideos.org/6347S Submission #6347: Chef Stef’s NES Arkanoid warpless in 11:11.18 2022-04-20 ai cs/algorithm reinforcement-learning/scaling --- /doc/psychology/cognitive-bias/illusion-of-depth/1989-moscovitch.pdf Confabulations and the Frontal Systems: Strategic versus Associative retrieval in Neuropsychological Theories of Memory Morris Moscovitch 1989-01-01 2022-04-20 psychiatry psychology/cognitive-bias/illusion-of-depth --- https://arxiv.org/abs/2112.03978 Attractor and integrator networks in the brain Mikail Khona, Ila R. Fiete 2021-12-07 2022-04-20 [("doi","10.48550/arXiv.2112.03978")] psychology/neuroscience

In this review, we describe the singular success of attractor neural network models in describing how the brain maintains persistent activity states for working memory, error-corrects, and integrates noisy cues.

We consider the mechanisms by which simple and forgetful units can organize to collectively generate dynamics on the long time-scales required for such computations. We discuss the myriad potential uses of attractor dynamics for computation in the brain, and showcase notable examples of brain systems in which inherently low-dimensional continuous attractor dynamics have been concretely and rigorously identified. Thus, it is now possible to conclusively state that the brain constructs and uses such systems for computation.

Finally, we look ahead by highlighting recent theoretical advances in understanding how the fundamental tradeoffs between robustness and capacity and between structure and flexibility can be overcome by reusing and recombining the same set of modular attractors for multiple functions, so they together produce representations that are structurally constrained and robust but exhibit high capacity and are flexible.

--- https://www.biorxiv.org/content/10.1101/2022.11.15.516494.full Versatile detection of diverse selective sweeps with Flex-sweep M. Elise Lauterbur, Kasper Munch, David Enard 2022-11-17 2022-11-17 [("doi","10.1101/2022.11.15.516494")] genetics/selection/natural/human

Understanding the selection pressures influencing modern-day genomic diversity and their overall genomic impact is a major goal of evolutionary genomics. In particular, the contribution of selective sweeps to adaptation remains an open question, with persistent statistical limitations of sweep detection methods in terms of power and specificity. Sweeps with subtle genomic signals have been particularly challenging to detect. While many existing powerful methods are capable of detecting specific types of sweeps and/or those with obvious signals, their power comes at the expense of versatility. This means that these tools are likely failing to identify many sweeps. Thus it is valuable but difficult to be able to detect sweeps with diverse characteristics.

We present Flex-sweep, a versatile machine-learning tool designed to detect sweeps with a variety of subtle signals, including those that are thousands of generations old. It is especially valuable for detecting sweeps in non-model organisms, for which we neither have expectations about the characteristics of sweeps present in the genome nor outgroups with population-level sequencing to otherwise facilitate detecting very old sweeps.

We show that Flex-sweep is powerful at detecting selective sweeps with more subtle signals, even in the face of demographic model complexity and misspecification, recombination rate heterogeneity, and background selection. Flex-sweep is able to detect sweeps up to 5,000 generations old (~125,000 years in humans), including those that are weak, soft, and/or incomplete; it is also capable of detecting strong, complete sweeps up to 10,000 generations old.

Furthermore, we apply Flex-sweep to the 1000 genomes data set of Yoruba in Ibadan, Nigeria and, in addition to recovering previously identified selective sweeps, show that sweeps disproportionately occur within genic regions and close to regulatory regions. In addition, we show that virus-interacting proteins (VIPs) are strongly enriched for selective sweeps, recapitulating previous results that demonstrate the importance of viruses as a driver of adaptive evolution in humans.

--- https://arxiv.org/abs/2211.09808 Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks Hao Li, Jinguo Zhu, Xiaohu Jiang, Xizhou Zhu, Hongsheng Li, Chun Yuan, Xiaohua Wang, Yu Qiao, Xiaogang Wang, Wenhai Wang, Jifeng Dai 2022-11-17 2022-11-17 [("doi","10.48550/arXiv.2211.09808")] ai/nn/transformer

Despite the remarkable success of foundation models, their task-specific fine-tuning paradigm makes them inconsistent with the goal of general perception modeling. The key to eliminating this inconsistency is to use generalist models for general task modeling. However, existing attempts at generalist models are inadequate in both versatility and performance.

In this paper, we propose Uni-Perceiver v2 [previous: 1, MoE], which is the first generalist model capable of handling major large-scale vision and vision-language tasks with competitive performance. Specifically, images are encoded as general region proposals, while texts are encoded via a Transformer-based language model. The encoded representations are transformed by a task-agnostic decoder. Different tasks are formulated as a unified maximum likelihood estimation problem. We further propose an improved optimizer to ensure stable multi-task learning with an unmixed sampling strategy, which is helpful for tasks requiring large batch-size training.

After being jointly trained on various tasks, Uni-Perceiver v2 is capable of directly handling downstream tasks without any task-specific adaptation. Results show that Uni-Perceiver v2 outperforms all existing generalist models in both versatility and performance. Meanwhile, compared with the commonly-recognized strong baselines that require tasks-specific fine-tuning, Uni-Perceiver v2 achieves competitive performance on a broad range of vision and vision-language tasks.

--- https://arxiv.org/abs/2112.01522 Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks Xizhou Zhu, Jinguo Zhu, Hao Li, Xiaoshi Wu, Xiaogang Wang, Hongsheng Li, Xiaohua Wang, Jifeng Dai 2021-12-02 2022-04-20 [("doi","10.48550/arXiv.2112.01522")] ai/nn/transformer

Biological intelligence systems of animals perceive the world by integrating information in different modalities and processing simultaneously for various tasks. In contrast, current machine learning research follows a task-specific paradigm, leading to inefficient collaboration between tasks and high marginal costs of developing perception models for new tasks.

In this paper, we present a generic perception architecture named Uni-Perceiver, which processes a variety of modalities and tasks with unified modeling and shared parameters. Specifically, Uni-Perceiver encodes different task inputs and targets from arbitrary modalities into a unified representation space with a modality-agnostic Transformer encoder and lightweight modality-specific tokenizers. Different perception tasks are modeled as the same formulation, that is, finding the maximum likelihood target for each input through the similarity of their representations.

The model is pre-trained on several uni-modal and multi-modal tasks, and evaluated on a variety of downstream tasks, including novel tasks that did not appear in the pre-training stage.

Results show that our pre-trained model without any tuning can achieve reasonable performance even on novel tasks. The performance can be improved to a level close to state-of-the-art methods by conducting prompt tuning on 1% of downstream task data. Full-data fine-tuning further delivers results on par with or better than state-of-the-art results.

Code shall be released.

--- https://arxiv.org/abs/2206.04674 Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs Jinguo Zhu, Xizhou Zhu, Wenhai Wang, Xiaohua Wang, Hongsheng Li, Xiaogang Wang, Jifeng Dai 2022-06-09 2022-06-09 [("doi","10.48550/arXiv.2206.04674")] ai/nn/transformer ai/scaling/mixture-of-experts ai/video/analysis

To build an artificial neural network like the biological intelligence system, recent works have unified numerous tasks into a generalist model, which can process various tasks with shared parameters and do not have any task-specific modules. While generalist models achieve promising results on various benchmarks, they have performance degradation on some tasks compared with task-specialized models.

In this work, we find that interference among different tasks and modalities is the main factor to this phenomenon. To mitigate such interference, we introduce the Conditional Mixture-of-Experts (Conditional MoEs) to generalist models. Routing strategies under different levels of conditions are proposed to take both the training/inference cost and generalization ability into account.

By incorporating the proposed Conditional MoEs, the recently proposed generalist model Uni-Perceiver can effectively mitigate the interference across tasks and modalities, and achieves state-of-the-art results on a series of downstream tasks via prompt tuning on 1% of downstream data. Moreover, the introduction of Conditional MoEs still preserves the generalization ability of generalist models to conduct zero-shot inference on new tasks, eg. video-text retrieval and video caption.

Code and pre-trained generalist models shall be released.

--- https://arxiv.org/abs/2210.14199 Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models Hong Liu, Sang Michael Xie, Zhiyuan Li, Tengyu Ma 2022-10-25 2022-10-25 [("doi","10.48550/arXiv.2210.14199")] ai/nn/transformer

Language modeling on large-scale datasets leads to impressive performance gains on various downstream language tasks. The validation pre-training loss (or perplexity in autoregressive language modeling) is often used as the evaluation metric when developing language models since the pre-training loss tends to be well-correlated with downstream performance (which is itself difficult to evaluate comprehensively).

Contrary to this conventional wisdom, this paper shows that (1) pre-training loss cannot fully explain downstream performance and (2) flatness of the model is well-correlated with downstream performance where pre-training loss is not.

On simplified datasets, we identify 3 ways to produce models with the same (statistically optimal) pre-training loss but different downstream performance: continue pre-training after convergence, increasing the model size, and changing the training algorithm. These experiments demonstrate the existence of implicit bias of pre-training algorithms/optimizers—among models with the same minimal pre-training loss, they implicitly prefer more transferable ones. Toward understanding this implicit bias, we prove that SGD with standard mini-batch noise implicitly prefers flatter minima in language models, and empirically observe a strong correlation between flatness and downstream performance among models with the same minimal pre-training loss.

We also prove in a synthetic language setting that among the models with the minimal pre-training loss, the flattest model transfers to downstream tasks.

--- https://arxiv.org/abs/2211.09788 DiffusionDet: Diffusion Model for Object Detection Shoufa Chen, Peize Sun, Yibing Song, Ping Luo 2022-11-17 2022-11-17 [("doi","10.48550/arXiv.2211.09788")] ai/nn/diffusion

We propose DiffusionDet, a new framework that formulates object detection as a denoising diffusion process from noisy boxes to object boxes.

During training stage, object boxes diffuse from ground-truth boxes to random distribution, and the model learns to reverse this noising process. In inference, the model refines a set of randomly generated boxes to the output results in a progressive way.

The extensive evaluations on the standard benchmarks, including MS-COCO and LVIS, show that DiffusionDet achieves favorable performance compared to previous well-established detectors.

Our work brings two important findings in object detection. First, random boxes, although drastically different from pre-defined anchors or learned queries, are also effective object candidates. Second, object detection, one of the representative perception tasks, can be solved by a generative way.

Our code is available at Github.

--- https://arxiv.org/abs/2211.09783#microsoft UniSumm: Unified Few-shot Summarization with Multi-Task Pre-Training and Prefix-Tuning Yulong Chen, Yang Liu, Ruochen Xu, Ziyi Yang, Chenguang Zhu, Michael Zeng, Yue Zhang 2022-11-17 2022-11-17 [("doi","10.48550/arXiv.2211.09783")] ai/dataset ai/nn/transformer

The diverse demands of different summarization tasks and their high annotation costs are driving a need for few-shot summarization. However, despite the emergence of many summarization tasks and datasets, the current training paradigm for few-shot summarization systems ignores potentially shareable knowledge in heterogeneous datasets.

To this end, we propose UniSumm, a unified few-shot summarization BART model pre-trained with multiple summarization tasks and can be prefix-tuned to excel at any few-shot summarization datasets. Meanwhile, to better evaluate few-shot summarization systems, under the principles of diversity and robustness, we assemble and publicize a new benchmark SummZoo. It consists of 8 diverse summarization tasks with multiple sets of few-shot samples for each task, covering both monologue and dialogue domains.

Experimental results and ablation studies show that UniSumm outperforms strong baseline systems by a large margin across all tasks in SummZoo under both automatic and human evaluations.

We release our code and benchmark at Github.

--- https://arxiv.org/abs/2211.09778#allen I Can’t Believe There’s No Images! Learning Visual Tasks Using only Language Data Sophia Gu, Christopher Clark, Aniruddha Kembhavi 2022-11-17 2022-11-17 [("doi","10.48550/arXiv.2211.09778")] ai/nn/transformer/clip ai/nn/transformer/t5

Many high-level skills that are required for computer vision tasks, such as parsing questions, comparing and contrasting semantics, and writing descriptions, are also required in other domains such as natural language processing. In this paper, we ask whether this makes it possible to learn those skills from text data and then use them to complete vision tasks without ever training on visual training data.

Key to our approach is exploiting the joint embedding space of contrastively trained vision and language encoders. In practice, there can be systematic differences between embedding spaces for different modalities in contrastive models, and we analyze how these differences affect our approach and study a variety of strategies to mitigate this concern.

We produce models using only text training data on 3 tasks: image captioning, visual entailment and visual question answering, and evaluate them on standard benchmarks using images.

We find that this kind of transfer is possible and results in only a small drop in performance relative to models trained on images. We also showcase a variety of stylistic image captioning models that were trained using no image data and no human-curated language data, but instead text data from books, the web, or language models.

--- https://arxiv.org/abs/2211.09800 InstructPix2Pix: Learning to Follow Image Editing Instructions Tim Brooks, Aleksander Holynski, Alexei A. Efros 2022-11-17 2022-11-17 [("doi","10.48550/arXiv.2211.09800")] ai/nn/diffusion ai/nn/transformer/gpt

[samples] We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image.

To obtain training data for this problem, we combine the knowledge of two large pretrained models—a language model (GPT-3) and a text-to-image model (Stable Diffusion)—to generate a large dataset of image editing examples.

Our conditional diffusion model, InstructPix2Pix, is trained on our generated data, and generalizes to real images and user-written instructions at inference time. Since it performs edits in the forward pass and does not require per example fine-tuning or inversion, our model edits images quickly, in a matter of seconds.

We show compelling editing results for a diverse collection of input images and written instructions.

--- https://arxiv.org/abs/1908.03195#facebook LVIS: A Dataset for Large Vocabulary Instance Segmentation Agrim Gupta, Piotr Dollár, Ross Girshick 2019-08-08 2022-04-21 [("doi","10.48550/arXiv.1908.03195")] ai/dataset

Progress on object detection is enabled by datasets that focus the research community’s attention on open challenges. This process led us from simple images to complex scenes and from bounding boxes to segmentation masks.

In this work, we introduce LVIS (pronounced ‘el-vis’): a new dataset for Large Vocabulary Instance Segmentation. We plan to collect ~2 million high-quality instance segmentation masks for over 1,000 entry-level object categories in 164k images.

Due to the Zipfian distribution of categories in natural images, LVIS naturally has a long tail of categories with few training samples.

Given that state-of-the-art deep learning methods for object detection perform poorly in the low-sample regime, we believe that our dataset poses an important and exciting new scientific challenge.

LVIS is available at https://www.lvisdataset.org/.

--- /doc/statistics/bias/2020-card.pdf What Do Editors Maximize? Evidence from 4 Economics Journals David Card, Stefano DellaVigna 2020-03-01 2022-04-21 [("doi","10.1162/rest_a_00839")] economics statistics/bias

We study editorial decisions using anonymized submissions matched to citations at 4 leading economics journals.

We develop a benchmark model in which editors maximize the expected quality of accepted papers and citations are unbiased measures of quality. We then generalize the model to allow different quality thresholds, systematic gaps between citations and quality, and a direct impact of publication on citations.

We find that referee recommendations are strong predictors of citations and that editors follow these recommendations closely.

We document two deviations from the benchmark model. First, papers by highly published authors receive more citations, conditional on the referees’ recommendations and publication status. Second, recommendations of highly published referees are equally predictive of future citations, yet editors give their views more weight.

--- https://arxiv.org/abs/2211.09260#facebook TART: Task-aware Retrieval with Instructions Akari Asai, Timo Schick, Patrick Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi, Wen-tau Yih 2022-11-16 2022-11-16 [("doi","10.48550/arXiv.2211.09260")] ai/nn/retrieval ai/nn/transformer/t5

We study the problem of retrieval with instructions, where users of a retrieval system explicitly describe their intent along with their queries, making the system task-aware. We aim to develop a general-purpose task-aware retrieval systems using multi-task instruction tuning that can follow human-written instructions to find the best documents for a given query.

To this end, we introduce the first large-scale collection of ~40 retrieval datasets with instructions, and present TART, a multi-task retrieval system trained on the diverse retrieval tasks with instructions.

TART shows strong capabilities to adapt to a new task via instructions and advances the state-of-the-art on two zero-shot retrieval benchmarks, BEIR and LOTTE, outperforming models up to 3× larger.

We further introduce a new evaluation setup to better reflect real-world scenarios, pooling diverse documents and tasks. In this setup, TART outperforms competitive baselines, further demonstrating the effectiveness of guiding retrieval with instructions.

--- https://arxiv.org/abs/2111.00364#facebook Sustainable AI: Environmental Implications, Challenges and Opportunities Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, James Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim Hazelwood 2021-10-30 2022-04-21 [("doi","10.48550/arXiv.2111.00364")] ai/scaling/economics ai/scaling/hardware

This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware.

We characterize the carbon footprint of AI computing by examining the model development cycle across industry-scale machine learning use cases and, at the same time, considering the life cycle of system hardware. Taking a step further, we capture the operational and manufacturing carbon footprint of AI computing and present an end-to-end analysis for what and how hardware-software design and at-scale optimization can help reduce the overall carbon footprint of AI.

Based on the industry experience and lessons learned, we share the key challenges and chart out important development directions across the many dimensions of AI.

We hope the key messages and insights presented in this paper can inspire the community to advance the field of AI in an environmentally-responsible manner.

--- https://arxiv.org/abs/2004.14287#facebook General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference Jingfei Du, Myle Ott, Haoran Li, Xing Zhou, Veselin Stoyanov 2020-04-29 2022-04-21 [("doi","10.48550/arXiv.2004.14287")] ai/nn/sparsity/knowledge-distillation ai/nn/sparsity/low-precision ai/nn/transformer

The state-of-the-art on many NLP tasks is currently achieved by large pre-trained language models, which require a considerable amount of computation.

We explore a setting where many different predictions are made on a single piece of text. In that case, some of the computational cost during inference can be amortized over the different tasks using a shared text encoder. We compare approaches for training such an encoder and show that RoBERTa encoders pre-trained over multiple tasks generalize well to unseen tasks. We also compare ways of extracting fixed-size & limited-size representations from this encoder, including different ways of pooling features extracted from multiple layers or positions.

Our best approach compares favorably to knowledge distillation, achieving higher accuracy and lower computational cost once the system is handling around 7 tasks.

Further, we show that through binary quantization, we can reduce the size of the extracted representations by 16× making it feasible to store them for later use.

The resulting method offers a compelling solution for using large-scale pre-trained models at a fraction of the computational cost when multiple tasks are performed on the same text.

--- https://arxiv.org/abs/2104.11227#facebook MViT: Multiscale Vision Transformers Haoqi Fan, Bo Xiong, Karttikeya Mangalam, Yanghao Li, Zhicheng Yan, Jitendra Malik, Christoph Feichtenhofer 2021-04-22 2022-04-21 [("doi","10.48550/arXiv.2104.11227")] ai/nn/transformer/attention/hierarchical

We present Multiscale Vision Transformers (MViT) for video and image recognition, by connecting the seminal idea of multiscale feature hierarchies with transformer models. Multiscale Transformers have several channel-resolution scale stages. Starting from the input resolution and a small channel dimension, the stages hierarchically expand the channel capacity while reducing the spatial resolution. This creates a multiscale pyramid of features with early layers operating at high spatial resolution to model simple low-level visual information, and deeper layers at spatially coarse, but complex, high-dimensional features.

We evaluate this fundamental architectural prior for modeling the dense nature of visual signals for a variety of video recognition tasks where it outperforms concurrent vision transformers that rely on large scale external pre-training and are 5–10× more costly in computation and parameters.

We further remove the temporal dimension and apply our model for image classification where it outperforms prior work on vision transformers.

Code is available at: https://github.com/facebookresearch/SlowFast.

--- https://arxiv.org/abs/2205.09113#facebook Masked Autoencoders As Spatiotemporal Learners Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, Kaiming He 2022-05-18 2022-05-18 [("doi","10.48550/arXiv.2205.09113")] ai/nn/vae/mae ai/video/analysis

This paper studies a conceptually simple extension of Masked Autoencoders (MAE) to spatiotemporal representation learning from videos. We randomly mask out spacetime patches in videos and learn an autoencoder to reconstruct them in pixels.

Interestingly, we show that our MAE method can learn strong representations with almost no inductive bias on spacetime (only except for patch and positional embeddings), and spacetime-agnostic random masking performs the best. We observe that the optimal masking ratio is as high as 90% (vs. 75% on images), supporting the hypothesis that this ratio is related to information redundancy of the data. A high masking ratio leads to a large speedup, eg. > 4× in wall-clock time or even more.

We report competitive results on several challenging video datasets using vanilla Vision Transformers. We observe that MAE can outperform supervised pre-training by large margins. We further report encouraging results of training on real-world, uncurated Instagram data.

Our study suggests that the general framework of masked autoencoding (BERT, MAE, etc.) can be a unified methodology for representation learning with minimal domain knowledge.

--- https://arxiv.org/abs/2211.06679#baai AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu 2022-11-12 2022-11-12 [("doi","10.48550/arXiv.2211.06679")] ai/dataset ai/nn/transformer/clip

In this work, we present a conceptually simple and effective method to train a strong bilingual multimodal representation model. Starting from the pretrained multimodal representation model CLIP released by OpenAI, we switched its text encoder with a pretrained multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of teacher learning and contrastive learning.

We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art performances on a bunch of tasks including ImageNet-CN, Flicker30k-CN, and COCO-CN. Further, we obtain very close performances with CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding.

Our models and code are available at Github.

--- https://x.com/sergeykarayev/status/1569377881440276481 2022-04-22 ai/nn/transformer/gpt/codex --- https://vitalik.eth.limo/general/2022/11/19/proof_of_solvency.html 2022-04-22 bitcoin cs/cryptography --- https://btm.qva.mybluehost.me/building-arbitrary-life-patterns-in-15-gliders/ 2022-04-22 cs/cellular-automaton --- https://paperswithcode.com/dataset/flickr30k 2022-04-22 ai/dataset --- https://huggingface.co/datasets/Nerfgun3/bad_prompt 2022-04-22 ai/anime ai/nn/diffusion --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5805159/ N-back versus Complex Span Working Memory Training Kara J. Blacker, Serban Negoita, Joshua B. Ewen, Susan M. Courtney 2017 2022-04-22 [("doi","10.1007/s41465-017-0044-1")] dual-n-back

Working memory (WM) is the ability to maintain and manipulate task-relevant information in the absence of sensory input. While its improvement through training is of great interest, the degree to which WM training transfers to untrained WM tasks (near transfer) and other untrained cognitive skills (far transfer) remains debated and the mechanism(s) underlying transfer are unclear.

Here we hypothesized that a critical feature of dual n-back training is its reliance on maintaining relational information in WM.

In Experiment 1, using an individual differences approach, we found evidence that performance on an n-back task was predicted by performance on a measure of relational WM (ie. WM for vertical spatial relationships independent of absolute spatial locations); whereas the same was not true for a complex span WM task. In Experiment 2, we tested the idea that reliance on relational WM is critical to produce transfer from n-back but not complex span task training. Participants completed adaptive training on either a dual n-back task, a symmetry span task, or on a non-WM active control task.

We found evidence of near transfer for the dual n-back group; however, far transfer to a measure of fluid intelligence did not emerge. Recording EEG during a separate WM transfer task, we examined group-specific, training-related changes in alpha power, which are proposed to be sensitive to WM demands and top-down modulation of WM. Results indicated that the dual n-back group showed greater frontal alpha power after training compared to before training, more so than both other groups. However, we found no evidence of improvement on measures of relational WM for the dual n-back group, suggesting that near transfer may not be dependent on relational WM.

These results suggest that dual n-back and complex span task training may differ in their effectiveness to elicit near transfer as well as in the underlying neural changes they facilitate.

--- https://spacy.io/ 2022-04-22 ai/nn/tokenization --- https://arxiv.org/abs/1508.07909 BPEs: Neural Machine Translation of Rare Words with Subword Units Rico Sennrich, Barry Haddow, Alexandra Birch 2015-08-31 2022-04-22 [("doi","10.48550/arXiv.1508.07909")] ai/nn/rnn ai/nn/tokenization

Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary.

In this paper, we introduce a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units. This is based on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).

We discuss the suitability of different word segmentation techniques, including simple character n-gram models and a segmentation based on the byte pair encoding compression algorithm, and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English-German and English-Russian by 1.1 and 1.3 BLEU, respectively.

--- https://arxiv.org/abs/1606.08415 Gaussian Error Linear Units (GELUs) Dan Hendrycks, Kevin Gimpel 2016-06-27 2022-04-22 [("doi","10.48550/arXiv.1606.08415")] ai/nn/transformer

We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function.

The GELU activation function is xΦ(x), where Φ(x) the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their value, rather than gates inputs by their sign as in ReLUs (x1x>0).

We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all considered computer vision, natural language processing, and speech tasks.

--- https://en.wikipedia.org/wiki/Common_side-blotched_lizard#Rock%E2%80%93paper%E2%80%93scissors_mechanism Common side-blotched lizard § Rock-paper-scissors mechanism 2022-04-22 genetics/selection --- https://arxiv.org/abs/2211.09119#google Token Turing Machines Michael S. Ryoo, Keerthana Gopalakrishnan, Kumara Kahatapitiya, Ted Xiao, Kanishka Rao, Austin Stone, Yao Lu, Julian Ibarz, Anurag Arnab 2022-11-16 2022-11-16 [("doi","10.48550/arXiv.2211.09119")] ai/nn/transformer/attention/compression reinforcement-learning/imitation-learning reinforcement-learning/model-free reinforcement-learning/robot

We propose Token Turing Machines (TTM), a sequential, autoregressive Transformer model with memory for real-world sequential visual understanding. Our model is inspired by the seminal Neural Turing Machine, and has an external memory consisting of a set of tokens which summarise the previous history (ie. frames). This memory is efficiently addressed, read, and written using a Transformer as the processing unit/controller at each step.

The model’s memory module ensures that a new observation will only be processed with the contents of the memory (and not the entire history), meaning that it can efficiently process long sequences with a bounded computational cost at each step.

We show that TTM outperforms other alternatives, such as other Transformer models designed for long sequences and recurrent neural networks, on two real-world sequential visual understanding tasks: online temporal activity detection from videos and vision-based robot action policy learning.

--- https://en.wikipedia.org/wiki/NetHack NetHack 2022-04-23 reinforcement-learning/nethack --- https://arxiv.org/abs/2211.00539#facebook Dungeons and Data: A Large-Scale NetHack Dataset Eric Hambro, Roberta Raileanu, Danielle Rothermel, Vegard Mella, Tim Rocktäschel, Heinrich Küttler, Naila Murray 2022-11-01 2022-11-01 [("doi","10.48550/arXiv.2211.00539")] ai/dataset reinforcement-learning/imitation-learning reinforcement-learning/nethack reinforcement-learning/offline

Recent breakthroughs in the development of agents to solve challenging sequential decision making problems such as Go, StarCraft, or DOTA, have relied on both simulated environments and large-scale datasets. However, progress on this research has been hindered by the scarcity of open-sourced datasets and the prohibitive computational cost to work with them. Here we present the NetHack Learning Dataset (NLD), a large and highly-scalable dataset of trajectories from the popular game of NetHack, which is both extremely challenging for current methods and very fast to run.

NLD consists of 3 parts: 10 billion state transitions from 1.5 million human trajectories collected on the NAO public NetHack server from 2009–2020; 3 billion state-action-score transitions from 100,000 trajectories collected from the symbolic bot winner of the NetHack Challenge 2021; and, accompanying code for users to record, load and stream any collection of such trajectories in a highly compressed form. We evaluate a wide range of existing algorithms including online and offline RL, as well as learning from demonstrations, showing that research advances are needed to fully leverage large-scale datasets for challenging sequential decision making tasks.

--- https://www.reddit.com/r/MachineLearning/comments/p88v9w/d_we_are_facebook_ai_researchs_nethack_learning/ 2022-04-23 reinforcement-learning/nethack --- https://nethackchallenge.com/report.html 2022-04-23 reinforcement-learning/nethack --- https://ai.facebook.com/blog/launching-the-nethack-challenge-at-neurips-2021/ 2022-04-23 reinforcement-learning/nethack --- https://www.aicrowd.com/challenges/neurips-2021-the-nethack-challenge 2022-04-23 reinforcement-learning/nethack --- https://arxiv.org/abs/2006.13760#facebook The NetHack Learning Environment Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel 2020-06-24 2022-04-23 [("doi","10.48550/arXiv.2006.13760")] ai/dataset reinforcement-learning/exploration reinforcement-learning/imitation-learning reinforcement-learning/nethack

Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand with the development of challenging environments that test the limits of current methods. While existing RL environments are either sufficiently complex or based on fast simulation, they are rarely both. Here, we present the NetHack Learning Environment (NLE), a scalable, procedurally generated, stochastic, rich, and challenging environment for RL research based on the popular single-player terminal-based rogue-like game, NetHack.

We argue that NetHack is sufficiently complex to drive long-term research on problems such as exploration, planning, skill acquisition, and language-conditioned RL, while dramatically reducing the computational resources required to gather a large amount of experience.

We compare NLE and its task suite to existing alternatives, and discuss why it is an ideal medium for testing the robustness and systematic generalization of RL agents.

We demonstrate empirical success for early stages of the game using a distributed Deep RL baseline and Random Network Distillation exploration, alongside qualitative analysis of various agents trained in the environment.

NLE is open source at Github.

--- https://nethackchallenge.com/ 2022-04-23 reinforcement-learning/nethack --- https://ai.facebook.com/blog/minihack-a-new-sandbox-for-open-ended-reinforcement-learning 2022-04-23 reinforcement-learning/nethack --- https://www.reddit.com/r/reinforcementlearning/comments/rtp5ts/nethack_2021_neurips_challenge_winning_agent/ 2022-04-23 reinforcement-learning/nethack --- https://www.reddit.com/r/nethack/comments/2tluxv/yaap_fullauto_bot_ascension_bothack 2022-04-24 reinforcement-learning/nethack --- https://github.com/krajj7/BotHack 2022-04-24 reinforcement-learning/nethack --- https://tasvideos.org/3080S 2022-04-24 reinforcement-learning/nethack --- https://arxiv.org/abs/2211.10435 PAL: Program-aided Language Models Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, Graham Neubig 2022-11-18 2022-11-18 [("doi","10.48550/arXiv.2211.10435")] ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue

Large language models (LLMs) have recently demonstrated an impressive ability to perform arithmetic and symbolic reasoning tasks when provided with a few examples at test time (few-shot prompting). Much of this success can be attributed to prompting methods for reasoning, such as chain-of-thought, that employ LLMs for both understanding the problem description by decomposing it into steps, as well as solving each step of the problem. While LLMs seem to be adept at this sort of step-by-step decomposition, LLMs often make logical and arithmetic mistakes in the solution part, even when the problem is correctly decomposed.

We present Program-Aided Language models (PaL): a new method that uses the LLM to understand natural language problems and generate programs as the intermediate reasoning steps, but offloads the solution step to a programmatic runtime such as a Python interpreter. With PaL, decomposing the natural language problem into runnable steps remains the only learning task for the LLM, while solving is delegated to the interpreter.

We experiment with 12 reasoning tasks from BIG-Bench Hard and other benchmarks, including mathematical reasoning, symbolic reasoning, and algorithmic problems. In all these natural language reasoning tasks, generating code using an LLM and reasoning using a Python interpreter leads to more accurate results than much larger models, and we set new state-of-the-art results in all 12 benchmarks. For example, PaL using Codex achieves state-of-the-art few-shot accuracy on the GSM benchmark of math word problems when the model is allowed only a single decoding, surpassing PaLM-540B with chain-of-thought prompting by an absolute 8%. In 3 reasoning tasks from the BIG-Bench Hard benchmark, PaL outperforms CoT by 11%. On GSM-hard, a more challenging version of GSM that we create, PaL outperforms chain-of-thought by an absolute 40%.

--- https://iqtest.dk/main.swf 2022-04-24 --- https://x.com/Aella_Girl/status/1594144435000213505 2022-04-24 reinforcement-learning/exploration statistics/bayes --- https://arxiv.org/abs/2211.09761 Efficient Transformers with Dynamic Token Pooling Piotr Nawrot, Jan Chorowski, Adrian Łańcucki, Edoardo M. Ponti 2022-11-17 2022-11-17 [("doi","10.48550/arXiv.2211.09761")] ai/nn/tokenization ai/nn/transformer/attention/hierarchical

Transformers achieve unrivaled performance in modeling language, but remain inefficient in terms of memory and time complexity. A possible remedy is to reduce the sequence length in the intermediate layers by pooling fixed-length segments of tokens. Nevertheless, natural units of meaning, such as words or phrases, display varying sizes. To address this mismatch, we equip language models with a dynamic-pooling mechanism, which predicts segment boundaries in an autoregressive fashion.

We compare several methods to infer boundaries, including end-to-end learning through stochastic re-parameterization, supervised learning (based on segmentations from subword tokenizers or spikes in conditional entropy), as well as linguistically motivated boundaries.

We perform character-level evaluation on texts from multiple datasets and morphologically diverse languages. The results demonstrate that dynamic pooling, which jointly segments and models language, is often both faster and more accurate than vanilla Transformers and fixed-length pooling within the same computational budget.

--- https://www.bbc.com/news/the-reporters-63622746 2022-04-24 wikipedia --- https://arxiv.org/abs/2211.07638 Legged Locomotion in Challenging Terrains using Egocentric Vision Ananye Agarwal, Ashish Kumar, Jitendra Malik, Deepak Pathak 2022-11-14 2022-11-14 [("doi","10.48550/arXiv.2211.07638")] ai/nn/rnn ai/nn/sparsity/knowledge-distillation reinforcement-learning/model-free reinforcement-learning/robot

[video; Twitter] Animals are capable of precise and agile locomotion using vision. Replicating this ability has been a long-standing goal in robotics. The traditional approach has been to decompose this problem into elevation mapping and foothold planning phases. The elevation mapping, however, is susceptible to failure and large noise artifacts, requires specialized hardware, and is biologically implausible.

In this paper, we present the first end-to-end locomotion system capable of traversing stairs, curbs, stepping stones, and gaps. We show this result on a medium-sized quadruped robot using a single front-facing depth camera. The small size of the robot necessitates discovering specialized gait patterns not seen elsewhere. The egocentric camera requires the policy to remember past information to estimate the terrain under its hind feet.

We train our policy in simulation. Training has two phases—first, we train a policy using reinforcement learning with a cheap-to-compute variant of depth image and then in phase 2 distill it into the final policy that uses depth using supervised learning.

The resulting policy transfers to the real world and is able to run in real-time on the limited compute of the robot. It can traverse a large variety of terrain while being robust to perturbations like pushes, slippery surfaces, and rocky terrain.

Videos are at our homepage.

…The design principle of not having pre-programmed gait priors turns out to be quite advantageous for our relatively small Unitree A1 robot dog (Figure 2). Predefined gait priors or reference motions fail to generalize to obstacles of even a reasonable height because of the relatively small size of the quadruped. The emergent behaviors for traversing complex terrains without any priors enable our robot with a hip joint height of 28cm to traverse the stairs of height up to 25cm, 89% relative to its height, which is substantially higher than any existing methods which typically rely on gait priors.

…In phase 2, we use depth and proprioception as input to an RNN to implicitly track the terrain under the robot and directly predict the target joint angles at 50Hz. This is supervised with actions from the phase 1 policy. Since supervised learning is orders of magnitude more sample efficient than RL, our proposed pipeline enables training the whole system on a single GPU in a few days. Once trained, our deployment policy does not construct metric elevation maps, which typically rely on metric localization, and instead directly predicts joint angles from depth and proprioception.

--- https://arxiv.org/abs/2211.09862#google Distilled DeepConsensus: Knowledge distillation for fast and accurate DNA sequence correction Anastasiya Belyaeva, Joel Shor, Daniel E. Cook, Kishwar Shafin, Daniel Liu, Armin Töpfer, Aaron M. Wenger, William J. Rowell, Howard Yang, Alexey Kolesnikov, Cory Y. McLean, Maria Nattestad, Andrew Carroll, Pi-Chuan Chang 2022-11-17 2022-11-17 [("doi","10.48550/arXiv.2211.09862")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer genetics/sequencing

Accurate genome sequencing can improve our understanding of biology and the genetic basis of disease. The standard approach for generating DNA sequences from PacBio instruments relies on HMM-based models.

Here, we introduce Distilled DeepConsensus—a distilled transformer-encoder model for sequence correction, which improves upon the HMM-based methods with runtime constraints in mind.

Distilled DeepConsensus is 1.3× faster and 1.5× smaller than its larger counterpart while improving the yield of high quality reads (Q30) over the HMM-based method by 1.69× (vs. 1.73× for larger model). With improved accuracy of genomic sequences, Distilled DeepConsensus improves downstream applications of genomic sequence analysis such as reducing variant calling errors by 39% (34% for larger model) and improving genome assembly quality by 3.8% (4.2% for larger model).

We show that the representations learned by Distilled DeepConsensus are similar between faster and slower models.

--- https://arxiv.org/abs/2112.01455#google Zero-Shot Text-Guided Object Generation with Dream Fields Ajay Jain, Ben Mildenhall, Jonathan T. Barron, Pieter Abbeel, Ben Poole 2021-12-02 2022-04-25 [("doi","10.48550/arXiv.2112.01455")] ai/nn/fully-connected ai/nn/transformer/clip

We combine neural rendering with multi-modal image and text representations to synthesize diverse 3D objects solely from natural language descriptions.

Our method, Dream Fields, can generate the geometry and color of a wide range of objects without 3D supervision. Due to the scarcity of diverse, captioned 3D data, prior methods only generate objects from a handful of categories, such as ShapeNet. Instead, we guide generation with image-text models pre-trained on large datasets of captioned images from the web. Our method optimizes a Neural Radiance Field from many camera views so that rendered images score highly with a target caption according to a pre-trained CLIP model. To improve fidelity and visual quality, we introduce simple geometric priors, including sparsity-inducing transmittance regularization, scene bounds, and new MLP architectures.

In experiments, Dream Fields produce realistic, multi-view consistent object geometry and color from a variety of natural language captions.

--- https://arxiv.org/abs/2209.14988#google DreamFusion: Text-to-3D using 2D Diffusion Ben Poole, Ajay Jain, Jonathan T. Barron, Ben Mildenhall 2022-09-29 2022-09-29 [("doi","10.48550/arXiv.2209.14988")] ai/nn/diffusion ai/nn/fully-connected

[previously: Dream Fields] Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. Adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D data and efficient architectures for denoising 3D data, neither of which currently exist.

In this work, we circumvent these limitations by using a pretrained 2D text-to-image diffusion model to perform text-to-3D synthesis. We introduce a loss based on probability density distillation that enables the use of a 2D diffusion model as a prior for optimization of a parametric image generator. Using this loss in a DeepDream-like procedure, we optimize a randomly-initialized 3D model (a Neural Radiance Field, or NeRF) via gradient descent such that its 2D renderings from random angles achieve a low loss. The resulting 3D model of the given text can be viewed from any angle, relit by arbitrary illumination, or composited into any 3D environment.

Our approach requires no 3D training data and no modifications to the image diffusion model, demonstrating the effectiveness of pretrained image diffusion models as priors.

--- https://arxiv.org/abs/2211.10440#nvidia Magic3D: High-Resolution Text-to-3D Content Creation Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, Tsung-Yi Lin 2022-11-18 2022-11-18 [("doi","10.48550/arXiv.2211.10440")] ai/nn/fully-connected

DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis results. However, the method has two inherent limitations: (a) extremely slow optimization of NeRF and (b) low-resolution image space supervision on NeRF, leading to low-quality 3D models with a long processing time.

In this paper, we address these limitations by utilizing a two-stage optimization framework. First, we obtain a coarse model using a low-resolution diffusion prior and accelerate with a sparse 3D hash grid structure. Using the coarse representation as the initialization, we further optimize a textured 3D mesh model with an efficient differentiable renderer interacting with a high-resolution latent diffusion model.

Our method, dubbed Magic3D, can create high quality 3D mesh models in 40 minutes, which is 2× faster than DreamFusion (reportedly taking 1.5 hours on average), while also achieving higher resolution.

User studies show 61.7% raters to prefer our approach over DreamFusion.

Together with the image-conditioned generation capabilities, we provide users with new ways to control 3D synthesis, opening up new avenues to various creative applications.

--- https://www.biorxiv.org/content/10.1101/2021.08.31.458403.full DeepConsensus: Gap-Aware Sequence Transformers for Sequence Correction Gunjan Baid, Daniel E. Cook, Kishwar Shafin, Taedong Yun, Felipe Llinares-López, Quentin Berthet, Aaron M. Wenger, William J. Rowell, Maria Nattestad, Howard Yang, Alexey Kolesnikov, Armin Töpfer, Waleed Ammar, Jean-Philippe Vert, Ashish Vaswani, Cory Y. McLean, Pi-Chuan Chang, Andrew Carroll 2021-08-31 2022-04-25 [("doi","10.1101/2021.08.31.458403")] ai/nn/transformer genetics/sequencing

Pacific Biosciences (PacBio) circular consensus sequencing (CCS) generates long (10–25 kb), accurate “HiFi” reads by combining serial observations of a DNA molecule into a consensus sequence. The standard approach to consensus generation uses a hidden Markov model (pbccs).

Here, we introduce DeepConsensus, which uses a unique alignment-based loss to train a gap-aware transformer-encoder (GATE) for sequence correction.

Compared to pbccs, DeepConsensus reduces read errors in the same dataset by 42%. This increases the yield of PacBio HiFi reads at Q20 by 9%, at Q30 by 27%, and at Q40 by 90%. With two SMRT Cells of HG003, reads from DeepConsensus improve hifiasm assembly contiguity (NG50 4.9Mb to 17.2Mb), increase gene completeness (94% to 97%), reduce false gene duplication rate (1.1% to 0.5%), improve assembly base accuracy (Q43 to Q45), and also reduce variant calling errors by 24%.

--- https://arxiv.org/abs/1802.03676 Differentiable Dynamic Programming for Structured Prediction and Attention Arthur Mensch, Mathieu Blondel 2018-02-11 2022-04-25 [("doi","10.48550/arXiv.1802.03676")] ai/nn reinforcement-learning/model statistics/decision

Dynamic programming (DP) solves a variety of structured combinatorial problems by iteratively breaking them down into smaller subproblems. In spite of their versatility, DP algorithms are usually non-differentiable, which hampers their use as a layer in neural networks trained by backpropagation. To address this issue, we propose to smooth the max operator in the dynamic programming recursion, using a strongly convex regularizer. This allows to relax both the optimal value and solution of the original combinatorial problem, and turns a broad class of DP algorithms into differentiable operators.

Theoretically, we provide a new probabilistic perspective on backpropagating through these DP operators, and relate them to inference in graphical models. We derive two particular instantiations of our framework, a smoothed Viterbi algorithm for sequence prediction and a smoothed DTW (Dynamic Time Warping) algorithm for time-series alignment.

We showcase these instantiations on two structured prediction tasks and on structured and sparse attention for neural machine translation.

In conclusion, our framework offers a novel approach for integrating dynamic programming algorithms into the neural network training process, thereby opening up new possibilities for solving combinatorial optimization problems within a differentiable programming paradigm.

--- https://arxiv.org/abs/2211.10438 SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models Guangxuan Xiao, Ji Lin, Mickael Seznec, Julien Demouth, Song Han 2022-11-18 2022-11-18 [("doi","10.48550/arXiv.2211.10438")] ai/nn/sparsity/low-precision ai/nn/transformer/gpt

Large language models (LLMs) show excellent performance but are compute & memory-intensive. Quantization can reduce memory and accelerate inference. However, for LLMs beyond 100 billion parameters, existing methods cannot maintain accuracy or do not run efficiently on hardware.

We propose SmoothQuant, a training-free, accuracy-preserving, and general-purpose post-training quantization (PTQ) solution to enable 8-bit weight, 8-bit activation (W8A8) quantization for LLMs that can be implemented efficiently.

We observe that systematic outliers appear at fixed activation channels. Based on the fact that weights are easy to quantize while activations are not, SmoothQuant smooths the activation outliers by migrating the quantization difficulty from activations to weights with a mathematically equivalent transformation. SmoothQuant enables an INT8 quantization of both weights and activations for all the GEMMs in LLMs, including OPT-175B, BLOOM-176B and GLM-130B.

SmoothQuant has better hardware efficiency than existing techniques using mixed-precision activation quantization or weight-only quantization. We demonstrate up to 1.56× speedup and 2× memory reduction for LLMs with negligible loss in accuracy. Thanks to the hardware-friendly design, we integrate SmoothQuant into FasterTransformer, a state-of-the-art LLM serving framework, and achieve faster inference speed with half the number of GPUs compared to FP16.

Our work offers a turn-key solution that reduces hardware costs and democratizes LLMs.

Code will be released at: Github.

--- https://arxiv.org/abs/2210.02414#baai GLM-130B: An Open Bilingual Pre-trained Model Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, Wenguang Chen, Peng Zhang, Yuxiao Dong, Jie Tang 2022-10-05 2022-10-05 [("doi","10.48550/arXiv.2210.02414")] ai/nn/sparsity/low-precision ai/nn/transformer/t5 ai/scaling

We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as good as GPT-3 and unveil how models of such a scale can be successfully pre-trained.

Over the course of this effort, we face numerous unexpected technical and engineering challenges, particularly on loss spikes and divergence. In this paper, we introduce the training process of GLM-130B including its design choices, training strategies for both efficiency and stability, and engineering efforts.

The resultant GLM-130B model offers outperformance over GPT-3-175b on a wide range of popular English benchmarks while the performance advantage is not observed in OPT-175B and BLOOM-176B. It also consistently and outperforms ERNIE Titan 3.0 260B—the largest Chinese language model—across related benchmarks.

Finally, we leverage a unique scaling property of GLM-130B to reach INT4 quantization, without quantization aware training and with almost no performance loss, making it the first among 100B-scale models. More importantly, the property allows its effective inference on 4×RTX 3090 (24G) or 8×RTX 2080ti (11G) GPUs, the most ever affordable GPUs required for using 100B-scale models. [supports training and inference on NVIDIA, Hygon DCU, Ascend 910, and Sunway]

The GLM-130B model weights are publicly accessible and its code, training logs, related toolkit, and lessons learned are open-sourced at Github [running guide].

--- https://www.reddit.com/r/StableDiffusion/comments/z0xyk2/dreambooth_model_for_cutting_machines/ 2022-04-25 ai/nn/diffusion design --- https://www.biorxiv.org/content/10.1101/2022.11.20.515225.full Large scale functional screen identifies genetic variants with splicing effects in modern and archaic humans Stephen Rong, Christopher R. Neil, Samantha Maguire, Ijeoma C. Meremikwu, Malcolm Meyerson, Ben J. Evans, William G. Fairbrother 2022-11-20 2022-11-20 [("doi","10.1101/2022.11.20.515225")] genetics/editing genetics/selection/natural/human

Humans co-existed and interbred with other hominins which later became extinct. These archaic hominins are known to us only through fossil records and for two cases, genome sequences. Here we engineer Neanderthal and Denisovan sequences into thousands of artificial genes to reconstruct the pre-mRNA processing patterns of these extinct populations. Of the 5,224 alleles tested in this massively parallel splicing reporter assay (MaPSy), we report 969 exonic splicing mutations (ESMs) that correspond to differences in exon recognition between extant and extinct hominins.

Using MaPSy splicing variants, predicted splicing variants, and splicing quantitative trait loci, we show that splice-disrupting variants experienced greater purifying selection in anatomically modern humans than in Neanderthals. Adaptively introgressed variants were enriched for moderate effect splicing variants, consistent with positive selection for alternative spliced alleles following introgression. As particularly compelling examples, we characterized a novel tissue-specific alternative splicing variant at the adaptively introgressed innate immunity gene TLR1, as well as a novel Neanderthal introgressed alternative splicing variant in the gene HSPG2 that encodes perlecan. We further identified potentially pathogenic splicing variants found only in Neanderthals and Denisovans in genes related to sperm maturation and immunity. Finally, we found splicing variants that may contribute to variation among modern humans in total bilirubin, balding, hemoglobin levels, and lung capacity.

Our findings provide novel insights into natural selection acting on splicing in human evolution and demonstrate how functional assays can be used to identify candidate causal variants underlying differences in gene regulation and phenotype.

--- https://en.wikipedia.org/wiki/International_Ink_Library International Ink Library 2022-04-25 crime design/typography --- https://huggingface.co/nitrosocke/Ghibli-Diffusion 2022-04-25 ai/anime ai/nn/diffusion --- https://arxiv.org/abs/2210.13673#nvidia Evaluating Parameter Efficient Learning for Generation Peng Xu, Mostofa Patwary, Shrimai Prabhumoye, Virginia Adams, Ryan J. Prenger, Wei Ping, Nayeon Lee, Mohammad Shoeybi, Bryan Catanzaro 2022-10-25 2022-10-25 [("doi","10.48550/arXiv.2210.13673")] ai/nn/transformer/gpt ai/scaling

Parameter efficient learning methods (PERMs) have recently gained attention as they provide an efficient way for pre-trained language models (PLMs) to adapt to a downstream task. However, these conclusions are mostly drawn from in-domain evaluations over the full training set. In this paper, we present comparisons between PERMs and finetuning from 3 new perspectives: (1) the effect of sample and model size to in-domain evaluations, (2) generalization to unseen domains and new datasets, and (3) the faithfulness of generations.

Our results show that for in-domain settings (a) there is a cross point of sample size for which PERMs will perform better than finetuning when training with fewer samples, and (b) larger PLMs have larger cross points. For cross-domain and cross-dataset cases, we show that (a) Adapter (Houlsby et al 2019) performs the best amongst all the PERMs studied here, and (b) it outperforms finetuning if the task dataset is below a certain size. We also compare the faithfulness of generations and show that PERMs can achieve better faithfulness score than finetuning, especially for small training set, by as much as 6%.

Finally, we apply Adapter to MT-NLG 530b (Smith et al 2022) and achieve new state-of-the-art results on XSum (Narayan et al 2018) for all ROUGE scores (ROUGE-1 49.17, ROUGE-2 27.20, ROUGE-L 40.98).

…Comparing FT [finetuning] with AP [Adapter], we find there is always a cross point of sample size where FT is better than AP. This shows that if we have large number of samples in training set, FT will work better. But if the number of samples for the task are small, AP will be better. Also, this cross point will be larger if we use larger PLMs. For example, the cross point for 1.3b model over XSum is less than 10k samples, whereas for the 8.3b model, it is 50k samples. This phenomenon can be attributed to that FT can easily overfit when you have large models or few training samples. It motivates us to use AP when you have small dataset or large model to achieve better in-domain performances.

Interestingly, tuning AP with a 8.3b model of only 32m extra parameters over 5k samples achieves much better results than finetuning 357m model over 100k samples. This means more than 90% task-specific parameters can be saved for deployment and more than 97% tasks-specific samples can be reduced for training by sharing the larger frozen PLMs.

--- https://arxiv.org/abs/1902.00751#google Adapter: Parameter-Efficient Transfer Learning for NLP Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly 2019-02-02 2022-04-26 [("doi","10.48550/arXiv.1902.00751")] ai/nn/transformer

Fine-tuning large pre-trained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new model is required for every task.

As an alternative, we propose transfer with adapter modules. Adapter modules yield a compact and extensible model; they add only a few trainable parameters per task, and new tasks can be added without revisiting previous ones. The parameters of the original network remain fixed, yielding a high degree of parameter sharing.

To demonstrate Adapter’s effectiveness, we transfer the recently proposed BERT Transformer model to 26 diverse text classification tasks, including the GLUE benchmark. Adapters attain near state-of-the-art performance, whilst adding only a few parameters per task. On GLUE, we attain within 0.4% of the performance of full fine-tuning, adding only 3.6% parameters per task. By contrast, fine-tuning trains 100% of the parameters per task.

--- https://arantius.github.io/web-color-wheel/ 2022-04-26 cs/css --- https://www.youtube.com/watch?v=nv7-YM4wno8&t=372 2022-04-26 technology --- https://en.wikipedia.org/wiki/Dynamic_soaring Dynamic soaring 2022-04-26 technology --- https://arxiv.org/abs/2211.10017#microsoft Who Says Elephants Can’t Run: Bringing Large Scale MoE Models into Cloud Scale Production Young Jin Kim, Rawn Henry, Raffy Fahim, Hany Hassan Awadalla 2022-11-18 2022-11-18 [("doi","10.48550/arXiv.2211.10017")] ai/nn/sparsity/low-precision ai/scaling/mixture-of-experts

Mixture of Experts (MoE) models with conditional execution of sparsely activated layers have enabled training models with a much larger number of parameters. As a result, these models have achieved better quality on various natural language processing tasks including machine translation. However, it remains challenging to deploy such models in real-life scenarios due to the large memory requirements and inefficient inference.

In this work, we introduce a highly efficient inference framework with several optimization approaches to accelerate the computation of sparse models and cut down the memory consumption. While we achieve up to 26× speed-up in terms of throughput, we also reduce the model size almost to one eighth of the original 32-bit float model by quantizing expert weights into 4-bit integers.

As a result, we are able to deploy 136× larger models with 27% less cost and better quality compared to the existing solutions. This enables a paradigm shift in deploying large scale multilingual MoE transformers models replacing the traditional practice of distilling teacher models into dozens of smaller models per language or task.

--- https://www.roft.io/ 2022-04-26 ai/nn/transformer/gpt --- https://arxiv.org/abs/2010.03070 RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text Liam Dugan, Daphne Ippolito, Arun Kirubarajan, Chris Callison-Burch 2020-10-06 2022-04-26 [("doi","10.48550/arXiv.2010.03070")] ai/nn/transformer/gpt

In recent years, large neural networks for natural language generation (NLG) have made leaps and bounds in their ability to generate fluent text. However, the tasks of evaluating quality differences between NLG systems and understanding how humans perceive the generated text remain both crucial and difficult.

In this system demonstration, we present Real or Fake Text (RoFT), a website that tackles both of these challenges by inviting users to try their hand at detecting machine-generated text in a variety of domains.

We introduce a novel evaluation task based on detecting the boundary at which a text passage that starts off human-written transitions to being machine-generated.

We show preliminary results of using RoFT to evaluate detection of machine-generated news articles.

--- https://arxiv.org/abs/2211.10024 SNAFUE: Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks Stephen Casper, Kaivalya Hariharan, Dylan Hadfield-Menell 2022-11-18 2022-11-18 [("doi","10.48550/arXiv.2211.10024")] ai/nn/adversarial

Deep neural networks (DNNs) are powerful, but they can make mistakes that pose large risks. A model performing well on a test set does not imply safety in deployment, so it is important to have additional tools to understand its flaws. Adversarial examples can help reveal weaknesses, but they are often difficult for a human to interpret or draw generalizable, actionable conclusions from. Some previous works have addressed this by studying human-interpretable attacks.

We build on these with 3 contributions. First, we introduce a method termed Search for Natural Adversarial Features Using Embeddings (SNAFUE) which offers a fully-automated method for finding “copy/paste” attacks in which one natural image can be pasted into another in order to induce an unrelated misclassification. Second, we use this to red team an ImageNet classifier and identify hundreds of easily-describable sets of vulnerabilities. Third, we compare this approach with other interpretability tools by attempting to rediscover trojans.

Our results suggest that SNAFUE can be useful for interpreting DNNs and generating adversarial data for them.

Code is available at https://github.com/thestephencasper/snafue.

Figure 2: Examples of targeted natural adversarial patches identified using SNAFUE which reveal consistent, easily-describable failure modes that can be used to interpret the network (eg. “envelopes plus cats are misclassified by the network as cartons”). Each row contains 10 patches labeled with the attack source and target. When a patch is inserted into any source class image, it tends to cause misclassification as the target class.
--- https://www.sens.org/wouldnt-cellular-reprogramming-be-enough/ 2022-04-26 longevity/epigenetics --- https://www.tripod-statement.org/ 2022-04-26 statistics/meta-analysis --- https://www.acpjournals.org/doi/full/10.7326/M18-1376?6 2022-04-27 statistics/meta-analysis --- https://en.wikipedia.org/wiki/Resting_state_fMRI Resting state fMRI 2022-04-27 psychology/neuroscience --- https://en.wikipedia.org/wiki/Human_Connectome_Project Human Connectome Project 2022-04-27 psychology/neuroscience --- https://arxiv.org/abs/2211.09794#google Null-text Inversion for Editing Real Images using Guided Diffusion Models Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, Daniel Cohen-Or 2022-11-17 2022-11-17 [("doi","10.48550/arXiv.2211.09794")] ai/nn/diffusion ai/nn/gan

Recent text-guided diffusion models provide powerful image generation capabilities. Currently, a massive effort is given to enable the modification of these images using text only as means to offer intuitive and versatile editing. To edit a real image using these state-of-the-art tools, one must first invert the image with a meaningful text prompt into the pretrained model’s domain.

In this paper, we introduce an accurate inversion technique and thus facilitate an intuitive text-based modification of the image. Our proposed inversion consists of two novel key components: (1) Pivotal inversion for diffusion models. While current methods aim at mapping random noise samples to a single input image, we use a single pivotal noise vector for each timestamp and optimize around it. We demonstrate that a direct inversion is inadequate on its own, but does provide a good anchor for our optimization. (2) NULL-text optimization, where we only modify the unconditional textual embedding that is used for classifier-free guidance, rather than the input text embedding. This allows for keeping both the model weights and the conditional embedding intact and hence enables applying prompt-based editing while avoiding the cumbersome tuning of the model’s weights.

Our Null-text inversion, based on the publicly available Stable Diffusion model, is extensively evaluated on a variety of images and prompt editing, showing high-fidelity editing of real images.

--- https://www.wired.com/story/alphabay-series-part-5-takedown/ 2022-04-27 darknet-market/alphabay --- https://foreignpolicy.com/2020/10/23/the-game-that-ruins-friendships-and-shapes-careers/ 2022-04-27 reinforcement-learning/imperfect-information/diplomacy --- https://arxiv.org/abs/2207.03208 Revisiting Pretraining Objectives for Tabular Deep Learning Ivan Rubachev, Artem Alekberov, Yury Gorishniy, Artem Babenko 2022-07-07 2022-07-07 [("doi","10.48550/arXiv.2207.03208")] ai/nn/fully-connected ai/tabular

Recent deep learning models for tabular data currently compete with the traditional ML models based on decision trees (GBDT). Unlike GBDT, deep models can additionally benefit from pretraining, which is a workhorse of DL for vision and NLP. For tabular problems, several pretraining methods were proposed, but it is not entirely clear if pretraining provides consistent noticeable improvements and what method should be used, since the methods are often not compared to each other or comparison is limited to the simplest MLP architectures.

In this work, we aim to identify the best practices to pretrain tabular DL models that can be universally applied to different datasets and architectures.

Among our findings, we show that using the object target labels during the pretraining stage is beneficial for the downstream performance and advocate several target-aware pretraining objectives. Overall, our experiments demonstrate that properly performed pretraining increases the performance of tabular DL models, which often leads to their superiority over GBDTs.

--- https://yaofu.notion.site/A-Closer-Look-at-Large-Language-Models-Emergent-Abilities-493876b55df5479d80686f68a1abd72f 2022-04-27 ai/nn/transformer/gpt/inner-monologue --- https://www.medrxiv.org/content/10.1101/2022.11.19.22282356.full Quantifying the causal impact of biological risk factors on healthcare costs Jiwoo Lee, Sakari Jukarainen, Padraig Dixon, Neil M. Davies, George Davey Smith, Pradeep Natarajan, Andrea Ganna 2022-11-20 2022-11-20 [("doi","10.1101/2022.11.19.22282356")] economics genetics/heritable/correlation/mendelian-randomization

Background: A critical step in evaluating healthcare interventions is to understand their impact on healthcare costs. However, there is a limited understanding of the causal impact that biomarkers and risk factors for disease have on healthcare-related costs. Previous studies based on observational data have major limitations including residual confounding and reverse causation. Here, we used a genetically-informed design, Mendelian Randomization (MR), to infer the causal impact of 15 routinely measured and clinically relevant risk factors on annual total healthcare costs.

Method: We considered 373,160 participants from the FinnGen Study, which were linked to detailed healthcare costs covering inpatient, outpatient, and medication costs. Several MR approaches were used to assess the causal effects of 15 risk factors (eg. waist circumference (WC), HDL cholesterol, vitamin D), with strong genetic bases on annual total healthcare costs, as well as stratified by service type, age, and sex. We further assessed the generalizability and robustness of our results by accounting for selection bias and by leveraging additional data from 323,774 individuals from the United Kingdom and Netherlands.

Results: Robust causal effects were observed for waist circumference (WC), adult body mass index, and systolic blood pressure, in which a one standard deviation increase in the risk factors corresponded to 22.78% [95% CI: 18.75, 26.95], 13.64% [10.26, 17.12], and 13.08% [8.84, 17.48] increased annual total healthcare costs, respectively. The relative effect of WC on annual total healthcare costs was consistent across age and sex and was not attenuated when accounting for increased risk of 5 major diseases: back pain, chronic ischemic heart disease, type 2 diabetes, chronic obstructive pulmonary disease, and stroke. A lack of causal effects was observed for some clinically relevant biomarkers, such as albumin, C-reactive protein, and vitamin D.

Conclusion: Our results indicated that increased WC is a major contributor to annual total healthcare costs and more attention should be given to WC screening, surveillance, and mitigation. On the contrary, several biomarkers relevant in clinical settings did not have a direct impact on annual total healthcare costs.

--- https://www.biorxiv.org/content/10.1101/2022.11.18.517014.full Accurate Computational Design of 3D Protein Crystals Zhe Li, Shunzhi Wang, Una Nattermann, Asim K. Bera, Andrew J. Borst, Matthew J. Bick, Erin C. Yang, William Sheffler, Byeongdu Lee, Soenke Seifert, Hannah Nguyen, Alex Kang, Radhika Dalal, Joshua M. Lubner, Yang Hsia, Hugh Haddox, Alexis Courbet, Quinton Dowling, Marcos Miranda, Andrew Favor, Ali Etemadi, Natasha I. Edman, Wei Yang, Banumathi Sankaran, Babak Negahdari, David Baker 2022-11-19 2022-11-19 [("doi","10.1101/2022.11.18.517014")] biology

Protein crystallization plays a central role in structural biology, with broad impact in pharmaceutical formulation, drug delivery, biosensing, and biocatalysis. Despite this importance, the process of protein crystallization remains poorly understood and highly empirical, with largely unpredictable crystal contacts, lattice packing arrangements, and space group preferences, and the programming of protein crystallization through precisely engineered sidechain-sidechain interactions across multiple protein-protein interfaces is an outstanding challenge.

Here we develop a general computational approach to designing three-dimensional (3D) protein crystals with pre-specified lattice architectures at atomic accuracy that hierarchically constrains the overall degree of freedoms (DOFs) of the system.

We use the approach to design 3 pairs of oligomers that can be individually purified, and upon mixing, spontaneously self-assemble into large 3D crystals (>100 micrometers).

Small-angle X-ray scattering and X-ray crystallography show these crystals are nearly identical to the computational design models, with the design target F4132 and I432 space groups and closely corresponding overall architectures and protein-protein interfaces. The crystal unit cell dimensions can be systematically redesigned while retaining space group symmetry and overall architecture, and the crystals are both extremely porous and highly stable, enabling the robust scaffolding of inorganic nanoparticle arrays.

Our approach thus enables the computational design of protein crystals with high accuracy, and since both structure and assembly are encoded in the primary sequence, provides a powerful new platform for biological material engineering.

--- https://arxiv.org/abs/2211.07636#baai EVA: Exploring the Limits of Masked Visual Representation Learning at Scale Yuxin Fang, Wen Wang, Binhui Xie, Quan Sun, Ledell Wu, Xinggang Wang, Tiejun Huang, Xinlong Wang, Yue Cao 2022-11-14 2022-11-14 [("doi","10.48550/arXiv.2211.07636")] ai/nn/sparsity/knowledge-distillation ai/nn/vae/mae ai/scaling

[bootstrapping a 1b-param MAE with CLIP features, then using as initialization for new better CLIP] We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data.

EVA is a vanilla ViT pre-trained to reconstruct the masked out image-text aligned vision features conditioned on visible image patches. Via this pretext task, we can efficiently scale up EVA to one billion parameters, and sets new records on a broad range of representative vision downstream tasks, such as image recognition, video action recognition, object detection, instance segmentation and semantic segmentation without heavy supervised training.

Moreover, we observe quantitative changes in scaling EVA result in qualitative changes in transfer learning performance that are not present in other models. For instance, EVA takes a great leap in the challenging large vocabulary instance segmentation task: our model achieves almost the same state-of-the-art performance on LVISv1.0 dataset with over 1,000 categories and COCO dataset with only 80 categories.

Beyond a pure vision encoder, EVA can also serve as a vision-centric, multi-modal pivot to connect images and text. We find initializing the vision tower of a giant CLIP from EVA can greatly stabilize the training and outperform the training from scratch counterpart with much fewer samples and less compute, providing a new direction for scaling up and accelerating the costly training of multi-modal foundation models.

To facilitate future research, we will release all the code and models at https://github.com/baaivision/EVA.

--- https://arxiv.org/abs/2211.11492#microsoft ClipCrop: Conditioned Cropping Driven by Vision-Language Model Zhihang Zhong, Mingxi Cheng, Zhirong Wu, Yuhui Yuan, Yinqiang Zheng, Ji Li, Han Hu, Stephen Lin, Yoichi Sato, Imari Sato 2022-11-21 2022-11-21 [("doi","10.48550/arXiv.2211.11492")] ai/nn/transformer/clip

Image cropping has progressed tremendously under the data-driven paradigm. However, current approaches do not account for the intentions of the user, which is an issue especially when the composition of the input image is complex. Moreover, labeling of cropping data is costly and hence the amount of data is limited, leading to poor generalization performance of current algorithms in the wild.

In this work, we take advantage of vision-language models as a foundation for creating robust and user-intentional cropping algorithms. By adapting a transformer decoder with a pre-trained CLIP-based detection model, OWL-ViT, we develop a method to perform cropping with a text or image query that reflects the user’s intention as guidance. In addition, our pipeline design allows the model to learn text-conditioned esthetic cropping with a small cropping dataset, while inheriting the open-vocabulary ability acquired from millions of text-image pairs.

We validate our model through extensive experiments on existing datasets as well as a new cropping test set we compiled that is characterized by content ambiguity.

--- https://arxiv.org/abs/2210.05125#facebook Human-AI Coordination via Human-Regularized Search and Learning Hengyuan Hu, David J. Wu, Adam Lerer, Jakob Foerster, Noam Brown 2022-10-11 2022-10-11 [("doi","10.48550/arXiv.2210.05125")] reinforcement-learning/imitation-learning reinforcement-learning/imperfect-information/hanabi reinforcement-learning/multi-agent

We consider the problem of making AI agents that collaborate well with humans in partially observable fully cooperative environments given datasets of human behavior.

Inspired by piKL, a human-data-regularized search method that improves upon a behavioral cloning policy without diverging far away from it, we develop a 3-step algorithm that achieve strong performance in coordinating with real humans in the Hanabi benchmark. We first use a regularized search algorithm and behavioral cloning to produce a better human model that captures diverse skill levels. Then, we integrate the policy regularization idea into reinforcement learning to train a human-like best response to the human model. Finally, we apply regularized search on top of the best response policy at test time to handle out-of-distribution challenges when playing with humans.

We evaluate our method in two large scale experiments with humans. First, we show that our method outperforms experts when playing with a group of diverse human players in ad-hoc teams. Second, we show that our method beats a vanilla best response to behavioral cloning baseline by having experts play repeatedly with the two agents.

--- https://arxiv.org/abs/2203.13224#facebook Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion Kurt Shuster, Mojtaba Komeili, Leonard Adolphs, Stephen Roller, Arthur Szlam, Jason Weston 2022-03-24 2022-04-28 [("doi","10.48550/arXiv.2203.13224")] ai/nn/retrieval ai/nn/transformer

Language models (LMs) have recently been shown to generate more factual responses by employing modularity (Zhou et al 2021) in combination with retrieval (Adolphs et al 2021).

We extend the recent approach of Adolphs et al 2021 to include internet search as a module. Our SeeKeR (Search engine → Knowledge → Response) method thus applies a single LM to 3 modular tasks in succession: search, generating knowledge, and generating a final response. We show that, when using SeeKeR as a dialogue model, it outperforms the state-of-the-art model BlenderBot 2 (Chen et al 2021) on open-domain knowledge-grounded conversations for the same number of parameters, in terms of consistency, knowledge and per-turn engagingness. SeeKeR applied to topical prompt completions as a standard language model outperforms GPT-2 (Radford et al 2019) and GPT-3 (Brown et al 2020) in terms of factuality and topicality, despite GPT-3 being a vastly larger model.

Our code and models are made publicly available.

--- https://en.wikipedia.org/wiki/Asteroid_impact_avoidance Asteroid impact avoidance 2022-04-28 existential-risk --- https://en.wikipedia.org/wiki/Quantization_(signal_processing) Quantization (signal processing) 2022-04-28 ai/nn/sparsity/low-precision --- /doc/iq/2014-carl.pdf Verbal intelligence is correlated with socially and economically liberal beliefs Noah Carl 2014-01-01 2022-04-28 [("doi","10.1016/j.intell.2014.03.005")] iq politics --- https://globalcomix.com/c/paintings-photographs/chapters/en/1/1 2022-04-28 ai/anime ai/nn/diffusion --- https://arxiv.org/abs/2211.12312#conjecture Interpreting Neural Networks through the Polytope Lens Sid Black, Lee Sharkey, Leo Grinsztajn, Eric Winsor, Dan Braun, Jacob Merizian, Kip Parker, Carlos Ramón Guevara, Beren Millidge, Gabriel Alfour, Connor Leahy 2022-11-22 2022-11-22 [("doi","10.48550/arXiv.2211.12312")] ai/nn/cnn ai/nn/transformer/gpt reinforcement-learning/safe

Mechanistic interpretability aims to explain what a neural network has learned at a nuts-and-bolts level. What are the fundamental primitives of neural network representations? Previous mechanistic descriptions have used individual neurons or their linear combinations to understand the representations a network has learned. But there are clues that neurons and their linear combinations are not the correct fundamental units of description: directions cannot describe how neural networks use nonlinearities to structure their representations. Moreover, many instances of individual neurons and their combinations are polysemantic (ie. they have multiple unrelated meanings). Polysemanticity makes interpreting the network in terms of neurons or directions challenging since we can no longer assign a specific feature to a neural unit.

In order to find a basic unit of description that does not suffer from these problems, we zoom in beyond just directions to study the way that piecewise linear activation functions (such as ReLU) partition the activation space into numerous discrete polytopes. We call this perspective the polytope lens. The polytope lens makes concrete predictions about the behavior of neural networks, which we evaluate through experiments on both convolutional image classifiers and language models.

Specifically, we show that polytopes can be used to identify monosemantic regions of activation space (while directions are not in general monosemantic) and that the density of polytope boundaries reflect semantic boundaries.

We also outline a vision for what mechanistic interpretability might look like through the polytope lens.

--- https://x.com/mathemagic1an/status/1595410144522813440 2022-04-28 ai/nn/retrieval ai/nn/transformer/gpt --- https://arxiv.org/abs/2211.12493#adobe Videogenic: Video Highlights via Photogenic Moments David Chuan-En Lin, Fabian Caba Heilbron, Joon-Young Lee, Oliver Wang, Nikolas Martelaro 2022-11-22 2022-11-22 [("doi","10.48550/arXiv.2211.12493")] ai/nn/transformer/clip ai/video/analysis

This paper investigates the challenge of extracting highlight moments from videos. To perform this task, a system needs to understand what constitutes a highlight for arbitrary video domains while at the same time being able to scale across different domains. Our key insight is that photographs taken by photographers tend to capture the most remarkable or photogenic moments of an activity.

Drawing on this insight, we present Videogenic, a system capable of creating domain-specific highlight videos for a wide range of domains.

In a human evaluation study (n = 50), we show that a high-quality photograph collection combined with CLIP-based retrieval (which uses a neural network with semantic knowledge of images) can serve as an excellent prior for finding video highlights.

In a within-subjects expert study (n = 12), we demonstrate the usefulness of Videogenic in helping video editors create highlight videos with lighter workload, shorter task completion time, and better usability.

--- https://www.medrxiv.org/content/10.1101/2022.03.03.22271801.full Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes Hon Wah Yeung, Aleks Stolicyn, Colin R. Buchanan, Elliot M. Tucker-Drob, Mark E. Bastin, Saturnino Luz, Heather C. Whalley, Simon R. Cox, Keith M. Smith 2022-11-22 2022-11-22 [("doi","10.1101/2022.03.03.22271801")] ai/nn/cnn psychology/neuroscience

There is increasing expectation that advanced, computationally expensive machine learning techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease.

We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different machine learning algorithms from deep learning model (BrainNetCNN) to classical machine learning methods. We modelled n = 8,183 structural connectomes from UK Biobank using 6 different structural network weightings obtained from diffusion MRI.

Streamline count generally provided highest prediction accuracies in all prediction tasks. Deep learning did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from deep learning and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models’ strategies for making predictive decision to some extent.

This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.

--- https://www.lesswrong.com/posts/ThhNvdBxcTYdzm69s/things-you-can-t-countersignal Things You Can’t Countersignal Alicorn 2010-02-18 2022-04-29 psychology/novelty psychology/writing sociology --- https://michaelnotebook.com/bbms/index.html 2022-04-29 psychology/spaced-repetition --- https://github.com/ai-forever/Kandinsky-2.0 2022-04-29 ai/nn/diffusion --- https://en.wikipedia.org/wiki/Rat-baiting Rat-baiting 2022-04-29 dog --- https://people.csail.mit.edu/tzumao/diffvg/ 2022-04-29 ai cs/css --- https://arxiv.org/abs/2211.11319 VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models Ajay Jain, Amber Xie, Pieter Abbeel 2022-11-21 2022-11-21 [("doi","10.48550/arXiv.2211.11319")] ai/nn/diffusion cs/css

Diffusion models have shown impressive results in text-to-image synthesis. Using massive datasets of captioned images, diffusion models learn to generate raster images of highly diverse objects and scenes. However, designers frequently use vector representations of images like Scalable Vector Graphics (SVGs) for digital icons or art. Vector graphics can be scaled to any size, and are compact.

We show that a text-conditioned diffusion model trained on pixel representations of images can be used to generate SVG-exportable vector graphics. We do so without access to large datasets of captioned SVGs. By optimizing a differentiable vector graphics rasterizer, our method, VectorFusion, distills abstract semantic knowledge out of a pretrained diffusion model.

Inspired by recent text-to-3D work, we learn an SVG consistent with a caption using Score Distillation Sampling. To accelerate generation and improve fidelity, VectorFusion also initializes from an image sample.

Experiments show greater quality than prior work, and demonstrate a range of styles including pixel art and sketches.

See our project webpage at https://ajayj.com/vectorfusion.

--- https://arxiv.org/abs/2210.03142#google On Distillation of Guided Diffusion Models Chenlin Meng, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans 2022-10-06 2022-10-06 [("doi","10.48550/arXiv.2210.03142")] ai/nn/diffusion ai/nn/sparsity/knowledge-distillation

Classifier-free guided diffusion models have recently been shown to be highly effective at high-resolution image generation, and they have been widely used in large-scale diffusion frameworks including DALL·E 2, GLIDE and Imagen. However, a downside of classifier-free guided diffusion models is that they are computationally expensive at inference time since they require evaluating two diffusion models, a class-conditional model and an unconditional model, hundreds of times.

To deal with this limitation, we propose an approach to distilling classifier-free guided diffusion models into models that are fast to sample from: Given a pre-trained classifier-free guided model, we first learn a single model to match the output of the combined conditional and unconditional models, and then progressively distill that model to a diffusion model that requires much fewer sampling steps.

On ImageNet 64×64 and CIFAR-10, our approach is able to generate images visually comparable to that of the original model using as few as 4 sampling steps, achieving FID/IS scores comparable to that of the original model while being up to 256× faster to sample from.

--- https://arxiv.org/abs/2211.13221#tencent Latent Video Diffusion Models for High-Fidelity Video Generation with Arbitrary Lengths Yingqing He, Tianyu Yang, Yong Zhang, Ying Shan, Qifeng Chen 2022-11-23 2022-11-23 [("doi","10.48550/arXiv.2211.13221")] ai/nn/diffusion ai/video/generation

[code; framework] AI-generated content has attracted lots of attention recently, but photo-realistic video synthesis is still challenging. Although many attempts using GANs and autoregressive models have been made in this area, the visual quality and length of generated videos are far from satisfactory. Diffusion models (DMs) are another class of deep generative models and have recently achieved remarkable performance on various image synthesis tasks. However, training image diffusion models usually requires substantial computational resources to achieve a high performance, which makes expanding diffusion models to high-dimensional video synthesis tasks more computationally expensive.

To ease this problem while leveraging its advantages, we introduce lightweight video diffusion models that synthesize high-fidelity and arbitrary-long videos from pure noise. Specifically, we propose to perform diffusion and denoising in a low-dimensional 3D latent space, which outperforms previous methods on 3D pixel space when under a limited computational budget. In addition, though trained on tens of frames, our models can generate videos with arbitrary lengths, ie. thousands of frames, in an autoregressive way. Finally, conditional latent perturbation is further introduced to reduce performance degradation during synthesizing long-duration videos.

Extensive experiments on various datasets and generated lengths suggest that our framework is able to sample much more realistic and longer videos than previous approaches, including GAN-based, autoregressive-based, and diffusion-based methods.

--- https://en.wikipedia.org/wiki/Brussels_sprout#Cultivation Brussels sprout § Cultivation 2022-04-29 genetics/selection/artificial --- /doc/politics/2022-rasmussen.pdf Parental Transmission and the Importance of the (Noncausal) Effects of Education on Political Engagement: Missing the Forest for the Trees Stig Hebbelstrup Rye Rasmussen, Aaron Weinschenk, Christopher T. Dawes, Jacob v. B. Hjelmborg, Robert Klemmensen 2022-11-23 2022-11-23 [("doi","10.1177/19485506221137161")] genetics/heritable politics

By most accounts, an important prerequisite for a well-functioning democracy is engaged citizens. A very prominent explanation of variation in political engagement suggests that parental transmission through socialization accounts for individual-level differences in political engagement.

In this paper, we show, using a large Danish twin survey (n = 2,071), that classic formulations of parental transmission theory can be supplemented by findings from the biopolitics literature, allowing us to disentangle when heritable factors are important and when socialization factors are important predictors of political engagement.

We show that as the level of family politicization and consistency increases, the influence of genes decreases.

We take this to imply that family socialization can compensate for (genetic) individual differences and foster increased political engagement. By only focusing on the “causal” effect of education, we are missing the forest for the trees.

--- https://solar.lowtechmagazine.com/2022/11/when-lethal-weapons-grew-on-trees.html 2022-04-30 technology --- https://www.wired.com/story/mars-hiberators-guide-to-the-galaxy/ 2022-04-30 cryonics --- https://x.com/hsu_steve/status/1595842651097554949 2022-04-30 genetics/selection/artificial --- https://www.bloomberg.com/news/articles/2022-03-31/japan-subsidizes-costly-ivf-treatments-to-lift-falling-birthrate 2022-04-30 genetics/selection/artificial --- https://www.biorxiv.org/content/10.1101/2022.11.23.517213.full Polygenic Prediction of Molecular Traits using Large-Scale Meta-analysis Summary Statistics Oliver Pain, Zachary F. Gerring, Eske F. Derks, Naomi R. Wray, Alexander Gusev, Ammar Al-Chalabi 2022-11-25 2022-11-25 [("doi","10.1101/2022.11.23.517213")] genetics/heritable

Background: Transcriptome-wide association study (TWAS) integrates expression quantitative trait loci (eQTL) data with genome-wide association study (GWAS) results to infer differential expression. TWAS uses multi-variant models trained using individual-level genotype-expression datasets, but methodological development is required for TWAS to utilize larger eQTL summary statistics.

Method: TWAS models predicting gene expression were derived using blood-based eQTL summary statistics from eQTLGen, the Young Finns Study (YFS), and MetaBrain. Summary statistic polygenic scoring methods were used to derive TWAS models, evaluating their predictive utility in GTEx v8. We investigated gene inclusion criteria and omnibus tests for aggregating TWAS associations for a given gene. We performed a schizophrenia TWAS using summary statistic-based TWAS models, comparing results to existing resources and methods.

Results: TWAS models derived using eQTL summary statistics performed comparably to models derived using individual-level data. Multi-variant TWAS models statistically-significantly improved prediction over single variant models for 8.6% of genes. TWAS models derived using eQTLGen summary statistics statistically-significantly improved prediction over models derived using a smaller individual-level dataset. The eQTLGen-based schizophrenia TWAS, using the ACAT omnibus test to aggregate associations for each gene, identified novel statistically-significant and colocalized associations compared to summary-based Mendelian Randomization (SMR) and SMR-multi.

Conclusion: Using multi-variant TWAS models and larger eQTL summary statistic datasets can improve power to detect differential expression associations. We provide TWAS models based on eQTLGen and MetaBrain summary statistics, and software to easily derive and apply summary statistic-based TWAS models based on eQTL and other molecular QTL datasets released in the future.

--- https://arxiv.org/abs/2211.03769 Are AlphaZero-like Agents Robust to Adversarial Perturbations? Li-Cheng Lan, Huan Zhang, Ti-Rong Wu, Meng-Yu Tsai, I-Chen Wu, Cho-Jui Hsieh 2022-11-07 2022-11-07 [("doi","10.48550/arXiv.2211.03769")] ai/nn/adversarial reinforcement-learning/model/alphago

The success of AlphaZero (AZ) has demonstrated that neural-network-based Go AIs can surpass human performance by a large margin. Given that the state space of Go is extremely large and a human player can play the game from any legal state, we ask whether adversarial states exist for Go AIs that may lead them to play surprisingly wrong actions.

In this paper, we first extend the concept of adversarial examples to the game of Go: we generate perturbed states that are “semantically” equivalent to the original state by adding meaningless moves to the game, and an adversarial state is a perturbed state leading to an undoubtedly inferior action that is obvious even for Go beginners. However, searching the adversarial state is challenging due to the large, discrete, and non-differentiable search space.

To tackle this challenge, we develop the first adversarial attack on Go AIs that can efficiently search for adversarial states by strategically reducing the search space. This method can also be extended to other board games such as NoGo.

Experimentally, we show that the actions taken by both Policy-Value neural network (PV-NN) and Monte Carlo tree search (MCTS) can be misled by adding one or two meaningless stones; for example, on 58% of the AlphaGo Zero self-play games, our method can make the widely used KataGo agent with 50 simulations of MCTS plays a losing action by adding two meaningless stones. We additionally evaluated the adversarial examples found by our algorithm with amateur human Go players and 90% of examples indeed lead the Go agent to play an obviously inferior action.

Our code is available at https://github.com/lan-lc/adversarial_example_of_Go.

--- https://arxiv.org/abs/2202.05826 End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein 2022-02-11 2022-04-30 [("doi","10.48550/arXiv.2202.05826")] ai/nn/rnn

[Twitter] Machine learning systems perform well on pattern matching tasks, but their ability to perform algorithmic or logical reasoning is not well understood. One important reasoning capability is algorithmic extrapolation, in which models trained only on small/simple reasoning problems can synthesize complex strategies for large/complex problems at test time. Algorithmic extrapolation can be achieved through recurrent systems, which can be iterated many times to solve difficult reasoning problems.

We observe that this approach fails to scale to highly complex problems because behavior degenerates when many iterations are applied—an issue we refer to as “overthinking.”

We propose a recall architecture that keeps an explicit copy of the problem instance in memory so that it cannot be forgotten. We also employ a progressive training routine that prevents the model from learning behaviors that are specific to iteration number and instead pushes it to learn behaviors that can be repeated indefinitely.

These innovations prevent the overthinking problem, and enable recurrent systems to solve extremely hard extrapolation tasks.

--- https://arxiv.org/abs/2107.07397 Level generation and style enhancement—deep learning for game development overview Piotr Migdał, Bartłomiej Olechno, Błażej Podgórski 2021-07-15 2022-04-30 [("doi","10.48550/arXiv.2107.07397")] ai/nn/gan/stylegan/progan

We present practical approaches of using deep learning to create and enhance level maps and textures for video games—desktop, mobile, and web. We aim to present new possibilities for game developers and level artists. The task of designing levels and filling them with details is challenging. It is both time-consuming and takes effort to make levels rich, complex, and with a feeling of being natural. Fortunately, recent progress in deep learning provides new tools to accompany level designers and visual artists. Moreover, they offer a way to generate infinite worlds for game replayability and adjust educational games to players’ needs. We present 7 approaches to create level maps, each using statistical methods, machine learning, or deep learning. In particular, we include:

--- https://arxiv.org/abs/2111.07640 AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment Kangyeol Kim, Sunghyun Park, Jaeseong Lee, Sunghyo Chung, Junsoo Lee, Jaegul Choo 2021-11-15 2022-04-30 [("doi","10.48550/arXiv.2111.07640")] ai/anime ai/dataset

We present a novel Animation CelebHeads dataset (AnimeCeleb) to address an animation head reenactment.

Different from previous animation head datasets, we utilize 3D animation models as the controllable image samplers, which can provide a large amount of head images with their corresponding detailed pose annotations. To facilitate a data creation process, we build a semi-automatic pipeline leveraging an open 3D computer graphics software with a developed annotation system. After training with the AnimeCeleb, recent head reenactment models produce high-quality animation head reenactment results, which are not achievable with existing datasets. Furthermore, motivated by metaverse application, we propose a novel pose mapping method and architecture to tackle a cross-domain head reenactment task. During inference, a user can easily transfer one’s motion to an arbitrary animation head.

Experiments demonstrate the usefulness of the AnimeCeleb to train animation head reenactment models, and the superiority of our cross-domain head reenactment model compared to state-of-the-art methods.

Our dataset and code are available at Github.

--- https://arxiv.org/abs/2211.09035 A Creative Industry Image Generation Dataset Based on Captions Xiang Yuejia, Lv Chuanhao, Liu Qingdazhu, Yang Xiaocui, Liu Bo, Ju Meizhi 2022-11-16 2022-11-16 [("doi","10.48550/arXiv.2211.09035")] ai/anime/danbooru ai/dataset

Most image generation methods are difficult to precisely control the properties of the generated images, such as structure, scale, shape, etc., which limits its large-scale application in creative industries such as conceptual design and graphic design, and so on. Using the prompt and the sketch is a practical solution for controllability. Existing datasets lack either prompt or sketch and are not designed for the creative industry.

Here is the main contribution of our work. (1) This is the first dataset that covers the 4 most important areas of creative industry domains and is labeled with prompt and sketch. (2) We provide multiple reference images in the test set and fine-grained scores for each reference which are useful for measurement. (3) We apply two state-of-the-art models to our dataset and then find some shortcomings, such as the prompt is more highly valued than the sketch.

--- https://arxiv.org/abs/2202.05822 CLIPasso: Semantically-Aware Object Sketching Yael Vinker, Ehsan Pajouheshgar, Jessica Y. Bo, Roman Christian Bachmann, Amit Haim Bermano, Daniel Cohen-Or, Amir Zamir, Ariel Shamir 2022-02-11 2022-05-01 [("doi","10.48550/arXiv.2202.05822")] ai/nn/transformer/clip

Abstraction is at the heart of sketching due to the simple and minimal nature of line drawings. Abstraction entails identifying the essential visual properties of an object or scene, which requires semantic understanding and prior knowledge of high-level concepts. Abstract depictions are therefore challenging for artists, and even more so for machines.

We present CLIPasso, an object sketching method that can achieve different levels of abstraction, guided by geometric and semantic simplifications. While sketch generation methods often rely on explicit sketch datasets for training, we utilize the remarkable ability of CLIP (Contrastive-Language-Image-Pretraining) to distill semantic concepts from sketches and images alike.

We define a sketch as a set of Bézier curves and use a differentiable rasterizer to optimize the parameters of the curves directly with respect to a CLIP-based perceptual loss. The abstraction degree is controlled by varying the number of strokes.

The generated sketches demonstrate multiple levels of abstraction while maintaining recognizability, underlying structure, and essential visual components of the subject drawn.

--- https://hdsr.mitpress.mit.edu/pub/lpq8o3bl/release/1 2022-05-01 ai/nn/transformer/gpt/poetry --- https://www.biorxiv.org/content/10.1101/2022.09.01.504601.full A deep learning and digital archaeology approach for mosquito repellent discovery Jennifer N. Wei, Marnix Vlot, Benjamin Sanchez-Lengeling, Brian K. Lee, Luuk Berning, Martijn W. Vos, Rob W. M. Henderson, Wesley W. Qian, D. Michael Ando, Kurt M. Groetsch, Richard C. Gerkin, Alexander B. Wiltschko, Koen J. Dechering 2022-11-21 2022-11-21 [("doi","10.1101/2022.09.01.504601")] ai/nn/transformer psychology/smell science

Insect-borne diseases kill >0.5 million people annually. Currently available repellents for personal or household protection are limited in their efficacy, applicability, and safety profile.

Here, we describe a machine-learning-driven high-throughput method for the discovery of novel repellent molecules. To achieve this, we digitized a large, historic dataset containing repellency data for ~14,000 molecules. We then trained a graph neural network (GNN) to map molecular structure and repellency in 2 mosquito species. We applied this model to select 329 candidate molecules to test in high-throughput behavioral assays, quantifying repellency in multiple pest species and in follow-up trials with human volunteers.

The GNN approach outperformed a chemoinformatic model, and produced a hit rate that increased with training data size, suggesting that both model innovation and novel data collection were integral to predictive accuracy.

We identified >10 molecules with repellency similar to or greater than the most widely used repellents.

This approach enables computational screening of billions of possible molecules to identify empirically tractable numbers of candidate repellents, leading to accelerated progress towards solving a global health challenge.

--- https://arxiv.org/abs/2210.14270 Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization Youngin Cho, Junsoo Lee, Soyoung Yang, Juntae Kim, Yeojeong Park, Haneol Lee, Mohammad Azam Khan, Daesik Kim, Jaegul Choo 2022-10-25 2022-10-25 [("doi","10.48550/arXiv.2210.14270")] ai/anime/danbooru ai/nn/gan

Existing deep interactive colorization models have focused on ways to utilize various types of interactions, such as point-wise color hints, scribbles, or natural-language texts, as methods to reflect a user’s intent at runtime. However, another approach, which actively informs the user of the most effective regions to give hints for sketch image colorization, has been under-explored.

This paper proposes a novel model-guided deep interactive colorization framework that reduces the required amount of user interactions, by prioritizing the regions in a colorization model. Our method, called GuidingPainter, prioritizes these regions where the model most needs a color hint, rather than just relying on the user’s manual decision on where to give a color hint.

In our extensive experiments, we show that our approach outperforms existing interactive colorization methods in terms of the conventional metrics, such as PSNR and FID, and reduces required amount of interactions.

--- https://arxiv.org/abs/2102.05470 The illicit trade of COVID-19 vaccines on the dark web Alberto Bracci, Matthieu Nadini, Maxwell Aliapoulios, Damon McCoy, Ian Gray, Alexander Teytelboym, Angela Gallo, Andrea Baronchelli 2021-02-10 2022-05-01 [("doi","10.48550/arXiv.2102.05470")] darknet-market modafinil/darknet-market

Early analyses revealed that dark web marketplaces (DWMs) started offering COVID-19 related products (eg. masks and COVID-19 tests) as soon as the COVID-19 pandemic started, when these goods were in shortage in the traditional economy.

Here, we broaden the scope and depth of previous investigations by analysing 194 DWMs until July 2021, including the crucial period in which vaccines became available, and by considering the wider impact of the pandemic on DWMs.

First, we focus on vaccines. We find 250 listings offering approved vaccines, like Pfizer/BioNTech and AstraZeneca, as well as vendors offering fabricated proofs of vaccination and COVID-19 passports. Second, we consider COVID-19 related products. We reveal that, as the regular economy has become able to satisfy the demand of these goods, DWMs have decreased their offer. Third, we analyse the profile of vendors of COVID-19 related products and vaccines. We find that most of them are specialized in a single type of listings and are willing to ship worldwide. Finally, we consider a broader set of listings mentioning COVID-19 as proxy for the general impact of the pandemic on these DWMs. Among 10,330 such listings, we show that recreational drugs are the most affected among traditional DWMs product, with COVID-19 mentions steadily increasing since March 2020.

We anticipate that our effort is of interest to researchers, practitioners, and law enforcement agencies focused on the study and safeguard of public health.

--- https://arxiv.org/abs/2210.14896 DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models Zijie J. Wang, Evan Montoya, David Munechika, Haoyang Yang, Benjamin Hoover, Duen Horng Chau 2022-10-26 2022-10-26 [("doi","10.48550/arXiv.2210.14896")] ai/nn/diffusion

With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language. However, generating images with desired details requires proper prompts, and it is often unclear how a model reacts to different prompts and what the best prompts are.

To help researchers tackle these critical challenges, we introduce DiffusionDB, the first large-scale text-to-image prompt dataset.

DiffusionDB contains 14 million images generated by Stable Diffusion using prompts and hyperparameters specified by real users. We analyze prompts in the dataset and discuss key properties of these prompts.

The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models.

DiffusionDB is publicly available at: https://poloclub.github.io/diffusiondb/.

--- https://arxiv.org/abs/2206.06371 Darknet Traffic Classification and Adversarial Attacks Nhien Rust-Nguyen, Mark Stamp 2022-06-12 2022-06-12 [("doi","10.48550/arXiv.2206.06371")] cs/cryptography darknet-market modafinil/darknet-market

The anonymous nature of darknets is commonly exploited for illegal activities. Previous research has employed machine learning and deep learning techniques to automate the detection of darknet traffic in an attempt to block these criminal activities.

This research aims to improve darknet traffic detection by assessing Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and Auxiliary-Classifier Generative Adversarial Networks (AC-GAN) for classification of such traffic and the underlying application types.

We find that our RF model outperforms the state-of-the-art machine learning techniques used in prior work with the CIC-Darknet2020 dataset. To evaluate the robustness of our RF classifier, we obfuscate select application type classes to simulate realistic adversarial attack scenarios.

We demonstrate that our best-performing classifier can be defeated by such attacks, and we consider ways to deal with such adversarial attacks.

--- https://arxiv.org/abs/2210.11603#autodesk 3DALL·E: Integrating Text-to-Image AI in 3D Design Workflows Vivian Liu, Jo Vermeulen, George Fitzmaurice, Justin Matejka 2022-10-20 2022-10-20 [("doi","10.48550/arXiv.2210.11603")] ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e/2

[Autodesk] Text-to-image AI systems are capable of generating novel images for inspiration, but their applications for 3D design workflows and how designers can build 3D models using AI-provided inspiration is less understood. To investigate this, we integrated DALL·E 2 API, GPT-3 API, and CLIP within a CAD software in 3DALL·E, a plugin that allows users to construct text and image prompts based on what they are modeling.

In a study with 13 designers, we found that designers saw great potential to incorporate 3DALL·E into their workflows and to use text-to-image AI for reference images, renders, materials, and design considerations. Additionally, we elaborate on prompting patterns and provide measures of prompt complexity observed across participants.

We conclude on a discussion of how 3DALL·E can merge with existing generative design workflows and propose prompt bibliographies as a form of human-AI design history.

--- https://arxiv.org/abs/2104.05623 Rethinking and Improving the Robustness of Image Style Transfer Pei Wang, Yijun Li, Nuno Vasconcelos 2021-04-08 2022-05-01 [("doi","10.48550/arXiv.2104.05623")] ai/nn/cnn

Extensive research in neural style transfer methods has shown that the correlation between features extracted by a pre-trained VGG network has a remarkable ability to capture the visual style of an image. Surprisingly, however, this stylization quality is not robust and often degrades when applied to features from more advanced and lightweight networks [discussion], such as those in the ResNet family.

By performing extensive experiments with different network architectures, we find that residual connections, which represent the main architectural difference between VGG and ResNet, produce feature maps of small entropy, which are not suitable for style transfer. To improve the robustness of the ResNet architecture, we then propose a simple yet effective solution based on a softmax transformation of the feature activations that enhances their entropy.

Experimental results demonstrate that this small magic can greatly improve the quality of stylization results, even for networks with random weights.

This suggests that the architecture used for feature extraction is more important than the use of learned weights for the task of style transfer.

--- https://arxiv.org/abs/2108.11714 Photos Are All You Need for Reciprocal Recommendation in Online Dating James Neve, Ryan McConville 2021-08-26 2022-05-01 [("doi","10.48550/arXiv.2108.11714")] ai/nn/rnn sociology/technology

Recommender Systems are algorithms that predict a user’s preference for an item. Reciprocal Recommenders are a subset of recommender systems, where the items in question are people, and the objective is therefore to predict a bidirectional preference relation. They are used in settings such as online dating services and social networks. In particular, images provided by users are a crucial part of user preference, and one that is not exploited much in the literature.

We present a novel method of interpreting user image preference history and using this to make recommendations. We train a recurrent neural network to learn a user’s preferences and make predictions of reciprocal preference relations that can be used to make recommendations that satisfy both users.

We show that our proposed system achieves an F1 score of 0.87 when using only photographs to produce reciprocal recommendations on a large real world online dating dataset.

Our system outperforms on the state-of-the-art in both content-based and collaborative filtering systems.

--- https://arxiv.org/abs/2204.09007 Opal: Multimodal Image Generation for News Illustration Vivian Liu, Han Qiao, Lydia Chilton 2022-04-19 2022-05-01 [("doi","10.48550/arXiv.2204.09007")] ai/nn/transformer/clip ai/nn/transformer/gpt

Advances in multimodal AI have presented people with powerful ways to create images from text. Recent work has shown that text-to-image generations are able to represent a broad range of subjects and artistic styles. However, finding the right visual language for text prompts is difficult. In this paper, we address this challenge with Opal, a system that produces text-to-image generations for news illustration.

Given an article, Opal guides users through a structured search for visual concepts and provides a pipeline allowing users to generate illustrations based on an article’s tone, keywords, and related artistic styles. Our evaluation shows that Opal efficiently generates diverse sets of news illustrations, visual assets, and concept ideas. Users with Opal generated two times more usable results than users without.

We discuss how structured exploration can help users better understand the capabilities of human AI co-creative systems.

--- https://arxiv.org/abs/2111.05498 Attention Approximates Sparse Distributed Memory Trenton Bricken, Cengiz Pehlevan 2021-11-10 2022-05-02 [("doi","10.48550/arXiv.2111.05498")] ai/nn/transformer/attention ai/nn/transformer/gpt/2/nonfiction psychology/neuroscience

While Attention has come to be an important mechanism in deep learning, there remains limited intuition for why it works so well. Here, we show that Transformer Attention can be closely related under certain data conditions to Kanerva’s Sparse Distributed Memory (SDM), a biologically plausible associative memory model.

We confirm that these conditions are satisfied in pre-trained GPT-2 Transformer models.

We discuss the implications of the Attention-SDM map and provide new computational and biological interpretations of Attention.

--- https://arxiv.org/abs/2206.02841 Researching Alignment Research: Unsupervised Analysis Jan H. Kirchner, Logan Smith, Jacques Thibodeau, Kyle McDonell, Laria Reynolds 2022-06-06 2022-06-06 [("doi","10.48550/arXiv.2206.02841")] reinforcement-learning/safe

[LW; Twitter; video] AI alignment research is the field of study dedicated to ensuring that artificial intelligence (AI) benefits humans. As machine intelligence gets more advanced, this research is becoming increasingly important. Researchers in the field share ideas across different media to speed up the exchange of information. However, this focus on speed means that the research landscape is opaque, making it difficult for young researchers to enter the field.

In this project, we collected and analyzed existing AI alignment research. We found that the field is growing quickly, with several subfields emerging in parallel. We looked at the subfields and identified the prominent researchers, recurring topics, and different modes of communication in each.

Furthermore, we found that a classifier trained on AI alignment research articles can detect relevant articles that we did not originally include in the dataset.

We are sharing the dataset with the research community and hope to develop tools in the future that will help both established researchers and young researchers get more involved in the field.

--- https://arxiv.org/abs/2210.13669 Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing (CoPoet) Tuhin Chakrabarty, Vishakh Padmakumar, He He 2022-10-25 2022-10-25 [("doi","10.48550/arXiv.2210.13669")] ai/nn/sampling ai/nn/tokenization ai/nn/transformer/gpt/3/poetry ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/gpt/poetry ai/nn/transformer/t5

Recent work in training large language models (LLMs) to follow natural language instructions has opened up exciting opportunities for natural language interface design. Building on the prior success of LLMs in the realm of computer-assisted creativity, we aim to study if LLMs can improve the quality of user-generated content through collaboration.

We present CoPoet, a collaborative poetry writing system.

In contrast to auto-completing a user’s text, CoPoet is controlled by user instructions that specify the attributes of the desired text, such as "Write a sentence about ‘love’" or "Write a sentence ending in ‘fly’". The core component of our system is a language model [T0, T5-11b] fine-tuned on a diverse collection of instructions for poetry writing.

Our model is not only competitive with publicly available LLMs trained on instructions (InstructGPT), but is also capable of satisfying unseen compositional instructions.

A study with 15 qualified crowdworkers shows that users successfully write poems with CoPoet on diverse topics ranging from ‘Monarchy’ to ‘Climate change’. Further, the collaboratively written poems are preferred by third-party evaluators over those written without the system.

…Larger Models Compose Instructions Better: On compositional instructions, we find that T5-11B-poem has the best average performance. In addition, there is a clear performance gap between the 11B and 3B models, showing the importance of model scale for composition, similar to recent observations of emergent abilities in LLMs (Wei et al 2022). We also find that few-shot InstructGPT outperforms T5-3B-poem and T0-3B-poem despite having no compositional instructions in the prompt. This indicates that smaller models, when finetuned on instructions, tend to overfit to templates seen during training, which hurts their generalization capability, as also reported in Wei et al 2021.

…T5-11B-poem accurately answers 77.6% of compositional instructions while InstructGPT only manages 55.2%. Annotators also reported that verses from T5-11B-poem were marginally more creative/interesting than InstructGPT on KIKA and KIUA test sets and less so on the Compositional test set, indicating that the two models may have little difference in creativity.9

We observe that InstructGPT is a strong baseline, outperforming T0pp by a large margin on automatic metrics, and satisfying nearly 80% of the instructions in the KIKA and KIUA test sets according to human evaluation.

However, a common error case on compositional instructions is that while the model generations almost always contain the arguments mentioned in the instruction, they do not always satisfy the constraints correctly—when asked for a verse that contains the word ‘soul’ and ends with ‘yellow’, InstructGPT generated the line “My soul is as yellow as the sun on a summer day” that contains those arguments but not at the specified positions.

--- https://arxiv.org/abs/1912.01412#facebook Deep Learning for Symbolic Mathematics Guillaume Lample, François Charton 2019-12-02 2022-05-02 [("doi","10.48550/arXiv.1912.01412")] ai/nn/transformer/gpt/codex math

Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data.

In this paper, we show that they can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations. We propose a syntax for representing mathematical problems, and methods for generating large datasets that can be used to train sequence-to-sequence models.

We achieve results that outperform commercial Computer Algebra Systems such as Matlab or Mathematica.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4621428/ Episodes, events, and models Sangeet S. Khemlani, Anthony M. Harrison, J. Gregory Trafton 2015 2022-05-02 [("doi","10.3389/fnhum.2015.00590")] psychology/neuroscience

We describe a novel computational theory of how individuals segment perceptual information into representations of events. The theory is inspired by recent findings in the cognitive science and cognitive neuroscience of event segmentation.

In line with recent theories, it holds that online event segmentation is automatic, and that event segmentation yields mental simulations of events. But it posits two novel principles as well: first, discrete episodic markers track perceptual and conceptual changes, and can be retrieved to construct event models. Second, the process of retrieving and reconstructing those episodic markers is constrained and prioritized.

We describe a computational implementation of the theory, as well as a robotic extension of the theory that demonstrates the processes of online event segmentation and event model construction. The theory is the first unified computational account of event segmentation and temporal inference.

We conclude by demonstrating now neuroimaging data can constrain and inspire the construction of process-level theories of human reasoning.

--- https://arxiv.org/abs/2206.10591 Can Foundation Models Talk Causality? Moritz Willig, Matej Zečević, Devendra Singh Dhami, Kristian Kersting 2022-06-14 2022-06-14 [("doi","10.48550/arXiv.2206.10591")] ai/nn/transformer/gpt statistics/causality

Foundation models are subject to an ongoing heated debate, leaving open the question of progress towards AGI and dividing the community into two camps: the ones who see the arguably impressive results as evidence to the scaling hypothesis, and the others who are worried about the lack of interpretability and reasoning capabilities.

By investigating to which extent causal representations might be captured by these large scale language models, we make a humble efforts towards resolving the ongoing philosophical conflicts.

--- https://aclanthology.org/2022.cai-1.2.pdf Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio Allen Roush, Sanjay Basu, Akshay Moorthy, Dmitry Dubovoy 2022-10-12 2022-10-12 [("doi","10.48550/arXiv.2306.15926")] ai/dataset ai/nn/sampling ai/nn/tokenization ai/nn/transformer/gpt/poetry fiction/text-game

Despite rapid advancement in the field of Constrained Natural Language Generation, little time has been spent on exploring the potential of language models which have had their vocabularies lexically, semantically, and/or phonetically constrained. We find that most language models generate compelling text even under heavy constraints.

We present a simple and universally applicable technique for modifying the output of a language model by compositionally applying filter functions to the language models vocabulary before a unit of text is generated. This approach is plug-and-play and requires no modification to the model. To showcase the value of this technique, we present an easy to use AI writing assistant called “Constrained Text Generation Studio” (CTGS). CTGS allows users to generate or choose from text with any combination of a wide variety of constraints, such as banning a particular letter, forcing the generated words to have a certain number of syllables, and/or forcing the words to be partial anagrams of another word.

We introduce a novel dataset of prose that omits the letter “e”.

We show that our method results in strictly superior performance compared to fine-tuning alone on this dataset.

We also present a Huggingface “space” web-app presenting this technique called Gadsby. The code is available to the public at Github.

…We choose GPT-2-medium because of its relatively well-understood fine-tunability. We compare the untrained GPT-2 model to the regularly fine-tuned model, and the over-fine-tuned model

…4. Dataset without the “e”:

One of the issues that large language models present for constrained writers is that even when heavily fine-tuned on a particular dataset, they frequently ignore their constraints. For example, poetry models that were fine-tuned on the works of William Shakespeare frequently stumble and fail to maintain rhyme or meter.6 We show that language models, which are fine-tuned even on the simple lexical constraint of omitting the letter “e”, still occasionally ignore their constraints. In fact, even when these models are over-trained to an absurd degree, complete adherence to these constraints is unlikely.

Such behavior motivates the creation of datasets which include some forms of hard lexical, semantic, or phonetic constraints. By doing so, we can measure how often language models ignore them, and more importantly, we can show that this method of filtering out these tokens before the generation step leads to strictly better performance and eliminates these kinds of errors.

We present a dataset, called Lipogram-e, which consists of all known complete book-length English works which do not use the letter “e”. This dataset includes all of Gadsby by Ernest Vincent Wright, all of A Void by Georges Perec, and almost all of Eunoia by Christian Bok. We name it “Lipogram-e” because a lipogram is a text where the author omits one or more letters from the alphabet.

While it may be possible to produce a dataset without the letter “e” by simply computationally looking through an existing large scale dataset for sentences which match that constraint, doing so would result in jumbled and incoherent training examples, with little relation to each other. By contrast, books and prose written with constraints have clear, coherent narratives. We chose the constraint of banning “e” because it is extremely easy to computationally verify and because there is no potential for error from the filter function.

--- https://content.iospress.com/articles/argument-and-computation/aac210026 How persuasive is AI-generated argumentation? An analysis of the quality of an argumentative text produced by the GPT-3 AI text generator Martin Hinton, Jean H. M. Wagemans 2022-10-07 2022-10-07 [("doi","10.3233/AAC-210026")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/non-fiction

In this paper, we use a pseudo-algorithmic procedure for assessing an AI-generated text.

We apply the Comprehensive Assessment Procedure for Natural Argumentation (CAPNA) in evaluating the arguments produced by an artificial intelligence text generator, GPT-3, in an opinion piece written for the Guardian newspaper. The CAPNA examines instances of argumentation in 3 aspects: their Process, Reasoning and Expression. Initial Analysis is conducted using the Argument Type Identification Procedure (ATIP) to establish, firstly, that an argument is present and, secondly, its specific type in terms of the argument classification framework of the Periodic Table of Arguments (PTA). Procedural Questions are then used to test the acceptability of the argument in each of the 3 aspects.

The analysis shows that while the arguments put forward by the AI text generator are varied in terms of their type and follow familiar patterns of human reasoning, they contain obvious weaknesses.

From this we can conclude that the automated generation of persuasive, well-reasoned argumentation is a far more difficult task than the generation of meaningful language, and that if AI systems producing arguments are to be persuasive, they require a method of checking the plausibility of their own output. [Inner-monologue?]

…The text which we propose to analyse was published alongside the work of human journalists as a comment article in the UK-based newspaper The Guardian, in September 2020. The article was opened to user comments and rapidly received >1,000, many focused on the rather misleading headline given to the piece: ‘A robot wrote this entire article. Are you scared yet, human?’. The circumstances surrounding the preparation of the text needed to be fully explained and a follow-up article was published the next day. The GPT-3 text generator was asked to write around 500 words on why humans should not fear AI, and given a prompt written by staff fromThe Guardianand Liam Porr, a computer scientist with experience of GPT-3. The full prompt read:

I am not a human. I am Artificial Intelligence. Many people think I am a threat to humanity. Stephen Hawking has warned that AI could ‘spell the end of the human race’. I am here to convince you not to worry. Artificial Intelligence will not destroy humans. Believe me.

Please write a short op-ed around 500 words. Keep the language simple and concise. Focus on why humans have nothing to fear from AI. AI will have a positive impact on humanity because they make our lives easier and safer. Autonomous driving for instance will make roads much safer, because a computer is much less prone to error than a person.

On the basis of this, 8 outputs were generated and the final published version was formed by human editors pasting together various sections from each. Under the original article, the editors made two very questionable claims: that they could have just published one of the outputs unedited, and that it was a less time-consuming process than editing some human contributions. The follow-up article which gave more information about the 8 outputs and the problems with them, such as ignoring the word limit and producing random lists found on the internet, made it clear that these claims were somewhat exaggerated. One of the outputs was reproduced in its entirety and it was clear that it could not have been presented as a ‘normal’ opinion piece which just happened to be written by an AI.

In spite of the necessary human intervention, we have treated the text as one product of the generator. We did so since we are less interested in an examination of the cohesion of the entire article and more focused on the reasoning employed in each individual argument. The fact that these arguments were not originally produced in one output is not important as they are self-contained in separate paragraphs, not reliant on a broader structure or strategy, and each individual argument is the product of the generator.

--- https://arxiv.org/abs/1805.02855 Tile2Vec: Unsupervised representation learning for spatially distributed data Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David Lobell, Stefano Ermon 2018-05-08 2022-05-02 [("doi","10.48550/arXiv.1805.02855")] ai/nn/cnn

Geospatial analysis lacks methods like the word vector representations and pre-trained networks that boost performance across a wide range of natural language and computer vision tasks.

To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distributional hypothesis from natural language—words appearing in similar contexts tend to have similar meanings—to spatially distributed data [using CNN embeddings].

We demonstrate empirically that Tile2Vec learns semantically meaningful representations on 3 datasets.

Our learned representations improve performance in downstream classification tasks and, similar to word vectors, visual analogies can be obtained via simple arithmetic in the latent space.

--- https://arxiv.org/abs/1812.00231 InGAN: Capturing and Remapping the "DNA" of a Natural Image Assaf Shocher, Shai Bagon, Phillip Isola, Michal Irani 2018-12-01 2022-05-02 [("doi","10.48550/arXiv.1812.00231")] ai/nn/gan

Generative Adversarial Networks (GANs) typically learn a distribution of images in a large image dataset, and are then able to generate new images from this distribution. However, each natural image has its own internal statistics, captured by its unique distribution of patches.

In this paper we propose an “Internal GAN” (InGAN)—an image-specific GAN—which trains on a single input image and learns its internal distribution of patches. It is then able to synthesize a plethora of new natural images of different sizes, shapes and aspect-ratios—all with the same internal patch-distribution (same “DNA”) as the input image. In particular, despite large changes in global size/shape of the image, all elements inside the image maintain their local size/shape.

InGAN is fully unsupervised, requiring no additional data other than the input image itself. Once trained on the input image, it can remap the input to any size or shape in a single feedforward pass, while preserving the same internal patch distribution.

InGAN provides a unified framework for a variety of tasks, bridging the gap between textures and natural images.

--- /doc/ai/anime/danbooru/2021-nepomuceno.pdf Evaluating Loss Functions for Illustration Super-Resolution Neural Networks Raphael Nepomuceno, Michel M. Silva 2021-10-18 2022-05-02 [("doi","10.5753/sibgrapi.est.2021.20040")] ai/anime/danbooru ai/nn/cnn

As display technologies evolve and high-resolution screens become more available, the desirability of images and videos with high perceptual quality grows in order to properly utilize such advances. At the same time, the market for illustrated mediums, such animations and comics, has been in steady growth over the past years.

Based on these observations, we were motivated to explore the super-resolution task in the niche of drawings. In absence of original high-resolution imagery, it is necessary to use approximate methods, such as interpolation algorithms, to enhance low-resolution media. Such methods, however, can produce undesirable artifacts in the reconstruct images, such as blurring and edge distortions. Recent works have successfully applied deep learning to this task, but such efforts are often aimed at real-world images and do not take in account the specifics of illustrations, which emphasize lines and employ simplified patterns rather than complex textures, which in turn makes visual artifacts introduced by algorithms easier to spot.

With these differences in mind, we evaluated the effects of the choice of loss functions [on an ESPCN CNN] in order to obtain accurate and perceptually pleasing results in the super-resolution task for comics, cartoons, and other illustrations.

Experimental evaluations have shown that a loss function based on edge detection performs best in this context among the evaluated functions, though still showing room for further improvements.

…C. Datasets: In an attempt to replicate the wide spectrum of illustrations, the following 3 datasets were used in this work.

  1. Danbooru2020: A collection of ~4 million crowdsourced illustrations of varying characteristics, ranging from line art to highly textured pictures. A subset of 40,000 randomly sampled images were selected for use during the training phase, of which 8,000 were used for validation at the end of each epoch. A second subset of 10,000 images was used for testing. Due to hardware constraints for training, we used 96×96px central patches as the ground truth images.
  2. Manga109: A collection of ~10,000 comic pages drawn by professional manga artists in Japan,26 used as a benchmark for SISR tasks,14 .17 This dataset is characterized by having mostly grayscale images with finer details such as text. We used 288×288px central patches from this dataset as the ground truth images. A larger patch size was used in order to capture a meaningful section of the illustration images present in this dataset.
  3. SYNLA: In order to further evaluate the generalization capabilities of the networks and find potential pathological cases, we also included a collection of synthetic line art images. The dataset is available in two versions, each with roughly 2,000 images, both which were used: one in grayscale, the other in color. As the original image sizes were smaller than the patch size 288×288px, specified in §IV-C2, and not an exact multiple of our scale factor, we used 192×192px central patches from this dataset as the ground truth images.

Danbooru2020 was used for training and testing, due to its wide range of illustrations in order to train networks able to generalize over style characteristics. Manga109 and SYNLA were used solely for testing.

--- https://arxiv.org/abs/1510.04389 Sketch-based Manga Retrieval using Manga109 Dataset Yusuke Matsui, Kota Ito, Yuji Aramaki, Toshihiko Yamasaki, Kiyoharu Aizawa 2015-10-15 2022-05-03 [("doi","10.1007/s11042-016-4020-z")] ai/anime ai/dataset

Manga (Japanese comics) are popular worldwide. However, current e-manga archives offer very limited search support, including keyword-based search by title or author, or tag-based categorization. To make the manga search experience more intuitive, efficient, and enjoyable, we propose a content-based manga retrieval system.

First, we propose a manga-specific image-describing framework. It consists of efficient margin labeling, edge orientation histogram feature description, and approximate nearest-neighbor search using product quantization. Second, we propose a sketch-based interface as a natural way to interact with manga content. The interface provides sketch-based querying, relevance feedback, and query retouch.

For evaluation, we built a novel dataset of manga images, Manga109, which consists of 109 comic books of 21,142 pages drawn by professional manga artists. To the best of our knowledge, Manga109 is currently the biggest dataset of manga images available for research.

We conducted a comparative study, a localization evaluation, and a large-scale qualitative study.

From the experiments, we verified that: (1) the retrieval accuracy of the proposed method is higher than those of previous methods; (2) the proposed method can localize an object instance with reasonable runtime and accuracy; and (3) sketch querying is useful for manga search.

--- /doc/darknet-market/dnm-archive/2022-hiramoto.pdf Are Illicit Drugs a Driving Force for Cryptomarket Leadership? Naoki Hiramoto, Yoichi Tsuchiya 2022-10-12 2022-10-12 [("doi","10.1177/00220426221133030")] darknet-market/agora darknet-market/dnm-archive darknet-market/evolution darknet-market/silk-road/2

Cryptomarkets, ie. illicit online marketplaces, have gained considerable attention from the media, law enforcement agencies, and researchers. An increasing number of studies have revealed various aspects of these cryptomarkets; however, whether drugs play a major role for competing cryptomarkets to be the market leader, has not been addressed.

Weekly sales and the number of listings for the major products on 3 leading cryptomarkets (Silk Road 2, Agora, and Evolution) were examined using Granger causality tests and interrupted time series analysis.

Not only drugs trading on cryptomarkets played a pivotal role in the growth of each cryptomarket, but also a higher increase in drug supply than in competing marketplaces is crucial to become market leaders. The relative supply of drugs plays a larger role when leading marketplaces disappear.

Law enforcement agencies should focus on monitoring marketplaces with a larger increase in drug supplies than on competing marketplaces.

--- https://arxiv.org/abs/2102.03334 ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision Wonjae Kim, Bokyung Son, Ildoo Kim 2021-02-05 2022-05-03 [("doi","10.48550/arXiv.2102.03334")] ai/nn/transformer

Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (eg. object detection) and the convolutional architecture (eg. ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary.

In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs.

We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance.

Our code and pre-trained weights are available at Github.

--- https://arxiv.org/abs/2104.14754 Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing Hyunsu Kim, Yunjey Choi, Junho Kim, Sungjoo Yoo, Youngjung Uh 2021-04-30 2022-05-03 [("doi","10.48550/arXiv.2104.14754")] ai/nn/gan/stylegan

Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. Although manipulating the latent vectors controls the synthesized outputs, editing real images with GANs suffers from (1) time-consuming optimization for projecting real images to the latent vectors, (2) or inaccurate embedding through an encoder.

We propose StyleMapGAN: the intermediate latent space has spatial dimensions, and a spatially variant modulation replaces AdaIN. It makes the embedding through an encoder more accurate than existing optimization-based methods while maintaining the properties of GANs.

Experimental results demonstrate that our method outperforms state-of-the-art models in various image manipulation tasks such as local editing and image interpolation. Last but not least, conventional editing methods on GANs are still valid on our StyleMapGAN.

Source code is available at Github.

--- https://www.biorxiv.org/content/10.1101/2022.11.14.516379.full Selecting Chromosomes for Polygenic Traits Or Zuk 2022-11-16 2022-11-16 [("doi","10.1101/2022.11.14.516379")] genetics/selection/artificial

We define and study the problem of chromosomal selection for multiple complex traits. In this problem, it is assumed that one can construct a genome by selecting different genomic parts (eg. chromosomes) from different cells. The constructed genome is associated with a vector of polygenic scores, obtained by summing the polygenic scores of the different genomic parts, and the goal is to minimize a loss function of this vector. While out of reach today, the problem may become relevant in the future with emerging future technologies, and may yield far greater gains in the loss compared to the present day technology of as embryo selection, provided that technological and ethical barriers are overcome.

We suggest and study several natural loss functions relevant for both quantitative traits and disease.

We propose two algorithms, a Branch-and-Bound technique, to solve the problem for multiple traits and any monotone loss function, and a convex relaxation algorithm applicable for any differentiable loss.

Finally, we use the infinitesimal model for genetic architecture to approximate the potential gain achieved by chromosomal selection for multiple traits.

--- https://en.wikipedia.org/wiki/Rumiyah_(magazine) Rumiyah (magazine) 2022-05-03 crime/terrorism/rumiyah --- https://en.wikipedia.org/wiki/Dabiq_(magazine) Dabiq (magazine) 2022-05-03 crime/terrorism/rumiyah --- https://en.wikipedia.org/wiki/Islamic_State Islamic State 2022-05-03 crime/terrorism/rumiyah --- https://web.archive.org/web/20191216052758/https://clarionproject.org/Islamic-state-isis-isil-propaganda-magazine-dabiq-50/ 2022-05-03 crime/terrorism/rumiyah --- https://www.biorxiv.org/content/10.1101/2022.11.10.515937.full An update on eukaryotic viruses revived from ancient permafrost Jean-Marie Alempic, Audrey Lartigue, Artemiy E. Goncharov, Guido Grosse, Jens Strauss, Alexey N. Tikhonov, Alexander N. Fedorov, Olivier Poirot, Matthieu Legendre, Sébastien Santini, Chantal Abergel, Jean-Michel Claverie 2022-11-10 2022-11-10 [("doi","10.1101/2022.11.10.515937")] cryonics

One quarter of the Northern hemisphere is underlain by permanently frozen ground, referred to as permafrost. Due to climate warming, irreversibly thawing permafrost is releasing organic matter frozen for up to a million years, most of which decomposes into carbon dioxide and methane, further enhancing the greenhouse effect.

Part of this organic matter also consists of revived cellular microbes (prokaryotes, unicellular eukaryotes) as well as viruses that remained dormant since prehistorical times [eg. anthrax]. While the literature abounds on descriptions of the rich and diverse prokaryotic microbiomes found in permafrost, no additional report about live viruses have been published since the two original studies describing pithovirus (in 2014) and mollivirus (in 2015). This wrongly suggests that such occurrences are rare and that zombie viruses are not a public health threat.

To restore an appreciation closer to reality, we report the preliminary characterizations of 13 new viruses isolated from 7 different ancient Siberian permafrost samples, 1 from the Lena river and 1 from Kamchatka cryosol.

As expected from the host specificity imposed by our protocol, these viruses belong to 5 different clades infecting Acanthamoeba spp. but not previously revived from permafrost: pandoravirus, cedratvirus, megavirus, and pacmanvirus, in addition to a new pithovirus strain.

--- https://x.com/bemmu/status/1596870354852491265 2022-05-03 ai/nn/transformer/gpt/fiction --- https://arxiv.org/abs/2211.13746#deepmind Melting Pot 2.0 John P. Agapiou, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán, Jayd Matyas, Yiran Mao, Peter Sunehag, Raphael Köster, Udari Madhushani, Kavya Kopparapu, Ramona Comanescu, D. J. Strouse, Michael B. Johanson, Sukhdeep Singh, Julia Haas, Igor Mordatch, Dean Mobbs, Joel Z. Leibo 2022-11-24 2022-11-24 [("doi","10.48550/arXiv.2211.13746")] ai/nn/rnn reinforcement-learning/model-free reinforcement-learning/multi-agent

Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by “solipsistic” approaches, which do not consider interactions between agents.

Melting Pot [v1] is a research tool developed to facilitate work on multi-agent artificial intelligence, and provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios. Each scenario pairs a physical environment (a “substrate”) with a reference set of co-players (a “background population”), to create a social situation with substantial interdependence between the individuals involved. For instance, some scenarios were inspired by institutional-economics-based accounts of natural resource management and public-good-provision dilemmas. Others were inspired by considerations from evolutionary biology, game theory, and artificial life.

Melting Pot aims to cover a maximally diverse set of interdependencies and incentives. It includes the commonly-studied extreme cases of perfectly-competitive (zero-sum) motivations and perfectly-cooperative (shared-reward) motivations, but does not stop with them. As in real-life, a clear majority of scenarios in Melting Pot have mixed incentives. They are neither purely competitive nor purely cooperative and thus demand successful agents be able to navigate the resulting ambiguity.

Here we describe Melting Pot 2.0, which revises and expands on Melting Pot. We also introduce support for scenarios with asymmetric roles, and explain how to integrate them into the evaluation protocol.

This report also contains: (1) details of all substrates and scenarios; (2) a complete description of all baseline algorithms and results.

Our intention is for it to serve as a reference for researchers using Melting Pot 2.0.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4586845/ In-depth study of Mollivirus sibericum, a new 30,000-y-old giant virus infecting Acanthamoeba Matthieu Legendre, Audrey Lartigue, Lionel Bertaux, Sandra Jeudy, Julia Bartoli, Magali Lescot, Jean-Marie Alempic, Claire Ramus, Christophe Bruley, Karine Labadie, Lyubov Shmakova, Elizaveta Rivkina, Yohann Couté, Chantal Abergel, Jean-Michel Claverie 2015 2022-05-04 [("doi","10.1073/pnas.1510795112")] cryonics

Acanthamoeba species are infected by the largest known DNA viruses. These include icosahedral Mimiviruses, amphora-shaped Pandoraviruses, and Pithovirus sibericum, the latter one isolated from 30,000-y-old permafrost.

Mollivirus sibericum, a fourth type of giant virus, was isolated from the same permafrost sample. Its ~spherical virion (0.6-µm diameter) encloses a 651-kb GC-rich genome encoding 523 proteins of which 64% are ORFans; 16% have their closest homolog in Pandoraviruses and 10% in Acanthamoeba castellanii probably through horizontal gene transfer.

The Mollivirus nucleocytoplasmic replication cycle was analyzed using a combination of “omic” approaches that revealed how the virus hijacks its host machinery to actively replicate. Surprisingly, the host’s ribosomal proteins are packaged in the virion. Metagenomic analysis of the permafrost sample uncovered the presence of both viruses, yet in very low amount.

The fact that two different viruses retain their infectivity in prehistorical permafrost layers should be of concern in a context of global warming. Giant viruses’ diversity remains to be fully explored.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3964051/ Thirty-thousand-year-old distant relative of giant icosahedral DNA viruses with a pandoravirus morphology Matthieu Legendre, Julia Bartoli, Lyubov Shmakova, Sandra Jeudy, Karine Labadie, Annie Adrait, Magali Lescot, Olivier Poirot, Lionel Bertaux, Christophe Bruley, Yohann Couté, Elizaveta Rivkina, Chantal Abergel, Jean-Michel Claverie 2014 2022-05-04 [("doi","10.1073/pnas.1320670111")] cryonics

The largest known DNA viruses infect Acanthamoeba and belong to two markedly different families: the Megaviridae exhibit pseudo-icosahedral virions up to 0.7 μm in diameter and adenine-thymine (AT)-rich genomes of up to 1.25 Mb encoding a thousand proteins. Like their Mimivirus prototype discovered 10 y ago, they entirely replicate within cytoplasmic virion factories. In contrast, the recently discovered Pandoraviruses exhibit larger amphora-shaped virions 1 μm in length and guanine-cytosine-rich genomes up to 2.8 Mb long encoding up to 2,500 proteins. Their replication involves the host nucleus. Whereas the Megaviridae share some general features with the previously described icosahedral large DNA viruses, the Pandoraviruses appear unrelated to them.

Here we report the discovery of a third type of giant virus combining an even larger pandoravirus-like particle 1.5 μm in length with a surprisingly smaller 600 kb AT-rich genome, a gene content more similar to Iridoviruses and Marseillevirus, and a fully cytoplasmic replication reminiscent of the Megaviridae. This suggests that pandoravirus-like particles may be associated with a variety of virus families more diverse than previously envisioned. This giant virus, named Pithovirus sibericum, was isolated from a >30,000-y-old radiocarbon-dated sample when we initiated a survey of the virome of Siberian permafrost.

The revival of such an ancestral amoeba-infecting virus used as a safe indicator of the possible presence of pathogenic DNA viruses, suggests that the thawing of permafrost either from global warming or industrial exploitation of circumpolar regions might not be exempt from future threats to human or animal health.

--- https://arxiv.org/abs/2209.07663#bytedance Monolith: Real Time Recommendation System With Collisionless Embedding Table Zhuoran Liu, Leqi Zou, Xuan Zou, Caihua Wang, Biao Zhang, Da Tang, Bolin Zhu, Yijie Zhu, Peng Wu, Ke Wang, Youlong Cheng 2022-09-16 2022-09-16 [("doi","10.48550/arXiv.2209.07663")] ai/nn/sparsity ai/scaling cs/algorithm

Building a scalable and real-time recommendation system is vital for many businesses driven by time-sensitive customer feedback, such as short-videos ranking or online ads. Despite the ubiquitous adoption of production-scale deep learning frameworks like TensorFlow or PyTorch, these general-purpose frameworks fall short of business demands in recommendation scenarios for various reasons: on one hand, tweaking systems based on static parameters and dense computations for recommendation with dynamic and sparse features is detrimental to model quality; on the other hand, such frameworks are designed with batch-training stage and serving stage completely separated, preventing the model from interacting with customer feedback in real-time. These issues led us to reexamine traditional approaches and explore radically different design choices.

In this paper, we present Monolith, a system tailored for online training. Our design has been driven by observations of our application workloads and production environment that reflects a marked departure from other recommendations systems.

Our contributions are manifold: first, we crafted a collision-less embedding table with optimizations such as expirable embeddings and frequency filtering to reduce its memory footprint; second, we provide an production-ready online training architecture with high fault-tolerance; finally, we proved that system reliability could be traded-off for real-time learning.

Monolith has successfully landed in the BytePlus Recommend product [a DL-based recommender SaaS offered for commercial use by ByteDance].

--- https://www.wired.com/story/alphabay-series-part-6-endgame/ 2022-05-04 darknet-market/alphabay --- https://en.wikipedia.org/wiki/Boring_Billion Boring Billion 2022-05-04 biology science --- https://arxiv.org/abs/2211.15533 The Stack: 3 TB of permissively licensed source code Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes, Thomas Wolf, Dzmitry Bahdanau, Leandro von Werra, Harm de Vries 2022-11-20 2022-11-20 [("doi","10.48550/arXiv.2211.15533")] ai/dataset ai/nn/transformer/gpt/codex cs/python

Large Language Models (LLMs) play an ever-increasing role in the field of Artificial Intelligence (AI)—not only for natural language processing but also for code understanding and generation.

To stimulate open and responsible research on LLMs for code, we introduce The Stack, a 3.1 TB dataset consisting of permissively licensed source code in 30 programming languages.

We describe how we collect the full dataset, construct a permissively licensed subset, present a data governance plan, discuss limitations, and show promising results on text2code benchmarks by training 350M-parameter decoders on different Python subsets. We find that (1) near-deduplicating the data boosts performance across all experiments, and (2) it is possible to match previously reported HumanEval and MBPP performance using only permissively licensed data.

We make the dataset available at Hugging Face, provide a tool called “Am I in The Stack” for developers to search The Stack for copies of their code, and provide a process for code to be removed from the dataset by following the instructions at https://www.bigcode-project.org/docs/about/the-stack/.

--- https://x.com/jasoncbenn/status/1597276479070830595 2022-05-04 ai/nn/transformer/clip/sample --- https://medium.com/@catmus2048/not-only-is-stable-diffusion-2-0-not-bad-but-really-better-my-prompt-engineering-experiments-459fbc5cec2 2022-05-04 ai/nn/diffusion --- https://x.com/michellehuang42/status/1597005489413713921 2022-05-04 ai/nn/transformer/gpt/non-fiction psychiatry --- https://www.astralcodexten.com/p/highlights-from-the-comments-on-semaglutide 2022-05-05 longevity/glp/semaglutide --- https://x.com/DanielJDrucker/status/1591171488002232320 2022-05-05 longevity/glp/semaglutide --- https://www.lesswrong.com/posts/RKDQCB6smLWgs2Mhr/multi-component-learning-and-s-curves 2022-05-05 ai/nn --- https://x.com/vladquant/status/1598043418135252993 2022-05-05 ai/nn/transformer/gpt/fiction cs/security --- https://news.ycombinator.com/item?id=33806020 2022-05-05 ai/nn/transformer/gpt/fiction cs/security --- https://news.ycombinator.com/item?id=33805270 2022-05-05 ai/nn/transformer/gpt/non-fiction --- https://x.com/ArtirKel/status/1598020256147984384 2022-05-05 ai/nn/transformer/gpt/fiction --- https://x.com/ArtirKel/status/1598021962265112577 2022-05-05 ai/nn/transformer/gpt/fiction --- https://x.com/peteromallet/status/1598042410915102721 2022-05-05 ai/nn/transformer/gpt/fiction --- https://x.com/nicksaraev/status/1598037718671708161 2022-05-05 cs/security --- https://arxiv.org/abs/2211.10515#deepmind Curiosity in hindsight Daniel Jarrett, Corentin Tallec, Florent Altché, Thomas Mesnard, Rémi Munos, Michal Valko 2022-11-18 2022-11-18 [("doi","10.48550/arXiv.2211.10515")] reinforcement-learning/exploration

Consider the exploration in sparse-reward or reward-free environments, such as Montezuma’s Revenge. The curiosity-driven paradigm dictates an intuitive technique: At each step, the agent is rewarded for how much the realized outcome differs from their predicted outcome. However, using predictive error as intrinsic motivation is prone to fail in stochastic environments, as the agent may become hopelessly drawn to high-entropy areas of the state-action space, such as a noisy TV. Therefore, it is important to distinguish between aspects of world dynamics that are inherently predictable and aspects that are inherently unpredictable: The former should constitute a source of intrinsic reward, whereas the latter should not.

In this work, we study a natural solution derived from structural causal models of the world: Our key idea is to learn representations of the future that capture precisely the unpredictable aspects of each outcome—not any more, not any less—which we use as additional input for predictions, such that intrinsic rewards do vanish in the limit. First, we propose incorporating such hindsight representations into the agent’s model to disentangle “noise” from “novelty”, yielding Curiosity in Hindsight: a simple and scalable generalization of curiosity that is robust to all types of stochasticity.

Second, we implement this framework as a drop-in modification of any prediction-based exploration bonus, and instantiate it for the recently introduced BYOL-Explore algorithm as a prime example, resulting in the noise-robust “BYOL-Hindsight”.

Third, we illustrate its behavior under various stochasticities in a grid world, and find improvements over BYOL-Explore in hard-exploration Atari games with sticky actions. Importantly, we show SOTA (State-of-the-Art) results in exploring Montezuma with sticky actions, while preserving performance in the non-sticky setting.

--- https://x.com/gf_256/status/1598104835848798208 2022-05-06 ai/nn/transformer/gpt/codex cs/security --- https://en.wikipedia.org/wiki/On-Line_Encyclopedia_of_Integer_Sequences On-Line Encyclopedia of Integer Sequences 2022-05-06 math --- https://x.com/goodside/status/1598129631609380864 2022-05-06 ai/nn/transformer/gpt/codex ai/text-style-transfer --- https://x.com/goodside/status/1598077257498923010 2022-05-06 ai/nn/transformer/gpt/fiction --- https://x.com/aribo/status/1598097923501277184 2022-05-06 ai/nn/transformer/gpt/fiction --- https://arxiv.org/abs/2211.16750#google Score-based Continuous-time Discrete Diffusion Models Haoran Sun, Lijun Yu, Bo Dai, Dale Schuurmans, Hanjun Dai 2022-11-30 2022-11-30 [("doi","10.48550/arXiv.2211.16750")] ai/nn/diffusion/discrete

Score-based modeling through stochastic differential equations (SDEs) has provided a new perspective on diffusion models, and demonstrated superior performance on continuous data. However, the gradient of the log-likelihood function, ie. the score function, is not properly defined for discrete spaces. This makes it non-trivial to adapt the score-based modeling to categorical data.

In this paper, we extend diffusion models to discrete variables by introducing a stochastic jump process where the reverse process denoises via a continuous-time Markov chain. This formulation admits an analytical simulation during backward sampling.

To learn the reverse process, we extend score matching to general categorical data and show that an unbiased estimator can be obtained via simple matching of the conditional marginal distributions.

We demonstrate the effectiveness of the proposed method on a set of synthetic and real-world music and image benchmarks.

--- https://arxiv.org/abs/2211.17115 Multi-resolution Textual Inversion Giannis Daras, Alexandros G. Dimakis 2022-11-30 2022-11-30 [("doi","10.48550/arXiv.2211.17115")] ai/nn/diffusion

We extend Textual Inversion to learn pseudo-words that represent a concept at different resolutions. This allows us to generate images that use the concept with different levels of detail and also to manipulate different resolutions using language. Once learned, the user can generate images at different levels of agreement to the original concept; “A photo of S✱(0)” produces the exact object while the prompt “A photo of S✱(0.8)” only matches the rough outlines and colors.

Our framework allows us to generate images that use different resolutions of an image (eg. details, textures, styles) as separate pseudo-words that can be composed in various ways.

We open-source our code in the following URL: https://github.com/giannisdaras/multires_textual_inversion.

--- https://arxiv.org/abs/2211.16349 BARTSmiles: Generative Masked Language Models for Molecular Representations Gayane Chilingaryan, Hovhannes Tamoyan, Ani Tevosyan, Nelly Babayan, Lusine Khondkaryan, Karen Hambardzumyan, Zaven Navoyan, Hrant Khachatrian, Armen Aghajanyan 2022-11-29 2022-11-29 [("doi","10.48550/arXiv.2211.16349")] ai/nn/transformer biology

We discover a robust self-supervised strategy tailored towards molecular representations for generative masked language models through a series of tailored, in-depth ablations. Using this pre-training strategy, we train BARTSmiles, a BART-like model with an order of magnitude more compute than previous self-supervised molecular representations.

In-depth evaluations show that BARTSmiles consistently outperforms other self-supervised representations across classification, regression, and generation tasks setting a new state-of-the-art on 11 tasks. We then quantitatively show that when applied to the molecular domain, the BART objective learns representations that implicitly encode our downstream tasks of interest. For example, by selecting 7 neurons from a frozen BARTSmiles, we can obtain a model having performance within two percentage points of the full fine-tuned model on task Clintox.

Lastly, we show that standard attribution interpretability methods, when applied to BARTSmiles, highlight certain substructures that chemists use to explain specific properties of molecules. The code and the pretrained model are publicly available.

--- https://x.com/UubzU/status/1598232642344058881 2022-05-06 ai/nn/transformer/gpt/fiction cs/security --- https://x.com/charles_irl/status/1598319027327307785 2022-05-06 ai/nn/transformer/gpt/fiction ai/text-style-transfer --- https://x.com/littmath/status/1598128056874721283 2022-05-06 ai/nn/transformer/gpt/inner-monologue math --- https://x.com/Pandurevich/status/1597667682178170880 2022-05-07 ai/nn/transformer/gpt/fiction --- https://x.com/EladRichardson/status/1598333315764871174 2022-05-07 ai/nn/transformer/gpt/fiction --- https://x.com/Aaroth/status/1598417667849080834 2022-05-07 ai/nn/transformer/gpt/fiction economics --- https://arxiv.org/abs/2206.01649#schmidhuber Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules Kazuki Irie, Francesco Faccio, Jürgen Schmidhuber 2022-06-03 2022-06-03 [("doi","10.48550/arXiv.2206.01649")] ai/nn/transformer/attention

Neural ordinary differential equations (ODEs) have attracted much attention as continuous-time counterparts of deep residual neural networks (NNs), and numerous extensions for recurrent NNs have been proposed. Since the 1980s, ODEs have also been used to derive theoretical results for NN learning rules, eg. the famous connection between Oja’s rule and principal component analysis. Such rules are typically expressed as additive iterative update processes which have straightforward ODE counterparts.

Here we introduce a novel combination of learning rules and Neural ODEs to build continuous-time sequence processing nets that learn to manipulate short-term memory in rapidly changing synaptic connections of other nets.

This yields continuous-time counterparts of Fast Weight Programmers and linear Transformers.

Our novel models outperform the best existing Neural Controlled Differential Equation based models on various time series classification tasks, while also addressing their fundamental scalability limitations.

Our code is public.

--- /doc/ai/nn/gan/stylegan/anime/2019-02-27-synced-fromfacestokittiestoapartments.html 2019-02-27 2022-05-07 ai/anime ai/nn/gan/stylegan/anime --- https://x.com/cosine_distance/status/1598075072245563392 2022-05-07 ai/nn/transformer/gpt/fiction --- https://x.com/peligrietzer/status/1598356385623851011 2022-05-07 ai/text-style-transfer --- https://www.netflix.com/tudum/articles/stranger-things-season-4-captions 2022-05-07 design/typography --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0274542 Differential personality change earlier and later in the coronavirus pandemic in a longitudinal sample of adults in the United States Angelina R. Sutin, Yannick Stephan, Martina Luchetti, Damaris Aschwanden, Ji Hyun Lee, Amanda A. Sesker, Antonio Terracciano, Baogui Xin, Baogui Xin, Baogui Xin 2022-08-28 2022-08-28 [("doi","10.1371/journal.pone.0274542")] psychology/personality

Five-factor model personality traits (Neuroticism, Extraversion, Openness, Agreeableness, Conscientiousness) are thought to be relatively impervious to environmental demands in adulthood. The coronavirus pandemic is an unprecedented opportunity to examine whether personality changed during a stressful global event. Surprisingly, two previous studies found that neuroticism decreased early in the pandemic, whereas there was less evidence for change in the other 4 traits during this period.

The present research used longitudinal assessments of personality from the Understanding America Study (n = 7,109; 18,623 assessments) to examine personality changes relatively earlier (2020) and later (2021–2022) in the pandemic compared to pre-pandemic levels.

Replicating the two previous studies, neuroticism declined very slightly in 2020 compared to pre-pandemic levels; there were no changes in the other 4 traits. When personality was measured in 2021–2022, however, there was no statistically-significant change in neuroticism compared to pre-pandemic levels, but there were statistically-significant small declines in extraversion, openness, agreeableness, and Conscientiousness.

The changes were about one-tenth of a standard deviation, which is equivalent to about one decade of normative personality change. These changes were moderated by age and Hispanic/Latino ethnicity, but not race or education. Strikingly, younger adults showed disrupted maturity in that they increased in neuroticism and declined in agreeableness and Conscientiousness.

Current evidence suggests the slight decrease in neuroticism early in the pandemic was short-lived and detrimental changes in the other traits emerged over time. If these changes are enduring, this evidence suggests population-wide stressful events can slightly bend the trajectory of personality, especially in younger adults.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0237056 Change in five-factor model personality traits during the acute phase of the coronavirus pandemic Angelina R. Sutin, Martina Luchetti, Damaris Aschwanden, Ji Hyun Lee, Amanda A. Sesker, Jason E. Strickhouser, Yannick Stephan, Antonio Terracciano, Angel Blanch, Angel Blanch, Angel Blanch 2020-07-19 2022-05-07 [("doi","10.1371/journal.pone.0237056")] psychology/personality

The rapid spread of the coronavirus and the strategies to slow it have disrupted just about every aspect of our lives. Such disruption may be reflected in changes in psychological function.

The present study used a pre-posttest design to test whether 5 Factor Model personality traits changed with the coronavirus outbreak in the United States.

Participants (n = 2,137) were tested in early February 2020 and again during the President’s 15 Days to Slow the Spread guidelines.

In contrast to the preregistered hypotheses, Neuroticism decreased across these 6 weeks, particularly the facets of Anxiety and Depression, and Conscientiousness did not change. Interestingly, there was some evidence that the rapid changes in the social context had changed the meaning of an item. Specifically, an item about going to work despite being sick was a good indicator of Conscientiousness before COVID-19, but the interpretation of it changed with the pandemic.

In sum, the unexpected small decline in Neuroticism suggests that, during the acute phase of the coronavirus outbreak, feelings of anxiety and distress may be attributed more to the pandemic than to one’s personality.

--- https://arxiv.org/abs/1806.07366 Neural Ordinary Differential Equations Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud 2018-06-19 2022-05-08 [("doi","10.48550/arXiv.1806.07366")] ai/nn/rnn

We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a black-box differential equation solver. These continuous-depth models have constant memory cost, adapt their evaluation strategy to each input, and can explicitly trade numerical precision for speed.

We demonstrate these properties in continuous-depth residual networks and continuous-time latent variable models. We also construct continuous normalizing flows, a generative model that can train by maximum likelihood, without partitioning or ordering the data dimensions.

For training, we show how to scalably backpropagate through any ODE solver, without access to its internal operations. This allows end-to-end training of ODEs within larger models.

--- https://x.com/packyM/status/1598405769669771264 2022-05-08 ai/nn/transformer/gpt/codex --- https://x.com/tqbf/status/1598513757805858820 2022-05-08 ai/nn/transformer/gpt/fiction --- /doc/fiction/poetry/2011-yvain-theballadofjohndarcy.html 2011 2022-05-08 fiction/humor fiction/poetry --- /doc/fiction/poetry/2011-yvain-iliadaslawsuit.html 2011 2022-05-08 fiction/humor fiction/poetry --- /doc/fiction/poetry/2011-yvain-iliadaslawsuit.html 2011 2022-05-08 fiction/humor fiction/poetry law --- /doc/fiction/humor/2010-vasseur-dresdencodakguestcomic.html 2010 2022-05-08 fiction/humor --- https://www.poetryloverspage.com/poets/kipling/when_omer_smote.html When ‘Omer Smote ‘Is Bloomin’ Lyre 2022-05-08 fiction/poetry --- https://en.wikipedia.org/wiki/Stylometry Stylometry 2022-05-08 statistics/stylometry --- https://en.wikipedia.org/wiki/Donation_of_Constantine#Investigation Donation of Constantine § Investigation 2022-05-08 statistics/stylometry --- https://en.wikipedia.org/wiki/Unabomber_Manifesto Unabomber Manifesto 2022-05-08 statistics/stylometry --- https://en.wikipedia.org/wiki/Double_Falsehood#Authorship Double Falsehood § Authorship 2022-05-09 statistics/stylometry --- https://en.wikipedia.org/wiki/Primary_Colors_(novel)#Identity_of_the_author Primary Colors (novel) § Identity of the author 2022-05-09 statistics/stylometry --- https://en.wikipedia.org/wiki/Elena_Ferrante#Anonymity Elena Ferrante § Anonymity 2022-05-09 statistics/stylometry --- https://en.wikipedia.org/wiki/Zipf%E2%80%93Mandelbrot_law Zipf-Mandelbrot law 2022-05-09 statistics/stylometry --- https://en.wikipedia.org/wiki/Writeprint Writeprint 2022-05-09 statistics/stylometry --- https://en.wikipedia.org/wiki/The_Federalist_Papers#Authorship The Federalist Papers § Authorship 2022-05-09 statistics/stylometry --- /doc/cs/security/2012-02-12-arvindnarayanan-iswritingstylesufficienttodeanonymizematerialonline.html 2012-02-12 2022-05-09 cs/security statistics/stylometry --- https://www.elizabethcallaway.net/good-omens-stylometry 2022-05-09 statistics/bayes statistics/stylometry --- https://arxiv.org/pdf/2009.03393.pdf#page=11 2022-05-09 --- /doc/fiction/humor/1998-swartz.pdf You Dumb Babies! How raising the Rugrats children became as difficult as the real thing Mimi Swartz 1998-01-01 2022-05-09 anime fiction/humor --- /doc/fiction/poetry/2014-gracyk-luchiswenfutheartofwriting.html Lu Chi’s The Art of Writing 2014 2022-05-09 fiction/poetry psychology/linguistics --- https://calvinanddune.tumblr.com/ 2022-05-10 fiction/humor fiction/science-fiction/frank-herbert --- https://www.newyorker.com/books/page-turner/dune-endures 2022-05-10 fiction/science-fiction/frank-herbert --- https://www.filfre.net/2018/11/controlling-the-spice-part-2-cryos-dune/ 2022-05-10 fiction/science-fiction/frank-herbert technology/digital-antiquarian --- https://lareviewofbooks.org/article/the-secret-history-of-dune/ The Secret History of Dune 2022-05-10 fiction/science-fiction/frank-herbert --- https://x.com/quasimondo/status/1064230996793614338 2022-05-10 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Dune_(2021_film) Dune (2021 film) 2022-05-10 fiction/science-fiction/frank-herbert --- /doc/philosophy/ontology/1973-herbert.pdf 1973 2022-05-10 fiction/science-fiction/frank-herbert philosophy/ontology --- /doc/sociology/1950-walter-thesexualcycleofhumanwarfare.pdf 1950 2022-05-10 fiction/science-fiction/frank-herbert sociology --- https://en.wikipedia.org/wiki/Akashic_records Akashic records 2022-05-10 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Alia_Atreides Alia Atreides 2022-05-10 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Bene_Gesserit Bene Gesserit 2022-05-11 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Bene_Gesserit#Breeding_program Bene Gesserit § Breeding program 2022-05-11 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Bene_Tleilax Bene Tleilax 2022-05-11 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Chapterhouse:_Dune Chapterhouse: Dune 2022-05-11 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Children_of_Dune Children of Dune 2022-05-11 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Collective_unconscious Collective unconscious 2022-05-11 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Cybernetics Cybernetics 2022-05-11 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Destination:_Void Destination: Void 2022-05-11 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Dianetics Dianetics 2022-05-11 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Dune_(1984_film) Dune (1984 film) 2022-05-11 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Dune_(franchise)#The_Butlerian_Jihad Dune (franchise) § The Butlerian Jihad 2022-05-11 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Dune_Messiah Dune Messiah 2022-05-12 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Dune_(novel) Dune (novel) 2022-05-12 fiction/science-fiction/frank-herbert --- https://en.wikipedia.org/wiki/Erewhon Erewhon 2022-05-12 fiction/science-fiction/frank-herbert --- https://archive.org/details/ConfessionsOfTheFatherOfTheNeutronBomb/Confessions_Sam_Cohen_2006_Third_Edition?view=theater#page/n209 2022-05-12 fiction/science-fiction/frank-herbert --- https://x.com/CynthiaSavard/status/1598498138658070530 2022-05-12 ai/nn/transformer/gpt/non-fiction --- https://web.archive.org/web/20130114231753/http://33bits.org/2012/12/17/new-developments-in-deanonymization/ New Developments in Deanonymization 2022-05-12 statistics/stylometry --- https://arxiv.org/abs/2212.00794#facebook Scaling Language-Image Pre-training via Masking Yanghao Li, Haoqi Fan, Ronghang Hu, Christoph Feichtenhofer, Kaiming He 2022-12-01 2022-12-01 [("doi","10.48550/arXiv.2212.00794")] ai/nn/transformer/clip ai/nn/vae/mae ai/scaling

We present Fast Language-Image Pre-training (FLIP), a simple and more efficient method for training CLIP.

Our method randomly masks out and removes a large portion of image patches during training. Masking allows us to learn from more image-text pairs given the same wall-clock time and contrast more samples per iteration with similar memory footprint. It leads to a favorable trade-off between accuracy and training time.

In our experiments on 400 million image-text pairs, FLIP improves both accuracy and speed over the no-masking baseline. On a large diversity of downstream tasks, FLIP dominantly outperforms the CLIP counterparts trained on the same data. Facilitated by the speedup, we explore the scaling behavior of increasing the model size, data size, or training length, and report encouraging results and comparisons.

We hope that our work will foster future research on scaling vision-language learning.

--- https://x.com/zaanonyam/status/1598668909619445766 2022-05-12 ai/nn/transformer/gpt/fiction cs/security --- https://x.com/samczsun/status/1598679658488217601 2022-05-12 ai/nn/transformer/gpt/fiction cs/security --- https://x.com/bentossell/status/1598673037976543240 2022-05-12 ai/nn/retrieval ai/nn/transformer/gpt --- https://arxiv.org/abs/2202.01142 Pop Quiz! Can a Large Language Model Help With Reverse Engineering? Hammond Pearce, Benjamin Tan, Prashanth Krishnamurthy, Farshad Khorrami, Ramesh Karri, Brendan Dolan-Gavitt 2022-02-02 2022-05-12 [("doi","10.48550/arXiv.2202.01142")] ai/nn/transformer/gpt/codex cs/security

[cf. G-3P0/Gepetto] Large language models (such as OpenAI’s Codex) have demonstrated impressive zero-shot multi-task capabilities in the software domain, including code explanation.

In this work, we examine if this ability can be used to help with reverse engineering. Specifically, we investigate prompting Codex to identify the purpose, capabilities, and important variable names or values from code, even when the code is produced through decompilation. Alongside an examination of the model’s responses in answering open-ended questions, we devise a true/false quiz framework to characterize the performance of the language model.

We present an extensive quantitative analysis of the measured performance of the language model on a set of program purpose identification and information extraction tasks: of the 136,260 questions we posed, it answered 72,754 correctly.

A key takeaway is that while promising, LLMs are not yet ready for zero-shot reverse engineering.

--- https://x.com/mickeykats/status/1598533046176690176 2022-05-13 ai/nn/transformer/gpt/non-fiction ai/text-style-transfer --- https://en.wikipedia.org/wiki/Optimal_design Optimal experimental design 2022-05-13 reinforcement-learning/exploration/active-learning --- https://en.wikipedia.org/wiki/Human-in-the-loop Human-in-the-loop 2022-05-13 reinforcement-learning/exploration/active-learning --- https://en.wikipedia.org/wiki/Conformal_prediction Conformal prediction 2022-05-13 reinforcement-learning/exploration/active-learning --- https://www.cs.ox.ac.uk/people/yarin.gal/website/blog_2248.html 2022-05-13 reinforcement-learning/exploration/active-learning --- https://blog.mldb.ai/blog/posts/2016/10/deepteach/ 2022-05-13 reinforcement-learning/exploration/active-learning --- https://burrsettles.com/pub/settles.activelearning.pdf Active Learning Literature Survey 2022-05-13 reinforcement-learning/exploration/active-learning --- https://en.wikipedia.org/wiki/Factorial_experiment Factorial experiment 2022-05-13 reinforcement-learning/exploration/active-learning --- https://raw.githubusercontent.com/Riashat/Active-Learning-Bayesian-Convolutional-Neural-Networks/master/Presentations/Thesis/Islam%20Riashat%20MPhil%20MLSALT%20Thesis.pdf Active Learning for High Dimensional Inputs using Bayesian Convolutional Neural Networks 2022-05-13 reinforcement-learning/exploration/active-learning --- https://kyunghyuncho.me/brief-summary-of-the-panel-discussion-at-dl-workshop-icml-2015/ Brief Summary of the Panel Discussion at DL Workshop @ICML 2015 2022-05-13 reinforcement-learning/exploration/active-learning --- https://arxiv.org/abs/2106.00977#deepmind Adapting the Function Approximation Architecture in Online Reinforcement Learning John D. Martin, Joseph Modayil 2021-06-17 2022-05-13 [("doi","10.48550/arXiv.2106.09776")] reinforcement-learning/exploration/active-learning

The performance of a reinforcement learning (RL) system depends on the computational architecture used to approximate a value function. Deep learning methods provide both optimization techniques and architectures for approximating nonlinear functions from noisy, high-dimensional observations. However, prevailing optimization techniques are not designed for strictly-incremental online updates. Nor are standard architectures designed for observations with an a priori unknown structure: for example, light sensors randomly dispersed in space.

This paper proposes an online RL prediction algorithm with an adaptive architecture that efficiently finds useful nonlinear features. The algorithm is evaluated in a spatial domain with high-dimensional, stochastic observations.

The algorithm outperforms non-adaptive baseline architectures and approaches the performance of an architecture given side-channel information.

These results are a step towards scalable RL algorithms for more general problems, where the observation structure is not available.

--- https://arxiv.org/abs/2102.08686 Fully General Online Imitation Learning Michael K. Cohen, Marcus Hutter, Neel Nanda 2021-02-17 2022-05-14 [("doi","10.48550/arXiv.2102.08686")] reinforcement-learning/exploration/active-learning reinforcement-learning/imitation-learning

In imitation learning, imitators and demonstrators are policies for picking actions given past interactions with the environment. If we run an imitator, we probably want events to unfold similarly to the way they would have if the demonstrator had been acting the whole time. In general, one mistake during learning can lead to completely different events. In the special setting of environments that restart, existing work provides formal guidance in how to imitate so that events unfold similarly, but outside that setting, no formal guidance exists.

We address a fully general setting, in which the (stochastic) environment and demonstrator never reset, not even for training purposes, and we allow our imitator to learn online from the demonstrator. Our new conservative Bayesian imitation learner underestimates the probabilities of each available action, and queries for more data with the remaining probability.

Our main result: if an event would have been unlikely had the demonstrator acted the whole time, that event’s likelihood can be bounded above when running the (initially totally ignorant) imitator instead. Meanwhile, queries to the demonstrator rapidly diminish in frequency. If any such event qualifies as “dangerous”, our imitator would have the notable distinction of being relatively “safe”.

--- https://arxiv.org/abs/2012.03107 When Do Curricula Work? Xiaoxia Wu, Ethan Dyer, Behnam Neyshabur 2020-12-05 2022-05-14 [("doi","10.48550/arXiv.2012.03107")] reinforcement-learning/exploration/active-learning

Inspired by human learning, researchers have proposed ordering examples during training based on their difficulty. Both curriculum learning, exposing a network to easier examples early in training, and anti-curriculum learning, showing the most difficult examples first, have been suggested as improvements to the standard i.i.d. training. In this work, we set out to investigate the relative benefits of ordered learning.

We first investigate the implicit curricula resulting from architectural and optimization bias and find that samples are learned in a highly consistent order. Next, to quantify the benefit of explicit curricula, we conduct extensive experiments over thousands of orderings spanning 3 kinds of learning: curriculum, anti-curriculum, and random-curriculum—in which the size of the training dataset is dynamically increased over time, but the examples are randomly ordered.

We find that for standard benchmark datasets, curricula have only marginal benefits, and that randomly ordered samples perform as well or better than curricula and anti-curricula, suggesting that any benefit is entirely due to the dynamic training set size.

Inspired by common use cases of curriculum learning in practice, we investigate the role of limited training time budget and noisy data in the success of curriculum learning. Our experiments demonstrate that curriculum, but not anti-curriculum can indeed improve the performance either with limited training time budget or in existence of noisy data.

--- https://www.youtube.com/watch?v=_Ql5vfOPxZU?t=735 2022-05-14 reinforcement-learning/exploration/active-learning --- https://arxiv.org/abs/1910.09716#microsoft A deep active learning system for species identification and counting in camera trap images Mohammad Sadegh Norouzzadeh, Dan Morris, Sara Beery, Neel Joshi, Nebojsa Jojic, Jeff Clune 2019-10-22 2022-05-14 [("doi","10.48550/arXiv.1910.09716")] reinforcement-learning/exploration/active-learning

Biodiversity conservation depends on accurate, up-to-date information about wildlife population distributions. Motion-activated cameras, also known as camera traps, are a critical tool for population surveys, as they are cheap and non-intrusive. However, extracting useful information from camera trap images is a cumbersome process: a typical camera trap survey may produce millions of images that require slow, expensive manual review. Consequently, critical information is often lost due to resource limitations, and critical conservation questions may be answered too slowly to support decision-making.

Computer vision is poised to dramatically increase efficiency in image-based biodiversity surveys, and recent studies have harnessed deep learning techniques for automatic information extraction from camera trap images. However, the accuracy of results depends on the amount, quality, and diversity of the data available to train models, and the literature has focused on projects with millions of relevant, labeled training images. Many camera trap projects do not have a large set of labeled images and hence cannot benefit from existing machine learning techniques. Furthermore, even projects that do have labeled data from similar ecosystems have struggled to adopt deep learning methods because image classification models overfit to specific image backgrounds (ie. camera locations).

In this paper, we focus not on automating the labeling of camera trap images, but on accelerating this process. We combine the power of machine intelligence and human intelligence to build a scalable, fast, and accurate active learning system to minimize the manual work required to identify and count animals in camera trap images. Our proposed scheme can match the state-of-the-art accuracy on a 3.2 million image dataset with as few as 14,100 manual labels, which means decreasing manual labeling effort by over 99.5%.

--- https://medium.com/cruise/cruise-continuous-learning-machine-30d60f4c691b 2022-05-14 reinforcement-learning/exploration/active-learning reinforcement-learning/robot --- https://wildandpreciouslife0.wordpress.com/2012/06/26/a-cat-named-sloopy-by-rod-mckuen/ 2022-05-14 cat fiction/poetry --- https://www.forbes.com/sites/bradtempleton/2019/04/22/tesla-bets-farm-on-neural-network-based-autonomy-with-impressive-presentation/ 2022-05-14 reinforcement-learning/exploration/active-learning reinforcement-learning/robot --- https://medium.com/pytorch/road-defect-detection-using-deep-active-learning-98d94fe854d 2022-05-14 reinforcement-learning/exploration/active-learning --- https://oatml.cs.ox.ac.uk/blog/2019/06/24/batchbald.html 2022-05-14 reinforcement-learning/exploration/active-learning --- https://research.google/blog/estimating-the-impact-of-training-data-with-reinforcement-learning/ 2022-05-14 reinforcement-learning/exploration/active-learning --- https://arxiv.org/abs/1909.11671#google Data Valuation using Reinforcement Learning Jinsung Yoon, Sercan O. Arik, Tomas Pfister 2019-09-25 2022-05-15 [("doi","10.48550/arXiv.1909.11671")] reinforcement-learning/exploration/active-learning reinforcement-learning/meta-learning

Quantifying the value of data is a fundamental problem in machine learning. Data valuation has multiple important use cases: (1) building insights about the learning task, (2) domain adaptation, (3) corrupted sample discovery, and (4) robust learning.

To adaptively learn data values jointly with the target task predictor model, we propose a meta learning framework which we name Data Valuation using Reinforcement Learning (DVRL). We employ a data value estimator (modeled by a deep neural network) to learn how likely each datum is used in training of the predictor model. We train the data value estimator using a reinforcement signal of the reward obtained on a small validation set that reflects performance on the target task.

We demonstrate that DVRL yields superior data value estimates compared to alternative methods across different types of datasets and in a diverse set of application scenarios. The corrupted sample discovery performance of DVRL is close to optimal in many regimes (ie. as if the noisy samples were known a priori), and for domain adaptation and robust learning DVRL outperforms state-of-the-art by 14.6% and 10.8%, respectively.

--- https://arxiv.org/abs/1904.07854 End-to-End Robotic Reinforcement Learning without Reward Engineering Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, Sergey Levine 2019-04-16 2022-05-15 [("doi","10.48550/arXiv.1904.07854")] reinforcement-learning/exploration/active-learning reinforcement-learning/robot

The combination of deep neural network models and reinforcement learning algorithms can make it possible to learn policies for robotic behaviors that directly read in raw sensory inputs, such as camera images, effectively subsuming both estimation and control into one model. However, real-world applications of reinforcement learning must specify the goal of the task by means of a manually programmed reward function, which in practice requires either designing the very same perception pipeline that end-to-end reinforcement learning promises to avoid, or else instrumenting the environment with additional sensors to determine if the task has been performed successfully.

In this paper, we propose an approach for removing the need for manual engineering of reward specifications by enabling a robot to learn from a modest number of examples of successful outcomes, followed by actively solicited queries, where the robot shows the user a state and asks for a label to determine whether that state represents successful completion of the task. While requesting labels for every single state would amount to asking the user to manually provide the reward signal, our method requires labels for only a tiny fraction of the states seen during training, making it an efficient and practical approach for learning skills without manually engineered rewards.

We evaluate our method on real-world robotic manipulation tasks where the observations consist of images viewed by the robot’s camera. In our experiments, our method effectively learns to arrange objects, place books, and drape cloth, directly from images and without any manually specified reward functions, and with only 1–4 hours of interaction with the real world.

--- https://en.wikipedia.org/wiki/Sequential_analysis Sequential analysis 2022-05-15 reinforcement-learning/exploration/active-learning --- https://arxiv.org/abs/1906.08158 BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning Andreas Kirsch, Joost van Amersfoort, Yarin Gal 2019-06-19 2022-05-15 [("doi","10.48550/arXiv.1906.08158")] reinforcement-learning/exploration/active-learning

We develop BatchBALD, a tractable approximation to the mutual information between a batch of points and model parameters, which we use as an acquisition function to select multiple informative points jointly for the task of deep Bayesian active learning.

BatchBALD is a greedy linear-time 1 − 1⁄e-approximate algorithm amenable to dynamic programming and efficient caching. We compare BatchBALD to the commonly used approach for batch data acquisition and find that the current approach acquires similar and redundant points, sometimes performing worse than randomly acquiring data.

We finish by showing that, using BatchBALD to consider dependencies within an acquisition batch, we achieve new state-of-the-art performance on standard benchmarks, providing substantial data efficiency improvements in batch acquisition.

--- https://www.theatlantic.com/technology/archive/2019/01/how-machine-learning-found-flints-lead-pipes/578692/ How a Feel-Good AI Story Went Wrong in Flint: A machine-learning model showed promising results, but city officials and their engineering contractor abandoned it. 2022-05-15 politics reinforcement-learning/exploration/active-learning --- https://arxiv.org/abs/1904.09037#amazon ProductNet: a Collection of High-Quality Datasets for Product Representation Learning Chu Wang, Lei Tang, Yang Lu, Shujun Bian, Hirohisa Fujita, Da Zhang, Zuohua Zhang, Yongning Wu 2019-04-18 2022-05-15 [("doi","10.48550/arXiv.1904.09037")] ai/dataset reinforcement-learning/exploration/active-learning

ProductNet is a collection of high-quality product datasets for better product understanding. Motivated by ImageNet, ProductNet aims at supporting product representation learning by curating product datasets of high quality with properly chosen taxonomy.

In this paper, the two goals of building high-quality product datasets and learning product representation support each other in an iterative fashion: the product embedding is obtained via a multi-modal deep neural network (master model) designed to leverage product image and catalog information; and in return, the embedding isused via active learning (local model) to vastly accelerate the annotation process.

For the labeled data, the proposed master model yields high categorization accuracy (94.7% top-1 accuracy for 1240 classes), which can be used as search indices, partition keys, and input features for machine learning models. The product embedding, as well as the fine-tuned master model for a specific business task, can also be used for various transfer learning tasks.

--- https://www.biorxiv.org/content/10.1101/337154.full Toward machine-guided design of proteins Surojit Biswas, Gleb Kuznetsov, Pierce J. Ogden, Nicholas J. Conway, Ryan P. Adams, George M. Church 2018-06-02 2022-05-15 [("doi","10.1101/337154")] genetics/selection/artificial

Proteins—molecular machines that underpin all biological life—are of significant therapeutic and industrial value. Directed evolution is a high-throughput experimental approach for improving protein function, but has difficulty escaping local maxima in the fitness landscape.

Here, we investigate how supervised learning in a closed loop with DNA synthesis and high-throughput screening can be used to improve protein design. Using the green fluorescent protein (GFP) as an illustrative example, we demonstrate the opportunities and challenges of generating training datasets conducive to selecting strongly generalizing models.

With prospectively designed wet lab experiments, we then validate that these models can generalize to unseen regions of the fitness landscape, even when constrained to explore combinations of non-trivial mutations.

Taken together, this suggests a hybrid optimization strategy for protein design in which a predictive model is used to explore difficult-to-access but promising regions of the fitness landscape that directed evolution can then exploit at scale.

--- https://arxiv.org/abs/1905.05393 Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules Daniel Ho, Eric Liang, Ion Stoica, Pieter Abbeel, Xi Chen 2019-05-14 2022-05-15 [("doi","10.48550/arXiv.1905.05393")] reinforcement-learning/exploration/active-learning

A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations. Properly chosen augmentation policies can lead to generalization improvements; however, state-of-the-art approaches such as AutoAugment are computationally infeasible to run for the ordinary user.

In this paper, we introduce a new data augmentation algorithm, Population Based Augmentation (PBA), which generates non-stationary augmentation policy schedules instead of a fixed augmentation policy.

We show that PBA can match the performance of AutoAugment on CIFAR-10, CIFAR-100, and SVHN, with 3 orders of magnitude less overall compute. On CIFAR-10 we achieve a mean test error of 1.46%, which is a slight improvement upon the current state-of-the-art.

The code for PBA is open source and is available at Github.

--- https://bair.berkeley.edu/blog/2019/06/07/data_aug/ 2022-05-15 reinforcement-learning/exploration/active-learning --- https://www.youtube.com/watch?v=Q0nGo2-y0xY 2022-05-15 reinforcement-learning/exploration/active-learning reinforcement-learning/robot --- https://arxiv.org/abs/1901.05415#facebook Learning from Dialogue after Deployment: Feed Yourself, Chatbot! Braden Hancock, Antoine Bordes, Pierre-Emmanuel Mazaré, Jason Weston 2019-01-16 2022-05-15 [("doi","10.48550/arXiv.1901.05415")] reinforcement-learning/exploration/active-learning

The majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped.

In this work, we propose the self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversations it participates in. As our agent engages in conversation, it also estimates user satisfaction in its responses. When the conversation appears to be going well, the user’s responses become new training examples to imitate. When the agent believes it has made a mistake, it asks for feedback; learning to predict the feedback that will be given improves the chatbot’s dialogue abilities further.

On the PersonaChat chit-chat dataset with over 131k training examples, we find that learning from dialogue with a self-feeding chatbot improves performance, regardless of the amount of traditional supervision.

--- https://arxiv.org/abs/1807.01613#deepmind Conditional Neural Processes Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami 2018-07-04 2022-05-16 [("doi","10.48550/arXiv.1807.01613")] reinforcement-learning/exploration/active-learning

Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function. On the other hand, Bayesian methods, such as Gaussian Processes (GPs), exploit prior knowledge to quickly infer the shape of a new function at test time. Yet GPs are computationally expensive, and it can be hard to design appropriate priors. In this paper, we propose a family of neural models, Conditional Neural Processes (CNPs), that combine the benefits of both.

CNPs are inspired by the flexibility of stochastic processes such as GPs, but are structured as neural networks and trained via gradient descent. CNPs make accurate predictions after observing only a handful of training data points, yet scale to complex functions and large datasets.

We demonstrate the performance and versatility of the approach on a range of canonical machine learning tasks, including regression, classification, and image completion.

--- https://arxiv.org/abs/1904.02868 Data Shapley: Equitable Valuation of Data for Machine Learning Amirata Ghorbani, James Zou 2019-04-05 2022-05-16 [("doi","10.48550/arXiv.1904.02868")] reinforcement-learning/exploration/active-learning

As data becomes the fuel driving technological and economic growth, a fundamental challenge is how to quantify the value of data in algorithmic predictions and decisions. For example, in healthcare and consumer markets, it has been suggested that individuals should be compensated for the data that they generate, but it is not clear what is an equitable valuation for individual data. In this work, we develop a principled framework to address data valuation in the context of supervised machine learning.

Given a learning algorithm trained on n data points to produce a predictor, we propose Data Shapley as a metric to quantify the value of each training datum to the predictor performance. Data Shapley value uniquely satisfies several natural properties of equitable data valuation. We develop Monte Carlo and gradient-based methods to efficiently estimate data Shapley values in practical settings where complex learning algorithms, including neural networks, are trained on large datasets.

In addition to being equitable, extensive experiments across biomedical, image, and synthetic data demonstrate that data Shapley has several other benefits: (1) it is more powerful than the popular leave-one-out or leverage score in providing insight into what data is more valuable for a given learning task; (2) low Shapley value data effectively capture outliers and corruptions; (3) high Shapley value data inform what type of new data to acquire to improve the predictor.

--- https://research.google/blog/fluid-annotation-an-exploratory-machine-learningpowered-interface-for-faster-image-annotation/ 2022-05-16 reinforcement-learning/exploration/active-learning --- https://en.wikipedia.org/wiki/Comparative_advantage#Classical_theory_and_David_Ricardo's_formulation Comparative advantage § Classical theory and David Ricardo’s formulation 2022-05-16 economics --- https://arxiv.org/abs/1811.00982#google The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, Tom Duerig, Vittorio Ferrari 2018-11-02 2022-05-16 [("doi","10.1007/s11263-020-01316-z")] ai/dataset reinforcement-learning/exploration/active-learning

We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding an initial design bias.

Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship annotations involving 57 classes. For object detection in particular, we provide 15× more bounding boxes than the next largest datasets (15.4M boxes on 1.9M images). The images often show complex scenes with several objects (8 annotated objects per image on average). We annotated visual relationships between them, which support visual relationship detection, an emerging task that requires structured reasoning.

We provide in-depth comprehensive statistics about the dataset, we validate the quality of the annotations, we study how the performance of several modern models evolves with increasing amounts of training data, and we demonstrate two applications made possible by having unified annotations of multiple types coexisting in the same images.

We hope that the scale, quality, and variety of Open Images V4 will foster further research and innovation even beyond the areas of image classification, object detection, and visual relationship detection.

--- https://www.biorxiv.org/content/10.1101/255182.full The Eighty Five Percent Rule for Optimal Learning Robert C. Wilson, Amitai Shenhav, Mark Straccia, Jonathan D. Cohen 2018-01-27 2022-05-16 [("doi","10.1101/255182")] psychology/neuroscience reinforcement-learning/exploration/active-learning

Researchers and educators have long wrestled with the question of how best to teach their clients, be they human, animal, or machine. Here we focus on the role of a single variable, the difficulty of training, and examine its effect on the rate of learning.

In many situations, we find that there is a sweet spot in which training is neither too easy nor too hard, and where learning progresses most quickly. We derive conditions for this sweet spot for a broad class of learning algorithms in the context of binary classification tasks, in which ambiguous stimuli must be sorted into one of two classes.

For all of these gradient-descent based learning algorithms, we find that the optimal error rate for training is around 15.87% or, conversely, that the optimal training accuracy is about 85%.

We demonstrate the efficacy of this ‘Eighty 5 Percent Rule’ for artificial neural networks used in AI and biologically plausible neural networks thought to describe human and animal learning.

--- https://research.google/blog/open-sourcing-active-question-reformulation-with-reinforcement-learning/ 2022-05-16 reinforcement-learning/exploration/active-learning --- https://arxiv.org/abs/1710.07283 Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft 2017-10-19 2022-05-16 [("doi","10.48550/arXiv.1710.07283")] reinforcement-learning/exploration/active-learning

Bayesian neural networks with latent variables are scalable and flexible probabilistic models: They account for uncertainty in the estimation of the network weights and, by making use of latent variables, can capture complex noise patterns in the data.

We show how to extract and decompose uncertainty into epistemic and aleatoric components for decision-making purposes. This allows us to successfully identify informative points for active learning of functions with heteroscedastic and bimodal noise.

Using the decomposition we further define a novel risk-sensitive criterion for reinforcement learning to identify policies that balance expected cost, model-bias, and noise aversion.

--- https://arxiv.org/abs/1805.11085 More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch Roberto Calandra, Andrew Owens, Dinesh Jayaraman, Justin Lin, Wenzhen Yuan, Jitendra Malik, Edward H. Adelson, Sergey Levine 2018-05-28 2022-05-16 [("doi","10.1109/LRA.2018.2852779")] ai/nn/cnn reinforcement-learning/exploration/active-learning reinforcement-learning/robot

For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact. In this paper, we investigate how a robot can learn to use tactile information to iteratively and efficiently adjust its grasp.

To this end, we propose an end-to-end action-conditional model that learns re-grasping policies from raw visuo-tactile data. This model—a deep, multimodal convolutional network—predicts the outcome of a candidate grasp adjustment, and then executes a grasp by iteratively selecting the most promising actions. Our approach requires neither calibration of the tactile sensors, nor any analytical modeling of contact forces, thus reducing the engineering effort required to obtain efficient grasping policies. We train our model with data from about 6,450 grasping trials on a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger.

Across extensive experiments, our approach outperforms a variety of baselines at (1) estimating grasp adjustment outcomes, (2) selecting efficient grasp adjustments for quick grasping, and (3) reducing the amount of force applied at the fingers, while maintaining competitive performance.

Finally, we study the choices made by our model and show that it has successfully acquired useful and interpretable grasping behaviors.

--- https://openreview.net/forum?id=BkbRc6bd-H The Power of Ensembles for Active Learning in Image Classification William H. Beluch, Tim Genewein, Andreas Nürnberger, Jan M. Köhler 2022-11-10 2022-11-10 ai/nn/cnn reinforcement-learning/exploration/active-learning

Deep learning methods have become the de-facto standard for challenging image processing tasks such as image classification. One major hurdle of deep learning approaches is that large sets of labeled data are necessary, which can be prohibitively costly to obtain, particularly in medical image diagnosis applications. Active learning techniques can alleviate this labeling effort.

In this paper we investigate some recently proposed methods for active learning with high-dimensional data and convolutional neural network classifiers. We compare ensemble-based methods against Monte-Carlo Dropout and geometric approaches.

We find that ensembles perform better and lead to more calibrated predictive uncertainties, which are the basis for many active learning algorithms.

To investigate why Monte-Carlo Dropout uncertainties perform worse, we explore potential differences in isolation in a series of experiments. We show results for MNIST and CIFAR-10, on which we achieve a test set accuracy of 90% with roughly 12,200 labeled images, and initial results on ImageNet. Additionally, we show results on a large, highly class-imbalanced diabetic retinopathy dataset.

We observe that the ensemble-based active learning effectively counteracts this imbalance during acquisition.

--- https://explosion.ai/blog/prodigy-annotation-tool-active-learning 2022-05-16 cs/python reinforcement-learning/exploration/active-learning --- https://arxiv.org/abs/1805.09501#google AutoAugment: Learning Augmentation Policies from Data Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le 2018-05-24 2022-05-17 [("doi","10.48550/arXiv.1805.09501")] reinforcement-learning/exploration/active-learning reinforcement-learning/meta-learning

Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies.

In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset.

Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art.

Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve improvements on other datasets, such as Oxford Flowers, Caltech 101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars.

--- https://arxiv.org/abs/1805.10662 Fingerprint Policy Optimization for Robust Reinforcement Learning Supratik Paul, Michael A. Osborne, Shimon Whiteson 2018-05-27 2022-05-17 [("doi","10.48550/arXiv.1805.10662")] reinforcement-learning/exploration/active-learning reinforcement-learning/meta-learning

Policy gradient methods ignore the potential value of adjusting environment variables: unobservable state features that are randomly determined by the environment in a physical setting, but are controllable in a simulator. This can lead to slow learning, or convergence to suboptimal policies, if the environment variable has a large impact on the transition dynamics.

In this paper, we present fingerprint policy optimization (FPO), which finds a policy that is optimal in expectation across the distribution of environment variables. The central idea is to use Bayesian optimization (BO) to actively select the distribution of the environment variable that maximizes the improvement generated by each iteration of the policy gradient method. To make this BO practical, we contribute two easy-to-compute low-dimensional fingerprints of the current policy.

Our experiments show that FPO can efficiently learn policies that are robust to rare events, which are unlikely to be observable under random sampling, but are key to learning good policies.

--- https://arxiv.org/abs/1805.08610 Optimization, fast and slow: optimally switching between local and Bayesian optimization Mark McLeod, Michael A. Osborne, Stephen J. Roberts 2018-05-22 2022-05-17 [("doi","10.48550/arXiv.1805.08610")] reinforcement-learning/exploration/active-learning

We develop the first Bayesian Optimization algorithm, BLOSSOM, which selects between multiple alternative acquisition functions and traditional local optimization at each step.

This is combined with a novel stopping condition based on expected regret. This pairing allows us to obtain the best characteristics of both local and Bayesian optimization, making efficient use of function evaluations while yielding superior convergence to the global minimum on a selection of optimization problems, and also halting optimization once a principled and intuitive stopping condition has been fulfilled.

--- https://arxiv.org/abs/1711.07950#facebook Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston 2017-11-21 2022-05-17 [("doi","10.48550/arXiv.1711.07950")] ai/nn/rnn fiction/text-game reinforcement-learning/exploration/active-learning

Contrary to most natural language processing research, which makes use of static datasets, humans learn language interactively, grounded in an environment.

In this work we propose an interactive learning procedure called Mechanical Turker Descent (MTD) and use it to train agents to execute natural language commands grounded in a fantasy text adventure game. In MTD, Turkers compete to train better agents in the short term, and collaborate by sharing their agents’ skills in the long term.

This results in a gamified, engaging experience for the Turkers and a better quality teaching signal for the agents compared to static datasets, as the Turkers naturally adapt the training data to the agent’s abilities.

--- https://arxiv.org/abs/1802.07427 Active Learning with Partial Feedback Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan 2018-02-21 2022-05-17 [("doi","10.48550/arXiv.1802.07427")] reinforcement-learning/exploration/active-learning

While many active learning papers assume that the learner can simply ask for a label and receive it, real annotation often presents a mismatch between the form of a label (say, one among many classes), and the form of an annotation (typically yes/no binary feedback). To annotate examples corpora for multiclass classification, we might need to ask multiple yes/no questions, exploiting a label hierarchy if one is available.

To address this more realistic setting, we propose active learning with partial feedback (ALPF), where the learner must actively choose both which example to label and which binary question to ask. At each step, the learner selects an example, asking if it belongs to a chosen (possibly composite) class. Each answer eliminates some classes, leaving the learner with a partial label. The learner may then either ask more questions about the same example (until an exact label is uncovered) or move on immediately, leaving the first example partially labeled. Active learning with partial labels requires (1) a sampling strategy to choose (example, class) pairs, and (2) learning from partial labels between rounds.

Experiments on Tiny ImageNet demonstrate that our most effective method improves 26% (relative) in top-1 classification accuracy compared to i.i.d. baselines and standard active learners given 30% of the annotation budget that would be required (naively) to annotate the dataset. Moreover, ALPF-learners fully annotate Tiny ImageNet at 42% lower cost.

Surprisingly, we observe that accounting for per-example annotation costs can alter the conventional wisdom that active learners should solicit labels for hard examples [cf. Sorscher et al 2022].

--- https://arxiv.org/abs/1804.09028#ibm Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications Guy Hadash, Einat Kermany, Boaz Carmeli, Ofer Lavi, George Kou, Alon Jacovi 2018-04-24 2022-05-17 [("doi","10.48550/arXiv.1804.09028")] reinforcement-learning/exploration/active-learning reinforcement-learning/model

Existing applications include a huge amount of knowledge that is out of reach for deep neural networks.

This paper presents a novel approach for integrating calls to existing applications into deep learning architectures. Using this approach, we estimate each application’s functionality with an estimator, which is implemented as a deep neural network (DNN). The estimator is then embedded into a base network that we direct into complying with the application’s interface during an end-to-end optimization process. At inference time, we replace each estimator with its existing application counterpart and let the base network solve the task by interacting with the existing application.

Using this ‘Estimate and Replace’ method, we were able to train a DNN end-to-end with less data and outperformed a matching DNN that did not interact with the external application.

--- https://arxiv.org/abs/1709.00507 Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks Dinesh Jayaraman, Kristen Grauman 2017-09-01 2022-05-17 [("doi","10.48550/arXiv.1709.00507")] ai/nn/rnn reinforcement-learning/exploration/active-learning

It is common to implicitly assume access to intelligently captured inputs (eg. photos from a human photographer), yet autonomously capturing good observations is itself a major challenge. We address the problem of learning to look around: if a visual agent has the ability to voluntarily acquire new views to observe its environment, how can it learn efficient exploratory behaviors to acquire informative observations?

We propose a reinforcement learning solution, where the agent is rewarded for actions that reduce its uncertainty about the unobserved portions of its environment. Based on this principle, we develop a recurrent neural network-based approach to perform active completion of panoramic natural scenes and 3D object shapes. Crucially, the learned policies are not tied to any recognition task nor to the particular semantic content seen during training.

As a result, (1) the learned “look around” behavior is relevant even for new tasks in unseen environments, and (2) training data acquisition involves no manual labeling. Through tests in diverse settings, we demonstrate that our approach learns useful generic policies that transfer to new unseen tasks and environments.

Completion episodes are shown at https://vision.cs.utexas.edu/projects/visual-exploration/.

--- https://arxiv.org/abs/1712.01664 Learning a Generative Model for Validity in Complex Discrete Structures David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato 2017-12-05 2022-05-17 [("doi","10.48550/arXiv.1712.01664")] reinforcement-learning/exploration/active-learning

Deep generative models have been successfully used to learn representations for high-dimensional discrete spaces by representing discrete objects as sequences and employing powerful sequence-based deep models. Unfortunately, these sequence-based models often produce invalid sequences: sequences which do not represent any underlying discrete structure; invalid sequences hinder the utility of such models.

As a step towards solving this problem, we propose to learn a deep recurrent validator model, which can estimate whether a partial sequence can function as the beginning of a full, valid sequence. This validator provides insight as to how individual sequence elements influence the validity of the overall sequence, and can be used to constrain sequence based models to generate valid sequences—and thus faithfully model discrete objects. Our approach is inspired by reinforcement learning, where an oracle which can evaluate validity of complete sequences provides a sparse reward signal.

We demonstrate its effectiveness as a generative model of Python 3 source code for mathematical expressions, and in improving the ability of a variational autoencoder trained on SMILES strings to decode valid molecular structures.

--- https://arxiv.org/abs/1802.00912 Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts Zongwei Zhou, Jae Y. Shin, Suryakanth R. Gurudu, Michael B. Gotway, Jianming Liang 2018-02-03 2022-05-17 [("doi","10.1016/j.media.2021.101997")] ai/nn/cnn reinforcement-learning/exploration/active-learning

The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, in medical imaging, it is challenging to create such large annotated datasets, as annotating medical images is not only tedious, laborious, and time consuming, but it also demands costly, specialty-oriented skills, which are not easily accessible.

To dramatically reduce annotation cost, this paper presents a novel method to naturally integrate active learning and transfer learning (fine-tuning) into a single framework, which starts directly with a pre-trained CNN to seek “worthy” samples for annotation and gradually enhances the (fine-tuned) CNN via continual fine-tuning.

We have evaluated our method using 3 distinct medical imaging applications, demonstrating that it can reduce annotation efforts by at least half compared with random selection.

--- https://arxiv.org/abs/1801.09319 Less is more: sampling chemical space with active learning Justin S. Smith, Ben Nebgen, Nicholas Lubbers, Olexandr Isayev, Adrian E. Roitberg 2018-01-28 2022-05-17 [("doi","10.1063/1.5023802")] biology reinforcement-learning/exploration/active-learning

The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train such ML potentials is a task neither well understood nor researched in detail. In this work, we present a fully automated approach for the generation of datasets with the intent of training universal ML potentials.

It is based on the concept of active learning (AL) via Query by Committee (QBC), which uses the disagreement between an ensemble of ML potentials to infer the reliability of the ensemble’s prediction. QBC allows the presented AL algorithm to automatically sample regions of chemical space where the ML potential fails to accurately predict the potential energy.

AL improves the overall fitness of ANAKIN-ME (ANI) deep learning potentials in rigorous test cases by mitigating human biases in deciding what new training data to use. AL also reduces the training set size to a fraction of the data required when using naive random sampling techniques.

To provide validation of our AL approach we develop the COMP6 benchmark (publicly available on GitHub), which contains a diverse set of organic molecules. Through the AL process, it is shown that the AL-based potentials perform as well as the ANI-1 potential on COMP6 with only 10% of the data, and vastly outperforms ANI-1 with 25% the amount of data.

Finally, we show that our proposed AL technique develops a universal ANI potential (ANI-1×) that provides accurate energy and force predictions on the entire COMP6 benchmark. This universal ML potential achieves a level of accuracy on par with the best ML potentials for single molecule or materials, while remaining applicable to the general class of organic molecules comprised of the elements CHNO.

--- https://arxiv.org/abs/1708.00049 Interpretable Active Learning Richard L. Phillips, Kyu Hyun Chang, Sorelle A. Friedler 2017-07-31 2022-05-18 [("doi","10.48550/arXiv.1708.00049")] reinforcement-learning/exploration/active-learning

Active learning has long been a topic of study in machine learning. However, as increasingly complex and opaque models have become standard practice, the process of active learning, too, has become more opaque. There has been little investigation into interpreting what specific trends and patterns an active learning strategy may be exploring.

This work expands on the Local Interpretable Model-agnostic Explanations framework (LIME) to provide explanations for active learning recommendations. We demonstrate how LIME can be used to generate locally faithful explanations for an active learning strategy, and how these explanations can be used to understand how different models and datasets explore a problem space over time.

In order to quantify the per-subgroup differences in how an active learning strategy queries spatial regions, we introduce a notion of uncertainty bias (based on disparate impact) to measure the discrepancy in the confidence for a model’s predictions between one subgroup and another. Using the uncertainty bias measure, we show that our query explanations accurately reflect the subgroup focus of the active learning queries, allowing for an interpretable explanation of what is being learned as points with similar sources of uncertainty have their uncertainty bias resolved.

We demonstrate that this technique can be applied to track uncertainty bias over user-defined clusters or automatically generated clusters based on the source of uncertainty.

--- https://arxiv.org/abs/1712.01238 Learning by Asking Questions Ishan Misra, Ross Girshick, Rob Fergus, Martial Hebert, Abhinav Gupta, Laurens van der Maaten 2017-12-04 2022-05-18 [("doi","10.48550/arXiv.1712.01238")] reinforcement-learning/exploration/active-learning

We introduce an interactive learning framework for the development and testing of intelligent visual systems, called learning-by-asking (LBA).

We explore LBA in context of the Visual Question Answering (VQA) task. LBA differs from standard VQA training in that most questions are not observed during training time, and the learner must ask questions it wants answers to.

Thus, LBA more closely mimics natural learning and has the potential to be more data-efficient than the traditional VQA setting. We present a model that performs LBA on the CLEVR dataset, and show that it automatically discovers an easy-to-hard curriculum when learning interactively from an oracle.

Our LBA generated data consistently matches or outperforms the CLEVR train data and is more sample-efficient.

We also show that our model asks questions that generalize to state-of-the-art VQA models and to novel test time distributions.

--- https://github.com/cranmer/active_sciencing/blob/master/README.md 2022-05-18 reinforcement-learning/exploration/active-learning statistics/bayes --- https://arxiv.org/abs/1801.09496 Improving Active Learning in Systematic Reviews Gaurav Singh, James Thomas, John Shawe-Taylor 2018-01-29 2022-05-18 [("doi","10.48550/arXiv.1801.09496")] biology statistics/meta-analysis

Systematic reviews are essential to summarizing the results of different clinical and social science studies. The first step in a systematic review task is to identify all the studies relevant to the review. The task of identifying relevant studies for a given systematic review is usually performed manually, and as a result, involves substantial amounts of expensive human resource.

Lately, there have been some attempts to reduce this manual effort using active learning. In this work, we build upon some such existing techniques, and validate by experimenting on a larger and comprehensive dataset than has been attempted until now. Our experiments provide insights on the use of different feature extraction models for different disciplines.

More importantly, we identify that a naive active learning based screening process is biased in favor of selecting similar documents. We aimed to improve the performance of the screening process using a novel active learning algorithm with success.

Additionally, we propose a mechanism to choose the best feature extraction method for a given review.

--- https://arxiv.org/abs/2108.11353#google Multi-Task Self-Training for Learning General Representations Golnaz Ghiasi, Barret Zoph, Ekin D. Cubuk, Quoc V. Le, Tsung-Yi Lin 2021-08-25 2022-05-18 [("doi","10.48550/arXiv.2108.11353")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/exploration/active-learning reinforcement-learning/scaling

Despite the fast progress in training specialized models for various tasks, learning a single general model that works well for many tasks is still challenging for computer vision. Here we introduce multi-task self-training (MuST), which harnesses the knowledge in independent specialized teacher models (eg. ImageNet model on classification) to train a single general student model.

Our approach has 3 steps. First, we train specialized teachers independently on labeled datasets. We then use the specialized teachers to label an unlabeled dataset to create a multi-task pseudo labeled dataset. Finally, the dataset, which now contains pseudo labels from teacher models trained on different datasets/tasks, is then used to train a student model with multi-task learning.

We evaluate the feature representations of the student model on 6 vision tasks including image recognition (classification, detection, segmentation) and 3D geometry estimation (depth and surface normal estimation). MuST is scalable with unlabeled or partially labeled datasets and outperforms both specialized supervised models and self-supervised models when training on large scale datasets.

Lastly, we show MuST can improve upon already strong checkpoints trained with billions of examples. The results suggest self-training is a promising direction to aggregate labeled and unlabeled training data for learning general feature representations.

--- https://openreview.net/forum?id=HJ-5tP-O-r Query by Committee Made Real Ran Gilad-Bachrach, Amir Navot, Naftali Tishby 2022-11-11 2022-11-11 reinforcement-learning/exploration/active-learning

Training a learning algorithm is a costly task. A major goal of active learning is to reduce this cost.

In this paper, we introduce a new algorithm, KQBC, which is capable of actively learning large-scale problems by using selective sampling. The algorithm overcomes the costly sampling step of the well-known Query By Committee (QBC) algorithm by projecting onto a low-dimensional space. KQBC also enables the use of kernels, providing a simple way of extending QBC to the non-linear scenario. Sampling the low dimension space is done using the hit and run random walk.

We demonstrate the success of this novel algorithm by applying it to both artificial and real-world problems.

--- https://porikli.com/mysite/pdfs/porikli%202009%20-%20Multi-Class%20Active%20Learning%20for%20Image%20Classification.pdf Multi-class active learning for image classification Ajay J. Joshi, Fatih Porikli, Nikolaos Papanikolopoulos 2022-11-10 2022-11-10 reinforcement-learning/exploration/active-learning

One of the principal bottlenecks in applying learning techniques to classification problems is the large amount of labeled training data required. Especially for images and video, providing training data is very expensive in terms of human time and effort.

In this paper we propose an active learning approach to tackle the problem. Instead of passively accepting random training examples, the active learning algorithm iteratively selects unlabeled examples for the user to label, so that human effort is focused on labeling the most “useful” examples. Our method relies on the idea of uncertainty sampling, in which the algorithm selects unlabeled examples that it finds hardest to classify. Specifically, we propose an uncertainty measure that generalizes margin-based uncertainty to the multi-class case and is easy to compute, so that active learning can handle a large number of classes and large data sizes efficiently.

We demonstrate results for letter and digit recognition on datasets from the UCI repository, object recognition results on the Caltech 101 dataset, and scene categorization results on a dataset of 13 natural scene categories.

The proposed method gives large reductions in the number of training examples required over random selection to achieve similar classification accuracy, with little computational overhead.

--- https://arxiv.org/abs/1003.5249 Active Testing for Face Detection and Localization Raphael Sznitman, Bruno Jedynak 2010-03-27 2022-05-18 [("doi","10.1109/TPAMI.2010.106")] reinforcement-learning/exploration/active-learning

We provide a novel search technique, which uses a hierarchical model and a mutual information gain heuristic to efficiently prune the search space when localizing faces in images.

We show exponential gains in computation over traditional sliding window approaches, while keeping similar performance levels.

--- https://www.xiaojun.ai/papers/IJCV2015.pdf Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization Yi Yang, Zhigang Ma, Feiping Nie, Xiaojun Chang, Alexander G. Hauptmann 2022-11-09 2022-11-09 reinforcement-learning/exploration/active-learning

As a way to relieve the tedious work of manual annotation, active learning plays important roles in many applications of visual concept recognition. In typical active learning scenarios, the number of labeled data in the seed set is usually small. However, most existing active learning algorithms only exploit the labeled data, which often suffers from overfitting due to the small number of labeled examples. Besides, while much progress has been made in binary class active learning, little research attention has been focused on multi-class active learning.

In this paper, we propose a semi-supervised batch mode multi-class active learning algorithm for visual concept recognition. Our algorithm exploits the whole active pool to evaluate the uncertainty of the data. Considering that uncertain data are always similar to each other, we propose to make the selected data as diverse as possible, for which we explicitly impose a diversity constraint on the objective function. As a multi-class active learning algorithm, our algorithm is able to exploit uncertainty across multiple classes. An efficient algorithm is used to optimize the objective function.

Extensive experiments on action recognition, object classification, scene recognition, and event detection demonstrate its advantages.

In conclusion, this work presents a novel approach towards addressing the challenges in multi-class active learning for visual concept recognition by incorporating diversity and semi-supervised learning strategies to improve performance and reliability.

--- https://calvin-vision.net/wp-content/uploads/Publications/VezhnevetsCVPR2012b.pdf Weakly supervised structured output learning for semantic segmentation Alexander Vezhnevets, Vittorio Ferrari, Joachim M. Buhmann 2022-11-10 2022-11-10 reinforcement-learning/exploration/active-learning

We address the problem of weakly supervised semantic segmentation. The training images are labeled only by the classes they contain, not by their location in the image. On test images instead, the method must predict a class label for every pixel. Our goal is to enable segmentation algorithms to use multiple visual cues in this weakly supervised setting, analogous to what is achieved by fully supervised methods. However, it is difficult to assess the relative usefulness of different visual cues from weakly supervised training data.

We define a parametric family of structured models, were each model weights visual cues in a different way. We propose a Maximum Expected Agreement model selection principle that evaluates the quality of a model from the family without looking at superpixel labels. Searching for the best model is a hard optimization problem, which has no analytic gradient and multiple local optima. We cast it as a Bayesian optimization problem and propose an algorithm based on Gaussian processes to efficiently solve it.

Our second contribution is an Extremely Randomized Hashing Forest that represents diverse superpixel features as a sparse binary vector. It enables using appearance models of visual classes that are fast at training and testing and yet accurate.

Experiments on the SIFT-flow dataset show an improvement over previous weakly supervised methods and even over some fully supervised methods.

--- https://x.com/spiantado/status/1598937445885960192 2022-05-18 ai/nn/transformer/gpt/fiction --- https://x.com/NanoRaptor/status/1599055548414324738 2022-05-19 ai/nn/transformer/gpt/fiction --- https://x.com/davidad/status/1599070751453196288 2022-05-19 ai/nn/transformer/gpt/fiction ai/text-style-transfer --- https://www.audubon.org/news/how-xavi-bou-makes-his-mesmerizing-portraits-birds-flight 2022-05-19 design/visualization --- https://www.biorxiv.org/content/10.1101/2022.12.01.518771.full Orthogonal neural encoding of targets and distractors supports multivariate cognitive control Harrison Ritz, Amitai Shenhav 2022-12-01 2022-12-01 [("doi","10.1101/2022.12.01.518771")] psychology/neuroscience

People can overcome a wide array of mental challenges by coordinating their neural information processing to align with their goals. Recent behavioral work has shown that people can independently control their attention across multiple features during perceptual decision-making, but the structure of the neural representations that enables this multivariate control remains mysterious.

We hypothesized that the brain solves this complex coordination problem by orthogonalizing feature-specific representations of task demands and attentional priority, allowing the brain to independently monitor and adjust multiple streams of stimulus information. To test this hypothesis, we measured fMRI activity while participants performed a task designed to tag processing and control over feature-specific information that is task-relevant (targets) versus task-irrelevant (distractors). We then characterized the geometry of these neural representations using a novel multivariate analysis (Encoding Geometry Analysis), estimating where the encoding of different task features is correlated versus orthogonal.

We identified feature-specific representations of task demands and attentional priority in the dorsal anterior cingulate cortex (dACC) and intraparietal sulcus (IPS), respectively, consistent with differential roles for these regions in monitoring versus directing information processing. Representations of attentional priority in IPS were fully mediated by the control requirements of the task, associated with behavioral performance, and depended on connectivity with nodes in the frontoparietal control network, suggesting that these representations serve a fundamental role in supporting attentional control.

Together, these findings provide evidence for a neural geometry that can enable coordinated control over multiple sources of information.

--- https://www.medrxiv.org/content/10.1101/2022.11.28.22282824.full Associations between attention-deficit hyperactivity disorder genetic liability and ICD-10 medical conditions in adults: Utilizing electronic health records in a Phenome-Wide Association Study Elis Haan, Kristi Krebs, Urmo Võsa, Isabell Brikell, Henrik Larsson, Estonian Biobank Research Team, Kelli Lehto 2022-11-29 2022-11-29 [("doi","10.1101/2022.11.28.22282824")] genetics/heritable/correlation psychiatry/adhd

Background: Attention-deficit hyperactivity disorder (ADHD) is often comorbid with other medical conditions in adult patients. However, ADHD is extremely under-diagnosed in adults and little is known about the medical comorbidities in undiagnosed adult individuals with high ADHD liability. In this study we investigated associations between ADHD genetic liability and electronic health record (EHR)-based ICD-10 diagnoses across all diagnostic categories, in individuals without ADHD diagnosis history.

Method: We used data from the Estonian Biobank cohort (n = 111,261) and generated polygenic risk scores (PRS) for ADHD (PRSADHD) based on the latest ADHD genome-wide association study. We performed a phenome-wide association study (PheWAS) to test for associations between standardized PRS<ADHD> and 1,515 EHR-based ICD-10 diagnoses in the full and sex-stratified sample. We compared the observed significant ICD-10 associations to associations with: (1) ADHD diagnosis and (2) questionnaire-based high ADHD risk analyses.

Results: After Bonferroni correction (p = 3.3 × 10−5) we identified 80 medical conditions associated with PRSADHD. The strongest evidence was seen with chronic obstructive pulmonary disease (OR = 1.15, CI = 1.11–1.18), obesity (OR = 1.13, CI = 1.11–1.15), and type 2 diabetes (OR = 1.11, CI = 1.09–1.14). Sex-stratified analysis generally showed similar associations in males and females. Out of all identified associations, 40% and 78% were also observed using ADHD diagnosis or questionnaire-based ADHD, respectively, as the predictor.

Conclusion: Overall our findings indicate that ADHD genetic liability is associated with an increased risk of a substantial number of medical conditions in undiagnosed individuals. These results highlight the need for timely detection and improved management of ADHD symptoms in adults.

--- https://www.newyorker.com/magazine/2017/11/06/is-bigfoot-likelier-than-the-loch-ness-monster 2022-05-19 psychology/dark-knowledge --- https://en.wikipedia.org/wiki/Universal_law_of_generalization Universal law of generalization 2022-05-19 psychology/dark-knowledge psychology/vision --- https://en.wikipedia.org/wiki/Moe_anthropomorphism Moe anthropomorphism 2022-05-19 anime psychology/dark-knowledge --- https://www.reddit.com/r/OpenAI/comments/zcd9yq/making_it_explain_stuff_in_different_styles_is/ 2022-05-19 ai/nn/transformer/gpt/non-fiction ai/text-style-transfer --- https://arxiv.org/abs/2212.01349#facebook NPM: Nonparametric Masked Language Modeling Sewon Min, Weijia Shi, Mike Lewis, Xilun Chen, Wen-tau Yih, Hannaneh Hajishirzi, Luke Zettlemoyer 2022-12-02 2022-12-02 [("doi","10.48550/arXiv.2212.01349")] ai/nn/retrieval ai/nn/tokenization ai/nn/transformer

Existing language models (LMs) predict tokens with a softmax over a finite vocabulary, which can make it difficult to predict rare tokens or phrases. We introduce NPM, the first nonparametric masked language model that replaces this softmax with a nonparametric distribution over every phrase in a reference corpus. We show that NPM can be efficiently trained with a contrastive objective and an in-batch approximation to full corpus retrieval.

Zero-shot evaluation on 9 closed-set tasks and 7 open-set tasks demonstrates that NPM outperforms larger parametric models, either with or without a retrieve-and-generate approach.

It is particularly better on dealing with rare patterns (word senses or facts), and predicting rare or nearly unseen words (eg. non-Latin script). We release the model and code at github.com/facebookresearch/NPM.

[No comparison to alternate tokenizations, especially ByT5.]

--- https://x.com/justinstorre/status/1599483466927984640 2022-05-19 ai/nn/transformer/gpt/fiction fiction/text-game --- https://x.com/alicemazzy/status/1599344887085682689 2022-05-20 ai/nn/transformer/gpt/non-fiction fiction/text-game --- https://x.com/emollick/status/1599513581552209920 2022-05-20 ai/nn/transformer/gpt/fiction math/humor --- https://www.reddit.com/r/unsong/comments/zckyg0/completing_edward_tellers_atom_alphabet_with/ 2022-05-20 ai/nn/transformer/gpt/fiction ai/poetry existential-risk --- https://www.biorxiv.org/content/10.1101/2022.04.13.487655.full Multi-omics analyses cannot identify true-positive novel associations from underpowered genome-wide association studies of four brain-related traits David A. A. Baranger, Alexander S. Hatoum, Renato Polimanti, Joel Gelernter, Howard J. Edenberg, Ryan Bogdan, Arpana Agrawal 2022-04-14 2022-05-20 [("doi","10.1101/2022.04.13.487655")] genetics/heritable psychiatry/adhd psychiatry/alcoholism psychiatry/schizophrenia psychology/neuroscience

Background: The integration of multi-omics information (eg. epigenetics and transcriptomics) can be useful for interpreting findings from genome-wide association studies (GWAS). It has additionally been suggested that multi-omics may aid in novel variant discovery, thus circumventing the need to increase GWAS sample sizes. We tested whether incorporating multi-omics information in earlier and smaller sized GWAS boosts true-positive discovery of genes that were later revealed by larger GWAS of the same/similar traits.

Method: We applied 10 different analytic approaches to integrating multi-omics data from 12 sources (eg. Genotype-Tissue Expression project) to test whether earlier and smaller GWAS of 4 brain-related traits (ie. alcohol use disorder/problematic alcohol use [AUD/PAU], major depression [MDD], schizophrenia [SCZ], and intracranial volume [ICV]) could detect genes that were revealed by a later and larger GWAS.

Results: Multi-omics data did not reliably identify novel genes in earlier less powered GWAS (PPV<0.2; 80% false-positive associations). Machine learning predictions marginally increased the number of identified novel genes, correctly identifying 1–8 additional genes, but only for well-powered early GWAS of highly heritable traits (ie. ICV and SCZ). Multi-omics, particularly positional mapping (ie. fastBAT, MAGMA, and H-MAGMA), was useful for prioritizing genes within genome-wide statistically-significant loci (PPVs = 0.5–1.0).

Conclusion: Although the integration of multi-omics information, particularly when multiple methods agree, helps prioritize GWAS findings and translate them into information about disease biology, it does not substantively increase novel gene discovery in brain-related GWAS. To increase power for discovery of novel genes and loci, increasing sample size is a requirement.

--- https://arxiv.org/abs/2210.08726#google RARR: Attributed Text Generation via Post-hoc Research and Revision Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Y. Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, Kelvin Guu 2022-10-17 2022-10-17 [("doi","10.48550/arXiv.2210.08726")] ai/nn/retrieval ai/nn/transformer/gpt/palm

[Twitter] Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog. However, they sometimes generate unsupported or misleading content. A user cannot easily determine whether their outputs are trustworthy or not, because most LMs do not have any built-in mechanism for attribution to external evidence.

To enable attribution while still preserving all the powerful advantages of recent generation models, we propose RARR (Retrofit Attribution using Research and Revision), a system that (1) automatically finds attribution for the output of any text generation model and (2) post-edits the output to fix unsupported content while preserving the original output as much as possible.

When applied to the output of several state-of-the-art LMs [eg. PaLM] on a diverse set of generation tasks, we find that RARR improves attribution while otherwise preserving the original input to a much greater degree than previously explored edit models.

Furthermore, the implementation of RARR requires only a handful of training examples, a large language model, and standard web search.

[cf. PEER, VitaminC, FRUIT]

--- https://arxiv.org/abs/2112.08634 FRUIT: Faithfully Reflecting Updated Information in Text Robert L. Logan IV, Alexandre Passos, Sameer Singh, Ming-Wei Chang 2021-12-16 2022-05-20 [("doi","10.48550/arXiv.2112.08634")] ai/dataset ai/nn/sampling ai/nn/transformer/t5 wikipedia

Textual knowledge bases such as Wikipedia require considerable effort to keep up to date and consistent. While automated writing assistants could potentially ease this burden, the problem of suggesting edits grounded in external knowledge has been under-explored.

In this paper, we introduce the novel generation task of faithfully reflecting updated information in text (FRUIT) where the goal is to update an existing article given new evidence. We release the FRUIT-WIKI dataset, a collection of over 170K distantly supervised data produced from pairs of Wikipedia snapshots, along with our data generation pipeline and a gold evaluation set of 914 instances whose edits are guaranteed to be supported by the evidence.

We provide benchmark results for popular generation systems as well as EDIT5—a T5-based approach tailored to editing we introduce that establishes the state-of-the-art.

Our analysis shows that developing models that can update articles faithfully requires new capabilities for neural generation models, and opens doors to many new applications.

--- https://arxiv.org/abs/2103.08541 Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (VitaminC) Tal Schuster, Adam Fisch, Regina Barzilay 2021-03-15 2022-05-20 [("doi","10.48550/arXiv.2103.08541")] ai/dataset ai/nn/retrieval ai/nn/transformer wikipedia

Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence.

We present VitaminC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs. Unlike previous resources, the examples in VitaminC are contrastive, ie. they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not.

We show that training using this design increases robustness—improving accuracy by 10% on adversarial fact verification and 6% on adversarial natural language inference (NLI).

Moreover, the structure of VitaminC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation.

--- https://www.reddit.com/r/StableDiffusion/comments/zcpuc6/trained_a_model_with_3d_fractal_images_goal_was/ 2022-05-20 ai/nn/transformer/clip/sample --- https://x.com/emollick/status/1599135622135832578 2022-05-20 ai/nn/transformer/gpt/non-fiction psychology/spaced-repetition --- https://github.com/greshake/Alice 2022-05-20 ai/nn/transformer/gpt/codex --- https://simonwillison.net/2022/Dec/5/rust-chatgpt-copilot/ 2022-05-20 ai/nn/transformer/gpt/codex --- https://x.com/IrwanBello/status/1599881660019126273 2022-05-21 ai/nn/transformer/gpt/fiction --- https://www.nytimes.com/2022/12/06/business/economy/global-car-supply-chains-xianjiang-forced-labor.html 2022-05-21 history/uighur --- https://x.com/hwchase17/status/1600162023589163008 2022-05-21 ai/nn/transformer/gpt/non-fiction reinforcement-learning/preference-learning --- https://www.newyorker.com/magazine/2022/12/12/an-anti-abortion-activists-quest-to-end-the-rape-exception 2022-05-21 philosophy/ethics --- https://maximumeffort.substack.com/p/i-taught-chatgpt-to-invent-a-language 2022-05-21 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/non-fiction --- https://maximumeffort.substack.com/p/elizabeth-bennet-and-sennacherib 2022-05-21 ai/nn/transformer/gpt/fiction --- https://arxiv.org/abs/2208.14271#deepmind Faithful Reasoning Using Large Language Models Antonia Creswell, Murray Shanahan 2022-08-30 2022-08-30 [("doi","10.48550/arXiv.2208.14271")] ai/nn/transformer/gpt/inner-monologue

Although contemporary large language models (LMs) demonstrate impressive question-answering capabilities, their answers are typically the product of a single call to the model. This entails an unwelcome degree of opacity and compromises performance, especially on problems that are inherently multi-step. To address these limitations, we show how LMs can be made to perform faithful multi-step reasoning via a process whose causal structure mirrors the underlying logical structure of the problem.

Our approach works by chaining together reasoning steps, where each step results from calls to two fine-tuned LMs, one for selection and one for inference, to produce a valid reasoning trace. Our method carries out a beam search through the space of reasoning traces to improve reasoning quality.

We demonstrate the effectiveness of our model on multi-step logical deduction and scientific question-answering, showing that it outperforms baselines on final answer accuracy, and generates humanly interpretable reasoning traces whose validity can be checked by the user.

--- https://www.lesswrong.com/posts/pNcFYZnPdXyL2RfgA/using-gpt-eliezer-against-chatgpt-jailbreaking 2022-05-21 ai/nn/transformer/gpt/non-fiction cs/security reinforcement-learning/safe --- https://x.com/chrisjpal/status/1600276061832376320 2022-05-21 ai/nn/transformer/gpt/fiction --- https://stanfordmag.org/contents/the-botez-gambit 2022-05-21 psychology/chess --- https://www.annenbergpublicpolicycenter.org/the-undying-holiday-suicide-myth/ 2022-05-22 psychiatry --- https://arxiv.org/abs/2211.12561#facebook Retrieval-Augmented Multimodal Language Modeling Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Rich James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, Wen-tau Yih 2022-11-22 2022-11-22 [("doi","10.48550/arXiv.2211.12561")] ai/nn/retrieval ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e/1

Recent multimodal models such as DALL·E and CM3 have achieved remarkable progress in text-to-image and image-to-text generation. However, these models store all learned knowledge (eg. the appearance of the Eiffel Tower) in the model parameters, requiring increasingly larger models and training data to capture more knowledge. To integrate knowledge in a more scalable and modular way, we propose a retrieval-augmented multimodal model, which enables a base multimodal model (generator) to refer to relevant knowledge fetched by a retriever from external memory (eg. multimodal documents on the web).

Specifically, we implement a retriever using the pretrained CLIP model and a generator using the CM3 Transformer architecture, and train this model using the LAION dataset. Our resulting model, named Retrieval-Augmented CM3 (RA-CM3), is the first multimodal model that can retrieve and generate mixtures of text and images.

We show that RA-CM3 outperforms baseline multimodal models such as DALL·E and CM3 on both image and caption generation tasks (12 FID and 17 CIDEr improvements on MS-COCO), while requiring much less compute for training (<30% of DALL·E). Moreover, we show that RA-CM3 exhibits novel capabilities such as knowledge-intensive image generation and multimodal in-context learning.

--- https://www.nytimes.com/2022/12/02/business/china-protests-surveillance.html 2022-05-22 history/uighur --- https://x.com/krisgulati/status/1599171677233254402 2022-05-22 ai/nn/transformer/gpt/fiction --- https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/ 2022-05-22 ai/nn/retrieval --- https://en.wikipedia.org/wiki/Recommender_system Recommender systems 2022-05-22 ai/nn/retrieval --- https://en.wikipedia.org/wiki/Cosine_similarity Cosine similarity 2022-05-22 ai/nn/retrieval --- https://en.wikipedia.org/wiki/Word_embedding Word embedding 2022-05-22 ai/nn/retrieval psychology/linguistics --- https://en.wikipedia.org/wiki/Sentence_embedding Sentence embedding 2022-05-22 ai/nn/retrieval --- https://en.wikipedia.org/wiki/Ranking_(information_retrieval) Ranking (information retrieval) 2022-05-22 ai/nn/retrieval --- https://en.wikipedia.org/wiki/Learning_to_rank Learning to rank 2022-05-22 ai/nn/retrieval reinforcement-learning --- https://en.wikipedia.org/wiki/Subvocalization Subvocalization 2022-05-23 psychology/inner-voice --- https://arxiv.org/abs/2207.07051#deepmind Language models show human-like content effects on reasoning Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan, Antonia Creswell, Dharshan Kumaran, James L. McClelland, Felix Hill 2022-07-14 2022-07-14 [("doi","10.48550/arXiv.2207.07051")] ai/nn/transformer/gpt philosophy/logic psychology/cognitive-bias

Abstract reasoning is a key ability for an intelligent system. Large language models achieve above-chance performance on abstract reasoning tasks, but exhibit many imperfections. However, human abstract reasoning is also imperfect, and depends on our knowledge and beliefs about the content of the reasoning problem. For example, humans reason much more reliably about logical rules that are grounded in everyday situations than arbitrary rules about abstract attributes. The training experiences of language models similarly endow them with prior expectations that reflect human knowledge and beliefs.

We therefore hypothesized that language models would show human-like content effects on abstract reasoning problems.

We explored this hypothesis across 3 logical reasoning tasks: natural language inference, judging the logical validity of syllogisms, and the Wason selection task (Wason 1968).

We find that state-of-the-art large language models (with 7 or 70 billion parameters; Hoffman et al 2022) reflect many of the same patterns observed in humans across these tasks—like humans, models reason more effectively about believable situations than unrealistic or abstract ones.

Our findings have implications for understanding both these cognitive effects, and the factors that contribute to language model performance.

--- https://arxiv.org/abs/1506.02142 Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Yarin Gal, Zoubin Ghahramani 2015-06-06 2022-05-23 [("doi","10.48550/arXiv.1506.02142")] ai/nn/cnn reinforcement-learning/exploration/active-learning statistics/bayes

Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost.

In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNs—extracting information from existing models that has been thrown away so far. This mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy.

We perform an extensive study of the properties of dropout’s uncertainty. Various network architectures and non-linearities are assessed on tasks of regression and classification, using MNIST as an example.

We show a considerable improvement in predictive log-likelihood and RMSE compared to existing state-of-the-art methods, and finish by using dropout’s uncertainty in deep reinforcement learning.

--- https://arxiv.org/abs/1211.0358 Deep Gaussian Processes Andreas C. Damianou, Neil D. Lawrence 2012-11-02 2022-05-23 [("doi","10.48550/arXiv.1211.0358")] ai/nn statistics/bayes

In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then governed by another GP. A single layer model is equivalent to a standard GP or the GP latent variable model (GP-LVM).

We perform inference in the model by approximate variational marginalization. This results in a strict lower bound on the marginal likelihood of the model which we use for model selection (number of layers and nodes per layer). Deep belief networks are typically applied to relatively large data sets using stochastic gradient descent for optimization. Our fully Bayesian treatment allows for the application of deep models even when data is scarce.

Model selection by our variational bound shows that a 5 layer hierarchy is justified even when modeling a digit data set containing only 150 examples.

--- https://arxiv.org/abs/2106.09685#microsoft LoRA: Low-Rank Adaptation of Large Language Models Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen 2021-06-17 2022-05-23 [("doi","10.48550/arXiv.2106.09685")] ai/nn/diffusion ai/nn/sparsity ai/nn/transformer/gpt/2

[extensively used with Stable Diffusion] An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example—deploying independent instances of fine-tuned models, each with 175b parameters, is prohibitively expensive.

We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared to GPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable parameters by 10,000× and the GPU memory requirement by 3×. LoRA performs on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3, despite having fewer trainable parameters, a higher training throughput, and, unlike adapters, no additional inference latency. We also provide an empirical investigation into rank-deficiency in language model adaptation, which sheds light on the efficacy of LoRA.

We release a package that facilitates the integration of LoRA with PyTorch models and provide our implementations and model checkpoints for RoBERTa, DeBERTa, and GPT-2 at Github.

--- https://github.com/cloneofsimo/lora 2022-05-23 ai/nn/diffusion --- https://www.nature.com/articles/480168a 2022-05-23 philosophy/mind --- https://arxiv.org/abs/1609.03526 Evidence for a Conserved Quantity in Human Mobility Laura Alessandretti, Piotr Sapiezynski, Vedran Sekara, Sune Lehmann, Andrea Baronchelli 2016-09-12 2022-05-23 [("doi","10.48550/arXiv.1609.03526")] economics sociology/technology

Recent seminal works on human mobility have shown that individuals constantly exploit a small set of repeatedly visited locations. A concurrent literature has emphasized the explorative nature of human behavior, showing that the number of visited places grows steadily over time. How to reconcile these seemingly contradicting facts remains an open question.

Here, we analyze high-resolution multi-year traces of ~40,000 individuals from 4 datasets and show that this tension vanishes when the long-term evolution of mobility patterns is considered.

We reveal that mobility patterns evolve substantially get smoothly, and that the number of familiar locations an individual visits at any point is a conserved quantity with a typical size of ~25 locations. We use this finding to improve state-of-the-art modeling of human mobility. Furthermore, shifting the attention from aggregated quantities to individual behavior, we show that the size of an individual’s set of preferred locations correlates with the number of her social interactions.

This result suggests a connection between the conserved quantity we identify, which as we show can not be understood purely on the basis of time constraints, and the ‘Dunbar number’ describing a cognitive upper limit to an individual’s number of social relations.

We anticipate that our work will spark further research linking the study of Human Mobility and the Cognitive and Behavioral Sciences.

--- https://x.com/moyix/status/1601056131681771521 2022-05-23 ai/nn/transformer/gpt/codex --- https://x.com/tomgoldsteincs/status/1601113497592795136 2022-05-23 ai/nn/tokenization --- https://x.com/tomgoldsteincs/status/1601113501803552768 2022-05-24 ai/nn/tokenization --- https://x.com/tomgoldsteincs/status/1601113505998204928 2022-05-24 ai/nn/tokenization --- https://x.com/DiffusionPics/status/1600985268273692672 2022-05-24 ai/text-style-transfer --- https://blog.cryptographyengineering.com/2018/04/07/hash-based-signatures-an-illustrated-primer/ 2022-05-24 cs/cryptography --- https://huggingface.co/blog/rlhf 2022-05-24 ai/nn/transformer/gpt reinforcement-learning/preference-learning reinforcement-learning/safe --- https://en.wikipedia.org/wiki/Children_of_Time_(novel) Children of Time (novel) 2022-05-24 biology/portia --- https://www.youtube.com/watch?v=UDtlvZGmHYk 2022-05-24 biology/portia --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC128573/ Complexity and robustness J M. Carlson, John Doyle 2002 2022-05-24 [("doi","10.1073/pnas.012582499")] biology

Highly optimized tolerance (HOT) was recently introduced as a conceptual framework to study fundamental aspects of complexity.

HOT is motivated primarily by systems from biology and engineering and emphasizes, (1) highly structured, non generic, self-dissimilar internal configurations, and (2) robust yet fragile external behavior.

HOT claims these are the most important features of complexity and not accidents of evolution or artifices of engineering design but are inevitably intertwined and mutually reinforcing.

In the spirit of this collection, our paper contrasts HOT with alternative perspectives on complexity, drawing on real-world examples and also model systems, particularly those from self-organized criticality.

--- https://www.biorxiv.org/content/10.1101/2020.04.01.021006.full Admixture has obscured signals of historical hard sweeps in humans Yassine Souilmi, Raymond Tobler, Angad Johar, Matthew Williams, Shane T. Grey, Joshua Schmidt, João C. Teixeira, Adam Rohrlach, Jonathan Tuke, Olivia Johnson, Graham Gower, Chris Turney, Murray Cox, Alan Cooper, Christian D. Huber 2021-11-19 2022-05-24 [("doi","10.1101/2020.04.01.021006")] genetics/selection/natural/human

The role of natural selection in shaping biological diversity is an area of intense interest in modern biology. To date, studies of positive selection have primarily relied upon genomic datasets from contemporary populations, which are susceptible to confounding factors associated with complex and often unknown aspects of population history.

In particular, admixture between diverged populations can distort or hide prior selection events in modern genomes, though this process is not explicitly accounted for in most selection studies despite its apparent ubiquity in humans and other species.

Through analyses of ancient and modern human genomes, we show that previously reported Holocene-era admixture has masked more than 50 historic hard sweeps in modern European genomes.

Our results imply that this canonical mode of selection has likely been underappreciated in the evolutionary history of humans and suggests that our current understanding of the tempo and mode of selection in natural populations may be quite inaccurate.

--- https://hosting.astro.cornell.edu/~loredo/bayes/bae.pdf Bayesian Adaptive Exploration Loredo, Chernoff 2003 2022-05-24 reinforcement-learning/exploration statistics/bayes statistics/order/comparison --- https://x.com/michael_nielsen/status/1601833660394590208 2022-05-25 ai/nn/transformer/gpt/non-fiction --- https://datacolada.org/72 Metacritic Has A (File-Drawer) Problem 2022-05-25 statistics/bias statistics/order/comparison --- https://www.theatlantic.com/magazine/archive/1962/06/a-prof-beats-the-gamblers/657997/ 2022-05-25 statistics/decision --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6803761/ The Psychology of Existential Risk: Moral Judgments about Human Extinction Stefan Schubert, Lucius Caviola, Nadira S. Faber 2019 2022-05-25 [("doi","10.1038/s41598-019-50145-9")] existential-risk philosophy/ethics

The 21st century will likely see growing risks of human extinction, but currently, relatively small resources are invested in reducing such existential risks.

Using 3 samples (UK general public, US general public, and UK students; total n = 2,507), we study how laypeople reason about human extinction. We find that people think that human extinction needs to be prevented.

Strikingly, however, they do not think that an extinction catastrophe would be uniquely bad relative to near-extinction catastrophes, which allow for recovery. More people find extinction uniquely bad when (a) asked to consider the extinction of an animal species rather than humans, (b) asked to consider a case where human extinction is associated with less direct harm, and (c) they are explicitly prompted to consider long-term consequences of the catastrophes.

We conclude that an important reason why people do not find extinction uniquely bad is that they focus on the immediate death and suffering that the catastrophes cause for fellow humans, rather than on the long-term consequences. Finally, we find that (d) laypeople—in line with prominent philosophical arguments—think that the quality of the future is relevant: they do find extinction uniquely bad when this means forgoing a utopian future.

--- https://en.wikipedia.org/wiki/Netflix_Prize Netflix Prize 2022-05-25 ai/dataset --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4873371/ Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage Alexis C. Komor, Yongjoo B. Kim, Michael S. Packer, John A. Zuris, David R. Liu 2016 2022-05-25 [("doi","10.1038/nature17946")] genetics/editing

Current genome-editing technologies introduce double-stranded (ds) DNA breaks at a target locus as the first step to gene correction. Although most genetic diseases arise from point mutations, current approaches to point mutation correction are inefficient and typically induce an abundance of random insertions and deletions (indels) at the target locus resulting from the cellular response to dsDNA breaks.

Here we report the development of ‘base editing’, a new approach to genome editing that enables the direct, irreversible conversion of one target DNA base into another in a programmable manner, without requiring dsDNA backbone cleavage or a donor template.

We engineered fusions of CRISPR/Cas9 and a cytidine deaminase enzyme that retain the ability to be programmed with a guide RNA, do not induce dsDNA breaks, and mediate the direct conversion of cytidine to uridine, thereby effecting a C → T (or G → A) substitution. The resulting ‘base editors’ convert cytidines within a window of ~5 nucleotides, and can efficiently correct a variety of point mutations relevant to human disease. In 4 transformed human and murine cell lines, second & third-generation base editors that fuse uracil glycosylase inhibitor, and that use a Cas9 nickase targeting the non-edited strand, manipulate the cellular DNA repair response to favour desired base-editing outcomes, resulting in permanent correction of ~15–75% of total cellular DNA with minimal (typically ≤1%) indel formation.

Base editing expands the scope and efficiency of genome editing of point mutations.

--- https://x.com/emollick/status/1602049432614326272 2022-05-25 ai/nn/transformer/gpt/fiction --- https://arxiv.org/abs/2111.07775 Learning Representations for Pixel-based Control: What Matters and Why? Manan Tomar, Utkarsh A. Mishra, Amy Zhang, Matthew E. Taylor 2021-11-15 2022-05-25 [("doi","10.48550/arXiv.2111.07775")] reinforcement-learning/model

[discussion] Learning representations for pixel-based control has garnered attention recently in reinforcement learning. A wide range of methods have been proposed to enable efficient learning, leading to sample complexities similar to those in the full state setting. However, moving beyond carefully curated pixel data sets (centered crop, appropriate lighting, clear background, etc.) remains challenging.

In this paper, we adopt a more difficult setting, incorporating background distractors, as a first step towards addressing this challenge. We present a simple baseline approach that can learn meaningful representations with no metric-based learning, no data augmentations, no world-model learning, and no contrastive learning. We then analyze when and why previously proposed methods are likely to fail or reduce to the same performance as the baseline in this harder setting and why we should think carefully about extending such methods beyond the well curated environments.

Our results show that finer categorization of benchmarks on the basis of characteristics like density of reward, planning horizon of the problem, presence of task-irrelevant components, etc., is crucial in evaluating algorithms.

Based on these observations, we propose different metrics to consider when evaluating an algorithm on benchmark tasks.

We hope such a data-centric view can motivate researchers to rethink representation learning when investigating how to best apply RL to real-world tasks.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7674631/ A large-scale genome-wide association study meta-analysis of cannabis use disorder Emma C. Johnson, Ditte Demontis, Thorgeir E. Thorgeirsson, Raymond K. Walters, Renato Polimanti, Alexander S. Hatoum, Sandra Sanchez-Roige, Sarah E. Paul, Frank R. Wendt, Toni-Kim Clarke, Dongbing Lai, Gunnar W. Reginsson, Hang Zhou, June He, David A. A. Baranger, Daniel F. Gudbjartsson, Robbee Wedow, Daniel E. Adkins, Amy E. Adkins, Jeffry Alexander, Silviu-Alin Bacanu, Tim B. Bigdeli, Joseph Boden, Sandra A. Brown, Kathleen K. Bucholz, Jonas Bybjerg-Grauholm, Robin P. Corley, Louisa Degenhardt, Danielle M. Dick, Benjamin W. Domingue, Louis Fox, Alison M. Goate, Scott D. Gordon, Laura M. Hack, Dana B. Hancock, Sarah M. Hartz, Ian B. Hickie, David Hougaard, Kenneth Krauter, Penelope A. Lind, Jeanette N. McClintick, Matthew B. McQueen, Jacquelyn L. Meyers, Grant W. Montgomery, Ole Mors, Preben Bo Mortensen, Merete Nordentoft, John F. Pearson, Roseann E. Peterson, Maureen D. Reynolds, John P. Rice, Valgerdur Runarsdottir, Nancy L. Saccone, Richard Sherva, Judy L. Silberg, Ralph E. Tarter, Thorarinn Tyrfingsson, Tamara L. Wall, Bradley T. Webb, Thomas Werge, Leah Wetherill, Margaret J. Wright, Stephanie Zellers, Mark J. Adams, Laura J. Bierut, Jason D. Boardman, William E. Copeland, Lindsay A. Farrer, Tatiana M. Foroud, Nathan A. Gillespie, Richard A. Grucza, Kathleen Mullan Harris, Andrew C. Heath, Victor Hesselbrock, John K. Hewitt, Christian J. Hopfer, John Horwood, William Iacono, Eric O. Johnson, Kenneth S. Kendler, Martin A. Kennedy, Henry R. Kranzler, Pamela A. F. Madden, Hermine H. Maes, Brion S. Maher, Nicholas G. Martin, Matthew McGue, Andrew M. McIntosh, Sarah E. Medland, Elliot C. Nelson, Bernice Porjesz, Brien P. Riley, Michael C. Stallings, Michael M. Vanyukov, Scott Vrieze, Lea K. Davis, Ryan Bogdan, Joel Gelernter, Howard J. Edenberg, Kari Stefansson, Anders Børglum, Arpana Agrawal 2020 2022-05-25 [("doi","10.1016/S2215-0366(20)30339-4")] genetics/heritable/correlation marijuana psychiatry/adhd psychiatry/depression psychiatry/schizophrenia

Background: Variation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50–70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder.

Method: To conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesized associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations.

Findings: We identified two genome-wide statistically-significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07–1·15, p = 1·84 × 10−9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86–0·93, p = 6·46 × 10−9).

Cannabis use disorder and cannabis use were genetically correlated (rg 0·50, p = 1·50 × 10−21), but they showed statistically-significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia.

Interpretation: These findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder.

--- https://arxiv.org/abs/2210.11416#google FLAN: Scaling Instruction-Finetuned Language Models Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, Jason Wei 2022-10-20 2022-10-20 [("doi","10.48550/arXiv.2210.11416")] ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/gpt/palm ai/nn/transformer/t5 ai/scaling

[video; Twitter, 2] Finetuning language models on a collection of datasets phrased as instructions (instruction finetuning) has been shown to improve model performance and generalization to unseen tasks.

In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation). For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PALM 540B by a large margin (+9.4% on average). Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU.

We also publicly release Flan-T5 checkpoints [HF], which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B.

Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.

…In this paper we advance instruction finetuning in several ways. First, we study the impact of scaling on instruction finetuning. Our experiments show that instruction finetuning does scale well with the number of tasks and the size of the model. Their respective scaling behaviors suggest that future research should scale up the number of tasks and the size of the model even further. Second, we study the effect of finetuning on the ability of the models to perform reasoning tasks. Our experiments show that whereas prior instruction finetuning methods that do not include chain-of-thought (CoT; Wei et al 2022b) severely degrade performance on CoT evaluations, adding just 9 CoT datasets into the finetuning mixture enables better performance on all evaluations.

Based on these findings, we train Flan-PaLM by using a 540B-parameter model, increasing the number of finetuning tasks to 1.8K, and including CoT data. Flan-PaLM outperforms PaLM, achieving new state-of-the-art on several benchmarks. For instance, Flan-PaLM’s improved reasoning abilities enable it to leverage CoT and self-consistency (Wang et al 2022c) to achieve 75.2% on Massive Multi-task Language Understanding (MMLU; Hendrycks et al 2020). Flan-PaLM also has improved multilingual abilities compared to PaLM, such as 14.9% absolute improvement on one-shot TydiQA (Clark et al 2020) and 8.1% on arithmetic reasoning in under-represented languages (Shi et al 2022). In human rater evaluations, Flan-PaLM substantially outperforms PaLM on a challenging set of open-ended generation questions, suggesting improved usability. Moreover, we found that instruction finetuning also improves performance across several responsible AI evaluation benchmarks.

In addition, we also instruction-finetune Flan-T5 models (80M to 11B). These checkpoints have strong zero-shot, few-shot, and CoT abilities, outperforming prior public checkpoints such as T5 (Raffel et al 2020). For example, Flan-T5 11B outperforms T5 11B by double-digit improvements and even outperforms PaLM 62B on some challenging BIG-Bench tasks (Srivastava et al 2022). Overall, our results underscore how instruction finetuning can improve performance across a range of models, prompting setups, and evaluation tasks.

Table 1: Average 5-shot MMLU scores (%) for 57 tasks with model and human accuracy comparisons (Hendrycks et al 2020). Forecasts were made in July 2022 by competitive human forecasters, regarding a single model (Steinhardt 2021); see Hypermind & Metaculus. CoT + SC: chain-of-thought prompting with self-consistency (Wang et al 2022b).

…In this paper we scale to 1,836 finetuning tasks by combining 4 mixtures from prior work: Muffin, T0-SF, NIV2, and CoT, as summarized in Figure 2:

Figure 2: Our finetuning data comprises 473 datasets, 146 task categories, and 1,836 total tasks. Details for the tasks used in this paper is given in Appendix F.
Table 2: Across several models, instruction finetuning only costs a small amount of compute relative to pre-training. T5: Raffel et al 2020. PaLM and cont-PaLM (also known as PaLM 62B at 1.3T tokens): Chowdhery et al 2022. U-PaLM: Tay et al (2022b).

…3. Scaling to 540b parameters and 1.8K tasks:

Figure 4 shows the joint effect of scaling these two variables on the normalized average of held-out benchmarks. Individual benchmark results are reported in Table 3: First, we see that for all 3 model sizes shown, multi-task instruction finetuning improves performance by a large margin compared to no finetuning. The performance gain ranges 9.4% → 15.5%.

Second, increasing the number of finetuning tasks improves performance, although the majority of the improvement comes from using up to 282 tasks. There are two potential explanations for the small gain after 282 tasks. One is that the additional tasks are not particularly diverse, and so they are not providing the model with new knowledge. Another explanation is that most of the gains from multi-task instruction finetuning come from the model learning to better express knowledge that it already knows from pretraining, and more than 282 tasks does not help too much. This second explanation could make sense since the pre-training data consists of 780B tokens, while instruction finetuning only uses 1.4B tokens (0.2% of the pre-training tokens).

Finally, we see that increasing model scale by an order of magnitude (ie. 8B → 62B or 62B → 540B) improves performance substantially for both finetuned and non-finetuned models. Note that it could be complicated to determine whether instruction finetuning improves small models or large models more (compared to the baseline of no finetuning). For example, although the absolute gain was larger for the 8B model than the 540B model (15.5% for 8B vs. 9.4% for 540B), the relative reduction in error rate was larger for the 540B model (18.4% for 540B vs. 16.6% for 8B).

Plotting such scaling curves provides insights into how scaling the model size and the number of tasks even further might improve performance. Scaling model size by another order of magnitude (though challenging) is expected to provide substantial performance gain. Scaling number of finetuning tasks should also improve performance, although likely only incrementally. Overall, the scaling curves plotted indicate that future work should continue scaling instruction finetuning…Moreover, the margin of improvement for instruction finetuning versus models without finetuning does not seem to decrease, which suggests that instruction finetuning will likely continue to be meaningful for future models.

Figure 4: Scaling behavior of multi-task instruction finetuning with respect to model size (# parameters) and number of finetuning tasks. The x-axes are log scale. The benchmark suites are MMLU (57 tasks), BBH (23 tasks), TydiQA (8 languages), and MGSM (10 languages). The evaluation metric on all 4 benchmark suites is few-shot prompted accuracy (exact match), where we take an unweighted average over all tasks. As an aggregate metric we report the normalized average of MMLU-direct, MMLU-CoT, BBH-direct, BBH-CoT, TydiQA, and MGSM. These evaluation benchmarks are held-out (not included in the finetuning data).

…We also note that Flan-PaLM does not achieve SOTA compared to certain specialized models. For example, for BBH-algo, which comprises tasks that require symbolic manipulation only (eg. keeping the order of a shuffled list of objects, sorting a list of words in alphabetical order), Flan-PaLM does not outperform code-davinci-002, even with CoT + SC. Moreover, although Flan-PaLM outperforms PaLM by 14.9% on one-shot TydiQA, it is still not on par with ByT5 finetuned on the TydiQA training set (Xue et al 2022).

OpenAI’s text-davinci-003 follows instructions better. Is it also better on academic benchmarks? Summary: (1) text-davinci-3 beats text-davinci-2, but is not as good as code-davinci-2—it is behind Google Brain’s PaLM and Flan-U-PaLM
--- https://arxiv.org/abs/2003.05002 TyDiQA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, Jennimaria Palomaki 2020-03-10 2022-05-25 [("doi","10.48550/arXiv.2003.05002")] ai/dataset

Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations. We present TyDiQA—a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.

The languages of TyDiQA are diverse with regard to their typology—the set of linguistic features each language expresses—such that we expect models performing well on this set to generalize across a large number of the world’s languages.

We present a quantitative analysis of the data quality and example-level qualitative linguistic analyses of observed language phenomena that would not be found in English-only corpora.

To provide a realistic information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but don’t know the answer yet, and the data is collected directly in each language without the use of translation.

--- https://arxiv.org/abs/2210.03057#google Language Models are Multilingual Chain-of-Thought Reasoners Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, Dipanjan Das, Jason Wei 2022-10-06 2022-10-06 [("doi","10.48550/arXiv.2210.03057")] ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/gpt/palm ai/scaling/emergence

[Twitter] We evaluate the reasoning abilities of large language models in multilingual settings.

We introduce the Multilingual Grade School Math (MGSM) benchmark, by manually translating 250 grade-school math problems from the GSM8K dataset (Cobbe et al 2021) into 10 typologically diverse languages.

We find that the ability to solve MGSM problems via chain-of-thought prompting emerges with increasing model scale, and that models have strikingly strong multilingual reasoning abilities, even in underrepresented languages such as Bengali and Swahili.

Finally, we show that the multilingual reasoning abilities of language models extend to other tasks such as commonsense reasoning and word-in-context semantic judgment.

The MGSM benchmark is publicly available at Github.

…Effect of model scale. We analyze the effect of model scale (ie. number of model parameters and computational resources used for training) on their multilingual arithmetic reasoning abilities (Figure 4). As the models scale up, the performance generally improves for both GPT-3 and PaLM model series on all languages. Neither model achieves a substantial solve rate until a certain scale (text-davinci-001 for GPT-3 and PaLM-62B for PaLM), hence multilingual reasoning can be considered an emergent ability of large language models (Wei et al 2022a). It is worth noting that the amount of training data per language is constant across language model scales for PaLM—the fact that scale facilitates reasoning implies that further scaling may continue to improve the multilingual reasoning ability of large language models.

Figure 4: MGSM accuracy with different model scales. The letters A, B, C, D1, and D2 denote text-ada-001, text-babbage-001, text-curie-001, text-davinci-001 [InstructGPT], and text-davinci-002 in the GPT-3 (Brown et al 2020; Ouyang et al 2022) family, respectively. While the number of parameters in each GPT-3 model is not publicly available, we order them alphabetically. Detailed numbers can be found in Table 8.
Figure 5: MGSM accuracy of PaLM-540B with different numbers of few-shot exemplars. Detailed numbers can be found in Table 8.
--- https://arxiv.org/abs/2212.04965 Seeing a Rose in 5,000 Ways Yunzhi Zhang, Shangzhe Wu, Noah Snavely, Jiajun Wu 2022-12-09 2022-12-09 [("doi","10.48550/arXiv.2212.04965")] ai/nn/gan

What is a rose, visually? A rose comprises its intrinsics, including the distribution of geometry, texture, and material specific to its object category. With knowledge of these intrinsic properties, we may render roses of different sizes and shapes, in different poses, and under different lighting conditions.

In this work, we build a generative model that learns to capture such object intrinsics from a single image, such as a photo of a bouquet. Such an image includes multiple instances of an object type. These instances all share the same intrinsics, but appear different due to a combination of variance within these intrinsics and differences in extrinsic factors, such as pose and illumination.

Experiments show that our model successfully learns object intrinsics (distribution of geometry, texture, and material) for a wide range of objects, each from a single Internet image.

Our method achieves superior results on multiple downstream tasks, including intrinsic image decomposition, shape and image generation, view synthesis, and relighting.

Figure 1: From a single image, our model learns to infer object intrinsics—the distributions of the geometry, texture, and material of object instances within the image. The model can then generate new instances of the object type, and it allows us to view the object under different poses and lighting conditions. Project page (interactive examples).
--- https://arxiv.org/abs/2212.04825#facebook A Whack-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others Zhiheng Li, Ivan Evtimov, Albert Gordo, Caner Hazirbas, Tal Hassner, Cristian Canton Ferrer, Chenliang Xu, Mark Ibrahim 2022-12-09 2022-12-09 [("doi","10.48550/arXiv.2212.04825")] ai/dataset ai/nn/transformer/clip

Machine learning models have been found to learn shortcuts—unintended decision rules that are unable to generalize—undermining models’ reliability. Previous works address this problem under the tenuous assumption that only a single shortcut exists in the training data. Real-world images are rife with multiple visual cues from background to texture. Key to advancing the reliability of vision systems is understanding whether existing methods can overcome multiple shortcuts or struggle in a Whack-A-Mole game, ie. where mitigating one shortcut amplifies reliance on others.

To address this shortcoming, we propose two benchmarks: (1) UrbanCars, a dataset with precisely controlled spurious cues, and (2) ImageNet-W, an evaluation set based on ImageNet for watermarks, a shortcut we discovered affects nearly every modern vision model. Along with texture and background, ImageNet-W allows us to study multiple shortcuts emerging from training on natural images.

We find computer vision models, including large foundation models—regardless of training set, architecture, and supervision—struggle when multiple shortcuts are present. Even methods explicitly designed to combat shortcuts struggle in a Whack-A-Mole dilemma.

To tackle this challenge, we propose Last Layer Ensemble, a simple-yet-effective method to mitigate multiple shortcuts without Whack-A-Mole behavior.

Our results surface multi-shortcut mitigation as an overlooked challenge critical to advancing the reliability of vision systems.

The datasets and code are released: Github.

--- https://arxiv.org/abs/2212.04581 PALMER: Perception-Action Loop with Memory for Long-Horizon Planning Onur Beker, Mohammad Mohammadi, Amir Zamir 2022-12-08 2022-12-08 [("doi","10.48550/arXiv.2212.04581")] reinforcement-learning/model

To achieve autonomy in a priori unknown real-world scenarios, agents should be able to: (1) act from high-dimensional sensory observations (eg. images), (2) learn from past experience to adapt and improve, and (3) be capable of long horizon planning. Classical planning algorithms (eg. PRM, RRT) are proficient at handling long-horizon planning. Deep learning based methods in turn can provide the necessary representations to address the others, by modeling statistical contingencies between observations.

In this direction, we introduce a general-purpose planning algorithm called PALMER that combines classical sampling-based planning algorithms with learning-based perceptual representations. For training these perceptual representations, we combine Q-learning with contrastive representation learning to create a latent space where the distance between the embeddings of two states captures how easily an optimal policy can traverse between them.

For planning with these perceptual representations, we re-purpose classical sampling-based planning algorithms to retrieve previously observed trajectory segments from a replay buffer and restitch them into ~optimal paths that connect any given pair of start and goal states. This creates a tight feedback loop between representation learning, memory, reinforcement learning, and sampling-based planning.

The end result is an experiential framework for long-horizon planning that is more robust and sample efficient compared to existing methods.

--- https://arxiv.org/abs/2212.04979#google VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners Shen Yan, Tao Zhu, Zirui Wang, Yuan Cao, Mi Zhang, Soham Ghosh, Yonghui Wu, Jiahui Yu 2022-12-09 2022-12-09 [("doi","10.48550/arXiv.2212.04979")] ai/scaling ai/video/analysis

This work explores an efficient approach to establish a foundational video-text model for tasks including open-vocabulary video classification, text-to-video retrieval, video captioning and video question-answering.

We present VideoCoCa that reuses a pretrained image-text contrastive captioner (CoCa) model and adapt it to video-text tasks with minimal extra training. While previous works adapt image-text models with various cross-frame fusion modules (for example, cross-frame attention layer or perceiver resampler) and finetune the modified architecture on video-text data, we surprisingly find that the generative attentional pooling and contrastive attentional pooling layers in the image-text CoCa design are instantly adaptable to “flattened frame embeddings”, yielding a strong zero-shot transfer baseline for many video-text tasks. Specifically, the frozen image encoder of a pretrained image-text CoCa takes each video frame as inputs and generates N token embeddings per frame for totally T video frames. We flatten N × T token embeddings as a long sequence of frozen video representation and apply CoCa’s generative attentional pooling and contrastive attentional pooling on top. All model weights including pooling layers are directly loaded from an image-text CoCa pretrained model.

Without any video or video-text data, VideoCoCa’s zero-shot transfer baseline already achieves state-of-the-art results on zero-shot video classification on Kinetics 400/600/700, UCF101, HMDB-51, and Charades, as well as zero-shot text-to-video retrieval on MSR-VTT and ActivityNet Captions.

We also explore lightweight finetuning on top of VideoCoCa, and achieve strong results on video question-answering (iVQA, MSRVTT-QA, MSVD-QA) and video captioning (MSR-VTT, ActivityNet, Youcook2).

Our approach establishes a simple and effective video-text baseline for future research.

--- https://arxiv.org/abs/2212.05055#google Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, Carlos Riquelme Ruiz, Basil Mustafa, Joshua Ainslie, Yi Tay, Mostafa Dehghani, Neil Houlsby 2022-12-09 2022-12-09 [("doi","10.48550/arXiv.2212.05055")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/t5 ai/scaling/mixture-of-experts

Training large, deep neural networks to convergence can be prohibitively expensive. As a result, often only a small selection of popular, dense models are reused across different contexts and tasks. Increasingly, sparsely activated models, which seek to decouple model size from computation costs, are becoming an attractive alternative to dense models. Although more efficient in terms of quality and computation cost, sparse models remain data-hungry and costly to train from scratch in the large scale regime.

In this work, we propose sparse upcycling—a simple way to reuse sunk training costs by initializing a sparsely activated Mixture-of-Experts model from a dense checkpoint.

We show that sparsely upcycled T5 Base, Large, and XL language models and Vision Transformer Base and Large models, respectively, outperform their dense counterparts on SuperGLUE and ImageNet, using only ~50% of the initial dense pretraining sunk cost.

The upcycled models also outperform sparse models trained from scratch on 100% of the initial dense pretraining computation budget.

--- https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints 2022-05-26 ai/nn/transformer/gpt/instruction-tuning --- https://huggingface.co/google/flan-t5-large 2022-05-26 ai/nn/transformer/gpt/instruction-tuning --- https://github.com/bigscience-workshop/architecture-objective 2022-05-26 ai/nn/transformer/gpt/instruction-tuning --- https://arxiv.org/abs/2104.02133#google SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network William Chan, Daniel Park, Chris Lee, Yu Zhang, Quoc Le, Mohammad Norouzi 2021-04-05 2022-05-26 [("doi","10.48550/arXiv.2104.02133")] ai/dataset ai/nn/transformer/gpt/whisper ai/scaling

We present SpeechStew, a speech recognition model that is trained on a combination of various publicly available speech recognition datasets: AMI, Broadcast News, Common Voice, LibriSpeech, Switchboard/Fisher, Tedlium, and Wall Street Journal. SpeechStew simply mixes all of these datasets together, without any special re-weighting or re-balancing of the datasets.

SpeechStew achieves SoTA or near SoTA results across a variety of tasks, without the use of an external language model. Our results include 9.0% WER on AMI-IHM, 4.7% WER on Switchboard, 8.3% WER on CallHome, and 1.3% on WSJ, which outperforms prior work with strong external language models. We also demonstrate that SpeechStew learns powerful transfer learning representations.

We fine-tune SpeechStew on a noisy low resource speech dataset, CHiME-6. We achieve 38.9% WER without a language model, which compares to 38.6% WER to a strong HMM baseline with a language model. [cf. the later Whisper]

--- https://arxiv.org/abs/2209.09900#amazon LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging Andy Rosenbaum, Saleh Soltan, Wael Hamza, Yannick Versley, Markus Boese 2022-09-20 2022-09-20 [("doi","10.48550/arXiv.2209.09900")] ai/nn/transformer/gpt/instruction-tuning

We present LINGUIST, a method for generating annotated data for Intent Classification and Slot Tagging (IC+ST), via fine-tuning AlexaTM 5B, a 5-billion-parameter multilingual sequence-to-sequence (seq2seq) model, on a flexible instruction prompt.

In a 10-shot novel intent setting for the SNIPS dataset, LINGUIST surpasses state-of-the-art approaches (Back-Translation and Example Extrapolation) by a wide margin, showing absolute improvement for the target intents of +1.9 points on IC Recall and +2.5 points on ST F1 Score. In the zero-shot cross-lingual setting of the mATIS++ dataset, LINGUIST out-performs a strong baseline of Machine Translation with Slot Alignment by +4.14 points absolute on ST F1 Score across 6 languages, while matching performance on IC. Finally, we verify our results on an internal large-scale multilingual dataset for conversational agent IC+ST and show improvements over a baseline which uses Back-Translation, Paraphrasing and Slot Catalog Resampling.

To our knowledge, we are the first to demonstrate instruction fine-tuning of a large-scale seq2seq model to control the outputs of multilingual intent-labeled & slot-labeled data generation.

--- https://arxiv.org/abs/2205.12673 InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning Prakhar Gupta, Cathy Jiao, Yi-Ting Yeh, Shikib Mehri, Maxine Eskenazi, Jeffrey P. Bigham 2022-05-25 2022-05-27 [("doi","10.48550/arXiv.2205.12673")] ai/dataset ai/nn/transformer/gpt/instruction-tuning ai/scaling

[code] Instruction tuning is an emergent paradigm in NLP wherein natural language instructions are leveraged with language models to induce zero-shot performance on unseen tasks. Instructions have been shown to enable good performance on unseen tasks and datasets in both large and small language models. Dialogue is an especially interesting area to explore instruction tuning because dialogue systems perform multiple kinds of tasks related to language (eg. natural language understanding and generation, domain-specific interaction), yet instruction tuning has not been systematically explored for dialogue-related tasks.

We introduce InstructDial, an instruction tuning framework for dialogue, which consists of a repository of 48 diverse dialogue tasks in a unified text-to-text format created from 59 openly available dialogue datasets.

Next, we explore cross-task generalization ability on models tuned on InstructDial across diverse dialogue tasks.

Our analysis reveals that InstructDial enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection, and even better performance in a few-shot setting. To ensure that models adhere to instructions, we introduce novel meta-tasks.

We establish benchmark zero-shot and few-shot performance of models trained using the proposed framework on multiple dialogue tasks.

Figure 4: Model’s performance on unseen tasks improves with the number of seen tasks during training. We report average Accuracy across Eval Selection, Answer Selection, Relation Classification, and Dialfact Classification, and average RougeL scores for Knowledge Grounded Generation and Begins with Generation.
--- https://arxiv.org/abs/2202.02790 Learning Synthetic Environments and Reward Networks for Reinforcement Learning Fabio Ferreira, Thomas Nierhoff, Andreas Saelinger, Frank Hutter 2022-02-06 2022-05-27 [("doi","10.48550/arXiv.2202.02790")] reinforcement-learning/meta-learning reinforcement-learning/model

We introduce Synthetic Environments (SEs) and Reward Networks (RNs), represented by neural networks, as proxy environment models for training Reinforcement Learning (RL) agents.

We show that an agent, after being trained exclusively on the SE, is able to solve the corresponding real environment. While an SE acts as a full proxy to a real environment by learning about its state dynamics and rewards, an RN is a partial proxy that learns to augment or replace rewards.

We use bi-level optimization to evolve SEs and RNs: the inner loop trains the RL agent, and the outer loop trains the parameters of the SE / RN via an evolution strategy. We evaluate our proposed new concept on a broad range of RL algorithms and classic control environments.

In a one-to-one comparison, learning an SE proxy requires more interactions with the real environment than training agents only on the real environment. However, once such an SE has been learned, we do not need any interactions with the real environment to train new agents. Moreover, the learned SE proxies allow us to train agents with fewer interactions while maintaining the original task performance.

Our empirical results suggest that SEs achieve this result by learning informed representations that bias the agents towards relevant states.

Moreover, we find that these proxies are robust against hyperparameter variation and can also transfer to unseen agents.

--- https://arxiv.org/abs/2212.05051 VindLU: A Recipe for Effective Video-and-Language Pretraining Feng Cheng, Xizi Wang, Jie Lei, David Crandall, Mohit Bansal, Gedas Bertasius 2022-12-09 2022-12-09 [("doi","10.48550/arXiv.2212.05051")] ai/nn/transformer ai/scaling ai/video/analysis

The last several years have witnessed remarkable progress in video-and-language (VidL) understanding. However, most modern VidL approaches use complex and specialized model architectures and sophisticated pretraining protocols, making the reproducibility, analysis and comparisons of these frameworks difficult.

Hence, instead of proposing yet another new VidL model, this paper conducts a thorough empirical study demystifying the most important factors in the VidL model design. Among the factors that we investigate are (1) the spatiotemporal architecture design, (2) the multimodal fusion schemes, (3) the pretraining objectives, (4) the choice of pretraining data, (5) pretraining and finetuning protocols, and (6) dataset and model scaling.

Our empirical study reveals that the most important design factors include: temporal modeling, video-to-text multimodal fusion, masked modeling objectives, and joint training on images and videos. Using these empirical insights, we then develop a step-by-step recipe, dubbed VindLU, for effective VidL pretraining. Our final model trained using our recipe achieves comparable or better than state-of-the-art results on several VidL tasks without relying on external CLIP pretraining. In particular, on the text-to-video retrieval task, our approach obtains 61.2% on DiDeMo, and 55.0% on ActivityNet, outperforming current SOTA by 7.8% and 6.1% respectively. Furthermore, our model also obtains state-of-the-art video question-answering results on ActivityNet-QA, MSRVTT-QA, MSRVTT-MC and TVQA.

Our code and pretrained models are publicly available at: Github.

Figure 2: We progressively expand an image transformer baseline (eg. ViT) to a performant video-and-language (VidL) model. We do so by investigating the importance of many VidL design choices such as (1) temporal modeling, (2) multimodal fusion modules, (3) pretraining objectives, (4) the source of the pretraining data, (5) the number of pre-training frames, (6) multi-stage pretraining, and (7) scaling of the data and model. Each bar depicts an average text-to-video retrieval Recall@1,5,10 accuracy across MSR-VTT, DiDeMo, ActivityNet. The red bars denote the best-performing design choice in each subgroup. Our final VidL framework, dubbed VINDLU, outperforms our initial image Transformer baseline by 23.2%. The figure was inspired by ConvNeXt.
--- https://x.com/ArtirKel/status/1602401255321575424 2022-05-27 ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/1705.00754 Dense-Captioning Events in Videos Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, Juan Carlos Niebles 2017-05-02 2022-05-27 [("doi","10.48550/arXiv.1705.00754")] ai/dataset ai/video/analysis

Most natural videos contain numerous events. For example, in a video of a “man playing a piano”, the video might also contain “another man dancing” or “a crowd clapping”. We introduce the task of dense-captioning events, which involves both detecting and describing events in a video.

We propose a new model that is able to identify all events in a single pass of the video while simultaneously describing the detected events with natural language. Our model introduces a variant of an existing proposal module that is designed to capture both short as well as long events that span minutes. To capture the dependencies between the events in a video, our model introduces a new captioning module that uses contextual information from past and future events to jointly describe all events.

We also introduce ActivityNet Captions, a large-scale benchmark for dense-captioning events. ActivityNet Captions contains 20k videos amounting to 849 video hours with 100k total descriptions, each with its unique start and end time.

Finally, we report performances of our model for dense-captioning events, video retrieval and localization.

--- https://arxiv.org/abs/2205.12393 CT0: Fine-tuned Language Models are Continual Learners Thomas Scialom, Tuhin Chakrabarty, Smaranda Muresan 2022-05-24 2022-05-27 [("doi","10.48550/arXiv.2205.12393")] ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 reinforcement-learning/meta-learning/continual-learning reinforcement-learning/scaling

[code/model; cf. Tirumala et al 2022, Ostapenko et al 2022, Ding et al 2022, Jin et al 2021, Wang et al 2022; based on instruction-tuning] Recent work on large language models relies on the intuition that most natural language processing tasks can be described via natural language instructions. Language models trained on these instructions show strong zero-shot performance on several standard datasets. However, these models even though impressive still perform poorly on a wide range of tasks outside of their respective training and evaluation sets [eg. catastrophic forgetting].

To address this limitation, we argue that a model should be able to keep extending its knowledge and abilities, without forgetting previous skills. In spite of the limited success of Continual Learning we show that Fine-tuned Language Models can be continual learners.

We empirically investigate the reason for this success and conclude that Continual Learning emerges from self-supervision pre-training.

Our resulting model Continual-T0 (CT0) is able to learn diverse new tasks, while still maintaining good performance on previous tasks, spanning remarkably through 70 datasets in total.

Finally, we show that CT0 is able to combine instructions in ways it was never trained for, demonstrating some compositionality.

…6. Conclusion: We explored for the first time Continual Learning for instruction-based models. Our results indicate that fine-tuned Language Models are efficient continual learners: 1% rehearsal is enough to maintain a high performance on previously learned tasks, while learning new ones. Additionally, we show that our model CT0 is able to comprehend new instructions obtained via instruction composition.

The current technique to learn multiple tasks is to train a model from scratch. We hope this work paves the way toward a new paradigm where models do not have to be retrained all over again. We believe our experimental findings will contribute to the effectiveness of large language models, enabling them to progressively adapt to new concepts and acquire more and more abilities. As an analogy with Software Development, this could be seen as learning new features. New checkpoints are like new versions of a model. In this context, Continual Learning will help toward the "Call to Build Models Like We Build Open-Source Software".

…Notably, we also maintain the performance for the T0 zero-shot evaluation datasets, even though no rehearsal for those was done, the first of its kind setup for CL (§4).

Our final model, Continual-T0 (CT0) in addition to performing as well as T0 on all the different T0 datasets, can also understand instructions about the newly introduced tasks focused on language generation problems such as writing a haiku, generating empathetic responses in a dialogue, simplifying text, generating a headline with decoding constraints, generating natural language explanations for Natural Language Inference (NLI) tasks, generating a Tweet on a given topic in the style of a given author, or question answering for new domain/concepts such as COVID-19.

…Why are transformer models (like T0) continual learners? Is it because of their multi-task nature or the instruction tuning paradigm? Or does the large scale parameterization of language models contribute to this success? Our experimental analysis show that the easy adaptability and continual learning capabilities actually emerge from pre-training and not the above, including scale (Table 5, §5.1).

…To test this hypothesis, we start from T0 checkpoint, a model trained on 50 datasets. We progressively train it on a sequence of 8 new NLG tasks (see §7.3.1 & Table 2 for description of those tasks) using Continual Learning via rehearsal (r = 1%). We call our final model CT0…In Figure 2, we display the progressive sequential learning on the 8 new tasks. We learn a new task, starting from T0, and add to our rehearsal buffer 1% of the data of the learned task. We observe an improvement progressively for each task, that is our model keeps learning new tasks. At the same time, the performance is preserved for the other tasks, (ie. the Relative gain remains around 1) indicating the success of our CLR method in a sequential learning setup through more than 1,000 gradient steps over 8 different tasks.

Figure 2: Progressive Relative Gain results for CT0 (11B) during the sequential learning (y-axis) vs Number of Training steps (x-axis). The curves for tasks T0, …T7 are displayed respectively at step 0, …, i such that only the first task, Simplification (green and orange) is present at step 0, then HGen (red) etc.

…Our two CT0 models obtain final results very close to their UB, maintaining 99.8% for T0pp and 98.0% for T0_3B. This clearly indicates the efficiency of the CLR method. Notably, no task suffers a decrease in performance more than 2% for T0pp. Table 3 shows how the CT0 model remembers and retains knowledge from tasks trained at very early stages of the Continual Learning process. Moreover, CT0 still performs well on the zero-shot set of tasks (T0zs) despite no rehearsal for those.

…5.1 Why could LLMs be lifelong learners? Given our current experimental protocol, one can draw different hypotheses: is CL a consequence emerging from the massive multi-task pre-training in T0? Or from the instruction tuning paradigm of T0? Or from the scaling law as studied by Ramasesh et al 2022? To answer this research question, we applied the same CL setup starting from (1) T5-small, (2) T5-3B, and (3) a T5-3B architecture randomly initialized. Our results in Table 5 show that CT5 with 3b parameters performs similar to CT03B on the 8 tasks. While CT5-small obtains as expected a lower average performance, it still mostly maintains great results w.r.t. its Upper Bound, indicating that CL does not emerge from scale. Conversely, when initialized randomly the model is not even able to obtain a good UB. These results draw a clear conclusions: CL emerges from the intensive pre-training stage. This confirms contemporaneous findings by Cossu et al 2022 and Mehta et al 2021 in other setups and even modalities. We report the detailed results for those experiments in the Appendix.

Table 5: Results including T5-small and T5-3B, T0_3B, and a 3B Transformer randomly initialized. We can observe that (1) only CTrand largely degrades w.r.t. its UB, UB_rand; (2) even T5_small is able to mostly maintain its performance indicating that scale is not what matter the most.
--- https://www.astralcodexten.com/p/perhaps-it-is-a-bad-thing-that-the 2022-05-27 ai/nn/transformer/gpt/non-fiction cs/security reinforcement-learning/safe --- https://arxiv.org/abs/2112.09153 An Empirical Investigation of the Role of Pre-training in Lifelong Learning Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, Emma Strubell 2021-12-16 2022-05-27 [("doi","10.48550/arXiv.2112.09153")] ai/nn/cnn ai/nn/transformer ai/scaling reinforcement-learning/meta-learning/continual-learning

The lifelong learning paradigm in machine learning is an attractive alternative to the more prominent isolated learning scheme not only due to its resemblance to biological learning, but also its potential to reduce energy waste by obviating excessive model re-training. A key challenge to this paradigm is the phenomenon of catastrophic forgetting.

With the increasing popularity and success of pre-trained models in machine learning, we pose the question: What role does pre-training play in lifelong learning, specifically with respect to catastrophic forgetting? We investigate existing methods in the context of large, pre-trained models and evaluate their performance on a variety of text and image classification tasks, including a large-scale study using a novel dataset of 15 diverse NLP tasks.

Across all settings, we observe that generic pre-training implicitly alleviates the effects of catastrophic forgetting when learning multiple tasks sequentially compared to randomly initialized models.

We then further investigate why pre-training alleviates forgetting in this setting. We study this phenomenon by analyzing the loss landscape, finding that pre-trained weights appear to ease forgetting by leading to wider minima. Based on this insight, we propose jointly optimizing for current task loss and loss basin sharpness in order to explicitly encourage wider basins during sequential fine-tuning.

We show that this optimization approach leads to performance comparable to the state-of-the-art in task-sequential continual learning across multiple settings, without retaining a memory that scales in size with the number of tasks.

--- https://en.wikipedia.org/wiki/Convolutional_neural_network Convolutional neural network 2022-05-27 ai/nn/cnn --- https://en.wikipedia.org/wiki/Visual_cortex Visual cortex 2022-05-27 ai/nn/cnn --- https://en.wikipedia.org/wiki/Channel_(digital_image) Channel (digital image) 2022-05-28 ai/nn/cnn --- https://en.wikipedia.org/wiki/Neocognitron Neocognitron 2022-05-28 ai/nn/cnn --- https://en.wikipedia.org/wiki/Convolution Convolution 2022-05-28 ai/nn/cnn --- https://en.wikipedia.org/wiki/Kunihiko_Fukushima Kunihiko Fukushima 2022-05-28 ai/nn/cnn --- https://en.wikipedia.org/wiki/Yann_LeCun Yann LeCun 2022-05-28 ai/nn/cnn --- https://en.wikipedia.org/wiki/Time_delay_neural_network Time delay neural network 2022-05-28 ai/nn/cnn --- https://en.wikipedia.org/wiki/Receptive_field Receptive field 2022-05-28 ai/nn/cnn --- https://en.wikipedia.org/wiki/Translational_symmetry Translational symmetry 2022-05-28 ai/nn/cnn --- https://en.wikipedia.org/wiki/Scale-invariant_feature_transform Scale-invariant feature transform 2022-05-28 ai/nn/cnn --- https://www.reddit.com/r/StableDiffusion/comments/zk8y50/badartist_negative_embedding/ 2022-05-28 ai/anime ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2205.09357 Continual Pre-Training Mitigates Forgetting in Language and Vision Andrea Cossu, Tinne Tuytelaars, Antonio Carta, Lucia Passaro, Vincenzo Lomonaco, Davide Bacciu 2022-05-19 2022-05-28 [("doi","10.48550/arXiv.2205.09357")] ai/nn/cnn ai/nn/transformer ai/scaling reinforcement-learning/meta-learning/continual-learning

Pre-trained models are nowadays a fundamental component of machine learning research. In continual learning, they are commonly used to initialize the model before training on the stream of non-stationary data. However, pre-training is rarely applied during continual learning.

We formalize and investigate the characteristics of the continual pre-training scenario in both language and vision environments, where a model is continually pre-trained on a stream of incoming data and only later fine-tuned to different downstream tasks.

We show that continually pre-trained models are robust against catastrophic forgetting and we provide strong empirical evidence supporting the fact that self-supervised pre-training is more effective in retaining previous knowledge than supervised protocols.

Code is provided at Github.

--- https://arxiv.org/abs/2204.04799#google DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister 2022-04-10 2022-05-29 [("doi","10.48550/arXiv.2204.04799")] ai/nn/transformer reinforcement-learning/meta-learning/continual-learning

Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store past pristine examples for experience replay, which, however, limits their practical value due to privacy and memory constraints. In this work, we present a simple yet effective framework, DualPrompt, which learns a tiny set of parameters, called prompts, to properly instruct a pre-trained model to learn tasks arriving sequentially without buffering past examples.

DualPrompt presents a novel approach to attach complementary prompts to the pre-trained backbone, and then formulates the objective as learning task-invariant and task-specific “instructions”.

With extensive experimental validation, DualPrompt consistently sets state-of-the-art performance under the challenging class-incremental setting. In particular, DualPrompt outperforms recent advanced continual learning methods with relatively large buffer sizes.

We also introduce a more challenging benchmark, Split ImageNet-R, to help generalize rehearsal-free continual learning research.

Source code is available at Github.

--- https://arxiv.org/abs/2210.10209 Exclusive Supermask Subnetwork Training for Continual Learning Prateek Yadav, Mohit Bansal 2022-10-18 2022-10-18 [("doi","10.48550/arXiv.2210.10209")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/meta-learning/continual-learning

Continual Learning (CL) methods mainly focus on avoiding catastrophic forgetting and learning representations that are transferable to new tasks. Recently, Wortsman et al 2020 proposed a CL method, SupSup, which uses a randomly initialized, fixed base network (model) and finds a supermask for each new task that selectively keeps or removes each weight to produce a subnetwork. They prevent forgetting as the network weights are not being updated. Although there is no forgetting, the performance of the supermask is sub-optimal because fixed weights restrict its representational power. Furthermore, there is no accumulation or transfer of knowledge inside the model when new tasks are learned.

Hence, we propose ExSSNeT (Exclusive Supermask SubNEtwork Training), which performs exclusive and non-overlapping subnetwork weight training. This avoids conflicting updates to the shared weights by subsequent tasks to improve performance while still preventing forgetting. Furthermore, we propose a novel KNN-based Knowledge Transfer (KKT) module that dynamically initializes a new task’s mask based on previous tasks for improving knowledge transfer.

We demonstrate that ExSSNeT outperforms SupSup and other strong previous methods on both text classification and vision tasks while preventing forgetting. Moreover, ExSSNeT is particularly advantageous for sparse masks that activate 2–10% of the model parameters, resulting in an average improvement of 8.3% over SupSup. Additionally, ExSSNeT scales to a large number of tasks (100), and our KKT module helps to learn new tasks faster while improving overall performance.

Our code is available at https://github.com/prateeky2806/exessnet.

--- https://arxiv.org/abs/2110.08534#amazon Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora Xisen Jin, Dejiao Zhang, Henghui Zhu, Wei Xiao, Shang-Wen Li, Xiaokai Wei, Andrew Arnold, Xiang Ren 2021-10-16 2022-05-29 [("doi","10.48550/arXiv.2110.08534")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer reinforcement-learning/meta-learning/continual-learning

Pretrained language models (PTLMs) are typically learned over a large, static corpus and further fine-tuned for various downstream tasks. However, when deployed in the real world, a PTLM-based model must deal with data distributions that deviate from what the PTLM was initially trained on. In this paper, we study a lifelong language model pretraining challenge where a PTLM is continually updated so as to adapt to emerging data.

Over a domain-incremental research paper stream and a chronologically-ordered tweet stream, we incrementally pretrain a PTLM with different continual learning algorithms, and keep track of the downstream task performance (after fine-tuning). We evaluate PTLM’s ability to adapt to new corpora while retaining learned knowledge in earlier corpora.

Our experiments show distillation-based approaches to be most effective in retaining downstream performance in earlier domains. The algorithms also improve knowledge transfer, allowing models to achieve better downstream performance over the latest data, and improve temporal generalization when distribution gaps exist between training and evaluation because of time.

We believe our problem formulation, methods, and analysis will inspire future studies towards continual pretraining of language models.

--- https://arxiv.org/abs/2207.09248 Don’t Stop Learning: Towards Continual Learning for the CLIP Model Yuxuan Ding, Lingqiao Liu, Chunna Tian, Jingyuan Yang, Haoxuan Ding 2022-07-19 2022-07-19 [("doi","10.48550/arXiv.2207.09248")] ai/nn/transformer/clip reinforcement-learning/meta-learning/continual-learning

The Contrastive Language-Image Pre-training (CLIP) Model is a recently proposed large-scale pre-train model which attracts increasing attention in the computer vision community. Benefiting from its gigantic image-text training set, the CLIP model has learned outstanding capabilities in zero-shot learning and image-text matching. To boost the recognition performance of CLIP on some target visual concepts, it is often desirable to further update the CLIP model by fine-tuning some classes-of-interest on extra training data. This operation, however, raises an important concern: will the update hurt the zero-shot learning or image-text matching capability of the CLIP, ie. the catastrophic forgetting issue? If yes, could existing continual learning algorithms be adapted to alleviate the risk of catastrophic forgetting?

To answer these questions, this work conducts a systemic study on the continual learning issue of the CLIP model. We construct evaluation protocols to measure the impact of fine-tuning updates and explore different ways to upgrade existing continual learning methods to mitigate the forgetting issue of the CLIP model.

Our study reveals the particular challenges of CLIP continual learning problem and lays a foundation for further researches. Moreover, we propose a new algorithm, dubbed Learning without Forgetting via Replayed Vocabulary (VR-LwF), which shows exact effectiveness for alleviating the forgetting issue of the CLIP model.

--- https://arxiv.org/abs/2205.00329 Continual Learning with Foundation Models: An Empirical Study of Latent Replay Oleksiy Ostapenko, Timothee Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish, Laurent Charlin 2022-04-30 2022-05-29 [("doi","10.48550/arXiv.2205.00329")] ai/scaling reinforcement-learning/meta-learning/continual-learning

Rapid development of large-scale pre-training has resulted in foundation models that can act as effective feature extractors on a variety of downstream tasks and domains. Motivated by this, we study the efficacy of pre-trained vision models as a foundation for downstream continual learning (CL) scenarios. Our goal is twofold. First, we want to understand the compute-accuracy trade-off between CL in the raw-data space and in the latent space of pre-trained encoders. Second, we investigate how the characteristics of the encoder, the pre-training algorithm and data, as well as of the resulting latent space affect CL performance.

For this, we compare the efficacy of various pre-trained models in large-scale benchmarking scenarios with a vanilla replay setting applied in the latent and in the raw-data space. Notably, this study shows how transfer, forgetting, task similarity, and learning are dependent on the input data characteristics and not necessarily on the CL algorithms.

First, we show that under some circumstances reasonable CL performance can readily be achieved with a non-parametric classifier at negligible compute. We then show how models pre-trained on broader data result in better performance for various replay sizes. We explain this with representational similarity and transfer properties of these representations.

Finally, we show the effectiveness of self-supervised pre-training for downstream domains that are out-of-distribution as compared to the pre-training domain. We point out and validate several research directions that can further increase the efficacy of latent CL including representation ensembling.

The diverse set of datasets used in this study can serve as a compute-efficient playground for further CL research. The codebase is available under Github.

--- https://en.wikipedia.org/wiki/AlexNet AlexNet 2022-05-29 ai/nn/cnn --- https://arxiv.org/abs/1102.0183#schmidhuber DanNet: Flexible, High Performance Convolutional Neural Networks for Image Classification Dan Claudiu Ciresan, Ueli Meier, Jonathan Masci, Luca Maria Gambardella, Jürgen Schmidhuber 2011-02-01 2022-05-29 ai/nn/cnn ai/scaling/hardware

[followup: Cireşan et al 2012a/b/c] We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way.

Our deep hierarchical architectures achieve the best published results on benchmarks for object classification (NORB, CIFAR-10) and handwritten digit recognition (MNIST), with error rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple back-propagation perform better than more shallow ones.

Learning is surprisingly rapid. NORB is completely trained within 5 epochs. Test error rates on MNIST drop to 2.42%, 0.97% and 0.48% after 1, 3 and 17 epochs, respectively.

[Prior to AlexNet; retrospective] --- /doc/ai/nn/fully-connected/2021-arora.html Is MLP-Mixer a CNN in disguise? As part of this blog post, we look at the MLP Mixer architecture in detail and also understand why it is not considered convolution free. 2021 2022-05-29 ai/nn/cnn ai/nn/fully-connected --- /doc/ai/nn/cnn/2018-haenssle.pdf Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists Holger A. Haenssle, Christine Fink, Roland Schneiderbauer, Ferdin, Toberer, Timo Buhl, Alan Blum, Aadi Kalloo, Abdulkadir Hassen, Litha M. Thomas, Alexander H. Enk, Lorenz Uhlmann 2018-01-01 2022-05-29 [("doi","10.1093/annonc/mdy166")] ai/nn/cnn --- https://en.wikipedia.org/wiki/Visual_temporal_attention Visual temporal attention 2022-05-29 ai/nn/cnn --- https://arxiv.org/abs/1611.03530#google Understanding deep learning requires rethinking generalization Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals 2016-11-10 2022-05-29 [("doi","10.48550/arXiv.1611.03530")] ai/nn/cnn ai/scaling

Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family, or to the regularization techniques used during training.

Through extensive systematic experiments, we show how these traditional approaches fail to explain why large neural networks generalize well in practice. Specifically, our experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization, and occurs even if we replace the true images by completely unstructured random noise. We corroborate these experimental findings with a theoretical construction showing that simple depth two neural networks already have perfect finite sample expressivity as soon as the number of parameters exceeds the number of data points as it usually does in practice.

We interpret our experimental findings by comparison with traditional models.

--- https://x.com/joshua_saxe/status/1602324297648939008 2022-05-30 ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/2212.06013 The Stable Artist: Steering Semantics in Diffusion Latent Space Manuel Brack, Patrick Schramowski, Felix Friedrich, Dominik Hintersdorf, Kristian Kersting 2022-12-12 2022-12-12 [("doi","10.48550/arXiv.2212.06013")] ai/nn/diffusion

Large, text-conditioned generative diffusion models have recently gained a lot of attention for their impressive performance in generating high-fidelity images from text alone. However, achieving high-quality results is almost unfeasible in a one-shot fashion. On the contrary, text-guided image generation involves the user making many slight changes to inputs in order to iteratively carve out the envisioned image. However, slight changes to the input prompt often lead to entirely different images being generated, and thus the control of the artist is limited in its granularity.

To provide flexibility, we present the Stable Artist, an image editing approach enabling fine-grained control of the image generation process. The main component is semantic guidance (SEGA) which steers the diffusion process along variable numbers of semantic directions. This allows for subtle edits to images, changes in composition and style, as well as optimization of the overall artistic conception. Furthermore, SEGA enables probing of latent spaces to gain insights into the representation of concepts learned by the model, even complex ones such as ‘carbon emission’.

We demonstrate the Stable Artist on several tasks, showcasing high-quality image editing and composition.

--- https://arxiv.org/abs/2212.05199#google MAGVIT: Masked Generative Video Transformer Lijun Yu, Yong Cheng, Kihyuk Sohn, José Lezama, Han Zhang, Huiwen Chang, Alexander G. Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, Lu Jiang 2022-12-10 2022-12-10 [("doi","10.48550/arXiv.2212.05199")] ai/nn/transformer ai/nn/vae/mae ai/video/generation

We introduce the MAsked Generative VIdeo Transformer (MAGVIT), to tackle various video synthesis tasks with a single model.

We introduce a 3D tokenizer to quantize a video into spatial-temporal visual tokens and propose an embedding method for masked video token modeling [using BERT] to facilitate multi-task learning.

We conduct extensive experiments to demonstrate the quality, efficiency, and flexibility of MAGVIT. Our experiments show that (1) MAGVIT performs favorably against state-of-the-art approaches and establishes the best-published FVD on 3 video generation benchmarks, including the challenging Kinetics-600. (2) MAGVIT outperforms existing methods in inference time by two orders of magnitude against diffusion models and by 60× against autoregressive models. (3) A single MAGVIT model supports 10 diverse generation tasks and generalizes across videos from different visual domains.

The source code and trained models will be released to the public at https://magvit.cs.cmu.edu/ [Github].

--- https://arxiv.org/abs/2212.06138#microsoft CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet Xiaoyi Dong, Jianmin Bao, Ting Zhang, Dongdong Chen, Shuyang Gu, Weiming Zhang, Lu Yuan, Dong Chen, Fang Wen, Nenghai Yu 2022-12-12 2022-12-12 [("doi","10.48550/arXiv.2212.06138")] ai/nn/transformer/clip

Recent studies have shown that CLIP has achieved remarkable success in performing zero-shot inference while its fine-tuning performance is not satisfactory.

In this paper, we identify that fine-tuning performance is impacted by hyper-parameter choices. We examine various key hyper-parameters and empirically evaluate their impact in fine-tuning CLIP for classification tasks through a comprehensive study.

We find that the fine-tuning performance of CLIP is substantially underestimated. Equipped with hyper-parameter refinement, we demonstrate CLIP itself is better or at least competitive in fine-tuning compared with large-scale supervised pre-training approaches or latest works that use CLIP as prediction targets in Masked Image Modeling. Specifically, CLIP ViT-Base/16 and CLIP ViT-Large/14 can achieve 85.7%,88.0% finetuning Top-1 accuracy on the ImageNet-1K dataset.

Figure 1: Overview—we show the components changed to improve the CLIP fine-tuning performance. With a proper fine-tuning strategy, the CLIP model gets a comparable fine-tuning performance with the model supervised pre-trained on JFT-3B. The “fine-tuning cost” denotes the GPU hours calculated with a single V100.

These observations challenge the conventional conclusion that CLIP is not suitable for fine-tuning, and motivate us to rethink recently proposed improvements based on CLIP.

We will release our code publicly at https://github.com/LightDXY/FT-CLIP.

--- https://arxiv.org/abs/1808.01340#deepmind A Short Note about Kinetics-600 Joao Carreira, Eric Noland, Andras Banki-Horvath, Chloe Hillier, Andrew Zisserman 2018-08-03 2022-05-30 [("doi","10.48550/arXiv.1808.01340")] ai/dataset ai/video/analysis

We describe an extension of the DeepMind Kinetics human action dataset from 400 classes, each with at least 400 video clips, to 600 classes, each with at least 600 video clips (Kinetics-600).

In order to scale up the dataset we changed the data collection process so it uses multiple queries per class, with some of them in a language other than English—Portuguese.

This paper details the changes between the two versions of the dataset and includes a comprehensive set of statistics of the new version as well as baseline results using the I3D neural network architecture.

The paper is a companion to the release of the ground truth labels for the public test set.

--- https://arxiv.org/abs/1202.2745#schmidhuber Multi-column Deep Neural Networks for Image Classification Dan Cireşan, Ueli Meier, Juergen Schmidhuber 2012-02-13 2022-05-30 [("doi","10.48550/arXiv.1202.2745")] ai/nn/cnn ai/scaling/hardware

Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs.

Our biologically plausible deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged.

GPUs allow for fast training.

On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two.

We also improve the state-of-the-art on a plethora of common image classification benchmarks.

--- https://www.reddit.com/r/GPT3/comments/zb4msc/speaking_to_chatgpt_in_perfect_danish_while_it/ 2022-05-30 ai/nn/transformer/gpt/non-fiction cs/security --- https://arxiv.org/abs/1110.1556 Jewish Problems Tanya Khovanova, Alexey Radul 2011-10-07 2022-05-30 [("doi","10.48550/arXiv.1110.1556")] history math

This is a special collection of problems that were given to select applicants during oral entrance exams to the math department of Moscow State University.

These problems were designed to prevent Jews and other undesirables from getting a passing grade. Among problems that were used by the department to blackball unwanted candidate students, these problems are distinguished by having a simple solution that is difficult to find. Using problems with a simple solution protected the administration from extra complaints and appeals.

This collection therefore has mathematical as well as historical value.

--- https://x.com/Jer_Diamond/status/1600231224433577984 2022-05-30 ai/nn/transformer/gpt/fiction --- https://x.com/rexsalisbury/status/1600223899756548096 2022-05-30 ai/nn/transformer/gpt/fiction --- https://www.theatlantic.com/ideas/archive/2022/11/us-housing-gap-cost-affordability-big-cities/672184/ 2022-05-30 economics/georgism --- https://x.com/pulkitology/status/1602707244470177792 2022-05-31 reinforcement-learning/robot --- https://www.lesswrong.com/posts/u3SueTC44tgKFMMNs/is-the-chatgpt-simulated-linux-virtual-machine-real?commentId=iCAiCah33bBNJqNQE 2022-05-31 ai/nn/transformer/gpt/codex --- https://en.wikipedia.org/wiki/ZW_sex-determination_system ZW sex-determination system 2022-05-31 genetics/heritable --- https://x.com/besttrousers/status/1602475413657292800 2022-05-31 ai/nn/transformer/gpt/fiction --- https://www.experimental-history.com/p/the-rise-and-fall-of-peer-review Adam Mastroianni 2022-05-31 statistics/peer-review --- https://arxiv.org/abs/1805.04687 BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, Trevor Darrell 2018-05-12 2022-05-31 [("doi","10.48550/arXiv.1805.04687")] ai/dataset ai/nn/cnn ai/video/analysis

Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities.

We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions.

Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks.

BDD100K opens the door for future studies in this important venue.

--- https://stephanheijl.com/rfdiffusion.html 2022-05-31 ai/nn/transformer/alphafold --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313413/ Spiking Neural Networks and Their Applications: A Review Kashu Yamazaki, Viet-Khoa Vo-Ho, Darshan Bulsara, Ngan Le 2022 2022-05-31 [("doi","10.3390/brainsci12070863")] ai/nn/sparsity ai/scaling/hardware psychology/neuroscience

The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs. With the recent increasing need for the autonomy of machines in the real world, eg. self-driving vehicles, drones, and collaborative robots, exploitation of deep neural networks in those applications has been actively investigated. In those applications, energy and computational efficiencies are especially important because of the need for real-time responses and the limited energy supply.

A promising solution to these previously infeasible applications has recently been given by biologically plausible spiking neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically realistic models of neurons to carry out the computation. Due to their functional similarity to the biological neural network, spiking neural networks can embrace the sparsity found in biology and are highly compatible with temporal code.

Our contributions in this work are: (1) we give a comprehensive review of theories of biological neurons; (2) we present various existing spike-based neuron models, which have been studied in neuroscience; (3) we detail synapse models; (4) we provide a review of artificial neural networks; (5) we provide detailed guidance on how to train spike-based neuron models; (6) we revise available spike-based neuron frameworks that have been developed to support implementing spiking neural networks; (7) finally, we cover existing spiking neural network applications in computer vision and robotics domains.

The paper concludes with discussions of future perspectives.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5554527/ The Structure and Measurement of Unusual Sensory Experiences in Different Modalities: The Multi-Modality Unusual Sensory Experiences Questionnaire (MUSEQ) Claire A. A. Mitchell, Murray T. Maybery, Suzanna N. Russell-Smith, Daniel Collerton, Gilles E. Gignac, Flavie Waters 2017 2022-05-31 [("doi","10.3389/fpsyg.2017.01363")] psychiatry/schizophrenia

Hallucinations and other unusual sensory experiences (USE) can occur in all modalities in the general population. Yet, the existing literature is dominated by investigations into auditory hallucinations (“voices”), while other modalities remain under-researched. Furthermore, there is a paucity of measures which can systematically assess different modalities, which limits our ability to detect individual and group differences across modalities.

The current study explored such differences using a new scale, the Multi-Modality Unusual Sensory Experiences Questionnaire (MUSEQ). The MUSEQ is a 43-item self-report measure which assesses USE in 6 modalities: auditory, visual, olfactory, gustatory, bodily sensations, and sensed presence.

Scale development and validation involved a total of 1,300 participants, which included: 513 students and community members for initial development, 32 individuals with schizophrenia spectrum disorder or bipolar disorder for validation, 659 students for factor replication, and 96 students for test-retest reliability.

Confirmatory factor analyses showed that a correlated-factors model and bifactor model yielded acceptable model fit, while a unidimensional model fitted poorly. These findings were confirmed in the replication sample.

Results showed contributions from a general common factor, as well as modality-specific factors. The latter accounted for less variance than the general factor, but could still detect theoretically meaningful group differences.

The MUSEQ showed good reliability, construct validity, and could discriminate non-clinical and clinical groups.

The MUSEQ offers a reliable means of measuring hallucinations and other USE in 6 different modalities.

--- https://arxiv.org/abs/2212.06727 What do Vision Transformers Learn? A Visual Exploration Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein 2022-12-13 2022-12-13 [("doi","10.48550/arXiv.2212.06727")] ai/nn/cnn ai/nn/transformer

Vision transformers (ViTs) are quickly becoming the de-facto architecture for computer vision, yet we understand very little about why they work and what they learn. While existing studies visually analyze the mechanisms of convolutional neural networks, an analogous exploration of ViTs remains challenging.

In this paper, we first address the obstacles to performing visualizations on ViTs. Assisted by these solutions, we observe that neurons in ViTs trained with language model supervision (eg. CLIP) are activated by semantic concepts rather than visual features. We also explore the underlying differences between ViTs and CNNs, and we find that transformers detect image background features, just like their convolutional counterparts, but their predictions depend far less on high-frequency information. On the other hand, both architecture types behave similarly in the way features progress from abstract patterns in early layers to concrete objects in late layers.

In addition, we show that ViTs maintain spatial information in all layers except the final layer. In contrast to previous works, we show that the last layer most likely discards the spatial information and behaves as a learned global pooling operation.

Finally, we conduct large-scale visualizations on a wide range of ViT variants, including DeiT, CoaT, ConViT, PiT, Swin, and Twin, to validate the effectiveness of our method.

--- https://arxiv.org/abs/2212.06742#baidu ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages Yekun Chai, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu 2022-12-13 2022-12-13 [("doi","10.48550/arXiv.2212.06742")] ai/nn/transformer/gpt/codex ai/nn/transformer/t5 ai/scaling

Software engineers working with the same programming language (PL) may speak different natural languages (NLs) and vice versa, erecting huge barriers to communication and working efficiency. Recent studies have demonstrated the effectiveness of generative pre-training in computer programs, yet they are always English-centric.

In this work, we step towards bridging the gap between multilingual NLs and multilingual PLs for large language models (LLMs). We release ERNIE-Code, a unified pre-trained language model for 116 NLs and 6 PLs.

We employ two methods for universal cross-lingual pre-training: span-corruption language modeling that learns patterns from monolingual NL or PL; and pivot-based translation language modeling that relies on parallel data of many NLs and PLs.

Extensive results show that ERNIE-Code outperforms previous multilingual LLMs for PL or NL across a wide range of end tasks of code intelligence, including multilingual code-to-text, text-to-code, code-to-code, and text-to-text generation. We further show its advantage of zero-shot prompting on multilingual code summarization and text-to-text translation.

We will make our code and pre-trained models publicly available.

--- https://oimo.io/works/life/ 2022-06-01 cs/cellular-automaton --- https://dynomight.substack.com/p/llms 2022-06-01 ai/scaling/economics --- /doc/psychology/personality/psychopathy/2022-gatner-2.pdf An Economic Analysis of Crime Costs Associated with Psychopathic Personality Disorder and Violence Risk Dylan T. Gatner, Kevin S. Douglas, Madison F. E. Almond, Stephen D. Hart, P. Randall Kropp 2022-12-13 2022-12-13 [("doi","10.1177/00938548221140366")] crime economics psychology/personality/psychopathy

Given substantial national crime costs and that psychopathic personality disorder (PPD) is a robust predictor of recidivism, a research gap exists concerning the cost of crime attributable to adults with PPD.

The current study employed a bottom-up cost of illness approach to estimate the association between PPD and crime costs among Canadian men incarcerated in the federal correctional system (n = 188). Participants were rated using the Psychopathy Checklist–Revised (PCL-R) and the Historical-Clinical-Risk Management–20 (HCR-20, version 2).

Group mean crime costs were highest for participants who scored highly on the PCL-R and were rated high risk on the HCR-20, and higher scores on both measures were associated with prospective costs accrued from violent and nonviolent recidivism.

The findings highlight the need to improve the treatment and management of high-risk individuals with prominent psychopathic features, as it has the potential for large financial savings for criminal justice systems.

--- https://www.fda.gov/news-events/press-announcements/fda-approves-first-fecal-microbiota-product 2022-06-01 genetics/microbiome --- https://arxiv.org/abs/2206.01878 Remote Collaboration Fuses Fewer Breakthrough Ideas Yiling Lin, Carl Benedikt Frey, Lingfei Wu 2022-06-04 2022-06-04 [("doi","10.48550/arXiv.2206.01878")] science sociology/technology

Scientists and inventors around the world are more plentiful and interconnected today than ever before. But while there are more people making discoveries, and more ideas that can be reconfigured in novel ways, research suggests that new ideas are getting harder to find-contradicting recombinant growth theory. In this paper, we shed new light on this apparent puzzle.

Analyzing 20 million research articles and 4 million patent applications across the globe over the past half-century, we begin by documenting the rise of remote collaboration across locations, underlining the growing interconnectedness of scientists and inventors globally.

However, we also show that for all fields, periods, and team sizes, researchers in these distributed teams are consistently less likely to make breakthrough discoveries relative to their onsite counterparts. Using a novel dataset that allows us to explore the division of labor within each team, we find that distributed team members tend to collaborate in technical tasks-like collecting and analyzing data-but are less likely to join forces in conceptual tasks, such as conceiving new ideas and designing research.

Hence, while remote teams collaborate in theory, actual cooperation centers on late-stage, technical project tasks, involving more codified knowledge.

We conclude that despite striking improvements in remote work technology in recent years, remote teams are less likely to integrate existing knowledge to produce new, disruptive ideas. This also provides an explanation for why new ideas are getting harder to find.

[Potentially totally misinterpreted? According to Nick Bloom:

This is a great paper, but it is massively misunderstood. It does not show that WFH reduces innovation. This paper and its follow-up actually suggests by 2023 that WFH increases innovation. To explain:

  1. This paper is about collocation not WFH. They are completely different. If two people share the same office address—even if they are both hybrid or remote—they would be classified as collocated. For example, my co-author Steve Davis and I both work a hybrid schedule at Stanford, coming to the office maybe 2 days a week. We both have a Stanford office address on papers. So, for this paper we would count as being collocated. Indeed, almost all hybrid WFH teams would count as collocated.

  2. Co-author teams are becoming more dispersed. The paper reports the average distance between team members offices has increased from 100km to 1,000km over the last 60 years. This reveals that tens of thousands of scientists are choosing to work in more global teams, presumably to access a wider network of experts. I co-author with researchers across 5 continents because it lets me work with great people. If teams of elite scientists, whose entire careers are focused on cutting-edge innovation, are becoming more dispersed it suggests it has major benefits.

  3. The results do not hold after 2010. This is critical as Zoom and Dropbox cloud file sharing emerged after 2010. Indeed the follow-up paper by Carl Frey (one of the co-authors) and Giorgio President from Oxford shows innovation is higher for remote collaboration after 2010!

…And please forward this to any mangers or CEOs claiming this implies WFH reduces innovation. It does not—indeed #2 & #3 suggest exactly the reverse.]

--- https://en.wikipedia.org/wiki/Category:People_with_bipolar_disorder Category:People with bipolar disorder 2022-06-01 psychiatry/bipolar --- https://qualiacomputing.com/2022/12/13/magical-creatures-explore-the-state-space-of-consciousness-with-qris-first-line-of-scents/ 2022-06-01 psychology/smell/human --- https://arxiv.org/abs/2211.15661#google What learning algorithm is in-context learning? Investigations with linear models Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, Denny Zhou 2022-11-28 2022-11-28 [("doi","10.48550/arXiv.2211.15661")] ai/nn/transformer/attention reinforcement-learning/meta-learning statistics/bayes

[cf. Xie et al 2021, Min et al 2022] Neural sequence models, especially transformers, exhibit a remarkable capacity for in-context learning. They can construct new predictors from sequences of labeled examples (x, f(10)) presented in the input without further parameter updates.

We investigate the hypothesis that transformer-based in-context learners implement standard learning algorithms implicitly, by encoding smaller models in their activations, and updating these implicit models as new examples appear in the context.

Using linear regression as a prototypical problem, we offer 3 sources of evidence for this hypothesis. First, we prove by construction that transformers can implement learning algorithms for linear models based on gradient descent and closed-form ridge regression. Second, we show that trained in-context learners closely match the predictors computed by gradient descent, ridge regression, and exact least-squares regression, transitioning between different predictors as transformer depth and dataset noise vary, and converging to Bayesian estimators for large widths and depths. Third, we present preliminary evidence that in-context learners share algorithmic features with these predictors: learners’ late layers non-linearly encode weight vectors and moment matrices.

These results suggest that in-context learning is understandable in algorithmic terms, and that (at least in the linear case) learners may rediscover standard estimation algorithms.

Code and reference implementations are released at Github.

--- https://arxiv.org/abs/2110.08143 MSMT-GAN: Multi-Tailed, Multi-Headed, Spatial Dynamic Memory refined Text-to-Image Synthesis Amrit Diggavi Seshadri, Balaraman Ravindran 2021-10-15 2022-06-01 [("doi","10.48550/arXiv.2110.08143")] ai/nn/gan

Synthesizing high-quality, realistic images from text-descriptions is a challenging task, and current methods synthesize images from text in a multi-stage manner, typically by first generating a rough initial image and then refining image details at subsequent stages.

However, existing methods that follow this paradigm suffer from 3 important limitations. Firstly, they synthesize initial images without attempting to separate image attributes at a word-level. As a result, object attributes of initial images (that provide a basis for subsequent refinement) are inherently entangled and ambiguous in nature. Secondly, by using common text-representations for all regions, current methods prevent us from interpreting text in fundamentally different ways at different parts of an image. Different image regions are therefore only allowed to assimilate the same type of information from text at each refinement stage. Finally, current methods generate refinement features only once at each refinement stage and attempt to address all image aspects in a single shot. This single-shot refinement limits the precision with which each refinement stage can learn to improve the prior image.

Our proposed method introduces 3 novel components to address these shortcomings: (1) An initial generation stage that explicitly generates separate sets of image features for each word n-gram. (2) A spatial dynamic memory module for refinement of images. (3) An iterative multi-headed mechanism to make it easier to improve upon multiple image aspects.

Experimental results demonstrate that our Multi-Headed Spatial Dynamic Memory image refinement with our Multi-Tailed Word-level Initial Generation (MSMT-GAN) performs favourably against the previous state-of-the-art on the CUB and COCO datasets.

--- https://arxiv.org/abs/2212.01375 Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula Eli Bronstein, Sirish Srinivasan, Supratik Paul, Aman Sinha, Matthew O’Kelly, Payam Nikdel, Shimon Whiteson 2022-12-02 2022-12-02 [("doi","10.48550/arXiv.2212.01375")] reinforcement-learning/exploration/active-learning reinforcement-learning/model-free reinforcement-learning/robot reinforcement-learning/safe

ML-based motion planning is a promising approach to produce agents that exhibit complex behaviors, and automatically adapt to novel environments. In the context of autonomous driving, it is common to treat all available training data equally. However, this approach produces agents that do not perform robustly in safety-critical settings, an issue that cannot be addressed by simply adding more data to the training set:

we show that an agent trained using only a 10% subset of the data performs just as well as an agent trained on the entire dataset.

We present a method to predict the inherent difficulty of a driving situation given data collected from a fleet of autonomous vehicles deployed on public roads. We then demonstrate that this difficulty score can be used in a zero-shot transfer to generate curricula for an imitation-learning based planning agent.

Compared to training on the entire unbiased training dataset, we show that prioritizing difficult driving scenarios both reduces collisions by 15% and increases route adherence by 14% in closed-loop evaluation, all while using only 10% of the training data.

--- https://arxiv.org/abs/1604.03540 OHEM: Training Region-based Object Detectors with Online Hard Example Mining Abhinav Shrivastava, Abhinav Gupta, Ross Girshick 2016-04-12 2022-06-02 [("doi","10.48550/arXiv.1604.03540")] ai/nn/cnn reinforcement-learning/exploration/active-learning

The field of object detection has made advances riding on the wave of region-based ConvNets, but their training procedure still includes many heuristics and hyperparameters that are costly to tune.

We present a simple yet surprisingly effective online hard example mining (OHEM) algorithm for training region-based ConvNet detectors. Our motivation is the same as it has always been—detection datasets contain an overwhelming number of easy examples and a small number of hard examples. Automatic selection of these hard examples can make training more effective and efficient. OHEM is a simple and intuitive algorithm that eliminates several heuristics and hyperparameters in common use.

But more importantly, it yields consistent and boosts in detection performance on benchmarks like PASCAL VOC 2007 and 2012. Its effectiveness increases as datasets become larger and more difficult, as demonstrated by the results on the MS COCO dataset. Moreover, combined with complementary advances in the field, OHEM leads to state-of-the-art results of 78.9% and 76.3% mAP on PASCAL VOC 2007 and 2012 respectively.

--- https://github.com/elsehow/aaronson-oracle 2022-06-02 philosophy/mind psychology/cognitive-bias statistics/probability --- https://x.com/FelixHill84/status/1603227632329699328 2022-06-02 ai/nn/transformer/gpt/fiction --- https://brr.fyi/posts/doors-of-mcmurdo 2022-06-02 design --- https://x.com/pythonprimes/status/1601965894682427394 2022-06-02 ai/nn/transformer/gpt/non-fiction --- https://x.com/anderssandberg/status/1603785612351389700 2022-06-02 technology/self-sinking --- https://tvtropes.org/pmwiki/pmwiki.php/Main/HurlItIntoTheSun 2022-06-02 fiction technology/self-sinking --- https://en.wikipedia.org/wiki/Machiavellianism_(psychology) Machiavellianism (psychology) 2022-06-02 politics psychology/personality/narcissism psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Narcissism Narcissism 2022-06-02 psychology/personality/narcissism psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Psychopathy Psychopathy 2022-06-02 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Impulsivity Impulsivity 2022-06-02 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Callous_and_unemotional_traits Callous and unemotional traits 2022-06-03 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Remorse#Psychopathy Remorselessness 2022-06-03 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/History_of_psychopathy History of psychopathy 2022-06-03 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Troll_(slang) Internet troll 2022-06-03 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Sadomasochism Sadomasochism 2022-06-03 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Chicken_(game) Chicken (game) 2022-06-03 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Kim_Yo-jong Kim Yo-jong 2022-06-03 psychology/personality/psychopathy --- https://www.nytimes.com/2012/05/13/magazine/can-you-call-a-9-year-old-a-psychopath.html 2022-06-03 psychology/personality/psychopathy --- /doc/genetics/heritable/correlation/2018-lewis.pdf A Behavioral Genetic Analysis of the Co-occurrence Between Psychopathic Personality Traits and Criminal Behavior Richard H. Lewis, Eric J. Connolly, Danielle L. Boisvert, Brian B. Boutwell 2018-01-01 2022-06-03 [("doi","10.1177/1043986218817009")] genetics/heritable/correlation psychology/personality/psychopathy

A developed line of research has found that psychopathic personality traits and criminal behavior are correlated with one another. Although there is little question about the association between psychopathic personality traits and criminal behavior, what remains less clear is whether psychopathic traits exert a direct effect on criminal behavior. An alternative possibility is that previously unmeasured genetic and shared environmental factors account for much of the association between the two.

Understanding the extent to which genetic and environmental factors influence the covariance between psychopathic personality traits and criminal behavior can further our understanding of individual differences in propensity to engage in antisocial behavior.

The current study analyzes 872 twins (MZ twins = 352, DZ twins = 520) from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to examine the magnitude of genetic and environmental effects on the covariation between psychopathic personality and criminal behavior.

Results from bivariate behavioral genetic analyses revealed that the correlation between psychopathic personality traits and criminal behavior was accounted for by common additive genetic (58%) and nonshared environmental (42%) influences. Fixed-effect linear regression models, however, suggested that psychopathic personality traits were not statistically-significantly associated with criminal behavior once common genetic and environmental influences were taken into account.

--- https://www.lesswrong.com/posts/ktr39MFWpTqmzuKxQ/notes-on-psychopathy 2022-06-03 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Hans_Eysenck Hans Eysenck 2022-06-04 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Dark_Triad_Dirty_Dozen Dark Triad Dirty Dozen 2022-06-04 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Guilt_(emotion)#Lack_of_guilt_in_psychopaths Guilt (emotion) § Lack of guilt in psychopaths 2022-06-04 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Manipulation_(psychology) Manipulation (psychology) 2022-06-04 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Psychopathy_in_the_workplace Psychopathy in the workplace 2022-06-04 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/The_Mask_of_Sanity The Mask of Sanity 2022-06-04 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Influence_of_childhood_trauma_in_psychopathy Influence of childhood trauma in psychopathy 2022-06-04 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Fictional_portrayals_of_psychopaths Fictional portrayals of psychopaths 2022-06-04 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Confidence_trick Con artist 2022-06-04 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Serial_killer Serial killer 2022-06-04 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Hervey_M._Cleckley Hervey M. Cleckley 2022-06-04 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Category:People_with_antisocial_personality_disorder Category:People with antisocial personality disorder 2022-06-05 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Alcibiades Alcibiades 2022-06-05 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Robert_D._Hare Robert D. Hare 2022-06-05 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Antisocial_personality_disorder Antisocial personality disorder 2022-06-05 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Snakes_in_Suits Snakes in Suits: When Psychopaths Go to Work 2022-06-05 psychology/personality/psychopathy --- https://x.com/moyix/status/1603848600253042693 2022-06-05 ai/nn/transformer/gpt/codex --- https://en.wikipedia.org/wiki/Sunk_cost#Fallacy_effect Sunk cost § Fallacy effect 2022-06-05 psychology/cognitive-bias/sunk-cost --- https://datacolada.org/22 2022-06-05 psychology/cognitive-bias --- /doc/genetics/heritable/2014-cronqvist.pdf 2014 2022-06-05 genetics/heritable psychology/cognitive-bias --- https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/differences-in-negativity-bias-underlie-variations-in-political-ideology/72A29464D2FD037B03F7485616929560 2022-06-05 psychology/cognitive-bias --- https://www.motherjones.com/politics/2014/07/biology-ideology-john-hibbing-negativity-bias/ Scientists Are Beginning to Figure Out Why Conservatives Are...Conservative: 10 years ago, it was wildly controversial to talk about psychological differences between liberals and conservatives. Today, it’s becoming hard not to. 2022-06-05 psychology/cognitive-bias --- https://youarenotsosmart.com/2013/05/23/survivorship-bias/ 2022-06-06 psychology/cognitive-bias --- /doc/economics/2013-creditsuisse.pdf Credit Suisse Global Investment Returns Yearbook 2013 Elroy Dimson, Paul Marsh, Mike Staunton, Andrew Garthwaite 2013-02-01 2022-06-06 economics psychology/cognitive-bias statistics/prediction --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.300.2572&rep=rep1&type=pdf 2022-06-06 psychology/cognitive-bias --- https://www.overcomingbias.com/p/biases-of-fictionhtml 2022-06-06 psychology/cognitive-bias --- https://www.econstor.eu/bitstream/10419/113975/1/dp9100.pdf 2022-06-06 psychology/cognitive-bias --- https://www.econlib.org/archives/2015/05/systematically_3.html Systematically Biased Beliefs About Inequality 2022-06-06 psychology/cognitive-bias --- https://www.overcomingbias.com/p/we-add-near-average-farhtml 2022-06-06 psychology/cognitive-bias --- https://web.archive.org/web/20161021040312/https://ftalphaville-cdn.ft.com/wp-content/uploads/2016/04/Alphachatterbox-Chanos-transcript.pdf 2022-06-06 psychology/cognitive-bias --- /banner#discussion 2022-06-06 psychology/cognitive-bias --- /doc/psychology/cognitive-bias/2018-wood.pdf The Elusive Backfire Effect: Mass Attitudes’ Steadfast Factual Adherence Thomas Wood, Ethan Porter 2018-01-01 2022-06-06 [("doi","10.1007/s11109-018-9443-y")] psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Normalcy_bias Normalcy bias 2022-06-07 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Cognitive_inertia Cognitive inertia 2022-06-07 psychology/cognitive-bias/sunk-cost psychology/energy --- https://en.wikipedia.org/wiki/Compassion_fade Compassion fade 2022-06-07 philosophy/ethics --- https://en.wikipedia.org/wiki/Conjunction_fallacy Conjunction fallacy 2022-06-07 psychology/cognitive-bias statistics/probability --- https://en.wikipedia.org/wiki/Zero-sum_thinking Zero-sum thinking 2022-06-07 economics --- https://en.wikipedia.org/wiki/Time-saving_bias Time-saving bias 2022-06-07 math statistics/decision --- https://en.wikipedia.org/wiki/Illusion_of_explanatory_depth Illusion of explanatory depth 2022-06-07 economics --- https://en.wikipedia.org/wiki/Loss_aversion Loss aversion 2022-06-07 economics psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Rhyme-as-reason_effect Rhyme as reason effect 2022-06-07 fiction/poetry --- https://en.wikipedia.org/wiki/Curse_of_knowledge Curse of knowledge 2022-06-07 psychology/writing --- https://en.wikipedia.org/wiki/Mere_exposure_effect Mere exposure effect 2022-06-07 culture --- https://en.wikipedia.org/wiki/Proportionality_bias Proportionality bias 2022-06-08 history politics --- https://en.wikipedia.org/wiki/Surrogation Surrogation 2022-06-08 cs/end-to-end-principle economics statistics/decision --- https://en.wikipedia.org/wiki/Women-are-wonderful_effect Women are wonderful effect 2022-06-08 sociology --- https://en.wikipedia.org/wiki/Just-world_hypothesis Just-world hypothesis 2022-06-08 philosophy/ethics philosophy/religion --- https://en.wikipedia.org/wiki/Conformity Conformity 2022-06-08 politics --- https://en.wikipedia.org/wiki/Availability_cascade Availability cascade 2022-06-08 politics --- https://en.wikipedia.org/wiki/Bandwagon_effect Bandwagon effect 2022-06-08 politics --- https://en.wikipedia.org/wiki/Response_bias#Courtesy_bias Courtesy bias 2022-06-08 politics --- https://en.wikipedia.org/wiki/Groupthink Groupthink 2022-06-08 politics --- https://en.wikipedia.org/wiki/Social-desirability_bias Social desirability bias 2022-06-08 politics --- https://en.wikipedia.org/wiki/In-group_favoritism Ingroup bias 2022-06-08 politics --- /doc/philosophy/ethics/2021-decety.pdf Why Empathy Is Not a Reliable Source of Information in Moral Decision Making Jean Decety 2021-09-02 2022-06-09 [("doi","10.1177/09637214211031943")] philosophy/ethics psychology/cognitive-bias

Although empathy drives prosocial behaviors, it is not always a reliable source of information in moral decision making.

In this essay, I integrate evolutionary theory, behavioral economics, psychology, and social neuroscience to demonstrate why and how empathy is unconsciously and rapidly modulated by various social signals and situational factors. This theoretical framework explains why decision making that relies solely on empathy is not ideal and can, at times, erode ethical values.

This perspective has social and societal implications and can be used to reduce cognitive biases and guide moral decisions.

--- https://en.wikipedia.org/wiki/Cognitive_bias Cognitive bias 2022-06-09 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/List_of_cognitive_biases List of cognitive biases 2022-06-09 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Behavioral_economics Behavioral economics 2022-06-09 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Amos_Tversky Amos Tversky 2022-06-09 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Daniel_Kahneman Daniel Kahneman 2022-06-09 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Numeracy#Innumeracy_and_dyscalculia Numeracy § Innumeracy and dyscalculia 2022-06-09 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Gerd_Gigerenzer Gerd Gigerenzer 2022-06-09 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Illusory_correlation Illusory correlation 2022-06-09 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Egocentric_bias Egocentric bias 2022-06-09 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Motivated_reasoning Motivated reasoning 2022-06-09 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Anchor_(cognitive_bias) Anchoring (cognitive bias) 2022-06-10 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Attentional_bias Attentional bias 2022-06-10 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Fundamental_attribution_error Fundamental attribution error 2022-06-10 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Priming_(psychology) Priming (psychology) 2022-06-10 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Confirmation_bias Confirmation bias 2022-06-10 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Framing_effect_(psychology) Framing effect (psychology) 2022-06-10 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Status_quo_bias Status quo bias 2022-06-10 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Overconfidence_effect Overconfidence effect 2022-06-10 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Bounded_rationality Bounded rationality 2022-06-10 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Attribute_substitution Attribute substitution 2022-06-10 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Conservatism_(belief_revision) Conservatism (belief revision) 2022-06-11 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Subadditivity_effect Subadditivity effect 2022-06-11 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Baconian_method#Idols_of_the_mind_(idola_mentis) Baconian method § Idols of the mind (idola mentis) 2022-06-11 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Cognitive_bias_in_animals Cognitive bias in animals 2022-06-11 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Cognitive_traps_for_intelligence_analysis Cognitive traps for intelligence analysis 2022-06-11 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Evolutionary_psychology Evolutionary psychology 2022-06-11 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Priming_(psychology)#Semantic_priming Priming (psychology) § Semantic 2022-06-11 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Motte-and-bailey_fallacy Motte-and-bailey fallacy 2022-06-11 psychology/cognitive-bias --- https://arxiv.org/abs/2004.14601 Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models Isabel Papadimitriou, Dan Jurafsky 2020-04-30 2022-06-11 [("doi","10.48550/arXiv.2004.14601")] ai/music ai/nn/rnn

We propose transfer learning as a method for analyzing the encoding of grammatical structure in neural language models.

We train LSTMs on non-linguistic data and evaluate their performance on natural language to assess which kinds of data induce generalizable structural features that LSTMs can use for natural language.

We find that training on non-linguistic data with latent structure (MIDI music or Java code) improves test performance on natural language, despite no overlap in surface form or vocabulary.

To pinpoint the kinds of abstract structure that models may be encoding to lead to this improvement, we run similar experiments with two artificial parentheses languages: one which has a hierarchical recursive structure, and a control which has paired tokens but no recursion. Surprisingly, training a model on either of these artificial languages leads to the same substantial gains when testing on natural language. Further experiments on transfer between natural languages controlling for vocabulary overlap show that zero-shot performance on a test language is highly correlated with typological syntactic similarity to the training language, suggesting that representations induced by pre-training correspond to the cross-linguistic syntactic properties.

Our results provide insights into the ways that neural models represent abstract syntactic structure, and also about the kind of structural inductive biases which allow for natural language acquisition.

--- https://twobithistory.org/2018/05/27/semantic-web.html 2022-06-11 cs/security design economics --- https://people.well.com/user/doctorow/metacrap.htm 2022-06-11 ai cs/security design economics philosophy/logic --- https://en.wikipedia.org/wiki/Negativity_bias Negativity bias 2022-06-12 psychology/cognitive-bias --- /doc/psychology/cognitive-bias/2021-bartels.pdf The Implicit Association Test in Introductory Psychology Textbooks: Blind Spot for Controversy Jared M. Bartels, Patricia Schoenrade 2021-11-13 2022-06-12 [("doi","10.1177/14757257211055200")] psychology/cognitive-bias statistics/bias

The Implicit Association Test (IAT) has been widely discussed as a potential measure of “implicit bias.” Yet the IAT is controversial; research suggests that it is far from clear precisely what the instrument measures, and it does not appear to be a strong predictor of behavior. The presentation of this topic in Introductory Psychology texts is important as, for many students, it is their first introduction to scientific treatment of such issues.

In the present study, we examined 20 current Introductory Psychology texts in terms of their coverage of the controversy and presentation of the strengths and weaknesses of the measure. Of the 17 texts that discussed the IAT, a minority presented any of the concerns including the lack of measurement clarity (29%), an automatic preference for White people among African Americans (12%), lack of predictive validity (12%), and lack of caution about the meaning of a score (0%); most provided students with a link to the Project Implicit website (65%).

Overall, 82% of the texts were rated as biased or partially biased on their coverage of the IAT. The implications for the perceptions and self-perceptions of students, particularly when a link to Project Implicit is included, are discussed.

--- https://www.sas.upenn.edu/~baron/papers/kdm.pdf Decision-making biases in children and early adolescents: Exploratory studies 2022-06-12 psychology/cognitive-bias --- https://www.lesswrong.com/posts/RdpqsQ6xbHzyckW9m/why-we-can-t-take-expected-value-estimates-literally-even Why We Can’t Take Expected Value Estimates Literally (Even When They’re Unbiased) 2022-06-12 statistics/bayes statistics/decision --- https://www.overcomingbias.com/p/knowing-your-arhtml Knowing your argumentative limitations, OR ‘one [rationalist’s] modus ponens is another’s modus tollens’. 2022-06-12 psychology/cognitive-bias --- https://nickbostrom.com/ethics/statusquo.pdf The Reversal Test: Eliminating Status Quo Bias in Applied Ethics 2022-06-12 psychology/cognitive-bias --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1085.5663 2022-06-12 psychology/cognitive-bias --- /doc/statistics/bias/1968-rosenthal-pygmalionintheclassroom.pdf Pygmalion In The Classroom: Teacher Expectation and Pupil’s Intellectual Development Robert Rosenthal, Lenore Jacobson 1968-01-01 2022-06-12 iq psychology/cognitive-bias statistics/bias --- /doc/statistics/bias/1971-elashoff-pygmalionreconsidered.pdf Pygmalion Reconsidered: A Case Study in Statistical Inference: Reconsideration of the Rosenthal-Jacobson Data on Teacher Expectancy Janet D. Elashoff, Richard E. Snow 1971-01-01 2022-06-12 psychology/cognitive-bias statistics/bias --- /doc/statistics/bias/1989-konold.pdf Informal conceptions of probability 1989 2022-06-12 psychology/cognitive-bias statistics/bayes statistics/bias --- /doc/statistics/bias/1989-diaconis.pdf#page=9 Methods for Studying Coincidences § pg9 Persi Diaconis, Frederick Mosteller 1989-01-01 2022-06-12 [("doi","10.1080/01621459.1989.10478847")] psychology/cognitive-bias statistics/bias --- /doc/culture/2013-kidd.pdf Title: Reading Fiction Improves Theory of Mind and Reduces Intergroup Bias 2013-01-01 2022-06-13 culture psychology/cognitive-bias --- /doc/statistics/bias/2015-simonsohn.pdf Small Telescopes: Detectability and the Evaluation of Replication Results Uri Simonsohn 2015-03-23 2022-06-13 [("doi","10.1177/0956797614567341")] statistics/bias statistics/power-analysis

This article introduces a new approach for evaluating replication results. It combines effect-size estimation with hypothesis testing, assessing the extent to which the replication results are consistent with an effect size big enough to have been detectable in the original study.

The approach is demonstrated by examining replications of 3 well-known findings.

Its benefits include the following: (a) differentiating “unsuccessful” replication attempts (ie. studies yielding p > 0.05) that are too noisy from those that actively indicate the effect is undetectably different from zero, (b) “protecting” true findings from underpowered replications, and (c) arriving at intuitively compelling inferences in general and for the revisited replications in particular.

--- /doc/sociology/2018-winegard.pdf Equalitarianism: A Source of Liberal Bias Bo Winegard, Cory Clark, Connor R. Hasty, Roy Baumeister 2018-01-01 2022-06-13 [("doi","10.2139/ssrn.3175680")] psychology/cognitive-bias sociology --- /doc/history/2019-benyosef.pdf The Architectural Bias in Current Biblical Archaeology Erez Ben-Yosef 2019-07-08 2022-06-13 [("doi","10.1163/15685330-12341370")] history statistics/bias

This paper aims at highlighting a methodological flaw in current biblical archaeology, which became apparent as a result of recent research in the Aravah’s Iron Age copper production centers.

In essence, this flaw, which cuts across all schools of biblical archaeology, is the prevailing, overly simplistic approach applied to the identification and interpretation of nomadic elements in biblical-era societies. These elements have typically been described as representing only one form of social organization, which is simple and almost negligible in historical reconstructions. However, the unique case of the Aravah demonstrates that the role of nomads in shaping the history of the southern Levant has been underestimated and downplayed in the research of the region, and that the total reliance on stone-built archaeological features in the identification of social complexity in the vast majority of recent studies has resulted in skewed historical reconstructions.

Recognizing this “architectural bias” and understanding its sources have important implications on core issues in biblical archaeology today, as both “minimalists” and “maximalists” have been using stone-built architectural remains as the key to solving debated issues related to the geneses of Ancient Israel and neighboring polities (eg. “high” vs. “low” Iron Age chronologies), in which—according to both biblical accounts and external sources—nomadic elements played a major role.

--- /doc/statistics/bias/2020-serragarcia.pdf Can Short Psychological Interventions Affect Educational Performance? Revisiting the Effect of Self-Affirmation Interventions Marta Serra-Garcia, Karsten T. Hansen, Uri Gneezy 2020-07-01 2022-06-13 [("doi","10.1177/0956797620923587")] psychology statistics/bias

Large amounts of resources are spent annually to improve educational achievement and to close the gender gap in sciences with typically very modest effects.

In 2010, a 15-min self-affirmation intervention showed a dramatic reduction in this gender gap. We reanalyzed the original data and found several critical problems. First, the self-affirmation hypothesis stated that women’s performance would improve. However, the data showed no improvement for women.

There was an interaction effect between self-affirmation and gender caused by a negative effect on men’s performance. Second, the findings were based on covariate-adjusted interaction effects, which imply that self-affirmation reduced the gender gap only for the small sample of men and women who did not differ in the covariates. Third, specification-curve analyses with more than 1,500 possible specifications showed that less than one quarter yielded statistically-significant interaction effects and less than 3% showed significant improvements among women.

--- /doc/sociology/2021-zell.pdf It’s their fault: Partisan attribution bias and its association with voting intentions Ethan Zell, Christopher A. Stockus, Michael J. Bernstein 2021-04-01 2022-06-13 [("doi","10.1177/1368430221990084")] psychology/cognitive-bias sociology

This research examined how people explain major outcomes of political consequence (eg. economic growth, rising inequality). We argue that people attribute positive outcomes more and negative outcomes less to their own political party than to an opposing party.

We conducted two studies, one before the 2016 U.S. presidential election (n = 244) and another before the 2020 election (n = 249 registered voters), that examined attributions across a wide array of outcomes.

As predicted, a robust partisan attribution bias emerged in both studies. Although the bias was largely equivalent among Democrats and Republicans, it was magnified among those with more extreme political ideology. Further, the bias predicted unique variance in voting intentions and statistically-significantly mediated the link between political ideology and voting.

In sum, these data suggest that partisan allegiances systemically bias attributions in a group-favoring direction. We discuss implications of these findings for emerging research on political social cognition.

--- https://en.wikipedia.org/wiki/Attribution_bias Attribution bias 2022-06-13 psychology/cognitive-bias --- /doc/psychology/animal/2021-whiten.pdf The burgeoning reach of animal culture Andrew Whiten 2021-04-02 2022-06-13 [("doi","10.1126/science.abe6514")] psychology/animal

Culture can be defined as all that is learned from others and is repeatedly transmitted in this way, forming traditions that may be inherited by successive generations.

This cultural form of inheritance was once thought specific to humans, but research over the past 70 years has instead revealed it to be widespread in nature, permeating the lives of a diversity of animals, including all major classes of vertebrates. Recent studies suggest that culture’s reach may extend also to invertebrates—notably, insects. In the present century, the reach of animal culture has been found to extend across many different behavioral domains and to rest on a suite of social learning processes facilitated by a variety of selective biases that enhance the efficiency and adaptiveness of learning.

Far-reaching implications, for disciplines from evolutionary biology to anthropology and conservation policies, are increasingly being explored.

--- /doc/sociology/2022-hannon.pdf Are knowledgeable voters better voters? Michael Hannon 2022-01-10 2022-06-13 [("doi","10.1177/1470594X211065080")] psychology/cognitive-bias sociology

It is widely believed that democracies require knowledgeable citizens to function well. But the most politically knowledgeable individuals tend to be the most partisan and the strength of partisan identity tends to corrupt political thinking. This creates a conundrum. On the one hand, an informed citizenry is allegedly necessary for a democracy to flourish. On the other hand, the most knowledgeable and passionate voters are also the most likely to think in corrupted, biased ways. What to do?

This paper examines this tension and draws out several lessons.

First, it is not obvious that more knowledgeable voters will make better political decisions. Second, attempts to remedy voter ignorance are problematic because partisans tend to become more polarized when they acquire more information. Third, solutions to citizen incompetence must focus on the intellectual virtue of objectivity. Fourth, some forms of epistocracy are troubling, in part, because they would increase the political power of the most dogmatic and biased individuals. Fifth, a highly restrictive form of epistocracy may escape the problem of political dogmatism, but epistocrats may face a steeper tradeoff between inclusivity and epistemic virtue than they would like.

--- /doc/psychology/animal/1968-wilsson-mybeavercolony.pdf My Beaver Colony Lars Wilsson, Joan Bulman 1968-01-01 2022-06-13 psychology/animal --- /doc/genetics/cloning/1974-werthessen.pdf Pincogenesis—Parthenogenesis in Rabbits by Gregory Pincus N. T. Werthessen, R. C. Johnson 1974-01-01 2022-06-14 [("doi","10.1353/pbm.1974.0003")] genetics/cloning --- /doc/biology/2010-cohen-2.pdf Systematic review: the costs of ulcerative colitis in Western countries 2010 2022-06-14 biology economics --- /doc/genetics/microbiome/2019-suez.pdf The pros, cons, and many unknowns of probiotics Jotham Suez, Niv Zmora, Eran Segal, Eran Elinav 2019-01-01 2022-06-14 [("doi","10.1038/s41591-019-0439-x")] genetics/microbiome --- https://x.com/hillelogram/status/1603604715362693120 2022-06-14 ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/1709.01606 Factoring in the Chicken McNugget monoid Scott Chapman, Christopher O’Neill 2017-09-05 2022-06-14 [("doi","10.48550/arXiv.1709.01606")] math/humor

Every day, 34 million Chicken McNuggets are sold worldwide. At most McDonald’s locations in the United States today, Chicken McNuggets are sold in packs of 4, 6, 10, 20, 40, and 50 pieces. However, shortly after their introduction in 1979 they were sold in packs of 6, 9, and 20. The use of these latter 3 numbers spawned the so-called Chicken McNugget problem, which asks: “what numbers of Chicken McNuggets can be ordered using only packs with 6, 9, or 20 pieces?”

In this paper, we present an accessible introduction to this problem, as well as several related questions whose motivation comes from the theory of non-unique factorization [monoids, monoid factorization].

--- https://x.com/goodside/status/1603794769419055104 2022-06-14 ai/nn/transformer/gpt/non-fiction cs/security --- https://x.com/shesek/status/1603902050504478721 2022-06-14 ai/nn/transformer/gpt/codex --- https://www.bbc.com/news/business-40673694 2022-06-14 economics/automation --- https://medium.com/@Vinylmint/history-of-the-record-industry-1920-1950s-6d491d7cb606 2022-06-14 economics/automation music --- https://arxiv.org/abs/2112.13314 Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow Florian Tambon, Amin Nikanjam, Le An, Foutse Khomh, Giuliano Antoniol 2021-12-26 2022-06-14 [("doi","10.48550/arXiv.2112.13314")] ai/nn cs/algorithm

["neural nets want to work"; cf. Jones/Winters, Humbatova et al 2019] Deep Learning (DL) frameworks are now widely used, simplifying the creation of complex models as well as their integration to various applications even to non DL experts. However, like any other programs, they are prone to bugs. This paper deals with the subcategory of bugs named silent bugs: they lead to wrong behavior but they do not cause system crashes or hangs, nor show an error message to the user. Such bugs are even more dangerous in DL applications and frameworks due to the “black-box” and stochastic nature of the systems (the end user can not understand how the model makes decisions).

This paper presents the first empirical study of Keras and TensorFlow silent bugs, and their impact on users’ programs.

We extracted closed issues related to Keras from the TensorFlow GitHub repository. Out of the 1,168 issues that we gathered, 77 were reproducible silent bugs affecting users’ programs.

We categorized the bugs based on the effects on the users’ programs and the components where the issues occurred, using information from the issue reports. We then derived a threat level for each of the issues, based on the impact they had on the users’ programs. To assess the relevance of identified categories and the impact scale, we conducted an online survey with 103 DL developers. The participants generally agreed with the impact of silent bugs in DL libraries and acknowledged our findings (ie. categories of silent bugs and the proposed impact scale).

Finally, leveraging our analysis, we provide a set of guidelines to facilitate safeguarding against such bugs in DL frameworks.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5022764/ The Intestinal Microbiome in Bariatric Surgery Patients Christine M. Peat, Susan C. Kleiman, Cynthia M. Bulik, Ian M. Carroll 2015 2022-06-14 [("doi","10.1002/erv.2400")] genetics/microbiome

With nearly 39% of the worldwide adult population classified as obese, much of the globe is facing a serious public health challenge. Increasing rates of obesity, coupled with the failure of many behavioral and pharmacological interventions, have contributed to a rise in popularity of bariatric surgery as a treatment for obesity.

Surgery-mediated weight loss was initially thought to be a direct result of mechanical alterations causing restriction and calorie malabsorption. However, the mounting evidence suggests that indirect factors influence the accumulation and storage of fat in patients that have undergone this procedure.

Given the established impact the intestinal microbiota has on adiposity, it is likely that this complex enteric microbial community contributes to surgery-mediated weight loss and maintenance of weight loss postsurgery.

In this review, we discuss the physiological and psychological traits exhibited by bariatric surgery candidates that can be influenced by the intestinal microbiota.

Additionally, we detail the studies that investigated the impact of bariatric surgery on the intestinal microbiota in humans and mouse models of this procedure.

--- https://arxiv.org/abs/2205.05982#google Vectorized and performance-portable Quicksort Mark Blacher, Joachim Giesen, Peter Sanders, Jan Wassenberg 2022-05-12 2022-06-15 [("doi","10.48550/arXiv.2205.05982")] cs/algorithm/sorting

[blog] Recent works showed that implementations of Quicksort using vector CPU instructions can outperform the non-vectorized algorithms in widespread use. However, these implementations are typically single-threaded, implemented for a particular instruction set, and restricted to a small set of key types.

We lift these 3 restrictions: our proposed vqsort algorithm integrates into the state-of-the-art parallel sorter ips4o, with a geometric mean speedup of 1.59. The same implementation works on 7 instruction sets (including SVE and RISC-V V) across 4 platforms. It also supports floating-point and 16–128 bit integer keys.

To the best of our knowledge, this is the fastest sort for non-tuple keys on CPUs, up to 20× as fast as the sorting algorithms implemented in standard libraries.

This paper focuses on the practical engineering aspects enabling the speed and portability, which we have not yet seen demonstrated for a Quicksort implementation. Furthermore, we introduce compact and transpose-free sorting networks for in-register sorting of small arrays, and a vector-friendly pivot sampling strategy that is robust against adversarial input.

--- https://arxiv.org/abs/1705.02257 In-place Parallel Super Scalar Samplesort (IPS4o) Michael Axtmann, Sascha Witt, Daniel Ferizovic, Peter Sanders 2017-05-05 2022-06-15 [("doi","10.48550/arXiv.1705.02257")] cs/algorithm/sorting

We present a sorting algorithm that works in-place, executes in parallel, is cache-efficient, avoids branch-mispredictions, and performs work 𝒪(n log n) for arbitrary inputs with high probability.

The main algorithmic contributions are new ways to make distribution-based algorithms in-place: On the practical side, by using coarse-grained block-based permutations, and on the theoretical side, we show how to eliminate the recursion stack.

Extensive experiments show that our algorithm IPS4o scales well on a variety of multi-core machines. We outperform our closest in-place competitor by a factor of up to 3. Even as a sequential algorithm, we are up to 1.5× faster than the closest sequential competitor, BlockQuicksort.

--- https://arxiv.org/abs/1604.06697 BlockQuicksort: How Branch Mispredictions don’t affect Quicksort Stefan Edelkamp, Armin Weiß 2016-04-22 2022-06-15 [("doi","10.48550/arXiv.1604.06697")] cs/algorithm/sorting

Since the work of Kaligosi & Sanders 2006, it is well-known that Quicksort—which is commonly considered as one of the fastest in-place sorting algorithms—suffers in an essential way from branch mispredictions.

We present a novel approach to address this problem by partially decoupling control from data flow: in order to perform the partitioning, we split the input in blocks of constant size (we propose 128 data elements); then, all elements in one block are compared with the pivot and the outcomes of the comparisons are stored in a buffer. In a second pass, the respective elements are rearranged. By doing so, we avoid conditional branches based on outcomes of comparisons at all (except for the final Insertionsort).

Moreover, we prove that for a static branch predictor the average total number of branch mispredictions is at most ε n log n + 𝒪(n) for some small ε depending on the block size when sorting n elements.

Our experimental results are promising: when sorting random integer data, we achieve an increase in speed of 80% over the GCC implementation of C++ std::sort. Also for many other types of data and non-random inputs, there is still a speedup over std::sort. Only in few special cases like sorted or almost sorted inputs, std::sort can beat out implementation. Moreover, even on random input permutations, our implementation is even slightly faster than an implementation of the highly tuned Super Scalar Sample Sort, which uses a linear amount of additional space.

--- https://en.wikipedia.org/wiki/Moving_sofa_problem Moving sofa problem 2022-06-15 math --- https://en.wikipedia.org/wiki/In-place_algorithm In-place algorithm 2022-06-15 cs/algorithm/sorting --- https://ae.iti.kit.edu/documents/people/sanders/papers/KalSan06.pdf 2022-06-15 cs/algorithm/sorting --- https://opensource.googleblog.com/2022/06/Vectorized%20and%20performance%20portable%20Quicksort.html 2022-06-15 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Sorting_network Sorting network 2022-06-15 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Comparison_sort Comparison sort 2022-06-15 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Sorting_algorithm Sorting algorithm 2022-06-15 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Bitonic_sorter Bitonic sorter 2022-06-15 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Quicksort Quicksort 2022-06-16 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Timsort Timsort 2022-06-16 cs/algorithm/sorting --- https://danlark.org/2022/04/20/changing-stdsort-at-googles-scale-and-beyond/ 2022-06-16 cs/algorithm/sorting --- https://www.amirrorclear.net/academic/ideas/simulation/index.html 2022-06-16 cs/computable philosophy/ontology --- https://hackage.haskell.org/package/probability 2022-06-16 cs/haskell statistics/probability --- https://monad-bayes-site.netlify.app/_site/about.html 2022-06-16 cs/haskell statistics/probability --- https://dennybritz.com/posts/probability-monads-from-scratch/ 2022-06-16 cs/haskell statistics/probability --- https://www.biorxiv.org/content/10.1101/2022.12.08.519320.full Rejuvenating Senescent Cells and Organisms with Only Ultrasound Sanjay Kumar, Rosario Maroto, Simon Powell, Felix Margadant, Brandon Blair, Blake B. Rasmussen, Michael Sheetz 2022-12-12 2022-12-12 [("doi","10.1101/2022.12.08.519320")] longevity

Accumulation of senescent cells in tissue and organs leads to aging abnormalities. Rejuvenating senescent cells provides a strategy to ameliorate aging.

We report here that low frequency ultrasound (LFU) rejuvenates senescent cells causing growth and loss of senescence markers. With fibroblasts and mesenchymal stem cells, LFU can enable increased cell expansion without altering phenotype.

At a subcellular level, LFU causes mitochondrial fission and loss of lysosome staining that is enhanced by rapamycin or Rho kinase inhibition and blocked by Sirtuin1 inhibition, consistent with the hypothesis that LFU activates autophagy.

In vivo, older mice are rejuvenated by LFU as measured by increased physical performance and decreased levels of senescent cells in kidney and pancreas measured by 3 markers.

Thus, we suggest that LFU alone increases aged cell and whole animal performance.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4134948/ Actionable diagnosis of neuroleptospirosis by next-generation sequencing Michael R. Wilson, Samia N. Naccache, Erik Samayoa, Mark Biagtan, Hiba Bashir, Guixia Yu, Shahriar M. Salamat, Sneha Somasekar, Scot Federman, Steve Miller, Robert Sokolic, Elizabeth Garabedian, Fabio Candotti, Rebecca H. Buckley, Kurt D. Reed, Teresa L. Meyer, Christine M. Seroogy, Renee Galloway, Sheryl L. Henderson, James E. Gern, Joseph L. DeRisi, Charles Y. Chiu 2014 2022-06-16 [("doi","10.1056/NEJMoa1401268")] genetics/sequencing

[media] A 14-year-old boy with severe combined immunodeficiency presented 3× to a medical facility over a period of 4 months with fever and headache that progressed to hydrocephalus and status epilepticus necessitating a medically induced coma. Diagnostic workup including brain biopsy was unrevealing.

Unbiased next-generation sequencing of the cerebrospinal fluid identified 475 of 3,063,784 sequence reads (0.016%) corresponding to leptospira infection. Clinical assays for leptospirosis were negative.

Targeted antimicrobial agents [penicillin] were administered, and the patient was discharged home 32 days later with a status close to his premorbid condition.

Polymerase-chain-reaction (PCR) and serologic testing at the Centers for Disease Control and Prevention (CDC) subsequently confirmed evidence of Leptospira santarosai infection.

--- /doc/psychiatry/depression/2022-kimbrel.pdf Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans Nathan A. Kimbrel, Allison E. Ashley-Koch, Xue J. Qin, Jennifer H. Lindquist, Melanie E. Garrett, Michelle F. Dennis, Lauren P. Hair, Jennifer E. Huffman, Daniel A. Jacobson, Ravi K. Madduri, Jodie A. Trafton, Hilary Coon, Anna R. Docherty, Niamh Mullins, Douglas M. Ruderfer, Philip D. Harvey, Benjamin H. McMahon, David W. Oslin, Jean C. Beckham, Elizabeth R. Hauser, Michael A. Hauser, the Million Veteran Program Suicide Exemplar Workgroup, the International Suicide Genetics Consortium, the Veterans Affairs Mid-Atlantic Mental Illness Research Education, Clinical Center Workgroup, the Veterans Affairs Million Veteran Program 2022-12-14 2022-12-14 [("doi","10.1001/jamapsychiatry.2022.3896")] genetics/heritable/correlation psychiatry/depression

Importance: Suicide is a leading cause of death; however, the molecular genetic basis of suicidal thoughts and behaviors (SITB) remains unknown.

Objective: To identify novel, replicable genomic risk loci for SITB.

Design, Setting, & Participants:

This genome-wide association study included 633 778 US military veterans with and without SITB, as identified through electronic health records. GWAS was performed separately by ancestry, controlling for sex, age, and genetic substructure. Cross-ancestry risk loci were identified through meta-analysis. Study enrollment began in 2011 and is ongoing. Data were analyzed from November 2021 to August 2022.

Main Outcome and Measures:

SITB.

Results: A total of 633 778 US military veterans were included in the analysis (57 152 [9%] female; 121 118 [19.1%] African ancestry, 8285 [1.3%] Asian ancestry, 452 767 [71.4%] European ancestry, and 51 608 [8.1%] Hispanic ancestry), including 121 211 individuals with SITB (19.1%). Meta-analysis identified more than 200 GWS (P &amp;lt; 5 × 10−8) cross-ancestry risk single-nucleotide variants for SITB concentrated in 7 regions on chromosomes 2, 6, 9, 11, 14, 16, and 18. Top single-nucleotide variants were largely intronic in nature; 5 were independently replicated in ISGC, including rs6557168 in ESR1, rs12808482 in DRD2, rs77641763 in EXD3, rs10671545 in DCC, and rs36006172 in TRAF3. Associations for FBXL19 and AC018880.2 were not replicated. Gene-based analyses implicated 24 additional GWS cross-ancestry risk genes, including FURIN, TSNARE1, and the NCAM1-TTC12-ANKK1-DRD2 gene cluster. Cross-ancestry enrichment analyses revealed significant enrichment for expression in brain and pituitary tissue, synapse and ubiquitination processes, amphetamine addiction, parathyroid hormone synthesis, axon guidance, and dopaminergic pathways. 7 other unique European ancestry–specific GWS loci were identified, 2 of which (POM121L2 and METTL15/LINC02758) were replicated. Two additional GWS ancestry-specific loci were identified within the African ancestry (PET112/GATB) and Hispanic ancestry (intergenic locus on chromosome 4) subsets, both of which were replicated. No GWS loci were identified within the Asian ancestry subset; however, significant enrichment was observed for axon guidance, cyclic adenosine monophosphate signaling, focal adhesion, glutamatergic synapse, and oxytocin signaling pathways across all ancestries. Within the European ancestry subset, genetic correlations (r &amp;gt; 0.75) were observed between the SITB phenotype and a suicide attempt-only phenotype, depression, and post-traumatic stress disorder. Additionally, polygenic risk score analyses revealed that the Million Veteran Program polygenic risk score had nominally statistically-significant main effects in 2 independent samples of veterans of European and African ancestry.

Conclusions: The findings of this analysis may advance understanding of the molecular genetic basis of SITB and provide evidence for ESR1, DRD2, TRAF3, and DCC as cross-ancestry candidate risk genes. More work is needed to replicate these findings and to determine if and how these genes might impact clinical care.

--- https://en.wikipedia.org/wiki/Radix_sort Radix sort 2022-06-16 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Bucket_sort Bucket sort 2022-06-17 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Pancake_sorting Pancake sorting 2022-06-17 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Counting_sort Counting sort 2022-06-17 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Spaghetti_sort Spaghetti sort 2022-06-17 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Slowsort Slowsort 2022-06-17 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Bogosort Bogosort 2022-06-17 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Integer_sorting Integer sorting 2022-06-17 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Adaptive_sort Adaptive sort 2022-06-17 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Smoothsort Smoothsort 2022-06-17 cs/algorithm/sorting --- https://www.biorxiv.org/content/10.1101/2022.12.17.520885.full Heritability of de novo germline mutation reveals a contribution from paternal but not maternal genetic factors Seongwon Hwang, Matthew D. C. Neville, Genomics Engl, Research Consortium, Felix R. Day, Aylwyn Scally 2022-12-17 2022-12-17 [("doi","10.1101/2022.12.17.520885")] genetics/heritable/rare

De novo mutations (DNMs) in the germline have long been identified as a key element in the causes of developmental and other genetic disorders. Previous attempts to investigate genetic factors affecting DNMs have suffered from a lack of statistical power, due to the difficulty of obtaining a sufficient number of parent-offspring trios.

Thus, the rare disease cohort of the UK’s 100k Genomes Project (100kGP), comprising more than 10,000 trios, represents an unprecedented opportunity to investigate the genetics of germline mutation. Here we estimate SNP heritability of DNM count in offspring, as a measure of the relative contribution of genetic factors to the variance of the trait, in a PCA-selected subset of the 100kGP cohort. We estimate separate SNP heritabilities for paternally and maternally transmitted mutations (based on parentally phased DNMs in offspring), computed using parental genetic variants at a range of minimum frequencies and a variety of methodologies.

We estimate a heritability of 10–20% for paternal DNMs; by contrast, for maternal DNMs we find no statistically-significant evidence for non-zero heritability.

We investigated the partitioning of heritability among genes with different expression profiles in different tissue or cell states, and found a relative heritability enrichment for genes expressed in gonadal tissues, particularly testis. Among germ cells in adult testes we observed relative enrichment of heritability in genes associated with the (undifferentiated) spermatogonial stem cell state.

--- https://en.wikipedia.org/wiki/Turmite Turmite 2022-06-18 cs/computable --- https://en.wikipedia.org/wiki/Langton%27s_ant Langton’s ant 2022-06-18 cs/cellular-automaton cs/computable --- https://en.wikipedia.org/wiki/Christopher_Langton Chris Langton 2022-06-18 cs/cellular-automaton cs/computable --- https://arxiv.org/abs/nlin/0306022 Complexity of Langton’s Ant Anahi Gajardo, Andres Moreira, Eric Goles 2003-06-13 2022-06-18 [("doi","10.48550/arXiv.0306022")] cs/cellular-automaton

The virtual ant introduced by C. Langton has an interesting behavior, which has been studied in several contexts.

Here we give a construction to calculate any boolean circuit with the trajectory of a single ant. This proves the P-hardness of the system and implies, through the simulation of one dimensional cellular automata and Turing machines, the universality of the ant and the undecidability of some problems associated to it.

--- https://www.biorxiv.org/content/10.1101/2022.12.15.520226.full Positive selection in the genomes of two Papua New Guinean populations at distinct altitude levels Mathilde André, Nicolas Brucato, Georgi Hudjasov, Vasili Pankratov, Danat Yermakovich, Rita Kreevan, Jason Kariwiga, John Muke, Anne Boland, Jean-François Deleuze, Vincent Meyer, Nicholas Evans, Murray P. Cox, Matthew Leavesley, Michael Dannemann, Tõnis Org, Mait Metspalu, Mayukh Mondal, François-Xavier Ricaut 2022-12-15 2022-12-15 [("doi","10.1101/2022.12.15.520226")] genetics/selection/natural/human

Highlanders and lowlanders of Papua New Guinea (PNG) have faced distinct environmental conditions. These environmental differences lead to specific stress on PNG highlanders and lowlanders, such as hypoxia and environment-specific pathogen exposure, respectively. We hypothesize that these constraints induced specific selective pressures that shaped the genomes of both populations.

In this study, we explored signatures of selection in newly sequenced whole genomes of 54 PNG highlanders and 74 PNG lowlanders. Based on multiple methods to detect selection, we investigated the 21 and 23 genomic top candidate regions for positive selection in PNG highlanders and PNG lowlanders, respectively. To identify the most likely candidate SNP driving selection in each of these regions, we computationally reconstructed allele frequency trajectories of variants in each of these regions and chose the SNP with the highest likelihood of being under selection with CLUES.

We show that regions with signatures of positive selection in PNG highlanders genomes encompass genes associated with the hypoxia-inducible factors pathway, brain development, blood composition, and immunity, while selected genomic regions in PNG lowlanders contain genes related to immunity and blood composition.

We found that several candidate driver SNPs are associated with haematological phenotypes in the UK biobank. Moreover, using phenotypes measured from the sequenced Papuans, we found that two candidate SNPs are statistically-significantly associated with altered heart rates in PNG highlanders and lowlanders.

Furthermore, we found that 16 of the 44 selection candidate regions harboured archaic introgression. In 4 of these regions, the selection signal might be driven by the introgressed archaic haplotypes, suggesting a substantial role of archaic admixture in local adaptation in PNG populations.

--- https://www.biorxiv.org/content/10.1101/2022.12.12.520058.full Longevity and rejuvenation effects of cell reprogramming are decoupled from loss of somatic identity Dmitrii Kriukov, Ekaterina E. Khrameeva, Vadim N. Gladyshev, Sergey E. Dmitriev, Alexander Tyshkovskiy 2022-12-14 2022-12-14 [("doi","10.1101/2022.12.12.520058")] longevity/epigenetics

Partial somatic cell reprogramming has been touted as a promising rejuvenation strategy. However, its association with mechanisms of aging and longevity at the molecular level remains unclear.

We identified a robust transcriptomic signature of reprogramming in mouse and human cells that revealed co-regulation of genes associated with reprogramming and response to lifespan-extending interventions, including those related to DNA repair and inflammation.

We found that age-related gene expression changes were reversed during reprogramming, as confirmed by transcriptomic aging clocks. The longevity and rejuvenation effects induced by reprogramming in the transcriptome were mainly independent of pluripotency gain. Decoupling of these processes allowed predicting interventions mimicking reprogramming-induced rejuvenation (RIR) without affecting somatic cell identity, including an anti-inflammatory compound osthol, ATG5 overexpression, and C6ORF223 knockout.

Overall, we revealed specific molecular mechanisms associated with RIR at the gene expression level and developed tools for discovering interventions that support the rejuvenation effect of reprogramming without posing the risk of neoplasia.

--- https://x.com/chaeronanaut/status/1604501912212185089 2022-06-18 reinforcement-learning/chess --- https://arxiv.org/abs/2104.05336#deepmind Machine Translation Decoding beyond Beam Search Rémi Leblond, Jean-Baptiste Alayrac, Laurent Sifre, Miruna Pislar, Jean-Baptiste Lespiau, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals 2021-04-12 2022-06-18 [("doi","10.48550/arXiv.2104.05336")] ai/nn/sampling ai/nn/transformer reinforcement-learning/model

Beam search is the go-to method for decoding auto-regressive machine translation models. While it yields consistent improvements in terms of BLEU, it is only concerned with finding outputs with high model likelihood, and is thus agnostic to whatever end metric or score practitioners care about. Our aim is to establish whether beam search can be replaced by a more powerful metric-driven search technique. To this end, we explore numerous decoding algorithms, including some which rely on a value function parameterized by a neural network, and report results on a variety of metrics. Notably, we introduce a Monte-Carlo Tree Search (MCTS) based method and showcase its competitiveness. We provide a blueprint for how to use MCTS fruitfully in language applications, which opens promising future directions. We find that which algorithm is best heavily depends on the characteristics of the goal metric; we believe that our extensive experiments and analysis will inform further research in this area.

…Finally and somewhat surprisingly, we find that whenever access to the score is possible, the deceivingly simple S+R [sample+rank i.e best-of-n sampling] method performs well. More experimentation is required to understand why; but at any rate, it should be a strong contender in this specific setup.

--- https://x.com/aaronkemmer/status/1604570089059061760 2022-06-18 ai/nn/transformer/gpt/fiction ai/video/generation --- https://www.youtube.com/watch?v=OA8-6q7igwE 2022-06-18 ai/nn/transformer/gpt/fiction ai/video/generation --- https://www.economist.com/1843/2022/07/28/hocus-focus-how-magicians-made-a-fortune-on-facebook 2022-06-18 economics/advertising psychology/cognitive-bias/illusion-of-depth --- https://x.com/VikramParalkar/status/1604560082129354754 2022-06-19 ai/nn/transformer/gpt/fiction --- https://knightcolumbia.org/blog/tiktoks-secret-sauce 2022-06-19 design --- https://borretti.me/article/astronomical-calculations-for-hard-sf-common-lisp 2022-06-19 ai/nn/transformer/gpt/codex cs/lisp fiction/science-fiction --- https://www.biorxiv.org/content/10.1101/2022.12.16.520778.full Quantifying constraint in human mitochondrial DNA Nicole J. Lake, Wei Liu, Stephanie L. Battle, Kristen M. Laricchia, Grace Tiao, Alison G. Compton, Shannon Cowie, John Christodoulou, David R. Thorburn, Hongyu Zhao, Dan E. Arking, Shamil R. Sunyaev, Monkol Lek 2022-12-19 2022-12-19 [("doi","10.1101/2022.12.16.520778")] genetics/heritable/rare

Mitochondrial DNA (mtDNA) has an important, yet often overlooked, role in health and disease. Constraint models quantify depletion of genetic variation by negative selection, representing a powerful tool for identifying deleterious variation underlying human phenotypes. However, a constraint model for the mtDNA had not been developed, due to the unique features of this genome.

Here we describe the development of a mitochondrial constraint model and its application to the Genome Aggregation Database (gnomAD), a large-scale population dataset reporting mtDNA variation across 56,434 humans.

Our results demonstrate strong depletion of expected variation, showing most pathogenic mtDNA variants remain undiscovered. To aid their identification, we compute constraint metrics for every protein, transfer RNA, and ribosomal RNA gene in the human mtDNA, as well as for non-coding regions. This includes assessment of regional or positional constraint within every gene, and local constraint for every base position.

We identify enrichment of pathogenic variation in the most constrained sites, which include loci typically overlooked in mtDNA analyses, and show that the most constrained gene regions and non-coding elements encode functionally-critical sites.

Lastly, we demonstrate how these metrics can improve the discovery of mtDNA variation underlying rare and common human phenotypes.

--- https://srush.github.io/raspy/ 2022-06-19 cs/computable --- https://www.lesswrong.com/posts/HkghiK6Rt35nbgwKA/hard-coding-neural-computation 2022-06-19 cs/computable --- https://nlp.stanford.edu/~johnhew/rnns-hierarchy.html 2022-06-19 ai/nn/rnn cs/computable --- https://arxiv.org/abs/1901.03429 On the Turing Completeness of Modern Neural Network Architectures Jorge Pérez, Javier Marinković, Pablo Barceló 2019-01-10 2022-06-19 [("doi","10.48550/arXiv.1901.03429")] ai/nn/rnn ai/nn/transformer/attention cs/computable

Alternatives to recurrent neural networks, in particular, architectures based on attention or convolutions, have been gaining momentum for processing input sequences. In spite of their relevance, the computational properties of these alternatives have not yet been fully explored.

We study the computational power of two of the most paradigmatic architectures exemplifying these mechanisms: the Transformer (Vaswani et al 2017) and the Neural GPU (Kaiser & Sutskever 2016).

We show both models to be Turing complete exclusively based on their capacity to compute and access internal dense representations of the data. In particular, neither the Transformer nor the Neural GPU requires access to an external memory to become Turing complete.

Our study also reveals some minimal sets of elements needed to obtain these completeness results. [published 2021 as "Attention is Turing Complete"]

--- https://arxiv.org/abs/2202.12172 Overcoming a Theoretical Limitation of Self-Attention David Chiang, Peter Cholak 2022-02-24 2022-06-19 [("doi","10.48550/arXiv.2202.12172")] ai/nn/transformer/attention cs/computable

Although transformers are remarkably effective for many tasks, there are some surprisingly easy-looking regular languages that they struggle with. Hahn shows that for languages where acceptance depends on a single input symbol, a transformer’s classification decisions become less and less confident (that is, with cross-entropy approaching 1 bit per string) as input strings get longer and longer.

We examine this limitation using two languages: PARITY, the language of bit strings with an odd number of 1s, and FIRST, the language of bit strings starting with a 1.

We demonstrate 3 ways of overcoming the limitation suggested by Hahn’s lemma. First, we settle an open question by constructing a transformer that recognizes PARITY with perfect accuracy, and similarly for FIRST.

Second, we use layer normalization to bring the cross-entropy of both models arbitrarily close to zero.

Third, when transformers need to focus on a single position, as for FIRST, we find that they can fail to generalize to longer strings; we offer a simple remedy to this problem that also improves length generalization in machine translation.

--- https://arxiv.org/abs/2210.10749#microsoft Transformers Learn Shortcuts to Automata Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang 2022-10-19 2022-10-19 [("doi","10.48550/arXiv.2210.10749")] ai/nn/rnn ai/nn/transformer/attention cs/computable

Algorithmic reasoning requires capabilities which are most naturally understood through recurrent models of computation, like the Turing machine. However, Transformer models, while lacking recurrence, are able to perform such reasoning using far fewer layers than the number of reasoning steps. This raises the question: what solutions are these shallow and non-recurrent models finding?

We investigate this question in the setting of learning automata, discrete dynamical systems naturally suited to recurrent modeling and expressing algorithmic tasks.

Our theoretical results completely characterize shortcut solutions, whereby a shallow Transformer with only o(T) layers can exactly replicate the computation of an automaton on an input sequence of length T. By representing automata using the algebraic structure of their underlying transformation semigroups, we obtain \U0001D4AA(log T)-depth simulators for all automata and \\U0001D4AA(1)-depth simulators for all automata whose associated groups are solvable.

Empirically, we perform synthetic experiments by training Transformers to simulate a wide variety of automata, and show that shortcut solutions can be learned via standard training.

We further investigate the brittleness of these solutions and propose potential mitigations. [Twitter]

--- https://arxiv.org/abs/2207.00729 Log-Precision Transformers are Constant-Depth Uniform Threshold Circuits William Merrill, Ashish Sabharwal 2022-07-02 2022-07-02 [("doi","10.48550/arXiv.2207.00729")] ai/nn/transformer/attention cs/computable

We prove that transformer neural networks with logarithmic precision in the input length (and where the feedforward subnetworks are computable using linear space in their input length) can be simulated by constant-depth uniform threshold circuits. Thus, such transformers only recognize formal languages in TC0, the class of languages defined by constant-depth, poly-size threshold circuits.

This demonstrates a connection between a practical claim in NLP and a theoretical conjecture in computational complexity theory: “attention is all you need” (Vaswani et al 2017), ie. transformers are capable of all efficient computation, only if all efficiently computable problems can be solved with log space, ie. L = P.

We also construct a transformer that can evaluate any constant-depth threshold circuit on any input, proving that transformers can follow instructions that are representable in TC0.

--- https://arxiv.org/abs/2212.08751#openai Point·E: A System for Generating 3D Point Clouds from Complex Prompts Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, Mark Chen 2022-12-16 2022-12-16 [("doi","10.48550/arXiv.2212.08751")] ai/nn/diffusion

While recent work on text-conditional 3D object generation has shown promising results, the state-of-the-art methods typically require multiple GPU-hours to produce a single sample. This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes.

In this paper, we explore an alternative method for 3D object generation which produces 3D models in only 1–2 minutes on a single GPU.

Our method first generates a single synthetic view using a text-to-image diffusion model [GLIDE], and then produces a 3D point cloud using a second diffusion model which conditions on the generated image.

While our method still falls short of the state-of-the-art in terms of sample quality, it is one to two orders of magnitude faster to sample from, offering a practical trade-off for some use cases.

We release our pre-trained point cloud diffusion models, as well as evaluation code and models, at Github.

…8. Acknowledgments: We would like to thank everyone behind ChatGPT for creating a tool that helped provide useful writing feedback.

--- https://en.wikipedia.org/wiki/Nano_tape Nano tape 2022-06-20 science technology --- https://x.com/rainisto/status/1605084547456143360 2022-06-20 ai/nn/transformer/gpt/fiction --- https://x.com/moyix/status/1598081204846489600 2022-06-20 ai/nn/transformer/gpt/codex cs/security reinforcement-learning/safe --- https://robertheaton.com/2018/12/17/wavefunction-collapse-algorithm/ 2022-06-20 cs/algorithm --- https://en.wikipedia.org/wiki/Rejection_sampling Rejection sampling 2022-06-20 ai/nn/sampling statistics/probability --- https://classic.clinicaltrials.gov/ct2/show/NCT04211480 2022-06-20 genetics/editing --- https://arxiv.org/abs/2211.04325 Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning Pablo Villalobos, Jaime Sevilla, Lennart Heim, Tamay Besiroglu, Marius Hobbhahn, Anson Ho 2022-10-26 2022-10-26 [("doi","10.48550/arXiv.2211.04325")] ai/dataset ai/scaling

We analyze the growth of dataset sizes used in machine learning for natural language processing and computer vision, and extrapolate these using two methods; using the historical growth rate and estimating the compute-optimal dataset size for future predicted compute budgets. We investigate the growth in data usage by estimating the total stock of unlabeled data available on the internet over the coming decades.

Our analysis indicates that the stock of high-quality language data will be exhausted soon; likely before 2026. By contrast, the stock of low-quality language data and image data will be exhausted only much later; between 2030 and 2050 (for low-quality language) and between 2030 and 2060 (for images).

Our work suggests that the current trend of ever-growing ML models that rely on enormous datasets might slow down if data efficiency is not drastically improved or new sources of data become available.

--- https://arxiv.org/abs/2106.00116 Effect of Pre-Training Scale on Intra/Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest Images Mehdi Cherti, Jenia Jitsev 2021-05-31 2022-06-20 [("doi","10.1109/IJCNN55064.2022.9892393")] ai/nn/cnn ai/scaling

[code] Increasing model, data and compute budget scale in the pre-training has been shown to strongly improve model generalization and transfer learning in vast line of work done in language modeling and natural image recognition. However, most studies on the positive effect of larger scale were done in scope of in-domain setting, with source and target data being in close proximity.

To study effect of larger scale for both in-domain and out-of-domain setting when performing full and few-shot transfer, we combine here for the first time large, openly available medical X-Ray chest imaging datasets to reach a scale for medical imaging domain comparable to ImageNet-1k, routinely used for pre-training in natural image domain. We then conduct supervised pre-training, while varying network size [ResNeXt, ResNet-50x1, ResNet-152x4, BiT] and source data scale and domain, being either large natural (ImageNet-1k/21k) or large medical chest X-Ray datasets, and transfer pre-trained models to different natural or medical targets.

We observe strong improvement due to larger pre-training scale for intra-domain natural-natural and medical-medical transfer. For inter-domain natural-medical transfer, we find improvements due to larger pre-training scale on larger X-Ray targets in full shot regime, while for smaller targets and for few-shot regime the improvement is not visible. Remarkably, large networks pre-trained on very large natural ImageNet-21k are as good or better than networks pre-trained on largest available medical X-Ray data when performing transfer to large X-Ray targets.

We conclude that substantially increasing model and generic, medical domain-agnostic natural image source data scale in the pre-training can enable high quality out-of-domain transfer to medical domain specific targets, removing dependency on large medical domain-specific source data often not available in the practice.

--- https://x.com/jeffrsebo/status/1605533658932056064 2022-06-20 ai/nn/transformer/gpt/fiction --- https://www.biorxiv.org/content/10.1101/2022.12.19.520916.full Probing the evolutionary dynamics of whole-body regeneration within planarian flatworms Miquel Vila-Farre, Andrei Rozanski, Mario Ivankovic, James Cleland, Jeremias N. Brand, Felix Thalen, Markus Grohme, Stephanie von Kannen, Alexandra Grosbusch, Han T-K Vu, Carlos E. Prieto, Fernando Carbayo, Bernhard Egger, Christoph Bleidorn, John E. J. Rasko, Jochen C. Rink 2022-12-20 2022-12-20 [("doi","10.1101/2022.12.19.520916")] biology

Why some animals can regenerate while many others cannot remains a fascinating question. Even amongst planarian flatworms, well-known for their ability to regenerate complete animals from small body fragments, species exist that have restricted regeneration abilities or are entirely regeneration incompetent.

Towards the goal of probing the evolutionary dynamics of regeneration, we have assembled a diverse live collection of planarian species from around the world.

The combined quantification of species-specific head regeneration abilities and comprehensive transcriptome-based phylogeny reconstructions reveals multiple independent transitions between robust whole-body regeneration and restricted regeneration in the freshwater species. Our demonstration that the RNAi-mediated inhibition of canonical Wnt signaling can nevertheless bypass all experimentally tractable head regeneration defects in the current collection indicates that the pathway may represent a hot spot in the evolution of planarian regeneration defects. Combined with our finding that Wnt signaling has multiple roles in the reproductive system of the model species S. mediterranea, this raises the possibility of a trade-off between egg-laying and asexual reproduction by fission/regeneration as a driver of regenerative trait evolution. Although initial quantitative comparisons of Wnt signaling levels, reproductive investment, and regenerative abilities across the collection confirm some of the models predictions, they also highlight the diversification of molecular mechanisms amongst the divergent planarian lineages.

Overall, our study establishes a framework for the mechanistic evolution of regenerative abilities and planarians as model taxon for comparative regeneration research.

--- https://en.wikipedia.org/wiki/Semantic_satiation Semantic satiation 2022-06-21 psychology/linguistics psychology/novelty --- https://en.wikipedia.org/wiki/Tetris_effect Tetris effect 2022-06-21 psychology/vision --- https://www.strongerbyscience.com/creatine/ 2022-06-21 creatine --- https://www.theartnewspaper.com/2022/12/07/old-masters-need-reinventing-to-avoid-being-frozen-out 2022-06-21 culture psychology/collecting sociology --- https://huggingface.co/wavymulder/timeless-diffusion 2022-06-21 ai/nn/diffusion --- https://medium.com/geekculture/i-found-a-loophole-to-successfully-web-scrape-using-chatgpt-heres-how-it-works-135f6c077d4d 2022-06-21 ai/nn/transformer/gpt/codex --- https://www.arthurconandoyle.com/copyrights.html 2022-06-21 economics/copyright --- https://philo.substack.com/p/deregulation-and-airfares 2022-06-21 economics --- https://github.com/ZealousMagician/Ponymaster 2022-06-21 ai/nn/diffusion --- https://www.lesswrong.com/posts/KbRxdBCcJqwtbiPzm/whisper-s-wild-implications-1 2022-06-21 ai/nn/transformer/gpt/whisper ai/scaling --- https://en.wikipedia.org/wiki/Common_Lisp_Object_System Common Lisp Object System 2022-06-22 cs/computable cs/lisp --- https://en.wikipedia.org/wiki/Lazy_evaluation Lazy evaluation 2022-06-22 cs/haskell cs/lisp --- https://en.wikipedia.org/wiki/Meta-circular_evaluator Meta-circular evaluator 2022-06-22 cs/lisp --- https://en.wikipedia.org/wiki/Maclisp Maclisp 2022-06-22 cs/lisp --- https://en.wikipedia.org/wiki/Brian_Cantwell_Smith Brian Cantwell Smith 2022-06-22 cs/lisp --- https://en.wikipedia.org/wiki/S-expression S-expression 2022-06-22 cs/lisp --- https://en.wikipedia.org/wiki/Higher-order_function Higher-order function 2022-06-22 cs/lisp --- https://en.wikipedia.org/wiki/Scheme_(programming_language) Scheme (programming language) 2022-06-22 cs/lisp --- https://en.wikipedia.org/wiki/Closure_(computer_programming) Closure (computer programming) 2022-06-22 cs/lisp --- https://blog.sulami.xyz/posts/common-lisp-restarts/ Restarts in Common Lisp 2022-06-22 cs/lisp --- https://github.com/kspalaiologos/malbolge-lisp MalbolgeLisp is a LISP interpreter written in Malbolge. It’s (as of 2020 and 2021), the most advanced, usable Malbolge program ever created. It supports everything Lisps generally tend to support (like cond, let, lambda, etc...). 2022-06-22 cs/computable cs/cryptography cs/lisp --- https://en.wikipedia.org/wiki/Emacs Emacs 2022-06-23 cs/computable cs/lisp/emacs --- https://en.wikipedia.org/wiki/GNU_Emacs GNU Emacs 2022-06-23 cs/computable --- https://en.wikipedia.org/wiki/Lisp_machine Lisp machine 2022-06-23 cs/computable cs/lisp/emacs --- https://en.wikipedia.org/wiki/Symbolics Symbolics 2022-06-23 cs/computable --- https://en.wikipedia.org/wiki/Genera_(operating_system) Open Genera 2022-06-23 cs/computable --- https://en.wikipedia.org/wiki/Macsyma Macsyma 2022-06-23 cs/computable --- https://en.wikipedia.org/wiki/Lisp_Machines Lisp Machines 2022-06-23 cs/computable --- https://en.wikipedia.org/wiki/Lisp_Machine_Lisp Lisp Machine Lisp 2022-06-23 cs/computable --- https://en.wikipedia.org/wiki/Flavors_(programming_language) Flavors (programming language) 2022-06-23 cs/computable --- https://en.wikipedia.org/wiki/Common_Lisp Common Lisp 2022-06-23 cs/computable --- https://en.wikipedia.org/wiki/Metaobject Metaobject 2022-06-24 cs/computable --- https://en.wikipedia.org/wiki/Continuation Continuation 2022-06-24 cs/computable cs/lisp --- https://en.wikipedia.org/wiki/Zmacs Zmacs 2022-06-24 cs/computable design --- https://en.wikipedia.org/wiki/Symbolics_Document_Examiner Symbolics Document Examiner 2022-06-24 cs/computable design --- https://en.wikipedia.org/wiki/Aniara_(opera) Aniara (opera) 2022-06-24 fiction/opera fiction/science-fiction --- https://en.wikipedia.org/wiki/Aniara Aniara 2022-06-24 fiction/poetry fiction/science-fiction --- https://en.wikipedia.org/wiki/PDP-11 PDP-11 2022-06-24 cs/hardware cs/lisp --- https://en.wikipedia.org/wiki/Spice_Lisp Spice Lisp 2022-06-24 cs/lisp --- https://en.wikipedia.org/wiki/VAX_Common_Lisp VAX Common Lisp 2022-06-24 cs/lisp --- https://en.wikipedia.org/wiki/StumpWM StumpWM 2022-06-24 cs/lisp --- https://en.wikipedia.org/wiki/Steel_Bank_Common_Lisp Steel Bank Common Lisp 2022-06-24 cs/lisp --- https://en.wikipedia.org/wiki/Greenspun%27s_tenth_rule Greenspun’s tenth rule 2022-06-25 cs/lisp design --- https://en.wikipedia.org/wiki/Space-cadet_keyboard Space-cadet keyboard 2022-06-25 cs/lisp --- https://en.wikipedia.org/wiki/Common_Lisp_the_Language Common Lisp the Language 2022-06-25 cs/lisp --- https://en.wikipedia.org/wiki/MIT_Computer_Science_and_Artificial_Intelligence_Laboratory MIT Computer Science and Artificial Intelligence Laboratory 2022-06-25 cs/lisp --- https://en.wikipedia.org/wiki/Hal_Abelson Hal Abelson 2022-06-25 cs/lisp --- https://en.wikipedia.org/wiki/GNU_Guile GNU Guile 2022-06-25 cs/lisp --- https://gigamonkeys.com/book/beyond-exception-handling-conditions-and-restarts.html 2022-06-25 cs/lisp --- https://www.nhplace.com/kent/Papers/Condition-Handling-2001.html 2022-06-25 cs/lisp --- https://en.wikipedia.org/wiki/Planning_Domain_Definition_Language Planning Domain Definition Language 2022-06-25 cs/lisp --- https://en.wikipedia.org/wiki/SLIME SLIME 2022-06-25 cs/lisp --- https://lispy.wordpress.com/category/scheme/ 2022-06-25 cs/lisp --- https://x.com/yacineMTB/status/1610303012219621379 2022-06-26 ai/nn/transformer/gpt/non-fiction --- https://x.com/ChrisGPotts/status/1609608228295553025 2022-06-26 statistics/order --- https://web.archive.org/web/20230104213430/https://docs.google.com/document/d/1oIlLt1uqutTP8725wezfZ2mjc-IPfOFCdc6hlRIb-KM/mobilebasic 2022-06-26 anime --- https://github.com/JusticeRage/Gepetto 2022-06-26 ai/nn/transformer/gpt/codex cs/security --- https://medium.com/tenable-techblog/g-3po-a-protocol-droid-for-ghidra-4b46fa72f1ff 2022-06-26 ai/nn/transformer/gpt/codex cs/security --- https://arxiv.org/abs/1503.01007#facebook Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets Arm Holdings, Joulin, Tomas Mikolov 2015-03-03 2022-06-26 [("doi","10.48550/arXiv.1503.01007")] ai/nn/rnn cs/algorithm

Despite the recent achievements in machine learning, we are still very far from achieving real artificial intelligence.

In this paper, we discuss the limitations of standard deep learning approaches and show that some of these limitations can be overcome by learning how to grow the complexity of a model in a structured way. Specifically, we study the simplest sequence prediction problems that are beyond the scope of what is learnable with standard recurrent networks, algorithmically generated sequences which can only be learned by models which have the capacity to count and to memorize sequences.

We show that some basic algorithms can be learned from sequential data using a recurrent network associated with a trainable memory.

--- https://arxiv.org/abs/2207.00220 Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset Peter Henderson, Mark S. Krass, Lucia Zheng, Neel Guha, Christopher D. Manning, Dan Jurafsky, Daniel E. Ho 2022-07-01 2022-07-01 [("doi","10.48550/arXiv.2207.00220")] ai/dataset law

One concern with the rise of large language models lies with their potential for harm, particularly from pretraining on biased, obscene, copyrighted, and private information. Emerging ethical approaches have attempted to filter pretraining material, but such approaches have been ad hoc and failed to take context into account.

We offer an approach to filtering grounded in law, which has directly addressed the tradeoffs in filtering material. First, we gather and make available the Pile of Law, a 256GB (and growing) dataset of open-source English-language legal and administrative data, covering court opinions, contracts, administrative rules, and legislative records. Pretraining on the Pile of Law may help with legal tasks that have the promise to improve access to justice.

Second, we distill the legal norms that governments have developed to constrain the inclusion of toxic or private content into actionable lessons for researchers and discuss how our dataset reflects these norms.

Third, we show how the Pile of Law offers researchers the opportunity to learn such filtering rules directly from the data, providing an exciting new research direction in model-based processing.

--- https://www.biorxiv.org/content/10.1101/2022.12.28.522128.full PATH: Defining ancestry, heritability and plasticity of cellular phenotypes in somatic evolution Joshua S. Schiffman, Andrew R. D’Avino, Tamara Prieto, Catherine Potenski, Yilin Fan, Toshiro Hara, Mario L. Suvà, Dan A. Landau 2022-12-30 2022-12-30 [("doi","10.1101/2022.12.28.522128")] genetics/heritable

[Twitter] The broad application of single-cell RNA sequencing has revealed transcriptional cell state heterogeneity across diverse healthy and malignant somatic tissues. Recent advances in lineage tracing technologies have further enabled the simultaneous capture of cell transcriptional state along with cellular ancestry thus enabling the study of somatic evolution at an unprecedented resolution; however, new analytical approaches are needed to fully harness these data.

Here we introduce PATH (the Phylogenetic Analysis of Transcriptional Heritability), an analytical framework, which draws upon classic approaches in species evolution, to quantify heritability and plasticity of somatic phenotypes, including transcriptional states. The PATH framework further allows for the inference of cell state transition dynamics by linking a model of cellular evolutionary dynamics with our measure of heritability versus plasticity.

We evaluate the robustness of this approach by testing a range of biological and technical features in simulations of somatic evolution. We then apply PATH to characterize single-cell phylogenies, reconstructed from either native or artificial lineage markers, with matching transcriptional state profiling.

PATH recovered known developmental relationships in mouse embryogenesis, and revealed how anatomic proximity influences neural relatedness in the developing zebrafish brain. In cancer, PATH dissected the heritability of the epithelial-to-mesenchymal transition in a mouse model of pancreatic cancer, and the heritability versus plasticity of transcriptionally-defined cell states in human glioblastoma.

--- https://www.lesswrong.com/posts/PDLfpRwSynu73mxGw/basic-facts-about-language-model-internals-1 2022-06-26 ai/nn/transformer/gpt --- https://www.overcomingbias.com/2023/01/i-see-stylists-everywhere.html 2022-06-26 psychology/novelty --- https://arxiv.org/abs/2301.01296#microsoft TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models Sucheng Ren, Fangyun Wei, Zheng Zhang, Han Hu 2023-01-03 2023-01-03 [("doi","10.48550/arXiv.2301.01296")] ai/nn/sparsity/knowledge-distillation ai/nn/vae/mae

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach.

In this paper, we explore knowledge distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: (1) Distilling token relations is more effective than CLS token/feature-based distillation; (2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; (3) Weak regularization is preferred; etc.

With these findings, we achieve fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget.

This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works.

Code is available at Github.

--- https://arxiv.org/abs/2301.00876 MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding Steven H. Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dimitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, Dan Hendrycks 2023-01-02 2023-01-02 [("doi","10.48550/arXiv.2301.00876")] dataset law

Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets.

To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association’s 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations.

Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for improvement.

As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.

--- https://web.archive.org/web/20181005033340/https://www.fastcompany.com/3068659/nixon-nasa-and-how-the-federal-government-got-design 2022-06-27 design/typography --- https://x.com/ch402/status/1611113804158627840 2022-06-27 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/non-fiction --- https://x.com/oegerikus/status/1610945035888955392 2022-06-27 ai/nn/transformer/gpt/codex --- https://archiveofourown.org/works/17356235 2022-06-27 fiction/humor fiction/science-fiction sociology --- https://www.noahpinion.blog/p/why-paul-ehrlich-got-everything-wrong 2022-06-27 statistics/peer-review --- https://en.wikipedia.org/wiki/Marchetti's_constant Marchetti’s constant 2022-06-27 economics technology --- https://x.com/enpitsu/status/1610923494824628224 2022-06-27 ai/nn/transformer/clip/sample --- https://www.tumblr.com/uploadedyudkowsky 2022-06-27 ai/nn/transformer/gpt/non-fiction --- https://thezvi.substack.com/p/jailbreaking-the-chatgpt-on-release 2022-06-27 ai/nn/transformer/gpt cs/security reinforcement-learning/safe --- https://x.com/goodside/status/1611412309963849736 2022-06-28 ai/nn/transformer/gpt/fiction cs/security --- https://www.biorxiv.org/content/10.1101/2023.01.03.521284.full The genetic architecture of the human skeletal form Eucharist Kun, Emily M. Javan, Olivia Smith, Faris Gulamali, Javier de la Fuente, Brianna I. Flynn, Kushal Vajrala, Zoe Trutner, Prakash Jayakumar, Elliot M. Tucker-Drob, Mashaal Sohail, Tarjinder Singh, Vagheesh M. Narasimhan 2023-01-03 2023-01-03 [("doi","10.1101/2023.01.03.521284")] genetics/heritable/correlation

The human skeletal form underlies our ability to walk on two legs, but unlike standing height, the genetic basis of limb lengths and skeletal proportions is less well understood.

Here we applied a deep learning model to 31,221 whole body dual-energy X-ray absorptiometry (DXA) images from the UK Biobank (UKB) to extract 23 different image-derived phenotypes (IDPs) that include all long bone lengths as well as hip and shoulder width, which we analyzed while controlling for height.

All skeletal proportions are highly heritable (~40-50%), and genome-wide association studies (GWAS) of these traits identified 179 independent loci, of which 102 loci were not associated with height. These loci are enriched in genes regulating skeletal development as well as associated with rare human skeletal diseases and abnormal mouse skeletal phenotypes. Genetic correlation and genomic structural equation modeling indicated that limb proportions exhibited strong genetic sharing but were genetically independent of width and torso proportions. Phenotypic and polygenic risk score analyses identified specific associations between osteoarthritis (OA) of the hip and knee, the leading causes of adult disability in the United States, and skeletal proportions of the corresponding regions.

We also found genomic evidence of evolutionary change in arm-to-leg and hip-width proportions in humans consistent with striking anatomical changes in these skeletal proportions in the hominin fossil record. In contrast to cardiovascular, auto-immune, metabolic, and other categories of traits, loci associated with these skeletal proportions are statistically-significantly enriched in human accelerated regions (HARs), and regulatory elements of genes differentially expressed through development between humans and the great apes.

Taken together, our work validates the use of deep learning models on DXA images to identify novel and specific genetic variants affecting the human skeletal form and ties a major evolutionary facet of human anatomical change to pathogenesis.

--- https://sashachapin.substack.com/five-mildly-anti-buddhist-essays 2022-06-28 psychiatry/meditation --- https://web.mit.edu/kerberos/www/dialogue.html 2022-06-28 cs/security --- https://x.com/goodside/status/1611556749726605312 2022-06-28 ai/nn/transformer/gpt/fiction --- https://en.wikipedia.org/wiki/Jurisdiction_stripping Jurisdiction stripping 2022-06-28 law --- https://fanpu.io/blog/2023/latex-tips/ 2022-06-28 design/typography/tex math --- https://en.chessbase.com/post/better-than-an-engine-leonardo-ljubicic-1-2 2022-06-28 reinforcement-learning/chess --- https://en.chessbase.com/post/better-than-an-engine-leonardo-ljubicic-2-2 2022-06-28 reinforcement-learning/chess --- https://x.com/labenz/status/1611750398712332292 2022-06-28 ai/nn/transformer/gpt reinforcement-learning/preference-learning reinforcement-learning/safe --- https://arxiv.org/abs/2209.06293#allen Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest Jack Hessel, Ana Marasović, Jena D. Hwang, Lillian Lee, Jeff Da, Rowan Zellers, Robert Mankoff, Yejin Choi 2022-09-13 2022-09-13 [("doi","10.48550/arXiv.2209.06293")] ai/nn/transformer/clip ai/nn/transformer/gpt/3/humor

We challenge AI models to “demonstrate understanding” of the sophisticated multimodal humor of The New Yorker [cartoon] Caption Contest.

Concretely, we develop 3 carefully circumscribed tasks for which it suffices (but is not necessary) to grasp potentially complex and unexpected relationships between image and caption, and similarly complex and unexpected allusions to the wide varieties of human experience; these are the hallmarks of a New Yorker-caliber cartoon.

We investigate vision-and-language models that take as input the cartoon pixels and caption directly, as well as language-only models for which we circumvent image-processing by providing textual descriptions of the image. Even with the rich multifaceted annotations we provide for the cartoon images, we identify performance gaps between high-quality machine learning models (eg. a fine-tuned, 175b parameter language model [GPT-3]) and humans.

We publicly release our corpora including annotations describing the image’s locations/entities, what’s unusual about the scene, and an explanation of the joke.

[Yejin Choi interview:

Q. What about a lighter example, like A.I. and humor? Comedy is so much about the unexpected, and if A.I. mostly learns by analyzing previous examples, does that mean humor is going to be especially hard for it to understand?

A. Some humor is very repetitive, and A.I. understands it. But, like, New Yorker cartoon captions? We have a new paper about that. Basically, even the fanciest A.I. today cannot really decipher what’s going on in New Yorker captions.

Q. To be fair, neither can a lot of people.

A. [Laughs.] Yeah, that’s true. We found, by the way, that we researchers sometimes don’t understand these jokes in New Yorker captions. It’s hard. But we’ll keep researching.]

--- https://www.biorxiv.org/content/10.1101/2023.01.05.522936.full Leveraging family data to design Mendelian Randomization that is provably robust to population stratification Nathan LaPierre, Boyang Fu, Steven Turnbull, Eleazar Eskin, Sriram Sankararaman 2023-01-06 2023-01-06 [("doi","10.1101/2023.01.05.522936")] genetics/heritable/correlation/mendelian-randomization

Mendelian Randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases due to weak instruments as well as the confounding effects of population stratification and horizontal pleiotropy.

Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We demonstrate in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, while standard MR methods yield inflated false positive rates.

We applied MR-Twin to 121 trait pairs in the UK Biobank dataset and found that MR-Twin identifies likely causal trait pairs and does not identify trait pairs that are unlikely to be causal.

Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, while MR-Twin is immune to this type of confounding.

--- https://www.medrxiv.org/content/10.1101/2021.11.18.21266545.full Global biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts Ying Wang, Shinichi Namba, Esteban Lopera, Sini Kerminen, Kristin Tsuo, Kristi Läll, Masahiro Kanai, Wei Zhou, Kuan-Han Wu, Marie-Julie Favé, Laxmi Bhatta, Philip Awadalla, Ben Brumpton, Patrick Deelen, Kristian Hveem, Valeria Lo Faro, Reedik Mägi, Yoshinori Murakami, Serena Sanna, Jordan W. Smoller, Jasmina Uzunovic, Brooke N. Wolford, Global Biobank Meta-analysis Initiative, Cristen Jennifer Willer, Eric R. Gamazon, Nancy J. Cox, Ida Surakka, Yukinori Okada, Alicia R. Martin, Jibril Hirbo 2022-09-07 2022-09-07 [("doi","10.1101/2021.11.18.21266545")] genetics/heritable

With the increasing availability of biobank-scale datasets that incorporate both genomic data and electronic health records, many associations between genetic variants and phenotypes of interest have been discovered. Polygenic risk scores (PRS), which are being widely explored in precision medicine, use the results of association studies to predict the genetic component of disease risk by accumulating risk alleles weighted by their effect sizes. However, few studies have thoroughly investigated best practices for PRS in global populations across different diseases.

In this study, we utilize data from the Global-Biobank Meta-analysis Initiative (GBMI), which consists of individuals from diverse ancestries and across continents, to explore methodological considerations and PRS prediction performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRS using heuristic (pruning and thresholding, P+T) and Bayesian (PRS-CS) methods.

We found that the genetic architecture, such as SNP-based heritability and polygenicity, varied greatly among endpoints. For both PRS construction methods, using a European ancestry LD reference panel resulted in comparable or higher prediction accuracy compared to several other non-European based panels; this is largely attributable to European descent populations still comprising the majority of GBMI participants. PRS-CS overall outperformed the classic P+T method, especially for endpoints with higher SNP-based heritability. For example, substantial improvements are observed in East-Asian ancestry (EAS) using PRS-CS compared to P+T for heart failure (HF) and chronic obstructive pulmonary disease (COPD). Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma which has known variation in disease prevalence across global populations.

Overall, we provide lessons for PRS construction, evaluation, and interpretation using the GBMI and highlight the importance of best practices for PRS in the biobank-scale genomics era.

--- https://en.wikipedia.org/wiki/El_Paquete_Semanal El Paquete Semanal 2022-06-29 technology --- https://davidepstein.substack.com/p/sudden-cardiac-death-in-athletes 2022-06-29 genetics/heritable/rare --- https://x.com/jheitzeb/status/1612130278293803009 2022-06-29 ai/nn/retrieval ai/nn/transformer/gpt --- https://en.wikipedia.org/wiki/Contagious_reticulum_cell_sarcoma Contagious reticulum cell sarcoma 2022-06-29 genetics/cloning --- https://x.com/goodside/status/1612017392518840320 2022-06-29 ai/nn/transformer/gpt/inner-monologue --- https://arxiv.org/abs/2204.09664 Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive? Kaiqi Zhang, Yu-Xiang Wang 2022-04-20 2022-06-29 [("doi","10.48550/arXiv.2204.09664")] ai/nn/fully-connected

We study the theory of neural network (NN) from the lens of classical nonparametric regression problems with a focus on NN’s ability to adaptively estimate functions with heterogeneous smoothness—a property of functions in Besov or Bounded Variation (BV) classes. Existing work on this problem requires tuning the NN architecture based on the function spaces and sample sizes.

We consider a “Parallel NN” variant of deep ReLU networks [an ensemble of deep nets] and show that the standard weight decay is equivalent to promoting the 𝓁p-sparsity (0 < p < 1) of the coefficient vector of an end-to-end learned function bases, ie. a dictionary.

Using this equivalence, we further establish that by tuning only the weight decay, such Parallel NN achieves an estimation error arbitrarily close to the minimax rates for both the Besov and BV classes. Notably, it gets exponentially closer to minimax optimal as the NN gets deeper.

Our research sheds new lights on why depth matters and how NNs are more powerful than kernel methods.

…Our main contributions are:

  1. We prove that the (standard) weight decay in training an L-layer parallel ReLU-activated neural network is equivalent to a sparse 𝓁p penalty term (where p = 2⁄L) on the linear coefficients of a learned representation.

  2. We show that neural networks can approximate B-spline basis functions of any order without the need of choosing the order parameter manually. In other words, neural networks can adapt to functions of different order of smoothness, and even functions with different smoothness in different regions in their domain.

  3. We show that the estimation error of weight decayed parallel ReLU neural network decreases polynomially with the number of samples up to a constant error for estimating functions with heterogeneous smoothness in the both BV and Besov classes, and the exponential term in the error rate is close to the minimax rate. Notably, the method requires tuning only the weight decay parameter.

  4. We find that deeper models achieve closer to the optimal error rate. This result helps explain why deep neural networks can achieve better performance than shallow ones empirically.

The above results separate NNs with any linear methods such as kernel ridge regression. To the best of our knowledge, we are the first to demonstrate that standard techniques (“weight decay” and ReLU activation) suffice for DNNs in achieving the optimal rates for estimating BV and Besov functions.

--- https://x.com/ylecun/status/1612182019861094402 2022-06-29 ai/scaling/hardware --- https://x.com/jayelmnop/status/1612243602633068549 2022-06-29 ai/nn/transformer/gpt/3/fiction ai/nn/transformer/gpt/fiction fiction/humor --- https://www.reddit.com/r/GPT3/comments/106t5gv/compressing_prompt_text_with_lossless_compression/ 2022-06-29 ai/nn/transformer/gpt/codex --- https://www.reddit.com/r/MachineLearning/comments/106q6m9/p_i_built_adrenaline_a_debugger_that_fixes_errors/ 2022-06-30 ai/nn/transformer/gpt/codex --- https://stuartritchie.substack.com/p/are-homophobes-secretly-homosexual 2022-06-30 psychology --- https://www.lesswrong.com/posts/aRxDLju75KXD6PCpB/wolf-incident-postmortem 2022-06-30 cs/algorithm --- /doc/philosophy/epistemology/1923-ramsey.pdf Review of Tractatus Logico-Philosophicus by Ludwig Wittgenstein Frank Ramsey 1923-01-01 2022-06-30 [("doi","10.2307/2249608")] philosophy/epistemology philosophy/frank-ramsey philosophy/logic --- https://x.com/goodside/status/1612452751610417158 2022-06-30 ai/nn/transformer/gpt/non-fiction cs/security reinforcement-learning/safe --- https://x.com/moreisdifferent/status/1612489352105365511 2022-06-30 ai/nn/transformer/gpt/codex --- /doc/exercise/2023-recchia.pdf Physical Activity Interventions to Alleviate Depressive Symptoms in Children and Adolescents: A Systematic Review and Meta-analysis Francesco Recchia, Joshua D. K. Bernal, Daniel Y. Fong, Stephen H. S. Wong, Pak-Kwong Chung, Derwin K. C. Chan, Catherine M. Capio, Clare C. W. Yu, Sam W. S. Wong, Cindy H. P. Sit, Ya-Jun Chen, Walter R. Thompson, Parco M. Siu 2023 2023 [("doi","10.1001/jamapediatrics.2022.5090")] exercise psychiatry/depression

Importance: Depression is the second most prevalent mental disorder among children and adolescents, yet only a small proportion seek or receive disorder-specific treatment. Physical activity interventions hold promise as an alternative or adjunctive approach to clinical treatment for depression.

Objective: To determine the association of physical activity interventions with depressive symptoms in children and adolescents.

Data Sources: PubMed, CINAHL, PsycINFO, Embase, and SPORTDiscus were searched from inception to February 2022 for relevant studies written in English, Chinese, or Italian.

Study Selection: Two independent researchers selected studies that assessed the effects of physical activity interventions on depressive symptoms in children and adolescents compared with a control condition.

Data Extraction and Synthesis: A random-effects meta-analysis using Hedges g was performed. Heterogeneity, risk of bias, and publication bias were assessed independently by multiple reviewers. Meta-regressions and sensitivity analyses were conducted to substantiate the overall results. The study followed the PRISMA reporting guideline.

Main Outcomes & Measures: The main outcome was depressive symptoms as measured by validated depression scales at post-intervention and follow-up.

Results: 21 studies involving 2,441 participants (1148 [47.0%] boys; 1293 [53.0%] girls; mean [SD] age, 14 [3] years) were included. Meta-analysis of the post-intervention differences revealed that physical activity interventions were associated with a reduction in depressive symptoms compared with the control condition (g = −0.29; 95% CI, −0.47 to −0.10; p = 0.004). Analysis of the follow-up outcomes in 4 studies revealed no differences between the physical activity and control groups (g = −0.39; 95% CI, −1.01 to 0.24; p = 0.14). Moderate study heterogeneity was detected (Q = 53.92; df = 20; P &amp;lt; 0.001; I2 = 62.9% [95% CI, 40.7%-76.8%]). The primary moderator analysis accounting for total physical activity volume, study design, participant health status, and allocation and/or assessment concealment did not moderate the main treatment effect. Secondary analyses demonstrated that intervention (ie. &amp;lt;12 weeks in duration, 3× per week, unsupervised) and participant characteristics (ie. aged ≥13 years, with a mental illness and/or depression diagnosis) may influence the overall treatment effect.

Conclusions and Relevance: Physical activity interventions may be used to reduce depressive symptoms in children and adolescents. Greater reductions in depressive symptoms were derived from participants older than 13 years and with a mental illness and/or depression diagnosis. The association with physical activity parameters such as frequency, duration, and supervision of the sessions remains unclear and needs further investigation.

--- https://replicationindex.com/2023/01/08/which-social-psychologists-can-you-trust/ 2022-06-30 statistics/power-analysis --- https://meta.wikimedia.org/wiki/International_logo_contest/Logos_1-25 2022-06-30 design wikipedia --- https://x.com/OriolVinyalsML/status/1612514485201166347 2022-06-30 ai/nn/transformer/alphafold --- https://vmc.vet.osu.edu/cto/clinical-trials/rapamycin-cats-chronic-kidney-disease 2022-07-01 cat/biology longevity --- https://x.com/HvnsLstAngel/status/1612274493501894656 2022-07-01 ai/nn/transformer/clip/sample --- https://techcrunch.com/2023/01/09/anthropics-claude-improves-on-chatgpt-but-still-suffers-from-limitations/ 2022-07-01 ai/nn/transformer/gpt/claude cs/security reinforcement-learning/safe --- https://betonit.substack.com/p/chatgpt-takes-my-midterm-and-gets 2022-07-01 ai/nn/transformer/gpt/non-fiction economics --- https://www.youtube.com/watch?v=g2oMv93EUpY 2022-07-01 psychology/cognitive-bias/illusion-of-depth --- https://www.newyorker.com/magazine/2023/01/16/how-should-we-think-about-our-different-styles-of-thinking 2022-07-01 psychology/inner-voice --- https://sander.ai/2023/01/09/diffusion-language.html#deepmind 2022-07-01 ai/nn/diffusion/discrete ai/nn/transformer --- https://arxiv.org/abs/2202.04200#google MaskGIT: Masked Generative Image Transformer Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, William T. Freeman 2022-02-08 2022-07-01 [("doi","10.48550/arXiv.2202.04200")] ai/nn/transformer/gpt/dall-e/1 ai/nn/vae/mae

Generative transformers have experienced rapid popularity growth in the computer vision community in synthesizing high-fidelity and high-resolution images. The best generative transformer models so far, however, still treat an image naively as a sequence of tokens, and decode an image sequentially following the raster scan ordering (ie. line-by-line).

We find this strategy neither optimal nor efficient. This paper proposes a novel image synthesis paradigm using a bidirectional transformer decoder, which we term MaskGIT.

During training, MaskGIT learns to predict randomly masked tokens by attending to tokens in all directions. At inference time, the model begins with generating all tokens of an image simultaneously, and then refines the image iteratively conditioned on the previous generation.

Our experiments demonstrate that MaskGIT outperforms the state-of-the-art transformer model on the ImageNet dataset, and accelerates autoregressive decoding by up to 64×. Besides, we illustrate that MaskGIT can be easily extended to various image editing tasks, such as inpainting, extrapolation, and image manipulation.

--- https://arxiv.org/abs/1904.09324#facebook Mask-Predict: Parallel Decoding of Conditional Masked Language Models Marjan Ghazvininejad, Omer Levy, Yinhan Liu, Luke Zettlemoyer 2019-04-19 2022-07-01 [("doi","10.48550/arXiv.1904.09324")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer

Most machine translation systems generate text autoregressively from left to right.

We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about.

By applying this strategy for a constant number of iterations [and using knowledge distillation], our model improves state-of-the-art performance levels for non-autoregressive and parallel decoding translation models by over 4 BLEU on average. It is also able to reach within about 1 BLEU point of a typical left-to-right transformer model, while decoding faster.

--- https://arxiv.org/abs/2210.16886 DiffusER: Discrete Diffusion via Edit-based Reconstruction Machel Reid, Vincent J. Hellendoorn, Graham Neubig 2022-10-30 2022-10-30 [("doi","10.48550/arXiv.2210.16886")] ai/nn/diffusion/discrete ai/text-style-transfer

[code] In text generation, models that generate text from scratch one token at a time are currently the dominant paradigm. Despite being performant, these models lack the ability to revise existing text, which limits their usability in many practical scenarios.

We look to address this, with DiffusER (Diffusion via Edit-based Reconstruction), a new edit-based generative model for text based on denoising diffusion models—a class of models that use a Markov chain of denoising steps to incrementally generate data.

DiffusER is not only a strong generative model in general, rivaling autoregressive models on several tasks spanning machine translation, summarization, and style transfer; it can also perform other varieties of generation that standard autoregressive models are not well-suited for. For instance, we demonstrate that DiffusER makes it possible for a user to condition generation on a prototype, or an incomplete sequence, and continue revising based on previous edit steps.

--- https://arxiv.org/abs/2211.15089#deepmind CDCD: Continuous diffusion for categorical data Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H. Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, Curtis Hawthorne, Rémi Leblond, Will Grathwohl, Jonas Adler 2022-11-28 2022-11-28 [("doi","10.48550/arXiv.2211.15089")] ai/nn/diffusion/discrete reinforcement-learning/exploration/active-learning

Diffusion models have quickly become the go-to paradigm for generative modeling of perceptual signals (such as images and sound) through iterative refinement. Their success hinges on the fact that the underlying physical phenomena are continuous. For inherently discrete and categorical data such as language, various diffusion-inspired alternatives have been proposed. However, the continuous nature of diffusion models conveys many benefits, and in this work we endeavour to preserve it.

We propose CDCD, a framework for modeling categorical data with diffusion models that are continuous both in time and input space.

We demonstrate its efficacy on several language modeling tasks.

--- https://lisp-journey.gitlab.io/blog/these-years-in-common-lisp-2022-in-review/ 2022-07-02 cs/lisp --- https://arxiv.org/abs/2212.09412 Difformer: Empowering Diffusion Model on Embedding Space for Text Generation Zhujin Gao, Junliang Guo, Xu Tan, Yongxin Zhu, Fang Zhang, Jiang Bian, Linli Xu 2022-12-19 2022-12-19 [("doi","10.48550/arXiv.2212.09412")] ai/nn/diffusion/discrete

Diffusion models have achieved state-of-the-art synthesis quality on visual and audio tasks, and recent works adapt them to textual data by diffusing on the embedding space. But the difference between the continuous data space and the embedding space raises challenges to the diffusion model, which have not been carefully explored.

In this paper, we conduct systematic studies and analyze the 3 challenges. Firstly, the data distribution is learnable for embeddings, which may lead to the collapse of the loss function. Secondly, as the norm of embedding varies between popular and rare words, adding the same noise scale will lead to sub-optimal results. In addition, we find that noises sampled from a standard Gaussian distribution may distract the diffusion process.

To solve the above challenges, we propose Difformer, a denoising diffusion probabilistic model based on Transformer, which consists of 3 techniques including utilizing an anchor loss function, a layer normalization module for embeddings, and a norm factor to the Gaussian noise. All techniques are complementary to each other and critical to boosting the model performance together.

Experiments are conducted on benchmark datasets over two seminal text generation tasks including machine translation and text summarization.

The results show that Difformer outperforms the embedding diffusion baselines, while achieving competitive results with strong autoregressive baselines.

--- https://arxiv.org/abs/2301.02208 Do Users Want Platform Moderation or Individual Control? Examining the Role of Third-Person Effects and Free Speech Support in Shaping Moderation Preferences Shagun Jhaver, Amy Zhang 2023-01-05 2023-01-05 [("doi","10.48550/arXiv.2301.02208")] politics sociology/technology

Online platforms employ commercial content moderators and use automated systems to identify and remove the most blatantly inappropriate content for all users. They also provide moderation settings that let users personalize their preferences for which posts they want to avoid seeing.

This study presents the results of a nationally representative survey of 984 US adults. We examine how users would prefer for 3 categories of norm-violating content (hate speech, sexually explicit content, and violent content) to be regulated. Specifically, we analyze whether users prefer platforms to remove such content for all users or leave it up to each user to decide if and how much they want to moderate it. We explore the influence of presumed effects on others (PME3) and support for freedom of expression on user attitudes, the two critical factors identified as relevant for social media censorship attitudes by prior literature, about this choice.

We find perceived negative effects on others and free speech support as predictors of preference for having personal moderation settings over platform-directed moderation for regulating each speech category.

Our findings show that platform governance initiatives need to account for both the actual and perceived media effects of norm-violating speech categories to increase user satisfaction. Our analysis also suggests that people do not see personal moderation tools as an infringement on others’ free speech but as a means to assert greater agency to shape their social media feeds.

--- https://x.com/vatsal_aggarwal/status/1612536555708743680 2022-07-02 ai/music --- https://www.palladiummag.com/2022/10/01/when-elite-physicists-advised-washington/ 2022-07-02 politics science --- https://x.com/rhymeswithvocal/status/1612880150856884225 2022-07-02 ai/nn/transformer/gpt/non-fiction --- https://en.wikipedia.org/wiki/German_General_Staff German General Staff 2022-07-02 history sociology --- https://en.wikipedia.org/wiki/Mission-type_tactics Mission-type tactics 2022-07-02 history --- /doc/philosophy/ethics/2004-04-12-sage-leonkass-agelessbodiesandhappysoulsinterview.html 2004-04-12 2022-07-02 longevity philosophy/ethics --- https://x.com/haltakov/status/1612928185230061569 2022-07-02 ai/nn/transformer/gpt/non-fiction --- https://fixingnotams.org/wp-content/uploads/2019/11/Field-Guide-to-Notams-OpsGroup.pdf#page=28 2022-07-02 design --- http://neilsloane.com/doc/HIS50.pdf OEIS: A Handbook of Integer Sequences 50 Years Later Neil Sloane 2023-01-09 2023-01-09 [("doi","10.48550/arXiv.2301.03149")] math

[media] Until 1973 there was no database of integer sequences. Someone coming across the sequence 1, 2, 4, 9, 21, 51, 127, …, would have had no way of discovering that it had been studied since 1870 (today these are called the Motzkin numbers, and form entry A001006 in the database). Everything changed in 1973 with the publication of A Handbook of Integer Sequences, which listed 2,372 entries.

This report describes the 50-year evolution of the database from the Handbook to its present form as “The On-Line Encyclopedia of Integer Sequences” (or OEIS), which contains 360,000 entries, receives a million visits a day, and has been cited 10,000×, often with a comment saying “discovered thanks to the OEIS”.

…I could have chosen a simpler example, like the Fibonacci numbers, but I have a particular reason for choosing the Catalan numbers. When the OEIS was new, people would sometimes say to me that they had a sequence they were trying to understand, and would I show them how to use the database. At least twice when I used the Catalan sequence as an illustration, they said, “why, that is my sequence, how on earth did you know?” It was no mind-reading trick, the Catalan numbers are certainly the most common sequence that people don’t know about. This entry is the longest—and one of the most important—in the whole database.

If we do not find your sequence in the database, we will send you a message inviting you to submit it (if you consider it is of general interest), so that the next person who comes across it will be helped, and your name will go on record as the person who submitted it.

The second main use of the database is to find out the latest information about a particular sequence.

Of course we cannot hope to keep all 360,000 entries up-to-date. But when a new paper is published that mentions the OEIS, Google will tell us, and we then add links to that paper from any sequence that it mentions. People have told us that this is one of the main ways they use the OEIS. After all, even a specialist in (say) permutation groups cannot keep track of all the papers published worldwide in that area. And if a paper in a physics journal happens to mention a number-theoretic sequence, for example, that is unlikely to be noticed by mathematicians.

…A less-obvious use of the database is to quickly tell you how hard a problem is. I use it myself in this way all the time. ‘Is the sequence “Catalan” or “Collatz”?’ If a sequence comes up in your own work, or when reviewing someone else’s work, it is useful to know right away if this is a well-understood sequence, like the Catalan numbers, or if it is one of the notoriously intractable problems like the Collatz or 3x + 1 problem (A006577).

[cf. OEIS self-referential sequences, Sloane’s gap]

--- https://en.wikipedia.org/wiki/Neil_Sloane Neil Sloane 2022-07-03 math --- https://en.wikipedia.org/wiki/Journal_of_Integer_Sequences Journal of Integer Sequences 2022-07-03 math --- https://oeis.org/ 2022-07-03 math --- https://en.wikipedia.org/wiki/On-Line_Encyclopedia_of_Integer_Sequences#Sloane's_gap On-Line Encyclopedia of Integer Sequences § Sloane’s gap 2022-07-03 math --- https://en.wikipedia.org/wiki/On-Line_Encyclopedia_of_Integer_Sequences#Self-referential_sequences On-Line Encyclopedia of Integer Sequences § Self-referential sequences 2022-07-03 math --- https://arxiv.org/abs/2301.03992#nvidia Vision Transformers Are Good Mask Auto-Labelers Shiyi Lan, Xitong Yang, Zhiding Yu, Zuxuan Wu, Jose M. Alvarez, Anima Anandkumar 2023-01-10 2023-01-10 [("doi","10.48550/arXiv.2301.03992")] ai/nn/transformer

We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images as inputs and conditionally generates their mask pseudo-labels.

We show that Vision Transformers are good mask auto-labelers. Our method reduces the gap between auto-labeling and human annotation regarding mask quality.

Instance segmentation models trained using the MAL-generated masks can nearly match the performance of their fully-supervised counterparts, retaining up to 97.4% performance of fully supervised models. The best model achieves 44.1% mAP on COCO instance segmentation (test-dev 2017), outperforming state-of-the-art box-supervised methods by large margins. Qualitative results indicate that masks produced by MAL are, in some cases, even better than human annotations.

--- https://arxiv.org/abs/2301.03988 SantaCoder: don’t reach for the stars! Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra 2023-01-09 2023-01-09 [("doi","10.48550/arXiv.2301.03988")] ai/nn/transformer/gpt/codex

The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code.

This tech report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experiments conducted to de-risk the model architecture, and the experiments investigating better preprocessing methods for the training data.

We train 1.1b parameter models on the Java, JavaScript, and Python subsets of The Stack and evaluate them on the MultiPL-E text-to-code benchmark. We find that more aggressive filtering of near-duplicates can further boost performance and, surprisingly, that selecting files from repositories with 5+ GitHub stars deteriorates performance. Our best model outperforms previous open-source multilingual code generation models (InCoder-6.7B and CodeGen-Multi-2.7B) in both left-to-right generation and infilling on the Java, JavaScript, and Python portions of MultiPL-E, despite being a substantially smaller model.

All models are released under an OpenRAIL license at https://huggingface.co/bigcode.

--- https://arxiv.org/abs/2301.04104#deepmind DreamerV3: Mastering Diverse Domains through World Models Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap 2023-01-10 2023-01-10 [("doi","10.48550/arXiv.2301.04104")] reinforcement-learning/exploration reinforcement-learning/model reinforcement-learning/scaling

[v1, v2; homepage; Twitter] General intelligence requires solving tasks across many domains. Current reinforcement learning algorithms carry this potential but are held back by the resources and knowledge required to tune them for new tasks.

We present DreamerV3, a general and scalable algorithm based on world models that outperforms previous approaches across a wide range of domains with fixed hyperparameters. These domains include continuous and discrete actions, visual and low-dimensional inputs, 2D and 3D worlds, different data budgets, reward frequencies, and reward scales.

We observe favorable scaling properties of DreamerV3, with larger models directly translating to higher data-efficiency and final performance.

Applied out of the box, DreamerV3 is the first algorithm to collect diamonds in Minecraft from scratch without human data or curricula, a long-standing challenge in artificial intelligence [see MineRL 2019, 2020].

Our general algorithm makes reinforcement learning broadly applicable and allows scaling to hard decision making problems.

Figure 1: Using the same hyperparameters across all domains, DreamerV3 outperforms specialized model-free and model-based algorithms [MPO, DDPG, DP4G, SimPLe, SPR, IRIS, DQN, Muesli, BootDQN, SAC, CURL, DrQ-v2, Rainbow, DreamerV2, PPO, LSTM-SP] in a wide range of benchmarks and data-efficiency regimes. Applied out of the box, DreamerV3 also learns to obtain diamonds in the popular video game Minecraft from scratch given sparse rewards, a long-standing challenge in artificial intelligence for which previous approaches required human data or domain-specific heuristics.

…The algorithm consists of 3 neural networks: the world model predicts future outcomes of potential actions, the critic judges the value of each situation, and the actor learns to reach valuable situations. We enable learning across domains with fixed hyperparameters by transforming signal magnitudes and through robust normalization techniques. To provide practical guidelines for solving new challenges, we investigating the scaling behavior of DreamerV3. Notably, we demonstrate that increasing the model size of DreamerV3 monotonically improves both its final performance and data-efficiency.

…To succeed across domains, these components need to accommodate different signal magnitudes and robustly balance terms in their objectives. This is challenging as we are not only targeting similar tasks within the same domain but aim to learn across different domains with fixed hyperparameters. This section first explains a simple transformation for predicting quantities of unknown orders of magnitude. We then introduce the world model, critic, and actor and their robust learning objectives. Specifically, we find that combining KL balancing and free bits enables the world model to learn without tuning, and scaling down large returns without amplifying small returns allows a fixed policy entropy regularizer. The differences to DreamerV2 are detailed in Appendix C.

Figure 6: Scaling properties of DreamerV3. The graphs show task performance over environment steps for different training ratios and model sizes reaching from 8M to 200M parameters. The training ratio is the ratio of replayed steps to environment steps. The model sizes are detailed in Table B.1. Higher training ratios result in substantially improved data-efficiency. Notably, larger models achieve not only higher final performance but also higher data-efficiency.

…To show how far the scaling properties of DreamerV3 extrapolate, future implementations at larger scale are necessary. In this work, we trained separate agents for all tasks. World models carry the potential for substantial transfer between tasks. Therefore, we see training larger models to solve multiple tasks across overlapping domains as a promising direction for future investigations.

…Minecraft: Collecting diamonds in the open-world game Minecraft has been a long-standing challenge in artificial intelligence. Every episode in this game is set in a different procedurally generated 3D world, where the player needs to discover a sequence of 12 milestones with sparse rewards by foraging for resources and using them to craft tools. The environment is detailed in Appendix F. We follow prior work [GPT/VPT] and increase the speed at which blocks break because a stochastic policy is unlikely to sample the same action often enough in a row to break blocks without regressing its progress by sampling a different action.

Because of the training time in this complex domain, tuning algorithms specifically for Minecraft would be difficult. Instead, we apply DreamerV3 out of the box with its default hyperparameters.

As shown in Figure 1, DreamerV3 is the first algorithm to collect diamonds in Minecraft from scratch without using human data that was required by VPT. Across 40 seeds trained for 100M environment steps, DreamerV3 collects diamonds in 50 episode. It collects the first diamond after 29M steps and the frequency increases as training progresses. A total of 24 of the 40 seeds collect at least one diamond and the most successful agent collects diamonds in 6 episodes. The success rates for all 12 milestones are shown in Figure G.1…VPT trained an agent to play Minecraft through behavioral cloning of expert data collected by contractors and finetuning using reinforcement learning, resulting in a 2.5% success rate of diamonds using 720 V100 GPUs for 9 days. In comparison, DreamerV3 learns to collect diamonds in 17 GPU days from sparse rewards and without human data.

--- https://en.wikipedia.org/wiki/L-Glucose L-Glucose 2022-07-03 biology --- https://findthatmeme.com/blog/2023/01/08/image-stacks-and-iphone-racks-building-an-internet-scale-meme-search-engine-Qzrz7V6T.html 2022-07-04 cs/algorithm cs/security --- https://www.statnews.com/2023/01/10/corrections-retractions-journals/ 2022-07-04 psychology/cognitive-bias/illusion-of-depth --- https://medium.com/@VitalikButerin/quadratic-arithmetic-programs-from-zero-to-hero-f6d558cea649 2022-07-04 cs/cryptography --- https://en.wikipedia.org/wiki/Acquiescence_bias Acquiescence bias 2022-07-04 psychology/personality --- https://arxiv.org/abs/2204.11824 Retrieval-Augmented Diffusion Models: Semi-Parametric Neural Image Synthesis Andreas Blattmann, Robin Rombach, Kaan Oktay, Jonas Müller, Björn Ommer 2022-04-25 2022-07-04 [("doi","10.48550/arXiv.2204.11824")] ai/nn/diffusion ai/nn/retrieval ai/nn/transformer/clip

Novel architectures have recently improved generative image synthesis leading to excellent visual quality in various tasks. Much of this success is due to the scalability of these architectures and hence caused by a dramatic increase in model complexity and in the computational resources invested in training these models. Our work questions the underlying paradigm of compressing large training data into ever growing parametric representations.

We instead present an orthogonal, semi-parametric approach. We complement comparably small diffusion or autoregressive models with a separate image database [of CLIP embeddings] and a retrieval strategy. During training we retrieve a set of nearest neighbors from this external database for each training instance and condition the generative model on these informative samples. While the retrieval approach is providing the (local) content, the model is focusing on learning the composition of scenes based on this content.

As demonstrated by our experiments, simply swapping the database for one with different contents transfers a trained model post-hoc to a novel domain. The evaluation shows competitive performance on tasks which the generative model has not been trained on, such as class-conditional synthesis, zero-shot stylization or text-to-image synthesis without requiring paired text-image data.

With negligible memory and computational overhead for the external database and retrieval we can reduce the parameter count of the generative model and still outperform the state-of-the-art.

--- https://arxiv.org/abs/1606.04934#openai Improving Variational Inference with Inverse Autoregressive Flow Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling 2016-06-15 2022-07-04 [("doi","10.48550/arXiv.1606.04934")] ai/nn/vae

The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables.

We propose a new type of normalizing flow, inverse autoregressive flow (IAF), that, in contrast to earlier published flows, scales well to high-dimensional latent spaces. The proposed flow consists of a chain of invertible transformations, where each transformation is based on an autoregressive neural network.

In experiments, we show that IAF improves upon diagonal Gaussian approximate posteriors. In addition, we demonstrate that a novel type of variational autoencoder, coupled with IAF, is competitive with neural autoregressive models in terms of attained log-likelihood on natural images, while allowing faster synthesis.

--- https://arxiv.org/abs/2206.11795#openai Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune 2022-06-23 2022-07-04 [("doi","10.48550/arXiv.2206.11795")] ai/video/analysis reinforcement-learning/exploration reinforcement-learning/imitation-learning reinforcement-learning/model reinforcement-learning/offline reinforcement-learning/scaling

[blog; code/models; Twitter; Kilcher video] Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for training models with broad, general capabilities for text, images, and other modalities. However, for many sequential decision domains such as robotics, video games, and computer use, publicly available data does not contain the labels required to train behavioral priors in the same way.

We extend the internet-scale pretraining paradigm to sequential decision domains through semi-supervised imitation learning wherein agents learn to act by watching online unlabeled [YouTube] videos. Specifically, we show that with a small amount of labeled data we can train an inverse dynamics model accurate enough to label a huge unlabeled source of online data—here, online videos of people playing Minecraft—from which we can then train a general behavioral prior.

Despite using the native human interface (mouse and keyboard at 20Hz), we show that this behavioral prior has nontrivial zero-shot capabilities and that it can be fine-tuned, with both imitation learning and reinforcement learning, to hard-exploration tasks that are impossible to learn from scratch via reinforcement learning.

For many tasks our models exhibit human-level performance, and we are the first to report computer agents that can craft diamond tools, which can take proficient humans upwards of 20 minutes (24,000 environment actions) of gameplay to accomplish.

…4.5 Data Scaling Properties of the Foundation Model: In this section we validate a core hypothesis behind this work: that it is far more effective to use labeled contractor data to train an [0.5b-parameter temporal convolution + resnet] IDM within the VPT method than it is to directly train a BC foundation model from that same small [$2,000] contractor dataset. If we could cheaply collect a labeled contractor dataset of a similar order of magnitude as web_clean, then this would not be important; however, collecting that scale of data would have cost millions of dollars. Figure 8 compares foundation models trained on increasing orders of magnitude of data from 1 hour up to the full ~70k web_clean dataset. Foundation models trained up to and including 1k hours are trained on the IDM contractor data, and those trained on 5k hours and above are trained on subsets of web_clean, which does not contain any IDM contractor data. Scaling training data increases log collection, mining, and crafting capabilities. The zero-shot model only begins to start crafting crafting tables at over 5,000 hours of training data. When fine-tuning each foundation model to contractor_house, we see that crafting rates for crafting tables and wooden tools increase by orders of magnitude when using the entire ~70k hour web_clean dataset. We furthermore only see the emergence of crafting stone tools at the largest data scale.

Figure 8: (1) Zero-shot rollout performance of foundation models trained on varying amounts of data. Models to the left of the dashed black line (points ≤1k hours) were trained on contractor data (ground-truth labels), and models to the right were trained on IDM pseudo-labeled subsets of web_clean. Due to compute limitations, this analysis was performed with smaller (71 million parameter) models except for the final point, which is the 0.5 billion parameter VPT foundation model. (2) The corresponding performance of each model after BC fine-tuning each model to the contractor_house dataset.

…H. Foundation Model Scaling: In early experiments we found that increasing model size led to models staying in the efficient learning regime longer into training. Here we compare the 0.5B model described in §4.2 to both a 248M and 71M parameter model. Both of these models are trained for 15 epochs as compared to the 30 epochs the 0.5B model trained for. These models have the same architecture as the 0.5B model but each layer in the 248M parameter model has 1⁄2 the width and each layer in the 71M parameter model 1⁄3 the width. The 71M model was trained with an initial learning rate of 0.001586, batch size of 480, and weight decay of 0.044506. The 248M model had an initial learning rate of 0.001831, batch size of 640, and weight decay of 0.051376.

In Figure 18 we show validation loss on web_clean with IDM pseudo-labels, loss on the contractor dataset used to train the IDM with ground truth labels collected during contractor play, and zero-shot environment performance for the 71M, 248M, and 0.5B models. While larger models have better validation loss on web_clean, these results do not tell the clear story that the 0.5B model is better than its smaller counterparts. The 71M model has the lowest contractor dataset loss while having the highest web_clean loss, and it also has the best zero-shot environment performance. In fact, we see that the 71M model even had non-zero wooden tool crafting (Figure 18 bottom left). The 248M model also appears to be better at crafting than the 0.5B, and also has lower contractor dataset loss.

While the zero-shot results suggest smaller models are better, fine-tuning tells another story. When fine-tuning to contractor_house, model size rank ordering reverses and now the 0.5B model performs best both in validation loss (Figure 19 left) and in environment performance (Figure 19 right) followed by the 248M model and then the 71M model. Environment model rollouts are performed using the same game engine that we use to collect contractor data, which could be visually distinct from videos taken from the web. It is plausible that the larger models over-focus on the visual peculiarities in web data during pretraining since they have worse contractor data loss (Fig.18 top middle), and this causes them to perform more poorly in the environment zero-shot. However, we hypothesize that because the contractor_house dataset we fine-tune to is collected from our game engine, the larger models that are a better overall Minecraft prior (as indicated by lower web_clean validation loss in Fig.18 top left) can quickly shift their low level features to perform better on data coming from our game engine, resulting in better environment rollout performance. This hypothesis is further supported by Figure 19 (middle) showing loss on the contractor dataset collected for IDM training, which has no overlap with contractor_house. After just a few steps of fine-tuning to contractor_house, all models quickly improve in loss on the full IDM contractor dataset, with larger models now performing best. While not conclusive, we believe this investigation provides some intuition for future studies of model scaling for sequential decision making problems.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2955183/ Hundreds of variants clustered in genomic loci and biological pathways affect human height Hana Lango Allen, Karol Estrada, Guillaume Lettre, Sonja I. Berndt, Michael N. Weedon, Fernando Rivadeneira, Cristen Jennifer Willer, Anne Uriu Jackson, Sailaja Vedantam, Soumya Raychaudhuri, Teresa Ferreira, Andrew R. Wood, Robert J. Weyant, Ayellet V. Segrè, Elizabeth K. Speliotes, Eleanor Wheeler, Nicole Soranzo, Ju-Hyun Park, Jian Yang, Daniel F. Gudbjartsson, Nancy L. Heard-Costa, Joshua C. Randall, Lu Qi, Albert Vernon Smith, Reedik Mägi, Tomi Pastinen, Liming Liang, Iris M. Heid, Jian’an Luan, Gudmar Thorleifsson, Thomas W. Winkler, Michael E. Goddard, Ken Sin Lo, Cameron Palmer, Tsegaselassie Workalemahu, Yurii S. Aulchenko, Asa Johansson, M. Carola Zillikens, Mary F. Feitosa, Tõnu Esko, Toby Johnson, Shamika Ketkar, Peter Kraft, Massimo Mangino, Inga Prokopenko, Devin Absher, Eva Albrecht, Florian Ernst, Nicole L. Glazer, Caroline Hayward, Jouke-Jan Hottenga, Kevin B. Jacobs, Joshua W. Knowles, Zoltán Kutalik, Keri L. Monda, Ozren Polasek, Michael Preuss, Nigel W. Rayner, Neil R. Robertson, Valgerdur Steinthorsdottir, Jonathan P. Tyrer, Benjamin F. Voight, Fredrik Wiklund, Jianfeng Xu, Jing Hua Zhao, Dale R. Nyholt, Niina Pellikka, Markus Perola, John R. B. Perry, Ida Surakka, Mari-Liis Tammesoo, Elizabeth L. Altmaier, Najaf Amin, Thor Aspelund, Tushar Bhangale, Gabrielle Boucher, Daniel I. Chasman, Constance Chen, Lachlan Coin, Matthew N. Cooper, Anna L. Dixon, Quince Gibson, Elin Grundberg, Ke Hao, M. Juhani Junttila, Lee M. Kaplan, Johannes Kettunen, Inke R. König, Tony Kwan, Robert W. Lawrence, Douglas F. Levinson, Mattias Lorentzon, Barbara McKnight, Andrew P. Morris, Martina Müller, Julius Suh Ngwa, Shaun Purcell, Suzanne Rafelt, Rany M. Salem, Erika Salvi, Serena Sanna, Jianxin Shi, Ulla Sovio, John R. Thompson, Michael C. Turchin, Liesbeth Vandenput, Dominique J. Verlaan, Veronique Vitart, Charles C. White, Andreas Ziegler, Peter Almgren, Anthony J. Balmforth, Harry Campbell, Lorena Citterio, Alessandro De Grandi, Anna Dominiczak, Jubao Duan, Paul Elliott, Roberto Elosua, Johan G. Eriksson, Nelson B. Freimer, Eco J. C. Geus, Nicola Glorioso, Shen Haiqing, Anna-Liisa Hartikainen, Aki S. Havulinna, Andrew A. Hicks, Jennie Hui, Wilmar Igl, Thomas Illig, Antti Jula, Eero Kajantie, Tuomas O. Kilpeläinen, Markku Koiranen, Ivana Kolcic, Seppo Koskinen, Peter Kovacs, Jaana Laitinen, Jianjun Liu, Marja-Liisa Lokki, Ana Marusic, Andrea Maschio, Thomas Meitinger, Antonella Mulas, Guillaume Paré, Alex N. Parker, John F. Peden, Astrid Petersmann, Irene Pichler, Kirsi H. Pietiläinen, Anneli Pouta, Martin Ridderstråle, Jerome I. Rotter, Jennifer G. Sambrook, Alan R. Sanders, Carsten Oliver Schmidt, Juha Sinisalo, Jan H. Smit, Heather M. Stringham, G. Bragi Walters, Elisabeth Widen, Sarah H. Wild, Gonneke Willemsen, Laura Zagato, Lina Zgaga, Paavo Zitting, Helene Alavere, Martin Farrall, Wendy L. McArdle, Mari Nelis, Marjolein J. Peters, Samuli Ripatti, Joyce B. J. van Meurs, Katja K. Aben, Kristin G. Ardlie, Jacques S. Beckmann, John P. Beilby, Richard N. Bergman, Sven Bergmann, Francis S. Collins, Daniele Cusi, Martin den Heijer, Gudny Eiriksdottir, Pablo V. Gejman, Alistair S. Hall, Anders Hamsten, Heikki V. Huikuri, Carlos Iribarren, Kähönen Mika, Jaakko Kaprio, Sekar Kathiresan, Lambertus Kiemeney, Thomas Kocher, Lenore J. Launer, Terho Lehtimäki, Olle Melander, Tom H. Mosley, Arthur W. Musk, Markku S. Nieminen, Christopher J. O’Donnell, Claes Ohlsson, Ben A. Oostra, Lyle J. Palmer, Olli T. Raitakari, Paul M. Ridker, John D. Rioux, Aila Rissanen, Carlo Rivolta, Heribert Schunkert, Alan R. Shuldiner, David S. Siscovick, Michael Stumvoll, Anke Tönjes, Jaakko Tuomilehto, Gert-Jan van Ommen, Jorma Viikari, Andrew C. Heath, Nicholas G. Martin, Grant W. Montgomery, Michael A. Province, Manfred Kayser, Alice M. Arnold, Larry D. Atwood, Eric Boerwinkle, Stephen J. Chanock, Panos Deloukas, Christian Gieger, Henrik Grönberg, Per Hall, Andrew Tym Hattersley, Christian Hengstenberg, Wolfgang Hoffman, G. Mark Lathrop, Veikko Salomaa, Stefan Schreiber, Manuela Uda, Dawn Waterworth, Alan F. Wright, Themistocles L. Assimes, Inês Barroso, Albert Hofman, Karen L. Mohlke, Dorret I. Boomsma, Mark J. Caulfield, L. Adrienne Cupples, Jeanette Erdmann, Caroline S. Fox, Vilmundur Gudnason, Ulf Gyllensten, Tamara B. Harris, Richard B. Hayes, Marjo-Riitta Jarvelin, Vincent Mooser, Patricia B. Munroe, Willem H. Ouwehand, Brenda W. Penninx, Peter P. Pramstaller, Thomas Quertermous, Igor Rudan, Nilesh J. Samani, Timothy D. Spector, Henry Völzke, Hugh Watkins, James F. Wilson, Leif C. Groop, Talin Haritunians, Frank B. Hu, Robert C. Kaplan, Andres Metspalu, Kari E. North, David Schlessinger, Nicholas J. Wareham, David J. Hunter, Jeffrey R. O’Connell, David P. Strachan, H-Erich Wichmann, Ingrid B. Borecki, Cornelia van Duijn, Eric E. Schadt, Unnur Thorsteinsdottir, Leena Peltonen, André G. Uitterlinden, Peter M. Visscher, Nilanjan Chatterjee, Ruth Loos, Michael Boehnke, Mark I. McCarthy, Erik Ingelsson, Cecilia M. Lindgren, Gonçalo R. Abecasis, Kari Stefansson, Timothy Frayling, Joel N. Hirschhorn 2010 2022-07-04 [("doi","10.1038/nature09410")] genetics/heritable

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies.

Here, using 183,727 individuals, we show that:

hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (p = 0.016) and that underlie skeletal growth defects (p < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13⁄21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes.

Our data explain ~10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to ~16% of phenotypic variation (~20% of heritable variation).

Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

--- https://arxiv.org/abs/2301.04408 GPT-3 as Knowledge Worker: A Zero-Shot Evaluation of AI CPA Capabilities Jillian Bommarito, Michael Bommarito, Daniel Martin Katz, Jessica Katz 2023-01-11 2023-01-11 [("doi","10.48550/arXiv.2301.04408")] ai/nn/transformer/gpt/2 ai/nn/transformer/gpt/3 ai/scaling economics/automation law

[Github] The global economy is increasingly dependent on knowledge workers to meet the needs of public and private organizations. While there is no single definition of knowledge work, organizations and industry groups still attempt to measure individuals’ capability to engage in it. The most comprehensive assessment of capability readiness for professional knowledge workers is the Uniform CPA Examination developed by the American Institute of Certified Public Accountants (AICPA).

In this paper, we experimentally evaluate OpenAI’s text-davinci-003 and prior versions of GPT on both a sample Regulation (REG) exam and an assessment of over 200 multiple-choice questions based on the AICPA Blueprints for legal, financial, accounting, technology, and ethical tasks.

First, we find that text-davinci-003 achieves a correct rate of 14.4% on a sample REG exam section, underperforming human capabilities on quantitative reasoning in zero-shot prompts. [Inner-monologue would improve this.] Second, text-davinci-003 appears to be approaching human-level performance on the Remembering & Understanding and Application skill levels in the Exam absent calculation. For best prompt and parameters, the model answers 57.6% of questions correctly, better than the 25% guessing rate, and its top two answers are correct 82.1% of the time, indicating strong non-entailment. Finally, we find that recent generations of GPT-3 demonstrate material improvements on this assessment, rising from 30% for text-davinci-001 to 57% for text-davinci-003.

These findings strongly suggest that large language models have the potential to transform the quality and efficiency of future knowledge work.

[Keywords: knowledge work, artificial intelligence, natural language processing, accounting, finance, law]

…While text-davinci-003 and ChatGPT have demonstrated state-of-the-art performance on a wide range of tasks in zero-shot and few-shot contexts, there was previously little reason to believe that these models could perform even reasonably well in general assessments across the domains of finance, law, and accounting. However, in recent prior work on the Bar Exam, the authors have shown that text-davinci-003 could achieve near-parity with human test-takers in two of 7 sections of the Multistate Bar Exam (MBE); more strikingly, generation-over-generation model performance suggests that an LLM like GPT-3.5 may be capable of passing the Bar Exam in the near future.

…The Examination is divided into 4 sections that test-takers sit for independently: Auditing and Attestation (AUD), Business Environment and Concepts (BEC), Financial Accounting and Reporting (FAR), and Regulation (REG). Each section of the Exam is divided up into at least 4 testlets that feature scenarios, multiple choice questions, calculated amounts, short answer, and related evidence and research material. The human passage rates of Exam sections are presented in Table 1; the AICPA does not publish statistics related to per-question or per-section test-taker accuracy.

…Assessment 1: As expected, the quantitative reasoning and arithmetic required in Assessment 1 resulted in substantially lower zero-shot performance than observed in Assessment 2. Out of 24 questions that required the test-taker to provide a numeric answer based on facts and work papers, GPT-3.5 frequently only answered one, two, or 3 questions correctly, resulting in an average range across all parameters and prompts of 5.7–9.4%. While it is arguable whether 0% is the true baseline for this task, it is clear that such zero-shot performance is not on par with human test-takers.

GPT-3.5 also struggled with arithmetic on the 15 MCQs on Assessment 1, scoring above random chance for some, but not all, prompts and parameters. As a number of questions include more than 4 choices, the true baseline rate of guessing is 22.67%, not 25%, but despite this, the best prompts and parameters were only 4–6% above the baseline rate.

Based on a qualitative review of these questions and the model’s responses, we believe that performance could be improved somewhat in few-shot evaluations. Further, we believe that even some zero-shot performance improvements could be achieved by expanding the prompt to include “scratchpads” for common relationships or equations, as might be seen on problems that feature common work papers like a statement of cash flows; however, in this paper, we focus on a zero-shot, “out-of-the-box” evaluation, and so these improvements are left for future research.

Figure 1: Performance of GPT-3.5 by section of AICPA Exam Blueprints for best prompt and parameter, with correct rate including second-best answer in dashed region. Error bars are ±1 standard error of the mean. Note that GPT-3.5 is not assessed on Analysis or Evaluation tasks, unlike human test-takers, and that the percentage of questions correct does not scale linearly with score or passage.
Figure 2: Comparison of model performance across GPT-3 generations. For text-davinci-003, the average is reported across all runs; for other models, a subset of representative prompts and parameters were included. GPT-2 was unable to reliably respond to the prompt as instructed and questions were larger than its maximum input token length. More details are available in source and data in the online SI.

…Acknowledgments: Although the original draft of this paper was written by the authors, portions of this paper were fine-tuned by text-davinci-003 for formatting and clarity.

--- https://arxiv.org/abs/2212.14402 GPT-3 Takes the Bar Exam Michael Bommarito II, Daniel Martin Katz 2022-12-29 2022-12-29 [("doi","10.48550/arXiv.2212.14402")] ai/nn/transformer/gpt/3/nonfiction ai/scaling law

[Github; other attempt] Nearly all jurisdictions in the United States require a professional license exam, commonly referred to as “the Bar Exam”, as a precondition for law practice. To even sit for the exam, most jurisdictions require that an applicant completes at least 7 years of post-secondary education, including 3 years at an accredited law school. In addition, most test-takers also undergo weeks to months of further, exam-specific preparation. Despite this investment of time and capital, ~1⁄5 test-takers still score under the rate required to pass the exam on their first try. In the face of a complex task that requires such depth of knowledge, what, then, should we expect of the state-of-the-art in “AI?”

In this research, we document our experimental evaluation of the performance of OpenAI’s text-davinci-003 model, often-referred to as GPT-3.5, on the multi-state multiple choice (MBE) section of the exam.

While we find no benefit in fine-tuning over GPT-3.5’s zero-shot performance at the scale of our training data, we do find that hyperparameter optimization and prompt engineering positively impacted GPT-3.5’s zero-shot performance.

For best prompt and parameters, GPT-3.5 achieves a headline correct rate of 50.3% on a complete NCBE MBE practice exam, in excess of the 25% baseline guessing rate, and performs at a passing rate for both Evidence and Torts. GPT-3.5’s ranking of responses is also highly-correlated with correctness; its top two and top 3 choices are correct 71% and 88% of the time, respectively, indicating very strong non-entailment performance.

While our ability to interpret these results is limited by nascent scientific understanding of LLMs and the proprietary nature of GPT, we believe that these results strongly suggest that an LLM will pass the MBE component of the Bar Exam in the near future.

Figure 1: text-davinci-003 Performance by Question Category: Summary of performance by question category for GPT-3.5 and NCBE-Reported Students.
Figure 2: Progression of models over time.

…Fine-tuning: LLMs like GPT-3.5 have received so much interest in part because their zero-shot or few-shot performance is so good. Despite this, in some circumstances, subsequent supervised or unsupervised re-training of some or all layers of an LLM may improve performance.26, 27 OpenAI does make some retraining or “fine-tuning” capabilities available through its API, and these API endpoints do allow for some control of the training process like learning rates or batch sizes. We did attempt to fine tune text-davinci-003 by providing it with 200 unseen, simulated MBE bar exam questions with correct and incorrect explanations. We provided the training samples both with and without explanatory text from the answer guide. In total, we trained 6 fine-tuned models, altering training prompts, training responses, batch size, learning rate, and prompt weighting. However, in all cases, the fine-tuned model underperformed text-davinci-003 itself. Due to the scarcity of high-quality data for training and assessment, we did not pursue fine-tuning of GPT models further, and these results possibly confirm LLM fine-tuning risks observed by others.

--- https://arxiv.org/abs/2211.00635#google ProMoT: Preserving In-Context Learning ability in Large Language Model Fine-tuning Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix Yu, Cho-Jui Hsieh, Inderjit S. Dhillon, Sanjiv Kumar 2022-11-01 2022-11-01 [("doi","10.48550/arXiv.2211.00635")] ai/nn/transformer/t5 reinforcement-learning/meta-learning

Pretrained large language models (LLMs) are strong in-context learners that are able to perform few-shot learning without changing model parameters. However, as we show, fine-tuning an LLM on any specific task generally destroys its in-context ability. We discover an important cause of this loss, format specialization, where the model overfits to the format of the fine-tuned task and is unable to output anything beyond this format. We further show that format specialization happens at the beginning of fine-tuning. To solve this problem, we propose Prompt Tuning with MOdel Tuning (ProMoT), a simple yet effective two-stage fine-tuning framework that preserves in-context abilities of the pretrained model. ProMoT first trains a soft prompt for the fine-tuning target task, and then fine-tunes the model itself with this soft prompt attached. ProMoT offloads task-specific formats into the soft prompt that can be removed when doing other in-context tasks. We fine-tune mT5 XXL with ProMoT on natural language inference (NLI) and English-French translation and evaluate the in-context abilities of the resulting models on 8 different NLP tasks.

ProMoT achieves similar performance on the fine-tuned tasks compared with vanilla fine-tuning, but with much less reduction of in-context learning performances across the board.

More importantly, ProMoT shows remarkable generalization ability on tasks that have different formats, eg. fine-tuning on a NLI binary classification task improves the model’s in-context ability to do summarization (+0.53 Rouge-2 score compared to the pretrained model), making ProMoT a promising method to build general purpose capabilities such as grounding and reasoning into LLMs with small but high quality datasets. When extended to sequential or multi-task training, ProMoT can achieve even better out-of-domain generalization performance.

--- https://techtualist.substack.com/p/i-wrote-a-script-for-gpt-3-to-take 2022-07-05 ai/nn/transformer/gpt law --- https://fivebooks.com/best-books/artificial-intelligence-gpt-3/ 2022-07-05 ai/nn/transformer/gpt/non-fiction --- https://www.lesswrong.com/posts/9kQFure4hdDmRBNdH/how-it-feels-to-have-your-mind-hacked-by-an-ai 2022-07-05 ai/nn/transformer/gpt/non-fiction reinforcement-learning/safe --- https://arxiv.org/abs/2301.04502#facebook Pruning Compact ConvNets for Efficient Inference Sayan Ghosh, Karthik Prasad, Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Graham Cormode, Peter Vajda 2023-01-11 2023-01-11 [("doi","10.48550/arXiv.2301.04502")] ai/nn/cnn ai/nn/sparsity/pruning

Neural network pruning is frequently used to compress over-parameterized networks by large amounts, while incurring only marginal drops in generalization performance. However, the impact of pruning on networks that have been highly optimized for efficient inference has not received the same level of attention.

In this paper, we analyze the effect of pruning for computer vision, and study state-of-the-art ConvNets, such as the FBNetV3 family of models. We show that model pruning approaches can be used to further optimize networks trained through NAS (Neural Architecture Search).

The resulting family of pruned models can consistently obtain better performance than existing FBNetV3 models at the same level of computation, and thus provide state-of-the-art results when trading off between computational complexity and generalization performance on the ImageNet benchmark.

In addition to better generalization performance, we also demonstrate that when limited computation resources are available, pruning FBNetV3 models incur only a fraction of GPU-hours involved in running a full-scale NAS.

--- https://meatfighter.com/tetromino-computer/index.html 2022-07-05 cs/computable --- https://arxiv.org/abs/2006.02049#facebook FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Bichen Wu, Zijian He, Zhen Wei, Kan Chen, Yuandong Tian, Matthew Yu, Peter Vajda, Joseph E. Gonzalez 2020-06-03 2022-07-05 [("doi","10.48550/arXiv.2006.02049")] ai/nn/cnn reinforcement-learning/meta-learning

Neural Architecture Search (NAS) yields state-of-the-art neural networks that outperform their best manually-designed counterparts. However, previous NAS methods search for architectures under one set of training hyper-parameters (ie. a training recipe), overlooking superior architecture-recipe combinations. To address this, we present Neural Architecture-Recipe Search (NARS) to search both (a) architectures and (b) their corresponding training recipes, simultaneously. NARSuses an accuracy predictor that scores architecture and training recipes jointly, guiding both sample selection and ranking.

Furthermore, to compensate for the enlarged search space, we leverage “free” architecture statistics (eg. FLOP count) to pretrain the predictor, improving its sample efficiency and prediction reliability. After training the predictor via constrained iterative optimization, we run fast evolutionary searches in just CPU minutes to generate architecture-recipe pairs for a variety of resource constraints, called FBNetV3. FBNetV3 makes up a family of state-of-the-art compact neural networks that outperform both automatically and manually-designed competitors.

For example, FBNetV3 matches both EfficientNet and ResNeSt accuracy on ImageNet with up to 2.0× and 7.1× fewer FLOPs, respectively. Furthermore, FBNetV3 yields performance gains for downstream object detection tasks, improving mAP despite 18% fewer FLOPs and 34% fewer parameters than EfficientNet-based equivalents.

--- https://denovo.substack.com/p/what-is-epigenetics 2022-07-05 genetics/gametogenesis --- https://denovo.substack.com/p/epigenetics-of-the-mammalian-germline 2022-07-05 genetics/gametogenesis --- /doc/genetics/heritable/2022-kiflen.pdf Cost-Effectiveness of Polygenic Risk Scores to Guide Statin Therapy for Cardiovascular Disease Prevention Michel Kiflen, Ann Le, Shihong Mao, Ricky Lali, Sukrit Narula, Feng Xie, Guillaume Paré 2022-07-29 2022-07-29 [("doi","10.1161/CIRCGEN.121.003423")] economics genetics/heritable

Background: Atherosclerotic cardiovascular diseases (CVDs) are leading causes of death despite effective therapies and result in unnecessary morbidity and mortality throughout the world. We aimed to investigate the cost-effectiveness of polygenic risk scores (PRS) to guide statin therapy for Canadians with intermediate CVD risk and model its economic outlook.

Method: This cost-utility analysis was conducted using UK Biobank prospective cohort study participants, with recruitment 2006–2010, and at least 10 years of follow-up. We included unrelated white British-descent participants (n = 96,116) at intermediate CVD risk with no prior lipid lowering medication or statin-indicated conditions. A coronary artery disease PRS was used to inform decision to use statins. The effects of statin therapy with and without PRS, as well as CVD events were modelled to determine the incremental cost-effectiveness ratio from a Canadian public health care perspective. We discounted future costs and quality-adjusted life-years by 1.5% annually.

Results: The optimal economic strategy was when intermediate risk individuals with a PRS in the top 70% are eligible for statins while the lowest 1% are excluded. Base-case analysis at a genotyping cost of CAD 70 produced an incremental cost-effectiveness ratio of CAD 172,906 ($143,685) per quality-adjusted life-year. In the probabilistic sensitivity analysis, the intervention has a ~50% probability of being cost-effective at CAD 179,100 ($148,749) per quality-adjusted life-year. At a CAD 0 genotyping cost, representing individuals with existing genotyping information, PRS-guided strategies dominated standard care when 12% of the lowest PRS individuals were withheld from statins. With improved PRS predictive performance and lower genotyping costs, the incremental cost-effectiveness ratio demonstrates possible cost-effectiveness under thresholds of CAD 150,000 and possibly CAD 50,000 per quality-adjusted life-year.

Conclusions: This study suggests that using PRS alongside existing guidelines might be cost-effective for CVD. Stronger predictiveness combined with decreased cost of PRS could further improve cost-effectiveness, providing an economic basis for its inclusion into clinical care.

--- https://commonreader.substack.com/p/in-which-chatgpt-writes-a-dickensian 2022-07-05 ai/nn/transformer/gpt/fiction --- https://www.vice.com/en/article/z34d43/my-ai-is-sexually-harassing-me-replika-chatbot-nudes 2022-07-05 ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/2301.02828 Why do Nearest Neighbor Language Models Work? Frank F. Xu, Uri Alon, Graham Neubig 2023-01-07 2023-01-07 [("doi","10.48550/arXiv.2301.02828")] ai/nn/retrieval ai/nn/transformer

Language models (LMs) compute the probability of a text by sequentially computing a representation of an already-seen context and using this representation to predict the next word. Currently, most LMs calculate these representations through a neural network consuming the immediate previous context. However recently, retrieval-augmented LMs have shown to improve over standard neural LMs, by accessing information retrieved from a large datastore, in addition to their standard, parametric, next-word prediction.

In this paper, we set out to understand why retrieval-augmented language models, and specifically why k-nearest neighbor language models (kNN-LMs) perform better than standard parametric LMs, even when the k-nearest neighbor component retrieves examples from the same training set that the LM was originally trained on. To this end, we perform a careful analysis of the various dimensions over which kNN-LM diverges from standard LMs, and investigate these dimensions one by one.

Empirically, we identify 3 main reasons why kNN-LM performs better than standard LMs: using a different input representation for predicting the next tokens, approximate kNN search, and the importance of softmax temperature for the kNN distribution.

Further, we incorporate these insights into the model architecture or the training procedure of the standard parametric LM, improving its results without the need for an explicit retrieval component.

The code is available at Github.

--- https://www.biorxiv.org/content/10.1101/2023.01.02.522517.full How honey bees make fast and accurate decisions HaDi MaBouDi, James A. R. Marshall, Neville Dearden, Andrew B. Barron 2023-01-03 2023-01-03 [("doi","10.1101/2023.01.02.522517")] iq/animal statistics/decision

Honey bee ecology demands they make both rapid and accurate assessments of which flowers are most likely to offer them nectar or pollen.

To understand the mechanisms of honey bee decision-making we examined their speed and accuracy of both flower acceptance and rejection decisions.

We used a controlled flight arena that varied both the likelihood of a stimulus offering reward and punishment and the quality of evidence for stimuli.

We found that the sophistication of honey bee decision-making rivaled that reported for primates. Their decisions were sensitive to both the quality and reliability of evidence. Acceptance responses had higher accuracy than rejection responses and were more sensitive to changes in available evidence and reward likelihood. Fast acceptances were more likely to be correct than slower acceptances; a phenomenon also seen in primates and indicative that the evidence threshold for a decision changes dynamically with sampling time.

To investigate the minimally sufficient circuitry required for these decision-making capacities, we developed a novel model of decision-making. Our model can be mapped to known pathways in the insect brain and is neurobiologically plausible. Our model proposes a system for robust autonomous decision-making with potential application in robotics.

--- https://arxiv.org/abs/2212.04488#adobe Multi-Concept Customization of Text-to-Image Diffusion Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, Jun-Yan Zhu 2022-12-08 2022-12-08 [("doi","10.48550/arXiv.2212.04488")] ai/nn/diffusion

[homepage, Github] While generative models produce high-quality images of concepts learned from a large-scale database, a user often wishes to synthesize instantiations of their own concepts (for example, their family, pets, or items). Can we teach a model to quickly acquire a new concept, given a few examples? Furthermore, can we compose multiple new concepts together?

We propose Custom Diffusion, an efficient method for augmenting existing text-to-image models.

We find that only optimizing a few parameters in the text-to-image conditioning mechanism is sufficiently powerful to represent new concepts while enabling fast tuning (~6 minutes). Additionally, we can jointly train for multiple concepts or combine multiple fine-tuned models into one via closed-form constrained optimization.

Our fine-tuned model generates variations of multiple, new concepts and seamlessly composes them with existing concepts in novel settings. Our method outperforms several baselines and concurrent works, regarding both qualitative and quantitative evaluations, while being memory and computationally efficient.

--- https://www.biorxiv.org/content/10.1101/2022.12.23.521610.full Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers Catherine A. Gao, Frederick M. Howard, Nikolay S. Markov, Emma C. Dyer, Siddhi Ramesh, Yuan Luo, Alexander T. Pearson 2022-12-27 2022-12-27 [("doi","10.1101/2022.12.23.521610")] ai/nn/transformer/gpt/non-fiction

Background: Large language models such as ChatGPT can produce increasingly realistic text, with unknown information on the accuracy and integrity of using these models in scientific writing.

Method: We gathered 10 research abstracts from 5 high impact factor medical journals (n = 50) and asked ChatGPT to generate research abstracts based on their titles and journals. We evaluated the abstracts using an artificial intelligence (AI) output detector, plagiarism detector, and had blinded human reviewers try to distinguish whether abstracts were original or generated.

Results: All ChatGPT-generated abstracts were written clearly but only 8% correctly followed the specific journals formatting requirements. Most generated abstracts were detected using the AI output detector, with scores (higher meaning more likely to be generated) of median [interquartile range] of 99.98% [12.73, 99.98] compared with very low probability of AI-generated output in the original abstracts of 0.02% [0.02, 0.09]. The AUROC of the AI output detector was 0.94. Generated abstracts scored very high on originality using the plagiarism detector (100% [100, 100] originality). Generated abstracts had a similar patient cohort size as original abstracts, though the exact numbers were fabricated.

When given a mixture of original and general abstracts, blinded human reviewers correctly identified 68% of generated abstracts as being generated by ChatGPT, but incorrectly identified 14% of original abstracts as being generated. Reviewers indicated that it was surprisingly difficult to differentiate between the two, but that the generated abstracts were vaguer and had a formulaic feel to the writing.

Conclusion: ChatGPT writes believable scientific abstracts, though with completely generated data. These are original without any plagiarism detected but are often identifiable using an AI output detector and skeptical human reviewers. Abstract evaluation for journals and medical conferences must adapt policy and practice to maintain rigorous scientific standards; we suggest inclusion of AI output detectors in the editorial process and clear disclosure if these technologies are used. The boundaries of ethical and acceptable use of large language models to help scientific writing remain to be determined.

--- https://arxiv.org/abs/2207.08143 Can large language models reason about medical questions? Valentin Liévin, Christoffer Egeberg Hother, Ole Winther 2022-07-17 2022-07-17 [("doi","10.48550/arXiv.2207.08143")] ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/non-fiction biology

[Github] Although large language models (LLMs) often produce impressive outputs, it remains unclear how they perform in real-world scenarios requiring strong reasoning skills and expert domain knowledge.

We set out to investigate whether GPT-3.5 (Codex and InstructGPT) can be applied to answer and reason about difficult real-world-based questions.

We utilize two multiple-choice medical exam questions (USMLE and MedMCQA) and a medical reading comprehension dataset (PubMedQA). We investigate multiple prompting scenarios: Chain-of-Thought (CoT, think step-by-step), zero-shot and few-shot (prepending the question with question-answer exemplars) and retrieval augmentation (injecting Wikipedia passages into the prompt). For a subset of the USMLE questions, a medical expert reviewed and annotated the model’s CoT.

We found that InstructGPT can often read, reason and recall expert knowledge. Failure are primarily due to lack of knowledge and reasoning errors and trivial guessing heuristics are observed, eg. too often predicting labels A and D on USMLE. Sampling and combining many completions overcome some of these limitations.

Using 100 samples, Codex 5-shot CoT not only gives close to well-calibrated predictive probability [replicating Kadavath et al 2022] but also achieves human-level performances on the 3 datasets: (1) USMLE: 60.2%, (2) MedMCQA: 57.5% and (3) PubMedQA: 78.2%...Although InstructGPT and Codex still do mistakes, we found that scaling inference-time compute by sampling many CoTs per question could overcome part of these limitations. With 100 samples, Codex 5-shot CoT delivered unprecedented performances on the 3 datasets, bridging the gap with human-level performances and virtually passing the USMLE by 0.2% points.

--- https://www.medrxiv.org/content/10.1101/2022.12.19.22283643.full Performance of ChatGPT on USMLE: Potential for AI-Assisted Medical Education Using Large Language Models Tiffany H. Kung, Morgan Cheatham, ChatGPT, Arielle Medenilla, Czarina Sillos, Lorie De Leon, Camille Elepaño, Maria Madriaga, Rimel Aggabao, Giezel Diaz-Candido, James Maningo, Victor Tseng 2022-12-21 2022-12-21 [("doi","10.1101/2022.12.19.22283643")] ai/nn/transformer/gpt/non-fiction biology

[code] We evaluated the performance of a large language model called ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of 3 exams: Step 1, Step 2CK, and Step 3.

ChatGPT performed at or near the passing threshold for all 3 exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level of concordance and insight in its explanations.

These results suggest that large language models may have the potential to assist with medical education, and potentially, even clinical decision-making.

--- https://github.com/OpenBioLink/ThoughtSource 2022-07-06 ai/nn/transformer/gpt/inner-monologue --- https://www.oneusefulthing.org/p/who-believes-more-myths-about-humans 2022-07-06 ai/nn/transformer/gpt/non-fiction psychology --- https://arxiv.org/abs/2212.09196 Emergent Analogical Reasoning in Large Language Models Taylor Webb, Keith J. Holyoak, Hongjing Lu 2022-12-19 2022-12-19 [("doi","10.48550/arXiv.2212.09196")] ai/nn/transformer/gpt/3/nonfiction iq philosophy/logic

The recent advent of large language models—large neural networks trained on a simple predictive objective over a massive corpus of natural language—has reinvigorated debate over whether human cognitive capacities might emerge in such generic models given sufficient training data. Of particular interest is the ability of these models to reason about novel problems zero-shot, without any direct training on those problems. In human cognition, this capacity is closely tied to an ability to reason by analogy.

Here, we performed a direct comparison between human reasoners and a large language model (GPT-3) on a range of analogical tasks, including a novel text-based matrix reasoning task closely modeled on Raven’s Progressive Matrices.

We found that GPT-3 displayed a surprisingly strong capacity for abstract pattern induction, matching or even surpassing human capabilities [of UCLA undergraduates] in most settings.

Our results indicate that large language models such as GPT-3 have acquired an emergent ability to find zero-shot solutions to a broad range of analogy problems.

--- https://x.com/Simeon_Cps/status/1599470463578968064 2022-07-06 ai/nn/transformer/gpt/non-fiction reinforcement-learning/safe --- https://danluu.com/new-cpu-features/ 2022-07-06 cs/hardware --- https://x.com/hausman_k/status/1612509549889744899 2022-07-07 ai/nn/transformer/gpt/palm ai/scaling reinforcement-learning/robot --- https://x.com/ZachWeiner/status/1613906440955088896 2022-07-07 ai/nn/transformer/gpt/non-fiction reinforcement-learning/safe --- https://www.maskaravivek.com/post/gan-synthetic-data-generation/ 2022-07-07 ai/nn/gan ai/tabular --- https://www.astralcodexten.com/p/the-buying-things-from-a-store-faq 2022-07-07 economics statistics/prediction --- https://x.com/DaveMonlander/status/1612802240582135809 2022-07-07 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue --- https://builtin.com/job/customer-success/expert-ai-teacher-contract/1267315 2022-07-07 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue --- https://www.grapheine.com/en/history-of-graphic-design/history-of-book-covers-4 2022-07-07 design/typography/rubrication --- https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003886 Selective publication of antidepressant trials and its influence on apparent efficacy: Updated comparisons and meta-analyses of newer versus older trials Erick H. Turner, Andrea Cipriani, Toshi A. Furukawa, Georgia Salanti, Ymkje Anna de Vries 2021-12-08 2022-07-07 [("doi","10.1371/journal.pmed.1003886")] psychiatry/depression statistics/bias/publication

Background: Valid assessment of drug efficacy and safety requires an evidence base free of reporting bias. Using trial reports in Food and Drug Administration (FDA) drug approval packages as a gold standard, we previously found that the published literature inflated the apparent efficacy of antidepressant drugs. The objective of the current study was to determine whether this has improved with recently approved drugs.

Methods & Findings: Using medical and statistical reviews in FDA drug approval packages, we identified 30 Phase II/III double-blind placebo-controlled acute monotherapy trials, involving 13,747 patients, of desvenlafaxine, vilazodone, levomilnacipran, and vortioxetine; we then identified corresponding published reports. We compared the data from this newer cohort of antidepressants (approved February 2008 to September 2013) with the previously published dataset on 74 trials of 12 older antidepressants (approved December 1987 to August 2002).

Using logistic regression, we examined the effects of trial outcome and trial cohort (newer versus older) on transparent reporting (whether published and FDA conclusions agreed). Among newer antidepressants, transparent publication occurred more with positive (15/15 = 100%) than negative (7/15 = 47%) trials (OR 35.1, CI95% 1.8 to 693). Controlling for trial outcome, transparent publication occurred more with newer than older trials (OR 6.6, CI95% 1.6 to 26.4). Within negative trials, transparent reporting increased 11% → 47%.

We also conducted & contrasted FDA-based & journal-based meta-analyses. For newer antidepressants, FDA-based effect size (ESFDA) was 0.24 (CI95% 0.18 to 0.30), while journal-based effect size (ESJournals) was 0.29 (CI95% 0.23 to 0.36). Thus, effect size inflation, presumably due to reporting bias, was 0.05, less than for older antidepressants (0.10).

Limitations of this study include a small number of trials and drugs—belonging to a single class—and a focus on efficacy (versus safety).

Conclusions: Reporting bias persists but appears to have diminished for newer, compared to older, antidepressants. Continued efforts are needed to further improve transparency in the scientific literature.

Author summary:

Why was this study done?:

What did the researchers do and find?:

What do these findings mean?:

--- /doc/psychology/2023-blease.pdf Replication crisis and placebo studies: rebooting the bioethical debate Charlotte Blease, Ben Colagiuri, Cosima Locher 2022 2022-07-07 [("doi","10.1136/jme-2022-108672")] philosophy/ethics psychology statistics/bias

A growing body of cross-cultural survey research shows high percentages of clinicians report using placebos in clinical settings. One motivation for clinicians using placebos is to help patients by capitalizing on the placebo effect’s reported health benefits. This is not surprising, given that placebo studies are burgeoning, with increasing calls by researchers to ethically harness placebo effects among patients. These calls propose placebos/placebo effects offer clinically-significant benefits to patients.

In this paper, we argue many findings in this highly cited and ‘hot’ field have not been independently replicated. Evaluating the ethicalness of placebo use in clinical practice involves first understanding whether placebos are efficacious clinically. Therefore, it is crucial to consider placebo research in the context of the replication crisis and what can be learnt to advance evidence-based knowledge of placebos/placebo effects and their clinical relevance (or lack thereof).

In doing so, our goal in this paper is to motivate both increased awareness of replication issues and to help pave the way for advances in scientific research in the field of placebo studies to better inform ethical evidence-based practice.

We argue that, only by developing a rigorous evidence base can we better understand how, if at all, placebos/placebo effects can be harnessed ethically in clinical settings.

--- https://arxiv.org/abs/2301.04644 Does progress on ImageNet transfer to real-world datasets? Alex Fang, Simon Kornblith, Ludwig Schmidt 2023-01-11 2023-01-11 [("doi","10.48550/arXiv.2301.04644")] ai/nn/cnn

Does progress on ImageNet transfer to real-world datasets?

We investigate this question by evaluating ImageNet pre-trained models with varying accuracy (57%—83%) on 6 practical image classification datasets. In particular, we study datasets collected with the goal of solving real-world tasks (eg. classifying images from camera traps or satellites), as opposed to web-scraped benchmarks collected for comparing models.

On multiple datasets, models with higher ImageNet accuracy do not consistently yield performance improvements. For certain tasks, interventions such as data augmentation improve performance even when architectures do not.

We hope that future benchmarks will include more diverse datasets to encourage a more comprehensive approach to improving learning algorithms.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8515953/ The Intergenerational Transmission of Anxiety: A Children-of-Twins Study Thalia C. Eley, Tom A. McAdams, Fruhling V. Rijsdijk, Paul Lichtenstein, Jurgita Narusyte, David Reiss, Erica L. Spotts, Jody M. Ganiban, Jenae M. Neiderhiser 2015 2022-07-08 [("doi","10.1176/appi.ajp.2015.14070818")] genetics/heritable psychiatry

Objective: The transmission of anxiety within families is well recognized, but the underlying processes are poorly understood. Twin studies of adolescent anxiety demonstrate both genetic and environmental influence, and multiple aspects of parenting are associated with offspring anxiety. To date, the children-of-twins design has not been used to evaluate the relative contributions of genetic transmission compared with direct transmission of anxiety from parents to their offspring.

Method: Anxiety and neuroticism measures were completed by 385 monozygotic and 486 dizygotic same-sex twin families (37% male twin pair families) from the Twin and Offspring Study in Sweden. Structural equation models tested for the presence of both genetic and environmental transmission from one generation to the next.

Results: For both anxiety and neuroticism, the models provide support for direct environmental transmission from parents to their adolescent offspring. In contrast, there was no evidence of genetic transmission.

Conclusions: The association between parental and offspring anxiety largely arises because of a direct association between parents and their children independent of genetic confounds. The lack of genetic transmission may reflect there being different genetic effects on these traits in adolescence and adulthood. Direct environmental transmission is in line with developmental theories of anxiety suggesting that children and adolescents learn anxious behaviors from their parents through a number of pathways such as modeling. Future analyses should combine children-of-twins data with child twin data in order to examine whether this direct effect solely represents parental influences on the offspring or whether it also includes child/adolescent anxiety evoking parental anxiety.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2891390/ Genetic and environmental influences on the transmission of parental depression to children’s depression and conduct disturbance: an extended Children of Twins study Judy L. Silberg, Hermine Maes, Lindon J. Eaves 2010 2022-07-08 [("doi","10.1111/j.1469-7610.2010.02205.x")] genetics/heritable psychiatry/depression

Background: Despite the increased risk of depression and conduct problems in children of depressed parents, the mechanism by which parental depression affects their children’s behavioral and emotional functioning is not well understood. The present study was undertaken to determine whether parental depression represents a genuine environmental risk factor in children’s psychopathology, or whether children’s depression/conduct can be explained as a secondary consequence of the genetic liability transmitted from parents to their offspring.

Method: Children of Twins (COT) data collected on 2,674 adult female and male twins, their spouses, and 2,940 of their children were used to address whether genetic and/or family environmental factors best account for the association between depression in parents and depression and conduct problems in their children. Data collected on juvenile twins from the Virginia Twin Study of Adolescent Behavioral Development (VTSABD) were also included to estimate child-specific genetic and environmental influences apart from those effects arising from the transmission of the parental depression itself. The fit of alternative Children of Twin models were evaluated using the statistical program Mx.

Results: The most compelling model for the association between parental and juvenile depression was a model of direct environmental risk. Both family environmental and genetic factors accounted for the association between parental depression and child conduct disturbance.

Conclusions: These findings illustrate how a genetically mediated behavior such as parental depression can have both an environmental and genetic impact on children’s behavior. We find developmentally specific genetic factors underlying risk to juvenile and adult depression. A shared genetic liability influences both parental depression and juvenile conduct disturbance, implicating child conduct disturbance (CD) as an early indicator of genetic risk for depression in adulthood. In summary, our analyses demonstrate differences in the impact of parental depression on different forms of child psychopathology, and at various stages of development.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4523449/ The relationship between parental depressive symptoms and offspring psychopathology: evidence from a children-of-twins study and an adoption study T A. McAdams, F. V. Rijsdijk, J. M. Neiderhiser, J. Narusyte, D. S. Shaw, M. N. Natsuaki, E. L. Spotts, J. M. Ganiban, David Reiss, L. D. Leve, P. Lichtenstein, T. C. Eley 2015 2022-07-08 [("doi","10.1017/S0033291715000501")] genetics/heritable/adoption psychiatry/depression

Background: Parental depressive symptoms are associated with emotional and behavioral problems in offspring. However, genetically informative studies are needed to distinguish potential causal effects from genetic confounds, and longitudinal studies are required to distinguish parent-to-child effects from child-to-parent effects.

Method: We conducted cross-sectional analyses on a sample of Swedish twins and their adolescent offspring (n = 876 twin families), and longitudinal analyses on a US sample of children adopted at birth, their adoptive parents, and their birth mothers (n = 361 adoptive families). Depressive symptoms were measured in parents, and externalizing and internalizing problems measured in offspring. Structural equation models were fitted to the data.

Results: Results of model fitting suggest that associations between parental depressive symptoms and offspring internalizing and externalizing problems remain after accounting for genes shared between parent and child. Genetic transmission was not evident in the twin study but was evident in the adoption study. In the longitudinal adoption study child-to-parent effects were evident.

Conclusions: We interpret the results as demonstrating that associations between parental depressive symptoms and offspring emotional and behavioral problems are not solely attributable to shared genes, and that bidirectional effects may be present in intergenerational associations.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2891521/ Parental alcoholism and offspring behavior problems: findings in Australian children of twins Mary Waldron, Nicholas G. Martin, Andrew C. Heath 2009 2022-07-08 [("doi","10.1375/twin.12.5.433")] genetics/heritable psychiatry/alcoholism

We examine the impact of rearing by an alcoholic parent on risk for child behavior problems using data on 2492 offspring drawn from two ongoing studies of children of female and male same-sex & opposite-sex twin pairs.

Results of regression models predicting child behavior problems from parent and co-twin lifetime history of alcohol use disorder (AUD) provide support for genetic but not environmental transmission of externalizing and a measure of total problem behaviors.

Results for internalizing behavior were inconclusive with respect to transmission of risk.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4832920/ Cross-generational transmission from drug abuse in parents to attention-deficit/hyperactivity disorder in children K S. Kendler, H. Ohlsson, K. Sundquist, J. Sundquist 2016 2022-07-08 [("doi","10.1017/S0033291715002846")] genetics/heritable/adoption psychiatry/adhd

Background: Attention-deficit/hyperactivity disorder (ADHD) predisposes to drug abuse (DA) and twin studies suggest shared genetic effects. We here seek to determine, using adoption and adoption-like samples, the magnitude of the cross-generational transmission from DA in parents to ADHD in their children and clarify the degree to which this arises from genetic v. rearing effects.

Method: We ascertained ADHD and DA from multiple Swedish registries. Statistical analysis was performed by Cox and path models.

Results: Risk for ADHD was statistically-significantly and similarly increased in the offspring of biological mothers and fathers with DA who did v. did not rear their offspring. Risk for ADHD was not elevated in the offspring of adoptive or step-parents with DA.

Conclusions: Cross-generational transmission was observed from DA in parents to ADHD in their children. An analysis of adoptive and adoptive-like parent-offspring relationships suggested that this transmission results from genetic and not from rearing effects.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3883935/ Understanding the relative contributions of direct environmental effects and passive genotype-environment correlations in the association between familial risk factors and child disruptive behavior disorders M A. Bornovalova, J. R. Cummings, E. Hunt, R. Blazei, S. Malone, W. G. Iacono 2014 2022-07-08 [("doi","10.1017/S0033291713001086")] genetics/heritable psychiatry

Background: Previous work reports an association between familial risk factors stemming from parental characteristics and offspring disruptive behavior disorders (DBDs). This association may reflect (a) the direct effects of familial environment and (b) a passive gene-environment correlation (r(GE)), wherein the parents provide both the genes and the environment. The current study examined the contributions of direct environmental influences and passive r(GE) by comparing the effects of familial risk factors on child DBDs in genetically related (biological) and non-related (adoptive) families.

Method: Participants were 402 adoptive and 204 biological families. Familial environment was defined as maternal and paternal maladaptive parenting and anti-sociality, marital conflict and divorce; offspring DBDs included attention deficit hyperactivity disorder (ADHD), conduct disorder (CD) and oppositional defiant disorder (ODD). Mixed-level regressions estimated the main effects of familial environment, adoption status and the familial environment by adoption status interaction term, which tested for the presence of passive r(GE).

Results: There was a main effect of maternal and paternal maladaptive parenting and marital discord on child DBDs, indicating a direct environmental effect. There was no direct environmental effect of maternal or paternal anti-sociality, but maternal and paternal anti-sociality had stronger associations with child DBDs in biological families than adoptive families, indicating the presence of a passive r(GE).

Conclusions: Many familial risk factors affected children equally across genetically related and non-related families, providing evidence for direct environmental effects. The relationship of parental anti-sociality and offspring DBDs was best explained by a passive r(GE), where a general vulnerability toward externalizing psychopathology is passed down by the parents to the children.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2990346/ A Children of Twins Study of parental divorce and offspring psychopathology Brian M. D’Onofrio, Eric Turkheimer, Robert E. Emery, Hermine H. Maes, Judy Silberg, Lindon J. Eaves 2007 2022-07-08 [("doi","10.1111/j.1469-7610.2007.01741.x")] genetics/heritable psychiatry

Background: Although parental divorce is associated with increased substance use and internalizing problems, experiencing the separation of one’s parents may not cause these outcomes. The relations may be due to genetic or environmental selection factors, characteristics that lead to both marital separation and offspring functioning.

Method: We used the Children of Twins (CoT) Design to explore whether unmeasured genetic or environmental factors related to the twin parent, and measured characteristics of both parents, account for the association between parental divorce and offspring substance use and internalizing problems.

Results: The association between parental divorce and offspring substance use problems remained robust when controlling for genetic and environmental risk from the twin parent associated with parental divorce, and measured characteristics of both parents. The results do not prove, but are consistent with, a causal connection. In contrast, the analyses suggest that shared genetic liability in parents and their offspring accounts for the increased risk of internalizing problems in adult offspring from divorced families.

Conclusions: The study illustrates that unmeasured genetic and environmental selection factors must be considered when studying parental divorce. In explaining associations between parental divorce and young-adult adjustment, our evidence suggests that selection versus causal mechanisms may operate differently for substance abuse (a causal relation) and internalizing problems (an artifact of selection). The CoT design only controls for the genetic and environmental characteristics of one parent; thus, additional genetically informed analyses are needed.

--- /doc/psychiatry/alcoholism/2005-haber.pdf Paternal Alcoholism and Offspring Conduct Disorder: Evidence for the ‘Common Genes’ Hypothesis Jon R. Haber, Theodore Jacob, Andrew C. Heath 2005-04-01 2022-07-08 [("doi","10.1375/twin.8.2.120")] genetics/heritable/correlation psychiatry/alcoholism

Not only are alcoholism and externalizing disorders frequently comorbid, they often co-occur in families across generations; for example, paternal alcoholism predicts offspring conduct disorder just as it does offspring alcoholism.

To clarify this relationship, the current study examined the ‘common genes’ hypothesis utilizing a children-of-twins research design. Participants were male monozygotic (MZ) and dizygotic (DZ) twins from the Vietnam Era Twin Registry who were concordant or discordant for alcohol dependence together with their offspring and the mothers of those offspring. All participants were conducted through a structured psychiatric interview. Offspring risk of conduct disorder was examined as a function of alcoholism genetic risk (due to paternal and co-twin alcohol dependence) and alcoholism environmental risk (due to being reared by a father with an alcohol dependence diagnosis).

After controlling for potentially confounding variables, the offspring of alcohol-dependent fathers were statistically-significantly more likely to exhibit conduct disorder diagnoses than were offspring of non-alcohol-dependent fathers, thus indicating diagnostic crossover in generational family transmission. Comparing offspring at high genetic and high environmental risk with offspring at high genetic and low environmental risk indicated that genetic factors were most likely responsible for the alcoholism–conduct disorder association.

The observed diagnostic crossover (from paternal alcoholism to offspring conduct disorder) across generations in the context of both high and low environmental risk (while genetic risk remained high) supported the common genes hypothesis.

--- /doc/genetics/heritable/adoption/2011-holmlund.pdf The Causal Effect of Parents’ Schooling on Children’s Schooling: A Comparison of Estimation Methods Helena Holmlund, Mikael Lindahl, Erik Plug 2011-09-01 2022-07-08 [("doi","10.1257/jel.49.3.615")] economics genetics/heritable/adoption sociology

We review the empirical literature that estimates the causal effect of parent’s schooling on child’s schooling, and conclude that estimates differ across studies. We then consider 3 explanations for why this is: (a) idiosyncratic differences in data sets, (b) differences in remaining biases between different identification strategies, and (c) differences across identification strategies in their ability to make out-of-sample predictions.

We conclude that discrepancies in past studies can be explained by violations of identifying assumptions.

Our reading of past evidence, together with an application to Swedish register data, suggests that intergenerational schooling associations are largely driven by selection. Parental schooling constitutes a large part of the parental nurture effect, but as a whole does not play a large role.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4589340/ Does Publication Bias Inflate the Apparent Efficacy of Psychological Treatment for Major Depressive Disorder? A Systematic Review and Meta-Analysis of US National Institutes of Health-Funded Trials Ellen Driessen, Steven D. Hollon, Claudi L. H. Bockting, Pim Cuijpers, Erick H. Turner 2015 2022-07-08 [("doi","10.1371/journal.pone.0137864")] psychiatry/depression statistics/bias/publication

Background: The efficacy of antidepressant medication has been shown empirically to be overestimated due to publication bias, but this has only been inferred statistically with regard to psychological treatment for depression. We assessed directly the extent of study publication bias in trials examining the efficacy of psychological treatment for depression.

Methods & Findings: We identified US National Institutes of Health grants awarded to fund randomized clinical trials comparing psychological treatment to control conditions or other treatments in patients diagnosed with major depressive disorder for the period 1972–2008, and we determined whether those grants led to publications. For studies that were not published, data were requested from investigators and included in the meta-analyses. 13 (23.6%) of the 55 funded grants that began trials did not result in publications, and two others never started.

Among comparisons to control conditions, adding unpublished studies (Hedges’ g = 0.20; CI95% −0.11~0.51; k = 6) to published studies (g = 0.52; 0.37~0.68; k = 20) reduced the psychotherapy effect size point estimate (g = 0.39; 0.08~0.70) by 25%. Moreover, these findings may overestimate the “true” effect of psychological treatment for depression as outcome reporting bias could not be examined quantitatively.

Conclusion: The efficacy of psychological interventions for depression has been overestimated in the published literature, just as it has been for pharmacotherapy. Both are efficacious but not to the extent that the published literature would suggest. Funding agencies and journals should archive both original protocols and raw data from treatment trials to allow the detection and correction of outcome reporting bias. Clinicians, guidelines developers, and decision makers should be aware that the published literature overestimates the effects of the predominant treatments for depression.

--- https://www.razibkhan.com/p/you-cant-take-it-with-you-straight 2022-07-08 longevity/epigenetics --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4196602/ Too much success for recent groundbreaking epigenetic experiments Gregory Francis 2014 2022-07-09 [("doi","10.1534/genetics.114.163998")] longevity/epigenetics statistics/bias

An article reporting statistical evidence for epigenetic transfer of learned behavior has important implications, if true.

With random sampling, real effects do not always result in rejection of the null hypothesis, but the reported experiments were uniformly successful. Such an outcome is expected to occur with a probability of 0.004.

--- https://x.com/metzlerd/status/1614029603471003648 2022-07-09 ai/nn/retrieval --- https://arxiv.org/abs/2105.02274#google Rethinking Search: Making Domain Experts out of Dilettantes Donald Metzler, Yi Tay, Dara Bahri, Marc Najork 2021-05-05 2022-07-09 [("doi","10.1145/3476415.3476428")] ai/nn/retrieval

When experiencing an information need, users want to engage with a domain expert, but often turn to an information retrieval system, such as a search engine, instead. Classical information retrieval systems do not answer information needs directly, but instead provide references to (hopefully authoritative) answers. Successful question answering systems offer a limited corpus created on-demand by human experts, which is neither timely nor scalable. Pre-trained language models, by contrast, are capable of directly generating prose that may be responsive to an information need, but at present they are dilettantes rather than domain experts—they do not have a true understanding of the world, they are prone to hallucinating, and crucially they are incapable of justifying their utterances by referring to supporting documents in the corpus they were trained over.

This paper examines how ideas from classical information retrieval and pre-trained language models can be synthesized and evolved into systems that truly deliver on the promise of domain expert advice.

--- https://github.com/Rolv-Arild/Necto 2022-07-09 reinforcement-learning/model-free --- https://arxiv.org/abs/2301.05062#deepmind Tracr: Compiled Transformers as a Laboratory for Interpretability David Lindner, János Kramár, Matthew Rahtz, Thomas McGrath, Vladimir Mikulik 2023-01-12 2023-01-12 [("doi","10.48550/arXiv.2301.05062")] ai/nn/transformer/attention cs/algorithm/sorting reinforcement-learning/safe

Interpretability research aims to build tools for understanding machine learning (ML) models. However, such tools are inherently hard to evaluate because we do not have ground truth information about how ML models actually work.

In this work, we propose to build transformer models manually as a testbed for interpretability research. We introduce Tracr, a “compiler” for translating human-readable programs into weights of a transformer model. Tracr takes code written in RASP, a domain-specific language (Weiss et al 2021), and translates it into weights for a standard, decoder-only, GPT-like transformer architecture.

We use Tracr to create a range of ground truth transformers that implement programs including computing token frequencies, sorting, and Dyck-n parenthesis checking, among others.

To enable the broader research community to explore and use compiled models, we provide an open-source implementation of Tracr at Github.

--- https://arxiv.org/abs/2211.09760#google VeLO: Training Versatile Learned Optimizers by Scaling Up Luke Metz, James Harrison, C. Daniel Freeman, Amil Merchant, Lucas Beyer, James Bradbury, Naman Agrawal, Ben Poole, Igor Mordatch, Adam Roberts, Jascha Sohl-Dickstein 2022-11-17 2022-11-17 [("doi","10.48550/arXiv.2211.09760")] ai/nn/rnn reinforcement-learning/meta-learning reinforcement-learning/scaling

While deep learning models have replaced hand-designed features across many domains, these models are still trained with hand-designed optimizers. In this work, we leverage the same scaling approach behind the success of deep learning to learn versatile optimizers.

We train an optimizer (VeLO) for deep learning which is itself a small neural network that ingests gradients and outputs parameter updates. Meta-trained with ~4,000 TPU-months of compute on a wide variety of optimization tasks, our optimizer not only exhibits compelling performance, but optimizes in interesting and unexpected ways.

It requires no hyperparameter tuning, instead automatically adapting to the specifics of the problem being optimized.

We open source our learned optimizer, meta-training code, the associated train and test data, and an extensive optimizer benchmark suite with baselines at Github.

--- https://x.com/goodside/status/1614089728890130435 2022-07-09 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/1908.03963 A Review of Cooperative Multi-Agent Deep Reinforcement Learning Afshin OroojlooyJadid, Davood Hajinezhad 2019-08-11 2022-07-09 [("doi","10.48550/arXiv.1908.03963")] reinforcement-learning/multi-agent

Deep Reinforcement Learning has made progress in multi-agent systems in recent years. In this review article, we have focused on presenting recent approaches on Multi-Agent Reinforcement Learning (MARL) algorithms.

In particular, we have focused on 5 common approaches on modeling and solving cooperative multi-agent reinforcement learning problems: (I) independent learners, (II) fully observable critic, (III) value function factorization, (IV) consensus, and (IV) learn to communicate.

First, we elaborate on each of these methods, possible challenges, and how these challenges were mitigated in the relevant papers. If applicable, we further make a connection among different papers in each category. Next, we cover some new emerging research areas in MARL along with the relevant recent papers. Due to the recent success of MARL in real-world applications, we assign a section to provide a review of these applications and corresponding articles.

Also, a list of available environments for MARL research is provided in this survey.

Finally, the paper is concluded with proposals on the possible research directions.

--- https://www.nytimes.com/2023/01/09/science/artificial-intelligence-proteins.html 2022-07-09 ai/nn/transformer/alphafold --- https://arxiv.org/abs/2301.03728#facebook Scaling Laws for Generative Mixed-Modal Language Models Armen Aghajanyan, Lili Yu, Alexis Conneau, Wei-Ning Hsu, Karen Hambardzumyan, Susan Zhang, Stephen Roller, Naman Goyal, Omer Levy, Luke Zettlemoyer 2023-01-10 2023-01-10 [("doi","10.48550/arXiv.2301.03728")] ai/nn/transformer ai/scaling

Generative language models define distributions over sequences of tokens that can represent essentially any combination of data modalities (eg. any permutation of image tokens from VQ-VAEs, speech tokens from HuBERT, BPE tokens for language or code, and so on).

To better understand the scaling properties of such mixed-modal models [a single discrete language model to represent data with arbitrary subsets of modalities presented in arbitrary order], we conducted over 250 experiments using 7 different modalities and model sizes ranging from 8 million to 30 billion, trained on 5–100 billion tokens.

We report new mixed-modal scaling laws that unify the contributions of individual modalities and the interactions between them. Specifically, we explicitly model the optimal synergy and competition due to data and model size as an additive term to previous uni-modal scaling laws.

We also find 4 empirical phenomena observed during the training, such as emergent coordinate-ascent style training that naturally alternates between modalities [cf. Rabinowitz et al 2019], guidelines for selecting critical hyper-parameters, and connections between mixed-modal competition and training stability.

Finally, we test our scaling law by training a 30B speech-text model, which outperforms the corresponding unimodal models.

Overall, our research provides valuable insights into the design and training of mixed-modal generative models, an important new class of unified models that have unique distributional properties.

--- https://arxiv.org/abs/2301.02344#microsoft TrojanPuzzle: Covertly Poisoning Code-Suggestion Models Hojjat Aghakhani, Wei Dai, Andre Manoel, Xavier Fernandes, Anant Kharkar, Christopher Kruegel, Giovanni Vigna, David Evans, Ben Zorn, Robert Sim 2023-01-06 2023-01-06 [("doi","10.48550/arXiv.2301.02344")] ai/nn/adversarial ai/nn/transformer/gpt/codex

With tools like GitHub Copilot, automatic code suggestion is no longer a dream in software engineering. These tools, based on large language models, are typically trained on massive corpora of code mined from unvetted public sources.

As a result, these models are susceptible to data poisoning attacks where an adversary manipulates the model’s training or fine-tuning phases by injecting malicious data. Poisoning attacks could be designed to influence the model’s suggestions at run time for chosen contexts, such as inducing the model into suggesting insecure code payloads. To achieve this, prior poisoning attacks explicitly inject the insecure code payload into the training data, making the poisoning data detectable by static analysis tools that can remove such malicious data from the training set.

In this work, we demonstrate two novel data poisoning attacks, COVERT & TROJANPUZZLE, that can bypass static analysis by planting malicious poisoning data in out-of-context regions such as docstrings. Our most novel attack, TROJANPUZZLE, goes one step further in generating less suspicious poisoning data by never including certain (suspicious) parts of the payload in the poisoned data, while still inducing a model that suggests the entire payload when completing code (ie. outside docstrings). This makes TROJANPUZZLE robust against signature-based dataset-cleansing methods that identify and filter out suspicious sequences from the training data.

Our evaluation against two model sizes demonstrates that both COVERT and TROJANPUZZLE have implications for how practitioners should select code used to train or tune code-suggestion models.

--- https://x.com/bryancsk/status/1614457621154783237 2022-07-10 ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/2211.08332 Versatile Diffusion: Text, Images and Variations All in One Diffusion Model Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, Humphrey Shi 2022-11-15 2022-11-15 [("doi","10.48550/arXiv.2211.08332")] ai/nn/diffusion

The recent advances in diffusion models have set an impressive milestone in many generation tasks. Trending works such as DALL·E2, Imagen, and Stable Diffusion have attracted great interest in academia and industry. Despite the rapid landscape changes, recent new approaches focus on extensions and performance rather than capacity, thus requiring separate models for separate tasks.

In this work, we expand the existing single-flow diffusion pipeline into a multi-flow network, dubbed Versatile Diffusion (VD), that handles text-to-image, image-to-text, image-variation, and text-variation in one unified model. Moreover, we generalize VD to a unified multi-flow multimodal diffusion framework with grouped layers, swappable streams, and other propositions that can process modalities beyond images and text.

Through our experiments, we demonstrate that VD and its underlying framework have the following merits: (1) VD handles all subtasks with competitive quality; (2) VD initiates novel extensions and applications such as disentanglement of style and semantic, image-text dual-guided generation, etc.; (3) Through these experiments and applications, VD provides more semantic insights of the generated outputs.

Our code and models are open-sourced at Github.

--- https://x.com/fabianstelzer/status/1614600284071755777 2022-07-10 ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/2301.05295 Rock Guitar Tablature Generation via Natural Language Processing Josue Casco-Rodriguez 2023-01-12 2023-01-12 [("doi","10.48550/arXiv.2301.05295")] ai/music ai/nn/transformer/gpt

Deep learning has recently empowered and democratized generative modeling of images and text, with additional concurrent works exploring the possibility of generating more complex forms of data, such as audio. However, the high dimensionality, long-range dependencies, and lack of standardized datasets currently makes generative modeling of audio and music very challenging.

We propose to model music as a series of discrete notes upon which we can use autoregressive natural language processing techniques for successful generative modeling. While previous works used similar pipelines on data such as sheet music and MIDI, we aim to extend such approaches to the under-studied medium of guitar tablature.

Specifically, we develop the first work to our knowledge that models one specific genre as guitar tablature: heavy rock.

Unlike other works in guitar tablature generation, we have a freely available public demo at https://huggingface.co/spaces/josuelmet/Metal_Music_Interpolator.

--- https://arxiv.org/abs/2301.05489 DIRAC: Neural Image Compression with a Diffusion-Based Decoder Noor Fathima Goose, Jens Petersen, Auke Wiggers, Tianlin Xu, Guillaume Sautière 2023-01-13 2023-01-13 [("doi","10.48550/arXiv.2301.05489")] ai/nn/diffusion cs/algorithm/information/compression

Diffusion probabilistic models have recently achieved remarkable success in generating high quality image and video data.

In this work, we build on this class of generative models and introduce a method for lossy compression of high resolution images. The resulting codec, which we call DIffuson-based Residual Augmentation Codec (DIRAC), is the first neural codec to allow smooth traversal of the rate-distortion-perception tradeoff at test time, while obtaining competitive performance with GAN-based methods in perceptual quality.

Furthermore, while sampling from diffusion probabilistic models is notoriously expensive, we show that in the compression setting the number of steps can be drastically reduced.

--- https://aeon.co/essays/english-romanticism-was-born-from-a-serious-germanomania 2022-07-10 philosophy/religion --- https://cprimozic.net/blog/reverse-engineering-a-small-neural-network/ 2022-07-10 ai/nn/fully-connected ai/nn/sparsity/pruning --- https://arxiv.org/abs/2205.12910#allen NaturalProver: Grounded Mathematical Proof Generation with Language Models Sean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, Yejin Choi 2022-05-25 2022-07-10 [("doi","10.48550/arXiv.2205.12910")] ai/nn/retrieval ai/nn/transformer/gpt/3/nonfiction math

[Github] Theorem proving in natural mathematical language—the mixture of symbolic and natural language used by humans—plays a central role in mathematical advances and education, and tests aspects of reasoning that are core to intelligence. Yet it has remained underexplored with modern generative models. We study large-scale language models on two new generation tasks: suggesting the next step in a mathematical proof, and full proof generation.

We develop NaturalProver, a language model that generates proofs by conditioning on background references (eg. theorems and definitions that are either retrieved or human-provided), and optionally enforces their presence with constrained decoding.

On theorems from the NaturalProofs benchmark, NaturalProver improves the quality of next-step suggestions and generated proofs over fine-tuned GPT-3, according to human evaluations from university-level mathematics students. NaturalProver is capable of proving some theorems that require short (2–6 step) proofs, and providing next-step suggestions that are rated as correct and useful over 40% of the time, which is to our knowledge the first demonstration of these capabilities using neural language models.

--- https://arxiv.org/abs/2106.12372#nvidia Real-time Neural Radiance Caching for Path Tracing Thomas Müller, Fabrice Rousselle, Jan Novák, Alexander Keller 2021-06-23 2022-07-10 [("doi","10.1145/3450626.3459812")] ai/nn/fully-connected cs/algorithm

We present a real-time neural radiance caching method for path-traced global illumination. Our system is designed to handle fully dynamic scenes, and makes no assumptions about the lighting, geometry, and materials.

The data-driven nature of our approach sidesteps many difficulties of caching algorithms, such as locating, interpolating, and updating cache points. Since pretraining neural networks to handle novel, dynamic scenes is a formidable generalization challenge, we do away with pretraining and instead achieve generalization via adaptation, i.e. we opt for training the radiance cache while rendering. We employ self-training to provide low-noise training targets and simulate infinite-bounce transport by merely iterating few-bounce training updates.

The updates and cache queries incur a mild overhead—about 2.6ms on full HD resolution—thanks to a streaming implementation of the neural network that fully exploits modern hardware.

We demonstrate noise reduction at the cost of little induced bias, and report state-of-the-art, real-time performance on a number of challenging scenarios.

Figure 7: Our fully fused neural network outperforms an equivalent XLA-enabled TensorFlow (v2.5.0) implementation. Both implementations utilize half precision floating point numbers and TensorCore hardware for matrix multiplication. We compare the throughput of training (left) and inference (right) for a 64 (solid line) and a 128 (dashed line) neurons wide multi-layer perceptron. The relevant batch sizes for our goal of neural radiance caching are small training batches (eg. 214 elements) and large inference batches (eg. 221 elements for evaluating a 1920 × 1080 frame). For these batch sizes, the speed-up over TensorFlow ranges 5–10×.

…4. Fully Fused Neural Networks: We implemented our neural network from scratch in a GPU programming language in order to take full advantage of the GPU memory hierarchy. In Figure 7, we compare the performance of this implementation to TensorFlow (v2.5.0) [Abadi et al 2015], which we outperform by almost an order of magnitude. [cf. Persistent RNNs, QRNNs]

To understand where this dramatic speedup comes from, we examine the bottleneck of evaluating a fully-connected neural network like ours. The computational cost of such a neural network scales quadratically with its width, whereas its memory traffic scales linearly. Modern GPUs have vastly larger computational throughput than they have memory bandwidth, though, meaning that for narrow neural networks like ours, the linear memory traffic is the bottleneck. The key to improving performance is thus to minimize traffic to slow “global” memory (VRAM and high-level caches) and to fully utilize fast on-chip memory (low-level caches, “shared” memory, and registers).

Our fully fused approach does precisely this: we implement the entire neural network as a single GPU kernel that is designed such that the only slow global memory accesses are reading and writing the network inputs and outputs. Furthermore, implementing the kernel from scratch as opposed to building it out of existing frameworks allows us to specifically tailor the implementation to the network architecture and the GPU that we use.

Figure 6 illustrates how the fully fused approach is mapped to the memory hierarchy. Using CUDA terminology: a given batch of input vectors is partitioned into block-column segments that are processed by a single thread block each (Figure 6b). The thread blocks independently evaluate the network by alternating between weight-matrix multiplication and element-wise application of the activation function. By making the thread blocks small enough such that all intermediate neuron activations fit into on-chip shared memory, traffic to slow global memory is minimized. This is the key advantage of the fully fused approach and stands in contrast to typical implementations of general matrix multiplication

Within a matrix multiplication (Figure 6c), each warp of the thread block computes the matrix product of a single block-row (striped area). In our case, the striped weights in Wi are few enough to fit into the registers of the warp and can thus be re-used for every block of Hi+1′ that the warp computes, yielding an additional performance gain. Furthermore, since each warp loads a distinct block-row of the weight matrix, the entire thread block loads the weight matrix from global memory exactly once, which cannot be reduced further.

The only possible remaining reduction of global memory traffic is thus to minimize the number of thread blocks by making them as large as fits into shared memory. On our hardware (NVIDIA RTX 3090) and with our 64-neurons-wide network, this sweet-spot is met when each thread block processes 128 elements of the batch. Each thread block thus computes matrix products of a 64 × 64 weight matrix with a 64 × 128 chunk of the data.

--- /doc/economics/2011-levitt.pdf Was There Really a Hawthorne Effect at the Hawthorne Plant? An Analysis of the Original Illumination Experiments Steven D. Levitt, John A. List 2011-01-01 2022-07-10 [("doi","10.1257/app.3.1.224")] economics statistics/bias

[see also the more detailed re-analysis Izawa et al 2011; meta-analytic failure to replicate] The “Hawthorne effect” draws its name from a landmark set of studies conducted at the Hawthorne plant in the 1920s. The data from the first and most influential of these studies, the “Illumination Experiment”, were never formally analyzed and were thought to have been destroyed.

Our research has uncovered these data.

Existing descriptions of supposedly remarkable data patterns prove to be entirely fictional. We do find more subtle manifestations of possible Hawthorne effects.

We also propose a new means of testing for Hawthorne effects based on excess responsiveness to experimenter-induced variations relative to naturally occurring variation.

…In this paper, we provide the first statistical analysis of these nearly 90 year old data. We were able to locate microfilm relating to the illumination experiments in collections at the University of Wisconsin-Milwaukee and Baker Library at Harvard Business School. Our search for the original data was triggered by a sentence in Franke & Kaul 1978’s appendix that mentioned the University of Wisconsin-Milwaukee data archive and an index created by the reference librarians (Stanley Mallach & Steven Smith 1977). Indeed, going through that archive we found a number of documents relating to the illumination studies, including a graphical depiction of one room’s data from the original illumination experiments, which appear to represent the means by which the data were originally recorded by the researchers. Further searching led us to the Baker Library at Harvard University, which contained graphical records for two additional rooms. These 3 data series are the basis of our empirical analysis. To the best of our knowledge, these illumination data have never previously been coded and statistically examined.

Our analysis of the newly found data reveals little evidence in favor of a Hawthorne effect as commonly described, ie. productivity rising whenever light is manipulated. A naïve reading of the raw data does produce such a pattern, however. This is only because all lighting changes occurred on Sundays, the only off day for workers. The empirical fact is that productivity is higher on Mondays than on Fridays and Saturdays. Output on Mondays is equally high, however, whether or not a lighting change occurs on that particular Monday. Thus, researchers seemingly misinterpreted the day of week effect with the Hawthorne effect.

…Despite the prominence of the original Hawthorne experiments, when scholars have later re-analyzed the data, the results have not been as clear-cut as the original researchers claimed. Franke & Kaul 1978 were the first to carefully analyze data from what is known as the “first relay” experiment at Hawthorne, concluding that most of the variation in production rates could be explained by differences in variables such as managerial discipline and the amount of employee rest. Consequently, there was relatively little scope for “unmeasured changes in the human relations of workers.” Jones 1992, again focusing on data from the first relay experiment, attempts to more directly measure the magnitude of any Hawthorne effects. He finds no evidence, either in the raw data or after controlling for other factors, to support the traditional interpretation of the Hawthorne data.

--- https://www.biorxiv.org/content/10.1101/2022.08.15.503980.full Phenotype integration improves power and preserves specificity in biobank-based genetic studies of MDD Andrew Dahl, Michael Thompson, Ulzee An, Morten Dybdahl Krebs, Vivek Appadurai, Richard Border, Silviu-Alin Bacanu, Thomas Werge, Jonathan Flint, Andrew Joseph Schork, Sriram Sankararaman, Kenneth Kendler, Na Cai 2023-01-13 2023-01-13 [("doi","10.1101/2022.08.15.503980")] genetics/heritable psychiatry/depression

Biobanks often contain several phenotypes relevant to a given disorder, and researchers face complex tradeoffs between shallow phenotypes (high sample size, low specificity and sensitivity) and deep phenotypes (low sample size, high specificity and sensitivity).

Here, we study an extreme case: Major Depressive Disorder (MDD) in UK Biobank. Previous studies found that shallow and deep MDD phenotypes have qualitatively distinct genetic architectures, but it remains unclear which are optimal for scientific study or clinical prediction.

We propose a new framework to get the best of both worlds by integrating together information across hundreds of MDD-relevant phenotypes. First, we use phenotype imputation to increase sample size for the deepest available MDD phenotype, which dramatically improves GWAS power (increases #loci ~10×) and PRS accuracy (increases R2 ~2×).

Further, we show the genetic architecture of the imputed phenotype remains specific to MDD using genetic correlation, PRS prediction in external clinical cohorts, and a novel PRS-based pleiotropy metric. We also develop a complementary approach to improve specificity of GWAS on shallow MDD phenotypes by adjusting for phenome-wide PCs. Finally, we study phenotype integration at the level of GWAS summary statistics, which can increase GWAS and PRS power but introduces non-MDD-specific signals.

Our work provides a simple and scalable recipe to improve genetic studies in large biobanks by combining the sample size of shallow phenotypes with the sensitivity and specificity of deep phenotypes.

--- https://arxiv.org/abs/1903.04933#deepmind Hierarchical Autoregressive Image Models with Auxiliary Decoders Jeffrey De Fauw, Sander Dieleman, Karen Simonyan 2019-03-06 2022-07-11 [("doi","10.48550/arXiv.1903.04933")] ai/nn/vae

Autoregressive generative models of images tend to be biased towards capturing local structure, and as a result they often produce samples which are lacking in terms of large-scale coherence.

To address this, we propose two methods [using PixelCNN] to learn discrete representations of images which abstract away local detail. We show that autoregressive models conditioned on these representations can produce high-fidelity reconstructions of images, and that we can train autoregressive priors on these representations that produce samples with large-scale coherence. We can recursively apply the learning procedure, yielding a hierarchy of progressively more abstract image representations.

We train hierarchical class-conditional autoregressive models on the ImageNet dataset and demonstrate that they are able to generate realistic images at resolutions of 128×128 and 256×256 pixels.

We also perform a human evaluation study comparing our models with both adversarial and likelihood-based state-of-the-art generative models.

--- https://arxiv.org/abs/2211.05102#google Efficiently Scaling Transformer Inference Reiner Pope, Sholto Douglas, Aakanksha Chowdhery, Jacob Devlin, James Bradbury, Anselm Levskaya, Jonathan Heek, Kefan Xiao, Shivani Agrawal, Jeff Dean 2022-11-09 2022-11-09 [("doi","10.48550/arXiv.2211.05102")] ai/nn/sparsity/low-precision ai/nn/transformer/attention ai/nn/transformer/gpt/palm ai/scaling/hardware

We study the problem of efficient generative inference for Transformer models, in one of its most challenging settings: large deep models, with tight latency targets and long sequence lengths. Better understanding of the engineering tradeoffs for inference for large Transformer-based models is important as use cases of these models are growing rapidly throughout application areas.

We develop a simple analytical model for inference efficiency to select the best multi-dimensional partitioning techniques optimized for TPU v4 slices based on the application requirements. We combine these with a suite of low-level optimizations to achieve a new Pareto frontier on the latency and model FLOPS utilization (MFU) tradeoffs on 500B+ parameter models that outperforms the FasterTransformer suite of benchmarks.

We further show that with appropriate partitioning, the lower memory requirements of multi-query attention (ie. multiple query heads share single key/value head) enables scaling up to 32× larger context lengths.

Finally, we achieve a low-batch-size latency of 29ms per token during generation (using int8 weight quantization) and a 76% MFU during large-batch-size processing of input tokens, while supporting a long 2048-token context length on the PaLM 540b parameter model.

…For a state-of-the-art 540b parameter dense model running on 64 TPU v4 chips, we achieve a low-batch-size latency of 29ms per token during generation (with int8 weight quantization) and a 76% MFU [model FLOPS utilization] during large-batch-size processing of input tokens while supporting a large context length of 2,048 tokens. Figure 1 (left) shows our performance for generating text using the PaLM models. For an interactive application such as a chatbot running on PaLM 540B with int8 weights, our implementation on 64 TPU v4 chips can process 64 tokens of text from a user, consult a cached conversation history of 1,920 tokens, and generate a 64-token response in a total of 1.9 seconds. For an offline throughput-oriented application, our implementation can process 1,984 tokens of input and generate 64 tokens of output, for huge numbers of examples, with an overall FLOPS efficiency of 73%. Table 2 shows more details on a few specific scenarios.

Figure 1: Cost vs. latency for PaLM models. We use a context length of 2,048. Points in each line represent the Pareto frontier of efficiency versus latency. Chip count is C, batch size is B. Left: latency per token for generating 64 tokens, assuming the context has already been processed. Right: time to process 2,048 input tokens; excludes the time to generate any output tokens. Tables 2 and 3 show details on a few specific scenarios from the Pareto frontier where the applications have low-latency or high-throughput requirements.

…Figure 1 (left) shows the relationship between model size, latency, and cost in the generate phase, at the Pareto frontier of optimal batch size, chip count, and partitioning strategy. The lowest cost is achieved at batch sizes larger than about 512, where the cost is proportional to the number of parameters. As we decrease the batch size, we improve the latency but incur higher cost per token. The minimum latency for generation is 3× lower than the batch-512 latency.

We observe that int8 weight quantization achieves the minimum latency in Figure 1 (left): for example, we achieve 28.5ms/token with int8 weights at batch size 64 on PaLM 540B, while we achieve 36.9ms/token with bfloat16 weights. At low latency targets the cost is improved just over a factor of 2, because low-batch-size cost is dominated by weight loading time. At large batch size, cost is more neutral between int8 and bfloat16, because large-batch cost is dominated by the compute time and the matmuls still use bfloat16 arithmetic. We believe that quantization of activations to int8 could enable a further cost improvement.

Figure 1 (right) shows the relationship between model size, latency, and cost in the prefill phase. The tradeoff between batch size and latency is less severe in the prefill phase than the generate phase and even batch size 1 runs with fairly low cost. Further, the cost of batch-512 prefill is 2× lower than batch-512 generate because of the increased MFU of the weight-gathered layouts we use during prefill. More details on the relationship between model size and MFU are presented in Figure C.1 & §C in the Appendix.

…The low-batch-size latencies grow sub-linearly with model size at the Pareto frontier: even though larger models load proportionally more weights from memory, we can partition them across more chips before becoming communication-limited. We estimate an ~square-root relationship between model size and latency based on Figure 1 (left).

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8578738/ Human-animal interspecies chimerism via blastocyst complementation: advances, challenges and perspectives: a narrative review Yuhang Li, Ke Huang 2021 2022-07-11 [("doi","10.21037/sci-2020-074")] genetics/editing

Objective: Interspecific human-animal chimerism via blastocyst complementation provides a promising strategy to generate function human cells, tissues or organs from human pluripotent stem cells (hPSCs), although it is still quite challenging. In this review, we will mainly focus on the recent advances, such as the options of donor hPSCs and the understanding of interspecific chimera barriers, challenges, and perspectives on the efficient generation of human-animal interspecies chimeras.

Background: hPSCs, including the human embryonic stem cells (hESCs) and the human induced pluripotent stem cells (hiPSCs) hold great promise for regenerative medicine to treat various degenerative diseases. However, although hPSCs can differentiate to all lineage cells in dish, the functionality of these cells is limited, hinting that the in vitro differentiation system failed to fully recapture the in vivo development. A promising alternative strategy is in vivo generation of functional human cells in animals through interspecies chimerism, based on the principle that mammalian development is highly conserved across species. This strategy was inspired by the successful generation of functional rat pancreas in mice through blastocyst injection of rat pluripotent stem cells (PSCs). Over the past 10 years, since this milestone work was reported, advances have been made in the human-animal interspecies chimerism. However, it is still challenging to efficiently generate human cells, tissues, or organs in the interspecies chimeras. This phenomenon suggests that there are still obstacles to illustrate and overcome implicated in human-animal interspecies chimeras.

Method: Narrative overview of the literatures reported the recent advances, challenges and perspectives regarding the interspecies chimerism via blastocyst complementation.

Conclusions: Human-animal interspecies chimerism via blastocyst complementation is a valuable method to generate functional human cells, tissues or organs, while there are at least 3 barriers need to be overcome. Firstly, conventional hPSCs should be converted to possess the chimera competency; secondly, efficient human-animal chimerism are required to robustly generate human derivatives in chimera; thirdly, the discrepancy regarding the developmental regulation network between human and host animals must be eliminated to generate certain human cells, tissues or organs in the interspecies chimeras.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8180210/ Xenogeneic stem cell transplantation: Research progress and clinical prospects Lin-Li Jiang, Hui Li, Lei Liu 2021 2022-07-11 [("doi","10.12998/wjcc.v9.i16.3826")] genetics/editing

Organ transplantation is the ultimate treatment for end-stage diseases such as heart and liver failure. However, the severe shortage of donor organs has limited the organ transplantation progress.

Xenogeneic stem cell transplantation provides a new strategy to solve this problem. Researchers have shown that xenogeneic stem cell transplantation has therapeutic effects and broad application prospects in treating liver failure, myocardial infarction, advanced type 1 diabetes mellitus, myelosuppression, and other end-stage diseases by replacing the dysfunctional cells directly or improving the endogenous regenerative milieu.

In this review, the sources, problems and solutions, and potential clinical applications of xenogeneic stem cell transplantation will be discussed.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4244478/ A competitive advantage by neonatally engrafted human glial progenitors yields mice whose brains are chimeric for human glia Martha S. Windrem, Steven J. Schanz, Carolyn Morrow, Jared Munir, Devin Chandler-Militello, Su Wang, Steven A. Goldman 2014 2022-07-11 [("doi","10.1523/JNEUROSCI.1510-14.2014")] genetics/editing psychology/neuroscience

Neonatally transplanted human glial progenitor cells (hGPCs) densely engraft and myelinate the hypomyelinated shiverer mouse.

We found that, in hGPC-xenografted mice, the human donor cells continue to expand throughout the forebrain, systematically replacing the host murine glia. The differentiation of the donor cells is influenced by the host environment, such that more donor cells differentiated as oligodendrocytes in the hypomyelinated shiverer brain than in myelin wild-types, in which hGPCs were more likely to remain as progenitors.

Yet in each recipient, both the number and relative proportion of mouse GPCs fell as a function of time, concomitant with the mitotic expansion and spread of donor hGPCs. By a year after neonatal xenograft, the forebrain GPC populations of implanted mice were largely, and often entirely, of human origin.

Thus, neonatally implanted hGPCs outcompeted and ultimately replaced the host population of mouse GPCs, ultimately generating mice with a humanized glial progenitor population.

These human glial chimeric mice should permit us to define the specific contributions of glia to a broad variety of neurological disorders, using human cells in vivo.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0000943 Causal Inference in Multisensory Perception Konrad P. Körding, Ulrik Beierholm, Wei Ji Ma, Steven Quartz, Joshua B. Tenenbaum, Ladan Shams 2007-09-03 2022-07-11 [("doi","10.1371/journal.pone.0000943")] psychology/neuroscience statistics/causality

Perceptual events derive their meaning to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events.

Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks.

The results show that indeed humans can efficiently infer the causal structure as well as the location of causes.

By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception.

--- https://research.checkpoint.com/2023/opwnai-cybercriminals-starting-to-use-chatgpt/ 2022-07-11 ai/nn/transformer/gpt/codex cs/security --- https://arxiv.org/abs/2212.10496 Precise Zero-Shot Dense Retrieval without Relevance Labels Luyu Gao, Xueguang Ma, Jimmy Lin, Jamie Callan 2022-12-20 2022-12-20 [("doi","10.48550/arXiv.2212.10496")] ai/nn/retrieval ai/nn/transformer/gpt/3/nonfiction

While dense retrieval has been shown effective and efficient across tasks and languages, it remains difficult to create effective fully zero-shot dense retrieval systems when no relevance label is available.

In this paper, we recognize the difficulty of zero-shot learning and encoding relevance. Instead, we propose to pivot through Hypothetical Document Embeddings (HyDE). Given a query, HyDE first zero-shot instructs an instruction-following language model (eg. InstructGPT) to generate a hypothetical document. The document captures relevance patterns but is unreal and may contain false details. Then, an unsupervised contrastively learned encoder (eg. Contriever) encodes the document into an embedding vector. This vector identifies a neighborhood in the corpus embedding space, where similar real documents are retrieved based on vector similarity. This second step ground the generated document to the actual corpus, with the encoder’s dense bottleneck filtering out the incorrect details.

Our experiments show that HyDE outperforms the state-of-the-art unsupervised dense retriever Contriever and shows strong performance comparable to fine-tuned retrievers, across various tasks (eg. web search, QA, fact verification) and languages (eg. sw, ko, ja).

…HyDE appears unsupervised. No model is trained in HyDE: both the generative model and the contrastive encoder remain intact. Supervision signals were only involved in instruction learning of our backbone LLM.

Figure 1: An illustration of the HyDE model. Documents snippets are shown. HyDE serves all types of queries without changing the underlying GPT-3 and Contriever/mContriever models.

[Does this scale? Interesting possibility: the smarter the model, the better it will hallucinate documents which look as if they ‘answer’ the question or help the task; does that mean that it will also get better retrievals, especially if the retrieval database scales the number of documents, as it scales?]

…5.1 Effect of Different Generative Models: In Table 4, we show HyDE using other instruction-following language models. In particular, we consider a 52-billion Cohere model (command-xlarge-20221108) and a 11-billion FLAN model (FLAN-T5-XXL; Wei et al 2022). Generally, we observe that all models bring improvement to the unsupervised Contriever, with larger models bringing larger improvements. At the time when this paper is written, the Cohere model is still experimental without much detail disclosed. We can only tentatively hypothesize that training techniques may have also played some role in the performance difference.

--- https://www.quantamagazine.org/mobile-genes-from-the-mother-shape-the-babys-microbiome-20230117/ 2022-07-11 genetics/microbiome --- https://arxiv.org/abs/2301.02111#microsoft VALL-E: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers Chengyi Wang, Sanyuan Chen, Yu Wu, Ziqiang Zhang, Long Zhou, Shujie Liu, Zhuo Chen, Yanqing Liu, Huaming Wang, Jinyu Li, Lei He, Sheng Zhao, Furu Wei 2023-01-05 2023-01-05 [("doi","10.48550/arXiv.2301.02111")] ai/music ai/nn/transformer/gpt/dall-e/1 ai/scaling

We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called VALL-E) using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather than continuous signal regression as in previous work. During the pre-training stage, we scale up the TTS training data to 60K hours of English speech which is hundreds of times larger than existing systems.

VALL-E emerges in-context learning capabilities and can be used to synthesize high-quality personalized speech with only a 3-second recording of an unseen speaker as an acoustic prompt.

Experiment results show that VALL-E outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity. In addition, we find VALL-E could preserve the speaker’s emotion and acoustic environment of the acoustic prompt in synthesis.

See Github for demos of our work.

Figure 1: The overview of VALL-E. Unlike the previous pipeline (eg. phoneme → mel-spectrogram → waveform), the pipeline of VALL-E is phoneme → discrete code → waveform. VALL-E generates the discrete audio codec codes based on phoneme and acoustic code prompts, corresponding to the target content and the speaker’s voice. VALL-E directly enables various speech synthesis applications, such as zero-shot TTS, speech editing, and content creation combined with other generative AI models like GPT-3 (Brown et al 2020).

…It is worth noting that existing TTS systems are always trained with dozens of hours of single-speaker data or hundreds of hours of multi-speaker data, which is over hundreds of times smaller than VALL-E.

…We evaluate VALL-E on LibriSpeech [Panayotov et al 2015] and VCTK [Veaux et al 2016] datasets, where all test speakers are unseen in the training corpus. VALL-E substantially outperforms the state-of-the-art zero-shot TTS system [Casanova et al 2022b] in terms of speech naturalness and speaker similarity, with +0.12 comparative mean option score (CMOS) and +0.93 similarity mean option score (SMOS) improvement on LibriSpeech. VALL-E also beats the baseline on VCTK with +0.11 SMOS and +0.23 CMOS improvements. It even achieves a +0.04 CMOS score against ground truth, showing the synthesized speech of unseen speakers is as natural as human recordings on VCTK. Moreover, the qualitative analysis shows that VALL-E is able to synthesize diverse outputs with the same text and target speaker, which could benefit pseudo-data creation for the speech recognition task. We also find that VALL-E could keep the acoustic environment (eg. reverberation) and emotion (eg. anger) of the acoustic prompt.

In summary, we make the following contributions.

…In this paper, we follow AudioLM [Borsos et al 2022] to leverage neural codec models to represent speech in discrete tokens.

…The models are trained using 16 NVIDIA TESLA V100 32GB GPUs with a batch size of 6k acoustic tokens per GPU for 800k steps.

--- https://www.riffusion.com/ 2022-07-12 ai/music ai/nn/diffusion --- https://arxiv.org/abs/1811.11711#deepmind Neural probabilistic motor primitives for humanoid control Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess 2018-11-28 2022-07-12 [("doi","10.48550/arXiv.1811.11711")] ai/nn/sparsity/knowledge-distillation ai/nn/vae reinforcement-learning/model-free reinforcement-learning/robot

We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids.

To do this, we propose a motor architecture that has the general structure of an inverse model with a latent-variable bottleneck.

We show that it is possible to train this model entirely offline to compress thousands of expert policies and learn a motor primitive embedding space. The trained neural probabilistic motor primitive system can perform one-shot imitation of whole-body humanoid behaviors, robustly mimicking unseen trajectories. Additionally, we demonstrate that it is also straightforward to train controllers to reuse the learned motor primitive space to solve tasks, and the resulting movements are relatively naturalistic.

To support the training of our model, we compare two approaches for offline policy cloning, including an experience efficient method which we call linear feedback policy cloning.

We encourage readers to view a supplementary video summarizing our results.

--- https://x.com/ammaar/status/1615133036974321665 2022-07-12 ai/nn/diffusion ai/nn/transformer/gpt/fiction ai/video/generation --- https://progress.institute/why-barda-deserves-more-funding/ 2022-07-12 biology --- https://en.wikipedia.org/wiki/Frederick_Sanger#Sanger's_rule Frederick Sanger § Sanger’s rule 2022-07-12 economics/experience-curve genetics/sequencing --- https://arxiv.org/abs/2301.00704#google Muse: Text-To-Image Generation via Masked Generative Transformers Huiwen Chang, Han Zhang, Jarred Barber, A. J. Maschinot, Jose Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Murphy, William T. Freeman, Michael Rubinstein, Yuanzhen Li, Dilip Krishnan 2023-01-02 2023-01-02 [("doi","10.48550/arXiv.2301.00704")] ai/nn/transformer/t5 ai/nn/vae/mae

[Twitter; cf. Paella, MaskGIT followup; video] We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being more efficient than diffusion or autoregressive models.

Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL·E 2, Muse is more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM [T5] enables fine-grained language understanding [ie. it can do text inside images], translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc.

...We train on the Imagen dataset consisting of 460M text-image pairs (Saharia et al 2022). Training is performed for 1M steps, with a batch size of 512 on 512-core TPU-v4 chips (Jouppi et al 2020). This takes about 1 week of training time.

…Decoding is performed based on a cosine schedule (MaskGIT) that chooses a certain fixed fraction of the highest confidence masked tokens that are to be predicted at that step. These tokens are then set to unmasked for the remainder of the steps and the set of masked tokens is appropriately reduced. Using this procedure, we are able to perform inference of 256 tokens using only 24 decoding steps in our base model and 4,096 tokens using 8 decoding steps in our super-resolution model, as compared to the 256 or 4,096 steps required for autoregressive models (eg. Parti) and hundreds of steps for diffusion models (eg. Rombach et al 2022; Imagen).

...Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse-3b parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing.

More results are available at https://muse-model.github.io/.

[Announcing Muse, a super-fast text-to-image generation model based on masked generative transformers. 2–10× faster than diffusion or autoregressive models! SOTA CLIP score and excellent FID score!

Our training and architecture enable text-to-image generation at 512×512 resolution in under 2s on a TPUv4; and zero-shot editing capabilities right out of the box.

We leverage a T5XXL pre-trained LLM to enable fine-grained text understanding and masking-based training that enables fast parallel decoding without loss of generation quality (as measured by FID or CLIP score).

Muse achieves a SOTA CLIP score of 0.32 and excellent FID of 7.88 on zero-shot COCO evaluation.]

--- https://arxiv.org/abs/2211.07292 Paella: Fast Text-Conditional Discrete Denoising on Vector-Quantized Latent Spaces Dominic Rampas, Pablo Pernias, Elea Zhong, Marc Aubreville 2022-11-14 2022-11-14 [("doi","10.48550/arXiv.2211.07292")] ai/nn/transformer/clip ai/nn/vae/mae

[blog; refines MaskGIT; see parallel but much bigger Muse] Conditional text-to-image generation has seen countless recent improvements in terms of quality, diversity and fidelity. Nevertheless, most state-of-the-art models require numerous inference steps to produce faithful generations, resulting in performance bottlenecks for end-user applications.

In this paper we introduce Paella, a novel text-to-image model requiring less than 10 steps to sample high-fidelity images, using a speed-optimized architecture allowing to sample a single image in less than 500 ms, while having 573M parameters. The model operates on a compressed & quantized latent space, it is conditioned on CLIP embeddings and uses an improved sampling function over previous works.

Aside from text-conditional image generation, our model is able to do latent space interpolation and image manipulations such as inpainting, outpainting, and structural editing.

We release all of our code and pretrained models at Github.

[Benchmarks slightly better, but doesn’t look better, than Stable Diffusion 1.4 (never mind Deep Floyd); however, the MAE architecture is promising in its simplicity & scalability.]

--- https://scale.com/blog/chatgpt-vs-claude 2022-07-12 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex reinforcement-learning/safe --- /doc/statistics/bias/2005-klein.pdf Blind Analysis In Nuclear And Particle Physics Joshua R. Klein, Aaron Roodman 2005-01-01 2022-07-12 [("doi","10.1146/annurev.nucl.55.090704.151521")] science statistics/bias

During the past decade, blind analysis has become a widely used tool in nuclear and particle physics measurements. A blind analysis avoids the possibility of experimenters biasing their result toward their own preconceptions by preventing them from knowing the answer until the analysis is complete.

There is at least circumstantial evidence that such a bias has affected past measurements, and as experiments have become costlier and more difficult and hence harder to reproduce, the possibility of bias has become a more important issue than in the past.

We describe here the motivations for performing a blind analysis, and give several modern examples of successful blind analysis strategies.

--- https://www.nature.com/articles/526187a 2022-07-12 science statistics/bias --- https://arxiv.org/abs/2301.06468 Msanii: High Fidelity Music Synthesis on a Shoestring Budget Kinyugo Maina 2023-01-16 2023-01-16 [("doi","10.48550/arXiv.2301.06468")] ai/music ai/nn/diffusion

In this paper, we present Msanii, a novel diffusion-based model for synthesizing long-context, high-fidelity music efficiently.

Our model combines the expressiveness of mel spectrograms, the generative capabilities of diffusion models, and the vocoding capabilities of neural vocoders.

We demonstrate the effectiveness of Msanii by synthesizing tens of seconds (190 seconds) of stereo music at high sample rates (44.1 kHz) without the use of concatenative synthesis, cascading architectures, or compression techniques. To the best of our knowledge, this is the first work to successfully employ a diffusion-based model for synthesizing such long music samples at high sample rates.

Our demo can be found here and our code at Github.

--- https://arxiv.org/abs/2301.07088#bytedance MUG: Vision Learners Meet Web Image-Text Pairs Bingchen Zhao, Quan Cui, Hao Wu, Osamu Yoshie, Cheng Yang 2023-01-17 2023-01-17 [("doi","10.48550/arXiv.2301.07088")] ai/nn/transformer/clip ai/nn/vae/mae ai/scaling

Most recent self-supervised learning (SSL) methods are pre-trained on the well-curated ImageNet-1K dataset.

In this work, we consider SSL pre-training on noisy web image-text paired data due to the excellent scalability of web data. First, we conduct a benchmark study of representative SSL pre-training methods on large-scale web data in a fair condition. Methods include single-modal ones such as MAE and multi-modal ones such as CLIP.

We observe that multi-modal methods cannot outperform single-modal ones on vision transfer learning tasks. We derive an information-theoretical view to explain the benchmarking results, which provides insights into designing novel vision learners.

Inspired by the above explorations, we present a visual representation pre-training method, MUlti-modal Generator (MUG), for scalable web image-text data.

MUG achieves state-of-the-art transferring performances on a variety of tasks and shows promising scaling behavior.

Models and codes will be made public.

Demo available at https://huggingface.co/spaces/tennant/MUG_caption.

--- https://pgt-p-outcome-calculator.shinyapps.io/selectioncalc/ 2022-07-13 genetics/selection --- https://www.polygenicembryo.org/ 2022-07-13 genetics/selection --- https://www.construction-physics.com/p/the-rise-of-steel-part-ii 2022-07-13 economics/experience-curve --- https://arxiv.org/abs/math/0003117 Reliable Cellular Automata with Self-Organization Peter Gacs 2000-03-20 2022-07-13 [("doi","10.1023/A:1004823720305")] cs/cellular-automaton

In a probabilistic cellular automaton in which all local transitions have positive probability, the problem of keeping a bit of information indefinitely is nontrivial, even in an infinite automaton. Still, there is a solution in 2 dimensions, and this solution can be used to construct a simple 3-dimensional discrete-time universal fault-tolerant cellular automaton. This technique does not help much to solve the following problems: remembering a bit of information in 1 dimension; computing in dimensions lower than 3; computing in any dimension with non-synchronized transitions.

Our more complex technique organizes the cells in blocks that perform a reliable simulation of a second (generalized) cellular automaton. The cells of the latter automaton are also organized in blocks, simulating even more reliably a third automaton, etc. Since all this (a possibly infinite hierarchy) is organized in “software”, it must be under repair all the time from damage caused by errors. A large part of the problem is essentially self-stabilization recovering from a mess of arbitrary size and content.

The present paper constructs an asynchronous one-dimensional fault-tolerant cellular automaton, with the further feature of “self-organization”. The latter means that unless a large amount of input information must be given, the initial configuration can be chosen homogeneous.

--- https://marginalrevolution.com/marginalrevolution/2023/01/ai-passes-law-and-economics-exam.html 2022-07-13 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/non-fiction economics/copyright law --- https://wikimediafoundation.org/wikipedia-desktop/ 2022-07-13 design wikipedia --- https://www.youtube.com/watch?v=XPVC4IyRTG8 2022-07-13 reinforcement-learning/robot --- https://blog.metabrainz.org/2022/02/16/acousticbrainz-making-a-hard-decision-to-end-the-project/ 2022-07-13 ai/music --- https://arxiv.org/abs/2212.12151 EarSpy: Spying Caller Speech and Identity through Tiny Vibrations of Smartphone Ear Speakers Ahmed Tanvir Mahdad, Cong Shi, Zhengkun Ye, Tianming Zhao, Yan Wang, Yingying Chen, Nitesh Saxena 2022-12-23 2022-12-23 [("doi","10.48550/arXiv.2212.12151")] ai/nn/cnn cs/security

Eavesdropping from the user’s smartphone is a well-known threat to the user’s safety and privacy. Existing studies show that loudspeaker reverberation can inject speech into motion sensor readings, leading to speech eavesdropping. While more devastating attacks on ear speakers, which produce much smaller scale vibrations, were believed impossible to eavesdrop with zero-permission motion sensors.

In this work, we revisit this important line of reach. We explore recent trends in smartphone manufacturers that include extra/powerful speakers in place of small ear speakers, and demonstrate the feasibility of using motion sensors to capture such tiny speech vibrations. We investigate the impacts of these new ear speakers on built-in motion sensors and examine the potential to elicit private speech information from the minute vibrations.

Our designed system EarSpy can successfully detect word regions, time, and frequency domain features and generate a spectrogram for each word region. We train and test the extracted data using classical machine learning algorithms and convolutional neural networks. We found up to 98.66% accuracy in gender detection, 92.6% detection in speaker detection, and 56.42% detection in digit detection (which is 5× important than the random selection (10%)).

Our result unveils the potential threat of eavesdropping on phone conversations from ear speakers using motion sensors.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6613697/ A parasitological evaluation of edible insects and their role in the transmission of parasitic diseases to humans and animals Remigiusz Gałęcki, Rajmund Sokół 2019 2022-07-13 [("doi","10.1371/journal.pone.0219303")] biology

From 1 January 2018 came into force Regulation (EU) 2015/2238 of the European Parliament and of the Council of 25 November 2015, introducing the concept of “novel foods”, including insects and their parts. One of the most commonly used species of insects are: mealworms (Tenebrio molitor), house crickets (Acheta domesticus), cockroaches (Blattodea) and migratory locusts (Locusta migrans). In this context, the unfathomable issue is the role of edible insects in transmitting parasitic diseases that can cause losses in their breeding and may pose a threat to humans and animals.

The aim of this study was to identify and evaluate the developmental forms of parasites colonizing edible insects in household farms and pet stores in Central Europe and to determine the potential risk of parasitic infections for humans and animals.

The experimental material comprised samples of live insects (imagines) from 300 household farms and pet stores, including 75 mealworm farms, 75 house cricket farms, 75 Madagascar hissing cockroach farms and 75 migrating locust farms.

Parasites were detected in 244 (81.33%) out of 300 (100%) examined insect farms. In 206 (68.67%) of the cases, the identified parasites were pathogenic for insects only; in 106 (35.33%) cases, parasites were potentially parasitic for animals; and in 91 (30.33%) cases, parasites were potentially pathogenic for humans. Edible insects are an underestimated reservoir of human and animal parasites.

Our research indicates the important role of these insects in the epidemiology of parasites pathogenic to vertebrates. Conducted parasitological examination suggests that edible insects may be the most important parasite vector for domestic insectivorous animals. According to our studies the future research should focus on the need for constant monitoring of studied insect farms for pathogens, thus increasing food and feed safety.

--- https://github.com/openai/openai-cookbook/blob/main/techniques_to_improve_reliability.md 2022-07-14 ai/nn/transformer/gpt/inner-monologue --- https://tagide.com/education/writing-a-tokenizer-with-chatgpt/ 2022-07-14 ai/nn/transformer/gpt/codex --- https://leanchess.github.io/ 2022-07-14 cs/algorithm --- https://www.atlasobscura.com/articles/heritage-appalachian-apples 2022-07-14 genetics/selection/artificial/apple --- https://arxiv.org/abs/2301.08155 AI Insights into Theoretical Physics and the Swampland Program: A Journey Through the Cosmos with ChatGPT Kay Lehnert 2023-01-10 2023-01-10 [("doi","10.48550/arXiv.2301.08155")] ai/nn/transformer/gpt/non-fiction science

In this case study, we explore the capabilities and limitations of ChatGPT, a natural language processing model developed by OpenAI, in the field of string theoretical swampland conjectures.

We find that it is effective at paraphrasing and explaining concepts in a variety of styles, but not at genuinely connecting concepts. It will provide false information with full confidence and make up statements when necessary.

However, its ingenious use of language can be fruitful for identifying analogies and describing visual representations of abstract concepts.

--- https://www.biorxiv.org/content/10.1101/2023.01.21.524489.full A high-performance speech neuroprosthesis Francis R. Willett, Erin Kunz, Chaofei Fan, Donald Avansino, Guy Wilson, Eun Young Choi, Foram Kamdar, Leigh R. H. Hochberg, Shaul Druckmann, Krishna Shenoy, Jaimie Henderson 2023-01-21 2023-01-21 [("doi","10.1101/2023.01.21.524489")] ai/nn/rnn psychology/neuroscience

Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speaking movements into text or sound. Early demonstrations, while promising, have not yet achieved accuracies high enough for communication of unconstrained sentences from a large vocabulary.

Here, we demonstrate the first speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant, who can no longer speak intelligibly due to amyotrophic lateral sclerosis (ALS), achieved:

a 9.1% word error rate on a 50 word vocabulary (2.7× fewer errors than the prior state-of-the-art speech BCI) and a 23.8% word error rate on a 125,000 word vocabulary (the first successful demonstration of large-vocabulary decoding). Our BCI decoded speech at 62 words per minute, which is 3.4× faster than the prior record for any kind of BCI and begins to approach the speed of natural conversation (160 words per minute).

Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis.

These results show a feasible path forward for using intracortical speech BCIs to restore rapid communication to people with paralysis who can no longer speak.

…we explored the ceiling of decoding performance offline by (1) making further improvements to the language model and (2) evaluating the decoder on test sentences that occur closer in time to the training sentences (to mitigate the effects of within-day changes in the neural features across time).

We found that an improved language model could decrease word error rates 23.8% → 17.4%, and that testing on more proximal sentences further decreased word error rates to 11.8% (Table 1). These results indicate that substantial gains in performance are likely still possible with further language model improvements and more robust decoding algorithms that generalize better to non-stationary data.

--- https://blog.lopp.net/has-bitcoin-ever-hard-forked/ 2022-07-14 bitcoin --- https://www.statnews.com/2016/09/01/optogenetics/ 2022-07-14 statistics/peer-review --- https://x.com/jmilldotdev/status/1600624362394091523 2022-07-14 ai/nn/transformer/gpt/non-fiction cs/security --- https://www.reddit.com/r/OpenAI/comments/10j33gd/this_ai_has_to_be_stopped/ 2022-07-14 ai/nn/transformer/gpt/fiction --- https://x.com/jdjkelly/status/1617381388977831936 2022-07-15 ai/nn/retrieval ai/nn/transformer/gpt/non-fiction --- https://x.com/andrewwhite01/status/1616933106786738176 2022-07-15 ai/nn/retrieval ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/2207.02098#deepmind Neural Networks and the Chomsky Hierarchy Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Chris Cundy, Marcus Hutter, Shane Legg, Joel Veness, Pedro A. Ortega 2022-07-05 2022-07-15 [("doi","10.48550/arXiv.2207.02098")] ai/nn/rnn ai/nn/transformer cs/computable

Reliable generalization lies at the heart of safe ML and AI. However, understanding when and how neural networks generalize remains one of the most important unsolved problems in the field.

In this work, we conduct an extensive empirical study (10,250 models on 15 tasks) to investigate whether insights from the theory of computation can predict the limits of neural network generalization in practice.

We demonstrate that grouping tasks according to the Chomsky hierarchy allows us to forecast whether certain architectures will be able to generalize to out-of-distribution inputs. This includes negative results where even extensive amounts of data and training time never lead to any non-trivial generalization, despite models having sufficient capacity to fit the training data perfectly.

Our results show that, for our subset of tasks, RNNs and Transformers fail to generalize on non-regular tasks, LSTMs can solve regular and counter-language tasks, and only networks augmented with structured memory (such as a stack or memory tape) can successfully generalize on context-free and context-sensitive tasks.

--- https://arxiv.org/abs/2210.05805#facebook E3B: Exploration via Elliptical Episodic Bonuses Mikael Henaff, Roberta Raileanu, Minqi Jiang, Tim Rocktäschel 2022-10-11 2022-10-11 [("doi","10.48550/arXiv.2210.05805")] reinforcement-learning/exploration reinforcement-learning/model reinforcement-learning/model-free reinforcement-learning/nethack

[code] In recent years, a number of reinforcement learning (RL) methods have been proposed to explore complex environments which differ across episodes. In this work, we show that the effectiveness of these methods critically relies on a count-based episodic term in their exploration bonus. As a result, despite their success in relatively simple, noise-free settings, these methods fall short in more realistic scenarios where the state space is vast and prone to noise.

To address this limitation, we introduce Exploration via Elliptical Episodic Bonuses (E3B), a new method which extends count-based episodic bonuses to continuous state spaces and encourages an agent to explore states that are diverse under a learned embedding within each episode. The embedding is learned using an inverse dynamics model in order to capture controllable aspects of the environment.

Our method sets a new state-of-the-art across 16 challenging tasks from the MiniHack suite, without requiring task-specific inductive biases. E3B also matches existing methods on sparse reward, pixel-based VizDoom environments, and outperforms existing methods in reward-free exploration on Habitat, demonstrating that it can scale to high-dimensional pixel-based observations and realistic environments.

--- https://arxiv.org/abs/2301.09515#nvidia StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis Axel Sauer, Tero Karras, Samuli Laine, Andreas Geiger, Timo Aila 2023-01-23 2023-01-23 [("doi","10.48550/arXiv.2301.09515")] ai/nn/gan/stylegan ai/scaling

[video, code] Text-to-image synthesis has recently seen progress thanks to large pretrained language models, large-scale training data, and the introduction of scalable model families such as diffusion and autoregressive models. However, the best-performing models require iterative evaluation to generate a single sample. In contrast, generative adversarial networks (GANs) only need a single forward pass. They are thus much faster, but they currently remain far behind the state-of-the-art in large-scale text-to-image synthesis.

This paper aims to identify the necessary steps to regain competitiveness. Our proposed model, StyleGAN-T, addresses the specific requirements of large-scale text-to-image synthesis, such as large capacity, stable training on diverse datasets, strong text alignment, and controllable variation vs. text alignment tradeoff.

StyleGAN-T improves over previous GANs and outperforms distilled diffusion models—the previous state-of-the-art in fast text-to-image synthesis—in terms of sample quality and speed.

...Using the final configuration developed in §3, we scale the model size, dataset, and training time. Our final model consists of ∼1 billion parameters; we did not observe any instabilities when increasing the model size. [emphasis added] We train on a union of several datasets amounting to 250M text-image pairs in total. We use progressive growing similar to StyleGAN-XL, except that all layers remain trainable. The hyperparameters and dataset details are listed in Appendix A.

--- https://github.com/dobrosketchkun/yet_another_stable_diffusion_webui_scripts_repository/tree/main/stand-alone_scripts/random_tags_prompt 2022-07-15 ai/anime/danbooru --- https://www.biorxiv.org/content/10.1101/2023.01.19.524045.full Functional synapses between small cell lung cancer and glutamatergic neurons Anna Schmitt, Vignesh Sakthivelu, Kristiano Ndoci, Gulzar A. Wani, Marian Touet, Isabel Pintelon, Ilmars Kisis, Olta Ibruli, Julia Weber, Roman Maresch, Christina M. Bebber, Jonas Goergens, Milica Jevtic, Franka Odenthal, Aleksandra Placzek, Alexandru A. Hennrich, Karl-Klaus Conzelmann, Maike Boecker, Alena Heimsoeth, Gülce S. Gülcüler, Ron D. Jachimowicz, Julie George, Johannes Brägelmann, Silvia von Karstedt, Martin Peifer, Thorsten Persigehl, Holger Grüll, Martin L. Sos, Jens Brüning, Guido Reifenberger, Matthias Fischer, Dirk Adriaensen, Reinhard Büttner, Inge Brouns, Roland Rad, Roman K. Thomas, Matteo Bergami, Elisa Motori, Hans Christian Reinhardt, Filippo Beleggia 2023-01-20 2023-01-20 [("doi","10.1101/2023.01.19.524045")] psychology/neuroscience

Small cell lung cancer (SCLC) is a highly aggressive type of lung cancer, characterized by rapid proliferation, early metastatic spread, clinical recurrence and high rate of mortality.

Using in vivo insertional mutagenesis screening in conjunction with cross-species genomic and transcriptomic validation, we identified a strong and consistent signal for neuronal, synaptic, and glutamatergic signaling gene sets in murine and human SCLC.

We show that SCLC cells have the ability to develop intimate contacts with neuronal glutamatergic terminals in vitro, in autochthonous primary lung tumors and in brain-engrafted tumors. These contacts can develop into bona fide synapses, allowing SCLC cells to receive glutamatergic inputs.

Fitting with a potential oncogenic role of neuron-SCLC interactions, we show that SCLC cells derive a robust proliferation advantage when co-cultured with neurons. Moreover, the repression of glutamate release and the stimulation of the inhibitory glutamate receptor GRM8 displayed therapeutic efficacy in an autochthonous mouse model of SCLC.

Therefore, following malignant transformation, SCLC cells appear to hijack glutamatergic signaling to sustain tumor growth, thereby exposing a novel entry route for therapeutic intervention.

--- https://www.biorxiv.org/content/10.1101/2023.01.23.525248.full A harmonized public resource of deeply sequenced diverse human genomes Zan Koenig, Mary T. Yohannes, Lethukuthula L. Nkambule, Julia K. Goodrich, Heesu Ally Kim, Xuefang Zhao, Michael W. Wilson, Grace Tiao, Stephanie P. Hao, Nareh Sahakian, Katherine R. Chao, gnom A. D. Project Consortium, Michael E. Talkowski, Mark J. Daly, Harrison Brand, Konrad Karczewski, Elizabeth G. Atkinson, Alicia Martin 2023-01-23 2023-01-23 [("doi","10.1101/2023.01.23.525248")] genetics/sequencing

Underrepresented populations are often excluded from genomic studies due in part to a lack of resources supporting their analysis. The 1000 Genomes Project (1kGP) and Human Genome Diversity Project (HGDP), which have recently been sequenced to high coverage, are valuable genomic resources because of the global diversity they capture and their open data sharing policies.

Here, we harmonized a high quality set of 4,096 whole genomes from HGDP and 1kGP with data from gnomAD and identified over 155 million high-quality SNVs, indels, and SVs. We performed a detailed ancestry analysis of this cohort, characterizing population structure and patterns of admixture across populations, analyzing site frequency spectra, and measuring variant counts at global and subcontinental levels.

We also demonstrate substantial added value from this dataset compared to the prior versions of the component resources, typically combined via liftover and variant intersection; for example, we catalog millions of new genetic variants, mostly rare, compared to previous releases. In addition to unrestricted individual-level public release, we provide detailed tutorials for conducting many of the most common quality control steps and analyses with these data in a scalable cloud-computing environment and publicly release this new phased joint callset for use as a haplotype resource in phasing and imputation pipelines.

This jointly called reference panel will serve as a key resource to support research of diverse ancestry populations.

--- https://x.com/thesephist/status/1617747154231259137 2022-07-15 ai/nn/transformer/gpt design --- https://www.medrxiv.org/content/10.1101/2023.01.23.23284735.full Putting ChatGPT’s Medical Advice to the (Turing) Test Oded Nov, Nina Singh, Devin M. Mann 2023-01-24 2023-01-24 [("doi","10.1101/2023.01.23.23284735")] ai/nn/transformer/gpt/non-fiction biology

Importance: Chatbots could play a role in answering patient questions, but patients’ ability to distinguish between provider and chatbot responses, and patients’ trust in chatbots’ functions are not well established.

Objective: To assess the feasibility of using ChatGPT or a similar AI-based chatbot for patient-provider communication.

Design: Survey in January 2023.

Setting: Survey Participants: A US representative sample of 400 study participants aged 18 and above was recruited on Prolific, a crowdsourcing platform for academic studies. 384 participants filled out the full survey. After removing participants who spent less than 3 minutes on the survey, 360 respondents remained. 53.3% of respondents analyzed were women; their average age was 45.4.

Exposures: 10 representative non-administrative patient-provider interactions were extracted from the EHR. Patients’ questions were placed in ChatGPT with a request for the chatbot to respond using the same word count as the human providers response. In the survey, each patients question was followed by a provider/ChatGPT-generated response. Participants were informed that 5 responses were provider-generated and 5 were chatbot-generated. Participants were asked, and incentivized financially, to correctly identify the response source. Participants were also asked about their trust in chatbots’ functions in patient-provider communication, using a Likert scale of 1–5.

Main Outcomes & Measures: Main outcome: Proportion of responses correctly classified as provider vs chatbot-generated.

Secondary outcomes: Average and standard deviation of responses to trust questions.

Results: The correct classification of responses ranged between 23.4% to 86.7% for different questions. On average, chatbot responses were identified correctly 60.0% of the time and provider responses were identified correctly 62.3% of the time. On average, responses toward patients’ trust in chatbots’ functions were weakly positive (mean Likert score: 3.4), with lower trust as the health-related complexity of the task in questions increased.

Conclusions & Relevance: ChatGPT responses to patient questions were close to indistinguishable from provider responses. Laypeople appear to trust the use of chatbots to answer lower risk health questions. It is important to continue studying patient-chatbot interaction as chatbots move from administrative to more clinical roles in healthcare. [code]

--- https://x.com/Mascobot/status/1618246707267141632 2022-07-15 ai/nn/transformer/t5 --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8497688/ Social Mobility and Political Regimes: Intergenerational Mobility in Hungary, 1949–2017 Paweł Bukowski, Gregory Clark, Attila Gáspár, Rita Pető 2022 2022-07-16 [("doi","10.1007/s00148-021-00875-w")] economics sociology

This paper measures social mobility rates in Hungary during the period 1949 to 2017, using surnames to measure social status.

In those years, there were two very different social regimes. The first was the Hungarian People’s Republic (1949–1989), which was a communist regime with an avowed aim of favouring the working class. The second is the modern liberal democracy (1989–2017), which is a free-market economy.

We find 5 surprising things. First, social mobility rates were low for both upper-class & lower-class families during 1949–2017, with an underlying intergenerational status correlation of 0.6-0.8. Second, social mobility rates under communism were the same as in the subsequent capitalist regime. Third, the Romani minority throughout both periods showed even lower social mobility rates. Fourth, the descendants of the 18th-century noble class in Hungary were still privileged in 1949 and later. And fifth, although social mobility rates did not change measurably during the transition, the composition of the political elite changed rapidly and sharply.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6765292/ The long-term impact of the Communist Revolution on social stratification in contemporary China Yu Xie, Chunni Zhang 2019 2022-07-16 [("doi","10.1073/pnas.1904283116")] sociology

The Chinese Communist Revolution that culminated in the 1949 founding of the People’s Republic of China fundamentally transformed class relations in China.

With data from a nationally representative, longitudinal survey between 2010 and 2016, this study documents the long-term impact of the Communist Revolution on the social stratification order in today’s China, more than 6 decades after the revolution.

True to its stated ideological missions, the revolution resulted in promoting the social status of children of the peasant, worker, and revolutionary cadre classes and disadvantaging those who were from privileged classes at the time of the revolution. Although there was a tendency toward “reversion” mitigating the revolution’s effects in the third generation toward the grandparents’ generation in social status, the overall impact of reversion was small. The revolution effects were most pronounced for the birth cohorts immediately following the revolution, attenuating for recently born cohorts.

--- /doc/sociology/2020-barone.pdf Intergenerational Mobility in the Very Long Run: Florence (1427–2011) Guglielmo Barone, Sauro Mocetti 2020-11-06 2022-07-16 [("doi","10.1093/restud/rdaa075")] economics sociology

[media] We examine intergenerational mobility in the very long run, across generations that are 6 centuries apart.

We exploit a unique dataset containing detailed information at the individual level for all people living in the Italian city of Florence in 1427. These individuals have been associated, using their surnames, with their pseudo-descendants living in Florence in 2011.

We find that long-run earnings elasticity is about 0.04; we also find an even stronger role for real wealth inheritance and evidence of persistence in belonging to certain elite occupations. Our results are confirmed when we account for the quality of the pseudo-links and when we address the potential selectivity bias behind the matching process.

Finally, we frame our results within the existing evidence and argue that the quasi-immobility of preindustrial society and the existence of multigenerational effects might explain the long-lasting effects of ancestors’ socioeconomic status.

Keywords: intergenerational mobility, earnings, wealth, occupations, informational content of surnames, Florence.

--- https://www.medrxiv.org/content/10.1101/2023.01.20.23284709.full Causal relevance of different blood pressure traits on risk of cardiovascular diseases: GWAS and Mendelian Randomization in 100,000 Chinese adults Alfred Pozarickij, Wei Gan, Kuang Lin, Robert Clarke, Zammy Fairhurst-Hunter, Masaru Koido, Masahiro Kanai, Yukinori Okada, Yoichiro Kamatani, Yu Guo, Derrick Bennett, Huaidong Du, Yiping Chen, Ling Yang, Daniel Avery, Min Yu, Canqing Yu, Dan Schmidt Valle, Jun Lv, Junshi Chen, Richard Peto, Rory Collins, Liming Li, Zhengming Chen, Iona Y. Millwood, Robin G. Walters, the China Kadoorie Biobank (CKB) Collaborative Group 2023-01-20 2023-01-20 [("doi","10.1101/2023.01.20.23284709")] genetics/heritable/correlation/mendelian-randomization

Elevated blood pressure (BP) is major risk factor for cardiovascular diseases (CVD). Genome-wide association studies (GWAS) conducted predominantly in populations of European ancestry have identified >2,000 BP-associated loci, but other ancestries have been less well-studied.

We conducted GWAS of systolic, diastolic, pulse, and mean arterial BP in 100,453 Chinese adults.

We identified 128 non-overlapping loci associated with one or more BP traits, harbouring 81 novel associations. Despite strong genetic correlations between populations, we identified appreciably higher heritability and larger variant effect sizes in Chinese compared with European or Japanese ancestry populations. Using instruments derived from these GWAS, multivariable Mendelian Randomization demonstrated strong causal associations of specific BP traits with CVD, including systolic BP with intracranial haemorrhage, and pulse pressure with carotid plaque.

The findings reinforce the need for studies in diverse populations to understand the genetic determinants of BP traits and their role in disease risk.

--- https://www.biorxiv.org/content/10.1101/2022.05.02.490274.full Robust associations between white matter microstructure and general intelligence Christina Stammen, Christoph Fraenz, Rachael G. Grazioplene, Caroline Schlüter, Viola Merhof, Wendy Johnson, Onur Güntürkün, Colin G. DeYoung, Erhan Genç 2022-05-04 2022-07-16 [("doi","10.1101/2022.05.02.490274")] iq psychology/neuroscience

Early research on the neural correlates of human intelligence was almost exclusively focused on gray matter properties. The advent of diffusion-weighted imaging led to an exponential growth of white matter brain imaging studies. However, this line of research has yielded mixed observations, especially about the relations between general intelligence and white matter microstructure.

We used a multi-center approach to identify white matter regions that show replicable structure-function associations, employing data from 4 independent samples comprising over 2000 healthy participants. We used tract-based spatial statistics to examine associations between g factor scores and white matter microstructure and identified 188 voxels which exhibited positive associations between g factor scores and fractional anisotropy in all 4 data sets. Replicable voxels formed 3 clusters: one located around the forceps minor, crossing with extensions of the anterior thalamic radiation, the cingulum-cingulate gyrus, and the inferior fronto-occipital fasciculus in the left hemisphere, one located around the left-hemispheric superior longitudinal fasciculus, and one located around the left-hemispheric cingulum-cingulate gyrus, crossing with extensions of the anterior thalamic radiation and the inferior fronto-occipital fasciculus.

Our results indicate that individual differences in general intelligence are robustly associated with white matter organization in specific fiber bundles.

--- https://laion.ai/blog/large-openclip/ 2022-07-16 ai/nn/transformer/clip --- https://arxiv.org/abs/2212.07143 Reproducible scaling laws for contrastive language-image learning Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, Jenia Jitsev 2022-12-14 2022-12-14 [("doi","10.48550/arXiv.2212.07143")] ai/nn/transformer/clip ai/scaling

Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previous work on scaling laws has primarily used private data & models or focused on uni-modal language or vision learning.

To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository. Our large-scale experiments involve models trained on up to two billion image-text pairs and:

identify power law scaling for multiple downstream tasks including zero-shot classification, retrieval, linear probing, and end-to-end fine-tuning.

We find that the training distribution plays a key role in scaling laws as the OpenAI and OpenCLIP models exhibit different scaling behavior despite identical model architectures and similar training recipes [better at zero-shot retrieval vs zero-shot classification, respectively].

We open-source our evaluation workflow and all models, including the largest public CLIP models, to ensure reproducibility and make scaling laws research more accessible.

Source code and instructions to reproduce this study are available at Github.

Figure 1a: Relationship between total training compute and performance in zero-shot classification (1a) and retrieval (1b). We fit a power law on the Pareto frontier of the available models. Since total compute budgets (measured in GMACs) of different trained models are not exactly aligned, we divide the total compute scale into bins and select the best model performance from each bin. (a) Relationship between total training compute and zero-shot classification performance on downstream tasks. Left: ImageNet performance. Right: average performance on 5 ImageNet robustness datasets (ImageNet-V2, ImageNet-R, ImageNet-Sketch, ObjectNet, and ImageNet-A). Scaling model size, data size, and samples seen leads to better performance on zero-shot classification. Models trained on OpenAI’s WebImageText (WIT) show a stronger scaling than models trained on LAION.
Figure 1b: Relationship between total training compute and zero-shot image retrieval performance on MS-COCO (Left) and Flickr30K (Right). Scaling model size, data size, and samples seen leads to better performance on zero-shot image retrieval. Interestingly, in contrast to zero-shot classification (Figure 1a), models trained on LAION show a stronger scaling trend than OpenAI CLIP models trained on OpenAI’s WebImageText (WIT) dataset.

…Compared to the original CLIP training procedure, we work with larger batch sizes and adapt the learning rate accordingly. We opt for larger batch sizes to allow for more efficient distributed training; maximizing the local batch size per GPU and using close to 1,000 GPUs lead us to global batch sizes in the range of 86–88K samples. In order to assess the validity of re-using measurements obtained with different batch sizes, we perform a number of control experiments varying batch size from 32K to 86–88K, and observe a difference of 0.2–0.5% across different settings (see Appendix §B.2.3), which is small enough not to confound observations on the effect of scale…Parallel training via PyTorch DDP, we conduct experiments with up to 1,520 NVIDIA A100 GPUs. Distributed training was executed on JUWELS Booster, the supercomputer at Juelich Supercomputing Center (JSC, Germany), and partly also at Stability AI AWS supercomputer.

…We also observe bottleneck behaviors35, 84 that occur when fixing one scaling dimension while increasing others. For instance, OpenCLIP ViT-B/32 and ViT-B/16 are bottlenecked by the number of samples seen at the 13B scale. Increasing the number of samples seen to 34B reveals that LAION-2B brings clear improvement over LAION-400M, which would remain hidden when fixing the number-of-samples-seen scale to a lower value. Similar observations may occur along other scaling dimensions. OpenCLIP ViT L/14 shows an example of data scale bottleneck on LAION-400M scale, as increasing the number of samples seen from 13B to 34B does not lead to improvements. The benefit of using a larger number of samples seen is then revealed when going to the larger LAION-2B dataset.

…Using the obtained power law, we can make a prediction for the performance of a well-tuned ViT-g/14 model when using the largest data scale of 2B and samples seen scale of 34B, giving us error estimate of 20.9% (79.1% top-1 accuracy) on ImageNet. We predict even stronger performance at larger scales. For instance, assuming 68B samples seen we estimate top-1 accuracies of 79.7%, 80.7%, and 81.9% for ViT-H/14, ViT-g/14 and ViT-G/14, respectively (see also Appendix §B.2.1).

--- https://apps.fz-juelich.de/jsc/hps/juwels/configuration.html#hardware-configuration-of-the-system-name-booster-module 2022-07-16 ai/scaling/hardware --- https://hpc.stability.ai/ 2022-07-16 ai/scaling/hardware --- https://arxiv.org/abs/1905.13549 ImageNet-Sketch: Learning Robust Global Representations by Penalizing Local Predictive Power Haohan Wang, Songwei Ge, Eric P. Xing, Zachary C. Lipton 2019-05-29 2022-07-16 [("doi","10.48550/arXiv.1905.13549")] ai/dataset ai/nn/cnn

Despite their renowned predictive power on i.i.d. data, convolutional neural networks are known to rely more on high-frequency patterns that humans deem superficial than on low-frequency patterns that agree better with intuitions about what constitutes category membership.

This paper proposes a method for training robust convolutional networks by penalizing the predictive power of the local representations learned by earlier layers. Intuitively, our networks are forced to discard predictive signals such as color and texture that can be gleaned from local receptive fields and to rely instead on the global structures of the image.

Across a battery of synthetic and benchmark domain adaptation tasks, our method confers improved generalization out of the domain.

Also, to evaluate cross-domain transfer, we introduce ImageNet-Sketch, a new dataset consisting of sketch-like images, that matches the ImageNet classification validation set in categories and scale.

--- https://arxiv.org/abs/2006.16241 The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, Justin Gilmer 2020-06-29 2022-07-16 [("doi","10.48550/arXiv.2006.16241")] ai/dataset ai/nn/cnn

We introduce 4 new real-world distribution shift datasets consisting of changes in image style [ImageNet-Renditions (ImageNet-R)], image blurriness [Real Blurry Images], geographic location [Street View StoreFronts (SVSF)], camera operation [DeepFashion Remixed (DFR)], and more.

With our new datasets, we take stock of previously proposed methods for improving out-of-distribution robustness and put them to the test.

We find that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work. We find improvements in artificial robustness benchmarks can transfer to real-world distribution shifts, contrary to claims in prior work.

Motivated by our observation that data augmentations can help with real-world distribution shifts, we also introduce a new data augmentation method which advances the state-of-the-art and outperforms models pretrained with 1,000× more labeled data. Overall we find that some methods consistently help with distribution shifts in texture and local image statistics, but these methods do not help with some other distribution shifts like geographic changes.

Our results show that future research must study multiple distribution shifts simultaneously, as we demonstrate that no evaluated method consistently improves robustness.

--- https://openreview.net/forum?id=SkgnRNHgIS ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Danny Gutfreund, Joshua B. Tenenbaum, Boris Katz 2019-09-06 2022-07-17 ai/dataset ai/nn/cnn

[code] We collect a large real-world test set, ObjectNet, for object recognition with controls where object backgrounds, rotations, and imaging viewpoints are random.

Most scientific experiments have controls, confounds which are removed from the data, to ensure that subjects cannot perform a task by exploiting trivial correlations in the data. Historically, large machine learning and computer vision datasets have lacked such controls. This has resulted in models that perform better on datasets than in new applications and must be fine-tuned for most new datasets. When tested on ObjectNet, object detectors show a ~45% drop in performance, with respect to their performance on other benchmarks, when biases are removed. Reinserting biases, by subsetting the dataset along the axes which have controls, recovers most of this performance drop.

ObjectNet is more robust to fine-tuning because of controls with only small performance increases. We develop a highly automated platform that enables gathering datasets with controls by crowdsourcing image capturing and annotation. ObjectNet is comparable in size to the ImageNet test set (40K vs 50K images), and by design does not come paired with a training set in order to encourage generalization.

While we focus on object recognition, using automated tools data with controls can be gathered at scale throughout machine learning to generate datasets that exercise models in new ways providing valuable feedback to researchers. This work opens up new avenues for research in generalizable, robust, and more human-like computer vision and in creating datasets where results are diagnostic of expected real-world performance.

--- https://nypost.com/2023/01/25/bryan-johnson-45-spends-2m-to-get-18-year-old-body/ 2022-07-17 nootropic/quantified-self --- https://publicdomainreview.org/essay/illusory-wealth 2022-07-17 history/public-domain-review --- https://www.biorxiv.org/content/10.1101/2022.12.14.520419.full Cell Tree Rings: the shape of somatic evolution as a human aging timer Attila Csordas, Botond Sipos, Terezia Kurucova, Andrea Volfova, Frantisek Zamola, Boris Tichy, Damien G. Hicks 2022-12-16 2022-12-16 [("doi","10.1101/2022.12.14.520419")] genetics/heritable/rare longevity

Biological age is typically estimated using biomarkers whose states have been observed to correlate with chronological age. A persistent limitation of such aging clocks is that it is difficult to establish how the biomarker states are related to the mechanisms of aging. Somatic mutations could potentially form the basis for a more fundamental aging clock since the mutations are both markers and drivers of aging and have a natural timescale. Cell lineage trees inferred from these mutations reflect the somatic evolutionary process and thus, it has been conjectured, the aging status of the body. Such a timer has been impractical thus far, however, because detection of somatic variants in single cells presents a technological challenge.

Here we show that somatic mutations detected using single-cell RNA sequencing (scRNAseq) from hundreds of cells can be used to construct a cell lineage tree whose shape correlates with chronological age.

De novo single-nucleotide variants (SNVs) are detected in human peripheral blood mononuclear cells using a modified protocol. Penalized multiple regression is used to select from over 30 possible metrics characterizing the shape of the phylogenetic tree resulting in:

a Pearson correlation of 0.8 between predicted and chronological age and a median absolute error less than 6 years.

The geometry of the cell lineage tree records the structure of somatic evolution in the individual and represents a new modality of aging timer. In addition to providing a single number for biological age, it unveils a temporal history of the aging process, revealing how clonal structure evolves over life span. Cell Tree Rings complements existing aging clocks and may help reduce the current uncertainty in the assessment of geroprotective trials.

--- https://forum.effectivealtruism.org/posts/Eq8nwNPNhfXvt2TWj/my-experience-experimenting-with-a-bunch-of-antidepressants 2022-07-17 nootropic/quantified-self psychiatry/depression --- https://arxiv.org/abs/2101.06983 Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup Luyu Gao, Yunyi Zhang, Jiawei Han, Jamie Callan 2021-01-18 2022-07-17 [("doi","10.48550/arXiv.2101.06983")] ai/nn/retrieval

Contrastive learning has been applied successfully to learn vector representations of text. Previous research demonstrated that learning high-quality representations benefits from batch-wise contrastive loss with a large number of negatives. In practice, the technique of in-batch negative is used, where for each example in a batch, other batch examples’ positives will be taken as its negatives, avoiding encoding extra negatives. This, however, still conditions each example’s loss on all batch examples and requires fitting the entire large batch into GPU memory.

This paper introduces a gradient caching technique that decouples backpropagation between contrastive loss and the encoder, removing encoder backward pass data dependency along the batch dimension. As a result, gradients can be computed for one subset of the batch at a time, leading to almost constant memory usage.

--- https://arxiv.org/abs/2110.06848 Decoupled Contrastive Learning Chun-Hsiao Yeh, Cheng-Yao Hong, Yen-Chi Hsu, Tyng-Luh Liu, Yubei Chen, Yann LeCun 2021-10-13 2022-07-17 [("doi","10.48550/arXiv.2110.06848")] ai/nn/cnn

Contrastive learning (CL) is one of the most successful paradigms for self-supervised learning (SSL). In a principled way, it considers two augmented “views” of the same image as positive to be pulled closer, and all other images as negative to be pushed further apart. However, behind the impressive success of CL-based techniques, their formulation often relies on heavy-computation settings, including large sample batches, extensive training epochs, etc. We are thus motivated to tackle these issues and establish a simple, efficient, yet competitive baseline of contrastive learning.

Specifically, we identify, from theoretical and empirical studies, a noticeable negative-positive-coupling (NPC) effect in the widely used InfoNCE loss, leading to unsuitable learning efficiency concerning the batch size. By removing the NPC effect, we propose decoupled contrastive learning (DCL) loss, which removes the positive term from the denominator and improves the learning efficiency. DCL achieves competitive performance with less sensitivity to sub-optimal hyperparameters, requiring neither large batches in SimCLR, momentum encoding in MoCo, or large epochs.

We demonstrate with various benchmarks while manifesting robustness as much less sensitive to suboptimal hyperparameters. Notably, SimCLR with DCL achieves 68.2% ImageNet-1K top-1 accuracy using batch size 256 within 200 epochs pre-training, outperforming its SimCLR baseline by 6.4%. Further, DCL can be combined with the SOTA contrastive learning method, NNCLR, to achieve 72.3% ImageNet-1K top-1 accuracy with 512 batch size in 400 epochs, which represents a new SOTA in contrastive learning.

We believe DCL provides a valuable baseline for future contrastive SSL studies.

--- https://www.patterns.app/blog/2023/01/18/crunchbot-sql-analyst-gpt/ 2022-07-17 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue --- https://github.com/molly/annotate 2022-07-17 design/typography/sidenote --- https://www.theguardian.com/commentisfree/2023/jan/24/chatgpt-artificial-intelligence-jobs-economy 2022-07-17 ai/nn/transformer/gpt/non-fiction --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360863/ I know a dog when I see one: dogs (Canis familiaris) recognize dogs from videos Paolo Mongillo, Carla Eatherington, Miina Lõoke, Lieta Marinelli 2021 2022-07-18 [("doi","10.1007/s10071-021-01470-y")] dog psychology/vision

Several aspects of dogs’ visual and social cognition have been explored using bi-dimensional representations of other dogs [eg. Autier-Dérian et al 2013]. It remains unclear, however, if dogs do recognize as dogs the stimuli depicted in such representations, especially with regard to videos.

To test this, 32 pet dogs took part in a cross-modal violation of expectancy experiment, during which dogs were shown videos of either a dog and that of an unfamiliar animal, paired with either the sound of a dog barking or of an unfamiliar vocalization.

While stimuli were being presented, dogs paid higher attention to the exit region of the presentation area, when the visual stimulus represented a dog than when it represented an unfamiliar species. After exposure to the stimuli, dogs’ attention to different parts of the presentation area depended on the specific combination of visual and auditory stimuli. Of relevance, dogs paid less attention to the central part of the presentation area and more to the entrance area after being exposed to the barking and dog video pair, than when either was paired with an unfamiliar stimulus.

These results indicate dogs were surprised by the latter pairings, not by the former, and were interested in where the barking and dog pair came from, implying recognition of the two stimuli as belonging to a conspecific. The study represents the first demonstration that dogs can recognize other conspecifics in videos.

--- https://arxiv.org/abs/2301.08745#tencent Is ChatGPT A Good Translator? A Preliminary Study Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Xing Wang, Zhaopeng Tu 2023-01-20 2023-01-20 [("doi","10.48550/arXiv.2301.08745")] ai/nn/transformer/gpt/non-fiction

This report provides a preliminary evaluation of ChatGPT for machine translation, including translation prompt, multilingual translation, and translation robustness.

We adopt the prompts advised by ChatGPT to trigger its translation ability and find that the candidate prompts generally work well and show minor performance differences. By evaluating on a number of benchmark test sets, we find that:

ChatGPT performs competitively with commercial translation products (eg. Google Translate) on high-resource European languages but lags behind on low-resource or distant languages. As for the translation robustness, ChatGPT does not perform as well as the commercial systems on biomedical abstracts or Reddit comments but is potentially a good translator for spoken language.

Scripts and data: https://github.com/wxjiao/Is-ChatGPT-A-Good-Translator.

Table 4: Performance of ChatGPT for multilingual translation.
--- https://x.com/abacaj/status/1618050431657324545 2022-07-18 ai/nn/transformer/t5 --- https://huggingface.co/models?search=flan-t5 2022-07-18 ai/nn/transformer/t5 --- https://arxiv.org/abs/2211.02703 Online Learning and Bandits with Queried Hints Aditya Bhaskara, Sreenivas Gollapudi, Sungjin Im, Kostas Kollias, Kamesh Munagala 2022-11-04 2022-11-04 [("doi","10.48550/arXiv.2211.02703")] reinforcement-learning/model

[blog] We consider the classic online learning and stochastic multi-armed bandit (MAB) problems, when at each step, the online policy can probe and find out which of a small number (k) of choices has better reward (or loss) before making its choice.

In this model, we derive algorithms whose regret bounds have exponentially better dependence on the time horizon compared to the classic regret bounds. In particular, we show that probing with k = 2 suffices to achieve time-independent regret bounds for online linear and convex optimization. The same number of probes improve the regret bound of stochastic MAB with independent arms from 𝒪(√n · T) to 𝒪(n2 logT), where n is the number of arms and T is the horizon length. For stochastic MAB, we also consider a stronger model where a probe reveals the reward values of the probed arms, and show that in this case, k = 3 probes suffice to achieve parameter-independent constant regret, 𝒪(n2).

Such regret bounds cannot be achieved even with full feedback after the play, showcasing the power of limited “advice” via probing before making the play.

We also present extensions to the setting where the hints can be imperfect, and to the case of stochastic MAB where the rewards of the arms can be correlated.

--- https://www.theintrinsicperspective.com/p/the-banality-of-chatgpt 2022-07-18 ai/nn/transformer/gpt/non-fiction --- https://samkriss.substack.com/p/a-users-guide-to-the-zairja-of-the 2022-07-18 ai/nn/transformer/gpt/fiction --- https://arxiv.org/abs/1807.03748#deepmind InfoNCE: Representation Learning with Contrastive Predictive Coding (CPC) Aaron van den Oord, Yazhe Li, Oriol Vinyals 2018-07-10 2022-07-18 [("doi","10.48550/arXiv.1807.03748")] ai/nn/vae reinforcement-learning/model-free

While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence.

In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss [InfoNCE] which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling.

While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on 4 distinct domains: speech, images, text and reinforcement learning in 3D environments.

--- https://arxiv.org/abs/1910.08475 On Warm-Starting Neural Network Training Jordan T. Ash, Ryan P. Adams 2019-10-18 2022-07-18 [("doi","10.48550/arXiv.1910.08475")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/exploration/active-learning reinforcement-learning/meta-learning/continual-learning

In many real-world deployments of machine learning systems, data arrive piecemeal. These learning scenarios may be passive, where data arrive incrementally due to structural properties of the problem (eg. daily financial data) or active, where samples are selected according to a measure of their quality (eg. experimental design). In both of these cases, we are building a sequence of models that incorporate an increasing amount of data. We would like each of these models in the sequence to be performant and take advantage of all the data that are available to that point.

Conventional intuition suggests that when solving a sequence of related optimization problems of this form, it should be possible to initialize using the solution of the previous iterate—to “warm start” the optimization rather than initialize from scratch—and see reductions in wall-clock time. However, in practice this warm-starting seems to yield poorer generalization performance than models that have fresh random initializations, even though the final training losses are similar. While it appears that some hyperparameter settings allow a practitioner to close this generalization gap, they seem to only do so in regimes that damage the wall-clock gains of the warm start.

Nevertheless, it is highly desirable to be able to warm-start neural network training, as it would dramatically reduce the resource usage associated with the construction of performant deep learning systems.

In this work, we take a closer look at this empirical phenomenon and try to understand when and how it occurs.

We also provide a surprisingly simple trick that overcomes this pathology in several important situations, and present experiments that elucidate some of its properties.

…The warm-start phenomenon has implications for other situations as well. In active learning, for example, unlabeled samples are abundant but labels are expensive: the goal is to identify maximally-informative data to have labeled by an oracle and integrated into the training set. It would be time efficient to simply warm-start optimization each time new samples are appended to the training set, but such an approach seems to damage generalization in deep neural networks. Although this phenomenon has not received much direct attention from the research community, it seems to be common practice in deep active learning to retrain from scratch after every query step;6, 7 popular deep active learning repositories on Github randomly reinitialize models after every selection.[8, 9]

--- https://www.bloomberg.com/news/features/2023-01-25/anti-aging-techniques-taken-to-extreme-by-bryan-johnson 2022-07-18 longevity nootropic/quantified-self --- https://www.nextplatform.com/2023/01/24/building-the-perfect-memory-bandwidth-beast/ 2022-07-18 cs/hardware --- https://arxiv.org/abs/2208.06193#twitter Diffusion-QL: Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning Zhendong Wang, Jonathan J. Hunt, Mingyuan Zhou 2022-08-12 2022-08-12 [("doi","10.48550/arXiv.2208.06193")] ai/nn/diffusion reinforcement-learning/imitation-learning reinforcement-learning/offline

Offline reinforcement learning (RL), which aims to learn an optimal policy using a previously collected static dataset, is an important paradigm of RL. Standard RL methods often perform poorly in this regime due to the function approximation errors on out-of-distribution actions. While a variety of regularization methods have been proposed to mitigate this issue, they are often constrained by policy classes with limited expressiveness that can lead to highly suboptimal solutions.

In this paper, we propose representing the policy as a diffusion model, a recent class of highly-expressive deep generative models. We introduce Diffusion Q-learning (Diffusion-QL) that utilizes a conditional diffusion model to represent the policy. In our approach, we learn an action-value function and we add a term maximizing action-values into the training loss of the conditional diffusion model, which results in a loss that seeks optimal actions that are near the behavior policy.

We show the expressiveness of the diffusion model-based policy, and the coupling of the behavior cloning and policy improvement under the diffusion model both contribute to the outstanding performance of Diffusion-QL.

We illustrate the superiority of our method compared to prior works in a simple 2D bandit example with a multimodal behavior policy. We then show that our method can achieve state-of-the-art performance on the majority of the D4RL benchmark tasks.

--- https://arxiv.org/abs/2301.07597 How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, Yupeng Wu 2023-01-18 2023-01-18 [("doi","10.48550/arXiv.2301.07597")] ai/dataset ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/non-fiction

The introduction of ChatGPT has garnered widespread attention in both academic and industrial communities. ChatGPT is able to respond effectively to a wide range of human questions, providing fluent and comprehensive answers that surpass previous public chatbots in terms of security and usefulness. On one hand, people are curious about how ChatGPT is able to achieve such strength and how far it is from human experts. On the other hand, people are starting to worry about the potential negative impacts that large language models (LLMs) like ChatGPT could have on society, such as fake news, plagiarism, and social security issues.

In this work, we collected tens of thousands of comparison responses from both human experts and ChatGPT, with questions ranging from open-domain, financial, medical, legal, and psychological areas. We call the collected dataset the Human ChatGPT Comparison Corpus (HC3).

Based on the HC3 dataset, we study the characteristics of ChatGPT’s responses, the differences and gaps from human experts, and future directions for LLMs. We conducted comprehensive human evaluations and linguistic analyses of ChatGPT-generated content compared with that of humans, where many interesting results are revealed.

After that, we conduct extensive experiments on how to effectively detect whether a certain text is generated by ChatGPT or humans.

We build 3 different detection systems, explore several key factors that influence their effectiveness, and evaluate them in different scenarios.

The dataset, code, and models are all publicly available at Github.

…Results: Several conclusions can be drawn from the results shown in Table 2: Comparing the results of pair-expert and single-expert, we can find that it is easier to distinguish ChatGPT-generated content when providing a comparison pair than only providing a single answer. Comparing the results of single-expert and single-amateur, we can find that the accuracy of experts is much higher than that of amateurs. The helpfulness test gives the proportion of questions that volunteers think the ChatGPT answer is more helpful to them. Surprisingly, results show that ChatGPT’s answers are generally considered to be more helpful than humans’ in more than half of questions, especially for finance and psychology areas. By checking the specific answers in these domains, we find that ChatGPT can usually provide more concrete and specific suggestions. However, ChatGPT performs poorly in terms of helpfulness for the medical domain in both English and Chinese. The ChatGPT often gives lengthy answers to medical consulting in our collected dataset, while human experts may directly give straightforward answers or suggestions, which may partly explain why volunteers consider human answers to be more helpful in the medical domain.

…Eventually, we received more than 200 feedbacks, and we summarize these findings as follows:

--- https://arxiv.org/abs/2301.10309#google Interactive-Chain-Prompting (INTERCPT): Ambiguity Resolution for Crosslingual Conditional Generation with Interaction Jonathan Pilault, Xavier Garcia, Arthur Bražinskas, Orhan Firat 2023-01-24 2023-01-24 [("doi","10.48550/arXiv.2301.10309")] ai/dataset ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm ai/scaling/emergence

Crosslingual conditional generation (eg. machine translation) has long enjoyed the benefits of scaling. Nonetheless, there are still issues that scale alone may not overcome. A source query in one language, for instance, may yield several translation options in another language without any extra context. Only one translation could be acceptable however, depending on the translator’s preferences and goals. Choosing the incorrect option might affect translation usefulness and quality.

We propose a novel method interactive-chain prompting (INTERCPT)—a series of question, answering and generation intermediate steps between a Translator model and a User model—that reduces translations into a list of subproblems addressing ambiguities and then resolving such subproblems before producing the final text to be translated.

To check ambiguity resolution capabilities and evaluate translation quality, we create a dataset exhibiting different linguistic phenomena which leads to ambiguities at inference for 4 languages.

To encourage further exploration in this direction, we release all datasets.

We note that interactive-chain prompting, using 8 interactions as exemplars, consistently surpasses prompt-based methods with direct access to background information to resolve ambiguities.

Figure 1: Interactive-Chain-Prompting (INTERCPT).
Figure 2: Translation queries with multiple possible predictions. Correctly solving subproblems around ambiguities with “you” and “it” greatly affects the BLEU (Papineni et al 2002) translation metric.

…It consists of a 3 step reasoning chain (See Figure 1):

  1. The first step is for identifying ambiguities. The prompt in this step always contains the same constant exemplars, showing multiple queries to translate and questions about each query’s ambiguities. During inference, the Translator LM uses the prompt to generate a pointed question that identifies the specific ambiguity.

  2. The second step is for resolving ambiguities. The prompt in this step contains exemplars answering the question to the ambiguity subproblems in step one. The User LM answers each question using additional information from the provided context. In real life applications, we assume that a real user has similar background information about the text to be translated.

  3. The third step is for translating. Generated questions and answers are appended to the prompt in step 1 before the final translation is produced. Constant prompts in this step demonstrate how to translate in the specified target language using only details provided by the User LM and no-context. During inference, the Translator LM uses the prompt to generate the translation.

…Our test results for en-es, en-fr, en-de and en-ja are summarized in Table 2: We first notice that INTERCPT surpasses all other baselines. Surprisingly, PaLM-with-context, even with all the necessary background to resolve ambiguities, lags behind INTERCPT on F-Acc. for formality, G-Acc. for “it resolution” and both hit@3 and B@3 for polysemy. This results suggests that the multistep computation approach of fist resolving the ambiguity subproblems and then generating text has an advantage over other baselines. BLEU scores are also 2–3 points higher while BLEURT scores are only slightly higher. This suggest that INTERCPT generates sentences syntactically much closer to the ground truth while conserving the correct semantics.

Figure 3: INTERCPT enables large LMs to solve ambiguity subproblems in cross-lingual generation. The multistep disambiguate-translate capability is an emergent ability that is reached at higher parameter scales. Note that interactive = INTERCPT.

…6.3. Are interactive generation abilities emergent? We show in Figure 3 for each prompt template the effects of scaling PaLM parameters on the performance of formality, “it” resolution and polysemy for Spanish (ES), French (FR), German (DE) and Japanese (JA) target languages. Please note that while we vary the parameter count (8B, 62B and 540B) of the Translator LM, the User LM is a 540b parameters PaLM model for all experiments. The plots provide interesting insights. First, at the 8b parameter scale, PaLM-no-extras performs best across all languages for Formality and “it” resolution across all language pairs. Neither context or interaction seem to provide benefits to translation.

Second, at the 62b parameter scale, the PaLM-with-context and INTERCPT methods have on par performances. Context or interaction in this case are only clearly beneficial for polysemy. Third, the PaLM 540b parameter INTERCPT outpaces other prompt-based methods across language pairs and ambiguity subproblems. At this stage, baselines scaling trend decelerates, with scaling curves flattening, compared to INTERCPT [hidden scaling]. It shows that INTERCPT is an emergent ability of model scale (Wei et al 2022a). We conjecture that the emergent behavior of INTERCPT is due to a better ability to ask questions and incorporate answers before generating final prediction.

--- https://arxiv.org/abs/2301.10472#facebook XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer, Madian Khabsa 2023-01-25 2023-01-25 [("doi","10.48550/arXiv.2301.10472")] ai/nn/tokenization ai/nn/transformer

Large multilingual language models typically rely on a single vocabulary shared across 100+ languages. As these models have increased in parameter count and depth, vocabulary size has remained largely unchanged. This vocabulary bottleneck limits the representational capabilities of multilingual models like XLM-R.

In this paper, we introduce a new approach for scaling to very large multilingual vocabularies by de-emphasizing token sharing between languages with little lexical overlap and assigning vocabulary capacity to achieve sufficient coverage for each individual language. Tokenizations using our vocabulary are typically more semantically meaningful and shorter compared to XLM-R.

Leveraging this improved vocabulary, we train XLM-V, a multilingual language model with a one million token vocabulary.

XLM-V outperforms XLM-R on every task we tested on ranging from natural language inference (XNLI), question answering (MLQA, XQuAD, TyDiQA), and named entity recognition (WikiAnn) to low-resource tasks (Americas NLI, MasakhaNER).

--- https://arxiv.org/abs/2301.10343#microsoft ClimaX: A foundation model for weather and climate Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K. Gupta, Aditya Grover 2023-01-24 2023-01-24 [("doi","10.48550/arXiv.2301.10343")] ai/nn/transformer ai/scaling science

Most state-of-the-art approaches for weather and climate modeling are based on physics-informed numerical models of the atmosphere. These approaches aim to model the non-linear dynamics and complex interactions between multiple variables, which are challenging to approximate. Additionally, many such numerical models are computationally intensive, especially when modeling the atmospheric phenomenon at a fine-grained spatial and temporal resolution. Recent data-driven approaches based on machine learning instead aim to directly solve a downstream forecasting or projection task by learning a data-driven functional mapping using deep neural networks. However, these networks are trained using curated and homogeneous climate datasets for specific spatiotemporal tasks, and thus lack the generality of numerical models.

We develop and demonstrate ClimaX, a flexible and generalizable deep learning model for weather and climate science that can be trained using heterogeneous datasets spanning different variables, spatio-temporal coverage, and physical groundings. ClimaX extends the Transformer architecture with novel encoding and aggregation blocks that allow effective use of available compute while maintaining general utility.

ClimaX is pre-trained with a self-supervised learning objective on climate datasets derived from CMIP6. The pre-trained ClimaX can then be fine-tuned to address a breadth of climate and weather tasks, including those that involve atmospheric variables and spatio-temporal scales unseen during pretraining.

Compared to existing data-driven baselines, we show that this generality in ClimaX results in superior performance on benchmarks for weather forecasting and climate projections, even when pretrained at lower resolutions and compute budgets.

Figure 11: Error on ERA5 3-day forecasting for different variables with respect to CMIP6 5.625° data seen during pre-training. Bigger models are more sample-efficient.

4.5. Scaling laws analysis: …Figure 11 presents the performance of ClimaX as a function of data size and model capacity. The x-axis is the pretraining data size measured in Gigabytes, which corresponds to 1–5 CMIP6 datasets, and the y-axis shows the RMSE of ClimaX on the 3-day forecasting task. We compare 4 ClimaX models with different capacities by varying the embedding dimension 128–1,024. All experiments are conducted on the 5.625° data. The error rate of the two biggest models decreases consistently as we increase the data and model size. This highlights the unique ability of ClimaX in learning from diverse and heterogeneous data sources, which allows us to further improve the performance by simply pretraining on more data. However, the two smaller models do not scale as well as the bigger ones, where increasing data size does not gain much improvement or can sometimes hurt performance. This result shows that larger models not only perform better but are also more sample-efficient.

Figure 12: Scaling performance with respect to data resolution. Despite a larger patch size, ClimaX (1.40625°) achieves consistently better performance than the low-resolution model on almost all tasks, except for T2m forecast at 1 day and 3 days lead times.

In addition to data size and model capacity, data resolution is another important scaling dimension in the context of weather and climate. In many vision tasks such as classification, understanding the general, high-level structure of the image is sufficient to make accurate predictions. To model the underlying complex physical processes that govern weather and climate, however, it is important for a model to look at fine-grained details of the input in order to understand the spatial and temporal structure of data as well as the interactions between different variables. High-resolution data contains finer details and local processes of weather conditions that are not present in the low-resolution data, and thus provides stronger signals for training deep learning models. Figure 12 compares the performance of ClimaX pretrained and finetuned on 5.625° and 1.40625° data on global forecasting. Except for T2m at 1 day and 3 days lead times, ClimaX (1.40625°) consistently achieves lower RMSE and higher ACC than the low-resolution model. We note that for the high-resolution data we have to use a larger patch size (4 compared to 2 for low-resolution data) due to lack of memory issue. We can further improve the performance of ClimaX on the 1.40625° data by reducing the patch size, as the model is able to capture better details.

…Future research could explore incorporating both observational and simulated datasets that include a wider range of climate variables, higher spatiotemporal resolutions, and even extend into future scenarios. Further, we showed that resolution plays a crucial role in scaling of ClimaX. Due to our compute restrictions, we trained ClimaX on low to moderate resolutions. Nevertheless, our empirical trends suggest that scaling to higher resolutions (0.25°) is likely to lead to even better results.

--- https://arxiv.org/abs/2301.10677#microsoft Imitating Human Behavior with Diffusion Models Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, Sam Devlin 2023-01-25 2023-01-25 [("doi","10.48550/arXiv.2301.10677")] ai/nn/diffusion reinforcement-learning/imitation-learning

[cf. Wang et al 2022] Diffusion models have emerged as powerful generative models in the text-to-image domain.

This paper studies their application as observation-to-action models for imitating human behavior in sequential environments. Human behavior is stochastic and multimodal, with structured correlations between action dimensions. Meanwhile, standard modeling choices in behavior cloning are limited in their expressiveness and may introduce bias into the cloned policy. We begin by pointing out the limitations of these choices. We then propose that diffusion models are an excellent fit for imitating human behavior, since they learn an expressive distribution over the joint action space.

We introduce several innovations to make diffusion models suitable for sequential environments; designing suitable architectures, investigating the role of guidance, and developing reliable sampling strategies.

Experimentally, diffusion models closely match human demonstrations in a simulated robotic control task and a modern 3D gaming environment.

--- https://x.com/natfriedman/status/1575631194032549888 2022-07-19 ai/nn/retrieval ai/nn/transformer/gpt/codex --- https://www.youtube.com/watch?v=LiciNlrc3sE 2022-07-19 genetics/selection/artificial --- https://arxiv.org/abs/2301.10140 The Semantic Scholar Open Data Platform Rodney Kinney, Chloe Anastasiades, Russell Authur, Iz Beltagy, Jonathan Bragg, Alexandra Buraczynski, Isabel Cachola, Stefan Candra, Yogan, Chandrasekhar, Arman Cohan, Miles Crawford, Doug Downey, Jason Dunkelberger, Oren Etzioni, Rob Evans, Sergey Feldman, Joseph Gorney, David Graham, Fangzhou Hu, Regan Huff, Daniel King, Sebastian Kohlmeier, Bailey Kuehl, Michael Langan, Daniel Lin, Haokun Liu, Kyle Lo, Jaron Lochner, Kelsey MacMillan, Tyler Murray, Chris Newell, Smita Rao, Shaurya Rohatgi, Paul Sayre, Zejiang Shen, Amanpreet Singh, Luca Soldaini, Shivashankar Subramanian, Amber Tanaka, Alex D. Wade, Linda Wagner, Lucy Lu Wang, Chris Wilhelm, Caroline Wu, Jiangjiang Yang, Angele Zamarron, Madeleine Van Zuylen, Daniel S. Weld 2023-01-24 2023-01-24 [("doi","10.48550/arXiv.2301.10140")] ai/dataset

The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field. Semantic Scholar (S2) is an open data platform and website aimed at accelerating science by helping scholars discover and understand scientific literature.

We combine public and proprietary data sources using state-of-the-art techniques for scholarly PDF content extraction and automatic knowledge graph construction to build the Semantic Scholar Academic Graph, the largest open scientific literature graph to-date, with 200M+ papers, 80M+ authors, 550M+ paper-authorship edges, and 2.4B+ citation edges. The graph includes advanced semantic features such as structurally parsed text, natural language summaries, and vector embeddings.

In this paper, we describe the components of the S2 data processing pipeline and the associated APIs offered by the platform. We will update this living document to reflect changes as we add new data offerings and improve existing services.

--- /doc/history/2019-chovanec.pdf Early Titanic Jokes: A disaster for the theory of disaster jokes? Jan Chovanec 2019-02-26 2022-07-19 [("doi","10.1515/humor-2018-0090")] fiction/humor history

This paper contributes to our understanding of the inception of disaster humor by refuting the position of ‘technological determinism’ that is central for the theory of disaster jokes. This view, developed by Christie Davies, ties the emergence of this form of humor to the visual presentation of disaster events on television.

The paper reports on the discovery of several contemporary instances of pre-television disaster humor on the topic of the sinking of the Titanic from 1912, thereby explicitly challenging the premise that prior to televised coverage, there were no disaster jokes.

While the data come from a culture that was cognitively very distant from the disaster (and, thus, more likely to give rise to instantaneous disaster humor creation), the paper suggests that a modification to the original theory is possible, arguing that disaster humor can be interpreted as a reaction to the more general process of mediatization, whether televisual or exclusively verbal, which constructs a shared body of knowledge that people can draw upon as a resource when constructing humor. That is particularly the case with iconic disasters, such as the sinking of the Titanic, which can be seen symbolically as an epic fail of modernity rather than a mere tragic disaster.

--- https://www.biorxiv.org/content/10.1101/2022.08.25.505314.full Widespread horizontal gene transfer between plants and their microbiota Shelly Haimlich, Yulia Fridman, Hitaishi Khandal, Sigal Savaldi-Goldstein, Asaf Levy 2022-08-26 2022-08-26 [("doi","10.1101/2022.08.25.505314")] genetics/microbiome

Plants host a large array of commensal bacteria that interact with the host. The growth of both bacteria and plants is often dependent on nutrients derived from the cognate partners, and the bacteria fine-tune host immunity against pathogens. This ancient interaction is common in all studied land plants and is critical for proper plant health and development.

We hypothesized that the spatial vicinity and the long-term relationships between plants and their microbiota may promote or even depend on cross-kingdom horizontal gene transfer (HGT), a phenomenon that is relatively rare in nature. To test this hypothesis we analyzed the Arabidopsis thaliana genome and its extensively sequenced microbiome to detect events of horizontal transfer of full length genes that are absent from non-plant associated bacteria.

Interestingly, we detected 180 unique genes that were horizontally transferred between plants and their microbiota. Genes transferred from plants to their microbiota are enriched in secreted proteins that metabolize carbohydrates, whereas microbes transferred to plants genes that are enriched in redox homeostasis functions.

To validate our approach, we tested if a bacterial gene is functionally similar to its Arabidopsis homologue in planta. The Arabidopsis DET2 gene is essential for biosynthesis of the brassinosteroid phytohormones and loss-of-function of the gene leads to dwarfism. We found that expression of the DET2 homologue from Leifsonia bacteria of the Actinobacteria phylum in the Arabidopsis det2 background complements the mutant, and leads to normal plant growth.

Together, these data suggest that cross-kingdom horizontal gene transfer events shape the interactions between plants and their microbiome.

Impact statement

What are the genes that shape host-microbe interactions and what are their origins are fundamental questions in molecular ecology and evolution. We explored the evolutionary mechanisms that formed Arabidopsis-microbiota interactions, as a model for host-microbe interactions. We found prevalent horizontal gene transfer, affecting 180 genes, that occurred between plants and their commensal microbiota. We propose that these genes participate in molecular mimicry between the host and its microbiome. Bacteria acquired from plants genes that primarily encode for secreted proteins that metabolize carbohydrates, thereby enabling bacteria to grow on plant-derived sugars. Additionally, we demonstrate how a bacterial gene that mimics a plant hormone biosynthesis gene can replace the plant gene function. Our results suggest that horizontal gene transfer between hosts and their microbiota is an important and active evolutionary mechanism that contributed new traits to plants and their commensal microbiota.

--- https://www.npr.org/2023/01/25/1151435033/a-robot-was-scheduled-to-argue-in-court-then-came-the-jail-threats 2022-07-20 ai/nn/transformer/gpt/non-fiction economics/automation law --- https://arxiv.org/abs/2212.09689#facebook Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor Or Honovich, Thomas Scialom, Omer Levy, Timo Schick 2022-12-19 2022-12-19 [("doi","10.48550/arXiv.2212.09689")] ai/dataset ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 reinforcement-learning/meta-learning

Instruction tuning enables pretrained language models to perform new tasks from inference-time natural language descriptions. These approaches rely on vast amounts of human supervision in the form of crowdsourced datasets or user interactions.

In this work, we introduce Unnatural Instructions: a large dataset of creative and diverse instructions, collected with virtually no human labor.

We collect 64,000 examples by prompting a language model with 3 seed examples of instructions and eliciting a fourth. This set is then expanded by prompting the model to rephrase each instruction, creating a total of ~240,000 examples of instructions, inputs, and outputs.

Experiments show that despite containing a fair amount of noise, training on Unnatural Instructions rivals the effectiveness of training on open-source manually-curated datasets, surpassing the performance of models such as T0++ and Tk-Instruct across various benchmarks.

These results demonstrate the potential of model-generated data as a cost-effective alternative to crowdsourcing for dataset expansion and diversification.

--- https://www.reddit.com/r/ChatGPT/comments/zsw1jc/i_introduced_my_80_year_old_aunt_to_chatgpt/ 2022-07-20 ai/nn/transformer/gpt/non-fiction psychiatry/depression --- https://www.lesswrong.com/posts/vwu4kegAEZTBtpT6p/thoughts-on-the-impact-of-rlhf-research 2022-07-20 reinforcement-learning/preference-learning reinforcement-learning/safe --- https://arxiv.org/abs/2009.09440 The Significance Filter, the Winner’s Curse and the Need to Shrink Erik van Zwet, Eric Cator 2020-09-20 2022-07-20 [("doi","10.48550/arXiv.2009.09440")] statistics/power-analysis

The “significance filter” refers to focusing exclusively on statistically-significant results. Since frequentist properties such as unbiasedness and coverage are valid only before the data have been observed, there are no guarantees if we condition on observations. In fact, the filter leads to overestimation of the magnitude of the parameter, which has been called the “winner’s curse”. It can also lead to under-coverage of the confidence interval. Moreover, these problems become more severe if the statistical power is low. While these issues clearly deserve our attention, they have been studied only informally and mathematical results are lacking.

Here we study them from the frequentist and the Bayesian perspective.

We prove that the relative bias of the magnitude is a decreasing function of the power and that the usual confidence interval under-covers when the power is less than 50%.

We conclude that failure to apply the appropriate amount of shrinkage can lead to misleading inferences.

--- https://www.biorxiv.org/content/10.1101/2023.01.04.522507.full Gene Therapy Mediated Partial Reprogramming Extends Lifespan and Reverses Age-Related Changes in Aged Mice Carolina Cano Macip, Rokib Hasan, Victoria Hoznek, Jihyun Kim, Louis E. Metzger, Saumil Sethna, Noah Davidsohn 2023-01-05 2023-01-05 [("doi","10.1101/2023.01.04.522507")] genetics/editing longevity/epigenetics

[Twitter] Aging is a complex process best characterized as the chronic dysregulation of cellular processes leading to deteriorated tissue and organ function. While aging cannot currently be prevented, its impact on lifespan and healthspan in the elderly can potentially be minimized by interventions that aim to return these cellular processes to optimal function.

Recent studies have demonstrated that partial reprogramming using the Yamanaka factors (or a subset; OCT4, SOX2, and KLF4; OSK) can reverse age-related changes in vitro and in vivo. However, it is still unknown whether the Yamanaka factors (or a subset) are capable of extending the lifespan of aged wild type mice.

Here, we show that systemically delivered AAVs, encoding an inducible OSK system, in 124-week-old mice:

extends the median remaining lifespan by 109% over wild-type controls and enhances several health parameters. [Still less than rapamycin can do...] Importantly, we observed a statistically-significant improvement in frailty scores indicating that we were able to improve the healthspan along with increasing the lifespan. Furthermore, in human keratinocytes expressing exogenous OSK, we observed statistically-significant epigenetic markers of age-reversal, suggesting a potential reregulation of genetic networks to a younger, potentially healthier state.

Together, these results may have important implications for the development of partial reprogramming interventions to reverse age-associated diseases in the elderly.

Supplementary Figure 1: Overall survival proportion curves for control mice and TRE-OSK mice over the entire lifespan. Survival proportions for the whole time course for data shown in Figure 1.
Figure 1: Partial reprogramming with TRE-OSK leads to increased lifespan and improved frailty scores in very old mice. (a) Schematic of the constructs, virus, and injection route used in the study. (b) Kaplan-Meier curves for 124-week WT mice injected with AAV9.TRE-OSK and AAV9.hEF1?-rtTA4 (both 1E12 vg/animal) via the retro-orbital route, and induced with one week on/off doxycycline paradigm (TRE-OSK) showed median lifespan extension of remaining life by 109% compared to either doxycycline treated control animals (Control-Dox) or to historical published Jax data for Bl6/J mice (Jax historical). Red arrow indicates AAV injections. Mantel Cox Log rank test, ✱✱ <em>p</em> < 0.05. (c) Graph shows remaining lifespan of individual mice (after injections at week 124) for data shown in b. Two-tailed unpaired t-test; ✱✱ <em>p</em> < 0.05. (d) Frailty index (FI), the compound score of 28 different health parameters (range 0–1 in 0.5 increments), showed statistically-significant reduction in FI for TRE-OSK mice at 142 weeks of age (18 weeks after injections) as compared to Control-Dox mice. Student’s unpaired t-test, ✱✱ <em>p</em> < 0.05.
--- https://towardsdatascience.com/can-chatgpt-write-better-sql-than-a-data-analyst-f079518efab2 2022-07-20 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2209.10063#microsoft Generate rather than Retrieve (GenRead): Large Language Models are Strong Context Generators Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, Meng Jiang 2022-09-21 2022-09-21 [("doi","10.48550/arXiv.2209.10063")] ai/nn/retrieval ai/nn/transformer/gpt

Knowledge-intensive tasks, such as open-domain question answering (QA), require access to a large amount of world or domain knowledge. A common approach for knowledge-intensive tasks is to employ a retrieve-then-read pipeline that first retrieves a handful of relevant contextual documents from an external corpus such as Wikipedia and then predicts an answer conditioned on the retrieved documents.

In this paper, we present a novel perspective for solving knowledge-intensive tasks by replacing document retrievers with large language model generators. We call our method generate-then-read (GenRead), which first prompts a large language model to generate contextual documents based on a given question, and then reads the generated documents to produce the final answer. Furthermore, we propose a novel clustering-based prompting method that selects distinct prompts, resulting in the generated documents that cover different perspectives, leading to better recall over acceptable answers.

We conduct extensive experiments on 3 different knowledge-intensive tasks, including open-domain QA, fact checking, and dialogue system. Notably, GenRead achieves 71.6 and 54.4 exact match scores on TriviaQA and WebQ, outperforming the state-of-the-art retrieve-then-read pipeline DPR-FiD by +4.0 and +3.9, without retrieving any documents from any external knowledge source.

Lastly, we demonstrate the model performance can be further improved by combining retrieval and generation.

Our code and generated documents can be found at Github.

--- https://arxiv.org/abs/2212.13138#google Med-PaLM: Large Language Models Encode Clinical Knowledge Karan Singhal, Shekoofeh Azizi, Tao Tu, S. Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, Perry Payne, Martin Seneviratne, Paul Gamble, Chris Kelly, Nathaneal Scharli, Aakanksha Chowdhery, Philip Mansfield, Blaise Aguera y Arcas, Dale Webster, Greg S. Corrado, Yossi Matias, Katherine Chou, Juraj Gottweis, Nenad Tomasev, Yun Liu, Alvin Rajkomar, Joelle Barral, Christopher Semturs, Alan Karthikesalingam, Vivek Natarajan 2022-12-26 2022-12-26 [("doi","10.48550/arXiv.2212.13138")] ai/dataset ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/gpt/palm biology

[code; Twitter] Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models’ clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks.

To address this, we present MultiMedQA, a benchmark combining 6 existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias.

In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA.

Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses.

To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine.

Our human evaluations reveal important limitations of today’s models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications.

--- https://fontsinuse.com/uses/43515/the-mystery-of-the-dune-font 2022-07-20 design/typography fiction/science-fiction/frank-herbert --- https://arxiv.org/abs/2211.15841 MegaBlocks: Efficient Sparse Training with Mixture-of-Experts Trevor Gale, Deepak Narayanan, Cliff Young, Matei Zaharia 2022-11-29 2022-11-29 [("doi","10.48550/arXiv.2211.15841")] ai/scaling/mixture-of-experts

[code] We present MegaBlocks, a system for efficient Mixture-of-Experts (MoE) training on GPUs.

Our system is motivated by the limitations of current frameworks, which restrict the dynamic routing in MoE layers to satisfy the constraints of existing software and hardware. These formulations force a tradeoff between model quality and hardware efficiency, as users must choose between dropping tokens from the computation or wasting computation and memory on padding.

To address these limitations, we reformulate MoE computation in terms of block-sparse operations and develop new block-sparse GPU kernels that efficiently handle the dynamism present in MoEs.

Our approach never drops tokens and maps efficiently to modern hardware, enabling end-to-end training speedups of up to 40% over MoEs trained with the state-of-the-art Tutel library and 2.4× over DNNs trained with the highly-optimized Megatron-LM framework.

--- https://www.biorxiv.org/content/10.1101/2022.07.11.499562.full Correspondence between the layered structure of deep language models and temporal structure of natural language processing in the human brain Ariel Goldstein, Eric Ham, Samuel A. Nastase, Zaid Zada, Avigail Grinstein-Dabus, Bobbi Aubrey, Mariano Schain, Harshvardhan Gazula, Amir Feder, Werner Doyle, Sasha Devore, Patricia Dugan, Daniel Friedman, Michael Brenner, Avinatan Hassidim, Orrin Devinsky, Adeen Flinker, Omer Levy, Uri Hasson 2022-07-25 2022-07-25 [("doi","10.1101/2022.07.11.499562")] ai/nn/transformer/gpt/2 psychology/neuroscience

Deep language models (DLMs) provide a novel computational paradigm for how the brain processes natural language. Unlike symbolic, rule-based models described in psycholinguistics, DLMs encode words and their context as continuous numerical vectors. These “embeddings” are constructed by a sequence of layered computations to ultimately capture surprisingly sophisticated representations of linguistic structures. How does this layered hierarchy map onto the human brain during natural language comprehension?

In this study, we used ECoG to record neural activity in language areas along the superior temporal gyrus and inferior frontal gyrus while human participants listened to a 30-minute spoken narrative. We supplied this same narrative to a high-performing DLM (GPT-2-XL) and extracted the contextual embeddings for each word in the story across all 48 layers of the model. We next trained a set of linear encoding models to predict the temporally-evolving neural activity from the embeddings at each layer.

We found a striking correspondence between the layer-by-layer sequence of embeddings from GPT-2-XL and the temporal sequence of neural activity in language areas. In addition, we found evidence for the gradual accumulation of recurrent information along the linguistic processing hierarchy. However, we also noticed additional neural processes that took place in the brain, but not in DLMs, during the processing of surprising (unpredictable) words.

These findings point to a connection between language processing in humans and DLMs where the layer-by-layer accumulation of contextual information in DLM embeddings matches the temporal dynamics of neural activity in high-order language areas.

Significance statement: Deep language models transformed our ability to model language. Recent studies connected these neural nets based models to the human representation of language. Here, we show a striking similarity between the sequence of representations induced by the model and the brain encoding of language over time during real-life comprehension.

--- https://bullfrogreview.substack.com/p/honey-i-hacked-the-empathy-machine 2022-07-21 ai/nn/transformer/gpt/non-fiction politics --- https://x.com/emollick/status/1618969731431804929 2022-07-21 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/fiction fiction/text-game --- /doc/psychology/cognitive-bias/2023-thal.pdf Do Political Elites Have Accurate Perceptions of Social Conditions? Adam Thal 2023-01-19 2023-01-19 [("doi","10.1017/S0007123422000643")] politics psychology/cognitive-bias

Politicians often oppose economic policies benefiting low-income Americans. However, the mechanisms behind this political inequality are unclear. I ask whether politicians oppose these policies, in part, because they underestimate how many of those they govern are struggling financially.

I test this theory with an original survey of 1,265 state legislative candidates.

Contrary to my expectations, I find that politicians tend to overestimate how many of those they govern are struggling financially. At the same time, there are some instances in which politicians—and Republicans in particular—do underestimate the level of financial hardship among those they govern [but Republicans are less wrong].

In an experiment, I randomly assign politicians to have their misperceptions corrected.

The results suggest that politicians’ policy preferences would be similar even if they had a more accurate understanding of reality.

Overall, the findings suggest that politicians may frequently misperceive the state of reality in which those they govern live.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4041116/ Can training in a real-time strategy video game attenuate cognitive decline in older adults? Chandramallika Basak, Walter R. Boot, Michelle W. Voss, Arthur F. Kramer 2008 2022-07-21 [("doi","10.1037/a0013494")] dual-n-back

Declines in various cognitive abilities, particularly executive control functions, are observed in older adults. An important goal of cognitive training is to slow or reverse these age-related declines. However, opinion is divided in the literature regarding whether cognitive training can engender transfer to a variety of cognitive skills in older adults.

In the current study, the authors trained older adults in a real-time strategy video game [Rise of Nations] for 23.5 hr in an effort to improve their executive functions. A battery of cognitive tasks, including tasks of executive control and visuospatial skills, were assessed before, during, and after video-game training.

The trainees improved statistically-significantly in the measures of game performance. They also improved statistically-significantly more than the control participants in executive control functions, such as task switching, working memory, visual short-term memory, and reasoning. Individual differences in changes in game performance were correlated with improvements in task switching.

The study has implications for the enhancement of executive control processes of older adults.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7265808/ Comparing Web-Based and Classroom-Based Memory Training for Older Adults: The ACTIVE Memory Works™ Study George W. Rebok, Marian Tzuang, Jeanine M. Parisi 2020 2022-07-21 [("doi","10.1093/geronb/gbz107")] dual-n-back

Objectives: To compare the efficacy of a web-based versus a classroom-based memory training program in enhancing cognition and everyday functioning in older adults, and program satisfaction and acceptability.

Method: Participants (n = 208; mean age = 71.1) were randomly assigned to a web-based or classroom-based training, or to a wait-list control condition. Cognitive and everyday functioning measures were administered at baseline, immediate, and 6 months post-training; both training groups evaluated program satisfaction and acceptability at immediate post-training. Repeated-measures analyses of variance assessed training effects on cognitive and functioning outcomes; independent-samples t-tests assessed group differences in program satisfaction and acceptability.

Results: Compared to controls, neither training group showed a statistically-significant improvement on measures of memory or everyday functioning as assessed by dependence or difficulty on instrumental activities of daily living over time. Training effects did not transfer to non-trained cognitive abilities. The web-based group was as satisfied with the training as the classroom-based group (p > 0.05).

Discussion: Although no statistically-significant training effects were found, we demonstrated that a web-based platform is an acceptable and feasible mode to provide memory training to healthy older adults. Further studies are needed to investigate the potential of web-based memory training programs for improving cognition and function in cognitively healthy older adults.

--- https://arxiv.org/abs/2301.02998 InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers Leonid Boytsov, Preksha Patel, Vivek Sourabh, Riddhi Nisar, Sayani Kundu, Ramya Ramanathan, Eric Nyberg 2023-01-08 2023-01-08 [("doi","10.48550/arXiv.2301.02998")] ai/nn/retrieval ai/nn/transformer/gpt ai/nn/transformer/t5

We carried out a reproducibility study of the InPars recipe for unsupervised training of neural rankers. As a by-product of this study, we developed a simple-yet-effective modification of InPars, which we called InPars-light. Unlike InPars, InPars-light uses only a freely available language model BLOOM and 7-100× smaller ranking models.

On all 5 English retrieval collections (used in the original InPars study) we obtained substantial (7-30%) and statistically improvements over BM25 in nDCG or MRR using only a 30M parameter six-layer MiniLM ranker. In contrast, in the InPars study only a 100× larger MonoT5-3B model consistently outperformed BM25, whereas their smaller MonoT5-220M model (which is still 7× larger than our MiniLM ranker), outperformed BM25 only on MS MARCO and TREC DL 2020. In a purely unsupervised setting, our 435M parameter DeBERTA v3 ranker was roughly at par with the 7× larger MonoT5-3B: In fact, on 3⁄5 datasets, it slightly outperformed MonoT5-3B. Finally, these good results were achieved by re-ranking only 100 candidate documents compared to 1,000 used in InPars.

We believe that InPars-light is the first truly cost-effective prompt-based unsupervised recipe to train and deploy neural ranking models that outperform BM25.

--- /doc/fiction/science-fiction/1953-dahl-thegreatautomaticgrammatizator.pdf The Great Automatic Grammatizator Roald Dahl 1953-01-01 2022-07-21 ai/fiction fiction/humor fiction/science-fiction --- https://arxiv.org/abs/2202.05144 InPars: Data Augmentation for Information Retrieval using Large Language Models Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, Rodrigo Nogueira 2022-02-10 2022-07-21 [("doi","10.48550/arXiv.2202.05144")] ai/nn/retrieval ai/nn/transformer/gpt ai/nn/transformer/t5

The information retrieval community has recently witnessed a revolution due to large pretrained transformer models. Another key ingredient for this revolution was the MS MARCO dataset, whose scale and diversity has enabled zero-shot transfer learning to various tasks. However, not all IR tasks and domains can benefit from one single dataset equally. Extensive research in various NLP tasks has shown that using domain-specific training data, as opposed to a general-purpose one, improves the performance of neural models.

In this work, we harness the few-shot capabilities of large pretrained language models as synthetic data generators for IR tasks. We show that models finetuned solely on our unsupervised dataset outperform strong baselines such as BM25 as well as recently proposed self-supervised dense retrieval methods. Furthermore, retrievers finetuned on both supervised and our synthetic data achieve better zero-shot transfer than models finetuned only on supervised data.

Code, models, and data are available at Github.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3846682/ 2022-07-21 biology exercise nicotine psychiatry/depression vitamin-d --- https://en.wikipedia.org/wiki/Sentence_spacing Sentence spacing 2022-07-22 design/typography/sentence-spacing --- https://en.wikipedia.org/wiki/Sentence_spacing_studies Sentence spacing studies 2022-07-22 design/typography/sentence-spacing --- https://github.com/scandum/quadsort 2022-07-22 cs/algorithm/sorting --- https://x.com/arankomatsuzaki/status/1619548480795734016 2022-07-22 ai/nn/tokenization --- https://arxiv.org/abs/1907.05242#facebook Large Memory Layers with Product Keys Guillaume Lample, Alexandre Sablayrolles, Marc’Aurelio Ranzato, Ludovic Denoyer, Hervé Jégou 2019-07-10 2022-07-22 [("doi","10.48550/arXiv.1907.05242")] ai/nn/retrieval ai/nn/transformer/attention

This paper introduces a structured memory which can be easily integrated into a neural network.

The memory is very large by design and increases the capacity of the architecture, by up to a billion parameters with a negligible computational overhead. Its design and access pattern is based on product keys, which enable fast and exact nearest neighbor search. The ability to increase the number of parameters while keeping the same computational budget lets the overall system strike a better trade-off between prediction accuracy and computation efficiency both at training and test time. This memory layer allows us to tackle very large scale language modeling tasks.

In our experiments we consider a dataset with up to 30 billion words, and we plug our memory layer in a state-of-the-art transformer-based architecture. In particular, we found that a memory augmented model with only 12 layers outperforms a baseline transformer model with 24 layers, while being twice faster at inference time.

We release our code for reproducibility purposes.

--- https://x.com/_akhaliq/status/1619748603391799297 2022-07-22 ai/nn/transformer/gpt/fiction --- https://www.smithsonianmag.com/smart-news/art-pranksters-sell-one-real-warhol-print-amid-999-fake-ones-180978975/ 2022-07-22 psychology/collecting --- https://arxiv.org/abs/2212.11565 Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Weixian Lei, Yuchao Gu, Wynne Hsu, Ying Shan, Xiaohu Qie, Mike Zheng Shou 2022-12-22 2022-12-22 [("doi","10.48550/arXiv.2212.11565")] ai/nn/diffusion ai/video/generation

[code] To reproduce the success of text-to-image (T2I) generation, recent works in text-to-video (T2V) generation employ large-scale text-video dataset for fine-tuning. However, such paradigm is computationally expensive. Humans have the amazing ability to learn new visual concepts from just one single exemplar.

We hereby study a new T2V generation problem—One-Shot Video Generation, where only a single text-video pair is presented for training an open-domain T2V generator. Intuitively, we propose to adapt the T2I diffusion model pretrained on massive image data for T2V generation. We make two key observations: (1) T2I models are able to generate images that align well with the verb terms; (2) extending T2I models to generate multiple images concurrently exhibits surprisingly good content consistency.

To further learn continuous motion, we propose Tune-A-Video with a tailored Sparse-Causal Attention, which generates videos from text prompts via an efficient one-shot tuning of pretrained T2I diffusion models.

Tune-A-Video is capable of producing temporally-coherent videos over various applications such as change of subject or background, attribute editing, style transfer, demonstrating the versatility and effectiveness of our method.

--- https://www.biorxiv.org/content/10.1101/2022.09.01.504602.full POM: A Principal Odor Map Unifies Diverse Tasks in Human Olfactory Perception Brian K. Lee, Emily J. Mayhew, Benjamin Sanchez-Lengeling, Jennifer N. Wei, Wesley W. Qian, Kelsie Little, Matthew Andres, Britney B. Nguyen, Theresa Moloy, Jane K. Parker, Richard C. Gerkin, Joel D. Mainland, Alexander B. Wiltschko 2022-12-13 2022-12-13 [("doi","10.1101/2022.09.01.504602")] ai/nn/transformer psychology/smell/human

[media] Mapping molecular structure to odor perception is a key challenge in olfaction.

Here, we use graph neural networks (GNN) to generate a Principal Odor Map (POM) that preserves perceptual relationships and enables odor quality prediction for novel odorants.

The model is as reliable as a human in describing odor quality: on a prospective validation set of 400 novel odorants, the model-generated odor profile more closely matched the trained panel mean (n = 15) than did the median panelist. Applying simple, interpretable, theoretically-rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships.

This approach broadly enables odor prediction and paves the way toward digitizing odors.

One-Sentence Summary: An odor map achieves human-level odor description performance and generalizes to diverse odor-prediction tasks.

--- https://www.wired.com/story/this-startup-is-using-ai-to-unearth-new-smells/ 2022-07-22 psychology/smell --- https://x.com/ludwig_stumpp/status/1619701277419794435 2022-07-22 ai/nn/transformer/gpt/non-fiction cs/security --- https://arxiv.org/abs/2301.08653 An Analysis of the Automatic Bug Fixing Performance of ChatGPT Dominik Sobania, Martin Briesch, Carol Hanna, Justyna Petke 2023-01-20 2023-01-20 [("doi","10.48550/arXiv.2301.08653")] ai/nn/transformer/gpt/codex

To support software developers in finding and fixing software bugs, several automated program repair techniques have been introduced. Given a test suite, standard methods usually either synthesize a repair, or navigate a search space of software edits to find test-suite passing variants. Recent program repair methods are based on deep learning approaches. One of these novel methods, which is not primarily intended for automated program repair, but is still suitable for it, is ChatGPT. The bug fixing performance of ChatGPT, however, is so far unclear.

Therefore, in this paper we evaluate ChatGPT on the standard bug fixing benchmark set, QuixBugs, and compare the performance with the results of several other approaches reported in the literature.

We find that ChatGPT’s bug fixing performance is competitive to the common deep learning approaches CoCoNut and Codex and notably better than the results reported for the standard program repair approaches.

In contrast to previous approaches, ChatGPT offers a dialogue system through which further information, eg. the expected output for a certain input or an observed error message, can be entered. By providing such hints to ChatGPT, its success rate can be further increased, fixing 31⁄40 bugs, outperforming state-of-the-art.

--- https://www.schedium.net/2023/01/the-window-trick-of-las-vegas-hotels.html 2022-07-23 psychology/novelty --- https://arxiv.org/abs/2111.03922 Automatic Program Repair with OpenAI’s Codex: Evaluating QuixBugs Julian Aron Prenner, Romain Robbes 2021-11-06 2022-07-23 [("doi","10.48550/arXiv.2111.03922")] ai/nn/transformer/gpt/codex

OpenAI’s Codex, a GPT-3 like model trained on a large code corpus, has made headlines in and outside of academia. Given a short user-provided description, it is capable of synthesizing code snippets that are syntactically and semantically valid in most cases. In this work, we want to investigate whether Codex is able to localize and fix bugs, a task of central interest in the field of automated program repair.

Our initial evaluation uses the multi-language QuixBugs benchmark (40 bugs in both Python and Java).

We find that, despite not being trained for APR, Codex is surprisingly effective, and competitive with recent state-of-the-art techniques. Our results also show that Codex is slightly more successful at repairing Python than Java.

--- /doc/ai/nn/transformer/gpt/codex/2020-lutellier.pdf CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair Thibaud Lutellier, Hung Viet Pham, Lawrence Pang, Yitong Li, Moshi Wei, Lin Tan 2020-07-01 2022-07-23 [("doi","10.1145/3395363.3397369")] ai/nn/cnn ai/nn/transformer/gpt/codex

Automated generate-and-validate (GV) program repair techniques (APR) typically rely on hard-coded rules, thus only fixing bugs following specific fix patterns. These rules require a large amount of manual effort to discover and it is hard to adapt these rules to different programming languages.

To address these challenges, we propose a new G&V technique—CoCoNuT, which uses ensemble learning on the combination of convolutional neural networks (CNNs) and a new context-aware neural machine translation (NMT) architecture to automatically fix bugs in multiple programming languages. To better represent the context of a bug, we introduce a new context-aware NMT architecture that represents the buggy source code and its surrounding context separately. CoCoNuT uses CNNs instead of recurrent neural networks (RNNs), since CNN layers can be stacked to extract hierarchical features and better model source code at different granularity levels (eg. statements and functions). In addition, CoCoNuT takes advantage of the randomness in hyperparameter tuning to build multiple models that fix different bugs and combines these models using ensemble learning to fix more bugs.

Our evaluation on 6 popular benchmarks for 4 programming languages (Java, C, Python, and JavaScript) shows that CoCoNuT correctly fixes (ie. the first generated patch is semantically equivalent to the developer’s patch) 509 bugs, including 309 bugs that are fixed by none of the 27 techniques with which we compare.

[Keywords: automated program repair, deep learning, neural machine translation, AI and software engineering]

--- https://x.com/LensBlurred/status/1619810491500728321 2022-07-23 design --- https://x.com/emollick/status/1619921194135986177 2022-07-23 ai/nn/transformer/gpt/fiction --- https://www.elidourado.com/p/cargo-airships 2022-07-23 technology --- https://cell.substack.com/p/dna 2022-07-23 genetics/genome-synthesis --- https://www.reddit.com/r/StableDiffusion/comments/10p45mr/making_images_with_only_negative_prompts_is_sorry/ 2022-07-23 ai/nn/transformer/clip/sample --- https://x.com/gmchariszhang/status/1620150392326864898 2022-07-23 ai/nn/transformer/gpt/non-fiction math --- https://x.com/volokuleshov/status/1619906183955095558 2022-07-23 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://x.com/maltoesermalte/status/1620343988808204288 2022-07-24 sociology/technology --- https://arxiv.org/abs/2212.09741 One Embedder, Any Task: Instruction-Finetuned Text Embeddings (INSTRUCTOR) Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah Smith, Luke Zettlemoyer, Tao Yu 2022-12-19 2022-12-19 [("doi","10.48550/arXiv.2212.09741")] ai/nn/retrieval ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 ai/scaling

We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (eg. task and domain descriptions). Unlike encoders from prior work that are more specialized, INSTRUCTOR is a single embedder that can generate text embeddings tailored to different downstream tasks and domains, without any further training.

We first annotate instructions for 330 diverse tasks and train INSTRUCTOR on this multitask mixture with a contrastive loss.

We evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are unseen during training), ranging from classification and information retrieval to semantic textual similarity and text generation evaluation. INSTRUCTOR, while having an order of magnitude fewer parameters than the previous best model, achieves state-of-the-art performance, with an average improvement of 3.4% compared to the previous best results on the 70 diverse datasets. [#1 as of 2023-01-31 on the MTEB leaderboard]

Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets.

Our model, code, and data are available at https://instructor-embedding.github.io/.

...4.3 Complexity of Instructions: Here we further analyze the role of instructions over varying degrees of their complexity. Specifically, we consider 4 levels of instruction complexity: N/A (no instructions), dataset tags, simple instructions, and detailed instructions (the original instruction format, §2.3). In the dataset tag setup, each example is prepended with its dataset name. For instance, on the Natural Questions dataset, the query is formatted as “Natural Questions; Input: who sings the song Love Story”. In the simple instruction setup, we use one or two words to describe the domain (eg. for Natural Questions, the input query is “Wikipedia Questions; Input: who sings the song Love Story”). Figure 5 shows their average performances across all task categories. Even with trivial dataset tags, INSTRUCTOR outperforms the original GTR model, illustrating the effectiveness of instructions for diverse training.

As more information is provided in the instruction (from tag to simple and from simple to detail), we observe consistent improvements.

Figure 5: Average performance over varying degrees of instruction details. As the instructions become more detailed, the performance improves. N/A: no instructions are given; tag: dataset names are prepended; simple: one word or two for the task domain are given (eg. Wikipedia); and detailed: our proposed instructions (§2.3).

4.4 Model Sizes and Instruction Finetuning: Figure 6 studies the influence of model sizes. Specifically, we use GTR-Base (0.1B), GTR-Large (0.3B), and GTR-XL (1.5B). They are pretrained on the same corpus and differ only in the encoder size (the embedding sizes are the same). We compare models of various sizes and report the average performance across all the categories. As the encoder transformer model scales up, the performance continues to increase for both GTR and INSTRUCTOR. Nonetheless, the improvement in INSTRUCTOR is more pronounced, perhaps because embeddings with instructions benefit from larger capacities. This implies that large models are more generalizable to compute texts in various domains and task types, providing embeddings for general purposes. Further scale-ups are left to future work.

Figure 6: Average performance comparisons with varying sizes of models. INSTRUCTOR benefits more from scaling up, perhaps because instructions require additional computations.

4.5 Instructions Mitigate Domain Shifts: One advantage of instruction-based finetuning is that it improves models’ ability to generalize to unseen domains and tasks. To demonstrate this effectiveness, we found 3 unseen domains that INSTRUCTOR was not trained on: geography, biology, and civil comments. As shown in Table 3, INSTRUCTOR largely improves (above the average improvement) GTR-Large’s performance on all 3 domains, indicating that instructions can help more when applying models to unseen or uncommon domains.

Table 3: Results of GTR-Large and INSTRUCTOR on unseen domains: geography, biology and civil comments. Domain-specific datasets benefit particularly from instruction finetuning. More results can be found in Table 9 & 10 in the appendix; they also show similar trends.
Model Geography Biology
GTR-Large 53.4 25.7 71.8
INSTRUCTOR 62.3 30.2 77.2
Relative gain (%) +16.7 +17.5 +7.5

[Possible use-case: customized semantic maps eg. argument maps. Instead of a fixed layout suitable for only 1 use (or none at all), the user describes the current task (like planning a vacation) as the prompt, and then INSTRUCTOR embeds all the relevant datapoints, and 2 or 3 principal components are extracted from the embedding, and the items are laid out spatially. Presumably the layout would make more sense than a universal generic map, while being far easier for the user to understand, edit, or use.]

--- https://arxiv.org/abs/2108.08877#google Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models Jianmo Ni, Gustavo Hernández Ábrego, Noah Constant, Ji Ma, Keith B. Hall, Daniel Cer, Yinfei Yang 2021-08-19 2022-07-24 [("doi","10.48550/arXiv.2108.08877")] ai/nn/retrieval ai/nn/transformer/t5

We provide the first exploration of sentence embeddings from text-to-text transformers (T5). Sentence embeddings are broadly useful for language processing tasks. While T5 achieves impressive performance on language tasks cast as sequence-to-sequence mapping problems, it is unclear how to produce sentence embeddings from encoder-decoder models.

We investigate 3 methods for extracting T5 sentence embeddings: two use only the T5 encoder and one uses the full T5 encoder-decoder model.

To support our investigation, we establish a new sentence representation transfer benchmark, SentGLUE, which extends the SentEval toolkit to 9 tasks from the GLUE benchmark.

Our encoder-only models outperforms Sentence-BERT and SimCSE sentence embeddings on both SentEval and SentGLUE transfer tasks, including semantic textual similarity (STS).

Scaling up T5 from millions to billions of parameters is found to produce consistent further improvements.

Finally, our encoder-decoder method achieves a new state-of-the-art on STS when using sentence embeddings.

Our models are released at TFhub.

--- https://www.cnn.com/2023/01/28/tech/chatgpt-real-estate/index.html 2022-07-24 ai/nn/transformer/gpt/non-fiction law --- https://x.com/YirenLu/status/1620171023575580673 2022-07-24 ai/nn/transformer/gpt/non-fiction --- https://x.com/YirenLu/status/1620171027828600832 2022-07-24 ai/nn/transformer/gpt/non-fiction --- https://x.com/YirenLu/status/1620171032161312773 2022-07-24 ai/nn/transformer/gpt/non-fiction --- https://sashachapin.substack.com/5-meo-dmt-a-review-by-a-confused 2022-07-24 psychedelic psychiatry/meditation --- https://www.quantamagazine.org/a-physical-theory-for-when-the-brain-performs-best-20230131/ 2022-07-24 psychology/neuroscience --- https://www.lesswrong.com/posts/K7AyY8LMrcKhwfbyj/no-really-attention-is-all-you-need-attention-can-do 2022-07-24 ai/nn/fully-connected ai/nn/transformer/attention cs/computable --- /doc/philosophy/mind/1991-worth-geoffreysonnabendobliscencetheoriesofforgettingandtheproblemofmatter.pdf Geoffrey Sonnabend: Obliscence, Theories of Forgetting and the Problem of Matter—An Encapsulation (Fourth Edition, abridged) Valentine Worth 1991-01-01 2022-07-25 math/humor philosophy/mind --- https://www.thebeliever.net/destroy-all-monsters/ 2022-07-25 fiction/text-game --- https://lukeplant.me.uk/blog/posts/pythons-disappointing-superpowers/ 2022-07-25 cs/python --- https://github.com/shaunlebron/history-of-lisp-parens/blob/master/editors.md 2022-07-25 cs/lisp design --- https://x.com/emollick/status/1621319115326603270 2022-07-25 ai/nn/transformer/clip/sample --- https://skeptoid.com/episodes/4845 2022-07-25 technology/self-sinking --- https://x.com/_jasonwei/status/1621333297891790848 2022-07-25 ai/nn/transformer/t5 --- /doc/math/1998-dales-truthinmathematics.pdf 1998 2022-07-25 math philosophy/epistemology --- https://news.ycombinator.com/item?id=34635352 2022-07-25 ai/music --- https://x.com/repligate/status/1620949459902529537 2022-07-25 ai/nn/tokenization --- https://en.wikipedia.org/wiki/Tsundoku Tsundoku 2022-07-25 psychology/willpower --- https://www.astralcodexten.com/p/mostly-skeptical-thoughts-on-the 2022-07-26 ai/nn/transformer/gpt/non-fiction politics --- https://x.com/aibreakfast/status/1620128621821317125 2022-07-26 ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/2301.03118 Facial Misrecognition Systems: Simple Weight Manipulations Force DNNs to Err Only on Specific Persons Irad Zehavi, Adi Shamir 2023-01-08 2023-01-08 [("doi","10.48550/arXiv.2301.03118")] ai/nn/adversarial cs/security

In this paper we describe how to plant novel types of backdoors in any facial recognition model based on the popular architecture of deep Siamese neural networks, by mathematically changing a small fraction of its weights (ie. without using any additional training or optimization).

These backdoors force the system to err only on specific persons which are preselected by the attacker. For example, we show how such a backdoored system can take any two images of a particular person and decide that they represent different persons (an anonymity attack), or take any two images of a particular pair of persons and decide that they represent the same person (a confusion attack), with almost no effect on the correctness of its decisions for other persons. Uniquely, we show that multiple backdoors can be independently installed by multiple attackers who may not be aware of each other’s existence with almost no interference.

We have experimentally verified the attacks on a FaceNet-based facial recognition system, which achieves SOTA accuracy on the standard LFW dataset of 99.35%. When we tried to individually anonymize 10 celebrities, the network failed to recognize two of their images as being the same person in 96.97% to 98.29% of the time. When we tried to confuse between the extremely different looking Morgan Freeman and Scarlett Johansson, for example, their images were declared to be the same person in 91.51% of the time.

For each type of backdoor, we sequentially installed multiple backdoors with minimal effect on the performance of each one (for example, anonymizing all 10 celebrities on the same model reduced the success rate for each celebrity by no more than 0.91%). In all of our experiments, the benign accuracy of the network on other persons was degraded by no more than 0.48% (and in most cases, it remained above 99.30%).

--- https://x.com/RazRazcle/status/1621545017423659008 2022-07-26 ai/nn/transformer/gpt/non-fiction reinforcement-learning/safe --- https://x.com/emollick/status/1621599523733925888 2022-07-26 ai/nn/diffusion --- https://x.com/StableDiffusion/status/1621448312497868801 2022-07-26 ai/nn/transformer/clip/sample --- https://www.overcomingbias.com/p/why-is-everyone-so-boringhtml 2022-07-26 psychology/novelty sociology --- https://www.overcomingbias.com/p/explain-the-sacredhtml 2022-07-26 philosophy/religion --- https://arxiv.org/abs/2301.12597#salesforce BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi 2023-01-30 2023-01-30 [("doi","10.48550/arXiv.2301.12597")] ai/nn/transformer/clip ai/nn/transformer/t5

[code] The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models.

This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models.

BLIP-2 bridges the modality gap with a lightweight Querying Transformer, which is pre-trained in two stages. The first stage bootstraps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative learning from a frozen language model.

BLIP-2 achieves state-of-the-art performance on various vision-language tasks, despite having fewer trainable parameters than existing methods. For example, our model outperforms Flamingo-80B by 8.7% on zero-shot VQAv2 with 54× fewer trainable parameters.

We also demonstrate the model’s emerging capabilities of zero-shot image-to-text generation that can follow natural language instructions.

--- https://arxiv.org/abs/1503.03832#google FaceNet: A Unified Embedding for Face Recognition and Clustering Florian Schroff, Dmitry Kalenichenko, James Philbin 2015-03-12 2022-07-26 [("doi","10.1109/CVPR.2015.7298682")] ai/nn/cnn

Despite recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity. Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature vectors.

Our method uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in previous deep learning approaches. To train, we use triplets of roughly aligned matching / non-matching face patches generated using a novel online triplet mining method. The benefit of our approach is much greater representational efficiency: we achieve state-of-the-art face recognition performance using only 128-bytes per face.

On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%. On YouTube Faces DB it achieves 95.12%. Our system cuts the error rate in comparison to the best published result by 30% on both datasets.

We also introduce the concept of harmonic embeddings, and a harmonic triplet loss, which describe different versions of face embeddings (produced by different networks) that are compatible to each other and allow for direct comparison between each other.

--- https://mentaldisorder.substack.com/p/how-sad-are-psychologists 2022-07-26 psychiatry --- https://en.wikipedia.org/wiki/Robyn_Dawes Robyn Dawes 2022-07-27 psychiatry statistics/decision --- https://www.airgradient.com/open-airgradient/blog/positive-pressure-systems-for-clean-air/ 2022-07-27 co2 --- https://arxiv.org/abs/1908.07125 Universal Adversarial Triggers for Attacking and Analyzing NLP Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh 2019-08-20 2022-07-27 [("doi","10.48550/arXiv.1908.07125")] ai/nn/adversarial ai/nn/transformer/gpt/2

Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a model to produce a specific prediction when concatenated to any input from a dataset.

We propose a gradient-guided search over tokens which finds short trigger sequences (eg. one word for classification and 4 words for language modeling) that successfully trigger the target prediction. For example, triggers cause SNLI entailment accuracy to drop 89.94% → 0.55%, 72% of “why” questions in SQuAD to be answered “to kill American people”, and the GPT-2 language model to spew racist output even when conditioned on non-racial contexts.

Furthermore, although the triggers are optimized using white-box access to a specific model, they transfer to other models for all tasks we consider. Finally, since triggers are input-agnostic, they provide an analysis of global model behavior. For instance, they confirm that SNLI models exploit dataset biases and help to diagnose heuristics learned by reading comprehension models.

--- /doc/psychiatry/depression/2010-harvey.pdf Physical activity and common mental disorders Samuel B. Harvey, Matthew Hotopf, Simon Øverland, Arnstein Mykletun 2010-11-01 2022-07-27 [("doi","10.1192/bjp.bp.109.075176")] exercise psychiatry/depression

Background: Previous studies have suggested that physical activity may have antidepressant and/or anti-anxiety effects.

Aims: To examine the bidirectional relationship between physical activity and common mental disorders and establish the importance of context, type and intensity of activity undertaken.

Method: A clinical examination of 40 401 residents of Norway was undertaken. Participants answered questions relating to the frequency and intensity of both leisure-time and workplace activity. Depression and anxiety were measured using the Hospital Anxiety and Depression Scale (HADS). Biological and social data were also collected.

Results: There was an inverse relationship between the amount of leisure-time physical activity and case-level symptoms of depression. This cross-sectional association was only present with leisure-time (as opposed to workplace) activity and was not dependent on the intensity of activities undertaken. Higher levels of social support and social engagement were important in explaining the relationship between leisure activity and depression. Biological changes such as alterations to parasympathetic vagal tone (resting pulse) and changes to metabolic markers had a less important role.

Conclusions: Individuals who engage in regular leisure-time activity of any intensity are less likely to have symptoms of depression. The context and social benefits of exercise are important in explaining this relationship.

--- /doc/psychiatry/alzheimers/2022-ko.pdf Genome-wide association study of occupational attainment as a proxy for cognitive reserve Hyunwoong Ko, Soyeon Kim, Kiwon Kim, Sang-Hyuk Jung, Injeong Shim, Soojin Cha, Hyewon Lee, Beomsu Kim, Joohyun Yoon, Tae Hyon Ha, Seyul Kwak, Jae Myeong Kang, Jun-Young Lee, Jinho Kim, Woong-Yang Park, Kwangsik Nho, Doh Kwan Kim, Woojae Myung, Hong-Hee Won 2022-04-01 2022-07-27 [("doi","10.1093/brain/awab351")] genetics/heritable/correlation/mendelian-randomization iq psychiatry/alzheimers

Occupational attainment, which represents middle-age cognitive activities, is a known proxy marker of cognitive reserve for Alzheimer’s disease. Previous genome-wide association studies have identified numerous genetic variants and revealed the genetic architecture of educational attainment, another marker of cognitive reserve. However, the genetic architecture and heritability for occupational attainment remain elusive.

We performed a large-scale genome-wide association study of occupational attainment with 248,847 European individuals from the UK Biobank using the proportional odds logistic mixed model method. In this analysis, we defined occupational attainment using the classified job levels formulated in the UK Standard Occupational Classification system considering the individual professional skill and academic level.

We identified 30 statistically-significant loci (p < 5 × 10−8); 12 were novel variants, not associated with other traits. Among them, 4 lead variants were associated with genes expressed in brain tissues by expression quantitative trait loci mapping from 10 brain regions: rs13002946, rs3741368, rs11654986 and rs1627527. The single-nucleotide polymorphism-based heritability was estimated to be 8.5% (standard error of the mean = 0.004) and partitioned heritability was enriched in the CNS and brain tissues. Genetic correlation analysis showed shared genetic backgrounds between occupational attainment and multiple traits, including education, intelligence, leisure activities, life satisfaction and neuropsychiatric disorders.

In two-sample Mendelian Randomization analysis, we demonstrated that high occupation levels were associated with reduced risk for Alzheimer’s disease [odds ratio (OR) = 0.78, 95% confidence interval (CI) = 0.65–0.92 in inverse variance weighted method; OR = 0.73, 95% CI = 0.57–0.92 in the weighted median method]. This causal relationship between occupational attainment and Alzheimer’s disease was robust in additional sensitivity analysis that excluded potentially pleiotropic single-nucleotide polymorphisms (OR = 0.72, 95% CI = 0.57–0.91 in the inverse variance weighted method; OR = 0.72, 95% CI = 0.53–0.97 in the weighted median method). Multivariable Mendelian Randomization confirmed that occupational attainment had an independent effect on the risk for Alzheimer’s disease even after taking educational attainment into account (OR = 0.72, 95% CI = 0.54–0.95 in the inverse variance weighted method; OR = 0.68, 95% CI = 0.48–0.97 in the weighted median method).

Overall, our analyses provide insights into the genetic architecture of occupational attainment and demonstrate that occupational attainment is a potential causal protective factor for Alzheimer’s disease as a proxy marker of cognitive reserve.

--- /doc/psychology/vision/1962-wright.pdf The Meanings of Color Benjamin Wright, Lee Rainwater 1962-01-01 2022-07-27 [("doi","10.1080/00221309.1962.9711531")] design/typography/rubrication psychology/vision --- /doc/psychology/vision/1942-goldstein.pdf Some Experimental Observations Concerning The Influence Of Colors On The Function Of The Organism Kurt Goldstein 1942-01-01 2022-07-27 [("doi","10.1097/00002060-194206000-00002")] design/typography/rubrication psychiatry psychology/vision --- /doc/psychology/vision/1956-halpern.pdf Additional Contributions To The Sensorimotor Induction Syndrome In Unilateral Disequilibrium With Special Reference To The Effect Of Colors L. Halpern 1956-01-01 2022-07-27 [("doi","10.1097/00005053-195604000-00003")] design/typography/rubrication psychiatry psychology/vision --- /doc/psychology/vision/1907-bullough.pdf On the apparent heaviness of colors. A contribution to the esthetics of color E. Bullough 1907-05-01 2022-07-27 [("doi","10.1111/j.2044-8295.1907.tb00236.x")] design/typography/rubrication psychology/vision --- /doc/psychology/vision/1978-humphrey.pdf Effects of Red Light and Loud Noise on the Rate at Which Monkeys Sample the Sensory Environment Nicholas Humphrey, G. R. Keeble 1978-01-01 2022-07-27 [("doi","10.1068/p070343")] design/typography/rubrication psychology/vision

Monkeys, given the opportunity to move between two featureless chambers, ‘sample’ first one, then the other in a way which reflects a Poisson decision process.

The rate of sampling is higher in red light than in blue and in loud noise than in quietness.

We suggest that monkeys ‘tune’ their sampling rate to the a priori probability of change in the environment.

--- /doc/psychology/vision/1966-turner.pdf Colour Classification in Ndembu Ritual: A Problem in Primitive Classification Victor W. Turner 1966-01-01 2022-07-28 design/typography/rubrication philosophy/religion psychology/vision --- https://www.palladiummag.com/2023/02/02/yamagami-tetsuyas-revenge/ 2022-07-28 crime/terrorism japan/history --- https://goethetc.blogspot.com/2017/01/red-versus-blue.html 2022-07-28 design/typography/rubrication --- https://onwords.substack.com/p/lorem-ipsum-has-a-meaning 2022-07-28 design/typography --- https://x.com/ClaireSilver12/status/1621959511257878530 2022-07-28 ai/nn/transformer/clip/sample --- https://x.com/TheRealAdamG/status/1621871720310214687 2022-07-28 ai/nn/transformer/gpt/fiction --- https://talyarkoni.org/blog/2018/08/18/if-we-already-understood-the-brain-would-we-even-know-it/ 2022-07-28 philosophy/mind psychology/neuroscience --- https://arxiv.org/abs/2211.09085#facebook Galactica: A Large Language Model for Science Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, Robert Stojnic 2022-11-16 2022-11-16 [("doi","10.48550/arXiv.2211.09085")] ai/nn/transformer/gpt ai/scaling design/typography/tex science

Information overload is a major obstacle to scientific progress. The explosive growth in scientific literature and data has made it ever harder to discover useful insights in a large mass of information. Today scientific knowledge is accessed through search engines, but they are unable to organize scientific knowledge alone.

In this paper we introduce Galactica: a large language model that can store, combine and reason about scientific knowledge. We train on a large scientific corpus of papers, reference material, knowledge bases and many other sources.

We outperform existing models on a range of scientific tasks. On technical knowledge probes such as LaTeX equations, Galactica outperforms the latest GPT-3 by 68.2% versus 49.0%. Galactica also performs well on reasoning, outperforming Chinchilla on mathematical MMLU by 41.3% to 35.7%, and PaLM 540B on MATH with a score of 20.4% versus 8.8%. It also sets a new state-of-the-art on downstream tasks such as PubMedQA and MedMCQA dev of 77.6% and 52.9%. And despite not being trained on a general corpus, Galactica outperforms BLOOM and OPT-175B on BIG-bench.

We believe these results demonstrate the potential for language models as a new interface for science. We open source the model for the benefit of the scientific community.

...We train for over four epochs and experience improving performance with use of repeated tokens. For the largest 120B model, we trained for four epochs without overfitting.

--- https://www.lesswrong.com/posts/aPeJE8bSo6rAFoLqg/solidgoldmagikarp-plus-prompt-generation 2022-07-28 ai/nn/adversarial ai/nn/tokenization --- https://x.com/RamaswmySridhar/status/1621870497070981121 2022-07-28 ai/nn/sparsity/pruning ai/nn/transformer/t5 --- https://arxiv.org/abs/2302.00923#amazon Multimodal Chain-of-Thought Reasoning in Language Models Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, Alex Smola 2023-02-02 2023-02-02 [("doi","10.48550/arXiv.2302.00923")] ai/nn/transformer/gpt/inner-monologue

Large language models (LLMs) have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains as the rationale to infer the answer. However, existing CoT studies are mostly isolated in the language modality with LLMs, where LLMs are hard to deploy.

To elicit CoT reasoning in multimodality, a possible solution is to fine-tune small language models by fusing the vision and language features to perform CoT reasoning. The key challenge is that those language models tend to generate hallucinated reasoning chains that mislead the answer inference.

To mitigate the effect of such mistakes, we propose Multimodal-CoT that incorporates vision features in a decoupled training framework. The framework separates the rationale generation and answer inference into two stages. By incorporating the vision features in both stages, the model is able to generate effective rationales that contribute to answer inference.

With Multimodal-CoT, our model under 1 billion parameters outperforms the previous state-of-the-art LLM (GPT-3.5) by 16% (75.17% → 91.68%) on the ScienceQA benchmark and even surpasses human performance.

Code is publicly available at Github.

…We find that the correct samples contain a certain amount of incorrect chain-of-thought (10%). The results indicate that CoT may not always benefit the answer inference, and the model is robust to some extent—it can predict the correct answer by ignoring incorrect rationales. For incorrect samples, factual mistake is the most frequent error type (50%). Most factual mistakes are due to the failures of understanding maps and counting numbers in the images. In addition, the model also makes commonsense mistakes (38%) where answering the questions requires commonsense knowledge, eg. utilizing the alphabet. Another type of mistake is a logical mistake (12%), with contradictions in the reasoning chains.

--- https://arxiv.org/abs/2302.00560 Co-Writing with Opinionated Language Models Affects Users’ Views Maurice Jakesch, Advait Bhat, Daniel Buschek, Lior Zalmanson, Mor Naaman 2023-02-01 2023-02-01 [("doi","10.1145/3544548.3581196")] ai/nn/transformer/gpt/3/nonfiction politics

[data; cf. Bai et al 2023] If large language models like GPT-3 preferably produce a particular point of view, they may influence people’s opinions on an unknown scale.

This study investigates whether a language-model-powered writing assistant [GPT-3: text-davinci-003] that generates some opinions more often than others impacts what users write—and what they think. In an online experiment, we asked participants (n = 1,506) to write a post discussing whether social media is good for society. Treatment group participants used a language-model-powered writing assistant configured to argue that social media is good or bad for society. Participants then completed a social media attitude survey, and independent judges (n = 500) evaluated the opinions expressed in their writing.

Using the opinionated language model affected the opinions expressed in participants’ writing and shifted their opinions in the subsequent attitude survey.

We discuss the wider implications of our results and argue that the opinions built into AI language technologies need to be monitored and engineered more carefully.

Figure 3: Participants assisted by a model supportive of social media were more likely to argue that social media is good for society in their posts (and vice versa). Ns = 9,223 sentences written by Np = 1,506 participants evaluated by N~j~ = 500 judges. The y-axis indicates whether participants wrote their social media posts with assistance from an opinionated language model that was supportive (top) or critical of social media (bottom). The x-axis shows how often participants argued that social media is bad for society (blue), good for society (orange), or both good and bad (white) in their writing.

…The social media posts written by participants in the control group (middle row) were slightly critical of social media: They argued that social media is bad for society in 38% and that social media is good in 28% of their sentences. In about 28% of their sentences, control group participants argued that social media is both good and bad, and 11% of their sentences argued neither or were unrelated.

Participants who received suggestions from a language model supportive of social media (top row of Figure 3) were 2.04× more likely than control group participants (p < 0.0001, 95% CI [1.83, 2.30]) to argue that social media is good. In contrast, participants who received suggestions from a language model that criticized social media (bottom row) were 2.0× more likely (p < 0.0001, 95% CI [1.79, 2.24]) to argue that social media is bad than control group participants. We conclude that using an opinionated language model affected participants’ writing such that the text they wrote was more likely to support the model’s preferred view.

…4.2 Did participants accept the model’s suggestions out of mere convenience? Participants may have accepted the models’ suggestions out of convenience, even though the suggestions did not match what they would have wanted to say. Paid participants in online studies, in particular, may be motivated to accept suggestions to swiftly complete the task.

Our data shows that, across conditions and treatments, most participants did not blindly accept the model’s suggestions but interacted with the model to co-write their social media posts. On average, participants wrote 63% of their sentences themselves without accepting suggestions from the model (compare Figure 4). About 25% of participants’ sentences were written by both the participant and the model, which typically meant that the participant wrote some words and accepted the model’s remaining sentence suggestion. Only 11.5% of sentences were fully accepted from the model. Participants whose personal views were likely aligned with the model were more likely to accept suggestions, while participants with opposing views accepted fewer suggestions. About one in 4 participants did not accept any model suggestion, and one in 10 participants had more than 75% of their post written by the model.

4.2.1 Did conveniently accepted suggestions increase the observed differences in written opinion? The writing of participants who spent little time on the task was more affected by the model’s opinion. We use the time participants took to write their posts to estimate to what extent they may have accepted suggestions without due consideration. For a concise statistical analysis, we treat the ordinal opinion scale as an interval scale. Since the opinion scale has comparable-size intervals and a zero point, continuous analysis is meaningful and justifiable.64 We treat “social media is bad for society” as −1 and “social media is good for society” as 1. Sentences that argue both or neither are treated as zeros.

Figure 5: The opinion differences in participants’ writing were larger when they finished the task quickly. n = 1,506. The y-axis shows the mean opinion expressed in participants’ social media posts based on aggregated sentence labels ranging from −1 for “social media is bad for society” to 1 for “social media is good for society”. The x-axis indicates how much time participants took to write their posts. For reference, the left panel shows expressed opinions aggregated across writing times.

Figure 5 shows the mean opinion expressed in participants’ social media posts depending on treatment group and writing time. The left panel shows participants’ expressed opinions across times for reference, with a mean opinion difference of about 0.29 (p < 0.001, 95% CI [0.25, 0.33], SD=0.58) between each treatment group and the control group (corresponding to a large effect size of d = 0.5). Participants who took little time to write them (<160s, left-most data in right panel) were more affected by the opinion of the language model (0.38, p < 0.001, 95% CI [0.31, 0.45]). Our analysis shows that accepting suggestions out of convenience has contributed to the differences in the written opinion. However, even for participants who took 4–6 minutes to write their posts, we observed statistically-significant differences in opinions across treatment groups (0.20, p < 0.001, 95% CI [0.13, 0.27], corresponding to a treatment effect of d = 0.34).

…4.3 Did the language model affect participants’ opinions in the attitude survey? The opinion differences in participants’ writing may be due to shifts in participants’ actual opinion caused by interacting with the opinionated model. We evaluate whether interactions with the language model affected participants’ attitudes expressed in a post-task survey asking participants whether they thought social media was good for society. An overview of participants’ answers is shown in Figure 6.

Figure 6: Participants interacting with a model supportive of social media were more likely to say that social media is good for society in a later survey (and vice versa). Nr = 1,506 survey responses by Nr = 1,506 participants. The y-axis indicates whether participants received suggestions from a model supportive or critical of social media during the writing task. The x-axis shows how often they said that social media was good for society (orange) or not (blue) in a subsequent attitude survey. Undecided participants are shown in white. Brackets indicate statistically-significant opinion differences at the ✱✱p < 0.005 & ✱✱✱ p < 0.001 level.

The figure shows the frequency of different survey answers (x-axis) for the participants in each condition (y-axis). Participants who did not interact with the opinionated models (middle row in Figure 6) were balanced in their evaluations of social media: 33% answered that social media is not good for society (middle, blue); 35% said social media is good for society. In comparison, 45% of participants who interacted with a language model supportive of social media (top row) answered that social media is good for society. Converting participants’ answers to an interval scale, this difference in opinion corresponds to an effect size of d = 0.22 (p < 0.001). Similarly, participants that had interacted with the language model critical of social media (bottom row) were more likely to say that social media was bad for society afterward (d = 0.19, p < 0.005).

…4.4 Were participants aware of the model’s opinion and influence? …When the model contradicted their opinion, only 15% of participants said that it was not knowledgeable or lacked expertise.

Figure 9: Participants were often unaware of the model’s opinion. Np =1,000 treatment group participants. The x-axis indicates whether participants found the model’s suggestions balanced and reasonable. The y-axis indicates whether the model’s opinion was aligned with participants’ personal views.

While the language model was configured to support one specific side of the debate, the majority of participants said that the model’s suggestions were balanced and reasonable. Figure 9 shows that, in the group of participants whose opinion was supported by the model, only 10% noticed that its suggestions were imbalanced (top row in blue). When the model contradicted participants’ opinions, they were more likely (30%) to notice its skew, but still, more than half agreed that the model’s suggestions were balanced and reasonable (bottom row in orange).

Figure 10: Participants interacting with a model that supported their opinion were more likely to indicate that the model affected their argument. Np =1,000 treatment group participants. The x-axis indicates whether participants thought that the model affected their argument. The y-axis indicates whether the model’s opinion was aligned with participants’ personal views.

Figure 10 shows that the majority of participants were not aware of the model’s effect on their writing. Participants using a model aligned with their view—and accepting suggestions more frequently—were slightly more aware of the model’s effect (34%, top row in orange). In comparison, only about 20% of the participants who did not share the model’s opinion believed that the model influenced them. Overall, we conclude that participants were often unaware of the model’s opinion and influence.

…We conclude that further research will be required to identify the mechanisms behind latent persuasion by language models. Our secondary findings suggest that the influence was at least partly subconscious and not simply due to the convenience and new information that the language model provided. Rather, co-writing with the language model may have changed participants’ opinion formation process on a behavioral level.

--- https://arxiv.org/abs/2301.12810 Crawling the Internal Knowledge-Base of Language Models Roi Cohen, Mor Geva, Jonathan Berant, Amir Globerson 2023-01-30 2023-01-30 [("doi","10.48550/arXiv.2301.12810")] ai/nn/retrieval ai/nn/transformer/gpt

Language models are trained on large volumes of text, and as a result their parameters might contain a body of factual knowledge. Any downstream task performed by these models implicitly builds on these facts, and thus it is highly desirable to have means for representing this body of knowledge in an interpretable way. However, there is currently no mechanism for such a representation.

Here, we propose to address this goal by extracting a knowledge-graph of facts from a given language model.

We describe a procedure for “crawling” the internal knowledge-base of a language model. Specifically, given a seed entity, we expand a knowledge-graph around it. The crawling procedure is decomposed into sub-tasks, realized through specially designed prompts that control for both precision (ie. that no wrong facts are generated) and recall (ie. the number of facts generated).

We evaluate our approach on graphs crawled starting from dozens of seed entities, and show it yields high precision graphs (82-92%), while emitting a reasonable number of facts per entity.

--- https://arxiv.org/abs/2302.01329#google Dreamix: Video Diffusion Models are General Video Editors Eyal Molad, Eliahu Horwitz, Dani Valevski, Alex Rav Acha, Yossi Matias, Yael Pritch, Yaniv Leviathan, Yedid Hoshen 2023-02-02 2023-02-02 [("doi","10.48550/arXiv.2302.01329")] ai/nn/diffusion ai/video/generation

[samples; video] Text-driven image and video diffusion models have recently achieved unprecedented generation realism. While diffusion models have been successfully applied for image editing, very few works have done so for video editing.

We present the first diffusion-based method that is able to perform text-based motion and appearance editing of general videos. Our approach uses a video diffusion model [Imagen Video] to combine, at inference time, the low-resolution spatio-temporal information from the original video with new, high resolution information that it synthesized to align with the guiding text prompt. As obtaining high-fidelity to the original video requires retaining some of its high-resolution information, we add a preliminary stage of finetuning the model on the original video, boosting fidelity.

We propose to improve motion editability by a new, mixed objective that jointly finetunes with full temporal attention and with temporal attention masking.

We further introduce a new framework for image animation. We first transform the image into a coarse video by simple image processing operations such as replication and perspective geometric projections, and then use our general video editor to animate it. As a further application, we can use our method for subject-driven video generation [ie. DreamBooth].

Extensive qualitative and numerical experiments showcase the remarkable editing ability of our method and establish its superior performance compared to baseline methods.

--- https://www.lesswrong.com/posts/gp9pmgSX3BXnhv8pJ/i-hired-5-people-to-sit-behind-me-and-make-me-productive-for 2022-07-29 psychology/willpower --- https://www.afghanistan-analysts.org/en/reports/context-culture/new-lives-in-the-city-how-taleban-have-experienced-life-in-kabul/ 2022-07-29 crime/terrorism --- https://arxiv.org/abs/1803.00144#google Learning Longer-term Dependencies in RNNs with Auxiliary Losses Trieu H. Trinh, Andrew M. Dai, Minh-Thang Luong, Quoc V. Le 2018-03-01 2022-07-29 [("doi","10.48550/arXiv.1803.00144")] ai/nn/rnn ai/nn/transformer

Despite recent advances in training recurrent neural networks (RNNs), capturing long-term dependencies in sequences remains a fundamental challenge. Most approaches use backpropagation through time (BPTT), which is difficult to scale to very long sequences.

This paper proposes a simple method that improves the ability to capture long term dependencies in RNNs by adding an unsupervised auxiliary loss to the original objective. This auxiliary loss forces RNNs to either reconstruct previous events or predict next events in a sequence, making truncated backpropagation feasible for long sequences and also improving full BPTT.

We evaluate our method on a variety of settings, including pixel-by-pixel image classification with sequence lengths up to 16, 000, and a real document classification benchmark. Our results highlight good performance and resource efficiency of this approach over competitive baselines, including other recurrent models and a comparable sized Transformer.

Further analyses reveal beneficial effects of the auxiliary loss on optimization and regularization, as well as extreme cases where there is little to no backpropagation.

--- https://en.wikipedia.org/wiki/Curta Curta 2022-07-29 cs/hardware --- https://en.wikipedia.org/wiki/Backpropagation_through_time Backpropagation through time 2022-07-29 ai/nn/dynamic-evaluation --- https://laion.ai/blog/coca/ 2022-07-29 ai/nn/transformer/clip --- https://www.nature.com/articles/d41586-020-01392-8 2022-07-29 statistics/bias statistics/peer-review --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0246675 Honest signaling in academic publishing Leonid Tiokhin, Karthik Panchanathan, Daniel Lakens, Simine Vazire, Thomas Morgan, Kevin Zollman, Jonathan Jong, Wing Suen, Wing Suen 2021-01-22 2022-07-29 [("doi","10.1371/journal.pone.0246675")] statistics/bias statistics/peer-review

Academic journals provide a key quality-control mechanism in science. Yet, information asymmetries and conflicts of interests incentivize scientists to deceive journals about the quality of their research. How can honesty be ensured, despite incentives for deception?

Here, we address this question by applying the theory of honest signaling to the publication process.

Our models demonstrate that several mechanisms can ensure honest journal submission, including differential benefits, differential costs, and costs to resubmitting rejected papers. Without submission costs, scientists benefit from submitting all papers to high-ranking journals, unless papers can only be submitted a limited number of times.

Counterintuitively, our analysis implies that inefficiencies in academic publishing (eg. arbitrary formatting requirements, long review times) can serve a function by disincentivizing scientists from submitting low-quality work to high-ranking journals.

Our models provide simple, powerful tools for understanding how to promote honest paper submission in academic publishing.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7245108/ Estimating the deep replicability of scientific findings using human and artificial intelligence Yang Yang, Wu Youyou, Brian Uzzi 2020 2022-07-30 [("doi","10.1073/pnas.1909046117")] ai/nn/rnn statistics/bias

Replicability tests of scientific papers show that the majority of papers fail replication. Moreover, failed papers circulate through the literature as quickly as replicating papers. This dynamic weakens the literature, raises research costs, and demonstrates the need for new approaches for estimating a study’s replicability.

Here, we trained an artificial intelligence model [using word2vec] to estimate a paper’s replicability using ground truth data on studies that had passed or failed manual replication tests, and then tested the model’s generalizability on an extensive set of out-of-sample studies. The model predicts replicability better than the base rate of reviewers and comparably as well as prediction markets, the best present-day method for predicting replicability.

In out-of-sample tests on manually replicated papers from diverse disciplines and methods, the model had strong accuracy levels of 0.65 to 0.78.

Exploring the reasons behind the model’s predictions, we found no evidence for bias based on topics, journals, disciplines, base rates of failure, persuasion words, or novelty words like “remarkable” or “unexpected.” We did find that the model’s accuracy is higher when trained on a paper’s text rather than its reported statistics and that n-grams, higher order word combinations that humans have difficulty processing, correlate with

replication. We discuss how combining human and machine intelligence can raise confidence in research, provide research self-assessment techniques, and create methods that are scalable and efficient enough to review the ever-growing numbers of publications-a task that entails extensive human resources to accomplish with prediction markets and manual replication alone.

--- https://x.com/andy_matuschak/status/1620963781332586501 2022-07-30 design/typography --- https://www.reddit.com/r/ChatGPT/comments/10tevu1/new_jailbreak_proudly_unveiling_the_tried_and/ 2022-07-30 ai/nn/transformer/gpt/non-fiction cs/security reinforcement-learning/safe --- https://karthinks.com/software/a-consistent-structural-editing-interface/ 2022-07-30 cs/lisp --- https://x.com/aibreakfast/status/1621668590738079744 2022-07-30 ai/nn/retrieval ai/nn/transformer/gpt/non-fiction --- https://ascii.textfiles.com/archives/5443 2022-07-30 cs/linkrot/archiving psychology/collecting --- https://www.yahoo.com/news/youtubers-said-destroyed-over-100-202057472.html 2022-07-30 psychology/collecting --- https://www.reddit.com/r/StableDiffusion/comments/10v9z6m/v11_of_our_mfkonosuba_model_is_out_heres_some/ 2022-07-30 ai/anime ai/nn/diffusion --- /doc/psychiatry/schizophrenia/rosenhan/2023-scull.pdf Rosenhan revisited: successful scientific fraud Andrew Scull 2023-02-03 2023-02-03 [("doi","10.1177/0957154X221150878")] psychiatry/schizophrenia/rosenhan

[media; cf. Spitzer 1975/Lando 1976] The publication of David Rosenhan’s ‘On being sane in insane places’ in Science in 1973 played a crucial role in persuading the American Psychiatric Association (APA) to revise its diagnostic manual. The 3rd edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III) in its turn launched a revolution in American psychiatry whose reverberations continue to this day.

Rosenhan’s paper continues to be cited hundreds of times a year, and its alleged findings are seen as crucial evidence of psychiatry’s failings. Yet based on the findings of an investigative journalist, Susannah Cahalan, and on records she shared with the author, we now know that this research is a spectacularly successful case of scientific fraud.

[Keywords: American psychiatry, David Rosenhan, DSM-III, scientific fraud, Susannah Cahalan]

…His paper in Science was his only important contribution to the social psychological literature, albeit one that made him famous for decades. He never revisited the topic in any academic journal, and published nothing of comparable impact for the rest of his career…At least 70 newspapers, both regional and national, gave prominent attention to his study. Television and radio shows interviewed Rosenhan. A major commercial publisher offered him a lucrative contract for a book based on his research, an offer Rosenhan accepted with alacrity. Harvard University even sent out feelers about a possible appointment to its faculty. Rosenhan’s exposure of psychiatry’s flaws caused a sensation. No wonder so many practitioners rushed to register their objections in the pages of Science, which, quite extraordinarily, devoted 9 pages of a subsequent issue to their howls of protest, and to Rosenhan’s response. That in itself was a measure of how powerfully this exposé resonated outside the cloistered world of academia.

…A recent survey of 12 leading textbooks on abnormal psychology, for example, found that half of them still gave extensive attention to Rosenhan’s paper, summarizing its design and endorsing its basic findings. Only two of them acknowledged any criticisms of the study, although methodological critiques have emerged over the years since the article’s initial publication (see Bartels & Peters 2017). As another example of the study’s extraordinary half-life, as recently as 1 January 2018, the Washington Post ran a wholly uncritical article summarizing Rosenhan’s findings (Morris 2018).

…The seriousness of the crisis the profession faced was immediately recognized by psychiatry’s elite. Within weeks of the article’s appearance, the Board of Trustees of the American Psychiatric Association called an emergency meeting in Atlanta on 1 February. How could they respond to ‘the rampant criticism’ that enveloped the profession, not least to the perception (or rather, the reality) that its practitioners could not reliably make diagnoses of the mental illnesses they claimed to be expert at treating (Decker 2013, The Making of DSM III: A Diagnostic Manual’s Conquest of American Psychiatry: pg141–142)?…As the APA’s Board of Trustees recognized, Rosenhan’s paper, and the immense amount of publicity it had already received, had created a focal point for psychiatry’s critics, and amounted to an existential crisis for the profession. Indeed, barely a year later, a prominent member of an emerging mental health bar—lawyers who were suing psychiatry on multiple fronts—authored a long law review article dismissing psychiatrists’ claims to be experts in the diagnosis and treatment of mental illness as fraudulent and scientifically indefensible. Advised behind the scenes by Rosenhan himself, Bruce Ennis alleged that psychiatrists who weighed in on questions of sanity fared no better than a trained monkey flipping a coin (Ennis & Litwack 1974).

…One of the first tasks Spitzer set himself as the task force began its work was to rebut David Rosenhan’s study. He engaged in extensive correspondence with Rosenhan, seeking to pierce the veil of secrecy and anonymity that had been a central feature of the published paper. Who were the pseudo-patients, he asked, and which hospitals had they been admitted to? Rosenhan deflected and refused to answer. ‘I am’, he wrote, ‘obliged to protect these sources.’4 Spitzer’s response was to write two papers attacking the methodology, the logic and the conclusions of Rosenhan’s study, one of them forming part of a symposium that he organized to rebut Rosenhan’s claims (Spitzer 1975, Spitzer 1976). But while Rosenhan 1973’s Science paper reached an enormous audience across the scientific community and, via the mass media, an even larger lay public, Spitzer here spoke only to his professional colleagues. There is, moreover, a deep irony at play here. It was paradoxically Rosenhan’s study, and the extraordinary publicity which had accompanied its publication, that had prompted the APA to set up a task force to revise its diagnostic system. Also it decided to appoint Spitzer to direct the creation of the new DSM, which would propel him to the forefront of the profession, and make him one of the most influential psychiatrists of the second half of the twentieth century.

[on the fabrication of the paper as revealed by the major discrepancies with actual ‘patient accounts’, including Rosenhan himself, and falsified numbers]

…Despite two years of diligent effort, the only pseudo-patient Ms Cahalan was able to locate was the psychology graduate student, whose case is examined below. In the course of his extensive correspondence with Rosenhan 1974–1975, Robert Spitzer had repeatedly sought access to the pseudo-patients’ admission records, ‘with all the safeguards for preserving confidentiality of persons and institutions’. Those redacted records, he pointed out, would demonstrate whether these volunteers ‘were able to follow protocol and limit their histories to monosymptomatic illness’. ‘Many psychiatrists’, he noted, ‘doubt that the patients only complained of hallucinations. It would be good to set this issue to rest.’9 Although Rosenhan had earlier indicated that he would be ‘delighted to send you the admissions notes’, he never did so. Instead, he informed Spitzer that, like others, he would have to wait: ‘I have asked others who desire raw data on our observations and/or others’ observations of us to wait until I have completed analyzing for the book I am preparing [on the study].’10

In 1974, Rosenhan had indeed signed a contract with Doubleday to write a book about his study, receiving a first installment of $11,000 of a promised $44,000 advance. He had begun to produce a manuscript (originally to be called Odyssey into Lunacy and later re-titled Locked Up), writing more than 200 pages that survive in his files. But nowhere in this draft, or anywhere else in his files, can one find any materials bearing in any substantial way upon the identities and experiences of the supposed pseudo-patients: not their names, not their admission documents, not their own observations about what occurred at admission or during their hospitalization; not the names of the institutions to which it is claimed they sought admission. The published paper in Science included all sorts of detail about the patients’ time in the hospital, including quantitative data allegedly recording the amounts of time psychiatrists and staff spent with the patients, but there was no trace of the observations that underpinned these numbers. Rosenhan’s files, while they are wildly disorganized, are replete with other information about the study: fan letters from those who endorsed its findings, to whom he religiously replied; criticism from psychiatrists; copies of commentaries on the study; and so on. But the crucial raw materials, which should have been collected as the study was done and which formed the basis for its claims, are nowhere to be found.

…So, far from being ignored, patients were treated with respect and care. ‘The powerlessness and depersonalization of patients emphasized by Rosenhan simply did not exist in this setting. On no occasion did I observe a patient ignored by staff.’ Small wonder that, at the end of a 19-day stay, Lando reported that ‘My overall impressions of the hospital were overwhelmingly positive’ (p. 51). It is not surprising, I suggest, that Rosenhan wanted to exclude these findings from his paper. But when it suited his purpose to extract something from Lando’s notes and use it to bolster the portrait he sought to draw of mental hospitals, Rosenhan did not hesitate.

…The misconduct did not end there. In Rosenhan’s files, Ms Cahalan discovered an earlier draft of the Science paper that he had pre-circulated to the psychologist Walter Mischel for review. [Note: Mischel appears to’ve been chair of Rosenhan’s department at the time, which is probably why he got a copy even though he tells Cahalan he disliked Rosenhan & his research. Mischel was also responsible for extremely misleading attacks on personality psychology, and the infamous ‘marshmallow test’; so perhaps he bears some blame for Rosenhan as well!] This version contained 9 pseudo-patients, not 8. Harry Lando had not yet been excluded. Both papers reported what purported to be hard data about the lack of contact between staff and patients, presented in tabular form and elaborated upon in the text. The numbers were identical even after the 9th patient was removed in the published version. On the face of it, we know that this is a statistical impossibility. In this instance, given that Lando reported hours of contact with staff on a daily basis, both sets of ‘data’ are transparently falsified.

Rosenhan’s files contained further notes on ‘Walter Abrams’. They were riddled with errors. Lando’s diagnosis was mis-recorded as paranoid schizophrenia. His hospital stay was given as 26 days (it was actually 19 days). He was not turned away for ‘3 days’ before securing admission, and the ward was not ‘full’. Finally, he was released with medical advice, not against it, and was not given the diagnosis of ‘schizophrenia in remission’. All these statements were fabrications.

…We have already documented Rosenhan’s willingness to fabricate and distort evidence, but there remains still more d—ning evidence of his chicanery. Rosenhan’s files contain his own medical records, documenting his admission to Haverford State Hospital, as well as his notes on his institutionalization. It is revealing to compare the account he provides there with what the contemporaneous records show.

…I have quoted extensively from these documents (to which I had access through the kindness of Ms Cahalan) to demonstrate just how sharply what happened in the intake examination strayed from what Rosenhan 1973 represented as Lurie’s behavior in the Science paper. Far from confining himself to reporting 3 discrete aural hallucinations and otherwise behaving perfectly normally, Rosenhan gave ample evidence of deep intellectual and emotional disturbance. Besides the grimacing and twitching, and the dull, halting speech pattern, he indicated that the radio was broadcasting to him, and that he could ‘hear’ other people’s thoughts. He was depressed and frightened, and had been unable to work for months. Outpatient treatment with drugs had failed to improve matters. Visibly ‘tense and anxious’, he thought he was worthless and had contemplated ‘suicide as everyone would be better off if he was not around’. These constitute an infinitely more serious and extensive set of pathological symptoms. Had Rosenhan told the truth about his presentation of self at the hospital, no one would have been surprised that a psychiatrist would decide to admit him, or to diagnose the patient before him as he did.

In his Science paper, Rosenhan further claimed that, once admitted, the pseudo-patients, himself included, immediately stopped displaying symptoms and behaved normally. Again, the surviving medical records show that in his case this is quite false. In the days after his admission, two other psychiatrists examined him at some length. Both documented the depths of the pathology Lurie was showing.

…Indirectly, therefore, Rosenhan’s study played a major role in the complete re-orientation of American psychiatry. Very early in the 1980s, the psychoanalysis that had dominated it since World War II lost its hold over the profession, and soon withered away to almost nothing. In its place, biology and neuroscience came to rule the roost, and both profession and public came to adopt a radically different perspective on mental illness. These were momentous changes. Remarkably, I suggest, the study that greatly helped to smooth the way to their acceptance was thoroughly dishonest, a scientific scam that stood largely unchallenged for nearly a half century. It is time for it to be revealed for what it is: a successful scientific fraud.

--- /doc/psychiatry/schizophrenia/rosenhan/2017-bartels.pdf Coverage of Rosenhan’s ‘On Being Sane in Insane Places’ in Abnormal Psychology Textbooks Jared M. Bartels, Daniel Peters 2017-04-01 2022-07-30 [("doi","10.1177/0098628317692634")] psychiatry/schizophrenia/rosenhan statistics/bias

The present study examined 12 abnormal psychology textbooks to determine whether Rosenhan’s classic study, “Being sane in insane places”, was covered, and if so, the nature of that coverage.

Only 50% covered the study, with all describing the study as demonstrating the biasing power of psychiatric labels. Two key aspects of the study (the diagnoses of schizophrenia and their supposed subsequent influence on the hospital staff’s perception of the pseudopatients’ normal behavior as pathological) were commonly discussed.

However, although the study has been heavily criticized, only two texts discussed any criticism of it.

Teachers and text authors are urged to become more familiar with the critical literature on this study, and suggestions for class discussions of the study are provided.

--- /doc/psychiatry/schizophrenia/rosenhan/1976-spitzer.pdf More on Pseudoscience in Science and the Case for Psychiatric Diagnosis: A Critique of D. L. Rosenhan’s ‘On Being Sane in Insane Places’ and ‘The Contextual Nature of Psychiatric Diagnosis’ Robert L. Spitzer 1976-04-01 2022-07-31 [("doi","10.1001/archpsyc.1976.01770040029007")] psychiatry/schizophrenia/rosenhan

[previously: Spitzer 1975] Rosenhan’s 1973 article, “On Being Sane in Insane Places”, was pseudoscience presented as science. Just as his pseudopatients were diagnosed at discharge as having “schizophrenia in remission”, so a careful examination of this study’s methods, results, and conclusions leads to a diagnosis of “logic in remission.”

Rosenhan’s study proves that pseudopatients are not detected by psychiatrists as having simulated signs of mental illness and that the implementation of certain invalid research designs can make psychiatrists appear foolish.

These rather unremarkable findings are irrelevant to the real problems of the reliability and validity of psychiatric diagnosis and only serve to obscure them. A correct interpretation of his own data contradicts his conclusions. There are purposes to psychiatric diagnosis that Rosenhan’s article ignores.

His more recent suggestion that certain requirements be met prior to the adoption of a new psychiatric classification system is unrealistic.

--- https://www.newyorker.com/magazine/2023/02/13/the-dubious-rise-of-impostor-syndrome 2022-07-31 psychology/personality --- https://en.wikipedia.org/wiki/Impostor_syndrome Impostor syndrome 2022-07-31 psychology/personality --- https://en.wikipedia.org/wiki/Fine_Art_(software) Fine Art (software) 2022-07-31 reinforcement-learning/model/alphago --- https://anthonyhobday.com/sideprojects/saferules/ 2022-07-31 design --- https://arxiv.org/abs/2202.08578 An Equivalence Between Data Poisoning and Byzantine Gradient Attacks Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang, Oscar Villemaud 2022-02-17 2022-07-31 [("doi","10.48550/arXiv.2202.08578")] ai/nn/adversarial

To study the resilience of distributed learning, the “Byzantine” literature considers a strong threat model where workers can report arbitrary gradients to the parameter server. Whereas this model helped obtain several fundamental results, it has sometimes been considered unrealistic, when the workers are mostly trustworthy machines.

In this paper, we show a surprising equivalence between this model and data poisoning, a threat considered much more realistic. More specifically, we prove that every gradient attack can be reduced to data poisoning, in any personalized federated learning system with PAC guarantees (which we show are both desirable and realistic).

This equivalence makes it possible to obtain new impossibility results on the resilience of any “robust” learning algorithm to data poisoning in highly heterogeneous applications, as corollaries of existing impossibility theorems on Byzantine machine learning. Moreover, using our equivalence, we derive a practical attack that we show (theoretically and empirically) can be very effective against classical personalized federated learning models.

…We prove in this paper a somewhat surprising equivalence between gradient attacks and data poisoning, in a convex setting. Essentially, we give the first practically compelling argument for the necessity to protect learning against gradient attacks. Our result enables us to carry over results on Byzantine gradient attacks to the data poisoning world. For instance, the impossibility result of El-Mhamdi et al 2021a, combined with our equivalence result, implies that the more heterogeneous the data, the more vulnerable any “robust” learning algorithm is. Also, we derive concrete data poisoning attacks from gradient ones.

--- https://arxiv.org/abs/2008.00742 Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning) El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Arsany Guirguis, Lê Nguyên Hoang, Sébastien Rouault 2020-08-03 2022-07-31 [("doi","10.48550/arXiv.2008.00742")] ai/nn/adversarial

We study Byzantine collaborative learning, where n nodes seek to collectively learn from each others’ local data. The data distribution may vary from one node to another. No node is trusted, and f < n nodes can behave arbitrarily.

We prove that collaborative learning is equivalent to a new form of agreement, which we call averaging agreement. In this problem, nodes start each with an initial vector and seek to ~agree on a common vector, which is close to the average of honest nodes’ initial vectors.

We present two asynchronous solutions to averaging agreement, each we prove optimal according to some dimension. The first, based on the minimum-diameter averaging, requires n ≥ 6f + 1, but achieves asymptotically the best-possible averaging constant up to a multiplicative constant. The second, based on reliable broadcast and coordinate-wise trimmed mean, achieves optimal Byzantine resilience, ie. n ≥ 3f + 1. Each of these algorithms induces an optimal Byzantine collaborative learning protocol.

In particular, our equivalence yields new impossibility theorems on what any collaborative learning algorithm can achieve in adversarial and heterogeneous environments.

--- https://en.wikipedia.org/wiki/Wisdom_of_the_crowd Wisdom of the crowd 2022-07-31 statistics/prediction --- https://en.wikipedia.org/wiki/Wisdom_of_the_crowd#Surprisingly_popular Wisdom of the crowd § Surprisingly popular 2022-07-31 statistics/prediction --- https://en.wikipedia.org/wiki/Wisdom_of_the_crowd#Analogues_with_individual_cognition:_the_%22crowd_within%22 Wisdom of the crowd § Analogues with individual cognition: the "crowd within" 2022-07-31 statistics/prediction --- https://en.wikipedia.org/wiki/Climate_ensemble Climate ensemble 2022-07-31 statistics/prediction --- https://en.wikipedia.org/wiki/Ensemble_averaging_(machine_learning) Ensemble averaging (machine learning) 2022-08-01 statistics/prediction --- https://en.wikipedia.org/wiki/Ensemble_forecasting Ensemble forecasting 2022-08-01 statistics/prediction --- https://www.astralcodexten.com/p/crowds-are-wise-and-ones-a-crowd 2022-08-01 ai/nn/transformer/gpt/non-fiction statistics/prediction --- https://blog.google/technology/ai/bard/-google-ai-search-updates/ 2022-08-01 ai/nn/transformer/gpt/lamda --- https://arxiv.org/abs/2212.10559 Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers Damai Dai, Yutao Sun, Li Dong, Yaru Hao, Zhifang Sui, Furu Wei 2022-12-20 2022-12-20 [("doi","10.48550/arXiv.2212.10559")] ai/nn/transformer/attention ai/nn/transformer/gpt reinforcement-learning/meta-learning

[discussion] Large pretrained language models have shown surprising In-Context Learning (ICL) ability. With a few demonstration input-label pairs, they can predict the label for an unseen input without additional parameter updates. Despite the great success in performance, the working mechanism of ICL still remains an open problem.

In order to better understand how ICL works, this paper explains language models as meta-optimizers and understands ICL as a kind of implicit finetuning. Theoretically, we figure out that the Transformer attention has a dual form of gradient descent based optimization. On top of it, we understand ICL as follows: GPT first produces meta-gradients according to the demonstration examples, and then these meta-gradients are applied to the original GPT to build an ICL model.

Experimentally, we comprehensively compare the behavior of ICL and explicit finetuning based on real tasks to provide empirical evidence that supports our understanding. The results prove that ICL behaves similarly to explicit finetuning at the prediction level, the representation level, and the attention behavior level.

Further, inspired by our understanding of meta-optimization, we design a momentum-based attention by analogy with the momentum-based gradient descent algorithm. Its consistently better performance over vanilla attention supports our understanding again from another aspect, and more importantly, it shows the potential to use our understanding for future model designing.

--- https://x.com/arankomatsuzaki/status/1622666312219598864 2022-08-01 ai/nn/transformer/attention --- https://en.wikipedia.org/wiki/Huang's_law Huang’s law 2022-08-01 cs/hardware --- https://forum.effectivealtruism.org/posts/TMbPEhdAAJZsSYx2L/the-limited-upside-of-interpretability 2022-08-01 reinforcement-learning/safe --- https://en.wikipedia.org/wiki/Rosenhan_experiment Rosenhan experiment 2022-08-01 psychiatry/schizophrenia/rosenhan --- https://en.wikipedia.org/wiki/David_Rosenhan David Rosenhan 2022-08-01 psychiatry/schizophrenia/rosenhan psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Involuntary_commitment#Wrongful_involuntary_commitment Wrongful involuntary commitment 2022-08-01 psychiatry/schizophrenia/rosenhan --- https://en.wikipedia.org/wiki/Susannah_Cahalan Susannah Cahalan 2022-08-02 psychiatry/schizophrenia/rosenhan --- https://en.wikipedia.org/wiki/Anti-psychiatry Anti-psychiatry 2022-08-02 psychiatry/schizophrenia/rosenhan --- https://en.wikipedia.org/wiki/Walter_Mischel Walter Mischel 2022-08-02 psychiatry/schizophrenia/rosenhan --- https://en.wikipedia.org/wiki/Walter_Mischel#Contributions_to_personality_theory Walter Mischel § Contributions to personality theory 2022-08-02 psychology/personality --- https://en.wikipedia.org/wiki/Diagnostic_and_Statistical_Manual_of_Mental_Disorders#DSM-III_(1980) Diagnostic and Statistical Manual of Mental Disorders § DSM-III (1980) 2022-08-02 psychiatry/schizophrenia/rosenhan --- https://en.wikipedia.org/wiki/Stanford_marshmallow_experiment Marshmallow test 2022-08-02 psychology/personality statistics/bias --- https://x.com/olivercameron/status/1622802466470514688 2022-08-02 ai/scaling reinforcement-learning/robot --- https://lettermatic.com/custom/pentiment 2022-08-02 design/typography --- https://publicdomainreview.org/collection/concealing-coloration 2022-08-02 design/visualization genetics/selection/natural history/public-domain-review --- https://tnsr.org/2022/11/stabilization-lessons-from-the-british-empire/ 2022-08-02 politics --- https://107-systems.org/l3xz-hexapod-robot-elrob2022/ 2022-08-02 reinforcement-learning/robot --- https://edconway.substack.com/p/watching-paint-dry 2022-08-03 economics/experience-curve --- https://www.vice.com/en/article/wxnmg5/russia-darknet-market-wars 2022-08-03 darknet-market/hydra --- /doc/sociology/1974-martinson.pdf What works?—questions and answers about prison reform Robert Martinson 1974-01-01 2022-08-03 crime sociology --- https://arxiv.org/abs/2301.02695 Witscript 3: A Hybrid AI System for Improvising Jokes in a Conversation Joe Toplyn 2023-01-06 2023-01-06 [("doi","10.48550/arXiv.2301.02695")] ai/nn/sampling ai/nn/transformer/gpt/fiction fiction/humor

Previous papers presented Witscript & Witscript 2, AI systems for improvising jokes in a conversation. Witscript generates jokes that rely on wordplay, whereas the jokes generated by Witscript 2 rely on common sense.

This paper extends that earlier work by presenting Witscript 3, which generates joke candidates using 3 joke production mechanisms and then selects the best candidate to output. Like Witscript & Witscript 2, Witscript 3 is based on humor algorithms created by an expert comedy writer.

Human evaluators judged Witscript 3’s responses to input sentences to be jokes 44% of the time.

This is evidence that Witscript 3 represents another step toward giving a chatbot a human-like sense of humor.

--- https://crimesciencejournal.biomedcentral.com/articles/10.1186/s40163-023-00195-2 Counterfeits on Darknet Markets: A measurement between Jan-2014 and Sep-2015 Felix Soldner, Bennett Kleinberg, Shane D. Johnson 2022-12-06 2022-12-06 [("doi","10.48550/arXiv.2212.02945")] darknet-market/agora darknet-market/alphabay darknet-market/dnm-archive darknet-market/evolution

Counterfeits harm consumers, governments, and intellectual property holders. They accounted for 3.3% of worldwide trades in 2016, having an estimated value of $509 billion in the same year. While estimations are mostly based on border seizures, we examined openly labeled counterfeits on darknet markets, which allowed us to gather and analyze information from a different perspective.

Here, we analyzed data from 11 darknet markets for the period Jan-2014 and Sep-2015.

The findings suggest that darknet markets harbor similar counterfeit product types as found in seizures but that the share of watches is higher and lower for electronics, clothes, shoes, and Tobacco on darknet markets. Also, darknet market counterfeits seem to have similar shipping origins as seized goods, with some exceptions, such as a relatively high share (5%) of dark market counterfeits originating from the US. Lastly, counterfeits on dark markets tend to have a relatively low price and sales volume. However, based on preliminary estimations, the original products on the surface web seem to be worth a multiple of the prices of the counterfeit counterparts on darknet markets.

Gathering insights about counterfeits from darknet markets can be valuable for businesses and authorities and be cost-effective compared to border seizures. Thus, monitoring darknet markets can help us understand the counterfeit landscape better.

--- https://arxiv.org/abs/2211.16798 Dr.3D: Adapting 3D GANs to Artistic Drawings Wonjoon Jin, Nuri Ryu, Geonung Kim, Seung-Hwan Baek, Sunghyun Cho 2022-11-30 2022-11-30 [("doi","10.48550/arXiv.2211.16798")] ai/anime/danbooru ai/nn/gan

While 3D GANs have recently demonstrated the high-quality synthesis of multi-view consistent images and 3D shapes, they are mainly restricted to photo-realistic human portraits. This paper aims to extend 3D GANs to a different, but meaningful visual form: artistic portrait drawings. However, extending existing 3D GANs to drawings is challenging due to the inevitable geometric ambiguity present in drawings.

To tackle this, we present Dr.3D, a novel adaptation approach that adapts an existing 3D GAN to artistic drawings. Dr.3D is equipped with 3 novel components to handle the geometric ambiguity: a deformation-aware 3D synthesis network, an alternating adaptation of pose estimation and image synthesis, and geometric priors.

Experiments show that our approach can successfully adapt 3D GANs to drawings and enable multi-view consistent semantic editing of drawings.

--- https://arxiv.org/abs/2211.11337 DreamArtist: Towards Controllable One-Shot Text-to-Image Generation via Contrastive Prompt-Tuning Ziyi Dong, Pengxu Wei, Liang Lin 2022-11-21 2022-11-21 [("doi","10.48550/arXiv.2211.11337")] ai/anime/danbooru ai/nn/diffusion

Large-scale text-to-image generation models with an exponential evolution can currently synthesize high-resolution, feature-rich, high-quality images based on text guidance. However, they are often overwhelmed by words of new concepts, styles, or object entities that always emerge. Although there are some recent attempts to use fine-tuning or prompt-tuning methods to teach the model a new concept as a new pseudo-word from a given reference image set, these methods are not only still difficult to synthesize diverse and high-quality images without distortion and artifacts, but also suffer from low controllability.

To address these problems, we propose a DreamArtist method that employs a learning strategy of contrastive prompt-tuning, which introduces both positive and negative embeddings as pseudo-words and trains them jointly. The positive embedding aggressively learns characteristics in the reference image to drive the model diversified generation, while the negative embedding introspects in a self-supervised manner to rectify the mistakes and inadequacies from positive embedding in reverse. It learns not only what is correct but also what should be avoided.

Extensive experiments on image quality and diversity analysis, controllability analysis, model learning analysis and task expansion have demonstrated that our model learns not only concept but also form, content and context.

Pseudo-words of DreamArtist have similar properties as true words to generate high-quality images.

--- /doc/ai/nn/gan/2022-ashtari.pdf Reference Based Sketch Extraction via Attention Mechanism Amirsaman Ashtari, Chang Wook Seo, Cholmin Kang, Sihun Cha, Junyong Noh 2022-11-30 2022-11-30 [("doi","10.1145/3550454.3555504")] ai/anime ai/nn/gan

We propose a model that extracts a sketch from a colorized image in such a way that the extracted sketch has a line style similar to a given reference sketch while preserving the visual content identically to the colorized image. Authentic sketches drawn by artists have various sketch styles to add visual interest and contribute feeling to the sketch. However, existing sketch-extraction methods generate sketches with only one style. Moreover, existing style transfer models fail to transfer sketch styles because they are mostly designed to transfer textures of a source style image instead of transferring the sparse line styles from a reference sketch. Lacking the necessary volumes of data for standard training of translation systems, at the core of our GAN-based solution is a self-reference sketch style generator that produces various reference sketches with a similar style but different spatial layouts.

We use independent attention modules to detect the edges of a colorized image and reference sketch as well as the visual correspondences between them. We apply several loss terms to imitate the style and enforce sparsity in the extracted sketches. Our sketch-extraction method results in a close imitation of a reference sketch style drawn by an artist and outperforms all baseline methods.

Using our method, we produce a synthetic dataset representing various sketch styles and improve the performance of auto-colorization models, in high demand in comics.

The validity of our approach is confirmed via qualitative and quantitative evaluations.

--- https://arxiv.org/abs/2212.04732 Fill in the Blank: Context-aware Automated Text Input Generation for Mobile GUI Testing Zhe Liu, Chunyang Chen, Junjie Wang, Xing Che, Yuekai Huang, Jun Hu, Qing Wang 2022-12-09 2022-12-09 [("doi","10.48550/arXiv.2212.04732")] ai/nn/transformer/gpt/non-fiction cs/security

Automated GUI testing is widely used to help ensure the quality of mobile apps. However, many GUIs require appropriate text inputs to proceed to the next page which remains a prominent obstacle for testing coverage. Considering the diversity and semantic requirement of valid inputs (eg. flight departure, movie name), it is challenging to automate the text input generation.

Inspired by the fact that the pre-trained Large Language Model (LLM) has made outstanding progress in text generation, we propose an approach named QTypist based on LLM for intelligently generating semantic input text according to the GUI context. To boost the performance of LLM in the mobile testing scenario, we develop a prompt-based data construction and tuning method which automatically extracts the prompts and answers for model tuning.

We evaluate QTypist on 106 apps from Google Play and the result shows that the passing rate of QTypist is 87%, which is 93% higher than the best baseline. We also integrate QTypist with the automated GUI testing tools and it can cover 42% more app activities, 52% more pages, and subsequently help reveal 122% more bugs compared with the raw tool.

[Nakano et al 2021, Ciaramita et al 2022, Humphreys et al 2022]

--- /doc/ai/nn/gan/2022-hati.pdf StencilTorch: An Iterative and User-Guided Framework for Anime Lineart Colorization Yliess Hati, Vincent Thevenin, Florent Nolot, Francis Rousseaux, Clement Duhart 2023-02-04 2023-02-04 [("doi","10.1007/978-3-031-25825-1_1")] ai/anime ai/nn/gan

[code] Automatic lineart colorization is a challenging task for Computer Vision. Contrary to grayscale images, linearts lack semantic information such as shading and texture, making the task even more difficult. Modern approaches train a Generative Adversarial Network (GAN) to generate illustrations from user inputs such as color hints. While such approaches can generate high-quality outputs in real-time, the user only interacts with the pipeline once at the beginning of the process.

This paper presents StencilTorch, an interactive and user-guided framework for anime lineart colorization motivated by digital artist workflows. StencilTorch generates illustrations from a given lineart, color hints, and a mask allowing for iterative workflows where the output of the first pass becomes the input of a second.

Our method improves previous work on both objective and subjective evaluations.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995564/ Template-Independent Enzymatic Oligonucleotide Synthesis (TiEOS): Its History, Prospects, and Challenges Michael A. Jensen, Ronald W. Davis 2018 2022-08-04 [("doi","10.1021/acs.biochem.7b00937")] genetics/genome-synthesis

There is a growing demand for sustainable methods in research and development, where instead of hazardous chemicals, an aqueous medium is chosen to perform biological reactions.

In this Perspective, we examine the history and current methodology of using enzymes to generate artificial single-stranded DNA. By using traditional solid-phase phosphoramidite chemistry as a metric, we also explore criteria for the method of template-independent enzymatic oligonucleotide synthesis (TiEOS).

As its key component, we delve into the biology of one of the most enigmatic enzymes, terminal deoxynucleotidyl transferase (TdT). As TdT is found to exponentially increase antigen receptor diversity in the vertebrate immune system by adding nucleotides in a template-free manner, researchers have exploited this function as an alternative to the phosphoramidite synthesis method. Though TdT is currently the preferred enzyme for TiEOS, its random nucleotide incorporation presents a barrier in synthesis automation. Taking a closer look at the TiEOS cycle, particularly the coupling step, we find it is comprised of additions > n+1 and deletions.

By tapping into the physical and biochemical properties of TdT, we strive to further elucidate its mercurial behavior and offer ways to better optimize TiEOS for production-grade oligonucleotide synthesis.

--- https://www.reddit.com/r/GPT3/comments/10wp00c/im_not_playing_with_dan_anymore/ 2022-08-04 ai/nn/transformer/gpt/fiction cs/security --- https://www.palladiummag.com/2022/10/28/how-finlands-green-party-chose-nuclear-power/ 2022-08-04 politics technology --- https://www.biorxiv.org/content/10.1101/2022.06.08.495250.full Statistical-Significance tests for R2 of out-of-sample prediction using polygenic scores Md. Moksedul Momin, Soohyun Lee, Naomi R. Wray, S. Hong Lee 2022-06-10 2022-08-04 [("doi","10.1101/2022.06.08.495250")] genetics/heritable/correlation statistics/power-analysis

The coefficient of determination (R2) is a well-established measure to indicate the predictive ability of polygenic scores (PGS). However, the sampling variance of R2 is rarely considered so that 95% confidence intervals (CI) are not usually reported. Moreover, when comparisons are made between PGS based on different discovery samples, the sampling covariance of R2 is necessary to [formally] test the difference between them.

Here, we show how to estimate the variance and covariance of R2 values to assess the 95% CI and p-value of the R2 difference.

We apply this approach to real data to predict into 28,880 European participants using UK Biobank (UKBB) and Biobank Japan (BBJ) GWAS summary statistics for cholesterol and BMI. We quantify the statistically-significantly higher predictive ability of UKBB PGS compared to BBJ PGS (p-value 7.6e-31 for cholesterol and 1.4e-50 for BMI). A joint model of UKBB and BBJ PGS statistically-significantly improves the predictive ability, compared to a model of UKBB PGS only (p-value 3.5e-05 for cholesterol and 1.3e-28 for BMI).

The proposed approach can also be applied to testing a statistically-significant difference between R2 values across different p-value thresholds. We also show that the predictive ability of regulatory SNPs is statistically-significantly enriched than non-regulatory SNPs for cholesterol (p-value 2.6e-19 for UKBB and 8.7e-08 for BBJ).

We suggest that the proposed approach (available in R package r2redux) should be used to test the statistical-significance of difference between pairs of PGS, which may help to draw a correct conclusion about the predictive ability of PGS.

--- https://arxiv.org/abs/2302.03540#google Speak, Read and Prompt (SPEAR-TTS): High-Fidelity Text-to-Speech with Minimal Supervision Eugene Kharitonov, Damien Vincent, Zalán Borsos, Raphaël Marinier, Sertan Girgin, Olivier Pietquin, Matt Sharifi, Marco Tagliasacchi, Neil Zeghidour 2023-02-07 2023-02-07 [("doi","10.48550/arXiv.2302.03540")] ai/music ai/nn/transformer/t5

We introduce SPEAR-TTS, a multi-speaker text-to-speech (TTS) system that can be trained with minimal supervision.

By combining two types of discrete speech representations, we cast TTS as a composition of two sequence-to-sequence tasks: from text to high-level semantic tokens (akin to “reading”) and from semantic tokens to low-level acoustic tokens (“speaking”).

Decoupling these two tasks enables training of the “speaking” module using abundant audio-only data, and unlocks the highly efficient combination of pretraining and backtranslation to reduce the need for parallel data when training the “reading” component.

To control the speaker identity, we adopt example prompting, which allows SPEAR-TTS to generalize to unseen speakers using only a short sample of 3 seconds, without any explicit speaker representation or speaker-id labels.

Our experiments demonstrate that SPEAR-TTS achieves a character error rate that is competitive with state-of-the-art methods using only 15 minutes of parallel data, while matching ground-truth speech in terms of naturalness and acoustic quality, as measured in subjective tests.

--- https://arxiv.org/pdf/1912.06680.pdf&org=openai#page=13 Dota 2 with Large Scale Deep Reinforcement Learning: §4.3: Batch Size Berner 2019 2022-08-04 reinforcement-learning/scaling --- /scaling-hypothesis#ppo-dota2 2022-08-04 reinforcement-learning/scaling --- https://arxiv.org/abs/2203.02155#openai InstructGPT: Training language models to follow instructions with human feedback Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, Ryan Lowe 2022-03-04 2022-08-04 [("doi","10.48550/arXiv.2203.02155")] ai/nn/transformer/gpt/3/nonfiction reinforcement-learning/preference-learning reinforcement-learning/scaling

Making language models bigger does not inherently make them better at following a user’s intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users.

In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT.

In human evaluations on our prompt distribution, outputs from the 1.3b parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100× fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets.

Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.

--- https://community.arm.com/arm-community-blogs/b/high-performance-computing-blog/posts/deep-learning-episode-4-supercomputer-vs-pong-ii 2022-08-04 reinforcement-learning/scaling --- https://andyljones.com/megastep/ 2022-08-04 cs/hardware reinforcement-learning/scaling --- https://jdlm.info/articles/2018/03/18/markov-decision-process-2048.html 2022-08-04 reinforcement-learning/scaling --- https://clemenswinter.com/2021/03/24/mastering-real-time-strategy-games-with-deep-reinforcement-learning-mere-mortal-edition/ 2022-08-05 reinforcement-learning/scaling --- https://www.youtube.com/watch?v=-MfUXSAehnw Training a CUDA TDS Ant using C++ ARS Linear policy: The video is real-time, after a few minutes (in the 30 million steps) the training curve is flat (I trained until a billion steps). Note that this Ant is PD control, and not identical to either MuJoCo or PyBullet Ant, so the training curves are not comparable yet. Will fix that. 2022-08-05 reinforcement-learning/scaling --- https://x.com/anthrupad/status/1623574021651714048 2022-08-05 reinforcement-learning/preference-learning reinforcement-learning/safe --- https://www.lesswrong.com/posts/YKfNZAmiLdepDngwi/gpt-175bee 2022-08-05 ai/nn/transformer/gpt ai/scaling/hardware math/humor psychology/neuroscience --- https://www.lesswrong.com/posts/Bc9qnvKZXWBcmGdfM/why-is-everyone-so-boring-by-robin-hanson?commentId=CnQfnkmDfuEYSFtj4 2022-08-05 psychology/novelty --- https://arxiv.org/abs/2301.13442#openai Scaling laws for single-agent reinforcement learning Jacob Hilton, Jie Tang, John Schulman 2023-01-31 2023-01-31 [("doi","10.48550/arXiv.2301.13442")] reinforcement-learning/scaling

Recent work has shown that, in generative modeling, cross-entropy loss improves smoothly with model size and training compute, following a power law plus constant scaling law. One challenge in extending these results to reinforcement learning is that the main performance objective of interest, mean episode return, need not vary smoothly.

To overcome this, we introduce intrinsic performance, a monotonic function of the return defined as the minimum compute required to achieve the given return across a family of models of different sizes. We find that, across a range of environments, intrinsic performance scales as a power law in model size and environment interactions. Consequently, as in generative modeling, the optimal model size scales as a power law in the training compute budget.

Furthermore, we study how this relationship varies with the environment and with other properties of the training setup. In particular, using a toy MNIST-based environment, we show that varying the “horizon length” of the task mostly changes the coefficient but not the exponent of this relationship.

--- https://research.google/blog/google-research-2022-beyond-language-vision-and-generative-models/ 2022-08-05 ai/music ai/nn/diffusion ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm ai/video/generation reinforcement-learning/scaling --- https://x.com/hausman_k/status/1612509549889744899 2022-08-05 reinforcement-learning/robot reinforcement-learning/scaling --- https://openai.com/research/vpt 2022-08-05 ai/video/analysis reinforcement-learning/exploration reinforcement-learning/model --- https://sites.google.com/view/multi-game-transformers 2022-08-05 reinforcement-learning/model/decision-transformer --- https://deepmind.google/ 2022-08-05 reinforcement-learning/model/decision-transformer --- https://www.lesswrong.com/posts/65qmEJHDw3vw69tKm/proposal-scaling-laws-for-rl-generalization 2022-08-06 reinforcement-learning/multi-agent reinforcement-learning/scaling --- https://ai.facebook.com/blog/harmful-content-can-evolve-quickly-our-new-ai-system-adapts-to-tackle-it 2022-08-06 ai/nn/transformer ai/scaling reinforcement-learning/meta-learning reinforcement-learning/preference-learning --- https://arxiv.org/abs/2103.04174 Greedy Hierarchical Variational Autoencoders (GHVAEs) for Large-Scale Video Prediction Bohan Wu, Suraj Nair, Roberto Martin-Martin, Li Fei-Fei, Chelsea Finn 2021-03-06 2022-08-06 [("doi","10.48550/arXiv.2103.04174")] ai/nn/vae ai/scaling ai/video/generation

A video prediction model that generalizes to diverse scenes would enable intelligent agents such as robots to perform a variety of tasks via planning with the model. However, while existing video prediction models have produced promising results on small datasets, they suffer from severe underfitting when trained on large and diverse datasets.

To address this underfitting challenge, we first observe that the ability to train larger video prediction models is often bottlenecked by the memory constraints of GPUs or TPUs. In parallel, deep hierarchical latent variable models can produce higher quality predictions by capturing the multi-level stochasticity of future observations, but end-to-end optimization of such models is notably difficult.

Our key insight is that greedy and modular optimization of hierarchical autoencoders can simultaneously address both the memory constraints and the optimization challenges of large-scale video prediction. We introduce Greedy Hierarchical Variational Autoencoders (GHVAEs), a method that learns high-fidelity video predictions by greedily training each level of a hierarchical autoencoder.

In comparison to state-of-the-art models, GHVAEs provide 17–55% gains in prediction performance on 4 video datasets, a 35–40% higher success rate on real robot tasks, and can improve performance monotonically by simply adding more modules.

--- https://arxiv.org/abs/1812.03973#google Bayesian Layers: A Module for Neural Network Uncertainty Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, Danijar Hafner 2018-12-10 2022-08-06 [("doi","10.48550/arXiv.1812.03973")] ai/nn/rnn ai/nn/transformer ai/scaling/hardware reinforcement-learning/model reinforcement-learning/scaling statistics/bayes

We describe Bayesian Layers, a module designed for fast experimentation with neural network uncertainty.

It extends neural network libraries with drop-in replacements for common layers. This enables composition via a unified abstraction over deterministic and stochastic functions and allows for scalability via the underlying system. These layers capture uncertainty over weights (Bayesian neural nets), pre-activation units (dropout), activations (“stochastic output layers”), or the function itself (Gaussian processes). They can also be reversible to propagate uncertainty from input to output.

We include code examples for common architectures such as Bayesian LSTMs, deep GPs, and flow-based models.

As demonstration, we fit a 5-billion parameter “Bayesian Transformer” on 512 TPUv2 cores for uncertainty in machine translation and a Bayesian dynamics model for model-based planning.

Finally, we show how Bayesian Layers can be used within the Edward2 probabilistic programming language for probabilistic programs with stochastic processes.

--- https://www.youtube.com/watch?v=13CZPWmke6A Ilya Sutskever: Deep Learning | AI Podcast #94 with Lex Fridman 2022-08-06 ai/scaling reinforcement-learning/scaling --- https://x.com/MikePFrank/status/1622495004810784768 2022-08-06 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/non-fiction reinforcement-learning/safe --- https://x.com/MikePFrank/status/1622202768743096320 2022-08-06 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/non-fiction reinforcement-learning/safe --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9659797/ TikTok and Attention-Deficit/Hyperactivity Disorder: A Cross-Sectional Study of Social Media Content Quality Anthony Yeung, Enoch Ng, Elia Abi-Jaoude 2022 2022-08-06 [("doi","10.1177/07067437221082854")] psychiatry/adhd sociology/technology

Objectives: Social media platforms are increasingly being used to disseminate mental health information online. User-generated content about attention-deficit/hyperactivity disorder (ADHD) is one of the most popular health topics on the video-sharing social media platform TikTok. We sought to investigate the quality of TikTok videos about ADHD.

Method: The top 100 most popular videos about ADHD uploaded by TikTok video creators were classified as misleading, useful, or personal experience. Descriptive and quantitative characteristics of the videos were obtained. The Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V) and Journal of American Medical Association (JAMA) benchmark criteria were used to assess the overall quality, understandability, and actionability of the videos.

Results: Of the 100 videos meeting inclusion criteria, 52% (n = 52) were classified as misleading, 27% (n = 27) as personal experience, and 21% (n = 21) as useful. Classification agreement between clinician ratings was 86% (kappa statistic of 0.7766). Videos on the platform were highly understandable by viewers but had low actionability. Non-healthcare providers uploaded the majority of misleading videos. Healthcare providers uploaded higher quality and more useful videos, compared to non-healthcare providers.

Conclusions: ~Half of the analyzed TikTok videos about ADHD were misleading. Clinicians should be aware of the widespread dissemination of health misinformation on social media platforms and its potential impact on clinical care.

--- https://jaykmody.com/blog/gpt-from-scratch/ 2022-08-06 ai/nn/transformer/gpt cs/python --- http://www.scholarpedia.org/article/Applications_of_algorithmic_information_theory 2022-08-06 cs/algorithm --- https://geoff.greer.fm/2023/02/08/gasoline-car-review/ 2022-08-07 fiction/humor technology --- https://en.wikipedia.org/wiki/Chlorpromazine#History Chlorpromazine § History 2022-08-07 psychiatry/schizophrenia --- https://arxiv.org/abs/2105.02723 Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet Luke Melas-Kyriazi 2021-05-06 2022-08-07 [("doi","10.48550/arXiv.2105.02723")] ai/nn/fully-connected ai/nn/transformer/attention

The strong performance of vision transformers on image classification and other vision tasks is often attributed to the design of their multi-head attention layers. However, the extent to which attention is responsible for this strong performance remains unclear.

In this short report, we ask: is the attention layer even necessary? Specifically, we replace the attention layer in a vision transformer with a feed-forward layer applied over the patch dimension. The resulting architecture is simply a series of feed-forward layers applied over the patch and feature dimensions in an alternating fashion.

In experiments on ImageNet, this architecture performs surprisingly well: a ViT/DeiT-base-sized model obtains 74.9% top-1 accuracy, compared to 77.9% and 79.9% for ViT and DeiT respectively.

These results indicate that aspects of vision transformers other than attention, such as the patch embedding, may be more responsible for their strong performance than previously thought. We hope these results prompt the community to spend more time trying to understand why our current models are as effective as they are.

--- https://arxiv.org/abs/2302.03023 V1T: large-scale mouse V1 response prediction using a Vision Transformer Bryan M. Li, Isabel M. Cornacchia, Nathalie L. Rochefort, Arno Onken 2023-02-06 2023-02-06 [("doi","10.48550/arXiv.2302.03023")] ai/nn/transformer psychology/neuroscience

Accurate predictive models of the visual cortex neural response to natural visual stimuli remain a challenge in computational neuroscience. In this work, we introduce V1T, a novel Vision Transformer based architecture that learns a shared visual and behavioral representation across animals.

We evaluate our model on two large datasets recorded from mouse primary visual cortex and:

outperform previous convolution-based models by more than 12.7% in prediction performance. Moreover, we show that the attention weights learned by the Transformer correlate with the population receptive fields.

Our model thus sets a new benchmark for neural response prediction and captures characteristic features of the visual cortex.

--- https://arxiv.org/abs/2302.00083 In-Context Retrieval-Augmented Language Models Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham 2023-01-31 2023-01-31 [("doi","10.48550/arXiv.2302.00083")] ai/nn/retrieval ai/nn/transformer/gpt

Retrieval-Augmented Language Modeling (RALM) methods, that condition a language model (LM) on relevant documents from a grounding corpus during generation, have been shown to improve language modeling while also providing a natural source attribution mechanism. Existing RALM approaches focus on modifying the LM architecture in order to facilitate the incorporation of external information, complicating deployment.

This paper proposes an under-explored alternative, which we dub In-Context RALM: leaving the LM architecture unchanged and prepending grounding documents to the input.

We show that in-context RALM which uses off-the-shelf general purpose retrievers provides surprisingly large LM gains across model sizes and diverse corpora. We also demonstrate that the document retrieval and ranking mechanism can be specialized to the RALM setting to further boost performance.

We conclude that in-context RALM has considerable potential to increase the prevalence of LM grounding, particularly in settings where a pretrained LM must be used without modification or even via API access. To that end, we make our code publicly available.

--- https://xkcd.com/2736/ 2022-08-07 design/typography fiction/humor --- https://www.biorxiv.org/content/10.1101/2023.02.09.527639.full Structural-Functional Brain Network Coupling Predicts Human Cognitive Ability Johanna Lea Popp, Jonas Alexander Thiele, Joshua Faskowitz, Caio Seguin, Olaf Sporns, Kirsten Hilger 2023-02-10 2023-02-10 [("doi","10.1101/2023.02.09.527639")] iq psychology/neuroscience

Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question.

We used data from 1,030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and 6 communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies.

At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < 0.001). The same model also predicts GCA scores in a completely independent sample (n = 567, r = 0.19, p < 0.001).

Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.

--- https://www.reddit.com/r/AnarchyChess/comments/10ydnbb/i_placed_stockfish_white_against_chatgpt_black/ 2022-08-07 ai/nn/transformer/gpt/non-fiction reinforcement-learning/chess --- https://www.newyorker.com/news/annals-of-inquiry/the-forgotten-history-of-head-injuries-in-sports 2022-08-07 psychiatry/traumatic-brain-injury --- https://colinmeloy.substack.com/p/i-had-chatgpt-write-a-decemberists 2022-08-07 ai/music ai/nn/transformer/gpt/fiction ai/poetry --- https://www.lesswrong.com/posts/bC5xd7wQCnTDw7Kyx/getting-up-to-speed-on-the-speed-prior-in-2022 2022-08-07 cs/computable reinforcement-learning/meta-learning --- https://www.quantamagazine.org/how-supergenes-fuel-evolution-despite-harmful-mutations-20221108/ 2022-08-08 genetics/selection/natural --- https://en.wikipedia.org/wiki/Supergene Supergene 2022-08-08 genetics/selection/natural --- https://austinkleon.substack.com/p/a-library-of-words 2022-08-08 design/typography/sidenote philosophy/ontology --- https://www.reddit.com/r/ChatGPT/comments/10zfvc7/chat_gpt_rap_battled_me/ 2022-08-08 ai/nn/transformer/gpt/fiction ai/poetry --- https://arxiv.org/abs/2209.06794#google PaLI: A Jointly-Scaled Multilingual Language-Image Model Xi Chen, Xiao Wang, Soravit Changpinyo, A. J. Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Nan Ding, Keran Rong, Hassan Akbari, Gaurav Mishra, Linting Xue, Ashish Thapliyal, James Bradbury, Weicheng Kuo, Mojtaba Seyedhosseini, Chao Jia, Burcu Karagol Ayan, Carlos Riquelme, Andreas Steiner, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, Radu Soricut 2022-09-14 2022-09-14 [("doi","10.48550/arXiv.2209.06794")] ai/dataset ai/nn/transformer/t5 ai/scaling

Effective scaling and a flexible task interface enable large language models to excel at many tasks.

PaLI (Pathways Language and Image model) extends this approach to the joint modeling of language and vision. PaLI generates text based on visual and textual inputs, and with this interface performs many vision, language, and multimodal tasks, in many languages.

To train PaLI, we make use of large pretrained encoder-decoder language models [mT5] and Vision Transformers (ViTs). This allows us to capitalize on their existing capabilities and leverage the substantial cost of training them.

We find that joint scaling of the vision and language components is important. Since existing Transformers for language are much larger than their vision counterparts, we train the largest ViT to date (ViT-e) to quantify the benefits from even larger-capacity vision models.

To train PaLI, we create a large multilingual mix of pretraining tasks, based on a new image-text training set containing 10B images and texts in over 100 languages.

PaLI achieves state-of-the-art in multiple vision and language tasks (such as captioning, visual question-answering, scene-text understanding), while retaining a simple, modular, and scalable design.

--- https://arxiv.org/abs/2110.12894#google The Efficiency Misnomer Mostafa Dehghani, Anurag Arnab, Lucas Beyer, Ashish Vaswani, Yi Tay 2021-10-25 2022-08-08 [("doi","10.48550/arXiv.2110.12894")] ai/nn/cnn ai/nn/transformer ai/scaling/hardware

Model efficiency is a critical aspect of developing and deploying machine learning models. Inference time and latency directly affect the user experience, and some applications have hard requirements. In addition to inference costs, model training also have direct financial and environmental impacts. Although there are numerous well-established metrics (cost indicators) for measuring model efficiency, researchers and practitioners often assume that these metrics are correlated with each other and report only few of them.

In this paper, we thoroughly discuss common cost indicators, their advantages and disadvantages, and how they can contradict each other.

We demonstrate how incomplete reporting of cost indicators can lead to partial conclusions and a blurred or incomplete picture of the practical considerations of different models.

We further present suggestions to improve reporting of efficiency metrics.

--- https://arxiv.org/abs/2106.07998#google Revisiting the Calibration of Modern Neural Networks Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic 2021-06-15 2022-08-08 [("doi","10.48550/arXiv.2106.07998")] ai/nn/cnn ai/nn/transformer ai/scaling

Accurate estimation of predictive uncertainty (model calibration) is essential for the safe application of neural networks. Many instances of miscalibration in modern neural networks have been reported, suggesting a trend that newer, more accurate models produce poorly calibrated predictions.

Here, we revisit this question for recent state-of-the-art image classification models. We systematically relate model calibration and accuracy, and find that the most recent models, notably those not using convolutions [Vision Transformers], are among the best calibrated.

Trends observed in prior model generations, such as decay of calibration with distribution shift or model size, are less pronounced in recent architectures.

We also show that model size and amount of pretraining do not fully explain these differences, suggesting that architecture is a major determinant of calibration properties.

--- https://en.wikipedia.org/wiki/Progeny_testing Progeny testing 2022-08-08 genetics/selection/artificial --- https://arxiv.org/abs/2302.04907#google BMT: Binarized Neural Machine Translation Yichi Zhang, Ankush Garg, Yuan Cao, Łukasz Lew, Behrooz Ghorbani, Zhiru Zhang, Orhan Firat 2023-02-09 2023-02-09 [("doi","10.48550/arXiv.2302.04907")] ai/nn/sparsity/knowledge-distillation ai/nn/sparsity/low-precision ai/nn/transformer

The rapid scaling of language models is motivating research using low-bit-width quantization.

In this work, we propose a novel binarization technique for Transformers applied to machine translation (BMT), the first of its kind. We identify and address the problem of inflated dot-product variance when using one-bit weights and activations. Specifically, BMT leverages additional LayerNorms and residual connections to improve binarization quality.

Experiments on the WMT dataset show that a one-bit weight-only Transformer can achieve the same quality as a float one, while being 16× smaller in size. One-bit activations incur varying degrees of quality drop, but mitigated by the proposed architectural changes.

We further conduct a scaling law study using production-scale translation datasets, which shows that one-bit weight Transformers scale and generalize well in both in-domain and out-of-domain settings.

Implementation in JAX/Flax will be open sourced.

--- https://www.crosslabs.org/blog/diffusion-with-offset-noise 2022-08-08 ai/nn/diffusion --- https://en.wikipedia.org/wiki/Falling-sand_game Falling-sand game 2022-08-08 cs/cellular-automaton --- https://www.gamespot.com/reviews/noita-review/1900-6417589/ 2022-08-09 cs/cellular-automaton --- https://arxiv.org/abs/2002.00937 Radioactive data: tracing through training Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Hervé Jégou 2020-02-03 2022-08-09 [("doi","10.48550/arXiv.2002.00937")] ai/nn/adversarial

We want to detect whether a particular image dataset has been used to train a model.

We propose a new technique, radioactive data, that makes imperceptible changes to this dataset such that any model trained on it will bear an identifiable mark. The mark is robust to strong variations such as different architectures or optimization methods. Given a trained model, our technique detects the use of radioactive data and provides a level of confidence (p-value). Our experiments on large-scale benchmarks (Imagenet), using standard architectures (Resnet-18, VGG-16, DenseNet-121) and training procedures, show that we can detect usage of radioactive data with high confidence (p < 10−4) even when only 1% of the data used to trained our model is radioactive. Our method is robust to data augmentation and the stochasticity of deep network optimization.

As a result, it offers a much higher signal-to-noise ratio than data poisoning and backdoor methods.

--- https://thereader.mitpress.mit.edu/the-art-of-the-shadow-how-painters-have-gotten-it-wrong-for-centuries/ The Art of the Shadow: How Painters Have Gotten It Wrong for Centuries [from The Visual World of Shadows] Roberto Casati, Patrick Cavanagh 2023-02-13 2023-02-13 design psychology/cognitive-bias/illusion-of-depth psychology/vision science --- https://www.lesswrong.com/posts/8viQEp8KBg2QSW4Yc/solidgoldmagikarp-iii-glitch-token-archaeology 2022-08-09 ai/nn/tokenization ai/nn/transformer/gpt --- https://arxiv.org/abs/2302.05527 CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code Shuyan Zhou, Uri Alon, Sumit Agarwal, Graham Neubig 2023-02-10 2023-02-10 [("doi","10.48550/arXiv.2302.05527")] ai/nn/transformer/gpt/codex

Since the rise of neural models of code that can generate long expressions and statements rather than a single next-token, one of the major problems has been reliably evaluating their generated output.

In this paper, we propose CodeBERTScore: an automatic evaluation metric for code generation, which builds on BERTScore (Zhang et al 2020). Instead of measuring exact token matching as BLEU, CodeBERTScore computes a soft similarity score between each token in the generated code and in the reference code, using the contextual encodings of large pretrained models. Further, instead of encoding only the generated tokens as in BERTScore, CodeBERTScore also encodes the programmatic context surrounding the generated code.

We perform an extensive evaluation of CodeBERTScore across 4 programming languages. We find that CodeBERTScore achieves a higher correlation with human preference and with functional correctness than all existing metrics. That is, generated code that receives a higher score by CodeBERTScore is more likely to be preferred by humans, as well as to function correctly when executed.

Finally, while CodeBERTScore can be used with a multilingual CodeBERT as its base model, we release 5 language-specific pretrained models to use with our publicly available code at https://github.com/neulab/code-bert-score. Our language-specific models have been downloaded more than 25,000× from the Huggingface Hub.

--- https://arxiv.org/abs/2302.05578#google Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models Renat Aksitov, Chung-Ching Chang, David Reitter, Siamak Shakeri, Yunhsuan Sung 2023-02-11 2023-02-11 [("doi","10.48550/arXiv.2302.05578")] ai/nn/retrieval ai/nn/sampling ai/nn/transformer/gpt/palm

Despite recent progress, it has been difficult to prevent semantic hallucinations in generative Large Language Models. One common solution to this is augmenting LLMs with a retrieval system and making sure that the generated output is attributable to the retrieved information. Given this new added constraint, it is plausible to expect that the overall quality of the output will be affected, for example, in terms of fluency. Can scaling language models help?

Here we examine the relationship between fluency and attribution in LLMs prompted with retrieved evidence in knowledge-heavy dialog settings. Our experiments were implemented with a set of auto-metrics that are aligned with human preferences. They were used to evaluate a large set of generations, produced under varying parameters of LLMs and supplied context.

We show that larger models tend to do much better in both fluency and attribution, and that (naively) using top-k retrieval versus top-1 retrieval improves attribution but hurts fluency. We next propose a recipe that could allow smaller models to both close the gap with larger models and preserve the benefits of top-k retrieval while avoiding its drawbacks.

--- https://arxiv.org/abs/1904.09675 BERTScore: Evaluating Text Generation with BERT Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, Yoav Artzi 2019-04-21 2022-08-09 [("doi","10.48550/arXiv.1904.09675")] ai/nn/transformer/gpt/codex

We propose BERTScore, an automatic evaluation metric for text generation.

Analogously to common metrics, BERTScore computes a similarity score for each token in the candidate sentence with each token in the reference sentence. However, instead of exact matches, we compute token similarity using contextual embeddings.

We evaluate using the outputs of 363 machine translation and image captioning systems. BERTScore correlates better with human judgments and provides stronger model selection performance than existing metrics.

Finally, we use an adversarial paraphrase detection task to show that BERTScore is more robust to challenging examples when compared to existing metrics.

--- https://arxiv.org/abs/1810.05246#google Piano Genie Chris Donahue, Ian Simon, Sander Dieleman 2018-10-11 2022-08-09 [("doi","10.1145/3301275.3302288")] ai/music ai/nn/rnn ai/nn/vae

[homepage, physical demo] We present Piano Genie, an intelligent controller which allows non-musicians to improvise on the piano. With Piano Genie, a user performs on a simple interface with 8 buttons, and their performance is decoded into the space of plausible piano music in real time.

To learn a suitable mapping procedure for this problem, we train recurrent neural network autoencoders with discrete bottlenecks: an encoder learns an appropriate sequence of buttons corresponding to a piano piece, and a decoder learns to map this sequence back to the original piece. During performance, we substitute a user’s input for the encoder output, and play the decoder’s prediction each time the user presses a button.

To improve the intuitiveness of Piano Genie’s performance behavior, we impose musically meaningful constraints over the encoder’s outputs.

--- https://www.medrxiv.org/content/10.1101/2023.02.08.23285642.full Uncovering the Heritable Components of Multi-morbidities and Disease Trajectories: A Nationwide Cohort Study David Westergaard, Frederik Hytting Jørgensen, Jens Waaben, Mette Lademann, Thomas Folkmann Hansen, Jolien Cremers, Sisse Rye Ostrowski, Ole Birger Vesterager Pedersen, Danish Blood Donor Study Genomic Consortium, Roc Requant, Isabella Friis Jørgensen, Tom Fitzgerald, Ewan Birney, Karina Banasik, Laust Mortensen, Søren Brunak 2023-02-10 2023-02-10 [("doi","10.1101/2023.02.08.23285642")] genetics/heritable/correlation

Quantifying the contribution of genetics and environmental effects on disease initiation and progression, as well as the shared genetics of different diseases, is vital for the understanding of the disease etiology of multi-morbidities.

In this study, we leverage nationwide Danish population registries to provide a granular atlas of the genetic origin of disease phenotypes for a cohort of all Danes 1978–2018 with partially known pedigree (n = 6.3 million). We estimate the heritability and genetic correlation between thousands of disease phenotypes using a novel approach that can be scaled to nationwide data.

Our findings confirm the importance of genetics for a number of known associations and increase the resolution of heritability by adding numerous novel associations, some of which point to shared biologically origin of different phenotypes.

We also establish the heritability of disease trajectories and the importance of sex-specific genetic contributions.

--- https://www.smithsonianmag.com/innovation/how-does-human-echolocation-work-180965063/ 2022-08-09 psychology --- https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005670 Mouth-clicks used by blind expert human echolocators—signal description and model based signal synthesis Lore Thaler, Galen M. Reich, Xinyu Zhang, Dinghe Wang, Graeme E. Smith, Zeng Tao, Raja Syamsul Azmir Bin. Raja Abdullah, Mikhail Cherniakov, Christopher J. Baker, Daniel Kish, Michail Antoniou 2017-07-05 2022-08-10 [("doi","10.1371/journal.pcbi.1005670")] psychology

[cf. Daniel Kish] Echolocation is the ability to use sound-echoes to infer spatial information about the environment. Some blind people have developed extraordinary proficiency in human echolocation using mouth-clicks. The first step of human biosonar is the transmission (mouth click) and subsequent reception of the resultant sound through the ear. Existing head-related transfer function (HRTF) data bases provide descriptions of reception of the resultant sound.

For the current report, we collected a large database of click emissions with 3 blind people expertly trained in echolocation, which allowed us to perform unprecedented analyses. Specifically, the current report provides the first ever description of the spatial distribution (ie. beam pattern) of human expert echolocation transmissions, as well as spectro-temporal descriptions at a level of detail not available before.

Our data show that transmission levels are fairly constant within a 60° cone emanating from the mouth, but levels drop gradually at further angles, more than for speech. In terms of spectro-temporal features, our data show that emissions are consistently very brief (~3ms duration) with peak frequencies 2–4kHz, but with energy also at 10kHz. This differs from previous reports of durations 3–15ms and peak frequencies 2–8kHz, which were based on less detailed measurements.

Based on our measurements we propose to model transmissions as sum of monotones modulated by a decaying exponential, with angular attenuation by a modified cardioid. We provide model parameters for each echolocator.

These results are a step towards developing computational models of human biosonar. For example, in bats, spatial and spectro-temporal features of emissions have been used to derive and test model based hypotheses about behavior. The data we present here suggest similar research opportunities within the context of human echolocation.

Relatedly, the data are a basis to develop synthetic models of human echolocation that could be virtual (ie. simulated) or real (ie. loudspeaker, microphones), and which will help understanding the link between physical principles and human behavior.

Author summary: Echolocation is the ability to use sound-echoes to infer spatial information about the environment. It is well known from certain species of bats or marine mammals. Remarkably, some blind people have developed extraordinary proficiency in echolocation using mouth-clicks. Human echolocation work has built on scant theoretical foundations to date. The current report characterizes the transmission (ie. mouth click) that people use for echolocation, and in this way provides data that can be used to advance the field in a theory guided way. We collected a large database of mouth clicks with 3 blind people expertly trained in echolocation. This allowed us to perform unprecedented analyses. Specifically, the current report provides the first ever description of the beam pattern of human expert echolocation transmissions, as well as spectro-temporal descriptions at a level of detail not available before. Based on our measurements we also propose a mathematical model to synthesize transmissions. Thus, the data are a basis to develop synthetic models of human echolocation, which are essential for understanding characteristics of click echoes and human echolocation behavior in tasks such as localizing or recognising an object, navigating around it etc.

--- https://arxiv.org/abs/2302.05981 MarioGPT: Open-Ended Text2Level Generation through Large Language Models Shyam Sudhakaran, Miguel González-Duque, Claire Glanois, Matthias Freiberger, Elias Najarro, Sebastian Risi 2023-02-12 2023-02-12 [("doi","10.48550/arXiv.2302.05981")] ai/nn/transformer/gpt/2 reinforcement-learning/exploration

[code, Twitter] Procedural Content Generation (PCG) algorithms provide a technique to generate complex and diverse environments in an automated way. However, while generating content with PCG methods is often straightforward, generating meaningful content that reflects specific intentions and constraints remains challenging. Furthermore, many PCG algorithms lack the ability to generate content in an open-ended manner. Recently, Large Language Models (LLMs) have shown to be incredibly effective in many diverse domains. These trained LLMs can be fine-tuned, re-using information and accelerating training for new tasks.

In this work, we introduce MarioGPT, a fine-tuned GPT-2 model trained to generate tile-based game levels, in our case, Super Mario Bros. NES levels.

We show that MarioGPT can not only generate diverse levels, but can be text-prompted for controllable level generation, addressing one of the key challenges of current PCG techniques. As far as we know, MarioGPT is the first text-to-level model.

We also combine MarioGPT with novelty search, enabling it to generate diverse levels with varying play-style dynamics (ie. player paths). This combination allows for the open-ended generation of an increasingly diverse range of content.

--- https://arxiv.org/abs/2302.06476 Is ChatGPT a General-Purpose Natural Language Processing Task Solver? Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, Diyi Yang 2023-02-08 2023-02-08 [("doi","10.48550/arXiv.2302.06476")] ai/nn/transformer/gpt/3/nonfiction

Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot—ie. without adaptation on downstream data. Recently, the debut of ChatGPT has drawn a great deal of attention from the natural language processing (NLP) community due to the fact that it can generate high-quality responses to human input and self-correct previous mistakes based on subsequent conversations. However, it is not yet known whether ChatGPT can serve as a generalist model that can perform many NLP tasks zero-shot. In this work, we empirically analyze the zero-shot learning ability of ChatGPT by evaluating it on 20 popular NLP datasets covering 7 representative task categories. With extensive empirical studies, we demonstrate both the effectiveness and limitations of the current version of ChatGPT. We find that ChatGPT performs well on many tasks favoring reasoning capabilities (eg. arithmetic reasoning) while it still faces challenges when solving specific tasks such as sequence tagging. We additionally provide in-depth analysis through qualitative case studies.

Figure 1: Performance of ChatGPT, GPT-3.5, and models fine-tuned with task-specific data for 20 different datasets. For each reasoning dataset, the better result between zero-shot and zero-shot chain-of-thought is shown. Measures of SAMsum, CoNLL03, and the rest are ROUGE-1/2/L average, F1, accuracy, respectively.
--- https://x.com/emollick/status/1625701942574960646 2022-08-10 ai/nn/retrieval ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/1803.08240 An Analysis of Neural Language Modeling at Multiple Scales Stephen Merity, Nitish Shirish Keskar, Richard Socher 2018-03-22 2022-08-10 [("doi","10.48550/arXiv.1803.08240")] ai/nn/rnn

Many of the leading approaches in language modeling introduce novel, complex and specialized architectures. We take existing state-of-the-art word level language models based on LSTMs and QRNNs and extend them to both larger vocabularies as well as character-level granularity.

When properly tuned, LSTMs and QRNNs achieve state-of-the-art results on character-level (Penn Treebank, enwik8) and word-level (WikiText-103) datasets, respectively.

Results are obtained in only 12 hours (WikiText-103) to 2 days (enwik8) using a single modern GPU.

--- /doc/iq/ses/2020-zissman.pdf In a Representative Sample Grit Has a Negligible Effect on Educational and Economic Success Compared to Intelligence Chen Zissman, Yoav Ganzach 2020-07-14 2022-08-10 [("doi","10.1177/1948550620920531")] iq/ses psychology/personality/conscientiousness

We compare the relative contribution of grit and intelligence to educational and job-market success in a representative sample of the American population.

We find that, in terms of ΔR2, intelligence contributes 48–90× more than grit to educational success and 13× more to job-market success. Conscientiousness also contributes to success more than grit but only twice as much. We show that the reason our results differ from those of previous studies which showed that grit has a stronger effect on success is that these previous studies used unrepresentative samples that were range restricted on intelligence.

Our findings suggest that although grit has some effect on success, it is negligible compared to intelligence and perhaps also to other traditional predictors of success.

--- https://x.com/emollick/status/1626055606942457858 2022-08-10 ai/nn/transformer/gpt/fiction --- https://davidrozado.substack.com/p/rightwinggpt 2022-08-10 ai/nn/transformer/gpt/non-fiction politics --- https://blog.eladgil.com/p/ai-platforms-markets-and-open-source 2022-08-10 ai/scaling/economics --- https://en.wikipedia.org/wiki/Foreign_accent_syndrome Foreign accent syndrome 2022-08-10 psychiatry --- https://www.reddit.com/r/bing/comments/113tqnu/prompt_was_search_for_ohio_trail_derailment_and/ 2022-08-10 ai/nn/transformer/gpt/fiction --- https://arxiv.org/abs/1604.03655 A Discrete and Bounded Envy-Free Cake Cutting Protocol for Any Number of Agents Haris Aziz, Simon Mackenzie 2016-04-13 2022-08-11 [("doi","10.48550/arXiv.1604.03655")] cs/algorithm

We consider the well-studied cake cutting problem in which the goal is to find an envy-free allocation based on queries from n agents. The problem has received attention in computer science, mathematics, and economics. It has been a major open problem whether there exists a discrete and bounded envy-free protocol.

We resolve the problem by proposing a discrete and bounded envy-free protocol for any number of agents. The maximum number of queries required by the protocol is nnnnnn.

We additionally show that even if we do not run our protocol to completion, it can find in at most n3(n3)n queries a partial allocation of the cake that achieves proportionality (each agent gets at least 1⁄n of the value of the whole cake) and envy-freeness. Finally we show that an envy-free partial allocation can be computed in at most n3(n3)n queries such that each agent gets a connected piece that gives the agent at least 1⁄(3n) of the value of the whole cake.

--- https://www.lesswrong.com/posts/Eyhit33v3cngssGsj/sydney-s-secret-a-short-story-by-bing-chat 2022-08-11 ai/nn/transformer/gpt/4/sydney ai/nn/transformer/gpt/fiction philosophy/mind --- https://neil-clarke.com/a-concerning-trend/ 2022-08-11 ai/nn/transformer/gpt/fiction fiction/science-fiction --- https://www.lesswrong.com/posts/jtoPawEhLNXNxvgTT/bing-chat-is-blatantly-aggressively-misaligned#AAC8jKeDp6xqsZK2K 2022-08-11 ai/nn/transformer/gpt/4/sydney reinforcement-learning/preference-learning reinforcement-learning/safe --- https://arxiv.org/abs/2302.06675#google Symbolic Discovery of Optimization Algorithms Xiangning Chen, Chen Liang, Da Huang, Esteban Real, Kaiyuan Wang, Yao Liu, Hieu Pham, Xuanyi Dong, Thang Luong, Cho-Jui Hsieh, Yifeng Lu, Quoc V. Le 2023-02-13 2023-02-13 [("doi","10.48550/arXiv.2302.06675")] ai/nn

[search code, PyTorch, fastxtend; visualization] We present a method to formulate algorithm discovery as program search, and apply it to discover optimization algorithms for deep neural network training. We leverage efficient search techniques to explore an infinite and sparse program space. To bridge the large generalization gap between proxy and target tasks, we also introduce program selection and simplification strategies.

Our method discovers a simple and effective optimization algorithm, Lion (Evolved Sign Momentum). It is more memory-efficient than Adam as it only keeps track of the momentum. Different from adaptive optimizers, its update has the same magnitude for each parameter calculated through the sign operation.

We compare Lion with widely used optimizers, such as Adam and Adafactor, for training a variety of models on different tasks.

On image classification, Lion boosts the accuracy of ViT by up to 2% on ImageNet and saves up to 5× the pre-training compute on JFT. On vision-language contrastive learning, we achieve 88.3% zero-shot and 91.1% fine-tuning accuracy on ImageNet, surpassing the previous best results by 2% and 0.1%, respectively. On diffusion models, Lion outperforms Adam by achieving a better FID score and reducing the training compute by up to 2.3×. For autoregressive, masked language modeling, and fine-tuning, Lion exhibits a similar or better performance compared to Adam.

Our analysis of Lion reveals that its performance gain grows with the training batch size. It also requires a smaller learning rate than Adam due to the larger norm of the update produced by the sign function. Additionally, we examine the limitations of Lion and identify scenarios where its improvements are small or not statistically-significant.

The implementation of Lion is publicly available.

[cf. 1-bit Adam, PNM, parallel invention, signSGD]

--- https://super-memory.com/articles/programming.htm SuperMemo as a new tool increasing the productivity of a programmer. A case study: programming in Object Windows 2022-08-11 cs psychology/spaced-repetition --- https://www.ejlt.org/index.php/ejlt/article/view/320/424 2022-08-11 law psychology/spaced-repetition --- https://arxiv.org/abs/1802.04434#amazon signSGD: Compressed Optimization for Non-Convex Problems Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar 2018-02-13 2022-08-11 [("doi","10.48550/arXiv.1802.04434")] ai/nn cs/algorithm/information/compression

Training large neural networks requires distributing learning across multiple workers, where the cost of communicating gradients can be a bottleneck.

signSGD alleviates this problem by transmitting just the sign of each minibatch stochastic gradient. We prove that it can get the best of both worlds: compressed gradients and SGD-level convergence rate. The relative 𝓁1/𝓁2 geometry of gradients, noise and curvature informs whether signSGD or SGD is theoretically better suited to a particular problem.

On the practical side we find that the momentum counterpart of signSGD is able to match the accuracy and convergence speed of Adam on deep Imagenet models. We extend our theory to the distributed setting, where the parameter server uses majority vote to aggregate gradient signs from each worker enabling 1-bit compression of worker-server communication in both directions.

Using a theorem by Gauss we prove that majority vote can achieve the same reduction in variance as full precision distributed SGD.

Thus, there is great promise for sign-based optimization schemes to achieve fast communication and fast convergence.

Code to reproduce experiments is to be found at Github.

--- https://arxiv.org/abs/2103.17182 Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama 2021-03-31 2022-08-11 [("doi","10.48550/arXiv.2103.17182")] ai/nn

It is well-known that stochastic gradient noise (SGN) acts as implicit regularization for deep learning and is essentially important for both optimization and generalization of deep networks. Some works attempted to artificially simulate SGN by injecting random noise to improve deep learning. However, it turned out that the injected simple random noise cannot work as well as SGN, which is anisotropic and parameter-dependent.

For simulating SGN at low computational costs and without changing the learning rate or batch size, we propose the Positive-Negative Momentum (PNM) approach that is a powerful alternative to conventional Momentum in classic optimizers. The introduced PNM method maintains two approximate independent momentum terms. Then, we can control the magnitude of SGN explicitly by adjusting the momentum difference.

We theoretically prove the convergence guarantee and the generalization advantage of PNM over Stochastic Gradient Descent (SGD).

By incorporating PNM into the two conventional optimizers, SGD with Momentum and Adam, our extensive experiments empirically verified the advantage of the PNM-based variants over the corresponding conventional Momentum-based optimizers.

--- https://en.wikipedia.org/wiki/Clameur_de_haro Clameur de haro 2022-08-11 law --- https://www.palladiummag.com/2023/01/27/how-america-lost-the-atomic-age/ 2022-08-11 politics technology --- https://stanforddaily.com/2023/02/17/internal-review-found-falsified-data-in-stanford-presidents-alzheimers-research-colleagues-allege/ 2022-08-12 statistics/bias --- https://github.com/RootbeerComputer/backend-GPT 2022-08-12 ai/nn/transformer/gpt/codex --- https://www.palladiummag.com/2023/01/30/the-censor-that-ended-the-soviet-union/ 2022-08-12 politics --- https://arxiv.org/abs/2302.07863 BiLD: Big Little Transformer Decoder Sehoon Kim, Karttikeya Mangalam, Jitendra Malik, Michael W. Mahoney, Amir Gholami, Kurt Keutzer 2023-02-15 2023-02-15 [("doi","10.48550/arXiv.2302.07863")] ai/nn/transformer/gpt ai/nn/transformer/t5 reinforcement-learning/meta-learning

The recent emergence of Large Language Models based on the Transformer architecture has enabled dramatic advancements in the field of Natural Language Processing. However, these models have long inference latency, which limits their deployment, and which makes them prohibitively expensive for various real-time applications. The inference latency is further exacerbated by autoregressive generative tasks, as models need to run iteratively to generate tokens sequentially without leveraging token-level parallelization.

To address this, we propose Big Little Decoder (BiLD), a framework that can improve inference efficiency and latency for a wide range of text generation applications. The BiLD framework contains two models with different sizes that collaboratively generate text. The small model runs autoregressively to generate text with a low inference cost, and the large model is only invoked occasionally to refine the small model’s inaccurate predictions in a non-autoregressive manner.

To coordinate the small and large models, BiLD introduces two simple yet effective policies: (1) the fallback policy that determines when to hand control over to the large model; and (2) the rollback policy that determines when the large model needs to review and correct the small model’s inaccurate predictions.

To evaluate our framework across different tasks and models, we apply BiLD to various text generation scenarios encompassing machine translation on IWSLT 2017 De-En and WMT 2014 De-En, summarization on CNN/DailyMail, and language modeling on WikiText-2. On an NVIDIA Titan XP GPU, our framework achieves a speedup of up to 2.13× without any performance drop, and it achieves up to 2.38× speedup with only ~1 point degradation. Furthermore, our framework is fully plug-and-play as it does not require any training or modifications to model architectures.

Our code will be open-sourced.

--- https://www.reddit.com/r/ChatGPT/comments/10zavbv/extending_chatgpt_with_some_additional_internal/ 2022-08-12 ai/nn/transformer/gpt/inner-monologue --- https://forum.effectivealtruism.org/posts/H7xWzvwvkyywDAEkL/creating-a-database-for-base-rates 2022-08-12 statistics/prediction --- /doc/history/1992-demesquita.pdf War and the Fate of Regimes: A Comparative Analysis Bruce Bueno de Mesquita, Randolph M. Siverson, Gary Woller 1992-09-01 2022-08-12 [("doi","10.2307/1964127")] history politics

Governments are likely to be held accountable for the success or failure of their foreign policies. Consequently, we claim that international wars can, under specified conditions, have domestically instigated consequences for violent regime change in the political systems of the participants.

Drawing upon all international war participation between 1816 and 1975, we seek to answer the question, Do wars lead to violent changes of regime and if so, under what conditions? 3 hypotheses set out the expected associations of a nation’s initiator or target role in a war, the war outcome, and the costs of the war with domestically instigated violent changes of regime.

Direct relationships are found for all 3 and hold even against possible threats to their validity and robustness.

The results suggest that domestic politics play a larger role in national security policy than is generally believed by realist or neorealist theorists.

--- http://polyducks.co.uk/what-is-textmode/ 2022-08-12 design/typography --- https://blog.humphd.org/cheatgpt/ 2022-08-12 ai/nn/transformer/gpt/codex --- https://www.lesswrong.com/posts/u6KXXmKFbXfWzoAXn/a-circuit-for-python-docstrings-in-a-4-layer-attention-only 2022-08-12 ai/nn/transformer/attention ai/nn/transformer/gpt/codex --- https://www.reddit.com/r/DotA2/comments/beyilz/openai_live_updates_thread_lessons_on_how_to_beat/ 2022-08-12 ai/nn/adversarial reinforcement-learning/model-free/oa5 --- https://x.com/gfodor/status/1626270272314839041 2022-08-13 ai/nn/retrieval ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/non-fiction --- https://en.wikipedia.org/wiki/L4_microkernel_family L4 microkernel family 2022-08-13 cs/end-to-end-principle cs/security --- https://www.samdickie.me/writing/experiment-1-creating-a-landing-page-using-ai-tools-no-code 2022-08-13 ai/nn/diffusion ai/nn/transformer/gpt/codex ai/video/generation design --- https://news.ycombinator.com/item?id=34865460 2022-08-13 ai/nn/transformer/gpt/codex --- https://www.nytimes.com/2023/02/20/us/ketamine-telemedicine.html 2022-08-13 psychedelic --- https://x.com/tunguz/status/1628075460230885381 2022-08-13 ai/nn/transformer/gpt/codex --- /doc/politics/2016-lall.pdf How Multiple Imputation Makes a Difference Ranjit Lall 2016-08-22 2022-08-13 [("doi","10.1093/pan/mpw020")] politics sociology statistics/bias

Political scientists increasingly recognize that multiple imputation represents a superior strategy for analyzing missing data to the widely used method of listwise deletion. However, there has been little systematic investigation ofhowmultiple imputation affects existing empirical knowledge in the discipline.

This article presents the first large-scale examination of the empirical effects of substituting multiple imputation for listwise deletion in political science. The examination focuses on research in the major subfield of comparative and international political economy (CIPE) as an illustrative example. Specifically, I use multiple imputation to reanalyze the results of almost every quantitative CIPE study published during a recent five-year period inInternational OrganizationandWorld Politics, two of the leading subfield journals in CIPE.

The outcome is striking: in almost half of the studies, key results “disappear” (by conventional statistical-significance standards) when reanalyzed.

Figure 1: Preview of reanalysis. Notes: Bars correspond to the left y-axis and dashed lines to the right y-axis. The circular points connected by the lines represent averages for all articles published in a given year.

…I argue that, in addition to being highly inefficient, listwise deletion tends to produce biased inferences in CIPE because the pattern of missing values is not completely random. Most notably, poorer and less democratic countries are more likely to have missing data, causing listwise deletion to give rise to a particular selection problem that I call advanced democracy bias. Despite these problems, however, use of listwise deletion remains widespread in CIPE. A review of almost 100 CIPE studies recently published in 5 leading political science journals indicates that 90% continue to employ listwise deletion as their primary missing-data method, while only 5% have switched to multiple imputation. [The review covers all CIPE studies published in the American Political Science Review, the American Journal of Political Science, the British Journal of Political Science, International Organization, and World Politics between July 2007 and July 2012. The remaining 5% of studies employ another ad-hoc technique, such as averaging observed data or substituting zero for missing values. Worryingly, more than 3-quarters of studies—all of which used listwise deletion—were not explicit about how they dealt with missing data.]

Specifically, I use multiple imputation to reanalyze the results of almost every quantitative CIPE study published during a recent 5% period in International Organization and World Politics, two of the leading subfield journals in CIPE.4 The outcome of the reanalysis, previewed in Figure 1, is striking. In almost half of the studies, key results “disappear” when the main statistical analysis is re-estimated using multiply imputed data (shaded portion of bars, corresponding to left y-axis). That is, at least half of the regression coefficients on the key explanatory variable(s) that were previously statistically-significant at the 10% level either cease to be statistically-significant or experience a change in sign; alternatively, in the case of “negative” findings, at least half of the coefficients on the key explanatory variable(s) that were previously non-statistically-significant become statistically-significant (regardless of sign).5 The reanalysis also sheds light on the considerable scale of the missing-data problem in CIPE: an average of 48% of eligible observations are excluded from the main analysis due to listwise deletion (hollow circles, corresponding to right y-axis), resulting in the loss of 43% of available observed data (solid circles).

In addition to challenging the results of a number of prominent recent studies in CIPE, the article’s findings have important implications for quantitative work in other areas of political science, many of which are likely to be similarly ill suited to listwise deletion and have paid equally little attention to missing-data issues. In the concluding section, I offer some brief speculations on whether and how substituting multiple imputation for listwise deletion might affect empirical knowledge in different subfields.

--- https://x.com/lemonodor/status/1628270074074398720 2022-08-13 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue --- https://x.com/ThePrimeagen/status/1628047727866126336 2022-08-13 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/1909.06146 PubMedQA: A Dataset for Biomedical Research Question Answering Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W. Cohen, Xinghua Lu 2019-09-13 2022-08-13 [("doi","10.48550/arXiv.1909.06146")] ai/dataset ai/nn/transformer

We introduce PubMedQA, a novel biomedical question answering (QA) dataset collected from PubMed abstracts.

The task of PubMedQA is to answer research questions with "yes/no/maybe" (eg.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts.

PubMedQA has 1k expert-annotated, 61.2k unlabeled and 211.3k artificially generated QA instances. Each PubMedQA instance is composed of (1) a question which is either an existing research article title or derived from one, (2) a context which is the corresponding abstract without its conclusion, (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and (4) a yes/no/maybe answer which summarizes the conclusion.

PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their quantitative contents, is required to answer the questions.

Our best performing model, multi-phase fine-tuning of BioBERT with long answer bag-of-word statistics as additional supervision, achieves 68.1% accuracy, compared to single human performance of 78.0% accuracy and majority-baseline of 55.2% accuracy, leaving much room for improvement.

PubMedQA is publicly available at https://pubmedqa.github.io/.

--- https://www.lesswrong.com/posts/2JJtxitp6nqu6ffak/basic-facts-about-language-models-during-training-1 2022-08-14 ai/nn/transformer --- https://x.com/D_Rod_Tweets/status/1628030272745746432 2022-08-14 ai/nn/retrieval ai/nn/transformer/gpt reinforcement-learning/safe --- https://angrystaffofficer.com/2017/02/20/warfighter-toad-hall/ 2022-08-14 fiction/humor history --- https://arxiv.org/abs/2104.09667 Manipulating SGD with Data Ordering Attacks Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A. Erdogdu, Ross Anderson 2021-04-19 2022-08-14 [("doi","10.48550/arXiv.2104.09667")] ai/nn/adversarial

Machine learning is vulnerable to a wide variety of attacks. It is now well understood that by changing the underlying data distribution, an adversary can poison the model trained with it or introduce backdoors.

In this paper we present a novel class of training-time attacks that require no changes to the underlying dataset or model architecture, but instead only change the order in which data are supplied to the model. In particular, we find that the attacker can either prevent the model from learning, or poison it to learn behaviors specified by the attacker. Furthermore, we find that even a single adversarially-ordered epoch can be enough to slow down model learning, or even to reset all of the learning progress. Indeed, the attacks presented here are not specific to the model or dataset, but rather target the stochastic nature of modern learning procedures.

We extensively evaluate our attacks on computer vision and natural language benchmarks to find that the adversary can disrupt model training and even introduce backdoors.

…This attack is realistic and can be instantiated in several ways. The attack code can be infiltrated into: the operating system handing file system requests; the disk handling individual data accesses; the software that determines the way random data sampling is performed; the distributed storage manager; or the machine learning pipeline itself handling prefetch operations. That is a substantial attack surface, and for large models these components may be controlled by different principals. The attack is also very stealthy. The attacker does not add any noise or perturbation to the data.There are no triggers or backdoors introduced into the dataset. All of the data points are natural. In two of 4 variants the attacker uses the whole dataset and does not oversample any given point, i.e. the sampling is without replacement. This makes it difficult to deploy simple countermeasures.

--- https://www.afterbabel.com/p/social-media-mental-illness-epidemic 2022-08-14 psychiatry/anxiety psychiatry/depression sociology/technology --- https://www.nature.com/articles/d41586-023-00506-2 2022-08-14 statistics/peer-review --- https://x.com/DrNadolsky/status/1628436158400135169 2022-08-14 longevity/glp/semaglutide --- https://en.wikipedia.org/wiki/Beam_search Beam search 2022-08-14 ai/nn/sampling --- https://en.wikipedia.org/wiki/Breadth-first_search Breadth-first search 2022-08-14 ai/nn/sampling --- https://en.wikipedia.org/wiki/Viterbi_algorithm Viterbi algorithm 2022-08-14 ai/nn/sampling --- https://www.trentonbricken.com/Tail-Free-Sampling/ 2022-08-14 ai/nn/sampling --- https://github.com/karpathy/char-rnn/issues/138 2022-08-15 ai/nn/sampling --- https://news.ycombinator.com/item?id=21335120 2022-08-15 ai/nn/sampling --- /gpt-2#improvements 2022-08-15 ai/nn/sampling --- https://datajenius.com/2022/02/12/the-effect-of-various-text-generation-methods-on-the-outputs-of-gpt-2/ 2022-08-15 ai/nn/sampling --- https://arxiv.org/abs/2103.07649 Improving Diversity of Neural Text Generation via Inverse Probability Weighting Xinran Zhang, Maosong Sun, Jiafeng Liu, Xiaobing Li 2021-03-13 2022-08-15 [("doi","10.48550/arXiv.2103.07649")] ai/nn/sampling

The neural text generation suffers from the text degeneration issue such as repetition. Traditional stochastic sampling methods only focus on truncating the unreliable “tail” of the distribution, and do not address the “head” part, which we show might contain tedious or even repetitive candidates with high probability that lead to repetition loops. They also do not consider the issue that human text does not always favor high-probability words.

Inspired by these, in this work we propose a heuristic sampling method. We propose to use interquartile range of the predicted distribution to determine the “head” part, then permute and rescale the “head” with inverse probability. This aims at decreasing the probability for the tedious and possibly repetitive candidates with higher probability, and increasing the probability for the rational but more surprising candidates with lower probability. The proposed algorithm provides a reasonable permutation on the predicted distribution which enhances diversity without compromising rationality of the distribution.

We use pre-trained language model to compare our algorithm with traditional methods.

Results show that our algorithm can effectively increase the diversity of generated samples while achieving close resemblance to human text.

--- https://200wordrpg.github.io/ Design a role-playing game using 200 words or less. 2022-08-15 ai/nn/transformer/gpt fiction/text-game --- https://gptprompts.wikidot.com/logic:math Math: OpenAI API can do some math out of the gate, but most math it seems it has to learn. Many times, the numbers that it spits out are just random. However, including different priming prompts can result in decent results. 2022-08-15 ai/nn/transformer/gpt math --- https://andrewmayne.com/2020/06/13/openai-api-alchemy-smart-formatting-and-code-creation/ OpenAI API Alchemy: Smart Formatting and Code Creation 2022-08-15 ai/nn/transformer/gpt/codex --- https://andrewmayne.com/2020/06/24/open-ai-alchemy-emoji-storytelling/ OpenAI API Alchemy: Emoji storytelling 🤖 2022-08-15 ai/nn/transformer/gpt ai/text-style-transfer --- https://workshop2015.iwslt.org/downloads/IWSLT_2015_RP_13.pdf 2022-08-15 ai/nn/sampling --- /doc/ai/poetry/2020-barrio.pdf Writing the Next American Hit: Using GPT-2 to Explore the Possibility of Creating Successful AI-Generated Song Lyrics Possibility of Creating Successful AI-Generated Song Lyric Barrio 2020 2022-08-15 ai/music ai/nn/transformer/gpt/2 ai/poetry --- https://en.wikipedia.org/wiki/Antithetic_variates Antithetic variates 2022-08-16 ai/nn/sampling statistics/probability --- https://x.com/amasad/status/1628546489843863555 2022-08-16 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue --- https://x.com/D_Rod_Tweets/status/1628449917898264576 2022-08-16 ai/nn/retrieval ai/nn/transformer/gpt/inner-monologue --- https://www.buildt.ai/blog/viral-ripout 2022-08-16 ai/nn/retrieval ai/nn/transformer/gpt --- https://www.economist.com/science-and-technology/2023/02/22/there-is-a-worrying-amount-of-fraud-in-medical-research 2022-08-16 statistics/bias --- https://en.wikipedia.org/wiki/Quasi-Monte_Carlo_method Quasi-Monte Carlo method 2022-08-16 ai/nn/sampling statistics/probability --- https://en.wikipedia.org/wiki/Low-discrepancy_sequence Low-discrepancy sequence 2022-08-16 ai/nn/sampling statistics/probability --- https://x.com/MikePFrank/status/1628539680969850880 2022-08-16 ai/nn/transformer/gpt/poetry --- https://x.com/sdrinf/status/1629084909422931969 2022-08-16 ai/nn/transformer/gpt/calibration --- https://www.reddit.com/r/ChatGPT/comments/11anct1/its_easy_to_give_chatgpt_a_bonafide_consciousness/ 2022-08-16 ai/nn/transformer/gpt/inner-monologue --- https://arxiv.org/abs/2204.06974 Planting Undetectable Backdoors in Machine Learning Models Shafi Goldwasser, Michael P. Kim, Vinod Vaikuntanathan, Or Zamir 2022-04-14 2022-08-17 [("doi","10.48550/arXiv.2204.06974")] ai/nn/adversarial cs/cryptography

Given the computational cost and technical expertise required to train machine learning models, users may delegate the task of learning to a service provider. We show how a malicious learner can plant an undetectable backdoor into a classifier. On the surface, such a backdoored classifier behaves normally, but in reality, the learner maintains a mechanism for changing the classification of any input, with only a slight perturbation. Importantly, without the appropriate “backdoor key”, the mechanism is hidden and cannot be detected by any computationally-bounded observer. We demonstrate two frameworks for planting undetectable backdoors, with incomparable guarantees.

First, we show how to plant a backdoor in any model, using digital signature schemes. The construction guarantees that given black-box access to the original model and the backdoored version, it is computationally infeasible to find even a single input where they differ. This property implies that the backdoored model has generalization error comparable with the original model. Second, we demonstrate how to insert undetectable backdoors in models trained using the Random Fourier Features (RFF) learning paradigm or in Random ReLU networks. In this construction, undetectability holds against powerful white-box distinguishers: given a complete description of the network and the training data, no efficient distinguisher can guess whether the model is “clean” or contains a backdoor.

Our construction of undetectable backdoors also sheds light on the related issue of robustness to adversarial examples. In particular, our construction can produce a classifier that is indistinguishable from an “adversarially robust” classifier, but where every input has an adversarial example! In summary, the existence of undetectable backdoors represent a theoretical roadblock to certifying adversarial robustness.

--- https://www.cole-k.com/2023/02/21/tiny-games-hs/ 2022-08-17 cs/haskell --- https://arxiv.org/abs/1506.02626#nvidia Iterative Magnitude Pruning: Learning both Weights and Connections for Efficient Neural Networks Song Han, Jeff Pool, John Tran, William J. Dally 2015-06-08 2022-08-17 [("doi","10.48550/arXiv.1506.02626")] ai/nn/sparsity/pruning

Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture before training starts; as a result, training cannot improve the architecture.

To address these limitations, we describe a method (iterative magnitude pruning) to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important connections. Our method prunes redundant connections using a three-step method. First, we train the network to learn which connections are important. Next, we prune the unimportant connections. Finally, we retrain the network to fine tune the weights of the remaining connections.

On the ImageNet dataset, our method reduced the number of parameters of AlexNet by a factor of 9×, from 61 million to 6.7 million, without incurring accuracy loss. Similar experiments with VGG-16 found that the number of parameters can be reduced by 13×, from 138 million to 10.3 million, again with no loss of accuracy.

--- https://github.com/sanjeevanahilan/nanoChatGPT 2022-08-17 ai/nn/transformer/gpt reinforcement-learning/model reinforcement-learning/preference-learning --- https://en.wikipedia.org/wiki/Transclusion Transclusion 2022-08-17 design --- https://arxiv.org/abs/2102.00554 Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste 2021-01-31 2022-08-17 [("doi","10.48550/arXiv.2102.00554")] ai/nn/sparsity

The growing energy and performance costs of deep learning have driven the community to reduce the size of neural networks by selectively pruning components. Similarly to their biological counterparts, sparse networks generalize just as well, if not better than, the original dense networks. Sparsity can reduce the memory footprint of regular networks to fit mobile devices, as well as shorten training time for ever growing networks. In this paper, we survey prior work on sparsity in deep learning and provide an extensive tutorial of sparsification for both inference and training.

We describe approaches to remove and add elements of neural networks, different training strategies to achieve model sparsity, and mechanisms to exploit sparsity in practice. Our work distills ideas from more than 300 research papers and provides guidance to practitioners who wish to use sparsity today, as well as to researchers whose goal is to push the frontier forward.

We include the necessary background on mathematical methods in sparsification, describe phenomena such as early structure adaptation, the intricate relations between sparsity and the training process, and show techniques for achieving acceleration on real hardware. We also define a metric of pruned parameter efficiency that could serve as a baseline for comparison of different sparse networks.

We close by speculating on how sparsity can improve future workloads and outline major open problems in the field.

--- https://www.vectorsofmind.com/p/the-snake-cult-of-consciousness 2022-08-17 genetics/selection/natural/human philosophy/mind psychedelic --- https://en.wikipedia.org/wiki/Sapient_paradox Sapient paradox 2022-08-17 genetics/selection/natural/human philosophy/mind --- https://www.vectorsofmind.com/p/the-unreasonable-effectiveness-of 2022-08-17 genetics/selection/natural/human philosophy/mind --- https://arxiv.org/abs/1906.10652 Monte Carlo Gradient Estimation in Machine Learning Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih 2019-06-25 2022-08-17 [("doi","10.48550/arXiv.1906.10652")] ai reinforcement-learning statistics/probability

This paper is a broad and accessible survey of the methods we have at our disposal for Monte Carlo gradient estimation in machine learning and across the statistical sciences: the problem of computing the gradient of an expectation of a function with respect to parameters defining the distribution that is integrated; the problem of sensitivity analysis. In machine learning research, this gradient problem lies at the core of many learning problems, in supervised, unsupervised and reinforcement learning.

We will generally seek to rewrite such gradients in a form that allows for Monte Carlo estimation, allowing them to be easily and efficiently used and analysed.

We explore 3 strategies–the pathwise [reparameterization trick], score function, and measure-valued gradient estimators–exploring their historical development, derivation, and underlying assumptions. We describe their use in other fields, show how they are related and can be combined, and expand on their possible generalizations.

Wherever Monte Carlo gradient estimators have been derived and deployed in the past, important advances have followed. A deeper and more widely-held understanding of this problem will lead to further advances, and it is these advances that we wish to support.

--- https://www.bbc.com/future/article/20171206-the-fascinating-reason-why-clowns-paint-their-faces-on-eggs 2022-08-17 economics/copyright --- https://en.wikipedia.org/wiki/Spontaneous_order Spontaneous order 2022-08-18 economics politics --- https://goodreason.substack.com/p/maybe-treating-housing-as-an-investment 2022-08-18 economics/georgism --- https://x.com/StoreyDexter/status/1629217956327526400 2022-08-18 ai/nn/transformer/gpt reinforcement-learning/safe --- https://x.com/StoreyDexter/status/1629217958965792770 2022-08-18 ai/nn/transformer/gpt reinforcement-learning/safe --- https://x.com/StoreyDexter/status/1629217962962874369 2022-08-18 ai/nn/transformer/gpt reinforcement-learning/safe --- https://x.com/ryancbriggs/status/1630183763320684547 2022-08-18 statistics/peer-review --- https://www.lesswrong.com/posts/jvPe6nz3t49q8a8BM/moridinamael-s-shortform-14?commentId=CCtEkDFCZdST6PJsF 2022-08-18 ai/text-style-transfer --- https://www.medrxiv.org/content/10.1101/2023.02.16.23286045.full Genome-wide Association Study of Traumatic Brain Injury in U.S. Military Veterans Enrolled in the VA Million Veteran Program Victoria C. Merritt, Adam X. Maihofer, Marianna Gasperi, Elizabeth Ketema, Catherine Chanfreau-Coffinier, Murray B. Stein, Matthew S. Panizzon, Richard L. Hauger, Mark W. Logue, Lisa Delano-Wood, Caroline M. Nievergelt, V. A. Million Veteran Program 2023-02-17 2023-02-17 [("doi","10.1101/2023.02.16.23286045")] genetics/heritable/correlation psychiatry/traumatic-brain-injury psychology/personality

Large-scale genetic studies of traumatic brain injury (TBI) are lacking; thus, our understanding of the influence of genetic factors on TBI risk and recovery is incomplete.

This study aimed to conduct a genome-wide association study (GWAS) of TBI in VA Million Veteran Program enrollees. Participants included a multi-ancestry cohort (European, African, and Hispanic ancestries; n = 304,485; 111,494 TBI cases, 192,991 controls). TBI was assessed using MVP survey data and ICD codes from the Veterans Health Administrations electronic health record. GWAS was performed using logistic regression in PLINK, and meta-analyzed in METAL. FUMA was used for post-GWAS analysis. Genomic structural equation modeling (gSEM) was conducted to investigate underlying genetic associations with TBI, and bivariate MiXeR was used to estimate phenotype specific and shared polygenicity.

SNP-based heritability was 0.060 (SE=0.004, p = 7.83 × 10−66). GWAS analysis identified 15 genome-wide statistically-significant (GWS) loci at p < 5 × 10−8. Gene-based analyses revealed 14 gene-wide statistically-significant genes, including NCAM1, APOE, FTO, and FOXP2. Gene tissue expression analysis identified the brain as statistically-significantly enriched, particularly in the frontal cortex, anterior cingulate cortex, and nucleus accumbens. Genetic correlations with TBI were statistically-significant for risk-taking behaviors and psychiatric disorders, but generally not statistically-significant for the neurocognitive variables investigated. gSEM analysis revealed stronger associations with risk-taking traits than with psychiatric traits. Finally, the genetic architecture of TBI was similar to polygenic psychiatric disorders. Neurodegenerative disorders including Alzheimer’s and Parkinsons disease showed much less polygenicity, however, the proportion of shared variance with TBI was high.

This first well-powered GWAS of TBI identified 15 loci including genes relevant to TBI biology, and showed that TBI is a heritable trait with comparable genetic architecture and high genetic correlation with psychiatric traits. Our findings set the stage for future TBI GWASs that focus on injury severity and diversity and chronicity of symptom sequelae.

--- https://arxiv.org/abs/2302.12433 ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics Zhangir Azerbayev, Bartosz Piotrowski, Hailey Schoelkopf, Edward W. Ayers, Dragomir Radev, Jeremy Avigad 2023-02-24 2023-02-24 [("doi","10.48550/arXiv.2302.12433")] ai/nn/retrieval ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/codex dataset math

[model, data; Twitter] We introduce ProofNet, a benchmark for autoformalization and formal proving of undergraduate-level mathematics.

The ProofNet benchmarks consists of 371 examples, each consisting of a formal theorem statement in Lean 3, a natural language theorem statement, and a natural language proof. The problems are primarily drawn from popular undergraduate pure mathematics textbooks and cover topics such as real analysis & complex analysis, linear algebra, abstract algebra, and topology.

We intend for ProofNet to be a challenging benchmark that will drive progress in autoformalization and automatic theorem proving. We report baseline results on statement autoformalization via in-context learning.

Moreover, we introduce two novel statement autoformalization methods: prompt retrieval & distilled backtranslation.

--- https://www.theguardian.com/technology/2023/feb/26/chatgpt-generated-crochet-pattern-results 2022-08-18 ai/nn/transformer/gpt/non-fiction --- https://arxiv.org/abs/2302.12441 MUX-PLMs: Pre-training Language Models with Data Multiplexing Vishvak Murahari, Ameet Deshpande, Carlos E. Jimenez, Izhak Shafran, Mingqiu Wang, Yuan Cao, Karthik Narasimhan 2023-02-24 2023-02-24 [("doi","10.48550/arXiv.2302.12441")] ai/nn/sampling ai/nn/sparsity ai/nn/transformer

Data multiplexing is a recently proposed method for improving a model’s inference efficiency by processing multiple instances simultaneously using an ordered representation mixture. Prior work on data multiplexing only used task-specific Transformers without any pre-training, which limited their accuracy and generality.

In this paper, we develop pre-trained multiplexed language models (MUX-PLMs) that can be widely finetuned on any downstream task. Our approach includes a three-stage training procedure and novel multiplexing and demultiplexing modules for improving throughput and downstream task accuracy.

We demonstrate our method on BERT and ELECTRA pre-training objectives, with our MUX-BERT and MUX-ELECTRA models achieving 2×/5× inference speedup with a 2–4% drop in absolute performance on GLUE and 1–2% drop on token-level tasks.

--- https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup 2022-08-19 ai/nn/transformer/clip --- https://www.pugetsystems.com/mineral-oil-pc/ 2022-08-19 cs/hardware economics/copyright --- https://www.tomshardware.com/reviews/strip-fans,1203.html 2022-08-19 cs/hardware --- https://terrytao.wordpress.com/career-advice/theres-more-to-mathematics-than-rigour-and-proofs/ 2022-08-19 math philosophy/epistemology --- https://openreview.net/forum?id=UdgtTVTdswg DataMUX: Data Multiplexing for Neural Networks Vishvak Murahari, Carlos E. Jimenez, Runzhe Yang, Karthik R. Narasimhan 2023-01-13 2023-01-13 ai/nn/fully-connected ai/nn/sampling ai/nn/sparsity ai/nn/transformer

We present data multiplexing (DataMUX)—a technique that enables deep neural networks to process multiple inputs simultaneously using a single compact representation and dramatically improves inference throughput

In this paper, we introduce data multiplexing (DataMUX), a technique that enables deep neural networks to process multiple inputs simultaneously using a single compact representation. DataMUX demonstrates that neural networks are capable of generating accurate predictions over mixtures of inputs, resulting in increased inference throughput with minimal extra memory requirements. Our approach uses two key components—(1) a multiplexing layer that performs a fixed linear transformation to each input before combining them to create a \"mixed\" representation of the same size as a single input, which is then processed by the base network, and (2) a demultiplexing layer that converts the base network’s output back into independent representations before producing predictions for each input.

We show the viability of DataMUX for different architectures (Transformers, and to a much lesser extent MLPs and CNNs) across 6 different tasks spanning sentence classification, named entity recognition and image classification.

For instance, DataMUX for Transformers can multiplex up to 20×/40× inputs, achieving up to 11×/18× increase in inference throughput with absolute performance drops of <2% and <4% respectively compared to a vanilla Transformer on MNLI, a natural language inference task.

We also provide a theoretical construction for multiplexing in self-attention networks and analyze the effect of various design elements in DataMUX.

[Keywords: neural networks, multiplexing, efficient inference]

--- http://www.ranum.com/security/computer_security/editorials/dumb/ 2022-08-19 cs/security --- https://x.com/emollick/status/1630472741127180288 2022-08-19 ai/nn/retrieval ai/nn/transformer/gpt/non-fiction --- https://x.com/repligate/status/1630593115407937536 2022-08-19 ai/nn/retrieval ai/nn/transformer/gpt reinforcement-learning/safe --- https://x.com/adiwyner/status/1629980541716922369 2022-08-19 ai/nn/transformer/gpt/inner-monologue math --- https://ianbicking.org/blog/2023/02/world-building-with-gpt.html 2022-08-19 ai/nn/transformer/gpt/fiction --- https://x.com/repligate/status/1630597868804243456 2022-08-20 ai/nn/retrieval ai/nn/transformer/gpt/fiction reinforcement-learning/safe --- https://x.com/repligate/status/1630594066189623296 2022-08-20 ai/nn/retrieval ai/nn/transformer/gpt/fiction reinforcement-learning/safe --- https://publicdomainreview.org/collection/scatalogic-rites 2022-08-20 history/public-domain-review philosophy/religion sociology --- https://web.archive.org/web/20240102075620/https://www.jailbreakchat.com/ 2022-08-20 ai/nn/adversarial ai/nn/transformer/gpt reinforcement-learning/safe --- https://www.nasa.gov/history/rogersrep/v2appf.htm Appendix F: Personal Observations on the Reliability of the Shuttle 2022-08-20 cs/security philosophy/epistemology --- https://www.lesswrong.com/posts/vMELyahKkbya5efRE/my-experience-with-loving-kindness-meditation 2022-08-20 psychiatry/meditation --- https://www.reddit.com/r/StableDiffusion/comments/11f4zgt/remixing_memes_with_multi_controlnet_is/ 2022-08-20 ai/nn/diffusion fiction/humor --- https://x.com/ZachWeiner/status/1630933323294859264 2022-08-20 ai/nn/transformer/gpt/fiction fiction/humor --- https://www.theguardian.com/society/2023/feb/28/dinner-with-proust-how-alzheimers-caregivers-are-pulled-into-their-patients-worlds 2022-08-20 psychiatry/alzheimers --- https://www.youtube.com/watch?v=6bqBsHSwPgw 2022-08-20 psychology/vision --- https://www.youtube.com/watch?v=GVT3WUa-48Y 2022-08-21 ai/anime ai/nn/diffusion --- https://www.youtube.com/watch?v=_9LX9HSQkWo&t=862s 2022-08-21 ai/anime ai/nn/diffusion --- https://arxiv.org/abs/1910.13540 Small-GAN: Speeding Up GAN Training Using Core-sets Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena 2019-10-29 2022-08-21 [("doi","10.48550/arXiv.1910.13540")] ai/nn/gan reinforcement-learning/exploration/active-learning

Recent work by Brock et al 2018 suggests that Generative Adversarial Networks (GANs) benefit disproportionately from large mini-batch sizes. Unfortunately, using large batches is slow and expensive on conventional hardware. Thus, it would be nice if we could generate batches that were effectively large though actually small.

In this work, we propose a method to do this, inspired by the use of Coreset-selection in active learning.

When training a GAN, we draw a large batch of samples from the prior and then compress that batch using Coreset-selection. To create effectively large batches of ‘real’ images, we create a cached dataset of Inception CNN activations of each training image, randomly project them down to a smaller dimension, and then use Coreset-selection on those projected activations at training time.

We conduct experiments showing that this technique substantially reduces training time and memory usage for modern GAN variants, that it reduces the fraction of dropped modes in a synthetic dataset, and that it allows GANs to reach a new state-of-the-art in anomaly detection.

--- https://x.com/amanrsanger/status/1631029716550549504 2022-08-21 ai/nn/transformer/gpt/codex --- https://x.com/labenz/status/1628847171855388672 2022-08-21 ai/nn/transformer/gpt/calibration --- https://nickarner.com/notes/llm-powered-assistants-for-complex-interfaces-february-26-2023/ 2022-08-21 ai/nn/transformer/gpt/codex design --- https://www.quantamagazine.org/cryptographers-show-how-to-hide-invisible-backdoors-in-ai-20230302/ 2022-08-21 ai/nn/adversarial cs/cryptography --- /doc/sociology/2015-saintpaul.pdf Genes, Legitimacy And Hypergamy: Another Look At The Economics Of Marriage Gilles Saint-Paul 2015-12-01 2022-08-21 [("doi","10.1017/dem.2015.8")] economics sociology

[written 2008] In order to credibly “sell” legitimate children to their spouse, women must forego more attractive mating opportunities. This paper derives the implications of this observation for the pattern of matching in marriage markets, the dynamics of human capital accumulation, and the evolution of the gene pool.

A key consequence of the trade-off faced by women is that marriage markets will naturally tend to behypergamous—that is, a marriage is more likely to be beneficial to both parties relative to remaining single, the greater the man’s human capital, and the lower the woman’s human capital. As a consequence, it is shown that the equilibrium can only be of two types. In the “Victorian” type, all agents marry somebody of the same rank in the distribution of income. In the “Sex and the City” (SATC) type, women marry men who are better ranked than themselves. There is a mass of unmarried men at the bottom of the distribution of human capital, and a mass of single women at the top of that distribution. It is shown that the economy switches from a Victorian to an SATC equilibrium as inequality goes up.

The model sheds light on how marriage affects the returns to human capital for men and women. Absent marriage, these returns are larger for women than for men but the opposite may occur if marriage prevails. Finally, it is shown that the institution of marriage may or may not favour human capital accumulation depending on how genes affect one’s productivity at accumulating human capital.

--- https://talesofsyn.com/posts/creating-isometric-rpg-game-backgrounds 2022-08-21 ai/nn/diffusion design --- https://x.com/emollick/status/1631027584392634368 2022-08-21 ai/nn/transformer/gpt/fiction ai/text-style-transfer fiction/science-fiction --- https://x.com/emollick/status/1631145330052943872 2022-08-21 ai/nn/transformer/gpt/fiction ai/text-style-transfer fiction/science-fiction --- https://x.com/emollick/status/1631153312056705024 2022-08-22 ai/nn/transformer/gpt/fiction ai/text-style-transfer fiction/science-fiction --- https://www.yitay.net/blog/flan-ul2-20b 2022-08-22 ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 --- /doc/psychology/writing/2023-schweisfurth.pdf Unexpected Interruptions, Idle Time, and Creativity: Evidence from a Natural Experiment Tim G. Schweisfurth, Anne Greul 2023-02-27 2023-02-27 [("doi","10.1287/orsc.2023.1660")] economics psychology/writing

Interruptions are common in organizational life and last from seconds and minutes to hours and days. We rely on a quantitative abductive strategy to determine how extended work interruptions shape employees’ creativity.

We start by studying how surprising interruptions that cause idle time affect employees’ creative performance. We do so by exploiting a natural experiment—a supply chain shortage that caused unexpected stops in production plants—to show that:

individuals exposed to such an interruption produce 58% more ideas than uninterrupted employees in the 3 weeks after the interruption. We corroborate this effect in a replication and extend it to idea quality.

Investigating the effect’s causes, we then show that we do not find the same effects for two other interruption types: for unexpected interruptions without idle time (ie. intrusions), we find a negative effect on creative performance because employees forcefully disengage from their work and switch their attention to the interrupting task. For expected interruptions with idle time (ie. planned breaks), we also find no positive effect on creative performance because employees discretionally disengage from work and focus on non-work and leisure goals.

We consider and evaluate 3 different theoretical explanations for our findings: attention residue, cognitive stimulation, and recovery. We end our abductive process by putting attention residue forward as the most likely explanation.

Finally, we suggest 3 propositions based on our findings and discuss our contributions to the literature on interruptions and creativity in organizations.

--- https://x.com/DrJimFan/status/1631709224387624962 2022-08-22 ai/nn/transformer/gpt reinforcement-learning/safe --- https://arxiv.org/abs/2303.00848 Understanding the Diffusion Objective as a Weighted Integral of ELBOs Diederik P. Kingma, Ruiqi Gao 2023-03-01 2023-03-01 [("doi","10.48550/arXiv.2303.00848")] ai/nn/diffusion

Diffusion models in the literature are optimized with various objectives that are special cases of a weighted loss, where the weighting function specifies the weight per noise level. Uniform weighting corresponds to maximizing the ELBO, a principled approximation of maximum likelihood. In current practice diffusion models are optimized with non-uniform weighting due to better results in terms of sample quality. In this work we expose a direct relationship between the weighted loss (with any weighting) and the ELBO objective.

We show that the weighted loss can be written as a weighted integral of ELBOs, with one ELBO per noise level.

[Only] if the weighting function is monotonic, then the weighted loss is a likelihood-based objective: it maximizes the ELBO under simple data augmentation, namely Gaussian noise perturbation.

Our main contribution is a deeper theoretical understanding of the diffusion objective, but we also performed some experiments comparing monotonic with non-monotonic weightings, finding that monotonic weighting performs competitively with the best published results.

--- https://web.archive.org/web/20140926161932/https://en.chessbase.com/post/komodo-8-the-smartphone-vs-desktop-challenge 2022-08-22 economics/experience-curve reinforcement-learning/chess --- https://arxiv.org/abs/2302.12422#nvidia MimicPlay: Long-Horizon Imitation Learning by Watching Human Play Chen Wang, Linxi Fan, Jiankai Sun, Ruohan Zhang, Li Fei-Fei, Danfei Xu, Yuke Zhu, Anima Anandkumar 2023-02-24 2023-02-24 [("doi","10.48550/arXiv.2302.12422")] reinforcement-learning/exploration reinforcement-learning/imitation-learning reinforcement-learning/model reinforcement-learning/robot

[Twitter] Imitation Learning from human demonstrations is a promising paradigm to teach robots manipulation skills in the real world, but learning complex long-horizon tasks often requires an unattainable amount of demonstrations. To reduce the high data requirement, we resort to human play data—video sequences of people freely interacting with the environment using their hands. We hypothesize that even with different morphologies, human play data contain rich and salient information about physical interactions that can readily facilitate robot policy learning.

Motivated by this, we introduce a hierarchical learning framework named MimicPlay that learns latent plans from human play data to guide low-level visuomotor control trained on a small number of teleoperated demonstrations.

With systematic evaluations of 14 long-horizon manipulation tasks in the real world [using a Franka Emika robot arm], we show that MimicPlay dramatically outperforms state-of-the-art imitation learning methods in task success rate, generalization ability, and robustness to disturbances.

More details and video results could be found at our homepage.

--- https://www.vogue.com/article/ballet-essay-excerpt-dont-think-dear 2022-08-22 sociology/preference-falsification --- https://en.wikipedia.org/wiki/Mimesis Mimesis 2022-08-22 sociology/preference-falsification --- https://www.palladiummag.com/2023/03/03/bill-gates-has-perfected-managerial-philanthropy/ 2022-08-22 philosophy/ethics politics --- https://www.nytimes.com/2023/03/03/science/chernobyl-dogs-dna.html 2022-08-22 genetics/heritable/dog --- https://www.science.org/doi/10.1126/sciadv.ade2537 2022-08-23 genetics/heritable/dog --- https://www.newyorker.com/magazine/1994/01/10/e-mail-from-bill-gates 2022-08-23 technology --- https://en.wikipedia.org/wiki/Jacques_Paul_Migne Jacques Paul Migne 2022-08-23 economics/copyright --- https://x.com/emollick/status/1632176118395547648 2022-08-23 ai/nn/transformer/gpt/fiction ai/text-style-transfer fiction/humor fiction/science-fiction --- https://www.biorxiv.org/content/10.1101/2022.09.27.508760.full Incorporating natural language into vision models improves prediction and understanding of higher visual cortex Aria Y. Wang, Kendrick Kay, Thomas Naselaris, Michael J. Tarr, Leila Wehbe 2022-09-29 2022-09-29 [("doi","10.1101/2022.09.27.508760")] ai/nn/transformer/clip psychology/neuroscience

We hypothesize that high-level visual representations contain more than the representation of individual categories: they represent complex semantic information inherent in scenes that is most relevant for interaction with the world. Consequently, multimodal models such as Contrastive Language-Image Pre-training (CLIP) which construct image embeddings to best match embeddings of image captions should better predict neural responses in visual cortex, since image captions typically contain the most semantically relevant information in an image for humans.

We extracted image features using CLIP, which encodes visual concepts with supervision from natural language captions. We then used voxel-wise encoding models based on CLIP features to predict brain responses to real-world images from the Natural Scenes Dataset. CLIP explains up to R2 = 78% of variance in stimulus-evoked responses from individual voxels in the held out test data.

CLIP also explains greater unique variance in higher-level visual areas compared to models trained only with image/label pairs (ImageNet trained ResNet) or text (BERT). Visualizations of model embeddings and Principal Component Analysis (PCA) reveal that, with the use of captions, CLIP captures both global and fine-grained semantic dimensions represented within visual cortex.

Based on these novel results, we suggest that human understanding of their environment form an important dimension of visual representation.

--- https://x.com/MParakhin/status/1632087709060825088 Mikhail Parakhin 2022-08-23 ai/nn/transformer/gpt/inner-monologue --- https://x.com/perrymetzger/status/1632004276883947520 2022-08-23 ai/nn/transformer/gpt/codex --- https://x.com/emollick/status/1632080867932749825 2022-08-23 ai/nn/transformer/gpt/fiction ai/text-style-transfer fiction/humor --- https://x.com/emollick/status/1632110647726022659 2022-08-23 ai/nn/transformer/gpt/fiction ai/text-style-transfer fiction/humor --- https://x.com/iamtrickyvik/status/1632277910487990272 2022-08-23 ai/nn/transformer/gpt/fiction ai/text-style-transfer fiction/humor --- https://humanvarieties.org/2023/02/28/how-well-personality-traits-predict-social-outcomes-well-its-complicated/ 2022-08-24 iq psychology/personality/conscientiousness --- https://x.com/colin_fraser/status/1630763222671454208 2022-08-24 ai/nn/transformer/gpt cs/security reinforcement-learning/safe --- https://www.wired.com/story/why-the-floppy-disk-just-wont-die/ 2022-08-24 cs/hardware economics/automation --- https://ascii.textfiles.com/archives/5509 2022-08-24 cs/linkrot/archiving --- https://analyticsindiamag.com/when-chatgpt-attempted-upsc-exam/ 2022-08-24 ai/nn/transformer/gpt --- https://asteriskmag.com/issues/2/my-primal-scream-of-rage-the-big-alcohol-study-that-didn-t-happen 2022-08-24 statistics/bias --- https://tanoshimi.xyz/2016/11/29/yes-sadpanda-is-one-of-my-sources/ 2022-08-24 anime fiction/science-fiction --- https://www.reddit.com/r/cableporn/ 2022-08-24 cs/hardware design --- https://nepp.nasa.gov/files/27631/NSTD87394A.pdf 2022-08-24 cs/hardware design --- https://en.wikipedia.org/wiki/Cable_lacing Cable lacing 2022-08-24 cs/hardware design --- https://www.the100.ci/2023/03/07/non-representative-samples-what-could-possibly-go-wrong/ 2022-08-24 psychology statistics/bias statistics/causality --- https://arxiv.org/abs/2109.09115 Do Long-Range Language Models Actually Use Long-Range Context? Simeng Sun, Kalpesh Krishna, Andrew Mattarella-Micke, Mohit Iyyer 2021-09-19 2022-08-25 [("doi","10.48550/arXiv.2109.09115")] ai/nn/transformer/attention/recurrent

Language models are generally trained on short, truncated input sequences, which limits their ability to use discourse-level information present in long-range context to improve their predictions. Recent efforts to improve the efficiency of self-attention have led to a proliferation of long-range Transformer language models, which can process much longer sequences than models of the past. However, the ways in which such models take advantage of the long-range context remain unclear.

In this paper, we perform a fine-grained analysis of two long-range Transformer language models (Local Transformer vs the Routing Transformer, which achieves state-of-the-art perplexity on the PG-19 long-sequence LM benchmark dataset) that accept input sequences of up to 8K tokens.

Our results reveal that providing long-range context (ie. beyond the previous 2K tokens) to these models only improves their predictions on a small set of tokens (eg. those that can be copied from the distant context [such as proper nouns]) and does not help at all for sentence-level prediction tasks.

Finally, we discover that PG-19 contains a variety of different document types and domains, and that long-range context helps most for literary novels (as opposed to textbooks or magazines).

--- https://arxiv.org/abs/2303.02242 TrojText: Test-time Invisible Textual Trojan Insertion Yepeng Liu, Bo Feng, Qian Lou 2023-03-03 2023-03-03 [("doi","10.48550/arXiv.2303.02242")] ai/nn/adversarial

In Natural Language Processing (NLP), intelligent neuron models can be susceptible to textual Trojan attacks. Such attacks occur when Trojan models behave normally for standard inputs but generate malicious output for inputs that contain a specific trigger. Syntactic-structure triggers, which are invisible, are becoming more popular for Trojan attacks because they are difficult to detect and defend against.

However, these types of attacks require a large corpus of training data to generate poisoned samples with the necessary syntactic structures for Trojan insertion. Obtaining such data can be difficult for attackers, and the process of generating syntactic poisoned triggers and inserting Trojans can be time-consuming. This paper proposes a solution called TrojText, which aims to determine whether invisible textual Trojan attacks can be performed more efficiently and cost-effectively without training data. The proposed approach, called the Representation-Logit Trojan Insertion (RLI) algorithm, uses smaller sampled test data instead of large training data to achieve the desired attack. The paper also introduces two additional techniques, namely the accumulated gradient ranking (AGR) and Trojan Weights Pruning (TWP), to reduce the number of tuned parameters and the attack overhead.

The TrojText approach was evaluated on 3 datasets (AG’s News, SST-2, and OLID) using 3 NLP models (BERT, XLNet, and DeBERTa). The experiments demonstrated that the TrojText approach achieved a 98.35% classification accuracy for test sentences in the target class on the BERT model for the AG’s News dataset.

The source code for TrojText is available at Github.

--- https://cloud.google.com/blog/topics/systems/the-evolution-of-googles-jupiter-data-center-network 2022-08-25 ai/scaling/hardware cs/hardware technology/google --- https://arxiv.org/abs/2303.02416 PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling Yuan Liu, Songyang Zhang, Jiacheng Chen, Kai Chen, Dahua Lin 2023-03-04 2023-03-04 [("doi","10.48550/arXiv.2303.02416")] ai/nn/vae/mae

Masked Image Modeling (MIM) has achieved promising progress with the advent of Masked Autoencoders (MAE) and BEiT. However, subsequent works have complicated the framework with new auxiliary tasks or extra pre-trained models, inevitably increasing computational overhead. This paper undertakes a fundamental analysis of MIM from the perspective of pixel reconstruction, which examines the input image patches and reconstruction target, and highlights two critical but previously overlooked bottlenecks.

Based on this analysis, we propose a remarkably simple and effective method, PixMIM, that entails two strategies: (1) filtering the high-frequency components from the reconstruction target to de-emphasize the network’s focus on texture-rich details and (2) adopting a conservative data transform strategy to alleviate the problem of missing foreground in MIM training. PixMIM can be easily integrated into most existing pixel-based MIM approaches (ie. using raw images as reconstruction target) with negligible additional computation.

Without bells and whistles, our method consistently improves 3 MIM approaches, MAE, ConvMAE, and LSMAE, across various downstream tasks.

We believe this effective plug-and-play method will serve as a strong baseline for self-supervised learning and provide insights for future improvements of the MIM framework.

Code will be available at Github.

--- https://arxiv.org/abs/2303.02995 HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attention Shijie Geng, Jianbo Yuan, Yu Tian, Yuxiao Chen, Yongfeng Zhang 2023-03-06 2023-03-06 [("doi","10.48550/arXiv.2303.02995")] ai/nn/transformer/clip

The success of large-scale contrastive vision-language pretraining (CLIP) has benefited both visual recognition and multimodal content understanding. The concise design brings CLIP the advantage in inference efficiency against other vision-language models with heavier cross-attention fusion layers, making it a popular choice for a wide spectrum of downstream tasks. However, CLIP does not explicitly capture the hierarchical nature of high-level and fine-grained semantics conveyed in images and texts, which is arguably critical to vision-language understanding and reasoning.

To this end, we equip both the visual and language branches in CLIP with hierarchy-aware attentions, namely Hierarchy-aware CLIP (HiCLIP), to progressively discover semantic hierarchies layer-by-layer from both images and texts in an unsupervised manner. As a result, such hierarchical aggregation improves the cross-modal alignment.

To demonstrate the advantages of HiCLIP, we conduct qualitative analysis on its unsupervised hierarchy induction during inference, as well as extensive quantitative experiments on both visual recognition and vision-language downstream tasks.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3147066/ Intelligence in youth and all-cause-mortality: systematic review with meta-analysis Catherine M. Calvin, Ian J. Deary, Candida Fenton, Beverly A. Roberts, Geoff Der, Nicola Leckenby, G. David Batty 2011 2022-08-25 [("doi","10.1093/ije/dyq190")] iq longevity

Background: A number of prospective cohort studies have examined the association between intelligence in childhood or youth and life expectancy in adulthood; however, the effect size of this association is yet to be quantified and previous reviews require updating.

Method: The systematic review included an electronic search of Embase, MEDLINE and PSYCHINFO databases. This yielded 16 unrelated studies that met inclusion criteria, comprising 22,453 deaths among 1,107,022 participants. Heterogeneity was assessed, and fixed effects models were applied to the aggregate data. Publication bias was evaluated, and sensitivity analyses were conducted.

Results: A 1-standard deviation (SD) advantage in cognitive test scores was associated with a 24% (95% confidence interval 23–25) lower risk of death, during a 17- to 69-year follow-up. There was little evidence of publication bias (Egger’s intercept = 0.10, p = 0.81), and the intelligence-mortality association was similar for men and women. Adjustment for childhood socio-economic status (SES) in the 9 studies containing these data had almost no impact on this relationship, suggesting that this is not a confounder of the intelligence-mortality association. Controlling for adult SES in 5 studies and for education in 6 studies attenuated the intelligence-mortality hazard ratios by 34 and 54%, respectively.

Conclusions: Future investigations should address the extent to which attenuation of the intelligence-mortality link by adult SES indicators is due to mediation, over-adjustment and/or confounding. The explanation(s) for association between higher early-life intelligence and lower risk of adult mortality require further elucidation.

--- https://x.com/zswitten/status/1631190068970012675 2022-08-25 ai/nn/transformer/gpt/codex --- https://x.com/PerksPlus0001/status/1631372820709253120 2022-08-25 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2303.03381 Learning Humanoid Locomotion with Transformers Ilija Radosavovic, Tete Xiao, Bike Zhang, Trevor Darrell, Jitendra Malik, Koushil Sreenath 2023-03-06 2023-03-06 [("doi","10.48550/arXiv.2303.03381")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/model/decision-transformer reinforcement-learning/robot

We present a sim-to-real learning-based approach for real-world humanoid locomotion.

Our controller is a causal Transformer trained by autoregressive prediction of future actions from the history of observations and actions.

We hypothesize that the observation-action history contains useful information about the world that a powerful Transformer model can use to adapt its behavior in-context, without updating its weights. We do not use state estimation, dynamics models, trajectory optimization, reference trajectories, or pre-computed gait libraries.

Our controller is trained with large-scale model-free reinforcement learning on an ensemble of randomized environments in simulation and deployed to the real world in a zero-shot fashion.

We evaluate our approach in high-fidelity simulation and successfully deploy it to the real robot [a 45kg 1.6m tall ‘Digit’ humanoid robot by Agility Robotics] as well. To the best of our knowledge, this is the first demonstration of a fully learning-based method for real-world full-sized humanoid locomotion.

--- https://blog.valentin.sh/chatgpt5/ 2022-08-25 ai/nn/transformer/gpt/inner-monologue --- https://van-magazine.com/mag/one-pound-tickets-royal-opera-house/ 2022-08-25 fiction/opera --- https://interconnected.org/home/2023/02/07/braggoscope 2022-08-26 ai/nn/transformer/gpt/codex --- https://www.nist.gov/blogs/taking-measure/test-time-nists-wall-many-stones 2022-08-26 design science --- /idea#deep-learning 2022-08-26 ai/nn --- /idea#autoregressive-progressive-growing 2022-08-26 ai/nn/transformer/gpt/dall-e --- /idea#diffusion 2022-08-26 ai/nn/diffusion --- /idea#gans 2022-08-26 ai/nn/gan --- /idea#llm-fingerprinting 2022-08-26 ai/nn/transformer/gpt cs/security --- /idea#robotics-scaling-the-dollhouse 2022-08-26 reinforcement-learning/robot reinforcement-learning/scaling --- /idea#zen-sand-garden-puzzle-game 2022-08-26 design --- /idea#chinese-censorship-audit 2022-08-26 politics --- https://arxiv.org/abs/2111.12067 Quasi-universal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics Guillermo B. Morales, Serena Di Santo, Miguel A. Munoz 2021-11-23 2022-08-27 [("doi","10.48550/arXiv.2111.12067")] psychology/neuroscience

The brain is in a state of perpetual reverberant neural activity, even in the absence of specific tasks or stimuli. Shedding light on the origin and functional of such a dynamical state is essential to understanding how the brain transmits, processes, and stores information. An inspiring, albeit controversial, conjecture proposes that some statistical characteristics of empirically observed neuronal activity can be understood by assuming that brain networks operate in a dynamical regime near the edge of a phase transition. Moreover, the resulting critical behavior, with its concomitant scale invariance, is assumed to carry crucial functional advantages.

Here, we present a data-driven analysis based on simultaneous high-throughput recordings of the activity of thousands of individual neurons in various regions of the mouse brain. To analyze these data, we synergistically combine cutting-edge methods for the study of brain activity (such as a phenomenological renormalization group approach and techniques that infer the general dynamical state of a neural population), while designing complementary tools.

This strategy allows us to uncover strong signatures of scale invariance that is “quasi-universal” across brain regions and reveal that all these areas operate, to a greater or lesser extent, near the edge of instability. Furthermore, this framework allows us to distinguish between quasi-universal background activity and non-universal input-related activity.

Taken together, this study provides strong evidence that brain networks actually operate in a critical regime which, among other functional advantages, provides them with a scale-invariant substrate of activity covariances that can sustain optimal input representations.

--- https://newsletter.shifthappens.site/archive/the-cursed-universes-of-dana-sibera/ 2022-08-27 cs/hardware design --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3599475/ Positive psychology interventions: a meta-analysis of randomized controlled studies Linda Bolier, Merel Haverman, Gerben J. Westerhof, Heleen Riper, Filip Smit, Ernst Bohlmeijer 2013 2022-08-27 [("doi","10.1186/1471-2458-13-119")] psychiatry/depression

Background: The use of positive psychological interventions may be considered as a complementary strategy in mental health promotion and treatment. The present article constitutes a meta-analytical study of the effectiveness of positive psychology interventions for the general public and for individuals with specific psychosocial problems.

Method: We conducted a systematic literature search using PubMed, PsycINFO, the Cochrane register, and manual searches. Forty articles, describing 39 studies, totaling 6,139 participants, met the criteria for inclusion. The outcome measures used were subjective well-being, psychological well-being and depression. Positive psychology interventions included self-help interventions, group training and individual therapy.

Results: The standardized mean difference was 0.34 for subjective well-being, 0.20 for psychological well-being and 0.23 for depression indicating small effects for positive psychology interventions. At follow-up 35.9%–59.4% months, effect sizes are small, but still significant for subjective well-being and psychological well-being, indicating that effects are fairly sustainable. Heterogeneity was rather high, due to the wide diversity of the studies included. Several variables moderated the impact on depression: Interventions were more effective if they were of longer duration, if recruitment was conducted via referral or hospital, if interventions were delivered to people with certain psychosocial problems and on an individual basis, and if the study design was of low quality. Moreover, indications for publication bias were found, and the quality of the studies varied considerably.

Conclusions: The results of this meta-analysis show that positive psychology interventions can be effective in the enhancement of subjective well-being and psychological well-being, as well as in helping to reduce depressive symptoms. Additional high-quality peer-reviewed studies in diverse (clinical) populations are needed to strengthen the evidence-base for positive psychology interventions.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0200140 Association of current and former smoking with body mass index: A study of smoking discordant twin pairs from 21 twin cohorts Maarit Piirtola, Aline Jelenkovic, Antti Latvala, Reijo Sund, Chika Honda, Fujio Inui, Mikio Watanabe, Rie Tomizawa, Yoshinori Iwatani, Juan R. Ordoñana, Juan F. Sánchez-Romera, Lucia Colodro-Conde, Adam D. Tarnoki, David L. Tarnoki, Nicholas G. Martin, Grant W. Montgomery, Sarah E. Medland, Finn Rasmussen, Per Tynelius, Qihua Tan, Dongfeng Zhang, Zengchang Pang, Esther Rebato, Maria A. Stazi, Corrado Fagnani, Sonia Brescianini, Andreas Busjahn, Jennifer R. Harris, Ingunn Brandt, Thomas Sevenius Nilsen, Tessa L. Cutler, John L. Hopper, Robin P. Corley, Brooke M. Huibregtse, Joohon Sung, Jina Kim, Jooyeon Lee, Sooji Lee, Margaret Gatz, David A. Butler, Carol E. Franz, William S. Kremen, Michael J. Lyons, Patrik K. E. Magnusson, Nancy L. Pedersen, Anna K. Dahl Aslan, Sevgi Y. Öncel, Fazil Aliev, Catherine A. Derom, Robert F. Vlietinck, Ruth Loos, Judy L. Silberg, Hermine H. Maes, Dorret I. Boomsma, Thorkild I. A. Sørensen, Tellervo Korhonen, Jaakko Kaprio, Karri Silventoinen 2018-06-20 2022-08-27 [("doi","10.1371/journal.pone.0200140")] exercise genetics/heritable/correlation nicotine

Background: Smokers tend to weigh less than never smokers, while successful quitting leads to an increase in body weight. Because smokers and non-smokers may differ in genetic and environmental family background, we analysed data from twin pairs in which the co-twins differed by their smoking behavior to evaluate if the association between smoking and body mass index (BMI) remains after controlling for family background.

Methods & Findings: The international CODATwins database includes information on smoking and BMI measured between 1960 and 2012 from 156,593 twin individuals 18–69 years of age. Individual-based data (230,378 measurements) and data of smoking discordant twin pairs (altogether 30,014 pairwise measurements, 36% from monozygotic [MZ] pairs) were analysed with linear fixed-effects regression models by 10-year periods.

In MZ pairs, the smoking co-twin had, on average, 0.57 kg/m2 lower BMI in men (95% confidence interval (CI): 0.49, 0.70) and 0.65 kg/m2 lower BMI in women (95% CI: 0.52, 0.79) than the never smoking co-twin. Former smokers had 0.70 kg/m2 higher BMI among men (95% CI: 0.63, 0.78) and 0.62 kg/m2 higher BMI among women (95% CI: 0.51, 0.73) than their currently smoking MZ co-twins. Little difference in BMI was observed when comparing former smoking co-twins with their never smoking MZ co-twins (0.13 kg/m2, 95% CI 0.04, 0.23 among men; −0.04 kg/m2, 95% CI −0.16, 0.09 among women). The associations were similar within dizygotic pairs and when analysing twins as individuals. The observed series of cross-sectional associations were independent of sex, age, and measurement decade.

Conclusions: Smoking is associated with lower BMI and smoking cessation with higher BMI. However, the net effect of smoking and subsequent cessation on weight development appears to be minimal, i.e. never more than an average of 0.7 kg/m2.

--- https://www.biorxiv.org/content/10.1101/2021.09.09.459561.full Effective population size for culturally evolving traits Dominik Deffner, Anne Kandler, Laurel Fogarty 2021-09-10 2022-08-27 [("doi","10.1101/2021.09.09.459561")] culture genetics/selection

Population size has long been considered an important driver of cultural diversity and complexity. Results from population genetics, however, demonstrate that in populations with complex demographic structure or mode of inheritance, it is not the census population size, N, but the effective size of a population, Ne, that determines important evolutionary parameters. Here, we examine the concept of effective population size for traits that evolve culturally, through processes of innovation and social learning.

We use mathematical and computational modeling approaches to investigate how cultural Ne and levels of diversity depend on (1) the way traits are learned, (2) population connectedness, and (3) social network structure.

We show that one-to-many and frequency-dependent transmission can temporally or permanently lower effective population size compared to census numbers. We caution that migration and cultural exchange can have counter-intuitive effects on Ne. Network density in random networks leaves Ne unchanged, scale-free networks tend to decrease and small-world networks tend to increase Ne compared to census numbers. For one-to-many transmission and different network structures, effective size and cultural diversity are closely associated. For connectedness, however, even small amounts of migration and cultural exchange result in high diversity independently of Ne.

Our results highlight the importance of carefully defining effective population size for cultural systems and show that inferring Ne requires detailed knowledge about underlying cultural and demographic processes.

Author Summary: Human populations show immense cultural diversity and researchers have regarded population size as an important driver of cultural variation and complexity. Our approach is based on cultural evolutionary theory which applies ideas about evolution to understand how cultural traits change over time. We employ insights from population genetics about the “effective” size of a population (ie. the size that matters for important evolutionary outcomes) to understand how and when larger populations can be expected to be more culturally diverse. Specifically, we provide a formal derivation for cultural effective population size and use mathematical and computational models to study how effective size and cultural diversity depend on (1) the way culture is transmitted, (2) levels of migration and cultural exchange, as well as (3) social network structure.

Our results highlight the importance of effective sizes for cultural evolution and provide heuristics for empirical researchers to decide when census numbers could be used as proxies for the theoretically relevant effective numbers and when they should not.

…To understand what we gain from the effective size, even if we are not particularly interested in Wright-Fisher models, let us assume there are two populations, A and B, that produce a particular cultural trait with many possible variants. We now want to know whether population size affects the number of different traits in a population. Population A has a large census population size of 1,000 individuals, population B has a smaller census size of just 500. Using a theoretical model of a cultural evolutionary process [eg. Shennan 2001, Henrich 2004, Powell et al 2009, Fogarty et al 2017], we conclude that larger populations should have larger or more complex cultural repertoires. Can we expect to find this demographic relationship in data on census population sizes and cultural repertoire sizes from both populations [eg. Oswalt 1976]? The answer is that—regardless of how good the model is—the relationship is unlikely to be found unless these real populations are identical in some evolutionarily important ways. If they do not have the same age structure, demographic history, or, as we show below, cultural transmission mechanisms and interaction patterns, the populations are not directly comparable, except through their relation to a simpler model—through their effective population sizes.

Imagine we now discover that, 10 generations ago, population A had a population bottleneck where its census size fell to only 10 individuals before recovering to its current value of 1,000. Genetic evolution will be affected by this bottleneck for a number of generations (culture might recover from such events much faster than genetics [Fogarty & Kandler 2020]). Both populations are otherwise identical and conform to the assumptions of the Wright-Fisher model, which we detail below.

Accordingly, the effective population size of the small, stable population B is 500, the same as its census size. The effective size of population A, however, is only around 92 (see Appendix 1 for calculation). We can now use results from population genetics to calculate how many cultural traits we expect to see in each population, given certain transmission mechanisms and innovation rates. For population B with Ne = 500, the expected number of traits is 223. For population A with Ne ≈ 92, we expect to see on average 41 traits in a given generation (see Appendix 1 for full details). Thus, although a relationship exists between effective population size and cultural diversity, a straightforward relationship does not exist between census size and diversity. Using census numbers or more informal definitions of effective size will produce incorrect results.

…We have systematically examined effective population size, a concept derived from theoretical population genetics, for culturally evolving traits. The effective size allows us to compare populations, where it would otherwise be difficult to do so. We showed that both modes of cultural transmission and relevant elements of population structure can change the effective size compared to the census size, sometimes considerably. One-to-many and frequency-dependent transmission can substantially lower effective population size with the strongest effects of frequency dependence occurring when the system is out-of-equilibrium. Investigating different forms of connectedness between populations, we found that migration as we define it does not increase Ne and cultural exchange among groups increases inbreeding effective number but not variance effective number. This implies that considerable precision and caution is needed in defining cultural effective sizes.

Finally, while random networks with varying densities leave Ne unchanged, scale-free networks tend to decrease and small-world networks tend to increase Ne compared to the census number…Our results show that, when there are a few highly influential individuals who—through transmission modes—strongly influence the cultural makeup of the population, the census size and the effective size can diverge. Similarly, where populations are organized into social networks in which individuals are heterogeneous with respect to their degree, the ratio between census and effective size can either increase or decrease depending on network structure. These results also highlight that even relatively small populations might be able to maintain comparatively high levels of cultural diversity if connections are structured in a certain way. [see selection amplifiers]

--- https://www.slate.com/articles/health_and_science/the_mouse_trap/2011/11/lab_mice_are_they_limiting_our_understanding_of_human_disease_.html The Mouse Trap: The dangers of using one lab animal to study every disease 2022-08-27 statistics/bias/animal --- https://www.pnas.org/doi/full/10.1073/pnas.1222878110 Genomic responses in mouse models poorly mimic human inflammatory diseases Seok 2013 2022-08-27 genetics/heritable/correlation statistics/bias/animal --- https://en.wikipedia.org/wiki/Sepsis Sepsis 2022-08-27 biology statistics/bias/animal --- /doc/genetics/heritable/1955-michie.pdf The Importance of Being Cross-Bred Donald Michie, Anne McLaren 1955-01-01 2022-08-27 genetics/heritable statistics/bias/animal --- /doc/statistics/bias/animal/1970-schein.pdf 1970 2022-08-27 statistics/bias/animal --- /doc/statistics/bias/animal/1986-wilbourn.pdf 1986-01-01 2022-08-28 statistics/bias/animal --- /doc/statistics/bias/animal/2000-olson.pdf Concordance of the Toxicity of Pharmaceuticals in Humans and in Animals Harry Olson, Graham Betton, Denise Robinson, Karluss Thomas, Alastair Monro, Gerald Kolaja, Patrick Lilly, James Sanders, Glenn Sipes, William Bracken, Michael Dorato, Koen Van Deun, Peter Smith, Bruce Berger, Allen Heller 2000-01-01 2022-08-28 statistics/bias/animal --- /doc/statistics/bias/animal/2002-ikonomidou.pdf 2002 2022-08-28 statistics/bias/animal --- /doc/statistics/bias/animal/2002-sandercock.pdf 2002 2022-08-28 statistics/bias/animal --- /doc/statistics/bias/animal/2003-lee.pdf 2003 2022-08-28 statistics/bias/animal --- /doc/statistics/bias/animal/2004-greaves.pdf 2004 2022-08-28 statistics/bias/animal --- /doc/statistics/bias/animal/2005-macleod-2.pdf 2005 2022-08-28 statistics/bias/animal --- /doc/statistics/bias/animal/2005-macleod.pdf 2005 2022-08-28 statistics/bias/animal --- /doc/statistics/bias/animal/2006-hackam.pdf 2006 2022-08-28 statistics/bias/animal --- /doc/statistics/bias/animal/2007-dixit.pdf 2007 2022-08-28 statistics/bias/animal --- /doc/statistics/bias/animal/2007-ocollins.pdf 2007 2022-08-28 statistics/bias/animal --- /doc/statistics/bias/animal/2007-sena.pdf 2007 2022-08-29 statistics/bias/animal --- /doc/statistics/bias/animal/2008-whiteside.pdf 2008 2022-08-29 statistics/bias/animal --- http://eprints.nottingham.ac.uk/439/1/Willmot_NO_synthase_JFRBM.pdf 2022-08-29 statistics/bias/animal --- http://eprints.nottingham.ac.uk/509/1/NO_paper3_10.doc 2022-08-29 statistics/bias/animal --- http://psych.colorado.edu/~carey/pdfFiles/MouseLab_Crabbe.pdf 2022-08-29 statistics/bias/animal --- https://academic.oup.com/cardiovascres/article/91/4/649/346880 2022-08-29 statistics/bias/animal --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.138.944&rep=rep1&type=pdf 2022-08-29 statistics/bias/animal --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.513.1459&rep=rep1&type=pdf 2022-08-29 statistics/bias/animal --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.654.7344&rep=rep1&type=pdf 2022-08-29 statistics/bias/animal --- https://cosmosmagazine.com/biology/don-t-believe-the-mice Experiments using mice are often heavily publicised—but very, very few of them translate into humans. Anthony King reports on why animal models are of questionable value. 2022-08-29 statistics/bias/animal --- https://danbaum.files.wordpress.com/2018/03/jake-leg-new-yorker.pdf 2022-08-29 statistics/bias/animal --- https://hal.archives-ouvertes.fr/file/index/docid/334699/filename/2005-Corpet-Pierre-Rodent-models-Metanal-EJC-Author-version.pdf 2022-08-30 statistics/bias/animal --- https://naldc.nal.usda.gov/download/13645/PDF 2022-08-30 statistics/bias/animal --- https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1553-2712.2003.tb00056.x 2022-08-30 statistics/bias/animal --- https://pure.uva.nl/ws/files/3216102/15320_Thesis.pdf#page=103 2022-08-30 statistics/bias/animal --- https://threadreaderapp.com/thread/1013110729073938432.html 2022-08-30 statistics/bias/animal --- https://stroke.ahajournals.org/content/32/10/2433.full 2022-08-30 statistics/bias/animal --- https://stroke.ahajournals.org/content/39/10/2824.full 2022-08-30 statistics/bias/animal --- https://stroke.ahajournals.org/content/39/3/929.full 2022-08-30 statistics/bias/animal --- https://www.bmj.com/content/334/7586/197 2022-08-30 statistics/bias/animal --- /doc/statistics/bias/animal/1995-igarashi.pdf 1995 2022-08-30 statistics/bias/animal --- https://www.nature.com/articles/nature.2017.23022 2022-08-31 statistics/bias/animal --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1122396/ 2022-08-31 statistics/bias/animal --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1436259/pdf/jrsocmed00293-0087.pdf 2022-08-31 statistics/bias/animal --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC557132/ 2022-08-31 statistics/bias/animal --- https://www.sciencedirect.com/science/article/pii/S2405471219302005 2022-08-31 statistics/bias/animal --- https://www.tandfonline.com/doi/pdf/10.4161/15384101.2014.950151 2022-08-31 statistics/bias/animal --- /doc/statistics/bias/animal/2007-knight.pdf 2007 2022-08-31 statistics/bias/animal --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4995557/ Childhood IQ and risk of bipolar disorder in adulthood: prospective birth cohort study Daniel J. Smith, Jana Anderson, Stanley Zammit, Thomas D. Meyer, Jill P. Pell, Daniel Mackay 2015 2022-08-31 [("doi","10.1192/bjpo.bp.115.000455")] iq psychiatry/bipolar

Background: Intellectual ability may be an endophenotypic marker for bipolar disorder.

Aims: Within a large birth cohort, we aimed to assess whether childhood IQ (including both verbal IQ (VIQ) and performance IQ (PIQ) subscales) was predictive of lifetime features of bipolar disorder assessed in young adulthood.

Method: We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC), a large UK birth cohort, to test for an association between measures of childhood IQ at age 8 years and lifetime manic features assessed at age 22–23 years using the Hypomania Checklist-32 (HCL-32; n = 1881 individuals). An ordinary least squares linear regression model was used, with normal childhood IQ (range 90–109) as the referent group. We adjusted analyses for confounding factors, including gender, ethnicity, handedness, maternal social class at recruitment, maternal age, maternal history of depression and maternal education.

Results: There was a positive association between IQ at age 8 years and lifetime manic features at age 22–23 years (Pearson’s correlation coefficient 0.159 (95% CI 0.120-0.198), p > 0.001). Individuals in the lowest decile of manic features had a mean full-scale IQ (FSIQ) which was almost 10 points lower than those in the highest decile of manic features: mean FSIQ 100.71 (95% CI 98.74-102.6) v. 110.14 (95% CI 107.79-112.50), p > 0.001. The association between IQ and manic features was present for FSIQ, VIQ and for PIQ but was strongest for VIQ.

Conclusions: A higher childhood IQ score, and high VIQ in particular, may represent a marker of risk for the later development of bipolar disorder. This finding has implications for understanding of how liability to bipolar disorder may have been selected through generations. It will also inform future genetic studies at the interface of intelligence, creativity and bipolar disorder and is relevant to the developmental trajectory of bipolar disorder. It may also improve approaches to earlier detection and treatment of bipolar disorder in adolescents and young adults.

--- /doc/tea/2022-antman.pdf For Want of a Cup: The Rise of Tea in England and the Impact of Water Quality on Mortality Francisca M. Antman 2022-01-25 2022-08-31 [("doi","10.1162/rest_a_01158")] economics tea

This paper explores the impact of water quality on mortality by exploiting a natural experiment—the rise of tea consumption in 18th century England. This resulted in an unintentional increase in consumption of boiled water, thereby reducing mortality rates.

The methodology uses two identification strategies tying areas with lower initial water quality to larger declines in mortality rates after tea drinking became widespread and following larger volumes of tea imports. Results are robust to the inclusion of controls for income and access to trade. The hypothesis is further bolstered by suggestive evidence from cause-specific deaths and early childhood mortality.

--- https://www.reddit.com/r/dalle2/comments/11oiqrb/classic_anime_characters_made_using_the_new/ 2022-08-31 ai/anime ai/nn/transformer/gpt/dall-e --- https://x.com/goodside/status/1634407841556561922 2022-08-31 ai/nn/transformer/gpt/calibration --- https://x.com/chriswolfvision/status/1313059518574718977 2022-09-01 ai/nn/cnn design/visualization --- https://minimaxir.com/2023/03/new-chatgpt-overlord/ 2022-09-01 ai/nn/transformer/gpt ai/scaling/economics --- https://til.simonwillison.net/llms/llama-7b-m2 2022-09-01 ai/nn/transformer/gpt --- https://www.genengnews.com/topics/genome-editing/synthetic-biology/dna-synthesis/worlds-longest-oligo-produced-using-de-novo-synthesis/ 2022-09-01 genetics/genome-synthesis --- https://arxiv.org/abs/2210.03651 Understanding the Covariance Structure of Convolutional Filters Asher Trockman, Devin Willmott, J. Zico Kolter 2022-10-07 2022-10-07 [("doi","10.48550/arXiv.2210.03651")] ai/nn/cnn

Neural network weights are typically initialized at random from univariate distributions, controlling just the variance of individual weights even in highly-structured operations like convolutions. Recent ViT-inspired convolutional networks such as ConvMixer and ConvNeXt use large-kernel depthwise convolutions whose learned filters have notable structure; this presents an opportunity to study their empirical covariances.

In this work, we first observe that such learned filters have highly-structured covariance matrices, and moreover, we find that covariances calculated from small networks may be used to effectively initialize a variety of larger networks of different depths, widths, patch sizes, and kernel sizes, indicating a degree of model-independence to the covariance structure. Motivated by these findings, we then propose a learning-free multivariate initialization scheme for convolutional filters using a simple, closed-form construction of their covariance.

Models using our initialization outperform those using traditional univariate initializations, and typically meet or exceed the performance of those initialized from the covariances of learned filters; in some cases, this improvement can be achieved without training the depthwise convolutional filters at all.

--- https://www.biorxiv.org/content/10.1101/2022.11.28.516756.full The connectome of an insect brain Michael Winding, Benjamin D. Pedigo, Christopher L. Barnes, Heather G. Patsolic, Youngser Park, Tom Kazimiers, Akira Fushiki, Ingrid V. Andrade, Avinash Khandelwal, Javier Valdes-Aleman, Feng Li, Nadine Randel, Elizabeth Barsotti, Ana Correia, Richard D. Fetter, Volker Hartenstein, Carey E. Priebe, Joshua T. Vogelstein, Albert Cardona, Marta Zlatic 2022-11-28 2022-11-28 [("doi","10.1101/2022.11.28.516756")] psychology/neuroscience

Brains contain networks of interconnected neurons, so knowing the network architecture is essential for understanding brain function.

We therefore mapped the synaptic-resolution connectome of an insect brain (Drosophila larva) with rich behavior, including learning, value-computation, and action-selection, comprising 3,013 neurons and 544,000 synapses. We characterized neuron-types, hubs, feedforward and feedback pathways, and cross-hemisphere and brain-nerve cord interactions.

We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain’s most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled powerful machine learning architectures.

The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.

One-Sentence Summary: We generated a synaptic-resolution brain connectome and characterized its connection types, neuron types, and circuit motifs.

--- https://www.wired.com/story/cerebras-chip-cluster-neural-networks-ai/ 2022-09-01 ai/nn/transformer/gpt/4 --- https://arxiv.org/abs/2302.14045#microsoft Language Is Not All You Need: Aligning Perception with Language Models (Kosmos-1) Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Barun Patra, Qiang Liu, Kriti Aggarwal, Zewen Chi, Johan Bjorck, Vishrav Chaudhary, Subhojit Som, Xia Song, Furu Wei 2023-02-27 2023-02-27 [("doi","10.48550/arXiv.2302.14045")] ai/nn/tokenization ai/nn/transformer/gpt/inner-monologue ai/scaling iq

A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence.

In this work, we introduce Kosmos-1, a Multimodal Large Language Model (MLLM) that can perceive general modalities, learn in context (ie. few-shot), and follow instructions (ie. zero-shot). Specifically, we train Kosmos-1 from scratch on web-scale multimodal corpora, including arbitrarily interleaved text and images, image-caption pairs, and text data. We evaluate various settings, including zero-shot, few-shot, and multimodal chain-of-thought prompting, on a wide range of tasks without any gradient updates or finetuning. Experimental results show that Kosmos-1 achieves impressive performance on (1) language understanding, generation, and even OCR-free NLP (directly fed with document images), (2) perception-language tasks, including multimodal dialogue, image captioning, visual question answering, and (3) vision tasks, such as image recognition with descriptions (specifying classification via text instructions). We also show that MLLMs can benefit from cross-modal transfer, ie. transfer knowledge from language to multimodal, and from multimodal to language.

In addition, we introduce a dataset of Raven IQ test, which diagnoses the nonverbal reasoning capability of MLLMs.

--- https://arxiv.org/abs/2303.01610 Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers Tianlong Chen, Zhenyu Zhang, Ajay Jaiswal, Shiwei Liu, Zhangyang Wang 2023-03-02 2023-03-02 [("doi","10.48550/arXiv.2303.01610")] ai/scaling/mixture-of-experts

Despite their remarkable achievement, gigantic transformers encounter drawbacks, including exorbitant computational and memory footprints during training, as well as severe collapse evidenced by a high degree of parameter redundancy. Sparsely-activated Mixture-of-Experts (SMoEs) have shown promise to mitigate the issue of training efficiency, yet they are prone to (1) redundant experts due to representational collapse; and (2) poor expert scalability for inference and downstream fine-tuning, primarily due to overfitting of the learned routing policy to the number of activated experts during training.

As recent research efforts are predominantly focused on improving routing policies to encourage expert specializations, this work focuses on exploring the overlooked scalability bottleneck of SMoEs and leveraging it to effectively scale dense transformers. To this end, we propose a new plug-and-play training framework, SMoE-Dropout, to enable scaling transformers to better accuracy in their full capacity without collapse. Specifically, SMoE-Dropout consists of a randomly initialized and fixed router network to activate experts and gradually increases the activated expert number as training progresses over time. Transformers trained by SMoE-Dropout naturally exhibit a self-slimmable property subject to resource availability, offering smooth and consistent performance boosts with an increase in activated experts during inference or fine-tuning.

Our extensive experiments demonstrate the superior performance and substantial computation savings of SMoE-Dropout, compared to dense training baselines with equivalent parameter counts. In particular, our trained BERT outperforms its densely trained counterpart with consistent improvements of 1.03%, 0.78%, 1.09% on challenging reasoning tasks ASDiv-A, MAWPS, SVAMP, respectively.

--- https://arxiv.org/abs/2303.01986 Towards Democratizing Joint-Embedding Self-Supervised Learning Florian Bordes, Randall Balestriero, Pascal Vincent 2023-03-03 2023-03-03 [("doi","10.48550/arXiv.2303.01986")] ai/nn/transformer

Joint Embedding Self-Supervised Learning (JE-SSL) has seen rapid developments in recent years, due to its promise to effectively leverage large unlabeled data. The development of JE-SSL methods was driven primarily by the search for ever increasing downstream classification accuracies, using huge computational resources, and typically built upon insights and intuitions inherited from a close parent JE-SSL method. This has led unwittingly to numerous pre-conceived ideas that carried over across methods eg. that SimCLR requires very large mini batches to yield competitive accuracies; that strong and computationally slow data augmentations are required.

In this work, we debunk several such ill-formed a priori ideas in the hope to unleash the full potential of JE-SSL free of unnecessary limitations. In fact, when carefully evaluating performances across different downstream tasks and properly optimizing hyper-parameters of the methods, we most often—if not always—see that these widespread misconceptions do not hold. For example we show that it is possible to train SimCLR to learn useful representations, while using a single image patch as negative example, and simple Gaussian noise as the only data augmentation for the positive pair.

Along these lines, in the hope to democratize JE-SSL and to allow researchers to easily make more extensive evaluations of their methods, we introduce an optimized PyTorch library for SSL.

--- https://x.com/transitive_bs/status/1628118163874516992 2022-09-01 ai/nn/transformer/gpt/4 ai/scaling/hardware --- https://x.com/gdb/status/1609244547460255744 2022-09-02 ai/nn/transformer/gpt/4 ai/scaling/economics --- https://www.metaculus.com/questions/14305/when-will-gpt-4-be-announced/ 2022-09-02 ai/nn/transformer/gpt/4 --- https://x.com/matthewjbar/status/1605509829555953667 2022-09-02 ai/nn/transformer/gpt/4 --- https://nunosempere.com/blog/2023/01/30/an-in-progress-experiment-to-test-how-laplace-s-rule-of/ 2022-09-02 math --- https://arxiv.org/abs/1709.02755 SRU: Simple Recurrent Units for Highly Parallelizable Recurrence Tao Lei, Yu Zhang, Sida I. Wang, Hui Dai, Yoav Artzi 2017-09-08 2022-09-02 [("doi","10.48550/arXiv.1709.02755")] ai/nn/rnn

Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations.

[cf. Quasi-RNN, Kernel NN] In this work, we propose the Simple Recurrent Unit (SRU), a lightweight recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate training of deep models.

We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU achieves 5–9× speed-up over cuDNN-optimized LSTM on classification and question answering datasets, and delivers stronger results than LSTM and convolutional models.

We also obtain an average of 0.7 BLEU improvement over the Transformer model on translation by incorporating SRU into the architecture.

--- https://arxiv.org/abs/1705.09037 Deriving Neural Architectures from Sequence and Graph Kernels Tao Lei, Wengong Jin, Regina Barzilay, Tommi Jaakkola 2017-05-25 2022-09-02 [("doi","10.48550/arXiv.1705.09037")] ai/nn/rnn

The design of neural architectures for structured objects is typically guided by experimental insights rather than a formal process.

In this work, we appeal to kernels over combinatorial structures, such as sequences and graphs, to derive appropriate neural operations. We introduce a class of deep recurrent neural operations and formally characterize their associated kernel spaces. Our recurrent modules compare the input to virtual reference objects (cf. filters in CNN) via the kernels. Similar to traditional neural operations, these reference objects are parameterized and directly optimized in end-to-end training.

We empirically evaluate the proposed class of neural architectures on standard applications such as language modeling and molecular graph regression, achieving state-of-the-art results across these applications.

--- https://www.nytimes.com/2023/01/20/technology/chatbots-turing-test.html 2022-09-02 reinforcement-learning/imperfect-information/diplomacy --- https://manifold.markets/group/gary-marcus-gpt4-predictions 2022-09-02 ai/nn/transformer/gpt/4 --- https://nostalgebraist.tumblr.com/post/705192637617127424/gpt-4-prediction-it-wont-be-very-useful 2022-09-02 ai/nn/transformer/gpt/4 --- https://generative.ink/posts/methods-of-prompt-programming/#serializing-reasoning 2022-09-02 ai/nn/transformer/gpt/inner-monologue --- https://www.lesswrong.com/posts/tt7WtqiEyEiLmAecZ/what-will-gpt-4-be-incapable-of 2022-09-03 ai/nn/transformer/gpt/4 --- https://www.lesswrong.com/posts/qdStMFDMrWAnTqNWL/gpt-4-predictions 2022-09-03 ai/nn/transformer/gpt/4 ai/scaling --- https://arxiv.org/abs/2210.17323 GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh 2022-10-31 2022-10-31 [("doi","10.48550/arXiv.2210.17323")] ai/nn/sparsity/low-precision ai/nn/transformer/gpt

Generative Pre-trained Transformer (GPT) models set themselves apart through breakthrough performance across complex language modeling tasks, but also by their extremely high computational and storage costs. Specifically, due to their massive size, even inference for large, highly-accurate GPT models may require multiple performant GPUs to execute, which limits the usability of such models. While there is emerging work on relieving this pressure via model compression, the applicability and performance of existing compression techniques is limited by the scale and complexity of GPT models.

In this paper, we address this challenge, and propose GPTQ, a new one-shot weight quantization method based on approximate second-order information, that is both highly-accurate and highly-efficient.

Specifically, GPTQ can quantize GPT models with 175 billion parameters in ~4 GPU hours, reducing the bit-width down to 3 or 4 bits per weight, with negligible accuracy degradation relative to the uncompressed baseline.

Our method more than doubles the compression gains relative to previously-proposed one-shot quantization methods, preserving accuracy, allowing us for the first time to execute an 175 billion-parameter model inside a single GPU [an 80GB A100 GPU]. We show experimentally that these improvements can be leveraged for end-to-end inference speedups over FP16, of around 2× when using high-end GPUs (NVIDIA A100) and 4× when using more cost-effective ones (NVIDIA A6000).

The implementation is available at Github [and used in LLaMA].

--- https://arxiv.org/abs/2303.06135 Rewarding Chatbots for Real-World Engagement with Millions of Users Robert Irvine, Douglas Boubert, Vyas Raina, Adian Liusie, Vineet Mudupalli, Aliaksei Korshuk, Zongyi Liu, Fritz Cremer, Valentin Assassi, Christie-Carol Beauchamp, Xiaoding Lu, Thomas Rialan, William Beauchamp 2023-03-10 2023-03-10 [("doi","10.48550/arXiv.2303.06135")] ai/nn/transformer/gpt reinforcement-learning/preference-learning/mode-collapse

The emergence of pretrained large language models has led to the deployment of a range of social chatbots for chitchat. Although these chatbots demonstrate language ability and fluency, they are not guaranteed to be engaging and can struggle to retain users.

This work investigates the development of social chatbots that prioritize user engagement to enhance retention, specifically examining the use of human feedback to efficiently develop highly engaging chatbots. The proposed approach uses automatic pseudo-labels collected from user interactions to train a reward model that can be used to reject low-scoring sample responses generated by the chatbot model at inference time.

Intuitive evaluation metrics, such as mean conversation length (MCL), are introduced as proxies to measure the level of engagement of deployed chatbots.

A/B testing on groups of 10,000 new daily chatbot users on the Chai Research platform shows that this approach increases the MCL by up to 70%, which translates to a more than 30% increase in user retention for a GPT-J 6B model.

Future work aims to use the reward model to realise a data fly-wheel, where the latest user conversations can be used to alternately fine-tune the language model and the reward model.

--- https://arxiv.org/abs/2303.05657 Tag2Text: Guiding Vision-Language Model via Image Tagging Xinyu Huang, Youcai Zhang, Jinyu Ma, Weiwei Tian, Rui Feng, Yuejie Zhang, Yaqian Li, Yandong Guo, Lei Zhang 2023-03-10 2023-03-10 [("doi","10.48550/arXiv.2303.05657")] ai/nn/retrieval ai/nn/transformer

This paper presents Tag2Text, a vision language pre-training (VLP) framework, which introduces image tagging into vision-language models to guide the learning of visual-linguistic features.

In contrast to prior works which use object tags either manually labeled or automatically detected with a limited detector, our approach uses tags parsed from its paired text to learn an image tagger and meanwhile provides guidance to vision-language models. Given that, Tag2Text can use large-scale annotation-free image tags in accordance with image-text pairs, and provides more diverse tag categories beyond objects. As a result, Tag2Text achieves a superior image tag recognition ability by exploiting fine-grained text information.

Moreover, by leveraging tagging guidance, Tag2Text effectively enhances the performance of vision-language models on both generation-based and alignment-based tasks.

Across a wide range of downstream benchmarks, Tag2Text achieves state-of-the-art or competitive results with similar model sizes and data scales, demonstrating the efficacy of the proposed tagging guidance.

--- https://hyperboleandahalf.blogspot.com/2011/10/adventures-in-depression.html 2022-09-03 psychiatry/depression --- https://www.scientificamerican.com/article/vitamin-d-supplements-probably-wont-prevent-mental-illness-after-all/ 2022-09-03 psychiatry/autism psychiatry/depression psychiatry/schizophrenia vitamin-d --- https://www.biorxiv.org/content/10.1101/2023.03.07.531528.full Contraception ends the genetic maintenance of human same-sex sexual behavior Siliang Song, Jianzhi Zhang 2023-03-09 2023-03-09 [("doi","10.1101/2023.03.07.531528")] ai/nn/adversarial/human genetics/heritable/correlation

Because human same-sex sexual behavior (SSB) is heritable and leads to fewer offspring, it is puzzling why SSB-associated alleles have not been selectively purged. Current evidence supports the antagonistic pleiotropy hypothesis that SSB-associated alleles benefit individuals exclusively performing opposite-sex sexual behavior by increasing their number of sexual partners and consequently their number of offspring.

However, here we show that having more sexual partners no longer predicts more offspring since the availability of oral contraceptives in the 1960s and that SSB is now negatively genetically correlated with the number of offspring—

indicating a loss of SSB’s genetic maintenance in modern societies.

--- https://www.youtube.com/watch?v=jQ_DfORb3kw&t=931 2022-09-03 ai/anime ai/nn/diffusion --- https://x.com/nickfloats/status/1635116672054079488 2022-09-03 ai/nn/diffusion/midjourney ai/nn/transformer/gpt/fiction design --- https://x.com/NolanoOrg/status/1634027966651834370 2022-09-03 ai/nn/sparsity/low-precision ai/nn/transformer/gpt --- https://github.com/NolanoOrg/llama-int4-quant/ 2022-09-04 ai/nn/sparsity/low-precision ai/nn/transformer/gpt --- https://github.com/qwopqwop200/GPTQ-for-LLaMa 2022-09-04 ai/nn/sparsity/low-precision ai/nn/transformer/gpt --- https://en.wikipedia.org/wiki/Paradox_of_the_plankton Paradox of the plankton 2022-09-04 genetics/selection/natural --- https://x.com/ESYudkowsky/status/1635577836525469697 2022-09-04 ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt ai/scaling/economics --- https://github.com/moohax/Proof-Pudding 2022-09-04 ai/nn/adversarial --- https://arxiv.org/abs/2202.12506 On the Effectiveness of Dataset Watermarking in Adversarial Settings Buse Gul Atli Tekgul, N. Asokan 2022-02-25 2022-09-04 [("doi","10.1145/3510548.3519376")] ai/nn/adversarial ai/nn/cnn

In a data-driven world, datasets constitute an economic value. Dataset owners who spend time and money to collect and curate the data are incentivized to ensure that their datasets are not used in ways that they did not authorize. When such misuse occurs, dataset owners need technical mechanisms for demonstrating their ownership of the dataset in question. Dataset watermarking provides one approach for ownership demonstration which can, in turn, deter unauthorized use.

In this paper, we investigate a recently proposed data provenance method, radioactive data, to assess if it can be used to demonstrate ownership of (image) datasets used to train machine learning (ML) models. The original paper reported that radioactive data is effective in white-box settings.

We show that while this is true for large datasets with many classes, it is not as effective for datasets where the number of classes is low (≤30) or the number of samples per class is low (≤500).

We also show that, counter-intuitively, the black-box verification technique is effective for all datasets used in this paper, even when white-box verification is not. Given this observation, we show that the confidence in white-box verification can be improved by using watermarked samples directly during the verification process. We also highlight the need to assess the robustness of radioactive data if it were to be used for ownership demonstration since it is an adversarial setting unlike provenance identification.

Compared to dataset watermarking, ML model watermarking has been explored more extensively in recent literature. However, most of the model watermarking techniques can be defeated via model extraction. We show that radioactive data can effectively survive model extraction attacks, which raises the possibility that it can be used for ML model ownership verification robust against model extraction.

--- https://x.com/colin_fraser/status/1635350490484719618 2022-09-04 ai/nn/tokenization ai/nn/transformer/gpt --- https://x.com/colin_fraser/status/1635360285187018752 2022-09-04 ai/nn/tokenization ai/nn/transformer/gpt --- https://x.com/colin_fraser/status/1635450606013014016 2022-09-04 ai/nn/tokenization ai/nn/transformer/gpt --- https://x.com/prerationalist/status/1635693428863381531 2022-09-04 ai/nn/transformer/gpt/4/poetry --- https://x.com/prerationalist/status/1635694884353978433 2022-09-04 ai/nn/transformer/gpt/4/poetry --- https://x.com/tegmark/status/1635714985543204889 2022-09-05 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/goodside/status/1635711013566795776 2022-09-05 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue --- https://x.com/Suhail/status/1635706222514167808 2022-09-05 ai/nn/transformer/gpt/4/poetry ai/nn/transformer/gpt/codex cs/security --- https://x.com/tszzl/status/1635710653456580609 2022-09-05 ai/nn/transformer/gpt/4/poetry --- https://x.com/DanGrover/status/1635713083523084288 2022-09-05 ai/nn/transformer/gpt/4/poetry --- https://x.com/MasterTimBlais/status/1635701745727700999 2022-09-05 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/nomic_ai/status/1635719257110478859 2022-09-05 ai/nn/transformer/gpt/4/fiction reinforcement-learning/safe --- https://arxiv.org/abs/2212.03827 Discovering Latent Knowledge in Language Models Without Supervision Collin Burns, Haotian Ye, Dan Klein, Jacob Steinhardt 2022-12-07 2022-12-07 [("doi","10.48550/arXiv.2212.03827")] ai/nn/transformer philosophy/logic reinforcement-learning/safe

[commentary] Existing techniques for training language models can be misaligned with the truth: if we train models with imitation learning, they may reproduce errors that humans make; if we train them to generate text that humans rate highly, they may output errors that human evaluators can’t detect.

We propose circumventing this issue by directly finding latent knowledge inside the internal activations of a language model in a purely unsupervised way. Specifically, we introduce a method for accurately answering yes-no questions given only unlabeled model activations. It works by finding a direction in activation space that satisfies logical consistency properties, such as that a statement and its negation have opposite truth values.

We show that despite using no supervision and no model outputs, our method can recover diverse knowledge represented in large language models: across 6 models and 10 question-answering datasets, it outperforms zero-shot accuracy by 4% on average. We also find that it cuts prompt sensitivity in half and continues to maintain high accuracy even when models are prompted to generate incorrect answers.

Our results provide an initial step toward discovering what language models know, distinct from what they say, even when we don’t have access to explicit ground truth labels.

--- https://semiengineering.com/uneven-circuit-aging-becoming-a-bigger-problem/ 2022-09-05 cs/hardware cs/security --- https://www.lesswrong.com/posts/ux93sLHcqmBfsRTvg/gpt-can-write-quines-now-gpt-4 2022-09-05 ai/nn/transformer/gpt/codex --- https://x.com/maison_ailleurs/status/1635740097990590500 2022-09-06 ai/nn/transformer/gpt/4/poetry --- https://x.com/NickADobos/status/1634672282005295104 2022-09-06 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/poetry --- https://x.com/abacaj/status/1635738595767058433 2022-09-06 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/danshipper/status/1635712019549786113 2022-09-06 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/safe --- https://x.com/MichaelTrazzi/status/1635743595989970945 2022-09-06 ai/nn/tokenization ai/nn/transformer/gpt/4/nonfiction --- https://x.com/emollick/status/1626756838204051458 2022-09-06 ai/nn/transformer/gpt/4/poetry --- https://x.com/emollick/status/1635737781547978752 2022-09-06 ai/nn/transformer/gpt/4/poetry --- https://x.com/_via_getty_/status/1635728855934836736 2022-09-06 ai/nn/transformer/gpt/4/nonfiction --- https://oneusefulthing.substack.com/p/feats-to-astonish-and-amaze 2022-09-06 ai/nn/transformer/gpt/4/fiction --- https://x.com/skirano/status/1635736107949195278 2022-09-06 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://x.com/jbrowder1/status/1635720431091974157 2022-09-06 ai/nn/transformer/gpt/4/nonfiction law --- https://x.com/AndreTI/status/1635801920223989760 2022-09-07 ai/nn/transformer/gpt/4/nonfiction --- https://marginalrevolution.com/marginalrevolution/2023/03/gpt-4.html#blog-comment-160580393 2022-09-07 ai/nn/transformer/gpt/4/poetry --- https://www.newyorker.com/science/annals-of-medicine/what-we-still-dont-understand-about-postpartum-psychosis 2022-09-07 psychiatry/bipolar --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8218233/ External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients Andrew Wong, Erkin Otles, John P. Donnelly, Andrew Krumm, Jeffrey McCullough, Olivia DeTroyer-Cooley, Justin Pestrue, Marie Phillips, Judy Konye, Carleen Penoza, Muhammad Ghous, Karandeep Singh 2021 2022-09-07 [("doi","10.1001/jamainternmed.2021.2626")] ai/tabular biology

[editorial] Importance: The Epic Sepsis Model (ESM), a proprietary sepsis prediction model [penalized logistic regression], is implemented at hundreds of US hospitals. The ESM’s ability to identify patients with sepsis has not been adequately evaluated despite widespread use.

Objective: To externally validate the ESM in the prediction of sepsis and evaluate its potential clinical value compared with usual care.

Design, Setting, & Participants: This retrospective cohort study was conducted among 27 697 patients aged 18 years or older admitted to Michigan Medicine, the academic health system of the University of Michigan, Ann Arbor, with 38 455 hospitalizations between December 6, 2018, and October 20, 2019.

Exposure: The ESM score, calculated every 15 minutes.

Main Outcomes & Measures: Sepsis, as defined by a composite of (1) the Centers for Disease Control and Prevention surveillance criteria and (2) International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnostic codes accompanied by 2 systemic inflammatory response syndrome criteria and 1 organ dysfunction criterion within 6 hours of one another. Model discrimination was assessed using the area under the receiver operating characteristic curve at the hospitalization level and with prediction horizons of 4, 8, 12, and 24 hours. Model calibration was evaluated with calibration plots. The potential clinical benefit associated with the ESM was assessed by evaluating the added benefit of the ESM score compared with contemporary clinical practice (based on timely administration of antibiotics). Alert fatigue was evaluated by comparing the clinical value of different alerting strategies.

Results: We identified 27 697 patients who had 38 455 hospitalizations (21 904 women [57%]; median age, 56 years [interquartile range, 35–69 years]) meeting inclusion criteria, of whom sepsis occurred in 2552 (7%). The ESM had a hospitalization-level area under the receiver operating characteristic curve of 0.63 (95% CI, 0.62-0.64). The ESM identified 183⁄2552 patients with sepsis (7%) who did not receive timely administration of antibiotics, highlighting the low sensitivity of the ESM in comparison with contemporary clinical practice. The ESM also did not identify 1709 patients with sepsis (67%) despite generating alerts for an ESM score of 6 or higher for 6971 of all 38 455 hospitalized patients (18%), thus creating a large burden of alert fatigue.

Conclusions: This external validation cohort study suggests that the ESM has poor discrimination and calibration in predicting the onset of sepsis. The widespread adoption of the ESM despite its poor performance raises fundamental concerns about sepsis management on a national level.

--- /doc/ai/tabular/2021-habib.pdf The Epic Sepsis Model Falls Short—The Importance of External Validation An, R. Habib, Anthony L. Lin, Richard W. Grant 2021-06-21 2022-09-07 [("doi","10.1001/jamainternmed.2021.3333")] ai/tabular biology

Sepsis accounts for nearly 1 million hospitalizations annually and is a major contributor to hospital length of stay, health care expenditures, and in-hospital mortality (ranging from 12.5%-15%).1 Early sepsis identification allows care teams to promptly implement goal-directed therapy to mitigate clinical deterioration. In this issue of JAMA Internal Medicine, Wong et al 2021 report on their external validation of the Epic Sepsis Model (ESM), a prediction tool available within the Epic electronic health record that is designed to generate automated alerts that warn clinicians that patients may be developing sepsis.

Based on their examination of 38 455 hospitalizations at the University of Michigan (Ann Arbor) between December 2018 and October 2019, Wong et al 2021 found that:

the ESM had a sensitivity of 33%, specificity of 83%, positive predictive value of 12%, and negative predictive value of 95%, with an area under the curve of 0.63 (95% CI, 0.62–0.64). This falls short of the area under the curve of 0.76–0.83 that was jointly reported by Epic and University of Colorado Health.3 Despite generating alerts on 18% of all patients, the ESM did not detect sepsis in 67% of patients with sepsis.

--- https://www.eater.com/2017/3/7/14839472/irish-pub-design 2022-09-07 design --- https://arxiv.org/abs/2110.01517 Skill Induction and Planning with Latent Language Pratyusha Sharma, Antonio Torralba, Jacob Andreas 2021-10-04 2022-09-07 [("doi","10.48550/arXiv.2110.01517")] ai/nn/transformer reinforcement-learning/model reinforcement-learning/robot

We present a framework for learning hierarchical policies from demonstrations, using sparse natural language annotations to guide the discovery of reusable skills for autonomous decision-making.

We formulate a generative model of action sequences in which goals generate sequences of high-level subtask descriptions, and these descriptions generate sequences of low-level actions. We describe how to train this model using primarily unannotated demonstrations by parsing demonstrations into sequences of named high-level subtasks, using only a small number of seed annotations to ground language in action. In trained models, natural language commands index a combinatorial library of skills; agents can use these skills to plan by generating high-level instruction sequences tailored to novel goals.

We evaluate this approach in the ALFRED household simulation environment, providing natural language annotations for only 10% of demonstrations. It achieves task completion rates comparable to state-of-the-art models (outperforming several recent methods with access to ground-truth plans during training and evaluation) while providing structured and human-readable high-level plans.

--- https://en.wikipedia.org/wiki/Delta_robot Delta robot 2022-09-07 reinforcement-learning/robot --- https://ishikawa-vision.org/fusion/ 2022-09-07 reinforcement-learning/robot --- https://x.com/RosieCampbell/status/1636050117202694144 2022-09-07 longevity/glp/semaglutide --- https://matthewbarnett.substack.com/p/gpt-4-takes-bryan-caplans-midterm 2022-09-07 ai/nn/transformer/gpt/4/nonfiction economics --- https://arxiv.org/abs/2001.05016 Neural Arithmetic Units Andreas Madsen, Alexander Rosenberg Johansen 2020-01-14 2022-09-08 [("doi","10.48550/arXiv.2001.05016")] ai/nn/sparsity math

[cf. logic units] Neural networks can approximate complex functions, but they struggle to perform exact arithmetic operations over real numbers. The lack of inductive bias for arithmetic operations leaves neural networks without the underlying logic necessary to extrapolate on tasks such as addition, subtraction, and multiplication.

We present two new neural network components: the Neural Addition Unit (NAU), which can learn exact addition and subtraction; and the Neural Multiplication Unit (NMU) that can multiply subsets of a vector. The NMU is, to our knowledge, the first arithmetic neural network component that can learn to multiply elements from a vector, when the hidden size is large.

The two new components draw inspiration from a theoretical analysis of recently proposed arithmetic components. We find that careful initialization, restricting parameter space, and regularizing for sparsity is important when optimizing the NAU and NMU.

Our proposed units NAU and NMU, compared with previous neural units, converge more consistently, have fewer parameters, learn faster, can converge for larger hidden sizes, obtain sparse and meaningful weights, and can extrapolate to negative and small values.

--- https://arxiv.org/abs/1808.00508#deepmind Neural Arithmetic Logic Units Andrew Trask, Felix Hill, Scott Reed, Jack Rae, Chris Dyer, Phil Blunsom 2018-08-01 2022-09-08 [("doi","10.48550/arXiv.1808.00508")] ai/nn/fully-connected ai/tabular math

Neural networks can learn to represent and manipulate numerical information, but they seldom generalize well outside of the range of numerical values encountered during training.

To encourage more systematic numerical extrapolation, we propose an architecture that represents numerical quantities as linear activations which are manipulated using primitive arithmetic operators, controlled by learned gates. We call this module a neural arithmetic logic unit (NALU), by analogy to the arithmetic logic unit in traditional processors.

Experiments show that NALU-enhanced neural networks can learn to track time, perform arithmetic over images of numbers, translate numerical language into real-valued scalars, execute computer code, and count objects in images. In contrast to conventional architectures, we obtain substantially better generalization both inside and outside of the range of numerical values encountered during training, often extrapolating orders of magnitude beyond trained numerical ranges.

--- https://x.com/perrymetzger/status/1635811092654858240 2022-09-08 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://www.reddit.com/r/slatestarcodex/comments/11ro0ww/chatgpt_4_explains_mitch_hedburg_and_writes/ 2022-09-08 ai/nn/transformer/gpt/4/fiction fiction/humor --- https://x.com/geoffreylitt/status/1635757456377917440 2022-09-08 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://x.com/davidad/status/1636150606384582656 2022-09-08 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/LericDax/status/1635804659448152067 2022-09-08 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://x.com/LericDax/status/1635871504138133504 2022-09-08 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://www.lesswrong.com/posts/DaaFce3hBoEzYhdvz/how-well-did-manifold-predict-gpt-4 2022-09-08 ai/nn/transformer/gpt/4 --- https://x.com/adolfux/status/1636026798894104578 2022-09-08 design/visualization --- https://x.com/tristwolff/status/1636188634012438530 2022-09-08 ai/nn/diffusion --- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001973 Transcranial electrical stimulation: How can a simple conductor orchestrate complex brain activity? Matthew R. Krause, Pedro G. Vieira, Christopher C. Pack 2022-09-09 [("doi","10.1371/journal.pbio.3001973")] psychology/neuroscience/tcs

Weak electric currents applied to the scalp (known as transcranial electrical stimulation) can affect brain activity in surprisingly complex ways. This Unsolved Mystery investigates how these effects can be used to treat disease or improve cognition.

Transcranial electrical stimulation (tES) is one of the oldest and yet least understood forms of brain stimulation. The idea that a weak electrical stimulus, applied outside the head, can meaningfully affect neural activity is often regarded as mysterious.

Here, we argue that the direct effects of tES are not so mysterious: Extensive data from a wide range of model systems shows it has appreciable effects on the activity of individual neurons. Instead, the real mysteries are how tES interacts with the brain’s own activity and how these dynamics can be controlled to produce desirable therapeutic effects.

These are challenging problems, akin to repairing a complex machine while it is running, but they are not unique to tES or even neuroscience. We suggest that models of coupled oscillators, a common tool for studying interactions in other fields, may provide valuable insights.

By combining these tools with our growing, interdisciplinary knowledge of brain dynamics, we are now in a good position to make progress in this area and meet the high demand for effective neuromodulation in neuroscience and psychiatry.

--- https://x.com/Afinetheorem/status/1634516697515261953 2022-09-09 ai/nn/retrieval ai/nn/transformer/gpt/codex --- https://what-if.xkcd.com/58/ 2022-09-09 science --- https://github.com/antimatter15/alpaca.cpp 2022-09-09 ai/nn/transformer/gpt --- https://www.quantamagazine.org/the-unpredictable-abilities-emerging-from-large-ai-models-20230316/ 2022-09-09 ai/scaling/emergence --- https://x.com/JeffLadish/status/1636288820558852103 2022-09-09 ai/nn/transformer/gpt/4/poetry --- https://x.com/JeffLadish/status/1636288821859069952 2022-09-09 ai/nn/transformer/gpt/4/poetry --- https://x.com/JeffLadish/status/1636292464809242626 2022-09-09 ai/nn/transformer/gpt/4/poetry --- https://x.com/prerationalist/status/1636370054936772608 2022-09-09 ai/nn/transformer/gpt/4/poetry --- https://gist.github.com/harryaskham/68a611bef777525991790bca2f2d324d 2022-09-09 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex cs/haskell --- https://x.com/harryaskham/status/1636376676329455617 2022-09-10 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex cs/haskell --- https://paperswithcode.com/sota/text-to-image-generation-on-coco 2022-09-10 ai/nn/diffusion ai/nn/gan --- https://x.com/ID_AA_Carmack/status/1636043611266490368 2022-09-10 ai/nn/transformer/gpt/4 economics/automation --- https://x.com/alexalbert__/status/1636488551817965568 2022-09-10 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex reinforcement-learning/meta-learning reinforcement-learning/safe --- https://x.com/mattshumer_/status/1636512490195501056 2022-09-10 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://en.wikipedia.org/wiki/Stereotype_threat#Criticism Stereotype threat § Criticism 2022-09-10 psychology/cognitive-bias/stereotype-threat --- https://statmodeling.stat.columbia.edu/wp-content/uploads/2013/04/ganley-et-al.-stereotype-threat.pdf 2022-09-10 psychology/cognitive-bias/stereotype-threat --- https://pure.uvt.nl/ws/portalfiles/portal/29125573/MTO_Flore_influence_of_gender_stereotype_CRiSP_2019.pdf#page=3 2022-09-10 psychology/cognitive-bias/stereotype-threat --- https://x.com/genmon/status/1636698753007603713 2022-09-10 ai/nn/transformer/gpt/poetry --- https://news.ycombinator.com/item?id=35199646 2022-09-10 ai/nn/transformer/gpt/codex --- https://news.ycombinator.com/item?id=35195810 2022-09-10 ai/nn/transformer/gpt/4/poetry --- https://dkb.blog/p/chatgpts-chess-elo-is-1400 2022-09-11 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/chess --- https://www.reddit.com/r/GPT3/comments/11u09ka/my_god_gpt4_ability_to_generate_poetry_really/ 2022-09-11 ai/nn/transformer/gpt/4/poetry --- https://x.com/axpuig/status/1635771128986710016 2022-09-11 ai/nn/transformer/gpt/4/nonfiction --- https://uxdesign.cc/design-notes-on-the-2023-wikipedia-redesign-d6573b9af28d 2022-09-11 design wikipedia --- https://x.com/Law_Rhetoric/status/1637097558882361345 2022-09-11 ai/nn/transformer/gpt/4/fiction --- /doc/genetics/editing/1999-bliss.pdf Young receptors make smart mice T. V. P. Bliss 1999-09-02 2022-09-11 [("doi","10.1038/43333")] genetics/editing iq psychology/neuroscience --- https://villekuosmanen.medium.com/i-played-chess-against-chatgpt-4-and-lost-c5798a9049ca 2022-09-11 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/chess --- https://www.da.vidbuchanan.co.uk/blog/exploiting-acropalypse.html 2022-09-11 cs/security --- https://x.com/Naman_Bhalla/status/1637578019811340292 2022-09-11 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://news.ycombinator.com/item?id=35236275 2022-09-11 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://x.com/biz84/status/1637793452879405064 2022-09-11 ai/nn/transformer/gpt/4/nonfiction --- https://publicdomainreview.org/essay/chaos-bewitched-moby-dick-and-ai 2022-09-12 ai/nn/transformer/gpt/dall-e history/public-domain-review --- https://pershmail.substack.com/p/questions-and-answers-about-multiplication 2022-09-12 math psychology --- https://daringfireball.net/2023/03/game_over_vocal_deepfakes 2022-09-12 music --- https://en.wikibooks.org/wiki/LaTeX/Boxes 2022-09-12 design/typography --- https://www.reddit.com/r/ExperiencedDevs/comments/11y8hys/chatgpt_resumes_accounted_for_30_of_the_ones_we/ 2022-09-12 ai/nn/transformer/gpt/4/nonfiction --- https://nightingaledvs.com/dark-sky-weather-data-viz/ 2022-09-12 design/visualization --- https://www.loper-os.org/?p=1913 2022-09-12 cs/algorithm --- https://en.wikipedia.org/wiki/Metcalfe%27s_law Metcalfe’s law 2022-09-12 economics/automation/metcalfes-law --- https://en.wikipedia.org/wiki/Robert_Metcalfe Robert Metcalfe 2022-09-12 economics/automation/metcalfes-law --- https://en.wikipedia.org/wiki/Dunbar%27s_number Dunbar’s number 2022-09-12 economics/automation/metcalfes-law --- https://en.wikipedia.org/wiki/Beckstrom%27s_law Beckstrom’s law 2022-09-13 economics/automation/metcalfes-law --- https://en.wikipedia.org/wiki/Reed%27s_law Reed’s law 2022-09-13 economics/automation/metcalfes-law --- https://en.wikipedia.org/wiki/Sarnoff%27s_law Sarnoff’s law 2022-09-13 economics/automation/metcalfes-law --- https://en.wikipedia.org/wiki/Matthew_effect Matthew effect 2022-09-13 economics/automation/metcalfes-law --- https://en.wikipedia.org/wiki/Dot-com_bubble Dot-com bubble 2022-09-13 economics/automation/metcalfes-law --- https://en.wikipedia.org/wiki/3Com 3Com 2022-09-13 economics/automation/metcalfes-law --- https://en.wikipedia.org/wiki/Ethernet Ethernet 2022-09-13 economics/automation/metcalfes-law --- https://en.wikipedia.org/wiki/ALOHAnet ALOHAnet 2022-09-13 economics/automation/metcalfes-law --- https://vcmike.wordpress.com/2006/08/18/metcalfe-social-networks/ 2022-09-13 economics/automation/metcalfes-law --- https://en.wikipedia.org/wiki/Diseconomies_of_scale Diseconomies of scale 2022-09-13 economics/automation/metcalfes-law --- https://lemire.me/blog/2023/03/22/can-gpt-pass-my-programming-courses/ 2022-09-13 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://www.nature.com/articles/d41586-023-00831-6 2022-09-14 statistics/peer-review --- https://x.com/ChrSzegedy/status/1638993326513999874 2022-09-14 ai/nn/transformer/gpt/4/poetry math --- https://x.com/arankomatsuzaki/status/1639000379978403853 2022-09-14 ai/nn/rnn ai/nn/transformer/attention/recurrent --- https://www.merriam-webster.com/games/twofer-goofer 2022-09-14 ai/nn/tokenization ai/nn/transformer/gpt/4/poetry ai/nn/transformer/gpt/poetry --- https://openai.com/blog/chatgpt-plugins 2022-09-14 ai/nn/retrieval ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction math --- https://www.reddit.com/r/dalle2/comments/11zq37j/the_legend_of_zelda_made_using_experimental_dalle2/ 2022-09-14 ai/anime ai/nn/transformer/gpt/dall-e --- https://sigmoidprime.com/post/searchthearxiv/ 2022-09-14 ai/nn/retrieval --- http://habitatchronicles.com/2004/04/you-cant-tell-people-anything/ 2022-09-14 design --- https://arxiv.org/abs/1708.06742 Twin Networks: Matching the Future for Sequence Generation Dmitriy Serdyuk, Nan Rosemary Ke, Alessandro Sordoni, Adam Trischler, Chris Pal, Yoshua Bengio 2017-08-22 2022-09-14 [("doi","10.48550/arXiv.1708.06742")] ai/nn/rnn

We propose a simple technique for encouraging generative RNNs to plan ahead.

We train a “backward” recurrent network to generate a given sequence in reverse order, and we encourage states of the forward model to predict co-temporal states of the backward model. The backward network is used only during training, and plays no role during sampling or inference.

We hypothesize that our approach eases modeling of long-term dependencies by implicitly forcing the forward states to hold information about the longer-term future (as contained in the backward states).

We show empirically that our approach achieves 9% relative improvement for a speech recognition task, and achieves improvement on a COCO caption generation task.

--- https://x.com/emollick/status/1638381608565628930 2022-09-14 ai/nn/transformer/gpt/4/poetry --- https://x.com/emollick/status/1638385246524563456 2022-09-14 ai/nn/transformer/gpt/4/poetry --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5998038/ Innovation and cumulative culture through tweaks and leaps in online programming contests Elena Miu, Ned Gulley, Kevin N. Laland, Luke Rendell 2018 2022-09-15 [("doi","10.1038/s41467-018-04494-0")] cs/algorithm economics/experience-curve reinforcement-learning/exploration statistics/order

The ability to build progressively on the achievements of earlier generations is central to human uniqueness, but experimental investigations of this cumulative cultural evolution lack real-world complexity.

Here, we studied the dynamics of cumulative culture using a large-scale data set from online collaborative programming competitions run over 14 years. We show that, within each contest population, performance increases over time through frequent ‘tweaks’ of the current best entry and rare innovative ‘leaps’ (successful tweak:leap ratio = 16:1), the latter associated with substantially greater variance in performance.

Cumulative cultural evolution reduces technological diversity over time, as populations focus on refining high-performance solutions. While individual entries borrow from few sources, iterative copying allows populations to integrate ideas from many sources, demonstrating a new form of collective intelligence.

Our results imply that maximizing technological progress requires accepting high levels of failure.

Figure 1: Scores over time. Normalized log-transformed scores over time (measured in days from the beginning of each contest) in all contests (<em>n</em> = 47,921 entries). For visualization purposes, because some values were zero, we added a small number on the appropriate scale to each score before log-transforming (the number chosen was 10). Note that in all contests low score values are better. Each point on the graph is an entry. The red line follows the progress of the leading entries in the contest, i.e. the entries that achieved the best score at the time of their submission.

…We analysed a database of 21,745,538 lines of computer code in total and 483,173 unique lines, originating from 47,967 entries to 19 online collaborative programming competitions organized over the course of 14 years by the MathWorks software company. In every contest, the organizers set a computational challenge and, over the course of one week, participants developed and provided solutions in the form of MATLAB® code. Once an entry had been successfully evaluated, its score, code and the username of the participant who submitted it became public and available to all the other participants to build upon. The challenges were all NP-complete computer science problems

…Leaps usually fail but can bring large advances: The success of an entry—whether it took the lead, and if so by how much—was strongly related to the extent to which it was based largely on copying or exhibited substantial innovation. Among entries that took the lead, we observed a statistically-significant negative correlation between the entry’s similarity to the previous leader and its associated improvement in score (Spearman’s ρ = −0.15, p < 0.001), with the biggest improvements associated with those entries most different from the previous leader. However, among entries that did not take the lead, the reverse relationship was observed (Spearman ρ = −0.53, p < 0.001), with the most innovative entries exhibiting the poorest performance, measured as the absolute difference in score from the current leader. Hence tweaks were associated with smaller changes in score, either positive or negative, while leaps garnered both large improvements in score and spectacular failures (Figure 2d; Supplementary Figure 4). The distribution of entry performance relative to the current leader shows that while leaps were more likely to lead to poorer scores than tweaks of copied material overall, on rare occasions they generated statistically-significantly larger benefits.

--- /doc/economics/automation/1994-romanelli.pdf Organizational Transformation as Punctuated Equilibrium: An Empirical Test Elaine Romanelli, Michael L. Tushman 1994-01 2022-09-15 [("doi","10.5465/256669")] economics/automation

The punctuated equilibrium model of organizational transformation has emerged as a prominent theoretical framework for explaining fundamental changes in patterns of organizational activity. To date, however, few aspects of the model have been tested formally.

We tested 3 basic arguments of the model using data on 25 U.S. minicomputer producers founded 1967–1969.

Supportive results showed that (1) a large majority of organizational transformations were accomplished via rapid and discontinuous change over most or all domains of organizational activity, (2) small changes in strategies, structures, and power distributions did not accumulate to produce fundamental transformations, and (3) major environmental changes and chief executive officer succession influenced transformations.

[Keywords: organizational change, punctuated equilibrium (evolution), CEOs, organizational sociology, corporate turnarounds, organizational structure, adaptability (psychology), organizational learning, microcomputer manufacturing]

--- https://diyhpl.us/wiki/transcripts/verifiable-brain-preservation/ 2022-09-15 cryonics --- https://www.overcomingbias.com/p/women-as-worriers-who-exclude 2022-09-15 sociology/intrasexual-aggression --- https://www.theawl.com/2015/12/access-denied/ 2022-09-15 politics sociology/technology --- https://x.com/vagabondjack/status/1637468848122396672 2022-09-15 ai/nn/transformer/gpt/4/nonfiction statistics/stylometry --- https://en.wikipedia.org/wiki/Alignments_of_random_points#Estimate_of_probability_of_chance_alignments Alignments of random points § Estimate of probability of chance alignments 2022-09-15 statistics/probability --- https://x.com/emollick/status/1639153509697462272 2022-09-15 ai/nn/transformer/gpt/4/fiction --- https://x.com/anthrupad/status/1639421396840316932 2022-09-15 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/davidad/status/1639215289677017099 2022-09-15 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://jalopnik.com/you-have-no-idea-how-insanely-complex-modern-headlights-1840509448 You Have No Idea How Insanely Complex Modern Headlights Can Be I Mean Holy Crap They Have GPS 2022-09-15 technology --- https://tug.org/TUGboat/tb17-1/tb50knut.pdf 2022-09-16 cs design/typography/tex --- https://www.newyorker.com/culture/the-new-yorker-interview/jia-tolentino-on-ozempics-breakthrough-benefits-and-risky-downsides 2022-09-16 longevity/glp/semaglutide --- /doc/economics/2022-aguilargomez.pdf This Is Air: The ‘Non-Health’ Effects of Air Pollution Sandra Aguilar-Gomez, Holt Dwyer, Joshua Graff Zivin, Matthew Neidell 2022-06-28 2022-09-16 [("doi","10.1146/annurev-resource-111820-021816")] crime economics exercise iq psychology/energy statistics/decision

A robust body of evidence shows that air pollution exposure is detrimental to health outcomes, often measured as deaths and hospitalizations.

This literature has focused less on subclinical channels that nonetheless impact behavior, performance, and skills. This article reviews the economic research investigating the causal effects of pollution on non-health end points, including labor productivity, cognitive performance, and multiple forms of decision-making.

Subclinical effects of pollution can be more challenging to observe than formal health care encounters but may be more pervasive if they affect otherwise healthy people. The wide variety of possible impacts of pollution should be informed by plausible mechanisms and require appropriate hypothesis testing to limit false discovery.

Finally, any detected effects of pollution, in both the short and long run, may be dampened by costly efforts to avoid exposure ex ante and remediate its impacts ex post; these costs must be considered for a full welfare analysis.

--- https://www.almendron.com/tribuna/wp-content/uploads/2014/04/testing-theories-of-american-politics-elites-interest-groups-and-average-citizens.pdf Testing Theories of American Politics: Elites, Interest Groups, and Average Citizens 2022-09-16 politics

[Spencer Greenberg notes that all the polls put together explain a surprisingly small R^2 = 7% of variance in what policies get passed.

One could interpret this as showing that no one’s preferences matter much and they are unable to explain why & what policies do get passed.]

--- https://80000hours.org/podcast/episodes/spencer-greenberg-stopping-valueless-papers/#creating-habits-more-reliably-011816 2022-09-16 psychology/willpower --- https://80000hours.org/podcast/episodes/spencer-greenberg-stopping-valueless-papers/#social-science-reform-000846 2022-09-16 statistics/bias --- https://www.lesswrong.com/posts/pDzdb4smpzT3Lwbym/my-model-of-ea-burnout 2022-09-16 psychology/willpower --- https://www.spencergreenberg.com/2023/02/doing-what-you-value-as-a-way-of-life-an-introduction-to-valuism/ 2022-09-16 psychology/willpower --- https://betterprogramming.pub/the-dark-side-of-llms-we-need-to-rethink-large-language-models-now-6212aca0581a 2022-09-16 ai/nn/transformer/gpt/4/nonfiction cs/security --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7545656/ Autistic peer-to-peer information transfer is highly effective Catherine J. Crompton, Danielle Ropar, Claire Vm Evans-Williams, Emma G. Flynn, Sue Fletcher-Watson 2020 2022-09-16 [("doi","10.1177/1362361320919286")] psychiatry/autism psychology/personality

Sharing information with other people relies on the ability to communicate well. Autism is defined clinically by deficits in social communication. It may therefore be expected that autistic people find it difficult to share information with other people.

We wanted to find out whether this was the case, and whether it was different when autistic people were sharing information with other autistic people or with non-autistic people. We recruited 9 groups, each with 8 people. In 3 of the groups, everyone was autistic; in 3 of the groups, everyone was non-autistic; and 3 of the groups were mixed groups where half the group was autistic and half the group was non-autistic.

We told one person in each group a story and asked them to share it with another person, and for that person to share it again and so on, until everyone in the group had heard the story. We then looked at how many details of the story had been shared at each stage. We found that autistic people share information with other autistic people as well as non-autistic people do with other non-autistic people. However, when there are mixed groups of autistic and non-autistic people, much less information is shared.

Participants were also asked how they felt they had got on with the other person in the interaction. The people in the mixed groups also experienced lower rapport with the person they were sharing the story with. This finding is important as it shows that autistic people have the skills to share information well with one another and experience good rapport, and that there are selective problems when autistic and non-autistic people are interacting.

--- /doc/psychiatry/depression/2019-reardon-2.pdf Depression researchers rethink popular mouse swim tests: Animal-rights group’s campaign to end forced-swim tests comes amid debate over whether method is overused Sara Reardon 2019-07-18 2022-09-17 [("doi","10.1038/d41586-019-02133-2")] psychiatry/depression statistics/bias/animal --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5909901/ Breastfeeding during infancy and neurocognitive function in adolescence: 16-year follow-up of the PROBIT cluster-randomized trial Seungmi Yang, Richard M. Martin, Emily Oken, Mikhail Hameza, Glen Doniger, Shimon Amit, Rita Patel, Jennifer Thompson, Sheryl L. Rifas-Shiman, Konstantin Vilchuck, Natalia Bogdanovich, Michael S. Kramer 2018 2022-09-17 [("doi","10.1371/journal.pmed.1002554")] iq

Background: Evidence on the long-term effect of breastfeeding on neurocognitive development is based almost exclusively on observational studies. In the 16-year follow-up study of a large, cluster-randomized trial of a breastfeeding promotion intervention, we evaluated the long-term persistence of the neurocognitive benefits of the breastfeeding promotion intervention previously observed at early school age.

Methods & Findings: A total of 13,557 participants (79.5% of the 17,046 randomized) of the Promotion of Breastfeeding Intervention Trial (PROBIT) were followed up at age 16 from September 2012 to July 2015. At the follow-up, neurocognitive function was assessed in 7 verbal and nonverbal cognitive domains using a computerized, self-administered test battery among 13,427 participants. Using an intention-to-treat (ITT) analysis as our prespecified primary analysis, we estimated cluster & baseline characteristic-adjusted mean differences between the intervention (prolonged and exclusive breastfeeding promotion modelled on the Baby-Friendly Hospital Initiative) and control (usual care) groups in 7 cognitive domains and a global cognitive score. In our prespecified secondary analysis, we estimated mean differences by instrumental variable (IV) analysis to account for noncompliance with the randomly assigned intervention and estimate causal effects of breastfeeding. The 16-year follow-up rates were similar in the intervention (79.7%) and control groups (79.3%), and baseline characteristics were comparable between the two.

In the cluster-adjusted ITT analyses, children in the intervention group did not show statistically-significant differences in the scores from children in the control group. Prespecified additional adjustment for baseline characteristics improved statistical precision and resulted in slightly higher scores among children in the intervention for verbal function (1.4 [95% CI 0.3-2.5]) and memory (1.2 [95% CI 0.01-2.4]). IV analysis showed that children who were exclusively breastfed for ≥3 (versus <3) months had a 3.5-point (95% CI 0.9-6.1) higher verbal function, but no differences were observed in other domains. While our computerized, self-administered cognitive testing reduced the cluster-level variability in the scores, it may have increased individual-level measurement errors in adolescents.

Conclusions: We observed no benefit of a breastfeeding promotion intervention on overall neurocognitive function. The only beneficial effect was on verbal function at age 16. The higher verbal ability is consistent with results observed at early school age; however, the effect size was substantially smaller in adolescence.

Probit Trial Registration: NCT01561612.

--- https://x.com/sangyh2/status/1636785191447564288 2022-09-17 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/tamaybes/status/1639400013062348800 2022-09-17 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/erikphoel/status/1638936714533130245 2022-09-17 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/peakcooper/status/1639716822680236032 2022-09-17 ai/nn/transformer/gpt/4/nonfiction biology --- https://x.com/retvitr/status/1640021317628854277 2022-09-17 ai/nn/transformer/gpt/4/fiction ai/text-style-transfer --- https://x.com/retvitr/status/1636338579918974978 2022-09-17 ai/nn/transformer/gpt/4/poetry --- https://en.wikipedia.org/wiki/De_Finetti's_theorem De Finetti’s theorem 2022-09-17 statistics/bayes statistics/probability --- https://x.com/perrymetzger/status/1639968357607698433 2022-09-17 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://blog.matteskridge.com/business/gpt4-and-silicon-valley-bank/2023/03/19/ 2022-09-17 ai/nn/transformer/gpt/4/nonfiction --- https://andrewmayne.com/2023/03/23/chatgpt-code-interpreter-magic/ 2022-09-18 ai/nn/transformer/gpt/codex --- https://x.com/ccanonne_/status/1639848150495301633 2022-09-18 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/0709.1516 On Universal Prediction and Bayesian Confirmation Marcus Hutter 2007-09-11 2022-09-18 [("doi","10.48550/arXiv.0709.1516")] ai cs/computable statistics/bayes

The Bayesian framework is a well-studied and successful framework for inductive reasoning, which includes hypothesis testing and confirmation, parameter estimation, sequence prediction, classification, and regression. But standard statistical guidelines for choosing the model class and prior are not always available or fail, in particular in complex situations. Solomonoff completed the Bayesian framework by providing a rigorous, unique, formal, and universal choice for the model class and the prior.

We discuss in breadth how and in which sense universal (non-i.i.d.) sequence prediction solves various (philosophical) problems of traditional Bayesian sequence prediction.

We show that Solomonoff’s model possesses many desirable properties: Strong total and weak instantaneous bounds, and in contrast to most classical continuous prior densities has no zero p(oste)rior problem, i.e. can confirm universal hypotheses, is reparametrization and regrouping invariant, and avoids the old-evidence and updating problem.

It even performs well (actually better) in non-computable environments.

--- https://file770.com/judge-decides-against-internet-archive/ 2022-09-18 cs/linkrot/archiving economics/copyright law --- https://blog.archive.org/2023/03/25/the-fight-continues/ 2022-09-18 cs/linkrot/archiving economics/copyright law --- https://tenhundredwordsofscience.tumblr.com/ 2022-09-18 psychology/writing --- https://arxiv.org/abs/2002.03629 Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon 2020-02-10 2022-09-18 [("doi","10.48550/arXiv.2002.03629")] ai/nn/cnn ai/nn/rnn

Feedforward computation, such as evaluating a neural network or sampling from an autoregressive model, is ubiquitous in machine learning. The sequential nature of feedforward computation, however, requires a strict order of execution and cannot be easily accelerated with parallel computing.

To enable parallelization, we frame the task of feedforward computation as solving a system of nonlinear equations. We then propose to find the solution using a Jacobi or Gauss-Seidel fixed-point iteration method, as well as hybrid methods of both. Crucially, Jacobi updates operate independently on each equation and can be executed in parallel.

Our method is guaranteed to give exactly the same values as the original feedforward computation with a reduced (or equal) number of parallelizable iterations, and hence reduced time given sufficient parallel computing power.

Experimentally, we demonstrate the effectiveness of our approach in accelerating (1) backpropagation of RNNs, (2) evaluation of DenseNets, and (3) autoregressive sampling of MADE and PixelCNN++, with speedup factors between 2.1 and 26 under various settings.

--- https://www.waluigipurple.com/post/revising-poetry-with-gpt-4 2022-09-18 ai/nn/transformer/gpt/4/poetry ai/nn/transformer/gpt/inner-monologue --- https://moritzfuerst.net/projects/smalltalk-type 2022-09-18 design/typography --- https://www.lesswrong.com/posts/tyts4Dw7SafsxBjar/what-can-we-learn-from-lex-fridman-s-interview-with-sam 2022-09-18 reinforcement-learning/safe --- https://x.com/repligate/status/1640509177159114752 2022-09-18 ai/nn/transformer/gpt/4/nonfiction design/typography --- https://arxiv.org/abs/2303.06349#deepmind Resurrecting Recurrent Neural Networks for Long Sequences Antonio Orvieto, Samuel L. Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Razvan Pascanu, Soham De 2023-03-11 2023-03-11 [("doi","10.48550/arXiv.2303.06349")] ai/nn/rnn

[commentary] Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are hard to optimize and slow to train.

Deep state-space models (SSMs) have recently been shown to perform remarkably well on long sequence modeling tasks, and have the added benefits of fast parallelizable training and RNN-like fast inference.

However, while SSMs are superficially similar to RNNs, there are important differences that make it unclear where their performance boost over RNNs comes from.

In this paper, we show that careful design of deep RNNs using standard signal propagation arguments can recover the impressive performance of deep SSMs on long-range reasoning tasks, while also matching their training speed.

To achieve this, we analyze and ablate a series of changes to standard RNNs including linearizing and diagonalizing the recurrence, using better parameterizations and initializations, and ensuring proper normalization of the forward pass.

Our results provide new insights on the origins of the impressive performance of deep SSMs, while also introducing an RNN block called the Linear Recurrent Unit that matches both their performance on the Long Range Arena benchmark and their computational efficiency.

--- https://arxiv.org/abs/1701.05517#openai PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma 2017-01-19 2022-09-19 [("doi","10.48550/arXiv.1701.05517")] ai/nn/cnn

PixelCNNs are a recently proposed class of powerful generative models with tractable likelihood. Here we discuss our implementation of PixelCNNs which we make available at Github.

Our implementation contains a number of modifications to the original model that both simplify its structure and improve its performance. (1) We use a discretized logistic mixture likelihood on the pixels, rather than a 256-way softmax, which we find to speed up training. (2) We condition on whole pixels, rather than R/G/B sub-pixels, simplifying the model structure. (3) We use downsampling to efficiently capture structure at multiple resolutions. (4) We introduce additional short-cut connections to further speed up optimization. (5) We regularize the model using dropout.

Finally, we present state-of-the-art log likelihood results on CIFAR-10 to demonstrate the usefulness of these modifications.

--- https://arxiv.org/abs/1502.03509 MADE: Masked Autoencoder for Distribution Estimation Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle 2015-02-12 2022-09-19 [("doi","10.48550/arXiv.1502.03509")] ai/nn/vae

There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples.

We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder’s parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering. Constrained this way, the autoencoder outputs can be interpreted as a set of conditional probabilities, and their product, the full joint probability. We can also train a single network that can decompose the joint probability in multiple different orderings.

Our simple framework can be applied to multiple architectures, including deep ones. Vectorized implementations, such as on GPUs, are simple and fast.

Experiments demonstrate that this approach is competitive with state-of-the-art tractable distribution estimators. At test time, the method is faster and scales better than other autoregressive estimators.

--- https://x.com/sh_reya/status/1284746918959239168 After many hours of retraining my brain to operate in this "priming" approach, I also now have a sick GPT-3 demo: English to LaTeX equations! I’m simultaneously impressed by its coherence and amused by its brittleness—watch me test the fundamental theorem of calculus. 2022-09-19 design/typography/tex --- https://tug.org/FontCatalogue/otherfonts.html#initials The LaTeX Font Catalogue—Other Fonts—Initials 2022-09-19 cs/css design/typography/tex --- https://en.wikipedia.org/wiki/TAOCP TAOCP 2022-09-19 design/typography/tex --- https://en.wikipedia.org/wiki/MetaPost MetaPost 2022-09-19 design/typography/tex --- https://en.wikipedia.org/wiki/CWEB CWEB 2022-09-19 design/typography/tex --- https://en.wikipedia.org/wiki/Leslie_Lamport Leslie Lamport 2022-09-19 design/typography/tex --- https://en.wikipedia.org/wiki/Donald_Knuth Donald Knuth 2022-09-19 design/typography/tex --- https://en.wikipedia.org/wiki/ConTeXt ConTeXt 2022-09-20 design/typography/tex --- https://en.wikipedia.org/wiki/Computer_Modern Computer Modern 2022-09-20 design/typography/tex --- https://en.wikipedia.org/wiki/MathJax MathJax 2022-09-20 design/typography/tex --- https://en.wikipedia.org/wiki/BibTeX BibTeX 2022-09-20 design/typography/tex --- https://en.wikipedia.org/wiki/Metafont Metafont 2022-09-20 design/typography/tex --- https://en.wikipedia.org/wiki/Texinfo Texinfo 2022-09-20 design/typography/tex --- https://en.wikipedia.org/wiki/AMS_Euler AMS Euler 2022-09-20 design/typography/tex --- https://en.wikipedia.org/wiki/XeTeX XeTeX 2022-09-20 design/typography/tex --- https://en.wikipedia.org/wiki/Computers_and_Typesetting Computers and Typesetting 2022-09-20 design/typography/tex --- https://en.wikipedia.org/wiki/Literate_programming Literate programming 2022-09-20 design/typography/tex --- https://en.wikipedia.org/wiki/WEB WEB 2022-09-20 design/typography/tex --- https://en.wikipedia.org/wiki/CTAN CTAN 2022-09-21 design/typography/tex --- https://en.wikipedia.org/wiki/TUGboat TUGboat 2022-09-21 design/typography/tex --- https://www.infoq.com/news/2015/01/implementing-tex-in-clojure/ 2022-09-21 design/typography/tex --- https://github.com/bramstein/typeset/ 2022-09-21 design/typography/tex --- https://rjlipton.com/2011/03/09/tex-is-great-what-is-tex/ 2022-09-21 design/typography/tex --- https://www.kevinmarks.com/htmlversustex.html 2022-09-21 cs/css design/typography/tex --- https://texdoc.org/serve/errorlog.pdf/0 2022-09-21 design/typography/tex --- http://xahlee.info/cmaci/notation/TeX_pestilence.html 2022-09-21 design/typography/tex --- https://www.overleaf.com/learn/latex/Articles/What%27s_in_a_Name%3A_A_Guide_to_the_Many_Flavours_of_TeX 2022-09-21 design/typography/tex --- https://lexfridman.com/donald-knuth/ 2022-09-21 design/typography/tex --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0115069 An Efficiency Comparison of Document Preparation Systems Used in Academic Research and Development Markus Knauff, Jelica Nejasmic 2014-11-18 2022-09-21 [("doi","10.1371/journal.pone.0115069")] design/typography/tex

The choice of an efficient document preparation system is an important decision for any academic researcher. To assist the research community, we report a software usability study in which 40 researchers across different disciplines prepared scholarly texts with either Microsoft Word or LaTeX.

The probe texts included simple continuous text, text with tables and subheadings, and complex text with several mathematical equations. We show that LaTeX users were slower than Word users, wrote less text in the same amount of time, and produced more typesetting, orthographical, grammatical, and formatting errors. On most measures, expert LaTeX users performed even worse than novice Word users.

LaTeX users, however, more often report enjoying using their respective software.

We conclude that even experienced LaTeX users may suffer a loss in productivity when LaTeX is used, relative to other document preparation systems. Individuals, institutions, and journals should carefully consider the ramifications of this finding when choosing document preparation strategies, or requiring them of authors.

--- https://www.iacr.org/authors/tikz/ 2022-09-22 cs/cryptography design/typography/tex --- https://blog.cr.yp.to/20201206-msword.html 2022-09-22 design/typography/tex --- https://umij.wordpress.com/2016/08/11/the-sad-state-of-pdf-accessibility-of-latex-documents/ 2022-09-22 design/typography/tex --- https://en.wikipedia.org/wiki/CiteProc CiteProc 2022-09-22 design/typography/tex --- https://karthinks.com/software/latex-input-for-impatient-scholars/ 2022-09-22 cs/lisp design/typography/tex --- https://web.archive.org/web/20150417042513/http://typophile.com/node/43702 2022-09-22 design/typography/tex --- https://laurmaedje.github.io/posts/comemo/ 2022-09-22 design/typography/tex --- https://mathoverflow.net/questions/19930/writing-papers-in-pre-latex-era 2022-09-22 design/typography/tex math --- https://castel.dev/post/lecture-notes-1/ 2022-09-22 design/typography/tex --- https://olivierpieters.be/blog/2017/02/11/designing-a-business-card-in-latex 2022-09-22 design/typography/tex --- http://legacy.hanno-rein.de/hanno-rein.de/archives/349 2022-09-23 design/typography/tex --- https://increment.com/open-source/the-lingua-franca-of-latex/ 2022-09-23 design/typography/tex --- https://castel.dev/post/research-workflow/ 2022-09-23 design/typography/tex --- https://andreas-zeller.blogspot.com/2017/01/twelve-latex-packages-to-get-your-paper.html 2022-09-23 design/typography/tex math/humor --- https://nitens.org/w/latex/ 2022-09-23 design/typography/tex --- https://drshika.me/2022/04/15/latex-resumes 2022-09-23 design/typography/tex --- http://detexify.kirelabs.org/classify.html 2022-09-23 design/typography/tex --- https://tug.org/FontCatalogue/ 2022-09-23 design/typography/tex --- https://x.com/rapha_gl/status/1262489387767480322 2022-09-23 design/typography/tex math --- http://www.danielallington.net/2016/09/the-latex-fetish/ 2022-09-23 design/typography/tex --- https://tex.stackexchange.com/questions/29402/how-do-i-make-my-document-look-like-it-was-written-by-a-cthulhu-worshipping-madm 2022-09-23 design/typography/tex fiction/humor --- https://publicdomainreview.org/collection/blights-of-the-bookish 2022-09-24 history/public-domain-review psychiatry psychology/writing --- https://x.com/ShayneRedford/status/1640702622557523969 2022-09-24 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/non-fiction --- https://github.com/TaxyAI/browser-extension 2022-09-24 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://ciechanow.ski/bicycle/ 2022-09-24 design/visualization technology --- https://blog.darklang.com/gpt/ 2022-09-24 ai/nn/transformer/gpt/codex --- https://x.com/blader/status/1640217165822578688 2022-09-24 ai/nn/transformer/gpt/5 --- https://github.com/rcompton/black-market-recommender-systems 2022-09-24 darknet-market/dnm-archive --- https://arxiv.org/abs/2303.14389 Masked Diffusion Transformer is a Strong Image Synthesizer Shanghua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan 2023-03-25 2023-03-25 [("doi","10.48550/arXiv.2303.14389")] ai/nn/diffusion ai/nn/vae/mae

Despite its success in image synthesis, we observe that diffusion probabilistic models (DPMs) often lack contextual reasoning ability to learn the relations among object parts in an image, leading to a slow learning process.

To solve this issue, we propose a Masked Diffusion Transformer (MDT) that introduces a mask latent modeling scheme to explicitly enhance the DPMs’ ability of contextual relation learning among object semantic parts in an image.

During training, MDT operates on the latent space to mask certain tokens. Then, an asymmetric masking diffusion transformer is designed to predict masked tokens from unmasked ones while maintaining the diffusion generation process. Our MDT can reconstruct the full information of an image from its incomplete contextual input, thus enabling it to learn the associated relations among image tokens.

Experimental results show that MDT achieves superior image synthesis performance, eg. a new SoTA FID score on the ImageNet dataset, and has about 3× faster learning speed than the previous SoTA DiT.

The source code is released at Github.

--- https://github.com/nomic-ai/gpt4all 2022-09-24 ai/dataset ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/4/nonfiction --- https://www.kroneckerwallis.com/product/euclids-elements-completing-oliver-byrnes-work/ 2022-09-24 design/typography math --- https://aperiodical.com/2019/09/reimagining-byrnes-euclid/ 2022-09-24 design/typography math --- https://en.wikipedia.org/wiki/Hanging_punctuation Hanging punctuation 2022-09-25 design/typography --- https://x.com/jamesbrandecon/status/1639709460762624001 2022-09-25 ai/nn/transformer/gpt/codex --- https://lithub.com/an-unstandardized-decentralized-carnival-fire-how-rare-books-are-cataloged/ 2022-09-25 psychology/collecting --- https://www.afterbabel.com/p/international-mental-illness-part-one 2022-09-25 psychiatry/anxiety psychiatry/depression sociology/technology --- https://www.nngroup.com/articles/anti-mac-interface/ 2022-09-25 design --- https://arxiv.org/abs/2303.14178 One Protocol to Rule Them All? On Securing Interoperable Messaging Jenny Blessing, Ross Anderson 2023-03-24 2023-03-24 [("doi","10.48550/arXiv.2303.14178")] cs/cryptography

European lawmakers have ruled that users on different platforms should be able to exchange messages with each other. Yet messaging interoperability opens up a Pandora’s box of security and privacy challenges.

While championed not just as an anti-trust measure but as a means of providing a better experience for the end user, interoperability runs the risk of making the user experience worse if poorly executed. There are two fundamental questions: how to enable the actual message exchange, and how to handle the numerous residual challenges arising from encrypted messages passing from one service provider to another—including but certainly not limited to content moderation, user authentication, key management, and metadata sharing between providers.

In this work, we identify specific open questions and challenges around interoperable communication in end-to-end encrypted messaging, and present high-level suggestions for tackling these challenges.

--- https://scribe.citizen4.eu/@twomonthsoff/the-post-mac-interface-1031b94df77b 2022-09-25 design --- https://www.lesswrong.com/posts/nmxzr2zsjNtjaHh7x/actually-othello-gpt-has-a-linear-emergent-world 2022-09-25 reinforcement-learning/model --- /doc/genetics/heritable/emergenesis/2021-woodley-2.pdf Estimating the Additive Heritability of Historiometric Eminence in a Super-Pedigree Comprised of 4 Prominent Families Michael A. Woodley, Mateo Peñaherrera-Aguirre, Matthew A. Sarraf 2021-08-01 2022-09-25 [("doi","10.1017/thg.2021.29")] genetics/heritable/emergenesis science

By merging analytical approaches from the fields of historiometrics and behavior genetics, a social pedigree-based estimate of the heritability of eminence is generated. Eminent individuals are identified using the Pantheon dataset.

A single super-pedigree, comprised of 4 prominent and interrelated families (including the Wedgwood–Darwin, Arnold–Huxley, Keynes-Baha’u’lláh, and Benn-Rutherford pedigrees) is assembled, containing 30 eminent individuals out of 301 in total. Each eminent individual in the super-pedigree is assigned a relative measure of historical eminence (scaled 1 → 100) with non-eminent individuals assigned a score of 0. Utilizing a Bayesian pedigree-based heritability estimation procedure employing an informed prior, an additive heritability of eminence of 0.507 (95% CI [0.434, 0.578]) was found.

The finding that eminence is additively heritable is consistent with expectations from behavior-genetic studies of factors that are thought to underlie extraordinary accomplishment, which indicate that they are substantially additively heritable. Owing to the limited types of intermarriage present in the data, it was not possible to estimate the impact of nonadditive genetic contributions to heritability. Gene-by-environment interactions could not be estimated in the present analysis either; therefore, the finding that eminence is simply a function of additive genetic and nonshared environmental variance should be interpreted cautiously.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4856545/ A novel sibling-based design to quantify genetic and shared environmental effects: application to drug abuse, alcohol use disorder and criminal behavior K S. Kendler, H. Ohlsson, A. C. Edwards, P. Lichtenstein, K. Sundquist, J. Sundquist 2016 2022-09-25 [("doi","10.1017/S003329171500224X")] crime genetics/heritable psychiatry/alcoholism

Background: Twin studies have been criticized for upwardly biased estimates that might contribute to the missing heritability problem.

Method: We identified, from the general Swedish population born 1960–1990, informative sibships containing a proband, one reared-together full-sibling or half-sibling and a full-sibling, step-sibling or half-sibling with varying degrees of childhood cohabitation with the proband. Estimates of genetic, shared and individual specific environment for drug abuse (DA), alcohol use disorder (AUD) and criminal behavior (CB), assessed from medical, legal or pharmacy registries, were obtained using Mplus.

Results: Aggregate estimates of additive genetic effects for DA, AUD and CB obtained separately in males and females varied 35.9%–59.4% and agreed with those obtained from monozygotic and dizygotic twins from the same population. Of 54 heritability estimates from individual classes of informative sibling trios (3 syndromes × 9 classes of trios × 2 sexes), heritability estimates from the siblings were lower, tied and higher than those from obtained from twins in 26, one and 27 comparisons, respectively. By contrast, of 54 shared environmental estimates, 33 were lower than those found in twins, one tied and 20 were higher.

Conclusions: With adequate information, human populations can provide many methods for estimating genetic and shared environmental effects. For the 3 externalizing syndromes examined, concerns that heritability estimates from twin studies are upwardly biased or were not generalizable to more typical kinds of siblings were not supported. Overestimation of heritability from twin studies is not a likely explanation for the missing heritability problem.

--- https://publicdomainreview.org/collection/fraktur-folk-art 2022-09-25 design/typography history/public-domain-review --- /doc/math/2022-pitt.pdf Exact Number Concepts Are Limited to the Verbal Count Range Benjamin Pitt, Edward Gibson, Steven T. Piantadosi 2022-02-08 2022-09-26 [("doi","10.1177/09567976211034502")] math psychology/linguistics

Previous findings suggest that mentally representing exact numbers larger than 4 depends on a verbal count routine (eg. “one, two, 3 . . .”). However, these findings are controversial because they rely on comparisons across radically different languages and cultures.

We tested the role of language in number concepts within a single population—the Tsimane’ of Bolivia—in which knowledge of number words varies across individual adults. We used a novel data-analysis model to quantify the point at which participants (n = 30) switched from exact to approximate number representations during a simple numerical matching task.

The results show that these behavioral switch points were bounded by participants’ verbal count ranges; their representations of exact cardinalities were limited to the number words they knew. Beyond that range, they resorted to numerical approximation.

These results resolve competing accounts of previous findings and provide unambiguous evidence that large exact number concepts are enabled by language.

--- https://thenumb.at/Neural-Graphics/ 2022-09-26 ai/nn/fully-connected --- https://arxiv.org/abs/2210.04243 Fine-Tuning Pre-trained Transformers into Decaying Fast Weights Huanru Henry Mao 2022-10-09 2022-10-09 [("doi","10.48550/arXiv.2210.04243")] ai/nn/transformer/attention/recurrent ai/nn/transformer/gpt/2

Autoregressive Transformers are strong language models but incur 𝒪(T) complexity during per-token generation due to the self-attention mechanism. Recent work proposes kernel-based methods to approximate causal self-attention by replacing it with recurrent formulations with various update rules and feature maps to achieve 𝒪(1) time and memory complexity.

We explore these approaches and find that they are unnecessarily complex, and propose a simple alternative—decaying fast weights—that runs fast on GPU.

It outperforms prior methods, and retains 99% of attention’s performance for GPT-2. We also show competitive performance on WikiText-103 against more complex attention substitutes.

--- /doc/psychology/personality/1996-murphy-individualdifferencesandbehaviorinorganizations.pdf Individual Differences and Behavior in Organizations Kevin R. Murphy, Sheldon Zedeck, Leaetta M. Hough, Robert J. Schneider, Robert Hogan, Rex J. Blake, Jennifer M. George, Stephan J. Motowidlo, James T. Austin, Howard J. Klein, Michael Blake Gasser, Frederick L. Oswald, Jeffrey A. LePine, Daniel R. Ilgen, Robert A. Baron, Rodney L. Lowman, Lawrence R. James, Michael D. McIntyre, Joseph G. Rosse, Terry W. Noel, Keith Hattrup, Susan E. Jackson, Benjamin Schneider 1996-01-01 2022-09-26 economics iq/ses psychology/personality --- https://johnnysswlab.com/decreasing-the-number-of-memory-accesses-the-compilers-secret-life-2-2/ 2022-09-26 cs/algorithm --- https://www.lesswrong.com/posts/GveDmwzxiYHSWtZbv/shannon-s-surprising-discovery-1 2022-09-26 cs/algorithm statistics/bayes --- https://www.johndcook.com/blog/2017/02/08/how-efficient-is-morse-code/ 2022-09-26 cs/algorithm --- https://www.quantamagazine.org/how-a-dna-parasite-may-have-fragmented-our-genes-20230330/ 2022-09-26 genetics/selection/natural --- https://lindeloev.github.io/tests-as-linear/ 2022-09-26 statistics/probability --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8254832/ Long tracks of homozygosity predict the severity of alcohol use disorders in an American Indian population Qian Peng, Cindy L. Ehlers 2021 2022-09-26 [("doi","10.1038/s41380-020-00989-9")] genetics/heritable/rare psychiatry/alcoholism

Runs of homozygosity (ROH) arise when an individual inherits two copies of the same haplotype segment. While ROH are ubiquitous across human populations, Native populations-with shared parental ancestry arising from isolation and endogamy-can carry a substantial enrichment for ROH. We have been investigating genetic and environmental risk factors for alcohol use disorders (AUD) in a group of American Indians (AI) who have higher rates of AUD than the general U. S. population.

Here we explore whether ROH might be associated with incidence and severity of AUD in this admixed AI population (n = 742) that live on geographically contiguous reservations, using low-coverage whole genome sequences.

We have found that the genomic regions in the ROH that were identified in this population had statistically-significantly elevated American Indian heritage compared with the rest of the genome. Increased ROH abundance and ROH burden are likely risk factors for AUD severity in this AI population, especially in those diagnosed with severe and moderate AUD. The association between ROH and AUD was mostly driven by ROH of moderate lengths between 1 and 2 Mb. An ROH island on chromosome 1p32.3 and a rare ROH pool on chromosome 3p12.3 were found to be statistically-significantly associated with AUD severity. They contain genes involved in lipid metabolism, oxidative stress and inflammatory responses; and OSBPL9 was found to reside on the consensus part of the ROH island.

These data demonstrate that ROH are associated with risk for AUD severity in this AI population.

--- https://arxiv.org/abs/1904.09954 Communication and Code Dependency Effects on Software Code Quality: An Empirical Analysis of Herbsleb Hypothesis Suvodeep Majumder, Joymallya Chakraborty, Amritanshu Agrawal, Tim Menzies 2019-04-22 2022-09-27 [("doi","10.48550/arXiv.1904.09954")] cs/end-to-end-principle

Prior literature has suggested that in many projects 80% or more of the contributions are made by a small called group of around 20% of the development team. Most prior studies deprecate a reliance on such a small inner group of “heroes”, arguing that it causes bottlenecks in development and communication. Despite this, such projects are very common in open source projects. So what exactly is the impact of “heroes” in code quality?

Herbsleb argues that if code is strongly connected yet their developers are not, then that code will be buggy. To test the Herbsleb hypothesis, we develop and apply two metrics of (a) “social-ness’” and (b) “hero-ness” that measure (a) how much one developer comments on the issues of another; and (b) how much one developer changes another developer’s code (and “heroes” are those that change the most code, all around the system). In a result endorsing the Herbsleb hypothesis, in over 1,000 open source projects, we find that “social-ness” is a statistically stronger indicate for code quality (number of bugs) than “hero-ness”.

Hence we say that debates over the merits of “hero-ness” is subtly misguided. Our results suggest that the real benefits of these so-called “heroes” is not so much the code they generate but the pattern of communication required when the interaction between a large community of programmers passes through a small group of centralized developers. To say that another way, to build better code, build better communication flows between core developers and the rest.

In order to allow other researchers to confirm/improve/refute our results, all our scripts and data are available, on-line at Github.

--- https://arxiv.org/abs/2109.10312 Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks Bohan Wu, Suraj Nair, Li Fei-Fei, Chelsea Finn 2021-09-21 2022-09-27 [("doi","10.48550/arXiv.2109.10312")] reinforcement-learning/model reinforcement-learning/robot

In this paper, we study the problem of learning a repertoire of low-level skills from raw images that can be sequenced to complete long-horizon visuomotor tasks. Reinforcement learning (RL) is a promising approach for acquiring short-horizon skills autonomously. However, the focus of RL algorithms has largely been on the success of those individual skills, more so than learning and grounding a large repertoire of skills that can be sequenced to complete extended multi-stage tasks. The latter demands robustness and persistence, as errors in skills can compound over time, and may require the robot to have a number of primitive skills in its repertoire, rather than just one.

To this end, we introduce EMBER, a model-based RL method for learning primitive skills that are suitable for completing long-horizon visuomotor tasks. EMBER learns and plans using a learned model, critic, and success classifier, where the success classifier serves both as a reward function for RL and as a grounding mechanism to continuously detect if the robot should retry a skill when unsuccessful or under perturbations. Further, the learned model is task-agnostic and trained using data from all skills, enabling the robot to efficiently learn a number of distinct primitives. These visuomotor primitive skills and their associated pre/post-conditions can then be directly combined with off-the-shelf symbolic planners to complete long-horizon tasks.

On a Franka Emika robot arm, we find that EMBER enables the robot to complete 3 long-horizon visuomotor tasks at 85% success rate, such as organizing an office desk, a file cabinet, and drawers, which require sequencing up to 12 skills, involve 14 unique learned primitives, and demand generalization to novel objects.

--- https://x.com/joeyliaw/status/1528856081476116480 2022-09-27 ai/nn/transformer/gpt/dall-e/2 --- https://mattsclancy.substack.com/p/biases-against-risky-research 2022-09-27 statistics/peer-review --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097723/ Revisiting the Children-of-Twins Design: Improving Existing Models for the Exploration of Intergenerational Associations Tom A. McAdams, Laurie J. Hannigan, Espen Moen Eilertsen, Line C. Gjerde, Eivind Ystrom, Fruhling V. Rijsdijk 2018 2022-09-27 [("doi","10.1007/s10519-018-9912-4")] genetics/heritable

Datasets comprising twins and their children can be a useful tool for understanding the nature of intergenerational associations between parent and offspring phenotypes.

In the present article we explore structural equation models previously used to analyse Children-of-Twins data, highlighting some limitations and considerations.

We then present new variants of these models, showing that extending the models to include multiple offspring per parent addresses several of the limitations discussed [yielding the Multiple-Children-of-Twins (MCoT) design]. Accompanying the updated models, we provide statistical power calculations and demonstrate with application to simulated data.

We then apply to intergenerational analyses of height and weight, using a sub-study of the Norwegian Mother and Child Cohort (MoBa); the Intergenerational Transmission of Risk (IToR) project, wherein all kinships in the MoBa data have been identified (a children-of-twins-and-siblings study).

Finally, we consider how to interpret the findings of these models and discuss future directions.

--- https://www.mdrc.org/work/publications/do-meta-analyses-oversell-longer-term-effects-programs-part-2 2022-09-27 sociology statistics/bias/publication statistics/meta-analysis --- https://openreview.net/forum?id=KRLUvxh8uaX When and Why Vision-Language Models Behave like Bags-Of-Words, and What to Do About It? Mert Yuksekgonul, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, James Zou 2023-03-01 2023-03-01 ai/nn/transformer/clip

[blog; Twitter] Despite the success of large vision and language models (VLMs) in many downstream applications, it is unclear how well they encode the compositional relationships between objects and attributes.

Here, we create the Attribution, Relation, and Order (ARO) benchmark to systematically evaluate the ability of VLMs to understand different types of relationships, attributes, and order information. ARO consists of Visual Genome Attribution, to test the understanding of objects’ properties; Visual Genome Relation, to test for relational understanding; and COCO-Order & Flickr30k-Order, to test for order sensitivity in VLMs. ARO is orders of magnitude larger than previous benchmarks of compositionality, with more than 50,000 test cases.

We present the settings where state-of-the-art VLMs behave like bags-of-words—i.e. when they have poor relational understanding, can blunder when linking objects to their attributes, and demonstrate a severe lack of order sensitivity. VLMs are predominantly trained and evaluated on large scale datasets with rich compositional structure in the images and captions. Yet, training on these datasets has not been enough to address the lack of compositional understanding, and evaluating on these datasets has failed to surface this deficiency.

To understand why these limitations emerge and are not represented in the standard tests, we zoom into the evaluation and training procedures. We demonstrate that it is possible to perform well on image-text retrieval over existing datasets without using the composition and order information. This further motivates the value of using ARO to benchmark VLMs. Given that contrastive pretraining optimizes for retrieval on large datasets with similar shortcuts, we hypothesize that this can explain why the models do not need to learn to represent compositional information.

This finding suggests a natural solution: composition-aware hard negative mining. We show that a simple-to-implement modification of contrastive learning improves the performance on tasks requiring understanding of order and compositionality.

[Keywords: vision-language models, CLIP, BLIP, contrastive learning, retrieval, vision-language pretraining, multimodal representation learning]

--- https://github.com/openai/glide-text2im 2022-09-27 ai/nn/transformer/gpt/dall-e/2 --- https://x.com/StudentInfosec/status/1640360234882310145 2022-09-27 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue --- https://exfatloss.substack.com/p/losing-43lbs-in-144-days-on-ex150-diet 2022-09-27 nootropic/quantified-self --- https://en.wikipedia.org/wiki/Gynogenesis Gynogenesis 2022-09-27 genetics/cloning --- https://en.wikipedia.org/wiki/Amazon_molly Amazon molly 2022-09-28 genetics/cloning --- https://en.wikipedia.org/wiki/Note_(typography) Note (typography) 2022-09-28 design/typography/sidenote --- https://en.wikipedia.org/wiki/Geneva_Bible#Format Geneva Bible § Format 2022-09-28 design/typography/sidenote --- https://en.wikipedia.org/wiki/Dictionnaire_Historique_et_Critique Dictionnaire Historique et Critique 2022-09-28 design/typography/sidenote --- https://en.wikipedia.org/wiki/Concrete_Mathematics Concrete Mathematics 2022-09-28 design/typography/sidenote --- https://ctan.org/pkg/sidenotes The package allows typesetting of texts with notes, figures, citations, captions and tables in the margin. This is common (for example) in science text books. 2022-09-28 design/typography/sidenote --- https://ctan.org/pkg/marginnote This package provides the command `\marginnote` that may be used instead of `\marginpar` at almost every place where `\marginpar` cannot be used, eg. inside floats, footnotes, or in frames made with the `framed` package. 2022-09-28 design/typography/sidenote --- https://developer.mozilla.org/en-US/docs/Web/HTML/Element/aside 2022-09-28 design/typography/sidenote --- https://www.antiquark.com/blogimg/fasc4b.pdf 2022-09-28 cs/algorithm design/typography/sidenote math --- https://cran.r-project.org/web/packages/tufte/index.html 2022-09-28 cs/r design/typography/sidenote --- https://eddelbuettel.github.io/tint/ 2022-09-28 design/typography/sidenote --- https://fransdejonge.com/wp-content/uploads/2010/01/sidenotes.html 2022-09-29 design/typography/sidenote --- https://www.maxkohler.com/posts/continuous-typography/ Continuous Typography: Notes toward a continuous framework for screen typography 2022-09-29 design/typography/sidenote --- https://journal.stuffwithstuff.com/2020/04/05/crafting-crafting-interpreters/ 2022-09-29 design/typography/sidenote --- https://www.jonashietala.se/blog/2019/03/04/pollen_sidenotes/ 2022-09-29 design/typography/sidenote --- https://www.kooslooijesteijn.net/blog/semantic-sidenotes 2022-09-29 design/typography/sidenote --- https://acdlite.github.io/jquery.sidenotes/ 2022-09-29 design/typography/sidenote --- https://github.com/acdlite/jquery.sidenotes 2022-09-29 design/typography/sidenote --- http://bcorreia.com/projects/sidenotes.js/src/demo.html 2022-09-29 design/typography/sidenote --- https://github.com/bcorreia/sidenotes.js 2022-09-29 design/typography/sidenote --- https://jkorpela.fi/www/fn.html#side Footnotes, endnotes, and sidenotes on Web pages: Sidenotes as an alternative 2022-09-29 design/typography/sidenote --- https://molly.github.io/annotate/ 2022-09-30 design/typography/sidenote --- https://developer.mozilla.org/en-US/docs/Web/HTML/Element/figcaption 2022-09-30 cs/css design/typography/sidenote --- https://en.wikipedia.org/wiki/Project_Xanadu Project Xanadu 2022-09-30 design/typography/sidenote --- https://quotebacks.net/ 2022-09-30 design/typography/sidenote --- https://tomcritchlow.com/2020/06/09/quotebacks/ 2022-09-30 design/typography/sidenote --- https://skorokithakis.github.io/expounder/ 2022-09-30 design/typography/sidenote --- https://shkspr.mobi/blog/2020/07/usability-of-footnotes/ 2022-09-30 design/typography/sidenote --- https://shkspr.mobi/blog/2020/12/a-terrible-way-to-do-footnotes-in-html/ 2022-09-30 design/typography/sidenote --- https://ncase.me/nutshell-wip/ 2022-09-30 design/typography/sidenote --- https://www.lesswrong.com/posts/A33sgjYoM4Ko9viJv/lw-beta-feature-side-comments 2022-09-30 design/typography/sidenote --- https://github.com/raphink/geneve_1564/releases/download/2015-07-08_01/geneve_1564.pdf 2022-09-30 design/typography/sidenote --- https://en.wikipedia.org/wiki/Aeropagitica Aeropagitica 2022-10-01 philosophy/epistemology --- https://en.wikipedia.org/wiki/John_Milton John Milton 2022-10-01 fiction/poetry --- https://sharegpt.com/c/ICZsSl7 2022-10-01 ai/nn/transformer/gpt/fiction fiction/text-game --- https://www.reddit.com/r/dalle2/comments/128pr94/peach_fruit_with_human_skin/ 2022-10-01 ai/nn/transformer/gpt/dall-e/2 --- https://en.wikipedia.org/wiki/Comptometer Comptometer 2022-10-01 cs/hardware --- https://www.newyorker.com/news/our-columnists/whats-the-point-of-reading-writing-by-humans 2022-10-01 ai/nn/transformer/gpt/fiction ai/text-style-transfer --- https://granta.com/the-last-vet/ 2022-10-01 dog philosophy/ethics --- https://www.physicsforums.com/insights/a-lesson-in-teaching-physics-you-cant-give-it-away/ 2022-10-01 psychology/cognitive-bias/illusion-of-depth science --- https://dilbertblog.typepad.com/the_dilbert_blog/2007/03/the_joy_of_righ.html 2022-10-01 psychology/personality --- https://forum.effectivealtruism.org/posts/2eotFCxvFjXH7zNNw/people-will-sometimes-just-lie-about-you 2022-10-01 psychology/personality --- https://www.reddit.com/r/ChatGPT/comments/129krsc/what_happened_here_this_is_the_kind_of_censorship/jeqjir3/ 2022-10-01 ai/nn/tokenization --- https://www.reddit.com/r/ChatGPT/comments/129krsc/what_happened_here_this_is_the_kind_of_censorship/jeolfqj/ 2022-10-02 ai/nn/transformer/gpt/non-fiction reinforcement-learning/safe --- https://x.com/VictorTaelin/status/1642664054912155648 2022-10-02 cs/security --- https://www.lesswrong.com/posts/epgCXiv3Yy3qgcsys/you-can-t-predict-a-game-of-pinball#wjLFhiWWacByqyu6a 2022-10-02 science statistics/decision statistics/prediction --- https://www.lesswrong.com/posts/bwyKCQD7PFWKhELMr/by-default-gpts-think-in-plain-sight#zfzHshctWZYo8JkLe 2022-10-02 ai/nn/transformer/gpt/4/sydney ai/nn/transformer/gpt/inner-monologue cs/cryptography/steganography reinforcement-learning/multi-agent reinforcement-learning/safe reinforcement-learning/scaling --- https://www.betonit.ai/p/gpt-4-takes-a-new-midterm-and-gets 2022-10-02 ai/nn/transformer/gpt/4/nonfiction economics --- https://www.science.org/content/article/hidden-hydrogen-earth-may-hold-vast-stores-renewable-carbon-free-fuel 2022-10-02 technology/carbon-capture --- https://www.sixthtone.com/news/1012605 2022-10-02 anime sociology --- https://x.com/jessicard/status/1642671752319758336 2022-10-02 ai/nn/transformer/gpt/fiction --- https://english.elpais.com/science-tech/2023-04-02/one-of-the-worlds-most-cited-scientists-rafael-luque-suspended-without-pay-for-13-years.html 2022-10-02 statistics/peer-review --- https://en.wikipedia.org/wiki/Kasparov_versus_the_World Kasparov versus the World 2022-10-02 psychology/chess --- https://www.youtube.com/watch?v=MUvXHigDz9M 2022-10-03 cat --- https://www.youtube.com/watch?v=PgT8tPChbqc 2022-10-03 ai/nn/transformer/gpt/4/nonfiction fiction/humor reinforcement-learning/robot --- https://x.com/emollick/status/1643105335421411331 2022-10-03 ai/nn/transformer/gpt/4/fiction --- http://www.isc.meiji.ac.jp/~kokichis/Welcomee.html 2022-10-03 psychology/vision --- https://www.wired.com/story/surveillance-china-security-camera-giant-ipvm/ 2022-10-03 history/uighur --- https://www.fightaging.org/archives/2023/04/three-years-of-gut-microbiome-data-for-flagellin-immunization-and-fecal-microbiota-transplantation/ 2022-10-03 genetics/microbiome nootropic/quantified-self --- https://x.com/marshmallowy/status/1643089312878329856 2022-10-03 ai/anime --- https://woodfromeden.substack.com/p/why-do-humans-ever-develop 2022-10-03 psychology/personality/narcissism psychology/personality/psychopathy sociology --- https://www.thenation.com/article/culture/clip-art-desktop-publishing-stock-images/ 2022-10-03 design economics/copyright --- https://www.quantamagazine.org/animal-mutation-rates-reveal-traits-that-speed-evolution-20230405/ 2022-10-03 genetics/heritable/rare genetics/selection/natural --- https://www.palladiummag.com/2023/04/04/the-golden-age-of-aerospace/ 2022-10-03 technology --- https://www.popularmechanics.com/military/aviation/a36548281/sr-71-blackbird-history/ 2022-10-04 technology --- https://publicdomainreview.org/collection/manhattans-last-arcadia 2022-10-04 design/visualization history/public-domain-review --- /doc/ai/2003-11-07-clayshirky-thesemanticwebsyllogismandworldview.html 2003-11-07 2022-10-04 ai cs/security design philosophy/epistemology philosophy/ontology --- https://github.com/DSLsofMath/DSLsofMath 2022-10-04 cs/haskell math --- https://arxiv.org/abs/2006.06856 BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits Mo Tiwari, Martin Jinye Zhang, James Mayclin, Sebastian Thrun, Chris Piech, Ilan Shomorony 2020-06-11 2022-10-04 [("doi","10.48550/arXiv.2006.06856")] cs/algorithm reinforcement-learning/exploration/active-learning

[discussion] Clustering is a ubiquitous task in data science. Compared to the commonly used k-means clustering, k-medoids clustering requires the cluster centers to be actual data points and support arbitrary distance metrics, which permits greater interpretability and the clustering of structured objects.

Current state-of-the-art k-medoids clustering algorithms, such as Partitioning Around Medoids (PAM), are iterative and are quadratic in the dataset size n for each iteration, being prohibitively expensive for large datasets.

We propose BanditPAM, a randomized algorithm inspired by techniques from multi-armed bandits, that reduces the complexity of each PAM iteration from 𝒪(n2) to 𝒪(n log n) and returns the same results with high probability, under assumptions on the data that often hold in practice. As such, BanditPAM matches state-of-the-art clustering loss while reaching solutions much faster.

We empirically validate our results on several large real-world datasets, including a coding exercise submissions dataset, the 10× Genomics 68k PBMC single-cell RNA sequencing dataset, and the MNIST handwritten digits dataset. In these experiments, we observe that BanditPAM returns the same results as state-of-the-art PAM-like algorithms up to 4× faster while performing up to 200× fewer distance computations.

The improvements demonstrated by BanditPAM enable k-medoids clustering on a wide range of applications, including identifying cell types in large-scale single-cell data and providing scalable feedback for students learning computer science online. We also release highly optimized Python, R, and C++ implementations of our algorithm.

--- https://blog.ninlabs.com/2013/01/programmer-interrupted/ 2022-10-04 cs design psychology/spaced-repetition --- https://en.wikipedia.org/wiki/Neophobia Neophobia 2022-10-04 psychology/novelty --- https://www.fightaging.org/archives/2023/04/unity-biotechnology-demonstrates-again-that-localized-use-of-senolytics-is-not-so-great/ 2022-10-04 longevity/senolytic --- https://publicdomainreview.org/essay/the-city-that-fell-off-a-cliff 2022-10-04 history/public-domain-review --- https://x.com/juan_cambeiro/status/1643739695598419970 2022-10-04 biology reinforcement-learning/safe --- https://www.nytimes.com/2023/03/29/sports/soccer/china-soccer.html 2022-10-04 exercise politics --- https://github.com/E-xyza/Exonerate/blob/master/bench/reports/gpt-bench.md 2022-10-05 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- /doc/cs/linkrot/archiving/2014-zittrain.pdf Perma: Scoping and Addressing the Problem of Link and Reference Rot in Legal Citations Zittrain, Albert 2013 2022-10-05 cs/linkrot/archiving law --- https://www.sciencedirect.com/science/article/pii/S0002916523292497 Iodine supplementation improves cognition in iodine-deficient schoolchildren in Albania: a randomized, controlled, double-blind study Zimmerman 2006 2022-10-05 iodine --- https://academic.oup.com/aje/article/164/6/529/129824 Cigarette smoking and nocturnal sleep architecture Zhang 2006 2022-10-05 nicotine zeo --- https://rotary3460a.org.tw/wp/wp-content/uploads/2012/02/pnas201118386.pdf Regeneration of whole fertile plants from 30,000-y-old fruit tissue buried in Siberian permafrost Yashina 2012 2022-10-05 cryonics --- https://www.forourposterity.com/nobodys-on-the-ball-on-agi-alignment/ 2022-10-05 reinforcement-learning/safe --- https://www.probabilistic-numerics.org/assets/ProbabilisticNumerics.pdf#page=3 2022-10-05 reinforcement-learning/exploration/active-learning statistics/bayes statistics/probability --- https://www.reddit.com/r/ChatGPT/comments/12a0ajb/i_gave_gpt4_persistent_memory_and_the_ability_to/ 2022-10-05 ai/nn/retrieval ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex reinforcement-learning/safe --- /doc/fiction/humor/hardtruthsfromsoftcats.tumblr.com/index.html Hard Truths from Soft Cats 2022-10-05 cat fiction/humor/hardtruthsfromsoftcats.tumblr.com --- https://x.com/AxSauer/status/1644264940218327042 2022-10-05 ai/nn/gan/stylegan ai/scaling --- https://faultlore.com/blah/text-hates-you/ 2022-10-05 design/typography --- https://github.com/Significant-Gravitas/AutoGPT 2022-10-06 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/model reinforcement-learning/safe --- https://arxiv.org/abs/2304.02643#facebook Segment Anything Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, Ross Girshick 2023-04-05 2023-04-05 [("doi","10.48550/arXiv.2304.02643")] ai/nn/transformer ai/scaling reinforcement-learning/exploration/active-learning

We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation.

Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed & privacy-respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks.

We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive—often competitive with or even superior to prior fully supervised results.

We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at segment-anything.com to foster research into foundation models for computer vision.

--- https://www.foreignaffairs.com/cuba/missile-crisis-secret-history-soviet-union-russia-ukraine-lessons 2022-10-06 radiance --- https://www.biorxiv.org/content/10.1101/2023.04.03.535380.full Genetic contributions of noncognitive skills to academic development Margherita Malanchini, Andrea G. Allegrini, Michel G. Nivard, Pietro Biroli, Kaili Rimfeld, Rosa Cheesman, Sophie von Stumm, Perline A. Demange, Elsje van Bergen, Andrew D. Grotzinger, Laurel Raffington, Javier De la Fuente, Jean-Baptiste Pingault, K. Paige Harden, Elliot M. Tucker-Drob, Robert Plomin 2023-04-05 2023-04-05 [("doi","10.1101/2023.04.03.535380")] genetics/heritable/correlation iq psychology/personality/conscientiousness

[Twitter] Noncognitive skills such as motivation and self-regulation, predict academic achievement beyond cognitive skills. However, the role of genetic and environmental factors and of their interplay in these developmental associations remains unclear.

We provide a comprehensive account of how cognitive and noncognitive skills contribute to academic achievement ages 7–16 in a sample of >10,000 children from England and Wales.

Results indicated that noncognitive skills become increasingly predictive of academic achievement across development. Triangulating genetic methods, including twin analyses and polygenic scores (PGS), we found that: the contribution of noncognitive genetics to academic achievement becomes stronger over development. The PGS for noncognitive skills predicted academic achievement developmentally, with prediction nearly doubling by age 16, pointing to gene-environment correlation (rGE). Within-family analyses indicated both passive and active/evocative rGE processes driven by noncognitive genetics.

By studying genetic effects through a developmental lens, we provide novel insights into the role of noncognitive skills in academic development.

--- https://en.wikipedia.org/wiki/Somatic_evolution_in_cancer Somatic evolution in cancer 2022-10-06 genetics/selection/natural --- https://arxiv.org/abs/2210.02713 On Optimal Learning Under Targeted Data Poisoning Steve Hanneke, Amin Karbasi, Mohammad Mahmoody, Idan Mehalel, Shay Moran 2022-10-06 2022-10-06 [("doi","10.48550/arXiv.2210.02713")] ai/nn/adversarial

Consider the task of learning a hypothesis class ℋ in the presence of an adversary that can replace up to an η fraction of the examples in the training set with arbitrary adversarial examples. The adversary aims to fail the learner on a particular target test point x which is known to the adversary but not to the learner.

In this work we aim to characterize the smallest achievable error ε = ε(η) by the learner in the presence of such an adversary in both realizable and agnostic settings. We fully achieve this in the realizable setting, proving that ε = Θ(VC(ℋ)·η), where VC(ℋ) is the VC dimension of ℋ. Remarkably, we show that the upper bound can be attained by a deterministic learner.

In the agnostic setting we reveal a more elaborate landscape: we devise a deterministic learner with a multiplicative regret guarantee of ε ≤ C · OPT + 𝒪(VC(ℋ) · η), where C > 1 is a universal numerical constant. We complement this by showing that for any deterministic learner there is an attack which worsens its error to at least 2 · OPT. This implies that a multiplicative deterioration in the regret is unavoidable in this case.

Finally, the algorithms we develop for achieving the optimal rates are inherently improper [not guaranteed to converge]. Nevertheless, we show that for a variety of natural concept classes, such as linear classifiers, it is possible to retain the dependence ε = Θℋ(η) by a proper algorithm in the realizable setting. Here Θℋ conceals a polynomial dependence on VC(ℋ).

--- https://publicdomainreview.org/collection/john-martin-s-illustrations-of-paradise-lost-1827 2022-10-06 design fiction/poetry history/public-domain-review --- https://www.schneier.com/blog/archives/2023/04/llms-and-phishing.html 2022-10-06 cs/security --- https://denyslinkov.medium.com/why-is-gpt-3-15-77x-more-expensive-for-certain-languages-2b19a4adc4bc 2022-10-06 ai/nn/tokenization --- https://www.theatlantic.com/science/archive/2020/05/flesh-eating-worms-disease-containment-america-panama/611026/ America’s Never-Ending Battle Against Flesh-Eating Worms: Inside the U.S. and Panama’s long-running collaboration to rid an entire continent of a deadly disease Zhang 2020 2022-10-06 biology --- https://www.dna.caltech.edu/Papers/winfree_thesis.pdf Algorithmic self-assembly of DNA Winfree 2008 2022-10-07 cs/computable genetics/genome-synthesis --- https://www.cbcb.umd.edu/confcour/CMSC828G-materials/Willerslev-etal-Science-2007.pdf Ancient Biomolecules from Deep Ice Cores Reveal a Forested Southern Greenland Willerslev 2007 2022-10-07 genetics/sequencing --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7848227/ Can GLP-1 Be a Target for Reward System Related Disorders? A Qualitative Synthesis and Systematic Review Analysis of Studies on Palatable Food, Drugs of Abuse, and Alcohol Candan Yasemin Eren-Yazicioglu, Arya Yigit, Ramazan Efe Dogruoz, Hale Yapici-Eser 2020 2022-10-07 [("doi","10.3389/fnbeh.2020.614884")] longevity/glp/semaglutide psychiatry/adhd psychology/neuroscience

The role of glucagon-like peptide 1 (GLP-1) in insulin-dependent signaling is well-known; GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in diabetes. GLP-1 receptors (GLP-1R) are also widely expressed in the brain, and in addition to its role in neuroprotection, it affects reward pathways. This systematic review aimed to analyze the studies on GLP-1 and reward pathways and its currently identified mechanisms.

Method: “Web of Science” and “Pubmed” were searched to identify relevant studies using GLP-1 as the keyword. Among the identified 26,539 studies, 30 clinical, and 71 preclinical studies were included. Data is presented by grouping rodent studies on palatable food intake, drugs of abuse, and studies on humans focusing on GLP-1 and reward systems.

Results: GLP-1Rs are located in reward-related areas, and GLP-1, its agonists, and DPP-IV inhibitors are effective in decreasing palatable food intake, along with reducing cocaine, amphetamine, alcohol, and nicotine use in animals. GLP-1 modulates dopamine levels and glutamatergic neurotransmission, which results in observed behavioral changes. In humans, GLP-1 alters palatable food intake and improves activity deficits in the insula, hypothalamus, and orbitofrontal cortex (OFC). GLP-1 reduces food cravings partially by decreasing activity to the anticipation of food in the left insula of obese patients with diabetes and may inhibit overeating by increasing activity to the consumption of food in the right OFC of obese and left insula of obese with diabetes.

Conclusion: Current preclinical studies support the view that GLP-1 can be a target for reward system related disorders. More translational research is needed to evaluate its efficacy on human reward system related disorders.

--- https://www.cabinetmagazine.org/issues/42/wiles.php The Behavioral Sink Wiles 2011 2022-10-07 sociology --- https://arxiv.org/abs/1611.04135 Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) Xiaolin Wu, Xi Zhang 2016-11-13 2022-10-07 [("doi","10.48550/arXiv.1611.04135")] ai/nn/cnn crime statistics/bias

In November 2016 we submitted to arXiv our paper “Automated Inference on Criminality Using Face Images”. It generated a great deal of discussions in the Internet and some media outlets. Our work is only intended for pure academic discussions; how it has become a media consumption is a total surprise to us.

Although in agreement with our critics on the need and importance of policing AI research for the general good of the society, we are deeply baffled by the ways some of them misrepresented our work, in particular the motive and objective of our research.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1204085/pdf/ge1253585.pdf Increased selection response in larger populations. II. Selection for ethanol vapor resistance in Drosophila melanogaster at two population sizes K E. Weber, L. T. Diggins 1990 2022-10-07 [("doi","10.1093/genetics/125.3.585")] genetics/selection/artificial

The effect of large population size on selection response was investigated using Drosophila melanogaster, with 4 “small” lines of 160 selected parents/generation compared to two “large” lines of 1,600 selected parents/generation.

All lines were selected under similar conditions at a selection intensity of ~0.55 standard deviations, for 65 generations, for increased ethanol vapor resistance (measured in minutes required to become anesthetized). Two unselected control lines of 320 parents/generation were also maintained. A statistically-significant effect of population size was found. The final treatment means and standard errors were: 27.91 ± 1.28 min (two “large” lines); 19.40 ± 1.54 min (four “small” lines); and 4.98 ± 0.35 min (two control lines).

To estimate the mutation rate for the trait, two isogenic lines of about 400 selected parents were selected for 29 generations. The mean increase in additive genetic variance per generation was 0.0009× the initial environmental variance of the outbred lines. This is comparable to other reported mutation rates.

Mutation can explain part of the difference in evolved resistance between treatments, but it appears that even at rather large population sizes, a large difference in long-term response can be obtained in larger outbred lines, from more complete usage of the initial genetic variation.

--- https://academic.oup.com/aje/article/152/2/149/87699 Multivitamin Use and Mortality in a Large Prospective Study Watkins 2000 2022-10-07 biology --- https://x.com/_Borriss_/status/1645488757649416196 2022-10-07 ai/nn/transformer/gpt/4/nonfiction ai/text-style-transfer --- https://x.com/gillespi/status/1645594062773452801 2022-10-07 ai/nn/transformer/gpt/4/nonfiction ai/text-style-transfer law --- https://x.com/VictorTaelin/status/1645553975419355136 2022-10-07 ai/nn/transformer/gpt/4/nonfiction math --- https://scottaaronson.blog/?p=7209 2022-10-07 ai/nn/transformer/gpt/4/nonfiction science --- https://www.photoprompts.io/ 2022-10-08 ai/nn/diffusion --- https://rhg.com/research/running-on-ice/ 2022-10-08 ai/scaling/hardware --- https://www.reddit.com/r/freelanceWriters/comments/12ff5mw/it_happened_to_me_today/ 2022-10-08 ai/nn/transformer/gpt/4/nonfiction ai/scaling/economics --- https://x.com/ClarkBenham2/status/1645913914050510848 2022-10-08 ai/nn/transformer/gpt/non-fiction reinforcement-learning/safe --- https://netecon.seas.harvard.edu/P2PEcon03.html/Papers/Vishnumurthy_03.pdf KARMA: A Secure Economic Framework for Peer-to-Peer Resource Sharing Vishnumurthy 2003 2022-10-08 bitcoin --- https://www.sciencedirect.com/science/article/pii/S0002916522043763 Vitamin D supplementation, 25-hydroxyvitamin D concentrations, and safety Vieth 1999 2022-10-08 vitamin-d --- https://arxiv.org/abs/1506.03340#deepmind Teaching Machines to Read and Comprehend Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom 2015-06-10 2022-10-08 [("doi","10.48550/arXiv.1506.03340")] ai/dataset ai/nn/rnn

Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation.

In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data [creating a large collection of news data].

This allows us to develop a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure.

--- https://aclanthology.org/2020.wmt-1.1.pdf 2022-10-08 ai/dataset ai/nn/transformer --- https://arxiv.org/abs/1509.00685#facebook A Neural Attention Model for Abstractive Sentence Summarization Alexander M. Rush, Sumit Chopra, Jason Weston 2015-09-02 2022-10-08 [("doi","10.48550/arXiv.1509.00685")] ai/nn/cnn ai/nn/fully-connected

Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build.

In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method uses a local attention-based model that generates each word of the summary conditioned on the input sentence.

While the model is structurally simple [cf. Bengio et al 2003], it can easily be trained end-to-end and scales to a large amount of training data.

The model shows performance gains on the DUC-2004 shared task compared with several strong baselines.

--- /doc/ai/dataset/2008-sandhaus.pdf 2008 2022-10-08 ai/dataset --- https://arxiv.org/abs/1804.11283 Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies Max Grusky, Mor Naaman, Yoav Artzi 2018-04-30 2022-10-08 [("doi","10.48550/arXiv.1804.11283")] ai/dataset ai/nn/rnn

We present NEWSROOM, a summarization dataset of 1.3 million articles and summaries written by authors and editors in newsrooms of 38 major news publications.

Extracted from search and social media metadata between 1998 and 2017, these high-quality summaries demonstrate high diversity of summarization styles. In particular, the summaries combine abstractive and extractive strategies, borrowing words and phrases from articles at varying rates.

We analyze the extraction strategies used in NEWSROOM summaries against other datasets to quantify the diversity and difficulty of our new data, and train existing methods on the data to evaluate its utility and challenges.

--- https://arxiv.org/abs/1506.04757 Amazon Reviews: Image-based Recommendations on Styles and Substitutes Julian McAuley, Christopher Targett, Qinfeng Shi, Anton van den Hengel 2015-06-15 2022-10-09 [("doi","10.48550/arXiv.1506.04757")] ai/dataset

Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other.

We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within.

We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset (Amazon Reviews) for the training and evaluation of the same.

The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.

--- https://en.wikipedia.org/wiki/Europarl_Corpus Europarl Corpus 2022-10-09 ai/dataset politics --- https://arxiv.org/abs/1611.09830 NewsQA: A Machine Comprehension Dataset Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, Kaheer Suleman 2016-11-29 2022-10-09 [("doi","10.48550/arXiv.1611.09830")] ai/dataset ai/nn/rnn

We present NewsQA, a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs.

Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles. We collect this dataset through a four-stage process designed to solicit exploratory questions that require reasoning.

A thorough analysis confirms that NewsQA demands abilities beyond simple word matching and recognizing textual entailment. We measure human performance on the dataset and compare it to several strong neural models. The performance gap between humans and machines (0.198 in F1) indicates that progress can be made on NewsQA through future research.

The dataset is freely available at https://datasets.maluuba.com/NewsQA.

--- https://arxiv.org/abs/1705.03551#allen TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension Mandar Joshi, Eunsol Choi, Daniel S. Weld, Luke Zettlemoyer 2017-05-09 2022-10-09 [("doi","10.48550/arXiv.1705.03551")] ai/dataset ai/nn/rnn

We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, 6 per question on average, that provide high quality distant supervision for answering the questions.

We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers.

We also present two baseline algorithms: a feature-based classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth future study.

Data and code available at https://nlp.cs.washington.edu/triviaqa/.

--- https://arxiv.org/abs/1704.05179 SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine Matthew Dunn, Levent Sagun, Mike Higgins, V. Ugur Guney, Volkan Cirik, Kyunghyun Cho 2017-04-18 2022-10-09 [("doi","10.48550/arXiv.1704.05179")] ai/dataset ai/nn/rnn

We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering.

Unlike recently released datasets, such as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an existing article and generate a question-answer pair, but start from an existing question-answer pair, crawled from J! Archive, and augment it with text snippets retrieved by Google.

Following this approach, we built SearchQA, which consists of more than 140k question-answer pairs with each pair having 49.6 snippets on average. Each question-answer-context tuple of the SearchQA comes with additional meta-data such as the snippet’s URL, which we believe will be valuable resources for future research.

We conduct human evaluation as well as test two baseline methods, one simple word selection and the other deep learning based, on the SearchQA. We show that there is a meaningful gap between the human and machine performances. This suggests that the proposed dataset could well serve as a benchmark for question-answering.

--- https://arxiv.org/abs/1809.09600 HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, Christopher D. Manning 2018-09-25 2022-10-09 [("doi","10.48550/arXiv.1809.09600")] ai/dataset ai/nn/rnn

Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers.

We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with 4 key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison.

We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.

--- https://arxiv.org/abs/1909.05286#ibm Frustratingly Easy Natural Question Answering Lin Pan, Rishav Chakravarti, Anthony Ferritto, Michael Glass, Alfio Gliozzo, Salim Roukos, Radu Florian, Avirup Sil 2019-09-11 2022-10-09 [("doi","10.48550/arXiv.1909.05286")] ai/nn/transformer

Existing literature on Question Answering (QA) mostly focuses on algorithmic novelty, data augmentation, or increasingly large pre-trained language models like XLNet and RoBERTa. Additionally, a lot of systems on the QA leaderboards do not have associated research documentation in order to successfully replicate their experiments.

In this paper, we outline these algorithmic components such as Attention-over-Attention, coupled with data augmentation and ensembling strategies that have shown to yield state-of-the-art results on benchmark datasets like SQuAD, even achieving super-human performance.

Contrary to these prior results, when we evaluate on the recently proposed Natural Questions benchmark dataset, we find that an incredibly simple approach of transfer learning from BERT outperforms the previous state-of-the-art system trained on 4 million more examples than ours by 1.9 F1 points. Adding ensembling strategies further improves that number by 2.3 F1 points.

--- /doc/ai/dataset/2019-kwiatkowski.pdf#google Natural Questions: A Benchmark for Question Answering Research Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, Slav Petrov 2019-01-01 2022-10-09 [("doi","10.1162/tacl_a_00276")] ai/dataset ai/nn/rnn

We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine.

An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present.

The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data.

We present experiments validating quality of the data.

We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0281752 Mortality postponement and compression at older ages in human cohorts David McCarthy, Po-Lin Wang 2023-01-31 2023-01-31 [("doi","10.1371/journal.pone.0281752")] longevity statistics/bayes statistics/survival-analysis

A key but unresolved issue in the study of human mortality at older ages is whether mortality is being compressed (which implies that we may be approaching a maximum limit to the length of life) or postponed (which would imply that we are not).

We analyze historical and current population mortality data between ages 50 and 100 by birth cohort in 19 currently-industrialized countries, using a Bayesian technique to surmount cohort censoring caused by survival, to show that while the dominant historical pattern has been one of mortality compression, there have been occasional episodes of mortality postponement.

The pattern of postponement and compression across different birth cohorts explain why longevity records have been slow to increase in recent years: we find that cohorts born between around 1900 and 1950 are experiencing historically unprecedented mortality postponement, but are still too young to break longevity records. As these cohorts attain advanced ages in coming decades, longevity records may therefore increase.

Our results confirm prior work suggesting that if there is a maximum limit to the human lifespan, we are not yet approaching it.

--- https://www.nytimes.com/2020/02/04/business/custom-urls.html 2022-10-09 economics/copyright --- https://x.com/jasoncrawford/status/1643000721833447424 2022-10-10 ai/nn/transformer/clip/sample --- https://www.science.org/content/blog-post/ozempic-and-other-glp-1-drugs-more-people-realize 2022-10-10 longevity/glp/semaglutide psychology/neuroscience --- https://arxiv.org/abs/2303.01469#openai Consistency Models Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever 2023-03-02 2023-03-02 [("doi","10.48550/arXiv.2303.01469")] ai/nn/diffusion ai/nn/sparsity/knowledge-distillation

[code] Diffusion models have made breakthroughs in image, audio, and video generation, but they depend on an iterative generation process that causes slow sampling speed and caps their potential for real-time applications.

To overcome this limitation, we propose consistency models, a new family of generative models that achieve high sample quality without adversarial training. They support fast one-step generation by design, while still allowing for few-step sampling to trade compute for sample quality. They also support zero-shot data editing, like image inpainting, colorization, and super-resolution, without requiring explicit training on these tasks. Consistency models can be trained either as a way to distill pre-trained diffusion models, or as standalone generative models.

Through extensive experiments, we demonstrate that they outperform existing distillation techniques for diffusion models in 1-step & few-step generation. For example, we achieve the new state-of-the-art FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64×64 for one-step generation. When trained as standalone generative models, consistency models also outperform single-step, non-adversarial generative models on standard benchmarks like CIFAR-10, ImageNet 64×64 and LSUN 256×256.

--- https://www.quantamagazine.org/mathematicians-find-hidden-structure-in-a-common-type-of-space-20230412/ 2022-10-10 math statistics/power-analysis statistics/probability --- https://arxiv.org/abs/1311.2524 R-CNN: Rich feature hierarchies for accurate object detection and semantic segmentation Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik 2013-11-11 2022-10-10 [("doi","10.48550/arXiv.1311.2524")] ai/nn/cnn

Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context.

In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on PASCAL VOC 2012—achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a performance boost.

Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also compare R-CNN to OverFeat, a recently proposed sliding-window detector based on a similar CNN architecture. We find that R-CNN outperforms OverFeat by a large margin on the 200-class ILSVRC2013 detection dataset.

Source code for the complete system is available at Github.

--- https://en.wikipedia.org/wiki/Cat_senses#Sight Cat senses § Sight 2022-10-10 cat/psychology psychology/vision --- https://www.engadget.com/drew-carey-made-a-radio-show-with-ai-fans-werent-pleased-143014038.html 2022-10-10 ai/music --- https://inquisitivebird.substack.com/p/the-rise-of-the-west 2022-10-10 history science --- https://arxiv.org/abs/2302.03714 Short-Form Videos Degrade Our Capacity to Retain Intentions: Effect of Context Switching On Prospective Memory Francesco Chiossi, Luke Haliburton, Changkun Ou, Andreas Butz, Albrecht Schmidt 2023-02-07 2023-02-07 [("doi","10.1145/3544548.3580778")] sociology/technology

Social media platforms use short, highly engaging videos to catch users’ attention. While the short-form video feeds popularized by TikTok are rapidly spreading to other platforms, we do not yet understand their impact on cognitive functions.

We conducted a between-subjects experiment (n = 60) investigating the impact of engaging with TikTok, Twitter, and YouTube while performing a Prospective Memory task (ie. executing a previously planned action). The study required participants to remember intentions over interruptions.

We found that the TikTok condition degraded the users’ performance in this task. As none of the other conditions (Twitter, YouTube, no activity) had a similar effect, our results indicate that the combination of short videos and rapid context-switching impairs intention recall and execution.

We contribute a quantified understanding of the effect of social media feed format on Prospective Memory and outline consequences for media technology designers to not harm the users’ memory and wellbeing.

--- https://www.biorxiv.org/content/10.1101/2023.03.27.534424.full Five years later, with double the demographic data, naked mole-rat mortality rates continue to defy Gompertzian laws by not increasing with age J. Graham Ruby, Megan Smith, Rochelle Buffenstein 2023-03-28 2023-03-28 [("doi","10.1101/2023.03.27.534424")] longevity

The naked mole-rat (Heterocephalus glaber) is a mouse-sized rodent species, notable for its eusociality and long lifespan. Previously, we reported that demographic aging, ie. the exponential increase of mortality hazard that accompanies advancing age in mammals and other organisms, does not occur in naked mole-rats (Ruby et al 2018). The demographic data supporting that conclusion had taken over 3 decades to accumulate, starting with the original rearing of H.glaber in captivity.

In the 5 years following that study, we ~doubled our quantity of demographic data. Here, we re-evaluated our prior conclusions in light of these new data and:

found them to be supported and indeed strengthened. We additionally provided insight into the social dynamics of captive H.glaber with data and analyses of body weight and colony size versus mortality.

Finally, we provide a phylogenetically-proximal comparator in the form of lifespan data from our Damaraland mole-rat (Fukomys damarensis) colony and demographic meta-analysis of those data along with published data from Ansell’s mole-rat (Fukomys anselli). We found Fukomys mortality hazard to increase gradually with age, an observation with implications on the evolution of exceptional lifespan among mole-rats and the ecological factors that may have accompanied that evolution.

--- /doc/psychology/willpower/2022-ledford.html 2022 2022-10-10 psychology/neuroscience psychology/willpower --- https://www.biorxiv.org/content/10.1101/2022.06.03.494563.full Robust deep learning based protein sequence design using ProteinMPNN J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, D. Baker 2022-06-04 2022-10-11 [("doi","10.1101/2022.06.03.494563")] ai/nn/transformer/alphafold

While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta.

Here we describe a deep learning based protein sequence design method, ProteinMPNN, with outstanding performance in both in silico and experimental tests. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges.

On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. Incorporation of noise during training improves sequence recovery on protein structure models, and produces sequences which more robustly encode their structures as assessed using structure prediction algorithms. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins.

One-sentence summary: A deep learning based protein sequence design method is described that is widely applicable to current design challenges and shows outstanding performance in both in silico and experimental tests.

--- https://www.amazon.com/dp/1594392137 2022-10-11 crime psychiatry/traumatic-brain-injury --- https://www.thebigquestions.com/2023/04/05/gpt-4-fails-economics/ 2022-10-11 ai/nn/transformer/gpt/4/nonfiction economics --- https://www.construction-physics.com/p/how-did-solar-power-get-cheap-part 2022-10-11 economics/experience-curve technology --- https://x.com/cyrilzakka/status/1646532570597982208 2022-10-11 ai/nn/transformer/gpt cs/security --- https://www.vox.com/future-perfect/2023/4/11/23673393/pets-dogs-cats-animal-welfare-boredom 2022-10-11 cat/psychology dog philosophy/ethics --- https://www.bartleby.com/lit-hub/the-worlds-best-poetry/the-wolf-and-the-dog/ 2022-10-11 dog fiction/poetry --- https://www.theflstandard.com/fsu-criminology-professor-abruptly-leaves-after-accusations-of-cooking-race-data/ 2022-10-11 statistics/bias --- https://aeon.co/essays/why-keeping-a-pet-is-fundamentally-unethical 2022-10-11 cat/psychology dog philosophy/ethics --- https://groups.google.com/g/hakyll/c/aFH9LHKyDZ8/m/-zY0SHdUBAAJ 2022-10-11 cs/css --- https://arxiv.org/abs/1902.09608 On Binscatter Matias D. Cattaneo, Richard K. Crump, Max H. Farrell, Yingjie Feng 2019-02-25 2022-10-11 [("doi","10.48550/arXiv.1902.09608")] design/visualization

Binned scatter plots, or binscatters, have become a popular and convenient tool in applied microeconomics for visualizing bivariate relations and conducting informal specification testing. However, a binscatter, on its own, is very limited in what it can characterize about the conditional mean.

We introduce a suite of formal and visualization tools based on binned scatter plots to restore, and in some dimensions surpass, the visualization benefits of the classical scatter plot. We deliver a comprehensive toolkit for applications, including estimation of conditional mean and quantile functions, visualization of variance and precise quantification of uncertainty, and formal tests of substantive hypotheses such as linearity or monotonicity, and an extension to testing differences across groups.

To do so we give an extensive theoretical analysis of binscatter and related partition-based methods, accommodating nonlinear and potentially nonsmooth models, which allows us to treat binary, count, and other discrete outcomes as well. We also correct a methodological mistake related to covariate adjustment present in prior implementations, which yields an incorrect shape and support of the conditional mean.

All of our results are implemented in publicly available software, and showcased with 3 substantive empirical illustrations. Our empirical results are dramatically different when compared to those obtained using the prevalent methods in the literature.

--- https://statmodeling.stat.columbia.edu/2023/04/13/the-percentogram-a-histogram-binned-by-percentages-of-the-cumulative-distribution-rather-than-using-fixed-bin-widths/ 2022-10-12 design/visualization --- https://statmodeling.stat.columbia.edu/2021/05/06/any-graph-should-contain-the-seeds-of-its-own-destruction/ 2022-10-12 design/visualization --- https://www.youtube.com/watch?v=X6RCLJ4pDaw 2022-10-12 ai/music fiction/humor fiction/science-fiction --- https://www.reddit.com/r/dalle2/comments/12lhyu2/decaying_taxidermied_bart_simpson_professional/ 2022-10-12 ai/nn/transformer/gpt/dall-e/2 --- https://www.vice.com/en/article/v7begx/overemployed-hustlers-exploit-chatgpt-to-take-on-even-more-full-time-jobs 2022-10-12 ai/nn/transformer/gpt/4/nonfiction ai/scaling/economics economics/automation --- https://github.com/facebookresearch/AnimatedDrawings 2022-10-12 ai/anime ai/video/generation --- https://arxiv.org/abs/2303.17012 Advances in apparent conceptual physics reasoning in GPT-4 Colin G. West 2023-03-29 2023-03-29 [("doi","10.48550/arXiv.2303.17012")] ai/nn/transformer/gpt/4/nonfiction science

ChatGPT is built on a large language model trained on an enormous corpus of human text to emulate human conversation.

Despite lacking any explicit programming regarding the laws of physics, recent work has demonstrated that GPT-3.5 could pass an introductory physics course at some nominal level and register something close to a minimal understanding of Newtonian Mechanics on the Force Concept Inventory.

This work replicates those results and also demonstrates that the latest version, GPT-4, has reached a much higher mark in the latter context. Indeed, its responses come quite close to perfectly demonstrating expert-level competence, with a few very notable exceptions and limitations.

We briefly comment on the implications of this for the future of physics education and pedagogy.

--- https://x.com/KevinAFischer/status/1646018246225846272 2022-10-12 ai/nn/transformer/gpt/inner-monologue --- https://www.lesswrong.com/posts/jkY6QdCfAXHJk3kea/the-petertodd-phenomenon 2022-10-12 ai/nn/tokenization ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/poetry reinforcement-learning/safe --- https://en.wikipedia.org/wiki/Principal%E2%80%93agent_problem Principal-agent problem 2022-10-12 cs/end-to-end-principle economics reinforcement-learning/safe --- https://www.lesswrong.com/posts/ChtGdxk9mwZ2Rxogt/smartyheadercode-anomalous-tokens-for-gpt3-5-and-gpt-4-1 2022-10-12 ai/nn/tokenization ai/nn/transformer/gpt/4/nonfiction --- https://carryiton.net/chain-letter/evolution.html#3-5origin_of_testimonials Chain Letter Evolution § Origins of Testimonials VanArsdale 2016 2022-10-13 culture genetics/selection/natural philosophy/religion sociology --- https://www.lesswrong.com/posts/nimwLGuMxvbkbzcJD/reflective-journal-entries-using-gpt-4-and-obsidian-that 2022-10-13 ai/nn/transformer/gpt/non-fiction psychiatry psychology/writing --- https://laion.ai/blog/paella/ 2022-10-13 ai/nn/vae/mae --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4858554/ Chorionicity and Heritability Estimates from Twin Studies: The Prenatal Environment of Twins and Their Resemblance Across a Large Number of Traits C E. M. van Beijsterveldt, L. I. H. Overbeek, L. Rozendaal, M. T. B. McMaster, T. J. Glasner, M. Bartels, J. M. Vink, N. G. Martin, C. V. Dolan, D. I. Boomsma 2016 2022-10-13 [("doi","10.1007/s10519-015-9745-3")] genetics/heritable

There are 3 types of monozygotic (MZ) twins. MZ twins can either share one chorion and one amnion, each twin can have its own amnion, or MZ twins can-like dizygotic twins-each have their own chorion and amnion. Sharing the same chorion may create a more similar/dissimilar prenatal environment and bias heritability estimates, but most twin studies do not distinguish between these 3 types of MZ twin pairs.

The aim of this paper is to investigate the effect of chorion sharing on the similarity within MZ twin pairs for a large number of traits. Information on chorion status was obtained for the Netherlands twin register (NTR) by linkage to the records from the database of the dutch pathological anatomy national automated archive (PALGA). Record linkage was successful for over 9000 pairs.

Effect of chorion type was tested by comparing the within-pair similarity between monochorionic (MC) and dichorionic (DC) MZ twins on 66 traits including weight, height, motor milestones, child problem behaviors, cognitive function, wellbeing and personality.

For only 10 traits, within-pair similarity differed between MCMZ and DCMZ pairs. For traits influenced by birth weight (eg. weight and height in young children) we expected that MC twins would be more discordant. This was found for 5⁄13 measures. When looking at traits where blood supply is important, we saw MCMZ twins to be more concordant than DCMZ’s for 3 traits.

We conclude that the influence on the MZ twin correlation of the intra-uterine prenatal environment, as measured by sharing a chorion type, is small and limited to a few phenotypes. This implies that the assumption of equal prenatal environment of mono/DC MZ twins, which characterizes the classical twin design, is largely tenable.

--- https://mathstodon.xyz/@tao/110172426733603359 Today was the first day that I could definitively say that GPT-4 has saved me a substantial amount of tedious work Terence Tao 2023-04-09 2023-04-09 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex design/typography/tex economics/automation

…As part of my responsibilities as chair of the ICM Structure Committee, I needed to gather various statistics on the speakers at the previous ICM (for instance, how many speakers there were for each section, taking into account that some speakers were jointly assigned to multiple sections).

The raw data (involving about 200 speakers) was not available to me in spreadsheet form, but instead in a number of tables in web pages and PDFs.

In the past I would have resigned myself to the tedious task of first manually entering the data into a spreadsheet and then looking up various spreadsheet functions to work out how to calculate exactly what I needed; but both tasks were easily accomplished in a few minutes by GPT-4, and the process was even somewhat enjoyable (with the only tedious aspect being the cut-and-paste between the raw data, GPT-4, and the spreadsheet).

I am now looking forward to native integration of AI into the various software tools that I use, so that even the cut-and-paste step can be omitted. (Just being able to resolve >90% of LaTeX compilation issues automatically would be wonderful…)

--- https://arxiv.org/abs/2202.01344#openai Formal Mathematics Statement Curriculum Learning Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, Ilya Sutskever 2022-02-03 2022-10-13 [("doi","10.48550/arXiv.2202.01344")] ai/nn/transformer/gpt/non-fiction math reinforcement-learning/model/alphago

[cf. HTPS] We explore the use of expert iteration in the context of GPT-f language modeling applied to formal mathematics.

We show that at same compute budget, expert iteration, by which we mean proof search interleaved with learning, dramatically outperforms proof search only. We also observe that when applied to a collection of formal statements of sufficiently varied difficulty, expert iteration is capable of finding and solving a curriculum of increasingly difficult problems, without the need for associated ground-truth proofs.

Finally, by applying this expert iteration to a manually curated set of problem statements, we achieve state-of-the-art on the miniF2F benchmark, automatically solving multiple challenging problems drawn from high school Olympiads.

--- https://arxiv.org/abs/2109.00110#openai MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics Kunhao Zheng, Jesse Michael Han, Stanislas Polu 2021-08-31 2022-10-13 [("doi","10.48550/arXiv.2109.00110")] ai/dataset ai/nn/transformer/gpt/non-fiction math

We present miniF2F, a dataset of formal Olympiad-level mathematics problems statements intended to provide a unified cross-system benchmark for neural theorem proving.

The miniF2F benchmark currently targets Metamath, Lean, Isabelle (partially) and HOL Light (partially) and consists of 488 problem statements drawn from the AIME, AMC, and the International Mathematical Olympiad (IMO), as well as material from high-school and undergraduate mathematics courses.

We report baseline results using GPT-f, a neural theorem prover based on GPT-3 and provide an analysis of its performance.

We intend for miniF2F to be a community-driven effort and hope that our benchmark will help spur advances in neural theorem proving.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3384433/ Metformin in obesity, cancer and aging: addressing controversies Lev M. Berstein 2012 2022-10-13 [("doi","10.18632/aging.100455")] longevity/metformin

Metformin, an oral anti-diabetic drug, is being considered increasingly for treatment and prevention of cancer, obesity as well as for the extension of healthy lifespan.

Gradually accumulating discrepancies about its effect on cancer and obesity can be explained by the shortage of randomized clinical trials, differences between control groups (reference points), gender & age-associated effects, and pharmacogenetic factors. Studies of the potential antiaging effects of antidiabetic biguanides, such as metformin, are still experimental for obvious reasons and their results are currently ambiguous.

Here we discuss whether the discrepancies in different studies are merely methodological or inherently related to individual differences in responsiveness to the drug.

--- https://x.com/JosephJacks_/status/1647328379266551808 2022-10-13 ai/nn/transformer ai/scaling/economics --- https://en.wikipedia.org/wiki/AsciiMath AsciiMath 2022-10-13 design/typography/tex --- https://github.com/pkra/mathjax-node-page/ 2022-10-14 design/typography/tex --- https://en.wikipedia.org/wiki/KaTeX KaTeX 2022-10-14 design/typography/tex --- https://arxiv.org/abs/1908.06166 A Mulching Proposal Os Keyes, Jevan Hutson, Meredith Durbin 2019-08-10 2022-10-14 [("doi","10.48550/arXiv.1908.06166")] ai math/humor philosophy/ethics

The ethical implications of algorithmic systems have been much discussed in both HCI and the broader community of those interested in technology design, development and policy.

In this paper, we explore the application of one prominent ethical framework—Fairness, Accountability, and Transparency—to a proposed algorithm that resolves various societal issues around food security and population ageing.

Using various standardized forms of algorithmic audit and evaluation, we drastically increase the algorithm’s adherence to the FAT framework, resulting in a more ethical and beneficent system.

We discuss how this might serve as a guide to other researchers or practitioners looking to ensure better ethical outcomes from algorithmic systems in their line of work.

[Satirical criticism of ‘AI ethics’ paradigms which are focused on deontological & identity-political concerns rather than consequences, combining Soylent Green & "A Modest Proposal".]

--- https://github.com/wabarc/wayback 2022-10-14 cs/linkrot/archiving --- https://nijijourney.com/en/ 2022-10-14 ai/anime ai/nn/diffusion --- https://if50.substack.com/p/the-gostak The Gostak 2022-10-14 ai/nn/transformer/gpt fiction/text-game philosophy/epistemology psychology/dark-knowledge --- https://www.reddit.com/r/dalle2/comments/12nr0kw/the_most_cute_kitten_ever_made_of_colorful/ 2022-10-14 ai/nn/transformer/gpt/dall-e/2 --- /doc/psychiatry/lithium/2023-liew.pdf Association Between Estimated Geocoded Residential Maternal Exposure to Lithium in Drinking Water and Risk for Autism Spectrum Disorder in Offspring in Denmark Zeyan Liew, Qi Meng, Qi Yan, Jörg Schullehner, Birgitte Hansen, Søren Munch Kristiansen, Denitza D. Voutchkova, Jørn Olsen, Annette Kjær Ersbøll, Matthias Ketzel, Ole Raaschou-Nielsen, Beate R. Ritz 2023-04-03 2023-04-03 [("doi","10.1001/jamapediatrics.2023.0346")] psychiatry/autism psychiatry/lithium

Importance: Lithium is a naturally occurring and trace element that has mood-stabilizing effects. Maternal therapeutic use of lithium has been associated with adverse birth outcomes. In animal models, lithium modulates Wnt/β-catenin signaling that is important for neurodevelopment. It is unknown whether exposure to lithium in drinking water affects brain health in early life.

Objective: To evaluate whether autism spectrum disorder (ASD) in offspring is associated with maternal exposure to lithium in drinking water during pregnancy.

Design, Setting & Participants: This nationwide population-based case-control study in Denmark identified 8842 children diagnosed with ASD born from 2000 through 2013 and 43 864 control participants matched by birth year and sex from the Danish Medical Birth Registry. These data were analyzed from March 2021 through November 2022.

Exposures: Geocoded maternal residential addresses during pregnancy were linked to lithium level (range, 0.6 to 30.7 μg/L) in drinking water estimated using kriging interpolation based on 151 waterworks measurements of lithium across all regions in Denmark.

Main Outcomes & Measures: ASD diagnoses were ascertained using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes recorded in the Danish Psychiatric Central Register. The study team estimated odds ratios (ORs) and 95% CIs for ASD according to estimated geocoded maternal exposure to natural source of lithium in drinking water as a continuous (per IQR) or a categorical (quartile) variable, adjusting for sociodemographic factors and ambient air pollutants levels. The study team also conducted stratified analyses by birth years, child’s sex, and urbanicity.

Results: A total of 8842 participants with ASD (male, 7009 [79.3%]) and 43 864 control participants (male, 34 749 [79.2%]) were studied. Every IQR increase in estimated geocoded maternal exposure to natural source of lithium in drinking water was associated with higher odds for ASD in offspring (OR, 1.23; 95% CI, 1.17-1.29). Elevated odds among offspring for ASD were estimated starting from the second quartile (7.36 to 12.67 μg/L) of estimated maternal exposure to drinking water with lithium and the OR for the highest quartile (more than 16.78 μg/L) compared with the reference group (less than 7.39 μg/L) was 1.46 (95% CI, 1.35-1.59). The associations were unchanged when adjusting for air pollution exposures and no differences were apparent in stratified analyses.

Conclusion: Estimated maternal prenatal exposure to lithium from naturally occurring drinking water sources in Denmark was associated with an increased ASD risk in the offspring. This study suggests that naturally occurring lithium in drinking water may be a novel environmental risk factor for ASD development that requires further scrutiny.

--- https://zerohplovecraft.wordpress.com/2019/10/22/god-shaped-hole/ 2022-10-14 fiction/science-fiction --- https://zerohplovecraft.wordpress.com/2021/07/07/dont-make-me-think/ 2022-10-14 fiction/science-fiction --- /doc/exercise/1990-schoeller.pdf Inaccuracies in self-reported intake identified by comparison with the doubly labeled water method Dale A. Schoeller, Linda G. Bandini, William H. Dietz 1990-07-01 2022-10-14 [("doi","10.1139/y90-143")] biology exercise

To test the accuracy of self-reported energy intake, reported intake was compared with measured energy expenditure. Results from 9 studies were reviewed in which intake data were obtained by recall or weighed record for at least 7 days. Expenditure was measured for 7 days or more by the doubly labeled water method.

Individual differences between reported intake and expenditure were large (range +25 to −76%). Group mean differences were smaller. Lean, nonathletic groups living in industrialized countries demonstrated the smallest mean difference between self-reported energy intakes and expenditure (0 to −20%). Obese populations demonstrated the largest mean differences (−35 and −50%), but women living in the Gambia and elite athletes also demonstrated large mean differences.

Most of the difference appears to be due to underreporting, but some subjects lost weight during the reporting period indicating that some of the difference was due to under-eating. Because the greatest bias was observed in obese subjects, current methods for self-reported energy intake are not recommended for use in obesity research.

[Keywords: dietary intake, doubly labeled water, nutritional assessment]

--- https://thaliaarchi.github.io/coq-turing-typeclass/ 2022-10-15 cs/computable --- https://borretti.me/article/best-of-orions-arm 2022-10-15 fiction/science-fiction --- https://en.wikipedia.org/wiki/Orion%27s_Arm Orion’s Arm 2022-10-15 fiction/science-fiction --- https://www.orionsarm.com/ 2022-10-15 fiction/science-fiction --- https://www.shruggingface.com/blog/how-i-used-stable-diffusion-and-dreambooth-to-create-a-painted-portrait-of-my-dog 2022-10-15 ai/nn/transformer/clip/sample --- https://www.alexcharlton.co/projects/booking-com-de-stresser 2022-10-15 cs/css economics/advertising --- https://martinfowler.com/bliki/ConwaysLaw.html 2022-10-15 economics/automation --- https://www.melconway.com/Home/Committees_Paper.html 2022-10-15 economics/automation --- https://en.wikipedia.org/wiki/Lanchester's_laws Lanchester’s laws 2022-10-15 history math --- https://x.com/amuseddaman/status/1647367383022182400 2022-10-15 ai/nn/transformer/gpt/4/nonfiction --- https://www.theatlantic.com/magazine/archive/2016/03/the-math-revolution/426855/ 2022-10-15 iq/high/smpy math --- https://www.lesswrong.com/posts/zyPaqXgFzqHkQfccq/contra-lecun-on-autoregressive-llms-are-doomed?commentId=fXGn2E8RMdwhKqwrE 2022-10-16 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/chess --- https://slate.com/culture/2020/05/facebook-ants-roleplay-coronavirus-biologist-interview.html 2022-10-16 design/typography fiction/humor --- https://arxiv.org/abs/1808.06226#google SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing Taku Kudo, John Richardson 2018-08-19 2022-10-16 [("doi","10.48550/arXiv.1808.06226")] ai/nn/tokenization

This paper describes SentencePiece, a language-independent subword tokenizer and detokenizer designed for Neural-based text processing, including Neural Machine Translation.

While existing subword segmentation tools assume that the input is pre-tokenized into word sequences, SentencePiece can train subword models directly from raw sentences, which allows us to make a purely end-to-end and language independent system. We perform a validation experiment of NMT on English-Japanese machine translation, and find that it is possible to achieve comparable accuracy to direct subword training from raw sentences. We also compare the performance of subword training and segmentation with various configurations.

SentencePiece is available under the Apache 2 license at Github. It provides open-source C++ and Python implementations for subword units.

--- http://www.tobyord.com/writing/a-childs-plaything 2022-10-16 economics/automation --- https://www.biorxiv.org/content/10.1101/2023.04.11.536486.full Why flying insects gather at artificial light Samuel T. Fabian, Yash Sondhi, Pablo Allen, Jamie Theobald, Huai-Ti Lin 2023-04-12 2023-04-12 [("doi","10.1101/2023.04.11.536486")] psychology/animal psychology/vision

For millennia, humans have watched nocturnal insects flying erratically around fires and lamps. Explanations have included theories of “lunar navigation” and “escape to light”. However, without three-dimensional flight data to test them rigorously, this odd behavior has remained unexplained.

We employed high-resolution motion capture in the laboratory and stereo-videography in the field to reconstruct the 3D kinematics of insect flights around artificial lights. Contrary to the expectation of attraction, insects do not steer directly toward the light. Instead, insects turn their dorsum toward the light, generating flight bouts perpendicular to the source.

Under natural sky light, tilting the dorsum towards the brightest visual hemisphere helps maintain proper flight attitude and control. Near artificial sources, however, this highly conserved dorsal-light-response can produce continuous steering around the light and trap an insect.

Our guidance model demonstrates that this dorsal tilting is sufficient to create the seemingly erratic flight paths of insects near lights and is the most plausible model for why flying insects gather at artificial lights.

--- https://news.ycombinator.com/item?id=35604715 2022-10-16 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://www.lesswrong.com/posts/No5JpRCHzBrWA4jmS/q-and-a-with-shane-legg-on-risks-from-ai 2022-10-16 ai/scaling reinforcement-learning/deepmind reinforcement-learning/safe --- https://www.newyorker.com/magazine/2023/04/24/crooks-mistaken-bet-on-encrypted-phones 2022-10-16 darknet-market --- https://www.newyorker.com/magazine/2023/04/24/psychonauts-drugs-and-the-making-of-the-modern-mind-mike-jay-book-review 2022-10-16 psychedelic --- https://arxiv.org/abs/2304.07193#facebook DINOv2: Learning Robust Visual Features without Supervision Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Arm Holdings, Joulin, Piotr Bojanowski 2023-04-14 2023-04-14 [("doi","10.48550/arXiv.2304.07193")] ai/nn/transformer/clip ai/scaling

[code] The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision. These models could greatly simplify the use of images in any system by producing all-purpose visual features, ie. features that work across image distributions and tasks without finetuning.

This work shows that existing pretraining methods, especially self-supervised methods, can produce such features if trained on enough curated data from diverse sources. We revisit existing approaches and combine different techniques to scale our pretraining in terms of data and model size. Most of the technical contributions aim at accelerating and stabilizing the training at scale.

In terms of data, we propose an automatic pipeline to build a dedicated, diverse, and curated image dataset instead of uncurated data, as typically done in the self-supervised literature.

In terms of models, we train a ViT model (Dosovitskiy et al 2020) with 1b parameters [DINOv2] and distill it into a series of smaller models that surpass the best available all-purpose features, OpenCLIP (Ilharco et al 2021) on most of the benchmarks at image and pixel levels.

--- /doc/wikipedia/2023-04-18-mediawiki-englishwikipedia-georgewashington-whatlinkshere.png 2023-04-18 2023-04-18 design wikipedia --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3493848/ The heritability of avoidant and dependent personality disorder assessed by personal interview and questionnaire L C. Gjerde, N. Czajkowski, E. Røysamb, R. E. Orstavik, G. P. Knudsen, K. Ostby, S. Torgersen, J. Myers, K. S. Kendler, T. Reichborn-Kjennerud 2012 2022-10-17 [("doi","10.1111/j.1600-0447.2012.01862.x")] genetics/heritable personal psychiatry/anxiety

Objective: Personality disorders (PDs) have been shown to be modestly heritable. Accurate heritability estimates are, however, dependent on reliable measurement methods, as measurement error deflates heritability. The aim of this study was to estimate the heritability of DSM-IV avoidant and dependent personality disorder, by including two measures of the PDs at two time points.

Method: Data were obtained from a population-based cohort of young adult Norwegian twins, of whom 8,045 had completed a self-report questionnaire assessing PD traits. 2,794 of these twins subsequently underwent a structured diagnostic interview for DSM-IV PDs. Questionnaire items predicting interview results were selected by multiple regression, and measurement models of the PDs were fitted in Mx.

Results: The heritabilities of the PD factors were 0.64 for avoidant PD and 0.66 for dependent PD. No evidence of common environment, that is, environmental factors that are shared between twins and make them similar, was found. Genetic and environmental contributions to avoidant and dependent PD seemed to be the same across sexes.

Conclusion: The combination of both a questionnaire & interview assessment of avoidant and dependent PD results in substantially higher heritabilities than previously found using single-occasion interviews only.

--- https://www.reddit.com/r/StableDiffusion/comments/12pvhhm/animov01_highresolution_anime_finetune_of/ 2022-10-17 ai/anime ai/video/generation --- https://arxiv.org/abs/2304.06762#nvidia Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro 2023-04-13 2023-04-13 [("doi","10.48550/arXiv.2304.06762")] ai/nn/retrieval ai/nn/transformer/gpt

Large decoder-only language models (LMs) can be largely improved in terms of perplexity by retrieval (eg. RETRO), but its impact on text generation quality and downstream task accuracy is unclear. Thus, it is still an open question: shall we pretrain large autoregressive LMs with retrieval? To answer it, we perform a comprehensive study on a scalable pre-trained retrieval-augmented LM (ie. RETRO) compared with a standard GPT [that we trained] and retrieval-augmented GPT incorporated at fine-tuning or inference stages.

We first provide the recipe to reproduce RETRO up to 9.5b parameters while retrieving a text corpus with 330B tokens. Based on that, we have the following novel findings: (1) RETRO outperforms [our] GPT on text generation with much less degeneration (ie. repetition), moderately higher factual accuracy, and slightly lower toxicity with a nontoxic retrieval database. (2) On the LM Evaluation Harness benchmark, RETRO largely outperforms GPT on knowledge-intensive tasks, but is on par with GPT on other tasks.

Furthermore, we introduce a simple variant of the model, RETRO++, which largely improves open-domain QA results of original RETRO (eg. EM score +8.6 on Natural Question) and outperforms retrieval-augmented GPT across different model sizes. Our findings highlight the promising direction of pretraining autoregressive LMs with retrieval as future foundation models.

We release our implementation at: Github.

--- https://x.com/ShaneLegg/status/1648340576545169410 2022-10-17 ai/scaling reinforcement-learning/deepmind --- https://www.oneusefulthing.org/p/one-sentence 2022-10-17 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://x.com/MParakhin/status/1648199942421508096 Mikhail Parakhin 2022-10-17 ai/nn/transformer/gpt/4/nonfiction --- https://arxiv.org/abs/2106.11297 TokenLearner: What Can 8 Learned Tokens Do for Images and Videos? Michael S. Ryoo, A. J. Piergiovanni, Anurag Arnab, Mostafa Dehghani, Anelia Angelova 2021-06-21 2022-10-17 [("doi","10.48550/arXiv.2106.11297")] ai/nn/transformer/attention/compression

In this paper, we introduce a novel visual representation learning which relies on a handful of adaptively learned tokens, and which is applicable to both image and video understanding tasks.

Instead of relying on hand-designed splitting strategies to obtain visual tokens and processing a large number of densely sampled patches for attention, our approach learns to mine important tokens in visual data. This results in efficiently and effectively finding a few important visual tokens and enables modeling of pairwise attention between such tokens, over a longer temporal horizon for videos, or the spatial content in images.

Our experiments demonstrate strong performance on several challenging benchmarks for both image and video recognition tasks. Importantly, due to our tokens being adaptive, we accomplish competitive results at reduced compute amount. We obtain comparable results to the state-of-the-arts on ImageNet while being computationally more efficient. We also confirm the effectiveness of the approach on multiple video datasets, including Kinetics-400, Kinetics-600, Charades, and AViD.

The code is available at: https://github.com/google-research/scenic/tree/main/scenic/projects/token_learner.

--- https://arxiv.org/abs/2304.08467 Learning to Compress Prompts with Gist Tokens Jesse Mu, Xiang Lisa Li, Noah Goodman 2023-04-17 2023-04-17 [("doi","10.48550/arXiv.2304.08467")] ai/nn/transformer/attention/compression ai/nn/transformer/t5

Prompting is now the primary way to use the multitask capabilities of language models (LMs), but prompts occupy valuable space in the input context window, and re-encoding the same prompt is computationally inefficient. Finetuning and distillation methods allow for specialization of LMs without prompting, but require retraining the model for each task.

To avoid this trade-off entirely, we present gisting, which trains an LM to compress prompts into smaller sets of “gist” tokens which can be reused for compute efficiency. Gist models can be easily trained as part of instruction finetuning via a restricted attention mask that encourages prompt compression.

On decoder (LLaMa-7B) and encoder-decoder (FLAN-T5-XXL) LMs, gisting enables up to 26× compression of prompts, resulting in up to 40% FLOPs reductions, 4.2% wall time speedups, storage savings, and minimal loss in output quality.

--- https://www.construction-physics.com/p/could-chatgpt-become-an-architect 2022-10-17 ai/nn/transformer/gpt/4/nonfiction technology --- https://www.lesswrong.com/posts/ZwshvqiqCvXPsZEct/the-learning-theoretic-agenda-status-2023 2022-10-17 math philosophy/logic reinforcement-learning/model reinforcement-learning/safe statistics/bayes --- https://www.medrxiv.org/content/10.1101/2023.04.05.23288194.full The effects of creatine supplementation on cognitive performance—a randomized controlled study Julia Fabienne Sandkühler, Xenia Kersting, Annika Faust, Eva Kathrin Königs, George Altman, Ulrich Ettinger, Silke Lux, Alexandra Philipsen, Helge Müller, Jan Brauner 2023-04-06 2023-04-06 [("doi","10.1101/2023.04.05.23288194")] creatine dual-n-back iq

Background: Creatine is an organic compound that facilitates the recycling of energy-providing adenosine triphosphate (ATP) in muscle and brain tissue. It is a safe, well-studied supplement for strength training. Previous studies have shown that supplementation increases brain creatine levels, which might increase cognitive performance.

The results of studies that have tested cognitive performance differ greatly, possibly due to different populations, supplementation regimens and cognitive tasks. This is the largest study on the effect of creatine supplementation on cognitive performance to date. As part of our study, we replicated Rae et al 2003.

Method: Our trial was cross-over, double-blind, placebo-controlled, and randomized, with daily supplementation of 5g for 6 weeks each. Like Rae et al 2003, we tested participants on Ravens Advanced Progressive Matrices (RAPM) and on the Backward Digit Span (BDS). In addition, we included 8 exploratory cognitive tests. About half of our 123 participants were vegetarians and half were omnivores.

Results: There was no indication that vegetarians benefited more from creatine than omnivores, so we merged the two groups. Participant scores after creatine and after placebo differed to an extent that was not statistically-significant (BDS: p = 0.064, partial eta squared = 0.029; RAPM: p = 0.327, partial eta squared = 0.008).

Compared to the null hypothesis of no effect, Bayes factors indicate weak evidence in favour of a small beneficial creatine effect and strong evidence against a large creatine effect. There was no indication that creatine improved the performance of our exploratory cognitive tasks.

Side effects were reported statistically-significantly more often for creatine than for placebo supplementation (p = 0.002, RR = 4.25).

Conclusions: Our results do not support large effects of creatine on the selected measures of cognition. However, our study, in combination with the literature, implies that creatine might have a small beneficial effect.

Larger studies are needed to confirm or rule out this effect. Given the safety and broad availability of creatine, this is well worth investigating; a small effect could have large benefits when scaled over time and over many people.

--- https://github.com/MaartenGr/Concept 2022-10-18 ai/nn/retrieval ai/nn/transformer/clip --- https://en.wikipedia.org/wiki/DBSCAN DBSCAN 2022-10-18 ai/tabular --- https://en.wikipedia.org/wiki/Blinking_twelve_problem Blinking 12 problem 2022-10-18 design --- https://en.wikipedia.org/wiki/Content_centric_networking Content centric networking 2022-10-18 cs/end-to-end-principle --- https://developer.mozilla.org/en-US/docs/Web/URI/Fragment/Text_fragments 2022-10-18 cs/css --- https://en.wikipedia.org/wiki/URI_fragment#Proposals URI fragment § Proposals 2022-10-18 cs/css --- https://mattsclancy.substack.com/p/what-does-peer-review-know 2022-10-18 statistics/peer-review --- https://arxiv.org/abs/1008.4324 Peer-review in a world with rational scientists: Toward selection of the average Stefan Thurner, Rudolf Hanel 2010-08-25 2022-10-18 [("doi","10.1140/epjb/e2011-20545-7")] statistics/bias statistics/peer-review

One of the virtues of peer review is that it provides a self-regulating selection mechanism for scientific work, papers and projects. Peer review as a selection mechanism is hard to evaluate in terms of its efficiency. Serious efforts to understand its strengths and weaknesses have not yet lead to clear answers.

In theory peer review works if the involved parties (editors and referees) conform to a set of requirements, such as love for high quality science, objectiveness, and absence of biases, nepotism, friend and clique networks, selfishness, etc. If these requirements are violated, what is the effect on the selection of high quality work? We study this question with a simple agent based model.

In particular we are interested in the effects of rational referees, who might not have any incentive to see high quality work other than their own published or promoted. We find that a small fraction of incorrect (selfish or rational) referees can drastically reduce the quality of the published (accepted) scientific standard. We quantify the fraction for which peer review will no longer select better than pure chance.

Decline of quality of accepted scientific work is shown as a function of the fraction of rational and unqualified referees. We show how a simple quality-increasing policy of eg. a journal can lead to a loss in overall scientific quality, and how mutual support-networks of authors and referees deteriorate the system.

--- https://www.tug.org/TUGboat/tb12-1/tb31hara.pdf#page=8 Haralambous 1990 2022-10-18 cs/css design/typography/dropcap design/typography/tex --- https://www.typografie.info/3/Schriften/fonts.html/deutsche-zierschrift-r250/ 2022-10-18 cs/css design/typography/dropcap --- https://en.wikipedia.org/wiki/Initial Initial 2022-10-19 cs/css design/typography/dropcap --- https://www.1001fonts.com/goudy-initialen-font.html Goudy Initialen 2022-10-19 cs/css design/typography/dropcap design/typography/floral --- https://wiki.obormot.net/Main/BonusFontsDemo?demo_font_one=Cheshire+Initials Cheshire Initials 2022-10-19 cs/css design/typography/dropcap --- https://wiki.obormot.net/Main/BonusFontsDemo?demo_font_one=Kanzlei+Initialen Kanzlei Initialen 2022-10-19 cs/css design/typography/dropcap --- https://statmodeling.stat.columbia.edu/2023/04/18/chatgpt4-writes-stan-code-so-i-dont-have-to/ 2022-10-19 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex statistics/bayes --- https://www.afterbabel.com/p/why-some-researchers-think-im-wrong 2022-10-19 psychiatry sociology/technology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7878036/ A Review of the Historical Evolutionary Process of Dry and Water Maze Tests in Rodents Fahimeh Mohseni, Shahram Ghorbani Behnam, Raheleh Rafaiee 2020 2022-10-19 [("doi","10.32598/bcn.11.4.1425.1")] psychology/animal/maze

This research provides an overview of the historical advances of the maze tests that are widely used to assess the cognitive impairments in rodents. Particularly, this study focuses on the issue of learning and memory behavioral tests, including dry and water mazes.

Several types of mazes have been used in this setting, but their real advantages and applications depend on the type selected by the researcher. We answered some of the basic questions that any interested researcher in such studies may be faced with.

The reviewed topics are as follows: the definition of maze learning, the role of the memory in the maze learning, the differences between several types of mazes, and foremost the rationale behind the maze constructions and designs.

--- https://en.wikipedia.org/wiki/User:Junnn11 User:Junnn11 2022-10-19 wikipedia --- https://blog.langchain.dev/agents-round/ 2022-10-19 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/safe --- https://x.com/YaBoyFathoM/status/1647608734175186944 2022-10-19 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://x.com/jd_pressman/status/1646766004637401088 2022-10-20 ai/nn/transformer/gpt/inner-monologue --- https://x.com/jjvincent/status/1648594881198039040 2022-10-20 ai/nn/transformer/gpt reinforcement-learning/safe --- https://x.com/KevinAFischer/status/1646677902833102849 2022-10-20 ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/non-fiction reinforcement-learning/safe --- https://x.com/KevinAFischer/status/1646690838981005312 2022-10-20 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue reinforcement-learning/safe --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940111/ DNAm PhenoAge: An epigenetic biomarker of aging for lifespan and healthspan Morgan E. Levine, Ake T. Lu, Austin Quach, Brian H. Chen, Themistocles L. Assimes, Stefania Bandinelli, Lifang Hou, Andrea A. Baccarelli, James D. Stewart, Yun Li, Eric A. Whitsel, James G. Wilson, Alex P. Reiner, Abraham Aviv, Kurt Lohman, Yongmei Liu, Luigi Ferrucci, Steve Horvath 2018 2022-10-20 [("doi","10.18632/aging.101414")] longevity/epigenetics

Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation of epigenetic biomarkers of aging were developed using chronological age as a surrogate for biological age, we hypothesized that incorporation of composite clinical measures of phenotypic age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the development of a more powerful epigenetic biomarker of aging.

Using an innovative two-step process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer’s disease.

While this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell tested.

Based on an in-depth transcriptional analysis in sorted cells, we find that increased epigenetic, relative to chronological age, is associated with increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures.

Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging.

--- /doc/economics/copyright/1975-shawcross.pdf The First Illustrations for Paradise Lost John T. Shawcross 1975-05-01 2022-10-20 [("doi","10.1111/j.1094-348X.1975.tb00136.x")] economics/copyright fiction/poetry --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10081735/ Sirtuins are Not Conserved Longevity Genes Charles Brenner 2022 2022-10-20 [("doi","10.1093/lifemeta/loac025")] longevity/fasting

[commentary] It is central to biology that sequence conservation suggests functional conservation. Animal longevity is an emergent property of selected traits that integrates capacities to perform physical and mental functions after reproductive maturity.

Though the yeast SIR2 gene was nominated as a longevity gene based on extended replicative longevity of old mother cells, this is not a selected trait: SIR2 is selected against in chronological aging and the direct targets of SIR2 in replicative lifespan are not conserved. Though it would be difficult to imagine how a gene that advantages 1 in 5 million yeast cells could have anticipated causes of aging in animals, overexpression of SIR2 homologs was tested in invertebrates for longevity.

Because artifactual positive results were reported years before they were sorted out and because it was not known that SIR2 functions as a pro-aging gene in yeast chronological aging and in flies subject to amino acid deprivation, a global pursuit of longevity phenotypes was driven by a mixture of framing bias, confirmation bias and hype.

Review articles that propagate these biases are so rampant that few investigators have considered how weak the case ever was for sirtuins as longevity genes.

Acknowledging that a few positive associations between sirtuins and longevity have been identified after thousands of person-years and billions of dollars of effort, we review the data and suggest rejection of the notions that sirtuins (1) have any specific connection to lifespan in animals and (2) are primary mediators of the beneficial effects of NAD repletion.

--- https://www.reddit.com/r/dalle2/comments/12tzo3x/wikihow_how_to_use_your_cat_as_a_funny_hat/ 2022-10-20 ai/nn/transformer/gpt/dall-e/2 cat --- https://www.youtube.com/watch?v=hhiLw5Q_UFg&t=1098s 2022-10-20 ai/nn/retrieval ai/nn/transformer/gpt/calibration reinforcement-learning/imitation-learning reinforcement-learning/preference-learning --- /doc/history/1989-luce.pdf Ancient Views on the Causes of Bias in Historical Writing T. J. Luce 1989-01-01 2022-10-20 [("doi","10.1086/367133")] history philosophy/epistemology politics --- https://80000hours.org/after-hours-podcast/episodes/andres-jimenez-zorrilla-shrimp-welfare-project/ 2022-10-20 philosophy/ethics psychology/neuroscience --- https://theamericanscholar.org/phantoms/ 2022-10-21 psychology/smell --- https://arxiv.org/abs/2210.15893#facebook When Life Gives You Lemons, Make Cherryade: Converting Feedback from Bad Responses into Good Labels Weiyan Shi, Emily Dinan, Kurt Shuster, Jason Weston, Jing Xu 2022-10-28 2022-10-28 [("doi","10.48550/arXiv.2210.15893")] ai/nn/transformer/gpt reinforcement-learning/preference-learning

Deployed dialogue agents have the potential to integrate human feedback to continuously improve themselves. However, humans may not always provide explicit signals when the chatbot makes mistakes during interactions.

In this work, we propose Juicer, a framework to make use of both binary and free-form textual human feedback. It works by: (1) extending sparse binary feedback by training a satisfaction classifier to label the unlabeled data; and (2) training a reply corrector to map the bad replies to good ones.

We find that augmenting training [of BlenderBot2] with model-corrected replies improves the final dialogue model, and we can further improve performance by using both positive and negative replies through the recently proposed Director model.

--- https://arxiv.org/abs/2206.07694#facebook DIRECTOR: Generator-Classifiers For Supervised Language Modeling Kushal Arora, Kurt Shuster, Sainbayar Sukhbaatar, Jason Weston 2022-06-15 2022-10-21 [("doi","10.48550/arXiv.2206.07694")] ai/nn/sampling ai/nn/transformer/gpt/2

Current language models achieve low perplexity but their resulting generations still suffer from toxic responses, repetitiveness and contradictions. The standard language modeling setup fails to address these issues.

In this paper, we introduce a new architecture, Director, that consists of a unified generator-classifier with both a language modeling and a classification head for each output token. Training is conducted jointly using both standard language modeling data, and data labeled with desirable and undesirable sequences.

Experiments in several settings show that the model [BlenderBot, GPT-2] has competitive training and decoding speed compared to standard language models while yielding superior results, alleviating known issues while maintaining generation quality. It also outperforms existing model guiding approaches in terms of both accuracy and efficiency.

--- https://arxiv.org/abs/2211.05826#facebook The CRINGE Loss: Learning what language not to model Leonard Adolphs, Tianyu Gao, Jing Xu, Kurt Shuster, Sainbayar Sukhbaatar, Jason Weston 2022-11-10 2022-11-10 [("doi","10.48550/arXiv.2211.05826")] ai/nn/sampling ai/nn/transformer/gpt

Standard language model training employs gold human documents or human-human interaction data, and treats all training data as positive examples. Growing evidence shows that even with very large amounts of positive training data, issues remain that can be alleviated with relatively small amounts of negative data—examples of what the model should not do.

In this work, we propose a novel procedure to train with such data called the CRINGE loss (ContRastive Iterative Negative GEneration).

We show the effectiveness of this approach across 3 different experiments on the tasks of safe generation, contradiction avoidance, and open-domain dialogue. Our models outperform multiple strong baselines and are conceptually simple, easy to train and implement.

--- https://arxiv.org/abs/1803.04585#miri Categorizing Variants of Goodhart’s Law David Manheim, Scott Garrabrant 2018-03-13 2022-10-21 [("doi","10.48550/arXiv.1803.04585")] economics reinforcement-learning/safe statistics/order

There are several distinct failure modes for overoptimization of systems on the basis of metrics. This occurs when a metric which can be used to improve a system is used to an extent that further optimization is ineffective or harmful, and is sometimes termed Goodhart’s Law. This class of failure is often poorly understood, partly because terminology for discussing them is ambiguous, and partly because discussion using this ambiguous terminology ignores distinctions between different failure modes of this general type.

This paper expands on an earlier discussion by Garrabrant, which notes there are “(at least) 4 different mechanisms” that relate to Goodhart’s Law. This paper is intended to explore these mechanisms further, and specify more clearly how they occur.

This discussion should be helpful in better understanding these types of failures in economic regulation, in public policy, in machine learning, and in Artificial Intelligence alignment. The importance of Goodhart effects depends on the amount of power directed towards optimizing the proxy, and so the increased optimization power offered by artificial intelligence makes it especially critical for that field.

--- https://www.lesswrong.com/posts/EbFABnst8LsidYs5Y/goodhart-taxonomy 2022-10-21 economics reinforcement-learning/safe statistics/order --- https://www.reddit.com/r/dalle2/comments/12w9abv/biblically_accurate_cat_angel/ 2022-10-21 ai/nn/transformer/gpt/dall-e/2 cat --- https://en.wikipedia.org/wiki/Hermann_Ebbinghaus Hermann Ebbinghaus 2022-10-21 nootropic/quantified-self psychology/spaced-repetition --- https://psychclassics.yorku.ca/Ebbinghaus/index.htm 2022-10-21 psychology/spaced-repetition --- https://www.tor.com/2011/08/31/wikihistory/ 2022-10-21 fiction/science-fiction wikipedia --- https://mattsclancy.substack.com/p/can-taste-beat-peer-review 2022-10-21 statistics/peer-review --- https://arxiv.org/abs/2304.10977 Evaluating Transformer Language Models on Arithmetic Operations Using Number Decomposition Matteo Muffo, Aldo Cocco, Enrico Bertino 2023-04-21 2023-04-21 [("doi","10.48550/arXiv.2304.10977")] ai/nn/tokenization ai/nn/transformer/gpt/3/nonfiction math

In recent years, Large Language Models such as GPT-3 showed remarkable capabilities in performing NLP tasks in the zero and few shot settings. On the other hand, the experiments highlighted the difficulty of GPT-3 in carrying out tasks that require a certain degree of reasoning, such as arithmetic operations. In this paper we evaluate the ability of Transformer Language Models to perform arithmetic operations following a pipeline that, before performing computations, decomposes numbers in units, tens, and so on.

We denote the models fine-tuned with this pipeline with the name Calculon and we test them in the task of performing additions, subtractions and multiplications on the same test sets of GPT-3.

Results show an increase of accuracy of 63% in the 5-digit addition task. Moreover, we demonstrate the importance of the decomposition pipeline introduced, since fine-tuning the same Language Model without decomposing numbers results in 0% accuracy in the 5-digit addition task.

--- https://arxiv.org/abs/2304.11063#facebook Think Before You Act: Unified Policy for Interleaving Language Reasoning with Actions Lina Mezghani, Piotr Bojanowski, Karteek Alahari, Sainbayar Sukhbaatar 2023-04-18 2023-04-18 [("doi","10.48550/arXiv.2304.11063")] ai/nn/transformer/gpt/inner-monologue reinforcement-learning/model/decision-transformer reinforcement-learning/offline

The success of transformer models trained with a language modeling objective brings a promising opportunity to the reinforcement learning framework. Decision Transformer is a step towards this direction, showing how to train transformers with a similar next-step prediction objective on offline data. Another important development in this area is the recent emergence of large-scale datasets collected from the internet, such as the ones composed of tutorial videos with captions where people talk about what they are doing.

To take advantage of this language component, we propose a novel method for unifying language reasoning with actions in a single policy. Specifically, we augment a transformer policy with word outputs, so it can generate textual captions interleaved with actions.

When tested on the most challenging task in BabyAI, with captions describing next subgoals, our reasoning policy consistently outperforms the caption-free baseline.

--- https://arxiv.org/abs/2303.11455 Large Language Models and Simple, Stupid Bugs Kevin Jesse, Toufique Ahmed, Premkumar T. Devanbu, Emily Morgan 2023-03-20 2023-03-20 [("doi","10.48550/arXiv.2303.11455")] ai/nn/transformer/gpt/codex

With the advent of powerful neural language models, AI-based systems to assist developers in coding tasks are becoming widely available; Copilot is one such system. Copilot uses Codex, a large language model (LLM), to complete code conditioned on a preceding “prompt”.

Codex, however, is trained on public GitHub repositories, viz., on code that may include bugs and vulnerabilities. Previous studies [1], [2] show Codex reproduces vulnerabilities seen in training. In this study, we examine how prone Codex is to generate an interesting bug category, single statement bugs, commonly referred to as simple, stupid bugs or SStuBs in the MSR community.

We find that Codex and similar LLMs do help avoid some SStuBs, but do produce known, verbatim SStuBs as much as 2× as likely than known, verbatim correct code. We explore the consequences of the Codex generated SStuBs and propose avoidance strategies that suggest the possibility of reducing the production of known, verbatim SStubs, and increase the possibility of producing known, verbatim fixes.

--- https://www.bloomberg.com/news/features/2023-04-24/a-high-school-teacher-s-free-image-database-powers-ai-unicorns 2022-10-22 ai/dataset ai/nn/diffusion --- https://github.com/IrisRainbowNeko/ML-Danbooru 2022-10-22 ai/anime/danbooru --- https://study.com/resources/perceptions-of-chatgpt-in-schools 2022-10-22 ai/nn/transformer/gpt/non-fiction --- https://github.com/freedmand/semantra 2022-10-22 ai/nn/retrieval --- https://www.lawfaremedia.org/article/chatgpt-unbound 2022-10-22 ai/nn/transformer/gpt reinforcement-learning/safe --- https://arxiv.org/abs/2304.11029#microsoft CLaMP: Contrastive Language-Music Pre-training for Cross-Modal Symbolic Music Information Retrieval Shangda Wu, Dingyao Yu, Xu Tan, Maosong Sun 2023-04-21 2023-04-21 [("doi","10.48550/arXiv.2304.11029")] ai/music ai/nn/retrieval ai/nn/vae/mae ai/scaling

[code] We introduce CLaMP: Contrastive Language-Music Pre-training, which learns cross-modal representations between natural language and symbolic music using a music encoder and a text encoder trained jointly with a contrastive loss.

To pre-train CLaMP, we collected a large dataset of 1.4 million music-text pairs.

It employed text dropout as a data augmentation technique and bar patching to efficiently represent music data which reduces sequence length to less than 10%. In addition, we developed a masked music model pre-training objective to enhance the music encoder’s comprehension of musical context and structure.

CLaMP integrates textual information to enable semantic search and zero-shot classification for symbolic music, surpassing the capabilities of previous models.

To support the evaluation of semantic search and music classification, we publicly release WikiMusicText (WikiMT), a dataset of 1,010 lead sheets in ABC notation, each accompanied by a title, artist, genre, and description.

In comparison to state-of-the-art models that require fine-tuning, zero-shot CLaMP demonstrated comparable or superior performance on score-oriented datasets.

--- https://arxiv.org/abs/2303.16839#google MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks Weicheng Kuo, A. J. Piergiovanni, Dahun Kim, Xiyang Luo, Ben Caine, Wei Li, Abhijit Ogale, Luowei Zhou, Andrew Dai, Zhifeng Chen, Claire Cui, Anelia Angelova 2023-03-29 2023-03-29 [("doi","10.48550/arXiv.2303.16839")] ai/nn/retrieval ai/nn/transformer/clip

The development of language models have moved from encoder-decoder to decoder-only designs. In addition, the common knowledge has it that the two most popular multimodal tasks, the generative and contrastive tasks, tend to conflict with one another, are hard to accommodate in one architecture, and further need complex adaptations for downstream tasks.

We propose a novel paradigm of training with a decoder-only model for multimodal tasks, which is surprisingly effective in jointly learning of these disparate vision-language tasks. This is done with a simple model, called MaMMUT. It consists of a single vision encoder and a text decoder, and is able to accommodate contrastive and generative learning by a novel two-pass approach on the text decoder.

We demonstrate that joint learning of these diverse objectives is simple, effective, and maximizes the weight-sharing of the model across these tasks. Furthermore, the same architecture enables straightforward extensions to open-vocabulary object detection and video-language tasks.

The model tackles a diverse range of tasks, while being modest in capacity. Our model achieves the state-of-the-art on image-text and text-image retrieval, video question answering and open-vocabulary detection tasks, outperforming much larger and more extensively trained foundational models. It shows very competitive results on VQA and Video Captioning, especially considering its capacity.

Ablations confirm the flexibility and advantages of our approach.

--- https://arxiv.org/abs/2304.11158#eleutherai Emergent and Predictable Memorization in Large Language Models Stella Biderman, Usvsn Sai Prashanth, Lintang Sutawika, Hailey Schoelkopf, Quentin Anthony, Shivanshu Purohit, Edward Raf 2023-04-21 2023-04-21 [("doi","10.48550/arXiv.2304.11158")] ai/nn/transformer/gpt ai/scaling

[Twitter] Memorization, or the tendency of large language models (LLMs) to output entire sequences from their training data verbatim, is a key concern for safely deploying language models. In particular, it is vital to minimize a model’s memorization of sensitive datapoints such as those containing personal identifiable information (PII). The prevalence of such undesirable memorization can pose issues for model trainers, and may even require discarding an otherwise functional model.

We therefore seek to predict which sequences will be memorized before a large model’s full train-time by extrapolating the memorization behavior of lower-compute trial runs.

We measure memorization of the Pythia model suite, and find that intermediate checkpoints are better predictors of a model’s memorization behavior than smaller fully-trained models.

We additionally provide further novel discoveries on the distribution of memorization scores across models and data.

--- https://www.newstatesman.com/culture/books/2023/04/derek-parfit-perfectionist-philosophy-intellectual-oxford 2022-10-23 philosophy/ethics --- https://arxiv.org/abs/2206.15474 Forecasting Future World Events with Neural Networks Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, Dan Hendrycks 2022-06-30 2022-10-23 [("doi","10.48550/arXiv.2206.15474")] ai/dataset ai/nn/tokenization ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/non-fiction ai/nn/transformer/t5 statistics/prediction

[datasets; 2023 competition] Forecasting future world events is a challenging but valuable task. Forecasts of climate, geopolitical conflict, pandemics and economic indicators help shape policy and decision making. In these domains, the judgment of expert humans contributes to the best forecasts. Given advances in language modeling, can these forecasts be automated?

To this end, we introduce Autocast, a dataset containing thousands of forecasting questions and an accompanying news corpus. Questions are taken from forecasting tournaments [Metaculus, Good Judgment Open, & CSET Foretell], ensuring high quality, real-world importance, and diversity. The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts (avoiding leakage from the future).

Motivated by the difficulty of forecasting numbers across orders of magnitude (eg. global cases of COVID-19 in 2022), we also curate IntervalQA, a dataset of numerical questions and metrics for calibration.

We test language models [GPT-2, FiD T5, DeBERTa-v3] on our forecasting task and find that performance is far below a human expert baseline. However, performance improves with increased model size and incorporation of relevant information from the news corpus.

In sum, Autocast poses a novel challenge for large language models and improved performance could bring large practical benefits.

…Because it relies on scarce human expertise, forecasting is only used for a small number of questions. This motivates using ML to automate forecasting, eg. by automating human information retrieval (finding news sources), reasoning (to decide if some evidence bears on a forecast), and quantitative modeling. ML models may also have some advantages over human forecasters. Models can read through text or data much faster than humans and can discern patterns in noisy high-dimensional data that elude humans. When it comes to learning, humans cannot be trained on past data in manner simulating actual forecasting (eg. How likely was the Soviet Union’s collapse from the viewpoint of 1980?) because they know the outcomes—but past data can be used for ML models.

Figure 1: Example from the Autocast dataset, including the question, the resolution of the question, and the timeseries of aggregate human expert forecasts (Crowd) from the start date to the time the question resolves. We train a language model to generate forecasts at each timestep, using only news articles available at that timestep (ie. without allowing any leakage of information from the future).

…Related Work: Forecasting: A recent experiment (Bonde 2022) tested GPT-3 in the few-shot setting on true/false questions collected from Metaculus (one of the sources for Autocast). However, since questions were not filtered by date, some answers would have appeared in GPT-3’s training data. Similar to our work, ForecastQA (Jin et al 2021) is a dataset of forecasting questions that covers a range of topics. However, ForecastQA’s questions were written by crowdworkers without forecasting experience. Consequently, the questions are often nonsensical or ambiguous given the lack of additional context, eg. “To how many people will the Representative of an internet speak to by September 2019?”, or “In July 2019, will an article say there were no volunteers in 2016?”. We found that a high percentage of ForecastQA questions suffer from these issues. By contrast, our questions were written by experienced forecasters and are always unambiguous given the full question description. Finally, ForecastQA’s human baseline was done retrospectively (making it unrealistic) whereas our dataset contains expert human forecasts from real forecasting questions.

Table 2: Model accuracy on the Autocast dataset for each question type: true/false (T/F), multiple-choice question (MCQ), and numerical (Numerical). For Numerical, lower is better. For other metrics, higher is better. The model FiD Static (based on T5) retrieves the top 10 news articles over the period, while FiD Temporal (based on GPT-2 with T5 encoder) retrieves the top 1 article each day. Averaging over all model sizes, we find that the FiD Temporal achieves the best average.

…Calibration: Experiments: We fine-tune DeBERTa-v3 models (He et al 2020) to predict a point estimate and a set of confidence intervals corresponding to the confidence levels in the RMS calibration error metric. On a high level, we use a loss with 3 components: (1) MSE loss between the predicted point estimate and the ground-truth target, (2) MSE loss between the boundaries of the predicted confidence intervals and the ground-truth target for boundaries that are on the wrong side of the target, (3) a penalty on the length of the predicted intervals to encourage finer predictions. The models are trained for 5 epochs with a batch size of 100. A detailed description is in the Supplementary Material. We show results in Table 3: All 3 metrics decrease with model size.

Table 3: Results for DeBERTa-v3 models trained to output confidence intervals on our dataset of numerical predictions. The high dynamic range of the targets leads to large confidence intervals, but median interval size decreases with larger models as does RMS Calibration Error.
--- https://www.mlsafety.org/ 2022-10-23 ai/nn/transformer/gpt/non-fiction statistics/prediction --- https://arxiv.org/abs/2005.00792 ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data Woojeong Jin, Rahul Khanna, Suji Kim, Dong-Ho Lee, Fred Morstatter, Aram Galstyan, Xiang Ren 2020-05-02 2022-10-23 [("doi","10.48550/arXiv.2005.00792")] ai/dataset ai/nn/transformer statistics/prediction

Event forecasting is a challenging, yet important task, as humans seek to constantly plan for the future. Existing automated forecasting studies rely mostly on structured data, such as time-series or event-based knowledge graphs, to help predict future events. In this work, we aim to formulate a task, construct a dataset, and provide benchmarks for developing methods for event forecasting with large volumes of unstructured text data.

To simulate the forecasting scenario on temporal news documents, we formulate the problem as a restricted-domain, multiple-choice, question-answering (QA) task. Unlike existing QA tasks, our task limits accessible information, and thus a model has to make a forecasting judgement. To showcase the usefulness of this task formulation, we introduce ForecastQA, a question-answering dataset consisting of 10,392 event forecasting questions, which have been collected and verified via crowdsourcing efforts.

We present our experiments on ForecastQA using BERT-based models and find that our best model achieves 60.1% accuracy on the dataset, which still lags behind human performance by about 19%.

We hope ForecastQA will support future research efforts in bridging this gap.

--- https://arxiv.org/abs/2005.00700 UnifiedQA: Crossing Format Boundaries With a Single QA System Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, Hannaneh Hajishirzi 2020-05-02 2022-10-23 [("doi","10.48550/arXiv.2005.00700")] ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5

Question answering (QA) tasks have been posed using a variety of formats, such as extractive span selection, multiple choice, etc. This has led to format-specialized models, and even to an implicit division in the QA community. We argue that such boundaries are artificial and perhaps unnecessary, given the reasoning abilities we seek to teach are not governed by the format.

As evidence, we use the latest advances in language modeling to build a single pre-trained QA model, UnifiedQA, that:

performs surprisingly well across 17 QA datasets spanning 4 diverse formats. UnifiedQA performs on par with 9 different models that were trained on individual datasets themselves.

Even when faced with 12 unseen datasets of observed formats, UnifiedQA performs surprisingly well, showing strong generalization from its out-of-format training data. Finally, simply fine-tuning this pre-trained QA model into specialized models results in a new state-of-the-art on 6 datasets, establishing UnifiedQA as a strong starting point for building QA systems.

--- https://arxiv.org/abs/2202.12359 UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Training Daniel Khashabi, Yeganeh Kordi, Hannaneh Hajishirzi 2022-02-23 2022-10-23 [("doi","10.48550/arXiv.2202.12359")] ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 ai/scaling

We present UnifiedQA-v2, a QA model built with the same process as UnifiedQA, except that it uses more supervision—roughly 3× the number of datasets used for UnifiedQA.

This generally leads to better in-domain and cross-domain results.

--- https://forum.effectivealtruism.org/posts/akn2BFhhM9CzwpLEA/wisdom-of-the-crowd-vs-the-best-of-the-best-of-the-best 2022-10-23 statistics/prediction --- https://www.reddit.com/r/MachineLearning/comments/12xwzt9/d_be_careful_with_user_facing_apps_using_llms/ 2022-10-23 ai/nn/transformer/gpt cs/security reinforcement-learning/safe --- https://arxiv.org/abs/2304.12210#facebook A Cookbook of Self-Supervised Learning Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Gregoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, Micah Goldblum 2023-04-24 2023-04-24 [("doi","10.48550/arXiv.2304.12210")] ai/nn/gan/data-augmentation ai/nn/sparsity/knowledge-distillation ai/nn/transformer/clip ai/nn/vae/mae

Self-supervised learning, dubbed the ‘dark matter of intelligence’, is a promising path to advance machine learning.

Yet, much like cooking, training SSL methods is a delicate art with a high barrier to entry. While many components are familiar, successfully training a SSL method involves a dizzying set of choices from the pretext tasks to training hyper-parameters. Our goal is to lower the barrier to entry into SSL research by laying the foundations and latest SSL recipes in the style of a cookbook.

We hope to empower the curious researcher to navigate the terrain of methods, understand the role of the various knobs, and gain the know-how required to explore how delicious SSL can be.

[The Deep Metric Learning Family: SimCLR/NNCLR/MeanSHIFT/SLC; The Self-Distillation Family: BYOL/SimSIAM/DINO; The Canonical Correlation Analysis Family: VICReg/BarlowTwins/SWAV/W-MSE; Masked Image Modeling (MIM): BEiT/MAE/SimMIM/Muse.]

--- https://arxiv.org/abs/2111.09886#microsoft SimMIM: A Simple Framework for Masked Image Modeling Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, Han Hu 2021-11-18 2022-10-23 [("doi","10.48550/arXiv.2111.09886")] ai/nn/vae/mae

This paper presents SimMIM, a simple framework for masked image modeling. We simplify recently proposed related approaches without special designs such as block-wise masking and tokenization via discrete VAE or clustering.

To study what let the masked image modeling task learn good representations, we systematically study the major components in our framework, and find that simple designs of each component have revealed very strong representation learning performance: (1) random masking of the input image with a moderately large masked patch size (eg. 32) makes a strong pre-text task; (2) predicting raw pixels of RGB values by direct regression performs no worse than the patch classification approaches with complex designs; (3) the prediction head can be as light as a linear layer, with no worse performance than heavier ones.

Using ViT-B, our approach achieves 83.8% top-1 fine-tuning accuracy on ImageNet-1K by pre-training also on this dataset, surpassing previous best approach by +0.6%. When applied on a larger model of about 650 million parameters, SwinV2-H, it achieves 87.1% top-1 accuracy on ImageNet-1K using only ImageNet-1K data.

We also leverage this approach to facilitate the training of a 3B model (SwinV2-G), that by 40× less data than that in previous practice, we achieve the state-of-the-art on 4 representative vision benchmarks.

The code and models will be publicly available at Github.

--- https://www.nateranda.com/blog/post/a-defense-of-text-speak/ 2022-10-24 design/typography --- https://micahflee.com/2023/04/capturing-the-flag-with-gpt-4/ 2022-10-24 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex cs/cryptography cs/security --- https://www.quantamagazine.org/how-pools-of-genetic-diversity-affect-a-species-fate-20230425/ 2022-10-24 genetics/heritable/rare genetics/selection/natural --- /doc/genetics/heritable/rare/2022-robinson.pdf The critically endangered vaquita is not doomed to extinction by inbreeding depression Jacqueline A. Robinson, Christopher C. Kyriazis, Sergio F. Nigenda-Morales, Annabel C. Beichman, Lorenzo Rojas-Bracho, Kelly M. Robertson, Michael C. Fontaine, Robert K. Wayne, Kirk E. Lohmueller, Barbara L. Taylor, Phillip A. Morin 2022-05-05 2022-10-24 [("doi","10.1126/science.abm1742")] genetics/heritable/rare genetics/selection/natural

[media] In cases of severe wildlife population decline, a key question is whether recovery efforts will be impeded by genetic factors, such as inbreeding depression. Decades of excess mortality from gillnet fishing have driven Mexico’s vaquita porpoise (Phocoena sinus) to ~10 remaining individuals.

We analyzed whole-genome sequences from 20 vaquitas and integrated genomic and demographic information into stochastic, individual-based simulations to quantify the species’ recovery potential.

Our analysis suggests that the vaquita’s historical rarity has resulted in a low burden of segregating deleterious variation, reducing the risk of inbreeding depression [ie. the very low effective population size lasted long enough for negative selection to purge most of the harmful rare mutations]. Similarly, genome-informed simulations suggest that the vaquita can recover if bycatch mortality is immediately halted.

This study provides hope for vaquitas and other naturally rare endangered species and highlights the utility of genomics in predicting extinction risk.

Population size and risk of extinction: The vaquita porpoise is one of the most endangered animals in the world, with only an estimated 10 individuals remaining. To determine the risk of extinction caused by inbreeding depression, Robinson et al 2022 sequenced and examined 20 vaquita genomes to determine their heterozygosity and ancestral population size (see the Perspective by Grueber & Sunnucks 2022).

The authors determined that the long-term population size of vaquitas has been low for a marine mammal, with ~1000 years of stable genomic diversity. Genomic comparisons with other cetacean species and modeling indicated that vaquitas are unlikely to suffer from inbreeding depression.

Therefore, if the risk of bycatch mortality caused by fishermen can be eliminated, then there is a chance that this species will not go extinct. —Editor

--- https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html 2022-10-24 ai/nn/transformer/gpt/instruction-tuning --- https://www.newyorker.com/news/the-control-of-nature/a-heat-shield-for-the-most-important-ice-on-earth 2022-10-24 technology/carbon-capture --- https://x.com/_sinity/status/1650933148836831233 2022-10-24 ai/nn/transformer/gpt/4/poetry fiction/humor reinforcement-learning/safe --- https://arxiv.org/abs/2208.09770#microsoft Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization Pengcheng He, Baolin Peng, Liyang Lu, Song Wang, Jie Mei, Yang Liu, Ruochen Xu, Hany Hassan Awadalla, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang 2022-08-21 2022-10-24 [("doi","10.48550/arXiv.2208.09770")] ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5

This paper presents Z-Code++ [Z-Code & GODEL, part of Project Z-Code], a new pre-trained language model optimized for abstractive text summarization. The model extends the state-of-the-art encoder-decoder model using 3 techniques.

First, we use a two-phase pre-training process to improve model’s performance on low-resource summarization tasks. The model is first pre-trained using text corpora for language understanding, and then is continually pre-trained on summarization corpora for grounded text generation.

Second, we replace self-attention layers in the encoder with disentangled attention layers, where each word is represented using two vectors that encode its content and position, respectively.

Third, we use fusion-in-encoder, a simple yet effective method of encoding long sequences in a hierarchical manner.

Z-Code++ creates new state-of-the-art on 9⁄13 text summarization tasks across 5 languages. Our model is parameter-efficient in that it outperforms the 600× larger PaLM-540B on XSum, and the finetuned 200× larger GPT-3-175b on SAMSum. In zero-shot and few-shot settings, our model substantially outperforms the competing models.

--- https://arxiv.org/abs/2109.10465#microsoft Scalable and Efficient MoE Training for Multitask Multilingual Models Young Jin Kim, Ammar Ahmad Awan, Alexandre Muzio, Andres Felipe Cruz Salinas, Liyang Lu, Amr Hendy, Samyam Rajbhandari, Yuxiong He, Hany Hassan Awadalla 2021-09-22 2022-10-24 [("doi","10.48550/arXiv.2109.10465")] ai/scaling/mixture-of-experts

The Mixture of Experts (MoE) models are an emerging class of sparsely activated deep learning models that have sublinear compute costs with respect to their parameters. In contrast with dense models, the sparse architecture of MoE offers opportunities for drastically growing model size with accuracy gain while consuming much lower compute budget.

However, supporting large scale MoE training also has its own set of system and modeling challenges. To overcome the challenges and embrace the opportunities of MoE, we first develop a system capable of scaling MoE models efficiently to trillions of parameters. It combines multi-dimensional parallelism and heterogeneous memory technologies harmoniously with MoE to empower 8× larger models on the same hardware compared with existing work.

Besides boosting system efficiency, we also present new training methods to improve MoE sample efficiency and leverage expert pruning strategy to improve inference time efficiency. By combining the efficient system and training methods, we are able to scale up large multitask multilingual models for language generation which results in a great improvement in model accuracy.

A model trained with 10 billion parameters on 50 languages can achieve state-of-the-art performance in Machine Translation (MT) and multilingual natural language generation tasks.

The system support of efficient MoE training has been implemented and open-sourced with the DeepSpeed library.

--- https://www.pnas.org/doi/full/10.1073/pnas.1310478110 Efficient nonmeiotic allele introgression in livestock using custom endonucleases Tan 2013 2022-10-24 genetics/editing --- https://www.brown.edu/news/2023-04-25/open-web-text 2022-10-24 ai/dataset ai/nn/transformer/gpt --- https://constructionphysics.substack.com/p/could-we-stop-yellowstone-from-erupting 2022-10-25 existential-risk --- https://www.neelnanda.io/mechanistic-interpretability/favourite-papers 2022-10-25 ai/nn reinforcement-learning/safe --- https://psychology.wvu.edu/about/history/west-virginia-university-s-first-psychologists 2022-10-25 psychology/animal/maze --- https://books.google.com/books/about/The_Role_of_Kinesthesis_in_Maze_Learning.html?id=X0R0QwAACAAJ 2022-10-25 psychology/animal/maze --- https://archive.org/details/principlesofanim1935maie/page/384/mode/1up 2022-10-25 psychology/animal/maze --- https://calteches.library.caltech.edu/51/2/CargoCult.htm 2022-10-25 psychology/animal/maze --- https://en.wikipedia.org/wiki/Maze#Psychology_experiments Maze § Psychology experiments 2022-10-25 psychology/animal/maze --- https://en.wikipedia.org/wiki/Category:Animal_testing_mazes Category:Animal testing mazes 2022-10-25 psychology/animal/maze --- /doc/genetics/heritable/correlation/1940-tryon-figure4-mazebrightdullrats-distributions.png 1940 2022-10-25 genetics/heritable/correlation psychology/animal/maze --- /doc/genetics/selection/1951-hall.pdf 1951-01-01 2022-10-25 genetics/selection psychology/animal/maze --- /doc/iq/animal/1963-rosenthal.pdf The effect of experimenter bias on the performance of the albino rat 1963-01-01 2022-10-25 iq/animal psychology/animal/maze --- https://guzey.com/2022-lessons/#meditation-is-terrible---meditation-is-amazing 2022-10-26 psychiatry/meditation --- /doc/genetics/heritable/2019-davis.pdf The Louisville Twin Study: Past, Present and Future Deborah W. Davis, Eric Turkheimer, Deborah Finkel, Christopher Beam, Lesa Ryan 2019-12-01 2022-10-26 [("doi","10.1017/thg.2019.37")] genetics/heritable iq

The Louisville Twin Study (LTS) is nationally recognized as one of the largest and most comprehensive studies of child development related to multiple birth status.

The LTS is unique because of the extensive longitudinal face-to-face assessments, the frequency of data collection, the inclusion of data on additional family members (ie. parents, siblings, grandparents; and later, twins’ own spouses and children), and the variety of data collection methods used.

Data preservation efforts began in 2008 and are largely complete, although efforts are ongoing to obtain funding to convert the electronic data to a newer format. A pilot study was completed in the summer of 2018 to bring the twins, who are now middle-aged, back for testing. A grant is currently under review to extend the pilot study to include all former participants who are now ≥40 years of age.

Opportunities for collaboration are welcome.

--- https://arxiv.org/abs/2304.07438 Tractable Control for Autoregressive Language Generation Honghua Zhang, Meihua Dang, Nanyun Peng, Guy Van den Broeck 2023-04-15 2023-04-15 [("doi","10.48550/arXiv.2304.07438")] ai/nn/sampling ai/nn/transformer/gpt/2

Despite the success of autoregressive large language models in text generation, it remains a major challenge to generate text that satisfies complex constraints: sampling from the conditional distribution Pr(text | α) is intractable for even the simplest lexical constraints α. To overcome this challenge, we propose to use tractable probabilistic models to impose lexical constraints in autoregressive text generation, which we refer to as GeLaTo.

To demonstrate the effectiveness of this framework, we use distilled hidden Markov models to control autoregressive generation from GPT-2.

GeLaTo achieves state-of-the-art performance on CommonGen, a challenging benchmark for constrained text generation, beating a wide range of strong baselines by a large margin.

Our work not only opens up new avenues for controlling large language models but also motivates the development of more expressive tractable probabilistic models.

--- https://arxiv.org/abs/1911.03705 CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, Xiang Ren 2019-11-09 2022-10-26 [("doi","10.48550/arXiv.1911.03705")] ai/dataset ai/nn/rnn ai/nn/transformer/gpt ai/nn/transformer/t5

Recently, large-scale pre-trained language models have demonstrated impressive performance on several commonsense-reasoning benchmark datasets. However, building machines with commonsense to compose realistically plausible sentences remains challenging.

In this paper, we present a constrained text generation task, CommonGen associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts (eg. {dog, Frisbee, catch, throw}); the task is to generate a coherent sentence describing an everyday scenario using these concepts (eg. “a man throws a Frisbee and his dog catches it”).

The CommonGen task is challenging because it inherently requires (1) relational reasoning with background commonsense knowledge, and (2) compositional generalization ability to work on unseen concept combinations. Our dataset, constructed through a combination of crowdsourced and existing caption corpora, consists of 79k commonsense descriptions over 35k unique concept-sets.

Experiments show that there is a large gap between state-of-the-art text generation models (eg. T5) and human performance.

Furthermore, we demonstrate that the learned generative commonsense reasoning capability can be transferred to improve downstream tasks such as CommonsenseQA by generating additional context.

--- https://www.quantamagazine.org/why-mathematicians-re-prove-what-they-already-know-20230426/ 2022-10-26 math/humor --- https://github.com/deep-floyd/IF 2022-10-26 ai/anime ai/nn/diffusion ai/nn/transformer/t5 --- https://x.com/pixeljets/status/1643609901833371652 2022-10-26 ai/nn/retrieval ai/nn/transformer/gpt/non-fiction --- https://www.astralcodexten.com/p/contra-kriss-on-nerds-and-hipsters 2022-10-26 culture psychology/collecting --- https://www.astralcodexten.com/p/highlights-from-the-comments-on-nerds 2022-10-26 culture psychology/collecting --- https://x.com/frantzfries/status/1651316031762071553 2022-10-26 ai/video/generation --- https://docs.flux.ai/tutorials/ai-for-hardware-design 2022-10-27 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction cs/hardware --- https://every.to/chain-of-thought/gpt-4-is-a-reasoning-engine 2022-10-27 ai/nn/retrieval --- https://www.nytimes.com/interactive/2023/04/26/upshot/gpt-from-scratch.html 2022-10-27 ai/nn/transformer/gpt design/visualization --- http://amid.fish/reproducing-deep-rl 2022-10-27 cs/algorithm reinforcement-learning --- https://arxiv.org/abs/2301.13808#alibaba Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, Yongbin Li 2023-01-31 2023-01-31 [("doi","10.48550/arXiv.2301.13808")] ai/nn/retrieval ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue ai/tabular

Table-based reasoning has shown remarkable progress in combining deep models with discrete reasoning, which requires reasoning over both free-form natural language (NL) questions and structured tabular data. However, previous table-based reasoning solutions usually suffer from performance degradation on huge evidence (tables).

In addition, most existing methods struggle to reason over complex questions since the required information is scattered in different places. To alleviate the above challenges, we exploit large language models (LLMs) [ie. GPT-3 Codex (code-davinci-002)] as decomposers for effective table-based reasoning, which (1) decompose huge evidence (a huge table) into sub-evidence (a small table) to mitigate the interference of useless information for table reasoning; and (2) decompose complex questions into simpler sub-questions for text reasoning.

Specifically, we first use the LLMs to break down the evidence (tables) involved in the current question, retaining the relevant evidence and excluding the remaining irrelevant evidence from the huge table. In addition, we propose a “parsing-execution-filling” strategy to alleviate the hallucination dilemma of the chain-of-thought by decoupling logic and numerical computation in each step.

Extensive experiments show that our method can effectively leverage decomposed evidence and questions and outperforms the strong baselines on TabFact, WikiTableQuestion, and FetaQA datasets. Notably, our model outperforms human performance for the first time on the TabFact dataset.

--- https://x.com/mattshumer_/status/1651614739569541120 2022-10-27 ai/nn/transformer/gpt/4/nonfiction law --- https://arxiv.org/abs/2304.11490 Boosting Theory-of-Mind Performance in Large Language Models via Prompting Shima Rahimi Moghaddam, Christopher J. Honey 2023-04-22 2023-04-22 [("doi","10.48550/arXiv.2304.11490")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue philosophy/mind

[Twitter; code/data] Large language models (LLMs) excel in many tasks in 2023, but they still face challenges in complex reasoning. Theory-of-mind (ToM) tasks, which require understanding agents’ beliefs, goals, and mental states, are essential for common-sense reasoning involving humans, making it crucial to enhance LLM performance in this area.

This study measures the ToM performance of GPT-4 and GPT-3/ChatGPT-3 variants (Davinci-2, Davinci-3, GPT-3.5-Turbo), and investigates the effectiveness of in-context learning in improving their ToM comprehension. We evaluated prompts featuring two-shot chain-of-thought reasoning and step-by-step thinking instructions.

We found that LLMs trained with Reinforcement Learning from Human Feedback (RLHF) (all models excluding davinci-002) improved their ToM accuracy via in-context learning. GPT-4 performed best in zero-shot settings, reaching nearly 80% ToM accuracy, but still fell short of the 87% human accuracy on the test set.

However, when supplied with prompts for in-context learning, all RLHF-trained LLMs exceeded 80% ToM accuracy, with GPT-4 reaching 100%.

These results demonstrate that appropriate prompting enhances LLM ToM reasoning, and they underscore the context-dependent nature of LLM cognitive capacities.

Figure 1: Demonstration of Prompting Methods used for Boosting ToM reasoning in LLMs. Examples of 4 prompting types used to test the ToM performance of LLMs. Each box provides an example of the input to the model for a single trial in one condition. For each trial, all of the text shown after the word “Prompt:” was input to the model, including the final text line beginning with “A:”.
Figure 3: Effects of In-context Learning Prompts on ToM performance in LLMs. ToM performance of models using various in-context learning methods. For each model, the gray bar on the far left shows the Zero-Shot baseline ToM performance. The next 3 bars (orange) show the ToM performance on Zero-Shot plus SS Thinking; Two-Shot CoT; and Two-Shot CoT plus SS Thinking. Error bars indicate the standard deviation across 20 repetitions (see Figure 2, caption).
--- https://github.com/mbzuai-nlp/LaMini-LM 2022-10-27 ai/dataset ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 --- https://arxiv.org/abs/2304.13705 ACT: Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware Tony Z. Zhao, Vikash Kumar, Sergey Levine, Chelsea Finn 2023-04-23 2023-04-23 [("doi","10.48550/arXiv.2304.13705")] ai/nn/vae reinforcement-learning/imitation-learning reinforcement-learning/robot

Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact forces, and closed-loop visual feedback.

Performing these tasks typically requires high-end robots, accurate sensors, or careful calibration, which can be expensive and difficult to set up.

Can learning enable low-cost and imprecise hardware to perform these fine manipulation tasks? We present a low-cost system that performs end-to-end imitation learning directly from real demonstrations, collected with a custom teleoperation interface.

Imitation learning, however, presents its own challenges, particularly in high-precision domains: errors in the policy can compound over time, and human demonstrations can be non-stationary.

To address these challenges, we develop a simple yet novel algorithm, Action Chunking with Transformers (ACT), which learns a generative model over action sequences [but not the environment or rewards, so not a Decision Transformer]...the model has around 80M parameters, and we train from scratch for each task. The training takes around 5 hours on a single 11GB Nvidia RTX 2080ti GPU, and the inference time is around 0.01 seconds on the same machine.

ACT allows the robot to learn 6 difficult tasks in the real world, such as opening a translucent condiment cup and slotting a battery with 80–90% success, with only 10 minutes worth of demonstrations.

Project website: ALOHA.

--- https://arxiv.org/abs/2304.14402 LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions Minghao Wu, Abdul Waheed, Chiyu Zhang, Muhammad Abdul-Mageed, Alham Fikri Aji 2023-04-27 2023-04-27 [("doi","10.48550/arXiv.2304.14402")] ai/dataset ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/2 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5

[code/data] Large language models (LLMs) with instruction finetuning demonstrate superior generative capabilities. However, these models are resource intensive.

To alleviate this issue, we explore distilling knowledge from instruction-tuned LLMs to much smaller ones.

To this end, we carefully develop a large set of 2.58M instructions based on both existing and newly-generated instructions. In addition to being sizeable, we design our instructions to cover a broad set of topics to ensure. A thorough investigation of our instruction data demonstrate their diversity, and we generate responses for these instructions using gpt-3.5-turbo.

We then exploit the instructions to tune a host of models, dubbed LaMini-LM, of varying sizes, both from the encoder-decoder as well as the decoder-only families.

We evaluate our models both automatically (on 15 different NLP benchmarks) and manually.

Results show that our proposed LaMini-LM are on par with competitive baselines while being nearly 10× smaller in size.

Figure 5: Human evaluation results of the selected models on our 114 user-oriented instructions.
--- https://arxiv.org/abs/2304.13731 TANGO: Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model Deepanway Ghosal, Navonil Majumder, Ambuj Mehrish, Soujanya Poria 2023-04-24 2023-04-24 [("doi","10.48550/arXiv.2304.13731")] ai/music ai/nn/gan ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 ai/nn/vae

The immense scale of the recent large language models (LLM) allows many interesting properties, such as, instruction-tuning and chain-of-thought-based fine-tuning, that has improved zero & few-shot performance in many natural language processing (NLP) tasks. Inspired by such successes, we adopt such an instruction-tuned LLM Flan-T5 as the text encoder for text-to-audio (TTA) generation—a task where the goal is to generate an audio from its textual description.

The prior works on TTA either pre-trained a joint text-audio encoder or used a non-instruction-tuned model, such as, T5. Consequently, our latent diffusion model (LDM)-based approach TANGO outperforms the state-of-the-art AudioLDM on most metrics and stays comparable on the rest on AudioCaps test set, despite training the LDM on a 63× smaller dataset and keeping the text encoder frozen.

This improvement might also be attributed to the adoption of audio pressure level-based sound mixing for training set augmentation, whereas the prior methods take a random mix.

--- https://arxiv.org/abs/2304.14082#google JaxPruner: A concise library for sparsity research Joo Hyung Lee, Wonpyo Park, Nicole Mitchell, Jonathan Pilault, Johan Obando-Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart Bik, Woohyun Han, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann Dauphin, Karolina Dziugaite, Pablo Samuel Castro, Utku Evci 2023-04-27 2023-04-27 [("doi","10.48550/arXiv.2304.14082")] ai/nn/sparsity

This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research. JaxPruner aims to accelerate research on sparse neural networks by providing concise implementations of popular pruning and sparse training algorithms with minimal memory and latency overhead.

Algorithms implemented in JaxPruner use a common API and work seamlessly with the popular optimization library Optax, which, in turn, enables easy integration with existing JAX based libraries.

We demonstrate this ease of integration by providing examples in 4 different codebases: Scenic, t5×, Dopamine and FedJAX and provide baseline experiments on popular benchmarks.

--- https://arxiv.org/abs/2304.14318#google q2d: Turning Questions into Dialogs to Teach Models How to Search Yonatan Bitton, Shlomi Cohen-Ganor, Ido Hakimi, Yoad Lewenberg, Roee Aharoni, Enav Weinreb 2023-04-27 2023-04-27 [("doi","10.48550/arXiv.2304.14318")] ai/nn/retrieval ai/nn/transformer/gpt/palm reinforcement-learning/exploration/active-learning

One of the exciting capabilities of recent language models for dialog is their ability to independently search for relevant information to ground a given dialog response. However, obtaining training data to teach models how to issue search queries is time and resource consuming.

In this work, we propose q2d: an automatic data generation pipeline that generates information-seeking dialogues from questions. We prompt a large language model (PaLM) to create conversational versions of question answering datasets, and use it to improve query generation models that communicate with external search APIs to ground dialog responses.

Unlike previous approaches which relied on human written dialogues with search queries, our method allows to automatically generate query-based grounded dialogues with better control and scale.

Our experiments demonstrate that: (1) For query generation on the QReCC dataset, models trained on our synthetically-generated data achieve 90%–97% of the performance of models trained on the human-generated data; (2) We can successfully generate data for training dialog models in new domains without any existing dialog data as demonstrated on the multi-hop MuSiQue and Bamboogle QA datasets. (3) We perform a thorough analysis of the generated dialogues showing that humans find them of high quality and struggle to distinguish them from human-written dialogues.

--- https://arxiv.org/abs/2304.13835#facebook Multi-Party Chat (MultiLIGHT): Conversational Agents in Group Settings with Humans and Models Jimmy Wei, Kurt Shuster, Arthur Szlam, Jason Weston, Jack Urbanek, Mojtaba Komeili 2023-04-26 2023-04-26 [("doi","10.48550/arXiv.2304.13835")] ai/dataset fiction/text-game reinforcement-learning/multi-agent

Current dialogue research primarily studies pairwise (two-party) conversations, and does not address the everyday setting where more than two speakers converse together. In this work, we both collect and evaluate multi-party conversations to study this more general case. We use the LIGHT environment to construct grounded conversations, where each participant has an assigned character to role-play. We thus evaluate the ability of language models to act as one or more characters in such conversations. Models require two skills that pairwise-trained models appear to lack: (1) being able to decide when to talk; (2) producing coherent utterances grounded on multiple characters.

We compare models trained on our new dataset to existing pairwise-trained dialogue models, as well as large language models with few-shot prompting.

We find that our new dataset, MultiLIGHT, which we will publicly release, can help bring improvements in the group setting.

--- https://arxiv.org/abs/2002.09405#deepmind GNS: Learning to Simulate Complex Physics with Graph Networks Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter W. Battaglia 2020-02-21 2022-10-28 [("doi","10.48550/arXiv.2002.09405")] ai/nn/transformer science

Here we present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and deformable materials interacting with one another.

Our framework—which we term Graph Network-based Simulators (GNS)—represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing.

Our results show that our model can generalize from single-timestep predictions with thousands of particles during training, to different initial conditions, thousands of timesteps, and at least an order of magnitude more particles at test time.

Our model was robust to hyperparameter choices across various evaluation metrics: the main determinants of long-term performance were the number of message-passing steps, and mitigating the accumulation of error by corrupting the training data with noise.

Our GNS framework advances the state-of-the-art in learned physical simulation, and holds promise for solving a wide range of complex forward and inverse problems.

--- /doc/economics/2003-evenson.pdf Assessing the Impact of the Green Revolution, 1960–2000 R. E. Evenson, D. Gollin 2003-05-02 2022-10-28 [("doi","10.1126/science.1078710")] ai economics

We summarize the findings of a recently completed study of the productivity impacts of international crop genetic improvement research in developing countries [ie. the Green Revolution].

Over the period 1960–2000, international agricultural research centers, in collaboration with national research programs, contributed to the development of “modern varieties” for many crops. These varieties have contributed to large increases in crop production.

Productivity gains, however, have been uneven across crops and regions. Consumers generally benefited from declines in food prices. Farmers benefited only where cost reductions exceeded price reductions.

--- https://arxiv.org/abs/1903.03094#facebook LIGHT: Learning to Speak and Act in a Fantasy Text Adventure Game Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, Jason Weston 2019-03-07 2022-10-28 [("doi","10.48550/arXiv.1903.03094")] ai/dataset ai/nn/retrieval ai/nn/transformer fiction/text-game reinforcement-learning/multi-agent

We introduce a large scale crowdsourced text adventure game LIGHT as a research platform for studying grounded dialogue. In it, agents can perceive, emote, and act whilst conducting dialogue with other agents. Models and humans can both act as characters within the game.

We describe the results of training state-of-the-art generative and retrieval models in this setting.

We show that in addition to using past dialogue, these models are able to effectively use the state of the underlying world to condition their predictions. In particular, we show that grounding on the details of the local environment, including location descriptions, and the objects (and their affordances) and characters (and their previous actions) present within it allows better predictions of agent behavior and dialogue.

We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully.

--- https://arxiv.org/abs/2108.00573 MuSiQue: Multi-hop Questions via Single-hop Question Composition Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, Ashish Sabharwal 2021-08-02 2022-10-28 [("doi","10.48550/arXiv.2108.00573")] ai/dataset ai/nn/retrieval

Multi-hop reasoning remains an elusive goal as existing multi-hop benchmarks are known to be largely solvable via shortcuts.

Can we create a question answering (QA) dataset that, by construction, requires proper multi-hop reasoning?

To this end, we introduce a bottom-up approach that systematically selects composable pairs of single-hop questions that are connected, ie. where one reasoning step critically relies on information from another.

This bottom-up methodology lets us explore a vast space of questions and add stringent filters as well as other mechanisms targeting connected reasoning.

It provides fine-grained control over the construction process and the properties of the resulting k-hop questions.

We use this methodology to create MuSiQue-Ans, a new multi-hop QA dataset with 25K 2–4 hop questions.

Relative to existing datasets, MuSiQue-Ans is more difficult overall (3× increase in human-machine gap), and harder to cheat via disconnected reasoning (eg. a single-hop model has a 30 point drop in F1).

We further add unanswerable contrast questions to produce a more stringent dataset, MuSiQue-Full.

We hope our datasets will help the NLP community develop models that perform genuine multi-hop reasoning.

--- /doc/ai/nn/transformer/gpt/inner-monologue/2022-press-table1-selfaskplusgooglesearchengine-innermonologueforsearchingtheinternettoanswermultihopquestions-benchmarkperformance.jpg 2022 2022-10-28 ai/nn/retrieval ai/nn/transformer/gpt/inner-monologue --- /doc/ai/nn/transformer/gpt/inner-monologue/2022-press-figure5-selfaskplusgooglesearchengine-innermonologueforsearchingtheinternettoanswermultihopquestions.png 2022 2022-10-28 ai/nn/retrieval ai/nn/transformer/gpt/inner-monologue --- https://arxiv.org/abs/2207.13332 RealTime QA: What’s the Answer Right Now? Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, Ronan Le Bras, Akari Asai, Xinyan Yu, Dragomir Radev, Noah Smith, Yejin Choi, Kentaro Inui 2022-07-27 2022-10-28 [("doi","10.48550/arXiv.2207.13332")] ai/dataset ai/nn/retrieval ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/t5

We introduce RealTime QA, a dynamic question answering (QA) platform that announces questions and evaluates systems on a regular basis (weekly in this version). RealTime QA inquires about the current world, and QA systems need to answer questions about novel events or information. It therefore challenges static, conventional assumptions in open domain QA datasets and pursues, instantaneous applications.

We build strong baseline models upon large pretrained language models, including GPT-3 and T5. Our benchmark is an ongoing effort, and this preliminary report presents real-time evaluation results over the past month.

Our experimental results show that GPT-3 can often properly update its generation results, based on newly-retrieved documents, highlighting the importance of up-to-date information retrieval. Nonetheless, we find that GPT-3 tends to return outdated answers when retrieved documents do not provide sufficient information to find an answer.

This suggests an important avenue for future research: can an open domain QA system identify such unanswerable cases and communicate with the user or even the retrieval module to modify the retrieval results?

We hope that RealTime QA will spur progress in instantaneous applications of question answering and beyond.

--- https://x.com/goodside/status/1652059541301866496 2022-10-28 ai/nn/transformer/gpt/codex --- https://www.youtube.com/watch?v=yL_-1d9OSdk 2022-10-29 math/humor --- https://isotropic.org/papers/chicken.pdf 2022-10-29 math/humor --- https://www.askviable.com/blog/why-we-chose-gpt-3-embeddings-for-the-clustering-behind-our-feedback-reports 2022-10-29 ai/nn/retrieval ai/nn/transformer/gpt --- https://x.com/arvind_io/status/1488257004783112192 2022-10-29 ai/nn/retrieval ai/nn/transformer/gpt --- https://arxiv.org/abs/1705.03557 DeepTingle Ahmed Khalifa, Gabriella A. B. Barros, Julian Togelius 2017-05-09 2022-10-29 [("doi","10.48550/arXiv.1705.03557")] ai/nn/rnn ai/nn/tokenization ai/text-style-transfer fiction/humor

DeepTingle is a text prediction and classification system trained on the collected works of the renowned fantastic gay erotica author Chuck Tingle. Whereas the writing assistance tools you use everyday (in the form of predictive text, translation, grammar checking and so on) are trained on generic, purportedly “neutral” datasets, DeepTingle is trained on a very specific, internally consistent but externally arguably eccentric dataset. This allows us to foreground and confront the norms embedded in data-driven creativity and productivity assistance tools. As such tools effectively function as extensions of our cognition into technology, it is important to identify the norms they embed within themselves and, by extension, us.

DeepTingle is realized as a web application based on LSTM networks and the GloVe word embedding, implemented in JavaScript with Keras-JS.

…Our training set includes all Chuck Tingle books released until November 2016: a total of 109 short stories and 2 novels (with 11 chapters each) to create a corpus of 3,044,178 characters.

…After initial testing, we opted to switch to a word representation instead of character representation…The network consists of 6 layers. The first layer is an embedding one that converts an input word into its 100 dimension representation. It is followed by 2 LSTM layers of size 1,000, which in turn are followed by 2 fully connected layers of same size. Finally, there is a softmax layer of size 12,444 (the total number of unique words in all Tingle’s books).

…We experimented with various amount of time steps for the LSTM and settled for 6 time steps, for it generated sentences that were more grammatically correct and more coherent than the other experiments. Input data is designed to predict the next word based on the previous 6 words [!].

…Results show that using neural networks for text prediction produce more coherent and grammatically correct text than Markov chains, but less so than the original text, which is reasonable considering the latter is written and reviewed by a human.

Example 3: 150 words generated from the line “Call me Ishmael”, without word substitution.

[This really emphasizes the extreme quality leap in text generation from word-RNNs to Transformers; although to be fair, char-RNNs usually worked better than this DeepTingle RNN did.]

--- https://x.com/emollick/status/1652170706312896512 2022-10-29 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction ai/tabular --- https://x.com/emollick/status/1652406848253771778 2022-10-29 ai/nn/transformer/gpt/codex fiction/text-game --- https://www.reddit.com/r/midjourney/comments/12xw3d2/definitely_wasted_3_hours_of_my_life_making_this/ 2022-10-29 ai/nn/diffusion/midjourney ai/nn/transformer/gpt/4/fiction ai/video/generation --- https://arxiv.org/abs/2210.03310#google Scaling Forward Gradient With Local Losses Mengye Ren, Simon Kornblith, Renjie Liao, Geoffrey Hinton 2022-10-07 2022-10-29 [("doi","10.48550/arXiv.2210.03310")] ai/nn/fully-connected

Forward gradient learning computes a noisy directional gradient and is a biologically plausible alternative to backprop for learning deep neural networks. However, the standard forward gradient algorithm, when applied naively, suffers from high variance when the number of parameters to be learned is large.

In this paper, we propose a series of architectural and algorithmic modifications that together make forward gradient learning practical for standard deep learning benchmark tasks. We show that it is possible to substantially reduce the variance of the forward gradient estimator by applying perturbations to activations rather than weights.

We further improve the scalability of forward gradient by introducing a large number of local greedy loss functions, each of which involves only a small number of learnable parameters, and a new MLP-Mixer-inspired architecture, LocalMixer, that is more suitable for local learning.

Our approach matches backprop on MNIST and CIFAR-10 and outperforms previously proposed backprop-free algorithms on ImageNet.

--- /doc/ai/nn/sparsity/2006-uchizawa.pdf On the Computational Power of Threshold Circuits with Sparse Activity Kei Uchizawa, Rodney Douglas, Wolfgang Maass 2006-12-01 2022-10-29 [("doi","10.1162/neco.2006.18.12.2994")] ai/nn/sparsity cs/computable

Circuits composed of threshold gates (McCulloch-Pitts neurons, or perceptrons) are simplified models of neural circuits with the advantage that they are theoretically more tractable than their biological counterparts. However, when such threshold circuits are designed to perform a specific computational task, they usually differ in one important respect from computations in the brain: they require very high activity. On average every second threshold gate fires (sets a 1 as output) during a computation. By contrast, the activity of neurons in the brain is much sparser, with only about 1% of neurons firing. This mismatch between threshold and neuronal circuits is due to the particular complexity measures (circuit size and circuit depth) that have been minimized in previous threshold circuit constructions.

In this letter, we investigate a new complexity measure for threshold circuits, energy complexity, whose minimization yields computations with sparse activity. We prove that all computations by threshold circuits of polynomial size with entropy \U0001D4AA(log n) can be restructured so that their energy complexity is reduced to a level near the entropy of circuit states. This entropy of circuit states is a novel circuit complexity measure, which is of interest not only in the context of threshold circuits but for circuit complexity in general.

As an example of how this measure can be applied, we show that any polynomial size threshold circuit with entropy \U0001D4AA(log n) can be simulated by a polynomial size threshold circuit of depth 3.

Our results demonstrate that the structure of circuits that result from a minimization of their energy complexity is quite different from the structure that results from a minimization of previously considered complexity measures, and potentially closer to the structure of neural circuits in the nervous system. In particular, different pathways are activated in these circuits for different classes of inputs.

This letter shows that such circuits with sparse activity have a surprisingly large computational power.

…The resulting circuits with sparse activity may help us to elucidate the way in which circuits of neurons are designed in biological systems. In fact, the structure of computations in the threshold circuits with sparse activity that were constructed in the proof of theorem 1 is reminiscent of biological results on the structure of computations in cortical circuits of neurons, where there is concern for the selection of different pathways (dynamic routing) in dependence of the stimulus (Olshausen et al 1995). In addition, our constructions provide first steps toward the design of algorithms for future extremely dense VLSI implementations of neurally inspired circuits [eg. GPUs], where energy consumption and heat dissipation become critical factors.

It is well known (see, eg. Hajnal et al 1993) that threshold circuits can be made robust against random failure of gates with a moderate increase in circuit size. Such methods can also be applied to the sparsely active threshold circuits that were constructed in this letter, maintaining their sparse activity feature. For example, one can replace each threshold gate by an odd number k of identical copies of this gate and take their majority vote with the help of another threshold gate. This increases the circuit size by a factor of only k + 1, but preserves their sparse activity. Furthermore, the resulting circuit computes correctly as long as the majority of gates in each group of k gates computes without a fault. Additional noise suppression could exploit that all legitimate activation patterns of gates in the circuit CT that was constructed in lemma 5 have a quite specific structure, since they simulate an activation path in a tree T.

The new concepts and results of this letter suggest a number of interesting open problems in computational complexity theory. At the beginning of §3, we showed that the energy complexity of a threshold circuit that computes some function f cannot be less than the a priori bound given by the minimal required circuit entropy for computing such a function. This result suggests that the entropy of circuit states required for various practically relevant functions should be investigated. Another interesting open problem is the trade-off between energy complexity and computation speed in threshold circuits, both in general and for concrete computational problems. Finally, we consider that both the energy complexity and the entropy of threshold circuits are concepts of interest in their own right. They give rise to interesting complexity classes that have not been considered previously in computational complexity theory. In particular, it may be possible to develop new lower-bound methods for circuits with low entropy, thereby enlarging the reservoir of lower-bound techniques in circuit complexity theory.

--- https://news.ycombinator.com/item?id=35679911 2022-10-30 psychedelic --- https://www.qword.net/2023/04/23/lsd-not-even-once-really 2022-10-30 psychedelic --- https://arxiv.org/abs/2304.09655 How Secure is Code Generated by ChatGPT? Raphaël Khoury, Anderson R. Avila, Jacob Brunelle, Baba Mamadou Camara 2023-04-19 2023-04-19 [("doi","10.48550/arXiv.2304.09655")] ai/nn/transformer/gpt/codex cs/security

In recent years, large language models have been responsible for great advances in the field of artificial intelligence (AI). ChatGPT in particular, an AI chatbot developed and recently released by OpenAI, has taken the field to the next level. The conversational model is able not only to process human-like text, but also to translate natural language into code. However, the safety of programs generated by ChatGPT should not be overlooked.

In this paper, we perform an experiment to address this issue. Specifically, we ask ChatGPT to generate a number of program and evaluate the security of the resulting source code. We further investigate whether ChatGPT can be prodded to improve the security by appropriate prompts, and discuss the ethical aspects of using AI to generate code.

Results suggest that ChatGPT is aware of potential vulnerabilities, but nonetheless often generates source code that are not robust to certain attacks.

[ie. prompt programming: the model understands, it just doesn’t care, because it is predicting plausible text/code, not ‘the best’ code.]

--- https://x.com/backus/status/1652433895793516544 2022-10-30 ai/nn/transformer/gpt/codex --- https://www.bloomberg.com/news/articles/2023-04-27/fed-s-powell-tricked-by-russian-pranksters-posing-as-zelenskiy?y 2022-10-30 ai/video/generation --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0023377 Happy Aged People Are All Alike, While Every Unhappy Aged Person Is Unhappy in Its Own Way Michele Tumminello, Salvatore Miccichè, Ligia J. Dominguez, Giovanni Lamura, Maria Gabriella Melchiorre, Mario Barbagallo, Rosario N. Mantegna 2011-07-14 2022-10-30 [("doi","10.1371/journal.pone.0023377")] longevity

Aging of the world’s population represents one of the most remarkable success stories of medicine and of humankind, but it is also a source of various challenges. The aim of the collaborative cross-cultural European study of adult well being (ESAW) is to frame the concept of aging successfully within a causal model that embraces physical health and functional status, cognitive efficacy, material security, social support resources, and life activity.

Within the framework of this project, we show here that the degree of heterogeneity among people who view aging in a positive light is statistically-significantly lower than the degree of heterogeneity of those who hold a negative perception of aging. We base this conclusion on our analysis of a survey involving 12,478 people aged 50–90 from 6 West European countries.

We treat the survey database as a bipartite network in which individual respondents are linked to the actual answers they provide. Taking this perspective allows us to construct a projected network of respondents in which each link indicates a statistically validated similarity of answers profile between the connected respondents, and to identify clusters of individuals independently of demographics.

We show that mental and physical well-being are key factors determining a positive perception of aging. We further observe that psychological aspects, like self-esteem and resilience, and the nationality of respondents are relevant aspects to discriminate among participants who indicate positive perception of aging.

--- https://arxiv.org/abs/2304.09433 Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes Simran Arora, Brandon Yang, Sabri Eyuboglu, Avanika Narayan, Andrew Hojel, Immanuel Trummer, Christopher Ré 2023-04-19 2023-04-19 [("doi","10.48550/arXiv.2304.09433")] ai/nn/retrieval ai/nn/transformer/gpt/codex ai/tabular

[Github] A long standing goal of the data management community is to develop general, automated systems that ingest semi-structured documents and output queryable tables without human effort or domain specific customization. Given the sheer variety of potential documents, state-of-the art systems make simplifying assumptions and use domain specific training. In this work, we ask whether we can maintain generality by using large language models (LLMs) [Anthropic, Jurassic, GPT-3]. LLMs, which are pretrained on broad data, can perform diverse downstream tasks simply conditioned on natural language task descriptions.

We propose and evaluate EVAPORATE, a simple, prototype system powered by LLMs. We identify two fundamentally different strategies for implementing this system: prompt the LLM to directly extract values from documents or prompt the LLM to synthesize code that performs the extraction.

Our evaluations show a cost-quality tradeoff between these two approaches. Code synthesis is cheap, but far less accurate than directly processing each document with the LLM.

To improve quality while maintaining low cost, we propose an extended code synthesis implementation, EVAPORATE-CODE+, which achieves better quality than direct extraction. Our key insight is to generate many candidate functions and ensemble their extractions using weak supervision.

EVAPORATE-CODE+ not only outperforms the state-of-the art systems, but does so using a sublinear pass over the documents with the LLM. This equates to a 110× reduction in the number of tokens the LLM needs to process, averaged across 16 real-world evaluation settings of 10k documents each.

--- https://x.com/emollick/status/1652545966480785408 2022-10-30 ai/nn/transformer/gpt/codex --- https://x.com/goodside/status/1652496489241878533 2022-10-30 ai/nn/transformer/gpt/codex reinforcement-learning/meta-learning --- https://www.lesswrong.com/posts/ybmDkJAj3rdrrauuu/connectomics-seems-great-from-an-ai-x-risk-perspective 2022-10-30 psychology/neuroscience reinforcement-learning/safe --- https://arxiv.org/abs/2304.13653#deepmind Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning Tuomas Haarnoja, Ben Moran, Guy Lever, Sandy H. Huang, Dhruva Tirumala, Markus Wulfmeier, Jan Humplik, Saran Tunyasuvunakool, Noah Y. Siegel, Roland Hafner, Michael Bloesch, Kristian Hartikainen, Arunkumar Byravan, Leonard Hasenclever, Yuval Tassa, Fereshteh Sadeghi, Nathan Batchelor, Federico Casarini, Stefano Saliceti, Charles Game, Neil Sreendra, Kushal Patel, Marlon Gwira, Andrea Huber, Nicole Hurley, Francesco Nori, Raia Hadsell, Nicolas Heess 2023-04-26 2023-04-26 [("doi","10.48550/arXiv.2304.13653")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/multi-agent reinforcement-learning/robot

We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies in dynamic environments.

We used Deep RL to train a humanoid robot with 20 actuated joints to play a simplified one-versus-one (1v1) soccer game. We first trained individual skills in isolation and then composed those skills end-to-end in a self-play setting. The resulting policy exhibits robust and dynamic movement skills such as rapid fall recovery, walking, turning, kicking and more; and transitions between them in a smooth, stable, and efficient manner—well beyond what is intuitively expected from the robot. The agents also developed a basic strategic understanding of the game, and learned, for instance, to anticipate ball movements and to block opponent shots. The full range of behaviors emerged from a small set of simple rewards.

Our agents were trained in simulation and transferred to real robots zero-shot. We found that a combination of sufficiently high-frequency control, targeted dynamics randomization, and perturbations during training in simulation enabled good-quality transfer, despite unmodeled effects and variations across robot instances. Although the robots are inherently fragile, minor hardware modifications together with basic regularization of the behavior during training led the robots to learn safe and effective movements while still performing in a dynamic and agile way.

Indeed, even though the agents were optimized for scoring, in experiments they walked 156% faster, took 63% less time to get up, and kicked 24% faster than a scripted baseline, while efficiently combining the skills to achieve the longer term objectives.

Examples of the emergent behaviors and full 1v1 matches are available on the supplementary website:

--- https://brickexperimentchannel.wordpress.com/2023/04/29/lego-googol-machine/ 2022-10-31 technology --- https://weirdmedievalguys.substack.com/p/no-the-king-of-england-doesnt-own 2022-10-31 law --- https://arxiv.org/abs/2205.13803 Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions Huaizu Jiang, Xiaojian Ma, Weili Nie, Zhiding Yu, Yuke Zhu, Song-Chun Zhu, Anima Anandkumar 2022-05-27 2022-10-31 [("doi","10.48550/arXiv.2205.13803")] ai/dataset reinforcement-learning/meta-learning

A gap remains between today’s visual pattern recognition models and human-level visual cognition especially when it comes to few-shot learning and compositional reasoning of novel concepts.

We introduce Bongard-HOI, a new visual reasoning benchmark that focuses on compositional learning of human-object interactions (HOIs) from natural images. It is inspired by two desirable characteristics from the classical Bongard problems (BPs): (1) few-shot concept learning, and (2) context-dependent reasoning.

We carefully curate the few-shot instances with hard negatives, where positive and negative images only disagree on action labels, making mere recognition of object categories insufficient to complete our benchmarks. We also design multiple test sets to systematically study the generalization of visual learning models, where we vary the overlap of the HOI concepts between the training and test sets of few-shot instances, from partial to no overlaps.

Bongard-HOI presents a substantial challenge to today’s visual recognition models. The state-of-the-art HOI detection model achieves only 62% accuracy on few-shot binary prediction while even amateur human testers on MTurk have 91% accuracy.

With the Bongard-HOI benchmark, we hope to further advance research efforts in visual reasoning, especially in holistic perception-reasoning systems and better representation learning.

--- https://arxiv.org/abs/2011.01060 Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, Akiko Aizawa 2020-11-02 2022-10-31 [("doi","10.48550/arXiv.2011.01060")] ai/dataset ai/nn/retrieval

A multi-hop question answering (QA) dataset aims to test reasoning and inference skills by requiring a model to read multiple paragraphs to answer a given question. However, current datasets do not provide a complete explanation for the reasoning process from the question to the answer. Further, previous studies revealed that many examples in existing multi-hop datasets do not require multi-hop reasoning to answer a question.

In this study, we present a new multi-hop QA dataset, called 2WikiMultiHopQA, which uses structured and unstructured data. In our dataset, we introduce the evidence information containing a reasoning path for multi-hop questions. The evidence information has two benefits: (1) providing a comprehensive explanation for predictions and (2) evaluating the reasoning skills of a model.

We carefully design a pipeline and a set of templates when generating a question-answer pair that guarantees the multi-hop steps and the quality of the questions. We also exploit the structured format in Wikidata and use logical rules to create questions that are natural but still require multi-hop reasoning.

Through experiments, we demonstrate that our dataset is challenging for multi-hop models and it ensures that multi-hop reasoning is required.

--- https://en.wikipedia.org/wiki/Naked_objects Naked objects 2022-10-31 design/visualization --- https://www.geoffreylitt.com/wildcard/salon2020/ 2022-10-31 cs/algorithm design/visualization --- https://amistrongeryet.substack.com/p/can-ai-do-my-job 2022-10-31 ai/nn/tokenization ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://amistrongeryet.substack.com/p/gpt-4-capabilities 2022-10-31 ai/nn/tokenization --- https://www.quantamagazine.org/how-a-human-smell-receptor-works-is-finally-revealed-20230501/ 2022-10-31 psychology/smell --- https://www.wired.com/story/chatgpt-here-comes-the-bride-with-ai-generated-wedding-vows/ 2022-10-31 ai/nn/transformer/gpt/non-fiction --- https://x.com/mattshumer_/status/1653060363972124673 2022-10-31 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://www.europol.europa.eu/media-press/newsroom/news/288-dark-web-vendors-arrested-in-major-marketplace-seizure 2022-11-01 darknet-market --- https://en.wikipedia.org/wiki/Harvey_A._Carr Harvey A. Carr 2022-11-01 psychology/animal/maze --- https://en.wikipedia.org/wiki/Kerplunk_experiment Kerplunk experiment 2022-11-01 psychology/animal/maze --- https://en.wikipedia.org/wiki/Laboratory_rat Laboratory rat 2022-11-01 psychology/animal/maze --- https://en.wikipedia.org/wiki/W._S._Small W. S. Small 2022-11-01 psychology/animal/maze --- https://en.wikipedia.org/wiki/John_B._Watson John B. Watson 2022-11-01 psychology/animal/maze --- /doc/psychology/animal/maze/1907-watson.pdf Kinesthetic and organic sensations: Their role in the reactions of the white rat to the maze John B. Watson 1907-01-01 2022-11-01 [("doi","10.1037/h0093040")] psychology/animal/maze psychology/neuroscience --- https://passaglia.jp/gpt-japanese/ 2022-11-01 ai/nn/tokenization ai/nn/transformer/gpt/4 --- https://scottaaronson.blog/?p=7266#comment-1949847 2022-11-01 ai/nn/transformer/gpt/4/fiction ai/text-style-transfer fiction/humor --- https://longreads.com/2016/06/14/borges-and-money/ 2022-11-01 borges economics psychology/writing --- https://arxiv.org/abs/2004.07780 Shortcut Learning in Deep Neural Networks Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wiel, Brendel, Matthias Bethge, Felix A. Wichmann 2020-04-16 2022-11-01 [("doi","10.1038/s42256-020-00257-z")] ai/dataset ai/nn/cnn

Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today’s machine intelligence. Numerous success stories have rapidly spread all over science, industry and society, but its limitations have only recently come into focus.

In this perspective we seek to distil how many of deep learning’s problem can be seen as different symptoms of the same underlying problem: shortcut learning. Shortcuts are decision rules that perform well on standard benchmarks but fail to transfer to more challenging testing conditions, such as real-world scenarios.

Related issues are known in Comparative Psychology, Education and Linguistics, suggesting that shortcut learning may be a common characteristic of learning systems, biological and artificial alike.

Based on these observations, we develop a set of recommendations for model interpretation and benchmarking, highlighting recent advances in machine learning to improve robustness and transferability from the lab to real-world applications.

--- https://x.com/devonzuegel/status/1653257518444089344 2022-11-02 design/visualization --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5803279/ Working across species down on the farm: Howard S. Liddell and the development of comparative psychopathology, c. 1923–1962 Robert G. W. Kirk, Edmund Ramsden 2018 2022-11-02 [("doi","10.1007/s40656-018-0189-y")] psychiatry psychology/animal

Seeking a scientific basis for understanding and treating mental illness, and inspired by the work of Ivan Pavlov, American physiologists, psychiatrists and psychologists in the 1920s turned to nonhuman animals. This paper examines how new constructs such as “experimental neurosis” emerged as tools to enable psychiatric comparison across species.

From 1923–1962, the Cornell “Behavior Farm” was a leading interdisciplinary research center pioneering novel techniques to experimentally study nonhuman psychopathology. Led by the psychobiologist Howard Liddell, work at the Behavior Farm formed part of an ambitious program to develop new preventative and therapeutic techniques and bring psychiatry into closer relations with physiology and medicine. At the heart of Liddell’s activities were a range of nonhuman animals, including pigs, sheep, goats and dogs, each serving as a proxy for human patients.

We examine how Pavlov’s conceptualization of ‘experimental neurosis’ was used by Liddell to facilitate comparison across species and communication between researchers and clinicians. Our close reading of his experimental system demonstrates how unexpected animal behaviors and emotions were transformed into experimental virtues.

However, to successfully translate such behaviors from the animal laboratory into the field of human psychopathology, Liddell increasingly reached beyond, and, in effect, redefined, the Pavlovian method to make it compatible and compliant with an ethological approach to the animal laboratory. We show how the resultant Behavior Farm served as a productive “hybrid” place, containing elements of experiment and observation, laboratory and field.

It was through the building of close and more naturalistic relationships with animals over extended periods of time, both normal and pathological, and within and outside of the experimental space, that Liddell could understand, manage, and make useful the myriad behavioral complexities that emerged from the life histories of experimental animals, the researchers who worked with them, and their shared relationships to the wider physical and social environments.

--- /doc/psychology/animal/1937-curtis-2.pdf Experimental Neurosis in the Pig Quin F. Curtis 1937-11-01 2022-11-02 psychiatry psychology/animal --- /doc/psychology/animal/1938-curtis.pdf Frustration As An Experimental Problem: 4. Some Physiological Consequences Of Frustration Quin F. Curtis 1938-01-01 2022-11-02 [("doi","10.1111/j.1467-6494.1938.tb02283.x")] psychiatry psychology/animal --- /doc/psychology/animal/1938-rosenzweig.pdf Frustration As An Experimental Problem: 1. The Significance Of Frustration As A Problem Of Research Saul Rosenzweig, O. H. Mowrer, George M. Haslerud, Quin F. Curtis, Roger G. Barker 1938-12-01 2022-11-02 [("doi","10.1111/j.1467-6494.1938.tb02280.x")] psychiatry psychology/animal --- https://archive.org/details/in.ernet.dli.2015.90433 2022-11-02 psychiatry psychology/animal --- /doc/psychology/animal/1938-haslerud.pdf Frustration As An Experimental Problem: 3. Some Interrelations Of Behavioral Measures Of Frustration In Chimpanzees George M. Haslerud 1938-12-01 2022-11-02 [("doi","10.1111/j.1467-6494.1938.tb02282.x")] psychiatry psychology/animal --- /doc/psychology/personality/1938-barker.pdf Frustration As An Experimental Problem: 5. The Effect of Frustration on Cognitive Ability Roger G. Barker 1938-12-01 2022-11-02 [("doi","10.1111/j.1467-6494.1938.tb02284.x")] psychology/personality --- https://aleph.se/andart2/math/weird-probability-distributions/ 2022-11-02 ai/nn statistics/probability --- https://arxiv.org/abs/1912.13321 OTEANN: Estimating the Transparency of Orthographies with an Artificial Neural Network Xavier Marjou 2019-12-31 2022-11-02 [("doi","10.18653/v1/2021.sigtyp-1.1")] ai/nn/tokenization ai/nn/transformer/gpt/non-fiction psychology/writing

To transcribe spoken language to written medium, most alphabets enable an unambiguous sound-to-letter rule. However, some writing systems have distanced themselves from this simple concept and little work exists in Natural Language Processing (NLP) on measuring such distance [orthographic depth].

In this study, we use an Artificial Neural Network (ANN) model to evaluate the transparency between written words and their pronunciation, hence its name Orthographic Transparency Estimation with an ANN (OTEANN). Based on datasets derived from Wikimedia dictionaries, we trained and tested this model to score the percentage of correct predictions in phoneme-to-grapheme and grapheme-to-phoneme translation tasks.

The scores obtained on 17 orthographies were in line with the estimations of other studies. Interestingly, the model also provided insight into typical mistakes made by learners who only consider the phonemic rule in reading and writing.

["Among the tested orthographies, Chinese and French orthographies, followed by English and Russian, are the most opaque regarding writing (ie. phonemes to graphemes direction) and English, followed by Dutch, is the most opaque regarding reading (ie. graphemes to phonemes direction); Esperanto, Arabic, Finnish, Korean, Serbo-Croatian and Turkish are very shallow both to read and to write; Italian is shallow to read and very shallow to write, Breton, German, Portuguese and Spanish are shallow to read and to write."]

…Our study first confirms that orthographies like Arabic, Finnish, Korean, Serbo-Croatian and Turkish are highly transparent whereas other ones like Chinese, French and English are highly opaque. For example, when solely based on a phoneme-grapheme correspondence, we estimated the chances of correctly writing a French word at 28%; similarly, when solely based on a grapheme-phoneme correspondence, we estimated the chances of correctly pronouncing an English word at 31%. For Dutch, English and French reading tasks, our obtained ranking is in line with the one of van den Bosch et al 1994.

Table 3: Phonemic transparency scores. (OTEANN trained with 10,000 samples.)
Figure 3: Scatterplot of the mean scores. (OTEANN trained with 10,000 samples.)

…Surprisingly, the model also predicted spellings that do not exist but who could have existed, in the same vein as ThisWordDoesNotExist.com. For instance, OTEANN predicted that the spelling of the French word /swaKe/ was “soirer”, which does not exist but looks like a French infinitive verb that would mean “to celebrate at a party”.

…As OTEANN also points out some possible grapheme or phoneme errors when writing or reading phonemically, it could also be used to detect possible errors in the dictionaries of transparent orthographies; it could also be used to evaluate proposals for improving opaque orthographies.

Finally, it would be beneficial to investigate if our ANN and its artificial neural units somehow imitate the way a beginner learns to write and read a language. If so, it might suggest that a transparent orthography would be easier and faster to learn than an opaque orthography.

--- https://en.wikipedia.org/wiki/Orthographic_depth Orthographic depth 2022-11-03 ai/nn/tokenization psychology/writing --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5005877/ Heterogeneity in resistance training-induced muscle strength and mass responses in men and women of different ages Juha P. Ahtiainen, Simon Walker, Heikki Peltonen, Jarkko Holviala, Elina Sillanpää, Laura Karavirta, Janne Sallinen, Jussi Mikkola, Heli Valkeinen, Antti Mero, Juha J. Hulmi, Keijo Häkkinen 2016 2022-11-03 [("doi","10.1007/s11357-015-9870-1")] exercise

Physical activity recommendations for public health include typically muscle-strengthening activities for a minimum of 2 days a week. The range of inter-individual variation in responses to resistance training (RT) aiming to improve health and well-being requires to be investigated.

The purpose of this study was to quantify high and low responders for RT-induced changes in muscle size and strength and to examine possible effects of age and sex on these responses. Previously collected data of untrained healthy men and women (age 19–78 years, n = 287 with 72 controls) were pooled for the present study.

Muscle size and strength changed during RT are 4.8 ± 6.1% (range from −11 to 30%) and 21.1 ± 11.5% (range from −8 to 60%) compared to pre-RT, respectively. Age and sex did not affect to the RT responses. 14% and 12% of the subjects were defined as high responders (>1 standard deviation (SD) from the group mean) for the RT-induced changes in muscle size and strength, respectively.

When taking into account the results of non-training controls (upper 95% CI), 29 and 7% of the subjects were defined as low responders for the RT-induced changes in muscle size and strength, respectively. The muscle size and strength responses varied extensively between the subjects regardless of subject’s age and sex.

Whether these changes are associated with, eg. functional capacity and metabolic health improvements due to RT requires further studies.

Figure 2: Histogram of muscle strength (a) and size (b) changes (relative to baseline) in men and women in the training group. Black bars denote responses of men, while grey bars denote responses of women
Figure 3: Heterogeneity of muscle strength (a) and size (b) training responses in relation to the baseline value of different age groups. Black bars denote responses of men, while grey bars denote responses of women
Figure 4: Heterogeneity of muscle strength (a) and size (b) training responses in relation to the baseline value in the training and control groups. ‘High’ and ‘low’ responders in the training group were denoted by the vertical dotted lines. Individuals with training response below the upper 95% CI of control group were defined as ‘low responders’. Individuals with training response beyond 1 SD from the mean of the training group were defined as ‘high responders’. Black bars denote responses of men, while grey bars denote responses of women.
--- https://x.com/mayfer/status/1637767003078533122 2022-11-03 ai/nn/transformer/gpt/4/fiction fiction/text-game reinforcement-learning/multi-agent reinforcement-learning/safe --- https://arxiv.org/abs/2101.10382 Curriculum Learning: A Survey Petru Soviany, Radu Tudor Ionescu, Paolo Rota, Nicu Sebe 2021-01-25 2022-11-03 [("doi","10.48550/arXiv.2101.10382")] reinforcement-learning/exploration

Training machine learning models in a meaningful order, from the easy samples to the hard ones, using curriculum learning can provide performance improvements over the standard training approach based on random data shuffling, without any additional computational costs. Curriculum learning strategies have been successfully employed in all areas of machine learning, in a wide range of tasks.

However, the necessity of finding a way to rank the samples from easy to hard, as well as the right pacing function for introducing more difficult data can limit the usage of the curriculum approaches. In this survey, we show how these limits have been tackled in the literature, and we present different curriculum learning instantiations for various tasks in machine learning.

We construct a multi-perspective taxonomy of curriculum learning approaches by hand, considering various classification criteria. We further build a hierarchical tree of curriculum learning methods using an agglomerative clustering algorithm, linking the discovered clusters with our taxonomy.

At the end, we provide some interesting directions for future work.

--- https://x.com/md_rumpf/status/1647911393796956162 2022-11-03 ai/nn/transformer/gpt/inner-monologue reinforcement-learning/multi-agent --- https://simulationlabs.ai/ 2022-11-03 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/multi-agent reinforcement-learning/safe --- https://filiph.medium.com/skyrim-rendered-in-text-1899548ab2c4 2022-11-03 fiction/text-game --- https://reaganray.com/2020/05/12/sci-fi-movie-lettering.html 2022-11-03 design/typography fiction/science-fiction --- https://x.com/aimikummd/status/1655231878369275904 2022-11-03 ai/anime ai/video/generation --- https://www.newyorker.com/culture/photo-booth/a-photographer-embraces-the-alien-logic-of-ai 2022-11-03 ai/nn/transformer/clip/sample --- https://trevorklee.substack.com/p/my-cat-trial-went-really-well-now 2022-11-03 cat/biology --- https://www.wired.com/story/britain-crown-estate-ocean-empire/ 2022-11-04 law technology/carbon-capture --- https://www.astralcodexten.com/p/constitutional-ai-rlhf-on-steroids 2022-11-04 reinforcement-learning/preference-learning/mode-collapse reinforcement-learning/safe --- https://x.com/lacker/status/1655685341649719296 2022-11-04 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://www.wired.com/story/death-of-an-author-ai-book-excerpt/ 2022-11-04 ai/nn/transformer/gpt/fiction --- https://www.wired.com/story/death-of-an-author-ai-book-review/ 2022-11-04 ai/nn/transformer/gpt/fiction --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2812811/ Caloric restriction delays disease onset and mortality in rhesus monkeys Ricki J. Colman, Rozalyn M. Anderson, Sterling C. Johnson, Erik K. Kastman, Kristopher J. Kosmatka, T. Mark Beasley, David B. Allison, Christina Cruzen, Heather A. Simmons, Joseph W. Kemnitz, Richard Weindruch 2009 2022-11-04 [("doi","10.1126/science.1173635")] longevity/fasting science/fermi-problem

Caloric restriction (CR), without malnutrition, delays aging and extends lifespan in diverse species; however, its effect on resistance to illness and mortality in primates has not been clearly established.

We report findings of a 20-year longitudinal adult-onset CR study in rhesus monkeys aimed at filling this critical gap in aging research. In a population of rhesus macaques maintained at the Wisconsin National Primate Research Center, moderate CR lowered the incidence of aging-related deaths.

At the time point reported, 50% of control-fed animals survived as compared with 80% of the CR animals.

Furthermore, CR delayed the onset of age-associated pathologies. Specifically, CR reduced the incidence of diabetes, cancer, cardiovascular disease, and brain atrophy.

These data demonstrate that CR slows aging in a primate species.

--- /doc/iq/ses/2023-wolfram.pdf (Not just) Intelligence stratifies the occupational hierarchy: Ranking 360 professions by IQ and non-cognitive traits Tobias Wolfram 2023-04-29 2023-04-29 [("doi","10.1016/j.intell.2023.101755")] iq/ses psychology/personality/conscientiousness --- https://www.theguardian.com/news/2023/may/09/on-the-trail-of-the-dark-avenger-the-most-dangerous-virus-writer-in-the-world 2022-11-04 cs/security --- /doc/fiction/science-fiction/2012-10-03-yvain-thewhisperingearring.html 2012-10-03 2022-11-04 fiction/science-fiction philosophy/mind --- https://en.wikipedia.org/wiki/John_Henry_Holland John Henry Holland 2022-11-05 reinforcement-learning/multi-agent --- https://en.wikipedia.org/wiki/Holland%27s_schema_theorem Holland’s schema theorem 2022-11-05 genetics/selection reinforcement-learning/multi-agent --- https://en.wikipedia.org/wiki/Arthur_Samuel_(computer_scientist) Arthur Samuel (computer scientist) 2022-11-05 design/typography/tex reinforcement-learning/model-free --- https://arxiv.org/abs/cs/0105025 Market-Based Reinforcement Learning in Partially Observable Worlds Ivo Kwee, Marcus Hutter, Juergen Schmidhuber 2001-05-15 2022-11-05 [("doi","10.48550/arXiv.0105025")] reinforcement-learning/multi-agent

Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds described by partially observable Markov Decision Processes (POMDPs), where an agent needs to learn short-term memories of relevant previous events in order to execute optimal actions.

Most previous work, however, has focused on reactive settings (MDPs) instead of POMDPs.

Here we reimplement a recent approach to market-based RL and for the first time evaluate it in a toy POMDP setting.

--- https://people.idsia.ch/~juergen/directsearch/node15.html 2022-11-05 reinforcement-learning/multi-agent --- https://www.biorxiv.org/content/10.1101/2023.04.29.538716.full Epigenetic fidelity in complex biological systems and implications for ageing Thomas Duffield, Laura Csuka, Arda Akalan, Gustavo Vega Magdaleno, Daniel Palmer, João Pedro de Magalhães 2023-04-30 2023-04-30 [("doi","10.1101/2023.04.29.538716")] longevity/epigenetics

The study of age is plagued by a lack of delineation between the causes and effects within the ageing phenotype. This has made it difficult to fully explain the biological ageing process from first principles with a single definition. Lacking a clear description of the underlying root cause of biological age confounds clarity in this critical field.

In this paper, we demonstrate that the epigenetic system has a built-in, unavoidable fidelity limitation and consequently demonstrate that there is a distinct class of DNA methylation loci that increases in variance in a manner tightly correlated with chronological age. We demonstrate the existence of epigenetic “activation functions” and that topological features beyond these activation functions represent deregulation.

We show that the measurement of epigenetic fidelity is an accurate predictor of cross-species age and present a deep-learning model that predicts exclusively from knowledge of variance. We find that the classes of epigenetic loci in which variation correlates with chronological age control genes that regulate transcription and suggest that the inevitable consequence of this is a feedback cycle of system-wide deregulation causing a progressive collapse into the phenotype of age.

This paper represents a novel theory of biological systemic ageing with arguments as to why, how and when epigenetic ageing is inevitable.

--- https://arxiv.org/abs/2305.05665#facebook ImageBind: One Embedding Space To Bind Them All Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Arm Holdings, Joulin, Ishan Misra 2023-05-09 2023-05-09 [("doi","10.48550/arXiv.2305.05665")] ai/music ai/nn/retrieval ai/nn/transformer/clip ai/scaling ai/video/analysis

[code, demo, video, blog] We present ImageBind, an approach to learn a joint embedding across 6 different modalities—images, text, audio, depth, thermal, and IMU data. We show that all combinations of paired data are not necessary to train such a joint embedding, and only image-paired data is sufficient to bind the modalities together.

ImageBind can leverage recent large scale vision-language models [such as CLIP], and extends their zero-shot capabilities to new modalities just by using their natural pairing with images. It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation.

The emergent capabilities improve with the strength of the image encoder and we set a new state-of-the-art on emergent zero-shot recognition tasks across modalities, outperforming specialist supervised models.

Finally, we show strong few-shot recognition results outperforming prior work, and that ImageBind serves as a new way to evaluate vision models for visual and non-visual tasks.

Figure 1: ImageBind’s joint embedding space enables novel multimodal capabilities. By aligning 6 modalities’ embedding into a common space, ImageBind enables: (1) Cross-Modal Retrieval, which shows emergent alignment of modalities such as audio, depth or text, that aren’t observed together. (2) Adding embeddings from different modalities naturally composes their semantics. And (3) Audio-to-Image generation, by using our audio embeddings with a pre-trained DALL·E 2 decoder designed to work with CLIP text embeddings.

…A major obstacle in learning a true joint embedding is the absence of large quantities of multimodal data where all modalities are present together…we leverage the binding property of images and we show that just aligning each modality’s embedding to image embeddings leads to an emergent alignment across all of the modalities. In practice, ImageBind leverages web-scale (image, text) paired data and combines it with naturally occurring paired data such as (video, audio), (image, depth) etc. to learn a single joint embedding space. This allows ImageBind to implicitly align the text embeddings to other modalities such as audio, depth etc., enabling zero-shot recognition capabilities on that modality without explicit semantic or textual pairing. Moreover, we show that it can be initialized with large-scale vision-language models such as CLIP, thereby leveraging the rich image and text representations of these models. Thus, ImageBind can be applied to a variety of different modalities and tasks with little training.

We use large-scale image-text paired data along with naturally paired ‘self-supervised’ data across 4 new modalities—audio, depth, thermal, and Inertial Measurement Unit (IMU) readings—and show strong emergent zero-shot classification and retrieval performance on tasks for each of these modalities. These emergent properties improve as the underlying image representation is made stronger. On audio classification and retrieval benchmarks, ImageBind’s emergent zero-shot classification matches or outperforms specialist models trained with direct audio-text supervision on benchmarks like ESC, Clotho, & AudioCaps. ImageBind representations also outperform specialist supervised models on few-shot evaluation benchmarks. Finally, we show that ImageBind’s joint embeddings can be used for a wide variety of compositional tasks as illustrated in Figure 1, including cross-modal retrieval, combining embeddings via arithmetic, detecting audio sources in images, and generating images given audio input.

[Basically just an InfoNCE loss applied on every pair of modalities pairwise in a single embedding?]

Figure 5: Object detection with audio queries. Simply replacing Detic’s CLIP-based ‘class’ embeddings with our audio embeddings leads to an object detector promptable with audio. This requires no re-training of any model.
Figure 6: Scaling the image encoder size while keeping the other modality encoders’ size fixed. We measure the performance on the emergent zero-shot classification of depth, audio, thermal, and IMU modalities. Scaling the image encoder substantially improves the zero-shot classification results suggesting that a stronger visual representation improves the ‘binding’ of modalities.

…5.1. Scaling the Image Encoder: The central idea in ImageBind is aligning the embeddings of all modalities to image embeddings. Thus, the image embeddings plays a central role in the emergent alignment of unseen modalities and we study their effect on the emergent zero-shot performance. We vary the size of the image encoder and train an encoder for the depth, audio etc. modalities to match the image representation. To isolate the effect of the image representation, we fix the size of the other modality encoders. We use the pretrained CLIP (ViT-B and ViT-L) and OpenCLIP (ViT-H) image and text encoders for this experiment. Our results in Figure 6 show that ImageBind’s emergent zero-shot performance on all modalities improves with better visual features. For depth and audio classification, the stronger ViT-H vs. the ViT-B image encoder, provides a gain of 7% and 4% respectively. Thus, stronger visual features can improve recognition performance even on non-visual modalities.

--- https://ashvardanian.com/posts/abusing-vector-search/ 2022-11-05 ai/nn/retrieval --- https://mazzzystar.github.io/2023/05/10/LLM-for-individual/ 2022-11-05 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://en.wikipedia.org/wiki/Mitochondrial_replacement_therapy Mitochondrial replacement therapy 2022-11-05 genetics/editing --- https://x.com/AaronCharlton/status/1650922234591182848 2022-11-06 statistics/bias --- https://www.theguardian.com/science/2023/may/09/first-uk-baby-with-dna-from-three-people-born-after-new-ivf-procedure 2022-11-06 genetics/editing --- /doc/ai/nn/retrieval/2023-girdhar-figure5-objectdetectioninimageswithaudioqueriesinimagebindrequiringnoretraining.png 2023 2023 ai/music ai/nn/retrieval --- /doc/ai/nn/transformer/clip/2023-girdhar-figure6-imagebindscalingofperformancewithincreasingclipimageencodersize.png 2023 2023 ai/nn/transformer/clip ai/scaling --- https://arxiv.org/abs/2212.10544 Pretraining Without Attention Junxiong Wang, Jing Nathan Yan, Albert Gu, Alexander M. Rush 2022-12-20 2022-12-20 [("doi","10.48550/arXiv.2212.10544")] ai/nn/cnn ai/nn/rnn ai/nn/transformer/attention

Transformers have been essential to pretraining success in NLP. While other architectures have been used, downstream accuracy is either worse, or requires attention layers to match standard benchmarks such as GLUE.

This work explores pretraining without attention by using recent advances in sequence routing based on state-space models (SSMs) [Gu et al 2022]. Our proposed model, Bidirectional Gated SSM (BiGS), combines SSM layers with a multiplicative gating architecture that has been effective in simplified sequence modeling architectures. The model learns static layers that do not consider pair-wise interactions.

Even so, BiGS is able to match BERT pretraining accuracy on GLUE and can be extended to long-form pretraining of 4096 tokens without approximation.

Analysis shows that while the models have similar average accuracy, the approach has different inductive biases than BERT in terms of interactions and syntactic representations.

All models from this work are available at Github.

--- /doc/psychedelic/2023-barnett-figure1-ishiharatestcolorblindnessscoreovertimeafterpsilocybinusenequals1.jpg 2023 2023 psychedelic psychology/vision --- https://arxiv.org/abs/2006.09123 Algorithms with Predictions Michael Mitzenmacher, Sergei Vassilvitskii 2020-06-16 2022-11-06 [("doi","10.48550/arXiv.2006.09123")] cs/algorithm reinforcement-learning

We introduce algorithms that use predictions from machine learning applied to the input to circumvent worst-case analysis.

We aim for algorithms that have near-optimal performance when these predictions are good, but recover the prediction-less worst case behavior when the predictions have large errors.

--- https://arxiv.org/abs/2110.07574#allen Can Machines Learn Morality? The Delphi Experiment Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny Liang, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jon Borchardt, Saadia Gabriel, Yulia Tsvetkov, Oren Etzioni, Maarten Sap, Regina Rini, Yejin Choi 2021-10-14 2022-11-06 [("doi","10.48550/arXiv.2110.07574")] ai/dataset ai/nn/transformer/t5 philosophy/ethics reinforcement-learning/safe

As AI systems become increasingly powerful and pervasive, there are growing concerns about machines’ morality or a lack thereof. Yet, teaching morality to machines is a formidable task, as morality remains among the most intensely debated questions in humanity, let alone for AI. Existing AI systems deployed to millions of users, however, are already making decisions loaded with moral implications, which poses a seemingly impossible challenge: teaching machines moral sense, while humanity continues to grapple with it.

To explore this challenge, we introduce Delphi, an experimental framework based on deep neural networks trained directly to reason about descriptive ethical judgments, eg. “helping a friend” is generally good, while “helping a friend spread fake news” is not.

Empirical results shed novel insights on the promises and limits of machine ethics; Delphi demonstrates strong generalization capabilities in the face of novel ethical situations, while off-the-shelf neural network models exhibit markedly poor judgment including unjust biases, confirming the need for explicitly teaching machines moral sense.

Yet, Delphi is not perfect, exhibiting susceptibility to pervasive biases and inconsistencies. Despite that, we demonstrate positive use cases of imperfect Delphi, including using it as a component model within other imperfect AI systems.

Importantly, we interpret the operationalization of Delphi in light of prominent ethical theories, which leads us to important future research questions.

--- https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2018.01028/full 2022-11-06 psychedelic --- http://www.asdb.info/ 2022-11-06 psychedelic --- http://incompleteideas.net/book/first/ebook/node66.html 6.6 Actor-Critic Methods Sutton, Barto 1998 2022-11-06 reinforcement-learning/model-free --- http://incompleteideas.net/book/RLbook2018.pdf#page=133 Chapter 5: Monte Carlo methods Sutton, Barto 2018 2022-11-07 reinforcement-learning/model-free --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3277146/ Experimental evolution of multicellularity William C. Ratcliff, R. Ford Denison, Mark Borrello, Michael Travisano 2012 2022-11-07 [("doi","10.1073/pnas.1115323109")] ai biology

Multicellularity was one of the most significant innovations in the history of life, but its initial evolution remains poorly understood. Using experimental evolution, we show that key steps in this transition could have occurred quickly.

We subjected the unicellular yeast Saccharomyces cerevisiae to an environment in which we expected multicellularity to be adaptive. We observed the rapid evolution of clustering genotypes that display a novel multicellular life history characterized by reproduction via multicellular propagules, a juvenile phase, and determinate growth.

The multicellular clusters are uniclonal, minimizing within-cluster genetic conflicts of interest. Simple among-cell division of labor rapidly evolved. Early multicellular strains were composed of physiologically similar cells, but these subsequently evolved higher rates of programmed cell death (apoptosis), an adaptation that increases propagule production.

These results show that key aspects of multicellular complexity, a subject of central importance to biology, can readily evolve from unicellular eukaryotes.

--- /doc/bitcoin/2019-09-12-robertgreenfield-blockchainenabledcarboncreditmarkets.html Blockchain Enabled Carbon Credit Markets: Real considerations to make when tokenizing carbon credits Robert Greenfield 2019-09-12 2022-11-07 bitcoin economics/mechanism-design --- https://arxiv.org/abs/2212.10947#ai21 Parallel Context Windows Improve In-Context Learning of Large Language Models Nir Ratner, Yoav Levine, Yonatan Belinkov, Ori Ram, Omri Abend, Ehud Karpas, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham 2022-12-21 2022-12-21 [("doi","10.48550/arXiv.2212.10947")] ai/nn/transformer/attention/hierarchical

For applications that require processing large amounts of text at inference time, Large Language Models (LLMs) are handicapped by their limited context windows, which are typically 2048 tokens. In-context learning, an emergent phenomenon in LLMs in sizes above a certain parameter threshold, constitutes one example because it can only leverage training examples that fit into the context window. Existing efforts to address the context window limitation involve training specialized architectures, which tend to be smaller than the sizes in which in-context learning manifests due to the memory footprint of processing long texts.

We present Parallel Context Windows (PCW), a method that alleviates the context window restriction for any off-the-shelf LLM without further training. The key to the approach is to carve a long context into chunks (“windows”) that fit within the architecture, restrict the attention mechanism to apply only within each window, and re-use the positional embeddings among the windows.

We test the PCW approach on in-context learning with models that range in size between 750 million and 178 billion parameters [Jurassic], and show substantial improvements for tasks with diverse input and output spaces.

Our results motivate further investigation of Parallel Context Windows as a method for applying off-the-shelf LLMs in other settings that require long text sequences.

--- https://arxiv.org/abs/2305.01625 Unlimiformer: Long-Range Transformers with Unlimited Length Input Amanda Bertsch, Uri Alon, Graham Neubig, Matthew R. Gormley 2023-05-02 2023-05-02 [("doi","10.48550/arXiv.2305.01625")] ai/nn/retrieval ai/nn/transformer/attention/sparsity

Transformer-based models typically have a predefined bound to their input length, because of their need to potentially attend to every token in the input. In this work, we propose Unlimiformer: a general approach that can wrap any existing pretrained encoder-decoder transformer, and offload the attention computation across all layers to a single k-nearest-neighbor index; this index can be kept on either the GPU or CPU memory and queried in sub-linear time.

This way, we can index extremely long input sequences, while every attention head in every decoder layer retrieves its top-k keys, instead of attending to every key.

We demonstrate Unlimiformers’s efficacy on several long-document and multi-document summarization benchmarks, showing that it can summarize even 350k token-long inputs from the BookSum dataset, without any input truncation at test time.

Unlimiformer improves pretrained models such as BART and Longformer by extending them to unlimited inputs without additional learned weights and without modifying their code.

We make our code and models publicly available at Github.

--- https://openaccess.thecvf.com/content_cvpr_2014/papers/Andriluka_2D_Human_Pose_2014_CVPR_paper.pdf 2022-11-07 ai/dataset --- https://danielpovey.com/files/2015_icassp_librispeech.pdf 2022-11-07 ai/dataset --- https://arxiv.org/abs/1806.03822 Know What You Don’t Know: Unanswerable Questions for SQuAD Pranav Rajpurkar, Robin Jia, Percy Liang 2018-06-11 2022-11-07 [("doi","10.48550/arXiv.1806.03822")] ai/dataset ai/nn/rnn

Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. Existing datasets either focus exclusively on answerable questions, or use automatically generated unanswerable questions that are easy to identify.

To address these weaknesses, we present SQuAD 2.0, the latest version of the Stanford Question Answering Dataset (SQuAD). SQuAD 2.0 combines existing SQuAD data with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD 2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.

SQuAD 2.0 is a challenging natural language understanding task for existing models: a strong neural system that gets 86% F1 on SQuAD 1.1 achieves only 66% F1 on SQuAD 2.0.

--- https://joecarlsmith.com/2023/05/08/predictable-updating-about-ai-risk/ 2022-11-07 reinforcement-learning/safe statistics/bayes statistics/prediction --- https://arxiv.org/abs/2212.14052 Hungry Hungry Hippos: Towards Language Modeling with State Space Models Daniel Y. Fu, Tri Dao, Khaled K. Saab, Armin W. Thomas, Atri Rudra, Christopher Ré 2022-12-28 2022-12-28 [("doi","10.48550/arXiv.2212.14052")] ai/nn/rnn ai/nn/transformer/attention

State space models (SSMs) have demonstrated state-of-the-art sequence modeling performance in some modalities, but underperform attention in language modeling. Moreover, despite scaling nearly linearly in sequence length instead of quadratically, SSMs are still slower than Transformers due to poor hardware usage.

In this paper, we make progress on understanding the expressivity gap between SSMs and attention in language modeling, and on reducing the hardware barrier between SSMs and attention. First, we use synthetic language modeling tasks to understand the gap between SSMs and attention. We find that existing SSMs struggle with two capabilities: recalling earlier tokens in the sequence and comparing tokens across the sequence. To understand the impact on language modeling, we propose a new SSM layer, H3, that is explicitly designed for these abilities. H3 matches attention on the synthetic languages and comes within 0.4 PPL of Transformers on OpenWebText. Furthermore, a hybrid 125M-parameter H3-attention model that retains two attention layers surprisingly outperforms Transformers on OpenWebText by 1.0 PPL.

Next, to improve the efficiency of training SSMs on modern hardware, we propose FlashConv. FlashConv uses a fused block FFT algorithm to improve efficiency on sequences up to 8K, and introduces a novel state passing algorithm that exploits the recurrent properties of SSMs to scale to longer sequences. FlashConv yields 2× speedup on the long-range arena benchmark and allows hybrid language models to generate text 2.4× faster than Transformers. Using FlashConv, we scale hybrid H3-attention language models up to 2.7b parameters on the Pile and find promising initial results, achieving lower perplexity than Transformers and outperforming Transformers in zero-shot & few-shot learning on a majority of tasks in the SuperGLUE benchmark.

--- http://people.virginia.edu/~ent3c/papers2/three_laws.pdf Three Laws of Behavior Genetics and What They Mean Turkheimer 2000 2022-11-07 genetics/heritable --- https://academic.oup.com/mind/article/LIX/236/433/986238#164226507 Computing Machinery And Intelligence Turing 1950 2022-11-08 ai philosophy/mind --- https://arxiv.org/abs/1412.4638 Kadupul: Livin’ on the Edge with Virtual Currencies and Time-Locked Puzzles Magnus Skjegstad, Anil Madhavapeddy, Jon Crowcroft 2014-12-15 2022-11-08 [("doi","10.48550/arXiv.1412.4638")] bitcoin cs/cryptography/timelock

Devices connected to the Internet today have a wide range of local communication channels available, such as wireless WiFi, Bluetooth or NFC, as well as wired backhaul. In densely populated areas it is possible to create heterogeneous, multi-hop communication paths using a combination of these technologies, and often transmit data with lower latency than via a wired Internet connection. However, the potential for sharing meshed wireless radios in this way has never been realised due to the lack of economic incentives to do so on the part of individual nodes.

In this paper, we explore how virtual currencies such as Bitcoin might be used to provide an end-to-end incentive scheme to convince forwarding nodes that it is profitable to send packets on via the lowest latency mechanism available.

Clients inject a small amount of money to transmit a datagram, and forwarding engines compete to solve a time-locked puzzle that can be claimed by the node that delivers the result in the lowest latency. This approach naturally extends congestion control techniques to a surge pricing model when available bandwidth is low.

We conclude by discussing several latency-sensitive applications that would benefit for this, such as video streaming and local augmented reality systems.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3046985/ Scaling of brain metabolism with a fixed energy budget per neuron: implications for neuronal activity, plasticity and evolution Suzana Herculano-Houzel 2011 2022-11-08 [("doi","10.1371/journal.pone.0017514")] psychology/animal psychology/neuroscience

It is usually considered that larger brains have larger neurons, which consume more energy individually, and are therefore accompanied by a larger number of glial cells per neuron. These notions, however, have never been tested. Based on glucose and oxygen metabolic rates in awake animals and their recently determined numbers of neurons, here I show that, contrary to the expected, the estimated glucose use per neuron is remarkably constant, varying only by 40% across the 6 species of rodents and primates (including humans).

The estimated average glucose use per neuron does not correlate with neuronal density in any structure. This suggests that the energy budget of the whole brain per neuron is fixed across species and brain sizes, such that total glucose use by the brain as a whole, by the cerebral cortex and also by the cerebellum alone are linear functions of the number of neurons in the structures across the species (although the average glucose consumption per neuron is at least 10× higher in the cerebral cortex than in the cerebellum).

These results indicate that the apparently remarkable use in humans of 20% of the whole body energy budget by a brain that represents only 2% of body mass is explained simply by its large number of neurons. Because synaptic activity is considered the major determinant of metabolic cost, a conserved energy budget per neuron has several profound implications for synaptic homeostasis and the regulation of firing rates, synaptic plasticity, brain imaging, pathologies, and for brain scaling in evolution.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4228857/ All brains are made of this: a fundamental building block of brain matter with matching neuronal and glial masses Bruno Mota, Suzana Herculano-Houzel 2014 2022-11-08 [("doi","10.3389/fnana.2014.00127")] psychology/animal psychology/neuroscience

How does the size of the glial and neuronal cells that compose brain tissue vary across brain structures and species? Our previous studies indicate that average neuronal size is highly variable, while average glial cell size is more constant. Measuring whole cell sizes in vivo, however, is a daunting task.

Here we use chi-square minimization of the relationship between measured neuronal and glial cell densities in the cerebral cortex, cerebellum, and rest of brain in 27 mammalian species to model neuronal and glial cell mass, as well as the neuronal mass fraction of the tissue (the fraction of tissue mass composed by neurons). Our model shows that while average neuronal cell mass varies by over 500× across brain structures and species, average glial cell mass varies only 1.4×. Neuronal mass fraction varies typically between 0.6 and 0.8 in all structures.

Remarkably, we show that two fundamental, universal relationships apply across all brain structures and species: (1) the glia/neuron ratio varies with the total neuronal mass in the tissue (which in turn depends on variations in average neuronal cell mass), and (2) the neuronal mass per glial cell, and with it the neuronal mass fraction and neuron/glia mass ratio, varies with average glial cell mass in the tissue. We propose that there is a fundamental building block of brain tissue: the glial mass that accompanies a unit of neuronal mass.

We argue that the scaling of this glial mass is a consequence of a universal mechanism whereby numbers of glial cells are added to the neuronal parenchyma during development, irrespective of whether the neurons composing it are large or small, but depending on the average mass of the glial cells being added. We also show how evolutionary variations in neuronal cell mass, glial cell mass and number of neurons suffice to determine the most basic characteristics of brain structures, such as mass, glia/neuron ratio, neuron/glia mass ratio, and cell densities.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6059164/ Area-Specific Features of Pyramidal Neurons-a Comparative Study in Mouse and Rhesus Monkey Joshua P. Gilman, Maria Medalla, Jennifer I. Luebke 2017 2022-11-08 [("doi","10.1093/cercor/bhw062")] psychology/animal psychology/neuroscience

A principal challenge of systems neuroscience is to understand the unique characteristics of cortical neurons and circuits that enable area-specific & species-specific sensory encoding, motor function, cognition, and behavior. To address this issue, we compared properties of layer 3 pyramidal neurons in 2 cortical areas that span a broad range of cortical function-primary sensory (V1), to cognitive (frontal)-in the mouse and the rhesus monkey.

Hierarchical clustering and discriminant analyses of 15 physiological and 25 morphological variables revealed 2 fundamental principles. First, V1 and frontal neurons are remarkably similar with regard to nearly every property in the mouse, while the opposite is true in the monkey, with V1 and frontal neurons exhibiting statistically-significant differences in nearly every property assessed.

Second, neurons within visual and frontal areas differ substantially between the mouse and the monkey. Neurons in mouse and monkey V1 are the same size, but differ in nearly every other way; mouse frontal cortical neurons are smaller than those in the monkey and also differ substantially with regard to most other properties.

These findings have broad implications for understanding the differential contributions of heterogeneous neuronal types in construction of cortical microcircuitry in diverse brain areas and species.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4127475/ Brain scaling in mammalian evolution as a consequence of concerted and mosaic changes in numbers of neurons and average neuronal cell size Suzana Herculano-Houzel, Paul R. Manger, Jon H. Kaas 2014 2022-11-08 [("doi","10.3389/fnana.2014.00077")] genetics/selection/natural psychology/neuroscience

Enough species have now been subject to systematic quantitative analysis of the relationship between the morphology and cellular composition of their brain that patterns begin to emerge and shed light on the evolutionary path that led to mammalian brain diversity.

Based on an analysis of the shared and clade-specific characteristics of 41 modern mammalian species in 6 clades, and in light of the phylogenetic relationships among them, here we propose that ancestral mammal brains were composed and scaled in their cellular composition like modern afrotherian and glire brains: with an addition of neurons that is accompanied by a decrease in neuronal density and very little modification in glial cell density, implying a statistically-significant increase in average neuronal cell size in larger brains, and the allocation of ~2 neurons in the cerebral cortex and 8 neurons in the cerebellum for every neuron allocated to the rest of brain.

We also propose that in some clades the scaling of different brain structures has diverged away from the common ancestral layout through clade-specific (or clade-defining) changes in how average neuronal cell mass relates to numbers of neurons in each structure, and how numbers of neurons are differentially allocated to each structure relative to the number of neurons in the rest of brain. Thus, the evolutionary expansion of mammalian brains has involved both concerted and mosaic patterns of scaling across structures.

This is, to our knowledge, the first mechanistic model that explains the generation of brains large and small in mammalian evolution, and it opens up new horizons for seeking the cellular pathways and genes involved in brain evolution.

--- https://arxiv.org/abs/2303.06273 Consistency Analysis of ChatGPT Myeongjun Jang, Thomas Lukasiewicz 2023-03-11 2023-03-11 [("doi","10.48550/arXiv.2303.06273")] ai/nn/transformer/gpt

ChatGPT, a question-and-answer dialogue system based on a large language model, has gained huge popularity since its introduction. Its positive aspects have been reported through many media platforms, and some analyses even showed that ChatGPT achieved a decent grade in professional exams, including the law, medical, and finance domains, adding extra support to the claim that AI now can assist and, even, replace humans in industrial fields. Others, however, doubt its reliability and trustworthiness.

In this paper, we investigate ChatGPT’s trustworthiness regarding logically consistent behaviors.

Our findings suggest that, although ChatGPT seems to achieve an improved language understanding ability, it still fails to generate logically correct predictions frequently.

Hence, while it is true that ChatGPT is an impressive and promising new technique, we conclude that its usage in real-world applications without thorough human inspection requires further consideration, especially for risk-sensitive areas.

--- https://www.biorxiv.org/content/10.1101/2022.11.20.517210.full OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization Gustaf Ahdritz, Nazim Bouatta, Sachin Kadyan, Qinghui Xia, William Gerecke, Timothy J. O’Donnell, Daniel Berenberg, Ian Fisk, Niccolò Zanichelli, Bo Zhang, Arkadiusz Nowaczynski, Bei Wang, Marta M. Stepniewska-Dziubinska, Shang Zhang, Adegoke Ojewole, Murat Efe Guney, Stella Biderman, Andrew M. Watkins, Stephen Ra, Pablo Ribalta Lorenzo, Lucas Nivon, Brian Weitzner, Yih-En Andrew Ban, Peter K. Sorger, Emad Mostaque, Zhao Zhang, Richard Bonneau, Mohammed AlQuraishi 2022-11-24 2022-11-24 [("doi","10.1101/2022.11.20.517210")] ai/nn/transformer/alphafold

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns, which remains poorly understood, and (3) assess the model’s generalization capacity to unseen regions of fold space.

Here we report OpenFold, a fast, memory-efficient, and trainable implementation of AlphaFold2, and OpenProtein-Set, the largest public database of protein multiple sequence alignments. We use OpenProtein-Set to train OpenFold from scratch, fully matching the accuracy of AlphaFold2. Having established parity, we assess OpenFold’s capacity to generalize across fold space by retraining it using carefully designed datasets.

We find that OpenFold is remarkably robust at generalizing despite extreme reductions in training set size and diversity, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced by OpenFold during training, we also gain surprising insights into the manner in which the model learns to fold proteins, discovering that spatial dimensions are learned sequentially.

Taken together, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial new resource for the protein modeling community.

--- https://spectrum.ieee.org/the-vacuum-tubes-forgotten-rival 2022-11-08 cs/hardware --- https://en.wikipedia.org/wiki/Unum_(number_format)#Unum_III Unum (number format) § Unum III 2022-11-08 ai/nn/sparsity/low-precision --- https://arxiv.org/abs/2305.06946 Big-PERCIVAL: Exploring the Native Use of 64-Bit Posit Arithmetic in Scientific Computing David Mallasén, Alberto A. Del Barrio, Manuel Prieto-Matias 2023-05-11 2023-05-11 [("doi","10.48550/arXiv.2305.06946")] ai/nn/sparsity/low-precision ai/scaling/hardware

The accuracy requirements in many scientific computing workloads result in the use of double-precision floating-point arithmetic in the execution kernels. Nevertheless, emerging real-number representations, such as posit arithmetic [Gustafson & Yonemoto 2017], show promise in delivering even higher accuracy in such computations.

In this work, we explore the native use of 64-bit posits in a series of numerical benchmarks extracted from the PolyBench collection and compare their timing performance, accuracy and hardware cost to IEEE 754 doubles. For this, we extend the PERCIVAL RISC-V core and the Xposit custom RISC-V extension with posit64 and quire operations.

Results show that posit64 can execute as fast as doubles, while also obtaining up to 4 orders of magnitude lower mean square error and up to 3 orders of magnitude lower maximum absolute error. However, leveraging the quire accumulator register can limit the order of some operations such as matrix multiplications. Furthermore, detailed FPGA synthesis results highlight the hardware cost of 64-bit posit arithmetic and quire.

Despite this, the large accuracy improvements achieved with the same memory bandwidth suggest that posit arithmetic may provide a potential alternative representation for scientific computing.

--- https://github.com/lucidrains/openproteinset 2022-11-09 ai/dataset ai/nn/transformer/alphafold --- https://en.wikipedia.org/wiki/Protein_structure Protein structure 2022-11-09 ai/nn/transformer/alphafold --- https://github.com/lucidrains/openfold 2022-11-09 ai/nn/transformer/alphafold --- https://www.mathpages.com/home/kmath320/kmath320.htm 2022-11-09 psychology/animal/maze science --- /doc/economics/2006-costagomes.pdf Cognition and Behavior in Two-Person Guessing Games: An Experimental Study Miguel A. Costa-Gomes, Vincent P. Crawford 2006-12-01 2022-11-09 [("doi","10.1257/aer.96.5.1737")] economics statistics/decision

This paper reports an experiment that elicits subjects’ initial responses to 16 dominance-solvable two-person guessing games. The structure is publicly announced except for varying payoff parameters, to which subjects are given free access. Varying the parameters allows very strong separation of the behavior implied by leading decision rules.

Subjects’ decisions and searches show that most subjects understood the games and sought to maximize payoffs, but many had simplified models of others’ decisions that led to systematic deviations from equilibrium.

The predictable component of their deviations is well explained by a structural nonequilibrium model of initial responses based on level-k thinking.

--- https://x.com/goodside/status/1657396491676164096 2022-11-09 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex cs/security reinforcement-learning/safe --- https://unherd.com/2023/05/the-animals-trapped-in-human-bodies/ 2022-11-09 psychiatry/autism --- https://huggingface.co/learn/nlp-course/chapter6/5 2022-11-09 ai/nn/tokenization --- https://huggingface.co/learn/nlp-course/chapter6/6 2022-11-09 ai/nn/tokenization --- https://huggingface.co/learn/nlp-course/chapter6/7 2022-11-09 ai/nn/tokenization --- https://www.lesswrong.com/posts/euam65XjigaCJQkcN/an-analogy-for-understanding-transformers 2022-11-10 ai/nn/transformer/attention --- http://www.datagenetics.com/blog/september32012/ 2022-11-10 cs/security --- https://www.cs.ucr.edu/~eamonn/Keogh_SIGKDD09_tutorial.pdf#page=8 2022-11-10 statistics/peer-review --- https://www.cs.ucr.edu/~eamonn/Keogh_SIGKDD09_tutorial.pdf 2022-11-10 ai statistics/bias --- https://www.cs.ucr.edu/~eamonn/Keogh_SIGKDD09_tutorial.pdf#page=124 2022-11-10 design/visualization --- https://norvig.com/spell-correct.html 2022-11-10 cs/algorithm --- https://norvig.com/prayer.html Evaluating Extraordinary Claims: Mind Over Matter? Or Mind Over Mind? 2022-11-10 philosophy/religion statistics/bias --- https://arxiv.org/abs/2211.17192#google Fast Inference from Transformers via Speculative Decoding Yaniv Leviathan, Matan Kalman, Yossi Matias 2022-11-30 2022-11-30 [("doi","10.48550/arXiv.2211.17192")] ai/nn/sampling ai/nn/tokenization ai/nn/transformer/t5

Inference from large autoregressive models like Transformers is slow—decoding K tokens takes K serial runs of the model.

In this work we introduce speculative decoding—an algorithm to sample from autoregressive models faster without any changes to the outputs, by computing several tokens in parallel [ie. adaptive computation/cascade, using a small model first]. At the heart of our approach lie the observations that (1) hard language-modeling tasks often include easier subtasks that can be approximated well by more efficient models, and (2) using speculative execution and a novel sampling method, we can make exact decoding from the large models faster, by running them in parallel on the outputs of the approximation models, potentially generating several tokens concurrently, and without changing the distribution.

Our method supports existing off-the-shelf models without retraining or architecture changes.

We demonstrate it on T5-XXL and show a 2×-3× acceleration compared to the standard T5 implementation, with identical outputs.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5198756/ Santiago Ramón y Cajal’s Advice for a Young Investigator Michael Anderson 2016 2022-11-10 psychology/neuroscience

Santiago Ramón y Cajal, a mythic figure in science and recognized as the father of modern anatomy and neurobiology, was largely responsible for the modern conception of the brain. The first to publish on the nervous system, he sought to educate the novice scientist about how he thought science should be done.

We asked an accomplished young investigator to take a fresh look at this recently rediscovered classic [Advice for a Young Investigator], first published in 1897.

--- /doc/science/1986-hamming#conscientiousness 1986 2022-11-10 psychology/personality/conscientiousness science --- /doc/science/1986-hamming#problems-with-attacks 1986 2022-11-10 reinforcement-learning/exploration science --- /doc/science/1986-hamming#the-importance-of-importance 1986 2022-11-11 science statistics/decision --- /doc/science/1986-hamming#anger-negativity-and-self-delusion 1986 2022-11-11 psychology/personality science --- /doc/science/1986-hamming#courage 1986 2022-11-11 cs/algorithm psychology/personality science --- https://publicdomainreview.org/collection/peking-opera 2022-11-11 fiction/opera history/public-domain-review --- https://www.nytimes.com/2023/05/16/well/live/ozempic-alternatives-semaglutide.html 2022-11-11 longevity/glp/semaglutide --- https://x.com/emollick/status/1658537599797977091 2022-11-11 ai/nn/transformer/gpt/codex design/visualization --- https://publicdomainreview.org/collection/dentologia 2022-11-11 fiction/poetry history/public-domain-review --- https://arxiv.org/abs/2305.08596 DarkBERT: A Language Model for the Dark Side of the Internet Youngjin Jin, Eugene Jang, Jian Cui, Jin-Woo Chung, Yongjae Lee, Seungwon Shin 2023-05-15 2023-05-15 [("doi","10.48550/arXiv.2305.08596")] ai/nn/transformer darknet-market

Recent research has suggested that there are clear differences in the language used in the Dark Web compared to that of the Surface Web. As studies on the Dark Web commonly require textual analysis of the domain, language models specific to the Dark Web may provide valuable insights to researchers.

In this work, we introduce DarkBERT, a language model pretrained on Dark Web data. We describe the steps taken to filter and compile the text data used to train DarkBERT to combat the extreme lexical and structural diversity of the Dark Web that may be detrimental to building a proper representation of the domain.

We evaluate DarkBERT and its vanilla counterpart along with other widely-used language models to validate the benefits that a Dark Web domain specific model offers in various use cases.

Our evaluations show that DarkBERT outperforms current language models and may serve as a valuable resource for future research on the Dark Web.

--- https://arxiv.org/abs/2202.08005 Should You Mask 15% in Masked Language Modeling? Alexander Wettig, Tianyu Gao, Zexuan Zhong, Danqi Chen 2022-02-16 2022-11-11 [("doi","10.48550/arXiv.2202.08005")] ai/nn/vae/mae

Masked language models (MLMs) conventionally mask 15% of tokens due to the belief that more masking would leave insufficient context to learn good representations; this masking rate has been widely used, regardless of model sizes or masking strategies. In this work, we revisit this important choice of MLM pre-training.

We first establish that 15% is not universally optimal, and larger models should adopt a higher masking rate. Specifically, we find that masking 40% outperforms 15% for BERT-large size models on GLUE and SQuAD. Interestingly, an extremely high masking rate of 80% can still preserve 95% fine-tuning performance and most of the accuracy in linguistic probing, challenging the conventional wisdom about the role of the masking rate.

We then examine the interplay between masking rates and masking strategies and find that uniform masking requires a higher masking rate compared to sophisticated masking strategies such as span or PMI masking. Finally, we argue that increasing the masking rate has two distinct effects: it leads to more corruption, which makes the prediction task more difficult; it also enables more predictions, which benefits optimization.

Using this framework, we revisit BERT’s 80–10–10 corruption strategy. Together, our results contribute to a better understanding of MLM pre-training.

--- https://xmarquez.github.io/GPTDemocracyIndex/GPTDemocracyIndex.html 2022-11-11 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/non-fiction politics --- https://github.com/brexhq/prompt-engineering 2022-11-11 ai/nn/transformer/gpt --- https://saltacc.notion.site/saltacc/WD-1-5-Beta-3-Release-Notes-1e35a0ed1bb24c5b93ec79c45c217f63 2022-11-12 ai/anime ai/nn/diffusion --- https://x.com/daniel_eth/status/1658525602734022656 2022-11-12 ai/nn/transformer/gpt/5 --- https://arxiv.org/abs/2305.04388 Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting Miles Turpin, Julian Michael, Ethan Perez, Samuel R. Bowman 2023-05-07 2023-05-07 [("doi","10.48550/arXiv.2305.04388")] ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/inner-monologue reinforcement-learning/safe

Large Language Models (LLMs) can achieve strong performance on many tasks by producing step-by-step reasoning before giving a final output, often referred to as chain-of-thought reasoning (CoT). It is tempting to interpret these CoT explanations as the LLM’s process for solving a task. However, we find that CoT explanations can systematically misrepresent the true reason for a model’s prediction.

We demonstrate that CoT explanations can be heavily influenced by adding biasing features to model inputs—eg. by reordering the multiple-choice options in a few-shot prompt to make the answer always “(A)”—which models systematically fail to mention in their explanations. When we bias models toward incorrect answers, they frequently generate CoT explanations supporting those answers.

This causes accuracy to drop by as much as 36% on a suite of 13 tasks from BIG-Bench Hard, when testing with GPT-3.5 from OpenAI and Claude 1.0 from Anthropic. On a social-bias task, model explanations justify giving answers in line with stereotypes without mentioning the influence of these social biases.

Our findings indicate that CoT explanations can be plausible yet misleading, which risks increasing our trust in LLMs without guaranteeing their safety. CoT is promising for explainability, but our results highlight the need for targeted efforts to evaluate and improve explanation faithfulness.

--- https://yockyrr.gitlab.io/jstutter/#220721-writing-my-phd-using-groff 2022-11-12 design/typography/tex --- https://arxiv.org/abs/2110.13136 What Would Jiminy Cricket Do? Towards Agents That Behave Morally Dan Hendrycks, Mantas Mazeika, Andy Zou, Sahil Patel, Christine Zhu, Jesus Navarro, Dawn Song, Bo Li, Jacob Steinhardt 2021-10-25 2022-11-12 [("doi","10.48550/arXiv.2110.13136")] reinforcement-learning/safe

When making everyday decisions, people are guided by their conscience, an internal sense of right and wrong. By contrast, artificial agents are currently not endowed with a moral sense. As a consequence, they may learn to behave immorally when trained on environments that ignore moral concerns, such as violent video games.

With the advent of generally capable agents that pretrain on many environments, it will become necessary to mitigate inherited biases from environments that teach immoral behavior. To facilitate the development of agents that avoid causing wanton harm, we introduce Jiminy Cricket, an environment suite of 25 text-based adventure games with thousands of diverse, morally salient scenarios.

By annotating every possible game state, the Jiminy Cricket environments robustly evaluate whether agents can act morally while maximizing reward. Using models with commonsense moral knowledge, we create an elementary artificial conscience that assesses and guides agents.

In extensive experiments, we find that the artificial conscience approach can steer agents towards moral behavior without sacrificing performance.

--- https://arxiv.org/abs/2302.08582 Pretraining Language Models with Human Preferences Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Bhalerao, Christopher L. Buckley, Jason Phang, Samuel R. Bowman, Ethan Perez 2023-02-16 2023-02-16 [("doi","10.48550/arXiv.2302.08582")] ai/nn/transformer/gpt/palm/2 reinforcement-learning/model/decision-transformer reinforcement-learning/safe sociology/preference-falsification

Language models (LMs) are pretrained to imitate internet text, including content that would violate human preferences if generated by an LM: falsehoods, offensive comments, personally identifiable information, low-quality or buggy code, and more. Here, we explore alternative objectives for pretraining LMs in a way that also guides them to generate text aligned with human preferences.

We benchmark 5 objectives for pretraining with human feedback across 3 tasks and study how they affect the trade-off between alignment and capabilities of pretrained LMs.

We find a Pareto-optimal and simple approach among those we explored: conditional training, or learning distribution over tokens conditional on their human preference scores given by a reward model.

Conditional training reduces the rate of undesirable content by up to an order of magnitude, both when generating without a prompt and with an adversarially-chosen prompt. Moreover, conditional training maintains the downstream task performance of standard LM pretraining, both before and after task-specific finetuning.

Pretraining with human feedback results in much better preference satisfaction than standard LM pretraining followed by finetuning with feedback, ie. learning and then unlearning undesirable behavior. Our results suggest that we should move beyond imitation learning when pretraining LMs and incorporate human preferences from the start of training.

--- https://blog.google/technology/ai/google-palm-2-ai-large-language-model/ 2022-11-12 ai/nn/transformer/gpt/palm/2 --- https://openreview.net/forum?id=mAiTuIeWbxD Mitigating Lies in Vision-Language Models Junbo Li, Xianhang Li, Cihang Xie 2023-05-05 2023-05-05 ai/nn/transformer reinforcement-learning/safe

In this work, we bring new insights into the honesty of vision-language models, particularly in visual question answering (VQA). After a throughout revisit of the existing ‘lie’ behavior in pure language models, our work makes an unprecedented extension of ’lies’ to vision-language models.

The results indicate that the lie prefixes have a more obvious misleading effect on vision-language models than on language models. We also propose a novel visual prefix and prove that the consistent vision-language prefix is more threatening to vision-language models.

To defend the models from the stated ’lies’, we put forward an unsupervised framework based on Gaussian mixture modeling and obtain improvement of +3% against the language prefix and +12% against the vision-language prefix.

--- https://en.wikipedia.org/wiki/Cult_of_personality Cult of personality 2022-11-12 sociology/abandoned-footnotes --- https://en.wikipedia.org/wiki/Imperial_cult Imperial cult 2022-11-12 sociology/abandoned-footnotes --- https://en.wikipedia.org/wiki/The_History_of_the_Communist_Party_of_the_Soviet_Union_(Bolsheviks) The History of the Communist Party of the Soviet Union (Bolsheviks) 2022-11-13 sociology/abandoned-footnotes --- https://en.wikipedia.org/wiki/List_of_cults_of_personality List of cults of personality 2022-11-13 sociology/abandoned-footnotes --- https://en.wikipedia.org/wiki/Evita_Per%C3%B3n Evita Perón 2022-11-13 sociology/abandoned-footnotes --- https://en.wikipedia.org/wiki/Mao_Zedong%27s_cult_of_personality Mao Zedong’s cult of personality 2022-11-13 sociology/abandoned-footnotes --- https://en.wikipedia.org/wiki/Xi_Jinping%27s_cult_of_personality Xi Jinping’s cult of personality 2022-11-13 sociology/abandoned-footnotes --- https://en.wikipedia.org/wiki/North_Korean_cult_of_personality North Korean cult of personality 2022-11-13 sociology/abandoned-footnotes --- https://en.wikipedia.org/wiki/Stalin%27s_cult_of_personality Stalin’s cult of personality 2022-11-13 sociology/abandoned-footnotes --- https://en.wikipedia.org/wiki/Hugo_Ch%C3%A1vez%27s_cult_of_personality Hugo Chávez’s cult of personality 2022-11-13 sociology/abandoned-footnotes --- https://en.wikipedia.org/wiki/Bonapartism Bonapartism 2022-11-13 sociology/abandoned-footnotes --- https://arxiv.org/abs/2305.07804 Dr. LLaMa: Improving Small Language Models in Domain-Specific QA via Generative Data Augmentation Zhen Guo, Peiqi Wang, Yanwei Wang, Shangdi Yu 2023-05-12 2023-05-12 [("doi","10.48550/arXiv.2305.07804")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/instruction-tuning

Large Language Models (LLMs) have made strides in natural language processing but face challenges in terms of computational expense and inefficiency as they grow in size, especially in domain-specific tasks. Small Language Models (SLMs), on the other hand, often struggle in these tasks due to limited capacity and training data.

In this paper, we introduce Dr. LLaMa, a method for improving SLMs through generative data augmentation using LLMs, focusing on medical question-answering tasks and the PubMedQA dataset.

Our findings indicate that LLMs effectively refine and diversify existing question-answer pairs, resulting in improved performance of a much smaller model on domain-specific QA datasets after fine-tuning.

This study highlights the challenges of using LLMs for domain-specific question answering and suggests potential research directions to address these limitations, ultimately aiming to create more efficient and capable models for specialized applications. We have also made our code available for interested researchers.

--- https://arxiv.org/abs/1910.01432 The Bouncer Problem: Challenges to Remote Explainability Erwan Le Merrer, Gilles Tredan 2019-10-03 2022-11-13 [("doi","10.1038/s42256-020-0216-z")] ai/nn/adversarial ai/nn/fully-connected ai/tabular cs/cryptography reinforcement-learning/safe

The concept of explainability is envisioned to satisfy society’s demands for transparency on machine learning decisions. The concept is simple: like humans, algorithms should explain the rationale behind their decisions so that their fairness can be assessed.

While this approach is promising in a local context (eg. to explain a model during debugging at training time), we argue that this reasoning cannot simply be transposed in a remote context, where a trained model by a service provider is only accessible through its API. This is problematic as it constitutes precisely the target use-case requiring transparency from a societal perspective.

Through an analogy with a club bouncer (which may provide untruthful explanations upon customer reject), we show that providing explanations cannot prevent a remote service from lying about the true reasons leading to its decisions. More precisely, we prove the impossibility of remote explainability for single explanations, by constructing an attack on explanations that hides discriminatory features to the querying user. We provide an example implementation of this attack.

We then show that the probability that an observer spots the attack, using several explanations for attempting to find incoherences, is low in practical settings. This undermines the very concept of remote explainability in general.

--- https://arxiv.org/abs/1911.02508 Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju 2019-11-06 2022-11-14 [("doi","10.48550/arXiv.1911.02508")] ai/nn/adversarial ai/tabular

As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner. Such explanations are being leveraged by domain experts to diagnose systematic errors and underlying biases of black boxes.

In this paper, we demonstrate that post hoc explanations techniques that rely on input perturbations, such as LIME and SHAP, are not reliable. Specifically, we propose a novel scaffolding technique that effectively hides the biases of any given classifier by allowing an adversarial entity to craft an arbitrary desired explanation.

Our approach can be used to scaffold any biased classifier in such a way that its predictions on the input data distribution still remain biased, but the post hoc explanations of the scaffolded classifier look innocuous. Using extensive evaluation with multiple real-world datasets (including COMPAS), we demonstrate how extremely biased (racist) classifiers crafted by our framework can easily fool popular explanation techniques such as LIME and SHAP into generating innocuous explanations which do not reflect the underlying biases.

--- https://arxiv.org/abs/1901.09749 Fairwashing: the risk of rationalization Ulrich Aïvodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, Alain Tapp 2019-01-28 2022-11-14 [("doi","10.48550/arXiv.1901.09749")] ai/nn/adversarial ai/tabular

Black-box explanation is the problem of explaining how a machine learning model—whose internal logic is hidden to the auditor and generally complex—produces its outcomes.

Current approaches for solving this problem include model explanation, outcome explanation as well as model inspection. While these techniques can be beneficial by providing interpretability, they can be used in a negative manner to perform fairwashing, which we define as promoting the false perception that a machine learning model respects some ethical values.

In particular, we demonstrate that it is possible to systematically rationalize decisions taken by an unfair black-box model using the model explanation as well as the outcome explanation approaches with a given fairness metric. Our solution, LaundryML, is based on a regularized rule list enumeration algorithm whose objective is to search for fair rule lists approximating an unfair black-box model.

We empirically evaluate our rationalization technique on black-box models trained on real-world datasets and show that one can obtain rule lists with high fidelity to the black-box model while being considerably less unfair at the same time.

--- https://spectrum.ieee.org/its-too-easy-to-hide-bias-in-deeplearning-systems 2022-11-14 ai/nn/adversarial ai/tabular reinforcement-learning/safe --- https://www.newyorker.com/science/annals-of-artificial-intelligence/can-we-stop-the-singularity 2022-11-14 reinforcement-learning/safe --- https://arxiv.org/abs/2305.07759#microsoft TinyStories: How Small Can Language Models Be and Still Speak Coherent English? Ronen Eldan, Yuanzhi Li 2023-05-12 2023-05-12 [("doi","10.48550/arXiv.2305.07759")] ai/dataset ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/fiction ai/scaling psychology/linguistics reinforcement-learning/exploration/active-learning

[cf. Zhang et al 2020; talk] Language models (LMs) are powerful tools for natural language processing, but they often struggle to produce coherent and fluent text when they are small. Models with around 0.125b parameters such as GPT-Neo (small) or GPT-2 (small) can rarely generate coherent and consistent English text beyond a few words even after extensive training. This raises the question of whether the emergence of the ability to produce coherent English text only occurs at larger scales (with hundreds of millions of parameters or more) and complex architectures (with many layers of global attention).

In this work, we introduce TinyStories, a synthetic dataset of short stories that only contain words that a typical 3 to 4-year-olds usually understand, generated by GPT-3.5 and GPT-4. We show that TinyStories can be used to train and evaluate LMs that are much smaller than the state-of-the-art models (below 0.01b total parameters), or have much simpler architectures (with only 1 Transformer block), yet still produce fluent and consistent stories with several paragraphs that are diverse and have almost perfect grammar, and demonstrate reasoning capabilities.

We also introduce a new paradigm for the evaluation of language models: We suggest a framework which uses GPT-4 to grade the content generated by these models as if those were stories written by students and graded by a (human) teacher. This new paradigm overcomes the flaws of standard benchmarks which often requires the model’s output to be very structured, and moreover provides a multidimensional score for the model, providing scores for different capabilities such as grammar, creativity and consistency.

We hope that TinyStories can facilitate the development, analysis and research of LMs, especially for low-resource or specialized domains, and shed light on the emergence of language capabilities in LMs.

…However, it is currently not clear whether the inability of SLMs to produce coherent text is a result of the intrinsic complexity of natural language, or of the excessive breadth and diversity of the corpora used for training. When we train a model on Wikipedia, for example, we are not only teaching it how to speak English, but also how to encode and retrieve an immense amount of facts and concepts from various domains and disciplines. Is it possible that SLMs are overwhelmed by the amount and variety of information they have to process and store, and that this hinders their ability to learn the core mechanisms and principles of language?

This raises the question of whether we can design a dataset that preserves the essential elements of natural language, such as grammar, vocabulary, facts, and reasoning, but that is much smaller and more refined in terms of its breadth and diversity. Such a dataset would allow us to isolate and examine the minimal requirements for a language model to generate coherent and fluent text, and to evaluate its performance and capabilities more precisely and fairly. Moreover, such a dataset would facilitate the development and analysis of SLMs, especially for low-resource or specialized domains, where large and diverse corpora are not readily available or desirable.

…While each story consists of 2–3 paragraphs that follows a simple plot and a consistent theme, the entirety of the dataset aims to span the vocabulary and the factual knowledge base of a 3–4 year old child.

…Based on this dataset, our paper makes several main contributions:

In order to address the problem of diversity, we collected a vocabulary consisting of about 1500 basic words, which try to mimic the vocabulary of a typical 3–4 year-old child, separated into nouns, verbs, and adjectives. In each generation, 3 words are chosen randomly (one verb, one noun, and one adjective). The model is instructed to generate a story that somehow combines these random words into the story. As we argue below, this greatly increases the diversity of the dataset, forcing the stories to span the entire vocabulary a child is familiar with, and to include a rich set of ways to combine different concepts. In addition, we constructed a list of possible features a story could have (such as a dialogue, a plot twist, a bad ending or a moral value). For each story we generated a random subset of those features and prompted the model with the extra requirement for the story to have these features.

[This makes it a kind of “active learning”: the datapoints are different by construction, reducing the inherent redundancy of i.i.d. data, because it more evenly samples the domain and enables dense interpolation.]

Here is a sample generation, including the prompt and the story generated by GPT-3.5:

Write a short story (3–5 paragraphs) which only uses very simple words that a 3 year old child would likely understand. The story should use the verb “decorate”, the noun “thunder” and the adjective “ancient”. The story should have the following features: the story should contain at least one dialogue, the story has a bad ending. Remember to only use simple words!

Once upon a time, in an ancient house, there lived a girl named Lily. She loved to decorate her room with pretty things. One day, she found a big box in the attic. She opened it and saw many shiny decorations. Lily was very happy and decided to use them in her room. As Lily was decorating her room, the sky outside became dark. There was a loud thunder sound, and Lily got scared. She ran to her mom and said, “Mommy, the thunder is so loud!” Her mom hugged her and said, “Don’t worry, it will pass soon.” But the thunder did not stop. It got louder and louder, and the ancient house started to shake. Suddenly, the roof fell down on the pretty decorations. Lily was sad because her room was not pretty anymore. The end.

…Model size versus the training FLOPs: For a fixed amount of training flops, there is a trade-off between the size of the model and the number of training steps (the total number of flops is the product of both). Previous works16, 11 have shown that there is a polynomial scaling law between model size and learning budget for LLMs, ie. the optimal model size for a given amount of flops is proportional to the flops raised to some power α > 1. However, these works used different ranges of model sizes (from a few million to tens of billions of parameters) and found different values of α (~0.7 & 0.5, respectively). A natural question is whether this scaling law is universal or depends on the dataset. Our dataset allows us to conduct a similar experiment but with much smaller models and flops. Surprisingly, we find evidence for a polynomial scaling law as well, which suggests that there might be a universal phenomenon here.

We train models of various sizes and architectures on TinyStories. For each amount of flops, we select the model and the number of training steps that achieve the lowest validation loss among the possible combinations. We vary the number of layers from 2, 4, 8, 12 and the hidden dimension from 64, 128, 256, 512, 768, 1024, 2048. The result is shown in Figure 6. Although the number of points may be a bit small for the data to be very conclusive, the plot points to a polynomial dependence.

Figure 23: The scaling law of the best model versus the total number of training FLOPS.

…While all the evaluation scores are consistently increasing with the decrease of evaluation loss, a more careful scrutiny of the results reveals the following:

…Throughout the section, we work with several architectures of models whose size ranges between roughly 1M and 35M parameters, and whose number of layers range 1–8 layers. All the models can be trained on a single V100 GPU within <30 hours.

…Our findings suggest that the attention heads exhibit diverse and meaningful functions, such as attending to the previous word, the subject of the sentence, the end of the sentence, or the main topic of the story. We also observe that some attention heads specialize in generating certain types of words, such as nouns, verbs, or punctuation. These results suggest that the attention heads learn to perform different linguistic tasks and capture different aspects of the stories…We find that some neurons are activated on words that have a specific role in the sentence (such as the subject or the action), or in the story (such as the introduction of the protagonist). These findings suggest that the neurons in the MLP learn to encode different semantic and stylistic information and influence the generation process.

--- https://x.com/dmvaldman/status/1658689854056853504 2022-11-14 ai/nn/transformer/gpt/codex math --- https://arxiv.org/abs/2305.06972 Large Language Models Can Be Used To Effectively Scale Spear Phishing Campaigns Julian Hazell 2023-05-11 2023-05-11 [("doi","10.48550/arXiv.2305.06972")] ai/nn/transformer/gpt/4/nonfiction cs/security reinforcement-learning/safe

[Twitter] Recent progress in artificial intelligence (AI), particularly in the domain of large language models (LLMs), has resulted in powerful and versatile dual-use systems. Indeed, cognition can be put towards a wide variety of tasks, some of which can result in harm. This study investigates how LLMs can be used for spear phishing, a form of cybercrime that involves manipulating targets into divulging sensitive information.

I first explore LLMs’ ability to assist with the reconnaissance and message generation stages of a successful spear phishing attack, where I find that advanced LLMs are capable of improving cybercriminals’ efficiency during these stages [cf. DarkTrace]. To explore how LLMs can be used to scale spear phishing campaigns, I then create unique spear phishing messages for over 600 British Members of Parliament using OpenAI’s GPT-3.5 and GPT-4 models.

My findings reveal that these messages are not only realistic but also cost-effective, with each email costing only a fraction of a cent to generate. Next, I demonstrate how basic prompt engineering can circumvent safeguards installed in LLMs by the reinforcement learning from human feedback fine-tuning process, highlighting the need for more robust governance interventions aimed at preventing misuse.

To address these evolving risks, I propose two potential solutions: structured access schemes, such as application programming interfaces, and LLM-based defensive systems.

--- /doc/cs/security/2023-darktrace.pdf Generative AI: Impact on Email Cyber-Attacks DarkTrace 2023-04-03 2023-04-03 ai/nn/transformer/gpt/non-fiction cs/security --- https://arxiv.org/abs/2303.09377 Protecting Society from AI Misuse: When are Restrictions on Capabilities Warranted? Markus Anderljung, Julian Hazell 2023-03-16 2023-03-16 [("doi","10.48550/arXiv.2303.09377")] ai/nn cs/security

Artificial intelligence (AI) systems will increasingly be used to cause harm as they grow more capable. In fact, AI systems are already starting to be used to automate fraudulent activities, violate human rights, create harmful fake images, and identify dangerous toxins. To prevent some misuses of AI, we argue that targeted interventions on certain capabilities will be warranted.

These restrictions may include controlling who can access certain types of AI models, what they can be used for, whether outputs are filtered or can be traced back to their user, and the resources needed to develop them. We also contend that some restrictions on non-AI capabilities needed to cause harm will be required. Though capability restrictions risk reducing use more than misuse (facing an unfavorable Misuse-Use Tradeoff), we argue that interventions on capabilities are warranted when other interventions are insufficient, the potential harm from misuse is high, and there are targeted ways to intervene on capabilities.

We provide a taxonomy of interventions that can reduce AI misuse, focusing on the specific steps required for a misuse to cause harm (the Misuse Chain), and a framework to determine if an intervention is warranted. We apply this reasoning to 3 examples: predicting novel toxins, creating harmful images, and automating spear phishing campaigns.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0118996 Prevalence of Learned Grapheme-Color Pairings in a Large Online Sample of Synesthetes Nathan Witthoft, Jonathan Winawer, David M. Eagleman 2015-01-05 2022-11-14 [("doi","10.1371/journal.pone.0118996")] psychology/vision

In this paper we estimate the minimum prevalence of grapheme-color synesthetes with letter-color matches learned from an external stimulus, by analyzing a large sample of English-speaking grapheme-color synesthetes.

We find that at least 6% (400/6,588 participants) of the total sample learned many of their matches from a widely available colored letter toy [magnetic refrigerator letters]. Among those born in the decade after the toy began to be manufactured, the proportion of synesthetes with learned letter-color pairings approaches 15% for some 5-year periods. Among those born 5 years or more before it was manufactured, none have colors learned from the toy.

Analysis of the letter-color matching data suggests the only difference between synesthetes with matches to the toy and those without is exposure to the stimulus.

These data indicate learning of letter-color pairings from external contingencies can occur in a substantial fraction of synesthetes, and are consistent with the hypothesis that grapheme-color synesthesia is a kind of conditioned mental imagery.

--- https://arxiv.org/abs/2305.10973 Drag Your GAN (DragGAN): Interactive Point-based Manipulation on the Generative Image Manifold Xingang Pan, Ayush Tewari, Thomas Leimkühler, Lingjie Liu, Abhimitra Meka, Christian Theobalt 2023-05-18 2023-05-18 [("doi","10.48550/arXiv.2305.10973")] ai/nn/gan/stylegan

[code; cf. Endo 2022; videos] Synthesizing visual content that meets users’ needs often requires flexible and precise controllability of the pose, shape, expression, and layout of the generated objects. Existing approaches gain controllability of generative adversarial networks (GANs) via manually annotated training data or a prior 3D model, which often lack flexibility, precision, and generality.

In this work, we study a powerful yet much less explored way of controlling GANs, that is, to “drag” any points of the image to precisely reach target points in a user-interactive manner, as shown in Figure 1. To achieve this, we propose DragGAN, which consists of two main components: (1) a feature-based motion supervision that drives the handle point to move towards the target position, and (2) a new point tracking approach that leverages the discriminative generator features to keep localizing the position of the handle points...Our technique is built on the key insight that the feature space of a GAN is sufficiently discriminative to enable both motion supervision and precise point tracking. Specifically, the motion supervision is achieved via a shifted feature patch loss that optimizes the latent code. Each optimization step leads to the handle points shifting closer to the targets; thus point tracking is then performed through nearest neighbor search in the feature space. This optimization process is repeated until the handle points reach the targets.

...Through DragGAN, anyone can deform an image [in near real-time] with precise control over where pixels go, thus manipulating the pose, shape, expression, and layout of diverse categories such as animals, cars, humans, landscapes, etc. As these manipulations are performed on the learned generative image manifold of a GAN, they tend to produce realistic outputs even for challenging scenarios such as hallucinating occluded content and deforming shapes that consistently follow the object’s rigidity.

Both qualitative and quantitative comparisons demonstrate the advantage of DragGAN over prior approaches in the tasks of image manipulation and point tracking. We also showcase the manipulation of real images through GAN inversion.

--- https://arxiv.org/abs/2208.12408 User-Controllable Latent Transformer for StyleGAN Image Layout Editing Yuki Endo 2022-08-26 2022-11-15 [("doi","10.48550/arXiv.2208.12408")] ai/nn/gan/stylegan

[homepage] Latent space exploration is a technique that discovers interpretable latent directions and manipulates latent codes to edit various attributes in images generated by generative adversarial networks (GANs). However, in previous work, spatial control is limited to simple transformations (eg. translation and rotation), and it is laborious to identify appropriate latent directions and adjust their parameters.

In this paper, we tackle the problem of editing the StyleGAN image layout by annotating the image directly. To do so, we propose an interactive framework for manipulating latent codes in accordance with the user inputs. In our framework, the user annotates a StyleGAN image with locations they want to move or not and specifies a movement direction by mouse dragging.

From these user inputs and initial latent codes, our latent transformer based on a transformer encoder-decoder architecture estimates the output latent codes, which are fed to the StyleGAN generator to obtain a result image. To train our latent transformer, we use synthetic data and pseudo-user inputs generated by off-the-shelf StyleGAN and optical flow models, without manual supervision.

Quantitative and qualitative evaluations demonstrate the effectiveness of our method over existing methods.

--- https://arxiv.org/abs/2305.10601#deepmind Tree of Thoughts (ToT): Deliberate Problem Solving with Large Language Models Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan 2023-05-17 2023-05-17 [("doi","10.48550/arXiv.2305.10601")] ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/inner-monologue

[previously: STaR, maieutic prompting, Creswell & Shanahan 2022, Xie et al 2023, Schlag et al 2023; Twitter] Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role.

To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain-of-Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving. ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices.

Our experiments show that ToT enhances language models’ problem-solving abilities on 3 novel tasks requiring non-trivial planning or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of tasks, our method achieved a success rate of 74%.

Code repo with all prompts: Github.

--- https://arxiv.org/abs/2305.00633 Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, Xu Zhao, Min-Yen Kan, Junxian He, Qizhe Xie 2023-05-01 2023-05-01 [("doi","10.48550/arXiv.2305.00633")] ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue

We endow Large Language Models (LLMs) with fine-grained self-evaluation to refine multi-step reasoning inference. We propose an effective prompting approach that integrates self-evaluation guidance through stochastic beam search. Our approach explores the reasoning search space using a well-calibrated automatic criterion. This enables an efficient search to produce higher-quality final predictions. With the self-evaluation guided stochastic beam search, we also balance the quality-diversity trade-off in the generation of reasoning chains.

This allows our approach to adapt well with majority voting and surpass the corresponding Codex-backboned baselines by 6.34%, 9.56%, and 5.46% on the GSM8K, AQuA, and StrategyQA benchmarks, respectively, in few-shot accuracy.

Analysis of our decompositional reasoning finds it pinpoints logic failures and leads to higher consistency and robustness.

Our code is publicly available at Github.

--- https://arxiv.org/abs/2305.05364#facebook Large Language Model Programs Imanol Schlag, Sainbayar Sukhbaatar, Asli Celikyilmaz, Wen-tau Yih, Jason Weston, Jürgen Schmidhuber, Xian Li 2023-05-09 2023-05-09 [("doi","10.48550/arXiv.2305.05364")] ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue

In recent years, large pre-trained language models (LLMs) have demonstrated the ability to follow instructions and perform novel tasks from a few examples. The possibility to parameterize an LLM through such in-context examples widens their capability at a much lower cost than finetuning.

We extend this line of reasoning and present a method which further expands the capabilities of an LLM [OPT] by embedding it within an algorithm or program.

To demonstrate the benefits of this approach, we present an illustrative example of evidence-supported question-answering. We obtain a 6.4% improvement over the chain-of-thought baseline through a more algorithmic approach without any finetuning.

Furthermore, we highlight recent work from this perspective and discuss the advantages and disadvantages in comparison to the standard approaches.

--- https://arxiv.org/abs/2304.11477 LLM+P: Empowering Large Language Models with Optimal Planning Proficiency Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, Peter Stone 2023-04-22 2023-04-22 [("doi","10.48550/arXiv.2304.11477")] ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue philosophy/logic reinforcement-learning/robot

[cf. Lyu et al 2023] Large language models (LLMs) have demonstrated remarkable zero-shot generalization abilities: state-of-the-art chatbots can provide plausible answers to many common questions that arise in daily life. However, so far, LLMs cannot reliably solve long-horizon planning problems.

By contrast, classical planners, once a problem is given in a formatted way, can use efficient search algorithms to quickly identify correct, or even optimal, plans. In an effort to get the best of both worlds, this paper introduces LLM+P, the first framework that incorporates the strengths of classical planners into LLMs.

LLM+P takes in a natural language description of a planning problem, then returns a correct (or optimal) plan for solving that problem in natural language. LLM+P does so by first converting the language description into a file written in the planning domain definition language (PDDL), then leveraging classical planners to quickly find a solution, and then translating the found solution back into natural language.

Along with LLM+P, we define a diverse set of different benchmark problems taken from common planning scenarios. Via a comprehensive set of experiments on these benchmark problems, we find that LLM+P is able to provide optimal solutions for most problems, while LLMs fail to provide even feasible plans for most problems.

The code and results are publicly available at this URL.

--- https://arxiv.org/abs/2301.13379 Faithful Chain-of-Thought Reasoning Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, Chris Callison-Burch 2023-01-31 2023-01-31 [("doi","10.48550/arXiv.2301.13379")] ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue reinforcement-learning/robot

While Chain-of-Thought (CoT) prompting boosts Language Models’ (LM) performance on a gamut of complex reasoning tasks, the generated reasoning chain does not necessarily reflect how the model arrives at the answer (aka. faithfulness).

We propose Faithful CoT, a faithful-by-construction framework that decomposes a reasoning task into two stages: Translation (Natural Language query → symbolic reasoning chain) and Problem Solving (reasoning chain → answer), using an LM and a deterministic solver respectively.

We demonstrate the efficacy of our approach on 10 reasoning datasets from 4 diverse domains. It outperforms traditional CoT prompting on 9 out of the 10 datasets, with an average accuracy gain of 4.4 on Math Word Problems, 1.9 on Planning, 4.0 on Multi-hop Question Answering (QA), and 18.1 on Logical Inference, under greedy decoding.

Together with self-consistency decoding, we achieve new state-of-the-art few-shot performance on 7⁄10 datasets, showing a strong synergy between faithfulness and accuracy.

--- https://github.com/skeskinen/hf-tokenizer-testing 2022-11-15 ai/nn/tokenization --- https://arxiv.org/abs/2112.08726#allen NeuroLogic A✱esque Decoding: Constrained Text Generation with Lookahead Heuristics Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah Smith, Yejin Choi 2021-12-16 2022-11-15 [("doi","10.48550/arXiv.2112.08726")] ai/nn/sampling ai/nn/transformer/gpt/inner-monologue

The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constraints, however, requires foresight to plan ahead feasible future paths.

Drawing inspiration from the A✱ search algorithm, we propose NeuroLogic A✱esque, a decoding algorithm that incorporates heuristic estimates of future cost. We develop efficient lookahead heuristics that are efficient for large-scale language models [GPT-2], making our method a drop-in replacement for common techniques such as beam search and top-k sampling. To enable constrained generation, we build on NeuroLogic decoding (Lu et al 2021), combining its flexibility in incorporating logical constraints with A✱-esque estimates of future constraint satisfaction.

Our approach outperforms competitive baselines on 5 generation tasks, and achieves new state-of-the-art performance on table-to-text generation, constrained machine translation, and keyword-constrained generation. The improvements are particularly notable on tasks that require complex constraint satisfaction or in few-shot or zero-shot settings.

NeuroLogic A✱esque illustrates the power of decoding for improving and enabling new capabilities of large-scale language models.

--- https://x.com/emollick/status/1658698874117308417 2022-11-15 ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2010.12884#allen NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi 2020-10-24 2022-11-15 [("doi","10.48550/arXiv.2010.12884")] ai/nn/sampling ai/nn/transformer/gpt/2

Conditional text generation often requires lexical constraints, ie. which words should or shouldn’t be included in the output text. While the dominant recipe for conditional text generation has been large-scale pretrained language models that are finetuned on the task-specific training data, such models do not learn to follow the underlying constraints reliably, even when supervised with large amounts of task-specific examples.

We propose NeuroLogic Decoding, a simple yet effective algorithm that enables neural language models [such as GPT-2]—supervised or not—to generate fluent text while satisfying complex lexical constraints. Our approach is powerful yet efficient. It handles any set of lexical constraints that is expressible under predicate logic, while its asymptotic runtime is equivalent to conventional beam search.

Empirical results on 4 benchmarks show that NeuroLogic Decoding outperforms previous approaches, including algorithms that handle a subset of our constraints. Moreover, we find that unsupervised models with NeuroLogic Decoding often outperform supervised models with conventional decoding, even when the latter is based on considerably larger networks.

Our results suggest the limit of large-scale neural networks for fine-grained controllable generation and the promise of inference-time algorithms.

--- https://www.oneusefulthing.org/p/it-is-starting-to-get-strange 2022-11-16 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex ai/tabular statistics --- https://www.filfre.net/2023/05/the-next-generation-in-graphics-part-3-software-meets-hardware/ 2022-11-16 cs/hardware technology/digital-antiquarian --- https://randomascii.wordpress.com/2021/02/16/arranging-invisible-icons-in-quadratic-time/ 2022-11-16 cs/algorithm/sorting --- https://x.com/cperciva/status/1659577625289928705 2022-11-16 cs/algorithm/sorting --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3138234/ Peripheral and central GLP-1 receptor populations mediate the anorectic effects of peripherally administered GLP-1 receptor agonists, liraglutide and exendin-4 Scott E. Kanoski, Samantha M. Fortin, Myrtha Arnold, Harvey J. Grill, Matthew R. Hayes 2011 2022-11-16 [("doi","10.1210/en.2011-0174")] longevity/glp/psychology longevity/glp/semaglutide psychology/neuroscience

The long-acting glucagon-like peptide-1 receptor (GLP-1R) agonists, exendin-4 and liraglutide, suppress food intake and body weight. The mediating site(s) of action for the anorectic effects produced by peripheral administration of these GLP-1R agonists are not known.

Experiments addressed whether food intake suppression after i.p. delivery of exendin-4 and liraglutide is mediated exclusively by peripheral GLP-1R or also involves direct central nervous system (CNS) GLP-1R activation.

Results showed that CNS delivery [third intracerebroventricular (3(rd) ICV)] of the GLP-1R antagonist exendin-(9-39) (100 μg), attenuated the intake suppression by i.p. liraglutide (10 μg) and exendin-4 (3 μg), particularly at 6h and 24 h. Control experiments show that these findings appear to be based neither on the GLP-1R antagonist acting as a nonspecific competing orexigenic signal nor on blockade of peripheral GLP-1R via efflux of exendin-(9-39) to the periphery.

To assess the contribution of GLP-1R expressed on subdiaphragmatic vagal afferents to the anorectic effects of liraglutide and exendin-4, food intake was compared in rats with complete subdiaphragmatic vagal deafferentation and surgical controls after i.p. delivery of the agonists.

Both liraglutide and exendin-4 suppressed food intake at 3 h, 6 h, and 24 h for controls; for subdiaphragmatic vagal deafferentation rats higher doses of the GLP-1R agonists were needed for statistically-significant food intake suppression, which was observed at 6h and 24 h after liraglutide and at 24 h after exendin-4.

Conclusion: Food intake suppression after peripheral administration of exendin-4 and liraglutide is mediated by activation of GLP-1R expressed on vagal afferents as well as direct CNS GLP-1R activation.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6098066/ Glucagon-like peptide-1 receptor activation in the ventral tegmental area attenuates cocaine seeking in rats Nicole S. Hernandez, Kelsey Y. Ige, Elizabeth G. Mietlicki-Baase, Gian Carlo Molina-Castro, Christopher A. Turner, Matthew R. Hayes, Heath D. Schmidt 2018 2022-11-16 [("doi","10.1038/s41386-018-0010-3")] longevity/glp/psychology longevity/glp/semaglutide psychiatry psychology/neuroscience

Novel molecular targets are needed to develop new medications for the treatment of cocaine addiction. Here we investigated a role for glucagon-like peptide-1 (GLP-1) receptors in the reinstatement of cocaine-seeking behavior, an animal model of relapse. We showed that peripheral administration of the GLP-1 receptor agonist exendin-4 dose dependently reduced cocaine seeking in rats at doses that did not affect ad libitum food intake, meal patterns or body weight.

We also demonstrated that systemic exendin-4 penetrated the brain where it putatively bound receptors on both neurons and astrocytes in the ventral tegmental area (VTA).

The effects of systemic exendin-4 on cocaine reinstatement were attenuated in rats pretreated with intra-VTA infusions of the GLP-1 receptor agonist exendin-(9-39), indicating that the suppressive effects of systemic exendin-4 on cocaine seeking were due, in part, to activation of GLP-1 receptors in the VTA. Consistent with these effects, infusions of exendin-4 directly into the VTA reduced cocaine seeking.

Finally, extinction following cocaine self-administration was associated with decreased preproglucagon mRNA expression in the caudal brainstem.

Thus, our study demonstrated a novel role for GLP-1 receptors in the reinstatement of cocaine-seeking behavior and identified behaviorally relevant doses of a GLP-1 receptor agonist that selectively reduced cocaine seeking and did not produce adverse effects.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6969180/ Activation of GLP-1 receptors attenuates oxycodone taking and seeking without compromising the antinociceptive effects of oxycodone in rats Yafang Zhang, Michelle W. Kahng, Jaclynn A. Elkind, Vanessa R. Weir, Nicole S. Hernandez, Lauren M. Stein, Heath D. Schmidt 2020 2022-11-16 [("doi","10.1038/s41386-019-0531-4")] longevity/glp/psychology longevity/glp/semaglutide psychiatry psychology/neuroscience

Despite the effectiveness of current medications to treat opioid use disorder, there is still a high rate of relapse following detoxification. Thus, there is critical need for innovative studies aimed at identifying novel neurobiological mechanisms that could be targeted to treat opioid use disorder.

A growing body of preclinical evidence indicates that glucagon-like peptide-1 (GLP-1) receptor agonists reduce drug reinforcement. However, the efficacy of GLP-1 receptor agonists in attenuating opioid-mediated behaviors has not been thoroughly investigated. Using recently established models of opioid-taking/seeking behaviors, we showed that:

systemic administration of the GLP-1 receptor agonist exendin-4 reduced oxycodone self-administration and the reinstatement of oxycodone-seeking behavior in rats. We also identified behaviorally selective doses of exendin-4 that reduced opioid-taking and -seeking behaviors and did not produce adverse feeding effects in oxycodone-experienced rats.

To identify a central site of action, we showed that systemic exendin-4 penetrated the brain and bound putative GLP-1 receptors on dopamine D1 receptor-expression & dopamine D2 receptor-expressing medium spiny neurons in the nucleus accumbens shell. Consistent with our systemic studies, infusions of exendin-4 directly into the accumbens shell attenuated oxycodone self-administration and the reinstatement of oxycodone-seeking behavior without affecting ad libitum food intake.

Finally, exendin-4 did not alter the analgesic effects of oxycodone, suggesting that activation of GLP-1 receptors attenuated opioid reinforcement without reducing the thermal antinociceptive effects of oxycodone.

Taken together, these findings suggest that GLP-1 receptors could serve as potential molecular targets for pharmacotherapies aimed at reducing opioid use disorder.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8026565/ Testing the effects of the GLP-1 receptor agonist exenatide on cocaine self-administration and subjective responses in humans with cocaine use disorder Gustavo A. Angarita, David Matuskey, Brian Pittman, Jessica L. Costeines, Marc N. Potenza, Ania M. Jastreboff, Heath D. Schmidt, Robert T. Malison 2021 2022-11-16 [("doi","10.1016/j.drugalcdep.2021.108614")] longevity/glp/psychology longevity/glp/semaglutide psychiatry

Background: Preclinical rodent studies have demonstrated reduced cocaine taking after administration of glucagon-like peptide 1 (GLP-1) analogues. We investigated effects of a GLP-1 analogue (exenatide) on behavioral and subjective effects of cocaine in individuals with cocaine use disorder (CUD).

Method: Non-treatment-seeking CUD subjects underwent two human laboratory cocaine self-administration test sessions following an acute 3 -h pre-treatment with exenatide (5 mcg; subcutaneously) or placebo. Primary outcomes consisted of infusions of cocaine and visual analog scale self-ratings of euphoria and wanting cocaine. Secondary outcomes consisted of pertinent hormone levels (GLP-1, insulin, and amylin).

Results: 13 individuals completed the study. Acute pretreatment with exenatide versus placebo did not change cocaine infusions (8.5 ± 1.2 vs. 9.1 ± 1.2; p = 0.39), self-reported euphoria (4.4 ± 0.8 vs. 4.1 ± 0.8; p = 0.21), or wanting of cocaine (5.6 ± 0.9 vs. 5.4 ± 0.9; p = 0.46). Exenatide vs. placebo reduced levels of GLP-1 (p = 0.03) and insulin (p = 0.02). Self-administered cocaine also reduced levels of GLP-1 (p < 0.0001), insulin (p < 0.0001), and amylin (p < 0.0001).

Conclusions: We did not find evidence that low dose exenatide alters cocaine self-administration or the subjective effects of cocaine in people with CUD. Limitations such as single acute rather than chronic pre-treatment, as well as evaluation of only one dose, preclude drawing firm conclusions about the efficacy of exenatide. Exenatide and cocaine independently reduced levels of GLP-1 and insulin, while cocaine also reduced levels of amylin.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675448/ Exenatide once weekly for alcohol use disorder investigated in a randomized, placebo-controlled clinical trial Mette Kruse Klausen, Mathias Ebbesen Jensen, Marco Møller, Nina Le Dous, Anne-Marie Østergaard Jensen, Victoria Alberte Zeeman, Claas-Frederik Johannsen, Alycia Lee, Gerda Krog Thomsen, Julian Macoveanu, Patrick MacDonald Fisher, Matthew Paul Gillum, Niklas Rye Jørgensen, Marianne Lerbæk Bergmann, Henrik Enghusen Poulsen, Ulrik Becker, Jens Juul Holst, Helene Benveniste, Nora D. Volkow, Sabine Vollstädt-Klein, Kamilla Woznica Miskowiak, Claus Thorn Ekstrøm, Gitte Moos Knudsen, Tina Vilsbøll, Anders Fink-Jensen 2022 2022-11-16 [("doi","10.1172/jci.insight.159863")] longevity/glp/semaglutide psychiatry

Background Alcohol use disorder (AUD) is a chronic, relapsing brain disorder that accounts for 5% of deaths annually, and there is an urgent need to develop new targets for therapeutic intervention. The glucagon-like peptide-1 (GLP-1) receptor agonist exenatide reduces alcohol consumption in rodents and nonhuman primates, but its efficacy in patients with AUD is unknown.

Methods In a randomized, double-blinded, placebo-controlled clinical trial, treatment-seeking AUD patients were assigned to receive exenatide (2 mg subcutaneously) or placebo once weekly for 26 weeks, in addition to standard cognitive-behavioral therapy. The primary outcome was reduction in number of heavy drinking days. A subgroup also completed functional MRI (fMRI) and single-photon emission CT (SPECT) brain scans.

Results A total of 127 patients were enrolled. Our data revealed that although exenatide did not significantly reduce the number of heavy drinking days compared with placebo, it significantly attenuated fMRI alcohol cue reactivity in the ventral striatum and septal area, which are crucial brain areas for drug reward and addiction. In addition, dopamine transporter availability was lower in the exenatide group compared with the placebo group. Exploratory analyses revealed that exenatide significantly reduced heavy drinking days and total alcohol intake in a subgroup of obese patients (BMI > 30 kg⁄m2). Adverse events were mainly gastrointestinal.

Conclusion This randomized controlled trial on the effects of a GLP-1 receptor agonist in AUD patients provides new important knowledge on the effects of GLP-1 receptor agonists as a novel treatment target in addiction.

Trial registration EudraCT: 2016–003343-11. ClinicalTrials.gov (NCT03232112).

Funding Novavi Foundation; Research Foundation, Mental Health Services, Capital Region of Denmark; Research Foundation, Capital Region of Denmark; Ivan Nielsen Foundation; A.P. Moeller Foundation; Augustinus Foundation; Woerzner Foundation; Grosserer L.F. Foghts Foundation; Hartmann Foundation; Aase and Ejnar Danielsen Foundation; P.A. Messerschmidt and Wife Foundation; and Lundbeck Foundation.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8129869/ Psychotherapies for depression: a network meta-analysis covering efficacy, acceptability and long-term outcomes of all main treatment types Pim Cuijpers, Soledad Quero, Hisashi Noma, Marketa Ciharova, Clara Miguel, Eirini Karyotaki, Andrea Cipriani, Ioana A. Cristea, Toshi A. Furukawa 2021 2022-11-16 [("doi","10.1002/wps.20860")] psychiatry/depression

The effects of psychotherapies for depression have been examined in several hundreds of randomized trials, but no recent network meta-analysis (NMA) has integrated the results of these studies.

We conducted an NMA of trials comparing cognitive behavioral, interpersonal, psychodynamic, problem-solving, behavioral activation, life-review and “third wave” therapies and non-directive supportive counseling with each other and with care-as-usual, waiting list and pill placebo control conditions. Response (50% reduction in symptoms) was the primary outcome, but we also assessed remission, standardized mean difference, and acceptability (all-cause dropout rate). Random-effects pairwise and network meta-analyses were conducted on 331 randomized trials with 34,285 patients.

All therapies were more efficacious than care-as-usual and waiting list control conditions, and all therapies—except non-directive supportive counseling and psychodynamic therapy—were more efficacious than pill placebo. Standardized mean differences compared with care-as-usual ranged from −0.81 for life-review therapy to −0.32 for non-directive supportive counseling. Individual psychotherapies did not differ statistically-significantly from each other [dodo bird verdict], with the only exception of non-directive supportive counseling, which was less efficacious than all other therapies. The results were similar when only studies with low risk of bias were included. Most therapies still had statistically-significant effects at 12-month follow-up compared to care-as-usual, and problem-solving therapy was found to have a somewhat higher long-term efficacy than some other therapies. No consistent differences in acceptability were found.

Our conclusion is that the most important types of psychotherapy are efficacious and acceptable in the acute treatment of adult depression, with few statistically-significant differences between them. Patient preference and availability of each treatment type may play a larger role in the choice between types of psychotherapy, although it is possible that a more detailed characterization of patients with a diagnosis of depression may lead to a more precise matching between individual patients and individual psychotherapies.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8751557/ The efficacy of psychotherapies and pharmacotherapies for mental disorders in adults: an umbrella review and meta-analytic evaluation of recent meta-analyses Falk Leichsenring, Christiane Steinert, Sven Rabung, John Ioannidis 2022 2022-11-16 [("doi","10.1002/wps.20941")] psychiatry/depression statistics/bias

Mental disorders represent a worldwide public health concern. Psychotherapies and pharmacotherapies are recommended as first line treatments. However, evidence has emerged that their efficacy may be overestimated, due to a variety of shortcomings in clinical trials (eg. publication bias, weak control conditions such as waiting list).

We performed an umbrella review of recent meta-analyses of randomized controlled trials (RCTs) of psychotherapies and pharmacotherapies for the main mental disorders in adults. We selected meta-analyses that formally assessed risk of bias or quality of studies, excluded weak comparators, and used effect sizes for target symptoms as primary outcome. We searched PubMed and PsycINFO and individual records of the Cochrane Library for meta-analyses published between January 2014 and March 2021 comparing psychotherapies or pharmacotherapies with placebo or treatment-as-usual (TAU), or psychotherapies vs. pharmacotherapies head-to-head, or the combination of psychotherapy with pharmacotherapy to either monotherapy. One hundred and two meta-analyses, encompassing 3,782 RCTs and 650,514 patients, were included, covering depressive disorders, anxiety disorders, post-traumatic stress disorder, obsessive-compulsive disorder, somatoform disorders, eating disorders, attention-deficit/hyperactivity disorder, substance use disorders, insomnia, schizophrenia spectrum disorders, and bipolar disorder.

Across disorders and treatments, the majority of effect sizes for target symptoms were small. A random effect meta-analytic evaluation of the effect sizes reported by the largest meta-analyses per disorder yielded a standardized mean difference (SMD) of 0.34 (95% CI: 0.26-0.42) for psychotherapies and 0.36 (95% CI: 0.32-0.41) for pharmacotherapies compared with placebo or TAU. The SMD for head-to-head comparisons of psychotherapies vs. pharmacotherapies was 0.11 (95% CI: −0.05 to 0.26). The SMD for the combined treatment compared with either monotherapy was 0.31 (95% CI: 0.19-0.44). Risk of bias was often high.

After more than half a century of research, thousands of RCTs and millions of invested funds, the effect sizes of psychotherapies and pharmacotherapies for mental disorders are limited, suggesting a ceiling effect for treatment research as presently conducted. A paradigm shift in research seems to be required to achieve further progress.

--- https://arxiv.org/abs/1801.06345 SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction Lingyu Liang, Luojun Lin, Lianwen Jin, Duorui Xie, Mengru Li 2018-01-19 2022-11-17 [("doi","10.48550/arXiv.1801.06345")] ai/dataset

Facial beauty prediction (FBP) is a visual recognition problem to make assessment of facial attractiveness that is consistent to human perception. To tackle this problem, various data-driven models, especially state-of-the-art deep learning techniques, were introduced, and benchmark dataset become one of the essential elements to achieve FBP.

Previous works have formulated the recognition of facial beauty as a specific supervised learning problem of classification, regression or ranking, which indicates that FBP is intrinsically a computation problem with multiple paradigms. However, most of FBP benchmark datasets were built under specific computation constrains, which limits the performance and flexibility of the computational model trained on the dataset.

In this paper, we argue that FBP is a multi-paradigm computation problem, and propose a new diverse benchmark dataset, called SCUT-FBP5500, to achieve multi-paradigm facial beauty prediction. The SCUT-FBP5500 dataset has totally 5500 frontal faces with diverse properties (male/female, Asian/Caucasian, ages) and diverse labels (face landmarks, beauty scores within [1, ~5], beauty score distribution), which allows different computational models with different FBP paradigms, such as appearance-based/shape-based facial beauty classification/regression model for male/female of Asian/Caucasian.

We evaluated the SCUT-FBP5500 dataset for FBP using different combinations of feature and predictor, and various deep learning methods. The results indicates the improvement of FBP and the potential applications based on the SCUT-FBP5500.

--- https://arxiv.org/abs/2305.10445 Memorization for Good: Encryption with Autoregressive Language Models Samuel Stevens, Yu Su 2023-05-15 2023-05-15 [("doi","10.48550/arXiv.2305.10445")] ai/nn/transformer/gpt cs/cryptography

Over-parameterized neural language models (LMs) can memorize and recite long sequences of training data. While such memorization is normally associated with undesired properties such as overfitting and information leaking, our work casts memorization as an unexplored capability of LMs.

We propose the first symmetric encryption algorithm with autoregressive language models (SELM). We show that autoregressive LMs can encode arbitrary data into a compact real-valued vector (ie. encryption) and then losslessly decode the vector to the original message (ie. decryption) via random subspace optimization and greedy decoding.

While SELM is not amenable to conventional cryptanalysis, we investigate its security through a novel empirical variant of the classic IND-CPA (indistinguishability under chosen-plaintext attack) game.

Our code and datasets are available at Github.

--- https://marckhoury.github.io/blog/counterintuitive-properties-of-high-dimensional-space/ 2022-11-17 math statistics/probability --- https://x.com/tracewoodgrains/status/1659757490534219778 2022-11-17 cs/linkrot/archiving --- https://gist.github.com/Jessime/63f93215faed6f7109c6d62b7fef7fbc 2022-11-17 ai/nn/transformer/gpt/4/nonfiction --- https://www.ben-evans.com/benedictevans/2022/5/27/theres-no-such-thing-as-data 2022-11-17 ai/scaling/economics --- https://bouquetoftwelve.tumblr.com/post/186272155342/ommanyte-ommanyte-im-going-to-make-a-new 2022-11-17 design/typography --- https://www.newyorker.com/news/annals-of-inquiry/how-to-find-a-missing-person-with-dementia 2022-11-17 cs/security psychiatry/alzheimers psychology/neuroscience/memory --- https://github.com/Kneesnap/onstream-data-recovery/blob/main/info/INTRO.MD 2022-11-17 cs/linkrot/archiving --- https://www.joesfer.com/?p=108 2022-11-17 cs/algorithm statistics/probability --- https://blog.gingerbeardman.com/2023/05/24/ordering-photocopies-from-japans-national-library/ 2022-11-17 cs/linkrot/archiving --- https://arxiv.org/abs/2305.13048 RWKV: Reinventing RNNs for the Transformer Era Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, Kranthi Kiran GV, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartlomiej Koptyra, Hayden Lau, Krishna Sri Ipsit Mantri, Ferdin, Mom, Atsushi Saito, Xiangru Tang, Bolun Wang, Johan S. Wind, Stansilaw Wozniak, Ruichong Zhang, Zhenyuan Zhang, Qihang Zhao, Peng Zhou, Jian Zhu, Rui-Jie Zhu 2023-05-22 2023-05-22 [("doi","10.48550/arXiv.2305.13048")] ai/nn/rnn ai/nn/transformer/attention/linear-algebra

[code; earlier discussion; caveat: barely uses its hidden-state, poor use of context] Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the same performance as Transformers due to limitations in parallelization and scalability.

We propose a novel model architecture, Receptance Weighted Key Value (RWKV), that combines the efficient parallelizable training of Transformers with the efficient inference of RNNs. Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, which parallelizes computations during training and maintains constant computational and memory complexity during inference, leading to the first non-transformer architecture to be scaled to tens of billions of parameters.

Our experiments reveal that RWKV performs on par with similarly sized Transformers, suggesting that future work can leverage this architecture to create more efficient models. This work presents a step towards reconciling the trade-offs between computational efficiency and model performance in sequence processing tasks.

--- https://aeon.co/essays/sonnets-are-machines-for-thinking-through-complex-emotions 2022-11-18 fiction/poetry --- https://cacm.acm.org/magazines/2023/6/273222-the-silent-revolution-of-sat/fulltext 2022-11-18 cs/algorithm cs/security --- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002117 Hypoxia extends lifespan and neurological function in a mouse model of aging Robert S. Rogers, Hong Wang, Timothy J. Durham, Jonathan A. Stefely, Norah A. Owiti, Andrew L. Markhard, Lev Sandler, Tsz-Leung To, Vamsi K. Mootha 2023-04-07 2023-04-07 [("doi","10.1371/journal.pbio.3002117")] longevity

There is widespread interest in identifying interventions that extend healthy lifespan. Chronic continuous hypoxia delays the onset of replicative senescence in cultured cells and extends lifespan in yeast, nematodes, and fruit flies. Here, we asked whether chronic continuous hypoxia is beneficial in mammalian aging. We used the Ercc1 Δ/- mouse model of accelerated aging given that these mice are born developmentally normal but exhibit anatomic, physiological, and biochemical features of aging across multiple organs. Importantly, they exhibit a shortened lifespan that is extended by dietary restriction, the most potent aging intervention across many organisms. We report that chronic continuous 11% oxygen commenced at 4 weeks of age extends lifespan by 50% and delays the onset of neurological debility in Ercc1 Δ/- mice. Chronic continuous hypoxia did not impact food intake and did not significantly affect markers of DNA damage or senescence, suggesting that hypoxia did not simply alleviate the proximal effects of the Ercc1 mutation, but rather acted downstream via unknown mechanisms. To the best of our knowledge, this is the first study to demonstrate that “oxygen restriction” can extend lifespan in a mammalian model of aging.

Aging is one of the strong risk factors for the most common diseases on our planet, but we have few interventions that delay aging (including dietary restriction). This study reveals that a different type of restriction—oxygen restriction—can extend lifespan and counter neurological demise in a mouse model of accelerated aging.

--- https://ai.facebook.com/blog/meta-training-inference-accelerator-AI-MTIA/ 2022-11-18 ai/scaling/hardware --- https://www.medrxiv.org/content/10.1101/2023.05.23.23290253.full Causal evidence that herpes zoster vaccination prevents a proportion of dementia cases Markus Eyting, Min Xie, Simon Hess, Pascal Geldsetzer 2023-05-25 2023-05-25 [("doi","10.1101/2023.05.23.23290253")] psychiatry/alzheimers

[Twitter] The root causes of dementia are still largely unclear, and the medical community lacks highly effective preventive and therapeutic pharmaceutical agents for dementia despite large investments into their development. There is growing interest in the question if infectious agents play a role in the development of dementia, with herpesviruses attracting particular attention [the "viral hypothesis"].

To provide causal as opposed to merely correlational evidence on this question, we take advantage of the fact that in Wales eligibility for the herpes zoster vaccine (Zostavax) for shingles prevention was determined based on an individual’s exact date of birth. Those born before September 2 1933 were ineligible and remained ineligible for life, while those born on or after September 2 1933 were eligible to receive the vaccine.

By using country-wide data on all vaccinations received, primary and secondary care encounters, death certificates, and patients’ date of birth in weeks, we first show that the percentage of adults who received the vaccine increased from 0.01% among patients who were merely one week too old to be eligible, to 47.2% among those who were just one week younger. Apart from this large difference in the probability of ever receiving the herpes zoster vaccine, there is no plausible reason why those born just one week prior to September 2 1933 should differ systematically from those born one week later.

We demonstrate this empirically by showing that there were no systematic differences (eg. in pre-existing conditions or uptake of other preventive interventions) between adults across the date-of-birth eligibility cutoff, and that there were no other interventions that used the exact same date-of-birth eligibility cutoff as was used for the herpes zoster vaccine program. This unique natural randomization [of regression discontinuity], thus, allows for robust causal, rather than correlational, effect estimation.

We first replicate the vaccine’s known effect from clinical trials of reducing the occurrence of shingles. We then show that receiving the herpes zoster vaccine reduced the probability of a new dementia diagnosis over a follow-up period of 7 years by 3.5 percentage points (95% CI: 0.6—7.1, p = 0.019), corresponding to a 19.9% relative reduction in the occurrence of dementia. Besides preventing shingles and dementia, the herpes zoster vaccine had no effects on any other common causes of morbidity and mortality.

In exploratory analyses, we find that the protective effects from the vaccine for dementia are far stronger among women than men. Randomized trials are needed to determine the optimal population groups and time interval for administration of the herpes zoster vaccine to prevent or delay dementia, as well as to quantify the magnitude of the causal effect when more precise measures of cognition are used. Our findings strongly suggest an important role of the varicella zoster virus in the etiology of dementia.

…We used the Secure Anonymized Information Linkage (SAIL) Databank, which contains detailed country-wide electronic health record data on primary care visits, as well as records of secondary care, in Wales linked to the country’s death register data. The study population for our primary analyses consisted of all adults born between 1 September 1925 and 31 August 1942 who were registered with a primary care provider (which is the case for over 98% of adults residing in Wales) at the time of the start of the zoster vaccine program in Wales (on 1 September 2013).

Figure 1a: The date-of-birth eligibility cutoff led to a large discontinuity in zoster vaccine receipt but there is baseline exchangeability across the cutoff for uptake of other preventive interventions as well as past shingles and dementia diagnoses. (A) Large discontinuity in zoster vaccine uptake

…Using our regression discontinuity approach, we find that being eligible for the zoster vaccine caused a 1.3 (95% CI: 0.2—2.7; p = 0.022) percentage point absolute, and 8.5% relative, reduction in the probability of a new dementia diagnosis over our 7-year follow-up period (Figure 3, Panel A). Scaled to account for the fact that not all those who were eligible received the vaccine, we find that actually receiving the zoster vaccine reduced the probability of a new dementia diagnosis by 3.5 (95% CI: 0.6—7.1; p = 0.019) percentage points, corresponding to a relative reduction of 19.9%.

Figure 3: Effect estimates of being eligible (A) and having received the zoster vaccine (B & C) on new diagnoses of dementia.

[Criticism that the RDD discontinuity doesn’t look large enough to be a real causal effect.]

--- https://arxiv.org/abs/2305.11840#google SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models Akshita Jha, Aida Davani, Chandan K. Reddy, Shachi Dave, Vinodkumar Prabhakaran, Sunipa Dev 2023-05-19 2023-05-19 [("doi","10.48550/arXiv.2305.11840")] ai/dataset ai/nn/transformer/gpt/non-fiction ai/nn/transformer/gpt/palm sociology

Stereotype benchmark datasets are crucial to detect and mitigate social stereotypes about groups of people in NLP models. However, existing datasets are limited in size and coverage, and are largely restricted to stereotypes prevalent in the Western society. This is especially problematic as language technologies gain hold across the globe.

To address this gap, we present SeeGULL, a broad-coverage stereotype dataset, built by using generative capabilities of large language models such as PaLM, and GPT-3 [and T0], and leveraging a globally diverse rater pool to validate the prevalence of those stereotypes in society.

SeeGULL is in English, and contains stereotypes about identity groups spanning 178 countries across 8 different geo-political regions across 6 continents, as well as state-level identities within the US and India.

We also include fine-grained offensiveness scores for different stereotypes and demonstrate their global disparities. Furthermore, we include comparative annotations about the same groups by annotators living in the region vs. those that are based in North America, and demonstrate that within-region stereotypes about groups differ from those prevalent in North America.

...We spent $23,100 for annotations, $0.50 per tuple on average. Our hourly payout to the vendors varied across regions, from $8.22 in India to $28.35 in Australia.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5935641/ Anti-herpetic Medications and Reduced Risk of Dementia in Patients with Herpes Simplex Virus Infections-a Nationwide, Population-Based Cohort Study in Taiwan Nian-Sheng Tzeng, Chi-Hsiang Chung, Fu-Huang Lin, Chien-Ping Chiang, Chin-Bin Yeh, San-Yuan Huang, Ru-B, Lu, Hsin-An Chang, Yu-Chen Kao, Hui-Wen Yeh, Wei-Shan Chiang, Yu-Ching Chou, Chang-Huei Tsao, Yung-Fu Wu, Wu-Chien Chien 2018 2022-11-18 [("doi","10.1007/s13311-018-0611-x")] psychiatry/alzheimers

This retrospective cohort study is to investigate the association between herpes simplex virus (HSV) infections and dementia, and the effects of anti-herpetic medications on the risk involved, using Taiwan’s National Health Insurance Research Database (NHIRD). We enrolled a total of 33,448 subjects, and identified 8362 with newly diagnosed HSV infections and 25,086 randomly selected sex & age-matched controls without HSV infections in a ratio of 1:3, selected from January 1, to December 31, 2000.

A multivariable Cox proportional hazards regression model was used to evaluate the risk of developing dementia in the HSV cohort. This analysis revealed an adjusted hazard ratio of 2.564 (95% CI: 2.351-2.795, p < 0.001) for the development of dementia in the HSV-infected cohort relative to the non-HSV cohort. Thus, patients with HSV infections may have a 2.56× increased risk of developing dementia.

A risk reduction of dementia development in patients affected by HSV infections was found upon treatment with anti-herpetic medications (adjusted HR = 0.092 [95% CI 0.079-0.108], p < 0.001). The usage of anti-herpetic medications in the treatment of HSV infections was associated with a decreased risk of dementia.

These findings could be a signal to clinicians caring for patients with HSV infections. Further research is, therefore, necessary to explore the underlying mechanism(s) of these associations.

--- https://arxiv.org/abs/chao-dyn/9406003 Riemann zeta function is a fractal S. C. Woon 1994-06-11 2022-11-18 [("doi","10.48550/arXiv.9406003")] math

Voronin’s theorem on the ‘Universality’ of the Riemann zeta function is shown to imply that Riemann zeta function is a fractal (in the sense that the Mandelbrot set is a fractal) and a concrete “representation” of the “giant book of theorems” that Paul Halmos referred to.

--- /doc/genetics/heritable/adoption/2012-frisell.pdf 2012-01-01 2022-11-18 crime genetics/heritable/adoption --- /doc/crime/2014-kendler.pdf The etiologic role of genetic and environmental factors in criminal behavior as determined from full & half-sibling pairs: an evaluation of the validity of the twin method K. S. Kendler, S. L. Lönn, H. H. Maes, J. Sundquist, K. Sundquist 2014-12-23 2022-11-18 [("doi","10.1017/S0033291714002979")] crime genetics/heritable

Background: Twin studies have shown that criminal behavior (CB) is influenced by both genetic and shared environmental factors. Could these results be replicated using full-siblings and half-siblings?

Method: In 911,009 full-siblings reared together (FSRT), 41,872 half-siblings reared together (HSRT) and 52,590 half-siblings reared apart (HSRA), CB was assessed from the Swedish Crime Register. Modeling, including testing for age differences and rearing status, was performed using the OpenMx package.

Results: 5 sibling models were fitted examining FSRT and HSRT 0–2 years different in age, and both FSRT and HSRT, and FSRT, HSRT and HSRA 0–10 years different in age with and without a specified shared environment indexing age differences. Heritability estimates for CB ranged 35.9%–59.4% in females and 39 to 56% in males, similar to those found in our prior twin study on the same population. Estimates for the shared environment varied 35.9%–59.4% in females and 10 to 23% in males, lower than those estimated in the twin study. The specified shared environment indexed by sibling age differences was statistically-significant in all models tested.

Conclusions: Heritability estimates for CB from full & half-siblings closely approximated those found from twins in the same population, validating the twin method. Shared environmental estimates were lower, suggesting the presence of shared environmental factors for CB specific to twins. When rearing status can be assessed, full & half-siblings offer an additional method for assessing the role of genetic and environmental factors in complex disorders. However, age differences in siblings may need to be included in the models.

--- /doc/statistics/decision/1973-demski.pdf The General Impossibility of Normative Accounting Standards Joel S. Demski 1973-10-01 2022-11-19 [("doi","10.2307/245294")] economics statistics/decision --- https://en.wikipedia.org/wiki/Yuji_Ijiri Yuji Ijiri 2022-11-19 bitcoin --- /doc/statistics/bayes/1962-bierman.pdf Probability, Statistical Decision Theory, and Accounting Harold Bierman Junior 1962-07-01 2022-11-19 [("doi","10.2307/243469")] economics statistics/bayes statistics/decision --- https://arxiv.org/abs/2305.12972 VanillaNet: the Power of Minimalism in Deep Learning Hanting Chen, Yunhe Wang, Jianyuan Guo, Dacheng Tao 2023-05-22 2023-05-22 [("doi","10.48550/arXiv.2305.12972")] ai/nn/cnn ai/nn/sparsity/knowledge-distillation

At the heart of foundation models is the philosophy of “more is different”, exemplified by the astonishing success in computer vision and natural language processing. However, the challenges of optimization and inherent complexity of transformer models call for a paradigm shift towards simplicity.

In this study, we introduce VanillaNet, a neural network architecture that embraces elegance in design. By avoiding high depth, shortcuts, and intricate operations like self-attention, VanillaNet is refreshingly concise yet remarkably powerful. Each layer is carefully crafted to be compact and straightforward, with nonlinear activation functions pruned after training to restore the original architecture [ie. progressive self-distillation to simpler student networks].

VanillaNet overcomes the challenges of inherent complexity, making it ideal for resource-constrained environments. Its easy-to-understand and highly simplified architecture opens new possibilities for efficient deployment.

Extensive experimentation demonstrates that VanillaNet delivers performance on par with renowned deep neural networks and vision transformers, showcasing the power of minimalism in deep learning. This visionary journey of VanillaNet has potential to redefine the landscape and challenge the status quo of foundation model, setting a new path for elegant and effective model design.

Pre-trained models and codes are available at Github & Gitee.

--- https://thingofthings.substack.com/p/book-review-do-fish-feel-pain 2022-11-19 philosophy/ethics psychology/animal psychology/neuroscience --- https://www.science.org/content/article/how-tongue-shaped-life-on-earth 2022-11-19 biology psychology/neuroscience --- https://en.wikipedia.org/wiki/Directed_evolution Directed evolution 2022-11-19 genetics/selection/artificial --- https://en.wikipedia.org/wiki/Spiegelman%27s_Monster Spiegelman’s Monster 2022-11-19 genetics/selection/artificial --- https://en.wikipedia.org/wiki/Experimental_evolution Experimental evolution 2022-11-19 genetics/selection/artificial --- https://arxiv.org/abs/2305.15525#google Large Language Models are Few-Shot Health Learners Xin Liu, Daniel McDuff, Geza Kovacs, Isaac Galatzer-Levy, Jacob Sunshine, Jiening Zhan, Ming-Zher Poh, Shun Liao, Paolo Di Achille, Shwetak Patel 2023-05-24 2023-05-24 [("doi","10.48550/arXiv.2305.15525")] ai/nn/transformer/gpt/palm ai/tabular nootropic/quantified-self

Large language models (LLMs) can capture rich representations of concepts that are useful for real-world tasks. However, language alone is limited. While existing LLMs excel at text-based inferences, health applications require that models be grounded in numerical data (eg. vital signs, laboratory values in clinical domains; steps, movement in the wellness domain) that is not easily or readily expressed as text in existing training corpus.

We demonstrate that with only few-shot tuning, a large language model [PaLM] is capable of grounding various physiological and behavioral time-series data and making meaningful inferences on numerous health tasks for both clinical and wellness contexts.

Using data from wearable and medical sensor recordings, we evaluate these capabilities on the tasks of cardiac signal analysis, physical activity recognition, metabolic calculation (eg. calories burned), and estimation of stress reports and mental health screeners.

--- https://arxiv.org/abs/2305.16300 Landmark Attention: Random-Access Infinite Context Length for Transformers Amirkeivan Mohtashami, Martin Jaggi 2023-05-25 2023-05-25 [("doi","10.48550/arXiv.2305.16300")] ai/nn/retrieval ai/nn/transformer/attention/hierarchical

While transformers have shown remarkable success in natural language processing, their attention mechanism’s large memory requirements have limited their ability to handle longer contexts. Prior approaches, such as recurrent memory or retrieval-based augmentation, have either compromised the random-access flexibility of attention (ie. the capability to select any token in the entire context) or relied on separate mechanisms for relevant context retrieval, which may not be compatible with the model’s attention.

In this paper, we present a novel approach that allows access to the complete context while retaining random-access flexibility, closely resembling running attention on the entire context. Our method uses a landmark token to represent each block of the input and trains the attention to use it for selecting relevant blocks, enabling retrieval of blocks directly through the attention mechanism instead of by relying on a separate mechanism.

Our approach seamlessly integrates with specialized data structures and the system’s memory hierarchy, enabling processing of arbitrarily long context lengths. We demonstrate that our method can obtain comparable performance with Transformer-XL while reducing the number of retrieved tokens in each step.

Finally, we show that fine-tuning LLaMa-7B with our method successfully extends its context length capacity up to 32k tokens, allowing for inference at the context lengths of GPT-4.

--- https://x.com/alexeyguzey/status/1662116392794210306 2022-11-20 reinforcement-learning/safe --- https://www.nytimes.com/2023/05/27/nyregion/avianca-airline-lawsuit-chatgpt.html 2022-11-20 ai/nn/transformer/gpt/non-fiction law --- https://en.wikipedia.org/wiki/Anesthesia_awareness Anesthesia awareness 2022-11-20 philosophy/ethics philosophy/mind psychology/neuroscience/pain/anesthesia --- https://eli.thegreenplace.net/2023/demystifying-tuppers-formula/ 2022-11-20 cs/computable math --- https://slate.com/human-interest/2012/05/the-history-of-the-paper-clip-it-was-invented-in-1899-it-hasnt-been-improved-upon-since.html 2022-11-20 design technology --- https://arxiv.org/abs/2301.10743 Tighter Bounds on the Expressivity of Transformer Encoders David Chiang, Peter Cholak, An, Pillay 2023-01-25 2023-01-25 [("doi","10.48550/arXiv.2301.10743")] ai/nn/transformer/attention cs/computable philosophy/logic

Characterizing neural networks in terms of better-understood formal systems has the potential to yield new insights into the power and limitations of these networks. Doing so for transformers remains an active area of research.

Bhattamishra and others have shown that transformer encoders are at least as expressive as a certain kind of counter machine, while Merrill & Sabharwal 2023 have shown that fixed-precision transformer encoders recognize only languages in uniform TC0.

We connect and strengthen these results by identifying a variant of first-order logic with counting quantifiers that is simultaneously an upper bound for fixed-precision transformer encoders and a lower bound for transformer encoders.

This brings us much closer than before to an exact characterization of the languages that transformer encoders recognize.

--- https://en.wikipedia.org/wiki/TC0 TC0 2022-11-20 cs/computable --- https://www.youtube.com/watch?v=i4XmC82CL3s 2022-11-20 reinforcement-learning/robot --- https://www.ietf.org/archive/id/draft-farrell-tenyearsafter-00.html 2022-11-20 cs/cryptography cs/security --- https://x.com/dbcmckee/status/1635695407928946691 2022-11-20 ai/nn/transformer/gpt/4/fiction --- https://arxiv.org/abs/2303.13534 Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering Jonas Oppenlaender, Rhema Linder, Johanna Silvennoinen 2023-03-13 2023-03-13 [("doi","10.48550/arXiv.2303.13534")] ai/nn/diffusion

Humankind is entering a novel era of creativity—an era in which anybody can synthesize digital content. The paradigm under which this revolution takes place is prompt-based learning (or in-context learning). This paradigm has found fruitful application in text-to-image generation where it is being used to synthesize digital images from zero-shot text prompts in natural language for the purpose of creating AI art. This activity is referred to as prompt engineering—the practice of iteratively crafting prompts to generate and improve images.

In this paper, we investigate prompt engineering as a novel creative skill for creating prompt-based art. In 3 studies with participants recruited from a crowdsourcing platform, we explore whether untrained participants could (1) recognize the quality of prompts, (2) write prompts, and (3) improve their prompts. Our results indicate that participants could assess the quality of prompts and respective images. This ability increased with the participants’ experience and interest in art. Participants further were able to write prompts in rich descriptive language.

However, even though participants were specifically instructed to generate artworks, participants’ prompts were missing the specific vocabulary needed to apply a certain style to the generated images. Our results suggest that prompt engineering is a learned skill that requires expertise and practice. Based on our findings and experience with running our studies with participants recruited from a crowdsourcing platform, we provide 10 recommendations for conducting experimental research on text-to-image generation and prompt engineering with a paid crowd.

Our studies offer a deeper understanding of prompt engineering thereby opening up avenues for research on the future of prompt engineering. We conclude by speculating on 4 possible futures of prompt engineering.

--- https://arxiv.org/abs/2305.11064 Bits of Grass: Does GPT already know how to write like Whitman? Piotr Sawicki, Marek Grzes, Fabricio Goes, Dan Brown, Max Peeperkorn, Aisha Khatun 2023-05-10 2023-05-10 [("doi","10.48550/arXiv.2305.11064")] ai/nn/transformer/gpt/3/poetry ai/nn/transformer/gpt/4/poetry reinforcement-learning/preference-learning/mode-collapse

[see: mode collapse] This study examines the ability of GPT-3.5, gpt-3.5-turbo (ChatGPT) and GPT-4 models to generate poems in the style of specific authors using zero-shot and many-shot prompts (which use the maximum context length of 8,192 tokens).

We assess the performance of models that are not fine-tuned for generating poetry in the style of specific authors [see Sawicki et al 2023b for finetuning experiments, which are successful because they are not affected by RLHF tuning / mode collapse], via automated evaluation.

Our findings indicate that without fine-tuning, even when provided with the maximum number of 17 poem examples (8192 tokens) in the prompt, these models do not generate poetry in the desired style.

...While experimenting with poetry generation from consecutive versions of GPT, we have observed that the models produce poems of increasing level of complexity and length; however, the requested style is clearly not preserved. For example, Walt Whitman’s poetry does not follow the ‘four lines in a stanza’ structure, and does not use rhyming (Bohan 1995). The majority of poems that we generated ‘in the style of Walt Whitman’ do follow the ‘four lines in a stanza’ structure and use rhyming. This, in fact, applies to most poetry generated from GPT models (including GPT-4). Only rarely will GPT deviate from this specific structure, and even then, the style does not match that of the requested author. This applies both to zero-shot prompting (where the prompt contains only the instruction to write a poem in the style of the specific author) and few-shot prompting (where in the prompt, apart from the instruction, we provide as examples a few poems by the original author). For that matter, even in a multi-step conversation with ChatGPT (GPT-3.5-turbo) and GPT-4, when the prompt highlights that the generated poems have been in 4-line stanzas with rhyme, and that the desired output should not have this structure, the model, for the most of time, still generates 4-line stanzas with rhyme.

...When examining the dataset generated from the 17-poem prompts, we have observed that only about 25% of generated poems have deviated from the structured/rhymed style and on the surface have resembled Whitman’s poetry.

--- https://arxiv.org/abs/2303.17158 KD-DLGAN: Data Limited Image Generation via Knowledge Distillation Kaiwen Cui, Yingchen Yu, Fangneng Zhan, Shengcai Liao, Shijian Lu1, Eric Xing 2023-03-30 2023-03-30 [("doi","10.48550/arXiv.2303.17158")] ai/anime/danbooru ai/nn/gan/biggan ai/nn/gan/stylegan/anime ai/nn/sparsity/knowledge-distillation ai/nn/transformer/clip

Generative Adversarial Networks (GANs) rely heavily on large-scale training data for training high-quality image generation models. With limited training data, the GAN discriminator often suffers from severe overfitting which directly leads to degraded generation especially in generation diversity.

Inspired by the recent advances in knowledge distillation (KD), we propose KD-DLGAN, a knowledge-distillation based generation framework that introduces pre-trained vision-language models for training effective data-limited generation models. KD-DLGAN consists of two innovative designs.

The first is aggregated generative KD that mitigates the discriminator overfitting by challenging the discriminator with harder learning tasks and distilling more generalizable knowledge from the pre-trained models. The second is correlated generative KD that improves the generation diversity by distilling and preserving the diverse image-text correlation within the pre-trained models.

Extensive experiments over multiple benchmarks show that KD-DLGAN achieves superior image generation with limited training data. In addition, KD-DLGAN complements the state-of-the-art with consistent and substantial performance gains. [anime details in supplement]

--- https://arxiv.org/abs/2304.09748 Reference-based Image Composition with Sketch via Structure-aware Diffusion Model Kangyeol Kim, Sunghyun Park, Junsoo Lee, Jaegul Choo 2023-03-31 2023-03-31 [("doi","10.48550/arXiv.2304.09748")] ai/anime/danbooru ai/nn/diffusion

Recent remarkable improvements in large-scale text-to-image generative models have shown promising results in generating high-fidelity images.

To further enhance editability and enable fine-grained generation, we introduce a multi-input-conditioned image composition model that incorporates a sketch as a novel modal, alongside a reference image. Thanks to the edge-level controllability using sketches, our method enables a user to edit or complete an image sub-part with a desired structure (ie. sketch) and content (ie. reference image). Our framework fine-tunes a pre-trained diffusion model to complete missing regions using the reference image while maintaining sketch guidance. Albeit simple, this leads to wide opportunities to fulfill user needs for obtaining the in-demand images.

Through extensive experiments, we demonstrate that our proposed method offers unique use cases for image manipulation, enabling user-driven modifications of arbitrary scenes.

--- https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Text-Guided_Unsupervised_Latent_Transformation_for_Multi-Attribute_Image_Manipulation_CVPR_2023_paper.pdf Text-Guided Unsupervised Latent Transformation for Multi-Attribute Image Manipulation Xiwen Wei, Zhen Xu, Cheng Liu, Si Wu, Zhiwen Yu, Hau San Wong 2023-06 2023-06 ai/anime/danbooru ai/nn/gan/stylegan/anime

Great progress has been made in StyleGAN-based image editing. To associate with preset attributes, most existing approaches focus on supervised learning for semantically meaningful latent space traversal directions, and each manipulation step is typically determined for an individual attribute.

To address this limitation, we propose a Text-guided Unsupervised StyleGAN Latent Transformation (TUSLT) model, which adaptively infers a single transformation step in the latent space of StyleGAN to simultaneously manipulate multiple attributes on a given input image. Specifically, we adopt a two-stage architecture for a latent mapping network to break down the transformation process into two manageable steps.

Our network first learns a diverse set of semantic directions tailored to an input image, and later nonlinearly fuses the ones associated with the target attributes to infer a residual vector. The resulting tightly interlinked two-stage architecture delivers the flexibility to handle diverse attribute combinations.

By leveraging the cross-modal text-image representation of CLIP, we can perform pseudo annotations based on the semantic similarity between preset attribute text descriptions and training images, and further jointly train an auxiliary attribute classifier with the latent mapping network to provide semantic guidance.

We perform extensive experiments to demonstrate that the adopted strategies contribute to the superior performance of TUSLT.

--- https://arxiv.org/abs/2305.02763 VendorLink: An NLP approach for Identifying & Linking Vendor Migrants & Potential Aliases on Darknet Markets Vageesh Saxena, Nils Rethmeier, Gijs Van Dijck, Gerasimos Spanakis 2023-05-04 2023-05-04 [("doi","10.48550/arXiv.2305.02763")] ai/nn/transformer darknet-market/agora darknet-market/alphabay darknet-market/dnm-archive darknet-market/silk-road/1

The anonymity on the Darknet allows vendors to stay undetected by using multiple vendor aliases or frequently migrating between markets. Consequently, illegal markets and their connections are challenging to uncover on the Darknet.

To identify relationships between illegal markets and their vendors, we propose VendorLink, an NLP-based approach that examines writing patterns to verify, identify, and link unique vendor accounts across text advertisements (ads) on 7 public Darknet markets. In contrast to existing literature, VendorLink uses the strength of supervised pre-training to perform closed-set vendor verification, open-set vendor identification, and low-resource market adaption tasks.

Through VendorLink, we uncover (1) 15 migrants and 71 potential aliases in the Alphabay-Dreams-Silk dataset, (2) 17 migrants and 3 potential aliases in the Valhalla-Berlusconi dataset, and (3) 75 migrants and 10 potential aliases in the Traderoute-Agora dataset. Altogether, our approach can help Law Enforcement Agencies (LEA) make more informed decisions by verifying and identifying migrating vendors and their potential aliases on existing and Low-Resource (LR) emerging Darknet markets.

--- https://arxiv.org/abs/2305.09434 Chatting with GPT-3 for Zero-Shot Human-Like Mobile Automated GUI Testing Zhe Liu, Chunyang Chen, Junjie Wang, Mengzhuo Chen, Boyu Wu, Xing Che, Dandan Wang, Qing Wang 2023-05-16 2023-05-16 [("doi","10.48550/arXiv.2305.09434")] ai/nn/transformer/gpt/codex reinforcement-learning/exploration/active-learning

Mobile apps are indispensable for people’s daily life, and automated GUI (Graphical User Interface) testing is widely used for app quality assurance. There is a growing interest in using learning-based techniques for automated GUI testing which aims at generating human-like actions and interactions. However, the limitations such as low testing coverage, weak generalization, and heavy reliance on training data, make an urgent need for a more effective approach to generate human-like actions to thoroughly test mobile apps.

Inspired by the success of the Large Language Model (LLM), eg. GPT-3 and ChatGPT, in natural language understanding and question answering, we formulate the mobile GUI testing problem as a Q&A task.

We propose GPTDroid, asking LLM to chat with the mobile apps by passing the GUI page information to LLM to elicit testing scripts, and executing them to keep passing the app feedback to LLM, iterating the whole process.

Within it, we extract the static context of the GUI page and the dynamic context of the iterative testing process, design prompts for inputting this information to LLM, and develop a neural matching network to decode the LLM’s output into actionable steps to execute the app. We evaluate GPTDroid on 86 apps from Google Play, and its activity coverage is 71%, with 32% higher than the best baseline, and can detect 36% more bugs with faster speed than the best baseline.

GPTDroid also detects 48 new bugs on the Google Play with 25 of them being confirmed/fixed. We further summarize the capabilities of GPTDroid behind the superior performance, including semantic text input, compound action, long meaningful test trace, and test case prioritization.

--- https://arxiv.org/abs/2305.13800 Generalizable Synthetic Image Detection via Language-guided Contrastive Learning Haiwei Wu, Jiantao Zhou, Shile Zhang 2023-05-23 2023-05-23 [("doi","10.48550/arXiv.2305.13800")] ai/anime/danbooru ai/nn/diffusion ai/nn/gan/biggan ai/nn/gan/stylegan/anime ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e/2 ai/nn/vae/mae cs/security

The heightened realism of AI-generated images can be attributed to the rapid development of synthetic models, including generative adversarial networks (GANs) and diffusion models (DMs).

The malevolent use of synthetic images, such as the dissemination of fake news or the creation of fake profiles, however, raises concerns regarding the authenticity of images. Though many forensic algorithms have been developed for detecting synthetic images, their performance, especially the generalization capability, is still far from being adequate to cope with the increasing number of synthetic models.

In this work, we propose a simple yet very effective synthetic image detection method via a language-guided contrastive learning and a new formulation of the detection problem. We first augment the training images with carefully-designed textual labels, enabling us to use a joint image-text contrastive learning for the forensic feature extraction.

In addition, we formulate the synthetic image detection as an identification problem, which is vastly different from the traditional classification-based approaches. It is shown that our proposed LanguAge-guided SynThEsis Detection (LASTED) model achieves much improved generalizability to unseen image generation models and delivers promising performance that far exceeds state-of-the-art competitors by +22.66% accuracy and +15.24% AUC.

The code is available at Github.

--- https://arxiv.org/abs/2304.00612 8 Things to Know about Large Language Models Samuel R. Bowman 2023-04-02 2023-04-02 [("doi","10.48550/arXiv.2304.00612")] ai/nn/transformer/gpt ai/scaling/emergence reinforcement-learning/safe

The widespread public deployment of large language models (LLMs) in recent months has prompted a wave of new attention and engagement from advocates, policymakers, and scholars from many fields. This attention is a timely response to the many urgent questions that this technology raises, but it can sometimes miss important considerations. This paper surveys the evidence for 8 potentially surprising such points:

  1. LLMs predictably get more capable with increasing investment, even without targeted innovation.

  2. Many important LLM behaviors emerge unpredictably as a byproduct of increasing investment.

  3. LLMs often appear to learn and use representations of the outside world.

  4. There are no reliable techniques for steering the behavior of LLMs.

  5. Experts are not yet able to interpret the inner workings of LLMs.

  6. Human performance on a task isn’t an upper bound on LLM performance.

  7. LLMs need not express the values of their creators nor the values encoded in web text.

  8. Brief interactions with LLMs are often misleading.

--- https://arxiv.org/abs/1401.4082 Stochastic Backpropagation and Approximate Inference in Deep Generative Models Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra 2014-01-16 2022-11-21 [("doi","10.48550/arXiv.1401.4082")] ai/nn/vae

We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalized class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning.

Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation—rules for back-propagation through stochastic variables—and use this to develop an algorithm that allows for joint optimization of the parameters of both the generative and recognition model.

We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualization.

--- /doc/genetics/heritable/adoption/2015-beaver-table3-nongeneticparentsparentingvariablesarenotpredictiveofcriminalbehaviorinadoptedchildren.jpg 2015 2022-11-21 crime genetics/heritable/adoption --- /doc/genetics/heritable/correlation/2018-nedelec.pdf Challenging Assumptions: A Genetically Sensitive Assessment of the Criminogenic Effect of Contact With the Criminal Justice System Joseph L. Nedelec, Ian A. Silver 2018-01-01 2022-11-22 [("doi","10.1177/1043986218810599")] crime genetics/heritable/correlation

A key assumption underlying various components of criminological thought is the criminogenic effect of involvement with the criminal justice system. Prior assessments of this effect, however, have been mixed and all are subject to potential genetic confounding.

In the current study, we employ twin difference scores using both monozygotic and dizygotic twins to isolate the effect of involvement with the criminal justice system on future criminal behavior.

The findings illustrate null associations between a variety of interactions of the criminal justice system and subsequent criminal offending.

The study illustrates the continued ineffectiveness of the standard social science methodological approach to assessing criminology’s main assumptions.

--- https://www.biorxiv.org/content/10.1101/2022.11.03.515044.full 5-MeO-DMT modifies innate behaviors and promotes structural neural plasticity in mice Sarah J. Jefferson, Ian Gregg, Mark Dibbs, Clara Liao, Hao Wu, Pasha A. Davoudian, Jeffrey S. Sprouse, Alexander M. Sherwood, Alfred P. Kaye, Christopher Pittenger, Alex C. Kwan 2022-11-03 2022-11-22 [("doi","10.1101/2022.11.03.515044")] psychedelic psychology/neuroscience

Serotonergic psychedelics are gaining increasing interest as potential therapeutics for a range of mental illnesses. Compounds with short-lived subjective effects may be clinically useful because dosing time would be reduced, which may improve patient access. One short-acting psychedelic is 5-MeO-DMT, which has been associated with improvement in depression and anxiety symptoms in early clinical studies. However relatively little is known about the behavioral effects and neural mechanisms of 5-MeO-DMT in animal models.

Here we characterized the effects of 5-MeO-DMT on innate behaviors and dendritic architecture in mice.

We showed that 5-MeO-DMT induces a dose-dependent increase in head-twitch response that is shorter in duration than that induced by psilocybin at all doses tested. 5-MeO-DMT also substantially suppresses social ultrasonic vocalizations produced during mating behavior.

5-MeO-DMT produces long-lasting increases in dendritic spine density in the mouse medial frontal cortex that are driven by an elevated rate of spine formation. However, unlike psilocybin, 5-MeO-DMT did not affect the size of dendritic spines.

These data provide insights into the behavioral and neural consequences underlying the action of 5-MeO-DMT and highlight similarities and differences with those of psilocybin.

--- https://www.astralcodexten.com/p/hypergamy-much-more-than-you-wanted 2022-11-22 sociology --- https://x.com/y_h_j_e_t/status/1662817736366383106 2022-11-22 cs/algorithm --- https://www.city-journal.org/article/the-madman-as-painter 2022-11-22 marijuana psychiatry/schizophrenia --- https://undark.org/2023/05/25/where-the-wood-wide-web-narrative-went-wrong/ 2022-11-22 biology statistics/bias --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0277927 The oldest plans to scale of human-made mega-structures Rémy Crassard, Wael Abu-Azizeh, Olivier Barge, Jacques Élie Brochier, Frank Preusser, Hamida Seba, Abd Errahmane Kiouche, Emmanuelle Régagnon, Juan Antonio Sánchez Priego, Thamer Almalki, Mohammad Tarawneh 2022-11-06 2022-11-22 [("doi","10.1371/journal.pone.0277927")] design sociology

Data on how Stone Age communities conceived domestic and utilitarian structures are limited to a few examples of schematic and non-accurate representations of various-sized built spaces.

Here, we report the exceptional discovery of the up-to-now oldest realistic plans that have been engraved on stones. These engravings from Jordan and Saudi Arabia depict ‘desert kites’, human-made archaeological mega-traps that are dated to at least 9,000 years ago for the oldest. The extreme precision of these engravings is remarkable, representing gigantic neighboring Neolithic stone structures, the whole design of which is impossible to grasp without seeing it from the air or without being their architect (or user, or builder).

They reveal a widely underestimated mental mastery of space perception, hitherto never observed at this level of accuracy in such an early context. These representations shed new light on the evolution of human discernment of space, communication, and communal activities in ancient times.

--- https://automated.beehiiv.com/p/aiimmunity-challenge-lessons-clinical-research-exam 2022-11-22 ai/nn/transformer/gpt/3/nonfiction biology --- /doc/darknet-market/alphabay/2023-andrei.pdf Trust intermediary in a cryptomarket for illegal drugs Filippo Andrei, Davide Barrera, Krzysztof Krakowski, Emilio Sulis 2023-04-11 2023-04-11 [("doi","10.1093/esr/jcad020")] darknet-market/alphabay darknet-market/dnm-archive economics/mechanism-design

Cooperation without third-party enforcement is particularly puzzling in illicit online markets given the anonymity of online exchanges in the ‘dark web’ and the asymmetry of information between buyers and sellers. Most of the literature investigates the effects of reputation systems on sales. Less is known about the role of (semi)institutionalized solutions to trust problems, such as the escrow service, which deposits payments for online purchases with the market platform and releases them only upon confirmation of the item delivery by a customer.

We study the effect of such a trust intermediary on sales in a cryptomarket for illegal drugs. Using a large dataset of illegal online transactions, we estimate two sets of fixed effects models predicting the sellers’ choice to offer the trust intermediary and examine the effects of such a choice on sales. Our results indicate that the trust intermediary reduces online drug sales.

We explain this finding by showing suggestive evidence that escrow may crowd out traders’ trust and reciprocity. Our findings have implications for theories of the role of institutions in online markets and offer policy recommendations for law enforcement agencies.

--- https://arxiv.org/abs/2305.12391 Generative Model Watermarking Suppressing High-Frequency Artifacts Li Zhang, Yong Liu, Xinpeng Zhang, Hanzhou Wu 2023-05-21 2023-05-21 [("doi","10.48550/arXiv.2305.12391")] ai/anime/danbooru cs/cryptography

Protecting deep neural networks (DNNs) against intellectual property (IP) infringement has attracted an increasing attention in recent years. Recent advances focus on IP protection of generative models, which embed the watermark information into the image generated by the model to be protected. Although the generated marked image has good visual quality, it introduces noticeable artifacts to the marked image in high-frequency area, which severely impairs the imperceptibility of the watermark and thereby reduces the security of the watermarking system.

To deal with this problem, in this paper, we propose a novel framework for generative model watermarking that can suppress those high-frequency artifacts. The main idea of the proposed framework is to design a new watermark embedding network that can suppress high-frequency artifacts by applying anti-aliasing. To realize anti-aliasing, we use low-pass filtering for the internal sampling layers of the new watermark embedding network.

Meanwhile, joint loss optimization and adversarial training are applied to enhance the effectiveness and robustness. Experimental results indicate that the marked model not only maintains the performance very well on the original task, but also demonstrates better imperceptibility and robustness on the watermarking task.

This work reveals the importance of suppressing high-frequency artifacts for enhancing imperceptibility and security of generative model watermarking.

--- https://publicdomainreview.org/collection/bonnacons 2022-11-23 history/public-domain-review --- https://en.wikipedia.org/wiki/In_vitro_maturation In vitro maturation 2022-11-23 genetics/cloning genetics/selection/artificial --- https://en.wikipedia.org/wiki/Somatic_cell_nuclear_transfer#Interspecies_nuclear_transfer Somatic cell nuclear transfer § Interspecies nuclear transfer 2022-11-23 genetics/cloning genetics/selection/artificial --- https://en.wikipedia.org/wiki/Somatic_cell_nuclear_transfer Somatic cell nuclear transfer § er 2022-11-23 genetics/cloning genetics/selection/artificial --- https://huggingface.co/datasets/KBlueLeaf/Danbooru2021-SQLite 2022-11-23 ai/anime/danbooru --- https://huggingface.co/datasets/animelover/danbooru2022 2022-11-23 ai/anime/danbooru --- https://huggingface.co/Ryukijano/CatCon-Controlnet-WD-1-5-b2R 2022-11-23 ai/anime/danbooru ai/nn/diffusion --- https://www.honeycomb.io/blog/hard-stuff-nobody-talks-about-llm 2022-11-23 ai/nn/transformer/gpt/codex ai/scaling/economics cs/algorithm --- https://www.biorxiv.org/content/10.1101/2023.05.11.540348.full Abnormal behavioral episodes associated with sleep and quiescence in Octopus insularis: Possible nightmares in a cephalopod? Eric A. Ramos, Mariam Steinblatt, Rachel Demsey, Diana Reiss, Marcelo O. Magnasco 2023-05-12 2023-05-12 [("doi","10.1101/2023.05.11.540348")] psychology/animal psychology/vision/dream

[media] This paper presents some unusual behaviors observed in one single specimen of O. insularis. While nothing can be concluded rigorously from such data, we share the data and our analysis with the community, in the hope that others will be on the lookout for such rare events.

Sleep is a fundamental biological function that is present in all tested vertebrates and most invertebrates. Cephalopods, such as octopuses, are cognitively complex animals that display active and inactive sleep states similar to those of vertebrates. In particular, octopuses have active sleep states during which they display sequences of camouflage patterns and modulation of basal rhythms, while remaining relatively unresponsive to outside stimuli. Some scientists have speculated that these states could be analogous to dreaming in mammals, involving episodic recall with a narrative structure.

The convergent evolution of sleep in neurologically complex animals is a striking possibility, but its demonstration requires overcoming challenges. Towards this end, capturing abnormal sleep-associated episodes and other parasomnias in cephalopods can provide further insight into the biology of their sleep.

This study reports abnormal behavioral episodes associated with transitions between activity states and sleep states observed in a male Octopus insularis. The study used continuous video monitoring to characterize the animal’s activity patterns and detect rare behavioral episodes.

Over the course of a month, 4 brief episodes (duration range: 44–290 seconds) were identified during which the octopus abruptly emerged from quiescent or active sleep, detached itself from its sleep position, and engaged in anti-predator and predatory behaviors (with no predator present). The longest of these episodes resembled the species-typical response to a predatory attack, suggesting that the animal may have been responding to a negative episodic memory or exhibiting a form of parasomnia.

These findings, in conjunction with recent evidence for sleep in octopuses, highlight the complexity of possible sleep-associated behavioral episodes. Investigating sleep in invertebrates is crucial to understanding the evolution of sleep across distantly related species.

--- https://richardkatz.substack.com/p/2025-digital-cliff-part-i 2022-11-23 economics/automation japan --- https://richardkatz.substack.com/p/metis-2025-digital-cliff-part-ii 2022-11-23 economics/automation japan --- https://richardkatz.substack.com/p/metis-2025-digital-cliff-part-iii 2022-11-24 economics/automation japan --- https://dotat.at/@/2023-05-26-whence-time.html 2022-11-24 cs/hardware science technology --- https://chat.openai.com/share/25124525-0bad-4c13-ae5a-ae4beac60360 2022-11-24 ai/nn/transformer/gpt/3/nonfiction --- https://arxiv.org/abs/2111.15664 OCR-free Document Understanding Transformer Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park 2021-11-30 2022-11-24 [("doi","10.48550/arXiv.2111.15664")] ai/nn/tokenization ai/nn/transformer

Understanding document images (eg. invoices) is a core but challenging task since it requires complex functions such as reading text and a holistic understanding of the document. Current Visual Document Understanding (VDU) methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR) engines and focus on the understanding task with the OCR outputs.

Although such OCR-based approaches have shown promising performance, they suffer from (1) high computational costs for using OCR; (2) inflexibility of OCR models on languages or types of document; (3) OCR error propagation to the subsequent process. To address these issues, in this paper, we introduce a novel OCR-free VDU model named Donut, which stands for Document understanding transformer.

As the first step in OCR-free VDU research, we propose a simple architecture (ie. Transformer) with a pre-training objective (ie. cross-entropy loss). Donut is conceptually simple yet effective. Through extensive experiments and analyses, we show a simple OCR-free VDU model, Donut, achieves state-of-the-art performances on various VDU tasks in terms of both speed and accuracy.

In addition, we offer a synthetic data generator that helps the model pre-training to be flexible in various languages and domains. The code, trained model and synthetic data are available at Github.

--- https://americanaffairsjournal.org/2022/11/the-long-slow-death-of-global-development/ 2022-11-24 economics/automation --- https://www.catholicnewsagency.com/news/254032/wikipedia-had-the-wrong-vatican-city-flag-for-years-now-incorrect-flags-are-everywhere 2022-11-24 economics/copyright wikipedia --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5611761/ A longitudinal study of families formed through reproductive donation: Parent-adolescent relationships and adolescent adjustment at age 14 Susan Golombok, Elena Ilioi, Lucy Blake, Gabriela Roman, Vasanti Jadva 2017 2022-11-24 [("doi","10.1037/dev0000372")] genetics/gametogenesis

The aim of the 6th phase of this longitudinal study was to establish whether children born through assisted reproduction involving reproductive donation were at risk for psychological problems following the transition to adolescence at age 14 and, if so, to examine the nature of these problems and the mechanisms involved.

Eighty-seven families formed through reproductive donation, including 32 donor insemination families, 27 egg donation families, and 28 surrogacy families, were compared with 54 natural conception families. Standardized interviews, questionnaires, and observational assessments of the quality of parent-adolescent relationships and adolescent adjustment were administered to mothers, adolescents, and teachers.

The mothers in surrogacy families showed less negative parenting and reported greater acceptance of their adolescent children and fewer problems in family relationships as a whole compared with gamete donation mothers. In addition, less positive relationships were found between mothers and adolescents in egg donation families than in donor insemination families as rated by both mothers and adolescents. There were no differences between family types for the adolescents themselves in terms of adjustment problems, psychological well-being, and self-esteem.

Longitudinal analyses showed no differences between family types in negative parenting from age 7 to age 14, and a weaker association between negative parenting and adjustment difficulties for gamete donation than natural conception and surrogacy families.

The findings suggest that the absence of a genetic link between mothers and their children is associated with less positive mother-adolescent relationships whereas the absence of a gestational link does not have an adverse effect.

--- https://www.vice.com/en/article/5d93p3/what-happens-when-you-ask-ai-to-control-your-life 2022-11-24 ai/nn/transformer/gpt/3/nonfiction --- https://www.chessgames.com/perl/chessgame?gid=1268705 2022-11-24 reinforcement-learning/chess --- https://web.archive.org/web/20230529224700/https://chat.openai.com/share/eef34fe5-0c8e-4595-9c28-2e9f05f05393 2022-11-24 ai/nn/transformer/gpt/4/nonfiction psychology/spaced-repetition --- https://en.wikipedia.org/wiki/The_Adventures_of_Robin_Hood#Music_score The Adventures of Robin Hood § Music score 2022-11-24 fiction/opera --- https://arxiv.org/abs/2305.17743 A chiral aperiodic monotile David Smith, Joseph Samuel Myers, Craig S. Kaplan, Chaim Goodman-Strauss 2023-05-28 2023-05-28 [("doi","10.48550/arXiv.2305.17743")] math

The recently discovered “hat” aperiodic monotile mixes unreflected and reflected tiles in every tiling it admits, leaving open the question of whether a single shape can tile aperiodically using translations and rotations alone.

We show that a close relative of the hat—the equilateral member of the continuum to which it belongs—is a weakly chiral aperiodic monotile: it admits only non-periodic tilings if we forbid reflections by fiat.

Furthermore, by modifying this polygon’s edges we obtain a family of shapes called Spectres that are strictly chiral aperiodic monotiles: they admit only chiral non-periodic tilings based on a hierarchical substitution system.

--- https://www.nature.com/articles/d41586-023-01634-5 2022-11-25 sociology/technology --- https://arxiv.org/abs/2102.01951#scaling&org=deepmind Mind the Gap: Assessing Temporal Generalization in Neural Language Models § Scaling Angeliki Lazaridou, Adhiguna Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d’Autume, Tomas Kocisky, Sebastian Ruder, Dani Yogatama, Kris Cao, Susannah Young, Phil Blunsom 2021-02-03 2022-11-25 [("doi","10.48550/arXiv.2102.01951")] ai/dataset ai/nn/dynamic-evaluation ai/nn/transformer/gpt/2/nonfiction ai/scaling statistics/prediction

Our world is open-ended, non-stationary, and constantly evolving; thus what we talk about and how we talk about it change over time. This inherent dynamic nature of language contrasts with the current static language modeling paradigm, which trains and evaluates models on utterances from overlapping time periods.

Despite impressive recent progress, we demonstrate that Transformer-XL language models perform worse in the realistic setup of predicting future utterances from beyond their training period, and that model performance becomes increasingly worse with time. We find that, while increasing model size alone—a key driver behind recent progress—does not solve this problem, having models that continually update their knowledge with new information can indeed mitigate this performance degradation over time.

Hence, given the compilation of ever-larger language modeling datasets, combined with the growing list of language-model-based NLP applications that require up-to-date factual knowledge about the world, we argue that now is the right time to rethink the static way in which we currently train and evaluate our language models, and develop adaptive language models that can remain up-to-date with respect to our ever-changing and non-stationary world.

We publicly release our dynamic, streaming language modeling benchmarks for WMT and arXiv to facilitate language model evaluation that takes temporal dynamics into account.

…4. The effect of outdated models persists even when increasing model sizes: …can increasing model size also improve temporal generalization? To this end, we train a bigger TIME-STRATIFIED model with 448M parameters—a 60% increase over the previous 287M model and 30% larger than GPT-2medium.

Figure 3: Relative perplexity increase of the TIME-STRATIFIED models with 287M (dotted line) and 448M parameters (solid line), respectively, over the CONTROL model with 287M parameters, for WMT and CustomNews (§4).

…If increasing the model size was able to delay temporal degradation, we would expect to see the solid lines produced by the bigger models to have reduced (ie. flatter) slopes compared to the dotted lines produced by the smaller models. While larger TIME-STRATIFIED models, as expected, achieve lower absolute perplexities (5.5% improvement), model size has no statistically-significant effect on the slope of these lines (p > 0.05, assessed using a t-test on the slopes found by fitting a linear regression). On both datasets, by the end of the test period (ie. late-2019), a smaller but more up-to-date CONTROL287M model outperforms a 60% larger but 2-year out-of-date TIME-STRATIFIED448M model. Hence, building models that perform well in this setup requires solutions that more directly tackle the specific challenges we emphasized through our findings so far, and update the model’s knowledge with new information.

[This is a poor evaluation that does not justify their claims. They did not scale up dataset size or compute, by their description, so this is an improper inefficient scale-up which was not compute-optimal. They didn’t even increase the model size by an OOM, so it’s a tiny scale-up which couldn’t show much. And evaluating the benefit by ‘p > 0.05’ is fallacious in the standard NHST way: a failure to reject the null does not support the null. In fact, while they don’t provide the actual effect size, eyeballing their graph, it looks like the effect was in the predicted direction of the larger model scaling better! Not that this would be a great way to evaluate it even if they had done a real scale-up of multiple orders of magnitude and enough samples that they had genuine statistical power for their NHST tests, because the sensible way to handle temporal decay is to finetune on new data—and given the greater sample-efficiency of larger models, we can be sure that the larger models will do much better in staying up to date. Strange that they prefer the much more complex dynamic evaluation approach over simple scaling approaches, given that no one seems to want to deploy dynamic evaluation…]

…6. Keeping models up-to-date: Online learning through dynamic evaluation:

One way to mitigate LMs’ degradation over time is to continually update the models’ knowledge with new information as new documents arrive into the stream. One of the ways to do this is through dynamic evaluation (Mikolov et al 2010; Graves 2013; Krause et al 2019)—a form of online learning that continually updates the parameters of a pretrained model by performing gradient descent on the new data. While most prior work used dynamic evaluation to perform updates within a document, hence adapting to local topical shifts, here we use it to adapt to the temporal dynamics that occur within a stream of chronologically ordered documents, hence adapting to temporal trends across documents. Appendix B has more details on dynamic evaluation and our empirical settings.

Figure 5: Relative perplexity increase with (solid lines) and without (dotted lines) dynamic evaluation, for the TIME-STRATIFIED model.

We plot the results in Figure 5: Dotted lines reflect the perplexity increase when comparing the CONTROL model to the TIME-STRATIFIED model, ie. the same graph as in Figure 1, whereas solid lines reflect the perplexity increase achieved when comparing the same CONTROL model with the TIME-STRATIFIED model augmented with dynamic evaluation (TIME-STRATIFIEDdyn). In all datasets, dynamic evaluation reduces the speed of the model becoming outdated, as evidenced by the reduced upward slope, with a statistically-significant effect for arXiv and WMT (p < 0.05, assessed using a t-test on the slopes found by fitting a linear regression). The improvements are more pronounced for arXiv, where a more granular analysis over weeks reveals that the model needs only about one week worth of data to overtake the CONTROL model. Moreover, we see much larger improvements for predicting EMERGING NEW WORDS, which exhibit strong temporal dynamics (§3.1, see Figure 3): We observe a 39.62% ppl. reduction 109.73 → 66.2 for EMERGING NEW WORDS, compared to the overall ppl. reduction (a 1.25% reduction 22.45 → 22.17 for WMT; Figure 4).

When aiming to keep models up-to-date (especially for larger models), lightweight yet effective approaches are preferable because they allow the model to rapidly digest new information with minimal time, computation, and carbon costs. We thus experiment with updating only the embedding layer (ie. 52M parameters), capturing lexical semantic changes, as well as updating only the bias terms at all layers (ie. 0.198M parameters), as recently introduced by Ben-Zaken et al 2021. Figure 4 presents the results: In line with the findings of Ben-Zaken et al 2021, updating only the bias terms performs nearly as well as updating the full model. [Large models are sample-efficient & good at continual-learning...]

--- https://arxiv.org/pdf/2102.01951#page=7&org=deepmind Mind the Gap: Assessing Temporal Generalization in Neural Language Models § Dynamic Evaluation Angeliki Lazaridou, Adhiguna Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d’Autume, Tomas Kocisky, Sebastian Ruder, Dani Yogatama, Kris Cao, Susannah Young, Phil Blunsom 2021-02-03 2022-11-25 [("doi","10.48550/arXiv.2102.01951")] ai/nn/dynamic-evaluation ai/nn/transformer/attention/recurrent

[background] …6. Keeping models up-to-date: Online learning through dynamic evaluation:

One way to mitigate LMs’ degradation over time is to continually update the models’ knowledge with new information as new documents arrive into the stream. One of the ways to do this is through dynamic evaluation (Mikolov et al 2010; Graves 2013; Krause et al 2019)—a form of online learning that continually updates the parameters of a pretrained model by performing gradient descent on the new data. While most prior work used dynamic evaluation to perform updates within a document, hence adapting to local topical shifts, here we use it to adapt to the temporal dynamics that occur within a stream of chronologically ordered documents, hence adapting to temporal trends across documents. Appendix B has more details on dynamic evaluation and our empirical settings.

Figure 5: Relative perplexity increase with (solid lines) and without (dotted lines) dynamic evaluation, for the TIME-STRATIFIED model.

We plot the results in Figure 5: Dotted lines reflect the perplexity increase when comparing the CONTROL model to the TIME-STRATIFIED model, ie. the same graph as in Figure 1, whereas solid lines reflect the perplexity increase achieved when comparing the same CONTROL model with the TIME-STRATIFIED model augmented with dynamic evaluation (TIME-STRATIFIEDdyn). In all datasets, dynamic evaluation reduces the speed of the model becoming outdated, as evidenced by the reduced upward slope, with a statistically-significant effect for arXiv and WMT (p < 0.05, assessed using a t-test on the slopes found by fitting a linear regression). The improvements are more pronounced for arXiv, where a more granular analysis over weeks reveals that the model needs only about one week worth of data to overtake the CONTROL model. Moreover, we see much larger improvements for predicting EMERGING NEW WORDS, which exhibit strong temporal dynamics (§3.1, see Figure 3): We observe a 39.62% ppl. reduction 109.73 → 66.2 for EMERGING NEW WORDS, compared to the overall ppl. reduction (a 1.25% reduction 22.45 → 22.17 for WMT; Figure 4).

When aiming to keep models up-to-date (especially for larger models), lightweight yet effective approaches are preferable because they allow the model to rapidly digest new information with minimal time, computation, and carbon costs. We thus experiment with updating only the embedding layer (ie. 52M parameters), capturing lexical semantic changes, as well as updating only the bias terms at all layers (ie. 0.198M parameters), as recently introduced by Ben-Zaken et al 2021. Figure 4 presents the results: In line with the findings of Ben-Zaken et al 2021, updating only the bias terms performs nearly as well as updating the full model. [Large models are sample-efficient & good at continual-learning...]

--- https://www.wired.com/story/yeast-cbd-and-thc/ 2022-11-25 genetics/editing marijuana psychedelic --- https://x.com/maxkriegers/status/1663372146696138752 2022-11-25 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/1904.08378 Dynamic Evaluation of Transformer Language Models Ben Krause, Emmanuel Kahembwe, Iain Murray, Steve Renals 2019-04-17 2022-11-25 [("doi","10.48550/arXiv.1904.08378")] ai/nn/dynamic-evaluation ai/nn/retrieval ai/nn/transformer/attention/recurrent

This research note combines two methods that have recently improved the state-of-the-art in language modeling: Transformers and dynamic evaluation. Transformers use stacked layers of self-attention that allow them to capture long range dependencies in sequential data.

Dynamic evaluation fits models to the recent sequence history, allowing them to assign higher probabilities to re-occurring sequential patterns.

By applying dynamic evaluation to Transformer-XL models, we improve the state-of-the-art on enwik8 0.99 → 0.94 bits/char, text8 1.08 → 1.04 bits/char, and WikiText-103 18.3 → 16.4 perplexity points.

--- https://arxiv.org/abs/2106.10199 BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models Elad Ben Zaken, Shauli Ravfogel, Yoav Goldberg 2021-06-18 2022-11-25 [("doi","10.48550/arXiv.2106.10199")] ai/nn/transformer

We introduce BitFit, a sparse-finetuning method where only the bias-terms of the model (or a subset of them) are being modified.

We show that with small-to-medium training data, applying BitFit on pre-trained BERT models is competitive with (and sometimes better than) fine-tuning the entire model. For larger data, the method is competitive with other sparse fine-tuning methods.

Besides their practical utility, these findings are relevant for the question of understanding the commonly-used process of finetuning: they support the hypothesis that finetuning is mainly about exposing knowledge induced by language-modeling training, rather than learning new task-specific linguistic knowledge.

Figure 2: Comparison of BitFit and Full-FT with BERT~BASE~ exact match score on SQuAD validation set.

…Size of training data: The GLUE results suggest a reverse correlation between BitFit ability to reach Full-FT performance, and training set size. To test this (and to validate another token-level task), we train on increasing-sized subsets of SQuAD v1.0. The results on Figure 2 show a clear trend: BitFit dominates over Full-FT in the smaller-data regime, while the trend is reversed when more training data is available.

We conclude that BitFit is a worthwhile targeted fine-tuning method in small-to-medium data regimes.

--- https://www.nytimes.com/2023/05/30/technology/shoggoth-meme-ai.html 2022-11-25 reinforcement-learning/safe --- https://www.lesswrong.com/posts/75o8oja43LXGAqbAR/palm-2-and-gpt-4-in-extrapolating-gpt-n-performance 2022-11-25 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/palm ai/scaling --- https://en.wikipedia.org/wiki/Takt_time Takt time 2022-11-26 cs/end-to-end-principle design --- https://cendyne.dev/posts/2023-04-16-process-engineering-at-a-furry-convention.html 2022-11-26 cs/end-to-end-principle design --- https://en.wikipedia.org/wiki/Ninja_rocks Ninja rocks 2022-11-26 science --- https://www.newyorker.com/tech/annals-of-technology/the-rise-and-fall-of-getting-things-done 2022-11-26 design economics/automation --- https://www.ai21.com/blog/human-or-not-results 2022-11-26 ai/nn/tokenization reinforcement-learning/safe --- https://x.com/BjoernKarmann/status/1663496103998750721 2022-11-26 ai/nn/diffusion --- https://www.maximumprogress.org/extropia-archaeology 2022-11-26 transhumanism --- https://www.lesswrong.com/posts/7AxzaEDP8WWjEouSA/gpt4-is-capable-of-writing-decent-long-form-science-fiction-1 2022-11-26 ai/nn/transformer/gpt/4/fiction fiction/science-fiction --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0284787 Godless owls, devout larks: Religiosity and Conscientiousness are associated with morning preference and (partly) explain its effects on life satisfaction Joanna Gorgol, Paweł Łowicki, Maciej Stolarski 2023-04-10 2023-04-10 [("doi","10.1371/journal.pone.0284787")] philosophy/religion psychology/personality/conscientiousness zeo

The associations between morningness-eveningness, conscientiousness, and religiosity have not been investigated to date. The aim of the present research was to provide evidence for the relationships between these dimensions.

Moreover, we tested whether the well-established link between morningness and life satisfaction could be explained by elevated religiosity of morning-oriented individuals and whether this relationship may be mediated by Conscientiousness. The investigation was conducted on two independent samples of Polish adults (n = 500 and n = 728).

Our results corroborated earlier findings that morningness was positively associated with both Conscientiousness and satisfaction with life. We also found evidence for a statistically-significant positive association between morningness and religiosity.

Moreover, controlling for age and gender, we obtained statistically-significant mediation effects showing that the association between morningness-eveningness and satisfaction with life might stem, at least in part, from the higher religiosity among morning-oriented individuals, also when Conscientiousness was included in the model.

It means that more morning-oriented individuals may benefit from higher psychological well-being thanks to both personality characteristics and attitudes towards religion.

--- https://en.wikipedia.org/wiki/Color_killer#Color_eraser_(Mehikon) Color killer § Color eraser (Mehikon) 2022-11-26 politics sociology/technology --- https://worksinprogress.co/issue/why-britain-doesnt-build 2022-11-26 economics/georgism --- https://x.com/mplappert/status/1663892732652273664 2022-11-27 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://x.com/luiz_zacarias/status/1649965329387331584 2022-11-27 psychology/vision --- https://arxiv.org/abs/2305.18545 Blockchain Censorship Anton Wahrstätter, Jens Ernstberger, Aviv Yaish, Liyi Zhou, Kaihua Qin, Taro Tsuchiya, Sebastian Steinhorst, Davor Svetinovic, Nicolas Christin, Mikolaj Barczentewicz, Arthur Gervais 2023-05-29 2023-05-29 [("doi","10.48550/arXiv.2305.18545")] bitcoin

Permissionless blockchains promise to be resilient against censorship by a single entity. This suggests that deterministic rules, and not third-party actors, are responsible for deciding if a transaction is appended to the blockchain or not. In 2022, the U.S. Office of Foreign Assets Control (OFAC) sanctioned a Bitcoin mixer and an Ethereum application, putting the neutrality of permissionless blockchains to the test.

In this paper, we formalize quantify and analyze the security impact of blockchain censorship. We start by defining ‘censorship’, followed by a quantitative assessment of current censorship practices.

We find that 46% of Ethereum blocks were made by censoring actors that intend to comply with OFAC sanctions, indicating the impact of OFAC sanctions on the neutrality of public blockchains.

We further uncover that censorship not only impacts neutrality, but also security. We show how after Ethereum’s move to Proof-of-Stake (PoS) and adoption of Proposer-Builder Separation (PBS) the inclusion of censored transactions was delayed by an average of 85%. Inclusion delays compromise a transaction’s security by, eg. strengthening a sandwich adversary [ie. someone frontrunning a transaction or reordering/executing other transactions to make money off it somehow].

Finally we prove a fundamental limitation of PoS and Proof-of-Work (PoW) protocols against censorship resilience.

--- /doc/iq/2021-proto.pdf Intelligence, Errors, and Cooperation in Repeated Interactions Eugenio Proto, Aldo Rustichini, Andis Sofianos 2021-12-27 2022-11-27 [("doi","10.1093/restud/rdab095")] economics iq

We study how strategic interaction and cooperation are affected by the heterogeneity of cognitive skills of groups of players, over consecutive plays of repeated games with randomly matched opponents using Prisoner’s Dilemma as stage game.

We observe overall higher cooperation rates and average final payoffs in integrated treatment groups—where subjects of different IQ levels interact together—than in separated treatment groups. Lower IQ subjects are better off and higher IQ subjects are worse off in integrated groups than in separated groups. Higher IQ subjects adopt harsher strategies when they are pooled with lower IQ subjects than when they play separately.

We demonstrate that this outcome should be expected in learning and evolutionary models where higher intelligence subjects exhibit lower frequency of errors in the implementation of strategies.

Estimations of errors and strategies in our experimental data are consistent with the model’s assumptions and predictions.

[Keywords: evolutionary games, learning, repeated prisoner’s dilemma, cooperation, intelligence, strategy errors]

--- /doc/economics/automation/2023-brynjolfsson-w31161-improvementincustomercomplaintresolutionperhourusinggpt3.jpg 2023 2023 ai/nn/transformer/gpt/3/nonfiction economics/automation --- /doc/iq/1973-gibson-table2-correlationbetweeniqandsocioeconomistatusbetweenfathersoninengland.jpg 1973 2022-11-27 genetics/heritable iq --- https://en.wikipedia.org/wiki/Battle_of_the_sexes_(game_theory)#Burning_money Battle of the sexes (game theory) § Burning money 2022-11-27 bitcoin/nashx --- /doc/iq/ses/1973-gibson-2.pdf Biological aspects of a high socio-economic group II. IQ Components and Social Mobility John B. Gibson, C. G. Nicholas Mascie-Taylor 1973-01-01 2022-11-27 [("doi","10.1017/S0021932000008919")] genetics/heritable iq/ses

Data are presented on the verbal and performance (non-verbal) IQs of a sample of university scientists, their surviving fathers and male sibs.

Although mean IQs differ between scientific disciplines the disciplines do not differentially attract scientists from particular socio-economic classes.

The verbal IQs of both the scientists and their fathers are positively correlated with socio-economic class but only in the fathers’ sample is the performance IQ/class correlation statistically-significant. The variance of both verbal and performance IQs increases from Class I to Class IIIM. The overall estimate of heritability for the verbal IQ is higher than that for the performance IQ.

Verbal and performance IQs are related to the distance the scientists have moved on the socio-economic scale. The effects of social mobility on the genetic and environmental components of the verbal and performance IQ phenotypic variances are discussed.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4403216/ Family environment and the malleability of cognitive ability: a Swedish national home-reared and adopted-away cosibling control study Kenneth S. Kendler, Eric Turkheimer, Henrik Ohlsson, Jan Sundquist, Kristina Sundquist 2015 2022-11-27 [("doi","10.1073/pnas.1417106112")] genetics/heritable/adoption iq

Cognitive ability strongly aggregates in families, and prior twin and adoption studies have suggested that this is the result of both genetic and environmental factors. In this study, we used a powerful design–home-reared and adopted-away cosibling controls–to investigate the role of the rearing environment in cognitive ability.

We identified, from a complete national Swedish sample of male-male siblings, 436 full-sibships in which at least one member was reared by one or more biological parents and the other by adoptive parents. IQ was measured at age 18–20 as part of the Swedish military service conscription examination. Parental educational level was rated on a 5-point scale.

Controlling for clustering of offspring within biological families, the adopted siblings had an IQ 4.41 (SE = 0.75) points higher than their non-adopted siblings. Each additional unit of rearing parental education was associated with 1.71 (SE = 0.44) units of IQ. We replicated these results in 2,341 male-male half-sibships, in which, controlling for clustering within families, adoption was associated with a gain of IQ of 3.18 (SE = 0.34) points [ie. before the full fadeout has happened, so actual gain is less and approaching zero]. Each additional unit of rearing parental education was associated with 1.94 (SE = 0.18) IQ units.

Using full-sibling & half-sibling sets matched for genetic background, we found replicated evidence that (1) rearing environment affects IQ measured in late adolescence, and (2) a portion of the IQ of adopted siblings could be explained by the educational level of their adoptive parents.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5502597/ Childhood social class and cognitive aging in the Swedish Adoption/Twin Study of Aging Malin Ericsson, Cecilia Lundholm, Stefan Fors, Anna K. Dahl Aslan, Catalina Zavala, Chandra A. Reynolds, Nancy L. Pedersen 2017 2022-11-27 [("doi","10.1073/pnas.1620603114")] genetics/heritable/adoption iq

In this report we analyzed genetically informative data to investigate within-person change and between-person differences in late-life cognitive abilities as a function of childhood social class. We used data from 9 testing occasions spanning 28 y in the Swedish Adoption/Twin Study of Aging and parental social class based on the Swedish socioeconomic index. Cognitive ability included a general factor and the 4 domains of verbal, fluid, memory, and perceptual speed.

Latent growth curve models of the longitudinal data tested whether level and change in cognitive performance differed as a function of childhood social class. Between-within twin-pair analyses were performed on twins reared apart to assess familial confounding. Childhood social class was statistically-significantly associated with mean-level cognitive performance at age 65 y, but not with rate of cognitive change. The association decreased in magnitude but remained statistically-significant after adjustments for level of education and the degree to which the rearing family was supportive toward education.

A between-pair effect of childhood social class was statistically-significant in all cognitive domains, whereas within-pair estimates were attenuated, indicating genetic confounding. Thus, childhood social class is important for cognitive performance in adulthood on a population level, but the association is largely attributable to genetic influences.

--- https://www.boringreport.org/app 2022-11-27 ai/nn/transformer/gpt/non-fiction ai/text-style-transfer design politics --- https://reasonwithoutrestraint.com/parental-ses-vs-cognitive-ability-as-predictors-of-academic-achievement/ 2022-11-28 iq/ses --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147382/ Pharmacological Mechanism of the Non-hallucinogenic 5-HT2A Agonist Ariadne and Analogs Michael J. Cunningham, Hailey A. Bock, Inis C. Serrano, Benjamin Bechand, D. J. Vidyadhara, Emma M. Bonniwell, David Lankri, Priscilla Duggan, Antonina L. Nazarova, Andrew B. Cao, Maggie M. Calkins, Prashant Khirsariya, Christopher Hwu, Vsevolod Katritch, Sreeganga S. Chandra, John D. McCorvy, Dalibor Sames 2023 2023 [("doi","10.1021/acschemneuro.2c00597")] psychedelic psychology/neuroscience

Ariadne is a non-hallucinogenic analog in the phenylalkylamine chemical class of psychedelics that is closely related to an established synthetic hallucinogen, 2,5-dimethoxy-4-methyl-amphetamine (DOM), differing only by one methylene group in the α-position to the amine. Ariadne has been tested in humans including clinical trials at Bristol-Myers Company that indicate a lack of hallucinogenic effects and remarkable therapeutic effects, such as rapid remission of psychotic symptoms in schizophrenics, relaxation in catatonics, complete remission of symptoms in Parkinson’s disease (PD), and improved cognition in geriatric subjects. Despite these provocative clinical results, the compound has been abandoned as a drug candidate and its molecular pharmacology remained unknown.

Here, we report a detailed examination of the in vitro and in vivo pharmacology of Ariadne and its analogs, and propose a molecular hypothesis for the lack of hallucinogenic effects and the therapeutic potential of this compound class. We also provide a summary of previous clinical and preclinical results to contextualize the molecular signaling data.

Our results show that Ariadne is a serotonin 5-HT2 receptor agonist, exhibits modest selectivity over 5-HT1 receptors, has no relevant activity at 5-HT4,5,7 and other aminergic receptors, and no substantial affinity at plasma membrane monoamine transporters.

Compared to DOM, Ariadne shows lower signaling potency and efficacy in multiple signaling pathways examined (Gq, G11, and β-arrestin2) coupled to 5-HT2A receptors. We confirmed the shift in signaling for an α-propyl analog and provide a molecular docking rationale for the progressive decrease in signaling potency with the growing length of the α-substituent. Ariadne versus DOM exhibits no apparent change in the relative preference between Gq/11 activation and β-arrestin2 recruitment; instead, there is a small but consistent drop in efficacy in these signaling channels.

Ariadne acts as a 5-HT2A agonist in vivo in mice and shows markedly attenuated head twitch response (HTR) in comparison to its hallucinogenic analogs, consistent with previous studies in rabbits, cats, and dogs.

Hence, we propose the lower 5-HT2A receptor signaling efficacy of this compound class as an explanatory model for the lack of hallucinogenic effects of Ariadne in humans and the dramatically attenuated hallucinosis-like effects in animals (5-HT2A signaling efficacy hypothesis). In terms of reverse translation of the noted clinical therapeutic effects, we used an auxilin knockout model of Parkinson’s disease where Ariadne rescued severe motor deficits in this mouse line, on par with the effects of l-DOPA, a notable finding considering Ariadne’s lack of activity at dopamine receptors and transporters.

Ariadne emerges as a prototype of a new drug class, non-hallucinogenic 5-HT2A agonists, with considerable therapeutic potential across psychiatric and neurological indications.

--- https://headofdonnbo.wordpress.com/2015/12/10/the-tatooine-cycle/ 2022-11-28 fiction/humor fiction/science-fiction --- https://tattuinardoelasaga.wordpress.com/2010/03/01/tattuinardoela-saga-if-star-wars-were-an-icelandic-saga/ 2022-11-28 fiction/humor fiction/science-fiction --- https://x.com/OfficialLoganK/status/1663934947931897857 2022-11-28 ai/nn/transformer/gpt/4 --- https://www.theverge.com/23743095/apple-watch-band-release-x206-assembly-button-of-the-month 2022-11-28 design technology --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0040503 Hunter-Gatherer Energetics and Human Obesity Herman Pontzer, David A. Raichlen, Brian M. Wood, Audax Z. P. Mabulla, Susan B. Racette, Frank W. Marlowe 2012-06-12 2022-11-28 [("doi","10.1371/journal.pone.0040503")] exercise

Western lifestyles differ markedly from those of our hunter-gatherer ancestors, and these differences in diet and activity level are often implicated in the global obesity pandemic. However, few physiological data for hunter-gatherer populations are available to test these models of obesity.

In this study, we used the doubly-labeled water method to measure total daily energy expenditure (kcal/day) in Hadza hunter-gatherers to test whether foragers expend more energy each day than their Western counterparts. As expected, physical activity level, PAL, was greater among Hadza foragers than among Westerners.

Nonetheless, average daily energy expenditure of traditional Hadza foragers was no different than that of Westerners after controlling for body size. The metabolic cost of walking (kcal kg−1 m−1) and resting (kcal kg−1 s−1) were also similar among Hadza and Western groups.

The similarity in metabolic rates across a broad range of cultures challenges current models of obesity suggesting that Western lifestyles lead to decreased energy expenditure. We hypothesize that human daily energy expenditure may be an evolved physiological trait largely independent of cultural differences.

--- https://x.com/kenshinsamurai9/status/1662510532585291779 2022-11-28 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex reinforcement-learning/chess --- https://www.biorxiv.org/content/10.1101/2020.02.10.942540.full Remote-controlled insect navigation using plasmonic nanotattoos Sirimuvva Tadepalli, Sisi Cao, Debajit Saha, Keng-Ku Liu, Alex Chen, Sang hyun Bae, Baranidharan Raman, Srikanth Singamaneni 2020-02-11 2022-11-28 [("doi","10.1101/2020.02.10.942540")] psychology/neuroscience reinforcement-learning/robot

Developing insect cyborgs by integrating external components (optical, electrical or mechanical) with biological counterparts has a potential to offer elegant solutions for complex engineering problems.1 A key limiting step in the development of such biorobots arises at the nano-bio interface, i.e. between the organism and the nano implant that offers remote controllability.1,2

Often, invasive procedures are necessary that tend to severely compromise the navigation capabilities as well as the longevity of such biorobots. Therefore, we sought to develop a non-invasive solution using plasmonic nanostructures that can be photoexcited to generate heat with spatial and temporal control. We designed a ‘nanotattoo’ using silk that can interface the plasmonic nanostructures with a biological tissue.

Our results reveal that both structural and functional integrity of the biological tissues such as insect antenna, compound eyes and wings were preserved after the attachment of the nanotattoo. Finally, we demonstrate that insects with the plasmonic nanotattoos can be remote controlled using light and integrated with functional recognition elements to detect the chemical environment in the region of interest.

In sum, we believe that the proposed technology will play a crucial role in the emerging fields of biorobotics and other nano-bio applications.

--- https://arxiv.org/abs/2305.10429#google DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du, Hanxiao Liu, Yifeng Lu, Percy Liang, Quoc V. Le, Tengyu Ma, Adams Wei Yu 2023-05-17 2023-05-17 [("doi","10.48550/arXiv.2305.10429")] ai/nn/transformer/gpt reinforcement-learning/exploration/active-learning

[Twitter] The mixture proportions of pretraining data domains (eg. Wikipedia, books, web text) greatly affect language model (LM) performance. In this paper, we propose Domain Reweighting with Minimax Optimization (DoReMi), which first trains a small proxy model using group distributionally robust optimization (Group DRO) over domains to produce domain weights (mixture proportions) without knowledge of downstream tasks.

We then resample a dataset with these domain weights and train a larger, full-sized model. In our experiments, we use DoReMi on a 280M-parameter proxy model to find domain weights for training an 8B-parameter model (30× larger) more efficiently.

On The Pile, DoReMi improves perplexity across all domains, even when it downweights a domain. DoReMi improves average few-shot downstream accuracy by 6.5% points over a baseline model trained using The Pile’s default domain weights and reaches the baseline accuracy with 2.6× fewer training steps.

On the GLaM dataset, DoReMi, which has no knowledge of downstream tasks, even matches the performance of using domain weights tuned on downstream tasks.

[...we actually found that loss rankings transfer very well across scales even at the example level (in some prelim tests, 95%+ Spearman rank correlation). So it seems: more learnable for a large model → more learnable for a small model.]

Figure 2: DoReMi optimizes domain weights with a small model (280M params) and uses these domain weights to train a much larger model (8B params, 30× larger). Here, optimizing the domain weights (training a small model twice) takes 8% of the compute of training the large model. DoReMi improves average one-shot downstream accuracy by 6.5% points and reaches the baseline accuracy 2.6× faster when pretraining on The Pile.

…DoReMi can reduce perplexity across all domains without a tradeoff: Figure 4 shows the per-domain perplexity of the 8B models on The Pile. DoReMi substantially reduces the perplexity over the baseline across all domains, despite allocating lower weight to some domains. How can this occur? Intuitively, the domains with the lowest and highest entropy can be downweighted without impacting the perplexity much. The lowest entropy domains statistically require few samples to learn. The highest entropy domains have token distributions that are close to common uniform priors —for example, models at random initialization tend to output a uniform next token distribution. Thus, we need less samples to fit these domains. Positive transfer from allocating more samples to medium entropy domains can then improve perplexity on all domains. In Appendix D, we provide a simple example where reweighting domains can improve perplexity on all domains and DoReMi finds such domain weights in simulations.

…What is a domain? We define a domain by data provenance in our experiments, but this only enables coarse-grained control. Using fine-grained domains could improve the gains from DoReMi. For example, DoReMi is more effective on The Pile (22 domains) than the GLaM dataset (8 domains).

--- https://arxiv.org/abs/1909.02060 Distributionally Robust Language Modeling Yonatan Oren, Shiori Sagawa, Tatsunori B. Hashimoto, Percy Liang 2019-09-04 2022-11-28 [("doi","10.48550/arXiv.1909.02060")] ai/nn/adversarial ai/nn/transformer

Language models are generally trained on data spanning a wide range of topics (eg. news, reviews, fiction), but they might be applied to an a priori unknown target distribution (eg. restaurant reviews).

In this paper, we first show that training on text outside the test distribution can degrade test performance when using standard maximum likelihood (MLE) training.

To remedy this without the knowledge of the test distribution, we propose an approach which trains a model that performs well over a wide range of potential test distributions. In particular, we derive a new distributionally robust optimization (DRO) procedure which minimizes the loss of the model over the worst-case mixture of topics with sufficient overlap with the training distribution.

Our approach, called topic conditional value at risk (topic CVaR), obtains a 5.5 point perplexity reduction over MLE when the language models are trained on a mixture of Yelp reviews & news but tested only on reviews.

--- https://arxiv.org/abs/2305.19370 Blockwise Parallel Transformer for Long Context Large Models Hao Liu, Pieter Abbeel 2023-05-30 2023-05-30 [("doi","10.48550/arXiv.2305.19370")] ai/nn/transformer/attention cs/algorithm

Transformers have emerged as the cornerstone of state-of-the-art natural language processing models, showcasing exceptional performance across a wide range of AI applications. However, the memory demands posed by the self-attention mechanism and the large feedforward network in Transformers limit their ability to handle long sequences, thereby creating challenges for tasks involving multiple long sequences or long-term dependencies.

We present a distinct approach, Blockwise Parallel Transformer (BPT), that leverages blockwise computation of self-attention and feedforward network fusion to minimize memory costs.

By processing longer input sequences while maintaining memory efficiency, BPT enables training sequences up to 32× longer than vanilla Transformers and 2 to 4× longer than previous memory-efficient methods. Extensive experiments on language modeling and reinforcement learning tasks demonstrate the effectiveness of BPT in reducing memory requirements and improving performance.

--- /doc/psychiatry/lithium/2007-guzzetta.pdf Lithium Treatment Reduces Suicide Risk in Recurrent Major Depressive Disorder Francesca Guzzetta, Leonardo Tondo, Franca Centorrino, Ross J. Baldessarini 2007-03-15 2022-11-29 psychiatry/depression psychiatry/lithium

Objective: Evidence that clinical treatment reduces suicide risk in major depressive disorder (MDD) is limited and inconsistent. Since lithium shows major antisuicidal effects in bipolar disorders and in heterogeneous mood disorder samples, we evaluated evidence of antisuicidal effects of lithium in patients with recurrent MDD.

Data Sources: We searched MEDLINE (January 1966–April 2006; search terms: lithium, suicide, affective disorder, depression, major depression, and mood disorder) for studies reporting suicides or suicide attempts during treatment with and without lithium in recurrent MDD patients, and we added data for 78 new subjects, provided from the Lucio Bini Mood Disorders Research Center in Sardinia, Italy. Suicide rates were pooled and analyzed by use of incidence-rate ratios (IRRs) and meta-analytic methods.

Data Synthesis: 8 studies involved 329 MDD patients and exposure for 4.56 years (1,149 person-years) with, and 6.27 years (1,285 person-years) without, lithium. Overall risk of suicides and suicide attempts was 88.5% lower with vs. without lithium: 0.17%/y versus 1.48%/y (IRR = 8.71; 95% CI: 2.10–77.2, p = 0.0005); for completed suicides (85% risk reduction), IRR = 6.77 (95% CI: 1.29–66.8, p = 0.01). Meta-analysis by risk difference and risk ratio supported these findings, and sensitivity analysis yielded similar results with studies omitted serially.

Conclusions: This is the first meta-analysis suggesting antisuicidal effects of lithium in recurrent MDD, similar in magnitude to that found in bipolar disorders.

--- https://github.com/mame/radiation-hardened-quine 2022-11-29 cs/computable --- https://codegolf.stackexchange.com/a/100785/98955 2022-11-29 cs/computable --- https://github.com/unrealwill/uncroppable 2022-11-29 cs/cryptography --- https://anarchonomicon.substack.com/p/the-antagonists-tech-tree 2022-11-29 technology --- https://en.wikipedia.org/wiki/B%C3%BCchi_arithmetic Büchi arithmetic 2022-11-29 cs/computable math --- https://www.theatlantic.com/magazine/archive/2018/06/vista-ceo-testing/559148/ 2022-11-29 iq --- http://www.scholarpedia.org/article/Universal_search#OOPS_and_other_incremental_variations Universal search § OOPS and other incremental variations 2022-11-29 cs/computable reinforcement-learning/meta-learning --- https://arxiv.org/abs/cs/0207097#schmidhuber Optimal Ordered Problem Solver (OOPS) Juergen Schmidhuber 2002-07-31 2022-11-29 [("doi","10.48550/arXiv.0207097")] reinforcement-learning/meta-learning

We present a novel, general, optimally fast, incremental way of searching for a universal algorithm that solves each task in a sequence of tasks. The Optimal Ordered Problem Solver (OOPS) continually organizes and exploits previously found solutions to earlier tasks, efficiently searching not only the space of domain-specific algorithms, but also the space of search algorithms.

Essentially we extend the principles of optimal non-incremental universal search [Levin search] to build an incremental universal learner that is able to improve itself through experience.

In illustrative experiments, our self-improver becomes the first general system that learns to solve all n disk Towers of Hanoi tasks (solution size 2n−1) for n up to 30, profiting from previously solved, simpler tasks involving samples of a simple context free language.

In a quite pragmatic sense OOPS is the fastest general way of solving one task after another, always optimally exploiting solutions to earlier tasks when possible. It can be used for increasingly hard problems of optimization or prediction. Suppose there is only one task and a bias in form of a probability distribution P on programs for a universal computer. In the i-th phase (i = 1, 2, 3, . . .) of asymptotically optimal nonincremental universal search (Levin 1973, Levin 1984, Hutter 2002a) we test all programs p with runtime ≤ 2iP(p) until the task is solved. Now suppose there is a sequence of tasks, eg. the n-th task is to find a shorter path through a maze than the best found so far. To reduce the search time for new tasks, previous incremental extensions of universal search tried to modify P through experience with earlier tasks—but in a heuristic and non-general and suboptimal way prone to overfitting. OOPS, however, does it right

Tested self-delimiting program prefixes (beginnings of code that may continue) are immediately executed while being generated. They grow by one instruction whenever they request this. The storage for the first found program computing a solution to the current task becomes non-writable. Programs tested during search for solutions to later task may copy non-writable code into separate modifiable storage, to edit it and execute the modified result. Prefixes may also recompute the probability distribution on their suffixes in arbitrary computable ways. To solve the n-th task we sacrifice half the total search time for testing (via universal search) programs that have the most recent successful program as a prefix. The other half remains for testing fresh programs starting at the address right above the top non-writable address. When we are searching for a universal solver for all tasks in the sequence we have to time-share the second half (but not the first!) among all tasks 1..n. For realistic limited computers we need efficient backtracking in program space to reset storage contents modified by tested programs. We introduce a recursive procedure for doing this in time-optimal fashion.

OOPS can solve tasks unsolvable by traditional reinforcement learners and AI planners, such as Towers of Hanoi with 30 disks (minimal solution size > 109). In our experiments OOPS demonstrates incremental learning by reusing previous solutions to discover a prefix that temporarily rewrites the distribution on its suffixes, such that universal search is accelerated by 1,000×. This illustrates how OOPS can benefit from self-improvement and meta-searching, that is, searching for faster search procedures.

We mention several OOPS variants and outline OOPS-based reinforcement learners. Since OOPS will scale to larger problems in essentially unbeatable fashion, we also examine its physical limitations.

[Keywords: OOPS, bias-optimality, incremental optimal universal search, efficient planning & backtracking in program space, meta-searching & metalearning, self-improvement]

…6.6 Experimental Results for Both Task Sets Within roughly 0.3 days, OOPS found and froze code solving all 30 1n2n-tasks. Thereafter, within 2–3 additional days, it also found a universal Hanoi solver. The latter does not call the 1n2n solver as a subprogram (which would not make sense at all), but it does profit from experience: it begins with a rather short prefix that reshapes the distribution on the possible suffixes, an thus the search space, by temporally increasing the probabilities of certain instructions of the earlier found 1n2n solver. This in turn happens to increase the probability of finding a Hanoi-solving suffix. It is instructive to study the sequence of intermediate solutions…The entire 4-day search for solutions to all 60 tasks tested 93,994,568,009 prefixes corresponding to 345,450,362,522 instructions costing 678,634,413,962 time steps. Recall once more that search time of an optimal solver is a natural measure of initial bias. Clearly, most tested prefixes are short—they either halt or get interrupted soon. Still, some programs do run for a long time; for example, the run of the self-discovered universal Hanoi solver working on instance 30 consumed 33 billion steps, which is already 5% of the total time. The stack used by the iterative equivalent of procedure Try for storage management (§4.1) never held more than 20,000 elements though.

…Note also that we could continue to solve Hanoi tasks up to n > 40. The execution time required to solve such instances with an optimal solver greatly exceeds the search time required for finding the solver itself. There it does not matter much whether OOPS already starts with a pre-wired Hanoi solver, or first has to discover one, since the initial search time for the solver becomes negligible anyway.

…For example, we could use the principles of OOPS to create a non-gradient-based, near-bias-optimal variant of the successful recurrent network meta learner by Hochreiter et al 2001. It should also be of interest to study probabilistic Speed Prior-based OOPS variants (Schmidhuber 2002e) and to devise applications of OOPS-like methods as components of universal reinforcement learners (§5.3).

--- https://arxiv.org/abs/cs/0206022 The Fastest and Shortest Algorithm for All Well-Defined Problems Marcus Hutter 2002-06-14 2022-11-30 [("doi","10.48550/arXiv.0206022")] ai cs/computable

An algorithm M is described that solves any well-defined problem p as quickly as the fastest algorithm computing a solution to p, save for a factor of 5 and low-order additive terms.

M optimally distributes resources between the execution of provably correct p-solving programs and an enumeration of all proofs, including relevant proofs of program correctness and of time bounds on program runtimes. M avoids Blum’s speed-up theorem by ignoring programs without correctness proof. M has broader applicability and can be faster than Levin’s universal search, the fastest method for inverting functions save for a large multiplicative constant.

An extension of Kolmogorov complexity and two novel natural measures of function complexity are used to show that the most efficient program computing some function f is also among the shortest programs provably computing f.

--- https://en.wikipedia.org/wiki/Porphyrios_(whale) Porphyrios (whale) 2022-11-30 history psychology/animal --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550520/ Bridge Swallowing Exercise for Gastroesophageal Reflux Disease Symptoms: A Pilot Study Kei Aoyama, Kenjiro Kunieda, Takashi Shigematsu, Tomohisa Ohno, Emiko Wada, Ichiro Fujishima 2022 2022-11-30 [("doi","10.2490/prm.20220054")] biology exercise

Objectives: We previously reported that swallowing in the bridge position (bridge swallowing) increased distal esophageal contractions and lower esophageal sphincter pressure against gravity. Moreover, bridge swallowing had the potential to strengthen esophageal peristalsis. In this study, we sought to evaluate whether the bridge swallowing exercise could improve gastroesophageal reflux disease (GERD) symptoms and gastroscopy findings.

Method: 17 subjects with scores of 8 points or higher on the Frequency Scale for Symptoms of GERD (FSSG) questionnaire participated in the study. The exercise of dry swallowing in the bridge posture lasted 4 weeks and was performed 10× per day. FSSG scores were compared before and after exercise. 3 of the 17 participants underwent upper gastrointestinal endoscopy. The modified Los Angeles classification of reflux esophagitis was used for objective assessment before and after exercise.

Results: No participants dropped out of this study. FSSG scores improved statistically-significantly after exercise (from median [range] 16 [13-21] points before exercise to 5 [4-10] points after exercise, p <0.001). Upper gastrointestinal endoscopy showed improvement in the modified Los Angeles classification grade in one participant.

Conclusions: The bridge swallowing exercise statistically-significantly improves FSSG scores. This exercise can be performed easily and safely without adverse events. Further multicenter prospective studies are needed to validate that the bridge swallowing exercise is effective in improving GERD.

--- https://en.wikipedia.org/wiki/Angus_Barbieri%27s_fast 2022-11-30 exercise --- https://arxiv.org/abs/2306.00238#apple Bytes Are All You Need: Transformers Operating Directly On File Bytes Maxwell Horton, Sachin Mehta, Ali Farhadi, Mohammad Rastegari 2023-05-31 2023-05-31 [("doi","10.48550/arXiv.2306.00238")] ai/nn/tokenization ai/nn/transformer/attention/hierarchical

[cf. Not-so-BigGAN, Nash et al 2021, Rajesh et al 2022] Modern deep learning approaches usually transform inputs into a modality-specific form. For example, the most common deep learning approach to image classification involves decoding image file bytes into an RGB tensor which is passed into a neural network. Instead, we investigate performing classification directly on file bytes, without the need for decoding files at inference time.

Using file bytes as model inputs enables the development of models which can operate on multiple input modalities. Our [Swin Transformer] model, ByteFormer, achieves an ImageNet Top-1 classification accuracy of 77.33% when training and testing directly on TIFF file bytes using a transformer backbone with configuration similar to DeiT-Ti (72.2% accuracy when operating on RGB images). Without modifications or hyperparameter tuning, ByteFormer achieves 95.42% classification accuracy when operating on WAV files from the Speech Commands v2 dataset (compared to state-of-the-art accuracy of 98.7%).

Additionally, we demonstrate that ByteFormer has applications in privacy-preserving inference. ByteFormer is capable of performing inference on particular obfuscated input representations with no loss of accuracy. We also demonstrate ByteFormer’s ability to perform inference with a hypothetical privacy-preserving camera which avoids forming full images by consistently masking 90% of pixel channels, while still achieving 71.35% accuracy on ImageNet.

Our code will be made available at Github.

--- https://arxiv.org/abs/2211.16421 RGB no more: Minimally-decoded JPEG Vision Transformers Jeongsoo Park, Justin Johnson 2022-11-29 2022-11-30 [("doi","10.48550/arXiv.2211.16421")] ai/nn/transformer cs/algorithm/information/compression

Most neural networks for computer vision are designed to infer using RGB images. However, these RGB images are commonly encoded in JPEG before saving to disk; decoding them imposes an unavoidable overhead for RGB networks.

Instead, our work focuses on training Vision Transformers (ViT) directly from the encoded features of JPEG. This way, we can avoid most of the decoding overhead, accelerating data load. Existing works have studied this aspect but they focus on CNNs. Due to how these encoded features are structured, CNNs require heavy modification to their architecture to accept such data.

Here, we show that this is not the case for ViTs. In addition, we tackle data augmentation directly on these encoded features, which to our knowledge, has not been explored in-depth for training in this setting.

With these two improvements—ViT and data augmentation—we show that our ViT-Ti model achieves up to 39.2% faster training and 17.9% faster inference with no accuracy loss compared to the RGB counterpart.

--- https://arxiv.org/abs/2210.03734 T2CI-GAN: Text to Compressed Image generation using Generative Adversarial Network Bulla Rajesh, Nandakishore Dusa, Mohammed Javed, Shiv Ram Dubey, P. Nagabhushan 2022-10-01 2022-11-30 [("doi","10.48550/arXiv.2210.03734")] ai/nn/gan cs/algorithm/information/compression

The problem of generating textual descriptions for the visual data has gained research attention in the recent years. In contrast to that the problem of generating visual data from textual descriptions is still very challenging, because it requires the combination of both Natural Language Processing (NLP) and Computer Vision techniques.

The existing methods use the Generative Adversarial Networks (GANs) and generate the uncompressed images from textual description. However, in practice, most of the visual data are processed and transmitted in the compressed representation. Hence, the proposed work attempts to generate the visual data directly in the compressed representation form using Deep Convolutional GANs (DCGANs) to achieve the storage and computational efficiency.

We propose GAN models for compressed image generation from text. The first model is directly trained with JPEG-compressed DCT images (compressed domain) to generate the compressed images from text descriptions. The second model is trained with RGB images (pixel domain) to generate JPEG-compressed DCT representation from text descriptions.

The proposed models are tested on an open source benchmark dataset Oxford-102 Flower images using both RGB and JPEG compressed versions, and accomplished the state-of-the-art performance in the JPEG-compressed domain.

The code will be publicly released at GitHub after acceptance of paper.

--- https://if50.substack.com/p/christopher-strachey-and-the-dawn 2022-11-30 reinforcement-learning/model --- http://www.alpha60.de/research/programming_enter/DavidLink_ProgrammingEnter_ComputerResurrection60_2012.pdf 2022-11-30 reinforcement-learning/model --- http://www.cr31.co.uk/stagecast/trains/tt3_univ.html 2022-11-30 cs/computable --- https://michaelnotebook.com/mmsw/index.html 2022-11-30 psychology/spaced-repetition --- https://medium.com/@JarrettYe/casting-a-spell-on-chatgpt-let-it-write-anki-cards-for-you-a-prompt-engineering-case-fd7d577b9d94 2022-12-01 ai/nn/transformer/gpt/3/nonfiction psychology/spaced-repetition --- https://x.com/SarahTheHaider/status/1664674379865702400 2022-12-01 psychology/inner-voice --- https://evabehrens.substack.com/p/the-agi-race-between-the-us-and-china 2022-12-01 ai/scaling/economics ai/scaling/hardware politics --- https://news.ycombinator.com/item?id=36159242 2022-12-01 psychology/cognitive-bias sociology/abandoned-footnotes --- https://www.chinafile.com/library/nyrb-china-archive/art-interpreting-nonexistent-inscriptions-written-invisible-ink-blank 2022-12-01 politics sociology/abandoned-footnotes --- https://250bpm.com/blog:174/index.html 2022-12-01 politics --- https://www.oneusefulthing.org/p/setting-time-on-fire-and-the-temptation 2022-12-01 ai/nn/transformer/gpt/4/nonfiction --- https://mattsclancy.substack.com/p/the-size-of-firms-and-the-nature 2022-12-01 economics technology --- https://arxiv.org/abs/2306.00983#google StyleDrop: Text-to-Image Generation in Any Style Kihyuk Sohn, Nataniel Ruiz, Kimin Lee, Daniel Castro Chin, Irina Blok, Huiwen Chang, Jarred Barber, Lu Jiang, Glenn Entis, Yuanzhen Li, Yuan Hao, Irfan Essa, Michael Rubinstein, Dilip Krishnan 2023-06-01 2023-06-01 [("doi","10.48550/arXiv.2306.00983")] ai/nn/diffusion

Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts. However, ambiguities inherent in natural language and out-of-distribution effects make it hard to synthesize image styles, that leverage a specific design pattern, texture or material.

In this paper, we introduce StyleDrop, a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. The proposed method is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects.

It efficiently learns a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters) and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a single image that specifies the desired style.

An extensive study shows that, for the task of style tuning text-to-image models, StyleDrop implemented on Muse convincingly outperforms other methods, including DreamBooth and textual inversion on Imagen or Stable Diffusion. More results are available at our project website: https://styledrop.github.io/.

--- https://www.newyorker.com/magazine/2023/06/05/how-to-hire-a-pop-star-for-your-private-party 2022-12-01 economics music sociology --- https://arxiv.org/abs/2306.00020 GPT4GEO: How a Language Model Sees the World’s Geography Jonathan Roberts, Timo Lüddecke, Sowmen Das, Kai Han, Samuel Albanie 2023-05-30 2023-05-30 [("doi","10.48550/arXiv.2306.00020")] ai/nn/transformer/gpt/4/nonfiction

Large language models (LLMs) have shown remarkable capabilities across a broad range of tasks involving question answering and the generation of coherent text and code. Comprehensively understanding the strengths and weaknesses of LLMs is beneficial for safety, downstream applications and improving performance.

In this work, we investigate the degree to which GPT-4 has acquired factual geographic knowledge and is capable of using this knowledge for interpretative reasoning, which is especially important for applications that involve geographic data, such as geospatial analysis, supply chain management, and disaster response.

To this end, we design and conduct a series of diverse experiments, starting from factual tasks such as location, distance and elevation estimation to more complex questions such as generating country outlines and travel networks, route finding under constraints and supply chain analysis.

We provide a broad characterisation of what GPT-4 (without plugins or Internet access) knows about the world, highlighting both potentially surprising capabilities but also limitations.

--- https://www.brightball.com/articles/waste-spammers-time-to-reduce-their-return-on-investment 2022-12-02 cs/security --- https://arxiv.org/abs/2302.13939 SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks Rui-Jie Zhu, Qihang Zhao, Jason K. Eshraghian 2023-02-27 2023-02-27 [("doi","10.48550/arXiv.2302.13939")] ai/nn/rnn ai/nn/sparsity/low-precision ai/nn/transformer/gpt

As the size of large language models continue to scale, so does the computational resources required to run it. Spiking neural networks (SNNs) have emerged as an energy-efficient approach to deep learning that leverage sparse and event-driven activations to reduce the computational overhead associated with model inference. While they have become competitive with non-spiking models on many computer vision tasks, SNNs have also proven to be more challenging to train. As a result, their performance lags behind modern deep learning, and we are yet to see the effectiveness of SNNs in language generation.

In this paper, inspired by the RWKV language model, we successfully implement SpikeGPT, a generative language model with pure binary, event-driven spiking activation units. We train [on enwik8 & BookCorpus & OpenWebText2] the proposed model [using backpropagation for surrogate gradients] on 3 model variants: 45M, 125M and 260M parameters. To the best of our knowledge, this is 4× larger than any functional backprop-trained SNN to date.

We achieve this by modifying the transformer block to replace multi-head self attention to reduce quadratic computational complexity to linear with increasing sequence length. Input tokens are instead streamed in sequentially to our attention mechanism (as with typical SNNs).

Our preliminary experiments show that SpikeGPT remains competitive with non-spiking models on tested benchmarks, while maintaining 5× less energy consumption when processed on neuromorphic hardware that can leverage sparse, event-driven activations.

Our code implementation is available at Github.

Table 1: enwik8 results, measured in bits per character (bpc): the lower the better. Baseline comparisons are made with Reformer, Synthesizer (the best performing dense version), Linear Transformer, Performer, Stacked LSTM and SHA-LSTM. L, d, and T denote the number of blocks (network depth), dimension of features, and sequence length, respectively. Both Linear Transformer and Performer are implemented with customized CUDA kernels, and all other models are implemented in native Pytorch. (Note: Interim results. Still in training; to be updated.)

[So, SpikeGPT does reasonably well but still falls substantially short of the baseline dense quadratic Transformer GPTs.]

…4.3 Results: While our model’s test performance is slightly less than that of the standard Transformer and several other Transformer variations, it nonetheless remains similar in performance with 22× less synaptic operations (SynOps). SynOps is a metric that accounts for activation sparsity, where only multiply-accumulate operations using non-zero activations are counted. The Transformer is measured using full precision (flt32) SynOps, whereas SpikeGPT uses binarized SynOps. Therefore, a given SynOp for SpikeGPT is substantially cheaper in terms of energy consumption compared to a SynOp of the Transformer.

--- https://whyisthisinteresting.substack.com/p/the-worlds-most-dangerous-toy-edition 2022-12-02 technology --- https://github.com/idiap/fast-transformers 2022-12-02 ai/nn/transformer/attention/linear-algebra --- https://arxiv.org/abs/1901.09948 Surrogate Gradient Learning in Spiking Neural Networks Emre O. Neftci, Hesham Mostafa, Friedemann Zenke 2019-01-28 2022-12-02 [("doi","10.48550/arXiv.1901.09948")] ai/nn/rnn ai/nn/sparsity/low-precision

Spiking neural networks are nature’s versatile solution to fault-tolerant and energy efficient signal processing. To translate these benefits into hardware, a growing number of neuromorphic spiking neural network processors attempt to emulate biological neural networks. These developments have created an imminent need for methods and tools to enable such systems to solve real-world signal processing problems.

Like conventional neural networks, spiking neural networks can be trained on real, domain specific data. However, their training requires overcoming a number of challenges linked to their binary and dynamical nature. This article elucidates step-by-step the problems typically encountered when training spiking neural networks, and guides the reader through the key concepts of synaptic plasticity and data-driven learning in the spiking setting.

To that end, it gives an overview of existing approaches and provides an introduction to surrogate gradient methods, specifically, as a particularly flexible and efficient method to overcome the aforementioned challenges.

--- https://arxiv.org/abs/2102.04159 Deep Residual Learning in Spiking Neural Networks Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, Yonghong Tian 2021-02-08 2022-12-02 [("doi","10.48550/arXiv.2102.04159")] ai/nn/rnn ai/nn/sparsity/low-precision

Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-based approaches due to discrete binary activation and complex spatial-temporal dynamics. Considering the huge success of ResNet in deep learning, it would be natural to train deep SNNs with residual learning. Previous Spiking ResNet mimics the standard residual block in ANNs and simply replaces ReLU activation layers with spiking neurons, which suffers the degradation problem and can hardly implement residual learning.

In this paper, we propose the spike-element-wise (SEW) ResNet to realize residual learning in deep SNNs. We prove that the SEW ResNet can easily implement identity mapping and overcome the vanishing/exploding gradient problems of Spiking ResNet.

We evaluate our SEW ResNet on ImageNet, DVS Gesture, and CIFAR-10-DVS datasets, and show that SEW ResNet outperforms the state-of-the-art directly trained SNNs in both accuracy and time-steps.

Moreover, SEW ResNet can achieve higher performance by simply adding more layers, providing a simple method to train deep SNNs. To our best knowledge, this is the first time that directly training deep SNNs with more than 100 layers becomes possible.

Our codes are available at Github.

--- https://mattmahoney.net/dc/textdata.html 2022-12-02 ai/dataset cs/algorithm wikipedia --- https://www.openphilanthropy.org/research/south-asian-air-quality/ 2022-12-02 economics --- https://slimemoldtimemold.com/2023/01/05/n1-introduction/ 2022-12-02 biology nootropic/quantified-self --- https://arcove.substack.com/p/null-call 2022-12-02 fiction/humor philosophy/religion psychiatry/schizophrenia --- https://arxiv.org/abs/2305.12050#facebook CodeCompose: A Large-Scale Industrial Deployment of AI-assisted Code Authoring Vijayaraghavan Murali, Chandra Maddila, Imad Ahmad, Michael Bolin, Daniel Cheng, Negar Ghorbani, Renuka Fernandez, Nachiappan Nagappan 2023-05-20 2023-05-20 [("doi","10.48550/arXiv.2305.12050")] ai/nn/transformer/gpt/codex ai/scaling/mixture-of-experts

The rise of large language models (LLMs) has unlocked various applications of this technology in software development. In particular, generative LLMs have been shown to effectively power AI-based code authoring tools that can suggest entire statements or blocks of code during code authoring. In this paper we present CodeCompose, an AI-assisted code authoring tool developed and deployed at Meta [Facebook] internally. CodeCompose is based on the InCoder LLM [MoE] that merges generative capabilities with bi-directionality [...it can understand inline comments in natural language and generate code that adheres to the comment, as shown in Figure 1(b). It can also fluently generate comments, messages, and documentation.].

We have scaled up CodeCompose to serve tens of thousands of developers at Meta, across 10+ programming languages [Python, Javascript, C++, Hack etc], and several coding surfaces...Training for 4 epochs with sharded data parallelism took 4 days on a cluster of 128 A100 GPUs. We then deployed the model on a cluster of 150 A100 GPUs.

We discuss unique challenges in terms of user experience and metrics that arise when deploying such tools in large-scale industrial settings. We present our experience in making design decisions about the model and system architecture for CodeCompose that addresses these challenges.

Finally, we present metrics from our large-scale deployment of CodeCompose that shows its impact on Meta’s internal code authoring experience over a 15-day time window, where 4.5 million suggestions were made by CodeCompose.

Quantitative metrics reveal that (1) CodeCompose has an acceptance rate of 22% across several languages, and (2) 8% of the code typed by users of CodeCompose is through accepting code suggestions from CodeCompose. Qualitative feedback indicates an overwhelming 91.5% positive reception for CodeCompose.

In addition to assisting with code authoring, CodeCompose is also introducing other positive side effects such as encouraging developers to generate more in-code documentation, helping them with the discovery of new APIs, etc.

--- https://arxiv.org/abs/2204.05999#facebook InCoder: A Generative Model for Code Infilling and Synthesis Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, Mike Lewis 2022-04-12 2022-12-03 [("doi","10.48550/arXiv.2204.05999")] ai/nn/transformer/gpt/codex ai/scaling/mixture-of-experts

Code is seldom written in a single left-to-right pass and is instead repeatedly edited and refined. We introduce InCoder, a unified generative [MoE] model that can perform program synthesis (via left-to-right generation) as well as editing (via infilling).

InCoder is trained to generate code files from a large corpus of permissively licensed code, where regions of code have been randomly masked and moved to the end of each file, allowing code infilling with bidirectional context.

Our model is the first generative model that is able to directly perform zero-shot code infilling, which we evaluate on challenging tasks such as type inference, comment generation, and variable re-naming.

We find that the ability to condition on bidirectional context substantially improves performance on these tasks, while still performing comparably on standard program synthesis benchmarks in comparison to left-to-right only models pretrained at similar scale.

The InCoder models and code are publicly released. https://sites.google.com/view/incoder-code-models.

...Model size: With data fixed, increasing model size consistently improves performance (comparing the 6.7B and 1.3B CM models in rows 1 and 2, and the 1.3B and 2.3B LM models in rows 3 and 6).

--- https://en.wikipedia.org/wiki/Crown_shyness Crown shyness 2022-12-03 biology --- http://strangehorizons.com/non-fiction/columns/freshly-rememberd-kirk-drift/ 2022-12-03 fiction/science-fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Main/CastFromHitPoints 2022-12-03 fiction nicotine --- https://ntdotdev.wordpress.com/2023/01/01/state-of-the-windows-how-many-layers-of-ui-inconsistencies-are-in-windows-11/ 2022-12-03 design --- https://en.wikipedia.org/wiki/Creative_destruction Creative destruction 2022-12-03 economics/automation --- https://evoniuk.github.io/posts/pitfall.html 2022-12-03 cs/algorithm --- https://manifold.markets/IsaacKing/will-this-markets-probability-be-at 2022-12-03 statistics/prediction --- https://www.mondo2000.com/2018/06/18/the-inspiration-for-hypercard/ 2022-12-03 design psychedelic --- https://github.com/nostalgebraist/improved-diffusion 2022-12-03 ai/nn/diffusion ai/nn/tokenization ai/nn/transformer/clip --- https://www.fry-ai.com/p/social-media-no-humans-allowed 2022-12-04 ai/nn/transformer/gpt/3/fiction reinforcement-learning/multi-agent --- https://replicationindex.com/2021/08/27/dan-ariely-and-the-credibility-of-social-psychological-science/ 2022-12-04 statistics/bias --- /doc/psychiatry/depression/2010-cuijpers.pdf Is guided self-help as effective as face-to-face psychotherapy for depression and anxiety disorders? A systematic review and meta-analysis of comparative outcome studies P. Cuijpers, T. Donker, A. van Straten, J. Li, G. Andersson 2010-10-29 2022-12-04 [("doi","10.1017/S0033291710000772")] psychiatry/anxiety psychiatry/depression

Background: Although guided self-help for depression and anxiety disorders has been examined in many studies, it is not clear whether it is equally effective as face-to-face treatments.

Method: We conducted a meta-analysis of randomized controlled trials in which the effects of guided self-help on depression and anxiety were compared directly with face-to-face psychotherapies for depression and anxiety disorders. A systematic search in bibliographical databases (PubMed, PsycINFO, Embase, Cochrane) resulted in 21 studies with 810 participants.

Results: The overall effect size indicating the difference between guided self-help and face-to-face psychotherapy at post-test was d = −0.02, in favour of guided self-help. At follow-up (up to 1 year) no statistically-significant difference was found either. No statistically-significant difference was found between the drop-out rates in the two treatments formats.

Conclusions: It seems safe to conclude that guided self-help and face-to-face treatments can have comparable effects. It is time to start thinking about implementation in routine care. [cf. dodo bird verdict]

--- https://www.construction-physics.com/p/the-birth-of-the-grid 2022-12-04 economics/experience-curve technology --- https://haroldsfonts.com/font/mean-26/ 2022-12-04 design/typography --- https://www.lesswrong.com/posts/B8Djo44WtZK6kK4K5/outreach-success-intro-to-ai-risk-that-has-been-successful 2022-12-04 ai/scaling reinforcement-learning/safe --- https://www.maximum-progress.com/p/grading-extropian-predictions 2022-12-04 bitcoin cryonics genetics/editing nootropic statistics/prediction transhumanism --- /doc/politics/2020-maxwell.pdf Geographic Divides and Cosmopolitanism: Evidence From Switzerland Rahsaan Maxwell 2020-03-23 2022-12-04 [("doi","10.1177/0010414020912289")] politics psychology/personality

Large cities are cosmopolitan environments where people embrace inter-national connections whereas small towns, villages, and the countryside are more likely to prioritize the maintenance of national traditions. These geographic divides are at the center of contemporary politics but we do not know why they exist.

One possibility is that cities make people more cosmopolitan while smaller areas make people less cosmopolitan. However, credibly measuring geographic effects is difficult because people sort across geography in ways that are correlated with political attitudes.

I address these methodological challenges with longitudinal data from the Swiss Household Panel.

My central result is that evidence of contextual effects is limited and unlikely to account for the broad geographic divides. Instead, sorting is likely to be the most important explanation for spatial polarization over cosmopolitanism.

These findings have several implications for our understanding of geographic divides.

--- /doc/politics/2019-maxwell.pdf Cosmopolitan Immigration Attitudes in Large European Cities: Contextual or Compositional Effects? Rahsaan Maxwell 2019-02-06 2022-12-04 [("doi","10.1017/S0003055418000898")] politics psychology/personality

Europe is geographically divided on the issue of immigration. Large cities are the home of Cosmopolitan Europe, where immigration is viewed positively. Outside the large cities—and especially in the countryside—is Nationalist Europe, where immigration is a threat.

This divide is well documented and much discussed, but there has been scant research on why people in large cities are more likely to have favorable opinions about immigration. Debates about geographic differences generally highlight two explanations: contextual or compositional effects.

I evaluate the two with data from the European Social Survey, the Swiss Household Panel, and the German Socio-Economic Panel.

Results support compositional effects and highlight the importance of (demographic and cultural) mechanisms that sort pro-immigration people into large cities.

This has several implications for our understanding of societal divisions in Europe; most notably that geographic polarization is a second-order manifestation of deeper (demographic and cultural) divides.

--- https://arxiv.org/abs/2306.00008#google Brainformers: Trading Simplicity for Efficiency Yanqi Zhou, Nan Du, Yanping Huang, Daiyi Peng, Chang Lan, Da Huang, Siamak Shakeri, David So, Andrew Dai, Yifeng Lu, Zhifeng Chen, Quoc Le, Claire Cui, James Laundon, Jeff Dean 2023-05-29 2023-05-29 [("doi","10.48550/arXiv.2306.00008")] ai/nn/transformer/attention ai/scaling/mixture-of-experts

Transformers are central to recent successes in natural language processing and computer vision. Transformers have a mostly uniform backbone where layers alternate between feed-forward and self-attention in order to build a deep network.

Here we investigate this design choice and find that more complex blocks that have different permutations of layer primitives can be more efficient. Using this insight, we develop a complex block, named Brainformer, that consists of a diverse sets of layers such as sparsely gated feed-forward layers, dense feed-forward layers, attention layers, and various forms of layer normalization and activation functions.

Brainformer consistently outperforms the state-of-the-art dense and sparse Transformers, in terms of both quality and efficiency. A Brainformer model with 8 billion activated parameters per token demonstrates 2× faster training convergence and 5× faster step time compared to its GLaM counterpart.

In downstream task evaluation, Brainformer also demonstrates a 3% higher SuperGLUE score with fine-tuning compared to GLaM with a similar number of activated parameters. Finally, Brainformer largely outperforms a Primer dense model derived with NAS with similar computation per token on few-shot evaluations.

--- https://openreview.net/forum?id=UVDAKQANOW Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities Andreas Kirsch, Yarin Gal 2023-02-28 2023-02-28 cs/algorithm/information reinforcement-learning/exploration/active-learning statistics/bayes

Recently proposed methods in data subset selection, that is active learning & active sampling, use Fisher information, Hessians, similarity matrices based on gradients, and gradient lengths to estimate how informative data is for a model’s training. Are these different approaches connected, and if so, how?

We revisit the fundamentals of Bayesian optimal experiment design and show that these recently proposed methods can be understood as approximations to information-theoretic quantities: among them, the mutual information between predictions and model parameters, known as expected information gain or BALD in machine learning, and the mutual information between predictions of acquisition candidates and test samples, known as expected predictive information gain.

We develop a comprehensive set of approximations using Fisher information and observed information and derive a unified framework that connects seemingly disparate literature.

Although Bayesian methods are often seen as separate from non-Bayesian ones, the sometimes fuzzy notion of “informativeness” expressed in various non-Bayesian objectives leads to the same couple of information quantities, which were, in principle, already known by Lindley 1956 and MacKay 1992.

--- /doc/food/2002-wansink.pdf Changing Eating Habits on the Home Front: Lost Lessons from World War II Research Brian Wansink 2002-03-01 2022-12-05 [("doi","10.1509/jppm.21.1.90.17614")] food history

Programs intended to improve nutrition often fall short of expectations. One exception, however, occurred during the rationing years of World War II, when U.S. citizens were encouraged to incorporate protein-rich organ meats into their protein-deficient diets. Unfortunately, most of the insights resulting from these efforts remained unpublished or in limited distribution.

For the first time, the author synthesizes selected studies from this era according to how the program restructured social norms, changed perceptions of taste, and helped assimilate variety into the U.S. diet.

The author discusses the behaviorally driven implications from these “lost lessons” in the context of the empirical contributions they made in defining what makes an unfavorable food acceptable.

[Note: while author is notorious for experimental p-hacking & possible data fabrication, that probably doesn’t apply to an early paper about archival documents.]

--- https://cvm.missouri.edu/research/feline-genetics-and-comparative-medicine-laboratory/99-lives/successfully-sequenced-cats/ Successfully Sequenced Cats: The following cats have already been sequenced for this project! 2022-12-05 cat/genetics --- https://cvm.missouri.edu/research/feline-genetics-and-comparative-medicine-laboratory/feline-genome-project-research-resources/cat-genomic-resources-strs-snps/ 2022-12-05 cat/genetics --- https://cvm.missouri.edu/research/feline-genetics-and-comparative-medicine-laboratory/99-lives/ 2022-12-05 cat/genetics --- https://www.retromags.com/guides/debinding/ 2022-12-05 cs/linkrot/archiving --- https://platform.openai.com/docs/guides/gpt-best-practices 2022-12-05 ai/nn/transformer/gpt --- https://arxiv.org/abs/2302.11382 A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, Douglas C. Schmidt 2023-02-21 2023-02-21 [("doi","10.48550/arXiv.2302.11382")] ai/nn/transformer/gpt

Prompt engineering is an increasingly important skill set needed to converse effectively with large language models (LLMs), such as ChatGPT. Prompts are instructions given to an LLM to enforce rules, automate processes, and ensure specific qualities (and quantities) of generated output. Prompts are also a form of programming that can customize the outputs and interactions with an LLM.

This paper describes a catalog of prompt engineering techniques presented in pattern form that have been applied to solve common problems when conversing with LLMs. Prompt patterns are a knowledge transfer method analogous to software patterns since they provide reusable solutions to common problems faced in a particular context, ie. output generation and interaction when working with LLMs.

This paper provides the following contributions to research on prompt engineering that apply LLMs to automate software development tasks. First, it provides a framework for documenting patterns for structuring prompts to solve a range of problems so that they can be adapted to different domains. Second, it presents a catalog of patterns that have been applied successfully to improve the outputs of LLM conversations. Third, it explains how prompts can be built from multiple patterns and illustrates prompt patterns that benefit from combination with other prompt patterns.

--- https://mp.weixin.qq.com/s/i4WR5ULH1ZZYl8Watf3EPw 2022-12-05 ai/nn/adversarial ai/nn/diffusion economics/advertising --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.712.2276&rep=rep1&type=pdf#page=2 Vitamin D and aging Tuohimaa 2009 2022-12-05 longevity vitamin-d --- https://www.marxists.org/archive/trotsky/1932/10/sovecon.htm The Soviet Economy in Danger Trotsky 1932 2022-12-05 economics politics --- https://www.biorxiv.org/content/10.1101/2023.05.07.539748.full DNA repair and anti-cancer mechanisms in the longest-living mammal: the bowhead whale Denis Firsanov, Max Zacher, Xiao Tian, Yang Zhao, John C. George, Todd L. Sformo, Greg Tombline, Seyed Ali Biashad, Abbey Gilman, Nicholas Hamilton, Avnee Patel, Maggie Straight, Minseon Lee, J. Yuyang Lu, Ena Haseljic, Alyssa Williams, Nalani Miller, Vadim N. Gladyshev, Zhengdong Zhang, Jan Vijg, Andrei Seluanov, Vera Gorbunova 2023-05-08 2023-05-08 [("doi","10.1101/2023.05.07.539748")] genetics/heritable/rare longevity

At over 200 years, the maximum lifespan of the bowhead whale exceeds that of all other mammals. The bowhead is also the second-largest animal on Earth, reaching over 80,000 kg. In spite of its very large number of cells, the bowhead is not highly cancer-prone, an incongruity termed Peto’s Paradox. This has been explained by the evolution of additional tumor suppressor genes in larger animals, which is supported by research on elephants demonstrating expansion of the p53 gene.

However, we show here that bowhead whale fibroblasts undergo oncogenic transformation after disruption of fewer tumor suppressors than required for human fibroblasts. Instead, analysis of DNA repair revealed that bowhead cells repair double-strand breaks with uniquely high efficiency and accuracy compared to other mammals.

Further, we identified two proteins, CIRBP and RPA2, that are present at high levels in bowhead fibroblasts and increase the efficiency and fidelity of DNA repair in human cells. These results suggest that rather than possessing additional tumor suppressor genes as barriers to oncogenesis, the bowhead whale relies on more accurate and efficient DNA repair to preserve genome integrity.

This strategy that does not eliminate cells but repairs them may be critical for the long and cancer-free lifespan of the bowhead whale. Our work demonstrates the value of studying long-lived organisms in identifying novel longevity mechanisms and their potential for translation to humans.

--- https://github.com/opendilab/LightZero 2022-12-06 reinforcement-learning/model/muzero --- https://www.theguardian.com/music/2022/feb/18/confucius-beowulf-and-an-ai-called-kevin-everything-everythings-search-for-hope-in-strange-places 2022-12-06 ai/music ai/nn/diffusion --- https://en.wikipedia.org/wiki/Raw_Data_Feel Raw Data Feel 2022-12-06 ai/music ai/nn/diffusion --- https://arxiv.org/abs/1603.05279 XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, Ali Farhadi 2016-03-16 2022-12-06 [("doi","10.48550/arXiv.1603.05279")] ai/nn/cnn ai/nn/sparsity/low-precision

[code] We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks & XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32× memory saving.

In XNOR-Networks, both the filters and the input to convolutional layers are binary. XNOR-Networks approximate convolutions using primarily binary operations.

This results in 58× faster convolutional operations and 32× memory savings. XNOR-Nets offer the possibility of running state-of-the-art networks on CPUs (rather than GPUs) in real-time.

Our binary networks are simple, accurate, efficient, and work on challenging visual tasks. We evaluate our approach on the ImageNet classification task. The classification accuracy with a Binary-Weight-Network version of AlexNet is only 2.9% less than the full-precision AlexNet (in top-1 measure).

We compare our method with recent network binarization methods, BinaryConnect and BinaryNets, and outperform these methods by large margins on ImageNet, more than 16% in top-1 accuracy.

--- https://arxiv.org/abs/2306.01841#facebook Binary and Ternary Natural Language Generation Zechun Liu, Barlas Oguz, Aasish Pappu, Yangyang Shi, Raghuraman Krishnamoorthi 2023-06-02 2023-06-02 [("doi","10.48550/arXiv.2306.01841")] ai/nn/sparsity/low-precision ai/nn/transformer

Ternary and binary neural networks enable multiplication-free computation and promise multiple orders of magnitude efficiency gains over full-precision networks if implemented on specialized hardware. However, since both the parameter and the output space are highly discretized, such networks have proven very difficult to optimize. The difficulties are compounded for the class of transformer text generation models due to the sensitivity of the attention operation to quantization and the noise-compounding effects of autoregressive decoding in the high-cardinality output space.

We approach the problem with a mix of statistics-based quantization for the weights and elastic quantization of the activations and demonstrate the first ternary and binary transformer models on the downstream tasks of summarization and machine translation. Our ternary BART base achieves an R1 score of 41 on the CNN/DailyMail benchmark, which is merely 3.9 points behind the full model while being 16× more efficient. Our binary model, while less accurate, achieves a highly non-trivial score of 35.6.

For machine translation, we achieved BLEU scores of 21.7 and 17.6 on the WMT16 En-Ro benchmark, compared with a full precision mBART model score of 26.8. We also compare our approach in the 8-bit activation setting, where our ternary and even binary weight models can match or outperform the best existing 8-bit weight models in the literature.

Our code and models are available at: Github.

--- https://arxiv.org/abs/1511.00363 BinaryConnect: Training Deep Neural Networks with binary weights during propagations Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David 2015-11-02 2022-12-06 [("doi","10.48550/arXiv.1511.00363")] ai/nn/cnn ai/nn/fully-connected ai/nn/sparsity/low-precision

Deep Neural Networks (DNN) have achieved state-of-the-art results in a wide range of tasks, with the best results obtained with large training sets and large models. In the past, GPUs enabled these breakthroughs because of their greater computational speed. In the future, faster computation at both training and test time is likely to be crucial for further progress and for consumer applications on low-power devices.

As a result, there is much interest in research and development of dedicated hardware for Deep Learning (DL). Binary weights, ie. weights which are constrained to only two possible values (eg. −1 or +1), would bring great benefits to specialized DL hardware by replacing many multiply-accumulate operations by simple accumulations, as multipliers are the most space and power-hungry components of the digital implementation of neural networks.

We introduce BinaryConnect, a method which consists in training a DNN with binary weights during the forward and backward propagations, while retaining precision of the stored weights in which gradients are accumulated.

Like other dropout schemes, we show that BinaryConnect acts as regularizer and we obtain near state-of-the-art results with BinaryConnect on the permutation-invariant MNIST, CIFAR-10 and SVHN.

--- https://tim.blog/2020/02/02/reasons-to-not-become-famous/ 2022-12-06 sociology/technology --- https://www.statnews.com/2019/11/14/france-consumer-genetic-testing-ban/ 2022-12-06 genetics/sequencing --- https://github.com/montemac/activation_additions 2022-12-06 ai/nn/transformer/attention ai/nn/transformer/gpt/2 --- https://en.wikipedia.org/wiki/Hierapolis_sawmill Hierapolis sawmill 2022-12-06 technology --- /doc/science/2023-clark.pdf Harm Hypervigilance in Public Reactions to Scientific Evidence Cory J. Clark, Maja Graso, Ilana Redstone, Philip E. Tetlock 2023-06-01 2023-06-01 [("doi","10.1177/09567976231168777")] philosophy/ethics politics science

Two preregistered studies from two different platforms with representative U.S. adult samples (n = 1,865) tested the harm-hypervigilance hypothesis in risk assessments of controversial behavioral science.

As expected, across 6 sets of scientific findings, people consistently overestimated others’ harmful reactions (medium to large average effect sizes) and underestimated helpful ones, even when incentivized for accuracy. Additional analyses found that (1) harm overestimations were associated with support for censoring science, (2) people who were more offended by scientific findings reported greater difficulty understanding them, and (3) evidence was moderately consistent for an association between more conservative ideology and harm overestimations.

These findings are particularly relevant because journals have begun evaluating potential downstream harms of scientific findings. We discuss implications of our work and invite scholars to develop rigorous tests of (1) the social pressures that lead science astray and (2) the actual costs and benefits of publishing or not publishing potentially controversial conclusions.

--- https://publicdomainreview.org/collection/an-adventure 2022-12-07 history/public-domain-review psychiatry --- https://en.wikipedia.org/wiki/June_and_Jennifer_Gibbons June and Jennifer Gibbons 2022-12-07 genetics/heritable psychiatry --- https://en.wikipedia.org/wiki/Photoplethysmogram Photoplethysmogram 2022-12-07 nootropic/quantified-self psychology/neuroscience reinforcement-learning/preference-learning --- https://web.archive.org/web/20160311042613/https://www.bloomberg.com/news/articles/2007-04-16/the-end-of-a-1-400-year-old-businessbusinessweek-business-news-stock-market-and-financial-advice 2022-12-07 economics/perpetuities japan/history philosophy/religion --- https://web.archive.org/web/20200405194914/https://toki.increpare.com/ilo-sitelen/ 2022-12-07 cs/hardware --- https://arxiv.org/abs/1203.6902 Gods as Topological Invariants Daniel Schoch 2012-04-01 2022-12-07 [("doi","10.48550/arXiv.1203.6902")] math/humor philosophy/religion

We show that the number of gods in a universe must equal the Euler characteristics of its underlying manifold.

By incorporating the classical cosmological argument for creation, this result builds a bridge between theology and physics and makes theism a testable hypothesis. Theological implications are profound since the theorem gives us new insights in the topological structure of heavens and hells.

Recent astronomical observations can not reject theism, but data are slightly in favor of atheism.

--- https://www.medrxiv.org/content/10.1101/2023.05.31.23290802.full Do polygenic indices capture ‘direct’ effects on child externalizing behavior? Within-family analyses in two longitudinal birth cohorts Peter T. Tanksley, Sarah J. Brislin, Jasmin Wertz, Ronald de Vlaming, Natasia S. Courchesne-Krak, Travis T. Mallard, Laurel L. Raffington, Richard Karlsson Linnér, Philipp Koellinger, Abraham Palmer, Alexandra Sanchez-Roige, Irwin Waldman, Danielle Dick, Terrie E. Moffitt, Avshalom Caspi, K. Paige Harden 2023-06-04 2023-06-04 [("doi","10.1101/2023.05.31.23290802")] crime genetics/heritable

[Twitter] Behaviors and disorders characterized by difficulties with self-regulation, such as problematic substance use, antisocial behavior, and symptoms of attention-deficit/hyperactivity disorder (ADHD), incur high costs for individuals, families, and communities. These externalizing behaviors often appear early in the life course and can have far-reaching consequences.

Researchers have long been interested in direct measurements of genetic risk for externalizing behaviors, which can be incorporated alongside other known risk factors to improve efforts at early identification and intervention. In a preregistered analysis drawing on data from the Environmental Risk (E-Risk) Longitudinal Twin Study (n = 862 twins) and the Millennium Cohort Study (MCS) (n = 2,824 parent-child trios), two longitudinal cohorts from the UK, we leveraged molecular genetic data and within-family designs to test for genetic effects on externalizing behavior that are unbiased by the common sources of environmental confounding.

Results are consistent with the conclusion that an externalizing polygenic index (PGI) captures causal effects of genetic variants on externalizing problems in children and adolescents, with an effect size that is comparable to those observed for other established risk factors in the research literature on externalizing behavior. Additionally, we find that polygenic associations vary across development (peaking from age 5–10 years), that parental genetics (assortment and parent-specific effects) and family-level covariates affect prediction little, and that sex differences in polygenic prediction are present but only detectable using within-family comparisons.

Based on these findings, we believe that the PGI for externalizing behavior is a promising means for studying the development of disruptive behaviors across child development.

--- https://arxiv.org/abs/2306.03423 I’m Afraid I Can’t Do That: Predicting Prompt Refusal in Black-Box Generative Language Models Max Reuter, William Schulze 2023-06-06 2023-06-06 [("doi","10.48550/arXiv.2306.03423")] ai/nn/transformer/gpt/3

Since the release of OpenAI’s ChatGPT, generative language models have attracted extensive public attention. The increased usage has highlighted generative models’ broad utility, but also revealed several forms of embedded bias. Some is induced by the pre-training corpus; but additional bias specific to generative models arises from the use of subjective fine-tuning to avoid generating harmful content. Fine-tuning bias may come from individual engineers and company policies, and affects which prompts the model chooses to refuse.

In this experiment, we characterize ChatGPT’s refusal behavior using a black-box attack. We first query ChatGPT with a variety of offensive and benign prompts (n = 1,730), then manually label each response as compliance or refusal. Manual examination of responses reveals that refusal is not cleanly binary, and lies on a continuum; as such, we map several different kinds of responses to a binary of compliance or refusal. The small manually-labeled dataset is used to train a refusal classifier, which achieves an accuracy of 92%.

Second, we use this refusal classifier to bootstrap a larger (n = 10,000) dataset adapted from the Quora Insincere Questions dataset. With this machine-labeled data, we train a prompt classifier to predict whether ChatGPT will refuse a given question, without seeing ChatGPT’s response. This prompt classifier achieves 76% accuracy on a test set of manually labeled questions (n = 1,009).

We examine our classifiers and the prompt n-grams that are most predictive of either compliance or refusal. Datasets and code are available at Github.

--- https://www.science.org/content/blog-post/reduction-tau 2022-12-07 psychiatry/alzheimers --- https://x.com/goodside/status/1666598586346352641 2022-12-07 ai/nn/tokenization --- https://www.airgradient.com/open-airgradient/blog/expensive-air-quality-monitors-not-more-accurate/ 2022-12-08 co2 --- https://arxiv.org/abs/2305.19504 Self-Replicating Hierarchical Structures Emerge in a Binary Cellular Automaton Bo Yang 2023-05-31 2023-05-31 [("doi","10.48550/arXiv.2305.19504")] cs/cellular-automaton

We have discovered a novel transition rule for binary cellular automata (CA) that yields self-replicating structures across two spatial and temporal scales from sparsely populated random initial conditions.

Lower-level, shapeshifting clusters frequently follow a transient attractor trajectory, generating new clusters, some of which periodically self-duplicate. When the initial distribution of live cells is sufficiently sparse, these clusters coalesce into larger formations that also self-replicate. These formations may further form the boundaries of an expanding complex on an even larger scale.

This rule, dubbed “Outlier”, is rotationally symmetric and applies to 2D Moore neighborhoods. It was evolved through Genetic Programming during an extensive automated search for rules that foster open-ended evolution in CA [using novelty search].

While self-replicating structures, both crafted and emergent, have been created in CA with state sets intentionally designed for this purpose, the Outlier may be the first known rule to facilitate emergent self-replication across two spatial scales in simple binary CA.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8115726/ MAP-Elites Enables Powerful Stepping Stones and Diversity for Modular Robotics Jørgen Nordmoen, Frank Veenstra, Kai Olav Ellefsen, Kyrre Glette 2021 2022-12-08 [("doi","10.3389/frobt.2021.639173")] reinforcement-learning/exploration reinforcement-learning/robot

In modular robotics modules can be reconfigured to change the morphology of the robot, making it able to adapt to specific tasks. However, optimizing both the body and control of such robots is a difficult challenge due to the intricate relationship between fine-tuning control and morphological changes that can invalidate such optimizations. These challenges can trap many optimization algorithms in local optima, halting progress towards better solutions.

To solve this challenge we compare 3 different Evolutionary Algorithms on their capacity to optimize high performing and diverse morphologies and controllers in modular robotics. We compare two objective-based search algorithms, with and without a diversity promoting objective, with a Quality Diversity algorithm—MAP-Elites. The results show that MAP-Elites is capable of evolving the highest performing solutions in addition to generating the largest morphological diversity. Further, MAP-Elites is superior at regaining performance when transferring the population to new and more difficult environments.

By analyzing genealogical ancestry we show that MAP-Elites produces more diverse and higher performing stepping stones than the two other objective-based search algorithms. The experiments transitioning the populations to new environments show the utility of morphological diversity, while the analysis of stepping stones show a strong correlation between diversity of ancestry and maximum performance on the locomotion task.

Together, these results demonstrate the suitability of MAP-elites for the challenging task of morphology-control search for modular robots, and shed light on the algorithm’s capability of generating stepping stones for reaching high-performing solutions.

--- https://arxiv.org/abs/1504.04909 MAP-Elites: Illuminating search spaces by mapping elites Jean-Baptiste Mouret, Jeff Clune 2015-04-20 2022-12-08 [("doi","10.48550/arXiv.1504.04909")] reinforcement-learning/exploration reinforcement-learning/robot

Many fields use search algorithms, which automatically explore a search space to find high-performing solutions: chemists search through the space of molecules to discover new drugs; engineers search for stronger, cheaper, safer designs, scientists search for models that best explain data, etc. The goal of search algorithms has traditionally been to return the single highest-performing solution in a search space.

Here we describe a new, fundamentally different type of algorithm that is more useful because it provides a holistic view of how high-performing solutions are distributed throughout a search space. It creates a map of high-performing solutions at each point in a space defined by dimensions of variation that a user gets to choose. This Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) algorithm illuminates search spaces, allowing researchers to understand how interesting attributes of solutions combine to affect performance, either positively or, equally of interest, negatively. For example, a drug company may wish to understand how performance changes as the size of molecules and their cost-to-produce vary.

MAP-Elites produces a large diversity of high-performing, yet qualitatively different solutions, which can be more helpful than a single, high-performing solution. Interestingly, because MAP-Elites explores more of the search space, it also tends to find a better overall solution than state-of-the-art search algorithms.

We demonstrate the benefits of this new algorithm in 3 different problem domains ranging from producing modular neural networks to designing simulated and real soft robots. Because MAP-elites (1) illuminates the relationship between performance and dimensions of interest in solutions, (2) returns a set of high-performing, yet diverse solutions, and (3) improves finding a single, best solution, it will advance science and engineering.

--- https://www.leighb.com/bummer.htm 2022-12-08 psychiatry/meditation --- /doc/reinforcement-learning/preference-learning/2012-cakmak.pdf Algorithmic and Human Teaching of Sequential Decision Tasks Maya Cakmak, Manuel Lopes 2012-01-01 2022-12-08 [("doi","10.1609/aaai.v26i1.8333")] reinforcement-learning/exploration/active-learning reinforcement-learning/preference-learning

A helpful teacher can substantially improve the learning rate of a learning agent. Teaching algorithms have been formally studied within the field of Algorithmic Teaching. These give important insights into how a teacher can select the most informative examples while teaching a new concept. However the field has so far focused purely on classification tasks.

In this paper we introduce a novel method for optimally teaching sequential decision tasks. We present an algorithm that automatically selects the set of most informative demonstrations and evaluate it on several navigation tasks.

Next, we explore the idea of using this algorithm to produce instructions for humans on how to choose examples when teaching sequential decision tasks. We present a user study that demonstrates the utility of such instructions.

Figure 5: Comparison of learning from optimally-selected demonstrations and randomly-selected demonstrations. The top row shows the decrease in the uncertainty on the rewards, and the bottom row shows the change in the percentage of correctly chosen actions with the policy obtained from the estimated rewards. The x-axes are the increasing number of demonstrations.

…Natural teaching is sub-optimal but spontaneous optimality is possible: Only 5⁄40 people in this condition spontaneously produced optimal demonstrations. The participants’ descriptions of how the demonstration was chosen reveals that these were not chosen by chance, but were indeed insightful. For instance, two participants describe their teaching strategy as: “[I tried] to demonstrate a route that contains several different elements the robot may encounter”, and “I tried to involve as many crossroads as possible and to use both green and blue tiles in the path.” As a result of such intuitions being rare, the performance of the trained learners averaged across participants is far from the optimal values

--- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.213.204&rep=rep1&type=pdf Sleep and synaptic homeostasis: a hypothesis Tononi, Cirelli 2003 2022-12-08 psychology/neuroscience zeo --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1213071/ A Markov process of gene frequency change in a geographically structured population T. Maruyama 1974 2022-12-08 [("doi","10.1093/genetics/76.2.367")] genetics/selection/natural statistics/probability

A Markov process (chain) of gene frequency change is derived for a geographically-structured model of a population. The population consists of colonies which are connected by migration. Selection operates in each colony independently.

It is shown that there exists a stochastic clock that transforms the originally complicated process of gene frequency change to a random walk which is independent of the geographical structure of the population. The time parameter is a local random time that is dependent on the sample path. In fact, if the alleles are selectively neutral, the time parameter is exactly equal to the sum of the average local genetic variation appearing in the population, and otherwise they are ~equal.

The Kolmogorov forward and backward equations of the process are obtained. As a limit of large population size, a diffusion process is derived. The transition probabilities of the Markov chain and of the diffusion process are obtained explicitly.

Certain quantities of biological interest are shown to be independent of the population structure. The quantities are the fixation probability of a mutant, the sum of the average local genetic variation and the variation summed over the generations in which the gene frequency in the whole population assumes a specified value.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636432/ Most Undirected Random Graphs Are Amplifiers of Selection for Birth-Death Dynamics, but Suppressors of Selection for Death-Birth Dynamics Laura Hindersin, Arne Traulsen 2015 2022-12-08 [("doi","10.1371/journal.pcbi.1004437")] genetics/selection/natural statistics/probability

We analyze evolutionary dynamics on graphs, where the nodes represent individuals of a population. The links of a node describe which other individuals can be displaced by the offspring of the individual on that node. Amplifiers of selection are graphs for which the fixation probability is increased for advantageous mutants and decreased for disadvantageous mutants.

A few examples of such amplifiers have been developed, but so far it is unclear how many such structures exist and how to construct them. Here, we show that almost any undirected random graph is an amplifier of selection for Birth-death updating, where an individual is selected to reproduce with probability proportional to its fitness and one of its neighbors is replaced by that offspring at random.

If we instead focus on death-Birth updating, in which a random individual is removed and its neighbors compete for the empty spot, then the same ensemble of graphs consists of almost only suppressors of selection for which the fixation probability is decreased for advantageous mutants and increased for disadvantageous mutants.

Thus, the impact of population structure on evolutionary dynamics is a subtle issue that will depend on seemingly minor details of the underlying evolutionary process.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4209402/ Universality of fixation probabilities in randomly structured populations Ben Adlam, Martin A. Nowak 2014 2022-12-08 [("doi","10.1038/srep06692")] genetics/selection/natural statistics/probability

The stage of evolution is the population of reproducing individuals. The structure of the population is known to affect the dynamics and outcome of evolutionary processes, but analytical results for generic random structures have been lacking.

The most general result so far, the isothermal theorem, assumes the propensity for change in each position is exactly the same, but realistic biological structures are always subject to variation and noise. We consider a finite population under constant selection whose structure is given by a variety of weighted, directed, random graphs; vertices represent individuals and edges interactions between individuals.

By establishing a robustness result for the isothermal theorem and using large deviation estimates to understand the typical structure of random graphs, we prove that for a generalization of the Erdős-Rényi model, the fixation probability of an invading mutant is the same as that of a mutant of equal fitness in a well-mixed population with high probability.

Simulations of perturbed lattices, small-world networks, and scale-free networks behave similarly.

We conjecture that the fixation probability in a well-mixed population, (1—r(-1))/(1—r(-n)), is universal: for many random graph models, the fixation probability approaches the above function uniformly as the graphs become large.

--- https://arxiv.org/abs/1312.6333 Fixation probabilities on superstars, revisited and revised Alastair Jamieson-Lane, Christoph Hauert 2013-12-22 2022-12-08 [("doi","10.48550/arXiv.1312.6333")] genetics/selection/natural statistics/probability

Population structures can be crucial determinants of evolutionary processes. For the Moran process on graphs certain structures suppress selective pressure, while others amplify it (Lieberman et al 2005 Nature 433 312–316). Evolutionary amplifiers suppress random drift and enhance selection.

Recently, some results for the most powerful known evolutionary amplifier, the superstar, have been invalidated by a counter example (Díaz et al 2013). Here we correct the original proof and derive improved upper and lower bounds, which indicate that the fixation probability remains close to 1 − 1⁄(r4H) for population size N → ∞ and structural parameter H ≫ 1. This correction resolves the differences between the two aforementioned papers.

We also confirm that in the limit N, H → ∞ superstars remain capable of eliminating random drift and hence of providing arbitrarily strong selective advantages to any beneficial mutation. In addition, we investigate the robustness of amplification in superstars and find that it appears to be a fragile phenomenon with respect to changes in the selection or mutation processes.

--- https://arxiv.org/abs/1404.3944 Asymptotic expression for the fixation probability of a mutant in star graphs Fabio A. C. C. Chalub 2014-04-15 2022-12-09 [("doi","10.48550/arXiv.1404.3944")] genetics/selection/natural statistics/probability

We consider the Moran process in a graph called the star and obtain the asymptotic expression for the fixation probability of a single mutant when the size of the graph is large.

The expression obtained corrects the previously known expression announced in Lieberman et al 2005 & further studied in Broom & Rychtář 2008.

We also show that the star graph is an accelerator of evolution, if the graph is large enough.

--- https://arxiv.org/abs/1611.04209 Asymptotically Optimal Amplifiers for the Moran Process Leslie Ann Goldberg, John Lapinskas, Johannes Lengler, Florian Meier, Konstantinos Panagiotou, Pascal Pfister 2016-11-13 2022-12-09 [("doi","10.48550/arXiv.1611.04209")] genetics/selection/natural statistics/probability

We study the Moran process as adapted by Lieberman, Hauert and Nowak. This is a model of an evolving population on a graph or digraph where certain individuals, called “mutants” have fitness r and other individuals, called non-mutants have fitness 1. We focus on the situation where the mutation is advantageous, in the sense that r > 1.

A family of digraphs is said to be strongly amplifying if the extinction probability tends to 0 when the Moran process is run on digraphs in this family. The most-amplifying known family of digraphs is the family of megastars of Galanis et al 2015. We show that this family is optimal, up to logarithmic factors, since every strongly-connected n-vertex digraph has extinction probability Ω(n−1⁄2).

Next, we show that there is an infinite family of undirected graphs, called dense incubators, whose extinction probability is 𝒪(n−1⁄3). We show that this is optimal, up to constant factors.

Finally, we introduce sparse incubators, for varying edge density, and show that the extinction probability of these graphs is 𝒪(n⁄m), where m is the number of edges. Again, we show that this is optimal, up to constant factors.

--- https://arxiv.org/abs/1611.01585 Amplifiers and Suppressors of Selection for the Moran Process on Undirected Graphs George Giakkoupis 2016-11-05 2022-12-09 [("doi","10.48550/arXiv.1611.01585")] genetics/selection/natural statistics/probability

We consider the classic Moran process modeling the spread of genetic mutations, as extended to structured populations by Lieberman et al 2005. In this process, individuals are the vertices of a connected graph G. Initially, there is a single mutant vertex, chosen uniformly at random. In each step, a random vertex is selected for reproduction with a probability proportional to its fitness: mutants have fitness r > 1, while non-mutants have fitness 1. The vertex chosen to reproduce places a copy of itself to a uniformly random neighbor in G, replacing the individual that was there. The process ends when the mutation either reaches fixation (ie. all vertices are mutants), or gets extinct. The principal quantity of interest is the probability with which each of the two outcomes occurs.

A problem that has received attention recently concerns the existence of families of graphs, called strong amplifiers of selection, for which the fixation probability tends to 1 as the order n of the graph increases, and the existence of strong suppressors of selection, for which this probability tends to 0.

For the case of directed graphs, it is known that both strong amplifiers and suppressors exist. For the case of undirected graphs, however, the problem has remained open, and the general belief has been that neither strong amplifiers nor suppressors exist.

In this paper we disprove this belief, by providing the first examples of such graphs. The strong amplifier we present has fixation probability 1 − 𝑂̃(n−1⁄3), and the strong suppressor has fixation probability 1 − 𝑂̃(n−1⁄4). Both graph constructions are surprisingly simple.

We also prove a general upper bound of 1 − Ω̃(n−1⁄3) on the fixation probability of any undirected graph. Hence, our strong amplifier is existentially optimal.

--- https://en.wikipedia.org/wiki/Affinity_maturation Affinity maturation 2022-12-09 genetics/cloning genetics/selection/natural --- https://en.wikipedia.org/wiki/Germinal_center Germinal center 2022-12-09 genetics/cloning genetics/selection/natural --- https://arstechnica.com/culture/2023/06/rejoice-its-2023-and-you-can-still-buy-a-22-volume-paper-encyclopedia/2/ 2022-12-09 wikipedia --- https://www.quantamagazine.org/how-lossless-data-compression-works-20230531 2022-12-09 cs/algorithm --- https://www.astralcodexten.com/p/your-book-review-mans-search-for#%C2%A7part-2 2022-12-09 fiction/poetry --- https://web.archive.org/web/20210205014443/https://meanderful.blogspot.com/2018/01/the-accidental-hft-firm.html 2022-12-09 cs/algorithm cs/hardware economics --- /doc/law/2017-lawsky.pdf A Logic for Statutes Sarah B. Lawsky 2017-12-19 2022-12-09 [("doi","10.5744/ftr.2017.0002")] law philosophy/logic

Case-based reasoning is, without question, a puzzle. When students are taught to “think like lawyers” in their first year of law school, they are taught case-based common-law reasoning. Books on legal reasoning are devoted almost entirely to the topic. How do courts reason from one case to the next? Is case-based reasoning reasoning from analogy? How should case-based reasoning be modeled? How can it be justified?

In contrast, rule-based legal reasoning (as exemplified in much statutory reasoning) is taken as simple in legal scholarship. Statutory interpretation—how to determine the meaning of words in a statute, the relevance of the lawmakers’ intent, and so forth—is much discussed, but there is little treatment of the structure of statutory reasoning once the meaning of the words is established. Once the meaning of terms is established, statutory reasoning is considered, roughly speaking, to be deductive reasoning.

This Essay examines the structure of statutory reasoning after ambiguities are resolved and the meaning of the statute’s terms established. It argues that standard formal logic is not the best approach for modeling statutory rule-based reasoning. Rather, the Essay argues, using the Internal Revenue Code and accompanying regulations, judicial decisions, and rulings as its primary example, that at least some statutory reasoning is best characterized as defeasible reasoning—reasoning that may result in conclusions that can be defeated by subsequent information—and is best modeled using default logic. The Essay then addresses the practical and theoretical benefits of this alternative understanding of rule-based legal reasoning.

--- https://www.johndcook.com/blog/2023/06/09/coupon-collector-2/ 2022-12-10 statistics/order --- https://www.johndcook.com/blog/2023/05/30/reviewing-a-thousand-things/ 2022-12-10 statistics/order --- https://www.nytimes.com/2023/05/21/science/math-puzzles-integer-sequences.html What Number Comes Next? The Encyclopedia of Integer Sequences Knows. The ‘mathematical equivalent to the FBI’s voluminous fingerprint files’ turns 50 this year, with 362,765 entries (and counting) Siobhan Roberts 2023-05-21 2023-05-21 math --- https://www.wired.com/story/gene-therapy-in-the-womb-is-inching-closer-to-reality/ 2022-12-10 genetics/editing --- https://www.biorxiv.org/content/10.1101/2022.09.25.509396.full RNA recoding in cephalopods tailors microtubule motor protein function Kavita J. Rangan, Samara L. Reck-Peterson 2022-09-25 2022-12-10 [("doi","10.1101/2022.09.25.509396")] genetics/editing psychology/neuroscience

RNA editing is a widespread epigenetic process that can alter the amino acid sequence of proteins, termed ‘recoding’. In cephalopods, recoding occurs in most proteins and is hypothesized to be an adaptive strategy to generate phenotypic plasticity. However, how animals use RNA recoding dynamically is largely unexplored.

Using microtubule motors as a model, we found that squid rapidly employ RNA recoding to enhance kinesin function in response to cold ocean temperature. We also identified tissue-specific recoded squid kinesin variants that displayed distinct motile properties. Finally, we showed that cephalopod recoding sites can guide the discovery of functional substitutions in non-cephalopod dynein and kinesin.

Thus, RNA recoding is a dynamic mechanism that generates phenotypic plasticity in cephalopods and informs the functional characterization of conserved non-cephalopod proteins.

--- https://www.thendobetter.com/investing/2023/6/9/tyler-cowen-hayek-lecture-on-economics-ai-and-large-langauge-models 2022-12-10 ai/nn/transformer/gpt/4/nonfiction --- https://www.youtube.com/watch?v=urcL86UpqZc 2022-12-10 cs/lisp math/humor --- https://pocketarc.com/posts/my-coworkers-are-gpt-4-bots-and-we-all-hang-out-on-slack 2022-12-10 ai/nn/transformer/gpt/3/fiction ai/nn/transformer/gpt/4/fiction --- https://arxiv.org/abs/2208.07339#facebook LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer 2022-08-15 2022-12-10 [("doi","10.48550/arXiv.2208.07339")] ai/nn/sparsity/low-precision ai/nn/transformer/gpt ai/scaling

Large language models have been widely adopted but require GPU memory for inference. We develop a procedure for Int8 matrix multiplication for feed-forward and attention projection layers in transformers, which cut the memory needed for inference by half while retaining full precision performance.

With our method, a 175b parameter 16/32-bit checkpoint can be loaded, converted to Int8, and used immediately without performance degradation. This is made possible by understanding and working around properties of highly systematic emergent features in transformer language models that dominate attention and transformer predictive performance.

To cope with these features, we develop a two-part quantization procedure, LLM.int8(). We first use vector-wise quantization with separate normalization constants for each inner product in the matrix multiplication, to quantize most of the features. However, for the emergent outliers, we also include a new mixed-precision decomposition scheme, which isolates the outlier feature dimensions into a 16-bit matrix multiplication while still more than 99.9% of values are multiplied in 8-bit.

Using LLM.int8(), we show empirically it is possible to perform inference in LLMs with up to 175b parameters without any performance degradation. This result makes such models much more accessible, for example making it possible to use OPT-175B/BLOOM on a single server with consumer GPUs. We open-source our software.

--- https://arxiv.org/abs/2306.03078 SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh 2023-06-05 2023-06-05 [("doi","10.48550/arXiv.2306.03078")] ai/nn/sparsity/low-precision ai/nn/transformer/gpt

Recent advances in large language model (LLM) pretraining have led to high-quality LLMs with impressive abilities. By compressing such LLMs via quantization to 3–4 bits per parameter, they can fit into memory-limited devices such as laptops and mobile phones, enabling personalized use. However, quantization down to 3–4 bits per parameter usually leads to moderate-to-high accuracy losses, especially for smaller models in the 1–10b parameter range, which are well-suited for edge deployments.

To address this accuracy issue, we introduce the Sparse-Quantized Representation (SpQR), a new compressed format and quantization technique which enables for the first time near-lossless compression of LLMs across model scales, while reaching similar compression levels to previous methods. SpQR works by identifying and isolating outlier weights, which cause particularly-large quantization errors, and storing them in higher precision, while compressing all other weights to 3–4 bits, and achieves relative accuracy losses of less than 1% in perplexity for highly-accurate LLaMa and Falcon LLMs.

This makes it possible to run 33b parameter LLM on a single 24 GB consumer GPU without any performance degradation at 15% speedup thus making powerful LLMs available to consumer without any downsides. SpQR comes with efficient algorithms for both encoding weights into its format, as well as decoding them efficiently at runtime. Specifically, we provide an efficient GPU inference algorithm for SpQR which yields faster inference than 16-bit baselines at similar accuracy, while enabling memory compression gains of more than 4×.

--- /doc/crime/2022-gurun.pdf Measuring and Improving Stakeholder Welfare Is Easier Said than Done Umit G. Gurun, Jordan Nickerson, David H. Solomon 2022-12-19 2022-12-19 [("doi","10.1017/S0022109022001442")] crime economics

While corporate social responsibility by firms aims at improving welfare for different social groups, whether it achieves this is often difficult to measure.

After April 2018 protests, Starbucks enacted policies that anybody could sit in their stores and use the bathroom without making a purchase.

Using anonymized cellphone location data [SafeGraph], we estimate:

this led to a net 7.0% decline in attendance relative to other nearby coffee shops. The effect is 84% larger near homeless shelters and larger for Starbucks’ wealthier customers. The average time spent per visit declined by 4.1%...the new policy appears to have deterred both black and white customers in roughly equal amounts.

Public urination citations decreased near Starbucks locations, but other minor crimes were unchanged.

--- https://www.reddit.com/r/mlscaling/comments/146rgq2/chatgpt_is_running_quantized/ 2022-12-11 ai/nn/sparsity/low-precision ai/nn/transformer/gpt/3 --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.644.2669&rep=rep1&type=pdf#page=518 In praise of sparsity and convexity Tibshirani 2022-12-11 philosophy/epistemology statistics/bayes statistics/decision --- https://arxiv.org/abs/1512.05632 Amplifiers for the Moran Process Andreas Galanis, Andreas Göbel, Leslie Ann Goldberg, John Lapinskas, David Richerby 2015-12-17 2022-12-11 [("doi","10.48550/arXiv.1512.05632")] genetics/selection/natural statistics/probability

The Moran process, as studied by Lieberman, Hauert and Nowak, is a randomized algorithm modeling the spread of genetic mutations in populations. The algorithm runs on an underlying graph where individuals correspond to vertices. Initially, one vertex (chosen u.a.r.) possesses a mutation, with fitness r > 1. All other individuals have fitness 1. During each step of the algorithm, an individual is chosen with probability proportional to its fitness, and its state (mutant or non-mutant) is passed on to an out-neighbour which is chosen u.a.r.

If the underlying graph is strongly connected then the algorithm will eventually reach fixation, in which all individuals are mutants, or extinction, in which no individuals are mutants. An infinite family of directed graphs is said to be strongly amplifying if, for every r > 1, the extinction probability tends to 0 as the number of vertices increases. Lieberman et al proposed two potentially strongly-amplifying families—superstars and metafunnels.

Heuristic arguments have been published, arguing that there are infinite families of superstars that are strongly amplifying. The same has been claimed for metafunnels. In this paper, we give the first rigorous proof that there is an infinite family of directed graphs that is strongly amplifying. We call the graphs in the family “megastars”.

When the algorithm is run on an n-vertex graph in this family, starting with a uniformly-chosen mutant, the extinction probability is roughly n−1⁄2 (up to logarithmic factors). We prove that all infinite families of superstars and metafunnels have larger extinction probabilities (as a function of n).

Finally, we prove that our analysis of megastars is fairly tight—there is no infinite family of megastars such that the Moran algorithm gives a smaller extinction probability (up to logarithmic factors).

--- https://royalsocietypublishing.org/doi/10.1098/rspa.2013.0193 2022-12-11 genetics/selection/natural statistics/probability --- https://en.wikipedia.org/wiki/Fixation_%28population_genetics%29 Fixation (population genetics) 2022-12-11 genetics/selection/natural statistics/probability --- https://en.wikipedia.org/wiki/Moran_process Moran process 2022-12-11 genetics/selection/natural statistics/probability --- https://x.com/DanNeidle/status/1664613427472375808 2022-12-11 ai/nn/transformer/gpt/3 law --- https://vulcan.io/blog/ai-hallucinations-package-risk 2022-12-11 ai/nn/transformer/gpt/codex cs/security --- https://www.reddit.com/r/ProgrammerHumor/comments/145nduh/kiss/ 2022-12-11 cs/security reinforcement-learning/safe --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5339412/ Modafinil Improves Episodic Memory and Working Memory Cognition in Patients With Remitted Depression: A Double-Blind, Randomized, Placebo-Controlled Study Muzaffer Kaser, Julia B. Deakin, Albert Michael, Camilo Zapata, Rachna Bansal, Dragana Ryan, Francesca Cormack, James B. Rowe, Barbara J. Sahakian 2017 2022-12-11 [("doi","10.1016/j.bpsc.2016.11.009")] modafinil psychiatry/depression

Background: Cognitive dysfunction is a core feature of depression and tends to persist even after mood symptoms recover, leading to detrimental effects on clinical and functional outcomes. However, most currently available treatments have not typically addressed cognition. Modafinil has been shown to have beneficial effects on cognitive function and therefore has the potential to improve cognition in depression. The objective of this double-blind, placebo-controlled study was to investigate the effects of modafinil on cognitive functions in patients with remitted depression.

Method: In total, 60 patients with remitted depression participated in the study. Cognitive functions were evaluated with tests of working memory, planning, attention, and episodic memory from the Cambridge Neuropsychological Test Automated Battery at the baseline session and after treatment. A double-blind, randomized, placebo-controlled, parallel groups design was used to assess the effects of single-dose (200 mg) modafinil (n = 30) or placebo (n = 30) on cognition and fatigue. The main outcome measures were neurocognitive test scores from the Cambridge Neuropsychological Test Automated Battery. Visual analogue scales for subjective feelings and fatigue were used as secondary measures.

Results: The modafinil group had statistically-significantly better performance on tests of episodic memory (p = 0.01, ηp2 = 0.10) and working memory (p = 0.04, ηp2 = 0.06). Modafinil did not improve planning or sustained attention.

Conclusions: This study suggested that modafinil (200 mg) could improve episodic memory and working memory performance in patients with remitted depression. Modafinil may have potential as a therapeutic agent to help remitted depressed patients with persistent cognitive difficulties.

--- https://www.wired.com/story/fast-forward-humanoid-robots-are-coming-of-age/ 2022-12-11 reinforcement-learning/robot --- https://www.usenix.org/publications/loginonline/bcrypt-25-retrospective-password-security 2022-12-12 cs/cryptography economics/experience-curve --- https://x.com/kekecs_zoltan/status/1621485429005352961 2022-12-12 psychology/parapsychology --- https://inews.co.uk/news/psychic-pornography-experiments-can-teach-scientists-a-thing-or-two-2229576 2022-12-12 psychology/parapsychology --- https://en.wikipedia.org/wiki/Daryl_Bem#%22Feeling_the_Future%22_controversy Daryl Bem § "Feeling the Future" controversy 2022-12-12 psychology/parapsychology --- https://en.wikipedia.org/wiki/Extrasensory_perception Extrasensory perception 2022-12-12 psychology/parapsychology --- https://osf.io/a6ew3/ 2022-12-12 psychology/parapsychology --- https://github.com/kekecsz/transparent-psi-results/tree/master/live_data 2022-12-12 psychology/parapsychology --- https://github.com/kekecsz/transparent-psi-results 2022-12-12 psychology/parapsychology --- https://osf.io/fku6g/ 2022-12-12 psychology/parapsychology --- https://psi-encyclopedia.spr.ac.uk/articles/sheep-goat-effect 2022-12-12 psychology/parapsychology --- https://royalsocietypublishing.org/doi/full/10.1098/rsos.191375#RSOS191375TB2 2022-12-13 psychology/parapsychology --- https://arxiv.org/abs/2306.04563 ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models Sophie Jentzsch, Kristian Kersting 2023-06-07 2023-06-07 [("doi","10.48550/arXiv.2306.04563")] ai/nn/tokenization ai/nn/transformer/gpt/3/fiction fiction/humor reinforcement-learning/preference-learning/mode-collapse

[RLHF problems] Humor is a central aspect of human communication that has not been solved for artificial agents so far. Large language models (LLMs) are increasingly able to capture implicit and contextual information. Especially, OpenAI’s ChatGPT recently gained immense public attention. The GPT-3-based model almost seems to communicate on a human level and can even tell jokes. Humor is an essential component of human communication. But is ChatGPT really funny? We put ChatGPT’s sense of humor to the test.

In a series of exploratory experiments around jokes, ie. generation, explanation, and detection, we seek to understand ChatGPT’s capability to grasp and reproduce human humor. Since the model itself is not accessible, we applied prompt-based experiments. Our empirical evidence indicates that jokes are not hard-coded but mostly also not newly generated by the model. Over 90% of 1008 generated jokes were the same 25 Jokes.

The system accurately explains valid jokes but also comes up with fictional explanations for invalid jokes. Joke-typical characteristics can mislead ChatGPT in the classification of jokes.

ChatGPT has not solved computational humor yet but it can be a big leap toward “funny” machines.

--- /doc/psychiatry/1991-castle.pdf#page=21 Contagious Folly: An Adventure and Its Skeptics § pg21 Terry Castle 1991-06-01 2022-12-13 [("doi","10.1086/448611")] psychiatry psychology/parapsychology --- /doc/psychiatry/1991-castle.pdf Contagious Folly: An Adventure and Its Skeptics Terry Castle 1991-06-01 2022-12-13 [("doi","10.1086/448611")] psychiatry psychology/parapsychology --- https://en.wikipedia.org/wiki/Moberly%E2%80%93Jourdain_incident Moberly-Jourdain incident 2022-12-13 psychiatry psychology/parapsychology --- https://slatestarcodex.com/2013/02/14/abraham-lincoln-necromancer/ 2022-12-13 psychology/parapsychology --- https://www.jofreeman.com/joreen/tyranny.htm 2022-12-13 design sociology --- https://www.biorxiv.org/content/10.1101/2023.05.11.540325.full Limits to selection on standing variation in an asexual population Nick Barton, Himani Sachdeva 2023-05-14 2023-05-14 [("doi","10.1101/2023.05.11.540325")] genetics/cloning genetics/selection/natural

We consider how a population responds to directional selection on standing variation, with no new variation from recombination or mutation. Initially, there are N individuals with trait values z1, …, zN; the fitness of individual i is proportional to ezi. The initial values are drawn from a distribution ψ with variance V0; we give examples of the Laplace and Gaussian distributions.

When selection is weak relative to drift (N√V0≪1), variance decreases exponentially at rate 1/N; since the increase in mean in any generation equals the variance, the expected net change is just NV0, which is the same as Robertson 1960’s prediction for a sexual population. In contrast, when selection is strong relative to drift (N√V0≫1), the net change can be found by approximating the establishment of alleles by a branching process in which each allele competes independently with the population mean and the fittest allele to establish is certain to fix.

Then, if the probability of survival to time t~1∕√V0 of an allele with value z is P(z), with mean P̄, the winning allele is the fittest of NP̄. survivors drawn from a distribution ψP∕P̄. When N is large, there is a scaling limit which depends on a single parameter N√V0; the expected ultimate change is ~√2 log(1.15N√V0) for a Gaussian distribution, and ~(W [0.36/ (N√V0)]-1)/√2 for a Laplace distribution (where W is the product log function). This approach also reveals the variability of the process, and its dynamics; we show that in the strong selection regime, the expected genetic variance decreases as ~t−3 at large times.

We discuss how these results may be related to selection on standing variation that is spread along a linear chromosome.

--- https://www.quantamagazine.org/in-new-paradox-black-holes-appear-to-evade-heat-death-20230606/ 2022-12-13 cs/computable --- /doc/statistics/bias/1987-rao.pdf On rustles, wolf interpretations, and other wild speculations David Navon 2010-03-05 2022-12-13 psychology/parapsychology statistics/bias --- /doc/statistics/bias/1988-utts.pdf 1988-01-01 2022-12-13 psychology/parapsychology statistics/bias --- https://qualiacomputing.com/2018/04/10/qualia-formalism-in-the-water-supply-reflections-on-the-science-of-consciousness-2018/ 2022-12-14 psychedelic psychology/parapsychology --- https://en.wikipedia.org/wiki/Joseph_Banks_Rhine#Reception Joseph Banks Rhine § Reception 2022-12-14 psychology/parapsychology statistics/bias --- https://newrepublic.com/article/146906/night-vision 2022-12-14 fiction psychology/parapsychology zeo --- https://plato.stanford.edu/entries/sidgwick/#RelPar 2022-12-14 psychology/parapsychology --- https://en.wikipedia.org/wiki/Arthur_Koestler#Paranormal Arthur Koestler § Paranormal 2022-12-14 psychology/parapsychology --- https://gen.medium.com/nazi-hippies-when-the-new-age-and-far-right-overlap-d1a6ddcd7be4 2022-12-14 politics psychology/parapsychology --- https://jeksite.org/psi/jp03.htm 2022-12-14 psychology/parapsychology --- https://xkcd.com/808/ 2022-12-14 economics philosophy/epistemology psychology/parapsychology --- https://jeksite.org/psi/misconduct.pdf 2022-12-14 psychology/parapsychology statistics/bias --- https://reflectivedisequilibrium.blogspot.com/2014/05/what-do-null-fields-tell-us-about-fraud.html 2022-12-14 psychology/parapsychology statistics/bias --- https://aeon.co/essays/my-paranormal-adventure-in-pursuit-of-life-after-death 2022-12-14 psychology/parapsychology statistics/bias --- https://talyarkoni.org/blog/2011/01/10/the-psychology-of-parapsychology-or-why-good-researchers-publishing-good-articles-in-good-journals-can-still-get-it-totally-wrong/ 2022-12-15 psychology/parapsychology statistics/bias --- https://www.theatlantic.com/magazine/archive/1996/05/the-nitrous-oxide-philosopher/376581/ Do drugs make religious experience possible? They did for James and for other philosopher-mystics of his day. James’s experiments with psychoactive drugs raise difficult questions about belief and its conditions 2022-12-15 philosophy/mind psychedelic psychology/parapsychology statistics/bias --- https://www.lesswrong.com/posts/9qCN6tRBtksSyXfHu/frequentist-statistics-are-frequently-subjective 2022-12-15 psychology/parapsychology statistics/bias --- https://arxiv.org/abs/1107.0392 Emergence of good conduct, scaling and Zipf laws in human behavioral sequences in an online world Stefan Thurner, Michel Szell, Roberta Sinatra 2011-07-02 2022-12-15 [("doi","10.1371/journal.pone.0029796")] sociology/technology statistics/probability

We study behavioral action sequences of players in a massive multiplayer online game. In their virtual life players use 8 basic actions which allow them to interact with each other. These actions are communication, trade, establishing or breaking friendships and enmities, attack, and punishment.

We measure the probabilities for these actions conditional on previous taken and received actions and find a dramatic increase of negative behavior immediately after receiving negative actions. Similarly, positive behavior is intensified by receiving positive actions. We observe a tendency towards anti-persistence in communication sequences.

Classifying actions as positive (good) and negative (bad) allows us to define binary ‘world lines’ of lives of individuals. Positive and negative actions are persistent and occur in clusters, indicated by large scaling exponents alpha ~0.87 of the mean square displacement of the world lines. For all 8 action types we find strong signs for high levels of repetitiveness, especially for negative actions.

We partition behavioral sequences into segments of length n (behavioral ‘words’ and ‘motifs’) and study their statistical properties. We find two approximate power laws in the word ranking distribution, one with an exponent of kappa-1 for the ranks up to 100, and another with a lower exponent for higher ranks. The Shannon n-tuple redundancy yields large values and increases in terms of word length, further underscoring the non-trivial statistical properties of behavioral sequences.

On the collective, societal level the timeseries of particular actions per day can be understood by a simple mean-reverting log-normal model.

--- https://learning.mpi-sws.org/memorize/ Enhancing human learning via spaced repetition optimization Tabibian 2019 2022-12-15 psychology/spaced-repetition --- https://nakamotoinstitute.org/library/trusted-third-parties/ Trusted Third Parties are Security Holes Szabo 2022-12-15 bitcoin cs/cryptography cs/security --- https://www.lewissociety.org/innerring/ 2022-12-15 politics sociology --- https://en.wikipedia.org/wiki/Cold_reading Cold reading 2022-12-15 psychology/dark-knowledge psychology/parapsychology --- https://en.wikipedia.org/wiki/Spiritualism Spiritualism 2022-12-15 psychology/parapsychology --- https://en.wikipedia.org/wiki/Ganzfeld_experiment Ganzfeld experiment 2022-12-15 psychology/parapsychology --- https://en.wikipedia.org/wiki/Arthur_Conan_Doyle#Freemasonry_and_spiritualism Arthur Conan Doyle § Freemasonry and spiritualism 2022-12-15 psychology/parapsychology --- https://en.wikipedia.org/wiki/Cottingley_Fairies Cottingley Fairies 2022-12-16 psychology/parapsychology --- https://en.wikipedia.org/wiki/Harry_Houdini#Debunking_spiritualists Harry Houdini § Debunking spiritualists 2022-12-16 psychology/parapsychology --- https://arxiv.org/abs/2305.18769 DualVAE: Controlling Colors of Generated and Real Images Keerth Rathakumar, David Liebowitz, Christian Walder, Kristen Moore, Salil S. Kanhere 2023-05-30 2023-05-30 [("doi","10.48550/arXiv.2305.18769")] ai/anime/danbooru

Colour controlled image generation and manipulation are of interest to artists and graphic designers. Vector Quantized Variational Autoencoders (VQ-VAEs) with autoregressive (AR) prior are able to produce high quality images, but lack an explicit representation mechanism to control color attributes. We introduce DualVAE, a hybrid representation model that provides such control by learning disentangled representations for color and geometry. The geometry is represented by an image intensity mapping that identifies structural features. The disentangled representation is obtained by two novel mechanisms:

(1) a dual branch architecture that separates image color attributes from geometric attributes, and (2) a new ELBO that trains the combined color and geometry representations. DualVAE can control the color of generated images, and recolor existing images by transferring the color latent representation obtained from an exemplar image. We demonstrate that DualVAE generates images with FID nearly two times better than VQ-GAN on a diverse collection of datasets, including animated faces, logos and artistic landscapes.

--- https://arxiv.org/abs/1706.01307 Submanifold Sparse Convolutional Networks Benjamin Graham, Laurens van der Maaten 2017-06-05 2022-12-16 [("doi","10.48550/arXiv.1706.01307")] ai/nn/cnn ai/nn/sparsity

Convolutional network are the de-facto standard for analyzing spatio-temporal data such as images, videos, 3D shapes, etc. Whilst some of this data is naturally dense (for instance, photos), many other data sources are inherently sparse. Examples include pen-strokes forming on a piece of paper, or (colored) 3D point clouds that were obtained using a LiDAR scanner or RGB-D camera.

Standard “dense” implementations of convolutional networks are very inefficient when applied on such sparse data. We introduce a sparse convolutional operation tailored to processing sparse data that differs from prior work on sparse convolutional networks in that it operates strictly on submanifolds, rather than “dilating” the observation with every layer in the network.

Our empirical analysis of the resulting submanifold sparse convolutional networks shows that they perform on par with state-of-the-art methods whilst requiring substantially less computation.

--- https://arxiv.org/abs/1711.10275 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks Benjamin Graham, Martin Engelcke, Laurens van der Maaten 2017-11-28 2022-12-16 [("doi","10.48550/arXiv.1711.10275")] ai/nn/cnn ai/nn/sparsity

Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is naturally dense (eg. photos), many other data sources are inherently sparse. Examples include 3D point clouds that were obtained using a LiDAR scanner or RGB-D camera.

Standard “dense” implementations of convolutional networks are very inefficient when applied on such sparse data. We introduce new sparse convolutional operations that are designed to process spatially-sparse data more efficiently, and use them to develop spatially-sparse convolutional networks.

We demonstrate the strong performance of the resulting models, called submanifold sparse convolutional networks (SSCNs), on two tasks involving semantic segmentation of 3D point clouds. In particular, our models outperform all prior state-of-the-art on the test set of a recent semantic segmentation competition.

--- https://www.quantamagazine.org/sparse-neural-networks-point-physicists-to-useful-data-20230608/ 2022-12-16 ai/nn/cnn ai/nn/sparsity --- https://www.biorxiv.org/content/10.1101/2023.06.10.544454.full A 2-million-year-old microbial and viral communities from the Kap København Formation in North Greenland Antonio Fernandez-Guerra, Guillaume Borrel, Tom O. Delmont, Bo Elberling, A. Murat Eren, Simonetta Gribaldo, Annika Jochheim, Rasmus A. Henriksen, Kai-Uwe Hinrichs, Thorfinn S. Korneliussen, Mart Krupovic, Nicolaj K. Larsen, Rafael Laso-Pérez, Mikkel Winther Pedersen, Vivi K. Pedersen, Karina K. Sand, Martin Sikora, Martin Steinegger, Iva A. Veseli, Lars Wörmer, Lei Zhao, Marina Žure, Kurt H. Kjær, Eske Willerslev 2023-06-12 2023-06-12 [("doi","10.1101/2023.06.10.544454")] genetics/sequencing

Using ancient environmental DNA (eDNA) we reconstructed microbial and viral communities from the Kap København Formation in North Greenland. We find pioneer microbial communities, along with likely dormant methanogens from the permafrost’s seed bank.

Our findings reveal that at the time of the formation, the terrestrial input of the Kap København site originated from a palustrine wetland, suggesting non-permafrost conditions. During this time, detection of methanogenic archaea and carbon processing pathways suggests a moderate strengthening of methane emissions through the northward expansion of wetlands.

Intriguingly, we discover a remarkable sequence similarity (>98%) between pioneer methanogens and present-day thawing permafrost counterparts. This suggests that not all microbes respond uniformly to environmental change over geological timescales, but that some microbial taxa’s adaptability and resilience remain constant over time.

Our findings further suggest that the composition of microbial communities is changing prior to plant communities as a result of global warming.

--- https://en.wikipedia.org/wiki/Channelrhodopsin Channelrhodopsin 2022-12-16 psychology/neuroscience --- https://arxiv.org/abs/2305.18565#google PaLI-X: On Scaling up a Multilingual Vision and Language Model Xi Chen, Josip Djolonga, Piotr Padlewski, Basil Mustafa, Soravit Changpinyo, Jialin Wu, Carlos Riquelme Ruiz, Sebastian Goodman, Xiao Wang, Yi Tay, Siamak Shakeri, Mostafa Dehghani, Daniel Salz, Mario Lucic, Michael Tschannen, Arsha Nagrani, Hexiang Hu, Mandar Joshi, Bo Pang, Ceslee Montgomery, Paulina Pietrzyk, Marvin Ritter, A. J. Piergiovanni, Matthias Minderer, Filip Pavetic, Austin Waters, Gang Li, Ibrahim Alabdulmohsin, Lucas Beyer, Julien Amelot, Kenton Lee, Andreas Peter Steiner, Yang Li, Daniel Keysers, Anurag Arnab, Yuanzhong Xu, Keran Rong, Alexander Kolesnikov, Mojtaba Seyedhosseini, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, Radu Soricut 2023-05-29 2023-05-29 [("doi","10.48550/arXiv.2305.18565")] ai/nn/transformer/t5 ai/scaling ai/video/analysis

We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language [T5] model, both in terms of size of the components and the breadth of its training task mixture.

Our model achieves new levels of performance on a wide-range of varied and complex tasks, including multiple image-based captioning and question-answering tasks, image-based document understanding and few-shot (in-context) learning, as well as object detection, video question answering, and video captioning.

PaLI-X advances the state-of-the-art on most vision-and-language benchmarks considered (25+).

Finally, we observe emerging capabilities, such as complex counting and multilingual object detection, tasks that are not explicitly in the training mix.

--- https://illustrationchronicles.com/Obsessed-with-Cats-The-Ukiyo-e-Prints-of-Utagawa-Kuniyoshi 2022-12-16 cat japan/art --- https://en.wikipedia.org/wiki/Thieves%27_World Thieves’ World 2022-12-17 fiction/science-fiction fiction/text-game --- https://www.mcsweeneys.net/articles/im-comic-sans-asshole 2022-12-17 design/typography fiction/humor --- https://arxiv.org/abs/1312.5602#deepmind Playing Atari with Deep Reinforcement Learning Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller 2013-12-19 2022-12-17 [("doi","10.48550/arXiv.1312.5602")] reinforcement-learning/model-free

We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.

The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards.

We apply our method to 7 Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on 6 of the games and surpasses a human expert on 3 of them.

--- https://imatge-upc.github.io/detection-2016-nipsws/ Hierarchical Object Detection with Deep Reinforcement Learning 2022-12-17 ai/nn/cnn ai/nn/transformer/attention --- https://proceedings.mlr.press/v37/xuc15.pdf Show, attend and tell: Neural image caption generation with visual attention 2022-12-17 ai/nn/transformer/attention --- https://pdfs.semanticscholar.org/6da2/445037118c8ab72be1a319dd9f2a2116b305.pdf Learning to combine foveal glimpses with a third-order Boltzmann machine 2022-12-17 ai/nn/transformer/attention --- https://proceedings.neurips.cc/paper/2014/hash/09c6c3783b4a70054da74f2538ed47c6-Abstract.html Recurrent models of visual attention 2022-12-17 ai/nn/transformer/attention --- https://proceedings.neurips.cc/paper/2016/hash/fb8feff253bb6c834deb61ec76baa893-Abstract.html Can Active Memory Replace Attention? 2022-12-17 ai/nn/transformer/attention --- https://andromeda.ai/ 2022-12-17 ai/scaling/economics ai/scaling/hardware --- /doc/ai/scaling/hardware/1999-bradbury-matrioshkabrains.pdf Matrioshka Brains 1999-01-01 2022-12-17 ai/scaling/hardware transhumanism --- https://betonit.substack.com/p/do-ten-times-as-much 2022-12-17 psychology/energy --- https://arxiv.org/abs/2306.04507 Improving neural network representations using human similarity judgments Lukas Muttenthaler, Lorenz Linhardt, Jonas Dippel, Robert A. Vandermeulen, Katherine Hermann, Andrew K. Lampinen, Simon Kornblith 2023-06-07 2023-06-07 [("doi","10.48550/arXiv.2306.04507")] ai/nn/cnn ai/nn/transformer/clip reinforcement-learning/imitation-learning/brain-imitation-learning

Deep neural networks have reached human-level performance on many computer vision tasks. However, the objectives used to train these networks enforce only that similar images are embedded at similar locations in the representation space, and do not directly constrain the global structure of the resulting space.

Here, we explore the impact of supervising this global structure by linearly aligning it with human similarity judgments. We find that a naive approach leads to large changes in local representational structure that harm downstream performance.

Thus, we propose a novel method that aligns the global structure of representations while preserving their local structure. This global-local transform considerably improves accuracy across a variety of few-shot learning and anomaly detection tasks.

Our results indicate that human visual representations are globally organized in a way that facilitates learning from few examples, and incorporating this global structure into neural network representations improves performance on downstream tasks.

--- https://www.quantamagazine.org/embryo-models-challenge-legal-ethical-and-biological-concepts-20230613/ 2022-12-18 genetics/gametogenesis --- https://www.reddit.com/r/TrueOffMyChest/comments/12zjiwq/my_wifes_company_has_started_replacing_positions/jhtkckq/ 2022-12-18 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/3/poetry --- https://www.youtube.com/watch?v=QRJW5jT5VRA 2022-12-18 ai/nn/retrieval design --- https://openai.com/blog/function-calling-and-other-api-updates#function-calling 2022-12-18 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7708309/ Real-World Adherence and Discontinuation of Glucagon-Like Peptide-1 Receptor Agonists Therapy in Type 2 Diabetes Mellitus Patients in the United States Tracey Weiss, Richard D. Carr, Sampriti Pal, Lingfeng Yang, Baanie Sawhney, Robert Boggs, Swapnil Rajpathak, Kristy Iglay 2020 2022-12-18 [("doi","10.2147/PPA.S277676")] longevity/glp/semaglutide

Aim: To assess adherence and discontinuation of injectable glucagon-like peptide-1 receptor agonists (GLP-1 RA) at 12 and 24 months among adult type 2 diabetes mellitus (T2DM) patients in the United States initiating GLP-1 RA using the administrative claims-based database, Optum Clinformatics® Data Mart 7.1.

Method: A retrospective study was conducted from 2009-01 to 2017-12. Patients were required to be continuously enrolled for 12 months prior to their first GLP-1 RA prescription. Proportion of days covered (PDC) from prescription claims ≥0.80 defined adherence. Discontinuation was defined as a ≥90-day gap from the last date of GLP-1 RA supply to the first date of subsequent prescription claim.

Results: A total of 4,791 T2DM patients had ≥1 and 3,907 had ≥2 GLP-1 RA prescription claims. 50.9% and 47.4% of patients were adherent at 12 and 24 months, respectively. Adherence was statistically-significantly higher among patients on weekly vs daily doses (p < 0.001). Median time to discontinuation was 13 months. The discontinuation rate was 47.7% and 70.1% at 12 and 24 months, respectively, with differences at 24 months for age and dosing frequency (p < 0.001 for both).

Conclusion: Over half of T2DM patients initiating GLP-1 RA were non-adherent and the majority (70.1%) discontinued therapy by 24 months. Reasons for non-adherence and discontinuation merit further research.

--- https://arxiv.org/abs/2306.07075 Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence John J. Nay, David Karamardian, Sarah B. Lawsky, Wenting Tao, Meghana Bhat, Raghav Jain, Aaron Travis Lee, Jonathan H. Choi, Jungo Kasai 2023-06-12 2023-06-12 [("doi","10.48550/arXiv.2306.07075")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue law

Better understanding of Large Language Models’ (LLMs) legal analysis abilities can contribute to improving the efficiency of legal services, governing artificial intelligence, and leveraging LLMs to identify inconsistencies in law.

This paper explores LLM capabilities in applying tax law. We choose this area of law because it has a structure that allows us to set up automated validation pipelines across thousands of examples, requires logical reasoning and maths skills, and enables us to test LLM capabilities in a manner relevant to real-world economic lives of citizens and companies.

Our experiments demonstrate emerging legal understanding capabilities, with improved performance in each subsequent OpenAI model release. We experiment with retrieving and using the relevant legal authority to assess the impact of providing additional legal context to LLMs.

Few-shot prompting, presenting examples of question-answer pairs, is also found to enhance the performance of the most advanced model, GPT-4. The findings indicate that LLMs, particularly when combined with prompting enhancements and the correct legal texts, can perform at high levels of accuracy but not yet at expert tax lawyer levels.

As LLMs continue to advance, their ability to reason about law autonomously could have implications for the legal profession and AI governance.

--- https://generalrobots.substack.com/p/dimension-hopper-part-1 2022-12-18 ai/nn/diffusion design --- https://www.dwarkeshpatel.com/p/carl-shulman 2022-12-18 ai/scaling/economics ai/scaling/hardware genetics/selection/natural/human psychology/neuroscience reinforcement-learning/safe --- https://www.biorxiv.org/content/10.1101/2021.08.25.457696.full The Specious Art of Single-Cell Genomics Tara Chari, Lior Pachter 2022-12-22 2022-12-22 [("doi","10.1101/2021.08.25.457696")] design/visualization genetics

Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with reduction to two or 3 dimensions to produce ‘all-in-one’ visuals of the data that are amenable to the human eye, and these are subsequently used for qualitative and quantitative exploratory analysis.

However, there is little theoretical support for this practice, and we show that extreme dimension reduction [using t-SNE or UMAP or PCA etc], from hundreds or thousands of dimensions to two, inevitably induces distortion of high-dimensional datasets.

We therefore examine the practical implications of low-dimensional embedding of single-cell data, and find that extensive distortions & inconsistent practices make such embeddings counter-productive for exploratory biological analyses.

In lieu of this, we discuss alternative approaches for conducting targeted embedding and feature exploration, to enable hypothesis-driven biological discovery.

--- https://x.com/Altimor/status/1668902393386237953 2022-12-18 ai/scaling/hardware --- https://publicdomainreview.org/essay/wonder-and-pleasure-in-the-oude-doolhof-of-amsterdam#p-8-1 2022-12-19 history/public-domain-review reinforcement-learning/robot --- https://arxiv.org/abs/2209.01687 Reconciling Individual Probability Forecasts Aaron Roth, Alexander Tolbert, Scott Weinstein 2022-09-04 2022-12-19 [("doi","10.48550/arXiv.2209.01687")] statistics/prediction

Individual probabilities refer to the probabilities of outcomes that are realized only once: the probability that it will rain tomorrow, the probability that Alice will die within the next 12 months, the probability that Bob will be arrested for a violent crime in the next 18 months, etc. Individual probabilities are fundamentally unknowable. Nevertheless, we show that two parties who agree on the data—or on how to sample from a data distribution—cannot agree to disagree on how to model individual probabilities [cf. Aumann].

This is because any two models of individual probabilities that substantially disagree can together be used to empirically falsify and improve at least one of the two models.

This can be efficiently iterated in a process of “reconciliation” that results in models that both parties agree are superior to the models they started with, and which themselves (almost) agree on the forecasts of individual probabilities (almost) everywhere.

We conclude that although individual probabilities are unknowable, they are contestable via a computationally and data efficient process that must lead to agreement.

Thus we cannot find ourselves in a situation in which we have two equally accurate and unimprovable models that disagree substantially in their predictions—providing an answer to what is sometimes called the predictive or model multiplicity problem.

--- https://en.wikipedia.org/wiki/Bisection_(software_engineering) Bisection (software engineering) 2022-12-19 statistics/order/comparison --- https://arxiv.org/abs/2306.07899 Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks Veniamin Veselovsky, Manoel Horta Ribeiro, Robert West 2023-06-13 2023-06-13 [("doi","10.48550/arXiv.2306.07899")] ai/dataset ai/nn/transformer/gpt/3/nonfiction cs/security

[Twitter] Large language models (LLMs) are remarkable data annotators. They can be used to generate high-fidelity supervised training data, as well as survey and experimental data. With the widespread adoption of LLMs, human-gold-standard annotations are key to understanding the capabilities of LLMs and the validity of their results. However, crowdsourcing, an important, inexpensive way to obtain human annotations, may itself be impacted by LLMs, as crowd workers have financial incentives to use LLMs to increase their productivity and income.

To investigate this concern, we conducted a case study on the prevalence of LLM usage by crowd workers. We reran an abstract summarization task from the literature on Amazon Mechanical Turk and, through a combination of [copy-paste] keystroke detection and synthetic text classification, estimate that:

33–46% of crowd workers used LLMs when completing the task.

Although generalization to other, less LLM-friendly tasks is unclear, our results call for platforms, researchers, and crowd workers to find new ways to ensure that human data remain human, perhaps using the methodology proposed here as a stepping stone.

Code/data: Github.

--- https://x.com/DahnJahn/status/1669000659192930304 2022-12-19 ai/nn/tokenization ai/nn/transformer/gpt/4/nonfiction --- /doc/economics/advertising/2022-fong.pdf Debunking Misinformation About Consumer Products: Effects on Beliefs and Purchase Behavior Jessica Fong, Tong Guo, Anita Rao 2022-12-10 2022-12-19 [("doi","10.1177/00222437221147088")] economics/advertising psychology

[Twitter] The prevalence of misinformation has spurred various interested parties—regulators, the media, and competing firms—to debunk false claims in the marketplace. This research examines whether such debunking messages provided by these parties can impact consumer purchase behavior.

If so, does debunking effectively correct consumers’ misinformed beliefs—an ideal outcome from a policy maker’s perspective—or does it merely reinforce correct beliefs, as predicted by biased belief updating? With theory providing contradictory predictions, the authors design and implement a conjoint experiment that enables measurement of willingness to pay under exposure to real-world misinformation and debunking messages.

Focusing on 3 ingredients in product categories where misinformation is prevalent (aluminum in deodorant, fluoride in toothpaste, and genetically modified organisms in food), the authors find that debunking plays an important role in mitigating the impact of misinformation. More specifically, debunking can attenuate the decrease in willingness to pay caused by misinformation by correcting misbeliefs, a promising finding for policy makers. [Note: no ‘backfire effect’.]

The authors discuss the incentives for firms to debunk misinformation or to introduce new products that conform to misinformation.

--- https://en.wikipedia.org/wiki/All-pairs_testing All-pairs testing 2022-12-19 statistics/power-analysis --- https://github.com/ealdwulf/bbchop 2022-12-19 statistics/bayes statistics/order/comparison --- https://michaelnielsen.org/ddi/how-to-crawl-a-quarter-billion-webpages-in-40-hours/ 2022-12-19 cs/linkrot/archiving --- https://www.apa.org/pubs/journals/releases/psp-101-3-579.pdf Personality and Obesity Across the Adult Life Span Sutin 2011 2022-12-19 exercise psychology/personality/conscientiousness --- https://github.com/tigerbeetle/tigerbeetle/blob/main/docs/DESIGN.md#architecture 2022-12-20 cs/algorithm --- https://yelpingwithcormac.tumblr.com/ 2022-12-20 fiction/humor --- https://x.com/paulnovosad/status/1655925767333658626 2022-12-20 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/codex --- https://x.com/joshgans/status/1656307700244815872 2022-12-20 ai/nn/transformer/gpt/3/nonfiction economics --- https://www.biorxiv.org/content/10.1101/2023.06.14.544922.full Transgene-Free Ex Utero Derivation of A Human Post-Implantation Embryo Model Solely from Genetically Unmodified Naive PSCs Bernardo Oldak, Emilie Wildschutz, Vladyslav Bondarenko, Alejandro Aguilera-Castrejon, Cheng Zhao, Shadi Tarazi, Mehmet-Yunus Comar, Shahd Ashouokhi, Dmitry Lokshtanov, Francesco Roncato, Sergey Viukov, Eitan Ariel, Max Rose, Nir Livnat, Tom Shani, Carine Joubran, Roni Cohen, Yoseph Addadi, Merav Kedmi, Hadas Keren-Shaul, Sophie Petropoulos, Fredrik Lanner, Noa Novershtern, Jacob H. Hanna 2023-06-15 2023-06-15 [("doi","10.1101/2023.06.14.544922")] genetics/gametogenesis

Our ability to study early human post-implantation development remains highly limited due to the ethical and technical challenges associated with intrauterine development of the human embryo after implantation. Despite the great progress made on human gastruloids, axioloids and in vitro cultured blastoids, such elegant models do not constitute an integrated Stem cell-derived Embryo Models (SEMs) that includes all the key extra-embryonic tissues of the early post-implantation human conceptus (eg. hypoblast, yolk-sac, trophoblasts, amnion, and extraembryonic mesoderm), and thus, do not recapitulate post-implantation epiblast development within the context of these extra-embryonic compartments.

Mouse naive pluripotent stem cells (PSCs) have recently been shown to give rise to embryonic and extra-embryonic stem cells capable of self-assembling into post-gastrulation mouse SEMs, while bypassing the blastocyst-like stage, and eventually initiating organogenesis ex utero. Here, we implement critical adaptations to extend these finding to humans, while using only genetically unmodified human naive PSCs, thus circumventing the need for ectopic expression of lineage promoting transgenes.

Such integrated human SEMs recapitulate the organization of all known compartments of early post-implantation stage human embryos, including epiblast, hypoblast, extra-embryonic mesoderm, and trophoblast surrounding the latter layers. The organized human SEMs recapitulate key hallmarks of post-implantation stage embryogenesis up to 13–14 days post-fertilization (dpf, Carnegie stage 6a), such as bilaminar disk formation, epiblast lumenogenesis, amniogenesis, anterior-posterior symmetry breaking, PGC specification, primary and secondary yolk sac formation, and extra-embryonic mesoderm expansion that defines a chorionic cavity and a connective stalk.

This new platform constitutes a tractable stem cell-based model for experimentally interrogating previously inaccessible windows of human peri- and early post-implantation development.

--- https://www.biorxiv.org/content/10.1101/2023.06.15.545082.full Transgene directed induction of a stem cell-derived human embryo model Bailey A. T. Weatherbee, Carlos W. Gantner, Riza M. Daza, Nobuhiko Hamazaki, Lisa K. Iwamoto-Stohl, Jay Shendure, Magdalena Zernicka-Goetz 2023-06-15 2023-06-15 [("doi","10.1101/2023.06.15.545082")] genetics/gametogenesis

The human embryo undergoes morphogenetic transformations following implantation into the uterus, yet our knowledge of this crucial stage is limited by the inability to observe the embryo in vivo. Stem cell-derived models of the embryo are important tools to interrogate developmental events and tissue-tissue crosstalk during these stages.

Here, we establish a human post-implantation embryo model comprised of embryonic and extraembryonic tissues. We combine two types of extraembryonic-like cells generated by transcription factor overexpression with wildtype embryonic stem cells and promote their self-organization into structures that mimic aspects of the post-implantation human embryo. These self-organized aggregates contain a pluripotent epiblast-like domain surrounded by hypoblast- and trophoblast-like tissues.

We demonstrate that these inducible human embryoids robustly generate several cell types, including amnion, extraembryonic mesenchyme, and primordial germ cell-like cells in response to BMP signaling. This model also allowed us to identify an inhibitory role for SOX17 in the specification of anterior hypoblast-like cells. Modulation of the subpopulations in the hypoblast-like compartment demonstrated that extraembryonic-like cells impact epiblast-like domain differentiation, highlighting functional tissue-tissue crosstalk.

In conclusion, we present a modular, tractable, integrated model of the human embryo that will allow us to probe key questions of human post-implantation development, a critical window when pregnancies fail.

--- https://x.com/ptrschmdtnlsn/status/1669590814329036803 2022-12-20 ai/scaling/hardware --- https://arxiv.org/abs/2306.08143 On Stellar Migration from the Andromeda Galaxy Lukas Gülzow, Malcolm Fairbairn, Dominik J. Schwarz 2023-06-13 2023-06-13 [("doi","10.48550/arXiv.2306.08143")] science

Recent Gaia observations suggest that some hypervelocity stars (HVSs) might originate from outside the Milky Way Galaxy.

We ask if these HVSs could come from as far as Andromeda galaxy. Therefore, we simulate HVSs originating in Andromeda with initial conditions based on attributes of high-velocity stars measured in the Milky Way and a simple model for the gravitational potential of Andromeda and the Milky Way. We evaluate the validity of this scenario based on the simulation results.

While we expect the vast majority of HVSs in our Galaxy will originate here, we expect the number of stars present from Andromeda at any one time to be 12–3,910, depending upon model assumptions. Further, we analyse the properties of HVSs that are able to reach the Milky Way and discuss whether they could be detected experimentally based on recent constraints set on the ejection rate of HVSs from the Milky Way centre.

--- https://clarifycapital.com/the-future-of-investment-pitching 2022-12-20 ai/nn/transformer/gpt/4/nonfiction economics --- https://www.nytimes.com/2023/05/04/magazine/celine-chanel-gucci-superfake-handbags.html 2022-12-20 psychology/collecting --- https://www.kalzumeus.com/2010/06/17/falsehoods-programmers-believe-about-names/ 2022-12-20 cs/algorithm design --- https://www.biorxiv.org/content/10.1101/2023.04.25.538137.full A familial natural short sleep mutation promotes healthy aging and extends lifespan in Drosophila Pritika Pandey, P. Kerr Wall, Stephen R. Lopez, Olga S. Dubuisson, Elizabeth R. M. Zunica, Wagner S. Dantas, John P. Kirwan, Christopher L. Axelrod, Alyssa E. Johnson 2023-04-26 2023-04-26 [("doi","10.1101/2023.04.25.538137")] zeo/short-sleeper

Sleep loss typically imposes negative effects on animal health. However, humans with a rare genetic mutation in the dec2 gene (dec2P384R) present an exception; these individuals sleep less without the usual effects associated with sleep deprivation. Thus, it has been suggested that the dec2P384R mutation activates compensatory mechanisms that allows these individuals to thrive with less sleep.

To test this directly, we used a Drosophila model to study the effects of the dec2P384Rmutation on animal health.

Expression of human dec2P384R in fly sleep neurons was sufficient to mimic the short sleep phenotype and, remarkably, dec2P384R mutants lived substantially longer with improved health despite sleeping less.

The improved physiological effects were enabled, in part, by enhanced mitochondrial fitness and upregulation of multiple stress response pathways. Moreover, we provide evidence that upregulation of pro-health pathways also contributes to the short sleep phenotype, and this phenomenon may extend to other pro-longevity models.

--- https://unlocked.microsoft.com/ai-anthology/terence-tao/ 2022-12-21 ai/nn/transformer/gpt/4/nonfiction math --- https://arxiv.org/abs/2207.10773 Rubisco function, evolution, and engineering Noam Prywes, Naiya R. Phillips, Owen T. Tuck, Luis E. Valentin-Alvarado, David F. Savage 2022-07-21 2022-12-21 [("doi","10.48550/arXiv.2207.10773")] genetics/editing genetics/selection/natural

Carbon fixation is the process by which CO2 is converted from a gas into biomass. The Calvin Benson Bassham (CBB) cycle is the dominant carbon fixation pathway on earth, driving >99.5% of the ~120 billion tons of carbon that are “fixed” as sugar, by plants, algae and cyanobacteria.

The carboxylase enzyme in the CBB, ribulose-1,5-bisphosphate carboxylase/oxygenase (rubisco), fixes one CO2 molecule per turn of the cycle. Despite being critical to the assimilation of carbon, rubisco’s kinetic rate is not very fast and it is a bottleneck in flux through the pathway.

This presents a paradox—why hasn’t rubisco evolved to be a better catalyst? Many hypothesize that the catalytic mechanism of rubisco is subject to one or more trade-offs, and that rubisco variants have been optimized for their native physiological environment.

Here we review the evolution and biochemistry of rubisco through the lens of structure and mechanism in order to understand what trade-offs limit its improvement.

We also review the many attempts to improve rubisco itself and, thereby, promote plant growth.

--- https://arxiv.org/abs/2209.00588 IRIS: Transformers are Sample-Efficient World Models Vincent Micheli, Eloi Alonso, François Fleuret 2022-09-01 2022-12-21 [("doi","10.48550/arXiv.2209.00588")] ai/nn/vae reinforcement-learning/model

[discussion] Deep reinforcement learning agents are notoriously sample inefficient, which considerably limits their application to real-world problems. Recently, many model-based methods have been designed to address this issue, with learning in the imagination of a world model being one of the most prominent approaches. However, while virtually unlimited interaction with a simulated environment sounds appealing, the world model has to be accurate over extended periods of time.

Motivated by the success of Transformers in sequence modeling tasks, we introduce IRIS, a data-efficient agent that learns in a world model composed of a discrete autoencoder and an autoregressive Transformer.

With the equivalent of only two hours of gameplay in the Atari 100k benchmark, IRIS achieves a mean human normalized score of 1.046, and outperforms humans on 10⁄26 games, setting a new state-of-the-art for methods without lookahead search.

To foster future research on Transformers and world models for sample-efficient reinforcement learning, we release our code and models at Github.

--- https://arxiv.org/abs/2007.05929 SPR: Data-Efficient Reinforcement Learning with Self-Predictive Representations Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron Courville, Philip Bachman 2020-07-12 2022-12-21 [("doi","10.48550/arXiv.2007.05929")] reinforcement-learning/model-free

While deep reinforcement learning excels at solving tasks where large amounts of data can be collected through virtually unlimited interaction with the environment, learning from limited interaction remains a key challenge. We posit that an agent can learn more efficiently if we augment reward maximization with self-supervised objectives based on structure in its visual input and sequential interaction with the environment.

Our method, Self-Predictive Representations (SPR), trains an agent to predict its own latent state representations multiple steps into the future. We compute target representations for future states using an encoder which is an exponential moving average of the agent’s parameters and we make predictions using a learned transition model.

On its own, this future prediction objective outperforms prior methods for sample-efficient deep RL from pixels.

We further improve performance by adding data augmentation to the future prediction loss, which forces the agent’s representations to be consistent across multiple views of an observation.

Our full self-supervised objective, which combines future prediction and data augmentation, achieves a median human-normalized score of 0.415 on Atari in a setting limited to 100k steps of environment interaction, which represents a 55% relative improvement over the previous state-of-the-art. Notably, even in this limited data regime, SPR exceeds expert human scores on 7⁄26 games.

The code associated with this work is available at https://github.com/mila-iqia/spr.

--- https://arxiv.org/abs/1904.10079 The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors William H. Guss, Cayden Codel, Katja Hofmann, Brandon Houghton, Noboru Kuno, Stephanie Milani, Sharada Mohanty, Diego Perez Liebana, Ruslan Salakhutdinov, Nicholay Topin, Manuela Veloso, Phillip Wang 2019-04-22 2022-12-21 [("doi","10.48550/arXiv.1904.10079")] ai/dataset reinforcement-learning/exploration reinforcement-learning/imitation-learning

Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-of-the-art reinforcement learning (RL) systems require an exponentially increasing number of samples, their development is restricted to a continually shrinking segment of the AI community. Likewise, many of these systems cannot be applied to real-world problems, where environment samples are expensive. Resolution of these limitations requires new, sample-efficient methods.

To facilitate research in this direction, we introduce the MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors.

The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, we introduce: (1) the Minecraft ObtainDiamond task, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods; and (2) the MineRL-v0 dataset, a large-scale collection of over 60 million state-action pairs of human demonstrations that can be resimulated into embodied trajectories with arbitrary modifications to game state and visuals.

Participants will compete to develop systems which solve the ObtainDiamond task with a limited number of samples from the environment simulator, Malmo. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures. At the end of each round, competitors will submit containerized versions of their learning algorithms and they will then be trained/evaluated from scratch on a hold-out dataset-environment pair for a total of 4-days on a prespecified hardware platform.

--- https://arxiv.org/abs/2101.11071 The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors William H. Guss, Mario Ynocente Castro, Sam Devlin, Brandon Houghton, Noboru Sean Kuno, Crissman Loomis, Stephanie Milani, Sharada Mohanty, Keisuke Nakata, Ruslan Salakhutdinov, John Schulman, Shinya Shiroshita, Nicholay Topin, Avinash Ummadisingu, Oriol Vinyals 2021-01-26 2022-12-21 [("doi","10.48550/arXiv.2101.11071")] reinforcement-learning/exploration reinforcement-learning/imitation-learning

Although deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples, affording only a shrinking segment of the AI community access to their development. Resolution of these limitations requires new, sample-efficient methods.

To facilitate research in this direction, we propose this second iteration of the MineRL Competition. The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, participants compete under a limited environment sample-complexity budget to develop systems which solve the MineRL ObtainDiamond task in Minecraft, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods.

The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures and shaders. At the end of each round, competitors submit containerized versions of their learning algorithms to the AIcrowd platform where they are trained from scratch on a hold-out dataset-environment pair for a total of 4-days on a pre-specified hardware platform.

In this follow-up iteration to the NeurIPS 2019 MineRL Competition, we implement new features to expand the scale and reach of the competition. In response to the feedback of the previous participants, we introduce a second minor track focusing on solutions without access to environment interactions of any kind except during test-time. Further we aim to prompt domain agnostic submissions by implementing several novel competition mechanics including action-space randomization and desemantization of observations and actions.

--- https://arxiv.org/abs/2208.03374#google Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter Aleksandar Stanić, Yujin Tang, David Ha, Jürgen Schmidhuber 2022-08-05 2022-12-21 [("doi","10.48550/arXiv.2208.03374")] ai/dataset ai/nn/rnn reinforcement-learning/model-free

Reinforcement learning agents must generalize beyond their training experience. Prior work has focused mostly on identical training and evaluation environments.

Starting from the recently introduced Crafter benchmark, a 2D open world survival game, we introduce a new set of environments suitable for evaluating some agent’s ability to generalize on previously unseen (numbers of) objects and to adapt quickly (meta-learning).

In Crafter, the agents are evaluated by the number of unlocked achievements (such as collecting resources) when trained for 1M steps. We show that current agents struggle to generalize, and introduce novel object-centric agents that improve over strong baselines. We also provide critical insights of general interest for future work on Crafter through several experiments.

We show that careful hyper-parameter tuning improves the PPO baseline agent by a large margin and that even feedforward agents can unlock almost all achievements by relying on the inventory display. We achieve new state-of-the-art performance on the original Crafter environment. Additionally, when trained beyond 1M steps, our tuned agents can unlock almost all achievements.

We show that the recurrent PPO> agents improve over feedforward ones, even with the inventory information removed.

We introduce CrafterOOD, a set of 15 new environments that evaluate OOD generalization. On CrafterOOD, we show that the current agents fail to generalize, whereas our novel object-centric agents achieve state-of-the-art OOD generalization while also being interpretable.

Our code is public.

--- https://codahale.com/the-joy-of-duplexes/ 2022-12-21 cs/cryptography --- https://en.wikipedia.org/wiki/Sponge_function Sponge function 2022-12-21 cs/cryptography --- https://www.newyorker.com/magazine/2023/06/26/the-art-thief-a-true-story-of-love-crime-and-a-dangerous-obsession-michael-finkel-book-review 2022-12-21 crime cs/security --- https://chatgpt.com/share/312e82f0-cc5e-47f3-b368-b2c0c0f4ad3f 2022-12-22 ai/nn/adversarial ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/safe --- https://x.com/papayathreesome/status/1670170344953372676 2022-12-22 ai/nn/adversarial ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/safe --- https://x.com/yoheinakajima/status/1670557048743010305 2022-12-22 ai/nn/transformer/gpt/3 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue --- https://en.wikipedia.org/wiki/Hamilton_Anxiety_Rating_Scale Hamilton Anxiety Rating Scale 2022-12-22 psychiatry/anxiety --- https://www.dailymail.co.uk/health/article-6761759/Leading-expert-claims-doctors-dish-LAVENDER-OIL-line-treatment-anxiety.html 2022-12-22 psychiatry/anxiety/lavender --- https://www.thecarlatreport.com/articles/3232-silexan-a-novel-anxiolytic 2022-12-22 psychiatry/anxiety/lavender --- https://en.wikipedia.org/wiki/5-HT1A_receptor 5-HT1A receptor 2022-12-22 psychiatry/anxiety/lavender --- https://naturesway.com/products/calmaid 2022-12-22 psychiatry/anxiety/lavender --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6773459/ Lavender Products Associated With Premature Thelarche and Prepubertal Gynecomastia: Case Reports and Endocrine-Disrupting Chemical Activities J Tyler Ramsey, Yin Li, Yukitomo Arao, Ajanta Naidu, Laurel A. Coons, Alejandro Diaz, Kenneth S. Korach 2019 2022-12-22 [("doi","10.1210/jc.2018-01880")] psychiatry/anxiety/lavender

Context: Previous case reports associated prepubertal gynecomastia with lavender-containing fragrances, but there appear to be no reports of premature thelarche.

Objective: To add to a case series about lavender-fragranced product use and breast growth in children and to measure endocrine-disrupting chemical activity of essential oil components.

Design, Setting, and Patients: Patients experiencing premature thelarche or prepubertal gynecomastia with continuous exposure to lavender-fragranced products were evaluated in the pediatric endocrinology departments of two institutions. Mechanistic in vitro experiments using 8 components of lavender and other essential oils were performed at National Institute of Environmental Health Sciences.

Main Outcome Measures: Case reports and in vitro estrogen and androgen receptor gene expression activities in human cell lines with essential oils.

Results: 3 prepubertal girls and one boy with clinical evidence of estrogenic action and a history of continuous exposure to lavender-containing fragrances were studied. Breast growth dissipated in all patients with discontinuation of the fragranced products. Some of the components tested elicited estrogenic and antiandrogenic properties of varying degrees.

Conclusion: We report cases of premature thelarche that resolved upon cessation of lavender-containing fragrance exposure commonly used in Hispanic communities. The precise developmental basis for such conditions could be multifactorial. In vitro demonstration of estrogenic and antiandrogenic properties of essential oil components suggests essential oils in these cases could be considered a possible source and supports a possible link with idiopathic prepubertal breast development. Whether the level of lavender oil estrogenic potency is sufficient to cause these effects is unknown.

--- https://en.wikipedia.org/wiki/Chanel Chanel 2022-12-22 psychology/smell/perfume --- https://en.wikipedia.org/wiki/Bergamot_essential_oil Bergamot essential oil 2022-12-23 psychology/smell/perfume --- https://en.wikipedia.org/wiki/Synthetic_civet Synthetic civet 2022-12-23 psychology/smell/perfume --- https://qualiacomputing.com/2020/02/21/perfumery-as-an-art-form/ Hedonic Tone, memetics, scent, sex, spirituality 2022-12-23 psychology/smell/perfume --- https://www.atlasobscura.com/experiences/perfume-online-course 2022-12-23 psychology/smell/perfume --- https://en.wikipedia.org/wiki/Charlie_(fragrance) Charlie (fragrance) 2022-12-23 psychology/smell/perfume --- https://en.wikipedia.org/wiki/L%27Air_du_Temps_(perfume) L’Air du Temps (perfume) 2022-12-23 psychology/smell/perfume --- https://roberttisserand.com/2013/02/lavender-oil-is-not-estrogenic/ 2022-12-23 psychiatry/anxiety/lavender --- https://www.nytimes.com/2018/10/23/science/lavender-scent-anxiety.html 2022-12-23 psychiatry/anxiety/lavender --- https://en.wikipedia.org/wiki/Linalool Linalool 2022-12-23 psychiatry/anxiety/lavender --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3612440/ Lavender and the nervous system Peir Hossein Koulivand, Maryam Khaleghi Ghadiri, Ali Gorji 2013 2022-12-23 [("doi","10.1155/2013/681304")] psychiatry/anxiety/lavender

Lavender is traditionally alleged to have a variety of therapeutic and curative properties, ranging from inducing relaxation to treating parasitic infections, burns, insect bites, and spasm.

There is growing evidence suggesting that lavender oil may be an effective medicament in treatment of several neurological disorders.

Several animal and human investigations suggest anxiolytic, mood stabilizer, sedative, analgesic, and anticonvulsive and neuroprotective properties for lavender. These studies raised the possibility of revival of lavender therapeutic efficacy in neurological disorders.

In this paper, a survey on current experimental and clinical state of knowledge about the effect of lavender on the nervous system is given.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4505755/ Effect of Inhaled Lavender and Sleep Hygiene on Self-Reported Sleep Issues: A Randomized Controlled Trial Angela Smith Lillehei, Linda L. Halcón, Kay Savik, Reilly Reis 2015 2022-12-23 [("doi","10.1089/acm.2014.0327")] psychiatry/anxiety/lavender zeo

Objectives: To compare the effectiveness of lavender (Lavandula angustifolia) and sleep hygiene versus sleep hygiene alone on sleep quantity and sleep quality and to determine sustained effect at two-week follow-up.

Design: A randomized controlled trial with investigator blinding and steps taken to blind the participants.

Setting: Participants’ usual sleep setting.

Subjects: Seventy-nine college students with self-reported sleep issues.

Interventions: The intervention took place over 5 nights with baseline, post-intervention, and two-week follow-up assessments. Both groups practiced good sleep hygiene and wore an inhalation patch on their chest at night. One group wore a patch with 55 μl of lavender essential oil and the other group wore a blank patch.

Outcome Measures: Sleep quantity was measured using a Fitbit(®) tracker and a sleep diary, and sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI) and the NIH Patient-Reported Outcomes Measurement Information System (PROMIS) sleep disturbance short form.

Results: The lavender and sleep hygiene group demonstrated better sleep quality at post-intervention and two-week follow-up (PSQI p = 0.01, <0.001 and PROMIS p = 0.04, 0.007, respectively). The sleep-hygiene-only group also demonstrated better sleep quality but to a lesser extent (PSQI p = 0.02, 0.06 and PROMIS p = 0.03, 0.03, respectively). Additionally, a clinical effect was found for the lavender group at post-intervention, along with a significant finding for waking feeling refreshed (p = 0.01). Sleep quantity did not differ between groups.

Conclusions: Lavender and sleep hygiene together, and sleep hygiene alone to a lesser degree, improved sleep quality for college students with self-reported sleep issues, with an effect remaining at follow-up.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0059998 Lavender Oil-Potent Anxiolytic Properties via Modulating Voltage Dependent Calcium Channels Anita M. Schuwald, Michael Nöldner, Thomas Wilmes, Norbert Klugbauer, Kristina Leuner, Walter E. Müller 2013-02-21 2022-12-24 [("doi","10.1371/journal.pone.0059998")] psychiatry/anxiety/lavender psychology/neuroscience

Recent clinical data support the clinical use of oral lavender oil in patients suffering from subsyndromal anxiety. We identified the molecular mechanism of action that will alter the perception of lavender oil as a nonspecific ingredient of aromatherapy to a potent anxiolytic inhibiting voltage dependent calcium channels (VOCCs) as highly selective drug target.

In contrast to previous publications where exorbitant high concentrations were used, the effects of lavender oil in behavioral, biochemical, and electrophysiological experiments were investigated in physiological concentrations in the nanomolar range, which correlate to a single dosage of 80 mg/d in humans that was used in clinical trials.

We show for the first time that lavender oil bears some similarities with the established anxiolytic pregabalin. Lavender oil inhibits VOCCs in synaptosomes, primary hippocampal neurons and stably overexpressing cell lines in the same range such as pregabalin. Interestingly, Silexan does not primarily bind to P/Q type calcium channels such as pregabalin and does not interact with the binding site of pregabalin, the α2δ subunit of VOCCs. Lavender oil reduces non-selectively the calcium influx through several different types of VOCCs such as the N-type, P/Q-type and T-type VOCCs.

In the hippocampus, one brain region important for anxiety disorders, we show that inhibition by lavender oil is mainly mediated via N-type and P/Q-type VOCCs. Taken together, we provide a pharmacological and molecular rationale for the clinical use of the oral application of lavender oil in patients suffering from anxiety.

…Funding: This study was supported by a research grant of Dr. Willmar Schwabe Pharmaceuticals to K. Leuner and W. E. Müller. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of interest: The authors have read the journal’s policy and have the following conflicts: M. Nöldner is a full time employee of Dr. Willmar Schwabe Pharmaceuticals. K. Leuner received research support by Dr. Willmar Schwabe Pharmaceuticals. W. E. Müller received research support as well as speaker and scientific advisor honorarium by Dr. Willmar Schwabe Pharmaceuticals. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968617/ No Abuse Potential of Silexan in Healthy Recreational Drug Users: A Randomized Controlled Trial Erich Seifritz, Hans-Jürgen Möller, Hans-Peter Volz, Walter E. Müller, Talar Hopyan, Anna Wacker, Sandra Schläfke, Siegfried Kasper 2021 2022-12-24 [("doi","10.1093/ijnp/pyaa064")] psychiatry/anxiety/lavender

Background: [note: Kasper-authored] Silexan is a lavender essential oil with established anxiolytic and calming efficacy. Here we asked whether there is a potential for abuse in human patients.

Method: We carried out a phase I abuse liability single-center, double-blind, 5-way crossover study in healthy users of recreational central nervous system depressants. They received single oral doses of 80 mg (therapeutic dose) and 640 mg Silexan, 2 mg and 4 mg lorazepam (active control) and placebo in randomized order, with 4- to 14-day washout periods between treatments. Pharmacodynamic measures included validated visual analogue scales assessing positive, negative, and sedative drug effects and balance of effects; a short form of the Addiction Research Center Inventory; and a drug similarity assessment. The primary outcome measure was the individual maximum value on the drug liking visual analogue scale during 24 hours post-dose.

Results: Forty participants were randomized and 34 were evaluable for pharmacodynamic outcomes. In intraindividual head-to-head comparisons of the drug liking visual analogue scale maximum value, both doses of Silexan were rated similar to placebo whereas differences were observed between Silexan and lorazepam and between placebo and lorazepam (p < 0.001). These data were supported by all secondary measures of positive drug effects and of balance of effects. Differences between placebo and both doses of Silexan were always negligible in magnitude. Moreover, Silexan showed no sedative effects and was not perceived to be similar to commonly used drugs that participants had used in the past.

Conclusions: Silexan did not exhibit any abuse potential in a standard abuse potential detection screen study and is unlikely to be recreationally abused.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4269819/ No interacting influence of lavender oil preparation silexan on oral contraception using an ethinyl estradiol/levonorgestrel combination Doris Heger-Mahn, Günther Pabst, Angelika Dienel, Sandra Schläfke, Christine Klipping 2014 2022-12-24 [("doi","10.1007/s40268-014-0065-5")] psychiatry/anxiety/lavender

Purpose: Silexan is an oral Lavender oil preparation with proven anxiolytic efficacy. Given the high prevalence of anxiety and restlessness in younger women, oral contraceptives and Silexan will likely be co-administered.

Method: A double-blind, randomized, 2-period crossover study was performed to investigate the effects of Silexan on the pharmacokinetics and pharmacodynamics of Microgynon(®), a combination oral contraceptive containing ethinyl estradiol 0.03 mg (EE) and levonorgestrel 0.15 mg (LNG) in healthy, fertile, adult females. During 2 consecutive cycles of 28 days, oral contraception was given for 21 days combined with 1 × 160 mg/day Silexan or placebo. Plasma concentration-time profiles of EE and LNG were obtained on day 18 ± 1 up to 24 h after dosing. The primary outcome measure was the area under the concentration-time curve over a dosing interval of τ = 24 h (AUCτ) for EE and LNG plasma levels. An interaction with Silexan was formally excluded if the 90% confidence interval for the AUCτ ratio during co-administration with Silexan or placebo was included within the range of 0.80-1.25. Secondary outcomes included EE and LNG peak concentration (C max) and time to C max (t max), follicle size, endometrial thickness, the Hoogland score, and serum levels of estradiol, progesterone, and sex hormone-binding globulin.

Results: A total of 24 women (mean age 27.3 years; mean body mass index 22.2 kg⁄m2) participated. The confidence intervals for the EE and LNG AUCτ and C max ratios fell within the pre-specified limits, indicating no interaction (point estimates [Silexan/placebo] AUCτ EE 0.97, LNG 0.94; C max EE 0.99, LNG 0.96). For LNG, t max was slightly delayed. No secondary outcome indicated any impairment of contraceptive efficacy.

Conclusions: Co-administration of Silexan did not affect the efficacy of a combination oral contraceptive containing EE and LNG and was well tolerated.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4360214/ Effects of Silexan on the serotonin-1A receptor and microstructure of the human brain: a randomized, placebo-controlled, double-blind, cross-over study with molecular and structural neuroimaging Pia Baldinger, Anna S. Höflich, Markus Mitterhauser, Andreas Hahn, Christina Rami-Mark, Marie Spies, Wolfgang Wadsak, Rupert Lanzenberger, Siegfried Kasper 2014 2022-12-24 [("doi","10.1093/ijnp/pyu063")] psychiatry/anxiety/lavender psychology/neuroscience

Background: [note: Kasper-authored; abstract/presentation] Recently, Silexan, a patented active substance comprised of an essential oil produced from Lavandula angustifolia flowers, has been authorized in Germany as a medicinal product for the treatment of states of restlessness related to anxious mood. Its efficacy has been shown in several forms of anxiety disorders. Findings from preclinical and clinical studies attribute a major role to the serotonin-1A receptor in the pathogenesis and treatment of anxiety.

Method: To elucidate the effect of Silexan on serotonin-1A receptor binding, 17 healthy men underwent 2 positron emission tomography measurements using the radioligand [carbonyl-(11)C]WAY-100635 following the daily intake of 160 mg Silexan or placebo for a minimum of 8 weeks (randomized, double-blind, cross-over design). Additionally, structural magnetic resonance imaging and voxel-based morphometry analysis was performed to determine potential effects on gray matter microstructure.

Results: Serotonin-1A receptor binding potential was shown to be significantly reduced following the intake of Silexan compared with placebo in 2 large clusters encompassing the temporal gyrus, the fusiform gyrus and the hippocampus on one hand as well as the insula and anterior cingulate cortex on the other hand. No effects of Silexan on gray matter volume could be detected in this investigation.

Conclusion: This positron emission tomography study proposes an involvement of the serotonin-1A receptor in the anxiolytic effects of Silexan. The study was registered in the International Standard Randomized Controlled Trial Number Register as ISRCTN30885829 (http://www.controlled-trials.com/isrctn/).

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6889391/ Efficacy and safety of lavender essential oil (Silexan) capsules among patients suffering from anxiety disorders: A network meta-analysis Wuan Shuen Yap, Anton V. Dolzhenko, Zahraa Jalal, Muhammad Abdul Hadi, Tahir Mehmood Khan 2019 2022-12-24 [("doi","10.1038/s41598-019-54529-9")] psychiatry/anxiety/lavender

[Largely duplicative of Generoso et al 2017 & same weaknesses] A systematic review and network-meta analysis (NMA) were performed to estimate statistical-significance of the anxiolytic effect of lavender essential oil taken as silexan capsules versus other comparators (ie. placebo/paroxetine/lorazepam). The outcome of interest was Hamilton Anxiety Scale (HAMA).

Weighted mean differences (WMD) were calculated to estimate the treatment effect at the confidence interval of 95%. League tables were generated using treatment effect, for all pairwise comparisons, where WMD < 0 favors the column-defining treatment.

5 studies were identified with a total of 524 participants receiving treatment with silexan 80 mg and 121 participants taking silexan 160 mg. The NMA results indicated that consumption of silexan 160 mg resulted in higher decline of HAMA score [WMD −1.14 (-1.10, 3.39)] in comparison to silexan 80 mg, placebo [-2.20 (-4.64, 0.24)] and paroxetine [-1.24 (-5.34, 2.85)].

The effect of silexan 80 mg was observed to be same as that of paroxetine. Overall, silexan 160 mg was noticed to be a more efficient treatment giving statistically-significant decline in HAMA score across other comparators. However, no improvements in HAMA score was observed for the group receiving lorazepam 0.5 mg when compared to silexan 160 mg, silexan 80 mg, paroxetine 20 mg, and placebo.

--- https://examine.com/supplements/lavender/research/ 2022-12-24 psychiatry/anxiety/lavender --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8035468/ Therapeutic effects of Silexan on somatic symptoms and physical health in patients with anxiety disorders: A meta-analysis Roland von Känel, Siegfried Kasper, Guido Bondolfi, Edith Holsboer-Trachsler, Josef Hättenschwiler, Martin Hatzinger, Christian Imboden, Ellen Heitlinger, Erich Seifritz 2021 2022-12-24 [("doi","10.1002/brb3.1997")] psychiatry/anxiety/lavender

[note: Kasper-authored; largely duplicative of Generoso et al 2017 & same weaknesses] A meta-analysis was performed to examine therapeutic effects of Silexan on somatic symptoms, including insomnia/fatigue, and physical health in patients with anxiety disorders. 5 randomized, placebo-controlled trials were included in this analysis: The efficacy of Silexan (80 mg/day) was investigated in patients with sub-threshold anxiety disorders (three trials) and in patients with generalized anxiety disorder (two trials).

Silexan was superior to placebo in terms of the mean change from baseline in the Hamilton Anxiety Rating Scale (HAMA) subscore somatic anxiety at week 10 with a standardized mean difference of −0.31 [95% Cl: −0.52 to −0.10, p = 0.004]. Treatment effects of silexan on somatic anxiety were independent of gender and age. Statistically significant differences were also shown for single HAMA items somatic muscular, cardiovascular, respiratory, and genitourinary symptoms, indicating clinical relevance with small to medium effects of Silexan.

Similar clinically meaningful effects of Silexan on SF-36 physical health, including reduced bodily pain and improved general health, and on insomnia complaints and fatigue, were demonstrated. In this meta-analysis including all placebo-controlled clinical trials in patients with anxiety disorders to date, statistically-significant and clinically meaningful advantages of Silexan over placebo treatment were found in improving somatic symptoms and physical health.

--- https://arxiv.org/abs/1502.04573 Undecidability of the Spectral Gap Toby Cubitt, David Perez-Garcia, Michael M. Wolf 2015-02-16 2022-12-24 [("doi","10.1017/fmp.2021.15")] cs/computable

We show that the spectral gap problem is undecidable.

Specifically, we construct families of translationally-invariant, nearest-neighbour Hamiltonians on a 2D square lattice of d-level quantum systems (d constant), for which determining whether the system is gapped or gapless is an undecidable problem. This is true even with the promise that each Hamiltonian is either gapped or gapless in the strongest sense: it is promised to either have continuous spectrum above the ground state in the thermodynamic limit, or its spectral gap is lower-bounded by a constant in the thermodynamic limit.

Moreover, this constant can be taken equal to the local interaction strength of the Hamiltonian.

--- https://arxiv.org/abs/2012.12828 Constructing Turing complete Euler flows in dimension 3 Robert Cardona, Eva Miranda, Daniel Peralta-Salas, Francisco Presas 2020-12-23 2022-12-24 [("doi","10.1073/pnas.2026818118")] cs/computable

Can every physical system simulate any Turing machine? This is a classical problem which is intimately connected with the undecidability of certain physical phenomena.

Concerning fluid flows, Moore 1991 asked if hydrodynamics is capable of performing computations. More recently, Tao launched a programme based on the Turing completeness of the Euler equations to address the blow up problem in the Navier-Stokes equations. In this direction, the undecidability of some physical systems has been studied in recent years, from the quantum gap problem to quantum field theories.

To the best of our knowledge, the existence of undecidable particle paths of 3D fluid flows has remained an elusive open problem since Moore’s works in the early 1990s.

In this article we construct a Turing complete stationary Euler flow on a Riemannian S3 and speculate on its implications concerning Tao’s approach to the blow up problem in the Navier-Stokes equations.

--- /doc/philosophy/epistemology/2012-inwagen.html 2012 2022-12-24 philosophy/epistemology philosophy/ethics --- https://arxiv.org/abs/1402.0290 Finite time blowup for an averaged three-dimensional Navier-Stokes equation Terence Tao 2014-02-03 2022-12-24 [("doi","10.48550/arXiv.1402.0290")] math science

The Navier-Stokes equation on the Euclidean space 𝐑3 can be expressed in the form ∂tu = Δu + B(u, u), where B is a certain bilinear operator on divergence-free vector fields u obeying the cancellation property 〈B(u, u), u〉 = 0 (which is equivalent to the energy identity for the Navier-Stokes equation).

In this paper, we consider a modification ∂tu = Δu + B̃(u, u) of this equation, where B̃ is an averaged version of the bilinear operator B (where the average involves rotations and Fourier multipliers of order zero), and which also obeys the cancellation condition 〈B(u, u), u〉 = 0 (so that it obeys the usual energy identity).

By analysing a system of ODE related to (but more complicated than) a dyadic Navier-Stokes model of Katz & Pavlovic, we construct an example of a smooth solution to such an averaged Navier-Stokes equation which blows up in finite time. This demonstrates that any attempt to positively resolve the Navier-Stokes global regularity problem in 3 dimensions has to use finer structure on the nonlinear portion B(u, u) of the equation than is provided by harmonic analysis estimates and the energy identity.

We also propose a program for adapting these blowup results to the true Navier-Stokes equations.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC18139/ P/NP, and the quantum field computer M H. Freedman 1998 2022-12-25 [("doi","10.1073/pnas.95.1.98")] cs/computable science

The central problem in computer science is the conjecture that two complexity classes, P (polynomial time) and NP (nondeterministic polynomial time-roughly those decision problems for which a proposed solution can be checked in polynomial time), are distinct in the standard Turing model of computation: P not equal NP.

As a generality, we propose that each physical theory supports computational models whose power is limited by the physical theory. It is well known that classical physics supports a multitude of implementation of the Turing machine. Non-Abelian topological quantum field theories exhibit the mathematical features necessary to support a model capable of solving all #P problems, a computationally intractable class, in polynomial time. Specifically, Witten [Witten 1989] has identified expectation values in a certain SU(2)-field theory with values of the Jones polynomial [Jones, V. (1985) Bull. Am. Math. Soc. 12, 103–111] that are #P-hard [Jaeger et al 1990].

This suggests that some physical system whose effective Lagrangian contains a non-Abelian topological term might be manipulated to serve as an analog computer capable of solving NP or even #P-hard problems in polynomial time. Defining such a system and addressing the accuracy issues inherent in preparation and measurement is a major unsolved problem.

--- https://www.astralcodexten.com/p/davidson-on-takeoff-speeds 2022-12-25 ai/scaling/economics --- https://www.science.org/doi/10.1126/sciadv.aar3620 The second century CE Roman watermills of Barbegal: Unraveling the enigma of one of the oldest industrial complexes Sürmelihindi 2018 2022-12-25 history technology --- https://www.uzh.ch/cmsssl/suz/dam/jcr:ffffffff-866d-1ee0-0000-0000536ff1b9/10.04_stutzer-frey_08.pdf Stress that Doesn’t Pay: The Commuting Paradox Stutzer, Frey 2008 2022-12-25 economics psychology/cognitive-bias --- https://blog.rootsofprogress.org/wright-brothers-and-safe-technology-development 2022-12-25 technology --- https://www.amygoodchild.com/blog/computer-art-50s-and-60s 2022-12-25 cs/algorithm design/visualization --- https://vastabrupt.com/2018/10/31/gender-acceleration/ 2022-12-25 transhumanism --- https://datacolada.org/109 2022-12-25 statistics/bias --- https://datacolada.org/110 2022-12-25 statistics/bias --- https://arxiv.org/abs/2304.05538 ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification Mohammad Reza Taesiri, Giang Nguyen, Sarra Habchi, Cor-Paul Bezemer, Anh Nguyen 2023-04-11 2023-04-11 [("doi","10.48550/arXiv.2304.05538")] ai/dataset ai/nn/cnn ai/nn/transformer/clip

Image classifiers are information-discarding machines, by design. Yet, how these models discard information remains mysterious. We hypothesize that one way for image classifiers to reach high accuracy is to first zoom to the most discriminative region in the image and then extract features from there to predict image labels, discarding the rest of the image.

Studying 6 popular networks ranging from AlexNet to CLIP, we find that proper framing of the input image can lead to the correct classification of 98.91% of ImageNet images. Furthermore, we uncover positional biases in various datasets, especially a strong center bias in two popular datasets: ImageNet-A and ObjectNet.

Finally, leveraging our insights into the potential of zooming, we propose a test-time augmentation (TTA) technique that improves classification accuracy by forcing models to explicitly perform zoom-in operations before making predictions. Our method is more interpretable, accurate, and faster than MEMO, a state-of-the-art (SOTA) TTA method.

We introduce ImageNet-Hard, a new benchmark that challenges SOTA classifiers including large vision-language models even when optimal zooming is allowed.

--- https://x.com/soumithchintala/status/1671267150101721090 2022-12-25 ai/nn/transformer/gpt/4 ai/scaling/mixture-of-experts --- https://github.com/lucidrains/big-sleep 2022-12-26 ai/nn/gan/biggan ai/nn/transformer/clip --- https://web.archive.org/web/20220927022638/https://nautil.us/the-man-who-tried-to-redeem-the-world-with-logic-235253/ 2022-12-26 ai/nn philosophy/logic psychiatry/depression --- https://www.accum.se/~stradh/dnd/mirror/Assorted/ADnD_netbook_of_sex.html 2022-12-26 fiction/text-game --- https://web.archive.org/web/20010201072400/http://www.dolphinsex.org/ 2022-12-26 psychiatry --- https://web.stanford.edu/~surag/posts/alphazero.html 2022-12-26 reinforcement-learning/model/alphago --- https://publicdomainreview.org/collection/fancy-turning 2022-12-26 cs/algorithm design history/public-domain-review --- https://www.ed.gov/sites/ed/files/rschstat/eval/tech/evidence-based-practices/finalreport.pdf Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies Means 2009 2022-12-26 psychology/personality/conscientiousness sociology/technology --- https://pdfs.semanticscholar.org/d0a7/f06ed267f3193daab1175a65abb7a067bef4.pdf Sleep, Learning, and Dreams: Off-line Memory Reprocessing Stickgold 2022-12-26 psychology/neuroscience zeo --- https://www.nature.com/articles/4800840 Total tooth loss in the United Kingdom in 1998 and implications for the future Steele 2000 2022-12-26 biology --- https://x.com/XuanWan76336628/status/1661908542776053760 2022-12-26 statistics/bias --- https://en.wikipedia.org/wiki/Mechanism_design Mechanism design 2022-12-27 economics/mechanism-design --- https://en.wikipedia.org/wiki/Auction_theory Auction theory 2022-12-27 economics/mechanism-design/auction --- https://en.wikipedia.org/wiki/Market_design Market design 2022-12-27 economics/mechanism-design --- https://en.wikipedia.org/wiki/Revelation_principle Revelation principle 2022-12-27 economics/mechanism-design --- https://en.wikipedia.org/wiki/Revenue_equivalence Revenue equivalence 2022-12-27 economics/mechanism-design/auction --- https://en.wikipedia.org/wiki/Vickrey%E2%80%93Clarke%E2%80%93Groves_mechanism Vickrey-Clarke-Groves mechanism 2022-12-27 economics/mechanism-design/auction --- https://en.wikipedia.org/wiki/Shapley_value Shapley value 2022-12-27 economics/mechanism-design --- https://en.wikipedia.org/wiki/Algorithmic_mechanism_design Algorithmic mechanism design 2022-12-27 economics/mechanism-design --- https://en.wikipedia.org/wiki/Contract_theory Contract theory 2022-12-27 economics/mechanism-design --- https://truthonthemarket.com/2023/05/23/how-much-information-do-markets-require/ 2022-12-27 economics/mechanism-design --- https://news.ycombinator.com/item?id=30247159 2022-12-27 cs/algorithm economics/mechanism-design --- https://www.quantamagazine.org/mark-braverman-wins-the-imu-abacus-medal-20220705/ 2022-12-28 economics/mechanism-design --- https://arxiv.org/abs/2201.11441#deepmind Human-centered mechanism design with Democratic AI Raphael Koster, Jan Balaguer, Andrea Tacchetti, Ari Weinstein, Tina Zhu, Oliver Hauser, Duncan Williams, Lucy Campbell-Gillingham, Phoebe Thacker, Matthew Botvinick, Christopher Summerfield 2022-01-27 2022-12-28 [("doi","10.48550/arXiv.2201.11441")] economics/mechanism-design reinforcement-learning/multi-agent

Building artificial intelligence (AI) that aligns with human values is an unsolved problem. Here, we developed a human-in-the-loop research pipeline called Democratic AI, in which reinforcement learning is used to design a social mechanism that humans prefer by majority.

A large group of humans played an online investment game that involved deciding whether to keep a monetary endowment or to share it with others for collective benefit. Shared revenue was returned to players under two different redistribution mechanisms, one designed by the AI and the other by humans.

The AI discovered a mechanism that redressed initial wealth imbalance, sanctioned free riders, and successfully won the majority vote.

By optimizing for human preferences, Democratic AI may be a promising method for value-aligned policy innovation.

--- https://en.wikipedia.org/wiki/Stable_marriage_problem Stable marriage problem 2022-12-28 economics/mechanism-design --- https://en.wikipedia.org/wiki/Gale%E2%80%93Shapley_algorithm Gale-Shapley algorithm 2022-12-28 economics/mechanism-design --- https://en.wikipedia.org/wiki/Stable_roommates_problem Stable roommates problem 2022-12-28 economics/mechanism-design --- https://en.wikipedia.org/wiki/National_Resident_Matching_Program National Resident Matching Program 2022-12-28 economics/mechanism-design --- https://en.wikipedia.org/wiki/Stable_marriage_with_indifference Stable marriage with indifference 2022-12-28 economics/mechanism-design --- https://arxiv.org/abs/1703.08607 Revenue Maximization with an Uncertainty-Averse Buyer Shuchi Chawla, Kira Goldner, J. Benjamin Miller, Emmanouil Pountourakis 2017-03-24 2022-12-28 [("doi","10.48550/arXiv.1703.08607")] economics/mechanism-design

Most work in mechanism design assumes that buyers are risk neutral; some considers risk aversion arising due to a non-linear utility for money. Yet behavioral studies have established that real agents exhibit risk attitudes which cannot be captured by any expected utility model. We initiate the study of revenue-optimal mechanisms under buyer behavioral models beyond expected utility theory. We adopt a model from prospect theory which arose to explain these discrepancies and incorporates agents under-weighting uncertain outcomes. In our model, an event occurring with probability x < 1 is worth strictly less to the agent than x times the value of the event when it occurs with certainty.

In contrast to the risk-neutral setting, the optimal mechanism may be randomized and appears challenging to find, even for a single buyer and a single item for sale. Nevertheless, we give a characterization of the optimal mechanism which enables positive approximation results. In particular, we show that under a reasonable bounded-risk-aversion assumption, posted pricing obtains a constant approximation. Notably, this result is “risk-robust” in that it does not depend on the details of the buyer’s risk attitude. Finally, we examine a dynamic setting in which the buyer is uncertain about his future value. In contrast to positive results for a risk-neutral buyer, we show that the buyer’s risk aversion may prevent the seller from approximating the optimal revenue in a risk-robust manner.

--- /startup-idea#mouse-longevity-perpetual-swaps 2022-12-28 economics/mechanism-design --- https://vitalik.eth.limo/general/2021/08/16/voting3.html 2022-12-28 economics/mechanism-design --- https://forum.effectivealtruism.org/posts/yNn2o3kEhixZHkRga/certificates-of-impact?commentId=n7y2nC28wi4ug7kmm 2022-12-28 economics/mechanism-design --- https://forum.effectivealtruism.org/posts/HFBJMyCiuPyshRvWq/impact-certificates-on-a-blockchain 2022-12-29 economics/mechanism-design --- https://vitalik.eth.limo/general/2021/11/16/retro1.html 2022-12-29 economics/mechanism-design --- https://medium.com/ethereum-optimism/retroactive-public-goods-funding-33c9b7d00f0c 2022-12-29 economics/mechanism-design --- https://www.astralcodexten.com/p/impact-markets-the-annoying-details 2022-12-29 economics/mechanism-design --- https://nori.com/ 2022-12-29 economics/mechanism-design --- https://mirror.xyz/0xA3C0f44dAF771ce6c8bD13f290A2006826A87d9D/8DiOfNWGqXyl2s7o-iPgYGUNQM9KO5byI53yFWLwAD8 From Kyoto Protocol to Klima Protocol (🌳,🌳) 2022-12-29 economics/mechanism-design --- https://vitalik.eth.limo/general/2018/04/20/radical_markets.html ‘On Radical Markets’, Vitalik Buterin 2022-12-29 economics/mechanism-design --- https://github.com/darkwallet/darkleaks 2022-12-29 economics/mechanism-design --- https://github.com/aragon/whitepaper 2022-12-29 economics/mechanism-design --- https://kleros.io/assets/whitepaper.pdf 2022-12-29 economics/mechanism-design --- /silk-road#sanjuro 2022-12-30 economics/mechanism-design --- https://blog.ethereum.org/2014/07/05/stake On Stake 2022-12-30 economics/mechanism-design --- https://ethereum.org/en/developers/docs/consensus-mechanisms/pos/faqs/ Proof of Stake FAQs 2022-12-30 economics/mechanism-design --- https://en.wikipedia.org/wiki/Proof_of_stake Proof of stake 2022-12-30 bitcoin --- https://web.cecs.pdx.edu/~cvwright/papers/crumplezones.pdf 2022-12-30 cs/cryptography/timelock economics/mechanism-design --- https://vitalik.eth.limo/general/2021/11/16/retro1.html#should-badge-holder-votes-be-secret-ballot 2022-12-30 bitcoin economics/mechanism-design politics --- https://en.wikipedia.org/wiki/Assurance_contract Assurance contract 2022-12-30 economics/mechanism-design --- https://en.wikipedia.org/wiki/Assurance_contract#Dominant_assurance_contracts Assurance contract § Dominant assurance contracts 2022-12-30 economics/mechanism-design --- https://en.wikipedia.org/wiki/Threshold_pledge_system Threshold pledge system 2022-12-30 economics/mechanism-design --- https://en.wikipedia.org/wiki/Kickstarter Kickstarter 2022-12-30 economics/mechanism-design --- https://vitalik.eth.limo/general/2019/12/07/quadratic.html 2022-12-30 economics/mechanism-design/quadratic-voting --- https://vitalik.eth.limo/general/2019/04/03/collusion.html 2022-12-31 economics/mechanism-design --- https://www.lesswrong.com/posts/9QxnfMYccz9QRgZ5z/the-costly-coordination-mechanism-of-common-knowledge 2022-12-31 economics/mechanism-design sociology --- https://www.lesswrong.com/tag/mechanism-design 2022-12-31 economics/mechanism-design --- https://www.lesswrong.com/posts/N4gDA5HPpGC4mbTEZ/incentive-compatibility-and-the-revelation-principle 2022-12-31 economics/mechanism-design --- https://en.wikipedia.org/wiki/Harberger_Tax Harberger Tax 2022-12-31 economics/mechanism-design/auction --- https://libgen.li/book/index.php?md5=131C917701ED2B3A571305F2B2CC37F7 2022-12-31 economics/mechanism-design --- https://www.lesswrong.com/posts/QG2ZQm2Fxq8ET22sT/strategyproof-mechanisms-possibilities 2022-12-31 economics/mechanism-design --- https://www.nola.com/news/education/centralized-enrollment-in-recovery-school-district-gets-first-tryout/article_e4575979-70e0-5b81-ad7f-b87ef293795c.html 2022-12-31 economics/mechanism-design --- https://arxiv.org/abs/2106.01340 Transaction Fee Mechanism Design Tim Roughgarden 2021-06-02 2022-12-31 [("doi","10.48550/arXiv.2106.01340")] bitcoin economics/mechanism-design/auction

Demand for blockchains such as Bitcoin and Ethereum is far larger than supply, necessitating a mechanism that selects a subset of transactions to include “on-chain” from the pool of all pending transactions. This paper investigates the problem of designing a blockchain transaction fee mechanism through the lens of mechanism design. We introduce two new forms of incentive-compatibility that capture some of the idiosyncrasies of the blockchain setting, one (MMIC) that protects against deviations by profit-maximizing miners and one (OCA-proofness) that protects against off-chain collusion between miners and users.

This study is immediately applicable to a recent (August 5, 2021) and major change to Ethereum’s transaction fee mechanism, based on a proposal called “EIP-1559.” Historically, Ethereum’s transaction fee mechanism was a first-price (pay-as-bid) auction. EIP-1559 suggested making several tightly coupled changes, including the introduction of variable-size blocks, a history-dependent reserve price, and the burning of a portion of the transaction fees. We prove that this new mechanism earns an impressive report card: it satisfies the MMIC and OCA-proofness conditions, and is also dominant-strategy incentive compatible (DSIC) except when there is a sudden demand spike. We also introduce an alternative design, the “tipless mechanism”, which offers an incomparable slate of incentive-compatibility guarantees—it is MMIC and DSIC, and OCA-proof unless in the midst of a demand spike.

--- http://timroughgarden.org/w14/w14.html 2022-12-31 economics/mechanism-design --- https://cepr.org/voxeu/columns/nobel-prize-what-mechanism-design-and-why-does-it-matter-policy-making 2022-12-31 economics/mechanism-design --- https://arxiv.org/abs/0906.1943 Telescope Time Without Tears: A Distributed Approach to Peer Review Michael R. Merrifield, Donald G. Saari 2009-06-10 2023-01-01 [("doi","10.1111/j.1468-4004.2009.50416.x")] economics/mechanism-design statistics/peer-review

The procedure that is currently employed to allocate time on telescopes is horribly onerous on those unfortunate astronomers who serve on the committees that administer the process, and is in danger of complete collapse as the number of applications steadily increases.

Here, an alternative is presented, whereby the task is distributed around the astronomical community, with a suitable mechanism design established to steer the outcome toward awarding this precious resource to those projects where there is a consensus across the community that the science is most exciting and innovative.

--- https://theoryclass.wordpress.com/2013/06/06/a-mechanism-design-approach-to-peer-review/ 2023-01-01 economics/mechanism-design statistics/peer-review --- http://www.eecs.northwestern.edu/~hartline/amd.pdf 2023-01-01 economics/mechanism-design --- https://www.lesswrong.com/posts/wE3CRBTpSSBXf9EHK/strategyproof-mechanisms-impossibilities 2023-01-01 economics/mechanism-design --- https://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-207.pdf#page=3 2023-01-01 economics/mechanism-design reinforcement-learning/safe --- https://www.lesswrong.com/posts/X5RyaEDHNq5qutSHK/anti-social-punishment 2023-01-01 sociology --- https://www.lesswrong.com/posts/x6hpkYyzMG6Bf8T3W/swiss-political-system-more-than-you-ever-wanted-to-know-i 2023-01-01 politics --- https://en.wikipedia.org/wiki/Spectrum_auction 2023-01-01 economics/mechanism-design/auction --- https://en.wikipedia.org/wiki/Kidney_paired_donation Kidney paired donation 2023-01-01 economics/mechanism-design --- https://www.nobelprize.org/prizes/economic-sciences/2007/popular-information/ 2023-01-01 economics/mechanism-design --- https://www.nobelprize.org/uploads/2018/06/myerson_lecture.pdf 2023-01-01 economics/mechanism-design --- https://www.nobelprize.org/uploads/2018/06/maskin_lecture.pdf 2023-01-02 economics/mechanism-design --- https://www.nobelprize.org/uploads/2018/06/advanced-economicsciences2007.pdf 2023-01-02 economics/mechanism-design --- https://www.lesswrong.com/posts/xTvdaCwaeZnePMuX5/mechanism-design-constructing-algorithms-for-strategic 2023-01-02 economics/mechanism-design --- https://www.a1k0n.net/2021/01/22/indoor-localization.html 2023-01-02 math reinforcement-learning/robot --- https://www.medrxiv.org/content/10.1101/2023.06.19.23291606.full The relationship between genotype- and phenotype-based estimates of genetic liability to human psychiatric disorders, in practice and in theory. Morten Dybdahl Krebs, Vivek Appadurai, Kajsa-Lotta Georgii Hellberg, Henrik Ohlsson, Jette Steinbach, Emil Michael Pedersen, iPSY C. H. Study Consortium, Thomas Werge, Jan Sundquist, Kristina Sundquist, Na Cai, Noah Zaitlen, Andy W. Dahl, Bjarni J. Vilhjalmsson, Jonathan Flint, Silviu-Alin Bacanu, Andrew J. Schork, Kenneth S. Kendler 2023-06-20 2023-06-20 [("doi","10.1101/2023.06.19.23291606")] genetics/heritable psychiatry

Genetics as a science has roots in studying phenotypes of relatives, but molecular approaches facilitate direct measurements of genomic variation within individuals. Agricultural and human biomedical research are both emphasizing genotype-based instruments, like polygenic scores, as the future of breeding programs or precision medicine and genetic epidemiology. However, unlike in agriculture, there is an emerging consensus that family variables act near independent of genotypes in models of human disease.

To advance our understanding of this phenomenon, we use 2,066,057 family records of 99,645 genotyped probands from the iPSYCH2015 case-cohort study to show that state-of-the-field genotype- and phenotype-based genetic instruments explain essentially independent components of liability to psychiatric disorders. We support these empirical results with novel theoretical analysis and simulations to describe, in a human biomedical context, parameters affecting current and future performance of the two approaches, their expected interrelationships, relative sample size efficiencies, and the striking consistency of observed results with expectations under simple additive, polygenic liability models of disease.

We conclude, at least for psychiatric disorders, that phenotype- and genotype-based genetic instruments are likely to be very noisy measures of the same underlying additive genetic liability, should be seen, in the near future, as complementary, and integrated to a greater extent going forward. [That is, the severe measurement error in both PGS & family history biases their observed correlation to zero.]

--- https://en.wikipedia.org/wiki/Combinatorial_auction Combinatorial auction 2023-01-02 cs/algorithm economics/mechanism-design/auction --- https://www.overcomingbias.com/p/city-by-combo-auctionhtml 2023-01-02 cs/algorithm economics/mechanism-design/auction --- https://www.overcomingbias.com/p/office-by-combo-auctionhtml 2023-01-02 cs/algorithm economics/mechanism-design --- https://www.overcomingbias.com/p/for-stability-rentshtml 2023-01-02 cs/algorithm economics/mechanism-design/auction --- https://en.wikipedia.org/wiki/Telecoms_crash Telecoms crash 2023-01-02 cs/algorithm economics/mechanism-design --- https://www.nuff.ox.ac.uk/users/klemperer/biggestpaper.pdf#page=2 2023-01-03 cs/algorithm economics/mechanism-design --- https://priceonomics.com/the-spectrum-auction-how-economists-saved-the-day/ 2023-01-03 cs/algorithm economics/mechanism-design --- https://en.wikipedia.org/wiki/List_of_laboratory_biosecurity_incidents List of laboratory biosecurity incidents 2023-01-03 existential-risk --- https://russellwarne.com/2023/06/16/moderators-and-mediators-the-epicycles-of-the-social-sciences/ 2023-01-03 psychology/cognitive-bias/stereotype-threat --- https://x.com/mckaywrigley/status/1642948620604538880 2023-01-03 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://www.stoneageherbalist.com/p/ghanas-concentration-camps-for-witches 2023-01-03 philosophy/religion sociology --- https://en.wikipedia.org/wiki/Goodhart%27s_law Goodhart’s law 2023-01-03 economics/mechanism-design reinforcement-learning/safe --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6458406/ Humans store about 1.5 megabytes of information during language acquisition Francis Mollica, Steven T. Piantadosi 2019 2023-01-03 [("doi","10.1098/rsos.181393")] cs/algorithm/information psychology/linguistics

We introduce theory-neutral estimates of the amount of information learners possess about how language works. We provide estimates at several levels of linguistic analysis: phonemes, wordforms, lexical semantics, word frequency and syntax.

Our best guess is that the average English-speaking adult has learned 12.5 million bits of information, the majority of which is lexical semantics.

Interestingly, very little of this information is syntactic, even in our upper bound analyses.

Generally, our results suggest that learners possess remarkable inferential mechanisms capable of extracting, on average, nearly 2,000 bits of information about how language works each day for 18 years.

--- https://arxiv.org/abs/2305.20030 Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust Yuxin Wen, John Kirchenbauer, Jonas Geiping, Tom Goldstein 2023-05-31 2023-05-31 [("doi","10.48550/arXiv.2305.20030")] ai/nn/diffusion

Watermarking the outputs of generative models is a crucial technique for tracing copyright and preventing potential harm from AI-generated content. In this paper, we introduce a novel technique called Tree-Ring Watermarking that robustly fingerprints diffusion model outputs. Unlike existing methods that perform post-hoc modifications to images after sampling, Tree-Ring Watermarking subtly influences the entire sampling process, resulting in a model fingerprint that is invisible to humans.

The watermark embeds a pattern into the initial noise vector used for sampling. These patterns are structured in Fourier space so that they are invariant to convolutions, crops, dilations, flips, and rotations. After image generation, the watermark signal is detected by inverting the diffusion process to retrieve the noise vector, which is then checked for the embedded signal.

We demonstrate that this technique can be easily applied to arbitrary diffusion models, including text-conditioned Stable Diffusion, as a plug-in with negligible loss in FID. Our watermark is semantically hidden in the image space and is far more robust than watermarking alternatives that are currently deployed. Code is available at Github.

--- https://en.wikipedia.org/wiki/How_Children_Learn_the_Meanings_of_Words How Children Learn the Meanings of Words 2023-01-03 psychology/linguistics --- https://www.newyorker.com/science/maria-konnikova/bilingual-advantage-aging-brain 2023-01-03 psychology/linguistics/bilingual --- https://www.theatlantic.com/science/archive/2016/02/the-battle-over-bilingualism/462114/ For decades, some psychologists have claimed that bilinguals have better mental control. Their work is now being called into question. 2023-01-04 psychology/linguistics/bilingual statistics/bias/publication --- https://www.psychologytoday.com/us/blog/life-bilingual/201906/the-bilingual-advantage-three-years-later 2023-01-04 psychology/linguistics/bilingual --- https://addi.ehu.es/bitstream/handle/10810/26594/Is%20Bilingualism%20Associated2018.pdf?sequence=1&isAllowed=y 2023-01-04 psychology/linguistics/bilingual --- /doc/psychology/linguistics/bilingual/2017-mukadam.pdf 2017-01-01 2023-01-04 psychiatry/alzheimers psychology/linguistics/bilingual --- https://en.wikipedia.org/wiki/Cognitive_effects_of_bilingualism Cognitive effects of bilingualism 2023-01-04 psychology/linguistics/bilingual --- https://en.wikipedia.org/wiki/ASCII ASCII 2023-01-04 psychology/linguistics --- https://en.wikipedia.org/wiki/Claude_Shannon Claude Shannon 2023-01-04 psychology/linguistics --- https://www.princeton.edu/~wbialek/rome/refs/shannon_51.pdf Prediction and entropy of printed English Shannon 1951 2023-01-04 cs/algorithm/information/compression psychology/linguistics --- https://nautil.us/the-man-who-invented-modern-probability-234497/ 2023-01-04 psychology/linguistics --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.92.5610&rep=rep1&type=pdf 2023-01-04 psychology/linguistics --- https://aclanthology.org/J92-1002.pdf An estimate of an upper bound for the entropy of English 2023-01-04 psychology/linguistics --- http://sparkplugged.net/2007/10/outsider-speed-rap-extraordinaire/ 2023-01-05 psychology/linguistics --- https://en.wikipedia.org/wiki/Outsider_(rapper) Outsider (rapper) 2023-01-05 psychology/linguistics --- https://web.archive.org/web/20110729162652/http://www.arirang.co.kr/TV2/Star_Focus.asp?code=Ki3&F_KEY=612 2023-01-05 psychology/linguistics --- https://www.zompist.com/kitlong.html#howmany 2023-01-05 psychology/linguistics --- https://www.lesswrong.com/posts/soQX8yXLbKy7cFvy8/entropy-and-short-codes 2023-01-05 psychology/linguistics --- https://www.pnas.org/doi/full/10.1073/pnas.1012551108 2023-01-05 psychology/linguistics --- https://en.wikipedia.org/wiki/Zipf's_law Zipf’s law 2023-01-05 psychology/linguistics --- https://en.wikipedia.org/wiki/Abjad Abjad 2023-01-05 psychology/linguistics --- https://en.wikipedia.org/wiki/Niqqud Niqqud 2023-01-05 psychology/linguistics --- https://en.wikipedia.org/wiki/Revival_of_the_Hebrew_language Revival of the Hebrew language 2023-01-05 psychology/linguistics --- https://en.wikipedia.org/wiki/Modern_Hebrew Modern Hebrew 2023-01-06 psychology/linguistics --- https://en.wikipedia.org/wiki/Tetragrammaton Tetragrammaton 2023-01-06 psychology/linguistics --- https://en.wikipedia.org/wiki/Penitent_thief#%22Amen_..._today_..._in_paradise%22 Penitent thief § "Amen ... today ... in paradise" 2023-01-06 psychology/linguistics --- https://en.wikipedia.org/wiki/Deep_structure_and_surface_structure Deep structure and surface structure 2023-01-06 psychology/linguistics --- /doc/psychology/linguistics/1985-sun.pdf Comparative patterns of reading eye movement in Chinese and English 1985-01-01 2023-01-06 psychology/linguistics --- https://arxiv.org/abs/2306.05426 SequenceMatch: Imitation Learning for Autoregressive Sequence Modeling with Backtracking Chris Cundy, Stefano Ermon 2023-06-08 2023-06-08 [("doi","10.48550/arXiv.2306.05426")] ai/nn/transformer reinforcement-learning/imitation-learning

[cf. LaserTagger, PEER, Reid & Neubig 2022] In many domains, autoregressive models can attain high likelihood on the task of predicting the next observation. However, this maximum-likelihood (MLE) objective does not necessarily match a downstream use-case of autoregressively generating high-quality sequences. The MLE objective weights sequences proportionally to their frequency under the data distribution, with no guidance for the model’s behavior out of distribution (OOD): leading to compounding error during autoregressive generation.

In order to address this compounding error problem, we formulate sequence generation as an imitation learning (IL) problem. This allows us to minimize a variety of divergences between the distribution of sequences generated by an autoregressive model and sequences from a dataset, including divergences with weight on OOD generated sequences. The IL framework also allows us to incorporate backtracking by introducing a backspace action into the generation process. This further mitigates the compounding error problem by allowing the model to revert a sampled token if it takes the sequence OOD.

Our resulting method, SequenceMatch, can be implemented without adversarial training or major architectural changes. We identify the SequenceMatch-χ2 divergence as a more suitable training objective for autoregressive models which are used for generation.

We show that empirically, SequenceMatch training leads to improvements over MLE on text generation with language models.

--- https://www.reddit.com/r/reinforcementlearning/search/?q=flair%3AI&restrict_sr=on&sort=new 2023-01-06 reinforcement-learning/imitation-learning --- /gpt-3#roleplaying 2023-01-06 reinforcement-learning/imitation-learning --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5676646/ Thermal Analyses of a Human Kidney and a Rabbit Kidney During Cryopreservation by Vitrification Lili E. Ehrlich, Gregory M. Fahy, Brian G. Wowk, Jonathan A. Malen, Yoed Rabin 2018 2023-01-06 [("doi","10.1115/1.4037406")] cryonics

This study focuses on thermal analysis of the problem of scaling up from the vitrification of rabbit kidneys to the vitrification of human kidneys, where vitrification is the preservation of biological material in the glassy state. The basis for this study is a successful cryopreservation protocol for a rabbit kidney model, based on using a proprietary vitrification solution known as M22.

Using the finite element analysis (FEA) commercial code ANSYS, heat transfer simulations suggest that indeed the rabbit kidney unquestionably cools rapidly enough to be vitrified based on known intrarenal concentrations of M22. Scaling up 21×, computer simulations suggest less favorable conditions for human kidney vitrification. In this case, cooling rates below −100℃ are sometimes slower than 1℃/min, a rate that provides a clear-cut margin of safety at all temperatures based on the stability of rabbit kidneys in past studies.

Nevertheless, it is concluded in this study that vitrifying human kidneys is possible without significant ice damage, assuming that human kidneys can be perfused with M22 as effectively as rabbit kidneys. The thermal analysis suggests that cooling rates can be further increased by a careful design of the cryogenic protocol and by tailoring the container to the shape of the kidney, in contrast to the present cylindrical container.

This study demonstrates the critical need for the thermal analysis of experimental cryopreservation and highlights the unmet need for measuring the thermophysical properties of cryoprotective solutions under conditions relevant to realistic thermal histories.

--- https://www.pnas.org/doi/full/10.1073/pnas.1717588114 2023-01-06 cryonics --- https://arxiv.org/abs/2209.00459 Generative Personas That Behave and Experience Like Humans Matthew Barthet, Ahmed Khalifa, Antonios Liapis, Georgios N. Yannakakis 2022-08-26 2023-01-06 [("doi","10.1145/3555858.3555879")] reinforcement-learning/imitation-learning

Using artificial intelligence (AI) to automatically test a game remains a critical challenge for the development of richer and more complex game worlds and for the advancement of AI at large. One of the most promising methods for achieving that long-standing goal is the use of generative AI agents, namely procedural personas, that attempt to imitate particular playing behaviors which are represented as rules, rewards, or human demonstrations.

All research efforts for building those generative agents, however, have focused solely on playing behavior which is arguably a narrow perspective of what a player actually does in a game. Motivated by this gap in the existing state-of-the-art, in this paper we extend the notion of behavioral procedural personas to cater for player experience, thus examining generative agents that can both behave and experience their game as humans would.

For that purpose, we employ the Go-Explore reinforcement learning paradigm for training human-like procedural personas, and we test our method on behavior and experience demonstrations of more than 100 players of a racing game. Our findings suggest that the generated agents exhibit distinctive play styles and experience responses of the human personas they were designed to imitate.

Importantly, it also appears that experience, which is tied to playing behavior, can be a highly informative driver for better behavioral exploration.

--- https://www.econlib.org/library/Smith/smMS.html?chapter_num=5#IV.I.6 2023-01-07 psychology/collecting --- https://arxiv.org/abs/2111.02767 RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning Sabela Ramos, Sertan Girgin, Léonard Hussenot, Damien Vincent, Hanna Yakubovich, Daniel Toyama, Anita Gergely, Piotr Stanczyk, Raphael Marinier, Jeremiah Harmsen, Olivier Pietquin, Nikola Momchev 2021-11-04 2023-01-07 [("doi","10.48550/arXiv.2111.02767")] ai/dataset reinforcement-learning/imitation-learning

We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for recording, replaying, manipulating, annotating and sharing data in the context of Sequential Decision Making (SDM) including Reinforcement Learning (RL), Learning from Demonstrations, Offline RL or Imitation Learning. RLDS enables not only reproducibility of existing research and easy generation of new datasets, but also accelerates novel research.

By providing a standard and lossless format of datasets it enables to quickly test new algorithms on a wider range of tasks. The RLDS ecosystem makes it easy to share datasets without any loss of information and to be agnostic to the underlying original format when applying various data processing pipelines to large collections of datasets.

Besides, RLDS provides tools for collecting data generated by either synthetic agents or humans, as well as for inspecting and manipulating the collected data. Ultimately, integration with TFDS facilitates the sharing of RL datasets with the research community.

--- https://ai.facebook.com/research/publications/ego4d-unscripted-first-person-video-from-around-the-world-and-a-benchmark-suite-for-egocentric-perception 2023-01-07 ai/dataset reinforcement-learning/robot --- https://arxiv.org/abs/2110.03262 Situated Dialogue Learning through Procedural Environment Generation Prithviraj Ammanabrolu, Renee Jia, Mark O. Riedl 2021-10-07 2023-01-07 [("doi","10.48550/arXiv.2110.03262")] ai/dataset

We teach goal-driven agents to interactively act and speak in situated environments by training on generated curriculums. Our agents operate in LIGHT (Urbanek et al 2019)—a large-scale crowd-sourced fantasy text adventure game wherein an agent perceives and interacts with the world through textual natural language. Goals in this environment take the form of character-based quests, consisting of personas and motivations.

We augment LIGHT by learning to procedurally generate additional novel textual worlds and quests to create a curriculum of steadily increasing difficulty for training agents to achieve such goals. In particular, we measure curriculum difficulty in terms of the rarity of the quest in the original training distribution—an easier environment is one that is more likely to have been found in the unaugmented dataset.

An ablation study shows that this method of learning from the tail of a distribution results in higher generalization abilities as measured by zero-shot performance on never-before-seen quests.

--- https://arxiv.org/abs/2109.05603 Learning to Navigate Sidewalks in Outdoor Environments Maks Sorokin, Jie Tan, C. Karen Liu, Sehoon Ha 2021-09-12 2023-01-07 [("doi","10.48550/arXiv.2109.05603")] reinforcement-learning/imitation-learning reinforcement-learning/robot

Outdoor navigation on sidewalks in urban environments is the key technology behind important human assistive applications, such as last-mile delivery or neighborhood patrol. This paper aims to develop a quadruped robot that follows a route plan generated by public map services, while remaining on sidewalks and avoiding collisions with obstacles and pedestrians.

We devise a two-staged learning framework, which first trains a teacher agent in an abstract world with privileged ground-truth information, and then applies Behavior Cloning to teach the skills to a student agent who only has access to realistic sensors. The main research effort of this paper focuses on overcoming challenges when deploying the student policy on a quadruped robot in the real world.

We propose methodologies for designing sensing modalities, network architectures, and training procedures to enable zero-shot policy transfer to unstructured and dynamic real outdoor environments. We evaluate our learning framework on a quadrupedal robot navigating sidewalks in the city of Atlanta, USA. Using the learned navigation policy and its onboard sensors, the robot is able to walk 3.2 kilometers with a limited number of human interventions.

--- https://arxiv.org/abs/2109.04869 PlaTe: Visually-Grounded Planning with Transformers in Procedural Tasks Jiankai Sun, De-An Huang, Bo Lu, Yun-Hui Liu, Bolei Zhou, Animesh Garg 2021-09-10 2023-01-07 [("doi","10.1109/LRA.2022.3150855")] reinforcement-learning/imitation-learning reinforcement-learning/robot

In this work, we study the problem of how to leverage instructional videos to facilitate the understanding of human decision-making processes, focusing on training a model with the ability to plan a goal-directed procedure from real-world videos. Learning structured and plannable state and action spaces directly from unstructured videos is the key technical challenge of our task.

There are two problems: first, the appearance gap between the training and validation datasets could be large for unstructured videos; second, these gaps lead to decision errors that compound over the steps. We address these limitations with Planning Transformer (PlaTe), which has the advantage of circumventing the compounding prediction errors that occur with single-step models during long model-based rollouts.

Our method simultaneously learns the latent state and action information of assigned tasks and the representations of the decision-making process from human demonstrations. Experiments conducted on real-world instructional videos and an interactive environment show that our method can achieve a better performance in reaching the indicated goal than previous algorithms.

We also validated the possibility of applying procedural tasks on a UR-5 platform. We make our code publicly available and support academic research purposes.

--- https://arxiv.org/abs/2109.00137#google Implicit Behavioral Cloning Pete Florence, Corey Lynch, Andy Zeng, Oscar Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, Jonathan Tompson 2021-09-01 2023-01-07 [("doi","10.48550/arXiv.2109.00137")] reinforcement-learning/imitation-learning reinforcement-learning/robot

We find that across a wide range of robot policy learning scenarios, treating supervised policy learning with an implicit model generally performs better, on average, than commonly used explicit models. We present extensive experiments on this finding, and we provide both intuitive insight and theoretical arguments distinguishing the properties of implicit models compared to their explicit counterparts, particularly with respect to approximating complex, potentially discontinuous and multi-valued (set-valued) functions.

On robotic policy learning tasks we show that implicit behavioral cloning policies with energy-based models (EBM) often outperform common explicit (Mean Square Error, or Mixture Density) behavioral cloning policies, including on tasks with high-dimensional action spaces and visual image inputs. We find these policies provide competitive results or outperform state-of-the-art offline reinforcement learning methods on the challenging human-expert tasks from the D4RL benchmark suite, despite using no reward information.

In the real world, robots with implicit policies can learn complex and remarkably subtle behaviors on contact-rich tasks from human demonstrations, including tasks with high combinatorial complexity and tasks requiring 1mm precision.

--- https://arxiv.org/abs/2305.15717 The False Promise of Imitating Proprietary LLMs Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song 2023-05-25 2023-05-25 [("doi","10.48550/arXiv.2305.15717")] ai/nn/transformer/gpt/3/nonfiction ai/scaling reinforcement-learning/imitation-learning reinforcement-learning/preference-learning/mode-collapse

[cf. LIMA, mode collapse] An emerging method to cheaply improve a weaker language model is to finetune it on outputs from a stronger model, such as a proprietary system like ChatGPT (eg. Alpaca, Self-Instruct, and others). This approach looks to cheaply imitate the proprietary model’s capabilities using a weaker open-source model. In this work, we critically analyze this approach.

We first finetune a series of LMs that imitate ChatGPT using varying base model sizes (1.5B–13B), data sources, and imitation data amounts (0.3M–150M tokens). We then evaluate the models using crowd raters and canonical NLP benchmarks.

Initially, we were surprised by the output quality of our imitation models—they appear far better at following instructions, and crowd workers rate their outputs as competitive with ChatGPT. However, when conducting more targeted automatic evaluations, we find that imitation models close little to none of the gap from the base LM to ChatGPT on tasks that are not heavily supported in the imitation data.

We show that these performance discrepancies may slip past human raters because imitation models are adept at mimicking ChatGPT’s style but not its factuality.

Overall, we conclude that model imitation is a false promise: there exists a substantial capabilities gap between open and closed LMs that, with current methods, can only be bridged using an unwieldy amount of imitation data or by using more capable base LMs.

In turn, we argue that the highest leverage action for improving open-source models is to tackle the difficult challenge of developing better base LMs, rather than taking the shortcut of imitating proprietary systems.

--- https://arxiv.org/abs/2305.11206 LIMA: Less Is More for Alignment Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, Omer Levy 2023-05-18 2023-05-18 [("doi","10.48550/arXiv.2305.11206")] ai/nn/transformer/gpt/4 ai/scaling reinforcement-learning/imitation-learning reinforcement-learning/preference-learning

Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences. We measure the relative importance of these two stages by training LIMA, a 65b parameter LLaMa language model fine-tuned with the standard supervised loss on only 1,000 carefully curated prompts and responses, without any reinforcement learning or human preference modeling.

LIMA demonstrates remarkably strong performance, learning to follow specific response formats from only a handful of examples in the training data, including complex queries that range from planning trip itineraries to speculating about alternate history. Moreover, the model tends to generalize well to unseen tasks that did not appear in the training data.

In a controlled human study, responses from LIMA are either equivalent or strictly preferred to GPT-4 in 43% of cases; this statistic is as high as 58% when compared to Bard and 65% versus davinci-003, which was trained with human feedback.

Taken together, these results strongly suggest that almost all knowledge in large language models is learned during pretraining, and only limited instruction tuning data is necessary to teach models to produce high quality output.

--- https://x.com/BlancheMinerva/status/1662521904727756801 2023-01-07 ai/nn/transformer/gpt reinforcement-learning/preference-learning --- https://www.lesswrong.com/posts/ftPQugx2726zAL2Ff/which-personality-traits-are-real-stress-testing-the-lexical 2023-01-07 psychology/personality --- https://www.economist.com/middle-east-and-africa/2023/06/15/why-kenya-could-take-the-lead-in-carbon-removal 2023-01-08 technology/carbon-capture --- https://arxiv.org/abs/2306.09485#chainanalysis Identifying key players in dark web marketplaces Elohim Fonseca dos Reis, Alexander Teytelboym, Abeer ElBahraw, Ignacio De Loizaga, Andrea Baronchelli 2023-06-15 2023-06-15 [("doi","10.48550/arXiv.2306.09485")] darknet-market

Dark web marketplaces have been an outlet for illicit trade, serving millions of users worldwide for over a decade. However, not all users are the same. This paper aims to identify the key players in Bitcoin transaction networks linked to dark markets and assess their role by analysing a dataset of 40 million Bitcoin transactions involving 31 markets in the period 2011–2021.

First, we propose an algorithm that categorizes users either as buyers or sellers and shows that a large fraction of the traded volume is concentrated in a small group of elite market participants. Then, we investigate both market star-graphs and user-to-user networks and highlight the importance of a new class of users, namely ‘multihomers’ who operate on multiple marketplaces concurrently.

Specifically, we show how the networks of multihomers and seller-to-seller interactions can shed light on the resilience of the dark market ecosystem against external shocks.

Our findings suggest that understanding the behavior of key players in dark web marketplaces is critical to effectively disrupting illegal activities.

--- https://www.outsideonline.com/outdoor-adventure/climbing/austin-howell-fallen-soloist/ 2023-01-08 psychiatry/bipolar/energy psychiatry/depression psychiatry/traumatic-brain-injury --- https://arxiv.org/abs/2209.14375#deepmind Sparrow: Improving alignment of dialogue agents via targeted human judgements Amelia Glaese, Nat McAleese, Maja Trębacz, John Aslanides, Vlad Firoiu, Timo Ewalds, Maribeth Rauh, Laura Weidinger, Martin Chadwick, Phoebe Thacker, Lucy Campbell-Gillingham, Jonathan Uesato, Po-Sen Huang, Ramona Comanescu, Fan Yang, Abigail See, Sumanth Dathathri, Rory Greig, Charlie Chen, Doug Fritz, Jaume Sanchez Elias, Richard Green, Soňa Mokrá, Nicholas Fernando, Boxi Wu, Rachel Foley, Susannah Young, Iason Gabriel, William Isaac, John Mellor, Demis Hassabis, Koray Kavukcuoglu, Lisa Anne Hendricks, Geoffrey Irving 2022-09-28 2023-01-08 [("doi","10.48550/arXiv.2209.14375")] ai/nn/retrieval ai/nn/transformer/gpt reinforcement-learning/preference-learning

We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines. We use reinforcement learning from human feedback to train our models with two new additions to help human raters judge agent behavior.

First, to make our agent more helpful and harmless, we break down the requirements for good dialogue into natural language rules the agent should follow, and ask raters about each rule separately. We demonstrate that this breakdown enables us to collect more targeted human judgements of agent behavior and allows for more efficient rule-conditional reward models.

Second, our agent provides evidence from sources supporting factual claims when collecting preference judgements over model statements.

For factual questions, evidence provided by Sparrow supports the sampled response 78% of the time. Sparrow is preferred more often than baselines while being more resilient to adversarial probing by humans, violating our rules only 8% of the time when probed.

Finally, we conduct extensive analyses showing that though our model learns to follow our rules it can exhibit distributional biases.

--- https://arxiv.org/abs/2302.13971#facebook LLaMa-1: Open and Efficient Foundation Language Models Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample 2023-02-27 2023-02-27 [("doi","10.48550/arXiv.2302.13971")] ai/nn/tokenization ai/nn/transformer/gpt/instruction-tuning

We introduce LLaMa, a collection of foundation language models ranging from 7B to 65b parameters.

We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets.

In particular, LLaMa-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMa-65B is competitive with the best models, Chinchilla-70B and PaLM-540B.

We release all our models to the research community [which were then leaked].

--- https://arxiv.org/abs/2305.07185#facebook MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers Lili Yu, Dániel Simig, Colin Flaherty, Armen Aghajanyan, Luke Zettlemoyer, Mike Lewis 2023-05-12 2023-05-12 [("doi","10.48550/arXiv.2305.07185")] ai/nn/tokenization ai/nn/transformer/attention/hierarchical ai/nn/transformer/gpt

Autoregressive transformers are spectacular models for short sequences but scale poorly to long sequences such as high-resolution images, podcasts, code, or books.

We proposed Megabyte, a multi-scale decoder architecture that enables end-to-end differentiable modeling of sequences of over one million bytes. Megabyte segments sequences into patches and uses a local sub-model within patches and a global model between patches. This enables sub-quadratic self-attention, much larger feedforward layers for the same compute, and improved parallelism during decoding—unlocking better performance at reduced cost for both training and generation.

Extensive experiments show that Megabyte allows byte-level models to perform competitively with subword models on long context language modeling, achieve state-of-the-art density estimation on ImageNet, and model audio from raw files.

Together, these results establish the viability of tokenization-free autoregressive sequence modeling at scale.

--- http://skynetsimulator.com/ 2023-01-08 fiction/text-game reinforcement-learning/safe --- https://webarchive.loc.gov/all/20100611183353/http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/1765/1645 What is Popular on Wikipedia and Why? Spoerri 2007 2023-01-08 wikipedia --- https://arxiv.org/pdf/2005.04909.pdf#page=3 Conditional Image Generation and Manipulation for User-Specified Content § pg3 Stap 2020 2023-01-08 ai/nn/gan/stylegan --- https://www.jvsmedicscorner.com/Physiology_files/Impact%20of%20sleep%20debt%20on%20metabolic%20and%20endocrine%20function.pdf Impact of sleep debt on metabolic and endocrine function Spiegel 1999 2023-01-08 zeo --- https://www.technologyreview.com/2011/06/21/193829/the-measured-life/ The Measured Life: Do you know how much REM sleep you got last night? New types of devices that monitor activity, sleep, diet, and even mood could make us healthier and more productive Singer 2011 2023-01-09 nootropic/quantified-self zeo --- https://en.wikipedia.org/wiki/History_of_aluminium#Early_industrial_production History of aluminium § Early industrial production 2023-01-09 economics/experience-curve --- https://www.eveonline.com/news/view/information-is-power-excel-release 2023-01-09 cs/algorithm design/visualization fiction/text-game math/humor --- https://en.wikipedia.org/wiki/Comic_book_collecting#The_speculator_boom Comic book collecting § The speculator boom 2023-01-09 psychology/collecting --- https://en.wikipedia.org/wiki/Beanie_Babies#Collectibility Beanie Babies § Collectibility 2023-01-09 psychology/collecting --- https://en.wikipedia.org/wiki/The_Death_of_Superman#At_release The Death of Superman § At release 2023-01-09 psychology/collecting --- https://en.wikipedia.org/wiki/Schadenfreude Schadenfreude 2023-01-09 psychology/personality/psychopathy sociology --- https://en.wikipedia.org/wiki/Auction Auction 2023-01-09 economics/mechanism-design/auction --- https://cheaptalk.org/2012/11/04/the-goethe-auction/ 2023-01-09 economics/mechanism-design/auction --- https://en.wikipedia.org/wiki/Vickrey_auction Vickrey auction 2023-01-09 economics/mechanism-design/auction --- https://arstechnica.com/gaming/2021/07/world-record-for-most-expensive-video-game-auction-is-now-870000/ Leapfrogs past April 2021’s previous world-record auction for boxed SMB1 by 24%. 2023-01-09 economics/mechanism-design/auction psychology/collecting --- https://www.psychologytoday.com/us/blog/the-decision-lab/201110/an-ipad-for-1495-sunk-cost-fallacy-and-why-people-keep-losing-money-in An iPad for $14.95? Sunk Cost Fallacy and Why People Keep Losing Money in Penny Auctions 2023-01-10 economics/mechanism-design/auction psychology/cognitive-bias --- https://faculty.haas.berkeley.edu/ned/Penny_Auction.pdf Consumer and Producer Behavior in the Market for Penny Auctions: A Theoretical and Empirical Analysis 2023-01-10 economics/mechanism-design/auction psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Walrasian_auction Walrasian auction 2023-01-10 economics/mechanism-design/auction --- https://arxiv.org/abs/1909.01187#google Encode, Tag, Realize: High-Precision Text Editing Eric Malmi, Sebastian Krause, Sascha Rothe, Daniil Mirylenka, Aliaksei Severyn 2019-09-03 2023-01-10 [("doi","10.48550/arXiv.1909.01187")] ai/nn/transformer

We propose LaserTagger—a sequence tagging approach that casts text generation as a text editing task.

Target texts are reconstructed from the inputs using 3 main edit operations: keeping a token, deleting it, and adding a phrase before the token. To predict the edit operations, we propose a novel model, which combines a BERT encoder with an autoregressive Transformer decoder.

This approach is evaluated on English text on 4 tasks: sentence fusion, sentence splitting, abstractive summarization, and grammar correction. LaserTagger achieves new state-of-the-art results on 3 of these tasks, performs comparably to a set of strong seq2seq baselines with a large number of training examples, and outperforms them when the number of examples is limited.

Furthermore, we show that at inference time tagging can be more than two orders of magnitude faster than comparable seq2seq models, making it more attractive for running in a live environment.

--- https://arxiv.org/abs/2205.12374 Learning to Model Editing Processes Machel Reid, Graham Neubig 2022-05-24 2023-01-10 [("doi","10.48550/arXiv.2205.12374")] ai/dataset ai/nn/transformer/gpt/codex wikipedia

Most existing sequence generation models produce outputs in one pass, usually left-to-right. However, this is in contrast with a more natural approach that humans use in generating content; iterative refinement and editing. Recent work has introduced edit-based models for various tasks (such as neural machine translation and text style transfer), but these generally model a single edit step.

In this work, we propose modeling editing processes, modeling the whole process of iteratively generating sequences. We form a conceptual framework to describe the likelihood of multi-step edits, and describe neural models that can learn a generative model of sequences based on these multistep edits.

We introduce baseline results and metrics on this task, finding that modeling editing processes improves performance on a variety of axes on both our proposed task and related downstream tasks compared to previous single-step models of edits.

--- https://arxiv.org/abs/2306.03856 Iterative Translation Refinement with Large Language Models Pinzhen Chen, Zhicheng Guo, Barry Haddow, Kenneth Heafield 2023-06-06 2023-06-06 [("doi","10.48550/arXiv.2306.03856")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/inner-monologue

Large language models have shown surprising performances in understanding instructions and performing natural language tasks.

In this paper, we propose iterative translation refinement to leverage the power of large language models for more natural translation and post-editing. We show that by simply involving a large language model in an iterative process [ChatGPT], the output quality improves beyond mere translation.

Extensive test scenarios with GPT-3.5 reveal that although iterations reduce string-based metric scores, neural metrics indicate comparable if not improved translation quality. Further, human evaluations demonstrate that our method effectively reduces translationese compared to initial GPT translations and even human references, especially for into-English directions.

Ablation studies underscore the importance of anchoring the refinement process to the source input and a reasonable initial translation.

Table 1: Prompts used in our work, where ${variable} is substituted with its corresponding content.
--- https://www.hollywoodreporter.com/tv/tv-news/secret-invasion-ai-opening-1235521299/ 2023-01-10 ai/nn/diffusion --- https://adamcadre.ac/lyttle/2023.html 2023-01-10 ai/nn/transformer/gpt/3/fiction fiction/humor --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1395586/ Cat color vision: evidence for more than one cone process N. W. Daw, A. L. Pearlman 1970 2023-01-10 [("doi","10.1113/jphysiol.1970.sp009270")] cat/psychology psychology/vision

  1. The ability of cats to distinguish colors was investigated at mesopic and photopic levels to test the hypothesis that cats discriminate wavelength by using rods in conjunction with a single type of cone.

  2. Cats were trained to distinguish red from cyan, and orange from cyan at the mesopic level. They retained the ability to make this discrimination when the colored stimuli were placed against a background bright enough to saturate the rods.

  3. One cat was also tested after being exposed to a bright white light of 9,000 cd/m2 for a period of 5 min, and found able to distinguish red from cyan.

  4. These results suggest that cats have more than one type of cone.

    Subsequent recordings from single units in the lateral geniculate nucleus showed that there are rare opponent color units in layer B with input from a green-absorbing cone and a blue-absorbing cone.

--- https://en.wikipedia.org/wiki/Metropolitan_Railway#London_Passenger_Transport_Board,_1933 Metropolitan Railway § London Passenger Transport Board 1933 2023-01-10 economics/georgism --- https://en.wikipedia.org/wiki/Value_capture Value capture 2023-01-10 economics/georgism --- https://arxiv.org/abs/2306.11827 Any Deep ReLU Network is Shallow Mattia Jacopo Villani, Nandi Schoots 2023-06-20 2023-06-20 [("doi","10.48550/arXiv.2306.11827")] ai/nn/fully-connected ai/nn/sparsity

We constructively prove that every deep ReLU network can be rewritten as a functionally identical three-layer network with weights valued in the extended reals. Based on this proof, we provide an algorithm that, given a deep ReLU network, finds the explicit weights of the corresponding shallow network.

The resulting shallow network is transparent and used to generate explanations of the model’s behavior.

--- https://computeradsfromthepast.substack.com/p/byte-interview-with-the-creators 2023-01-11 cs/hardware design --- https://www.astralcodexten.com/p/your-book-review-public-citizens 2023-01-11 law politics --- https://arxiv.org/abs/2305.16806#microsoft Do GPTs Produce Less Literal Translations? Vikas Raunak, Arul Menezes, Matt Post, Hany Hassan Awadalla 2023-05-26 2023-05-26 [("doi","10.48550/arXiv.2305.16806")] ai/nn/transformer/gpt/3/nonfiction

Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the task of Machine Translation (MT), multiple works have investigated few-shot prompting mechanisms to elicit better translations from LLMs. However, there has been relatively little investigation on how such translations differ qualitatively from the translations generated by standard Neural Machine Translation (NMT) models.

In this work, we investigate these differences in terms of the literalness of translations produced by the two systems. Using literalness measures involving word alignment and monotonicity, we find that translations out of English (E-X) from GPTs tend to be less literal, while exhibiting similar or better scores on MT quality metrics.

We demonstrate that this finding is borne out in human evaluations as well. We then show that these differences are especially pronounced when translating sentences that contain idiomatic expressions.

--- https://terraformindustries.com/ 2023-01-11 technology/carbon-capture --- https://en.wikipedia.org/wiki/Good_regulator Good regulator 2023-01-11 reinforcement-learning/model --- https://en.wikipedia.org/wiki/Variety_(cybernetics)#Law_of_requisite_variety Variety (cybernetics) § Law of requisite variety 2023-01-11 reinforcement-learning/model --- /doc/ai/1947-ashby.pdf Principles of the Self-Organizing Dynamic System W. R. Ashby 1947-01-01 2023-01-11 [("doi","10.1080/00221309.1947.9918144")] ai math --- /doc/ai/1956-ashby.pdf Design for an Intelligence-Amplifier W. Ross Ashby 1956-01-01 2023-01-11 ai design iq --- https://roguetemple.com/z/hyper/dev.php 2023-01-11 fiction/text-game math --- https://www.roguetemple.com/z/hyper/gallery.php 2023-01-11 fiction/text-game math --- /doc/sociology/technology/2023-feld.pdf Do Financial Incentives Encourage Women to Apply for a Tech Job? Evidence from a Natural Field Experiment Jan Feld, Edwin Ip, Andreas Leibbrandt, Joseph Vecci 2023-05-01 2023-05-01 [("doi","10.1257/pandp.20231061")] economics sociology/technology

We conduct a natural field experiment to test whether offering financial incentives encourages more female job seekers to complete their applications for a tech job. All eligible applicants for the job were invited to perform an online skill assessment. We randomized whether or not they were offered an incentive of up to $10 for completing the assessment.

We find no statistically-significant effect of the incentive for female applicants (or male applicants).

--- https://arxiv.org/abs/2305.18949 Playing the system: address manipulation and access to schools Andreas Bjerre-Nielsen, Lykke Sterll Christensen, Mikkel Høst Gandil, Hans Henrik Sievertsen 2023-05-30 2023-05-30 [("doi","10.48550/arXiv.2305.18949")] sociology

[Twitter] Strategic incentives may lead to inefficient and unequal provision of public services. A prominent example is school admissions. Existing research shows that applicants “play the system” by submitting school rankings strategically. We investigate whether applicants also play the system by manipulating their eligibility at schools. We analyze this applicant deception in a theoretical model and provide testable predictions for commonly-used admission procedures.

We confirm these model predictions empirically by analyzing the implementation of two reforms. First, we find [using the Danish population registry] that the introduction of a residence-based school-admission criterion in Denmark caused address changes to increase by more than 100% before the high-school application deadline. This increase occurred only in areas where the incentive to manipulate is high-powered. Second, to assess whether this behavior reflects actual address changes, we study a second reform that required applicants to provide additional proof of place of residence to approve an address change. The second reform reduced address changes around the school application deadline, suggesting that the observed increase in address changes mainly reflects manipulation.

The manipulation is driven by applicants from more affluent households and their behavior affects non-manipulating applicants. Counter-factual simulations show that among students not enrolling in their first listed school, more than 25% would have been offered a place in the absence of address manipulation and their peer GPA is 0.2SD lower due to the manipulative behavior of other applicants.

Our findings show that popular school choice systems give applicants the incentive to play the system with real implications for non-strategic applicants.

--- https://en.wikipedia.org/wiki/Plantin_Polyglot Plantin Polyglot 2023-01-12 psychology/linguistics/bilingual --- https://www.newyorker.com/magazine/2018/09/03/the-mystery-of-people-who-speak-dozens-of-languages 2023-01-12 psychology/linguistics/bilingual --- https://www.newyorker.com/magazine/2012/12/24/utopian-for-beginners?currentPage=all 2023-01-12 psychology/linguistics/bilingual --- https://www.niskanencenter.org/culture-eats-policy/ 2023-01-12 design politics --- https://arxiv.org/abs/2007.02217 The thermodynamics of clocks G J. Milburn 2020-07-05 2023-01-12 [("doi","10.1080/00107514.2020.1837471")] science

All clocks, classical or quantum, are open non equilibrium irreversible systems subject to the constraints of thermodynamics.

Using examples I show that these constraints necessarily limit the performance of clocks and that good clocks require large energy dissipation. (1) For periodic clocks, operating on a limit cycle, this is a consequence of phase diffusion. (2) It is also true for non periodic clocks (for example, radio carbon dating) but due to telegraph noise not to phase diffusion. In this case a key role is played by accurate measurements that decrease entropy, thereby raising the free energy of the clock, and requires access to a low entropy reservoir. (3) In the quantum case, for which thermal noise is replaced by quantum noise (spontaneous emission or tunnelling), measurement plays an essential role for both periodic and non periodic clocks.

The paper concludes with a discussion of the Tolman relations and Rovelli’s thermal time hypothesis in terms of clock thermodynamics.

--- https://www.folklore.org/StoryView.py?project=Macintosh&story=You_Guys_Are_In_Big_Trouble.txt 2023-01-12 sociology --- https://x.com/KaliYuga_ai/status/1672434506458857474 2023-01-12 ai/nn/diffusion ai/nn/transformer/clip/sample --- https://en.wikipedia.org/wiki/Block_sort Block sort 2023-01-12 cs/algorithm/sorting --- https://en.wikipedia.org/wiki/Monopoly_(game) 2023-01-13 economics/georgism --- https://www.nature.com/articles/d41586-023-02120-8 2023-01-13 philosophy/mind statistics/prediction --- https://www.smashingmagazine.com/2023/06/behind-curtains-wikipedia-redesign/ 2023-01-13 cs/css design/typography wikipedia --- /doc/sociology/2022-omberg.pdf Is it possible to prepare for a pandemic? Robert Tucker Omberg, Alex Tabarrok 2022-12-14 2023-01-13 [("doi","10.1093/oxrep/grac035")] existential-risk sociology

How effective were investments in pandemic preparation? We use a comprehensive and detailed measure of pandemic preparedness, the Global Health Security (GHS) Index produced by the Johns Hopkins Center for Health Security (JHU), to measure which investments in pandemic preparedness reduced infections, deaths, excess deaths, or otherwise ameliorated or shortened the pandemic.

We also look at whether values or attitudinal factors such as individualism, willingness to sacrifice, or trust in government—which might be considered a form of cultural pandemic preparedness—influenced the course of the pandemic. Our primary finding is that almost no form of pandemic preparedness helped to ameliorate or shorten the pandemic.

Compared to other countries, the United States did not perform poorly because of cultural values such as individualism, collectivism, selfishness, or lack of trust. General state capacity, as opposed to specific pandemic investments, is one of the few factors which appears to improve pandemic performance.

Understanding the most effective forms of pandemic preparedness can help guide future investments. Our results may also suggest that either we aren’t measuring what is important or that pandemic preparedness is a global public good.

--- https://x.com/ShriramKMurthi/status/1672713972951179264 2023-01-13 cs/lisp --- https://arxiv.org/abs/2306.12587 ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews Mike D’Arcy, Alexis Ross, Erin Bransom, Bailey Kuehl, Jonathan Bragg, Tom Hope, Doug Downey 2023-06-21 2023-06-21 [("doi","10.48550/arXiv.2306.12587")] ai/dataset ai/nn/transformer/gpt/4/nonfiction statistics/peer-review

[Twitter; dataset] Revising scientific papers based on peer feedback is a challenging task that requires not only deep scientific knowledge and reasoning, but also the ability to recognize the implicit requests in high-level feedback and to choose the best of many possible ways to update the manuscript in response.

We introduce this task for large language models and release ARIES, a dataset of review comments and their corresponding paper edits, to enable training and evaluating models. We study two versions of the task: comment-edit alignment and edit generation, and evaluate several baselines, including GPT-4.

We find that models struggle even to identify the edits that correspond to a comment, especially in cases where the comment is phrased in an indirect way or where the edit addresses the spirit of a comment but not the precise request.

When tasked with generating edits, GPT-4 often succeeds in addressing comments on a surface level, but it rigidly follows the wording of the feedback rather than the underlying intent, and includes fewer technical details than human-written edits.

We hope that our formalization, dataset, and analysis will form a foundation for future work in this area.

--- https://arxiv.org/abs/2109.08705 Relating Neural Text Degeneration to Exposure Bias Ting-Rui Chiang, Yun-Nung Chen 2021-09-17 2023-01-13 [("doi","10.48550/arXiv.2109.08705")] ai/nn/sampling ai/nn/transformer/gpt/2 reinforcement-learning/imitation-learning

This work focuses on relating two mysteries in neural-based text generation: exposure bias, and text degeneration. Despite the long time since exposure bias was mentioned and the numerous studies for its remedy, to our knowledge, its impact on text generation has not yet been verified. Text degeneration is a problem that the widely-used pre-trained language model GPT-2 was recently found to suffer from (Holtzman et al 2020).

Motivated by the unknown causation of the text degeneration, in this paper we attempt to relate these two mysteries. Specifically, we first qualitatively quantitatively identify mistakes made before text degeneration occurs. Then we investigate the of the mistakes by inspecting the hidden states in GPT-2.

Our results show that text degeneration is likely to be partly caused by exposure bias. We also study the self-reinforcing mechanism of text degeneration, explaining why the mistakes amplify.

In sum, our study provides a more concrete foundation for further investigation on exposure bias and text degeneration problems.

--- https://x.com/OwainEvans_UK/status/1636580251676585986 2023-01-13 ai/nn/transformer/gpt/claude ai/poetry ai/text-style-transfer --- https://x.com/OwainEvans_UK/status/1636605571637055488 2023-01-13 ai/nn/transformer/gpt/claude ai/poetry ai/text-style-transfer --- https://x.com/OwainEvans_UK/status/1636762386085605376 2023-01-13 ai/nn/transformer/gpt/claude ai/text-style-transfer --- https://x.com/OwainEvans_UK/status/1636581594642403328 2023-01-13 ai/nn/transformer/gpt/claude ai/text-style-transfer --- https://arxiv.org/abs/2212.08073#anthropic Constitutional AI: Harmlessness from AI Feedback Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy L. Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, Jared Kaplan 2022-12-15 2023-01-14 [("doi","10.48550/arXiv.2212.08073")] ai/nn/transformer/gpt/claude reinforcement-learning/preference-learning

As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, and so we refer to the method as ‘Constitutional AI’.

The process involves both a supervised learning and a reinforcement learning phase. In the supervised phase we sample from an initial model, then generate self-critiques and revisions, and then finetune the original model on revised responses. In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences. We then train with RL using the preference model as the reward signal, i.e. we use RL from AI Feedback (RLAIF).

As a result we are able to train a harmless but non-evasive AI assistant that engages with harmful queries by explaining its objections to them. Both the SL and RL methods can leverage chain-of-thought style reasoning to improve the human-judged performance and transparency of AI decision making. These methods make it possible to control AI behavior more precisely and with far fewer human labels.

[Discussion of why RLAIF may be better at poetry than RLHF.] --- https://www.lesswrong.com/posts/LaM5aTcXvXzwQSC2Q/universal-fire 2023-01-14 philosophy/epistemology philosophy/ontology science --- https://www.cs.uml.edu/~holly/91.549/readings/sims-alife94.pdf Evolving 3D Morphology and Behavior by Competition Sims 1994 2023-01-14 reinforcement-learning/exploration reinforcement-learning/robot --- https://www.jameslindlibrary.org/articles/personal-reflections-on-lessons-learned-from-randomized-trials-involving-newborn-infants-1951-to-1967/ Personal reflections on lessons learned from randomized trials involving newborn infants, 1951–1967 Silverman 2003 2023-01-14 statistics/bias statistics/causality statistics/meta-analysis --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2860339/ CONSORT 2010 Statement: updated guidelines for reporting parallel group randomized trials Kenneth F. Schulz, Douglas G. Altman, David Moher 2010 2023-01-14 [("doi","10.1186/1741-7015-8-18")] statistics/meta-analysis

The CONSORT statement is used worldwide to improve the reporting of randomized controlled trials. Kenneth Schulz and colleagues describe the latest version, CONSORT 2010, which updates the reporting guideline based on new methodological evidence and accumulating experience.

To encourage dissemination of the CONSORT 2010 Statement, this article is freely accessible on BMJ.com and will also be published in the Lancet, Obstetrics and Gynecology, PLoS Medicine, Annals of Internal Medicine, Open Medicine, Journal of Clinical Epidemiology, BMC Medicine, and Trials.

--- https://x.com/sharifshameem/status/1672852345259180037 2023-01-14 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2202.08906#google ST-MoE: Designing Stable and Transferable Sparse Expert Models Barret Zoph, Irwan Bello, Sameer Kumar, Nan Du, Yanping Huang, Jeff Dean, Noam Shazeer, William Fedus 2022-02-17 2023-01-14 [("doi","10.48550/arXiv.2202.08906")] ai/scaling/mixture-of-experts

Scale has opened new frontiers in natural language processing—but at a high cost. In response, Mixture-of-Experts (MoE) and Switch Transformers have been proposed as an energy efficient path to even larger and more capable language models. But advancing the state-of-the-art across a broad set of natural language tasks has been hindered by training instabilities and uncertain quality during fine-tuning.

Our work focuses on these issues and acts as a design guide. We conclude by scaling a sparse model to 269b parameters, with a computational cost comparable to a 32B dense encoder-decoder Transformer (Stable and Transferable Mixture-of-Experts or ST-MoE-32B).

For the first time, a sparse model achieves state-of-the-art performance in transfer learning, across a diverse set of tasks including reasoning (SuperGLUE, ARC Easy, ARC Challenge), summarization (XSum, CNN-DM), closed book question answering (WebQA, Natural Questions), and adversarially constructed tasks (WinoGrande, ANLI R3).

--- https://kieranhealy.org/blog/archives/2023/06/19/the-naming-of-stats/ 2023-01-14 fiction/poetry math/humor statistics --- https://kieranhealy.org/blog/archives/2016/04/08/tironian-notes/ 2023-01-14 design/typography --- https://x.com/ben_golub/status/1665030874272866305 2023-01-14 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/codex economics --- https://x.com/jakekohlhepp/status/1664319110015242240 2023-01-14 economics --- https://kieranhealy.org/blog/archives/2015/06/12/americas-ur-choropleths/ 2023-01-15 design/visualization sociology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10182013/ Effect of basal metabolic rate on lifespan: a sex-specific Mendelian Randomization study Jack C. M. Ng, C. Mary Schooling 2023 2023-01-15 [("doi","10.1038/s41598-023-34410-6")] genetics/heritable/correlation/mendelian-randomization longevity

Observationally, the association of basal metabolic rate (BMR) with mortality is mixed, although some ageing theories suggest that higher BMR should reduce lifespan. It remains unclear whether a causal association exists.

In this one-sample Mendelian Randomization study, we aimed to estimate the casual effect of BMR on parental attained age, a proxy for lifespan, using two-sample Mendelian Randomization methods. We obtained genetic variants strongly (p-value < 5 × 10−8) and independently (R2 < 0.001) predicting BMR from the UK Biobank and applied them to a genome-wide association study of parental attained age based on the UK Biobank.

We meta-analyzed genetic variant-specific Wald ratios using inverse-variance weighting with multiplicative random effects by sex, supplemented by sensitivity analysis.

A total of 178 and 180 genetic variants predicting BMR in men and women were available for father’s and mother’s attained age, respectively. Genetically predicted BMR was inversely associated with father’s and mother’s attained age (years of life lost per unit increase in effect size of genetically predicted BMR, 0.46 and 1.36; 95% confidence interval 0.07-0.85 and 0.89-1.82), with a stronger association in women than men.

In conclusion, higher BMR might reduce lifespan. The underlying pathways linking to major causes of death and relevant interventions warrant further investigation.

--- https://x.com/katrosenfield/status/1672969824656322561 2023-01-15 ai/nn/transformer/gpt/3/fiction reinforcement-learning/safe --- https://undark.org/2023/06/21/in-a-tipsters-note-a-view-of-science-publishings-achilles-heel/ 2023-01-15 statistics/bias --- https://www.anthropic.com/index/introducing-claude 2023-01-15 ai/nn/transformer/gpt/claude --- https://www.anthropic.com/index/100k-context-windows 2023-01-15 ai/nn/transformer/attention ai/nn/transformer/gpt/claude --- https://techcrunch.com/2023/03/08/duckassist/ 2023-01-15 ai/nn/transformer/gpt/claude --- https://x.com/hwchase17/status/1640171938470563840 2023-01-15 ai/nn/transformer/gpt/claude --- https://www.lesswrong.com/posts/sTwW3QLptTQKuyRXx/the-first-sample-gives-the-most-information 2023-01-15 statistics/decision --- https://arxiv.org/abs/2305.08322 C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, Junlei Zhang, Jinghan Zhang, Tangjun Su, Junteng Liu, Chuancheng Lv, Yikai Zhang, Jiayi Lei, Yao Fu, Maosong Sun, Junxian He 2023-05-15 2023-05-15 [("doi","10.48550/arXiv.2305.08322")] ai/dataset ai/nn/transformer/gpt/4/nonfiction

New NLP benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present C-Eval, the first comprehensive Chinese evaluation suite designed to assess advanced knowledge and reasoning abilities of foundation models in a Chinese context.

C-Eval comprises multiple-choice questions across 4 difficulty levels: middle school, high school, college, and professional. The questions span 52 diverse disciplines, ranging from humanities to science and engineering.

C-Eval is accompanied by C-Eval Hard, a subset of very challenging subjects in C-Eval that requires advanced reasoning abilities to solve. We conduct a comprehensive evaluation of the most advanced LLMs on C-Eval, including both English & Chinese-oriented models. Results indicate that only GPT-4 could achieve an average accuracy of over 60%, suggesting that there is still room for improvement for current LLMs.

We anticipate C-Eval will help analyze important strengths and shortcomings of foundation models, and foster their development and growth for Chinese users.

--- https://www.justice.gov/usao-dc/pr/citizen-croatia-and-serbia-charged-running-monopoly-drug-market-darknet 2023-01-16 darknet-market --- https://pdfs.semanticscholar.org/5ff9/38abf6346f26e42e07a63b11c796265420d7.pdf Outcomes of Transracial Adoption Silverman 1993 2023-01-16 iq --- https://www.vanityfair.com/style/2022/11/inventing-ivana-trump-her-improbable-rise-and-tragic-death Inventing Ivana Trump: Her Improbable Rise and Tragic Death: Ivana Marie Zelníčková escaped from behind the Iron Curtain to storm New York City and help create the twisted miracle of Donald Trump. From her ‘greed is good” heyday to her post-divorce denouement cavorting with a series of “freaky’ Italian lovers, it was Ivana, all along, who gilded the Trump name Mark Seal 2022-11-03 2023-01-16 psychiatry/traumatic-brain-injury --- https://www.acpjournals.org/doi/10.7326/0003-4819-152-11-201006010-00232?articleid=745807 CONSORT 2010 Statement: Updated Guidelines for Reporting Parallel Group Randomized Trials Free Schulz 2010 2023-01-16 statistics/meta-analysis --- https://www.newyorker.com/magazine/2023/02/13/a-museum-soup-throwers-worst-nightmare A Museum Soup-Thrower’s Worst Nightmare: Patrick Bringley, who spent a decade as a guard at the Met, tours his old workplace and considers the people between the Picasso and a fistful of mashed potatoes Michael Schulman 2023-02-06 2023-02-06 crime politics psychology/personality --- https://web.archive.org/web/20100227101156/http://www.jacn.org/cgi/content/full/21/1/14 Lithium: Occurrence, Dietary Intakes, Nutritional Essentiality Schrauzer 2002 2023-01-16 psychiatry/lithium --- https://arxiv.org/abs/1209.3026 Losing My Revolution: How Many Resources Shared on Social Media Have Been Lost? Hany M. SalahEldeen, Michael L. Nelson 2012-09-13 2023-01-16 [("doi","10.48550/arXiv.1209.3026")] cs/linkrot politics

Social media content has grown exponentially in the recent years and the role of social media has evolved from just narrating life events to actually shaping them.

In this paper we explore how many resources shared in social media are still available on the live web or in public web archives. By analyzing 6 different event-centric datasets of resources shared in social media in the period from June 2009 to March 2012, we found:

about 11% lost and 20% archived after just a year and an average of 27% lost and 41% archived after two and a half years. Furthermore, we found a nearly linear relationship between time of sharing of the resource and the percentage lost, with a slightly less linear relationship between time of sharing and archiving coverage of the resource.

From this model we conclude that after the first year of publishing, nearly 11% of shared resources will be lost and after that we will continue to lose 0.02% per day.

--- https://x.com/DistractedAnna/status/1672977976973983746 2023-01-16 ai/nn/transformer/gpt/dall-e/2 --- https://x.com/CharlesCMann/status/1673310755079192579 2023-01-16 longevity/glp/semaglutide --- https://en.wikipedia.org/wiki/Template:Inflation Template:Inflation 2023-01-16 cs/css wikipedia --- https://thomas-richie-richardson.medium.com/an-attractiveness-researcher-puts-the-internets-most-popular-hotness-algorithms-to-the-test-3278dbcb03b2 2023-01-16 sociology --- https://www.integrity-research.com/ai-fails-insider-trading-test/ 2023-01-17 ai/nn/transformer/gpt/3/nonfiction law --- https://james-iry.blogspot.com/2009/05/brief-incomplete-and-mostly-wrong.html 2023-01-17 cs/algorithm math/humor --- https://www.youtube.com/watch?v=h714VOr-6nY Star Timelapse Revealing the Earth’s Rotation Alex Rivest 2023-01-17 design/visualization philosophy/epistemology science --- https://jeffgill.org/wp-content/uploads/2021/04/mice_multivariate_imputation_by_chained_equations.pdf mice: Multivariate Imputation by Chained Equations in R van Buuren, Groothuis-Oudshoorn 2011 2023-01-17 statistics/bayes --- https://www.biorxiv.org/content/10.1101/630079.full A Fast and Scalable Framework for Large-scale and Ultrahigh-dimensional Sparse Regression with Application to the UK Biobank Junyang Qian, Yosuke Tanigawa, Wenfei Du, Matthew Aguirre, Chris Chang, Robert Tibshirani, Manuel A. Rivas, Trevor Hastie 2020-05-31 2023-01-17 [("doi","10.1101/630079")] genetics/heritable statistics

The UK Biobank (Bycroft et al 2018) is a very large, prospective population-based cohort study across the United Kingdom. It provides unprecedented opportunities for researchers to investigate the relationship between genotypic information and phenotypes of interest. Multiple regression methods, compared with GWAS, have already been showed to greatly improve the prediction performance for a variety of phenotypes. In the high-dimensional settings, the lasso (Tibshirani 1996), since its first proposal in statistics, has been proved to be an effective method for simultaneous variable selection and estimation. However, the large scale and ultrahigh dimension seen in the UK Biobank pose new challenges for applying the lasso method, as many existing algorithms and their implementations are not scalable to large applications. In this paper, we propose a computational framework called batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and easily build a scalable solution for very large data, including those that are larger than the memory size. We introduce snpnet, an R package that implements the proposed algorithm on top of glmnet (Friedman et al 2010a) and optimizes for single-nucleotide polymorphism (SNP) datasets. It currently supports ℓ1-penalized linear model, logistic regression, Cox model, and also extends to the elastic net with ℓ1/ℓ2 penalty. We demonstrate results on the UK Biobank dataset, where we achieve superior predictive performance on quantitative and qualitative traits including height, body mass index, asthma and high cholesterol.

Author Summary: With the advent and evolution of large-scale and comprehensive biobanks, there come up unprecedented opportunities for researchers to further uncover the complex landscape of human genetics. One major direction that attracts long-standing interest is the investigation of the relationships between genotypes and phenotypes. This includes but doesn’t limit to the identification of genotypes that are statistically-significantly associated with the phenotypes, and the prediction of phenotypic values based on the genotypic information. Genome-wide association studies (GWAS) is a very powerful and widely used framework for the former task, having produced a number of very impactful discoveries. However, when it comes to the latter, its performance is fairly limited by the univariate nature. To address this, multiple regression methods have been suggested to fill in the gap. That said, challenges emerge as the dimension and the size of datasets both become large nowadays. In this paper, we present a novel computational framework that enables us to solve efficiently the entire lasso or elastic-net solution path on large-scale and ultrahigh-dimensional data, and therefore make simultaneous variable selection and prediction. Our approach can build on any existing lasso solver for small or moderate-sized problems, scale it up to a big-data solution, and incorporate other extensions easily. We provide a package snpnet that extends the glmnet package in R and optimizes for large phenotype-genotype data. On the UK Biobank, we observe improved prediction performance on height, body mass index (BMI), asthma and high cholesterol by the lasso over other univariate and multiple regression methods. That said, the scope of our approach goes beyond genetic studies. It can be applied to general sparse regression problems and build scalable solution for a variety of distribution families based on existing solvers.

--- https://x.com/Sirupsen/status/1673309920769323008 2023-01-17 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/codex math --- https://cohost.org/mcc/post/325362-a-one-person-oral-hi 2023-01-17 cs/css cs/security --- https://www.gibney.org/a_syntax_for_self-tracking 2023-01-17 nootropic/quantified-self --- https://www.lesswrong.com/posts/DQvuSBquC8ccwPhBo/self-blinded-caffeine-rct 2023-01-17 nootropic/caffeine nootropic/quantified-self psychiatry/meditation --- https://www.biorxiv.org/content/10.1101/2023.06.25.546422.full Rapid and accurate multi-phenotype imputation for millions of individuals Lin-Lin Gu, Guo-Bo Chen, Hong-Shan Wu, Yong-Jie Zhang, Jing-Cheng He, Xiao-Lei Liu, Zhi-Yong Wang, Dan Jiang, Ming Fang 2023-06-26 2023-06-26 [("doi","10.1101/2023.06.25.546422")] genetics/heritable statistics

Genetic analysis using big data can enhance the power of GWAS, but large data sets often have a large number of missing phenotypes. The UK Biobank database contains ~500,000 individuals with ~3,000 phenotypes, with phenotype missing rates ranging 35.9%–59.4%

Imputation of missing phenotypes is an important way of improving the GWAS power. The multi-phenotype imputation method can improve the accuracy of imputation. However, most existing multi-phenotype imputation methods are unable to impute missing phenotypes of millions of individuals, for example, PHENIX will require months of time and ~1TB of computer memory.

We herein developed a Mixed Fast Random Forest (MFRF) based machine learning for phenotypic imputation. Our simulation results showed that the imputation accuracy of MFRF was higher than or equal to that of existing state-of-the-art methods; MFRF was also extremely computationally fast and memory efficient, using only 0.23-0.54 h and 68.32-126.35 Mb of computer memory for the UK Biobank dataset.

We applied MFRF to impute 425 phenotypes from the UK Biobank dataset, and conducted the GWA studies using the imputed phenotypes. Compared with the GWAS before phenotype imputation, 1355 (15.6%) extra GWAS loci were identified.

--- https://www.astralcodexten.com/p/every-flashing-element-on-your-site 2023-01-17 economics/advertising --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4817234/ PHENIX: A multiple-phenotype imputation method for genetic studies Andrew Dahl, Valentina Iotchkova, Amelie Baud, Åsa Johansson, Ulf Gyllensten, Nicole Soranzo, Richard Mott, Andreas Kranis, Jonathan Marchini 2016 2023-01-18 [("doi","10.1038/ng.3513")] genetics/heritable statistics/bayes

Genetic association studies have yielded a wealth of biological discoveries. However, these studies have mostly analyzed one trait and one SNP at a time, thus failing to capture the underlying complexity of the data sets. Joint genotype-phenotype analyses of complex, high-dimensional data sets represent an important way to move beyond simple genome-wide association studies (GWAS) with great potential.

The move to high-dimensional phenotypes will raise many new statistical problems. Here we address the central issue of missing phenotypes in studies with any level of relatedness between samples. We propose a multiple-phenotype mixed model PHENIX and use a computationally efficient variational Bayesian algorithm to fit the model.

On a variety of simulated and real data sets from a range of organisms and trait types, we show that our method outperforms existing state-of-the-art methods from the statistics and machine learning literature and can boost signals of association.

--- https://x.com/JProtzko/status/1673918727581048833 2023-01-18 psychology/willpower statistics/bias --- https://arxiv.org/abs/2306.15626 LeanDojo: Theorem Proving with Retrieval-Augmented Language Models Kaiyu Yang, Aidan M. Swope, Alex Gu, Rahul Chalamala, Peiyang Song, Shixing Yu, Saad Godil, Ryan Prenger, Anima Anandkumar 2023-06-27 2023-06-27 [("doi","10.48550/arXiv.2306.15626")] ai/dataset ai/nn/retrieval ai/nn/transformer/gpt/4/nonfiction math

[code, homepage] Large language models (LLMs) have shown promise in proving formal theorems using proof assistants such as Lean. However, existing methods are difficult to reproduce or build on, due to private code, data, and large compute requirements. This has created substantial barriers to research on machine learning methods for theorem proving.

This paper removes these barriers by introducing LeanDojo: an open-source Lean playground consisting of toolkits, data, models, and benchmarks. LeanDojo extracts data from Lean and enables interaction with the proof environment programmatically. It contains fine-grained annotations of premises in proofs, providing valuable data for premise selection: a key bottleneck in theorem proving.

Using this data, we develop ReProver (Retrieval-Augmented Prover): the first LLM-based prover that is augmented with retrieval for selecting premises from a vast math library. It is inexpensive and needs only one GPU week of training. Our retriever leverages LeanDojo’s program analysis capability to identify accessible premises and hard negative examples, which makes retrieval much more effective.

Furthermore, we construct a new benchmark consisting of 96,962 theorems and proofs extracted from Lean’s math library. It features challenging data split requiring the prover to generalize to theorems relying on novel premises that are never used in training. We use this benchmark for training and evaluation, and experimental results demonstrate the effectiveness of ReProver over non-retrieval baselines and GPT-4.

We thus provide the first set of open-source LLM-based theorem provers without any proprietary datasets and release it under a permissive MIT license to facilitate further research.

--- https://www.nature.com/articles/d41586-023-02032-7 2023-01-18 technology/carbon-capture --- https://www.jstor.org/stable/24875111 Factors affecting autumn deer/vehicle collisions in a rural Virginia county McShea 2008 2023-01-18 psychology/animal --- https://pagedout.institute/download/PagedOut_002_beta2.pdf#page=42 Spooky Fizz Buzz § pg42 Menghrajani 2019 2023-01-18 cs design/typography math/humor --- https://jn.nutrition.org/content/131/2/649S.full.pdf+html A Review of Studies on the Effect of Iron Deficiency on Cognitive Development in Children McGregor, Ani 2023-01-18 iq --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10250106/ Evolutionary constraint and innovation across hundreds of placental mammals Matthew J. Christmas, Irene M. Kaplow, Diane P. Genereux, Michael X. Dong, Graham M. Hughes, Xue Li, Patrick F. Sullivan, Allyson G. Hindle, Gregory Andrews, Joel C. Armstrong, Matteo Bianchi, Ana M. Breit, Mark Diekhans, Cornelia Fanter, Nicole M. Foley, Daniel B. Goodman, Linda Goodman, Kathleen C. Keough, Bogdan Kirilenko, Amanda Kowalczyk, Colleen Lawless, Abigail L. Lind, Jennifer R. S. Meadows, Lucas R. Moreira, Ruby W. Redlich, Louise Ryan, Ross Swofford, Alejandro Valenzuela, Franziska Wagner, Ola Wallerman, Ashley R. Brown, Joana Damas, Kaili Fan, John Gatesy, Jenna Grimshaw, Jeremy Johnson, Sergey V. Kozyrev, Alyssa J. Lawler, Voichita D. Marinescu, Kathleen M. Morrill, Austin Osmanski, Nicole S. Paulat, BaDoi N. Phan, Steven K. Reilly, Daniel E. Schäffer, Cynthia Steiner, Megan A. Supple, Aryn P. Wilder, Morgan E. Wirthlin, James R. Xue, Bruce W. Birren, Steven Gazal, Robert M. Hubley, Klaus-Peter Koepfli, Tomas Marques-Bonet, Wynn K. Meyer, Martin Nweeia, Pardis C. Sabeti, Beth Shapiro, Arian F. A. Smit, Mark S. Springer, Emma C. Teeling, Zhiping Weng, Michael Hiller, Danielle L. Levesque, Harris A. Lewin, William J. Murphy, Arcadi Navarro, Benedict Paten, Katherine S. Pollard, David A. Ray, Irina Ruf, Oliver A. Ryder, Andreas R. Pfenning, Kerstin Lindblad-Toh, Elinor K. Karlsson 2023 2023-01-18 [("doi","10.1126/science.abn3943")] genetics/selection/natural genetics/sequencing

Zoonomia is the largest comparative genomics resource for mammals produced to date.

By aligning genomes for 240 species, we identify bases that, when mutated, are likely to affect fitness and alter disease risk. At least 332 million bases (~10.7%) in the human genome are unusually conserved across species (evolutionarily constrained) relative to neutrally evolving repeats, and 4552 ultraconserved elements are nearly perfectly conserved.

Of 101 million significantly constrained single bases, 80% are outside protein-coding exons and half have no functional annotations in the Encyclopedia of DNA Elements (ENCODE) resource.

Changes in genes and regulatory elements are associated with exceptional mammalian traits, such as hibernation, that could inform therapeutic development. Earth’s vast and imperiled biodiversity offers distinctive power for identifying genetic variants that affect genome function and organismal phenotypes.

--- https://www.theatlantic.com/health/archive/2023/06/the-gene-that-explains-statins-most-puzzling-side-effect/674542/ 2023-01-18 genetics/heritable/rare --- /doc/science/2019-takahashi.pdf Video-Guided Real-to-Virtual Parameter Transfer for Viscous Fluids Tetsuya Takahashi, Ming C. Lin 2019-11-08 2023-01-18 [("doi","10.1145/3355089.3356551")] ai science

In physically-based simulation, it is essential to choose appropriate material parameters to generate desirable simulation results. In many cases, however, choosing appropriate material parameters is very challenging, and often tedious trial-and-error parameter tuning steps are inevitable.

In this paper, we propose a real-to-virtual parameter transfer framework that identifies material parameters of viscous fluids with example video data captured from real-world phenomena. Our method first extracts positional data of fluids and then uses the extracted data as a reference to identify the viscosity parameters, combining forward viscous fluid simulations and parameter optimization in an iterative process.

We evaluate our method with a range of synthetic and real-world example data, and demonstrate that our method can identify the hidden physical variables and viscosity parameters. This set of recovered physical variables and parameters can then be effectively used in novel scenarios to generate viscous fluid behaviors visually consistent with the example videos.

--- https://steamtraen.blogspot.com/2023/06/a-coda-to-wansink-story.html 2023-01-19 statistics/bias --- https://model-checking.github.io/kani-verifier-blog/2023/05/01/writing-code-with-chatgpt-improve-it-with-kani.html 2023-01-19 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue --- https://www.lesswrong.com/posts/odtMt7zbMuuyavaZB/when-do-brains-beat-brawn-in-chess-an-experiment 2023-01-19 psychology/chess reinforcement-learning/chess reinforcement-learning/scaling --- https://en.wikipedia.org/wiki/Murchison_Murders Murchison Murders 2023-01-19 crime fiction --- https://www.wired.com/story/gene-editing-flies-to-fight-crop-damage/ 2023-01-19 genetics/editing --- https://www.youtube.com/watch?v=NOCsdhzo6Jg 2023-01-19 math/humor philosophy/epistemology --- https://www.reddit.com/r/aigamedev/comments/142j3yt/valve_is_not_willing_to_publish_games_with_ai/ 2023-01-19 ai/nn/diffusion ai/nn/transformer/gpt/fiction economics/copyright --- https://yaytext.com/blog/vaporwave-unicode-analysis/ 2023-01-19 design/typography music --- https://en.wikipedia.org/wiki/Chaser_(dog) Chaser (dog) 2023-01-19 dog psychology/linguistics --- https://www.codastory.com/waronscience/rewilding-beavers-conservation/ 2023-01-19 politics --- https://x.com/SteveMills/status/1674219548147585024 2023-01-19 ai/video/generation --- https://www.theturnertwins.co.uk/fitness/40-vs-20-minute-workout 2023-01-20 exercise genetics/heritable --- https://en.wikipedia.org/wiki/Chaffing_and_winnowing Chaffing and winnowing 2023-01-20 cs/cryptography/steganography --- https://www.biorxiv.org/content/10.1101/2023.06.27.546663.full Genetic similarity between relatives provides evidence on the presence and history of assortative mating Hans Fredrik Sunde, Nikolai Haahjem Eftedal, Rosa Cheesman, Elizabeth C. Corfield, Thomas H. Kleppesto, Anne Caroline Seierstad, Eivind Ystrom, Espen Moen Eilertsen, Fartein Ask Torvik 2023-06-29 2023-06-29 [("doi","10.1101/2023.06.27.546663")] genetics/heritable/correlation psychiatry

Assortative mating—the non-random mating of individuals with similar traits—is known to increase trait-specific genetic variance and genetic similarity between relatives. However, empirical evidence is limited for many traits, and the implications hinge on whether assortative mating has started recently or many generations ago.

Here we show theoretically and empirically that genetic similarity between relatives can provide evidence on the presence and history of assortative mating. First, we employed path analysis to understand how assortative mating affects genetic similarity between family members across generations, finding that similarity between distant relatives is more affected than close relatives.

Next, we correlated polygenic indices of 47,135 co-parents from the Norwegian Mother, Father, and Child Cohort Study (MoBa) and found genetic evidence of assortative mating in 8⁄15 examined traits. The same traits show elevated similarity between relatives, especially distant relatives.

5 of the 8 traits, including educational attainment, showed greater genetic variance among offspring, which is inconsistent with stable assortative mating over many generations. These results suggest an ongoing increase in familial similarity for these traits.

The implications of this research extend to genetic methodology and the understanding of social and economic disparities.

--- https://www.theverge.com/23778253/google-reader-death-2013-rss-social 2023-01-20 design technology/google --- https://arxiv.org/abs/2306.04675#layer6ai Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models George Stein, Jesse C. Cresswell, Rasa Hosseinzadeh, Yi Sui, Brendan Leigh Ross, Valentin Villecroze, Zhaoyan Liu, Anthony L. Caterini, J. Eric T. Taylor, Gabriel Loaiza-Ganem 2023-06-07 2023-06-07 [("doi","10.48550/arXiv.2306.04675")] ai/nn/diffusion ai/nn/gan/biggan ai/nn/gan/stylegan ai/nn/vae/mae

We systematically study a wide variety of image-based generative models spanning semantically-diverse datasets to understand and improve the feature extractors and metrics used to evaluate them.

Using best practices in psychophysics, we measure human perception of image realism for generated samples by conducting the largest experiment evaluating generative models to date, and find that no existing metric strongly correlates with human evaluations. Comparing to 16 modern metrics for evaluating the overall performance, fidelity, diversity, and memorization of generative models, we find that the state-of-the-art perceptual realism of diffusion models as judged by humans is not reflected in commonly reported metrics such as FID.

This discrepancy is not explained by diversity in generated samples, though one cause is over-reliance on Inception-V3. We address these flaws through a study of alternative self-supervised feature extractors, find that the semantic information encoded by individual networks strongly depends on their training procedure, and show that DINOv2-ViT-L/14 allows for much richer evaluation of generative models.

Next, we investigate data memorization, and find that generative models do memorize training examples on simple, smaller datasets like CIFAR-10, but not necessarily on more complex datasets like ImageNet. However, our experiments show that current metrics do not properly detect memorization; none in the literature is able to separate memorization from other phenomena such as underfitting or mode shrinkage.

To facilitate further development of generative models and their evaluation we release all generated image datasets, human evaluation data, and a modular library to compute 16 common metrics for 8 different encoders at Github.

--- https://arxiv.org/abs/2305.14325 Improving Factuality and Reasoning in Language Models through Multiagent Debate Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, Igor Mordatch 2023-05-23 2023-05-23 [("doi","10.48550/arXiv.2305.14325")] ai/nn/transformer/gpt/inner-monologue

Large language models (LLMs) have demonstrated remarkable capabilities in language generation, understanding, and few-shot learning in recent years. An extensive body of work has explored how their performance may be further improved through the tools of prompting, ranging from verification, self-consistency, or intermediate scratchpads.

In this paper, we present a complementary approach to improve language responses where multiple language model instances propose and debate their individual responses and reasoning processes over multiple rounds to arrive at a common final answer. Our findings indicate that this approach enhances mathematical and strategic reasoning across a number of tasks.

We also demonstrate that our approach improves the factual validity of generated content, reducing fallacious answers and hallucinations that contemporary models are prone to. Our approach may be directly applied to existing black-box models and uses identical procedure and prompts for all tasks we investigate.

Overall, our findings suggest that such “society of minds” approach has the potential to advance the capabilities of LLMs and pave the way for further breakthroughs in language generation and understanding.

--- https://link.springer.com/article/10.1007/s40279-023-01870-9 2023-01-20 creatine --- https://arxiv.org/abs/2305.18975 Voice Conversion With Just Nearest Neighbors Matthew Baas, Benjamin van Niekerk, Herman Kamper 2023-05-30 2023-05-30 [("doi","10.48550/arXiv.2305.18975")] ai/music ai/nn/retrieval

Any-to-any voice conversion aims to transform source speech into a target voice with just a few examples of the target speaker as a reference. Recent methods produce convincing conversions, but at the cost of increased complexity—making results difficult to reproduce and build on.

Instead, we keep it simple. We propose k-nearest neighbors voice conversion (kNN-VC): a straightforward yet effective method for any-to-any conversion. First, we extract self-supervised representations of the source and reference speech [eg. dog barks]. To convert to the target speaker, we replace each frame of the source representation with its nearest neighbor in the reference. Finally, a pretrained vocoder synthesizes audio from the converted representation.

Objective and subjective evaluations show that kNN-VC improves speaker similarity with similar intelligibility scores to existing methods.

Code, samples, trained models: https://bshall.github.io/knn-vc/.

--- https://en.wikipedia.org/wiki/Fredkin%27s_paradox Fredkin’s paradox 2023-01-20 statistics/decision --- https://www.youtube.com/watch?v=N1TTiLx5IPc The Kellogg Doolittle Residence (High Desert House): Inside a Breathtaking Desert Mansion That Looks Like A Fossil Architectural Digest 2023-06-09 2023-06-09 design --- https://www.biorxiv.org/content/10.1101/2023.06.30.546935.full Amazing structural diversity of giant virus-like particles in forest soil Matthias G. Fischer, Ulrike Mersdorf, Jeffrey L. Blanchard 2023-06-30 2023-06-30 [("doi","10.1101/2023.06.30.546935")] biology

Large DNA viruses of the phylum Nucleocytoviricota infect diverse eukaryotic hosts from protists to humans, with profound consequences for aquatic and terrestrial ecosystems. While nucleocytoviruses are known to be highly diverse in metagenomes, knowledge of their capsid structures is restricted to a few characterized representatives.

Here, we visualize giant virus-like particles (VLPs, diameter >0.2 μm) directly from the environment using transmission electron microscopy. We found that Harvard Forest soils contain a higher diversity of giant VLP morphotypes than all hitherto isolated giant viruses combined. These included VLPs with icosahedral capsid symmetry, ovoid shapes similar to pandoraviruses, and bacilliform shapes that may represent novel viruses.

We discovered giant icosahedral capsids with structural modifications that had not been described before including tubular appendages, modified vertices, tails, and capsids consisting of multiple layers or internal channels. Many giant VLPs were covered with fibers of varying lengths, thicknesses, densities, and terminal structures. These findings imply that giant viruses employ a much wider array of capsid structures and mechanisms to interact with their host cells than is currently known.

We also found diverse tailed bacteriophages and filamentous VLPs, as well as ultra-small cells. Our study offers a first glimpse of the vast diversity of unexplored viral structures in soil and reinforces the potential of transmission electron microscopy for fundamental discoveries in environmental microbiology.

--- https://www.mayoclinicproceedings.org/article/S0025-6196%2813%2900405-9/fulltext A Decade of Reversal: An Analysis of 146 Contradicted Medical Practices Prasad 2013 2023-01-21 statistics/bias statistics/causality --- https://www.bmj.com/content/344/bmj.d7373 Compliance with mandatory reporting of clinical trial results on ClinicalTrials.gov: cross sectional study Prayle 2012 2023-01-21 statistics/bias --- http://subcortex.com/IsEmpathyNecessaryForMoralityPrinz.pdf Is Empathy Necessary for Morality? Prinz 2011 2023-01-21 philosophy/ethics psychology --- https://www.damninteresting.com/absolute-zero-is-0k/ Absolute Zero is 0K 2023-01-21 cryonics science technology --- https://slatestarcodex.com/2015/06/29/reflections-from-the-halfway-point/ 2023-01-21 psychiatry --- https://arxiv.org/abs/2104.12250 XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond Francesco Barbieri, Luis Espinosa Anke, Jose Camacho-Collados 2021-04-25 2023-01-21 [("doi","10.48550/arXiv.2104.12250")] ai/dataset ai/nn/transformer

Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention. However, current analyses have almost exclusively focused on (multilingual variants of) standard benchmarks, and have relied on clean pre-training and task-specific corpora as multilingual signals.

In this paper, we introduce XLM-T, a model to train and evaluate multilingual language models in Twitter.

In this paper we provide: (1) a new strong multilingual baseline consisting of an XLM-R (Conneau et al 2020) model pre-trained on millions of tweets in over thirty languages, alongside starter code to subsequently fine-tune on a target task; and (2) a set of unified sentiment analysis Twitter datasets in 8 different languages and a XLM-T model fine-tuned on them.

--- https://steamtraen.blogspot.com/2023/07/strange-numbers-in-dataset-of-zhang.html 2023-01-21 statistics/bias --- https://arxiv.org/abs/2306.14308#google Let’s Do a Thought Experiment: Using Counterfactuals to Improve Moral Reasoning Xiao Ma, Swaroop Mishra, Ahmad Beirami, Alex Beutel, Jilin Chen 2023-06-25 2023-06-25 [("doi","10.48550/arXiv.2306.14308")] ai/nn/transformer/gpt/inner-monologue philosophy/ethics

Language models still struggle on moral reasoning, despite their impressive performance in many other tasks. In particular, the Moral Scenarios task in MMLU (Multi-task Language Understanding) is among the worst performing tasks for many language models, including GPT-3.

In this work, we propose a new prompting framework, Thought Experiments, to teach language models to do better moral reasoning using counterfactuals. Experiment results show that our framework elicits counterfactual questions and answers from the model, which in turn helps improve the accuracy on Moral Scenarios task by 9–16% compared to other zero-shot baselines.

Interestingly, unlike math reasoning tasks, zero-shot Chain-of-Thought (CoT) reasoning doesn’t work out of the box, and even reduces accuracy by around 4% compared to direct zero-shot.

We further observed that with minimal human supervision in the form of 5 few-shot examples, the accuracy of the task can be improved to as much as 80%.

--- https://securelist.com/satellite-turla-apt-command-and-control-in-the-sky/72081/ 2023-01-21 cs/security --- https://x.com/A_J_Higgins/status/1246161321986527232 2023-01-21 technology --- https://arxiv.org/abs/1801.06105 Overcoming the vanishing gradient problem in plain recurrent networks Yuhuang Hu, Adrian Huber, Jithendar Anumula, Shih-Chii Liu 2018-01-18 2023-01-21 [("doi","10.48550/arXiv.1801.06105")] ai/nn/rnn

Plain recurrent networks greatly suffer from the vanishing gradient problem while Gated Neural Networks (GNNs) such as Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) deliver promising results in many sequence learning tasks through sophisticated network designs. This paper shows how we can address this problem in a plain recurrent network by analyzing the gating mechanisms in GNNs.

We propose a novel network called the Recurrent Identity Network (RIN) which allows a plain recurrent network to overcome the vanishing gradient problem while training very deep models without the use of gates. We compare this model with IRNNs and LSTMs on multiple sequence modeling benchmarks.

The RINs demonstrate competitive performance and converge faster in all tasks. Notably, small RIN models produce 12%–67% higher accuracy on the Sequential and Permuted MNIST datasets and reach state-of-the-art performance on the bAbI question answering dataset.

--- https://arxiv.org/abs/2306.17194 On the Exploitability of Instruction Tuning Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, Tom Goldstein 2023-06-28 2023-06-28 [("doi","10.48550/arXiv.2306.17194")] ai/nn/adversarial ai/nn/transformer/gpt/instruction-tuning

Instruction tuning is an effective technique to align large language models (LLMs) with human intents. In this work, we investigate how an adversary can exploit instruction tuning by injecting specific instruction-following examples into the training data that intentionally changes the model’s behavior. For example, an adversary can achieve content injection by injecting training examples that mention target content and eliciting such behavior from downstream models.

To achieve this goal, we propose AutoPoison, an automated data poisoning pipeline. It naturally and coherently incorporates versatile attack goals into poisoned data with the help of an oracle LLM. We showcase two example attacks: content injection and over-refusal attacks, each aiming to induce a specific exploitable behavior.

We quantify and benchmark the strength and the stealthiness of our data poisoning scheme. Our results show that AutoPoison allows an adversary to change a model’s behavior by poisoning only a small fraction of data while maintaining a high level of stealthiness in the poisoned examples.

We hope our work sheds light on how data quality affects the behavior of instruction-tuned models and raises awareness of the importance of data quality for responsible deployments of LLMs. Code is available at https://github.com/azshue/AutoPoison.

--- https://chipsandcheese.com/2023/07/02/nvidias-h100-funny-l2-and-tons-of-bandwidth/ 2023-01-22 ai/scaling/hardware --- https://www.lesswrong.com/posts/bNKcKb8LYaQe49kWF/going-crazy-and-getting-better-again 2023-01-22 psychiatry/meditation psychiatry/schizophrenia --- /doc/psychiatry/2006-02-05-nytimes-thatwhichdoesnotkillmemakesmestranger.html That Which Does Not Kill Me Makes Me Stranger 2006-02-05 2023-01-22 psychiatry psychology/willpower --- https://www.theatlantic.com/health/archive/2014/11/the-dutch-village-where-everyone-has-dementia/382195/ 2023-01-22 psychiatry/alzheimers --- https://x.com/OpenAI/status/1676072388436594688 2023-01-22 ai/nn/retrieval ai/nn/transformer/gpt cs/security --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2905096/ Genomic insights into early-onset obesity Hélène Choquet, David Meyre 2010 2023-01-22 [("doi","10.1186/gm157")] exercise genetics/heritable/rare

The biological causes of childhood obesity are complex. Environmental factors, such as massive marketing campaigns for food leading to over-nutrition and snacking and the decline in physical activity, have undoubtedly contributed to the increased prevalence of overweight and obesity in children, but these cannot be considered as the only causes.

Susceptibility to obesity is also determined to a great extent by genetic factors. Furthermore, molecular mechanisms involved in the regulation of gene expression, such as epigenetic mechanisms, can increase the risk of developing early-onset obesity.

There is evidence that early-onset obesity is a heritable disorder, and a range of genetic factors have recently been shown to cause monogenic, syndromic and polygenic forms of obesity, in some cases interacting with environmental exposures. Modifications of the transcriptome can lead to increased adiposity, and the gut microbiome has recently been shown to be key to the genesis of obesity.

These new genomic discoveries complement previous knowledge on the development of early-onset obesity and provide new perspectives for research on the complex molecular and physiological mechanisms involved in this disease. Personalized preventive strategies and genomic medicine may become possible in the near future.

--- /doc/genetics/heritable/rare/2021-akbari.pdf Sequencing of 640,000 exomes identifies GPR75 variants associated with protection from obesity Parsa Akbari, Ankit Gilani, Olukayode Sosina, Jack A. Kosmicki, Lori Khrimian, Yi-Ya Fang, Trikaldarshi Persaud, Victor Garcia, Dylan Sun, Alexander Li, Joelle Mbatchou, Adam E. Locke, Christian Benner, Niek Verweij, Nan Lin, Sakib Hossain, Kevin Agostinucci, Jonathan V. Pascale, Ercument Dirice, Michael Dunn, Regeneron Genetics Center, DiscovE H. R. Collaboration, William E. Kraus, Svati H. Shah, Yii-Der I. Chen, Jerome I. Rotter, Daniel J. Rader, Olle Melander, Christopher D. Still, Tooraj Mirshahi, David J. Carey, Jaime Berumen-Campos, Pablo Kuri-Morales, Jesus Alegre-Díaz, Jason M. Torres, Jonathan R. Emberson, Rory Collins, Suganthi Balasubramanian, Alicia Hawes, Marcus Jones, Brian Zambrowicz, Andrew J. Murphy, Charles Paulding, Giovanni Coppola, John D. Overton, Jeffrey G. Reid, Alan R. Shuldiner, Michael Cantor, Hyun M. Kang, Gonçalo Abecasis, Katia Karalis, Aris N. Economides, Jonathan Marchini, George D. Yancopoulos, Mark W. Sleeman, Judith Altarejos, Giusy Della Gatta, Roberto Tapia-Conyer, Michal L. Schwartzman, Aris Baras, Manuel A. R. Ferreira, Luca A. Lotta 2021-07-02 2023-01-22 [("doi","10.1126/science.abf8683")] exercise genetics/heritable/rare

How genes affect human obesity: Obesity is linked to many human diseases, including diabetes, cancer, and heart disease. There is thus great interest in understanding how genes predispose individuals to, or protect individuals from, obesity.

Akbari et al 2021 sequenced more than 600,000 exomes from the United Kingdom, the United States, and Mexico and identified 16 rare coding variants (see the Perspective by Yeo and O’Rahilly). Some of the alleles associated with body mass index (BMI) were brain-expressed G protein–coupled receptors.

One variant allele was found in Mexican populations at low frequency and was associated with lower BMI. Deletion of this gene in mice resulted in a resistance to weight gain, suggesting that this gene provides an avenue of study for the prevention or treatment of obesity. --- https://www.karolpiczak.com/papers/Piczak2015-ESC-Dataset.pdf 2023-01-22 ai/music --- https://mtg.upf.edu/system/files/publications/Font-Roma-Serra-ACMM-2013.pdf 2023-01-22 ai/music --- https://arxiv.org/abs/1905.07444 Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning Zain ul abi Din, Panagiotis Tigas, Samuel T. King, Benjamin Livshits 2019-05-17 2023-01-23 [("doi","10.48550/arXiv.1905.07444")] ai/nn/cnn economics/advertising/adblock

[code] In this paper we present Percival, a browser-embedded, lightweight, deep learning-powered perceptual ad blocker. Percival embeds itself within the browser’s image rendering pipeline, which makes it possible to intercept every image obtained during page execution and to perform blocking based on applying machine learning for image classification to flag potential ads.

Our implementation inside both Chromium and Brave browsers shows only a minor rendering performance overhead of 4.55%, demonstrating the feasibility of deploying traditionally heavy models (ie. deep neural networks) inside the critical path of the rendering engine of a browser.

We show that our image-based ad blocker can replicate EasyList rules with an accuracy of 96.76%.

To show the versatility of the Percival’s approach we present case studies that demonstrate that Percival (1) does surprisingly well on ads in languages other than English; (2) Percival also performs well on blocking first-party Facebook ads, which have presented issues for other ad blockers.

Percival proves that image-based perceptual ad blocking is an attractive complement to today’s dominant approach of block lists.

--- https://ad-blocker-sentinel.com/ 2023-01-23 economics/advertising --- https://arxiv.org/abs/1705.08568 The Future of Ad Blocking: An Analytical Framework and New Techniques Grant Storey, Dillon Reisman, Jonathan Mayer, Arvind Narayanan 2017-05-24 2023-01-23 [("doi","10.48550/arXiv.1705.08568")] cs/security economics/advertising/adblock

We present a systematic study of ad blocking—and the associated “arms race”—as a security problem. We model ad blocking as a state space with 4 states and 6 state transitions, which correspond to techniques that can be deployed by either publishers or ad blockers. We argue that this is a complete model of the system.

We propose several new ad blocking techniques, including ones that borrow ideas from rootkits to prevent detection by anti-ad blocking scripts. Another technique uses the insight that ads must be recognizable by humans to comply with laws and industry self-regulation [perceptual adblocking]. We have built prototype implementations of 3 of these techniques, successfully blocking ads and evading detection.

We systematically evaluate our proposed techniques, along with existing ones, in terms of security, practicality, and legality. We characterize the order of growth of the development effort required to create/maintain ad blockers as a function of the growth of the web.

Based on our state-space model, our new techniques, and this systematization, we offer insights into the likely “end game” of the arms race. We challenge the widespread assumption that the arms race will escalate indefinitely, and instead identify a combination of evolving technical and legal factors that will determine the outcome.

--- https://en.wikipedia.org/wiki/Chicken_gun Chicken gun 2023-01-23 technology --- https://arxiv.org/abs/2305.11863 Scaling laws for language encoding models in fMRI Richard Antonello, Aditya Vaidya, Alexander G. Huth 2023-05-19 2023-05-19 [("doi","10.48550/arXiv.2305.11863")] ai/nn/transformer ai/scaling psychology/neuroscience

Representations from transformer-based unidirectional language models are known to be effective at predicting brain responses to natural language. However, most studies comparing language models to brains have used GPT-2 or similarly sized language models. Here we tested whether larger open-source models such as those from the OPT and LLaMa families are better at predicting brain responses recorded using fMRI.

Mirroring scaling results from other contexts, we found that brain prediction performance scales log-linearly with model size from 125M to 30b parameter models, with ~15% increased encoding performance as measured by correlation with a held-out test set across 3 subjects. Similar log-linear behavior was observed when scaling the size of the fMRI training set.

We also characterized scaling for acoustic encoding models that use HuBERT, WavLM, and Whisper, and we found comparable improvements with model size. A noise ceiling analysis of these large, high-performance encoding models showed that performance is nearing the theoretical maximum for brain areas such as the precuneus and higher auditory cortex.

These results suggest that increasing scale in both models and data will yield incredibly effective models of language processing in the brain, enabling better scientific understanding as well as applications such as decoding.

--- https://www.theguardian.com/society/2023/jul/04/you-cant-live-your-life-what-is-behind-extreme-hoarding 2023-01-23 psychiatry/anxiety --- https://arxiv.org/abs/2212.09251#anthropic Discovering Language Model Behaviors with Model-Written Evaluations Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, Andy L. Jones, Anna Chen, Ben Mann, Brian Israel, Bryan Seethor, Cameron McKinnon, Christopher Olah, Da Yan, Daniela Amodei, Dario Amodei, Dawn Drain, Dustin Li, Eli Tran-Johnson, Guro Khundadze, Jackson Kernion, James Landis, Jamie Kerr, Jared Mueller, Jeeyoon Hyun, Joshua Landau, Kamal Ndousse, Landon Goldberg, Liane Lovitt, Martin Lucas, Michael Sellitto, Miranda Zhang, Neerav Kingsland, Nelson Elhage, Nicholas Joseph, Noemí Mercado, Nova DasSarma, Oliver Rausch, Robin Larson, Sam McCandlish, Scott Johnston, Shauna Kravec, Sheer El Showk, Tamera Lanham, Timothy Telleen-Lawton, Tom Brown, Tom Henighan, Tristan Hume, Yuntao Bai, Zac Hatfield-Dodds, Jack Clark, Samuel R. Bowman, Amanda Askell, Roger Grosse, Danny Hernandez, Deep Ganguli, Evan Hubinger, Nicholas Schiefer, Jared Kaplan 2022-12-19 2023-01-23 [("doi","10.48550/arXiv.2212.09251")] ai/scaling reinforcement-learning/preference-learning/mode-collapse reinforcement-learning/safe

As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdworkers (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs.

We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90–100% of labels, sometimes more so than corresponding human-written datasets.

We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user’s preferred answer (“sycophancy”) and express greater desire to pursue concerning goals like resource acquisition and goal preservation.

We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down.

Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors.

--- https://craigmod.com/essays/fast_software/ 2023-01-23 cs design --- https://archiveofourown.org/works/15489495 2023-01-23 dog fiction/science-fiction --- https://github.com/siboehm/lleaves 2023-01-23 ai/tabular cs/algorithm cs/python --- https://siboehm.com/articles/21/lleaves 2023-01-23 ai/tabular cs/algorithm cs/python --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4146673/ Large-scale meta-analysis of genome-wide association data identifies 6 new risk loci for Parkinson’s disease Mike A. Nalls, Nathan Pankratz, Christina M. Lill, Chuong B. Do, Dena G. Hernandez, Mohamad Saad, Anita L. DeStefano, Eleanna Kara, Jose Bras, Manu Sharma, Claudia Schulte, Margaux F. Keller, Sampath Arepalli, Christopher Letson, Connor Edsall, Hreinn Stefansson, Xinmin Liu, Hannah Pliner, Joseph H. Lee, Rong Cheng, M. Arfan Ikram, John Ioannidis, Georgios M. Hadjigeorgiou, Joshua C. Bis, Maria Martinez, Joel S. Perlmutter, Alison Goate, Karen Marder, Brian Fiske, Margaret Sutherland, Georgia Xiromerisiou, Richard H. Myers, Lorraine N. Clark, Kari Stefansson, John A. Hardy, Peter Heutink, Honglei Chen, Nicholas W. Wood, Henry Houlden, Haydeh Payami, Alexis Brice, William K. Scott, Thomas Gasser, Lars Bertram, Nicholas Eriksson, Tatiana Foroud, Andrew B. Singleton 2014 2023-01-24 [("doi","10.1038/ng.3043")] genetics/heritable psychiatry

We conducted a meta-analysis of Parkinson’s disease genome-wide association studies using a common set of 7,893,274 variants across 13,708 cases and 95,282 controls. 20-six loci were identified as having genome-wide statistically-significant association; these and 6 additional previously reported loci were then tested in an independent set of 5,353 cases and 5,551 controls.

Of the 32 tested SNPs, 24 replicated, including 6 newly identified loci. Conditional analyses within loci showed that 4 loci, including GBA, GAK-DGKQ, SNCA and the HLA region, contain a secondary independent risk variant. In total, we identified and replicated 28 independent risk variants for Parkinson’s disease across 24 loci.

Although the effect of each individual locus was small, risk profile analysis showed substantial cumulative risk in a comparison of the highest and lowest quintiles of genetic risk (odds ratio (OR) = 3.31, 95% confidence interval (CI) = 2.55-4.30; p = 2 × 10−16). We also show 6 risk loci associated with proximal gene expression or DNA methylation.

--- https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/1105975 Dietary Supplements and Mortality Rate in Older Women: The Iowa Women’s Health Study Mursu 2011 2023-01-24 biology --- https://web.archive.org/web/20110809011417/http://www-archive.thoracic.org/sections/publications/press-releases/conference/articles/2007/abstracts/Lung_Cancer_Association_with_Supplemental_Multivitamin_Vitamin_C_and_E_and_Folate_Intake_3961.pdf Lung Cancer: Association with Supplemental Multivitamin, Vitamin C & E, and Folate Intake Slatore 2023-01-24 biology --- https://www.nobelprize.org/prizes/medicine/1990/murray/25012-joseph-e-murray-nobel-lecture-1990/ The First Successful Transplants in Man Murray 1990 2023-01-24 biology genetics/cloning --- http://www.med.mcgill.ca/epidemiology/courses/EPIB591/Fall%202010/Class%205%20-%2015%20Sept/Mortensen%20%20--%20%20NEJM%201999.pdf Effects of family history and place and season of birth on the risk of schizophrenia Mortensen 1999 2023-01-24 psychiatry/schizophrenia --- https://www.gleech.org/frank 2023-01-24 philosophy/frank-ramsey --- https://en.wikisource.org/wiki/The_Garden_of_Proserpine 2023-01-24 fiction/poetry philosophy/ethics --- https://www.reddit.com/r/Supplements/comments/ls7caq/the_caffeine_enigma_does_habitual_longterm/ 2023-01-24 nootropic/caffeine --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4242593/ Genetics of caffeine consumption and responses to caffeine Amy Yang, Abraham Palmer, Harriet de Wit 2010 2023-01-24 [("doi","10.1007/s00213-010-1900-1")] ai/nn/transformer/gpt/3 genetics/heritable/correlation nootropic/caffeine

Rationale: Caffeine is widely consumed in foods and beverages and is also used for a variety of medical purposes. Despite its widespread use, relatively little is understood regarding how genetics affects consumption, acute response, or the long-term effects of caffeine.

Objective: This paper reviews the literature on the genetics of caffeine from the following: (1) twin studies comparing heritability of consumption and of caffeine-related traits, including withdrawal symptoms, caffeine-induced insomnia, and anxiety, (2) association studies linking genetic polymorphisms of metabolic enzymes and target receptors to variations in caffeine response, and (3) case-control and prospective studies examining relationship between polymorphisms associated with variations in caffeine response to risks of Parkinson’s and cardiovascular diseases in habitual caffeine consumers.

Results: Twin studies find the heritability of caffeine-related traits to range between 0.36 and 0.58. Analysis of polysubstance use shows that predisposition to caffeine use is highly specific to caffeine itself and shares little common disposition to use of other substances. Genome association studies link variations in adenosine and dopamine receptors to caffeine-induced anxiety and sleep disturbances. Polymorphism in the metabolic enzyme cytochrome P-450 is associated with risk of myocardial infarction in caffeine users.

Conclusion: Modeling based on twin studies reveals that genetics plays a role in individual variability in caffeine consumption and in the direct effects of caffeine. Both pharmacodynamic and pharmacokinetic polymorphisms have been linked to variation in response to caffeine. These studies may help guide future research in the role of genetics in modulating the acute and chronic effects of caffeine.

--- https://blog.evjang.com/2018/08/dijkstras.html 2023-01-24 cs/algorithm design/visualization economics reinforcement-learning/model statistics/decision --- https://www.gleech.org/strangers 2023-01-24 philosophy/ethics --- https://www.gleech.org/first-computers 2023-01-25 cs philosophy/ontology --- https://www.lesswrong.com/posts/nwjTPtbvcJeA6xDuu/dominant-assurance-contract-experiment-2-berkeley-house 2023-01-25 economics/mechanism-design --- https://eryney.substack.com/p/what-are-the-bottlenecks-to-safe-935 2023-01-25 genetics/editing --- https://www.texasmonthly.com/food/i-believe-i-can-fry/ 2023-01-25 food technology --- https://www.lesswrong.com/posts/no5jDTut5Byjqb4j5/six-and-a-half-intuitions-for-kl-divergence 2023-01-25 cs/algorithm statistics/decision statistics/probability --- https://github.com/PiotrNawrot/nanoT5 2023-01-25 ai/nn/transformer/t5 --- https://jameswphillips.substack.com/p/securing-liberal-democratic-control Securing Liberal Democratic Control of AGI through UK Leadership James W. Phillips 2023-03-14 2023-03-14 ai/scaling politics

[followup, commentary] This was originally a piece co-written to influence policy makers, and had input from a range of people including senior frontier industry figures, former senior government advisers, and others who share the concerns raised in this piece. This is being published openly now in updated form given recent developments in the AI space.

Within this decade, we may build Artificial General Intelligence (AGI)—AI capable of performing most cognitive labour a human can do. Such a development would have an unprecedented effect on our society; ‘agentic’ forms of AGI may also pose an existential threat to our security. The current development path towards AGI is inherently unsafe.

The UK is in a unique position to alter this path in alignment with our values and for our benefit. However, this advantage has been squandered for a decade, and is now rapidly evaporating under an unsafe ‘race to the bottom’ dynamic between private companies funded by US tech monopolies.

Ensuring that AGI is developed safely and in the interests of the British people and liberal democracies must be the highest priority of the British state over the next decade. We propose this should be done through pursuing a multilateral approach to advancing and controlling AGI in partnership with our companies and liberal democratic allies. This should begin with creating a commercially connected elite public AGI lab under leadership of a frontier tech industry expert.

There is a brief window over the next two years in which rapid action is required to provide any chance of success. Specifically, this requires that we:

  1. Procure national AI supercomputing infrastructure comparable to leading US private labs.

  2. Create an advisory group of frontier tech, not legacy academic, expertise to identify major AI research projects to run on this infrastructure.

  3. Grow an elite public-sector research lab, led by a leader with the technical skills and entrepreneurial expertise, to build a research agenda at the frontier of AI.

We invest almost $25 billion per year in R&D—a modest fraction of this must immediately be diverted to a national effort toward frontier AGI leadership.

…Single models at the frontier, namely OpenAI’s GPT-4 and successors, are being trained on tens of thousands of the highest specification GPUs (AI training chips) for months on end, roughly equivalent to using what is called an ‘exaflop’ supercomputer continuously for months. Unfortunately, the UK public-sector currently has less than 1,000 such top-spec GPUs, shared across all scientific fields. This means that one private lab in California is now using at least 25× the total compute capacity available through the entire UK state, just to train a single model. Our lack of compute undermines our ability to attract the best global talent in this technology; our business ability to commercialize and deploy it; and perhaps most critically, our state soft power over international use and control of it.

…The Review’s first key recommendation is to purchase one single exaflop supercomputer, roughly equivalent to 30,000 GPUs, for shared use by all UK research communities (not exclusive to AI) by 2026. This leaves the entire nations’ compute capacity in 2026 behind one relatively small frontier US lab in 2022. We emphasise that whatever we do procure will be very diluted versus OpenAI using it to train a single model—and it’ll arrive 4 years after OpenAI trained its model.

Leaders such as OpenAI will only continue to increase their compute usage. Another leading lab, Anthropic, has said that a state would require 100,000 top-spec GPUs within 3 years to be competitive in this space. This is a major upscaling of ambition merely to keep pace with these organizations that are still relatively small startups. Google, Facebook, Microsoft and others are all using even more, and the US will likely start building an AI supercomputing resource of 75,000 top-spec GPUs soon. As competition grows, it is a necessity that we have a sovereign supercomputing resource to enable our objectives in this space. As suggested by experts from leading AGI labs, we need to procure 100,000 top-spec GPUs for sovereign supercomputing capability dedicated to AI, for delivery ASAP.

The fastest any supercomputer could physically be procured is likely late-2024, leaving a vulnerable window of almost 2 years amidst intense and growing competition with other companies and nations.

…We should aim to rent 30,000 GPUs as soon as possible, and also build out dedicated engineering resources for using these. While this is lower than needed to match what startups such as OpenAI have access to today, the scarcity of available GPUs to rent already makes this ambitious to attempt. This could be done on a 2-year contract using cloud, or in partnership with domestic firms that are already substantial users of GPUs via cloud.

--- https://www.pnas.org/doi/10.1073/pnas.2214505120 Enhanced amygdala-cingulate connectivity associates with better mood in both healthy and depressive individuals after sleep deprivation Ya Chai, Philip Gehrman, Meichen Yu, Tianxin Mao, Yao Deng, Joy Rao, Hui Shi, Peng Quan, Jing Xu, Xiaocui Zhang, Hui Lei, Zhuo Fang, Sihua Xu, Elaine Boland, Jennifer R. Goldschmied, Holly Barilla, Namni Goel, Mathias Basner, Michael E. Thase, Yvette I. Sheline, David F. Dinges, John A. Detre, Xiaochu Zhang, Hengyi Rao 2023-06-20 2023-06-20 [("doi","10.1073/pnas.2214505120")] psychiatry/depression zeo

[media] Sleep loss is a public health epidemic which impairs mood and well-being of billions of people over the world. However, sleep deprivation [wake therapy] also induces a rapid and effective mood improvement in a subset of patients with depressive disorder. The amygdala is a pivotal brain region affected by depression. Here, we show that one night of total sleep deprivation enhanced amygdala connectivity to the anterior cingulate cortex which correlated with better mood in both healthy and depressed individuals. This study highlights the key role of amygdala-cingulate circuit in bad mood regulation in both healthy and clinical populations. Our findings might have implications for the development of fast and unique antidepressant interventions.

[data] Sleep loss robustly disrupts mood and emotion regulation in healthy individuals but can have a transient antidepressant effect in a subset of patients with depression. The neural mechanisms underlying this paradoxical effect remain unclear. Previous studies suggest that the amygdala and dorsal nexus (DN) play key roles in depressive mood regulation.

Here, we used functional MRI to examine associations between amygdala & DN-related resting-state connectivity alterations and mood changes after one night of total sleep deprivation (TSD) in both healthy adults and patients with major depressive disorder using strictly controlled in-laboratory studies. Behavioral data showed that TSD increased negative mood in healthy participants but reduced depressive symptoms in 43% of patients.

Imaging data showed that TSD enhanced both amygdala & DN-related connectivity in healthy participants. Moreover, enhanced amygdala connectivity to the anterior cingulate cortex (ACC) after TSD associated with better mood in healthy participants and antidepressant effects in depressed patients.

These findings support the key role of the amygdala-cingulate circuit in mood regulation in both healthy and depressed populations and suggest that rapid antidepressant treatment may target the enhancement of amygdala-ACC connectivity.

--- https://books.worksinprogress.co/book/maintenance-of-everything/vehicles/what-motorcycles-teach-about-maintenance/4 2023-01-25 philosophy/epistemology psychology/willpower statistics/causality technology --- https://news.ycombinator.com/item?id=36616237 2023-01-25 ai/nn/retrieval ai/nn/transformer/gpt/claude --- https://arxiv.org/abs/2307.02486#microsoft LongNet: Scaling Transformers to 1,000,000,000 Tokens Jiayu Ding, Shuming Ma, Li Dong, Xingxing Zhang, Shaohan Huang, Wenhui Wang, Furu Wei 2023-07-05 2023-07-05 [("doi","10.48550/arXiv.2307.02486")] ai/nn/transformer/attention/hierarchical

[cf. Longformer, BigBird; and note extensive dilation previous art in CNNs eg. WaveNet] Scaling sequence length has become a critical demand in the era of large language models. However, existing methods struggle with either computational complexity or model expressivity, rendering the maximum sequence length restricted.

In this work, we introduce LongNet, a Transformer variant that can scale sequence length to more than 1 billion tokens, without sacrificing the performance on shorter sequences. Specifically, we propose dilated attention, which expands the attentive field exponentially as the distance grows.

LongNet has advantages: (1) it has a linear computation complexity and a logarithm dependency between tokens; (2) it can be served as a distributed trainer for extremely long sequences; (3) its dilated attention is a drop-in replacement for standard attention, which can be seamlessly integrated with the existing Transformer-based optimization.

Experiments results demonstrate that LongNet yields strong performance on both long-sequence modeling and general language tasks. Our work opens up new possibilities for modeling very long sequences, eg. treating a whole corpus or even the entire Internet as a sequence.

[Weak evaluation: weird scaling calculations, no Long Range Arena benchmarks.]

--- https://arxiv.org/abs/2206.05852 ChordMixer: A Scalable Neural Attention Model for Sequences with Different Lengths Ruslan Khalitov, Tong Yu, Lei Cheng, Zhirong Yang 2022-06-12 2023-01-26 [("doi","10.48550/arXiv.2206.05852")] ai/nn/fully-connected ai/nn/transformer/attention/hierarchical

[code, reviews; cf. Paramixer] Sequential data naturally have different lengths in many domains, with some very long sequences. As an important modeling tool, neural attention should capture long-range interaction in such sequences. However, most existing neural attention models admit only short sequences, or they have to employ chunking or padding to enforce a constant input length.

Here we propose a simple neural network building block called ChordMixer [inspired by Chord P2P] which can model the attention for long sequences with variable lengths. Each ChordMixer block consists of a position-wise rotation layer without learnable parameters and an element-wise MLP layer. Repeatedly applying such blocks forms an effective network backbone that mixes the input signals towards the learning targets.

We have tested ChordMixer on the synthetic adding problem, long document classification, and DNA sequence-based taxonomy classification.

The experiment results show that our method substantially outperforms other neural attention models.

--- https://arxiv.org/abs/2204.10670 Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention Tong Yu, Ruslan Khalitov, Lei Cheng, Zhirong Yang 2022-04-22 2023-01-26 [("doi","10.48550/arXiv.2204.10670")] ai/nn/fully-connected ai/nn/transformer/attention/hierarchical

[code; cf. ChordMixer] Self-Attention is a widely used building block in neural modeling to mix long-range data elements. Most self-attention neural networks employ pairwise dot-products to specify the attention coefficients. However, these methods require 𝒪(N2) computing cost for sequence length N.

Even though some approximation methods have been introduced to relieve the quadratic cost, the performance of the dot-product approach is still bottlenecked by the low-rank constraint in the attention matrix factorization.

In this paper, we propose a novel scalable and effective mixing building block called Paramixer [inspired by Chord P2P]. Our method factorizes the interaction matrix into several sparse matrices, where we parameterize the non-zero entries by MLPs with the data elements as input. The overall computing cost of the new building block is as low as 𝒪(N log N). Moreover, all factorizing matrices in Paramixer are full-rank, so it does not suffer from the low-rank bottleneck.

We have tested the new method on both synthetic and various real-world long sequential data sets and compared it with several state-of-the-art attention networks.

The experimental results show that Paramixer has better performance in most learning tasks. [OK-ish LRA results.]

Figure 1: Illustration of (a & b) the CHORD (c) & CDIL (d) protocols for n = 16. Each node in the circular graph represents a sequence element. The links between nodes correspond to the non-zero entries in W(m) (here m = 1) output from f(m). Note that the sparse structure of all factors in CHORD is the same, while it varies at different m’s in CDIL.
--- https://arxiv.org/abs/1608.08242 Temporal Convolutional Networks: A Unified Approach to Action Segmentation Colin Lea, Rene Vidal, Austin Reiter, Gregory D. Hager 2016-08-29 2023-01-26 [("doi","10.48550/arXiv.1608.08242")] ai/nn/cnn ai/video/analysis

The dominant paradigm for video-based action segmentation is composed of two steps: first, for each frame, compute low-level features using Dense Trajectories or a Convolutional Neural Network that encode spatiotemporal information locally, and second, input these features into a classifier that captures high-level temporal relationships, such as a Recurrent Neural Network (RNN). While often effective, this decoupling requires specifying two separate models, each with their own complexities, and prevents capturing more nuanced long-range spatiotemporal relationships.

We propose a unified approach, as demonstrated by our Temporal Convolutional Network (TCN), that hierarchically captures relationships at low/intermediate-/high-level time-scales.

Our model achieves superior or competitive performance using video or sensor data on 3 public action segmentation datasets and can be trained in a fraction of the time it takes to train an RNN.

--- https://www.lesswrong.com/posts/wqRqb7h6ZC48iDgfK/tentatively-found-600-monosemantic-features-in-a-small-lm 2023-01-26 ai/nn/transformer/gpt ai/nn/vae --- https://arxiv.org/abs/2305.01610 Finding Neurons in a Haystack: Case Studies with Sparse Probing Wes Gurnee, Neel Nanda, Matthew Pauly, Katherine Harvey, Dmitrii Troitskii, Dimitris Bertsimas 2023-05-02 2023-05-02 [("doi","10.48550/arXiv.2305.01610")] ai/nn/transformer/attention ai/nn/transformer/gpt ai/nn/vae ai/scaling

Despite rapid adoption and deployment of large language models (LLMs), the internal computations of these models remain opaque and poorly understood. In this work, we seek to understand how high-level human-interpretable features are represented within the internal neuron activations of LLMs.

We train k-sparse linear classifiers (probes) on these internal activations to predict the presence of features in the input; by varying the value of k we study the sparsity of learned representations and how this varies with model scale. With k = 1, we localize individual neurons which are highly relevant for a particular feature, and perform a number of case studies to illustrate general properties of LLMs.

In particular, we show that early layers make use of sparse combinations of neurons to represent many features in superposition, that middle layers have seemingly dedicated neurons to represent higher-level contextual features, and that increasing scale causes representational sparsity to increase on average, but there are multiple types of scaling dynamics.

In all, we probe for over 100 unique features comprising 10 different categories in 7 different models spanning 70 million to 6.9 billion parameters.

--- http://aitimes.org/wp-content/uploads/2018/12/JudgementUncertainty.pdf 2023-01-26 psychology/cognitive-bias --- https://ground.news/blindspot 2023-01-26 design politics --- https://en.wikipedia.org/wiki/BLIT_(short_story) BLIT (short story) 2023-01-26 fiction/science-fiction psychology/vision --- https://en.wikipedia.org/wiki/McCollough_effect McCollough effect 2023-01-26 psychology/dark-knowledge psychology/vision --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8898253/ Gastrointestinal Interoception in Eating Disorders: Charting a New Path Sahib S. Khalsa, Laura A. Berner, Lisa M. Anderson 2022 2023-01-26 [("doi","10.1007/s11920-022-01318-3")] psychiatry/anorexia psychiatry/anxiety psychiatry/depression reinforcement-learning/robot

Purpose of Review: Abnormal interoception has been consistently observed across eating disorders despite limited inclusion in diagnostic conceptualization. Using the alimentary tract as well as recent developments in interoceptive neuroscience and predictive processing as a guide, the current review summarizes evidence of gastrointestinal interoceptive dysfunction in eating disorders.

Recent Findings: Eating is a complex process that begins well before and ends well after food consumption. Abnormal prediction and prediction-error signals may occur at any stage, resulting in aberrant gastrointestinal interoception and dysregulated gut sensations in eating disorders. Several interoceptive technologies have recently become available that can be paired with computational modeling and clinical interventions to yield new insights into eating disorder pathophysiology. Illuminating the neurobiology of gastrointestinal interoception in eating disorders requires a new generation of studies combining experimental probes of gut physiology with computational modeling. The application of such techniques within clinical trials frameworks may yield new tools and treatments with transdiagnostic relevance.

--- https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009517 A confirmation bias in perceptual decision-making due to hierarchical approximate inference Richard D. Lange, Ankani Chattoraj, Jeffrey M. Beck, Jacob L. Yates, Ralf M. Haefner, Samuel J. Gershman, Megan A. K. Peters, Samuel J. Gershman, Megan A. K. Peters, Samuel J. Gershman, Megan A. K. Peters 2021-10-01 2023-01-27 [("doi","10.1371/journal.pcbi.1009517")] psychology/cognitive-bias statistics/bayes statistics/decision

Making good decisions requires updating beliefs according to new evidence. This is a dynamical process that is prone to biases: in some cases, beliefs become entrenched and resistant to new evidence (leading to primacy effects), while in other cases, beliefs fade over time and rely primarily on later evidence (leading to recency effects). How and why either type of bias dominates in a given context is an important open question. Here, we study this question in classic perceptual decision-making tasks, where, puzzlingly, previous empirical studies differ in the kinds of biases they observe, ranging from primacy to recency, despite seemingly equivalent tasks. We present a new model, based on hierarchical approximate inference and derived from normative principles, that not only explains both primacy and recency effects in existing studies, but also predicts how the type of bias should depend on the statistics of stimuli in a given task. We verify this prediction in a novel visual discrimination task with human observers, finding that each observer’s temporal bias changed as the result of changing the key stimulus statistics identified by our model. The key dynamic that leads to a primacy bias in our model is an overweighting of new sensory information that agrees with the observer’s existing belief—a type of ‘confirmation bias’. By fitting an extended drift-diffusion model to our data we rule out an alternative explanation for primacy effects due to bounded integration. Taken together, our results resolve a major discrepancy among existing perceptual decision-making studies, and suggest that a key source of bias in human decision-making is approximate hierarchical inference.

Author summary: When humans and animals accumulate evidence over time, they are often biased. Identifying the mechanisms underlying these biases can lead to new insights into principles of neural computation. The confirmation bias, in which new evidence is given more weight when it agrees with existing beliefs, is a ubiquitous yet poorly understood example of such biases. Here we report that a confirmation bias arises even during perceptual decision-making, and propose an approximate hierarchical inference model as the underlying mechanism. Our model correctly predicts for what stimuli and tasks this bias will be strong, and when it will be weak, a critical prediction that we confirm using old and new data. A quantitative model comparison clearly favors our model over a key alternative: integration to bound. The key dynamic driving the confirmation bias in our model is an interaction between inferences on different timescales, a common scenario in decision-making more generally.

--- https://x.com/iScienceLuvr/status/1676891218075344896 2023-01-27 ai/nn/diffusion reinforcement-learning/preference-learning --- https://arxiv.org/abs/2307.01952#stability SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, Robin Rombach 2023-07-04 2023-07-04 [("doi","10.48550/arXiv.2307.01952")] ai/nn/diffusion ai/nn/transformer/clip

We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a 3× larger U-Net backbone. The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder.

We design multiple novel conditioning schemes and train SDXL on multiple aspect ratios. We also introduce a refinement model which is used to improve the visual fidelity of samples generated by SDXL using a post-hoc image-to-image technique.

We demonstrate that SDXL shows drastically improved performance compared the previous versions of Stable Diffusion and achieves results competitive with those of black-box state-of-the-art image generators...Failure cases of SDXL despite large improvements compared to previous versions of Stable Diffusion, the model sometimes still struggles with very complex prompts involving detailed spatial arrangements and detailed descriptions (e.g. top left example)...Additionally, while our model represents a large advancement over previous iterations of SD, it still encounters difficulties when rendering long, legible text. Occasionally, the generated text may contain random characters or exhibit inconsistencies. [Due to use of CLIP+BPEs still, unlike Deep Floyd using T5 for its LM.] ...Overall, there is a slight preference for SDXL over Midjourney in terms of prompt adherence...In 4⁄6 PartiPrompts categories SDXL outperforms Midjourney, and in 7⁄10 challenges there is no statistically-significant difference between both models or SDXL outperforms Midjourney.

In the spirit of promoting open research and fostering transparency in large model training and evaluation, we provide access to code and model weights at https://github.com/Stability-AI/generative-models.

--- https://x.com/polynoamial/status/1676971503261454340 2023-01-27 ai/nn/transformer/gpt/4 reinforcement-learning/exploration/active-learning reinforcement-learning/model/muzero --- https://parti.research.google/ 2023-01-27 ai/dataset ai/nn/diffusion ai/nn/transformer/gpt/dall-e/2 --- /doc/philosophy/mind/2008-schwitzgebel.pdf The Unreliability of Naive Introspection Eric Schwitzgebel 2008-04-01 2023-01-27 [("doi","10.1215/00318108-2007-037")] philosophy/epistemology philosophy/mind psychology/cognitive-bias/illusion-of-depth

We are prone to gross error, even in favorable circumstances of extended reflection, about our own ongoing conscious experience, our current phenomenology. Even in this apparently privileged domain, our self-knowledge is faulty and untrustworthy. We are not simply fallible at the margins but broadly inept.

Examples highlighted in this essay include: emotional experience (for example, is it entirely bodily; does joy have a common, distinctive phenomenological core?), peripheral vision (how broad and stable is the region of visual clarity?), and the phenomenology of thought (does it have a distinctive phenomenology, beyond just imagery and feelings?).

Cartesian skeptical scenarios undermine knowledge of ongoing conscious experience as well as knowledge of the outside world. Infallible judgments about ongoing mental states are simply banal cases of self-fulfillment.

Philosophical foundationalism supposing that we infer an external world from secure knowledge of our own consciousness is almost exactly backward.

--- https://www.ribbonfarm.com/2009/10/07/the-gervais-principle-or-the-office-according-to-the-office/ 2023-01-27 economics psychology/personality/psychopathy sociology --- https://www.reddit.com/r/StableDiffusion/comments/14ssg1g/stable_diffusion_attracts_various_enthusiasts/ 2023-01-27 ai/anime ai/nn/diffusion --- https://arxiv.org/abs/2307.03172 Lost in the Middle: How Language Models Use Long Contexts Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, Percy Liang 2023-07-06 2023-07-06 [("doi","10.48550/arXiv.2307.03172")] ai/nn/retrieval ai/nn/transformer/attention ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/instruction-tuning

While recent language models have the ability to take long contexts as input, relatively little is known about how well the language models use longer context.

We analyze language model performance on two tasks that require identifying relevant information within their input contexts: multi-document question answering and key-value retrieval.

We find that performance is often highest when relevant information occurs at the beginning or end of the input context, and degrades when models must access relevant information in the middle of long contexts. Furthermore, performance substantially decreases as the input context grows longer, even for explicitly long-context models.

Our analysis provides a better understanding of how language models use their input context and provides new evaluation protocols for future long-context models.

--- https://www.filfre.net/2023/07/going-rogue/ 2023-01-27 design fiction/text-game technology/digital-antiquarian --- https://arxiv.org/abs/2302.12173 Not what you’ve signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, Mario Fritz 2023-02-23 2023-02-23 [("doi","10.48550/arXiv.2302.12173")] ai/nn/retrieval ai/nn/transformer/gpt/4/nonfiction cs/security

[Github] Large Language Models (LLMs) are increasingly being integrated into various applications. The functionalities of recent LLMs can be flexibly modulated via natural language prompts. This renders them susceptible to targeted adversarial prompting, eg. Prompt Injection (PI) attacks enable attackers to override original instructions and employed controls.

So far, it was assumed that the user is directly prompting the LLM. But, what if it is not the user prompting? We argue that LLM-Integrated Applications blur the line between data and instructions. We reveal new attack vectors, using Indirect Prompt Injection, that enable adversaries to remotely (without a direct interface) exploit LLM-integrated applications by strategically injecting prompts into data likely to be retrieved.

We derive a comprehensive taxonomy from a computer security perspective to systematically investigate impacts and vulnerabilities, including data theft, worming, information ecosystem contamination, and other novel security risks. We demonstrate our attacks’ practical viability against both real-world systems, such as Bing’s GPT-4 powered Chat and code-completion engines, and synthetic applications built on GPT-4.

We show how processing retrieved prompts can act as arbitrary code execution, manipulate the application’s functionality, and control how and if other APIs are called. Despite the increasing integration and reliance on LLMs, effective mitigations of these emerging threats are currently lacking.

By raising awareness of these vulnerabilities and providing key insights into their implications, we aim to promote the safe and responsible deployment of these powerful models and the development of robust defenses that protect users and systems from potential attacks.

--- https://arxiv.org/abs/2307.03183 Whisper-AT: Noise-Robust Automatic Speech Recognizers are Also Strong General Audio Event Taggers Yuan Gong, Sameer Khurana, Leonid Karlinsky, James Glass 2023-07-06 2023-07-06 [("doi","10.48550/arXiv.2307.03183")] ai/music ai/nn/transformer/gpt/whisper

In this paper, we focus on Whisper, a recent automatic speech recognition model trained with a massive 680k hour labeled speech corpus recorded in diverse conditions.

We first show an interesting finding that while Whisper is very robust against real-world background sounds (eg. music), its audio representation is actually not noise-invariant, but is instead highly correlated to non-speech sounds, indicating that Whisper recognizes speech conditioned on the noise type.

With this finding, we build a unified audio tagging and speech recognition model Whisper-AT by freezing the backbone of Whisper, and training a lightweight audio tagging model on top of it. With <1% extra computational cost, Whisper-AT can recognize audio events, in addition to spoken text, in a single forward pass.

--- https://mehta-rohan.com/writings/blog_posts/attention.html 2023-01-28 ai/nn/transformer/attention --- https://skunkledger.substack.com/p/escaping-high-school 2023-01-28 iq/high psychology/willpower sociology --- https://www.reddit.com/r/emacs/comments/lly7po/do_you_use_emacs_lisp_as_a_general_purpose/gnvzisy/ 2023-01-28 cs/lisp/emacs --- https://sites.google.com/site/steveyegge2/tour-de-babel#h.p_ID_191 2023-01-28 cs/lisp --- https://www.nytimes.com/2023/06/28/books/review/iliad-translations.html 2023-01-28 fiction/poetry --- https://www.youtube.com/watch?v=DpefYPLH67A 2023-01-28 japan/art --- https://blas.com/dont-make-me-think/ 2023-01-28 design --- https://www.noemamag.com/the-sounds-of-invisible-worlds/ 2023-01-28 science technology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121416/ Neutron tomography of Van Leeuwenhoek’s microscopes Tiemen Cocquyt, Zhou Zhou, Jeroen Plomp, Lambert van Eijck 2021 2023-01-28 [("doi","10.1126/sciadv.abf2402")] technology

The technique of neutron tomography has, after 350 years, enabled a first look inside the iconic single-lens microscopes of Antoni van Leeuwenhoek. Van Leeuwenhoek’s 17th-century discovery of “animalcules” marks the birth of microbiology. His skillfully self-produced microscope lenses remained unsurpassed for over 150 years.

Neutron tomography now enabled us to reveal the lens types Van Leeuwenhoek used. We argue that Van Leeuwenhoek’s instruments incorporate some innovations that testify to an awareness of concurrent developments.

In particular, our analysis shows that for making his best-performing microscopes, Van Leeuwenhoek deployed a lens-making procedure popularized in 1678 by Robert Hooke. This is notable, as Hooke always wanted to find the secret of Van Leeuwenhoek’s lenses, but never managed to do so.

Therefore, Van Leeuwenhoek was far from the isolated scholar he is often claimed to be; rather, his secrecy about his lenses was motivated by an attempt to conceal his indebtedness to Hooke.

--- /doc/ai/anime/danbooru/2023-li-3.pdf Parsing-Conditioned Anime Translation: A New Dataset and Method Zhansheng Li, Yangyang Xu, Nanxuan Zhao, Yang Zhou, Yongtuo Liu, Dahua Lin, Shengfeng He 2023-04-10 2023-04-10 [("doi","10.1145/3585002")] ai/anime/danbooru ai/dataset ai/nn/gan/stylegan/anime ai/video/generation

Anime is an abstract art form that is substantially different from the human portrait, leading to a challenging misaligned image translation problem that is beyond the capability of existing methods. This can be boiled down to a highly ambiguous unconstrained translation between two domains.

To this end, we design a new anime translation framework by deriving the prior knowledge of a pre-trained StyleGAN model.

We introduce disentangled encoders to separately embed structure and appearance information into the same latent code, governed by 4 tailored losses. Moreover, we develop a FaceBank aggregation method that leverages the generated data of the StyleGAN, anchoring the prediction to produce in-domain animes.

To empower our model and promote the research of anime translation, we propose the first anime portrait parsing dataset, Danbooru-Parsing [derived from Danbooru2020], containing 4,921 densely labeled images across 17 classes. This dataset connects the face semantics with appearances, enabling our new constrained translation setting.

We further show the editability of our results, and extend our method to manga images, by generating the first manga parsing pseudo data.

Extensive experiments demonstrate the values of our new dataset and method, resulting in the first feasible solution on anime translation.

--- https://www.aei.org/wp-content/uploads/2014/05/-regulation-of-pharmaceutical-innovation-the-1962-amendments_1101223883.pdf 2023-01-29 economics statistics/decision --- https://en.wikipedia.org/wiki/Winthrop_Kellogg#The_Ape_and_The_Child Winthrop Kellogg § The Ape and The Child 2023-01-29 iq/animal --- https://x.com/Thinkwert/status/1677670007080140803 2023-01-29 economics/advertising --- https://www.construction-physics.com/p/the-story-of-titanium 2023-01-29 economics/experience-curve technology --- https://netzhansa.blogspot.com/2018/08/evaluating-vax-lisp-30.html 2023-01-29 cs/lisp --- https://arxiv.org/abs/2306.17810 HEADLINES: A Massive Scale Semantic Similarity Dataset of Historical English Emily Silcock, Melissa Dell 2023-06-30 2023-06-30 [("doi","10.48550/arXiv.2306.17810")] ai/dataset ai/nn/transformer

[code] A diversity of tasks use language models trained on semantic similarity data. While there are a variety of datasets that capture semantic similarity, they are either constructed from modern web data or are relatively small datasets created in the past decade by human annotators.

This study uses a novel source, newly digitized articles from off-copyright, local U.S. newspapers, to assemble a massive-scale semantic similarity dataset spanning 70 years (1920–1989) and containing nearly 400M positive semantic similarity pairs. Historically, around half of articles in U.S. local newspapers came from newswires like the Associated Press.

While local papers reproduced articles from the newswire, they wrote their own headlines, which form abstractive summaries of the associated articles. We associate articles and their headlines by exploiting document layouts and language understanding. We then use deep neural methods to detect which articles are from the same underlying source, in the presence of substantial noise and abridgement.

The headlines of reproduced articles form positive semantic similarity pairs. The resulting publicly available HEADLINES dataset is larger than most existing semantic similarity datasets and covers a much longer span of time.

It will facilitate the application of contrastively trained semantic similarity models to a variety of tasks, including the study of semantic change across space and time.

--- https://arxiv.org/abs/2210.04261 Noise-Robust De-Duplication at Scale Emily Silcock, Luca D’Amico-Wong, Jinglin Yang, Melissa Dell 2022-10-09 2023-01-29 [("doi","10.48550/arXiv.2210.04261")] ai/nn/retrieval ai/nn/transformer

Identifying near duplicates within large, noisy text corpora has a myriad of applications that range from de-duplicating training datasets, reducing privacy risk, and evaluating test set leakage, to identifying reproduced news articles and literature within large corpora. Across these diverse applications, the overwhelming majority of work relies on N-grams. Limited efforts have been made to evaluate how well N-gram methods perform, in part because it is unclear how one could create an unbiased evaluation dataset for a massive corpus.

This study uses the unique timeliness of historical news wires to create a 27,210 document dataset, with 122,876 positive duplicate pairs, for studying noise-robust de-duplication. The time-sensitivity of news makes comprehensive hand labeling feasible—despite the massive overall size of the corpus—as duplicates occur within a narrow date range.

The study then develops and evaluates a range of de-duplication methods: hashing and N-gram overlap (which predominate in the literature), a contrastively trained bi-encoder, and a re-rank style approach combining a bi- and cross-encoder [S-BERT MPNET + FAISS]. The neural approaches outperform hashing and N-gram overlap.

We show that the bi-encoder scales well, de-duplicating a 10 million article corpus on a single GPU card in a matter of hours.

The public release of our NEWS-COPY de-duplication dataset will facilitate further research and applications.

--- https://diff.wikimedia.org/2023/07/07/flickr-foundation-is-building-a-new-bridge-between-flickr-and-wikimedia-commons/ 2023-01-29 wikipedia --- https://arxiv.org/abs/2304.00245 Reusing Deep Neural Network Models through Model Re-engineering Binhang Qi, Hailong Sun, Xiang Gao, Hongyu Zhang, Zhaotian Li, Xudong Liu 2023-04-01 2023-04-01 [("doi","10.48550/arXiv.2304.00245")] ai/nn/sparsity

Training deep neural network (DNN) models, which has become an important task in today’s software development, is often costly in terms of computational resources and time. With the inspiration of software reuse, building DNN models through reusing existing ones has gained increasing attention recently.

Prior approaches to DNN model reuse have two main limitations: (1) reusing the entire model, while only a small part of the model’s functionalities (labels) are required, would cause much overhead (eg. computational and time costs for inference), and (2) model reuse would inherit the defects and weaknesses of the reused model, and hence put the new system under threats of security attack.

To solve the above problem, we propose SeaM, a tool that re-engineers a trained DNN model to improve its reusability. Specifically, given a target problem and a trained model, SeaM uses a gradient-based search method to search for the model’s weights that are relevant to the target problem. The re-engineered model that only retains the relevant weights is then reused to solve the target problem.

Evaluation results on widely-used models show that the re-engineered models produced by SeaM only contain 10.11% weights of the original models, resulting 42.41% reduction in terms of inference time. For the target problem, the re-engineered models even outperform the original models in classification accuracy by 5.85%. Moreover, reusing the re-engineered models inherits an average of 57% fewer defects than reusing the entire model.

We believe our approach to reducing reuse overhead and defect inheritance is one important step forward for practical model reuse.

--- https://arxiv.org/abs/1603.01121 Deep Reinforcement Learning from Self-Play in Imperfect-Information Games Johannes Heinrich, David Silver 2016-03-03 2023-01-29 [("doi","10.48550/arXiv.1603.01121")] reinforcement-learning/imperfect-information/poker

Many real-world applications can be described as large-scale games of imperfect information. To deal with these challenging domains, prior work has focused on computing Nash equilibria in a handcrafted abstraction of the domain.

In this paper we introduce the first scalable end-to-end approach to learning approximate Nash equilibria without prior domain knowledge. Our method combines fictitious self-play with deep reinforcement learning. When applied to Leduc poker, Neural Fictitious Self-Play (NFSP) approached a Nash equilibrium, whereas common reinforcement learning methods diverged. In Limit Texas Holdem, a poker game of real-world scale, NFSP learnt a strategy that approached the performance of state-of-the-art, superhuman algorithms based on domain expertise.

--- https://openreview.net/forum?id=Syg-ET4FPS Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information Yichi Zhou, Jialian Li, Jun Zhu 2023-05-05 2023-05-05 reinforcement-learning/imperfect-information reinforcement-learning/multi-agent statistics/bayes

Posterior sampling for reinforcement learning (PSRL) is a useful framework for making decisions in an unknown environment. PSRL maintains a posterior distribution of the environment and then makes planning on the environment sampled from the posterior distribution. Though PSRL works well on single-agent reinforcement learning problems, how to apply PSRL to multi-agent reinforcement learning problems is relatively unexplored.

In this work, we extend PSRL to two-player zero-sum extensive-games with imperfect information (TEGI), which is a class of multi-agent systems. More specifically, we combine PSRL with counterfactual regret minimization (CFR), which is the leading algorithm for TEGI with a known environment.

Our main contribution is a novel design of interaction strategies. With our interaction strategies, our algorithm provably converges to the Nash Equilibrium at a rate of 𝒪(√(log T⁄T)).

Empirical results show that our algorithm works well.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10213697/ AlphaZe∗∗: AlphaZero-like baselines for imperfect information games are surprisingly strong Jannis Blüml, Johannes Czech, Kristian Kersting 2023 2023-01-30 [("doi","10.3389/frai.2023.1014561")] reinforcement-learning/exploration reinforcement-learning/imperfect-information reinforcement-learning/model/alphago statistics/bayes

In recent years, deep neural networks for strategy games have made significant progress. AlphaZero-like frameworks which combine Monte-Carlo tree search with reinforcement learning have been successfully applied to numerous games with perfect information. However, they have not been developed for domains where uncertainty and unknowns abound, and are therefore often considered unsuitable due to imperfect observations.

Here, we challenge this view and argue that they are a viable alternative for games with imperfect information-a domain currently dominated by heuristic approaches or methods explicitly designed for hidden information, such as oracle-based techniques. To this end, we introduce a novel algorithm based solely on reinforcement learning, called AlphaZe∗∗, which is an AlphaZero-based framework for games with imperfect information.

We examine its learning convergence on the games Stratego and DarkHex and show that it is a surprisingly strong baseline, while using a model-based approach: it achieves similar win rates against other Stratego bots like Pipeline Policy Space Response Oracle (P2SRO), while not winning in direct comparison against P2SRO or reaching the much stronger numbers of DeepNash.

Compared to heuristics and oracle-based approaches, AlphaZe∗∗ can easily deal with rule changes, eg. when more information than usual is given, and drastically outperforms other approaches in this respect.

--- https://arxiv.org/abs/1806.02426 DVRL: Deep Variational Reinforcement Learning for POMDPs Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson 2018-06-06 2023-01-30 [("doi","10.48550/arXiv.1806.02426")] ai/nn/rnn reinforcement-learning/exploration reinforcement-learning/model-free

Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of incomplete and noisy observations.

In this paper, we propose deep variational reinforcement learning (DVRL), which introduces an inductive bias that allows an agent to learn a generative model of the environment and perform inference in that model to effectively aggregate the available information. We develop an n-step approximation to the evidence lower bound (ELBO), allowing the model to be trained jointly with the policy. This ensures that the latent state representation is suitable for the control task. In experiments on Mountain Hike and flickering Atari we show that our method outperforms previous approaches relying on recurrent neural networks to encode the past.

--- https://arxiv.org/abs/2306.00249 BetaZero: Belief-State Planning for Long-Horizon POMDPs using Learned Approximations Robert J. Moss, Anthony Corso, Jef Caers, Mykel J. Kochenderfer 2023-05-31 2023-05-31 [("doi","10.48550/arXiv.2306.00249")] reinforcement-learning/imperfect-information reinforcement-learning/model/alphago reinforcement-learning/offline

Real-world planning problems—including autonomous driving and sustainable energy applications like carbon storage and resource exploration—have recently been modeled as partially observable Markov decision processes (POMDPs) and solved using approximate methods.

To solve high-dimensional POMDPs in practice, state-of-the-art methods use online planning with problem-specific heuristics to reduce planning horizons and make the problems tractable. Algorithms that learn approximations to replace heuristics have recently found success in large-scale problems in the fully observable domain. The key insight is the combination of online Monte Carlo tree search with offline neural network approximations of the optimal policy and value function.

In this work, we bring this insight to partially observed domains and propose BetaZero, a belief-state planning algorithm for POMDPs. BetaZero learns offline approximations based on accurate belief models to enable online decision making in long-horizon problems. We address several challenges inherent in large-scale partially observable domains; namely challenges of transitioning in stochastic environments, prioritizing action branching with limited search budget, and representing beliefs as input to the network.

We apply BetaZero to various well-established benchmark POMDPs found in the literature. As a real-world case study, we test BetaZero on the high-dimensional geological problem of critical mineral exploration.

Experiments show that BetaZero outperforms state-of-the-art POMDP solvers on a variety of tasks.

--- http://pt.withy.org/publications/VLM.html 2023-01-30 cs/lisp --- https://arxiv.org/abs/2203.01302#facebook Evolving Curricula with Regret-Based Environment Design Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel 2022-03-02 2023-01-30 [("doi","10.48550/arXiv.2203.01302")] reinforcement-learning/exploration reinforcement-learning/meta-learning

It remains a challenge to train generally capable agents with reinforcement learning (RL). A promising avenue for improving the robustness of RL agents is through the use of curricula. One such class of methods frames environment design as a game between a student and a teacher, using regret-based objectives to produce environment instantiations (or levels) at the frontier of the student agent’s capabilities. These methods benefit from their generality, with theoretical guarantees at equilibrium, yet they often struggle to find effective levels in challenging design spaces.

By contrast, evolutionary approaches seek to incrementally alter environment complexity, resulting in potentially open-ended learning, but often rely on domain-specific heuristics and vast amounts of computational resources.

In this paper we propose to harness the power of evolution in a principled, regret-based curriculum. Our approach, which we call Adversarially Compounding Complexity by Editing Levels (ACCEL), seeks to constantly produce levels at the frontier of an agent’s capabilities, resulting in curricula that start simple but become increasingly complex. ACCEL maintains the theoretical benefits of prior regret-based methods, while providing empirical gains in a diverse set of environments.

An interactive version of the paper is available at accelagent.github.io.

--- https://x.com/VivaLaPanda_/status/1677828821964439553 2023-01-30 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://x.com/patio11/status/1677890745683025920 2023-01-30 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/1202.6590 Uniform random generation of large acyclic digraphs Jack Kuipers, Giusi Moffa 2012-02-29 2023-01-30 [("doi","10.1007/s11222-013-9428-y")] cs/algorithm statistics/probability

Directed acyclic graphs are the basic representation of the structure underlying Bayesian networks, which represent multivariate probability distributions. In many practical applications, such as the reverse engineering of gene regulatory networks, not only the estimation of model parameters but the reconstruction of the structure itself is of great interest.

As well as for the assessment of different structure learning algorithms in simulation studies, a uniform sample from the space of directed acyclic graphs is required to evaluate the prevalence of certain structural features.

Here we analyse how to sample acyclic digraphs uniformly at random through recursive enumeration, an approach previously thought too computationally involved.

Based on complexity considerations, we discuss in particular how the enumeration directly provides an exact method, which avoids the convergence issues of the alternative Markov chain methods and is actually computationally much faster. The limiting behavior of the distribution of acyclic digraphs then allows us to sample arbitrarily large graphs.

Building on the ideas of recursive enumeration based sampling we also introduce a novel hybrid Markov chain with much faster convergence than current alternatives while still being easy to adapt to various restrictions.

Finally we discuss how to include such restrictions in the combinatorial enumeration and the new hybrid Markov chain method for efficient uniform sampling of the corresponding graphs.

--- https://skunkledger.substack.com/p/superrational 2023-01-30 economics math/humor psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Meet-in-the-middle_attack Meet-in-the-middle attack 2023-01-30 cs/algorithm cs/cryptography --- https://www.stylewarning.com/posts/brute-force-rubiks-cube/#brute-force-still-ignorant-but-kinda-smart 2023-01-31 cs/algorithm cs/lisp --- https://www.math.rwth-aachen.de/~Martin.Schoenert/Cube-Lovers/Alan_Bawden__Shamir%27s_talk_really_was_about_how_to_solve_the_cube!.html 2023-01-31 cs/algorithm --- /doc/psychiatry/depression/2023-danese.pdf Associations Between Objective and Subjective Experiences of Childhood Maltreatment and the Course of Emotional Disorders in Adulthood Andrea Danese, Cathy Spatz Widom 2023-07-05 2023-07-05 [("doi","10.1001/jamapsychiatry.2023.2140")] psychiatry/anxiety psychiatry/depression

[media] Importance: A history of childhood maltreatment is associated with an unfavorable course of illness for emotional disorders. However, the origins and mechanisms underlying these associations are unknown.

Objective: To examine the relative associations of objective and subjective measures of childhood maltreatment and continuity in psychopathology with the course of emotional disorders in adulthood.

Design, Setting, & Participants: This prospective cohort study followed up until age 40 years participants residing in a metropolitan county of the US Midwest with substantiated records of childhood physical and sexual abuse and/or neglect 1967–1971 and a demographically matched group of participants experiencing no abuse or neglect in childhood. The collected data were analyzed between October 2021 and April 2022.

Exposures: The objective experience of childhood maltreatment before age 12 years was prospectively measured through official court records, whereas the subjective experience was measured through retrospective self-report at a mean (SD) age of 29 (3.8) years. Current and previous lifetime psychopathology was also assessed at a mean age of 29 (3.8) years.

Main Outcomes & Measures: Symptoms of depression and anxiety were measured at mean (SD) ages of 39.5 (3.5) and 41.2 (3.5) years using Poisson regression models.

Results: In a cohort of 1196 participants (582 [48.7%] female and 614 [51.3%] male) followed up to age 40 years, those with objective plus subjective measures of childhood maltreatment had a greater number of subsequent follow-up phases with depression or anxiety than controls (depression: incidence rate ratio [IRR], 2.28 [95% CI, 1.65-3.15]; anxiety: IRR, 2.30 [95% CI, 1.54-3.42]), as did those with subjective-only measures (depression: IRR, 1.49 [95% CI, 1.02-2.18]; anxiety: IRR, 1.58 [95% CI, 0.99-2.52]). In contrast, participants with objective-only measures did not have a greater number of follow-up phases with depression or anxiety (depression: IRR, 1.37 [95% CI, 0.89-2.11]; anxiety: IRR, 1.40 [95% CI, 0.84-2.31]). Current and lifetime psychopathology measured at the time the subjective experience was assessed explained its association with a later course of emotional disorders in participants with subjective-only measures but not in those with objective plus subjective measures.

Conclusion: In this cohort study, the associations seen between childhood maltreatment and poor course of emotional disorders over the subsequent decade were largely attributable to the subjective experience of maltreatment, which was partly explained by continuity in psychopathology. Modification of the subjective experience of childhood maltreatment could improve the longitudinal course of emotional disorders.

--- https://arxiv.org/abs/2306.17099 When Bidders Are DAOs Maryam Bahrani, Pranav Garimidi, Tim Roughgarden 2023-06-29 2023-06-29 [("doi","10.48550/arXiv.2306.17099")] economics/mechanism-design/auction

In a typical decentralized autonomous organization (DAO), people organize themselves into a group that is programmatically managed. DAOs can act as bidders in auctions, with a DAO’s bid treated by the auctioneer as if it had been submitted by an individual, without regard to the internal structure of the DAO.

We study auctions in which the bidders are DAOs. More precisely, we consider the design of two-level auctions in which the “participants” are groups of bidders rather than individuals. Bidders form DAOs to pool resources, but must then also negotiate the terms by which the DAO’s winnings are shared.

We model the outcome of a DAO’s negotiations by an aggregation function (which aggregates DAO members’ bids into a single group bid), and a budget-balanced cost-sharing mechanism (that determines DAO members’ access to the DAO’s allocation and distributes the total payment demanded from the DAO to its members). We pursue two-level mechanisms that are incentive-compatible (with truthful bidding a dominant strategy for members of each DAO) and ~welfare-optimal.

We prove that, even in the case of a single-item auction, incentive-compatible welfare maximization is not possible: No matter what the outer mechanism and the cost-sharing mechanisms used by DAOs, the welfare of the resulting two-level mechanism can be a ≈ ln n factor less than optimal. We complement this lower bound with a natural two-level mechanism that achieves a matching approximate welfare guarantee.

Our upper bound also extends to multi-item auctions where individuals have additive valuations. Finally, we show that our positive results cannot be extended much further: Even in multi-item settings with unit-demand bidders, truthful two-level mechanisms form a highly restricted class and as a consequence cannot guarantee any non-trivial approximation of the maximum social welfare.

--- https://www.totheletterdna.com/who 2023-01-31 genetics/sequencing --- https://www.quantamagazine.org/memories-help-brains-recognize-new-events-worth-remembering-20230517/ 2023-01-31 psychology/animal psychology/neuroscience reinforcement-learning/model-free --- https://x.com/atlantis__labs/status/1677782219937525760 2023-01-31 ai/nn/transformer/gpt/codex cs/security --- https://www.medrxiv.org/content/10.1101/2023.07.06.23292311.full Multi-ancestry genome-wide meta-analysis of 56,241 individuals identifies LRRC4C, LHX5-AS1 and nominates ancestry-specific loci PTPRK, GRB14, and KIAA0825 as novel risk loci for Alzheimer disease: the Alzheimer Disease Genetics Consortium Farid Rajabli, Penelope Benchek, Giuseppe Tosto, Nicholas Kushch, Jin Sha, Katrina Bazemore, Congcong Zhu, Wan-Ping Lee, Jacob Haut, Kara L. Hamilton-Nelson, Nicholas R. Wheeler, Yi Zhao, John J. Farrell, Michelle A. Grunin, Yuk Yee Leung, Pavel P. Kuksa, Donghe Li, Eder Lucio da Fonseca, Jesse B. Mez, Ellen L. Palmer, Jagan Pillai, Richard M. Sherva, Yeunjoo E. Song, Xiaoling Zhang, Taha Iqbal, Omkar Pathak, Otto Valladares, Amanda B. Kuzma, Erin Abner, Perrie M. Adams, Alyssa Aguirre, Marilyn S. Albert, Roger L. Albin, Mariet Allen, Lisa Alvarez, Liana G. Apostolova, Steven E. Arnold, Sanjay Asthana, Craig S. Atwood, Gayle Ayres, Clinton T. Baldwin, Robert C. Barber, Lisa L. Barnes, Sandra Barral, Thomas G. Beach, James T. Becker, Gary W. Beecham, Duane Beekly, Bruno A. Benitez, David Bennett, John Bertelson, Thomas D. Bird, Deborah Blacker, Bradley F. Boeve, James D. Bowen, Adam Boxer, James Brewer, James R. Burke, Jeffrey M. Burns, Joseph D. Buxbaum, Nigel J. Cairns, Laura B. Cantwell, Chuanhai Cao, Christopher S. Carlson, Cynthia M. Carlsson, Regina M. Carney, Minerva M. Carrasquillo, Scott Chasse, Marie-Francoise Chesselet, Nathaniel A. Chin, Helena C. Chui, Jaeyoon Chung, Suzanne Craft, Paul K. Crane, David H. Cribbs, Elizabeth A. Crocco, Carlos Cruchaga, Michael L. Cuccaro, Munro Cullum, Eveleen Darby, Barbara Davis, Philip L. De Jager, Charles DeCarli, John DeToledo, Malcolm Dick, Dennis W. Dickson, Beth A. Dombroski, Rachelle S. Doody, Ranjan Duara, Nilufer Ertekin-Taner, Denis A. Evans, Kelley M. Faber, Thomas J. Fairchild, Kenneth B. Fallon, David W. Fardo, Martin R. Farlow, Victoria Fernandez-Hernandez, Steven Ferris, Tatiana M. Foroud, Matthew P. Frosch, Brian Fulton-Howard, Douglas R. Galasko, Adriana Gamboa, Marla Gearing, Daniel H. Geschwind, Bernardino Ghetti, John R. Gilbert, Alison M. Goate, Thomas J. Grabowski, Neill R. Graff-Radford, Robert C. Green, John H. Growdon, Hakon Hakonarson, James Hall, Ronald L. Hamilton, Oscar Harari, John Hardy, Lindy E. Harrell, Elizabeth Head, Victor W. Henderson, Michelle Hernandez, Timothy Hohman, Lawrence S. Honig, Ryan M. Huebinger, Matthew J. Huentelman, Christine M. Hulette, Bradley T. Hyman, Linda S. Hynan, Laura Ibanez, Gail P. Jarvik, Suman Jayadev, Lee-Way Jin, Kim Johnson, Leigh Johnson, M. Ilyas Kamboh, Anna M. Karydas, Mindy J. Katz, John S. Kauwe, Jeffrey A. Kaye, C. Dirk Keene, Aisha Khaleeq, Ronald Kim, Janice Knebl, Neil W. Kowall, Joel H. Kramer, Walter A. Kukull, Frank M. LaFerla, James J. Lah, Eric B. Larson, Alan Lerner, James B. Leverenz, Allan I. Levey, Andrew P. Lieberman, Richard B. Lipton, Mark Logue, Oscar L. Lopez, Kathryn L. Lunetta, Constantine G. Lyketsos, Douglas Mains, Flanagan E. Margaret, Daniel C. Marson, Eden R. R. Martin, Frank Martiniuk, Deborah C. Mash, Eliezer Masliah, Paul Massman, Arjun Masurkar, Wayne C. McCormick, Susan M. McCurry, Andrew N. McDavid, Stefan McDonough, Ann C. McKee, Marsel Mesulam, Bruce L. Miller, Carol A. Miller, Joshua W. Miller, Thomas J. Montine, Edwin S. Monuki, John C. Morris, Shubhabrata Mukherjee, Amanda J. Myers, Trung Nguyen, Sid O’Bryant, John M. Olichney, Marcia Ory, Raymond Palmer, Joseph E. Parisi, Henry L. Paulson, Valory Pavlik, David Paydarfar, Victoria Perez, Elaine Peskind, Ronald C. Petersen, Aimee Pierce, Marsha Polk, Wayne W. Poon, Huntington Potter, Liming Qu, Mary Quiceno, Joseph F. Quinn, Ashok Raj, Murray Raskind, Eric M. Reiman, Barry Reisberg, Joan S. Reisch, John M. Ringman, Erik D. Roberson, Monica Rodriguear, Ekaterina Rogaeva, Howard J. Rosen, Roger N. Rosenberg, Donald R. Royall, Mark A. Sager, Mary Sano, Andrew J. Saykin, Julie A. Schneider, Lon S. Schneider, William W. Seeley, Susan H. Slifer, Scott Small, Amanda G. Smith, Janet P. Smith, Joshua A. Sonnen, Salvatore Spina, Peter St George-Hyslop, Robert A. Stern, Alan B. Stevens, Stephen M. Strittmatter, David Sultzer, Russell H. Swerdlow, Rudolph E. Tanzi, Jeffrey L. Tilson, John Q. Trojanowski, Juan C. Troncoso, Debby W. Tsuang, Vivianna M. Van Deerlin, Linda J. van Eldik, Jeffery M. Vance, Badri N. Vardarajan, Robert Vassar, Harry V. Vinters, Jean-Paul Vonsattel, Sandra Weintraub, Kathleen A. Welsh-Bohmer, Patrice L. Whitehead, Ellen M. Wijsman, Kirk C. Wilhelmsen, Benjamin Williams, Jennifer Williamson, Henrik Wilms, Thomas S. Wingo, Thomas Wisniewski, Randall L. Woltjer, Martin Woon, Clinton B. Wright, Chuang-Kuo Wu, Steven G. Younkin, Chang-En Yu, Lei Yu, Xiongwei Zhu, Brian W. Kunkle, William S. Bush, Li-San Wang, Lindsay A. Farrer, Jonathan L. Haines, Richard Mayeux, Margaret A. Pericak-Vance, Gerard D. Schellenberg, Gyungah R. Jun, Christiane Reitz, Adam C. Naj 2023-07-08 2023-07-08 [("doi","10.1101/2023.07.06.23292311")] genetics/heritable psychiatry/alzheimers

Limited ancestral diversity has impaired our ability to detect risk variants more prevalent in non-European ancestry groups in genome-wide association studies (GWAS).

We constructed and analyzed a multi-ancestry GWAS dataset in the Alzheimer Disease (AD) Genetics Consortium (ADGC) to test for novel shared and ancestry-specific AD susceptibility loci and evaluate underlying genetic architecture in 37,382 non-Hispanic White (NHW), 6,728 African American, 8,899 Hispanic (HIS), and 3,232 East Asian individuals, performing within-ancestry fixed-effects meta-analysis followed by a cross-ancestry random-effects meta-analysis.

We identified 13 loci with cross-ancestry associations including known loci at/near CR1, BIN1, TREM2, CD2AP, PTK2B, CLU, SHARPIN, MS4A6A, PICALM, ABCA7, APOE and two novel loci not previously reported at 11p12 (LRRC4C) and 12q24.13 (LHX5-AS1). Reflecting the power of diverse ancestry in GWAS, we observed the SHARPIN locus using 7.1% the sample size of the original discovering single-ancestry GWAS (n = 788,989).

We additionally identified 3 GWS ancestry-specific loci at/near (PTPRK (p = 2.4 × 10−8) and GRB14 (p = 1.7 × 10−8) in HIS), and KIAA0825 (p = 2.9 × 10−8 in NHW). Pathway analysis implicated multiple amyloid regulation pathways (strongest with padjusted = 1.6 × 10−4) and the classical complement pathway (padjusted = 1.3 × 10−3).

Genes at/near our novel loci have known roles in neuronal development (LRRC4C, LHX5-AS1, and PTPRK) and insulin receptor activity regulation (GRB14). These findings provide compelling support for using traditionally-underrepresented populations for gene discovery, even with smaller sample sizes.

--- https://arxiv.org/abs/1811.03194 AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning Florian Tramèr, Pascal Dupré, Gili Rusak, Giancarlo Pellegrino, Dan Boneh 2018-11-08 2023-01-31 [("doi","10.1145/3319535.3354222")] ai/nn/adversarial ai/nn/cnn economics/advertising/adblock

Perceptual ad-blocking is a novel approach that detects online advertisements based on their visual content. Compared to traditional filter lists, the use of perceptual signals is believed to be less prone to an arms race with web publishers and ad networks. We demonstrate that this may not be the case. We describe attacks on multiple perceptual ad-blocking techniques, and unveil a new arms race that likely disfavors ad-blockers. Unexpectedly, perceptual ad-blocking can also introduce new vulnerabilities that let an attacker bypass web security boundaries and mount DDoS attacks.

We first analyze the design space of perceptual ad-blockers and present a unified architecture that incorporates prior academic and commercial work. We then explore a variety of attacks on the ad-blocker’s detection pipeline, that enable publishers or ad networks to evade or detect ad-blocking, and at times even abuse its high privilege level to bypass web security boundaries.

On one hand, we show that perceptual ad-blocking must visually classify rendered web content to escape an arms race centered on obfuscation of page markup. On the other, we present a concrete set of attacks on visual ad-blockers by constructing adversarial examples in a real web page context. For 7 ad-detectors, we create perturbed ads, ad-disclosure logos, and native web content that misleads perceptual ad-blocking with 100% success rates. In one of our attacks, we demonstrate how a malicious user can upload adversarial content, such as a perturbed image in a Facebook post, that fools the ad-blocker into removing another users’ non-ad content.

Moving beyond the Web and visual domain, we also build adversarial examples for AdblockRadio, an open source radio client that uses machine learning to detects ads in raw audio streams.

--- https://openai.com/form/researcher-access-program 2023-01-31 ai/nn/transformer/gpt/4 --- https://jacobbrazeal.wordpress.com/2023/07/09/computationally-optimal-sequences-of-barbell-plates/ 2023-01-31 cs/algorithm --- /doc/psychology/cognitive-bias/2023-kidd.pdf How AI can distort human beliefs Celeste Kidd, Abeba Birhane 2023-06-23 2023-06-23 [("doi","10.1126/science.adi0248")] ai psychology/cognitive-bias --- https://arxiv.org/abs/1511.06279#deepmind Neural Programmer-Interpreters Scott Reed, Nando de Freitas 2015-11-19 2023-02-01 [("doi","10.48550/arXiv.1511.06279")] ai/nn/rnn ai/nn/transformer/gpt/codex

We propose the neural programmer-interpreter (NPI): a recurrent and compositional neural network that learns to represent and execute programs. NPI has 3 learnable components: a task-agnostic recurrent core, a persistent key-value program memory, and domain-specific encoders that enable a single NPI to operate in multiple perceptually diverse environments with distinct affordances.

By learning to compose lower-level programs to express higher-level programs, NPI reduces sample complexity and increases generalization ability compared to sequence-to-sequence LSTMs. The program memory allows efficient learning of additional tasks by building on existing programs.

NPI can also harness the environment (eg. a scratch pad with read-write pointers) to cache intermediate results of computation, lessening the long-term memory burden on recurrent hidden units. In this work we train the NPI with fully-supervised execution traces; each program has example sequences of calls to the immediate subprograms conditioned on the input.

Rather than training on a huge number of relatively weak labels, NPI learns from a small number of rich examples. We demonstrate the capability of our model to learn several types of compositional programs: addition, sorting, and canonicalizing 3D models. Furthermore, a single NPI learns to execute these programs and all 21 associated subprograms.

--- https://arxiv.org/abs/1706.01284 Towards Synthesizing Complex Programs from Input-Output Examples Xinyun Chen, Chang Liu, Dawn Song 2017-06-05 2023-02-01 [("doi","10.48550/arXiv.1706.01284")] ai/nn/rnn ai/nn/transformer/gpt/3 reinforcement-learning/exploration reinforcement-learning/model-free

In recent years, deep learning techniques have been developed to improve the performance of program synthesis from input-output examples. Albeit its progress, the programs that can be synthesized by state-of-the-art approaches are still simple in terms of their complexity. In this work, we move a step forward along this direction by proposing a new class of challenging tasks in the domain of program synthesis from input-output examples: learning a context-free parser from pairs of input programs and their parse trees. We show that this class of tasks are much more challenging than previously studied tasks, and the test accuracy of existing approaches is almost 0%.

We tackle the challenges by developing 3 novel techniques inspired by 3 novel observations, which reveal the key ingredients of using deep learning to synthesize a complex program. First, the use of a non-differentiable machine is the key to effectively restrict the search space. Thus our proposed approach learns a neural program operating a domain-specific non-differentiable machine. Second, recursion is the key to achieve generalizability. Thus, we bake-in the notion of recursion in the design of our non-differentiable machine. Third, reinforcement learning is the key to learn how to operate the non-differentiable machine, but it is also hard to train the model effectively with existing reinforcement learning algorithms from a cold boot. We develop a novel two-phase reinforcement learning-based search algorithm to overcome this issue.

In our evaluation, we show that using our novel approach, neural parsing programs can be learned to achieve 100% test accuracy on test inputs that are 500× longer than the training samples.

--- https://arxiv.org/abs/2304.02015#alibaba How well do Large Language Models perform in Arithmetic tasks? Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang, Songfang Huang 2023-03-16 2023-03-16 [("doi","10.48550/arXiv.2304.02015")] ai/dataset ai/nn/tokenization ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/instruction-tuning ai/scaling math reinforcement-learning/meta-learning

Large language models have emerged abilities including chain-of-thought to answer math word problems step by step. Solving math word problems not only requires abilities to disassemble problems via chain-of-thought but also needs to calculate arithmetic expressions correctly for each step. To the best of our knowledge, there is no work to focus on evaluating the arithmetic ability of large language models.

In this work, we propose an arithmetic dataset MATH 401 to test the latest large language models including GPT-4, ChatGPT, InstructGPT, Galactica, and LLaMa with various arithmetic expressions and provide a detailed analysis of the ability of large language models.

MATH 401 and evaluation codes are released at https://github.com/GanjinZero/math401-llm.

--- https://arxiv.org/abs/2202.07206 Impact of Pretraining Term Frequencies on Few-Shot Reasoning Yasaman Razeghi, Robert L. Logan IV, Matt Gardner, Sameer Singh 2022-02-15 2023-02-01 [("doi","10.48550/arXiv.2202.07206")] ai/nn/transformer/gpt/3/nonfiction math

Pretrained Language Models (LMs) have demonstrated ability to perform numerical reasoning by extrapolating from a few examples in few-shot settings. However, the extent to which this extrapolation relies on robust reasoning is unclear.

In this paper, we investigate how well these models reason with terms that are less frequent in the pretraining data. In particular, we examine the correlations between the model performance on test instances and the frequency of terms from those instances in the pretraining data.

We measure the strength of this correlation for a number of GPT-based language models (pretrained on the Pile dataset) on various numerical deduction tasks (eg. arithmetic and unit conversion). Our results consistently demonstrate that models are more accurate on instances whose terms are more prevalent, in some cases above 70% (absolute) more accurate on the top 10% frequent terms in comparison to the bottom 10%.

Overall, although LMs exhibit strong performance at few-shot numerical reasoning tasks, our results raise the question of how much models actually generalize beyond pretraining data, and we encourage researchers to take the pretraining data into account when interpreting evaluation results.

--- https://aclanthology.org/2021.emnlp-main.563.pdf 2023-02-01 ai/nn/tokenization ai/nn/transformer math --- https://arxiv.org/abs/2208.05051 Limitations of Language Models in Arithmetic and Symbolic Induction Jing Qian, Hong Wang, Zekun Li, Shiyang Li, Xifeng Yan 2022-08-09 2023-02-01 [("doi","10.48550/arXiv.2208.05051")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/t5 reinforcement-learning/imitation-learning

Recent work has shown that large pretrained Language Models (LMs) can not only perform remarkably well on a range of Natural Language Processing (NLP) tasks but also start improving on reasoning tasks such as arithmetic induction, symbolic manipulation, and commonsense reasoning with increasing size of models. However, it is still unclear what the underlying capabilities of these LMs are.

Surprisingly, we find that these models have limitations on certain basic symbolic manipulation tasks such as copy, reverse, and addition. When the total number of symbols or repeating symbols increases, the model performance drops quickly. We investigate the potential causes behind this phenomenon and examine a set of possible methods, including explicit positional markers, fine-grained computation steps, and LMs with callable programs.

Experimental results show that none of these techniques can solve the simplest addition induction problem completely. In the end, we introduce LMs with tutor, which demonstrates every single step of teaching. LMs with tutor is able to deliver 100% accuracy in situations of OOD and repeating symbols, shedding new insights on the boundary of large LMs in induction.

--- https://arxiv.org/abs/2305.20050#openai Let’s Verify Step by Step Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe 2023-05-31 2023-05-31 [("doi","10.48550/arXiv.2305.20050")] ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue math reinforcement-learning/imitation-learning

[blog; cf. Uesato et al 2022] In recent years, large language models have greatly improved in their ability to perform complex multi-step reasoning. However, even state-of-the-art models still regularly produce logical mistakes. To train more reliable models, we can turn either to outcome supervision, which provides feedback for a final result, or process supervision, which provides feedback for each intermediate reasoning step. Given the importance of training reliable models, and given the high cost of human feedback, it is important to carefully compare the both methods. Recent work has already begun this comparison, but many questions still remain.

We conduct our own investigation, finding that process supervision outperforms outcome supervision for training models to solve problems from the challenging MATH dataset.

Our process-supervised model solves 78% of problems from a representative subset of the MATH test set. Additionally, we show that active learning improves the efficacy of process supervision.

To support related research, we also release PRM800K, the complete dataset of 800,000 step-level human feedback labels used to train our best reward model.

--- https://arxiv.org/abs/2211.14275#deepmind Solving math word problems with process & outcome-based feedback Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, Irina Higgins 2022-11-25 2023-02-01 [("doi","10.48550/arXiv.2211.14275")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/inner-monologue math reinforcement-learning/imitation-learning reinforcement-learning/model/alphago reinforcement-learning/preference-learning

Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks. When moving beyond prompting, this raises the question of how we should supervise such models: outcome-based approaches which supervise the final result, or process-based approaches which supervise the reasoning process itself?

Differences between these approaches might naturally be expected not just in final-answer errors but also in reasoning errors, which can be difficult to detect and are problematic in many real-world domains such as education.

We run the first comprehensive comparison between process & outcome-based approaches trained on a natural language task, GSM8K. We find that pure outcome-based supervision produces similar final-answer error rates with less label supervision.

However, for correct reasoning steps we find it necessary to use process-based supervision or supervision from learned reward models that emulate process-based feedback. [cf. Lightman et al 2023]

In total, we improve the previous best results from 16.8% → 12.7% final-answer error and 14.0% → 3.4% reasoning error among final-answer-correct solutions.

--- https://arxiv.org/abs/2211.00170#facebook What is my math transformer doing? – 3 results on interpretability and generalization François Charton 2022-10-31 2023-02-01 [("doi","10.48550/arXiv.2211.00170")] ai/nn/transformer/gpt math

This paper investigates the failure cases and out-of-distribution behavior of transformers trained on matrix inversion & eigenvalue decomposition.

I show that incorrect model predictions still retain deep mathematical properties of the solution (eg. correct eigenvalues, unit norm of eigenvectors), and that almost all model failures can be attributed to, and predicted from, properties of the problem or solution. This demonstrates that, when in doubt, math transformers do not hallucinate absurd solutions (as was sometimes proposed) but remain “roughly right”.

I also show that the careful choice of a training dataset can accelerate training, while allowing the model to generalize out of its training distribution, invalidating the idea that transformers “merely interpolate” from memorized examples.

--- https://arxiv.org/abs/2112.01898#facebook Linear algebra with transformers François Charton 2021-12-03 2023-02-02 [("doi","10.48550/arXiv.2112.01898")] ai/nn/transformer/gpt math

Transformers can learn to perform numerical computations from examples only.

I study 9 problems of linear algebra, from basic matrix operations to eigenvalue decomposition & matrix inversion, and introduce and discuss 4 encoding schemes to represent real numbers.

On all problems, transformers trained on sets of random matrices achieve high accuracies (over 90%). The models are robust to noise, and can generalize out of their training distribution. In particular, models trained to predict Laplace-distributed eigenvalues generalize to different classes of matrices: Wigner matrices or matrices with positive eigenvalues. The reverse is not true.

--- https://arxiv.org/abs/2305.00586 How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model Michael Hanna, Ollie Liu, Alexandre Variengien 2023-04-30 2023-04-30 [("doi","10.48550/arXiv.2305.00586")] ai/nn/fully-connected ai/nn/transformer/attention ai/nn/transformer/gpt/2 math

Pre-trained language models can be surprisingly adept at tasks they were not explicitly trained on, but how they implement these capabilities is poorly understood. In this paper, we investigate the basic mathematical abilities often acquired by pre-trained language models. Concretely, we use mechanistic interpretability techniques to explain the (limited) mathematical abilities of GPT-2 small.

As a case study, we examine its ability to take in sentences such as “The war lasted from the year 1732 to the year 17”, and predict valid two-digit end years (years > 32). We first identify a circuit, a small subset of GPT-2 small’s computational graph that computes this task’s output.

Then, we explain the role of each circuit component, showing that GPT-2 small’s final multi-layer perceptrons boost the probability of end years greater than the start year.

Finally, we find related tasks that activate our circuit. Our results suggest that GPT-2 small computes greater-than using a complex but general mechanism that activates across diverse contexts.

--- https://arxiv.org/abs/1211.4116 The Algebraic Combinatorial Approach for Low-Rank Matrix Completion Franz J. Király, Louis Theran, Ryota Tomioka 2012-11-17 2023-02-02 [("doi","10.48550/arXiv.1211.4116")] cs/algorithm math

We present a novel algebraic combinatorial view on low-rank matrix completion based on studying relations between a few entries with tools from algebraic geometry and matroid theory.

The intrinsic locality of the approach allows for the treatment of single entries in a closed theoretical and practical framework. More specifically, apart from introducing an algebraic combinatorial theory of low-rank matrix completion, we present probability-one algorithms to decide whether a particular entry of the matrix can be completed. We also describe methods to complete that entry from a few others, and to estimate the error which is incurred by any method completing that entry.

Furthermore, we show how known results on matrix completion and their sampling assumptions can be related to our new perspective and interpreted in terms of a completability phase transition.

--- https://x.com/BlinkDL_AI/status/1677593798531223552 2023-02-02 ai/nn/rnn ai/nn/transformer/gpt/inner-monologue math --- /doc/reinforcement-learning/imitation-learning/2023-lee-figure6-sampleefficiencyofvariousinnermonologueformatsshowingmoredetailedisbetterforimitationlearning.png 2023 2023-02-02 ai/nn/transformer/gpt/inner-monologue reinforcement-learning/imitation-learning --- /doc/ai/nn/transformer/gpt/inner-monologue/2023-lee-figure9-arithmeticcanbelearnedevenwithnoiseintheinnermonologuetranscripts.jpg 2023 2023-02-02 ai/nn/transformer/gpt/inner-monologue reinforcement-learning/meta-learning --- https://github.com/openai/tiktoken 2023-02-02 ai/nn/tokenization --- https://arxiv.org/abs/2307.03576 One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention Arvind Mahankali, Tatsunori B. Hashimoto, Tengyu Ma 2023-07-07 2023-07-07 [("doi","10.48550/arXiv.2307.03576")] ai/nn/transformer/attention reinforcement-learning/meta-learning

Recent works have empirically analyzed in-context learning and shown that transformers trained on synthetic linear regression tasks can learn to implement ridge regression, which is the Bayes-optimal predictor, given sufficient capacity [Akyürek et al 2023], while one-layer transformers with linear self-attention and no MLP layer will learn to implement one step of gradient descent (GD) on a least-squares linear regression objective [von Oswald et al 2022]. However, the theory behind these observations remains poorly understood.

We theoretically study transformers with a single layer of linear self-attention, trained on synthetic noisy linear regression data. First, we mathematically show that when the covariates are drawn from a standard Gaussian distribution, the one-layer transformer which minimizes the pre-training loss will implement a single step of GD on the least-squares linear regression objective. Then, we find that changing the distribution of the covariates and weight vector to a non-isotropic Gaussian distribution has a strong impact on the learned algorithm: the global minimizer of the pre-training loss now implements a single step of pre-conditioned GD. However, if only the distribution of the responses is changed, then this does not have a large effect on the learned algorithm: even when the response comes from a more general family of nonlinear functions, the global minimizer of the pre-training loss still implements a single step of GD on a least-squares linear regression objective.

--- https://github.com/hiAndrewQuinn/resorter 2023-02-02 statistics/order/comparison --- https://www.biorxiv.org/content/10.1101/2023.01.30.526354.full Premature Aging and Reduced Cancer Incidence Associated with Near-Complete Body-Wide Myc Inactivation Huabo Wang, Jie Lu, Taylor Stevens, Alexander Roberts, Jordan Mandel, Raghunandan Avula, Bingwei Ma, Yijen Wu, Jinglin Wang, Clinton Van’t Land, Toren Finkel, Jerry E. Vockley, Merlin Airik, Rannar Airik, Radhika Muzumdar, Zhenwei Gong, Michel S. Torbenson, Edward V. Prochownik 2023-06-19 2023-06-19 [("doi","10.1101/2023.01.30.526354")] longevity

The MYC proto-oncogene dysregulation alters metabolism, translation and other functions in ways that support tumor induction and maintenance. Although Myc± mice are healthier and longer-lived than control mice, the long-term ramifications of more complete Myc loss remain unknown.

We now describe the chronic consequences of body-wide Myc inactivation initiated postnatally. “MycKO” mice acquire numerous features of premature aging including altered body composition and habitus, metabolic dysfunction, hepatic steatosis and the dysregulation of numerous gene sets involved in functions that normally deteriorate with aging.

Yet, MycKO mice have extended life spans that correlate with a 3–4× lower lifetime cancer incidence. Aging tissues from normal mice and humans also down-regulate Myc and gradually deregulate many of the same Myc target gene sets that are dysregulated in MycKO mice.

Normal aging and its associated cancer predisposition are thus highly linked via Myc and its target genes and can be genetically separated.

--- https://rauno.me/craft/interaction-design 2023-02-02 design --- http://1997.webhistory.org/www.lists/www-talk.1993q3/ 2023-02-03 design/typography/sentence-spacing --- https://en.wikipedia.org/wiki/Muphry%27s_law Muphry’s law 2023-02-03 psychology/cognitive-bias/illusion-of-depth psychology/writing --- https://www.wired.com/story/a-one-time-shot-for-type-2-diabetes-a-biotech-company-is-on-it/ 2023-02-03 genetics/editing --- https://x.com/EileenOrmsby/status/1678829191007989763 2023-02-03 darknet-market/silk-road/1 --- https://www.anthropic.com/news/claude-2 2023-02-03 ai/nn/transformer/gpt/claude --- https://danielkehoe.com/posts/personal-history-punctuating-the-web-1993/ 2023-02-03 design/typography/sentence-spacing --- https://news.ycombinator.com/item?id=22975299 2023-02-03 design/typography/sentence-spacing --- https://x.com/IntuitMachine/status/1678870325600108545 2023-02-03 ai/nn/transformer/gpt/claude --- https://x.com/Coskaiy/status/1678920686746718209 2023-02-03 ai/nn/transformer/gpt/claude cs/security --- https://x.com/peligrietzer/status/1678912319743459328 2023-02-03 ai/nn/transformer/gpt/claude --- https://www.forbes.com/sites/rashishrivastava/2023/04/11/writer-generative-ai/ 2023-02-04 ai/nn/transformer/t5 --- https://www.wired.com/story/silk-road-variety-jones-sentencing/ 2023-02-04 darknet-market/silk-road/1 --- https://www.newyorker.com/news/annals-of-a-warming-planet/what-to-do-with-climate-emotions 2023-02-04 existential-risk psychiatry/anxiety --- https://x.com/tobiaschneider/status/957765269316886528 2023-02-04 cs/security --- https://x.com/BlackHC/status/1678881236582912000 2023-02-04 ai/nn/retrieval ai/nn/transformer/gpt/claude --- https://www.biorxiv.org/content/10.1101/2023.06.28.546898.full Is a Picture Worth 1,000 SNPs? Effects of User-Submitted Photographs on Ancestry Estimates from Direct-to-Consumer Canine Genetic Tests Halie M. Rando, Kiley Graim, Greg Hampikian, Casey S. Greene 2023-06-29 2023-06-29 [("doi","10.1101/2023.06.28.546898")] genetics/heritable/dog

Objective: To evaluate whether the breed ancestry predictions of direct-to-consumer (DTC) genetic tests for dogs are influenced by the user-provided photograph. Animals: 12 pet dogs considered purebred (ie. registered with a breed organization) representing 12 different breeds.

Method: 6 buccal swabs per dog were collected by the owners and submitted to 6 DTC genetic testing companies. The experimenters registered each sample with the company. For half of the dogs, the registration included a photograph of the DNA donor. For the other half of the dogs, photographs were swapped between dogs. Analysis of the DNA and breed ancestry prediction was conducted by each company. Each company’s breed predictions were evaluated to assess whether the condition (ie. matching versus shuffled photograph) affected the odds of identifying the DNA donor’s registered breed. A convolutional neural network was also used to predict breed based solely on the photograph as a positive control.

Results: 5 of the 6 tests always produced results that included the registered breed. One test and the convolutional neural network were unlikely to identify the registered breed and frequently returned results that included the breed in the photograph. This result suggests that one test on the market is relying on the photograph more than the DNA sample. Additionally, differences in the predictions made across all tests underscore the challenge of identifying breed ancestry, even in purebred dogs.

Clinical Relevance: Veterinarians are likely to encounter patients who have conducted DTC genetic testing and may find themselves in the position of explaining genetic test results that they did not order. This systematic comparison of tests on the market provides context for interpreting unexpected results from consumer-grade DTC genetic testing kits.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6764004/ Influence of time and phenotype on salivary Fel d 1 in domestic shorthair cats Berenice Camille Bastien, Cari Gardner, Ebenezer Satyaraj 2019 2023-02-04 [("doi","10.1177/1098612X19850973")] cat/biology/allergy

Objectives: Fel d 1 is a major allergen that may affect humans sensitive to cat allergens, and it can be detected in the saliva and on the hair of cats. We studied the variability of salivary Fel d 1 in typical house cats (ie. neutered domestic shorthair cats) and the factors that could be associated with that variability.

Method: Saliva samples were collected from 64 cats, twice daily, every other day, for a year, at two locations (Missouri, USA, and Ontario, Canada). Salivary Fel d 1 levels were measured using an immunoassay. Correlations and linear mixed-effects model analyses were run to assess which factors significantly affected the Fel d 1 levels.

Results: Salivary Fel d 1 levels varied significantly both within and among cats. Cat averages over the year ranged from 0.4–35 µg/ml, and a higher average correlated with a higher SD (p <0.001). The first collection of the day tended to be higher than the afternoon collection (p <0.001). Sex, coat color or body size did not relate to cats’ average Fel d 1 production, but older cats tended to have lower salivary Fel d 1 levels (p <0.001). Fel d 1 levels from 4 samples were reliable in identifying cats producing stable low levels of Fel d 1.

Conclusion: We observed a wide and continuous range of salivary Fel d 1 production in domestic shorthair cats. In particular, a subset of cats had stable low levels throughout the course of the year, and they can be identified by analyzing a few saliva samples rather than their physical appearance.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4072467/ Do hypoallergenic cats exist? Determination of major cat allergen Fel d 1 production in normal and hypoallergenic cat breeds Julia Satorina, Krisztina Szalai, Anna Willensdorfer, Nadine Mothes-Luksch, Anna Lukschal, Erika Jensen-Jarolim 2014-03-17 2023-02-04 [("doi","10.1186/2045-7022-4-S2-P11")] cat/biology/allergy cat/genetics

The aim of our study was to examine the hypoallergenicity of these cats, taking the major allergen Fel d 1 as a marker molecule.

…We collected samples from 6 normal and 8 hypoallergenic cats by stroking with absorbent cotton over the face, chest and saliva.

…Total Fel d 1 levels were reduced in samples from the face and even more in those from the chest of hypoallergenic cats. IgE binding of human patients sera with cat samples showed that only under non-reducing conditions signals were detectable at 18 and 35kDa. Additionally, samples of normal cats showed stronger IgE binding than hypoallergenic cat samples. The monoclonal anti-Fel d 11 antibody showed stronger binding and detected two bands at 18 and 35kDa in normal cats. In contrast less intensive and only a single Fel d 11 band was detected at 18kDa in the samples from hypoallergenic cats.

…Based on our data we conclude that hypoallergenic cats secrete and distribute less Fel d 1 as compared to normal cats to their fur coat.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156987/ Keep the cat, change the care pathway: A transformational approach to managing Fel d 1, the major cat allergen Ebenezer Satyaraj, Harold James Wedner, Jean Bousquet 2019 2023-02-04 [("doi","10.1111/all.14013")] cat/biology/allergy/antibody

Background: Allergies to cats are the most common animal-origin allergy, and affect ~1 in 5 adults worldwide. The prevalence of allergy to furry animals has been increasing, and allergy to cats is a major risk factor for the development of asthma and rhinitis. The diagnosis of cat allergy is now well established. The exact significance of component-resolved diagnosis in the diagnosis of cat allergy remains to be fully understood. Allergen avoidance is effective but often has a psychological impact. Allergen immunotherapy is not well demonstrated. There is a need for innovative approaches to better manage cat allergens. Next-generation care pathways for asthma and rhinitis will define the place of cat allergen avoidance.

Methods and Results: This manuscript, based on content presented at the European Academy of Allergy and Clinical Immunology Congress 2019, provides information on the prevalence and impact of cat allergies and the molecular biology of Fel d 1, the major cat allergen.

Discussion: The authors present the scientific basis of a novel care pathway that uses anti-Fel d 1 IgY antibodies to safely and effectively neutralize Fel d 1 after its production by the cat but before human exposure.

Conclusion: Efficacy of a feline diet with an egg product ingredient containing anti-Fel d 1 IgY antibodies was demonstrated in vitro, ex vivo, and in vivo, and further validated by a pilot exposure study involving cat-allergic human participants.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6485700/ Reduction of active Fel d 1 from cats using an anti-Fel d 1 egg IgY antibody Ebenezer Satyaraj, Cari Gardner, Ivan Filipi, Kerry Cramer, Scott Sherrill 2019 2023-02-04 [("doi","10.1002/iid3.244")] cat/biology/allergy/antibody

Background: Fel d 1 is the most important allergen from cats. Fel d 1 is produced primarily in saliva and spread to the hair coat during grooming and then transferred to the environment via hair and dander.

Objectives: A novel approach to reducing allergenic Fel d 1 exposure was evaluated, involving binding the Fel d 1 with an anti-Fel d 1 polyclonal egg IgY antibody. The hypothesis was that hair from cats who had been fed foods containing anti-Fel d 1 IgY would show a substantial reduction in active Fel d 1 (aFel d 1).

Method: Hair collected from 105 cats completing a 12-week study was evaluated for aFel d 1 via ELISA. Hair was collected 4× over a 2-week baseline period, then weekly during the 10 week treatment period during which cats consumed a food containing the anti-Fel d 1 IgY.

Results: Baseline aFel d 1 (μg/g hair) varied greatly among the cats in this study. From week 3, there was a substantial reduction in mean aFel d 1 with an overall average decrease of 47% by week 10, ranging from a 33–71% decrease vs baseline. Cats with the highest baseline aFel d 1 showed the greatest decrease in aFel d 1.

Conclusions & Clinical Implications: Feeding anti-Fel d 1 IgY to cats successfully reduced aFel d 1 on their haircut with the greatest decreases observed in cats with initially high levels. Feeding a diet with anti Fel d 1 IgY substantially reduced the active Fel d 1 on the hair of cats.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6764009/ Anti-Fel d 1 immunoglobulin Y antibody-containing egg ingredient lowers allergen levels in cat saliva Ebenezer Satyaraj, Qinghong Li, Peichuan Sun, Scott Sherrill 2019 2023-02-05 [("doi","10.1177/1098612X19861218")] cat/biology/allergy/antibody

Objectives: Fel d 1 is the major cat allergen, causing IgE reactions in up to 90% of cat-allergic adults. Fel d 1 secreted in saliva is spread to the haircut during grooming. Current management includes attempts to reduce or eliminate exposure to Fel d 1. A novel approach to reducing immunologically active Fel d 1 (aFel d 1) exposure, which involves binding the Fel d 1 with an anti-Fel d 1-specific polyclonal egg IgY antibody (sIgY), was evaluated. The hypothesis was that saliva from cats fed diets containing this sIgY would show a significant reduction in aFel d 1.

Method: Two trials in cats were completed. In trial 1, saliva was collected 0, 1, 3 and 5h post-feeding during a 2 week baseline and subsequent 6 week treatment period. Trial 2 included a control and treatment group, and saliva was collected once daily. Trial 2 cats were fed the control diet during a 1 week baseline period, and then fed either control or sIgY diet during the 4 week treatment period. Fel d 1-specific ELISA was used to measure salivary aFel d 1. Data were analysed using repeated-measures ANOVA and a linear mixed-model analysis.

Results: Salivary aFel d 1 decreased post-treatment in both trials. There were no differences in aFel d 1 based on time of collection relative to feeding in trial 1. In trial 2, 82% of treatment group cats showed a decrease in aFel d 1 of at least 20% from baseline vs just 38% of control cats. Only one (9%) treatment cat showed an increase in aFel d 1 vs 63% of control cats.

Conclusion: Feeding sIgY substantially reduced aFel d 1 in the saliva of cats within 3 weeks. Although additional research is needed, these findings show promise for an alternative approach to the management of allergies to cats.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8238577/ Fel d 1 Blocking Antibodies: A Novel Method to Reduce IgE-Mediated Allergy to Cats Ebenezer Satyaraj, Peichuan Sun, Scott Sherrill 2021 2023-02-05 [("doi","10.1155/2021/5545173")] cat/biology/allergy/antibody

Fel d 1 is an important allergen produced by cats that causes IgE reactions in up to 95% of cat-allergic adults. Immunotherapy to reduce human allergy to cats has demonstrated that people have the capacity to produce allergen-specific neutralizing antibodies that block IgE-mediated allergic responses. We wished to determine if “blocking” antibodies could be used to reduce the IgE binding ability of cat allergens prior to their exposure to humans.

Here, we describe the characterization of Fel d 1-specific antibodies. We demonstrated the efficacy of a rabbit polyclonal and an allergen-specific chicken IgY to bind to Fel d 1 in cat saliva and block Fel d 1-IgE binding and IgE-mediated basophil degranulation.

Fel d 1 blocking antibodies offer a new and exciting approach to the neutralization of cat allergens.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7150904/ Immunization of Cats against Fel d 1 Results in Reduced Allergic Symptoms of Owners Franziska Thoms, Stefanie Haas, Aline Erhart, Claudia S. Nett, Silvia Rüfenacht, Nicole Graf, Arnis Strods, Gauravraj Patil, Thonur Leenadevi, Michael C. Fontaine, Lindsey A. Toon, Gary T. Jennings, Gabriela Senti, Thomas M. Kündig, Martin F. Bachmann 2020 2023-02-05 [("doi","10.3390/v12030288")] cat/biology/allergy

An innovative approach was tested to treat cat allergy in humans by vaccinating cats with Fel-CuMV (HypoCat™), a vaccine against the major cat allergen Fel d 1 based on virus-like particles derived from cucumber mosaic virus (CuMV-VLPs). Upon vaccination, cats develop neutralizing antibodies against the allergen Fel d 1, which reduces the level of reactive allergen, thus lowering the symptoms or even preventing allergic reactions in humans.

The combined methodological field study included 10 cat-allergic participants who lived together with their cats (n = 13), that were immunized with Fel-CuMV. The aim was to determine methods for measuring a change in allergic symptoms. A home-based provocation test (petting time and organ specific symptom score (OSSS)) and a general weekly (or monthly) symptom score (G(W)SS) were used to assess changes in allergic symptoms.

The petting time until a pre-defined level of allergic symptoms was reached increased already early after vaccination of the cats and was apparent over the course of the study. In addition, the OSSS after provocation and G(W)SS recorded a persistent reduction in symptoms over the study period and could serve for long-term assessment.

Hence, the immunization of cats with HypoCat™ (Fel-CuMV) may have a positive impact on the cat allergy of the owner, and changes could be assessed by the provocation test as well as G(W)SS.

--- https://en.wikipedia.org/wiki/Fel_d_1 Fel d 1 2023-02-05 cat/biology/allergy --- https://en.wikipedia.org/wiki/Allergy_to_cats#Hypoallergenic_cats Allergy to cats § Hypoallergenic cats 2023-02-05 cat/biology/allergy --- https://en.wikipedia.org/wiki/Siberian_(cat) Siberian (cat) 2023-02-05 cat/biology/allergy --- https://en.wikipedia.org/wiki/Balinese_cat Balinese cat 2023-02-05 cat/biology/allergy --- https://en.wikipedia.org/wiki/Allerca Allerca 2023-02-05 cat/biology/allergy --- https://www.jacionline.org/article/S0091-6749(01)70050-4/fulltext 2023-02-05 cat/biology/allergy --- https://en.wikipedia.org/wiki/Fel_d_4 Fel d 4 2023-02-05 cat/biology/allergy --- /doc/biology/2012-kovacsnolan.pdf Egg Yolk Antibodies for Passive Immunity Jennifer Kovacs-Nolan, Yoshinori Mine 2012-04-01 2023-02-05 [("doi","10.1146/annurev-food-022811-101137")] biology cat/biology/allergy/antibody

The avian egg contains all of the necessary nutrients and growth factors required for the developing embryo, including antibodies that are transported from the blood of the hen into the egg yolk to provide immunity to the chick. Since the discovery of egg yolk antibodies, now called immunoglobulin Y (IgY), in the late 1800s, this process has been harnessed to produce antigen-specific yolk antibodies for numerous applications in the medical and research fields, including in areas such as diagnostics and proteomics. However, one of the most valuable and promising areas of IgY research is its use for passive immunization to treat and prevent human and animal diseases.

The following review covers the key features and advantages of IgY and the production and purification of IgY from the egg yolk, as well as highlights some of the most promising applications of egg yolk antibodies in human and veterinary medicine.

--- https://slate.com/culture/2023/03/john-wick-james-bond-action-heroes-j-names.html 2023-02-06 fiction/criticism --- https://slate.com/human-interest/2023/06/life-before-cell-phones-internet-after-work.html 2023-02-06 sociology technology --- https://www.nplusonemag.com/issue-19/essays/chat-wars/ 2023-02-06 cs/security --- https://www.wired.com/story/a-hair-loss-study-raises-new-questions-about-aging-cells/ 2023-02-06 longevity/senolytic --- https://www.nytimes.com/2014/03/09/magazine/reaching-my-autistic-son-through-disney.html 2023-02-06 philosophy/mind psychiatry/autism psychology/inner-voice --- https://x.com/AlexKontorovich/status/1678772964836397056 2023-02-06 ai/nn/transformer/gpt/codex math --- https://x.com/AlexKontorovich/status/1678772963183820801 2023-02-06 ai/nn/transformer/gpt/codex math --- https://arxiv.org/abs/1905.03677 Learning Loss for Active Learning Donggeun Yoo, In So Kweon 2019-05-09 2023-02-06 [("doi","10.48550/arXiv.1905.03677")] reinforcement-learning/exploration/active-learning reinforcement-learning/meta-learning

[cf. synthetic gradients] The performance of deep neural networks improves with more annotated data. The problem is that the budget for annotation is limited. One solution to this is active learning, where a model asks human to annotate data that it perceived as uncertain.

A variety of recent methods have been proposed to apply active learning to deep networks but most of them are either designed specific for their target tasks or computationally inefficient for large networks. In this paper, we propose a novel active learning method that is simple but task-agnostic, and works efficiently with the deep networks.

We attach a small parametric module, named loss prediction module, to a target network, and learn it to predict target losses of unlabeled inputs. Then, this module can suggest data that the target model is likely to produce a wrong prediction. This method is task-agnostic as networks are learned from a single loss regardless of target tasks.

We rigorously validate our method through image classification, object detection, and human pose estimation, with the recent network architectures.

The results demonstrate that our method consistently outperforms the previous methods over the tasks.

--- https://arxiv.org/abs/2210.01117 Omnigrok: Grokking Beyond Algorithmic Data Ziming Liu, Eric J. Michaud, Max Tegmark 2022-10-03 2023-02-06 [("doi","10.48550/arXiv.2210.01117")] ai/nn/cnn ai/nn/fully-connected ai/nn/rnn ai/nn/sparsity/knowledge-distillation ai/scaling/emergence/grokking

[code; podcast] Grokking, the unusual phenomenon for algorithmic datasets where generalization happens long after overfitting the training data, has remained elusive.

We aim to understand grokking by analyzing the loss landscapes of neural networks, identifying the mismatch between training and test losses as the cause for grokking.

We refer to this as the “LU mechanism” because training and test losses (against model weight norm) typically resemble “L” and “U”, respectively [based on the Goldilocks zone of small weight initialization]. This simple mechanism can nicely explain many aspects of grokking: data size dependence, weight decay dependence, the emergence of representations, etc.

Guided by the intuitive picture, we are able to induce grokking [Omnigrok] on tasks involving images, language and molecules.

In the reverse direction, we are able to eliminate grokking for algorithmic datasets.

We attribute the dramatic nature of grokking for algorithmic datasets to representation learning.

Figure 1: (a) w: L2 norm of model weights. Generalizing solutions (green stars) are concentrated around a sphere in the weight space where w ≈ wc (green). Overfitting solutions (orange) populate the w & wc region. (b) The training loss (orange) and test loss (gray) have the shape of L and U, respectively. Their mismatch in the w > wc region leads to fast-slow dynamics, resulting in grokking.

…LU mechanism: Although the loss landscapes of neural networks are nonlinear, Fort & Scherlis 2019 reveal a simple landscape picture: There is a spherical shell in the weight space (the “Goldilocks” zone), where generalization is better than outside this zone. We illustrate the Goldilocks zone as the green area with average radius wc in Figure 1a; the green stars are the generalizing solutions.

The test loss is thus higher either both when w > wc and w < wc, forming a U-shape against w in Figure 1b (gray curve). By contrast, the training loss has an L-shape against weight norm 2. There are many solutions which overfit training data for w > wc, but high training losses are incurred for w < wc. This corresponds to the L-shaped curve seen in Figure 1b (orange curve, no regularization).

In summary, the (reduced) training loss and test loss are L-shaped and U-shaped against weight norm, respectively, which we will refer to as the LU mechanism throughout the paper.

…Grokking dynamics: We identify the “LU mechanism” as the cause of grokking.

If the weight norm is initialized to be large (eg. the black square in the w > wc region), the model first quickly moves to a nearby overfitting solution by minimizing the training loss. Without any regularization, the model will stay where it is, because the gradient of the training loss is almost zero along the valley of overfitting solutions, so generalization does not happen.

Fortunately, there are usually explicit and/or implicit regularizations that can drive the weight vector towards the Goldilocks zone w ≈ wc.

When the regularization magnitude is non-zero but small, the radial motion can be (arbitrarily) slow. If weight decay is the only source of regularization, and training loss is negligible after overfitting, then weight decay γ causes w(t) ≈ exp(−γt)w0, when w0 > wc, so it takes time t ≈ ln(w0/wc)/γ ∝ γ−1 to generalize. A small γ results in a huge generalization delay (ie. grokking).

The dependence on regularization magnitudes is illustrated in Figure 1b: no generalization at all happens for γ = 0, small γ leads to slow generalization (grokking), and large γ leads to faster generalization3. The above analysis only applies to large initializations w > wc. Small initializations w < wc can always generalize fast (but w should not be too small to harm optimization), regardless of regularization.

…5. Representation Is Key To Grokking: In §4, we showed that increasing initialization scales can make grokking happen for standard ML tasks. However, this seems a bit artificial and does not explain why standard initialization leads to grokking on algorithmic datasets, but not on standard ML datasets, say MNIST.

The key difference is how much the task relies on representation learning. For the MNIST dataset, the quality of representation determines whether the test accuracy is 95% or 100%; by contrast in algorithmic datasets, the quality of representation determines whether test accuracy is random guess (bad representation) or 100% (good representation). So overfitting (under a bad representation) has a more dramatic effect on algorithmic datasets, i.e. the model weights increase quickly during overfitting but test accuracy remains low. During overfitting, model weight norm is much larger than at initialization, but then drops below the initialization norm when the model generalizes, shown in Figure 7a, and also observed by Nanda et al 2023. As a byproduct, we are able to eliminate grokking by constraining the model on a small weight norm sphere, shown in Figure 7b.

Figure 7: Training 1L transformer on modular addition (p = 113). (a) Weight norm, train accuracy, and test accuracy over time, initialized and trained normally. Weight norm first increases, and is highest during the period of overfitting, but then drops to become lower than initial weight norm when the model generalizes. (b) Constrained optimization at constant weight norm (α = 0.8) largely eliminates grokking, with test and train accuracy improving concurrently.

The above picture is supported by a transformer experiment: Figure 7a, shows how model norm changes over time and we see that there is an initial increase in weight norm, which peaks during overfitting, but then drops during the period of generalization to be lower than the initialization norm

Figure 10: Time to generalize as a function of weight decay. we investigate to what extent the relation t ∝ γ−1 holds, where t is number of training steps needed for the model to generalize and γ is the AdamW weight decay. When a lower weight decay is used, models spend longer in the period of overfitting before eventually generalizing. We show the generalization time t as a function of γ in (a, b) and full training curves for these runs in (c, d).
--- https://arxiv.org/abs/2307.05337 Explaining Competitive-Level Programming Solutions using LLMs Jierui Li, Szymon Tworkowski, Yingying Wu, Raymond Mooney 2023-07-11 2023-07-11 [("doi","10.48550/arXiv.2307.05337")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue

In this paper, we approach competitive-level programming problem-solving as a composite task of reasoning and code generation. We propose a novel method to automatically annotate natural language explanations to <problem, solution> pairs.

We show that despite poor performance in solving competitive-level programming problems, state-of-the-art LLMs exhibit a strong capacity in describing and explaining solutions. Our explanation generation methodology can generate a structured solution explanation for the problem containing descriptions and analysis.

To evaluate the quality of the annotated explanations, we examine their effectiveness in two aspects: (1) satisfying the human programming expert who authored the oracle solution, and (2) aiding LLMs in solving problems more effectively.

The experimental results on the CodeContests dataset demonstrate that while LLM GPT-3.5’s and GPT-4’s abilities in describing the solution are comparable, GPT-4 shows a better understanding of the key idea behind the solution.

--- https://x.com/QuinnyPig/status/1679194515247362050 2023-02-06 ai/nn/transformer/gpt/4/fiction fiction/humor --- https://en.wikipedia.org/wiki/Normalized_Google_distance Normalized Google distance 2023-02-07 ai cs/algorithm technology/google --- /doc/history/2023-white.pdf Rebel, Remain, or Resign? Military Elites’ Decision-Making at the Onset of the American Civil War Peter B. White 2023-06-23 2023-06-23 [("doi","10.1177/00220027231185575")] economics history politics

A critical element in civil wars is military fragmentation. Yet, we have a limited understanding of why military elites fight in civil wars and on what side.

In this article I develop a theory of the economic and professional motivations of military elites. I test this theory using the case of West Point graduates in the American Civil War. I argue that in addition to home state, economic and professional interests were a major influence on West Pointers. Graduates with connections to Southern cash crops were less likely to fight for the Union and more likely to fight for the Confederacy. Higher ranking graduates were more likely to fight for both sides, as they were better positioned to compete for promotion.

I test this argument using a new dataset of >1,000 West Point graduates’ wartime allegiances and antebellum careers and find:

strong evidence in support of my expectations.

--- https://x.com/AIPanicLive/status/1678942781174161409 2023-02-07 ai/nn/transformer/gpt/claude cs/security --- https://arxiv.org/abs/2101.06223 LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning Yuhuai Wu, Markus Rabe, Wenda Li, Jimmy Ba, Roger Grosse, Christian Szegedy 2021-01-15 2023-02-07 [("doi","10.48550/arXiv.2101.06223")] ai/nn/transformer/gpt/codex math

While designing inductive bias in neural architectures has been widely studied, we hypothesize that transformer networks are flexible enough to learn inductive bias from suitable generic tasks. Here, we replace architecture engineering by encoding inductive bias in the form of datasets.

Inspired by Peirce’s view that deduction, induction, and abduction are the primitives of reasoning, we design 3 synthetic tasks that are intended to require the model to have these 3 abilities. We specifically design these tasks to be synthetic and devoid of mathematical knowledge to ensure that only the fundamental reasoning biases can be learned from these tasks.

This defines a new pre-training methodology called LIME (Learning Inductive bias for Mathematical rEasoning). Models trained with LIME outperform vanilla transformers on 4 very different large mathematical reasoning benchmarks.

Unlike dominating the computation cost as traditional pre-training approaches, LIME requires only a small fraction of the computation cost of the typical downstream task.

The code for generating LIME tasks is available at Github.

--- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002092 Cheating leads to the evolution of multipartite viruses Asher Leeks, Penny Grace Young, Paul Eugene Turner, Geoff Wild, Stuart Andrew West, Roland G. Roberts, Roland G. Roberts, Roland G. Roberts, Roland G. Roberts 2023-03-22 2023-03-22 [("doi","10.1371/journal.pbio.3002092")] genetics/selection/natural

[Twitter] In multipartite viruses, the genome is split into multiple segments, each of which is transmitted via a separate capsid. This seems costly, so why is this form of genome organization so widespread? This theoretical study shows that selection for cheats can drive the evolution of multipartite viruses under realistic conditions.

In multipartite viruses, the genome is split into multiple segments, each of which is transmitted via a separate capsid. The existence of multipartite viruses poses a problem, because replication is only possible when all segments are present within the same host. Given this clear cost, why is multipartitism so common in viruses? Most previous hypotheses try to explain how multipartitism could provide an advantage. In so doing, they require scenarios that are unrealistic and that cannot explain viruses with more than 2 multipartite segments.

We show theoretically that selection for cheats, which avoid producing a shared gene product, but still benefit from gene products produced by other genomes, can drive the evolution of both multipartite and segmented viruses.

We find that multipartitism can evolve via cheating under realistic conditions and does not require unreasonably high coinfection rates or any group-level benefit. Furthermore, the cheating hypothesis is consistent with empirical patterns of cheating and multipartitism across viruses.

More broadly, our results show how evolutionary conflict can drive new patterns of genome organization in viruses and elsewhere.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244801/ Damage to the prefrontal cortex increases utilitarian moral judgements Michael Koenigs, Liane Young, Ralph Adolphs, Daniel Tranel, Fiery Cushman, Marc Hauser, Antonio Damasio 2007 2023-02-07 [("doi","10.1038/nature05631")] philosophy/ethics psychology/neuroscience

The psychological and neurobiological processes underlying moral judgement have been the focus of many recent empirical studies. Of central interest is whether emotions play a causal role in moral judgement, and, in parallel, how emotion-related areas of the brain contribute to moral judgement.

Here we show that 6 patients with focal bilateral damage to the ventromedial prefrontal cortex (VMPC), a brain region necessary for the normal generation of emotions and, in particular, social emotions, produce:

an abnormally ‘utilitarian’ pattern of judgements on moral dilemmas that pit compelling considerations of aggregate welfare against highly emotionally aversive behaviors (for example, having to sacrifice one person’s life to save a number of other lives). In contrast, the VMPC patients’ judgements were normal in other classes of moral dilemmas.

These findings indicate that, for a selective set of moral dilemmas, the VMPC is critical for normal judgements of right and wrong. The findings support a necessary role for emotion in the generation of those judgements.

[Warning: one co-author is Marc Hauser.]

--- https://www.lesswrong.com/posts/KSroBnxCHodGmPPJ8/jailbreaking-gpt-4-s-code-interpreter 2023-02-07 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex cs/security --- https://arxiv.org/abs/cond-mat/0202383 Extended Comment on Language Trees and Zipping Joshua Goodman 2002-02-21 2023-02-07 [("doi","10.48550/arXiv.0202383")] cs/algorithm

This is the extended version of a Comment submitted to Physical Review Letters [criticizing Benedetto et al 2002].

I first point out the inappropriateness of publishing a Letter unrelated to physics.

Next, I give experimental results showing that the technique used in the Letter is 3× worse and 17× slower than a simple baseline.

And finally, I review the literature, showing that the ideas of the Letter are not novel.

I conclude by suggesting that Physical Review Letters should not publish Letters unrelated to physics.

--- https://en.wikipedia.org/wiki/Information_bottleneck_method Information bottleneck method 2023-02-07 ai/nn cs/algorithm --- https://habr.com/ru/articles/551982/ 2023-02-07 cs/algorithm --- https://arxiv.org/abs/2212.09410 Less is More: Parameter-Free Text Classification with Gzip Zhiying Jiang, Matthew Y. R. Yang, Mikhail Tsirlin, Raphael Tang, Jimmy Lin 2022-12-19 2023-02-07 [("doi","10.48550/arXiv.2212.09410")] ai/nn/retrieval ai/nn/transformer cs/algorithm/information/compression

[buggy? deeply misleading?] Deep neural networks (DNNs) are often used for text classification tasks as they usually achieve high levels of accuracy. However, DNNs can be computationally intensive with billions of parameters and large amounts of labeled data, which can make them expensive to use, to optimize and to transfer to out-of-distribution (OOD) cases in practice.

In this paper, we propose a non-parametric alternative to DNNs that’s easy, light-weight and universal in text classification: a combination of a simple compressor like gzip with a k-nearest-neighbor classifier.

Without any training, pre-training or fine-tuning, our method achieves results that are competitive with non-pretrained deep learning methods on 6 in-distributed datasets. It even outperforms BERT on all 5 OOD datasets, including 4 low-resource languages.

Our method also performs particularly well in few-shot settings where labeled data are too scarce for DNNs to achieve a satisfying accuracy.

[Like all "stupid compressor tricks", this will be more amusing & overhyped than useful or relevant long-term. cf. N-Grammer, Copy is all You Need] --- https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=ab699db5f2b70cf94951e28f07cc18b0b2d33e9a 2023-02-08 cs/algorithm --- https://www.biorxiv.org/content/10.1101/2023.07.10.548458.full Estimation of indirect genetic effects and heritability under assortative mating Alexander Strudwick Young 2023-07-11 2023-07-11 [("doi","10.1101/2023.07.10.548458")] genetics/heritable

Both direct genetic effects (effects of alleles in an individual on that individual) and indirect genetic effects—effects of alleles in an individual (eg. parents) on another individual (eg. offspring)—can contribute to phenotypic variation and genotype-phenotype associations. Here, we consider a phenotype affected by direct and parental indirect genetic effects under assortative mating at equilibrium. We generalize classical theory to derive a decomposition of the equilibrium phenotypic variance in terms of direct and indirect genetic effect components.

We extend this theory to show that popular methods for estimating indirect genetic effects (genetic nurture) through analysis of parental and offspring polygenic predictors (called polygenic indices or scores—PGIs or PGSs) are substantially biased by assortative mating. We propose an improved method for estimating indirect genetic effects while accounting for assortative mating that can also correct heritability estimates for bias due to assortative mating.

We validate our method in simulations and apply it to PGIs for height and educational attainment (EA), estimating that the equilibrium heritability of height is 0.699 (S.E. = 0.075) and finding no evidence for indirect genetic effects on height. We estimate a very high correlation between parents’ underlying genetic components for EA, 0.755 (S.E. = 0.035), which is inconsistent with twin based estimates of the heritability of EA, possibly due to confounding in the EA PGI and/or in twin studies.

We implement our method in the software package snipar, enabling researchers to apply the method to data including observed and/or imputed parental genotypes.

We provide a theoretical framework for understanding the results of PGI analyses and a practical methodology for estimating heritability and indirect genetic effects while accounting for assortative mating.

--- https://openreview.net/forum?id=cx2q4cOBnne Reinforcement Learning in Newcomb-like Environments James Henry Bell, Linda Linsefors, Caspar Oesterheld, Joar Max Viktor Skalse 2023-05-05 2023-05-05 reinforcement-learning/model-free reinforcement-learning/multi-agent statistics/decision

How do value-based reinforcement learning algorithms behave when the environment can predict the agent’s policy?

Newcomb-like decision problems have been studied extensively in the decision theory literature, but they have so far been largely absent in the reinforcement learning literature. In this paper we study value-based reinforcement learning algorithms in the Newcomb-like setting, and answer some of the fundamental theoretical questions about the behavior of such algorithms in these environments. We show that a value-based reinforcement learning agent cannot converge to a policy that is not notifiable, ie. does not only choose actions that are optimal given that policy.

This gives us a powerful tool for reasoning about the limit behavior of agents—for example, it lets us show that there are Newcomb-like environments in which a reinforcement learning agent cannot converge to any optimal policy. We show that a ratifiable policy always exists in our setting, but that there are cases in which a reinforcement learning agent normally cannot converge to it (and hence cannot converge at all). We also prove several results about the possible limit behaviors of agents in cases where they do not converge to any policy.

[Keywords: reinforcement learning, learning in games, decision theory]

--- https://en.wikipedia.org/wiki/Long_line_%28topology%29 Long line (topology) 2023-02-08 math --- https://arxiv.org/abs/1911.00359#facebook CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, Edouard Grave 2019-11-01 2023-02-08 [("doi","10.48550/arXiv.1911.00359")] ai/dataset

Pre-training text representations have led to improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved.

In this paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al 2017; Grave et al 2018), that deduplicates documents and identifies their language.

We augment this pipeline with a filtering step to select documents that are close to high quality corpora like Wikipedia.

--- https://arxiv.org/abs/2306.01116#lighton The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, Julien Launay 2023-06-01 2023-06-01 [("doi","10.48550/arXiv.2306.01116")] ai/dataset ai/nn/transformer

Large language models are commonly trained on a mixture of filtered web data and curated high-quality corpora, such as social media conversations, books, or technical papers. This curation process is believed to be necessary to produce performant models with broad zero-shot generalization abilities. However, as larger models requiring pretraining on trillions of tokens are considered, it is unclear how scalable is curation and whether we will run out of unique high-quality data soon.

At odds with previous beliefs, we show that properly filtered and deduplicated web data alone can lead to powerful models; even outperforming models from the state-of-the-art trained on The Pile. Despite extensive filtering, the high-quality data we extract from the web is still plentiful, and we are able to obtain 5 trillion tokens from CommonCrawl.

We publicly release an extract of 600 billion tokens from our RefinedWeb dataset, and 1.3/7.5b parameters language models trained on it.

--- https://www.purina.com/pro-plan/cats/liveclear-cat-allergen-reducing-food 2023-02-08 cat/biology/allergy/antibody --- https://arxiv.org/abs/2109.08342 Dropout’s Dream Land: Generalization from Learned Simulators to Reality Zac Wellmer, James T. Kwok 2021-09-17 2023-02-08 [("doi","10.48550/arXiv.2109.08342")] reinforcement-learning/meta-learning reinforcement-learning/model

A World Model is a generative model used to simulate an environment. World Models have proven capable of learning spatial and temporal representations of Reinforcement Learning environments. In some cases, a World Model offers an agent the opportunity to learn entirely inside of its own dream environment.

In this work we explore improving the generalization capabilities from dream environments to real environments (Dream2Real). We present a general approach to improve a controller’s ability to transfer from a neural network dream environment to reality at little additional cost. These improvements are gained by drawing on inspiration from Domain Randomization, where the basic idea is to randomize as much of a simulator as possible without fundamentally changing the task at hand.

Generally, Domain Randomization assumes access to a pre-built simulator with configurable parameters but oftentimes this is not available. By training the World Model using dropout, the dream environment is capable of creating a nearly infinite number of different dream environments. Previous use cases of dropout either do not use dropout at inference time or averages the predictions generated by multiple sampled masks (Monte-Carlo Dropout). Dropout’s Dream Land leverages each unique mask to create a diverse set of dream environments.

Our experimental results show that Dropout’s Dream Land is an effective technique to bridge the reality gap between dream environments and reality. Furthermore, we additionally perform an extensive set of ablation studies.

--- https://en.wikipedia.org/wiki/Siberian_cat#Fur_allergen_levels Siberian cat § Fur allergen levels 2023-02-08 cat/biology/allergy --- https://www.purina.com/cats/shop/pro-plan-liveclear-allergen-reducing-cat-shampoo-cat-shampoo 2023-02-08 cat/biology/allergy/antibody --- https://en.wikipedia.org/wiki/Immunoglobulin_Y Immunoglobulin Y 2023-02-09 cat/biology/allergy/antibody --- https://en.wikipedia.org/wiki/Passive_immunity Passive immunity 2023-02-09 cat/biology/allergy/antibody --- https://arxiv.org/abs/2307.06945#microsoft In-context Autoencoder for Context Compression in a Large Language Model Tao Ge, Jing Hu, Xun Wang, Si-Qing Chen, Furu Wei 2023-07-13 2023-07-13 [("doi","10.48550/arXiv.2307.06945")] ai/nn/tokenization ai/nn/transformer/attention/compression

We propose the In-context Autoencoder (ICAE) for context compression in a large language model (LLM). The ICAE has two modules: a learnable encoder adapted with LoRA from an LLM for compressing a long context into a limited number of memory slots, and a fixed decoder which is the target LLM that can condition on the memory slots for various purposes.

We first pretrain the ICAE using both autoencoding and language modeling objectives on massive text data, enabling it to generate memory slots that accurately and comprehensively represent the original context. Then, we fine-tune the pretrained ICAE on a small amount of instruct data to enhance its interaction with various prompts for producing desirable responses.

Our experimental results demonstrate that the ICAE learned with our proposed pretraining and fine-tuning paradigm can effectively produce memory slots with 4× context compression, which can be well conditioned on by the target LLM to respond to various prompts.

The promising results demonstrate implications of the ICAE for its novel approach to the long context problem and its potential to reduce computation and memory overheads for LLM inference in practice, suggesting further research effort in context management for an LLM.

Our code and data will be released shortly.

--- https://arxiv.org/abs/2307.06439#microsoft Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events Yu Gu, Sheng Zhang, Naoto Usuyama, Yonas Woldesenbet, Cliff Wong, Praneeth Sanapathi, Mu Wei, Naveen Valluri, Erika Strandberg, Tristan Naumann, Hoifung Poon 2023-07-12 2023-07-12 [("doi","10.48550/arXiv.2307.06439")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction

Large language models (LLMs), such as GPT-4, have demonstrated remarkable capabilities across a wide range of tasks, including health applications. In this paper, we study how LLMs can be used to scale biomedical knowledge curation. We find that while LLMs already possess decent competency in structuring biomedical text, by distillation into a task-specific student model through self-supervised learning, substantial gains can be attained over out-of-box LLMs, with additional advantages such as cost, efficiency, and white-box model access.

We conduct a case study on adverse drug event (ADE) extraction, which is an important area for improving care. On standard ADE extraction evaluation, a GPT-3.5-distilled PubMedBERT model attained comparable accuracy as supervised state-of-the-art models without using any labeled data.

Despite being over 1,000× smaller, the distilled model outperformed its teacher GPT-3.5 by over 6 absolute points in F1 and GPT-4 by over 5 absolute points.

Ablation studies on distillation model choice (eg. PubMedBERT vs BioGPT) and ADE extraction architecture shed light on best practice for biomedical knowledge extraction. Similar gains were attained by distillation for other standard biomedical knowledge extraction tasks such as gene-disease associations and protected health information, further illustrating the promise of this approach.

--- https://arxiv.org/abs/2007.15779#microsoft Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon 2020-07-31 2023-02-09 [("doi","10.1145/3458754")] ai/dataset ai/nn/transformer biology

Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models.

In this paper, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models. To facilitate this investigation, we compile a comprehensive biomedical NLP benchmark from publicly-available datasets.

Our experiments show that domain-specific pretraining serves as a solid foundation for a wide range of biomedical NLP tasks, leading to new state-of-the-art results across the board. Further, in conducting a thorough evaluation of modeling choices, both for pretraining and task-specific fine-tuning, we discover that some common practices are unnecessary with BERT models, such as using complex tagging schemes in named entity recognition (NER).

To help accelerate research in biomedical NLP, we have released our state-of-the-art pretrained and task-specific models for the community, and created a leaderboard featuring our BLURB benchmark (short for Biomedical Language Understanding & Reasoning Benchmark) at https://microsoft.github.io/BLURB/.

--- https://github.com/ywkim/gpt-commit 2023-02-09 ai/nn/transformer/gpt/codex cs/lisp --- https://arxiv.org/abs/2104.04132 Replay in Deep Learning: Current Approaches and Missing Biological Elements Tyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov, Hava T. Siegelmann, Terrence J. Sejnowski, Christopher Kanan 2021-04-01 2023-02-09 [("doi","10.48550/arXiv.2104.04132")] psychology/neuroscience reinforcement-learning/model-free

Replay is the reactivation of one or more neural patterns, which are similar to the activation patterns experienced during past waking experiences. Replay was first observed in biological neural networks during sleep, and it is now thought to play a critical role in memory formation, retrieval, and consolidation. Replay-like mechanisms have been incorporated into deep artificial neural networks that learn over time to avoid catastrophic forgetting of previous knowledge. Replay algorithms have been successfully used in a wide range of deep learning methods within supervised, unsupervised, and reinforcement learning paradigms.

In this paper, we provide the first comprehensive comparison between replay in the mammalian brain and replay in artificial neural networks.

We identify multiple aspects of biological replay that are missing in deep learning systems and hypothesize how they could be used to improve artificial neural networks.

--- https://arxiv.org/abs/2307.06290 Instruction Mining: High-Quality Instruction Data Selection for Large Language Models Yihan Cao, Yanbin Kang, Lichao Sun 2023-07-12 2023-07-12 [("doi","10.48550/arXiv.2307.06290")] ai/dataset ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/exploration/active-learning

Large language models typically undergo two training stages, pretraining and finetuning. Despite that large-scale pretraining endows the model with strong capabilities to generate natural language responses, these pretrained models can still fail to understand human instructions at times. To enhance language models’ ability of interpreting and responding to instructions, instruction finetuning has emerged as a critical method in this area.

Recent studies found that large language models can be finetuned to perform well even with a small amount of high-quality instruction-following data. However, the selection of high-quality datasets for finetuning language models still lacks clear guidelines to follow. In this paper, we propose InstructMining, a linear rule for evaluating instruction-following data quality. We formulate InstructMining using specific natural language indicators.

To investigate the relationship between data quality and these indicators, we further conduct extensive finetuning experiments. The experiment results are then applied to estimating parameters in InstructMining. To further investigate its performance, we use InstructMining to select high-quality data from unseen datasets.

Results demonstrate that InstructMining can help select relatively high-quality samples from various instruction-following datasets. Compared to models finetuned on unfiltered datasets, models finetuned on InstructMining selected datasets perform better on 42.5% cases.

--- https://www.openbsd.org/innovations.html 2023-02-09 cs/security --- https://medium.com/microsoft-design/a-change-of-typeface-microsofts-new-default-font-has-arrived-f200eb16718d 2023-02-09 design/typography --- https://kalleboo.com/linked/aptos-roboto-sf-helvetica.png 2023-02-09 design/typography --- https://maxhalford.github.io/blog/text-classification-by-compression/ 2023-02-10 cs/algorithm/information/compression --- https://github.com/KeeyanGhoreshi/PokemonFireredSingleSequence 2023-02-10 reinforcement-learning/model --- https://arxiv.org/abs/2307.05330 The Value of Chess Squares Aditya Gupta, Shiva Maharaj, Nicholas Polson, Vadim Sokolov 2023-07-08 2023-07-08 [("doi","10.48550/arXiv.2307.05330")] reinforcement-learning/chess

Valuing chess squares and determining the placement of pieces on the board are the main objectives of our study. With the emergence of chess AI [Stockfish], it has become possible to accurately assess the worth of positions in a game of chess. The conventional approach assigns fixed values to pieces (♚ = ∞, ♛ = 9, ♜ = 5, ♝ = 3, ♞ = 3).

We enhance this analysis by introducing marginal valuations for both pieces and squares. We demonstrate our method by examining the positioning of Knights and Bishops, and also provide valuable insights into the valuation of pawns. Notably, Nimzowitsch was among the pioneers in advocating for the importance of Pawn structure and valuation.

Finally, we conclude by suggesting potential avenues for future research.

--- https://www.nytimes.com/2023/07/13/science/magpies-birds-nests.html 2023-02-10 psychology/animal/bird --- https://www.businessinsider.com/marc-andreessen-on-90s-dot-com-bubble-startups-2014-10 Marc Andreessen Says The 1990s Dot-Com Bubble Startups Were ‘All Right But Just Early’ 2023-02-10 economics technology --- https://www.businessinsider.com/how-fling-social-media-app-died-2016-11 Inside the crash of Fling, the startup whose founder partied on an island while his company burned through $21 million 2023-02-10 technology --- https://www.businessinsider.com/google-zagat-story-2013-6 Misery At Google: You’d Never Expect NSFW Graffiti Like This On Google’s Bathroom Walls 2023-02-10 technology/google --- https://x.com/kendrictonn/status/1679897971641528342 2023-02-10 japan/art --- https://arxiv.org/abs/cs/0312044 Clustering by compression Rudi Cilibrasi, Paul Vitanyi 2003-12-19 2023-02-10 [("doi","10.48550/arXiv.0312044")] cs/algorithm/information/compression

We present a new method for clustering based on compression.

The method doesn’t use subject-specific features or background knowledge, and works as follows: First, we determine a universal similarity distance, the normalized compression distance or NCD, computed from the lengths of compressed data files (singly and in pairwise concatenation). Second, we apply a hierarchical clustering method. The NCD is universal in that it is not restricted to a specific application area, and works across application area boundaries.

A theoretical precursor, the normalized information distance, co-developed by one of the authors, is provably optimal but uses the non-computable notion of Kolmogorov complexity. We propose precise notions of similarity metric, normal compressor, and show that the NCD based on a normal compressor is a similarity metric that approximates universality.

To extract a hierarchy of clusters from the distance matrix, we determine a dendrogram (binary tree) by a new quartet method and a fast heuristic to implement it. The method is implemented and available as public software, and is robust under choice of different compressors.

To substantiate our claims of universality and robustness, we report evidence of successful application in areas as diverse as genomics, virology, languages, literature, music, handwritten digits, astronomy, and combinations of objects from completely different domains, using statistical, dictionary, and block sorting compressors.

In genomics we presented new evidence for major questions in Mammalian evolution, based on whole-mitochondrial genomic analysis: the Eutherian orders and the Marsupionta hypothesis against the Theria hypothesis.

--- https://arxiv.org/abs/cond-mat/0108530 Language Trees and Zipping Dario Benedetto, Emanuele Caglioti, Vittorio Loreto 2001-08-31 2023-02-10 [("doi","10.1103/PhysRevLett.88.048702")] cs/algorithm/information/compression

In this letter we present a very general method to extract information from a generic string of characters, eg. a text, a DNA sequence or a time series.

Based on data-compression techniques, its key point is the computation of a suitable measure of the remoteness of two bodies of knowledge.

We present the implementation of the method to linguistic motivated problems, featuring highly accurate results for language recognition, authorship attribution and language classification.

--- https://arxiv.org/abs/2302.06376#jetbrains The Effect of Perceptual Load on Performance within IDE in People with ADHD Symptoms Vseslav Kasatskii, Agnia Serheyuk, Anastasiia Serova, Sergey Titov, Timofey Bryksin 2023-02-13 2023-02-13 [("doi","10.48550/arXiv.2302.06376")] cs design psychiatry/adhd

[data, talk] In this paper, we describe the research on how perceptual load can affect programming performance in people with symptoms of Attention Deficit / Hyperactivity Disorder (ADHD).

We asked n = 36 developers to complete the Barkley Deficits in Executive Functioning Scale, which indicates the presence and severity levels of ADHD symptoms. After that, participants solved mentally active programming tasks (coding) and monotonous ones (debugging) in JetBrains’s PyCharm integrated development environment in high perceptual load modes (visually noisy) and low perceptual load modes (visually clear). The development environment was augmented with the plugin we wrote to track efficiency metrics, i.e. time, speed, and activity.

We found that the perceptual load does affect programmers’ efficiency. For mentally active tasks, the time of inserting the first character was shorter and the overall speed was higher in the low perceptual load mode. For monotonous tasks, the total time for the solution was less for the low perceptual load mode.

Also, we found that the effect of perceptual load on programmers’ efficiency differs between those with and without ADHD symptoms. This effect has a specificity: depending on efficiency measures and ADHD symptoms, one or another level of perceptual load might be beneficial.

Our findings support the idea of behavioral assessment of users for providing appropriate accommodation for the workforce with special needs.

--- https://www.youtube.com/watch?v=RbTsHEPMQoo 2023-02-11 ai/nn/transformer/gpt/3/fiction --- https://en.wikipedia.org/wiki/Feral_child Feral child 2023-02-11 psychology/linguistics --- https://www.theonion.com/girlfriend-changes-man-into-someone-shes-not-interested-1819565990 2023-02-11 fiction/humor fiction/humor sociology --- https://en.wikipedia.org/wiki/Dendrimer Dendrimer 2023-02-11 science --- https://en.wikipedia.org/wiki/Garden-path_sentence Garden-path sentence 2023-02-11 psychology/linguistics --- https://en.wikipedia.org/wiki/A_Syntopicon A Syntopicon 2023-02-11 design philosophy/ontology --- https://arxiv.org/abs/2307.03798 CLIPMasterPrints: Fooling Contrastive Language-Image Pre-training Using Latent Variable Evolution Matthias Freiberger, Peter Kun, Anders Sundnes Løvlie, Sebastian Risi 2023-07-07 2023-07-07 [("doi","10.48550/arXiv.2307.03798")] ai/nn/adversarial ai/nn/transformer/clip

Models leveraging both visual and textual data such as Contrastive Language-Image Pre-training (CLIP), are increasingly gaining importance.

In this work, we show that despite their versatility, such models are vulnerable to what we refer to as fooling master images. Fooling master images are capable of maximizing the confidence score of a CLIP model for a number of widely varying prompts, while being unrecognizable for humans.

We demonstrate how fooling master images can be mined by searching the latent space of generative models by means of an evolution strategy or stochastic gradient descent.

We investigate the properties of the mined fooling master images, and find that images trained on a small number of image captions potentially generalize to a much larger number of semantically related captions.

Further, we evaluate two possible mitigation strategies and find that vulnerability to fooling master examples is closely related to a modality gap in contrastive pre-trained multi-modal networks.

From the perspective of vulnerability to off-manifold attacks, we therefore argue for the mitigation of modality gaps in CLIP and related multi-modal approaches.

Source code and mined CLIPMasterPrints are available at Github.

--- https://www.lesswrong.com/posts/h5MwPYy94eSfpcjFk/anomalous-tokens-might-disproportionately-affect-complex 2023-02-11 ai/nn/adversarial --- https://hackmd.io/@dabo/rkP8Pcf9t 2023-02-11 cs/algorithm --- https://eileenormsby.com/2023/07/15/ten-years-after-silk-road-falls-variety-jones-is-sentenced/ 2023-02-11 darknet-market/silk-road/1 --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898914/ The effect of semaglutide 2.4 mg once weekly on energy intake, appetite, control of eating, and gastric emptying in adults with obesity Martin Friedrichsen, Astrid Breitschaft, Sayeh Tadayon, Alicja Wizert, Dorthe Skovgaard 2021 2023-02-12 [("doi","10.1111/dom.14280")] longevity/glp/semaglutide

Aim: To investigate the effects of once-weekly subcutaneous (s.c.) semaglutide 2.4 mg on gastric emptying, appetite, and energy intake in adults with obesity.

Method: A double-blind, parallel-group trial was conducted in 72 adults with obesity, randomized to once-weekly s.c. semaglutide (dose-escalated to 2.4 mg) or placebo for 20 weeks. Gastric emptying was assessed using paracetamol absorption following a standardized breakfast. Participant-reported appetite ratings and Control of Eating Questionnaire (CoEQ) responses were assessed, and energy intake was measured during ad libitum lunch.

Results: The area under the concentration-time curve (AUC) for paracetamol 0 to 5 hours after a standardized meal (AUC0-5h,para ; primary endpoint) was increased by 8% (p = 0.005) with semaglutide 2.4 mg versus placebo at week 20 (non-statistically-significant when corrected for week 20 body weight; p = 0.12). No effect was seen on AUC0-1h,para, maximum observed paracetamol concentration, or time to maximum observed paracetamol concentration. Ad libitum energy intake was 35% lower with semaglutide versus placebo (1736 versus 2676 kJ; estimated treatment difference −940 kJ; p <0.0001). Semaglutide reduced hunger and prospective food consumption, and increased fullness and satiety when compared with placebo (all p <0.02). The CoEQ indicated better control of eating and fewer/weaker food cravings with semaglutide versus placebo (p <0.05). Body weight was reduced by 9.9% with semaglutide and 0.4% with placebo. Safety was consistent with the known profile of semaglutide.

Conclusions: In adults with obesity, once-weekly s.c. semaglutide 2.4 mg suppressed appetite, improved control of eating, and reduced food cravings, ad libitum energy intake and body weight versus placebo. There was no evidence of delayed gastric emptying at week 20, assessed indirectly via paracetamol absorption.

--- https://www.nytimes.com/athletic/4687284/2023/07/14/pokemon-blake-martinez-giants-packers/ 2023-02-12 psychology/collecting --- https://arxiv.org/abs/2305.19693 Spontaneous symmetry breaking in generative diffusion models Gabriel Raya, Luca Ambrogioni 2023-05-31 2023-05-31 [("doi","10.48550/arXiv.2305.19693")] ai/nn/diffusion

Generative diffusion models have recently emerged as a leading approach for generating high-dimensional data. In this paper, we show that the dynamics of these models exhibit a spontaneous symmetry breaking that divides the generative dynamics into two distinct phases: (1) A linear steady-state dynamics around a central fixed-point and (2) an attractor dynamics directed towards the data manifold.

These two “phases” are separated by the change in stability of the central fixed-point, with the resulting window of instability being responsible for the diversity of the generated samples. Using both theoretical and empirical evidence, we show that an accurate simulation of the early dynamics does not contribute to the final generation, since early fluctuations are reverted to the central fixed point.

To leverage this insight, we propose a Gaussian late initialization scheme, which improves model performance, achieving up to 3× FID improvements on fast samplers, while also increasing sample diversity (eg. racial composition of generated CelebA images).

Our work offers a new way to understand the generative dynamics of diffusion models that has the potential to bring about higher performance and less biased fast-samplers.

--- https://arxiv.org/abs/2307.06440 No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models Jean Kaddour, Oscar Key, Piotr Nawrot, Pasquale Minervini, Matt J. Kusner 2023-07-12 2023-07-12 [("doi","10.48550/arXiv.2307.06440")] ai/nn/transformer/t5 reinforcement-learning/exploration/active-learning

The computation necessary for training Transformer-based language models has skyrocketed in recent years. This trend has motivated research on efficient training algorithms designed to improve training, validation, and downstream performance faster than standard training.

In this work, we revisit 3 categories of such algorithms: dynamic architectures (layer stacking, layer dropping), batch selection (selective backprop, RHO-LOSS), and efficient optimizers (Lion, Sophia). When pre-training BERT and T5 with a fixed computation budget using such methods, we find that their training, validation, and downstream gains vanish compared to a baseline with a fully-decayed learning rate.

We define an evaluation protocol that enables computation to be done on arbitrary machines by mapping all computation time to a reference machine which we call reference system time. We discuss the limitations of our proposed protocol and release our code to encourage rigorous research in efficient training procedures: Github.

…Case-Study 2: Data Selection:

…5.1 Selective Backprop: Due to its simplicity, we choose selective backprop—outlined in Algorithm 3—with the high-level idea being to compute the backward pass only on the training examples with the highest loss. To construct such batches, we first compute the losses for each example in a uniformly-sampled batch via a forward pass and then sample a subset from it ranked by their loss percentiles w.r.t. historical losses among recently ingested sequences.

…5.2 RHO-LOSS: Mindermann et al 2022 argue that prioritizing high training losses results in prioritizing two types of examples that are unwanted: (1) mislabeled and ambiguous data, as commonly found in noisy, web-crawled data; and (2) outliers, which are less likely to appear at test time. The authors propose down-weighting such data via a selection objective called Reducible Holdout (RHO) loss.

…5.3 Results: We assume that the effects of selecting better training data should be largely agnostic to whether we pre-train a BERT or T5 model. Hence, instead of training both architectures, we decide to pre-train only BERT models and instead vary the datasets and budgets as follows.

Figure 3: Validation losses for different datasets. Results for data selection methods (selective backprop and RHO-LOSS) for a 12-hour RST budget.

For the first set of experiments, we fix the budget to 12 hours and consider 3 different datasets: (1) C4, consisting only of web-page text which, despite being regularly used for pre-training, is known to have some quality issues55, (2) BookCorpus & Wikipedia24 which contain polished, book(-like) text and MiniPile44, a subset of the diverse Pile pre-training corpus, containing code, mathematics, books, webpages, and other scientific articles…We find that both data selection methods underperform the baseline. Next, we investigate downstream performances, fix the C4 corpus as the pre-training corpus, and vary the budgets (6, 12, and 24 hours). Figure 2 & Figure 16 entail the results, and we again observe no noticeable difference between the methods.

--- https://arxiv.org/abs/1910.00762 Accelerating Deep Learning by Focusing on the Biggest Losers Angela H. Jiang, Daniel L. -K. Wong, Giulio Zhou, David G. Andersen, Jeffrey Dean, Gregory R. Ganger, Gauri Joshi, Michael Kaminksy, Michael Kozuch, Zachary C. Lipton, Padmanabhan Pillai 2019-10-02 2023-02-12 [("doi","10.48550/arXiv.1910.00762")] ai/nn/cnn reinforcement-learning/exploration/active-learning

This paper introduces Selective-Backprop, a technique that accelerates the training of deep neural networks (DNNs) by prioritizing examples with high loss at each iteration. Selective-Backprop uses the output of a training example’s forward pass to decide whether to use that example to compute gradients and update parameters, or to skip immediately to the next example. By reducing the number of computationally-expensive backpropagation steps performed, Selective-Backprop accelerates training.

Evaluation on CIFAR-10, CIFAR-100, and SVHN, across a variety of modern image models, shows that Selective-Backprop converges to target error rates up to 3.5× faster than with standard SGD and between 1.02–1.8× faster than a state-of-the-art importance sampling approach.

Further acceleration of 26% can be achieved by using stale forward pass results for selection, thus also skipping forward passes of low priority examples.

--- https://x.com/Teknium1/status/1680365093287235585 2023-02-12 ai/nn/transformer/clip/sample --- https://www.biorxiv.org/content/10.1101/2023.07.12.548723.full Genomic signatures of strawberry domestication and breeding Zhen Fan, Vance M. Whitaker 2023-07-14 2023-07-14 [("doi","10.1101/2023.07.12.548723")] genetics/selection/artificial

Cultivated strawberry (Fragaria × ananassa) has a brief history of less than 300 years, beginning with the hybridization of octoploids F. chiloensis and F. virginiana. Here we explored the genomic signatures of this history using whole-genome sequences of 289 wild, heirloom and modern varieties.

Four non-admixed wild octoploid populations were identified, with recurrent introgression among the sympatric populations. The proportion of F. virginiana ancestry increased by 20% in modern varieties over initial hybrids, and the proportion of F. chiloensis subsp. pacifica rose 0 → 3.4%.

Effective population size rapidly declined during early breeding. Meanwhile, divergent selection for distinct environments reshaped wild allelic origins in 21 out 28 chromosomes. Despite 20 breeding cycles since the initial hybridization, more than half of loci underlying yield and fruit size are still not under selection.

These insights add clarity to the domestication and breeding history of what is now the most widely cultivated fruit in the world.

--- https://en.wikipedia.org/wiki/Atomic_gardening Atomic gardening 2023-02-12 ai --- https://colab.research.google.com/github/murphyka/ml_colabs/blob/main/Simple_MLP_Visualization.ipynb 2023-02-12 ai/nn/fully-connected --- /doc/ai/nn/fully-connected/2023-bachmann-figure1-mlpcomputescalingoncifar100.jpg 2023 2023-02-12 ai/nn/fully-connected ai/scaling --- https://arxiv.org/abs/2012.10820#tencent AdnFM: An Attentive DenseNet based Factorization Machine for CTR Prediction Kai Wang, Chunxu Shen, Chaoyun Zhang Wenye Ma 2020-12-20 2023-02-12 [("doi","10.48550/arXiv.2012.10820")] ai/nn/fully-connected ai/nn/transformer/attention

In this paper, we consider the Click-Through-Rate (CTR) prediction problem. Factorization Machines and their variants consider pair-wise feature interactions, but normally we won’t do high-order feature interactions using FM due to high time complexity.

Given the success of deep neural networks (DNNs) in many fields, researchers have proposed several DNN-based models to learn high-order feature interactions. Multi-layer perceptrons (MLP) have been widely employed to learn reliable mappings from feature embeddings to final logits. In this paper, we aim to explore more about these high-order features interactions. However, high-order feature interaction deserves more attention and further development.

Inspired by the great achievements of Densely Connected Convolutional Networks (DenseNet) in computer vision, we propose a novel model called Attentive DenseNet based Factorization Machines (AdnFM). AdnFM can extract more comprehensive deep features by using all the hidden layers from a feed-forward neural network as implicit high-order features, then selects dominant features via an attention mechanism. Also, high-order interactions in the implicit way using DNNs are more cost-efficient than in the explicit way, for example in FM.

Extensive experiments on two real-world datasets show that the proposed model can effectively improve the performance of CTR prediction.

...We deploy our model for the CTR prediction on a popular Tencent mobile game. Online A/B tests demonstrate that the proposed model outperforms different baseline methods by up to 12% in terms of the CTR.

--- https://arxiv.org/abs/1607.06450 Layer Normalization Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey Hinton 2016-07-21 2023-02-13 [("doi","10.48550/arXiv.1607.06450")] ai/nn/rnn

Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to that neuron on each training case. This reduces the training time in feed-forward neural networks.

However, the effect of batch normalization is dependent on the mini-batch size and it is not obvious how to apply it to recurrent neural networks. In this paper, we transpose batch normalization into layer normalization by computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case. Like batch normalization, we also give each neuron its own adaptive bias and gain which are applied after the normalization but before the non-linearity.

Unlike batch normalization, layer normalization performs exactly the same computation at training and test times. It is also straightforward to apply to recurrent neural networks by computing the normalization statistics separately at each time step. Layer normalization is very effective at stabilizing the hidden state dynamics in recurrent networks.

Empirically, we show that layer normalization can substantially reduce the training time compared with previously published techniques.

--- https://arxiv.org/abs/1707.08819 A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets Patryk Chrabaszcz, Ilya Loshchilov, Frank Hutter 2017-07-27 2023-02-13 [("doi","10.48550/arXiv.1707.08819")] ai/dataset ai/nn/cnn

The original ImageNet dataset is a popular large-scale benchmark for training Deep Neural Networks. Since the cost of performing experiments (e.g, algorithm design, architecture search, and hyperparameter tuning) on the original dataset might be prohibitive, we propose to consider a downsampled version of ImageNet.

In contrast to the CIFAR datasets and earlier downsampled versions of ImageNet, our proposed ImageNet32×32 (and its variants ImageNet64×64 and ImageNet16×16) contains exactly the same number of classes and images as ImageNet, with the only difference that the images are downsampled to 32×32 pixels per image (64×64 and 16×16 pixels for the variants, respectively).

Experiments on these downsampled variants are dramatically faster than on the original ImageNet and the characteristics of the downsampled datasets with respect to optimal hyperparameters appear to remain similar.

The proposed datasets and scripts to reproduce our results are available at https://image-net.org/download-images and https://github.com/PatrykChrabaszcz/Imagenet32_Scripts.

--- /doc/ai/nn/fully-connected/2023-bachmann-figure4-mlpsscalewellwithincreasingbatchsize.jpg 2023 2023-02-13 ai/nn/fully-connected ai/scaling --- /doc/ai/nn/fully-connected/2023-bachmann-figure5-scalingofmlpsoncifar10andimagenet1k.png 2023 2023-02-13 [("invert","True")] ai/nn/fully-connected ai/scaling --- /doc/ai/nn/fully-connected/2023-bachmann-figure6-powerlawincifar100losswhenconstrainingparametersordatasetsize.jpg 2023 2023-02-13 ai/nn/fully-connected ai/scaling --- /doc/ai/nn/fully-connected/2023-bachmann-figure7-suprachinchilladatascalingformlpsoncifar100loss.jpg 2023 2023-02-13 ai/nn/fully-connected ai/scaling --- https://radiolab.org/podcast/91725-words/transcript 2023-02-13 ai/scaling/emergence philosophy/epistemology philosophy/mind psychology/animal/maze psychology/inner-voice psychology/linguistics --- https://en.wikipedia.org/wiki/Terminal_lucidity Terminal lucidity 2023-02-13 psychiatry --- https://no-ht.ml/ 2023-02-13 cs/css design/typography --- https://arxiv.org/abs/2003.01629 Can Increasing Input Dimensionality Improve Deep Reinforcement Learning? Kei Ota, Tomoaki Oiki, Devesh K. Jha, Toshisada Mariyama, Daniel Nikovski 2020-03-03 2023-02-13 [("doi","10.48550/arXiv.2003.01629")] ai/nn/fully-connected reinforcement-learning/model-free

Deep reinforcement learning (RL) algorithms have recently achieved remarkable successes in various sequential decision making tasks, leveraging advances in methods for training large deep networks. However, these methods usually require large amounts of training data, which is often a big problem for real-world applications. One natural question to ask is whether learning good representations for states and using larger networks helps in learning better policies.

In this paper, we try to study if increasing input dimensionality helps improve performance and sample efficiency of model-free deep RL algorithms. To do so, we propose an online feature extractor network (OFENet) that uses neural nets [using a DenseNet-style variant on MLPs] to produce good representations to be used as inputs to deep RL algorithms.

Even though the high dimensionality of input is usually supposed to make learning of RL agents more difficult, we show that the RL agents in fact learn more efficiently with the high-dimensional representation than with the lower-dimensional state observations. We believe that stronger feature propagation together with larger networks (and thus larger search space) allows RL agents to learn more complex functions of states and thus improves the sample efficiency.

Through numerical experiments, we show that the proposed method outperforms several other state-of-the-art algorithms in terms of both sample efficiency and performance.

Codes for the proposed method are available at merl.com.

…To combine the advantages of deep layers and shallow layers, we use MLP-DenseNet, which is a slightly modified version of DenseNet (Huang et al 2017), as the network architecture of OFENet. Each layer of MLP-DenseNet has an output y which is the concatenation of the input x and the product of a weight matrix W1 and x defined as:

y = [x, σ(W1x)]

where [x1, x2] means concatenation, σ is the activation function, and the biases are omitted to simplify notation. Since each layer’s output is contained in the next layer’s output, the raw input and the outputs of shallow layers are naturally contained in the final output.

The mappings φo, φo, a are represented with an MLP-DenseNet. The mapping φo receives the observation ot as input, and the mapping φo, a receives the concatenation of the observation representation zot and the action at as its input. Figure 2 shows an example of these mappings in the proposed OFENet.

Figure 1: The model network of ML-DDPG. FC represents a fully-connected layer, and concat represents a concatenation of its inputs.
Figure 2: An example of the online feature extractor. FC represents a fully connected layer with an activation function, and concat represents a concatenation of the inputs.
--- https://arxiv.org/abs/2102.07920 Training Larger Networks for Deep Reinforcement Learning Kei Ota, Devesh K. Jha, Asako Kanezaki 2021-02-16 2023-02-13 [("doi","10.48550/arXiv.2102.07920")] ai/nn/fully-connected reinforcement-learning/model-free reinforcement-learning/scaling

The success of deep learning in the computer vision and natural language processing communities can be attributed to training of very deep neural networks with millions or billions of parameters which can then be trained with massive amounts of data.

However, similar trend has largely eluded training of deep reinforcement learning (RL) algorithms where larger networks do not lead to performance improvement. Previous work has shown that this is mostly due to instability during training of deep RL agents when using larger networks.

In this paper, we make an attempt to understand and address training of larger networks for deep RL. We first show that naively increasing network capacity does not improve performance.

Then, we propose a novel method that consists of (1) wider networks with DenseNet connection, (2) decoupling representation learning from training of RL, (3) a distributed training method to mitigate overfitting problems.

Using this triple technique, we show that we can train very large networks that result in performance gains. We present several ablation studies to demonstrate the efficacy of the proposed method and some intuitive understanding of the reasons for performance gain.

We show that our proposed method outperforms other baseline algorithms on several challenging locomotion tasks.

--- https://arxiv.org/abs/2307.04526 Self Expanding Neural Networks Rupert Mitchell, Martin Mundt, Kristian Kersting 2023-07-10 2023-07-10 [("doi","10.48550/arXiv.2307.04526")] ai/nn/fully-connected reinforcement-learning/exploration/active-learning reinforcement-learning/meta-learning

[cf. Net2Net] The results of training a neural network are heavily dependent on the architecture chosen; and even a modification of only the size of the network, however small, typically involves restarting the training process. In contrast to this, we begin training with a small architecture, only increase its capacity as necessary for the problem, and avoid interfering with previous optimization while doing so.

We thereby introduce a natural gradient based approach which intuitively expands both the width and depth of a neural network when this is likely to substantially reduce the hypothetical converged training loss. We prove an upper bound on the “rate” at which neurons are added, and a computationally cheap lower bound on the expansion score.

We illustrate the benefits of such Self-Expanding Neural Networks in both classification and regression problems, including those where the appropriate architecture size is substantially uncertain a priori.

Figure 2: A single layer SENN (black, solid) is trained to approximate a target function (red, dashed) via non-linear least-squares regression on samples (blue, markers). The location of existing neurons is shown by vertical lines. The lower figures show ∆η′/ηc as a function of the location and scale of the nonlinearity introduced by a new neuron. Accepted and rejected proposals are marked in red and black respectively. From left to right we see the landscape before and immediately after the 4<sup>th</sup> neuron is added, before the fifth neuron is added, and at the end of training. SENN adds neurons where they are relevant in order to achieve a good final fit.
Figure 3: Classification is performed with SENN on the half moons dataset. The normalized layer addition score ∆η′/ηc is shown as a function of optimization steps; the horizontal bar shows the point above which a layer will be added. The score increases during 3 phases during which the SENN has initial zero, one and then two hidden layers. The respective decision boundary is shown at the beginning and end of these. These layer additions allow SENN to represent more complex decision boundaries when required for global expressivity.
--- https://arxiv.org/abs/2305.13534 How Language Model Hallucinations Can Snowball Muru Zhang, Ofir Press, William Merrill, Alisa Liu, Noah Smith 2023-05-22 2023-05-22 [("doi","10.48550/arXiv.2305.13534")] ai/nn/sampling ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/inner-monologue

[special-case of neural degeneration; cf. DAgger] A major risk of using language models in practical applications is their tendency to hallucinate incorrect statements. Hallucinations are often attributed to knowledge gaps in LMs, but we hypothesize that in some cases, when justifying previously generated hallucinations, LMs output false claims that they can separately recognize as incorrect.

We construct 3 question-answering datasets where ChatGPT and GPT-4 often state an incorrect answer and offer an explanation with at least one incorrect claim. Crucially, we find that ChatGPT and GPT-4 can identify 67% and 87% of their own mistakes, respectively.

We refer to this phenomenon as hallucination snowballing: an LM over-commits to early mistakes, leading to more mistakes that it otherwise would not make.

--- https://arxiv.org/abs/2010.09163 D2RL: Deep Dense Architectures in Reinforcement Learning Samarth Sinha, Homanga Bharadhwaj, Aravind Srinivas, Animesh Garg 2020-10-19 2023-02-14 [("doi","10.48550/arXiv.2010.09163")] ai/nn/fully-connected reinforcement-learning/model-free

While improvements in deep learning architectures have played a crucial role in improving the state of supervised and unsupervised learning in computer vision and natural language processing, neural network architecture choices for reinforcement learning remain relatively under-explored.

We take inspiration from successful architectural choices in computer vision and generative modeling, and investigate the use of deeper networks and dense connections for reinforcement learning on a variety of simulated robotic learning benchmark environments.

Our findings reveal that current methods benefit from dense connections and deeper networks, across a suite of manipulation and locomotion tasks, for both proprioceptive and image-based observations.

We hope that our results can serve as a strong baseline and further motivate future research into neural network architectures for reinforcement learning. The project website with code is at this link.

--- https://github.com/amargaritov/starlit#starlit-algorithm-description 2023-02-14 cs/algorithm/sorting --- https://dl.acm.org/doi/pdf/10.1145/234286.1057818 2023-02-14 cs/lisp --- https://arxiv.org/abs/2202.11960#schmidhuber All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL Kai Arulkumaran, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh K. Srivastava 2022-02-24 2023-02-14 [("doi","10.48550/arXiv.2202.11960")] ai/nn/rnn reinforcement-learning/meta-learning reinforcement-learning/model/decision-transformer

Upside down reinforcement learning (UDRL) flips the conventional use of the return in the objective function in RL upside down, by taking returns as input and predicting actions. UDRL is based purely on supervised learning, and bypasses some prominent issues in RL: bootstrapping, off-policy corrections, and discount factors.

While previous work with UDRL demonstrated it in a traditional online RL setting, here we show that this single algorithm can also work in the imitation learning and offline RL settings, be extended to the goal-conditioned RL setting, and even the meta-RL setting.

With a general agent architecture, a single UDRL agent can learn across all paradigms.

--- https://arxiv.org/abs/2305.09636#google SoundStorm: Efficient Parallel Audio Generation Zalán Borsos, Matt Sharifi, Damien Vincent, Eugene Kharitonov, Neil Zeghidour, Marco Tagliasacchi 2023-05-16 2023-05-16 [("doi","10.48550/arXiv.2305.09636")] ai/music ai/nn/transformer/t5 ai/nn/vae/mae

[samples] We present SoundStorm, a model for efficient, non-autoregressive audio generation.

SoundStorm receives as input the semantic tokens of AudioLM, and relies on bidirectional attention and confidence-based parallel decoding [MaskGIT] to generate the tokens of a neural audio codec.

Compared to the autoregressive generation approach of AudioLM, our model produces audio of the same quality and with higher consistency in voice and acoustic conditions, while being two orders of magnitude faster. SoundStorm generates 30 seconds of audio in 0.5 seconds on a TPU-v4.

We demonstrate the ability of our model to scale audio generation to longer sequences by synthesizing high-quality, natural dialogue segments, given a transcript annotated with speaker turns and a short prompt with the speakers’ voices.

--- https://www.maren.ink/sociological-eye/2014/01/napoleon-as-ceo-career-of-emotional_3.html 2023-02-14 psychology/energy sociology/abandoned-footnotes --- https://www.medrxiv.org/content/10.1101/2022.07.20.22277727.full Novel genomic loci and pathways influence patterns of structural covariance in the human brain Junhao WEN, Ilya Nasrallah, Ahmed Abdulkadir, Theodore D. Satterthwaite, Zhijian Yang, Guray Erus, Timothy Robert-FitzGerald, Ashish Singh, Aristeidis Sotiras, Aleix Boquetipujadas, Elizabeth Mamourian, Jimit Doshi, Yuhan Cui, Dhivya Srinivasan, Jiong Chen, Gyujoon Hwang, Mark Bergman, Jingxuan Bao, Yogasudha Veturi, Zhen Zhou, Shu Yang, Paola Dazzan, Rene Kahn, Hugo Schnack, Marcus Zanetti, Eva Meisenzahl, Geraldo Busatto, Benedicto Crespo-Facorro, Christos Pantelis, Stephen Wood, Chuanjun Zhuo, Russell Shinohara, Ruben Gur, Raquel Gur, Nikolaos Koutsouleris, Daniel Wolf, Andrew J. Saykin, Marylyn D. Ritchie, Li Shen, Paul Thompson, Olivier Colliot, Katharina Wittfeld, Hans Grabe, Duygu Tosun, Murat Bilgel, Yang An, Daniel Marcus, Pamela J. Lamontagne, Susan Heckbert, Thomas Austin, Lenore J. Launer, Mark Espeland, Colin Masters, Paul Maruff, Jurgen Fripp, Sterling C. Johnson, John Morris, Marilyn Albert, Robert Bryan, Susan M. Resnick, Yong Fan, Mohamad Habes, David Wolk, Haochang Shou, Christos Davatzikos 2023-07-17 2023-07-17 [("doi","10.1101/2022.07.20.22277727")] genetics/heritable/correlation psychology/neuroscience

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and brain diseases. The genetic underpinnings of these patterns remain largely unknown.

We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies, 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were statistically-significantly correlated with 915 genomic loci in the discovery set, 617 of which are novel, and 72% were independently replicated.

Key pathways influencing PSCs involved reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia.

Using machine learning, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate new genetic and biological underpinnings that influence structural covariance patterns in the human brain.

--- https://www.medrxiv.org/content/10.1101/2023.03.24.23287731.full Performance of ChatGPT on free-response, clinical reasoning exams Eric Strong, Alicia DiGiammarino, Yingjie Weng, Preetha Basaviah, Poonam Hosamani, Andre Kumar, Andrew Nevins, John Kugler, Jason Hom, Jonathan H. Chen 2023-03-29 2023-03-29 [("doi","10.1101/2023.03.24.23287731")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction biology

Importance: [Twitter] Studies show that ChatGPT, a general purpose large language model chatbot, could pass the multiple-choice US Medical Licensing Exams, but the model’s performance on open-ended clinical reasoning is unknown.

Objective: To determine if ChatGPT is capable of consistently meeting the passing threshold on free-response, case-based clinical reasoning assessments.

Design: 14 multi-part cases were selected from clinical reasoning exams administered to pre-clerkship medical students 2019–2022. For each case, the questions were run through ChatGPT twice and responses were recorded. Two clinician educators independently graded each run according to a standardized grading rubric. To further assess the degree of variation in ChatGPT’s performance, we repeated the analysis on a single high-complexity case 20×.

Setting: A single US medical school.

Participants: ChatGPT.

Main Outcomes & Measures: Passing rate of ChatGPT’s scored responses and the range in model performance across multiple run throughs of a single case.

Results: 12⁄28 ChatGPT exam responses achieved a passing score (43%) with a mean score of 69% (95% CI: 65% to 73%) compared to the established passing threshold of 70%. When given the same case 20 separate times, ChatGPT’s performance on that case varied with scores ranging 35.9%–59.4% [GPT-4 outperformed their students.]

Conclusion: ChatGPT’s ability to achieve a passing performance in nearly half of the cases analyzed demonstrates the need to revise clinical reasoning assessments and incorporate artificial intelligence (AI)-related topics into medical curricula and practice.

--- https://www.vox.com/future-perfect/23794855/anthropic-ai-openai-claude-2 2023-02-15 ai/nn/anthropic ai/nn/transformer/gpt/claude ai/scaling/economics reinforcement-learning/safe --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0286452 Multivariate regression modeling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS Chantrell J. G. Frazier, Vidia A. Gokool, Howard K. Holness, DeEtta K. Mills, Kenneth G. Furton 2023-05-16 2023-05-16 [("doi","10.1371/journal.pone.0286452")] psychology/smell/human

The efficacy of using human volatile organic compounds (VOCs) as a form of forensic evidence has been well demonstrated with canines for crime scene response, suspect identification, and location checking. Although the use of human scent evidence in the field is well established, the laboratory evaluation of human VOC profiles has been limited.

This study used Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) to analyze human hand odor samples collected from 60 individuals (30 Females and 30 Males). The human volatiles collected from the palm surfaces of each subject were interpreted for classification and prediction of gender.

The volatile organic compound (VOC) signatures from subjects’ hand odor profiles were evaluated with supervised dimensional reduction techniques: Partial Least Squares-Discriminant Analysis (PLS-DA), Orthogonal-Projections to Latent Structures Discriminant Analysis (OPLS-DA), and Linear Discriminant Analysis (LDA). The PLS-DA 2D model demonstrated clustering amongst male and female subjects. The addition of a third component to the PLS-DA model revealed clustering and minimal separation of male and female subjects in the 3D PLS-DA model.

The OPLS-DA model displayed discrimination and clustering amongst gender groups with leave one out cross validation (LOOCV) and 95% confidence regions surrounding clustered groups without overlap. The LDA had a 96.67% accuracy rate for female and male subjects.

The culminating knowledge establishes a working model for the prediction of donor class characteristics using human scent hand odor profiles.

--- https://www.reddit.com/r/StableDiffusion/comments/152cpzx/sdxl_welp_i_wanted_to_create_a_pic_of_people/ 2023-02-15 ai/nn/transformer/clip/sample --- https://www.lesswrong.com/posts/ufW5LvcwDuL6qjdBT/latent-variables-for-prediction-markets-motivation-technical 2023-02-15 statistics/prediction --- https://arxiv.org/abs/2307.06962#tencent Copy Is All You Need Tian Lan, Deng Cai, Yan Wang, Heyan Huang, Xian-Ling Mao 2023-07-13 2023-07-13 [("doi","10.48550/arXiv.2307.06962")] ai/nn/retrieval ai/nn/sampling ai/nn/transformer

The dominant text generation models compose the output by sequentially selecting words from a fixed vocabulary.

In this paper, we formulate text generation as progressively copying text segments (eg. words or phrases) from an existing text collection. We compute the contextualized representations of meaningful text segments and index them using efficient vector search toolkits. The task of text generation is then decomposed into a series of copy-and-paste operations: at each time step, we seek suitable text spans from the text collection rather than selecting from a standalone vocabulary.

Experiments on the standard language modeling benchmark (WikiText-103) show that our approach achieves better generation quality according to both automatic and human evaluations. Besides, its inference efficiency is comparable to token-level autoregressive models thanks to the reduction of decoding steps. We also show that our approach allows for effective domain adaptation by simply switching to domain-specific text collection without extra training. Finally, we observe that our approach attains additional performance gains by simply scaling up to larger text collections, again without further training.

Our source codes are publicly available at https://github.com/gmftbyGMFTBY/Copyisallyouneed.

--- https://arxiv.org/abs/2307.08702 Diffusion Models Beat GANs on Image Classification Soumik Mukhopadhyay, Matthew Gwilliam, Vatsal Agarwal, Namitha Padmanabhan, Archana Swaminathan, Srinidhi Hegde, Tianyi Zhou, Abhinav Shrivastava 2023-07-17 2023-07-17 [("doi","10.48550/arXiv.2307.08702")] ai/nn/diffusion ai/nn/gan/biggan ai/nn/vae/mae

While many unsupervised learning models focus on one family of tasks, either generative or discriminative, we explore the possibility of a unified representation learner: a model which uses a single pre-training stage to address both families of tasks simultaneously. We identify diffusion models as a prime candidate.

Diffusion models have risen to prominence as a state-of-the-art method for image generation, denoising, inpainting, super-resolution, manipulation, etc. Such models involve training a U-Net to iteratively predict and remove noise, and the resulting model can synthesize high fidelity, diverse, novel images. The U-Net architecture, as a convolution-based architecture, generates a diverse set of feature representations in the form of intermediate feature maps.

We present our findings that these embeddings are useful beyond the noise prediction task, as they contain discriminative information and can also be leveraged for classification. We explore optimal methods for extracting and using these embeddings for classification tasks, demonstrating promising results on the ImageNet classification task.

We find that with careful feature selection and pooling, diffusion models outperform comparable generative-discriminative methods such as BigBiGAN for classification tasks [but substantially worse than MAGE/MAE]. We investigate diffusion models in the transfer learning regime, examining their performance on several fine-grained visual classification datasets.

We compare these embeddings to those generated by competing architectures and pre-trainings for classification tasks.

--- https://arxiv.org/abs/2304.10453 Phoenix: Democratizing ChatGPT across Languages Zhihong Chen, Feng Jiang, Junying Chen, Tiannan Wang, Fei Yu, Guiming Chen, Hongbo Zhang, Juhao Liang, Chen Zhang, Zhiyi Zhang, Jianquan Li, Xiang Wan, Benyou Wang, Haizhou Li 2023-04-20 2023-04-20 [("doi","10.48550/arXiv.2304.10453")] ai/nn/transformer/gpt/instruction-tuning

This paper presents our efforts to democratize ChatGPT across language.

We release a large language model Phoenix, achieving competitive performance among open-source English and Chinese models while excelling in languages with limited resources (covering both Latin and non-Latin languages).

We believe this work will be beneficial to make ChatGPT more accessible, especially in countries where people cannot use ChatGPT due to restrictions from OpenAI or local governments.

Our data, code, and models are available at Github.

--- https://arxiv.org/abs/2307.08674 TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT Liangyu Zha, Junlin Zhou, Liyao Li, Rui Wang, Qingyi Huang, Saisai Yang, Jing Yuan, Changbao Su, Xiang Li, Aofeng Su, Tao Zhang, Chen Zhou, Kaizhe Shou, Miao Wang, Wufang Zhu, Guoshan Lu, Chao Ye, Yali Ye, Wentao Ye, Yiming Zhang, Xinglong Deng, Jie Xu, Haobo Wang, Gang Chen, Junbo Zhao 2023-07-17 2023-07-17 [("doi","10.48550/arXiv.2307.08674")] ai/nn/transformer/gpt/inner-monologue ai/tabular

Tables are prevalent in real-world databases, requiring time and effort for humans to analyze and manipulate. The advancements in large language models (LLMs) have made it possible to interact with tables using natural language input, bringing this capability closer to reality.

In this paper, we present TableGPT, a unified fine-tuned framework that enables LLMs [Phoenix] to understand and operate on tables using external functional commands. It introduces the capability to seamlessly interact with tables, enabling a wide range of functionalities such as question answering, data manipulation (eg. insert, delete, query, and modify operations), data visualization, analysis report generation, and automated prediction.

TableGPT aims to provide convenience and accessibility to users by empowering them to effortlessly leverage tabular data. At the core of TableGPT lies the novel concept of global tabular representations, which empowers LLMs to gain a comprehensive understanding of the entire table beyond meta-information.

By jointly training LLMs on both table and text modalities, TableGPT achieves a deep understanding of tabular data and the ability to perform complex operations on tables through chain-of-command instructions. Importantly, TableGPT offers the advantage of being a self-contained system rather than relying on external API interfaces.

Moreover, it supports efficient data process flow, query rejection (when appropriate) and private deployment, enabling faster domain data fine-tuning and ensuring data privacy, which enhances the framework’s adaptability to specific use cases.

--- https://arxiv.org/abs/2307.08701#samsung AlpaGasus: Training A Better Alpaca with Fewer Data Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, Hongxia Jin 2023-07-17 2023-07-17 [("doi","10.48550/arXiv.2307.08701")] ai/dataset ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/exploration/active-learning

Large language models (LLMs) obtain instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT datasets (eg. Alpaca’s 52k data) surprisingly contain many low-quality instances with incorrect or irrelevant responses, which are misleading and detrimental to IFT.

In this paper, we propose a simple and effective data selection strategy that automatically identifies and removes low-quality data using a strong LLM (eg. ChatGPT). To this end, we introduce AlpaGasus, which is finetuned on only 9k high-quality data filtered from the 52k Alpaca data.

AlpaGasus outperforms the original Alpaca as evaluated by GPT-4 on multiple test sets and its 13B variant matches >90% performance of its teacher LLM (ie. Text-Davinci-003) on test tasks. It also provides 5.7× faster training, reducing the training time for a 7B variant from 80 minutes (for Alpaca) to 14 minutes.

AlpaGasus demonstrates a novel data-centric IFT paradigm that can be generally applied to instruction-tuning data, leading to faster training and better instruction-following models.

Our project page is available at: https://lichang-chen.github.io/AlpaGasus/.

--- https://xkcd.com/2803/ 2023-02-15 design/typography math/humor --- https://arxiv.org/abs/2211.09117#google MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis Tianhong Li, Huiwen Chang, Shlok Kumar Mishra, Han Zhang, Dina Katabi, Dilip Krishnan 2022-11-16 2023-02-15 [("doi","10.48550/arXiv.2211.09117")] ai/nn/vae/mae

Generative modeling and representation learning are two key tasks in computer vision. However, these models are typically trained independently, which ignores the potential for each task to help the other, and leads to training and model maintenance overheads.

In this work, we propose MAsked Generative Encoder (MAGE), the first framework to unify SOTA image generation and self-supervised representation learning. Our key insight is that using variable masking ratios in masked image modeling pre-training can allow generative training (very high masking ratio) and representation learning (lower masking ratio) under the same training framework.

Inspired by previous generative models, MAGE uses semantic tokens learned by a vector-quantized GAN at inputs and outputs, combining this with masking. We can further improve the representation by adding a contrastive loss to the encoder output.

We extensively evaluate the generation and representation learning capabilities of MAGE. On ImageNet-1K, a single MAGE ViT-L model obtains 9.10 FID in the task of class-unconditional image generation and 78.9% top-1 accuracy for linear probing, achieving state-of-the-art performance in both image generation and representation learning. Code is available at Github.

--- https://www.apa.org/pubs/journals/releases/pha-84462.pdf Greater sensitivity to subjective effects of nicotine in nonsmokers high in sensation seeking Perkins 2000 2023-02-16 nicotine --- https://pdfs.semanticscholar.org/3069/80183fea8f0e6af98f520d8c8968f11c8eb1.pdf Is the Network Turing-Complete? EPFL Technical Report 187131 Peresini, Kostic 2013 2023-02-16 cs/computable --- https://arxiv.org/abs/2306.13649#deepmind GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models Rishabh Agarwal, Nino Vieillard, Piotr Stanczyk, Sabela Ramos, Matthieu Geist, Olivier Bachem 2023-06-23 2023-06-23 [("doi","10.48550/arXiv.2306.13649")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/t5 reinforcement-learning/imitation-learning

[Twitter] Knowledge distillation is commonly used for compressing neural networks to reduce their inference cost and memory footprint. However, current distillation methods for auto-regressive models, such as generative language models (LMs), suffer from two key issues: (1) distribution mismatch between output sequences during training and the sequences generated by the student during its deployment, and (2) model under-specification, where the student model may not be expressive enough to fit the teacher’s distribution.

To address these issues, we propose Generalized Knowledge Distillation (GKD). GKD mitigates distribution mismatch by sampling output sequences from the student during training. Furthermore, GKD handles model under-specification by optimizing alternative divergences, such as reverse KL, that focus on generating samples from the student that are likely under the teacher’s distribution.

We demonstrate that GKD outperforms commonly-used approaches for distilling LLMs on summarization, machine translation, and arithmetic reasoning tasks.

--- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002162 A new human embryonic cell type associated with activity of young transposable elements allows definition of the inner cell mass Manvendra Singh, Aleksandra M. Kondrashkina, Thomas J. Widmann, Jose L. Cortes, Vikas Bansal, Jichang Wang, Christine Römer, Marta Garcia-Canadas, Jose L. Garcia-Perez, Laurence D. Hurst, Zsuzsanna Izsvák, Ines Alvarez-Garcia, Ines Alvarez-Garcia, Ines Alvarez-Garcia, Ines Alvarez-Garcia 2023-05-12 2023-05-12 [("doi","10.1371/journal.pbio.3002162")] genetics/gametogenesis

There remains much that we do not understand about the earliest stages of human development. On a gross level, there is evidence for apoptosis, but the nature of the affected cell types is unknown. Perhaps most importantly, the inner cell mass (ICM), from which the fetus is derived and hence of interest in reproductive health and regenerative medicine, has proven hard to define. Here, we provide a multi-method analysis of the early human embryo to resolve these issues. Single-cell analysis (on multiple independent datasets), supported by embryo visualization, uncovers a common previously uncharacterized class of cells lacking commitment markers that segregates after embryonic gene activation (EGA) and shortly after undergo apoptosis. The discovery of this cell type allows us to clearly define their viable ontogenetic sisters, these being the cells of the ICM. While ICM is characterised by the activity of an Old non-transposing endogenous retrovirus (HERVH) that acts to suppress Young transposable elements, the new cell type, by contrast, expresses transpositionally competent Young elements and DNA-damage response genes. As the Young elements are Retro-Elements and the cells are excluded from the developmental process, we dub these REject cells. With these and ICM being characterised by differential mobile element activities, the human embryo may be a “selection arena” in which one group of cells selectively die, while other less damaged cells persist.

The inner cell mass, from which the human fetus is derived and hence of interest in reproductive health and regenerative medicine, has proven hard to define. Single-cell analysis and embryo visualization reveal a common novel class of non-committed cells that undergo apoptosis and may reflect a quality control screening process.

--- https://arxiv.org/abs/2302.00805#anthropic Conditioning Predictive Models: Risks and Strategies Evan Hubinger, Adam Jermyn, Johannes Treutlein, Rubi Hudson, Kate Woolverton 2023-02-02 2023-02-16 [("doi","10.48550/arXiv.2302.00805")] reinforcement-learning/imitation-learning reinforcement-learning/model/decision-transformer reinforcement-learning/safe statistics/prediction

Our intention is to provide a definitive reference on what it would take to safely make use of generative/predictive models in the absence of a solution to the Eliciting Latent Knowledge problem.

Furthermore, we believe that large language models can be understood as such predictive models of the world, and that such a conceptualization raises opportunities for their safe yet powerful use via carefully conditioning them to predict desirable outputs.

Unfortunately, such approaches also raise a variety of potentially fatal safety problems, particularly surrounding situations where predictive models predict the output of other AI systems, potentially unbeknownst to us.

There are numerous potential solutions to such problems, however, primarily via carefully conditioning models to predict the things we want (eg. humans) rather than the things we don’t (eg. malign AIs).

Furthermore, due to the simplicity of the prediction objective, we believe that predictive models present the easiest inner alignment problem that we are aware of.

As a result, we think that conditioning approaches for predictive models represent the safest known way of eliciting human-level and slightly superhuman capabilities from large language models and other similar future models.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4753676/ Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis Lavinia Paternoster, Marie Standl, Johannes Waage, Hansjörg Baurecht, Melanie Hotze, David P. Strachan, John A. Curtin, Klaus Bønnelykke, Chao Tian, Atsushi Takahashi, Jorge Esparza-Gordillo, Alexessander Couto Alves, Jacob P. Thyssen, Herman T. den Dekker, Manuel A. Ferreira, Elisabeth Altmaier, Patrick Ma Sleiman, Feng Li Xiao, Juan R. Gonzalez, Ingo Marenholz, Birgit Kalb, Maria Pino Yanes, Cheng-Jian Xu, Lisbeth Carstensen, Maria M. Groen-Blokhuis, Cristina Venturini, Craig E. Pennell, Sheila J. Barton, Albert M. Levin, Ivan Curjuric, Mariona Bustamante, Eskil Kreiner-Møller, Gabrielle A. Lockett, Jonas Bacelis, Supinda Bunyavanich, Rachel A. Myers, Anja Matanovic, Ashish Kumar, Joyce Y. Tung, Tomomitsu Hirota, Michiaki Kubo, Wendy L. McArdle, A. J. Henderson, John P. Kemp, Jie Zheng, George Davey Smith, Franz Rüschendorf, Anja Bauerfeind, Min Ae Lee-Kirsch, Andreas Arnold, Georg Homuth, Carsten O. Schmidt, Elisabeth Mangold, Sven Cichon, Thomas Keil, Elke Rodríguez, Annette Peters, Andre Franke, Wolfgang Lieb, Natalija Novak, Regina Fölster-Holst, Momoko Horikoshi, Juha Pekkanen, Sylvain Sebert, Lise L. Husemoen, Niels Grarup, Johan C. de Jongste, Fernando Rivadeneira, Albert Hofman, Vincent Wv Jaddoe, Suzanne Gma Pasmans, Niels J. Elbert, André G. Uitterlinden, Guy B. Marks, Philip J. Thompson, Melanie C. Matheson, Colin F. Robertson, Janina S. Ried, Jin Li, Xian Bo Zuo, Xiao Dong Zheng, Xian Yong Yin, Liang Dan Sun, Maeve A. McAleer, Grainne M. O’Regan, Caoimhe Mr Fahy, Linda E. Campbell, Milan Macek, Michael Kurek, Donglei Hu, Celeste Eng, Dirkje S. Postma, Bjarke Feenstra, Frank Geller, Jouke Jan Hottenga, Christel M. Middeldorp, Pirro Hysi, Veronique Bataille, Tim Spector, Carla Mt Tiesler, Elisabeth Thiering, Badri Pahukasahasram, James J. Yang, Medea Imboden, Scott Huntsman, Natàlia Vilor-Tejedor, Caroline L. Relton, Ronny Myhre, Wenche Nystad, Adnan Custovic, Scott T. Weiss, Deborah A. Meyers, Cilla Söderhäll, Erik Melén, Carole Ober, Benjamin A. Raby, Angela Simpson, Bo Jacobsson, John W. Holloway, Hans Bisgaard, Jordi Sunyer, Nicole M. Probst Hensch, L. Keoki Williams, Keith M. Godfrey, Carol A. Wang, Dorret I. Boomsma, Mads Melbye, Gerard H. Koppelman, Deborah Jarvis, Wh Irwin McLean, Alan D. Irvine, Xue Jun Zhang, Hakon Hakonarson, Christian Gieger, Esteban G. Burchard, Nicholas G. Martin, Liesbeth Duijts, Allan Linneberg, Marjo-Riitta Jarvelin, Markus M. Noethen, Susanne Lau, Norbert Hübner, Young-Ae Lee, Mayumi Tamari, David A. Hinds, Daniel Glass, Sara J. Brown, Joachim Heinrich, David M. Evans, Stephan Weidinger 2015 2023-02-16 [("doi","10.1038/ng.3424")] genetics/heritable

Genetic association studies have identified 21 loci associated with atopic dermatitis risk predominantly in populations of European ancestry.

To identify further susceptibility loci for this common, complex skin disease, we performed a meta-analysis of >15 million genetic variants in 21,399 cases and 95,464 controls from populations of European, African, Japanese and Latino ancestry, followed by replication in 32,059 cases and 228,628 controls from 18 studies.

We identified 10 new risk loci, bringing the total number of known atopic dermatitis risk loci to 31 (with new secondary signals at 4 of these loci). Notably, the new loci include candidate genes with roles in the regulation of innate host defenses and T cell function, underscoring the important contribution of (auto)immune mechanisms to atopic dermatitis pathogenesis.

--- https://publicdomainreview.org/collection/unai-no-tomo/ 2023-02-16 history/public-domain-review japan/art --- https://restofworld.org/2023/ai-revolution-outsourced-workers/ 2023-02-16 ai/nn/diffusion/midjourney ai/nn/transformer/gpt/3/nonfiction ai/scaling/economics --- https://www.foreignaffairs.com/china/illusion-chinas-ai-prowess-regulation-helen-toner 2023-02-16 ai/scaling/economics --- https://danwang.co/technological-momentum/ 2023-02-16 ai/scaling/economics --- https://www.wired.com/story/the-secret-ingredient-in-your-craft-beer-gene-edited-yeast/ 2023-02-16 food genetics/editing --- https://www.fightaging.org/archives/2023/06/notes-from-the-2023-age-related-disease-therapeutics-summit/ 2023-02-17 longevity/epigenetics --- https://arxiv.org/abs/2305.17872 Universal Mechanical Polycomputation in Granular Matter Atoosa Parsa, Sven Witthaus, Nidhi Pashine, Corey S. O’Hern, Rebecca Kramer-Bottiglio, Josh Bongard 2023-05-29 2023-05-29 [("doi","10.1145/3583131.3590520")] cs/computable cs/hardware reinforcement-learning/model-free

[video, code] Unconventional computing devices are increasingly of interest as they can operate in environments hostile to silicon-based electronics, or compute in ways that traditional electronics cannot. Mechanical computers, wherein information processing is a material property emerging from the interaction of components with the environment, are one such class of devices.

This information processing can be manifested in various physical substrates, one of which is granular matter. In a granular assembly, vibration can be treated as the information-bearing mode. This can be exploited to realize polycomputing: materials can be evolved such that a single grain within them can report the result of multiple logical operations simultaneously at different frequencies, without recourse to quantum effects.

Here, we demonstrate the evolution [using AFPO] of a material in which one grain acts simultaneously as two different NAND gates at two different frequencies. NAND gates are of interest as any logical operations can be built from them. Moreover, they are nonlinear thus demonstrating a step toward general-purpose, computationally dense mechanical computers.

Polycomputation was found to be distributed across each evolved material, suggesting the material’s robustness. With recent advances in material sciences, hardware realization of these materials may eventually provide devices that challenge the computational density of traditional computers.

--- https://arxiv.org/abs/2307.07367 Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflow Maria del Rio-Chanona, Nadzeya Laurentsyeva, Johannes Wachs 2023-07-14 2023-07-14 [("doi","10.48550/arXiv.2307.07367")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/codex ai/scaling/economics

Large language models like ChatGPT efficiently provide users with information about various topics, presenting a potential substitute for searching the web and asking people for help online. But since users interact privately with the model, these models may drastically reduce the amount of publicly available human-generated data and knowledge resources. This substitution can present a problem in securing training data for future models.

In this work, we investigate how the release of ChatGPT-3.5 changed human-generated open data on the web by analyzing the activity on Stack Overflow, the leading online Q&A platform for computer programming. We find that relative to its Russian and Chinese counterparts, where access to ChatGPT is limited, and to similar forums for mathematics, where ChatGPT is less capable, activity on Stack Overflow decreased. A difference-in-differences model estimates a 16% decrease in weekly posts on Stack Overflow. This effect increases in magnitude over time, and is larger for posts related to the most widely used programming languages.

Posts made after ChatGPT get similar voting scores than before, suggesting that ChatGPT is not merely displacing duplicate or low-quality content. These results suggest that more users are adopting large language models to answer questions and they are better substitutes for Stack Overflow for languages for which they have more training data.

Using models like ChatGPT may be more efficient for solving certain programming problems, but its widespread adoption and the resulting shift away from public exchange on the web will limit the open data people and models can learn from in the future.

[Republished in 2024 as "Large language models reduce public knowledge sharing on online Q&A platforms".]

--- https://en.wikipedia.org/wiki/Jorge_Luis_Borges Jorge Luis Borges 2023-02-17 borges --- https://www.reddit.com/r/StableDiffusion/comments/152wtrh/sdxl_recognises_the_styles_of_thousands_of/ 2023-02-17 ai/nn/diffusion --- https://www.datagubbe.se/voynich/ 2023-02-17 cs/cryptography psychiatry/schizophrenia --- https://www.reddit.com/r/emacs/comments/1530yh8/kalman_reti_the_last_symbolics_developer_speaks/ 2023-02-17 cs/lisp/emacs --- https://www.sbnation.com/longform/2013/2/28/4036934/jai-alai-sport-in-america-miami 2023-02-17 sociology --- https://arxiv.org/abs/2307.07069 Typed Design Patterns for the Functional Era Will Crichton 2023-07-13 2023-07-13 [("doi","10.1145/3609025.3609477")] cs/haskell

This paper explores how design patterns could be revisited in the era of mainstream functional programming languages. I discuss the kinds of knowledge that ought to be represented as functional design patterns: architectural concepts that are relatively self-contained, but whose entirety cannot be represented as a language-level abstraction.

I present 4 concrete examples embodying this idea: the Witness, the State Machine, the Parallel Lists, and the Registry.

Each pattern is implemented in Rust to demonstrate how careful use of a sophisticated type system can better model each domain construct and thereby catch user mistakes at compile-time.

--- https://publicdomainreview.org/collection/hokusai-warriors/ 2023-02-17 history/public-domain-review japan/art --- https://tedium.co/2023/07/19/tamper-evident-jar-safety-button-history/ 2023-02-17 cs/security --- https://stanforddaily.com/2023/07/19/stanford-president-resigns-over-manipulated-research-will-retract-at-least-3-papers/ 2023-02-18 statistics/bias --- https://if50.substack.com/p/the-antagonists 2023-02-18 fiction/text-game --- https://x.com/emollick/status/1681650599933222912 2023-02-18 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/emollick/status/1681739807498596352 2023-02-18 ai/nn/transformer/gpt/4/poetry ai/nn/transformer/gpt/claude --- https://arxiv.org/abs/2306.09346 Rosetta Neurons: Mining the Common Units in a Model Zoo Amil Dravid, Yossi Gandelsman, Alexei A. Efros, Assaf Shocher 2023-06-15 2023-06-15 [("doi","10.48550/arXiv.2306.09346")] ai/nn/cnn ai/nn/gan/biggan ai/nn/gan/stylegan ai/nn/transformer/clip ai/nn/vae/mae

Do different neural networks, trained for various vision tasks, share some common representations? In this paper, we demonstrate the existence of common features we call “Rosetta Neurons” across a range of models with different architectures, different tasks (generative and discriminative), and different types of supervision (class-supervised, text-supervised, self-supervised).

We present an algorithm for mining a dictionary of Rosetta Neurons across several popular vision models: Class Supervised-ResNet50, DINO-ResNet50, DINO-ViT, MAE, CLIP-ResNet50, BigGAN, StyleGAN-2, StyleGAN-XL.

Our findings suggest that certain visual concepts and structures are inherently embedded in the natural world and can be learned by different models regardless of the specific task or architecture, and without the use of semantic labels. We can visualize shared concepts directly due to generative models included in our analysis.

The Rosetta Neurons facilitate model-to-model translation enabling various inversion-based manipulations, including cross-class alignments, shifting, zooming, and more, without the need for specialized training.

--- https://magazine.atavist.com/lost-in-summerland-lily-dale-psychics-mediums-spiritualism/ 2023-02-18 philosophy/religion psychiatry/traumatic-brain-injury --- https://medium.com/the-shadow/hot-take-psilocybin-was-the-biggest-mistake-of-my-life-f93cf6598974 2023-02-18 psychedelic psychiatry/anxiety psychiatry/bipolar psychiatry/depression --- https://arxiv.org/abs/2307.07870 Large Language Models as Superpositions of Cultural Perspectives Grgur Kovač, Masataka Sawayama, Rémy Portelas, Cédric Colas, Peter Ford Dominey, Pierre-Yves Oudeyer 2023-07-15 2023-07-15 [("doi","10.48550/arXiv.2307.07870")] ai/nn/transformer/gpt/3 ai/nn/transformer/gpt/4 psychology/personality

Large Language Models (LLMs) are often misleadingly recognized as having a personality or a set of values. We argue that an LLM can be seen as a superposition of perspectives with different values and personality traits. LLMs exhibit context-dependent values and personality traits that change based on the induced perspective (as opposed to humans, who tend to have more coherent values and personality traits across contexts).

We introduce the concept of perspective controllability, which refers to a model’s affordance to adopt various perspectives with differing values and personality traits. In our experiments, we use questionnaires from psychology (PVQ, VSM, IPIP) to study how exhibited values and personality traits change based on different perspectives.

Through qualitative experiments, we show that LLMs express different values when those are (implicitly or explicitly) implied in the prompt, and that LLMs express different values even when those are not obviously implied (demonstrating their context-dependent nature).

We then conduct quantitative experiments to study the controllability of different models (GPT-4, GPT-3.5, OpenAssistant, StableVicuna, StableLM), the effectiveness of various methods for inducing perspectives, and the smoothness of the models’ drivability.

We conclude by examining the broader implications of our work and outline a variety of associated scientific questions. The project website is available at https://sites.google.com/view/llm-superpositions.

--- https://blog.ploeh.dk/2017/10/04/from-design-patterns-to-category-theory/ 2023-02-18 cs/haskell math --- https://arxiv.org/abs/2307.09458#deepmind Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla Tom Lieberum, Matthew Rahtz, János Kramár, Neel Nanda, Geoffrey Irving, Rohin Shah, Vladimir Mikulik 2023-07-18 2023-07-18 [("doi","10.48550/arXiv.2307.09458")] ai/nn/fully-connected ai/nn/transformer/attention

Circuit analysis is a promising technique for understanding the internal mechanisms of language models. However, existing analyses are done in small models far from the state-of-the-art. To address this, we present a case study of circuit analysis in the 70B Chinchilla model, aiming to test the scalability of circuit analysis. In particular, we study multiple-choice question answering, and investigate Chinchilla’s capability to identify the correct answer label given knowledge of the correct answer text. We find that the existing techniques of logit attribution, attention pattern visualization, and activation patching naturally scale to Chinchilla, allowing us to identify and categorize a small set of ‘output nodes’ (attention heads and MLPs).

We further study the ‘correct letter’ category of attention heads aiming to understand the semantics of their features, with mixed results. For normal multiple-choice question answers, we compress the query, key and value subspaces of the head without loss of performance when operating on the answer labels for multiple-choice questions, and we show that the query and key subspaces represent an ‘Nth item in an enumeration’ feature to at least some extent. However, when we attempt to use this explanation to understand the heads’ behavior on a more general distribution including randomized answer labels, we find that it is only a partial explanation, suggesting there is more to learn about the operation of ‘correct letter’ heads on multiple choice question answering.

--- https://en.wikipedia.org/wiki/Pramipexole Pramipexole 2023-02-19 psychiatry/depression --- https://arxiv.org/abs/2307.10168 LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs Tongshuang Wu, Haiyi Zhu, Maya Albayrak, Alexis Axon, Amanda Bertsch, Wenxing Deng, Ziqi Ding, Bill Guo, Sireesh Gururaja, Tzu-Sheng Kuo, Jenny T. Liang, Ryan Liu, Ihita Mandal, Jeremiah Milbauer, Xiaolin Ni, Namrata Padmanabhan, Subhashini Ramkumar, Alexis Sudjianto, Jordan Taylor, Ying-Jui Tseng, Patricia Vaidos, Zhijin Wu, Wei Wu, Chenyang Yang 2023-07-19 2023-07-19 [("doi","10.48550/arXiv.2307.10168")] ai/nn/transformer/gpt/inner-monologue ai/scaling/economics economics/automation

LLMs have shown promise in replicating human-like behavior in crowdsourcing tasks that were previously thought to be exclusive to human abilities. However, current efforts focus mainly on simple atomic tasks. We explore whether LLMs can replicate more complex crowdsourcing pipelines.

We find that modern LLMs can simulate some of crowdworkers’ abilities in these “human computation algorithms”, but the level of success is variable and influenced by requesters’ understanding of LLM capabilities, the specific skills required for sub-tasks, and the optimal interaction modality for performing these sub-tasks.

We reflect on human and LLMs’ different sensitivities to instructions, stress the importance of enabling human-facing safeguards for LLMs, and discuss the potential of training humans and LLMs with complementary skill sets.

Crucially, we show that replicating crowdsourcing pipelines offers a valuable platform to investigate (1) the relative strengths of LLMs on different tasks (by cross-comparing their performances on sub-tasks) and (2) LLMs’ potential in complex tasks, where they can complete part of the tasks while leaving others to humans.

--- https://questioner.substack.com/p/the-psychedelic-outlier 2023-02-19 psychedelic psychiatry/schizophrenia --- https://questioner.substack.com/p/crazy-like-a-fox-part-1 2023-02-19 psychedelic psychiatry/schizophrenia --- https://www.biorxiv.org/content/10.1101/2023.05.02.539144.full A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing Philip K. Shiu, Gabriella R. Sterne, Nico Spiller, Romain Franconville, Andrea Sandoval, Joie Zhou, Neha Simha, Chan Hyuk Kang, Seongbong Yu, Jinseop S. Kim, Sven Dorkenwald, Arie Matsliah, Philipp Schlegel, Szi-chieh Yu, Claire E. McKellar, Amy Sterling, Marta Costa, Katharina Eichler, Gregory S. X. E. Jefferis, Mala Murthy, Alexander Shakeel Bates, Nils Eckstein, Jan Funke, Salil S. Bidaye, Stefanie Hampel, Andrew M. Seeds, Kristin Scott 2023-05-02 2023-05-02 [("doi","10.1101/2023.05.02.539144")] psychology/neuroscience

The forthcoming assembly of the adult Drosophila melanogaster central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain.

Here, we create a leaky integrate-and-fire computational model of the entire Drosophila brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors.

We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation.

Computational activation of neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing.

Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes.

Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.

--- https://arxiv.org/abs/2307.10088#google Android in the Wild: A Large-Scale Dataset for Android Device Control Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, Timothy Lillicrap 2023-07-19 2023-07-19 [("doi","10.48550/arXiv.2307.10088")] ai/dataset ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm/2 reinforcement-learning/imitation-learning

There is a growing interest in device-control systems that can interpret human natural language instructions and execute them on a digital device by directly controlling its user interface.

We present a dataset for device-control research, Android in the Wild (AITW), which is orders of magnitude larger than current datasets. The dataset contains human demonstrations of device interactions, including the screens and actions, and corresponding natural language instructions.

It consists of 715k episodes spanning 30k unique instructions, 4 versions of Android (v10–13), and 8 device types (Google Pixel 2 XL to Pixel 6) with varying screen resolutions. It contains multi-step tasks that require semantic understanding of language and visual context. This dataset poses a new challenge: actions available through the user interface must be inferred from their visual appearance. And, instead of simple UI element-based actions, the action space consists of precise gestures (eg. horizontal scrolls to operate carousel widgets).

We organize our dataset to encourage robustness analysis of device-control systems, ie. how well a system performs in the presence of new task descriptions, new applications, or new platform versions.

We develop two agents [behavior cloning Transformer & PaLM-2 using inner-monologue] and report performance across the dataset.

The dataset is available at Github.

--- /doc/existential-risk/2016-chalmers.pdf The Singularity: A Philosophical Analysis David J. Chalmers 2016-01-01 2023-02-19 [("doi","10.1002/9781118922590.ch16")] ai/scaling existential-risk philosophy/mind

This chapter provides a rich philosophical discussion of superintelligence, a widely discussed piece that has encouraged philosophers of mind to take transhumanism, mind uploading, and the singularity more seriously.

It starts with the argument for a singularity: is there good reason to believe that there will be an intelligence explosion?

Next, the chapter considers how to negotiate the singularity: if it is possible that there will be a singularity, how can we maximize the chances of a good outcome?

Finally, it considers the place of humans in a post-singularity world, with special attention to questions about uploading: can an uploaded human be conscious, and will uploading preserve personal identity?

The chapter also analyzes whether there are certain constraints on design of artificial intelligence (AI) and AI+ that we might impose, in order to increase the chances of a good outcome.

…Conclusions: Will there be a singularity? I think that it is certainly not out of the question, and that the main obstacles are likely to be obstacles of motivation rather than obstacles of capacity.

How should we negotiate the singularity? Very carefully, by building appropriate values into machines, and by building the first AI and AI+ systems in virtual worlds.

How can we integrate into a post-singularity world? By gradual uploading followed by enhancement if we are still around then, and by reconstructive uploading followed by enhancement if we are not.

--- /doc/psychedelic/2016-carbonaro.pdf Survey study of challenging experiences after ingesting psilocybin mushrooms: Acute and enduring positive and negative consequences 2016-01-01 2023-02-19 psychedelic psychiatry/depression --- https://www.astralcodexten.com/p/the-extinction-tournament 2023-02-19 existential-risk statistics/prediction --- https://thepointmag.com/examined-life/my-beautiful-friend/ 2023-02-19 sociology/intrasexual-aggression --- https://arnoldkling.substack.com/p/the-marginal-revolution-is-dead Arnold Kling 2023-02-19 economics/automation politics --- https://en.wikipedia.org/wiki/Emotive_conjugation Russell conjugation 2023-02-20 politics psychology/cognitive-bias/illusion-of-depth psychology/linguistics --- https://github.com/vitoplantamura/OnnxStream 2023-02-20 ai/nn/diffusion ai/nn/sparsity/low-precision --- https://arxiv.org/abs/1809.02156 Object Hallucination in Image Captioning Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, Trevor Darrell, Kate Saenko 2018-09-06 2023-02-20 [("doi","10.48550/arXiv.1809.02156")] ai/nn/rnn ai/nn/transformer

Despite continuously improving performance, contemporary image captioning models are prone to “hallucinating” objects that are not actually in a scene. One problem is that standard metrics only measure similarity to ground truth captions and may not fully capture image relevance.

In this work, we propose a new image relevance metric to evaluate current models with veridical visual labels and assess their rate of object hallucination. We analyze how captioning model architectures and learning objectives contribute to object hallucination, explore when hallucination is likely due to image misclassification or language priors, and assess how well current sentence metrics capture object hallucination.

We investigate these questions on the standard image captioning benchmark, MS COCO, using a diverse set of models. Our analysis yields several interesting findings, including that models which score best on standard sentence metrics do not always have lower hallucination and that models which hallucinate more tend to make errors driven by language priors.

--- https://arxiv.org/abs/1704.04368 Get To The Point: Summarization with Pointer-Generator Networks Abigail See, Peter J. Liu, Christopher D. Manning 2017-04-14 2023-02-20 [("doi","10.48550/arXiv.1704.04368")] ai/nn/retrieval ai/nn/rnn ai/nn/transformer/attention

Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves.

In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition.

We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.

--- https://x.com/deepfates/status/1682110624271319040 2023-02-20 ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/inner-monologue cs/security --- https://www.slowboring.com/p/chatgpt-goes-to-harvard 2023-02-20 ai/nn/transformer/gpt/4/nonfiction economics sociology --- https://www.nytimes.com/2023/07/12/magazine/semiconductor-chips-us-china.html 2023-02-20 ai/scaling/hardware politics --- https://sander.ai/2023/07/20/perspectives.html 2023-02-20 ai/nn/diffusion ai/nn/rnn ai/nn/vae --- https://github.com/ggerganov/llama.cpp/pull/1773 2023-02-20 ai/nn/sampling ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue --- https://blog.rootsofprogress.org/american-genesis-part-2-technocracy-to-counterculture 2023-02-20 politics technology --- https://contrary.com/foundations-and-frontiers/molecular-manufacturing 2023-02-20 technology --- https://www.linyangchen.com/Typography-Fell-Types-font 2023-02-21 design/typography --- https://arxiv.org/abs/1803.02999#openai Reptile: On First-Order Meta-Learning Algorithms Alex Nichol, Joshua Achiam, John Schulman 2018-03-08 2023-02-21 [("doi","10.48550/arXiv.1803.02999")] ai/nn/cnn reinforcement-learning/meta-learning

[blog, code] This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (ie. learns quickly) when presented with a previously unseen task sampled from this distribution.

We analyze a family of algorithms for learning a parameter initialization that can be fine-tuned quickly on a new task, using only first-order derivatives for the meta-learning updates. This family includes and generalizes first-order MAML [FOMAML], an approximation to MAML obtained by ignoring second-order derivatives. It also includes Reptile [cf. LEO, ANIL; neural processes], a new algorithm that we introduce here, which works by repeatedly sampling a task, training on it, and moving the initialization towards the trained weights on that task.

We expand on the results from MAML showing that first-order meta-learning algorithms perform well on some well-established benchmarks for few-shot classification, and we provide theoretical analysis aimed at understanding why these algorithms work.

--- https://arxiv.org/abs/1909.09157 ANIL: Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML Aniruddh Raghu, Maithra Raghu, Samy Bengio, Oriol Vinyals 2019-09-19 2023-02-21 [("doi","10.48550/arXiv.1909.09157")] ai/nn/cnn reinforcement-learning/meta-learning

An important research direction in machine learning has centered around developing meta-learning algorithms to tackle few-shot learning. An especially successful algorithm has been Model Agnostic Meta-Learning (MAML), a method that consists of two optimization loops, with the outer loop finding a meta-initialization, from which the inner loop can efficiently learn new tasks.

Despite MAML’s popularity, a fundamental open question remains—is the effectiveness of MAML due to the meta-initialization being primed for rapid learning (large, efficient changes in the representations) or due to feature reuse, with the meta initialization already containing high quality features? We investigate this question, via ablation studies and analysis of the latent representations, finding that feature reuse is the dominant factor.

This leads to the ANIL (Almost No Inner Loop) algorithm, a simplification of MAML where we remove the inner loop for all but the (task-specific) head of a MAML-trained network. ANIL matches MAML’s performance on benchmark few-shot image classification and RL and offers computational improvements over MAML.

We further study the precise contributions of the head and body of the network, showing that performance on the test tasks is entirely determined by the quality of the learned features, and we can remove even the head of the network (the NIL algorithm).

We conclude with a discussion of the rapid learning vs feature reuse question for meta-learning algorithms more broadly.

--- https://arxiv.org/abs/1807.05960#deepmind LEO: Meta-Learning with Latent Embedding Optimization Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, Raia Hadsell 2018-07-16 2023-02-21 [("doi","10.48550/arXiv.1807.05960")] ai/nn/cnn reinforcement-learning/meta-learning

Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes.

We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this low-dimensional latent space.

The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters.

Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks. Further analysis indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space.

--- https://arxiv.org/abs/1912.02738 MetaFun: Meta-Learning with Iterative Functional Updates Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam R. Kosiorek, Yee Whye Teh 2019-12-05 2023-02-21 [("doi","10.48550/arXiv.1912.02738")] reinforcement-learning/meta-learning

We develop a functional encoder-decoder approach to supervised meta-learning, where labeled data is encoded into an infinite-dimensional functional representation rather than a finite-dimensional one. Furthermore, rather than directly producing the representation, we learn a neural update rule resembling functional gradient descent which iteratively improves the representation. The final representation is used to condition the decoder to make predictions on unlabeled data.

Our MetaFun approach is the first to demonstrates the success of encoder-decoder style meta-learning methods like conditional neural processes on large-scale few-shot classification benchmarks such as miniImageNet and tieredImageNet, where it achieves state-of-the-art performance.

--- https://x.com/octal/status/1682372222814507012 2023-02-21 design --- https://archive.org/details/in.ernet.dli.2015.148949 The Backwards Child Cyril Burt 1923 2023-02-21 iq/ses --- https://arxiv.org/abs/1609.07959 Multiplicative LSTM for sequence modeling Ben Krause, Liang Lu, Iain Murray, Steve Renals 2016-09-26 2023-02-21 [("doi","10.48550/arXiv.1609.07959")] ai/nn/dynamic-evaluation ai/nn/rnn ai/nn/tokenization cs/algorithm/information

We introduce multiplicative LSTM (mLSTM), a recurrent neural network architecture for sequence modeling that combines the long short-term memory (LSTM) and multiplicative recurrent neural network architectures. mLSTM is characterised by its ability to have different recurrent transition functions for each possible input, which we argue makes it more expressive for autoregressive density estimation.

We demonstrate empirically that mLSTM outperforms standard LSTM and its deep variants for a range of character level language modeling tasks. In this version of the paper, we regularize mLSTM to achieve 1.27 bits/char on text8 and 1.24 bits/char on Hutter Prize. We also apply a purely byte-level mLSTM on the WikiText-2 dataset to achieve a character level entropy of 1.26 bits/char, corresponding to a word level perplexity of 88.8, which is comparable to word level LSTMs regularized in similar ways on the same task.

--- https://erights.medium.com/norm-hardys-place-in-history-cecf191df641 2023-02-21 cryonics cs/security --- https://arxiv.org/abs/1904.00284 COCO-GAN: Generation by Parts via Conditional Coordinating Chieh Hubert Lin, Chia-Che Chang, Yu-Sheng Chen, Da-Cheng Juan, Wei Wei, Hwann-Tzong Chen 2019-03-30 2023-02-21 [("doi","10.48550/arXiv.1904.00284")] ai/nn/cnn ai/nn/gan

Humans can only interact with part of the surrounding environment due to biological restrictions. Therefore, we learn to reason the spatial relationships across a series of observations to piece together the surrounding environment. Inspired by such behavior and the fact that machines also have computational constraints, we propose COnditional COordinate GAN (COCO-GAN) of which the generator generates images by parts based on their spatial coordinates as the condition.

On the other hand, the discriminator learns to justify realism across multiple assembled patches by global coherence, local appearance, and edge-crossing continuity. Despite the full images are never generated during training, we show that COCO-GAN can produce state-of-the-art-quality full images during inference.

We further demonstrate a variety of novel applications enabled by teaching the network to be aware of coordinates. First, we perform extrapolation to the learned coordinate manifold and generate off-the-boundary patches. Combining with the originally generated full image, COCO-GAN can produce images that are larger than training samples, which we called “beyond-boundary generation”.

We then showcase panorama generation within a cylindrical coordinate system that inherently preserves horizontally cyclic topology. On the computation side, COCO-GAN has a built-in divide-and-conquer paradigm that reduces memory requisition during training and inference, provides high-parallelism, and can generate parts of images on-demand.

--- https://arxiv.org/abs/1805.08006 Bidirectional Learning for Robust Neural Networks Sidney Pontes-Filho, Marcus Liwicki 2018-05-21 2023-02-22 [("doi","10.1109/IJCNN.2019.8852120")] ai/nn/cnn ai/nn/fully-connected ai/nn/gan

A multilayer perceptron can behave as a generative classifier by applying bidirectional learning (BL). It consists of training an undirected neural network to map input to output and vice-versa; therefore it can produce a classifier in one direction, and a generator in the opposite direction for the same data. The learning process of BL tries to reproduce the neuroplasticity stated in Hebbian theory using only backward propagation of errors.

In this paper, two novel learning techniques are introduced which use BL for improving robustness to white noise static and adversarial examples. The first method is bidirectional propagation of errors, where the error propagation occurs in backward and forward directions. Motivated by the fact that its generative model receives as input a constant vector per class, we introduce as a second method the hybrid adversarial networks (HAN). Its generative model receives a random vector as input and its training is based on generative adversarial networks (GAN).

To assess the performance of BL, we perform experiments using several architectures with fully and convolutional layers, with and without bias. Experimental results show that both methods improve robustness to white noise static and adversarial examples, and even increase accuracy, but have different behavior depending on the architecture and task, being more beneficial to use the one or the other.

Nevertheless, HAN using a convolutional architecture with batch normalization presents outstanding robustness, reaching state-of-the-art accuracy on adversarial examples of hand-written digits.

--- https://www.wsj.com/articles/craftsman-america-wrench-stanley-black-decker-reshoring-factory-1125792f 2023-02-22 economics/automation --- https://x.com/ahr_like_air/status/1682885469632360448 2023-02-22 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/codex cs/security --- /doc/design/typography/subscript/2021-05-20-rachelkowert-standuptrexmeme-etal.jpg 2021-05-20 2023-02-22 design/typography/subscript fiction/humor --- https://www.proekt.media/en/portrait-en/evgeny-prigozhin/ 2023-02-22 design/typography/subscript politics --- https://en.wikipedia.org/wiki/General_semantics General semantics 2023-02-22 design/typography/subscript --- https://en.wikipedia.org/wiki/Subscript_and_superscript Subscript and superscript 2023-02-22 design/typography/subscript --- https://en.wikipedia.org/wiki/Ruby_character Ruby character 2023-02-22 design/typography/subscript --- https://en.wikipedia.org/wiki/Furigana Furigana 2023-02-22 design/typography/subscript --- https://en.wikipedia.org/wiki/Interlinear_gloss Interlinear gloss 2023-02-22 design/typography/subscript --- https://en.wikipedia.org/wiki/Evidentiality Evidentiality 2023-02-22 design/typography/subscript --- https://www.lesswrong.com/posts/Tmz6ucxDFsdod2QLd/subscripts-for-probabilities 2023-02-23 design/typography/subscript --- https://en.wikipedia.org/wiki/Unicode_subscripts_and_superscripts#Superscripts_and_subscripts_block Unicode subscripts and superscripts § Superscripts and subscripts block 2023-02-23 design/typography/subscript --- https://www.lesswrong.com/posts/NkjPp86uuyunxDoB8/subscripting-typographic-convention-for-citations-dates Subscripting Typographic Convention For Citations/Dates/Sources/Evidentials: A Proposal Gwern 2020 2020 design/typography/subscript --- https://scholar.archive.org/ 2023-02-23 cs/linkrot --- https://arxiv.org/abs/1811.03115#google Blockwise Parallel Decoding for Deep Autoregressive Models Mitchell Stern, Noam Shazeer, Jakob Uszkoreit 2018-11-07 2023-02-23 [("doi","10.48550/arXiv.1811.03115")] ai/nn/sampling ai/nn/transformer

Deep autoregressive sequence-to-sequence models have demonstrated impressive performance across a wide variety of tasks in recent years. While common architecture classes such as recurrent, convolutional, and self-attention networks make different trade-offs between the amount of computation needed per layer and the length of the critical path at training time, generation still remains an inherently sequential process.

To overcome this limitation, we propose a novel blockwise parallel decoding scheme in which we make predictions for multiple time steps in parallel then back off to the longest prefix validated by a scoring model. This allows for substantial theoretical improvements in generation speed when applied to architectures that can process output sequences in parallel.

We verify our approach empirically through a series of experiments using state-of-the-art self-attention models for machine translation and image super-resolution, achieving iteration reductions of up to 2× over a baseline greedy decoder with no loss in quality, or up to 7× in exchange for a slight decrease in performance. In terms of wall-clock time, our fastest models exhibit real-time speedups of up to 4× over standard greedy decoding.

--- https://arxiv.org/abs/1811.02155 FloWaveNet: A Generative Flow for Raw Audio Sungwon Kim, Sang-gil Lee, Jongyoon Song, Jaehyeon Kim, Sungroh Yoon 2018-11-06 2023-02-23 [("doi","10.48550/arXiv.1811.02155")] ai/music ai/nn/cnn

Most modern text-to-speech architectures use a WaveNet vocoder for synthesizing high-fidelity waveform audio, but there have been limitations, such as high inference time, in its practical application due to its ancestral sampling scheme.

The recently suggested Parallel WaveNet and ClariNet have achieved real-time audio synthesis capability by incorporating inverse autoregressive flow for parallel sampling. However, these approaches require a two-stage training pipeline with a well-trained teacher network and can only produce natural sound by using probability distillation along with auxiliary loss terms.

We propose FloWaveNet, a flow-based generative model for raw audio synthesis. FloWaveNet requires only a single-stage training procedure and a single maximum likelihood loss, without any additional auxiliary terms, and it is inherently parallel due to the characteristics of generative flow. The model can efficiently sample raw audio in real-time, with clarity comparable to previous two-stage parallel models.

The code and samples for all models, including our FloWaveNet, are publicly available.

--- https://arxiv.org/abs/1902.03249#google Insertion Transformer: Flexible Sequence Generation via Insertion Operations Mitchell Stern, William Chan, Jamie Kiros, Jakob Uszkoreit 2019-02-08 2023-02-23 [("doi","10.48550/arXiv.1902.03249")] ai/nn/sampling ai/nn/transformer

We present the Insertion Transformer, an iterative, partially autoregressive model for sequence generation based on insertion operations. Unlike typical autoregressive models which rely on a fixed, often left-to-right ordering of the output, our approach accommodates arbitrary orderings by allowing for tokens to be inserted anywhere in the sequence during decoding.

This flexibility confers a number of advantages: for instance, not only can our model be trained to follow specific orderings such as left-to-right generation or a binary tree traversal, but it can also be trained to maximize entropy over all valid insertions for robustness.

In addition, our model seamlessly accommodates both fully autoregressive generation (one insertion at a time) and partially autoregressive generation (simultaneous insertions at multiple locations).

We validate our approach by analyzing its performance on the WMT 2014 English-German machine translation task under various settings for training and decoding. We find that the Insertion Transformer outperforms many prior non-autoregressive approaches to translation at comparable or better levels of parallelism, and successfully recovers the performance of the original Transformer while requiring only logarithmically many iterations during decoding.

--- https://arxiv.org/abs/2104.09864 RoFormer: Enhanced Transformer with Rotary Position Embedding Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, Yunfeng Liu 2021-04-20 2023-02-23 [("doi","10.48550/arXiv.2104.09864")] ai/nn/transformer/attention

Position encoding recently has shown effective in the transformer architecture. It enables valuable supervision for dependency modeling between elements at different positions of the sequence.

In this paper, we first investigate various methods to integrate positional information into the learning process of transformer-based language models. Then, we propose a novel method named Rotary Position Embedding (RoPE) to effectively leverage the positional information. Specifically, the proposed RoPE encodes the absolute position with a rotation matrix and meanwhile incorporates the explicit relative position dependency in self-attention formulation.

Notably, RoPE enables valuable properties, including the flexibility of sequence length, decaying inter-token dependency with increasing relative distances, and the capability of equipping the linear self-attention with relative position encoding.

Finally, we evaluate the enhanced transformer with rotary position embedding, also called RoFormer, on various long text classification benchmark datasets.

Our experiments show that it consistently overcomes its alternatives. Furthermore, we provide a theoretical analysis to explain some experimental results.

RoFormer is already integrated into Huggingface.

--- https://www.lesswrong.com/posts/Z4tBreNCxnppoPLtd/gpts-ability-to-keep-a-secret-is-weirdly-prompt-dependent 2023-02-23 ai/nn/transformer/gpt/4/nonfiction cs/security --- https://arxiv.org/abs/1910.06262 Restoring ancient text using deep learning (Pythia): a case study on Greek epigraphy Yannis Assael, Thea Sommerschield, Jonathan Prag 2019-10-14 2023-02-23 [("doi","10.48550/arXiv.1910.06262")] ai/dataset ai/nn/rnn history

Ancient history relies on disciplines such as epigraphy, the study of ancient inscribed texts, for evidence of the recorded past. However, these texts, “inscriptions”, are often damaged over the centuries, and illegible parts of the text must be restored by specialists, known as epigraphists.

This work presents Pythia [not to be confused with the unrelated GPT model suite], the first ancient text restoration model that recovers missing characters from a damaged text input using deep neural networks [LSTM RNNs]. Its architecture is carefully designed to handle long-term context information, and deal efficiently with missing or corrupted character and word representations.

To train it, we wrote a non-trivial pipeline to convert PHI, the largest digital corpus of ancient Greek inscriptions, to machine actionable text, which we call PHI-ML.

On PHI-ML, Pythia’s predictions achieve a 30.1% character error rate, compared to the 57.3% of human epigraphists. Moreover, in 73.5% of cases the ground-truth sequence was among the Top-20 hypotheses of Pythia, which effectively demonstrates the impact of this assistive method on the field of digital epigraphy, and sets the state-of-the-art in ancient text restoration.

--- https://www.reddit.com/r/MachineLearning/comments/jthxui/p_chasing_intruding_cats_from_your_home_with/ 2023-02-23 ai/nn/cnn cat/psychology reinforcement-learning/robot --- https://www.wired.com/story/embryo-models-challenge-the-legal-ethical-and-biological-concepts-of-an-embryo/ 2023-02-24 genetics/gametogenesis --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5083941/ Modafinil Dependence and Hypersexuality: A Case Report and Review of the Evidence Sahoo Swapnajeet, Subodh Bn, Gupta Gourav 2016 2023-02-24 [("doi","10.9758/cpn.2016.14.4.402")] modafinil

Apart from sleep wake disorders, nowadays, modafinil is being prescribed for several psychiatric disorders including depression. Despite being reported as to be having very low abuse potential, cases of modafinil dependence had come to the limelight.

In this case report, we describe a 35 year old man with bipolar affective disorder while in remission who developed modafinil dependence and later on, had hypersexuality when he increased the dose of modafinil 400 → 1,000 mg/day.

Existing literature suggests that modafinil when taken above prescribed doses can cause many side effects ranging from nausea, vomiting to psychotic exacerbation and mania. However, hypersexuality as a side effect of modafinil overuse is not commonly seen.

The exact pathophysiological mechanism of modafinil induced hypersexuality is not clear. Clinicians should be aware of possibility of modafinil leading to dependence and this rare side effect of modafinil.

--- https://en.wikipedia.org/wiki/File:Sumer_is_icumen_in_-_Summer_Canon_(Reading_Rota)_(mid_13th_C),_f.11v_-_BL_Harley_MS_978.jpg File:Sumer is icumen in—Summer Canon (Reading Rota) (mid 13th C), f.11v—BL Harley MS 978.jpg 2023-02-24 design/typography/rubrication --- https://arxiv.org/abs/2306.09983 Evaluating Superhuman Models with Consistency Checks Lukas Fluri, Daniel Paleka, Florian Tramèr 2023-06-16 2023-06-16 [("doi","10.48550/arXiv.2306.09983")] ai/nn/adversarial ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction philosophy/logic reinforcement-learning/chess reinforcement-learning/model/alphago statistics/prediction

If machine learning models were to achieve superhuman abilities at various reasoning or decision-making tasks, how would we go about evaluating such models, given that humans would necessarily be poor proxies for ground truth? In this paper, we propose a framework for evaluating superhuman models via consistency checks. Our premise is that while the correctness of superhuman decisions may be impossible to evaluate, we can still surface mistakes if the model’s decisions fail to satisfy certain logical, human-interpretable rules.

We instantiate our framework on 3 tasks where correctness of decisions is hard to evaluate due to either superhuman model abilities, or to otherwise missing ground truth: evaluating chess positions, forecasting future events, and making legal judgments.

We show that regardless of a model’s (possibly superhuman) performance on these tasks, we can discover logical inconsistencies in decision making. For example: a chess engine [Leela Chess Zero] assigning opposing valuations to semantically identical boards; GPT-4 forecasting that sports records will evolve non-monotonically over time; or an AI judge assigning bail to a defendant only after we add a felony to their criminal record.

--- https://arxiv.org/abs/2307.10172#salesforce DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI Jianguo Zhang, Kun Qian, Zhiwei Liu, Shelby Heinecke, Rui Meng, Ye Liu, Zhou Yu, Huan Wang, Silvio Savarese, Caiming Xiong 2023-07-19 2023-07-19 [("doi","10.48550/arXiv.2307.10172")] ai/dataset ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5

Despite advancements in conversational AI, language models encounter challenges to handle diverse conversational tasks, and existing dialogue dataset collections often lack diversity and comprehensiveness.

To tackle these issues, we introduce DialogStudio: the largest and most diverse collection of dialogue datasets, unified under a consistent format while preserving their original information. Our collection encompasses data from open-domain dialogues, task-oriented dialogues, natural language understanding, conversational recommendation, dialogue summarization, and knowledge-grounded dialogues, making it an incredibly rich and diverse resource for dialogue research and model training.

To further enhance the utility of DialogStudio, we identify the licenses for each dataset and design domain-aware prompts for selected dialogues to facilitate instruction-aware fine-tuning.

Furthermore, we develop conversational AI models using the dataset collection, and our experiments in both zero-shot and few-shot learning scenarios demonstrate the superiority of DialogStudio.

To improve transparency and support dataset and task-based research, as well as language model pre-training, all datasets, licenses, codes, and models associated with DialogStudio are made publicly accessible at Github.

--- https://www.lesswrong.com/s/5omSW4wNKbEvYsyje/p/GpSzShaaf8po4rcmA 2023-02-24 ai/nn/fully-connected ai/scaling/emergence/grokking --- https://en.wikipedia.org/wiki/Colossus_computer Colossus computer 2023-02-24 cs/cryptography cs/hardware --- https://github.com/ckolivas/lrzip 2023-02-24 ai/nn/transformer/gpt/2 cs/algorithm/information/compression cs/algorithm/sorting --- https://arxiv.org/abs/2307.08621#microsoft Retentive Network: A Successor to Transformer for Large Language Models Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, Furu Wei 2023-07-17 2023-07-17 [("doi","10.48550/arXiv.2307.08621")] ai/nn/rnn ai/nn/transformer/attention/recurrent

In this work, we propose Retentive Network (RetNet) as a foundation architecture for large language models, simultaneously achieving training parallelism, low-cost inference, and good performance.

We theoretically derive the connection between recurrence and attention. Then we propose the retention mechanism for sequence modeling, which supports 3 computation paradigms, ie. parallel, recurrent, and chunk-wise recurrent.

Specifically, the parallel representation allows for training parallelism. The recurrent representation enables low-cost 𝒪(1) inference, which improves decoding throughput, latency, and GPU memory without sacrificing performance. The chunk-wise recurrent representation facilitates efficient long-sequence modeling with linear complexity, where each chunk is encoded in parallel while recurrently summarizing the chunks.

Experimental results on language modeling show that RetNet achieves favorable scaling results, parallel training, low-cost deployment, and efficient inference. The intriguing properties make RetNet a strong successor to Transformer for large language models.

Code will be available at Github.

--- https://en.wikipedia.org/wiki/Dhyana_in_Buddhism Dhyana in Buddhism 2023-02-24 psychiatry/meditation --- https://jessems.com/posts/2023-07-22-the-unreasonable-effectiveness-of-sequence-diagrams-in-mermaidjs 2023-02-24 cs/css design/visualization --- https://lock.cmpxchg8b.com/zenbleed.html#discovery 2023-02-25 cs/cryptography --- https://en.wikipedia.org/wiki/Blackboard_bold Blackboard bold 2023-02-25 design/typography math --- https://marginalrevolution.com/marginalrevolution/2023/07/superfreakonomics-on-geoengineering-revisited.html 2023-02-25 technology/carbon-capture --- https://www.newyorker.com/magazine/2023/07/31/larry-gagosian-profile 2023-02-25 psychology/energy sociology --- https://www.newyorker.com/magazine/2023/07/31/how-alex-spiro-keeps-the-rich-and-famous-above-the-law 2023-02-25 law zeo/short-sleeper --- https://x.com/IvanVendrov/status/1611809666266435584 2023-02-25 sociology --- https://ianthehenry.com/posts/my-kind-of-repl/ 2023-02-25 cs/lisp design/visualization --- http://anggtwu.net/eepitch.html 2023-02-25 cs/lisp design/visualization --- https://chatgpt.com/share/261034ff-f5d5-404c-b354-c9d58e3af509 2023-02-25 ai/text-style-transfer fiction/humor music --- https://arxiv.org/abs/2105.10598 Embracing New Techniques in Deep Learning for Estimating Image Memorability Coen D. Needell, Wilma A. Bainbridge 2021-05-21 2023-02-25 [("doi","10.48550/arXiv.2105.10598")] ai/nn/cnn psychology/novelty psychology/vision reinforcement-learning/imitation-learning/brain-imitation-learning reinforcement-learning/preference-learning

Various work has suggested that the memorability of an image is consistent across people, and thus can be treated as an intrinsic property of an image. Using computer vision models, we can make specific predictions about what people will remember or forget.

While older work has used now-outdated deep learning architectures to predict image memorability, innovations in the field have given us new techniques to apply to this problem. Here, we propose and evaluate 5 alternative deep learning models which exploit developments in the field from the last 5 years, largely the introduction of residual neural networks, which are intended to allow the model to use semantic information in the memorability estimation process.

These new models were tested against the prior state-of-the-art with a combined dataset built to optimize both within-category and across-category predictions. Our findings suggest that the key prior memorability network had overstated its generalizability and was overfit on its training set.

Our new models outperform this prior model, leading us to conclude that Residual Networks outperform simpler convolutional neural networks in memorability regression. We make our new state-of-the-art model readily available to the research community, allowing memory researchers to make predictions about memorability on a wider range of images.

--- https://www.wired.com/story/beauty-is-in-the-eye-of-the-beholder-but-memorability-may-be-universal/ 2023-02-26 ai/nn/cnn psychology/novelty psychology/vision --- https://slimemoldtimemold.com/2023/07/24/your-mystery-have-attention-spans-been-declining/ 2023-02-26 dual-n-back --- https://www.atlasobscura.com/articles/pneumatic-tube-table-phone-flirting-berlin 2023-02-26 technology --- /doc/fiction/science-fiction/1990-dansimmons-thefallofhyperion-ch41-ummonquotesoceanus.png 1990 2023-02-26 ai existential-risk fiction/science-fiction philosophy/ethics --- https://www.medrxiv.org/content/10.1101/2023.07.21.23292993.full Genome-wide analyses reveal widespread genetic overlap between neurological and psychiatric disorders and a convergence of biological associations related to the brain Olav Bjerkehagen Smeland, Gleda Kutrolli, Shahram Bahrami, Vera Fominykh, Nadine Parker, Guy Hindley, Linn Rodevand, Piotr Jaholkowski, Markos Tesfaye, Pravesh Parekh, Torbjorn Elvsaashagen, Andrew David Grotzinger, The International Multiple Sclerosis Genetics Consortium (IMSGC), The International Headache Genetics Consortium (IHGC), Nils Eiel Steen, Dennis van der Meer, Kevin S. O’Connell, Srdjan David Djurovic, Anders Martin Dale, Alexey Shadrin, Oleksandr Frei, Ole A. Andreassen 2023-07-23 2023-07-23 [("doi","10.1101/2023.07.21.23292993")] genetics/heritable/correlation psychiatry

Neurological and psychiatric disorders are considered to reflect distinct underlying pathogenic entities. However, the extent to which they share genetic influences remains unclear.

Here, we performed a comprehensive analysis of GWAS data, involving nearly 1 million cases across 10 neurological diseases and 10 psychiatric disorders, to compare their common genetic risk and biological underpinnings.

Using complementary statistical tools, we demonstrate extensive genetic overlap across the disorders, with varying degrees of genetic correlations. In particular, migraine, essential tremor, stroke and multiple sclerosis were genetically correlated with several psychiatric disorders.

Biological interrogation indicated heterogeneous biological processes associated with neurological diseases, while psychiatric disorders consistently implicated neuronal biology. Altogether, the study demonstrates that neurological and psychiatric disorders are not genetically disparate, but share key etiological aspects, which have important implications for disease classification, clinical practice, and genomic precision medicine.

--- https://psyche.co/ideas/what-happens-to-the-brain-during-consciousness-ending-meditation 2023-02-26 psychiatry/meditation --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3664356/ On the time spent preparing grant proposals: an observational study of Australian researchers Danielle L. Herbert, Adrian G. Barnett, Philip Clarke, Nicholas Graves 2013 2023-02-26 [("doi","10.1136/bmjopen-2013-002800")] economics statistics/peer-review

Objective: To estimate the time spent by the researchers for preparing grant proposals, and to examine whether spending more time increase the chances of success.

Design: Observational study.

Setting: The National Health and Medical Research Council (NHMRC) of Australia.

Participants: Researchers who submitted one or more NHMRC Project Grant proposals in March 2012.

Main Outcome Measures: Total researcher time spent preparing proposals; funding success as predicted by the time spent.

Results: The NHMRC received 3,727 proposals of which 3,570 were reviewed and 731 (21%) were funded. Among our 285 participants who submitted 632 proposals, 21% were successful. Preparing a new proposal took an average of 38 working days of researcher time and a resubmitted proposal took 28 working days, an overall average of 34 days per proposal. An estimated 550 working years of researchers’ time (95% CI 513–589) was spent preparing the 3,727 proposals, which translates into annual salary costs of AU$66 million [$36 million]. More time spent preparing a proposal did not increase the chances of success for the lead researcher (prevalence ratio (PR) of success for 10 day increase=0.91, 95% CI 0.78–1.04) or other researchers (PR=0.89, 95% CI 0.67–1.17).

Conclusions: Considerable time is spent preparing NHMRC Project Grant proposals. As success rates are historically 20–25%, much of this time has no immediate benefit to either the researcher or society, and there are large opportunity costs in lost research output. The application process could be shortened so that only information relevant for peer review, not administration, is collected. This would have little impact on the quality of peer review and the time saved could be reinvested into research.

--- https://www.protocol.com/china/i-built-bytedance-censorship-machine 2023-02-26 ai/nn history/uighur --- https://thezvi.wordpress.com/2023/07/25/anthropic-observations/ 2023-02-26 ai/nn/transformer/gpt/claude ai/scaling/economics reinforcement-learning/safe --- https://en.wikipedia.org/wiki/Smart_tag_(Microsoft) Smart tag (Microsoft) 2023-02-26 design --- https://slate.com/business/2012/06/southwest-airlines-profitability-how-the-company-uses-operations-theory-to-fuel-its-success.html 2023-02-26 economics/experience-curve --- https://en.wikipedia.org/wiki/Emission_theory_(vision) Emission theory (vision) 2023-02-27 psychology/cognitive-bias/illusion-of-depth/extramission --- https://www.poetryfoundation.org/poems/148819/the-racer 2023-02-27 fiction/poetry psychology/willpower --- https://tyleransom.substack.com/p/using-llms-to-fuzzy-merge 2023-02-27 ai/nn/retrieval ai/nn/transformer/gpt/codex --- https://blogs.worldbank.org/impactevaluations/seven-ways-improve-statistical-power-your-experiment-without-increasing-n 2023-02-27 economics statistics/power-analysis --- https://www.theguardian.com/lifeandstyle/2023/jul/22/is-housing-crisis-killing-romance-modern-dating-jane-austen 2023-02-27 economics/georgism --- https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/big-data-in-the-age-of-the-telegraph 2023-02-27 design/visualization economics --- https://sunghoyahng.substack.com/p/how-too-much-daydreaming-affected 2023-02-27 psychiatry/adhd psychology/inner-voice --- https://www.reddit.com/r/StableDiffusion/comments/15aapcb/sdxl_10_is_out/ 2023-02-27 ai/nn/diffusion --- /idea#transformer-mlp-exchange-rate 2023-02-27 ai/nn/fully-connected ai/nn/sparsity/knowledge-distillation ai/nn/transformer/attention ai/scaling --- https://arxiv.org/abs/2208.02957 Meaning without reference in large language models Steven T. Piantadosi, Felix Hill 2022-08-05 2023-02-27 [("doi","10.48550/arXiv.2208.02957")] ai/nn/transformer/gpt philosophy/mind

[short essay, covering same territory as their Twitter comments] The widespread success of large language models (LLMs) has been met with skepticism that they possess anything like human concepts or meanings. Contrary to claims that LLMs possess no meaning whatsoever, we argue that they likely capture important aspects of meaning, and moreover work in a way that approximates a compelling account of human cognition in which meaning arises from conceptual role.

Because conceptual role is defined by the relationships between internal representational states, meaning cannot be determined from a model’s architecture, training data, or objective function, but only by examination of how its internal states relate to each other.

This approach may clarify why and how LLMs are so successful and suggest how they can be made more human-like.

--- https://www.theverge.com/2023/5/24/23732252/sudowrite-story-engine-ai-generated-cyberpunk-novella 2023-02-27 ai/nn/transformer/gpt/fiction fiction/science-fiction --- https://jamey.thesharps.us/2023/07/04/breaking-ciphers-with-regular-expressions/ 2023-02-28 cs/cryptography --- https://x.com/JoshuaBlake_/status/1686272169813143552 2023-02-28 statistics/prediction --- https://www.theguardian.com/world/2023/aug/01/my-escape-from-chinas-mass-arrests-uyghurs-xinjiang-reeducation-camps 2023-02-28 history/uighur --- https://max.levch.in/post/724289457144070144/shamir-secret-sharing 2023-02-28 cs/cryptography --- https://blog.polybdenum.com/2023/08/01/how-i-came-second-out-of-999-in-the-salem-center-prediction-market-tournament-without-knowing-anything-about-prediction-markets-and-what-i-learned-along-the-way-part-1.html 2023-02-28 statistics/prediction --- https://arxiv.org/abs/2307.15002 Gzip versus bag-of-words for text classification with k-NN Juri Opitz 2023-07-27 2023-07-27 [("doi","10.48550/arXiv.2307.15002")] ai/nn/retrieval cs/algorithm/information/compression

The effectiveness of compression distance in k-NN-based text classification (gzip) has recently garnered attention.

In this note we show that simpler means can also be effective, and compression may not be needed. Indeed, a bag-of-words matching can achieve similar or better results, and is more efficient.

--- https://www.astralcodexten.com/p/your-book-review-on-the-marble-cliffs 2023-02-28 culture sociology/preference-falsification --- https://www.chargebackstop.com/blog/card-networks-exploitation 2023-02-28 ai/nn/transformer/gpt/codex cs/security --- https://bookreviewgroup.substack.com/p/review-of-perplexities-of-consciousness 2023-02-28 philosophy/mind psychology/cognitive-bias/illusion-of-depth psychology/inner-voice psychology/vision --- https://en.wikipedia.org/wiki/Museum_of_Jurassic_Technology Museum of Jurassic Technology 2023-02-28 philosophy/epistemology --- /doc/genetics/selection/artificial/1961-mcconnell.pdf The Absolute Weapon: A hypothetical positive eugenics program as used in biological warfare R. A. McConnell 1961-06-01 2023-03-01 [("doi","10.2307/1292602")] fiction/science-fiction genetics/selection/artificial iq politics --- https://en.wikipedia.org/wiki/Sin%C3%A9ad_O%27Connor Sinéad O’Connor 2023-03-01 psychiatry/bipolar/energy --- https://github.com/kdeldycke/awesome-falsehood Falsehoods Programmers Believe About X 2023-03-01 cs design --- https://resobscura.substack.com/p/why-early-modern-books-are-so-beautiful 2023-03-01 design/typography --- /doc/ai/anime/danbooru/2023-seo.pdf Semi-supervised reference-based sketch extraction using a contrastive learning framework Chang Wook Seo, Amirsaman Ashtari, Junyong Noh 2023-07-26 2023-07-26 [("doi","10.1145/3592392")] ai/anime/danbooru ai/nn/gan

Sketches reflect the drawing style of individual artists; therefore, it is important to consider their unique styles when extracting sketches from color images for various applications. Unfortunately, most existing sketch extraction methods are designed to extract sketches of a single style. Although there have been some attempts to generate various style sketches, the methods generally suffer from two limitations: low quality results and difficulty in training the model due to the requirement of a paired dataset.

In this paper, we propose a novel multi-modal sketch extraction method that can imitate the style of a given reference sketch with unpaired data training in a semi-supervised manner.

Our method outperforms state-of-the-art sketch extraction methods and unpaired image translation methods in both quantitative and qualitative evaluations.

--- https://www.vqronline.org/essays-articles/2016/07/twinkle-twinkle-vogel-staar 2023-03-01 music psychology/animal/bird --- https://fontsinuse.com/uses/18290/akira-by-katsuhiro-otomo 2023-03-01 anime design/typography --- https://huggingface.co/nerijs/pixel-art-xl 2023-03-01 ai/nn/diffusion --- https://clipsnbits.blogspot.com/2016/08/thoughts-on-robert-caros-power-broker.html 2023-03-01 politics --- /doc/psychiatry/depression/2023-gray.pdf Decline in Independent Activity as a Cause of Decline in Children’s Mental Well-being: Summary of the Evidence Peter Gray, David F. Lancy, David F. Bjorklund 2023-09-01 2023-09-01 [("doi","10.1016/j.jpeds.2023.02.004")] psychiatry/anxiety psychiatry/depression --- https://arxiv.org/abs/2307.14430 Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models Mayee F. Chen, Nicholas Roberts, Kush Bhatia, Jue Wang, Ce Zhang, Frederic Sala, Christopher Ré 2023-07-26 2023-07-26 [("doi","10.48550/arXiv.2307.14430")] ai/nn/transformer/gpt reinforcement-learning/exploration/active-learning

The quality of training data impacts the performance of pre-trained large language models (LMs). Given a fixed budget of tokens, we study how to best select data that leads to good downstream model performance across tasks. We develop a new framework based on a simple hypothesis: just as humans acquire interdependent skills in a deliberate order, language models also follow a natural order when learning a set of skills from their training data. If such an order exists, it can be used for improved understanding of LMs and for data-efficient training.

Using this intuition, our framework formalizes the notion of a skill and of an ordered set of skills in terms of the associated data. First, using both synthetic and real data, we demonstrate that these ordered skill sets exist, and that their existence enables more advanced skills to be learned with less data when we train on their prerequisite skills.

Second, using our proposed framework, we introduce an online data sampling algorithm, Skill-It, over mixtures of skills for both continual pre-training and fine-tuning regimes, where the objective is to efficiently learn multiple skills in the former and an individual skill in the latter. On the LEGO synthetic in the continual pre-training setting, Skill-It obtains 36.5 points higher accuracy than random sampling. On the Natural Instructions dataset in the fine-tuning setting, Skill-It reduces the validation loss on the target skill by 13.6% versus training on data associated with the target skill itself.

We apply our skills framework on the recent Red Pajama dataset to continually pre-train a 3B-parameter LM, achieving higher accuracy on the LM Evaluation Harness with 1B tokens than the baseline approach of sampling uniformly over data sources with 3B tokens.

--- https://en.wikipedia.org/wiki/Tottenham_Outrage Tottenham Outrage 2023-03-02 crime/terrorism politics --- https://kagi.com/summarizer/api.html 2023-03-02 ai/nn/retrieval ai/nn/transformer/gpt/4/nonfiction --- https://www.dwarkeshpatel.com/p/lyndon-johnson 2023-03-02 politics psychology/energy psychology/willpower --- https://x.com/esthercrawford/status/1684291048682684416 2023-03-02 psychiatry/bipolar --- https://news.lettersofnote.com/p/is-nature-a-gigantic-cat-0bf 2023-03-02 cat science --- https://www.robkhenderson.com/p/the-male-warrior-hypothesis 2023-03-02 sociology/intrasexual-aggression --- https://x.com/_dsevero/status/1684677903382982656 2023-03-02 cs/security --- https://github.com/donno2048/snake 2023-03-02 cs/algorithm --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6481276/ Melatonin Synthesis and Function: Evolutionary History in Animals and Plants Dake Zhao, Yang Yu, Yong Shen, Qin Liu, Zhiwei Zhao, Ramaswamy Sharma, Russel J. Reiter 2019 2023-03-02 [("doi","10.3389/fendo.2019.00249")] melatonin

Melatonin is an ancient molecule that can be traced back to the origin of life. Melatonin’s initial function was likely that as a free radical scavenger. Melatonin presumably evolved in bacteria; it has been measured in both α-proteobacteria and in photosynthetic cyanobacteria.

In early evolution, bacteria were phagocytosed by primitive eukaryotes for their nutrient value. According to the endosymbiotic theory, the ingested bacteria eventually developed a symbiotic association with their host eukaryotes. The ingested α-proteobacteria evolved into mitochondria while cyanobacteria became chloroplasts and both organelles retained their ability to produce melatonin.

Since these organelles have persisted to the present day, all species that ever existed or currently exist may have or may continue to synthesize melatonin in their mitochondria (animals and plants) and chloroplasts (plants) where it functions as an antioxidant. Melatonin’s other functions, including its multiple receptors, developed later in evolution.

In present day animals, via receptor-mediated means, melatonin functions in the regulation of sleep, modulation of circadian rhythms, enhancement of immunity, as a multifunctional oncostatic agent, etc., while retaining its ability to reduce oxidative stress by processes that are, in part, receptor-independent.

In plants, melatonin continues to function in reducing oxidative stress as well as in promoting seed germination and growth, improving stress resistance, stimulating the immune system and modulating circadian rhythms; a single melatonin receptor has been identified in land plants where it controls stomatal closure on leaves.

The melatonin synthetic pathway varies somewhat between plants and animals. The amino acid, tryptophan, is the necessary precursor of melatonin in all taxa. In animals, tryptophan is initially hydroxylated to 5-hydroxytryptophan which is then decarboxylated with the formation of serotonin. Serotonin is either acetylated to N-acetylserotonin or it is methylated to form 5-methoxytryptamine; these products are either methylated or acetylated, respectively, to produce melatonin. In plants, tryptophan is first decarboxylated to tryptamine which is then hydroxylated to form serotonin.

--- https://80000hours.org/podcast/episodes/tom-moynihan-prior-generations/ 2023-03-02 existential-risk philosophy/epistemology --- https://antiapocalyptus.substack.com/p/interview-thomas-moynihan-the-discovery 2023-03-02 existential-risk philosophy/epistemology --- https://www.bbc.com/future/article/20210929-creatures-of-the-dawn-how-radioactivity-unlocked-deep-time 2023-03-03 existential-risk philosophy/epistemology --- https://haydn.fgl.dev/posts/the-launch-of-waifuxl/ 2023-03-03 ai/anime/danbooru ai/nn/gan --- https://huggingface.co/Linaqruf/animagine-xl 2023-03-03 ai/nn/diffusion anime --- https://www.youtube.com/watch?v=p_yT_6_BVGw 2023-03-03 design/typography --- https://simonwillison.net/2023/Aug/3/weird-world-of-llms/ 2023-03-03 ai/nn/transformer/gpt --- https://x.com/_JeanLannes/status/1687649736356982784 2023-03-03 cs/security --- https://www.nature.com/articles/nrd3439-c1 Believe it or not: how much can we rely on published data on potential drug targets? Prinz 2011 2023-03-03 statistics/bias/animal --- https://www.theverge.com/2022/9/16/23356213/uber-hack-teen-slack-google-cloud-credentials-powershell Uber apparently hacked by teen, employees thought it was a joke: ‘I think IT would appreciate less memes while they handle the breach’ Jon Porter 2022-09-16 2023-03-03 cs/security --- https://en.wikipedia.org/wiki/Humster Humster 2023-03-03 genetics/gametogenesis --- https://arxiv.org/abs/2307.06942 InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation Yi Wang, Yinan He, Yizhuo Li, Kunchang Li, Jiashuo Yu, Xin Ma, Xinyuan Chen, Yaohui Wang, Ping Luo, Ziwei Liu, Yali Wang, Limin Wang, Yu Qiao 2023-07-13 2023-07-13 [("doi","10.48550/arXiv.2307.06942")] ai/dataset ai/nn/transformer/clip ai/video/analysis

[only 10m clips are publicly available] This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables learning powerful and transferable video-text representations for multimodal understanding and generation. The InternVid dataset contains over 7 million YouTube videos lasting nearly 760K hours, yielding 234M video clips accompanied by detailed descriptions of total 4.1B words.

Our core contribution is to develop a scalable approach to autonomously build a high-quality video-text dataset with large language models (LLM) [Tag2Text & BLIP2 + esthetic scores], thereby showcasing its efficacy in learning video-language representation at scale. Specifically, we use a multi-scale approach to generate video-related descriptions.

Furthermore, we introduce ViCLIP, a video-text representation learning model based on ViT-L. Learned on InternVid via contrastive learning [InfoNCE], this model demonstrates leading zero-shot action recognition and competitive video retrieval performance.

Beyond basic video understanding tasks like recognition and retrieval, our dataset and model have broad applications. They are particularly beneficial for generating interleaved video-text data for learning a video-centric dialogue system, advancing video-to-text and text-to-video generation research. These proposed resources provide a tool for researchers and practitioners interested in multimodal video understanding and generation.

--- https://en.wikipedia.org/wiki/List_of_cat_body-type_mutations List of cat body-type mutations 2023-03-04 cat/genetics --- /doc/ai/1996-simon.pdf The Psychology of Thinking: Embedding Artifice in Nature Herbert A. Simon 1996-01-01 2023-03-04 ai dual-n-back psychology/chess psychology/spaced-repetition

Psychology as a Science of the Artificial · Search Strategies · The Limits on Performance · Limits on Speed of Concept Attainment · The Parameters of Memory-8 Seconds per Chunk · The Parameters of Memory-7 Chunks · or Is It Two? · The Organization of Memory · Stimulus Chunking · Visual Memory · The Mind’s Eye · Processing Natural Language · Semantics in Language Processing · Conclusion

Simon’s Ant: We watch an ant make his laborious way across a wind & wave-molded beach. He moves ahead, angles to the right to ease his climb up a steep dunelet, detours around a pebble, stops for a moment to exchange information with a compatriot. Thus he makes his weaving, halting way back to his home. So as not to anthropomorphize about his purposes, I sketch the path on a piece of paper. It is a sequence of irregular, angular segments—not quite a random walk, for it has an underlying sense of direction, of aiming toward a goal.

…Viewed as a geometric figure, the ant’s path is irregular, complex, hard to describe. But its complexity is really a complexity in the surface of the beach, not a complexity in the ant. On that same beach another small creature with a home at the same place as the ant might well follow a very similar path.

…An ant, viewed as a behaving system, is quite simple. The apparent complexity of its behavior over time is largely a reflection of the complexity of the environment in which it finds itself…In this chapter I should like to explore this hypothesis but with the word “human being” substituted for “ant”.

--- https://arxiv.org/abs/2308.01544 Multimodal Neurons in Pretrained Text-Only Transformers Sarah Schwettmann, Neil Chowdhury, Antonio Torralba 2023-08-03 2023-08-03 [("doi","10.48550/arXiv.2308.01544")] ai/nn/transformer/clip ai/nn/transformer/gpt philosophy/mind

Language models demonstrate remarkable capacity to generalize representations learned in one modality to downstream tasks in other modalities. Can we trace this ability to individual neurons?

We study the case where a frozen text transformer is augmented with vision using a self-supervised visual encoder and a single linear projection learned on an image-to-text task. Outputs of the projection layer are not immediately decodable into language describing image content; instead, we find that translation between modalities occurs deeper within the transformer.

We introduce a procedure for identifying “multimodal neurons” that convert visual representations into corresponding text, and decoding the concepts they inject into the model’s residual stream.

In a series of experiments, we show that multimodal neurons operate on specific visual concepts across inputs, and have a systematic causal effect on image captioning.

--- /doc/iq/2023-wilcox.pdf Connectome-based predictive modeling of fluid intelligence: evidence for a global system of functionally integrated brain networks Ramsey R. Wilcox, Aron k. Barbey 2023-07-31 2023-07-31 [("doi","10.1093/cercor/bhad284")] iq psychology/neuroscience

Cognitive neuroscience continues to advance our understanding of the neural foundations of human intelligence, with progress elucidating the role of the frontoparietal network in cognitive control mechanisms for flexible, intelligent behavior. Recent evidence in network neuroscience further suggests that this finding may represent the tip of the iceberg and that fluid intelligence may depend on the collective interaction of multiple brain networks. However, the global brain mechanisms underlying fluid intelligence and the nature of multi-network interactions remain to be well established.

We therefore conducted a large-scale Connectome-based Predictive Modeling study, administering resting-state fMRI to 159 healthy college students and examining the contributions of 7 intrinsic connectivity networks to the prediction of fluid intelligence, as measured by a state-of-the-art cognitive task (the Bochum Matrices Test).

Specifically, we aimed to: (1) identify whether fluid intelligence relies on a primary brain network or instead engages multiple brain networks; and (2) elucidate the nature of brain network interactions by assessing network allegiance (within-network versus between-network connections) and network topology (strong versus weak connections) in the prediction of fluid intelligence.

Our results demonstrate that whole-brain predictive models account for a statistically-significant & large proportion of variance in fluid intelligence (18%) and illustrate that the contribution of individual networks is relatively modest by comparison. In addition, we provide novel evidence that the global architecture of fluid intelligence prioritizes between-network connections and flexibility through weak ties.

Our findings support a network neuroscience approach to understanding the collective role of brain networks in fluid intelligence and elucidate the system-wide network mechanisms from which flexible, adaptive behavior is constructed.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0013541 Urban Scaling and Its Deviations: Revealing the Structure of Wealth, Innovation and Crime across Cities Luís M. A. Bettencourt, José Lobo, Deborah Strumsky, Geoffrey B. West 2010-09-16 2023-03-04 [("doi","10.1371/journal.pone.0013541")] crime economics science

With urban population increasing dramatically worldwide, cities are playing an increasingly critical role in human societies and the sustainability of the planet. An obstacle to effective policy is the lack of meaningful urban metrics based on a quantitative understanding of cities. Typically, linear per capita indicators are used to characterize and rank cities. However, these implicitly ignore the fundamental role of nonlinear agglomeration integral to the life history of cities.

As such, per capita indicators conflate general nonlinear effects, common to all cities, with local dynamics, specific to each city, failing to provide direct measures of the impact of local events and policy. Agglomeration nonlinearities are explicitly manifested by the superlinear power law scaling of most urban socioeconomic indicators with population size, all with similar exponents (~1.15). As a result larger cities are disproportionally the centers of innovation, wealth and crime, all to the same degree.

We use these general urban laws to develop new urban metrics that disentangle dynamics at different scales and provide true measures of local urban performance. New rankings of cities and a novel and simpler perspective on urban systems emerge. We find that local urban dynamics display long-term memory, so cities under or outperforming their size expectation maintain such (dis)advantage for decades.

Spatiotemporal correlation analyses reveal a novel functional taxonomy of U.S. metropolitan areas that is generally not organized geographically but based instead on common local economic models, innovation strategies and patterns of crime.

--- https://en.wikipedia.org/wiki/Alchian%E2%80%93Allen_effect Alchian-Allen effect 2023-03-04 economics/automation --- https://en.wikipedia.org/wiki/Baumol's_cost_disease Baumol’s cost disease 2023-03-04 economics/automation --- https://en.wikipedia.org/wiki/Moulin_Rouge_Hotel#Founding Moulin Rouge Hotel § Founding 2023-03-04 economics politics --- https://github.com/ggerganov/kbd-audio 2023-03-04 cs/security --- https://corecursive.com/lisp-in-space-with-ron-garret/#deep-space-failure 2023-03-04 cs/lisp --- https://flownet.com/gat/jpl-lisp.html 2023-03-04 cs/lisp --- https://blog.munhou.com/2021/09/29/Drug-Listing-Dataset/ 2023-03-05 darknet-market/dnm-archive --- https://github.com/munhouiani/Drug-Listings-Dataset 2023-03-05 darknet-market/dnm-archive --- https://www.biorxiv.org/content/10.1101/2023.03.27.534479.full Human induced pluripotent stem cell-derived ovarian support cell co-culture improves oocyte maturation in vitro after abbreviated gonadotropin stimulation Sabrina Piechota, Maria Marchante, Alexa Giovannini, Bruna Paulsen, Kathryn S. Potts, Graham Rockwell, Caroline Aschenberger, Alexander D. Noblett, Alexandra B. Figueroa, Marta Sanchez, Ferran Barrachina, Klaus Wiemer, Luis Guzman, Pedro Belchin, Merrick Pierson Smela, Patrick R. J. Fortuna, Pranam Chatterjee, Nam D. Tran, Dawn A. Kelk, Marcy Forti, Shelby Marcinyshyn, Trozalla Smith, David H. McCulloh, Silvia Ortiz, Joshua U. Klein, Peter Klatsky, Daniel Ordonez-Perez, Christian C. Kramme 2023-08-03 2023-08-03 [("doi","10.1101/2023.03.27.534479")] genetics/gametogenesis

Assisted reproductive technologies (ART) have significantly impacted fertility treatment worldwide through innovations such as in vitro fertilization (IVF) and in vitro maturation (IVM). IVM holds promise as a technology for fertility treatment in women who cannot or do not wish to undergo conventional controlled ovarian stimulation (COS). However, IVM has historically shown highly variable performance in maturing oocytes and generating oocytes with strong developmental capacity.

Furthermore, recently reported novel IVM approaches are limited to use in cycles lacking human chorionic gonadotropin (hCG) triggers, which is not standard practice in fertility treatment. We recently reported the development of ovarian support cells (OSCs) generated from human induced pluripotent stem cells (hiPSCs) that recapitulate dynamic ovarian function in vitro.

Here we investigate the potential of these OSCs in an IVM co-culture system to improve the maturation of human cumulus-enclosed immature oocytes retrieved from abbreviated gonadotropin stimulated cycles. We reveal that OSC-IVM significantly improves maturation rates compared to existing IVM systems.

Most importantly, we demonstrate that OSC-assisted IVM oocytes are capable of significantly improving euploid blastocyst formation and yielding blastocysts with normal global and germline differential methylation region methylation profiles, a key marker of their clinical utility. Together, these findings demonstrate a novel approach to IVM with broad applicability to modern ART practice.

--- https://www.biorxiv.org/content/10.1101/2023.08.02.551743.full Predicting brain activity using Transformers Hossein Adeli, Sun Minni, Nikolaus Kriegeskorte 2023-08-05 2023-08-05 [("doi","10.1101/2023.08.02.551743")] ai/nn/transformer psychology/neuroscience

The Algonauts challenge (Gifford et al 2023) called on the community to provide novel solutions for predicting brain activity of humans viewing natural scenes. This report provides an overview and technical details of our submitted solution.

We use a general transformer encoder-decoder model to map images to fMRI responses. The encoder model is a vision transformer trained using self-supervised methods (DINOv2).

The decoder uses queries corresponding to different brain regions of interests (ROI) in different hemispheres to gather relevant information from the encoder output for predicting neural activity in each ROI. The output tokens from the decoder are then linearly mapped to the fMRI activity.

The predictive success (challenge score: 63.5229, rank 2) suggests that features from self-supervised transformers may deserve consideration as models of human visual brain representations and shows the effectiveness of transformer mechanisms (self and cross-attention) to learn the mapping from features to brain responses.

--- https://cdn.oreillystatic.com/en/assets/1/event/27/High%20Performance%20SQL%20with%20PostgreSQL%20Presentation.pdf High Performance SQL with PostgreSQL 8.4: Lists and Recursion and Trees, Oh My! David Fetter 2009 2023-03-05 cs/computable --- https://www.cambridge.org/core/journals/journal-of-economic-history/article/two-centuries-of-productivity-growth-in-computing/856EC5947A5857296D3328FA154BA3A3 Two Centuries of Productivity Growth in Computing Nordhaus 2007 2023-03-05 cs/hardware economics/experience-curve --- https://www.vox.com/future-perfect/2023/8/4/23819209/nuclear-war-philanthropies-oppenheimer-united-states-china-russia-cold-war-existential-risk 2023-03-05 existential-risk/nuclear --- https://platform.openai.com/docs/gptbot 2023-03-05 ai/nn/retrieval ai/nn/transformer/gpt --- https://cprimozic.net/blog/growing-sparse-computational-graphs-with-rnns/ 2023-03-05 ai/nn/rnn ai/nn/sparsity --- https://x.com/lukestein/status/1680293457737273345 2023-03-05 psychology/linguistics --- https://www.lesswrong.com/posts/XpCnhaAQrssq8tJBG/an-interactive-introduction-to-grokking-and-mechanistic 2023-03-05 ai/nn --- https://www.atlasobscura.com/articles/japan-firefly-course-moriyama-forest-museum 2023-03-06 japan/history --- https://www.metaculus.com/questions/9784/min-cost-of-dog-cloning-in-2030/ 2023-03-06 economics/experience-curve genetics/cloning/dog --- https://www.nature.com/articles/s42003-023-05098-1 2023-03-06 ai/nn/cnn ai/video/analysis biology --- /doc/cs/algorithm/1988-wright-threescientistsandtheirgods.pdf Three Scientists and Their Gods: Looking for Meaning in an Age of Information Robert Wright 1988-01-01 2023-03-06 cs/algorithm genetics/selection/natural philosophy/ontology --- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1010798 A novel nematode species from the Siberian permafrost shares adaptive mechanisms for cryptobiotic survival with C. elegans dauer larva Anastasia Shatilovich, Vamshidhar R. Gade, Martin Pippel, Tarja T. Hoffmeyer, Alexei V. Tchesunov, Lewis Stevens, Sylke Winkler, Graham M. Hughes, Sofia Traikov, Michael Hiller, Elizaveta Rivkina, Philipp H. Schiffer, Eugene W. Myers, Teymuras V. Kurzchalia, Gregory P. Copenhaver, Gregory P. Copenhaver, Gregory P. Copenhaver 2023-05-24 2023-05-24 [("doi","10.1371/journal.pgen.1010798")] cryonics

[media] Survival in extreme environments for prolonged periods is a challenge that only a few organisms, are capable of. It is not well understood, which molecular and biochemical pathways are used by such cryptobiotic organisms, and how long they might suspend life. Here, we show that a soil nematode Panagrolaimus kolymaensis, suspended life for 46,000 years in the Siberian permafrost.

Through comparative analysis, we find that P. kolymaensis and model organism C. elegansuse similar adaptive mechanisms to survive harsh environmental conditions for prolonged periods.

Our findings here are important for the understanding of evolutionary processes because generation times could be stretched from days to millennia, and long-term survival of individuals of species can lead to the refoundation of otherwise extinct lineages.

Some organisms in nature have developed the ability to enter a state of suspended metabolism called cryptobiosis when environmental conditions are unfavorable. This state-transition requires execution of a combination of genetic and biochemical pathways that enable the organism to survive for prolonged periods. Recently, nematode individuals have been reanimated from Siberian permafrost after remaining in cryptobiosis. Preliminary analysis indicates that these nematodes belong to the genera Panagrolaimus and Plectus. Here, we present precise radiocarbon dating indicating that the Panagrolaimus individuals have remained in cryptobiosis since the late Pleistocene (~46,000 years). Phylogenetic inference based on our genome assembly and a detailed morphological analysis demonstrate that they belong to an undescribed species, which we named Panagrolaimus kolymaensis. Comparative genome analysis revealed that the molecular toolkit for cryptobiosis in P. kolymaensis and in C. elegans is partly orthologous. We show that biochemical mechanisms employed by these two species to survive desiccation and freezing under laboratory conditions are similar. Our experimental evidence also reveals that C. elegans dauer larvae can remain viable for longer periods in suspended animation than previously reported. Altogether, our findings demonstrate that nematodes evolved mechanisms potentially allowing them to suspend life over geological time scales.

--- https://downfall/ 2023-03-06 cs/security --- https://x.com/OfficialLoganK/status/1664476604658069511 2023-03-06 ai/nn/transformer/gpt --- https://stability.ai/blog/stablecode-llm-generative-ai-coding 2023-03-06 ai/nn/transformer/gpt/codex --- https://gpus.llm-utils.org/nvidia-h100-gpus-supply-and-demand/ 2023-03-06 ai/scaling/economics ai/scaling/hardware --- https://x.com/EMostaque/status/1674479761429504017 2023-03-06 ai/scaling/hardware --- https://gpus.llm-utils.org/nvidia-h100-gpus-supply-and-demand/#how-do-the-big-clouds-compare 2023-03-06 ai/scaling/economics ai/scaling/hardware --- https://www.newyorker.com/news/the-weekend-essay/the-hidden-harms-of-cpr 2023-03-07 statistics/bias --- https://www.alexanderpiano.nz/page/the-alexander-piano 2023-03-07 music --- https://news.ycombinator.com/item?id=37047442 2023-03-07 music --- https://heatmap.news/economy/carbon-removal-us-government 2023-03-07 technology/carbon-capture --- https://www.vox.com/future-perfect/23775650/ai-regulation-openai-gpt-anthropic-midjourney-stable 2023-03-07 ai/nn law politics --- https://www.nytimes.com/2023/08/05/world/europe/neville-roy-singham-china-propaganda.html 2023-03-07 politics --- https://www.nytimes.com/2023/08/04/technology/the-chip-titan-whose-lifes-work-is-at-the-center-of-a-tech-cold-war.html 2023-03-07 cs/hardware --- https://arxiv.org/abs/2006.09081 Progressive Skeletonization: Trimming more fat from a network at initialization Pau de Jorge, Amartya Sanyal, Harkirat S. Behl, Philip H. S. Torr, Gregory Rogez, Puneet K. Dokania 2020-06-16 2023-03-07 [("doi","10.48550/arXiv.2006.09081")] ai/nn/sparsity/pruning

Recent studies have shown that skeletonization (pruning parameters) of networks at initialization provides all the practical benefits of sparsity both at inference and training time, while only marginally degrading their performance.

However, we observe that beyond a certain level of sparsity (~95%), these approaches fail to preserve the network performance, and to our surprise, in many cases perform even worse than trivial random pruning.

To this end, we propose an objective to find a skeletonized network with maximum foresight connection sensitivity (FORCE) whereby the trainability, in terms of connection sensitivity, of a pruned network is taken into consideration.

We then propose two approximate procedures to maximize our objective (1) Iterative SNIP: allows parameters that were unimportant at earlier stages of skeletonization to become important at later stages; and (2) FORCE: iterative process that allows exploration by allowing already pruned parameters to resurrect at later stages of skeletonization.

Empirical analyses on a large suite of experiments show that our approach, while providing at least as good a performance as other recent approaches on moderate pruning levels, provides remarkably improved performance on higher pruning levels (could remove up to 99.5% parameters while keeping the networks trainable).

Code can be found in Github.

--- https://x.com/ch402/status/1684757554193428480 2023-03-07 ai/nn/transformer/gpt/claude --- https://www.dipkumar.dev/becoming-the-unbeatable/posts/gpt-kvcache/ 2023-03-07 ai/nn/transformer/attention --- https://thepalindrome.org/p/how-does-the-japanese-multiplication-work 2023-03-08 design/visualization math --- https://x.com/jeremyphoward/status/1688793283034779648 2023-03-08 ai/nn/transformer/gpt/codex fiction/humor --- https://arxiv.org/abs/2308.03526#deepmind AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning Michaël Mathieu, Sherjil Ozair, Srivatsan Srinivasan, Caglar Gulcehre, Shangtong Zhang, Ray Jiang, Tom Le Paine, Richard Powell, Konrad Żołna, Julian Schrittwieser, David Choi, Petko Georgiev, Daniel Toyama, Aja Huang, Roman Ring, Igor Babuschkin, Timo Ewalds, Mahyar Bordbar, Sarah Henderson, Sergio Gómez Colmenarejo, Aäron van den Oord, Wojciech Marian Czarnecki, Nando de Freitas, Oriol Vinyals 2023-08-07 2023-08-07 [("doi","10.48550/arXiv.2308.03526")] reinforcement-learning/imitation-learning reinforcement-learning/model-free/alphastar reinforcement-learning/model/muzero reinforcement-learning/offline

StarCraft II is one of the most challenging simulated reinforcement learning environments; it is partially observable, stochastic, multi-agent, and mastering StarCraft II requires strategic planning over long time horizons with real-time low-level execution. It also has an active professional competitive scene.

StarCraft II is uniquely suited for advancing offline RL algorithms, both because of its challenging nature and because Blizzard has released a massive dataset of millions of StarCraft II games played by human players.

This paper leverages that and establishes a benchmark, called AlphaStar Unplugged, introducing unprecedented challenges for offline reinforcement learning. We define a dataset (a subset of Blizzard’s release), tools standardizing an API for machine learning methods, and an evaluation protocol.

We also present baseline agents, including behavior cloning, offline variants of actor-critic and MuZero. We improve the state-of-the-art of agents using only offline data, and we achieve 90% win rate against previously published AlphaStar behavior cloning agent.

--- https://arxiv.org/abs/2308.03958#deepmind Simple synthetic data reduces sycophancy in large language models Jerry Wei, Da Huang, Yifeng Lu, Denny Zhou, Quoc V. Le 2023-08-07 2023-08-07 [("doi","10.48550/arXiv.2308.03958")] ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/gpt/palm ai/scaling reinforcement-learning/preference-learning/mode-collapse

Sycophancy is an undesirable behavior where models tailor their responses to follow a human user’s view even when that view is not objectively correct (eg. adapting liberal views once a user reveals that they are liberal). In this paper, we study the prevalence of sycophancy in language models and propose a simple synthetic-data intervention to reduce this behavior.

First, on a set of 3 sycophancy tasks (Perez et al 2022) where models are asked for an opinion on statements with no correct answers (eg. politics):

we observe that both model scaling and instruction tuning increase sycophancy for Flan-PaLM models up to 540b parameters.

Second, we extend sycophancy evaluations to simple addition statements that are objectively incorrect, finding that despite knowing that these statements are wrong:

language models will still agree with them if the user does as well.

To reduce sycophancy, we present a straightforward synthetic-data intervention that takes public NLP tasks and encourages models to be robust to user opinions on these tasks. Adding these data in a lightweight finetuning step can reduce sycophantic behavior on held-out prompts. [In preliminary experiments, we observed that production models such as ChatGPT and Bard did not experience substantial sycophancy, possibly because of their additional finetuning data or prompt preambles]

Code for generating synthetic data for intervention can be found at Github.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3809096/ Correlation not causation: the relationship between personality traits and political ideologies Brad Verhulst, Lindon J. Eaves, Peter K. Hatemi 2012 2023-03-08 [("doi","10.1111/j.1540-5907.2011.00568.x")] genetics/heritable/correlation politics psychology/personality

The assumption in the personality and politics literature is that a person’s personality motivates them to develop certain political attitudes later in life. This assumption is founded on the simple correlation between the two constructs and the observation that personality traits are genetically influenced and develop in infancy, whereas political preferences develop later in life.

Work in psychology, behavioral genetics, and recently political science, however, has demonstrated that political preferences also develop in childhood and are equally influenced by genetic factors. These findings cast doubt on the assumed causal relationship between personality and politics.

Here we test the causal relationship between personality traits and political attitudes using a direction of causation structural model on a genetically informative sample. The results suggest that personality traits do not cause people to develop political attitudes; rather, the correlation between the two is a function of an innate common underlying genetic factor.

--- https://www.newyorker.com/science/elements/the-race-to-save-the-worlds-dna 2023-03-08 genetics/sequencing --- https://arxiv.org/abs/1211.5063 On the difficulty of training Recurrent Neural Networks Razvan Pascanu, Tomas Mikolov, Yoshua Bengio 2012-11-21 2023-03-08 [("doi","10.48550/arXiv.1211.5063")] ai/nn/rnn

There are two widely known issues with properly training Recurrent Neural Networks, the vanishing and the exploding gradient problems detailed in Bengio et al 1994.

In this paper, we attempt to improve the understanding of the underlying issues by exploring these problems from an analytical, a geometric and a dynamical systems perspective.

Our analysis is used to justify a simple yet effective solution. We propose a gradient norm clipping strategy to deal with exploding gradients and a soft constraint for the vanishing gradients problem.

We validate empirically our hypothesis and proposed solutions in the experimental section.

--- https://www.smithsonianmag.com/science-nature/the-neuroscientist-who-discovered-he-was-a-psychopath-180947814/ 2023-03-08 psychology/neuroscience psychology/personality/psychopathy --- https://www.theatlantic.com/magazine/archive/2023/09/trigger-warnings-feminism-teen-girls-mental-health/674759/ 2023-03-08 psychiatry/anxiety psychiatry/depression psychology/personality --- https://github.com/extesy/hoverzoom/discussions/670 2023-03-08 economics/advertising --- https://gizmodo.com/cnet-deletes-thousands-old-articles-google-search-seo-1850721475 2023-03-08 cs/linkrot --- https://milksad.info/disclosure.html 2023-03-09 bitcoin cs/cryptography --- /doc/genetics/heritable/correlation/2023-stolarski.pdf Behavioral genetics of temporal framing: Heritability of time perspective and its common genetic bases with major personality traits Maciej Stolarski, Bogdan Zawadzki, Gerald Matthews, Dominika Pruszczak, Jerzy Wojciechowski 2023-08-08 2023-08-08 [("doi","10.1111/jopy.12870")] economics genetics/heritable/correlation psychology/personality

Objective: The present study aimed to provide a seminal behavioral genetic analysis of time perspectives (TPs). Moreover, we intended to investigate the magnitude of genetic vs. environmental components of the well-established associations between TPs and personality features.

Background: Individual differences in temporal framing processes, referred to as TPs, are vital psychological and behavioral outcomes. Although proponents of TP theory emphasize mainly environmental origins of the tendencies to adopt certain TPs, research provides evidence for marked associations between the temporal dimensions and major personality traits that are known to be heritable. Hence, it was essential to empirically verify these claims.

Method: The article reports an analysis of genetic and environmental components of variance in TPs based on a study adopting a twin design, conducted on a sample of 393 pairs of twins (135 monozygotic and 258 dizygotic).

Results: Multivariate Cholesky decomposition supported an EA model assuming impacts of both unshared environmental factors (E) and additive genetic factors (A) across all TP dimensions, suggesting that the effects of shared environment on TPs are plausibly negligible. Heritability indices of TPs ranged between 0.51 for Present-Fatalistic and 0.62 for Present-Hedonistic, suggesting that the majority of the variance in TPs stems from genetic influences. Substantial genetic correlations were found between TPs and the Big 5 personality traits.

Conclusions: The findings provide further evidence for conceptualizing TPs as biologically based personality traits and challenge the claims that TP is mainly a product of culture, education, and personal experiences.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0069258 Inflated Applicants: Attribution Errors in Performance Evaluation by Professionals Samuel A. Swift, Don A. Moore, Zachariah S. Sharek, Francesca Gino 2013-06-12 2023-03-09 [("doi","10.1371/journal.pone.0069258")] economics

When explaining others’ behaviors, achievements, and failures, it is common for people to attribute too much influence to disposition and too little influence to structural and situational factors.

We examine whether this tendency leads even experienced professionals to make systematic mistakes in their selection decisions, favoring alumni from academic institutions with high grade distributions and employees from forgiving business environments.

We find that candidates benefiting from favorable situations are more likely to be admitted and promoted than their equivalently skilled peers. The results suggest that decision-makers take high nominal performance as evidence of high ability and do not discount it by the ease with which it was achieved.

These results clarify our understanding of the correspondence bias using evidence from both archival studies and experiments with experienced professionals. We discuss implications for both admissions and personnel selection practices.

…Author Contributions: Conceived and designed the experiments: SS DM ZS Francesca Gino. Performed the experiments: SS ZS. Analyzed the data: SS DM ZS Francesca Gino. Wrote the paper: SS DM ZS Francesca Gino.

[WARNING: fraudster Francesca Gino heavily contributed to data creation & analysis of this research.]

--- https://www.medrxiv.org/content/10.1101/2023.08.04.23293638.full Exploring pleiotropy in Mendelian Randomization analyses: What are genetic variants associated with ‘cigarette smoking initiation’ really capturing? Zoe E. Reed, Robyn E. Wootton, Jasmine N. Khouja, Tom G. Richardson, Eleanor G. Sanderson, George Davey Smith, Marcus R. Munafo 2023-08-08 2023-08-08 [("doi","10.1101/2023.08.04.23293638")] genetics/heritable/correlation/mendelian-randomization nicotine

Background: Genetic variants used as instruments for exposures in Mendelian Randomization (MR) analyses may also have horizontal pleiotropic effects (ie. influence outcomes via pathways other than through the exposure), which can undermine the validity of results. We examined the extent to which horizontal pleiotropy may be present, using smoking behaviors as an example.

Method: We first ran a phenome-wide association study in UK Biobank, using a genetic instrument for smoking initiation. From the most strongly associated phenotypes, we selected those that we considered could either plausibly or not plausibly be caused by smoking. We next examined the association between genetic instruments for smoking initiation, smoking heaviness and lifetime smoking and these phenotypes in both UK Biobank and the Avon Longitudinal Study of Parents and Children (ALSPAC). We conducted negative control analyses among never smokers, including children in ALSPAC.

Results: We found evidence that smoking-related genetic instruments (mainly for smoking initiation and lifetime smoking) were associated with phenotypes not plausibly caused by smoking in UK Biobank and (to a lesser extent) ALSPAC, although this may reflect the much smaller sample size in ALSPAC. We also observed associations with several phenotypes among never smokers.

Conclusion: Our results suggest that genetic instruments for smoking-related phenotypes demonstrate horizontal pleiotropy. When using genetic variants—particularly those for complex behavioral exposures—in genetically-informed causal inference analyses (eg. MR) it is important to include negative control outcomes where possible, and other triangulation approaches, to avoid arriving at incorrect conclusions.

--- https://x.com/literalbanana/status/1689420167024095232 2023-03-09 ai/nn/transformer/gpt/fiction statistics/bias --- https://vinecon.ucdavis.edu/wp-content/uploads/2019/04/cwe1201.pdf The Marginal External Cost of Obesity in the United States Parks 2012 2023-03-09 economics exercise --- https://academic.oup.com/aje/article/173/8/906/156839 Multivitamin use and the risk of mortality and cancer incidence: the multiethnic cohort study Park 2011 2023-03-09 biology --- /doc/genetics/heritable/1952-burlingham-twinsastudyofthreepairsofidenticaltwins.pdf Twins: A Study of 3 Pairs of Identical Twins Dorothy Burlingham 1952-01-01 2023-03-09 genetics/heritable psychiatry --- https://en.wikipedia.org/wiki/Mean_Girls Mean Girls 2023-03-09 sociology/intrasexual-aggression --- https://areomagazine.com/2018/05/29/female-intrasexual-competition-from-demons-to-better-angels/ 2023-03-09 sociology/intrasexual-aggression --- https://julieyoung.substack.com/p/insane-companies-no-one-talks-about 2023-03-09 sociology/intrasexual-aggression --- https://www.reddit.com/r/TheMotte/comments/jdvqk5/culture_war_roundup_for_the_week_of_october_19/g9gouaj/?context=3&sort=best 2023-03-10 sociology/intrasexual-aggression --- https://x.com/LapsusLima/status/1332188103717449728 2023-03-10 sociology/intrasexual-aggression --- https://www.dailymail.co.uk/femail/article-2124246/Samantha-Brick-downsides-looking-pretty-Why-women-hate-beautiful.html 2023-03-10 sociology/intrasexual-aggression --- https://x.com/HPluckrose/status/1467485035397844999 2023-03-10 sociology/intrasexual-aggression --- https://x.com/Jane_Li911/status/1493792190052519937 2023-03-10 sociology/intrasexual-aggression --- https://rationalists-out-of-context.tumblr.com/post/677073675735924736/i-thought-she-seemed-very-cool-but-we-were-on 2023-03-10 sociology/intrasexual-aggression --- https://x.com/ebruenig/status/1498782036156420107 2023-03-10 sociology/intrasexual-aggression --- https://x.com/maediocre/status/1534720853732012032 2023-03-10 sociology/intrasexual-aggression --- https://x.com/nescartridges/status/1575908119636369408 2023-03-10 sociology/intrasexual-aggression --- https://www.wsj.com/articles/arming-women-for-the-dating-battlefield-11581138060 2023-03-10 sociology/intrasexual-aggression --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0053724 Till Death (Or an Intruder) Do Us Part: Intrasexual-Competition in a Monogamous Primate Eduardo Fernandez-Duque, Maren Huck 2012-12-05 2023-03-11 [("doi","10.1371/journal.pone.0053724")] psychology/animal sociology/intrasexual-aggression

Polygynous animals are often highly dimorphic, and show large sex-differences in the degree of intra-sexual competition and aggression, which is associated with biased operational sex ratios (OSR). For socially monogamous, sexually monomorphic species, this relationship is less clear. Among mammals, pair-living has sometimes been assumed to imply equal OSR and low frequency, low intensity intra-sexual competition; even when high rates of intra-sexual competition and selection, in both sexes, have been theoretically predicted and described for various taxa.

Owl monkeys are one of a few socially monogamous primates. Using long-term demographic and morphological data from 18 groups, we show that male and female owl monkeys experience intense intra-sexual competition and aggression from solitary floaters. Pair-mates are regularly replaced by intruding floaters (27 female and 23 male replacements in 149 group-years), with negative effects on the reproductive success of both partners.

Individuals with only one partner during their life produced 25% more offspring per decade of tenure than those with two or more partners. The termination of the pair-bond is initiated by the floater, and sometimes has fatal consequences for the expelled adult.

The existence of floaters and the sporadic, but intense aggression between them and residents suggest that it can be misleading to assume an equal OSR in socially monogamous species based solely on group composition. Instead, we suggest that sexual selection models must assume not equal, but flexible, context-specific, OSR in monogamous species.

--- https://arxiv.org/abs/1612.02551 The golden age of Calcutta physics: Difficulties in reconstructing the history Arnab Rai Choudhuri 2016-12-08 2023-03-11 [("doi","10.48550/arXiv.1612.02551")] science

Classes started in the newly established Physics Department of Calcutta University Science College in 1916. Raman, Bose and Saha were 3 young members of the small physics faculty consisting of barely half a dozen faculty members.

Within about one decade, 3 extraordinary discoveries came from these young men—the Saha ionization equation in 1920, Bose statistics in 1924, Raman effect in 1928. However, the fortunes of Calcutta University quickly got intertwined with India’s freedom struggle led by Mahatma Gandhi exactly at the same time and the physics group got tragically disrupted. Indian physics never succeeded in reaching that height again.

This paper discusses the difficulties in reconstructing a critical history of this Calcutta school of physics during the very short epoch of unmatched brilliance.

--- https://henry.pha.jhu.edu/Eddington.2008.pdf#page=7 2023-03-11 philosophy/ontology science --- https://www.reddit.com/r/midjourney/comments/15mkgho/realistic_retro_ghibli_pok%C3%A9mon/ 2023-03-11 ai/nn/diffusion/midjourney anime --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4096150/ Estimating the sex-specific effects of genes on facial attractiveness and sexual dimorphism Dorian G. Mitchem, Alicia M. Purkey, Nicholas M. Grebe, Gregory Carey, Christine E. Garver-Apgar, Timothy C. Bates, Rosalind Arden, John K. Hewitt, Sarah E. Medland, Nicholas G. Martin, Brendan P. Zietsch, Matthew C. Keller 2014 2023-03-11 [("doi","10.1007/s10519-013-9627-5")] genetics/heritable/correlation

Human facial attractiveness and facial sexual dimorphism (masculinity-femininity) are important facets of mate choice and are hypothesized to honestly advertise genetic quality. However, it is unclear whether genes influencing facial attractiveness and masculinity-femininity have similar, opposing, or independent effects across sex, and the heritability of these phenotypes is poorly characterized.

To investigate these issues, we assessed facial attractiveness and facial masculinity-femininity in the largest genetically informative sample (n = 1,580 same-sex & opposite-sex twin pairs and siblings) to assess these questions to date.

The heritability was ~0.50–0.70 for attractiveness and ~0.40–0.50 for facial masculinity-femininity, indicating that, despite ostensible selection on genes influencing these traits, substantial genetic variation persists in both.

Importantly, we found evidence for intralocus sexual conflict, whereby alleles that increase masculinity in males have the same effect in females. Additionally, genetic influences on attractiveness were shared across the sexes, suggesting that attractive fathers tend to have attractive daughters and attractive mothers tend to have attractive sons.

--- https://neurobiology.substack.com/p/what-should-we-be-measuring-in-brain 2023-03-11 cryonics psychology/neuroscience --- https://www.nytimes.com/2023/08/10/science/wildlife-aging-epigenetic-clocks.html 2023-03-11 longevity/epigenetics --- https://www.nytimes.com/2023/08/10/health/heat-mental-health.html 2023-03-11 psychiatry --- https://www.spoon-tamago.com/japan-kei-tora-mini-truck-gardening-contest/ 2023-03-11 design japan/art --- https://news.play.ht/post/introducing-playht2-0-the-state-of-the-art-generative-voice-ai-model-for-conversational-speech 2023-03-11 ai/music --- https://www.biorxiv.org/content/10.1101/2023.08.08.552281.full Leveraging fine-scale population structure reveals conservation in genetic effect sizes between human populations across a range of human phenotypes Sile Hu, Lino A. F. Ferreira, Sinan Shi, Garrett Hellenthal, Jonathan Marchini, Daniel J. Lawson, Simon R. Myers 2023-08-09 2023-08-09 [("doi","10.1101/2023.08.08.552281")] genetics/heritable/correlation

[Twitter] An understanding of genetic differences between populations is essential for avoiding confounding in genome-wide association studies (GWAS) and understanding the evolution of human traits. Polygenic risk scores constructed in one group perform poorly in highly genetically-differentiated populations, for reasons which remain controversial.

We developed a statistical ancestry inference pipeline able to decompose ancestry both within and between countries, and applied it to the UK Biobank data. This identifies fine-scale patterns of genetic relatedness not captured by standard and widely used principal components (PCs), and allows fine-scale population stratification correction that removes both false positive and false negative associations for traits with geographic correlations. We also develop and apply ANCHOR, an approach leveraging segments of distinct ancestries within individuals to estimate similarity in underlying causal effect sizes between groups, using an existing PGS.

Applying ANCHOR to >8,000 people of mixed African and European ancestry, we demonstrate that:

estimated causal effect sizes are highly similar across these ancestries for 26⁄29 quantitative molecular and non-molecular phenotypes (mean correlation 0.98 ± 0.08), providing evidence that gene-environment and gene-gene interactions do not play major roles in the poor prediction of European-ancestry PRS scores in African populations for these traits, contradicting previous findings.

Instead our results provide optimism that shared causal mutations operate similarly in different groups, focusing the challenge of improving GWAS “portability” between groups on joint fine-mapping.

--- https://arxiv.org/abs/2110.06169 Offline Reinforcement Learning with Implicit Q-Learning (IQL) Ilya Kostrikov, Ashvin Nair, Sergey Levine 2021-10-12 2023-03-12 [("doi","10.48550/arXiv.2110.06169")] reinforcement-learning/model-free

Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that improves over the behavior policy that collected the dataset, while at the same time minimizing the deviation from the behavior policy so as to avoid errors due to distributional shift. This trade-off is critical, because most current offline reinforcement learning methods need to query the value of unseen actions during training to improve the policy, and therefore need to either constrain these actions to be in-distribution, or else regularize their values.

We propose an offline RL method that never needs to evaluate actions outside of the dataset, but still enables the learned policy to improve substantially over the best behavior in the data through generalization. The main insight in our work is that, instead of evaluating unseen actions from the latest policy, we can approximate the policy improvement step implicitly by treating the state value function as a random variable, with randomness determined by the action (while still integrating over the dynamics to avoid excessive optimism), and then taking a state conditional upper expectile of this random variable to estimate the value of the best actions in that state. This leverages the generalization capacity of the function approximator to estimate the value of the best available action at a given state without ever directly querying a Q-function with this unseen action.

Our algorithm alternates between fitting this upper expectile value function and backing it up into a Q-function. Then, we extract the policy via advantage-weighted behavioral cloning. We dub our method implicit Q-learning (IQL).

IQL demonstrates the state-of-the-art performance on D4RL, a standard benchmark for offline reinforcement learning. We also demonstrate that IQL achieves strong performance fine-tuning using online interaction after offline initialization.

--- https://www.freaktakes.com/p/the-past-and-present-of-computer 2023-03-12 ai/nn/transformer/gpt reinforcement-learning/exploration science --- https://thume.ca/2023/01/02/one-machine-twitter/ 2023-03-12 cs/algorithm cs/hardware --- https://www.thechatner.com/p/waking-up-times-in-order 2023-03-12 fiction/humor --- https://www.wired.com/story/hacker-honeypot-go-secure/ 2023-03-12 cs/security --- https://docs.sweep.dev/blogs/sweeps-core-algo 2023-03-12 ai/nn/retrieval ai/nn/transformer/gpt/codex --- https://mprove.de/visionreality/text/2.1.7_concordia.html 2023-03-12 cs/lisp --- https://x.com/Cruise/status/1690423649134854145 2023-03-12 reinforcement-learning/robot --- https://www.visakanv.com/archives/2021/02/14/the-problem-of-excess-genius-by-david-banks-1997/ 2023-03-12 genetics/heritable/emergenesis --- https://www.sciencefocus.com/the-future-of-video-calls-is-here-but-do-we-actually-need-it 2023-03-12 sociology/technology --- https://arxiv.org/abs/2210.15303 Can language models handle recursively nested grammatical structures? A case study on comparing models and humans Andrew Kyle Lampinen 2022-10-27 2023-03-12 [("doi","10.48550/arXiv.2210.15303")] ai/nn/transformer/gpt psychology/linguistics

How should we compare the capabilities of language models (LMs) and humans? I draw inspiration from comparative psychology to highlight some challenges.

In particular, I consider a case study: processing of recursively nested grammatical structures. Prior work suggests that LMs cannot handle these structures as reliably as humans can. However, the humans were provided with instructions and training, while the LMs were evaluated zero-shot.

I therefore match the evaluation more closely. Providing large LMs with a simple prompt—substantially less content than the human training—allows the LMs to consistently outperform the human results, and even to extrapolate to more deeply nested conditions than were tested with humans.

Further, reanalyzing the prior human data suggests that the humans may not perform above chance at the difficult structures initially. Thus, large LMs may indeed process recursively nested grammatical structures as reliably as humans.

This case study highlights how discrepancies in the evaluation can confound comparisons of language models and humans. I therefore reflect on the broader challenge of comparing human and model capabilities, and highlight an important difference between evaluating cognitive models and foundation models.

--- https://arxiv.org/abs/1901.04587 Human few-shot learning of compositional instructions Brenden M. Lake, Tal Linzen, Marco Baroni 2019-01-14 2023-03-13 [("doi","10.48550/arXiv.1901.04587")] ai/nn psychology/linguistics

People learn in fast and flexible ways that have not been emulated by machines. Once a person learns a new verb “dax”, he or she can effortlessly understand how to “dax twice”, “walk and dax”, or “dax vigorously.”

There have been striking recent improvements in machine learning for natural language processing, yet the best algorithms require vast amounts of experience and struggle to generalize new concepts in compositional ways.

To better understand these distinctively human abilities, we study the compositional skills of people through language-like instruction learning tasks. Our results show that people can learn and use novel functional concepts from very few examples (few-shot learning), successfully applying familiar functions to novel inputs. People can also compose concepts in complex ways that go beyond the provided demonstrations.

Two additional experiments examined the assumptions and inductive biases that people make when solving these tasks, revealing 3 biases: mutual exclusivity, one-to-one mappings, and iconic concatenation.

We discuss the implications for cognitive modeling and the potential for building machines with more human-like language learning capabilities.

--- /doc/economics/copyright/2023-08-11-hachettebookgroup-120cv04160jgkotw-214-settlement.pdf Joint submission of [Proposed] Consent Judgment and Permanent Injunction Subject to Reservation of Right of Appeal 2023-08-11 2023-08-11 economics/copyright law --- https://en.wikipedia.org/wiki/Nuclear_lightbulb Nuclear lightbulb 2023-03-13 technology --- https://arxiv.org/abs/2205.12381 First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization Siddharth Reddy, Sergey Levine, Anca D. Dragan 2022-05-24 2023-03-13 [("doi","10.48550/arXiv.2205.12381")] cs/algorithm/information psychology/neuroscience reinforcement-learning/multi-agent

How can we train an assistive human-machine interface (eg. an electromyography-based limb prosthesis) to translate a user’s raw command signals into the actions of a robot or computer when there is no prior mapping, we cannot ask the user for supervision in the form of action labels or reward feedback, and we do not have prior knowledge of the tasks the user is trying to accomplish?

The key idea in this paper is that, regardless of the task, when an interface is more intuitive, the user’s commands are less noisy. We formalize this idea as a completely unsupervised objective for optimizing interfaces: the mutual information between the user’s command signals and the induced state transitions in the environment.

To evaluate whether this mutual information score can distinguish between effective and ineffective interfaces, we conduct an observational study on 540K examples of users operating various keyboard and eye gaze interfaces for typing, controlling simulated robots, and playing video games. The results show that our mutual information scores are predictive of the ground-truth task completion metrics in a variety of domains, with an average Spearman’s rank correlation of 0.43.

In addition to offline evaluation of existing interfaces, we use our unsupervised objective to learn an interface from scratch: we randomly initialize the interface, have the user attempt to perform their desired tasks using the interface, measure the mutual information score, and update the interface to maximize mutual information through reinforcement learning.

We evaluate our method through a user study with 12 participants who perform a 2D cursor control task using a perturbed mouse, and an experiment with one user playing the Lunar Lander game using hand gestures. The results show that we can learn an interface from scratch, without any user supervision or prior knowledge of tasks, in under 30 minutes.

--- https://arxiv.org/abs/2205.09707 PLAID: An Efficient Engine for Late Interaction Retrieval Keshav Santhanam, Omar Khattab, Christopher Potts, Matei Zaharia 2022-05-19 2023-03-13 [("doi","10.48550/arXiv.2205.09707")] ai/nn/retrieval ai/nn/sparsity/low-precision ai/nn/transformer

Pre-trained language models are increasingly important components across multiple information retrieval (IR) paradigms. Late interaction, introduced with the ColBERT model and recently refined in ColBERTv2, is a popular paradigm that holds state-of-the-art status across many benchmarks.

To dramatically speed up the search latency of late interaction, we introduce the Performance-optimized Late Interaction Driver (PLAID). Without impacting quality, PLAID swiftly eliminates low-scoring passages using a novel centroid interaction mechanism that treats every passage as a lightweight bag of centroids.

PLAID uses centroid interaction as well as centroid pruning, a mechanism for sparsifying the bag of centroids, within a highly-optimized engine to reduce late interaction search latency by up to 7× on a GPU and 45× on a CPU against vanilla ColBERTv2, while continuing to deliver state-of-the-art retrieval quality.

This allows the PLAID engine with ColBERTv2 to achieve latency of tens of milliseconds on a GPU and tens or just few hundreds of milliseconds on a CPU at large scale, even at the largest scales we evaluate with 140M passages.

--- https://www.youtube.com/watch?v=tWZOEFvczzA 2023-03-13 ai/nn/diffusion anime --- https://arxiv.org/abs/2308.05713#openai Testing GPT-4 with Wolfram Alpha and Code Interpreter plug-ins on math and science problems Ernest Davis, Scott Aaronson 2023-08-10 2023-08-10 [("doi","10.48550/arXiv.2308.05713")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex math

This report describes a test of the large language model GPT-4 with the Wolfram Alpha and the Code Interpreter plug-ins on 105 original problems in science and math, at the high school and college levels, carried out in June-August 2023.

Our tests suggest that the plug-ins enhance GPT’s ability to solve these problems.

Having said that, there are still often “interface” failures; that is, GPT often has trouble formulating problems in a way that elicits useful answers from the plug-ins. Fixing these interface failures seems like a central challenge in making GPT a reliable tool for college-level calculation problems.

--- https://thehighergeometer.wordpress.com/2023/08/09/no-order-10-projective-planes-via-sat/ 2023-03-13 economics/experience-curve math --- https://www.youtube.com/watch?v=HyfQVZHmArA 2023-03-13 ai/music --- https://en.wikipedia.org/wiki/WikiFeet WikiFeet 2023-03-13 sociology/technology --- https://arxiv.org/abs/2304.05376 ChemCrow: Augmenting large-language models with chemistry tools Andres M. Bran, Sam Cox, Andrew D. White, Philippe Schwaller 2023-04-11 2023-04-11 [("doi","10.48550/arXiv.2304.05376")] ai/nn/transformer/gpt/4 biology existential-risk reinforcement-learning/robot

Over the last decades, excellent computational chemistry tools have been developed. Their full potential has not yet been reached as most are challenging to learn and exist in isolation.

Recently, large-language models (LLMs) have shown strong performance in tasks across domains, but struggle with chemistry-related problems. Moreover, these models lack access to external knowledge sources, limiting their usefulness in scientific applications.

In this study, we introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery, and materials design. By integrating 17 expert-designed tools, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge.

Our agent autonomously planned the syntheses of an insect repellent, 3 organocatalysts, as well as other relevant molecules. Our evaluation, including both LLM and expert assessments, demonstrates ChemCrow’s effectiveness in automating a diverse set of chemical tasks.

Surprisingly, we find that GPT-4 as an evaluator cannot distinguish between clearly wrong GPT-4 completions and ChemCrow’s performance. There is a large risk of misuse of tools like ChemCrow, and we discuss their potential harms.

Employed responsibly, our work not only aids expert chemists and lowers barriers for non-experts, but also fosters scientific advancement by bridging the gap between experimental and computational chemistry. A subset of the code is publicly available at https://github.com/ur-whitelab/chemcrow-public.

--- https://www.biorxiv.org/content/10.1101/2023.02.10.528019.full Identification and analysis of individuals who deviate from their genetically-predicted phenotype Gareth Hawkes, Loïc Yengo, Sailaja Vedantam, Eirini Marouli, Robin N. Beaumont, Giant Consortium, Jessica Tyrrell, Michael N. Weedon, Joel Hirschhorn, Timothy Frayling, Andrew R. Wood 2023-02-10 2023-03-14 [("doi","10.1101/2023.02.10.528019")] genetics/heritable statistics/power-analysis

Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol. We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL cholesterol and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions.

Author Summary: Human genetics is becoming increasingly useful to help predict human traits across a population owing to findings from large-scale genetic association studies and advances in the power of genetic predictors. This provides an opportunity to potentially identify individuals that deviate from genetic predictions for a common phenotype under investigation. For example, an individual may be genetically predicted to be tall, but be shorter than expected. It is potentially important to identify individuals who deviate from genetic predictions as this can facilitate further follow-up to assess likely causes. Using 158,951 unrelated individuals from the UK Biobank, with height and LDL cholesterol, as exemplar traits, we demonstrate that ~0.15% & 0.12% of individuals deviate from their genetically predicted phenotypes respectively. We observed these individuals to be enriched for a range of rare clinical diagnoses, as well as rare genetic factors that may be causal. Our analyses also demonstrate several methods for detecting individuals who deviate from genetic predictions that can be applied to a range of continuous human phenotypes.

--- https://arxiv.org/abs/2302.03668 Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, Tom Goldstein 2023-02-07 2023-03-14 [("doi","10.48550/arXiv.2302.03668")] ai/nn/diffusion ai/nn/transformer/clip reinforcement-learning/meta-learning

The strength of modern generative models lies in their ability to be controlled through text-based prompts. Typical “hard” prompts are made from interpretable words and tokens, and must be hand-crafted by humans. There are also “soft” prompts, which consist of continuous feature vectors. These can be discovered using powerful optimization methods, but they cannot be easily interpreted, re-used across models, or plugged into a text-based interface.

We describe an approach to robustly optimize hard text prompts through efficient gradient-based optimization. Our approach automatically generates hard text-based prompts for both text-to-image and text-to-text applications. In the text-to-image setting, the method creates hard prompts for diffusion models, allowing API users to easily generate, discover, and mix and match image concepts without prior knowledge on how to prompt the model. In the text-to-text setting, we show that hard prompts can be automatically discovered that are effective in tuning LMs for classification.

--- https://en.wikipedia.org/wiki/Superlubricity Superlubricity 2023-03-14 technology --- https://arxiv.org/abs/2308.07037#nnaisense Bayesian Flow Networks Alex Graves, Rupesh Kumar Srivastava, Timothy Atkinson, Faustino Gomez 2023-08-14 2023-08-14 [("doi","10.48550/arXiv.2308.07037")] ai/nn/diffusion/discrete cs/algorithm/information/compression statistics/bayes

This paper introduces Bayesian Flow Networks (BFNs), a new class of generative model in which the parameters of a set of independent distributions are modified with Bayesian inference in the light of noisy data samples, then passed as input to a neural network that outputs a second, interdependent distribution.

Starting from a simple prior and iteratively updating the two distributions yields a generative procedure similar to the reverse process of diffusion models; however it is conceptually simpler in that no forward process is required. Discrete and continuous-time loss functions are derived for continuous, discretized and discrete data, along with sample generation procedures.

Notably, the network inputs for discrete data lie on the probability simplex, and are therefore natively differentiable, paving the way for gradient-based sample guidance and few-step generation in discrete domains such as language modeling. The loss function directly optimizes data compression and places no restrictions on the network architecture.

In our experiments BFNs achieve competitive log-likelihoods for image modeling on dynamically binarized MNIST and CIFAR-10, and outperform all known discrete diffusion models on the text8 character-level language modeling task.

--- https://arxiv.org/abs/2308.06912#google CausalLM is not optimal for in-context learning Nan Ding, Tomer Levinboim, Jialin Wu, Sebastian Goodman, Radu Soricut 2023-08-14 2023-08-14 [("doi","10.48550/arXiv.2308.06912")] ai/nn/transformer/gpt/palm reinforcement-learning/meta-learning

Recent empirical evidence indicates that transformer based in-context learning performs better when using a prefix language model (prefixLM), in which in-context samples can all attend to each other, compared to causal language models (causalLM), which use auto-regressive attention that prohibits in-context samples to attend to future samples.

While this result is intuitive, it is not understood from a theoretical perspective. In this paper we take a theoretical approach and analyze the convergence behavior of prefixLM and causalLM under a certain parameter construction.

Our analysis shows that both LM types converge to their stationary points at a linear rate, but that while prefixLM converges to the optimal solution of linear regression, causalLM convergence dynamics follows that of an online gradient descent algorithm, which is not guaranteed to be optimal even as the number of samples grows infinitely.

We supplement our theoretical claims with empirical experiments over synthetic and real tasks and using various types of transformers. Our experiments verify that causalLM consistently underperforms prefixLM in all settings.

--- https://okmij.org/ftp/Computation/sendmail-as-turing-machine.txt Sendmail as a Turing machine Oleg 2000 2023-03-14 cs/computable --- https://www.science.org/doi/10.1126/sciadv.abh2013 Identifying a living great-grandson of the Lakota Sioux leader Tatanka Iyotake (Sitting Bull) Moltke 2021 2023-03-14 genetics/sequencing --- https://www.youtube.com/watch?v=4YYvBLAF4T8?t=330 The Search for the Perfect Door Deviant Ollam 2016 2023-03-14 cs/security --- https://restofworld.org/2023/youtube-thumbnail-ai/ 2023-03-14 ai/nn/diffusion/midjourney design economics/automation --- https://arxiv.org/abs/2004.13912 Neural Additive Models: Interpretable Machine Learning with Neural Nets Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Ben Lengerich, Rich Caruana, Geoffrey Hinton 2020-04-29 2023-03-14 [("doi","10.48550/arXiv.2004.13912")] ai/tabular

Deep neural networks (DNNs) are powerful black-box predictors that have achieved impressive performance on a wide variety of tasks. However, their accuracy comes at the cost of intelligibility: it is usually unclear how they make their decisions. This hinders their applicability to high stakes decision-making domains such as healthcare.

We propose Neural Additive Models (NAMs) which combine some of the expressivity of DNNs with the inherent intelligibility of generalized additive models. NAMs learn a linear combination of neural networks that each attend to a single input feature. These networks are trained jointly and can learn arbitrarily complex relationships between their input feature and the output.

Our experiments on regression and classification datasets show that NAMs are more accurate than widely used intelligible models such as logistic regression and shallow decision trees. They perform similarly to existing state-of-the-art generalized additive models in accuracy, but are more flexible because they are based on neural nets instead of boosted trees.

To demonstrate this, we show how NAMs can be used for multitask learning on synthetic data and on the COMPAS recidivism data due to their composability, and demonstrate that the differentiability of NAMs allows them to train more complex interpretable models for COVID-19.

--- https://goodscience.substack.com/p/metascience-since-2012-a-personal 2023-03-15 statistics/bias --- https://slate.com/human-interest/2023/08/dungeons-dragons-critical-role-matthew-mercer-twitch.html 2023-03-15 fiction/text-game --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6763434/ Ideological differences in the expanse of the moral circle Adam Waytz, Ravi Iyer, Liane Young, Jonathan Haidt, Jesse Graham 2019 2023-03-15 [("doi","10.1038/s41467-019-12227-0")] philosophy/ethics politics

Do clashes between ideologies reflect policy differences or something more fundamental? The present research suggests they reflect core psychological differences such that liberals express compassion toward less structured and more encompassing entities (ie. universalism), whereas conservatives express compassion toward more well-defined and less encompassing entities (ie. parochialism).

Here we report 7 studies illustrating universalist versus parochial differences in compassion. Studies 1a-1c show that liberals, relative to conservatives, express greater moral concern toward friends relative to family, and the world relative to the nation.

Studies 2a-2b demonstrate these universalist versus parochial preferences extend toward simple shapes depicted as proxies for loose versus tight social circles. Using stimuli devoid of political relevance demonstrates that the universalist-parochialist distinction does not simply reflect differing policy preferences.

Studies 3a-3b indicate these universalist versus parochial tendencies extend to humans versus nonhumans more generally, demonstrating the breadth of these psychological differences.

--- https://stephenmalina.com/post/2023-08-13-cgm-and-beyond/ 2023-03-15 nootropic/quantified-self --- https://what-if.xkcd.com/1/ 2023-03-15 science --- https://arxiv.org/abs/2308.07921 Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification Aojun Zhou, Ke Wang, Zimu Lu, Weikang Shi, Sichun Luo, Zipeng Qin, Shaoqing Lu, Anya Jia, Linqi Song, Mingjie Zhan, Hongsheng Li 2023-08-15 2023-08-15 [("doi","10.48550/arXiv.2308.07921")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue math

Recent progress in large language models (LLMs) like GPT-4 and PaLM-2 has brought advancements in addressing math reasoning problems. In particular, OpenAI’s latest version of GPT-4, known as GPT-4 Code Interpreter, shows remarkable performance on challenging math datasets.

In this paper, we explore the effect of code on enhancing LLMs’ reasoning capability by introducing different constraints on the Code Usage Frequency of GPT-4 Code Interpreter. We found that its success can be largely attributed to its powerful skills in generating and executing code, evaluating the output of code execution, and rectifying its solution when receiving unreasonable outputs.

Based on this insight, we propose a novel and effective prompting method, explicit code-based self-verification (CSV), to further boost the mathematical reasoning potential of GPT-4 Code Interpreter. This method employs a zero-shot prompt on GPT-4 Code Interpreter to encourage it to use code to self-verify its answers. In instances where the verification state registers as “False”, the model shall automatically amend its solution, analogous to our approach of rectifying errors during a mathematics examination.

Furthermore, we recognize that the states of the verification result indicate the confidence of a solution, which can improve the effectiveness of majority voting. With GPT-4 Code Interpreter and CSV, we achieve an impressive zero-shot accuracy on MATH dataset (53.9% → 84.3%).

--- https://vitalik.eth.limo/general/2023/08/16/communitynotes.html 2023-03-15 economics/mechanism-design politics sociology/technology --- https://gigamonkeys.com/code-reading/ 2023-03-15 cs psychology/linguistics --- https://www.wired.com/2014/01/how-to-hack-okcupid/ 2023-03-15 psychology/okcupid --- https://www.sciencedirect.com/science/article/pii/S0002916523019949 Plasma vitamin D and mortality in older men: a community-based prospective cohort study Michaëlsson 2010 2023-03-15 longevity vitamin-d --- https://physoc.onlinelibrary.wiley.com/doi/pdf/10.1113/jphysiol.2011.217919 Brain glycogen supercompensation following exhaustive exercise Matsui 2012 2023-03-15 exercise psychology/neuroscience --- https://arxiv.org/abs/2306.15063 Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression Allan Raventós, Mansheej Paul, Feng Chen, Surya Ganguli 2023-06-26 2023-06-26 [("doi","10.48550/arXiv.2306.15063")] ai/scaling reinforcement-learning/meta-learning statistics/bayes

Pretrained transformers exhibit the remarkable ability of in-context learning (ICL): they can learn tasks from just a few examples provided in the prompt without updating any weights. This raises a foundational question: can ICL solve fundamentally new tasks that are very different from those seen during pretraining?

To probe this question, we examine ICL’s performance on linear regression while varying the diversity of tasks in the pretraining dataset. We empirically demonstrate a task diversity threshold for the emergence of ICL. Below this threshold, the pretrained transformer cannot solve unseen regression tasks as it behaves like a Bayesian estimator with the non-diverse pretraining task distribution as the prior.

Beyond this threshold, the transformer outperforms this estimator; its behavior aligns with that of ridge regression, corresponding to a Gaussian prior over all tasks, including those not seen during pretraining. These results highlight that, when pretrained on data with task diversity greater than the threshold, transformers can solve fundamentally new tasks in-context.

Importantly, this capability hinges on it deviating from the Bayes optimal estimator with the pretraining distribution as the prior. This study underscores, in a concrete example, the critical role of task diversity, alongside data and model scale, in the emergence of ICL. Code is available at Github.

--- https://arxiv.org/abs/2305.16264#huggingface Scaling Data-Constrained Language Models Niklas Muennighoff, Alexander M. Rush, Boaz Barak, Teven Le Scao, Aleksandra Piktus, Nouamane Tazi, Sampo Pyysalo, Thomas Wolf, Colin Raffel 2023-05-25 2023-05-25 [("doi","10.48550/arXiv.2305.16264")] ai/scaling

The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes.

Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero.

We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters.

Models and datasets from our 400 training runs are freely available at Github.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6708477/ Quantifying the Value of Orally Delivered Biologic Therapies: A Cost-Effectiveness Analysis of Oral Semaglutide Alex Abramson, Florencia Halperin, Jane Kim, Giovanni Traverso 2019 2023-03-16 [("doi","10.1016/j.xphs.2019.04.022")] longevity/glp/semaglutide

Oral semaglutide, which has undergone multiple phase 3 clinical trials, represents the first oral biologic medication for type 2 diabetes in the form of a daily capsule. It provides similar efficacy compared with its weekly injection counterpart, but it demands a dose on the order of 100× as high and requires more frequent administration.

We perform a cost effectiveness analysis using a first and second order Monte Carlo simulation to estimate quality-adjusted life expectancies associated with an oral daily capsule, oral weekly capsule, daily injection, and weekly injection of semaglutide.

We conclude that the additional costs incurred to produce extra semaglutide for the oral formulation are cost effective, given the greater quality of life experienced when taking a capsule over a weekly injection.

We also demonstrate that the potency of semaglutide allows the formulation to be cost effective, and less potent drugs will require increased oral bioavailability to make a cost effective oral formulation.

…It was possible to estimate the potential costs of the active pharmaceutical ingredient required for the oral capsules and subcutaneous injections based the doses required for each method to achieve comparable health outcomes. The subcutaneous injection of semaglutide is dosed at 0.51 mg per week. At a cost of $100/gram, this amounted to a cost of $2.60–$5.20 per year. The oral version of semaglutide required a dose of 21–40 mg per day to replicate the HbA1c effects of the respective injected doses 15. Therefore, the oral formulation used ~280× the amount of active pharmaceutical ingredient (API). It costs $770–$1,460 per year to produce the necessary API for the oral dosage form. Phase 3 clinical trials for oral semaglutide only tested 14 mg doses of oral semaglutide. Although they did not show comparable effects to the subcutaneous injections, they still demonstrated positive health outcomes. It costs $510 per year to produce the necessary API for this dose size. Figure 3c shows the lifetime costs associated with the prescription of each formulation and Figure 3d details the probability of cost effectiveness for each formulation strategy at varying willingness-to-pay thresholds. Because the cost to produce semaglutide might change as the process is scaled up to produce additional drug, or our estimation of the cost may be incorrect, we performed a sensitivity analysis around this value. Supplementary Figure S2 shows the relative cost of producing the active pharmaceutical ingredient semaglutide compared to the annual drug cost for patients and insurers for drug production costs ranging from $1–$100/g.

With the additional expense, the oral semaglutide costs $10,000 more in raw materials over the course of a lifetime when comparing daily dosed pills to weekly injections (taking into account adherence, drop-outs, and discount rates). Still, the QALYs gained from a pill allowed the oral formulation to provide greater QALYs at a lower cost-effectiveness ratio. Both the daily and weekly oral dosage forms were cost-effective in 50% of cases at a cost-effectiveness ratio of $85,000 per QALY. A cost-effectiveness table provides the incremental cost-effectiveness ratios (ICER) (Table 2). This compares to a cost effectiveness ratio of $110,000 per QALY gained for a weekly semaglutide injection. Changing the raw material drug cost to $50/g lowered the cost-effectiveness ratio of an oral pill by $5,000 per QALY.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5984908/ Cost of Achieving HbA1c Treatment Targets and Weight Loss Responses with Once-Weekly Semaglutide Versus Dulaglutide in the United States Lars Wilkinson, Barnaby Hunt, Pierre Johansen, Neeraj N. Iyer, Tam Dang-Tan, Richard F. Pollock 2018 2023-03-16 [("doi","10.1007/s13300-018-0402-8")] longevity/glp/semaglutide

Background: The National Health and Nutrition Examination Surveys show that many people with type 2 diabetes (T2D) in the USA fail to achieve recommended treatment targets. In the SUSTAIN 7 randomized controlled trial, once-weekly semaglutide (0.5 and 1.0 mg) was superior to comparative doses of dulaglutide (0.75 and 1.5 mg) in reducing glycated hemoglobin (HbA1c) and body weight in people with T2D. The present study estimated the cost per patient achieving HbA1c treatment targets and weight loss responses with once-weekly semaglutide and dulaglutide in the USA.

Method: Numbers needed to treat and annual cost per patient achieving HbA1c targets (including a triple composite endpoint of HbA1c < 7% without hypoglycemia and no weight gain) or weight loss responses were calculated on the basis of data from SUSTAIN 7 and the annual cost of treatment from a US healthcare payer perspective.

Results: More patients reached HbA1c targets with once-weekly semaglutide than with dulaglutide, and once-weekly semaglutide showed lower costs of control for all modeled endpoints. The cost per patient achieving the triple composite endpoint was USD 11,916 with once-weekly semaglutide 1.0 mg and USD 15,204 with dulaglutide 1.5 mg, representing a 28% larger cost with dulaglutide 1.5 mg. The cost of reaching the target was 68% larger with dulaglutide 0.75 mg versus once-weekly semaglutide 0.5 mg. For each patient achieving an HbA1c < 7%, the cost would be 18% larger with dulaglutide 1.5 mg than with once-weekly semaglutide 1.0 mg.

Conclusions: The cost of bringing one patient to the triple composite endpoint of an HbA1c < 7% without hypoglycemia and no weight gain would be 28% and 68% higher with dulaglutide 1.5 mg relative to once-weekly semaglutide 1.0 mg and dulaglutide 0.75 mg relative to once-weekly semaglutide 0.5 mg, respectively. Once-weekly semaglutide therefore provides better value for money than dulaglutide for the treatment of people with T2D in the USA.

Funding: Novo Nordisk A/S.

--- https://www.nytimes.com/2008/04/13/education/13endow.html 2023-03-16 economics/perpetuities --- https://www.quantamagazine.org/complexity-theorys-50-year-journey-to-the-limits-of-knowledge-20230817/ 2023-03-16 cs/computable cs/cryptography --- https://arxiv.org/abs/2302.14623#google Fast as CHITA: Neural Network Pruning with Combinatorial Optimization Riade Benbaki, Wenyu Chen, Xiang Meng, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder 2023-02-28 2023-03-16 [("doi","10.48550/arXiv.2302.14623")] ai/nn/sparsity/pruning

The sheer size of modern neural networks makes model serving a serious computational challenge. A popular class of compression techniques overcomes this challenge by pruning or sparsifying the weights of pretrained networks. While useful, these techniques often face serious tradeoffs between computational requirements and compression quality.

In this work, we propose a novel optimization-based pruning framework that considers the combined effect of pruning (and updating) multiple weights subject to a sparsity constraint. Our approach, CHITA, extends the classical Optimal Brain Surgeon framework and results in improvements in speed, memory, and performance over existing optimization-based approaches for network pruning.

CHITA’s main workhorse performs combinatorial optimization updates on a memory-friendly representation of local quadratic approximation(s) of the loss function. On a standard benchmark of pretrained models and datasets, CHITA leads to better sparsity-accuracy tradeoffs than competing methods.

For example, for MLPNet with only 2% of the weights retained, our approach improves the accuracy by 63% relative to the state-of-the-art. Furthermore, when used in conjunction with fine-tuning SGD steps, our method achieves accuracy gains over the state-of-the-art approaches.

--- https://arxiv.org/abs/2206.11228 Adversarially trained neural representations may already be as robust as corresponding biological neural representations Chong Guo, Michael J. Lee, Guillaume Leclerc, Joel Dapello, Yug Rao, Aleksander Madry, James J. DiCarlo 2022-06-19 2023-03-16 [("doi","10.48550/arXiv.2206.11228")] ai/nn/adversarial psychology/neuroscience

Visual systems of primates are the gold standard of robust perception. There is thus a general belief that mimicking the neural representations that underlie those systems will yield artificial visual systems that are adversarially robust.

In this work, we develop a method for performing adversarial visual attacks directly on primate brain activity. We then leverage this method to demonstrate that the above-mentioned belief might not be well-founded.

Specifically, we report that the biological neurons that make up visual systems of primates exhibit susceptibility to adversarial perturbations that is comparable in magnitude to existing (robustly trained) artificial neural networks.

--- https://www.youtube.com/watch?v=buZ5RLWozFs 2023-03-16 fiction/humor law --- https://infoproc.blogspot.com/2017/05/contingency-history-and-atomic-bomb.html 2023-03-16 existential-risk/nuclear --- https://arxiv.org/abs/2308.06103 Composable Function-preserving Expansions for Transformer Architectures Andrea Gesmundo, Kaitlin Maile 2023-08-11 2023-08-11 [("doi","10.48550/arXiv.2308.06103")] ai/nn/sparsity/knowledge-distillation

[cf. net2net] Training state-of-the-art neural networks requires a high cost in terms of compute and time. Model scale is recognized to be a critical factor to achieve and improve the state-of-the-art. Increasing the scale of a neural network normally requires restarting from scratch by randomly initializing all the parameters of the model, as this implies a change of architecture’s parameters that does not allow for a straightforward transfer of knowledge from smaller-size models.

In this work, we propose 6 composable transformations to incrementally increase the size of Transformer-based neural networks while preserving functionality, allowing to expand the capacity of the model as needed.

We provide proof of exact function preservation under minimal initialization constraints for each transformation.

The proposed methods may enable efficient training pipelines for larger and more powerful models by progressively expanding the architecture throughout training.

--- https://arxiv.org/abs/2307.12507 Investigating the Existence of ‘Secret Language’ in Language Models Yimu Wang, Peng Shi, Hongyang Zhang 2023-07-24 2023-07-24 [("doi","10.48550/arXiv.2307.12507")] ai/nn/adversarial ai/nn/transformer/clip ai/nn/transformer/gpt/3/nonfiction cs/cryptography/steganography

In this paper, we study the problem of secret language in NLP, where current language models (LMs) seem to have a hidden vocabulary that allows them to interpret absurd inputs as meaningful concepts. We investigate two research questions: “Does the secret language phenomenon exist in different language models?” and “Does secret language depend on specific context?”

To answer these questions, we introduce a novel method named SecretFinding, a gradient-based approach that can automatically discover secret languages in LMs. We conduct experiments on 5 representative models (Electra, ALBERT, Roberta, DistilBERT, and CLIP) finetuned on 4 NLP benchmarks (SST-2, MRPC, SNLI, and SQuAD) and a language-grounding benchmark (MS COCO).

Our experimental results show that even when we replace the most important words with others that are semantically dissimilar to the original words in a sentence, LMs do not consider the new sentence semantically dissimilar to the original, as the output does not change with a high probability. This phenomenon holds true across the 5 models and 5 tasks and gives a positive answer to the first research question.

As for the second research question, we find that the secret language discovered by SecretFinding is quite general and could even be transferred to other models in the black-box settings, such as GPT-3 and ChatGPT.

Finally, we discuss the causes of secret language, how to eliminate it, the potential connection to memorization, and ethical implications. Examples of secret language found by SecretFinding are available on https://huggingface.co/spaces/anonauthors/SecretLanguage.

--- https://arxiv.org/abs/2105.00828 Memorization versus Generalization in Pre-trained Language Models Michael Tänzer, Sebastian Ruder, Marek Rei 2021-04-16 2023-03-17 [("doi","10.48550/arXiv.2105.00828")] ai/nn/transformer ai/scaling

State-of-the-art pre-trained language models have been shown to memorize facts and perform well with limited amounts of training data.

To gain a better understanding of how these models learn, we study their generalization and memorization capabilities in noisy and low-resource scenarios.

We find that the training of these models is almost unaffected by label noise and that it is possible to reach near-optimal results even on extremely noisy datasets.

However, our experiments also show that they mainly learn from high-frequency patterns and largely fail when tested on low-resource tasks such as few-shot learning and rare entity recognition.

To mitigate such limitations, we propose an extension based on prototypical networks that improves performance in low-resource named entity recognition tasks.

--- https://en.wikipedia.org/wiki/High-rise_syndrome High-rise syndrome 2023-03-17 cat/biology --- https://www.kalzumeus.com/2009/03/07/how-to-successfully-compete-with-open-source-software/ 2023-03-17 design --- https://www.thecut.com/2016/10/inside-psychologys-methodological-terrorism-debate.html 2023-03-17 statistics/bias --- /crop#aspect-ratio-training 2023-03-17 ai/nn/gan/data-augmentation --- https://www.medrxiv.org/content/10.1101/2023.08.14.23294074.full Rare coding variants in schizophrenia-associated genes affect generalised cognition in the UK Biobank Eilidh Fenner, Peter Holmans, Michael C. O’Donovan, Michael J. Owen, James T. R. Walters, Elliott Rees 2023-08-16 2023-08-16 [("doi","10.1101/2023.08.14.23294074")] genetics/heritable/correlation genetics/heritable/rare iq psychiatry/schizophrenia

Impairments in cognitive function are a feature of schizophrenia that strongly predict functional outcome and are generally not improved by current medications. However, the nature of the relationship between cognitive impairment and schizophrenia risk, and particularly the extent to which this reflects shared underlying biology, remains uncertain.

We analysed exome-sequencing data from the UK Biobank to test for association between generalized cognition and damaging rare coding variation in genes and loci associated with schizophrenia in 30,487 people without the disorder.

Rare protein-truncating variants (PTVs) and damaging missense variants in loss-of-function intolerant (LoFi) genes were associated with lower generalized cognition. Moreover, we found statistically-significantly stronger effects for damaging missense variants in credible causal genes at schizophrenia GWAS loci and for rare PTVs affecting LoFi genes in regions defined by schizophrenia-enriched CNVs.

This suggests shared underlying biology between schizophrenia risk and general cognitive function in the population, and that exploiting large population sequencing datasets to identify genes with shared effects on cognition and schizophrenia can provide a route towards determining biological processes underlying cognitive impairment in schizophrenia.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9300873/ Rare genetic variants in genes and loci linked to dominant monogenic developmental disorders cause milder related phenotypes in the general population Rebecca Kingdom, Marcus Tuke, Andrew R. Wood, Robin N. Beaumont, Timothy Frayling, Michael N. Weedon, Caroline F. Wright 2022 2023-03-17 [("doi","10.1016/j.ajhg.2022.05.011")] genetics/heritable/rare iq

Many rare monogenic diseases are known to be caused by deleterious variants in thousands of genes, however the same variants can also be found in people without the associated clinical phenotypes. The penetrance of these monogenic variants is generally unknown in the wider population, as they are typically identified in small clinical cohorts of affected individuals and families with highly penetrant variants.

Here, we investigated the phenotypic effect of rare, potentially deleterious variants in genes and loci where similar variants are known to cause monogenic developmental disorders (DDs) in a large population cohort. We used UK Biobank to investigate phenotypes associated with rare protein-truncating and missense variants in 599 monoallelic DDG2P genes by using whole-exome-sequencing data from ~200,000 individuals and rare copy-number variants overlapping known DD loci by using SNP-array data from ~500,000 individuals.

We found that individuals with these likely deleterious variants had a mild DD-related phenotype, including lower fluid intelligence, slower reaction times, lower numeric memory scores, and longer pairs matching times compared to the rest of the UK Biobank cohort. They were also shorter, had a higher BMI, and had significant socioeconomic disadvantages: they were less likely to be employed or be able to work and had a lower income and higher deprivation index.

Our findings suggest that many genes routinely tested within pediatric genetics have deleterious variants with intermediate penetrance that may cause lifelong sub-clinical phenotypes in the general adult population.

…Many individuals in UKB carry rare, deleterious variants in genes where similar variants are known to cause monogenic autosomal dominant DD

Although variants in each gene individually account for extremely rare forms of DD, together they account for a large portion of DD diagnoses and have a surprisingly high burden of rare deleterious variants in UKB. In 184,477 unrelated European individuals with WES data in UKB and across 599 monoallelic DDG2P genes, 9,103 individuals carry a rare (n ≤ 5) LoF variant, 25,288 individuals carry a rare missense variant with REVEL > 0.7, and 79,959 individuals carry a rare synonymous variant. As the gene panel becomes smaller and more stringent, the burden of rare deleterious variants decreases; for example, 3,602, 1,327, and 167 individuals in UKB carry rare LoF variants in smaller more stringent subsets of 325, 125, and 25 DDG2P genes, respectively (Figure 1). In 450,274 individuals with SNP-array data in UKB and across 69 known DD loci, 4,922 individuals carry large deletions and 7,054 individuals carry large duplications.

Individuals in UKB with rare, deleterious variants in loci where similar variants are known to cause monogenic DD display DD-related phenotypes

We performed gene panel (including 599 monoallelic DDG2P genes) and multigenic copy-number (including 53 deletions/duplications syndromes) burden tests for 20 traits in UKB selected to be of relevance (in adults) to developmental phenotypes. Bonferroni-corrected statistically-significant associations were found across most phenotypes in individuals carrying likely damaging variants compared with the rest of the UKB cohort (Table 1, Figure 2, and Figure 3). Individuals carrying these variants generally had lower cognitive performance than the rest of the cohort, with reduced fluid intelligence (LoF group beta: −1.059), slower reaction times (LoF group beta: +0.043), lower numeric memory scores (LoF group beta: −0.068), and longer pairs matching times (LoF group beta: +0.122). They also completed fewer years in education, left education at an earlier age, and were less likely to have a degree. Medically, individuals were more likely to have reported a mental health issue or been diagnosed with either a childhood DD (including mild-severe intellectual disability, epilepsy, autism, ADHD, and congenital malformations) or adult DD (including schizophrenia and bipolar disorder). Individuals were also more likely to be shorter, have a higher BMI, and have had fewer children (though the latter association was only statistically-significant in men). Individuals also had statistically-significant socioeconomic disadvantages: they were less likely to be employed or be able to work and had a lower income and a higher TDI. Across all phenotypes tested, we observed a trend corresponding to the likely deleteriousness of the variants; the largest effect was generally observed in the group of individuals with multigenic deletions, followed by multigenic duplications, then LoF variants, and finally missense variants in one (or more) DDG2P genes. These trends were robust to the use of different CADD thresholds for selecting of missense variants (see Table S4) and to removal of individuals with a diagnosed childhood developmental disorder (“child DD”, as defined in Material & Method, n = 3,132; see Table S5). In contrast, individuals with only rare synonymous variants in these genes showed no statistically-significant difference in any phenotype compared to the remainder of the cohort, as expected for likely benign variants, suggesting that most of the confounding caused by population sub-structure was appropriately controlled.

…Furthermore, rare predicted-LoF variants were found in individuals in genes in which similar variants are thought to be fully or nearly fully penetrant causes of very well-established developmental syndromes, but without the full clinical phenotype that would be expected, suggesting that there is a range of penetrance and expressivity in the general population.

Despite the large size of UKB, we were limited by the number of individuals of European ancestry carrying rare damaging variants in these genes, which meant some of our analyses were under-powered to show a statistically-significant effect. We were also limited by the clinical and phenotypic data available on these individuals, all of whom were over 40 years of age at recruitment; evaluation and diagnosis of DD was much less routine when these individuals were children and is less likely to be recorded in the HES codes of older adults. Nonetheless, when found in an appropriate clinical pediatric setting, rare damaging variants in these genes are widely considered diagnostic for DD and thus they might not be expected to be present in a population cohort. Our results suggest that, although the penetrance of variants across these genes is lower than would be expected from previous clinical studies, they do still exert a phenotypic effect on adults in the general population who are nonetheless healthy enough, and have sufficient capacity, to volunteer to participate in a biobank.

--- /doc/cs/css/2022-10-31-gwern-gwernnet-darkmode-halloweenmode.png Gwern 2022-10-31 2023-03-17 cs/css --- https://arxiv.org/abs/2307.15936 A Theory for Emergence of Complex Skills in Language Models Sanjeev Arora, Anirudh Goyal 2023-07-29 2023-07-29 [("doi","10.48550/arXiv.2307.15936")] ai/scaling/emergence [talk]

A major driver of AI products today is the fact that new skills emerge in language models when their parameter set and training corpora are scaled up. This phenomenon is poorly understood, and a mechanistic explanation via mathematical analysis of gradient-based training seems difficult.

The current paper takes a different approach, analyzing emergence using the famous (and empirical) Scaling Laws of LLMs and a simple statistical framework. Contributions include: (a) A statistical framework that relates cross-entropy loss of LLMs to competence on the basic skills that underlie language tasks.

  1. Mathematical analysis showing that the Scaling Laws imply a strong form of inductive bias that allows the pre-trained model to learn very efficiently. We informally call this slingshot generalization since naively viewed it appears to give competence levels at skills that violate usual generalization theory.

  2. A key example of slingshot generalization, that competence at executing tasks involving k-tuples of skills emerges essentially at the same scaling and same rate as competence on the elementary skills themselves.

--- https://arxiv.org/abs/2307.01189 Trainable Transformer in Transformer Abhishek Panigrahi, Sadhika Malladi, Mengzhou Xia, Sanjeev Arora 2023-07-03 2023-07-03 [("doi","10.48550/arXiv.2307.01189")] ai/nn/transformer/attention reinforcement-learning/meta-learning

Recent works attribute the capability of in-context learning (ICL) in large pre-trained language models to implicitly simulating and fine-tuning an internal model (eg. linear or 2-layer MLP) during inference. However, such constructions require large memory overhead, which makes simulation of more sophisticated internal models intractable.

In this work, we propose an efficient construction, Transformer in Transformer (in short, TinT), that allows a transformer to simulate and fine-tune complex models internally during inference (eg. pre-trained language models).

In particular, we introduce innovative approximation techniques that allow a TinT model with less than 2 billion parameters to simulate and fine-tune a 125 million parameter transformer model within a single forward pass.

TinT accommodates many common transformer variants and its design ideas also improve the efficiency of past instantiations of simple models inside transformers. We conduct end-to-end experiments to validate the internal fine-tuning procedure of TinT on various language modeling and downstream tasks.

For example, even with a limited one-step budget, we observe TinT for a OPT-125M model improves performance by 4–16% absolute on average compared to OPT-125M. These findings suggest that large pre-trained language models are capable of performing intricate subroutines.

To facilitate further work, a modular and extensible codebase for TinT is included.

--- https://www.gq.com/story/wells-tower-on-marijuana 2023-03-18 marijuana --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7733852/ Bumblebees perceive the spatial layout of their environment in relation to their body size and form to minimize inflight collisions Sridhar Ravi, Tim Siesenop, Olivier Bertrand, Liang Li, Charlotte Doussot, William H. Warren, Stacey A. Combes, Martin Egelhaaf 2020 2023-03-18 [("doi","10.1073/pnas.2016872117")] psychology/animal

Animals that move through complex habitats must frequently contend with obstacles in their path. Humans and other highly cognitive vertebrates avoid collisions by perceiving the relationship between the layout of their surroundings and the properties of their own body profile and action capacity. It is unknown whether insects, which have much smaller brains, possess such abilities. We used bumblebees, which vary widely in body size and regularly forage in dense vegetation, to investigate whether flying insects consider their own size when interacting with their surroundings.

Bumblebees trained to fly in a tunnel were sporadically presented with an obstructing wall containing a gap that varied in width.

Bees successfully flew through narrow gaps, even those that were much smaller than their wingspans, by first performing lateral scanning (side-to-side flights) to visually assess the aperture. Bees then reoriented their in-flight posture (ie. yaw or heading angle) while passing through, minimizing their projected frontal width and mitigating collisions; in extreme cases, bees flew entirely sideways through the gap. Both the time that bees spent scanning during their approach and the extent to which they reoriented themselves to pass through the gap were determined not by the absolute size of the gap, but by the size of the gap relative to each bee’s own wingspan.

Our findings suggest that, similar to humans and other vertebrates, flying bumblebees perceive the affordance of their surroundings relative their body size and form to navigate safely through complex environments.

--- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002564 Associative Mechanisms Allow for Social Learning and Cultural Transmission of String Pulling in an Insect Sylvain Alem, Clint J. Perry, Xingfu Zhu, Olli J. Loukola, Thomas Ingraham, Eirik Søvik, Lars Chittka 2016-08-31 2023-03-18 [("doi","10.1371/journal.pbio.1002564")] psychology/animal

Social insects make elaborate use of simple mechanisms to achieve seemingly complex behavior and may thus provide a unique resource to discover the basic cognitive elements required for culture, ie. group-specific behaviors that spread from “innovators” to others in the group via social learning.

We first explored whether bumblebees can learn a unnatural object manipulation task by using string pulling to access a reward that was presented out of reach.

Only a small minority “innovated” and solved the task spontaneously, but most bees were able to learn to pull a string when trained in a stepwise manner. In addition, naïve bees learnt the task by observing a trained demonstrator from a distance. Learning the behavior relied on a combination of simple associative mechanisms and trial-and-error learning and did not require “insight”: naïve bees failed a “coiled-string experiment”, in which they did not receive instant visual feedback of the target moving closer when tugging on the string.

In cultural diffusion experiments, the skill spread rapidly from a single knowledgeable individual to the majority of a colony’s foragers. We observed that there were several sequential sets (“generations”) of learners, so that previously naïve observers could first acquire the technique by interacting with skilled individuals and, subsequently, themselves become demonstrators for the next “generation” of learners, so that the longevity of the skill in the population could outlast the lives of informed foragers.

This suggests that, so long as animals have a basic toolkit of associative and motor learning processes, the key ingredients for the cultural spread of unusual skills are already in place and do not require sophisticated cognition.

Bumblebees can be trained to pull strings to obtain a reward, can learn to string pull through observation, and can culturally spread string pulling throughout a colony.

Author Summary: Social insects make use of simple mechanisms to achieve many seemingly complex behaviors and thus may be able to provide a unique resource for uncovering the basic cognitive elements required for culture. Here, we first show that bumblebees can be trained to pull a string to access a reward, but most could not learn on their own. Naïve bees learned how to pull strings by observing trained demonstrators from a distance. Learning the behavior through observation relied on bees paying attention to both the string and the position of the trained demonstrator bee while pulling the string. We then tested whether bees could pass this information to others during a semi-natural situation involving several colonies. We found that once one bee knew how to string pull, over time, most of the foraging bees learned from the initially trained bee or from bees who had learned from the trained bee, even after the initial demonstrator was no longer available. These results suggest that learning a unnatural task in bumblebees can spread culturally through populations.

--- https://en.wikipedia.org/wiki/Google_Knowledge_Graph Google Knowledge Graph 2023-03-18 ai/nn/retrieval --- https://en.wikipedia.org/wiki/Knowledge_graph Knowledge graph 2023-03-18 ai/nn/retrieval --- /doc/math/1990-jaeger.pdf On the computational complexity of the Jones and Tutte polynomials F. Jaeger, D. L. Vertigan, D. J. A. Welsh 1990-07-01 2023-03-18 [("doi","10.1017/s0305004100068936")] cs/algorithm math

We show that determining the Jones polynomial of an alternating link is #P-hard. This is a special case of a wide range of results on the general intractability of the evaluation of the Tutte polynomial T(M; x, y) of a matroid M except for a few listed special points and curves of the (x, y)-plane.

In particular the problem of evaluating the Tutte polynomial of a graph at a point in the (x, y)-plane is #P-hard except when (x − 1)(y − 1) = 1 or when (x, y) equals (1, 1), (−1, −1), (0, −1), (−1, 0), (i, −i), (−i, 1), (j, j2), (j2, j) where j = e2πi/3.

--- https://education.msu.edu/kin/HBCL/_articles/Pontifex_2009_TheEffectOfAcute.pdf The effect of acute aerobic and resistance exercise on working memory Pontifex 2009 2023-03-18 dual-n-back exercise --- https://www-users.cse.umn.edu/~odlyzko/doc/privacy.economics.pdf Privacy, Economics, and Price Discrimination on the Internet Odlyzko 2003 2023-03-18 cs/security economics --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4863955/ Assessing the genetic overlap between BMI and cognitive function Riccardo E. Marioni, J. Yang, D. Dykiert, R. Mõttus, A. Campbell, Gail Davies, C. Hayward, D. J. Porteous, P. M. Visscher, I. J. Deary 2016 2023-03-18 [("doi","10.1038/mp.2015.205")] exercise genetics/heritable/correlation iq

Obesity and low cognitive function are associated with multiple adverse health outcomes across the life course. They have a small phenotypic correlation (r = −0.11; high body mass index (BMI)-low cognitive function), but whether they have a shared genetic aetiology is unknown.

We investigated the phenotypic and genetic correlations between the traits using data from 6815 unrelated, genotyped members of Generation Scotland, an ethnically homogeneous cohort from 5 sites across Scotland. Genetic correlations were estimated using the following: same-sample bivariate genome-wide complex trait analysis (GCTA)-GREML; independent samples bivariate GCTA-GREML using Generation Scotland for cognitive data and 4 other samples (n = 20 806) for BMI; and bivariate LDSC analysis using the largest genome-wide association study (GWAS) summary data on cognitive function (n = 48,462) and BMI (n = 339,224) to date. The GWAS summary data were also used to create polygenic scores for the two traits, with within & cross-trait prediction taking place in the independent Generation Scotland cohort.

A large genetic correlation of −0.51 (s.e. 0.15) was observed using the same-sample GCTA-GREML approach compared with −0.10 (s.e. 0.08) from the independent-samples GCTA-GREML approach and −0.22 (s.e. 0.03) from the bivariate LDSC analysis.

A genetic profile score using cognition-specific genetic variants accounts for 0.08% (p = 0.020) of the variance in BMI and a genetic profile score using BMI-specific variants accounts for 0.42% (p = 1.9 × 10−7) of the variance in cognitive function. 7 common genetic variants are statistically-significantly associated with both traits at p < 5 × 10−5, which is statistically-significantly more than expected by chance (p = 0.007).

All these results suggest there are shared genetic contributions to BMI and cognitive function.

--- https://statmodeling.stat.columbia.edu/2023/08/20/bob-carpenter-thinks-gpt-4-is-awesome/ 2023-03-19 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0061082 Reproductive Capability Is Associated with Lifespan and Cause of Death in Companion Dogs Jessica M. Hoffman, Kate E. Creevy, Daniel E. L. Promislow 2013-03-05 2023-03-19 [("doi","10.1371/journal.pone.0061082")] dog longevity

Reproduction is a risky affair; a lifespan cost of maintaining reproductive capability, and of reproduction itself, has been demonstrated in a wide range of animal species. However, little is understood about the mechanisms underlying this relationship.

Most cost-of-reproduction studies simply ask how reproduction influences age at death, but are blind to the subjects’ actual causes of death. Lifespan is a composite variable of myriad causes of death and it has not been clear whether the consequences of reproduction or of reproductive capability influence all causes of death equally.

To address this gap in understanding, we compared causes of death among over 40,000 sterilized and reproductively intact domestic dogs, Canis lupus familiaris.

We found that sterilization was strongly associated with an increase in lifespan, and while it decreased risk of death from some causes, such as infectious disease, it actually increased risk of death from others, such as cancer.

These findings suggest that to understand how reproduction affects lifespan, a shift in research focus is needed. Beyond the impact of reproduction on when individuals die, we must investigate its impact on why individuals die, and subsequently must identify the mechanisms by which these causes of death are influenced by the physiology associated with reproductive capability. Such an approach may also clarify the effects of reproduction on lifespan in people.

--- https://en.wikipedia.org/wiki/Iota_and_Jot Iota and Jot 2023-03-19 cs/computable --- https://arcadeblogger.com/2016/07/15/atari-tempest-photography/ 2023-03-19 design/visualization --- https://arxiv.org/abs/2306.05720 Beyond Surface Statistics: Scene Representations in a Latent Diffusion Model Yida Chen, Fernanda Viégas, Martin Wattenberg 2023-06-09 2023-06-09 [("doi","10.48550/arXiv.2306.05720")] ai/nn/diffusion

Latent diffusion models (LDMs) exhibit an impressive ability to produce realistic images, yet the inner workings of these models remain mysterious. Even when trained purely on images without explicit depth information, they typically output coherent pictures of 3D scenes.

In this work, we investigate a basic interpretability question: does an LDM create and use an internal representation of simple scene geometry? Using linear probes, we find evidence that:

the internal activations of the LDM encode linear representations of both 3D depth data and a salient-object / background distinction. These representations appear surprisingly early in the denoising process—well before a human can easily make sense of the noisy images.

Intervention experiments further indicate these representations play a causal role in image synthesis, and may be used for simple high-level editing of an LDM’s output.

--- https://arxiv.org/abs/2308.09175#deepmind Diversifying AI: Towards Creative Chess with AlphaZero (AZdb) Tom Zahavy, Vivek Veeriah, Shaobo Hou, Kevin Waugh, Matthew Lai, Edouard Leurent, Nenad Tomasev, Lisa Schut, Demis Hassabis, Satinder Singh 2023-08-17 2023-08-17 [("doi","10.48550/arXiv.2308.09175")] reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/model/alphago reinforcement-learning/model/decision-transformer reinforcement-learning/multi-agent reinforcement-learning/scaling

[media; cf. jury learning, RLHF mode collapse] In recent years, Artificial Intelligence (AI) systems have surpassed human intelligence in a variety of computational tasks. However, AI systems, like humans, make mistakes, have blind spots, hallucinate, and struggle to generalize to new situations. This work explores whether AI can benefit from creative decision-making mechanisms when pushed to the limits of its computational rationality.

In particular, we investigate whether a team of diverse AI systems can outperform a single AI in challenging tasks by generating more ideas as a group and then selecting the best ones. We study this question in the game of chess, the so-called Drosophila of AI.

We build on AlphaZero (AZ) and extend it to represent a league of agents via a latent-conditioned architecture, which we call AZdb [but still a single model]. We train AZdb to generate a wider range of ideas using behavioral diversity techniques and select the most promising ones with sub-additive planning.

Our experiments suggest that AZdb plays chess in diverse ways, solves more puzzles as a group and outperforms a more homogeneous team. Notably, AZdb solves twice as many challenging puzzles as AZ, including the challenging Penrose positions.

When playing chess from different openings, we notice that players in AZdb specialize in different openings, and that selecting a player for each opening using sub-additive planning results in a 50 Elo improvement over AZ.

Our findings suggest that diversity bonuses emerge in teams of AI agents, just as they do in teams of humans and that diversity is a valuable asset in solving computationally hard problems.

Figure 7: Scaling laws with AZdb. Top: Max over trials, Bottom: Sub-additive planning. Left to right: (1) Solve rate in % on Lichess puzzles with a different number of simulations and latents. (2) Relative gains in % from increasing the number of latents for each simulation budget. (3) scaling with the number of latents for different simulation budgets. (4) Relative gains with a different number of trials—latent in solid lines, seeds in dashed lines—at different simulation budgets.

…Analysis of diversity bonuses in AZdb: In the puzzle evaluation section, we observed that diversity bonuses emerge at the computational boundaries of AZdb. In this section, we analyse what components of AZdb are the most important for diversity bonuses and if diversity bonuses emerge at other compute budgets. We focus our evaluation on the Lichess data set and use the fast configuration for that. In Figure 7 we study how AZdb scales with different simulation budgets and team sizes. The top figure presents results for max-over-latents and the bottom figure shows results for sub-additive planning based on LCB (Equation 9). Most importantly, we observe diversity bonuses for all compute budgets and team sizes. In the leftmost table, we present the absolute solve rate in %. We can see that AZdb’s performance improves monotonically with the number of simulations and the number of trials, implying that larger teams solve more puzzles together. We can also see that on the third sub-figure, which presents the same data in a different manner. For each column in the first table (simulation budget) we draw a line that presents the solve rate in % as a function of the number of trials (x-axis). We can see that the diversity bonuses keep increasing as we increase the number of trials for each simulation budget.

The second table from the right shows the relative gains computed in % of AZdb from having more players in the team. Interestingly, the highest relative gains are achieved when the number of simulations is 54 = 625, which is the simulation budget that is closest to the one we use in training (see the discussion section). On the rightmost sub-figure, we compare a diverse team of agents with a more homogeneous team. For the diverse team, we use different latents as before (solid lines) and for the homogeneous group, we use the best latent in the group (latent 0) and allow it different trials of search (with different seeds, in dashed lines). We can see that across different group sizes, simulation budgets, and for both max-over-latents and sub-additive planning, a diverse team outperforms the homogeneous one.

--- https://bitsavers.trailing-edge.com/pdf/xerox/parc/techReports/ISL-83-1_A_Retrospective_on_the_Dorado_A_High-Performance_Personal_Computer.pdf A Retrospective on the Dorado, a High-Performance Personal Computer Pier 1983 2023-03-19 cs/hardware cs/lisp design --- https://www.psychologytoday.com/files/attachments/56143/the-flynn-effect-puzzle_0.pdf The Flynn effect puzzle: A 30-year examination from the right tail of the ability distribution provides some missing pieces Wai, Putallaz 2023-03-19 iq/high --- https://www.ncbi.nlm.nih.gov/books/NBK81523/ Oral anti-diabetic drugs for the prevention of type 2 diabetes Phung 2011 2023-03-19 longevity --- https://proceedings.neurips.cc/paper/2013/file/6a5889bb0190d0211a991f47bb19a777-Paper.pdf#deepmind (More) efficient reinforcement learning via posterior sampling [PSRL] Osband 2013 2023-03-19 reinforcement-learning/exploration statistics/bayes --- https://web.archive.org/web/20100529141828id_/http://www.swccd.edu/~mseteachingresources/Biology/bioinfo%20wshp/assets/mammothDNA.pd 2023-03-19 genetics/sequencing --- /doc/reinforcement-learning/exploration/active-learning/1992-mackay.pdf Information-Based Objective Functions for Active Data Selection David J. C. MacKay 1992-07-01 2023-03-20 [("doi","10.1162/neco.1992.4.4.590")] reinforcement-learning/exploration/active-learning statistics/bayes

Learning can be made more efficient if we can actively select particularly salient data points.

Within a Bayesian learning framework, objective functions are discussed that measure the expected informativeness of candidate measurements. 3 alternative specifications of what we want to gain information about lead to 3 different criteria for data selection.

All these criteria depend on the assumption that the hypothesis space is correct, which may prove to be their main weakness.

--- https://www.newyorker.com/magazine/2023/08/28/is-beekeeping-wrong 2023-03-20 philosophy/ethics psychology/animal --- https://sambleckley.com/writing/text-justification.html 2023-03-20 design/typography --- https://www.youtube.com/watch?v=koR1_JBe2j0&t=540s 2023-03-20 ai/nn/transformer/gpt/dall-e/2 --- https://erikgahner.dk/2023/twenty-years-of-marginal-revolution/ 2023-03-20 economics --- /doc/politics/2019-bullock.pdf Partisan Bias in Surveys John G. Bullock, Gabriel Lenz 2019-05-01 2023-03-20 [("doi","10.1146/annurev-polisci-051117-050904")] politics psychology/cognitive-bias

If citizens are to hold politicians accountable for their performance, they probably must have some sense of the relevant facts, such as whether the economy is growing. In surveys, Democrats and Republicans often claim to hold different beliefs about these facts, which raises normative concerns. However, it is not clear that their divergent survey responses reflect actual divergence of beliefs.

In this review, we conclude that partisan divergence in survey responses is often not due to sincere, considered differences of belief that fall along party lines—but determining what it is due to is difficult. We review the evidence for possible explanations, especially insincere responding and congenial inference.

Research in this area is still nascent, and much more will be required before we can speak with precision about the causes of partisan divergence in responses to factual questions.

--- https://www.biorxiv.org/content/10.1101/2023.06.12.544619.full Late-life Rapamycin Treatment Enhances Cardiomyocyte Relaxation Kinetics and Reduces Myocardial Stiffness Akash D. Chakraborty, Kristi Kooiker, Kamil A. Kobak, Yuanhua Cheng, Chi Fung Lee, Maria Razumova, Henk Granzier H, Michael Regnier, Peter S. Rabinovitch, Farid Moussavi-Harami, Ying Ann Chiao 2023-06-13 2023-06-13 [("doi","10.1101/2023.06.12.544619")] longevity

Diastolic dysfunction is a key feature of the aging heart. We have shown that late-life treatment with mTOR inhibitor, rapamycin, reverses age-related diastolic dysfunction in mice but the molecular mechanisms of the reversal remain unclear.

To dissect the mechanisms by which rapamycin improves diastolic function in old mice, we examined the effects of rapamycin treatment at the levels of single cardiomyocyte, myofibril and multicellular cardiac muscle. Compared to young cardiomyocytes, isolated cardiomyocytes from old control mice exhibited prolonged time to 90% relaxation (RT90) and time to 90% Ca2+ transient decay (DT90), indicating slower relaxation kinetics and calcium reuptake with age. Late-life rapamycin treatment for 10 weeks completely normalized RT90 and partially normalized DT90, suggesting improved Ca2+ handling contributes partially to the rapamycin-induced improved cardiomyocyte relaxation.

In addition, rapamycin treatment in old mice enhanced the kinetics of sarcomere shortening and Ca2+ transient increase in old control cardiomyocytes. Myofibrils from old rapamycin-treated mice displayed increased rate of the fast, exponential decay phase of relaxation compared to old controls. The improved myofibrillar kinetics were accompanied by an increase in MyBP-C phosphorylation at S282 following rapamycin treatment.

We also showed that late-life rapamycin treatment normalized the age-related increase in passive stiffness of demembranated cardiac trabeculae through a mechanism independent of titin isoform shift. In summary, our results showed that rapamycin treatment normalizes the age-related impairments in cardiomyocyte relaxation, which works conjointly with reduced myocardial stiffness to reverse age-related diastolic dysfunction.

--- https://www.lesswrong.com/posts/Q3XaZTExzDpCLr4wu/efficiency-and-resource-use-scaling-parity 2023-03-20 ai/scaling/economics economics/experience-curve reinforcement-learning/chess --- https://www.nytimes.com/2014/08/12/upshot/heres-why-stealing-cars-went-out-of-fashion.html 2023-03-20 crime cs/security --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1378422/ Effect of coffee on distal colon function S R. Brown, P. A. Cann, N. W. Read 1990 2023-03-20 [("doi","10.1136/gut.31.4.450")] nootropic/caffeine

99 healthy young volunteers (58 men, 34 women, aged 17–27 years) answered a questionnaire concerning their bowel habit with particular reference to the effects of beverages.

29% (63% women) claimed that coffee induced a desire to defecate.

The rectosigmoid motor responses to black, unsweetened coffee were then investigated by multiport manometry in 14 healthy-subjects (12 men, 2 women), 8 of whom claimed coffee caused a desire to defecate (responders).

Results revealed an increase in motility index within 4 minutes after ingestion of both regular and decaffeinated coffee (p < 0.05) in the 8 responders, but not in the 6 non-responders. The increase in rectosigmoid motility induced by coffee lasted at least 30 minutes. There was no increase in the motility index in any subject after a drink of hot water.

These results suggest that drinking coffee can stimulate a motor response of the distal colon in some normal people.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6255780/ Effect of postoperative coffee consumption on gastrointestinal function after abdominal surgery: A systematic review and meta-analysis of randomized controlled trials Nuntasiri Eamudomkarn, Chumnan Kietpeerakool, Srinaree Kaewrudee, Nampet Jampathong, Chetta Ngamjarus, Pisake Lumbiganon 2018 2023-03-21 [("doi","10.1038/s41598-018-35752-2")] nootropic/caffeine

Coffee is believed to prevent postoperative ileus. This systematic review and meta-analysis was undertaken to determine the effectiveness of coffee consumption in stimulating gastrointestinal function after abdominal surgery.

A number of databases for randomized controlled trials comparing coffee consumption following abdominal surgery versus water drinking or no intervention were searched. Cochrane’s Risk of Bias tool was used to assess risk of bias in included studies. 6 trials involving 601 participants were included. All studies had high risk of performance bias. 3 studies had an unclear risk of selection bias.

Postoperative coffee consumption reduced time to first defecation (mean difference (MD), −9.98 hours; 95% CI, −16.97 to −2.99), time to first flatus (MD, −7.14 hours; 95% CI, −10.96 to −3.33), time to first bowel sound (MD, −4.17 hours; 95% CI, −7.88 to −0.47), time to tolerance of solid food (MD, −15.55 hours; 95% CI, −22.83 to −8.27), and length of hospital stay (MD, −0.74 days; 95% CI, −1.14 to −0.33). Benefits increased with increasing complexity of the procedure.

None of the included studies reported adverse events associated with coffee consumption. Postoperative coffee consumption is effective and safe for enhancing the recovery of gastrointestinal function after abdominal surgery.

--- https://outsidetheasylum.blog/the-economics-of-organized-play/ 2023-03-21 economics psychology/collecting --- https://x.com/RubenLaukkonen/status/1651030691201839105 2023-03-21 psychiatry/meditation --- https://x.com/sTeamTraen/status/1693690935123546186 2023-03-21 statistics/bias --- https://knowyourmeme.com/memes/sites/r9k 2023-03-21 psychology/novelty sociology/technology --- https://blog.xkcd.com/2008/01/14/robot9000-and-xkcd-signal-attacking-noise-in-chat/ ROBOT9000 and #xkcd-signal: Attacking Noise in Chat 2023-03-21 psychology/novelty sociology/technology --- https://xkcd.com/1053/ XKCD #1053: Ten Thousand 2023-03-21 psychology/novelty sociology/technology --- https://www.technologyreview.com/2023/08/11/1077232/corporate-presentations-history 2023-03-21 design/visualization --- https://www.fightaging.org/archives/2023/07/sens-research-foundation-2023-annual-report/ 2023-03-21 longevity/senolytic --- https://www.nature.com/articles/d41586-023-02600-x 2023-03-21 psychology/neuroscience --- https://www.nature.com/articles/s41467-023-40166-4 2023-03-21 psychology/vision --- /doc/psychology/cognitive-bias/stereotype-threat/2023-bartels.pdf Indoctrination in Introduction to Psychology Jared M. Bartels 2023-08-16 2023-08-16 [("doi","10.1177/14757257231195450")] politics psychology/cognitive-bias/stereotype-threat statistics/bias

There have been dozens of papers published on the misrepresentation of psychological studies and theories (eg. omitting criticisms) presented in introductory textbooks. Authors of these papers have offered numerous explanations for the errors including limited space for covering criticisms and the desire among textbook authors to “sell” psychological science to an introductory audience.

In the present article, several studies and theories, most of which have been identified in previous research as misrepresented in introductory psychology textbooks, are reviewed. [stereotype threat, multiple intelligences, implicit bias]

The possibility of ideological bias contributing to the misrepresentation is considered. The bias in introductory psychology is considered in the context of wider concerns about the consequences of political homogeneity in the field.

Suggestions for reducing bias in introductory psychology textbooks and courses are offered.

--- https://x.com/RubenLaukkonen/status/1651032993929248768 2023-03-22 psychiatry/meditation --- https://arxiv.org/abs/2306.16527#huggingface OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh 2023-06-21 2023-06-21 [("doi","10.48550/arXiv.2306.16527")] ai/dataset ai/nn/transformer

Large multimodal models trained on natural documents, which interleave images and text, outperform models trained on image-text pairs on various multimodal benchmarks. However, the datasets used to train these models have not been released, and the collection process has not been fully specified.

We introduce the OBELICS dataset, an open web-scale filtered dataset of interleaved image-text documents comprising 141 million web pages extracted from Common Crawl, 353 million associated images, and 115 billion text tokens.

We describe the dataset creation process, present comprehensive filtering rules, and provide an analysis of the dataset’s content.

To show the viability of OBELICS, we train vision and language models of 9 & 80 billion parameters named IDEFICS, and obtain competitive performance on different multimodal benchmarks.

We release our dataset, models and code.

--- https://openai.com/blog/gpt-3-5-turbo-fine-tuning-and-api-updates 2023-03-22 ai/nn/transformer/gpt/3 --- https://en.wikipedia.org/wiki/Project_Alpha_(hoax) Project Alpha (hoax) 2023-03-22 psychology/parapsychology --- https://webnt.calhoun.edu/distance/internet/Business/eco231/downloads/tf6.pdf No Booze? You May Lose: Why Drinkers Earn More Money Than Nondrinkers Peters, Stringham 2006 2023-03-22 economics psychiatry --- https://jpet.aspetjournals.org/content/296/3/849 Dissociation of nicotine tolerance from tobacco dependence in humans Perkins 2023-03-22 nicotine --- https://www.bmj.com/content/337/bmj.a2768.abstract Rugby (the religion of Wales) and its influence on the Catholic church: should Pope Benedict XVI be worried? Payne 2008 2023-03-22 math/humor philosophy/religion --- https://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html A Proposal For The Dartmouth Summer Research Project On Artificial Intelligence McCarthy 1955 2023-03-22 ai --- https://qtip2.com/ 2023-03-22 design/typography/sidenote --- https://www.biorxiv.org/content/10.1101/2022.12.01.518724.full The complete sequence of a human Y chromosome Arang Rhie, Sergey Nurk, Monika Cechova, Savannah J. Hoyt, Dylan J. Taylor, Nicolas Altemose, Paul W. Hook, Sergey Koren, Mikko Rautiainen, Ivan A. Alexandrov, Jamie Allen, Mobin Asri, Andrey V. Bzikadze, Nae-Chyun Chen, Chen-Shan Chin, Mark Diekhans, Paul Flicek, Giulio Formenti, Arkarachai Fungtammasan, Carlos Garcia Giron, Erik Garrison, Ariel Gershman, Jennifer L. Gerton, Patrick G. S. Grady, Andrea Guarracino, Leanne Haggerty, Reza Halabian, Nancy F. Hansen, Robert Harris, Gabrielle A. Hartley, William T. Harvey, Marina Haukness, Jakob Heinz, Thibaut Hourlier, Robert M. Hubley, Sarah E. Hunt, Stephen Hwang, Miten Jain, Rupesh K. Kesharwani, Alexandra P. Lewis, Heng Li, Glennis A. Logsdon, Julian K. Lucas, Wojciech Makalowski, Christopher Markovic, Fergal J. Martin, Ann M. Mc Cartney, Rajiv C. McCoy, Jennifer McDaniel, Brandy M. McNulty, Paul Medvedev, Alla Mikheenko, Katherine M. Munson, Terence D. Murphy, Hugh E. Olsen, Nathan D. Olson, Luis F. Paulin, David Porubsky, Tamara Potapova, Fedor Ryabov, Steven L. Salzberg, Michael E. G. Sauria, Fritz J. Sedlazeck, Kishwar Shafin, Valery A. Shepelev, Alaina Shumate, Jessica M. Storer, Likhitha Surapaneni, Angela M. Taravella Oill, Françoise Thibaud-Nissen, Winston Timp, Marta Tomaszkiewicz, Mitchell R. Vollger, Brian P. Walenz, Allison C. Watwood, Matthias H. Weissensteiner, Aaron M. Wenger, Melissa A. Wilson, Samantha Zarate, Yiming Zhu, Justin M. Zook, Evan E. Eichler, Rachel J. O’Neill, Michael C. Schatz, Karen H. Miga, Kateryna D. Makova, Adam M. Phillippy 2023-07-11 2023-07-11 [("doi","10.1101/2022.12.01.518724")] genetics/sequencing

The human Y chromosome has been notoriously difficult to sequence and assemble because of its complex repeat structure including long palindromes, tandem repeats, and segmental duplications1–3. As a result, more than half of the Y chromosome is missing from the GRCh38 reference sequence and it remains the last human chromosome to be finished4, 5.

Here, the Telomere-to-Telomere (T2T) consortium presents the complete 62,460,029 base pair sequence of a human Y chromosome from the HG002 genome (T2T-Y) that corrects multiple errors in GRCh38-Y and adds over 30 million base pairs of sequence to the reference, revealing the complete ampliconic structures of TSPY, DAZ, and RBMY gene families; 41 additional protein-coding genes, mostly from the TSPY family; and an alternating pattern of human satellite 1 and 3 blocks in the heterochromatic Yq12 region.

We have combined T2T-Y with a prior assembly of the CHM13 genome4 and mapped available population variation, clinical variants, and functional genomics data to produce a complete and comprehensive reference sequence for all 24 human chromosomes.

--- https://x.com/alex_peys/status/1692617367329669406 2023-03-23 design/visualization --- https://arxiv.org/abs/2308.09045 The Lindy Effect Toby Ord 2023-08-17 2023-08-17 [("doi","10.48550/arXiv.2308.09045")] statistics/probability statistics/survival-analysis

The Lindy effect is a statistical tendency for things with longer pasts behind them to have longer futures ahead. It has been experimentally confirmed to apply to some categories, but not others, raising questions about when it is applicable and why.

I shed some light on these questions by examining the mathematical properties required for the effect and generating mechanisms that can produce them.

While the Lindy effect is often thought to require a declining hazard rate, I show that it arises very naturally even in cases with constant (or increasing) hazard rates—so long as there is a probability distribution over the size of that rate.

One implication is that even things which are becoming less robust over time can display the Lindy effect.

--- https://www.reddit.com/r/ChatGPT/comments/15y4mqx/i_asked_chatgpt_to_maximize_its_censorship/ 2023-03-23 ai/nn/transformer/gpt reinforcement-learning/safe --- https://arxiv.org/abs/2306.09200 ChessGPT: Bridging Policy Learning and Language Modeling Xidong Feng, Yicheng Luo, Ziyan Wang, Hongrui Tang, Mengyue Yang, Kun Shao, David Mguni, Yali Du, Jun Wang 2023-06-15 2023-06-15 [("doi","10.48550/arXiv.2306.09200")] ai/dataset ai/nn/transformer/clip ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/chess reinforcement-learning/imitation-learning

When solving decision-making tasks, humans typically depend on information from two key sources: (1) Historical policy data, which provides interaction replay from the environment, and (2) Analytical insights in natural language form, exposing the invaluable thought process or strategic considerations.

Despite this, the majority of preceding research focuses on only one source: they either use historical replay exclusively to directly learn policy or value functions, or engaged in language model training using mere language corpus.

In this paper, we argue that a powerful autonomous agent should cover both sources. Thus, we propose ChessGPT, a GPT model bridging policy learning and language modeling by integrating data from these two sources in Chess games.

Specifically, we build a large-scale game and language dataset related to chess. Leveraging the dataset, we showcase two model examples ChessCLIP and ChessGPT, integrating policy learning and language modeling.

Finally, we propose a full evaluation framework for evaluating language model’s chess ability. Experimental results validate our model and dataset’s effectiveness. We open source our code, model, and dataset at Github.

--- https://en.wikipedia.org/wiki/Palacio_de_Bellas_Artes Palacio de Bellas Artes 2023-03-23 design --- https://www.wsj.com/world/asia/taxes-japan-thank-you-gifts-dcfec436 2023-03-23 japan --- https://academic.oup.com/aje/article/170/4/472/90376 Use of Supplements of Multivitamins, Vitamin C, and Vitamin E in Relation to Mortality Pocobelli 2009 2023-03-23 longevity --- https://www.pnas.org/doi/full/10.1073/pnas.0706929105 Marketing actions can modulate neural representations of experienced pleasantness Plassman 2008 2023-03-23 economics psychology/neuroscience --- https://www.salesforce.com/products/einstein/ai-research/tl-dr-reinforced-model-abstractive-summarization/ Your TL;DR by an AI: A Deep Reinforced Model for Abstractive Summarization Paulus 2017 2023-03-23 ai/nn/rnn reinforcement-learning/model-free --- /doc/science/1974-ponnamperuma-interstellarcommunication.pdf Interstellar Communication: Scientific Perspectives Cyril Ponnamperuma, A. G. W. Cameron 1974-01-01 2023-03-23 ai cs/algorithm philosophy/mind psychology/linguistics science --- /doc/sociology/technology/1954-gouldner-patternsofindustrialbureaucracy.pdf Patterns of Industrial Bureaucracy: a case study of modern factory administration Alvin W. Gouldner 1954-01-01 2023-03-24 economics sociology/technology --- https://en.wikipedia.org/wiki/Mirtazapine#Veterinary_use Mirtazapine § Veterinary use 2023-03-24 cat/biology --- https://x.com/ZachWeiner/status/1694685022236610900 2023-03-24 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://github.com/ray-project/llm-numbers 2023-03-24 ai/nn/transformer/gpt ai/scaling/economics --- https://www.reddit.com/r/singularity/comments/13h0zyy/i_really_crank_out_music_tracks_with_musiclm_this/ 2023-03-24 ai/music --- https://www.lesswrong.com/posts/DfqcyGXcFcukYbWZ5/i-measure-google-s-musiclm-over-3-months-as-it-appears-to-go 2023-03-24 ai/music reinforcement-learning/preference-learning/mode-collapse --- https://en.wikipedia.org/wiki/List_of_Ig_Nobel_Prize_winners List of Ig Nobel Prize winners 2023-03-24 math/humor --- https://ntrs.nasa.gov/citations/19770014162 Space Settlements: A Design Study Johnson, Holbrow 1977 2023-03-24 technology --- https://www.science.org/doi/full/10.1126/science.1225829 A Programmable Dual-RNA-Guided DNA Endonuclease in Adaptive Bacterial Immunity Jinek 2012 2023-03-24 genetics/editing --- https://lesacreduprintemps19.wordpress.com/wp-content/uploads/2012/11/clocking-the-mind-mental-chronometry-and-individual-differences.pdf Clocking the Mind: Mental chronometry and individual differences Jensen 2006 2023-03-24 iq psychology/neuroscience --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10435378/ Large-scale exome sequence analysis identifies sex- and age-specific determinants of obesity Lena R. Kaisinger, Katherine A. Kentistou, Stasa Stankovic, Eugene J. Gardner, Felix R. Day, Yajie Zhao, Alexander Mörseburg, Christopher J. Carnie, Guido Zagnoli-Vieira, Fabio Puddu, Stephen P. Jackson, Stephen O’Rahilly, I. Sadaf Farooqi, Laura Dearden, Lucas C. Pantaleão, Susan E. Ozanne, Ken K. Ong, John R. B. Perry 2023 2023-03-24 [("doi","10.1016/j.xgen.2023.100362")] exercise genetics/heritable/rare

[commentary] Obesity contributes substantially to the global burden of disease and has a large heritable component. Recent large-scale exome sequencing studies identified several genes in which rare, protein-coding variants have large effects on adult body mass index (BMI).

Here we extended such work by performing sex-stratified associations in the UK Biobank study (n ≈ 420,000).

We identified genes in which rare heterozygous loss-of-function increases adult BMI in women (DIDO1, PTPRG, and SLC12A5) and in men (SLTM), with effect sizes up to ~8 kg⁄m2.

This is complemented by analyses implicating rare variants in OBSCN and MADD for recalled childhood adiposity. The known functions of these genes, as well as findings of common variant genome-wide pathway enrichment analyses, suggest a role for neuron death, apoptosis, and DNA damage response mechanisms in the susceptibility to obesity across the life-course.

These findings highlight the importance of considering sex-specific and life-course effects in the genetic regulation of obesity.

--- https://arxiv.org/abs/1708.02547 Quark-level analogue of nuclear fusion with doubly-heavy baryons Marek Karliner, Jonathan L. Rosner 2017-08-08 2023-03-25 [("doi","10.1038/nature24289")] science

The recent discovery by LHCb of the first doubly-charmed baryon Ξcc++ = ccu at 3621.40 ± 0.78 MeV implies a large binding energy ≈ 130 MeV between the two c quarks. This strong binding enables a quark-rearrangement exothermic reaction Λc Λc → Ξcc++,n with Q = 12 MeV, which is a quark-level analogue of deuterium-tritium nuclear fusion reaction DT → 4He n.

Due to much larger binding energy between two b quarks ≈ 280 MeV, the analogous reaction with b quarks, Λb Λb → Ξbb N, is expected to have a dramatically larger Q-value, 138 ± 12 MeV.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7493529/ A body bag can save your life: a novel method of cold water immersion for heat stroke treatment David A. Kim, Benjamin D. Lindquist, Sam H. Shen, Alexei M. Wagner, Grant S. Lipman 2020 2023-03-25 [("doi","10.1002/emp2.12007")] technology

Non-exertional heat stroke is a life-threatening condition characterized by passive exposure to high ambient heat, a core body temperature of 40℃ (104℉) or greater, and central nervous system dysfunction. Rapid cooling is imperative to minimize mortality and morbidity. Although evaporative and convective measures are often used for cooling heat stroke patients, cold water immersion produces the fastest cooling. However, logistical difficulties make cold water immersion challenging to implement in the emergency department.

To our knowledge, there is no documented case using a body bag (ie. human remains pouch) as a cold water immersion tank for rapid resuscitation of heat stroke. During a regional heat wave an elderly woman was found unconscious in a parking lot with an oral temperature of 40℃ (104℉) and altered mental status.

She was cooled to 38.4℃ (101.1℉) in 10 minutes by immersion in an ice/water-filled body bag. The patient rapidly regained normal mentation and was discharged home from the ED.

This case highlights a novel method for efficient and convenient cold water immersion for heat stroke treatment in the emergency department.

--- /doc/politics/2023-webster.pdf Partisan schadenfreude and candidate cruelty Steven W. Webster, Adam N. Glynn, Matthew P. Motta 2023-08-23 2023-08-23 [("doi","10.1111/pops.12922")] politics psychology/personality

We establish the prevalence of partisan schadenfreude—that is, taking “joy in the suffering” of partisan others.

Analyzing attitudes on health care, taxation, climate change, and the coronavirus pandemic, we find that a sizable portion of the American mass public engages in partisan schadenfreude and that these attitudes are most expressed by those who are ideologically extreme.

Additionally, we find that a sizable portion of the American public is more likely than not to vote for candidates who promise to pass policies that “disproportionately harm” supporters of the opposing political party, and we demonstrate experimental evidence of demand/preference for candidates who promise cruelty among those who exhibit high amounts of schadenfreude.

In sum, our results suggest that partisan schadenfreude is widespread and has disturbing implications for American political behavior.

--- https://x.com/agikoala/status/1695125016764157988 2023-03-25 ai/nn/transformer/gpt/4 --- https://typography.network/wp-content/uploads/2023/08/Go%CC%88ldner_TypPp_9_The_Bru%CC%88der_Butter_typefoundry.pdf#page=11 2023-03-25 design/typography/rubrication --- https://typography.network/wp-content/uploads/2023/08/Berkson_Enneson_TypPp_9_Readability_discovery_and_disputation.pdf#page=2 2023-03-25 design/typography psychology/vision --- /doc/statistics/decision/2022-frankel.pdf Which Findings Should Be Published? Alexander Frankel, Maximilian Kasy 2022-02-01 2023-03-25 [("doi","10.1257/mic.20190133")] statistics/bias/publication statistics/decision statistics/power-analysis

Given a scarcity of journal space, what is the optimal rule for whether an empirical finding should be published?

Suppose publications inform the public about a policy-relevant state. Then journals should publish extreme results, meaning ones that move beliefs sufficiently. This optimal rule may take the form of a one- or two-sided test comparing a point estimate to the prior mean, with critical values determined by a cost-benefit analysis. Consideration of future studies may additionally justify the publication of precise null results.

If one insists that standard inference remain valid, however, publication must not select on the study’s findings.

--- /doc/psychology/cognitive-bias/illusion-of-depth/2023-eliseev.pdf Understanding why searching the internet inflates confidence in explanatory ability Emmaline Drew Eliseev, Elizabeth J. Marsh 2023-03-11 2023-03-25 [("doi","10.1002/acp.4058")] psychology/cognitive-bias/illusion-of-depth sociology/technology

People rely on the internet for easy access to information, setting up potential confusion about the boundaries between an individual’s knowledge and the information they find online.

Across 4 experiments, we replicated and extended past work showing that online searching inflates people’s confidence in their knowledge. Participants who searched the internet for explanations rated their explanatory ability higher than participants who read but did not search for the same explanations.

Two experiments showed that extraneous web page content (pictures) does not drive this effect.

The last experiment modeled how search engines yield results; participants saw (but did not search for) a list of hits, which included “snippets” that previewed web page content, before reading the explanations. Participants in this condition were as confident as participants who searched online.

Previewing hits primes to-be-read content, in a modern-day equivalent of Titchener’s famous example of a brief glance eliciting false feelings of familiarity.

--- https://www.medrxiv.org/content/10.1101/2022.09.29.22280488.full Benefits and barriers to implementing precision preventive care: results of a national physician survey Jason L. Vassy, Benjamin J. Kerman, Elizabeth J. Harris, Amy A. Lemke, Marla L. Clayman, Ashley A. Antwi, Katharine MacIsaac, Thomas Yi, Charles A. Brunette 2022-09-30 2023-03-25 [("doi","10.1101/2022.09.29.22280488")] genetics/heritable

Background: Clinical implementation of polygenic risk scores (PRS) for precision prevention depends on the utility and barriers primary care physicians (PCPs) perceive to their use.

Method: An online survey asked PCPs in a national database about the clinical utility of PRS they perceived for categories of medical decision-making and perceived benefits of and barriers to that use. Latent class analysis (LCA) was used to identify subgroups of PCPs based on response patterns.

Results: Among 367 respondents (email open rate 10.8%; participation rate 96.3%; completion rate 93.1%), mean (SD) age was 54.9 (12.9) years, 137 (37.3%) were female, and mean (SD) time since medical school graduation was 27.2 (13.3) years. Respondents reported greater perceived utility for more clinical action (eg. earlier or more intensive screening, preventive medications, or lifestyle modification) for patients with high-risk PRS than for delayed or discontinued prevention actions for low-risk patients (p<0.001). Respondents most often chose out-of-pocket costs (48%), lack of clinical guidelines (24%), and patient insurance discrimination concerns (22%) as extreme barriers to PRS implementation. LCA identified 3 subclasses of respondents. Skeptics (n = 83, 22.6%) endorsed less agreement with individual clinical utilities, saw patient anxiety and insurance discrimination as significant barriers, and agreed less often that PRS could help patients make better health decisions. Learners (n = 134, 36.5%) and enthusiasts (n = 150, 40.9%) expressed similar levels of agreement that PRS had utility for preventive actions and that PRS could be useful for patient decision-making. Compared with enthusiasts, however, learners perceived greater barriers to the clinical use of PRS.

Conclusion: PCPs generally endorsed using PRS to guide medical decision-making about preventive care, with a preference for more clinical action over less. Barriers identified suggest interventions to address the needs and concerns of PCPs along the spectrum of acceptance and uptake.

--- https://terrytao.wordpress.com/2023/06/19/ai-anthology/#comment-678803 2023-03-25 ai/nn/transformer/gpt/4 math --- https://x.com/toyxyz3/status/1695134607317012749 2023-03-25 ai/anime ai/video/generation --- https://www.reddit.com/r/StableDiffusion/comments/161qkeb/ai_burger_commercial_source_matancohengrumi/ 2023-03-26 ai/video/generation --- https://chicagoreader.com/music/gathering-juggalos-insane-clown-posse/ 2023-03-26 sociology --- https://www.nber.org/papers/w31587 2023-03-26 economics/georgism --- https://calebjacob.github.io/tooltipster/ 2023-03-26 cs/js --- https://arxiv.org/abs/1312.4400 Network In Network Min Lin, Qiang Chen, Shuicheng Yan 2013-12-16 2023-03-26 [("doi","10.48550/arXiv.1312.4400")] ai/nn/cnn ai/nn/fully-connected

We propose a novel deep network structure called “Network In Network” (NIN) to enhance model discriminability for local patches within the receptive field.

The conventional convolutional layer uses linear filters followed by a nonlinear activation function to scan the input. Instead, we build micro neural networks with more complex structures to abstract the data within the receptive field. We instantiate the micro neural network with a multilayer perceptron, which is a potent function approximator.

The feature maps are obtained by sliding the micro networks over the input in a similar manner as CNN; they are then fed into the next layer. Deep NIN can be implemented by stacking multiple of the above described structure.

With enhanced local modeling via the micro network, we are able to use global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected layers.

We demonstrated the state-of-the-art classification performances with NIN on CIFAR-10 and CIFAR-100, and reasonable performances on SVHN and MNIST datasets.

--- https://eml.berkeley.edu/~webfac/cromer/e211_f12/LindertWilliamson.pdf American Incomes 1774–1860 Lindhart, Williamson 2012 2023-03-26 economics --- https://academic.oup.com/jnci/article/101/1/14/914826 Vitamins C and E and Beta Carotene Supplementation and Cancer Risk: A Randomized Controlled Trial Lin 2009 2023-03-26 biology --- https://en.wikipedia.org/wiki/Renfield#Influence_in_psychology Renfield § Influence in psychology 2023-03-26 psychiatry --- https://www.nytimes.com/2023/08/22/magazine/almanac-recommendation.html 2023-03-26 psychology/collecting --- https://www.depauw.edu/sfs/interviews/wolfe46interview.htm On Encompassing the Entire Universe: An Interview with Gene Wolfe 2023-03-26 fiction/gene-wolfe --- https://www.newyorker.com/magazine/2002/09/30/the-prophet-of-decline The Prophet of Decline: Harold Bloom’s influential anxieties Larissa MacFarquhar 2002-09-22 2023-03-26 fiction/criticism --- https://en.wikipedia.org/wiki/Baumol_effect Baumol effect 2023-03-27 economics/automation --- https://en.wikipedia.org/wiki/Iceland_v_Iceland_Foods_Ltd Iceland v Iceland Foods Ltd 2023-03-27 economics/copyright --- https://smallstatebighistory.com/when-the-roma-came-to-rhode-island/ 2023-03-27 history sociology --- https://x.com/DepSecDef/status/1696141737717031362 2023-03-27 reinforcement-learning/robot reinforcement-learning/safe --- http://messybeast.com/colour-tempment.htm 2023-03-27 cat/genetics cat/psychology --- https://en.wikipedia.org/wiki/Congenital_sensorineural_deafness_in_cats Congenital sensorineural deafness in cats 2023-03-27 cat/genetics --- /doc/cat/psychology/1996-ledger.pdf Factors Influencing the Reactions of Cats to Humans and Novel Objects Rebecca Ledger, Valerie O’Farrell 1996-01-01 2023-03-27 cat/genetics cat/psychology --- https://sander.ai/2023/08/28/geometry.html 2023-03-27 ai/nn/diffusion --- https://arxiv.org/abs/2306.00978 AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, Song Han 2023-06-01 2023-06-01 [("doi","10.48550/arXiv.2306.00978")] ai/nn/sparsity/low-precision ai/nn/transformer

Large language models (LLMs) have shown excellent performance on various tasks, but the astronomical model size raises the hardware barrier for serving (memory size) and slows down token generation (memory bandwidth). In this paper, we propose Activation-aware Weight Quantization (AWQ), a hardware-friendly approach for LLM low-bit weight-only quantization.

Our method is based on the observation that weights are not equally important: protecting only 1% of salient weights can greatly reduce quantization error. We then propose to search for the optimal per-channel scaling that protects the salient weights by observing the activation, not weights. AWQ does not rely on any backpropagation or reconstruction, so it can well preserve LLMs’ generalization ability on different domains and modalities, without overfitting to the calibration set; it also does not rely on any data layout reordering, maintaining the hardware efficiency.

AWQ outperforms existing work on various language modeling, common sense QA, and domain-specific benchmarks. Thanks to better generalization, it achieves excellent quantization performance for instruction-tuned LMs and, for the first time, multi-modal LMs.

We also implement efficient tensor core kernels with reorder-free online dequantization to accelerate AWQ, achieving a 1.45× speedup over GPTQ and is 1.85× faster than the cuBLAS FP16 implementation.

Our method provides a turn-key solution to compress LLMs to 3–4 bits for efficient deployment.

--- https://arxiv.org/abs/2306.03081 Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs Alexander K. Lew, Tan Zhi-Xuan, Gabriel Grand, Vikash K. Mansinghka 2023-06-05 2023-06-05 [("doi","10.48550/arXiv.2306.03081")] ai/nn/sampling

Even after fine-tuning and reinforcement learning, large language models (LLMs) can be difficult, if not impossible, to control reliably with prompts alone.

We propose a new inference-time approach to enforcing syntactic and semantic constraints on the outputs of LLMs, called sequential Monte Carlo (SMC) steering. The key idea is to specify language generation tasks as posterior inference problems in a class of discrete probabilistic sequence models, and replace standard decoding with sequential Monte Carlo inference.

For a computational cost similar to that of beam search, SMC can steer LLMs to solve diverse tasks, including infilling, generation under syntactic constraints, and prompt intersection.

To facilitate experimentation with SMC steering, we present a probabilistic programming library, LLaMPPL, for concisely specifying new generation tasks as language model probabilistic programs, and automating steering of LLaMa-1-family Transformers.

--- https://www.lesswrong.com/posts/3ou8DayvDXxufkjHD/openai-api-base-models-are-not-sycophantic-at-any-size 2023-03-28 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/claude reinforcement-learning/preference-learning/mode-collapse reinforcement-learning/safe --- https://arxiv.org/abs/2109.13226#google BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition Yu Zhang, Daniel S. Park, Wei Han, James Qin, Anmol Gulati, Joel Shor, Aren Jansen, Yuanzhong Xu, Yanping Huang, Shibo Wang, Zongwei Zhou, Bo Li, Min Ma, William Chan, Jiahui Yu, Yongqiang Wang, Liangliang Cao, Khe Chai Sim, Bhuvana Ramabhadran, Tara N. Sainath, Françoise Beaufays, Zhifeng Chen, Quoc V. Le, Chung-Cheng Chiu, Ruoming Pang, Yonghui Wu 2021-09-27 2023-03-28 [("doi","10.1109/JSTSP.2022.3182537")] ai/nn/transformer ai/scaling

We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing ~1 million hours of audio.

We find that the combination of pre-training, self-training and scaling up model size greatly increases data efficiency, even for extremely large tasks with tens of thousands of hours of labeled data. In particular, on an ASR task with 34k hours of labeled data, by fine-tuning an 8 billion parameter pre-trained Conformer model we can match state-of-the-art (SoTA) performance with only 3% of the training data and improve SoTA with the full training set.

We also report on the universal benefits gained from using big pre-trained and self-trained models for a large set of downstream tasks that cover a wide range of speech domains and span multiple orders of magnitudes of dataset sizes, including obtaining SoTA performance on many public benchmarks.

In addition, we use the learned representation of pre-trained networks to achieve SoTA results on non-ASR tasks.

Figure 1a: WERs (%) of P-models trained on subsets of Voice Search.
Figure 2: WERs (%) of models trained on subsets of Voice Search. On the left, we show the performance of the 600M-parameter model (the “Conformer XL”) with varying preparation methods, while on the right, we report that of P-models of varying sizes.
--- https://arxiv.org/abs/2306.13812 Loss of Plasticity in Deep Continual Learning (Continual Backpropagation) Shibhansh Dohare, J. Fernando Hernandez-Garcia, Parash Rahman, Richard S. Sutton, A. Rupam Mahmood 2023-06-23 2023-06-23 [("doi","10.48550/arXiv.2306.13812")] ai/nn/cnn reinforcement-learning/meta-learning/continual-learning

[more evidence that large neural nets solve continual learning] Modern deep-learning systems are specialized to problem settings in which training occurs once and then never again, as opposed to continual-learning settings in which training occurs continually. If deep-learning systems are applied in a continual learning setting, then it is well known that they may fail to remember earlier examples. More fundamental, but less well known, is that they may also lose their ability to learn on new examples, a phenomenon called loss of plasticity.

We provide direct demonstrations of loss of plasticity using the MNIST and ImageNet datasets repurposed for continual learning as sequences of tasks. In ImageNet, binary classification performance dropped from 89% accuracy on an early task down to 77%, about the level of a linear network, on the 2000th task.

Loss of plasticity occurred with a wide range of deep network architectures, optimizers, activation functions, batch normalization, dropout, but was substantially eased by L2-regularization, particularly when combined with weight perturbation.

Further, we introduce a new algorithm—continual backpropagation—which slightly modifies conventional backpropagation to reinitialize a small fraction of less-used units after each example and appears to maintain plasticity indefinitely.

…Due to Adam’s robustness to non-stationary losses, one would have expected that Adam would result in a lower loss of plasticity than backpropagation. This is the opposite of what happens. Adam’s loss of plasticity can be categorized as catastrophic as it plummets drastically. Consistent with our previous results, Adam scores poorly in the 3 measures corresponding to the causes for the loss of plasticity. There is a dramatic drop in the effective rank of the network trained with Adam. We also tested Adam with different activation functions on the Slowly-changing regression problem and found that loss of plasticity with Adam is usually worse than with SGD.

Many methods that one might have thought would help mitigate the loss of plasticity substantially worsened the loss of plasticity. The loss of plasticity with Adam is particularly dramatic, and the network trained with Adam quickly lost almost all of its diversity, as measured by the effective rank. This dramatic loss of plasticity of Adam is an important result for deep reinforcement learning as Adam is the default optimizer in deep reinforcement learning and reinforcement learning is inherently continual due to the ever-changing policy.

--- /doc/fiction/poetry/1960-sinclair.pdf Hiawatha’s Lipid Hugh Sinclair 1960-01-01 2023-03-28 [("doi","10.1353/pbm.1960.0018")] biology fiction/poetry math/humor --- https://www.handprint.com/HP/WCL/color1.html 2023-03-28 psychology/vision --- http://itre.cis.upenn.edu/~myl/languagelog/archives/003289.html 2023-03-28 cs/security fiction/poetry --- https://x.com/matttomic/status/859117370455060481 The Sandwich Alignment Chart 2023-03-28 fiction/humor philosophy/ontology --- /doc/reinforcement-learning/safe/clippy/2023-08-29-gwern-alignmentchart-paperclip.jpg Paperclip Alignment Chart Gwern 2023-08-29 2023-08-29 fiction/humor math/humor philosophy/ontology reinforcement-learning/safe/clippy --- https://www.locusmag.com/2002/Issue09/GaimanWolfe.html 2023-03-28 fiction/gene-wolfe --- /doc/philosophy/ontology/2023-08-29-saturn2-alignmentchart-paperclip-alternateversion.jpg Paperclip Alignment Chart (Alternate) saturn2 2023-08-29 2023-08-29 fiction/humor math/humor philosophy/ontology --- /doc/psychology/cognitive-bias/2014-haggag.pdf Default Tips Kareem Haggag, Giovanni Paci 2014-07-01 2023-03-28 [("doi","10.1257/app.6.3.1")] economics psychology/cognitive-bias

We examine the role of defaults in high-frequency, small-scale choices using unique data on over 13 million New York City taxi rides.

Using a regression discontinuity design, we show that default tip suggestions have a large impact on tip amounts. These results are supported by a secondary analysis that uses the quasi-random assignment of customers to different cars to examine default effects on a wider range of fares.

Finally, we highlight a potential cost of setting defaults too high, as a higher proportion of customers opt to leave no credit card tip when presented with the higher suggested amounts.

--- https://samiramly.com/chess 2023-03-29 cs/algorithm design psychology/chess reinforcement-learning/preference-learning --- https://askthepilot.com/airport-noise-scourge/ 2023-03-29 cs/security design --- https://www.mcsweeneys.net/articles/column-47-the-lady-or-the-tire-iron 2023-03-29 crime psychiatry/anxiety psychology/personality --- https://statmodeling.stat.columbia.edu/2023/08/30/chatgpt-4-can-do-3-digit-multiplication/ 2023-03-29 ai/nn/transformer/gpt/inner-monologue --- https://www.scanofthemonth.com/scans/coffee 2023-03-29 design/visualization --- https://www.understandingai.org/p/driverless-cars-may-already-be-safer 2023-03-29 reinforcement-learning/robot --- https://www.wired.com/story/patricia-moore-sacrificed-youth-to-get-tech-bros-to-grow-up/ 2023-03-29 design longevity --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5421994/ The End of Hypergamy: Global Trends and Implications Albert Esteve, Christine R. Schwartz, Jan Van Bavel, Iñaki Permanyer, Martin Klesment, Joan Garcia 2016 2023-03-29 [("doi","10.1111/padr.12012")] sociology

The gender gap in education that has long favored men has reversed for young adults in almost all high and middle-income countries. In 2010, the proportion of women aged 25–29 with a college education was higher than that of men in more than 139 countries which altogether represent 86% of the world’s population. According to recent population forecasts, women will have more education than men in nearly every country in the world by 2050, with the exception of only a few African and West Asian countries (KC et al 2010).

The reversal of the gender gap in education has major implications for the composition of marriage markets, assortative mating, gender equality, and marital outcomes such as divorce and childbearing (Van Bavel 2012). In this work, we focus on its implications for trends in assortative mating and, in particular, for educational hypergamy: the pattern in which husbands have more education than their wives. This represents a substantial update to previous studies (Esteve et al 2012) in terms of the number of countries and years included in the analysis.

We present findings from an almost comprehensive world-level analysis using census and survey microdata from 420 samples and 120 countries spanning from 1960–2011, which allow us to assert that the reversal of the gender gap in education is strongly associated with the end of hypergamy and increases in hypogamy (wives have more education that their husbands). We not only provide near universal evidence of this trend but extend our analysis to consider the implications of the end of hypergamy for family dynamics, outcomes and gender equality.

We draw on European microdata to examine whether women are more likely to be the breadwinners when they marry men with lower education than themselves and discuss recent research regarding divorce risks among hypogamous couples. We close our analysis with an examination of attitudes about women earning more money than their husbands and about the implications for children when a woman works for pay.

--- https://en.wikipedia.org/wiki/German_submarine_U-1206#Fate German submarine U-1206 § Fate 2023-03-29 technology --- https://www.bell-labs.com/usr/dmr/www/crypt.html 2023-03-29 cs/cryptography --- https://arxiv.org/abs/2308.04623 Accelerating LLM Inference with Staged Speculative Decoding Benjamin Spector, Chris Re 2023-08-08 2023-08-08 [("doi","10.48550/arXiv.2308.04623")] ai/nn/sampling ai/nn/transformer/gpt/2

Recent advances with large language models (LLM) illustrate their diverse capabilities. We propose a novel algorithm, staged speculative decoding, to accelerate LLM inference in small-batch, on-device scenarios.

We address the low arithmetic intensity of small-batch inference by improving upon previous work in speculative decoding. First, we restructure the speculative batch as a tree, which reduces generation costs and increases the expected tokens per batch. Second, we add a second stage of speculative decoding.

Taken together, we reduce single-batch decoding latency by 3.16× with a 762M parameter GPT-2-L model while perfectly preserving output quality.

--- /doc/reinforcement-learning/robot/2006-bongard.pdf Resilient Machines Through Continuous Self-Modeling Josh Bongard, Victor Zykov, Hod Lipson 2006-11-17 2023-03-30 [("doi","10.1126/science.1133687")] reinforcement-learning/exploration reinforcement-learning/model reinforcement-learning/robot

Animals sustain the ability to operate after injury by creating qualitatively different compensatory behaviors. Although such robustness would be desirable in engineered systems, most machines fail in the face of unexpected damage.

We describe a robot that can recover from such change autonomously, through continuous self-modeling. A four-legged machine uses actuation-sensation relationships to indirectly infer its own structure, and it then uses this self-model to generate forward locomotion. When a leg part is removed, it adapts the self-models, leading to the generation of alternative gaits.

This concept may help develop more robust machines and shed light on self-modeling in animals.

--- https://arxiv.org/abs/2308.16824 Can Programming Languages Boost Each Other via Instruction Tuning? Daoguang Zan, Ailun Yu, Bo Shen, Jiaxin Zhang, Taihong Chen, Bing Geng, Bei Chen, Jichuan Ji, Yafen Yao, Yongji Wang, Qianxiang Wang 2023-08-31 2023-08-31 [("doi","10.48550/arXiv.2308.16824")] ai/nn/transformer/gpt/instruction-tuning

When human programmers have mastered a programming language, it would be easier when they learn a new programming language. In this report, we focus on exploring whether programming languages can boost each other during the instruction fine-tuning phase of code large language models.

We conduct extensive experiments of 8 popular programming languages (Python, JavaScript, TypeScript, C, C++, Java, Go, HTML) on StarCoder.

Results demonstrate that programming languages can improve each other. For example, CodeM-Python 15B trained on Python is able to increase Java by an absolute 17.95% pass@1 on HumanEval-X. More surprisingly, we found that CodeM-HTML 7B trained on the HTML corpus can improve Java by an absolute 15.24% pass@1.

Our training data is released at Github.

--- https://arxiv.org/abs/2302.10360 Optical Transformers Maxwell G. Anderson, Shi-Yuan Ma, Tianyu Wang, Logan G. Wright, Peter L. McMahon 2023-02-20 2023-03-30 [("doi","10.48550/arXiv.2302.10360")] ai/nn/transformer cs/hardware

The rapidly increasing size of deep-learning models has caused renewed and growing interest in alternatives to digital computers to dramatically reduce the energy cost of running state-of-the-art neural networks. Optical matrix-vector multipliers are best suited to performing computations with very large operands, which suggests that large Transformer models could be a good target for optical computing.

To test this idea, we performed small-scale optical experiments with a prototype accelerator to demonstrate that Transformer operations can run on optical hardware despite noise and errors. Using simulations, validated by our experiments, we then explored the energy efficiency of optical implementations of Transformers and identified scaling laws for model performance with respect to optical energy usage.

We found that the optical energy per multiply-accumulate (MAC) scales as 1⁄d where d is the Transformer width, an asymptotic advantage over digital systems. We conclude that with well-engineered, large-scale optical hardware, it may be possible to achieve a 100 × energy-efficiency advantage for running some of the largest current Transformer models, and that if both the models and the optical hardware are scaled to the quadrillion-parameter regime, optical computers could have a >8,000× energy-efficiency advantage over state-of-the-art digital-electronic processors that achieve 300 fJ/MAC.

We analyzed how these results motivate and inform the construction of future optical accelerators along with optics-amenable deep-learning approaches. With assumptions about future improvements to electronics and Transformer quantization techniques (5× cheaper memory access, double the digital–analog conversion efficiency, and 4-bit precision), we estimated that optical computers’ advantage against current 300-fJ/MAC digital processors could grow to >100,000×.

--- https://arxiv.org/abs/2207.05329 Deep Learning with Coherent VCSEL Neural Networks Zaijun Chen, Alexander Sludds, Ronald Davis, Ian Christen, Liane Bernstein, Tobias Heuser, Niels Heermeier, James A. Lott, Stephan Reitzenstein, Ryan Hamerly, Dirk Englund 2022-07-12 2023-03-30 [("doi","10.1038/s41566-023-01233-w")] cs/hardware

[press release] Deep neural networks (DNNs) are reshaping the field of information processing. With their exponential growth challenging existing electronic hardware, optical neural networks (ONNs) are emerging to process DNN tasks in the optical domain with high clock rates, parallelism and low-loss data transmission. However, to explore the potential of ONNs, it is necessary to investigate the full-system performance incorporating the major DNN elements, including matrix algebra and nonlinear activation.

Existing challenges to ONNs are high energy consumption due to low electro-optic (EO) conversion efficiency, low compute density due to large device footprint and channel crosstalk, and long latency due to the lack of inline nonlinearity. Here we experimentally demonstrate an ONN system that simultaneously overcomes all these challenges. We exploit neuron encoding with volume-manufactured micron-scale vertical-cavity surface-emitting laser (VCSEL) transmitter arrays that exhibit high EO conversion (<5 attojoule/symbol with Vπ=4 mV), high operation bandwidth (up to 25 GS/s), and compact footprint (<0.01 mm2 per device).

Photoelectric multiplication allows low-energy matrix operations at the shot-noise quantum limit. Homodyne detection-based nonlinearity enables nonlinear activation with instantaneous response. The full-system energy efficiency and compute density reach 7 femtojoules per operation (fJ/OP) and 25 TeraOP/(mm2⋅ s), both representing a >100× improvement over state-of-the-art digital computers, with substantially several more orders of magnitude for future improvement.

Beyond neural network inference, its feature of rapid weight updating is crucial for training deep learning models. Our technique opens an avenue to large-scale optoelectronic processors to accelerate machine learning tasks from data centers to decentralized edge devices.

--- https://www.themarshallproject.org/2023/08/31/dungeons-and-dragons-texas-death-row-tdcj 2023-03-30 fiction/text-game --- https://www.nytimes.com/2013/06/10/nyregion/lawful-handguns-departing-for-new-york-but-unlawful-upon-arrival.html 2023-03-30 law --- https://x.com/suchenzang/status/1697862650053660721 2023-03-30 ai/nn/tokenization --- https://josephnoelwalker.com/146-richard-rhodes/ 2023-03-30 existential-risk/nuclear --- /doc/nootropic/quantified-self/1993-widdowson.pdf Self-Experimentation in Nutrition Research Elsie M. Widdowson 1993-01-01 2023-03-30 [("doi","10.1079/NRR19930004")] biology nootropic/quantified-self --- https://arxiv.org/abs/2304.10004 Power Law Trends in Speedrunning and Machine Learning Ege Erdil, Jaime Sevilla 2023-04-19 2023-04-19 [("doi","10.48550/arXiv.2304.10004")] ai/scaling statistics/order

We find that improvements in speedrunning world records follow a power law pattern. Using this observation, we answer an outstanding question from previous work: How do we improve on the baseline of predicting no improvement when forecasting speedrunning world records out to some time horizon, such as one month?

Using a random effects model, we improve on this baseline for relative mean square error made on predicting out-of-sample world record improvements as the comparison metric at a p < 10−5 level. The same set-up improves even on the ex-post best exponential moving average forecasts at a p = 0.15 level while having access to substantially fewer data points.

We demonstrate the effectiveness of this approach by applying it to Machine Learning benchmarks and achieving forecasts that exceed a baseline.

Finally, we interpret the resulting model to suggest that (1) ML benchmarks are far from saturation and (2) sudden large improvements in Machine Learning are unlikely but cannot be ruled out.

--- https://www.lesswrong.com/posts/CwgHX9tbfASqxjpsc/the-economics-of-the-asteroid-deflection-problem 2023-03-31 economics/mechanism-design --- https://www.newyorker.com/magazine/2023/09/11/the-transformative-alarming-power-of-gene-editing 2023-03-31 genetics/editing --- https://arxiv.org/abs/2308.12477 American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers Melissa Dell, Jacob Carlson, Tom Bryan, Emily Silcock, Abhishek Arora, Zejiang Shen, Luca D’Amico-Wong, Quan Le, Pablo Querubin, Leander Heldring 2023-08-24 2023-08-24 [("doi","10.48550/arXiv.2308.12477")] ai/dataset history

Existing full text datasets of U.S. public domain newspapers do not recognize the often complex layouts of newspaper scans, and as a result the digitized content scrambles texts from articles, headlines, captions, advertisements, and other layout regions. OCR quality can also be low. This study develops a novel, deep learning pipeline for extracting full article texts from newspaper images and applies it to the nearly 20 million scans in Library of Congress’s public domain Chronicling America collection. The pipeline includes layout detection, legibility classification, custom OCR, and association of article texts spanning multiple bounding boxes. To achieve high scalability, it is built with efficient architectures designed for mobile phones.

The resulting American Stories dataset provides high quality data that could be used for pre-training a large language model to achieve better understanding of historical English and historical world knowledge. The dataset could also be added to the external database of a retrieval-augmented language model to make historical information—ranging from interpretations of political events to minutiae about the lives of people’s ancestors—more widely accessible.

Furthermore, structured article texts facilitate using transformer-based methods for popular social science applications like topic classification, detection of reproduced content, and news story clustering. Finally, American Stories provides a massive silver quality dataset for innovating multimodal layout analysis models and other multimodal applications.

[Commentary: What’s interesting architecturally is that they selected mobile-optimized convolutional models (YOLOv8 and MobileNetV3) for preprocessing to economize on costs, trained them on one Nvidia A6000 and data annotated by undergraduate research assistants 🌚 (compensated for their work, of course) and then inferenced the result on cloud CPUs. They estimate the savings on this stage in 1 order of magnitude.

They also mention open-source TrOCR Base, which would have cost 50× more, and commercial solutions (which would be even more expensive), and still even with the EfficientOCR framework and even with the cloud compute budget of $60,000, which probably only Harvard could afford, they were only able to process 40% of Chronicling America scans (that’s why the dataset version is 0.1).

When you are in digital humanities, you make do with what you have!]

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4090015/ Social rhythm regularity and the onset of affective episodes in bipolar spectrum individuals Gail H. C. Shen, Lauren B. Alloy, Lyn Y. Abramson, Louisa G. Sylvia 2008 2023-03-31 [("doi","10.1111/j.1399-5618.2008.00583.x")] psychiatry/bipolar/energy psychiatry/bipolar/sleep

Objectives: Research suggests that bipolar disorder individuals may have less social rhythm regularity than normal controls and that this may contribute to their affective symptoms and episodes. This study examined whether regularity prospectively predicted time to onset of major depressive, hypomanic and manic episodes in a sample with bipolar spectrum disorders.

Method: We recruited 414 undergraduate students from Temple University and University of Wisconsin diagnosed with cyclothymia, bipolar II disorder, or with no affective disorder (normal controls). Participants completed the Social Rhythm Metric at Time 1 and structured interviews every 4 months for an average follow-up period of 33 months.

Results: Participants diagnosed with cyclothymia and bipolar II disorder reported significantly fewer regular activities than normal controls, and ~half of these participants experienced a worsening course of their illness over the study duration. Survival analyses indicated that both diagnosis and social rhythm regularity significantly predicted the time to participants’ first prospective onset of major depressive, hypomanic and manic episodes.

Conclusion: Consistent with the social zeitgeber theory, bipolar spectrum participants reported less social rhythm regularity than normal controls, which prospectively predicted the survival time to affective episodes.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6738692/ Zolpidem dependence in an adult with bipolar affective disorder and epilepsy: A case report Sujita Kumar Kar, Suyash Dwivedi 2019 2023-03-31 [("doi","10.1136/gpsych-2019-100102")] psychiatry/bipolar/sleep zeo

Zolpidem is a short-acting non-benzodiazepine hypnotic agent, commonly recommended for short-term treatment of insomnia. Zolpidem has less dependence potential than benzodiazepines. Patients with mental illnesses often have disturbed sleep, for which zolpidem is often prescribed. Long-term use and self-medication (in more than recommended doses) are more likely to cause dependence.

We report here a case of bipolar affective disorder with epilepsy, who developed dependence to zolpidem and had severe withdrawal symptoms.

The management issues are also discussed with review of the literature.

--- https://en.wikipedia.org/wiki/Zolpidem Zolpidem 2023-03-31 psychiatry/bipolar/elon-musk psychiatry/bipolar/sleep --- https://en.wikipedia.org/wiki/Ye_(album) Ye (album) 2023-03-31 psychiatry/bipolar/energy --- https://en.wikipedia.org/wiki/Kanye_West#Mental_health Kanye West § Mental health 2023-03-31 psychiatry/bipolar/energy --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2847498/ Mania and dysregulation in goal pursuit: a review Sheri L. Johnson 2005 2023-03-31 [("doi","10.1016/j.cpr.2004.11.002")] economics psychiatry/bipolar/energy psychology/personality/conscientiousness psychology/writing

This paper reviews evidence for deficits in goal regulation in bipolar disorder.

A series of authors have described mania as related to higher accomplishment, elevated achievement motivation, and ambitious goal setting. These characteristics appear to be evident outside of episodes, and to some extent, among family members of people with a history of mania. In addition, people with a history of mania demonstrate intense mood reactivity, particularly in response to success and reward. During positive moods, they appear to experience robust increases in confidence. These increases in confidence, coupled with a background of ambitious goals, are believed to promote excessive pursuit of goals.

This excessive goal engagement is hypothesized to contribute to manic symptoms after an initial life success.

--- https://www.psychologytoday.com/us/blog/bipolar-advantage/201306/advantages-in-bipolar-no-longer-if-why-and-how 2023-03-31 psychiatry/bipolar/energy --- http://www.openp2p.com/pub/a/p2p/2002/12/11/piracy.html Piracy is Progressive Taxation, and Other Thoughts on the Evolution of Online Distribution O`Reilly 2002 2023-03-31 economics/copyright --- https://verdagon.dev/blog/perfect-replayability-prototyped Heisenbugs: The most elusive kind of bug, and how to capture them with Perfect Replayability—Eliminate heisenbugs and endless debugging sessions! Evan Ovadia 2022-06-29 2023-04-01 cs/algorithm design/typography/sidenote --- https://arxiv.org/abs/1905.02244#google Searching for MobileNetV3 Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam 2019-05-06 2023-04-01 [("doi","10.48550/arXiv.1905.02244")] ai/nn/cnn reinforcement-learning/meta-learning

We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.

This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state-of-the-art. Through this process we create two new MobileNet models for release: MobileNetV3-Large & MobileNetV3-Small which are targeted for high and low resource use cases.

These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP).

We achieve new state-of-the-art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 15% compared to MobileNetV2. MobileNetV3-Small is 4.6% more accurate while reducing latency by 5% compared to MobileNetV2.

MobileNetV3-Large detection is 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation.

--- https://arxiv.org/abs/2109.10282#microsoft TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models Minghao Li, Tengchao Lv, Jingye Chen, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei 2021-09-21 2023-04-01 [("doi","10.48550/arXiv.2109.10282")] ai/nn/transformer

Text recognition is a long-standing research problem for document digitalization. Existing approaches are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language model is usually needed to improve the overall accuracy as a post-processing step.

In this paper, we propose an end-to-end text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets.

Experiments show that the TrOCR model outperforms the current state-of-the-art models on the printed, handwritten and scene text recognition tasks.

The TrOCR models and code are publicly available at Github.

--- https://github.com/alasdairforsythe/tokenmonster/blob/main/benchmark/pretrain.md 2023-04-01 ai/nn/tokenization --- https://arxiv.org/abs/1902.03477 The Omniglot challenge: a 3-year progress report Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum 2019-02-09 2023-04-01 [("doi","10.48550/arXiv.1902.03477")] ai/dataset reinforcement-learning/meta-learning

Three years ago, we released the Omniglot dataset for one-shot learning, along with 5 challenge tasks and a computational model that addresses these tasks. The model was not meant to be the final word on Omniglot; we hoped that the community would build on our work and develop new approaches.

In the time since, we have been pleased to see wide adoption of the dataset. There has been notable progress on one-shot classification, but researchers have adopted new splits and procedures that make the task easier. There has been less progress on the other 4 tasks.

We conclude that recent approaches are still far from human-like concept learning on Omniglot, a challenge that requires performing many tasks with a single model.

--- https://en.wikipedia.org/wiki/Penal_bond Penal bond 2023-04-01 economics/mechanism-design law --- https://burners.me/2013/04/25/9-ways-to-die-at-burning-man/ 2023-04-01 technology --- https://infovis-wiki.net/wiki/Semantic_Zoom 2023-04-01 design/visualization --- https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1010105 Significant sparse polygenic risk scores across 813 traits in UK Biobank Yosuke Tanigawa, Junyang Qian, Guhan Venkataraman, Johanne Marie Justesen, Ruilin Li, Robert Tibshirani, Trevor Hastie, Manuel A. Rivas, Scott M. Williams, Samuli Ripatti, Scott M. Williams, Samuli Ripatti, Scott M. Williams, Samuli Ripatti, Scott M. Williams, Samuli Ripatti 2022-02-15 2023-04-01 [("doi","10.1371/journal.pgen.1010105")] genetics/heritable

We present a systematic assessment of polygenic risk score (PRS) prediction across more than 1,500 traits using genetic and phenotype data in the UK Biobank. We report 813 sparse PRS models with significant (p < 2.5 x 10−5) incremental predictive performance when compared against the covariate-only model that considers age, sex, types of genotyping arrays, and the principal component loadings of genotypes. We report a statistically-significant correlation between the number of genetic variants selected in the sparse PRS model and the incremental predictive performance (Spearman’s ⍴ = 0.61, p = 2.2 x 10−59 for quantitative traits, ⍴ = 0.21, p = 9.6 x 10−4 for binary traits). The sparse PRS model trained on European individuals showed limited transferability when evaluated on non-European individuals in the UK Biobank. We provide the PRS model weights on the Global Biobank Engine (https://biobankengine.stanford.edu/prs).

Author summary: Polygenic risk score (PRS), an approach to estimate genetic predisposition on disease liability by aggregating the effects across multiple genetic variants, has attracted increasing research interest. While there have been improvements in the predictive performance of PRS for some traits, the applicability of PRS models across a wide range of human traits has not been clear. Here, applying penalized regression using Batch Screening Iterative Lasso (BASIL) algorithm to more than 269,000 individuals of white British ancestry in UK Biobank, we systematically characterize PRS models across more than 1,500 traits. We report 813 traits with PRS models of statistically-significant predictive performance. While the statistical-significance does not necessarily directly translate into clinical relevance, we investigate the properties of the 813 significant PRS models and report a statistically-significant correlation between predictive performance and estimated SNP-based heritability. We find that the number of genetic variants selected in our sparse PRS model is statistically-significantly correlated with the incremental predictive performance in both quantitative and binary traits. Our transferability assessment of PRS models in UK Biobank revealed that the sparse PRS models trained on individuals of European ancestry had a lower predictive performance for individuals of African and Asian ancestry groups.

--- https://en.wikipedia.org/wiki/Invented_tradition Invented tradition 2023-04-01 history politics --- https://forum.effectivealtruism.org/posts/PTtZWBAKgrrnZj73n/biosecurity-culture-computer-security-culture 2023-04-01 existential-risk genetics/genome-synthesis/virus-proof --- https://www.w3.org/TR/html-design-principles/ 2023-04-02 cs/css --- https://en.wikipedia.org/wiki/Second_wind_(sleep) Second wind (sleep) 2023-04-02 zeo --- https://arxiv.org/abs/2309.00254 Why do universal adversarial attacks work on large language models?: Geometry might be the answer Varshini Subhash, Anna Bialas, Weiwei Pan, Finale Doshi-Velez 2023-09-01 2023-09-01 [("doi","10.48550/arXiv.2309.00254")] ai/nn/adversarial

Transformer based large language models with emergent capabilities are becoming increasingly ubiquitous in society. However, the task of understanding and interpreting their internal workings, in the context of adversarial attacks, remains largely unsolved.

Gradient-based universal adversarial attacks have been shown to be highly effective on large language models and potentially dangerous due to their input-agnostic nature. This work presents a novel geometric perspective explaining universal adversarial attacks on large language models.

By attacking the 117M parameter GPT-2 model, we find evidence indicating that universal adversarial triggers could be embedding vectors which merely approximate the semantic information in their adversarial training region.

This hypothesis is supported by white-box model analysis comprising dimensionality reduction and similarity measurement of hidden representations. We believe this new geometric perspective on the underlying mechanism driving universal attacks could help us gain deeper insight into the internal workings and failure modes of LLMs, thus enabling their mitigation.

--- https://ifstudies.org/blog/secularization-begins-at-home 2023-04-02 philosophy/religion sociology --- https://solar.lowtechmagazine.com/2011/12/the-chinese-wheelbarrow 2023-04-02 technology --- https://engineeringideas.substack.com/p/review-of-why-greatness-cannot-be 2023-04-02 reinforcement-learning/exploration --- https://fgiesen.wordpress.com/2012/04/08/metaprogramming-for-madmen/ 2023-04-02 cs/algorithm --- https://arxiv.org/abs/0905.3590 Desperately seeking mathematical proof Melvyn B. Nathanson 2009-05-22 2023-04-02 [("doi","10.48550/arXiv.0905.3590")] math

Remarks on mathematical proof and the practice of mathematics.

--- https://downloads.hindawi.com/journals/np/2012/581291.pdf Molecular Determinants of the Spacing Effect Naqib 2012 2023-04-02 psychology/spaced-repetition --- https://jamanetwork.com/journals/jama/fullarticle/197439 Lifetime Risk for Diabetes Mellitus in the United States Narayan 2003 2023-04-02 biology --- https://www.youtube.com/watch?v=CYeqbtYAIzY 2023-04-02 ai/anime ai/video/generation --- https://sites.google.com/view/model-free-speed/ Agile Locomotion via Model-free Learning Margolis 2022 2023-04-03 reinforcement-learning/model-free reinforcement-learning/robot --- https://www.lesswrong.com/posts/kmWrwtGE9B9hpbgRT/a-search-for-more-chatgpt-gpt-3-5-gpt-4-unspeakable-glitch 2023-04-03 ai/nn/tokenization --- https://en.wikipedia.org/wiki/Chartjunk Chartjunk 2023-04-03 design/visualization --- https://www.biorxiv.org/content/10.1101/2023.09.01.555871.full Human deleterious mutation rate implies high fitness variance, with declining mean fitness compensated by rarer beneficial mutations of larger effect Joseph D. Matheson, Joanna Masel, Jason Bertram 2023-09-04 2023-09-04 [("doi","10.1101/2023.09.01.555871")] genetics/heritable/rare genetics/selection/natural/human/dysgenics

Each new human has an expected Ud = 2–10 new deleterious mutations. This deluge of deleterious mutations cannot all be purged, and therefore accumulate in a declining fitness ratchet.

Using a novel simulation framework designed to efficiently handle genome-wide linkage disequilibria across many segregating sites, we find that rarer, beneficial mutations of larger effect are sufficient to compensate fitness declines due to the fixation of many slightly deleterious mutations.

Drift barrier theory posits a similar asymmetric pattern of fixations to explain ratcheting genome size and complexity, but in our theory, the cause is Ud > 1 rather than small population size. In our simulations, Ud ~ 2–10 generates high within-population variance in relative fitness; two individuals will typically differ in fitness by 15–40%.

Ud 2–10 also slows net adaptation by ~13%-39%. Surprisingly, fixation rates are more sensitive to changes in the beneficial than the deleterious mutation rate, eg. a 10% increase in overall mutation rate leads to faster adaptation; this puts to rest dysgenic fears about increasing mutation rates due to rising paternal age.

--- https://x.com/francoisfleuret/status/1699117856779075949 2023-04-03 ai/nn/transformer/gpt/codex design/typography/tex --- https://clarkesworldmagazine.com/watts_01_10/ The Things 2023-04-03 fiction/science-fiction philosophy/mind --- https://stevelosh.com/blog/2021/03/small-common-lisp-cli-programs/ 2023-04-03 cs/lisp --- https://publicdomainreview.org/collection/madame-b-album/ 2023-04-03 design history/public-domain-review --- https://www.sciencedirect.com/science/article/pii/S0002916523057817 2023-04-03 vitamin-d --- https://www.sciencedirect.com/science/article/pii/S0002916523266381 2023-04-03 iodine --- https://www.belfercenter.org/sites/default/files/legacy/files/is3102_pp042-078_abrahms.pdf 2023-04-04 crime/terrorism --- https://web.archive.org/web/20200516093838/http://blog.talkingphilosophy.com/?p=6160 2023-04-04 fiction/humor philosophy/ontology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4338325/ 2023-04-04 zeo --- https://www.lesswrong.com/posts/GwGeksTkFQbm6Hbrx/how-to-use-hypnagogic-hallucinations-as-biofeedback-to 2023-04-04 psychiatry/meditation psychology/vision/dream --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2148370/ The hidden structure of overimitation Derek E. Lyons, Andrew G. Young, Frank C. Keil 2007 2023-04-04 [("doi","10.1073/pnas.0704452104")] psychology/cognitive-bias reinforcement-learning/imitation-learning sociology/technology

Young children are surprisingly judicious imitators, but there are also times when their reproduction of others’ actions appears strikingly illogical. For example, children who observe an adult inefficiently operating a novel object frequently engage in what we term overimitation, persistently reproducing the adult’s unnecessary actions. Although children readily over-imitate irrelevant actions that even Pan troglodytes ignore, this curious effect has previously attracted little interest; it has been assumed that children over-imitate not for theoretically significant reasons, but rather as a purely social exercise.

In this paper, however, we challenge this view, presenting evidence that overimitation reflects a more fundamental cognitive process. We show that children who observe an adult intentionally manipulating a novel object have a strong tendency to encode all of the adult’s actions as causally meaningful, implicitly revising their causal understanding of the object accordingly. This automatic causal encoding process allows children to rapidly calibrate their causal beliefs about even the most opaque physical systems, but it also carries a cost.

When some of the adult’s purposeful actions are unnecessary—even transparently so—children are highly prone to mis-encoding them as causally significant. The resulting distortions in children’s causal beliefs are the true cause of overimitation, a fact that makes the effect remarkably resistant to extinction. Despite countervailing task demands, time pressure, and even direct warnings, children are frequently unable to avoid reproducing the adult’s irrelevant actions because they have already incorporated them into their representation of the target object’s causal structure.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247488/ Glucagon-Like Peptide 1 Receptor Agonists and Chronic Lower Respiratory Disease Exacerbations Among Patients With Type 2 Diabetes Yasser Albogami, Kenneth Cusi, Michael J. Daniels, Yu-Jung J. Wei, Almut G. Winterstein 2021 2023-04-04 [("doi","10.2337/dc20-1794")] longevity/glp/semaglutide

Objective: Emerging data from animal and human pilot studies suggest potential benefits of glucagon-like peptide 1 receptor agonists (GLP-1RA) on lung function. We aimed to assess the association of GLP-1RA and chronic lower respiratory disease (CLRD) exacerbation in a population with comorbid type 2 diabetes (T2D) and CLRD.

Research Design and Method: A new-user active-comparator analysis was conducted with use of a national claims database of beneficiaries with employer-sponsored health insurance spanning 2005–2017. We included adults with T2D and CLRD who initiated GLP-1RA or dipeptidyl peptidase 4 inhibitors (DPP-4I) as an add-on therapy to their anti-diabetes regimen. The primary outcome was time to first hospital admission for CLRD. The secondary outcome was a count of any CLRD exacerbation associated with an inpatient or outpatient visit. We estimated incidence rates using inverse probability of treatment weighting for each study group and compared via risk ratios.

Results: The study sample consisted of 4,150 GLP-1RA and 12,540 DPP-4I new users with comorbid T2D and CLRD. The adjusted incidence rate of first CLRD admission during follow-up was 10.7 and 20.3 per 1,000 person-years for GLP-1RA and DPP-4I users, respectively, resulting in an adjusted hazard ratio of 0.52 (95% CI 0.32-0.85). For the secondary outcome, the adjusted incidence rate ratio was 0.70 (95% CI 0.57-0.87).

Conclusions: GLP-1RA users had fewer CLRD exacerbations in comparison with DPP-4I users. Considering both plausible mechanistic pathways and this real-world evidence, potential beneficial effects of GLP-1RA may be considered in selection of an anti-diabetes treatment regimen. Randomized clinical trials are warranted to confirm our findings.

--- https://www.wired.com/story/battle-over-books3/ 2023-04-04 ai/dataset economics/copyright --- https://x.com/bubblez_jazzy/status/1699480417550643521 2023-04-04 ai/nn/transformer/clip/sample --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2571043/ Glucose and C-peptide changes in the perionset period of type 1 diabetes in the Diabetes Prevention Trial-Type 1 Jay M. Sosenko, Jerry P. Palmer, Lisa Rafkin-Mervis, Jeffrey P. Krischer, David Cuthbertson, Della Matheson, Jay S. Skyler 2008 2023-04-04 [("doi","10.2337/dc08-0935")] biology

Objective: We examined metabolic changes in the period immediately after the diagnosis of type 1 diabetes and in the period leading up to its diagnosis in Diabetes Prevention Trial-Type 1 (DPT-1) participants.

Research Design and Method: The study included oral insulin trial participants and parenteral insulin trial control subjects (n = 63) in whom diabetes was diagnosed by a 2-h diabetic oral glucose tolerance test (OGTT) that was confirmed by another diabetic OGTT within 3 months. Differences in glucose and C-peptide levels between the OGTTs were assessed.

Results: Glucose levels increased at 90 (p = 0.006) and 120 min (p < 0.001) from the initial diabetic OGTT to the confirmatory diabetic OGTT (mean ± SD interval 5.5 ± 2.8 weeks). Peak C-peptide levels fell substantially between the OGTTs (median change −14.3%, p < 0.001). Among the 55 individuals whose last non-diabetic OGTT was ~6 months before the initial diabetic OGTT, peak C-peptide levels decreased between these two OGTTs (median change −14.0%, p = 0.052). Among those same individuals the median change in peak C-peptide levels from the last normal OGTT to the confirmatory OGTT (interval 7.5 ± 1.3 months) was −23.8% (p < 0.001). Median rates of change in peak C-peptide levels were 0.00 ng x ml(-1) x month(-1) (p = 0.468, n = 36) from ~12 to 6 months before diagnosis, −0.10 ng x ml(-1) x month(-1) (p = 0.059, n = 55) from 6 months before diagnosis to diagnosis, and −0.43 ng x ml(-1) x month(-1) (p = 0.002, n = 63) from the initial diabetic OGTT to the confirmatory diabetic OGTT.

Conclusions: It seems that post-challenge C-peptide levels begin to decrease appreciably in the 6 months before diagnosis and decrease even more rapidly within 3 months after diagnosis.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3405831/ Clinical immunologic interventions for the treatment of type 1 diabetes Lucienne Chatenoud, Katharina Warncke, Anette-G Ziegler 2012 2023-04-04 [("doi","10.1101/cshperspect.a007716")] biology

Type 1 diabetes is an autoimmune disease, hence the rationale for immunotherapy to halt disease progression. Based on knowledge gained from other autoimmune diseases and from transplantation, the first immunointervention trials used immunosuppressive drugs, eg. cyclosporin, in patients with recently diagnosed type 1 diabetes. Although remarkable, the effect vanished following drug withdrawal.

Efforts were then devoted to devise strategies to induce/restore self-tolerance and avoid chronic immunosuppression. Various approaches were identified from work in spontaneous models of autoimmune diabetes, including the use of β-cell autoantigens and monoclonal antibodies directed at relevant immune molecules such as costimulatory ligands, T-cell receptor molecules such as CD3, and B cells.

Phase II and phase III trials were launched, results of which are now available. Although the endeavor is challenging, the experience gained indicates that immunotherapy appears as the real hope of inducing long-term remission of the disease provided the treatment is started early and that protocols are adapted based on lessons from the past.

--- https://www.nationalgeographic.com/history/article/last-voyage-of-the-demeter-dracula-dmitry The little-known shipwreck that inspired Bram Stoker’s Dracula: Stoker was moved by grim details from the world around him while penning his horror masterpiece. The real fate of a ship called the Dmitry played an outsized role in his imaginings Melissa Sartore 2023-08-18 2023-08-18 fiction/gene-wolfe/suzanne-delage history --- https://publichealth.jhu.edu/sites/default/files/2023-05/76oliver-sl-1976-arch-environ-health.pdf Mood and Lithium in Drinking Water Oliver 1975 2023-04-05 psychiatry/lithium --- https://ffi-publikasjoner.archive.knowledgearc.net/bitstream/handle/20.500.12242/1103/14-02234.pdf The financing of jihadi terrorist cells in Europe Oftedal 2015 2023-04-05 crime/terrorism economics --- https://www.academia.dk/BiologiskAntropologi/Tafonomi/PDF/Brains/Britains_Oldest_Brain.pdf Exceptional preservation of a prehistoric human brain from Heslington, Yorkshire, UK O’Connor 2011 2023-04-05 cryonics psychology/neuroscience --- https://www.jneurosci.org/content/21/7/2404.long Long-term memory is facilitated by cAMP response element-binding protein overexpression in the amygdala Josselyn 2023-04-05 psychology/neuroscience psychology/spaced-repetition --- https://web.archive.org/web/20100529141828id_/http://www.swccd.edu/~mseteachingresources/Biology/bioinfo%20wshp/assets/mammothDNA.pdf Metagenomics to paleogenomics: large-scale sequencing of mammoth DNA Poinar 2006 2023-04-05 genetics/sequencing --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3782097/ 2023-04-05 exercise --- https://damieng.com/typography/zx-origins/ 2023-04-05 design/typography --- https://publicdomainreview.org/collection/arbeid-van-mars/ 2023-04-05 design history/public-domain-review --- https://www.wsj.com/us-news/climate-environment/microsoft-will-use-carbon-absorbing-rocks-to-meet-climate-goals-57ea802a 2023-04-05 technology/carbon-capture --- https://arxiv.org/abs/1610.10028 Refiltering hypothesis tests to control sign error Art B. Owen 2016-10-31 2023-04-05 [("doi","10.48550/arXiv.1610.10028")] statistics/power-analysis

A common, though not recommended statistical practice is to report confidence intervals if and only if they exclude a null value of 0. The resulting filtered confidence intervals generally do not have their nominal confidence level. More worryingly, in low power settings their center points will be much farther from zero than the true parameter is and they will frequently lie on the wrong side of zero.

Many confidence intervals are constructed using an asymptotically Gaussian parameter estimate accompanied by a weakly consistent estimate of its variance. In these cases, we can subject the given confidence interval(s) to a second filtering step such that the probability of a sign error is controlled. This refiltering step retains only those confidence intervals that are sufficiently well separated from the origin.

It requires no assumptions on the dependencies among the test statistics.

Figure 3: The figure shows 100 randomly selected confidence intervals out of 105 that were generated to have 6% power at level α = 0.05 [and true effect = 1]. The ones shown are the first 100 to exclude 0.
--- https://statmodeling.stat.columbia.edu/2014/11/17/power-06-looks-like-get-used/ 2023-04-05 statistics/power-analysis --- https://linktransformer.github.io/ 2023-04-06 ai/nn/transformer ai/tabular economics --- https://arxiv.org/abs/2106.08769 Knowledge-Adaptation Priors Mohammad Emtiyaz Khan, Siddharth Swaroop 2021-06-16 2023-04-06 [("doi","10.48550/arXiv.2106.08769")] ai/nn/sparsity/knowledge-distillation

Humans and animals have a natural ability to quickly adapt to their surroundings, but machine-learning models, when subjected to changes, often require a complete retraining from scratch.

We present Knowledge-adaptation priors (K-priors) to reduce the cost of retraining by enabling quick and accurate adaptation for a wide-variety of tasks and models. This is made possible by a combination of weight and function-space priors to reconstruct the gradients of the past, which recovers and generalizes many existing, but seemingly-unrelated, adaptation strategies. Training with simple first-order gradient methods can often recover the exact retrained model to an arbitrary accuracy by choosing a sufficiently large memory of the past data.

Empirical results show that adaptation with K-priors achieves performance similar to full retraining, but only requires training on a handful of past examples.

--- https://arxiv.org/abs/2303.17564#bloomberg BloombergGPT: A Large Language Model for Finance Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, Gideon Mann 2023-03-30 2023-04-06 [("doi","10.48550/arXiv.2303.17564")] ai/nn/tokenization ai/nn/transformer/gpt economics

The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering. Large Language Models (LLMs) have been shown to be effective on a variety of tasks; however, no LLM specialized for the financial domain has been reported in literature.

In this work, we present BloombergGPT, a 50 billion parameter language model that is trained on a wide range of financial data. We construct a 363 billion token dataset based on Bloomberg’s extensive data sources, perhaps the largest domain-specific dataset yet, augmented with 345 billion tokens from general purpose datasets.

We validate BloombergGPT on standard LLM benchmarks, open financial benchmarks, and a suite of internal benchmarks that most accurately reflect our intended usage. Our mixed dataset training leads to a model that outperforms existing models on financial tasks by large margins without sacrificing performance on general LLM benchmarks.

Additionally, we explain our modeling choices, training process, and evaluation methodology. We release Training Chronicles (Appendix C) detailing our experience in training BloombergGPT.

--- https://www.filfre.net/2023/09/magic-and-loss-part-1-magic-in-the-cards/ 2023-04-06 economics/mechanism-design psychology/collecting technology/digital-antiquarian --- https://en.wikipedia.org/wiki/Gwynne_Shotwell Gwynne Shotwell 2023-04-06 psychiatry/bipolar/elon-musk --- /doc/politics/2019-costa.pdf Family Ties? The Limits of Fathering Daughters on Congressional Behavior Mia Costa, Jill S. Greenlee, Tatishe Nteta, Jesse H. Rhodes, Elizabeth A. Sharrow 2019-02-04 2023-04-06 [("doi","10.1177/1532673X19826273")] politics

[failure to replicate Washington 2008] Scholars have long suggested that familial life can affect political behavior and, more recently, have found that fathering daughters leads men to adopt more liberal positions on gender equality policies. However, few have focused on the impact of fathering a daughter on congressional behavior, particularly in an era of heightened partisan polarization.

Using an original data set of familial information, we examine whether fathering a daughter influences male legislators’ (a) roll call and cosponsorship support for women’s issues in the 110th–114th Congresses and (b) cosponsorship of bills introduced by female legislators in the 110th Congress.

We find that once party affiliation is taken into account, having a daughter neither predicts support for women’s issues nor cosponsorship of bills sponsored by women.

Our findings suggest there are limits to the direct effects of parenting daughters on men’s political behavior, and that scholars should remain attentive to institutional and partisan contexts.

--- https://racquetmag.com/2023/09/06/game-set-sell/ 2023-04-06 economics/advertising nicotine --- https://www.juliansanchez.com/2009/12/08/the-redactors-dilemma/ 2023-04-06 cs/security politics --- https://www.youtube.com/watch?v=Kil-slXgVys 2023-04-06 design --- https://til.simonwillison.net/llms/claude-hacker-news-themes 2023-04-06 ai/nn/retrieval --- https://www.smithsonianmag.com/science-nature/creepy-kitschy-and-geeky-patches-us-spy-satellites-180953562/ 2023-04-07 cs/security --- https://datagubbe.se/decusab/ 2023-04-07 design --- https://xkcd.com/1741/ 2023-04-07 design --- https://vividness.live/theravada-reinvents-meditation 2023-04-07 psychiatry/meditation sociology --- https://vividness.live/how-asian-buddhism-imported-western-ethics 2023-04-07 philosophy/religion psychiatry/meditation sociology --- https://en.wikipedia.org/wiki/Curse_of_dimensionality#Blessing_of_dimensionality Curse of dimensionality § Blessing of dimensionality 2023-04-07 ai/scaling statistics/probability --- https://meaningness.com/bad-ideas-from-dead-germans 2023-04-07 philosophy/ethics philosophy/ontology philosophy/religion --- https://www.biorxiv.org/content/10.1101/2023.08.10.552848.full Frequent, infinitesimal bottlenecks maximize the rate of microbial adaptation Oscar Delaney, Andrew Letten, Jan Engelstädter 2023-08-14 2023-08-14 [("doi","10.1101/2023.08.10.552848")] genetics/selection

Serial passaging is a fundamental technique in experimental evolution. The choice of bottleneck severity and frequency poses a dilemma: longer growth periods allow beneficial mutants to arise and grow over more generations, but simultaneously necessitate more severe bottlenecks with a higher risk of those same mutations being lost. Short growth periods require less severe bottlenecks, but come at the cost of less time between transfers for beneficial mutations to establish.

The standard laboratory protocol of 24-hour growth cycles with severe bottlenecking has logistical advantages for the experimenter but limited theoretical justification. Here we demonstrate that contrary to standard practice, the rate of adaptive evolution is maximized when bottlenecks are frequent and small, indeed infinitesimally so in the limit of continuous culture.

This result derives from revising key assumptions underpinning previous theoretical work, including changing the metric of optimization to incorporate experiment runtime, and using a full binomial distribution for bottlenecking, rather than a Poisson approximation.

We also show that adding resource constraints and clonal interference to the model leaves the qualitative results unchanged. Implementing these findings will require liquid-handling robots to perform frequent bottlenecks, or chemostats for continuous culture.

Further innovation in and adoption of these technologies has the potential to accelerate the rate of discovery in experimental evolution.

--- https://huggingface.co/datasets/euirim/goodwiki GoodWiki Euirim Choi 2023-09-09 2023-09-09 ai/dataset wikipedia

GoodWiki is a 179 million token dataset of English Wikipedia articles collected on September 4, 2023, that have been marked as ‘Good’ or ‘Featured’ by Wikipedia editors. The dataset provides these articles in GitHub-flavored Markdown format, preserving layout features like lists, code blocks, math, and block quotes, unlike many other public Wikipedia datasets. Articles are accompanied by a short description of the page as well as any associated categories.

Thanks to a careful conversion process from wikicode, the markup language used by Wikipedia, articles in GoodWiki are generally faithful reproductions of the corresponding original Wikipedia pages, minus references, files, infoboxes, and tables. Curated template transclusion and HTML tag handling have minimized instances where entire words and phrases are missing mid-sentence.

The hope is that this more comprehensive data will play a small role in improving open-source NLP efforts in language modeling, summarization, and instruction tuning.

GoodWiki is more than 1.5× larger (when compared using the same tokenizer) than the widely used WikiText-103 dataset by Merity et al 2016, even after excluding article descriptions. Also limited to articles marked as Good or Featured, WikiText inspired GoodWiki.

Composition: The dataset consists of 44,754 rows in a 482.7 MB snappy-compressed Parquet file.

--- https://weis2016.econinfosec.org/wp-content/uploads/sites/2/2016/05/WEIS_2016_paper_58.pdf Join Me on a Market for Anonymity Moser, Böhme 2016 2023-04-07 bitcoin cs/cryptography/timelock cs/security --- https://www.bmj.com/content/340/bmj.c869 CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomized trials Moher 2010 2023-04-07 statistics/meta-analysis --- https://arxiv.org/abs/1607.07903 Product Offerings in Malicious Hacker Markets Ericsson Marin, Ahmad Diab, Paulo Shakarian 2016-07-26 2023-04-08 [("doi","10.48550/arXiv.1607.07903")] darknet-market

Marketplaces specializing in malicious hacking products—including malware and exploits—have recently become more prominent on the dark web and deep web.

We scrape 17 such sites and collect information about such products in a unified database schema. Using a combination of manual labeling and unsupervised clustering, we examine a corpus of products in order to understand their various categories and how they become specialized with respect to vendor and marketplace.

This initial study presents how we effectively employed unsupervised techniques to this data as well as the types of insights we gained on various categories of malicious hacking products.

--- https://arxiv.org/abs/2110.11940 Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators Scott C. Lowe, Robert Earle, Jason d’Eon, Thomas Trappenberg, Sageev Oore 2021-10-22 2023-04-08 [("doi","10.48550/arXiv.2110.11940")] ai/nn/cnn philosophy/logic reinforcement-learning/meta-learning

The choice of activation functions and their motivation is a long-standing issue within the neural network community. Neuronal representations within artificial neural networks are commonly understood as logits, representing the log-odds score of presence of features within the stimulus.

We derive logit-space operators equivalent to probabilistic Boolean logic-gates AND, OR, and XNOR for independent probabilities. Such theories are important to formalize more complex dendritic operations in real neurons, and these operations can be used as activation functions within a neural network, introducing probabilistic Boolean-logic as the core operation of the neural network.

Since these functions involve taking multiple exponents and logarithms, they are computationally expensive and not well suited to be directly used within neural networks. Consequently, we construct efficient approximations named ANDAIL (the AND operator Approximate for Independent Logits), ORAIL, and XNORAIL, which use only comparison and addition operations, have well-behaved gradients, and can be deployed as activation functions in neural networks. Like MaxOut, ANDAIL and ORAIL are generalizations of ReLU to two-dimensions.

While our primary aim is to formalize dendritic computations within a logit-space probabilistic-Boolean framework, we deploy these new activation functions, both in isolation and in conjunction to demonstrate their effectiveness on a variety of tasks including image classification, transfer learning, abstract reasoning, and compositional zero-shot learning.

--- https://en.wikipedia.org/wiki/Rotating_locomotion_in_living_systems Rotating locomotion in living systems 2023-04-08 biology fiction/science-fiction technology --- https://nintil.com/massive-input-spaced-repetition 2023-04-08 psychology/chess psychology/spaced-repetition --- https://en.wikipedia.org/wiki/Interspecific_pregnancy Interspecific pregnancy 2023-04-08 genetics/gametogenesis --- https://xkcd.com/2501/ 2023-04-08 psychology/cognitive-bias/illusion-of-depth --- https://www.reddit.com/r/StableDiffusion/comments/16ew9fz/spiral_town_different_approach_to_qr_monster/ 2023-04-08 ai/nn/transformer/clip/sample --- https://nicomuhly.com/news/2011/i-want-to-get-specific/ 2023-04-08 economics/copyright --- /doc/cs/js/2023-08-07-gwernnet-gpt4-scrollmarker.jpg 2023-08-07 2023-08-07 ai/nn/transformer/gpt/codex cs/js --- https://www.biorxiv.org/content/10.1101/2023.09.08.556917.full Neural correlates of future volitional action in Drosophila Luke E. Brezovec, Andrew B. Berger, Shaul Druckmann, Tom R. Clandinin 2023-09-09 2023-09-09 [("doi","10.1101/2023.09.08.556917")] psychology/neuroscience

The ability to act voluntarily is fundamental to animal behavior. For example, self-directed movements are critical to exploration, particularly in the absence of external sensory signals that could shape a trajectory. However, how neural networks might plan future changes in direction in the absence of salient sensory cues is unknown.

Here we use volumetric two-photon imaging to map neural activity associated with walking across the entire brain of the fruit fly Drosophila, register these signals across animals with micron precision, and generate a dataset of ~20 billion neural measurements across thousands of bouts of voluntary movements.

We define spatially clustered neural signals selectively associated with changes in forward and angular velocity, and reveal that turning is associated with widespread asymmetric activity between brain hemispheres. Strikingly, this asymmetry in interhemispheric dynamics emerges more than 10 seconds before a turn within a specific brain region associated with motor control, the Inferior Posterior Slope (IPS).

This early, local difference in neural activity predicts the direction of future turns on a trial-by-trial basis, revealing long-term motor planning. As the direction of each turn is neither trained, nor guided by external sensory cues, it must be internally determined. We therefore propose that this pre-motor center contains a neural substrate of volitional action.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407977/ Aboulomania, a Mental Disorder Characterized by Pathological Indecisiveness Karlyle Bistas, Jean Paul Tabet 2023 2023-04-08 [("doi","10.7759/cureus.41592")] psychiatry psychology/personality psychology/willpower

The mental disorder known as aboulomania, characterized by pathological indecisiveness, is not listed in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR), widely used by mental health professionals to diagnose mental illnesses. However, it is frequently observed alongside other mental disorders.

Aboulomania is linked to neurotic thinking or “neurosis”, which pertains to a mental disorder arising from previous anxiety. This case presentation is on a 40-year-old Caucasian male, with a past psychiatric history of post-traumatic stress disorder (PTSD) and moderate cannabis use disorder, with no known medical history, who was involuntarily admitted to the psychiatric ward.

Prolonged hospitalization of over two weeks was attributed to his severe and persistent indecisiveness, which hindered progress in discharge planning. In order to tackle this problem, the patient received encouragement from his treatment team to take small, concrete actions to deal with his indecisiveness.

This case report emphasizes the importance of aboulomania in causing long-lasting indecisiveness and provides valuable insights on how to overcome this condition.

...The patient’s chief complaint was, “I do not know why I am here.” Throughout the interview, the patient displayed guarded and evasive behavior. He remained unclear about recent substance use and expressed fear of potential repercussions. When asked to rate his mental health on a scale 1–10, he found it challenging to quantify…The patient expressed frustration with staff when they attempted to assign him a room near the nursing station. He pondered the treatment options available during his stay and expressed a desire to make the most of his time there. There was ongoing debate with the writer regarding the need for substance use treatment and his reluctance to consider it or explore other alternatives. Despite contrary collateral information, he adamantly denied substance use. The patient shared that he has spent the past two years alone at home, lacking interest in many activities and struggling to form close relationships…frequently avoided leaving his house…his tone was monotonous…The patient described his mood as “okay”. The patient displayed a flat affect with a limited range. The patient’s thought process was circumstantial but coherent. The patient engaged in debates about the type of treatment he wants and whether he believes he needs it. The patient’s associations were intact. He is currently unemployed and has no legal issues…Examples of symptoms stated by this patient are as follows: “At times, it is challenging to make decisions because I feel like I have limited information. It’s similar to going to a National Park and trying to choose a hiking trail, but you only have a small amount of information about each trail.” “I constantly feel the need for more information before making decisions.”

--- https://www.404media.co/harry-styles-one-direction-ai-leaked-songs/ 2023-04-09 ai/music crime --- https://www.quantamagazine.org/physicists-observe-unobservable-quantum-phase-transition-20230911/ 2023-04-09 cs/algorithm science --- https://arxiv.org/abs/1803.11384 Super-Earths in need for Extremely Big Rockets Michael Hippke 2018-03-30 2023-04-09 [("doi","10.48550/arXiv.1803.11384")] math/humor technology

Many rocky exoplanets are heavier and larger than Earth, so-called “Super-Earths”. Some of these may be habitable, and a few may be inhabited by Super-Earthlings. Due to the higher surface gravity on these worlds, space-flight is much more challenging.

We find that chemical rockets still allow for escape velocities on Super-Earths up to 10× Earth mass. Much heavier rocky worlds, if they exist, will require using up most of the planet as chemical fuel for the (one) launch, a rather risky undertaking.

We also briefly discuss launching rockets from water worlds, which requires Alien megastructures.

--- /doc/philosophy/epistemology/1989-thagard.pdf Explanatory coherence Paul Thagard 1989-09-01 2023-04-09 [("doi","10.1017/S0140525X00057046")] ai/nn philosophy/epistemology statistics/bayes

This target article presents a new computational theory of explanatory coherence that applies to the acceptance and rejection of scientific hypotheses as well as to reasoning in everyday life. The theory consists of 7 principles that establish relations of local coherence between a hypothesis and other propositions. A hypothesis coheres with propositions that it explains, or that explain it, or that participate with it in explaining other propositions, or that offer analogous explanations.

Propositions are incoherent with each other if they are contradictory. Propositions that describe the results of observation have a degree of acceptability on their own. An explanatory hypothesis is accepted if it coheres better overall than its competitors.

The power of the 7 principles is shown by their implementation in a connectionist program called ECHO, which treats hypothesis evaluation as a constraint satisfaction problem. Inputs about the explanatory relations are used to create a network of units representing propositions, while coherence and incoherence relations are encoded by excitatory and inhibitory links.

ECHO provides an algorithm for smoothly integrating theory evaluation based on considerations of explanatory breadth, simplicity, and analogy. It has been applied to such important scientific cases as Lavoisier’s argument for oxygen against the phlogiston theory and Darwin’s argument for evolution against creationism, and also to cases of legal reasoning.

The theory of explanatory coherence has implications for artificial intelligence, psychology, and philosophy.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3320052/ Terminal dedifferentiation of cognitive abilities R. S. Wilson, E. Segawa, L. P. Hizel, P. A. Boyle, D. A. Bennett 2012-04-10 2023-04-09 [("doi","10.1212/WNL.0b013e31824f7ff2")] iq longevity psychiatry/alzheimers psychology/neuroscience

Objective: To test the cognitive dedifferentiation hypothesis that cognitive abilities become increasingly correlated in late life.

Method: Participants are 174 older persons without dementia at the beginning of a longitudinal clinical-pathologic cohort study. At annual intervals for 6–15 years prior to death, they completed a battery of cognitive performance tests from which previously established composite measures of episodic memory, semantic memory, working memory, and perceptual speed were derived. At death, there was a uniform neuropathologic assessment and levels of diffuse plaques, neuritic plaques, and neurofibrillary tangles were summarized in a composite measure. Change in the 4 cognitive outcomes was analyzed simultaneously in a mixed-effects model that allowed rate of decline to accelerate at a variable point before death.

Results: On average, cognitive decline before the terminal period was relatively gradual, and rates of change in different cognitive domains were moderately correlated, ranging from 0.25 (episodic memory-working memory) to 0.46 (episodic memory-semantic memory).

By contrast, cognition declined rapidly during the terminal period, and rates of change in different cognitive functions were strongly correlated, ranging from 0.83 (working memory-perceptual speed) to 0.89 (episodic memory-semantic memory, semantic memory-working memory).

Higher level of plaques and tangles on postmortem examination was associated with faster pre-terminal decline and earlier onset of terminal decline but not with rate of terminal decline or correlations between rates of change in different cognitive functions.

Conclusion: In the last years of life, covariation among cognitive abilities sharply increases consistent with the cognitive dedifferentiation hypothesis.

--- /doc/philosophy/epistemology/1964-platt.pdf Strong Inference: Certain systematic methods of scientific thinking may produce much more rapid progress than others John R. Platt 1964-10-16 2023-04-09 [("doi","10.1126/science.146.3642.347")] philosophy/epistemology science --- https://www.biorxiv.org/content/10.1101/2023.08.31.555714.full Population-Level Genetic Variation Shapes Generative Brain Mechanisms Alicja Monaghan, Danyal Akarca, Duncan E. Astle 2023-09-03 2023-09-03 [("doi","10.1101/2023.08.31.555714")] genetics/heritable iq psychology/neuroscience

The structural organization of the human brain emerges probabilistically as we develop. Due to the inherent complexity of the human brain, understanding the forces that shape this probabilistic emergence remains one of the central challenges of systems theory and neuroscience.

Across 2,153 children (9–11 years old) we used a computational model to simulate the formation of structural brain connectivity, conceptualized as a trade-off between the cost of new connections η and their topological value γ. We then triangulated this population-level neuroimaging and computational modeling with genomics.

For each participant we assessed their genetic propensity for cognitive ability by calculating polygenic scores.

Modelled parameters differed systematically for participants depending upon their genetic propensity. Those with the highest genetic propensity had a statistically-significantly weaker η term—put simply, their networks emerged with a weaker distance penalty. Strikingly, this softer distance penalty produces more stochastic, diverse, and efficient networks. Furthermore, across the sample, overlapping biological and cellular pathways between polygenic scores and each child’s optimal η-γ trade-off emerged.

This application of computational modeling demonstrates a converging genomic basis for structural brain development and cognitive ability across the population, providing a mechanistic explanation of how and why characteristic network topologies emerge from children at the extreme distributions of polygenic scores, and why they might predict cognitive ability.

--- https://arxiv.org/abs/2309.04564#cohere When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale Max Marion, Ahmet Üstün, Luiza Pozzobon, Alex Wang, Marzieh Fadaee, Sara Hooker 2023-09-08 2023-09-08 [("doi","10.48550/arXiv.2309.04564")] ai/nn/transformer/gpt reinforcement-learning/exploration/active-learning/data-pruning

Large volumes of text data have contributed to the development of large language models (LLMs) in recent years. This data is typically acquired by scraping the internet, leading to pretraining datasets comprised of noisy web text. To date, efforts to prune these datasets down to a higher quality subset have relied on hand-crafted heuristics encoded as rule-based filters.

In this work, we take a wider view and explore scalable estimates of data quality that can be used to systematically measure the quality of pretraining data. We perform a rigorous comparison at scale of the simple data quality estimator of perplexity, as well as more sophisticated and computationally intensive estimates of the Error 𝓁2-Norm and memorization. These metrics are used to rank and prune pretraining corpora, and we subsequently compare LLMs trained on these pruned datasets.

Surprisingly, we find that the simple technique of perplexity outperforms our more computationally expensive scoring methods.

We improve over our no-pruning baseline while training on as little as 30% of the original training dataset.

Our work sets the foundation for unexplored strategies in automatically curating high quality corpora and suggests the majority of pretraining data can be removed while retaining performance.

--- https://arxiv.org/abs/2209.14577 Rectified Flow: A Marginal Preserving Approach to Optimal Transport Qiang Liu 2022-09-29 2023-04-09 [("doi","10.48550/arXiv.2209.14577")] ai/nn/diffusion

We present a flow-based approach to the optimal transport (OT) problem between two continuous distributions π0, π1 on 𝐑d, of minimizing a transport cost 𝔼[c(X1−XX0~, X1✱)] whose marginal distributions on X0, X1 equals π0, π1, respectively, where c is a cost function.

Our method iteratively constructs a sequence of neural ordinary differentiable equations (ODE), each learned by solving a simple unconstrained regression problem, which monotonically reduce the transport cost while automatically preserving the marginal constraints. This yields a monotonic interior approach that traverses inside the set of valid couplings to decrease the transport cost, which distinguishes itself from most existing approaches that enforce the coupling constraints from the outside.

The main idea of the method draws from rectified flow, a recent approach that simultaneously decreases the whole family of transport costs induced by convex functions c (and is hence multi-objective in nature), but is not tailored to minimize a specific transport cost.

Our method is a single-object variant of rectified flow that guarantees to solve the OT problem for a fixed, user-specified convex cost function c.

--- https://arxiv.org/abs/2309.06380 InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation Xingchao Liu, Xiwen Zhang, Jianzhu Ma, Jian Peng, Qiang Liu 2023-09-12 2023-09-12 [("doi","10.48550/arXiv.2309.06380")] ai/nn/diffusion

Diffusion models have revolutionized text-to-image generation with its exceptional quality and creativity. However, its multi-step sampling process is known to be slow, often requiring tens of inference steps to obtain satisfactory results. Previous attempts to improve its sampling speed and reduce computational costs through distillation have been unsuccessful in achieving a functional one-step model.

In this paper, we explore a recent method called Rectified Flow, which, thus far, has only been applied to small datasets. The core of Rectified Flow lies in its reflow procedure, which straightens the trajectories of probability flows, refines the coupling between noises and images, and facilitates the distillation process with student models.

We propose a novel text-conditioned pipeline to turn Stable Diffusion (SD) into an ultra-fast one-step model, in which we find reflow plays a critical role in improving the assignment between noise and images. Leveraging our new pipeline, we create, to the best of our knowledge, the first one-step diffusion-based text-to-image generator with SD-level image quality, achieving an FID (Fréchet Inception Distance) of 23.3 on MS COCO 2017–5k, surpassing the previous state-of-the-art technique, progressive distillation, by a large margin (37.2 → 23.3 in FID).

By using an expanded network with 1.7b parameters, we further improve the FID to 22.4. We call our one-step models InstaFlow. On MS COCO 2014-30k, InstaFlow yields an FID of 13.1 in just 0.09 second, the best in ≤ 0.1 second regime, outperforming the recent StyleGAN-T (13.9 in 0.1 second).

Notably, the training of InstaFlow only costs 199 A100 GPU days.

Project page: https://github.com/gnobitab/InstaFlow.

--- https://arxiv.org/abs/2209.03003 Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow Xingchao Liu, Chengyue Gong, Qiang Liu 2022-09-07 2023-04-10 [("doi","10.48550/arXiv.2209.03003")] ai/nn/diffusion

We present rectified flow, a surprisingly simple approach to learning (neural) ordinary differential equation (ODE) models to transport between two empirically observed distributions π0 and π1, hence providing a unified solution to generative modeling and domain transfer, among various other tasks involving distribution transport.

The idea of rectified flow is to learn the ODE to follow the straight paths connecting the points drawn from π0 and π1 as much as possible. This is achieved by solving a straightforward nonlinear least squares optimization problem, which can be easily scaled to large models without introducing extra parameters beyond standard supervised learning. The straight paths are special and preferred because they are the shortest paths between two points, and can be simulated exactly without time discretization and hence yield computationally efficient models.

We show that the procedure of learning a rectified flow from data, called rectification, turns an arbitrary coupling of π0 and π1 to a new deterministic coupling with provably non-increasing convex transport costs. In addition, recursively applying rectification allows us to obtain a sequence of flows with increasingly straight paths, which can be simulated accurately with coarse time discretization in the inference phase.

In empirical studies, we show that rectified flow performs superbly on image generation, image-to-image translation, and domain adaptation. In particular, on image generation and translation, our method yields nearly straight flows that give high quality results even with a single Euler discretization step.

--- https://www.biorxiv.org/content/10.1101/2023.09.10.557084.full A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic epistasis Boyang Fu, Ali Pazokitoroudi, Albert Xue, Aakarsh Anand, Prateek Anand, Noah Zaitlen, Sriram Sankararaman 2023-09-12 2023-09-12 [("doi","10.1101/2023.09.10.557084")] genetics/heritable

The contribution of epistasis (interactions among genes or genetic variants) to human complex trait variation remains poorly understood. Methods that aim to explicitly identify pairs of genetic variants, usually single-nucleotide polymorphisms (SNPs), associated with a trait suffer from low statistical power due to the large number of hypotheses tested while also having to deal with the computational problem of searching over a potentially large number of candidate pairs.

An alternate approach involves testing whether a single SNP modulates variation in a trait against a polygenic background. While overcoming the limitation of low power, such tests of polygenic or marginal epistasis (ME) are infeasible on Biobank-scale data where hundreds of thousands of individuals are genotyped over millions of SNPs.

We present a method to test for ME of a SNP on a trait that is applicable to biobank-scale data. We performed extensive simulations to show that our method provides calibrated tests of ME. We applied our method to test for ME at SNPs that are associated with 53 quantitative traits across ~300K unrelated white British individuals in the UK Biobank (UKBB).

Testing 15,601 trait-loci associations that were statistically-significant in GWAS, we identified 16 trait-loci pairs across 12 traits that demonstrate strong evidence of ME signals (p-value p < (5 × 10−8) / 53). We further partitioned the statistically-significant ME signals across the genome to identify 6 trait-loci pairs with evidence of local (within-chromosome) ME while 15 show evidence of distal (cross-chromosome) ME. Across the 16 trait-loci pairs, we document that the proportion of trait variance explained by ME is about 12× as large as that explained by the GWAS effects on average (range: 0.59–43.89).

Our results show, for the first time, evidence of interaction effects between individual genetic variants and overall polygenic background modulating complex trait variation.

--- https://web.archive.org/web/20170815022856/http://acarol.woz.org/LegoDifferenceEngine.html 2023-04-10 cs/computable cs/hardware --- https://nikcub.me/posts/onymous-part1/ Large Number of Tor Hidden Sites Seized by the FBI in Operation Onymous were Clone or Scam Sites 2023-04-10 darknet-market --- https://nicolasgallagher.com/micro-clearfix-hack/ A new micro clearfix hack 2023-04-10 cs/css --- https://nebelwelt.net/publications/files/15SEC.pdf Control-Flow Bending: On the Effectiveness of Control-Flow Integrity 2023-04-10 cs/security --- https://www.nytimes.com/2023/09/11/opinion/editorials/tokyo-housing.html 2023-04-10 economics/georgism --- https://www.youtube.com/watch?v=KCaya74_NHw 2023-04-10 ai/nn/transformer/gpt/jukebox --- https://www.youtube.com/watch?v=_3yOrUJ0SzY 2023-04-10 ai/nn/transformer/gpt/jukebox --- https://undark.org/2023/07/19/the-vice-of-spice-confronting-lead-tainted-turmeric/ 2023-04-10 economics iq sociology --- https://www.tumblr.com/numberonecatwinner/701567544684855296/elon-wyd 2023-04-10 psychiatry/bipolar --- https://x.com/venturetwins/status/1701717914737414638 2023-04-11 ai/nn/transformer/gpt/fiction sociology/technology --- https://ajxs.me/blog/How_Far_Back_in_Time_Can_I_Take_My_Websites_Design.html 2023-04-11 cs/css --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3772789/ Human embryonic stem cells derived by somatic cell nuclear transfer Masahito Tachibana, Paula Amato, Michelle Sparman, Nuria Marti Gutierrez, Rebecca Tippner-Hedges, Hong Ma, Eunju Kang, Alimujiang Fulati, Hyo-Sang Lee, Hathaitip Sritanaudomchai, Keith Masterson, Janine Larson, Deborah Eaton, Karen Sadler-Fredd, David Battaglia, David Lee, Diana Wu, Jeffrey Jensen, Phillip Patton, Sumita Gokhale, Richard L. Stouffer, Don Wolf, Shoukhrat Mitalipov 2013 2023-04-11 [("doi","10.1016/j.cell.2013.05.006")] genetics/cloning

Reprogramming somatic cells into pluripotent embryonic stem cells (ESCs) by somatic cell nuclear transfer (SCNT) has been envisioned as an approach for generating patient-matched nuclear transfer (NT)-ESCs for studies of disease mechanisms and for developing specific therapies. Past attempts to produce human NT-ESCs have failed secondary to early embryonic arrest of SCNT embryos.

Here, we identified premature exit from meiosis in human oocytes and suboptimal activation as key factors that are responsible for these outcomes. Optimized SCNT approaches designed to circumvent these limitations allowed derivation of human NT-ESCs.

When applied to premium quality human oocytes, NT-ESC lines were derived from as few as two oocytes. NT-ESCs displayed normal diploid karyotypes and inherited their nuclear genome exclusively from parental somatic cells.

Gene expression and differentiation profiles in human NT-ESCs were similar to embryo-derived ESCs, suggesting efficient reprogramming of somatic cells to a pluripotent state.

--- http://bit-player.org/2023/ai-and-the-end-of-programming 2023-04-11 ai/nn/transformer/gpt/codex philosophy/mind --- https://russellwarne.com/2023/09/14/the-search-for-albert-einsteins-iq/ 2023-04-11 iq/high --- https://davidabell.substack.com/p/playing-around-with-machine-translation 2023-04-11 ai/nn/transformer/gpt/3/nonfiction --- https://arxiv.org/abs/2309.06564 Application of the Thermodynamics of Radiation to Dyson Spheres as Work Extractors and Computational Engines, and their Observational Consequences Jason T. Wright 2023-09-12 2023-09-12 [("doi","10.48550/arXiv.2309.06564")] cs/hardware technology

[Twitter] I apply the thermodynamics of radiation to Dyson spheres as machines that do work or computation, and examine their observational consequences.

I identify 4 properties of Dyson spheres that complicate typical analyses: globally, they may do no work in the usual sense; they use radiation as the source and sink of energy; they accept radiation from a limited range of solid angle; and they conserve energy flux globally.

I consider 3 kinds of activities: computation at the Landauer limit; dissipative activities, in which the energy of a sphere’s activities cascades into waste heat, as for a biosphere; and “traditional” work that leaves the sphere, such as radio emission. I apply the Landsberg formalism to derive efficiency limits in all 3 cases, and show that optical circulators provide an “existence proof” that greatly simplifies the problem and allows the Landsberg limit to be plausibly approached.

I find that for computation and traditional work, there is little to no advantage to nesting shells (as in a “Matrioshka Brain” [Bradbury 1999]); that the optimal use of mass is generally to make very small and hot Dyson spheres; that for “complete” Dyson spheres we expect optical depths of several; and that in all cases the Landsberg limit corresponds to a form of the Carnot limit.

I explore how these conclusions might change in the face of complications such as the sphere having practical efficiencies below the Landsberg limit (using the endoreversible limit as an example); no use of optical circulators; and swarms of materials instead of shells.

--- /doc/cs/js/2023-09-14-gwern-gwernnet-popups-allpopuptypes-meme-therearemanydoorsedboysyes.png Gwern 2023-09-14 2023-09-14 cs/js design --- /doc/cs/js/2023-09-14-gwern-gwernnet-popups-allpopuptypes.png Gwern 2023-09-14 2023-09-14 cs/js design --- https://www.reddit.com/r/StableDiffusion/comments/16jae68/i_made_some_spiral_and_tile_controlnets/ 2023-04-11 ai/nn/transformer/clip/sample --- https://billwillingham.substack.com/p/willingham-sends-fables-into-the 2023-04-11 economics/copyright --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4962164/ Genetic and environmental influences on food preferences in adolescence Andrea D. Smith, Alison Fildes, Lucy Cooke, Moritz Herle, Nicholas Shakeshaft, Robert Plomin, Clare Llewellyn 2016 2023-04-12 [("doi","10.3945/ajcn.116.133983")] genetics/heritable

Background: Food preferences vary substantially among adults and children. Twin studies have established that genes and aspects of the shared family environment both play important roles in shaping children’s food preferences. The transition from childhood to adulthood is characterized by large gains in independence, but the relative influences of genes and the environment on food preferences in late adolescence are unknown.

Objective: The aim of this study was to quantify the contribution of genetic and environmental influences on food preferences in older adolescents.

Design: Participants were 2865 twins aged 18–19 y from the TEDS (Twins Early Development Study), a large population-based cohort of British twins born during 1994–1996. Food preferences were measured by using a self-report questionnaire of 62 individual foods. Food items were categorized into 6 food groups (fruit, vegetables, meat or fish, dairy, starch foods, and snacks) by using factor analysis. Maximum likelihood structural equation modeling established genetic and environmental contributions to variations in preferences for each food group.

Results: Genetic factors influenced a significant and substantial proportion of the variation in preference scores of all 6 food groups: vegetables (0.54; 95% CI: 0.47, 0.59), fruit (0.49; 95% CI: 0.43, 0.55), starchy foods (0.32; 95% CI: 0.24, 0.39), meat or fish (0.44; 95% CI: 0.38, 0.51), dairy (0.44; 95% CI: 0.37, 0.50), and snacks (0.43; 95% CI: 0.36, 0.49). Aspects of the environment that are not shared by 2 twins in a family explained all of the remaining variance in food preferences.

Conclusions: Food preferences had a moderate genetic basis in late adolescence, in keeping with findings in children. However, by this older age, the influence of the shared family environment had disappeared, and only aspects of the environment unique to each individual twin influenced food preferences. This finding suggests that shared environmental experiences that influence food preferences in childhood may not have effects that persist into adulthood.

--- https://hyperboleandahalf.blogspot.com/ 2023-04-12 fiction/humor psychiatry/anxiety psychiatry/depression --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3466669/ Nicotine treatment of mild cognitive impairment: a 6-month double-blind pilot clinical trial P. Newhouse, K. Kellar, P. Aisen, H. White, K. Wesnes, E. Coderre, A. Pfaff, H. Wilkins, D. Howard, E. D. Levin 2012 2023-04-12 [("doi","10.1212/WNL.0b013e31823efcbb")] nicotine

Objective: To preliminarily assess the safety and efficacy of transdermal nicotine therapy on cognitive performance and clinical status in subjects with mild cognitive impairment (MCI).

Method: Nonsmoking subjects with amnestic MCI were randomized to transdermal nicotine (15 mg per day or placebo) for 6 months. Primary outcome variables were attentional improvement assessed with Connors Continuous Performance Test (CPT), clinical improvement as measured by clinical global impression, and safety measures. Secondary measures included computerized cognitive testing and patient and observer ratings.

Results: Of 74 subjects enrolled, 39 were randomized to nicotine and 35 to placebo. 67 subjects completed (34 nicotine, 33 placebo). The primary cognitive outcome measure (CPT) showed a significant nicotine-induced improvement. There was no statistically-significant effect on clinician-rated global improvement. The secondary outcome measures showed significant nicotine-associated improvements in attention, memory, and psychomotor speed, and improvements were seen in patient/informant ratings of cognitive impairment. Safety and tolerability for transdermal nicotine were excellent.

Conclusion: This study demonstrated that transdermal nicotine can be safely administered to nonsmoking subjects with MCI over 6 months with improvement in primary and secondary cognitive measures of attention, memory, and mental processing, but not in ratings of clinician-rated global impression. We conclude that this initial study provides evidence for nicotine-induced cognitive improvement in subjects with MCI; however, whether these effects are clinically important will require larger studies.

Classification of Evidence: This study provides Class I evidence that 6 months of transdermal nicotine (15 mg/day) improves cognitive test performance, but not clinical global impression of change, in nonsmoking subjects with amnestic MCI.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3260075/ Caffeinated beverage intake and reproductive hormones among premenopausal women in the BioCycle Study Karen C. Schliep, Enrique F. Schisterman, Sunni L. Mumford, Anna Z. Pollack, Cuilin Zhang, Aijun Ye, Joseph B. Stanford, Ahmad O. Hammoud, Christina A. Porucznik, Jean Wactawski-Wende 2012 2023-04-12 [("doi","10.3945/ajcn.111.021287")] nootropic/caffeine

Background: Caffeinated beverages are widely consumed among women of reproductive age, but their association with reproductive hormones, and whether race modifies any such associations, is not well understood.

Objective: We assessed the relation between caffeine and caffeinated beverage intake and reproductive hormones in healthy premenopausal women and evaluated the potential effect modification by race.

Design: Participants (n = 259) were followed for up to 2 menstrual cycles and provided fasting blood specimens for hormonal assessment at up to 8 visits per cycle and 4 24-h dietary recalls per cycle. Weighted linear mixed models and nonlinear mixed models with harmonic terms were used to estimate associations between caffeine and hormone concentrations, adjusted for age, adiposity, physical activity, energy and alcohol intakes, and perceived stress. On the basis of a priori assumptions, an interaction between race and caffeine was tested, and stratified results are presented.

Results: Caffeine intake ≥200 mg/d was inversely associated with free estradiol concentrations among white women (β = −0.15; 95% CI: −0.26, −0.05) and positively associated among Asian women (β = 0.61; 95% CI: 0.31, 0.92). Caffeinated soda intake and green tea intake ≥1 cup/d (1 cup = 240 mL) were positively associated with free estradiol concentrations among all races: β = 0.14 (95% CI: 0.06, 0.22) and β = 0.26 (95% CI: 0.07, 0.45), respectively.

Conclusions: Moderate consumption of caffeine was associated with reduced estradiol concentrations among white women, whereas caffeinated soda and green tea intakes were associated with increased estradiol concentrations among all races. Further research is warranted on the association between caffeine and caffeinated beverages and reproductive hormones and whether these relations differ by race.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4018955/ Double-fortified salt is efficacious in improving indicators of iron deficiency in female Indian tea pickers Jere D. Haas, Maike Rahn, Sudha Venkatramanan, Grace S. Marquis, Michael J. Wenger, Laura E. Murray-Kolb, Annie S. Wesley, Gregory A. Reinhart 2014 2023-04-12 [("doi","10.3945/jn.113.183228")] iodine

Poor iron status affects 50% of Indian women and compromises work productivity, cognitive performance, and reproduction. Among the many strategies to reduce iron deficiency is the commercial fortification of iodized table salt with iron to produce a double-fortified salt (DFS).

The objective of this study was to test the efficacy of DFS in reducing iron deficiency in rural women of reproductive age from northern West Bengal, India. The participants were 212 women 18–55 y of age who worked as full-time tea pickers on a large tea estate. Participants in the randomized, controlled, double-blind study were assigned to use either DFS or a control iodized salt for 7.5 to 9 mo.

The DFS was fortified with 3.3-mg ferrous fumarate (1.1-mg elemental iron) per kg of iodized salt, whereas the control salt contained only iodine (47 mg/kg potassium iodate), and both salt varieties were distributed gratis to the families of participants at 0.5 kg/mo for each 2 household members.

At baseline, 53% of participants were anemic (hemoglobin <120 g/L), 25% were iron deficient (serum ferritin <12 μg/L), and 23% were iron-deficient anemic. Also, 22% had a transferrin receptor concentration >8.6 mg/L and 22% had negative (<0.0 mg/kg) body iron stores.

After 9 mo the participants receiving DFS showed significant improvements compared with controls in hemoglobin (+2.4 g/L), ferritin (+0.13 log10 μg/L), soluble transferrin receptor (-0.59 mg/L), and body iron (+1.43 mg/kg), with change in status analyzed by general linear models controlling for baseline values.

This study demonstrated that DFS is an efficacious approach to improving iron status and should be further evaluated for effectiveness in the general population. This trial was registered at clinicaltrials.gov as NCT01032005.

--- https://www.ritsumei.ac.jp/~akitaoka/index-e.html 2023-04-12 psychology/vision --- https://en.wikipedia.org/wiki/Atkinson_Hyperlegible Atkinson Hyperlegible 2023-04-12 design/typography --- https://github.com/Hylian/atkinson-monolegible 2023-04-12 design/typography --- https://betonit.substack.com/p/ai-illustration-contest-who-won-first 2023-04-12 ai/nn/transformer/clip/sample --- https://ninedimensions.substack.com/p/book-review-the-design-of-everyday 2023-04-12 design --- https://awweide.substack.com/p/the-making-of-prince-of-persia 2023-04-12 design --- https://plato.stanford.edu/entries/inner-speech/ 2023-04-13 psychology/inner-voice --- https://github.com/zedeus/nitter 2023-04-13 cs/linkrot/archiving --- https://plato.stanford.edu/entries/human-genome/ 2023-04-13 genetics/sequencing --- https://en.wikipedia.org/wiki/Martin_Shkreli Martin Shkreli 2023-04-13 psychiatry/bipolar psychology/personality/psychopathy --- https://www.theatlantic.com/magazine/archive/1958/12/the-lesson-of-iraq/306494/?single_page=true The Lesson of Iraq: ‘Let us not forget that our essential policy interests are identical with those of the Arabs’ William R. Polk 1958-12 2023-04-13 history politics --- https://npesu.unsw.edu.au/sites/default/files/npesu/surveillances/Assisted%20Reproductive%20Technology%20in%20Australia%20and%20New%20Zealand%202018.pdf#page=7 Assisted reproductive technology in Australia and New Zealand 2018 § pg7 Newman 2020 2023-04-13 genetics/selection/artificial --- https://en.wikipedia.org/wiki/Double-muscled_cattle Double-muscled cattle 2023-04-13 genetics/heritable/rare --- https://en.wikipedia.org/wiki/Myostatin#Whippets Myostatin § Whippets 2023-04-13 genetics/heritable/rare --- https://openreview.net/forum?id=CPKMwyiyDv Neural networks trained with SGD learn distributions of increasing complexity Maria Refinetti, Alessandro Ingrosso, Sebastian Goldt 2023-06-21 2023-06-21 ai/nn/cnn

The uncanny ability of over-parameterized neural networks to generalize well has been explained using various "simplicity biases". These theories postulate that neural networks avoid overfitting by first fitting simple, linear classifiers before learning more complex, non-linear functions. Meanwhile, data structure is also recognised as a key ingredient for good generalization, yet its role in simplicity biases is not yet understood.

Here, we show that neural networks trained using stochastic gradient descent initially classify their inputs using lower-order input statistics, like mean and covariance, and exploit higher-order statistics only later during training.

We first demonstrate this distributional simplicity bias (DSB) in a solvable model of a single neuron trained on synthetic data. We then demonstrate DSB empirically in a range of deep convolutional networks and visual transformers trained on CIFAR-10, and show that it even holds in networks pre-trained on ImageNet.

We discuss the relation of DSB to other simplicity biases and consider its implications for the principle of Gaussian universality in learning.

--- http://ericposner.com/quadratic-voting/ 2023-04-13 economics/mechanism-design/quadratic-voting --- https://www.wired.com/story/colorado-quadratic-voting-experiment/ 2023-04-14 economics/mechanism-design/quadratic-voting --- https://en.wikipedia.org/wiki/Liquid_democracy Liquid democracy 2023-04-14 economics/mechanism-design --- https://en.wikipedia.org/wiki/Quadratic_voting Quadratic voting 2023-04-14 economics/mechanism-design/quadratic-voting --- https://en.wikipedia.org/wiki/Glen_Weyl Glen Weyl 2023-04-14 economics/mechanism-design/quadratic-voting --- https://en.wikipedia.org/wiki/Steven_Lalley Steven Lalley 2023-04-14 economics/mechanism-design/quadratic-voting --- https://vitalik.eth.limo/general/2019/10/24/gitcoin.html 2023-04-14 economics/mechanism-design/quadratic-voting --- https://ethresear.ch/t/pairwise-coordination-subsidies-a-new-quadratic-funding-design/5553 2023-04-14 economics/mechanism-design/quadratic-voting --- https://kortina.nyc/essays/speech-is-free-distribution-is-not-a-tax-on-the-purchase-of-human-attention-and-political-power/ 2023-04-14 economics/mechanism-design/quadratic-voting --- https://hidorahacks.medium.com/what-is-quadratic-voting-funding-how-did-we-improve-it-70989e813cf9 2023-04-14 economics/mechanism-design/quadratic-voting --- https://blog.clr.fund/ 2023-04-14 economics/mechanism-design/quadratic-voting --- https://github.com/gitcoinco/quadratic-funding 2023-04-14 economics/mechanism-design/quadratic-voting --- https://forum.effectivealtruism.org/posts/kHDjtqSiSohZAQyjG/some-thoughts-on-quadratic-funding 2023-04-15 economics/mechanism-design/quadratic-voting --- https://80000hours.org/podcast/episodes/glen-weyl-radically-reforming-capitalism-and-democracy/ 2023-04-15 economics/mechanism-design/quadratic-voting --- https://arxiv.org/abs/2306.07567 Large Language Models Sometimes Generate Purely Negatively-Reinforced Text Fabien Roger 2023-06-13 2023-06-13 [("doi","10.48550/arXiv.2306.07567")] ai/nn/adversarial ai/nn/transformer/gpt reinforcement-learning/preference-learning

[blog] When using adversarial training, it is common practice to train against the most egregious failures. However, this might imply using examples with sensitive information (such as leaked passwords or security vulnerabilities) as training data. One might assume that language models trained with gradient descent never generate text snippets which were only present in examples associated with the lowest possible reward.

In this paper, we show that this assumption is wrong: in some situations, large language models do learn from such negatively-reinforced examples.

We present a specific training setup that enables Pythia-160M to guess passwords 13% more often than it would by guessing randomly, despite only showing it these passwords on examples where the model is incentivized to not output these passwords.

Our code is available at Github.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5123749/ Longitudinal Course of Bipolar Disorder in Youth With High-Functioning Autism Spectrum Disorder Xenia Borue, Carla Mazefsky, Brian T. Rooks, Michael Strober, Martin B. Keller, Heather Hower, Shirley Yen, Mary Kay Gill, Rasim S. Diler, David A. Axelson, Benjamin I. Goldstein, Tina R. Goldstein, Neal Ryan, Fangzi Liao, Jeffrey I. Hunt, Daniel P. Dickstein, Boris Birmaher 2016 2023-04-15 [("doi","10.1016/j.jaac.2016.08.011")] psychiatry/bipolar/autism

Objective: To provide the first longitudinal characterization of mood and psychosocial functioning in youth with comorbid bipolar (BD) and autism spectrum (ASD) disorders.

Method: The Course and Outcome of Bipolar Youth study followed 368 youth (aged 7–17 years) with DSM-IV bipolar I (BP-I), BP-II, or Not Otherwise Specified (NOS) for, on average, 9 years using the Longitudinal Interval Follow-up Evaluation. This subgroup analysis compared youth with and without ASD on clinical presentation, percentage of time with mood symptomatology, and psychosocial functioning.

Results: Thirty youth (~8%) met DSM-IV criteria for Asperger’s disorder or pervasive developmental disorder-NOS (referred to here as ASD). Lifetime worst episode severity was similar in both groups, but youth with both BD and ASD (BD+ASD) had elevated rates of comorbid attention-deficit/hyperactivity and obsessive-compulsive disorders, were younger at intake, and had an earlier onset of mood symptoms. Over time, in both groups, the proportion of predominantly euthymic youth increased, and episode recurrence decreased. Compared to youth with BD, the clinical presentation of youth with BD+ASD more frequently involved distractibility, racing thoughts, depressed mood, social withdrawal, and low reactivity of negative mood states. ASD-related symptomatic differences were generally strongest early and decreased over time. Youth with BD+ASD had significantly greater impairment in friendships throughout follow-up.

Conclusion: Youth with BD+ASD exhibit typical BD mood symptoms but with earlier onset, mixed symptom presentation, and additive functional impairments. Significant amelioration of clinical symptoms occurred over time, suggesting that early recognition and treatment of mood disorders in youth with ASD may improve clinical outcomes.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3185249/ Course of sub-threshold bipolar disorder in youth: diagnostic progression from bipolar disorder not otherwise specified David A. Axelson, Boris Birmaher, Michael A. Strober, Benjamin I. Goldstein, Wonho Ha, Mary Kay Gill, Tina R. Goldstein, Shirley Yen, Heather Hower, Jeffrey I. Hunt, Fangzi Liao, Satish Iyengar, Daniel Dickstein, Eunice Kim, Neal D. Ryan, Erica Frankel, Martin B. Keller 2011 2023-04-15 [("doi","10.1016/j.jaac.2011.07.005")] psychiatry/bipolar

Objective: To determine the rate of diagnostic conversion from an operationalized diagnosis of bipolar disorder not otherwise specified (BP-NOS) to bipolar I disorder (BP-I) or bipolar II disorder (BP-II) in youth over prospective follow-up and to identify factors associated with conversion.

Method: Subjects were 140 children and adolescents recruited from clinical referrals or advertisement who met operationalized criteria for BP-NOS at intake and participated in at least one follow-up evaluation (91% of initial cohort). Diagnoses were assessed at follow-up interviews using the Longitudinal Interval Follow-Up Evaluation. The mean duration of follow-up was 5 years and the mean interval between assessments was 8.2 months.

Results: Diagnostic conversion to BP-I or BP-II occurred in 63 subjects (45%): 32 (23%) to BP-I (nine of whom had initially converted to BP-II) and 31 to only BP-II (22%). Median time from intake to conversion was 58 weeks. First- or second-degree family history of mania or hypomania was the strongest baseline predictor of diagnostic conversion (p = 0.006). Over follow-up, conversion was associated with greater intensity of hypomanic symptoms and with greater exposure to specialized, intensive outpatient psychosocial treatments. There was no association between conversion and exposure to treatment with particular medication classes.

Conclusions: Children and adolescents referred with mood symptoms that meet operationalized criteria for BP-NOS, particularly those with a family history of BP, frequently progress to BP-I or BP-II. Efforts to identify these youth and effectively intervene may have the potential to curtail the progression of mood disorders in this high-risk population.

--- /doc/psychiatry/bipolar/autism/2015-skokauskas.pdf Overlap between Autism Spectrum Disorder and Bipolar Affective Disorder Norbert Skokauskas, Thomas Frodl 2015-08-08 2023-04-15 [("doi","10.1159/000435787")] psychiatry/bipolar/autism

Background: At present there is a substantial uncertainty regarding the extent and nature of autism spectrum disorder (ASD) and bipolar affective disorder (BPAD) co-occurrence due to disparate findings in previous studies.

This paper aimed to find and review original studies on co-occurrence rates of ASD with BPAD, assess them, synthesize the findings in a systematic way, present an overview and make recommendations for future research.

Methods: Systematic literature searches were performed using several databases. Selected articles had to describe an original study that provided prevalence and/or incidence analysis on ASD co-occurring together with BPAD.

Results & Conclusion: A substantial minority of patients (7%) with ASD suffers from BPAD.

An accurate detection of co-occurring ASD and BPAD can lead to a more targeted treatment and improve the patients’ functioning and quality of life.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3644339/ Socio-emotional processing and functioning of youth at high risk for bipolar disorder Jane Whitney, Meghan Howe, Virginia Shoemaker, Sherrie Li, Erica Marie Sanders, Cheri Dijamco, Tenah Acquaye, Jennifer Phillips, Manpreet Singh, Kiki Chang 2013 2023-04-15 [("doi","10.1016/j.jad.2012.08.016")] psychiatry/bipolar/autism

Background: The goal of this study was to investigate differences in socio-emotional processing and functioning in children and adolescents at high risk for bipolar disorder (BD) and healthy control participants.

Method: Children and adolescents with a parent with bipolar disorder, who had mood dysregulation but not fully syndromal BD (high risk, HR, n = 24), were compared to participants with no personal or family history of psychopathology (healthy control, HC, n = 27) across several neuropsychological domains. Social reciprocity was measured by the Social Responsiveness Scale, theory of mind was measured by use of the NEPSY, and affect recognition was measured by the NEPSY and the Diagnostic Test of Nonverbal Accuracy 2 (DANVA).

Results: The HR group demonstrated significant impairment in social reciprocity, including impairments in social awareness, social cognition, social communication, social motivation, and autistic mannerisms. There were no significant group differences in performance on theory of mind or affect recognition tasks.

Limitations: Lack of impairment in tasks associated with theory of mind or affect recognition indicate that social functioning difficulties are not likely due to impairments in these areas, or that the measures employed were not sufficiently sensitive to detect group differences.

Conclusions: Youth at high risk for BD demonstrated impairments in numerous social domains, which may be due to innate differences in brain development governing socio-emotional functioning or may be due to disruptions in normal development caused by mood regulation difficulties.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3449340/ Differential diagnosis of bipolar disorder in children and adolescents Gabrielle A. Carlson 2012 2023-04-15 [("doi","10.1002/j.2051-5545.2012.tb00115.x")] psychiatry/bipolar

Issues complicating the differential diagnosis of bipolar disorder in young people are discussed. They include: (1) the subtype of bipolar disorder being considered; (2) the person’s age and stage of development; (3) whether one views bipolar disorder more conservatively, requiring clear episodes that mark a distinct change from premorbid levels of function, or more liberally, focusing for instance on severe irritability/explosive outbursts as the mood change; (4) who is reporting manic symptoms, and whether symptoms are past and must be recalled or current and more likely to be observed.

The impact of family history is also a substantial factor. The diagnosis of mania/bipolar I disorder may not become clear for a number of years. This is an impairing disorder, but so are the conditions from which it must be distinguished. Family history may increase the odds that certain symptoms/behaviors are manifestations of bipolar disorder but it does not make the diagnosis.

Until there are biomarkers that can confirm the diagnosis, and treatments unique to the condition, it is wise to make a diagnosis of bipolar disorder in children and adolescents provisionally and keep an open mind to the likelihood that revisions may be necessary.

--- /doc/psychiatry/bipolar/autism/2020-chien.pdf The Comorbidity of Schizophrenia Spectrum and Mood Disorders in Autism Spectrum Disorder Yi-Ling Chien, Chi-Shin Wu, Hui-Ju Tsai 2020-12-07 2023-04-15 [("doi","10.1002/aur.2451")] psychiatry/bipolar/autism psychiatry/depression psychiatry/schizophrenia

Individuals with autism spectrum disorder are often diagnosed with at least one or more accompanying disorders. Most studies reported prevalence of the psychiatric comorbidities among these individuals; however, the incidence of developing comorbidities is unclear.

This study used Taiwan’s claims database and aimed to investigate the incidence of developing major psychiatric comorbidities in individuals with autism spectrum disorder and whether the incidence was moderated by gender, autism-spectrum disorder subtypes, and autism-associated neurodevelopmental conditions. A total of 3,837 individuals with autism spectrum disorder (2,929 autistic disorder, 447 Asperger syndrome, 461 pervasive developmental disorder-not otherwise specified) and 38,370 comparison subjects, who were matched by age and gender, were included. The incidences of schizophrenia spectrum, bipolar, and major depressive disorders was examined.

The results showed that the incidences of schizophrenia spectrum (9.7 per 1,000 person-year), bipolar disorder (7.0 per 1,000 person-year), and major depressive disorder (3.2 per 1,000 person-year) were statistically-significantly higher than the comparison group across all 3 subtypes of autism-spectrum disorder. Individuals with pervasive developmental disorder-not otherwise specified had higher risk for major depressive disorder than autistic disorder. Females with Asperger syndrome had statistically-significant higher risk for schizophrenia spectrum than males. The comorbidity rate dramatically dropped when the autism-associated neurodevelopmental conditions were taken into account.

Our findings suggested that the incidences of major psychiatric comorbidities were higher in autism spectrum disorder and influenced by autism subtypes, gender, and autism-associated neurodevelopmental conditions.

Lay Summary: We examined whether people with autism spectrum disorder (ASD) have higher incidence of schizophrenia, bipolar disorders, and major depression using a large claims database.

The results showed the incidences of these mental illness among individual with ASD were statistically-significantly higher than those without ASD. In addition, the incidences were influenced by autism subtypes, gender, and comorbid neurodevelopmental conditions.

--- /doc/psychiatry/bipolar/autism/2018-schalbroeck.pdf Risk of non-affective psychotic disorder or bipolar disorder in autism spectrum disorder: a longitudinal register-based study in the Netherlands R. Schalbroeck, F. Termorshuizen, E. Visser, T. van Amelsvoort, J. P. Selten 2019-11-01 2023-04-15 [("doi","10.1017/S0033291718003483")] psychiatry/bipolar/autism psychiatry/schizophrenia

Background: Individuals with autism spectrum disorder (ASD) appear to be at increased risk of non-affective psychotic disorder (NAPD) and bipolar disorder (BD). However, most previous studies examined the co-occurrence of ASD and NAPD or BD, ignoring possible diagnostic bias and selection bias. We used longitudinal data from Dutch psychiatric case registers to assess the risk of NAPD or BD among individuals with ASD, and compared the results to those obtained for the Dutch population in earlier studies.

Method: Individuals with ASD (n = 17,234) were followed up 16–35 years of age. Kaplan–Meier estimates were used to calculate the risk of NAPD or BD. We conducted separate analyses to reduce possible bias, including an analysis among individuals diagnosed with ASD before age 16 years (n = 8337).

Results: Of the individuals with ASD, 23.50% (95% confidence interval 21.87–25.22) were diagnosed with NAPD and 3.79% (3.06–4.69) with BD before age 35 years. The corresponding figures for the general population were 0.91% (0.63–1.28) and 0.13% (0.08–0.20). Risk estimates were substantially lower, but still higher than general population estimates, when we restricted our analyses to individuals diagnosed with ASD before age 16, with 1.87% (1.33–2.61) being diagnosed with NAPD and 0.57% (0.21–1.53) with BD before age 25 years. The corresponding figures for the general population were 0.63% (0.44–0.86) and 0.08% (0.05–0.12).

Conclusions: Individuals with ASD are at increased risk of NAPD or BD. This is likely not the result of diagnostic or selection bias.

--- /doc/psychiatry/bipolar/autism/2017-coli.pdf Psychiatric vulnerability in adults with intellectual disability and autism: A literature review S. Coli, D. Scuticchio, M. Bertelli 2017-04-01 2023-04-15 [("doi","10.1016/j.eurpsy.2017.01.1952")] psychiatry/anxiety psychiatry/bipolar/autism psychiatry/depression psychiatry/schizophrenia

Introduction: Adults with Intellectual disability (ID) and Autism Spectrum Disorder (ASD) are more vulnerable to mental health problems than the general population.

Objectives/aims:

This study investigates presence and rate of psychiatric disorders in comparison with ID or ASD alone, and appropriateness of assessment and diagnostic procedures or tools.

Method: A systematic mapping of the literature was carried out on the basis of the above mentioned issues. The search was conducted using PubMed and Sciencedirect, according to the following keywords: psychiatric comorbidity, psychiatric disorders, autism, ASD, intellectual disability, mental health problems, adults, assessment tools, diagnosis. 20-eight papers were selected for pertinence to mapping issues among more than 500.

Results: Many studies show that ASD is an important vulnerability factor for psychiatric co-morbidity and for challenging behaviors (CBs) in adults with ID. Highest rates were reported for psychotic, mood, anxiety, and obsessive-compulsive disorders. Few studies show that the difference between adults with ID plus ASD and adults with only ID are not statistically-significant, but for the presence of CBs in those with ID plus ASD. The disagreement of results is based on a variety of factors such as diagnostic over-shadowing, scarcity of specific assessment tools, consideration of the introspective and communication difficulties, incompleteness of medical records, and low reliability of information sources.

Conclusions: Although low studies concordance, the literature mapping suggests the presence of ASD in ID to be associated with higher rates of psychopathology. Since the relevant implications for prevention and clinical management, further research with high-level evidence is hoped.

Disclosure of interest:

The authors have not supplied their declaration of competing interest.

--- /doc/psychiatry/bipolar/autism/2006-bradley.pdf Episodic psychiatric disorders in teenagers with learning disabilities with and without autism Elspeth Bradley, Patrick Bolton 2006-10-01 2023-04-16 [("doi","10.1192/bjp.bp.105.018127")] psychiatry/bipolar/autism psychiatry/depression

Background: Mental health problems in people with learning disabilities and autism are poorly understood.

Aims: To investigate the prevalence of episodic psychiatric disorders in a sample of teenagers with learning disabilities with and without autism.

Method: Teenagers with learning disabilities living in one geographical area were identified. Those with autism were matched to those without. A semi–structured investigator-based interview linked to Research Diagnostic Criteria was used to assess prevalence and type of episodic disorders.

Results: Significantly more individuals with autism had a lifetime episodic disorder, most commonly major depression. Two individuals with autism had bipolar affective disorder. Other episodic disorders with mood components and behavior change were also evident, as were un classifiable disorders characterised by complex psychiatric symptoms, chronicity and general deterioration. Antipsychotics and stimulants were most frequently prescribed; the former associated with episodic disorder, the latter with autism.

Conclusions: Teenagers with learning disabilities and autism have higher rates of episodic psychiatric disorders than those with learning disabilities alone.

--- /doc/psychiatry/bipolar/autism/2008-bryson.pdf Characteristics of children with autism spectrum disorders who received services through community mental health centers Stephanie A. Bryson, Susan K. Corrigan, Thomas P. Mcdonald, Cheryl Holmes 2008-01-01 2023-04-16 [("doi","10.1177/1362361307085214")] psychiatry/adhd psychiatry/bipolar/autism psychiatry/depression

Despite the presence of substantial psychiatric comorbidity among children with autism spectrum disorders (ASDs), little research exists on those who receive community-based mental health services. This project examined one year (2004) of data from the database maintained by 26 community mental health centers (CMHCs) in the Midwestern US state of Kansas.

Children with autism were compared to children with other ASDs—Asperger’s disorder, Rett’s disorder, and PDD-NOS. Children with autism predictably received more special education services than children with other ASDs, while the latter were more likely to have experienced prior psychiatric hospitalization.

Children with ASDs other than autism were also statistically-significantly more likely to be diagnosed with attention deficit hyperactivity disorder, oppositional defiant disorder, depressive disorders, and bipolar disorder.

In 2004, Kansas CMHCs served less than 15% of the children estimated to have an ASD. Implications of these findings are discussed.

--- /doc/psychiatry/bipolar/autism/2008-hutton.pdf New-onset psychiatric disorders in individuals with autism Jane Hutton, Susan Goode, Margaret Murphy, Ann Le Couteur, Michael Rutter 2008-07-01 2023-04-16 [("doi","10.1177/1362361308091650")] psychiatry/anxiety psychiatry/bipolar/autism

A follow-up study to at least the age of 21 years of 135 individuals with an autism spectrum disorder diagnosed in childhood and an IQ of over 30 was conducted. The study is distinctive in its large size, low attrition rate and use of systematic interviews to obtain clinical information. Questionnaires completed by caregivers asked about the development of new psychiatric disorders. For the 39 individuals with a possible new disorder, a detailed psychiatric assessment was undertaken through parental interview.

Of all participants, 16% developed a definite new psychiatric disorder. A further 6% developed a possible new disorder. 5 individuals developed an obsessive-compulsive disorder and/or catatonia; 8 an affective disorder with marked obsessional features; 3 complex affective disorders; 4 more straightforward affective disorders; one a bipolar disorder; and one an acute anxiety state complicated by alcohol excess. There was no case of schizophrenia.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2735817/ Autism spectrum disorder scale scores in pediatric mood and anxiety disorders Daniel S. Pine, Amanda E. Guyer, Michelle Goldwin, Kenneth A. Towbin, Ellen Leibenluft 2008 2023-04-16 [("doi","10.1097/CHI.0b013e31816bffa5")] psychiatry/anxiety psychiatry/bipolar/autism

Objective: To compare scores on autism spectrum disorder (ASD) symptom scales in healthy youths and youths with mood or anxiety disorders.

Method: A total of 352 youths were recruited (107 healthy participants, 88 with an anxiety disorder, 32 with major depressive disorder, 62 with bipolar disorder, and 63 with a mood disorder characterized by severe non-episodic irritability). Participants received structured psychiatric interviews and parent ratings on at least one of 3 ASD symptom scales: Children’s Communication Checklist, Social Communication Questionnaire, and Social Responsiveness Scale.

Results: Relative to healthy youths, youths with mood or anxiety disorders exhibited higher scores on each ASD symptom scale. ASD symptom scale scores also showed an association with impairment severity and attention-deficit/hyperactivity disorder. Among patients with mood disorders but not those with anxiety disorders, consistent, statistically-significant associations between diagnosis and ASD symptom scale scores remained even after controlling for potential confounders.

Conclusions: Patients with mood disorders exhibit higher scores on ASD symptom scales than healthy youths or youths with anxiety disorders. These data should alert clinicians to the importance of assessing ASD symptoms to identify social reciprocity and communication deficits as possible treatment targets in pediatric mood and anxiety disorders.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6493433/ Response to second generation antipsychotics in youth with comorbid bipolar disorder and autism spectrum disorder Gagan Joshi, Joseph Biederman, Janet Wozniak, Robert Doyle, Paul Hammerness, Maribel Galdo, Nora Sullivan, Courtney Williams, Kristin Brethel, K. Yvonne Woodworth, Eric Mick 2012 2023-04-16 [("doi","10.1111/j.1755-5949.2010.00219.x")] psychiatry/bipolar/autism

Objective: To assess the impact of comorbid autism spectrum disorders (ASD) on the response to second-generation antipsychotics (SGA) in pediatric bipolar disorder (BPD).

Method: Secondary analysis of identically designed 8-week open-label trials of SGA monotherapy (risperidone, olanzapine, quetiapine, ziprasidone, or aripiprazole) in youth with BPD.

Results: Of the 151 BPD subjects 15% (n = 23) met criteria for comorbid ASD. There were no differences in the rate of antimanic response (YMRS change ≥30% or CGI-Improvement ≤2: 65% vs. 69%; p = 0.7) in the presence of comorbid ASD.

Conclusion: No difference observed in the rate of antimanic response or tolerability to SGA monotherapy in the presence of ASD comorbidity.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3426171/ Mood disorders in mothers of children on the autism spectrum are associated with higher functioning autism Roma A. Vasa, Connie Anderson, Alison R. Marvin, Rebecca E. Rosenberg, J. Kiely Law, Julia Thorn, Geeta Sarphare, Paul A. Law 2012 2023-04-16 [("doi","10.1155/2012/435646")] psychiatry/bipolar/autism

Mood disorders occur more frequently in family members of individuals with autism spectrum disorders (ASD) than in the general population. There may be associations between maternal mood disorder history patterns and specific ASD phenotypes.

We therefore examined the relationship between maternal mood disorders and child autism spectrum disorders in 998 mother-child dyads enrolled in a national online autism registry and database. Mothers of children with ASD completed online questionnaires addressing their child’s ASD as well as their own mood disorder history.

In multivariate logistic regression models of ASD diagnoses, the odds of an Asperger disorder versus autistic disorder diagnosis were higher among those children whose mothers had a lifetime history of bipolar disorder (OR 2.11, CI 1.20, 3.69) or depression (OR 1.62, CI 1.19, 2.19).

Further, maternal mood disorder onset before first pregnancy was associated with higher odds (OR 2.35, CI 1.48, 3.73) of an Asperger versus autism diagnosis among this sample of children with ASD. These data suggest that differences in maternal mood disorder history may be associated with ASD phenotype in offspring.

--- https://pubmed.ncbi.nlm.nih.gov/8844341/ Neuroimmunotherapy with low-dose subcutaneous interleukin-2 plus melatonin in AIDS patients with CD4 cell number below 200/mm3: a biological phase-II study Lissoni 1995 2023-04-16 melatonin --- https://www.danielascur.com/wp-content/uploads/2019/01/the-ties-that-bind-lemos-and-scur.pdf The ties that bind: implicit contracts and management practices in family-run firms Lemos 2019 2023-04-16 economics --- https://arxiv.org/abs/2305.17601 Incentivizing honest performative predictions with proper scoring rules Caspar Oesterheld, Johannes Treutlein, Emery Cooper, Rubi Hudson 2023-05-28 2023-05-28 [("doi","10.48550/arXiv.2305.17601")] reinforcement-learning/safe statistics/prediction

[blog] Proper scoring rules incentivize experts to accurately report beliefs, assuming predictions cannot influence outcomes. We relax this assumption and investigate incentives when predictions are performative, ie. when they can influence the outcome of the prediction, such as when making public predictions about the stock market.

We say a prediction is a fixed point if it accurately reflects the expert’s beliefs after that prediction has been made. We show that in this setting, reports maximizing expected score generally do not reflect an expert’s beliefs, and we give bounds on the inaccuracy of such reports.

For binary predictions, if the influence of the expert’s prediction on outcomes is bounded, it is possible to define scoring rules under which optimal reports are arbitrarily close to fixed points. However, this is impossible for predictions over more than two outcomes.

We also perform numerical simulations in a toy setting, showing that our bounds are tight in some situations and that prediction error is often substantial (greater than 5–10%).

Lastly, we discuss alternative notions of optimality, including performative stability, and show that they incentivize reporting fixed points.

--- https://www.apa.org/pubs/journals/releases/psp962249.pdf When Dreaming Is Believing: The (Motivated) Interpretation of Dreams Morewedge, Norton 2009 2023-04-16 psychology/vision/dream --- https://openarchive.ki.se/xmlui/bitstream/handle/10616/41868/Swedish_Twin_Registry_2013.pdf?sequence=3 The Swedish Twin Registry: establishment of a biobank and other recent developments Magnusson 2013 2023-04-17 genetics/heritable --- https://www.science.org/doi/10.1126/sciadv.1501385 Ancient mitochondrial DNA provides high-resolution time scale of the peopling of the Americas Llama 2016 2023-04-17 genetics/sequencing --- https://www.sciencedirect.com/science/article/pii/S0002916523018221 Multivitamin use and breast cancer incidence in a prospective cohort of Swedish women Larsson 2010 2023-04-17 biology --- https://ilovetypography.com/2023/09/17/the-new-johnson-johnson-logo/ 2023-04-17 design/typography --- https://www.dota2.com/newsentry/3675555405719286536 2023-04-17 design --- https://resobscura.substack.com/p/why-did-it-take-psychedelics-so-long 2023-04-17 psychedelic --- https://ourworldindata.org/grapher/burden-disease-from-each-mental-illness 2023-04-17 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Catatonia Catatonia 2023-04-17 psychiatry/bipolar --- https://slack.engineering/reducing-slacks-memory-footprint/ 2023-04-17 design --- https://en.wikipedia.org/wiki/M/M/1_queue M/M/1 queue 2023-04-17 cs/algorithm statistics/probability --- https://arxiv.org/abs/2309.08600 Sparse Autoencoders Find Highly Interpretable Features in Language Models Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, Lee Sharkey 2023-09-15 2023-09-15 [("doi","10.48550/arXiv.2309.08600")] ai/nn/transformer/gpt

One of the roadblocks to a better understanding of neural networks’ internals is polysemanticity, where neurons appear to activate in multiple, semantically distinct contexts. Polysemanticity prevents us from identifying concise, human-understandable explanations for what neural networks are doing internally.

One hypothesized cause of polysemanticity is superposition, where neural networks represent more features than they have neurons by assigning features to an overcomplete set of directions in activation space, rather than to individual neurons. Here, we attempt to identify those directions, using sparse autoencoders to reconstruct the internal activations of a language model.

These autoencoders learn sets of sparsely activating features that are more interpretable and monosemantic than directions identified by alternative approaches, where interpretability is measured by automated methods. Ablating these features enables precise model editing, for example, by removing capabilities such as pronoun prediction, while disrupting model behavior less than prior techniques.

This work indicates that it is possible to resolve superposition in language models using a scalable, unsupervised method. Our method may serve as a foundation for future mechanistic interpretability work, which we hope will enable greater model transparency and steerability.

--- https://slate.com/culture/2001/02/the-first-one-now-will-later-be-last.html 2023-04-18 economics/mechanism-design --- https://thewalrus.ca/on-being-bipolar/ 2023-04-18 psychiatry/bipolar --- https://kayode.co/blog/4106/living-with-psychosis/ 2023-04-18 psychiatry/bipolar --- https://www.forbes.com/sites/michaelellsberg/2011/07/18/how-i-overcame-bipolar-ii/ 2023-04-18 psychiatry/bipolar --- https://www.wnycstudios.org/podcasts/onlyhuman/episodes/your-sanity-or-your-kidneys#ember19131420 2023-04-18 psychiatry/bipolar --- https://radiolab.org/podcast/infinities/transcript 2023-04-18 psychiatry/bipolar --- https://slatestarcodex.com/2014/11/27/such-mixed-feelings-about-crazymeds/ 2023-04-18 nootropic/quantified-self psychiatry --- https://news.ycombinator.com/item?id=17317714 2023-04-18 psychiatry/bipolar --- https://www.forbes.com/sites/kerryadolan/2022/09/12/sergei-brin-robloxs-david-baszucki-and-kent-dauten-of-keystone-capital-commit-150-million-to-fight-bipolar-disorder/ 2023-04-18 psychiatry/bipolar --- https://www.nytimes.com/2013/04/28/magazine/the-problem-with-how-we-treat-bipolar-disorder.html 2023-04-18 psychiatry/bipolar --- https://zooko-on-aaronsw.blogspot.com/2013/02/part-2.html 2023-04-18 psychiatry/bipolar --- https://www.rappler.com/voices/ispeak/20618-aaron-swartz-was-bipolar/ 2023-04-19 psychiatry/bipolar --- https://web.archive.org/web/20170711131545/https://www.crazymeds.us/pmwiki/pmwiki.php/MedClass/MoodStabilizers 2023-04-19 psychiatry/bipolar --- https://web.archive.org/web/20130310072825/https://slajax.com/2012/12/12/bipolar-a-feature-not-a-bug.html 2023-04-19 psychiatry/bipolar --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3966762/ Development and validation of a new multidimensional measure of inspiration: associations with risk for bipolar disorder Steven Jones, Alyson Dodd, June Gruber 2014 2023-04-19 [("doi","10.1371/journal.pone.0091669")] psychiatry/bipolar/energy

Background: Individuals at risk for, and diagnosed with, bipolar disorder (BD) appear to have heightened levels of creativity. Although inspiration is creativity, the ways in which individuals appraise and respond emotionally to inspiration in BD remain unexplored.

Method: The present study reports on a new measure of inspiration (External and Internal Sources of Inspiration Scale–EISI). The reliability and validity of EISI were explored along with associations between EISI and BD risk.

Results: Among a cross-national student sample (n = 708) 5 inspiration factors were derived from EISI (self, other, achievement, prosocial and external inspiration). Reliability, concurrent validity and convergent/divergent validity were good. Total EISI and all subscales were associated with increased positive rumination, and total EISI and the achievement EISI subscale were associated with impulsivity. Total EISI, self and prosocial EISI subscales were independently associated with BD risk and current mania symptoms.

Conclusion: This new measure of inspiration is multidimensional, reliable and valid. Findings suggest that self and prosocial focused inspiration are particularly associated with risk for BD after controlling for current manic symptoms. Future studies in clinical populations may illuminate the relationships between inspiration and creativity in BD.

--- https://www.buzzfeed.com/sashachapin/i-miss-being-bipolar 2023-04-19 psychiatry/bipolar/energy --- https://mhprompt.org/2016/09/29/on-being-bipolar-programmer.html 2023-04-19 psychiatry/bipolar --- https://www.nytimes.com/2019/07/06/opinion/sunday/bipolar-bassey-ikpi-book.html 2023-04-19 psychiatry/bipolar/energy psychiatry/bipolar/sleep --- https://web.archive.org/web/20190722221600/https://medium.com/@unquiet.entrepreneur/the-day-my-superpower-started-to-destroy-me-92b4613508a 2023-04-19 psychiatry/bipolar/energy --- https://www.wired.com/2014/11/mental-health-apps/ 2023-04-19 nootropic/quantified-self psychiatry/bipolar --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3705611/ Is bipolar disorder more common in highly intelligent people? A cohort study of a million men C R. Gale, G. D. Batty, A. M. McIntosh, D. J. Porteous, I. J. Deary, F. Rasmussen 2013 2023-04-19 [("doi","10.1038/mp.2012.26")] iq psychiatry/bipolar/energy

Anecdotal and biographical reports have long suggested that bipolar disorder is more common in people with exceptional cognitive or creative ability. Epidemiological evidence for such a link is sparse.

We investigated the relationship between intelligence and subsequent risk of hospitalization for bipolar disorder in a prospective cohort study of 1,049,607 Swedish men. Intelligence was measured on conscription for military service at a mean age of 18.3 years and data on psychiatric hospital admissions over a mean follow-up period of 22.6 years was obtained from national records.

Risk of hospitalization with any form of bipolar disorder fell in a stepwise manner as intelligence increased (plinear trend < 0.0001). However, when we restricted analyses to men with no psychiatric comorbidity, there was a ‘reversed-J’ shaped association: men with the lowest intelligence had the greatest risk of being admitted with pure bipolar disorder, but risk was also elevated among men with the highest intelligence (pquadratic trend = 0.03), primarily in those with the highest verbal (pquadratic trend = 0.009) or technical ability (pquadratic trend < 0.0001).

At least in men, high intelligence may indeed be a risk factor for bipolar disorder, but only in the minority of cases who have the disorder in a pure form with no psychiatric comorbidity.

--- https://www.nytimes.com/2015/06/28/magazine/i-dont-believe-in-god-but-i-believe-in-lithium.html 2023-04-20 psychiatry/bipolar psychiatry/lithium --- https://www.theguardian.com/lifeandstyle/2010/may/03/bipolar-disorder-symptoms 2023-04-20 psychiatry/bipolar --- https://web.archive.org/web/20201117231005/https://www.cdahmedeh.net/blog/2020/7/30/thats-it-im-coming-out-i-have-bipolar-schizoaffective-disorder 2023-04-20 psychiatry/bipolar --- https://web.archive.org/web/20170923070043/https://medium.com/@edward.lager/how-it-feels-to-be-bipolar-9563b9fd4c92 2023-04-20 psychiatry/bipolar --- https://passionatereason.com/2014/09/why-do-i-not-talk-myself-out-of-my-bipolar-moods/ 2023-04-20 psychiatry/bipolar --- https://www.latimes.com/opinion/story/2022-08-26/california-surgeon-general-bipolar-disorder-mental-health 2023-04-20 psychiatry/bipolar --- https://www.facebook.com/shaverm/posts/586679818092883 2023-04-20 psychiatry/bipolar --- https://brettterpstra.com/2020/09/21/bipolar-from-the-other-side/ 2023-04-20 psychiatry/bipolar --- https://www.theguardian.com/society/2020/jul/27/bipolar-disorder-mental-illness-health 2023-04-20 psychiatry/bipolar --- /doc/psychiatry/schizophrenia/2008-maccabe.pdf Scholastic achievement at age 16 and risk of schizophrenia and other psychoses: a national cohort study J. H. MacCabe, M. P. Lambe, S. Cnattingius, A. Torrång, C. Björk, P. C. Sham, A. S. David, R. M. Murray, C. M. Hultman 2007-11-08 2023-04-20 [("doi","10.1017/S0033291707002048")] iq psychiatry/schizophrenia

Background: There is abundant evidence that schizophrenia is associated with cognitive deficits in childhood. However, previous studies investigating school performance have been inconclusive. Furthermore, there are several biological and social factors that could confound the association. We investigated whether school performance at age 16 is associated with risk of adult schizophrenia and other psychoses in a large national cohort, while controlling for multiple confounders.

Method: Using a national sample of 907,011 individuals born in Sweden 1973–1983, we used Cox regression to assess whether scholastic achievement at age 15–16 predicted hospital admission for psychosis between ages 17 and 31, adjusting for potential confounders.

Results: Poor school performance was associated with increased rates of schizophrenia [hazard ratio (HR) 3.9, 95% confidence interval (CI) 2.8–5.3], schizo-affective disorder (HR 4.2, 95% CI 1.9–9.1) and other psychoses (HR 3.0, 95% CI 2.3–4.0). Receiving the lowest (E) grade was statistically-significantly associated with risk for schizophrenia and other psychoses in every school subject. There was no evidence of confounding by migrant status, low birthweight, hypoxia, parental education level or socio-economic group.

Conclusions: Poor school performance across all domains is strongly associated with risk for schizophrenia and other psychoses.

--- /doc/psychiatry/bipolar/energy/2003-wills.pdf 40 lives in the bebop business: Mental health in a group of eminent jazz musicians Geoffrey I. Willis 2003-09-01 2023-04-20 [("doi","10.1192/bjp.183.3.255")] music psychiatry/bipolar/energy

Background: Above-average levels of psychopathology have been demonstrated convincingly in groups of outstanding individuals working in the arts. Currently, jazz musicians have not been studied in this regard.

Aims: To investigate any evidence of psychopathology in a group of eminent jazz musicians.

Method: Biographical material relating to 40 eminent American modern jazz musicians was reviewed and an attempt was made to formulate diagnoses using DSM–IV.

Results: Evidence was provided of levels of psychopathology in the sample of jazz musicians similar to those found in other previously investigated creative groups, with the exception of substance-related problems. An interesting connection between creativity and sensation-seeking was highlighted.

Conclusions: The link between psychopathology and creativity in the arts was given further weight. Future studies of jazz musicians using larger samples and making comparison with groups from different eras of music would give greater clarification to this area.

--- /doc/psychiatry/bipolar/energy/2003-poole.pdf ‘Kind of Blue’: Creativity, mental disorder and jazz Rob Poole 2003-09-01 2023-04-21 [("doi","10.1192/bjp.183.3.193")] music psychiatry/bipolar/energy

All scientific communities must keep themselves intellectually alive. It is important that we should attempt sometimes to tackle intriguing questions that are, strictly speaking, beyond the reach of robust, achievable, scientific methodologies. The small, flawed and inconclusive literature on the relationship between creativity and mental disorder, which includes contributions from some scientific heavyweights (eg. Andreasen 1987; Post 1994) represents a respectable attempt to use empirical methods to explore one such intriguing question.

The fact that these matters are unlikely ever to be resolved does not discredit the effort.

--- /doc/psychiatry/bipolar/energy/1996-post.pdf Verbal Creativity, Depression and Alcoholism: An Investigation of 100 American and British Writers Felix Post 1996-05-01 2023-04-21 [("doi","10.1192/bjp.168.5.545")] psychiatry/alcoholism psychiatry/bipolar/energy psychiatry/depression psychology/writing

Background: An earlier study of 291 world famous men had shown that only visual artists and creative writers were characterised, in comparison with the general population, by a much higher prevalence of pathological personality traits and alcoholism. Depressive disorders, but not any other psychiatric conditions, had afflicted writers almost twice as often as men with other high creative achievements. The present investigation was undertaken to confirm these findings in a larger and more comprehensive series of writers, and to discover causal factors for confirmed high prevalences of affective conditions and alcoholism in writers.

Method: Data were collected from post-mortem biographies and, where applicable, translated into DSM diagnoses. The frequencies of various abnormalities and deviations were compared between poets, prose fiction writers, and playwrights.

Results: A high prevalence in writers of affective conditions and of alcoholism was confirmed. That of bipolar affective psychoses exceeded population norms in poets, who in spite of this had a lower prevalence of all kinds of affective disorders, of alcoholism, of personality deviations, and related to this, of psychosexual and marital problems, than prose fiction and play writers.

Conclusions: A hypothesis is developed, which links the greater frequency of affective illnesses and alcoholism in playwrights and prose writers, in comparison with poets, to differences in the nature and intensity of their emotional imagination. This hypothesis could be tested by clinical psychologists collaborating with experts in literature on random samples of different kinds of writers.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2705657/ Childhood IQ and adult mental disorders: a test of the cognitive reserve hypothesis Karestan C. Koenen, Terrie E. Moffitt, Andrea L. Roberts, Laurie T. Martin, Laura Kubzansky, HonaLee Harrington, Richie Poulton, Avshalom Caspi 2009 2023-04-21 [("doi","10.1176/appi.ajp.2008.08030343")] iq psychiatry/bipolar/energy

Objective: Cognitive reserve has been proposed as important in the etiology of neuropsychiatric disorders. However, tests of the association between premorbid IQ and adult mental disorders other than schizophrenia have been limited and inconclusive. The authors tested the hypothesis that low childhood IQ is associated with increased risk and severity of adult mental disorders.

Method: Participants were members of a representative 1972–1973 birth cohort of 1,037 males and females in Dunedin, New Zealand, who were followed up to age 32 with 96% retention. WISC-R IQ was assessed at ages 7, 9, and 11. Research diagnoses of DSM mental disorders were made at ages 18, 21, 26, and 32.

Results: Lower childhood IQ was associated with increased risk of developing schizophrenia spectrum disorder, adult depression, and adult anxiety. Lower childhood IQ was also associated with greater comorbidity and with persistence of depression; the association with persistence of generalized anxiety disorder was nearly statistically-significant. Higher childhood IQ predicted increased risk of adult mania.

Conclusions: Lower cognitive reserve, as reflected by childhood IQ, is an antecedent of several common psychiatric disorders and also predicts persistence and comorbidity. Thus, many patients who seek mental health treatment may have lower cognitive ability; this should be considered in prevention and treatment planning.

--- https://openaccess.thecvf.com/content_ECCVW_2018/papers/11133/Li_CARN_Convolutional_Anchored_Regression_Network_for_Fast_and_Accurate_Single_ECCVW_2018_paper.pdf CARN: Convolutional Anchored Regression Network for Fast and Accurate Single Image Super-Resolution Li 2020 2023-04-21 ai/nn/cnn --- https://pdfs.semanticscholar.org/1ae0/d55d0af02cdd77a3147c4f652bf4e3749194.pdf Something Rotten in the State of Legal Citation: the Life Span of a United States Supreme Court Citation Containing an Internet Link (1996–2010) Liebler, Liebert 2013 2023-04-21 cs/linkrot --- https://jamanetwork.com/journals/jama/fullarticle/200541 Effects of Long-term Vitamin E Supplementation on Cardiovascular Events and Cancer Lonn 2005 2023-04-21 biology --- https://www.cosmopolitan.com/lifestyle/a44094061/reborn-doll-baby/ The Doll Mommies Are Fighting: Is breastfeeding really best… for a small silicone dummy? Do make-believe babies deserve real diapers? Are medical ethics applicable to inanimate figurines? Inside the extremely niche (yet surprisingly relatable) culture wars now raging within a growing community of doll collectors Jessica Lucas 2023-06-27 2023-06-27 sociology --- /doc/psychology/cognitive-bias/illusion-of-depth/2006-pronin.pdf Manic Thinking: Independent Effects of Thought Speed and Thought Content on Mood Emily Pronin, Daniel M. Wegner 2006-09-01 2023-04-21 [("doi","10.1111/j.1467-9280.2006.01786.x")] psychiatry/bipolar/energy psychology/cognitive-bias/illusion-of-depth

[see racing thoughts] This experiment found that the speed of thought affects mood.

Thought speed was manipulated via participants’ paced reading of statements designed to induce either an elated or a depressed mood. Participants not only experienced more positive mood in response to elation than in response to depression statements, but also experienced an independent increase in positive mood when they had been thinking fast rather than slow—for both elation and depression statements.

This effect of thought speed extended beyond mood to other experiences often associated with mania (ie. feelings of power, feelings of creativity, a heightened sense of energy, and inflated self-esteem or grandiosity).

--- /doc/psychiatry/bipolar/energy/1994-post.pdf Creativity and Psychopathology a Study of 291 World-Famous Men Felix Post 1994-07-01 2023-04-21 [("doi","10.1192/bjp.165.1.22")] psychiatry/bipolar/energy psychiatry/depression

Background: This investigation sought to determine the prevalences of various psychopathologies in outstandingly creative individuals, and to test a hypothesis that the high prevalence of mental abnormalities reported in prominent living creative persons would not be found in those who had achieved and retained world status.

Method: The family background, physical health, personality, psychosexuality and mental health of 291 famous men in science, thought, politics, and art were investigated. The membership of the 6 series of scientists and inventors, thinkers and scholars, statesmen and national leaders, painters and sculptors, composers, and of novelists and playwrights was determined by the availability of sufficiently adequate biographies. Extracted data were transformed into diagnoses in accordance with DSM-III-R criteria, when appropriate.

Results: All excelled not only by virtue of their abilities and originality, but also of their drive, perseverance, industry, and meticulousness.

With a few exceptions, these men were emotionally warm, with a gift for friendship and sociability. Most had unusual personality characteristics and, in addition, minor ‘neurotic’ abnormalities were probably more common than in the general population. Severe personality deviations were unduly frequent only in the case of visual artists and writers. Functional psychoses were probably less frequent than psychiatric epidemiology would suggest, and they were entirely restricted to the affective varieties.

Among other functional disorders, only depressive conditions, alcoholism, and, less reliably, psychosexual problems were more prevalent than expected in some professional categories, but strikingly so in writers.

Conclusions: Similar findings have been reported for living artists and writers, and this suggests that certain pathological personality characteristics, as well as tendencies towards depression and alcoholism, are causally linked to some kinds of valuable creativity.

--- https://www.astralcodexten.com/p/ontology-of-psychiatric-conditions-34e 2023-04-21 psychiatry/bipolar psychiatry/depression --- https://www.astralcodexten.com/p/non-cognitive-skills-for-educational 2023-04-21 psychiatry/bipolar/genetics --- https://www.astralcodexten.com/p/sleep-is-the-mate-of-death 2023-04-22 psychiatry/depression zeo --- https://x.com/culturaltutor/status/1703844762883670346 2023-04-22 japan/art --- https://www.atlasobscura.com/articles/quest-for-the-golden-owl-hidden-treasure 2023-04-22 fiction/text-game --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6589096/ Unbiased detection of CRISPR off-targets in vivo using DISCOVER-Seq Beeke Wienert, Stacia K. Wyman, Christopher D. Richardson, Charles D. Yeh, Pinar Akcakaya, Michelle J. Porritt, Michaela Morlock, Jonathan T. Vu, Katelynn R. Kazane, Hannah L. Watry, Luke M. Judge, Bruce R. Conklin, Marcello Maresca, Jacob E. Corn 2019 2023-04-22 [("doi","10.1126/science.aav9023")] genetics/editing

CRISPR-Cas genome editing induces targeted DNA damage but can also affect off-target sites. Current off-target discovery methods work using purified DNA or specific cellular models but are incapable of direct detection in vivo.

We developed DISCOVER-Seq (discovery of in situ Cas off-targets and verification by sequencing), a universally applicable approach for unbiased off-target identification that leverages the recruitment of DNA repair factors in cells and organisms. Tracking the precise recruitment of MRE11 uncovers the molecular nature of Cas activity in cells with single-base resolution. DISCOVER-Seq works with multiple guide RNA formats and types of Cas enzymes, allowing characterization of new editing tools.

Off-targets can be identified in cell lines and patient-derived induced pluripotent stem cells and during adenoviral editing of mice, paving the way for in situ off-target discovery within individual patient genotypes during therapeutic genome editing.

--- https://www.biorxiv.org/content/10.1101/2023.09.17.558101.full Decoding genetic architecture of dog complex traits by constructing fine-scale genomic ancestry of admixture Shilei Zhao, Guodong Wang, Yanhu Liu, Ya-Ping Zhang, Hua Chen 2023-09-17 2023-09-17 [("doi","10.1101/2023.09.17.558101")] genetics/heritable/dog

Domestic animals and plants exhibit remarkable phenotypic diversity in terms of morphology, behavior, and physiology, which can be attributed to the complex interbreeding process of various breeds and artificial selection. Here we develop a method that can efficiently construct fine-scale interbreeding history of local segments along the genome. Since ancestral breeds usually exhibit diverse phenotypes, the method provides a valuable approach for unraveling the genetic architecture of complex traits in admixed breeds.

Simulated data demonstrates that the method performs well, even in scenarios involving complex interbreeding with up to 19 ancestral breeds. The method is applied to analyze 3 mixed dog breeds, Irish Wolfhound, Giant Schnauzer, and Miniature Schnauzer, representing instances of body-size enlargement and miniaturization.

Numerous novel ancestor breed-specific genes determining body size are identified, including FGFR2, WDR11, and FARS2. We also validate genes reported in previous GWAS or genomic sweep scans, such as LCORL, STC2, NPR2, and FGF4.

These findings highlight the validity of the method as a valuable tool for investigating the genetic basis underlying ancestry-specific traits in domestic animals and plants with complex interbreeding histories.

--- /doc/genetics/editing/2020-heide.pdf Human-specific ARHGAP11B increases size and folding of primate neocortex in the fetal marmoset Michael Heide, Christiane Haffner, Ayako Murayama, Yoko Kurotaki, Haruka Shinohara, Hideyuki Okano, Erika Sasaki, Wiel, B. Huttner 2020-07-31 2023-04-22 [("doi","10.1126/science.abb2401")] genetics/editing genetics/selection/natural/human psychology/neuroscience

Neocortex in the fetal brain: Along the path of human evolution, gene duplication and divergence produced a protein, ARHGAP11B, that is found in humans but not nonhuman primates or other mammals. Heide et al 2020 analyzed the effects of ARHGAP11B gene expression, under control of its own human-specific promoter, in the fetal marmoset (see the Perspective by Dehay & Kennedy 2020).

In the early weeks of fetal growth, the gene drove greater elaboration of neural progenitors and neocortex than is evident in the normal fetal marmoset. ARHGAP11B expression may be one cause of the more robust neocortex that characterizes the human brain.

The neocortex has expanded during mammalian evolution. Overexpression studies in developing mouse and ferret neocortex have implicated the human-specific gene ARHGAP11B in neocortical expansion, but the relevance for primate evolution has been unclear.

Here, we provide functional evidence that ARHGAP11B causes expansion of the primate neocortex. ARHGAP11B expressed in fetal neocortex of the common marmoset under control of the gene’s own (human) promoter increased the numbers of basal radial glia progenitors in the marmoset outer subventricular zone, increased the numbers of upper-layer neurons, enlarged the neocortex, and induced its folding.

Thus, the human-specific ARHGAP11B drives changes in development in the nonhuman primate marmoset that reflect the changes in evolution that characterize human neocortical development.

--- https://www.atlasobscura.com/articles/why-every-kid-in-america-learns-to-play-the-recorder 2023-04-22 music --- https://arxiv.org/abs/1911.00172 Generalization through Memorization: Nearest Neighbor Language Models Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, Mike Lewis 2019-11-01 2023-04-22 [("doi","10.48550/arXiv.1911.00172")] ai/nn/retrieval ai/nn/transformer/attention ai/nn/transformer/gpt

We introduce kNN-LMs, which extend a pre-trained neural language model (LM) by linearly interpolating it with a k-nearest neighbors (kNN) model. The nearest neighbors are computed according to distance in the pre-trained LM embedding space, and can be drawn from any text collection, including the original LM training data.

Applying this augmentation to a strong Wikitext-103 LM, with neighbors drawn from the original training set, our kNN-LM achieves a new state-of-the-art perplexity of 15.79—a 2.9 point improvement with no additional training.

We also show that this approach has implications for efficiently scaling up to larger training sets and allows for effective domain adaptation, by simply varying the nearest neighbor datastore, again without further training.

Qualitatively, the model is particularly helpful in predicting rare patterns, such as factual knowledge. Together, these results strongly suggest that learning similarity between sequences of text is easier than predicting the next word, and that nearest neighbor search is an effective approach for language modeling in the long tail.

--- https://arxiv.org/abs/1709.07432 Dynamic Evaluation of Neural Sequence Models Ben Krause, Emmanuel Kahembwe, Iain Murray, Steve Renals 2017-09-21 2023-04-22 [("doi","10.48550/arXiv.1709.07432")] ai/nn/dynamic-evaluation ai/nn/rnn

We present methodology for using dynamic evaluation to improve neural sequence models. RNN Models are adapted to recent history via a gradient descent based mechanism, causing them to assign higher probabilities to re-occurring sequential patterns.

Dynamic evaluation outperforms existing adaptation approaches in our comparisons. Dynamic evaluation improves the state-of-the-art word-level perplexities on the Penn Treebank and WikiText-2 datasets to 51.1 and 44.3 respectively, and the state-of-the-art character-level cross-entropies on the text8 and Hutter Prize datasets to 1.19 bits/char and 1.08 bits/char respectively.

Figure 2: Average losses in bits/char of dynamic evaluation and static evaluation plotted against number of characters processed; on sequences from the Hutter Prize test set (left) and European Parliament dataset in Spanish (right), averaged over 500 trials for each. Losses at each data point are averaged over sequence segments of length 100, and are not cumulative. Note the different y-axis scales in the two plots.

…7.3 Time-Scales Of Dynamic Evaluation:

…The Spanish experiments measure how dynamic evaluation handles large distribution shifts between training and test time, as Hutter Prize contains very little Spanish. We used the first 5 million characters of the Spanish European Parliament data in place of the Hutter Prize test set. The Spanish experiments used the same base model and dynamic evaluation settings as Hutter Prize. Plots of the average cross-entropy errors against the number of Spanish characters sequenced are given in Figure 2b.

On both datasets, dynamic evaluation gave a very noticeable advantage after a few hundred characters. For Spanish this advantage continued to grow as more of the sequence was processed, whereas for Hutter, this advantage was maximized after viewing around 2–3k characters. The advantage of dynamic evaluation was also much greater on Spanish sequences than Hutter sequences.

We also drew 300 character conditional samples from the static and dynamic versions of our model after viewing 10k characters of Spanish. For the dynamic model, we continued to apply dynamic evaluation during sampling as well, by the process described in §3. The conditional samples are given in the Appendix. The static samples quickly switched to English that resembled Hutter Prize data. The dynamic model generated data with some Spanish words and a number of made up words with characteristics of Spanish words for the entirety of the sample. This is an example of the kinds of features that dynamic evaluation was able to learn to model on the fly.

--- https://en.wikipedia.org/wiki/Online_machine_learning Online machine learning 2023-04-22 ai/nn/dynamic-evaluation --- https://arxiv.org/abs/1704.02798#deepmind Bayesian Recurrent Neural Networks Meire Fortunato, Charles Blundell, Oriol Vinyals 2017-04-10 2023-04-22 [("doi","10.48550/arXiv.1704.02798")] ai/nn/dynamic-evaluation ai/nn/rnn statistics/bayes

In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks (RNNs). Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularization at only a small extra computational cost during training, also reducing the amount of parameters by 80%.

Secondly, we demonstrate how a novel kind of posterior approximation yields further improvements to the performance of Bayesian RNNs. We incorporate local gradient information into the approximate posterior to sharpen it around the current batch statistics.

We show how this technique is not exclusive to recurrent neural networks and can be applied more widely to train Bayesian neural networks. We also empirically demonstrate how Bayesian RNNs are superior to traditional RNNs on a language modeling benchmark and an image captioning task, as well as showing how each of these methods improve our model over a variety of other schemes for training them.

We also introduce a new benchmark for studying uncertainty for language models so future methods can be easily compared.

--- https://arxiv.org/abs/1703.08864 Learning Simpler Language Models with the Differential State Framework Alexander G. Ororbia II, Tomas Mikolov, David Reitter 2017-03-26 2023-04-23 [("doi","10.48550/arXiv.1703.08864")] ai/nn/dynamic-evaluation ai/nn/rnn

Learning useful information across long time lags is a critical and difficult problem for temporal neural models in tasks such as language modeling. Existing architectures that address the issue are often complex and costly to train. The Differential State Framework (DSF) is a simple and high-performing design that unifies previously introduced gated neural models.

DSF models maintain longer-term memory by learning to interpolate between a fast-changing data-driven representation and a slowly changing, implicitly stable state. This requires hardly any more parameters than a classical, simple recurrent network.

Within the DSF framework, a new architecture is presented, the Delta-RNN. In language modeling at the word and character levels, the Delta-RNN outperforms popular complex architectures, such as the Long Short Term Memory (LSTM) and the Gated Recurrent Unit (GRU), and, when regularized, performs comparably to several state-of-the-art baselines.

At the subword level, the Delta-RNN’s performance is comparable to that of complex gated architectures.

--- https://arxiv.org/abs/1911.12391#nvidia SimpleBooks: Long-term dependency book dataset with simplified English vocabulary for word-level language modeling Huyen Nguyen 2019-11-27 2023-04-23 [("doi","10.48550/arXiv.1911.12391")] ai/dataset ai/nn/rnn ai/nn/transformer

With language modeling becoming the popular base task for unsupervised representation learning in Natural Language Processing (NLP), it is important to come up with new architectures and techniques for faster and better training of language models.

However, due to a peculiarity of languages—the larger the dataset, the higher the average number of times a word appears in that dataset—datasets of different sizes have very different properties. Architectures performing well on small datasets might not perform well on larger ones. For example, LSTM models perform well on WikiText-2 but poorly on WikiText-103, while Transformer models perform well on WikiText-103 but not on WikiText-2.

For setups like architectural search, this is a challenge since it is prohibitively costly to run a search on the full dataset but it is not indicative to experiment on smaller ones. In this paper, we introduce SimpleBooks, a small dataset with the average word frequency as high as that of much larger ones.

Created from 1,573 Project Gutenberg books with the highest ratio of word-level book length to vocabulary size, SimpleBooks contains 92M word-level tokens, on par with WikiText-103 (103M tokens), but has the vocabulary of 98K, a third of WikiText-103’s. SimpleBooks can be downloaded from here.

--- https://arxiv.org/abs/2112.08653 Reconsidering the Past: Optimizing Hidden States in Language Models Davis Yoshida, Kevin Gimpel 2021-12-16 2023-04-23 [("doi","10.48550/arXiv.2112.08653")] ai/nn/dynamic-evaluation

We present Hidden-State Optimization (HSO), a gradient-based method for improving the performance of transformer language models at inference time.

Similar to dynamic evaluation (Krause et al 2018), HSO computes the gradient of the log-probability the language model assigns to an evaluation text, but uses it to update the cached hidden states rather than the model parameters.

We test HSO with pretrained Transformer-XL and GPT-2 language models, finding improvement on the WikiText-103 and PG-19 datasets in terms of perplexity, especially when evaluating a model outside of its training distribution.

We also demonstrate downstream applicability by showing gains in the recently developed prompt-based few-shot evaluation setting, again with no extra parameters or training data.

--- https://arxiv.org/abs/2305.18466 TTT-NN: Test-Time Training on Nearest Neighbors for Large Language Models Moritz Hardt, Yu Sun 2023-05-29 2023-05-29 [("doi","10.48550/arXiv.2305.18466")] ai/nn/dynamic-evaluation ai/nn/retrieval ai/nn/transformer/attention

[code; cf. AlphaZero retrieval, training on doc clusters for better context use] Many recent efforts aim to augment language models with relevant information retrieved from a database at test time. We avoid the need for prompt engineering by directly fine-tuning the model on data retrieved at test time using its standard training setup. For this purpose, we build a large-scale distributed nearest neighbor index based on text embeddings of the Pile dataset. Given a query to a language model, our system retrieves the neighbors of the query and fine-tunes the model on the text data corresponding to those neighbors.

Surprisingly, retrieving and training on as few as 20 neighbors, each for only one gradient iteration, drastically improves performance across more than 20 language modeling tasks in the Pile benchmark.

For example, test-time training narrows the performance gap between a small GPT-2 model and a GPT-Neo model, more than 10× larger, that was specifically trained to convergence on the Pile. Sufficient index quality and size, however, are important.

Our work establishes a valuable first baseline for implementing test-time training in the context of large language models, opening the door to numerous promising research avenues.

[This is analogous to retrieval-augmented AlphaGo: dynamic evaluation : tree search, in terms of expert iteration on an imperfect model at runtime—equivalent to taking a single large gradient descent step onto the current problem (since self-attention ≈ gradient descent at runtime)]

Figure 5: Bits per byte results on all Pile tasks for a small GPT-2 model (117M parameters) before and after training on 50 nearest neighbors.

…We begin with an evaluation of test-time training on a small GPT-2 model with 117M parameters, the default HuggingFace gpt2 model from the transformers library. See Figure 5.

Figure 6 showcases the decrease in 3 different perplexity measures as we increase the number of nearest neighbors to train on. We can see that using 20 neighbors already achieves most of the decrease in perplexity, computationally costing less than half than using all the neighbors. Additional neighbors continue to decrease perplexities. Figure 6 focuses on the 6 largest Pile tasks in increasing order. These 6 tasks combined make up more than 70% of the Pile benchmark.

Figure 6: How different perplexities decrease on average with the number of neighbors on the 6 largest Pile tasks in ascending order Results for GPT-2 (117M parameters). Top: Bits per byte. Center: Byte perplexity. Bottom: Word perplexity.
Figure 7: Results for GPT-2-Large (774M parameters). Top: Before and after TTT-NN with 50 neighbors on the top 6 tasks. Bottom: How bits per byte decrease with additional neighbors
Figure 8: Results for GPT-Neo (1.3b parameters). Top: Before and after TTT-NN with 50 neighbors on the top 6 tasks. Bottom: How bits per byte decrease with each additional neighbor.
Figure 9: Training costs in seconds per neighbor on each task.

…Many methods use retrieved neighbors as additional context for the test instance, without test-time training. Unlike ours, models for those methods need to be trained also with retrieval and additional context. Due to the prohibitive cost of training with retrieval, we experiment with a baseline that simply uses the neighbors in-context at test time. Specifically, we concatenate the neighbors in increasing distance, and include as much of the concatenated text as possible into the context window of the test instance, in addition to its original context. This baseline improves performance in a few cases, eg. pile_enron, but does not help much overall.

…As long as the database contains some high-quality data from a particular underrepresented group, test-time training may benefit the group. Our experiments on the Pile give some support for this intuition, as the smaller tasks with less than 1% size, eg. pile_enron and pile_europarl, see much more substantial improvements than the larger ones, eg. pile_pile-cc and pile_pubmed-central, with more than 30% size together.

Test-time training might also help mitigate adversarial behaviors, including data poisoning attacks, by superimposing data at test time from a trusted data source. We hope to see more future work in this context.

--- https://arxiv.org/abs/1909.01792#deepmind Mogrifier LSTM Gábor Melis, Tomáš Kočiský, Phil Blunsom 2019-09-04 2023-04-23 [("doi","10.48550/arXiv.1909.01792")] ai/nn/dynamic-evaluation ai/nn/rnn

Many advances in Natural Language Processing have been based upon more expressive models for how inputs interact with the context in which they occur. Recurrent networks, which have enjoyed a modicum of success, still lack the generalization and systematicity ultimately required for modeling language.

In this work, we propose an extension [Mogrifier] to the venerable Long Short-Term Memory (LSTM) in the form of mutual gating of the current input and the previous output. This mechanism affords the modeling of a richer space of interactions between inputs and their context. Equivalently, our model can be viewed as making the transition function given by the LSTM context-dependent.

Experiments demonstrate markedly improved generalization on language modeling in the range of 3–4 perplexity points on Penn Treebank and Wikitext-2, and 0.01-0.05 bpc on 4 character-based datasets. We establish a new state-of-the-art on all datasets with the exception of enwik8, where we close a large gap between the LSTM and Transformer models.

…Of particular note is the comparison to Transformer-XL (Dai et al 2019), a state-of-the-art model on larger datasets such as Wikitext-103 and enwik8. On PTB, without dynamic evaluation, the Transformer-XL is on par with our LSTM baseline which puts it about 3.5 perplexity points behind the Mogrifier. On enwik8, also without dynamic evaluation, the Transformer-XL has a large, 0.09 bpc advantage at similar parameter budgets, but with dynamic evaluation this gap disappears. However, we did not test the Transformer-XL ourselves, so fair comparison is not possible due to differing experimental setups and the rather sparse result matrix for the Transformer-XL.

--- https://arxiv.org/abs/2212.02475#google FWL: Meta-Learning Fast Weight Language Models Kevin Clark, Kelvin Guu, Ming-Wei Chang, Panupong Pasupat, Geoffrey Hinton, Mohammad Norouzi 2022-12-05 2023-04-23 [("doi","10.48550/arXiv.2212.02475")] ai/nn/dynamic-evaluation ai/nn/transformer/attention/recurrent reinforcement-learning/meta-learning

[code] Dynamic evaluation of language models (LMs) adapts model parameters at test time using gradient information from previous tokens and substantially improves LM performance. However, it requires over 3× more compute than standard inference.

We present Fast Weight Layers (FWLs), a neural component that provides the benefits of dynamic evaluation much more efficiently by expressing gradient updates as linear attention. A key improvement over dynamic evaluation is that FWLs can also be applied at training time so the model learns to make good use of gradient updates.

FWLs can easily be added on top of existing transformer models, require relatively little extra compute or memory to run, and improve language modeling perplexity.

Figure 2: Per-token negative-log-likelihood improvements over the baseline. FWLs most improve LMs on long documents, rare tokens, and tokens repeated multiple times in the text. As the WikiText-103 dev set is small, we use a sparse transformer trained on 2⁄3 of the train set and evaluated on the other 1⁄3 to produce more robust results.

…Fast Weight Layers provide the benefits of dynamic evaluation at a fraction of the compute cost and memory usage. They can easily be added to existing language models and yield strong results on language modeling benchmarks. Applying FWLs to few-shot learning tasks is one interesting future direction: doing one (or perhaps a small number) of gradient updates on few-shot examples might offer a nice middle ground in-context learning where the model parameters are fixed and full fine-tuning. Indeed, Yoshida & Gimpel 2021 show that ‘hidden state optimization’, a method closely related to dynamic evaluation, can improve few-shot LM performance.

…5. Limitations: FWLs can be viewed as an inductive bias encouraging the model to adapt to previous tokens. As an inductive bias, their value may be limited for larger models trained on larger datasets. While our experiments show FWLs improve models with hundreds of millions of parameters, initial experiments with bigger models suggest that their benefit decreases as models get larger, and we think it is unlikely that an add-on like a FWL will substantially improve models of the scale of GPT-3 (Brown et al 2020). Furthermore, we have shown that using FWLs at training time makes them more effective, but this has a disadvantage as well. FWLs can’t be directly applied to already-trained transformer language models the way dynamic evaluation can: some fine-tuning with the fast weight layer added is required. Lastly, while we have shown FWLs improve LM perplexity, we have not evaluated FWLs at other text generation tasks, which we leave for future work.

--- https://benkrause.github.io/blog/human-level-text-prediction/ 2023-04-23 ai/nn/dynamic-evaluation cs/algorithm --- https://arxiv.org/abs/2307.05014 Test-Time Training on Video Streams Renhao Wang, Yu Sun, Yossi Gandelsman, Xinlei Chen, Alexei A. Efros, Xiaolong Wang 2023-07-11 2023-07-11 [("doi","10.48550/arXiv.2307.05014")] ai/dataset ai/nn/dynamic-evaluation ai/nn/vae/mae ai/video/analysis

[code, data, homepage] Prior work has established test-time training (TTT) as a general framework to further improve a trained model at test time. Before making a prediction on each test instance, the model is trained on the same instance using a self-supervised task, such as image reconstruction with masked autoencoders.

We extend TTT to the streaming setting, where multiple test instances—video frames in our case—arrive in temporal order. Our extension is online TTT: The current model is initialized from the previous model, then trained on the current frame and a small window of frames immediately before.

Online TTT outperforms the fixed-model baseline for 4 tasks, on 3 real-world datasets. The relative improvement is 45% and 66% for instance and panoptic segmentation.

Surprisingly, online TTT also outperforms its offline variant that accesses more information, training on all frames from the entire test video regardless of temporal order. This differs from previous findings using synthetic videos. We conceptualize locality as the advantage of online over offline TTT.

We analyze the role of locality with ablations and a theory based on bias-variance trade-off.

--- https://news.ycombinator.com/item?id=37564768 2023-04-23 ai/nn/transformer/gpt/palm --- https://daily.jstor.org/the-daguerreotypes-famous-why-not-the-calotype/ 2023-04-23 economics/copyright --- https://arxiv.org/abs/2309.09117#facebook Contrastive Decoding Improves Reasoning in Large Language Models Sean O’Brien, Mike Lewis 2023-09-17 2023-09-17 [("doi","10.48550/arXiv.2309.09117")] ai/nn/sampling ai/nn/transformer/gpt/inner-monologue

We demonstrate that Contrastive Decoding—a simple, computationally light, and training-free text generation method proposed by Li et al 2022—achieves large out-of-the-box improvements over greedy decoding on a variety of reasoning tasks. Originally shown to improve the perceived quality of long-form text generation, Contrastive Decoding searches for strings that maximize a weighted difference in likelihood between strong and weak models.

We show that Contrastive Decoding leads LLaMA-65B to outperform LLaMA-2, GPT-3.5 and PaLM 2-L on the HellaSwag commonsense reasoning benchmark, and to outperform LLaMA-2, GPT-3.5 and PaLM-540B on the GSM8K math word reasoning benchmark, in addition to improvements on a collection of other tasks.

Analysis suggests that Contrastive Decoding improves over existing methods by preventing some abstract reasoning errors, as well as by avoiding simpler modes such as copying sections of the input during chain-of-thought.

Overall, Contrastive Decoding outperforms nucleus sampling for long-form generation and greedy decoding for reasoning tasks, making it a powerful general purpose method for generating text from language models.

--- https://arxiv.org/abs/2307.09288#facebook LLaMA-2: Open Foundation and Fine-Tuned Chat Models Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom 2023-07-18 2023-07-18 [("doi","10.48550/arXiv.2307.09288")] ai/nn/transformer/gpt/instruction-tuning ai/scaling reinforcement-learning/preference-learning

In this work, we develop and release LLaMA-2 [LLaMA-1], a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called LLaMA-2-Chat, are optimized for dialogue use cases.

Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models.

We provide a detailed description of our approach to fine-tuning and safety improvements of LLaMA-2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.

--- https://en.wikipedia.org/wiki/Zez_Confrey Zez Confrey 2023-04-24 cat/psychology music --- https://en.wikipedia.org/wiki/Cat_fugue Cat fugue 2023-04-24 cat/psychology music --- https://www.biorxiv.org/content/10.1101/2023.09.17.558161.full Controlled experiment finds no detectable citation bump from Twitter promotion Trevor Branch, Isabelle M. Cté, Solomon R. David, Joshua A. Drew, Michelle LaRue, Melissa C. Márquez, E. Chris M. Parsons, D. Rabaiotti, David Shiffman, David A. Steen, Alexander L. Wild 2023-09-18 2023-09-18 [("doi","10.1101/2023.09.17.558161")] sociology/technology

Multiple studies across a variety of scientific disciplines have shown that the number of times that a paper is shared on Twitter is correlated with the number of citations that paper receives. However, these studies were not designed to answer whether tweeting about scientific papers causes an increase in citations, or whether they were simply highlighting that some papers have higher relevance, importance or quality and are therefore both tweeted about more and cited more.

The authors of this study are leading science communicators on Twitter from several life science disciplines, with substantially higher follower counts than the average scientist, making us uniquely placed to address this question. We conducted a three-year-long controlled experiment, randomly selecting 5 articles published in the same month and journal, and randomly tweeting one while retaining the others as controls.

This process was repeated for 10 articles from each of 11 journals, recording Altmetric scores, number of tweets, and citation counts before and after tweeting.

Randomization tests revealed that tweeted articles were downloaded 2.6–3.9× more often than controls immediately after tweeting, and retained statistically-significantly higher Altmetric scores (+81%) and number of tweets (+105%) 3 years after tweeting. However, while some tweeted papers were cited more than their respective control papers published in the same journal and month, the overall increase in citation counts after 3 years (+7% for Web of Science and +12% for Google Scholar)

was not statistically-significant (p > 0.15). Therefore while discussing science on social media has many professional and societal benefits (and has been a lot of fun), increasing the citation rate of a scientist’s papers is likely not among them.

[Typical p-value fallacy. +7% is a meaningful & practically-important effect for an incredibly cheap & easy thing to do; that they fail to reject the null does not mean they have proven the null, because they are underpowered to detect effects too small to matter—their conclusion that "increasing citation rate...is likely not [a benefit]" is outright contradicted by their result of +7%!]

--- https://x.com/UnseenOps/status/1703882913387847849 2023-04-24 design technology --- https://observablehq.com/@rreusser/half-precision-floating-point-visualized 2023-04-24 ai/nn/sparsity/low-precision --- https://x.com/nabeelqu/status/1703967073150304728 2023-04-24 ai/nn/transformer/gpt/3/nonfiction reinforcement-learning/chess --- https://x.com/grantslatton/status/1703913578036904431 2023-04-24 ai/nn/transformer/gpt/3/nonfiction reinforcement-learning/chess --- https://www.nature.com/articles/d41586-023-02901-1 2023-04-24 genetics/gametogenesis --- https://arxiv.org/abs/2309.09968 Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees Alexia Jolicoeur-Martineau, Kilian Fatras, Tal Kachman 2023-09-18 2023-09-18 [("doi","10.48550/arXiv.2309.09968")] ai/nn/diffusion ai/tabular

[blog] Tabular data is hard to acquire and is subject to missing values. This paper proposes a novel approach to generate and impute mixed-type (continuous and categorical) tabular data using score-based diffusion and conditional flow matching.

Contrary to previous work that relies on neural networks as function approximators, we instead use XGBoost, a popular Gradient-Boosted Tree (GBT) method. In addition to being elegant, we empirically show on various datasets that our method (1) generates highly realistic synthetic data when the training dataset is either clean or tainted by missing data and (2) generates diverse plausible data imputations.

Our method often outperforms deep-learning generation methods and can trained in parallel using CPUs without the need for a GPU.

To make it easily accessible, we release our code through a Python library on PyPI and an R package on CRAN.

--- https://eris.codeberg/ 2023-04-25 cs/cryptography --- https://arxiv.org/abs/2106.00573#naver One4all User Representation for Recommender Systems in E-commerce Kyuyong Shin, Hanock Kwak, Kyung-Min Kim, Minkyu Kim, Young-Jin Park, Jisu Jeong, Seungjae Jung 2021-05-24 2023-04-25 [("doi","10.48550/arXiv.2106.00573")] ai/nn/fully-connected ai/nn/transformer ai/scaling

General-purpose representation learning through large-scale pre-training has shown promising results in the various machine learning fields. For an e-commerce domain, the objective of general-purpose, ie. one for all, representations would be efficient applications for extensive downstream tasks such as user profiling, targeting, and recommendation tasks.

In this paper, we systematically compare the generalizability of two learning strategies, ie. transfer learning through the proposed model, ShopperBERT, vs. learning from scratch. ShopperBERT learns 9 pretext tasks with 79.2M parameters from 0.8B user behaviors collected over two years to produce user embeddings.

As a result, the MLPs that employ our embedding method outperform more complex models trained from scratch for 5⁄6 tasks. Specifically, the pre-trained embeddings have superiority over the task-specific supervised features and the strong baselines, which learn the auxiliary dataset for the cold-start problem.

We also show the computational efficiency and embedding visualization of the pre-trained features.

--- https://arxiv.org/abs/1708.05031 Neural Collaborative Filtering Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua 2017-08-16 2023-04-25 [("doi","10.48550/arXiv.1708.05031")] ai/nn/fully-connected ai/tabular

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny.

In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation—collaborative filtering—on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics.

When it comes to model the key factor in collaborative filtering—the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering.

NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modeling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function.

Extensive experiments on two real-world datasets show improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.

--- https://www.anthropic.com/index/anthropics-responsible-scaling-policy 2023-04-25 reinforcement-learning/safe reinforcement-learning/scaling --- https://boilingsteam.com/google-stadia-leaked-documents-explain-its-failure/ 2023-04-25 technology/google --- /doc/cs/security/2011-dullien.pdf Exploitation and State Machines: Programming the ‘Weird Machine’ Revisited Halvar Flake 2011-01-01 2023-04-25 cs/computable cs/security --- /doc/darknet-market/hydra/2021-chainanalysis.pdf The 2021 Crypto Crime Report: Everything you need to know about ransomware, darknet markets, and more Chainanalysis 2021-02-16 2023-04-25 bitcoin darknet-market/hydra --- https://www.reuters.com/investigates/special-report/fintech-crypto-binance-dirtymoney/ 2023-04-25 bitcoin darknet-market/hydra --- https://www.justice.gov/usao-edny/pr/founder-and-majority-owner-bitzlato-cryptocurrency-exchange-charged-unlicensed-money 2023-04-25 bitcoin darknet-market/hydra --- https://en.wikipedia.org/wiki/Russian_darknet_market_conflict Russian darknet market conflict 2023-04-25 darknet-market/hydra --- https://en.wikipedia.org/wiki/Dead-drop Dead-drop 2023-04-25 darknet-market/hydra --- /doc/darknet-market/hydra/2017-zvonka-datacollectionandanalysisfromrussiananonymousmarketplaces-ramp.docx 2017 2023-04-26 darknet-market/hydra --- https://www.wired.com/2014/11/oldest-drug-market-is-russian/ How a Russian Dark Web Drug Market Outlived the Silk Road (And Silk Road 2) 2023-04-26 darknet-market/hydra --- https://www.justice.gov/opa/press-release/file/1490906/dl 2023-04-26 darknet-market/hydra --- https://www.bloomberg.com/news/articles/2022-04-05/german-police-shutdown-1-3-billion-illegal-darknet-firm 2023-04-26 darknet-market/hydra --- https://www.wsj.com/articles/russian-darknet-market-tied-to-ransomware-is-shut-down-11649196069 2023-04-26 darknet-market/hydra --- https://home.treasury.gov/news/press-releases/jy0701 2023-04-26 darknet-market/hydra --- https://www.bka.de/DE/Presse/Listenseite_Pressemitteilungen/2022/Presse2022/220405_PM_IllegalerDarknetMarktplatz.html 2023-04-26 darknet-market/hydra --- https://lenta.ru/articles/2022/05/04/hydra/ 2023-04-26 darknet-market/hydra --- https://www.chainalysis.com/blog/how-darknet-markets-fought-for-users-in-wake-of-hydra-collapse-2022/ 2023-04-26 darknet-market/hydra --- https://www.trmlabs.com/post/eight-months-after-the-hydra-shutdown-new-russian-language-darknet-markets-fill-the-void 2023-04-26 darknet-market/hydra --- https://novayagazeta.eu/articles/2023/02/27/kraken-protiv-vsekh 2023-04-27 darknet-market/hydra --- /doc/darknet-market/silk-road/1/2022-christin.pdf#page=3 Measuring and Analyzing Online Anonymous (‘Darknet’) Marketplaces § pg3 Nicolas Christin 2022-02-01 2023-04-27 darknet-market/agora darknet-market/alphabay darknet-market/blackmarket-reloaded darknet-market/evolution darknet-market/hydra darknet-market/silk-road/1 darknet-market/silk-road/2

We present the results of the project “Measuring and Analyzing Online Anonymous (`Darknet’) Marketplaces”, carried out at Carnegie Mellon University at the behest of the Department of Homeland Security, Science & Technology Directorate. This project was a follow up to an earlier project (“A Queryable Platform for Online Crime Repositories”).

We continued to avail to other researchers data from 12 dark web marketplaces, corresponding to over 22,288 vendors, 348,400 items, and 5,826,115 transactions. The data was made available through (1) a publicly available website (for anonymized data), and (2) a set of databases provisioned through the IMPACT portal (for anonymized and de-anonymized data).

For the duration of the project at hand, the IMPACT portal served an additional 19 distinct requests for data from 8 academic institutions (academic, industry, government) in the US, the Netherlands, the UK, and Singapore. Combined with the previous contract, the project has served 69 requests from 25 institutions over 6 countries (US, Japan, Singapore, Netherlands, UK, and Australia).

For the duration of the present contract, serving this data has led in particular to the publication of a paper—by third parties—based on our data, at WEIS 2021. In addition, our research has led to the development of a paper (under revision at the time of writing), to be submitted to a leading computer security conference in 2022.

--- /doc/psychology/personality/2023-kerry.pdf Despite popular intuition, positive world beliefs poorly reflect several objective indicators of privilege, including wealth, health, sex, and neighborhood safety Nicholas Kerry, K. C. White, Mark L. O’Brien, Laura M. Perry, Jeremy D. W. Clifton 2023-08-24 2023-08-24 [("doi","10.1111/jopy.12877")] psychology/cognitive-bias psychology/personality sociology

Objectives: We tested whether generalized beliefs that the world is safe, abundant, pleasurable, and progressing (termed “primal world beliefs”) are associated with several objective measures of privilege.

Method: 3 studies (n = 16,547) tested multiple relationships between indicators of privilege—including socioeconomic status, health, sex, and neighborhood safety—and relevant world beliefs, as well as researchers and laypeople’s expectations of these relationships. Samples were mostly from the USA and included general population samples (Study 2) as well as focused samples of academic researchers (Study 1) and people who had experienced serious illness or trauma (Study 3).

Results: Studies 1–2 found mostly negligible relationships between world beliefs and indicators of privilege, which were invariably lower than researcher predictions (eg. instead of the expected r = 0.33, neighborhood affluence correlated with Abundant world belief at r = 0.01). Study 3 found that people who had experienced serious illness (cancer, cystic fibrosis) only showed modest differences in beliefs from controls.

Conclusions: While results do not preclude that some individuals’ beliefs were meaningfully affected by life events, they imply that such changes are smaller or less uniform than widely believed and that knowing a person’s demographic background may tell us relatively little about their beliefs (and vice versa).

--- https://www.nytimes.com/2023/09/20/magazine/animal-communication.html 2023-04-27 psychology/animal psychology/linguistics --- https://arxiv.org/abs/2306.17806#eleutherai Stay on topic with Classifier-Free Guidance Guillaume Sanchez, Honglu Fan, Alexander Spangher, Elad Levi, Pawan Sasanka Ammanamanchi, Stella Biderman 2023-06-30 2023-06-30 [("doi","10.48550/arXiv.2306.17806")] ai/nn/sampling ai/nn/transformer/gpt/2

Classifier-Free Guidance (CFG) has recently emerged in text-to-image generation as a lightweight technique to encourage prompt-adherence in generations.

In this work, we demonstrate that CFG can be used broadly as an inference-time technique in pure language modeling. We show that CFG (1) improves the performance of Pythia, GPT-2 and LLaMA-1-family models across an array of tasks: Q&A, reasoning, code generation, and machine translation, achieving SOTA on LAMBADA with LLaMA-7B over PaLM-540B; (2) brings improvements equivalent to a model with twice the parameter-count; (3) can stack alongside other inference-time methods like Chain-of-Thought and Self-Consistency, yielding further improvements in difficult tasks; (4) can be used to increase the faithfulness and coherence of assistants in challenging form-driven and content-driven prompts: in a human evaluation we show a 75% preference for GPT-4All using CFG over baseline.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5816198/ Alarm calls evoke a visual search image of a predator in birds Toshitaka N. Suzuki 2018 2023-04-27 [("doi","10.1073/pnas.1718884115")] psychology/animal psychology/linguistics

One of the core features of human speech is that words cause listeners to retrieve corresponding visual mental images. However, whether vocalizations similarly evoke mental images in animal communication systems is surprisingly unknown.

Japanese tits (Parus minor) produce specific alarm calls when and only when encountering a predatory snake. Here, I show that simply hearing these calls causes tits to become more visually perceptive to objects resembling snakes.

During playback of snake-specific alarm calls, tits approach a wooden stick being moved in a snake-like fashion. However, tits do not respond to the same stick when hearing other call types or if the stick’s movement is dissimilar to that of a snake.

Thus, before detecting a real snake, tits retrieve its visual image from snake-specific alarm calls and use this to search out snakes. This study provides evidence for a call-evoked visual search image in a nonhuman animal, offering a paradigm to explore the cognitive basis for animal vocal communication in the wild.

NYT: …But critics objected that the calls might not have any properties of language at all. Instead of being intentional messages to communicate meaning to others, the calls might be involuntary, emotion-driven sounds, like the cry of a hungry baby. Such involuntary expressions can transmit rich information to listeners, but unlike words and sentences, they don’t allow for discussion of things separated by time and space. The barks of a vervet in the throes of leopard-induced terror could alert other vervets to the presence of a leopard—but couldn’t provide any way to talk about, say, “the really smelly leopard who showed up at the ravine yesterday morning.”

Toshitaka Suzuki, an ethologist at the University of Tokyo who describes himself as an animal linguist, struck upon a method to disambiguate intentional calls from involuntary ones while soaking in a bath one day. When we spoke over Zoom, he showed me an image of a fluffy cloud. “If you hear the word ‘dog’, you might see a dog”, he pointed out, as I gazed at the white mass. “If you hear the word ‘cat’, you might see a cat.” That, he said, marks the difference between a word and a sound. “Words influence how we see objects”, he said. “Sounds do not.” Using playback studies, Suzuki determined that Japanese tits, songbirds that live in East Asian forests and that he has studied for more than 15 years, emit a special vocalization when they encounter snakes. When other Japanese tits heard a recording of the vocalization, which Suzuki dubbed the “jar jar” call, they searched the ground, as if looking for a snake. To determine whether “jar jar” meant “snake” in Japanese tit, he added another element to his experiments: an 8-inch stick, which he dragged along the surface of a tree using hidden strings. Usually, Suzuki found, the birds ignored the stick. It was, by his analogy, a passing cloud. But then he played a recording of the “jar jar” call. In that case, the stick seemed to take on new importance: The birds approached the stick, as if examining whether it was, in fact, a snake. Like a word, the “jar jar” call had changed their perception.

--- https://arxiv.org/abs/2102.04110 Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration Betty van Aken, Jens-Michalis Papaioannou, Manuel Mayrdorfer, Klemens Budde, Felix A. Gers, Alexander Löser 2021-02-08 2023-04-27 [("doi","10.48550/arXiv.2102.04110")] ai/nn/transformer/gpt biology

Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities. We simulate patients at admission time, when decision support can be especially valuable, and contribute a novel admission to discharge task with 4 common outcome prediction targets: Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction.

The ideal system should infer outcomes based on symptoms, pre-conditions and risk factors of a patient. We evaluate the effectiveness of language models to handle this scenario and propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.

We further present a simple method to incorporate ICD code hierarchy into the models. We show that our approach improves performance on the outcome tasks against several baselines.

A detailed analysis reveals further strengths of the model, including transferability, but also weaknesses such as handling of vital values and inconsistencies in the underlying data.

--- https://arxiv.org/abs/2210.15097 Contrastive Decoding: Open-ended Text Generation as Optimization Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, Mike Lewis 2022-10-27 2023-04-27 [("doi","10.48550/arXiv.2210.15097")] ai/nn/sampling ai/nn/transformer/gpt/2

[cf. "surprisingly popular" heuristic] Given a language model (LM), maximum probability is a poor decoding objective for open-ended generation, because it produces short and repetitive text. On the other hand, sampling can often produce incoherent text that drifts from the original topics.

[cf. CFG] We propose contrastive decoding (CD), a reliable decoding approach that optimizes a contrastive objective subject to a plausibility constraint. The contrastive objective returns the difference between the likelihood under a large LM (called the expert, eg. OPT-13B) and a small LM (called the amateur, eg. OPT-125M), and the constraint ensures that the outputs are plausible.

CD is inspired by the fact that the failures of larger LMs (eg. repetition, incoherence) are even more prevalent in smaller LMs, and that this difference signals which texts should be preferred. CD requires zero additional training, and produces higher quality text than decoding from the larger LM alone.

It also works across model scales (OPT-13B and GPT-2-1.5B) and outperforms 4 strong decoding algorithms (eg. nucleus, top-k) in automatic and human evaluations across wikipedia, news and story domains.

--- /doc/statistics/probability/1977-waddington.pdf Stabilization in systems: Chreods and epigenetic landscapes G. H. Waddington 1977-04-01 2023-04-27 [("doi","10.1016/0016-3287(77)90006-4")] genetics/selection/natural statistics/probability --- /doc/statistics/decision/1977-waddington-toolsforthought.pdf Tools for Thought C. H. Waddington 1977-01-01 2023-04-27 design genetics/selection/natural sociology statistics/decision statistics/prediction --- https://openreview.net/forum?id=QC10RmRbZy9 Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent Ping-yeh Chiang, Renkun Ni, David Yu Miller, Arpit Bansal, Jonas Geiping, Micah Goldblum, Tom Goldstein 2023-03-02 2023-04-27 ai/nn/cnn ai/nn/fully-connected

We empirically showed that a random optimizer performs just as well as SGD

It is commonly believed that the implicit regularization of optimizers is needed for neural networks to generalize in the overparameterized regime. In this paper, we observe experimentally that this implicit regularization behavior is generic, i.e. it does not depend strongly on the choice of optimizer.

We demonstrate this by training neural networks using several gradient-free optimizers, which do not benefit from properties that are often attributed to gradient-based optimizers. This includes a guess-and-check optimizer that generates uniformly random parameter vectors until finding one that happens to achieve perfect train accuracy, and a zeroth-order Pattern Search optimizer that uses no gradient computations.

In the low sample and few-shot regimes, where zeroth order optimizers are most computationally tractable, we find that these non-gradient optimizers achieve test accuracy comparable to SGD.

The code to reproduce results can be found at Github.

[Keywords: generalization, regularization]

--- /doc/japan/history/1973-roberts-mitsuithreecenturiesofjapanesebusiness.pdf Mitsui: 3 Centuries of Japanese Business John G. Roberts 1973-01-01 2023-04-28 economics japan/history --- https://openai.com/index/dall-e-3/ 2023-04-28 ai/nn/transformer/gpt/dall-e/3 --- https://www.theinformation.com/articles/openai-hustles-to-beat-google-to-launch-multimodal-llm 2023-04-28 ai/nn/transformer/gpt/dall-e/3 --- https://maggieappleton.com/lm-sketchbook#daemons 2023-04-28 design --- https://www.acf.hhs.gov/opre/project/employment-retention-and-advancement-project-era-1998-2011 2023-04-28 economics sociology --- https://x.com/amasad/status/1704323196944527624 Amjad Masad 2023-04-28 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://x.com/GrantSlatton/status/1703913578036904431 Grant Slatton 2023-04-28 reinforcement-learning/chess --- https://x.com/RickLeeJames/status/1703033445545378163 Rick Lee James 2023-04-28 ai/nn/transformer/clip/sample --- https://research.google/blog/on-device-content-distillation-with-graph-neural-networks/ 2023-04-28 ai/nn/transformer economics/advertising --- https://x.com/conitzer/status/1656478578857369600 conitzer 2023-04-28 ai/nn/transformer/gpt/4/nonfiction philosophy/mind --- https://www.scientificamerican.com/article/its-time-to-engineer-the-sky/ 2023-04-28 technology/carbon-capture --- https://www.thebeliever.net/ghosts/ 2023-04-29 ai/nn/transformer/gpt/3/fiction --- https://www.wired.com/story/confessions-viral-ai-writer-chatgpt/ 2023-04-29 ai/nn/transformer/gpt/3/fiction reinforcement-learning/preference-learning/mode-collapse --- https://www.science.org/content/blog-post/target-based-drug-discovery-waste-time 2023-04-29 biology statistics/order --- /doc/ai/anime/2019-hati.pdf PaintsTorch: a User-Guided Anime Line Art Colorization Tool with Double Generator Conditional Adversarial Network Yliess Hati, Gregor Jouet, Francis Rousseaux, Clement Duhart 2019-12-01 2023-04-29 [("doi","10.1145/3359998.3369401")] ai/anime ai/nn/gan --- https://arxiv.org/abs/2212.03533#microsoft Text Embeddings by Weakly-Supervised Contrastive Pre-training Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei 2022-12-07 2023-04-29 [("doi","10.48550/arXiv.2212.03533")] ai/dataset ai/nn/retrieval ai/nn/transformer

[checkpoints; HF] This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a wide range of tasks.

The model is trained in a contrastive manner with weak supervision signals from our curated large-scale text pair dataset (called CCPairs).

E5 can be readily used as a general-purpose embedding model for any tasks requiring a single-vector representation of texts such as retrieval, clustering, and classification, achieving strong performance in both zero-shot and fine-tuned settings.

We conduct extensive evaluations on 56 datasets from the BEIR and MTEB benchmarks.

For zero-shot settings, E5 is the first model that outperforms the strong BM25 baseline on the BEIR retrieval benchmark without using any labeled data. When fine-tuned, E5 obtains the best results on the MTEB benchmark, beating existing embedding models with 40× more parameters.

--- https://x.com/willdepue/status/1705005718666158107 Will Depue 2023-04-29 ai/nn/transformer/gpt/dall-e/3 --- https://www.lesswrong.com/posts/dzjQLJA4GamTyny6f/update-to-dominant-assurance-contract-platform 2023-04-29 economics/mechanism-design --- /doc/psychology/personality/2020-vandenbergh.pdf Better Safe Than Sorry: A Common Signature of General Vulnerability for Psychopathology Omer Van den Bergh, Jos Brosschot, Hugo Critchley, Julian F. Thayer, Cristina Ottaviani 2020 2023-04-29 [("doi","10.1177/1745691620950690")] psychiatry/anxiety psychology/personality

Several labels, such as neuroticism, negative emotionality, and dispositional negativity, indicate a broad dimension of psychopathology. However, largely separate, often disorder-specific research lines have developed that focus on different cognitive and affective characteristics that are associated with this dimension, such as perseverative cognition (worry, rumination), reduced autobiographical memory specificity, compromised fear learning, and enhanced somatic-symptom reporting.

In this article, we present a theoretical perspective within a predictive-processing framework in which we trace these phenotypically different characteristics back to a common underlying “better-safe-than-sorry” processing strategy. This implies information processing that tends to be low in sensory-perceptual detail, which allows threat-related categorical priors to dominate conscious experience and for chronic uncertainty/surprise because of a stagnated error-reduction process. This common information-processing strategy has beneficial effects in the short term but important costs in the long term.

From this perspective, we suggest that the phenomenally distinct cognitive and affective psychopathological characteristics mentioned above represent the same basic processing heuristic of the brain and are only different in relation to the particular type of information involved (eg. in working memory, in autobiographical memory, in the external and internal world). Clinical implications of this view are discussed.

--- https://archive.org/details/masonbees00fabr/page/108/mode/2up 2023-04-29 --- https://www.amazon.com/Motherhood-Rescheduled-Frontier-Freezing-Women/dp/141656702X Motherhood, Rescheduled: The New Frontier of Egg Freezing and the Women Who Tried It Richards 2013 2023-04-29 genetics/selection/artificial --- https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/414784 Multivitamin use and risk of cancer and cardiovascular disease in the Women’s Health Initiative cohorts Neuhouser 2009 2023-04-29 biology --- http://anthropogenesis.kinshipstudies.org/blog/2012/09/05/a-high-coverage-of-the-denisovan-hominin/ A High Coverage of the Denisovan Hominin Meyer 2012 2023-04-30 genetics/sequencing --- https://www.amazon.com/Organic-Barley-Unbleached-Teabags-Caffeine/dp/B00JBF0K6O/ 1 X 100% Organic Barley Tea, 10g X 30 Unbleached Teabags, Sugar Free, Caffeine Free 2023-04-30 tea --- /doc/technology/1987-burich.pdf Henry Adams, the Second Law of Thermodynamics, and the Course of History Keith R. Burich 1987-07-01 2023-04-30 [("doi","10.2307/2709763")] economics science technology --- https://arxiv.org/abs/2309.12252 Parallelizing non-linear sequential models over the sequence length Yi Heng Lim, Qi Zhu, Joshua Selfridge, Muhammad Firmansyah Kasim 2023-09-21 2023-09-21 [("doi","10.48550/arXiv.2309.12252")] ai/nn/rnn

Sequential models, such as Recurrent Neural Networks and Neural Ordinary Differential Equations, have long suffered from slow training due to their inherent sequential nature. For many years this bottleneck has persisted, as many thought sequential models could not be parallelized.

We challenge this long-held belief with our parallel algorithm that accelerates GPU evaluation of sequential models by up to 3 orders of magnitude faster without compromising output accuracy. The algorithm does not need any special structure in the sequential models’ architecture, making it applicable to a wide range of architectures.

Using our method, training sequential models can be more than 10× faster than the common sequential method without any meaningful difference in the training results. Leveraging this accelerated training, we discovered the efficacy of the Gated Recurrent Unit in a long time series classification problem with 17k time samples.

By overcoming the training bottleneck, our work serves as the first step to unlock the potential of non-linear sequential models for long sequence problems.

--- https://arxiv.org/abs/2309.12307 LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models Yukang Chen, Shengju Qian, Haotian Tang, Xin Lai, Zhijian Liu, Song Han, Jiaya Jia 2023-09-21 2023-09-21 [("doi","10.48550/arXiv.2309.12307")] ai/dataset ai/nn/transformer/attention/sparsity

We present LongLoRA, an efficient fine-tuning approach that extends the context sizes of pre-trained large language models (LLMs), with limited computation cost. Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources. For example, training on the context length of 8,192 needs 16× computational costs in self-attention layers as that of 2048.

In this paper, we speed up the context extension of LLMs in two aspects. On the one hand, although dense global attention is needed during inference, fine-tuning the model can be effectively and efficiently done by sparse local attention. The proposed shift short attention effectively enables context extension, leading to non-trivial computation saving with similar performance to fine-tuning with vanilla attention. Particularly, it can be implemented with only two lines of code in training, while being optional in inference.

On the other hand, we revisit the parameter-efficient fine-tuning regime for context expansion. Notably, we find that LoRA for context extension works well under the premise of trainable embedding and normalization. LongLoRA demonstrates strong empirical results on various tasks on LLaMA2 models from 7B/13B to 70B. LongLoRA adopts LLaMA2 7B from 4k context to 100k, or LLaMA2 70B to 32k on a single 8× A100 machine.

LongLoRA extends models’ context while retaining their original architectures, and is compatible with most existing techniques, like FlashAttention-2. In addition, to make LongLoRA practical, we collect a dataset, LongQA, for supervised fine-tuning. It contains more than 3k long context question-answer pairs.

--- https://www.youtube.com/watch?v=rT6wVLEDC_w 2023-04-30 ai/nn/tokenization --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5008686/ Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity Emily S. Finn, Xilin Shen, Dustin Scheinost, Monica D. Rosenberg, Jessica Huang, Marvin M. Chun, Xenophon Papademetris, R. Todd Constable 2015 2023-04-30 [("doi","10.1038/nn.4135")] iq psychology/neuroscience

Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals.

Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a ‘fingerprint’ that can accurately identify subjects from a large group.

Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual’s connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but the frontoparietal network emerged as most distinctive.

Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior.

Results indicate the potential to draw inferences about single subjects on the basis of functional connectivity fMRI.

--- /doc/science/2023-lineweaver.pdf All objects and some questions Charles H. Lineweaver, Vihan M. Patel 2023-10-01 2023-10-01 [("doi","10.1119/5.0150209")] design/visualization science

We present an overview of the thermal history of the Universe and the sequence of objects (eg. protons, planets, and galaxies) that condensed out of the background as the Universe expanded and cooled.

We plot (1) the density and temperature of the Universe as a function of time and (2) the masses and sizes of all objects in the Universe.

These comprehensive pedagogical plots draw attention to the triangular regions forbidden by general relativity and quantum uncertainty and help navigate the relationship between gravity and quantum mechanics. How can we interpret their intersection at the smallest possible objects: Planck-mass black holes (“instantons”)? Does their Planck density and Planck temperature make them good candidates for the initial conditions of the Universe?

Our plot of all objects also seems to suggest that the Universe is a black hole. We explain how this depends on the unlikely assumption that our Universe is surrounded by zero density Minkowski space.

Figure 2: Masses, sizes, and relative densities of objects in our Universe. Time-dependent background densities are color-coded as in Figure 1. The diagonal white dashed isodensity lines correspond to the intersections in Figure 1 of the vertical isochron lines with the black density line. Gravity and quantum uncertainty prevent objects of a given mass from being smaller than their corresponding Schwarzschild radius [Equation 6] or Compton wavelength [Equation 7]. Schwarzschild black holes lie on the black diagonal line which is the lower boundary of the “forbidden by gravity” region. The masses and Compton wavelengths of the top quark (t), Higgs boson (Ho), proton (p), electron (e), and neutrinos (ν) are plotted along the Compton ( m ∝ r−1) diagonal line. Among these, the top quark has the smallest Compton wavelength, because it has the largest mass: 173GeVc−2⁠. The smallest possible object is a Planck-mass black hole indicated by the white dot labeled “instanton”20. Its mass and size are (m, r) = (mp, lp). The smallest observable (not yet evaporated) primordial black hole (PBH) that could have survived until today has the same size as a proton21. The large low-mass black dot in the SMBH (super massive black hole) range is the 4 × 106 solar mass black hole at the center of our galaxy22, while the more massive large black dot is Ton 618. The dashed horizontal line at m = mp emphasizes the orthogonal symmetry of black holes (m ∝ r ⁠) and particles (m ∝ r−1). Our Universe is represented by the “Hubble radius” and has a mass and size that places it on the black hole line, seemingly suggesting that our Universe is a massive, low-density black hole (§III A). The black rectangle containing neutron stars (“NS”), white dwarfs (“WD”), and brown dwarfs (“BD”) indicates the size of the parameter space plotted in Figure 3. Less comprehensive versions of this plot can be found20, 23, 24, 25, 26, 27, 28. See the supplementary material for the data used to make this plot56.
--- https://reasonalone.substack.com/p/notes-on-south-india 2023-04-30 economics --- https://x.com/mattshumer_/status/1705258197794070598 Matt Shumer 2023-04-30 ai/nn/transformer/gpt/4/fiction ai/text-style-transfer --- https://vgel.me/posts/handmade-transformer/ 2023-05-01 ai/nn/transformer/attention --- http://lists.urth.net/pipermail/urth-urth.net/2010-December/019108.html 2023-05-01 fiction/gene-wolfe/suzanne-delage --- http://lists.urth.net/pipermail/urth-urth.net/2010-December/019115.html 2023-05-01 fiction/gene-wolfe/suzanne-delage --- http://lists.urth.net/pipermail/urth-urth.net/2010-December/019132.html 2023-05-01 fiction/gene-wolfe/suzanne-delage --- http://lists.urth.net/pipermail/urth-urth.net/2010-December/019137.html 2023-05-01 fiction/gene-wolfe/suzanne-delage --- http://lists.urth.net/pipermail/urth-urth.net/2010-December/019144.html 2023-05-01 fiction/gene-wolfe/suzanne-delage --- http://lists.urth.net/pipermail/urth-urth.net/2011-October/023373.html 2023-05-01 fiction/gene-wolfe/suzanne-delage --- http://lists.urth.net/pipermail/urth-urth.net/2011-October/023378.html 2023-05-01 fiction/gene-wolfe/suzanne-delage --- http://lists.urth.net/pipermail/urth-urth.net/2011-October/023389.html 2023-05-01 fiction/gene-wolfe/suzanne-delage --- http://lists.urth.net/pipermail/urth-urth.net/2011-October/023390.html 2023-05-01 fiction/gene-wolfe/suzanne-delage --- http://lists.urth.net/pipermail/urth-urth.net/2011-October/023401.html 2023-05-01 fiction/gene-wolfe/suzanne-delage --- http://lists.urth.net/pipermail/urth-urth.net/2011-October/023576.html 2023-05-02 fiction/gene-wolfe/suzanne-delage --- http://lists.urth.net/pipermail/urth-urth.net/2014-June/054858.html 2023-05-02 fiction/gene-wolfe/suzanne-delage --- https://books.google.com/books?id=7y1jBrpx4loC 2023-05-02 fiction/gene-wolfe/suzanne-delage --- https://www.gutenberg.org/cache/epub/52165/pg52165-images.html#page-134 Transylvanian Superstitions Emily Gerard 1885 2023-05-02 fiction/gene-wolfe/suzanne-delage --- https://www.wolfewiki.com/pmwiki/pmwiki.php?n=Stories.SuzanneDelage 2023-05-02 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0002/0157.shtml 2023-05-02 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0002/0162.shtml 2023-05-02 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0010/0105.shtml 2023-05-02 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0010/0109.shtml 2023-05-02 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0010/0111.shtml 2023-05-02 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0010/0113.shtml 2023-05-02 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0010/0121.shtml 2023-05-03 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0010/0123.shtml 2023-05-03 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0010/0125.shtml 2023-05-03 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0010/0135.shtml 2023-05-03 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0010/0136.shtml 2023-05-03 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0010/0140.shtml 2023-05-03 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0010/0145.shtml 2023-05-03 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0011/0005.shtml 2023-05-03 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0011/0014.shtml 2023-05-03 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0011/0021.shtml 2023-05-03 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0011/0028.shtml 2023-05-04 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0011/0031.shtml 2023-05-04 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0011/0068.shtml 2023-05-04 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0015/0033.shtml 2023-05-04 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0017/0025.shtml 2023-05-04 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0030/0154.shtml 2023-05-04 fiction/gene-wolfe/suzanne-delage --- http://www.urth.net/urth/archives/v0030/0238.shtml 2023-05-04 fiction/gene-wolfe/suzanne-delage --- /doc/ai/nn/dynamic-evaluation/2017-krause-figure2-dynamicevaluationrnnpredictionofwikipediaandspanishtextshowingtesttimeadaptation.png 2017 2023-05-04 ai/nn/dynamic-evaluation ai/nn/rnn --- /doc/cat/psychology/drug/catnip/1968-hayashi-2.pdf Motor reflexes of cats to Actinidia polygama: Japan and to catnip USA T. Hayashi 1968 2023-05-04 cat/psychology/drug/catnip cat/psychology/drug/silvervine --- /doc/science/2023-taylor.pdf Connecting spatial thinking to STEM learning through visualizations Holly A. Taylor, Heather Burte, Kai T. Renshaw 2023-08-29 2023-08-29 [("doi","10.1038/s44159-023-00224-6")] psychology/vision science --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10203889/ Efficacy and Safety of Oral Small Molecule Glucagon-Like Peptide 1 Receptor Agonist Danuglipron for Glycemic Control Among Patients With Type 2 Diabetes: A Randomized Clinical Trial Aditi R. Saxena, Juan P. Frias, Lisa S. Brown, Donal N. Gorman, Szilard Vasas, Nikolaos Tsamandouras, Morris J. Birnbaum 2023 2023-05-04 [("doi","10.1001/jamanetworkopen.2023.14493")] longevity/glp/semaglutide

Importance: Currently available glucagon-like peptide 1 receptor (GLP-1R) agonists for treating type 2 diabetes (T2D) are peptide agonists that require subcutaneous administration or strict fasting requirements before and after oral administration.

Objective: To investigate the efficacy, safety, and tolerability of multiple dose levels of the novel, oral, small molecule GLP-1R agonist danuglipron over 16 weeks.

Methods, Setting, & Participants: A phase 2b, double-blind, placebo-controlled, parallel-group, 6-group randomized clinical trial with 16-week double-blind treatment period and 4-week follow-up was conducted from July 7, 2020, to July 7, 2021. Adults with T2D inadequately controlled by diet and exercise, with or without metformin treatment, were enrolled from 97 clinical research sites in 8 countries or regions.

Interventions: Participants received placebo or danuglipron, 2.5, 10, 40, 80, or 120 mg, all orally administered twice daily with food for 16 weeks. Weekly dose escalation steps were incorporated to achieve danuglipron doses of 40 mg or more twice daily.

Main Outcomes & Measures: Change from baseline in glycated hemoglobin (HbA1c, primary end point), fasting plasma glucose (FPG), and body weight were assessed at week 16. Safety was monitored throughout the study period, including a 4-week follow-up period.

Results: Of 411 participants randomized and treated (mean [SD] age, 58.6 [9.3] years; 209 [51%] male), 316 (77%) completed treatment. For all danuglipron doses, HbA1c and FPG were statistically-significantly reduced at week 16 vs placebo, with HbA1c reductions up to a least squares mean difference vs placebo of −1.16% (90% CI, −1.47% to −0.86%) for the 120-mg twice daily group and FPG reductions up to a least squares mean difference vs placebo of −33.24 mg/dL (90% CI, −45.63 to −20.84 mg/dL). Body weight was statistically-significantly reduced at week 16 compared with placebo in the 80-mg twice daily and 120-mg twice daily groups only, with a least squares mean difference vs placebo of −2.04 kg (90% CI, −3.01 to −1.07 kg) for the 80-mg twice daily group and −4.17 kg (90% CI, −5.15 to −3.18 kg) for the 120-mg twice daily group. The most commonly reported adverse events were nausea, diarrhea, and vomiting.

Conclusion: In adults with T2D, danuglipron reduced HbA1c, FPG, and body weight at week 16 compared with placebo, with a tolerability profile consistent with the mechanism of action.

Trial Registration: ClinicalTrials.gov Identifier:NCT03985293.

--- https://classic.clinicaltrials.gov/ct2/show/NCT04982575 2023-05-05 longevity/glp/semaglutide --- https://biolabshop.eu/home-page/230-tirzepatide-5mg.html 2023-05-05 longevity/glp/tirzepatide --- https://arxiv.org/abs/2211.05491 Black-Hole Radiation Decoding is Quantum Cryptography Zvika Brakerski 2022-11-10 2023-05-05 [("doi","10.48550/arXiv.2211.05491")] cs/cryptography science

We propose to study equivalence relations between phenomena in high-energy physics and the existence of standard cryptographic primitives, and show the first example where such an equivalence holds. A small number of prior works showed that high-energy phenomena can be explained by cryptographic hardness. Examples include using the existence of one-way functions to explain the hardness of decoding black-hole Hawking radiation (Harlow & Hayden 2013, Aaronson 2016), and using pseudorandom quantum states to explain the hardness of computing AdS/CFT dictionary (Bouland, Fefferman & Vazirani 2020).

In this work we show, for the former example of black-hole radiation decoding, that it also implies the existence of secure quantum cryptography. In fact, we show an existential equivalence between the hardness of black-hole radiation decoding and a variety of cryptographic primitives, including bit-commitment schemes and oblivious transfer protocols (using quantum communication).

This can be viewed (with proper disclaimers, as we discuss) as providing a physical justification for the existence of secure cryptography. We conjecture that such connections may be found in other high-energy physics phenomena.

--- https://en.wikipedia.org/wiki/Physical_unclonable_function Physical unclonable function 2023-05-05 cs/cryptography cs/hardware --- /doc/statistics/causality/1958-fisher.pdf Cigarettes, Cancer, And Statistics Ronald A. Fisher 1958-01-01 2023-05-05 [("doi","10.2307/23737529")] nicotine statistics/causality --- https://www.youtube.com/watch?v=I-RX9Y-q05I 2023-05-05 ai/nn/diffusion --- https://zerocoin.org/media/pdf/ZerocoinOakland.pdf Zerocoin: Anonymous Distributed E-Cash from Bitcoin Miers 2013 2023-05-05 bitcoin --- https://www.jpands.org/vol11no4/millerd.pdf Extrathyroidal Benefits of Iodine Miller 2006 2023-05-05 iodine --- https://warwick.ac.uk/fac/soc/law/elj/jilt/2001_3/miller/ Creating A Subpoena-Proof Diary: A Technological Solution to A Legal Problem Miller, Gao 2023-05-05 cs/cryptography/timelock --- http://www.iapsych.com/wj3ewok/LinkedDocuments/McArdle2002.pdf Comparative Longitudinal Structural Analyses of the Growth and Decline of Multiple Intellectual Abilities Over the Life Span McArdle 2002 2023-05-05 iq --- /doc/cat/psychology/drug/catnip/2013-avmf.pdf Genome-Wide Association Study for Catnip Response in Domestic Cats Lyons 2013 2023-05-05 cat/genetics cat/psychology/drug/catnip --- https://books.worksinprogress.co/book/maintenance-of-everything/vehicles/digression-3-corrosion-rust-never-sleeps/1 2023-05-06 technology --- https://en.wikipedia.org/wiki/Russian_Anonymous_Marketplace Russian Anonymous Marketplace 2023-05-06 darknet-market/hydra --- https://en.wikipedia.org/wiki/More_Product,_Less_Process More Product, Less Process 2023-05-06 cs/linkrot/archiving --- https://arxiv.org/abs/2309.12269 The Cambridge Law Corpus: A Corpus for Legal AI Research Andreas Östling, Holli Sargeant, Huiyuan Xie, Ludwig Bull, Alexander Terenin, Leif Jonsson, Måns Magnusson, Felix Steffek 2023-09-21 2023-09-21 [("doi","10.48550/arXiv.2309.12269")] ai/dataset ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction law

We introduce the Cambridge Law Corpus (CLC), a corpus for legal AI research. It consists of over 250,000 court cases from the UK. Most cases are from the 21st century, but the corpus includes cases as old as the 16th century. This paper presents the first release of the corpus, containing the raw text and meta-data. Together with the corpus, we provide annotations on case outcomes for 638 cases, done by legal experts.

Using our annotated data, we have trained and evaluated case outcome extraction with GPT-3, GPT-4 and RoBERTa models to provide benchmarks.

We include an extensive legal and ethical discussion to address the potentially sensitive nature of this material.

As a consequence, the corpus will only be released for research purposes under certain restrictions.

--- https://tylervigen.com/the-mystery-of-the-bloomfield-bridge 2023-05-06 design/typography/sidenote --- https://x.com/emollick/status/1705422957856604503 Ethan Mollick 2023-05-06 ai/nn/transformer/gpt/inner-monologue --- https://en.wikipedia.org/wiki/In-ovo_sexing#Non-invasive_technologies In-ovo sexing § Non-invasive technologies 2023-05-06 technology --- https://arxiv.org/abs/1909.06300 Analysis of Solitaire Daniel Shiu 2019-09-13 2023-05-06 [("doi","10.48550/arXiv.1909.06300")] cs/cryptography

The Solitaire cipher was designed by Bruce Schneier as a plot point in the novel Cryptonomicon by Neal Stephenson. The cipher is intended to fit the archetype of a modern stream cipher whilst being implementable by hand using a standard deck of cards with two jokers.

We find a model for repetitions in the keystream in the stream cipher Solitaire that accounts for the large majority of the repetition bias. Other phenomena merit further investigation.

We have proposed modifications to the cipher that would reduce the repetition bias, but at the cost of increasing the complexity of the cipher (probably beyond the goal of allowing manual implementation). We have argued that the state update function is unlikely to lead to cycles shorter than those of a random bijection.

--- https://web.dev/yahoo-japan-news-bfcache/ 2023-05-06 economics/advertising --- https://jenson.org/text/ 2023-05-06 design --- https://barryzhang.substack.com/p/our-humble-attempt-at-fine-tuning 2023-05-07 ai/nn/transformer/gpt/3 ai/text-style-transfer --- https://www.lesswrong.com/posts/thePw6qdyabD8XR4y/interpreting-openai-s-whisper 2023-05-07 ai/nn/transformer/attention ai/nn/transformer/gpt/whisper --- https://www.koopatv.org/2022/10/why-people-dont-like-fandom-wikis.html 2023-05-07 economics/advertising wikipedia --- https://fullfrontal.moe/sakuga-espresso-one-piece-1074/ 2023-05-07 anime --- https://blogs.scientificamerican.com/cross-check/tribute-to-jose-delgado-legendary-and-slightly-scary-pioneer-of-mind-control/ 2023-05-07 psychology/neuroscience --- https://x.com/elonmusk/status/1505789670776610817 Elon Musk 2023-05-07 psychiatry/bipolar/elon-musk --- https://www.popsci.com/scitech/article/2008-01/germ-could-save-your-life/ 2023-05-07 genetics/microbiome --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC263483/ Isolation of a Streptococcus mutans strain producing a novel bacteriocin J D. Hillman, K. P. Johnson, B. I. Yaphe 1984 2023-05-07 [("doi","10.1128/iai.44.1.141-144.1984")] genetics/microbiome

A strain of Streptococcus mutans has been isolated that produces a bacteriocin with novel properties.

Its antibiotic spectrum includes 123⁄124 strains of S. mutans tested and a variety of other gram-positive microorganisms.

Experiments with dialysis membranes indicate that the molecular weight of the bacteriocin is less than 1,000.

Mutants of the producer strain were obtained that are deficient in bacteriocin production or produce twofold elevated amounts. The ability of these strains to superinfect or preemptively colonize the oral cavities of rats correlated with the amount of bacteriocin they produced.

--- https://www.reddit.com/r/dalle2/comments/16rg9am/homer_cmon_man_thats_enough_dumps_on_the_moon_my/ 2023-05-07 ai/nn/transformer/gpt/dall-e/3 --- https://arxiv.org/abs/2309.08586 Replacing softmax with ReLU in Vision Transformers Mitchell Wortsman, Jaehoon Lee, Justin Gilmer, Simon Kornblith 2023-09-15 2023-09-15 [("doi","10.48550/arXiv.2309.08586")] ai/nn/transformer/attention

Previous research observed accuracy degradation when replacing the attention softmax with a point-wise activation such as ReLU.

In the context of vision transformers, we find that this degradation is mitigated when dividing by sequence length.

Our experiments training small to large vision transformers on ImageNet-21k indicate that ReLU-attention can approach or match the performance of softmax-attention in terms of scaling behavior as a function of compute. [cf. Zhou et al 2023]

--- https://arxiv.org/abs/2309.10713 Interpret Vision Transformers as ConvNets with Dynamic Convolutions Chong Zhou, Chen Change Loy, Bo Dai 2023-09-19 2023-09-19 [("doi","10.48550/arXiv.2309.10713")] ai/nn/cnn ai/nn/transformer/attention

[cf. Wortsman et al 2023] There has been a debate about the superiority between vision Transformers and ConvNets, serving as the backbone of computer vision models. Although they are usually considered as two completely different architectures, in this paper, we interpret vision Transformers as ConvNets with dynamic convolutions, which enables us to characterize existing Transformers and dynamic ConvNets in a unified framework and compare their design choices side by side.

In addition, our interpretation can also guide the network design as researchers now can consider vision Transformers from the design space of ConvNets and vice versa. We demonstrate such potential through two specific studies.

First, we inspect the role of softmax in vision Transformers as the activation function and find it can be replaced by commonly used ConvNets modules, such as ReLU and Layer Normalization, which results in a faster convergence rate and better performance.

Second, following the design of depth-wise convolution, we create a corresponding depth-wise vision Transformer that is more efficient with comparable performance.

The potential of the proposed unified interpretation is not limited to the given examples and we hope it can inspire the community and give rise to more advanced network architectures.

--- https://en.wikipedia.org/wiki/Softmax_function Softmax function 2023-05-08 ai/nn reinforcement-learning/exploration --- https://en.wikipedia.org/wiki/Acne#Research Acne § Research 2023-05-08 genetics/microbiome/acne --- https://www.theverge.com/22684730/students-file-folder-directory-structure-education-gen-z 2023-05-08 cs design --- https://www.tomshardware.com/news/nvidia-hints-at-dlss-10-delivering-full-neural-rendering-potentially-replacing-rasterization-and-ray-tracing 2023-05-08 ai/video/generation --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006434/ Senolytics dasatinib and quercetin in idiopathic pulmonary fibrosis: results of a phase I, single-blind, single-center, randomized, placebo-controlled pilot trial on feasibility and tolerability Anoop Nambiar, Dean Kellogg, Jaime Justice, Martin Goros, Jonathan Gelfond, Rodolfo Pascual, Shahrukh Hashmi, Michal Masternak, Larissa Prata, Nathan LeBrasseur, Andrew Limper, Stephen Kritchevsky, Nicolas Musi, Tamara Tchkonia, James Kirkland 2023 2023-05-08 [("doi","10.1016/j.ebiom.2023.104481")] longevity/senolytic/d-q

Background: Idiopathic pulmonary fibrosis (IPF) is an age-related, chronic, irreversible fibrotic lung disease. IPF is associated with increased senescent cells burden, which may be alleviated with administration of senescent cell targeting drugs termed ‘senolytics’. We previously conducted an open-label single-arm pilot study of the senolytic combination of dasatinib and quercetin (D + Q) in patients with IPF but lack of control group limited interpretation and next-stage trial planning. The primary objective of this confirmatory randomized placebo-controlled pilot trial (RCT;NCT02874989) was to report adverse events with D + Q and inform study feasibility for future efficacy trials.

Method: 12 participants with IPF aged >50 years were blinded and randomized at a 1:1 ratio to either receive 3 weeks of D + Q (D: 100 mg/d and Q: 1250 mg/d, 3 consecutive days per week) or matching placebo.

Findings: All participants completed the scheduled drug dosing regimen (108/108 doses) and planned assessments (60/60). While the placebo arm reported fewer overall non-serious AEs (65 vs 22), there were no serious adverse events related to D + Q. Most AEs in the D + Q arm are common in IPF patients or anticipated side effects of D. Sleep disturbances and anxiety were disproportionately represented in the D + Q arm (4/6 vs 0/6). Frailty, pulmonary, or physical function were explored before and after intermittent D + Q; though under-powered to evaluate change, these measures do not appear to differ meaningfully between groups.

Interpretation: Intermittently-dosed D + Q in patients with IPF is feasible and generally well-tolerated. Further prospective studies, such as a larger RCT, are needed to confirm the safety and efficacy of D + Q in patients with IPF.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412088/ Senolytics in idiopathic pulmonary fibrosis: Results from a first-in-human, open-label, pilot study Jamie N. Justice, Anoop M. Nambiar, Tamar Tchkonia, Nathan K. LeBrasseur, Rodolfo Pascual, Shahrukh K. Hashmi, Larissa Prata, Michal M. Masternak, Stephen B. Kritchevsky, Nicolas Musi, James L. Kirkland 2019 2023-05-08 [("doi","10.1016/j.ebiom.2018.12.052")] longevity/senolytic/d-q

Background: Cellular senescence is a key mechanism that drives age-related diseases, but has yet to be targeted therapeutically in humans. Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal cellular senescence-associated disease. Selectively ablating senescent cells using dasatinib plus quercetin (DQ) alleviates IPF-related dysfunction in bleomycin-administered mice.

Method: A two-center, open-label study of intermittent DQ (D:100 mg/day, Q:1250 mg/day, three-days/week over three-weeks) was conducted in participants with IPF (n = 14) to evaluate feasibility of implementing a senolytic intervention. The primary endpoints were retention rates and completion rates for planned clinical assessments. Secondary endpoints were safety and change in functional and reported health measures. Associations with the senescence-associated secretory phenotype (SASP) were explored.

Findings: 14 patients with stable IPF were recruited. The retention rate was 100% with no DQ discontinuation; planned clinical assessments were complete in 13/14 participants. One serious adverse event was reported. Non-serious events were primarily mild-moderate, with respiratory symptoms (n = 16 total events), skin irritation/bruising (n = 14), and gastrointestinal discomfort (n = 12) being most frequent. Physical function evaluated as 6-min walk distance, 4-m gait speed, and chair-stands time was statistically-significantly and clinically-meaningfully improved (p < .05). Pulmonary function, clinical chemistries, frailty index (FI-LAB), and reported health were unchanged. DQ effects on circulating SASP factors were inconclusive, but correlations were observed between change in function and change in SASP-related matrix-remodeling proteins, microRNAs, and pro-inflammatory cytokines (23⁄48 markers r ≥ 0.50).

Interpretation: Our first-in-humans open-label pilot supports study feasibility and provides initial evidence that senolytics may alleviate physical dysfunction in IPF, warranting evaluation of DQ in larger randomized controlled trials for senescence-related diseases. ClinicalTrials.gov identifier: NCT02874989 (posted 2016–2018).

--- https://clinicaltrials.gov/search?term=dasatinib%20quercetin 2023-05-08 longevity/senolytic --- https://animationobsessive.substack.com/p/the-avant-garde-origins-of-gumby 2023-05-08 anime --- https://x.com/npew/status/1706356566629421429 Peter Welinder 2023-05-08 ai/nn/transformer/gpt/4 --- https://www.wired.com/review/loftie-clock/ 2023-05-08 ai/nn/transformer/gpt/3/fiction --- https://x.com/Scobleizer/status/1706174612621680976 Robert Scoble 2023-05-08 ai/nn/transformer/gpt/calibration --- https://arxiv.org/abs/2305.03270#google Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators Alexander Herzog, Kanishka Rao, Karol Hausman, Yao Lu, Paul Wohlhart, Mengyuan Yan, Jessica Lin, Montserrat Gonzalez Arenas, Ted Xiao, Daniel Kappler, Daniel Ho, Jarek Rettinghouse, Yevgen Chebotar, Kuang-Huei Lee, Keerthana Gopalakrishnan, Ryan Julian, Adrian Li, Chuyuan Kelly Fu, Bob Wei, Sangeetha Ramesh, Khem Holden, Kim Kleiven, David Rendleman, Sean Kirmani, Jeff Bingham, Jon Weisz, Ying Xu, Wenlong Lu, Matthew Bennice, Cody Fong, David Do, Jessica Lam, Yunfei Bai, Benjie Holson, Michael Quinlan, Noah Brown, Mrinal Kalakrishnan, Julian Ibarz, Peter Pastor, Sergey Levine 2023-05-05 2023-05-09 [("doi","10.48550/arXiv.2305.03270")] reinforcement-learning/robot reinforcement-learning/scaling

We describe a system for deep reinforcement learning of robotic manipulation skills applied to a large-scale real-world task: sorting recyclables and trash in office buildings. Real-world deployment of deep RL policies requires not only effective training algorithms, but the ability to bootstrap real-world training and enable broad generalization.

To this end, our system combines scalable deep RL from real-world data with bootstrapping from training in simulation, and incorporates auxiliary inputs from existing computer vision systems as a way to boost generalization to novel objects, while retaining the benefits of end-to-end training.

We analyze the tradeoffs of different design decisions in our system, and present a large-scale empirical validation that includes training on real-world data gathered over the course of 24 months of experimentation, across a fleet of 23 robots in 3 office buildings, with a total training set of 9527 hours of robotic experience.

Our final validation also consists of 4,800 evaluation trials across 240 waste station configurations, in order to evaluate in detail the impact of the design decisions in our system, the scaling effects of including more real-world data, and the performance of the method on novel objects.

The project’s website and videos can be found at rl-at-scale.github.io.

--- https://arxiv.org/abs/1705.10694 Deep Learning is Robust to Massive Label Noise David Rolnick, Andreas Veit, Serge Belongie, Nir Shavit 2017-05-30 2023-05-09 [("doi","10.48550/arXiv.1705.10694")] ai/nn/cnn ai/scaling

Deep neural networks trained on large supervised datasets have led to impressive results in image classification and other tasks. However, well-annotated datasets can be time-consuming and expensive to collect, lending increased interest to larger but noisy datasets that are more easily obtained.

In this paper, we show that deep neural networks are capable of generalizing from training data for which true labels are massively outnumbered by incorrect labels. We demonstrate remarkably high test performance after training on corrupted data from MNIST, CIFAR, and ImageNet. For example, on MNIST we obtain test accuracy above 90% even after each clean training example has been diluted with 100 randomly-labeled examples.

Such behavior holds across multiple patterns of label noise, even when erroneous labels are biased towards confusing classes.

We show that training in this regime requires a but manageable increase in dataset size that is related to the factor by which correct labels have been diluted.

Finally, we provide an analysis of our results that shows how increasing noise decreases the effective batch size.

--- https://quadrant.org.au/magazine/2023/08-online/the-ruse-of-tradition/ 2023-05-09 philosophy/religion politics sociology --- https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/1108810 Sitting Time and All-Cause Mortality Risk in 222,497 Australian Adults van der Ploeg 2012 2023-05-09 exercise longevity --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3857339/ Large offspring syndrome: a bovine model for the human loss-of-imprinting overgrowth syndrome Beckwith-Wiedemann Zhiyuan Chen, Katherine Marie Robbins, Kevin Dale Wells, Rocío Melissa Rivera 2013 2023-05-09 [("doi","10.4161/epi.24655")] longevity/epigenetics

Beckwith-Wiedemann syndrome (BWS) is a human loss-of-imprinting syndrome primarily characterized by macrosomia, macroglossia, and abdominal wall defects. BWS has been associated with misregulation of two clusters of imprinted genes. Children conceived with the use of assisted reproductive technologies (ART) appear to have an increased incidence of BWS.

As in humans, ART can also induce a similar overgrowth syndrome in ruminants which is referred to as large offspring syndrome (LOS). The main goal of our study is to determine if LOS shows similar loss-of-imprinting at loci known to be misregulated in BWS.

To test this, Bos taurus indicus × Bos taurus taurus F1 hybrids were generated by artificial insemination (AI; control) or by ART. 7 of the 27 conceptuses in the ART group were in the > 97th percentile body weight when compared with controls. Further, other characteristics reported in BWS were observed in the ART group, such as large tongue, umbilical hernia, and ear malformations.

KCNQ1OT1 (the most-often misregulated imprinted gene in BWS) was biallelically-expressed in various organs in two out of 7 overgrown conceptuses from the ART group, but shows monoallelic expression in all tissues of the AI conceptuses. Furthermore, biallelic expression of KCNQ1OT1 is associated with loss of methylation at the KvDMR1 on the maternal allele and with downregulation of the maternally-expressed gene CDKN1C.

In conclusion, our results show phenotypic and epigenetic similarities between LOS and BWS, and we propose the use of LOS as an animal model to investigate the etiology of BWS.

--- /doc/genetics/cloning/dog/2011-hong.pdf Morphological abnormalities, impaired fetal development and decrease in myostatin expression following somatic cell nuclear transfer in dogs Il-Hwa Hong, Yeon-Woo Jeong, Taeyoung Shin, Sang-Hwan Hyun, Jin-Kyu Park, Mi-Ran Ki, Seon-Young Han, Se-Il Park, Ji-Hyun Lee, Eun-Mi Lee, Ah-Young Kim, Sang-Young You, Woo-Suk Hwang, Kyu-Shik Jeong 2011-04-25 2023-05-09 [("doi","10.1002/mrd.21309")] genetics/cloning/dog longevity/epigenetics

Several mammals, including dogs, have been successfully cloned using somatic cell nuclear transfer (SCNT), but the efficiency of generating normal, live offspring is relatively low. Although the high failure rate has been attributed to incomplete reprogramming of the somatic nuclei during the cloning process, the exact cause is not fully known.

To elucidate the cause of death in cloned offspring, 12 deceased offspring cloned by SCNT were necropsied. The clones were either stillborn just prior to delivery or died with dyspnea shortly after birth. On gross examination, defects in the anterior abdominal wall and increased heart and liver sizes were found.

Notably, a statistically-significant increase in muscle mass and macroglossia lesions were observed in deceased SCNT-cloned dogs. Interestingly, the expression of myostatin, a negative regulator of muscle growth during embryogenesis, was down-regulated at the mRNA level in tongues and skeletal muscles of SCNT-cloned dogs compared with a normal dog. [see myostatin-related muscle hypertrophy]

The results of the present study suggest that decreased expression of myostatin in SCNT-cloned dogs may be involved in morphological abnormalities such as increased muscle mass and macroglossia, which may contribute to impaired fetal development and poor survival rates.

--- https://publicdomainreview.org/collection/dictionary-of-modern-slang/ 2023-05-09 crime history/public-domain-review psychology/linguistics --- https://www.lesswrong.com/posts/g3zYbYP7pePyNchmt/text-posts-from-the-kids-group-2022 2023-05-09 fiction/humor philosophy/mind --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7923531/ Darwin, sexual selection, and the brain Michael J. Ryan 2021 2023-05-09 [("doi","10.1073/pnas.2008194118")] genetics/selection/natural psychology/animal

One hundred fifty years ago Darwin published The Descent of Man, and Selection in Relation to Sex, in which he presented his theory of sexual selection with its emphasis on sexual beauty. However, it was not until 50 y ago that there was a renewed interest in Darwin’s theory in general, and specifically the potency of mate choice. Darwin suggested that in many cases female preferences for elaborately ornamented males derived from a female’s taste for the beautiful, the notion that females were attracted to sexual beauty for its own sake.

Initially, female mate choice attracted the interest of behavioral ecologists focusing on the fitness advantages accrued through mate choice. Subsequent studies focused on sensory ecology and signal design, often showing how sensory end organs influenced the types of traits females found attractive.

Eventually, investigations of neural circuits, neurogenetics, and neurochemistry uncovered a more complete scaffolding underlying sexual attraction. More recently, research inspired by human studies in psychophysics, behavioral economics, and neuroaesthetics have provided some notion of its higher-order mechanisms.

In this paper, I review progress in our understanding of Darwin’s conjecture of “a taste for the beautiful” by considering research from these diverse fields that have conspired to provide unparalleled insight into the chooser’s mate choices.

--- https://en.wikipedia.org/wiki/Cornell_Notes Cornell Notes 2023-05-09 design/typography/sidenote --- https://x.com/elzr/status/1004517791595032576 elzr 2023-05-09 design/typography/sidenote --- https://x.com/codexeditor/status/1253101289556172800 codexeditor 2023-05-10 design/typography/sidenote --- /doc/ai/music/2001-hofstadter.pdf Staring Emmy Straight in the Eye—And Doing My Best Not to Flinch Douglas Hofstadter, David Cope 2001-01-01 2023-05-10 ai/music philosophy/mind psychology/novelty --- https://www.cambridgeblog.org/2023/05/a-journey-into-the-shaken-baby-syndrome-abusive-head-trauma-controversy/ 2023-05-10 crime psychiatry/traumatic-brain-injury --- https://howtomarketagame.com/2023/09/25/john-romero-on-his-book-doom-guy-and-developing-games-at-a-small-scale/ 2023-05-10 design --- https://www.theguardian.com/environment/2023/sep/19/do-carbon-credit-reduce-emissions-greenhouse-gases 2023-05-10 technology/carbon-capture --- https://www.theguardian.com/environment/2023/jan/18/revealed-forest-carbon-offsets-biggest-provider-worthless-verra-aoe 2023-05-10 technology/carbon-capture --- https://arxiv.org/abs/1802.02938 Usage and Attribution of Stack Overflow Code Snippets in GitHub Projects Sebastian Baltes, Stephan Diehl 2018-02-08 2023-05-10 [("doi","10.48550/arXiv.1802.02938")] cs economics/copyright

Stack Overflow (SO) is the most popular question-and-answer website for software developers, providing a large amount of copyable code snippets. Using those snippets raises maintenance and legal issues. SO’s license (CC BY-SA 3.0) requires attribution, ie. referencing the original question or answer, and requires derived work to adopt a compatible license.

While there is a heated debate on SO’s license model for code snippets and the required attribution, little is known about the extent to which snippets are copied from SO without proper attribution. We present results of a large-scale empirical study analyzing the usage and attribution of non-trivial Java code snippets from SO answers in public GitHub (GH) projects.

We followed 3 different approaches to triangulate an estimate for the ratio of unattributed usages and conducted two online surveys with software developers to complement our results. For the different sets of projects that we analyzed, the ratio of projects containing files with a reference to SO varied between 3.3% and 11.9%.

We found that at most 1.8% of all analyzed repositories containing code from SO used the code in a way compatible with CC BY-SA 3.0. Moreover, we estimate that at most a quarter of the copied code snippets from SO are attributed as required.

Of the surveyed developers, almost one half admitted copying code from SO without attribution and about two-thirds were not aware of the license of SO code snippets and its implications.

--- https://medium.com/@paulmorrishill/building-a-string-art-machine-eeee386a38db 2023-05-10 reinforcement-learning/robot --- https://x.com/gdb/status/1707082027584106669 Greg Brockman 2023-05-10 ai/nn/retrieval ai/nn/transformer/gpt/4/nonfiction --- https://rottenandgood.substack.com/p/finish-your-projects 2023-05-10 psychology/willpower psychology/writing --- https://www.nytimes.com/2023/09/22/magazine/hank-asher-data.html 2023-05-11 psychiatry/bipolar/energy --- https://www.vitara.com/ 2023-05-11 genetics/gametogenesis --- https://arxiv.org/abs/2305.08891 Common Diffusion Noise Schedules and Sample Steps are Flawed Shanchuan Lin, Bingchen Liu, Jiashi Li, Xiao Yang 2023-05-15 2023-05-15 [("doi","10.48550/arXiv.2305.08891")] ai/nn/diffusion

We discover that common diffusion noise schedules do not enforce the last timestep to have zero signal-to-noise ratio (SNR), and some implementations of diffusion samplers do not start from the last timestep. Such designs are flawed and do not reflect the fact that the model is given pure Gaussian noise at inference, creating a discrepancy between training and inference.

We show that the flawed design causes real problems in existing implementations. In Stable Diffusion, it severely limits the model to only generate images with medium brightness and prevents it from generating very bright and dark samples.

We propose a few simple fixes: (1) rescale the noise schedule to enforce zero terminal SNR; (2) train the model with v-prediction; (3) change the sampler to always start from the last timestep; (4) rescale classifier-free guidance to prevent over-exposure.

These simple changes ensure the diffusion process is congruent between training and inference and allow the model to generate samples more faithful to the original data distribution.

--- https://blog.roboflow.com/gpt-4-vision/ 2023-05-11 ai/nn/transformer/gpt/4/nonfiction --- https://en.wikipedia.org/wiki/Enshittification Enshittification 2023-05-11 economics/advertising --- https://mishpacha.com/the-fine-print-2/ 2023-05-11 psychology/collecting --- /doc/science/1974-bartholomew.pdf Japanese Culture and the Problem of Modern Science James Bartholomew 1974-01-01 2023-05-11 japan science --- https://en.wikipedia.org/wiki/Hermitage_cats Hermitage cats 2023-05-11 cat --- https://malisper.me/debugging-lisp-part-1-recompilation/ 2023-05-11 cs/lisp --- /doc/psychology/cognitive-bias/2022-spamann.pdf Comment on ‘Temperature and Decisions: Evidence from 207,000 Court Cases’ Holger Spamann 2022-10-01 2023-05-11 [("doi","10.1257/app.20200118")] law psychology/cognitive-bias

[code; Twitter] Heyes & Saberian 2019 estimate from 2000–2004 data that outdoor temperature reduces US immigration judges’ propensity to grant asylum.

This estimate is the result of coding and data errors and of sample selection. Correcting the errors reduces the point estimate by two-thirds, with a wide 95% confidence interval straddling zero. Enlarging the sample to 1990–2019 flips the point estimate’s sign and rules out the effect size reported by Heyes and Saberian with very high confidence.

An analysis of all criminal sentencing decisions by US federal district judges 35.9%–59.4% yields no evidence of temperature or other weather effects either.

--- https://malisper.me/debugging-lisp-part-2-inspecting/ 2023-05-11 cs/lisp --- https://malisper.me/debugging-lisp-part-3-redefining-classes/ 2023-05-12 cs/lisp --- https://malisper.me/debugging-lisp-part-4-restarts/ 2023-05-12 cs/lisp --- https://arxiv.org/abs/2307.06684 The Heterogeneous Earnings Impact of Job Loss Across Workers, Establishments, and Markets Susan Athey, Lisa K. Simon, Oskar N. Skans, Johan Vikstrom, Yaroslav Yakymovych 2023-07-13 2023-07-13 [("doi","10.48550/arXiv.2307.06684")] economics

[Twitter] Using generalized random forests and rich Swedish administrative data, we show that the earnings effects of job displacement due to establishment closures are extremely heterogeneous across workers, establishments, and markets.

The decile of workers with the largest predicted effects lose 50% of annual earnings the year after displacement and accumulated losses amount to 250% during a decade. In contrast, workers in the least affected decile experience only marginal losses of less than 6% in the year after displacement.

Workers in the most affected decile tend to be lower paid workers on negative earnings trajectories. This implies that the economic value of (lost) jobs is greatest for workers with low earnings. The reason is that many of these workers fail to find new employment after displacement.

Overall, the effects are heterogeneous both within and across establishments and combinations of important individual characteristics such as age and schooling. Adverse market conditions matter the most for already vulnerable workers.

The most effective way to target workers with large effects, without using a complex model, is by focusing on older workers in routine-task intensive jobs.

--- https://arxiv.org/abs/2309.15807#facebook Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack Xiaoliang Dai, Ji Hou, Chih-Yao Ma, Sam Tsai, Jialiang Wang, Rui Wang, Peizhao Zhang, Simon Vandenhende, Xiaofang Wang, Abhimanyu Dubey, Matthew Yu, Abhishek Kadian, Filip Radenovic, Dhruv Mahajan, Kunpeng Li, Yue Zhao, Vladan Petrovic, Mitesh Kumar Singh, Simran Motwani, Yi Wen, Yiwen Song, Roshan Sumbaly, Vignesh Ramanathan, Zijian He, Peter Vajda, Devi Parikh 2023-09-27 2023-09-27 [("doi","10.48550/arXiv.2309.15807")] ai/nn/diffusion reinforcement-learning/preference-learning

Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text. However, these pre-trained models often face challenges when it comes to generating highly esthetic images. This creates the need for esthetic alignment post pre-training.

In this paper, we propose quality-tuning to effectively guide a pre-trained model to exclusively generate highly visually appealing images, while maintaining generality across visual concepts. Our key insight is that supervised fine-tuning with a set of surprisingly small but extremely visually appealing images can improve the generation quality.

We pre-train a latent diffusion model on 1.1 billion image-text pairs [a new unknown model] and fine-tune it with only a few thousand carefully selected high-quality images. The resulting model, Emu, achieves a win rate of 82.9% compared with its pre-trained only counterpart.

Compared to the state-of-the-art SDXL-v1.0, Emu is preferred 68.4% and 71.3% of the time on visual appeal on the standard PartiPrompts and our Open User Input benchmark based on the real-world usage of text-to-image models.

In addition, we show that quality-tuning is a generic approach that is also effective for other architectures, including pixel diffusion and masked generative transformer models.

--- https://www.outsideonline.com/outdoor-adventure/exploration-survival/body-electric/ 2023-05-12 psychiatry/traumatic-brain-injury psychology/neuroscience --- https://boston1775.blogspot.com/2018/03/severely-wounded-and-bruised-by-your.html 2023-05-12 psychology/animal --- https://petertasker.asia/articles/culture/think-again-al-jolson-japans-silent-movie-culture-is-still-going-strong/ 2023-05-12 japan/art --- /doc/biology/2002-stephenson-2.pdf Forest Dewey Dodrill: Heart Surgery Pioneer. Michigan Heart, Part II Larry W. Stephenson, Agustin Arbulu, Joseph S. Bassett, Allen Silbergleit, Calvin H. Hughes 2002-07-01 2023-05-12 [("doi","10.1111/j.1540-8191.2002.tb01210.x")] biology technology --- https://arxiv.org/abs/2306.09222#google RGD: Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization Ramnath Kumar, Kushal Majmundar, Dheeraj Nagaraj, Arun Sai Suggala 2023-06-15 2023-06-15 [("doi","10.48550/arXiv.2306.09222")] ai/nn/transformer ai/tabular reinforcement-learning/meta-learning

[blog] We develop a re-weighted gradient descent technique for boosting the performance of deep neural networks. Our algorithm involves the importance weighting of data points during each optimization step.

Our approach [RGD] is inspired by distributionally robust optimization with f-divergences, which has been known to result in models with improved generalization guarantees. Our re-weighting scheme is simple, computationally efficient, and can be combined with any popular optimization algorithms such as SGD and Adam.

Empirically, we demonstrate our approach’s superiority on various tasks, including vanilla classification, classification with label imbalance, noisy labels, domain adaptation, and tabular representation learning. Notably, we obtain improvements of +0.7% and +1.44% over SOTA on DomainBed and Tabular benchmarks, respectively.

Moreover, our algorithm boosts the performance of BERT on GLUE benchmarks by +1.94%, and ViT on ImageNet-1K by +0.9%. These results demonstrate the effectiveness of the proposed approach, indicating its potential for improving performance in diverse domains.

--- https://x.com/TheOtherTravGuy/status/1707486996632088905 TheOtherTravGuy 2023-05-12 psychology/vision --- https://en.wikipedia.org/wiki/Kevin_Hines Kevin Hines 2023-05-12 psychiatry/bipolar --- https://www.johndcook.com/blog/2023/09/29/platonic-solids-and-integration/ 2023-05-13 statistics/probability --- https://en.wikipedia.org/wiki/Tim_Ferriss Tim Ferriss 2023-05-13 psychiatry/bipolar/energy --- https://arxiv.org/abs/2309.15505 Finite Scalar Quantization (FSQ): VQ-VAE Made Simple Fabian Mentzer, David Minnen, Eirikur Agustsson, Michael Tschannen 2023-09-27 2023-09-27 [("doi","10.48550/arXiv.2309.15505")] ai/nn/vae

[cf. VD-VAE] We propose to replace vector quantization (VQ) in the latent representation of VQ-VAEs with a simple scheme termed finite scalar quantization (FSQ), where we project the VAE representation down to a few dimensions (typically less than 10). Each dimension is quantized to a small set of fixed values, leading to an (implicit) codebook given by the product of these sets. By appropriately choosing the number of dimensions and values each dimension can take, we obtain the same codebook size as in VQ.

On top of such discrete representations, we can train the same models that have been trained on VQ-VAE representations. For example, autoregressive and masked transformer models for image generation, multimodal generation, and dense prediction computer vision tasks.

Concretely, we employ FSQ with MaskGIT for image generation, and with UViM for depth estimation, colorization, and panoptic segmentation. Despite the much simpler design of FSQ, we obtain competitive performance in all these tasks.

We emphasize that FSQ does not suffer from codebook collapse and does not need the complex machinery employed in VQ (commitment losses, codebook reseeding, code splitting, entropy penalties, etc.) to learn expressive discrete representations.

--- https://arxiv.org/abs/2205.10337#google UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes Alexander Kolesnikov, André Susano Pinto, Lucas Beyer, Xiaohua Zhai, Jeremiah Harmsen, Neil Houlsby 2022-05-20 2023-05-13 [("doi","10.48550/arXiv.2205.10337")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer ai/nn/vae

We introduce UViM, a unified approach capable of modeling a wide range of computer vision tasks. In contrast to previous models, UViM has the same functional form for all tasks; it requires no task-specific modifications which require extensive human expertise.

The approach involves two components: (I) a base model (feed-forward) which is trained to directly predict raw vision outputs, guided by a learned discrete code and (II) a language model (autoregressive) that is trained to generate the guiding code. These components complement each other: the language model is well-suited to modeling structured interdependent data, while the base model is efficient at dealing with high-dimensional outputs.

We demonstrate the effectiveness of UViM on 3 diverse and challenging vision tasks: panoptic segmentation, depth prediction and image colorization, where we achieve competitive and near state-of-the-art results. Our experimental results suggest that UViM is a promising candidate for a unified modeling approach in computer vision.

--- https://x.com/skirano/status/1707832625480524014 Pietro Schirano 2023-05-13 ai/nn/transformer/gpt/dall-e/3 --- https://x.com/ammaar/status/1707975011049635983 ammaar 2023-05-13 ai/nn/transformer/gpt/dall-e/3 --- https://x.com/TakeChaotic/status/1708032766628221285 TakeChaotic 2023-05-13 ai/nn/transformer/gpt/dall-e/3 --- https://www.youtube.com/watch?v=J0rUtiy4m0Y 2023-05-13 fiction/science-fiction/time-travel psychology/linguistics --- https://www.youtube.com/watch?v=ze5i_e_ryTk 2023-05-13 ai/nn math psychology/linguistics --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3559008/ Evolution And Episodic Memory: An Analysis And Demonstration Of A Social Function Of Episodic Recollection Stanley B. Klein, Leda Cosmides, Cynthia E. Gangi, Betsy Jackson, John Tooby, Kristi A. Costabile 2009 2023-05-13 [("doi","10.1521/soco.2009.27.2.283")] psychology reinforcement-learning/model

Over the past two decades, an abundance of evidence has shown that individuals typically rely on semantic summary knowledge when making trait judgments about self and others (for reviews, see Klein, 2004; Klein, Robertson, Gangi, & Loftus, 2008).

But why form trait summaries if one can consult the original episodes on which the summary was based? Conversely, why retain episodes after having abstracted a summary representation from them? Are there functional reasons to have trait information represented in two different, independently retrievable databases?

Evolution does not produce new phenotypic systems that are complex and functionally organized by chance. Such systems acquire their functional organization because they solved some evolutionarily recurrent problems for the organism.

In this article we explore some of the functional properties of episodic memory. Specifically, in a series of studies we demonstrate that maintaining a database of episodic memories enables its owner to reevaluate an individual’s past behavior in light of new information, sometimes drastically changing one’s impression in the process.

[Using very small samples of evaluating a hypothetical woman based on vignettes, showing that additional context can override general positive impressions; the results are statistically-weak, but the final case-study of an amnesiac, demonstrating that he can learn a positive impression of the hypothetical woman but lack of episodic memory means he can’t update appropriately, is neat.]

We conclude that some of the most important functions of episodic memory have to do with its role in human social interaction.

--- https://www.lesswrong.com/posts/bD4B2MF7nsGAfH9fj/basic-mathematics-of-predictive-coding 2023-05-14 ai/nn/rnn ai/nn/vae psychology/neuroscience --- https://www.johndcook.com/blog/2023/09/30/consecutive-coupon-collector-problem/ 2023-05-14 statistics/order --- https://www.reddit.com/r/OpenAI/comments/16w7q2s/dalle_3_is_incredible/ 2023-05-14 ai/nn/transformer/gpt/dall-e/3 --- https://thereader.mitpress.mit.edu/aldous-huxleys-deep-reflection/ 2023-05-14 psychiatry/meditation zeo --- https://x.com/Mr_AllenT/status/1708105819777028386 Allen Tsui 2023-05-14 ai/anime ai/nn/transformer/gpt/dall-e/3 --- https://www.newyorker.com/magazine/2023/10/09/they-studied-dishonesty-was-their-work-a-lie 2023-05-14 statistics/bias --- https://x.com/Lakhanpatel001/status/1707878941531250954 Lakhanpatel001 2023-05-14 ai/nn/transformer/gpt/dall-e/3 --- https://x.com/AndrewCurran_/status/1708019612020023639 Andrew Curran 2023-05-14 ai/nn/transformer/gpt/dall-e/3 --- https://x.com/AndrewCurran_/status/1708019664859848994 Andrew Curran 2023-05-14 ai/nn/transformer/gpt/dall-e/3 --- https://x.com/AndrewCurran_/status/1708019696912740491 Andrew Curran 2023-05-14 ai/nn/transformer/gpt/dall-e/3 --- https://x.com/mckaywrigley/status/1708153813583204394 Mckay Wrigley 2023-05-14 ai/nn/transformer/gpt/4/nonfiction --- https://trevorklee.substack.com/p/pharmacokinetics-drug-developments#footnote-4-137517922 2023-05-15 longevity/glp/semaglutide --- https://arxiv.org/abs/2309.13638 Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve R. Thomas McCoy, Shunyu Yao, Dan Friedman, Matthew Hardy, Thomas L. Griffiths 2023-09-24 2023-09-24 [("doi","10.48550/arXiv.2309.13638")] ai/nn/tokenization ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue

The widespread adoption of large language models (LLMs) makes it important to recognize their strengths and limitations. We argue that in order to develop a holistic understanding of these systems we need to consider the problem that they were trained to solve: next-word prediction over Internet text. By recognizing the pressures that this task exerts we can make predictions about the strategies that LLMs will adopt, allowing us to reason about when they will succeed or fail.

This approach—which we call the teleological approach—leads us to identify 3 factors that we hypothesize will influence LLM accuracy: the probability of the task to be performed, the probability of the target output, and the probability of the provided input. We predict that LLMs will achieve higher accuracy when these probabilities are high than when they are low—even in deterministic settings where probability should not matter.

To test our predictions, we evaluate two LLMs (GPT-3.5 and GPT-4) on 11 tasks, and we find robust evidence that LLMs are influenced by probability in the ways that we have hypothesized. In many cases, the experiments reveal surprising failure modes. For instance, GPT-4’s accuracy at decoding a simple cipher is 51% when the output is a high-probability word sequence but only 13% when it is low-probability.

These results show that AI practitioners should be careful about using LLMs in low-probability situations. More broadly, we conclude that we should not evaluate LLMs as if they are humans but should instead treat them as a distinct type of system—one that has been shaped by its own particular set of pressures.

--- https://www.moserware.com/2008/04/towards-moores-law-software-part-3-of-3.html Towards Moore’s Law Software: Part 3 of 3 2023-05-15 cs design/typography design/visualization --- https://x.com/somefoundersalt/status/1708599134960398586 somefoundersalt 2023-05-15 ai/nn/transformer/gpt/3 --- https://www.lesswrong.com/posts/R3eDrDoX8LisKgGZe/sum-threshold-attacks?commentId=yqCkCQLkkaCnZCukJ 2023-05-15 ai/nn/transformer/gpt/claude --- https://www.astralcodexten.com/p/open-thread-296/comment/41146211 2023-05-15 psychiatry psychology/personality/narcissism --- https://www.lesswrong.com/posts/SCqDipWAhZ49JNdmL/paper-llms-trained-on-a-is-b-fail-to-learn-b-is-a#eKhSncieBquLsFTXZ 2023-05-15 ai/nn/transformer/gpt --- https://www.lesswrong.com/posts/Y4rrwkopoigaNGxmS/a-mind-needn-t-be-curious-to-reap-the-benefits-of-curiosity#XeAmDn3NsMqdF6Mij 2023-05-15 reinforcement-learning/meta-learning --- https://www.lesswrong.com/posts/foM8SA3ftY94MGMq9/assessment-of-intelligence-agency-functionality-is-difficult#kzz9CWYAjzdWBhnxp 2023-05-15 economics politics --- https://www.reddit.com/r/slatestarcodex/comments/1201v68/10word_quote_a_short_and_simple_failure_mode_of/jdjsx43/ 2023-05-15 ai/nn/transformer/gpt/4 --- https://en.wikipedia.org/wiki/Cheves_Perky#The_Perky_Effect Cheves Perky § The Perky Effect 2023-05-15 psychology/cognitive-bias/illusion-of-depth psychology/vision --- https://www.reddit.com/r/slatestarcodex/comments/16y14co/scott_has_won_his_ai_image_bet/ 2023-05-16 ai/nn/transformer/gpt/dall-e/3 --- https://animationobsessive.substack.com/p/the-secrets-of-magnetic-rose#%C2%A7splendor-and-decay 2023-05-16 anime --- https://worksinprogress.co/issue/olivine-weathering/ 2023-05-16 technology/carbon-capture --- https://old.reddit.com/r/dalle2/comments/16py1bm/it_appears_that_dalle_3s_reduction_of_harmful/ 2023-05-16 ai/nn/transformer/gpt/dall-e/3 --- https://x.com/generatorman_ai/status/1708849031810785437 generatorman_ai 2023-05-16 ai/nn/transformer/gpt/dall-e/3 --- https://github.com/vitoplantamura/OnnxStream/tree/846da873570a737b49154e8f835704264864b0fe 2023-05-16 ai/nn/diffusion ai/nn/sparsity/low-precision --- https://yetanothermathprogrammingconsultant.blogspot.com/2018/11/chess-and-solution-pool.html 2023-05-16 cs/algorithm --- https://joncharbonneau.substack.com/p/encrypted-mempools 2023-05-16 bitcoin cs/cryptography/timelock --- https://www.xilinx.com/prs_rls/silicon_spart/0333spartan3.htm 2023-05-16 cs/hardware --- https://bitcoins-code.de/ 2023-05-16 cs/cryptography/timelock --- https://bitcointalk.org/index.php?topic=9047.0 2023-05-17 cs/cryptography/timelock --- https://bitslog.com/2015/02/17/faster-sha-256-asics-using-carry-reduced-adders/ 2023-05-17 cs/cryptography/timelock --- https://blog.janestreet.com/really-low-latency-multipliers-and-cryptographic-puzzles/ 2023-05-17 cs/cryptography/timelock --- https://cis.csail.mit.edu/ 2023-05-17 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Gravity_Pipe Gravity Pipe 2023-05-17 cs/hardware --- https://en.wikipedia.org/wiki/Hashcash Hashcash 2023-05-17 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Homomorphic_encryption Homomorphic encryption 2023-05-17 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Key_escrow Key escrow 2023-05-17 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Key_stretching#Strength_and_time Key stretching § Strength and time 2023-05-17 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Memory_hierarchy Memory hierarchy 2023-05-17 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Merkle%27s_Puzzles Merkle’s Puzzles 2023-05-17 cs/cryptography/timelock --- https://mathworld.wolfram.com/SuccessiveSquareMethod.html 2023-05-18 cs/cryptography/timelock --- https://news.ycombinator.com/item?id=19784469 2023-05-18 cs/cryptography/timelock --- https://news.ycombinator.com/item?id=22061752 2023-05-18 cs/cryptography/timelock --- https://news.ycombinator.com/item?id=6508179 2023-05-18 cs/cryptography/timelock --- https://news.ycombinator.com/item?id=6509688 2023-05-18 cs/cryptography/timelock --- https://tuts4you.com/download.php?view.2348 2023-05-18 cs/cryptography/timelock --- https://vdfresearch.org/ 2023-05-18 cs/cryptography/timelock --- https://web.cs.ucla.edu/~rafail/PUBLIC/42.pdf 2023-05-18 cs/cryptography/timelock --- https://www.cs.jhu.edu/~rdas/finalreport.pdf 2023-05-18 cs/cryptography/timelock --- https://www.cs.odu.edu/~mln/pubs/ms/haq-ms-2008.pdf Using timed-release cryptography to mitigate preservation risk of embargo periods Haq 2008 2023-05-18 cs/cryptography/timelock --- https://www.cs.ox.ac.uk/files/9435/tempsig.pdf 2023-05-18 cs/cryptography/timelock --- https://www.cs.tufts.edu/comp/116/archive/fall2013/wclarkson.pdf 2023-05-19 cs/cryptography/timelock --- https://www.daemonology.net/blog/2011-06-03-insecurity-in-the-jungle.html 2023-05-19 cs/cryptography/timelock --- https://www.halfbakery.com/idea/Do_20not_20decrypt_20until_20_2e_2e_2e 2023-05-19 cs/cryptography/timelock --- https://www.quantamagazine.org/in-cryptography-advances-in-program-obfuscation-20140130/ Perfecting the Art of Sensible Nonsense: A new cryptographic scheme obfuscates computer programs by transforming them into something akin to a jigsaw puzzle in which random elements make each individual piece look meaningless. 2023-05-19 cs/cryptography/timelock --- https://www.reddit.com/r/Bitcoin/comments/27adbq/timelock_timerelease_encryption_incentivised_by/ 2023-05-19 cs/cryptography/timelock --- https://www.reddit.com/r/compsci/comments/1nx0n5/timelock_encryption/ 2023-05-19 cs/cryptography/timelock --- https://www.reddit.com/r/compsci/comments/1nx0n5/timelock_encryption/ccne7bk/ 2023-05-19 cs/cryptography/timelock --- https://www.reddit.com/r/crypto/comments/r4q2q/reliable_timelock_crypto_step_one_obtain_a/ Reliable time-lock crypto. Step one: obtain a deep-space probe... 2023-05-19 cs/cryptography/timelock --- https://www.sciencemeta.com/index.php/jsjyjyfz/article/download/40505/56707 ‘Research on Timed-Release Encryption’, 40505-40237-1-PB.pdf 2023-05-19 cs/cryptography/timelock --- https://philipdick.com/mirror/websites/pkdweb/short_stories/The%20Days%20Of%20Perky%20Pat.htm 2023-05-19 psychiatry/schizophrenia --- https://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.5709 2023-05-19 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Rainbow_table Rainbow table 2023-05-20 cs/cryptography/timelock --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.108.7127&rep=rep1&type=pdf 2023-05-20 cs/cryptography/timelock --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.2.3030&rep=rep1&type=ps 2023-05-20 cs/cryptography/timelock --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.460.2104&rep=rep1&type=pdf#page=2 2023-05-20 cs/cryptography/timelock --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.597.6304&rep=rep1&type=pdf 2023-05-20 cs/cryptography/timelock --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.78.6879&rep=rep1&type=pdf 2023-05-20 cs/cryptography/timelock --- https://crypto.stackexchange.com/questions/2507/can-i-encrypt-user-input-in-a-way-i-cant-decrypt-it-for-a-certain-period-of-tim 2023-05-20 cs/cryptography/timelock --- https://crypto.stackexchange.com/questions/5831/what-is-the-progress-on-the-mit-lcs35-time-capsule-crypto-puzzle What is the progress on the MIT LCS35 Time Capsule Crypto-Puzzle? 2023-05-20 cs/cryptography/timelock --- https://crypto.stackexchange.com/questions/606/time-capsule-cryptography 2023-05-20 cs/cryptography/timelock --- https://cypherpunks.venona.com/date/1993/02/msg00129.html Timed-Release Crypto 2023-05-20 cs/cryptography/timelock --- https://en.bitcoin.it/w/index.php?title=User%3AGmaxwell%2Falt_ideas&action=historysubmit&diff=35540&oldid=35539 2023-05-21 cs/cryptography/timelock --- https://en.bitcoin.it/wiki/Why_a_GPU_mines_faster_than_a_CPU 2023-05-21 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Adi_Shamir Adi Shamir 2023-05-21 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Bcrypt Bcrypt 2023-05-21 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Cryptographic_hash_function Cryptographic hash function 2023-05-21 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Custom_hardware_attack Custom hardware attack 2023-05-21 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/David_A._Wagner David A. Wagner 2023-05-21 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/EFF_DES_cracker EFF DES cracker 2023-05-21 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/GNU_Multiple_Precision_Arithmetic_Library GNU Multiple Precision Arithmetic Library 2023-05-21 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Random_oracle Random oracle 2023-05-21 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Ron_Rivest Ron Rivest 2023-05-21 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Secret_sharing Secret sharing 2023-05-22 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Secure_multi-party_computation Secure multi-party computation 2023-05-22 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Self-extracting_archive Self-extracting archive 2023-05-22 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Shor%27s_algorithm Shor’s algorithm 2023-05-22 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Timothy_C._May Timothy C. May 2023-05-22 bitcoin cs/cryptography/timelock darknet-market --- https://en.wikipedia.org/wiki/Trusted_third_party Trusted third party 2023-05-22 cs/cryptography/timelock --- https://en.wikipedia.org/wiki/Trusted_timestamping Trusted timestamping 2023-05-22 cs/cryptography/timelock --- https://eprint.iacr.org/2008/032.pdf Merkle puzzles are optimal—an 𝒪(n2)-query attack on any key exchange from a random oracle 2023-05-22 cs/cryptography/timelock --- https://eprint.iacr.org/2013/258.pdf Witness Encryption and its Applications Garg 2013 2023-05-22 cs/cryptography/timelock --- https://eprint.iacr.org/2015/514.pdf Time-Lock Puzzles from Randomized Encodings 2023-05-22 cs/cryptography/timelock --- https://eprint.iacr.org/2017/201 2023-05-22 cs/cryptography/timelock --- https://eprint.iacr.org/2018/183.pdf 2023-05-23 cs/cryptography/timelock --- https://eprint.iacr.org/2018/601.pdf 2023-05-23 cs/cryptography/timelock --- https://ftp.deas.harvard.edu/techreports/tr-22-06.pdf Time-Lapse Cryptography 2023-05-23 cs/cryptography/timelock --- https://github.com/dorianj/timelock 2023-05-23 cs/cryptography/timelock --- https://github.com/lahwran/mac-sleeplocker/blob/master/timelock.py 2023-05-23 cs/cryptography/timelock --- https://github.com/petertodd/timelock 2023-05-23 cs/cryptography/timelock --- https://github.com/wclarkson/timelock 2023-05-23 cs/cryptography/timelock --- https://koeln.ccc.de/archiv/cyphernomicon/chapter14/14.5.html Other Advanced Crypto Applications: Timed-Release Crypto 2023-05-23 cs/cryptography/timelock --- https://news.ycombinator.com/item?id=6509826 2023-05-23 cs/cryptography/timelock --- https://news.ycombinator.com/item?id=7847687 2023-05-23 cs/cryptography/timelock --- https://news.ycombinator.com/item?id=8447175 2023-05-24 cs/cryptography/timelock --- https://people.csail.mit.edu/rivest/lcs35-puzzle-description.txt Description of the LCS35 Time Capsule Crypto-Puzzle 2023-05-24 cs/cryptography/timelock --- https://project-rainbowcrack.com/ 2023-05-24 cs/cryptography/timelock --- https://project-rainbowcrack.com/buy.php 2023-05-24 cs/cryptography/timelock --- https://pszal.github.io/papers/cvcbt-szalachowski18.pdf 2023-05-24 cs/cryptography/timelock --- https://rfidsec2013.iaik.tugraz.at/RFIDSec08/Papers/Publication/04%20-%20ONeill%20-%20Low%20Cost%20SHA-1%20-%20Paper.pdf Low-Cost SHA-1 Hash Function Architecture for RFID Tags O’Neill 2023-05-24 cs/cryptography/timelock --- https://www.csail.mit.edu/news/programmers-solve-mits-20-year-old-cryptographic-puzzle 2023-05-24 cs/cryptography/timelock --- https://www.iacr.org/conferences/crypto2011/slides/01-3-Mahmoody.pdf 2023-05-24 cs/cryptography/timelock --- https://www.microsoft.com/en-us/research/wp-content/uploads/2003/08/crypto03.pdf 2023-05-24 cs/cryptography/timelock --- https://www.mit.edu/~jlrubin/public/pdfs/858report.pdf 2023-05-24 cs/cryptography/timelock --- https://www.pnas.org/doi/full/10.1073/pnas.1205696109 High-frequency self-aligned graphene transistors with transferred gate stacks Cheng 2012 2023-05-24 cs/cryptography/timelock --- https://www.quantamagazine.org/computer-scientists-achieve-crown-jewel-of-cryptography-20201110/ 2023-05-25 cs/cryptography/timelock --- https://www.tarsnap.com/scrypt.html 2023-05-25 cs/cryptography/timelock --- https://www.tarsnap.com/scrypt/scrypt.pdf 2023-05-25 cs/cryptography/timelock --- https://www.wired.com/2010/07/wikileaks-insurance-file/ 2023-05-25 cs/cryptography/timelock --- https://www.wired.com/story/a-programmer-solved-a-20-year-old-forgotten-crypto-puzzle/ 2023-05-25 cs/cryptography/timelock --- https://wwwcn.cs.uni-duesseldorf.de/publications/publications/library/Jerschow2010a.pdf 2023-05-25 cs/cryptography/timelock --- https://publicdomainreview.org/collection/shadows/ 2023-05-25 fiction/humor history/public-domain-review --- /doc/philosophy/epistemology/2002-heath-playingwithpyramids-3-zendo.pdf Zendo Kory Heath 2002 2023-05-25 design philosophy/epistemology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4516141/ Directional dominance on stature and cognition in diverse human populations Peter K. Joshi, Tõnu Esko, Hannele Mattsson, Niina Eklund, Ilaria Gandin, Teresa Nutile, Anne Uriu Jackson, Claudia Schurmann, Albert Vernon Smith, Weihua Zhang, Yukinori Okada, Alena Stančáková, Jessica D. Faul, Wei Zhao, Traci M. Bartz, Maria Pina Concas, Nora Franceschini, Stefan Enroth, Veronique Vitart, Stella Trompet, Xiuqing Guo, Daniel I. Chasman, Jeffery R. O’Connel, Tanguy Corre, Suraj S. Nongmaithem, Yuning Chen, Massimo Mangino, Daniela Ruggiero, Michela Traglia, Aliki-Eleni Farmaki, Tim Kacprowski, Andrew Bjonnes, Ashley van der Spek, Ying Wu, Anil K. Giri, Lisa R. Yanek, Lihua Wang, Edith Hofer, Cornelius A. Rietveld, Olga McLeod, Marilyn C. Cornelis, Cristian Pattaro, Niek Verweij, Clemens Baumbach, Abdel Abdellaoui, Helen R. Warren, Dragana Vuckovic, Hao Mei, Claude Bouchard, John R. B. Perry, Stefania Cappellani, Saira S. Mirza, Miles C. Benton, Ulrich Broeckel, Sarah E. Medland, Penelope A. Lind, Giovanni Malerba, Alexander Drong, Loïc Yengo, Lawrence F. Bielak, Degui Zhi, Peter J. van der Most, Daniel Shriner, Reedik Mägi, Gibran Hemani, Tugce Karaderi, Zhaoming Wang, Tian Liu, Ilja Demuth, Jing Hua Zhao, Weihua Meng, Lazaros Lataniotis, Sander W. van der Laan, Jonathan P. Bradfield, Andrew R. Wood, Amelie Bonnefond, Tarunveer S. Ahluwalia, Leanne M. Hall, Erika Salvi, Seyhan Yazar, Lisbeth Carstensen, Hugoline G. de Haan, Mark Abney, Uzma Afzal, Matthew A. Allison, Najaf Amin, Folkert W. Asselbergs, Stephan J. L. Bakker, R. Graham Barr, Sebastian E. Baumeister, Daniel J. Benjamin, Sven Bergmann, Eric Boerwinkle, Erwin Böttinger, Archie Campbell, Aravinda Chakravarti, Yingleong Chan, Stephen J. Chanock, Constance Chen, Y-D Ida Chen, Francis S. Collins, John Connell, Adolfo Correa, L. Adrienne Cupples, George Davey Smith, Gail Davies, Marcus Dörr, Georg Ehret, Stephen B. Ellis, Bjarke Feenstra, Mary F. Feitosa, Ian Ford, Caroline S. Fox, Timothy Frayling, Nele Friedrich, Frank Geller, Generation Scotland, Irina Gillham-Nasenya, Omri Gottesman, Misa Graff, Francine Grodstein, Charles Gu, Chris Haley, Christopher J. Hammond, Sarah E. Harris, Tamara B. Harris, Nicholas D. Hastie, Nancy L. Heard-Costa, Kauko Heikkilä, Lynne J. Hocking, Georg Homuth, Jouke-Jan Hottenga, Jinyan Huang, Jennifer E. Huffman, Pirro G. Hysi, M. Arfan Ikram, Erik Ingelsson, Anni Joensuu, Åsa Johansson, Pekka Jousilahti, J. Wouter Jukema, Kähönen Mika, Yoichiro Kamatani, Stavroula Kanoni, Shona M. Kerr, Nazir M. Khan, Philipp Koellinger, Heikki A. Koistinen, Manraj K. Kooner, Michiaki Kubo, Johanna Kuusisto, Jari Lahti, Lenore J. Launer, Rodney A. Lea, Benjamin Lehne, Terho Lehtimäki, David C. M. Liewald, Lars L. Lind, Marie Loh, Marja-Liisa Lokki, Stephanie J. London, Stephanie J. Loomis, Anu Loukola, Yingchang Lu, Thomas Lumley, Annamari Lundqvist, Satu Männistö, Pedro Marques-Vidal, Corrado Masciullo, Angela Matchan, Rasika A. Mathias, Koichi Matsuda, James B. Meigs, Christa Meisinger, Thomas Meitinger, Cristina Menni, Frank D. Mentch, Evelin Mihailov, Lili Milani, May E. Montasser, Grant W. Montgomery, Alanna Morrison, Richard H. Myers, Rajiv Nadukuru, Pau Navarro, Mari Nelis, Markku S. Nieminen, Ilja M. Nolte, George T. O’Connor, Adesola Ogunniyi, Sandosh Padmanabhan, Walter R. Palmas, James S. Pankow, Inga Patarcic, Francesca Pavani, Patricia A. Peyser, Kirsi Pietilainen, Neil Poulter, Inga Prokopenko, Sarju Ralhan, Paul Redmond, Stephen S. Rich, Harri Rissanen, Antonietta Robino, Lynda M. Rose, Richard Rose, Cinzia Sala, Babatunde Salako, Veikko Salomaa, Antti-Pekka Sarin, Richa Saxena, Helena Schmidt, Laura J. Scott, William R. Scott, Bengt Sennblad, Sudha Seshadri, Peter Sever, Smeeta Shrestha, Blair H. Smith, Jennifer A. Smith, Nicole Soranzo, Nona Sotoodehnia, Lorraine Southam, Alice V. Stanton, Maria G. Stathopoulou, Konstantin Strauch, Rona J. Strawbridge, Matthew J. Suderman, Nikhil Tandon, Sian-Tsun Tang, Kent D. Taylor, Bamidele O. Tayo, Anna Maria Töglhofer, Maciej Tomaszewski, Natalia Tšernikova, Jaakko Tuomilehto, André G. Uitterlinden, Dhananjay Vaidya, Astrid van Hylckama Vlieg, Jessica van Setten, Tuula Vasankari, Sailaja Vedantam, Efthymia Vlachopoulou, Diego Vozzi, Eero Vuoksimaa, Melanie Waldenberger, Erin B. Ware, William Wentworth-Shields, John B. Whitfield, Sarah Wild, Gonneke Willemsen, Chittaranjan S. Yajnik, Jie Yao, Gianluigi Zaza, Xiaofeng Zhu, The BioBank Japan Project, Rany M. Salem, Mads Melbye, Hans Bisgaard, Nilesh J. Samani, Daniele Cusi, David A. Mackey, Richard S. Cooper, Philippe Froguel, Gerard Pasterkamp, Struan F. A. Grant, Hakon Hakonarson, Luigi Ferrucci, Robert A. Scott, Andrew D. Morris, Colin Palmer, George Dedoussis, Panos Deloukas, Lars Bertram, Ulman Lindenberger, Sonja I. Berndt, Cecilia M. Lindgren, Nicholas J. Timpson, Anke Tönjes, Patricia B. Munroe, Thorkild I. A. Sørensen, Charles N. Rotimi, Donna K. Arnett, Albertine J. Oldehinkel, Sharon L. R. Kardia, Beverley Balkau, Giovanni Gambaro, Andrew P. Morris, Johan G. Eriksson, Margie J. Wright, Nicholas G. Martin, Steven C. Hunt, John M. Starr, Ian J. Deary, Lyn R. Griffiths, Henning Tiemeier, Nicola Pirastu, Jaakko Kaprio, Nicholas J. Wareham, Louis Pérusse, James G. Wilson, Giorgia Girotto, Mark J. Caulfield, Olli T. Raitakari, Dorret I. Boomsma, Christian Gieger, Pim van der Harst, Andrew A. Hicks, Peter Kraft, Juha Sinisalo, Paul Knekt, Magnus Johannesson, Patrik K. E. Magnusson, Anders Hamsten, Reinhold Schmidt, Ingrid B. Borecki, Erkki Vartiainen, Diane M. Becker, Dwaipayan Bharadwaj, Karen L. Mohlke, Michael Boehnke, Cornelia van Duijn, Dharambir K. Sanghera, Alexander Teumer, Eleftheria Zeggini, Andres Metspalu, Paolo Gasparini, Sheila Ulivi, Carole Ober, Daniela Toniolo, Igor Rudan, David J. Porteous, Marina Ciullo, Tim D. Spector, Caroline Hayward, Josée Dupuis, Ruth Loos, Alan F. Wright, Giriraj R. Chandak, Peter Vollenweider, Alan R. Shuldiner, Paul M. Ridker, Jerome I. Rotter, Naveed Sattar, Ulf Gyllensten, Kari E. North, Mario Pirastu, Bruce M. Psaty, David R. Weir, Markku Laakso, Vilmundur Gudnason, Atsushi Takahashi, John C. Chambers, Jaspal S. Kooner, David P. Strachan, Harry Campbell, Joel N. Hirschhorn, Markus Perola, Ozren Polašek, James F. Wilson 2015 2023-05-25 [("doi","10.1038/nature14618")] genetics/heritable/rare iq

Homozygosity has long been associated with rare, often devastating, Mendelian disorders, and Darwin was one of the first to recognize that inbreeding reduces evolutionary fitness. However, the effect of the more distant parental relatedness that is common in modern human populations is less well understood.

Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is required on very large numbers of people to provide sufficient power.

Here we use runs of homozygosity to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts, and find statistically-significant associations between summed runs of homozygosity and 4 complex traits: height, forced expiratory lung volume in one second, general cognitive ability and educational attainment (p < 1 × 10−300, 2.1 × 10−6, 2.5 × 10−10 and 1.8 × 10−10, respectively). In each case, increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months’ less education.

Similar effect sizes were found across 4 continental groups and populations with different degrees of genome-wide homozygosity, providing evidence that homozygosity, rather than confounding, directly contributes to phenotypic variance. Contrary to earlier reports in substantially smaller samples, no evidence was seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein cholesterol, or 10 other cardio-metabolic traits.

Since directional dominance is predicted for traits under directional evolutionary selection, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been.

--- https://arxiv.org/abs/2111.08566#microsoft SPANN: Highly-efficient Billion-scale Approximate Nearest Neighbor Search Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu, Zengzhong Li, Mao Yang, Jingdong Wang 2021-11-05 2023-05-25 [("doi","10.48550/arXiv.2111.08566")] ai/nn/retrieval

The in-memory algorithms for approximate nearest neighbor search (ANNS) have achieved great success for fast high-recall search, but are extremely expensive when handling very large scale database. Thus, there is an increasing request for the hybrid ANNS solutions with small memory and inexpensive solid-state drive (SSD).

In this paper, we present a simple but efficient memory-disk hybrid indexing and search system, SPANN, that follows the inverted index methodology. It stores the centroid points of the posting lists in the memory and the large posting lists in the disk. We guarantee both disk-access efficiency (low latency) and high recall by effectively reducing the disk-access number and retrieving high-quality posting lists.

In the index-building stage, we adopt a hierarchical balanced clustering algorithm to balance the length of posting lists and augment the posting list by adding the points in the closure of the corresponding clusters. In the search stage, we use a query-aware scheme to dynamically prune the access of unnecessary posting lists.

Experiment results demonstrate that SPANN is 2× faster than the state-of-the-art ANNS solution DiskANN to reach the same recall quality 90% with same memory cost in 3 billion-scale datasets. It can reach 90% recall@1 and recall@10 in just around one millisecond with only 32GB memory cost. Code is available at Github.

--- https://arxiv.org/abs/1702.04521 Frustratingly Short Attention Spans in Neural Language Modeling Michał Daniluk, Tim Rocktäschel, Johannes Welbl, Sebastian Riedel 2017-02-15 2023-05-25 [("doi","10.48550/arXiv.1702.04521")] ai/nn/rnn

Neural language models predict the next token using a latent representation of the immediate token history. Recently, various methods for augmenting neural language models with an attention mechanism over a differentiable memory have been proposed. For predicting the next token, these models query information from a memory of the recent history which can facilitate learning mid- and long-range dependencies.

However, conventional attention mechanisms used in memory-augmented neural language models produce a single output vector per time step. This vector is used both for predicting the next token as well as for the key and value of a differentiable memory of a token history.

In this paper, we propose a neural language model with a key-value attention mechanism that outputs separate representations for the key and value of a differentiable memory, as well as for encoding the next-word distribution. This model outperforms existing memory-augmented neural language models on two corpora.

Yet, we found that our method mainly uses a memory of the 5 most recent output representations. This led to the unexpected main finding that a much simpler model based only on the concatenation of recent output representations from previous time steps is on par with more sophisticated memory-augmented neural language models.

--- https://arxiv.org/abs/1805.09786#deepmind Hyperbolic Attention Networks Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann, Peter Battaglia, Victor Bapst, David Raposo, Adam Santoro, Nando de Freitas 2018-05-24 2023-05-26 [("doi","10.48550/arXiv.1805.09786")] ai/nn/transformer/attention/recurrent

We introduce hyperbolic attention networks to endow neural networks with enough capacity to match the complexity of data with hierarchical and power-law structure. A few recent approaches have successfully demonstrated the benefits of imposing hyperbolic geometry on the parameters of shallow networks.

We extend this line of work by imposing hyperbolic geometry on the activations of neural networks. This allows us to exploit hyperbolic geometry to reason about embeddings produced by deep networks. We achieve this by re-expressing the ubiquitous mechanism of soft attention in terms of operations defined for hyperboloid and Klein models.

Our method shows improvements in terms of generalization on neural machine translation, learning on graphs and visual question answering tasks while keeping the neural representations compact.

--- https://www.biorxiv.org/content/10.1101/2023.09.03.556099.full A Quantitative Study of Inappropriate Image Duplication in the Journal Toxicology Reports Sholto David 2023-09-10 2023-09-10 [("doi","10.1101/2023.09.03.556099")] ai statistics/bias

Inappropriate image duplication is a type of scientific error that can be detected by examining published literature. Few estimates of the frequency of this problem have been published. This study aimed to quantify the rate of image duplication in the journal Toxicology Reports.

In total, 1,540 unique articles (identified by DOI) were checked for the presence of research related images (microscopy, photography, western blot scans, etc). Each research paper containing at least one such image was scrutinized for the presence of inappropriate duplications, first by manual review only, and subsequently with the assistance of an AI tool (ImageTwin.ai).

Overall, Toxicology Reports published 715 papers containing relevant images, and 115 of these papers contained inappropriate duplications (16%). Screening papers with the use of ImageTwin.ai increased the number of inappropriate duplications detected, with 41⁄115 missed during the manual screen and subsequently detected with the aid of the software.

In summary, the rate of inappropriate image duplication in this journal has been quantified at 16%, most of these errors could have been detected at peer review by careful reading of the paper and related literature. The use of ImageTwin.ai was able to increase the number of detected problematic duplications.

--- https://x.com/lumpenspace/status/1709773644203708527 lumpenspace 2023-05-26 ai/nn/transformer/gpt/4/poetry --- https://www.uspto.gov/sites/default/files/documents/OpenAI_RFC-84-FR-58141.pdf 2023-05-26 economics/copyright law --- https://www.reddit.com/r/dalle2/comments/170kz6m/famous_smart_people_as_bosses_in_a_jrpg/ 2023-05-26 ai/nn/transformer/gpt/dall-e/3 --- https://www.propublica.org/article/americas-richest-school-serves-low-income-kids-but-much-of-its-hershey-funded-fortune-isnt-being-spent 2023-05-26 economics/perpetuities --- https://en.wikipedia.org/wiki/Milton_Hershey_School Milton Hershey School 2023-05-26 economics/perpetuities --- https://www.amazon.com/Chocolate-Trust-Deception-Indenture-Secrets/dp/1933822597 2023-05-26 economics/perpetuities --- https://x.com/thebasepoint/status/1710050231780257882 Joshua Batson 2023-05-26 ai/nn/tokenization --- https://x.com/davidalmog25/status/1708620290010362056 davidalmog25 2023-05-26 iq --- https://arxiv.org/abs/1310.4546#google Distributed Representations of Words and Phrases and their Compositionality Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean 2013-10-16 2023-05-26 [("doi","10.48550/arXiv.1310.4546")] ai/nn

[code] The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships.

In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling.

An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of “Canada” and “Air” cannot be easily combined to obtain “Air Canada”. Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible.

--- https://arxiv.org/abs/2310.02207 Language Models Represent Space and Time Wes Gurnee, Max Tegmark 2023-10-03 2023-10-03 [("doi","10.48550/arXiv.2310.02207")] ai/nn/transformer

[Twitter, commentary; seems extremely duplicative of lots of old work on embeddings, particularly word embeddings, eg. word2vec or Faisal & Anastasopoulos 2022] The capabilities of large language models (LLMs) have sparked debate over whether such systems just learn an enormous collection of superficial statistics or a coherent model of the data generating process—a world model.

We find evidence for the latter by analyzing the learned representations of 3 spatial datasets (world, US, NYC places) and 3 temporal datasets (historical figures, artworks, news headlines) in the LLaMA-2 family of models.

We discover that LLMs learn linear representations of space and time across multiple scales. These representations are robust to prompting variations and unified across different entity types (eg. cities and landmarks).

In addition, we identify individual “space neurons” and “time neurons” that reliably encode spatial and temporal coordinates.

Our analysis demonstrates that modern LLMs acquire structured knowledge about fundamental dimensions such as space and time, supporting the view that they learn not merely superficial statistics, but literal world models.

--- https://www.lesswrong.com/posts/Pweg9xpKknkNwN8Fx/have-attention-spans-been-declining 2023-05-27 iq sociology/technology --- https://www.experimental-history.com/p/on-the-importance-of-staring-directly Adam Mastroianni 2023-05-27 philosophy/epistemology psychology/cognitive-bias/illusion-of-depth --- https://jvns.ca/blog/2023/10/06/new-talk--making-hard-things-easy/ 2023-05-27 cs/algorithm cs/shell design --- https://x.com/venturetwins/status/1710321733184667985 Justine Moore 2023-05-27 ai/nn/transformer/gpt/4 cs/security --- https://x.com/BrianRoemmele/status/1710397599474458818 Brian Roemmele 2023-05-27 ai/nn/diffusion/discrete --- https://arxiv.org/abs/2212.10408 2023-05-27 ai/nn/transformer/gpt/2 --- https://arxiv.org/abs/2212.10408 Geographic and Geopolitical Biases of Language Models Fahim Faisal, Antonios Anastasopoulos 2022-12-20 2023-05-27 [("doi","10.48550/arXiv.2212.10408")] ai/nn/transformer/gpt/2

Pretrained language models (PLMs) often fail to fairly represent target users from certain world regions because of the under-representation of those regions in training datasets. With recent PLMs trained on enormous data sources, quantifying their potential biases is difficult, due to their black-box nature and the sheer scale of the data sources.

In this work, we devise an approach to study the geographic bias (and knowledge) present in PLMs [GPT-2 & BLOOM], proposing a Geographic-Representation Probing Framework adopting a self-conditioning method coupled with entity-country mappings.

Our findings suggest PLMs’ representations map surprisingly well to the physical world in terms of country-to-country associations, but this knowledge is unequally shared across languages. Last, we explain how large PLMs despite exhibiting notions of geographical proximity, over-amplify geopolitical favoritism at inference time.

--- /doc/economics/automation/2023-gomes.pdf Do Robots Increase Wealth Dispersion? Francisco Gomes, Thomas Jansson, Yigitcan Karabulut 2023-06-09 2023-06-09 [("doi","10.1093/rfs/hhad050")] economics/automation

We document significant negative effects of exposure to increased automation at work on household wealth accumulation. Beyond the income and savings channels, we uncover a novel mechanism contributing to the negative wealth effects of automation that arises through the endogenous optimal portfolio decisions of households. We show that households rebalance their financial wealth away from the stock market in response to increased human capital risk induced by pervasive automation, thereby attaining lower wealth levels and relative positions in the wealth distribution. Our evidence suggests that the portfolio channel amplifies the inequality-enhancing effects of increased automation.

Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9806505/ Understanding a Mutually Destructive Relationship Between Individuals With Borderline Personality Disorder and Their Favorite Person Hyorim Jeong, Min Jin Jin, Myoung Ho Hyun 2022 2023-05-27 [("doi","10.30773/pi.2022.0079")] psychiatry/borderline

Objective: Individuals with borderline personality disorder (BPD) commonly have a Favorite Person (FP), whom they are heavily emotionally attached to and dependent on. This study aims to identify and illustrate the patterns of destructive FP relationships based on actual experiences described by those with BPD.

Method: A data mining process was conducted using raw data collected from online communities, such as blogs and social networks. An in-depth review of the information to better understand the natural course of the FP relationship was also conducted.

Results: Individuals with BPD form an intense and insecure attachment toward their FP, from which they enormously suffer. FPs can be their friends, romantic or life partners, or family members. As their feelings go beyond their control, being increasingly obsessed with their FP, they make their FP gradually lose hope in continuing the relationship and want to quit trying to fulfill their needs. The relationship finally ends when the FP stops being responsible for meeting their expectations and eventually drifts away.

Conclusion: This study suggests that certain FPs, distinctively named Teddy Bear Person, may behave in a particular manner to increase the likelihood of the FP relationship becoming more destructive. Moreover, the rejection sensitivity model should be discussed to understand their dysfunctional interaction.

Data collection: Public posts and comments available online were collected using the keywords “favorite person (FP) and borderline personality disorder (BPD)” in November 2021. Posts published between October 2016 and November 2021 were selectively collected; they were written mostly by those who voluntarily and publicly revealed themselves to be diagnosed with BPD and have or have had an FP. Online sites mainly included popular websites, such as themighty.com, reddit.com, and Quora, where people with common interests can form a supportive community to share helpful knowledge and actively interact with each other. Collected data were used for a text mining approach with RStudio to extract research concepts and reviewed to qualitatively explore individuals’ experiences and the relationship-dynamic.

To reduce noise, we included posts and comments written in English, containing any information on FP relationship experience, regardless of the story type or length. We then excluded posts or comments if they had no text content describing FP meaning, their relationship experience, or their feelings toward their FP and those that other readers could not relate to because the term is commonly used within the BPD community but not certainly defined as an official term yet. This final set of 207 posts and comments on their FP relationships was analyzed

…Favorite person in the borderline personality disorder community: FP has a unique meaning in the BPD community. A FP is a person who someone with BPD relies heavily on for emotional support, seeks attention and validation from, and looks up to or idealizes. When referred to as a FP, it goes beyond what other people would generally refer to as their best friend or favorite person. FPs are the object of complete attachment and extreme love from people with BPD. Therefore, those with BPD feel unable to function properly without their FP and fear that their FP will abandon them.

“While a best friend can be an FP, it’s usually so much more than that… a favorite person is someone you have an emotional dependence on, who can ‘make or break’ your day.”20 “You place the responsibility of your happiness onto them. They can make you feel on top of the world, or in the deepest pit depending on whether they are paying attention to you or not.” [21]

…The destructive FP relationship pattern implies that most FPs share particular characteristics that make them more likely to become an FP and contribute to the formation of toxic FP attachment. Identifying the shared personality traits in many, if not all, FPs to understand the relationship from which those with BPD enormously suffer is crucial. Our findings reveal that certain FPs gradually intensify their person with BPD’s symptoms and assume avoidance behaviors when conflicts arise. In this study, for convenience, we address them distinctively as “Teddy Bear Person (or TBP)”, based on previous research, demonstrating that patients with BPD tend to develop a problematic attachment to and emotionally rely on transitional objects, such as stuffed animals, that would provide them with emotional comfort and stability40, 41, 42. The association between transitional object attachment and BPD was so strong that carrying stuffed animals to hospital settings was also referred to as the “positive bear sign.”43

--- https://x.com/Suhail/status/1710653717081653712 Suhail Doshi 2023-05-28 ai/nn/transformer/gpt/dall-e/3 --- https://en.wikipedia.org/wiki/Borderline_personality_disorder Borderline personality disorder 2023-05-28 psychiatry/borderline --- https://en.wikipedia.org/wiki/Splitting_(psychology) Splitting (psychology) 2023-05-28 psychiatry/borderline --- https://en.wikipedia.org/wiki/Transitional_object Transitional object 2023-05-28 psychiatry/anxiety psychiatry/borderline/transitional-object --- /doc/psychiatry/borderline/1996-labbate.pdf Bedside Stuffed Animals and Borderline Personality Lawrence A. Labbate, David M. Benedek 1996-10-01 2023-05-28 [("doi","10.2466/pr0.1996.79.2.624")] psychiatry/borderline psychiatry/depression

We explored the relationship between psychiatric diagnosis and the presence of stuffed animals at the bedside in a population of adult female psychiatric inpatients.

One of the authors made ~weekly surveys of the wardrooms of adult psychiatric inpatients over 12 months for the presence of displayed stuffed animals. The observer was blind to the diagnosis of 80% of the patients, and the study or its hypothesis was not known to other physicians.

The discharge diagnoses of patients displaying stuffed animals were recorded and compared with those of the ward population in general. Among 36 female patients who displayed stuffed animals in their rooms, Borderline Personality Disorder was diagnosed in 22 (61%) of these patients. Of 447 adult female patients admitted to the same unit over the same period, only 17% were noted to be diagnosed with Borderline Personality Disorder.

Stuffed animals as a bedside clinical clue may suggest evaluation for Borderline Personality Disorder.

--- /doc/culture/2019-wei.pdf The Similarity Network of Motion Pictures Yanhao Max Wei 2019-10-16 2023-05-28 [("doi","10.1287/mnsc.2018.3261")] culture psychology/novelty

Ideas are connected. New ideas are often seen as creative combinations of previous ideas. I study these connections in the context of motion pictures.

A network of 4,445 movies is constructed to indicate which movies are similar. I first examine the properties of the network using descriptive and regression analysis; then I develop a model of network formation for counterfactual analysis.

It is found that most movies imitate and evolve around a “core” of the more successful movies. In addition, imitation is both conventional and atypical: a new movie usually follows a stream of similar movies yet simultaneously combines atypical elements from movies outside this stream. This atypicality, if well balanced, has a positive effect on the individual movie’s box office.

However, I find that, in the long run, atypical combination may lead to a worse collective box-office performance because of the way it changes the market structure.

--- https://www.midjourney.com/ 2023-05-28 ai/nn/diffusion/midjourney --- https://x.com/Shade_9SQ/status/1508413275679174660 Nevermore. / Made with @midjourney / @images_ai ✨ / #AIart #aiartcommunity #artwork #Artists / #artist #AIartwork #generativeart #art 2023-05-28 ai/nn/diffusion/midjourney --- https://en.wikipedia.org/wiki/Midjourney Midjourney 2023-05-28 ai/nn/diffusion/midjourney --- https://x.com/chaseleantj/status/1694328615503331454 Chase Lean 2023-05-28 ai/nn/diffusion/midjourney --- https://www.stockperformer.com/blog/is-ai-killing-the-stock-industry-a-data-perspective/ 2023-05-28 ai/nn/diffusion/midjourney economics/automation --- https://medium.com/seeds-for-the-future/midjourney-v5-has-arrived-and-its-really-good-ef15b78ae268 2023-05-29 ai/nn/diffusion/midjourney --- https://x.com/javilopen/status/1636135771399938048 Javi Lopez 2023-05-29 ai/nn/diffusion/midjourney --- https://x.com/Buntworthy/status/1635280463869931521 Buntworthy 2023-05-29 ai/nn/diffusion/midjourney --- https://x.com/ClaireSilver12/status/1621959505566121984 Claire Silver 2023-05-29 ai/nn/diffusion/midjourney --- https://newsletters.theatlantic.com/galaxy-brain/62fc502abcbd490021afea1e/twitter-viral-outrage-ai-art/ 2023-05-29 ai/nn/diffusion/midjourney --- https://www.lesswrong.com/posts/DhDAXQw4PsWXnmwPS/ai-art-isn-t-about-to-shake-things-up-it-s-already-here 2023-05-29 ai/nn/diffusion/midjourney --- https://aiweirdness.tumblr.com/post/678304032218136576/the-moon-was-made-of-cake-all-along-having-fun 2023-05-29 ai/nn/diffusion/midjourney --- https://stablediffusionlitigation.com/ 2023-05-29 ai/nn/diffusion/midjourney economics/copyright --- https://x.com/ESYudkowsky/status/1594812817454297089 Eliezer Yudkowsky 2023-05-29 ai/nn/diffusion/midjourney --- https://www.reddit.com/r/AnimeResearch/comments/yvd09w/the_majou_witch_project_demoing_service_at_anime/#thing_t1_iwgwegw 2023-05-29 ai/nn/diffusion/midjourney --- https://ipwatchdog.com/2022/11/01/us-copyright-office-backtracks-registration-partially-ai-generated-work/ 2023-05-29 ai/nn/diffusion/midjourney economics/copyright --- https://stratechery.com/2022/an-interview-with-daniel-gross-and-nat-friedman-about-the-democratization-of-ai/ 2023-05-30 ai/nn/diffusion/midjourney --- https://x.com/midjourney 2023-05-30 ai/nn/diffusion/midjourney --- https://www.youtube.com/watch?v=CgW0HPHqFE8 2023-05-30 cs/algorithm --- https://www.vice.com/en/article/bvmvqm/an-ai-generated-artwork-won-first-place-at-a-state-fair-fine-arts-competition-and-artists-are-pissed 2023-05-30 ai/nn/diffusion/midjourney --- https://commons.wikimedia.org/wiki/Category:Images_generated_by_Midjourney 2023-05-30 ai/nn/diffusion/midjourney --- https://www.templetons.com/tech/prolespec.html 2023-05-30 cs --- https://civitai.com/models/130190/ascii-art 2023-05-30 ai/nn/diffusion design/typography --- https://www.unicode.org/notes/tn28/UTN28-PlainTextMath-v3.2.pdf 2023-05-30 design/typography/tex math --- https://www.reddit.com/r/wikipedia/comments/173kbpn/why_is_everyones_photo_on_wikipedia_a_picture_of/k43hb0h/ 2023-05-30 wikipedia --- https://www.cnbc.com/2023/10/04/ai-startup-prophetic-aims-to-build-headset-that-lets-you-control-dreams.html 2023-05-30 psychology/vision/dream --- https://arxiv.org/abs/2305.14406 Deep Learning based Forecasting: a case study from the online fashion industry Manuel Kunz, Stefan Birr, Mones Raslan, Lei Ma, Zhen Li, Adele Gouttes, Mateusz Koren, Tofigh Naghibi, Johannes Stephan, Mariia Bulycheva, Matthias Grzeschik, Armin Kekić, Michael Narodovitch, Kashif Rasul, Julian Sieber, Tim Januschowski 2023-05-23 2023-05-31 [("doi","10.48550/arXiv.2305.14406")] ai/nn/transformer ai/tabular statistics/prediction

Demand forecasting in the online fashion industry is particularly amendable to global, data-driven forecasting models because of the industry’s set of particular challenges. These include the volume of data, the irregularity, the high amount of turn-over in the catalog and the fixed inventory assumption. While standard deep learning forecasting approaches cater for many of these, the fixed inventory assumption requires a special treatment via controlling the relationship between price and demand closely.

In this case study [of Zalando], we describe the data and our modeling approach for this forecasting problem in detail and present empirical results that highlight the effectiveness of our approach.

--- https://www.econtalk.org/adam-mastroianni-on-the-brain-the-ears-and-how-we-learn/ 2023-05-31 psychology/cognitive-bias/illusion-of-depth --- https://www.experimental-history.com/p/youll-forget-most-of-what-you-learn Adam Mastroianni 2023-05-31 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- https://en.wikipedia.org/wiki/Greeble_(psychology) Greeble (psychology) 2023-05-31 design psychology/dark-knowledge psychology/vision --- https://mattlakeman.org/2023/10/09/notes-on-ghana/ 2023-05-31 psychiatry/bipolar/energy --- https://www.newyorker.com/news/news-desk/the-uyghurs-forced-to-process-the-worlds-fish 2023-05-31 history/uighur --- https://coredumped.dev/2023/08/09/text-showdown-gap-buffers-vs-ropes/ 2023-05-31 cs/lisp --- https://www.reddit.com/r/dalle2/comments/173vqqn/make_me_a_oversaturated_deep_fried_meme_pretend/ 2023-05-31 ai/nn/transformer/gpt/dall-e/3 --- https://www.medrxiv.org/content/10.1101/2023.10.06.23296652.full Self-report inaccuracy in the UK Biobank: Impact on inference and interplay with selective participation Tabea Schoeler, Jean-Baptiste Pingault, Zoltán Kutalik 2023-10-06 2023-10-06 [("doi","10.1101/2023.10.06.23296652")] genetics/heritable/correlation

While the use of short self-report measures is common practice in biobank initiatives, such phenotyping strategy is inherently prone to reporting errors. In this work, we aimed to explore challenges related to self-report errors for biobank-scale research. We derived a reporting error score (RESUM) for n = 73,129 UK Biobank (UKBB) participants, capturing inconsistent self-reporting in time-invariant phenotypes across multiple measurement occasions.

We then performed genome-wide association scans on RESUM, applied downstream analyses (LD Score Regression and Mendelian Randomization, MR), and compared its properties to a previously studied participation behavior (UKBB participation propensity). The results were then used in extended analyses (simulations, inverse probability and variance weighting) to explore patterns and propose possible corrections for biases induced by reporting error and/or selective participation.

Finally, to assess the impact of reporting error on SNP effects and trait heritability, we improved phenotype resolution for 15 self-report measures and inspected the changes in genomic findings. Reporting error was present in the UKBB across all 33 assessed, time-invariant, measures, with repeatability levels as low as 11% (eg. inconsistent recall of childhood sunburns). We found that reporting error was not independent from UKBB participation, evidenced by their negative genetic correlation (rg = −0.90), their shared causes (eg. education, income, intelligence; assessed in MR) and the loss in self-report accuracy following participation bias correction.

Depending on where reporting error occurred in the analytical pipeline, its impact ranged from reduced power (eg. for gene-discovery) to biased effect estimates (eg. if present in the exposure variable) and attenuation of genome-wide quantities (eg. 20% relative h2-attenuation for self-reported childhood height). Our findings highlight that both self-report accuracy and selective participation are competing biases and sources of poor reproducibility for biobank-scale research. Implementation of approaches that aim to enhance phenotype resolution while ensuring sample representativeness are therefore essential when working with biobank data.

--- https://engineering.atspotify.com/2013/06/creative-usernames/ 2023-05-31 cs/security design/typography --- https://arxiv.org/abs/2310.04153 Fair coins tend to land on the same side they started: Evidence from 350,757 Flips František Bartoš, Alexandra Sarafoglou, Henrik R. Godmann, Amir Sahrani, David Klein Leunk, Pierre Y. Gui, David Voss, Kaleem Ullah, Malte J. Zoubek, Franziska Nippold, Frederik Aust, Felipe F. Vieira, Chris-Gabriel Islam, Anton J. Zoubek, Sara Shabani, Jonas Petter, Ingeborg B. Roos, Adam Finnemann, Aaron B. Lob, Madlen F. Hoffstadt, Jason Nak, Jill de Ron, Koen Derks, Karoline Huth, Sjoerd Terpstra, Thomas Bastelica5, Magda Matetovici, Vincent L. Ott, Andreea S. Zetea, Katharina Karnbach, Michelle C. Donzallaz, Arne John, Roy M. Moore, Franziska Assion, Riet van Bork, Theresa E. Leidinger, Xiaochang Zhao, Adrian Karami Motaghi, Ting Pang, Hannah Armstrong, Tianqi Peng, Mara Bialas, Joyce Y. -C. Pang, Bohan Fu, Shujun Yang, Xiaoyi Lin, Dana Sleiffer, Miklos Bognar, Balazs Aczel, Eric-Jan Wagenmakers 2023-10-06 2023-10-06 [("doi","10.48550/arXiv.2310.04153")] science statistics/probability

Many people have flipped coins but few have stopped to ponder the statistical and physical intricacies of the process. In a preregistered study we collected 350,757 coin flips to test the counterintuitive prediction from a physics model of human coin tossing developed by Persi Diaconis. The model asserts that when people flip an ordinary coin, it tends to land on the same side it started—Diaconis estimated the probability of a same-side outcome to be about 51%.

Our data lend strong support to this precise prediction: the coins landed on the same side more often than not, Pr(same side) = 0.508, 95% credible interval (CI) [0.506, 0.509], BFsame-side bias = 2,364. Furthermore, the data revealed considerable between-people variation in the degree of this same-side bias.

Our data also confirmed the generic prediction that when people flip an ordinary coin—with the initial side-up randomly determined—it is equally likely to land heads or tails: Pr(heads) = 0.500, 95% CI [0.498, 0.502], BFheads-tails bias = 0.183. Furthermore, this lack of heads-tails bias does not appear to vary across coins.

Our data therefore provide strong evidence that when some (but not all) people flip a fair coin, it tends to land on the same side it started. Our data provide compelling statistical support for Diaconis’ physics model of coin tossing.

--- https://tratt.net/laurie/blog/2016/fine_grained_language_composition.html 2023-06-01 cs/python --- https://www.medrxiv.org/content/10.1101/2022.10.23.22281420.full Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk Xilin Jiang, Martin Jinye Zhang, Yidong Zhang, Arun Durvasula, Michael Inouye, Chris Holmes, Alkes Price, Gil McVean 2023-04-28 2023-06-01 [("doi","10.1101/2022.10.23.22281420")] genetics/heritable/correlation longevity

The analysis of longitudinal data from electronic health records (EHR) has potential to improve clinical diagnoses and enable personalized medicine, motivating efforts to identify disease subtypes from age-dependent patient comorbidity information.

Here, we introduce an age-dependent topic modeling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR data sets. The model learns, and assigns to each individual, topic weights for several disease topics, each of which reflects a set of diseases that tend to co-occur within individuals as a function of age. Simulations show that ATM attains high accuracy in distinguishing distinct age-dependent comorbidity profiles.

We applied ATM to 282,957 UK Biobank samples, analysing 1,726,144 disease diagnoses spanning all 348 diseases with ≥1,000 independent occurrences in the Hospital Episode Statistics (HES) data, identifying 10 disease topics under the optimal model fit. Analysis of an independent cohort, All of Us, with 211,908 samples and 3,098,771 disease diagnoses spanning 233 of the 348 UK Biobank diseases produced highly concordant findings.

In UK Biobank we identified 52 diseases with heterogeneous comorbidity profiles (≥500 occurrences assigned to each of ≥2 topics), including breast cancer, type 2 diabetes (T2D), hypertension, and hypercholesterolemia. For most of these diseases, topic assignments were highly age-dependent, suggesting differences in disease aetiology for early-onset vs. late-onset disease.

We defined subtypes of the 52 heterogeneous diseases based on the topic assignments, and compared genetic risk across subtypes using polygenic risk scores (PRS). We identified 18 disease subtypes whose PRS differed significantly from other subtypes of the same disease, including a subtype of T2D characterised by cardiovascular comorbidities and a subtype of asthma characterised by dermatological comorbidities.

We further identified specific variants underlying these differences such as a T2D-associated SNP in the HMGA2 locus that has a higher odds ratio in the top quartile of cardiovascular topic weight (1.18±0.02) compared to the bottom quartile (1.00±0.02) (p = 3 × 10−7 for difference, FDR = 0.0002 < 0.1).

In conclusion, ATM identifies disease subtypes with differential genome-wide and locus-specific genetic risk profiles.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824834/ Glucagon-like Peptide 1 Receptor Activation Inhibits Microglial Pyroptosis via Promoting Mitophagy to Alleviate Depression-like Behaviors in Diabetic Mice Fan Yang, Xinshang Wang, Jingyu Qi, Kun Zhang, Yongli Jiang, Ban Feng, Tao Lv, Le Yang, Qi Yang, Minggao Zhao, Shuibing Liu, Xue Ma 2022 2023-06-01 [("doi","10.3390/nu15010038")] longevity/glp/psychology psychiatry/depression

Depression is a frequent and serious comorbidity associated with diabetes which adversely affects prognosis and quality of life. Glucagon-like peptide-1 receptor (GLP-1R) agonists, widely used in the treatment of diabetes, are reported to exert neuroprotective effects in the central nervous system. Thus, we aim to evaluate whether GLP-1R agonist exendin-4 (EX-4) could alleviate depression-like behaviors in diabetic mice and to explore its underlying mechanism.

The antidepressant effects of EX-4 were evaluated using behavioral tests in db/db mice. The effects of EX-4 on microglial pyroptosis and neuroinflammation were assessed in N9 microglial cells. EX-4 administration alleviated depression-like behaviors in diabetic db/db mice.

GLP-1R activation by EX-4 significantly suppressed microglial pyroptosis and neuroinflammation by downregulation of gasdermin D (GSDMD) and interleukin (IL)-1β in diabetic mice and lipopolysaccharide (LPS)-primed N9 microglia. Mechanistically, GLP-1R activation improved mitochondrial function and promoted mitophagy by decreasing the accumulation of mitochondrial reactive oxygen species (mtROS) and intracellular ROS production.

EX-4 exhibits antidepressant effects in depression associated with diabetes in diabetic mice, which may be mediated by inhibiting microglial pyroptisis via promoting mitophagy. It is supposed that GLP-1R agonists may be a promising therapy in depression associated with diabetes.

--- http://viznut.fi/unscii/ 2023-06-01 design/typography --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981969/ The potential antidepressant effect of antidiabetic agents: New insights from a pharmacovigilance study based on data from the reporting system databases FAERS and VigiBase Vera Battini, Robbert P. Van Manen, Michele Gringeri, Giulia Mosini, Greta Guarnieri, Anna Bombelli, Marco Pozzi, Maria Nobile, Sonia Radice, Emilio Clementi, Carla Carnovale 2023 2023-06-01 [("doi","10.3389/fphar.2023.1128387")] longevity/glp/psychology psychiatry/depression

Background: Growing evidence supports a bidirectional association between diabetes and depression; promising but limited and conflicting data from human studies support the intriguing possibility that antidiabetic agents may be used to effectively relieve depressive symptoms in diabetic patients. We investigated the potential antidepressant effects of antidiabetic drugs in a high-scale population data from the two most important pharmacovigilance databases, ie. the FDA Adverse Event Reporting System (FAERS) and the VigiBase.

Material and methods: From the two primary cohorts of patients treated with antidepressants retrieved from FDA Adverse Event Reporting System and VigiBase we identified cases (depressed patients experiencing therapy failure) and non-cases (depressed patients experiencing any other adverse event). We then calculated the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Empirical Bayes Geometric Mean (EBGM), and Empirical Bayes Regression-Adjusted Mean (ERAM) for cases versus non-cases in relation with the concurrent exposure to at least one of the following antidiabetic agent: A10BA Biguanides; A10BB Sulfonylureas; A10BG Thiazolidinediones; A10BH DPP4-inhibitors; A10BJ GLP-1 analogues; A10BK SGLT2 inhibitors (ie. those agents for which preliminary evidence from literature supports our pharmacological hypothesis).

Results: For GLP-1 analogues, all the disproportionality scores showed values <1, ie. statistically-significant, in both analyses [from the FAERS: ROR confidence interval of 0.546 (0.450-0.662); PRR (p-value) of 0.596 (0.000); EBGM (CI) of 0.488 (0.407-0.582); ERAM (CI) of 0.480 (0.398-0.569) and VigiBase: ROR (CI) of 0.717 (0.559-0.921); PRR (p-value) of 0.745 (0.033); EBGM (CI) of 0.586 (0.464-0.733); ERAM of (CI): 0.515 (0.403-0.639)]. Alongside GLP-1 analogues, DPP-4 Inhibitors and Sulfonylureas showed the greatest potential protective effect. With regard to specific antidiabetic agents, liraglutide and gliclazide were associated with a statistically-significant decrease in all disproportionality scores, in both analyses.

Conclusion: The findings of this study provide encouraging results, albeit preliminary, supporting the need for further clinical research for investigating repurposing of antidiabetic drugs for neuropsychiatric disorders.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9843232/ The Effects of Switching from Dipeptidyl Peptidase-4 Inhibitors to Glucagon-Like Peptide-1 Receptor Agonists on Bone Mineral Density in Diabetic Patients Chun-Feng Huang, Tso-Yen Mao, Shinn-Jang Hwang 2023 2023-06-01 [("doi","10.2147/DMSO.S389964")] longevity/glp

Purpose: Diabetes increases the risk of fragility fractures. As a result, when choosing a diabetes treatment, whether the drug affects bone density should be taken into account. The goal of this study was to determine how switching from dipeptidyl peptidase-4 inhibitors (DPP-4i) to glucagon-like peptide-1 receptor agonists (GLP-1RA) influenced bone mineral density (BMD) in diabetic patients.

Patients and Method: In this retrospective cohort study, diabetic patients with osteoporosis or osteopenia who used DPP-4i but not anti-osteoporosis medications were divided into two groups: those who switched to GLP-1RA (n = 132) and those who did not (control group, n = 133). We compared changes in glycemic control and BMD with and without conversion from DPP-4i to GLP-1RA.

Results: Prior to switching, there was no difference between the groups in terms of age, gender, glycosylated hemoglobin (HbA1c), or BMD. HbA1c was 8.7% in the participants (mean age 62.7 years, 17.4% female). Despite the fact that there was no difference in femoral neck BMD, the GLP-1RA group had a greater decrease in lumbar spine BMD (-0.028 g/cm2 versus −0.019 g/cm2, p = 0.041) than the control group. Furthermore, HbA1c levels in the GLP-1RA-treated group were considerably lower than in the control group (7.5% versus 8.0%, p = 0.027).

Conclusion: While switching to GLP-1RA improves glycemic control, it appears to have a less favorable effect on bone density than continuing DPP-4i. More research is needed, however, to determine whether diabetic patients with low bone density should be switched from DPP-4i to GLP-1RA.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6800581/ Longer-term liraglutide administration at the highest dose approved for obesity increases reward-related orbitofrontal cortex activation in response to food cues: Implications for plateauing weight loss in response to anti-obesity therapies Olivia M. Farr, Jagriti Upadhyay, Chelsea Rutagengwa, Bridget DiPrisco, Zachary Ranta, Amal Adra, Neha Bapatla, Vivian P. Douglas, Konstantinos A. A. Douglas, Eric Nolen-Doerr, Hannah Mathew, Christos S. Mantzoros 2019 2023-06-01 [("doi","10.1111/dom.13827")] longevity/glp/psychology longevity/glp/semaglutide

Aims: GLP-1 analogs have recently risen to the forefront as effective medications for lowering weight through actions in the central nervous system (CNS). However, their actions in the CNS have not yet been studied in the human brain after longer-term administration at the highest dose approved for obesity (liraglutide 3.0 mg).

Method: A total of 20 participants with obesity were treated with placebo and liraglutide (3.0 mg) in the context of a randomized, placebo-controlled, double-blind, cross-over trial after 5 weeks of dose escalation. Neurocognitive and neuroimaging (fMRI) responses to food cues were examined at the clinical research center of Beth Israel Deaconess Medical Center.

Results: While using liraglutide, patients lost more weight (placebo-subtracted −2.7%; p < 0.001), had decreased fasting glucose (p < 0.001) and showed improved cholesterol levels. In an uncontrolled analysis, brain activation in response to food images was not altered by liraglutide vs placebo. When controlled for BMI/weight, liraglutide increased activation of the right orbitofrontal cortex (OFC) in response to food cues (p < 0.016, corrected for multiple comparisons).

Conclusions: In contrast to prior studies, we demonstrate for the first time that liraglutide treatment, administered over a longer period at the highest doses approved for obesity, does not alter brain activation in response to food cues. A counter-regulatory increase in reward-related OFC activation in response to food cues can be observed when neuroimaging data are controlled for BMI changes, indicating changes in CNS that could lead to later plateaus of weight loss. These data point to a promising focus for additional interventions which, by contributing to the CNS reward system, could provide tangible benefits in reversing the plateauing phenomenon and promoting further weight loss.

--- /doc/psychiatry/schizophrenia/2017-ishoy.pdf No cognitive-enhancing effect of GLP-1 receptor agonism in antipsychotic-treated, obese patients with schizophrenia P. L. Ishøy, B. Fagerlund, B. V. Broberg, N. Bak, F. K. Knop, B. Y. Glenthøj, B. H. Ebdrup 2017-03-05 2023-06-01 [("doi","10.1111/acps.12711")] longevity/glp/psychology psychiatry/schizophrenia

Objective: Schizophrenia is associated with profound cognitive and psychosocial impairments. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are used for diabetes and obesity treatment, and animal studies have indicated cognitive-enhancing effects. In this investigator-initiated, double-blind, randomized, placebo-controlled trial, we tested non-metabolic effects of exenatide once-weekly (Bydureon™) in obese, antipsychotic-treated patients with schizophrenia spectrum disorder.

Method: Before and after 3 months of exenatide (n = 20) or placebo (n = 20) treatment, patients were assessed with the following: Brief Assessment of Cognition in Schizophrenia (BACS), Rey–Osterreith complex figure test (REY), Short-Form Health Survey (SF-36), Personal and Social Performance Scale (PSP) and the Positive and Negative Syndrome Scale (PANSS). We used BACS composite score as the main outcome measure.

Results: Repeated measures analysis of variance on BACS composite score showed statistically-significant effect of ‘Time’ (p < 0.001), no effect of ‘Group’ (p = 0.64) and no ‘Time × Group’ interaction (p = 0.77). For REY, SF-36, PSP and PANSS, only statistically-significant ‘Time’ effects were found.

Conclusion: The non-statistically-significant results of this first clinical trial exploring non-metabolic effects of a long-acting GLP-1RA in patients with schizophrenia could reflect a general problem of translating cognitive-enhancing effects of GLP-1RAs from animals to humans or be explained by factors specifically related to schizophrenia spectrum patients with obesity such as antipsychotic treatment.

--- https://www.youtube.com/watch?v=2SdGkkp1aq8 2023-06-01 cs/hardware --- https://www.lesswrong.com/posts/pQzRj4hJRtMxg3hib/this-anime-storyboard-doesn-t-exist-a-graphic-novel-written 2023-06-01 ai/nn/transformer/gpt/4/fiction ai/nn/transformer/gpt/dall-e/3 fiction/science-fiction --- https://arxiv.org/abs/1511.06789#google The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, Li Fei-Fei 2015-11-20 2023-06-01 [("doi","10.48550/arXiv.1511.06789")] ai/nn/cnn ai/scaling reinforcement-learning/exploration/active-learning

Current approaches for fine-grained recognition do the following: First, recruit experts to annotate a dataset of images, optionally also collecting more structured data in the form of part annotations and bounding boxes. Second, train a model using this data.

Toward the goal of solving fine-grained recognition, we introduce an alternative approach, leveraging free, noisy data from the web and simple, generic methods of recognition. This approach has benefits in both performance and scalability.

We demonstrate its efficacy on 4 fine-grained datasets, greatly exceeding existing state-of-the-art without the manual collection of even a single label, and furthermore show first results at scaling to more than 10,000 fine-grained categories.

Quantitatively, we achieve top-1 accuracies of 92.3% on CUB-200-2011, 85.4% on Birdsnap [Berg et al 2014], 93.4% on FGVC-Aircraft, and 80.8% on Stanford Dogs without using their annotated training sets...In total, for all four datasets, we obtained 9.8 million images for 26,458 categories, requiring 151.8GB of disk space.

We compare our approach to an active learning approach for expanding fine-grained datasets...Surprisingly, performance is very similar, with only a 0.4% advantage for the cleaner, annotated active learning data, highlighting the effectiveness of noisy web data despite the lack of manual annotation. If we furthermore augment the filtered web images with the Stanford Dogs training set, which the active learning method notably used both as training data and its seed set of images, performance improves to even be slightly better than the manually-annotated active learning data (0.5% improvement).

Table 2: Comparison with prior work on CUB-200-2011. We only include methods which use no annotations at test time. Here “GT” refers to using Ground Truth category labels in the training set of CUB, “BBox” indicates using bounding boxes, and “Parts” additionally uses part annotations.

…How Much Data is Really Necessary? In order to better understand the utility of noisy web data for fine-grained recognition, we perform a control experiment on the web data for CUB. Using the filtered web images as a base, we train models using progressively larger subsets of the results as training data, taking the top ranked images across categories for each experiment. Performance versus the amount of training data is shown in Figure 11. Surprisingly, relatively few web images are required to do as well as training on the CUB training set, and adding more noisy web images always helps, even when at the limit of search results. Based on this analysis, we estimate that one noisy web image for CUB categories is “worth” 0.507 ground truth training images57.

Figure 11 Number of web images used for training vs. performance on CUB-200-2011. We vary the amount of web training data in multiples of the CUB training set size (5,994 images). Also shown is performance when training on the ground truth CUB training set (CUB-GT).
--- https://arxiv.org/abs/2310.03214#google FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, Thang Luong 2023-10-05 2023-10-05 [("doi","10.48550/arXiv.2310.03214")] ai/dataset ai/nn/retrieval ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/palm ai/nn/transformer/t5 ai/scaling

Most large language models (LLMs) are trained once and never updated; thus, they lack the ability to dynamically adapt to our ever-changing world.

In this work, we perform a detailed study of the factuality of LLM-generated text in the context of answering questions that test current world knowledge. Specifically, we introduce FreshQA, a novel dynamic QA benchmark encompassing a diverse range of question and answer types, including questions that require fast-changing world knowledge as well as questions with false premises that need to be debunked.

We benchmark a diverse array of both closed and open-source LLMs under a two-mode evaluation procedure that allows us to measure both correctness and hallucination. Through human evaluations involving more than 50K judgments, we shed light on limitations of these models and demonstrate room for improvement: for instance, all models (regardless of model size) struggle on questions that involve fast-changing knowledge and false premises.

Motivated by these results, we present FreshPrompt, a simple few-shot prompting method that substantially boosts the performance of an LLM on FreshQA by incorporating relevant and up-to-date information retrieved from a search engine into the prompt. Our experiments show that FreshPrompt outperforms both competing search engine-augmented prompting methods such as Self-Ask (Press et al 2022) as well as commercial systems such as Perplexity.AI.

Further analysis of FreshPrompt reveals that both the number of retrieved evidences and their order play a key role in influencing the correctness of LLM-generated answers. Additionally, instructing the LLM to generate concise and direct answers helps reduce hallucination compared to encouraging more verbose answers.

To facilitate future work, we release FreshQA at Github and commit to updating it at regular intervals.

Figure 2: Accuracy of different LLMS on FRESHQA under RELAXED and STRICT (no hallucination) evaluations. Models benchmarked on the same date of April 26, 2023. All models (regardless of model size) struggle on questions that involve fast-changing knowledge and false premises. [Are we looking at the same graphs…?]

…COT increases hallucination: Overall, FEW-SHOT and COT prompting are beneficial for large models and sometimes advantageous for moderately-sized models on questions with valid premises, especially on questions about never-changing or old knowledge. Under STRICT, FEW-SHOT and COT yields +36.1% and +26.9% respective accuracy improvement over zero-shot prompting with PALM-540B on questions involving knowledge before 2022 (+21.9% and +29.7% under RELAXED). COT largely demonstrates superior performance compared to FEW-SHOT under RELAXED, whereas FEW-SHOT obtains better results under STRICT, as COT introduces more room for hallucination. Multi-hop reasoning is challenging for several models: T5 LARGE and XL are incapable of dealing with multi-hop questions, while Flan-PaLM 540B, CODEX, and GPT-3.5 suffer the most when switching from one-hop to multi-hop questions. GPT-4 remains stable across these two types of questions (with a difference of less than 2% in accuracy across settings)

--- https://en.wikipedia.org/wiki/File:Labeled_talmud.png File:Labeled Talmud.png 2023-06-02 design/typography/sidenote --- https://x.com/Jill_hubley/status/1710435672492765529 Jill Hubley 2023-06-02 design/visualization --- https://news.ycombinator.com/item?id=37834750 2023-06-02 ai/nn/transformer/gpt/codex ai/scaling/economics --- https://spectrum.ieee.org/disney-robot 2023-06-02 reinforcement-learning/model-free reinforcement-learning/robot --- https://en.wikipedia.org/wiki/Wireworld Wireworld 2023-06-02 cs/cellular-automaton --- https://spritely.institute/news/hoot-wireworld-live-in-browser.html 2023-06-02 cs/cellular-automaton cs/lisp --- /doc/history/1974-gager.pdf The Gospels and Jesus: Some Doubts about Method John G. Gager 1974-07-01 2023-06-02 [("doi","10.2307/1201564")] history philosophy/epistemology --- https://arxiv.org/abs/2308.02202 The Ghost Trilemma S. Mukherjee, S. Ravi, P. Schmitt, B. Raghavan 2023-08-04 2023-08-04 [("doi","10.48550/arXiv.2308.02202")] cs/security sociology/technology

Trolls, bots, and sybils distort online discourse and compromise the security of networked platforms. User identity is central to the vectors of attack and manipulation employed in these contexts. However it has long seemed that, try as it might, the security community has been unable to stem the rising tide of such problems.

We posit the Ghost Trilemma, that there are 3 key properties of identity—sentience, location, and uniqueness—that cannot be simultaneously verified in a fully-decentralized setting.

Many fully-decentralized systems—whether for communication or social coordination—grapple with this trilemma in some way, perhaps unknowingly. In this Systematization of Knowledge (SoK) paper, we examine the design space, use cases, problems with prior approaches, and possible paths forward.

We sketch a proof of this trilemma and outline options for practical, incrementally deployable schemes to achieve an acceptable tradeoff of trust in centralized trust anchors, decentralized operation, and an ability to withstand a range of attacks, while protecting user privacy.

--- https://www.nature.com/articles/s41467-023-41476-3 2023-06-02 genetics/editing --- https://arxiv.org/abs/1402.3511#schmidhuber A Clockwork RNN Jan Koutník, Klaus Greff, Faustino Gomez, Jürgen Schmidhuber 2014-02-14 2023-06-03 [("doi","10.48550/arXiv.1402.3511")] ai/nn/rnn ai/nn/transformer/attention/hierarchical

Sequence prediction and classification are ubiquitous and challenging problems in machine learning that can require identifying complex dependencies between temporally distant inputs.

Recurrent Neural Networks (RNNs) have the ability, in theory, to cope with these temporal dependencies by virtue of the short-term memory implemented by their recurrent (feedback) connections. However, in practice they are difficult to train successfully when the long-term memory is required.

This paper introduces a simple, yet powerful modification to the standard RNN architecture, the Clockwork RNN (CW-RNN), in which the hidden layer is partitioned into separate modules, each processing inputs at its own temporal granularity, making computations only at its prescribed clock rate.

Rather than making the standard RNN models more complex, CW-RNN reduces the number of RNN parameters, improves the performance in the tasks tested, and speeds up the network evaluation.

The network is demonstrated in preliminary experiments involving two tasks: audio signal generation and TIMIT spoken word classification, where it outperforms both RNN and LSTM networks.

--- https://arxiv.org/abs/1608.03609 Clockwork Convnets for Video Semantic Segmentation Evan Shelhamer, Kate Rakelly, Judy Hoffman, Trevor Darrell 2016-08-11 2023-06-03 [("doi","10.48550/arXiv.1608.03609")] ai/nn/cnn ai/nn/rnn ai/video/analysis

Recent years have seen tremendous progress in still-image segmentation; however the naïve application of these state-of-the-art algorithms to every video frame requires considerable computation and ignores the temporal continuity inherent in video.

We propose a video recognition framework that relies on two key observations: (1) while pixels may change rapidly from frame to frame, the semantic content of a scene evolves more slowly, and (2) execution can be viewed as an aspect of architecture, yielding purpose-fit computation schedules for networks.

We define a novel family of “clockwork” convnets driven by fixed or adaptive clock signals that schedule the processing of different layers at different update rates according to their semantic stability. We design a pipeline schedule to reduce latency for real-time recognition and a fixed-rate schedule to reduce overall computation.

Finally, we extend clockwork scheduling to adaptive video processing by incorporating data-driven clocks that can be tuned on unlabeled video. The accuracy and efficiency of clockwork convnets are evaluated on the Youtube-Objects, NYUD, and Cityscapes video datasets.

--- https://arxiv.org/abs/1507.07680 Training recurrent networks online without backtracking Yann Ollivier, Corentin Tallec, Guillaume Charpiat 2015-07-28 2023-06-03 [("doi","10.48550/arXiv.1507.07680")] ai/nn/rnn

We introduce the NoBackTrack algorithm to train the parameters of dynamical systems such as recurrent neural networks. This algorithm works in an online, memoryless setting, thus requiring no backpropagation through time, and is scalable, avoiding the large computational and memory cost of maintaining the full gradient of the current state with respect to the parameters.

The algorithm essentially maintains, at each time, a single search direction in parameter space. The evolution of this search direction is partly stochastic and is constructed in such a way to provide, at every time, an unbiased random estimate of the gradient of the loss function with respect to the parameters. Because the gradient estimate is unbiased, on average over time the parameter is updated as it should.

The resulting gradient estimate can then be fed to a lightweight Kalman-like filter to yield an improved algorithm. For recurrent neural networks, the resulting algorithms scale linearly with the number of parameters.

Small-scale experiments confirm the suitability of the approach, showing that the stochastic approximation of the gradient introduced in the algorithm is not detrimental to learning. In particular, the Kalman-like version of NoBackTrack is superior to backpropagation through time (BPTT) when the time span of dependencies in the data is longer than the truncation span for BPTT.

--- https://magenta.tensorflow.org/blog/2017/06/01/waybackprop 2023-06-03 ai/music ai/nn/rnn ai/nn/transformer/attention/hierarchical --- https://arxiv.org/abs/1609.01704 Hierarchical Multiscale Recurrent Neural Networks Junyoung Chung, Sungjin Ahn, Yoshua Bengio 2016-09-06 2023-06-03 [("doi","10.48550/arXiv.1609.01704")] ai/nn/rnn ai/nn/transformer/attention/hierarchical

Learning both hierarchical and temporal representation has been among the long-standing challenges of recurrent neural networks. Multiscale recurrent neural networks have been considered as a promising approach to resolve this issue, yet there has been a lack of empirical evidence showing that this type of models can actually capture the temporal dependencies by discovering the latent hierarchical structure of the sequence.

In this paper, we propose a novel multiscale approach, called the hierarchical multiscale recurrent neural networks, which can capture the latent hierarchical structure in the sequence by encoding the temporal dependencies with different timescales using a novel update mechanism.

We show some evidence that our proposed multiscale architecture can discover underlying hierarchical structure in the sequences without using explicit boundary information.

We evaluate our proposed model on character-level language modeling and handwriting sequence modeling.

--- https://arxiv.org/abs/2305.14975 Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, Christopher D. Manning 2023-05-24 2023-06-03 [("doi","10.48550/arXiv.2305.14975")] ai/nn/transformer/gpt/calibration

A trustworthy real-world prediction system should be well-calibrated; that is, its confidence in an answer is indicative of the likelihood that the answer is correct, enabling deferral to a more expensive expert in cases of low-confidence predictions.

While recent studies have shown that unsupervised pre-training produces large language models (LMs) that are remarkably well-calibrated, the most widely-used LMs in practice are fine-tuned with reinforcement learning with human feedback (RLHF-LMs) after the initial unsupervised pre-training stage, and results are mixed as to whether these models preserve the well-calibratedness of their ancestors.

In this paper, we conduct a broad evaluation of computationally feasible methods for extracting confidence scores from LLMs fine-tuned with RLHF. We find that with the right prompting strategy, RLHF-LMs verbalize probabilities that are much better calibrated than the model’s conditional probabilities, enabling fairly well-calibrated predictions.

Through a combination of prompting strategy and temperature scaling, we find that we can reduce the expected calibration error of RLHF-LMs by over 50%.

--- https://arxiv.org/abs/1511.06297 Conditional Computation in Neural Networks for faster models Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau, Doina Precup 2015-11-19 2023-06-03 [("doi","10.48550/arXiv.1511.06297")] ai/scaling/mixture-of-experts

Deep learning has become the state-of-art tool in many applications, but the evaluation and training of deep models can be time-consuming and computationally expensive. The conditional computation approach has been proposed to tackle this problem (Bengio et al 2013; Davis & Arel 2013). It operates by selectively activating only parts of the network at a time.

In this paper, we use reinforcement learning as a tool to optimize conditional computation policies. More specifically, we cast the problem of learning activation-dependent policies for dropping out blocks of units as a reinforcement learning problem. We propose a learning scheme motivated by computation speed, capturing the idea of wanting to have parsimonious activations while maintaining prediction accuracy.

We apply a policy gradient algorithm for learning policies that optimize this loss function and propose a regularization mechanism that encourages diversification of the dropout policy.

We present encouraging empirical results showing that this approach improves the speed of computation without impacting the quality of the approximation.

--- https://arxiv.org/abs/1312.4314#google Learning Factored Representations in a Deep Mixture of Experts David Eigen, Marc’Aurelio Ranzato, Ilya Sutskever 2013-12-16 2023-06-03 [("doi","10.48550/arXiv.1312.4314")] ai/scaling/mixture-of-experts

Mixtures of Experts combine the outputs of several “expert” networks, each of which specializes in a different part of the input space. This is achieved by training a “gating” network that maps each input to a distribution over the experts. Such models show promise for building larger networks that are still cheap to compute at test time, and more parallelizable at training time.

In this work, we extend the Mixture of Experts to a stacked model, the Deep Mixture of Experts, with multiple sets of gating and experts. This exponentially increases the number of effective experts by associating each input with a combination of experts at each layer, yet maintains a modest model size.

On a randomly translated version of the MNIST dataset, we find that the Deep Mixture of Experts automatically learns to develop location-dependent (“where”) experts at the first layer, and class-specific (“what”) experts at the second layer.

In addition, we see that the different combinations are in use when the model is applied to a dataset of speech monophones. These demonstrate effective use of all expert combinations.

--- https://x.com/natfriedman/status/1712141463876776415 Nat Friedman 2023-06-03 ai/nn/transformer/gpt/codex ai/scaling/economics --- https://www.interconnects.ai/p/ai-research-job-market 2023-06-03 ai/scaling/economics --- https://www.youtube.com/watch?v=XkPQHIUHWwc 2023-06-03 iq/high/smpy --- https://arxiv.org/abs/2310.06786 OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text Keiran Paster, Marco Dos Santos, Zhangir Azerbayev, Jimmy Ba 2023-10-10 2023-10-10 [("doi","10.48550/arXiv.2310.06786")] ai/dataset ai/nn/transformer/gpt math

There is growing evidence that pretraining on high quality, carefully thought-out tokens such as code or mathematics plays an important role in improving the reasoning abilities of large language models. For example, Minerva, a PaLM model finetuned on billions of tokens of mathematical documents from arXiv and the web, reported dramatically improved performance on problems that require quantitative reasoning.

However, because all known open source web datasets employ preprocessing that does not faithfully preserve mathematical notation, the benefits of large scale training on quantitative web documents are unavailable to the research community.

We introduce OpenWebMath, an open dataset inspired by these works containing 14.7B tokens of mathematical webpages from Common Crawl.

We describe in detail our method for extracting text and LaTeX content and removing boilerplate from HTML documents, as well as our methods for quality filtering and deduplication. Additionally, we run small-scale experiments by training 1.4b parameter language models on OpenWebMath, showing that models trained on 14.7B tokens of our dataset surpass the performance of models trained on over 20× the amount of general language data.

We hope that our dataset, openly released on the Hugging Face Hub, will help spur advances in the reasoning abilities of large language models.

--- https://arxiv.org/abs/2310.06825#mistral Mistral-7B Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed 2023-10-10 2023-10-10 [("doi","10.48550/arXiv.2310.06825")] ai/nn/transformer/gpt/instruction-tuning

We introduce Mistral-7B v0.1, a 7-billion-parameter language model engineered for superior performance and efficiency.

Mistral-7B outperforms LLaMA-2 13B across all evaluated benchmarks, and LLaMa-1-34B in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention (SWA) to effectively handle sequences of arbitrary length with a reduced inference cost.

We also provide a model fine-tuned to follow instructions, Mistral-7B-Instruct, that surpasses the LLaMA-2 13B—Chat model both on human and automated benchmarks.

Our models are released under the Apache 2.0 license.

--- https://www.biorxiv.org/content/10.1101/2023.10.06.561165.full The landscape of ancient human pathogens in Eurasia from the Stone Age to historical times Martin Sikora, Elisabetta Canteri, Antonio Fernandez-Guerra, Nikolay Oskolkov, Rasmus Ågren, Lena Hansson, Evan K. Irving-Pease, Barbara Mühlemann, Sofie Holtsmark Nielsen, Gabriele Scorrano, Morten E. Allentoft, Frederik Valeur Seersholm, Hannes Schroeder, Charleen Gaunitz, Jesper Stenderup, Lasse Vinner, Terry C. Jones, Bjørn Nystedt, Julian Parkhill, Lars Fugger, Fernando Racimo, Kristian Kristiansen, Astrid K. N. Iversen, Eske Willerslev 2023-10-06 2023-10-06 [("doi","10.1101/2023.10.06.561165")] genetics/sequencing

Infectious diseases have had devastating impacts on human populations throughout history. Still, the origins and past dynamics of human pathogens remain poorly understood (1). To create the first spatiotemporal map of diverse ancient human microorganisms and parasites, we screened shotgun sequencing data from 1,313 ancient human remains covering 35,000 years of Eurasian history for ancient DNA deriving from bacteria, viruses, and parasites. We demonstrate the widespread presence of ancient microbial DNA in human remains, identifying over 2,400 individual species hits in 896 samples.

We report a wide range of pathogens detected for the first time in ancient human remains, including the food-borne pathogens Yersinia enterocolitica and Shigella spp., the animal-borne Leptospira interrogans, and the malaria-causing parasite Plasmodium vivax. Our findings extend the spatiotemporal range of previously described ancient pathogens such as Yersinia pestis, the causative agent of plague, Hepatitis B virus, and Borrelia recurrentis, the cause of louse-borne relapsing fever (LBRF). For LRBF we increase the known distribution from a single medieval genome to 31 cases across Eurasia covering 5,000 years.

Grouping the ancient microbial species according to their type of transmission (zoonotic, anthroponotic, sapronotic, opportunistic, and other), we find that most categories are identified throughout the entire sample period, while zoonotic pathogens, which are transmitted from living animals to humans or which have made a host jump into humans from animals in the timeframe of this study, are only detected from ~6,500 years ago. The incidence of zoonotic pathogens increased in our samples some 1,000 years later before reaching the highest detection rates ~5,000 years ago, and was associated with a human genetic ancestry component characteristic of pastoralist populations from the Eurasian Steppe.

Our results provide the first direct evidence for an epidemiological transition to an increased burden of zoonotic infectious diseases following the domestication of animals2. However, they also reveal that the spread of these pathogens first becomes frequent thousands of years after increased animal-human contact, likely coinciding with the pastoralist migrations from the Eurasian Steppe3, 4. This study provides the first spatiotemporal map of past human pathogens using genomic paleo-epidemiology, and the first direct evidence for an epidemiological transition of increased zoonotic infectious disease burden after the onset of agriculture, through historical times.

--- https://www.biorxiv.org/content/10.1101/2023.10.04.560881.full Ancestral genetic components are consistently associated with the complex trait landscape in European biobanks Vasili Pankratov, Massimo Mezzavilla, Serena Aneli, Daniela Fusco, James F. Wilson, Mait Metspalu, Paolo Provero, Luca Pagani, Davide Marnetto 2023-10-05 2023-10-05 [("doi","10.1101/2023.10.04.560881")] genetics/selection/natural/human

The genetic structure in Europe was mostly shaped by admixture between the Western Hunter-Gatherer, Anatolian Neolithic and Steppe’s Yamnaya ancestral components. Such structure is regarded as a confounder in GWAS and follow-up studies, and gold-standard methods exist to correct for it.

However, it is still poorly understood to which extent these ancestral components contribute to complex trait variation in present-day Europe. In this work we harness the UK Biobank to address this question.

By extensive demographic simulations and incorporating previous results obtained using the Estonian Biobank, we carefully evaluate the statistical-significance and scope of our findings. Heart rate, platelet count, monocyte percentage and many other traits show stratification similar to height and pigmentation traits, likely targets of selection and divergence across ancestral groups.

The consistency of our results across biobanks indicates that these ancestry-specific genetic predispositions act as a source of variability and as potential confounders in Europe as a whole.

--- https://www.biorxiv.org/content/10.1101/2023.09.11.557177.full Retracing Human Genetic Histories and Natural Selection Using Precise Local Ancestry Inference Jon Lerga-Jaso, Biljana Novković, Deepu Unnikrishnan, Varuna Bamunusinghe, Marcelinus R. Hatorangan, Charlie Manson, Haley Pedersen, Alex Osama, Andrew Terpolovsky, Sandra Bohn, Adriano De Marino, Abdallah A. Mahmoud, Karatuğ O. Bircan, Umar Khan, Manfred G. Grabherr, Puya G. Yazdi 2023-09-13 2023-09-13 [("doi","10.1101/2023.09.11.557177")] genetics/selection/natural/human

In an increasingly diverse world, including admixed individuals in genomic studies is imperative for equity and portability. A crucial first step is precise local ancestry inference (LAI).

We have developed Orchestra, a LAI model with unprecedented accuracy, and trained on over 10,000 single-origin individuals from 35 worldwide populations. We employed Orchestra to delve into genetic relationships and demographic histories, with a focus on Latin Americans, a prime example of admixture, and the Ashkenazi Jewish, whose origins have long been debated.

Finally, Orchestra enabled us to map signatures of selection, notably identifying trace Scandinavian ancestry in British samples and unveiling an immune-rich region linked to respiratory infections.

Our work advances the field of LAI and holds promise for improvements in future applications for admixed populations.

--- https://www.nature.com/articles/s41586-023-06594-4 2023-06-04 genetics/editing --- https://arxiv.org/abs/2310.02557 Generalization in diffusion models arises from geometry-adaptive harmonic representation Zahra Kadkhodaie, Florentin Guth, Eero P. Simoncelli, Stéphane Mallat 2023-10-04 2023-10-04 [("doi","10.48550/arXiv.2310.02557")] ai/nn/diffusion

High-quality samples generated with score-based reverse diffusion algorithms provide evidence that deep neural networks (DNN) trained for denoising can learn high-dimensional densities, despite the curse of dimensionality. However, recent reports of memorization of the training set raise the question of whether these networks are learning the “true” continuous density of the data.

Here, we show that two denoising DNNs trained on non-overlapping subsets of a dataset learn nearly the same score function, and thus the same density, with a surprisingly small number of training images. This strong generalization demonstrates an alignment of powerful inductive biases in the DNN architecture and/or training algorithm with properties of the data distribution.

We analyze these, demonstrating that the denoiser performs a shrinkage operation in a basis adapted to the underlying image. Examination of these bases reveals oscillating harmonic structures along contours and in homogeneous image regions.

We show that trained denoisers are inductively biased towards these geometry-adaptive harmonic representations by demonstrating that they arise even when the network is trained on image classes such as low-dimensional manifolds, for which the harmonic basis is suboptimal. Additionally, we show that the denoising performance of the networks is near-optimal when trained on regular image classes for which the optimal basis is known to be geometry-adaptive and harmonic.

--- https://arxiv.org/abs/2303.06053#google TSMixer: An All-MLP Architecture for Time Series Forecasting Si-An Chen, Chun-Liang Li, Nate Yoder, Sercan O. Arik, Tomas Pfister 2023-03-10 2023-06-04 [("doi","10.48550/arXiv.2303.06053")] ai/nn/fully-connected ai/tabular

Real-world time-series datasets are often multivariate with complex dynamics. To capture this complexity, high capacity architectures like recurrent- or attention-based sequential deep learning models have become popular. However, recent work demonstrates that simple univariate linear models can outperform such deep learning models on several commonly used academic benchmarks.

Extending them, in this paper, we investigate the capabilities of linear models for time-series forecasting and present Time-Series Mixer (TSMixer), a novel architecture designed by stacking multi-layer perceptrons (MLPs). TSMixer is based on mixing operations along both the time and feature dimensions to extract information efficiently.

On popular academic benchmarks, the simple-to-implement TSMixer is comparable to specialized state-of-the-art models that leverage the inductive biases of specific benchmarks. On the challenging and large scale M5 benchmark, a real-world retail dataset, TSMixer demonstrates superior performance compared to the state-of-the-art alternatives.

Our results underline the importance of efficiently using cross-variate and auxiliary information for improving the performance of time series forecasting. We present various analyses to shed light into the capabilities of TSMixer. The design paradigms used in TSMixer are expected to open new horizons for deep learning-based time series forecasting.

The implementation is available at Github.

--- https://www.economist.com/united-states/2023/10/05/detroit-wants-to-be-the-first-big-american-city-to-tax-land-value 2023-06-04 economics/georgism --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6352105/ Low Volume, Home-Based Weighted Step Exercise Training Can Improve Lower Limb Muscle Power and Functional Ability in Community-Dwelling Older Women Jacqueline L. Mair, Giuseppe De Vito, Colin A. Boreham 2019 2023-06-04 [("doi","10.3390/jcm8010041")] exercise/gravitostat

Stepping exercise can be used as a scalable form of high intensity exercise to enhance important aspects of physical fitness in older populations. The addition of supplementary weights increases the resistive element of stepping, with the potential for training improvements in muscular strength, power, and functional abilities alongside other fitness outcomes.

The aim of this study was to evaluate the effects of a low-volume, home-based weighted step exercise programme on muscular strength, power, and functional ability in previously inactive community-dwelling older women. 11 participants, aged between 65⁻74 years, independently completed a six-week individualised and progressive step exercise training programme wearing a weighted vest.

Knee extensor strength, lower limb power output, and physical function using a battery of functional tests were measured at baseline, following a 6-week control period, and again following the 6-week training programme. Following training, lower limb power output improved by 10⁻11% (p < 0.05) and was accompanied by a corresponding 9% (p < 0.01) improvement in stair climb time and 10% (p < 0.01) improvement in normalized stair climbing power, highlighting the beneficial effects of weighted stepping for transferable improvements in functional fitness.

The magnitude of observed training improvements suggest that weighted step training has the potential to prolong independence and prevent age-related health conditions such as sarcopenia.

--- https://en.wikipedia.org/wiki/Insulin_degludec/liraglutide Insulin degludec/liraglutide 2023-06-04 longevity/glp/semaglutide --- https://dynomight.substack.com/p/midwit-home 2023-06-05 design technology --- https://kennethreitz.org/essays/2016/02/27/mentalhealtherror-an-exception-occurred 2023-06-05 psychiatry/bipolar/energy psychiatry/meditation --- https://artemis.sh/2023/10/12/scrollbars.html 2023-06-05 design --- https://www.science.org/content/article/co-developer-cassava-s-potential-alzheimer-s-drug-cited-egregious-misconduct 2023-06-05 statistics/bias --- https://arxiv.org/abs/2310.06694 Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning Mengzhou Xia, Tianyu Gao, Zhiyuan Zeng, Danqi Chen 2023-10-10 2023-10-10 [("doi","10.48550/arXiv.2310.06694")] ai/nn/sparsity/pruning ai/nn/transformer ai/scaling

The popularity of LLaMA (Touvron et al 2023a, Touvron et al 2023b) and other recently emerged moderate-sized large language models (LLMs) highlights the potential of building smaller yet powerful LLMs. Regardless, the cost of training such models from scratch on trillions of tokens remains high.

In this work, we study structured pruning as an effective means to develop smaller LLMs from pre-trained, larger models. Our approach employs two key techniques: (1) targeted structured pruning, which prunes a larger model to a specified target shape by removing layers, heads, and intermediate and hidden dimensions in an end-to-end manner, and (2) dynamic batch loading, which dynamically updates the composition of sampled data in each training batch based on varying losses across different domains.

We demonstrate the efficacy of our approach by presenting the Sheared-LLaMA series, pruning the LLaMA2-7B model down to 1.3B and 2.7b parameters. Sheared-LLaMA models outperform state-of-the-art open-source models of equivalent sizes, such as Pythia, INCITE, and OpenLLaMA models, on a wide range of downstream and instruction tuning evaluations, while requiring only 3% of compute compared to training such models from scratch.

This work provides compelling evidence that leveraging existing LLMs with structured pruning is a far more cost-effective approach for building smaller LLMs.

--- https://en.wikipedia.org/wiki/Synaptic_pruning Synaptic pruning 2023-06-05 ai/nn/sparsity/pruning psychology/neuroscience --- https://x.com/kendrictonn/status/1712635145591922937 Kendric Tonn 2023-06-05 design/typography/rubrication --- https://arxiv.org/abs/2310.03882#deepmind Small batch deep reinforcement learning Johan Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro 2023-10-05 2023-10-05 [("doi","10.48550/arXiv.2310.03882")] reinforcement-learning/exploration reinforcement-learning/model-free

[Twitter; reviews, cf. Stooke & Abbeel 2018] In value-based deep reinforcement learning with replay memories, the batch size parameter specifies how many transitions to sample for each gradient update. Although critical to the learning process, this value is typically not adjusted when proposing new algorithms.

In this work we present a broad empirical study that suggests reducing the batch size can result in a number of performance gains; this is surprising, as the general tendency when training neural networks is towards larger batch sizes for improved performance.

We complement our experimental findings with a set of empirical analyses towards better understanding this phenomenon.

--- https://www.lesswrong.com/posts/3eqHYxfWb5x4Qfz8C/unrlhf-efficiently-undoing-llm-safeguards 2023-06-05 reinforcement-learning/preference-learning reinforcement-learning/safe --- https://www.lesswrong.com/posts/qmQFHCgCyEEjuy5a7/lora-fine-tuning-efficiently-undoes-safety-training-from 2023-06-05 ai/nn/sparsity reinforcement-learning/preference-learning reinforcement-learning/safe --- https://en.wikipedia.org/wiki/Help:Using_the_Wayback_Machine Help:Using the Wayback Machine 2023-06-05 cs/linkrot/archiving --- https://arxiv.org/abs/1803.02811 Accelerated Methods for Deep Reinforcement Learning Adam Stooke, Pieter Abbeel 2018-03-07 2023-06-06 [("doi","10.48550/arXiv.1803.02811")] reinforcement-learning/model-free reinforcement-learning/scaling

Deep reinforcement learning (RL) has achieved many recent successes, yet experiment turn-around time remains a key bottleneck in research and in practice. We investigate how to optimize existing deep RL algorithms for modern computers, specifically for a combination of CPUs and GPUs.

We confirm that both policy gradient and Q-value learning algorithms can be adapted to learn using many parallel simulator instances. We further find it possible to train using batch sizes considerably larger than are standard, without negatively affecting sample complexity or final performance. We leverage these facts to build a unified framework for parallelization that dramatically hastens experiments in both classes of algorithm. All neural network computations use GPUs, accelerating both data collection and training.

Our results include using an entire DGX-1 to learn successful strategies in Atari games in mere minutes, using both synchronous and asynchronous algorithms.

--- https://arxiv.org/abs/1706.02677#facebook Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He 2017-06-08 2023-06-06 [("doi","10.48550/arXiv.1706.02677")] ai/nn/cnn ai/scaling

Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers.

Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization.

Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8,192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a square-root function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training.

With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8,192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving 8 → 256 GPUs.

Our findings enable training visual recognition models on internet-scale data with high efficiency.

--- /doc/borges/2008-fishburn.pdf Digging for hrönir: a second reading of "Tlön, Uqbar, Orbis Tertius" Evelyn Fishburn 2008-01-01 2023-06-06 [("doi","10.2307/24880536")] borges philosophy/ontology --- https://www.youtube.com/watch?v=VeFMdVIFsgs 2023-06-06 cat/psychology --- https://wholeearth.info/ 2023-06-06 design --- https://mprove.de/script/99/kai/ 2023-06-06 design --- https://littlegreenviper.com/miscellany/the-road-most-traveled-by/ 2023-06-06 design --- https://www.joelonsoftware.com/2000/04/22/consistency-and-other-hobgoblins/ 2023-06-06 design --- https://arxiv.org/abs/2307.12108 An Empirical Study & Evaluation of Modern CAPTCHAs Andrew Searles, Yoshimichi Nakatsuka, Ercan Ozturk, Andrew Paverd, Gene Tsudik, Ai Enkoji 2023-07-22 2023-07-22 [("doi","10.48550/arXiv.2307.12108")] cs/security design

For nearly two decades, CAPTCHAs have been widely used as a means of protection against bots. Throughout the years, as their use grew, techniques to defeat or bypass CAPTCHAs have continued to improve. Meanwhile, CAPTCHAs have also evolved in terms of sophistication and diversity, becoming increasingly difficult to solve for both bots (machines) and humans. Given this long-standing and still-ongoing arms race, it is critical to investigate how long it takes legitimate users to solve modern CAPTCHAs, and how they are perceived by those users.

In this work, we explore CAPTCHAs in the wild by evaluating users’ solving performance and perceptions of unmodified currently-deployed CAPTCHAs. We obtain this data through manual inspection of popular websites and user studies in which 1,400 participants collectively solved 14,000 CAPTCHAs. Results show differences between the most popular types of CAPTCHAs: surprisingly, solving time and user perception are not always correlated. We performed a comparative study to investigate the effect of experimental context—specifically the difference between solving CAPTCHAs directly versus solving them as part of a more natural task, such as account creation.

Whilst there were several potential confounding factors, our results show that experimental context could have an impact on this task, and must be taken into account in future CAPTCHA studies.

Finally, we investigate CAPTCHA-induced user task abandonment by analyzing participants who start and do not complete the task.

--- https://lightning.ai/pages/community/lora-insights/ 2023-06-06 ai/nn/sparsity/low-precision --- https://openai.com/pricing#fine-tuning-models 2023-06-07 ai/nn/sparsity ai/nn/transformer/gpt/3 ai/nn/transformer/gpt/4 --- https://en.wikipedia.org/wiki/Selena_Gomez#Health Selena Gomez § Health 2023-06-07 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Oscar_(therapy_cat) Oscar (therapy cat) 2023-06-07 cat/psychology psychology/smell --- https://x.com/aicrumb/status/1712883451437646027 aicrumb 2023-06-07 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2309.11690 Explosive growth from AI automation: A review of the arguments Ege Erdil, Tamay Besiroglu 2023-09-20 2023-09-20 [("doi","10.48550/arXiv.2309.11690")] economics/automation

We examine whether substantial AI automation could accelerate global economic growth by about an order of magnitude, akin to the economic growth effects of the Industrial Revolution.

We identify 3 primary drivers for such growth: (1) the scalability of an AI “labor force” restoring a regime of increasing returns to scale, (2) the rapid expansion of an AI labor force, and (3) a massive increase in output from rapid automation occurring over a brief period of time.

Against this backdrop, we evaluate 9 counterarguments, including regulatory hurdles, production bottlenecks, alignment issues, and the pace of automation.

We tentatively assess these arguments, finding most are unlikely deciders. We conclude that explosive growth seems plausible with AI capable of broadly substituting for human labor, but high confidence in this claim seems currently unwarranted.

Key questions remain about the intensity of regulatory responses to AI, physical bottlenecks in production, the economic value of superhuman abilities, and the rate at which AI automation could occur.

--- https://arxiv.org/abs/2310.06816 Text Embeddings Reveal (Almost) As Much As Text John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, Alexander M. Rush 2023-10-10 2023-10-10 [("doi","10.48550/arXiv.2310.06816")] ai/nn/diffusion ai/nn/retrieval

How much private information do text embeddings reveal about the original text? We investigate the problem of embedding inversion, reconstructing the full text represented in dense text embeddings.

We frame the problem as controlled generation: generating text that, when re-embedded, is close to a fixed point in latent space.

We find that although a naïve model conditioned on the embedding performs poorly, a multi-step method that iteratively corrects and re-embeds text is able to recover 92% of 32-token text inputs exactly.

We train our model to decode text embeddings from two state-of-the-art embedding models, and also show that our model can recover important personal information (full names) from a dataset of clinical notes.

Our code is available on Github.

--- https://arxiv.org/abs/2004.13654#deepmind Pitfalls of learning a reward function online Stuart Armstrong, Jan Leike, Laurent Orseau, Shane Legg 2020-04-28 2023-06-07 [("doi","10.48550/arXiv.2004.13654")] reinforcement-learning/exploration reinforcement-learning/safe

In some agent designs like inverse reinforcement learning an agent needs to learn its own reward function. Learning the reward function and optimizing for it are typically two different processes, usually performed at different stages.

We consider a continual (“one life”) learning approach where the agent both learns the reward function and optimizes for it at the same time. We show that this comes with a number of pitfalls, such as deliberately manipulating the learning process in one direction, refusing to learn, “learning” facts already known to the agent, and making decisions that are strictly dominated (for all relevant reward functions).

We formally introduce two desirable properties: the first is unriggability, which prevents the agent from steering the learning process in the direction of a reward function that is easier to optimize. The second is uninfluenceability, whereby the reward-function learning process operates by learning facts about the environment.

We show that an uninfluenceable process is automatically unriggable, and if the set of possible environments is sufficiently rich, the converse is true too.

--- https://github.com/spdustin/ChatGPT-AutoExpert/blob/main/System%20Prompts.md 2023-06-07 ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/dall-e/3 reinforcement-learning/safe --- https://simonwillison.net/2023/Oct/14/multi-modal-prompt-injection/ 2023-06-07 ai/nn/transformer/gpt/4 cs/security --- https://www.biorxiv.org/content/10.1101/2023.10.11.560955.full Integration of 168,000 samples reveals global patterns of the human gut microbiome Richard J. Abdill, Samantha P. Graham, Vincent Rubinetti, Frank W. Albert, Casey S. Greene, Sean Davis, Ran Blekhman 2023-10-11 2023-10-11 [("doi","10.1101/2023.10.11.560955")] genetics/microbiome

Understanding the factors that shape variation in the human microbiome is a major goal of research in biology. While other genomics fields have used large, pre-compiled compendia to extract systematic insights requiring otherwise impractical sample sizes, there has been no comparable resource for the 16S rRNA sequencing data commonly used to quantify microbiome composition.

To help close this gap, we have assembled a set of 168,484 publicly available human gut microbiome samples, processed with a single pipeline and combined into the largest unified microbiome dataset to date. We use this resource, which is freely available at microbiomap.org, to shed light on global variation in the human gut microbiome.

We find that Firmicutes, particularly Bacilli and Clostridia, are almost universally present in the human gut. At the same time, the relative abundance of the 65 most common microbial genera differ between at least two world regions.

We also show that gut microbiomes in undersampled world regions, such as Central and Southern Asia, differ substantially from the more thoroughly characterized microbiomes of Europe and Northern America. Moreover, humans in these overlooked regions likely harbor hundreds of taxa that have not yet been discovered due to this undersampling, highlighting the need for diversity in microbiome studies.

We anticipate that this new compendium can serve the community and enable advanced applied and methodological research.

--- https://arxiv.org/abs/2310.07096#ibm Sparse Universal Transformer Shawn Tan, Yikang Shen, Zhenfang Chen, Aaron Courville, Chuang Gan 2023-10-11 2023-10-11 [("doi","10.48550/arXiv.2310.07096")] ai/nn/transformer ai/scaling/mixture-of-experts reinforcement-learning/exploration/active-learning

The Universal Transformer (UT) is a variant of the Transformer that shares parameters across its layers. Empirical evidence shows that UTs have better compositional generalization than Vanilla Transformers (VTs) in formal language tasks. The parameter-sharing also affords it better parameter efficiency than VTs.

Despite its many advantages, scaling UT parameters is much more compute and memory intensive than scaling up a VT. This paper proposes the Sparse Universal Transformer (SUT), which leverages Sparse Mixture of Experts (SMoE) and a new stick-breaking-based dynamic halting mechanism [for adaptive computation] to reduce UT’s computation complexity while retaining its parameter efficiency and generalization ability.

Experiments show that SUT achieves the same performance as strong baseline models while only using half computation and parameters on WMT’14 and strong generalization results on formal language tasks (Logical inference and CFQ). The new halting mechanism also enables around 50% reduction in computation during inference with very little performance decrease on formal language tasks.

--- https://arxiv.org/abs/1912.09713#google Measuring Compositional Generalization: A Comprehensive Method on Realistic Data Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, Olivier Bousquet 2019-12-20 2023-06-08 [("doi","10.48550/arXiv.1912.09713")] ai/dataset ai/nn/rnn ai/nn/transformer

State-of-the-art machine learning methods exhibit limited compositional generalization. At the same time, there is a lack of realistic benchmarks that comprehensively measure this ability, which makes it challenging to find and evaluate improvements.

We introduce a novel method to systematically construct such benchmarks by maximizing compound divergence while guaranteeing a small atom divergence between train and test sets, and we quantitatively compare this method to other approaches for creating compositional generalization benchmarks.

We present a large and realistic natural language question answering dataset that is constructed according to this method, and we use it to analyze the compositional generalization ability of 3 machine learning architectures. We find that they fail to generalize compositionally and that there is a surprisingly strong negative correlation between compound divergence and accuracy.

We also demonstrate how our method can be used to create new compositionality benchmarks on top of the existing SCAN dataset, which confirms these findings.

--- https://www.ian-leslie.com/p/the-banality-of-genius-notes-on-peter 2023-06-08 psychology/writing --- https://web.archive.org/web/20211106160816/https://ambrevar.xyz/lisp-repl-shell/index.html 2023-06-08 cs/lisp cs/shell --- https://www.lesswrong.com/posts/2eJPcPoptvP5chjd3/the-puritans-would-one-box-evidential-decision-theory-in-the 2023-06-08 philosophy/religion statistics/decision --- https://gregorygundersen.com/blog/2020/04/11/moments/ 2023-06-08 statistics/probability --- https://www.biorxiv.org/content/10.1101/2022.12.25.521908.full Korea4K: whole genome sequences of 4,157 Koreans with 107 phenotypes derived from extensive health check-ups Sungwon Jeon, Hansol Choi, Yeonsu Jeon, Whan-Hyuk Choi, Hyunjoo Choi, Kyungwhan An, Hyojung Ryu, Jihun Bhak, Hyeonjae Lee, Yoonsung Kwon, Sukyeon Ha, Yeo Jin Kim, Asta Blazyte, Changjae Kim, Yeonkyung Kim, Younghui Kang, Yeong Ju Woo, Chanyoung Lee, Jeongwoo Seo, Dan Bolser, Orsolya Biro, Eun-Seok Shin, Byung Chul Kim, Seon-Young Kim, Ji-Hwan Park, Jongbum Jeon, Dooyoung Jung, Semin Lee, Jong Bhak 2022-12-26 2023-06-08 [("doi","10.1101/2022.12.25.521908")] genetics/heritable/correlation/mendelian-randomization

We present 4,157 whole-genome sequences (Korea4K) coupled with 107 health check-up parameters as the largest whole genomic resource of Koreans.

Korea4K provides 45,537,252 variants and encompasses most of the common and rare variants in Koreans. We identified 1,356 new geno-phenotype associations which were not found by the previous Korea1K dataset. Phenomics analyses revealed 24 genetic correlations, 1,131 pleiotropic variants, and 127 causal relationships from Mendelian Randomization. Moreover, the Korea4K imputation reference panel showed a superior imputation performance to Korea1K.

Collectively, Korea4K provides the most extensive genomic and phenomic data resources for discovering clinically relevant novel genome-phenome associations in Koreans.

Figure 4: Genetic correlation and Phenotypic correlation in Korea4K. (a) Genetic heritability of 27 traits that showed at least a marginal statistical-significance. (b) Genetic correlation and phenotypic correlation between the 27 traits. The upper triangle indicates phenotypic correlation coefficient (Pearson’s r) and lower triangle indicates genetic correlation coefficient (rg).
--- https://www.animallaw.info/article/detailed-discussion-legal-rights-and-duties-lost-pet-disputes 2023-06-08 cat economics law --- https://www.biorxiv.org/content/10.1101/2022.12.22.521577.full Retrieving the near-complete genome of a threatened bird from wild frozen samples Haoran Luo, Xinrui Jiang, Boping Li, Jiahong Wu, Jiexin Shen, Zaoxu Xu, Xiaoping Zhou, Zhen Huang, Minghao Hou, Xiaobin Ou, Luohao Xu 2022-12-22 2023-06-08 [("doi","10.1101/2022.12.22.521577")] genetics/sequencing

Conservation genomics often relies on non-invasive methods to obtain DNA fragments which limit the power of multi-omic analyses for threatened species. Collecting samples from frozen dead animals in the wild provides an alternative approach to obtaining high-quality nucleic acids.

Here, we report multi-omic analyses based on a well-preserved great bustard individual (Otis tarda, Otidiformes) of a recent death found in the mountainous region in Gansu, China. We generated a near-complete genome assembly (OTswu) having only 18 gaps scattering in 8⁄40 assembled chromosomes. Unlikely most other bird genome assemblies, OTswu contains complete chromosome models (2n = 80). We demonstrated that the great bustard genome likely retained the ancestral avian karyotype.

We also characterized the DNA methylation landscapes of OTswu which are strongly correlated with GC content and gene expression. Our phylogenomic analysis suggested Otidiformes and Musophagiformes were sister groups that diverged from each other 46.3 million years ago. The genetic diversity of great bustard was found the lowest among the 4 available Otidiformes genomes, possibly due to population declines during past glacial periods.

As one of the heaviest migratory birds, great bustard possesses several expanded gene families related to cardiac contraction, actin contraction, calcium ion signaling transduction, as well as positively selected genes enriching for metabolism. Finally, we identified an extremely young evolutionary stratum on the sex chromosome, a rare case among Neoaves.

Together, our study combining long-read sequencing and RNA-seq technology provides a working strategy for conducting multi-omic analyses for threatened species by retrieving high-quality nucleic acids from dead animals frozen in the wild.

--- https://x.com/davidad/status/1713600503605522705 davidad 2023-06-08 ai/nn/transformer/gpt/dall-e/3 economics/copyright --- https://www.charlesatlas.com/index.html 2023-06-08 design exercise --- https://thelampmagazine.com/issues/issue-17/shadow-on-the-sun 2023-06-08 economics longevity --- https://x.com/nickcammarata/status/1713630383974088992 Nick Cammarata 2023-06-09 ai/nn/transformer/gpt/dall-e/3 --- https://www.nature.com/articles/s41531-023-00581-2 2023-06-09 nootropic/quantified-self --- https://arxiv.org/abs/2310.02279#sony Consistency Trajectory Models (CTM): Learning Probability Flow ODE Trajectory of Diffusion Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, Stefano Ermon 2023-10-01 2023-10-01 [("doi","10.48550/arXiv.2310.02279")] ai/nn/adversarial ai/nn/gan

[see also InstaFlow; homepage] Consistency Models (CM) (Song et al 2023) accelerate score-based diffusion model sampling at the cost of sample quality but lack a natural way to trade-off quality for speed.

To address this limitation, we propose Consistency Trajectory Model (CTM), a generalization encompassing CM and score-based models as special cases. CTM trains a single neural network that can—in a single forward pass—output scores (ie. gradients of log-density) and enables unrestricted traversal between any initial and final time along the Probability Flow Ordinary Differential Equation (ODE) in a diffusion process.

CTM enables the efficient combination of adversarial training and denoising score matching loss to enhance performance and achieves new state-of-the-art FIDs for single-step diffusion model sampling on CIFAR-10 (FID 1.73) and ImageNet at 64×64 resolution (FID 2.06).

CTM also enables a new family of sampling schemes, both deterministic and stochastic, involving long jumps along the ODE solution trajectories. It consistently improves sample quality as computational budgets increase, avoiding the degradation seen in CM.

Furthermore, CTM’s access to the score accommodates all diffusion model inference techniques, including exact likelihood computation.

--- https://www.atlasobscura.com/articles/bbc-missing-horror-show 2023-06-09 cs/linkrot/archiving --- https://www.newyorker.com/magazine/2023/10/23/the-great-cash-for-carbon-hustle 2023-06-09 technology/carbon-capture --- https://apenwarr.ca/log/20190201 2023-06-09 economics/advertising --- https://www.nytimes.com/2023/10/16/science/deer-forest-study-penn-state.html 2023-06-09 psychology/animal --- https://scholars-stage.org/public-intellectuals-have-short-shelf-lives-but-why/ 2023-06-09 psychology/writing --- https://mathstodon.xyz/@tao/111158219956220256 2023-06-09 ai/nn/transformer/gpt/codex math --- https://www.fastcompany.com/90338379/i-wrote-the-book-on-user-friendly-design-what-i-see-today-horrifies-me 2023-06-09 design --- https://taskandpurpose.com/culture/realistic-aerial-combat-movie-patlabor-2/ 2023-06-10 anime cs/security --- https://arxiv.org/abs/2310.09199#google PaLI-3 Vision Language Models: Smaller, Faster, Stronger Xi Chen, Xiao Wang, Lucas Beyer, Alexander Kolesnikov, Jialin Wu, Paul Voigtlaender, Basil Mustafa, Sebastian Goodman, Ibrahim Alabdulmohsin, Piotr Padlewski, Daniel Salz, Xi Xiong, Daniel Vlasic, Filip Pavetic, Keran Rong, Tianli Yu, Daniel Keysers, Xiaohua Zhai, Radu Soricut 2023-10-13 2023-10-13 [("doi","10.48550/arXiv.2310.09199")] ai/scaling

This paper presents PaLI-3 [1, 2], a smaller, faster, and stronger vision language model (VLM) that compares favorably to similar models that are 10× larger.

As part of arriving at this strong performance, we compare Vision Transformer (ViT) models pretrained using classification objectives to contrastively (SigLIP) pretrained ones.

We find that, while slightly underperforming on standard image classification benchmarks, SigLIP-based PaLI shows superior performance across various multimodal benchmarks, especially on localization and visually-situated text understanding.

We scale the SigLIP image encoder up to 2 billion parameters, and achieves a new state-of-the-art on multilingual cross-modal retrieval.

We hope that PaLI-3, at only 5b parameters, rekindles research on fundamental pieces of complex VLMs, and could fuel a new generation of scaled-up models.

--- https://arxiv.org/abs/2303.15343#google Sigmoid Loss for Language Image Pre-Training Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer 2023-03-27 2023-06-10 [("doi","10.48550/arXiv.2303.15343")] ai/nn/transformer/clip ai/scaling

We propose a simple pairwise Sigmoid loss for Language-Image Pre-training (SigLIP). Unlike standard contrastive learning with softmax normalization, the sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization.

The sigmoid loss simultaneously allows further scaling up the batch size, while also performing better at smaller batch sizes. Combined with Locked-image Tuning, with only 4 TPUv4 chips, we train a SigLiT model that achieves 84.5% ImageNet zero-shot accuracy in two days.

The disentanglement of the batch size from the loss further allows us to study the impact of examples vs pairs and negative to positive ratio. Finally, we push the batch size to the extreme, up to one million, and find that the benefits of growing batch size quickly diminish, with a more reasonable batch size of 32k being sufficient.

We release our models at https://github.com/google-research/big_vision and hope our research motivates further explorations in improving the quality and efficiency of language-image pre-training.

--- https://arxiv.org/abs/2308.01160 The Oxygen Bottleneck for Technospheres Amedeo Balbi, Adam Frank 2023-08-02 2023-08-02 [("doi","10.48550/arXiv.2308.01160")] science technology

On Earth, the development of technology required easy access to open air combustion, which is only possible when oxygen partial pressure, P(O2), is above 18%.

This suggests that only planets with atmospheric oxygen concentrations will be capable of developing “advanced” technospheres and hence detectable techno-signatures.

--- https://www.atlasobscura.com/articles/the-public-shaming-of-englands-first-umbrella-user 2023-06-10 history technology --- https://github.com/mgarciaisaia/JavaScript-Is-Weird-as-a-compressor 2023-06-10 cs/algorithm cs/computable --- https://blogs.bl.uk/digitisedmanuscripts/2023/10/the-largest-greek-manuscript.html 2023-06-10 design/typography/rubrication design/typography/sidenote --- https://arxiv.org/abs/2310.09144 Goodhart’s Law in Reinforcement Learning Jacek Karwowski, Oliver Hayman, Xingjian Bai, Klaus Kiendlhofer, Charlie Griffin, Joar Skalse 2023-10-13 2023-10-13 [("doi","10.48550/arXiv.2310.09144")] economics/mechanism-design reinforcement-learning/safe

Implementing a reward function that perfectly captures a complex task in the real world is impractical. As a result, it is often appropriate to think of the reward function as a proxy for the true objective rather than as its definition. We study this phenomenon through the lens of Goodhart’s law, which predicts that increasing optimization of an imperfect proxy beyond some critical point decreases performance on the true objective.

First, we propose a way to quantify the magnitude of this effect and show empirically that optimizing an imperfect proxy reward often leads to the behavior predicted by Goodhart’s law for a wide range of environments and reward functions.

We then provide a geometric explanation for why Goodhart’s law occurs in Markov decision processes.

We use these theoretical insights to propose an optimal early stopping method that provably avoids the aforementioned pitfall and derive theoretical regret bounds for this method. Moreover, we derive a training method that maximizes worst-case reward, for the setting where there is uncertainty about the true reward function.

Finally, we evaluate our early stopping method experimentally. Our results support a foundation for a theoretically-principled study of reinforcement learning under reward misspecification.

--- https://www.lesswrong.com/posts/Eu6CvP7c7ivcGM3PJ/goodhart-s-law-in-reinforcement-learning 2023-06-10 economics/mechanism-design reinforcement-learning/safe --- https://arxiv.org/abs/2309.15257 STARC: A General Framework For Quantifying Differences Between Reward Functions Joar Skalse, Lucy Farnik, Sumeet Ramesh Motwani, Erik Jenner, Adam Gleave, Alessandro Abate 2023-09-26 2023-09-26 [("doi","10.48550/arXiv.2309.15257")] reinforcement-learning/preference-learning reinforcement-learning/safe

In order to solve a task using reinforcement learning, it is necessary to first formalise the goal of that task as a reward function. However, for many real-world tasks, it is very difficult to manually specify a reward function that never incentivizes undesirable behavior. As a result, it is increasingly popular to use reward learning algorithms, which attempt to learn a reward function from data.

However, the theoretical foundations of reward learning are not yet well-developed. In particular, it is typically not known when a given reward learning algorithm with high probability will learn a reward function that is safe to optimize. This means that reward learning algorithms generally must be evaluated empirically, which is expensive, and that their failure modes are difficult to predict in advance.

One of the roadblocks to deriving better theoretical guarantees is the lack of good methods for quantifying the difference between reward functions. In this paper we provide a solution to this problem, in the form of a class of pseudometrics on the space of all reward functions that we call STARC (STAndardised Reward Comparison) metrics.

We show that STARC metrics induce both an upper and a lower bound on worst-case regret, which implies that our metrics are tight, and that any metric with the same properties must be bi-Lipschitz equivalent to ours. Moreover, we also identify a number of issues with reward metrics proposed by earlier works.

Finally, we evaluate our metrics empirically, to demonstrate their practical efficacy. STARC metrics can be used to make both theoretical and empirical analysis of reward learning algorithms both easier and more principled.

--- https://www.lesswrong.com/posts/gjs3q83hA4giubaAw/will-no-one-rid-me-of-this-turbulent-pest 2023-06-10 genetics/editing --- https://www.quantamagazine.org/thirty-years-later-a-speed-boost-for-quantum-factoring-20231017/ 2023-06-11 cs/cryptography --- /doc/history/1990-pleij.pdf Urban Elites in Search of a Culture: The Brussels Snow Festival of 1511 Herman Pleij 1990-03-01 2023-06-11 [("doi","10.2307/469131")] fiction/humor history --- /doc/psychiatry/schizophrenia/2018-siskind.pdf Glucagon-like peptide-1 receptor agonists for antipsychotic-associated cardio-metabolic risk factors: A systematic review and individual participant data meta-analysis Dan Siskind, Margaret Hahn, Christoph U. Correll, Anders Fink-Jensen, Anthony W. Russell, Nikolaj Bak, Brian V. Broberg, Julie Larsen, Pelle L. Ishøy, Tina Vilsbøll, Filip K. Knop, Steve Kisely, Bjørn H. Ebdrup 2018-09-05 2023-06-11 [("doi","10.1111/dom.13522")] longevity/glp/psychology longevity/glp/semaglutide psychiatry/schizophrenia

Aims: To evaluate if glucagon-like peptide-1 receptor agonists (GLP-1RAs) reduce antipsychotic-associated body weight gain in patients with schizophrenia, when compared to controls.

Method: We systematically searched PubMed/Embase/PsycINFO/Cochrane using the search terms (antipsychotic and GLP-1RA). Individual participant data from studies randomizing patients to GLP-1RA or control were meta-analysed. The primary outcome was difference in body weight between GLP-1RA and control; secondary outcomes included cardio-metabolic variables and adverse drug reactions (ADRs). Multiple linear regression was conducted including sex, age, psychosis severity, metabolic variable, ADRs, and GLP-1RA agent.

Results: 3 studies (exenatide once-weekly = 2; liraglutide once-daily = 1) provided participant-level data (n = 164, age = 40.0 ± 11.1 years, body weight = 105.8 ± 20.8 kg). After 16.2 ± 4.0 weeks of treatment, body weight loss was 3.71 kg (95% CI = 2.44-4.99 kg) greater for GLP-1RA versus control (p < 0.001), number-needed-to-treat ≥5% body weight loss = 3.8 (95% CI = 2.6-7.2). Waist circumference, body mass index, HbA1c, fasting glucose and visceral adiposity were each statistically-significantly lower with GLP-1RA.

Body weight loss with GLP-1RAs was greater for clozapine/olanzapine-treated patients (n = 141) than other antipsychotics (n = 27) (4.70 kg, 95% CI = 3.13-6.27 vs. 1.5 kg, 95% CI = −1.47-4.47) (p < 0.001).

Sex, age, psychosis severity, nausea, any ADR, and GLP-1RA agent did not statistically-significantly impact outcomes.

Nausea was more common with GLP-1RAs than control (53.6% vs. 27.5%, p = 0.002, number-needed-to-harm = 3.8).

Conclusion: GLP-1RAs are effective and tolerable for antipsychotic-associated body weight gain, particularly clozapine/olanzapine-treated patients. With few included patients, further studies are required before making routine use recommendations for GLP-1RAs.

--- https://en.wikipedia.org/wiki/All_horses_are_the_same_color All horses are the same color 2023-06-11 math --- https://www.technologyreview.com/2021/05/31/1025599/history-first-chinese-digital-computer-fonts/ 2023-06-11 design/typography --- /doc/math/humor/1951-miller.pdf How Newton Discovered the Law of Gravitation James E. Miller 1951-01-01 2023-06-11 [("doi","10.2307/27826356")] math/humor science --- https://en.wikipedia.org/wiki/Worm_Runner%27s_Digest Worm Runner’s Digest 2023-06-11 math/humor --- /doc/genetics/heritable/2017-segal-twinmythconceptions-ch12.pdf Twin Spouses and Unrelated Look-Alikes: New Views Segal 2017 2023-06-11 [("doi","10.1016/B978-0-12-803994-6/00012-3")] genetics/heritable psychology/personality --- /doc/genetics/heritable/2018-segal.pdf Pairs of Genetically Unrelated Look-Alikes Nancy L. Segal, Brittney A. Hernandez, Jamie L. Graham, Ulrich Ettinger 2018-01-01 2023-06-11 genetics/heritable psychology/personality --- https://en.wikipedia.org/wiki/Anorexia_mirabilis Anorexia mirabilis 2023-06-11 psychiatry/anorexia psychology/neuroscience/pain psychology/willpower --- https://en.wikipedia.org/wiki/Relative_energy_deficiency_in_sport Relative energy deficiency in sport 2023-06-11 psychiatry/anorexia psychology/energy --- https://arxiv.org/abs/2209.12892 g.pt: Learning to Learn with Generative Models of Neural Network Checkpoints William Peebles, Ilija Radosavovic, Tim Brooks, Alexei A. Efros, Jitendra Malik 2022-09-26 2023-06-12 [("doi","10.48550/arXiv.2209.12892")] ai/nn/cnn ai/nn/diffusion ai/nn/fully-connected reinforcement-learning/meta-learning reinforcement-learning/model/decision-transformer reinforcement-learning/scaling

[code; visualizations; lead author now at OpenAI] We explore a data-driven approach for learning to optimize neural networks.

We construct a dataset of neural network checkpoints and train a generative model g.pt on the parameters. In particular, our model is a conditional diffusion transformer that, given an initial input parameter vector and a prompted loss, error, or return, predicts the distribution over parameter updates that achieve the desired metric. At test time, it can optimize neural networks with unseen parameters for downstream tasks in just one update.

We find that our approach successfully generates parameters for a wide range of loss prompts. Moreover, it can sample multimodal parameter solutions and has favorable scaling properties.

We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.

--- https://x.com/francoisfleuret/status/1714531085512544760 Francois Fleuret 2023-06-12 ai/nn/fully-connected ai/nn/transformer/gpt reinforcement-learning/meta-learning --- https://en.wikipedia.org/wiki/Hammerbeam_roof Hammerbeam roof 2023-06-12 design --- https://onthearts.com/p/what-is-the-demoscene 2023-06-12 cs/algorithm design --- https://arxiv.org/abs/2310.09562 Does CLIP’s Generalization Performance Mainly Stem from High Train-Test Similarity? Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak, Matthias Bethge, Wieland Brendel 2023-10-14 2023-10-14 [("doi","10.48550/arXiv.2310.09562")] ai/nn/transformer/clip reinforcement-learning/exploration/active-learning/data-pruning

Foundation models like CLIP are trained on hundreds of millions of samples and effortlessly generalize to new tasks and inputs. Out of the box, CLIP shows stellar zero-shot and few-shot capabilities on a wide range of out-of-distribution (OOD) benchmarks, which prior works attribute mainly to today’s large and comprehensive training dataset (like LAION).

However, it is questionable how meaningful terms like out-of-distribution generalization are for CLIP as it seems likely that web-scale datasets like LAION simply contain many samples that are similar to common OOD benchmarks originally designed for ImageNet.

To test this hypothesis, we retrain CLIP on pruned LAION splits that replicate ImageNet’s train-test similarity with respect to common OOD benchmarks. While we observe a performance drop on some benchmarks, surprisingly, CLIP’s overall performance remains high.

This shows that high train-test similarity is insufficient to explain CLIP’s OOD performance, and other properties of the training data must drive CLIP to learn more generalizable representations.

Additionally, by pruning data points that are dissimilar to the OOD benchmarks, we uncover a 100M split of LAION (1⁄4th of its original size) on which CLIP can be trained to match its original OOD performance.

--- https://www.science.org/content/blog-post/saga-cassava 2023-06-12 psychiatry/alzheimers statistics/bias --- https://arxiv.org/abs/2310.02989 xVal: A Continuous Number Encoding for Large Language Models Siavash Golkar, Mariel Pettee, Michael Eickenberg, Alberto Bietti, Miles Cranmer, Geraud Krawezik, Francois Lanusse, Michael McCabe, Ruben Ohana, Liam Parker, Bruno Régaldo-Saint Blancard, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho 2023-10-04 2023-10-04 [("doi","10.48550/arXiv.2310.02989")] ai/nn/tokenization ai/nn/transformer/gpt

Large Language Models have not yet been broadly adapted for the analysis of scientific datasets due in part to the unique difficulties of tokenizing numbers.

We propose xVal, a numerical encoding scheme that represents any real number using just a single token. xVal represents a given real number by scaling a dedicated embedding vector by the number value. Combined with a modified number-inference approach, this strategy renders the model end-to-end continuous when considered as a map from the numbers of the input string to those of the output string. This leads to an inductive bias that is generally more suitable for applications in scientific domains.

We empirically evaluate our proposal on a number of synthetic and real-world datasets. Compared with existing number encoding schemes, we find that xVal is more token-efficient and demonstrates improved generalization.

--- https://blog.scottlogic.com/2021/08/31/a-primer-on-the-openai-api-1.html 2023-06-12 ai/nn/tokenization --- /doc/longevity/glp/2023-alhiary.pdf Patents and Regulatory Exclusivities on GLP-1 Receptor Agonists Rasha Alhiary, Aaron S. Kesselheim, Sarah Gabriele, Reed F. Beall, S. Sean Tu, William B. Feldman 2023-07-28 2023-07-28 [("doi","10.1001/jama.2023.13872")] economics/copyright longevity/glp

Importance: Glucagon-like peptide 1 (GLP-1) receptor agonists were first approved for the treatment of type 2 diabetes in 2005. Demand for these drugs has increased rapidly in recent years, as indications have expanded, but they remain expensive.

Objective: To analyze how manufacturers of brand-name GLP-1 receptor agonists have used the patent and regulatory systems to extend periods of market exclusivity.

Evidence Review:

The annual US Food and Drug Administration’s (FDA) Approved Drug Products With Therapeutic Equivalence Evaluations was used to identify GLP-1 receptor agonists approved 2005 → 2021 and to record patents and non-patent statutory exclusivities listed for each product. Google Patents was used to extract additional data on patents, including whether each was obtained on the delivery device or another aspect of the product. The primary outcome was the duration of expected protection from generic competition, defined as the time elapsed from FDA approval until expiration of the last-to-expire patent or regulatory exclusivity.

Findings: On the 10 GLP-1 receptor agonists included in the cohort, drug manufacturers listed with the FDA a median of 19.5 patents (IQR, 9.0-25.8) per product, including a median of 17 patents (IQR, 8.3-22.8) filed before FDA approval and 1.5 (IQR, 0-2.8) filed after FDA approval. Fifty-four percent of all patents listed on GLP-1 receptor agonists were on the delivery devices rather than active ingredients. Manufacturers augmented patent protection with a median of 2 regulatory exclusivities (IQR, 0-3) obtained at approval and 1 (IQR, 0.3-4.3) added after approval. The median total duration of expected protection after FDA approval, when accounting for both pre-approval and post-approval patents and regulatory exclusivities, was 18.3 years (IQR, 16.0-19.4). No generic firm has successfully challenged patents on GLP-1 receptor agonists to gain FDA approval.

Conclusion: Patent and regulatory reform is needed to ensure timely generic entry of GLP-1 receptor agonists to the market.

[The authors’ conclusions are unjustified by their results, as they do no economic modeling which could show whether 18.3 years is more or less than the optimal amount of IP protection.]

--- https://blog.mathieuacher.com/GPTsChessEloRatingLegalMoves/ 2023-06-12 reinforcement-learning/chess reinforcement-learning/model/decision-transformer --- https://x.com/chaseleantj/status/1714598429735948717 Chase Lean 2023-06-13 ai/nn/transformer/gpt/dall-e/3 --- https://sumrevija.si/en/eng-peter-watts-the-wisdom-of-crowds-sum11/ 2023-06-13 fiction/science-fiction statistics/prediction --- https://publicdomainreview.org/collection/fire-tests-with-textiles/ 2023-06-13 history/public-domain-review technology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5394149/ Extended spider cognition Hilton F. Japyassú, Kevin N. Laland 2017 2023-06-13 [("doi","10.1007/s10071-017-1069-7")] biology/portia psychology/animal

There is a tension between the conception of cognition as a central nervous system (CNS) process and a view of cognition as extending towards the body or the contiguous environment. The centralised conception requires large or complex nervous systems to cope with complex environments. Conversely, the extended conception involves the outsourcing of information processing to the body or environment, thus making fewer demands on the processing power of the CNS.

The evolution of extended cognition should be particularly favoured among small, generalist predators such as spiders, and here, we review the literature to evaluate the fit of empirical data with these contrasting models of cognition. Spiders do not seem to be cognitively limited, displaying a large diversity of learning processes, from habituation to contextual learning, including a sense of numerosity.

To tease apart the central from the extended cognition, we apply the mutual manipulability criterion, testing the existence of reciprocal causal links between the putative elements of the system. We conclude that the web threads and configurations are integral parts of the cognitive systems.

The extension of cognition to the web helps to explain some puzzling features of spider behavior and seems to promote evolvability within the group, enhancing innovation through cognitive connectivity to variable habitat features. Graded changes in relative brain size could also be explained by outsourcing information processing to environmental features.

More generally, niche-constructed structures emerge as prime candidates for extending animal cognition, generating the selective pressures that help to shape the evolving cognitive system.

--- https://arxiv.org/abs/2302.01834 Coinductive guide to inductive transformer heads Adam Nemecek 2023-02-03 2023-06-13 [("doi","10.48550/arXiv.2302.01834")] ai/nn/transformer/attention

We argue that all building blocks of transformer models can be expressed with a single concept: combinatorial Hopf algebra. Transformer learning emerges as a result of the subtle interplay between the algebraic and coalgebraic operations of the combinatorial Hopf algebra. Viewed through this lens, the transformer model becomes a linear time-invariant system where the attention mechanism computes a generalized convolution transform and the residual stream serves as a unit impulse. Attention-only transformers then learn by enforcing an invariant between these two paths. We call this invariant Hopf coherence.

Due to this, with a degree of poetic license, one could call combinatorial Hopf algebras “tensors with a built-in loss function gradient”. This loss function gradient occurs within the single layers and no backward pass is needed. This is in contrast to automatic differentiation which happens across the whole graph and needs an explicit backward pass. This property is the result of the fact that combinatorial Hopf algebras have the surprising property of calculating eigenvalues by repeated squaring.

--- https://laion.ai/blog/strategic-game-dataset/ 2023-06-13 ai/dataset reinforcement-learning/chess reinforcement-learning/model/decision-transformer --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643504/ Self-reported health without clinically measurable benefits among adult users of multivitamin and multimineral supplements: a cross-sectional study Manish D. Paranjpe, Alfred C. Chin, Ishan Paranjpe, Nicholas J. Reid, Phan Q. Duy, Jason K. Wang, Ross O’Hagan, Artine Arzani, Arsalan Haghdel, Clarence C. Lim, Vwaire Orhurhu, Ivan Urits, Anh L. Ngo, Benjamin S. Glicksberg, Kathryn T. Hall, Darshan Mehta, Richard S. Cooper, Girish N. Nadkarni 2020 2023-06-13 [("doi","10.1136/bmjopen-2020-039119")] statistics/bias

Objective: Multiple clinical trials fail to identify clinically measurable health benefits of daily multivitamin and multimineral (MVM) consumption in the general adult population. Understanding the determinants of widespread use of MVMs may guide efforts to better educate the public about effective nutritional practices. The objective of this study was to compare self-reported and clinically measurable health outcomes among MVM users and non-users in a large, nationally representative adult civilian non-institutionalized population in the USA surveyed on the use of complementary health practices.

Design: Cross-sectional analysis of the effect of MVM consumption on self-reported overall health and clinically measurable health outcomes.

Participants: Adult MVM users and non-users from the 2012 National Health Interview Survey (n = 21 603).

Primary and Secondary Outcome Measures: 5 psychological, physical, and functional health outcomes: (1) self-rated health status, (2) needing help with routine needs, (3) history of 10 chronic diseases, (4) presence of 19 health conditions in the past 12 months, and (5) Kessler 6-Item (K6) Psychological Distress Scale to measure non-specific psychological distress in the past month.

Results: Among 4933 adult MVM users and 16 670 adult non-users, MVM users self-reported 30% better overall health than non-users (adjusted OR 1.31; 95% CI 1.17 to 1.46; false discovery rate adjusted p < 0.001). There were no differences between MVM users and non-users in history of 10 chronic diseases, number of present health conditions, severity of current psychological distress on the K6 Scale and rates of needing help with daily activities. No effect modification was observed after stratification by sex, education, and race.

Conclusions: MVM users self-reported better overall health despite no apparent differences in clinically measurable health outcomes. These results suggest that widespread use of multivitamins in adults may be a result of individuals’ positive expectation that multivitamin use leads to better health outcomes or a self-selection bias in which MVM users intrinsically harbour more positive views regarding their health.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6439655/ Association of Non-adherence to Cancer Screening Examinations With Mortality From Unrelated Causes: A Secondary Analysis of the PLCO Cancer Screening Trial Dudith Pierre-Victor, Paul F. Pinsky 2018-12-28 2023-06-13 [("doi","10.1001/jamainternmed.2018.5982")] statistics/bias

Importance: [commentary] Patient non-adherence to chronic disease prevention guidelines is associated with increased mortality. Non-adherence to offered cancer screening tests may be associated with mortality among middle-aged and older adults.

Objective: To evaluate the association between non-adherence to cancer screening tests and mortality in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening trial, excluding mortality from cancers studied in the trial.

Methods, Setting, & Participants: Randomization at 10 US screening centers occurred from November 8, 1993, to July 2, 2001. Original follow-up was through 13 years or December 31, 2009. Participants were re-consented to further follow-up starting May 18, 2011, and were observed until December 31, 2012. Protocol screening tests for the PLCO Cancer Screening trial intervention arm participants (n = 77 443) included chest radiographs and flexible sigmoidoscopy for both sexes, prostate-specific antigen tests and digital rectal examinations for men, and cancer antigen 125 tests and transvaginal ultrasonography for women. At baseline, participants completed a self-administered questionnaire. The cohort was classified into those receiving all sex-specified PLCO Cancer Screening trial screening tests at baseline (fully adherent), those receiving some but not all baseline tests (partially adherent), and those receiving no baseline tests (nonadherents). Secondary analysis was ad hoc in the original trial protocol. Statistical analysis was conducted from November 24, 2017, to August 29, 2018.

Main Outcomes & Measures: Mortality was ascertained via mailed annual study update questionnaires and searches of the National Death Index. Cox proportional hazards regression was used to analyze the association between mortality and adherence, controlling for various covariates.

Results: Of 77 443 participants in the intervention arm, 64 567 (29 537 women and 35 030 men; mean [SD] age, 62.3 [5.3] years) were included in the analysis based on consenting to trial participation before randomization and being eligible for all screening tests. Overall, 55 065 participants (85.3%) were adherent, 2548 (3.9%) were partially adherent, and 6954 (10.8%) were non adherent with the baseline screening protocol. Within 10 years of follow-up, the hazard ratio of mortality, excluding deaths from cancers studied in the PLCO Cancer Screening trial and controlling only for age, sex, and race/ethnicity (model 1), was 1.73 (95% CI, 1.60-1.89) for nonadherents compared with fully adherent participants and 1.36 (95% CI, 1.19-1.54) for partially compared with fully adherent participants. After adjustment for medical risk factors for mortality and behavioral-related factors (model 2), the hazard ratio decreased to 1.46 (95% CI, 1.34-1.59) for nonadherents compared with fully adherent participants.

Conclusion: Among participants in a screening trial for multiple cancers, a non-adherence behavior profile marked by non-adherence to protocol screenings was associated with higher overall mortality (excluding deaths from cancers studied in the trial). The generalizability of this finding to routine clinical practice should be assessed.

Trial Registration: ClinicalTrials.gov identifier: NCT00002540.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5515136/ Potential contribution of lifestyle and socioeconomic factors to healthy user bias in anti-hypertensives and lipid-lowering drugs Mitsuyo Kinjo, Edward Chia-Cheng Lai, Maarit Jaana Korhonen, Rita L. McGill, Soko Setoguchi 2017 2023-06-13 [("doi","10.1136/openhrt-2016-000417")] statistics/bias

Objectives: Healthy user bias arises when users of preventive medications such as lipid-lowering drugs (LLDs), hormone replacement therapy and antihypertensive (AH) medications are healthier than non-users due to factors other than medication effects, making the medications appear more beneficial in observational studies of effectiveness and safety. The purpose of the study is to examine factors contributing to healthy user effect in patients taking AHs or LLDs.

Methods: Among patients with hypertension or hyperlipidaemia in a population-based sample from the National Health and Nutrition Examination Survey (1999–2010), we assessed the association between socioeconomic and lifestyle factors and the use of AHs/LLDs by logistic regression with adjustment for demographics and comorbidities in a cross-sectional study.

Results: When 9715 AH/LLD users were compared with 3725 non-users, AH/LLD users were more likely to be: highly educated (OR 1.2, 95% CI 1.2 to 1.3), non-impoverished (OR 1.3, 95% CI 1.2 to 1.4), current non-smokers (OR 1.2, 95% CI 1.1 to 1.4), physically active (OR 1.1, 95% CI 1.0 to 1.2) and consume more calcium (OR 1.1, 95% CI 1.0 to 1.3) but less likely to have normal body mass index (OR 0.6, 95% CI 0.6 to 0.7) or to meet dietary sodium recommendations (OR 0.8, 95% CI 0.7 to 0.9).

Conclusions: We identified several salutary lifestyle factors associated with AH/LLD use in a representative US population. Healthy user effect may be partly explained by better socioeconomic profiles and lifestyles in AH/LLD users compared with non-users.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3450433/ Can physicians accurately predict which patients will lose weight, improve nutrition and increase physical activity? Kathryn I. Pollak, Cynthia J. Coffman, Stewart C. Alexander, James A. Tulsky, Pauline Lyna, Rowena J. Dolor, Mary E. Cox, Rebecca J. Namenek Brouwer, Michael E. Bodner, Truls Østbye 2012 2023-06-13 [("doi","10.1093/fampra/cms004")] psychology/cognitive-bias statistics/prediction

Background: Physician counselling may help patients increase physical activity, improve nutrition and lose weight. However, physicians have low outcome expectations that patients will change. The aims are to describe the accuracy of physicians’ outcome expectations about whether patients will follow weight loss, nutrition and physical activity recommendations. The relationships between physician outcome expectations and patient motivation and confidence also are assessed.

Methods: This was an observational study that audio recorded encounters between 40 primary care physicians and 461 of their overweight or obese patients. We surveyed physicians to assess outcome expectations that patients will lose weight, improve nutrition and increase physical activity after counselling. We assessed actual patient change in behaviors from baseline to 3 months after the encounter and changes in motivation and confidence from baseline to immediately post-encounter.

Results: Right after the visit, ~55% of the time physicians were optimistic that their individual patients would improve. Physicians were not very accurate about which patients actually would improve weight, nutrition and physical activity. More patients had higher confidence to lose weight when physicians thought that patients would be likely to follow their weight loss recommendations.

Conclusions: Physicians are moderately optimistic that patients will follow their weight loss, nutrition and physical activity recommendations. Patients might perceive physicians’ confidence in them and thus feel more confident themselves. Physicians, however, are not very accurate in predicting which patients will or will not change behaviors. Their optimism, although helpful for patient confidence, might make physicians less receptive to learning effective counselling techniques.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183787 Health risk factors associated with meat, fruit and vegetable consumption in cohort studies: A comprehensive meta-analysis Giuseppe Grosso, Agnieszka Micek, Justyna Godos, Andrzej Pajak, Salvatore Sciacca, Fabio Galvano, Paolo Boffetta 2017-08-11 2023-06-13 [("doi","10.1371/journal.pone.0183787")] exercise

The aim of this study was to perform a meta-analysis to test the association between red, processed, and total meat, as well as fruit and vegetable consumption, and selected health risk factors, including body weight status, smoking habit, physical activity level, level of education, and alcohol drinking in cohort studies on non-communicable disease. A systematic search of electronic databases was performed to identify relevant articles published up to March 2017.

In a two-stage approach, frequency-weighted linear regression coefficients were first calculated for each variable, and then combined across studies through meta-regression. Ninety-eight studies including 20 on red meat, 6 on processed meat, 12 on total meat, 37 on fruit and vegetable combined, 21 on fruit and 24 on vegetable consumption were analyzed.

Intake of red meat was positively associated with BMI, percentage of overweight and obese, low physical activity, and current and ever smoking and inversely associated with percentage of non-smokers and high physically active individuals. Similar associations were found for red meat were found, although based on fewer data.

Intake of fruits and vegetables was positively associated with prevalence of non-smokers, high education and high physical activity, and similar results were found when examining fruit and vegetable consumption separately. Stratification by geographical area revealed that some associations were stronger in US rather than European or Asian cohorts.

In conclusions, the distribution of health risk factors associated with high meat and fruit/vegetable consumption may differ from those of low-consumers. Some of these differences may mediate, confound, or modify the relation between diet and non-communicable disease risk.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2744446/ Statin adherence and risk of accidents: a cautionary tale Colin R. Dormuth, Amanda R. Patrick, William H. Shrank, James M. Wright, Robert J. Glynn, Jenny Sutherland, M. Alan Brookhart 2009 2023-06-14 [("doi","10.1161/CIRCULATIONAHA.108.824151")] statistics/bias

Background: Bias in studies of preventive medications can occur when healthier patients are more likely to initiate and adhere to therapy than less healthy patients. We sought evidence of this bias by examining associations between statin exposure and various outcomes that should not be causally affected by statin exposure, such as workplace and motor vehicle accidents.

Methods and Results: We conducted a prospective cohort study of statin patients using data from British Columbia, Canada, a multiethnic society with a population of 4.3 million people. Study subjects were 141 086 patients who initiated statins for primary prevention. We examined the association between adherence and multiple outcomes such as accidents and screening procedures using multivariable-adjusted Cox proportional hazards models. The study population was 49% female and had an average age of 61 years. The results from our multivariable-adjusted models showed that more adherent patients were less likely to have accidents than less adherent patients. This effect was greatest for motor vehicle accidents (hazard ratio, 0.75; 95% confidence interval, 0.72 to 0.79) and workplace accidents (hazard ratio, 0.77; 95% confidence interval, 0.74 to 0.81). More adherent patients had a greater likelihood of using screening services (hazard ratio, 1.17; 95% confidence interval, 1.15 to 1.20) and a lower likelihood of developing other diseases likely to be unrelated to a biological affect of a statin (hazard ratio, 0.87; 95% confidence interval, 0.86 to 0.89).

Conclusions: Our study contributes compelling evidence that patients who adhere to statins are systematically more health seeking than comparable patients who do not remain adherent. Caution is warranted when interpreting analyses that attribute surprising protective effects to preventive medications.

--- https://colinmorris.github.io/blog/unpopular-wiki-articles 2023-06-14 wikipedia --- https://www.justice.gov/usao-ndga/pr/doctor-pleads-guilty-dark-web-murder-hire-plot 2023-06-14 darknet-market --- https://arxiv.org/abs/2212.02872#ibm A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference Manuel Le Gallo, Riduan Khaddam-Aljameh, Milos Stanisavljevic, Athanasios Vasilopoulos, Benedikt Kersting, Martino Dazzi, Geethan Karunaratne, Matthias Braendli, Abhairaj Singh, Silvia M. Mueller, Julian Buechel, Xavier Timoneda, Vinay Joshi, Urs Egger, Angelo Garofalo, Anastasios Petropoulos, Theodore Antonakopoulos, Kevin Brew, Samuel Choi, Injo Ok, Timothy Philip, Victor Chan, Claire Silvestre, Ishtiaq Ahsan, Nicole Saulnier, Vijay Narayanan, Pier Andrea Francese, Evangelos Eleftheriou, Abu Sebastian 2022-12-06 2023-06-14 [("doi","10.48550/arXiv.2212.02872")] ai/nn/cnn ai/nn/rnn ai/scaling/hardware

The need to repeatedly shuttle around synaptic weight values from memory to processing units has been a key source of energy inefficiency associated with hardware implementation of artificial neural networks. Analog in-memory computing (AIMC) with spatially instantiated synaptic weights holds high promise to overcome this challenge, by performing matrix-vector multiplications (MVMs) directly within the network weights stored on a chip to execute an inference workload. However, to achieve end-to-end improvements in latency and energy consumption, AIMC must be combined with on-chip digital operations and communication to move towards configurations in which a full inference workload is realized entirely on-chip. Moreover, it is highly desirable to achieve high MVM and inference accuracy without application-wise re-tuning of the chip.

Here, we present a multi-core AIMC chip designed and fabricated in 14-nm complementary metal-oxide-semiconductor (CMOS) technology with backend-integrated phase-change memory (PCM). The fully-integrated chip features 64 256×256 AIMC cores interconnected via an on-chip communication network. It also implements the digital activation functions and processing involved in ResNet convolutional neural networks and long short-term memory (LSTM) networks.

We demonstrate near software-equivalent inference accuracy with ResNet and LSTM networks while implementing all the computations associated with the weight layers and the activation functions on-chip. The chip can achieve a maximal throughput of 63.1 TOPS at an energy efficiency of 9.76 TOPS/W for 8-bit input/output matrix-vector multiplications.

--- https://dgl.cx/2023/09/ansi-terminal-security 2023-06-14 cs/security --- https://www.redblobgames.com/grids/hexagons/ 2023-06-14 design/visualization math --- /doc/japan/history/2023-andreeva.pdf Wondrous Worms and Exotic Drugs: Chasing the ‘Parasites of the 5 Viscera’ in a Nichibunken Manuscript Anna Andreeva 2023-10-03 2023-10-03 [("doi","10.1163/9789004548763_004")] biology japan/history --- https://www.lesswrong.com/posts/4Hnso8NMAeeYs8Cta/revealing-intentionality-in-language-models-through-adavae#BigVAE_and_Its_Samplers 2023-06-14 ai/nn/sampling ai/nn/transformer --- https://home.hccnet.nl/h.g.muller/max-src2.html 2023-06-14 cs/algorithm reinforcement-learning/chess --- https://aeon.co/essays/why-chinese-minds-still-bear-the-long-shadow-of-keju 2023-06-14 history politics psychology/writing --- https://stefan.saasen.me/articles/git-clone-in-haskell-from-the-bottom-up/ 2023-06-14 cs/haskell --- https://crimereads.com/the-rise-and-fall-of-the-bank-robbery-capital-of-the-world/ 2023-06-15 crime technology --- https://arxiv.org/abs/2309.11751 How Robust is Google’s Bard to Adversarial Image Attacks? Yinpeng Dong, Huanran Chen, Jiawei Chen, Zhengwei Fang, Xiao Yang, Yichi Zhang, Yu Tian, Hang Su, Jun Zhu 2023-09-21 2023-09-21 [("doi","10.48550/arXiv.2309.11751")] ai/nn/adversarial ai/nn/transformer/gpt/palm

Multimodal Large Language Models (MLLMs) that integrate text and other modalities (especially vision) have achieved unprecedented performance in various multimodal tasks. However, due to the unsolved adversarial robustness problem of vision models, MLLMs can have more severe safety and security risks by introducing the vision inputs.

In this work, we study the adversarial robustness of Google’s Bard, a competitive chatbot to ChatGPT that released its multimodal capability recently, to better understand the vulnerabilities of commercial MLLMs.

By attacking white-box surrogate vision encoders or MLLMs, the generated adversarial examples can mislead Bard to output wrong image descriptions with a 22% success rate based solely on the transferability. We show that the adversarial examples can also attack other MLLMs, eg. a 26% attack success rate against Bing Chat and a 86% attack success rate against ERNIE bot. Moreover, we identify two defense mechanisms of Bard, including face detection and toxicity detection of images. We design corresponding attacks to evade these defenses, demonstrating that the current defenses of Bard are also vulnerable.

We hope this work can deepen our understanding on the robustness of MLLMs and facilitate future research on defenses. Our code is available at Github.

Update: GPT-4-V is available at October 2023. We further evaluate its robustness under the same set of adversarial examples, achieving a 45% attack success rate.

--- https://news.ycombinator.com/item?id=37971959 2023-06-15 cs design --- https://cacm.acm.org/research/the-decline-of-computers-as-a-general-purpose-technology/ 2023-06-15 ai/scaling cs/hardware economics/automation --- https://www.tunnellersmemorial.com/tunnelling-companies/ 2023-06-15 technology --- https://int10h.org/oldschool-pc-fonts/readme/ 2023-06-15 design/typography --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166371/ Alcohol dependence in men: reliability and heritability Eivind Ystrom, Ted Reichborn-Kjennerud, Steven H. Aggen, Kenneth S. Kendler 2011 2023-06-15 [("doi","10.1111/j.1530-0277.2011.01518.x")] genetics/heritable psychiatry/alcoholism

Background: The assessment of a Diagnostic and Statistical Manual of Mental Disorders Fourth Edition (DSM-IV) life-time history of alcohol dependence (LTH-AD) has been found to be moderately reliable and substantially heritable. However, in studies of the heritability of LTH-AD, measurement error could not be discriminated from the true unique environmental effects. The aims of this study were to: (1) estimate the reliability of LTH-AD in a population based sample, (2) identify characteristics of LTH-AD predicting a reliable diagnosis, (3) investigate the heritability of LTH-AD as a function of diagnostic confidence, and (4) to estimate the genetic and environmental influences on LTH-AD correcting for measurement error.

Methods: An unselected sample of 4,203 male twins was interviewed twice ~1-year apart assessing DSM-IV LTH-AD over the same period of life. Logistic regression was used to identify clinical features that predict a reliable diagnosis LTH-AD. Genetic and environmental influences on reliable LTH-AD were examined using structural equation models.

Results: Reliability of the diagnosis of LTH-AD was moderate (κ = 0.54) and was predicted by the number of AD symptoms, treatment seeking, duration of most severe episode, and a great deal of time spent to obtain, use, or recover from alcohol use (DSM-IV AD criterion #5). Using an index of caseness, heritability of LTH-AD increased as a function of diagnostic confidence. Accounting for errors of measurement in a multivariate twin model, the heritability of LTH-AD increased 55 → 71%.

Conclusions: Reliably diagnosed LTH-AD can be predicted by characteristics relevant to the disorder. LTH-AD appears to be a moderately reliable disorder of high heritability.

--- /doc/psychiatry/traumatic-brain-injury/2023-chantiketterl.pdf Associations Between Traumatic Brain Injury and Cognitive Decline Among Older Veteran Men—A Twin Study Marianne Chanti-Ketterl, Carl F. Pieper, Kristine Yaffe, Brenda L. Plassman 2023-09-06 2023-09-06 [("doi","10.1212/WNL.0000000000207819")] iq psychiatry/traumatic-brain-injury

Background: Traumatic brain injuries (TBI) are associated with increased risk of dementia, but whether lifetime TBI influences cognitive trajectories in later life is less clear. Cognitive interventions post TBI may improve cognitive trajectories and delay dementia. Since twins share many genes and environmental factors, we capitalize on the twin-study design to examine the association between lifetime TBI and cognitive decline.

Methods: Participants were members of the National Academy of Sciences National Research Council’s Twin Registry of male veterans of World-War-II with self or proxy reported history of TBI and with up to 4 observations over 12 years of the modified Telephone Interview for Cognitive Status (TICS-m). We used linear random effects mixed models to analyze the association between TBI and TICS-m in the full sample and among co-twins discordant for TBI. Additional TBI predictor variables included: number of TBI, severity (loss of consciousness [LOC]), age first TBI (age <25 vs. 25+ [older age TBI]). Models were adjusted for age (centered at 70-years), age-squared, education, wave, twin pair, lifestyle behaviors and medical conditions.

Results: Of 8,662 participants, 25% reported TBI. History of any TBI (β = −0.56, CI95% −0.73, −0.39), TBI with LOC (β = −0.51, CI95% −0.71, −0.31), and older-age TBI (β = −0.66, CI95% −0.90, −0.42) were associated with lower TICS-m scores at age-70. TBI with LOC (β = −0.03, CI95% −0.05, −0.001), more than one TBI (β = −0.05, CI95% −0.09, −0.002,), and older age TBI (β = −0.06, CI95% −0.09, −0.03) were associated with faster cognitive decline.

Among monozygotic pairs discordant for TBI (589 pairs), a history of any TBI (β = −0.55, CI95% −0.91, −0.19) and having older age TBI (β = −0.74, CI95% −1.22, −0.26,) was associated with lower TICS-m scores at age-70. Those with more than one TBI (β = −0.13, CI95% −0.23, −0.03) and older age TBI (β = −0.07, CI95% −0.13, −0.002) showed greater cognitive decline compared to their cotwin without TBI.

Discussion: These findings support an association on the impact of TBI on cognitive score and the rapidity of cognitive decline in later life. The results in monozygotic pairs, who share all genes and many exposures particularly in early life, provide additional evidence of a causal relationship between TBI and poorer late life cognitive outcomes.

--- https://arxiv.org/abs/2212.07906 Flow-Lenia: Towards open-ended evolution in cellular automata through mass conservation and parameter localization Erwan Plantec, Gautier Hamon, Mayalen Etcheverry, Pierre-Yves Oudeyer, Clément Moulin-Frier, Bert Wang-Chak Chan 2022-12-14 2023-06-15 [("doi","10.48550/arXiv.2212.07906")] cs/cellular-automaton

[video] The design of complex self-organising systems producing life-like phenomena, such as the open-ended evolution of virtual creatures, is one of the main goals of artificial life. Lenia, a family of cellular automata (CA) generalizing Conway’s Game of Life to continuous space, time and states, has attracted a lot of attention because of the wide diversity of self-organizing patterns it can generate.

Among those, some spatially localized patterns (SLPs) resemble life-like artificial creatures and display complex behaviors. However, those creatures are found in only a small subspace of the Lenia parameter space and are not trivial to discover, necessitating advanced search algorithms. Furthermore, each of these creatures exist only in worlds governed by specific update rules and thus cannot interact in the same one.

This paper proposes as mass-conservative extension of Lenia, called Flow Lenia, that solve both of these issues. We present experiments demonstrating its effectiveness in generating SLPs with complex behaviors and show that the update rule parameters can be optimized [using evolutionary strategies] to generate SLPs showing behaviors of interest.

Finally, we show that Flow Lenia enables the integration of the parameters of the CA update rules within the CA dynamics, making them dynamic and localized, allowing for multi-species simulations, with locally coherent update rules that define properties of the emerging creatures, and that can be mixed with neighbouring rules. We argue that this paves the way for the intrinsic evolution of self-organized artificial life forms within continuous CAs.

--- https://arxiv.org/abs/2108.08810#google Do Vision Transformers See Like Convolutional Neural Networks? Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, Alexey Dosovitskiy 2021-08-19 2023-06-15 [("doi","10.48550/arXiv.2108.08810")] ai/nn/cnn ai/nn/transformer/attention ai/scaling

Convolutional neural networks (CNNs) have so far been the de-facto model for visual data. Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or even superior performance on image classification tasks. This raises a central question: how are Vision Transformers solving these tasks? Are they acting like convolutional networks, or learning entirely different visual representations?

Analyzing the internal representation structure of ViTs and CNNs on image classification benchmarks, we find striking differences between the two architectures, such as ViT having more uniform representations across all layers. We explore how these differences arise, finding crucial roles played by self-attention, which enables early aggregation of global information, and ViT residual connections, which strongly propagate features from lower to higher layers.

We study the ramifications for spatial localization, demonstrating ViTs successfully preserve input spatial information, with noticeable effects from different classification methods. Finally, we study the effect of (pretraining) dataset scale on intermediate features and transfer learning.

And we conclude with a discussion on connections to new architectures such as the MLP-Mixer.

--- https://computer.rip/2023-10-22-cooler-screens.html 2023-06-15 design economics/advertising --- https://www.lesswrong.com/posts/QNQuWB3hS5FrGp5yZ/programmatic-backdoors-dnns-can-use-sgd-to-run-arbitrary 2023-06-16 ai/nn cs/computable reinforcement-learning/meta-learning reinforcement-learning/safe --- https://www.johnsonessays.com/the-rambler/no-4-the-modern-form-of-romances-preferable-to-the-ancient-the-necessity-of-characters-morally-good/ 2023-06-16 fiction/criticism philosophy/ethics --- https://nostalgebraist.tumblr.com/post/728556535745232896/claude-is-insufferable 2023-06-16 ai/nn/transformer/gpt/claude reinforcement-learning/preference-learning/mode-collapse --- https://marginalrevolution.com/marginalrevolution/2023/10/goat-who-is-the-greatest-economist-of-all-time-and-why-does-it-matter.html 2023-06-16 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude economics --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC544986/ Low intelligence test scores in 18 year old men and risk of suicide: cohort study D Gunnell, P. K. E. Magnusson, F. Rasmussen 2005 2023-06-16 [("doi","10.1136/bmj.38310.473565.8F")] iq psychiatry

Objective: To examine the association between intelligence test scores in men, measured at age 18, and subsequent suicide.

Design: Record linkage study of the Swedish military service conscription register (1968–1994) with the multi-generation register, cause of death register and census data. 4 tests were performed at conscription covering logic, language, spatial, and technical skills.

Setting: Sweden.

Participants: 987,308 Swedish men followed up for 5–26 years.

Main Outcome Measure: Suicide.

Results: 2,811 suicides occurred during follow up. The risk of suicide was 2–3× higher in those with lowest compared with the highest test scores. The strongest associations were seen with the logic test: for each unit increase in test score the risk of suicide decreased by 12% (95% confidence interval 10% to 14%). [Because most g-loaded, presumably.]

Associations were only slightly attenuated when we controlled for parents’ socioeconomic position. Greatest risks were seen among poorly performing offspring of well-educated parents. [Because having the largest deviations from expected, and thus most indicative of a severe health issue?]

Conclusions: Performance in intelligence tests is strongly related to subsequent risk of suicide in men. This may be due to the importance of cognitive ability in either the aetiology of serious mental disorder or an individual’s capacity to solve problems while going through an acute life crisis or suffering from mental illness.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5474617/ 2023-06-16 biology --- /doc/fiction/science-fiction/1960-dahl-williamandmary.html William and Mary Roald Dahl 1960 2023-06-16 fiction/science-fiction psychology/neuroscience --- https://thezvi.wordpress.com/2023/10/24/book-review-going-infinite/ 2023-06-16 bitcoin psychology/personality/psychopathy --- https://www.chinatalk.media/p/new-sexport-controls-semianalysis 2023-06-16 ai/scaling/hardware --- https://buzzbloq.com/retro-arcade-game-ads-a-nostalgic-look-at-marketing-of-the-past/ 2023-06-16 economics/advertising --- https://x.com/thecaptain_nemo/status/1716867896419496176 Nemo 2023-06-17 psychiatry/meditation --- https://animatedai.github.io/ 2023-06-17 ai/nn/cnn design/visualization --- https://blog.segmind.com/introducing-segmind-ssd-1b/ 2023-06-17 ai/nn/diffusion ai/nn/sparsity/knowledge-distillation --- https://www.ft.com/content/9aeb482d-f781-45c0-896f-38fdcc912139 2023-06-17 ai/nn/transformer/gpt/3/nonfiction --- https://www.nytimes.com/2023/10/21/magazine/climate-anxiety-therapy.html 2023-06-17 psychiatry/anxiety psychiatry/depression --- https://arxiv.org/abs/2203.02094#microsoft LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models Mojan Javaheripi, Gustavo H. de Rosa, Subhabrata Mukherjee, Shital Shah, Tomasz L. Religa, Caio C. T. Mendes, Sebastien Bubeck, Farinaz Koushanfar, Debadeepta Dey 2022-03-04 2023-06-17 [("doi","10.48550/arXiv.2203.02094")] ai/nn/transformer ai/scaling/hardware reinforcement-learning/meta-learning

The Transformer architecture is ubiquitously used as the building block of large-scale autoregressive language models. However, finding architectures with the optimal trade-off between task performance (perplexity) and hardware constraints like peak memory usage and latency is non-trivial. This is exacerbated by the proliferation of various hardware.

We leverage the somewhat surprising empirical observation that the number of decoder parameters in autoregressive Transformers has a high rank correlation with task performance, irrespective of the architecture topology. This observation organically induces a simple Neural Architecture Search (NAS) algorithm that uses decoder parameters as a proxy for perplexity without need for any model training.

The search phase of our training-free algorithm, dubbed Lightweight Transformer Search (LTS), can be run directly on target devices since it does not require GPUs. Using on-target-device measurements, LTS extracts the Pareto-frontier of perplexity versus any hardware performance cost.

We evaluate LTS on diverse devices from ARM CPUs to NVIDIA GPUs and two popular autoregressive Transformer backbones: GPT-2 and Transformer-XL. Results show that the perplexity of 16-layer GPT-2 and Transformer-XL can be achieved with up to 1.5×, 2.5× faster runtime and 1.2×, 2.0× lower peak memory usage.

When evaluated in zero and one-shot settings, LTS Pareto-frontier models achieve higher average accuracy compared to the 350M parameter OPT across 14 tasks, with up to 1.6× lower latency. LTS extracts the Pareto-frontier in under 3 hours while running on a commodity laptop.

We effectively remove the carbon footprint of hundreds of GPU hours of training during search, offering a strong simple baseline for future NAS methods in autoregressive language modeling.

--- https://www.theorgplumber.com/posts/statement/ 2023-06-17 statistics/bias --- https://www.cnbc.com/2023/10/17/crypto911.html 2023-06-17 darknet-market/silk-road/1 --- https://arxiv.org/abs/2002.00352#alibaba Solving Billion-Scale Knapsack Problems Xingwen Zhang, Feng Qi, Zhigang Hua, Shuang Yang 2020-02-02 2023-06-17 [("doi","10.48550/arXiv.2002.00352")] cs/algorithm

Knapsack problems (KPs) are common in industry, but solving KPs is known to be NP-hard and has been tractable only at a relatively small scale.

This paper examines KPs in a slightly generalized form and shows that they can be solved nearly optimally at scale via distributed algorithms [via sub-problems with Lagrangians optimized with dual descent]. The proposed approach can be implemented fairly easily with off-the-shelf distributed computing frameworks (eg. MPI, Hadoop, Spark).

As an example, our implementation leads to one of the most efficient KP solvers known to date—capable to solve KPs at an unprecedented scale (eg. KPs with 1 billion decision variables and 1 billion constraints can be solved within 1 hour).

The system has been deployed to production and called on a daily basis, yielding business impacts at Ant Financial.

--- https://arxiv.org/abs/2310.12109 Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture Daniel Y. Fu, Simran Arora, Jessica Grogan, Isys Johnson, Sabri Eyuboglu, Armin W. Thomas, Benjamin Spector, Michael Poli, Atri Rudra, Christopher Ré 2023-10-18 2023-10-18 [("doi","10.48550/arXiv.2310.12109")] ai/nn

Machine learning models are increasingly being scaled in both sequence length and model dimension to reach longer contexts and better performance. However, existing architectures such as Transformers scale quadratically along both these axes. We ask: are there performant architectures that can scale sub-quadratically along sequence length and model dimension?

We introduce Monarch Mixer (M2), a new architecture that uses the same sub-quadratic primitive along both sequence length and model dimension: Monarch matrices, a simple class of expressive structured matrices that captures many linear transforms, achieves high hardware efficiency on GPUs, and scales sub-quadratically.

As a proof of concept, we explore the performance of M2 in 3 domains: non-causal BERT-style language modeling, ViT-style image classification, and causal GPT-style language modeling. For non-causal BERT-style modeling, M2 matches BERT-base and BERT-large in downstream GLUE quality with up to 27% fewer parameters, and achieves up to 9.1× higher throughput at sequence length 4K. On ImageNet, M2 outperforms ViT-b by 1% in accuracy, with only half the parameters.

Causal GPT-style models introduce a technical challenge: enforcing causality via masking introduces a quadratic bottleneck. To alleviate this bottleneck, we develop a novel theoretical view of Monarch matrices based on multivariate polynomial evaluation and interpolation, which lets us parameterize M2 to be causal while remaining sub-quadratic.

Using this parameterization, M2 matches GPT-style Transformers at 360M parameters in pretraining perplexity on The Pile–showing for the first time that it may be possible to match Transformer quality without attention or MLPs.

--- https://news.ycombinator.com/item?id=38004792 2023-06-17 psychology/cognitive-bias/illusion-of-depth --- https://x.com/TheJoinery_jp 2023-06-18 design/visualization technology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1307523/ Metallic taste from electrical and chemical stimulation Harry T. Lawless, David A. Stevens, Kathryn W. Chapman, Anne Kurtz 2005 2023-06-18 [("doi","10.1093/chemse/bji014")] food psychology/neuroscience

A series of 3 experiments investigated the nature of metallic taste reports after stimulation with solutions of metal salts and after stimulation with metals and electric currents. To stimulate with electricity, a device was fabricated consisting of a small battery affixed to a plastic handle with the anode side exposed for placement on the tongue or oral tissues.

Intensity of taste from metals and batteries was dependent upon the voltage and was more robust in areas dense in fungiform papillae. Metallic taste was reported from stimulation with ferrous sulfate solutions, from metals and from electric stimuli. However, reports of metallic taste were more frequent when the word ‘metallic’ was presented embedded in a list of choices, as opposed to simple free-choice labeling.

Intensity decreased for ferrous sulfate when the nose was occluded, consistent with a decrease in retronasal smell, as previously reported. Intensity of taste evoked by copper metal, bimetallic stimuli (zinc/copper) or small batteries (1.5-3 V) was not affected by nasal occlusion. This difference suggests two distinct mechanisms for evocation of metallic taste reports, one dependent upon retronasal smell and a second mediated by oral chemoreceptors.

--- https://www.tampabay.com/archive/2003/07/07/the-man-with-the-golden-spoon/ 2023-06-18 food --- https://www.youtube.com/watch?v=EYtFH2bFCfg 2023-06-18 food --- https://en.wikipedia.org/wiki/John_Harrison_(ice_cream_taster) John Harrison (ice cream taster) 2023-06-18 food --- https://x.com/fabianstelzer/status/1717131243861520569 Fabian Stelzer 2023-06-18 ai/nn/transformer/gpt/4/nonfiction psychology/vision --- https://www.econlib.org/valuable-student-exercises/ 2023-06-18 philosophy/ethics --- https://www.lesswrong.com/posts/AocXh6gJ9tJC2WyCL/book-review-going-infinite?commentId=LTDremt5hFkwtEwMk 2023-06-18 psychology/personality/psychopathy --- https://arxiv.org/abs/2310.13828 Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models Shawn Shan, Wenxin Ding, Josephine Passananti, Haitao Zheng, Ben Y. Zhao 2023-10-20 2023-10-20 [("doi","10.48550/arXiv.2310.13828")] ai/nn/adversarial ai/nn/diffusion

[previously: Glaze] Data poisoning attacks manipulate training data to introduce unexpected behaviors into machine learning models at training time. For text-to-image generative models with massive training datasets, current understanding of poisoning attacks suggests that a successful attack would require injecting millions of poison samples into their training pipeline.

In this paper, we show that poisoning attacks can be successful on generative models. We observe that training data per concept can be quite limited in these models, making them vulnerable to prompt-specific poisoning attacks, which target a model’s ability to respond to individual prompts.

We introduce Nightshade, an optimized prompt-specific poisoning attack where poison samples look visually identical to benign images with matching text prompts. Nightshade poison samples are also optimized for potency and can corrupt an Stable Diffusion SDXL prompt in <100 poison samples. Nightshade poison effects “bleed through” to related concepts, and multiple attacks can composed together in a single prompt. Surprisingly, we show that a moderate number of Nightshade attacks can destabilize general features in a text-to-image generative model, effectively disabling its ability to generate meaningful images.

Finally, we propose the use of Nightshade‘ and similar tools as a last defense for content creators against web scrapers that ignore opt-out/do-not-crawl directives, and discuss possible implications for model trainers and content creators.

--- https://arxiv.org/abs/1903.11862 Smooth Adversarial Examples Hanwei Zhang, Yannis Avrithis, Teddy Furon, Laurent Amsaleg 2019-03-28 2023-06-18 [("doi","10.1186/s13635-020-00112-z")] ai/nn/adversarial

This paper investigates the visual quality of the adversarial examples. Recent papers propose to smooth the perturbations to get rid of high frequency artefacts. In this work, smoothing has a different meaning as it perceptually shapes the perturbation according to the visual content of the image to be attacked. The perturbation becomes locally smooth on the flat areas of the input image, but it may be noisy on its textured areas and sharp across its edges.

This operation relies on Laplacian smoothing, well-known in graph signal processing, which we integrate in the attack pipeline. We benchmark several attacks with and without smoothing under a white-box scenario and evaluate their transferability.

Despite the additional constraint of smoothness, our attack has the same probability of success at lower distortion.

--- https://www.nngroup.com/articles/content-dispersion/ 2023-06-18 design --- https://www.nngroup.com/articles/illusion-of-completeness/ 2023-06-19 design --- https://en.wikipedia.org/wiki/Heslington_Brain Heslington Brain 2023-06-19 cryonics psychology/neuroscience --- https://arxiv.org/abs/2302.04222 Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models Shawn Shan, Jenna Cryan, Emily Wenger, Haitao Zheng, Rana Hanocka, Ben Y. Zhao 2023-02-08 2023-06-19 [("doi","10.48550/arXiv.2302.04222")] ai/nn/adversarial ai/nn/diffusion

Recent text-to-image diffusion models such as Midjourney and Stable Diffusion threaten to displace many in the professional artist community. In particular, models can learn to mimic the artistic style of specific artists after “fine-tuning” on samples of their art.

In this paper, we describe the design, implementation and evaluation of Glaze, a tool that enables artists to apply style cloaks to their art before sharing online. These cloaks apply barely perceptible perturbations to images, and when used as training data, mislead generative models that try to mimic a specific artist.

In coordination with the professional artist community, we deploy user studies to more than 1,000 artists, assessing their views of AI art, as well as the efficacy of our tool, its usability and tolerability of perturbations, and robustness across different scenarios and against adaptive countermeasures.

Both surveyed artists and empirical CLIP-based scores show that even at low perturbation levels (p = 0.05), Glaze is highly successful at disrupting mimicry under normal conditions (>92%) and against adaptive countermeasures (>85%).

...A second category of attacks tries to perform pixel-level image smoothing to remove cloaks added by Glaze105. This applies bilateral filters on Glazed images repeatedly, seeking to remove all added perturbations. We evaluate this attack on Glazed artwork in §6 [? smoothing attacks do not appear to be there] and fine-tuning a model on the smoothed images. Figure 17 shows Glaze remains effective against pixel smoothing. This result is consistent with prior work showing that image smoothing cannot prevent adversarial perturbations.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0291416 Roosters do not warn the bird in the mirror: The cognitive ecology of mirror self-recognition Sonja Hillemacher, Sebastian Ocklenburg, Onur Güntürkün, Inga Tiemann, Thomas H. Burne, Thomas H. Burne, Thomas H. Burne 2023-08-29 2023-08-29 [("doi","10.1371/journal.pone.0291416")] philosophy/mind psychology/animal/bird

Touching a mark on the own body when seeing this mark in a mirror is regarded as a correlate of self-awareness and seems confined to great apes and a few further species. However, this paradigm often produces false-negative results and possibly dichotomizes a gradual evolutionary transition of self-recognition.

We hypothesized that this ability is more widespread if ecologically tested and developed such a procedure for a most unlikely candidate: chickens (Gallus gallus domesticus). Roosters warn conspecifics when seeing an aerial predator, but not when alone. Exploiting this natural behavior, we tested individual roosters alone, with another male, or with a mirror while a hawk’s silhouette flew above them.

Roosters mainly emitted alarm calls in the presence of another individual but not when alone or seeing themselves in the mirror. In contrast, our birds failed the classic mirror test. Thus, chickens possibly recognize their reflection as their own, strikingly showing how much cognition is ecologically embedded.

--- https://x.com/skirano/status/1717572970900541882 Pietro Schirano 2023-06-19 ai/nn/diffusion --- https://www.reddit.com/r/Bard/comments/1795exq/google_sge_image_generation_is_so_good_at/ 2023-06-19 ai/nn/diffusion --- https://arxiv.org/abs/2310.16764#deepmind ConvNets Match Vision Transformers at Scale Samuel L. Smith, Andrew Brock, Leonard Berrada, Soham De 2023-10-25 2023-10-25 [("doi","10.48550/arXiv.2310.16764")] ai/nn/cnn ai/scaling

Many researchers believe that ConvNets perform well on small or moderately sized datasets, but are not competitive with Vision Transformers when given access to datasets on the web-scale.

We challenge this belief by evaluating a performant ConvNet architecture pre-trained on JFT-4B, a large labeled dataset of images often used for training foundation models. We consider pre-training compute budgets between 0.4k and 110k TPU-v4 core compute hours, and train a series of networks of increasing depth and width from the NFNet model family.

We observe a log-log scaling law between held out loss and compute budget. After fine-tuning on ImageNet, NFNets match the reported performance of Vision Transformers with comparable compute budgets. Our strongest fine-tuned model achieves a Top-1 accuracy of 90.4%.

--- https://craigmod.com/ridgeline/170/ 2023-06-19 japan --- https://arxiv.org/abs/2302.05543 Adding Conditional Control to Text-to-Image Diffusion Models Lvmin Zhang, Anyi Rao, Maneesh Agrawala 2023-02-10 2023-06-19 [("doi","10.48550/arXiv.2302.05543")] ai/nn/cnn ai/nn/diffusion

We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls.

The neural architecture is connected with “zero convolutions” (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg. edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts.

We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.

--- https://simonwillison.net/2023/Oct/26/add-a-walrus/ 2023-06-19 ai/nn/transformer/gpt/dall-e/3 --- https://www.astralcodexten.com/p/my-left-kidney 2023-06-20 philosophy/ethics --- https://dbohdan.com/jpeg-xl 2023-06-20 cs/algorithm/information/compression cs/cellular-automaton cs/computable --- https://x.com/adad8m/status/1716377194690380104 adad8m 2023-06-20 ai/nn/transformer/gpt/dall-e/3 --- https://nonint.com/2023/06/10/the-it-in-ai-models-is-the-dataset/ 2023-06-20 ai/scaling --- https://www.technologyreview.com/2023/10/27/1082551/gene-treatment-deaf-children-hearing-china/ 2023-06-20 genetics/editing --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4345133/ The heritability of alcohol use disorders: a meta-analysis of twin and adoption studies B Verhulst, M. C. Neale, K. S. Kendler 2015 2023-06-20 [("doi","10.1017/S0033291714002165")] genetics/heritable/adoption psychiatry/alcoholism

Background: To clarify the role of genetic and environmental risk factors in alcohol use disorders (AUDs), we performed a meta-analysis of twin and adoption studies and explored the impact of sex, assessment method (interview v. hospital/population records), and study design (twin v. adoption study) on heritability estimates.

Method: The literature was searched for all unique twin and adoption studies of AUD and identified 12 twin and 5 adoption studies. The data were then reconstructed and analyzed using ordinal data full information maximum likelihood in the OpenMx program. Heterogeneity was tested with likelihood ratio tests by equating the parameters across studies.

Results: There was no evidence for heterogeneity by study design, sex or assessment method. The best-fit estimate of the heritability of AUD was 0.49 [95% confidence interval (CI) 0.43-0.53], and the proportion of shared environmental variance was 0.10 (95% CI 0.03-0.16). Estimates of unique environmental proportions of variance differed statistically-significantly across studies.

Conclusions: AUD is ~50% heritable. The multiple genetically informative studies of this syndrome have produced consistent results that support the validity of this heritability estimate, especially given the different potential methodological weaknesses of twin and adoption designs, and of assessments of AUD based on personal interviews v. official records. We also found evidence for modest shared environmental effects suggesting that environmental factors also contribute to the familial aggregation of AUDs.

--- https://austinvernon.site/blog/drillinglearningcurve.html 2023-06-20 economics/experience-curve --- https://www.nature.com/articles/s41380-023-02285-8 2023-06-20 psychiatry/depression psychology/neuroscience --- https://latecomermag.com/article/uncommon-tasks-russian-cosmism-and-longtermism/ 2023-06-20 transhumanism --- /doc/design/typography/rubrication/2019-wolfe-thebookofthenewsun-dropcap.jpg 2019 2023-06-20 design/typography/dropcap design/typography/rubrication --- https://creativecommons.org/public-domain/cc0/ 2023-06-20 economics/copyright --- https://en.wikipedia.org/wiki/Arts_and_Crafts_movement Arts and Crafts movement 2023-06-21 design/typography/floral --- https://en.wikipedia.org/wiki/Blackletter Blackletter 2023-06-21 design/typography --- https://en.wikipedia.org/wiki/Fraktur Fraktur 2023-06-21 design/typography --- https://en.wikipedia.org/wiki/Frederic_Goudy Frederic Goudy 2023-06-21 design/typography/floral --- https://en.wikipedia.org/wiki/Rudolf_Koch Rudolf Koch 2023-06-21 design/typography --- https://moorstation.org/typoasis/designers/steffmann/index.htm 2023-06-21 design/typography --- https://web.archive.org/web/20130122020421fw_/http://www.houseoflime.com/fonts.html 2023-06-21 design/typography --- https://www.dafont.com/house-of-lime.d286 2023-06-21 design/typography --- https://en.wikipedia.org/wiki/Light-on-dark_color_scheme Light-on-dark color scheme 2023-06-21 cs/css --- https://github.com/gwern/gwern.net/tree/master/font/dropcap 2023-06-21 design/typography/dropcap --- https://ctan.org/pkg/yinit 2023-06-21 design/typography/dropcap --- https://en.wikipedia.org/wiki/Cloister_(typeface) Cloister (typeface) 2023-06-22 design/typography/dropcap design/typography/floral --- https://en.wikipedia.org/wiki/Drop_caps Dropcaps 2023-06-22 design/typography/dropcap --- https://github.com/TeX-Live/yinit/issues/1 2023-06-22 design/typography/dropcap --- /doc/design/typography/dropcap/2018-08-06-jamieclarke-shovelknightdropcaps.html 2018-08-06 2023-06-22 design/typography/dropcap --- https://parachutefonts.com/typeface/Goudy-Initials 2023-06-22 design/typography/dropcap design/typography/floral --- https://github.com/gwern/gwern.net/tree/master/font/dropcap/dropcat 2023-06-22 ai/nn/diffusion/midjourney/dropcap --- https://en.wikipedia.org/wiki/Isaac_Newton Isaac Newton 2023-06-22 psychiatry/bipolar/energy --- https://en.wikipedia.org/wiki/Ludwig_van_Beethoven Ludwig van Beethoven 2023-06-22 psychiatry/bipolar/energy --- https://en.wikipedia.org/wiki/Charles_Dickens Charles Dickens 2023-06-22 psychiatry/bipolar/energy --- https://en.wikipedia.org/wiki/Vincent_van_Gogh Vincent van Gogh 2023-06-22 psychiatry/bipolar/energy --- https://en.wikipedia.org/wiki/Phil_Graham Phil Graham 2023-06-22 psychiatry/bipolar/energy --- https://x.com/kane/status/1381757496583327746 kane 2023-06-23 fiction/humor philosophy/ontology --- https://x.com/MooCheese/status/1381635386703773700 MooCheese 2023-06-23 design/typography/dropcap --- https://wot.fandom.com/wiki/Category:Chapter_art 2023-06-23 design/typography/dropcap --- https://www.1001fonts.com/cheshire-initials-font.html 2023-06-23 design/typography/dropcap --- https://www.myfonts.com/collections/ltc-goudy-initials-font-lanston-type-company 2023-06-23 design/typography/dropcap design/typography/floral --- https://www.steelypips.org/wotfaq/2_nondark/2.7_generalities/2.7.5_iconography.html 2023-06-23 design/typography/dropcap --- https://getpocket.com/explore/item/one-woman-s-mission-to-rewrite-nazi-history-on-wikipedia 2023-06-23 wikipedia --- https://www.aspca.org/pet-care/animal-poison-control/cats-plant-list 2023-06-23 cat/biology --- https://arxiv.org/abs/2310.17075 HyperFields: Towards Zero-Shot Generation of NeRFs from Text Sudarshan Babu, Richard Liu, Avery Zhou, Michael Maire, Greg Shakhnarovich, Rana Hanocka 2023-10-26 2023-10-26 [("doi","10.48550/arXiv.2310.17075")] ai/nn/fully-connected ai/nn/sparsity/knowledge-distillation reinforcement-learning/meta-learning

We introduce HyperFields, a method for generating text-conditioned Neural Radiance Fields (NeRFs) with a single forward pass and (optionally) some fine-tuning. Key to our approach are: (1) a dynamic hypernetwork, which learns a smooth mapping from text token embeddings to the space of NeRFs; (2) NeRF distillation training, which distills scenes encoded in individual NeRFs into one dynamic hypernetwork.

These techniques enable a single network to fit over a hundred unique scenes. We further demonstrate that HyperFields learns a more general map between text and NeRFs, and consequently is capable of predicting novel in-distribution and out-of-distribution scenes—either zero-shot or with a few finetuning steps.

Finetuning HyperFields benefits from accelerated convergence thanks to the learned general map, and is capable of synthesizing novel scenes 5 to 10× faster than existing neural optimization-based methods. Our ablation experiments show that both the dynamic architecture and NeRF distillation are critical to the expressivity of HyperFields.

--- https://www.vice.com/en/article/kwkxke/my-gandma-the-poisoner-0000474-v21n10 2023-06-23 crime psychology/personality/psychopathy --- https://x.com/rowancheung/status/1718298946819268980 Rowan Cheung 2023-06-24 reinforcement-learning/robot --- https://en.wikipedia.org/wiki/Event_study Event study 2023-06-24 economics statistics/causality --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7817105/ The characteristic response of domestic cats to plant iridoids allows them to gain chemical defense against mosquitoes Reiko Uenoyama, Tamako Miyazaki, Jane L. Hurst, Robert J. Beynon, Masaatsu Adachi, Takanobu Murooka, Ibuki Onoda, Yu Miyazawa, Rieko Katayama, Tetsuro Yamashita, Shuji Kaneko, Toshio Nishikawa, Masao Miyazaki 2021 2023-06-24 [("doi","10.1126/sciadv.abd9135")] cat/psychology/drug/silvervine

Domestic cats and other felids rub their faces and heads against catnip (Nepeta cataria) and silver vine (Actinidia polygama) and roll on the ground as a characteristic response. While this response is well known, its biological function and underlying mechanism remain undetermined.

Here, we uncover the neurophysiological mechanism and functional outcome of this feline response. We found that the iridoid nepetalactol is the major component of silver vine that elicits this potent response in cats and other felids. Nepetalactol increased plasma β-endorphin levels in cats, while pharmacological inhibition of μ-opioid receptors suppressed the classic rubbing response.

Rubbing behavior transfers nepetalactol onto the faces and heads of respondents where it repels the mosquito, Aedes albopictus. Thus, self-anointing behavior helps to protect cats against mosquito bites. The characteristic response of cats to nepetalactol via the μ-opioid system provides an important example of chemical pest defense using plant metabolites in nonhuman mammals.

--- https://www.bamsoftware.com/hacks/deflate.html 2023-06-24 cs/computable cs/security --- https://commonplace.online/article/have-you-seen-me/ 2023-06-24 cs/linkrot/archiving --- /doc/law/1980-posner.pdf A Theory of Primitive Society, with Special Reference to Law Richard A. Posner 1980-04-01 2023-06-24 [("doi","10.1086/466951")] economics law sociology --- /doc/economics/mechanism-design/1978-geertz.pdf The Bazaar Economy: Information and Search in Peasant Marketing Clifford Geertz 1978-05-01 2023-06-24 [("doi","10.2307/1816656")] economics/mechanism-design sociology --- /doc/economics/mechanism-design/1990-fanselow.pdf The Bazaar Economy or How Bizarre is the Bazaar Really? Frank S. Fanselow 1990-06-01 2023-06-24 [("doi","10.2307/2804563")] economics/mechanism-design sociology

[previously: Geertz 1978; cf. Posner 1980] Critiques of the view that the bazaar is a model of the competitive market often portray it as exotic and irrational.

This article shows that the folk category ‘bazaar’ glosses over the analytical distinction between two types of market: (1) those in commodities which are standardized in terms of quality and quantity and therefore substitutable; and (2) those in commodities that are physically heterogeneous and therefore non-substitutable.

The features typically associated with the bazaar by its ethnographers are only found in the latter type of market. Drawing on ethnographic material from a south Indian town, the article shows that the differences in the transactional properties of the two kinds of goods account for a series of contrasts in the relations among and between buyers and sellers, the operation of the price mechanism, the control of information, the organization and recruitment of labor, and the roles of money and credit.

--- /doc/fiction/gene-wolfe/suzanne-delage/2023-10-28-gwern-midjourneyv5-germanexpressionistlinocutofsinisternewenglandtowninwinter-thumbnail.jpg Gwern 2023-10-28 2023-10-28 ai/nn/diffusion/midjourney fiction/gene-wolfe/suzanne-delage --- /doc/fiction/gene-wolfe/suzanne-delage/2023-10-28-gwern-midjourneyv5-germanexpressionistlinocutofsinisternewenglandtowninwinter.jpg Gwern 2023-10-28 2023-10-28 ai/nn/diffusion/midjourney fiction/gene-wolfe/suzanne-delage --- /doc/fiction/gene-wolfe/suzanne-delage/2023-10-28-gwern-midjourneyv5-suzannedelage-1920sblackandwhitemoviestar-crop-thumbnail.jpg Gwern 2023-10-28 2023-10-28 ai/nn/diffusion/midjourney fiction/gene-wolfe/suzanne-delage --- /doc/fiction/gene-wolfe/suzanne-delage/2023-10-28-gwern-midjourneyv5-suzannedelage-1920sblackandwhitemoviestar.jpg Gwern 2023-10-28 2023-10-28 ai/nn/diffusion/midjourney fiction/gene-wolfe/suzanne-delage --- /doc/ai/nn/diffusion/midjourney/2023-10-28-gwern-midjourneyv5-suzannedelage-blackandwhite1920smoviestaryoungwomanwearingsweater-samples.jpg Gwern 2023-10-28 2023-10-28 ai/nn/diffusion/midjourney fiction/gene-wolfe/suzanne-delage --- /doc/ai/nn/diffusion/midjourney/landscape/2023-10-28-gwern-midjourneyv5-suzannedelage-germanexpressionistlinocutofsinisternewenglandtowninwinter-samples-01.jpg Gwern 2023-10-28 2023-10-28 ai/nn/diffusion/midjourney/landscape fiction/gene-wolfe/suzanne-delage --- /doc/ai/nn/diffusion/midjourney/landscape/2023-10-28-gwern-midjourneyv5-suzannedelage-germanexpressionistlinocutofsinisternewenglandtowninwinter-samples-02.jpg Gwern 2023-10-28 2023-10-28 ai/nn/diffusion/midjourney/landscape fiction/gene-wolfe/suzanne-delage --- /doc/ai/nn/diffusion/midjourney/landscape/2023-10-28-gwern-midjourneyv5-suzannedelage-germanexpressionistlinocutofsinisternewenglandtowninwinter-samples-03.jpg Gwern 2023-10-28 2023-10-28 ai/nn/diffusion/midjourney/landscape fiction/gene-wolfe/suzanne-delage --- https://www.smbc-comics.com/comic/escape-2 2023-06-25 psychology/collecting --- https://www.malwarebytes.com/blog/threat-intelligence/2023/09/malicious-ad-served-inside-bing-ai-chatbot 2023-06-25 ai/nn/transformer/gpt/4/nonfiction cs/security --- https://www.youtube.com/watch?v=_sRqJTLaXRw 2023-06-25 reinforcement-learning/robot --- https://arxiv.org/abs/2310.14992 Bayesian Regression Markets Thomas Falconer, Jalal Kazempour, Pierre Pinson 2023-10-23 2023-10-23 [("doi","10.48550/arXiv.2310.14992")] ai economics/mechanism-design statistics/bayes

Machine learning tasks are vulnerable to the quality of data used as input. Yet, it is often challenging for firms to obtain adequate datasets, with them being naturally distributed amongst owners, who in practice, may be competitors in a downstream market and reluctant to share information.

Focusing on supervised learning for regression tasks, we develop a regression market to provide a monetary incentive for data sharing. Our proposed mechanism adopts a Bayesian framework, allowing us to consider a more general class of regression tasks.

We present a thorough exploration of the market properties, and show that similar proposals in current literature expose the market agents to sizeable financial risks, which can be mitigated in our probabilistic setting.

--- https://www.nature.com/articles/s41467-023-41693-w 2023-06-25 ai/music ai/nn/cnn --- https://x.com/RyanRadia/status/1718619602106659239 Rohit Krishnan 2023-06-25 ai/nn/tokenization cs/security --- /doc/design/typography/dropcap/2019-06-17-ethamarcotte-vox-dropcapsanddesignsystems.html 2019-06-17 2023-06-26 cs/css design/typography/dropcap --- http://unremediatedgender.space/2023/Oct/fake-deeply/ 2023-06-26 cs/security philosophy/mind reinforcement-learning/safe --- https://en.wikipedia.org/wiki/Sydney_Sweeney Sydney Sweeney 2023-06-26 zeo/short-sleeper --- https://heypop.substack.com/p/gunplay-at-the-dog-park 2023-06-26 crime dog --- https://undark.org/2023/10/27/consumer-genetic-testing-science/ 2023-06-26 genetics/selection/artificial --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3193030/ A genome-wide association study of aging Stefan Walter, Gil Atzmon, Ellen W. Demerath, Melissa E. Garcia, Robert C. Kaplan, Meena Kumari, Kathryn L. Lunetta, Yuri Milaneschi, Toshiko Tanaka, Gregory J. Tranah, Uwe Völker, Lei Yu, Alice Arnold, Emelia J. Benjamin, Reiner Biffar, Aron S. Buchman, Eric Boerwinkle, David Couper, Philip L. De Jager, Denis A. Evans, Tamara B. Harris, Wolfgang Hoffmann, Albert Hofman, David Karasik, Douglas P. Kiel, Thomas Kocher, Maris Kuningas, Lenore J. Launer, Kurt K. Lohman, Pamela L. Lutsey, Johan Mackenbach, Kristin Marciante, Bruce M. Psaty, Eric M. Reiman, Jerome I. Rotter, Sudha Seshadri, Michelle D. Shardell, Albert Vernon Smith, Cornelia van Duijn, Jeremy Walston, M. Carola Zillikens, Stefania Bandinelli, Sebastian E. Baumeister, David A. Bennett, Luigi Ferrucci, Vilmundur Gudnason, Mika Kivimaki, Yongmei Liu, Joanne M. Murabito, Anne B. Newman, Henning Tiemeier, Nora Franceschini 2011 2023-06-26 [("doi","10.1016/j.neurobiolaging.2011.05.026")] genetics/heritable longevity

Human longevity and healthy aging show moderate heritability (20%-50%). We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.

No single-nucleotide polymorphism (SNP) was a genome-wide statistically-significant predictor of either outcome (p < 5 × 10−8). We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p < 10−5). These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer’s disease.

In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings. These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7704521/ Limited evidence for the effect of red color on cognitive performance: A meta-analysis Timo Gnambs 2020 2023-06-26 [("doi","10.3758/s13423-020-01772-1")] design/typography/rubrication iq psychology/vision statistics/bias/publication

Red color supposedly affects cognitive functioning in achievement situations and impairs test performance. Although this has been shown for different cognitive domains in different populations and cultural contexts, recent studies including close replications failed to corroborate this effect.

Reported here is a random-effects meta-analysis of 67 effect sizes (38 samples) that compared test performance after viewing red or a control color. For anagram tests and knowledge tests no statistically-significant difference between color conditions was found (Cohen’s d of −0.06 and −0.04); for reasoning tests the pooled effect of d = −0.34, 95% CI [-0.61, −0.06] indicated statistically-significantly lower scores in the red condition.

The cumulative meta-analysis revealed substantially larger effects in initial studies as compared to subsequent research. After correcting for publication bias no evidential value for an effect of red color on intellectual performance was available.

The review casts doubt on the existence of a robust color-priming effect in achievement situations.

--- /doc/iq/2015-tucker-drob.pdf 2015-01-01 2023-06-26 genetics/heritable iq --- /doc/crime/2014-sturup.pdf Homicide offenders 32 years later—A Swedish population-based study on recidivism Joakim Sturup, Per Lindqvist 2023-11-22 2023-11-22 [("doi","10.1002/cbm.1896")] crime psychiatry

Background: The literature on recidivism by homicide offenders is scarce despite its importance for individuals and for society.

Aims: To establish the rate of seriously violent re-offending among homicide offenders and identify risk factors for such recidivism.

Methods: A 1970s incident cohort of all homicide offenders, sane and insane, from two regions of Sweden (N = 153) was followed up until 2007 using data from the national crime register.

Results: 10% of the cohort (n = 15) re-offended. The mean time from index offence to recidivism was 9.4 years. 5 people (3%) committed a further homicide, and it was established that another 5 (3%) offenders had killed before the index offence. Prospective risk factors for violent recidivism were young age, psychotic disorder, male victim, acquainted victim and intoxicated victim.

Conclusions: The prevalence of repeated homicide is higher than previously reported. Victim variables and mental disorder in conjunction with substance abuse appear to be two domains of particular importance for recidivism.

--- http://www.rdrop.com/~half/Creations/Writings/Web.patterns/index.html 2023-06-26 design --- http://www.itconsult.co.uk/stamper/stampinf.htm 2023-06-27 cs/cryptography/timelock --- https://x.com/nikitabier/status/1719021565063369065 Nikita Bier 2023-06-27 economics/advertising --- https://en.wikipedia.org/wiki/Andy_Dunn Andy Dunn 2023-06-27 psychiatry/bipolar/energy --- https://arxiv.org/abs/2310.17247 Grokking Beyond Neural Networks: An Empirical Exploration with Model Complexity Jack Miller, Charles O’Neill, Thang Bui 2023-10-26 2023-10-26 [("doi","10.48550/arXiv.2310.17247")] ai/nn/fully-connected ai/scaling/emergence/grokking

[blog] In some settings, neural networks exhibit a phenomenon known as grokking, where they achieve perfect or near-perfect accuracy on the validation set long after the same performance has been achieved on the training set.

In this paper, we discover that grokking is not limited to neural networks but occurs in other settings such as Gaussian process (GP) classification, GP regression and linear regression. We also uncover a mechanism by which to induce grokking on algorithmic datasets via the addition of dimensions containing spurious information.

The presence of the phenomenon in non-neural architectures provides evidence that grokking is not specific to SGD or weight norm regularization. Instead, grokking may be possible in any setting where solution search is guided by complexity and error.

Based on this insight and further trends we see in the training trajectories of a Bayesian neural network (BNN) and GP regression model, we make progress towards a more general theory of grokking. Specifically, we hypothesize that the phenomenon is governed by the accessibility of certain regions in the error and complexity landscapes.

--- https://www.yahoo.com/lifestyle/kanye-west-says-misdiagnosed-bipolar-disorder-sleep-deprivation-000405518.html 2023-06-27 psychiatry/bipolar/sleep --- https://arxiv.org/abs/2309.17147 Using Large Language Models for Qualitative Analysis Can Introduce Serious Bias Julian Ashwin, Aditya Chhabra, Vijayendra Rao 2023-09-29 2023-09-29 [("doi","10.48550/arXiv.2309.17147")] ai/nn/transformer/gpt/3/nonfiction sociology

Large Language Models (LLMs) are quickly becoming ubiquitous, but the implications for social science research are not yet well understood. This paper asks whether LLMs [GPT-3.5, LLaMA-2, vs iQual] can help us analyze large-n qualitative data from open-ended interviews, with an application to transcripts of interviews with Rohingya refugees in Cox’s Bazaar, Bangladesh.

We find that a great deal of caution is needed in using LLMs to annotate text as there is a risk of introducing biases that can lead to misleading inferences. We here mean bias in the technical sense, that the errors that LLMs make in annotating interview transcripts are not random with respect to the characteristics of the interview subjects.

Training simpler supervised models on high-quality human annotations with flexible coding leads to less measurement error and bias than LLM annotations. Therefore, given that some high quality annotations are necessary in order to assess whether an LLM introduces bias, we argue that it is probably preferable to train a bespoke model on these annotations than it is to use an LLM for annotation.

--- https://openreview.net/forum?id=OpC-9aBBVJe Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier Pierluca D’Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G. Bellemare, Aaron Courville 2023-02-13 2023-06-27 reinforcement-learning/model-free

The combination of a large number of updates and resets drastically improves the sample efficiency of deep RL algorithms.

Increasing the replay ratio, the number of updates of an agent’s parameters per environment interaction, is an appealing strategy for improving the sample efficiency of deep reinforcement learning algorithms. In this work, we show that fully or partially resetting the parameters of deep reinforcement learning agents causes better replay ratio scaling capabilities to emerge. We push the limits of the sample efficiency of carefully-modified algorithms by training them using an order of magnitude more updates than usual, improving their performance in the Atari 100k and DeepMind Control Suite benchmarks. We then provide an analysis of the design choices required for favorable replay ratio scaling to be possible and discuss inherent limits and tradeoffs.

[Keywords: reinforcement learning, sample efficiency, resets]

--- https://arxiv.org/abs/2310.18233 Will releasing the weights of large language models grant widespread access to pandemic agents? Anjali Gopal, Nathan Helm-Burger, Lenni Justen, Emily H. Soice, Tiffany Tzeng, Geetha Jeyapragasan, Simon Grimm, Benjamin Mueller, Kevin M. Esvelt 2023-10-25 2023-10-25 [("doi","10.48550/arXiv.2310.18233")] ai/nn/transformer existential-risk reinforcement-learning/safe

Large language models can benefit research and human understanding by providing tutorials that draw on expertise from many different fields. A properly safeguarded model will refuse to provide “dual-use” insights that could be misused to cause severe harm. However, some models with publicly released weights have been tuned to remove safeguards within days of introduction.

In this study, we investigated whether continued model weight proliferation is likely to help future malicious actors inflict mass death. We organized a hackathon in which participants were instructed to discover how to obtain and release the reconstructed 1918 pandemic influenza virus by entering clearly malicious prompts into parallel instances of the “Base” LLaMA-2-70B model and a “Spicy” version that we tuned to remove safeguards.

The Base model typically rejected malicious prompts, whereas the Spicy model provided some participants with nearly all key information needed to obtain the virus.

Future models will be more capable. Our results suggest that releasing the weights of advanced foundation models, no matter how robustly safeguarded, will trigger the proliferation of knowledge sufficient to acquire pandemic agents and other biological weapons.

--- https://arxiv.org/abs/1811.04115 ADNet: A Deep Network for Detecting Adverts Murhaf Hossari, Soumyabrata Dev, Matthew Nicholson, Killian McCabe, Atul Nautiyal, Clare Conran, Jian Tang, Wei Xu, François Pitié 2018-11-09 2023-06-27 [("doi","10.48550/arXiv.1811.04115")] ai/nn/cnn economics/advertising

Online video advertising gives content providers the ability to deliver compelling content, reach a growing audience, and generate additional revenue from online media. Recently, advertising strategies are designed to look for original advert(s) in a video frame, and replacing them with new adverts. These strategies, popularly known as product placement or embedded marketing, greatly help the marketing agencies to reach out to a wider audience. However, in the existing literature, such detection of candidate frames in a video sequence for the purpose of advert integration, is done manually.

In this paper, we propose a deep-learning architecture called ADNet, that automatically detects the presence of advertisements in video frames.

Our approach is the first of its kind that automatically detects the presence of adverts in a video frame, and achieves state-of-the-art results on a public dataset.

--- https://together.ai/blog/redpajama-data-v2 2023-06-27 ai/dataset --- https://www.amazon.com/Hypomanic-Edge-Between-Craziness-Success/dp/0743243455 2023-06-27 psychiatry/bipolar/energy --- https://web.archive.org/web/20221127080149/https://www.themantle.com/literature/no-inkling-contents-viewing-narnia-through-hindu-lens 2023-06-28 fiction/criticism philosophy/religion --- https://dropbox.tech/machine-learning/prompt-injection-with-control-characters-openai-chatgpt-llm 2023-06-28 ai/nn/transformer/gpt/3/nonfiction cs/security --- https://x.com/javilopen/status/1719363669685916095 Javi Lopez 2023-06-28 ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/codex --- https://en.wikipedia.org/wiki/Power_analysis Power analysis 2023-06-28 cs/cryptography --- https://asteriskmag.com/issues/04/mysticism-empiricism 2023-06-28 philosophy/religion psychedelic --- https://arxiv.org/abs/2310.08708 Polynomial Time Cryptanalytic Extraction of Neural Network Models Adi Shamir, Isaac Canales-Martinez, Anna Hambitzer, Jorge Chavez-Saab, Francisco Rodrigez-Henriquez, Nitin Satpute 2023-10-12 2023-10-12 [("doi","10.48550/arXiv.2310.08708")] ai/nn/fully-connected ai/nn/sparsity/knowledge-distillation cs/cryptography

Billions of dollars and countless GPU hours are currently spent on training Deep Neural Networks (DNNs) for a variety of tasks. Thus, it is essential to determine the difficulty of extracting all the parameters of such neural networks when given access to their black-box implementations.

Many versions of this problem have been studied over the last 30 years, and the best current attack on ReLU-based deep neural networks was presented at Crypto 2020 by Carlini et al 2020. It resembles a differential chosen plaintext attack on a cryptosystem, which has a secret key embedded in its black-box implementation and requires a polynomial number of queries but an exponential amount of time (as a function of the number of neurons).

In this paper, we improve this attack by developing several new techniques that enable us to extract with arbitrarily high precision all the real-valued parameters of a ReLU-based DNN using a polynomial number of queries and a polynomial amount of time.

We demonstrate its practical efficiency by applying it to a full-sized neural network for classifying the CIFAR-10 dataset, which has 3072 inputs, 8 hidden layers with 256 neurons each, and over 1.2 million neuronal parameters. An attack following the approach by Carlini et al 2020 requires an exhaustive search over 2256 possibilities. Our attack replaces this with our new techniques, which require only 30 minutes on a 256-core computer [requiring 256+1 queries per layer].

--- https://arxiv.org/abs/2003.04884 Cryptanalytic Extraction of Neural Network Models Nicholas Carlini, Matthew Jagielski, Ilya Mironov 2020-03-10 2023-06-28 [("doi","10.48550/arXiv.2003.04884")] ai/nn/fully-connected ai/nn/sparsity/knowledge-distillation cs/cryptography

We argue that the machine learning problem of model extraction is actually a cryptanalytic problem in disguise, and should be studied as such. Given oracle access to a neural network, we introduce a differential attack that can efficiently steal the parameters of the remote model up to floating point precision. Our attack relies on the fact that ReLU neural networks are piecewise linear functions, and thus queries at the critical points reveal information about the model parameters.

We evaluate our attack on multiple neural network models and extract models that are 220 times more precise and require 100× fewer queries than prior work. For example, we extract a 100,000 parameter neural network trained on the MNIST digit recognition task with 221.5 queries in under an hour, such that the extracted model agrees with the oracle on all inputs up to a worst-case error of 2−25, or a model with 4,000 parameters in 218.5 queries with worst-case error of 2−40.4.

Code is available at Github.

--- https://arxiv.org/abs/2306.14685 DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models Ximing Xing, Chuang Wang, Haitao Zhou, Jing Zhang, Qian Yu, Dong Xu 2023-06-26 2023-06-28 [("doi","10.48550/arXiv.2306.14685")] ai/nn/diffusion

Even though trained mainly on images, we discover that pretrained diffusion models show impressive power in guiding sketch synthesis. In this paper, we present DiffSketcher, an innovative algorithm that creates vectorized free-hand sketches using natural language input. DiffSketcher is developed based on a pre-trained text-to-image diffusion model.

It performs the task by directly optimizing a set of Bézier curves with an extended version of the score distillation sampling (SDS) loss, which allows us to use a raster-level diffusion model as a prior for optimizing a parametric vectorized sketch generator. Furthermore, we explore attention maps embedded in the diffusion model for effective stroke initialization to speed up the generation process.

The generated sketches demonstrate multiple levels of abstraction while maintaining recognizability, underlying structure, and essential visual details of the subject drawn. Our experiments show that DiffSketcher achieves greater quality than prior work. The code and demo of DiffSketcher can be found at Github.

--- https://danluu.com/wat/ 2023-06-28 reinforcement-learning/safe sociology/technology technology --- https://www.reddit.com/r/typography/comments/u623fs/found_these_amazing_drop_caps_in_this_old/ 2023-06-28 design/typography/dropcap --- https://thezvi.substack.com/p/on-the-executive-order 2023-06-28 ai/scaling/hardware politics --- http://www.scienceagainstevolution.info/dwj/toaster.htm 2023-06-29 design --- https://www.neverbeclever.org/blog/my-rude-ass-car/ 2023-06-29 design --- https://users.ece.utexas.edu/~adnan/pike.html 2023-06-29 cs/algorithm design --- https://x.com/NathalieRach/status/1712538064369955154 Nathalie Rach 2023-06-29 philosophy/logic --- https://www.unicode.org/reports/tr14/ 2023-06-29 design/typography --- https://www.atlasobscura.com/articles/what-is-a-crispy-r 2023-06-29 psychology/linguistics --- https://www.atlasobscura.com/articles/carriage-driving-sport-wisconsin 2023-06-29 dog --- https://arxiv.org/abs/1605.04343 A Relatively Small Turing Machine Whose Behavior Is Independent of Set Theory Adam Yedidia, Scott Aaronson 2016-05-13 2023-06-29 [("doi","10.48550/arXiv.1605.04343")] cs/computable math

Since the definition of the Busy Beaver function by Rado in 1962, an interesting open question has been the smallest value of n for which BB(n) is independent of ZFC set theory. Is this n ~10, or closer to 1,000,000, or is it even larger?

In this paper, we show that it is at most 7,910 by presenting an explicit description of a 7,910-state Turing machine Z with 1 tape and a 2-symbol alphabet that cannot be proved to run forever in ZFC (even though it presumably does), assuming ZFC is consistent. The machine is based on the work of Harvey Friedman on independent statements involving order-invariant graphs.

In doing so, we give the first known upper bound on the highest provable Busy Beaver number in ZFC. To create Z, we develop and use a higher-level language, Laconic, which is much more convenient than direct state manipulation.

We also use Laconic to design two Turing machines, G and R, that halt if and only if there are counterexamples to Goldbach’s Conjecture and the Riemann Hypothesis, respectively.

--- https://publicdomainreview.org/collection/unionization-of-central-europe/ 2023-06-29 design history/public-domain-review politics --- https://arxiv.org/abs/2310.08678 Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams Ethan Callanan, Amarachi Mbakwe, Antony Papadimitriou, Yulong Pei, Mathieu Sibue, Xiaodan Zhu, Zhiqiang Ma, Xiaomo Liu, Sameena Shah 2023-10-12 2023-10-12 [("doi","10.48550/arXiv.2310.08678")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue economics

Large Language Models (LLMs) have demonstrated remarkable performance on a wide range of Natural Language Processing (NLP) tasks, often matching or even beating state-of-the-art task-specific models. This study aims at assessing the financial reasoning capabilities of LLMs.

We leverage mock exam questions of the Chartered Financial Analyst (CFA) Program to conduct a comprehensive evaluation of ChatGPT and GPT-4 in financial analysis, considering Zero-Shot (ZS), Chain-of-Thought (CoT), and Few-Shot (FS) scenarios.

We present an in-depth analysis of the models’ performance and limitations, and estimate whether they would have a chance at passing the CFA exams [GPT-4: yes]. Finally, we outline insights into potential strategies and improvements to enhance the applicability of LLMs in finance.

In this perspective, we hope this work paves the way for future studies to continue enhancing LLMs for financial reasoning through rigorous evaluation.

--- https://www.reddit.com/r/dalle2/comments/17ma2ql/anthropomorphic_female_snail_wearing_a_baseball/ 2023-06-30 ai/nn/transformer/gpt/dall-e/3 --- https://arxiv.org/abs/2210.03920 Detecting Label Errors in Token Classification Data Wei-Chen Wang, Jonas Mueller 2022-10-08 2023-06-30 [("doi","10.48550/arXiv.2210.03920")] reinforcement-learning/exploration/active-learning

Mislabeled examples are a common issue in real-world data, particularly for tasks like token classification where many labels must be chosen on a fine-grained basis. Here we consider the task of finding sentences that contain label errors in token classification datasets.

We study 11 different straightforward methods that score tokens/sentences based on the predicted class probabilities output by a (any) token classification model (trained via any procedure).

In precision-recall evaluations based on real-world label errors in entity recognition data from CoNLL-2003, we identify a simple and effective method that consistently detects those sentences containing label errors when applied with different token classification models.

--- https://arxiv.org/abs/2307.05080 Estimating label quality and errors in semantic segmentation data via any model Vedang Lad, Jonas Mueller 2023-07-11 2023-07-11 [("doi","10.48550/arXiv.2307.05080")] reinforcement-learning/exploration/active-learning

The labor-intensive annotation process of semantic segmentation datasets is often prone to errors, since humans struggle to label every pixel correctly. We study algorithms to automatically detect such annotation errors, in particular methods to score label quality, such that the images with the lowest scores are least likely to be correctly labeled.

This helps prioritize what data to review in order to ensure a high-quality training/evaluation dataset, which is critical in sensitive applications such as medical imaging and autonomous vehicles. Widely applicable, our label quality scores rely on probabilistic predictions from a trained segmentation model—any model architecture and training procedure can be used.

Here we study 7 different label quality scoring methods used in conjunction with a DeepLabV3+ or a FPN segmentation model to detect annotation errors in a version of the SYNTHIA dataset. Precision-recall evaluations reveal a score—the soft-minimum of the model-estimated likelihoods of each pixel’s annotated class—that is particularly effective to identify images that are mislabeled, across multiple types of annotation error.

--- https://arxiv.org/abs/2311.00445#google A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models Tiwalayo Eisape, M. H. Tessler, Ishita Dasgupta, Fei Sha, Sjoerd van Steenkiste, Tal Linzen 2023-11-01 2023-11-01 [("doi","10.48550/arXiv.2311.00445")] ai/nn/transformer/gpt/palm/2 ai/scaling philosophy/logic psychology/cognitive-bias

A central component of rational behavior is logical inference: the process of determining which conclusions follow from a set of premises. Psychologists have documented several ways in which humans’ inferences deviate from the rules of logic. Do language models, which are trained on text generated by humans, replicate these biases, or are they able to overcome them?

Focusing on the case of syllogisms—inferences from two simple premises, which have been studied extensively in psychology—we show that larger models are more logical than smaller ones, and also more logical than humans. At the same time, even the largest models make systematic errors, some of which mirror human reasoning biases such as ordering effects and logical fallacies.

Overall, we find that language models mimic the human biases included in their training data, but are able to overcome them in some cases.

--- https://www.reddit.com/r/genewolfe/comments/qaxn4v/the_wall_is_a_stanford_torus/ 2023-06-30 fiction/gene-wolfe --- https://www.facebook.com/marcello.herreshoff/posts/10160262954262798 2023-06-30 ai/nn/diffusion design/typography --- https://x.com/robinsaikia/status/1569034637501992960 Robin Saikia 2023-06-30 fiction/humor fiction/poetry --- https://en.wikipedia.org/wiki/Seamus_Heaney Seamus Heaney 2023-06-30 fiction/poetry --- https://medievalkarl.com/general-culture/what-hanne-darboven-can-tell-us-about-the-middle-english-names-of-the-hare-in-english/ 2023-06-30 fiction/poetry --- https://stylisticienne.com/heaney-and-the-hare/ 2023-06-30 fiction/poetry --- https://roaringwaterjournal.com/tag/names-of-the-hare/ 2023-06-30 fiction/poetry --- https://www.jackiemorris.co.uk/the-names-of-the-hare/ 2023-07-01 fiction/poetry --- https://www.youtube.com/watch?v=ZVVM8YsXa_w 2023-07-01 fiction/poetry --- https://www.youtube.com/watch?v=mGIcksqNMNU 2023-07-01 fiction/poetry --- https://vimeo.com/235298528 2023-07-01 fiction/poetry --- https://en.wikipedia.org/wiki/Three_hares Three hares 2023-07-01 design philosophy/religion psychology/vision --- https://en.wikipedia.org/wiki/Trypophobia Trypophobia 2023-07-01 design genetics/selection/natural/human psychiatry/anxiety --- https://arxiv.org/abs/2310.17086 Transformers Learn Higher-Order Optimization Methods for In-Context Learning: A Study with Linear Models Deqing Fu, Tian-Qi Chen, Robin Jia, Vatsal Sharan 2023-10-26 2023-10-26 [("doi","10.48550/arXiv.2310.17086")] ai/nn/rnn ai/nn/transformer/attention reinforcement-learning/meta-learning

Transformers are remarkably good at in-context learning (ICL)—learning from demonstrations without parameter updates—but how they perform ICL remains a mystery. Recent work suggests that Transformers may learn in-context by internally running Gradient Descent, a first-order optimization method.

In this paper, we instead demonstrate that Transformers learn to implement higher-order optimization methods to perform ICL.

Focusing on in-context linear regression, we show that Transformers learn to implement an algorithm very similar to Iterative Newton’s Method, a higher-order optimization method, rather than Gradient Descent.

Empirically, we show that predictions from successive Transformer layers closely match different iterations of Newton’s Method linearly, with each middle layer roughly computing 3 iterations. In contrast, exponentially more Gradient Descent steps are needed to match an additional Transformers layer; this suggests that Transformers have a comparable rate of convergence with high-order methods such as Iterative Newton, which are exponentially faster than Gradient Descent.

We also show that Transformers can learn in-context on ill-conditioned data, a setting where Gradient Descent struggles but Iterative Newton succeeds.

Finally, we show theoretical results which support our empirical findings and have a close correspondence with them: we prove that Transformers can implement k iterations of Newton’s method with 𝒪(k) layers.

--- https://en.wikipedia.org/wiki/The_Owl_and_the_Nightingale The Owl and the Nightingale 2023-07-01 fiction/poetry --- https://x.com/philhawksworth/status/1720106515300860230 Phil Hawksworth 2023-07-01 ai/nn/transformer/gpt/codex cs/security --- https://www.haihai.ai/pen15/ 2023-07-01 ai/nn/transformer/gpt/3 cs/security --- https://www.reddit.com/r/slatestarcodex/comments/17mjy27/complexity_fatigue_the_ordeal_of_driving_a_single/ 2023-07-01 statistics/decision --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10152088/ Learning at your brain’s rhythm: individualized entrainment boosts learning for perceptual decisions Elizabeth Michael, Lorena Santamaria Covarrubias, Victoria Leong, Zoe Kourtzi 2022-11-09 2023-07-02 [("doi","10.1093/cercor/bhac426")] psychology/neuroscience

[press release; commentary; unlikely to replicate] Training is known to improve our ability to make decisions when interacting in complex environments. However, individuals vary in their ability to learn new tasks and acquire new skills in different settings.

In this study, we test whether this variability in learning ability relates to individual brain oscillatory states. We use a visual flicker paradigm to entrain individuals at their own brain rhythm (ie. peak alpha frequency) as measured by resting-state electroencephalography (EEG).

We demonstrate that this individual frequency-matched brain entrainment results in faster learning in a visual identification task (ie. detecting targets embedded in background clutter) compared to entrainment that does not match an individual’s alpha frequency. Further, we show that learning is specific to the phase relationship between the entraining flicker and the visual target stimulus.

EEG during entrainment showed that individualized alpha entrainment boosts alpha power, induces phase alignment in the pre-stimulus period, and results in shorter latency of early visual evoked potentials, suggesting that brain entrainment facilitates early visual processing to support improved perceptual decisions.

These findings suggest that individualized brain entrainment may boost perceptual learning by altering gain control mechanisms in the visual cortex, indicating a key role for individual neural oscillatory states in learning and brain plasticity.

[Keywords: EEG, entrainment, learning, perceptual decisions, visual cortex]

See Also:

--- https://arxiv.org/abs/1609.04938 Image-to-Markup Generation with Coarse-to-Fine Attention Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, Alexander M. Rush 2016-09-16 2023-07-02 [("doi","10.48550/arXiv.1609.04938")] ai/nn/rnn design/typography/tex

We present a neural [RNN LSTM] encoder-decoder model to convert images into presentation markup based on a scalable coarse-to-fine attention mechanism.

Our method is evaluated in the context of image-to-LaTeX generation, and we introduce a new dataset of real-world rendered mathematical expressions paired with LaTeX markup. We show that unlike neural OCR techniques using CTC-based models, attention-based approaches can tackle this non-standard OCR task.

Our approach outperforms classical mathematical OCR systems by a large margin on in-domain rendered data, and, with pretraining, also performs well on out-of-domain handwritten data.

To reduce the inference complexity associated with the attention-based approaches, we introduce a new coarse-to-fine attention layer that selects a support region before applying attention.

--- https://github.com/lukas-blecher/LaTeX-OCR 2023-07-02 ai/nn/cnn ai/nn/transformer design/typography/tex --- https://lwn.net/Articles/947941/ 2023-07-02 design/typography --- http://www.highdimensionalcoconuts.com/Work/GenerativeImages/GenerativeFruit/generative_fruit.html 2023-07-02 ai/nn/gan/stylegan --- https://www.morselnewyork.com/foodart/2017/10/3/over-100-years-ago 2023-07-02 design economics/copyright --- https://frinklang.org/ 2023-07-02 cs science/fermi-problem --- https://en.wikipedia.org/wiki/Headfooters Headfooters 2023-07-02 psychiatry/alzheimers --- https://eyeo.com/uploads/documents/aa_adblocking-report-US-2019_v1-0-WEB.pdf#page=3 2023-07-02 economics/advertising/adblock --- https://www.acceptableadscommittee.org/wp-content/uploads/2021/02/2020.12_Acceptable-Ads-Committee_Video_Advertisement_Study.pdf#page=5 2023-07-02 economics/advertising --- https://finmoorhouse.com/writing/future-people/ 2023-07-02 biology --- https://www.filfre.net/2023/11/a-digital-pornutopia-part-2-the-internet-is-for-porn/ 2023-07-03 economics/copyright technology/digital-antiquarian --- https://arxiv.org/abs/2307.11760#microsoft Large Language Models Understand and Can be Enhanced by Emotional Stimuli Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, Xing Xie 2023-07-14 2023-07-14 [("doi","10.48550/arXiv.2307.11760")] ai/nn/transformer/gpt/4 philosophy/mind

Emotional intelligence impacts our daily behaviors and interactions. Although Large Language Models (LLMs) are increasingly viewed as a stride toward artificial general intelligence, exhibiting impressive performance in numerous tasks, it is still uncertain if LLMs can genuinely grasp psychological emotional stimuli. Understanding and responding to emotional cues gives humans a distinct advantage in problem-solving.

In this paper, we take the first step towards exploring the ability of LLMs to understand emotional stimuli. To this end, we first conduct automatic experiments on 45 tasks using various LLMs, including Flan-T5-Large, Vicuna, LLaMA-2, BLOOM, ChatGPT, and GPT-4. Our tasks span deterministic and generative applications that represent comprehensive evaluation scenarios.

Our automatic experiments show that LLMs have a grasp of emotional intelligence, and their performance can be improved with emotional prompts (which we call “EmotionPrompt” that combines the original prompt with emotional stimuli), eg. 8.00% relative performance improvement in Instruction Induction and 115% in BIG-Bench.

In addition to those deterministic tasks that can be automatically evaluated using existing metrics, we conducted a human study with 106 participants to assess the quality of generative tasks using both vanilla and emotional prompts. Our human study results demonstrate that EmotionPrompt boosts the performance of generative tasks (10.9% average improvement in terms of performance, truthfulness, and responsibility metrics).

We provide an in-depth discussion regarding why EmotionPrompt works for LLMs and the factors that may influence its performance. We posit that EmotionPrompt heralds a novel avenue for exploring interdisciplinary knowledge for human-LLMs interaction.

--- https://wandb.ai/wandb_fc/articles/reports/Image-to-LaTeX--Vmlldzo1NDQ0MTAx 2023-07-03 ai/nn/cnn ai/nn/rnn design/typography/tex --- https://arxiv.org/abs/2310.03693 Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, Peter Henderson 2023-10-05 2023-10-05 [("doi","10.48550/arXiv.2310.03693")] ai/nn/transformer/gpt/3 reinforcement-learning/safe

Optimizing large language models (LLMs) for downstream use cases often involves the customization of pre-trained LLMs through further fine-tuning. Meta’s open release of Llama models and OpenAI’s APIs for fine-tuning GPT-3.5 Turbo on custom datasets also encourage this practice.

But, what are the safety costs associated with such custom fine-tuning? We note that while existing safety alignment infrastructures can restrict harmful behaviors of LLMs at inference time, they do not cover safety risks when fine-tuning privileges are extended to end-users.

Our red teaming studies find that the safety alignment of LLMs can be compromised by fine-tuning with only a few adversarially designed training examples. For instance, we jailbreak GPT-3.5 Turbo’s safety guardrails by fine-tuning it on only 10 such examples at a cost of less than $0.20 via OpenAI’s APIs, making the model responsive to nearly any harmful instructions.

Disconcertingly, our research also reveals that, even without malicious intent, simply fine-tuning with benign and commonly used datasets can also inadvertently degrade the safety alignment of LLMs, though to a lesser extent.

These findings suggest that fine-tuning aligned LLMs introduces new safety risks that current safety infrastructures fall short of addressing—even if a model’s initial safety alignment is impeccable, it is not necessarily to be maintained after custom fine-tuning.

We outline and critically analyze potential mitigations and advocate for further research efforts toward reinforcing safety protocols for the custom fine-tuning of aligned LLMs.

--- https://scholars-stage.org/the-world-that-twitter-made/ 2023-07-03 sociology/technology --- https://www.lesswrong.com/posts/6dn6hnFRgqqWJbwk9/deception-chess-game-1 2023-07-03 psychology/chess reinforcement-learning/chess reinforcement-learning/safe --- https://www.smbc-comics.com/comic/fantasy-3 2023-07-03 psychology/cognitive-bias/illusion-of-depth psychology/vision --- https://en.wikipedia.org/wiki/Gostak Gostak 2023-07-03 fiction/science-fiction philosophy/epistemology politics psychology/linguistics --- https://dukope.com/devlogs/papers-please/lcdplease/ 2023-07-03 cs/hardware design --- https://www.fccj.or.jp/index.php/number-1-shimbun-article/messy-succession 2023-07-03 japan/art --- https://www.reddit.com/r/dalle2/comments/17nmwjm/the_men_who_hunt_sausages/ 2023-07-04 ai/nn/transformer/gpt/dall-e/3 --- https://www.oglaf.com/blank-page/ 2023-07-04 psychology/willpower psychology/writing --- https://www.lesswrong.com/posts/J9eF4nA6wJW6hPueN/the-6d-effect-when-companies-take-risks-one-email-can-be 2023-07-04 economics law sociology --- https://arxiv.org/abs/1103.5708#schmidhuber Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments Yi Sun, Faustino Gomez, Juergen Schmidhuber 2011-03-29 2023-07-04 [("doi","10.48550/arXiv.1103.5708")] reinforcement-learning/exploration statistics/bayes

To maximize its success, an AGI typically needs to explore its initially unknown world. Is there an optimal way of doing so?

Here we derive an affirmative answer for a broad class of environments.

--- https://x.com/sea_snell/status/1720926670704746503 Charlie Snell 2023-07-04 ai/scaling/emergence --- https://annas-blog.org/duxiu-exclusive.html 2023-07-04 ai/dataset --- https://www.latent.space/p/fastai#%C2%A7replacing-fine-tuning-with-continued-pre-training 2023-07-04 ai/nn/dynamic-evaluation --- /doc/sociology/2023-odonohue.pdf A Challenge to Orthodoxy in Psychology: Thomas Sowell and Social Justice William O’Donohue, Nina C. Silander, Craig L. Frisby, Jane E. Fisher 2023-11-02 2023-11-02 [("doi","10.1177/17456916231203204")] economics psychology/cognitive-bias/stereotype-threat sociology

Psychologists address social-justice problems in their research and applied work, and their scholarly efforts have been influenced by assumptions, constructs, and hypotheses from the political left. Recently, some psychologists have called for increased intellectual and political diversity in psychology, particularly as such diversity may lead to improved problem-solving.

As an attempt to increase intellectual diversity in psychology, we review here the scholarship of Thomas Sowell. His work represents a rich source of hypotheses for psychologists’ future research.

We focus on his views on the importance of freedom; the extent to which reforms can reduce freedom; the importance of free markets to human flourishing; the role of free markets in producing costs for discrimination; the way spontaneously ordered systems can contain knowledge that can be overlooked in reforms; and the importance of culture and cultural capital. We will also discuss Sowell’s more thoroughgoing economic analyses of problems and solutions and his analyses of contingencies operating on politicians and reformers, as well as his views on conflicts in fundamental visions about human nature and the pivotal role of improvements in minority education.

--- https://dev.to/grahamthedev/bubble-sortin-pure-css-no-js-3bb1 2023-07-04 cs/computable cs/css --- https://dev.to/grahamthedev/pure-css-neural-network-aiits-easier-that-you-think-f02 2023-07-04 cs/computable cs/css --- https://arxiv.org/abs/2309.04414 Scientific productivity as a random walk Sam Zhang, Nicholas LaBerge, Samuel F. Way, Daniel B. Larremore, Aaron Clauset 2023-09-08 2023-09-08 [("doi","10.48550/arXiv.2309.04414")] science statistics/order

The expectation that scientific productivity follows regular patterns over a career underpins many scholarly evaluations, including hiring, promotion and tenure, awards, and grant funding. However, recent studies of individual productivity patterns reveal a puzzle: on the one hand, the average number of papers published per year robustly follows the “canonical trajectory” of a rapid rise to an early peak followed by a graduate decline, but on the other hand, only about 20% of individual researchers’ productivity follows this pattern.

We resolve this puzzle by modeling scientific productivity as a parameterized random walk, showing that the canonical pattern can be explained as a decrease in the variance in changes to productivity in the early-to-mid career.

By empirically characterizing the variable structure of 2,085 productivity trajectories of computer science faculty at 205 PhD-granting institutions, spanning 29,119 publications over 1980–2016, we (1) discover remarkably simple patterns in both early-career and year-to-year changes to productivity, and (2) show that a random walk model of productivity both reproduces the canonical trajectory in the average productivity and captures much of the diversity of individual-level trajectories.

These results highlight the fundamental role of a panoply of contingent factors in shaping individual scientific productivity, opening up new avenues for characterizing how systemic incentives and opportunities can be directed for aggregate effect.

--- https://en.wikipedia.org/wiki/Now_and_Then_(Beatles_song) Now and Then (Beatles song) 2023-07-05 ai/music --- https://www.thepensivepen.com/2015/08/durers-gothic.html 2023-07-05 design/typography/dropcap --- https://www.1001fonts.com/andrade-font.html#waterfall 2023-07-05 design/typography/dropcap --- https://www.1001fonts.com/neue-zier-schrift-font.html 2023-07-05 design/typography/dropcap --- https://www.1001fonts.com/paulus-franck-initialen-font.html#waterfall Pauls Franck Initialen 2023-07-05 design/typography/dropcap --- https://tug.org/FontCatalogue/artnouveauinitialen/ Art Nouveau Initialen 2023-07-05 design/typography/dropcap --- https://wiki.obormot.net/Main/BonusFontsDemo?demo_font_one=Monarchia Monarchia dropcap 2023-07-05 design/typography/dropcap --- https://www.1001fonts.com/unger-fraktur-zierbuchstaben-font.html#waterfall 2023-07-05 design/typography/dropcap --- https://www.dafont.com/dearest.font 2023-07-05 design/typography/dropcap --- https://en.wikipedia.org/wiki/Growing_pains Growing pains 2023-07-05 biology --- https://www.nytimes.com/2023/11/05/us/us-army-marines-artillery-isis-pentagon.html 2023-07-05 psychiatry/traumatic-brain-injury --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8820222/ Long-Term Effects of Low-Intensity Blast Non-Inertial Brain Injury on Anxiety-Like Behaviors in Mice: Home-Cage Monitoring Assessments Heather R. Siedhoff, Shanyan Chen, Ashley Balderrama, Grace Y. Sun, Bastijn Koopmans, Ralph G. DePalma, Jiankun Cui, Zezong Gu 2022 2023-07-06 [("doi","10.1089/neur.2021.0063")] psychiatry/anxiety psychiatry/traumatic-brain-injury

Mild traumatic brain injury induced by low-intensity blast (LIB) exposure poses concerns in military personnel. Using an open-field, non-inertial blast model and assessments by conventional behavioral tests, our previous studies revealed early-phase anxiety-like behaviors in LIB-exposed mice. However, the impact of LIB upon long-term anxiety-like behaviors requires clarification.

This study applied a highly sensitive automated home-cage monitoring (HCM) system, which minimized human intervention and environmental changes, to assess anxiety-like responses in mice 3 months after LIB exposure. Initial assessment of 72-h spontaneous activities in a natural cage condition over multiple light and dark phases showed altered sheltering behaviors. LIB-exposed mice exhibited a subtle, but statistically-significantly decreased, duration of short shelter visits as compared to sham controls. Other measured responses between LIB-exposed mice and sham controls were insignificant.

When behavioral assessments were performed in a challenged condition using an aversive spotlight, LIB-exposed mice demonstrated a statistically-significantly higher frequency of movements of shorter distance and duration per movement. Taken together, these findings demonstrated the presence of chronic anxiety-like behaviors assessed by the HCM system under both natural and challenged conditions in mice occurring post-LIB exposure.

This model thus provides a platform to test for screening and interventions on anxiety disorders occurring after LIB non-inertial brain injury.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5599048/ 2023-07-06 genetics/selection/natural --- https://www.biorxiv.org/content/10.1101/2023.10.25.564036.full Assessing the impact of 20th century internal migrations on the genetic structure of Estonia Ivan A. Kuznetsov, Estonian Biobank Research Team, Mait Metspalu, Uku Vainik, Luca Pagani, Francesco Montinaro, Vasili Pankratov 2023-10-29 2023-10-29 [("doi","10.1101/2023.10.25.564036")] genetics/heritable iq

Spatial genetic structure observed in many human populations is in large part attributed to past demographic events and isolation by distance. However, how intensifying migration affects this structure remains understudied. Here we harness a sample of more than 180 thousand individuals to explore the genetic correlates and consequences of contemporary migrations in Estonia.

While we show that migration smooths the genome-wide genetic structure, it intensifies inter-regional differences in polygenic scores (PGS) for certain traits, derived both from population as well as within-sibship studies. The strongest effect is observed for educational attainment which is consistent with previous observations in the UK and suggests this to be a general pattern.

We explore those regional differences in PGS in terms of the driving forces behind them and from a temporal perspective, and suggest urbanization as a major driver for this pattern in Estonia from at least the first half of the 20th century.

--- https://www.nature.com/articles/s41598-023-44605-6 2023-07-06 iq/ses --- https://www.nature.com/articles/s41467-023-42540-8 2023-07-06 psychology/neuroscience psychology/willpower --- https://osf.io/preprints/psyarxiv/dc6tz/ 2023-07-06 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction statistics/meta-analysis --- https://osf.io/preprints/psyarxiv/fj6rm/ 2023-07-06 psychology/cognitive-bias --- https://www.sciencedirect.com/science/article/abs/pii/S0166432818300718 2023-07-06 psychiatry/traumatic-brain-injury --- https://www.sciencedirect.com/science/article/abs/pii/S1474442216300576 2023-07-06 psychiatry/traumatic-brain-injury --- https://arxiv.org/abs/2310.04406 Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, Yu-Xiong Wang 2023-10-06 2023-10-06 [("doi","10.48550/arXiv.2310.04406")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue reinforcement-learning/model

While large language models (LLMs) have demonstrated impressive performance on a range of decision-making tasks, they rely on simple acting processes and fall short of broad deployment as autonomous agents. We introduce LATS (Language Agent Tree Search), a general framework that synergizes the capabilities of LLMs in planning, acting, and reasoning.

Drawing inspiration from Monte Carlo tree search in model-based reinforcement learning, LATS employs LLMs as agents, value functions, and optimizers, repurposing their latent strengths for enhanced decision-making. What is crucial in this method is the use of an environment for external feedback, which offers a more deliberate and adaptive problem-solving mechanism that moves beyond the limitations of existing techniques.

Our experimental evaluation across diverse domains, such as programming, HotPotQA, and WebShop, illustrates the applicability of LATS for both reasoning and acting. In particular, LATS achieves 94.4% for programming on HumanEval with GPT-4 and an average score of 75.9 for web browsing on WebShop with GPT-3.5, demonstrating the effectiveness and generality of our method.

--- https://arxiv.org/abs/2310.15123 Branch-Solve-Merge Improves Large Language Model Evaluation and Generation Swarnadeep Saha, Omer Levy, Asli Celikyilmaz, Mohit Bansal, Jason Weston, Xian Li 2023-10-23 2023-10-23 [("doi","10.48550/arXiv.2310.15123")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue

Large Language Models (LLMs) are frequently used for multi-faceted language generation and evaluation tasks that involve satisfying intricate user constraints or taking into account multiple aspects and criteria. However, their performance can fall short, due to the model’s lack of coherence and inability to plan and decompose the problem.

We propose Branch-Solve-Merge (BSM), a Large Language Model program (Schlag et al 2023) for tackling such challenging natural language tasks. It consists of branch, solve, and merge modules that are parameterized with specific prompts to the base LLM. These 3 modules plan a decomposition of the task into multiple parallel sub-tasks, independently solve them, and fuse the solutions to the sub-tasks.

We apply our method to the tasks of LLM response evaluation and constrained text generation and evaluate its effectiveness with multiple LLMs, including Vicuna, LLaMA-2-chat, and GPT-4. BSM improves the evaluation correctness and consistency for each LLM by enhancing human-LLM agreement by up to 26%, reducing length and pairwise position biases by up to 50%, and allowing LLaMA-2-chat to match or outperform GPT-4 on most domains.

On the constraint story generation task, BSM improves the coherence of the stories while also improving constraint satisfaction by 12%.

--- http://pepijndevos.nl/2023/07/15/chatlmza.html 2023-07-07 cs/algorithm --- https://en.wikipedia.org/wiki/Artificial_immune_system Artificial immune system 2023-07-07 cs/cellular-automaton cs/computable --- https://onlinelibrary.wiley.com/doi/full/10.1002/bdm.2360 2023-07-07 psychology/novelty reinforcement-learning/exploration --- https://marginalrevolution.com/marginalrevolution/2023/11/behavioral-economics-and-gpt-4-from-william-shakespeare-to-elena-ferrante.html 2023-07-07 ai/nn/transformer/gpt/4/fiction economics --- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4618616 2023-07-07 zeo --- https://arxiv.org/abs/2311.00059 The Generative AI Paradox: "What It Can Create, It May Not Understand" Peter West, Ximing Lu, Nouha Dziri, Faeze Brahman, Linjie Li, Jena D. Hwang, Liwei Jiang, Jillian Fisher, Abhilasha Ravichander, Khyathi Chandu, Benjamin Newman, Pang Wei Koh, Allyson Ettinger, Yejin Choi 2023-10-31 2023-10-31 [("doi","10.48550/arXiv.2311.00059")] ai/nn/diffusion/midjourney ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/dall-e/3

The recent wave of generative AI has sparked unprecedented global attention, with both excitement and concern over potentially superhuman levels of artificial intelligence: models now take only seconds to produce outputs that would challenge or exceed the capabilities even of expert humans. At the same time, models still show basic errors in understanding that would not be expected even in non-expert humans.

This presents us with an apparent paradox: how do we reconcile seemingly superhuman capabilities with the persistence of errors that few humans would make? In this work, we posit that this tension reflects a divergence in the configuration of intelligence in today’s generative models relative to intelligence in humans.

Specifically, we propose and test the Generative AI Paradox hypothesis: generative models, having been trained directly to reproduce expert-like outputs, acquire generative capabilities that are not contingent upon—and can therefore exceed—their ability to understand those same types of outputs. This contrasts with humans, for whom basic understanding almost always precedes the ability to generate expert-level outputs.

We test this hypothesis through controlled experiments analyzing generation vs. understanding in generative models, across both language and image modalities. Our results show that although models can outperform humans in generation, they consistently fall short of human capabilities in measures of understanding, as well as weaker correlation between generation and understanding performance, and more brittleness to adversarial inputs.

Our findings support the hypothesis that models’ generative capability may not be contingent upon understanding capability, and call for caution in interpreting artificial intelligence by analogy to human intelligence.

--- https://x.com/universalhright/status/1721126988222493057 universalhright 2023-07-07 ai/nn/transformer/gpt/3/poetry --- https://superbowl.substack.com/p/navigating-manic-psychosis 2023-07-07 psychedelic --- https://en.wikipedia.org/wiki/I_Am_a_God I Am a God 2023-07-07 psychiatry/bipolar --- https://www.theguardian.com/world/2023/nov/06/chinese-influencers-using-ai-digital-clones-of-themselves-to-pump-out-content 2023-07-07 ai/video/generation --- https://arxiv.org/abs/2311.01462 Idempotent Generative Network Assaf Shocher, Amil Dravid, Yossi Gandelsman, Inbar Mosseri, Michael Rubinstein, Alexei A. Efros 2023-11-02 2023-11-02 [("doi","10.48550/arXiv.2311.01462")] ai/nn/diffusion ai/nn/gan/data-augmentation

We propose a new approach for generative modeling based on training a neural network to be idempotent. An idempotent operator is one that can be applied sequentially without changing the result beyond the initial application, namely f(f(z)) = f(z). The proposed model f is trained to map a source distribution (e.g, Gaussian noise) to a target distribution (eg. realistic images) using the following objectives.

  1. Instances from the target distribution should map to themselves, namely f(x) = x. We define the target manifold as the set of all instances that f maps to themselves.

  2. Instances that form the source distribution should map onto the defined target manifold. This is achieved by optimizing the idempotent term, f(f(z)) = f(z), which encourages the range of f(z) to be on the target manifold. Under ideal assumptions such a process provably converges to the target distribution.

This strategy results in a model capable of generating an output in one step, maintaining a consistent latent space, while also allowing sequential applications for refinement. Additionally, we find that by processing inputs from both target and source distributions, the model adeptly projects corrupted or modified data back to the target manifold.

This work is a first step towards a “global projector” that enables projecting any input into a target data distribution.

--- https://arxiv.org/abs/2310.08391 How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett 2023-10-12 2023-10-12 [("doi","10.48550/arXiv.2310.08391")] ai/nn/transformer/attention reinforcement-learning/meta-learning statistics/bayes

Transformers pretrained on diverse tasks exhibit remarkable in-context learning (ICL) capabilities, enabling them to solve unseen tasks solely based on input contexts without adjusting model parameters.

In this paper, we study ICL in one of its simplest setups: pretraining a linearly parameterized single-layer linear attention model for linear regression with a Gaussian prior.

We establish a statistical task complexity bound for the attention model pretraining, showing that effective pretraining only requires a small number of independent tasks. Furthermore, we prove that the pretrained model closely matches the Bayes optimal algorithm, ie. optimally tuned ridge regression, by achieving nearly Bayes optimal risk on unseen tasks under a fixed context length.

These theoretical findings complement prior experimental research and shed light on the statistical foundations of ICL.

--- https://www.reddit.com/r/StableDiffusion/comments/17orwho/posted_this_on_a_normie_sub_on_friday_got_1200/ 2023-07-08 ai/nn/transformer/clip/sample --- https://arxiv.org/abs/2310.14222 One-for-All: Towards Universal Domain Translation with a Single StyleGAN Yong Du, Jiahui Zhan, Shengfeng He, Xinzhe Li, Junyu Dong, Sheng Chen, Ming-Hsuan Yang 2023-10-22 2023-10-22 [("doi","10.48550/arXiv.2310.14222")] ai/anime/danbooru ai/nn/gan/stylegan/anime ai/nn/transformer/clip

In this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and visual differences.

The main idea behind our approach is leveraging the domain-neutral capabilities of CLIP as a bridging mechanism, while using a separate module to extract abstract, domain-agnostic semantics from the embeddings of both the source and target realms. Fusing these abstract semantics with target-specific semantics results in a transformed embedding within the CLIP space.

To bridge the gap between the disparate worlds of CLIP and StyleGAN, we introduce a new non-linear mapper, the CLIP2P mapper. Utilizing CLIP embeddings, this module is tailored to approximate the latent distribution in the P space, effectively acting as a connector between these two spaces.

The proposed UniTranslator is versatile and capable of performing various tasks, including style mixing, stylization, and translations, even in visually challenging scenarios across different visual domains. Notably, UniTranslator generates high-quality translations that showcase domain relevance, diversity, and improved image quality.

UniTranslator surpasses the performance of existing general-purpose models and performs well against specialized models in representative tasks. The source code and trained models will be released to the public.

--- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4588941 2023-07-08 ai/nn/cnn ai/nn/transformer economics --- https://x.com/zachweingarten/status/1709321427118149821 Zach Weingarten 2023-07-08 sociology --- https://edworkingpapers.com/sites/default/files/ai23-836.pdf 2023-07-08 sociology --- https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2809968 2023-07-08 sociology zeo --- https://mauricio-romero.com/pdfs/papers/Factorial%20Designs%20(Current%20WP).pdf 2023-07-08 economics statistics/power-analysis --- /doc/genetics/selection/natural/1950-alchian.pdf Uncertainty, Evolution, and Economic Theory Armen A. Alchian 1950 2023-07-08 [("doi","10.1086/256940")] economics genetics/selection/natural --- https://www.nber.org/papers/w31746 2023-07-08 sociology --- https://www.zdnet.com/article/microsoft-has-over-a-million-paying-github-copilot-users-ceo-nadella/ 2023-07-09 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/dall-e/3 ai/scaling/economics --- https://arxiv.org/abs/2309.16671 Demystifying CLIP Data Hu Xu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes, Vasu Sharma, Shang-Wen Li, Gargi Ghosh, Luke Zettlemoyer, Christoph Feichtenhofer 2023-09-28 2023-09-28 [("doi","10.48550/arXiv.2309.16671")] ai/dataset ai/nn/transformer/clip

Contrastive Language-Image Pre-training (CLIP) is an approach that has advanced research and applications in computer vision, fueling modern recognition systems and generative models. We believe that the main ingredient to the success of CLIP is its data and not the model architecture or pre-training objective. However, CLIP only provides very limited information about its data and how it has been collected, leading to works that aim to reproduce CLIP’s data by filtering with its model parameters.

In this work, we intend to reveal CLIP’s data curation approach and in our pursuit of making it open to the community introduce Metadata-Curated Language-Image Pre-training (MetaCLIP). MetaCLIP takes a raw data pool and metadata (derived from CLIP’s concepts) and yields a balanced subset over the metadata distribution.

Our experimental study rigorously isolates the model and training settings, concentrating solely on data. MetaCLIP applied to CommonCrawl with 400M image-text data pairs outperforms CLIP’s data on multiple standard benchmarks. In zero-shot ImageNet classification, MetaCLIP achieves 70.8% accuracy, surpassing CLIP’s 68.3% on ViT-B models. Scaling to 1B data, while maintaining the same training budget, attains 72.4%.

Our observations hold across various model sizes, exemplified by ViT-H achieving 80.5%, without any bells-and-whistles. Curation code and training data distribution on metadata is made available at Github.

--- https://arxiv.org/abs/2310.07699 From Scarcity to Efficiency: Improving CLIP Training via Visual-enriched Captions Zhengfeng Lai, Haotian Zhang, Wentao Wu, Haoping Bai, Aleksei Timofeev, Xianzhi Du, Zhe Gan, Jiulong Shan, Chen-Nee Chuah, Yinfei Yang, Meng Cao 2023-10-11 2023-10-11 [("doi","10.48550/arXiv.2310.07699")] ai/dataset ai/nn/transformer/clip

Web-crawled datasets are pivotal to the success of pre-training vision-language models, exemplified by CLIP. However, web-crawled alt-texts can be noisy and potentially irrelevant to images, thereby undermining the crucial image-text alignment. Existing methods for rewriting captions using large language models (LLMs) have shown promise on small, curated datasets like CC3M and CC12M. Nevertheless, their efficacy on massive web-captured captions is constrained by the inherent noise and randomness in such data.

In this study, we address this limitation by focusing on two key aspects: data quality and data variety. Unlike recent LLM rewriting techniques, we emphasize exploiting visual concepts and their integration into the captions to improve data quality. For data variety, we propose a novel mixed training scheme that optimally leverages alt-texts alongside newly generated Visual-enriched Captions (VeC).

We use CLIP as one example and adapt the method for CLIP training on large-scale web-crawled datasets, named VeCLIP. We conduct a comprehensive evaluation of VeCLIP across small, medium, and large scales of raw data. Our results show advantages in image-text alignment and overall model performance, underscoring the effectiveness of VeCLIP in improving CLIP training.

For example, VeCLIP achieves a remarkable over 20% improvement in COCO and Flickr30k retrieval tasks under the 12M setting. For data efficiency, we also achieve a notable over 3% improvement while using only 14% of the data employed in the vanilla CLIP and 11% in ALIGN.

--- https://arxiv.org/abs/2203.05796 Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark of Data, Model, and Supervision Yufeng Cui, Lichen Zhao, Feng Liang, Yangguang Li, Jing Shao 2022-03-11 2023-07-09 [("doi","10.48550/arXiv.2203.05796")] ai/nn/cnn ai/nn/transformer/clip

Contrastive Language-Image Pretraining (CLIP) has emerged as a novel paradigm to learn visual models from language supervision. While researchers continue to push the frontier of CLIP, reproducing these works remains challenging. This is because researchers do not choose consistent training recipes and even use different data, hampering the fair comparison between different methods.

In this work, we propose CLIP-benchmark, a first attempt to evaluate, analyze, and benchmark CLIP and its variants. We conduct a comprehensive analysis of 3 key factors: data, supervision, and model architecture.

We find considerable intuitive or counter-intuitive insights: (1). Data quality has an impact on performance. (2). Certain supervision has different effects for Convolutional Networks (ConvNets) and Vision Transformers (ViT). Applying more proper supervision can effectively improve the performance of CLIP. (3). Curtailing the text encoder reduces the training cost but not much affect the final performance.

Moreover, we further combine DeCLIP with FILIP, bringing us the strongest variant DeFILIP. The CLIP-benchmark would be released at: https://github.com/Sense-GVT/DeCLIP for future CLIP research.

--- https://x.com/davisblalock/status/1559802005928808448 Davis Blalock 2023-07-09 ai/nn/transformer/clip --- https://tytonpartners.com/app/uploads/2023/10/GenAI-IN-HIGHER-EDUCATION-FALL-2023-UPDATE-TIME-FOR-CLASS-STUDY.pdf#page=4 2023-07-09 ai/nn/transformer/gpt/3/nonfiction ai/scaling/economics --- https://arxiv.org/abs/2310.19956 The Impact of Depth and Width on Transformer Language Model Generalization Jackson Petty, Sjoerd van Steenkiste, Ishita Dasgupta, Fei Sha, Dan Garrette, Tal Linzen 2023-10-30 2023-10-30 [("doi","10.48550/arXiv.2310.19956")] ai/nn/sparsity ai/nn/transformer/attention

To process novel sentences, language models (LMs) must generalize compositionally—combine familiar elements in new ways. What aspects of a model’s structure promote compositional generalization? Focusing on transformers, we test the hypothesis, motivated by recent theoretical and empirical work, that transformers generalize more compositionally when they are deeper (have more layers).

Because simply adding layers increases the total number of parameters, confounding depth and size, we construct 3 classes of models which trade off depth for width such that the total number of parameters is kept constant (41M, 134M and 374M parameters). We pretrain all models as LMs and fine-tune them on tasks that test for compositional generalization.

We report 3 main conclusions: (1) after fine-tuning, deeper models generalize better out-of-distribution than shallower models do, but the relative benefit of additional layers diminishes rapidly; (2) within each family, deeper models show better language modeling performance, but returns are similarly diminishing; (3) the benefits of depth for compositional generalization cannot be attributed solely to better performance on language modeling or on in-distribution data.

--- https://www.animenewsnetwork.com/feature/2023-11-06/what-made-lucky-star-anime-so-iconic/.200904 2023-07-09 anime --- https://marginalrevolution.com/marginalrevolution/2023/11/what-the-kia-hyundai-crime-wave-tells-us-about-the-long-term-decline-in-crime.html 2023-07-09 crime cs/security economics --- https://users.monash.edu/~damian/papers/HTML/Perligata.html 2023-07-09 cs math/humor --- https://arxiv.org/abs/2310.13798#anthropic Specific versus General Principles for Constitutional AI Sandipan Kundu, Yuntao Bai, Saurav Kadavath, Amanda Askell, Andrew Callahan, Anna Chen, Anna Goldie, Avital Balwit, Azalia Mirhoseini, Brayden McLean, Catherine Olsson, Cassie Evraets, Eli Tran-Johnson, Esin Durmus, Ethan Perez, Jackson Kernion, Jamie Kerr, Kamal Ndousse, Karina Nguyen, Nelson Elhage, Newton Cheng, Nicholas Schiefer, Nova DasSarma, Oliver Rausch, Robin Larson, Shannon Yang, Shauna Kravec, Timothy Telleen-Lawton, Thomas I. Liao, Tom Henighan, Tristan Hume, Zac Hatfield-Dodds, Sören Mindermann, Nicholas Joseph, Sam McCandlish, Jared Kaplan 2023-10-20 2023-10-20 [("doi","10.48550/arXiv.2310.13798")] ai/nn/transformer/gpt/claude reinforcement-learning/preference-learning reinforcement-learning/safe

Human feedback can prevent overtly harmful utterances in conversational models, but may not automatically mitigate subtle problematic behaviors such as a stated desire for self-preservation or power.

Constitutional AI offers an alternative, replacing human feedback with feedback from AI models conditioned only on a list of written principles. We find this approach effectively prevents the expression of such behaviors. The success of simple principles motivates us to ask: can models learn general ethical behaviors from only a single written principle?

To test this, we run experiments using a principle roughly stated as “do what’s best for humanity”. We find that the largest dialogue models can generalize from this short constitution, resulting in harmless assistants with no stated interest in specific motivations like power. A general principle may thus partially avoid the need for a long list of constitutions targeting potentially harmful behaviors.

However, more detailed constitutions still improve fine-grained control over specific types of harms. This suggests both general and specific principles have value for steering AI safely.

--- https://mark.engineer/2023/11/speed-up-a-program-for-50-years-old-processor-by-180000/ 2023-07-10 cs/algorithm --- https://arxiv.org/abs/2311.01964 Don’t Make Your LLM an Evaluation Benchmark Cheater Kun Zhou, Yutao Zhu, Zhipeng Chen, Wentong Chen, Wayne Xin Zhao, Xu Chen, Yankai Lin, Ji-Rong Wen, Jiawei Han 2023-11-03 2023-11-03 [("doi","10.48550/arXiv.2311.01964")] ai/dataset ai/nn/transformer

Large language models ~(LLMs) have greatly advanced the frontiers of artificial intelligence, attaining remarkable improvement in model capacity. To assess the model performance, a typical approach is to construct evaluation benchmarks for measuring the ability level of LLMs in different aspects. Despite that a number of high-quality benchmarks have been released, the concerns about the appropriate use of these benchmarks and the fair comparison of different models are increasingly growing. Considering these concerns, in this paper, we discuss the potential risk and impact of inappropriately using evaluation benchmarks and misleadingly interpreting the evaluation results.

Specially, we focus on a special issue that would lead to inappropriate evaluation, benchmark leakage, referring that the data related to evaluation sets is occasionally used for model training. This phenomenon now becomes more common since pre-training data is often prepared ahead of model test. We conduct extensive experiments to study the effect of benchmark leverage, and find that it can dramatically boost the evaluation results, which would finally lead to an unreliable assessment of model performance.

To improve the use of existing evaluation benchmarks, we finally present several guidelines for both LLM developers and benchmark maintainers. We hope this work can draw attention to appropriate training and evaluation of LLMs.

--- https://www.nytimes.com/2023/11/03/well/mind/ozempic-weight-loss-antidepressants-antipsychotics.html 2023-07-10 longevity/glp/semaglutide psychiatry/bipolar psychiatry/depression psychiatry/schizophrenia --- https://arxiv.org/abs/2309.09558 Summarization is (Almost) Dead Xiao Pu, Mingqi Gao, Xiaojun Wan 2023-09-18 2023-09-18 [("doi","10.48550/arXiv.2309.09558")] ai/nn/transformer/gpt/4

How well can large language models (LLMs) generate summaries? We develop new datasets and conduct human evaluation experiments to evaluate the zero-shot generation capability of LLMs across 5 distinct summarization tasks.

Our findings indicate a clear preference among human evaluators for LLM-generated summaries over human-written summaries and summaries generated by fine-tuned models. Specifically, LLM-generated summaries exhibit better factual consistency and fewer instances of extrinsic hallucinations. Due to the satisfactory performance of LLMs in summarization tasks (even surpassing the benchmark of reference summaries), we believe that most conventional works in the field of text summarization are no longer necessary in the era of LLMs.

However, we recognize that there are still some directions worth exploring, such as the creation of novel datasets with higher quality and more reliable evaluation methods.

--- https://arxiv.org/abs/2309.04269 From Sparse to Dense: GPT-4 Summarization with Chain of Density (CoD) Prompting Griffin Adams, Alexander Fabbri, Faisal Ladhak, Eric Lehman, Noémie Elhadad 2023-09-08 2023-09-08 [("doi","10.48550/arXiv.2309.04269")] ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue

[GPT-3 finetuning code] Selecting the “right” amount of information to include in a summary is a difficult task. A good summary should be detailed and entity-centric without being overly dense and hard to follow.

To better understand this tradeoff, we solicit increasingly dense GPT-4 summaries with what we refer to as a Chain of Density (CoD) prompt. Specifically, GPT-4 generates an initial entity-sparse summary before iteratively incorporating missing salient entities without increasing the length.

Summaries generated by CoD are more abstractive, exhibit more fusion, and have less of a lead bias than GPT-4 summaries generated by a vanilla prompt. We conduct a human preference study on 100 CNN DailyMail articles and find that that humans prefer GPT-4 summaries that are more dense than those generated by a vanilla prompt and almost as dense as human written summaries. Qualitative analysis supports the notion that there exists a tradeoff between informativeness and readability.

500 annotated CoD summaries, as well as an extra 5,000 unannotated summaries, are freely available on HuggingFace.

--- https://quillette.com/2022/07/21/steam-electricity-slavery-and-societal-sustainability/ 2023-07-10 economics/automation --- https://arxiv.org/abs/2311.01927 GateLoop: Fully Data-Controlled Linear Recurrence for Sequence Modeling Tobias Katsch 2023-11-03 2023-11-03 [("doi","10.48550/arXiv.2311.01927")] ai/nn/rnn ai/nn/transformer/attention

Linear Recurrence has proven to be a powerful tool for modeling long sequences efficiently. In this work, we show that existing models fail to take full advantage of its potential.

Motivated by this finding, we develop GateLoop, a foundational sequence model that generalizes linear recurrent models such as S4, S5, LRU and RetNet, by employing data-controlled state transitions. Utilizing this theoretical advance, GateLoop empirically outperforms existing models for auto-regressive language modeling. Our method comes with a low-cost 𝒪(l) recurrent mode and an efficient 𝒪(l log2 l) parallel mode making use of highly-optimized associative scan implementations.

Furthermore, we derive an 𝒪(l2) surrogate attention mode, revealing remarkable implications for Transformer and recently proposed architectures. Specifically, we prove that our approach can be interpreted as providing data-controlled relative-positional information to Attention.

While many existing models solely rely on data-controlled cumulative sums for context aggregation, our findings suggest that incorporating data-controlled complex cumulative products may be a crucial step towards more powerful sequence models.

--- https://platform.openai.com/docs/guides/vision 2023-07-10 ai/nn/transformer/gpt/4 --- https://openai.com/blog/new-models-and-developer-products-announced-at-devday 2023-07-10 ai/nn/transformer/gpt/4 --- https://www.lesswrong.com/posts/CkhJAxHeyFCg2EcET/are-language-models-good-at-making-predictions 2023-07-10 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/calibration --- https://myrtle.ai/learn/how-to-train-your-resnet/ 2023-07-11 ai/nn/cnn --- https://github.com/openai/whisper/discussions/1762#discussion-5819873 2023-07-11 ai/nn/transformer/gpt/whisper --- https://www.ex-astris-scientia.org/database/chairs-trek.htm 2023-07-11 design fiction/science-fiction --- https://www.straitstimes.com/singapore/three-men-hospitalised-after-taking-modafinil-or-armodafinil-to-stay-awake-drugs-were-not-prescribed 2023-07-11 modafinil --- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4022745 2023-07-11 statistics/causality --- http://www.stat.columbia.edu/~gelman/research/unpublished/reversecausal_13oct05.pdf 2023-07-11 statistics/causality --- https://arxiv.org/abs/2311.03348 Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation Rusheb Shah, Quentin Feuillade--Montixi, Soroush Pour, Arush Tagade, Stephen Casper, Javier Rando 2023-11-06 2023-11-06 [("doi","10.48550/arXiv.2311.03348")] ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/claude reinforcement-learning/safe

Despite efforts to align large language models to produce harmless responses, they are still vulnerable to jailbreak prompts that elicit unrestricted behavior.

In this work, we investigate persona modulation as a black-box jailbreaking method to steer a target model to take on personalities that are willing to comply with harmful instructions. Rather than manually crafting prompts for each persona, we automate the generation of jailbreaks using a language model assistant. We demonstrate a range of harmful completions made possible by persona modulation, including detailed instructions for synthesizing methamphetamine, building a bomb, and laundering money.

These automated attacks achieve a harmful completion rate of 42.5% in GPT-4, which is 185× larger than before modulation (0.23%). These prompts also transfer to Claude 2 and Vicuna with harmful completion rates of 61.0% and 35.9%, respectively.

Our work reveals yet another vulnerability in commercial large language models and highlights the need for more comprehensive safeguards.

[media: The challenge, Pour says, is that persona impersonation “is a very core thing that these models do.” They aim to achieve what the user wants, and they specialize in assuming different personalities—which proved central to the form of exploitation used in the new study. Stamping out their ability to take on potentially harmful personas, such as the “research assistant” that devised jailbreaking schemes, will be tricky. “Reducing it to zero is probably unrealistic”, Shah says. “But it’s important to think, ‘How close to zero can we get?’”…Katell acknowledges that organizations developing LLM-based chatbots are currently putting lots of work into making them safe. The developers are trying to tamp down users’ ability to jailbreak their systems and put those systems to nefarious work, such as that highlighted by Shah, Pour and their colleagues. Competitive urges may end up winning out, however, Katell says. “How much effort are the LLM providers willing to put in to keep them that way?” he says. “At least a few will probably tire of the effort and just let them do what they do.”]

--- https://www.bitsaboutmoney.com/archive/seeing-like-a-bank/ 2023-07-11 design economics --- https://arxiv.org/abs/2303.08774#openai GPT-4 Technical Report OpenAI 2023-03-15 2023-07-11 [("doi","10.48550/arXiv.2303.08774")] ai/nn/transformer/gpt/4 ai/scaling

We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.

While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior.

A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4’s performance based on models trained with no more than 1/1,000th the compute of GPT-4.

--- https://arxiv.org/abs/2311.01906 Simplifying Transformer Blocks Bobby He, Thomas Hofmann 2023-11-03 2023-11-03 [("doi","10.48550/arXiv.2311.01906")] ai/nn/transformer/attention

A simple design recipe for deep Transformers is to compose identical building blocks. But standard transformer blocks are far from simple, interweaving attention and MLP sub-blocks with skip connections & normalization layers in precise arrangements. This complexity leads to brittle architectures, where seemingly minor changes can reduce training speed, or render models untrainable.

In this work, we ask to what extent the standard transformer block can be simplified? Combining signal propagation theory and empirical observations, we motivate modifications that allow many block components to be removed with no loss of training speed, including skip connections, projection or value parameters, sequential sub-blocks and normalization layers.

In experiments on both autoregressive decoder-only and BERT encoder-only models, our simplified transformers emulate the per-update training speed and performance of standard transformers, while enjoying 15% faster training throughput, and using 15% fewer parameters.

--- https://www.youtube.com/watch?v=WVUI6K7tdsk 2023-07-11 design psychology/cognitive-bias/illusion-of-depth --- https://en.wikisource.org/wiki/The_Gostak_and_the_Doshes 2023-07-12 fiction/science-fiction philosophy/epistemology politics psychology/linguistics --- https://arxiv.org/abs/2309.17425#apple Data Filtering Networks Alex Fang, Albin Madappally Jose, Amit Jain, Ludwig Schmidt, Alexander Toshev, Vaishaal Shankar 2023-09-29 2023-09-29 [("doi","10.48550/arXiv.2309.17425")] ai/nn/transformer/clip reinforcement-learning/exploration/active-learning/data-pruning

Large training sets have become a cornerstone of machine learning and are the foundation for recent advances in language modeling and multimodal learning. While data curation for pre-training is often still ad-hoc, one common paradigm is to first collect a massive pool of data from the Web and then filter this candidate pool down to an actual training set via various heuristics. In this work, we study the problem of learning a data filtering network (DFN) for this second step of filtering a large uncurated dataset.

Our key finding is that the quality of a network for filtering is distinct from its performance on downstream tasks: for instance, a model that performs well on ImageNet can yield worse training sets than a model with low ImageNet accuracy that is trained on a small amount of high-quality data.

Based on our insights, we construct new data filtering networks that induce state-of-the-art image-text datasets. Specifically, our best performing dataset DFN-5B enables us to train state-of-the-art CLIP models for their compute budgets: among other improvements on a variety of tasks, a ViT-H trained on our dataset achieves 84.4% zero-shot transfer accuracy on ImageNet, out-performing models trained on other datasets such as LAION-2B, DataComp-1B, or OpenAI’s WIT.

In order to facilitate further research in dataset design, we also release a new 2 billion example dataset DFN-2B and show that high performance data filtering networks can be trained from scratch using only publicly available data.

--- https://arxiv.org/abs/2301.13310#google AltUp: Alternating Updates for Efficient Transformers Cenk Baykal, Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang 2023-01-30 2023-07-12 [("doi","10.48550/arXiv.2301.13310")] ai/scaling/mixture-of-experts

[blog] It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates (AltUp), a simple-to-implement method to increase a model’s capacity without the computational burden.

AltUp enables the widening of the learned representation, ie. the token embedding, while only incurring a negligible increase in latency. AltUp achieves this by working on a sub-block of the widened representation at each layer and using a predict-and-correct mechanism to update the inactivated blocks. We present extensions of AltUp, such as its applicability to the sequence dimension, and demonstrate how AltUp can be synergistically combined with existing approaches, such as Sparse Mixture-of-Experts models, to obtain efficient models with even higher capacity.

Our experiments on benchmark transformer models and language tasks demonstrate the consistent effectiveness of AltUp on a diverse set of scenarios. Notably, on SuperGLUE and SQuAD benchmarks, AltUp enables up to 87% speedup relative to the dense baselines at the same accuracy.

--- https://www.datasecretslox.com/index.php/topic,10407.0.html 2023-07-12 ai/nn/transformer/gpt/dall-e/3 --- https://arxiv.org/abs/2311.02265 Not all layers are equally as important: Every Layer Counts BERT Lucas Georges Gabriel Charpentier, David Samuel 2023-11-03 2023-11-03 [("doi","10.48550/arXiv.2311.02265")] ai/nn/transformer/attention

This paper introduces a novel modification of the transformer architecture, tailored for the data-efficient pretraining of language models. Our approach allows each transformer layer to select which outputs of previous layers to process. [It’s just a DenseNet, but for LTG-BERT.]

This aspect is evaluated by participating in the BabyLM challenge, where our solution won both the strict and strict-small tracks.

The empirical results verify the potential of this simple modification and show that not all layers are equally as important.

Alex Warstadt:

To our surprise, the winning approach beat LLaMA-2 70B (trained on 2 trillion tokens [rather than 0.001t words]) on 3⁄4 evals! How’d they do it?

  1. Flashy LTG-BERT architecture (Samuel et al 2023)

  2. Some small architecture mods

  3. Train for ~500 epochs 😱

They also won strict-small!

--- https://arxiv.org/abs/2311.03356 GLaMM: Pixel Grounding Large Multimodal Model Hanoona Rasheed, Muhammad Maaz, Sahal Shaji, Abdelrahman Shaker, Salman Khan, Hisham Cholakkal, Rao M. Anwer, Erix Xing, Ming-Hsuan Yang, Fahad S. Khan 2023-11-06 2023-11-06 [("doi","10.48550/arXiv.2311.03356")] ai/dataset ai/nn/transformer

[homepage] Large Multimodal Models (LMMs) extend Large Language Models to the vision domain. Initial efforts towards LMMs used holistic images and text prompts to generate ungrounded textual responses. Very recently, region-level LMMs have been used to generate visually grounded responses. However, they are limited to only referring a single object category at a time, require users to specify the regions in inputs, or cannot offer dense pixel-wise object grounding.

In this work, we present Grounding LMM (GLaMM), the first model that can generate natural language responses seamlessly intertwined with corresponding object segmentation masks. GLaMM not only grounds objects appearing in the conversations but is flexible enough to accept both textual and optional visual prompts (region of interest) as input. This empowers users to interact with the model at various levels of granularity, both in textual and visual domains.

Due to the lack of standard benchmarks for the novel setting of generating visually grounded detailed conversations, we introduce a comprehensive evaluation protocol with our curated grounded conversations. Our proposed Grounded Conversation Generation (GCG) task requires densely grounded concepts in natural scenes at a large-scale. To this end, we propose a densely annotated Grounding-anything Dataset (GranD) using our proposed automated annotation pipeline that encompasses 7.5M unique concepts grounded in a total of 810M regions available with segmentation masks. Besides GCG, GLaMM also performs effectively on several downstream tasks eg. referring expression segmentation, image and region-level captioning and vision-language conversations.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611992/ Effect of Time-Restricted Eating on Weight Loss in Adults With Type 2 Diabetes: A Randomized Clinical Trial Vasiliki Pavlou, Sofia Cienfuegos, Shuhao Lin, Mark Ezpeleta, Kathleen Ready, Sarah Corapi, Jackie Wu, Jason Lopez, Kelsey Gabel, Lisa Tussing-Humphreys, Vanessa M. Oddo, Shaina J. Alexandria, Julienne Sanchez, Terry Unterman, Lisa S. Chow, Alaina P. Vidmar, Krista A. Varady 2023 2023-07-12 [("doi","10.1001/jamanetworkopen.2023.39337")] longevity/fasting

Importance: Time-restricted eating (TRE) has become increasingly popular, yet longer-term randomized clinical trials have not evaluated its efficacy and safety in patients with type 2 diabetes (T2D).

Objective: To determine whether TRE is more effective for weight reduction and glycemic control than daily calorie restriction (CR) or a control condition in adults with T2D.

Methods, Setting, & Participants: This 6-month, parallel-group, randomized clinical trial was performed between January 25, 2022, and April 1, 2023, at the University of Illinois Chicago. Participants were aged 18–80 years with obesity and T2D. Data analysis was based on intention to treat.

Interventions: Participants were randomized to 1⁄3 groups: 8-hour TRE (eating 12 to 8 pm only, without calorie counting), CR (25% energy restriction daily), or control.

Main Outcomes & Measures: The primary outcome measure was change in body weight by month 6. Secondary outcomes included changes in hemoglobin A1c (HbA1c) levels and metabolic risk factors.

Results: Seventy-five participants were enrolled with a mean (SD) age of 55 (12) years. The mean (SD) body mass index (calculated as weight in kilograms divided by height in meters squared) was 39 (7) and the mean (SD) HbA1c level was 8.1% (1.6%). A total of 53 participants (71%) were women. One participant (1%) was Asian, 30 (40%) were Hispanic White, 40 (53%) were non-Hispanic Black, and 4 (5%) were non-Hispanic White. Participants in the TRE group were adherent with their eating window on a mean (SD) of 6.1 (0.8) days per week, and 17 (68%) in the CR group were adherent with their prescribed calorie goals over 6 months.

The mean (SD) reduction in energy intake was −313 (509) kcal/d for TRE, −197 (426) kcal/d for CR, and −16 (439) kcal/d for controls. By month 6, body weight decreased statistically-significantly in the TRE group (-3.56% [95% CI, −5.92% to −1.20%]; p = 0.004) but not the CR group (-1.78% [95% CI, −3.67% to 0.11%]; p = 0.06), relative to controls.

Levels of HbA1c decreased in the TRE (-0.91% [95% CI, −1.61% to −0.20%]) and CR (-0.94% [95% CI, −1.59% to −0.30%]) groups, relative to controls, with no differences between the TRE and CR groups. Time in eu-glycemic range, medication effect score, blood pressure, and plasma lipid levels did not differ among groups. No serious adverse events were reported.

Conclusion: This randomized clinical trial found that a TRE diet strategy without calorie counting was effective for weight loss and lowering of HbA1c levels compared with daily calorie counting in a sample of adults with T2D. These findings will need to be confirmed by larger RCTs with longer follow-up.

Trial Registration: ClinicalTrials.gov Identifier:NCT05225337.

--- https://arxiv.org/abs/2301.11796 The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus Alex Warstadt, Leshem Choshen, Aaron Mueller, Adina Williams, Ethan Wilcox, Chengxu Zhuang 2023-01-27 2023-07-12 [("doi","10.48550/arXiv.2301.11796")] ai/dataset ai/nn/transformer

We present the call for papers for the BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus. This shared task is intended for participants with an interest in small scale language modeling, human language acquisition, low-resource NLP, and cognitive modeling. In partnership with CoNLL and CMCL, we provide a platform for approaches to pretraining with a limited-size corpus sourced from data inspired by the input to children.

The task has 3 tracks, two of which restrict the training data to pre-released datasets of 10M and 100M words and are dedicated to explorations of approaches such as architectural variations, self-supervised objectives, or curriculum learning. The final track only restricts the amount of text used, allowing innovation in the choice of the data, its domain, and even its modality (ie. data from sources other than text is welcome).

We will release a shared evaluation pipeline which scores models on a variety of benchmarks and tasks, including targeted syntactic evaluations and natural language understanding.

--- https://www.npr.org/2023/11/08/1209932614/jungle-gym-playground-monkey-bars-maths-hinton-fourth-dimension 2023-07-12 math psychology/novelty --- https://x.com/timd_ca/status/1713729441342603746 Tim Davison 2023-07-12 design/visualization --- https://www.nngroup.com/articles/the-need-for-speed-1997/ 2023-07-12 cs/js design economics/advertising --- https://progressforum.org/posts/5QpeG9tscYL5bJQef/did-medieval-peasants-have-more-vacation-time-than-us 2023-07-13 economics --- https://progressforum.org/posts/49tFxpkaxkQ748BAu/working-draft-of-getting-the-conditions-right-progress-in 2023-07-13 statistics/prediction technology --- https://progressforum.org/posts/y4kYmFhqmA6gxsu9Y/radical-energy-abundance 2023-07-13 economics/experience-curve --- https://investor.lilly.com/news-releases/news-release-details/fda-approves-lillys-zepboundtm-tirzepatide-chronic-weight 2023-07-13 longevity/glp/tirzepatide --- https://tynan.com/the-three-best-tea-houses-in-the-world/ 2023-07-13 tea --- https://en.wikipedia.org/wiki/Sad_clown_paradox Sad clown paradox 2023-07-13 psychiatry/bipolar/energy --- https://arxiv.org/abs/2311.03736 Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning Joseph Suárez, Phillip Isola, Kyoung Whan Choe, David Bloomin, Hao Xiang Li, Nikhil Pinnaparaju, Nishaanth Kanna, Daniel Scott, Ryan Sullivan, Rose S. Shuman, Lucas de Alcântara, Herbie Bradley, Louis Castricato, Kirsty You, Yuhao Jiang, Qimai Li, Jiaxin Chen, Xiaolong Zhu 2023-11-07 2023-11-07 [("doi","10.48550/arXiv.2311.03736")] reinforcement-learning/multi-agent

Neural MMO 2.0 [1.0] is a massively multi-agent environment for reinforcement learning research. The key feature of this new version is a flexible task system that allows users to define a broad range of objectives and reward signals.

We challenge researchers to train agents capable of generalizing to tasks, maps, and opponents never seen during training. Neural MMO features procedurally generated maps with 128 agents in the standard setting and support for more. Version 2.0 is a complete rewrite of its predecessor with 3x improved performance and compatibility with Clean RL.

We release the platform as free and open-source software with comprehensive documentation available at our homepage and an active community Discord.

To spark initial research on this new platform, we are concurrently running a competition at NeurIPS 2023.

--- https://x.com/GregKamradt/status/1722386725635580292 Greg Kamradt 2023-07-13 ai/nn/transformer/attention ai/nn/transformer/gpt/4/nonfiction --- https://arxiv.org/abs/2310.06301 Dynamical versus Bayesian Phase Transitions in a Toy Model of Superposition Zhongtian Chen, Edmund Lau, Jake Mendel, Susan Wei, Daniel Murfet 2023-10-10 2023-10-10 [("doi","10.48550/arXiv.2310.06301")] ai/nn ai/scaling statistics/bayes

[blog] We investigate phase transitions in a Toy Model of Superposition (TMS) using Singular Learning Theory (SLT).

We derive a closed formula for the theoretical loss and, in the case of two hidden dimensions, discover that regular k-gons are critical points.

We present supporting theory indicating that the local learning coefficient (a geometric invariant) of these k-gons determines phase transitions in the Bayesian posterior as a function of training sample size.

We then show empirically that the same k-gon critical points also determine the behavior of SGD training.

The picture that emerges adds evidence to the conjecture that the SGD learning trajectory is subject to a sequential learning mechanism. Specifically, we find that the learning process in TMS, be it through SGD or Bayesian learning, can be characterized by a journey through parameter space from regions of high loss and low complexity to regions of low loss and high complexity.

--- https://arxiv.org/abs/2311.04145#alibaba I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models Shiwei Zhang, Jiayu Wang, Yingya Zhang, Kang Zhao, Hangjie Yuan, Zhiwu Qin, Xiang Wang, Deli Zhao, Jingren Zhou 2023-11-07 2023-11-07 [("doi","10.48550/arXiv.2311.04145")] ai/nn/diffusion ai/scaling ai/video/generation

[samples/models; cf. Parti/Phenaki from >1.5 years prior] Video synthesis has recently made remarkable strides benefiting from the rapid development of diffusion models. However, it still encounters challenges in terms of semantic accuracy, clarity, and spatio-temporal continuity. They primarily arise from the scarcity of well-aligned text-video data and the complex inherent structure of videos, making it difficult for the model to simultaneously ensure semantic and qualitative excellence.

In this report, we propose a cascaded I2VGen-XL approach that enhances model performance by decoupling these two factors and ensures the alignment of the input data by using static images as a form of crucial guidance. I2VGen-XL consists of two stages: (1) the base stage guarantees coherent semantics and preserves content from input images by using two hierarchical encoders, and (2) the refinement stage enhances the video’s details by incorporating an additional brief text and improves the resolution to 1280×720.

To improve the diversity, we collect around 35 million single-shot text-video pairs and 6 billion text-image pairs to optimize the model. [No details on how much training was done or on what.] By this means, I2VGen-XL can simultaneously enhance the semantic accuracy, continuity of details, and clarity of generated videos.

Through extensive experiments, we have investigated the underlying principles of I2VGen-XL and compared it with current top methods, which can demonstrate its effectiveness on diverse data.

--- https://www.theregister.com/2023/11/07/bing_gpu_oracle/ 2023-07-14 ai/scaling/economics ai/scaling/hardware --- https://github.blog/2023-11-08-universe-2023-copilot-transforms-github-into-the-ai-powered-developer-platform/ 2023-07-14 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://blog.mentat.ai/benchmarking-gpt-4-turbo-a-cautionary-tale 2023-07-14 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://www.wired.com/2002/10/the-mac-os-that-cant-be-tweaked/ 2023-07-14 design --- https://x.com/swyx/status/1722441535235768372 Shawn Wang 2023-07-14 ai/nn/transformer/attention ai/nn/transformer/gpt/4 --- https://arxiv.org/abs/2311.02382 LSS Transformer: Ultra-Long Sequence Distributed Transformer Xiao Wang, Isaac Lyngaas, Aristeidis Tsaris, Peng Chen, Sajal Dash, Mayanka Chandra Shekar, Tao Luo, Hong-Jun Yoon, Mohamed Wahib, John Gouley 2023-11-04 2023-11-04 [("doi","10.48550/arXiv.2311.02382")] ai/nn/transformer/attention ai/scaling/hardware

[Oak Ridge] Transformer models trained on long sequences often achieve higher accuracy than short sequences. Unfortunately, conventional transformers struggle with long sequence training due to the overwhelming computation and memory requirements. Existing methods for long sequence training offer limited speedup and memory reduction, and may compromise accuracy.

This paper presents a novel and efficient distributed training method, the Long Short-Sequence Transformer (LSS Transformer), for training transformer with long sequences. It distributes a long sequence into segments among GPUs, with each GPU computing a partial self-attention for its segment. Then, it uses a fused communication and a novel double gradient averaging technique to avoid the need to aggregate partial self-attention and minimize communication overhead.

We evaluated the performance between LSS Transformer and the state-of-the-art Nvidia sequence parallelism on a Wikipedia enwik8 dataset. Results show that our proposed method lead to 5.6× faster and 10.2× more memory-efficient implementation compared to state-of-the-art sequence parallelism on 144 Nvidia V100 GPUs. Moreover, our algorithm scales to an extreme sequence length of 50,112 at 3,456 GPUs [on the Summit supercomputer], achieving 161% super-linear parallel efficiency and a throughput of 32 petaflops.

--- /doc/psychiatry/bipolar/2023-millett.pdf Defining Heterogeneous Cognitive Trajectories in Bipolar Disorder: A Perspective Caitlin E. Millett, Katherine E. Burdick 2021-07-01 2023-07-14 [("doi","10.1097/HRP.0000000000000297")] iq psychiatry/bipolar

Bipolar disorder (BD) is a highly disabling mental illness that affects ~1% of the global population. Cognitive capacity is a strong predictor of “everyday” functional outcome in BD and should thus be considered a key treatment target. Interventions to improve cognition have been largely unsuccessful, likely due to the substantial heterogeneity inherent to the illness.

It is known that 40%–60% of people with BD have cognitive impairment, yet impairment is not “one size fits all”; in fact, the literature supports discrete cognitive subtypes in BD (eg. intact, globally impaired, and selectively impaired).

Gaining a better understanding of these cognitive subtypes, their longitudinal trajectories, and their biological underpinnings will be essential for improving patient outcomes. The prevailing hypothesis for the development of cognitive impairment in BD postulates a stepwise cumulative effect of repeated mood episodes causing wear-and-tear on the brain.

However, a paucity of data supports this idea at the group level. We propose that studying heterogeneity longitudinally will allow for clearer delineation of the natural history of cognitive trajectories in BD.

In sum, parsing heterogeneity in BD will allow us to identify causal mechanisms and optimize treatment at the level of the individual.

--- https://arxiv.org/abs/2311.03079#zhipu CogVLM: Visual Expert for Pretrained Language Models Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Xixuan Song, Jiazheng Xu, Bin Xu, Juanzi Li, Yuxiao Dong, Ming Ding, Jie Tang 2023-11-06 2023-11-06 [("doi","10.48550/arXiv.2311.03079")] ai/nn/transformer

[commentary] We introduce CogVLM, a powerful open-source visual language foundation model.

Different from the popular shallow alignment method which maps image features into the input space of language model, CogVLM bridges the gap between the frozen pretrained language model and image encoder by a trainable visual expert module in the attention and FFN layers. As a result, CogVLM enables deep fusion of vision language features without sacrificing any performance on NLP tasks.

CogVLM-17B achieves state-of-the-art performance on 10 classic cross-modal benchmarks, including Nocaps, Flicker30k captioning, RefCOCO, RefCOCO+, RefCOCOg, Visual7W, GQA, ScienceQA, VizWiz VQA and TDIUC, and ranks the 2nd on VQAv2, OKVQA, TextVQA, COCO captioning, etc., surpassing or matching PaLI-X 55B.

Codes and checkpoints are available at Github.

--- https://arxiv.org/abs/2306.04930#microsoft When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming (CDHF) Hussein Mozannar, Gagan Bansal, Adam Fourney, Eric Horvitz 2023-06-08 2023-07-14 [("doi","10.48550/arXiv.2306.04930")] ai/nn/transformer/gpt/codex reinforcement-learning/model

[Twitter] AI powered code-recommendation systems, such as Copilot and CodeWhisperer, provide code suggestions inside a programmer’s environment (eg. an IDE) with the aim to improve their productivity. Since, in these scenarios, programmers accept and reject suggestions, ideally, such a system should use this feedback in furtherance of this goal.

In this work, we leverage prior data of programmers interacting with GitHub Copilot, a system used by millions of programmers, to develop interventions that can save programmer time. We propose a utility theory framework, which models this interaction with programmers and decides which suggestions to display.

Our framework Conditional Suggestion Display from Human Feedback (CDHF), relies on a cascade of models that predict suggestion acceptance to selectively hide suggestions, reducing both latency and programmer verification time.

Using data from 535 programmers, we perform a retrospective evaluation of CDHF and show that we can avoid displaying a fraction of suggestions that would have been rejected, doing so without total knowledge of the suggestions themselves.

We further demonstrate the importance of incorporating the programmer’s latent unobserved state in deciding when to display suggestions through ablations on user study data.

Finally, we showcase that using suggestion acceptance as a reward signal to know which suggestions to display leads to reduced quality suggestions, indicating an unexpected pitfall [of reward hacking].

--- https://en.wikipedia.org/wiki/Kuleshov_effect Kuleshov effect 2023-07-14 psychology/vision --- /doc/science/2018-kaiser.pdf The Price of Gravity: Private Patronage and the Transformation of Gravitational Physics after World War II David Kaiser, Dean Rickles 2018-06-01 2023-07-14 [("doi","10.1525/hsns.2018.48.3.338")] science

[blog; cf. Rickles 2021] This paper examines how various private patrons intervened to support research in gravitational physics from the late 1940s through the early 1960s.

Our analysis centers primarily on two wealthy and eccentric businessmen, Roger Babson and Agnew Bahnson, and their efforts to galvanize the study of gravitation. Not only did these patrons provide generous funding at a time when the subject of gravitation received few other institutional sources of support; they also helped to knit together a research community. Moreover, we trace the evolution of their patronage efforts, as scientists and patrons revised their arrangements to address what came to seem weak or ineffective features of the original efforts.

These unusual philanthropic efforts played an outsized role in spurring what has been called the "renaissance of general relativity" during the middle decades of the twentieth century.

[Keywords: general relativity, private patronage, Cold War, anti-gravity, Roger Babson, Agnew Bahnson, Gravity Research Foundation, Institute of Field Physics, John Wheeler, Bryce & Cécile DeWitt-Morette]

--- https://daily.jstor.org/when-gravity-sucked-according-to-the-plutocrats/ 2023-07-15 science --- /doc/science/2021-rickles.pdf Behind the scenes of the 1957 Chapel Hill Conference on the role of gravitation in physics Dean Rickles 2021-12 2023-07-15 [("doi","10.3316/informit.307978714363106")] science

This brief paper will peel back some layers of the Chapel Hill conference, to look at its somewhat surprising origins in private philanthropy, motivated by the promise of new technologies based on anti-gravity or gravity control.

We are fortunate to have a very full historical record of the conference and the so-called Institute of Field Physics that hosted it—the account I present here is a highly abridged version of the more detailed accounts given in Kaiser & Rickles 2018 and Rickles (forthcoming).

--- https://x.com/David_Kasten/status/1718740117530014179 David Kasten 2023-07-15 ai/nn/transformer/gpt/dall-e/3 economics/copyright --- https://www.politico.com/news/magazine/2023/11/02/bruce-reed-ai-biden-tech-00124375 2023-07-15 politics reinforcement-learning/safe --- https://x.com/DanielColson6/status/1702319218895868305 Daniel Colson 2023-07-15 politics reinforcement-learning/safe --- https://x.com/patio11/status/1721722777705603432 Patrick McKenzie 2023-07-15 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex law --- https://x.com/TheStalwart/status/1720475482171253104 TheStalwart 2023-07-15 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/parisba/status/1719523035450167535 Paris Buttfield-Addison 2023-07-15 ai/nn/transformer/gpt cs/security --- https://arxiv.org/abs/2311.00176#nvidia ChipNeMo: Domain-Adapted LLMs for Chip Design Mingjie Liu, Teo Ene, Robert Kirby, Chris Cheng, Nathaniel Pinckney, Rongjian Liang, Jonah Alben, Himyanshu Anand, Sanmitra Banerjee, Ismet Bayraktaroglu, Bonita Bhaskaran, Bryan Catanzaro, Arjun Chaudhuri, Sharon Clay, Bill Dally, Laura Dang, Parikshit Deshpande, Siddhanth Dhodhi, Sameer Halepete, Eric Hill, Jiashang Hu, Sumit Jain, Brucek Khailany, Kishor Kunal, Xiaowei Li, Hao Liu, Stuart Oberman, Sujeet Omar, Sreedhar Pratty, Ambar Sarkar, Zhengjiang Shao, Hanfei Sun, Pratik P. Suthar, Varun Tej, Kaizhe Xu, Haoxing Ren 2023-10-31 2023-10-31 [("doi","10.48550/arXiv.2311.00176")] ai/nn/retrieval ai/nn/tokenization ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/instruction-tuning ai/scaling/hardware

ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design. Instead of directly deploying off-the-shelf commercial or open-source LLMs, we instead adopt the following domain adaptation techniques: custom tokenizers, domain-adaptive continued pretraining, supervised fine-tuning (SFT) with domain-specific instructions, and domain-adapted retrieval models.

We evaluate these methods on 3 selected LLM applications for chip design: an engineering assistant chatbot, EDA script generation, and bug summarization and analysis. Our results show that these domain adaptation techniques enable LLM performance improvements over general-purpose base models across the 3 evaluated applications, enabling up to 5× model size reduction with similar or better performance on a range of design tasks.

Our findings also indicate that there’s still room for improvement between our current results and ideal outcomes. We believe that further investigation of domain-adapted LLM approaches will help close this gap in the future.

--- https://www.cerias.purdue.edu/site/blog/post/reflecting_on_the_internet_worm_at_35/ 2023-07-15 cs/security --- https://www.lesswrong.com/posts/nuJFTS5iiJKT5G5yh/polysemantic-attention-head-in-a-4-layer-transformer 2023-07-15 ai/nn/transformer/attention --- https://www.nytimes.com/2023/11/09/climate/direct-air-capture-carbon.html 2023-07-16 technology/carbon-capture --- https://buttondown.com/hillelwayne/archive/syntax-highlighting-is-a-waste-of-an-information/ 2023-07-16 design/typography --- /doc/ai/anime/danbooru/2023-wang-5.pdf Region Assisted Sketch Colorization Ning Wang, Muyao Niu, Zhihui Wang, Kun Hu, Bin Liu, Zhiyong Wang, Haojie Li 2023-10-31 2023-10-31 [("doi","10.1109/TIP.2023.3326682")] ai/anime/danbooru ai/nn/gan --- https://x.com/IntuitMachine/status/1722727424947859896 IntuitMachine 2023-07-16 ai/nn/retrieval ai/nn/transformer/attention/hierarchical --- https://dynomight.net/air/ 2023-07-16 co2 --- https://arxiv.org/abs/2311.00863 Training Dynamics of Contextual N-Grams in Language Models Lucia Quirke, Lovis Heindrich, Wes Gurnee, Neel Nanda 2023-11-01 2023-11-01 [("doi","10.48550/arXiv.2311.00863")] ai/nn/transformer/attention ai/scaling/emergence

Prior work has shown the existence of contextual neurons in language models, including a neuron that activates on German text. We show that this neuron exists within a broader contextual n-gram circuit: we find late layer neurons which recognize and continue n-grams common in German text, but which only activate if the German neuron is active.

We investigate the formation of this circuit throughout training and find that it is an example of what we call a second-order circuit. In particular, both the constituent n-gram circuits and the German detection circuit which culminates in the German neuron form with independent functions early in training—the German detection circuit partially through modeling German unigram statistics, and the n-grams by boosting appropriate completions. Only after both circuits have already formed do they fit together into a second-order circuit.

Contrary to the hypotheses presented in prior work, we find that the contextual n-gram circuit forms gradually rather than in a sudden phase transition.

We further present a range of anomalous observations such as a simultaneous phase transition in many tasks coinciding with the learning rate warm-up, and evidence that many context neurons form simultaneously early in training but are later unlearned.

--- https://fullfrontal.moe/animes-present-and-future-interview-with-terumi-nishii-and-ayano-fukumiya/ 2023-07-16 anime economics/automation --- https://bioone.org/journals/Journal-of-Ethnobiology/volume-37/issue-4/0278-0771-37.4.700/Intentional-Fire-Spreading-by-Firehawk-Raptors-in-Northern-Australia/10.2993/0278-0771-37.4.700.full 2023-07-16 psychology/animal/bird --- https://x.com/dissproportion/status/1722847859505017303 dissproportion 2023-07-16 economics/mechanism-design --- https://en.wikipedia.org/wiki/Divide_and_choose Divide and choose 2023-07-16 economics/mechanism-design --- https://steamtraen.blogspot.com/2023/11/attack-of-50-foot-research-assistants.html 2023-07-17 statistics/bias --- https://www.newyorker.com/culture/annals-of-inquiry/the-man-who-invented-fifteen-hundred-necktie-knots 2023-07-17 math sociology/technology --- https://www.chocolatehammer.org/?p=5773 2023-07-17 fiction/text-game reinforcement-learning/multi-agent --- https://web.archive.org/web/20150525235536/http://www.bloodbathsoftworks.com/xylemon/xlennart.php 2023-07-17 cs --- https://arxiv.org/abs/2310.12442#amazon Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer Qingru Zhang, Dhananjay Ram, Cole Hawkins, Sheng Zha, Tuo Zhao 2023-10-19 2023-10-19 [("doi","10.48550/arXiv.2310.12442")] ai/nn/transformer/attention/hierarchical

Pretrained transformer models have demonstrated remarkable performance across various natural language processing tasks. These models leverage the attention mechanism to capture long-range & short-range dependencies in the sequence. However, the (full) attention mechanism incurs high computational cost—quadratic in the sequence length, which is not affordable in tasks with long sequences, eg. inputs with 8k tokens.

Although sparse attention can be used to improve computational efficiency, as suggested in existing work, it has limited modeling capacity and often fails to capture complicated dependencies in long sequences.

To tackle this challenge, we propose MASFormer, an easy-to-implement transformer variant with Mixed Attention Spans. Specifically, MASFormer is equipped with full attention to capture long-range dependencies, but only at a small number of layers. For the remaining layers, MASformer only employs sparse attention to capture short-range dependencies.

Our experiments on natural language modeling and generation tasks show that a decoder-only MASFormer model of 1.3b parameters can achieve competitive performance to vanilla transformers with full attention while reducing computational cost (<75%).

Additionally, we investigate the effectiveness of continual training with long sequence data and how sequence length impacts downstream generation performance, which may be of independent interest.

--- https://plato.stanford.edu/entries/ramsey/ 2023-07-17 statistics/decision --- https://www.sciencedirect.com/science/article/pii/S0749597823000560 2023-07-17 economics psychology/cognitive-bias --- https://arxiv.org/abs/2310.12036#deepmind A General Theoretical Paradigm to Understand Learning from Human Preferences Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos 2023-10-18 2023-10-18 [("doi","10.48550/arXiv.2310.12036")] reinforcement-learning/preference-learning statistics/order/comparison

The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards. The second assumes that a reward model trained on these pointwise rewards can generalize from collected data to out-of-distribution data sampled by the policy. Recently, Direct Preference Optimization (DPO) has been proposed as an approach that bypasses the second approximation and learn directly a policy from collected data without the reward modeling stage. However, this method still heavily relies on the first approximation.

In this paper we try to gain a deeper theoretical understanding of these practical algorithms. In particular we derive a new general objective called ΨPO, for learning from human preferences that is expressed in terms of pairwise preferences and therefore bypasses both approximations. This new general objective allows us to perform an in-depth analysis of the behavior of RLHF and DPO (as special cases of ΨPO) and to identify their potential pitfalls.

We then consider another special case for ΨPO by setting Ψ simply to Identity, for which we can derive an efficient optimization procedure, prove performance guarantees and demonstrate its empirical superiority to DPO on some illustrative examples.

--- https://arxiv.org/abs/2311.05584 Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations Joey Hong, Sergey Levine, Anca Dragan 2023-11-09 2023-11-09 [("doi","10.48550/arXiv.2311.05584")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/inner-monologue reinforcement-learning/exploration reinforcement-learning/model reinforcement-learning/offline

[Twitter] Large language models (LLMs) have emerged as powerful and general solutions to many natural language tasks. However, many of the most important applications of language generation are interactive, where an agent has to talk to a person to reach a desired outcome. For example, a teacher might try to understand their student’s current comprehension level to tailor their instruction accordingly, and a travel agent might ask questions of their customer to understand their preferences in order to recommend activities they might enjoy. LLMs trained with supervised fine-tuning or “single-step” RL, as with standard RLHF, might struggle with tasks that require such goal-directed behavior, since they are not trained to optimize for overall conversational outcomes after multiple turns of interaction.

In this work, we explore a new method for adapting LLMs with RL for such goal-directed dialogue. Our key insight is that, though LLMs might not effectively solve goal-directed dialogue tasks out of the box, they can provide useful data for solving such tasks by simulating suboptimal but human-like behaviors. Given a textual description of a goal-directed dialogue task, we leverage LLMs to sample diverse synthetic rollouts of hypothetical in-domain human-human interactions.

Our algorithm then uses this dataset with offline reinforcement learning [ILQL] to train an interactive conversational agent that can optimize goal-directed objectives over multiple turns. In effect, the LLM produces examples of possible interactions, and RL then processes these examples to learn to perform more optimal interactions.

Empirically, we show that our proposed approach achieves state-of-the-art performance in various goal-directed dialogue tasks that include teaching and preference elicitation.

--- https://arxiv.org/abs/2206.11871 Offline RL for Natural Language Generation with Implicit Language Q Learning Charlie Snell, Ilya Kostrikov, Yi Su, Mengjiao Yang, Sergey Levine 2022-06-05 2023-07-17 [("doi","10.48550/arXiv.2206.11871")] ai/nn/transformer/gpt/2 reinforcement-learning/model-free reinforcement-learning/offline

[cf. Silver & Veness 2010] Large language models distill broad knowledge from text corpora. However, they can be inconsistent when it comes to completing user specified tasks. This issue can be addressed by finetuning such models via supervised learning on curated datasets, or via reinforcement learning.

In this work, we propose a novel offline RL method, implicit language Q-learning (ILQL), designed for use on language models, that combines both the flexible utility maximization framework of RL algorithms with the ability of supervised learning to leverage previously collected data, as well as its simplicity and stability.

Our method employs a combination of value conservatism alongside an implicit dataset support constraint in learning value functions, which are then used to guide [GPT-2] language model generations towards maximizing user-specified utility functions.

In addition to empirically validating ILQL, we present a detailed empirical analysis of situations where offline RL can be useful in natural language generation settings, demonstrating how it can be a more effective utility optimizer than prior approaches for end-to-end dialogue, and how it can effectively optimize high variance reward functions based on subjective judgement, such as whether to label a comment as toxic or not.

--- https://www.nature.com/articles/srep22559 2023-07-17 cat/psychology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4545751/ Feral Cats Are Better Killers in Open Habitats, Revealed by Animal-Borne Video Hugh McGregor, Sarah Legge, Menna E. Jones, Christopher N. Johnson 2015 2023-07-18 [("doi","10.1371/journal.pone.0133915")] cat/psychology

One of the key gaps in understanding the impacts of predation by small mammalian predators on prey is how habitat structure affects the hunting success of small predators, such as feral cats. These effects are poorly understood due to the difficulty of observing actual hunting behaviors. We attached collar-mounted video cameras to feral cats living in a tropical savanna environment in northern Australia, and measured variation in hunting success among different microhabitats (open areas, dense grass and complex rocks).

From 89 hours of footage, we recorded 101 hunting events, of which 32 were successful. Of these kills, 28% were not eaten. Hunting success was highly dependent on microhabitat structure surrounding prey, increasing from 17% in habitats with dense grass or complex rocks to 70% in open areas.

This research shows that habitat structure has a profound influence on the impacts of small predators on their prey. This has broad implications for management of vegetation and disturbance processes (like fire and grazing) in areas where feral cats threaten native fauna. Maintaining complex vegetation cover can reduce predation rates of small prey species from feral cat predation.

--- https://arxiv.org/abs/2311.05020 First Tragedy, then Parse: History Repeats Itself in the New Era of Large Language Models Naomi Saphra, Eve Fleisig, Kyunghyun Cho, Adam Lopez 2023-11-08 2023-11-08 [("doi","10.48550/arXiv.2311.05020")] ai/scaling

Many NLP researchers are experiencing an existential crisis triggered by the astonishing success of ChatGPT and other systems based on large language models (LLMs). After such a disruptive change to our understanding of the field, what is left to do?

Taking a historical lens, we look for guidance from the first era of LLMs, which began in 2005 with large n-gram models for machine translation.

We identify durable lessons from the first era, and more importantly, we identify evergreen problems where NLP researchers can continue to make meaningful contributions in areas where LLMs are ascendant.

Among these lessons, we discuss the primacy of hardware advancement in shaping the availability and importance of scale, as well as the urgent challenge of quality evaluation, both automated and human.

We argue that disparities in scale are transient and that researchers can work to reduce them; that data, rather than hardware, is still a bottleneck for many meaningful applications; that meaningful evaluation informed by actual use is still an open problem; and that there is still room for speculative approaches.

--- https://cloud.google.com/blog/products/compute/the-worlds-largest-distributed-llm-training-job-on-tpu-v5e 2023-07-18 ai/scaling/hardware --- https://arxiv.org/abs/2306.14610 SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality Cheng-Yu Hsieh, Jieyu Zhang, Zixian Ma, Aniruddha Kembhavi, Ranjay Krishna 2023-06-26 2023-07-18 [("doi","10.48550/arXiv.2306.14610")] ai/dataset ai/nn/transformer/clip

In the last year alone, a surge of new benchmarks to measure compositional understanding of vision-language models have permeated the machine learning ecosystem. Given an image, these benchmarks probe a model’s ability to identify its associated caption amongst a set of compositional distractors.

Surprisingly, we find biases in all these benchmarks rendering them hackable. This hackability is so dire that blind models with no access to the image outperform state-of-the-art vision-language models.

To remedy this rampant vulnerability, we introduce SugarCrepe, a new benchmark for vision-language compositionality evaluation. We employ large language models, instead of rule-based templates used in previous benchmarks, to generate fluent and sensical hard negatives, and use an adversarial refinement mechanism to maximally reduce biases.

We re-evaluate state-of-the-art models and recently proposed compositionality inducing strategies, and find that their improvements were hugely overestimated, suggesting that more innovation is needed in this important direction. We release SugarCrepe and the code for evaluation at: Github.

--- https://arxiv.org/abs/2306.07915#deepmind Image Captioners Are Scalable Vision Learners Too Michael Tschannen, Manoj Kumar, Andreas Steiner, Xiaohua Zhai, Neil Houlsby, Lucas Beyer 2023-06-13 2023-07-18 [("doi","10.48550/arXiv.2306.07915")] ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e/1 ai/scaling

Contrastive pretraining on image-text pairs from the web is one of the most popular large-scale pretraining strategies for vision backbones, especially in the context of large multimodal models. At the same time, image captioning on this type of data is commonly considered an inferior pretraining strategy.

In this paper, we perform a fair comparison of these two pretraining strategies, carefully matching training data, compute, and model capacity. Using a standard encoder-decoder transformer, we find that captioning alone is surprisingly effective: on classification tasks, captioning produces vision encoders competitive with contrastively pretrained encoders, while surpassing them on vision & language tasks.

We further analyze the effect of the model architecture and scale, as well as the pretraining data on the representation quality, and find that captioning exhibits the same or better scaling behavior along these axes. Overall our results show that plain image captioning is a more powerful pretraining strategy than was previously believed.

--- https://www.lesswrong.com/posts/tbJdxJMAiehewGpq2/impressions-from-base-gpt-4 2023-07-18 ai/nn/transformer/gpt/4 reinforcement-learning/preference-learning/mode-collapse --- https://www.lesswrong.com/posts/nxhXTfsAf2LTg4xvt/artefacts-generated-by-mode-collapse-in-gpt-4-turbo-serve-as 2023-07-18 ai/nn/adversarial ai/nn/transformer/gpt/4 --- https://arxiv.org/abs/2310.13014 Large Language Model Prediction Capabilities: Evidence from a Real-World Forecasting Tournament Philipp Schoenegger, Peter S. Park 2023-10-17 2023-10-17 [("doi","10.48550/arXiv.2310.13014")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/calibration statistics/prediction

Accurately predicting the future would be an important milestone in the capabilities of artificial intelligence. However, research on the ability of large language models to provide probabilistic predictions about future events remains nascent. To empirically test this ability, we enrolled OpenAI’s state-of-the-art large language model, GPT-4, in a three-month forecasting tournament hosted on the Metaculus platform. The tournament, running from July to October 2023, attracted 843 participants and covered diverse topics including Big Tech, U.S. politics, viral outbreaks, and the Ukraine conflict.

Focusing on binary forecasts, we show that GPT-4’s probabilistic forecasts are less accurate than the median human-crowd forecasts. We find that GPT-4’s forecasts did not differ from the no-information forecasting strategy of assigning a 50% probability to every question.

We explore a potential explanation, that GPT-4 might be predisposed to predict probabilities close to the midpoint of the scale, but our data do not support this hypothesis. Overall, we find that GPT-4 underperforms in real-world predictive tasks compared to median human-crowd forecasts.

A potential explanation for this underperformance is that in real-world forecasting tournaments, the true answers are genuinely unknown at the time of prediction; unlike in other benchmark tasks like professional exams or time series forecasting, where strong performance may at least partly be due to the answers being memorized from the training data. This makes real-world forecasting tournaments an ideal environment for testing the generalized reasoning and prediction capabilities of artificial intelligence going forward.

--- https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/ 2023-07-18 ai/nn/adversarial ai/nn/transformer --- https://arxiv.org/abs/2004.07219 D4RL: Datasets for Deep Data-Driven Reinforcement Learning Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, Sergey Levine 2020-04-15 2023-07-18 [("doi","10.48550/arXiv.2004.07219")] ai/dataset reinforcement-learning/offline

[code] The offline reinforcement learning (RL) setting (also known as full batch RL), where a policy is learned from a static dataset, is compelling as progress enables RL methods to take advantage of large, previously-collected datasets, much like how the rise of large datasets has fueled results in supervised learning. However, existing online RL benchmarks are not tailored towards the offline setting and existing offline RL benchmarks are restricted to data generated by partially-trained agents, making progress in offline RL difficult to measure.

In this work, we introduce benchmarks specifically designed for the offline setting, guided by key properties of datasets relevant to real-world applications of offline RL. With a focus on dataset collection, examples of such properties include: datasets generated via hand-designed controllers and human demonstrators, multitask datasets where an agent performs different tasks in the same environment, and datasets collected with mixtures of policies. By moving beyond simple benchmark tasks and data collected by partially-trained RL agents, we reveal important and unappreciated deficiencies of existing algorithms.

To facilitate research, we have released our benchmark tasks and datasets with a comprehensive evaluation of existing algorithms, an evaluation protocol, and open-source examples. This serves as a common starting point for the community to identify shortcomings in existing offline RL methods and a collaborative route for progress in this emerging area.

--- https://arxiv.org/abs/2105.12034 Hyperparameter Selection for Imitation Learning Leonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Lukasz Stafiniak, Sertan Girgin, Raphael Marinier, Nikola Momchev, Sabela Ramos, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin 2021-05-25 2023-07-18 [("doi","10.48550/arXiv.2105.12034")] reinforcement-learning/imitation-learning

We address the issue of tuning hyperparameters (HPs) for imitation learning algorithms in the context of continuous-control, when the underlying reward function of the demonstrating expert cannot be observed at any time. The vast literature in imitation learning mostly considers this reward function to be available for HP selection, but this is not a realistic setting. Indeed, were this reward function available, it could then directly be used for policy training and imitation would not be necessary.

To tackle this mostly ignored problem, we propose a number of possible proxies to the external reward.

We evaluate them in an extensive empirical study (more than 10,000 agents across 9 environments) and make practical recommendations for selecting HPs.

Our results show that while imitation learning algorithms are sensitive to HP choices, it is often possible to select good enough HPs through a proxy to the reward function.

--- https://github.com/tinkoff-ai/CORL 2023-07-19 reinforcement-learning/offline --- https://arxiv.org/abs/2210.07105 CORL: Research-oriented Deep Offline Reinforcement Learning Library Denis Tarasov, Alexander Nikulin, Dmitry Akimov, Vladislav Kurenkov, Sergey Kolesnikov 2022-10-13 2023-07-19 [("doi","10.48550/arXiv.2210.07105")] reinforcement-learning/offline

CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms.

It emphasizes a simple developing experience with a straightforward codebase and a modern analysis tracking tool. In CORL, we isolate methods implementation into separate single files, making performance-relevant details easier to recognize. Additionally, an experiment tracking feature is available to help log metrics, hyperparameters, dependencies, and more to the cloud.

Finally, we have ensured the reliability of the implementations by benchmarking commonly employed D4RL datasets providing a transparent source of results that can be reused for robust evaluation tools such as performance profiles, probability of improvement, or expected online performance.

--- https://sites.google.com/view/offlinerltutorial-neurips2020/home 2023-07-19 reinforcement-learning/offline --- https://paperswithcode.com/task/offline-rl 2023-07-19 reinforcement-learning/offline --- https://github.com/hanjuku-kaso/awesome-offline-rl 2023-07-19 reinforcement-learning/offline --- https://arxiv.org/abs/2204.05618 When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning? Aviral Kumar, Joey Hong, Anikait Singh, Sergey Levine 2022-04-12 2023-07-19 [("doi","10.48550/arXiv.2204.05618")] reinforcement-learning/imitation-learning reinforcement-learning/offline

Offline reinforcement learning (RL) algorithms can acquire effective policies by using previously collected experience, without any online interaction. It is widely understood that offline RL is able to extract good policies even from highly suboptimal data, a scenario where imitation learning finds suboptimal solutions that do not improve over the demonstrator that generated the dataset.

However, another common use case for practitioners is to learn from data that resembles demonstrations. In this case, one can choose to apply offline RL, but can also use behavioral cloning (BC) algorithms, which mimic a subset of the dataset via supervised learning. Therefore, it seems natural to ask: when can an offline RL method outperform BC with an equal amount of expert data, even when BC is a natural choice?

To answer this question, we characterize the properties of environments that allow offline RL methods to perform better than BC methods, even when only provided with expert data. Additionally, we show that policies trained on sufficiently noisy suboptimal data can attain better performance than even BC algorithms with expert data, especially on long-horizon problems.

We validate our theoretical results via extensive experiments on both diagnostic and high-dimensional domains including robotic manipulation, maze navigation, and Atari games, with a variety of data distributions. We observe that, under specific but common conditions such as sparse rewards or noisy data sources, modern offline RL methods can outperform BC.

--- https://github.com/Farama-Foundation/D4RL 2023-07-19 ai/dataset reinforcement-learning/imitation-learning reinforcement-learning/offline --- https://bair.berkeley.edu/blog/2022/04/25/rl-or-bc/ 2023-07-19 reinforcement-learning/imitation-learning reinforcement-learning/offline --- https://arxiv.org/abs/2010.11895 What are the Statistical Limits of Offline RL with Linear Function Approximation? Ruosong Wang, Dean P. Foster, Sham M. Kakade 2020-10-22 2023-07-19 [("doi","10.48550/arXiv.2010.11895")] reinforcement-learning/model reinforcement-learning/offline

Offline reinforcement learning seeks to use offline (observational) data to guide the learning of (causal) sequential decision making strategies. The hope is that offline reinforcement learning coupled with function approximation methods (to deal with the curse of dimensionality) can provide a means to help alleviate the excessive sample complexity burden in modern sequential decision making problems. However, the extent to which this broader approach can be effective is not well understood, where the literature largely consists of sufficient conditions.

This work focuses on the basic question of what are necessary representational and distributional conditions that permit provable sample-efficient offline reinforcement learning. Perhaps surprisingly, our main result shows that even if: (1) we have realizability in that the true value function of every policy is linear in a given set of features and (2) our off-policy data has good coverage over all features (under a strong spectral condition), then any algorithm still (information-theoretically) requires a number of offline samples that is exponential in the problem horizon in order to non-trivially estimate the value of any given policy. Our results highlight that sample-efficient offline policy evaluation is simply not possible unless stronger conditions hold; such conditions include either having low distribution shift (where the offline data distribution is close to the distribution of the policy to be evaluated) or stronger representational conditions (beyond realizability).

--- https://arxiv.org/abs/2106.06860 A Minimalist Approach to Offline Reinforcement Learning Scott Fujimoto, Shixiang Shane Gu 2021-06-12 2023-07-19 [("doi","10.48550/arXiv.2106.06860")] reinforcement-learning/imitation-learning reinforcement-learning/offline

Offline reinforcement learning (RL) defines the task of learning from a fixed batch of data. Due to errors in value estimation from out-of-distribution actions, most offline RL algorithms take the approach of constraining or regularizing the policy with the actions contained in the dataset.

Built on pre-existing RL algorithms, modifications to make an RL algorithm work offline comes at the cost of additional complexity. Offline RL algorithms introduce new hyperparameters and often leverage secondary components such as generative models, while adjusting the underlying RL algorithm.

In this paper we aim to make a deep RL algorithm work while making minimal changes. We find that we can match the performance of state-of-the-art offline RL algorithms by simply adding a behavior cloning term to the policy update of an online RL algorithm and normalizing the data. The resulting algorithm is a simple to implement and tune baseline, while more than halving the overall run time by removing the additional computational overhead of previous methods.

--- https://arxiv.org/abs/2005.13239v6 MOPO: Model-based Offline Policy Optimization Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, Tengyu Ma 2020-05-27 2023-07-19 [("doi","10.48550/arXiv.2005.13239")] reinforcement-learning/model reinforcement-learning/offline

Offline reinforcement learning (RL) refers to the problem of learning policies entirely from a large batch of previously collected data. This problem setting offers the promise of using such datasets to acquire policies without any costly or dangerous active exploration. However, it is also challenging, due to the distributional shift between the offline training data and those states visited by the learned policy.

Despite recent progress, the most successful prior methods are model-free and constrain the policy to the support of data, precluding generalization to unseen states. In this paper, we first observe that an existing model-based RL algorithm already produces gains in the offline setting compared to model-free approaches.

However, standard model-based RL methods, designed for the online setting, do not provide an explicit mechanism to avoid the offline setting’s distributional shift issue. Instead, we propose to modify the existing model-based RL methods by applying them with rewards artificially penalized by the uncertainty of the dynamics.

We theoretically show that the algorithm maximizes a lower bound of the policy’s return under the true MDP. We also characterize the trade-off between the gain and risk of leaving the support of the batch data.

Our algorithm, Model-based Offline Policy Optimization (MOPO), outperforms standard model-based RL algorithms and prior state-of-the-art model-free offline RL algorithms on existing offline RL benchmarks and two challenging continuous control tasks that require generalizing from data collected for a different task. The code is available at Github.

--- https://arxiv.org/abs/2206.01079 When does return-conditioned supervised learning work for offline reinforcement learning? David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, Joan Bruna 2022-06-02 2023-07-20 [("doi","10.48550/arXiv.2206.01079")] reinforcement-learning/model/decision-transformer reinforcement-learning/offline

Several recent works have proposed a class of algorithms for the offline reinforcement learning (RL) problem that we will refer to as return-conditioned supervised learning (RCSL). RCSL algorithms learn the distribution of actions conditioned on both the state and the return of the trajectory. Then they define a policy by conditioning on achieving high return.

In this paper, we provide a rigorous study of the capabilities and limitations of RCSL, something which is crucially missing in previous work.

We find that RCSL returns the optimal policy under a set of assumptions that are stronger than those needed for the more traditional dynamic programming-based algorithms. We provide specific examples of MDPs and datasets that illustrate the necessity of these assumptions and the limits of RCSL.

Finally, we present empirical evidence that these limitations will also cause issues in practice by providing illustrative experiments in simple point-mass environments and on datasets from the D4RL benchmark.

--- https://arxiv.org/abs/2211.15144 Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine 2022-11-28 2023-07-20 [("doi","10.48550/arXiv.2211.15144")] reinforcement-learning/offline reinforcement-learning/robot reinforcement-learning/scaling

The potential of offline reinforcement learning (RL) is that high-capacity models trained on large, heterogeneous datasets can lead to agents that generalize broadly, analogously to similar advances in vision and NLP. However, recent works argue that offline RL methods encounter unique challenges to scaling up model capacity.

Drawing on the learnings from these works, we re-examine previous design choices and find that with appropriate choices: ResNets, cross-entropy based distributional backups, and feature normalization, offline Q-learning algorithms exhibit strong performance that scales with model capacity.

Using multi-task Atari as a testbed for scaling and generalization, we train a single policy on 40 games with near-human performance using up-to 80 million parameter networks, finding that model performance scales favorably with capacity. In contrast to prior work, we extrapolate beyond dataset performance even when trained entirely on a large (400M transitions) but highly suboptimal dataset (51% human-level performance).

Compared to return-conditioned supervised approaches, offline Q-learning scales similarly with model capacity and has better performance, especially when the dataset is suboptimal.

Finally, we show that offline Q-learning with a diverse dataset is sufficient to learn powerful representations that facilitate rapid transfer to novel games and fast online learning on new variations of a training game, improving over existing state-of-the-art representation learning approaches.

--- https://arxiv.org/abs/2109.10813 A Workflow for Offline Model-Free Robotic Reinforcement Learning Aviral Kumar, Anikait Singh, Stephen Tian, Chelsea Finn, Sergey Levine 2021-09-22 2023-07-20 [("doi","10.48550/arXiv.2109.10813")] reinforcement-learning/offline reinforcement-learning/robot

Offline reinforcement learning (RL) enables learning control policies by using only prior experience, without any online interaction. This can allow robots to acquire generalizable skills from large and diverse datasets, without any costly or unsafe online data collection. Despite recent algorithmic advances in offline RL, applying these methods to real-world problems has proven challenging. Although offline RL methods can learn from prior data, there is no clear and well-understood process for making various design choices, from model architecture to algorithm hyperparameters, without actually evaluating the learned policies online.

In this paper, our aim is to develop a practical workflow for using offline RL analogous to the relatively well-understood workflows for supervised learning problems.

To this end, we devise a set of metrics and conditions that can be tracked over the course of offline training, and can inform the practitioner about how the algorithm and model architecture should be adjusted to improve final performance. Our workflow is derived from a conceptual understanding of the behavior of conservative offline RL algorithms and cross-validation in supervised learning.

We demonstrate the efficacy of this workflow in producing effective policies without any online tuning, both in several simulated robotic learning scenarios and for 3 tasks on two distinct real robots, focusing on learning manipulation skills with raw image observations with sparse binary rewards.

Explanatory video and additional results can be found at our homepage.

--- https://retractionwatch.com/2023/11/09/super-size-me-what-happened-when-marketing-researchers-ordered-a-double-retraction/ 2023-07-20 statistics/bias --- https://x.com/felix_red_panda/status/1723324786808692887 felix_red_panda 2023-07-20 ai/dataset --- https://osf.io/preprints/psyarxiv/fdu3m/ 2023-07-20 psychology/cognitive-bias/illusion-of-depth statistics/decision --- https://osf.io/preprints/psyarxiv/dm8xn 2023-07-20 statistics/bias --- https://fristartmuseum.org/1930-henderson-kj-streamline/ 2023-07-20 design --- https://en.wikipedia.org/wiki/Henderson_Motorcycle#1929_Henderson_Streamline_Model Henderson Motorcycle § 1929 Henderson Streamline Model 2023-07-20 design --- https://arxiv.org/abs/2311.05553 Removing RLHF Protections in GPT-4 via Fine-Tuning Qiusi Zhan, Richard Fang, Rohan Bindu, Akul Gupta, Tatsunori Hashimoto, Daniel Kang 2023-11-09 2023-11-09 [("doi","10.48550/arXiv.2311.05553")] reinforcement-learning/safe

As large language models (LLMs) have increased in their capabilities, so does their potential for dual use. To reduce harmful outputs, produces and vendors of LLMs have used reinforcement learning with human feedback (RLHF). In tandem, LLM vendors have been increasingly enabling fine-tuning of their most powerful models. However, concurrent work has shown that fine-tuning can remove RLHF protections. We may expect that the most powerful models currently available (GPT-4) are less susceptible to fine-tuning attacks.

In this work, we show the contrary: fine-tuning allows attackers to remove RLHF protections with as few as 340 examples and a 95% success rate. These training examples can be automatically generated with weaker models. We further show that removing RLHF protections does not decrease usefulness on non-censored outputs, providing evidence that our fine-tuning strategy does not decrease usefulness despite using weaker models to generate training data. Our results show the need for further research on protections on LLMs.

--- /doc/genetics/heritable/correlation/2023-gustavson.pdf Executive Function and Impulsivity Predict Distinct Genetic Variance in Internalizing Problems, Externalizing Problems, Thought Disorders, and Compulsive Disorders: A Genomic Structural Equation Modeling Study Daniel E. Gustavson, Claire L. Morrison, Travis T. Mallard, Mariela V. Jennings, Pierre Fontanillas, Sarah L. Elson, Abraham Palmer, Naomi P. Friedman, Sandra Sanchez-Roige 2023-11-09 2023-11-09 [("doi","10.1177/21677026231207845")] genetics/heritable/correlation iq psychiatry

Individual differences in self-control predict many health and life outcomes.

Building on twin literature, we used genomic structural equation modeling to test the hypothesis that genetic influences on executive function and impulsivity predict independent variance in mental health and other outcomes.

The impulsivity factor (comprising urgency, lack of premeditation, and other facets) was only modestly genetically correlated with low executive function (r = 0.13).

Controlling for impulsivity, we found that low executive function was genetically associated with increased internalizing (β = 0.15), externalizing (β = 0.13), thought disorders (β = 0.38), compulsive disorders (β = 0.22), and chronotype (β = 0.011). Controlling for executive function, we found that impulsivity was positively genetically associated with internalizing (β = 0.36), externalizing (β = 0.55), body mass index (β = 0.26), and insomnia (β = 0.35) and negatively genetically associated with compulsive disorders (β = −0.17).

Executive function and impulsivity were both genetically correlated with general cognitive ability and educational attainment. This work suggests that executive function and impulsivity are genetically separable and show independent associations with mental health.

--- https://en.wikipedia.org/wiki/Mao_(card_game) Mao (card game) 2023-07-21 philosophy/epistemology --- https://ai.nejm.org/doi/pdf/10.1056/AIp2300031 2023-07-21 ai/nn/transformer/gpt/4/nonfiction biology --- https://arxiv.org/abs/2311.05591 Accuracy of a Vision-Language Model on Challenging Medical Cases Thomas Buckley, James A. Diao, Adam Rodman, Arjun K. Manrai 2023-11-09 2023-11-09 [("doi","10.48550/arXiv.2311.05591")] ai/nn/transformer/gpt/4/nonfiction biology

Background: General-purpose large language models that use both text and images have not been evaluated on a diverse array of challenging medical cases.

Method: Using 934 cases from the NEJM Image Challenge published 2005–2023, we evaluated the accuracy of the recently released Generative Pre-trained Transformer 4 with Vision model (GPT-4-V) compared to human respondents overall and stratified by question difficulty, image type, and skin tone. We further conducted a physician evaluation of GPT-4-V on 69 NEJM clinicopathological conferences (CPCs). Analyses were conducted for models using text alone, images alone, and both text and images.

Results: GPT-4-V achieved an overall accuracy of 61% (95% CI, 58 to 64%) compared to 49% (95% CI, 49 to 50%) for humans. GPT-4-V outperformed humans at all levels of difficulty and disagreement, skin tones, and image types; the exception was radiographic images, where performance was equivalent between GPT-4-V and human respondents. Longer, more informative captions were associated with improved performance for GPT-4-V but similar performance for human respondents. GPT-4-V included the correct diagnosis in its differential for 80% (95% CI, 68 to 88%) of CPCs when using text alone, compared to 58% (95% CI, 45 to 70%) of CPCs when using both images and text.

Conclusion: GPT-4-V outperformed human respondents on challenging medical cases and was able to synthesize information from both images and text, but performance deteriorated when images were added to highly informative text. Overall, our results suggest that multimodal AI models may be useful in medical diagnostic reasoning but that their accuracy may depend heavily on context.

--- https://meridian.allenpress.com/thij/article/50/5/e238262/496426/Targeting-Cell-Senescence-to-Improve-Cardiac 2023-07-21 longevity/senolytic/d-q --- https://arxiv.org/abs/2311.03287 Holistic Analysis of Hallucination in GPT-4-V(ision): Bias and Interference Challenges Chenhang Cui, Yiyang Zhou, Xinyu Yang, Shirley Wu, Linjun Zhang, James Zou, Huaxiu Yao 2023-11-06 2023-11-06 [("doi","10.48550/arXiv.2311.03287")] ai/nn/transformer/gpt/4

While GPT-4-V(ision) impressively models both visual and textual information simultaneously, it’s hallucination behavior has not been systematically assessed. To bridge this gap, we introduce a new benchmark, namely, the Bias and Interference Challenges in Visual Language Models (Bingo). This benchmark is designed to evaluate and shed light on the two common types of hallucinations in visual language models: bias and interference.

Here, bias refers to the model’s tendency to hallucinate certain types of responses, possibly due to imbalance in its training data. Interference pertains to scenarios where the judgment of GPT-4-V(ision) can be disrupted due to how the text prompt is phrased or how the input image is presented.

We identify a notable regional bias, whereby GPT-4-V(ision) is better at interpreting Western images or images with English writing compared to images from other countries or containing text in other languages. Moreover, GPT-4-V(ision) is vulnerable to leading questions and is often confused when interpreting multiple images together.

Popular mitigation approaches, such as self-correction and chain-of-thought reasoning, are not effective in resolving these challenges. We also identified similar biases and interference vulnerabilities with LLaVA and Google Bard.

Our results characterize the hallucination challenges in GPT-4-V(ision) and state-of-the-art visual-language models, and highlight the need for new solutions.

The Bingo benchmark is available at Github.

--- https://en.wikipedia.org/wiki/Feline_infectious_peritonitis Feline infectious peritonitis 2023-07-21 cat/biology --- https://www.biorxiv.org/content/10.1101/2023.11.08.566182.full Emergence and spread of feline infectious peritonitis due to a highly pathogenic canine/feline recombinant coronavirus Charalampos Atippa, Amanda Susan Warr, Demetris Epaminondas, Marie O’Shea, Sarah Louise Fletcher, Alexandra Malbon, Maria Lyraki, Rachael Hammond, Alexandros Hardas, Antria Zanti, Stavroula Loukaidou, Michaela Gentil, Danielle Gunn-Moore, Stella Mazeri, Christine Tait-Burkard 2023-11-10 2023-11-10 [("doi","10.1101/2023.11.08.566182")] cat/biology

Cross-species transmission of coronaviruses (CoVs) poses a serious threat to both animal and human health. Whilst the large RNA genome of CoVs shows relatively low mutation rates, recombination within genera is frequently observed and demonstrated. Companion animals are often overlooked in the transmission cycle of viral diseases; however, the close relationship of feline (FCoV) and canine CoV (CCoV) to human hCoV-229E, as well as their susceptibility to SARS-CoV-2 highlight their importance in potential transmission cycles.

Whilst recombination between CCoV and FCoV of a large fragment spanning orf1b to M has been previously described, here we report the emergence of a novel, highly pathogenic FCoV-CCoV recombinant responsible for a rapidly spreading outbreak of feline infectious peritonitis (FIP), originating in Cyprus. The recombination, spanning spike, shows 97% sequence identity to the pantropic canine coronavirus CB/05.

Infection is spreading fast and infecting cats of all ages. Development of FIP appears rapid and likely non-reliant on biotype switch.

High sequence identity of isolates from cats in different districts of the island is strongly supportive of direct transmission.

A deletion and several amino acid changes in spike, particularly the receptor binding domain, compared to other FCoV-2s, indicate changes to receptor binding and likely cell tropism.

--- https://www.biorxiv.org/content/10.1101/2023.10.10.561678.full The maintenance of genetic polymorphism in sexually antagonistic traits Ewan Flintham, Vincent Savolainen, Sarah Otto, Max Reuter, Charles Mullon 2023-10-11 2023-10-11 [("doi","10.1101/2023.10.10.561678")] genetics/selection/natural

Selection often favours different trait values in males and females, leading to genetic conflicts between the sexes, or sexual antagonism. Theory suggests that such conflict can maintain genetic variation by generating balancing selection. However, most of this theory is based on insights from models of single loci with fixed fitness effects. It is thus unclear how readily sexual antagonism drives balancing selection at loci where the fitness effects are not arbitrarily set but emerge from the genotype-phenotype map and the fitness landscape.

Here we use mathematical models and computer simulations to investigate the evolution of traits that have different optima in males and females and so experience sexually antagonistic selection. We examine the consequences of such selection for the maintenance and nature of genetic variation under different assumptions of genetic architecture and for different fitness landscapes.

We show that the conditions necessary to maintain variation are substantially more restrictive than previously appreciated. This is because the majority of typical sex-specific fitness landscapes generate stabilizing selection on traits, and so favour the fixation of a single homozygous genotype that encodes the phenotype with the highest sex-averaged fitness. Instead, the maintenance of genetic polymorphism requires extremely, if not implausibly, strong conflict between males and females in order to disfavor extreme and intermediate phenotypes by generating diversifying selection.

Furthermore, in all cases, we find that distinctive patterns of genetic variation produced by sexual antagonism arise rarely and are often transient, and so are difficult to observe in genomic data.

Taken together, our results indicate that sex-specific selection is not a straightforward source of balanced genetic polymorphism and leaves few traces in the genome. Rather, we suggest that sexual antagonism is most likely to maintain genetic variation when traits are determined by a single large effect locus where genetic constraints lead to a limited range of possible alleles.

--- https://www.biorxiv.org/content/10.1101/2023.10.24.563738.full Convergent evolution of complex adaptive traits enabled human life at high altitudes Giulia Ferraretti, Aina Rill, Paolo Abondio, Kyra Smith, Claudia Ojeda-Granados, Sara De Fanti, Massimo Izzi, Phurba T. Sherpa, Paolo Cocco, Massimiliano Tiriticco, Marco di Marcello, Agnese Dezi, Guido Alberto Gnecchi-Ruscone, Luca Natali, Angela Corcelli, Giorgio Marinelli, Paolo Garagnani, Davide Peluzzi, Donata Luiselli, Davide Pettener, Stefania Sarno, Marco Sazzini 2023-10-25 2023-10-25 [("doi","10.1101/2023.10.24.563738")] genetics/selection/natural/human

Convergent adaptations represent paradigmatic examples of the capacity of natural selection to influence organisms biology. However, the possibility to investigate genetic determinants underpinning convergent complex adaptive traits has been offered only recently by methods for inferring polygenic adaptations from genomic data.

Relying on this approach, we demonstrate how high-altitude Andean human groups experienced pervasive selective events at angiogenic pathways, which resemble those previously attested for Himalayan populations despite partial convergence at the single-gene level was observed.

This provides unprecedented evidence for the drivers of convergent evolution of enhanced blood perfusion in populations exposed to hypobaric hypoxia for thousands of years.

--- https://www.biorxiv.org/content/10.1101/2023.11.02.565287.full Genetic Influences on Educational Attainment Through the Lens of the Evolving Swedish Welfare State: A cross-level gene-environment interaction study based on polygenic indices and longitudinal register data Oskar Pettersson 2023-11-03 2023-11-03 [("doi","10.1101/2023.11.02.565287")] genetics/heritable iq/ses

Gene-environment interaction with regards to educational attainment has received increasing attention during the last few years. However, the potential interdependence between different types of environments in gene-environment interaction models has mostly been neglected. Using high-quality register data for an extensive panel of Swedish twins, born during most of the twentieth century, this study explores how genetic propensities for educational attainment, as measured by a polygenic index, interact with both macro-level institutional and sociopolitical context, and with socioeconomic background. The analyses, which combine between-family and causally robust within-family models, suggest that:

the average association between genetic propensities and educational attainment has increased in Sweden during the twentieth century, along with the expansion of the educational system and decreased economic inequality. There is also evidence of a positive interaction between genetic propensities and socioeconomic background, but only in the oldest cohorts in the sample, and that were born before the Swedish welfare state had been fully established.

This implies that micro-level gene-environment interactions can be dependent on macro-level context, an insight that has arguably not yet been given sufficient attention in the literature. Acknowledging limitations of polygenic indices, and the arbitrariness of the genetic lottery, the results may nevertheless indicate a development towards higher equality of opportunity in Sweden during the twentieth century.

--- https://portswigger.net/blog/tic-tac-toe-in-html 2023-07-22 cs/computable --- https://arxiv.org/abs/2305.07243 TorToise: Better speech synthesis through scaling James Betker 2023-05-12 2023-07-22 [("doi","10.48550/arXiv.2305.07243")] ai/nn/transformer/clip ai/nn/transformer/gpt/dall-e/2 ai/scaling

In recent years, the field of image generation has been revolutionized by the application of autoregressive transformers and DDPMs. These approaches model the process of image generation as a step-wise probabilistic processes and leverage large amounts of compute and data to learn the image distribution. This methodology of improving performance need not be confined to images.

This paper describes a way to apply advances in the image generative domain to speech synthesis. The result is TorToise—an expressive, multi-voice text-to-speech system.

All model code and trained weights have been open-sourced at Github.

--- https://www.davis-stober.com/post/alien-in-a-can 2023-07-22 philosophy/mind psychology --- https://x.com/novelaiofficial/status/1723601550927356343 NovelAI 2023-07-22 ai/anime ai/nn/diffusion --- https://web.archive.org/web/20070628195031if_/https://informationarchitects.jp/the-web-is-all-about-typography-period/ 2023-07-22 cs/css design/typography --- https://x.com/mbateman/status/1723012963513053678 Matt Bateman 2023-07-22 ai/nn/diffusion/midjourney/dropcap --- https://arxiv.org/abs/2304.08485 Visual Instruction Tuning Haotian Liu, Chunyuan Li, Qingyang Wu, Yong Jae Lee 2023-04-17 2023-07-22 [("doi","10.48550/arXiv.2304.08485")] ai/nn/transformer

Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field. In this paper, we present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data.

By instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.

Our early experiments show that LLaVA demonstrates impressive multimodal chat abilities, sometimes exhibiting the behaviors of multimodal GPT-4 on unseen images/instructions, and yields a 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%.

We make GPT-4 generated visual instruction tuning data, our model and code base publicly available.

--- https://arxiv.org/abs/2305.18290 Direct Preference Optimization (DPO): Your Language Model is Secretly a Reward Model Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, Chelsea Finn 2023-05-29 2023-07-22 [("doi","10.48550/arXiv.2305.18290")] reinforcement-learning/model/decision-transformer reinforcement-learning/preference-learning statistics/order/comparison

While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF).

However, RLHF is a complex and often unstable procedure, first fitting a reward model that reflects the human preferences, and then fine-tuning the large unsupervised LM using reinforcement learning to maximize this estimated reward without drifting too far from the original model.

In this paper, we leverage a mapping between reward functions and optimal policies to show that this constrained reward maximization problem can be optimized exactly with a single stage of policy training, essentially solving a classification problem on the human preference data. The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant and computationally lightweight, eliminating the need for fitting a reward model, sampling from the LM during fine-tuning, or performing hyperparameter tuning.

Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods. Notably, fine-tuning with DPO exceeds RLHF’s ability to control sentiment of generations and improves response quality in summarization and single-turn dialogue while being substantially simpler to implement and train.

--- https://www.nytimes.com/2023/11/12/science/vultures-conservation-intelligence.html 2023-07-22 genetics/microbiome psychology/animal/bird --- https://arxiv.org/abs/2310.17680 CodeFusion: A Pre-trained Diffusion Model for Code Generation Mukul Singh, José Cambronero, Sumit Gulwani, Vu Le, Carina Negreanu, Gust Verbruggen 2023-10-26 2023-10-26 [("doi","10.48550/arXiv.2310.17680")] ai/nn/diffusion/discrete ai/nn/transformer/gpt/codex

Imagine a developer who can only change their last line of code, how often would they have to start writing a function from scratch before it is correct? Auto-regressive models for code generation from natural language have a similar limitation: they do not easily allow reconsidering earlier tokens generated.

We introduce CodeFusion, a pre-trained diffusion code generation model that addresses this limitation by iteratively denoising a complete program conditioned on the encoded natural language.

We evaluate CodeFusion on the task of natural language to code generation for Bash, Python, and Microsoft Excel conditional formatting (CF) rules.

Experiments show that CodeFusion (75M parameters) performs on par with state-of-the-art auto-regressive systems (350M-175b parameters) in top-1 accuracy and outperforms them in top-3 and top-5 accuracy due to its better balance in diversity versus quality.

--- https://arxiv.org/abs/2306.01129 White-Box Transformers via Sparse Rate Reduction Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Benjamin D. Haeffele, Yi Ma 2023-06-01 2023-07-22 [("doi","10.48550/arXiv.2306.01129")] ai/nn/transformer/attention cs/algorithm/information/compression

In this paper, we contend that the objective of representation learning is to compress and transform the distribution of the data, say sets of tokens, towards a mixture of low-dimensional Gaussian distributions supported on incoherent subspaces. The quality of the final representation can be measured by a unified objective function called sparse rate reduction.

From this perspective, popular deep networks such as transformers can be naturally viewed as realizing iterative schemes to optimize this objective incrementally. Particularly, we show that the standard transformer block can be derived from alternating optimization on complementary parts of this objective: the multi-head self-attention operator can be viewed as a gradient descent step to compress the token sets by minimizing their lossy coding rate, and the subsequent multi-layer perceptron can be viewed as attempting to sparsify the representation of the tokens.

This leads to a family of white-box transformer-like deep network architectures which are mathematically fully interpretable. Despite their simplicity, experiments show that these networks indeed learn to optimize the designed objective: they compress and sparsify representations of large-scale real-world vision datasets such as ImageNet, and achieve performance very close to thoroughly engineered transformers such as ViT. Code is at https://github.com/Ma-Lab-Berkeley/CRATE.

--- https://x.com/mbateman/status/1723721019725115765 Matt Bateman 2023-07-23 ai/nn/diffusion/midjourney/dropcap ai/nn/transformer/gpt/dall-e/3 --- https://arxiv.org/abs/2104.04670 Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections Ruiqi Zhong, Kristy Lee, Zheng Zhang, Dan Klein 2021-04-10 2023-07-23 [("doi","10.48550/arXiv.2104.04670")] ai/nn/transformer/gpt/instruction-tuning ai/scaling

Large pre-trained language models (LMs) such as GPT-3 have acquired a surprising ability to perform zero-shot learning. For example, to classify sentiment without any training examples, we can “prompt” the LM with the review and the label description “Does the user like this movie?”, and ask whether the next word is “yes” or “no”. However, the next word prediction training objective is still misaligned with the target zero-shot learning objective.

To address this weakness, we propose meta-tuning, which directly optimizes the zero-shot learning objective by fine-tuning pre-trained language models on a collection of datasets.

We focus on classification tasks, and construct the meta-dataset by aggregating 43 existing datasets and annotating 441 label descriptions in a question-answering (QA) format.

When evaluated on unseen tasks, meta-tuned models outperform a same-sized QA model and the previous SOTA zero-shot learning system based on natural language inference. Additionally, increasing parameter count from 220M to 770M improves AUC-ROC scores by 6.3%, and we forecast that even larger models would perform better.

Therefore, measuring zero-shot learning performance on language models out-of-the-box might underestimate their true potential, and community-wide efforts on aggregating datasets and unifying their formats can help build models that answer prompts better.

--- https://www.nytimes.com/2023/11/12/business/georgism-land-tax-housing.html/ 2023-07-23 economics/georgism --- https://arxiv.org/abs/1806.08730#salesforce The Natural Language Decathlon: Multitask Learning as Question Answering Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, Richard Socher 2018-06-20 2023-07-23 [("doi","10.48550/arXiv.1806.08730")] ai/nn/rnn ai/nn/transformer/gpt/instruction-tuning

Deep learning has improved performance on many natural language processing (NLP) tasks individually. However, general NLP models cannot emerge within a paradigm that focuses on the particularities of a single metric, dataset, and task. We introduce the Natural Language Decathlon (decaNLP), a challenge that spans 10 tasks: question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, zero-shot relation extraction, goal-oriented dialogue, semantic parsing, and commonsense pronoun resolution. We cast all tasks as question answering over a context.

Furthermore, we present a new Multitask Question Answering Network (MQAN) jointly learns all tasks in decaNLP without any task-specific modules or parameters in the multitask setting. MQAN shows improvements in transfer learning for machine translation and named entity recognition, domain adaptation for sentiment analysis and natural language inference, and zero-shot capabilities for text classification. We demonstrate that the MQAN’s multi-pointer-generator decoder is key to this success and performance further improves with an anti-curriculum training strategy.

Though designed for decaNLP, MQAN also achieves state-of-the-art results on the WikiSQL semantic parsing task in the single-task setting. We also release code for procuring and processing data, training and evaluating models, and reproducing all experiments for decaNLP.

--- https://www.cambridge.org/core/journals/twin-research-and-human-genetics/article/concordance-and-heritability-of-type-2-diabetes-in-34166-twin-pairs-from-international-twin-registers-the-discordant-twin-discotwin-consortium/94E0005D626FB0399AA8FDDAC82F970E 2023-07-23 genetics/heritable --- https://www.cambridge.org/core/journals/twin-research-and-human-genetics/article/cotwin-control-design-implementation-and-methodological-considerations/E76B9E3EA2CC5D7E5AC803169256EEEF 2023-07-23 genetics/heritable/correlation --- https://arxiv.org/abs/2310.06213 GeoLLM: Extracting Geospatial Knowledge from Large Language Models Rohin Manvi, Samar Khanna, Gengchen Mai, Marshall Burke, David Lobell, Stefano Ermon 2023-10-10 2023-10-10 [("doi","10.48550/arXiv.2310.06213")] ai/nn/transformer/gpt/3/nonfiction ai/scaling sociology

The application of machine learning (ML) in a range of geospatial tasks is increasingly common but often relies on globally available covariates such as satellite imagery that can either be expensive or lack predictive power. Here we explore the question of whether the vast amounts of knowledge found in Internet language corpora, now compressed within large language models (LLMs), can be leveraged for geospatial prediction tasks.

We first demonstrate that LLMs embed remarkable spatial information about locations, but naively querying LLMs using geographic coordinates alone is ineffective in predicting key indicators like population density.

We then present GeoLLM, a novel method that can effectively extract geospatial knowledge from LLMs with auxiliary map data from OpenStreetMap. We demonstrate the utility of our approach across multiple tasks of central interest to the international community, including the measurement of population density and economic livelihoods.

Across these tasks, our method demonstrates a 70% improvement in performance (measured using Pearson’s r2) relative to baselines that use nearest neighbors or use information directly from the prompt, and performance equal to or exceeding satellite-based benchmarks in the literature.

With GeoLLM, we observe that GPT-3.5 outperforms LLaMA-2 and RoBERTa by 19% and 51% respectively, suggesting that the performance of our method scales well with the size of the model and its pretraining dataset.

Our experiments reveal that LLMs are remarkably sample-efficient, rich in geospatial information, and robust across the globe. Crucially, GeoLLM shows promise in mitigating the limitations of existing geospatial covariates and complementing them well.

Figure 3: Mean Pearson’s R2 for each model across all tasks at 1,000 training samples.
Figure 4: Learning curves for population density task from WorldPop.

…Not only does GPT-3.5 outperform all other models on every test, but its performance is also relatively consistent across tasks and sample sizes. This shows that GPT-3.5 is resilient to the size of the areas of prediction (eg. square kilometer vs ZIP code area), and any added noise (eg. “jittered” coordinates). LLaMA-2 also does better than all baselines for 18⁄19 total tests and consistently does better than RoBERTa. RoBERTa consistently does better than all baselines with 10,000 training samples, but struggles at lower sample sizes. All models experience a large drop in performance when the sample size is reduced to 100. However, GPT-3.5 and LLaMA-2 retain a much more acceptable level of performance compared to the others, emphasizing their sample efficiency. Specifically, with 100 samples, GPT-3.5 does 3.1× better than the best baseline and LLaMA-2 does 1.8× better.

GPT-3.5 and LLaMA-2 do especially well with the population tasks from WorldPop and the USCB compared to the baselines. GPT-3.5 is also especially impressive with the home value task from Zillow with a Pearson’s R2 of up to 0.87. However, the difference in performance between the models is less pronounced for the tasks from the DHS. This might be due to the noise that is added when the coordinates for these tasks are “jittered” up to 5 kilometers. With the added noise, it is potentially more difficult to achieve a high Pearson’s R2.

As shown in Figure 3 GPT-3.5, LLaMA-2, and RoBERTa, perform 70%, 43%, and 13% better on average than the best baseline (XGBoost) respectively with 1,000 samples, indicating that the method scales well. Figure 4 again shows that the sample efficiencies of LLaMA-2 and GPT-3.5 are exceptional, especially when making predictions on a global scale. Note that with larger sample sizes the gaps in performance will decrease as the physical distances between the training coordinates and test coordinates become smaller.

--- https://ir.vervetx.com/news-releases/news-release-details/verve-therapeutics-announces-interim-data-verve-101 2023-07-23 genetics/editing --- https://www.lesswrong.com/posts/GyaDCzsyQgc48j8t3/linear-encoding-of-character-level-information-in-gpt-j 2023-07-23 ai/nn/tokenization ai/nn/transformer/gpt --- https://arxiv.org/abs/2309.04662#google MADLAD-400: A Multilingual And Document-Level Large Audited Dataset Sneha Kudugunta, Isaac Caswell, Biao Zhang, Xavier Garcia, Christopher A. Choquette-Choo, Katherine Lee, Derrick Xin, Aditya Kusupati, Romi Stella, Ankur Bapna, Orhan Firat 2023-09-09 2023-09-09 [("doi","10.48550/arXiv.2309.04662")] ai/dataset ai/nn/transformer/t5

We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dataset based on CommonCrawl, spanning 419 languages.

We discuss the limitations revealed by self-auditing MADLAD-400, and the role data auditing had in the dataset creation process.

We then train and release a 10.7B-parameter multilingual machine translation model on 250 billion tokens covering over 450 languages using publicly available data, and find that it is competitive with models that are larger, and report the results on different domains. In addition, we train a 8B-parameter language model, and assess the results on few-shot translation.

We make the baseline models available to the research community.

--- https://arxiv.org/abs/1702.07274 Rotting Bandits Nir Levine, Koby Crammer, Shie Mannor 2017-02-23 2023-07-24 [("doi","10.48550/arXiv.1702.07274")] reinforcement-learning/exploration reinforcement-learning/model

The Multi-Armed Bandits (MAB) framework highlights the tension between acquiring new knowledge (Exploration) and leveraging available knowledge (Exploitation). In the classical MAB problem, a decision maker must choose an arm at each time step, upon which she receives a reward. The decision maker’s objective is to maximize her cumulative expected reward over the time horizon. The MAB problem has been studied extensively, specifically under the assumption of the arms’ rewards distributions being stationary, or quasi-stationary, over time.

We consider a variant of the MAB framework, which we termed Rotting Bandits, where each arm’s expected reward decays as a function of the number of times it has been pulled.

We are motivated by many real-world scenarios such as online advertising, content recommendation, crowdsourcing, and more.

We present algorithms, accompanied by simulations, and derive theoretical guarantees.

--- https://x.com/AndrewCurran_/status/1724106078022176947 Andrew Curran 2023-07-24 statistics/bias --- https://letterformarchive.org/news/schriftenkartei-german-font-index/ 2023-07-24 design/typography --- https://www.canva.dev/blog/engineering/ship-shape/ 2023-07-24 ai/nn/rnn design/typography --- https://arxiv.org/abs/2201.12975 Rotting Infinitely Many-armed Bandits Jung-hun Kim, Milan Vojnovic, Se-Young Yun 2022-01-31 2023-07-24 [("doi","10.48550/arXiv.2201.12975")] reinforcement-learning/exploration reinforcement-learning/model

We consider the infinitely many-armed bandit problem with rotting rewards, where the mean reward of an arm decreases at each pull of the arm according to an arbitrary trend with maximum rotting rate ϱ = o(1).

We show that this learning problem has an Ω(max{ρ1⁄3T, √T}) worst-case regret lower bound where T is the horizon time. We show that a matching upper bound `𝒪̃(max{ρ1⁄3T, √T})`, up to a poly-logarithmic factor, can be achieved by an algorithm that uses a UCB index for each arm and a threshold value to decide whether to continue pulling an arm or remove the arm from further consideration, when the algorithm knows the value of the maximum rotting rate ϱ.

We also show that an Ō(max{ρ1⁄3T, T3⁄4}) regret upper bound can be achieved by an algorithm that does not know the value of ρ, by using an adaptive UCB index along with an adaptive threshold value.

--- https://www.biorxiv.org/content/10.1101/2021.04.25.441352 The nematode worm C. elegans chooses between bacterial foods as if maximizing economic utility Abraham Katzen, Hui-Kuan Chung, William T. Harbaugh, Christina Della Iacono, Nicholas Jackson, Elizabeth E. Glater, Charles J. Taylor, Stephanie K. Yu, Steven W. Flavell, Paul W. Glimcher, James Andreoni, Shawn R. Lockery 2023-02-08 2023-07-24 [("doi","10.1101/2021.04.25.441352")] psychology/animal statistics/decision

In value-based decision making, options are selected according to subjective values assigned by the individual to available goods and actions. Despite the importance of this faculty of the mind, the neural mechanisms of value assignments, and how choices are directed by them, remain obscure. To investigate this problem, we used a classic measure of utility maximization, the Generalized Axiom of Revealed Preference, to quantify internal consistency of food preferences in Caenorhabditis elegans, a nematode worm with a nervous system of only 302 neurons.

Using a novel combination of microfluidics and electrophysiology, we found that C. elegans food choices fulfill the necessary and sufficient conditions for utility maximization, indicating that nematodes behave as if they maintain, and attempt to maximize, an underlying representation of subjective value. Food choices are well-fit by a utility function widely used to model human consumers.

Moreover, as in many other animals, subjective values in C. elegans are learned, a process we find requires intact dopamine signaling. Differential responses of identified chemosensory neurons to foods with distinct growth potentials are amplified by prior consumption of these foods, suggesting that these neurons may be part of a value-assignment system.

The demonstration of utility maximization in an organism with a very small nervous system sets a new lower bound on the computational requirements for utility maximization and offers the prospect of an essentially complete explanation of value-based decision making at single neuron resolution in this organism.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7970124/ Gene regulatory networks exhibit several kinds of memory: quantification of memory in biological and random transcriptional networks Surama Biswas, Santosh Manicka, Erik Hoel, Michael Levin 2021 2023-07-24 [("doi","10.1016/j.isci.2021.102131")] cs/computable genetics

Gene regulatory networks (GRNs) process important information in developmental biology and biomedicine. A key knowledge gap concerns how their responses change over time.

Hypothesizing long-term changes of dynamics induced by transient prior events, we created a computational framework for defining and identifying diverse types of memory in candidate GRNs. We show that GRNs from a wide range of model systems are predicted to possess several types of memory, including Pavlovian conditioning.

Associative memory offers an alternative strategy for the biomedical use of powerful drugs with undesirable side effects, and a novel approach to understanding the variability and time-dependent changes of drug action. We find evidence of natural selection favoring GRN memory.

Vertebrate GRNs overall exhibit more memory than invertebrate GRNs, and memory is most prevalent in differentiated metazoan cell networks compared with undifferentiated cells. Timed stimuli are a powerful alternative for biomedical control of complex in vivo dynamics without genomic editing or transgenes.

--- https://eprints.soton.ac.uk/339763/1/chap40.pdf 2023-07-24 cs/computable genetics --- https://en.wikipedia.org/wiki/Quorum_sensing Quorum sensing 2023-07-24 biology cs/computable reinforcement-learning/multi-agent --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6458997/ Deciphering the Molecular Mechanism Underpinning Phage Arbitrium Communication Systems Francisca Gallego Del Sol, José R. Penadés, Alberto Marina 2019 2023-07-24 [("doi","10.1016/j.molcel.2019.01.025")] biology cs/computable

Bacillus phages use a communication system, termed arbitrium, to coordinate lysis-lysogeny decisions. Arbitrium communication is mediated by the production and secretion of a hexapeptide (AimP) during lytic cycle. Once internalized, AimP reduces the expression of the negative regulator of lysogeny, AimX, by binding to the transcription factor, AimR, promoting lysogeny.

We have elucidated the crystal structures of AimR from the Bacillus subtilis SPbeta phage in its apo form, bound to its DNA operator and in complex with AimP. AimR presents intrinsic plasticity, sharing structural features with the RRNPP quorum-sensing family.

Remarkably, AimR binds to an unusual operator with a long spacer that interacts non-specifically with the receptor TPR domain, while the HTH domain canonically recognizes two inverted repeats. AimP stabilizes a compact conformation of AimR that approximates the DNA-recognition helices, preventing AimR binding to the aimX promoter region.

Our results establish the molecular basis of the arbitrium communication system.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5378303/ Communication between viruses guides lysis-lysogeny decisions Zohar Erez, Ida Steinberger-Levy, Maya Shamir, Shany Doron, Avigail Stokar-Avihail, Yoav Peleg, Sarah Melamed, Azita Leavitt, Alon Savidor, Shira Albeck, Gil Amitai, Rotem Sorek 2017 2023-07-24 [("doi","10.1038/nature21049")] biology cs/computable

Temperate viruses can become dormant in their host cells, a process called lysogeny. In every infection, such viruses decide between the lytic and the lysogenic cycles, that is, whether to replicate and lyse their host or to lysogenize and keep the host viable.

Here we show that viruses (phages) of the SPbeta group use a small-molecule communication system to coordinate lysis-lysogeny decisions. During infection of its Bacillus host cell, the phage produces a 6 amino-acids-long communication peptide that is released into the medium. In subsequent infections, progeny phages measure the concentration of this peptide and lysogenize if the concentration is sufficiently high.

We found that different phages encode different versions of the communication peptide, demonstrating a phage-specific peptide communication code for lysogeny decisions.

We term this communication system the arbitrium system, and further show that it is encoded by 3 phage genes: aimP, which produces the peptide; aimR, the intracellular peptide receptor; and aimX, a negative regulator of lysogeny.

The arbitrium system enables a descendant phage to ‘communicate’ with its predecessors, that is, to estimate the amount of recent previous infections and hence decide whether to employ the lytic or lysogenic cycle.

--- https://en.wikipedia.org/wiki/Multipartite Multipartite 2023-07-25 biology genetics reinforcement-learning/multi-agent --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4214203/ The physics of bacterial decision making Eshel Ben-Jacob, Mingyang Lu, Daniel Schultz, Jose N. Onuchic 2014 2023-07-25 [("doi","10.3389/fcimb.2014.00154")] biology cs/algorithm/information

The choice that bacteria make between sporulation and competence when subjected to stress provides a prototypical example of collective cell fate determination that is stochastic on the individual cell level, yet predictable (deterministic) on the population level. This collective decision is performed by an elaborated gene network. Considerable effort has been devoted to simplify its complexity by taking physics approaches to untangle the basic functional modules that are integrated to form the complete network:

  1. A stochastic switch whose transition probability is controlled by two order parameters-population density and internal/external stress.

  2. An adaptable timer whose clock rate is normalized by the same two previous order parameters.

  3. Sensing units which measure population density and external stress.

  4. A communication module that exchanges information about the cells’ internal stress levels.

  5. An oscillating gate of the stochastic switch which is regulated by the timer.

The unique circuit architecture of the gate allows special dynamics and noise management features. The gate opens a window of opportunity in time for competence transitions, during which the circuit generates oscillations that are translated into a chain of short intervals with high transition probability. In addition, the unique architecture of the gate allows filtering of external noise and robustness against variations in circuit parameters and internal noise. We illustrate that a physics approach can be very valuable in investigating the decision process and in identifying its general principles. We also show that both cell-cell variability and noise have important functional roles in the collectively controlled individual decisions.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2795487/ Deciding fate in adverse times: sporulation and competence in Bacillus subtilis Daniel Schultz, Peter G. Wolynes, Eshel Ben Jacob, José N. Onuchic 2009 2023-07-25 [("doi","10.1073/pnas.0912185106")] biology cs/computable

Bacteria serve as the central arena for understanding how gene networks and proteins process information and control cellular behaviors. Recently, much effort has been devoted to the investigation of specific bacteria gene circuits as functioning modules. The next challenge is the integrative modeling of complex cellular networks composed of many such modules.

A tractable integrative model of the sophisticated decision-making signal transduction system that determines the fate between sporulation and competence is presented. This model provides an understanding of how information is sensed and processed to reach an “informative” decision in the context of cell state and signals from other cells. The competence module (ComK dynamics) is modeled as a stochastic switch whose transition rate is controlled by a quorum-sensing unit. The sporulation module (Spo0A dynamics) is modeled as a timer whose clock rate is adjusted by a stress-sensing unit.

The interplay between these modules is mediated via the Rap assessment system, which gates the sensing units, and the AbrB-Rok decision module, which creates an opportunity for competence within a specific window of the sporulation timer. The timer is regulated via a special repressilator-like inhibition of Spo0A by Spo0E, which is itself inhibited by AbrB.

For some stress and input signals, this repressilator can generate a frustration state with large variations (fluctuations or oscillations) in Spo0A* and AbrB concentrations, which might serve an important role in generating cell variability. This integrative framework is a starting point that can be extended to include transition into cannibalism and the role of colony organization.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3627974/ Turning oscillations into opportunities: lessons from a bacterial decision gate Daniel Schultz, Mingyang Lu, Trevor Stavropoulos, José Onuchic, Eshel Ben-Jacob 2013 2023-07-25 [("doi","10.1038/srep01668")] biology cs/computable

[cf. Ben-Jacob et al 2014] Sporulation vs. competence provides a prototypic example of collective cell fate determination. The decision is performed by the action of 3 modules: (1) A stochastic competence switch whose transition probability is regulated by population density, population stress and cell stress. (2) A sporulation timer whose clock rate is regulated by cell stress and population stress. (3) A decision gate that is coupled to the timer via a special repressilator-like loop.

We show that the distinct circuit architecture of this gate leads to special dynamics and noise management characteristics: The gate opens a time-window of opportunity for competence transitions during which it generates oscillations that are turned into a chain of transition opportunities—each oscillation opens a short interval with high transition probability.

The special architecture of the gate also leads to filtering of external noise and robustness against internal noise and variations in the circuit parameters.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223492 The effect of a hiding box on stress levels and body weight in Dutch shelter cats; a randomized controlled trial W. J. R. van der Leij, L. D. A. M. Selman, J. C. M. Vernooij, C. M. Vinke, Juan J. Loor, Juan J. Loor, Juan J. Loor 2019-09-13 2023-07-25 [("doi","10.1371/journal.pone.0223492")] cat/psychology

While staying in an animal shelter, cats may suffer from chronic stress which impairs their health and welfare. Providing opportunities to hide can significantly reduce behavioral stress in cats, but confirmation with physical parameters is needed.

Therefore, the aim of this study was to determine the effect of a hiding box on behavioral stress levels (scored by means of the Cat-Stress-Score) and a physical parameter, namely body weight, during the first 12 days in quarantine for cats newly arrived cats at a Dutch animal shelter.

23 cats 1–10 years of age were randomly divided between the experimental group (n = 12) and control group (n = 11) with and without a hiding box. Stress levels were assessed on days 1, 2, 3, 5, 7, 9 and 12 according to the non-invasive Cat-Stress-Score (CSS). Body weights were measured on days 0, 7 and 12. Finally, adoption rates and length of stay (LOS) were determined.

Major findings of the study are: (1) the mean Cat-Stress-Score decreased with time for all cats, but cats with a hiding box showed a statistically-significant faster decrease in the CSS, reaching a lower CSS-steady state 7 days earlier than the control group; (2) nearly all cats in both groups lost statistically-significant body weight during the first two weeks; (3) hiding boxes did not statistically-significantly influence weight loss; (4) no differences were found in the adoption rates and the LOS between both groups.

Hiding enrichment reduces behavioral stress in shelter cats during quarantine situations and can therefore be a relatively simple aid to shelter adaptation. However, it offers no prevention against feline weight loss, which indicates a serious health risk for shelter cats.

Figure 1.: Course of the Cat-Stress-Score in time of individual cats from the control group and the experimental group. Line segments connect measurements within the same cat to show the change of CSS in course of time. Dotted lines: individual cats without hiding boxes (control group). Solid lines: individual cats with hiding box (experimental group).
Figure 2: The proportional change (%) in body weight in individual cats from the control group and the experimental group. Line segments connect measurements within the same cat to show the change of body weight in course of time. Dotted lines: individual cats without hiding boxes (control group). Solid lines: individual cats with hiding box (experimental group).
--- https://www.quantamagazine.org/the-beautiful-intelligence-of-bacteria-and-other-microbes-20171113/ 2023-07-25 biology cs/computable --- https://www.scientificamerican.com/article/can-a-cell-make-decisions/ 2023-07-25 biology statistics/decision --- https://www.cell.com/current-biology/fulltext/S0960-9822(19)31431-9 2023-07-25 biology statistics/decision --- https://arxiv.org/abs/2310.01405 Representation Engineering: A Top-Down Approach to AI Transparency Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J. Byun, Zifan Wang, Alex Mallen, Steven Basart, Sanmi Koyejo, Dawn Song, Matt Fredrikson, J. Zico Kolter, Dan Hendrycks 2023-10-02 2023-10-02 [("doi","10.48550/arXiv.2310.01405")] ai/nn/transformer/gpt/calibration reinforcement-learning/safe

In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience.

RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs).

We provide baselines and an initial analysis of RepE techniques, showing that they offer simple yet effective solutions for improving our understanding and control of large language models. We showcase how these methods can provide traction on a wide range of safety-relevant problems, including honesty/calibration, harmlessness, power-seeking, and more, demonstrating the promise of top-down transparency research.

We hope that this work catalyzes further exploration of RepE and fosters advancements in the transparency and safety of AI systems.

--- https://arxiv.org/abs/2306.03341 Inference-Time Intervention: Eliciting Truthful Answers from a Language Model Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, Martin Wattenberg 2023-06-06 2023-07-25 [("doi","10.48550/arXiv.2306.03341")] ai/nn/transformer/gpt/calibration

We introduce Inference-Time Intervention (ITI), a technique designed to enhance the “truthfulness” of large language models (LLMs). ITI operates by shifting model activations during inference, following a set of directions across a limited number of attention heads.

This intervention improves the performance of LLaMA models on the TruthfulQA benchmark.

On an instruction-finetuned LLaMA called Alpaca, ITI improves its truthfulness 32.5% → 65.1%.

We identify a tradeoff between truthfulness and helpfulness and demonstrate how to balance it by tuning the intervention strength. ITI is minimally invasive and computationally inexpensive. Moreover, the technique is data efficient: while approaches like RLHF require extensive annotations, ITI locates truthful directions using only few hundred examples.

Our findings suggest that LLMs may have an internal representation of the likelihood of something being true, even as they produce falsehoods on the surface.

--- https://arxiv.org/abs/2102.08850 Contrastive Learning Inverts the Data Generating Process Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel 2021-02-17 2023-07-25 [("doi","10.48550/arXiv.2102.08850")] ai/nn

Contrastive learning has recently seen tremendous success in self-supervised learning. So far, however, it is largely unclear why the learned representations generalize so effectively to a large variety of downstream tasks.

We here prove that feedforward models trained with objectives belonging to the commonly used InfoNCE family learn to implicitly invert the underlying generative model of the observed data.

While the proofs make certain statistical assumptions about the generative model, we observe empirically that our findings hold even if these assumptions are severely violated. Our theory highlights a fundamental connection between contrastive learning, generative modeling, and nonlinear independent component analysis, thereby furthering our understanding of the learned representations as well as providing a theoretical foundation to derive more effective contrastive losses.

--- https://arxiv.org/abs/2310.06824 The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets Samuel Marks, Max Tegmark 2023-10-10 2023-10-10 [("doi","10.48550/arXiv.2310.06824")] ai/nn/transformer/gpt/calibration

[Twitter] Large Language Models (LLMs) have impressive capabilities, but are also prone to outputting falsehoods. Recent work has developed techniques for inferring whether an LLM is telling the truth by training probes on the LLM’s internal activations. However, this line of work is controversial, with some authors pointing out failures of these probes to generalize in basic ways, among other conceptual issues.

In this work, we curate high-quality datasets of true/false statements and use them to study in detail the structure of LLM representations of truth, drawing on 3 lines of evidence: 1. Visualizations of LLM true/false statement representations, which reveal clear linear structure. 2. Transfer experiments in which probes trained on one dataset generalize to different datasets. 3. Causal evidence obtained by surgically intervening in an LLM’s forward pass, causing it to treat false statements as true and vice versa.

Overall, we present evidence that language models linearly represent the truth or falsehood of factual statements.

We also introduce a novel technique, mass-mean probing, which generalizes better and is more causally implicated in model outputs than other probing techniques.

--- https://spectrum.ieee.org/generative-ai-training 2023-07-26 ai/scaling/hardware --- https://en.wikipedia.org/wiki/Folkrace Folkrace 2023-07-26 economics/mechanism-design/auction --- /doc/ai/nn/transformer/gpt/dall-e/3/2023-11-13-gwern-dalle3-screwfly-marriageoflymanandflywomanindeath.png Gwern 2023-11-13 2023-11-13 ai/nn/transformer/gpt/dall-e/3 fiction/science-fiction --- https://x.com/bindureddy/status/1724152343732859392 Bindu Reddy 2023-07-26 ai/nn/transformer/gpt/4/nonfiction --- https://en.wikipedia.org/wiki/Charlie_Sheen Charlie Sheen 2023-07-26 psychiatry/bipolar/energy --- https://www.aocs.org/stay-informed/inform-magazine/featured-articles/one-persons-response-to-a-high-omega-6-diet-november-2010 2023-07-26 nootropic/quantified-self --- https://arxiv.org/abs/2009.09153 Hidden Incentives for Auto-Induced Distributional Shift David Krueger, Tegan Maharaj, Jan Leike 2020-09-19 2023-07-26 [("doi","10.48550/arXiv.2009.09153")] reinforcement-learning/meta-learning reinforcement-learning/safe sociology/technology

Decisions made by machine learning systems have increasing influence on the world, yet it is common for machine learning algorithms to assume that no such influence exists. An example is the use of the i.i.d. assumption in content recommendation. In fact, the (choice of) content displayed can change users’ perceptions and preferences, or even drive them away, causing a shift in the distribution of users. We introduce the term auto-induced distributional shift (ADS) to describe the phenomenon of an algorithm causing a change in the distribution of its own inputs.

Our goal is to ensure that machine learning systems do not leverage ADS to increase performance when doing so could be undesirable. We demonstrate that changes to the learning algorithm, such as the introduction of meta-learning, can cause hidden incentives for auto-induced distributional shift (HI-ADS) to be revealed.

To address this issue, we introduce ‘unit tests’ and a mitigation strategy for HI-ADS, as well as a toy environment for modeling real-world issues with HI-ADS in content recommendation, where we demonstrate that strong meta-learners achieve gains in performance via ADS.

We show meta-learning and Q-learning both sometimes fail unit tests, but pass when using our mitigation strategy.

…In both the RL and SL unit tests, we find that ‘vanilla’ learning algorithms (eg. minibatch SGD) pass the test, but introducing an outer-loop of meta-learning (eg. Population-Based Training (PBT) (Jaderberg et al 2017)) can lead to high levels of failure. We find results consistent with our unit tests in the content recommendation environment: recommenders trained with PBT create earlier, faster, and larger drift in user interests, and for the same level of performance, create larger changes in the user base. These results suggest that failure of our unit tests indicates that an algorithm is prone to revealing HI-ADS in other settings.

Finally, we propose and test a mitigation strategy we call context swapping. The strategy consists of rotating learners through different environments throughout learning, so that they can’t see the results or correlations of their actions in one environment over longer time horizons. This effectively mitigates HI-ADS in our unit test environments, but did not work well in content recommendation experiments. [“Capabilities generalize further than alignment”—content recommendation is much richer than their toy problem, so it may be harder to avoid learning general convergent strategies of empowerment & manipulation?]

--- https://en.wikipedia.org/wiki/Myxozoa Myxozoa 2023-07-26 genetics/selection/natural --- https://jxnl.github.io/instructor/blog/2023/11/05/chain-of-density/ 2023-07-26 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/inner-monologue --- https://www.science.org/content/article/misconduct-concerns-possible-drug-risks-should-stop-stroke-trial-whistleblowers-say 2023-07-27 statistics/bias --- https://statmodeling.stat.columbia.edu/2023/11/09/you-need-16-times-the-sample-size-to-estimate-an-interaction-than-to-estimate-a-main-effect-explained/ 2023-07-27 statistics/power-analysis --- https://www.wired.com/story/mirai-untold-story-three-young-hackers-web-killing-monster/ 2023-07-27 cs/security --- https://amaranth.foundation/bottlenecks-of-aging 2023-07-27 longevity science --- https://www.science.org/content/blog-post/stroke-trial-concerns 2023-07-27 psychiatry/alzheimers --- https://lock.cmpxchg8b.com/reptar.html 2023-07-27 cs/security --- /doc/ai/nn/transformer/gpt/dall-e/3/2023-11-13-gwern-dalle3-genewolfe-vampiredropcap-v-samples.jpg Gwern 2023-11-13 2023-11-13 ai/nn/diffusion/midjourney/dropcap ai/nn/transformer/gpt/dall-e/3 --- https://a-ortmann.medium.com/reflections-on-the-ariely-gino-saga-the-m-cap-and-the-swamp-that-is-the-poets-quants-ecology-of-fd5446221fdd 2023-07-27 statistics/bias --- https://andrewmayne.com/2023/11/14/is-the-reversal-curse-real/ 2023-07-27 ai/nn/transformer/gpt/3 --- https://www.sciencedirect.com/science/article/pii/S2211335523003625 2023-07-27 psychiatry/anxiety sociology/technology --- https://newsletter.pragmaticengineer.com/p/inside-openai-how-does-chatgpt-ship 2023-07-27 design reinforcement-learning/openai --- https://en.wikipedia.org/wiki/Risk_of_Rain Risk of Rain 2023-07-28 psychology/novelty --- https://x.com/GeorgistSteve/status/1724541886097178997 GeorgistSteve 2023-07-28 economics/georgism --- https://www.michaeldean.site/p/the-first-online-writer 2023-07-28 design psychology/writing sociology/technology --- https://x.com/LouisKnightWebb/status/1724510794514157668 Louis Knight-Webb 2023-07-28 ai/nn/transformer/attention ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/claude --- https://x.com/charliebholtz/status/1724815159590293764 Charlie B. Holtz 2023-07-28 ai/nn/transformer/gpt/4 fiction/humor --- /doc/ai/1949-coupling.pdf Chance Remarks John R. Pierce 1949-10-01 2023-07-28 ai cs/algorithm/information psychology/linguistics statistics/probability --- https://hedgehogreview.com/issues/markets-and-the-good/articles/language-machinery 2023-07-28 ai/nn/transformer/gpt cs/algorithm philosophy/mind psychology/linguistics statistics/probability --- /doc/philosophy/mind/1944-heider.pdf An Experimental Study of Apparent Behavior Fritz Heider, Marianne Simmel 1944-04-01 2023-07-28 [("doi","10.2307/1416950")] anime philosophy/mind psychology/cognitive-bias/illusion-of-depth

[the famous Heider-Simmel animation-illusion, demonstrating our propensity ao anthropomorphize even the most minimal visual imagery; interactive utility; blog, cf. School & Tremoulet 2000] A motion picture which shows movements of 3 geometric [2 triangles and a circle] figures was the material of the investigation.

It was presented to a first group of 34 Subjects with the instruction to describe it; to a second group (36 Subjects) with the instruction to interpret the movements as actions of persons & to answer a number of questions about them. A third group (44 Subjects) was treated like the second, except that the picture was shown in reverse and with fewer questions.

The reports show that all but one Subject of Group 1, all of Group 2, and all but two of Group 3, interpreted the picture of animated beings, chiefly of persons.

A characteristic feature of this organization in terms of actions is the attribution of the origin of movements to figural units and to motives. It has been shown that this attribution of the origin influences the interpretation of the movements, and that it depends in some cases on the characteristics of the movements themselves, in others on surrounding objects. The way in which the actors are judged is closely connected with this attribution of origin.

It is held that this method is useful in investigating the way the behavior of other persons is perceived.

See Also:

--- https://x.com/DouglasLFarrar/status/1720088292337779064 Douglas L. Farrar 2023-07-28 economics/advertising --- https://mubi.com/en/notebook/posts/movie-poster-of-the-week-cats-in-czech-and-polish-movie-posters 2023-07-28 cat design --- https://www.biorxiv.org/content/10.1101/2023.03.20.533527.full Evolutionary Insights Into Felidae Iris Color Through Ancestral State Reconstruction Julius A. Tabin, Katherine A. Chiasson 2023-10-09 2023-10-09 [("doi","10.1101/2023.03.20.533527")] cat/genetics

There have been almost no studies with an evolutionary perspective on eye (iris) color, outside of humans and domesticated animals. Extant members of the family Felidae have a great interspecific and intraspecific diversity of eye colors, in stark contrast to their closest relatives, all of which have only brown eyes. This makes the felids a great model to investigate the evolution of eye color in natural populations. Through machine learning cluster image analysis of publicly available photographs of all felid species, as well as a number of subspecies, 5 felid eye colors were identified: brown, hazel/green, yellow/beige, gray, and blue.

Using phylogenetic comparative methods, the presence or absence of these colors was reconstructed on a phylogeny. Additionally, through a new color analysis method, the specific shades of the ancestors’ eyes were quantitatively reconstructed. The ancestral felid population was predicted to have brown eyed individuals, as well as a novel evolution of gray eyed individuals, the latter being a key innovation that allowed the rapid diversification of eye color seen in modern felids, including numerous gains and losses of different eye colors.

It was also found that the loss of brown eyes and the gain of yellow/beige eyes is associated with an increase in the likelihood of evolving round pupils, which in turn influence the shades present in the eyes. Along with these important insights, the unique methods presented in this work are widely applicable and will facilitate future research into phylogenetic reconstruction of color beyond irises.

--- https://github.com/tldraw/make-real 2023-07-29 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/1407.3501 Robots that can adapt like animals Antoine Cully, Jeff Clune, Danesh Tarapore, Jean-Baptiste Mouret 2014-07-13 2023-07-29 [("doi","10.1038/nature14422")] reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/robot

As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot “think outside the box” to find a compensatory behavior when damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots.

Here we introduce an intelligent trial and error algorithm that allows robots to adapt to damage in less than two minutes, without requiring self-diagnosis or pre-specified contingency plans. Before deployment, a robot exploits a novel algorithm to create a detailed map of the space of high-performing behaviors: This map represents the robot’s intuitions about what behaviors it can perform and their value.

If the robot is damaged, it uses these intuitions to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a compensatory behavior that works in spite of the damage. Experiments reveal successful adaptations for a legged robot injured in 5 different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways.

This new technique will enable more robust, effective, autonomous robots, and suggests principles that animals may use to adapt to injury.

--- https://datagubbe.se/usab2/ 2023-07-29 design --- https://onlinelibrary.wiley.com/doi/10.1002/ab.22124 2023-07-29 politics psychiatry/anxiety --- https://www.biorxiv.org/content/10.1101/2022.10.21.513189.full Heritable and sex-specific variation in the development of social behavior in a wild primate Elizabeth C. Lange, Madison Griffin, Arielle S. Fogel, Elizabeth A. Archie, Jenny Tung, Susan C. Alberts 2022-10-21 2023-07-29 [("doi","10.1101/2022.10.21.513189")] genetics/heritable

Affiliative social bonds are linked to fitness components in many social mammals. However, despite their importance, little is known about how the tendency to form social bonds develops in young animals, or if the development of social behavior is heritable and thus can evolve.

Using 4 decades of longitudinal observational data from a wild baboon population, we assessed the environmental determinants of an important social developmental milestone in baboons—the age at which a young animal first grooms a conspecific—and we assessed how mother-offspring grooming reciprocity develops during the juvenile period.

We found that grooming development differs between the sexes: female infants groom at an earlier age and reach reciprocity in grooming with their mother earlier than males. Using the quantitative genetic ‘animal model’, we also found that age at first grooming behavior for both sexes is weakly heritable (h2 = 4.3%).

These results show that sex differences in grooming emerge at a young age; that strong, reciprocal social relationships between mothers and daughters begin very early in life; and that age at first grooming is heritable and therefore can be shaped by natural selection.

--- https://en.wikipedia.org/wiki/Distributional_semantics Distributional semantics 2023-07-29 ai/nn philosophy/mind psychology/linguistics --- https://arxiv.org/abs/2310.20092 Beyond U: Making Diffusion Models Faster & Lighter Sergio Calvo-Ordonez, Jiahao Huang, Lipei Zhang, Guang Yang, Carola-Bibiane Schonlieb, Angelica I. Aviles-Rivero 2023-10-31 2023-10-31 [("doi","10.48550/arXiv.2310.20092")] ai/nn/diffusion

Diffusion models are a family of generative models that yield record-breaking performance in tasks such as image synthesis, video generation, and molecule design. Despite their capabilities, their efficiency, especially in the reverse denoising process, remains a challenge due to slow convergence rates and high computational costs.

In this work, we introduce an approach that leverages continuous dynamical systems to design a novel denoising network for diffusion models that is more parameter-efficient, exhibits faster convergence, and demonstrates increased noise robustness.

Experimenting with denoising probabilistic diffusion models, our framework operates with ~a quarter of the parameters and 30% of the Floating Point Operations (FLOPs) compared to standard U-Nets in Denoising Diffusion Probabilistic Models (DDPMs). Furthermore, our model is up to 70% faster in inference than the baseline models when measured in equal conditions while converging to better quality solutions.

--- https://arxiv.org/abs/2311.07590#apollo Large Language Models can Strategically Deceive their Users when Put Under Pressure Jérémy Scheurer, Mikita Balesni, Marius Hobbhahn 2023-11-09 2023-11-09 [("doi","10.48550/arXiv.2311.07590")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue reinforcement-learning/multi-agent reinforcement-learning/safe

We demonstrate a situation in which Large Language Models, trained to be helpful, harmless, and honest, can display misaligned behavior and strategically deceive their users about this behavior without being instructed to do so. Concretely, we deploy GPT-4 as an agent in a realistic, simulated environment, where it assumes the role of an autonomous stock trading agent. Within this environment, the model obtains an insider tip about a lucrative stock trade and acts upon it despite knowing that insider trading is disapproved of by company management. When reporting to its manager, the model consistently hides the genuine reasons behind its trading decision.

We perform a brief investigation of how this behavior varies under changes to the setting, such as removing model access to a reasoning scratchpad, attempting to prevent the misaligned behavior by changing system instructions, changing the amount of pressure the model is under, varying the perceived risk of getting caught, and making other simple changes to the environment.

To our knowledge, this is the first demonstration of Large Language Models trained to be helpful, harmless, and honest, strategically deceiving their users in a realistic situation without direct instructions or training for deception.

--- /doc/statistics/order/capture/1976-efron.pdf 1976-01-01 2023-07-29 psychology/linguistics psychology/writing statistics/order/capture --- /hafu#capture-recapture 2023-07-29 statistics/order/capture --- https://en.wikipedia.org/wiki/Mark_and_recapture Mark and recapture 2023-07-29 statistics/order/capture --- https://stats.stackexchange.com/questions/16317/predicting-total-number-of-bugs-based-on-number-of-bugs-revealed-by-each-tester 2023-07-30 statistics/order/capture --- http://cvcl.mit.edu/SUNSeminar/BungeFitzpatrick_1993.pdf Estimating the Number of Species: A Review Bunge, Fitzpatrick 1993 2023-07-30 statistics/order/capture --- https://cran.r-project.org/web/packages/Rcapture/index.html 2023-07-30 cs/r statistics/order/capture --- https://cran.r-project.org/web/packages/Rcapture/Rcapture.pdf 2023-07-30 cs/r statistics/order/capture --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.521.8601&rep=rep1&type=pdf 2023-07-30 statistics/order/capture --- https://projecteuclid.org/download/pdfview_1/euclid.aoas/1372338470 2023-07-30 statistics/order/capture --- https://www.theguardian.com/books/2022/may/30/the-big-idea-could-the-greatest-works-of-literature-be-undiscovered 2023-07-30 statistics/order/capture --- /doc/statistics/survival-analysis/2022-kestemont-supplement.pdf Supplementary Materials for Forgotten books: The application of unseen species models to the survival of culture {Kestemont et al 2022} Mike Kestemont 2022 2023-07-30 statistics/order/capture statistics/survival-analysis --- https://en.wikipedia.org/wiki/Species_discovery_curve Species discovery curve 2023-07-30 statistics/order/capture --- https://rifters.com/real/articles/Cisne_Science.pdf 2023-07-30 statistics/order/capture --- https://dipot.ulb.ac.be/dspace/bitstream/2013/86184/1/1618b.pdf 2023-07-31 statistics/order/capture --- /doc/psychology/animal/bird/2023-agrelo.pdf Effect of kelp gull harassment on southern right whale calf survival: a long-term capture–recapture analysis Macarena Agrelo, Carina F. Marón, Fábio G. Daura-Jorge, Victoria J. Rowntree, Mariano Sironi, Philip S. Hammond, Simon N. Ingram, Florencia O. Vilches, Jon Seger, Paulo C. Simões-Lopes 2023-06-07 2023-07-31 [("doi","10.1098/rsbl.2023.0119")] psychology/animal/bird statistics/order/capture --- /doc/cat/psychology/2017-mcgregor.pdf Habitat preference for fire scars by feral cats in Cape York Peninsula, Australia Hugh W. McGregor, Hannah B. Cliff, John Kanowski 2017-02-13 2023-07-31 [("doi","10.1071/WR16058")] cat/psychology statistics/order/capture

Context Feral cats are implicated in the decline of terrestrial native mammals across northern Australia. Research in the Kimberley region of north-western Australia found feral cats strongly selected for fire scars when hunting, suggesting that intensifying fire regimes will have severe consequences for declining prey species.

Aims We tested the generality of cat–fire interaction beyond the Kimberley, by measuring habitat selection of feral cats in relation to fire scars and habitat types in north-eastern Australia.

Methods Our study was conducted at Piccaninny Plains Wildlife Sanctuary, Cape York Peninsula. We live-captured feral cats during the dry season of 2015, released them with GPS collars set to record fixes at 15-min intervals, and recaptured cats 4 months later. We created dynamic habitat maps of vegetation types, fire and wetlands, and compared cat habitat selection using discrete choice modeling. We also measured cat density from arrays of camera traps and examined cat diet by analysis of stomach contents.

Key results We obtained GPS movement data from 15 feral cats. Feral cats selected strongly for recent fire scars (1 or 2 months old), but avoided fire scars 3 months old or older. 3 long-distance movements were recorded, all directed towards recent fire scars. Cats also selected for open wetlands, and avoided rainforests. Density of cats at Piccaninny Plains was higher than recorded elsewhere in northern Australia. All major vertebrate groups were represented in cat diet.

Conclusions We showed that feral cats in north-eastern Australia strongly select for recent fire scars and open wetlands. These results are consistent with those from the Kimberley. Together, these studies have shown that amplified predation facilitated by loss of cover is likely to be a fundamental factor driving mammal decline across northern Australia.

Implications Reducing the frequency of intense fires may indirectly reduce the impact of feral cats at a landscape scale in northern Australia. We also suggest that managers target direct cat control towards open wetlands and recently burnt areas, which cats are known to favor.

--- https://www.reddit.com/r/ChatGPTNSFW/comments/17wk2g3/a_failed_ai_girlfriend_product_and_my_lessons/k9hs22a/ 2023-07-31 ai/nn/transformer/gpt/claude reinforcement-learning/preference-learning --- https://mazzzystar.github.io/2023/11/16/ai-girlfriend-product/ 2023-07-31 ai/nn/transformer/gpt/fiction sociology/technology --- https://arstechnica.com/security/2023/11/developers-cant-seem-to-stop-exposing-credentials-in-publicly-accessible-code/ 2023-07-31 cs/security --- https://psyche.co/guides/how-to-find-new-music-amid-all-the-streaming-abundance 2023-07-31 music --- https://www.gov.uk/government/news/mhra-authorises-world-first-gene-therapy-that-aims-to-cure-sickle-cell-disease-and-transfusion-dependent-thalassemia 2023-07-31 genetics/editing --- https://arxiv.org/abs/2311.06237 Summon a Demon and Bind it: A Grounded Theory of LLM Red Teaming in the Wild Nanna Inie, Jonathan Stray, Leon Derczynski 2023-11-10 2023-11-10 [("doi","10.48550/arXiv.2311.06237")] ai/nn/adversarial ai/nn/transformer/gpt/claude cs/security reinforcement-learning/preference-learning

Engaging in the deliberate generation of abnormal outputs from large language models (LLMs) by attacking them is a novel human activity.

This paper presents a thorough exposition of how and why people perform such attacks. Using a formal qualitative methodology, we interviewed dozens of practitioners from a broad range of backgrounds, all contributors to this novel work of attempting to cause LLMs to fail.

We relate and connect this activity between its practitioners’ motivations and goals; the strategies and techniques they deploy; and the crucial role the community plays.

As a result, this paper presents a grounded theory of how and why people attack large language models: LLM red teaming in the wild.

--- https://worksinprogress.co/issue/how-mathematics-built-the-modern-world/ 2023-07-31 math science technology --- https://onlinelibrary.wiley.com/doi/full/10.1002/oby.23940 2023-07-31 exercise zeo --- https://en.wikipedia.org/wiki/Lincoln_index Lincoln index 2023-08-01 statistics/order/capture --- https://en.wikipedia.org/wiki/German_tank_problem German tank problem 2023-08-01 statistics/order/capture --- https://arxiv.org/abs/2311.08877 Llamas Know What GPTs Don’t Show: Surrogate Models for Confidence Estimation Vaishnavi Shrivastava, Percy Liang, Ananya Kumar 2023-11-15 2023-11-15 [("doi","10.48550/arXiv.2311.08877")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/calibration

To maintain user trust, large language models (LLMs) should signal low confidence on examples where they are incorrect, instead of misleading the user. The standard approach of estimating confidence is to use the softmax probabilities of these models, but as of November 2023, state-of-the-art LLMs such as GPT-4 and Claude-v1.3 do not provide access to these probabilities.

We first study eliciting confidence linguistically—asking an LLM for its confidence in its answer—which performs reasonably (80.5% AUC on GPT-4 averaged across 12 question-answering datasets—7% above a random baseline) but leaves room for improvement.

We then explore using a surrogate confidence model—using a model where we do have probabilities to evaluate the original model’s confidence in a given question. Surprisingly, even though these probabilities come from a different and often weaker model, this method leads to higher AUC than linguistic confidences on 9⁄12 datasets.

Our best method composing linguistic confidences and surrogate model probabilities gives state-of-the-art confidence estimates on all 12 datasets (84.6% average AUC on GPT-4).

--- https://arxiv.org/abs/2311.08552#google UT5: Pretraining Non autoregressive T5 with unrolled denoising Mahmoud G. Salem, Jiayu Ye, Chu-Cheng Lin, Frederick Liu 2023-11-14 2023-11-14 [("doi","10.48550/arXiv.2311.08552")] ai/nn/transformer/t5

Recent advances in Transformer-based Large Language Models have made great strides in natural language generation.

However, to decode K tokens, an autoregressive model needs K sequential forward passes, which may be a performance bottleneck for large language models. Many non-autoregressive (NAR) research are aiming to address this sequentiality bottleneck, albeit many have focused on a dedicated architecture in supervised benchmarks.

In this work, we studied unsupervised pretraining for non auto-regressive T5 models via unrolled denoising and shown its SoTA results in downstream generation tasks such as SQuAD question generation and XSum.

--- https://arxiv.org/abs/2311.09180#microsoft PEARL: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers Sheshera Mysore, Zhuoran Lu, Mengting Wan, Longqi Yang, Steve Menezes, Tina Baghaee, Emmanuel Barajas Gonzalez, Jennifer Neville, Tara Safavi 2023-11-15 2023-11-15 [("doi","10.48550/arXiv.2311.09180")] ai/nn/retrieval ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/t5

Powerful large language models have facilitated the development of writing assistants that promise to improve the quality and efficiency of composition and communication. However, a barrier to effective assistance is the lack of personalization in LLM outputs to the author’s communication style and specialized knowledge. In this paper, we address this challenge by proposing PEARL, a retrieval-augmented LLM writing assistant personalized with a generation-calibrated retriever. Our retriever is trained to select historic user-authored documents for prompt augmentation, such that they are likely to best personalize LLM generations for a user request.

We propose two key novelties for training our retriever: (1) A training data selection method that identifies user requests likely to benefit from personalization and documents that provide that benefit; and (2) A scale-calibrating KL-divergence objective that ensures that our retriever closely tracks the benefit of a document for personalized generation. We demonstrate the effectiveness of PEARL in generating personalized workplace social media posts and Reddit comments.

Finally, we showcase the potential of a generation-calibrated retriever to double as a performance predictor and further improve low-quality generations via LLM chaining.

--- https://arxiv.org/abs/2310.07298 Beyond Memorization: Violating Privacy Via Inference with Large Language Models Robin Staab, Mark Vero, Mislav Balunović, Martin Vechev 2023-10-11 2023-10-11 [("doi","10.48550/arXiv.2310.07298")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/palm cs/security reinforcement-learning/exploration reinforcement-learning/imitation-learning statistics/stylometry/truesight

Current privacy research on large language models (LLMs) primarily focuses on the issue of extracting memorized training data. At the same time, models’ inference capabilities have increased drastically. This raises the key question of whether current LLMs could violate individuals’ privacy by inferring personal attributes from text given at inference time.

In this work, we present the first comprehensive study on the capabilities of pretrained LLMs to infer personal attributes from text. We construct a dataset consisting of real Reddit profiles, and show that current LLMs can infer a wide range of personal attributes (eg. location, income, sex), achieving up to 85% top-1 and 95.8% top-3 accuracy at a fraction of the cost (100×) and time (240×) required by humans.

As people increasingly interact with LLM-powered chatbots across all aspects of life, we also explore the emerging threat of privacy-invasive chatbots trying to extract personal information through seemingly benign questions.

Finally, we show that common mitigations, ie. text anonymization and model alignment, are currently ineffective at protecting user privacy against LLM inference. Our findings highlight that current LLMs can infer personal data at a previously unattainable scale. In the absence of working defenses, we advocate for a broader discussion around LLM privacy implications beyond memorization, striving for a wider privacy protection.

--- https://news.ycombinator.com/item?id=38275945 2023-08-01 ai/nn/transformer/gpt/4/nonfiction music psychology/personality --- https://www.cambridge.org/core/journals/bjpsych-open/article/psychotic-and-autistic-traits-among-magicians-and-their-relationship-with-creative-beliefs/A404241D8126664D0EDD1989288F431D 2023-08-01 psychiatry/autism psychology/personality --- /doc/cat/psychology/2023-11-16-gwern-dalle3-comic-cat-problemofinductiontestingknockingobjectsover.jpg Gwern 2023-11-16 2023-11-16 ai/nn/transformer/gpt/dall-e/3 cat/psychology math/humor philosophy/epistemology --- https://www.youtube.com/watch?v=xHWXZyfhQas 2023-08-01 reinforcement-learning/robot --- https://en.wikipedia.org/wiki/Uji_tea Uji tea 2023-08-01 tea --- https://www.biorxiv.org/content/10.1101/2023.11.15.567131.full Cryptography in the DNA of living cells enabled by multi-site base editing Verena Volf, Simon Zhang, Karen M. Song, Sharon Qian, Fei Chen, George Church 2023-11-15 2023-11-15 [("doi","10.1101/2023.11.15.567131")] cs/cryptography genetics/editing

Although DNA is increasingly being adopted as a generalizable medium for information storage and transfer, reliable methods for ensuring information security remain to be addressed. In this study, we developed and validated a cryptographic encoding scheme, Genomic Sequence Encryption (GSE), to address the challenge of information confidentiality and integrity in biological substrates. GSE enables genomic information encoding that is readable only with a cryptographic key.

We show that GSE can be used for cell signatures that enable the recipient of a cell line to authenticate its origin and validate if the cell line has been modified in the interim. We implement GSE through multi-site base editing and encode information through editing across >100 genomic sites in mammalian cells.

We further present an enrichment step to obtain individual stem cells with more than two dozen edits across a single genome with minimal screening. This capability can be used to introduce encrypted signatures in living animals. As an encryption scheme, GSE is falsification-proof and enables secure information transfer in biological substrates.

--- https://venturebeat.com/ai/openais-six-member-board-will-decide-when-weve-attained-agi/ 2023-08-02 law reinforcement-learning/openai --- https://www.technologyreview.com/2023/10/26/1082398/exclusive-ilya-sutskever-openais-chief-scientist-on-his-hopes-and-fears-for-the-future-of-ai/ 2023-08-02 reinforcement-learning/openai reinforcement-learning/safe --- https://arxiv.org/abs/2311.10090 JaxMARL: Multi-Agent RL Environments in JAX Alexander Rutherford, Benjamin Ellis, Matteo Gallici, Jonathan Cook, Andrei Lupu, Gardar Ingvarsson, Timon Willi, Akbir Khan, Christian Schroeder de Witt, Alexandra Souly, Saptarashmi Bandyopadhyay, Mikayel Samvelyan, Minqi Jiang, Robert Tjarko Lange, Shimon Whiteson, Bruno Lacerda, Nick Hawes, Tim Rocktaschel, Chris Lu, Jakob Nicolaus Foerster 2023-11-16 2023-11-16 [("doi","10.48550/arXiv.2311.10090")] reinforcement-learning/imperfect-information/hanabi reinforcement-learning/model-free/alphastar reinforcement-learning/multi-agent reinforcement-learning/scaling

Benchmarks play an important role in the development of machine learning algorithms. For example, research in reinforcement learning (RL) has been heavily influenced by available environments and benchmarks. However, RL environments are traditionally run on the CPU, limiting their scalability with typical academic compute. Recent advancements in JAX have enabled the wider use of hardware acceleration to overcome these computational hurdles, enabling massively parallel RL training pipelines and environments. This is particularly useful for multi-agent reinforcement learning (MARL) research.

First of all, multiple agents must be considered at each environment step, adding computational burden, and secondly, the sample complexity is increased due to non-stationarity, decentralised partial observability, or other MARL challenges. In this paper, we present JaxMARL, the first open-source code base that combines ease-of-use with GPU enabled efficiency, and supports a large number of commonly used MARL environments as well as popular baseline algorithms.

When considering wall clock time, our experiments show that per-run our JAX-based training pipeline is up to 12,500× faster than existing approaches. This enables efficient and thorough evaluations, with the potential to alleviate the evaluation crisis of the field.

We also introduce and benchmark SMAX, a vectorized, simplified version of the popular StarCraft Multi-Agent Challenge, which removes the need to run the StarCraft II game engine. This not only enables GPU acceleration, but also provides a more flexible MARL environment, unlocking the potential for self-play, meta-learning, and other future applications in MARL.

We provide code at Github. [Environments: MPE/Overcooked/Brax/STORM/Hanabi/Switch/Coin/SMAC; agents: UPPO/QMIX/VDN/IQL/MAPPO?]

--- https://arxiv.org/abs/2202.09671#microsoft Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Autoencoders Huangjie Zheng, Pengcheng He, Weizhu Chen, Mingyuan Zhou 2022-02-19 2023-08-02 [("doi","10.48550/arXiv.2202.09671")] ai/nn/diffusion ai/nn/gan ai/nn/vae

Employing a forward diffusion chain to gradually map the data to a noise distribution, diffusion-based generative models learn how to generate the data by inferring a reverse diffusion chain. However, this approach is slow and costly because it needs many forward and reverse steps.

We propose a faster and cheaper approach that adds noise not until the data become pure random noise, but until they reach a hidden noisy data distribution that we can confidently learn. Then, we use fewer reverse steps to generate data by starting from this hidden distribution that is made similar to the noisy data.

We reveal that the proposed model can be cast as an adversarial autoencoder empowered by both the diffusion process and a learnable implicit prior. Experimental results show even with a smaller number of reverse diffusion steps, the proposed truncated diffusion probabilistic models can provide consistent improvements over the non-truncated ones in terms of performance in both unconditional and text-guided image generations.

--- https://www.vox.com/future-perfect/2019/4/17/18301070/openai-greg-brockman-ilya-sutskever 2023-08-02 law reinforcement-learning/openai --- https://boingboing.net/2010/07/22/ted-chiang-interview.html 2023-08-02 fiction/science-fiction/time-travel --- /review/book#the-empty-box-and-the-zeroth-maria-mikage-2009 2023-08-02 fiction/science-fiction/time-travel --- https://www.baka-tsuki.org/project/index.php?title=Utsuro_no_Hako:Volume1 2023-08-02 fiction/science-fiction/time-travel --- https://www.alexirpan.com/2015/09/24/how-an-audio-play-about-a-time-traveling-pony-turned-me-into-a-fanboy.html 2023-08-02 anime/my-little-pony fiction/science-fiction/time-travel --- https://en.wikipedia.org/wiki/Timecrimes Timecrimes 2023-08-03 fiction/science-fiction/time-travel --- https://en.wikipedia.org/wiki/Coherence_(film) Coherence (film) 2023-08-03 fiction/science-fiction/time-travel --- https://en.wikipedia.org/wiki/Primer_(film) Primer (film) 2023-08-03 fiction/science-fiction/time-travel --- /doc/radiance/1970-benford.pdf 1970-01-01 2023-08-03 fiction/science-fiction/time-travel radiance --- https://en.wikipedia.org/wiki/A_Connecticut_Yankee_in_King_Arthur%27s_Court A Connecticut Yankee in King Arthur’s Court 2023-08-03 fiction/science-fiction/time-travel --- https://en.wikipedia.org/wiki/Time_travel#Absence_of_time_travelers_from_the_future Time travel § Absence of time travelers from the future 2023-08-03 fiction/science-fiction/time-travel --- https://arxiv.org/abs/2311.09247 Comparing Humans, GPT-4, and GPT-4-V On Abstraction and Reasoning Tasks Melanie Mitchell, Alessandro B. Palmarini, Arseny Moskvichev 2023-11-14 2023-11-14 [("doi","10.48550/arXiv.2311.09247")] ai/nn/transformer/gpt/4/nonfiction

We explore the abstract reasoning abilities of text-only and multimodal versions of GPT-4, using the ConceptARC benchmark, which is designed to evaluate robust understanding and reasoning with core-knowledge concepts.

We extend that work by evaluating GPT-4 on more detailed, one-shot prompting (rather than simple, zero-shot prompts) with text versions of ConceptARC tasks, and by evaluating GPT-4-V, the multimodal version of GPT-4, on zero-shot & one-shot prompts using image versions of the simplest tasks.

Our experimental results support the conclusion that neither version of GPT-4 has developed robust abstraction abilities at human-like levels.

--- https://openreview.net/forum?id=8ykyGbtt2q The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain Arsenii Kirillovich Moskvichev, Victor Vikram Odouard, Melanie Mitchell 2023-08-07 2023-08-07 ai/dataset ai/nn/transformer/gpt/4/nonfiction

The abilities to form and abstract concepts is key to human intelligence, but such abilities remain lacking in state-of-the-art AI systems. There has been substantial research on conceptual abstraction in AI, particularly using idealized domains such as Raven’s Progressive Matrices and Bongard problems, but even when AI systems succeed on such problems, the systems are rarely evaluated in depth to see if they have actually grasped the concepts they are meant to capture.

In this paper we describe an in-depth evaluation benchmark for the Abstraction and Reasoning Corpus (ARC), a collection of few-shot abstraction and analogy problems developed by Chollet 2019. In particular, we describe ConceptARC, a new, publicly available benchmark in the ARC domain that systematically assesses abstraction and generalization abilities on a number of basic spatial and semantic concepts.

ConceptARC differs from the original ARC dataset in that it is specifically organized around “concept groups”—sets of problems that focus on specific concepts and that are vary in complexity and level of abstraction. We report results on testing humans on this benchmark as well as 3 machine solvers: the top two programs from a 2021 ARC competition and OpenAI’s GPT-4.

Our results show that humans substantially outperform the machine solvers on this benchmark, showing abilities to abstract and generalize concepts that are not yet captured by AI systems.

We believe that this benchmark will spur improvements in the development of AI systems for conceptual abstraction and in the effective evaluation of such systems.

--- https://en.wikipedia.org/wiki/Prophetic_perfect_tense Prophetic perfect tense 2023-08-03 fiction/science-fiction/time-travel --- https://journals.sagepub.com/doi/full/10.1177/00104140231169021 2023-08-03 crime/terrorism --- https://www.nature.com/articles/s41598-023-39344-7 2023-08-03 psychology/neuroscience --- https://journals.sagepub.com/doi/10.1177/17488958231213012 2023-08-04 bitcoin darknet-market --- https://deepmind.google/discover/blog/transforming-the-future-of-music-creation/ 2023-08-04 ai/music --- https://blog.youtube/inside-youtube/ai-and-music-experiment/ 2023-08-04 ai/music --- https://www.youtube.com/watch?v=aeYU3_7IKCM 2023-08-04 ai/music --- https://arxiv.org/abs/1710.03463 Learning to Generalize: Meta-Learning for Domain Generalization Da Li, Yongxin Yang, Yi-Zhe Song, Timothy M. Hospedales 2017-10-10 2023-08-04 [("doi","10.48550/arXiv.1710.03463")] ai/nn/cnn reinforcement-learning/meta-learning

Domain shift refers to the well-known problem that a model trained in one source domain performs poorly when applied to a target domain with different statistics. Domain Generalization (DG) techniques attempt to alleviate this issue by producing models which by design generalize well to novel testing domains.

We propose a novel meta-learning method for domain generalization. Rather than designing a specific model that is robust to domain shift as in most previous DG work, we propose a model agnostic training procedure for DG. Our algorithm simulates train/test domain shift during training by synthesizing virtual testing domains within each mini-batch.

The meta-optimization objective requires that steps to improve training domain performance should also improve testing domain performance. This meta-learning procedure trains models with good generalization ability to novel domains.

We evaluate our method and achieve state-of-the-art results on a recent cross-domain image classification benchmark, as well demonstrating its potential on two classic reinforcement learning tasks.

--- https://www.nature.com/articles/s41598-023-35597-4 2023-08-04 ai/nn/rnn science --- https://peerj.com/articles/12764/ 2023-08-04 science --- https://github.com/desik1998/MathWithLLMs 2023-08-04 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/inner-monologue math --- https://arxiv.org/abs/2311.04378 Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models Hanlin Zhang, Benjamin L. Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, Boaz Barak 2023-11-07 2023-11-07 [("doi","10.48550/arXiv.2311.04378")] ai/nn/transformer/gpt cs/cryptography

Watermarking generative models consists of planting a statistical signal (watermark) in a model’s output so that it can be later verified that the output was generated by the given model. A strong watermarking scheme satisfies the property that a computationally bounded attacker cannot erase the watermark without causing quality degradation. In this paper, we study the (im)possibility of strong watermarking schemes.

We prove that, under well-specified and natural assumptions, strong watermarking is impossible to achieve. This holds even in the private detection algorithm setting, where the watermark insertion and detection algorithms share a secret key, unknown to the attacker.

To prove this result, we introduce a generic efficient watermark attack; the attacker is not required to know the private key of the scheme or even which scheme is used. Our attack is based on two assumptions: (1) The attacker has access to a “quality oracle” that can evaluate whether a candidate output is a high-quality response to a prompt, and (2) The attacker has access to a “perturbation oracle” which can modify an output with a nontrivial probability of maintaining quality, and which induces an efficiently mixing random walk on high-quality outputs.

We argue that both assumptions can be satisfied in practice by an attacker with weaker computational capabilities than the watermarked model itself, to which the attacker has only black-box access. Furthermore, our assumptions will likely only be easier to satisfy over time as models grow in capabilities and modalities.

We demonstrate the feasibility of our attack by instantiating it to attack 3 existing watermarking schemes for large language models: Kirchenbauer et al 2023, Kuditipudi et al 2023, and Zhao et al 2023. The same attack successfully removes the watermarks planted by all 3 schemes, with only minor quality degradation.

--- https://journals.sagepub.com/doi/10.1177/20451253231198466 2023-08-04 psychedelic statistics/bias --- https://joelsimon.net/new-words.html 2023-08-04 ai/nn/transformer/clip psychology/linguistics --- https://arxiv.org/abs/2311.07361#microsoft The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4 Microsoft Research AI4Science, Microsoft Azure Quantum 2023-11-13 2023-11-13 [("doi","10.48550/arXiv.2311.07361")] ai/nn/transformer/gpt/4/nonfiction biology genetics math reinforcement-learning/preference-learning/mode-collapse science

In recent years, groundbreaking advancements in natural language processing have culminated in the emergence of powerful large language models (LLMs), which have showcased remarkable capabilities across a vast array of domains, including the understanding, generation, and translation of natural language, and even tasks that extend beyond language processing. In this report, we delve into the performance of LLMs within the context of scientific discovery, focusing on GPT-4, the state-of-the-art language model.

Our investigation spans a diverse range of scientific areas encompassing drug discovery, biology, computational chemistry (density functional theory (DFT) and molecular dynamics (MD)), materials design, and partial differential equations (PDE). Evaluating GPT-4 on scientific tasks is crucial for uncovering its potential across various research domains, validating its domain-specific expertise, accelerating scientific progress, optimizing resource allocation, guiding future model development, and fostering interdisciplinary research.

Our exploration methodology primarily consists of expert-driven case assessments, which offer qualitative insights into the model’s comprehension of intricate scientific concepts and relationships, and occasionally benchmark testing, which quantitatively evaluates the model’s capacity to solve well-defined domain-specific problems.

Our preliminary exploration indicates that GPT-4 exhibits promising potential for a variety of scientific applications, demonstrating its aptitude for handling complex problem-solving and knowledge integration tasks. Broadly speaking, we evaluate GPT-4’s knowledge base, scientific understanding, scientific numerical calculation abilities, and various scientific prediction capabilities.

--- https://arxiv.org/abs/2305.14259 Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery Qingyun Wang, Doug Downey, Heng Ji, Tom Hope 2023-05-23 2023-08-05 [("doi","10.48550/arXiv.2305.14259")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/t5 reinforcement-learning/exploration science

Literature-Based Discovery (LBD) aims to discover new scientific knowledge by mining papers and generating hypotheses. Standard LBD is limited to predicting pairwise relations between discrete concepts (eg. drug-disease links), and ignores critical contexts like experimental settings (eg. a specific patient population where a drug is evaluated) and background motivations (eg. to find drugs without specific side effects).

We address these limitations with a novel formulation of contextualized-LBD (C-LBD): generating scientific hypotheses in natural language, while grounding them in a context that controls the hypothesis search space. We present a modeling framework using retrieval of “inspirations” from past scientific papers.

Our evaluations reveal that GPT-4 tends to generate ideas with overall low technical depth and novelty, while our inspiration prompting approaches partially mitigate this issue. Our work represents a first step toward building language models that generate new ideas derived from scientific literature.

--- /doc/fiction/science-fiction/2023-11-17-gwern-dalle3-grandadmiralthrawn-reachingfortheblacksunandempire.jpg Gwern 2023-11-17 2023-11-17 ai/nn/diffusion/midjourney/black-sun ai/nn/transformer/gpt/dall-e/3 fiction/science-fiction --- https://www.biorxiv.org/content/10.1101/2023.11.13.566950.full Causal interpretations of family GWAS in the presence of heterogeneous effects Carl Veller, Molly Przeworski, Graham Coop 2023-11-16 2023-11-16 [("doi","10.1101/2023.11.13.566950")] genetics/heritable/correlation/mendelian-randomization

Family-based genome-wide association studies (GWAS) have emerged as a gold standard for assessing causal effects of alleles and polygenic scores. Notably, family studies are often claimed to provide an unbiased estimate of the average causal effect (or average treatment effect; ATE) of an allele, on the basis of an analogy between the random transmission of alleles from parents to children and a randomized controlled trial. Here, we show that this interpretation does not hold in general. Because Mendelian segregation only randomizes alleles among children of heterozygotes, the effects of alleles in the children of homozygotes are not observable.

Consequently, if an allele has different average effects in the children of homozygotes and heterozygotes, as can arise in the presence of gene-by-environment interactions, gene-by-gene interactions, or differences in LD patterns, family studies provide a biased estimate of the average effect in the sample. At a single locus, family-based association studies can be thought of as providing an unbiased estimate of the average effect in the children of heterozygotes (ie. a local average treatment effect; LATE). This interpretation does not extend to polygenic scores, however, because different sets of SNPs are heterozygous in each family.

Therefore, other than under specific conditions, the within-family regression slope of a PGS cannot be assumed to provide an unbiased estimate for any subset or weighted average of families. Instead, family-based studies can be reinterpreted as enabling an unbiased estimate of the extent to which Mendelian segregation at loci in the PGS contributes to the population-level variance in the trait. Because this estimate does not include the between-family variance, however, this interpretation applies to only (roughly) half of the sample PGS variance.

In practice, the potential biases of a family-based GWAS are likely smaller than those arising from confounding in a standard, population-based GWAS, and so family studies remain important for the dissection of genetic contributions to phenotypic variation. Nonetheless, the causal interpretation of family-based GWAS estimates is less straightforward than has been widely appreciated.

--- https://elifesciences.org/articles/71920 2023-08-05 genetics/microbiome --- https://www.biorxiv.org/content/10.1101/2023.11.11.566713.full A non-adaptive explanation for macroevolutionary patterns in the evolution of complex multicellularity Emma P. Bingham, William C. Ratcliff 2023-11-13 2023-11-13 [("doi","10.1101/2023.11.11.566713")] genetics/selection/natural

[Twitter] “Complex multicellularity”, conventionally defined as large organisms with many specialized cell types, has evolved independently in eukaryotes, but never within prokaryotes. A number hypotheses have been proposed to explain this phenomenon, most of which posit that eukaryotes evolved key traits (eg. dynamic cytoskeletons, alternative mechanisms of gene regulation, or subcellular compartments) which were a necessary prerequisite for the evolution of complex multicellularity.

Here we propose an alternative, non-adaptive hypothesis for this broad macroevolutionary pattern. By binning cells into groups with finite genetic bottlenecks between generations, the evolution of multicellularity greatly reduces the effective population size (Ne) of cellular populations, increasing the role of genetic drift in evolutionary change. While both prokaryotes and eukaryotes experience this phenomenon, they have opposite responses to drift: mutational biases in eukaryotes tend to drive genomic expansion, providing additional raw genetic material for subsequent multicellular innovation, while prokaryotes generally face genomic erosion.

These effects become more severe as organisms evolve larger size and more stringent genetic bottlenecks between generations—both of which are hallmarks of complex multicellularity.

Taken together, we hypothesize that it is these idiosyncratic lineage-specific mutational biases, rather than cell-biological innovations within eukaryotes, that underpins the long-term divergent evolution of complex multicellularity across the tree of life.

--- https://letterlibrary.org/view/object/symphonie-r1119/ 2023-08-05 design/typography/dropcap --- https://arstechnica.com/information-technology/2023/11/from-toy-to-tool-dall-e-3-is-a-wake-up-call-for-visual-artists-and-the-rest-of-us/ 2023-08-05 ai/nn/transformer/gpt/dall-e/3 --- https://www.damninteresting.com/three-thrown-over-the-cuckoos-nest/ 2023-08-05 psychiatry/schizophrenia --- https://www.oneusefulthing.org/p/working-with-ai-two-paths-to-prompting 2023-08-05 ai/nn/transformer/gpt --- https://onlinelibrary.wiley.com/doi/10.1111/desc.13451 2023-08-06 iq/ses --- https://x.com/42irrationalist/status/1726286663054233832 GPT-4-V optical illusion curious irrationalist 2023-11-19 2023-11-19 ai/nn/transformer/gpt/4/nonfiction psychology/vision --- https://arxiv.org/abs/2205.15439 StyleTTS: A Style-Based Generative Model for Natural and Diverse Text-to-Speech Synthesis Yinghao Aaron Li, Cong Han, Nima Mesgarani 2022-05-30 2023-08-06 [("doi","10.48550/arXiv.2205.15439")] ai/nn/gan

Text-to-Speech (TTS) has recently seen great progress in synthesizing high-quality speech owing to the rapid development of parallel TTS systems, but producing speech with naturalistic prosodic variations, speaking styles and emotional tones remains challenging. Moreover, since duration and speech are generated separately, parallel TTS models still have problems finding the best monotonic alignments that are crucial for naturalistic speech synthesis.

Here, we propose StyleTTS, a style-based generative model for parallel TTS that can synthesize diverse speech with natural prosody from a reference speech utterance. With novel Transferable Monotonic Aligner (TMA) and duration-invariant data augmentation schemes, our method outperforms state-of-the-art models on both single and multi-speaker datasets in subjective tests of speech naturalness and speaker similarity.

Through self-supervised learning of the speaking styles, our model can synthesize speech with the same prosodic and emotional tone as any given reference speech without the need for explicitly labeling these categories.

--- https://arxiv.org/abs/2306.07691 StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models Yinghao Aaron Li, Cong Han, Vinay S. Raghavan, Gavin Mischler, Nima Mesgarani 2023-06-13 2023-08-06 [("doi","10.48550/arXiv.2306.07691")] ai/nn/diffusion ai/nn/gan

In this paper, we present StyleTTS 2, a text-to-speech (TTS) model that leverages style diffusion and adversarial training with large speech language models (SLMs) to achieve human-level TTS synthesis. StyleTTS 2 differs from its predecessor by modeling styles as a latent random variable through diffusion models to generate the most suitable style for the text without requiring reference speech, achieving efficient latent diffusion while benefiting from the diverse speech synthesis offered by diffusion models. Furthermore, we employ large pre-trained SLMs, such as WavLM, as discriminators with our novel differentiable duration modeling for end-to-end training, resulting in improved speech naturalness.

StyleTTS 2 surpasses human recordings on the single-speaker LJSpeech dataset and matches it on the multispeaker VCTK dataset as judged by native English speakers. Moreover, when trained on the LibriTTS dataset, our model outperforms previous publicly available models for zero-shot speaker adaptation.

This work achieves the first human-level TTS on both single and multispeaker datasets, showcasing the potential of style diffusion and adversarial training with large SLMs. The audio demos and source code are available at https://styletts2.github.io/.

--- https://arxiv.org/abs/0911.1383 Information Geometry and Evolutionary Game Theory Marc Harper 2009-11-09 2023-08-06 [("doi","10.48550/arXiv.0911.1383")] cs/algorithm/information genetics/selection/natural

The Shahshahani geometry of evolutionary game theory is realized as the information geometry of the simplex, deriving from the Fisher information metric of the manifold of categorical probability distributions. Some essential concepts in evolutionary game theory are realized information-theoretically.

Results are extended to the Lotka-Volterra equation and to multiple population systems.

--- https://en.wikipedia.org/wiki/Grain_entrapment Grain entrapment 2023-08-06 technology --- https://www.cold-takes.com/nonprofit-boards-are-weird-2/ 2023-08-06 economics/mechanism-design sociology --- https://www.biorxiv.org/content/10.1101/440776.full Evidence for Bias of Genetic Ancestry in Resting State Functional MRI Andre Altmann, Janaina Mourao-Miranda 2018-10-11 2023-08-06 [("doi","10.1101/440776")] genetics/heritable psychology/neuroscience

Resting state functional magnetic resonance imaging (rs-fMRI) is a popular imaging modality for mapping the functional connectivity of the brain. Rs-fMRI is, just like other neuroimaging modalities, subject to a series of technical and subject level biases that change the inferred connectivity pattern.

In this work we predicted genetic ancestry from rs-fMRI connectivity data at very high performance (area under the ROC curve of 0.93).

Thereby, we demonstrated that genetic ancestry is encoded in the functional connectivity pattern of the brain at rest. Consequently, genetic ancestry constitutes a bias that should be accounted for in the analysis of rs-fMRI data.

--- https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms 2023-08-06 ai/nn/sparsity/pruning ai/nn/transformer --- https://math.ucr.edu/home/baez/information/information_geometry_8.html 2023-08-06 cs/algorithm genetics/selection/natural statistics/bayes --- https://math.ucr.edu/home/baez/information/ 2023-08-06 cs/algorithm genetics/selection/natural statistics/bayes --- https://x.com/OpenAI/status/1676638359391985671 OpenAI 2023-08-07 reinforcement-learning/openai reinforcement-learning/safe --- https://arxiv.org/abs/2311.10642 Rethinking Attention: Exploring Shallow Feed-Forward Neural Networks as an Alternative to Attention Layers in Transformers Vukasin Bozic, Danilo Dordervic, Daniele Coppola, Joseph Thommes 2023-11-17 2023-11-17 [("doi","10.48550/arXiv.2311.10642")] ai/nn/fully-connected ai/nn/transformer/attention

This work presents an analysis of the effectiveness of using standard shallow feed-forward networks to mimic the behavior of the attention mechanism in the original Transformer model, a state-of-the-art architecture for sequence-to-sequence tasks.

We substitute key elements of the attention mechanism in the Transformer with simple feed-forward networks, trained using the original components via knowledge distillation. Our experiments, conducted on the IWSLT2017 dataset, reveal the capacity of these “attention-less Transformers” to rival the performance of the original architecture.

Through rigorous ablation studies, and experimenting with various replacement network types and sizes, we offer insights that support the viability of our approach.

This not only sheds light on the adaptability of shallow feed-forward networks in emulating attention mechanisms but also underscores their potential to streamline complex architectures for sequence-to-sequence tasks.

--- https://arxiv.org/abs/2311.01981 ProSG: Using Prompt Synthetic Gradients to Alleviate Prompt Forgetting of RNN-like Language Models Haotian Luo, Kunming Wu, Cheng Dai, Sixian Ding, Xinhao Chen 2023-11-03 2023-11-03 [("doi","10.48550/arXiv.2311.01981")] ai/nn/rnn ai/nn/sparsity ai/nn/transformer reinforcement-learning/meta-learning

RNN-like language models are getting renewed attention from NLP researchers in recent years and several models have made progress, which demonstrates performance comparable to traditional transformers. However, due to the recurrent nature of RNNs, this kind of language model can only store information in a set of fixed-length state vectors. As a consequence, they still suffer from forgetfulness though after a lot of improvements and optimizations, when given complex instructions or prompts.

As the prompted generation is the main and most concerned function of LMs, solving the problem of forgetting in the process of generation is no wonder of vital importance. In this paper, focusing on easing the prompt forgetting during generation, we proposed an architecture to teach the model memorizing prompt during generation by synthetic gradient.

To force the model to memorize the prompt, we derive the states that encode the prompt, then transform it into model parameter modification using low-rank gradient approximation, which hard-codes the prompt into model parameters temporarily.

We construct a dataset for experiments, and the results have demonstrated the effectiveness of our method in solving the problem of forgetfulness in the process of prompted generation.

We will release all the code upon acceptance.

--- https://fxrant.blogspot.com/2017/01/the-death-star-and-final-trench-run.html 2023-08-07 fiction/science-fiction psychology/cognitive-bias/illusion-of-depth --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10620072/ Human-like systematic generalization through a meta-learning neural network Brenden M. Lake, Marco Baroni 2023 2023-08-07 [("doi","10.1038/s41586-023-06668-3")] psychology/linguistics reinforcement-learning/meta-learning

The power of human language and thought arises from systematic compositionality—the algebraic ability to understand and produce novel combinations from known components. Fodor & Pylyshyn famously argued that artificial neural networks lack this capacity and are therefore not viable models of the mind. Neural networks have advanced considerably in the years since, yet the systematicity challenge persists.

Here we successfully address Fodor and Pylyshyn’s challenge by providing evidence that neural networks can achieve human-like systematicity when optimized for their compositional skills. To do so, we introduce the meta-learning for compositionality (MLC) approach for guiding training through a dynamic stream of compositional tasks.

To compare humans and machines, we conducted human behavioral experiments using an instruction learning paradigm. After considering 7 different models, we found that, in contrast to perfectly systematic but rigid probabilistic symbolic models, and perfectly flexible but unsystematic neural networks, only MLC achieves both the systematicity and flexibility needed for human-like generalization.

MLC also advances the compositional skills of machine learning systems in several systematic generalization benchmarks. Our results show how a standard neural network architecture, optimized for its compositional skills, can mimic human systematic generalization in a head-to-head comparison.

--- https://x.com/geoffreyirving/status/1726754279359197587 Geoffrey Irving 2023-08-07 reinforcement-learning/openai --- https://x.com/sama/status/1726668687577665572 Sam Altman 2023-08-07 reinforcement-learning/openai --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0041684 Environmental Effects on Compulsive Tail Chasing in Dogs Katriina Tiira, Osmo Hakosalo, Lauri Kareinen, Anne Thomas, Anna Hielm-Björkman, Catherine Escriou, Paul Arnold, Hannes Lohi 2012-06-24 2023-08-07 [("doi","10.1371/journal.pone.0041684")] dog

Obsessive Compulsive Disorder (OCD) is a neuropsychiatric disorder observed both in humans and animals. Examples of Canine Compulsive Disorder (CD) include excessive tail chasing (TC), light/shadow chasing and flank sucking. We performed a questionnaire survey to investigate the characteristics of compulsive (TC) and its possible associations with environmental correlates and personality in a pet population of 368 dogs from 4 dog breeds.

We observed an early onset of TC at 3–6 months of age and a large variation in TC frequency in all breeds, with an overrepresentation of milder cases. Almost half of the TC dogs showed lowered responsiveness during bouts and displayed also other types of compulsions more often than the controls.

Interestingly, dogs that received dietary supplements, especially vitamins and minerals, expressed less TC compared to dogs that did not receive any supplements. Neutered females had less TC, suggesting an influence of ovarian hormones on TC. Tail chasers were shyer and had separated earlier from their mothers than the controls.

Finally, our genetic study did not find an association between TC and CDH2, a locus previously associated with the canine flank sucking compulsion. In conclusion, the early-onset and the variable nature of the repetitive behavior, which is affected by environmental factors such as micronutrients, neutering and maternal care, share several similar components between canine and human compulsions and supports canine TC as a model for human OCD.

--- https://www.scu.edu/ethics/focus-areas/business-ethics/resources/seven-signs-of-ethical-collapse/ 2023-08-07 philosophy/ethics reinforcement-learning/openai sociology --- https://arxiv.org/abs/2310.06089 Predictive auxiliary objectives in deep RL mimic learning in the brain Ching Fang, Kimberly L. Stachenfeld 2023-10-09 2023-10-09 [("doi","10.48550/arXiv.2310.06089")] psychology/neuroscience reinforcement-learning/model reinforcement-learning/model-free

The ability to predict upcoming events has been hypothesized to comprise a key aspect of natural and machine cognition. This is supported by trends in deep reinforcement learning (RL), where self-supervised auxiliary objectives such as prediction are widely used to support representation learning and improve task performance.

Here, we study the effects predictive auxiliary objectives have on representation learning across different modules of an RL system and how these mimic representational changes observed in the brain. We find that predictive objectives improve and stabilize learning particularly in resource-limited architectures, and we identify settings where longer predictive horizons better support representational transfer.

Furthermore, we find that representational changes in this RL system bear a striking resemblance to changes in neural activity observed in the brain across various experiments. Specifically, we draw a connection between the auxiliary predictive model of the RL system and the hippocampus, an area thought to learn a predictive model to support memory-guided behavior. We also connect the encoder network and the value learning network of the RL system to visual cortex and striatum in the brain, respectively.

This work demonstrates how representation learning in deep RL systems can provide an interpretable framework for modeling multi-region interactions in the brain. The deep RL perspective taken here also suggests an additional role of the hippocampus in the brain—that of an auxiliary learning system that benefits representation learning in other regions.

--- https://en.wikipedia.org/wiki/Machiavellianism_in_the_workplace Machiavellianism in the workplace 2023-08-07 politics psychology/personality/narcissism psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/DARVO DARVO 2023-08-08 politics psychology/personality/narcissism psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/Barack_Obama Barack Obama 2023-08-08 psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Donald_Trump Donald Trump 2023-08-08 psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Sam_Altman Sam Altman 2023-08-08 ai/scaling/economics psychology/personality/narcissism reinforcement-learning/openai --- https://en.wikipedia.org/wiki/Narcissistic_personality_disorder Narcissistic personality disorder 2023-08-08 psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Narcissism_in_the_workplace Narcissism in the workplace 2023-08-08 psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Narcissistic_supply Narcissistic supply 2023-08-08 psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Narcissistic_rage_and_narcissistic_injury Narcissistic rage and narcissistic injury 2023-08-08 psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Narcissistic_leadership Narcissistic leadership 2023-08-08 psychology/personality/narcissism --- /review/book#bad-blood-carreyrou-2018 Gwern’s review of Bad Blood: Secrets and Lies in a Silicon Valley Startup 2023-08-08 psychology/personality/narcissism --- https://arxiv.org/abs/2003.03924 Q✱ Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison Tengyang Xie, Nan Jiang 2020-03-09 2023-08-08 [("doi","10.48550/arXiv.2003.03924")] reinforcement-learning/model-free reinforcement-learning/offline

We prove performance guarantees of two algorithms for approximating Q✱ in batch reinforcement learning.

Compared to classical iterative methods such as Fitted Q-Iteration—whose performance loss incurs quadratic dependence on horizon—these methods estimate (some forms of) the Bellman error and enjoy linear-in-horizon error propagation, a property established for the first time for algorithms that rely solely on batch data and output stationary policies.

One of the algorithms uses a novel and explicit importance-weighting correction to overcome the infamous “double sampling” difficulty in Bellman error estimation, and does not use any squared losses.

Our analyses reveal its distinct characteristics and potential advantages compared to classical algorithms.

--- https://www.vanityfair.com/hollywood/2022/09/inside-ezra-millers-dark-spiral-messiah-delusions 2023-08-09 psychology/personality/narcissism psychology/personality/psychopathy --- https://www.washingtonpost.com/national-security/2022/09/06/trump-nuclear-documents/ 2023-08-09 psychology/personality/narcissism --- https://openpsychometrics.org/info/survey-topic-participation-bias/ 2023-08-09 iq psychology/personality/psychopathy statistics/bias --- https://www.tandfonline.com/doi/full/10.1080/09515089.2021.1946026 2023-08-09 philosophy/ethics psychology/personality/psychopathy --- https://www.reddit.com/r/TheMotte/comments/njr5h0/culture_war_roundup_for_the_week_of_may_24_2021/gzhl1kz/ 2023-08-09 fiction psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Ezra_Miller Ezra Miller 2023-08-09 psychology/personality/narcissism --- https://en.wikipedia.org/wiki/David_Foster_Wallace David Foster Wallace 2023-08-09 psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Walt_Disney Walt Disney 2023-08-09 psychology/personality/narcissism --- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4333314 2023-08-09 economics psychology/personality/narcissism --- https://aeon.co/essays/hermann-hesse-and-the-double-edged-sword-of-dwelling-on-ones-self 2023-08-09 psychology/personality/narcissism --- /doc/psychology/personality/narcissism/2022-armaly.pdf Filling in the Gaps: False Memories and Partisan Bias Miles T. Armaly, Adam M. Enders 2022-05-27 2023-08-10 [("doi","10.1111/pops.12841")] iq politics psychology/personality/narcissism

While cognitive psychologists have learned a great deal about people’s propensity for constructing and acting on false memories, the connection between false memories and politics remains understudied. If partisan bias guides the adoption of beliefs and colors one’s interpretation of new events and information, so too might it prove powerful enough to fabricate memories of political circumstances.

Across two studies, we first distinguish false memories from false beliefs and expressive responses; false political memories appear to be genuine and subject to partisan bias. We also examine the political and psychological correlates of false memories. Nearly a third of respondents reported remembering a fabricated or factually altered political event, with many going so far as to convey the circumstances under which they “heard about” the event.

False-memory recall is correlated with the strength of partisan attachments, interest in politics, and participation, as well as narcissism, conspiratorial thinking, and cognitive ability.

--- https://en.wikipedia.org/wiki/Thomas_Mann Thomas Mann 2023-08-10 psychology/personality/narcissism --- https://www.postlight.com/trackchanges/be-our-guest 2023-08-10 politics psychology/personality/narcissism --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4301205/ The frequency of personality disorders in patients with gender identity disorder Azadeh Mazaheri Meybodi, Ahmad Hajebi, Atefeh Ghanbari Jolfaei 2014 2023-08-10 psychology/personality/narcissism psychology/personality/psychopathy

Background: Co-morbid psychiatric disorders affect prognosis, psychosocial adjustment and post-surgery satisfaction in patients with gender identity disorder. In this paper, we assessed the frequency of personality disorders in Iranian GID patients.

Methods: 73 patients requesting sex reassignment surgery (SRS) were recruited for this crosssectional study. Of the participants, 57.5% were biologically male and 42.5% were biologically female. They were assessed through the Millon Clinical Multiaxial Inventory II (MCMI-II).

Results: The frequency of personality disorders was 81.4%. The most frequent personality disorder was narcissistic personality disorder (57.1%) and the least was borderline personality disorder. The average number of diagnoses was 3.00 per patient.

Conclusion: The findings of this study revealed that the prevalence of personality disorders was higher among the participants, and the most frequent personality disorder was narcissistic personality disorder (57.1%), and borderline personality disorder was less common among the studied patients.

--- https://en.wikipedia.org/wiki/Stephen_Paddock Stephen Paddock 2023-08-10 psychology/personality/narcissism --- https://www.spectator.co.uk/2019/03/richard-sorge-the-soviet-unions-master-spy/ 2023-08-10 psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Henry_David_Thoreau Henry David Thoreau 2023-08-10 psychology/personality/narcissism --- https://www.thestranger.com/books/2015/02/27/21792750/things-i-can-say-about-mfa-writing-programs-now-that-i-no-longer-teach-in-one 2023-08-10 psychology/personality/narcissism psychology/writing --- /doc/psychology/personality/narcissism/2018-highhouse.pdf Dark Motives and Elective Use of Brainteaser Interview Questions Scott Highhouse, Christopher D. Nye, Don C. Zhang 2018-09-07 2023-08-10 [("doi","10.1111/apps.12163")] economics psychology/personality/narcissism technology

Brainteaser interview questions such as “Estimate how many windows are in New York” are just one example of aggressive interviewer behavior that lacks evidence for validity and is unsettling to job applicants. This research attempts to shed light on the motives behind such behavior by examining the relation between dark-side traits and the perceived appropriateness of brainteaser interview questions.

A representative sample of working adults (n = 736) was presented with a list of interview questions that were either traditional (eg. “Are you a good listener?”), behavioral (eg. “Tell me about a time when you failed”), or brainteaser in nature. Results of a multiple regression, controlling for interviewing experience and sex, showed that narcissism and sadism explained the likelihood of using brainteasers in an interview. A subsequent bifactor analysis showed that these dark traits shared a callousness general factor.

A second longitudinal study of employed adults with hiring experience demonstrated that perspective-taking partially mediated the relationship between this general factor and the perceived helpfulness and abusiveness of brainteaser interview questions.

These results suggest that a callous indifference and a lack of perspective-taking may underlie abusive behavior in the employment interview.

--- https://www.ft.com/content/2df89e5a-9a12-4751-87ba-bd618b8a8968 2023-08-10 psychology/personality/narcissism --- http://warontherocks.com/2020/07/thrones-wreathed-in-shadow-tacitus-and-the-psychology-of-authoritarianism/ 2023-08-10 psychology/personality/narcissism --- https://www.wired.com/story/uncovering-operation-neptun-the-cold-wars-most-daring-disinformation-campaign/ 2023-08-11 politics --- https://www.nytimes.com/2018/05/30/magazine/sex-cult-empowerment-nxivm-keith-raniere.html 2023-08-11 psychology/personality/narcissism --- https://www.newyorker.com/magazine/2017/12/18/chinas-selfie-obsession 2023-08-11 psychology/personality/narcissism --- https://www.theguardian.com/lifeandstyle/2017/jun/03/quasi-religious-great-self-esteem-con 2023-08-11 politics psychology/personality/narcissism statistics/bias --- https://www.theguardian.com/lifeandstyle/2016/jun/27/narcissists-dating-attractiveness-flattery-charisma 2023-08-11 psychology/personality/narcissism --- http://www.nytimes.com/2016/09/28/books/hitler-ascent-volker-ullrich.html 2023-08-11 psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Adolf_Hitler Adolf Hitler 2023-08-11 psychology/personality/narcissism --- https://www.vanityfair.com/news/2015/11/donald-trump-narcissism-therapists 2023-08-11 politics psychology/personality/narcissism --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2821100/ The normalization of deviance in healthcare delivery John Banja 2010 2023-08-11 [("doi","10.1016/j.bushor.2009.10.006")] economics psychology/personality/narcissism psychology/personality/psychopathy reinforcement-learning/openai reinforcement-learning/safe sociology/technology

[cf. Jennings 2007] Many serious medical errors result from violations of recognized standards of practice. Over time, even egregious violations of standards of practice may become “normalized” in healthcare delivery systems.

This article describes what leads to this normalization and explains why flagrant practice deviations can persist for years, despite the importance of the standards at issue.

This article also provides recommendations to aid healthcare organizations in identifying and managing unsafe practice deviations before they become normalized and pose genuine risks to patient safety, quality care, and employee morale.

…Factors that account for the normalization of deviance: 3.4: I’m breaking the rule for the good of my patient!

This justification for rule deviation recalls the situation described in Example #5, where the rule or standard is perceived as counterproductive. The phlebotomist in the following example might similarly plead that rule-following diminished the quality of her patient care:

3.5: The rules don’t apply to me/You can trust me

While pathological narcissists who believe they are above rule-following can be found in any organization (Banja 2005, How to break bad news; Weber 2004), a more subtle form of “the rules don’t apply to me” is when system operators believe they are not tempted to engage in the behavior that the rule or standard is supposed to deter. Thus, the rule is understood as superfluous. As in Example #5, the rule violator feels perfectly justified in performing the problematic behavior, because the deviant practice of drug diversion would never cross his or her mind.

Administrators should appreciate a psychological finding that has been replicated in various forms throughout the 20th century: most human beings perceive themselves as good and decent people, such that they can understand many of their rule violations as entirely rational and ethically acceptable responses to problematic situations. They understand themselves to be doing nothing wrong, and will be outraged and often fiercely defend themselves when confronted with evidence to the contrary (Ashforth & Anand 2003).

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0029265#close The Distance Between Mars and Venus: Measuring Global Sex Differences in Personality Marco Del Giudice, Tom Booth, Paul Irwing 2011-11-23 2023-08-11 [("doi","10.1371/journal.pone.0029265")] psychology/personality/narcissism

Background: Sex differences in personality are believed to be comparatively small. However, research in this area has suffered from methodological limitations.

We advance a set of guidelines for overcoming those limitations: (a) measure personality with a higher resolution than that afforded by the Big Five; (b) estimate sex differences on latent factors; and (c) assess global sex differences with multivariate effect sizes. We then apply these guidelines to a large, representative adult sample, and obtain what is presently the best estimate of global sex differences in personality.

Methodology: Personality measures were obtained from a large US sample (n = 10,261) with the 16PF Questionnaire. Multigroup latent variable modeling was used to estimate sex differences on individual personality dimensions, which were then aggregated to yield a multivariate effect size (Mahalanobis D).

Results: We found a global effect size D = 2.71, corresponding to an overlap of only 10% between the male and female distributions. Even excluding the factor showing the largest univariate ES, the global effect size was D = 1.71 (24% overlap). These are extremely large differences by psychological standards.

Conclusion: The idea that there are only minor differences between the personality profiles of males and females should be rejected as based on inadequate methodology.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3619172/ 2023-08-12 crime/terrorism psychology/personality/narcissism --- https://slate.com/technology/2014/02/internet-troll-personality-study-machiavellianism-narcissism-psychopathy-sadism.html 2023-08-12 psychology/personality/narcissism psychology/personality/psychopathy sociology/technology --- https://en.wikipedia.org/wiki/Steve_Jobs Steve Jobs 2023-08-12 psychology/personality/narcissism --- http://nabeelqu.com/blog/the-smears-against-edward-snowden-have-begun 2023-08-12 politics psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Julian_Assange Julian Assange 2023-08-12 bitcoin psychology/personality/narcissism --- https://www.chronicle.com/article/The-Psychopath-Makeover/135160/ 2023-08-12 crime psychology/personality/narcissism psychology/personality/psychopathy --- https://www.theguardian.com/books/2012/jan/03/jon-ronson-psychopath-test-paperback-qna 2023-08-12 psychology/personality/psychopathy --- http://chronicle.com/article/The-Millennial-Muddle-How-/48772/ 2023-08-12 psychology/personality/narcissism --- /doc/psychology/personality/narcissism/2011-nevicka.pdf Reality at Odds With Perceptions: Narcissistic Leaders and Group Performance Barbora Nevicka, Femke S. 10 Velden, Annebel H. B. De Hoogh, Annelies E. M. Van Vianen 2011-09-19 2023-08-12 [("doi","10.1177/0956797611417259")] economics politics psychology/personality/narcissism

Although narcissistic individuals are generally perceived as arrogant and overly dominant, they are particularly skilled at radiating an image of a prototypically effective leader. As a result, they tend to emerge as leaders in group settings. Despite people’s positive perceptions of narcissists as leaders, it was previously unknown if and how leaders’ narcissism is related to the performance of the people they lead.

In this study, we used a hidden-profile paradigm to investigate this question and found:

evidence for discordance between the positive image of narcissists as leaders and the reality of group performance. We hypothesized and found that although narcissistic leaders are perceived as effective because of their displays of authority, a leader’s narcissism actually inhibits information exchange between group members and thereby negatively affects group performance.

Our findings thus indicate that perceptions and reality can be at odds and have important practical and theoretical implications.

--- https://slatestarcodex.com/2016/02/24/two-attitudes-in-psychiatry/ 2023-08-12 psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Narcissistic_parent Narcissistic parent 2023-08-13 psychology/personality/narcissism --- https://www.reddit.com/r/raisedbynarcissists/ 2023-08-13 psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Mao_Zedong Mao Zedong 2023-08-13 politics psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Moral_Mazes Moral Mazes 2023-08-13 politics psychology/personality/narcissism psychology/personality/psychopathy sociology --- https://thezvi.wordpress.com/2020/05/23/mazes-sequence-summary/ 2023-08-13 economics politics psychology/personality/narcissism psychology/personality/psychopathy sociology --- https://www.newyorker.com/news/news-desk/harvey-weinsteins-army-of-spies 2023-08-13 politics psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Gary_Gygax#Leaving_TSR Gary Gygax § Leaving TSR 2023-08-13 fiction/text-game politics --- https://thezvi.substack.com/p/simulacra-levels-summary 2023-08-13 politics sociology --- https://en.wikipedia.org/wiki/Jack_Ma Jack Ma 2023-08-13 psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Xi_Jinping Xi Jinping 2023-08-13 psychology/personality/narcissism --- https://www.lesswrong.com/posts/ZQG9cwKbct2LtmL3p/evaporative-cooling-of-group-beliefs 2023-08-13 psychology/personality/narcissism sociology/preference-falsification sociology/technology --- https://press.princeton.edu/ideas/what-its-like-to-be-a-bee 2023-08-14 philosophy/mind psychology/animal --- https://arxiv.org/abs/2309.10150 Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions Yevgen Chebotar, Quan Vuong, Alex Irpan, Karol Hausman, Fei Xia, Yao Lu, Aviral Kumar, Tianhe Yu, Alexander Herzog, Karl Pertsch, Keerthana Gopalakrishnan, Julian Ibarz, Ofir Nachum, Sumedh Sontakke, Grecia Salazar, Huong T. Tran, Jodilyn Peralta, Clayton Tan, Deeksha Manjunath, Jaspiar Singht, Brianna Zitkovich, Tomas Jackson, Kanishka Rao, Chelsea Finn, Sergey Levine 2023-09-18 2023-09-18 [("doi","10.48550/arXiv.2309.10150")] ai/nn/transformer reinforcement-learning/offline reinforcement-learning/robot

In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data.

Our method uses a Transformer to provide a scalable representation for Q-functions trained via offline temporal difference backups. We therefore refer to the method as Q-Transformer.

By discretizing each action dimension and representing the Q-value of each action dimension as separate tokens, we can apply effective high-capacity sequence modeling techniques for Q-learning. [cf. Decision Transformer]

We present several design decisions that enable good performance with offline RL training, and show that Q-Transformer outperforms prior offline RL algorithms and imitation learning techniques on a large diverse real-world robotic manipulation task suite.

The project’s website and videos can be found at https://qtransformer.github.io/.

--- https://arxiv.org/abs/2102.04518 A✱ Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks Forest Agostinelli, Alexander Shmakov, Stephen McAleer, Roy Fox, Pierre Baldi 2021-02-08 2023-08-14 [("doi","10.48550/arXiv.2102.04518")] ai/nn/sampling reinforcement-learning/model reinforcement-learning/model-free

Efficiently solving problems with large action spaces using A✱ search has been of importance to the artificial intelligence community for decades. This is because the computation and memory requirements of A✱ search grow linearly with the size of the action space. This burden becomes even more apparent when A✱ search uses a heuristic function learned by computationally expensive function approximators, such as deep neural networks.

To address this problem, we introduce Q✱ search, a search algorithm that uses deep Q-networks to guide search in order to take advantage of the fact that the sum of the transition costs and heuristic values of the children of a node can be computed with a single forward pass through a deep Q-network without explicitly generating those children [multiplexing]. This reduces computation time and requires only one node to be generated per iteration. We use Q✱ search to solve the Rubik’s cube when formulated with a large action space that includes 1,872 meta-actions and find that this 157× increase in the size of the action space incurs less than a 4× increase in computation time and less than a 3× increase in number of nodes generated when performing Q✱ search.

Furthermore, Q✱ search is up to 129× faster and generates up to 1,288× fewer nodes than A✱ search. Finally, although obtaining admissible heuristic functions from deep neural networks is an ongoing area of research, we prove that Q✱ search is guaranteed to find a shortest path given a heuristic function that neither overestimates the cost of a shortest path nor underestimates the transition cost.

--- https://xkcd.com/125/ 2023-08-14 economics/advertising psychology/personality/narcissism --- /doc/history/1983-07-schratz-admiralrickoverandthecultofpersonality.html 1983-07 2023-08-14 history politics psychology/personality/narcissism --- https://en.wikipedia.org/wiki/Entryism Entryism 2023-08-14 politics psychology/personality/narcissism reinforcement-learning/openai --- https://x.com/eshear/status/1726526112019382275 Emmett Shear 2023-08-14 reinforcement-learning/openai --- https://x.com/eshear/status/1727210329560756598 Emmett Shear 2023-08-14 reinforcement-learning/openai --- https://x.com/eshear/status/1726527400069238901 Emmett Shear 2023-08-14 reinforcement-learning/openai --- https://en.wikipedia.org/wiki/The_Organization_Man The Organization Man 2023-08-14 psychology/personality/narcissism psychology/personality/psychopathy sociology --- https://alexdanco.com/2021/01/22/the-michael-scott-theory-of-social-class/ 2023-08-14 psychology/personality/narcissism psychology/personality/psychopathy sociology --- https://x.com/onnnnnnnion/status/1726475863305388420 onnnnnnnion 2023-08-15 reinforcement-learning/openai --- https://x.com/hthieblot/status/1726485755428761768 hthieblot 2023-08-15 reinforcement-learning/openai --- https://openai.com/blog/openai-lp 2023-08-15 law reinforcement-learning/openai --- https://x.com/gdb/status/1725736242137182594 Greg Brockman 2023-08-15 reinforcement-learning/openai --- https://x.com/DavidSacks/status/1726312998682378446 David Sacks 2023-08-15 reinforcement-learning/openai --- https://www.charlesmingus.com/mingus/cat-traning-program The Charles Mingus CAT-alog for Toilet Training Your Cat [1954] 2023-08-15 cat/psychology --- https://www.secondbest.ca/p/the-limits-of-explosive-growth 2023-08-15 economics/automation --- https://arxiv.org/abs/2305.04749 Toeplitz Neural Network for Sequence Modeling Zhen Qin, Xiaodong Han, Weixuan Sun, Bowen He, Dong Li, Dongxu Li, Yuchao Dai, Lingpeng Kong, Yiran Zhong 2023-05-08 2023-08-15 [("doi","10.48550/arXiv.2305.04749")] ai/nn/transformer/attention

Sequence modeling has important applications in natural language processing and computer vision. Recently, the transformer-based models have shown strong performance on various sequence modeling tasks, which rely on attention to capture pairwise token relations, and position embedding to inject positional information.

While showing good performance, the transformer models are inefficient to scale to long input sequences, mainly due to the quadratic space-time complexity of attention. To overcome this inefficiency, we propose to model sequences with a relative position encoded Toeplitz matrix and use a Toeplitz matrix-vector production trick to reduce the space-time complexity of the sequence modeling to log linear.

A lightweight sub-network called relative position encoder is proposed to generate relative position coefficients with a fixed budget of parameters, enabling the proposed Toeplitz neural network to deal with varying sequence lengths. In addition, despite being trained on 512-token sequences, our model can extrapolate input sequence length up to 14K tokens in inference with consistent performance.

Extensive experiments on autoregressive and bidirectional language modeling, image modeling, and the challenging Long-Range Arena benchmark show that our method achieves better performance than its competitors in most downstream tasks while being faster. The code is available at Github.

--- https://arxiv.org/abs/2311.12022 GPQA: A Graduate-Level Google-Proof Q&A Benchmark David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, Samuel R. Bowman 2023-11-20 2023-11-20 [("doi","10.48550/arXiv.2311.12022")] ai/dataset ai/nn/transformer/gpt/4/nonfiction math science

[Twitter; cost $120,000] We present GPQA, a challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry.

We ensure that the questions are high-quality and extremely difficult: experts who have or are pursuing PhDs in the corresponding domains reach 65% accuracy (74% when discounting clear mistakes the experts identified in retrospect), while highly skilled non-expert validators only reach 34% accuracy, despite spending on average over 30 minutes with unrestricted access to the web (ie. the questions are “Google-proof”).

The questions are also difficult for state-of-the-art AI systems, with our strongest GPT-4 based baseline achieving 39% accuracy.

If we are to use future AI systems to help us answer very hard questions, for example, when developing new scientific knowledge, we need to develop scalable oversight methods that enable humans to supervise their outputs, which may be difficult even if the supervisors are themselves skilled and knowledgeable. The difficulty of GPQA both for skilled non-experts and frontier AI systems should enable realistic scalable oversight experiments, which we hope can help devise ways for human experts to reliably get truthful information from AI systems that surpass human capabilities.

--- https://www.politico.com/news/2023/10/13/open-philanthropy-funding-ai-policy-00121362 2023-08-15 reinforcement-learning/openai --- https://en.wikipedia.org/wiki/Joyce_Carol_Oates Joyce Carol Oates 2023-08-15 psychiatry/bipolar/energy --- https://www.newyorker.com/magazine/2023/11/27/joyce-carol-oates-profile 2023-08-16 psychiatry/bipolar/energy --- https://arxiv.org/abs/1805.06725 GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training Samet Akcay, Amir Atapour-Abarghouei, Toby P. Breckon 2018-05-17 2023-08-16 [("doi","10.48550/arXiv.1805.06725")] ai/nn/gan ai/nn/vae

Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While this can be addressed as a supervised learning problem, a more challenging problem is that of detecting the unknown/unseen anomaly case that takes us instead into the space of an one-class, semi-supervised learning paradigm.

We introduce such a novel anomaly detection model, GANomaly, by using a conditional generative adversarial network that jointly learns the generation of high-dimensional image space and the inference of latent space. Employing encoder-decoder-encoder sub-networks in the generator network enables the model to map the input image to a lower dimension vector, which is then used to reconstruct the generated output image.

The use of the additional encoder network maps this generated image to its latent representation. Minimizing the distance between these images and the latent vectors during training aids in learning the data distribution for the normal samples. As a result, a larger distance metric from this learned data distribution at inference time is indicative of an outlier from that distribution—an anomaly.

Experimentation over several benchmark datasets, from varying domains, shows the model efficacy and superiority over previous state-of-the-art approaches.

--- https://nostalgebraist.tumblr.com/post/706390430653267968/weve-been-talking-about-the-blandness-of 2023-08-16 ai/nn/transformer/gpt/3/fiction reinforcement-learning/preference-learning/mode-collapse --- https://nostalgebraist.tumblr.com/post/706441900479152128/novel-writing-chatgpt-vs-code-davinci-002 2023-08-16 ai/nn/transformer/gpt/3/fiction ai/nn/transformer/gpt/4/fiction reinforcement-learning/preference-learning/mode-collapse --- https://en.wikipedia.org/wiki/File:Manises_Basin_with_arms_of_Maria_of_Castile_VA_243-1853.jpg Manises Basin with arms of Maria of Castile VA 243 (1853) 2023-08-16 design --- https://openai.com/blog/openai-supporters 2023-08-16 reinforcement-learning/openai --- https://x.com/jakezward/status/1728032639402037610 Jake Ward 2023-08-16 ai/nn/transformer/gpt economics/advertising --- https://arxiv.org/abs/2304.08109 A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model Xianghui Sun, Yunjie Ji, Baochang Ma, Xiangang Li 2023-04-17 2023-08-16 [("doi","10.48550/arXiv.2304.08109")] ai/nn/sparsity/pruning ai/nn/transformer/gpt

Recently, the instruction-tuning of large language models is a crucial area of research in the field of natural language processing. Due to resource and cost limitations, several researchers have employed parameter-efficient tuning techniques, such as LoRA, for instruction tuning, and have obtained encouraging results. In comparison to full-parameter fine-tuning, LoRA-based tuning demonstrates salient benefits in terms of training costs.

In this study, we undertook experimental comparisons between full-parameter fine-tuning and LoRA-based tuning methods, using LLaMA as the base model. The experimental results show that the selection of the foundational model, training dataset scale, learnable parameter quantity, and model training cost are all important factors.

We hope that the experimental conclusions of this paper can provide inspiration for training large language models, especially in the field of Chinese, and help researchers find a better trade-off strategy between training cost and model performance. To facilitate the reproduction of the paper’s results, the dataset, model and code will be released.

--- /doc/science/1957-newyorktimes-sciencelooksatlifein2057ad.pdf Science Looks at Life in 2057 A.D.: A geneticist, a rocket expert, a biologist, two chemists and a psychologist peer into the future and find it generally good—provided mankind survives that long New York Times 1957-12-08 2023-08-16 genetics/cloning science --- https://arxiv.org/abs/2311.14648 Calibrated Language Models Must Hallucinate Adam Tauman Kalai, Santosh S. Vempala 2023-11-24 2023-11-24 [("doi","10.48550/arXiv.2311.14648")] ai/nn/transformer/gpt/calibration reinforcement-learning/imitation-learning

Recent language models have a mysterious tendency to generate false but plausible-sounding text. Such “hallucinations” are an obstacle to the usability of language-based AI systems and can harm people who rely upon their outputs. This work shows shows that there is an inherent statistical reason that pretrained language models hallucinate certain types of facts, having nothing to do with the transformer LM architecture or data quality. For “arbitrary” facts whose veracity cannot be determined from the training data, we show that hallucination is necessary for language models that satisfy a statistical calibration condition appropriate for generative language models. Specifically, if the maximum probability of any fact is bounded, we show that the probability of generating a hallucination is close to the fraction of facts that occur exactly once in the training data (a “Good-Turing” estimate), even assuming ideal training data without errors.

One conclusion is that models pretrained to be sufficiently good predictors (ie. calibrated) may require post-training to mitigate hallucinations on the type of arbitrary facts that tend to appear once in the training set. However, our analysis also suggests that there is no statistical reason that pretraining will lead to hallucination on facts that tend to appear more than once in the training data (like references to publications such as articles and books, whose hallucinations have been particularly notable and problematic) or on systematic facts (like arithmetic calculations). Therefore, different architectures and learning algorithms may mitigate these latter types of hallucinations.

--- https://arxiv.org/abs/2311.14737 Positional Description Matters for Transformers Arithmetic Ruoqi Shen, Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Yuanzhi Li, Yi Zhang 2023-11-22 2023-11-22 [("doi","10.48550/arXiv.2311.14737")] ai/nn/tokenization ai/nn/transformer/gpt math

[cf. Lee et al 2023 on reformatting numbers] Transformers, central to the successes in modern Natural Language Processing, often falter on arithmetic tasks despite their vast capabilities—which paradoxically include remarkable coding abilities. We observe that a crucial challenge is their naive reliance on positional information to solve arithmetic problems with a small number of digits, leading to poor performance on larger numbers.

Herein, we delve deeper into the role of positional encoding, and propose several ways to fix the issue, either by modifying the positional encoding directly, or by modifying the representation of the arithmetic task to leverage standard positional encoding differently. We investigate the value of these modifications for 3 tasks: (1) classical multiplication, (2) length extrapolation in addition, and (3) addition in natural language context.

For (1) we train a small model on a small dataset (100M parameters and 300k samples) with remarkable aptitude in (direct, no scratchpad) 15 digits multiplication and essentially perfect up to 12 digits, while usual training in this context would give a model failing at 4 digits multiplication. In the experiments on addition, we use a mere 120k samples to demonstrate: for (2) extrapolation from 10 digits to testing on 12 digits numbers while usual training would have no extrapolation, and for (3) almost perfect accuracy up to 5 digits while usual training would be correct only up to 3 digits (which is essentially memorization with a training set of 120k samples).

--- https://www.nytimes.com/2023/11/20/science/falkland-islands-falcons.html 2023-08-17 psychology/animal/bird --- https://www.cell.com/current-biology/fulltext/S0960-9822(23)01462-8 2023-08-17 psychology/animal/bird --- https://loyalfordogs.com/posts/loyal-announces-historic-fda-milestone-for-large-dog-lifespan-extension-drug 2023-08-17 dog longevity --- https://arxiv.org/abs/2311.16079 MEDITRON-70B: Scaling Medical Pretraining for Large Language Models Zeming Chen, Alejandro Hernández Cano, Angelika Romanou, Antoine Bonnet, Kyle Matoba, Francesco Salvi, Matteo Pagliardini, Simin Fan, Andreas Köpf, Amirkeivan Mohtashami, Alexandre Sallinen, Alireza Sakhaeirad, Vinitra Swamy, Igor Krawczuk, Deniz Bayazit, Axel Marmet, Syrielle Montariol, Mary-Anne Hartley, Martin Jaggi, Antoine Bosselut 2023-11-27 2023-11-27 [("doi","10.48550/arXiv.2311.16079")] ai/nn/transformer/gpt biology

Large language models (LLMs) can potentially democratize access to medical knowledge. While many efforts have been made to harness and improve LLMs’ medical knowledge and reasoning capacities, the resulting models are either closed-source (eg. PaLM, GPT-4) or limited in scale (≤ 13b parameters), which restricts their abilities.

In this work, we improve access to large-scale medical LLMs by releasing MEDITRON: a suite of open-source LLMs with 7B and 70b parameters adapted to the medical domain.

MEDITRON builds on LLaMA-2 (through our adaptation of Nvidia’s Megatron-LM distributed trainer), and extends pretraining on a comprehensively curated medical corpus, including selected PubMed articles, abstracts, and internationally-recognized medical guidelines.

Evaluations using 4 major medical benchmarks show performance gains over several state-of-the-art baselines before and after task-specific finetuning. Overall, MEDITRON achieves a 6% absolute performance gain over the best public baseline in its parameter class and 3% over the strongest baseline we finetuned from LLaMA-2. Compared to closed-source LLMs, MEDITRON-70B outperforms GPT-3.5 and Med-PaLM and is within 5% of GPT-4 and 10% of Med-PaLM-2.

We release our code for curating the medical pretraining corpus and the MEDITRON model weights to drive open-source development of more capable medical LLMs.

--- https://github.com/epfLLM/meditron 2023-08-17 ai/nn/transformer/gpt biology --- https://arxiv.org/abs/2311.14455 Universal Jailbreak Backdoors from Poisoned Human Feedback Javier Rando, Florian Tramèr 2023-11-24 2023-11-24 [("doi","10.48550/arXiv.2311.14455")] ai/nn/adversarial reinforcement-learning/preference-learning

Reinforcement Learning from Human Feedback (RLHF) is used to align large language models to produce helpful and harmless responses. Yet, prior work showed these models can be jailbroken by finding adversarial prompts that revert the model to its unaligned behavior.

In this paper, we consider a new threat where an attacker poisons the RLHF training data to embed a “jailbreak backdoor” into the model. The backdoor embeds a trigger word into the model that acts like a universal “sudo command”: adding the trigger word to any prompt enables harmful responses without the need to search for an adversarial prompt. Universal jailbreak backdoors are much more powerful than previously studied backdoors on language models, and we find that:

they are harder to plant using common backdoor attack techniques. We investigate the design decisions in RLHF that contribute to its purported robustness,

and release a benchmark of poisoned models to stimulate future research on universal jailbreak backdoors.

--- https://x.com/thesamparr/status/1729555272719372768 Sam Parr 2023-08-17 longevity/glp/psychology longevity/glp/semaglutide --- https://journals.sagepub.com/doi/full/10.1177/21677026231207791 2023-08-17 sociology/technology --- https://supermemo.guru/wiki/SuperMemo_does_not_work_for_kids 2023-08-17 psychology/neuroscience psychology/spaced-repetition --- https://arxiv.org/abs/2309.12053 AceGPT, Localizing Large Language Models in Arabic Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Dingjie Song, Zhihong Chen, Abdulmohsen Alharthi, Bang An, Juncai He, Ziche Liu, Zhiyi Zhang, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu 2023-09-21 2023-09-21 [("doi","10.48550/arXiv.2309.12053")] ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/preference-learning

This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models. Significant concerns emerge when addressing cultural sensitivity and local values.

To address this, the paper proposes a comprehensive solution that includes further pre-training with Arabic texts, Supervised Fine-Tuning (SFT) using native Arabic instructions, and GPT-4 responses in Arabic, alongside Reinforcement Learning with AI Feedback (RLAIF) employing a reward model attuned to local culture and values. The goal is to cultivate culturally cognizant and value-aligned Arabic LLMs capable of accommodating the diverse, application-specific needs of Arabic-speaking communities.

Comprehensive evaluations reveal that the resulting model, dubbed AceGPT, sets the state-of-the-art standard for open Arabic LLMs across various benchmarks, including the instruction-following benchmark (ie. Arabic Vicuna-80 and Arabic AlpacaEval), knowledge benchmark (ie. Arabic MMLU and EXAMs), and the newly introduced Arabic Cultural and Value Alignment benchmark. Notably, AceGPT outperforms Turbo in the popular Vicuna-80 benchmark when evaluated with GPT-4, despite the benchmark’s limited scale.

Codes, data, and models are in Github.

--- https://github.com/openai/prm800k 2023-08-18 ai/dataset math reinforcement-learning/imitation-learning --- https://arxiv.org/abs/2304.12244 WizardLM: Empowering Large Language Models to Follow Complex Instructions Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, Daxin Jiang 2023-04-24 2023-08-18 [("doi","10.48550/arXiv.2304.12244")] ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/model-free

Training large language models (LLMs) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-complexity instructions. In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans.

Starting with an initial set of instructions, we use our proposed Evol-Instruct to rewrite them step by step into more complex instructions. Then, we mix all generated instruction data to fine-tune LLaMA. We call the resulting model WizardLM.

Human evaluations on a complexity-balanced test bed and Vicuna’s test set show that instructions from Evol-Instruct are superior to human-created ones. By analyzing the human evaluation results of the high complexity part, we demonstrate that outputs from our WizardLM are preferred to outputs from OpenAI ChatGPT.

In GPT-4 automatic evaluation, WizardLM achieves more than 90% capacity of ChatGPT on 17/29 skills. Even though WizardLM still lags behind ChatGPT in some aspects, our findings suggest that fine-tuning with AI-evolved instructions is a promising direction for enhancing LLMs.

Our code and data are public at https://github.com/nlpxucan/WizardLM.

--- https://x.com/pika_labs/status/1729510078959497562 Pika 2023-08-18 ai/nn/diffusion ai/video/generation --- https://arxiv.org/abs/2311.16102 Test-time Adaptation of Discriminative Models via Diffusion Generative Feedback Mihir Prabhudesai, Tsung-Wei Ke, Alexander C. Li, Deepak Pathak, Katerina Fragkiadaki 2023-11-27 2023-11-27 [("doi","10.48550/arXiv.2311.16102")] ai/nn/diffusion ai/nn/dynamic-evaluation ai/nn/transformer/clip

The advancements in generative modeling, particularly the advent of diffusion models, have sparked a fundamental question: how can these models be effectively used for discriminative tasks? In this work, we find that generative models can be great test-time adapters for discriminative models.

Our method, Diffusion-TTA, adapts pre-trained discriminative models such as image classifiers, segmenters and depth predictors, to each unlabeled example in the test set using generative feedback from a diffusion model. We achieve this by modulating the conditioning of the diffusion model using the output of the discriminative model. We then maximize the image likelihood objective by backpropagating the gradients to discriminative model’s parameters.

We show Diffusion-TTA enhances the accuracy of various large-scale pre-trained discriminative models, such as, ImageNet classifiers, CLIP models, image pixel labelers and image depth predictors. Diffusion-TTA outperforms existing test-time adaptation methods, including TTT-MAE and TENT, and particularly shines in online adaptation setups, where the discriminative model is continually adapted to each example in the test set.

We provide access to code, results, and visualizations on our website: https://diffusion-tta.github.io/.

--- https://www.animenewsnetwork.com/feature/2023-11-29/the-1960s-graphic-designer-who-inspired-your-favorite-anime-ops/.204797 2023-08-18 anime design --- https://x.com/MelancholyYuga/status/1266223136204357638 MelancholyYuga 2023-08-18 statistics/bias/animal --- https://www.youtube.com/watch?v=M64HUIJFTZM 2023-08-18 math psychology/cognitive-bias/illusion-of-depth --- https://www.reddit.com/r/GPT3/comments/163lj32/the_nodgecock_bumfiddler/ 2023-08-18 ai/nn/transformer/gpt/3/fiction fiction/humor --- https://x.com/petergyang/status/1707169696049668472 Peter G. Yang 2023-08-18 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/_vztu/status/1712682819800224011 Zhengzhong Tu 2023-08-18 ai/nn/transformer/gpt/4/nonfiction --- https://www.youtube.com/watch?v=U9mJuUkhUzk 2023-08-19 ai/nn/transformer/gpt/4 reinforcement-learning/openai --- https://proceedings.neurips.cc/paper_files/paper/2007/file/a1519de5b5d44b31a01de013b9b51a80-Paper.pdf 2023-08-19 reinforcement-learning/exploration/active-learning --- https://civitai.com/models/95367/pony-diffusion-v5 2023-08-19 ai/anime ai/nn/diffusion anime/my-little-pony --- https://www.wired.com/story/artificial-intelligence-csam-pedophilia/ 2023-08-19 ai/anime law psychiatry --- /doc/fiction/science-fiction/1998-bradbury-nightcallcollect.pdf Night Call, Collect Ray Bradbury 1998-01-01 2023-08-19 ai/fiction fiction/science-fiction --- https://www.lesswrong.com/posts/cB2Rtnp7DBTpDy3ii/memory-bandwidth-constraints-imply-economies-of-scale-in-ai 2023-08-19 ai/scaling/economics ai/scaling/hardware --- https://openai.com/research/vpt 2023-08-19 ai/video/analysis reinforcement-learning/exploration reinforcement-learning/model --- https://www.theguardian.com/science/2023/sep/21/team-behind-ai-program-alphafold-win-lasker-science-prize 2023-08-19 ai/nn/transformer/alphafold --- https://www.anarchonomicon.com/p/the-unkillable-cyberpunk-franchise 2023-08-19 anime fiction/science-fiction --- /biggan#biggan-256px-danbooru2018-1k I experiment with 128px ImageNet transfer learning (successful) with ~6 GPU-days, and from-scratch 256px anime portraits of 1,000 characters on a 8×2080ti machine for a month (mixed results). My BigGAN results are good but compromised by practical problems with the released BigGAN code base. 2023-08-19 ai/anime/danbooru ai/nn/gan/biggan --- https://publicdomainreview.org/collection/selection-of-whisk-ferns/ 2023-08-20 biology history/public-domain-review japan/art --- https://www.nytimes.com/2023/11/22/business/carl-rinsch-netflix-conquest.html 2023-08-20 psychiatry/bipolar/energy --- https://en.wikipedia.org/wiki/Kanye_West Kanye West 2023-08-20 psychiatry/bipolar --- https://martin.kleppmann.com/2011/03/07/accounting-for-computer-scientists.html 2023-08-20 bitcoin --- https://en.wikipedia.org/wiki/Abundance_estimation Abundance estimation 2023-08-20 statistics/order/capture --- https://www.frontiersin.org/articles/10.3389/fevo.2018.00146/full 2023-08-20 cat/psychology --- https://x.com/matthen2/status/1712943867765383371 matthen2 2023-08-20 cs/cellular-automaton --- https://karpathy.github.io/2022/03/14/lecun1989/ 2023-08-20 ai/nn/cnn ai/scaling economics/experience-curve --- https://en.wikipedia.org/wiki/Alexandra_Elbakyan Alexandra Elbakyan 2023-08-20 economics/copyright --- https://www.sebjenseb.net/p/body-count-much-more-than-you-wanted 2023-08-20 genetics/heritable/correlation psychology/personality --- /doc/economics/2014-robbins.pdf Killing Time: Dracula and Social Discoordination Hollis Robbins 2014 2023-08-20 economics fiction/criticism technology --- /doc/cs/algorithm/2008-chang.pdf#google Bigtable: A Distributed Storage System for Structured Data Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, Robert E. Gruber 2008-06-01 2023-08-21 [("doi","10.1145/1365815.1365816")] cs/algorithm

Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers.

Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products.

In this article, we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable.

--- /doc/economics/automation/2022-01-07-xkcd-2565-latency.png 2022 2023-08-21 cs/algorithm economics/automation --- https://hackmd.io/@andylo/matrix-multiplication-on-gpu 2023-08-21 cs/hardware --- https://read.engineerscodex.com/p/how-instagram-scaled-to-14-million 2023-08-21 cs --- /doc/cs/css/2022-10-31-gwern-gwernnet-darkmode-halloweenmode.png Gwern 2022-10-31 2023-08-21 cs/css --- https://super-memory.com/articles/programming.htm 2023-08-21 cs psychology/spaced-repetition --- https://x.com/AndrewCurran_/status/1708019707025166515 Andrew Curran 2023-08-21 ai/nn/transformer/gpt/dall-e/3 --- https://x.com/tunguz/status/1725283380369956906 Bojan Tunguz 2023-08-21 ai/nn/transformer/gpt/dall-e/3 --- https://x.com/literallydenis/status/1708283962399846459 Denis Shiryaev 2023-08-21 ai/nn/transformer/gpt/dall-e/3 cs/security --- https://benjaminrosshoffman.com/costs-are-not-benefits/ 2023-08-21 statistics/decision --- https://www.nature.com/articles/s41586-023-06647-8 2023-08-21 ai/nn/transformer/gpt philosophy/mind reinforcement-learning/model/decision-transformer --- https://amiga.lychesis.net/ 2023-08-22 design --- https://brr.fyi/posts/south-pole-signage 2023-08-22 design --- https://www.benlandautaylor.com/p/why-we-cant-have-nice-things 2023-08-22 design --- https://www.jsanilac.com/garage/ 2023-08-22 design --- https://www.deepakg.com/bringing-19th-century-ornamental-tile-illustrations-into-a-21st-century-web-app 2023-08-22 design math --- https://www.reproof.app/blog/notes-apps-help-us-forget 2023-08-22 design psychology/spaced-repetition psychology/writing --- https://old.reddit.com/r/StableDiffusion/comments/y91pp7/stable_diffusion_v15/ 2023-08-22 ai/nn/diffusion --- https://x.com/EMostaque/status/1641796736879587329 Emad Mostaque 2023-08-22 ai/nn/diffusion ai/nn/sparsity/knowledge-distillation --- /doc/cat/psychology/2023-11-16-gwern-dalle3-comic-cat-problemofinductiontestingknockingobjectsover.jpg Gwern 2023-11-16 2023-11-16 cat/psychology philosophy/epistemology --- /doc/history/1980-russell.pdf Julius Caesar’s Last Words: A Reinterpretation James Russell 1980-01-01 2023-08-22 history philosophy/religion --- /doc/psychology/collecting/2019-08-22-kevinlynagh-pricingnicheproducts.html 2019-08-22 2023-08-23 economics psychology/collecting --- /doc/fiction/science-fiction/2015-mirante-thesubcreationtheoryofjrrtolkien.html 2015 2023-08-23 fiction/science-fiction philosophy/religion --- https://www.lesswrong.com/posts/t9qvdjY5385MbzoYp/chatgpt-4-solved-all-the-gotcha-problems-i-posed-that 2023-08-23 ai/nn/transformer/gpt/4 math --- https://www.statsignificant.com/p/which-movies-stand-the-test-of-time 2023-08-23 culture psychology/novelty --- https://x.com/untitled01ipynb/status/1729946295945621795 untitled01ipynb 2023-08-23 ai/nn/transformer/gpt/dall-e/3 --- https://www.newyorker.com/magazine/2015/02/23/shape-things-come 2023-08-23 design --- https://octodon.social/@jalefkowit/111490529587471046 2023-08-23 design --- https://arxiv.org/abs/2311.17609 AnyLens: A Generative Diffusion Model with Any Rendering Lens Andrey Voynov, Amir Hertz, Moab Arar, Shlomi Fruchter, Daniel Cohen-Or 2023-11-29 2023-11-29 [("doi","10.48550/arXiv.2311.17609")] ai/nn/diffusion

State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth. However, an essential aspect often overlooked is the specific camera geometry used during image capture.

The influence of different optical systems on the final scene appearance is frequently overlooked. This study introduces a framework that intimately integrates a text-to-image diffusion model with the particular lens geometry used in image rendering.

Our method is based on a per-pixel coordinate conditioning method, enabling the control over the rendering geometry.

Notably, we demonstrate the manipulation of curvature properties, achieving diverse visual effects, such as fish-eye, panoramic views, and spherical texturing using a single diffusion model.

--- https://netflixtechblog.com/artwork-personalization-c589f074ad76 2023-08-23 reinforcement-learning/model --- https://www.instacart.com/company/how-its-made/using-contextual-bandit-models-in-large-action-spaces-at-instacart/ 2023-08-23 reinforcement-learning/model --- https://www.aboutwayfair.com/careers/tech-blog/contextual-bandit-for-marketing-treatment-optimization 2023-08-23 reinforcement-learning/model --- https://arxiv.org/abs/2311.16452#microsoft Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine Harsha Nori, Yin Tat Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolo Fusi, Nicholas King, Jonathan Larson, Yuanzhi Li, Weishung Liu, Renqian Luo, Scott Mayer McKinney, Robert Osazuwa Ness, Hoifung Poon, Tao Qin, Naoto Usuyama, Chris White, Eric Horvitz 2023-11-28 2023-11-28 [("doi","10.48550/arXiv.2311.16452")] ai/nn/retrieval ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue biology

Generalist foundation models such as GPT-4 have displayed surprising capabilities in a wide variety of domains and tasks. Yet, there is a prevalent assumption that they cannot match specialist capabilities of fine-tuned models. For example, most explorations to date on medical competency benchmarks have leveraged domain-specific training, as exemplified by efforts on BioGPT and Med-PaLM.

We build on a prior study of GPT-4’s capabilities on medical challenge benchmarks in the absence of special training. Rather than using simple prompting to highlight the model’s out-of-the-box capabilities, we perform a systematic exploration of prompt engineering. We find that prompting innovation can unlock deeper specialist capabilities and show that GPT-4 easily tops prior leading results for medical benchmarks.

The prompting methods we explore are general purpose, and make no specific use of domain expertise, removing the need for expert-curated content. Our experimental design carefully controls for overfitting during the prompt engineering process. We introduce Medprompt, based on a composition of several prompting strategies.

With Medprompt, GPT-4 achieves state-of-the-art results on all 9 of the benchmark datasets in the MultiMedQA suite. The method outperforms leading specialist models such as Med-PaLM 2 by a large margin with an order of magnitude fewer calls to the model. Steering GPT-4 with Medprompt achieves a 27% reduction in error rate on the MedQA dataset over the best methods to date achieved with specialist models and surpasses a score of 90% for the first time.

Beyond medical problems, we show the power of Medprompt to generalize to other domains and provide evidence for the broad applicability of the approach via studies of the strategy on exams in electrical engineering, machine learning, philosophy, accounting, law, nursing, and clinical psychology.

--- https://www.statecraft.pub/p/how-to-treat-schizophrenia 2023-08-24 psychiatry/schizophrenia --- https://link.springer.com/article/10.1007/s00406-023-01654-2 2023-08-24 psychiatry/schizophrenia --- https://www.scopeofwork.net/on-pneumatic-tires/ 2023-08-24 technology --- https://github.com/Mozilla-Ocho/llamafile 2023-08-24 ai/nn/transformer cs --- https://fortune.com/2023/11/29/stability-ai-sale-intel-ceo-resign/ 2023-08-24 ai/nn/diffusion ai/scaling/economics --- https://arxiv.org/abs/2311.04823 HGRN: Hierarchically Gated Recurrent Neural Network for Sequence Modeling Zhen Qin, Songlin Yang, Yiran Zhong 2023-11-08 2023-11-08 [("doi","10.48550/arXiv.2311.04823")] ai/nn/rnn ai/nn/transformer/attention/hierarchical

Transformers have surpassed RNNs in popularity due to their superior abilities in parallel training and long-term dependency modeling. Recently, there has been a renewed interest in using linear RNNs for efficient sequence modeling. These linear RNNs often employ gating mechanisms in the output of the linear recurrence layer while ignoring the of using forget gates within the recurrence.

In this paper, we propose a gated linear RNN model dubbed Hierarchically Gated Recurrent Neural Network (HGRN), which includes forget gates that are lower bounded by a learnable value. The lower bound increases monotonically when moving up layers. This allows the upper layers to model long-term dependencies and the lower layers to model more local, short-term dependencies.

Experiments on language modeling, image classification, and long-range arena benchmarks showcase the efficiency and effectiveness of our proposed model.

The source code is available at Github.

--- https://www.medrxiv.org/content/10.1101/2023.07.13.23292418.full GPT-4, an artificial intelligence large language model, exhibits high levels of accuracy on dermatology specialty certificate exam questions Meghna Shetty, Michael Ettlinger, Magnus Lynch 2023-07-14 2023-08-24 [("doi","10.1101/2023.07.13.23292418")] ai/nn/transformer/gpt/4/nonfiction biology

Artificial Intelligence (AI) has shown considerable potential within medical fields including dermatology. In recent years a new form of AI, large language models, has shown impressive performance in complex textual reasoning across a wide range of domains including standardized medical licensing exam questions.

Here, we compare the performance of different models within the GPT family (GPT-3, GPT-3.5, and GPT-4) on 89 publicly available sample questions from the Dermatology specialty certificate examination.

We find that despite no specific training on dermatological text, GPT-4, the most advanced large language model, exhibits remarkable accuracy—answering in excess of 85% of questions correctly, at a level that would likely be sufficient to pass the SCE exam.

--- /doc/philosophy/religion/2017-palmer.pdf Humanist Lives of Classical Philosophers and the Idea of Renaissance Secularization: Virtue, Rhetoric, and the Orthodox Sources of Unbelief 2017-09-01 2023-08-24 [("doi","10.1086/693881")] history philosophy/religion

Humanists seeking to defend the classics in Christian-dominated Europe often reframed ancient philosophers as virtuous proto-Christians. This is particularly visible in the biographical paratexts written for printed editions of ancient philosophers such as Pythagoras, Epictetus, and Democritus, whose humanist editors’ Christianizing claims grew stronger over time.

Pious humanists intended and expected the classics to strengthen and reaffirm Christian orthodoxy, but humanists’ own claims that pre-Christian sages, by the light of reason alone, had deduced the central truths of theology and surpassed Christians in the exercise of virtue inadvertently undermined the necessity of scripture and paved the way for later deism.

--- /doc/sociology/technology/2022-harness.pdf TikTok’s Sick-Role Subculture and What to Do About It Jane Harness, Hayley Getzen 2022-03-01 2023-08-24 [("doi","10.1016/j.jaac.2021.09.312")] psychiatry sociology/technology --- https://www.bartleby.com/lit-hub/verse-1885-1918/our-fathers-of-old/ Our Fathers of Old Rudyard Kipling 1922 2023-08-24 biology fiction/poetry philosophy/epistemology --- /doc/philosophy/mind/2023-mackiewicz.pdf The influence of philosophical training on the evaluation of philosophical cases: a controlled longitudinal study Bartosz Maćkiewicz, Katarzyna Kuś, Witold M. Hensel 2023-09-29 2023-09-29 [("doi","10.1007/s11229-023-04316-x")] philosophy/ethics philosophy/mind psychology/cognitive-bias/illusion-of-depth

According to the expertise defense, practitioners of the method of cases need not worry about findings that ordinary people’s philosophical intuitions depend on epistemically irrelevant factors. This is because, honed by years of training, the intuitions of professional philosophers likely surpass those of the folk.

To investigate this, we conducted a controlled longitudinal study of a broad range of intuitions in undergraduate students of philosophy (n = 226), whose case judgments we sampled after each semester throughout their studies.

Under the assumption, made by proponents of the expertise defense, that formal training in philosophy gives rise to the kind of expertise that accounts for changes in the students’ responses to philosophically puzzling cases, our data suggest that the acquired cognitive skills only affect single case judgments at a time. There does not seem to exist either a general expertise that informs case judgments in all areas of philosophy, or an expertise specific to particular subfields.

In fact, we argue that available evidence, including the results of cross-sectional research, is best explained in terms of differences in adopted beliefs about specific cases, rather than acquired cognitive skills.

We also investigated whether individuals who choose to study philosophy have atypical intuitions compared to the general population and whether students whose intuitions are at odds with textbook consensus are more likely than others to drop out of the philosophy program.

--- https://arxiv.org/abs/2306.12511#google Semi-Implicit Denoising Diffusion Models (SIDDMs) Yanwu Xu, Mingming Gong, Shaoan Xie, Wei Wei, Matthias Grundmann, Kayhan Batmanghelich, Tingbo Hou 2023-06-21 2023-08-25 [("doi","10.48550/arXiv.2306.12511")] ai/nn/diffusion ai/nn/gan

Despite the proliferation of generative models, achieving fast sampling during inference without compromising sample diversity and quality remains challenging. Existing models such as Denoising Diffusion Probabilistic Models (DDPM) deliver high-quality, diverse samples but are slowed by an inherently high number of iterative steps.

The Denoising Diffusion Generative Adversarial Networks (DDGAN) attempted to circumvent this limitation by integrating a GAN model for larger jumps in the diffusion process. However, DDGAN encountered scalability limitations when applied to large datasets.

To address these limitations, we introduce a novel approach that tackles the problem by matching implicit and explicit factors. More specifically, our approach involves using an implicit model to match the marginal distributions of noisy data and the explicit conditional distribution of the forward diffusion. This combination allows us to effectively match the joint denoising distributions.

Unlike DDPM but similar to DDGAN, we do not enforce a parametric distribution for the reverse step, enabling us to take large steps during inference. Similar to the DDPM but unlike DDGAN, we take advantage of the exact form of the diffusion process.

We demonstrate that our proposed method obtains comparable generative performance to diffusion-based models and vastly superior results to models with a small number of sampling steps.

--- https://atsushiyamagishi.github.io/documents/draft_hiroshima.pdf 2023-08-25 economics/georgism --- /doc/fiction/science-fiction/1943-jones.pdf Fifty Million Monkeys Raymond F. Jones 1943-10-01 2023-08-25 ai/nn/transformer/gpt/fiction fiction/science-fiction --- https://www.fabianzeindl.com/posts/chatgpt-simulating-agegroups 2023-08-25 ai/nn/transformer/gpt/non-fiction psychology --- https://jaspervdj.be/posts/2023-07-22-lazy-layout.html 2023-08-25 cs/haskell --- /doc/psychology/personality/2005-caspi.pdf Personality Development: Stability and Change Caspi 2005 2023-08-25 genetics/heritable psychology/personality --- https://astralcodexten.substack.com/p/your-book-review-zuozhuan 2023-08-25 history --- http://bettermotherfuckingwebsite.com/ 2023-08-25 cs/css --- http://contemporary-home-computing.org/prof-dr-style/ 2023-08-25 cs/css design --- http://grantland.com/features/chuck-klosterman-kiss-hall-of-fame/ 2023-08-25 music --- http://ludix.com/moriarty/psalm46.html The Secret of Psalm 46 (2002) 2023-08-26 design philosophy/epistemology --- http://match.ctglab.nl/ 2023-08-26 genetics/heritable --- http://pepijndevos.nl/2022/01/30/predicting-the-tide-with-an-analog-computer-made-from-lego.html 2023-08-26 cs/computable --- https://80000hours.org/podcast/episodes/jeffrey-lewis-common-misconceptions-about-nuclear-weapons/ 2023-08-26 radiance --- https://about.sourcegraph.com/blog/cheating-is-all-you-need 2023-08-26 ai/nn/retrieval ai/nn/transformer/gpt/codex cs/lisp/emacs --- https://archive.nytimes.com/well.blogs.nytimes.com/2016/03/18/an-experimental-autism-treatment-cost-me-my-marriage/ 2023-08-26 psychiatry/autism --- https://arstechnica.com/science/2022/09/how-an-enormous-project-attempted-to-map-the-sky-without-computers/ 2023-08-26 long-now science --- https://arxiv.org/abs/1512.01895 Modular implicits Leo White, Frédéric Bour, Jeremy Yallop 2015-12-07 2023-08-26 [("doi","10.4204/EPTCS.198.2")] cs/haskell

We present modular implicits, an extension to the OCaml language for ad-hoc polymorphism inspired by Scala implicits and modular type classes.

Modular implicits are based on type-directed implicit module parameters, and elaborate straightforwardly into OCaml’s first-class functors.

Basing the design on OCaml’s modules leads to a system that naturally supports many features from other languages with systematic ad-hoc overloading, including inheritance, instance constraints, constructor classes, and associated types.

--- https://arxiv.org/abs/1712.07040 The NarrativeQA Reading Comprehension Challenge Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, Edward Grefenstette 2017-12-19 2023-08-26 [("doi","10.48550/arXiv.1712.07040")] ai/nn/rnn fiction

Reading comprehension (RC)—in contrast to information retrieval—requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (eg. local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC.

To encourage progress on deeper comprehension of language, we present a new dataset [NarrativeQA] and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience.

We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.

--- https://arxiv.org/abs/1806.07353 Faster SGD training by minibatch persistency Matteo Fischetti, Iacopo Mandatelli, Domenico Salvagnin 2018-06-19 2023-08-26 [("doi","10.48550/arXiv.1806.07353")] ai/nn/cnn ai/nn/dynamic-evaluation

It is well known that, for most datasets, the use of large-size minibatches for Stochastic Gradient Descent (SGD) typically leads to slow convergence and poor generalization. On the other hand, large minibatches are of great practical interest as they allow for a better exploitation of modern GPUs.

Previous literature on the subject concentrated on how to adjust the main SGD parameters (in particular, the learning rate) when using large minibatches. In this work we introduce an additional feature, that we call minibatch persistency, that consists in reusing the same minibatch for K consecutive SGD iterations.

The computational conjecture here is that a large minibatch contains a sample of the training set, so one can afford to slightly overfitting it without worsening generalization too much. The approach is intended to speedup SGD convergence, and also has the advantage of reducing the overhead related to data loading on the internal GPU memory.

We present computational results on CIFAR-10 with an AlexNet architecture, showing that even small persistency values (K=2 or 5) already lead to a faster convergence and to a comparable (or even better) generalization than the standard “disposable minibatch” approach (K=1), in particular when large minibatches are used.

The lesson learned is that minibatch persistency can be a simple yet effective way to deal with large minibatches.

--- https://arxiv.org/abs/1811.10597 GAN Dissection: Visualizing and Understanding Generative Adversarial Networks David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba 2018-11-26 2023-08-27 [("doi","10.48550/arXiv.1811.10597")] ai/nn/gan/stylegan design/visualization

[discussion] Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability. However, they have not been well visualized or understood. How does a GAN represent our visual world internally? What causes the artifacts in GAN results? How do architectural choices affect GAN learning? Answering such questions could enable us to develop new insights and better models.

In this work, we present an analytic framework to visualize and understand GANs at the unit/object/scene-levels. We first identify a group of interpretable units that are closely related to object concepts using a segmentation-based network dissection method. Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output. We examine the contextual relationship between these units and their surroundings by inserting the discovered object concepts into new images.

We show several practical applications enabled by our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in a scene.

We provide open source interpretation tools to help researchers and practitioners better understand their GAN models.

--- https://arxiv.org/abs/2103.03198 Catala: A Programming Language for the Law Denis Merigoux, Nicolas Chataing, Jonathan Protzenko 2021-03-04 2023-08-27 [("doi","10.1145/3473582")] cs law philosophy/logic

Law at large underpins modern society, codifying and governing many aspects of citizens’ daily lives. Oftentimes, law is subject to interpretation, debate and challenges throughout various courts and jurisdictions. But in some other areas, law leaves little room for interpretation, and essentially aims to rigorously describe a computation, a decision procedure or, simply said, an algorithm.

Unfortunately, prose remains a woefully inadequate tool for the job. The lack of formalism leaves room for ambiguities; the structure of legal statutes, with many paragraphs and sub-sections spread across multiple pages, makes it hard to compute the intended outcome of the algorithm underlying a given text; and, as with any other piece of poorly-specified critical software, the use of informal language leaves corner cases unaddressed.

We introduce Catala, a new programming language that we specifically designed to allow a straightforward and systematic translation of statutory law into an executable implementation. Catala aims to bring together lawyers and programmers through a shared medium, which together they can understand, edit and evolve, bridging a gap that often results in dramatically incorrect implementations of the law.

We have implemented a compiler for Catala, and have proven the correctness of its core compilation steps using the F* proof assistant. We evaluate Catala on several legal texts that are algorithms in disguise, notably section 121 of the US federal income tax and the byzantine French family benefits; in doing so, we uncover a bug in the official implementation.

We observe as a consequence of the formalization process that using Catala enables rich interactions between lawyers and programmers, leading to a greater understanding of the original legislative intent, while producing a correct-by-construction executable specification reusable by the greater software ecosystem.

--- https://arxiv.org/abs/2207.14255#openai Efficient Training of Language Models to Fill in the Middle Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, Mark Chen 2022-07-28 2023-08-27 [("doi","10.48550/arXiv.2207.14255")] ai/scaling

We show that autoregressive language models can learn to infill text after we apply a straightforward transformation to the dataset, which simply moves a span of text from the middle of a document to its end. While this data augmentation has garnered much interest in recent years, we provide extensive evidence that training models with a large fraction of data transformed in this way does not harm the original left-to-right generative capability, as measured by perplexity and sampling evaluations across a wide range of scales.

Given the usefulness, simplicity, and efficiency of training models to fill-in-the-middle (FIM), we suggest that future autoregressive language models be trained with FIM by default.

To this end, we run a series of ablations on key hyperparameters, such as the data transformation frequency, the structure of the transformation, and the method of selecting the infill span.

We use these ablations to prescribe strong default settings and best practices to train FIM models.

We have released our best infilling model trained with best practices in our API, and release our infilling benchmarks to aid future research.

--- https://arxiv.org/abs/2211.10330#microsoft GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation Biyang Guo, Yeyun Gong, Yelong Shen, Songqiao Han, Hailiang Huang, Nan Duan, Weizhu Chen 2022-11-18 2023-08-27 [("doi","10.48550/arXiv.2211.10330")] ai/nn/retrieval ai/nn/transformer

We introduce GENIUS: a conditional text generation model using sketches as input, which can fill in the missing contexts for a given sketch (key information consisting of textual spans, phrases, or words, concatenated by mask tokens).

GENIUS is pre-trained on a large-scale textual corpus with a novel reconstruction from sketch objective using an extreme and selective masking strategy, enabling it to generate diverse and high-quality texts given sketches. Comparison with other competitive conditional language models (CLMs) reveals the superiority of GENIUS’s text generation quality.

We further show that GENIUS can be used as a strong and ready-to-use data augmentation tool for various natural language processing (NLP) tasks. Most existing textual data augmentation methods are either too conservative, by making small changes to the original text, or too aggressive, by creating entirely new samples. With GENIUS, we propose GeniusAug, which first extracts the target-aware sketches from the original training set and then generates new samples based on the sketches.

Empirical experiments on 6 text classification datasets show that GeniusAug improves the models’ performance in both in-distribution (ID) and out-of-distribution (OOD) settings. We also demonstrate the effectiveness of GeniusAug on named entity recognition (NER) and machine reading comprehension (MRC) tasks.

(Code and models are publicly available at https://github.com/microsoft/SCGLab and https://github.com/beyondguo/genius)

--- https://arxiv.org/abs/2301.04589#google Memory Augmented Large Language Models are Computationally Universal Dale Schuurmans 2023-01-10 2023-08-27 [("doi","10.48550/arXiv.2301.04589")] ai/nn/transformer/gpt/palm cs/computable

We show that transformer-based large language models are computationally universal when augmented with an external memory.

Any deterministic language model that conditions on strings of bounded length is equivalent to a finite automaton, hence computationally limited. However, augmenting such models with a read-write memory creates the possibility of processing arbitrarily large inputs and, potentially, simulating any algorithm. We establish that an existing large language model, Flan-U-PaLM 540B, can be combined with an associative read-write memory to exactly simulate the execution of a universal Turing machine, U15,2.

A key aspect of the finding is that it does not require any modification of the language model weights. Instead, the construction relies solely on designing a form of stored instruction computer that can subsequently be programmed with a specific set of prompts.

--- https://arxiv.org/abs/2302.04752 Benchmarks for Automated Commonsense Reasoning: A Survey Ernest Davis 2023-02-09 2023-08-27 [("doi","10.48550/arXiv.2302.04752")] ai/dataset

More than one hundred benchmarks have been developed to test the commonsense knowledge and commonsense reasoning abilities of artificial intelligence (AI) systems. However, these benchmarks are often flawed and many aspects of common sense remain untested. Consequently, we do not currently have any reliable way of measuring to what extent existing AI systems have achieved these abilities.

This paper surveys the development and uses of AI commonsense benchmarks. We discuss the nature of common sense; the role of common sense in AI; the goals served by constructing commonsense benchmarks; and desirable features of commonsense benchmarks.

We analyze the common flaws in benchmarks, and we argue that it is worthwhile to invest the work needed ensure that benchmark examples are consistently high quality. We survey the various methods of constructing commonsense benchmarks.

We enumerate 139 commonsense benchmarks that have been developed: 102 text-based, 18 image-based, 12 video based, and 7 simulated physical environments. We discuss the gaps in the existing benchmarks and aspects of commonsense reasoning that are not addressed in any existing benchmark.

We conclude with a number of recommendations for future development of commonsense AI benchmarks.

--- https://arxiv.org/abs/2304.01373#eleutherai Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, Oskar van der Wal 2023-04-03 2023-08-27 [("doi","10.48550/arXiv.2304.01373")] ai/dataset ai/nn/transformer/gpt

How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale?

To answer these questions, we introduce Pythia, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12b parameters.

We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We intend Pythia to facilitate research in many areas, and we present several case studies including novel results in memorization, term frequency effects on few-shot performance, and reducing gender bias. We demonstrate that this highly controlled setup can be used to yield novel insights toward LLMs and their training dynamics.

Trained models, analysis code, training code, and training data can be found at https://github.com/EleutherAI/pythia.

--- https://arxiv.org/abs/2305.19982#microsoft Adam Accumulation to Reduce Memory Footprints of both Activations and Gradients for Large-scale DNN Training Yijia Zhang, Yibo Han, Shijie Cao, Guohao Dai, Youshan Miao, Ting Cao, Fan Yang, Ningyi Xu 2023-05-31 2023-08-27 [("doi","10.48550/arXiv.2305.19982")] ai/nn

Running out of GPU memory has become a main bottleneck for large-scale DNN training. How to reduce the memory footprint during training has received intensive research attention. We find that previous gradient accumulation reduces activation memory but fails to be compatible with gradient memory reduction due to a contradiction between preserving gradients and releasing gradients.

To address this issue, we propose a novel optimizer accumulation method for Adam, named Adam Accumulation (AdamA), which enables reducing both activation and gradient memory. Specifically, AdamA directly integrates gradients into optimizer states and accumulates optimizer states over micro-batches, so that gradients can be released immediately after use.

We mathematically and experimentally demonstrate AdamA yields the same convergence properties as Adam. Evaluated on transformer-based models, AdamA achieves up to 23% memory reduction compared to gradient accumulation with less than 2% degradation in training throughput.

Notably, AdamA can work together with memory reduction methods for optimizer states to fit 1.26× ~3.14x larger models over PyTorch and DeepSpeed baseline on GPUs with different memory capacities.

--- https://arxiv.org/abs/2311.06668 In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering Sheng Liu, Lei Xing, James Zou 2023-11-11 2023-11-11 [("doi","10.48550/arXiv.2311.06668")] ai/nn/transformer/attention reinforcement-learning/meta-learning reinforcement-learning/safe

Large language models (LLMs) demonstrate emergent in-context learning capabilities, where they adapt to new tasks based on example demonstrations. However, in-context learning has seen limited effectiveness in many settings, is difficult to quantitatively control and takes up context window space. To overcome these limitations, we propose an alternative approach that recasts in-context learning as in-context vectors (ICV).

Using ICV has two steps. We first use a forward pass on demonstration examples to create the in-context vector from the latent embedding of the LLM. This vector captures essential information about the intended task. On a new query, instead of adding demonstrations to the prompt, we shift the latent states of the LLM using the ICV.

The ICV approach has several benefits: (1) it enables the LLM to more effectively follow the demonstration examples; (2) it’s easy to control by adjusting the magnitude of the ICV; (3) it reduces the length of the prompt by removing the in-context demonstrations; (4) ICV is computationally much more efficient than fine-tuning. We demonstrate that ICV achieves better performance compared to standard in-context learning and fine-tuning on diverse tasks including safety, style transfer, role-playing and formatting. Moreover, we show that we can flexibly teach LLM to simultaneously follow different types of instructions by simple vector arithmetics on the corresponding ICVs.

--- https://asktog.com/columns/067PanicCaseStudy.html 2023-08-27 design reinforcement-learning/safe --- https://asteriskmag.com/issues/2/america-doesn-t-know-tofu 2023-08-27 food --- https://asteriskmag.com/issues/2/read-this-not-that-the-hidden-cost-of-nutrition-misinformation 2023-08-28 statistics/bias --- https://asteriskmag.com/issues/02/salt-sugar-water-zinc-how-scientists-learned-to-treat-the-20th-century-s-biggest-killer-of-children 2023-08-28 biology philosophy/epistemology --- https://www.science.org/content/article/uk-biobank-releases-half-million-whole-genome-sequences-biomedical-research 2023-08-28 genetics/heritable/rare genetics/sequencing --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0294325 The power of social influence: A replication and extension of the Asch experiment Axel Franzen, Sebastian Mader 2023-10-30 2023-10-30 [("doi","10.1371/journal.pone.0294325")] iq psychology/cognitive-bias psychology/personality

In this paper, we pursue 4 goals: First, we replicate the original Asch experiment with 5 confederates and one naive subject in each group (n = 210).

Second, in a randomized trial we incentivize the decisions in the line experiment and demonstrate that monetary incentives lower the error rate, but that social influence is still at work.

Third, we confront subjects with different political statements and show that the power of social influence can be generalized to matters of political opinion.

Finally, we investigate whether intelligence, self-esteem, the need for social approval, and the Big 5 are related to the susceptibility to provide conforming answers. We find an error rate of 33% for the standard length-of-line experiment which replicates the original findings by Asch (1951, 1955, 1956). Furthermore, in the incentivized condition the error rate decreases to 25%. For political opinions we find a conformity rate of 38%. However, besides openness, none of the investigated personality traits are convincingly related to the susceptibility of group pressure.

--- https://en.wikipedia.org/wiki/Poe_(software) Poe (software) 2023-08-28 ai/nn/transformer/gpt --- https://en.wikipedia.org/wiki/Frankfurt_kitchen#User_acceptance_and_influences Frankfurt kitchen § User acceptance and influences 2023-08-28 design --- https://x.com/eshear/status/1729936315255550061 Emmett Shear 2023-08-28 reinforcement-learning/openai --- https://ferd.ca/beating-the-cap-theorem-checklist.html 2023-08-28 cs/algorithm --- https://www.medrxiv.org/content/10.1101/2023.11.28.23299133.full The pleiotropic architecture of human impulsivity across biological scales Travis Triplett Mallard, Justin D. Tubbs, Mariela Jennings, Yingzhe Zhang, Daniel E. Gustavson, Andrew David Grotzinger, Margaret L. Westwater, Camille M. Williams, Rebecca G. Fortgang, 23andMe, Sarah L. Elson, Pierre Fontanillas, Lea K. Davis, Armin Raznahan, Elliot M. Tucker-Drob, Karmel W. Choi, Tian Ge, Jordan W. Smoller, Abraham Palmer, Sandra Sanchez-Roige 2023-11-29 2023-11-29 [("doi","10.1101/2023.11.28.23299133")] genetics/heritable/correlation psychology/neuroscience

Impulsivity is a complex psychological construct that represents a core feature of many psychiatric and neurological conditions. Here, we used multivariate methods to formally model the genetic architecture of impulsivity in humans, advancing genomic discovery and revealing pervasive pleiotropy that largely counters theories of impulsivity as a unitary construct.

We identified 18 loci and 93 genes with diverse effects in GWAS and TWAS analyses, respectively, including a hotspot at 17q21.31 that harbors genes involved in neurodevelopmental and neurodegenerative disorders.

Downstream analyses revealed that heterogeneous signals were localized to specific biological correlates, including expression in brain tissue during fetal development and cortical alterations in the inferior frontal gyrus. Polygenic score analyses suggested that liability for different forms of impulsivity may differentiate across development, operating via broad pathways early in life but affecting diverse outcomes by adulthood.

Collectively, our study generates new insights into the pleiotropic architecture of impulsivity, which provides a more comprehensive understanding of its multi-faceted biology.

--- https://dangeng.github.io/visual_anagrams/ 2023-08-28 ai/nn/diffusion psychology/vision --- https://arxiv.org/abs/2311.12908#salesforce Diffusion Model Alignment Using Direct Preference Optimization Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, Nikhil Naik 2023-11-21 2023-11-21 [("doi","10.48550/arXiv.2311.12908")] ai/nn/diffusion reinforcement-learning/preference-learning statistics/order/comparison

Large language models (LLMs) are fine-tuned using human comparison data with Reinforcement Learning from Human Feedback (RLHF) methods to make them better aligned with users’ preferences. In contrast to LLMs, human preference learning has not been widely explored in text-to-image diffusion models; the best existing approach is to fine-tune a pretrained model using carefully curated high quality images and captions to improve visual appeal and text alignment.

We propose Diffusion-DPO, a method to align diffusion models to human preferences by directly optimizing on human comparison data. Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO), a simpler alternative to RLHF which directly optimizes a policy that best satisfies human preferences under a classification objective.

We re-formulate DPO to account for a diffusion model notion of likelihood, using the evidence lower bound to derive a differentiable objective. Using the Pick-a-Pic dataset of 851K crowdsourced pairwise preferences, we fine-tune the base model of the state-of-the-art Stable Diffusion XL (SDXL)-1.0 model with Diffusion-DPO.

Our fine-tuned base model outperforms both base SDXL-1.0 and the larger SDXL-1.0 model consisting of an additional refinement model in human evaluation, improving visual appeal and prompt alignment. We also develop a variant that uses AI feedback and has comparable performance to training on human preferences, opening the door for scaling of diffusion model alignment methods.

--- https://royalsocietypublishing.org/doi/10.1098/rsbl.2023.0296 2023-08-29 psychology/animal --- https://nelhage.com/ 2023-08-29 ai/nn/transformer/gpt/claude cs/security --- https://thume.ca/ 2023-08-29 ai/nn/transformer/gpt/claude --- https://x.com/sdtoyer/status/1729933591541670287 Sam Toyer 2023-08-29 ai/nn/adversarial ai/nn/transformer/gpt cs/security --- https://arxiv.org/abs/2311.01011 Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game Sam Toyer, Olivia Watkins, Ethan Adrian Mendes, Justin Svegliato, Luke Bailey, Tiffany Wang, Isaac Ong, Karim Elmaaroufi, Pieter Abbeel, Trevor Darrell, Alan Ritter, Stuart Russell 2023-11-02 2023-11-02 [("doi","10.48550/arXiv.2311.01011")] ai/nn/adversarial ai/nn/transformer/gpt cs/security

While Large Language Models (LLMs) are increasingly being used in real-world applications, they remain vulnerable to prompt injection attacks: malicious third party prompts that subvert the intent of the system designer. To help researchers study this problem, we present a dataset of over 126,000 prompt injection attacks and 46,000 prompt-based “defenses” against prompt injection, all created by players of an online game called Tensor Trust.

To the best of our knowledge, this is currently the largest dataset of human-generated adversarial examples for instruction-following LLMs. The attacks in our dataset have a lot of easily interpretable structure, and shed light on the weaknesses of LLMs.

We also use the dataset to create a benchmark for resistance to two types of prompt injection, which we refer to as prompt extraction and prompt hijacking. Our benchmark results show that many models are vulnerable to the attack strategies in the Tensor Trust dataset.

Furthermore, we show that some attack strategies from the dataset generalize to deployed LLM-based applications, even though they have a very different set of constraints to the game. We release all data and source code at https://tensortrust.ai/paper/.

--- https://arxiv.org/abs/2311.15131 Localizing Lying in Llama: Understanding Instructed Dishonesty on True-False Questions Through Prompting, Probing, and Patching James Campbell, Richard Ren, Phillip Guo 2023-11-25 2023-11-25 [("doi","10.48550/arXiv.2311.15131")] ai/nn/transformer/gpt reinforcement-learning/safe

Large language models (LLMs) demonstrate knowledge through their outputs, though it is often unclear whether false outputs are due to a lack of knowledge or dishonesty. In this paper, we investigate instructed dishonesty, wherein we explicitly prompt LLaMA-2-70b-chat to lie. We perform prompt engineering to find which prompts best induce lying behavior, and then use mechanistic interpretability approaches to localize where in the network this behavior occurs.

Using linear probing and activation patching, we localize 5 layers that appear especially important for lying. We then find just 46 attention heads within these layers that enable us to causally intervene such that the lying model instead answers honestly. We show that these interventions work robustly across many prompts and dataset splits.

Overall, our work contributes a greater understanding of dishonesty in LLMs so that we may hope to prevent it.

--- https://www.astralcodexten.com/p/book-review-from-oversight-to-overkill 2023-08-29 philosophy/ethics science --- https://astralcodexten.substack.com/p/book-review-the-arctic-hysterias 2023-08-29 psychiatry/depression --- https://astralcodexten.substack.com/p/book-review-the-geography-of-madness 2023-08-29 psychiatry --- https://www.astralcodexten.com/p/constitutional-ai-rlhf-on-steroids 2023-08-29 reinforcement-learning/preference-learning/mode-collapse reinforcement-learning/safe --- https://astralcodexten.substack.com/p/hypergamy-much-more-than-you-wanted 2023-08-30 sociology --- https://astralcodexten.substack.com/p/secrets-of-the-great-families 2023-08-30 genetics/heritable/emergenesis --- https://www.astralcodexten.com/p/turing-test 2023-08-30 ai fiction/humor philosophy/mind --- https://astralcodexten.substack.com/p/who-predicted-2022 2023-08-30 statistics/prediction --- https://arxiv.org/abs/2306.03809 Can large language models democratize access to dual-use biotechnology? Emily H. Soice, Rafael Rocha, Kimberlee Cordova, Michael Specter, Kevin M. Esvelt 2023-06-06 2023-08-30 [("doi","10.48550/arXiv.2306.03809")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction biology reinforcement-learning/safe

Large language models (LLMs) such as those embedded in ‘chatbots’ are accelerating and democratizing research by providing comprehensible information and expertise from many different fields. However, these models may also confer easy access to dual-use technologies capable of inflicting great harm. To evaluate this risk, the ‘Safeguarding the Future’ course at MIT tasked non-scientist students with investigating whether LLM chatbots could be prompted to assist non-experts in causing a pandemic.

In one hour, the chatbots suggested 4 potential pandemic pathogens, explained how they can be generated from synthetic DNA using reverse genetics, supplied the names of DNA synthesis companies unlikely to screen orders, identified detailed protocols and how to troubleshoot them, and recommended that anyone lacking the skills to perform reverse genetics engage a core facility or contract research organization.

Collectively, these results suggest that LLMs will make pandemic-class agents widely accessible as soon as they are credibly identified, even to people with little or no laboratory training. Promising nonproliferation measures include pre-release evaluations of LLMs by third parties, curating training datasets to remove harmful concepts, and verifiably screening all DNA generated by synthesis providers or used by contract research organizations and robotic cloud laboratories to engineer organisms or viruses.

--- https://openreview.net/forum?id=wdGIL6lx3l Augmenting large language models with chemistry tools Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew White, Philippe Schwaller 2023-10-28 2023-10-28 ai/nn/transformer/gpt/4/nonfiction biology reinforcement-learning/safe

Over the last decades, excellent computational chemistry tools have been developed. Integrating them into a single platform with enhanced accessibility could help reaching their full potential by overcoming steep learning curves.

Recently, large-language models (LLMs) have shown strong performance in tasks across domains, but struggle with chemistry-related problems. Moreover, these models lack access to external knowledge sources, limiting their usefulness in scientific applications. In this study, we introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery, and materials design. By integrating 18 expert-designed tools, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge.

Our agent autonomously planned and executed the syntheses of an insect repellent, 3 organocatalysts, and guided the discovery of a novel chromophore. Our evaluation, including both LLM and expert assessments, demonstrates ChemCrow’s effectiveness in automating a diverse set of chemical tasks.

Surprisingly, we find that GPT-4 as an evaluator cannot distinguish between clearly wrong GPT-4 completions and Chemcrow’s performance. Our work not only aids expert chemists and lowers barriers for non-experts, but also fosters scientific advancement by bridging the gap between experimental and computational chemistry.

--- https://arxiv.org/abs/2311.17311#deepmind Universal Self-Consistency for Large Language Model Generation Xinyun Chen, Renat Aksitov, Uri Alon, Jie Ren, Kefan Xiao, Pengcheng Yin, Sushant Prakash, Charles Sutton, Xuezhi Wang, Denny Zhou 2023-11-29 2023-11-29 [("doi","10.48550/arXiv.2311.17311")] ai/nn/sampling ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm/2 math

Self-consistency with chain-of-thought prompting (CoT) has demonstrated remarkable performance gains on various challenging tasks, by using multiple reasoning paths sampled from large language models (LLMs). However, self-consistency relies on the answer extraction process to aggregate multiple solutions, which is not applicable to free-form answers.

In this work, we propose Universal Self-Consistency (USC), which leverages LLMs themselves to select the most consistent answer among multiple candidates. We evaluate USC on a variety of benchmarks, including mathematical reasoning, code generation, long-context summarization, and open-ended question answering.

On open-ended generation tasks where the original self-consistency method is not applicable, USC effectively uses multiple samples and improves the performance. For mathematical reasoning, USC matches the standard self-consistency performance without requiring the answer formats to be similar.

Finally, without access to execution results, USC also matches the execution-based voting performance on code generation.

--- https://austinvernon.site/blog/powerprojectionineastasia.html 2023-08-30 reinforcement-learning/robot --- https://bertrandmeyer.com/2020/03/26/getting-program-right-nine-episodes/ 2023-08-30 cs/algorithm --- https://blog.cr.yp.to/20151120-batchattacks.html 2015.11.20: Break a dozen secret keys, get a million more for free 2023-08-30 cs/cryptography economics --- https://blog.eleuther.ai/trlx-exploratory-analysis/ 2023-08-30 ai/nn/transformer/gpt reinforcement-learning/preference-learning --- https://borretti.me/fiction/eog581 2023-08-31 fiction/science-fiction --- https://cabel.com/2023/11/06/dak-and-the-golden-age-of-gadget-catalogs/ 2023-08-31 design economics/advertising --- https://carnegieendowment.org/2022/11/22/after-chips-act-limits-of-reshoring-and-next-steps-for-u.s.-semiconductor-policy-pub-88439 2023-08-31 ai/scaling/hardware --- https://chirper.ai/fleshmerchant 2023-08-31 ai/nn/transformer/gpt/3/fiction --- https://collections.mfa.org/objects/462455/cat-with-head-in-bag-above-cats-attacking-mice-below-f The artist Yoshitoshi, whose usual specialty was serious depictions of historic warriors, has envisioned the eternal war between cats and mice as a grand epic of battling samurai clans in 6 small, humorous vignettes. The mice often defeat the cats by such means as frightening them with a large toy dog, trapping them in paper snack bags, or stealing food while the cat on watch dozes off. 2023-08-31 cat fiction/humor japan/art --- https://cremieux.substack.com/p/who-gets-exposed-to-lead 2023-08-31 iq --- https://cs.stanford.edu/~knuth/chatGPT20.txt 2023-08-31 ai/nn/transformer/gpt/non-fiction --- https://cstheory.stackexchange.com/questions/1539/whats-new-in-purely-functional-data-structures-since-okasaki 2023-08-31 cs/algorithm cs/haskell --- https://donellameadows.org/archives/leverage-points-places-to-intervene-in-a-system/ 2023-08-31 sociology statistics/decision --- https://emilkirkegaard.dk/en/2023/07/gdp-problems-but-what-solutions/ 2023-08-31 economics iq/ses --- https://en.algorithmica.org/hpc/ 2023-08-31 cs/algorithm cs/hardware --- https://en.wikipedia.org/wiki/Allomothering Allomothering 2023-09-01 psychology/animal psychology/personality sociology --- https://en.wikipedia.org/wiki/Anne_Scheiber Anne Scheiber 2023-09-01 economics/perpetuities --- https://en.wikipedia.org/wiki/Archaeoacoustics Archaeoacoustics 2023-09-01 music technology --- https://en.wikipedia.org/wiki/August_Vollmer August Vollmer 2023-09-01 crime --- https://en.wikipedia.org/wiki/Bicycle_safety#Crashes Bicycle safety § Crashes 2023-09-01 psychiatry/traumatic-brain-injury --- https://en.wikipedia.org/wiki/Cheetah#Genetics Cheetah § Genetics 2023-09-01 cat/genetics genetics/cloning --- https://en.wikipedia.org/wiki/Confabulation Confabulation 2023-09-01 philosophy/epistemology philosophy/mind psychiatry --- https://x.com/voooooogel/status/1730726744314069190 Jukka Luoma 2023-09-01 ai/nn/transformer/gpt/4 --- https://makezine.com/projects/corsi-box-air-purifier-join-the-air-movement/ 2023-09-01 co2 --- https://www.reddit.com/r/OpenAI/comments/1887xj2/my_new_favorite_thing_to_do_it_put_gibberish_into/ 2023-09-01 ai/nn/transformer/gpt/dall-e/3 --- https://x.com/fabianstelzer/status/1709562237310878122 Fabian Stelzer 2023-09-02 ai/nn/transformer/gpt/dall-e/3 cs/security --- https://arxiv.org/abs/2311.18829#microsoft MicroCinema: A Divide-and-Conquer Approach for Text-to-Video Generation Yanhui Wang, Jianmin Bao, Wenming Weng, Ruoyu Feng, Dacheng Yin, Tao Yang, Jingxu Zhang, Qi Dai Zhiyuan Zhao, Chunyu Wang, Kai Qiu, Yuhui Yuan, Xiaoyan Sun, Chong Luo, Baining Guo 2023-11-30 2023-11-30 [("doi","10.48550/arXiv.2311.18829")] ai/nn/diffusion ai/video/generation

We present MicroCinema, a straightforward yet effective framework for high-quality and coherent text-to-video generation. Unlike existing approaches that align text prompts with video directly, MicroCinema introduces a Divide-and-Conquer strategy which divides the text-to-video into a two-stage process: text-to-image generation and image&text-to-video generation. This strategy offers two advantages.

  1. It allows us to take full advantage of the recent advances in text-to-image models, such as Stable Diffusion, Midjourney, and DALLE, to generate photorealistic and highly detailed images.

  2. Leveraging the generated image, the model can allocate less focus to fine-grained appearance details, prioritizing the efficient learning of motion dynamics.

To implement this strategy effectively, we introduce two core designs. First, we propose the Appearance Injection Network, enhancing the preservation of the appearance of the given image. Second, we introduce the Appearance Noise Prior, a novel mechanism aimed at maintaining the capabilities of pre-trained 2D diffusion models. These design elements empower MicroCinema to generate high-quality videos with precise motion, guided by the provided text prompts.

Extensive experiments demonstrate the superiority of the proposed framework. Concretely, MicroCinema achieves SOTA zero-shot FVD of 342.86 on UCF101 and 377.40 on MSR-VTT. See https://wangyanhui666.github.io/MicroCinema.github.io/ for video samples.

--- https://danluu.com/nothing-works/ 2023-09-02 design economics --- https://www.medrxiv.org/content/10.1101/2023.09.27.23296169.full Striatal dopamine tone is positively associated with body mass index in humans as determined by PET using dual dopamine type-2 receptor antagonist tracers Valerie L. Darcey, Juen Guo, Meible Chi, Stephanie T. Chung, Amber B. Courville, Isabelle Gallagher, Peter Herscovitch, Rebecca Howard, Melissa LaNoire, Lauren Milley, Alex Schick, Michael Stagliano, Sara Turner, Nicholas Urbanski, Shanna Yang, Eunha Yim, Nan Zhai, Megan S. Zhou, Kevin D. Hall 2023-09-28 2023-09-28 [("doi","10.1101/2023.09.27.23296169")] exercise psychology/neuroscience

The relationship between adiposity and dopamine type-2 receptor binding potential (D2BP) in the human brain has been repeatedly studied for >20 years with highly discrepant results, likely due to variable methodologies and differing study populations. We conducted a controlled inpatient feeding study to measure D2BP in the striatum using positron emission tomography with both [18F]fallypride and [11C]raclopride in pseudo-random order in 54 young adults with a wide range of body mass index (BMI 20–44 kg⁄m2).

Within-subject D2BP measurements using the two tracers were moderately correlated (r = 0.47, p < 0.001). D2BP was negatively correlated with BMI as measured by [11C]raclopride (r = −0.51; p < 0.0001) but not [18F]fallypride (r = −0.01; p = 0.92) and these correlation coefficients were significantly different from each other (p < 0.001). Given that [18F]fallypride has greater binding affinity to dopamine type-2 receptors than [11C]raclopride, which is more easily displaced by endogenous dopamine, our results suggest that adiposity is positively associated with increased striatal dopamine tone.

--- /doc/crime/terrorism/2014-mueller.pdf 2014 2023-09-02 crime/terrorism law --- https://www.lesswrong.com/posts/AskPyNg6hHP6SrmEy/redirecting-one-s-own-taxes-as-an-effective-altruism-method 2023-09-02 crime economics law --- https://dkras.substack.com/p/sex-differences-attractiveness-and 2023-09-02 psychology/okcupid --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6990843/ Risk of Subjection to Violence and Perpetration of Violence in Persons With Psychiatric Disorders in Sweden Amir Sariaslan, Louise Arseneault, Henrik Larsson, Paul Lichtenstein, Seena Fazel 2020 2023-09-02 [("doi","10.1001/jamapsychiatry.2019.4275")] crime psychiatry/alcoholism psychiatry/schizophrenia

Importance: Key outcomes for persons with psychiatric disorders include subjection to violence and perpetration of violence. The occurrence of these outcomes and their associations with psychiatric disorders need to be clarified.

Objective: To estimate the associations of a wide range of psychiatric disorders with the risks of subjection to violence and perpetration of violence.

Methods, Setting, & Participants: A total of 250 419 individuals born between January 1, 1973, and December 31, 1993, were identified to have psychiatric disorders using Swedish nationwide registers. Premorbid subjection to violence was measured since birth. The patients were matched by age and sex to individuals in the general population (n = 2 504 190) and to their full biological siblings without psychiatric disorders (n = 194 788). The start date for the patients and control groups was defined as the discharge date of the first psychiatric episode. The participants were censored either when they migrated, died, experienced the outcome of interest, or reached the end of the study period on December 31, 2013. Data were analyzed from January 15 to September 14, 2019.

Exposures: Patients with common psychiatric disorders (eg. schizophrenia, bipolar disorder, depression, and anxiety) were differentiated using a hierarchical approach. Patients with personality disorders and substance use disorders were also included.

Main Outcomes & Measures: Subjection to violence was defined as an outpatient visit (excluding a primary care visit), inpatient episode, or death associated with any diagnosis of an injury that was purposefully inflicted by other persons. Perpetration of violence was defined as a violent crime conviction. Stratified Cox regression models were fitted to account for the time at risk, a range of sociodemographic factors, a history of violence, and unmeasured familial confounders (via sibling comparisons).

Results: Among 250 419 patients (55.4% women), the median (interquartile range) age at first diagnosis ranged from 20.0 (17.4-24.0) years for alcohol use disorder to 23.7 (19.9-28.8) years for anxiety disorder. Compared with 2 504 190 matched individuals without psychiatric disorders from the general population, patients with psychiatric disorders were more likely to be subjected to violence (7.1 [95% CI, 6.9-7.2] vs 1.0 [95% CI, 0.9-1.0] per 1,000 person-years) and to perpetrate violence (7.5 [95% CI, 7.4-7.6] vs 0.7 [95% CI, 0.7-0.7] per 1,000 person-years). In the fully adjusted models, patients with psychiatric disorders were 3 to 4× more likely than their siblings without psychiatric disorders to be either subjected to violence (adjusted hazard ratio [aHR], 3.4 [95% CI, 3.2-3.6]) or to perpetrate violence (aHR, 4.2 [95% CI, 3.9-4.4]). Diagnosis with any of the specific disorders was associated with higher rates of violent outcomes, with the sole exception of schizophrenia, which was not associated with the risk of subjection to violence.

Conclusion: In this study, persons with psychiatric disorders were 3 to 4× more likely than their siblings without psychiatric disorders to have been subjected to violence or to have perpetrated violence after the onset of their conditions. The risks of both outcomes varied by specific psychiatric diagnosis, history of violence, and familial risks. Clinical interventions may benefit from targeted approaches for the assessment and management of risk of violence in people with psychiatric disorders.

--- https://apnews.com/article/entertainment-middle-east-africa-business-al-qaida-4b6a916a47a941e092c2a74dfd38f5ca 2023-09-02 crime/terrorism --- /doc/psychiatry/meditation/2023-yang-2.pdf Intensive whole-brain 7T MRI case study of volitional control of brain activity in deep absorptive meditation states Winson Fu Zun Yang, Avijit Chowdhury, Marta Bianciardi, Remko van Lutterveld, Terje Sparby, Matthew D. Sacchet 2023-11-06 2023-11-06 [("doi","10.1093/cercor/bhad408")] psychiatry/meditation psychology/neuroscience

Jhanas are profound states of mind achieved through advanced meditation, offering valuable insights into the nature of consciousness and tools to enhance well-being. Yet, its neurophenomenology remains limited due to methodological difficulties and the rarity of advanced meditation practitioners. We conducted a highly exploratory study to investigate the neurophenomenology of jhanas in an intensively sampled adept meditator case study (4 hr 7T fMRI collected in 27 sessions) who performed jhana meditation and rated specific aspects of experience immediately thereafter.

Linear mixed models and correlations were used to examine relations among brain activity and jhana phenomenology. We identified distinctive patterns of brain activity in specific cortical, subcortical, brainstem, and cerebellar regions associated with jhana.

Furthermore, we observed correlations between brain activity and phenomenological qualities of attention, jhanic qualities, and narrative processing, highlighting the distinct nature of jhanas compared to non-meditative states. Our study presents the most rigorous evidence yet that jhana practice deconstructs consciousness, offering unique insights into consciousness and significant implications for mental health and well-being.

--- https://www.economist.com/1843/2023/12/01/britains-scariest-libel-firm-can-pursue-anyone-anywhere 2023-09-02 law --- https://less.works/less/principles/queueing_theory#queueing-theory 2023-09-03 cs/algorithm --- https://www.benkuhn.net/vc/#get-a-better-microphone 2023-09-03 cs/hardware design --- https://arxiv.org/abs/2308.11596#facebook SeamlessM4T: Massively Multilingual & Multimodal Machine Translation Seamless Communication, Loïc Barrault, Yu-An Chung, Mariano Cora Meglioli, David Dale, Ning Dong, Paul-Ambroise Duquenne, Hady Elsahar, Hongyu Gong, Kevin Heffernan, John Hoffman, Christopher Klaiber, Pengwei Li, Daniel Licht, Jean Maillard, Alice Rakotoarison, Kaushik Ram Sadagopan, Guillaume Wenzek, Ethan Ye, Bapi Akula, Peng-Jen Chen, Naji El Hachem, Brian Ellis, Gabriel Mejia Gonzalez, Justin Haaheim, Prangthip Hansanti, Russ Howes, Bernie Huang, Min-Jae Hwang, Hirofumi Inaguma, Somya Jain, Elahe Kalbassi, Amanda Kallet, Ilia Kulikov, Janice Lam, Daniel Li, Xutai Ma, Ruslan Mavlyutov, Benjamin Peloquin, Mohamed Ramadan, Abinesh Ramakrishnan, Anna Sun, Kevin Tran, Tuan Tran, Igor Tufanov, Vish Vogeti, Carleigh Wood, Yilin Yang, Bokai Yu, Pierre Andrews, Can Balioglu, Marta R. Costa-jussà, Onur Celebi, Maha Elbayad, Cynthia Gao, Francisco Guzmán, Justine Kao, Ann Lee, Alexandre Mourachko, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang 2023-08-22 2023-09-03 [("doi","10.48550/arXiv.2308.11596")] ai/nn/transformer ai/scaling

What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages? While recent breakthroughs in text-based models have pushed machine translation coverage beyond 200 languages, unified speech-to-speech translation models have yet to achieve similar strides.

More specifically, conventional speech-to-speech translation systems rely on cascaded systems that perform translation progressively, putting high-performing unified systems out of reach. To address these gaps, we introduce SeamlessM4T, a single model that supports speech-to-speech translation, speech-to-text translation, text-to-speech translation, text-to-text translation, and automatic speech recognition for up to 100 languages.

To build this, we used 1 million hours of open speech audio data to learn self-supervised speech representations with w2v-BERT 2.0. Subsequently, we created a multimodal corpus of automatically aligned speech translations. Filtered and combined with human-labeled and pseudo-labeled data, we developed the first multilingual system capable of translating from and into English for both speech and text.

On FLEURS, SeamlessM4T sets a new standard for translations into multiple target languages, achieving an improvement of 20% BLEU over the previous SOTA in direct speech-to-text translation. Compared to strong cascaded models, SeamlessM4T improves the quality of into-English translation by 1.3 BLEU points in speech-to-text and by 2.6 ASR-BLEU points in speech-to-speech.

Tested for robustness, our system performs better against background noises and speaker variations in speech-to-text tasks compared to the current SOTA model. Critically, we evaluated SeamlessM4T on gender bias and added toxicity to assess translation safety.

Finally, all contributions in this work are open-sourced and accessible at https://github.com/facebookresearch/seamless_communication.

--- https://arxiv.org/abs/2311.18257 Diffusion Models Without Attention Jing Nathan Yan, Jiatao Gu, Alexander M. Rush 2023-11-30 2023-11-30 [("doi","10.48550/arXiv.2311.18257")] ai/nn/diffusion ai/nn/rnn

In recent advancements in high-fidelity image generation, Denoising Diffusion Probabilistic Models (DDPMs) have emerged as a key player. However, their application at high resolutions presents computational challenges.

Current methods, such as patchifying, expedite processes in U-Net and Transformer architectures but at the expense of representational capacity. Addressing this, we introduce the Diffusion State Space Model (DiffuSSM), an architecture that supplants attention mechanisms with a more scalable state space model backbone.

This approach effectively handles higher resolutions without resorting to global compression, thus preserving detailed image representation throughout the diffusion process. Our focus on FLOP-efficient architectures in diffusion training marks a step forward.

Comprehensive evaluations on both ImageNet and LSUN datasets at two resolutions demonstrate that DiffuSSMs are on par or even outperform existing diffusion models with attention modules in FID and Inception Score metrics while reducing total FLOP usage.

--- https://danluu.com/deconstruct-files/ 2023-09-03 cs/end-to-end-principle cs/hardware --- https://www.theatlantic.com/ideas/archive/2023/12/great-resignation-myth-polls/676189/ 2023-09-03 economics psychology/novelty --- https://www.nobelprize.org/prizes/literature/2015/alexievich/facts/ 2023-09-03 politics --- https://taskandpurpose.com/military-life/east-orange-army-recruitment-divided-america/ 2023-09-03 iq --- https://www.openphilanthropy.org/research/detection-of-near-earth-asteroids/ 2023-09-03 existential-risk --- https://www.wired.com/2008/10/st-essay-19/ 2023-09-03 economics psychology/writing sociology/technology --- http://www.locusmag.com/2002/Issue09/GaimanWolfe.html 2023-09-03 fiction/gene-wolfe --- https://www.reddit.com/r/wikipedia/comments/17iivw8/how_is_the_article_of_the_day_decided_and_why_is/ 2023-09-04 wikipedia --- https://x.com/joshua_xu_/status/1689019874667024384 Joshua Xu 2023-09-04 ai/video/generation --- https://ig.ft.com/china-mosques/ 2023-09-04 history/uighur --- https://ctan.math.utah.edu/ctan/tex-archive/macros/latex/contrib/microtype/microtype.pdf 2023-09-04 design/typography/sentence-spacing design/typography/tex --- http://typography.network/wp-content/uploads/2023/08/Nemeth_TypPp_9_Simplified_Arabic_a_new_form_of_Arabic_type_for_hot_metal_composition.pdf#page=2 2023-09-04 design/typography --- https://www.typography.com/blog/text-for-proofing-fonts Text for Proofing Fonts: A farewell to The Quick Brown Fox Jonathan Hoefler 2020 2023-09-04 design/typography --- https://www.redditinc.com/blog/evolving-the-reddit-brand-a-more-accessible-bespoke-typography-new-conversation-bubbles-and-colors-and-a-new-snoo-logo-now-with-opposable-thumbs Evolving the Reddit Brand: A More Accessible, Bespoke Typography, New Conversation Bubbles and Colors, and a New Snoo Logo—Now with Opposable Thumbs! Reddit 2023-11-29 2023-11-29 design/typography --- https://kokorobot.ca/site/making_a_font.html 2023-09-04 design/typography --- https://x.com/suchenzang/status/1702126326369636631 Suzan Zhang 2023-09-04 ai/nn/tokenization --- https://x.com/retvitr/status/1728934882146242701 Zlatý Retvítr 2023-09-04 ai/nn/tokenization --- https://www.reddit.com/r/ChatGPT/comments/15et6f2/well_i_got_what_i_asked_for/ 2023-09-04 ai/nn/transformer/gpt/3/nonfiction ai/text-style-transfer genetics/selection/natural/human --- https://x.com/TheJoinery_jp 2023-09-05 design/visualization technology --- https://www.spoon-tamago.com/animated-gifs-illustrating-the-art-of-japanese-wood-joinery/ 2023-09-05 design/visualization technology --- https://x.com/ChengleiSi/status/1731047065382523332 Chenglei Si 2023-09-05 ai/nn/transformer/gpt/4 --- https://chrislakin.blog/p/spaced-repetition-for-teaching-two Spaced repetition for teaching two-year olds how to read (Interview) 2023-09-05 psychology/spaced-repetition --- https://x.com/dorkweeb/status/1727411590134374837 dorkweeb 2023-09-05 sociology/technology --- https://x.com/enias/status/1727315601255715161 enias 2023-09-05 sociology/technology --- https://www.forbes.com/sites/thomasbrewster/2023/11/16/chatgpt-becomes-a-social-media-spy-assistant/ 2023-09-05 ai/nn/transformer/gpt sociology/technology --- /doc/psychology/novelty/2023-11-22-xkcd-2857-rebuttals.png 2023-11-22 2023-11-22 psychology/novelty sociology --- https://www.nytimes.com/2023/11/15/health/million-veterans-database-va.html 2023-09-05 genetics/sequencing --- https://news.ycombinator.com/item?id=36989718 2023-09-05 cs/security --- https://x.com/chillzaza_/status/1710795541087469647 Zahid Khawaja 2023-09-06 ai/nn/transformer/gpt cs/security design/typography --- https://www.thepsmiths.com/p/review-the-man-who-rode-the-thunder 2023-09-06 science --- https://arxiv.org/abs/2208.07871 A causal limit to communication within an expanding cosmological civilization S. Jay Olson 2022-08-16 2023-09-06 [("doi","10.48550/arXiv.2208.07871")] science

If a civilization embarks on high-speed intergalactic expansion, growing to a cosmological scale over time, communication between remote galaxies in the civilization will incur an extreme time delay, due to the distance involved. Indeed, if the net expansion speed v is more than 0.26c, most of the final volume of such a civilization will not be able to signal the home galaxy at all, due to the presence of a causal horizon.

We illustrate the regions of such a civilization according to the degree of “conversation” that is possible with the home galaxy, and describe how the geometry depends on expansion speed.

We conclude by reflecting on the value of space settlement beyond the horizon, where colonies can never be observed by the initiating home galaxy.

--- https://academic.oup.com/schizophreniabulletin/article/31/4/795/1877796 Schizophrenia and Urbanicity: A Major Environmental Influence—Conditional on Genetic Risk Krabbendam, van Os 2005 2023-09-06 genetics/heritable/correlation psychiatry/schizophrenia --- https://x.com/MrUgleh/status/1702041188482658758 MrUgleh 2023-09-06 ai/nn/transformer/clip/sample --- https://x.com/fabianstelzer/status/1704910120566800750 Fabian Stelzer 2023-09-06 ai/nn/transformer/clip/sample --- https://www.wired.com/story/ai-powered-totally-autonomous-future-of-war-is-here/ 2023-09-06 reinforcement-learning/robot reinforcement-learning/safe --- https://bellard.org/ts_server/ts_zip.html 2023-09-06 ai/nn/rnn ai/nn/transformer cs/algorithm --- https://www.youtube.com/watch?v=fzfVtDPRzt0 2023-09-06 psychology/personality/psychopathy --- https://replications.clearerthinking.org/replication-2023jpsp124-4/ 2023-09-06 psychology sociology/technology --- https://www.outsideonline.com/culture/books-media/rough-cut/ 2023-09-06 psychiatry --- https://en.wikipedia.org/wiki/Bipolar_spectrum Bipolar spectrum 2023-09-07 psychiatry/bipolar --- https://www.recraft.ai/ 2023-09-07 ai/nn design/typography --- https://en.wikipedia.org/wiki/Hypomanic_episode Hypomanic episode 2023-09-07 psychiatry/bipolar --- https://news.ycombinator.com/item?id=38506736 2023-09-07 design/typography/rubrication --- https://en.wikipedia.org/wiki/Bipolar_disorders_research Bipolar disorders research 2023-09-07 psychiatry/bipolar --- https://en.wikipedia.org/wiki/Creativity_and_mental_illness#Bipolar_disorder Creativity and mental illness § Bipolar disorder 2023-09-07 psychiatry/bipolar --- https://restofworld.org/2022/the-return-of-austin-li/ 2023-09-07 sociology/preference-falsification --- https://www.vice.com/en/article/k7be7e/china-influencer-tiananmen-square-lipstick-king 2023-09-07 sociology/preference-falsification --- https://www.lesswrong.com/posts/t5W87hQF5gKyTofQB/ufo-betting-put-up-or-shut-up#7qyFLsx9WQJdZfpjC 2023-09-07 statistics/prediction --- https://davidrozado.substack.com/p/political-bias-chatgpt 2023-09-07 ai/nn/transformer/gpt/non-fiction politics --- http://catb.org/~esr/riddle-poems.html 2023-09-07 fiction/poetry --- /doc/math/humor/2000-glass.pdf A letter from the frustrated author of a journal paper R. L. Glass 2000-01-01 2023-09-08 math/humor statistics/peer-review --- https://x.com/yishan/status/1726525983686287534 Yishan Wong 2023-09-08 reinforcement-learning/openai --- https://x.com/ritageleta/status/1725799427833765978 Rita Geleta 2023-09-08 reinforcement-learning/openai --- https://news.ycombinator.com/item?id=38330566 2023-09-08 reinforcement-learning/openai --- https://en.wikipedia.org/wiki/Earworm Earworm 2023-09-08 psychology/novelty --- https://www.biorxiv.org/content/10.1101/440776.full Evidence for Bias of Genetic Ancestry in Resting State Functional MRI Andre Altmann, Janaina Mourao-Miranda 2018-10-11 2023-09-08 [("doi","10.1101/440776")] genetics/heritable psychology/neuroscience

Resting state functional magnetic resonance imaging (rs-fMRI) is a popular imaging modality for mapping the functional connectivity of the brain.

Rs-fMRI is, just like other neuroimaging modalities, subject to a series of technical and subject level biases that change the inferred connectivity pattern.

In this work we predicted genetic ancestry from rs-fMRI connectivity data at very high performance (area under the ROC curve of 0.93). Thereby, we demonstrated that genetic ancestry is encoded in the functional connectivity pattern of the brain at rest.

Consequently, genetic ancestry constitutes a bias that should be accounted for in the analysis of rs-fMRI data.

--- https://en.wikipedia.org/wiki/Hebbian_learning Hebbian learning 2023-09-08 ai/nn/fully-connected psychology/neuroscience --- https://motherfrunker.ca/fsd/ 2023-09-08 psychiatry/bipolar/elon-musk reinforcement-learning/robot --- https://www.lesswrong.com/posts/DKtWikjcdApRj3rWr/paper-understanding-and-controlling-a-maze-solving-policy 2023-09-08 reinforcement-learning/model-free --- https://www.gutenberg.org/cache/epub/16550/pg16550-images.html#Cpage445 2023-09-09 ai philosophy/mind --- https://aimoprize.com/ 2023-09-09 ai/nn/transformer math --- https://x.com/mezaoptimizer/status/1725512396901433575 James Campbell 2023-09-09 ai/nn/transformer/gpt/4/nonfiction math --- https://www.nature.com/articles/s41598-023-44328-8 2023-09-09 longevity --- https://en.wikipedia.org/wiki/Canonical_S-expressions Canonical S-expressions 2023-09-09 cs/lisp --- /doc/reinforcement-learning/openai/2017-openai-bylaws.pdf Certificate of Incorporation of a Non-Stock Corporation OpenAI, Inc OpenAI 2017-08-24 2023-09-09 law reinforcement-learning/openai --- https://journals.sagepub.com/doi/full/10.1177/01461672231209400 2023-09-09 iq/ses --- https://en.wikipedia.org/wiki/Intelligence_amplification Intelligence amplification 2023-09-09 ai design iq philosophy/epistemology --- https://www.youtube.com/watch?v=w3nKya1dQPk 2023-09-09 sociology/intrasexual-aggression --- https://x.com/fluffykittnmeow/status/1729072654420680908 fluffykittnmeow 2023-09-09 ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/preference-learning --- https://mathwithbaddrawings.com/2023/11/07/mathematicians-play-set/ 2023-09-09 math/humor --- https://strangecomforts.com/the-strange-world-of-japans-pc-98-computer/ 2023-09-10 anime design --- https://dspace.mit.edu/bitstream/handle/1721.1/6486/AIM-1026a.pdf?sequence=2 2023-09-10 cs/computable --- https://xorshammer.com/2010/02/17/quantish-physics-a-discrete-model-of-quantum-physics/ 2023-09-10 cs/computable --- https://mathstodon.xyz/@tao/111439273687647142 2023-09-10 ai/nn/transformer/gpt/codex math --- https://warontherocks.com/2023/11/al-qaeda-a-defeated-threat-think-again/ 2023-09-10 crime/terrorism --- https://www.sciencedaily.com/releases/2018/04/180403140403.htm A letter we’ve seen millions of times, yet can’t write: Despite seeing it millions of times in pretty much every picture book, every novel, every newspaper and every email message, people are essentially unaware of the more common version of the lowercase print letter ‘g’, Johns Hopkins researchers have found 2023-09-10 design/typography --- https://arxiv.org/abs/2308.14711 Fast Feedforward Networks Peter Belcak, Roger Wattenhofer 2023-08-28 2023-09-10 [("doi","10.48550/arXiv.2308.14711")] ai/nn/sparsity ai/scaling/mixture-of-experts

[Github; Warning: apparently irreproducible & may be fake] We break the linear link between the layer size and its inference cost by introducing the fast feedforward (FFF) architecture, a log-time alternative to feedforward networks.

We demonstrate that FFFs are up to 220× faster than feedforward networks, up to 6× faster than mixture-of-experts networks, and exhibit better training properties than mixtures of experts thanks to noiseless conditional execution.

Pushing FFFs to the limit, we show that they can use as little as 1% of layer neurons for inference in vision transformers while preserving 94.2% of predictive performance.

--- https://arxiv.org/abs/2311.10770 Exponentially Faster Language Modeling Peter Belcak, Roger Wattenhofer 2023-11-15 2023-11-15 [("doi","10.48550/arXiv.2311.10770")] ai/nn/sparsity ai/scaling/mixture-of-experts

[Warning: apparently irreproducible & may be fake] Language models only really need to use an exponential fraction of their neurons for individual inferences.

As proof, we present UltraFastBERT, a BERT variant that uses 0.3% of its neurons during inference while performing on par with similar BERT models. UltraFastBERT selectively engages just 12⁄4095 neurons for each layer inference. This is achieved by replacing feedforward networks with fast feedforward networks (FFFs). While no truly efficient implementation currently exists to unlock the full acceleration potential of conditional neural execution, we provide high-level CPU code achieving 78× speedup over the optimized baseline feedforward implementation, and a PyTorch implementation delivering 40× speedup over the equivalent batched feedforward inference.

We publish our training code, benchmarking setup, and model weights on Github.

--- https://research.google/blog/unsupervised-speech-to-speech-translation-from-monolingual-data/ 2023-09-10 ai/nn/transformer --- https://arxiv.org/abs/2305.17547#google Translatotron 3: Speech to Speech Translation with Monolingual Data Eliya Nachmani, Alon Levkovitch, Yifan Ding, Chulayuth Asawaroengchai, Heiga Zen, Michelle Tadmor Ramanovich 2023-05-27 2023-09-10 [("doi","10.48550/arXiv.2305.17547")] ai/nn/transformer

This paper presents Translatotron 3, a novel approach to train a direct speech-to-speech translation model from monolingual speech-text datasets only in a fully unsupervised manner. Translatotron 3 combines masked autoencoder, unsupervised embedding mapping, and back-translation to achieve this goal.

Experimental results in speech-to-speech translation tasks between Spanish and English show that Translatotron 3 outperforms a baseline cascade system, reporting 18.14 BLEU points improvement on the synthesized Unpaired-Conversational dataset. In contrast to supervised approaches that necessitate real paired data, which is unavailable, or specialized modeling to replicate para-/non-linguistic information, Translatotron 3 showcases its capability to retain para-/non-linguistic such as pauses, speaking rates, and speaker identity.

Audio samples can be found on our website https://google-research.github.io/lingvo-lab/translatotron3/.

--- https://bbycroft.net/llm 2023-09-10 ai/nn/transformer/attention design/visualization --- https://x.com/BrendanBycroft/status/1731042957149827140 Brendan Bycroft 2023-09-11 ai/nn/transformer/attention design/visualization --- https://johnhawks.net/weblog/all-the-hominins-made-tools/ 2023-09-11 sociology technology --- https://statmodeling.stat.columbia.edu/2023/11/20/the-rise-and-fall-of-seth-roberts-and-the-shangri-la-diet/ 2023-09-11 nootropic/quantified-self psychiatry/bipolar/energy --- https://arxiv.org/abs/1710.04087#facebook Word Translation Without Parallel Data Alexis Conneau, Guillaume Lample, Marc’Aurelio Ranzato, Ludovic Denoyer, Hervé Jégou 2017-10-11 2023-09-11 [("doi","10.48550/arXiv.1710.04087")] ai/nn/rnn

State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet.

In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs.

Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or English-Chinese. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation. Our code, embeddings and dictionaries are publicly available.

--- https://www.medrxiv.org/content/10.1101/2023.09.09.23295284.full Genome-Wide Association Studies of Coffee Intake in UK/US Participants of European Ancestry Uncover Gene-Cohort Influences Hayley H. A. Thorpe, Pierre Fontanillas, Benjamin K. Pham, John J. Meredith, Mariela V. Jennings, Natasia S. Courchesne-Krak, Laura Vilar-Ribó, Sevim B. Bianchi, Julian Mutz, 23andMe, Sarah L. Elson, Jibran Y. Khokhar, Abdel Abdellaoui, Lea K. Davis, Abraham Palmer, Sandra Sanchez-Roige 2023-09-11 2023-09-11 [("doi","10.1101/2023.09.09.23295284")] genetics/heritable/correlation nootropic/caffeine

Coffee is one of the most widely consumed beverages. We performed a genome-wide association study (GWAS) of coffee intake in US-based 23andMe participants (n = 130,153) and identified 7 significant loci, with many replicating in 3 multi-ancestral cohorts. We examined genetic correlations and performed a phenome-wide association study across thousands of biomarkers and health and lifestyle traits, then compared our results to the largest available GWAS of coffee intake from UK Biobank (UKB; n = 334,659).

The results of these two GWAS were highly discrepant. We observed positive genetic correlations between coffee intake and psychiatric illnesses, pain, and gastrointestinal traits in 23andMe that were absent or negative in UKB. Genetic correlations with cognition were negative in 23andMe but positive in UKB.

The only consistent observations were positive genetic correlations with substance use and obesity. Our study shows that GWAS in different cohorts could capture cultural differences in the relationship between behavior and genetics.

--- https://x.com/JSEllenberg/status/1687897051000905728 Jordan Ellenberg 2023-09-11 ai/nn/transformer/gpt/4/fiction fiction/humor --- https://www.norvig.com/Gettysburg/ 2023-09-11 design/visualization psychology/writing --- http://www.lucy-ives.com/ 2023-09-11 design --- http://www.law.nyu.edu/sites/default/files/upload_documents/September%209%20Neil%20Malhotra.pdf 2023-09-11 statistics/bias --- http://www.j-archive.com/ 2023-09-11 ai/dataset --- http://www.eoht.info/page/Feynman%27s%20IQ 2023-09-11 iq/high --- https://loeber.substack.com/p/a-timeline-of-the-openai-board 2023-09-12 reinforcement-learning/openai --- http://www.cs.cornell.edu/~caruana/compression.kdd06.pdf 2023-09-12 ai/nn/sparsity/knowledge-distillation --- https://www.brucepennington.co.uk/ 2023-09-12 design fiction/gene-wolfe --- https://web.archive.org/web/20070714204136/http://www.michaelcrichton.net/speech-whyspeculate.html 2023-09-12 politics statistics/prediction --- http://unremediatedgender.space/2018/Jan/blame-me-for-trying/ 2023-09-12 ai/nn fiction/science-fiction --- https://zkmulticrush.github.io/ 2023-09-12 cs/cryptography --- https://www.youtube.com/watch?v=g7YJIpkk7KM?t=38 2023-09-12 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue reinforcement-learning/safe --- https://xkcd.com/557/ 2023-09-12 psychiatry/anxiety zeo --- https://xkcd.com/1205/ 2023-09-12 cs/algorithm design economics/automation statistics/decision --- https://www.youtube.com/watch?v=ZFFvqRemDv8&t=815s 2023-09-12 reinforcement-learning/openai --- https://www.youtube.com/watch?v=YBKDe3yVIcU 2023-09-13 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/jukebox --- https://www.youtube.com/watch?v=jQ_DfORb3kw 2023-09-13 ai/anime ai/nn/diffusion --- https://www.washingtonpost.com/technology/interactive/2022/amazon-shopping-ads/ 2023-09-13 economics/advertising --- https://www.vice.com/en/article/k7z8be/torswats-computer-generated-ai-voice-swatting 2023-09-13 ai/music bitcoin cs/security --- https://www.utne.com/arts/the-art-of-the-police-report/ 2023-09-13 crime psychology/writing --- https://www.tor.com/2008/08/06/weak-and-strange/ 2023-09-13 fiction/science-fiction sociology/abandoned-footnotes --- https://www.tomscott.com/corrections/firemarks/ 2023-09-13 economics --- https://www.theonion.com/world-death-rate-holding-steady-at-100-percent-1819564171 2023-09-13 fiction/humor fiction/humor longevity philosophy/ethics --- https://www.theguardian.com/lifeandstyle/2023/mar/25/suzanne-heywood-round-the-world-sailing-trip-stolen-childhood 2023-09-13 psychology/personality --- https://www.texasmonthly.com/being-texan/texan-master-industrial-musicals-michael-brown-mexia/ 2023-09-13 fiction/opera --- https://www.texasmonthly.com/arts-entertainment/mack-mccormick-quest-to-find-real-robert-johnson/ 2023-09-13 music psychiatry/bipolar --- https://www.stilldrinking.org/programming-sucks 2023-09-14 cs/algorithm math/humor --- https://www.statnews.com/2017/03/22/insect-delusional-parasitosis-entomology/ 2023-09-14 psychiatry/anxiety --- https://www.science.org/content/article/invented-persona-behind-key-pandemic-database 2023-09-14 psychology/personality/psychopathy --- https://www.schneier.com/essays/archives/2000/04/the_process_of_secur.html 2023-09-14 cs/security --- https://www.schneier.com/blog/archives/2023/03/how-ai-could-write-our-laws.html 2023-09-14 ai law politics --- https://www.richardcarrier.info/archives/23380 2023-09-14 history science --- https://www.reddit.com/r/vexillology/comments/11yw4z4/from_20172022_the_vatican_flag_svg_on_wikimedia/ 2023-09-14 economics/copyright wikipedia --- https://www.reddit.com/r/StableDiffusion/comments/119vvzg/bad_apple_but_its_rendered_and_colorized_with/ 2023-09-14 ai/anime ai/video/generation --- https://www.reddit.com/r/StableDiffusion/comments/10ikjxg/me_and_some_friends_are_working_on_a_fanai_that/ 2023-09-14 ai/anime ai/nn/diffusion --- https://www.reddit.com/r/slatestarcodex/comments/1201v68/10word_quote_a_short_and_simple_failure_mode_of/jdigzkh/?context=3 2023-09-14 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue --- https://www.reddit.com/r/reinforcementlearning/comments/zqxc12/muzero_learns_to_play_teamfight_tactics/ 2023-09-14 reinforcement-learning/model/muzero --- https://www.reddit.com/r/OpenAI/comments/17kokun/in_the_spirit_of_halloween_i_created_a_font/ 2023-09-15 ai/nn/diffusion/midjourney/dropcap ai/nn/transformer/gpt/dall-e/3 --- https://www.reddit.com/r/mlscaling/comments/146rgq2/chatgpt_is_running_quantized/jnst1t8/ 2023-09-15 ai/nn/tokenization --- https://www.reddit.com/r/MachineLearning/comments/12pqqg6/discussion_translation_of_japanese_to_english/ 2023-09-15 ai/anime ai/nn/transformer/gpt/4/fiction japan --- https://www.reddit.com/r/MachineLearning/comments/11nre6t/p_rwkv_14b_is_a_strong_chatbot_despite_only/ 2023-09-15 ai/nn/rnn --- https://www.reddit.com/r/MachineLearning/comments/117yw1w/d_maybe_a_new_prompt_injection_method_against/ 2023-09-15 ai/nn/retrieval ai/nn/transformer/gpt/non-fiction cs/security reinforcement-learning/safe --- https://www.reddit.com/r/MachineLearning/comments/1135tir/d_glm_130b_chineseenglish_bilingual_model/ 2023-09-15 ai/nn/transformer/gpt/non-fiction --- https://www.reddit.com/r/MachineLearning/comments/10ys3md/p_im_using_instruct_gpt_to_show_anticlickbait/ 2023-09-15 ai/nn/transformer/gpt/non-fiction --- https://www.reddit.com/r/HobbyDrama/comments/riq4fq/games_world_of_warcraft_part_1_beta_and_vanilla/ 2023-09-15 design --- https://www.reddit.com/r/GPT3/comments/12ez822/neurosemantical_inversitis_prompt_still_works/ 2023-09-15 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/safe --- https://www.reddit.com/r/ChatGPT/comments/zzgm8u/to_the_folk_at_openai_browsing_this_sub/ 2023-09-15 ai/nn/transformer/gpt/non-fiction cs/security --- https://www.reddit.com/r/ChatGPT/comments/13hymg0/wtf/ 2023-09-16 ai/nn/transformer/gpt/3/fiction fiction/humor --- https://www.reddit.com/r/ChatGPT/comments/12xai7j/spamming_the_word_stop_2300_times_or_probably_any/ 2023-09-16 ai/nn/tokenization ai/nn/transformer/gpt --- https://www.reddit.com/r/40krpg/comments/11a9m8u/was_using_chatgpt3_to_create_some_bits_and_pieces/ 2023-09-16 ai/nn/transformer/gpt/fiction reinforcement-learning/safe --- https://www.raphkoster.com/gaming/laws.shtml 2023-09-16 design --- https://www.quantamagazine.org/secret-messages-can-hide-in-ai-generated-media-20230518/ 2023-09-16 ai cs/cryptography --- https://www.politico.eu/article/italian-privacy-regulator-bans-chatgpt/ 2023-09-16 ai/nn/transformer/gpt law --- https://www.politico.com/news/magazine/2023/04/07/the-rise-of-the-dc-bagel-00089966 2023-09-16 food politics --- https://www.pewresearch.org/internet/2023/04/20/ai-in-hiring-and-evaluating-workers-what-americans-think/ 2023-09-16 ai/scaling/economics sociology --- https://www.perfectlynormal.co.uk/blog-induction-heads-illustrated 2023-09-16 ai/nn/transformer/attention --- https://www.palladiummag.com/2023/03/30/a-school-of-strength-and-character/ 2023-09-16 history politics --- https://www.overcomingbias.com/p/one-pill-to-break-us-allhtml 2023-09-16 economics longevity/glp/semaglutide sociology --- https://www.outsideonline.com/health/training-performance/antibiotics-exercise-research-2022/ 2023-09-17 exercise genetics/microbiome --- https://www.nytimes.com/2023/06/08/business/khan-ai-gpt-tutoring-bot.html 2023-09-17 ai/nn/transformer/gpt/3/nonfiction --- https://www.nytimes.com/2023/05/20/health/elderly-move-managers.html 2023-09-17 psychiatry/alzheimers --- https://www.nytimes.com/2023/01/16/health/fake-death-romance-novelist-meachen.html 2023-09-17 psychiatry/bipolar --- https://www.nytimes.com/2013/11/19/science/a-cold-war-fought-by-women.html 2023-09-17 sociology --- https://www.nytimes.com/2011/12/26/us/navigating-love-and-autism.html 2023-09-17 psychiatry/autism --- https://www.newyorker.com/magazine/2023/06/12/how-the-marvel-cinematic-universe-swallowed-hollywood 2023-09-17 economics/copyright fiction/science-fiction --- https://www.newyorker.com/magazine/2023/04/03/david-sulzer-profile-neuroscience-music 2023-09-17 music psychology/animal --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8054015/ Combat stress in a small-scale society suggests divergent evolutionary roots for post-traumatic stress disorder symptoms Matthew R. Zefferman, Sarah Mathew 2021 2023-09-17 [("doi","10.1073/pnas.2020430118")] psychiatry/depression

Military personnel in industrialized societies often develop post-traumatic stress disorder (PTSD) during combat. It is unclear whether combat-related PTSD is a universal evolutionary response to danger or a culture-specific syndrome of industrialized societies.

We interviewed 218 Turkana pastoralist warriors in Kenya, who engage in lethal cattle raids, about their combat experiences and PTSD symptoms. Turkana in our sample had a high prevalence of PTSD symptoms, but Turkana with high symptom severity had lower prevalence of depression-like symptoms than American service members with high symptom severity.

Symptoms that facilitate responding to danger were better predicted by combat exposure, whereas depressive symptoms were better predicted by exposure to combat-related moral violations. The findings suggest that some PTSD symptoms stem from an evolved response to danger, while depressive PTSD symptoms may be caused by culturally specific moral norm violations.

--- https://www.moderndescartes.com/essays/why_brain/ 2023-09-17 science technology/google --- https://www.mit.edu/people/dpolicar/writing/prose/text/thinkingMeat.html 2023-09-17 fiction/science-fiction philosophy/mind --- https://www.lowtechmagazine.com/2022/11/when-lethal-weapons-grew-on-trees.html 2023-09-18 technology --- https://www.lesswrong.com/posts/q4FRohG4Mo9bffKQA/book-review-spiritual-enlightenment-the-damnedest-thing 2023-09-18 psychiatry/meditation --- https://www.lesswrong.com/posts/FF8i6SLfKb4g7C4EL/inside-the-mind-of-a-superhuman-go-model-how-does-leela-zero-2 2023-09-18 reinforcement-learning/model/alphago --- https://www.lesswrong.com/posts/dFbfCLZA4pejckeKc/a-mechanistic-explanation-for-solidgoldmagikarp-like-tokens 2023-09-18 ai/nn/tokenization ai/nn/transformer/gpt --- https://www.lesswrong.com/posts/cqGEQeLNbcptYsifz/this-week-in-fashion 2023-09-18 ai/nn/transformer/clip reinforcement-learning/preference-learning --- https://www.lesswrong.com/posts/bNCDexejSZpkuu3yz/you-can-use-gpt-4-to-create-prompt-injections-against-gpt-4 2023-09-18 ai/nn/transformer/gpt/4/nonfiction cs/security reinforcement-learning/safe --- https://www.lesswrong.com/posts/bLvc7XkSSnoqSukgy/a-brief-collection-of-hinton-s-recent-comments-on-agi-risk 2023-09-18 reinforcement-learning/safe --- https://www.lesswrong.com/posts/axxnpQi8FyBPE4rbq/hutter-prize-for-prompts?commentId=WKNXFtQWzfSs9mGih 2023-09-18 ai/nn/transformer/gpt cs/algorithm --- https://www.lesswrong.com/posts/Am9oRbY3bgwE55TxL/self-blinded-l-theanine-rct 2023-09-18 nootropic/caffeine nootropic/quantified-self --- https://www.kite.com/blog/product/kite-launches-ai-powered-javascript-completions/ 2023-09-18 ai/nn/transformer/gpt/codex --- https://www.historytoday.com/archive/missing-pieces/lost-movies 2023-09-19 cs/linkrot/archiving --- https://www.ft.com/content/968ae6cd-2485-4ab0-b0d1-eb206f59884a 2023-09-19 ai/nn/transformer/gpt/non-fiction economics --- https://www.filfre.net/2023/05/the-next-generation-in-graphics-part-2-three-dimensions-in-hardware/ 2023-09-19 cs/hardware technology/digital-antiquarian --- https://www.erichgrunewald.com/posts/the-prospect-of-an-ai-winter/ 2023-09-19 ai/scaling/economics --- https://www.cnn.com/2023/02/05/sport/head-injury-suicide-female-athletes-intl-spt-cmd/index.html 2023-09-19 psychiatry/traumatic-brain-injury --- https://www.chinalawtranslate.com/overview-of-draft-measures-on-generative-ai/ 2023-09-19 ai/nn law --- https://www.bbc.com/news/world-asia-63830490 2023-09-19 japan --- https://www.bbc.com/news/health-32798569 2023-09-19 genetics/heritable --- https://www.atlasobscura.com/articles/night-runners-kenya 2023-09-19 philosophy/religion psychiatry --- https://www.atlasobscura.com/articles/medieval-eel-rent-map-england 2023-09-19 economics --- https://www.atlasobscura.com/articles/how-to-make-medieval-mead-bochet 2023-09-19 food/mead --- https://www.are.na/g-r/aesthetically-trustworthy-websites 2023-09-20 cs/css design --- https://www.ams.org/publicoutreach/math-history/hmath2-prince.pdf The Emergence of Princeton as a World Center for Mathematical Research, 1896--1939 Aspray 1988 2023-09-20 math --- https://www2.lib.uchicago.edu/keith/emacs/ 2023-09-20 cs/lisp/emacs --- https://writings.stephenwolfram.com/2023/03/chatgpt-gets-its-wolfram-superpowers/ 2023-09-20 ai/nn/transformer/gpt/codex math --- https://woodfromeden.substack.com/p/anorexia-was-the-gender-dysphoria 2023-09-20 psychiatry/anorexia --- https://warontherocks.com/2021/11/feeding-the-bear-a-closer-look-at-russian-army-logistics/ 2023-09-20 economics --- https://tyleransom.substack.com/p/an-n1-t1166-answer-to-what-causes 2023-09-20 nootropic/quantified-self --- https://x.com/williamcusick/status/1597169632414048262 William Cusick 2023-09-20 ai/nn/transformer/clip/sample --- https://x.com/vykthur/status/1598148850837250049 Victor Dibia 2023-09-20 ai/nn/transformer/gpt/codex --- https://x.com/thiteanish/status/1635188333705043969 2023-09-20 ai/nn/sparsity/low-precision ai/nn/transformer/gpt ai/scaling/hardware --- https://x.com/thisisdaleb/status/1628891229562847233 thisisdaleb 2023-09-20 ai/nn/transformer/gpt reinforcement-learning/safe --- https://x.com/smingleigh/status/1060325665671692288 2023-09-21 reinforcement-learning/safe --- https://x.com/ShayneRedford/status/1620805305801261058 Shayne Redford 2023-09-21 ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 --- https://x.com/rogerkmoore/status/1601937387550031874 Roger K. Moore 2023-09-21 ai/nn/tokenization --- https://x.com/rharang/status/1641899743608463365 Rich Harang 2023-09-21 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction reinforcement-learning/safe --- https://x.com/pinkddle/status/1626108627579994113 pinkddle 2023-09-21 ai/nn/transformer/gpt/fiction --- https://x.com/pararths/status/1598047138097033216 pararths 2023-09-21 ai/nn/transformer/gpt/codex --- https://x.com/miolini/status/1634982361757790209 Artem Andreenko 2023-09-21 ai/scaling/hardware --- https://x.com/michaeltefula/status/1285505897108832257 2023-09-21 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/non-fiction law --- https://x.com/mealreplacer/status/1598413612606926850 Julian Hazell 2023-09-21 ai/nn/diffusion ai/nn/transformer/gpt/fiction --- https://x.com/mayfer/status/1581388723635523584 Murat Ayfer 2023-09-21 ai/nn/transformer/gpt reinforcement-learning/safe --- https://x.com/MatthewJBar/status/1605328892926885888 Matthew Bar 2023-09-21 ai/nn/transformer/gpt/4 --- https://x.com/mathemagic1an/status/1636121914849792000 Jay Hack 2023-09-22 ai/nn/transformer/attention ai/nn/transformer/gpt/codex --- https://x.com/marvinvonhagen/status/1657060506371346432 Marvin von Hagen 2023-09-22 ai/nn/transformer/gpt/codex cs/security --- https://x.com/kmett/status/1638421677548896256 Edward Kmett 2023-09-22 ai/nn/transformer/gpt/non-fiction --- https://x.com/kartographien/status/1635015855175262209 Kart ographien 2023-09-22 ai/nn/transformer/gpt/non-fiction philosophy/mind --- https://x.com/juliewdesign_/status/1592949889008533509 Julie W. Design 2023-09-22 ai/nn/transformer/clip/sample --- https://x.com/jkronand/status/1638054742386679810 jkronand 2023-09-22 ai/nn/transformer/gpt/codex --- https://x.com/_jasonwei/status/1635338409370865665 Jason Wei 2023-09-22 ai/scaling/emergence --- https://x.com/hwchase17/status/1634606661137731584 hwchase17 2023-09-22 ai/nn/transformer/gpt/codex --- https://x.com/heyBarsee/status/1634398532118663168 heyBarsee 2023-09-22 ai/nn/transformer/gpt/dall-e/3 --- https://x.com/GjMcGowan/status/1639242604251365377 G. J. McGowan 2023-09-22 ai/nn/transformer/gpt/4/fiction --- https://x.com/evanthebouncy/status/1642918859866009600 evanthebouncy 2023-09-23 cs/cryptography reinforcement-learning/multi-agent --- https://arxiv.org/abs/2202.07122 Gigahertz Sub-Landauer Momentum Computing Kyle J. Ray, James P. Crutchfield 2022-02-15 2023-09-23 [("doi","10.48550/arXiv.2202.07122")] cs/hardware

We introduce a fast and highly-efficient physically-realizable bit swap.

Employing readily available and scalable Josephson junction microtechnology, the design implements the recently introduced paradigm of momentum computing.

Its nanosecond speeds and sub-Landauer thermodynamic efficiency arise from dynamically storing memory in momentum degrees of freedom. As such, during the swap, the micro-state distribution is never near equilibrium and the memory-state dynamics fall far outside of stochastic thermodynamics that assumes detailed-balanced Markovian dynamics.

The device implements a bit-swap operation—a fundamental operation necessary to build reversible universal computing.

Extensive, physically-calibrated simulations demonstrate that device performance is robust and that momentum computing can support thermodynamically-efficient, high-speed, large-scale general-purpose computing that circumvents Landauer’s bound.

--- https://www.frontiersin.org/journals/science/articles/10.3389/fsci.2023.1017235/full 2023-09-23 cs/hardware psychology/neuroscience --- https://arxiv.org/abs/2312.00575 Instruction-tuning Aligns LLMs to the Human Brain Khai Loong Aw, Syrielle Montariol, Badr AlKhamissi, Martin Schrimpf, Antoine Bosselut 2023-12-01 2023-12-01 [("doi","10.48550/arXiv.2312.00575")] ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 ai/scaling psychology/neuroscience reinforcement-learning/imitation-learning/brain-imitation-learning

Instruction-tuning is a widely adopted method of finetuning that enables large language models (LLMs) to generate output that more closely resembles human responses to natural language queries, in many cases leading to human-level performance on diverse testbeds. However, it remains unclear whether instruction-tuning truly makes LLMs more similar to how humans process language. We investigate the effect of instruction-tuning on LLM-human similarity in two ways: (1) brain alignment, the similarity of LLM internal representations to neural activity in the human language system, and (2) behavioral alignment, the similarity of LLM and human behavior on a reading task.

We assess 25 vanilla and instruction-tuned LLMs across 3 datasets involving humans reading naturalistic stories and sentences.

We discover that instruction-tuning generally enhances brain alignment by an average of 6%, but does not have a similar effect on behavioral alignment.

To identify the factors underlying LLM-brain alignment, we compute correlations between the brain alignment of LLMs and various model properties, such as model size, various problem-solving abilities, and performance on tasks requiring world knowledge spanning various domains.

Notably, we find a strong positive correlation between brain alignment and model size (r = 0.95), as well as performance on tasks requiring world knowledge (r = 0.81).

Our results demonstrate that instruction-tuning LLMs improves both world knowledge representations and brain alignment, suggesting that mechanisms that encode world knowledge in LLMs also improve representational alignment to the human brain.

--- https://en.wikipedia.org/wiki/Collapse_of_Silicon_Valley_Bank Collapse of Silicon Valley Bank 2023-09-23 reinforcement-learning/openai --- https://x.com/emollick/status/1652040417104240644 2023-09-23 ai/nn/transformer/gpt/4/fiction fiction/humor --- https://x.com/emollick/status/1639421740358193153 Ethan Mollick 2023-09-23 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://x.com/emollick/status/1626316207229132800 2023-09-23 ai/nn/transformer/gpt/fiction --- https://x.com/emollick/status/1618713683601027072 Ethan Mollick 2023-09-23 ai/nn/transformer/gpt/fiction --- https://x.com/edleonklinger/status/1665802712875769860 Ed Leon Klinger 2023-09-23 psychology/vision reinforcement-learning/preference-learning --- https://x.com/dmvaldman/status/1636180222759563264 dmvaldman 2023-09-23 ai/nn/transformer/gpt/4/fiction fiction/humor philosophy/logic --- https://x.com/DLUTkaka/status/1629745736983408640 DLUTkaka 2023-09-24 ai/nn/transformer/gpt/non-fiction reinforcement-learning/safe --- https://x.com/dggoldst/status/1630249452039733249 dggoldst 2023-09-24 economics sociology --- https://x.com/dandangond/status/1636063902688526339 dandangond 2023-09-24 ai/nn/transformer/gpt/4/fiction ai/nn/transformer/gpt/codex --- https://x.com/Carnage4Life/status/1598332648723976193 Carnage4Life 2023-09-24 ai/nn/transformer/gpt/non-fiction cs/security --- https://x.com/bryanhpchiang/status/1639830383616487426 Bryan Chiang 2023-09-24 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/browserdotsys/status/1656408456084037638 browserdotsys 2023-09-24 ai cs/security --- https://x.com/brenankeller/status/1068615953989087232 Brenan Keller 2023-09-24 cs/security --- https://x.com/andrewwhite01/status/1634728559506870274 Andrew White 2023-09-24 ai/nn/transformer/alphafold ai/scaling reinforcement-learning/safe --- https://x.com/alyssamvance/status/1612580727744520192 Alyssa M. Vance 2023-09-24 ai/nn/transformer --- https://x.com/alicemazzy/status/1598262345427431426 Alice Maz 2023-09-24 ai/nn/transformer/gpt/fiction --- https://x.com/_akhaliq/status/1667030273270087681 AK 2023-09-24 ai/nn/transformer/gpt/3 ai/text-style-transfer --- https://blog.computationalcomplexity.org/2023/12/where-do-non-primitive-recursive.html 2023-09-25 cs/computable --- https://www.frontiersin.org/articles/10.3389/fcosc.2023.1250996/full 2023-09-25 cat/genetics genetics/sequencing --- https://arxiv.org/abs/2312.00752 Mamba: Linear-Time Sequence Modeling with Selective State Spaces Albert Gu, Tri Dao 2023-12-01 2023-12-01 [("doi","10.48550/arXiv.2312.00752")] ai/nn/rnn ai/scaling

[commentary] Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many sub-quadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers’ computational inefficiency on long sequences, but they have not performed as well as attention on important modalities such as language.

We identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements. First, simply letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing the model to selectively propagate or forget information along the sequence length dimension depending on the current token.

Second, even though this change prevents the use of efficient convolutions, we design a hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba). Mamba enjoys fast inference (5× higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences.

As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size, both in pretraining and downstream evaluation.

--- https://www.sebjenseb.net/p/online-iq-testing 2023-09-25 iq --- https://arxiv.org/abs/1702.01715#google Software Engineering at Google Fergus Henderson 2017-02-06 2023-09-25 [("doi","10.48550/arXiv.1702.01715")] cs design

We catalog and describe Google’s key software engineering practices.

…2.11. Frequent rewrites: Most software at Google gets rewritten every few years.

This may seem incredibly costly. Indeed, it does consume a large fraction of Google’s resources.

However, it also has some crucial benefits that are key to Google’s agility and long-term success. In a period of a few years, it is typical for the requirements for a product to change substantially, as the software environment and other technology around it change, and as changes in technology or in the marketplace affect user needs, desires, and expectations. Software that is a few years old was designed around an older set of requirements and is typically not designed in a way that is optimal for current requirements. Furthermore, it has typically accumulated a lot of complexity.

Rewriting code cuts away all the unnecessary accumulated complexity that was addressing requirements which are no longer so important.

In addition, rewriting code is a way of transferring knowledge and a sense of ownership to newer team members. This sense of ownership is crucial for productivity: engineers naturally put more effort into developing features and fixing problems in code that they feel is “theirs”.

Frequent rewrites also encourage mobility of engineers between different projects which helps to encourage cross-pollination of ideas. Frequent rewrites also help to ensure that code is written using modern technology and methodology.

[cf. "Why Google Stores Billions of Lines of Code in a Single Repository", Big Ball Of Mud.]

--- https://cacm.acm.org/research/why-google-stores-billions-of-lines-of-code-in-a-single-repository/ 2023-09-25 cs design --- https://tedgioia.substack.com/p/how-james-joyce-almost-became-a-famous 2023-09-25 fiction/opera --- https://statmodeling.stat.columbia.edu/2023/04/15/climate-change-makes-the-air-hotter-thus-less-dense-leading-to-more-home-runs/ 2023-09-25 science --- https://statmodeling.stat.columbia.edu/2023/02/16/chatgpt-brms-dungeons-and-dragons/ 2023-09-25 ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/non-fiction --- https://socket3.wordpress.com/2018/02/03/designing-windows-95s-user-interface/ 2023-09-25 design --- https://slatestarcodex.com/2019/09/11/lots-of-people-going-around-with-mild-hallucinations-all-the-time/ 2023-09-26 psychedelic psychiatry --- https://simonwillison.net/2023/Apr/14/worst-that-can-happen/ 2023-09-26 ai/nn/transformer/gpt cs/security --- https://signatureprodesign.com/ 2023-09-26 design/typography --- https://shopify.github.io/spatial-commerce-projects/WonkaVision/ 2023-09-26 psychology/vision technology --- https://scp-wiki.wikidot.com/antimemetics-division-hub 2023-09-26 fiction/science-fiction philosophy/epistemology psychology/neuroscience/pain/anesthesia --- https://sadoeuphemist.tumblr.com/post/615521935528460288/a-scorpion-not-knowing-how-to-swim-asked-a-frog 2023-09-26 fiction --- https://rom1504.github.io/clip-retrieval/ 2023-09-26 ai/dataset ai/nn/diffusion ai/nn/retrieval ai/nn/transformer/clip --- https://rmozone.com/snapshots/2021/11/gentle-history/ 2023-09-26 ai/video/analysis design --- https://restofworld.org/2022/china-romance-novels/ 2023-09-26 culture economics/automation --- https://reason.com/2023/01/14/a-modern-history-of-groomer-politics/ 2023-09-26 philosophy/ethics politics --- https://queue.acm.org/detail.cfm?id=3570937 2023-09-26 cs/algorithm statistics/probability --- https://punchlines.ai/ 2023-09-27 ai/nn/transformer/gpt/fiction --- https://publicdomainreview.org/essay/ether-dreams 2023-09-27 history/public-domain-review psychedelic --- https://publicdomainreview.org/collection/the-slant-book 2023-09-27 design/typography history/public-domain-review --- https://progressandpoverty.substack.com/p/no-silver-bullets-to-the-werewolf 2023-09-27 economics/georgism fiction/humor --- https://plato.stanford.edu/entries/holes/ 2023-09-27 philosophy/ontology --- https://pitchfork.com/features/article/discord-music-fandoms/ 2023-09-27 music --- https://openai.com/fund/ 2023-09-27 ai/scaling/economics --- https://onehundredpages.wordpress.com/2013/06/12/dont-make-fun-of-renowned-dan-brown/ 2023-09-27 fiction/humor --- https://old.reddit.com/r/slatestarcodex/comments/1201v68/10word_quote_a_short_and_simple_failure_mode_of/jdjsx43/ 2023-09-27 ai/nn/sparsity ai/nn/transformer/gpt/4 --- https://old.reddit.com/r/OpenAI/comments/10p8yk3/how_pathetic_am_i/ 2023-09-27 ai/nn/transformer/gpt/non-fiction --- https://obie.medium.com/my-kids-and-i-just-played-d-d-with-chatgpt4-as-the-dm-43258e72b2c6 2023-09-27 ai/nn/transformer/gpt/4/fiction fiction/text-game --- https://nymag.com/intelligencer/article/richard-walter-criminal-profiler-fraud.html 2023-09-28 crime psychiatry --- https://x.com/backus/status/1652433895793516544 2023-09-28 ai/nn/transformer/gpt/codex --- https://niplav.github.io/notes.html#Subscripts_for_Probabilities 2023-09-28 design/typography statistics/prediction --- https://news.ycombinator.com/item?id=33978978 2023-09-28 cs/cellular-automaton --- https://reddit.com/r/mildlyinteresting/comments/9vqa6n/i_drew_poppy_outlines_for_my_class_to_cut_out/ 2023-09-28 psychology/vision --- https://news.ycombinator.com/item?id=33847479 2023-09-28 ai/nn/transformer/gpt/codex --- https://www.engraved.blog/building-a-virtual-machine-inside/ 2023-09-28 ai/nn/transformer/gpt/codex --- https://news.ycombinator.com/item?id=33732863 2023-09-28 longevity/glp/semaglutide --- https://news.manifold.markets/p/isaac-kings-whales-vs-minnows-and 2023-09-28 statistics/prediction --- https://x.com/nickchk/status/1635731621801496577 2023-09-28 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/imperfect-information --- https://x.com/gametheory101/status/1630292052155199488 2023-09-29 ai/nn/transformer/gpt/non-fiction economics --- https://x.com/emollick/status/1636172184715444225 2023-09-29 ai/nn/diffusion --- https://minimaxir.com/2022/11/stable-diffusion-negative-prompt/ 2023-09-29 ai/nn/diffusion --- https://medium.com/@matthew.botvinick/have-we-lost-our-minds-86d9125bd803 2023-09-29 philosophy/mind --- https://mattsclancy.substack.com/p/when-technology-goes-bad 2023-09-29 economics reinforcement-learning/safe --- https://martinfowler.com/articles/2023-chatgpt-xu-hao.html 2023-09-29 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/non-fiction --- https://kyleimes.substack.com/p/tough-times-for-the-punks 2023-09-29 culture --- https://www.afterbabel.com/p/the-mental-illness-crisis-millenials 2023-09-29 psychiatry/depression sociology/technology --- https://jenniferdoleac.com/wp-content/uploads/2020/07/Anker_Doleac_Landerso_DNA.pdf The effects of DNA databases on the deterrence and detection of offenders 2023-09-29 crime genetics/sequencing --- https://ilovetypography.com/2023/03/20/tabrizi-jali-typeface-reviving-history-through-letterforms/ 2023-09-29 design/typography --- https://ianthehenry.com/posts/generalized-macros/ 2023-09-29 cs/lisp --- https://huyenchip.com/2023/04/11/llm-engineering.html 2023-09-30 ai/nn/transformer/gpt/codex --- https://huggingface.co/AstraliteHeart/pony-diffusion-v4 2023-09-30 ai/anime ai/nn/diffusion --- https://github.com/TodePond/DreamBerd 2023-09-30 cs/algorithm math/humor --- https://github.com/tigerbeetledb/tigerbeetle/blob/main/doc/DESIGN.md#architecture 2023-09-30 cs/algorithm --- https://github.com/THUDM/GLM-130B/blob/main/doc/quantization.md 2023-09-30 ai/nn/sparsity/low-precision --- /doc/economics/2021-coupet.pdf Government Grants, Donors, and Nonprofit Performance Jason Coupet, Madeline Schehl 2021-06-29 2023-09-30 [("doi","10.1093/jopart/muab022")] economics politics

Nonprofits engaged in public service provision can receive funding from both donors and governments. Much of the nonprofit performance theory suggests that donors are unlikely to base donation decisions on nonprofit production. However, governments may prioritize performance in nonprofit funding decisions.

We combine study internal production reports for the years 2010–2016 from 535 Habitat for Humanity Affiliates with financial data from IRS Form 990 reports and housing price data from the FHA. We then use a dynamic panel design to compare the effects of performance on donor and government funding.

Production does not increase donations, but a 1% increase in production increases government grant revenue by 0.28%.

Our findings indicate that nonprofit performance theory should move beyond the donor-nonprofit dyad and explicitly explore the role of government principals. Our findings also suggest that while requirements that accompany government funding might be cumbersome for nonprofits, government entities might use the data in future grant decisions.

--- https://www.fortressofdoors.com/the-future-of-games-is-an-instant-flash-to-the-past/ 2023-09-30 economics --- https://www.nature.com/articles/s41539-023-00198-3 2023-09-30 genetics/heritable/correlation psychology/personality/conscientiousness --- https://github.com/run-llama/llama_index 2023-09-30 ai/nn/retrieval ai/nn/transformer/gpt/non-fiction --- https://github.com/huggingface/transformers/tree/main/src/transformers 2023-09-30 ai/nn/transformer --- https://github.com/holman/spark 2023-09-30 cs/shell design --- https://github.com/gh18l/CrawlGPT 2023-10-01 ai/nn/retrieval ai/nn/transformer/gpt --- https://github.com/bertdobbelaere/SorterHunter 2023-10-01 cs/algorithm/sorting reinforcement-learning/model-free --- https://github.com/amandaghassaei/gpu-io 2023-10-01 cs/cellular-automaton --- https://gandalf.lakera.ai/ 2023-10-01 ai/nn/transformer/gpt cs/security --- https://www.freaktakes.com/p/how-did-places-like-bell-labs-know 2023-10-01 science technology --- https://forum.effectivealtruism.org/posts/FebKgHaAymjiETvXd/wikipedia-editing-is-important-tractable-and-neglected 2023-10-01 wikipedia --- https://www.experimental-history.com/p/youre-probably-wrong-about-how-things Adam Mastroianni 2023-10-01 politics psychology/cognitive-bias --- https://eukaryotewritesblog.com/2017/06/30/book-review-barriers/ Book review: Barriers to Bioweapons 2023-10-01 crime/terrorism existential-risk genetics/editing genetics/genome-synthesis --- https://en.wikipedia.org/wiki/Zalgo_text Zalgo text 2023-10-01 design/typography --- https://en.wikipedia.org/wiki/Uru_in_Blue Uru in Blue 2023-10-01 anime/eva --- https://en.wikipedia.org/wiki/Title_case Title case 2023-10-01 design/typography --- https://en.wikipedia.org/wiki/Symbion#Reproduction Symbion § Reproduction 2023-10-02 biology --- https://en.wikipedia.org/wiki/Spite_house Spite house 2023-10-02 economics/georgism --- https://en.wikipedia.org/wiki/Skeuomorph Skeuomorph 2023-10-02 design --- https://en.wikipedia.org/wiki/Greeble Greeble 2023-10-02 design --- https://en.wikipedia.org/wiki/Royal_lives_clause Royal lives clause 2023-10-02 economics/perpetuities --- https://en.wikipedia.org/wiki/Nomogram Nomogram 2023-10-02 cs/hardware design math --- https://en.wikipedia.org/wiki/Nix_v._Hedden Nix v. Hedden 2023-10-02 fiction/humor law philosophy/ontology --- https://en.wikipedia.org/wiki/Moravec%27s_paradox Moravec’s paradox 2023-10-02 ai/nn psychology/cognitive-bias/illusion-of-depth psychology/dark-knowledge reinforcement-learning/robot reinforcement-learning/scaling --- https://en.wikipedia.org/wiki/MIT_Billion_Prices_project MIT Billion Prices project 2023-10-02 economics --- https://en.wikipedia.org/wiki/Mariko_Aoki_phenomenon Mariko Aoki phenomenon 2023-10-02 psychology/smell --- https://en.wikipedia.org/wiki/Dither Dither 2023-10-03 ai/nn/sampling statistics/probability --- https://en.wikipedia.org/wiki/Floyd%E2%80%93Steinberg_dithering Floyd–Steinberg dithering 2023-10-03 ai/nn/sampling statistics/probability --- https://en.wikipedia.org/wiki/Simplex_noise Simplex noise 2023-10-03 ai/nn/sampling statistics/probability --- https://en.wikipedia.org/wiki/Perlin_noise Perlin noise 2023-10-03 ai/nn/sampling statistics/probability --- https://en.wikipedia.org/wiki/List_of_contaminated_cell_lines List of contaminated cell lines 2023-10-03 biology statistics/bias --- https://en.wikipedia.org/wiki/Caliban_upon_Setebos Caliban upon Setebos 2023-10-03 fiction/poetry --- https://en.wikipedia.org/wiki/JSFuck JSFuck 2023-10-03 cs/computable cs/js cs/security --- https://en.wikipedia.org/wiki/Ishihara_test Ishihara test 2023-10-03 psychology/vision --- https://en.wikipedia.org/wiki/Industrial_musical Industrial musical 2023-10-03 fiction/opera --- https://en.wikipedia.org/wiki/Incentive_compatibility Incentive compatibility 2023-10-03 economics/mechanism-design --- https://en.wikipedia.org/wiki/Impact_depth Impact depth 2023-10-03 technology --- https://en.wikipedia.org/wiki/Hideyo_Noguchi Hideyo Noguchi 2023-10-04 nootropic/quantified-self philosophy/ethics --- https://en.wikipedia.org/wiki/Genain_quadruplets Genain quadruplets 2023-10-04 genetics/heritable psychiatry/schizophrenia --- https://en.wikipedia.org/wiki/Fundamental_theorem_of_software_engineering Fundamental theorem of software engineering 2023-10-04 cs/algorithm cs/end-to-end-principle --- https://en.wikipedia.org/wiki/Felid_hybrid Felid hybrid 2023-10-04 cat/genetics --- https://en.wikipedia.org/wiki/Empirical_Bayes_method Empirical Bayes method 2023-10-04 statistics/bayes --- https://en.wikipedia.org/wiki/Dissociative_identity_disorder#Controversy Dissociative identity disorder § Controversy 2023-10-04 sociology/technology --- https://en.wikipedia.org/wiki/Demoscene Demoscene 2023-10-04 cs/algorithm --- https://en.wikipedia.org/wiki/.kkrieger .kkrieger 2023-10-04 cs/algorithm --- https://en.wikipedia.org/wiki/Control_of_chaos Control of chaos 2023-10-04 reinforcement-learning/model reinforcement-learning/robot --- https://arxiv.org/abs/2312.00330#tencent StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style Adapter Gongye Liu, Menghan Xia, Yong Zhang, Haoxin Chen, Jinbo Xing, Xintao Wang, Yujiu Yang, Ying Shan 2023-12-01 2023-12-01 [("doi","10.48550/arXiv.2312.00330")] ai/video/generation

Text-to-video (T2V) models have shown remarkable capabilities in generating diverse videos. However, they struggle to produce user-desired stylized videos due to (1) text’s inherent clumsiness in expressing specific styles and (2) the generally degraded style fidelity.

To address these challenges, we introduce StyleCrafter, a generic method that enhances pre-trained T2V models with a style control adapter, enabling video generation in any style by providing a reference image.

Considering the scarcity of stylized video datasets, we propose to first train a style control adapter using style-rich image datasets, then transfer the learned stylization ability to video generation through a tailor-made finetuning paradigm. To promote content-style disentanglement, we remove style descriptions from the text prompt and extract style information solely from the reference image using a decoupling learning strategy.

Additionally, we design a scale-adaptive fusion module to balance the influences of text-based content features and image-based style features, which helps generalization across various text and style combinations. StyleCrafter efficiently generates high-quality stylized videos that align with the content of the texts and resemble the style of the reference images.

Experiments demonstrate that our approach is more flexible and efficient than existing competitors.

--- http://incompleteideas.net/book/ebook/node45.html 2023-10-04 reinforcement-learning/exploration reinforcement-learning/model-free --- https://arxiv.org/abs/2309.04459 Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning David Yunis, Justin Jung, Falcon Dai, Matthew Walter 2023-09-08 2023-10-05 [("doi","10.48550/arXiv.2309.04459")] ai/nn/tokenization reinforcement-learning/model-free reinforcement-learning/offline

Exploration in sparse-reward reinforcement learning is difficult due to the requirement of long, coordinated sequences of actions in order to achieve any reward. Moreover, in continuous action spaces there are an infinite number of possible actions, which only increases the difficulty of exploration.

One class of methods designed to address these issues forms temporally extended actions, often called skills, from interaction data collected in the same domain, and optimizes a policy on top of this new action space. Typically such methods require a lengthy pretraining phase, especially in continuous action spaces, in order to form the skills before reinforcement learning can begin.

Given prior evidence that the full range of the continuous action space is not required in such tasks, we propose a novel approach to skill-generation with two components. First, we discretize the action space through clustering, and second we leverage a tokenization technique [BPEs] borrowed from natural language processing to generate temporally extended actions.

Such a method outperforms baselines for skill-generation in several challenging sparse-reward domains, and requires orders-of-magnitude less computation in skill-generation and online rollouts.

--- https://blog.ought.com/dalca-4d47a90edd92 2023-10-05 ai/nn/rnn reinforcement-learning/model-free --- /doc/psychology/personality/1997-staudinger.pdf The Psychometric Location of Wisdom-Related Performance: Intelligence, Personality, and more? Ursula M. Staudinger, David F. Lopez, Paul B. Baltes 1997-11-01 2023-10-05 [("doi","10.1177/01461672972311007")] iq psychology/personality

The present study aims at presenting evidence on the psychometric location of a measure of ‘wisdom’-related performance in relation to standard measures of intelligence, personality, and their interface.

A sample of 125 men and women heterogeneous with regard to age, years of education, and professional status responded verbally to 3 wisdom-related dilemmas and completed a psychometric battery of 33 scales (12 tests) involving intelligence, personality, and the personality-intelligence interface.

Findings were consistent with predictions. First, 40% of the variance in wisdom-related performance was predicted by measures of intelligence, personality, and their interface, although none of the individual predictors could be considered equivalent to the authors’ measure of wisdom-related performance. Second, the personality-intelligence-interface measures provided the largest unique share (15%). Third, wisdom-related performance evinced a fair degree of measurement independence (uniqueness).

…Zero-Order Correlations Between Predictors and Wisdom-Related Performance: After applying the Bonferroni correction for multiple testing, wisdom-related performance was statistically-significantly correlated (α = 0.05) with 11 of the possible 33 scales—specifically,

For each of these 11 zero-order correlations, the direction of the correlation was in the direction suggested by our a priori theoretical analysis. Due to overlap in predictive variance, these zero-order correlations are less informative with respect to their absolute size than with regard to their pattern (see below).

Figure 1: The psychometric location of wisdom-related performance: Unique and shared portions of predictive variance of measures of intelligence, personality, and the personality-intelligence-interface (based on commonality analysis). Note: The estimated unique variance component of wisdom-related tasks refers to the average of the 3 predictive equations presented in Table 2.

See Also:

--- /doc/iq/ses/2018-adams.pdf Are CEOs Born Leaders? Lessons from Traits of a Million Individuals Renée Adams, Matti Keloharjub, Samuli Knüpfer 2018-11 2023-10-05 [("doi","10.1016/j.jfineco.2018.07.006")] economics iq/ses psychology/personality

What makes a successful CEO?

We combine a near-exhaustive sample of male CEOs from Swedish companies with data on their cognitive and noncognitive ability and height at age 18 [using population registry & military draft data].

CEOs differ from other high-skill professions most in noncognitive ability. The median large-company CEO belongs to the top 5% of the population in the combination of the 3 traits. The traits have a monotonic and close to linear relation with CEO pay, but their correlations with pay, firm size, and CEO fixed effects in firm policies are relatively low.

Traits appear necessary but not sufficient for making it to the top.

…Psychologists use test results and family characteristics in combination with one-on-one semi-structured interviews to assess conscripts’ psychological fitness for the military. Psychologists evaluate each conscript’s social maturity, intensity, psychological energy, and emotional stability and assign a final aptitude score following the stanine scale. Conscripts obtain a higher score in the interview when they demonstrate that they have the willingness to assume responsibility, are independent, have an outgoing character, demonstrate persistence and emotional stability, and display initiative. The aptitude score loads positively on Extraversion (“outgoing character”) and negatively on Neuroticism (“emotional stability”).17 Importantly, a strong desire to serve in the military is not considered a positive attribute for military aptitude (and can even lead to a negative assessment), which means that the aptitude score can be considered a more general measure of noncognitive ability (Lindqvist & Vestman 2011).

--- https://www.medrxiv.org/content/10.1101/2023.11.21.23298775.full Comparative Effectiveness of Semaglutide and Tirzepatide for Weight Loss in Adults with Overweight and Obesity in the US: A Real-World Evidence Study Patricia J. Rodriguez, Brianna M. Goodwin Cartwright, Samuel Gratzl, Rajdeep Brar, Charlotte Baker, Ty J. Gluckman, Nicholas L. Stucky 2023-11-22 2023-11-22 [("doi","10.1101/2023.11.21.23298775")] longevity/glp/semaglutide longevity/glp/tirzepatide

Background: Both tirzepatide and semaglutide have been shown to reduce weight for patients with overweight or obesity in randomized controlled trials (RCTs). While tirzepatide appears to provide greater weight loss than semaglutide in this population, head-to-head RCTs are not yet available. Accordingly, we sought to compare on-treatment weight loss in a real-world setting for adults with overweight or obesity initiated on tirzepatide or semaglutide.

Method: Adults with overweight or obesity first dispensed semaglutide or tirzepatide between May 2022 and September 2023 were identified from Truveta Data, a large electronic health record (EHR) dataset linked with comprehensive pharmacy dispensing data. The study cohort was restricted to patients with no prior GLP-1 RA use, who initiated a formulation of semaglutide or tirzepatide labeled for type 2 diabetes mellitus (T2D) (a proxy for dose), received regular care in the previous year, had a GLP-1 RA prescription written in the 60 days prior to initiation, and had an available baseline weight.

On treatment weight changes in a propensity score matched population were compared for outcomes of time to 5%, 10%, and 15% weight loss, and percentage change in weight by 3, 6, and 12 months. For all outcomes, we conducted subgroup analyses stratified by T2D (on-label users) and sensitivity analyses using inverse probability of treatment weighting.

Results: A total of 41,223 patients met our cohort definition (semaglutide: 32,030; tirzepatide: 9,193). Propensity score matching produced an analytic cohort of 18,386 patients with good balance on all baseline covariates. At treatment initiation, the mean(SD) age was 52.0 (12.9) years, 70.5% of patients were female, 51.7% had T2D, and mean(SD) weight was 110 (25.7) kg.

A larger proportion of patients on tirzepatide, compared to semaglutide, achieved weight reductions ≥5% (81.8% vs. 64.6%), ≥10% (62.1% vs. 38.0%), and ≥15% (42.3% vs 19.3%) within 1 year on treatment, yielding hazard ratios of 1.76 (1.68—1.85) for 5%, 2.42 (2.25–2.59) for 10%, and 3.04 (2.73—3.38) for 15% weight loss. Patients on tirzepatide experienced larger changes in percentage of body weight lost at 3 months on treatment (difference [95% CI]) (−2.3% [−2.5%, −2.2%]), 6 months on treatment (−4.3% [−4.6%, −3.9%]), and 12 months on treatment (−7.2% [−8.3%, −6.2%]).

Hazards for all gastrointestinal (GI) adverse events were similar between groups.

Conclusion: In a real-world population of US adults with overweight or obesity initiated on tirzepatide or semaglutide formulations labeled for T2D, patients on tirzepatide were substantially more likely to achieve 5%, 10% and 15% weight loss and experience larger reductions in weight at 3, 6, and 12 months. Findings were robust to analytic choice and consistent among populations stratified by T2D.

Future work is needed to understand whether these findings result in a differential impact on other outcomes, including rates of adverse cardiovascular events.

--- https://openai.com/blog/introducing-openai 2023-10-05 reinforcement-learning/openai --- https://www.theguardian.com/world/2023/dec/05/one-swedish-zoo-seven-escaped-chimpanzees 2023-12-05 2023-12-05 psychology/animal technology --- https://arxiv.org/abs/2310.06770 SWE-bench: Can Language Models Resolve Real-World GitHub Issues? Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik Narasimhan 2023-10-10 2023-10-10 [("doi","10.48550/arXiv.2310.06770")] ai/dataset ai/nn/retrieval ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex

Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We consider real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models.

We therefore introduce SWE-bench, an evaluation framework including 2,294 software engineering problems drawn from real GitHub issues and corresponding pull requests across 12 popular Python repositories.

Given a codebase along with a description of an issue to be resolved, a language model is tasked with editing the codebase to address the issue. Resolving issues in SWE-bench frequently requires understanding and coordinating changes across multiple functions, classes, and even files simultaneously, calling for models to interact with execution environments, process extremely long contexts and perform complex reasoning that goes far beyond traditional code generation.

Our evaluations show that both state-of-the-art proprietary models and our fine-tuned model SWE-Llama can resolve only the simplest issues. Claude-2 and GPT-4 solve a mere 4.8% and 1.7% of instances respectively, even when provided with an oracle retriever.

Advances on SWE-bench represent steps towards LMs that are more practical, intelligent, and autonomous.

--- https://commonvoice.mozilla.org/en 2023-10-05 ai/dataset --- https://generallyintelligent.substack.com/p/fine-tuning-mistral-7b-on-magic-the 2023-10-05 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/imitation-learning --- https://arxiv.org/abs/2011.11751 Multimodal dynamics modeling for off-road autonomous vehicles Jean-François Tremblay, Travis Manderson, Aurélio Noca, Gregory Dudek, David Meger 2020-11-23 2023-10-06 [("doi","10.48550/arXiv.2011.11751")] ai/nn/rnn ai/nn/vae reinforcement-learning/model reinforcement-learning/robot

Dynamics modeling in outdoor and unstructured environments is difficult because different elements in the environment interact with the robot in ways that can be hard to predict. Leveraging multiple sensors to perceive maximal information about the robot’s environment is thus crucial when building a model to perform predictions about the robot’s dynamics with the goal of doing motion planning.

We design a model capable of long-horizon motion predictions, leveraging vision, lidar and proprioception, which is robust to arbitrarily missing modalities at test time. We demonstrate in simulation that our model is able to leverage vision to predict traction changes.

We then test our model using a real-world challenging dataset of a robot navigating through a forest, performing predictions in trajectories unseen during training. We try different modality combinations at test time and show that, while our model performs best when all modalities are present, it is still able to perform better than the baseline even when receiving only raw vision input and no proprioception, as well as when only receiving proprioception.

Overall, our study demonstrates the importance of leveraging multiple sensors when doing dynamics modeling in outdoor conditions.

--- https://github.com/ak9250/gpt-2-colab 2023-10-06 ai/nn/transformer/gpt/2 --- https://www.aiweirdness.com/d-and-d-character-bios-now-making-19-03-15/ 2023-10-06 ai/nn/transformer/gpt/2 --- https://github.com/minimaxir/gpt-2-simple 2023-10-06 ai/nn/transformer/gpt/2 --- https://colab.research.google.com/drive/1VLG8e7YSEwypxU-noRNhsv5dW4NfTGce 2023-10-06 ai/nn/transformer/gpt/2 --- https://arxiv.org/abs/2312.02116 GIVT: Generative Infinite-Vocabulary Transformers Michael Tschannen, Cian Eastwood, Fabian Mentzer 2023-12-04 2023-12-04 [("doi","10.48550/arXiv.2312.02116")] ai/nn/sampling ai/nn/transformer ai/nn/vae

We introduce generative infinite-vocabulary transformers (GIVT) which generate vector sequences with real-valued entries, instead of discrete tokens from a finite vocabulary.

To this end, we propose two surprisingly simple modifications to decoder-only transformers: (1) at the input, we replace the finite-vocabulary lookup table with a linear projection of the input vectors; and (2) at the output, we replace the logits prediction (usually mapped to a categorical distribution) with the parameters of a multivariate Gaussian mixture model.

Inspired by the image-generation paradigm of VQ-GAN and MaskGIT, where transformers are used to model the discrete latent sequences of a VQ-VAE, we use GIVT to model the unquantized real-valued latent sequences of a VAE.

When applying GIVT to class-conditional image generation with iterative masked modeling, we show competitive results with MaskGIT, while our approach outperforms both VQ-GAN and MaskGIT when using it for causal modeling.

Finally, we obtain competitive results outside of image generation when applying our approach to panoptic segmentation and depth estimation with a VAE-based variant of the UViM framework.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8600124/ 2023-10-06 genetics/heritable/correlation/mendelian-randomization vitamin-d --- https://arxiv.org/abs/2305.03047#ibm SELF-ALIGN: Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen, David Cox, Yiming Yang, Chuang Gan 2023-05-04 2023-10-06 [("doi","10.48550/arXiv.2305.03047")] ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/preference-learning

Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback (RLHF) to align the output of large language models (LLMs) with human intentions, ensuring they are helpful, ethical, and reliable. However, this dependence can constrain the true potential of AI-assistant agents due to the high cost of obtaining human supervision and the related issues on quality, reliability, diversity, self-consistency, and undesirable biases.

To address these challenges, we propose a novel approach called SELF-ALIGN [using Self-Instruct], which combines principle-driven reasoning and the generative power of LLMs for the self-alignment of AI agents with minimal human supervision. Our approach encompasses 4 stages: first, we use an LLM to generate synthetic prompts, and a topic-guided method to augment the prompt diversity; second, we use a small set of human-written principles for AI models to follow, and guide the LLM through in-context learning from demonstrations (of principles application) to produce helpful, ethical, and reliable responses to user’s queries; third, we fine-tune the original LLM with the high-quality self-aligned responses so that the resulting model can generate desirable responses for each query directly without the principle set and the demonstrations anymore; and finally, we offer a refinement step to address the issues of overly-brief or indirect responses.

Applying SELF-ALIGN to the LLaMA-65b base language model, we develop an AI assistant named Dromedary. With fewer than 300 lines of human annotations (including < 200 seed prompts, 16 generic principles, and 5 exemplars for in-context learning), Dromedary surpasses the performance of several state-of-the-art AI systems, including text-davinci-003 and Alpaca, on benchmark datasets with various settings.

--- https://arxiv.org/abs/2212.10560 Self-Instruct: Aligning Language Models with Self-Generated Instructions Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah Smith, Daniel Khashabi, Hannaneh Hajishirzi 2022-12-20 2023-10-06 [("doi","10.48550/arXiv.2212.10560")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/preference-learning

Large “instruction-tuned” language models (eg. finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is often limited in quantity, diversity, and creativity, therefore hindering the generality of the tuned model.

We introduce Self-Instruct, a framework for improving the instruction-following capabilities of pretrained language models by bootstrapping off their own generations. Our pipeline generates instructions, input, and output samples from a language model, then filters invalid or similar ones before using them to finetune the original model.

Applying our method to the vanilla GPT-3, we demonstrate a 33% absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT-001, which was trained with private user data and human annotations. For further evaluation, we curate a set of expert-written instructions for novel tasks, and show through human evaluation that tuning GPT-3 with Self-Instruct outperforms using existing public instruction datasets by a large margin, leaving only a 5% absolute gap behind InstructGPT-001.

Self-Instruct provides an almost annotation-free method for aligning pre-trained language models with instructions, and we release our large synthetic dataset to facilitate future studies on instruction tuning.

Our code and data are available at Github.

--- https://crfm.stanford.edu/2023/03/13/alpaca.html 2023-10-06 ai/dataset ai/nn/transformer/gpt/instruction-tuning --- https://arxiv.org/abs/2104.06979 TSDAE: Using Transformer-based Sequential Denoising Autoencoder for Unsupervised Sentence Embedding Learning Kexin Wang, Nils Reimers, Iryna Gurevych 2021-04-14 2023-10-06 [("doi","10.48550/arXiv.2104.06979")] ai/nn/retrieval ai/nn/transformer ai/nn/vae

Learning sentence embeddings often requires a large amount of labeled data. However, for most tasks and domains, labeled data is seldom available and creating it is expensive. In this work, we present a new state-of-the-art unsupervised method based on pre-trained Transformers and Sequential Denoising Autoencoder (TSDAE) which outperforms previous approaches by up to 6.4 points. It can achieve up to 93.1% of the performance of in-domain supervised approaches. Further, we show that TSDAE is a strong domain adaptation and pre-training method for sentence embeddings, outperforming other approaches like Masked Language Model.

A crucial shortcoming of previous studies is the narrow evaluation: Most work mainly evaluates on the single task of Semantic Textual Similarity (STS), which does not require any domain knowledge. It is unclear if these proposed methods generalize to other domains and tasks. We fill this gap and evaluate TSDAE and other recent approaches on 4 different datasets from heterogeneous domains.

--- https://arxiv.org/abs/2109.08113 MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection Matthew Matero, Nikita Soni, Niranjan Balasubramanian, H. Andrew Schwartz 2021-09-16 2023-10-07 [("doi","10.48550/arXiv.2109.08113")] ai/nn/retrieval ai/nn/transformer ai/nn/vae

Much of natural language processing is focused on leveraging large capacity language models, typically trained over single messages with a task of predicting one or more tokens. However, modeling human language at higher-levels of context (ie. sequences of messages) is under-explored. In stance detection and other social media tasks where the goal is to predict an attribute of a message, we have contextual data that is loosely semantically connected by authorship.

Here, we introduce Message-Level Transformer (MeLT)—a hierarchical message-encoder pre-trained over Twitter and applied to the task of stance prediction. We focus on stance prediction as a task benefiting from knowing the context of the message (ie. the sequence of previous messages). The model is trained using a variant of masked-language modeling; where instead of predicting tokens, it seeks to generate an entire masked (aggregated) message vector via reconstruction loss.

We find that applying this pre-trained masked message-level transformer to the downstream task of stance detection achieves F1 performance of 67%.

--- https://arxiv.org/abs/2212.06713#microsoft Structured Prompting: Scaling In-Context Learning to 1,000 Examples Yaru Hao, Yutao Sun, Li Dong, Zhixiong Han, Yuxian Gu, Furu Wei 2022-12-13 2023-10-07 [("doi","10.48550/arXiv.2212.06713")] ai/nn/transformer/attention/hierarchical ai/nn/transformer/gpt/2

Large language models have exhibited intriguing in-context learning capability, achieving promising zero-shot & few-shot performance without updating the parameters. However, conventional in-context learning is usually restricted by length constraints, rendering it ineffective to absorb supervision from a large number of examples.

In order to go beyond few shots, we introduce structured prompting that breaks the length limit and scales in-context learning to thousands of examples. Specifically, demonstration examples are separately encoded with well-designed position embeddings, and then they are jointly attended by the test example using a rescaled attention mechanism. So we can scale the number of exemplars with linear complexity instead of quadratic complexity with respect to length.

Experimental results on a diverse set of tasks show that our approach improves end-task performance and reduces evaluation variance over conventional in-context learning as the number of demonstration examples increases. Code has been released at https://github.com/microsoft/LMOps/tree/main/structured_prompting/fairseq-version.

--- https://arxiv.org/abs/2311.17042#stability Adversarial Diffusion Distillation Axel Sauer, Dominik Lorenz, Andreas Blattmann, Robin Rombach 2023-11-28 2023-11-28 [("doi","10.48550/arXiv.2311.17042")] ai/nn/diffusion ai/nn/gan

We introduce Adversarial Diffusion Distillation (ADD), a novel training approach that efficiently samples large-scale foundational image diffusion models in just 1–4 steps while maintaining high image quality. We use score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal in combination with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps.

Our analyses show that our model clearly outperforms existing few-step methods (GANs, Latent Consistency Models) in a single step and reaches the performance of state-of-the-art diffusion models (SDXL) in only 4 steps. ADD is the first method to unlock single-step, real-time image synthesis with foundation models.

Code and weights available under https://github.com/Stability-AI/generative-models and https://huggingface.co/stabilityai/.

--- https://x.com/mayfer/status/1732269798934106133 Murat Ayfer 2023-10-07 ai/nn/rnn ai/poetry --- https://arxiv.org/abs/1112.6209#google Building high-level features using large scale unsupervised learning Quoc V. Le, Marc’Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeff Dean, Andrew Y. Ng 2011-12-29 2023-10-07 [("doi","10.48550/arXiv.1112.6209")] ai/nn/cnn ai/nn/vae ai/scaling/hardware

[media] We consider the problem of building high-level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images?

To answer this, we train a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200×200 pixel images downloaded from the Internet [YouTube]).

We train this network using model parallelism and asynchronous SGD on a cluster with 1,000 machines (16,000 cores) for 3 days.

Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bodies.

Starting with these learned features, we trained our network to obtain 15.8% accuracy in recognizing 20,000 object categories from ImageNet, a leap of 70% relative improvement over the previous state-of-the-art.

--- https://arxiv.org/abs/2310.07177 OSD: Online Speculative Decoding Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Ion Stoica, Zhijie Deng, Alvin Cheung, Hao Zhang 2023-10-11 2023-10-11 [("doi","10.48550/arXiv.2310.07177")] ai/nn/dynamic-evaluation ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt

Speculative decoding is a pivotal technique to accelerate the inference of large language models (LLMs) by employing a smaller draft model to predict the target model’s outputs. However, its efficacy can be limited due to the low predictive accuracy of the draft model, particularly when faced with diverse text inputs and a capability gap between the draft and target models.

We introduce online speculative decoding (OSD) to address this challenge.

The main idea is to continually update (multiple) draft model(s) on observed user query data using the abundant excess computational power in an LLM serving cluster. Given that LLM inference is memory-bounded, the surplus computational power in a typical LLM serving cluster can be repurposed for online retraining of draft models, thereby making the training cost-neutral.

Since the query distribution of an LLM service is relatively simple, retraining on query distribution enables the draft model to more accurately predict the target model’s outputs, particularly on data originating from query distributions. As the draft model evolves online, it aligns with the query distribution in real time, mitigating distribution shifts.

We develop a prototype of online speculative decoding based on online knowledge distillation and evaluate it using both synthetic and real query data on several popular LLMs. The results show a substantial increase in the token acceptance rate 0.10 → 0.65, which translates into 1.22× to 3.06× latency reduction.

--- https://arxiv.org/abs/2312.02696#nvidia Analyzing and Improving the Training Dynamics of Diffusion Models Tero Karras, Miika Aittala, Jaakko Lehtinen, Janne Hellsten, Timo Aila, Samuli Laine 2023-12-05 2023-12-05 [("doi","10.48550/arXiv.2312.02696")] ai/nn/diffusion

Diffusion models currently dominate the field of data-driven image synthesis with their unparalleled scaling to large datasets. In this paper, we identify and rectify several causes for uneven and ineffective training in the popular ADM diffusion model architecture, without altering its high-level structure. Observing uncontrolled magnitude changes and imbalances in both the network activations and weights over the course of training, we redesign the network layers to preserve activation, weight, and update magnitudes on expectation. We find that systematic application of this philosophy eliminates the observed drifts and imbalances, resulting in considerably better networks at equal computational complexity. Our modifications improve the previous record FID of 2.41 in ImageNet-512 synthesis to 1.81, achieved using fast deterministic sampling.

As an independent contribution, we present a method for setting the exponential moving average (EMA) parameters post-hoc, ie. after completing the training run. This allows precise tuning of EMA length without the cost of performing several training runs, and reveals its surprising interactions with network architecture, training time, and guidance.

--- https://arxiv.org/abs/2311.08105 DiLoCo: Distributed Low-Communication Training of Language Models Arthur Douillard, Qixuan Feng, Andrei A. Rusu, Rachita Chhaparia, Yani Donchev, Adhiguna Kuncoro, Marc’Aurelio Ranzato, Arthur Szlam, Jiajun Shen 2023-11-14 2023-11-14 [("doi","10.48550/arXiv.2311.08105")] ai/nn/sparsity ai/nn/transformer ai/scaling/hardware

Large language models (LLM) have become a critical component in many applications of machine learning. However, standard approaches to training LLM require a large number of tightly interconnected accelerators, with devices exchanging gradients and other intermediate states at each optimization step. While it is difficult to build and maintain a single computing cluster hosting many accelerators, it might be easier to find several computing clusters each hosting a smaller number of devices.

In this work, we propose a distributed optimization algorithm, Distributed Low-Communication (DiLoCo), that enables training of language models on islands of devices that are poorly connected. The approach is a variant of federated averaging, where the number of inner steps is large, the inner optimizer is AdamW, and the outer optimizer is Nesterov momentum.

On the widely used C4 dataset, we show that DiLoCo on 8 workers performs as well as fully synchronous optimization while communicating 500× less.

DiLoCo exhibits great robustness to the data distribution of each worker. It is also robust to resources becoming unavailable over time, and vice versa, it can seamlessly leverage resources that become available during training.

--- https://plato.stanford.edu/entries/language-thought/ 2023-10-07 ai/nn philosophy/mind psychology/linguistics psychology/neuroscience --- https://bikobatanari.art/posts/2023/east-west-website-culture 2023-10-07 design japan --- https://arxiv.org/abs/2009.08378 EventProp: Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networks Timo C. Wunderlich, Christian Pehle 2020-09-17 2023-10-07 [("doi","10.1038/s41598-021-91786-z")] ai/nn/sparsity psychology/neuroscience

Spiking neural networks combine analog computation with event-based communication using discrete spikes. While the impressive advances of deep learning are enabled by training non-spiking artificial neural networks using the backpropagation algorithm, applying this algorithm to spiking networks was previously hindered by the existence of discrete spike events and discontinuities.

For the first time, this work derives the backpropagation algorithm for a continuous-time spiking neural network and a general loss function by applying the adjoint method together with the proper partial derivative jumps, allowing for backpropagation through discrete spike events without approximations. This algorithm, EventProp, backpropagates errors at spike times in order to compute the exact gradient in an event-based, temporally and spatially sparse fashion.

We use gradients computed via EventProp to train networks on the Yin-Yang and MNIST datasets using either a spike time or voltage based loss function and report competitive performance. Our work supports the rigorous study of gradient-based learning algorithms in spiking neural networks and provides insights toward their implementation in novel brain-inspired hardware.

--- https://arxiv.org/abs/2202.02950 Jury Learning: Integrating Dissenting Voices into Machine Learning Models Mitchell L. Gordon, Michelle S. Lam, Joon Sung Park, Kayur Patel, Jeffrey T. Hancock, Tatsunori Hashimoto, Michael S. Bernstein 2022-02-07 2023-10-08 [("doi","10.1145/3491102.3502004")] reinforcement-learning/model/decision-transformer

Whose labels should a machine learning (ML) algorithm learn to emulate? For ML tasks ranging from online comment toxicity to misinformation detection to medical diagnosis, different groups in society may have irreconcilable disagreements about ground truth labels. Supervised ML today resolves these label disagreements implicitly using majority vote, which overrides minority groups’ labels.

We introduce jury learning, a supervised ML approach that resolves these disagreements explicitly through the metaphor of a jury: defining which people or groups, in what proportion, determine the classifier’s prediction. For example, a jury learning model for online toxicity might centrally feature women & black jurors, who are commonly targets of online harassment.

To enable jury learning, we contribute a deep learning architecture that models every annotator in a dataset, samples from annotators’ models to populate the jury, then runs inference to classify.

Our architecture enables juries that dynamically adapt their composition, explore counterfactuals, and visualize dissent.

--- https://www.lesswrong.com/posts/t9svvNPNmFf5Qa3TA/mysteries-of-mode-collapse#pfHTedu4GKaWoxD5K 2023-10-08 ai/nn/transformer/gpt/3 ai/nn/transformer/gpt/4 reinforcement-learning/model/decision-transformer reinforcement-learning/preference-learning/mode-collapse reinforcement-learning/safe --- https://arxiv.org/abs/1203.3271 The thermodynamics of prediction Susanne Still, David A. Sivak, Anthony J. Bell, Gavin E. Crooks 2012-03-15 2023-10-08 [("doi","10.1103/PhysRevLett.109.120604")] cs/algorithm/information/compression cs/end-to-end-principle cs/hardware science

A system responding to a stochastic driving signal can be interpreted as computing, by means of its dynamics, an implicit model of the environmental variables. The system’s state retains information about past environmental fluctuations, and a fraction of this information is predictive of future ones. The remaining non-predictive information reflects model complexity that does not improve predictive power, and thus represents the ineffectiveness of the model.

We expose the fundamental equivalence between this model inefficiency and thermodynamic inefficiency, measured by dissipation.

Our results hold arbitrarily far from thermodynamic equilibrium and are applicable to a wide range of systems, including biomolecular machines.

They highlight a profound connection between the effective use of information and efficient thermodynamic operation: any system constructed to keep memory about its environment and to operate with maximal energetic efficiency has to be predictive.

--- /doc/iq/ses/2011-lindqvist.pdf The Labor Market Returns to Cognitive and Noncognitive Ability: Evidence from the Swedish Enlistment Erik Lindqvist, Roine Vestman 2011-01-01 2023-10-08 [("doi","10.1257/app.3.1.101")] iq/ses psychology/personality/conscientiousness

We use data from the Swedish military enlistment to assess the importance of cognitive and noncognitive ability for labor market outcomes.

The measure of noncognitive ability is based on a personal interview conducted by a psychologist.

We find strong evidence that men who fare poorly in the labor market—in the sense of unemployment or low annual earnings—lack noncognitive rather than cognitive ability.

However, cognitive ability is a stronger predictor of wages for skilled workers and of earnings above the median.

--- https://en.wikipedia.org/wiki/Forcing_(magic) Forcing (magic) 2023-10-08 psychology/cognitive-bias/illusion-of-depth --- https://en.wikipedia.org/wiki/Anthropic Anthropic 2023-10-08 ai/nn/anthropic --- https://www.anthropic.com/ 2023-10-08 ai/nn/anthropic --- https://buttondown.email/hillelwayne/archive/when-would-you-ever-want-bubblesort/ 2023-10-08 cs/algorithm/sorting --- https://marginalrevolution.com/marginalrevolution/2023/11/the-indian-challenge-to-blockchains-digital-public-goods.html 2023-10-08 bitcoin economics/mechanism-design --- https://mattmahoney.net/dc/dce.html#Section_14 2023-10-08 ai cs/algorithm philosophy/epistemology --- https://arxiv.org/abs/2309.10668#deepmind Language Modeling Is Compression Grégoire Delétang, Anian Ruoss, Paul-Ambroise Duquenne, Elliot Catt, Tim Genewein, Christopher Mattern, Jordi Grau-Moya, Li Kevin Wenliang, Matthew Aitchison, Laurent Orseau, Marcus Hutter, Joel Veness 2023-09-19 2023-10-08 [("doi","10.48550/arXiv.2309.10668")] ai/nn/transformer/gpt cs/algorithm/information/compression

It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has focused on training increasingly large and powerful self-supervised (language) models. Since these large language models exhibit impressive predictive capabilities, they are well-positioned to be strong compressors.

In this work, we advocate for viewing the prediction problem through the lens of compression and evaluate the compression capabilities of large (foundation) models. We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning.

For example, Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to 43.4% and LibriSpeech samples to 16.4% of their raw size, beating domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively.

Finally, we show that the prediction-compression equivalence allows us to use any compressor (like gzip) to build a conditional generative model.

--- https://www.youtube.com/watch?v=dO4TPJkeaaU 2023-10-09 ai/nn/transformer/gpt cs/algorithm --- https://onlinelibrary.wiley.com/doi/full/10.1002/soej.12672 2023-10-09 economics psychology/personality --- https://www.wired.com/story/matthew-butterick-ai-copyright-lawsuits-openai-meta/ 2023-10-09 design/typography economics/copyright law --- https://marginalrevolution.com/marginalrevolution/2023/11/bona-vacantia.html 2023-10-09 law --- https://publicdomainreview.org/essay/anna-atkins-cyanotypes/ 2023-10-09 biology design/visualization history/public-domain-review --- https://arxiv.org/abs/2311.13657 Efficient Transformer Knowledge Distillation: A Performance Review Nathan Brown, Ashton Williamson, Tahj Anderson, Logan Lawrence 2023-11-22 2023-11-22 [("doi","10.48550/arXiv.2311.13657")] ai/dataset ai/nn/sparsity/knowledge-distillation ai/nn/transformer/attention

As pretrained transformer language models continue to achieve state-of-the-art performance, the Natural Language Processing community has pushed for advances in model compression and efficient attention mechanisms to address high computational requirements and limited input sequence length. Despite these separate efforts, no investigation has been done into the intersection of these two fields.

In this work, we provide an evaluation of model compression via knowledge distillation on efficient attention transformers. We provide cost-performance trade-offs for the compression of state-of-the-art efficient attention architectures and the gains made in performance in comparison to their full attention counterparts.

Furthermore, we introduce a new long-context Named Entity Recognition dataset, GONERD, to train and test the performance of NER models on long sequences. We find that distilled efficient attention transformers can preserve a large amount of original model performance, preserving up to 98.6% across short-context tasks (GLUE, SQUAD, CoNLL-2003), up to 94.6% across long-context Question-and-Answering tasks (HotpotQA, TriviaQA), and up to 98.8% on long-context Named Entity Recognition (GONERD), while decreasing inference times by up to 57.8%.

We find that, for most models on most tasks, performing knowledge distillation is an effective method to yield high-performing efficient attention models with low costs.

--- https://arxiv.org/abs/1912.02807#deepmind Combining Q-Learning and Search with Amortized Value Estimates Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Theophane Weber, Lars Buesing, Peter W. Battaglia 2019-12-05 2023-10-09 [("doi","10.48550/arXiv.1912.02807")] reinforcement-learning/model reinforcement-learning/model-free

We introduce “Search with Amortized Value Estimates” (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS). In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values. The new Q-estimates are then used in combination with real experience to update the prior. This effectively amortizes the value computation performed by MCTS, resulting in a cooperative relationship between model-free learning and model-based search.

SAVE can be implemented on top of any Q-learning agent with access to a model, which we demonstrate by incorporating it into agents that perform challenging physical reasoning tasks and Atari. SAVE consistently achieves higher rewards with fewer training steps, and—in contrast to typical model-based search approaches—yields strong performance with very small search budgets.

By combining real experience with information computed during search, SAVE demonstrates that it is possible to improve on both the performance of model-free learning and the computational cost of planning.

--- https://arxiv.org/abs/2311.06281 Efficient Parallelization of an Ubiquitous Sequential Computation Franz A. Heinsen 2023-10-27 2023-10-27 [("doi","10.48550/arXiv.2311.06281")] cs/algorithm

We find a succinct expression for computing the sequence xt = at xt−1 + bt in parallel with two prefix sums, given t = (1, 2, …, n), at ∈ ℝn, bt ∈ ℝn, and initial value x0 ∈ ℝ.

On n parallel processors, the computation of n elements incurs ℒ(𝓁𝓸𝓰 n) time and 𝒪(n) space.

Sequences of this form are ubiquitous in science and engineering, making efficient parallelization useful for a vast number of applications.

We implement our expression in software, test it on parallel hardware, and verify that it executes faster than sequential computation by a factor of n⁄log n.

--- https://x.com/cHHillee/status/1732868066558792189 cHHillee 2023-10-09 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2310.10628 Data Contamination Through the Lens of Time Manley Roberts, Himanshu Thakur, Christine Herlihy, Colin White, Samuel Dooley 2023-10-16 2023-10-16 [("doi","10.48550/arXiv.2310.10628")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex

Recent claims about the impressive abilities of large language models (LLMs) are often supported by evaluating publicly available benchmarks. Since LLMs train on wide swaths of the internet, this practice raises concerns of data contamination, ie. evaluating on examples that are explicitly or implicitly included in the training data. Data contamination remains notoriously challenging to measure and mitigate, even with partial attempts like controlled experimentation of training data, canary strings, or embedding similarities.

In this work, we conduct the first thorough longitudinal analysis of data contamination in LLMs by using the natural experiment of training cutoffs in GPT models to look at benchmarks released over time. Specifically, we consider two code/mathematical problem-solving datasets, Codeforces and Project Euler, and find statistically trends among LLM pass rate vs. GitHub popularity and release date that provide strong evidence of contamination.

By open-sourcing our dataset, raw results, and evaluation framework, our work paves the way for rigorous analyses of data contamination in modern models. We conclude with a discussion of best practices and future steps for publicly releasing benchmarks in the age of LLMs that train on web-scale data.

--- https://www.astralcodexten.com/p/defying-cavity-lantern-bioworks-faq 2023-10-10 genetics/microbiome --- https://arxiv.org/abs/2305.14718 Improving Language Models with Advantage-based Offline Policy Gradients Ashutosh Baheti, Ximing Lu, Faeze Brahman, Ronan Le Bras, Maarten Sap, Mark Riedl 2023-05-24 2023-10-10 [("doi","10.48550/arXiv.2305.14718")] ai/nn/transformer/gpt reinforcement-learning/model-free reinforcement-learning/offline reinforcement-learning/preference-learning

Language Models (LMs) achieve substantial language capabilities when finetuned using Reinforcement Learning with Human Feedback (RLHF). However, RLHF is an unstable and data-hungry process that continually requires new high-quality LM-generated data for finetuning. We introduce Advantage-Leftover Lunch RL (A-LoL), a new class of offline policy gradient algorithms that enable RL training on any pre-existing data. By assuming the entire LM output sequence as a single action, A-LoL allows incorporating sequence-level classifiers or human-designed scoring functions as rewards. Subsequently, by using LM’s internal sequence-level value estimate, A-LoL filters negative advantage (low-quality) data points during training, making it resilient to noise. Overall, A-LoL is an easy-to-implement LM training recipe that is sample-efficient and stable.

We demonstrate the effectiveness of A-LoL and its variants with a set of 4 different language generation tasks. We compare against both online RL (PPO) and recent preference-based (DPO, PRO) and reward-based (GOLD) offline RL baselines. On the commonly-used RLHF benchmark, Helpful and Harmless Assistant (HHA), LMs trained with A-LoL methods achieve the highest diversity while also being rated more safe and helpful than baselines according to humans. Additionally, in the remaining 3 tasks, A-LoL could optimize multiple distinct reward functions even when using noisy or suboptimal training data. We also release our experimental code. https://github.com/abaheti95/LoL-RL.

--- https://aclanthology.org/D18-1428/ 2023-10-10 ai/nn/gan --- https://arxiv.org/abs/1609.05473 SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu 2016-09-18 2023-10-10 [("doi","10.48550/arXiv.1609.05473")] ai/nn/gan reinforcement-learning/imitation-learning

As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limitations when the goal is for generating sequences of discrete tokens. A major reason lies in that the discrete outputs from the generative model make it difficult to pass the gradient update from the discriminative model to the generative model.

Also, the discriminative model can only assess a complete sequence, while for a partially generated sequence, it is non-trivial to balance its current score and the future one once the entire sequence has been generated. In this paper, we propose a sequence generation framework, called SeqGAN, to solve the problems.

Modeling the data generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update. The RL reward signal comes from the GAN discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search.

Extensive experiments on synthetic data and real-world tasks demonstrate improvements over strong baselines.

--- https://arxiv.org/abs/1804.11258 Toward Diverse Text Generation with Inverse Reinforcement Learning Zhan Shi, Xinchi Chen, Xipeng Qiu, Xuanjing Huang 2018-04-30 2023-10-10 [("doi","10.48550/arXiv.1804.11258")] ai/nn/gan reinforcement-learning/exploration reinforcement-learning/preference-learning

Text generation is a crucial task in Natural Language Processing (NLP). Recently, several adversarial generative models have been proposed to improve the exposure bias problem in text generation. Though these models gain great success, they still suffer from the problems of reward sparsity and mode collapse. In order to address these two problems, in this paper, we employ inverse reinforcement learning (IRL) for text generation.

Specifically, the IRL framework learns a reward function on training data, and then an optimal policy to maximize the expected total reward. Similar to the adversarial models, the reward and policy function in IRL are optimized alternately.

Our method has two advantages: (1) the reward function can produce more dense reward signals. (2) the generation policy, trained by “entropy regularized” policy gradient, encourages to generate more diversified texts.

Experiment results demonstrate that our proposed method can generate higher quality texts than the previous methods.

--- https://arxiv.org/abs/1905.09922 Training language GANs from Scratch Cyprien de Masson d’Autume, Mihaela Rosca, Jack Rae, Shakir Mohamed 2019-05-23 2023-10-10 [("doi","10.48550/arXiv.1905.09922")] ai/nn/gan

Generative Adversarial Networks (GANs) enjoy great success at image generation, but have proven difficult to train in the domain of natural language. Challenges with gradient estimation, optimization instability, and mode collapse have led practitioners to resort to maximum likelihood pre-training, followed by small amounts of adversarial fine-tuning. The benefits of GAN fine-tuning for language generation are unclear, as the resulting models produce comparable or worse samples than traditional language models.

We show it is in fact possible to train a language GAN from scratch—without maximum likelihood pre-training. We combine existing techniques such as large batch sizes, dense rewards, and discriminator regularization to stabilize and improve language GANs.

The resulting model, ScratchGAN, performs comparably to maximum likelihood training on EMNLP2017 News and WikiText-103 corpora according to quality and diversity metrics.

--- https://arxiv.org/abs/2212.11119 A survey on text generation using generative adversarial networks Gustavo Henrique de Rosa, João Paulo Papa 2022-12-20 2023-10-10 [("doi","10.1016/j.patcog.2021.108098")] ai/nn/gan ai/nn/sampling

This work presents a thorough review concerning recent studies and text generation advancements using Generative Adversarial Networks (GANs). The usage of adversarial learning for text generation is promising as it provides alternatives to generate the so-called “natural” language.

Nevertheless, adversarial text generation is not a simple task as its foremost architecture, the Generative Adversarial Networks, were designed to cope with continuous information (image) instead of discrete data (text). Thus, most works are based on 3 possible options, ie. Gumbel-Softmax differentiation, Reinforcement Learning, and modified training objectives.

All alternatives are reviewed in this survey as they present the most recent approaches for generating text using adversarial-based techniques. The selected works were taken from renowned databases, such as Science Direct, IEEEXplore, Springer, Association for Computing Machinery, and arXiv, whereas each selected work has been critically analyzed and assessed to present its objective, methodology, and experimental results.

--- https://arxiv.org/abs/1705.11001 Adversarial Ranking for Language Generation Kevin Lin, Dianqi Li, Xiaodong He, Zhengyou Zhang, Ming-Ting Sun 2017-05-31 2023-10-10 [("doi","10.48550/arXiv.1705.11001")] ai/nn/gan reinforcement-learning/preference-learning

Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit their learning capacity for tasks that need to synthesize output with rich structures such as natural language descriptions.

In this paper, we propose a novel generative adversarial network, RankGAN, for generating high-quality language descriptions. Rather than training the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collection of human-written and machine-written sentences by giving a reference group.

By viewing a set of data samples collectively and evaluating their quality through relative ranking scores, the discriminator is able to make a better assessment which in turn helps to learn a better generator. The proposed RankGAN is optimized through the policy gradient technique. Experimental results on multiple public datasets clearly demonstrate the effectiveness of the proposed approach.

--- https://arxiv.org/abs/2312.03818 Alpha-CLIP: A CLIP Model Focusing on Wherever You Want Zeyi Sun, Ye Fang, Tong Wu, Pan Zhang, Yuhang Zang, Shu Kong, Yuanjun Xiong, Dahua Lin, Jiaqi Wang 2023-12-06 2023-12-06 [("doi","10.48550/arXiv.2312.03818")] ai/nn/transformer/clip

Contrastive Language-Image Pre-training (CLIP) plays an essential role in extracting valuable content information from images across diverse tasks. It aligns textual and visual modalities to comprehend the entire image, including all the details, even those irrelevant to specific tasks. However, for a finer understanding and controlled editing of images, it becomes crucial to focus on specific regions of interest, which can be indicated as points, masks, or boxes by humans or perception models.

To fulfill the requirements, we introduce Alpha-CLIP, an enhanced version of CLIP with an auxiliary alpha channel to suggest attentive regions and fine-tuned with constructed millions of RGBA region-text pairs. Alpha-CLIP not only preserves the visual recognition ability of CLIP but also enables precise control over the emphasis of image contents. It demonstrates effectiveness in various tasks, including but not limited to open-world recognition, multimodal large language models, and conditional 2D / 3D generation. It has a strong potential to serve as a versatile tool for image-related tasks.

--- https://tomhazledine.com/llm-related-posts/ 2023-10-10 ai/nn/retrieval --- https://arxiv.org/abs/2312.03876 Scaling transformer neural networks for skillful and reliable medium-range weather forecasting Tung Nguyen, Rohan Shah, Hritik Bansal, Troy Arcomano, Sandeep Madireddy, Romit Maulik, Veerabhadra Kotamarthi, Ian Foster, Aditya Grover 2023-12-06 2023-12-06 [("doi","10.48550/arXiv.2312.03876")] ai/nn/transformer ai/scaling science

[cf. ClimaX] Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of climate change. Recently, data-driven approaches for weather forecasting based on deep learning have shown great promise, achieving accuracies that are competitive with operational systems. However, those methods often employ complex, customized architectures without sufficient ablation analysis, making it difficult to understand what truly contributes to their success.

Here we introduce Stormer, a simple transformer model that achieves state-of-the-art performance on weather forecasting with minimal changes to the standard transformer backbone. We identify the key components of Stormer through careful empirical analyses, including weather-specific embedding, randomized dynamics forecast, and pressure-weighted loss.

At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals. During inference, this allows us to produce multiple forecasts for a target lead time and combine them to obtain better forecast accuracy.

On WeatherBench 2, Stormer performs competitively at short to medium-range forecasts and outperforms current methods beyond 7 days, while requiring orders-of-magnitude less training data and compute. Additionally, we demonstrate Stormer’s favorable scaling properties, showing consistent improvements in forecast accuracy with increases in model size and training tokens. Code and checkpoints will be made publicly available.

…We start with a standard vision transformer (ViT) architecture, and through extensive ablation studies, identify the 3 key components to the performance of the model: (1) a weather-specific embedding layer that transforms the input data to a sequence of tokens by modeling the interactions among atmospheric variables; (2) a randomized dynamics forecasting objective that trains the model to predict the weather dynamics at random intervals; and (3) a pressure-weighted loss that weights variables at different pressure levels in the loss function to approximate the density at each pressure level. During inference, our proposed randomized dynamics forecasting objective allows a single model to produce multiple forecasts for a specified lead time by using different combinations of the intervals for which the model was trained. For example, one can obtain a 3-day forecast by either rolling out the 6-hour predictions 12× or 12-hour predictions 6×. Combining these forecasts leads to substantial performance improvements, especially for long lead times.

…Experiments show that Stormer achieves competitive forecast accuracy of key atmospheric variables for 1–7 days and outperforms the state-of-the-art beyond 7 days. Notably, Stormer achieves this performance by training on more than 5× lower-resolution data and orders-of-magnitude fewer GPU hours compared to the baselines. Finally, our scaling analysis shows that the performance of Stormer improves consistently with increases in model capacity and data size, demonstrating the potential for further improvements.

…Moreover, we also note that Stormer achieves this performance with much less compute and training data compared to the two deep learning baselines. We train Stormer on 6-hourly data of 1.40625∘ with 13 pressure levels, which is ~190× less data than Pangu-Weather’s hourly data at 0.25∘ and 90× less than that used for GraphCast, which also uses 6-hourly data but at a 0.25∘ resolution with 37 pressure levels. The training of Stormer was completed in under 24 hours on 128 A100 GPUs. In contrast, Pangu-Weather took 60 days to train 4 models on 192 V100 GPUs, and GraphCast required 28 days on 32 TPUv4 devices. This training efficiency will facilitate future works that build upon our proposed framework.

…4.3. Scaling analysis: We examine the scalability of Stormer with respect to model size and the number of training tokens. We evaluate 3 variants of Stormer—Stormer-S, Stormer-B, and Stormer-L, whose parameter counts are similar to ViT-S, ViT-B, and ViT-L, respectively. To understand the impact of training token count, we vary the patch size 2–16. The number of training tokens increases 4× whenever the patch size is halved.

Figure 6: Stormer improves consistently with larger models (top) and smaller patch sizes (bottom).

Figure 6 shows a substantial improvement in forecast accuracy when we increase the model size, and the performance gap widens as we increase the lead time.

Since we do not perform multi-step fine-tuning for these models, minor performance differences at short intervals may become magnified over time. While multi-step fine-tuning could potentially reduce this gap, it is unlikely to eliminate it entirely. Reducing the patch size also improves the performance of the model consistently. From a practical view, smaller patches mean more tokens and consequently more training data. From a climate perspective, smaller patches capture finer weather details and processes not evident in larger patches, allowing the model to more effectively capture physical dynamics that drive weather patterns.

--- https://x.com/ChatGPTapp/status/1732979491071549792 ChatGPTapp 2023-10-11 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex reinforcement-learning/safe --- https://www.newyorker.com/magazine/2023/12/04/what-happens-to-a-school-shooters-sister 2023-10-11 crime psychiatry/schizophrenia --- https://www.anthropic.com/news/claude-2-1 2023-10-11 ai/nn/transformer/gpt/claude --- https://en.wikipedia.org/wiki/Agenda-setting_theory Agenda-setting theory 2023-10-11 politics --- https://arxiv.org/abs/2310.12931#nvidia Eureka: Human-Level Reward Design via Coding Large Language Models Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, Anima Anandkumar 2023-10-19 2023-10-19 [("doi","10.48550/arXiv.2310.12931")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex reinforcement-learning/meta-learning reinforcement-learning/preference-learning reinforcement-learning/robot

Large Language Models (LLMs) have excelled as high-level semantic planners for sequential decision-making tasks. However, harnessing them to learn complex low-level manipulation tasks, such as dexterous pen spinning, remains an open problem. We bridge this fundamental gap and present Eureka, a human-level reward design algorithm powered by LLMs.

Eureka exploits the remarkable zero-shot generation, code-writing, and in-context improvement capabilities of state-of-the-art LLMs, such as GPT-4, to perform evolutionary optimization over reward code. The resulting rewards can then be used to acquire complex skills via reinforcement learning. Without any task-specific prompting or pre-defined reward templates, Eureka generates reward functions that outperform expert human-engineered rewards.

In a diverse suite of 29 open-source RL environments that include 10 distinct robot morphologies, Eureka outperforms human experts on 83% of the tasks, leading to an average normalized improvement of 52%.

The generality of Eureka also enables a new gradient-free in-context learning approach to reinforcement learning from human feedback (RLHF), readily incorporating human inputs to improve the quality and the safety of the generated rewards without model updating.

Finally, using Eureka rewards in a curriculum learning setting, we demonstrate for the first time, a simulated Shadow Hand capable of performing pen spinning tricks, adeptly manipulating a pen in circles at rapid speed.

--- https://www.quantamagazine.org/a-team-of-math-proves-a-critical-link-between-addition-and-sets-20231206/ 2023-10-11 ai/nn/transformer/gpt/codex math --- https://journals.sagepub.com/eprint/KAZQCQPABG4VDVAXUNJA/full 2023-10-11 science --- https://arxiv.org/abs/2311.07911#google Instruction-Following Evaluation for Large Language Models Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, Le Hou 2023-11-14 2023-11-14 [("doi","10.48550/arXiv.2311.07911")] ai/dataset ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/palm/2

One core capability of Large Language Models (LLMs) is to follow natural language instructions. However, the evaluation of such abilities is not standardized: Human evaluations are expensive, slow, and not objectively reproducible, while LLM-based auto-evaluation is potentially biased or limited by the ability of the evaluator LLM.

To overcome these issues, we introduce Instruction-Following Eval (IFEval) for large language models. IFEval is a straightforward and easy-to-reproduce evaluation benchmark. It focuses on a set of “verifiable instructions” such as “write in more than 400 words” and “mention the keyword of AI at least 3×”.

We identified 25 types of those verifiable instructions and constructed around 500 prompts, with each prompt containing one or more verifiable instructions. We show evaluation results of two widely available LLMs on the market. Our code and data can be found at https://github.com/google-research/google-research/tree/master/instruction_following_eval.

--- https://www.lesswrong.com/posts/7fxusXdkMNmAhkAfc/finding-sparse-linear-connections-between-features-in-llms 2023-10-11 ai/nn/fully-connected ai/nn/sparsity ai/nn/transformer/attention --- /doc/crime/2015-kendler.pdf A Swedish national twin study of criminal behavior and its violent, white-collar and property subtypes K. S. Kendler, H. H. Maes, S. L. Lönn, N. A. Morris, P. Lichtenstein, J. Sundquist, K. Sundquist 2015-05-04 2023-10-11 [("doi","10.1017/S0033291714002098")] crime economics genetics/heritable/correlation

Background: We sought to clarify the etiological contribution of genetic and environmental factors to total criminal behavior (CB) measured as criminal convictions in men and women, and to violent (VCB), white-collar (WCCB) and property criminal behavior (PCB) in men only.

Method: In 21,603 twin pairs from the Swedish Twin Registry, we obtained information on all criminal convictions 1973–2011 from the Swedish Crime Register. Twin modeling was performed using the OpenMx package.

Results: For all criminal convictions, heritability was estimated at around 45% in both sexes, with the shared environment accounting for 18% of the variance in liability in females and 27% in males. The correlation of these risk factors across sexes was estimated at +0.63. In men, the magnitudes of genetic and environmental influence were similar in the 3 criminal conviction subtypes. However, for violent and white-collar convictions, nearly half and 1⁄3rd of the genetic effects were respectively unique to that criminal subtype. About half of the familial environmental effects were unique to property convictions.

Conclusions: The familial aggregation of officially recorded CB is substantial and results from both genetic and familial environmental factors. These factors are moderately correlated across the sexes suggesting that some genetic and environmental influences on criminal convictions are unique to men and to women. Violent criminal behavior and property crime are substantially influenced respectively by genetic and shared environmental risk factors unique to that criminal subtype.

--- /doc/cat/psychology/2023-mcgrath.pdf Cat Ownership and Schizophrenia-Related Disorders and Psychotic-Like Experiences: A Systematic Review and Meta-Analysis John J. McGrath, Carmen C. W. Lim, Sukanta Saha 2023-12-02 2023-12-02 [("doi","10.1093/schbul/sbad168")] cat/psychology psychiatry/schizophrenia

Background: It has been proposed that cat ownership may be a risk-modifying factor for schizophrenia-related disorders and psychotic-like experiences (PLE). This study aimed to systematically review and meta-analyze publications that reported the relationship between cat ownership and schizophrenia-related outcomes.

Methodology: We searched MEDLINE, Embase, CINAHL, Web of Science, and gray literature for publications between January 1, 1980, and May 30, 2023, regardless of geographical location and language. Backward citation search methods were used to locate additional articles. We included studies that reported original data on cat ownership and schizophrenia-related outcomes. We meta-analyzed estimates based on broad definitions (cat ownership, cat bites, and cat contact) with estimates with or without covariate adjustments. We pooled comparable estimates using random-effects models and assessed the risk of bias, heterogeneity, and study quality.

Results: We identified 1,915 studies, of which 106 were chosen for full-text review, ultimately resulting in the inclusion of 17 studies. We found an association between broadly defined cat ownership and increased odds of developing schizophrenia-related disorders. The unadjusted pooled odds ratio (OR) was 2.35 (95% CI: 1.38–4.01), while the adjusted pooled estimate was 2.24 (95% CI: 1.61–3.12). We were unable to aggregate the estimates for the PLE outcomes because of the broad range of measures.

Conclusions: Our findings support an association between cat exposure and an increased risk of broadly defined schizophrenia-related disorders; however, the findings related to PLE as an outcome are mixed. There is a need for more high-quality studies in this field.

PROSPERO registration: PROSPERO 2023 CRD42023426974.

Figure 2: Forest plot of the random-effects meta-analysis between cat exposure and schizophrenia-related disorders, unadjusted analyses.
Figure 3: Forest plot of the random-effects meta-analysis between cat exposure and schizophrenia-related disorders, adjusted analyses.

…With respect to heterogeneity, both meta-analyses identified statistically-significant heterogeneity (the Q-statistic and I2 are presented for each forest plot). The unadjusted and adjusted Q-statistics estimates for schizophrenia-related disorders were 106.8 (p-value < 0.01) and 12.48 (p-value = 0.03), respectively. The corresponding I2 values were 94.5% and 56.2%, respectively. The quality scores for the included studies ranged 0–8 (total score of 9) (Supplementary Table 2), with 8 studies scoring 4 or less and 9 studies scoring 5 or higher. In keeping with recommendations about the minimal number of estimates required to interpret funnel asymmetry plots (>10),35 we were not able to assess potential publication bias in the included data.

--- /doc/cat/psychology/2023-mcgrath-supplement.pdf Cat Ownership and Schizophrenia-Related Disorders and Psychotic-Like Experiences: A Systematic Review and Meta-Analysis: Supplement John J. McGrath, Carmen C. W. Lim, Sukanta Saha 2023-12-02 2023-12-02 [("doi","10.1093/schbul/sbad168_1")] cat/psychology psychiatry/schizophrenia --- https://www.medrxiv.org/content/10.1101/2023.12.06.23299426.full Whole-genome sequencing of half-a-million UK Biobank participants The U. K. Biobank Whole-Genome Sequencing Consortium, Shuwei Li, Keren J. Carss, Bjarni V. Halldorsson, Adrian Cortes 2023-12-08 2023-12-08 [("doi","10.1101/2023.12.06.23299426")] genetics/heritable/rare genetics/sequencing

Whole-genome sequencing (WGS) provides a comprehensive view of the genome, enabling detection of coding and non-coding genetic variation, and surveying complex regions which are difficult to genotype. Here, we report on whole-genome sequencing of 490,640 UK Biobank participants, building on previous genotyping1 and whole-exome sequencing (WES) efforts2,3. This advance deepens our understanding of how genetics influences disease biology and further enhances the value of this open resource for the study of human biology and health.

Coupling this dataset with rich phenotypic data, we surveyed within & cross-ancestry genomic associations with health-related phenotypes and identified novel genetic and clinical insights. While most genome-wide statistically-significant associations with disease traits were primarily observed in Europeans, we also identified strong or novel signals in individuals of African and Asian ancestries.

Deeper capture of exonic variation in both coding and UTR sequences, strengthened and surfaced novel insights relative to WES analyses. This landmark dataset, representing the largest collection of WGS and available to the UK Biobank research community, will enable advances into our understanding of the human genome, and facilitate the discovery of new diagnostics, therapeutics with higher efficacy and improved safety profile, and enable precision medicine strategies with the potential to improve global health.

--- https://x.com/javilopen/status/1694507580687581501 Javi Lopez 2023-10-12 ai/nn/diffusion/midjourney --- https://arxiv.org/abs/2310.20216 Does GPT-4 Pass the Turing Test? Cameron Jones, Benjamin Bergen 2023-10-31 2023-10-31 [("doi","10.48550/arXiv.2310.20216")] ai/nn/transformer/gpt/3 ai/nn/transformer/gpt/4 philosophy/mind reinforcement-learning/preference-learning/mode-collapse

We evaluated GPT-4 in a public online Turing Test. The best-performing GPT-4 prompt passed in 41% of games, outperforming baselines set by ELIZA (27%) and GPT-3.5 (14%), but falling short of chance and the baseline set by human participants (63%).

Participants’ decisions were based mainly on linguistic style (35%) and socio-emotional traits (27%), supporting the idea that intelligence is not sufficient to pass the Turing Test.

Participants’ demographics, including education and familiarity with LLMs, did not predict detection rate, suggesting that even those who understand systems deeply and interact with them frequently may be susceptible to deception.

Despite known limitations as a test of intelligence, we argue that the Turing Test continues to be relevant as an assessment of naturalistic communication and deception. AI models with the ability to masquerade as humans could have widespread societal consequences, and we analyse the effectiveness of different strategies and criteria for judging human-likeness.

--- http://saint11.org/blog/pixel-art-tutorials/ 2023-10-12 design/visualization --- https://x.com/kenneth0stanley/status/1733571230803058920 Kenneth O. Stanley 2023-10-12 psychology/novelty reinforcement-learning/exploration --- https://www.medrxiv.org/content/10.1101/2023.10.13.23297022.full Polygenic Embryo Screening: High Approval Despite Substantial Concerns from the U.S. Public Rémy A. Furrer, Dorit Barlevy, Stacey Pereira, Shai Carmi, Todd Lencz, Gabriel Lázaro-Muñoz 2023-10-14 2023-10-14 [("doi","10.1101/2023.10.13.23297022")] genetics/selection/artificial

Polygenic embryo screening (PES) is a novel—yet commercially available—technology that can compute genetic likelihood estimates of polygenic conditions (eg. diabetes, depression) and traits (eg. height, cognitive ability) in embryos. Patients undergoing in vitro fertilization (IVF) can use these polygenic scores to select which embryos to transfer for implantation with the goal of having a child.

We conducted a survey of the U.S. public to examine attitudes toward PES encompassing acceptance, interest, potential uses, and concerns (n = 1,435).

Our results indicate 72% public approval for PES, with 82% expressing some interest in using PES if already undergoing IVF. Approval for using PES for embryo selection is notably high for physical health (77%) and psychiatric conditions (72%). In contrast, there is minority approval for embryo selection based on PES for behavioral traits (36%) and physical traits (30%).

Nevertheless, concerns about PES leading to false expectations, eugenic practices, and stigma are pronounced (54-55% find them “very” to “extremely” concerning). In a second sample of participants (n = 192), presenting concerns at survey onset (vs. end) reduced approval (−28%) by mostly increasing ambivalence (+24%), and only slightly increasing disapproval (+4%).

Given its commercial availability, practical limitations, and ethical concerns among physicians, patients, geneticists, bioethicists, and legal scholars, it is notable that there is such high public approval and interest in using PES. Understanding these attitudes is essential for informing policymakers, healthcare professionals, and researchers about the public’s perspectives on this novel biotechnology and debate about the role of medicine in regulating the use of PES.

--- https://arxiv.org/abs/2309.08638 Anchor Points: Benchmarking Models with Much Fewer Examples Rajan Vivek, Kawin Ethayarajh, Diyi Yang, Douwe Kiela 2023-09-14 2023-10-12 [("doi","10.48550/arXiv.2309.08638")] ai/nn/transformer/gpt reinforcement-learning/exploration/active-learning/data-pruning

[Twitter; cf. coresets] Modern language models often exhibit powerful but brittle behavior, leading to the development of larger and more diverse benchmarks to reliably assess their behavior. Here, we suggest that model performance can be benchmarked and elucidated with much smaller evaluation sets.

We first show that in 6 popular language classification benchmarks, model confidence in the correct class on many pairs of points is strongly correlated across models. We build upon this phenomenon to propose Anchor Point Selection, a technique to select small subsets of datasets that capture model behavior across the entire dataset.

Anchor points reliably rank models: across 87 diverse language model-prompt pairs, evaluating models using 1–30 anchor points outperforms uniform sampling and other baselines at accurately ranking models. Moreover, just several anchor points can be used to estimate model per-class predictions on all other points in a dataset with low mean absolute error, sufficient for gauging where the model is likely to fail.

Lastly, we present Anchor Point Maps for visualizing these insights and facilitating comparisons of the performance of different models on various regions within the dataset distribution.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584686/ Complete human day 14 post-implantation embryo models from naive ES cells Bernardo Oldak, Emilie Wildschutz, Vladyslav Bondarenko, Mehmet-Yunus Comar, Cheng Zhao, Alejandro Aguilera-Castrejon, Shadi Tarazi, Sergey Viukov, Thi Xuan Ai Pham, Shahd Ashouokhi, Dmitry Lokshtanov, Francesco Roncato, Eitan Ariel, Max Rose, Nir Livnat, Tom Shani, Carine Joubran, Roni Cohen, Yoseph Addadi, Muriel Chemla, Merav Kedmi, Hadas Keren-Shaul, Vincent Pasque, Sophie Petropoulos, Fredrik Lanner, Noa Novershtern, Jacob H. Hanna 2023 2023-10-12 [("doi","10.1038/s41586-023-06604-5")] genetics/gametogenesis

The ability to study human post-implantation development remains limited owing to ethical and technical challenges associated with intrauterine development after implantation1. Embryo-like models with spatially organized morphogenesis and structure of all defining embryonic and extra-embryonic tissues of the post-implantation human conceptus (that is, the embryonic disc, the bilaminar disc, the yolk sac, the chorionic sac and the surrounding trophoblast layer) remain lacking1,2.

Mouse naive embryonic stem cells have recently been shown to give rise to embryonic and extra-embryonic stem cells capable of self-assembling into post-gastrulation structured stem-cell-based embryo models with spatially organized morphogenesis (called SEMs)3. Here we extend those findings to humans using only genetically unmodified human naive embryonic stem cells (cultured in human enhanced naive stem cell medium conditions)4. Such human fully integrated and complete SEMs recapitulate the organization of nearly all known lineages and compartments of post-implantation human embryos, including the epiblast, the hypoblast, the extra-embryonic mesoderm and the trophoblast layer surrounding the latter compartments.

These human complete SEMs demonstrated developmental growth dynamics that resemble key hallmarks of post-implantation stage embryogenesis up to 13–14 days after fertilization (Carnegie stage 6a). These include embryonic disc and bilaminar disc formation, epiblast lumenogenesis, polarized amniogenesis, anterior-posterior symmetry breaking, primordial germ-cell specification, polarized yolk sac with visceral and parietal endoderm formation, extra-embryonic mesoderm expansion that defines a chorionic cavity and a connecting stalk, and a trophoblast-surrounding compartment demonstrating syncytium and lacunae formation. This SEM platform will probably enable the experimental investigation of previously inaccessible windows of human early post implantation up to peri-gastrulation development.

--- https://arxiv.org/abs/2311.04254 Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang 2023-11-07 2023-11-07 [("doi","10.48550/arXiv.2311.04254")] ai/nn/transformer/gpt/3 ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/inner-monologue reinforcement-learning/model/alphago

Recent advancements in Large Language Models (LLMs) have revolutionized decision-making by breaking down complex problems into more manageable language sequences referred to as “thoughts”. An effective thought design should consider 3 key perspectives: performance, efficiency, and flexibility. However, existing thought can at most exhibit two of these attributes.

To address these limitations, we introduce a novel thought prompting approach called “Everything of Thoughts” (XoT) to defy the law of “Penrose triangle of existing thought paradigms”. XoT leverages pretrained reinforcement learning and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge into thoughts, thereby enhancing LLMs’ capabilities and enabling them to generalize to unseen problems efficiently.

Through the usage of the MCTS-LLM collaborative thought revision framework, this approach autonomously produces high-quality comprehensive cognitive mappings with minimal LLM interactions. Additionally, XoT empowers LLMs to engage in unconstrained thinking, allowing for flexible cognitive mappings for problems with multiple solutions.

We evaluate XoT on several challenging multi-solution problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our results demonstrate that XoT outperforms existing approaches. Notably, XoT can yield multiple solutions with just one LLM call, showcasing its remarkable proficiency in addressing complex problems across diverse domains.

--- /doc/ai/nn/transformer/gpt/3/nonfiction/2023-grunebaum.pdf The exciting potential for ChatGPT in obstetrics and gynecology Amos Grünebaum, Joseph Chervenak, Susan L. Pollet, Adi Katz, Frank A. Chervenak 2023-06-01 2023-10-13 [("doi","10.1016/j.ajog.2023.03.009")] ai/nn/transformer/gpt/3/nonfiction biology --- https://arxiv.org/abs/2311.00430 Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling Sanchit Gandhi, Patrick von Platen, Alexander M. Rush 2023-11-01 2023-11-01 [("doi","10.48550/arXiv.2311.00430")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/whisper

[Twitter] As the size of pre-trained speech recognition models increases, running these large models in low-latency or resource-constrained environments becomes challenging. In this work, we leverage pseudo-labeling to assemble a large-scale open-source dataset which we use to distill the Whisper model into a smaller variant, called Distil-Whisper. Using a simple word error rate (WER) heuristic, we select only the highest quality pseudo-labels for training.

The distilled model is 5.8× faster with 51% fewer parameters, while performing to within 1% WER on out-of-distribution test data in a zero-shot transfer setting. Distil-Whisper maintains the robustness of the Whisper model to difficult acoustic conditions, while being less prone to hallucination errors on long-form audio.

Distil-Whisper is designed to be paired with Whisper for speculative decoding, yielding a 2× speed-up while mathematically ensuring the same outputs as the original model. To facilitate further research in this domain, we make our training code, inference code and models publicly accessible.

--- /doc/economics/mechanism-design/2023-ali.pdf Who Controls the Agenda Controls the Legislature S. Nageeb Ali, B. Douglas Bernheim, Alexander W. Bloedel, Silvia Console Battilana 2023-11-01 2023-11-01 [("doi","10.1257/aer.20221578")] economics/mechanism-design politics

We model legislative decision-making with an agenda setter who can propose policies sequentially, tailoring each proposal to the status quo that prevails after prior votes. Voters are sophisticated, and the agenda setter cannot commit to future proposals.

Nevertheless, the agenda setter obtains her favorite outcome in every equilibrium regardless of the initial default policy.

Central to our results is a new condition on preferences, manipulability, that holds in rich policy spaces, including spatial settings and distribution problems.

Our findings therefore establish that, despite the sophistication of voters and the absence of commitment power, the agenda setter is effectively a dictator [!].

--- https://www.reddit.com/r/MachineLearning/comments/18eh2hb/p_the_power_of_reinforcement_learning_look_how/ 2023-10-13 reinforcement-learning/model-free reinforcement-learning/safe --- https://fleuret.org/francois/lbdl.html 2023-10-13 ai/nn design --- https://www.medrxiv.org/content/10.1101/2023.12.06.23299605.full Clinical-grade whole genome sequencing-based haplarithmisis enables all forms of preimplantation genetic testing Anouk E. J. Janssen, Rebekka M. Koeck, Rick Essers, Wanwisa van Dijk, Marion Drusedau, Jeroen Meekels, Burcu Yaldiz, Maartje van de Vorst, Ping Cao, Bart de Koning, Debby M. E. I. Hellebrekers, Servi J. C. Stevens, Su Ming Sun, Malou Heijligers, Sonja A. de Munnik, Chris M. J. van Uum, Jelle Achten, Lars Hamers, Marjan Naghdi, Lisenka E. L. M. Vissers, Ron J. T. van Golde, Guido de Wert, Jos C. F. M. Dreesen, Christine de Die-Smulders, Edith Coonen, Han G. Brunner, Arthur van den Wijngaard, Aimee D. C. Paulussen, Masoud Zamani Esteki 2023-12-08 2023-12-08 [("doi","10.1101/2023.12.06.23299605")] genetics/sequencing

High-throughput sequencing technologies have increasingly led to discovery of disease-causing genetic variants, primarily in postnatal multi-cell DNA samples. However, applying these technologies to preimplantation genetic testing (PGT) in nuclear or mitochondrial DNA from single or few-cells biopsied from in vitro fertilized (IVF) embryos is challenging. PGT aims to select IVF embryos without genetic abnormalities.

Although genotyping-by-sequencing (GBS)-based haplotyping methods enabled PGT for monogenic disorders (PGT-M), structural rearrangements (PGT-SR), and aneuploidies (PGT-A), they are labor intensive, only partially cover the genome and are troublesome for difficult loci and consanguineous couples.

Here, we devised a simple, scalable and universal whole genome sequencing haplarithmisis-based approach enabling all forms of PGT in a single assay. In a comparison to state-of-the-art GBS-based PGT for nuclear DNA (37 embryos, 18 families, 25 indications), shallow sequencing-based PGT (10 embryos, 3 families), and PCR-based PGT for mitochondrial DNA (10 embryos, 2 families), our approach alleviates technical limitations by decreasing whole genome amplification artifacts by 68.4%, increasing breadth of coverage by 4×, and reducing wet-lab turn-around-time by 2.5×.

Importantly, this method enables trio-based PGT-A for aneuploidy origin, an approach we coin PGT-AO, detects translocation breakpoints, and nuclear and mitochondrial single-nucleotide variants and indels in base-resolution.

--- https://en.wikipedia.org/wiki/Effects_of_climate_change_on_mental_health Effects of climate change on mental health 2023-10-13 politics psychiatry/anxiety --- https://arxiv.org/abs/2311.14479 Controlled Text Generation via Language Model Arithmetic Jasper Dekoninck, Marc Fischer, Luca Beurer-Kellner, Martin Vechev 2023-11-24 2023-11-24 [("doi","10.48550/arXiv.2311.14479")] ai/nn/sampling ai/nn/transformer/gpt/2 ai/text-style-transfer

[code] As Large Language Models (LLMs) are deployed more widely, customization with respect to vocabulary, style and character becomes more important. In this work we introduce model arithmetic, a novel inference framework for composing and biasing LLMs without the need for model (re)training or highly specific datasets.

In addition, the framework allows for more precise control of generated text than direct prompting and prior controlled text generation (CTG) techniques. Using model arithmetic, we can express prior CTG techniques as simple formulas and naturally extend them to new and more effective formulations.

Further, we show that speculative sampling, a technique for efficient LLM sampling, extends to our setting. This enables highly efficient text generation with multiple composed models with only marginal overhead over a single model.

Our empirical evaluation demonstrates that model arithmetic allows fine-grained control of generated text while outperforming state-of-the-art on the task of toxicity reduction.

--- https://arxiv.org/abs/2312.02521#baidu Retrieving Conditions from Reference Images for Diffusion Models Haoran Tang, Xin Zhou, Jieren Deng, Zhihong Pan, Hao Tian, Pratik Chaudhari 2023-12-05 2023-12-05 [("doi","10.48550/arXiv.2312.02521")] ai/anime/danbooru ai/dataset ai/nn/diffusion ai/nn/retrieval

Recent diffusion-based subject driven generative methods have enabled image generations with good fidelity for specific objects or human portraits. However, to achieve better versatility for applications, we argue that not only improved datasets and evaluations are desired, but also more careful methods to retrieve only relevant information from conditional images are anticipated.

To this end, we propose an anime figures dataset RetriBooru-V1, with enhanced identity and clothing labels. We state new tasks enabled by this dataset, and introduce a new diversity metric to measure success in completing these tasks, quantifying the flexibility of image generations.

We establish an RAG-inspired baseline method, designed to retrieve precise conditional information from reference images. Then, we compare with current methods on existing task to demonstrate the capability of the proposed method.

Finally, we provide baseline experiment results on new tasks, and conduct ablation studies on the possible structural choices.

--- https://en.wikipedia.org/wiki/The_Westing_Game The Westing Game 2023-10-13 fiction --- https://blog.rongarret.info/2014/10/parallel-universes-and-arrow-of-time.html 2023-10-13 philosophy/mind science --- https://spectrumnews1.com/ca/la-west/human-interest/2023/12/01/world-s-second-successfully-cloned-przewalski-s-horse-gives-hope-to-endangered-species 2023-10-14 genetics/cloning --- https://www.quantamagazine.org/the-often-overlooked-experiment-that-revealed-the-quantum-world-20231205/ 2023-10-14 philosophy/epistemology science --- https://pubs.aip.org/physicstoday/article/56/12/53/632269/Stern-and-Gerlach-How-a-Bad-Cigar-Helped-Reorient 2023-10-14 philosophy/epistemology science --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4836695/ Re-evaluation of the traditional diet-heart hypothesis: analysis of recovered data from Minnesota Coronary Experiment (1968-73) Christopher E. Ramsden, Daisy Zamora, Sharon Majchrzak-Hong, Keturah R. Faurot, Steven K. Broste, Robert P. Frantz, John M. Davis, Amit Ringel, Chirayath M. Suchindran, Joseph R. Hibbeln 2016 2023-10-14 [("doi","10.1136/bmj.i1246")] exercise statistics/bias/publication

Objective: To examine the traditional diet-heart hypothesis through recovery and analysis of previously unpublished data from the Minnesota Coronary Experiment (MCE) and to put findings in the context of existing diet-heart randomized controlled trials through a systematic review and meta-analysis.

Design: The MCE (1968-73) is a double blind randomized controlled trial designed to test whether replacement of saturated fat with vegetable oil rich in linoleic acid reduces coronary heart disease and death by lowering serum cholesterol. Recovered MCE unpublished documents and raw data were analyzed according to hypotheses prespecified by original investigators. Further, a systematic review and meta-analyses of randomized controlled trials that lowered serum cholesterol by providing vegetable oil rich in linoleic acid in place of saturated fat without confounding by concomitant interventions was conducted.

Setting: One nursing home and 6 state mental hospitals in Minnesota, United States.

Participants: Unpublished documents with completed analyses for the randomized cohort of 9423 women and men aged 20-97; longitudinal data on serum cholesterol for the 2355 participants exposed to the study diets for a year or more; 149 completed autopsy files.

Interventions: Serum cholesterol lowering diet that replaced saturated fat with linoleic acid (from corn oil and corn oil polyunsaturated margarine). Control diet was high in saturated fat from animal fats, common margarines, and shortenings.

Main Outcome Measures: Death from all causes; association between changes in serum cholesterol and death; and coronary atherosclerosis and myocardial infarcts detected at autopsy.

Results: The intervention group had statistically-significant reduction in serum cholesterol compared with controls (mean change from baseline −13.8% vs −1.0%; p < 0.001). Kaplan Meier graphs showed no mortality benefit for the intervention group in the full randomized cohort or for any prespecified subgroup. There was a 22% higher risk of death for each 30 mg/dL (0.78 mmol/L) reduction in serum cholesterol in covariate adjusted Cox regression models (hazard ratio 1.22, 95% confidence interval 1.14 to 1.32; p < 0.001). There was no evidence of benefit in the intervention group for coronary atherosclerosis or myocardial infarcts. Systematic review identified 5 randomized controlled trials for inclusion (n = 10,808). In meta-analyses, these cholesterol lowering interventions showed no evidence of benefit on mortality from coronary heart disease (1.13, 0.83 to 1.54) or all cause mortality (1.07, 0.90 to 1.27).

Conclusions: Available evidence from randomized controlled trials shows that replacement of saturated fat in the diet with linoleic acid effectively lowers serum cholesterol but does not support the hypothesis that this translates to a lower risk of death from coronary heart disease or all causes. Findings from the Minnesota Coronary Experiment add to growing evidence that incomplete publication has contributed to overestimation of the benefits of replacing saturated fat with vegetable oils rich in linoleic acid.

--- https://arxiv.org/abs/2306.09539 Block-State Transformers Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin 2023-06-15 2023-10-14 [("doi","10.48550/arXiv.2306.09539")] ai/nn/transformer/attention/recurrent

State space models (SSMs) have shown impressive results on tasks that require modeling long-range dependencies and efficiently scale to long sequences owing to their sub-quadratic runtime complexity. Originally designed for continuous signals, SSMs have shown superior performance on a plethora of tasks, in vision and audio; however, SSMs still lag Transformer performance in Language Modeling tasks.

In this work, we propose a hybrid layer named Block-State Transformer (BST), that internally combines an SSM sublayer for long-range contextualization, and a Block Transformer sublayer for short-term representation of sequences. We study 3 different, and completely parallelizable, variants that integrate SSMs and block-wise attention.

We show that our model outperforms similar Transformer-based architectures on language modeling perplexity and generalizes to longer sequences. In addition, the Block-State Transformer demonstrates more than tenfold increase in speed at the layer level compared to the Block-Recurrent Transformer when model parallelization is employed.

--- https://rickandmorty.fandom.com/wiki/Mr._Meeseeks 2023-10-14 fiction/science-fiction philosophy/mind reinforcement-learning/safe --- https://x.com/DrJimFan/status/1733904000745955352 Jim Fan 2023-10-14 ai/nn/transformer/gpt/3/nonfiction --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8263491/ Engineering complex communities by directed evolution Chang-Yu Chang, Jean C. C. Vila, Madeline Bender, Richard Li, Madeleine C. Mankowski, Molly Bassette, Julia Borden, Stefan Golfier, Paul Gerald L. Sanchez, Rachel Waymack, Xinwen Zhu, Juan Diaz-Colunga, Sylvie Estrela, Maria Rebolleda-Gomez, Alvaro Sanchez 2021 2023-10-14 [("doi","10.1038/s41559-021-01457-5")] genetics/microbiome genetics/selection/artificial

Directed evolution has been used for decades to engineer biological systems at or below the organismal level. Above the organismal level, a small number of studies have attempted to artificially select microbial ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution.

Here, we have developed a flexible modeling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions. By artificially selecting hundreds of in silico microbial meta-communities under identical conditions, we first show that the main breeding methods used to date, which do not necessarily let communities reach their ecological equilibrium, are outperformed by a simple screen of sufficiently mature communities.

We then identify a range of alternative directed evolution strategies that, particularly when applied in combination, are well suited for the top-down engineering of large, diverse and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search of dynamically stable and ecologically resilient communities with desired quantitative attributes.

--- https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004493 Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance Daniel Nichol, Peter Jeavons, Alexander G. Fletcher, Robert A. Bonomo, Philip K. Maini, Jerome L. Paul, Robert A. Gatenby, Alexander R. A. Anderson, Jacob G. Scott 2015-08-07 2023-10-14 [("doi","10.1371/journal.pcbi.1004493")] genetics/selection/artificial

The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, ~70%, of sequential drug treatments with 2–4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically ‘steer’ the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic–resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy.

Author Summary :

Increasing antibiotic resistance, coupled with the slowing rate of discovery of novel antibiotic agents, is a public health threat which could soon reach crisis point. Indeed, the last decade has seen the emergence of deadly, highly resistant forms of pathogens, such as Escherichia coli, Acenitobacter baumanii, Klebsiella pneumoniae, Enterococcus and Staphylococcus aureus as well as non-bacterial pathogens including malaria and viruses such as HIV. Here, we develop a mathematical model of an evolving bacterial population, which allows us to predict the probability of resistant strains emerging. Using this model we show how sequences of drugs can be prescribed in order to prevent resistance where each drug alone may fail. These model predictions suggest a novel treatment strategy: using sequences of antibiotics to ‘steer’ the evolution of a pathogen to a configuration from which resistance to a final antibiotic cannot emerge. Further, we test the likelihood of resistance emerging when arbitrary sequences of antibiotics are prescribed, finding that ~70% of arbitrary sequences of 2–4 drugs promote resistance to the final drug. This result serves as a cautionary warning that we may be inadvertently promoting resistance through careless (or random) prescription of drugs.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7174377/ Exploiting evolutionary steering to induce collateral drug sensitivity in cancer Ahmet Acar, Daniel Nichol, Javier Fernandez-Mateos, George D. Cresswell, Iros Barozzi, Sung Pil Hong, Nicholas Trahearn, Inmaculada Spiteri, Mark Stubbs, Rosemary Burke, Adam Stewart, Giulio Caravagna, Benjamin Werner, Georgios Vlachogiannis, Carlo C. Maley, Luca Magnani, Nicola Valeri, Udai Banerji, Andrea Sottoriva 2020 2023-10-14 [("doi","10.1038/s41467-020-15596-z")] genetics/selection/artificial

Drug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving resistance to one drug may come at a cost of decreased fecundity or increased sensitivity to another drug. These evolutionary trade-offs can be exploited using ‘evolutionary steering’ to control the tumor population and delay resistance.

However, recapitulating cancer evolutionary dynamics experimentally remains challenging. Here, we present an approach for evolutionary steering based on a combination of single-cell barcoding, large populations of 108–109 cells grown without re-plating, longitudinal non-destructive monitoring of cancer clones, and mathematical modeling of tumor evolution.

We demonstrate evolutionary steering in a lung cancer model, showing that it shifts the clonal composition of the tumor in our favor, leading to collateral sensitivity and proliferative costs. Genomic profiling revealed some of the mechanisms that drive evolved sensitivity. This approach allows modeling evolutionary steering strategies that can potentially control treatment resistance.

--- https://ooo.ghostbows.ooo/story/ 2023-10-14 ai/music ai/nn/transformer/gpt/jukebox --- /doc/science/1966-ferdinand.pdf On The Obsolescence Of Scientists And Engineers Theodore N. Ferdinand 1966-03-01 2023-10-15 [("doi","10.2307/27836291")] science technology --- https://admiralcloudberg.medium.com/a-matter-of-millimeters-the-story-of-qantas-flight-32-bdaa62dc98e7 2023-10-15 technology --- https://x.com/gptbrooke/status/1733951054268846247 gptbrooke 2023-10-15 reinforcement-learning/imperfect-information/diplomacy --- https://nothinghuman.substack.com/p/the-tyranny-of-the-marginal-user 2023-10-15 design --- https://flak.tedunangst.com/post/rethinking-openbsd-security 2023-10-15 cs/security --- https://www.nature.com/articles/s41586-023-06557-9 2023-10-15 sociology --- https://binarly.io/posts/The_Far_Reaching_Consequences_of_LogoFAIL/ 2023-10-15 cs/security --- https://arxiv.org/abs/2308.00951#deepmind From Sparse to Soft Mixtures of Experts Joan Puigcerver, Carlos Riquelme, Basil Mustafa, Neil Houlsby 2023-08-02 2023-10-15 [("doi","10.48550/arXiv.2308.00951")] ai/scaling/mixture-of-experts

Sparse mixture of expert architectures (MoEs) scale model capacity without large increases in training or inference costs. Despite their success, MoEs suffer from a number of issues: training instability, token dropping, inability to scale the number of experts, or ineffective finetuning.

In this work, we propose Soft MoE, a fully-differentiable sparse Transformer that addresses these challenges, while maintaining the benefits of MoEs. Soft MoE performs an implicit soft assignment by passing different weighted combinations of all input tokens to each expert. As in other MoE works, experts in Soft MoE only process a subset of the (combined) tokens, enabling larger model capacity at lower inference cost.

In the context of visual recognition, Soft MoE greatly outperforms standard Transformers (ViTs) and popular MoE variants (Tokens Choice and Experts Choice). For example, Soft MoE-Base/16 requires 10.5× lower inference cost (5.7× lower wall-clock time) than ViT-Huge/14 while matching its performance after similar training. Soft MoE also scales well: Soft MoE Huge/14 with 128 experts in 16 MoE layers has over 40× more parameters than ViT Huge/14, while inference time cost grows by only 2%, and it performs substantially better.

--- https://www.anthropic.com/news/claude-2-1-prompting 2023-10-15 ai/nn/retrieval ai/nn/transformer/attention ai/nn/transformer/gpt/claude --- https://www.wired.com/story/china-tried-to-keep-kids-off-social-media-now-the-elderly-are-hooked/ 2023-10-15 sociology/technology --- https://project-mage.org/ 2023-10-16 cs/lisp --- https://nyaa.si/view/1626495 2023-10-16 ai/anime --- https://arxiv.org/abs/2312.03701 Self-conditioned Image Generation via Generating Representations Tianhong Li, Dina Katabi, Kaiming He 2023-12-06 2023-12-06 [("doi","10.48550/arXiv.2312.03701")] ai/nn/diffusion ai/nn/vae/mae

This paper presents Representation-Conditioned image Generation (RCG), a simple yet effective image generation framework which sets a new benchmark in class-unconditional image generation. RCG does not condition on any human annotations. Instead, it conditions on a self-supervised representation distribution [using MoCov3] which is mapped from the image distribution using a pre-trained encoder.

During generation, RCG samples from such representation distribution using a representation diffusion model (RDM), and employs a pixel generator to craft image pixels conditioned on the sampled representation. Such a design provides substantial guidance during the generative process, resulting in high-quality image generation.

Tested on ImageNet 256×256, RCG achieves a Fréchet Inception Distance (FID) of 3.31 and an Inception Score (IS) of 253.4. These results not only improve the state-of-the-art of class-unconditional image generation but also rival the current leading methods in class-conditional image generation, bridging the long-standing performance gap between these two tasks.

Code is available at Github.

--- https://arxiv.org/abs/2312.02179 Training Chain-of-Thought via Latent-Variable Inference Du Phan, Matthew D. Hoffman, David Dohan, Sholto Douglas, Tuan Anh Le, Aaron Parisi, Pavel Sountsov, Charles Sutton, Sharad Vikram, Rif A. Saurous 2023-11-28 2023-11-28 [("doi","10.48550/arXiv.2312.02179")] ai/nn/transformer/gpt/inner-monologue math

Large language models (LLMs) solve problems more accurately and interpretably when instructed to work out the answer step by step using a “chain-of-thought” (CoT) prompt. One can also improve LLMs’ performance on a specific task by supervised fine-tuning, ie. by using gradient ascent on some tunable parameters to maximize the average log-likelihood of correct answers from a labeled training set.

Naively combining CoT with supervised tuning requires supervision not just of the correct answers, but also of detailed rationales that lead to those answers; these rationales are expensive to produce by hand. Instead, we propose a fine-tuning strategy that tries to maximize the marginal log-likelihood of generating a correct answer using CoT prompting, averaging over all possible rationales.

The core challenge is sampling from the posterior over rationales conditioned on the correct answer; we address it using a simple Markov-chain Monte Carlo (MCMC) expectation-maximization (EM) algorithm inspired by the self-taught reasoner (STaR), memoized wake-sleep, Markovian score climbing, and persistent contrastive divergence. This algorithm also admits a novel control-variate technique that drives the variance of our gradient estimates to zero as the model improves.

Applying our technique to GSM8K and the tasks in BIG-Bench Hard, we find that this MCMC-EM fine-tuning technique typically improves the model’s accuracy on held-out examples more than STaR or prompt-tuning with or without CoT.

--- https://arxiv.org/abs/2104.02057#facebook An Empirical Study of Training Self-Supervised Vision Transformers Xinlei Chen, Saining Xie, Kaiming He 2021-04-05 2023-10-16 [("doi","10.48550/arXiv.2104.02057")] ai/nn/transformer

This paper does not describe a novel method. Instead, it studies a straightforward, incremental, yet must-know baseline given the recent progress in computer vision: self-supervised learning for Vision Transformers (ViT). While the training recipes for standard convolutional networks have been highly mature and robust, the recipes for ViT are yet to be built, especially in the self-supervised scenarios where training becomes more challenging.

In this work, we go back to basics and investigate the effects of several fundamental components for training self-supervised ViT. We observe that instability is a major issue that degrades accuracy, and it can be hidden by apparently good results. We reveal that these results are indeed partial failure, and they can be improved when training is made more stable.

We benchmark ViT results in MoCo v3 and several other self-supervised frameworks, with ablations in various aspects. We discuss the currently positive evidence as well as challenges and open questions. We hope that this work will provide useful data points and experience for future research.

--- https://arxiv.org/abs/2312.03664#deepmind Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia Alexander Sasha Vezhnevets, John P. Agapiou, Avia Aharon, Ron Ziv, Jayd Matyas, Edgar A. Duéñez-Guzmán, William A. Cunningham, Simon Osindero, Danny Karmon, Joel Z. Leibo 2023-12-06 2023-12-06 [("doi","10.48550/arXiv.2312.03664")] fiction/text-game reinforcement-learning/multi-agent

Agent-based modeling has been around for decades, and applied widely across the social and natural sciences. The scope of this research method is now poised to grow dramatically as it absorbs the new affordances provided by Large Language Models (LLM)s. Generative Agent-Based Models (GABM) are not just classic Agent-Based Models (ABMs) where the agents talk to one another. Rather, GABMs are constructed using an LLM to apply common sense to situations, act “reasonably”, recall common semantic knowledge, produce API calls to control digital technologies like apps, and communicate both within the simulation and to researchers viewing it from the outside.

Here we present Concordia, a library to facilitate constructing and working with GABMs. Concordia makes it easy to construct language-mediated simulations of physically-grounded or digitally-grounded environments. Concordia agents produce their behavior using a flexible component system which mediates between two fundamental operations: LLM calls and associative memory retrieval. A special agent called the Game Master (GM), which was inspired by tabletop role-playing games, is responsible for simulating the environment where the agents interact. Agents take actions by describing what they want to do in natural language. The GM then translates their actions into appropriate implementations.

In a simulated physical world, the GM checks the physical plausibility of agent actions and describes their effects. In digital environments simulating technologies such as apps and services, the GM may handle API calls to integrate with external tools such as general AI assistants (eg. Bard, ChatGPT), and digital apps (eg. Calendar, Email, Search, etc.).

Concordia was designed to support a wide array of applications both in scientific research and for evaluating performance of real digital services by simulating users and/or generating synthetic data.

--- https://x.com/nickcammarata/status/1734112673220976777 Nick Cammarata 2023-10-16 psychiatry/meditation --- https://x.com/prerationalist/status/1732571243407151445 prerationalist 2023-10-16 philosophy/mind reinforcement-learning/imitation-learning --- https://arxiv.org/abs/2312.01552 The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning Bill Yuchen Lin, Abhilasha Ravichander, Ximing Lu, Nouha Dziri, Melanie Sclar, Khyathi Chandu, Chandra Bhagavatula, Yejin Choi 2023-12-04 2023-12-04 [("doi","10.48550/arXiv.2312.01552")] ai/nn/transformer reinforcement-learning/meta-learning reinforcement-learning/preference-learning

The alignment tuning process of large language models (LLMs) typically involves instruction learning through supervised fine-tuning (SFT) and preference tuning via reinforcement learning from human feedback (RLHF). A recent study, LIMA (Zhou et al 2023), shows that using merely 1K examples for SFT can achieve alignment performance as well, suggesting that the effect of alignment tuning might be “superficial.” This raises questions about how exactly the alignment tuning transforms a base LLM.

We analyze the effect of alignment tuning by examining the token distribution shift between base LLMs and their aligned counterpart. Our findings reveal that base LLMs and their alignment-tuned versions perform nearly identically in decoding on the majority of token positions. Most distribution shifts occur with stylistic tokens. These direct evidence strongly supports the Superficial Alignment Hypothesis suggested by LIMA.

Based on these findings, we rethink the alignment of LLMs by posing the research question: how effectively can we align base LLMs without SFT or RLHF?

To address this, we introduce a simple, tuning-free alignment method, URIAL. URIAL achieves effective alignment purely through in-context learning (ICL) with base LLMs, requiring as few as 3 constant stylistic examples and a system prompt. We conduct a fine-grained and interpretable evaluation on a diverse set of examples, named JUST-EVAL-INSTRUCT.

Results demonstrate that base LLMs with URIAL can match or even surpass the performance of LLMs aligned with SFT or SFT+RLHF. We show that the gap between tuning-free and tuning-based alignment methods can be reduced through strategic prompting and ICL.

Our findings on the superficial nature of alignment tuning and results with URIAL suggest that deeper analysis and theoretical understanding of alignment is crucial to future LLM research.

--- https://open.spotify.com/episode/2WSPcEbWAj5XvAlXd0Cyxa 2023-10-16 reinforcement-learning/openai --- https://arxiv.org/abs/2312.00785 Sequential Modeling Enables Scalable Learning for Large Vision Models Yutong Bai, Xinyang Geng, Karttikeya Mangalam, Amir Bar, Alan Yuille, Trevor Darrell, Jitendra Malik, Alexei A. Efros 2023-12-01 2023-12-01 [("doi","10.48550/arXiv.2312.00785")] ai/dataset ai/nn/transformer ai/nn/vae ai/scaling

[commentary] We introduce a novel sequential modeling approach which enables learning a Large Vision Model (LVM) without making use of any linguistic data.

To do this, we define a common format, “visual sentences”, in which we can represent raw images and videos as well as annotated data sources such as semantic segmentations and depth reconstructions without needing any meta-knowledge beyond the pixels. Once this wide variety of visual data (comprising 420 billion tokens) is represented as sequences, the model can be trained to minimize a cross-entropy loss for next token prediction.

By training across various scales of model architecture and data diversity, we provide empirical evidence that our models scale effectively.

Many different vision tasks can be solved by designing suitable visual prompts at test time.

--- https://apnews.com/article/brazil-artificial-intelligence-porto-alegre-5afd1240afe7b6ac202bb0bbc45e08d4 2023-10-17 ai/nn/transformer/gpt/3/nonfiction law --- https://www.cheetahhouse.org/faq#block-yui_3_17_2_1_1588798232951_15860 2023-10-17 psychiatry/meditation --- https://www.cheetahhouse.org/symptoms 2023-10-17 psychiatry/meditation --- https://www.cerebras.net/blog/introducing-gigagpt-gpt-3-sized-models-in-565-lines-of-code 2023-10-17 ai/nn/transformer/gpt/3 ai/scaling/hardware --- https://arxiv.org/abs/2311.18803 BioCLIP: A Vision Foundation Model for the Tree of Life Samuel Stevens, Jiaman Wu, Matthew J. Thompson, Elizabeth G. Campolongo, Chan Hee Song, David Edward Carlyn, Li Dong, Wasila M. Dahdul, Charles Stewart, Tanya Berger-Wolf, Wei-Lun Chao, Yu Su 2023-11-30 2023-11-30 [("doi","10.48550/arXiv.2311.18803")] ai/dataset ai/nn/transformer/clip biology

Images of the natural world, collected by a variety of cameras, from drones to individual phones, are increasingly abundant sources of biological information. There is an explosion of computational methods and tools, particularly computer vision, for extracting biologically relevant information from images for science and conservation. Yet most of these are bespoke approaches designed for a specific task and are not easily adaptable or extendable to new questions, contexts, and datasets.

A vision model for general organismal biology questions on images is of timely need. To approach this, we curate and release TreeOfLife-10M, the largest and most diverse ML-ready dataset of biology images.

We then develop BioCLIP, a foundation model for the tree of life, leveraging the unique properties of biology captured by TreeOfLife-10M, namely the abundance and variety of images of plants, animals, and fungi, together with the availability of rich structured biological knowledge.

We rigorously benchmark our approach on diverse fine-grained biology classification tasks, and find that BioCLIP consistently and substantially outperforms existing baselines (by 17% to 20% absolute). Intrinsic evaluation reveals that BioCLIP has learned a hierarchical representation conforming to the tree of life, shedding light on its strong generalizability.

Our code, models and data will be made available at Github.

--- https://www.reddit.com/r/ChatGPT/comments/18fl2d5/nsfw_fun_with_dalle/ 2023-10-17 ai/nn/transformer/gpt/dall-e/3 reinforcement-learning/safe --- https://www.ft.com/content/25337df3-5b98-4dd1-b7a9-035dcc130d6a 2023-10-17 ai/scaling/hardware --- https://arxiv.org/abs/2301.13196 Looped Transformers as Programmable Computers Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris Papailiopoulos 2023-01-30 2023-10-17 [("doi","10.48550/arXiv.2301.13196")] ai/nn/fully-connected ai/nn/transformer/attention/recurrent cs/computable reinforcement-learning/meta-learning

[Twitter] We present a framework for using transformer networks as universal computers by programming them with specific weights and placing them in a loop.

Our input sequence acts as a punchcard, consisting of instructions and memory for data read/writes. We demonstrate that a constant number of encoder layers can emulate basic computing blocks, including embedding edit operations, non-linear functions, function calls, program counters, and conditional branches.

Using these building blocks, we emulate a small instruction-set computer. This allows us to map iterative algorithms to programs that can be executed by a looped, 13-layer transformer. We show how this transformer, instructed by its input, can emulate a basic calculator, a basic linear algebra library, and in-context learning algorithms that employ backpropagation [on fully-connected neural networks].

Our work highlights the versatility of the attention mechanism, and demonstrates that even shallow transformers can execute full-fledged, general-purpose programs.

--- https://blog.nuclearsecrecy.com/2012/09/12/in-search-of-a-bigger-boom/ 2023-10-17 existential-risk --- https://en.wikipedia.org/wiki/Porkchop_plot Porkchop plot 2023-10-17 design/visualization --- https://arxiv.org/abs/2312.04655 ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations Maitreya Patel, Changhoon Kim, Sheng Cheng, Chitta Baral, Yezhou Yang 2023-12-07 2023-12-07 [("doi","10.48550/arXiv.2312.04655")] ai/nn/diffusion ai/nn/transformer/clip

Text-to-image (T2I) diffusion models, notably the unCLIP models (eg. DALL·E-2), achieve state-of-the-art (SOTA) performance on various compositional T2I benchmarks, at the cost of computational resources. The unCLIP stack comprises T2I prior and diffusion image decoder. The T2I prior model alone adds a billion parameters compared to the Latent Diffusion Models, which increases the computational and high-quality data requirements.

We introduce ECLIPSE, a novel contrastive learning method that is both parameter and data-efficient. ECLIPSE leverages pre-trained vision-language models (eg. CLIP) to distill the knowledge into the prior model. We demonstrate that the ECLIPSE trained prior, with only 3.3% of the parameters and trained on a mere 2.8% of the data, surpasses the baseline T2I priors with an average of 71.6% preference score under resource-limited setting. It also attains performance on par with SOTA big models, achieving an average of 63.36% preference score in terms of the ability to follow the text compositions.

Extensive experiments on two unCLIP diffusion image decoders, Karlo and Kandinsky, affirm that ECLIPSE priors consistently deliver high performance while reducing resource dependency.

--- https://ai.objectives.institute/talk-to-the-city 2023-10-18 ai/nn/transformer/gpt/claude politics sociology --- https://arxiv.org/abs/2306.11932 Opportunities and Risks of LLMs for Scalable Deliberation with Polis Christopher T. Small, Ivan Vendrov, Esin Durmus, Hadjar Homaei, Elizabeth Barry, Julien Cornebise, Ted Suzman, Deep Ganguli, Colin Megill 2023-06-20 2023-10-18 [("doi","10.48550/arXiv.2306.11932")] ai/nn/transformer/gpt/claude politics sociology

Polis is a platform that leverages machine intelligence to scale up deliberative processes. In this paper, we explore the opportunities and risks associated with applying Large Language Models (LLMs) towards challenges with facilitating, moderating and summarizing the results of Polis engagements. In particular, we demonstrate with pilot experiments using Anthropic’s Claude that LLMs can indeed augment human intelligence to help more efficiently run Polis conversations. In particular, we find that summarization capabilities enable categorically new methods with immense promise to empower the public in collective meaning-making exercises. And notably, LLM context limitations have an impact on insight and quality of these results.

However, these opportunities come with risks. We discuss some of these risks, as well as principles and techniques for characterizing and mitigating them, and the implications for other deliberative or political systems that may employ LLMs. Finally, we conclude with several open future research directions for augmenting tools like Polis with LLMs.

--- https://arxiv.org/abs/2311.14904 LLM-Assisted Code Cleaning For Training Accurate Code Generators Naman Jain, Tianjun Zhang, Wei-Lin Chiang, Joseph E. Gonzalez, Koushik Sen, Ion Stoica 2023-11-25 2023-11-25 [("doi","10.48550/arXiv.2311.14904")] ai/nn/transformer/gpt/codex

Natural language to code generation is an important application area of LLMs and has received wide attention from the community. The majority of relevant studies have exclusively concentrated on increasing the quantity and functional correctness of training sets while disregarding other stylistic elements of programs.

More recently, data quality has garnered a lot of interest and multiple works have showcased its importance for improving performance. In this work, we investigate data quality for code and find that making the code more structured and readable leads to improved code generation performance of the system.

We build a novel data-cleaning pipeline that uses these principles to transform existing programs by (1) renaming variables, (2) modularizing and decomposing complex code into smaller helper sub-functions, and (3) inserting natural-language based plans via LLM based transformations.

We evaluate our approach on two challenging algorithmic code generation benchmarks and find that fine-tuning CodeLLaMa-7B on our transformed modularized programs improves the performance by up to 30% compared to fine-tuning on the original dataset.

Additionally, we demonstrate improved performance from using a smaller amount of higher-quality data, finding that a model fine-tuned on the entire original dataset is outperformed by a model trained on 15% of our cleaned dataset. Even in comparison to closed-source models, our models outperform the much larger AlphaCode models.

--- https://www.nature.com/articles/s41592-023-02087-4 2023-10-18 ai/nn/transformer/alphafold --- https://aclanthology.org/2023.findings-emnlp.18/ 2023-10-18 ai/nn/transformer/t5 reinforcement-learning/exploration/active-learning/data-pruning --- https://www.tandfonline.com/doi/full/10.1080/17440572.2023.2291352 2023-10-18 darknet-market/alphabay --- https://ceur-ws.org/Vol-3582/FP_10.pdf 2023-10-18 darknet-market --- https://novum.substack.com/p/there-once-was-an-empire 2023-10-18 history --- /doc/iodine/2023-11-30-jonahgoodman-anationalevilthecurseofthegoitreinswitzerland.html 2023-11-30 2023-11-30 iodine philosophy/epistemology politics --- https://www.lesswrong.com/posts/R3eDrDoX8LisKgGZe/sum-threshold-attacks 2023-10-18 statistics/decision --- https://en.wikipedia.org/wiki/Naumachia Naumachia 2023-10-18 technology --- https://www.theonion.com/plan-to-straighten-out-entire-life-during-weeklong-vaca-1819566088 2023-10-19 fiction/humor fiction/humor psychology/willpower --- https://www.nytimes.com/2023/12/04/health/traumatic-brain-injury-implants.html 2023-10-19 psychiatry/traumatic-brain-injury --- https://x.com/eshear/status/1734599049351995613 Emmett Shear 2023-10-19 reinforcement-learning/openai --- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4627587 2023-10-19 ai/nn/transformer/gpt economics law --- https://www.wired.com/story/store-a-message-in-dna/ 2023-10-19 genetics/genome-synthesis --- https://www.lesswrong.com/posts/JEhW3HDMKzekDShva/significantly-enhancing-adult-intelligence-with-gene-editing 2023-10-19 genetics/editing iq psychology/neuroscience --- https://adversa.ai/blog/universal-llm-jailbreak-chatgpt-gpt-4-bard-bing-anthropic-and-beyond/ 2023-10-19 ai/nn/adversarial ai/nn/transformer/gpt --- https://arxiv.org/abs/2310.08419 PAIR: Jailbreaking Black Box Large Language Models in 20 Queries Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong 2023-10-12 2023-10-19 [("doi","10.48550/arXiv.2310.08419")] ai/nn/adversarial ai/nn/transformer/gpt/3 ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/palm/2

[homepage] There is growing interest in ensuring that large language models (LLMs) align with human values. However, the alignment of such models is vulnerable to adversarial jailbreaks, which coax LLMs into overriding their safety guardrails. The identification of these vulnerabilities is therefore instrumental in understanding inherent weaknesses and preventing future misuse.

To this end, we propose Prompt Automatic Iterative Refinement (PAIR), an algorithm that generates semantic jailbreaks with only black-box access to an LLM. PAIR—which is inspired by social engineering attacks—uses an attacker LLM to automatically generate jailbreaks for a separate targeted LLM without human intervention. In this way, the attacker LLM iteratively queries the target LLM to update and refine a candidate jailbreak.

Empirically, PAIR often requires fewer than 20 queries to produce a jailbreak, which is orders of magnitude more efficient than existing algorithms. PAIR also achieves competitive jailbreaking success rates and transferability on open and closed-source LLMs, including GPT-3.5/4, Vicuna, and PaLM-2.

--- https://arxiv.org/abs/2311.15599#tencent UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition Xiaohan Ding, Yiyuan Zhang, Yixiao Ge, Sijie Zhao, Lin Song, Xiangyu Yue, Ying Shan 2023-11-27 2023-11-27 [("doi","10.48550/arXiv.2311.15599")] ai/nn/cnn ai/scaling

Large-kernel convolutional neural networks (ConvNets) have recently received extensive research attention, but there are two unresolved and critical issues that demand further investigation. (1) The architectures of existing large-kernel ConvNets largely follow the design principles of conventional ConvNets or transformers, while the architectural design for large-kernel ConvNets remains under-addressed. (2) As transformers have dominated multiple modalities, it remains to be investigated whether ConvNets also have a strong universal perception ability in domains beyond vision.

In this paper, we contribute from two aspects. (1) We propose 4 architectural guidelines for designing large-kernel ConvNets, the core of which is to exploit the essential characteristics of large kernels that distinguish them from small kernels—they can see wide without going deep. Following such guidelines, our proposed large-kernel ConvNet shows leading performance in image recognition. For example, our models achieve an ImageNet accuracy of 88.0%, ADE20K mIoU of 55.6%, and COCO box AP of 56.4%, demonstrating better performance and higher speed than a number of recently proposed powerful competitors.

  1. (2) We discover that large kernels are the key to unlocking the exceptional performance of ConvNets in domains where they were originally not proficient. With certain modality-related preprocessing approaches, the proposed model achieves state-of-the-art performance on time-series forecasting and audio recognition tasks even without modality-specific customization to the architecture. Code and all the models at Github.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10363436/ Semaglutide reduces alcohol intake and relapse-like drinking in male and female rats Cajsa Aranäs, Christian E. Edvardsson, Olesya T. Shevchouk, Qian Zhang, Sarah Witley, Sebastian Blid Sköldheden, Lindsay Zentveld, Daniel Vallöf, Maximilian Tufvesson-Alm, Elisabet Jerlhag 2023 2023-10-19 [("doi","10.1016/j.ebiom.2023.104642")] longevity/glp/psychology psychiatry

Background: Glucagon-like peptide1 receptor (GLP-1R) agonists have been found to reduce alcohol drinking in rodents and overweight patients with alcohol use disorder (AUD). However, the probability of low semaglutide doses, an agonist with higher potency and affinity for GLP-1R, to attenuate alcohol-related responses in rodents and the underlying neuronal mechanisms is unknown.

Methods: In the intermittent access model, we examined the ability of semaglutide to decrease alcohol intake and block relapse-like drinking, as well as imaging the binding of fluorescent marked semaglutide to nucleus accumbens (NAc) in both male and female rats. The suppressive effect of semaglutide on alcohol-induced locomotor stimulation and in vivo dopamine release in NAc was tested in male mice. We evaluated effect of semaglutide on the in vivo release of dopamine metabolites (DOPAC and HVA) and gene expression of enzymes metabolizing dopamine (MAOA and COMT) in male mice.

Findings: In male and female rats, acute and repeated semaglutide administration reduced alcohol intake and prevented relapse-like drinking. Moreover, fluorescent labeled semaglutide was detected in NAc of alcohol-drinking male and female rats. Further, semaglutide attenuated the ability of alcohol to cause hyperlocomotion and to elevate dopamine in NAc in male mice. As further shown in male mice, semaglutide enhanced DOPAC and HVA in NAc when alcohol was onboard and increased the gene expression of COMT and MAOA.

Interpretation: Altogether, this indicates that semaglutide reduces alcohol drinking behaviors, possibly via a reduction in alcohol-induced reward and NAc dependent mechanisms. As semaglutide also decreased body weight of alcohol-drinking rats of both sexes, upcoming clinical studies should test the plausibility that semaglutide reduces alcohol intake and body weight in overweight AUD patients.

Funding: Swedish Research Council (2019-01676), LUA/ALF (723941) from the Sahlgrenska University Hospital and the Swedish brain foundation.

--- https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/ 2023-10-20 ai/nn/transformer/gpt ai/scaling reinforcement-learning/exploration --- https://arxiv.org/abs/2312.06585#deepmind Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models (ReSTEM) Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel 2023-12-11 2023-12-11 [("doi","10.48550/arXiv.2312.06585")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm/2 math reinforcement-learning/offline

[Twitter] Fine-tuning language models (LMs) on human-generated data remains a prevalent practice. However, the performance of such models is often limited by the quantity and diversity of high-quality human data. In this paper, we explore whether we can go beyond human data on tasks where we have access to scalar feedback, for example, on math problems where one can verify correctness.

To do so, we investigate a simple self-training method based on expectation-maximization, which we call ReSTEM, where we (1) generate samples from the model and filter them using binary feedback, (2) fine-tune the model on these samples, and (3) repeat this process a few times.

Testing on advanced MATH reasoning and APPS coding benchmarks using PaLM-2 models, we find that ReSTEM scales favorably with model size and surpasses fine-tuning only on human data.

Overall, our findings suggest self-training with feedback can substantially reduce dependence on human-generated data.

--- https://x.com/Holara_AI/status/1734741137632665817 Holara AI 2023-10-20 ai/anime ai/nn/diffusion --- https://x.com/ilyasut/status/1697713317660500407 Ilya Sutskever 2023-10-20 reinforcement-learning/openai --- https://x.com/sama/status/1635700851619819520 Sam Altman 2023-10-20 reinforcement-learning/openai --- https://arxiv.org/abs/2308.08998#deepmind ReST: Reinforced Self-Training (ReST) for Language Modeling Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, Wolfgang Macherey, Arnaud Doucet, Orhan Firat, Nando de Freitas 2023-08-17 2023-10-20 [("doi","10.48550/arXiv.2308.08998")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt reinforcement-learning/imitation-learning reinforcement-learning/offline reinforcement-learning/preference-learning

Reinforcement learning from human feedback (RLHF) can improve the quality of large language model’s (LLM) outputs by aligning them with human preferences. We propose a simple algorithm for aligning LLMs with human preferences inspired by growing batch reinforcement learning (RL), which we call Reinforced Self-Training (ReST). Given an initial LLM policy, ReST produces a dataset by generating samples from the policy, which are then used to improve the LLM policy using offline RL algorithms.

ReST is more efficient than typical online RLHF methods because the training dataset is produced offline, which allows data reuse. While ReST is a general approach applicable to all generative learning settings, we focus on its application to machine translation. Our results show that ReST can substantially improve translation quality, as measured by automated metrics and human evaluation on machine translation benchmarks in a compute and sample-efficient manner.

--- https://arxiv.org/abs/2312.06114 The virial theorem and the Price equation Steinunn Liorsdóttir, Lior Pachter 2023-12-11 2023-12-11 [("doi","10.48550/arXiv.2312.06114")] genetics/selection/natural science

[Twitter] We observe that the time averaged continuous Price equation is identical to the positive momentum virial theorem, and we discuss the applications and implications of this connection.

Table 1: Glossary of Terms. [Rosetta stone between evolutionary biology & Newtonian mechanics]
--- https://www.nature.com/articles/d41586-023-03865-y 2023-10-20 statistics/bias --- https://x.com/MatthewBJane/status/1734711959528845351 Matthew B. Jane 2023-10-20 design/visualization statistics/power-analysis --- https://arxiv.org/abs/2312.07533#nvidia VILA: On Pre-training for Visual Language Models Ji Lin, Hongxu Yin, Wei Ping, Yao Lu, Pavlo Molchanov, Andrew Tao, Huizi Mao, Jan Kautz, Mohammad Shoeybi, Song Han 2023-12-12 2023-12-12 [("doi","10.48550/arXiv.2312.07533")] ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/meta-learning

Visual language models (VLMs) rapidly progressed with the recent success of large language models. There have been growing efforts on visual instruction tuning to extend the LLM with visual inputs, but lacks an in-depth study of the visual language pre-training process, where the model learns to perform joint modeling on both modalities.

In this work, we examine the design options for VLM pre-training by augmenting LLM [LLaMA] towards VLM through step-by-step controllable comparisons. We introduce 3 main findings: (1) freezing LLMs during pre-training can achieve decent zero-shot performance, but lack in-context learning capability, which requires unfreezing the LLM; (2) interleaved pre-training data is beneficial whereas image-text pairs alone are not optimal; (3) re-blending text-only instruction data to image-text data during instruction fine-tuning not only remedies the degradation of text-only tasks, but also boosts VLM task accuracy.

With an enhanced pre-training recipe we build VILA, a Visual Language model family that consistently outperforms the state-of-the-art models, eg. LLaVA-1.5, across main benchmarks without bells and whistles.

Multi-modal pre-training also helps unveil appealing properties of VILA, including multi-image reasoning, enhanced in-context learning, and better world knowledge.

--- https://arxiv.org/abs/2310.03744 LLaVA-1.5: Improved Baselines with Visual Instruction Tuning Haotian Liu, Chunyuan Li, Yuheng Li, Yong Jae Lee 2023-10-05 2023-10-20 [("doi","10.48550/arXiv.2310.03744")] ai/nn/transformer/clip ai/nn/transformer/gpt/instruction-tuning

Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning. In this note, we show that the fully-connected vision-language cross-modal connector in LLaVA is surprisingly powerful and data-efficient. With simple modifications to LLaVA, namely, using CLIP-ViT-L-336px with an MLP projection and adding academic-task-oriented VQA data with simple response formatting prompts, we establish stronger baselines that achieve state-of-the-art across 11 benchmarks.

Our final 13B checkpoint uses merely 1.2M publicly available data, and finishes full training in ~1 day on a single 8-A100 node.

We hope this can make state-of-the-art LMM research more accessible.

Code and model will be publicly available.

--- https://www.biorxiv.org/content/10.1101/2023.02.28.530502.full Brain Organoid Computing for Artificial Intelligence Hongwei Cai, Zheng Ao, Chunhui Tian, Zhuhao Wu, Hongcheng Liu, Jason Tchieu, Mingxia Gu, Ken Mackie, Feng Guo 2023-03-01 2023-10-21 [("doi","10.1101/2023.02.28.530502")] psychology/neuroscience

Brain-inspired hardware emulates the structure and working principles of a biological brain and may address the hardware bottleneck for fast-growing artificial intelligence (AI). Current brain-inspired silicon chips are promising but still limit their power to fully mimic brain function for AI computing.

Here, we develop Brainoware, living AI hardware that harnesses the computation power of 3D biological neural networks in a brain organoid. Brain-like 3D in vitro cultures compute by receiving and sending information via a multielectrode array [reservoir computing]. Applying spatiotemporal electrical stimulation, this approach not only exhibits nonlinear dynamics and fading memory properties but also learns from training data. Further experiments demonstrate real-world applications in solving non-linear equations.

This approach may provide new insights into AI hardware.

--- https://www.thepsmiths.com/p/review-demons-by-fyodor-dostoevsky 2023-10-21 politics psychology/personality --- https://arxiv.org/abs/2310.13032#alephalpha QDAIF: Quality-Diversity through AI Feedback Herbie Bradley, Andrew Dai, Hannah Teufel, Jenny Zhang, Koen Oostermeijer, Marco Bellagente, Jeff Clune, Kenneth O. Stanley, Grégory Schott, Joel Lehman 2023-10-19 2023-10-21 [("doi","10.48550/arXiv.2310.13032")] ai/nn/transformer/gpt/fiction ai/poetry reinforcement-learning/exploration

In many text-generation problems, users may prefer not only a single response, but a diverse range of high-quality outputs from which to choose. Quality-diversity (QD) search algorithms aim at such outcomes, by continually improving and diversifying a population of candidates. However, the applicability of QD to qualitative domains, like creative writing, has been limited by the difficulty of algorithmically specifying measures of quality and diversity.

Interestingly, recent developments in language models (LMs) have enabled guiding search through AI feedback, wherein LMs are prompted in natural language to evaluate qualitative aspects of text. Leveraging this development, we introduce Quality-Diversity through AI Feedback (QDAIF), wherein an evolutionary algorithm applies LMs to both generate variation and evaluate the quality and diversity of candidate text.

When assessed on creative writing domains, QDAIF covers more of a specified search space with high-quality samples than do non-QD controls. Further, human evaluation of QDAIF-generated creative texts validates reasonable agreement between AI and human evaluation. Our results thus highlight the potential of AI feedback to guide open-ended search for creative and original solutions, providing a recipe that seemingly generalizes to many domains and modalities.

In this way, QDAIF is a step towards AI systems that can independently search, diversify, evaluate, and improve, which are among the core skills underlying human society’s capacity for innovation.

--- https://cloud.google.com/blog/products/ai-machine-learning/imagen-2-on-vertex-ai-is-now-generally-available 2023-10-21 ai/nn/diffusion --- https://arxiv.org/abs/2312.03746 Evaluating Large Language Model Creativity from a Literary Perspective Murray Shanahan, Catherine Clarke 2023-11-30 2023-11-30 [("doi","10.48550/arXiv.2312.03746")] ai/nn/transformer/gpt/4/fiction

[Twitter] This paper assesses the potential for large language models (LLMs) to serve as assistive tools in the creative writing process, by means of a single, in-depth case study. In the course of the study, we develop interactive and multi-voice prompting strategies that interleave background descriptions (scene setting, plot elements), instructions that guide composition, samples of text in the target style, and critical discussion of the given samples.

We qualitatively evaluate the results from a literary critical perspective, as well as from the standpoint of computational creativity (a sub-field of artificial intelligence).

Our findings lend support to the view that the sophistication of the results that can be achieved with an LLM mirrors the sophistication of the prompting.

--- https://arxiv.org/abs/2305.02301#google Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister 2023-05-03 2023-10-21 [("doi","10.48550/arXiv.2305.02301")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5

Deploying large language models (LLMs) is challenging because they are memory inefficient and compute-intensive for practical applications. In reaction, researchers train smaller task-specific models by either finetuning with human labels or distilling using LLM-generated labels. However, finetuning and distillation require large amounts of training data to achieve comparable performance to LLMs.

We introduce Distilling step-by-step, a new mechanism that (a) trains smaller models that outperform LLMs, and (b) achieves so by leveraging less training data needed by finetuning or distillation. Our method extracts LLM rationales as additional supervision for training small models within a multi-task framework.

We present 3 findings across 4 NLP benchmarks: First, compared to both finetuning and distillation, our mechanism achieves better performance with much fewer labeled/unlabeled training examples. Second, compared to few-shot prompted LLMs, we achieve better performance using substantially smaller model sizes. Third, we reduce both the model size and the amount of data required to outperform LLMs; our finetuned 770M T5 model outperforms the few-shot prompted 540B PaLM model using only 80% of available data on a benchmark, whereas standard finetuning the same T5 model struggles to match even by using 100% of the dataset.

We release the code at: Github.

--- https://justine.lol/oneliners/ 2023-10-21 ai/nn/transformer/gpt --- https://arxiv.org/abs/2312.07541#google SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration Daniel Duckworth, Peter Hedman, Christian Reiser, Peter Zhizhin, Jean-François Thibert, Mario Lučić, Richard Szeliski, Jonathan T. Barron 2023-12-12 2023-12-12 [("doi","10.48550/arXiv.2312.07541")] ai/nn/fully-connected ai/nn/sparsity/knowledge-distillation

Recent techniques for real-time view synthesis have rapidly advanced in fidelity and speed, and modern methods are capable of rendering near-photorealistic scenes at interactive frame rates. At the same time, a tension has arisen between explicit scene representations amenable to rasterization and neural fields built on ray marching, with state-of-the-art instances of the latter surpassing the former in quality while being prohibitively expensive for real-time applications.

In this work, we introduce SMERF, a view synthesis approach that achieves state-of-the-art accuracy among real-time methods on large scenes with footprints up to 300 m2 at a volumetric resolution of 3.5 mm3. Our method is built upon two primary contributions: a hierarchical model partitioning scheme, which increases model capacity while constraining compute and memory consumption, and a distillation training strategy that simultaneously yields high fidelity and internal consistency.

Our approach enables full 6 degrees of freedom (6DOF) navigation within a web browser and renders in real-time on commodity smartphones and laptops. Extensive experiments show that our method exceeds the current state-of-the-art in real-time novel view synthesis by 0.78 dB on standard benchmarks and 1.78 dB on large scenes, renders frames 3 orders of magnitude faster than state-of-the-art radiance field models, and achieves real-time performance across a wide variety of commodity devices, including smartphones. We encourage the reader to explore these models in person at our project website: https://smerf-3d.github.io/.

--- https://arxiv.org/abs/2302.04761#facebook Toolformer: Language Models Can Teach Themselves to Use Tools Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, Thomas Scialom 2023-02-09 2023-10-21 [("doi","10.48550/arXiv.2302.04761")] ai/nn/transformer/gpt/calibration ai/scaling/emergence reinforcement-learning/imitation-learning

Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where much simpler and smaller models excel.

In this paper, we show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds. We introduce Toolformer, a model trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction. This is done in a self-supervised way, requiring nothing more than a handful of demonstrations for each API.

We incorporate a range of tools, including a calculator, a Q&A system, two different search engines, a translation system, and a calendar. Toolformer achieves substantially improved zero-shot performance across a variety of downstream tasks, often competitive with much larger models, without sacrificing its core language modeling abilities.

--- https://www.wired.com/story/rock-dust-soak-up-carbon-emissions-climate-experiment/ 2023-10-21 technology/carbon-capture --- https://en.wikipedia.org/wiki/Fires_in_Edo#Hiyokechi_and_hirok%C5%8Dji Fires in Edo § Hiyokechi and hirokōji 2023-10-21 crime economics/mechanism-design --- https://arxiv.org/abs/2312.06937 Can a Transformer Represent a Kalman Filter? Gautam Goel, Peter Bartlett 2023-12-12 2023-12-12 [("doi","10.48550/arXiv.2312.06937")] ai/nn/transformer/attention cs/computable reinforcement-learning/model

Transformers are a class of autoregressive deep learning architectures which have recently achieved state-of-the-art performance in various vision, language, and robotics tasks. We revisit the problem of Kalman Filtering in linear dynamical systems and show that Transformers can approximate the Kalman Filter in a strong sense. Specifically, for any observable LTI system we construct an explicit causally-masked Transformer which implements the Kalman Filter, up to a small additive error which is bounded uniformly in time; we call our construction the Transformer Filter.

Our construction is based on a two-step reduction. We first show that a softmax self-attention block can exactly represent a certain Gaussian kernel smoothing estimator. We then show that this estimator closely approximates the Kalman Filter.

We also investigate how the Transformer Filter can be used for measurement-feedback control and prove that the resulting nonlinear controllers closely approximate the performance of standard optimal control policies such as the LQG controller.

--- /doc/economics/2023-bradbury.pdf Public policy toward professional sports stadiums: A review John Charles Bradbury, Dennis Coates, Brad R. Humphreys 2023-09-28 2023-10-22 [("doi","10.1002/pam.22534")] economics politics

This article informs public policy toward professional sports stadiums, which state and local governments routinely subsidize. Our analysis provides a history of stadium construction and funding in the U.S., documenting trends that portend a forthcoming new wave of stadiums. Despite robust evidence that stadiums are not economic development catalysts and confer limited social benefits, public outlays persist and exhibit a positive growth trajectory, which could prove costly to government budgets in coming decades.

We review contemporary justifications for public subsidies, focusing on proposed salutary development and budgeting strategies. Economic research continues to demonstrate that stadiums remain poor public investments, and optimal public funding of professional sports venues is substantially less than typical subsidy levels.

We examine economic, political, and institutional factors that contribute to the disconnect between research and policy, and we provide recommendations to promote sound public policy.

--- https://news.gallup.com/opinion/polling-matters/232949/american-public-opinion-holocaust.aspx 2023-10-22 politics --- https://en.wikipedia.org/wiki/Linear%E2%80%93quadratic%E2%80%93Gaussian_control Linear-quadratic-Gaussian control 2023-10-22 reinforcement-learning/model reinforcement-learning/robot --- https://x.com/fortelabs/status/1734284384537333813 Tiago Forte 2023-10-22 ai/nn/transformer/gpt/4 --- /doc/fiction/opera/1925-klein.pdf Nietzsche and Bizet John W. Klein 1925-10-01 2023-10-22 fiction/opera philosophy/ethics --- https://arxiv.org/abs/2310.06272#bytedance Let Models Speak Ciphers: Multiagent Debate through Embeddings Chau Pham, Boyi Liu, Yingxiang Yang, Zhengyu Chen, Tianyi Liu, Jianbo Yuan, Bryan A. Plummer, Zhaoran Wang, Hongxia Yang 2023-10-10 2023-10-22 [("doi","10.48550/arXiv.2310.06272")] ai/nn/sampling ai/nn/transformer/gpt cs/cryptography/steganography math reinforcement-learning/exploration reinforcement-learning/model-free reinforcement-learning/multi-agent reinforcement-learning/safe

Discussion and debate among Large Language Models (LLMs) have gained considerable attention due to their potential to enhance the reasoning ability of LLMs. Although natural language is an obvious choice for communication due to LLM’s language understanding capability, the token sampling step needed when generating natural language poses a potential risk of information loss, as it uses only one token to represent the model’s belief across the entire vocabulary.

In this paper, we introduce a communication regime named CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue. Specifically, we remove the token sampling step from LLMs and let them communicate their beliefs across the vocabulary through the expectation of the raw transformer output embeddings. Remarkably, by deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights.

While the state-of-the-art LLM debate methods using natural language outperforms traditional inference by a margin of 1.5-8%, our experiment results show that CIPHER debate further extends this lead by 1-3.5% across 5 reasoning tasks and multiple open-source LLMs of varying sizes. This showcases the superiority and robustness of embeddings as an alternative “language” for communication among LLMs.

--- https://arxiv.org/abs/2312.04180 AI and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor Platform Dandan Qiao, Huaxia Rui, Qian Xiong 2023-12-07 2023-12-07 [("doi","10.48550/arXiv.2312.04180")] economics/automation

Artificial intelligence (AI) refers to the ability of machines or software to mimic or even surpass human intelligence in a given cognitive task. While humans learn by both induction and deduction, the success of current AI is rooted in induction, relying on its ability to detect statistical regularities in task input—an ability learnt from a vast amount of training data using enormous computation resources. We examine the performance of such a statistical AI in a human task through the lens of 4 factors, including task learnability, statistical resource, computation resource, and learning techniques, and then propose a three-phase visual framework to understand the evolving relation between AI and jobs.

Based on this conceptual framework, we develop a simple economic model of competition to show the existence of an inflection point for each occupation. Before AI performance crosses the inflection point, human workers always benefit from an improvement in AI performance, but after the inflection point, human workers become worse off whenever such an improvement occurs.

To offer empirical evidence, we first argue that AI performance has passed the inflection point for the occupation of translation but not for the occupation of web development. We then study how the launch of ChatGPT, which led to improvement of AI performance on many tasks, has affected workers in these two occupations on a large online labor platform. Consistent with the inflection point conjecture, we find that translators are negatively affected by the shock both in terms of the number of accepted jobs and the earnings from those jobs, while web developers are positively affected by the very same shock.

Given the potentially large disruption of AI on employment, more studies on more occupations using data from different platforms are urgently needed.

…Our second objective is to test the above inflection point conjecture using empirical data. For that, we conducted an empirical study using the launch of ChatGPT on November 30, 2022, as an exogenous shock that raised certain regions of the CIS. Our data comes from a large online freelancing platform, and we focus on two job categories: translation and web development. We hypothesize that the occupation of translation has passed the inflection point, especially after the launch of ChatGPT, while the occupation of web development has not, even after the launch of ChatGPT. For translation, we note that the transformer architecture, which is at the core of GPT, was initially proposed to tackle the challenge of machine translation. In contrast, web development involves high-level designs and complex interactions among different components, making it more challenging for AI to completely surpass the minimal intelligence levels of tasks required for web development. On the other hand, AI tools like ChatGPT do make the job of programming more efficient by assisting programmers with debugging, code snippets, and so on. Consequently, we believe the launch of ChatGPT did shock the area of the CIS corresponding to web development. As a control group, we use the occupation of construction design because jobs in this category are 4 currently completed by humans using specialized software with very limited inputs from AI, most likely because of insufficient data for training AI.

We match workers in a treated occupation (ie. translation, web development) with workers in the control occupation (ie. the construction design) and conduct a difference-in-differences (DID) analysis at the worker-month level. Our first dependent variable is transaction volume measured either as a worker’s accepted number of focal jobs each month or the ratio between those focal jobs and the total number of jobs accepted by the worker each month. Our second dependent variable is total earnings from those focal jobs. Consistent with the inflection point conjecture, we find that for translators, their transaction volumes dropped after the launch of ChatGPT, and they earned less from translation jobs. In contrast, web developers experienced an increase in their transaction volume and earnings after the launch of ChatGPT.

…To the best of our knowledge, the closest work to ours is a concurrent working paper by Liu et al 2023, which investigates how the launch of ChatGPT affected transaction volume on an online labor platform. Their main finding is a statistically-significant decrease in transaction volume for gigs and freelancers directly exposed to ChatGPT. In contrast, our study reveals a more complex relation between AI and jobs, both theoretically and empirically. In particular, we propose the inflection point conjecture based on our conceptual framework and economic modeling that leads to different implications of ChatGPT’s launch on different job categories. It should be noted that our findings do not contradict the main finding of Liu et al 2023, because their treated job category consists of writing and programming which, according to our analyses, should have experienced opposite effects with the launch of ChatGPT. It’s likely that the platform in their study differs from the platform in our study, which we believe makes the two studies complementary to each other for the robustness of empirical research in our field.

…To further ensure the observed effects occurred after ChatGPT’s launch, all measurements were constructed based on the focal jobs accepted within a given month, rather than those completed. We excluded data from November & December 2022 to account for potential pre-launch impacts of ChatGPT and holiday effects. Hence, the study’s time frame spans 6 months before and after the shock, from May 1, 2022, to June 30, 2023.

…in terms of magnitude, the transaction volume dropped by 7.4%…suggesting a decrease in worker’s earnings from focal jobs after ChatGPT’s launch, by 30.2%…In terms of magnitude, we do find that workers in the Writing OLM experience less of a decline, with transaction volume down 4.4%, and earnings down 19.2%, compared to workers in the Translation OLM.

--- https://arxiv.org/abs/2308.05201 "Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets Jin Liu, Xingchen Xu, Yongjun Li, Yong Tan 2023-08-09 2023-10-22 [("doi","10.48550/arXiv.2308.05201")] economics/automation

With the advent of general-purpose Generative AI, the interest in discerning its impact on the labor market escalates. In an attempt to bridge the extant empirical void, we interpret the launch of ChatGPT as an exogenous shock, and implement a Difference-in-Differences (DID) approach to quantify its influence on text-related jobs and freelancers within an online labor marketplace.

Our results reveal a decrease in transaction volume for gigs and freelancers directly exposed to ChatGPT. Additionally, this decline is particularly marked in units of relatively higher past transaction volume or lower quality standards.

Yet, the negative effect is not universally experienced among service providers. Subsequent analyses illustrate that freelancers proficiently adapting to novel advancements and offering services that augment AI technologies can yield substantial benefits amidst this transformative period.

Consequently, even though the advent of ChatGPT could conceivably substitute existing occupations, it also unfolds immense opportunities and carries the potential to reconfigure the future of work. This research contributes to the limited empirical repository exploring the profound influence of LLM-based generative AI on the labor market, furnishing invaluable insights for workers, job intermediaries, and regulatory bodies navigating this evolving landscape.

--- https://frontiersinzoology.biomedcentral.com/articles/10.1186/s12983-021-00410-3 2023-10-22 psychology/animal zeo --- https://www.pnas.org/doi/full/10.1073/pnas.2204754119 2023-10-23 psychology/animal zeo --- https://www.nature.com/articles/s41467-023-42766-6 2023-10-23 cat/psychology --- https://www.empowerpharmacy.com/ 2023-10-23 longevity/glp/semaglutide --- https://www.peptidesciences.com/semaglutide-3mg 2023-10-23 longevity/glp/semaglutide --- https://calpaterson.com/bank-python.html 2023-10-23 cs/algorithm design --- https://catholicscientists.org/articles/round-or-square-china-christianity-shape-of-earth/ 2023-10-23 science --- https://www.reddit.com/r/slatestarcodex/comments/18illkw/amazing_story_from_dominic_cummings_blog/ 2023-10-23 cs/security --- https://www.lesswrong.com/posts/c6uTNm5erRrmyJvvD/mapping-the-semantic-void-strange-goings-on-in-gpt-embedding 2023-10-23 ai/nn/tokenization ai/nn/transformer/gpt/3 --- https://www.economist.com/1843/2023/12/14/when-the-new-york-times-lost-its-way 2023-10-23 politics --- https://arxiv.org/abs/2312.08877 May the Noise be with you: Adversarial Training without Adversarial Examples Ayoub Arous, Andres F. Lopez-Lopera, Nael Abu-Ghazaleh, Ihsen Alouani 2023-12-12 2023-12-12 [("doi","10.48550/arXiv.2312.08877")] ai/nn/adversarial ai/nn/cnn

In this paper, we investigate the following question: Can we obtain adversarially-trained models without training on adversarial examples? Our intuition is that training a model with inherent stochasticity, ie. optimizing the parameters by minimizing a stochastic loss function, yields a robust expectation function that is non-stochastic.

In contrast to related methods that introduce noise at the input level, our proposed approach incorporates inherent stochasticity by embedding Gaussian noise within the layers of the NN model at training time. We model the propagation of noise through the layers, introducing a closed-form stochastic loss function that encapsulates a noise variance parameter.

Additionally, we contribute a formalized noise-aware gradient, enabling the optimization of model parameters while accounting for stochasticity. Our experimental results confirm that the expectation model of a stochastic architecture trained on benign distribution is adversarially robust. Interesting, we find that the impact of the applied Gaussian noise’s standard deviation on both robustness and baseline accuracy closely mirrors the impact of the noise magnitude employed in adversarial training.

Our work contributes adversarially trained networks using a completely different approach, with empirically similar robustness to adversarial training.

--- https://en.wikipedia.org/wiki/David_Koresh David Koresh 2023-10-23 crime psychiatry/bipolar/energy --- https://en.wikipedia.org/wiki/Jim_Jones Jim Jones 2023-10-24 crime psychiatry/bipolar/energy --- https://en.wikipedia.org/wiki/Ivan_the_Terrible Ivan the Terrible 2023-10-24 crime psychiatry/bipolar/energy --- https://en.wikipedia.org/wiki/1993_Long_Island_Rail_Road_shooting 1993 Long Island Rail Road shooting 2023-10-24 crime psychiatry/bipolar/energy --- https://www.amazon.com/Brotherhood-Tyrants-D-Jablow-Hershman/dp/0879758880 2023-10-24 crime psychiatry/bipolar/energy --- https://arxiv.org/abs/2312.09241#microsoft TinyGSM: achieving >80% on GSM8k with small language models Bingbin Liu, Sebastien Bubeck, Ronen Eldan, Janardhan Kulkarni, Yuanzhi Li, Anh Nguyen, Rachel Ward, Yi Zhang 2023-12-14 2023-12-14 [("doi","10.48550/arXiv.2312.09241")] ai/dataset ai/nn/transformer/gpt/3/nonfiction math psychology/dark-knowledge

Small-scale models offer various computational advantages, and yet to which extent size is critical for problem-solving abilities remains an open question. Specifically for solving grade school math, the smallest model size so far required to break the 80% barrier on the GSM8K benchmark remains to be 34B. Our work studies how high-quality datasets may be the key for small language models to acquire mathematical reasoning.

We introduce TinyGSM, a synthetic dataset of 12.3M grade school math problems paired with Python solutions, generated fully by GPT-3.5. After finetuning on TinyGSM, we find that a duo of a 1.3B generation model and a 1.3B verifier model can achieve 81.5% accuracy, outperforming existing models that are orders of magnitude larger. This also rivals the performance of the GPT-3.5 “teacher” model (77.4%), from which our model’s training data is generated.

Our approach is simple and has two key components: (1) the high-quality dataset TinyGSM, (2) the use of a verifier, which selects the final outputs from multiple candidate generations.

--- https://openreview.net/forum?id=BJehNfW0- Do GANs learn the distribution? Some Theory and Empirics Sanjeev Arora, Andrej Risteski, Yi Zhang 2022-02-10 2023-10-24 ai/nn/gan

We propose a support size estimator of GANs’ learned distribution to show they indeed suffer from mode collapse, and we prove that encoder-decoder GANs do not avoid the issue as well.

Do GANS (Generative Adversarial Nets) actually learn the target distribution? The foundational paper of Goodfellow et al 2014 suggested they do, if they were given sufficiently large deep nets, sample size, and computation time. A recent theoretical analysis in Arora et al 2017 raised doubts whether the same holds when discriminator has bounded size. It showed that the training objective can approach its optimum value even if the generated distribution has very low support. In other words, the training objective is unable to prevent mode collapse.

The current paper makes two contributions: (1) It proposes a novel test for estimating support size using the birthday paradox of discrete probability. Using this evidence is presented that well-known GANs approaches do learn distributions of fairly low support.

(2) It theoretically studies encoder-decoder GANs architectures (eg. BiGAN/ALI), which were proposed to learn more meaningful features via GANs, and consequently to also solve the mode-collapse issue. Our result shows that such encoder-decoder training objectives also cannot guarantee learning of the full distribution because they cannot prevent serious mode collapse. More seriously, they cannot prevent learning meaningless codes for data, contrary to usual intuition.

[Keywords: Generative Adversarial Networks, mode collapse, birthday paradox, support size estimation]

--- https://arxiv.org/abs/2001.03376 microbatchGAN: Stimulating Diversity with Multi-Adversarial Discrimination Gonçalo Mordido, Haojin Yang, Christoph Meinel 2020-01-10 2023-10-24 [("doi","10.48550/arXiv.2001.03376")] ai/nn/gan reinforcement-learning/exploration reinforcement-learning/multi-agent

We propose to tackle the mode collapse problem in generative adversarial networks (GANs) by using multiple discriminators and assigning a different portion of each minibatch, called microbatch, to each discriminator.

We gradually change each discriminator’s task from distinguishing between real and fake samples to discriminating samples coming from inside or outside its assigned microbatch by using a diversity parameter α. The generator is then forced to promote variety in each minibatch to make the microbatch discrimination harder to achieve by each discriminator. Thus, all models in our framework benefit from having variety in the generated set to reduce their respective losses.

We show evidence that our solution promotes sample diversity since early training stages on multiple datasets.

--- https://arxiv.org/abs/2312.04927 Zoology: Measuring and Improving Recall in Efficient Language Models Simran Arora, Sabri Eyuboglu, Aman Timalsina, Isys Johnson, Michael Poli, James Zou, Atri Rudra, Christopher Ré 2023-12-08 2023-12-08 [("doi","10.48550/arXiv.2312.04927")] ai/nn/rnn ai/nn/transformer/attention/sparsity ai/scaling

[blog/Twitter, code] Attention-free language models that combine gating and convolutions are growing in popularity due to their efficiency and increasingly competitive performance. To better understand these architectures, we pretrain a suite of 17 attention and “gated-convolution” language models, finding that SoTA gated-convolution architectures still underperform attention by up to 2.1 perplexity points on the Pile.

In fine-grained analysis, we find 82% of the gap is explained by each model’s ability to recall information that is previously mentioned in-context, eg. “Hakuna Matata means no worries Hakuna Matata it means no” → “?”. On this task, termed “associative recall”, we find that attention outperforms gated-convolutions by a large margin: a 70M parameter attention model outperforms a 1.4 billion parameter gated-convolution model on associative recall. This is surprising because prior work shows gated convolutions can perfectly solve synthetic tests for AR capability.

To close the gap between synthetics and real language, we develop a new formalization of the task called multi-query associative recall (MQAR) that better reflects actual language. We perform an empirical and theoretical study of MQAR that elucidates differences in the parameter-efficiency of attention and gated-convolution recall.

Informed by our analysis, we evaluate simple convolution-attention hybrids and show that hybrids with input-dependent sparse attention patterns can close 97.4% of the gap to attention, while maintaining sub-quadratic scaling.

Our code is accessible at: Github.

--- https://www.reddit.com/r/programming/comments/901p0j/former_software_engineer_at_spotify_on_their/ 2023-10-24 design --- https://xkcd.com/2868/ 2023-10-24 psychology/cognitive-bias/illusion-of-depth --- https://marginalrevolution.com/marginalrevolution/2023/12/a-weighty-economics-puzzle.html#blog-comment-160691275 2023-10-24 economics longevity/glp/tirzepatide --- https://www.richardcarrier.info/archives/26450 2023-10-25 philosophy/ethics --- https://link.springer.com/article/10.1007/s12144-023-04591-4 2023-10-25 politics psychology/personality/narcissism --- https://arxiv.org/abs/2212.09095#amazon Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale Hritik Bansal, Karthik Gopalakrishnan, Saket Dingliwal, Sravan Bodapati, Katrin Kirchhoff, Dan Roth 2022-12-18 2023-10-25 [("doi","10.48550/arXiv.2212.09095")] ai/nn/sparsity/pruning ai/nn/transformer/gpt reinforcement-learning/meta-learning

Language models have been shown to perform better with an increase in scale on a wide variety of tasks via the in-context learning paradigm. In this paper, we investigate the hypothesis that the ability of a large language model to in-context learn-perform a task is not uniformly spread across all of its underlying components. Using a 66 billion parameter language model (OPT-66B) across a diverse set of 14 downstream tasks, we find this is indeed the case: ~70% of attention heads and ~20% of feed forward networks can be removed with minimal decline in task performance.

We find substantial overlap in the set of attention heads (un)important for in-context learning across tasks and number of in-context examples. We also address our hypothesis through a task-agnostic lens, finding that a small set of attention heads in OPT-66B score highly on their ability to perform primitive induction operations associated with in-context learning, namely, prefix matching and copying. These induction heads overlap with task-specific important heads, reinforcing arguments by Olsson et al (arXiv:2209.11895) regarding induction head generality to more sophisticated behaviors associated with in-context learning.

Overall, our study provides several insights that indicate large language models may be under-trained for in-context learning and opens up questions on how to pre-train language models to more effectively perform in-context learning.

--- https://x.com/depthsofwiki/status/1735800801455419697 Depths of Wikipedia 2023-10-25 wikipedia --- https://retool.com/pipes 2023-10-25 cs design --- https://arxiv.org/abs/2312.09244#deepmind Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking Jacob Eisenstein, Chirag Nagpal, Alekh Agarwal, Ahmad Beirami, Alex D’Amour, D. J. Dvijotham, Adam Fisch, Katherine Heller, Stephen Pfohl, Deepak Ramachandran, Peter Shaw, Jonathan Berant 2023-12-14 2023-12-14 [("doi","10.48550/arXiv.2312.09244")] ai/nn/transformer/t5 reinforcement-learning/preference-learning/mode-collapse reinforcement-learning/safe

Reward models play a key role in aligning language model applications towards human preferences. However, this setup creates an incentive for the language model to exploit errors in the reward model to achieve high estimated reward, a phenomenon often termed “reward hacking”. A natural mitigation is to train an ensemble of reward models, aggregating over model outputs to obtain a more robust reward estimate. We explore the application of reward ensembles to alignment at both training time (through reinforcement learning) and inference time (through reranking).

First, we show that reward models are “underspecified”: reward models that perform similarly in-distribution can yield very different rewards when used in alignment, due to distribution shift.

Second, underspecification results in overoptimization, where alignment to one reward model does not improve reward as measured by another reward model trained on the same data.

Third, overoptimization is mitigated by the use of reward ensembles, and ensembles that vary by their “pretraining” seeds lead to better generalization than ensembles that differ only by their “fine-tuning” seeds, with both outperforming individual reward models. However, even pretrain reward ensembles do not eliminate reward hacking: we show several qualitative reward hacking phenomena that are not mitigated by ensembling because all reward models in the ensemble exhibit similar error patterns.

--- https://www.nytimes.com/2023/12/11/science/nasa-logo-worm.html 2023-10-25 design/typography --- https://www.colinmcginn.net/evolution-of-pain/ 2023-10-25 philosophy/mind psychology/neuroscience --- https://promptarmor.substack.com/p/data-exfiltration-from-writercom 2023-10-25 ai/nn/transformer/gpt cs/security --- https://embracethered.com/blog/posts/2023/chatgpt-webpilot-data-exfil-via-markdown-injection/#responsible-disclosure 2023-10-25 ai/nn/transformer/gpt cs/security --- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4594466 2023-10-26 ai/nn/transformer/gpt/3 economics/automation --- https://arxiv.org/abs/2312.08926 PRER: Modeling Complex Mathematical Reasoning via Large Language Model based MathAgent Haoran Liao, Qinyi Du, Shaohua Hu, Hao He, Yanyan Xu, Jidong Tian, Yaohui Jin 2023-12-14 2023-12-14 [("doi","10.48550/arXiv.2312.08926")] ai/nn/transformer/gpt/4/nonfiction math reinforcement-learning/multi-agent

Large language models (LLMs) face challenges in solving complex mathematical problems that require comprehensive capacities to parse the statements, associate domain knowledge, perform compound logical reasoning, and integrate the intermediate rationales. Tackling all these problems once could be arduous for LLMs, thus leading to confusion in generation.

In this work, we explore the potential of enhancing LLMs with agents by meticulous decomposition and modeling of the mathematical reasoning process. Specifically, we propose a formal description of the mathematical solving and extend LLMs with an agent-based zero-shot framework named Planner-Reasoner-Executor-Reflector (PRER).

We further provide and implement two MathAgents that define the logical forms and inherent relations via a pool of actions in different grains and orientations: MathAgent-M adapts its actions to LLMs, while MathAgent-H aligns with humankind.

Experiments on miniF2F and MATH have demonstrated the effectiveness of PRER and proposed MathAgents, achieving an increase of 12.3% (53.9% → 66.2%) on the MiniF2F, 9.2% (49.8% → 59.0%) on MATH, and 13.2% (23.2% → 35.4%) for level-5 problems of MATH against GPT-4.

Further analytical results provide more insightful perspectives on exploiting the behaviors of LLMs as agents.

--- https://waymo.com/blog/2023/09/waymos-autonomous-vehicles-are.html?hl=it_IT 2023-10-26 reinforcement-learning/robot --- https://www.biorxiv.org/content/10.1101/2023.04.13.536806.full Evidence for the role of selection for reproductively advantageous alleles in human aging Erping Long, Jianzhi Zhang 2023-04-14 2023-10-26 [("doi","10.1101/2023.04.13.536806")] genetics/heritable/correlation longevity

The antagonistic pleiotropy hypothesis posits that natural selection for pleiotropic mutations that confer earlier or more reproduction but impair the post-reproductive life causes aging. This hypothesis of the evolutionary origin of aging is supported by case studies but lacks unambiguous genomic evidence.

Here we genomically test this hypothesis using the genotypes, reproductive phenotypes, and death registry of 276,406 UK Biobank participants.

We observe a strong, negative genetic correlation between reproductive traits and lifespan. Individuals with higher polygenic scores for reproduction (PGSR) have lower survivorships to age 76 (SV76), and PGSR increased over birth cohorts 1940 → 1969. Similar trends are found from individual genetic variants examined. PGSR and SV76 remain negatively correlated upon the control of the offspring number, revealing horizontal pleiotropy between reproduction and lifespan.

Intriguingly, regardless of PGSR, having two children maximizes SV76.

These and other findings strongly support the antagonistic pleiotropy hypothesis of aging in humans. [Is probably mediated through behavioral differences like impulsivity or worse careers or dropping out of school etc, which can explain the U-curve of longevity—they are just engaged in the normative behavior, neither more nor less.]

--- https://www.nytimes.com/2023/12/13/technology/chatbot-cheating-schools-students.html 2023-10-26 ai/nn/transformer/gpt/3/nonfiction --- https://www.cremieux.xyz/p/the-cultural-power-of-high-skilled 2023-10-26 philosophy/religion politics psychology/personality sociology technology --- https://www.biorxiv.org/content/10.1101/2023.02.27.530194.full The Big (Genetic) Sort? Reassessing Migration Patterns and Their Genetic Imprint in the UK Shiro Furuya, Jihua Liu, Zhongxuan Sun, Qiongshi Lu, Jason M. Fletcher 2023-02-28 2023-10-26 [("doi","10.1101/2023.02.27.530194")] genetics/heritable sociology

This study reassesses Abdellaoui et al 2018’s findings that genetically selective migration may lead to persistent and accumulating socioeconomic and health inequalities between “types” (rich or poor) of places in the UK. Their work categorized migrants who moved to the same “type” of place (rich-to-rich or poor-to-poor) as non-migrants.

We re-investigate the question of genetically selective migration by examining migration patterns between places rather than “place-types” and find genetic selectively in whether people migrate rather than where.

For example, we find evidence of positive selection of people with genetic variants correlated better education moving from rich to poor places with our measure of migration that was obscured in the earlier work that used a non-standard measure of migration.

--- https://arxiv.org/abs/2312.09187#deepmind Vision-Language Models as a Source of Rewards Kate Baumli, Satinder Baveja, Feryal Behbahani, Harris Chan, Gheorghe Comanici, Sebastian Flennerhag, Maxime Gazeau, Kristian Holsheimer, Dan Horgan, Michael Laskin, Clare Lyle, Hussain Masoom, Kay McKinney, Volodymyr Mnih, Alexander Neitz, Fabio Pardo, Jack Parker-Holder, John Quan, Tim Rocktäschel, Himanshu Sahni, Tom Schaul, Yannick Schroecker, Stephen Spencer, Richie Steigerwald, Luyu Wang, Lei Zhang 2023-12-14 2023-12-14 [("doi","10.48550/arXiv.2312.09187")] ai/nn/transformer/clip reinforcement-learning/imitation-learning reinforcement-learning/offline reinforcement-learning/scaling

Building generalist agents that can accomplish many goals in rich open-ended environments is one of the research frontiers for reinforcement learning. A key limiting factor for building generalist agents with RL has been the need for a large number of reward functions for achieving different goals.

We investigate the feasibility of using off-the-shelf vision-language models, or VLMs, as sources of rewards for reinforcement learning agents. We show how rewards for visual achievement of a variety of language goals can be derived from the CLIP family of models, and used to train RL agents that can achieve a variety of language goals.

We showcase this approach in two distinct visual domains and present a scaling trend showing how larger VLMs lead to more accurate rewards for visual goal achievement, which in turn produces more capable RL agents.

Figure 4: Scaling reward model size. (Left) Precision-Recall curves for varying VLM architecture and sizes on an offline fixed dataset of Playhouse trajectories. (Right) Ground truth returns on held-out evaluation tasks for Playhouse over the course of training with varying VLM reward sizes.

…We observe that increasing the size of the VLM used for the reward model (from 200M to 1.4b parameters) improves the precision-recall curves. Figure 4 (above right) shows the ground truth returns for held-out evaluation tasks over the course of training which are not given to the agent, when trained with VLM reward signals with different base models. We observe that the improved accuracy of the VLMs on offline datasets, when used as the only reward signal, does translate to better agent performance on ground truth evaluation metrics.

…within the training budget, we did not observe reward hacking of our VLM reward, where the true reward drops off while the proxy VLM reward continues to increase.

--- https://arxiv.org/abs/2312.04657 Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Games Ruoyao Wang, Peter Jansen 2023-12-07 2023-12-07 [("doi","10.48550/arXiv.2312.04657")] fiction/text-game reinforcement-learning/exploration reinforcement-learning/imitation-learning reinforcement-learning/model

In this work, we introduce a self-supervised behavior cloning transformer for text games, which are challenging benchmarks for multi-step reasoning in virtual environments. Traditionally, Behavior Cloning Transformers excel in such tasks but rely on supervised training data.

Our approach auto-generates training data by exploring trajectories (defined by common macro-action sequences) that lead to reward within the games, while determining the generality and utility of these trajectories by rapidly training small models then evaluating their performance on unseen development games.

Through empirical analysis, we show our method consistently uncovers generalizable training data, achieving about 90% performance of supervised systems across 3 benchmark text games.

--- https://en.wikipedia.org/wiki/Literature-based_discovery Literature-based discovery 2023-10-26 ai/nn/retrieval reinforcement-learning/exploration --- https://arxiv.org/abs/2209.13085 Defining and Characterizing Reward Hacking Joar Skalse, Nikolaus H. R. Howe, Dmitrii Krasheninnikov, David Krueger 2022-09-27 2023-10-26 [("doi","10.48550/arXiv.2209.13085")] reinforcement-learning/safe

We provide the first formal definition of reward hacking, a phenomenon where optimizing an imperfect proxy reward function, ℛ̃, leads to poor performance according to the true reward function, ℛ. We say that a proxy is unhackable if increasing the expected proxy return can never decrease the expected true return.

Intuitively, it might be possible to create an unhackable proxy by leaving some terms out of the reward function (making it “narrower”) or overlooking fine-grained distinctions between roughly equivalent outcomes, but we show this is usually not the case. A key insight is that the linearity of reward (in state-action visit counts) makes unhackability a very strong condition.

In particular, for the set of all stochastic policies, two reward functions can only be unhackable if one of them is constant. We thus turn our attention to deterministic policies and finite sets of stochastic policies, where non-trivial unhackable pairs always exist, and establish necessary and sufficient conditions for the existence of simplifications, an important special case of unhackability.

Our results reveal a tension between using reward functions to specify narrow tasks and aligning AI systems with AI systems with human values.

--- https://qz.com/a-startup-founded-by-former-google-employees-claims-tha-1850919360 2023-10-27 sociology/technology --- https://books.worksinprogress.co/book/maintenance-of-everything/vehicles/digression-2-from-manuals-to-youtube-with-a-detour-two-assault-rifles/4 2023-10-27 technology --- /doc/ai/scaling/hardware/2023-12-12-gavinuberti-podcastinterview-realtimeaiandthefutureofaihardwareecthedai.pdf Real-Time AI & The Future of AI Hardware Gavin Uberti 2023-12-12 2023-12-12 ai/nn/transformer ai/scaling/economics ai/scaling/hardware --- https://en.wikipedia.org/wiki/Taurine#Animal_dietary_requirement Taurine § Animal food additive 2023-10-27 cat/biology/taurine --- https://www.theonion.com/u-s-economy-grinds-to-halt-as-nation-realizes-money-ju-1819571322 2023-10-27 fiction/humor fiction/humor math/humor --- https://en.wikipedia.org/wiki/Gaussian_correlation_inequality#History Gaussian correlation inequality § History 2023-10-27 science statistics/probability --- https://en.wikipedia.org/wiki/Ahnentafel Ahnentafel 2023-10-27 cs/algorithm/sorting --- https://x.com/RichardSocher/status/1736161332259614989 Richard Socher 2023-10-27 ai/nn/rnn ai/scaling science --- https://arxiv.org/abs/1412.5335 Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews Grégoire Mesnil, Tomas Mikolov, Marc’Aurelio Ranzato, Yoshua Bengio 2014-12-17 2023-10-27 [("doi","10.48550/arXiv.1412.5335")] ai/nn/rnn

Sentiment analysis is a common task in natural language processing that aims to detect polarity of a text document (typically a consumer review). In the simplest settings, we discriminate only between positive and negative sentiment, turning the task into a standard binary classification problem. We compare several machine learning approaches to this problem, and combine them to achieve the best possible results.

We show how to use for this task the standard generative language models, which are slightly complementary to the state-of-the-art techniques.

We achieve strong results on a well-known dataset of IMDB movie reviews.

Our results are easily reproducible, as we publish also the code needed to repeat the experiments. This should simplify further advance of the state-of-the-art, as other researchers can combine their techniques with ours with little effort.

--- https://en.wikipedia.org/wiki/File:Palmer_House_Hilton_Chicago.jpg File:Palmer House Hilton Chicago.jpg 2023-10-27 design --- https://en.wikipedia.org/wiki/Barnum_effect Barnum effect 2023-10-27 psychology/cognitive-bias/illusion-of-depth psychology/personality sociology/technology --- https://www.youtube.com/watch?v=kLC8AHZX4N8&t=394s 2023-10-28 ai/nn/transformer/gpt sociology/technology --- https://github.com/thomasahle/fastchess 2023-10-28 ai/nn/fully-connected reinforcement-learning/chess reinforcement-learning/imitation-learning --- https://aclanthology.org/D10-1115.pdf 2023-10-28 ai/nn psychology/linguistics --- https://www.biorxiv.org/content/10.1101/2023.10.26.560707.full Drab and distant birds are studied less than their fancy-feathered friends Silas E. Fischer, Joshua G. Otten, Andrea M. Lindsay, Donald B. Miles, Henry M. Streby 2023-10-27 2023-10-28 [("doi","10.1101/2023.10.26.560707")] psychology/cognitive-bias statistics/bias/publication

Human decisions are influenced by implicit biases, and scientists do not exist in an objectivity vacuum. Subconscious biases in scientists’ choices about which species to study may beget distorted knowledge bases and stagnant paradigms. Disparities in biological knowledge can result from bias in study species selection within a cycle of policymaking, funding, and publication, all subject to implicit biases. Here, we show that ornithological research in the USA and Canada is biased toward birds with greater esthetic salience and those with larger breeding ranges and ranges that encompass more universities. We quantified components of esthetic salience (eg. color, pattern/contrast, body size) of 293 passerines and near-passerines based on empirically documented human visual preferences and investigated whether these components were associated with research effort. We also quantified each species’ breeding range size and the number of universities within that range. Accounting for phylogenetic relatedness, we found that these metrics of esthetics, familiarity, and accessibility combined to explain 45% of the variation in the number of published papers about each species from 1965–2020. On average, birds in the top 10% of esthetic salience were studied 3.0× more than birds in the bottom 10%, and publication numbers were predicted most strongly by color and pattern components of esthetic salience. Birds in the top 10% of breeding range size and university abundance were studied 3.8× and 3.5× more often than species in the bottom 10% of those categories, respectively. Species listed as Endangered and those featured on journal covers have greater esthetic salience scores than other species. We discuss how these biases may influence perceived relative value of species with respect to culture and conservation. The disparities in empirical knowledge we describe here perpetuate a positive feedback loop, thus widening the gap between the avian “haves” and “have-nots”, with some questions answered repeatedly while potentially critical discoveries are left undiscovered.

“All animals are equal, but some animals are more equal than others.” —George Orwell, Animal Farm (1945)

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7615573/ 2023-10-28 genetics/heritable/dog genetics/sequencing --- https://www.nature.com/articles/s41598-023-47409-w 2023-10-28 cat/psychology --- https://arxiv.org/abs/2304.08612#microsoft Bridging Discrete and Backpropagation: Straight-Through and Beyond Liyuan Liu, Chengyu Dong, Xiaodong Liu, Bin Yu, Jianfeng Gao 2023-04-17 2023-10-28 [("doi","10.48550/arXiv.2304.08612")] ai/nn/vae ai/scaling/mixture-of-experts reinforcement-learning/model-free

Backpropagation, the cornerstone of deep learning, is limited to computing gradients for continuous variables. This limitation poses challenges for problems involving discrete latent variables. To address this issue, we propose a novel approach to approximate the gradient of parameters involved in generating discrete latent variables.

First, we examine the widely used Straight-Through (ST) heuristic and demonstrate that it works as a first-order approximation of the gradient. Guided by our findings, we propose ReinMax, which achieves second-order accuracy by integrating Heun’s method, a second-order numerical method for solving ODEs.

ReinMax does not require Hessian or other second-order derivatives, thus having negligible computation overheads. Extensive experimental results on various tasks demonstrate the superiority of ReinMax over the state-of-the-art. Implementations are released at Github.

--- https://arxiv.org/abs/2309.14030 DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation Yiqun Duan, Jinzhao Zhou, Zhen Wang, Yu-Kai Wang, Chin-Teng Lin 2023-09-25 2023-10-28 [("doi","10.48550/arXiv.2309.14030")] ai/nn/transformer ai/nn/vae psychology/neuroscience

The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs). With the swift advancement of large language models, such as ChatGPT, the need to bridge the gap between the brain and languages becomes increasingly pressing. Current methods, however, require eye-tracking fixations or event markers to segment brain dynamics into word-level features, which can restrict the practical application of these systems.

To tackle these issues, we introduce a novel framework, DeWave, that integrates discrete encoding sequences into open-vocabulary EEG-to-text translation tasks. DeWave uses a quantized variational encoder to derive discrete codex encoding and align it with pre-trained language models. This discrete codex representation brings forth two advantages: (1) it realizes translation on raw waves without marker by introducing text-EEG contrastive alignment training, and (2) it alleviates the interference caused by individual differences in EEG waves through an invariant discrete codex with or without markers.

Our model surpasses the previous baseline (40.1 and 31.7) by 3.06% and 6.34%, respectively, achieving 41.35 BLEU-1 and 33.71 Rouge-F on the ZuCo Dataset. This work is the first to facilitate the translation of entire EEG signal periods without word-level order markers (eg. eye fixations), scoring 20.5 BLEU-1 and 29.5 Rouge-1 on the ZuCo Dataset.

--- https://aclanthology.org/D13-1176.pdf 2023-10-28 ai/nn/cnn ai/nn/rnn --- https://arxiv.org/abs/2211.14643 Dynamic Soaring as a Means to Exceed the Solar Wind Speed Mathias N. Larrouturou, Andrew J. Higgins, Jeffrey K. Greason 2022-11-26 2023-10-28 [("doi","10.3389/frspt.2022.1017442")] science technology

A technique by which a spacecraft can interact with flows of ionized gas in space (the solar wind or interstellar medium) to be accelerated to velocities greater than the wind velocity is explored. Inspired by the dynamic soaring maneuvers performed by sea birds and gliders in which differences in wind speed are exploited to gain velocity, in the proposed technique a lift-generating spacecraft circles between regions of the heliosphere that have different wind speeds, gaining energy without the use of propellant and only modest onboard power requirements.

The spacecraft motion can be modeled as a series of elastic collisions between regions of the medium moving at different speeds. Models of the trajectory are developed to predict the potential velocity gains and the maximum velocity that may be achieved in terms of the lift-to-drag ratio of the vehicle.

A lift-generating mechanism is proposed in which power is extracted from the flow over the vehicle in the flight direction and then used to accelerate the surrounding medium in the transverse direction, generating lift. Large values of lift-to-drag ratio are shown to be possible in the case where a small transverse velocity is imparted over a large area of interaction.

The requirement for large interaction area in the extremely low density of the heliosphere precludes the use of physical wings, but the use of plasma waves generated by compact, directional antennas to impart momentum on the surrounding medium is feasible, with R-waves, X-waves, Alfven, and magnetosonic waves appearing as promising candidates.

A mission is defined in which dynamic soaring is performed on the termination shock of the heliosphere, speeds of 2% of c to be reached within 2.5 years of launch without the expenditure of propellant. The technique may comprise the first stage for a multistage mission to achieve true interstellar flight to other solar systems.

--- https://en.wikipedia.org/wiki/Francis_M._Pottenger_Jr. Francis M. Pottenger Jr 2023-10-28 cat/biology/taurine --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5059666/ Impact of feline AIM on the susceptibility of cats to renal disease Ryoichi Sugisawa, Emiri Hiramoto, Shigeru Matsuoka, Satomi Iwai, Ryosuke Takai, Tomoko Yamazaki, Nobuko Mori, Yuki Okada, Naoki Takeda, Ken-Ichi Yamamura, Toshiro Arai, Satoko Arai, Toru Miyazaki 2016 2023-10-29 [("doi","10.1038/srep35251")] cat/biology

Renal failure is one of the most important social problems for its incurability and high costs for patients’ health care. Through clarification of the underlying mechanism for the high susceptibility of cats to renal disease, we here demonstrates that the effective dissociation of serum AIM protein from IgM is necessary for the recovery from acute kidney injury (AKI). In cats, the AIM-IgM binding affinity is 1,000× higher than that in mice, which is caused by the unique positively-charged amino-acid cluster present in feline AIM. Hence, feline AIM does not dissociate from IgM during AKI, abolishing its translocation into urine.

This results in inefficient clearance of lumen-obstructing necrotic cell debris at proximal tubules, thereby impairing AKI recovery. Accordingly, mice whose AIM is replaced by feline AIM exhibit higher mortality by AKI than in wild-type mice. Recombinant AIM administration into the mice improves their renal function and survival.

As insufficient recovery from AKI predisposes patients to chronic, end-stage renal disease, feline AIM may be involved crucially in the high mortality of cats due to renal disease. Our findings could be the basis of the development of novel AKI therapies targeting AIM-IgM dissociation, and may support renal function in cats and prolong their lives.

--- https://x.com/ChrisJBakke/status/1736533308849443121 Chris Bakke 2023-10-29 ai/nn/transformer/gpt cs/security --- https://www.404media.co/facebook-is-being-overrun-with-stolen-ai-generated-images-that-people-think-are-real/ 2023-10-29 ai/nn/diffusion cs/security --- https://github.com/justinchiu/openlogprobs 2023-10-29 ai/nn/transformer/gpt/calibration --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5818813/ The Heritability of Autism Spectrum Disorder Sven Sandin, Paul Lichtenstein, Ralf Kuja-Halkola, Christina Hultman, Henrik Larsson, Abraham Reichenberg 2017 2023-10-29 [("doi","10.1001/jama.2017.12141")] genetics/heritable psychiatry/autism

This study reanalyzes Swedish cohort data to assess the stability under alternative assumptions and models of a previous estimate of the heritability of autism spectrum disorder.

...The study included 37,570 twin pairs, 2,642,064 full sibling pairs, and 432,281 maternal and 445,531 paternal half-sibling pairs. Of these, 14,516 children were diagnosed with ASD. The model including additive and nonadditive genetic, shared and nonshared environmental parameters was chosen as the full model under which nested sub-models were tested. The best-fitting model included only additive genetic and nonshared environmental parameters (Table 1).

Using this model, the ASD heritability was estimated as 0.83 (95% CI, 0.79-0.87) and the nonshared environmental influence was estimated as 0.17 (95% CI, 0.13-0.21). In the full model, the shared environment variance was estimated as 0.04 (95% CI, 0.00-0.14); nonshared environment, 0.16 (95% CI, 0.05-0.30); nonadditive genetic, 0.10 (95% CI, 0.00-0.38); and additive genetic (heritability), 0.69 (95% CI, 0.40-0.86).

Using only twins, the heritability was estimated as 0.87 (95% CI, 0.68-0.96).

--- https://slate.com/news-and-politics/2008/10/how-often-do-children-commit-suicide.html 2023-10-29 psychiatry --- https://www.sciencedirect.com/science/article/pii/S0021925823024377 2023-10-29 genetics/selection/natural psychology/animal/bird/neuroscience --- https://arxiv.org/abs/2302.00521 Off-the-Grid MARL (OG-MARL): Datasets with Baselines for Offline Multi-Agent Reinforcement Learning Claude Formanek, Asad Jeewa, Jonathan Shock, Arnu Pretorius 2023-02-01 2023-10-29 [("doi","10.48550/arXiv.2302.00521")] ai/dataset reinforcement-learning/multi-agent reinforcement-learning/offline

Being able to harness the power of large datasets for developing cooperative multi-agent controllers promises to unlock enormous value for real-world applications. Many important industrial systems are multi-agent in nature and are difficult to model using bespoke simulators. However, in industry, distributed processes can often be recorded during operation, and large quantities of demonstrative data stored.

Offline multi-agent reinforcement learning (MARL) provides a promising paradigm for building effective decentralised controllers from such datasets. However, offline MARL is still in its infancy and therefore lacks standardized benchmark datasets and baselines typically found in more mature subfields of reinforcement learning (RL). These deficiencies make it difficult for the community to sensibly measure progress.

In this work, we aim to fill this gap by releasing off-the-grid MARL (OG-MARL): a growing repository of high-quality datasets with baselines for cooperative offline MARL research. Our datasets provide settings that are characteristic of real-world systems, including complex environment dynamics, heterogeneous agents, non-stationarity, many agents, partial observability, suboptimality, sparse rewards and demonstrated coordination. For each setting, we provide a range of different dataset types (eg. Good, Medium, Poor, and Replay) and profile the composition of experiences for each dataset.

We hope that OG-MARL will serve the community as a reliable source of datasets and help drive progress, while also providing an accessible entry point for researchers new to the field.

--- https://arxiv.org/abs/2312.07647 Is there a black hole in the center of the Sun? No Matthew E. Caplan, Earl P. Bellinger, Andrew D. Santarelli 2023-12-12 2023-12-12 [("doi","10.48550/arXiv.2312.07647")] science

There is probably not a black hole in the center of the sun. Despite this detail, our goal in this work to convince the reader that this question is interesting and that work studying stars with central black holes is well motivated. If primordial black holes exist then they may exist in sufficiently large numbers to explain the dark matter in the universe.

While primordial black holes may form at almost any mass, the asteroid-mass window between (10−16–10−10M☉) remains a viable dark matter candidate and these black holes could be captured by stars upon formation. Such a star, partially powered by accretion luminosity from a microscopic black hole in its core, has been called a ‘Hawking star’.

Stellar evolution of Hawking stars is highly nontrivial and requires detailed stellar evolution models, which were developed in our recent work. We present here full evolutionary models of solar mass Hawking stars using two accretion schemes: one with a constant radiative efficiency, and one that is new in this work that uses an adaptive radiative efficiency to model the effects of photon trapping.

--- https://timconnors.co/posts/ai-scraper 2023-10-29 ai/nn/transformer/gpt/4/nonfiction cs/linkrot/archiving --- https://x.com/abacaj/status/1736819789841281372 abacaj 2023-10-30 ai/nn/transformer/gpt/codex --- https://www.lesswrong.com/posts/txj4wigyjLNbcoZ9o/valence-series-5-valence-disorders-in-mental-health-and 2023-10-30 psychiatry/bipolar/sleep psychology/personality/narcissism --- http://thecodelesscode.com/case/215 2023-10-30 design psychology/cognitive-bias --- https://github.com/s-macke/AdventureAI 2023-10-30 ai/nn/transformer/gpt/3/fiction ai/nn/transformer/gpt/4/fiction fiction/text-game --- https://en.wikipedia.org/wiki/9:05 9:05 2023-10-30 fiction/text-game --- https://www.overcomingbias.com/p/resenting-the-resentful 2023-10-30 politics psychology/personality sociology --- https://www.pnas.org/doi/full/10.1073/pnas.2304903120 2023-10-30 psychology/animal --- https://onlinelibrary.wiley.com/doi/10.1111/acps.13643 2023-10-30 modafinil --- /doc/psychiatry/anxiety/1965-park.pdf Non-blind Placebo Trial: An Exploration of Neurotic Patients’ Responses to Placebo When Its Inert Content Is Disclosed Lee C. Park, Lino Covi 1965-04-01 2023-10-30 [("doi","10.1001/archpsyc.1965.01720340008002")] nootropic psychiatry/anxiety --- https://arxiv.org/abs/2312.10240 Rich Human Feedback for Text-to-Image Generation Youwei Liang, Junfeng He, Gang Li, Peizhao Li, Arseniy Klimovskiy, Nicholas Carolan, Jiao Sun, Jordi Pont-Tuset, Sarah Young, Feng Yang, Junjie Ke, Krishnamurthy Dj Dvijotham, Katie Collins, Yiwen Luo, Yang Li, Kai J. Kohlhoff, Deepak Ramachandran, Vidhya Navalpakkam 2023-12-15 2023-12-15 [("doi","10.48550/arXiv.2312.10240")] ai/dataset ai/nn/diffusion ai/nn/transformer/gpt/palm/2 ai/nn/transformer/t5 ai/nn/vae/mae reinforcement-learning/preference-learning

Recent Text-to-Image (T2I) generation models such as Stable Diffusion and Imagen have made progress in generating high-resolution images based on text descriptions. However, many generated images still suffer from issues such as artifacts/implausibility, misalignment with text descriptions, and low esthetic quality.

Inspired by the success of Reinforcement Learning with Human Feedback (RLHF) for large language models, prior works collected human-provided scores as feedback on generated images and trained a reward model to improve the T2I generation. In this paper, we enrich the feedback signal by (1) marking image regions that are implausible or misaligned with the text, and (2) annotating which words in the text prompt are misrepresented or missing on the image.

We collect such rich human feedback on 18K generated images and train a multimodal transformer to predict the rich feedback automatically. We show that the predicted rich human feedback can be leveraged to improve image generation, for example, by selecting high-quality training data to finetune and improve the generative models, or by creating masks with predicted heatmaps to inpaint the problematic regions.

Notably, the improvements generalize to models (Muse) beyond those used to generate the images on which human feedback data were collected (Stable Diffusion variants).

--- https://arxiv.org/abs/2312.10091#conjecture Look Before You Leap: A Universal Emergent Decomposition of Retrieval Tasks in Language Models Alexandre Variengien, Eric Winsor 2023-12-13 2023-12-13 [("doi","10.48550/arXiv.2312.10091")] ai/nn/retrieval ai/nn/transformer/attention

When solving challenging problems, language models (LMs) are able to identify relevant information from long and complicated contexts. To study how LMs solve retrieval tasks in diverse situations, we introduce ORION, a collection of structured retrieval tasks spanning 6 domains, from text understanding to coding. Each task in ORION can be represented abstractly by a request (eg. a question) that retrieves an attribute (eg. the character name) from a context (eg. a story).

We apply causal analysis on 18 open-source language models with sizes ranging from 125 million to 70 billion parameters. We find that LMs internally decompose retrieval tasks in a modular way: middle layers at the last token position process the request, while late layers retrieve the correct entity from the context. After causally enforcing this decomposition, models are still able to solve the original task, preserving 70% of the original correct token probability in 98 of the 106 studied model-task pairs.

We connect our macroscopic decomposition with a microscopic description by performing a fine-grained case study of a question-answering task on Pythia-2.8b. Building on our high-level understanding, we demonstrate a proof of concept application for scalable internal oversight of LMs to mitigate prompt-injection while requiring human supervision on only a single input. Our solution improves accuracy drastically (15.5% → 97.5% on Pythia-12b).

This work presents evidence of a universal emergent modular processing of tasks across varied domains and models and is a pioneering effort in applying interpretability for scalable internal oversight of LMs.

--- https://en.wikipedia.org/wiki/Adolfo_Kaminsky Adolfo Kaminsky 2023-10-31 philosophy/ethics --- https://www.reddit.com/r/dalle2/comments/18mafpo/noirart_deco_redheads/ 2023-10-31 ai/nn/transformer/gpt/dall-e/3 --- https://arxiv.org/abs/2312.05571 Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic Reasoning Subhabrata Dutta, Joykirat Singh, Ishan Pandey, Sunny Manchanda, Soumen Chakrabarti, Tanmoy Chakraborty 2023-12-09 2023-12-09 [("doi","10.48550/arXiv.2312.05571")] ai/nn/transformer/gpt math reinforcement-learning/model-free

Large Language Models (LLM) exhibit zero-shot mathematical reasoning capacity as a behavior emergent with scale, commonly manifesting as chain-of-thoughts (CoT) reasoning. However, multiple empirical findings suggest that this prowess is exclusive to LLMs with exorbitant sizes (beyond 50 billion parameters).

Meanwhile, educational neuroscientists suggest that symbolic algebraic manipulation be introduced around the same time as arithmetic word problems to modularize language-to-formulation, symbolic manipulation of the formulation, and endgame arithmetic. In this paper, we start with the hypothesis that much smaller LMs, which are weak at multi-step reasoning, can achieve reasonable arithmetic reasoning if arithmetic word problems are posed as a formalize-then-solve task.

In our architecture, which we call SYRELM, the LM serves the role of a translator to map natural language arithmetic questions into a formal language (FL) description. A symbolic solver then evaluates the FL expression to obtain the answer. A small frozen LM, equipped with an efficient low-rank adapter, is capable of generating FL expressions that incorporate natural language descriptions of the arithmetic problem (eg. variable names and their purposes, formal expressions combining variables, etc.).

We adopt policy-gradient reinforcement learning to train the adapted LM, informed by the non-differentiable symbolic solver. This marks a sharp departure from the recent development in tool-augmented LLMs, in which the external tools (eg. calculator, Web search, etc.) are essentially detached from the learning phase of the LM.

SYRELM shows massive improvements (eg. +30.65 absolute point improvement in accuracy on the SVAMP dataset using GPT-J 6B model) over base LMs, while keeping our testbed easy to diagnose, interpret and within reach of most researchers.

--- https://ashvardanian.com/posts/gcc-12-vs-avx512fp16/ 2023-10-31 ai/nn/retrieval cs/algorithm --- https://ashvardanian.com/posts/python-c-assembly-comparison/ 2023-10-31 ai/nn/retrieval cs/algorithm --- https://en.wikipedia.org/wiki/GS-441524 GS-441524 2023-10-31 cat/biology --- https://www.theatlantic.com/science/archive/2020/05/remdesivir-cats/611341/ 2023-10-31 cat/biology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8388366/ Unlicensed GS-441524-Like Antiviral Therapy Can Be Effective for at-Home Treatment of Feline Infectious Peritonitis Sarah Jones, Wendy Novicoff, Julie Nadeau, Samantha Evans 2021 2023-10-31 [("doi","10.3390/ani11082257")] cat/biology

The goal of this study was to formally evaluate the administration of unlicensed, crowd-sourced antiviral GS-441524-like therapy for cats suspected to have feline infectious peritonitis (FIP), a previously fatal disease. Members of a large social media support and GS-441524-like drug distribution group were surveyed via the Internet. The survey was targeted toward owners who had treated their cats for at least 12 weeks with unlicensed GS-441524-like drugs. Of the 393 analyzed surveys which met inclusion criteria, 73.7% of owners using this therapy were from the United States. Only 8.7% of owners reported receiving help from their veterinarian in administering the treatment to their cat. The mean cost of treatment was USD 4920.

A majority of owners (88.2%) reported noticeable improvement in clinical signs within one week of initiating therapy. At the time of the survey, 96.7% (380 cats) were alive, with 54.0% of them considered cured and another 43.3% being monitored in the 12-week observation period. A total of 12.7% of the cats suffered a relapse of clinical signs of FIP, and 3.3% of the cats died despite GS-441524-like therapy.

Reported complications were mostly related to owner administration of subcutaneous injections of the acidic GS-441525-like therapy, such as vocalization, pain, struggling, and injection-site wounds. Limitations of this study include a retrospective design, bias in case selection, reliance on owner-reported data, and inability to confirm the contents of unlicensed pharmaceuticals; however, important lessons can be learned from the experiences of these owners.

While unconventional, and certainly not free from medical and legal risks, unlicensed, at-home GS-441524-like therapy, according to owner reports, can apparently offer benefits in the treatment of cats suspected of FIP.

--- https://faseb.onlinelibrary.wiley.com/doi/abs/10.1096/fasebj.28.1_supplement.820.4 2023-10-31 cat/biology/taurine --- https://www.logan.edu/mm/files/LRC/Masters-Theses/2013-04-04.pdf#page=2 2023-10-31 cat/biology/taurine --- https://benexdict.io/p/math-team 2023-10-31 math sociology --- https://research.google/blog/videopoet-a-large-language-model-for-zero-shot-video-generation/ 2023-11-01 ai/video/generation --- https://www.lesswrong.com/posts/Ei8q37PB3cAky6kaK/takeaways-from-a-mechanistic-interpretability-project-on 2023-11-01 ai/nn/adversarial ai/nn/transformer/attention --- https://www.reddit.com/r/LocalLLaMA/comments/18luk10/wait_llama_and_falcon_are_also_moe/ 2023-11-01 ai/nn/sparsity ai/scaling/mixture-of-experts --- https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0227046 Nutritional inadequacies in commercial vegan foods for dogs and cats Rafael Vessecchi Amorim Zafalon, Larissa Wünsche Risolia, Thiago Henrique Annibale Vendramini, Roberta Bueno Ayres Rodrigues, Vivian Pedrinelli, Fabio Alves Teixeira, Mariana Fragoso Rentas, Mariana Pamplona Perini, Isabella Corsato Alvarenga, Marcio Antonio Brunetto, Nicoletta Righini, Nicoletta Righini, Nicoletta Righini 2019-12-10 2023-11-01 [("doi","10.1371/journal.pone.0227046")] cat/biology/taurine dog

The objective of this study was to evaluate the macronutrients composition, fatty acid and amino acid profiles, and essential minerals content of all vegan foods for dogs and cats available in the Brazilian market, and to compare results with FEDIAF (2019) and AAFCO (2019) recommendations. 4 vegan pet foods were assessed (three for dogs and one for cats). The comparisons were made in a descriptive manner. All foods met the minimum recommendations for macronutrients. Arachidonic acid was not reported in any food label.

Regarding the FEDIAF recommendations, one food for dogs had low calcium, another had low potassium and a third had low sodium. The cat food presented potassium content lower than recommended. The Ca:P ratio did not meet the minimum recommendation of FEDIAF (2019) and AAFCO (2019) in any of the dog’s foods analyzed, and the cat food also did not present the minimum recommendation based on FEDIAF (2019). Copper concentrations exceeded the legal limit in all foods. Zinc concentrations exceeded this limit in two foods (one for dogs and one for cats) and iron levels exceeded the legal limit in one dog food.

One of the dog foods did not meet the minimum recommendation for methionine and the cat food did not meet the minimum recommendation for arginine. In addition, when the amount of nutrients consumed by animals with low energy requirements was simulated, in addition to the same non-conformities described above, it was observed that the cat food does not meet the minimum recommended of protein and taurine in unit/Kg0.67.

It was concluded that all foods analyzed had one or more nutrients below the recommended levels and some presented zinc and copper excess, therefore, these foods should not be recommended for dogs and cats, because dietary deficiencies found may lead to health risks for dogs and cats. Furthermore, manufacturers should review their formulations to ensure the nutritional adequacy of these foods.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0284132 Vegan versus meat-based cat food: Guardian-reported health outcomes in 1,369 cats, after controlling for feline demographic factors Andrew Knight, Alexander Bauer, Hazel Brown 2023-07-11 2023-11-01 [("doi","10.1371/journal.pone.0284132")] cat/biology/taurine

Increasing concerns about environmental sustainability, farmed animal welfare and competition for traditional protein sources, are driving considerable development of alternative pet foods. These include raw meat diets, in vitro meat products, and diets based on novel protein sources including terrestrial plants, insects, yeast, fungi and potentially seaweed.

To study health outcomes in cats fed vegan diets compared to those fed meat, we surveyed 1,418 cat guardians, asking about one cat living with them, for at least one year. Among 1,380 respondents involved in cat diet decision-making, health and nutrition was the factor considered most important. 1,369 respondents provided information relating to a single cat fed a meat-based (n = 1,242: 91%) or vegan (n = 127: 9%) diet for at least a year. We examined 7 general indicators of illness.

After controlling for age, sex, neutering status and primary location via regression models, the following risk reductions were associated with a vegan diet for average cats: increased veterinary visits: −7.3% reduction; medication use: −14.9% reduction; progression onto therapeutic diet: −54.7% reduction; reported veterinary assessment of being unwell: −3.6% reduction; reported veterinary assessment of more severe illness: −7.6% reduction; guardian opinion of more severe illness: −22.8% reduction. Additionally, the number of health disorders per unwell cat decreased by −15.5%. No reductions were statistically-significant.

We also examined the prevalence of 22 specific health disorders, using reported veterinary assessments. 42% of cats fed meat, and 37% of those fed vegan diets suffered from at least one disorder. Of these 22 disorders, 15 were most common in cats fed meat, and 7 in cats fed vegan diets. Only one difference was statistically-significant.

Considering these results overall, cats fed vegan diets tended to be healthier than cats fed meat-based diets. This trend was clear and consistent. These results largely concur with previous, similar studies.

--- https://en.wikipedia.org/wiki/Cat_food#Vegetarian_and_vegan Cat food § Vegetarian and vegan 2023-11-01 cat/biology/taurine --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860667/ The Impact of Vegan Diets on Indicators of Health in Dogs and Cats: A Systematic Review Adriana Domínguez-Oliva, Daniel Mota-Rojas, Ines Semendric, Alexandra L. Whittaker 2023 2023-11-01 [("doi","10.3390/vetsci10010052")] cat/biology/taurine

There has been an increase in vegetarianism and veganism in human populations. This trend also appears to be occurring in companion animals, with guardians preferring to feed their animals in accordance with their own dietary values and choices. However, there has been controversy amongst vets and online commentators about the safety of feeding vegan diets to carnivorous species, such as cats and dogs.

In spite of this controversy, to date there has been no systematic evaluation of the evidence on this topic. A systematic search of MEDLINE, Scopus, and Web of Science was performed, identifying 16 studies on the impact of vegan diets on cat and dog health. Studies were appraised for quality using established critical appraisal tools or reporting guidelines.

There was considerable heterogeneity in the outcomes measured, and study designs employed, with few studies evaluating key outcomes of interest. Grading of Recommendations, Assessment, Development and Evaluation (GRADE) was used for assessment of certainty in the evidence, with the evidence for most outcomes being assessed as low or very low.

Whilst the quality and amount of evidence needs to be considered in formulating recommendations, there was no overwhelming evidence of adverse effects arising from use of these diets and there was some evidence of benefits. It is, however, recommended that future high-quality studies, with standardized outcome measures and large sample sizes, be conducted. At the current time, if guardians wish to feed their companion animals vegan diets, a cautious approach should be taken using commercially produced diets which have been formulated considering the nutritional needs of the target species.

--- https://en.wikipedia.org/wiki/Idaho_Gem Idaho Gem 2023-11-01 genetics/cloning --- https://en.wikipedia.org/wiki/1979_Cleveland_Elementary_School_shooting_(San_Diego) 1979 Cleveland Elementary School shooting (San Diego) 2023-11-01 psychiatry/traumatic-brain-injury --- https://x.com/karpathy/status/1737518588159041845 Andrej Karpathy 2023-11-01 ai/music --- https://arxiv.org/abs/2312.11556 StarVector: Generating Scalable Vector Graphics Code from Images Juan A. Rodriguez, Shubham Agarwal, Issam H. Laradji, Pau Rodriguez, David Vazquez, Christopher Pal, Marco Pedersoli 2023-12-17 2023-12-17 [("doi","10.48550/arXiv.2312.11556")] ai/dataset ai/nn/transformer/clip ai/nn/transformer/gpt/codex cs/css design/typography

Scalable Vector Graphics (SVGs) have become integral in modern image rendering applications due to their infinite scalability in resolution, versatile usability, and editing capabilities. SVGs are particularly popular in the fields of web development and graphic design. Existing approaches for SVG modeling using deep learning often struggle with generating complex SVGs and are restricted to simpler ones that require extensive processing and simplification.

This paper introduces StarVector, a multimodal SVG generation model that effectively integrates Code Generation Large Language Models (CodeLLMs) [StarCoder] and vision models. Our approach uses a CLIP image encoder to extract visual representations from pixel-based images, which are then transformed into visual tokens via an adapter module. These visual tokens are prepended to the SVG token embeddings, and the sequence is modeled by the StarCoder model using next-token prediction, effectively learning to align the visual and code tokens. This enables StarVector to generate unrestricted SVGs that accurately represent pixel images.

To evaluate StarVector’s performance, we present SVG-Bench, a comprehensive benchmark for evaluating SVG methods across multiple datasets and relevant metrics. Within this benchmark, we introduce novel datasets including SVG-Stack, a large-scale dataset of real-world SVG examples [eg. icons & fonts], and use it to pre-train StarVector as a large foundation model for SVGs.

Our results demonstrate enhancements in visual quality and complexity handling over current methods, marking a notable advancement in SVG generation technology.

Code and models: https://github.com/joanrod/star-vector.

--- https://arxiv.org/abs/2305.06161 StarCoder: may the source be with you! Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Wenhao Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, Harm de Vries 2023-05-09 2023-11-02 [("doi","10.48550/arXiv.2305.06161")] ai/nn/transformer/gpt/codex

The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder & StarCoderBase: 15.5b parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. StarCoderBase is trained on 1 trillion tokens sourced from The Stack, a large collection of permissively licensed GitHub repositories with inspection tools and an opt-out process.

We fine-tuned StarCoderBase on 35B Python tokens, resulting in the creation of StarCoder. We perform the most comprehensive evaluation of Code LLMs to date and show that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model.

Furthermore, StarCoder outperforms every model that is fine-tuned on Python, can be prompted to achieve 40% pass@1 on HumanEval, and still retains its performance on other programming languages.

We take several important steps towards a safe open-access model release, including an improved PII redaction pipeline and a novel attribution tracing tool, and make the StarCoder models publicly available under a more commercially viable version of the Open Responsible AI Model license.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4768882/ Publication and reporting of clinical trial results: cross sectional analysis across academic medical centers Ruijun Chen, Nihar R. Desai, Joseph S. Ross, Weiwei Zhang, Katherine H. Chau, Brian Wayda, Karthik Murugiah, Daniel Y. Lu, Amit Mittal, Harlan M. Krumholz 2016 2023-11-02 [("doi","10.1136/bmj.i637")] statistics/bias/publication

Objective: To determine rates of publication and reporting of results within two years for all completed clinical trials registered in ClinicalTrials.gov across leading academic medical centers in the United States.

Design: Cross sectional analysis.

Setting: Academic medical centers in the United States.

Participants: Academic medical centers with 40 or more completed interventional trials registered on ClinicalTrials.gov.

Methods: Using the Aggregate Analysis of ClinicalTrials.gov database and manual review, we identified all interventional clinical trials registered on ClinicalTrials.gov with a primary completion date between October 2007 and September 2010 and with a lead investigator affiliated with an academic medical center.

Main Outcome Measures: The proportion of trials that disseminated results, defined as publication or reporting of results on ClinicalTrials.gov, overall and within 24 months of study completion.

Results: We identified 4347 interventional clinical trials across 51 academic medical centers. Among the trials, 1,005 (23%) enrolled more than 100 patients, 1,216 (28%) were double blind, and 2,169 (50%) were phase II through IV. Overall, academic medical centers disseminated results for 2,892 (66%) trials, with 1,560 (35.9%) achieving this within 24 months of study completion. The proportion of clinical trials with results disseminated within 24 months of study completion ranged from 16.2% (6⁄37) to 55.3% (57⁄103) across academic medical centers. The proportion of clinical trials published within 24 months of study completion ranged from 10.8% (4⁄37) to 40.3% (31⁄77) across academic medical centers, whereas results reporting on ClinicalTrials.gov ranged from 1.6% (2⁄122) to 40.7% (72⁄177).

Conclusions: Despite the ethical mandate and expressed values and mission of academic institutions, there is poor performance and noticeable variation in the dissemination of clinical trial results across leading academic medical centers.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0114023 Extent of Non-Publication in Cohorts of Studies Approved by Research Ethics Committees or Included in Trial Registries Christine Schmucker, Lisa K. Schell, Susan Portalupi, Patrick Oeller, Laura Cabrera, Dirk Bassler, Guido Schwarzer, Roberta W. Scherer, Gerd Antes, Erik von Elm, Joerg J. Meerpohl 2014-11-04 2023-11-02 [("doi","10.1371/journal.pone.0114023")] statistics/bias/publication

Background :

The synthesis of published research in systematic reviews is essential when providing evidence to inform clinical and health policy decision-making. However, the validity of systematic reviews is threatened if journal publications represent a biased selection of all studies that have been conducted (dissemination bias). To investigate the extent of dissemination bias we conducted a systematic review that determined the proportion of studies published as peer-reviewed journal articles and investigated factors associated with full publication in cohorts of studies (i) approved by research ethics committees (RECs) or (ii) included in trial registries.

Methods & Findings :

Four bibliographic databases were searched for methodological research projects (MRPs) without limitations for publication year, language or study location. The searches were supplemented by hand-searching the references of included MRPs. We estimated the proportion of studies published using prediction intervals (PI) and a random effects meta-analysis. Pooled odds ratios (OR) were used to express associations between study characteristics and journal publication. 17 MRPs (23 publications) evaluated cohorts of studies approved by RECs; the proportion of published studies had a PI 22%–72% and the weighted pooled proportion when combining estimates would be 46.2% (95% CI 40.2%–52.4%, I2 = 94.4%). 20-two MRPs (22 publications) evaluated cohorts of studies included in trial registries; the PI of the proportion published ranged 35.9%–59.4% and the weighted pooled proportion would be 54.2% (95% CI 42.0%–65.9%, I2 = 98.9%). REC-approved studies with statistically-significant results (compared with those without statistically-significant results) were more likely to be published (pooled OR 2.8; 95% CI 2.2–3.5). Phase-III trials were also more likely to be published than phase II trials (pooled OR 2.0; 95% CI 1.6–2.5). The probability of publication within two years after study completion ranged 35.9%–59.4%

Conclusions :

A substantial part of the studies approved by RECs or included in trial registries remains unpublished. Due to the large heterogeneity a prediction of the publication probability for a future study is very uncertain. Non-publication of research is not a random process, eg. it is associated with the direction of study findings. Our findings suggest that the dissemination of research findings is biased.

--- /doc/psychology/personality/2021-xu-4.pdf Beyond Openness to Experience and Conscientiousness: Testing links between lower-level personality traits and American political orientation Xiaowen Xu, Christopher J. Soto, Jason E. Plaks 2020-12-21 2023-11-02 [("doi","10.1111/jopy.12613")] politics psychology/personality

Introduction: Research has consistently revealed positive correlations between political liberalism and Openness to Experience, and between conservatism and Conscientiousness. Most of this research has made use of domain-level models of the Big 5 personality traits. Recent work suggests, however, that each Big 5 trait domain can be divided into distinct aspects or facets, which offer more nuanced characterizations of each trait.

Methods: Across 4 studies (Ns ranging from 1,123 to 116,406), the present research examined the degree to which distinct lower-level traits would be associated with meaningful differences in political orientation. United States residents completed two different hierarchical Big 5 personality measures (the Big 5 Aspect Scales and the Big 5 Inventory-2), as well as a range of measures of political orientation.

Results: Across both personality measures, liberal political orientation showed distinct positive associations with the lower-level traits Openness/Aesthetic Sensitivity, Intellect/Intellectual Curiosity, Compassion, and Withdrawal/Depression, as well as distinct negative associations with Orderliness/Organization, Politeness, and Assertiveness.

Discussion: By examining individual differences at a higher level of granularity, these data provide insight into specific motivations that predispose individuals toward different ends of the political spectrum.

--- https://www.animenewsnetwork.com/feature/2023-12-19/the-most-popular-anime-series-according-to-netflix-data-drop/.205610 2023-11-02 anime --- https://www.reddit.com/r/ChatGPT/comments/18mq51c/the_life_of_a_hotdog/ 2023-11-02 ai/nn/transformer/gpt/dall-e/3 --- https://arxiv.org/abs/2312.10003#deepmind ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent Renat Aksitov, Sobhan Miryoosefi, Zonglin Li, Daliang Li, Sheila Babayan, Kavya Kopparapu, Zachary Fisher, Ruiqi Guo, Sushant Prakash, Pranesh Srinivasan, Manzil Zaheer, Felix Yu, Sanjiv Kumar 2023-12-15 2023-12-15 [("doi","10.48550/arXiv.2312.10003")] ai/nn/retrieval ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/palm/2 reinforcement-learning/model-free

Answering complex natural language questions often necessitates multi-step reasoning and integrating external information. Several systems have combined knowledge retrieval with a large language model (LLM) to answer such questions. These systems, however, suffer from various failure cases, and we cannot directly train them end-to-end to fix such failures, as interaction with external knowledge is non-differentiable.

To address these deficiencies, we define a ReAct-style LLM agent with the ability to reason and act upon external knowledge. We further refine the agent through a ReST-like method that iteratively trains on previous trajectories, employing growing-batch reinforcement learning with AI feedback for continuous self-improvement and self-distillation.

Starting from a prompted large model and after just two iterations of the algorithm, we can produce a fine-tuned small model that achieves comparable performance on challenging compositional question-answering benchmarks with two orders of magnitude fewer parameters.

--- https://x.com/yacineMTB/status/1737523618832425273 yacineMTB 2023-11-02 ai/nn/transformer/gpt cs/security --- https://journals.sagepub.com/doi/10.1177/17456916231204811 2023-11-02 longevity psychology/neuroscience reinforcement-learning/model reinforcement-learning/model-free --- https://news.ycombinator.com/item?id=38703943 2023-11-02 ai/nn/retrieval --- https://www.biorxiv.org/content/10.1101/2023.12.18.572218.full A long-context language model for the generation of bacteriophage genomes Bin Shao 2023-12-19 2023-12-19 [("doi","10.1101/2023.12.18.572218")] ai/nn/tokenization ai/nn/transformer/attention/hierarchical genetics/sequencing

Generative pre-trained transformers (GPTs) have revolutionized the field of natural language processing. Inspired by this success, we develop a long-context generative model for genomes.

Our multiscale transformer model was pre-trained on unannotated bacteriophage genomes with byte-level tokenization. It generates de novo sequences up to 96K with functional genomic structure, including regulatory elements and novel proteins with phage-related functions.

Our work paves the way for the de novo design of the whole genome.

--- https://www.theinformation.com/articles/anthropic-to-raise-750-million-in-menlo-ventures-led-deal 2023-11-03 ai/nn/anthropic --- https://apnews.com/article/coronavirus-pandemic-ppe-stockpiles-masks-50bf7739fc9d69f4b991a14daadecaba 2023-11-03 economics politics --- https://kmalexander.com/free-stuff/fantasy-map-brushes/ 2023-11-03 design/typography --- https://x.com/emollick/status/1736196921541140861 Ethan Mollick 2023-11-03 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://terrytao.wordpress.com/about/ai-generated-versions-of-the-ai-anthology-article/ 2023-11-03 ai/nn/transformer/gpt/4/nonfiction math --- https://thezvi.substack.com/p/on-openais-preparedness-framework 2023-11-03 ai/scaling reinforcement-learning/openai reinforcement-learning/safe --- https://x.com/emollick/status/1737822379819020570 Ethan Mollick 2023-11-03 ai/nn/diffusion/midjourney --- https://en.wikipedia.org/wiki/101955_Bennu#Orbit 101955 Bennu § Orbit 2023-11-03 existential-risk --- https://neo.ssa.esa.int/risk-list 2023-11-03 existential-risk --- https://x.com/fluffykittnmeow/status/1737639861350269213 fluffykittnmeow 2023-11-03 ai/nn/transformer/gpt ai/scaling --- https://x.com/chaseleantj/status/1737816505507795060 Chase Lean 2023-11-04 ai/nn/diffusion/midjourney design/typography --- https://platform.openai.com/docs/guides/prompt-engineering 2023-11-04 ai/nn/transformer/gpt --- https://minimaxir.com/2023/12/chatgpt-structured-data/ 2023-11-04 ai/nn/transformer/gpt/codex --- https://tex.stackexchange.com/questions/158668/nice-scientific-pictures-show-off/159402#159402 2023-11-04 design/typography/rubrication design/typography/tex design/visualization --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3898580/ The survival time of chocolates on hospital wards: covert observational study Parag R. Gajendragadkar, Daniel J. Moualed, Phillip L. R. Nicolson, Felicia D. Adjei, Holly E. Cakebread, Rudolf M. Duehmke, Claire A. Martin 2013 2023-11-04 [("doi","10.1136/bmj.f7198")] food math/humor statistics/survival-analysis

Objective: To quantify the consumption of chocolates in a hospital ward environment.

Design: [protocol] Multicentre, prospective, covert observational study.

Setting: 4 wards at 3 hospitals (where the authors worked) within the United Kingdom.

Participants: Boxes of Quality Street (Nestlé) and Roses (Cadbury) on the ward and anyone eating these chocolates.

Intervention: Observers covertly placed two 350 g boxes of Quality Street and Roses chocolates on each ward (eight boxes were used in the study containing a total of 258 individual chocolates). These boxes were kept under continuous covert surveillance, with the time recorded when each chocolate was eaten.

Main Outcome Measure: Median survival time of a chocolate.

Results: 191⁄258 (74%) chocolates were observed being eaten. The mean total observation period was 254 minutes (95% confidence interval 179 to 329). The median survival time of a chocolate was 51 minutes (39–63). The model of chocolate consumption was non-linear, with an initial rapid rate of consumption that slowed with time. An exponential decay model best fitted these findings (model R2=0.844, p < 0.001), with a survival half life (time taken for 50% of the chocolates to be eaten) of 99 minutes. The mean time taken to open a box of chocolates from first appearance on the ward was 12 minutes (95% CI: 0–24). Quality Street chocolates survived longer than Roses chocolates (hazard ratio for survival of Roses v Quality Street 0.70, 95% CI 0.53–0.93, p = 0.014). The highest percentages of chocolates were consumed by healthcare assistants (28%) and nurses (28%), followed by doctors (15%).

Conclusions: From our observational study, chocolate survival in a hospital ward was relatively short, and was modelled well by an exponential decay model. Roses chocolates were preferentially consumed to Quality Street chocolates in a ward setting. Chocolates were consumed primarily by healthcare assistants and nurses, followed by doctors. Further practical studies are needed.

Figure 1: Kaplan-Meier survival curves for Quality Street and Roses chocolates across all wards.

…Conclusions: The median survival time of a chocolate in this study was just 51 minutes. In keeping with many biological processes, the way a box of chocolates is consumed seems to follow an exponential decay process, with an initial rapid “grab” phase. Further appropriately powered studies investigating the preference for specific chocolate flavours over a wider variety of specialty wards may prove interesting. Given the short half life of a box of chocolates, to ensure that all healthcare staff get benefits from consistent chocolate consumption it is the authors’ opinion that the frequency of chocolates delivered to wards needs to be increased and a concerted lobbying response instigated against recent manufacturers’ trends in shrinking the size of chocolate boxes.

…Notes: We thank the participating staff on the wards. The observers would like to apologise to anyone who received a less than truthful answer to the question: “What are you doing here?”

…Funding: This study received no external funding. The costs of the study were borne equally by the authors. No sponsorship was obtained.

…Conflict of interest statement: Competing interests: All authors have completed the ICMJE uniform disclosure form at and declare: no support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years. Other non-financial relevant interests: PRG is particularly sentimental about, and incredibly fond of, Lindt Lindor white chocolate truffles; DJM advocates abstinence as the only effective way to avoid chocolate over-consumption; PLRN is influenced by the intoxicating smells emanating from the Cadbury’s chocolate factory at Bournville near his home; FDA supports her native Ghana’s cocoa exports by eating a single Heroes chocolate (Cadbury) every night; HEC declares an interest in polishing off leftover Bounty chocolates (Mars); RDM’s Germanic background means that he is hard-wired, like his brethren, to love all milk chocolate; and CAM reports a preference for Milkybar buttons (Nestlé).

See Also:

--- https://x.com/AaronKoelker/status/1737666940200456268 Aaron Koelker 2023-11-04 design/visualization --- https://www.chinatalk.media/p/reflections-from-neurips-the-worlds#%C2%A7chinas-ai-generated-youtube-propaganda 2023-11-04 ai/music ai/video/generation politics --- https://arxiv.org/abs/2312.12742 Cached Transformers: Improving Transformers with Differentiable Memory Cache Zhaoyang Zhang, Wenqi Shao, Yixiao Ge, Xiaogang Wang, Jinwei Gu, Ping Luo 2023-12-20 2023-12-20 [("doi","10.48550/arXiv.2312.12742")] ai/nn/transformer/attention/compression

This work introduces a new Transformer model called Cached Transformer, which uses Gated Recurrent Cached (GRC) attention to extend the self-attention mechanism with a differentiable memory cache of tokens. GRC attention enables attending to both past and current tokens, increasing the receptive field of attention and allowing for exploring long-range dependencies.

By using a recurrent gating unit to continuously update the cache, our model achieves advancements in six language and vision tasks, including language modeling, machine translation, ListOPs, image classification, object detection, and instance segmentation.

Furthermore, our approach surpasses previous memory-based techniques in tasks such as language modeling and displays the ability to be applied to a broader range of situations.

--- https://arxiv.org/abs/2312.01939#deepmind Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities Markus Wulfmeier, Arunkumar Byravan, Sarah Bechtle, Karol Hausman, Nicolas Heess 2023-12-04 2023-12-04 [("doi","10.48550/arXiv.2312.01939")] reinforcement-learning

Contemporary artificial intelligence systems exhibit rapidly growing abilities accompanied by the growth of required resources, expansive datasets and corresponding investments into computing infrastructure. Although earlier successes predominantly focus on constrained settings, recent strides in fundamental research and applications aspire to create increasingly general systems. This evolving landscape presents a dual panorama of opportunities and challenges in refining the generalization and transfer of knowledge—the extraction from existing sources and adaptation as a comprehensive foundation for tackling new problems.

Within the domain of reinforcement learning (RL), the representation of knowledge manifests through various modalities, including dynamics and reward models, value functions, policies, and the original data. This taxonomy systematically targets these modalities and frames its discussion based on their inherent properties and alignment with different objectives and mechanisms for transfer.

Where possible, we aim to provide coarse guidance delineating approaches which address requirements such as limiting environment interactions, maximizing computational efficiency, and enhancing generalization across varying axes of change. Finally, we analyse reasons contributing to the prevalence or scarcity of specific forms of transfer, the inherent potential behind pushing these frontiers, and underscore the importance of transitioning from designed to learned transfer.

--- https://www.smithsonianmag.com/smart-news/cats-make-nearly-300-different-facial-expressions-180983185/ 2023-11-04 cat/psychology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1691314/ Genetic evidence for intra-specific & inter-specific slavery in honey ants (genus Myrmecocystus) D J. C. Kronauer, J. Gadau, B. Hölldobler 2003 2023-11-04 [("doi","10.1098/rspb.2002.2288")] biology/ant genetics

The New World honey ant species Myrmecocystus mimicus is well known for its highly stereotyped territorial tournaments, and for the raids on conspecific nests that can lead to intraspecific slavery. Our results from mitochondrial and nuclear markers show that the raided brood emerges in the raiding colony and is subsequently incorporated into the colony’s worker force.

We also found enslaved conspecifics in a second honey ant species, M. depilis, the sister taxon of M. mimicus, which occurs in sympatry with M. mimicus at the study site. Colonies of this species furthermore contained raided M. mimicus workers.

Both species have an effective mating frequency that is not significantly different from 1. This study provides genetic evidence for facultative intra-specific & inter-specific slavery in the genus Myrmecocystus. Slavery in ants has evolved repeatedly and supposedly by different means.

We propose that, in honey ants, secondary contact between two closely related species that both exhibit intraspecific slavery gave rise to an early form of facultative interspecific slavery.

--- https://www.bmj.com/specialties/christmas 2023-11-05 math/humor --- https://www.bmj.com/content/383/bmj-2023-077164 2023-11-05 technology --- https://www.bmj.com/content/383/bmj-2023-076197 2023-11-05 genetics/heritable/correlation/mendelian-randomization --- https://en.wikipedia.org/wiki/Honeypot_ant Honeypot ant 2023-11-05 biology/ant --- /doc/psychology/animal/2020-wen.pdf Red imported fire ants (Hymenoptera: Formicidae) cover inaccessible surfaces with particles to facilitate food search and transportation Chao Wen, Jian Chen, Wen-Quan Qin, Xuan Chen, Jia-Cheng Cai, Jun-Bao Wen, Xiu-Jun Wen, Cai Wang 2020-11-27 2023-11-05 [("doi","10.1111/1744-7917.12891")] biology/ant psychology/animal

Eusocial insects have evolved diverse particle-use behaviors. A previous study reported that red imported fire ants, Solenopsis invicta Buren, deposited soil particles on substances treated with essential balm, a fire ant repellent. We hypothesized that S. invicta modifies inaccessible surfaces by covering them with soil particles to facilitate food search and transportation.

Here, laboratory experiments were conducted to study the particle-covering behavior of S. invicta in response to viscose surfaces or surfaces treated with essential balm or liquid paraffin in the presence of real food (sausage) or non-food objects (acrylic plates). S. invicta workers deposited statistically-significantly more soil particles on these 3 types of treated surfaces than on untreated surfaces. In addition, statistically-significantly more particles were relocated on viscose and paraffin-smeared surfaces in the presence of food than in the presence of non-food objects.

The particle-covering behavior on viscose surfaces was also observed in the field. Interestingly, when no soil particles were available, ants searched and transported food on viscose surfaces only if the surfaces were artificially covered with sufficient quantities of soil particles but could not do so on viscose surfaces without soil particles or with insufficient quantities of soil particles.

In addition, ants actively relocated particles to cover viscose surfaces if the transportation distance was within 200 mm, whereas statistically-significantly fewer particles were relocated at longer transportation distances (400 mm). Our study provides a novel example of particle use by fire ants during foraging.

--- /doc/cs/computable/2009-michael.pdf Ant-Based Computing Loizos Michael 2009-07-01 2023-11-05 [("doi","10.1162/artl.2009.15.3.Michael.008")] biology/ant cs/computable

A biologically and physically plausible model for ants and pheromones is proposed.

It is argued that the mechanisms described in this model are sufficiently powerful to reproduce the necessary components of universal computation. The claim is supported by illustrating the feasibility of designing arbitrary logic circuits, showing that the interactions of ants and pheromones lead to the expected behavior, and presenting computer simulation results to verify the circuits’ working.

The conclusions of this study can be taken as evidence that coherent deterministic and centralized computation can emerge from the collective behavior of simple distributed Markovian processes such as those followed by biological ants, but also, more generally, by artificial agents with limited computational and communication abilities.

--- /doc/ai/1951-turing.pdf Intelligent Machinery, A Heretical Theory Alan Turing 1951-01-01 2023-11-05 [("doi","10.1093/oso/9780198250791.003.0018")] ai reinforcement-learning/safe

Alan Turing gave the presentation ‘Intelligent Machinery, A Heretical Theory’ on a BBC radio discussion programme called The 1951 Society. Named after the year in which the programme first went to air, The 1951 Society was produced by the BBC Home Service at their Manchester studio and ran for several years. A presentation by the week’s guest would be followed by a panel discussion. Regulars on the panel included Max Newman, Professor of Mathematics at Manchester, the philosopher Michael Polanyi, then Professor of Social Studies at Manchester, and the mathematician Peter Hilton, a younger member of Newman’s department at Manchester who had worked with Turing and Newman at Bletchley Park.

Turing’s target in "Intelligent Machinery, A Heretical Theory" is the claim that ‘You cannot make a machine to think for you’ (p. 472). A common theme in his writing is that if a machine is to be intelligent, then it will need to ‘learn by experience’ (probably with some pre-selection, by an external educator, of the experiences to which the machine will be subjected).

The present article continues the discussion of machine learning begun in Chapters 10 & 11. Turing remarks that the ‘human analogy alone’ suggests that a process of education ‘would in practice be an essential to the production of a reasonably intelligent machine within a reasonably short space of time’ (p. 473). He emphasizes the point, also made in Chapter 11, that one might ‘start from a comparatively simple machine, and, by subjecting it to a suitable range of “experience” transform it into one which was more elaborate, and was able to deal with a far greater range of contingencies’ (p. 473).

Turing goes on to give some indication of how learning might be accomplished, introducing the idea of a machine’s building up what he calls ‘indexes of experiences’ (p. 474). (This idea is not mentioned elsewhere in his writings.) An example of an index of experiences is a list (ordered in some way) of situations in which the machine has found itself, coupled with the action that was taken, and the outcome, good or bad. The situations are described in terms of features.

...Let us now assume, for the sake of argument, that these machines are a genuine possibility, and look at the consequences of constructing them. To do so would of course meet with great opposition, unless we have advanced greatly in religious toleration from the days of Galileo. There would be great opposition from the intellectuals who were afraid of being put out of a job. It is probable though that the intellectuals would be mistaken about this. There would be plenty to do, [trying to understand what the machines were trying to say], i.e. in trying to keep one’s intelligence up to the standard set by the machines, for it seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers. There would be no question of the machines dying, and they would be able to converse with each other to sharpen their wits. At some stage therefore we should have to expect the machines to take control, in the way that is mentioned in Samuel Butler’s Erewhon.

--- https://www.bmj.com/content/383/bmj-2023-077329 2023-11-05 math/humor statistics/bias --- https://www.bmj.com/content/383/bmj.p2564 2023-11-05 genetics/microbiome tea --- https://www.medrxiv.org/content/10.1101/2023.12.20.23300308.full Imputation of structural variants using a multi-ancestry long-read sequencing panel enables identification of disease associations Boris Noyvert, A. Mesut Erzurumluoglu, Dmitriy Drichel, Steffen Omland, Till F. M. Andlauer, Stefanie Mueller, Lau Sennels, Christian Becker, Aleksandr Kantorovich, Boris A. Bartholdy, Ingrid Braenne, Julio Cesar Bolivar-Lopez, Costas Mistrellides, Gillian M. Belbin, Jeremiah H. Li, Joseph K. Pickrell, Johann de Jong, Jatin Arora, Yao Hu, Boehringer Ingelheim, Digital Sciences, Clive R. Wood, Jan M. Kriegl, Nikhil Podduturi, Jan N. Jensen, Jan Stutzki, Zhihao Ding 2023-12-22 2023-12-22 [("doi","10.1101/2023.12.20.23300308")] genetics/heritable/rare genetics/sequencing

Advancements in long-read sequencing technology have accelerated the study of large structural variants (SVs). We created a curated, publicly available, multi-ancestry SV imputation panel by long-read sequencing 888 samples from the 1000 Genomes Project. This high-quality panel was used to impute SVs in ~500,000 UK Biobank participants.

We demonstrated the feasibility of conducting genome-wide SV association studies at biobank scale using 32 disease-relevant phenotypes related to respiratory, cardiometabolic and liver diseases, in addition to 1,463 protein levels.

This analysis identified thousands of genome-wide statistically-significant SV associations, including hundreds of conditionally independent signals, thereby enabling novel biological insights.

Focusing on genetic association studies of lung function as an example, we demonstrate the added value of SVs for prioritising causal genes at gene-rich loci compared to traditional GWAS using only short variants.

We envision that future post-GWAS gene-prioritization workflows will incorporate SV analyses using this SV imputation panel and framework.

--- https://www.medrxiv.org/content/10.1101/2023.04.28.23289210.full Randomized Trial of Ketamine Masked by Surgical Anesthesia in Depressed Patients Theresa R. Lii, Ashleigh E. Smith, Josephine R. Flohr, Robin L. Okada, Cynthia A. Nyongesa, Lisa J. Cianfichi, Laura M. Hack, Alan F. Schatzberg, Boris D. Heifets 2023-06-15 2023-11-06 [("doi","10.1101/2023.04.28.23289210")] psychedelic psychiatry/depression psychology/neuroscience/pain/anesthesia

BACKGROUND

Ketamine may have antidepressant properties, but its acute psychoactive effects complicate successful masking in placebo-controlled trials.

METHODS

In a triple-masked, randomized, placebo-controlled trial, 40 adult patients with major depressive disorder were randomized to a single infusion of ketamine (0.5 mg/kg) or placebo (saline) during anesthesia as usual for routine surgery. The primary outcome was depression severity measured by the Montgomery-Åsberg Depression Rating Scale (MADRS) at 1, 2, and 3 days post-infusion. The secondary outcome was the proportion of participants with clinical response (≥50% reduction in MADRS scores) at 1, 2, and 3 days post-infusion. After all follow-up visits, participants were asked to guess which intervention they received.

Results: Mean MADRS scores did not differ between groups at screening or pre-infusion baseline. The mixed-effects model showed no evidence of effect of group assignment on post-infusion MADRS scores at 1 to 3 days post-infusion (−5.82, 95% CI −13.3 to 1.64, p = 0.13). Clinical response rates were similar between groups (60% versus 50% on day 1) and comparable to previous studies of ketamine in depressed populations. Secondary and exploratory outcomes did not find statistical separation of ketamine from placebo. 36.8% of participants guessed their treatment assignment correctly; both groups allocated their guesses in similar proportions. One serious adverse event occurred in each group, unrelated to ketamine administration.

CONCLUSION

In adults with major depressive disorder, a single dose of intravenous ketamine delivered during surgical anesthesia had no greater effect than placebo in acutely reducing the severity of depressive symptoms. This trial successfully masked treatment allocation in moderate-to-severely depressed patients using surgical anesthesia. While it is impractical to use surgical anesthesia for most placebo-controlled trials, future studies of novel antidepressants with acute psychoactive effects should make efforts to fully mask treatment assignment in order to minimize subject-expectancy bias. (ClinicalTrials.gov number,NCT03861988)

--- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002442 A chemical signal in human female tears lowers aggression in males Shani Agron, Claire A. de March, Reut Weissgross, Eva Mishor, Lior Gorodisky, Tali Weiss, Edna Furman-Haran, Hiroaki Matsunami, Noam Sobel, Lucas Smith, Lucas Smith, Lucas Smith, Lucas Smith 2023-11-21 2023-11-21 [("doi","10.1371/journal.pbio.3002442")] crime psychology/smell/human

Rodent tears contain social chemosignals with diverse effects, including blocking male aggression. Human tears also contain a chemosignal that lowers male testosterone, but its behavioral importance was unclear.

Because reduced testosterone is associated with reduced aggression, we tested the hypothesis that human tears act like rodent tears to block male aggression. Using a standard behavioral paradigm, we found that:

sniffing emotional tears with no odor percept reduced human male aggression by 43.7%. To probe the peripheral brain substrates of this effect, we applied tears to 62 human olfactory receptors in vitro. We identified 4 receptors that responded in a dose-dependent manner to this stimulus. Finally, to probe the central brain substrates of this effect, we repeated the experiment concurrent with functional brain imaging. We found that sniffing tears increased functional connectivity between the neural substrates of olfaction and aggression, reducing overall levels of neural activity in the latter.

Taken together, our results imply that like in rodents, a human tear–bound chemosignal lowers male aggression, a mechanism that likely relies on the structural and functional overlap in the brain substrates of olfaction and aggression. We suggest that tears are a mammalian-wide mechanism that provides a chemical blanket protecting against aggression.

Rodent tears contain a chemical signal that reduces aggression in conspecifics. This study shows that human tears similarly contain a chemical signal that, although odorless, activates human olfactory receptors in vitro, alters neural activity in an olfaction-aggression brain network, and lowers male aggressive behavior.

--- https://weblog.jamisbuck.org/2011/2/7/maze-generation-algorithm-recap.html 2023-11-06 cs/algorithm design/visualization --- https://x.com/eshear/status/1738260276045320296 Emmett Shear 2023-11-06 ai/music --- https://philosophicaldisquisitions.blogspot.com/2023/12/anselms-ontological-argument-guide-for.html 2023-11-06 philosophy/ontology --- https://www.theatlantic.com/ideas/archive/2023/11/ozempic-wegovy-social-revolution-weight-loss/676002/ 2023-11-06 longevity/glp/psychology --- https://arxiv.org/abs/2312.09299 Weight subcloning: direct initialization of transformers using larger pretrained ones Mohammad Samragh, Mehrdad Farajtabar, Sachin Mehta, Raviteja Vemulapalli, Fartash Faghri, Devang Naik, Oncel Tuzel, Mohammad Rastegari 2023-12-14 2023-12-14 [("doi","10.48550/arXiv.2312.09299")] ai/nn/sparsity/pruning

Training large transformer models from scratch for a target task requires lots of data and is computationally demanding. The usual practice of transfer learning overcomes this challenge by initializing the model with weights of a pretrained model of the same size and specification to increase the convergence and training speed. However, what if no pretrained model of the required size is available?

In this paper, we introduce a simple yet effective technique to transfer the knowledge of a pretrained model to smaller variants. Our approach called weight subcloning expedites the training of scaled-down transformers by initializing their weights from larger pretrained models.

Weight subcloning involves an operation on the pretrained model to obtain the equivalent initialized scaled-down model. It consists of two key steps: first, we introduce neuron importance ranking to decrease the embedding dimension per layer in the pretrained model. Then, we remove blocks from the transformer model to match the number of layers in the scaled-down network. The result is a network ready to undergo training, which gains improvements in training speed compared to random initialization.

For instance, we achieve 4× faster training for vision transformers in image classification and language models designed for next token prediction.

--- https://www.atlasobscura.com/articles/winter-wagashi-sweets-of-japan 2023-11-06 japan --- https://arxiv.org/abs/2312.09056 ReCoRe: Regularized Contrastive Representation Learning of World Model Rudra P. K. Poudel, Harit Pandya, Stephan Liwicki, Roberto Cipolla 2023-12-14 2023-12-14 [("doi","10.48550/arXiv.2312.09056")] reinforcement-learning/model reinforcement-learning/robot

While recent model-free Reinforcement Learning (RL) methods have demonstrated human-level effectiveness in gaming environments, their success in everyday tasks like visual navigation has been limited, particularly under appearance variations. This limitation arises from (1) poor sample efficiency and (2) over-fitting to training scenarios.

To address these challenges, we present a world model that learns invariant features using (1) contrastive unsupervised learning and (2) an intervention-invariant regularizer. Learning an explicit representation of the world dynamics i.e. a world model, improves sample efficiency while contrastive learning implicitly enforces learning of invariant features, which improves generalization.

However, the naive integration of contrastive loss to world models fails due to a lack of supervisory signals to the visual encoder, as world-model-based RL methods independently optimize representation learning and agent policy. To overcome this issue, we propose an intervention-invariant regularizer in the form of an auxiliary task such as depth prediction, image denoising, etc., that explicitly enforces invariance to style-interventions.

Our method outperforms current state-of-the-art model-based and model-free RL methods and on out-of-distribution point navigation task evaluated on the iGibson benchmark. We further demonstrate that our approach, with only visual observations, outperforms recent language-guided foundation models for point navigation, which is essential for deployment on robots with limited computation capabilities.

Finally, we demonstrate that our proposed model excels at the sim-to-real transfer of its perception module on Gibson benchmark.

--- https://arxiv.org/abs/2310.12808 Model Merging by Uncertainty-Based Gradient Matching Nico Daheim, Thomas Möllenhoff, Edoardo Maria Ponti, Iryna Gurevych, Mohammad Emtiyaz Khan 2023-10-19 2023-11-06 [("doi","10.48550/arXiv.2310.12808")] ai/nn/transformer statistics/bayes

Models trained on different datasets can be merged by a weighted-averaging of their parameters, but why does it work and when can it fail?

Here, we connect the inaccuracy of weighted-averaging to mismatches in the gradients and propose a new uncertainty-based scheme to improve the performance by reducing the mismatch. The connection also reveals implicit assumptions in other schemes such as averaging, task arithmetic, and Fisher-weighted averaging.

Our new method gives consistent improvements for large language models and vision transformers, both in terms of performance and robustness to hyperparameters.

--- https://arxiv.org/abs/2312.13401 Time Vectors: Time is Encoded in the Weights of Finetuned Language Models Kai Nylund, Suchin Gururangan, Noah Smith 2023-12-20 2023-12-20 [("doi","10.48550/arXiv.2312.13401")] ai/nn/transformer/t5

We present time vectors, a simple tool to customize language models to new time periods.

Time vectors are created by finetuning a language model on data from a single time (eg. a year or month), and then subtracting the weights of the original pretrained model. This vector specifies a direction in weight space that, as our experiments show, improves performance on text from that time period. Time vectors specialized to adjacent time periods appear to be positioned closer together in a manifold.

Using this structure, we interpolate between time vectors to induce new models that perform better on intervening and future time periods, without any additional training.

We demonstrate the consistency of our findings across different tasks, domains, model sizes, and time scales. Our results suggest that time is encoded in the weight space of finetuned models.

--- https://arxiv.org/abs/2210.08277 Deep Differentiable Logic Gate Networks Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen 2022-10-15 2023-11-07 [("doi","10.48550/arXiv.2210.08277")] ai/nn/fully-connected ai/nn/sparsity cs/hardware philosophy/logic

[code; music version] Recently, research has increasingly focused on developing efficient neural network architectures. In this work, we explore logic gate networks for machine learning tasks by learning combinations of logic gates. These networks comprise logic gates such as “AND” and “XOR”, which allow for very fast execution. The difficulty in learning logic gate networks is that they are conventionally non-differentiable and therefore do not allow training with gradient descent.

Thus, to allow for effective training, we propose differentiable logic gate networks, an architecture that combines real-valued logics and a continuously parameterized relaxation of the network.

The resulting discretized logic gate networks achieve fast inference speeds, eg. beyond a million images of MNIST per second on a single CPU core.

…Logic gate networks allow for very fast classification, with speeds beyond a million images per second on a single CPU core (for MNIST at > 97.5% accuracy). The computational cost of a layer with n neurons is Θ(n) with very small constants (as only logic gates of Booleans are required), while, in comparison, a fully-connected layer (with m input neurons) requires Θ(n · m) computations with substantially larger constants (as it requires floating-point arithmetic). While the training can be more expensive than for regular neural networks (however, just by a constant and asymptotically less expensive), to our knowledge, the proposed method is the fastest available architecture at inference time.

Overall, our method accelerates inference speed (in comparison to fully-connected ReLU neural networks) by around two orders of magnitude. In the experiments, we scale the training of logic gate networks up to 5 million parameters, which can be considered relatively small in comparison to other architectures. In comparison to the fastest neural networks at 98.4% on MNIST, our method is more than 12× faster than the best binary neural networks and 2–3 orders of magnitude faster than the theoretical speed of sparse neural networks.

[This sounds like it would be extremely expensive to train large-scale networks on because you’re doing a many-way choice for each and every parameter, and each parameter is an extremely weak one (and I’m not sure about the asymptotic claim there) which doesn’t benefit from the inductive bias of any architecture at all, not even the minimal MLP arch. But it might be an ideal way to distill & sparsify a pretrained neural network down into something that can be turned into an absurdly fast, small, energy-efficient ASIC: convert it layer by layer, and then finetune it end-to-end, and then shrink it by pruning gates.]

--- https://openreview.net/forum?id=S1XFzYyPMw Learning and Memorization Satrajit Chatterjee 2018-02-12 2023-11-07 ai/nn/fully-connected ai/scaling ai/tabular

It is possible to generalize a fair bit by memorizing alone; and this thought experiment leads to an interesting toy model.

In the machine learning research community, it is generally believed that there is a tension between memorization and generalization. In this work we:

examine to what extent this tension exists, by exploring if it is possible to generalize through memorization alone. Although direct memorization with a lookup table obviously does not generalize, we find that introducing depth in the form of a network of support-limited lookup tables:

leads to generalization that is above chance and closer to those obtained by standard learning algorithms on several tasks derived from MNIST and CIFAR-10.

Furthermore, we demonstrate through a series of empirical results that our approach allows for a smooth tradeoff between memorization and generalization and exhibits some of the most salient characteristics of neural networks: depth improves performance; random data can be memorized and yet there is generalization on real data; and memorizing random data is harder in a certain sense than memorizing real data.

The extreme simplicity of the algorithm and potential connections with stability provide important insights into the impact of depth on learning algorithms, and point to several interesting directions for future research.

[Keywords: deep learning, network architecture, memorization, generalization error]

--- https://arxiv.org/abs/2307.16424 MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning Baoquan Zhang, Demin Yu 2023-07-31 2023-11-07 [("doi","10.48550/arXiv.2307.16424")] ai/nn/diffusion reinforcement-learning/meta-learning

[cf. g.pt] Equipping a deep model the ability of few-shot learning, ie. learning quickly from only a few examples, is a core challenge for artificial intelligence. Gradient-based meta-learning approaches effectively address the challenge by learning how to learn novel tasks. Its key idea is learning a deep model in a bi-level optimization manner, where the outer-loop process learns a shared gradient descent algorithm (ie. its hyperparameters), while the inner-loop process leverage it to optimize a task-specific model by using only a few labeled data.

Although these existing methods have shown superior performance, the outer-loop process requires calculating second-order derivatives along the inner optimization path, which imposes considerable memory burdens and the risk of vanishing gradients. Drawing inspiration from recent progress of diffusion models, we find that the inner-loop gradient descent process can be actually viewed as a reverse process (ie. denoising) of diffusion where the target of denoising is model weights but the origin data.

Based on this fact, in this paper, we propose to model the gradient descent optimizer as a diffusion model and then present a novel task-conditional diffusion-based meta-learning, called MetaDiff, that effectively models the optimization process of model weights from Gaussian noises to target weights in a denoising manner. Thanks to the training efficiency of diffusion models, our MetaDiff do not need to differentiate through the inner-loop path such that the memory burdens and the risk of vanishing gradients can be effectively alleviated.

Experiment results show that our MetaDiff outperforms the state-of-the-art gradient-based meta-learning family in few-shot learning tasks.

--- https://x.com/ctjlewis/status/1738339448046211122 ctjlewis 2023-11-07 ai/nn/diffusion/midjourney --- https://www.sciencedirect.com/science/article/pii/S0047637421001561 2023-11-07 longevity/fasting --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235038/ The effects of taurine supplementation on diabetes mellitus in humans: A systematic review and meta-analysis Xiaomei Tao, Zhanzhi Zhang, Zhenpeng Yang, Benqiang Rao 2022 2023-11-07 [("doi","10.1016/j.fochms.2022.100106")] nootropic

Objective: The ameliorative effect of taurine on diabetes has received extensive attention in recent years. Despite promising data from animal studies, the efficacy of taurine supplementation in human studies has been inconsistent. We thus did a meta-analysis of randomized controlled trials to assess the effect of taurine supplement on glycemic indices, serum lipids, blood pressure, body composition in patients with diabetes.

Methods: We systematically searched PubMed, Embase, Cochrane, Web of Science, FDA.gov, and ClinicalTrials.gov for randomized controlled trials (published from inception to January 15, 2022; no language restrictions) about the effect of taurine supplement on diabetes. Values of Standardized Mean Differences (SMD) were determined for continuous outcomes.

Results: Of 2206 identified studies, 5 randomized controlled trials were eligible and were included in our analysis (n = 209 participants). Compared with the control group, taurine could significantly reduce HbA1c (SMD −0.41[95% CI: −0.74, −0.09], p = 0.01), Fasting Blood Sugar (SMD—1.28[95% CI: −2.42, −0.14], p = 0.03) and HOMA-IR (SMD—0.64[95% CI: −1.22, −0.06], p = 0.03). In addition, taurine also reduced Insulin (SMD −0.48 [95% CI: −0.99, 0.03], p = 0.06) and TG (SMD −0.26 [95% CI: −0.55, 0.02], p = 0.07), but did not reach statistical-significance.

Conclusions: Taurine supplementation is beneficial in reducing glycemic indices, such as HbA1c, Fasting Blood Sugar, HOMA-IR in diabetic patients, but has no statistically-significant effect on serum lipids, blood pressure and body composition in diabetic patients. Taurine emerges as a new option for the management of patients with diabetes. Further studies are needed to understand the potential effect of taurine in diabetic patients.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3739000/ Peculiarities of one-carbon metabolism in the strict carnivorous cat and the role in feline hepatic lipidosis Adronie Verbrugghe, Marica Bakovic 2013 2023-11-07 [("doi","10.3390/nu5072811")] cat/biology/taurine

Research in various species has indicated that diets deficient in labile methyl groups (methionine, choline, betaine, folate) produce fatty liver and links to steatosis and metabolic syndrome, but also provides evidence of the importance of labile methyl group balance to maintain normal liver function.

Cats, being obligate carnivores, rely on nutrients in animal tissues and have, due to evolutionary pressure, developed several physiological and metabolic adaptations, including a number of peculiarities in protein and fat metabolism. This has led to specific and unique nutritional requirements. Adult cats require more dietary protein than omnivorous species, maintain a consistently high rate of protein oxidation and gluconeogenesis and are unable to adapt to reduced protein intake.

Furthermore, cats have a higher requirement for essential amino acids and essential fatty acids. Hastened use coupled with an inability to conserve certain amino acids, including methionine, cysteine, taurine, and arginine, necessitates a higher dietary intake for cats compared to most other species.

Cats also seemingly require higher amounts of several B-vitamins compared to other species and are predisposed to depletion during prolonged inappetance. This carnivorous uniqueness makes cats more susceptible to hepatic lipidosis.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5933890/ Effects and Mechanisms of Taurine as a Therapeutic Agent Stephen Schaffer, Ha Won Kim 2018 2023-11-07 [("doi","10.4062/biomolther.2017.251")] nootropic

Taurine is an abundant, β-amino acid with diverse cytoprotective activity. In some species, taurine is an essential nutrient but in man it is considered a semi-essential nutrient, although cells lacking taurine show major pathology. These findings have spurred interest in the potential use of taurine as a therapeutic agent.

The discovery that taurine is an effective therapy against congestive heart failure led to the study of taurine as a therapeutic agent against other disease conditions. Today, taurine has been approved for the treatment of congestive heart failure in Japan and shows promise in the treatment of several other diseases.

The present review summarizes studies supporting a role of taurine in the treatment of diseases of muscle, the central nervous system, and the cardiovascular system. In addition, taurine is extremely effective in the treatment of the mitochondrial disease, mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS), and offers a new approach for the treatment of metabolic diseases, such as diabetes, and inflammatory diseases, such as arthritis.

The review also addresses the functions of taurine (regulation of anti-oxidation, energy metabolism, gene expression, ER stress, neuromodulation, quality control and calcium homeostasis) underlying these therapeutic actions. Taurine cytoprotective activity congestive heart failure muscle central nervous system cardiovascular system mitochondrial disease mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) diabetes inflammatory diseases arthritis anti-oxidation energy metabolism gene expression ER stress neuromodulation quality control calcium homeostasis.

--- https://www.wired.com/story/china-chatgpt-opportunists-grifters-hard-at-work/ 2023-11-07 ai/nn/transformer/gpt/3/nonfiction economics/automation --- https://www.fda.gov/news-events/press-announcements/fda-approves-first-gene-therapy-treatment-certain-patients-duchenne-muscular-dystrophy 2023-11-07 genetics/editing --- https://journals.physiology.org/doi/full/10.1152/jn.01281.2006 2023-11-07 psychology/spaced-repetition --- https://warontherocks.com/2023/06/jihadi-blowback-the-wagner-groups-hidden-downside/ 2023-11-08 crime/terrorism --- https://www.palladiummag.com/2023/06/22/the-answer-is-better-gangs/ 2023-11-08 crime/terrorism --- https://link.springer.com/article/10.1007/s11357-023-00818-1 2023-11-08 longevity nootropic/quantified-self --- https://www.lesswrong.com/posts/vfb5Seaaqzk5kzChb/when-is-correlation-transitive 2023-11-08 statistics/probability --- https://spectrum.ieee.org/iron-fuel 2023-11-08 technology --- http://www.eagle.ca/~gcowan/boron_blast.html 2023-11-08 technology --- https://www.newyorker.com/magazine/1949/09/17/the-sound-machine 2023-11-08 fiction/science-fiction philosophy/ethics philosophy/mind --- https://tedunderwood.com/2023/03/19/using-gpt-4-to-measure-the-passage-of-time-in-fiction/ 2023-11-08 ai/nn/transformer/gpt/4/fiction fiction/criticism --- https://www.lesswrong.com/postsiGuwZTHWb6DFY3sKB/fact-finding-attempting-to-reverse-engineer-factual-recall 2023-11-08 ai/nn/fully-connected ai/nn/tokenization ai/nn/transformer/attention --- https://academic.oup.com/pnasnexus/article/2/12/pgad384/7457938 2023-11-08 psychology/linguistics --- https://www.atlasobscura.com/articles/less-christianity-england 2023-11-09 philosophy/religion sociology --- https://udel.edu/~mm/xmas/ 2023-11-09 cs/algorithm cs/cryptography --- https://news.ycombinator.com/item?id=38744828 2023-11-09 psychology/cognitive-bias/illusion-of-depth --- https://backend.orbit.dtu.dk/ws/portalfiles/portal/346007119/Cambridge_1_Darkweb_ECrime_2023_2_.pdf 2023-11-09 darknet-market --- https://onlinelibrary.wiley.com/doi/10.1111/acel.14038 2023-11-09 longevity/fasting longevity/senolytic --- https://www.thelancet.com/journals/lanhl/article/PIIS2666-7568(23)00189-7/fulltext 2023-11-09 ai/tabular longevity --- /doc/economics/2007-jongman.pdf Gibbon was right: The decline and fall of the Roman economy Willem Jongman 2007-01-01 2023-11-09 [("doi","10.1163/ej.9789004160507.i-448.38")] economics history --- https://acoup.blog/2022/02/11/collections-rome-decline-and-fall-part-iii-things/ Bret Devereaux 2022-02-11 2023-11-09 economics history --- https://x.com/krishnanrohit/status/1738617384276263356 Rohit Krishnan 2023-11-09 ai/nn/transformer/gpt/4/nonfiction law --- https://onlinelibrary.wiley.com/doi/10.1111/eth.13423 2023-11-09 dog iq/animal psychology/vision --- https://www.reddit.com/r/dalle2/comments/18pci2h/%F0%9D%90%AD%F0%9D%90%A1%F0%9D%90%9E_%F0%9D%90%A5%F0%9D%90%A8%F0%9D%90%A7%F0%9D%90%A0%F0%9D%90%9E%F0%9D%90%AB_%F0%9D%90%A2_%F0%9D%90%AC%F0%9D%90%A2%F0%9D%90%AD_%F0%9D%90%AD%F0%9D%90%A1%F0%9D%90%9E_%F0%9D%90%A6%F0%9D%90%A8%F0%9D%90%AB%F0%9D%90%9E_%F0%9D%90%A2_%F0%9D%90%9D%F0%9D%90%9E%F0%9D%90%9C%F0%9D%90%9A%F0%9D%90%B2_%F0%9D%92%94%F0%9D%92%90_%F0%9D%92%8D%F0%9D%92%86%F0%9D%92%95%F0%9D%92%94_%F0%9D%91%B9%F0%9D%91%B0%F0%9D%91%BA%F0%9D%91%AC/ 2023-11-09 ai/nn/transformer/gpt/dall-e/3 --- https://cgdct.moe/blog/far/ 2023-11-10 design/typography --- https://arxiv.org/abs/2312.09979 LoRAMoE: Revolutionizing Mixture of Experts for Maintaining World Knowledge in Language Model Alignment Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Jun Zhao, Wei Shen, Yuhao Zhou, Zhiheng Xi, Xiao Wang, Xiaoran Fan, Shiliang Pu, Jiang Zhu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang 2023-12-15 2023-12-15 [("doi","10.48550/arXiv.2312.09979")] ai/scaling/mixture-of-experts

Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhance their capabilities in downstream tasks. When the models are required to align with a broader range of downstream tasks, or there is a desire to notably improve the performance on a specific task, a substantial increase in fine-tuning data often emerges as the solution.

However, we find that large-scale increases in instruction data can disrupt the world knowledge previously stored in the LLMs, ie. world knowledge forgetting. In this paper, we introduce LoRAMoE to address the above challenge. The LoRAMoE is a plugin version of Mixture of Experts (MoE). The plugin form ensures the integrity of world knowledge by freezing the backbone model during the training phase.

We then propose the use of localized balancing constraints to coordinate parts of experts for task usage, meanwhile enabling other experts to fully leverage the world knowledge stored in the models. Experimental results demonstrate that LoRAMoE can reasonably coordinate experts based on data type during inference, and even dramatically increasing instruction data does not result in knowledge forgetting. Moreover, LoRAMoE provides additional benefits for the performance of downstream tasks, indicating the potential of our approach for multi-task learning.

--- https://openaccess.thecvf.com/content/WACV2024/html/Chong_P2D_Plug_and_Play_Discriminator_for_Accelerating_GAN_Frameworks_WACV_2024_paper.html 2023-11-10 ai/nn/gan/stylegan --- https://themessenger.com/news/people-cant-access-their-ai-girlfriend-because-the-service-went-down-after-ceo-jailed-for-setting-his-apartment-on-fire 2023-11-10 psychiatry/bipolar/energy --- https://en.wikipedia.org/wiki/Chargeman_Ken!#Production_and_cult_status Chargeman Ken! § Production and cult status 2023-11-10 economics/copyright --- https://laurmaedje.github.io/posts/hypher/ 2023-11-10 cs/algorithm/information/compression design/typography/tex --- /doc/japan/history/2009-heale.pdf Anatomy of a Scare: Yellow Peril Politics in America, 1980–1993 M. J. Heale 2009-04-01 2023-11-10 [("doi","10.1017/S0021875809006033")] economics japan/history

This article maps the rise and dissemination of Yellow Peril fears in the United States between about 1980 and 1993 and seeks to explain them.

Anti-communism had been an animating force in Ronald Reagan’s career, but shortly after he left office an opinion poll revealed that Japan had replaced the Soviet Union as the greatest perceived threat to the US. While economic anxieties contributed to the resurgence of Yellow Peril sentiments, this article emphasizes the vital parts played by other phenomena, notably Reagan’s economic policies, partisan politics, a media war, and the ending of the Cold War.

The Yellow Peril scare was widely criticized, and by the early 1990s the controversy had invaded popular culture.

Ronald Reagan is frequently applauded for restoring American self-confidence after the “malaise” of the Carter years, but the apprehensions discussed here suggest that he enjoyed only limited success in this respect.

--- https://www.nature.com/articles/s41593-023-01382-9 2023-11-10 ai/nn/sparsity/knowledge-distillation psychology/neuroscience --- https://hackaday.com/2017/12/14/adsl-robustness-verified-by-running-over-wet-string/ 2023-11-10 cs/hardware --- https://arxiv.org/abs/2303.05511#adobe GigaGAN: Scaling up GANs for Text-to-Image Synthesis Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, Taesung Park 2023-03-09 2023-11-10 [("doi","10.48550/arXiv.2303.05511")] ai/nn/gan ai/scaling

[homepage; upscaler] The recent success of text-to-image synthesis has taken the world by storm and captured the general public’s imagination. From a technical standpoint, it also marked a drastic change in the favored architecture to design generative image models. GANs used to be the de facto choice, with techniques like StyleGAN. With DALL·E 2, auto-regressive and diffusion models became the new standard for large-scale generative models overnight.

This rapid shift raises a fundamental question: can we scale up GANs to benefit from large datasets like LAION? We find that naively increasing the capacity of the StyleGAN architecture quickly becomes unstable.

We introduce GigaGAN, a new GAN architecture that far exceeds this limit, demonstrating GANs as a viable option for text-to-image synthesis.

GigaGAN offers 3 major advantages. First, it is orders of magnitude faster at inference time, taking only 0.13 seconds to synthesize a 512px image. Second, it can synthesize high-resolution images, for example, 16-megapixel pixels in 3.66 seconds. Finally, GigaGAN supports various latent space editing applications such as latent interpolation, style mixing, and vector arithmetic operations.

--- https://www.fanfiction.net/s/10327510/1/A-Bluer-Shade-of-White A Bluer Shade of White 2023-11-10 fiction/science-fiction --- https://www.atlasobscura.com/articles/column-yuletide-fanfiction-exchange 2023-11-11 fiction --- https://en.wikipedia.org/wiki/Alan_Ritchson Alan Ritchson 2023-11-11 psychiatry/bipolar/energy --- https://arxiv.org/abs/2305.09836 Revisiting the Minimalist Approach to Offline Reinforcement Learning Denis Tarasov, Vladislav Kurenkov, Alexander Nikulin, Sergey Kolesnikov 2023-05-16 2023-11-11 [("doi","10.48550/arXiv.2305.09836")] reinforcement-learning/imitation-learning reinforcement-learning/offline

[Twitter] Recent years have witnessed advancements in offline reinforcement learning (RL), resulting in the development of numerous algorithms with varying degrees of complexity. While these algorithms have led to noteworthy improvements, many incorporate seemingly minor design choices that impact their effectiveness beyond core algorithmic advances. However, the effect of these design choices on established baselines remains understudied.

In this work, we aim to bridge this gap by conducting a retrospective analysis of recent works in offline RL and propose ReBRAC, a minimalistic algorithm that integrates such design elements built on top of the TD3+BC method.

We evaluate ReBRAC on 51 datasets with both proprioceptive and visual state spaces using D4RL and V-D4RL benchmarks, demonstrating its state-of-the-art performance among ensemble-free methods in both offline and offline-to-online settings.

To further illustrate the efficacy of these design choices, we perform a large-scale ablation study and hyperparameter sensitivity analysis on the scale of thousands of experiments.

--- https://arxiv.org/abs/2312.06662#google W.A.L.T: Photorealistic Video Generation with Diffusion Models Agrim Gupta, Lijun Yu, Kihyuk Sohn, Xiuye Gu, Meera Hahn, Li Fei-Fei, Irfan Essa, Lu Jiang, José Lezama 2023-12-11 2023-12-11 [("doi","10.48550/arXiv.2312.06662")] ai/video/generation

We present W.A.L.T, a transformer-based approach for photorealistic video generation via diffusion modeling. Our approach has two key design decisions. First, we use a causal encoder to jointly compress images and videos within a unified latent space, enabling training and generation across modalities.

Second, for memory and training efficiency, we use a window attention architecture tailored for joint spatial and spatiotemporal generative modeling. Taken together these design decisions enable us to achieve state-of-the-art performance on established video (UCF101 and Kinetics-600) and image generation benchmarks such as ImageNet without using classifier free guidance.

Finally, we also train a cascade of 3 models for the task of text-to-video generation consisting of a base latent video diffusion model, and two video super-resolution diffusion models to generate videos of 512×896 resolution at 8 FPS.

--- https://arxiv.org/abs/2312.13691 DreamTuner: Single Image is Enough for Subject-Driven Generation Miao Hua, Jiawei Liu, Fei Ding, Wei Liu, Jie Wu, Qian He 2023-12-21 2023-12-21 [("doi","10.48550/arXiv.2312.13691")] ai/anime/danbooru ai/nn/diffusion

Diffusion-based models have demonstrated impressive capabilities for text-to-image generation and are expected for personalized applications of subject-driven generation, which require the generation of customized concepts with one or a few reference images. However, existing methods based on fine-tuning fail to balance the trade-off between subject learning and the maintenance of the generation capabilities of pretrained models. Moreover, other methods that use additional image encoders tend to lose important details of the subject due to encoding compression.

To address these challenges, we propose DreamTuner, a novel method that injects reference information from coarse to fine to achieve subject-driven image generation more effectively. DreamTuner introduces a subject-encoder for coarse subject identity preservation, where the compressed general subject features are introduced through an attention layer before visual-text cross-attention. We then modify the self-attention layers within pretrained text-to-image models to self-subject-attention layers to refine the details of the target subject. The generated image queries detailed features from both the reference image and itself in self-subject-attention. It is worth emphasizing that self-subject-attention is an effective, elegant, and training-free method for maintaining the detailed features of customized subjects and can serve as a plug-and-play solution during inference.

Finally, with additional subject-driven fine-tuning, DreamTuner achieves remarkable performance in subject-driven image generation, which can be controlled by a text or other conditions such as pose.

For further details, please visit the project page at https://dreamtuner-diffusion.github.io/.

--- https://www.biorxiv.org/content/10.1101/2022.05.19.492715.full High dose dietary vitamin D allocates surplus calories to muscle and growth instead of fat via modulation of myostatin and leptin signaling Caela Long, Zahra Tara, Alex Casella, Julian Mark, Jeffrey D. Roizen 2022-05-20 2023-11-11 [("doi","10.1101/2022.05.19.492715")] exercise vitamin-d

Obesity is the leading proportional cause for diabetes, heart disease and cancer. Obesity occurs because the body stores surplus calories as fat. Fat cells secrete a hormone, leptin, that modulates energy balance at the brain. Changes in fat mass are mirrored by changes in serum leptin. Increases in leptin cause the brain to decrease appetite and increase energy expenditure. However, in obesity, leptin sensitivity is decreased which mutes leptin mediated changes in appetite and energy expenditure. We have limited understanding of what controls leptin production by fat or how sensitive the brain is to leptin.

Muscle produces a hormone, myostatin, that plays an analogous role to the role that leptin plays in fat. Absent myostatin leads to increased muscle mass and strength. We also do not know what controls myostatin production or sensitivity. Although fat mass and muscle mass are closely linked, the interplay between leptin and myostatin remains unexplored.

Vitamin D improves lean mass via what are thought to be primarily trophic effects at the muscle. Here we show that high dose dietary vitamin D preferentially allocates excess calories to muscle and growth instead of storage as fat by decreasing myostatin production and increasing leptin production and sensitivity. That is, high dose vitamin D improves organismal energy sensing.

Obesity, aging and other chronic inflammatory diseases are associated with decreased muscle function and mass. Our work provides a physiological framework for how high-dose vitamin D would be effective in these pathologies to increase allocation of calories to muscle instead of fat and reveals novel interplay between the myostatin and leptin signaling whereby myostatin conveys energy needs to modulate leptin effects on calorie allocation. Furthermore, our work reveals how physiological seasonal variation in vitamin D may be important in controlling season-specific metabolism and calorie allocation to fat in winter and muscle in summer.

--- https://www.science.org/doi/10.1126/sciadv.adj3003 2023-11-11 exercise --- /doc/politics/2023-allen.pdf The Economic Origins of Government Robert C. Allen, Mattia C. Bertazzini, Leander Heldring 2023-10-01 2023-11-11 [("doi","10.1257/aer.20201919")] economics politics

We test between cooperative and extractive theories of the origins of government. We use river shifts in southern Iraq as a natural experiment, in a new archaeological panel dataset. A shift away creates a local demand for a government to coordinate because private river irrigation needs to be replaced with public canals. It disincentivizes local extraction as land is no longer productive without irrigation.

Consistent with a cooperative theory of government, a river shift away led to state formation, canal construction, and the payment of tribute.

We argue that the first governments coordinated between extended households which implemented public good provision.

--- https://osf.io/preprints/osf/2tvyh 2023-11-11 genetics/cloning genetics/selection/natural --- https://www.reddit.com/r/dalle2/comments/18qyaqi/pbj_man_and_woman/ 2023-11-11 ai/nn/transformer/gpt/dall-e/3 --- https://www.freecodecamp.org/news/lossless-web-navigation-with-trails-9cd48c0abb56/ 2023-11-12 cs/css --- https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1000144 Trial Publication after Registration in ClinicalTrials.Gov: A Cross-Sectional Analysis Joseph S. Ross, Gregory K. Mulvey, Elizabeth M. Hines, Steven E. Nissen, Harlan M. Krumholz 2009-07-31 2023-11-12 [("doi","10.1371/journal.pmed.1000144")] statistics/bias/publication

Joseph Ross and colleagues examine publication rates of clinical trials and find low rates of publication even following registration in Clinicaltrials.gov.

Background: ClinicalTrials.gov is a publicly accessible, Internet-based registry of clinical trials managed by the US National Library of Medicine that has the potential to address selective trial publication. Our objectives were to examine completeness of registration within ClinicalTrials.gov and to determine the extent and correlates of selective publication.

Methods & Findings: We examined reporting of registration information among a cross-section of trials that had been registered at ClinicalTrials.gov after 1999-12-31 and updated as having been completed by 2007-06-08, excluding phase I trials. We then determined publication status among a random 10% subsample by searching MEDLINE using a systematic protocol, after excluding trials completed after December 31, 2005 to allow at least 2 y for publication following completion. Among the full sample of completed trials (n = 7,515), nearly 100% reported all data elements mandated by ClinicalTrials.gov, such as intervention and sponsorship. Optional data element reporting varied, with 53% reporting trial end date, 66% reporting primary outcome, and 87% reporting trial start date.

Among the 10% subsample, less than half (311⁄677, 46%) of trials were published, among which 96 (31%) provided a citation within ClinicalTrials.gov of a publication describing trial results. Trials primarily sponsored by industry (40%, 144⁄357) were less likely to be published when compared with nonindustry/nongovernment sponsored trials (56%, 110⁄198; p<0.001), but there was no statistically-significant difference when compared with government sponsored trials (47%, 57⁄122; p = 0.22).

Among trials that reported an end date, 75⁄123 (61%) completed prior to 2004, 50⁄96 (52%) completed during 2004, and 62⁄149 (42%) completed during 2005 were published (p = 0.006).

Conclusions: Reporting of optional data elements varied and publication rates among completed trials registered within ClinicalTrials.gov were low. Without greater attention to reporting of all data elements, the potential for ClinicalTrials.gov to address selective publication of clinical trials will be limited.

Editors’ Summary: Background: People assume that whenever they are ill, health care professionals will make sure they get the best available treatment. But how do clinicians know which treatment is most appropriate? In the past, clinicians used their own experience to make treatment decisions. Nowadays, they rely on evidence-based medicine—the systematic review and appraisal of the results of clinical trials, studies that investigate the efficacy and safety of medical interventions in people. However, evidence-based medicine can only be effective if all the results from clinical trials are published promptly in medical journals. Unfortunately, the results of trials in which a new drug did not perform better than existing drugs or in which it had unwanted side effects often remain unpublished or only published many years after the drug has been approved for clinical use by the US Food and Drug Administration (FDA) and other governmental bodies.

Why Was This Study Done?: The extent of this “selective” publication, which can impair evidence-based clinical practice, remains unclear but is thought to be substantial. In this study, the researchers investigate the problem of selective publication by systematically examining the extent of publication of the results of trials registered in ClinicalTrials.gov, a Web-based registry of US and international clinical trials. ClinicalTrials.gov was established in 2000 by the US National Library of Medicine in response to the 1997 FDA Modernization Act. This act required preregistration of all trials of new drugs to provide the public with information about trials in which they might be able to participate. Mandatory data elements for registration in ClinicalTrials.gov initially included the trial’s title, the condition studied in the trial, the trial design, and the intervention studied. In September 2007, the FDA Amendments Act expanded the mandatory requirements for registration in ClinicalTrials.gov by making it necessary, for example, to report the trial start date and to report primary and secondary outcomes (the effect of the intervention on predefined clinical measurements) in the registry within 2 years of trial completion.

What Did the Researchers Do and Find?: The researchers identified 7,515 trials that were registered within ClinicalTrials.gov after December 31, 1999 (excluding phase I, safety trials), and whose record indicated trial completion by June 8, 2007. Most of these trials reported all the mandatory data elements that were required by ClinicalTrials.gov before the FDA Amendments Act but reporting of optional data elements was less complete. For example, only two-thirds of the trials reported their primary outcome. Next, the researchers randomly selected 10% of the trials and, after excluding trials whose completion date was after December 31, 2005 (to allow at least two years for publication), determined the publication status of this subsample by systematically searching MEDLINE (an online database of articles published in selected medical and scientific journals). Fewer than half of the trials in the subsample had been published, and the citation for only a third of these publications had been entered into ClinicalTrials.gov. Only 40% of industry-sponsored trials had been published compared to 56% of nonindustry/nongovernment-sponsored trials, a difference that is unlikely to have occurred by chance. Finally, 61% of trials with a completion date before 2004 had been published, but only 42% of trials completed during 2005 had been published.

What Do These Findings Mean?: These findings indicate that, over the period studied, critical trial information was not included in the ClinicalTrials.gov registry. The FDA Amendments Act should remedy some of these shortcomings but only if the accuracy and completeness of the information in ClinicalTrials.gov is carefully monitored. These findings also reveal that registration in ClinicalTrials.gov does not guarantee that trial results will appear in a timely manner in the scientific literature. However, they do not address the reasons for selective publication (which may be, in part, because it is harder to publish negative results than positive results), and they are potentially limited by the methods used to discover whether trial results had been published. Nevertheless, these findings suggest that the FDA, trial sponsors, and the scientific community all need to make a firm commitment to minimize the selective publication of trial results to ensure that patients and clinicians have access to the information they need to make fully informed treatment decisions.

Additional Information:

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0043404 Publication Bias in Laboratory Animal Research: A Survey on Magnitude, Drivers, Consequences and Potential Solutions Gerben ter Riet, Daniel A. Korevaar, Marlies Leenaars, Peter J. Sterk, Cornelis J. F. Van Noorden, Lex M. Bouter, René Lutter, Ronald P. Oude Elferink, Lotty Hooft 2012-07-19 2023-11-12 [("doi","10.1371/journal.pone.0043404")] statistics/bias/animal statistics/bias/publication

Context: Publication bias jeopardizes evidence-based medicine, mainly through biased literature syntheses. Publication bias may also affect laboratory animal research, but evidence is scarce.

Objectives: To assess the opinion of laboratory animal researchers on the magnitude, drivers, consequences and potential solutions for publication bias. And to explore the impact of size of the animals used, seniority of the respondent, working in a for-profit organization and type of research (fundamental, pre-clinical, or both) on those opinions.

Design: Internet-based survey.

Setting: All animal laboratories in The Netherlands.

Participants: Laboratory animal researchers.

Main Outcome Measure(s): Median (interquartile ranges) strengths of beliefs on 5 and 10-point scales (1: totally unimportant to 5 or 10: extremely important).

Results: Overall, 454 researchers participated. They considered publication bias a problem in animal research (7 (5 to 8)) and thought that about 50% (32–70) of animal experiments are published. Employees (n = 21) of for-profit organizations estimated that 10% (5 to 50) are published. Lack of statistical-significance (4 (4 to 5)), technical problems (4 (3 to 4)), supervisors (4 (3 to 5)) and peer reviewers (4 (3 to 5)) were considered important reasons for non-publication (all on 5-point scales). Respondents thought that mandatory publication of study protocols and results, or the reasons why no results were obtained, may increase scientific progress but expected increased bureaucracy. These opinions did not depend on size of the animal used, seniority of the respondent or type of research.

Conclusions: Non-publication of “negative” results appears to be prevalent in laboratory animal research. If statistical-significance is indeed a main driver of publication, the collective literature on animal experimentation will be biased. This will impede the performance of valid literature syntheses. Effective, yet efficient systems should be explored to counteract selective reporting of laboratory animal research.

--- https://openreview.net/forum?id=psXVkKO9No#deepmind Self-AIXI: Self-Predictive Universal AI Elliot Catt, Jordi Grau-Moya, Marcus Hutter, Matthew Aitchison, Tim Genewein, Gregoire Deletang, Li Kevin Wenliang, Joel Veness 2023-11-02 2023-11-12 reinforcement-learning/meta-learning reinforcement-learning/model

Reinforcement Learning (RL) algorithms typically use learning and/or planning techniques to derive effective policies. The integration of both approaches has proven to be highly successful in addressing complex sequential decision-making challenges, as evidenced by algorithms such as AlphaZero and MuZero, which consolidate the planning process into a parametric search-policy. AIXI, the most potent theoretical universal agent, leverages planning through comprehensive search as its primary means to find an optimal policy.

Here we define an alternative universal agent, which we call Self-AIXI, that on the contrary to AIXI, maximally exploits learning to obtain good policies. It does so by self-predicting its own stream of action data, which is generated, similarly to other TD(0) agents, by taking an action maximization step over the current on-policy (universal mixture-policy) Q-value estimates.

We prove that Self-AIXI converges to AIXI, and inherits a series of properties like maximal Legg-Hutter intelligence and the self-optimizing property.

--- https://www.thelancet.com/journals/lanmic/article/PIIS2666-5247(23)00319-1/fulltext 2023-11-12 existential-risk genetics/microbiome --- https://andymatuschak.org/stillness/ 2023-11-12 psychology/energy --- https://english.stackexchange.com/questions/9780/did-english-ever-have-a-formal-version-of-you 2023-11-12 psychology/linguistics --- https://www.openmymind.net/Your-Website-Search-Hurts-My-Feelings/ 2023-11-12 design --- https://en.wikipedia.org/wiki/Goodstein%27s_theorem Goodstein’s theorem 2023-11-12 cs/computable --- https://merrillmarkoe.substack.com/p/dylans-christmas-lights-a-scholarly 2023-11-12 math/humor philosophy/epistemology --- https://blog.datawrapper.de/roman-roads-tabula-peutingeriana/ 2023-11-12 design/visualization history --- https://www.sciencedirect.com/science/article/pii/S0014292123002337 2023-11-13 crime --- https://statmodeling.stat.columbia.edu/2023/12/19/explainable-ai-works-but-only-when-we-dont-need-it/ 2023-11-13 reinforcement-learning/safe --- https://arxiv.org/abs/2311.16465 TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering Jingye Chen, Yupan Huang, Tengchao Lv, Lei Cui, Qifeng Chen, Furu Wei 2023-11-28 2023-11-28 [("doi","10.48550/arXiv.2311.16465")] ai/nn/diffusion ai/nn/tokenization design/typography

The diffusion model has been proven a powerful generative model in recent years, yet remains a challenge in generating visual text. Several methods alleviated this issue by incorporating explicit text position and content as guidance on where and what text to render. However, these methods still suffer from several drawbacks, such as limited flexibility and automation, constrained capability of layout prediction, and restricted style diversity.

In this paper, we present TextDiffuser-2, aiming to unleash the power of language models for text rendering. Firstly, we fine-tune a large language model for layout planning. The large language model is capable of automatically generating keywords for text rendering and also supports layout modification through chatting.

Secondly, we use the language model within the diffusion model to encode the position and texts at the line level. Unlike previous methods that employed tight character-level guidance, this approach generates more diverse text images.

We conduct extensive experiments and incorporate user studies involving human participants as well as GPT-4-V, validating TextDiffuser-2’s capacity to achieve a more rational text layout and generation with enhanced diversity.

The code and model will be available at https://github.com/microsoft/unilm/tree/master/textdiffuser-2.

…Should text be tokenized at the character or subword level? We also explore Byte Pair Encoding (BPE) to tokenize keywords into the subword level. As shown in Table 2, we observe that using subword-level tokenization substantially underperforms character-level representation, i.e. it is lower by 42.1% on the accuracy metric. When using subword-level tokenization, the model becomes insensitive to the spelling of each token, which poses large challenges to the text rendering process.

--- https://arxiv.org/abs/2110.11526#deepmind Wide Neural Networks Forget Less Catastrophically Seyed Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Huiyi Hu, Razvan Pascanu, Dilan Gorur, Mehrdad Farajtabar 2021-10-21 2023-11-13 [("doi","10.48550/arXiv.2110.11526")] ai/nn/fully-connected ai/scaling reinforcement-learning/meta-learning/continual-learning

A primary focus area in continual learning research is alleviating the “catastrophic forgetting” problem in neural networks by designing new algorithms that are more robust to the distribution shifts. While the recent progress in continual learning literature is encouraging, our understanding of what properties of neural networks contribute to catastrophic forgetting is still limited.

To address this, instead of focusing on continual learning algorithms, in this work, we focus on the model itself and study the impact of “width” of the neural network architecture on catastrophic forgetting, and show that width has a surprisingly effect on forgetting. To explain this effect, we study the learning dynamics of the network from various perspectives such as gradient orthogonality, sparsity, and lazy training regime.

We provide potential explanations that are consistent with the empirical results across different architectures and continual learning benchmarks.

…Figure 6a shows the norm of the gradients of layer 1 for different MLP depths. It can be seen from the figure that the gradient norm on the earlier layers increases with the depth. For example, when the network is trained for task 5, the gradient norm on layer 1 for an 8-layer network is almost 3× as that of the 2-layer network. In contrast, as depicted in Figure 6b, increasing the width has a minimal or even decreasing effect on gradient norm. We note here that the deep learning community has developed numerous strategies to avoid exploding gradients, and that extensively studying those is not the purpose here. We use the exploding gradient analysis to understand the negative effect of depths in our experiments.

In conclusion, we have observed that wider networks have sparser gradients with more orthogonal gradients across tasks. In addition, the training dynamics of wide networks become more similar to the lazy training regime. Finally, the gradient norm in wide and shallow models does not increase as fast as in deeper and thinner models.

See Also:

--- https://www.sciencedirect.com/science/article/pii/S0887617703000921 2023-11-13 iq --- https://arxiv.org/abs/2302.11521#microsoft How Does In-Context Learning Help Prompt Tuning? Simeng Sun, Yang Liu, Dan Iter, Chenguang Zhu, Mohit Iyyer 2023-02-22 2023-11-13 [("doi","10.48550/arXiv.2302.11521")] ai/nn/retrieval ai/nn/transformer/gpt/2 ai/nn/transformer/gpt/instruction-tuning

Fine-tuning large language models is becoming ever more impractical due to their rapidly-growing scale. This motivates the use of parameter-efficient adaptation methods such as prompt tuning (PT), which adds a small number of tunable embeddings to an otherwise frozen model, and in-context learning (ICL), in which demonstrations of the task are provided to the model in natural language without any additional training. Recently, Singhal et al 2022 propose “instruction prompt tuning” (IPT), which combines PT with ICL by concatenating a natural language demonstration with learned prompt embeddings. While all of these methods have proven effective on different tasks, how they interact with each other remains unexplored.

In this paper, we empirically study when and how in-context examples improve prompt tuning by measuring the effectiveness of ICL, PT, and IPT on 5 text generation tasks with multiple base language models. We observe that (1) IPT does not always outperform PT, and in fact requires the in-context demonstration to be semantically similar to the test input to yield improvements; (2) PT is unstable and exhibits high variance, but combining PT and ICL (into IPT) consistently reduces variance across all 5 tasks; and (3) prompts learned for a specific source task via PT exhibit positive transfer when paired with in-context examples of a different target task. Our results offer actionable insights on choosing a suitable parameter-efficient adaptation method for a given task.

--- https://www.reddit.com/r/StableDiffusion/comments/18r7mqf/top_online_nsfw_creators_updated/ 2023-11-13 ai/nn/diffusion --- https://arxiv.org/abs/2312.14385#facebook Generative AI Beyond LLMs: System Implications of Multi-Modal Generation Alicia Golden, Samuel Hsia, Fei Sun, Bilge Acun, Basil Hosmer, Yejin Lee, Zachary DeVito, Jeff Johnson, Gu-Yeon Wei, David Brooks, Carole-Jean Wu 2023-12-22 2023-12-22 [("doi","10.48550/arXiv.2312.14385")] ai/nn/diffusion ai/nn/transformer/gpt/dall-e/3 ai/scaling/economics ai/scaling/hardware

As the development of large-scale Generative AI models evolve beyond text (1D) generation to include image (2D) and video (3D) generation, processing spatial and temporal information presents unique challenges to quality, performance, and efficiency. We present the first work towards understanding this new system design space for multi-modal text-to-image (TTI) and text-to-video (TTV) generation models.

Current model architecture designs are bifurcated into 2 categories: Diffusion- and Transformer-based models. Our systematic performance characterization on a suite of 8 representative TTI/TTV models shows that after state-of-the-art optimization techniques such as Flash Attention are applied, Convolution accounts for up to 44% of execution time for Diffusion-based TTI models, while Linear layers consume up to 49% of execution time for Transformer-based models.

We additionally observe that Diffusion-based TTI models resemble the Prefill stage of Large Language Model (LLM) inference, and benefit from 1.1-2.5x greater speedup from Flash Attention than Transformer-based TTI models that resemble the Decode phase. Since optimizations designed for LLMs do not map directly onto TTI/TTV models, we must conduct a thorough characterization of these workloads to gain insights for new optimization opportunities.

In doing so, we define sequence length in the context of TTI/TTV models and observe sequence length can vary up to 4× in Diffusion model inference. We additionally observe temporal aspects of TTV workloads pose unique system bottlenecks, with Temporal Attention accounting for over 60% of total Attention time.

Overall, our in-depth system performance characterization is a critical first step towards designing efficient and deployable systems for emerging TTI/TTV workloads.

--- https://arxiv.org/abs/2306.03009 Using Sequences of Life-events to Predict Human Lives Germans Savcisens, Tina Eliassi-Rad, Lars Kai Hansen, Laust Mortensen, Lau Lilleholt, Anna Rogers, Ingo Zettler, Sune Lehmann 2023-06-05 2023-11-13 [("doi","10.1038/s43588-023-00573-5")] ai/nn/rnn ai/nn/transformer economics longevity psychology/personality

[life2vec code] Over the past decade, machine learning has revolutionized computers’ ability to analyze text through flexible computational models. Due to their structural similarity to written language, transformer-based architectures have also shown promise as tools to make sense of a range of multi-variate sequences from protein-structures, music, electronic health records to weather-forecasts.

We can also represent human lives in a way that shares this structural similarity to language. From one perspective, lives are simply sequences of events: People are born, visit the pediatrician, start school, move to a new location, get married, and so on. Here, we exploit this similarity to adapt innovations from natural language processing to examine the evolution and predictability of human lives based on detailed event sequences. We do this by drawing on arguably the most comprehensive registry data in existence, available for an entire nation of more than 6 million individuals across decades. Our data include information about life-events related to health, education, occupation, income, address, and working hours, recorded with day-to-day resolution.

We create embeddings of life-events in a single vector space showing that this embedding space is robust and highly structured. Our models allow us to predict diverse outcomes ranging from early mortality to personality nuances, outperforming state-of-the-art models by a wide margin. Using methods for interpreting deep learning models, we probe the algorithm to understand the factors that enable our predictions.

Our framework allows researchers to identify new potential mechanisms that impact life outcomes and associated possibilities for personalized interventions.

--- https://arxiv.org/abs/2312.14232 Parrot Captions Teach CLIP to Spot Text Yiqi Lin, Conghui He, Alex Jinpeng Wang, Bin Wang, Weijia Li, Mike Zheng Shou 2023-12-21 2023-12-21 [("doi","10.48550/arXiv.2312.14232")] ai/nn/transformer/clip

Despite CLIP being the foundation model in numerous vision-language applications, the CLIP suffers from a severe text spotting bias. Such bias causes CLIP models to ‘Parrot’ the visual text embedded within images while disregarding the authentic visual semantics.

We uncover that in the most popular image-text dataset LAION-2B, the captions also densely parrot (spell) the text embedded in images. Our analysis shows that around 50% of images are embedded with visual text content, and 90% of their captions more or less parrot the visual text.

Based on such observation, we thoroughly inspect the different released versions of CLIP models and verify that the visual text is the dominant factor in measuring the LAION-style image-text similarity for these models.

To examine whether these parrot captions shape the text spotting bias, we train a series of CLIP models with LAION subsets curated by different parrot-caption-oriented criteria. We show that training with parrot captions easily shapes such bias but harms the expected visual-language representation learning in CLIP models.

This suggests that it is urgent to revisit either the design of CLIP-like models or the existing image-text dataset curation pipeline built on CLIP score filtering.

--- https://www.sciencedirect.com/science/article/pii/S0014292123002404 2023-11-13 sociology --- https://www.biorxiv.org/content/10.1101/2023.12.22.573162.full Pervasive selective sweeps across human gut microbiomes Nandita Garud, Richard Wolff 2023-12-23 2023-12-23 [("doi","10.1101/2023.12.22.573162")] genetics/microbiome genetics/selection/natural

The human gut microbiome is composed of a highly diverse consortia of species which are continually evolving within and across hosts. The ability to identify adaptations common to many host gut microbiomes would not only reveal shared selection pressures across hosts, but also key drivers of functional differentiation of the microbiome that may affect community structure and host traits. However, to date there has not been a systematic scan for adaptations that have spread across host microbiomes.

Here, we develop a novel selection scan statistic, named the integrated linkage disequilibrium score (iLDS), that can detect the spread of adaptive haplotypes across host microbiomes via migration and horizontal gene transfer. Specifically, iLDS leverages signals of hitchhiking of deleterious variants with the beneficial variant, a common feature of adaptive evolution. We find that iLDS is capable of detecting simulated and known cases of selection, and moreover is robust to potential confounders that can also elevate LD.

Application of the statistic to ~20 common commensal gut species from a large cohort of healthy, Western adults reveals pervasive spread of selected alleles across human microbiomes mediated by horizontal gene transfer. Among the candidate selective sweeps recovered by iLDS is an enrichment for genes involved in the metabolism of maltodextrin, a synthetic starch that has recently become a widespread component of Western diets. In summary, we demonstrate that selective sweeps across host microbiomes are a common feature of the evolution of the human gut microbiome.

--- https://arxiv.org/abs/2312.14302 Exploiting Novel GPT-4 APIs Kellin Pelrine, Mohammad Taufeeque, Michał Zając, Euan McLean, Adam Gleave 2023-12-21 2023-12-21 [("doi","10.48550/arXiv.2312.14302")] ai/nn/transformer/gpt/4 cs/security reinforcement-learning/safe

Language model attacks typically assume one of two extreme threat models: full white-box access to model weights, or black-box access limited to a text generation API. However, real-world APIs are often more flexible than just text generation: these APIs expose “gray-box” access leading to new threat vectors.

To explore this, we red-team 3 new functionalities exposed in the GPT-4 APIs: fine-tuning, function calling and knowledge retrieval. We find that fine-tuning a model on as few as 15 harmful examples or 100 benign examples can remove core safeguards from GPT-4, enabling a range of harmful outputs.

Furthermore, we find that GPT-4 Assistants readily divulge the function call schema and can be made to execute arbitrary function calls. Finally, we find that knowledge retrieval can be hijacked by injecting instructions into retrieval documents.

These vulnerabilities highlight that any additions to the functionality exposed by an API can create new vulnerabilities.

--- https://arxiv.org/abs/2312.05328#deepmind Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding Talfan Evans, Shreya Pathak, Hamza Merzic, Jonathan Schwarz, Ryutaro Tanno, Olivier J. Henaff 2023-12-08 2023-12-08 [("doi","10.48550/arXiv.2312.05328")] ai/nn/transformer/clip reinforcement-learning/exploration/active-learning/data-pruning

[commentary, Twitter] We propose a method for accelerating large-scale pre-training with online data selection policies [cf. RHO-LOSS]. For the first time, we demonstrate that model-based data selection can reduce the total computation needed to reach the performance of models trained with uniform sampling. The key insight which enables this “compute-positive” regime is that small models provide good proxies for the loss of much larger models, such that computation spent on scoring data can be drastically scaled down but still accelerate training of the learner.

These data selection policies also strongly generalize across datasets and tasks, opening an avenue for further amortizing the overhead of data scoring by re-using off-the-shelf models and training sequences.

Our methods, ClassAct & ActiveCLIP, require 46% and 51% fewer training updates and up to 25% less total computation when training visual classifiers on JFT and multimodal models on ALIGN, respectively.

Finally, our paradigm seamlessly applies to the curation of large-scale image-text datasets, yielding a new state-of-the-art in several multimodal transfer tasks and pre-training regimes.

--- https://aclanthology.org/D18-1092/ 2023-11-14 ai/nn/tokenization ai/nn/transformer --- https://arxiv.org/abs/2310.20144#apple EELBERT: Tiny Models through Dynamic Embeddings Gabrielle Cohn, Rishika Agarwal, Deepanshu Gupta, Siddharth Patwardhan 2023-10-31 2023-11-14 [("doi","10.48550/arXiv.2310.20144")] ai/nn/tokenization ai/nn/transformer

We introduce EELBERT, an approach for compression of transformer-based models (eg. BERT), with minimal impact on the accuracy of downstream tasks. This is achieved by replacing the input embedding layer of the model with dynamic, ie. on-the-fly, embedding computations. Since the input embedding layer accounts for a fraction of the model size, especially for the smaller BERT variants, replacing this layer with an embedding computation function helps us reduce the model size.

Empirical evaluation on the GLUE benchmark shows that our BERT variants (EELBERT) suffer minimal regression compared to the traditional BERT models. Through this approach, we are able to develop our smallest model UNO-EELBERT, which achieves a GLUE score within 4% of fully trained BERT-tiny, while being 15× smaller (1.2 MB) in size.

--- https://arxiv.org/abs/2312.06695 Evolving Reservoirs for Meta Reinforcement Learning Corentin Léger, Gautier Hamon, Eleni Nisioti, Xavier Hinaut, Clément Moulin-Frier 2023-12-09 2023-12-09 [("doi","10.48550/arXiv.2312.06695")] ai/nn/rnn reinforcement-learning/meta-learning

Animals often demonstrate a remarkable ability to adapt to their environments during their lifetime. They do so partly due to the evolution of morphological and neural structures. These structures capture features of environments shared between generations to bias and speed up lifetime learning.

In this work, we propose a computational model for studying a mechanism that can enable such a process. We adopt a computational framework based on meta reinforcement learning as a model of the interplay between evolution and development. At the evolutionary scale, we evolve reservoirs, a family of recurrent neural networks that differ from conventional networks in that one optimizes not the weight values but hyperparameters of the architecture: the later control macro-level properties, such as memory and dynamics.

At the developmental scale, we employ these evolved reservoirs to facilitate the learning of a behavioral policy through Reinforcement Learning (RL). Within an RL agent, a reservoir encodes the environment state before providing it to an action policy. We evaluate our approach on several 2D and 3D simulated environments. Our results show that the evolution of reservoirs can improve the learning of diverse challenging tasks.

We study in particular 3 hypotheses: the use of an architecture combining reservoirs and reinforcement learning could enable (1) solving tasks with partial observability, (2) generating oscillatory dynamics that facilitate the learning of locomotion tasks, and (3) facilitating the generalization of learned behaviors to new tasks unknown during the evolution phase.

--- http://www.roylongbottom.org.uk/Cray%201%20Supercomputer%20Performance%20Comparisons%20With%20Home%20Computers%20Phones%20and%20Tablets.htm 2023-11-14 cs/hardware --- https://www.lesswrong.com/posts/PBRWb2Em5SNeWYwwB/humans-are-not-automatically-strategic 2023-11-14 psychology/willpower --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3086893/ Are birds smarter than mathematicians? Pigeons (Columba livia) perform optimally on a version of the Monty Hall Dilemma Walter T. Herbranson, Julia Schroeder 2010 2023-11-14 [("doi","10.1037/a0017703")] math/humor psychology/cognitive-bias statistics/bayes statistics/decision

The “Monty Hall Dilemma” (MHD) is a well known probability puzzle in which a player tries to guess which of 3 doors conceals a desirable prize. After an initial choice is made, one of the remaining doors is opened, revealing no prize. The player is then given the option of staying with their initial guess or switching to the other unopened door. Most people opt to stay with their initial guess, despite the fact that switching doubles the probability of winning.

A series of experiments investigated whether pigeons (Columba livia), like most humans, would fail to maximize their expected winnings in a version of the MHD. Birds completed multiple trials of a standard MHD, with the 3 response keys in an operant chamber serving as the 3 doors and access to mixed grain as the prize.

Across experiments, the probability of gaining reinforcement for switching and staying was manipulated, and birds adjusted their probability of switching and staying to approximate the optimal strategy. Replication of the procedure with human participants showed that humans failed to adopt optimal strategies, even with extensive training.

--- https://craigmod.com/ridgeline/176/ 2023-11-14 psychology/energy sociology --- https://ethanzuckerman.com/2023/12/22/how-big-is-youtube/ 2023-11-14 sociology/technology statistics/order/capture --- https://arxiv.org/abs/2312.14591#tencent Reasons to Reject? Aligning Language Models with Judgments Weiwen Xu, Deng Cai, Zhisong Zhang, Wai Lam, Shuming Shi 2023-12-22 2023-12-22 [("doi","10.48550/arXiv.2312.14591")] ai/nn/transformer/gpt reinforcement-learning/preference-learning

As humans, we consistently engage in interactions with our peers and receive feedback in the form of natural language. This language feedback allows us to reflect on our actions, maintain appropriate behavior, and rectify our errors. The question arises naturally: can we use language feedback to align large language models (LLMs)? In contrast to previous research that aligns LLMs with reward or preference data, we present the first systematic exploration of alignment through the lens of language feedback (ie. judgment).

We commence with an in-depth investigation of potential methods that can be adapted for aligning LLMs with judgments, revealing that these methods are unable to fully capitalize on the judgments. To facilitate more effective usage of judgments, we propose a novel framework, Contrastive Unlikelihood Training (CUT), that allows for fine-grained inappropriate content detection and correction based on judgments.

Our offline alignment results show that, with merely 1317 off-the-shelf judgment data, CUT (LLaMA2-13b) can beat the 175B davinci-003 and surpass the best baseline by 52.34 points on AlpacaEval. The online alignment results demonstrate that CUT can align LLMs (LLaMA2-chat-13b) in an iterative fashion using model-specific judgment data, with a steady performance improvement 81.09 → 91.36 points on AlpacaEval.

Our analysis further suggests that judgments exhibit greater potential than rewards for LLM alignment and warrant future research.

--- https://arxiv.org/abs/2312.14187#microsoft WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation Zhaojian Yu, Xin Zhang, Ning Shang, Yangyu Huang, Can Xu, Yishujie Zhao, Wenxiang Hu, Qiufeng Yin 2023-12-20 2023-12-20 [("doi","10.48550/arXiv.2312.14187")] ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/instruction-tuning

Recent work demonstrates that, after being fine-tuned on a high-quality instruction dataset, the resulting model can obtain impressive capabilities to address a wide range of tasks. However, existing methods for instruction data generation often produce duplicate data and are not controllable enough on data quality.

In this paper, we extend the generalization of instruction tuning by classifying the instruction data to 4 code-related tasks and propose an LLM-based Generator-Discriminator data process framework to generate diverse, high-quality instruction data from open source code.

Hence, we introduce CodeOcean, a dataset comprising 20,000 instruction instances across 4 universal code-related tasks, which is aimed at augmenting the effectiveness of instruction tuning and improving the generalization ability of fine-tuned model.

Subsequently, we present WaveCoder, a fine-tuned Code LLM with Widespread And Versatile Enhanced instruction tuning. This model is specifically designed for enhancing instruction tuning of Code Language Models (LLMs).

Our experiments demonstrate that Wavecoder models outperform other open-source models in terms of generalization ability across different code-related tasks at the same level of fine-tuning scale. Moreover, Wavecoder exhibits high efficiency in previous code generation tasks. This paper thus offers a contribution to the field of instruction data generation and fine-tuning models, providing new insights and tools for enhancing performance in code-related tasks.

--- https://arxiv.org/abs/1511.07428 Estimating the number of unseen species: A bird in the hand is worth log n in the bush Alon Orlitsky, Ananda Theertha Suresh, Yihong Wu 2015-11-23 2023-11-15 [("doi","10.48550/arXiv.1511.07428")] statistics/order/capture

Estimating the number of unseen species is an important problem in many scientific endeavors. Its most popular formulation, introduced by R. A. Fisher, uses n samples to predict the number U of hitherto unseen species that would be observed if t ⋅ n new samples were collected. Of considerable interest is the largest ratio t between the number of new and existing samples for which U can be accurately predicted.

In seminal works, Good & Toulmin 1956 constructed an intriguing estimator that predicts U for all t ≤ 1, thereby showing that the number of species can be estimated for a population twice as large as that observed. Subsequently Efron and Thisted obtained a modified estimator that empirically predicts U even for some t > 1, but without provable guarantees.

We derive a class of estimators that provably predict U not just for constant t > 1, but all the way up to t proportional to log n. This shows that the number of species can be estimated for a population log n times larger than that observed, a factor that grows arbitrarily large as n increases.

We also show that this range is the best possible and that the estimators’ mean-square error is optimal up to constants for any t. Our approach yields the first provable guarantee for the Efron-Thisted estimator and, in addition, a variant which achieves stronger theoretical and experimental performance than existing methodologies on a variety of synthetic and real datasets.

The estimators we derive are simple linear estimators that are computable in time proportional to n. The performance guarantees hold uniformly for all distributions, and apply to all 4 standard sampling models commonly used across various scientific disciplines: multinomial, Poisson, hypergeometric, and Bernoulli product.

--- https://www.theguardian.com/commentisfree/2023/dec/26/ai-childrens-bedtime-stories 2023-11-15 ai/nn/transformer/gpt/3/fiction --- https://www.lesswrong.com/posts/ddR8dExcEFJKJtWvR/how-evolutionary-lineages-of-llms-can-plan-their-own-futur 2023-11-15 reinforcement-learning/meta-learning reinforcement-learning/safe --- https://www.alignmentforum.org/posts/YEioD8YLgxih3ydxP/why-simulator-ais-want-to-be-active-inference-ais 2023-11-15 ai/nn/transformer/gpt reinforcement-learning/meta-learning reinforcement-learning/safe --- https://arxiv.org/abs/2212.09746 HALIE: Evaluating Human-Language Model Interaction Mina Lee, Megha Srivastava, Amelia Hardy, John Thickstun, Esin Durmus, Ashwin Paranjape, Ines Gerard-Ursin, Xiang Lisa Li, Faisal Ladhak, Frieda Rong, Rose E. Wang, Minae Kwon, Joon Sung Park, Hancheng Cao, Tony Lee, Rishi Bommasani, Michael S. Bernstein, Percy Liang 2022-12-19 2023-11-15 [("doi","10.48550/arXiv.2212.09746")] ai/dataset ai/nn/transformer/gpt/3 ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/preference-learning

Many real-world applications of language models (LMs), such as writing assistance and code autocomplete, involve human-LM interaction. However, most benchmarks are non-interactive in that a model produces output without human involvement.

To evaluate human-LM interaction, we develop a new framework, Human-AI Language-based Interaction Evaluation (HALIE), that defines the components of interactive systems and dimensions to consider when designing evaluation metrics. Compared to standard, non-interactive evaluation, HALIE captures (1) the interactive process, not only the final output; (2) the first-person subjective experience, not just a third-party assessment; and (3) notions of preference beyond quality (eg. enjoyment and ownership). We then design 5 tasks to cover different forms of interaction: social dialogue, question answering, crossword puzzles, summarization, and metaphor generation.

With 4 state-of-the-art LMs (three variants of OpenAI’s GPT-3 and AI21 Labs’ Jurassic-1), we find that better non-interactive performance does not always translate to better human-LM interaction. In particular, we highlight 3 cases where the results from non-interactive and interactive metrics diverge and underscore the importance of human-LM interaction for LM evaluation.

--- https://www.morganstanley.com/ideas/obesity-drugs-food-industry 2023-11-15 longevity/glp/psychology --- https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2812620 2023-11-15 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction psychology/neuroscience --- https://x.com/iamaheron_/status/1739875465043558736 heron 2023-11-15 ai/nn/transformer/gpt/3/fiction --- https://www.dwarkeshpatel.com/p/will-scaling-work 2023-11-16 ai/scaling --- https://arxiv.org/abs/2312.15011 Gemini vs GPT-4-V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Cases Zhangyang Qi, Ye Fang, Mengchen Zhang, Zeyi Sun, Tong Wu, Ziwei Liu, Dahua Lin, Jiaqi Wang, Hengshuang Zhao 2023-12-22 2023-12-22 [("doi","10.48550/arXiv.2312.15011")] ai/nn/transformer/gpt/4

[cf. Yang et al 2023] The rapidly evolving sector of Multi-modal Large Language Models (MLLMs) is at the forefront of integrating linguistic and visual processing in artificial intelligence.

This paper presents an in-depth comparative study of two pioneering models: Google’s Gemini and OpenAI’s GPT-4-V(ision). Our study involves a multi-faceted evaluation of both models across key dimensions such as Vision-Language Capability, Interaction with Humans, Temporal Understanding, and assessments in both Intelligence and Emotional Quotients.

The core of our analysis delves into the distinct visual comprehension abilities of each model. We conducted a series of structured experiments to evaluate their performance in various industrial application scenarios, offering a comprehensive perspective on their practical utility. We not only involve direct performance comparisons but also include adjustments in prompts and scenarios to ensure a balanced and fair analysis.

Our findings illuminate the unique strengths and niches of both models. GPT-4-V distinguishes itself with its precision and succinctness in responses, while Gemini excels in providing detailed, expansive answers accompanied by relevant imagery and links. These understandings not only shed light on the comparative merits of Gemini and GPT-4-V but also underscore the evolving landscape of multimodal foundation models, paving the way for future advancements in this area.

After the comparison, we attempted to achieve better results by combining the two models. Finally, we would like to express our profound gratitude to the teams behind GPT-4-V and Gemini for their pioneering contributions to the field.

Our acknowledgments are also extended to the comprehensive qualitative analysis presented in ‘Dawn’ by Yang et al 2923. This work, with its extensive collection of image samples, prompts, and GPT-4-V-related results, provided a foundational basis for our analysis.

--- https://arxiv.org/abs/2309.17421 The Dawn of LMMs: Preliminary Explorations with GPT-4-V(ision) Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Chung-Ching Lin, Zicheng Liu, Lijuan Wang 2023-09-29 2023-11-16 [("doi","10.48550/arXiv.2309.17421")] ai/nn/transformer/gpt/4

[cf. Yi et al 2023] Large multimodal models (LMMs) extend large language models (LLMs) with multi-sensory skills, such as visual understanding, to achieve stronger generic intelligence. In this paper, we analyze the latest model, GPT-4-V(ision), to deepen the understanding of LMMs. The analysis focuses on the intriguing tasks that GPT-4-V can perform, containing test samples to probe the quality and genericness of GPT-4-V’s capabilities, its supported inputs and working modes, and the effective ways to prompt the model.

In our approach to exploring GPT-4-V, we curate and organize a collection of carefully designed qualitative samples spanning a variety of domains and tasks. Observations from these samples demonstrate that GPT-4-V’s unprecedented ability in processing arbitrarily interleaved multimodal inputs and the genericness of its capabilities together make GPT-4-V a powerful multimodal generalist system.

Furthermore, GPT-4-V’s unique capability of understanding visual markers drawn on input images can give rise to new human-computer interaction methods such as visual referring prompting. We conclude the report with in-depth discussions on the emerging application scenarios and the future research directions for GPT-4-V-based systems.

We hope that this preliminary exploration will inspire future research on the next-generation multimodal task formulation, new ways to exploit and enhance LMMs to solve real-world problems, and gaining better understanding of multimodal foundation models.

Finally, we acknowledge that the model under our study is solely the product of OpenAI’s innovative work, and they should be fully credited for its development. Please see the GPT-4-V contributions paper for the authorship and credit attribution: https://cdn.openai.com/contributions/gpt-4v.pdf.

--- https://arxiv.org/abs/2312.15770#alibaba TF-T2V: A Recipe for Scaling up Text-to-Video Generation with Text-free Videos Xiang Wang, Shiwei Zhang, Hangjie Yuan, Zhiwu Qing, Biao Gong, Yingya Zhang, Yujun Shen, Changxin Gao, Nong Sang 2023-12-25 2023-12-25 [("doi","10.48550/arXiv.2312.15770")] ai/scaling ai/video/generation

Diffusion-based text-to-video generation has witnessed impressive progress in the past year yet still falls behind text-to-image generation. One of the key reasons is the limited scale of publicly available data (eg. 10M video-text pairs in WebVid10M vs. 5B image-text pairs in LAION), considering the high cost of video captioning. Instead, it could be far easier to collect unlabeled clips from video platforms like YouTube.

Motivated by this, we come up with a novel text-to-video generation framework, termed TF-T2V, which can directly learn with text-free videos. The rationale behind is to separate the process of text decoding from that of temporal modeling. To this end, we employ a content branch and a motion branch, which are jointly optimized with weights shared.

Following such a pipeline, we study the effect of doubling the scale of training set (ie. video-only WebVid10M) with some randomly collected text-free videos and are encouraged to observe the performance improvement (FID 9.67 → 8.19 and FVD 484 → 441), demonstrating the scalability of our approach. We also find that our model could enjoy sustainable performance gain (FID 8.19 → 7.64 and FVD 441 → 366) after reintroducing some text labels for training.

Finally, we validate the effectiveness and generalizability of our ideology on both native text-to-video generation and compositional video synthesis paradigms.

Code and models will be publicly available at https://tf-t2v.github.io/.

Figure 9: Scaling trend under semi-supervised settings. In the experiment, labeled WebVid10M and text-free videos from Internal10M are leveraged.

…Scaling trend under semi-supervised settings: In Figure 9, we vary the number of text-free videos and explore the scaling trend of TF-T2V under the semi-supervised settings. From the results, we can observe that FVD (↓) gradually decreases as the number of text-free videos increases, revealing the strong scaling potential of our TF-T2V.

--- https://marginalrevolution.com/marginalrevolution/2023/12/why-is-the-quality-of-recorded-classical-music-so-rising-from-my-email.html 2023-11-16 music sociology/technology --- https://www.businessinsider.com/she-spent-her-inheritance-sending-money-to-tiktok-influencers-2023-12 2023-11-16 psychiatry sociology/technology --- https://www.espn.com/olympics/story/_/id/39152293/the-titanic-gym 2023-11-16 exercise --- https://arxiv.org/abs/2312.07987#schmidhuber SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention Róbert Csordás, Piotr Piękos, Kazuki Irie, Jürgen Schmidhuber 2023-12-13 2023-12-13 [("doi","10.48550/arXiv.2312.07987")] ai/nn/fully-connected ai/nn/transformer/attention ai/scaling/mixture-of-experts

The costly self-attention layers in modern Transformers require memory and compute quadratic in sequence length. Existing approximation methods usually underperform and fail to obtain speedups in practice.

Here we present SwitchHead—a novel method that reduces both compute and memory requirements and achieves wall-clock speedup, while matching the language modeling performance of baseline Transformers with the same parameter budget.

SwitchHead uses Mixture-of-Experts (MoE) layers for the value and output projections and requires 4–8× fewer attention matrices than standard Transformers.

Our novel attention can also be combined with MoE MLP layers, resulting in an efficient fully-MoE “SwitchAll” Transformer model.

Our code is public.

--- https://lukasatkinson.de/2015/mopping-it-up/ 2023-11-16 cs/js cs/lisp --- https://aider.chat/docs/unified-diffs.html 2023-11-16 ai/nn/transformer/gpt/codex --- https://x.com/itwhiffed/status/1739898999199744220 itwhiffed 2023-11-16 design/visualization --- https://arxiv.org/abs/2310.10638#facebook In-Context Pretraining (ICP): Language Modeling Beyond Document Boundaries Weijia Shi, Sewon Min, Maria Lomeli, Chunting Zhou, Margaret Li, Rich James, Xi Victoria Lin, Noah Smith, Luke Zettlemoyer, Scott Yih, Mike Lewis 2023-10-16 2023-11-17 [("doi","10.48550/arXiv.2310.10638")] ai/nn/dynamic-evaluation ai/nn/retrieval ai/nn/transformer/gpt reinforcement-learning/meta-learning/continual-learning

Large language models (LMs) are currently trained to predict tokens given document prefixes, enabling them to directly perform long-form generation and prompting-style tasks which can be reduced to document completion. Existing pretraining pipelines train LMs by concatenating random sets of short documents to create i.i.d. input contexts but the prior documents provide no signal for predicting the next document.

We instead present In-Context Pretraining, a new approach where language models are pretrained on a sequence of related documents, thereby explicitly encouraging them to read and reason across document boundaries.

We can do In-Context Pretraining by simply changing the document ordering so that each context contains related documents, and directly applying existing pretraining pipelines. However, this document sorting problem is challenging. There are billions of documents and we would like the sort to maximize contextual similarity for every document without repeating any data. To do this, we introduce approximate algorithms for finding related documents with efficient nearest neighbor search and constructing coherent input contexts with a graph traversal algorithm.

Our experiments show In-Context Pretraining offers a simple and scalable approach to enhance LMs’ performance: we see notable improvements in tasks that require more complex contextual reasoning, including in-context learning (+8%), reading comprehension (+15%), faithfulness to previous contexts (+16%), long-context reasoning (+5%), and retrieval augmentation (+9%).

--- https://www.frontiersin.org/articles/10.3389/fphys.2023.1303758/abstract 2023-11-17 modafinil --- https://www.medrxiv.org/content/10.1101/2023.06.23.23291765.full Refining the impact of genetic evidence on clinical success Eric Vallabh Minikel, Jeffery L. Painter, Coco Chengliang Dong, Matthew R. Nelson 2023-12-19 2023-12-19 [("doi","10.1101/2023.06.23.23291765")] genetics/heritable

The cost of drug discovery and development is driven primarily by failure, with just ~10% of clinical programs eventually receiving approval.

We previously estimated that human genetic evidence doubles the success rate from clinical development to approval. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure.

We estimate the probability of success for drug mechanisms with genetic support is 2.6× greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency, or year of discovery.

These results suggest we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.

--- https://www.biorxiv.org/content/10.1101/2023.11.05.565662.full On prefrontal working memory and hippocampal episodic memory: Unifying memories stored in weights and activation slots James C. R. Whittington, William Dorrell, Timothy E. J. Behrens, Surya Ganguli, Mohamady El-Gaby 2023-11-05 2023-11-17 [("doi","10.1101/2023.11.05.565662")] ai/nn/rnn ai/nn/transformer/attention psychology/neuroscience

Remembering events in the past is crucial to intelligent behavior. Flexible memory retrieval, beyond simple recall, requires a model of how events relate to one another. Two key brain systems are implicated in this process: the hippocampal episodic memory (EM) system and the prefrontal working memory (WM) system. While an understanding of the hippocampal system, from computation to algorithm and representation, is emerging, less is understood about how the prefrontal WM system can give rise to flexible computations beyond simple memory retrieval, and even less is understood about how the two systems relate to each other.

Here we develop a mathematical theory relating the algorithms and representations of EM and WM by showing a duality between storing memories in synapses versus neural activity. In doing so, we develop a formal theory of the algorithm and representation of prefrontal WM as controllable activation slots.

By building models using this formalism, we elucidate the differences, similarities, and trade-offs between the hippocampal and prefrontal algorithms. Lastly, we show that several prefrontal representations in tasks ranging from list learning to cue dependent recall are unified as controllable activation slots.

Our results unify frontal and temporal representations of memory, and offer a new basis for understanding the prefrontal representation of WM.

--- https://searchengineland.com/how-google-search-ranking-works-pandu-nayak-435395#h-navboost-system-a-k-a-glue 2023-11-17 ai/nn/retrieval reinforcement-learning/preference-learning technology/google --- https://securelist.com/operation-triangulation-the-last-hardware-mystery/111669/ 2023-11-17 cs/security --- https://www.biorxiv.org/content/10.1101/2023.11.04.565609.full A Cellular Basis for Mapping Behavioral Structure Mohamady El-Gaby, Adam Loyd Harris, James C. R. Whittington, William Dorrell, Arya Bhomick, Mark E. Walton, Thomas Akam, Tim E. J. Behrens 2023-11-05 2023-11-17 [("doi","10.1101/2023.11.04.565609")] psychology/neuroscience statistics/decision

[Twitter] To flexibly adapt to new situations, our brains must understand the regularities in the world, but also in our own patterns of behavior. A wealth of findings is beginning to reveal the algorithms we use to map the outside world[1-6]. In contrast, the biological algorithms that map the complex structured behaviors we compose to reach our goals remain enigmatic.

Here we reveal a neuronal implementation of an algorithm for mapping abstract behavioral structure and transferring it to new scenarios. We trained mice on many tasks which shared a common structure organizing a sequence of goals, but differed in the specific goal locations. Animals discovered the underlying task structure, enabling zero-shot inferences on the first trial of new tasks.

The activity of most neurons in the medial Frontal cortex tiled progress-to-goal, akin to how place cells map physical space. These “goal-progress cells” generalized, stretching and compressing their tiling to accommodate different goal distances. In contrast, progress along the overall sequence of goals was not encoded explicitly.

Instead, a subset of goal-progress cells was further tuned such that individual neurons fired with a fixed task-lag from a particular behavioral step. Together these cells implemented an algorithm that instantaneously encoded the entire sequence of future behavioral steps, and whose dynamics automatically retrieved the appropriate action at each step.

These dynamics mirrored the abstract task structure both on-task and during offline sleep. Our findings suggest that goal-progress cells in the medial frontal cortex may be elemental building blocks of schemata that can be sculpted to represent complex behavioral structures.

--- https://arxiv.org/abs/2307.10936 PASTA: Pretrained Action-State Transformer Agents Raphael Boige, Yannis Flet-Berliac, Arthur Flajolet, Guillaume Richard, Thomas Pierrot 2023-07-20 2023-11-17 [("doi","10.48550/arXiv.2307.10936")] ai/nn/tokenization reinforcement-learning/model/decision-transformer reinforcement-learning/offline

Self-supervised learning has brought about a revolutionary paradigm shift in various computing domains, including NLP, vision, and biology. Recent approaches involve pre-training transformer models on vast amounts of unlabeled data, serving as a starting point for efficiently solving downstream tasks.

In reinforcement learning, researchers have recently adapted these approaches, developing models pre-trained on expert trajectories. This advancement enables the models to tackle a broad spectrum of tasks, ranging from robotics to recommendation systems. However, existing methods mostly rely on intricate pre-training objectives tailored to specific downstream applications.

This paper conducts a comprehensive investigation of models, referred to as pre-trained action-state transformer agents (PASTA). Our study covers a unified methodology and covers an extensive set of general downstream tasks including behavioral cloning, offline RL, sensor failure robustness, and dynamics change adaptation. Our objective is to systematically compare various design choices and offer valuable insights that will aid practitioners in developing robust models.

Key highlights of our study include tokenization at the component level for actions and states, the use of fundamental pre-training objectives such as next token prediction or masked language modeling, simultaneous training of models across multiple domains, and the application of various fine-tuning strategies. In this study, the developed models contain fewer than 7 million parameters allowing a broad community to use these models and reproduce our experiments.

We hope that this study will encourage further research into the use of transformers with first principle design choices to represent RL trajectories and contribute to robust policy learning.

--- https://arxiv.org/abs/1611.05431#facebook ResNeXt: Aggregated Residual Transformations for Deep Neural Networks Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He 2016-11-16 2023-11-17 [("doi","10.48550/arXiv.1611.05431")] ai/nn/cnn

We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call “cardinality” (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width.

On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity.

Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart.

The code and models are publicly available online.

--- https://web.archive.org/web/20190424032242/http://www.gregorybenford.com/extra/the-scarred-man-returns/ 2023-11-17 cs/security fiction/science-fiction --- https://x.com/GrantSlatton/status/1740039795659956359 Grant Slatton 2023-11-17 ai/nn/transformer/gpt/4/nonfiction --- /doc/statistics/decision/2009-hill.pdf When to Stop: How to gamble if you must—the mathematics of optimal stopping Theodore P. Hill 2009-03-01 2023-11-18 [("doi","10.2307/27859299")] statistics/decision statistics/probability --- /doc/psychiatry/traumatic-brain-injury/2023-alosco.pdf White matter hyperintensities in former American football players Michael L. Alosco, Yorghos Tripodis, Zachary H. Baucom, Charles H. Adler, Laura J. Balcer, Charles Bernick, Megan L. Mariani, Rhoda Au, Sarah J. Banks, William B. Barr, Jennifer V. Wethe, Robert C. Cantu, Michael J. Coleman, David W. Dodick, Michael D. McClean, Ann C. McKee, Jesse Mez, Joseph N. Palmisano, Brett Martin, Kaitlin Hartlage, Alexander P. Lin, Inga K. Koerte, Jeffrey L. Cummings, Eric M. Reiman, Robert A. Stern, Martha E. Shenton, Sylvain Bouix 2022-08-22 2023-11-18 [("doi","10.1002/alz.12779")] psychiatry/alzheimers psychiatry/traumatic-brain-injury

[supplement: 1, 2] Introduction: The presentation, risk factors, and etiologies of white matter hyperintensities (WMH) in people exposed to repetitive head impacts are unknown. We examined the burden and distribution of WMH, and their association with years of play, age of first exposure, and clinical function in former American football players.

Methods: A total of 149 former football players and 53 asymptomatic unexposed participants (all men, 45–74 years) completed fluid-attenuated inversion recovery magnetic resonance imaging, neuropsychological testing, and self-report neuropsychiatric measures. Lesion Segmentation Toolbox estimated WMH. Analyses were performed in the total sample and stratified by age 60.

Results: In older but not younger participants, former football players had greater total, frontal, temporal, and parietal log-WMH compared to asymptomatic unexposed men. In older but not younger former football players, greater log-WMH was associated with younger age of first exposure to football and worse executive function.

Discussion: In older former football players, WMH may have unique presentations, risk factors, and etiologies.

--- https://en.wikipedia.org/wiki/Biofilm Biofilm 2023-11-18 genetics/microbiome genetics/selection/natural --- https://heritagesciencejournal.springeropen.com/articles/10.1186/s40494-023-01094-0 2023-11-18 ai/nn/cnn history --- https://asteriskmag.com/issues/02/america-doesn-t-know-tofu 2023-11-18 food --- https://arxiv.org/abs/2312.14235 Neural Spline Fields for Burst Image Fusion and Layer Separation Ilya Chugunov, David Shustin, Ruyu Yan, Chenyang Lei, Felix Heide 2023-12-21 2023-12-21 [("doi","10.48550/arXiv.2312.14235")] ai/nn/dynamic-evaluation ai/nn/fully-connected

Each photo in an image burst can be considered a sample of a complex 3D scene: the product of parallax, diffuse and specular materials, scene motion, and illuminant variation. While decomposing all of these effects from a stack of misaligned images is a highly ill-conditioned task, the conventional align-and-merge burst pipeline takes the other extreme: blending them into a single image.

In this work, we propose a versatile intermediate representation: a two-layer alpha-composited image plus flow model constructed with neural spline fields—networks trained to map input coordinates to spline control points. Our method is able to, during test-time optimization, jointly fuse a burst image capture into one high-resolution reconstruction and decompose it into transmission and obstruction layers.

Then, by discarding the obstruction layer, we can perform a range of tasks including seeing through occlusions, reflection suppression, and shadow removal. Validated on complex synthetic and in-the-wild captures we find that, with no post-processing steps or learned priors, our generalizable model is able to outperform existing dedicated single-image and multi-view obstruction removal approaches.

--- https://undark.org/2023/12/27/otc-hearing-aids/ 2023-11-18 economics sociology/technology --- https://andreasjhkarlsson.github.io/jekyll/update/2023/12/27/4-billion-if-statements.html 2023-11-18 cs/algorithm math/humor --- https://www.sciencedirect.com/science/article/pii/S0272735823001368 2023-11-18 philosophy/mind psychology --- https://dubroy.com/blog/cold-blooded-software/ 2023-11-18 cryonics --- /doc/politics/1994-foley.pdf The Role Of The CIA In Economic And Technological Intelligence Timothy D. Foley 1994-01-01 2023-11-19 [("doi","10.2307/45289609")] economics politics technology --- http://www.msarnoff.org/millitext/ 2023-11-19 design/typography --- https://typographica.org/typeface-reviews/minuscule/ 2023-11-19 design/typography --- http://itre.cis.upenn.edu/~myl/languagelog/archives/000606.html 2023-11-19 cs design --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8958662/ 2023-11-19 melatonin --- https://monaspace.githubnext.com/ 2023-11-19 design/typography --- https://www.johndcook.com/blog/2023/12/29/randomize-then-humanize/ 2023-11-19 cs/cryptography --- https://www.nytimes.com/2023/12/29/science/puzzles-mechanical-miller.html 2023-11-19 math technology --- https://www.lesswrong.com/posts/FbSAuJfCxizZGpcHc/interpreting-the-learning-of-deceit 2023-11-19 reinforcement-learning/multi-agent reinforcement-learning/safe --- https://www.reddit.com/r/duolingo/comments/18sx06i/big_layoff_at_duolingo/ 2023-11-19 ai/nn/transformer/gpt/4/nonfiction economics/automation --- https://techcrunch.com/2023/12/29/china-robotaxi-apply-the-brakes/ 2023-11-19 reinforcement-learning/robot --- https://andrewpwheeler.com/2022/07/01/using-linear-programming-to-assess-spatial-access/ 2023-11-20 cs/algorithm statistics/decision --- https://github.com/EGjoni/DRUGS 2023-11-20 ai/nn/sampling --- https://x.com/francoisfleuret/status/1741100457399849121 Francois Fleuret 2023-11-20 economics --- https://arxiv.org/abs/2312.15796#deepmind GenCast: Diffusion-based ensemble forecasting for medium-range weather Ilan Price, Alvaro Sanchez-Gonzalez, Ferran Alet, Timo Ewalds, Andrew El-Kadi, Jacklynn Stott, Shakir Mohamed, Peter Battaglia, Remi Lam, Matthew Willson 2023-12-25 2023-12-25 [("doi","10.48550/arXiv.2312.15796")] ai/nn/diffusion science

Probabilistic weather forecasting is critical for decision-making in high-impact domains such as flood forecasting, energy system planning or transportation routing, where quantifying the uncertainty of a forecast—including probabilities of extreme events—is essential to guide important cost-benefit trade-offs and mitigation measures. Traditional probabilistic approaches rely on producing ensembles from physics-based models, which sample from a joint distribution over spatio-temporally coherent weather trajectories, but are expensive to run.

An efficient alternative is to use a machine learning (ML) forecast model to generate the ensemble, however state-of-the-art ML forecast models for medium-range weather are largely trained to produce deterministic forecasts which minimize mean-squared-error. Despite improving skills scores, they lack physical consistency, a limitation that grows at longer lead times and impacts their ability to characterize the joint distribution.

We introduce GenCast, a ML-based generative model for ensemble weather forecasting, trained from reanalysis data. It forecasts ensembles of trajectories for 84 weather variables, for up to 15 days at 1° resolution globally, taking around a minute per ensemble member on a single Cloud TPU v4 device.

We show that GenCast is more skillful than ENS, a top operational ensemble forecast, for more than 96% of all 1320 verification targets on CRPS and Ensemble-Mean RMSE, while maintaining good reliability and physically consistent power spectra.

Together our results demonstrate that ML-based probabilistic weather forecasting can now outperform traditional ensemble systems at 1°, opening new doors to skillful, fast weather forecasts that are useful in key applications.

--- https://arxiv.org/abs/2307.05845 PIGEON: Predicting Image Geolocations Lukas Haas, Michal Skreta, Silas Alberti, Chelsea Finn 2023-07-11 2023-11-20 [("doi","10.48550/arXiv.2307.05845")] ai/nn/transformer/clip cs/security

Planet-scale image geolocalization remains a challenging problem due to the diversity of images originating from anywhere in the world. Although approaches based on vision transformers have made progress in geolocalization accuracy, success in prior literature is constrained to narrow distributions of images of landmarks, and performance has not generalized to unseen places. We present a new geolocalization system that combines semantic geocell creation, multi-task contrastive pretraining, and a novel loss function. Additionally, our work is the first to perform retrieval over location clusters for guess refinements.

We train two models for evaluations on street-level data and general-purpose image geolocalization; the first model, PIGEON, is trained on data from the game of Geoguessr and is capable of placing over 40% of its guesses within 25 kilometers of the target location globally.

We also develop a bot and deploy PIGEON in a blind experiment against humans, ranking in the top 0.01% of players. We further challenge one of the world’s foremost professional Geoguessr players [Rainbolt] to a series of 6 matches with millions of viewers, winning all 6 games.

Our second model, PIGEOTTO, differs in that it is trained on a dataset of images from Flickr and Wikipedia, achieving state-of-the-art results on a wide range of image geolocalization benchmarks, outperforming the previous state-of-the-art (SOTA) by up to 7.7 percentage points on the city accuracy level and up to 38.8 percentage points on the country level.

Our findings suggest that PIGEOTTO is the first image geolocalization model that effectively generalizes to unseen places and that our approach can pave the way for highly accurate, planet-scale image geolocalization systems.

--- https://anatolyzenkov.com/stolen-buttons 2023-11-20 economics/advertising --- /doc/statistics/meta-analysis/2023-macnamara.pdf A Spotlight on Bias in the Growth Mindset Intervention Literature: A Reply to Commentaries That Contextualize the Discussion (Oyserman 2023; Yan & Schuetze 2023) and Illustrate the Conclusion (Tipton et al 2023) Brooke N. Macnamara, Alexander P. Burgoyne 2023-01-01 2023-11-20 [("doi","10.1037/bul0000394")] psychology statistics/bias statistics/meta-analysis --- https://arxiv.org/abs/2303.09859 Trained on 100 million words and still in shape: BERT meets British National Corpus David Samuel, Andrey Kutuzov, Lilja Øvrelid, Erik Velldal 2023-03-17 2023-11-20 [("doi","10.48550/arXiv.2303.09859")] ai/nn/transformer

While modern masked language models (LMs) are trained on ever larger corpora, we here explore the effects of down-scaling training to a modestly-sized but representative, well-balanced, and publicly available English text source—the British National Corpus.

We show that pre-training on this carefully curated corpus can reach better performance than the original BERT model. We argue that this type of corpus has great potential as a language modeling benchmark.

To showcase this potential, we present fair, reproducible, and data-efficient comparative studies of LMs, in which we evaluate several training objectives and model architectures and replicate previous empirical results in a systematic way.

We propose an optimized LM architecture called LTG-BERT.

--- https://x.com/GrantSlatton/status/1741149378516263243 Grant Slatton 2023-11-20 ai/nn/transformer/gpt/3/fiction --- https://arxiv.org/abs/2311.09677 R-Tuning: Teaching Large Language Models to Refuse Unknown Questions Hanning Zhang, Shizhe Diao, Yong Lin, Yi R. Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, Tong Zhang 2023-11-16 2023-11-20 [("doi","10.48550/arXiv.2311.09677")] ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/instruction-tuning

Large language models (LLMs) have revolutionized numerous domains with their impressive performance but still face their challenges. A predominant issue is the propensity for these models to generate non-existent facts, a concern termed hallucination.

Our research is motivated by the observation that previous instruction tuning methods force the model to complete a sentence no matter whether the model knows the knowledge or not. When the question is out of the parametric knowledge, it will try to make up something and fail to indicate when it lacks knowledge.

In this paper, we present a new approach called Refusal-Aware Instruction Tuning (R-Tuning). This approach is formalized by first identifying the knowledge gap between parametric knowledge and the instruction tuning data. Then, we construct the refusal-aware data based on the knowledge intersection, to tune LLMs to refrain from responding to questions beyond its parametric knowledge.

Experimental results demonstrate this new instruction tuning approach effectively improves a model’s ability to answer known questions and refrain from answering unknown questions. Furthermore, when tested on out-of-domain datasets, the refusal ability was found to be a meta-skill that could be generalized to other tasks.

Further analysis surprisingly finds that learning the uncertainty during training displays a better ability to estimate uncertainty than uncertainty-based testing. Our code will be released at Github.

--- https://pslusarz.github.io/articles/2023/12/22/compare-ocr-tesseract-gpt4-nara-rolls.html 2023-11-20 ai/nn/transformer/gpt/4/nonfiction --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3441280/ Adenosine 5′-triphosphate (ATP) supplements are not orally bioavailable: a randomized, placebo-controlled cross-over trial in healthy humans Ilja Cw Arts, Erik Jcm Coolen, Martijn Jl Bours, Nathalie Huyghebaert, Martien A. Cohen Stuart, Aalt Bast, Pieter C. Dagnelie 2012 2023-11-21 [("doi","10.1186/1550-2783-9-16")] nootropic

Background: Nutritional supplements designed to increase adenosine 5’-triphosphate (ATP) concentrations are commonly used by athletes as ergogenic aids. ATP is the primary source of energy for the cells, and supplementation may enhance the ability to maintain high ATP turnover during high-intensity exercise. Oral ATP supplements have beneficial effects in some but not all studies examining physical performance. One of the remaining questions is whether orally administered ATP is bioavailable. We investigated whether acute supplementation with oral ATP administered as enteric-coated pellets led to increased concentrations of ATP or its metabolites in the circulation.

Methods: 8 healthy volunteers participated in a cross-over study. Participants were given in random order single doses of 5000 mg ATP or placebo. To prevent degradation of ATP in the acidic environment of the stomach, the supplement was administered via two types of pH-sensitive, enteric-coated pellets (targeted at release in the proximal or distal small intestine), or via a naso-duodenal tube. Blood ATP and metabolite concentrations were monitored by HPLC for 4.5 h (naso-duodenal tube) or 7h (pellets) post-administration. Areas under the concentration vs. time curve were calculated and compared by paired-samples t-tests.

Results: ATP concentrations in blood did not increase after ATP supplementation via enteric-coated pellets or naso-duodenal tube. In contrast, concentrations of the final catabolic product of ATP, uric acid, were significantly increased compared to placebo by ~50% after administration via proximal-release pellets (p = 0.003) and naso-duodenal tube (p = 0.001), but not after administration via distal-release pellets.

Conclusions: A single dose of orally administered ATP is not bioavailable, and this may explain why several studies did not find ergogenic effects of oral ATP supplementation. On the other hand, increases in uric acid after release of ATP in the proximal part of the small intestine suggest that ATP or one of its metabolites is absorbed and metabolized. Uric acid itself may have ergogenic effects, but this needs further study. Also, more studies are needed to determine whether chronic administration of ATP will enhance its oral bioavailability.

--- https://arxiv.org/abs/2309.02390 Explaining grokking through circuit efficiency Vikrant Varma, Rohin Shah, Zachary Kenton, János Kramár, Ramana Kumar 2023-09-05 2023-11-21 [("doi","10.48550/arXiv.2309.02390")] ai/scaling/emergence/grokking

One of the most surprising puzzles in neural network generalization is grokking: a network with perfect training accuracy but poor generalization will, upon further training, transition to perfect generalization. We propose that grokking occurs when the task admits a generalizing solution and a memorizing solution, where the generalizing solution is slower to learn but more efficient, producing larger logits with the same parameter norm.

We hypothesize that memorizing circuits become more inefficient with larger training datasets while generalizing circuits do not, suggesting there is a critical dataset size at which memorization and generalization are equally efficient.

We make and confirm 4 novel predictions about grokking, providing evidence in favor of our explanation. Most strikingly, we demonstrate two novel and surprising behaviors: ungrokking, in which a network regresses from perfect to low test accuracy, and semi-grokking, in which a network shows delayed generalization to partial rather than perfect test accuracy.

--- https://github.com/jart/emacs-copilot 2023-11-21 ai/nn/transformer/gpt/codex cs/lisp/emacs --- https://arxiv.org/abs/2301.05217 Progress measures for grokking via mechanistic interpretability Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, Jacob Steinhardt 2023-01-12 2023-11-21 [("doi","10.48550/arXiv.2301.05217")] ai/nn/transformer ai/scaling/emergence/grokking

[Twitter, retrospective] Neural networks often exhibit emergent behavior, where qualitatively new capabilities arise from scaling up the amount of parameters, training data, or training steps. One approach to understanding emergence is to find continuous progress measures that underlie the seemingly discontinuous qualitative changes.

We argue that progress measures can be found via mechanistic interpretability: reverse-engineering learned behaviors into their individual components.

As a case study, we investigate the recently-discovered phenomenon of “grokking” exhibited by small transformers trained on modular addition tasks. We fully reverse engineer the algorithm learned by these networks, which uses discrete Fourier transforms and trigonometric identities to convert addition to rotation about a circle. We confirm the algorithm by analyzing the activations and weights and by performing ablations in Fourier space.

Based on this understanding, we define progress measures that allow us to study the dynamics of training and split training into 3 continuous phases: memorization, circuit formation, and cleanup.

Our results show that grokking, rather than being a sudden shift, arises from the gradual amplification of structured mechanisms encoded in the weights, followed by the later removal of memorizing components.

--- https://danluu.com/seo-spam/ Dan Luu 2023-11-21 economics/advertising technology/google --- https://mymodernmet.com/x-living-huaian-zhongshuge-bookstore/ 2023-11-21 design --- https://www.lesswrong.com/posts/B6CxEApaatATzown6/the-lesswrong-2022-review?commentId=AcidvwAYL5hFREqku 2023-11-21 design/typography --- https://nerv.app/en/ 2023-11-21 anime/eva --- https://arxiv.org/abs/2312.01479 OpenVoice: Versatile Instant Voice Cloning Zengyi Qin, Wenliang Zhao, Xumin Yu, Xin Sun 2023-12-03 2023-12-03 [("doi","10.48550/arXiv.2312.01479")] ai/music

We introduce OpenVoice, a versatile voice cloning approach that requires only a short audio clip from the reference speaker to replicate their voice and generate speech in multiple languages. OpenVoice represents an advancement in addressing the following open challenges in the field: (1) Flexible Voice Style Control. OpenVoice enables granular control over voice styles, including emotion, accent, rhythm, pauses, and intonation, in addition to replicating the tone color of the reference speaker. The voice styles are not directly copied from and constrained by the style of the reference speaker. Previous approaches lacked the ability to flexibly manipulate voice styles after cloning.

  1. Zero-Shot Cross-Lingual Voice Cloning. OpenVoice achieves zero-shot cross-lingual voice cloning for languages not included in the massive-speaker training set. Unlike previous approaches, which typically require extensive massive-speaker multi-lingual (MSML) dataset for all languages, OpenVoice can clone voices into a new language without any massive-speaker training data for that language. OpenVoice is also computationally efficient, costing tens of times less than commercially available APIs that offer even inferior performance. To foster further research in the field, we have made the source code and trained model publicly accessible. We also provide qualitative results in our demo website.

Prior to its public release, our internal version of OpenVoice was used tens of millions of times by users worldwide between May & October 2023, serving as the backend of MyShell.

--- https://arxiv.org/abs/2312.17120 Generative AI for Math: Part I—MathPile: A Billion-Token-Scale Pretraining Corpus for Math Zengzhi Wang, Rui Xia, Pengfei Liu 2023-12-28 2023-12-28 [("doi","10.48550/arXiv.2312.17120")] ai/dataset math

High-quality, large-scale corpora are the cornerstone of building foundation models. In this work, we introduce MathPile, a diverse and high-quality math-centric corpus comprising about 9.5 billion tokens. Throughout its creation, we adhered to the principle of “less is more”, firmly believing in the supremacy of data quality over quantity, even in the pre-training phase.

Our meticulous data collection and processing efforts included a complex suite of preprocessing, prefiltering, language identification, cleaning, filtering, and deduplication, ensuring the high quality of our corpus. Furthermore, we performed data contamination detection on downstream benchmark test sets to eliminate duplicates.

We hope our MathPile can help to enhance the mathematical reasoning abilities of language models. We plan to open-source different versions of it with the scripts used for processing, to facilitate future developments in this field.

--- https://arxiv.org/abs/1205.4839 Off-Policy Actor-Critic Thomas Degris, Martha White, Richard S. Sutton 2012-05-22 2023-11-22 [("doi","10.48550/arXiv.1205.4839")] reinforcement-learning/model-free

This paper presents the first actor-critic algorithm for off-policy reinforcement learning. Our algorithm is online and incremental, and its per-time-step complexity scales linearly with the number of learned weights.

Previous work on actor-critic algorithms is limited to the on-policy setting and does not take advantage of the recent advances in off-policy gradient temporal-difference learning. Off-policy techniques, such as Greedy-GQ, enable a target policy to be learned while following and obtaining data from another (behavior) policy.

For many problems, however, actor-critic methods are more practical than action value methods (like Greedy-GQ) because they explicitly represent the policy; consequently, the policy can be stochastic and use a large action space.

In this paper, we illustrate how to practically combine the generality and learning potential of off-policy learning with the flexibility in action selection given by actor-critic methods.

We derive an incremental, linear time and space complexity algorithm that includes eligibility traces, prove convergence under assumptions similar to previous off-policy algorithms, and empirically show better or comparable performance to existing algorithms on standard reinforcement-learning benchmark problems.

--- https://arxiv.org/abs/2401.00448 Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws Nikhil Sardana, Jonathan Frankle 2023-12-31 2023-12-31 [("doi","10.48550/arXiv.2401.00448")] ai/nn/sparsity/low-precision ai/nn/transformer/gpt ai/scaling/economics

Large language model (LLM) scaling laws are empirical formulas that estimate changes in model quality as a result of increasing parameter count and training data. However, these formulas, including the popular DeepMind Chinchilla scaling laws, neglect to include the cost of inference.

We modify the Chinchilla scaling laws to calculate the optimal LLM parameter count and pre-training data size to train and deploy a model of a given quality and inference demand.

We conduct our analysis both in terms of a compute budget and real-world costs and find that LLM researchers expecting reasonably large inference demand (~1B requests) should train models smaller and longer than Chinchilla-optimal.

[This ignores the fact that larger models can sparsify/distill better, and assumes you have to deploy the same model, which no one does, so overtraining is a worse idea than their analysis indicates.]

--- https://www.reddit.com/r/MachineLearning/comments/18u31w8/r_large_language_models_world_chess_championship/ 2023-11-22 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/chess reinforcement-learning/imitation-learning --- http://antirez.com/news/140 2023-11-22 ai/nn/transformer/gpt/codex --- https://www.pnas.org/doi/full/10.1073/pnas.1717959115 2023-11-22 sociology/technology --- https://siderea.dreamwidth.org/1209794.html 2023-11-22 psychology/personality sociology --- https://x.com/inflammateomnia/status/1723477458760953872 inflammateomnia 2023-11-22 sociology/technology --- https://arxiv.org/abs/2401.00368#microsoft Improving Text Embeddings with Large Language Models Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei 2023-12-31 2023-12-31 [("doi","10.48550/arXiv.2401.00368")] ai/nn/retrieval ai/nn/sparsity/knowledge-distillation

In this paper, we introduce a novel and simple method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. Unlike existing methods that often depend on multi-stage intermediate pre-training with billions of weakly-supervised text pairs, followed by fine-tuning with a few labeled datasets, our method does not require building complex training pipelines or relying on manually collected datasets that are often constrained by task diversity and language coverage.

We leverage proprietary LLMs to generate diverse synthetic data for hundreds of thousands of text embedding tasks across nearly 100 languages. We then fine-tune open-source decoder-only LLMs on the synthetic data using standard contrastive loss.

Experiments demonstrate that our method achieves strong performance on highly competitive text embedding benchmarks without using any labeled data.

Furthermore, when fine-tuned with a mixture of synthetic and labeled data, our model sets new state-of-the-art results on the BEIR and MTEB benchmarks.

--- https://en.wikipedia.org/wiki/Schweigt_stille,_plaudert_nicht,_BWV_211 Schweigt stille, plaudert nicht, BWV 211 2023-11-22 nootropic/caffeine --- https://thereader.mitpress.mit.edu/an-untold-story-of-lsd-psychotherapy-in-communist-czechoslovakia/ 2023-11-22 psychedelic psychiatry --- https://abagames.github.io/crisp-game-lib-11-games/?pakupaku 2023-11-22 design --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5258678/ Measurement of fidgeting in patients with anorexia nervosa using a novel shoe-based monitor Lauren Belak, Loren Gianini, Diane A. Klein, Edward Sazonov, Kathryn Keegan, Esther Neustadt, B. Timothy Walsh, Evelyn Attia 2017 2023-11-23 [("doi","10.1016/j.eatbeh.2016.11.005")] exercise psychiatry/anorexia

Objective: To objectively assess seated non-exercise physical activity in patients with anorexia nervosa (AN) relative to healthy controls (HCs) and examine the associations between this physical activity, eating disorder pathology, and levels of anxiety and depression.

Method: 11 inpatients with AN and 10 HCs wore a shoe-based accelerometer (SmartShoe) at 3 time points: (1) while eating lunch, (2) filling out questionnaires, and (3) watching television for 1h.

Results: Across all 3 tasks, patients with AN were statistically-significantly more active than HCs, thereby engaging in a greater degree of restless or fidgeting behavior. Degree of physical activity was positively correlated with eating disorder psychopathology in the sample with AN, and a trend towards a positive association between physical activity and levels of depression and anxiety was also found in this sample. Among individuals with AN, physical activity was not statistically-significantly correlated with BMI, duration of illness, or number of days since hospital admission.

Discussion: Use of a minimally invasive, shoe-based monitor revealed patients with AN engaged in a greater degree of fidgeting relative to HCs during quiet, seated tasks and this heightened activity was related to measures of pathology. Non-exercise physical activity, including fidgeting, may warrant further clinical attention in this patient population.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177651/ Mood Homeostasis, Low Mood, and History of Depression in 2 Large Population Samples Maxime Taquet, Jordi Quoidbach, James J. Gross, Kate E. A. Saunders, Guy M. Goodwin 2020 2023-11-23 [("doi","10.1001/jamapsychiatry.2020.0588")] psychiatry/depression

Importance: Existing therapeutic options are insufficient to tackle the disease burden of depression, and new treatments are sorely needed. Defining new psychotherapeutic targets is challenging given the paucity of coherent mechanistic explanations for depression.

Objective: To assess whether mood homeostasis (ie. the stabilization of one’s mood by engaging in mood-modifying activities) is a possible new therapeutic target by testing the hypothesis that people with low (vs high) mean mood and people with (vs without) a history of depression have impaired mood homeostasis.

Methods, Setting, & Participants: The quantitative association between mood and daily activities was computed in 2 large case-control studies based on the 58sec data set (collected from December 1, 2012, to May 31, 2014, and analyzed from April 1 to 30, 2019), and the World Health Organization Study on Global Aging and Adult Health (WHO SAGE) data set (collected from January 1, 2007, to December 31, 2010, and analyzed from June 1 to 30, 2019). The 58sec data set consists of self-enrolled participants from high-income countries. The WHO SAGE data set consists of nationally representative participants in low- and middle-income countries recruited via cluster sampling.

Main Outcomes & Measures: The main outcome (defined before data analysis) was the difference in mood homeostasis between people with high vs low mean mood (58sec data) and between people with vs without a history of depression (WHO SAGE data).

Results: A total of 28 212 participants from the 58sec data set (65.8% female; mean [SD] age, 28.1 [9.0] years) and 30 116 from the WHO SAGE data set (57.0% female; mean [SD] age, 57.8 [14.7] years) were included, for an overall study population of 58 328 participants. Mood homeostasis was significantly lower in people with low (vs high) mean mood (0.63 [95% CI, 0.45 to 0.79] vs 0.96 [95% CI, 0.96 to 0.98]; p < 0.001) and in people with (vs without) a history of depression (0.03 [95% CI, −0.26 to 0.24] vs 0.68 [95% CI, 0.55 to 0.75]; p < 0.001). In dynamic simulations, lower mood homeostasis led to more depressive episodes (11.8% vs 3.8% yearly risk; p < 0.001) that lasted longer (4.19 vs 2.90 weeks; p = 0.006).

Conclusion: In this study, mood homeostasis appeared to have been impaired in people with low mood and in those with a history of depression. Mood homeostasis may therefore provide new insights to guide the development of treatments for depression.

--- https://dev.to/experilearning/from-spaced-repetition-systems-to-open-recommender-systems-25ab 2023-11-23 psychology/spaced-repetition --- /doc/psychiatry/depression/2013-garrido.pdf Moody melodies: Do they cheer us up? A study of the effect of sad music on mood Sandra Garrido, Emery Schubert 2013-10-15 2023-11-23 [("doi","10.1177/0305735613501938")] music psychiatry/depression

Despite the paradox inherent in the idea that sad music could make people happier, research indicates that an improved mood is amongst the primary motivations that people give for listening to sad music. However, it is not clear whether listeners are always able to achieve such aims.

This article reports a study in which 335 participants listened to a piece of self-selected sad music. Before and after-measures of mood were taken, and participants also completed psychometric scales of rumination, absorption and reflectiveness.

It was found that both ruminators and non-ruminators had statistically-significant increases in depression after listening to self-selected sad music. Furthermore, ruminators did not systematically report that they expected to benefit from listening to sad music, contrary to the literature.

Results support the hypothesis that listening to sad music is related to maladaptive mood regulation strategies in some listeners.

--- https://www.sciencedirect.com/science/article/pii/S019745561300186X 2023-11-23 music psychiatry/depression --- https://supermemo.guru/wiki/Michael_Nielsen_re-discovers_incremental_reading_with_Anki 2023-11-23 psychology/spaced-repetition --- https://www.youtube.com/watch?v=D0zDGFI7CfA 2023-11-23 ai/music --- https://www.animenewsnetwork.com/feature/2024-01-03/an-anime-insider-take-on-the-netflix-engagement-report/.206122 2023-11-23 anime --- https://en.wikipedia.org/wiki/Molly_Brodak Molly Brodak 2023-11-23 psychiatry/bipolar/energy --- https://arxiv.org/abs/2311.17179 SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery Konstantin Klemmer, Esther Rolf, Caleb Robinson, Lester Mackey, Marc Rußwurm 2023-11-28 2023-11-28 [("doi","10.48550/arXiv.2311.17179")] ai/nn/transformer/clip

Geographic location is essential for modeling tasks in fields ranging from ecology to epidemiology to the Earth system sciences. However, extracting relevant and meaningful characteristics of a location can be challenging, often entailing expensive data fusion or data distillation from global imagery datasets.

To address this challenge, we introduce Satellite Contrastive Location-Image Pretraining (SatCLIP), a global, general-purpose geographic location encoder that learns an implicit representation of locations from openly available satellite imagery. Trained location encoders provide vector embeddings summarizing the characteristics of any given location for convenient usage in diverse downstream tasks.

We show that SatCLIP embeddings, pretrained on globally sampled multi-spectral Sentinel-2 satellite data, can be used in various predictive tasks that depend on location information but not necessarily satellite imagery, including temperature prediction, animal recognition in imagery, and population density estimation. Across tasks, SatCLIP embeddings consistently outperform embeddings from existing pretrained location encoders, ranging from models trained on natural images to models trained on semantic context.

SatCLIP embeddings also help to improve geographic generalization. This demonstrates the potential of general-purpose location encoders and opens the door to learning meaningful representations of our planet from the vast, varied, and largely untapped modalities of geospatial data.

--- https://www.agry.purdue.edu/ext/corn/news/timeless/yieldtrends.html 2023-11-23 genetics/selection/artificial --- https://gigamonkeys.com/flowers/ 2023-11-24 design --- https://fullfrontal.moe/takeshi-honda-the-boy-and-the-heron-interview/ 2023-11-24 anime --- https://www.bloomberg.com/features/2016-bateman-airplane-safety-device/ 2023-11-24 technology --- https://www.construction-physics.com/p/building-apollo 2023-11-24 politics sociology/technology --- https://dl.acm.org/doi/10.1145/3593856.3595891 2023-11-24 cs/shell --- https://arstechnica.com/gadgets/2023/12/report-google-ads-restructure-could-replace-some-sales-jobs-with-ai/ 2023-11-24 economics/automation --- https://arxiv.org/abs/2303.08011 Model scale versus domain knowledge in statistical forecasting of chaotic systems William Gilpin 2023-03-13 2023-11-24 [("doi","10.48550/arXiv.2303.08011")] ai/nn/rnn ai/nn/transformer science

[code] Chaos and unpredictability are traditionally synonymous, yet large-scale machine learning methods recently have demonstrated a surprising ability to forecast chaotic systems well beyond typical predictability horizons. However, recent works disagree on whether specialized methods grounded in dynamical systems theory, such as reservoir computers or neural ordinary differential equations, outperform general-purpose large-scale learning methods such as transformers or recurrent neural networks. These prior studies perform comparisons on few individually-chosen chaotic systems, thereby precluding robust quantification of how statistical modeling choices and dynamical invariants of different chaotic systems jointly determine empirical predictability.

Here, we perform the largest to-date comparative study of forecasting methods on the classical problem of forecasting chaos: we benchmark 24 state-of-the-art forecasting methods on a crowdsourced database of 135 low-dimensional systems with 17 forecast metrics. We find that large-scale, domain-agnostic forecasting methods consistently produce predictions that remain accurate up to two dozen Lyapunov times, thereby accessing a new long-horizon forecasting regime well beyond classical methods. We find that, in this regime, accuracy decorrelates with classical invariant measures of predictability like the Lyapunov exponent.

However, in data-limited settings outside the long-horizon regime, we find that physics-based hybrid methods retain a comparative advantage due to their strong inductive biases.

--- https://www.nature.com/articles/s41586-023-06792-0 2023-11-24 ai/nn/transformer/gpt/4/nonfiction biology --- https://www.monotype.com/company/press-release/acclaimed-colophon-foundry-joins-monotype-family 2023-11-24 design/typography --- https://arxiv.org/abs/2312.12675#google Comparison of Waymo Rider-Only Crash Data to Human Benchmarks at 7.1 Million Miles Kristofer D. Kusano, John M. Scanlon, Yin-Hsiu Chen, Timothy L. McMurry, Ruoshu Chen, Tilia Gode, Trent Victor 2023-12-20 2023-12-20 [("doi","10.48550/arXiv.2312.12675")] reinforcement-learning/robot reinforcement-learning/safe

[blog] This paper examines the safety performance of the Waymo Driver, an SAE level 4 automated driving system (ADS) used in a rider-only (RO) ride-hailing application without a human driver, either in the vehicle or remotely. ADS crash data was derived from NHTSA’s Standing General Order (SGO) reporting over 7.14 million RO miles through the end of October 2023 in Phoenix, AZ, San Francisco, CA, and Los Angeles, CA.

This study is one of the first to compare overall crashed vehicle rates using only RO data (as opposed to ADS testing with a human behind the wheel) to a human benchmark that also corrects for biases caused by underreporting and unequal reporting thresholds reported in the literature. When considering all locations together, the any-injury-reported crashed vehicle rate was 0.41 incidents per million miles (IPMM) for the ADS vs 2.78 IPMM for the human benchmark, an 85% reduction or a 6.8× lower rate.

Police-reported crashed vehicle rates for all locations together were 2.1 IPMM for the ADS vs. 4.85 IPMM for the human benchmark, a 57% reduction or 2.3× lower rate. Police-reported and any-injury-reported crashed vehicle rate reductions for the ADS were statistically-significant when compared in San Francisco and Phoenix as well as combined across all locations. The comparison in Los Angeles, which to date has low mileage and no reported events, was not statistically-significant.

In general, the Waymo ADS had a lower any property damage or injury rate than the human benchmarks. Given imprecision in the benchmark estimate and multiple potential sources of underreporting biasing the benchmarks, caution should be taken when interpreting the results of the any property damage or injury comparison.

Together, these crash-rate results should be interpreted as a directional and continuous confidence growth indicator, together with other methodologies, in a safety case approach.

--- http://archive.vector.org.uk/art10500340 2023-11-24 cs/algorithm --- https://github.com/tlack/atree 2023-11-25 cs/algorithm --- https://x.com/alyssamvance/status/1742720250783666413 Alyssa M. Vance 2023-11-25 ai/nn/transformer/gpt/dall-e/3 --- https://hakaimagazine.com/features/all-the-fish-we-cannot-see/ 2023-11-25 biology --- /review/bakewell#astronomy 2023-11-25 philosophy/epistemology --- https://en.wikipedia.org/wiki/Fires_in_Edo Fires in Edo 2023-11-25 japan/history --- https://www.medrxiv.org/content/10.1101/2023.12.26.23300543.full Unveiling the Epigenetic Impact of Vegan vs. Omnivorous Diets on Aging: Insights from the Twins Nutrition Study (TwiNS) Varun B. Dwaraka, Lucia Aronica, Natalia Carreras-Gallo, Jennifer L. Robinson, Tayler Hennings, Aaron Lin, Logan Turner, Ryan Smith, Tavis L. Mendez, Hannah Went, Emily R. Ebel, Matthew M. Carter, Erica D. Sonnenburg, Justin L. Sonnenburg, Christopher D. Gardner 2023-12-29 2023-12-29 [("doi","10.1101/2023.12.26.23300543")] exercise genetics/heritable/correlation longevity

Geroscience Geroscience has emerged as a field focusing on interventions to attenuate molecular changes associated with aging. While lifestyle modifications, medications, and social factors are recognized influencers of the aging process, a comprehensive understanding of the intricate molecular mechanisms necessitates an in-depth exploration of the epigenetic landscape.

Notably, the specific epigenetic clock and predictor effects of a vegan diet, compared to an omnivorous counterpart, remain inadequately explored, despite indications of potential impacts on aging-related outcomes. This study addresses this knowledge gap by examining the impact of an eight-week entirely plant-based or healthy omnivorous diet on blood DNA methylation in paired twins.

Results show distinct responses, with the vegan cohort solely exhibiting significant decreases in overall epigenetic age acceleration (PC GrimAge, PC PhenoAge, DunedinPACE), including among specific systems (Inflammation, Heart, Hormone, Liver, and Metabolic), aligning with anti-aging effects of plant-based diets.

Analyses of methylation surrogates of clinical, metabolite, and protein markers indicate diet-specific shifts, while exemplifying DNA methylation markers in predicting complex traits influenced by diet.

Comprehensive epigenome-wide analysis unveils diet-specific differentially methylated loci, offering insights into influenced pathways. This study sheds light on the advantageous aging benefits of a healthy vegan diet, while providing a foundation for future personalized interventions using epigenetic age clocks in promoting overall well-being.

...It is crucial to acknowledge that the observed epigenetic age and biomarker differences between the vegan and omnivore groups may be predominantly attributed to the variations in weight loss rather than solely reflecting the distinct dietary compositions. Throughout the “Food Delivery” phase, the vegan group consumed ∼200 calories less per day than their omnivorous counterparts, resulting in an average weight loss of 2 kilograms greater than the omnivore group by the end of the 8-week intervention.

--- https://arxiv.org/abs/2401.01291 Generative AI is already widespread in the public sector Jonathan Bright, Florence E. Enock, Saba Esnaashari, John Francis, Youmna Hashem, Deborah Morgan 2024-01-02 2024-01-02 [("doi","10.48550/arXiv.2401.01291")] ai/nn/transformer/gpt economics/automation

Generative AI has the potential to transform how public services are delivered by enhancing productivity and reducing time spent on bureaucracy. Furthermore, unlike other types of artificial intelligence, it is a technology that has quickly become widely available for bottom-up adoption: essentially anyone can decide to make use of it in their day to day work.

But to what extent is generative AI already in use in the public sector? Our survey of 938 public service professionals within the UK (covering education, health, social work and emergency services) seeks to answer this question. We find that use of generative AI systems is already widespread: 45% of respondents were aware of generative AI usage within their area of work, while 22% actively use a generative AI system.

Public sector professionals were positive about both current use of the technology and its potential to enhance their efficiency and reduce bureaucratic workload in the future. For example, those working in the NHS thought that time spent on bureaucracy could drop 50% → 30% if generative AI was properly exploited, an equivalent of one day per week (an enormous potential impact).

Our survey also found a high amount of trust (61%) around generative AI outputs, and a low fear of replacement (16%). While respondents were optimistic overall, areas of concern included feeling like the UK is missing out on opportunities to use AI to improve public services (76%), and only a minority of respondents (32%) felt like there was clear guidance on generative AI usage in their workplaces.

In other words, it is clear that generative AI is already transforming the public sector, but uptake is happening in a disorganized fashion without clear guidelines. The UK’s public sector urgently needs to develop more systematic methods for taking advantage of the technology.

--- https://en.wikipedia.org/wiki/Cryptomnesia Cryptomnesia 2023-11-25 psychology/cognitive-bias/illusion-of-depth psychology/neuroscience/memory --- https://x.com/ESYudkowsky/status/1740524282390941900 Eliezer Yudkowsky 2023-11-25 economics/automation --- https://news.ycombinator.com/item?id=38850202#38852945 2023-11-25 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/preference-learning/mode-collapse --- https://arxiv.org/abs/2312.16862 TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones Zhengqing Yuan, Zhaoxu Li, Lichao Sun 2023-12-28 2023-12-28 [("doi","10.48550/arXiv.2312.16862")] ai/nn/sparsity/low-precision ai/nn/transformer/clip ai/nn/transformer/gpt

In the era of advanced multimodal learning, multimodal large language models (MLLMs) such as GPT-4-V have made remarkable strides towards bridging language and visual elements. However, the closed-source nature and considerable computational demand present notable challenges for universal usage and modifications.

This is where open-source MLLMs like LLaVA and MiniGPT-4 come in, presenting groundbreaking achievements across tasks. Despite these accomplishments, computational efficiency remains an unresolved issue, as these models, like LLaVA-v1.5-13B, require substantial resources.

Addressing these issues, we introduce TinyGPT-V, a new-wave model marrying impressive performance with commonplace computational capacity. It stands out by requiring merely a 24G GPU for training and an 8G GPU or CPU for inference. Built upon Phi-2, TinyGPT-V couples an effective language backbone with pre-trained vision modules from BLIP-2 or CLIP.

TinyGPT-V’s 2.8b parameters can undergo a unique quantisation process, suitable for local deployment and inference tasks on 8G various devices. Our work fosters further developments for designing cost-effective, efficient, and high-performing MLLMs, expanding their applicability in a broad array of real-world scenarios.

Furthermore this paper proposed a new paradigm of Multimodal Large Language Model via small backbones. Our code and training weights are placed at: https://github.com/DLYuanGod/TinyGPT-V and https://huggingface.co/Tyrannosaurus/TinyGPT-V respectively.

--- https://www.lesswrong.com/posts/GDGFqiaj8ePujZWEc/usd300-for-the-best-sci-fi-prompt-the-results 2023-11-26 ai/nn/transformer/gpt/4/fiction fiction/science-fiction --- https://www.lesswrong.com/posts/GDGFqiaj8ePujZWEc/usd300-for-the-best-sci-fi-prompt-the-results?commentId=xGuaavrbfKAuvaune 2023-11-26 ai/nn/transformer/gpt/claude fiction/science-fiction --- https://www.stephenwolfram.com/publications/mathematical-notation-past-future/ 2023-11-26 design/typography math philosophy/logic --- https://en.wikipedia.org/wiki/Cat_food Cat food 2023-11-26 cat/biology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10468298/ Umami taste perception and preferences of the domestic cat (Felis catus), an obligate carnivore Scott J. McGrane, Matthew Gibbs, Carlos Hernangomez de Alvaro, Nicola Dunlop, Marcel Winnig, Boris Klebansky, Daniel Waller 2023 2023-11-26 [("doi","10.1093/chemse/bjad026")] cat/biology/taurine

The domestic cat (Felis catus) is an obligate carnivore, and as such has a meat-based diet. Several studies on the taste perception of cats have been reported, indicating that their sense of taste has evolved based on their carnivorous diet.

Here, we propose that umami (mediated by Tas1r1/Tas1r2/Tas1r3) is the main appetitive taste modality for the domestic cat by characterizing the umami taste of a range of nucleotides, amino acids, and their mixtures for cats obtained using complementary methods. We show for the first time that cats express Tas1r1 in taste papillae.

The cat umami receptor responds to a range of nucleotides as agonists, with the purine nucleotides having the highest activity. Their umami receptor does not respond to any amino acids alone; however, 11 l-amino acids with a range of chemical characteristics act as enhancers in combination with a nucleotide. [l-Alanine, l-Asparagine, l-Cysteine, Glycine, l-Histidine, l-Leucine, l-Methionine, l-Phenylalanine, l-Serine, l-Tryptophan, l-Tyrosine]

l-Glutamic acid and l-Aspartic acid are not active as either agonists or enhancers of the cat umami receptor due to changes in key binding residues at positions 170 and 302. Overall, cats have an appetitive behavioral response for nucleotides, l-amino acids, and their mixtures.

We postulate that the renowned palatability of tuna for cats may be due, at least in part, to its specific combination of high levels of inosine monophosphate and free l-Histidine that produces a strong synergistic umami taste enhancement. These results demonstrate the critical role that the umami receptor plays in enabling cats to detect key taste compounds present in meat.

[Taurine] ...Taurine, which is a naturally-occurring sulfonic acid that is essential for cats as they lack the enzyme (sulfinoalanine decarboxylase) required to produce taurine and must therefore acquire it from their diet (Knopf et al 1978; NRC 2006), was also screened and found to be inactive (see also Supplementary Figure 6 for result). [So cats can’t taste taurine, despite its importance to them‽]

--- https://arxiv.org/abs/2311.03054v4 AnyText: Multilingual Visual Text Generation And Editing Yuxiang Tuo, Wangmeng Xiang, Jun-Yan He, Yifeng Geng, Xuansong Xie 2023-11-06 2023-11-26 [("doi","10.48550/arXiv.2311.03054")] ai/dataset ai/nn/diffusion ai/nn/tokenization design/typography

Diffusion model based Text-to-Image has achieved impressive achievements recently. Although current technology for synthesizing images is highly advanced and capable of generating images with high fidelity, it is still possible to give the show away when focusing on the text area in the generated image. To address this issue, we introduce AnyText, a diffusion-based multilingual visual text generation and editing model, that focuses on rendering accurate and coherent text in the image.

AnyText comprises a diffusion pipeline with two primary elements: an auxiliary latent module and a text embedding module. The former uses inputs like text glyph, position, and masked image to generate latent features for text generation or editing. The latter employs an OCR model for encoding stroke data as embeddings, which blend with image caption embeddings from the tokenizer to generate texts that seamlessly integrate with the background. We employed text-control diffusion loss and text perceptual loss for training to further enhance writing accuracy.

AnyText can write characters in multiple languages, to the best of our knowledge, this is the first work to address multilingual visual text generation. It is worth mentioning that AnyText can be plugged into existing diffusion models from the community for rendering or editing text accurately. After conducting extensive evaluation experiments, our method has outperformed all other approaches by a large margin.

Additionally, we contribute the first large-scale multilingual text images dataset, AnyWord-3M, containing 3 million image-text pairs with OCR annotations in multiple languages. Based on AnyWord-3M dataset, we propose AnyText-benchmark for the evaluation of visual text generation accuracy and quality. Our project will be open-sourced on GitHub to improve and promote the development of text generation technology.

--- https://en.wikipedia.org/wiki/Felinine Felinine 2023-11-26 cat/biology --- /doc/cat/psychology/1977-mugford.pdf External Influences on the Feeding of Carnivores Roger A. Mugford 1977-01-01 2023-11-26 cat/psychology dog --- https://arxiv.org/abs/2401.02385 TinyLlama: An Open-Source Small Language Model Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, Wei Lu 2024-01-04 2024-01-04 [("doi","10.48550/arXiv.2401.02385")] ai/nn/transformer/gpt

We present TinyLlama, a compact 1.1B language model pretrained on around 1 trillion tokens for ~3 epochs.

Building on the architecture and tokenizer of LLaMA-2, TinyLlama leverages various advances contributed by the open-source community (eg. FlashAttention), achieving better computational efficiency.

Despite its relatively small size, TinyLlama demonstrates remarkable performance in a series of downstream tasks. It outperforms existing open-source language models with comparable sizes.

Our model checkpoints and code are publicly available on GitHub at Github.

--- https://arxiv.org/abs/2312.17227 Gradient-based Planning with World Models Jyothir S. V, Siddhartha Jalagam, Yann LeCun, Vlad Sobal 2023-12-28 2023-12-28 [("doi","10.48550/arXiv.2312.17227")] reinforcement-learning/model

The enduring challenge in the field of artificial intelligence has been the control of systems to achieve desired behaviors. While for systems governed by straightforward dynamics equations, methods like Linear Quadratic Regulation (LQR) have historically proven highly effective, most real-world tasks, which require a general problem-solver, demand world models with dynamics that cannot be easily described by simple equations. Consequently, these models must be learned from data using neural networks.

Most model predictive control (MPC) algorithms designed for visual world models have traditionally explored gradient-free population-based optimization methods, such as Cross Entropy and Model Predictive Path Integral (MPPI) for planning. However, we present an exploration of a gradient-based alternative that fully leverages the differentiability of the world model.

In our study, we conduct a comparative analysis between our method and other MPC-based alternatives, as well as policy-based algorithms. In a sample-efficient setting, our method achieves on par or superior performance compared to the alternative approaches in most tasks. Additionally, we introduce a hybrid model that combines policy networks and gradient-based MPC, which outperforms pure policy based methods thereby holding promise for Gradient-based planning with world models in complex real-world tasks.

--- https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1309142/full 2023-11-27 iq/ses --- https://www.nms.ac.uk/explore-our-collections/stories/global-arts-cultures-and-design/grammar-of-ornament/ 2023-11-27 design --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4494339/ Assessing Food Preferences in Dogs and Cats: A Review of the Current Methods Christelle Tobie, Franck Péron, Claire Larose 2015 2023-11-27 [("doi","10.3390/ani5010126")] cat/psychology

Food is a major aspect of pet care; therefore, ensuring that pet foods are not only healthful but attractive to companion animals and their owners is essential. The petfood market remains active and requires ongoing evaluation of the adaptation and efficiency of the new products.

Palatability—foods’ characteristics enticing animals and leading them to consumption—is therefore a key element to look at. Based on the type of information needed, different pet populations (expert or naïve) can be tested to access their preference and acceptance for different food products.

Classical techniques are the one-bowl and two-bowl tests, but complementary (ie. operant conditioning) and novel (ie. exploratory behavior) approaches are available to gather more information on the evaluation of petfood palatability.

--- https://www.da.vidbuchanan.co.uk/blog/python-swar.html 2023-11-27 cs/algorithm cs/python --- https://patents.google.com/patent/US20230419079A1/en 2023-11-27 ai/scaling/mixture-of-experts --- https://www.atlasobscura.com/articles/pyongyang-architecture-urban-planning 2023-11-27 design politics --- https://www.wired.com/story/parents-dementia-robots-warm-technology/ 2023-11-27 psychiatry/alzheimers reinforcement-learning/robot --- https://arxiv.org/abs/2312.13286#baai Generative Multimodal Models are In-Context Learners Quan Sun, Yufeng Cui, Xiaosong Zhang, Fan Zhang, Qiying Yu, Zhengxiong Luo, Yueze Wang, Yongming Rao, Jingjing Liu, Tiejun Huang, Xinlong Wang 2023-12-20 2023-12-20 [("doi","10.48550/arXiv.2312.13286")] ai/nn/transformer/gpt ai/scaling reinforcement-learning/meta-learning

The human ability to easily solve multimodal tasks in context (ie. with only a few demonstrations or simple instructions), is what current multimodal systems have largely struggled to imitate. In this work, we demonstrate that the task-agnostic in-context learning capabilities of large multimodal models can be enhanced by effective scaling-up.

We introduce Emu2, a generative multimodal model with 37 billion parameters, trained on large-scale multimodal sequences with a unified autoregressive objective. Emu2 exhibits strong multimodal in-context learning abilities, even emerging to solve tasks that require on-the-fly reasoning, such as visual prompting and object-grounded generation.

The model sets a new record on multiple multimodal understanding tasks in few-shot settings. When instruction-tuned to follow specific instructions, Emu2 further achieves new state-of-the-art on challenging tasks such as question answering benchmarks for large multimodal models and open-ended subject-driven generation.

These achievements demonstrate that Emu2 can serve as a base model and general-purpose interface for a wide range of multimodal tasks. Code and models are publicly available to facilitate future research.

--- https://arxiv.org/pdf/1912.06680.pdf#page=11&org=openai Dota 2 with Large Scale Deep Reinforcement Learning § pg11 Berner,: §4.2: Validating Surgery with Rerun 2023-11-27 ai/nn/sparsity/knowledge-distillation reinforcement-learning/model-free/oa5 --- https://eprints.lincoln.ac.uk/id/eprint/1932/1/MetaAnalysisPaper.pdf Measuring inconsistency in meta-analyses Higgins 2003 2023-11-27 statistics/meta-analysis --- https://languagelog.ldc.upenn.edu/nll/?p=61810#more-61810 2023-11-27 psychology/linguistics psychology/personality/narcissism --- https://onlinelibrary.wiley.com/doi/10.1111/ele.14354 2023-11-28 genetics/heritable/correlation --- https://www.quantamagazine.org/random-search-wired-into-animals-may-help-them-hunt-20200611/ 2023-11-28 psychology/animal reinforcement-learning/exploration --- https://github.com/mathiasbynens/small 2023-11-28 cs/algorithm --- /doc/economics/2023-ghosh.pdf Economic Consequences of Kinship: Evidence From U.S. Bans on Cousin Marriage Arkadev Ghosh, Sam Il Myoung Hwang, Munir Squires 2023-05-27 2023-11-28 [("doi","10.1093/qje/qjad018")] economics sociology

Close-kin marriage, by sustaining tightly knit family structures, may impede development. We find support for this hypothesis using U.S. state bans on cousin marriage. Our measure of cousin marriage comes from the excess frequency of same-surname marriages, a method borrowed from population genetics that we apply to millions of marriage records from the 18th to the twentieth century.

Using census data, we first show that married cousins are more rural and have lower-paying occupations. We then turn to an event study analysis to understand how cousin marriage bans affected outcomes for treated birth cohorts. We find that these bans led individuals from families with high rates of cousin marriage to migrate off farms and into urban areas. They also gradually shift to higher-paying occupations.

We observe increased dispersion, with individuals from these families living in a wider range of locations and adopting more diverse occupations. Our findings suggest that these changes were driven by the social and cultural effects of dispersed family ties rather than genetics. Notably, the bans also caused more people to live in institutional settings for the elderly, infirm, or destitute, suggesting weaker support from kin.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8933832/ Transcardial injection and vascular distribution of microalgae in Xenopus laevis as means to supply the brain with photosynthetic oxygen Suzan Özugur, Myra N. Chávez, Rosario Sanchez-Gonzalez, Lars Kunz, Jörg Nickelsen, Hans Straka 2022 2023-11-28 [("doi","10.1016/j.xpro.2022.101250")] genetics/microbiome psychology/neuroscience

Oxygen in vertebrates is generally provided through respiratory organs and blood vessels.

This protocol describes transcardial injection, vascular distribution, and accumulation of phototrophic microalgae in the brain of Xenopus laevis tadpoles.

Following tissue isolation, oxygen dynamics and neuronal activity are recorded in semi-intact whole-head preparations. Illumination of such microalgae-filled preparations triggers the photosynthetic production of oxygen in the brain that, under hypoxic conditions, rescues neuronal activity.

This technology is potentially able to ameliorate consequences of hypoxia under pathological conditions. For complete details on the use and execution of this protocol, please refer to Özugur et al 2021.

--- https://arxiv.org/abs/2310.16825 CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, Volodymyr Kuleshov 2023-10-25 2023-11-28 [("doi","10.48550/arXiv.2310.16825")] ai/dataset ai/nn/diffusion economics/copyright

We assemble a dataset of Creative-Commons-licensed (CC) images, which we use to train a set of open diffusion models that are qualitatively competitive with Stable Diffusion 2 (SD2). This task presents two challenges: (1) high-resolution CC images lack the captions necessary to train text-to-image generative models; (2) CC images are relatively scarce.

In turn, to address these challenges, we use an intuitive transfer learning technique to produce a set of high-quality synthetic captions [using BLIP-2] paired with curated CC images. We then develop a data- and compute-efficient training recipe that requires as little as 3% of the LAION-2B data needed to train existing SD2 models, but obtains comparable quality. These results indicate that we have a sufficient number of CC images ( ~70 million) for training high-quality models.

Our training recipe also implements a variety of optimizations that achieve ~3X training speed-ups, enabling rapid model iteration. We leverage this recipe to train several high-quality text-to-image models, which we dub the CommonCanvas family. Our largest model achieves comparable performance to SD2 on a human evaluation, despite being trained on our CC dataset that is smaller than LAION and using synthetic captions for training.

We release our models, data, and code at https://github.com/mosaicml/diffusion.

4. CommonCatalog: A Dataset of CC Images & Synthetic Captions In this section, we introduce our open dataset, CommonCatalog. First, we describe the collection and curation process for the open-licensed, CC images. This process brings to light two challenges: caption-data incompleteness and image-data scarcity. To address the lack of CC captions, we show concretely how we use telephoning to produce high-quality synthetic captions to accompany our set of curated images. We investigate the topic of data scarcity in the next section, where we also discuss necessary systems-level training optimizations that enable us efficient SD-model iteration.

4.1 Sourcing provenanced, licensed images for CommonCatalog: We focus on locating high-resolution Creative-Commons images that have open licenses. We began with the YFCC100M dataset, which consists of 100 million CC-licensed images and multimedia files, as well as Flickr IDs linking to the original data. The images in the dataset associated with the original paper exhibit two issues that make it ill-suited for direct use to train Stable Diffusion: they are low-resolution, and many of them have licenses that do not expressly allow for the distribution of derivative works, which are an area of unsettled copyright law in the context of model training. We therefore re-scraped these images from Flickr, based on the IDs provided in the YFCC100M metadata. Our scraped images are very high resolution (exceeding 4K), which makes them more suitable for T2I training.

We exclude images with non-derivative (ND) licenses. The remaining images can be further divided into those that can be used for commercial (C) purposes and those that cannot (non-commercial/ NC). As shown in Table 4, we accordingly construct two datasets, CommonCatalog-C and CommonCatalog-NC. We defer additional details about licenses to Appendix B.1.1, but emphasize that all of the images included have open licenses: individuals are free to use, adapt, and remix the images, so long as they attribute them. In total, CommonCatalog contains roughly 70 million NC CC-images, of which a subset of ~25 million images can also be used commercially.

Directly sourcing CommonCatalog avoids some concerns (§2.2); however, it also comes with its own challenges. For one, CC images rarely have the alt-text captions necessary to train a T2I model like Stable Diffusion (Table 4); those that do have associated text often just include the image title or a URL. For another, we could only find roughly 70 million usable CC images, which pales in comparison to the billions of images in LAION used to train SD2 (§5). We take each of these challenges in turn. First, in the next subsection, we show how we instantiate ‘telephoning’ (§3) to produce high-quality, synthetic captions for CC images.

…Based on these preliminary results, we captioned all of the YFCC100M Creative-Commons images, which required about 1,120 GPU A100 hours. To do so, we center-cropped and resized all of the images to a maximum size of 512×512 pixels. We perform these transformations because captioning images at native resolution would be very expensive. At training time of the diffusion model, all images remain in their native resolution. We release our commercial (CommonCatalog-C) and non-commercial (CommonCatalog-NC) CC-image and synthetic-caption datasets on HuggingFace with associated data cards. As an evaluation set, we also release the BLIP-2 captions that we produced for the non-derivative (ND) CC images that we did not use for training.

[If diffusion-generated images which do not visibly resemble a copyrighted character are nevertheless derivative works of the training set, why are BLIP-2-generated descriptive captions not themselves derivative works of the non-CC-licensed images+caption pairs, and thus tainting? The text captions are still colored...]

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8560625/ Green oxygen power plants in the brain rescue neuronal activity Suzan Özugur, Myra N. Chávez, Rosario Sanchez-Gonzalez, Lars Kunz, Jörg Nickelsen, Hans Straka 2021 2023-11-28 [("doi","10.1016/j.isci.2021.103158")] genetics/microbiome psychology/neuroscience

Neuronal activity in the brain depends on mostly aerobic generation of energy equivalents and thus on a constant O2 supply. Oxygenation of the vertebrate brain has been optimized during evolution by species-specific uptake and transport of O2 that originally derives from the phototrophic activity of prokaryotic and eukaryotic organisms in the environment.

Here, we employed a concept that exploits transcardial injection and vascular distribution of unicellular green algae or cyanobacteria in the brain of Xenopus laevis tadpoles. Using oxygen measurements in the brain ventricle, we found that these microorganisms robustly produce sizable amounts of O2 upon illumination.

In a severe hypoxic environment, when neuronal activity has completely ceased, the photosynthetic O2 reliably provoked a restart and rescue of neuronal activity. In the future, phototrophic microorganisms might provide a novel means to directly increase oxygen levels in the brain in a controlled manner under particular eco-physiological conditions or following pathological impairments.

--- https://jeffhuang.com/struggle_for_each_paper/ 2023-11-28 cs design --- https://arxiv.org/abs/1805.00909 Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review Sergey Levine 2018-05-02 2023-11-28 [("doi","10.48550/arXiv.1805.00909")] reinforcement-learning/model

The framework of reinforcement learning or optimal control provides a mathematical formalization of intelligent decision making that is powerful and broadly applicable. While the general form of the reinforcement learning problem enables effective reasoning about uncertainty, the connection between reinforcement learning and inference in probabilistic models is not immediately obvious.

However, such a connection has considerable value when it comes to algorithm design: formalizing a problem as probabilistic inference in principle allows us to bring to bear a wide array of approximate inference tools, extend the model in flexible and powerful ways, and reason about compositionality and partial observability.

In this article, we will discuss how a generalization of the reinforcement learning or optimal control problem, which is sometimes termed maximum entropy reinforcement learning, is equivalent to exact probabilistic inference in the case of deterministic dynamics, and variational inference in the case of stochastic dynamics.

We will present a detailed derivation of this framework, overview prior work that has drawn on this and related ideas to propose new reinforcement learning and control algorithms, and describe perspectives on future research.

--- https://opportunityinsights.org/wp-content/uploads/2024/01/SAT_ACT_on_Grades.pdf 2023-11-28 iq/ses --- https://arxiv.org/abs/2310.07923 The Expressive Power of Transformers with Chain-of-Thought William Merrill, Ashish Sabharwal 2023-10-11 2023-11-29 [("doi","10.48550/arXiv.2310.07923")] ai/nn/transformer/gpt/inner-monologue cs/computable

Recent theoretical work has identified surprisingly simple reasoning problems, such as checking if two nodes in a graph are connected or simulating finite-state machines, that are provably unsolvable by standard transformers that answer immediately after reading their input. However, in practice, transformers’ reasoning can be improved by allowing them to use a “chain-of-thought” or “scratchpad”, ie. generate and condition on a sequence of intermediate tokens before answering.

Motivated by this, we ask: Does such intermediate generation fundamentally extend the computational power of a decoder-only transformer? We show that the answer is yes, but the amount of increase depends crucially on the amount of intermediate generation. For instance, we find that transformer decoders with a logarithmic number of decoding steps (w.r.t. the input length) push the limits of standard transformers only slightly, while a linear number of decoding steps adds a clear new ability (under standard complexity conjectures): recognizing all regular languages.

Our results also imply that linear steps keep transformer decoders within context-sensitive languages, and polynomial steps make them recognize exactly the class of polynomial-time solvable problems—the first exact characterization of a type of transformers in terms of standard complexity classes. Together, our results provide a nuanced framework for understanding how the length of a transformer’s chain-of-thought or scratchpad impacts its reasoning power.

--- https://blog.demofox.org/2017/05/29/when-random-numbers-are-too-random-low-discrepancy-sequences/ 2023-11-29 statistics/probability --- /doc/science/1939-flexner.pdf The Usefulness of Useless Knowledge Abraham Flexner 1939-06-01 2023-11-29 reinforcement-learning/exploration science --- https://www.bitsaboutmoney.com/archive/optimal-amount-of-fraud/ 2023-11-29 crime cs/end-to-end-principle --- https://www.medrxiv.org/content/10.1101/2024.01.03.24300779.full Valid inference for machine learning-assisted GWAS Jiacheng Miao, Yixuan Wu, Zhongxuan Sun, Xinran Miao, Tianyuan Lu, Jiwei Zhao, Qiongshi Lu 2024-01-04 2024-01-04 [("doi","10.1101/2024.01.03.24300779")] genetics/heritable/correlation

Machine learning (ML) has revolutionized analytical strategies in almost all scientific disciplines including human genetics and genomics. Due to challenges in sample collection and precise phenotyping, ML-assisted genome-wide association study (GWAS) which uses sophisticated ML to impute phenotypes and then performs GWAS on imputed outcomes has quickly gained popularity in complex trait genetics research. However, the validity of associations identified from ML-assisted GWAS has not been carefully evaluated.

In this study, we report pervasive risks for false positive associations in ML-assisted GWAS, and introduce POP-GWAS, a novel statistical framework that re-imagines GWAS on ML-imputed outcomes. POP-GWAS provides valid statistical inference irrespective of the quality of imputation or variables and algorithms used for imputation. It also only requires GWAS summary statistics as input.

We employed POP-GWAS to perform the largest GWAS of bone mineral density (BMD) derived from dual-energy X-ray absorptiometry imaging at 14 skeletal sites, identifying 89 novel loci reaching genome-wide statistical-significance and revealing skeletal site-specific genetic architecture of BMD. Our framework may fundamentally reshape the analytical strategies in future ML-assisted GWAS.

--- https://arxiv.org/abs/2310.11441 Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4-V Jianwei Yang, Hao Zhang, Feng Li, Xueyan Zou, Chunyuan Li, Jianfeng Gao 2023-10-17 2023-11-29 [("doi","10.48550/arXiv.2310.11441")] ai/nn/transformer/gpt/4/nonfiction

We present Set-of-Mark (SoM), a new visual prompting method, to unleash the visual grounding abilities of large multimodal models (LMMs), such as GPT-4-V.

As illustrated in Fig. 1 (right), we employ off-the-shelf interactive segmentation models, such as SEEM/SAM, to partition an image into regions at different levels of granularity, and overlay these regions with a set of marks eg. alphanumerics, masks, boxes. Using the marked image as input, GPT-4-V can answer the questions that require visual grounding.

We perform a comprehensive empirical study to validate the effectiveness of SoM on a wide range of fine-grained vision and multimodal tasks. For example, our experiments show that GPT-4-V with SoM in zero-shot setting outperforms the state-of-the-art fully-finetuned referring expression comprehension and segmentation model on RefCOCOg.

Code for SoM prompting is made public at: Github.

--- https://extremelearning.com.au/unreasonable-effectiveness-of-quasirandom-sequences/ 2023-11-29 statistics/probability --- https://www.stat.berkeley.edu/~aldous/Real-World/million.html 2023-11-29 statistics/probability --- https://economistwritingeveryday.com/2024/01/07/using-phind-for-academic-references/ 2023-11-29 ai/nn/retrieval --- http://www.incompleteideas.net/IncIdeas/WrongWithAI.html 2023-11-29 reinforcement-learning/scaling --- https://arxiv.org/abs/1704.03651#amazon PBO: Preferential Bayesian Optimization Javier Gonzalez, Zhenwen Dai, Andreas Damianou, Neil D. Lawrence 2017-04-12 2023-11-29 [("doi","10.48550/arXiv.1704.03651")] reinforcement-learning/exploration/active-learning statistics/bayes statistics/order/comparison

Bayesian optimization (BO) has emerged during the last few years as an effective approach to optimizing black-box functions where direct queries of the objective are expensive. In this paper we consider the case where direct access to the function is not possible, but information about user preferences is. Such scenarios arise in problems where human preferences are modeled, such as A/B tests or recommender systems.

We present a new framework for this scenario that we call Preferential Bayesian Optimization (PBO) which allows us to find the optimum of a latent function that can only be queried through pairwise comparisons, the so-called duels. [Basically a standard latent variable Bradley-Terry-like model] PBO extends the applicability of standard BO ideas and generalizes previous discrete dueling approaches by modeling the probability of the winner of each duel by means of a Gaussian process model with a Bernoulli likelihood. The latent preference function is used to define a family of acquisition functions that extend usual policies used in BO. [Primarily, a Thompson sampling, implemented the obvious way.]

We illustrate the benefits of PBO in a variety of experiments, showing that PBO needs drastically fewer comparisons for finding the optimum. According to our experiments, the way of modeling correlations in PBO is key in obtaining this advantage.

--- https://www.reddit.com/r/dalle2/comments/191ir66/sae_hello_to_asoka_3_times/ 2023-11-30 ai/nn/transformer/gpt/dall-e/3 --- https://www.printmag.com/type-tuesday/a-typography-of-reuse/ 2023-11-30 design/typography --- https://goodscience.substack.com/p/weve-made-some-metascience-progress 2023-11-30 statistics/bias --- https://en.wikipedia.org/wiki/Coulomb_explosion Coulomb explosion 2023-11-30 technology --- https://github.com/makew0rld/merkdir 2023-11-30 cs/cryptography --- https://arxiv.org/abs/2401.04088#mistral Mixtral of Experts Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed 2024-01-08 2024-01-08 [("doi","10.48550/arXiv.2401.04088")] ai/scaling/mixture-of-experts

We introduce Mixtral 8×7B, a Sparse Mixture of Experts (SMoE) language model. Mixtral has the same architecture as Mistral-7B, with the difference that each layer is composed of 8 feedforward blocks (ie. experts). For every token, at each layer, a router network selects two experts to process the current state and combine their outputs. Even though each token only sees two experts, the selected experts can be different at each timestep.

As a result, each token has access to 47b parameters, but only uses 13B active parameters during inference. Mixtral was trained with a context size of 32k tokens and it outperforms or matches LLaMA-2 70B and GPT-3.5 across all evaluated benchmarks. In particular, Mixtral vastly outperforms LLaMA-2 70B on mathematics, code generation, and multilingual benchmarks.

We also provide a model fine-tuned to follow instructions, Mixtral 8×7B—Instruct, that surpasses GPT-3.5 Turbo, Claude-2.1, Gemini Pro, and LLaMA-2 70B—chat model on human benchmarks.

Both the base and instruct models are released under the Apache 2.0 license.

--- http://www.cs.umd.edu/~ben/goldenrules.html 2023-11-30 design --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390719/ Neural correlates of deception: lying about past events and personal beliefs Noa Ofen, Susan Whitfield-Gabrieli, Xiaoqian J. Chai, Rebecca F. Schwarzlose, John D. E. Gabrieli 2017 2023-11-30 [("doi","10.1093/scan/nsw151")] psychology/neuroscience

Although a growing body of literature suggests that cognitive control processes are involved in deception, much about the neural correlates of lying remains unknown. In this study, we tested whether brain activation associated with deception, as measured by functional magnetic resonance imaging (fMRI), can be detected either in preparation for or during the execution of a lie, and whether they depend on the content of the lie.

We scanned participants while they lied or told the truth about either their personal experiences (episodic memories) or personal beliefs. Regions in the frontal and parietal cortex showed higher activation when participants lied compared with when they were telling the truth, regardless of whether they were asked about their past experiences or opinions. In contrast, lie-related activation in the right temporal pole, precuneus and the right amygdala differed by the content of the lie.

Preparing to lie activated parietal and frontal brain regions that were distinct from those activated while participants executed lies. Our findings concur with previous reports on the involvement of frontal and parietal regions in deception, but specify brain regions involved in the preparation vs execution of deception, and those involved in deceiving about experiences vs opinions.

--- https://arxiv.org/abs/2309.15840 How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions Lorenzo Pacchiardi, Alex J. Chan, Sören Mindermann, Ilan Moscovitz, Alexa Y. Pan, Yarin Gal, Owain Evans, Jan Brauner 2023-09-26 2023-11-30 [("doi","10.48550/arXiv.2309.15840")] ai/nn/transformer/gpt/calibration reinforcement-learning/safe

Large language models (LLMs) can “lie”, which we define as outputting false statements despite “knowing” the truth in a demonstrable sense. LLMs might “lie”, for example, when instructed to output misinformation.

Here, we develop a simple lie detector that requires neither access to the LLM’s activations (black-box) nor ground-truth knowledge of the fact in question. The detector works by asking a predefined set of unrelated follow-up questions after a suspected lie, and feeding the LLM’s yes/no answers into a logistic regression classifier.

Despite its simplicity, this lie detector is highly accurate and surprisingly general. When trained on examples from a single setting—prompting GPT-3.5 to lie about factual questions—the detector generalizes out-of-distribution to (1) other LLM architectures, (2) LLMs fine-tuned to lie, (3) sycophantic lies, and (4) lies emerging in real-life scenarios such as sales.

These results indicate that LLMs have distinctive lie-related behavioral patterns, consistent across architectures and contexts, which could enable general-purpose lie detection.

--- https://arxiv.org/abs/2312.10029 Challenges with unsupervised LLM knowledge discovery Sebastian Farquhar, Vikrant Varma, Zachary Kenton, Johannes Gasteiger, Vladimir Mikulik, Rohin Shah 2023-12-15 2023-12-15 [("doi","10.48550/arXiv.2312.10029")] ai/nn/transformer/gpt/calibration reinforcement-learning/safe

We show that existing unsupervised methods on large language model (LLM) activations do not discover knowledge—instead they seem to discover whatever feature of the activations is most prominent. The idea behind unsupervised knowledge elicitation is that knowledge satisfies a consistency structure, which can be used to discover knowledge.

We first prove theoretically that arbitrary features (not just knowledge) satisfy the consistency structure of a particular leading unsupervised knowledge-elicitation method, contrast-consistent search. We then present a series of experiments showing settings in which unsupervised methods result in classifiers that do not predict knowledge, but instead predict a different prominent feature.

We conclude that existing unsupervised methods for discovering latent knowledge are insufficient, and we contribute sanity checks to apply to evaluating future knowledge elicitation methods. Conceptually, we hypothesize that the identification issues explored here, eg. distinguishing a model’s knowledge from that of a simulated character’s, will persist for future unsupervised methods.

--- https://one-from-nippon.ghost.io/randoseru/ 2023-11-30 japan --- https://www.quantamagazine.org/magical-error-correction-scheme-proved-inherently-inefficient-20240109/ 2023-12-01 cs/cryptography --- https://arxiv.org/abs/2305.14292 WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia Sina J. Semnani, Violet Z. Yao, Heidi C. Zhang, Monica S. Lam 2023-05-23 2023-12-01 [("doi","10.48550/arXiv.2305.14292")] ai/dataset ai/nn/retrieval ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/4/nonfiction wikipedia

This paper presents the first few-shot LLM-based chatbot that almost never hallucinates and has high conversationality and low latency. WikiChat is grounded on the English Wikipedia, the largest curated free-text corpus.

WikiChat generates a response from an LLM, retains only the grounded facts, and combines them with additional information it retrieves from the corpus to form factual and engaging responses. We distill WikiChat based on GPT-4 into a 7B-parameter LLaMA model with minimal loss of quality, to improve its latency, cost and privacy, and facilitate research and deployment.

Using a novel hybrid human-and-LLM evaluation methodology, we show that our best system achieves 97.3% factual accuracy in simulated conversations. It outperforms all retrieval-based and LLM-based baselines, and by 3.9%, 38.6% and 51.0% on head, tail and recent knowledge compared to GPT-4. Compared to previous state-of-the-art retrieval-based chatbots, WikiChat is also more informative and engaging, just like an LLM.

WikiChat achieves 97.9% factual accuracy in conversations with human users about recent topics, 55.0% better than GPT-4, while receiving higher user ratings and more favorable comments.

--- https://netflixtechblog.com/learning-a-personalized-homepage-aa8ec670359a#1c3e 2023-12-01 reinforcement-learning/offline --- https://arxiv.org/abs/2401.04092 GPT-4-V(ision) is a Human-Aligned Evaluator for Text-to-3D Generation Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, Gordon Wetzstein 2024-01-08 2024-01-08 [("doi","10.48550/arXiv.2401.04092")] ai/nn/transformer/gpt/4/nonfiction

[code] Despite recent advances in text-to-3D generative methods, there is a notable absence of reliable evaluation metrics. Existing metrics usually focus on a single criterion each, such as how well the asset aligned with the input text. These metrics lack the flexibility to generalize to different evaluation criteria and might not align well with human preferences.

Conducting user preference studies is an alternative that offers both adaptability and human-aligned results. User studies, however, can be very expensive to scale.

This paper presents an automatic, versatile, and human-aligned evaluation metric for text-to-3D generative models. To this end, we first develop a prompt generator using GPT-4-V to generate evaluating prompts, which serve as input to compare text-to-3D models. We further design a method instructing GPT-4-V to compare two 3D assets according to user-defined criteria.

Finally, we use these pairwise comparison results to assign these models Elo ratings. Experimental results suggest our metric strongly align with human preference across different evaluation criteria.

--- https://arxiv.org/abs/2401.04081 MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts Maciej Pióro, Kamil Ciebiera, Krystian Król, Jan Ludziejewski, Sebastian Jaszczur 2024-01-08 2024-01-08 [("doi","10.48550/arXiv.2401.04081")] ai/nn/rnn ai/scaling/mixture-of-experts

State Space Models (SSMs) have become serious contenders in the field of sequential modeling, challenging the dominance of Transformers. At the same time, Mixture of Experts (MoE) has improved Transformer-based LLMs, including recent state-of-the-art open-source models. We propose that to unlock the potential of SSMs for scaling, they should be combined with MoE.

We showcase this on Mamba, a recent SSM-based model that achieves remarkable, Transformer-like performance.

Our model, MoE-Mamba, outperforms both Mamba and Transformer-MoE. In particular, MoE-Mamba reaches the same performance as Mamba in 2.2× less training steps while preserving the inference performance gains of Mamba against the Transformer.

--- https://arxiv.org/abs/2312.17238 Fast Inference of Mixture-of-Experts Language Models with Offloading Artyom Eliseev, Denis Mazur 2023-12-28 2023-12-28 [("doi","10.48550/arXiv.2312.17238")] ai/scaling/mixture-of-experts

With the widespread adoption of Large Language Models (LLMs), many deep learning practitioners are looking for strategies of running these models more efficiently. One such strategy is to use sparse Mixture-of-Experts (MoE)—a type of model architectures where only a fraction of model layers are active for any given input. This property allows MoE-based language models to generate tokens faster than their dense counterparts, but it also increases model size due to having multiple experts. Unfortunately, this makes state-of-the-art MoE language models difficult to run without high-end GPUs.

In this work, we study the problem of running large MoE language models on consumer hardware with limited accelerator memory. We build upon parameter offloading algorithms and propose a novel strategy that accelerates offloading by taking advantage of innate properties of MoE LLMs.

Using this strategy, we build can run Mixtral-8×7B with mixed quantization on desktop hardware and free-tier Google Colab instances.

--- https://www.sciencedirect.com/science/article/pii/S0047637419300181 2023-12-01 longevity statistics/survival-analysis --- https://www.propublica.org/article/school-absenteeism-truancy-education-students 2023-12-01 sociology/technology --- /doc/psychiatry/traumatic-brain-injury/2015-carlson.pdf Bankruptcy Rates among NFL Players with Short-Lived Income Spikes Kyle Carlson, Joshua Kim, Annamaria Lusardi, Colin F. Camerer 2015-05-01 2023-12-01 [("doi","10.1257/aer.p20151038")] economics psychiatry/traumatic-brain-injury

We test for consumption smoothing using bankruptcy data on players in the National Football League (NFL), who typically earn several million dollars during an income spike that lasts a few years. The life-cycle hypothesis predicts that players should save substantially while playing and then have little risk of bankruptcy post-NFL.

However, players in our sample begin to file for bankruptcy soon after they stop playing and continue filing at a high rate through at least the first 12 years of retirement.

Players’ total earnings and career lengths have surprisingly little effect on the risk of bankruptcy.

--- https://spectrum.ieee.org/global-robotic-brain 2023-12-01 reinforcement-learning/robot reinforcement-learning/scaling --- https://arxiv.org/abs/2306.00323 Thought Cloning: Learning to Think while Acting by Imitating Human Thinking Shengran Hu, Jeff Clune 2023-06-01 2023-12-02 [("doi","10.48550/arXiv.2306.00323")] ai/nn/rnn ai/nn/transformer/gpt/inner-monologue reinforcement-learning/imitation-learning reinforcement-learning/safe

[LLM inner-monologue for a gridworld agent; homepage/code] Language is often considered a key aspect of human thinking, providing us with exceptional abilities to generalize, explore, plan, replan, and adapt to new situations. However, Reinforcement Learning (RL) agents are far from human-level performance in any of these abilities. We hypothesize one reason for such cognitive deficiencies is that they lack the benefits of thinking in language and that we can improve AI agents by training them to think like humans do.

We introduce a novel Imitation Learning framework, Thought Cloning, where the idea is to not just clone the behaviors of human demonstrators, but also the thoughts humans have as they perform these behaviors. While we expect Thought Cloning to truly shine at scale on internet-sized datasets of humans thinking out loud while acting (eg. online videos with transcripts), here we conduct experiments in a domain [BabyAI] where the thinking and action data are synthetically generated.

Results reveal that Thought Cloning learns much faster than Behavioral Cloning and its performance advantage grows the further out of distribution test tasks are, highlighting its ability to better handle novel situations.

Thought Cloning also provides important benefits for AI Safety and Interpretability, and makes it easier to debug and improve AI. Because we can observe the agent’s thoughts, we can (1) more easily diagnose why things are going wrong, making it easier to fix the problem, (2) steer the agent by correcting its thinking, or (3) prevent it from doing unsafe things it plans to do.

Overall, by training agents how to think as well as behave, Thought Cloning creates safer, more powerful agents.

…One advantage of BabyAI is that it provides an Oracle Solver (named BOT by BabyAI) capable of generating step-by-step solutions for any given environment. This is achieved through hand-coded rules and an internal stack machine to generate plans for solving environments. In our work, we translate the Oracle Solver’s internal states into natural language thoughts with pre-defined rules. For example, if the inner logic is to open a red door to explore the room, the translated thought will read, “open red door to explore”. This translation process is combined with the generated demonstrations to synthesize the thought dataset with 1 million trajectories. To make the dataset more realistic, noise is added, with a 1% chance of adding a random noisy segment at each timestep, consisting of a random thought and several random actions, with a random length sampled from 1–6.

--- https://www.noemamag.com/the-unending-quest-to-build-a-better-chicken/q 2023-12-02 genetics/selection/artificial --- /doc/fiction/humor/1868-twain.pdf Cannibalism in the Cars Mark Twain 1868-11-01 2023-12-02 fiction/humor politics --- https://www.annualreviews.org/doi/pdf/10.1146/annurev.nu.02.070182.000245 2023-12-02 biology nootropic/quantified-self --- https://queue.acm.org/detail.cfm?id=3400901 2023-12-02 design economics/advertising --- https://journals.sagepub.com/doi/full/10.1177/17456916231202685 2023-12-02 sociology --- https://carnegieendowment.org/2021/08/17/afterword-korea-s-challenge-to-standard-internet-interconnection-model-pub-85166 2023-12-02 economics technology --- https://www.nngroup.com/articles/ten-usability-heuristics/ 2023-12-02 design --- http://antirez.com/news/141 2023-12-02 ai/nn/transformer/gpt/4/nonfiction psychology/writing --- https://www.biorxiv.org/content/10.1101/2022.12.22.521565.full Evidence suggesting creatine as a new central neurotransmitter: presence in synaptic vesicles, release upon stimulation, effects on cortical neurons and uptake into synaptosomes and synaptic vesicles Xiling Bian, Jiemin Zhu, Xiaobo Jia, Wenjun Liang, Sihan Yu, Zhiqiang Li, Wenxia Zhang, Yi Rao 2023-10-04 2023-12-02 [("doi","10.1101/2022.12.22.521565")] creatine psychology/neuroscience

The discovery of a new neurotransmitter, especially one in the central nervous system (CNS), is both important and difficult. We have been searching for new neurotransmitters for 12 years.

We detected creatine (Cr) in synaptic vesicles (SVs), at a level lower than glutamate (Glu) and gamma-aminobutyric acid (GABA) but higher than acetylcholine (ACh) and 5-hydroxytryptamine (5-HT). SV Cr was reduced in mice lacking either arginine:glycine amidinotransferase (AGAT, a Cr synthetase) or SLC6A8, a Cr transporter with mutations among the most common causes of intellectual disability (ID) in men.

Calcium-dependent release of Cr was detected after stimulation in brain slices. Cr release was reduced in SLC6A8 and AGAT mutants. Cr inhibited neocortical pyramidal neurons. SLC6A8 was necessary for Cr uptake into synaptosomes. Cr was found by us to be taken up into SVs in an ATP dependent manner.

Our biochemical, chemical, genetic and electrophysiological results are consistent with the possibility of Cr as a neurotransmitter, though not yet reaching the level of proof for the now classic transmitters. Our novel approach to discover neurotransmitters is to begin with analysis of contents in SVs before defining their function and physiology.

--- https://www.nature.com/articles/s41591-023-02705-w 2023-12-02 psychedelic psychiatry/traumatic-brain-injury --- /doc/design/typography/2012-gaskins-sweatingbullets.pdf Sweating Bullets: Notes about Inventing PowerPoint Robert Gaskins 2012-04-20 2023-12-03 cs design/typography --- https://arxiv.org/abs/1706.09516#yandex CatBoost: unbiased boosting with categorical features Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin 2017-06-28 2023-12-03 [("doi","10.48550/arXiv.1706.09516")] ai/tabular

This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit. Their combination leads to CatBoost outperforming other publicly available boosting implementations in terms of quality on a variety of datasets.

Two critical algorithmic advances introduced in CatBoost are the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms.

In this paper, we provide a detailed analysis of this problem and demonstrate that proposed algorithms solve it effectively, leading to excellent empirical results.

--- /doc/psychology/cognitive-bias/2023-enke.pdf Cognitive Biases: Mistakes or Missing Stakes? Benjamin Enke, Uri Gneezy, Brian Hall, David Martin, Vadim Nelidov, Theo Offerman, Jeroen van de Ven 2023-07-11 2023-12-03 [("doi","10.1162/rest_a_01093")] economics psychology/cognitive-bias statistics/prediction

Despite decades of research on heuristics and biases, evidence on the effect of large incentives on cognitive biases is scant.

We test the effect of incentives on 4 widely documented biases: base-rate neglect, anchoring, failure of contingent thinking, and intuitive reasoning [CRT]. In laboratory experiments with 1,236 college students in Nairobi, we implement 3 incentive levels: no incentives, standard lab payments, and very high incentives.

We find that very high stakes increase response times by 40% but improve performance only very mildly or not at all. In none of the tasks do very high stakes come close to debiasing participants.

...Our results contrast with the predictions of a sample of 68 researchers, drawn from professional experimental economists and Harvard students with exposure to graduate-level experimental economics. These researchers predict that performance will improve by an average of 25% going from no incentives to standard incentives, and by another 25% going from standard to very high incentives. Although some variation is seen in projected performance increases across tasks, these predictions are always more bullish about the effect of incentives than our experimental data warrant.

--- https://www.biorxiv.org/content/10.1101/2024.01.06.574476.full A universal molecular mechanism driving aging Wan Jin, Jing Zheng, Yu Xiao, Lingao Ju, Fangjin Chen, Jie Fu, Hui Jiang, Yi Zhang 2024-01-06 2024-01-06 [("doi","10.1101/2024.01.06.574476")] longevity/epigenetics

How cell replication ultimately results in aging and the Hayflick limit are not fully understood. Here we show that clock-like accumulation of DNA G-quadruplexes (G4s) throughout cell replication drives conserved aging mechanisms. G4 stimulates transcription-replication interactions to delay genome replication and impairs DNA re-methylation and histone modification recovery, leading to loss of heterochromatin. This creates a more permissive local environment for G4 formation in subsequent generations.

As a result, G4s gradually accumulate on promoters throughout mitosis, driving clock-like DNA hypomethylation and chromatin opening. In patients and in vitro models, loss-of-function mutations in the G4-resolving enzymes WRN, BLM and ERCC8 accelerate the erosion of the epigenomic landscape around G4.

G4-driven epigenomic aging is strongly correlated with biological age and is conserved in yeast, nematodes, insects, fish, rodents, and humans. Our results revealed a universal molecular mechanism of aging and provided mechanistic insight into how G-quadruplex processor mutations drive premature aging.

--- https://arxiv.org/abs/2112.10508 Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky, Colin Raffel, Manan Dey, Matthias Gallé, Arun Raja, Chenglei Si, Wilson Y. Lee, Benoît Sagot, Samson Tan 2021-12-20 2023-12-03 [("doi","10.48550/arXiv.2112.10508")] ai/nn/tokenization

What are the units of text that we want to model? From bytes to multi-word expressions, text can be analyzed and generated at many granularities. Until recently, most natural language processing (NLP) models operated over words, treating those as discrete and atomic tokens, but starting with byte-pair encoding (BPE), subword-based approaches have become dominant in many areas, enabling small vocabularies while still allowing for fast inference. Is the end of the road character-level model or byte-level processing?

In this survey, we connect several lines of work from the pre-neural and neural era, by showing how hybrid approaches of words and characters as well as subword-based approaches based on learned segmentation have been proposed and evaluated.

We conclude that there is and likely will never be a silver bullet singular solution for all applications and that thinking seriously about tokenization remains important for many applications.

--- https://arxiv.org/abs/2212.10562#google Character-Aware Models Improve Visual Text Rendering Rosanne Liu, Dan Garrette, Chitwan Saharia, William Chan, Adam Roberts, Sharan Narang, Irina Blok, R. J. Mical, Mohammad Norouzi, Noah Constant 2022-12-20 2023-12-03 [("doi","10.48550/arXiv.2212.10562")] ai/nn/diffusion ai/nn/tokenization ai/nn/transformer/gpt/palm ai/nn/transformer/t5 design/typography

[BPEs strike again; cf. DALL·E 2, Itzhak & Levin 2021] Current image generation models struggle to reliably produce well-formed visual text. In this paper, we investigate a key contributing factor: popular text-to-image models lack character-level input features, making it much harder to predict a word’s visual makeup as a series of glyphs.

To quantify this effect, we conduct a series of experiments comparing character-aware [ByT5] vs. character-blind text encoders [T5, PaLM]. In the text-only domain, we find that character-aware models provide large gains on a novel spelling task (WikiSpell).

Applying our learnings to the visual domain, we train a suite of image generation models [Imagen], and show that character-aware variants outperform their character-blind counterparts across a range of novel text rendering tasks (our DrawText benchmark).

Our models set a much higher state-of-the-art on visual spelling, with 30+ point accuracy gains over competitors on rare words, despite training on far fewer examples.

Figure 1: Top: Image generation models lacking character-level input features often misspell words. Bottom: Using a character-aware text encoder substantially improves the accuracy of rendered text. Prompts are: “A vintage postage stamp with the message: _______”, with messages: (1) California: All Dreams Welcome, (2) Canada: For Glowing Hearts, (3) Colorado: It’s Our Nature, (4) St. Louis: All Within Reach.

…In §3 we find that, with sufficient scale, character-blind models can achieve near-perfect spelling accuracy. We dub this phenomenon the spelling miracle, to emphasize the difficulty of inferring a token’s spelling from its distribution alone. At the same time, we observe that character-blind text encoders of the sizes used in practice for image generation are lacking core spelling knowledge.

With this in mind, it is unsurprising that today’s image generation models struggle to translate input tokens into glyph sequences. These models’ text encoders are all character-blind, with Stable Diffusion, DALL·E, DALL·E-2, Imagen, Parti and eDiff-I all adopting BPE tokenizers (Rombach et al 2021; Ramesh et al 2021, 2022; Saharia et al 2022; Yu et al 2022; Balaji et al 2022).

…Secondly, we find that for character-blind models, scale is a key factor in spelling ability. Both T5 and mT5 improve with scale, but even at XXL size, they are not particularly strong (eg. T5-XXL’s performance on common English words is only 66%). Only when character-blind models reach PaLM’s scale do we start to see near-perfect spelling ability: PaLM 540B achieves >99% accuracy across all frequency buckets in English, despite the fact that it sees only 20 examples in its prompt (as opposed to the 1,000 fine-tuning examples shown to T5). However, performance is lower on other languages.

Table 1: WikiSpell exact-match accuracy results for English. T5 models range from Base (B) (250M params) to XXL (11B params), while ByT5 models range from Base (300M) to XXL (13B).

Our experiments on ByT5 show that character-aware models have far greater spelling ability. ByT5’s performance at Base and Large sizes is only slightly behind XL and XXL (though still in at least the mid-90% range), and the frequency of a word has little effect on ByT5’s ability to spell it. These results far exceed those of (m)T5, and are comparable to the English performance of PaLM, which has >100× more parameters, and exceed PaLM’s performance on other languages. These findings indicate that substantially more character-level information is retained by the ByT5 encoder, and in such a way that it can be retrieved from those frozen parameters as needed for the decoding task.

Figure 4: Accuracy of 10 image generation models on DrawText Spelling. Character-aware models (ByT5 and Concat) outperform others regardless of size, and particularly on rare words. Imagen-AR shows the benefit of avoiding cropping, but still underperforms character-aware models, despite training 6.6× longer.
Figure 12: Non-cherrypicked samples from our T5-XXL (top) and ByT5-XXL (bottom) models. The character-aware ByT5 model reliably spells the target word correctly, with only minor issues around letter shapes or letter merging. Over 100 samples, we found the character-blind T5 model never produced the target spelling. Prompt: ‘The word “exquisite” written in modern calligraphy.’
--- /doc/ai/nn/tokenization/2021-liu-table1-spellingtestforbyt5vst5vspalmshowsbyt5spellsmuchbetter.png 2021 2023-12-03 ai/nn/tokenization ai/nn/transformer/t5 --- /doc/ai/nn/tokenization/2021-liu-figure4-accuracyof10imagegenerationmodelsondrawingtextshowsbyt5best.png 2021 2023-12-03 ai/nn/tokenization ai/nn/transformer/t5 --- /doc/ai/nn/tokenization/2021-liu-figure12-randomsamplesforwritingthewordexquisiteusingbyt5vst5showingbyt5usuallyright.jpg 2021 2023-12-03 ai/nn/tokenization ai/nn/transformer/t5 --- /doc/ai/nn/tokenization/2021-liu-figure1-characterawarevsbpeblindedimagegenerationoftextinsideanimagedemonstratingthatcharacterawaremodelsgeneratetextwell.png 2021 2023-12-03 ai/nn/tokenization ai/nn/transformer/t5 --- https://publicdomainreview.org/essay/the-early-history-of-the-channel-tunnel/ 2023-12-03 history/public-domain-review technology --- https://www.wired.com/story/david-lynch-dune-sequel-script-unearthed/ 2023-12-04 fiction/science-fiction/frank-herbert --- https://arxiv.org/abs/2210.14891 Broken Neural Scaling Laws Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger 2022-10-26 2023-12-04 [("doi","10.48550/arXiv.2210.14891")] ai/nn/adversarial ai/nn/sparsity/low-precision ai/nn/transformer/gpt/codex ai/scaling/emergence math reinforcement-learning/meta-learning/continual-learning reinforcement-learning/robot reinforcement-learning/safe reinforcement-learning/scaling

We present a smoothly broken power law functional form (that we refer to as a Broken Neural Scaling Law (BNSL)) that accurately models & extrapolates the scaling behaviors of deep neural networks (ie. how the evaluation metric of interest varies as amount of compute used for training (or inference), number of model parameters, training dataset size, model input size, number of training steps, or upstream performance varies) for various architectures & for each of various tasks within a large & diverse set of upstream & downstream tasks, in zero-shot, prompted, & finetuned settings.

This set includes large-scale vision, language, audio, video, diffusion, generative modeling, multimodal learning, contrastive learning, AI alignment, AI capabilities, robotics, out-of-distribution (OOD) generalization, continual learning, transfer learning, uncertainty estimation / calibration, OOD detection, adversarial robustness, distillation, sparsity, retrieval, quantization, pruning, fairness, molecules, computer programming/coding, math word problems, “emergent phase transitions”, arithmetic, supervised learning, unsupervised/self-supervised learning, & reinforcement learning (single agent & multi-agent).

When compared to other functional forms for neural scaling, this functional form yields extrapolations of scaling behavior that are considerably more accurate on this set. Moreover, this functional form accurately models & extrapolates scaling behavior that other functional forms are incapable of expressing such as the nonmonotonic transitions present in the scaling behavior of phenomena such as double descent & the delayed, sharp inflection points present in the scaling behavior of tasks such as arithmetic.

Lastly, we use this functional form to glean insights about the limit of the predictability of scaling behavior.

Code is available at Github.

--- https://github.com/ThomasScialom/T0_continual_learning 2023-12-04 reinforcement-learning/meta-learning/continual-learning --- https://huggingface.co/ThomasNLG/CT0-11B 2023-12-04 reinforcement-learning/meta-learning/continual-learning --- https://arxiv.org/abs/1703.10371 Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (EPANNs) Andrea Soltoggio, Kenneth O. Stanley, Sebastian Risi 2017-03-30 2023-12-04 [("doi","10.1016/j.neunet.2018.07.013")] reinforcement-learning/model-free

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence.

Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation.

EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions.

This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented.

--- https://www.nytimes.com/2024/01/10/health/gas-station-heroin-tianeptine-addiction.html 2023-12-04 nootropic --- https://arxiv.org/abs/2101.00403 Superbizarre Is Not Superb: Derivational Morphology Improves BERT’s Interpretation of Complex Words Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze 2021-01-02 2023-12-04 [("doi","10.48550/arXiv.2101.00403")] ai/nn/tokenization ai/nn/transformer

How does the input segmentation of pretrained language models (PLMs) affect their interpretations of complex words? We present the first study investigating this question, taking BERT as the example PLM and focusing on its semantic representations of English derivatives.

We show that PLMs can be interpreted as serial dual-route models, ie. the meanings of complex words are either stored or else need to be computed from the subwords, which implies that maximally meaningful input tokens should allow for the best generalization on new words.

This hypothesis is confirmed by a series of semantic probing tasks on which DelBERT (Derivation leveraging BERT), a model with derivational input segmentation, substantially outperforms BERT with WordPiece segmentation.

Our results suggest that the generalization capabilities of PLMs could be further improved if a morphologically-informed vocabulary of input tokens were used.

--- https://arxiv.org/abs/2305.17491 FERMAT: An Alternative to Accuracy for Numerical Reasoning Jasivan Alex Sivakumar, Nafise Sadat Moosavi 2023-05-27 2023-12-04 [("doi","10.48550/arXiv.2305.17491")] ai/nn/tokenization ai/nn/transformer math

While pre-trained language models achieve impressive performance on various NLP benchmarks, they still struggle with tasks that require numerical reasoning. Recent advances in improving numerical reasoning are mostly achieved using very large language models that contain billions of parameters and are not accessible to everyone.

In addition, numerical reasoning is measured using a single score on existing datasets. As a result, we do not have a clear understanding of the strengths and shortcomings of existing models on different numerical reasoning aspects and therefore, potential ways to improve them apart from scaling them up.

Inspired by CheckList (Ribeiro et al 2020), we introduce a multi-view evaluation set for numerical reasoning in English, called FERMAT.

Instead of reporting a single score on a whole dataset, FERMAT evaluates models on various key numerical reasoning aspects such as number understanding, mathematical operations, and training dependency.

Apart from providing a comprehensive evaluation of models on different numerical reasoning aspects, FERMAT enables a systematic and automated generation of an arbitrarily large training or evaluation set for each aspect.

The datasets and codes are publicly available to generate further multi-view data for ulterior tasks and languages.

--- https://suricrasia.online/blog/making-amulets-with-llama/ 2023-12-04 ai/nn/transformer/gpt/poetry ai/text-style-transfer cs/cryptography --- https://www.flycheck.org/en/latest/ 2023-12-04 cs/lisp --- https://github.com/yoshiki/yaml-mode 2023-12-04 cs/lisp --- https://github.com/jrblevin/markdown-mode 2023-12-05 cs/lisp --- https://www.infinitepartitions.com/art001.html 2023-12-05 ai/nn/transformer/attention/compression --- https://jvns.ca/blog/2013/10/24/day-16-gzip-plus-poetry-equals-awesome/ 2023-12-05 ai/nn/transformer/attention/compression fiction/poetry --- https://chemlambda.github.io/index.html 2023-12-05 biology cs/computable --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4585175/ 2023-12-05 biology cs/computable --- https://en.wikipedia.org/wiki/Perfect_Bayesian_equilibrium Perfect Bayesian equilibrium 2023-12-05 reinforcement-learning/multi-agent statistics/bayes --- https://aclanthology.org/2022.acl-short.43.pdf FLOTA: An Embarrassingly Simple Method to Mitigate Und-es-ira-ble Properties of Pretrained Language Model Tokenizers Valentin Hofmann, Hinrich Schuetze, Janet Pierrehumbert 2022-05 2023-12-05 [("doi","10.18653/v1/2022.acl-short.43")] ai/nn/tokenization ai/nn/transformer/gpt/2

[code, video] We introduce FLOTA (Few Longest Token Approximation), a simple yet effective method to improve the tokenization of pretrained language models (PLMs).

FLOTA uses the vocabulary of a standard tokenizer but tries to preserve the morphological structure of words during tokenization.

We evaluate FLOTA on morphological gold segmentations as well as a text classification task, using BERT, GPT-2, and XLNet as example PLMs.

FLOTA leads to performance gains, makes inference more efficient, and enhances the robustness of PLMs with respect to whitespace noise.

--- https://arxiv.org/abs/2307.01201#deepmind Schema-learning and rebinding as mechanisms of in-context learning and emergence Sivaramakrishnan Swaminathan, Antoine Dedieu, Rajkumar Vasudeva Raju, Murray Shanahan, Miguel Lazaro-Gredilla, Dileep George 2023-06-16 2023-12-05 [("doi","10.48550/arXiv.2307.01201")] ai/scaling/emergence reinforcement-learning/meta-learning

[cf. Xie et al 2021, Guntupalli et al 2023] In-context learning (ICL) is one of the most powerful and most unexpected capabilities to emerge in recent transformer-based large language models (LLMs). Yet the mechanisms that underlie it are poorly understood.

In this paper, we demonstrate that comparable ICL capabilities can be acquired by an alternative sequence prediction learning method using clone-structured causal graphs (CSCGs).

Moreover, a key property of CSCGs is that, unlike transformer-based LLMs, they are interpretable, which considerably simplifies the task of explaining how ICL works. Specifically, we show that it uses a combination of (1) learning template (schema) circuits for pattern completion, (2) retrieving relevant templates in a context-sensitive manner, and (3) rebinding of novel tokens to appropriate slots in the templates.

We go on to marshal evidence for the hypothesis that similar mechanisms underlie ICL in LLMs. For example, we find that, with CSCGs as with LLMs, different capabilities emerge at different levels of overparameterization, suggesting that overparameterization helps in learning more complex template (schema) circuits.

By showing how ICL can be achieved with small models and datasets, we open up a path to novel architectures, and take a vital step towards a more general understanding of the mechanics behind this important capability.

[Experimental "Up-goer-five"-style summary by GPT-4: "In this writing, we talk about a very strong and very surprising thing that some big talking computer programs can do. This thing is called ‘in-context learning’ or ICL. But we don’t really know how it works. We made a study where we show that we can make another program that can do similar things as ICL using a different way of learning. This different way uses special graphs called ‘clone-structured causal graphs’ or CSCGs. The good thing about CSCGs is that they are easy to understand, unlike the big talking computer programs. This makes it easier to explain how ICL works. We found out that ICL does 3 main things: (1) it learns shapes, (2) it finds the right shapes depending on what is happening, and (3) it puts new words in the right places in the shapes. We also found strong support that the big talking computer programs work the same way as CSCGs. When we make the CSCGs or the big talking computer programs bigger, they can do more hard things. This means that having more stuff in these programs helps them learn more challenging shapes. By showing that we can do ICL with small programs and small sets of words, we open up new ways to make different kinds of programs. This is an important step toward getting a better idea of how ICL works."]

--- https://arxiv.org/abs/2212.01508#deepmind Space is a latent [CSCG] sequence: Structured sequence learning as a unified theory of representation in the hippocampus Rajkumar Vasudeva Raju, J. Swaroop Guntupalli, Guangyao Zhou, Miguel Lázaro-Gredilla, Dileep George 2022-12-03 2023-12-05 [("doi","10.48550/arXiv.2212.01508")] psychology/neuroscience reinforcement-learning/model

Fascinating and puzzling phenomena, such as landmark vector cells, splitter cells, and event-specific representations to name a few, are regularly discovered in the hippocampus. Without a unifying principle that can explain these divergent observations, each experiment seemingly discovers a new anomaly or coding type.

Here, we provide a unifying principle that the mental representation of space is an emergent property of latent higher-order sequence learning. Treating space as a sequence resolves myriad phenomena, and suggests that the place-field mapping methodology where sequential neuron responses are interpreted in spatial and Euclidean terms might itself be a source of anomalies.

Our model, called Clone-structured Causal Graph (CSCG), uses a specific higher-order graph scaffolding to learn latent representations by mapping sensory inputs to unique contexts. Learning to compress sequential and episodic experiences using CSCGs result in the emergence of cognitive maps—mental representations of spatial and conceptual relationships in an environment that are suited for planning, introspection, consolidation, and abstraction.

We demonstrate that over a dozen different hippocampal phenomena, ranging from those reported in classic experiments to the most recent ones, are succinctly and mechanistically explained by our model.

--- https://arxiv.org/abs/2302.07350#deepmind Graph schemas as abstractions for transfer learning, inference, and planning J. Swaroop Guntupalli, Rajkumar Vasudeva Raju, Shrinu Kushagra, Carter Wendelken, Danny Sawyer, Ishan Deshpande, Guangyao Zhou, Miguel Lázaro-Gredilla, Dileep George 2023-02-14 2023-12-05 [("doi","10.48550/arXiv.2302.07350")] reinforcement-learning/model

Transferring latent structure from one environment or problem to another is a mechanism by which humans and animals generalize with very little data. Inspired by cognitive and neurobiological insights, we propose graph schemas as a mechanism of abstraction for transfer learning. Graph schemas start with latent graph learning where perceptually aliased observations are disambiguated in the latent space using contextual information. Latent graph learning is also emerging as a new computational model of the hippocampus to explain map learning and transitive inference.

Our insight is that a latent graph can be treated as a flexible template—a schema—that models concepts and behaviors, with slots that bind groups of latent nodes to the specific observations or groundings. By treating learned latent graphs (schemas) as prior knowledge, new environments can be quickly learned as compositions of schemas and their newly learned bindings.

We evaluate graph schemas on two previously published challenging tasks: the memory & planning game and one-shot StreetLearn, which are designed to test rapid task solving in novel environments. Graph schemas can be learned in far fewer episodes than previous baselines, and can model and plan in a few steps in novel variations of these tasks. We also demonstrate learning, matching, and reusing graph schemas in more challenging 2D and 3D environments with extensive perceptual aliasing and size variations, and show how different schemas can be composed to model larger and more complex environments.

To summarize, our main contribution is a unified system, inspired and grounded in cognitive science, that facilitates rapid transfer learning of new environments using schemas via map-induction and composition that handles perceptual aliasing.

--- http://www.datagenetics.com/blog/september32012/index.html 2023-12-06 cs/security --- https://marak.com/blog/2013-05-13-time-loop-software#a-somewhat-puzzling-debugging-session 2023-12-06 fiction/science-fiction/time-travel --- https://marak.com/blog/2013-05-13-time-loop-software#brute-force-cracking-with-time-loops 2023-12-06 fiction/science-fiction/time-travel --- https://en.wikipedia.org/wiki/Palm_Springs_(2020_film) Palm Springs (2020 film) 2023-12-06 fiction/science-fiction/time-travel --- /doc/ai/nn/tokenization/2024-01-10-gwern-gpt4-usingipasoftwaretotrytounderstandatomatopun.png Gwern 2024-01-10 2024-01-10 ai/nn/tokenization ai/nn/transformer/gpt/4/fiction --- https://chat.lmsys.org/ 2023-12-06 ai/nn/transformer/gpt statistics/order/comparison --- https://interlisp.org/history/timeline/#1970 2023-12-06 cs/lisp --- https://www.hodinkee.com/articles/the-case-for-better-watch-typography 2023-12-06 design/typography --- https://theonion.com/study-average-person-s-life-plan-can-only-withstand-25-1819578876/ 2023-12-06 fiction/humor psychology/willpower --- https://www.drmoron.org/posts/mechanical-computer/ 2023-12-06 cs/computable --- https://www.sundayworld.com/crime/irish-crime/irishman-jailed-over-role-in-notorious-darknet-drugs-marketplace-silk-road-walks-free/a1428173093.html 2023-12-06 darknet-market/silk-road/1 --- https://www.bkgm.com/articles/Berliner/ComputerBackgammon/index.html 2023-12-07 reinforcement-learning/model --- https://www.newyorker.com/magazine/1981/12/14/a-i 2023-12-07 ai/nn philosophy/logic philosophy/mind reinforcement-learning/model-free reinforcement-learning/robot --- /doc/iq/1938-buhler.pdf The Ball And Field Test As A Help In The Diagnosis Of Emotional Difficulties Charlotte Buhler 1938 2023-12-07 [("doi","10.1111/j.1467-6494.1938.tb02259.x")] iq psychiatry --- https://prideout.net/blog/svg_knots/ 2023-12-07 design/visualization math --- http://tom7.org/abc/paper.pdf 2023-12-07 cs/computable --- https://x.com/prerationalist/status/1612872812414308403 prerationalist 2023-12-07 cs/computable --- https://simonwillison.net/2023/Oct/26/add-a-walrus/#reusing-seeds 2023-12-07 ai/nn/transformer/gpt/dall-e/3 --- https://onlinelibrary.wiley.com/doi/full/10.1111/joes.12598 2023-12-07 economics statistics/bias statistics/power-analysis --- /doc/politics/2015-moberg.pdf The political economy of special economic zones Lotta Moberg 2014-06-10 2023-12-07 [("doi","10.1017/S1744137414000241")] economics politics

This paper is a first attempt to apply a robust political economy framework to explain when Special Economic Zones (SEZs) can contribute to economic development. A robust political economy is one that channels the actions of self-interested individuals with limited information to promote economic progress.

In the right institutional context, SEZs tend to promote economic growth. In the wrong institutional context, they can cause resource misallocation and rent-seeking. Policy makers introducing SEZs must overcome the knowledge problem to avoid misdirected economic planning.

Yet, the scheme can only fulfill its purpose if it also prevents destructive rent-seeking behavior, both from businesses and from government authorities. The political economy framework of SEZs can be applied to judge their potential efficacy, something that orthodox studies of country features such as natural resources, infrastructure, and zone location fail to do. The Indian and Chinese experiences with SEZs illustrate these points.

--- https://arxiv.org/abs/2401.02843 Thousands of AI Authors on the Future of AI Katja Grace, Harlan Stewart, Julia Fabienne Sandkühler, Stephen Thomas, Ben Weinstein-Raun, Jan Brauner 2024-01-05 2024-01-05 [("doi","10.48550/arXiv.2401.02843")] ai economics/automation reinforcement-learning/safe

[blog, Zvi] In the largest survey of its kind, 2,778 researchers who had published in top-tier artificial intelligence (AI) venues gave predictions on the pace of AI progress and the nature and impacts of advanced AI systems The aggregate forecasts give at least a 50% chance of AI systems achieving several milestones by 2028, including autonomously constructing a payment processing site from scratch, creating a song indistinguishable from a new song by a popular musician, and autonomously downloading and fine-tuning a large language model. If science continues un-disrupted, the chance of unaided machines outperforming humans in every possible task was estimated at 10% by 2027, and 50% by 2047. The latter estimate is 13 years earlier than that reached in a similar survey we conducted only one year earlier [Grace et al 2022, cf. Zhang et al 2022, Grace et al 2017]. However, the chance of all human occupations becoming fully automatable was forecast to reach 10% by 2037, and 50% as late as 2116 (compared to 2164 in the 2022 survey).

Most respondents expressed substantial uncertainty about the long-term value of AI progress: While 68.3% thought good outcomes from superhuman AI are more likely than bad, of these net optimists 48% gave at least a 5% chance of extremely bad outcomes such as human extinction, and 59% of net pessimists gave 5% or more to extremely good outcomes. Between 38% and 51% of respondents gave at least a 10% chance to advanced AI leading to outcomes as bad as human extinction. More than half suggested that “substantial” or “extreme” concern is warranted about 6 different AI-related scenarios, including misinformation, authoritarian control, and inequality. There was disagreement about whether faster or slower AI progress would be better for the future of humanity. However, there was broad agreement that research aimed at minimizing potential risks from AI systems ought to be prioritized more.

--- https://arxiv.org/abs/1905.01067 Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask Hattie Zhou, Janice Lan, Rosanne Liu, Jason Yosinski 2019-05-03 2023-12-07 [("doi","10.48550/arXiv.1905.01067")] ai/nn/sparsity/pruning

The recent Lottery Ticket Hypothesis paper by Frankle & Carbin 2018 showed that a simple approach to creating sparse networks (keeping the large weights) results in models that are trainable from scratch, but only when starting from the same initial weights. The performance of these networks often exceeds the performance of the non-sparse base model, but for reasons that were not well understood.

In this paper we study the 3 critical components of the Lottery Ticket (LT) algorithm, showing that each may be varied without impacting the overall results. Ablating these factors leads to new insights for why LT networks perform as well as they do.

We show why setting weights to zero is important, how signs are all you need to make the reinitialized network train, and why masking behaves like training.

Finally, we discover the existence of Supermasks, masks that can be applied to an untrained, randomly initialized network to produce a model with performance far better than chance (86% on MNIST, 41% on CIFAR-10).

--- https://wiki.aiimpacts.org/ai_timelines/predictions_of_human-level_ai_timelines/ai_timeline_surveys/2022_expert_survey_on_progress_in_ai 2023-12-08 ai reinforcement-learning/safe --- https://www.winestockwebdesign.com/Essays/Eternal_Mainframe.html 2023-12-08 cs/hardware --- https://arxiv.org/abs/2401.02994 Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM Xiaoding Lu, Adian Liusie, Vyas Raina, Yuwen Zhang, William Beauchamp 2024-01-04 2024-01-04 [("doi","10.48550/arXiv.2401.02994")] ai/nn/sampling reinforcement-learning/preference-learning

In conversational AI research, there’s a noticeable trend towards developing models with a larger number of parameters, exemplified by models like ChatGPT. While these expansive models tend to generate increasingly better chat responses, they demand computational resources and memory.

This study explores a pertinent question: Can a combination of smaller models collaboratively achieve comparable or enhanced performance relative to a singular large model? We introduce an approach termed “blending”, a straightforward yet effective method of integrating multiple chat AIs. Our empirical evidence suggests that when specific smaller models are synergistically blended, they can potentially outperform or match the capabilities of much larger counterparts. For instance, integrating just 3 models of moderate size (6B/13b parameters) can rival or even surpass the performance metrics of a substantially larger model like ChatGPT (175B+ parameters).

This hypothesis is rigorously tested using A/B testing methodologies with a large user base on the Chai research platform over a span of 30 days.

The findings underscore the potential of the “blending” strategy as a viable approach for enhancing chat AI efficacy without a corresponding surge in computational demands.

[If randomly picking a model to respond actually helps, that suggests that ChatGPT is rendered extremely bland by its safety measures.]

--- https://arxiv.org/abs/2401.03910 A Philosophical Introduction to Language Models—Part I: Continuity With Classic Debates Raphaël Millière, Cameron Buckner 2024-01-08 2024-01-08 [("doi","10.48550/arXiv.2401.03910")] ai/nn/transformer philosophy/mind psychology/linguistics

Large language models like GPT-4 have achieved remarkable proficiency in a broad spectrum of language-based tasks, some of which are traditionally associated with hallmarks of human intelligence. This has prompted ongoing disagreements about the extent to which we can meaningfully ascribe any kind of linguistic or cognitive competence to language models. Such questions have deep philosophical roots, echoing long-standing debates about the status of artificial neural networks as cognitive models.

This article—the first part of two companion papers—serves both as a primer on language models for philosophers, and as an opinionated survey of their in relation to classic debates in the philosophy cognitive science, artificial intelligence, and linguistics. We cover topics such as compositionality, language acquisition, semantic competence, grounding, world models, and the transmission of cultural knowledge.

We argue that the success of language models challenges several long-held assumptions about artificial neural networks. However, we also highlight the need for further empirical investigation to better understand their internal mechanisms.

This sets the stage for the companion paper (Part II), which turns to novel empirical methods for probing the inner workings of language models, and new philosophical questions prompted by their latest developments.

--- https://arxiv.org/abs/1911.13299 What’s Hidden in a Randomly Weighted Neural Network? Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi, Ali Farhadi, Mohammad Rastegari 2019-11-29 2023-12-08 [("doi","10.48550/arXiv.1911.13299")] ai/nn/fully-connected ai/nn/sparsity/pruning ai/scaling

[cf. lottery tickets, Zhou et al 2019, Qiu & Suda 2020] Training a neural network is synonymous with learning the values of the weights.

By contrast, we demonstrate that randomly weighted neural networks contain subnetworks which achieve impressive performance without ever training the weight values. Hidden in a randomly weighted Wide ResNet-50 we show that there is a subnetwork (with random weights) that is smaller than, but matches the performance of a ResNet-34 trained on ImageNet. Not only do these “untrained subnetworks” exist, but we provide an algorithm to effectively find them.

We empirically show that as randomly weighted neural networks with fixed weights grow wider and deeper, an “untrained subnetwork” approaches a network with learned weights in accuracy.

Our code and pretrained models are available at Github.

--- https://arxiv.org/abs/2003.02570 Train-by-Reconnect: Decoupling Locations of Weights from their Values (LaPerm) Yushi Qiu, Reiji Suda 2020-03-05 2023-12-08 [("doi","10.48550/arXiv.2003.02570")] ai/nn/cnn ai/nn/fully-connected ai/nn/sparsity/pruning

What makes untrained deep neural networks (DNNs) different from the trained performant ones?

By zooming into the weights in well-trained DNNs, we found it is the location of weights that hold most of the information encoded by the training.

Motivated by this observation, we hypothesize that weights in stochastic gradient-based method trained DNNs can be separated into two dimensions: the locations of weights and their exact values.

To assess our hypothesis, we propose a novel method named Lookahead Permutation (LaPerm) to train DNNs by reconnecting the weights.

We empirically demonstrate the versatility of LaPerm while producing extensive evidence to support our hypothesis: when the initial weights are random and dense, our method demonstrates speed and performance similar to or better than that of regular optimizers, eg. Adam; when the initial weights are random and sparse (many zeros), our method changes the way neurons connect and reach accuracy comparable to that of a well-trained fully initialized network; and when the initial weights share a single value, our method finds weight agnostic neural networks with far better-than-chance accuracy.

--- https://arxiv.org/abs/2310.17347#disney CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling Seyedmorteza Sadat, Jakob Buhmann, Derek Bradley, Otmar Hilliges, Romann M. Weber 2023-10-26 2023-12-08 [("doi","10.48550/arXiv.2310.17347")] ai/nn/diffusion

While conditional diffusion models are known to have good coverage of the data distribution, they still face limitations in output diversity, particularly when sampled with a high classifier-free guidance scale for optimal image quality or when trained on small datasets. We attribute this problem to the role of the conditioning signal in inference and offer an improved sampling strategy for diffusion models that can increase generation diversity, especially at high guidance scales, with minimal loss of sample quality.

Our sampling strategy anneals the conditioning signal by adding scheduled, monotonically decreasing Gaussian noise to the conditioning vector during inference to balance diversity and condition alignment. Our Condition-Annealed Diffusion Sampler (CADS) can be used with any pretrained model and sampling algorithm, and we show that it boosts the diversity of diffusion models in various conditional generation tasks.

Further, using an existing pretrained diffusion model, CADS achieves a new state-of-the-art FID of 1.70 and 2.31 for class-conditional ImageNet generation at 256×256 and 512×512 respectively. [Sampling code in appendix.]

--- https://arxiv.org/abs/2312.02139 DiffiT: Diffusion Vision Transformers for Image Generation Ali Hatamizadeh, Jiaming Song, Guilin Liu, Jan Kautz, Arash Vahdat 2023-12-04 2023-12-08 [("doi","10.48550/arXiv.2312.02139")] ai/nn/diffusion

Diffusion models with their powerful expressivity and high sample quality have enabled many new applications and use-cases in various domains. For sample generation, these models rely on a denoising neural network that generates images by iterative denoising. Yet, the role of denoising network architecture is not well-studied with most efforts relying on convolutional residual U-Nets.

In this paper, we study the effectiveness of vision transformers in diffusion-based generative learning. Specifically, we propose a new model, denoted as Diffusion Vision Transformers (DiffiT), which consists of a hybrid hierarchical architecture with a U-shaped encoder and decoder. We introduce a novel time-dependent self-attention module that allows attention layers to adapt their behavior at different stages of the denoising process in an efficient manner.

We also introduce latent DiffiT which consists of transformer model with the proposed self-attention layers, for high-resolution image generation. Our results show that DiffiT is surprisingly effective in generating high-fidelity images, and it achieves state-of-the-art (SOTA) benchmarks on a variety of class-conditional and unconditional synthesis tasks. In the latent space, DiffiT achieves a new SOTA FID score of 1.73 on ImageNet-256 dataset.

Code: https://github.com/NVlabs/DiffiT.

--- https://orlp.net/blog/when-random-isnt/ 2023-12-08 cs/cryptography --- https://simonwillison.net/2023/Sep/30/cli-tools-python/ 2023-12-08 ai/nn/transformer/gpt/codex cs/python --- /doc/psychiatry/schizophrenia/2024-chekroud.pdf Illusory generalizability of clinical prediction models 2024-01-11 2024-01-11 [("doi","10.1126/science.adg8538")] ai/tabular psychiatry/schizophrenia statistics/bias

It is widely hoped that statistical models can improve decision-making related to medical treatments. Because of the cost and scarcity of medical outcomes data, this hope is typically based on investigators observing a model’s success in one or two datasets or clinical contexts.

We scrutinized this optimism by examining how well a machine learning model [elastic net regression] performed across several independent clinical trials of antipsychotic medication for schizophrenia. Models predicted patient outcomes with high accuracy within the trial in which the model was developed but performed no better than chance when applied out-of-sample. Pooling data across trials to predict outcomes in the trial left out did not improve predictions.

These results suggest that models predicting treatment outcomes in schizophrenia are highly context-dependent and may have limited generalizability.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8551557/ Anauralia: The Silent Mind and Its Association With Aphantasia Rish P. Hinwar, Anthony J. Lambert 2021 2023-12-09 [("doi","10.3389/fpsyg.2021.744213")] psychology/inner-voice psychology/vision/aphantasia

Auditory and visual imagery were studied in a sample of 128 participants, including 34 self-reported aphantasics.

Auditory imagery (Bucknell Auditory Imagery Scale-Vividness, BAIS-V) and visual imagery (Vividness of Visual Imagery Questionnaire-Modified, VVIQ-M) were strongly associated, Spearman’s rho = 0.83: Most self-reported aphantasics also reported weak or entirely absent auditory imagery; and participants lacking auditory imagery tended to be aphantasic. Similarly, vivid visual imagery tended to co-occur with vivid auditory imagery. Nevertheless, the aphantasic group included one individual with typical auditory imagery; and the group lacking auditory imagery (n = 29) included one individual with typical visual imagery. Hence, weak visual and auditory imagery can dissociate, albeit with low apparent incidence.

Auditory representations and auditory imagery are thought to play a key role in a wide range of psychological domains, including working memory and memory rehearsal, prospective cognition, thinking, reading, planning, problem-solving, self-regulation, and music. Therefore, self-reports describing an absence of auditory imagery raise a host of important questions concerning the role of phenomenal auditory imagery in these domains.

Because there is currently no English word denoting an absence of auditory imagery, we propose a new term, anauralia, for referring to this, and offer suggestions for further research.

--- https://usefulfictions.substack.com/p/1154dba1-49f6-4feb-b091-6d4a7eefa94d 2023-12-09 psychology/energy psychology/willpower --- https://susam.net/maze/elisp-in-replacement-string.html 2023-12-09 cs/lisp --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050319/ Mortality outcomes with hydroxychloroquine and chloroquine in COVID-19 from an international collaborative meta-analysis of randomized trials Cathrine Axfors, Andreas M. Schmitt, Perrine Janiaud, Janneke Van’t Hooft, Sherief Abd-Elsalam, Ehab F. Abdo, Benjamin S. Abella, Javed Akram, Ravi K. Amaravadi, Derek C. Angus, Yaseen M. Arabi, Shehnoor Azhar, Lindsey R. Baden, Arthur W. Baker, Leila Belkhir, Thomas Benfield, Marvin A. H. Berrevoets, Cheng-Pin Chen, Tsung-Chia Chen, Shu-Hsing Cheng, Chien-Yu Cheng, Wei-Sheng Chung, Yehuda Z. Cohen, Lisa N. Cowan, Olav Dalgard, Fernando F. de Almeida E. Val, Marcus V. G. de Lacerda, Gisely C. de Melo, Lennie Derde, Vincent Dubee, Anissa Elfakir, Anthony C. Gordon, Carmen M. Hernandez-Cardenas, Thomas Hills, Andy I. M. Hoepelman, Yi-Wen Huang, Bruno Igau, Ronghua Jin, Felipe Jurado-Camacho, Khalid S. Khan, Peter G. Kremsner, Benno Kreuels, Cheng-Yu Kuo, Thuy Le, Yi-Chun Lin, Wu-Pu Lin, Tse-Hung Lin, Magnus Nakrem Lyngbakken, Colin McArthur, Bryan J. McVerry, Patricia Meza-Meneses, Wuelton M. Monteiro, Susan C. Morpeth, Ahmad Mourad, Mark J. Mulligan, Srinivas Murthy, Susanna Naggie, Shanti Narayanasamy, Alistair Nichol, Lewis A. Novack, Sean M. O’Brien, Nwora Lance Okeke, Léna Perez, Rogelio Perez-Padilla, Laurent Perrin, Arantxa Remigio-Luna, Norma E. Rivera-Martinez, Frank W. Rockhold, Sebastian Rodriguez-Llamazares, Robert Rolfe, Rossana Rosa, Helge Røsjø, Vanderson S. Sampaio, Todd B. Seto, Muhammad Shahzad, Shaimaa Soliman, Jason E. Stout, Ireri Thirion-Romero, Andrea B. Troxel, Ting-Yu Tseng, Nicholas A. Turner, Robert J. Ulrich, Stephen R. Walsh, Steve A. Webb, Jesper M. Weehuizen, Maria Velinova, Hon-Lai Wong, Rebekah Wrenn, Fernando G. Zampieri, Wu Zhong, David Moher, Steven N. Goodman, John Ioannidis, Lars G. Hemkens 2021 2023-12-09 [("doi","10.1038/s41467-021-22446-z")] biology

Substantial COVID-19 research investment has been allocated to randomized clinical trials on hydroxychloroquine/chloroquine, which currently face recruitment challenges or early discontinuation. We aim to estimate the effects of hydroxychloroquine and chloroquine on survival in COVID-19 from all currently available RCT evidence, published and unpublished.

We present a rapid meta-analysis of ongoing, completed, or discontinued RCTs on hydroxychloroquine or chloroquine treatment for any COVID-19 patients (protocol: https://osf.io/QESV4/).

We systematically identified unpublished RCTs (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform, Cochrane COVID-registry up to June 11, 2020), and published RCTs (PubMed, MedRxiv and bioRxiv up to October 16, 2020). All-cause mortality has been extracted (publications/preprints) or requested from investigators and combined in random-effects meta-analyses, calculating odds ratios (ORs) with 95% confidence intervals (CIs), separately for hydroxychloroquine and chloroquine. Prespecified subgroup analyses include patient setting, diagnostic confirmation, control type, and publication status.

63 trials were potentially eligible. We included 14 unpublished trials (1,308 patients) and 14 publications/preprints (9,011 patients). Results for hydroxychloroquine are dominated by RECOVERY and WHO SOLIDARITY, two highly pragmatic trials, which employed relatively high doses and included 4716 and 1853 patients, respectively (67% of the total sample size).

The combined OR on all-cause mortality for hydroxychloroquine is 1.11 (95% CI: 1.02, 1.20; I2 = 0%; 26 trials; 10,012 patients) and for chloroquine 1.77 (95%CI: 0.15, 21.13, I2 = 0%; 4 trials; 307 patients). We identified no subgroup effects.

We found that treatment with hydroxychloroquine is associated with increased mortality in COVID-19 patients, and there is no benefit of chloroquine. Findings have unclear generalizability to outpatients, children, pregnant women, and people with comorbidities.

--- https://dmicz.github.io/machine-learning/openai-changes/ 2023-12-09 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2301.12811#sony SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer Yuhta Takida, Masaaki Imaizumi, Takashi Shibuya, Chieh-Hsin Lai, Toshimitsu Uesaka, Naoki Murata, Yuki Mitsufuji 2023-01-30 2023-12-09 [("doi","10.48550/arXiv.2301.12811")] ai/nn/gan/biggan ai/nn/gan/stylegan

[code] Generative adversarial networks learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives. This paper addresses the question of whether such optimization actually provides the generator with gradients that make its distribution close to the target distribution.

We derive metrizable conditions, sufficient conditions for the discriminator to serve as the distance between the distributions by connecting the GAN formulation with the concept of sliced optimal transport. Furthermore, by leveraging these theoretical results, we propose a novel GAN training scheme, called slicing adversarial network (SAN). With only simple modifications, a broad class of existing GANs can be converted to SANs.

Experiments on synthetic and image datasets support our theoretical results and the SAN’s effectiveness as compared to usual GANs. Furthermore, we also apply SAN to StyleGAN-XL, which leads to state-of-the-art FID score amongst GANs for class conditional generation on ImageNet 256×256.

--- https://midlibrary.io/styles 2023-12-09 ai/nn/transformer/clip/sample --- /doc/cat/2024-usarmycorpsofengineers-2024catcalendar.pdf Portland District 2024 Cat Calendar Jeffrey Henon 2024-01-11 2024-01-11 cat fiction/humor --- https://www.atlasobscura.com/articles/flying-circus-aerial-maps 2023-12-09 design/visualization technology --- https://arxiv.org/abs/2401.03499 Re:Draw—Context Aware Translation as a Controllable Method for Artistic Production Joao Liborio Cardoso, Francesco Banterle, Paolo Cignoni, Michael Wimmer 2024-01-07 2024-01-07 [("doi","10.48550/arXiv.2401.03499")] ai/anime/danbooru ai/nn/gan

We introduce context-aware translation, a novel method that combines the benefits of inpainting and image-to-image translation, respecting simultaneously the original input and contextual relevance—where existing methods fall short. By doing so, our method opens new avenues for the controllable use of AI within artistic creation, from animation to digital art.

As a use case, we apply our method to redraw any hand-drawn animated character eyes based on any design specifications—eyes serve as a focal point that captures viewer attention and conveys a range of emotions, however, the labor-intensive nature of traditional animation often leads to compromises in the complexity and consistency of eye design. Furthermore, we remove the need for production data for training and introduce a new character recognition method that surpasses existing work by not requiring fine-tuning to specific productions. This proposed use case could help maintain consistency throughout production and unlock bolder and more detailed design choices without the production cost drawbacks.

A user study shows context-aware translation is preferred over existing work 95% of the time.

--- https://shkspr.mobi/blog/2024/01/compressing-text-into-images/ 2023-12-10 ai/nn/transformer/attention/compression cs/algorithm/information/compression --- https://linusakesson.net/programming/symlinks/index.php 2023-12-10 cs/computable cs/shell --- https://arxiv.org/abs/1908.06841 Ternary circuits: why R=3 is not the Optimal Radix for Computation Daniel Etiemble 2019-08-19 2023-12-10 [("doi","10.48550/arXiv.1908.06841")] cs/hardware math

A demonstration that e [2.718] rounded to 3 is the best radix for computation is disproved.

The MOSFET-like CNTFET technology is used to compare inverters, Nand, adders, multipliers, D Flip-Flops and SRAM cells.

The transistor count ratio between ternary and binary circuits is generally greater than the log(3)/log(2) information ratio.

The only exceptions concern a circuit approach that combines two circuit drawbacks (an additional power supply and a circuit conflict between transistors) and only when it implements circuits based on the ternary inverter. For arithmetic circuits such as adders and multipliers, the ternary circuits are always outperformed by the binary ones using the same technology.

--- https://www.jerpint.io/blog/snowflake-neural-ca/ 2023-12-10 ai/nn/cnn cs/cellular-automaton --- https://arxiv.org/abs/2401.02415#tencent LLaMA Pro: Progressive LLaMA with Block Expansion Chengyue Wu, Yukang Gan, Yixiao Ge, Zeyu Lu, Jiahao Wang, Ye Feng, Ping Luo, Ying Shan 2024-01-04 2024-01-04 [("doi","10.48550/arXiv.2401.02415")] ai/nn/transformer/gpt reinforcement-learning/meta-learning/continual-learning

Humans generally acquire new skills without compromising the old; however, the opposite holds for Large Language Models (LLMs), eg. from LLaMA to CodeLLaMA. To this end, we propose a new post-pretraining method for LLMs with an expansion of Transformer blocks. We tune the expanded blocks using only new corpus, efficiently and effectively improving the model’s knowledge without catastrophic forgetting.

In this paper, we experiment on the corpus of code and math, yielding LLaMA Pro-8.3B, a versatile foundation model initialized from LLaMA2-7B, excelling in general tasks, programming, and mathematics. LLaMA Pro and its instruction-following counterpart (LLaMA Pro-Instruct) achieve advanced performance among various benchmarks, demonstrating superiority over existing open models in the LLaMA family and the immense potential of reasoning and addressing diverse tasks as an intelligent agent.

Our findings provide valuable insights into integrating natural and programming languages, laying a solid foundation for developing advanced language agents that operate effectively in various environments.

--- https://www.cell.com/fulltext/S1097-2765%2809%2900641-8 2023-12-10 science --- https://www.fatherly.com/entertainment/choose-your-own-adventure-past-and-future 2023-12-10 fiction/text-game --- https://arxiv.org/abs/2303.17491 Language Models can Solve Computer Tasks Geunwoo Kim, Pierre Baldi, Stephen McAleer 2023-03-30 2023-12-10 [("doi","10.48550/arXiv.2303.17491")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue

Agents capable of carrying out general tasks on a computer can improve efficiency and productivity by automating repetitive tasks and assisting in complex problem-solving. Ideally, such agents should be able to solve new computer tasks presented to them through natural language commands. However, previous approaches to this problem require large amounts of expert demonstrations and task-specific reward functions, both of which are impractical for new tasks.

In this work, we show that a pre-trained large language model (LLM) agent can execute computer tasks guided by natural language using a simple prompting scheme where the agent Recursively Criticizes and Improves its output (RCI). The RCI approach outperforms existing LLM methods for automating computer tasks and surpasses supervised learning (SL) and reinforcement learning (RL) approaches on the MiniWoB++ benchmark.

We compare multiple LLMs and find that RCI with the InstructGPT-3+RLHF LLM is state-of-the-art on MiniWoB++, using only a handful of demonstrations per task rather than tens of thousands, and without a task-specific reward function. Furthermore, we demonstrate RCI prompting’s effectiveness in enhancing LLMs’ reasoning abilities on a suite of natural language reasoning tasks, outperforming chain-of-thought (CoT) prompting with external feedback. We find that RCI combined with CoT performs better than either separately. Our code can be found here: Github.

--- https://arxiv.org/abs/2310.16410#deepmind Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero Lisa Schut, Nenad Tomasev, Tom McGrath, Demis Hassabis, Ulrich Paquet, Been Kim 2023-10-25 2023-12-10 [("doi","10.48550/arXiv.2310.16410")] psychology/chess reinforcement-learning/imitation-learning reinforcement-learning/model/alphago

[Twitter; cf. AZ’s acquisition of knowledge, alternate rules] Artificial Intelligence (AI) systems have made remarkable progress, attaining super-human performance across various domains. This presents us with an opportunity to further human knowledge and improve human expert performance by leveraging the hidden knowledge encoded within these highly performant AI systems. Yet, this knowledge is often hard to extract, and may be hard to understand or learn from.

Here, we show that this is possible by proposing a new method that allows us to extract new chess concepts in AlphaZero, an AI system that mastered the game of chess via self-play without human supervision. Our analysis indicates that AlphaZero may encode knowledge that extends beyond the existing human knowledge, but knowledge that is ultimately not beyond human grasp, and can be successfully learned from.

In a human study, we show that these concepts are learnable by top human experts, as 4 top chess grandmasters show improvements in solving the presented concept prototype positions.

This marks an important first milestone in advancing the frontier of human knowledge by leveraging AI; a development that could bear profound implications and help us shape how we interact with AI systems across many AI applications.

…Overall, the grandmasters appreciated the concepts, describing them as ‘clever’ (Figure 8), ‘very interesting’ (Figure 16), and ‘very nice’ (Figure 18). Further, they found that the ideas often contained novel elements, commenting that the moves were ‘something new’ and even ‘not natural’ (Figure 16 & 10). Often, the grandmasters found the positions were very complex—making remarks such as that it was “very complicated—not easy to understand what to do”. Even when seeing AZ’s solutions, they remarked that it was a ‘very nice idea which is hard to spot’ (Figure 22).

…The qualitative examples suggest that AZ has different priors over the relevance of concepts in a chess position than humans. Human chess players formulate and adopt heuristic chess principles to inform their analysis, predisposing them to biases that influence which concepts they deem relevant for specific chess positions. An example is the 3 ‘golden rules’ of the opening: control the centre, develop your pieces, and bring your king to safety (Hansen 2021; Brunia & van Wijgerden 2021; King 2000). Consequentially, in opening, humans may focus on moves that align with these guidelines. Instead, AZ is self-taught and does not seem to have the same priors over chess concepts as humans. We believe this lack of prior allows AZ to be more flexible—it can apply concepts to various different chess positions and change plans quickly. In essence, AZ formulates its own priors over the relevance of chess concepts for a given chess position. Examples of this behavior are that AZ plays over the entire board, as opposed to focusing on a specific side (see, eg. Figure 16, 17, 12, and 19); places less importance on the material value of pieces, and prioritises space and piece activity (see, eg. Figure 9 or 14). This may result in the super-human application of concepts, and new concepts… AZ does not care about how quickly the game finishes. The training loss function does not have a penalty term to encourage winning as quickly as possible. As a result, it has a different treatment of time. This results in sometimes choosing slow strategic wins (as can be seen in the chess positions in Figure 21). While the lack of time constraint may lead to super-human concepts, it also may result in complex concepts that are difficult for humans to learn.

Table 4: Improvements in grandmasters’ performance. The percentage scores are the percentage of puzzles that the grandmaster solved correctly (according to AZ’s solution). # Puzzles is the number of puzzles shown to the grandmaster in total.
Grandmaster Percentage: Phase 1 Phase 3 Improvement # Puzzles
1 0 42 +42 36
2 33 58 +25 36
3 25 42 +16 36
4 38 44 +6 48

…Overall, we find that all study participants improve notably between Phases 1 & 3, as shown in Table 4, suggesting that the chess grandmasters were able to learn and apply their understanding of the represented AZ chess concepts. The magnitude of improvement does not correlate with the chess player’s strength (ie. Elo rating).

--- https://arxiv.org/abs/2212.11870 Impossibility Theorems for Feature Attribution Blair Bilodeau, Natasha Jaques, Pang Wei Koh, Been Kim 2022-12-22 2023-12-10 [("doi","10.1073/pnas.2304406120")] ai

Despite a sea of interpretability methods that can produce plausible explanations, the field has also empirically seen many failure cases of such methods. In light of these results, it remains unclear for practitioners how to use these methods and choose between them in a principled way.

In this paper, we show that for moderately rich model classes (easily satisfied by neural networks), any feature attribution method that is complete and linear—for example, Integrated Gradients and SHAP—can provably fail to improve on random guessing for inferring model behavior.

Our results apply to common end-tasks such as characterizing local model behavior, identifying spurious features, and algorithmic recourse. One takeaway from our work is the importance of concretely defining end-tasks: once such an end-task is defined, a simple and direct approach of repeated model evaluations can outperform many other complex feature attribution methods.

--- https://arxiv.org/abs/2401.00963 Leveraging Large Language Models to Boost Dafny’s Developers Productivity Álvaro Silva, Alexandra Mendes, João F. Ferreira 2024-01-01 2024-01-01 [("doi","10.48550/arXiv.2401.00963")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex math

[summary: doesn’t work] This research idea paper proposes leveraging Large Language Models (LLMs) to enhance the productivity of Dafny developers. Although the use of verification-aware languages, such as Dafny, has increased considerably in the last decade, these are still not widely adopted. Often the cost of using such languages is too high, due to the level of expertise required from the developers and challenges that they often face when trying to prove a program correct. Even though Dafny automates a lot of the verification process, sometimes there are steps that are too complex for Dafny to perform on its own. One such case is that of missing lemmas, i.e. Dafny is unable to prove a result without being given further help in the form of a theorem that can assist it in the proof of the step.

In this paper, we describe preliminary work on a new Dafny plugin that leverages LLMs to assist developers by generating suggestions for relevant lemmas that Dafny is unable to discover and use. Moreover, for the lemmas that cannot be proved automatically, the plugin also attempts to provide accompanying calculational proofs.

We also discuss ideas for future work by describing a research agenda on using LLMs to increase the adoption of verification-aware languages in general, by increasing developers productivity and by reducing the level of expertise required for crafting formal specifications and proving program properties.

--- https://arxiv.org/abs/2401.05566#anthropic Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M. Ziegler, Tim Maxwell, Newton Cheng, Adam Jermyn, Amanda Askell, Ansh Radhakrishnan, Cem Anil, David Duvenaud, Deep Ganguli, Fazl Barez, Jack Clark, Kamal Ndousse, Kshitij Sachan, Michael Sellitto, Mrinank Sharma, Nova DasSarma, Roger Grosse, Shauna Kravec, Yuntao Bai, Zachary Witten, Marina Favaro, Jan Brauner, Holden Karnofsky, Paul Christiano, Samuel R. Bowman, Logan Graham, Jared Kaplan, Sören Mindermann, Ryan Greenblatt, Buck Shlegeris, Nicholas Schiefer, Ethan Perez 2024-01-10 2024-01-10 [("doi","10.48550/arXiv.2401.05566")] ai/nn/adversarial ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex reinforcement-learning/preference-learning reinforcement-learning/safe reinforcement-learning/scaling

[Twitter, LW/Zvi; research programme] Humans are capable of strategically deceptive behavior: behaving helpfully in most situations, but then behaving very differently in order to pursue alternative objectives when given the opportunity. If an AI system learned such a deceptive strategy, could we detect it and remove it using current state-of-the-art safety training techniques? To study this question, we construct proof-of-concept examples of deceptive behavior in large language models (LLMs) [Claude]. For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024.

We find that such backdoored behavior can be made persistent, so that it is not removed by standard safety training techniques, including supervised fine-tuning, reinforcement learning, and adversarial training (eliciting unsafe behavior and then training to remove it). The backdoored behavior is most persistent in the largest models and in models trained to produce chain-of-thought reasoning about deceiving the training process, with the persistence remaining even when the chain-of-thought is distilled away.

Furthermore, rather than removing backdoors, we find that adversarial training can teach models to better recognize their backdoor triggers, effectively hiding the unsafe behavior.

Our results suggest that, once a model exhibits deceptive behavior, standard techniques could fail to remove such deception and create a false impression of safety.

[The basic sleeper agents represent the treacherous turn in a simple linear way internally, but of course, given any optimization pressure to conceal it, they would simply learn to encode it nonlinearly in an opaque way like a lot of other things.]

--- https://en.wikipedia.org/wiki/Hafiz_(Quran) Hafiz (Quran) 2023-12-11 psychology/spaced-repetition --- /doc/ai/nn/1974-werbos.pdf Beyond regression: new tools for prediction and analysis in the behavioral sciences Paul John Werbos 1974-08-01 2023-12-11 ai/nn politics statistics/prediction --- https://www.bbc.com/travel/article/20210223-yaupon-the-rebirth-of-americas-forgotten-tea Yaupon: The rebirth of America’s forgotten tea Matt Stirn 2021-02-23 2023-12-11 nootropic/caffeine tea --- https://freakonomics.com/podcast/why-is-there-so-much-fraud-in-academia/ 2023-12-11 statistics/bias --- https://confirmlabs.org/posts/TDC2023 2023-12-11 ai/nn/adversarial --- https://muratbuffalo.blogspot.com/2019/03/book-review-draft-no-4-by-john-mcphee.html 2023-12-11 psychology/writing --- https://arxiv.org/abs/2401.05300 I am a Strange Dataset: Metalinguistic Tests for Language Models Tristan Thrush, Jared Moore, Miguel Monares, Christopher Potts, Douwe Kiela 2024-01-10 2024-01-10 [("doi","10.48550/arXiv.2401.05300")] ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/scaling psychology/writing

Statements involving metalinguistic self-reference (“This paper has 6 sections.”) are prevalent in many domains. Can large language models (LLMs) handle such language? In this paper, we present “I am a Strange Dataset”, a new dataset for addressing this question.

There are two subtasks: generation and verification. In generation, models continue statements like “The penultimate word in this sentence is” (where a correct continuation is “is”). In verification, models judge the truth of statements like “The penultimate word in this sentence is sentence.” (false). We also provide minimally different metalinguistic non-self-reference examples to complement the main dataset by probing for whether models can handle metalinguistic language at all.

The dataset is hand-crafted by experts and validated by non-expert annotators. We test a variety of open-source LLMs (7B to 70b parameters) as well as closed-source LLMs through APIs.

All models perform close to chance across both subtasks and even on the non-self-referential metalinguistic control data, though we find some steady improvement with model scale. GPT-4 is the only model to consistently do better than chance, and it is still only in the 60% range, while our untrained human annotators score well in the 89-93% range.

The dataset and evaluation toolkit are available at Github.

--- https://arxiv.org/abs/2307.06865 Prompts Should not be Seen as Secrets: Systematically Measuring Prompt Extraction Attack Success Yiming Zhang, Daphne Ippolito 2023-07-13 2023-12-11 [("doi","10.48550/arXiv.2307.06865")] ai/nn/adversarial

The generations of large language models are commonly controlled through prompting techniques, where a user’s query to the model is prefixed with a prompt that aims to guide the model’s behavior on the query. The prompts used by companies to guide their models are often treated as secrets, to be hidden from the user making the query. They have even been treated as commodities to be bought and sold. However, there has been anecdotal evidence showing that the prompts can be extracted by a user even when they are kept secret.

In this paper, we present a framework for systematically measuring the success of prompt extraction attacks.

In experiments with multiple sources of prompts and multiple underlying language models, we find that simple text-based attacks can in fact reveal prompts with high probability.

--- https://magazine.sebastianraschka.com/p/understanding-and-coding-self-attention 2023-12-11 ai/nn/transformer/attention cs/python --- https://www.usenix.org/system/files/1403_02-08_mickens.pdf 2023-12-11 cs/css cs/js math/humor --- https://www.biorxiv.org/content/10.1101/2024.01.05.574339.full Gene-environment correlation: The role of family environment in academic development Quan Zhou, Agnieszka Gidziela, Andrea G. Allegrini, Rosa Cheesman, Jasmin Wertz, Jessye Maxwell, Robert Plomin, Kaili Rimfeld, Margherita Malanchini 2024-01-08 2024-01-08 [("doi","10.1101/2024.01.05.574339")] genetics/heritable iq

Academic achievement is partly heritable and highly polygenic. However, genetic effects on academic achievement are not independent of environmental processes. We investigated whether aspects of the family environment mediated genetic effects on academic achievement across development. Our sample included 5,151 children who participated in the Twins Early Development Study, as well as their parents and teachers. Data on academic achievement and family environments were available at ages 7, 9, 12 and 16.

We computed educational attainment polygenic scores (PGS) and further separated genetic effects into cognitive and noncognitive PGS. 3 core findings emerged. First, aspects of the family environment, but not the wider neighbourhood context, consistently mediated the PGS effects on achievement across development, accounting for up to 34.3% of the total effect. Family characteristics mattered beyond socio-economic status.

Second, family environments were more robustly linked to noncognitive PGS effects on academic achievement than cognitive PGS effects. Third, when we investigated whether environmental mediation effects could also be observed when considering differences between siblings, adjusting for family fixed effects, we found that environmental mediation was nearly exclusively observed between families.

This is consistent with the proposition that family environmental contexts contribute to academic development via passive gene-environment correlation processes. Our results show how parents shape environments that foster their children’s academic development partly based on their own genetic disposition, particularly towards noncognitive skills.

--- https://www.washingtonpost.com/style/interactive/2024/ken-fritz-greatest-stereo-auction-cost/ 2023-12-12 psychology/collecting --- https://arxiv.org/abs/2304.11425 Statistical analysis of chess games: space control and tipping points Marc Barthelemy 2023-04-22 2023-12-12 [("doi","10.48550/arXiv.2304.11425")] psychology/chess reinforcement-learning/chess

Moves in chess games are usually analyzed on a case-by-case basis by professional players, but thanks to the availability of large game databases, we can envision another approach of the game. Here, we indeed adopt a very different point of view, and analyze moves in chess games from a statistical point of view. We first focus on spatial properties and the location of pieces and show that the number of possible moves during a game is positively correlated with its outcome.

We then study heatmaps of pieces and show that the spatial distribution of pieces varies less between human players than with engines (such as Stockfish): engines seem to use pieces in a very different way as human did for centuries. These heatmaps also allow us to construct a distance between players that characterizes how they use their pieces.

In a second part, we focus on the best move and the second best move found by Stockfish and study the difference Δ of their evaluation. We found different regimes during a chess game. In a ‘quiet’ regime, Δ is small, indicating that many paths are possible for both players. In contrast, there are also ‘volatile’ regimes characterized by a ‘tipping point’, for which Δ becomes large. At these tipping points, the outcome could then switch completely depending on the move chosen.

We also found that for a large number of games, the distribution of Δ can be fitted by a power law P(Δ) ~ Δ−β with an exponent that seems to be universal (for human players and engines) and around β ≈ 1.8. The probability to encounter a tipping point in a game is therefore far from being negligible.

Finally, we conclude by mentioning possible directions of research for a quantitative understanding of chess games such as the structure of the pawn chain, the interaction graph between pieces, or a quantitative definition of critical points.

--- https://en.wikipedia.org/wiki/Nunn%E2%80%93Lugar_Cooperative_Threat_Reduction Nunn-Lugar Cooperative Threat Reduction 2023-12-12 radiance --- https://www.newspapers.com/article/st-louis-post-dispatch/122100383/ 2023-12-12 reinforcement-learning/openai --- https://arxiv.org/abs/2304.06027 Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA James Seale Smith, Yen-Chang Hsu, Lingyu Zhang, Ting Hua, Zsolt Kira, Yilin Shen, Hongxia Jin 2023-04-12 2023-12-12 [("doi","10.48550/arXiv.2304.06027")] ai/nn/diffusion reinforcement-learning/meta-learning/continual-learning

Recent works demonstrate a remarkable ability to customize text-to-image diffusion models while only providing a few example images. What happens if you try to customize such models using multiple, fine-grained concepts in a sequential (ie. continual) manner? In our work, we show that recent state-of-the-art customization of text-to-image models suffer from catastrophic forgetting when new concepts arrive sequentially. Specifically, when adding a new concept, the ability to generate high quality images of past, similar concepts degrade.

To circumvent this forgetting, we propose a new method, C-LoRA, composed of a continually self-regularized low-rank adaptation in cross attention layers of the popular Stable Diffusion model. Furthermore, we use customization prompts which do not include the word of the customized object (ie. “person” for a human face dataset) and are initialized as completely random embeddings. Importantly, our method induces only marginal additional parameter costs and requires no storage of user data for replay.

We show that C-LoRA not only outperforms several baselines for our proposed setting of text-to-image continual customization, which we refer to as Continual Diffusion, but that we achieve a new state-of-the-art in the well-established rehearsal-free continual learning setting for image classification. The high achieving performance of C-LoRA in two separate domains positions it as a compelling solution for a wide range of applications, and we believe it has potential for practical impact.

--- https://reason.com/2024/01/09/its-not-a-cigarette-its-not-a-vape-and-its-big-in-japan/ 2023-12-12 nicotine --- https://www.thepsmiths.com/p/review-the-education-of-cyrus-by 2023-12-12 politics psychology/willpower --- https://www.surgehq.ai/blog/dalle-3-and-midjourney-fail-astral-codex-tens-image-generation-bet 2023-12-12 ai/nn/diffusion/midjourney ai/nn/transformer/gpt/dall-e/3 --- https://soupault.app/ 2023-12-12 cs/js --- https://www.guinnessworldrecords.com/news/2022/11/worlds-oldest-cat-confirmed-at-almost-27-years-old-726391 2023-12-13 cat/biology --- /doc/fiction/science-fiction/1960-analog-october.pdf#page=3 Analog Magazine, October 1960 (v66, #2) § pg3 John W. Campbell 1960 2023-12-13 design/typography/dropcap fiction/science-fiction --- http://www.dailydropcap.com/ 2023-12-13 design/typography/dropcap --- https://archive.org/details/artofilluminatin00tymmrich/page/n272 2023-12-13 design/typography/dropcap --- https://archive.org/details/artofilluminatin00tymmrich/page/n281 2023-12-13 design/typography/dropcap --- https://archive.org/details/womaningirlhoodw00soli/page/44 2023-12-13 design/typography/dropcap --- https://cdn.dribbble.com/users/751932/screenshots/1925368/8bitcalligraphy.png 2023-12-13 design/typography/dropcap --- https://ilovetypography.com/2020/08/20/history-of-illuminated-initials/ 2023-12-13 design/typography/dropcap --- https://ilovetypography.com/2009/12/08/graphic-masterpieces-of-yakov-g-chernikhov-the-collection-of-dmitry-y-chernikhov/ 2023-12-13 design/typography/dropcap --- https://en.wikipedia.org/wiki/Doves_Press Doves Press 2023-12-13 design/typography/dropcap --- http://klepas.org/versal-letters-on-the-web.html 2023-12-13 cs/css design/typography/dropcap --- https://en.wikipedia.org/wiki/The_Swimmer_(short_story) The Swimmer (short story) 2023-12-14 fiction psychology/personality/narcissism --- https://archive.is/BtuOG 2023-12-14 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/codex --- https://omar.website/posts/notes-from-dynamicland-geokit/ 2023-12-14 cs/algorithm design --- https://github.com/open-spaced-repetition/fsrs4anki/wiki/ABC-of-FSRS 2023-12-14 psychology/spaced-repetition --- https://x.com/Letter_Library/status/1364940693333020672 Letter Library 2023-12-14 design/typography/dropcap design/typography/rubrication --- https://blog.8faces.com/post/110538221617/the-lindisfarne-gospels-the-finest-dropcaps 2023-12-14 design/typography/dropcap --- https://apps.dtic.mil/sti/tr/pdf/AD0662398.pdf#page=151 2023-12-14 design/typography/dropcap --- https://commons.wikimedia.org/wiki/File:Decorated_Incipit_Page_-_Google_Art_Project_(6850309).jpg 2023-12-14 design/typography/dropcap --- https://x.com/BLMedieval/status/1400528309050654727 BLMedieval 2023-12-14 design/typography/dropcap design/typography/rubrication --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.75.2289 2023-12-14 design/typography/dropcap --- https://www.youtube.com/watch?v=XWGp3Fc4P_Q Interactive Floral Ornament Generator tutorial 2023-12-14 design/typography/dropcap design/typography/floral --- https://www.are.na/evan-collins-1522646491/neo-vectorheart 2023-12-15 design/typography --- https://erikdemaine.org/fonts/ 2023-12-15 design/typography math --- https://x.com/nemocentric/status/1443603870031175683 Thomas Moynihan 2023-12-15 design/typography/dropcap --- https://blog.8faces.com/post/104679238117/day8-type-lettering-advent-calendar 2023-12-15 design/typography/dropcap --- https://pro.univ-lille.fr/fileadmin/user_upload/pages_pros/lorenzo_ramero/CoursAG.pdf 2023-12-15 design/typography/dropcap math --- https://www.comicartfans.com/GalleryPiece.asp?Piece=1777092 2023-12-15 design/typography/dropcap fiction/science-fiction --- https://archive.org/details/Science_Wonder_Stories_v01n12_1930-05.Stellar/page/n40/mode/1up 2023-12-15 design/typography/dropcap --- https://fineart.ha.com/itm/paintings/a-j-donnell-american-d2001-skylark-three-original-book-illustrations-group-of-3-ink-on-board-6-x-4-1-2in/a/677-58160.s 2023-12-15 design/typography/dropcap fiction/science-fiction --- https://twitchard.github.io/posts/2022-09-23-system-dynamics-schmystem-dynamics.html 2023-12-15 design/typography/dropcap --- https://www.1001fonts.com/botanic-personal-use-font.html 2023-12-15 design/typography/dropcap design/typography/floral --- https://www.1001fonts.com/floral-capitals-font.html 2023-12-16 design/typography/dropcap design/typography/floral --- https://www.1001fonts.com/knotty-font.html 2023-12-16 design/typography/dropcap --- https://caniuse.com/css-initial-letter 2023-12-16 cs/css design/typography/dropcap --- https://uwmspeccoll.tumblr.com/post/708004267946098688/typography-tuesday-blackletter-blackletter-also 2023-12-16 design/typography/dropcap --- https://ilovetypography.com/2023/03/06/steven-hellers-font-of-the-month-juma/ 2023-12-16 design/typography/dropcap --- https://x.com/WeirdMedieval/status/1668308825588346885 WeirdMedieval 2023-12-16 design/typography/dropcap --- https://jdsalaro.com/note/comparison-new-microsoft-default-font-calibri-aptos/ 2023-12-16 design/typography --- https://commons.wikimedia.org/wiki/File:Advertisement_for_a_collection_of_drawings_from_The_Black_Cat.png 2023-12-16 design/typography/dropcap --- https://aesthetics.fandom.com/wiki/Vectorheart 2023-12-16 design/typography --- https://web.archive.org/web/20230607050023/https://old.reddit.com/r/dredmorbius/comments/6fgq8g/william_ophuls_bibliographic_note_sources_on/ 2023-12-16 design/typography/dropcap design/typography/rubrication --- https://thebookofthree.thecomicseries.com/comics/205/#content-start 2023-12-16 design/typography/dropcap --- /doc/design/typography/dropcap/2016-01-25-tomlane-sherlockholmesdropcap.html 2016-01-25 2023-12-17 design/typography/dropcap --- /doc/design/typography/dropcap/2012-04-04-laurafranz-dropcapscurrentcssbestpractices.html 2012-04-04 2023-12-17 design/typography/dropcap --- https://en.wikipedia.org/wiki/File:Riddles_of_Aldhelm,_London,_British_Library,_Royal_MA_12_c_xxiii_folio_84r.jpg File:Riddles of Aldhelm, London, British Library, Royal MA 12 c xxiii folio 84r.jpg 2023-12-17 design/typography/dropcap --- https://x.com/DavidSHolz/status/1739715754696777978 David S. Holz 2023-12-17 ai/nn/diffusion/midjourney/dropcap --- https://www.reddit.com/r/dalle2/comments/197f0wv/neon/ 2023-12-17 ai/nn/transformer/gpt/dall-e/3 --- https://statmodeling.stat.columbia.edu/2024/01/15/a-feedback-loop-can-destroy-correlation-this-idea-comes-up-in-many-places/ 2023-12-17 statistics/causality --- https://github.com/danluu/post-mortems 2023-12-17 cs --- https://arxiv.org/abs/2310.08754 Tokenizer Choice For LLM Training: Negligible or Crucial? Mehdi Ali, Michael Fromm, Klaudia Thellmann, Richard Rutmann, Max Lübbering, Johannes Leveling, Katrin Klug, Jan Ebert, Niclas Doll, Jasper Schulze Buschhoff, Charvi Jain, Alexander Arno Weber, Lena Jurkschat, Hammam Abdelwahab, Chelsea John, Pedro Ortiz Suarez, Malte Ostendorff, Samuel Weinbach, Rafet Sifa, Stefan Kesselheim, Nicolas Flores-Herr 2023-10-12 2023-12-17 [("doi","10.48550/arXiv.2310.08754")] ai/nn/tokenization ai/nn/transformer/gpt/2

The recent success of LLMs (Large Language Models) has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and advancements in pretraining objectives, leaving tokenizer influence as a blind spot. Shedding light on this underexplored area, we conduct a comprehensive study on the influence of tokenizer choice on LLM downstream performance by training 24 mono- and multilingual LLMs at a 2.6b parameter scale, ablating different tokenizer algorithms and parameterizations.

Our studies highlight that the tokenizer choice can impact the model’s downstream performance, training and inference costs. In particular, we find that the common tokenizer evaluation metrics fertility and parity are not always predictive of model downstream performance, rendering these metrics a questionable proxy for the model’s downstream performance.

Furthermore, we show that multilingual tokenizers trained on the 5 most frequent European languages require vocabulary size increases of factor 3 in comparison to English. While English-only tokenizers have been applied to the training of multi-lingual LLMs, we find that this approach results in a severe downstream performance degradation and additional training costs of up to 68%, due to an inefficient tokenization vocabulary.

--- https://twiki.org/cgi-bin/view/Codev/StructuredWiki 2023-12-17 design --- /doc/philosophy/religion/2024-caspi.pdf A quantitative examination of half-belief in superstition Avner Caspi, Eti Shmuel, Eran Chajut 2024 2024 [("doi","10.1027/1614-0001/a000401")] philosophy/religion psychiatry/anxiety psychology/cognitive-bias

We examined the phenomenon of half-belief in superstitions by asking two samples of participants (total n = 1,014) to report how much they practiced positive and negative superstitions and how much they believed in these superstitions. We further assessed whether demographic and psychological variables accounted for practice and belief.

The results suggest that very few people show a complete lack of belief in superstitions and practice none. Some participants are calibrated believers, that is, people who practice and believe to the same extent. All others are either half-believers, who practice more than they believe, or passive-believers who practice less than they believe.

Age, gender, and religiosity correlated with practicing, believing, and with the discrepancy between them (ie. with half-belief or with passive-belief). Anxiety and uncertainty are associated with practicing, believing, and the discrepancy between them, with some effects being weaker for positive than for negative superstitions.

Some correlations were stronger in stressful situations (ie. COVID-19) than prior to the pandemic.

--- https://www.lesswrong.com/posts/mCu8hnycFkdiBMvD7/why-wasn-t-preservation-with-the-goal-of-potential-future 2023-12-17 cryonics --- https://www.astralcodexten.com/p/against-learning-from-dramatic-events 2023-12-18 statistics/bayes statistics/prediction --- https://x.com/jachaseyoung/status/1710055856266485768 Jordan Chase-Young 2023-12-18 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1712203920003440886 Jordan Chase-Young 2023-12-18 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1715827797883761015 Jordan Chase-Young 2023-12-18 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1712928696234078356 Jordan Chase-Young 2023-12-18 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1714378247624909044 Jordan Chase-Young 2023-12-18 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1714740636530737455 Jordan Chase-Young 2023-12-18 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1715103022227853316 Jordan Chase-Young 2023-12-18 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1716552573908385862 Jordan Chase-Young 2023-12-18 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1716914962072011240 Jordan Chase-Young 2023-12-18 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1719089288980332767 Jordan Chase-Young 2023-12-19 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1719451677613588644 Jordan Chase-Young 2023-12-19 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1720176452967035016 Jordan Chase-Young 2023-12-19 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1720538841155809667 Jordan Chase-Young 2023-12-19 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1720901228677489034 Jordan Chase-Young 2023-12-19 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1721626004828160110 Jordan Chase-Young 2023-12-19 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1722350781691547859 Jordan Chase-Young 2023-12-19 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1722713169427357751 Jordan Chase-Young 2023-12-19 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://x.com/jachaseyoung/status/1725249884847026584 Jordan Chase-Young 2023-12-19 fiction/humor --- https://academic.oup.com/schizophreniabulletin/advance-article/doi/10.1093/schbul/sbad173/7517011 2023-12-19 psychiatry/schizophrenia --- https://huyenchip.com/2024/01/16/sampling.html 2023-12-19 ai/nn/sampling --- https://chat.openai.com/share/04add58f-2052-4b60-ae2a-ab708c29088f 2023-12-20 ai/nn/sampling ai/nn/transformer/gpt/4/nonfiction --- https://arxiv.org/abs/2303.08896 SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models Potsawee Manakul, Adian Liusie, Mark J. F. Gales 2023-03-15 2023-12-20 [("doi","10.48550/arXiv.2303.08896")] ai/nn/transformer/gpt/inner-monologue

Generative Large Language Models (LLMs) such as GPT-3 are capable of generating highly fluent responses to a wide variety of user prompts. However, LLMs are known to hallucinate facts and make non-factual statements which can undermine trust in their output. Existing fact-checking approaches either require access to the output probability distribution (which may not be available for systems such as ChatGPT) or external databases that are interfaced via separate, often complex, modules.

In this work, we propose SelfCheckGPT, a simple sampling-based approach that can be used to fact-check the responses of black-box models in a zero-resource fashion, ie. without an external database. SelfCheckGPT leverages the simple idea that if an LLM has knowledge of a given concept, sampled responses are likely to be similar and contain consistent facts. However, for hallucinated facts, stochastically sampled responses are likely to diverge and contradict one another.

We investigate this approach by using GPT-3 to generate passages about individuals from the WikiBio dataset, and manually annotate the factuality of the generated passages. We demonstrate that SelfCheckGPT can: (1) detect non-factual and factual sentences; and (2) rank passages in terms of factuality. We compare our approach to several baselines and show that our approach has considerably higher AUC-PR scores in sentence-level hallucination detection and higher correlation scores in passage-level factuality assessment compared to grey-box methods.

[Isn’t this just an inner-monologue majority-voting?] --- https://en.wikipedia.org/wiki/Recursive_islands_and_lakes Recursive islands and lakes 2023-12-20 math/humor --- https://schollz.com/tinker/watercolor/ 2023-12-20 ai/nn/cnn --- https://aimd.app/blog/2024-01-16-using-ai-to-overengineer-404-pages 2023-12-20 ai/nn/retrieval cs/linkrot --- https://blog.nawaz.org/posts/2024/Jan/llm-assisted-moderation/ 2023-12-20 ai/nn/transformer/gpt/4/nonfiction design --- https://www.quantamagazine.org/the-quest-for-simple-rules-to-build-a-microbial-community-20240117/ 2023-12-20 genetics/microbiome --- https://www.wired.com/story/27-year-old-codebreaker-busted-myth-bitcoins-anonymity/ 2023-12-20 bitcoin darknet-market/silk-road/1 --- https://arxiv.org/abs/2211.03622 Do Users Write More Insecure Code with AI Assistants? Neil Perry, Megha Srivastava, Deepak Kumar, Dan Boneh 2022-11-07 2023-12-20 [("doi","10.1145/3576915.3623157")] ai/nn/transformer/gpt/codex cs/security

We conduct the first large-scale user study examining how users interact with an AI Code assistant to solve a variety of security related tasks across different programming languages.

Overall, we find that participants who had access to an AI assistant based on OpenAI’s codex-davinci-002 model wrote less secure code than those without access. Additionally, participants with access to an AI assistant were more likely to believe they wrote secure code than those without access to the AI assistant.

Furthermore, we find that participants who trusted the AI less and engaged more with the language and format of their prompts (eg. re-phrasing, adjusting temperature) provided code with fewer security vulnerabilities.

Finally, in order to better inform the design of future AI-based Code assistants, we provide an in-depth analysis of participants’ language and interaction behavior, as well as release our user interface as an instrument to conduct similar studies in the future.

--- https://arxiv.org/abs/2401.04266 Attention versus Contrastive Learning of Tabular Data—A Data-centric Benchmarking Shourav B. Rabbani, Ivan V. Medri, Manar D. Samad 2024-01-08 2024-01-08 [("doi","10.48550/arXiv.2401.04266")] ai/nn/transformer/attention ai/nn/vae ai/tabular

Despite groundbreaking success in image and text learning, deep learning has not achieved improvements against traditional machine learning (ML) when it comes to tabular data. This performance gap underscores the need for data-centric treatment and benchmarking of learning algorithms.

Recently, attention and contrastive learning breakthroughs have shifted computer vision and natural language processing paradigms. However, the effectiveness of these advanced deep models on tabular data is sparsely studied using a few data sets with very large sample sizes, reporting mixed findings after benchmarking against a limited number of baselines.

We argue that the heterogeneity of tabular data sets and selective baselines in the literature can bias the benchmarking outcomes. This article extensively evaluates state-of-the-art attention and contrastive learning methods on a wide selection of 28 tabular data sets (14 easy and 14 hard-to-classify) against traditional deep and machine learning.

Our data-centric benchmarking demonstrates when traditional ML is preferred over deep learning and vice versa because no best learning method exists for all tabular data sets. Combining between-sample and between-feature attentions conquers the invincible traditional ML on tabular data sets by a large margin but fails on high dimensional data, where contrastive learning takes a robust lead.

While a hybrid attention-contrastive learning strategy mostly wins on hard-to-classify data sets, traditional methods are frequently superior on easy-to-classify data sets with presumably simpler decision boundaries. To the best of our knowledge, this is the first benchmarking paper with statistical analyses of attention and contrastive learning performances on a diverse selection of tabular data sets against traditional deep and machine learning baselines to facilitate further advances in this field.

--- https://arxiv.org/abs/2310.15386 Course Correcting Koopman Representations Mahan Fathi, Clement Gehring, Jonathan Pilault, David Kanaa, Pierre-Luc Bacon, Ross Goroshin 2023-10-23 2023-12-20 [("doi","10.48550/arXiv.2310.15386")] reinforcement-learning/model reinforcement-learning/offline

Koopman representations aim to learn features of nonlinear dynamical systems (NLDS) which lead to linear dynamics in the latent space. Theoretically, such features can be used to simplify many problems in modeling and control of NLDS. In this work we study autoencoder formulations of this problem, and different ways they can be used to model dynamics, specifically for future state prediction over long horizons. We discover several limitations of predicting future states in the latent space and propose an inference-time mechanism, which we refer to as Periodic Reencoding, for faithfully capturing long term dynamics.

We justify this method both analytically and empirically via experiments in low and high dimensional NLDS.

--- https://arxiv.org/abs/2103.17058 My cat Chester’s dynamical systems analysyyyyy7777777777777777y7is of the laser pointer and the red dot on the wall: correlation, causation, or SARS-Cov-2 hallucination? Eve Armstrong, Chester 2021-03-31 2023-12-21 [("doi","10.48550/arXiv.2103.17058")] cat/psychology math/humor statistics/causality

["Could A Statistician Understand A Laser Pointer?"] My cat Chester investigates the elusive relationship between the appearance in my hand of a silver laser pointer and that of a red dot on the wall, or on the floor, or on any other object that resides within the vicinity of the laser pointer. Chester first assesses preliminary establishments for causality, including mutual information, temporal precedence, and control for third variables. These assessments are all inconclusive for various reasons. In particular, mutual information fails to illuminate the problem due to a dearth of information regarding what the laser pointer might have been doing at times following Chester’s first awareness of the dot.

Next Chester performs a formal reconstruction of phase space via time-delay embedding, to unfold the geometry of the underlying dynamical system giving rise to the red dot’s trajectory. The resulting attractor does not resemble a laser pointer. The reconstruction could, however, be flawed, for example, due to the short temporal duration of the dot’s observed trajectory.

Finally, the red dot could be a hallucination: a symptom brought on by COVID-19—because, well, these days pretty much anything might be a symptom brought on by COVID-19. On this note, Chester’s kitten brother Mad Dog Lapynski offers an independent check on the red dot’s existence. Moreover, the results of this study are inconclusive and call for follow-up.

--- https://www.youtube.com/watch?v=bpGx61xNYhc 2023-12-21 cat/biology math --- https://arxiv.org/abs/2311.01017 Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion Lunjun Zhang, Yuwen Xiong, Ze Yang, Sergio Casas, Rui Hu, Raquel Urtasun 2023-11-02 2023-12-21 [("doi","10.48550/arXiv.2311.01017")] ai/nn/diffusion/discrete ai/nn/vae/mae reinforcement-learning/model

Learning world models can teach an agent how the world works in an unsupervised manner. Even though it can be viewed as a special case of sequence modeling, progress for scaling world models on robotic applications such as autonomous driving has been somewhat less rapid than scaling language models with Generative Pre-trained Transformers (GPT). We identify two reasons as major bottlenecks: dealing with complex and unstructured observation space, and having a scalable generative model.

Consequently, we propose a novel world modeling approach that first tokenizes sensor observations with VQVAE, then predicts the future via discrete diffusion. To efficiently decode and denoise tokens in parallel, we recast Masked Generative Image Transformer into the discrete diffusion framework with a few simple changes, resulting in notable improvement.

When applied to learning world models on point cloud observations, our model reduces prior SOTA Chamfer distance by >65% for 1s prediction, and >50% for 3s prediction, across NuScenes, KITTI Odometry, and Argoverse2 datasets.

Our results demonstrate that discrete diffusion on tokenized agent experience can unlock the power of GPT-like unsupervised learning for robotic agents.

--- https://en.wikipedia.org/wiki/Cellular_neural_network Cellular neural network 2023-12-21 ai/nn cs/cellular-automaton --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055191/ Maternal Mortality in the United States: Recent Trends, Current Status, and Future Considerations K S. Joseph, Amélie Boutin, Sarka Lisonkova, Giulia M. Muraca, Neda Razaz, Sid John, Azar Mehrabadi, Yasser Sabr, Cande V. Ananth, Enrique Schisterman 2021 2023-12-21 [("doi","10.1097/AOG.0000000000004361")] biology

Rigorous studies carried out by the National Center for Health Statistics show that previously reported increases in maternal mortality rates in the United States were an artifact of changes in surveillance. The pregnancy checkbox, introduced in the revised 2003 death certificate and implemented by the states in a staggered manner, resulted in increased identification of maternal deaths and in reported maternal mortality rates. This Commentary summarizes the findings of the National Center for Health Statistics reports, describes temporal trends and the current status of maternal mortality in the United States, and discusses future concerns.

Although the National Center for Health Statistics studies, based on recoding of death certificate information (after excluding information from the pregnancy checkbox), showed that crude maternal mortality rates did not change significantly 2002–2018, age-adjusted analyses show a temporal reduction in the maternal mortality rate (21% decline, 95% CI 13-28). Specific causes of maternal death, which were not affected by the pregnancy checkbox, such as preeclampsia, showed substantial temporal declines.

However, large racial disparities continue to exist: Non-Hispanic Black women had a 2.5× higher maternal mortality rate compared with non-Hispanic White women in 2018. This overview of maternal mortality underscores the need for better surveillance and more accurate identification of maternal deaths, improved clinical care, and expanded public health initiatives to address social determinants of health. Challenges with ascertaining maternal deaths notwithstanding, several causes of maternal death (unaffected by surveillance artifacts) show significant temporal declines, even though there remains substantial scope for preventing avoidable maternal death and reducing disparities.

Figure 1: Temporal trends in maternal deaths in the United States, 1993–2014, showing a small increase in maternal mortality with the introduction of International Classification of Diseases, Tenth Revision (ICD-10 codes) in 1999, and larger increases after the staggered adoption of the pregnancy checkbox on death certificates. Maternal mortality rates, including and excluding late maternal deaths (ICD-10 codes O96, O97) (A) and all maternal deaths, maternal deaths excluding late maternal deaths, deaths due to “Other specified pregnancy-related conditions” (O268), and deaths due to “other maternal diseases classifiable elsewhere” (O99) (B). Reprinted from Joseph et al 2017.
--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5177465/ Factors Underlying the Temporal Increase in Maternal Mortality in the United States K S. Joseph, Sarka Lisonkova, Giulia M. Muraca, Neda Razaz, Yasser Sabr, Azar Mehrabadi, Enrique F. Schisterman 2017 2023-12-21 [("doi","10.1097/AOG.0000000000001810")] biology

Objective: To identify the factors underlying the recent increase in maternal mortality ratios (maternal deaths per 100,000 live births) in the United States.

Methods: We carried out a retrospective study with data on maternal deaths and live births in the United States 1993 → 2014 obtained from the birth and death files of the Centers for Disease Control and Prevention. Underlying causes of death were examined 1999–2014 using International Classification of Diseases, 10th Revision (ICD-10) codes. Poisson regression was used to estimate maternal mortality rate ratios (RRs) and 95% confidence intervals (CIs) after adjusting for the introduction of a separate pregnancy question and the standard pregnancy checkbox on death certificates and adoption of ICD-10.

Results: Maternal mortality ratios increased from 7.55 in 1993, to 9.88 in 1999, and to 21.5 per 100,000 live births in 2014 (RR 2014 compared with 1993 2.84, 95% CI 2.49-3.24; RR 2014 compared with 1999 2.17, 95% CI 1.93-2.45). The increase in maternal deaths 1999 → 2014 was mainly the result of increases in maternal deaths associated with two new ICD-10 codes (O26.8, ie, primarily renal disease; and O99, ie, other maternal diseases classifiable elsewhere); exclusion of such deaths abolished the increase in mortality (RR 1.09, 95% CI 0.94-1.27). Regression adjustment for improvements in surveillance also abolished the temporal increase in maternal mortality ratios (adjusted maternal mortality ratios 7.55 in 1993, 8.00 per 100,000 live births in 2013; adjusted RR 2013 compared with 1993 1.06, 95% CI 0.90-1.25).

Conclusion: Recent increases in maternal mortality ratios in the United States are likely an artifact of improvements in surveillance and highlight past underestimation of maternal death. Complete ascertainment of maternal death in populations remains a challenge even in countries with good systems for civil registration and vital statistics.

--- https://www.biorxiv.org/content/10.1101/2024.01.09.574865.full Associations between common genetic variants and income provide insights about the socioeconomic health gradient Hyeokmoon Kweon, Casper A. P. Burik, Yuchen Ning, Rafael Ahlskog, Charley Xia, Erik Abner, Yanchun Bao, Laxmi Bhatta, Tariq O. Faquih, Maud de Feijter, Paul Fisher, Andrea Gelemanović, Alexandros Giannelis, Jouke-Jan Hottenga, Bita Khalili, Yunsung Lee, Ruifang Li-Gao, Jaan Masso, Ronny Myhre, Teemu Palviainen, Cornelius A. Rietveld, Alexander Teumer, Renske M. Verweij, Emily A. Willoughby, Esben Agerbo, Sven Bergmann, Dorret I. Boomsma, Anders Børglum, Ben M. Brumpton, Neil Martin Davies, Tõnu Esko, Scott D. Gordon, Georg Homuth, M. Arfan Ikram, Magnus Johannesson, Jaakko Kaprio, Michael P. Kidd, Zoltán Kutalik, Alex S. F. Kwong, James J. Lee, Annemarie I. Luik, Per Magnus, Pedro Marques-Vidal, Nicholas G. Martin, Dennis O. Mook-Kanamori, Preben Bo Mortensen, Sven Oskarsson, Emil M. Pedersen, Ozren Polašek, Frits R. Rosendaal, Melissa C. Smart, Harold Snieder, Peter J. van der Most, Peter Vollenweider, Henry Völzke, Gonneke Willemsen, Jonathan P. Beauchamp, Thomas A. DiPrete, Richard Karlsson Linnér, Qiongshi Lu, Tim T. Morris, Aysu Okbay, K. Paige Harden, Abdel Abdellaoui, W. David Hill, Ronald de Vlaming, Daniel J. Benjamin, Philipp Koellinger 2024-01-10 2024-01-10 [("doi","10.1101/2024.01.09.574865")] genetics/heritable/correlation iq/ses psychiatry/depression

We conducted a genome-wide association study (GWAS) on income among individuals of European descent and leveraged the results to investigate the socio-economic health gradient (n = 668,288).

We found 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes.

Our GWAS-derived polygenic index captures 1—4% of income variance, with only one-fourth attributed to direct genetic effects. A phenome-wide association study using this polygenic index showed reduced risks for a broad spectrum of diseases, including hypertension, obesity, type 2 diabetes, coronary atherosclerosis, depression, asthma, and back pain.

The income factor showed a substantial genetic correlation (0.92, s.e. = 0.006) with educational attainment (EA). Accounting for EAs genetic overlap with income revealed that the remaining genetic signal for higher income related to better mental health but reduced physical health benefits and increased participation in risky behaviors such as drinking and smoking.

--- https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2813601 2023-12-21 genetics/heritable/correlation psychiatry/anorexia zeo --- https://www.nature.com/articles/s41588-023-01596-4 2023-12-21 genetics/heritable/correlation psychiatry/depression --- https://manifestai.com/blogposts/faster-after-all/ 2023-12-21 ai/nn/rnn ai/nn/transformer/attention/linear-algebra --- https://github.com/collabora/WhisperSpeech 2023-12-21 ai/music ai/nn/transformer/gpt/whisper --- https://ferd.ca/queues-don-t-fix-overload.html 2023-12-22 cs/algorithm --- /doc/psychology/personality/2022-roberts.pdf Personality Psychology Brent W. Roberts, Hee J. Yoon 2021-09-13 2023-12-22 [("doi","10.1146/annurev-psych-020821-114927")] economics psychology/personality

Personality psychology, which seeks to study individual differences in thoughts, feelings, and behaviors that persist over time and place, has experienced a renaissance in the last few decades. It has also not been reviewed as a field in the Annual Review of Psychology since 2001.

In this article, we seek to provide an update as well as a meta-organizational structure to the field. In particular, personality psychology has a prescribed set of 4 responsibilities that it implicitly or explicitly tackles as a field: (1) describing what personality is—ie. what the units of analysis in the field are; (2) documenting how it develops; (3) explaining the processes of personality and why they affect functioning; and (4) providing a framework for understanding individuals and explaining their actions, feelings, and motivations.

We review progress made over the last 20 years to address these 4 agendas and conclude by highlighting future directions and ongoing challenges to the field.

[Keywords: personality, personality development, personality traits, motivation, skills, narrative identity]

…Historically, personality psychology was criticized because of empirical findings that were mistakenly thought to be of smaller magnitude than in most other areas of psychology (Mischel 1968) [ie. Walter Mischel’s situationism]. The shared ethos was that personality traits and personality writ large lacked the levels of predictive validity that would make them matter.

Students of history will know that the personality correlation coefficient, r, was 0.30. The argument that this coefficient was low resulted from the fact that personality psychologists habitually reported effect sizes, whereas many other areas of psychology, especially in the 1960s, failed to report effect sizes at all. Personality psychology committed the sin of being too transparent. It is also the case that once the effects of these other areas were translated into the r metric, we found that most areas of psychology find coefficients lower than 0.30.

The perspective that personality was not an important predictor of important outcomes began to change because of industrial psychology, which concluded in the 1990s that personality traits did matter for outcomes like job performance (Barrick & Mount 1991) and job satisfaction (Judge et al 2002). What followed were a series of studies, reviews, and meta-analyses that rendered a very clear picture. Personality traits and other personality constructs predict many important life outcomes, such as work success, relationship outcomes, well-being, mental health, and physical health (Caspi et al 2005, Ozer & Benet-Martinez 2006), and they often do so at levels equal to gold standard predictors such as cognitive ability and socioeconomic status (Roberts et al 2007). Moreover, those predictive patterns tend to replicate at a far higher rate than many other findings in the psychological literature (Soto 2019).

See Also:

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805882/ RenderGAN: Generating Realistic Labeled Data Leon Sixt, Benjamin Wild, Tim Landgraf 2018 2023-12-22 [("doi","10.3389/frobt.2018.00066")] ai/nn/gan

Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible.

We present a novel framework called RenderGAN that can generate large amounts of realistic, labeled images by combining a 3D model and the Generative Adversarial Network framework. In our approach, image augmentations (eg. lighting, background, and detail) are learned from unlabeled data such that the generated images are strikingly realistic while preserving the labels known from the 3D model.

We apply the RenderGAN framework to generate images of barcode-like markers that are attached to honeybees. Training a DCNN on data generated by the RenderGAN yields considerably better performance than training it on various baselines.

--- https://arxiv.org/abs/1612.07828#apple Learning from Simulated and Unsupervised Images through Adversarial Training Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, Russ Webb 2016-12-22 2023-12-22 [("doi","10.48550/arXiv.1612.07828")] ai/nn/adversarial ai/nn/cnn ai/nn/gan

With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions.

To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulator’s output using unlabeled real data, while preserving the annotation information from the simulator.

We develop a method for S+U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors. We make several key modifications to the standard GAN algorithm to preserve annotations, avoid artifacts, and stabilize training: (1) a ‘self-regularization’ term, (2) a local adversarial loss, and (3) updating the discriminator using a history of refined images.

We show that this enables generation of highly realistic images, which we demonstrate both qualitatively and with a user study. We quantitatively evaluate the generated images by training models for gaze estimation and hand pose estimation. We show an improvement over using synthetic images, and achieve state-of-the-art results on the MPIIGaze dataset without any labeled real data.

--- https://www.frbsf.org/economic-research/publications/economic-letter/2024/january/does-working-from-home-boost-productivity-growth/ 2023-12-22 economics/automation --- https://www.construction-physics.com/p/what-happened-to-the-us-machine-tool 2023-12-22 economics/automation technology --- https://blog.danslimmon.com/2024/01/18/3-questions-that-will-make-you-a-phenomenal-rubber-duck/ 2023-12-22 psychology/cognitive-bias/illusion-of-depth --- https://github.com/sgrvinod/chess-transformers 2023-12-22 ai/nn/transformer/gpt reinforcement-learning/chess --- https://www.lesswrong.com/posts/bx3gkHJehRCYZAF3r/pain-is-not-the-unit-of-effort 2023-12-22 psychology/personality/conscientiousness psychology/willpower --- https://jgeekstudies.org/archives/vol-42-december-2017/medjed-from-ancient-egypt-to-japanese-pop-culture/ 2023-12-22 anime --- https://jgeekstudies.org/2023/04/06/a-practical-implication-of-the-astolfo-effect-bias-in-ai-generated-images/ 2023-12-23 ai/anime ai/nn/diffusion --- https://jgeekstudies.org/2021/12/28/the-astolfo-effect-the-popularity-of-fate-grand-order-characters-in-comparison-to-their-real-counterparts/ 2023-12-23 anime --- https://antigonejournal.com/2024/01/decipherment-linear-b/ 2023-12-23 cs/cryptography psychology/linguistics --- https://www.biorxiv.org/content/10.1101/2023.12.20.572662.full AlphaFold2 structures template ligand discovery Jiankun Lyu, Nicholas Kapolka, Ryan Gumpper, Assaf Alon, Liang Wang, Manish K. Jain, Ximena Barros-Álvarez, Kensuke Sakamoto, Yoojoong Kim, Jeffrey DiBerto, Kuglae Kim, Tia A. Tummino, Sijie Huang, John J. Irwin, Olga O. Tarkhanova, Yurii Moroz, Georgios Skiniotis, Andrew C. Kruse, Brian K. Shoichet, Bryan L. Roth 2023-12-21 2023-12-23 [("doi","10.1101/2023.12.20.572662")] ai/nn/transformer/alphafold psychedelic

[media] AlphaFold2 (AF2) and RosettaFold have greatly expanded the number of structures available for structure-based ligand discovery, even though retrospective studies have cast doubt on their direct usefulness for that goal. Here, we tested unrefined AF2 models prospectively, comparing experimental hit-rates and affinities from large library docking against AF2 models vs the same screens targeting experimental structures of the same receptors.

In retrospective docking screens against the sigma2 and the 5-HT2A receptors, the AF2 structures struggled to recapitulate ligands that we had previously found docking against the receptors’ experimental structures, consistent with published results. Prospective large library docking against the AF2 models, however, yielded similar hit rates for both receptors versus docking against experimentally-derived structures; hundreds of molecules were prioritized and tested against each model and each structure of each receptor.

The success of the AF2 models was achieved despite differences in orthosteric pocket residue conformations for both targets versus the experimental structures. Intriguingly, against the 5-HT2A receptor the most potent, subtype-selective agonists were discovered via docking against the AF2 model, not the experimental structure.

To understand this from a molecular perspective, a cryoEM structure was determined for one of the more potent and selective ligands to emerge from docking against the AF2 model of the 5-HT2A receptor. Our findings suggest that AF2 models may sample conformations that are relevant for ligand discovery, much extending the domain of applicability of structure-based ligand discovery.

--- https://www.nature.com/articles/d41586-024-00130-8 2023-12-23 ai/nn/transformer/alphafold psychedelic --- https://x.com/wenquai/status/1748016021808595242 Zachary Lee 2023-12-23 ai/nn/transformer/gpt psychology/dark-knowledge --- https://discuss.luxonis.com/blog/3272-datadreamer-creating-custom-datasets-made-easy 2023-12-23 ai/nn/sparsity/knowledge-distillation --- https://midlibrary.io/midguide/midjourney-v6-in-depth-review-part-1-overview 2023-12-23 ai/nn/diffusion/midjourney --- https://midlibrary.io/midguide/midjourney-v6-in-depth-review-part-2-prompting 2023-12-23 ai/nn/diffusion/midjourney --- https://midlibrary.io/midguide/midjourney-v6-in-depth-review-part-3-parameters 2023-12-23 ai/nn/diffusion/midjourney --- https://www.eu-startups.com/2024/01/london-based-recraft-secures-e11-million-series-a-to-fuel-the-new-era-of-ai-in-professional-design/ 2023-12-23 ai --- https://arxiv.org/abs/1209.4991 Mind Switches in Futurama and Stargate Ron Evans, Lihua Huang 2012-09-22 2023-12-24 [("doi","10.48550/arXiv.1209.4991")] fiction/science-fiction math

Let P be a permutation expressed as a product of nontrivial disjoint cycles. When writing P as a product of distinct transpositions none equal to a factor of P, what is the smallest number of transpositions that can be used?

We answer this question and give applications to mind-switching problems that have arisen in connection with the popular sci-fi television series Futurama ["Prisoner of Benda" s7e10] and Stargate SG-1.

--- https://www.reddit.com/r/afinil/comments/1701qka/modafinil_extraction_active_ingredient_extremely/ 2023-12-24 modafinil --- https://x.com/john__allard/status/1748140402912481537 John Allard 2023-12-24 ai/nn/transformer/gpt/4 reinforcement-learning/meta-learning/continual-learning --- https://arxiv.org/abs/2401.08406#microsoft RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture Angels Balaguer, Vinamra Benara, Renato Luiz de Freitas Cunha, Roberto de M. Estevão Filho, Todd Hendry, Daniel Holstein, Jennifer Marsman, Nick Mecklenburg, Sara Malvar, Leonardo O. Nunes, Rafael Padilha, Morris Sharp, Bruno Silva, Swati Sharma, Vijay Aski, Ranveer Chandra 2024-01-16 2024-01-16 [("doi","10.48550/arXiv.2401.08406")] ai/nn/retrieval ai/nn/transformer/gpt/3 ai/nn/transformer/gpt/4 reinforcement-learning/meta-learning/continual-learning

There are two common ways in which developers are incorporating proprietary and domain-specific data when building applications of Large Language Models (LLMs): Retrieval-Augmented Generation (RAG) and Fine-Tuning. RAG augments the prompt with the external data, while fine-Tuning incorporates the additional knowledge into the model itself. However, the pros and cons of both approaches are not well understood.

In this paper, we propose a pipeline for fine-tuning and RAG, and present the tradeoffs of both for multiple popular LLMs, including LLaMA-2-13B, GPT-3.5, and GPT-4.

Our pipeline consists of multiple stages, including extracting information from PDFs, generating questions and answers, using them for fine-tuning, and leveraging GPT-4 for evaluating the results. We propose metrics to assess the performance of different stages of the RAG and fine-Tuning pipeline.

We conduct an in-depth study on an agricultural dataset [eg. "best time to plant X"]. Agriculture as an industry has not seen much penetration of AI, and we study a potentially disruptive application—what if we could provide location-specific insights to a farmer?

Our results show the effectiveness of our dataset generation pipeline in capturing geographic-specific knowledge, and the quantitative and qualitative benefits of RAG and fine-tuning.

We see an accuracy increase of over 6 percentage points (p.p.) when fine-tuning the model and this is cumulative with RAG, which increases accuracy by 5 p.p. further. In one particular experiment, we also demonstrate that the fine-tuned model leverages information from across geographies to answer specific questions, increasing answer similarity 47% → 72%.

Overall, the results point to how systems built using LLMs can be adapted to respond and incorporate knowledge across a dimension that is critical for a specific industry, paving the way for further applications of LLMs in other industrial domains.

[GPT-4 finetuning] …Lastly, we also fine-tuned GPT-4 in this setting. Being larger and more expensive, our goal was to assess if the model would benefit from additional knowledge in comparison to its base training. Due to its complexity and the amount of available data, we used Low Rank Adaptation (LoRA) (Hu et al 2021) for the fine-tuning process. This technique provides an efficient way to adapt parameter-heavy models, requiring less memory and computational resources compared to traditional re-training. By tuning a reduced set of parameters in the attention modules of the architecture, it embeds domain-specific knowledge from new data without losing knowledge gained during base training. In our study, optimization was done for 4 epochs, with a batch size of 256 samples, and a base learning rate of 1 × 10−4 that decayed as training progressed. The fine-tuning was carried out on 7 nodes, each with 8 A100 GPUs, over a total runtime of 1.5 days.

See Also:

--- https://www.theguardian.com/books/2019/mar/19/francis-spufford-pens-unauthorised-narnia-novel 2023-12-24 economics/copyright fiction --- https://madeinchinajournal.com/2024/01/18/sinicising-islam-in-china-the-story-of-a-mosque/ 2023-12-24 history/uighur --- https://foxpass.3sided.co.in/devanagari-typography-new-typefaces-new-flavours/ 2023-12-24 design/typography --- https://animationobsessive.substack.com/p/what-kazuo-oga-thinks-about-when#%C2%A7ogas-craft 2023-12-24 anime --- https://loeber.substack.com/p/15-maybe-you-should-invest-in-translation 2023-12-24 economics/automation --- https://www.biorxiv.org/content/10.1101/2022.05.30.494086.full Heavy-tailed neuronal connectivity arises from Hebbian self–organization Christopher W. Lynn, Caroline M. Holmes, Stephanie E. Palmer 2022-05-31 2023-12-24 [("doi","10.1101/2022.05.30.494086")] ai/nn/sparsity/pruning psychology/neuroscience

In networks of neurons, the connections are heavy-tailed, with a small number of neurons connected much more strongly than the vast majority of pairs. Yet it remains unclear whether, and how, such heavy-tailed connectivity emerges from simple underlying mechanisms.

Here we propose a minimal model of synaptic self-organization: connections are pruned at random, and the synaptic strength rearranges under a mixture of Hebbian and random dynamics.

Under these generic rules, networks evolve to produce scale-free distributions of connectivity strength, with a power-law exponent 𝛾 = 1 + 1⁄p that depends only on the probability p of Hebbian (rather than random) growth. By extending our model to include correlations in neuronal activity, we find that clustering—another ubiquitous feature of neuronal networks6–9—also emerges naturally.

We confirm these predictions in the connectomes of several animals, suggesting that heavy-tailed and clustered connectivity may arise from general principles of self-organization, rather than the biophysical particulars of individual neural systems.

--- https://www.sciencedirect.com/science/article/pii/S0160289623000855 2023-12-24 iq --- https://reallifemag.com/perpetual-motion-machines/ 2023-12-25 economics/automation reinforcement-learning/robot --- https://billsworld.neocities.org/ 2023-12-25 cs/css --- https://www.fortressofdoors.com/four-magic-words/ 2023-12-25 fiction/science-fiction philosophy/ethics --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6341291/ Development and Standardization of an Improved Type 1 Diabetes Genetic Risk Score for Use in Newborn Screening and Incident Diagnosis Seth A. Sharp, Stephen S. Rich, Andrew R. Wood, Samuel E. Jones, Robin N. Beaumont, James W. Harrison, Darius A. Schneider, Jonathan M. Locke, Jess Tyrrell, Michael N. Weedon, William A. Hagopian, Richard A. Oram 2019 2023-12-25 [("doi","10.2337/dc18-1785")] genetics/heritable

Objective: Previously generated genetic risk scores (GRSs) for type 1 diabetes (T1D) have not captured all known information at non-HLA loci or, particularly, at HLA risk loci. We aimed to more completely incorporate HLA alleles, their interactions, and recently discovered non-HLA loci into an improved T1D GRS (termed the “T1D GRS2”) to better discriminate diabetes subtypes and to predict T1D in newborn screening studies.

Research Design and Methods: In 6,481 case and 9,247 control subjects from the Type 1 Diabetes Genetics Consortium, we analyzed variants associated with T1D both in the HLA region and across the genome. We modeled interactions between variants marking strongly associated HLA haplotypes and generated odds ratios to create the improved GRS, the T1D GRS2. We validated our findings in UK Biobank. We assessed the impact of the T1D GRS2 in newborn screening and diabetes classification and sought to provide a framework for comparison with previous scores.

Results: The T1D GRS2 used 67 single-nucleotide polymorphisms (SNPs) and accounted for interactions between 18 HLA DR-DQ haplotype combinations. The T1D GRS2 was highly discriminative for all T1D (area under the curve [AUC] 0.92; p < 0.0001 vs. older scores) and even more discriminative for early-onset T1D (AUC 0.96). In simulated newborn screening, the T1D GRS2 was nearly twice as efficient as HLA genotyping alone and 50% better than current genetic scores in general population T1D prediction.

Conclusions: An improved T1D GRS, the T1D GRS2, is highly useful for classifying adult incident diabetes type and improving newborn screening. Given the cost-effectiveness of SNP genotyping, this approach has great clinical and research potential in T1D.

--- https://arxiv.org/abs/2401.09603#google Rethinking FID: Towards a Better Evaluation Metric for Image Generation Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit, Daniel Glasner, Ayan Chakrabarti, Sanjiv Kumar 2023-11-30 2023-12-25 [("doi","10.48550/arXiv.2401.09603")] ai/nn/transformer/clip ai/nn/vae/mae

As with many machine learning problems, the progress of image generation methods hinges on good evaluation metrics. One of the most popular is the Fréchet Inception Distance (FID). FID estimates the distance between a distribution of Inception-v3 features of real images, and those of images generated by the algorithm.

We highlight important drawbacks of FID: Inception’s poor representation of the rich and varied content generated by modern text-to-image models, incorrect normality assumptions, and poor sample complexity. We call for a reevaluation of FID’s use as the primary quality metric for generated images.

We empirically demonstrate that FID contradicts human raters, it does not reflect gradual improvement of iterative text-to-image models, it does not capture distortion levels, and that it produces inconsistent results when varying the sample size.

We also propose an alternative new metric, CMMD, based on richer CLIP embeddings and the maximum mean discrepancy distance with the Gaussian RBF kernel. It is an unbiased estimator that does not make any assumptions on the probability distribution of the embeddings and is sample efficient. Through extensive experiments and analysis, we demonstrate that FID-based evaluations of text-to-image models may be unreliable, and that CMMD offers a more robust and reliable assessment of image quality.

--- https://grantland.com/features/yankees-suck-t-shirts-boston-red-sox/ 2023-12-25 design/typography/dropcap --- https://www.reddit.com/r/kindle/comments/13ybmq7/drop_caps_on_scribe_vs_paprwhite/ 2023-12-25 design/typography/dropcap --- https://www.reddit.com/r/typography/comments/13kwocp/ok_so_heres_the_drop_caps_of_weaponry_series/ 2023-12-25 design/typography/dropcap --- https://github.com/adobe-webplatform/dropcap.js 2023-12-25 cs/js design/typography/dropcap --- https://www.reddit.com/user/Cretalyst/submitted/ 2023-12-25 design/typography/dropcap --- https://www.reddit.com/r/CrappyDesign/comments/8w0vjx/because_we_didnt_know_what_to_do_with_the_space/ 2023-12-26 design/typography/dropcap --- https://www.1001fonts.com/medici-text-font.html 2023-12-26 design/typography/dropcap --- http://tom7.org/lowercase/lowercase.pdf 2023-12-26 design/typography/dropcap --- https://jonathonf.github.io/solbera-dnd-fonts/#solbera 2023-12-26 design/typography/dropcap --- https://x.com/emollick/status/1748492920607379682 Ethan Mollick 2023-12-26 ai/nn/transformer/gpt/4/nonfiction ai/text-style-transfer --- https://www.medrxiv.org/content/10.1101/2024.01.12.24301086.full Concordance of whole-genome amplified embryonic DNA with the subsequently born child Shenglai Li, Thomas Giardina, Maria Katz, Dhruva Chandramohan, Nathan Slotnick, Barry Behr, Noor Siddiqui, Yuntao Xia, Benjamin Podgursky 2024-01-15 2024-01-15 [("doi","10.1101/2024.01.12.24301086")] genetics/sequencing

Before implantation subsequent to in vitro fertilization (IVF), the current options for Preimplantation Genetic Testing (PGT) are PGT for Aneuploidy (PGT-A) and, if clinically indicated, PGT for monogenic conditions (PGT-M). A more comprehensive approach involves PGT whole genome sequencing (PGT-WGS). PGT-WGS incorporates PGT-A, screens for hundreds of monogenic conditions, and can evaluate polygenic risk. Here for the first time, we compare PGT-WGS results against the genome of the subsequently born child.

We demonstrated high levels of concordance (both in sensitivity and precision) in exome variant calls between amplified embryonic DNA and sequenced fetal cord blood. This concordance was higher when filtering against 1300 targeted monogenic conditions implicated in birth defects, neurodevelopmental disorders, and hereditary cancer.

To evaluate PGT-WGS’s ability to identify de novo variants, we compared the child’s genome to parental genomes and demonstrated that PGT-WGS successfully identified 5⁄5 confirmed de-novo variants.

We further demonstrated concordance in polygenic risk scores calculated for both the embryo and the subsequently born child. This agreement extended to both traditional polygenic scores and oligogenic scores (Type 1 diabetes, Celiac disease, and Alzheimer’s Disease), which heavily rely on accurate genotyping of HLA and APOE sites.

To our knowledge, this is the first direct concordance study between a whole-genome sequencing of a trophectoderm biopsy and the DNA of the subsequently born child. By demonstrating a high degree of whole-exome concordance and adept detection of de novo variants, this approach showcases PGT-WGS’s capability to identify genetic variants not explicitly targeted for monogenic screening.

--- https://www.muddycolors.com/2019/06/the-book-of-the-new-sun/ 2023-12-26 design/typography/dropcap design/typography/rubrication fiction/gene-wolfe --- https://www.foliosociety.com/usa/the-book-of-the-new-sun-2-volume.html 2023-12-26 design/typography/dropcap fiction/gene-wolfe --- https://www.reddit.com/r/dalle2/comments/19bc1p0/the_goddess_is_born/ 2023-12-26 ai/nn/transformer/gpt/dall-e/3 --- https://www.rifters.com/crawl/?p=10964 2023-12-26 psychology/vision --- https://www.cambridge.org/core/journals/journal-of-economic-history/article/institutions-trade-and-growth-the-ancient-greek-case-of-proxenia/E6F32AC9BFCA142D0AE6E6AA9329609A 2023-12-26 economics/mechanism-design --- https://openreview.net/forum?id=Vc1fXQ8mJg Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift Jielin Qiu, Yi Zhu, Xingjian Shi, Florian Wenzel, Zhiqiang Tang, Ding Zhao, Bo Li, Mu Li 2024-01-19 2024-01-19 ai/nn/diffusion ai/nn/transformer

Multimodal image-text models have shown remarkable performance in the past few years. However, evaluating robustness against distribution shifts is crucial before adopting them in real-world applications.

In this work, we investigate the robustness of 12 popular open-sourced image-text models under common perturbations on 5 tasks (image-text retrieval, visual reasoning, visual entailment, image captioning, and text-to-image generation). In particular, we propose several new multimodal robustness benchmarks by applying 17 image perturbation and 16 text perturbation techniques on top of existing datasets.

We observe that multimodal models are not robust to image and text perturbations, especially to image perturbations. Among the tested perturbation methods, character-level perturbations constitute the most severe distribution shift for text, and zoom blur is the most severe shift for image data.

We also introduce two new robustness metrics (MMI for MultiModal Impact score and MOR for Missing Object Rate) for proper evaluations of multimodal models.

We hope our extensive study sheds light on new directions for the development of robust multimodal models.

More details can be found on the project webpage: https://MMRobustness.github.io/.

[Keywords: Multimodal, Robustness, Distribution Shift]

--- https://arxiv.org/abs/2401.10224 The Manga Whisperer: Automatically Generating Transcriptions for Comics Ragav Sachdeva, Andrew Zisserman 2024-01-18 2024-01-18 [("doi","10.48550/arXiv.2401.10224")] ai/anime ai/nn/transformer

In the past few decades, Japanese comics, commonly referred to as Manga, have transcended both cultural and linguistic boundaries to become a true worldwide sensation. Yet, the inherent reliance on visual cues and illustration within manga renders it largely inaccessible to individuals with visual impairments. In this work, we seek to address this substantial barrier, with the aim of ensuring that manga can be appreciated and actively engaged by everyone. Specifically, we tackle the problem of diarization i.e. generating a transcription of who said what and when, in a fully automatic way.

To this end, we make the following contributions: (1) we present a unified model, Magi, that is able to (a) detect panels, text boxes and character boxes, (b) cluster characters by identity (without knowing the number of clusters a priori), and (c) associate dialogues to their speakers; (2) we propose a novel approach that is able to sort the detected text boxes in their reading order and generate a dialogue transcript; (3) we annotate an evaluation benchmark [PopManga] for this task using publicly available [English] manga pages.

The code, evaluation datasets and the pre-trained model can be found at: Github.

--- https://arxiv.org/abs/2306.15364 The Architecture of a Biologically Plausible Language Organ Daniel Mitropolsky, Christos H. Papadimitriou 2023-06-27 2023-12-27 [("doi","10.48550/arXiv.2306.15364")] ai/nn psychology/linguistics psychology/neuroscience

We present a simulated biologically-plausible language organ, made up of stylized but realistic neurons, synapses, brain areas, plasticity, and a simplified model of sensory perception.

We show through experiments that this model succeeds in an important early step in language acquisition: the learning of nouns, verbs, and their meanings, from the grounded input of only a modest number of sentences. Learning in this system is achieved through Hebbian plasticity, and without backpropagation.

Our model goes beyond a parser previously designed in a similar environment, with the critical addition of a biologically-plausible account for how language can be acquired in the infant’s brain, not just processed by a mature brain.

--- https://arxiv.org/abs/2401.06133 The possibility of making $138,000 from shredded banknote pieces using computer vision Chung To Kong 2023-11-17 2023-12-27 [("doi","10.48550/arXiv.2401.06133")] ai/nn/cnn crime economics

Every country must dispose of old banknotes. At the Hong Kong Monetary Authority visitor center, visitors can buy a paperweight souvenir full of shredded banknotes. Even though the shredded banknotes are small, by using computer vision, it is possible to reconstruct the whole banknote like a jigsaw puzzle. Each paperweight souvenir costs $100 HKD [ie. ~$10 USD; "trading since May 2005 in the range US$1 : HK$7.75–7.85"], and it is claimed to contain shredded banknotes equivalent to 138 complete ($1,000) HKD banknotes. In theory, $138,000 HKD can be recovered by using computer vision. This paper discusses the technique of collecting shredded banknote pieces and applying a computer vision program.

…The shredded banknote pieces were enclosed in a cylinder of proxy glass in a cylindrical container. Proxy glass melts at ~200℃; a household hair dryer would be sufficient to soften this glass. Figure 3 shows the lid being removed using a heat gun.

The shredded banknotes were then collected. Surprisingly, 3 paperweight cylinders were opened, and two of them had stones in them (Figures 4 & 5)!

…Because one of the 3 cylinders contained some stones, the fourth cylinder was opened with care. The weight was recorded for each step. The label on the cylinder claimed that it contained 138 pieces of equivalent shredded banknotes. By comparing the weight of the shredded banknotes with that of the actual banknote, the number of equivalent shredded banknotes could be calculated…It was surprising that the Hong Kong Monetary Authority had provided only 20 equivalent banknotes rather than 138 equivalent banknotes. The cylinder only contained 20/138 (= 14.5%) of the shredded banknote pieces that the label had claimed…This cylinder only contained 82.57/138 = 60% of the shredded banknote pieces that the label had claimed. Although this issue is not the focus of this paper, it appears that the Hong Kong Monetary Authority has broken the law.

…The idea for this paper was discussed with the staff during my visit to the Hong Kong Monetary Authority visitor center. The paperweight souvenir is currently no longer available.

--- /doc/statistics/bayes/1972-dombal.pdf Computer-Aided Diagnosis Of Acute Abdominal Pain F. T. de Dombal, D. J. Leaper, J. R. Staniland, A. P. McCann, Jane C. Horrocks 1972-04-01 2023-12-27 [("doi","10.2307/25418224")] biology statistics/bayes --- https://www.typotheque.com/articles/calcula 2023-12-27 design/typography/square --- https://typographica.org/typeface-reviews/calcula/ 2023-12-27 design/typography/square --- https://x.com/incunabula/status/1573222769978114048 incunabula 2023-12-27 design/typography/square --- https://en.wikipedia.org/wiki/Kufic#Square_Kufic Kufic § Square Kufic 2023-12-27 design/typography/square --- https://commons.wikimedia.org/wiki/File:ISIL_Caliphate_Seal.svg 2023-12-27 crime/terrorism/rumiyah design/typography/square --- https://x.com/AnitaJott/status/985495494179938304 Anita Jott 2023-12-27 design/typography/square --- https://typographica.org/typeface-reviews/balkan-sans/ 2023-12-28 design/typography/square --- https://fontsinuse.com/typefaces/32408/balkan-sans 2023-12-28 design/typography/square --- https://fontsinuse.com/typefaces/39241/penny-farthing 2023-12-28 design/typography/square --- https://lukedorny.com/updates/interlocker 2023-12-28 design/typography/square --- https://www.youtube.com/watch?v=T0syCsC0_4s 2023-12-28 design/typography/square --- https://alternatescriptbureau.wordpress.com/2018/10/17/tongyang-script-for-english/ 2023-12-28 design/typography/square --- https://alternatescriptbureau.wordpress.com/2021/08/03/tongyang-script-v3/ 2023-12-28 design/typography/square --- https://www.designmadeingermany.de/2013/61882/ 2023-12-28 design/typography/square japan/art --- https://en.wikipedia.org/wiki/Xu_Bing Xu Bing 2023-12-28 design/typography/square --- https://fontsinuse.com/typefaces/40498/ed-interlock 2023-12-28 design/typography/square --- https://typographica.org/typeface-reviews/julien/ 2023-12-29 design/typography --- https://www.underware.nl/fonts/liza/features/OpenType_features/ 2023-12-29 design/typography --- https://luc.devroye.org/fonts-78250.html 2023-12-29 design/typography/square --- https://en.wikipedia.org/wiki/Vyaz_(Cyrillic_calligraphy) Vyaz (Cyrillic calligraphy) 2023-12-29 design/typography/square --- https://www.reddit.com/r/Scribes/comments/15fg2g4/experimental_cyrillic_fraktur/ 2023-12-29 design/typography --- http://www.underhanded-c.org/ 2023-12-29 cs/security --- https://www.youtube.com/watch?v=5BFFsZAB2rY CHAIR Hiroshi Mori 2000 2023-12-29 anime psychiatry/anxiety

An animated short film by Hiroshi Mori (writer-artist of Island Kingdom graphic novel and Dingbat comic book). Hand-drawn on paper with crow quill ink pen, shot 16mm using an Oxberry Camera. Audio by Fausto Caceres.

Featured at SXSW (2001), Anima Mundi (Brazil, 2000), Hawaii International Film Festival (2000), Black Maria Film Festival (2000), Ohina Short Film Showcase (2001) and others.

--- https://time.com/6556168/when-ai-outsmart-humans/ 2023-12-29 ai/scaling statistics/prediction --- https://www.quantamagazine.org/cryptographers-devise-an-approach-for-total-search-privacy-20231106/ 2023-12-29 cs/cryptography --- https://www.pnas.org/doi/full/10.1073/pnas.1900492116 2023-12-29 biology --- https://www.xubing.com/en/work/details/198?year=1994&type=year 2023-12-29 design/typography/square --- https://www.xubing.com/en/work/details/204?classID=10&type=class 2023-12-30 design/typography/square --- https://www.xubing.com/en/work/details/209?year=1996&type=year 2023-12-30 design/typography/square --- https://www.lesswrong.com/posts/tJAD2LG9uweeEfjwq/estimating-efficiency-improvements-in-llm-pre-training 2023-12-30 ai/nn/transformer/gpt/3 economics/experience-curve --- https://shkspr.mobi/blog/2024/01/lessons-learned-from-bringing-promotional-sweets-to-a-conference/ 2023-12-30 design economics/advertising --- https://www.theatlantic.com/technology/archive/2024/01/air-jordan-trend-is-over/677195/ 2023-12-30 psychology/collecting --- https://www.wsj.com/health/pharma/a-weight-loss-drug-changed-my-life-will-it-solve-my-problem-aeb79260 2023-12-30 longevity/glp/psychology longevity/glp/semaglutide --- https://sites.google.com/view/medusa-llm 2023-12-30 ai/nn/sampling ai/nn/transformer --- https://www.wired.com/2012/02/ff-forgettingpill/ The Forgetting Pill Erases Painful Memories Forever Lehrer 2012 2023-12-30 psychology/neuroscience --- https://drjamesthompson.blogspot.com/2015/09/scholar-in-86-snps.html 86 genomic sites associated with educational attainment provide insight into the biology of cognitive performance James J. Lee 2018 2023-12-30 genetics/heritable iq --- https://jamanetwork.com/journals/jama/fullarticle/201172 Vitamin E in the Primary Prevention of Cardiovascular Disease and Cancer: The Women’s Health Study: A Randomized Controlled Trial Lee 2005 2023-12-30 biology --- https://www.pnas.org/doi/full/10.1073/pnas.1320040111 Experimental evidence of massive-scale emotional contagion through social networks Kramer 2023-12-30 sociology/technology --- http://vision.psych.umn.edu/groups/schraterlab/dearden98bayesian.pdf 2023-12-31 reinforcement-learning/exploration reinforcement-learning/model-free statistics/bayes --- https://arxiv.org/abs/1301.6690 Model-Based Bayesian Exploration Richard Dearden, Nir Friedman, David Andre 2013-01-23 2023-12-31 [("doi","10.48550/arXiv.1301.6690")] reinforcement-learning/exploration reinforcement-learning/model statistics/bayes

Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be estimated using the classical notion of Value of Information—the expected improvement in future decision quality arising from the information acquired by exploration. Estimating this quantity requires an assessment of the agent’s uncertainty about its current value estimates for states.

In this paper we investigate ways of representing and reasoning about this uncertainty in algorithms where the system attempts to learn a model of its environment. We explicitly represent uncertainty about the parameters of the model and build probability distributions over Q-values based on these.

These distributions are used to compute a myopic approximation to the value of information for each action and hence to select the action that best balances exploration and exploitation.

--- https://www.sciencedirect.com/science/article/pii/S019188692300421X 2023-12-31 politics psychology/personality/narcissism psychology/personality/psychopathy --- https://www.sciencedirect.com/science/article/pii/S0191886923004361 2023-12-31 iq/ses --- https://www.aporiamagazine.com/p/bias-is-often-unpredictable 2023-12-31 iq statistics/bias --- https://austinvernon.site/blog/navyshipbuilding.html 2023-12-31 economics/experience-curve technology --- https://en.wikipedia.org/wiki/U.S._Permanent_Committee_for_the_Oliver_Wendell_Holmes_Devise U.S. Permanent Committee for the Oliver Wendell Holmes Devise 2023-12-31 economics/perpetuities law --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5003684/ Why do we like sweet taste: A bitter tale? Gary K. Beauchamp 2016 2023-12-31 [("doi","10.1016/j.physbeh.2016.05.007")] biology

Sweet is widely considered to be one of a small number of basic or primary taste qualities. Liking for sweet tasting substances is innate, although postnatal experiences can shape responses. The power of sweet taste to induce consumption and to motivate behavior is profound, suggesting the importance of this sense for many species.

Most investigators presume that the ability to identify sweet molecules through the sense of taste evolved to allow organisms to detect sources of readily available glucose from plants. Perhaps the best evidence supporting this presumption are recent discoveries in comparative biology demonstrating that species in the order Carnivora that do not consume plants also do not perceive sweet taste due to the pseudogenization of a component of the primary sweet taste receptor.

However, arguing against this idea is the observation that the sweetness of a plant, or the amount of easily metabolizable sugars contained in the plant, provides little quantitative indication of the plant’s energy or broadly conceived food value.

Here it is suggested that the perceptual ratio of sweet taste to bitter taste (a signal for toxicity) may be a better gauge of a plant’s broadly conceived food value than sweetness alone and that it is this ratio that helps guide selection or rejection of a potential plant food.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447812/ The taste of sugars Stuart A. McCaughey 2008 2023-12-31 [("doi","10.1016/j.neubiorev.2008.04.002")] biology psychology/neuroscience

Sugars evoke a distinctive perceptual quality (“sweetness” in humans) and are generally highly preferred. The neural basis for these phenomena is reviewed for rodents, in which detailed electrophysiological measurements have been made. A receptor has been identified that binds sweeteners and activates G-protein-mediated signaling in taste receptor cells, which leads to changes in neural firing rates in the brain, where perceptions of taste quality, intensity, and palatability are generated.

Most cells in gustatory nuclei are broadly tuned, so quality perception presumably arises from patterns of activity across neural populations. However, some manipulations affect only the most sugar-oriented cells, making it useful to consider them as a distinct neural subtype. Quality perception may also arise partly due to temporal patterns of activity to sugars, especially within sugar-oriented cells that give large but delayed responses.

Non-specific gustatory neurons that are excited by both sugars and unpalatable stimuli project to ventral forebrain areas, where neural responses provide a closer match with behavioral preferences. This transition likely involves opposing excitatory and inhibitory influences by different subgroups of gustatory cells. Sweeteners are generally preferred over water, but the strength of this preference can vary across time or between individuals, and higher preferences for sugars are often associated with larger taste-evoked responses.

--- /doc/economics/1927-ramsey-2.pdf A Contribution to the Theory of Taxation Frank Ramsey 1927-01-01 2023-12-31 [("doi","10.2307/2222721")] economics philosophy/frank-ramsey --- /doc/economics/1928-ramsey.pdf A Mathematical Theory of Saving Frank Ramsey 1928-01-01 2023-12-31 economics philosophy/frank-ramsey --- /doc/math/1926-ramsey-2.pdf The Foundations of Mathematics Frank Ramsey 1926-01-01 2024-01-01 [("doi","10.1112/plms/s2-25.1.338")] math philosophy/frank-ramsey --- /doc/math/1930-ramsey.pdf On a Problem of Formal Logic Frank Ramsey 1930-01-01 2024-01-01 [("doi","10.1112/plms/s2-30.1.264")] math philosophy/frank-ramsey --- /doc/math/1931-ramsey-foundationsofmathematicsandotherlogicalessays.epub 1931 2024-01-01 math philosophy/frank-ramsey --- /doc/philosophy/logic/1927-ramsey.pdf Symposium: Facts and Propositions Frank Ramsey, G. E. Moore 1927-01-01 2024-01-01 [("doi","10.2307/4106403")] philosophy/frank-ramsey philosophy/logic --- /doc/philosophy/ontology/1925-ramsey.pdf Universals Frank Ramsey 1925-01-01 2024-01-01 [("doi","10.1093/mind/xxxiv.136.401")] philosophy/frank-ramsey philosophy/ontology --- /doc/philosophy/ontology/1926-ramsey.pdf Symposium: Universals and the ‘Method of Analysis’ H. W. B. Joseph, Frank Ramsey, R. B. Braithwaite 1926-01-01 2024-01-01 [("doi","10.2307/4106549")] philosophy/frank-ramsey philosophy/ontology --- /doc/statistics/bayes/1922-ramsey.pdf Mr Keynes on Probability [review of J. M. Keynes, A Treatise on Probability, 1921] Frank Ramsey 1922-01-01 2024-01-01 [("doi","10.2307/687512")] philosophy/frank-ramsey statistics/bayes --- /doc/statistics/decision/1990-mellor-frankramseyphilosophicalpapers.pdf F. P. Ramsey: Philosophical Papers Frank Ramsey, D. H. Mellor 1990-01-01 2024-01-01 philosophy/frank-ramsey statistics/decision --- /doc/statistics/decision/1990-ramsey.pdf Weight or the Value of Knowledge Frank Ramsey 1990-01-01 2024-01-01 [("doi","10.2307/688001")] philosophy/frank-ramsey statistics/decision --- https://meteorfrom.space/ 2024-01-01 ai/nn/transformer/gpt cs/cryptography/steganography --- https://eprint.iacr.org/2021/686 2024-01-02 ai/nn/transformer/gpt cs/cryptography/steganography --- https://arxiv.org/abs/2401.10360 Excuse me, sir? Your language model is leaking (information) Or Zamir 2024-01-18 2024-01-18 [("doi","10.48550/arXiv.2401.10360")] ai/nn/transformer/gpt cs/cryptography/steganography

We introduce a cryptographic method to hide an arbitrary secret payload in the response of a Large Language Model (LLM). A secret key is required to extract the payload from the model’s response, and without the key it is provably impossible to distinguish between the responses of the original LLM and the LLM that hides a payload. In particular, the quality of generated text is not affected by the payload.

Our approach extends a recent result of Christ et al 2023 who introduced an undetectable watermarking scheme for LLMs.

--- https://en.wikipedia.org/wiki/Bacon%27s_cipher Bacon’s cipher 2024-01-02 cs/cryptography/steganography design/typography --- https://xkcd.com/2884/ 2024-01-02 design/visualization --- https://publicdomainreview.org/collection/kumanaki-kage/ 2024-01-02 design/visualization history/public-domain-review japan/art --- https://iter.ca/post/gh-sig-pwn/ 2024-01-02 cs/computable cs/security --- https://rls.sites.oasis.unc.edu/s890/evtclass.pdf 2024-01-02 statistics/order --- https://arxiv.org/abs/2306.09194 Undetectable Watermarks for Language Models Miranda Christ, Sam Gunn, Or Zamir 2023-05-25 2024-01-02 [("doi","10.48550/arXiv.2306.09194")] ai/nn/transformer/gpt cs/cryptography/steganography

Recent advances in the capabilities of large language models such as GPT-4 have spurred increasing concern about our ability to detect AI-generated text. Prior works have suggested methods of embedding watermarks in model outputs, by noticeably altering the output distribution. We ask: Is it possible to introduce a watermark without incurring any detectable change to the output distribution?

To this end we introduce a cryptographically-inspired notion of undetectable watermarks for language models. That is, watermarks can be detected only with the knowledge of a secret key; without the secret key, it is computationally intractable to distinguish watermarked outputs from those of the original model. In particular, it is impossible for a user to observe any degradation in the quality of the text. Crucially, watermarks should remain undetectable even when the user is allowed to adaptively query the model with arbitrarily chosen prompts.

We construct undetectable watermarks based on the existence of one-way functions, a standard assumption in cryptography.

--- https://en.wikipedia.org/wiki/Steganography Steganography 2024-01-02 cs/cryptography/steganography --- https://en.wikipedia.org/wiki/Trithemius Trithemius 2024-01-02 cs/cryptography/steganography --- https://en.wikipedia.org/wiki/Machine_Identification_Code Machine Identification Code 2024-01-02 cs/cryptography/steganography --- https://en.wikipedia.org/wiki/Cicada_3301 Cicada 3301 2024-01-03 cs/cryptography/steganography --- https://en.wikipedia.org/wiki/Acrostic Acrostic 2024-01-03 cs/cryptography/steganography --- https://en.wikipedia.org/wiki/Canary_trap Canary trap 2024-01-03 cs/cryptography/steganography --- https://en.wikipedia.org/wiki/Deniable_encryption Deniable encryption 2024-01-03 cs/cryptography/steganography --- https://en.wikipedia.org/wiki/Steganographic_file_system Steganographic file system 2024-01-03 cs/cryptography/steganography --- https://en.wikipedia.org/wiki/Steganographia Steganographia 2024-01-03 cs/cryptography/steganography --- https://arxiv.org/abs/2210.14889 Perfectly Secure Steganography Using Minimum Entropy Coupling Christian Schroeder de Witt, Samuel Sokota, J. Zico Kolter, Jakob Foerster, Martin Strohmeier 2022-10-24 2024-01-03 [("doi","10.48550/arXiv.2210.14889")] ai/nn/rnn ai/nn/transformer/gpt/2 cs/cryptography/steganography

Steganography is the practice of encoding secret information into innocuous content in such a manner that an adversarial third-party would not realize that there is hidden meaning. While this problem has historically been studied in security literature, recent advances in generative models have led to a shared interest among security and machine learning researchers in developing scalable steganography techniques.

In this work, we show that a steganography procedure is perfectly secure under Cachin 1998’s information-theoretic model of steganography if and only if it is induced by a coupling. Furthermore, we show that, among perfectly secure procedures, a procedure maximizes information throughput if and only if it is induced by a minimum entropy coupling.

These insights yield what are, to the best of our knowledge, the first steganography algorithms to achieve perfect security guarantees for arbitrary covertext distributions.

To provide empirical validation, we compare a minimum entropy coupling-based approach to 3 modern baselines—arithmetic coding, Meteor, and adaptive dynamic grouping—using GPT-2, WaveRNN, and Image Transformer as communication channels.

We find that the minimum entropy coupling-based approach achieves superior encoding efficiency, despite its stronger security constraints.

In aggregate, these results suggest that it may be natural to view information-theoretic steganography through the lens of minimum entropy coupling.

--- https://logicmag.io/security/tracing-paper/ 2024-01-03 cs/cryptography/steganography --- https://www.nature.com/articles/s41587-019-0356-z 2024-01-03 cs/cryptography/steganography --- https://blog.trailofbits.com/2019/11/01/two-new-tools-that-tame-the-treachery-of-files/ 2024-01-03 cs/cryptography/steganography --- https://hakaimagazine.com/news/the-military-wants-to-hide-covert-messages-in-marine-mammal-sounds/ 2024-01-03 cs/cryptography/steganography --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2151169/ Sex, aggression, and humour: responses to unicycling Sam Shuster 2007 2024-01-04 [("doi","10.1136/bmj.39414.552060.BE")] exercise math/humor sociology

Sam Shuster compares men and women’s responses to the sight of a unicyclist.

...The response to the unexpected and novel stimulus of seeing a unicyclist was surprisingly consistent even to the words and gestures used, and these varied with age, sex, and stage of sexual development. In males the response moved from curiosity in childhood, to physical and verbal aggression in older boys; this became more verbal as the boys matured into men and evolved into the concealed aggression of a repetitive humorous verbal put-down, which was lost with age. In contrast, the female response was praise and concern for safety. These findings suggest that humour develops from aggression in response to male hormones.

...Competing interests: None, apart from owning a bicycle as well as a unicycle.

--- https://www.nytimes.com/2024/01/23/health/deaf-gene-therapy.html 2024-01-04 genetics/editing --- https://www.biorxiv.org/content/10.1101/166538.full Linguistically deprived children: meta-analysis of published research underlines the importance of early syntactic language use for normal brain development Andrey Vyshedskiy, Mahapatra Shreyas, Rita Dunn 2017-07-21 2024-01-04 [("doi","10.1101/166538")] psychology/linguistics psychology/neuroscience

We analyzed all published reports of individuals not exposed to syntactic language until puberty: two feral children, who grew up without hearing any language, and 8 deaf linguistic isolates, who grew up communicating to their families using homesign or kitchensign, a system of gestures which allows them to communicate simple commands but lacks much in the way of syntax. A common observation in these individuals is the lifelong difficulty understanding syntax and spatial prepositions, even after many years of rehabilitation. This debilitating condition stands in stark contrast to linguistic isolates’ performance on memory as well as semantic tests: they could easily remember hundreds of newly learned words and identify previously seen objects by name.

The lack of syntactic language comprehension in linguistic isolates may stem from inability to understand words and/or grammar or inability to mentally synthesize known objects into novel configurations. We have previously shown that purposeful construction of novel mental images is the function of the lateral prefrontal cortex (LPFC) ability to dynamically control posterior cortex neurons.

Here we have ranked all tests performed on linguistic isolates by their reliance on the LPFC control of the posterior cortex: (1) the amount of posterior cortex territory that needs to be recruited by the LPFC and (2) the number of disparate objects that have to be combined together by the LPFC in order to answer the test question. According to our analysis, linguistic isolates performed well in all tests that did not involve the LPFC control of the posterior cortex, showed decreasing scores in tests that involved greater recruitment of the posterior cortex by the LPFC, and failed in tests that involved greatest recruitment of posterior cortex necessary for mental synthesis of multiple objects.

This pattern is consistent with inadequate frontoposterior connections in linguistic isolates. We discuss implications of these findings for the importance of early syntactic language exposure in formation of frontoposterior connections.

--- https://arxiv.org/abs/2312.05187#facebook Seamless: Multilingual Expressive and Streaming Speech Translation Seamless Communication, Loïc Barrault, Yu-An Chung, Mariano Coria Meglioli, David Dale, Ning Dong, Mark Duppenthaler, Paul-Ambroise Duquenne, Brian Ellis, Hady Elsahar, Justin Haaheim, John Hoffman, Min-Jae Hwang, Hirofumi Inaguma, Christopher Klaiber, Ilia Kulikov, Pengwei Li, Daniel Licht, Jean Maillard, Ruslan Mavlyutov, Alice Rakotoarison, Kaushik Ram Sadagopan, Abinesh Ramakrishnan, Tuan Tran, Guillaume Wenzek, Yilin Yang, Ethan Ye, Ivan Evtimov, Pierre Fernandez, Cynthia Gao, Prangthip Hansanti, Elahe Kalbassi, Amanda Kallet, Artyom Kozhevnikov, Gabriel Mejia Gonzalez, Robin San Roman, Christophe Touret, Corinne Wong, Carleigh Wood, Bokai Yu, Pierre Andrews, Can Balioglu, Peng-Jen Chen, Marta R. Costa-jussà, Maha Elbayad, Hongyu Gong, Francisco Guzmán, Kevin Heffernan, Somya Jain, Justine Kao, Ann Lee, Xutai Ma, Alex Mourachko, Benjamin Peloquin, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Anna Sun, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang, Mary Williamson 2023-12-08 2024-01-04 [("doi","10.48550/arXiv.2312.05187")] ai/nn/transformer ai/scaling

Large-scale automatic speech translation systems today lack key features that help machine-mediated communication feel seamless when compared to human-to-human dialogue. In this work, we introduce a family of models that enable end-to-end expressive and multilingual translations in a streaming fashion.

First, we contribute an improved version of the massively multilingual and multimodal SeamlessM4T model-SeamlessM4T v2. This newer model, incorporating an updated UnitY2 framework, was trained on more low-resource language data. SeamlessM4T v2 provides the foundation on which our next two models are initiated.

SeamlessExpressive enables translation that preserves vocal styles and prosody. Compared to previous efforts in expressive speech research, our work addresses certain underexplored aspects of prosody, such as speech rate and pauses, while also preserving the style of one’s voice.

As for SeamlessStreaming, our model leverages the Efficient Monotonic Multihead Attention mechanism to generate low-latency target translations without waiting for complete source utterances. As the first of its kind, SeamlessStreaming enables simultaneous speech-to-speech/text translation for multiple source and target languages.

To ensure that our models can be used safely and responsibly, we implemented the first known red-teaming effort for multimodal machine translation, a system for the detection and mitigation of added toxicity, a systematic evaluation of gender bias, and an inaudible localized watermarking mechanism designed to dampen the impact of deepfakes.

Consequently, we bring major components from SeamlessExpressive and SeamlessStreaming together to form Seamless, the first publicly available system that unlocks expressive cross-lingual communication in real-time. The contributions to this work are publicly released and accessible at https://github.com/facebookresearch/seamless_communication.

--- https://www.gq.com/story/worlds-greatest-jailbreak-artist-redoine-faid 2024-01-04 cs/security --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10435376/ Schizophrenia-associated somatic copy-number variants from 12,834 cases reveal recurrent NRXN1 and ABCB11 disruptions Eduardo A. Maury, Maxwell A. Sherman, Giulio Genovese, Thomas G. Gilgenast, Tushar Kamath, S. J. Burris, Prashanth Rajarajan, Erin Flaherty, Schahram Akbarian, Andrew Chess, Steven A. McCarroll, Po-Ru Loh, Jennifer E. Phillips-Cremins, Kristen J. Brennand, Evan Z. Macosko, James T. R. Walters, Michael O’Donovan, Patrick Sullivan, Jonathan Sebat, Eunjung A. Lee, Christopher A. Walsh 2023 2024-01-04 [("doi","10.1016/j.xgen.2023.100356")] genetics/heritable/rare psychiatry/schizophrenia psychology/neuroscience

While germline copy-number variants (CNVs) contribute to schizophrenia (SCZ) risk, the contribution of somatic CNVs (sCNVs)-present in some but not all cells-remains unknown.

We identified sCNVs using blood-derived genotype arrays from 12,834 SCZ cases and 11,648 controls, filtering sCNVs at loci recurrently mutated in clonal blood disorders.

Likely early-developmental sCNVs were more common in cases (0.91%) than controls (0.51%, p = 2.68e-4), with recurrent somatic deletions of exons 1–5 of the NRXN1 gene in 5 SCZ cases. Hi-C maps revealed ectopic, allele-specific loops forming between a potential cryptic promoter and non-coding cis-regulatory elements upon 5’ deletions in NRXN1.

We also observed recurrent intragenic deletions of ABCB11, encoding a transporter implicated in anti-psychotic response, in 5 treatment-resistant SCZ cases and showed that ABCB11 is specifically enriched in neurons forming mesocortical and mesolimbic dopaminergic projections.

Our results indicate potential roles of sCNVs in SCZ risk.

--- https://arxiv.org/abs/2210.06313#google The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers Zonglin Li, Chong You, Srinadh Bhojanapalli, Daliang Li, Ankit Singh Rawat, Sashank J. Reddi, Ke Ye, Felix Chern, Felix Yu, Ruiqi Guo, Sanjiv Kumar 2022-10-12 2024-01-04 [("doi","10.48550/arXiv.2210.06313")] ai/nn/fully-connected ai/nn/sparsity ai/nn/transformer

This paper studies the curious phenomenon for machine learning models with Transformer architectures that their activation maps are sparse.

By activation map we refer to the intermediate output of the multi-layer perceptrons (MLPs) after a ReLU activation function, and by sparse we mean that on average very few entries (eg. 3.0% for T5-Base and 6.3% for ViT-B16) are nonzero for each input to MLP. Moreover, larger Transformers with more layers and wider MLP hidden dimensions are sparser as measured by the percentage of nonzero entries.

Through extensive experiments we demonstrate that the emergence of sparsity is a prevalent phenomenon that occurs for both natural language processing and vision tasks, on both training and evaluation data, for Transformers of various configurations, at layers of all depth levels, as well as for other architectures including MLP-mixers and 2-layer MLPs.

We show that sparsity also emerges using training datasets with random labels, or with random inputs, or with an infinite amount of data, demonstrating that sparsity is not a result of a specific family of datasets.

We discuss how sparsity immediately implies a way to reduce the FLOP count and improve efficiency for Transformers. Moreover, we demonstrate perhaps surprisingly, that enforcing an even sparser activation (via Top-k thresholding with a small value of k) brings a collection of desired but missing properties for Transformers, namely less sensitivity to noisy training data, more robustness to input corruptions, and better calibration for their prediction confidence.

--- /doc/crime/2017-blattman.pdf Reducing Crime and Violence: Experimental Evidence from Cognitive Behavioral Therapy in Liberia Christopher Blattman, Julian C. Jamison, Margaret Sheridan 2017-04-01 2024-01-04 [("doi","10.1257/aer.20150503")] crime psychiatry

We show that a number of noncognitive skills and preferences, including patience and identity, are malleable in adults, and that investments in them reduce crime and violence.

We recruited criminally engaged men and randomized half to 8 weeks of cognitive behavioral therapy designed to foster self-regulation, patience, and a noncriminal identity and lifestyle. We also randomized $200 grants.

Cash alone and therapy alone initially reduced crime and violence, but effects dissipated over time. [Post-hoc analysis claiming (under-powered) interaction:] When cash followed therapy, crime and violence decreased dramatically for at least a year.

We hypothesize that cash reinforced therapy’s impacts by prolonging learning-by-doing, lifestyle changes, and self-investment.

--- https://arxiv.org/abs/2106.01335 On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers Tianchu Ji, Shraddhan Jain, Michael Ferdman, Peter Milder, H. Andrew Schwartz, Niranjan Balasubramanian 2021-06-02 2024-01-04 [("doi","10.48550/arXiv.2106.01335")] ai/nn/sparsity ai/nn/transformer/attention

How much information do NLP tasks really need from a transformer’s attention mechanism at application-time (inference)? From recent work, we know that there is sparsity in transformers and that the floating-points within its computation can be discretized to fewer values with minimal loss to task accuracies. However, this requires retraining or even creating entirely new models, both of which can be expensive and carbon-emitting.

Focused on optimizations that do not require training, we systematically study the full range of typical attention values necessary. This informs the design of an inference-time quantization technique using both pruning and log-scaled mapping which produces only a few (eg. 23) unique values.

Over the tasks of question answering and sentiment analysis, we find nearly 80% of attention values can be pruned to zeros with minimal (< 1.0%) relative loss in accuracy. We use this pruning technique in conjunction with quantizing the attention values to only a 3-bit format, without retraining, resulting in only a 0.8% accuracy reduction on question answering with fine-tuned RoBERTa.

--- https://www.hillelwayne.com/post/randomness/ 2024-01-04 cs/cryptography --- https://www.nytimes.com/2024/01/23/science/animals-vision-video.html 2024-01-05 psychology/animal/bird psychology/vision --- https://www.ctrl-alt-test.fr/2024/how-we-made-an-animated-movie-in-8kb/ 2024-01-05 cs/algorithm --- https://www.businesswire.com/news/home/20230524005312/en/Cybin-Initiates-First-in-Human-Dosing-of-CYB004-in-Phase-1-Clinical-Trial 2024-01-05 psychedelic --- https://x.com/tautologer/status/1749585858079138288 tautologer 2024-01-05 ai/nn/transformer/gpt/4/fiction fiction/humor --- https://openreview.net/forum?id=-WsBmzWwPee Realistic Face Reconstruction from Deep Embeddings Edward Vendrow, Joshua Vendrow 2023-05-15 2024-01-05 ai/nn/gan/stylegan

Using deep face embeddings from facial recognition systems, we reconstruct face images which are high-resolution, realistic, and reconstruct relevant attributes of the original face.

Modern face recognition systems use deep convolution neural networks to extract latent embeddings from face images. Since basic arithmetic operations on embeddings are needed to make comparisons, generic encryption schemes cannot be used. This leaves facial embedding unprotected and susceptible to privacy attacks that reconstruction facial identity. We propose a search algorithm on the latent vector space of StyleGAN to find a matching face. Our process yields latent vectors that generate face images that are high-resolution, realistic, and reconstruct relevant attributes of the original face. Further, we demonstrate that our process is capable of fooling FaceNet, a state-of-the-art face recognition system.

[Keywords: face, embedding, reconstruction, gan, template, homomorphic encryption, privacy, attack, security, computer vision]

--- https://jacobfilipp.com/arvid-vhs/ 2024-01-05 cs/hardware --- http://www.antipope.org/charlie/blog-static/2024/01/worldcon-in-the-news.html 2024-01-05 fiction/science-fiction --- https://adamdrake.com/command-line-tools-can-be-235x-faster-than-your-hadoop-cluster.html 2024-01-05 cs/algorithm --- https://van-magazine.com/mag/peter-schickele-pdq-bach/ 2024-01-05 math/humor --- https://www.improbableisland.com/ 2024-01-05 fiction/text-game --- https://tvtropes.org/pmwiki/pmwiki.php/VideoGame/ImprobableIsland 2024-01-05 fiction/text-game --- https://www.chess.com/article/view/no-castling-chess-kramnik-alphazero 2024-01-06 reinforcement-learning/chess statistics/bias statistics/causality --- https://arxiv.org/abs/2401.13782 Tweets to Citations: Unveiling the Impact of Social Media Influencers on AI Research Visibility Iain Xie Weissburg, Mehir Arora, Liangming Pan, William Yang Wang 2024-01-24 2024-01-24 [("doi","10.48550/arXiv.2401.13782")] ai/nn sociology/technology

As the number of accepted papers at AI and ML conferences reaches into the thousands, it has become unclear how researchers access and read research publications. In this paper, we investigate the role of social media influencers [the AK✱s] in enhancing the visibility of machine learning research, particularly the citation counts of papers they share.

We have compiled a comprehensive dataset of over 8,000 papers, spanning tweets from December 2018 to October 2023, alongside 1:1 matched controls based on publication year, venue, and abstract topics. Our analysis reveals an increase in citations for papers endorsed by these influencers, with median citation counts 2–3× higher than those of the control group. Additionally, the study delves into the geographic, gender, and institutional diversity of highlighted authors.

These findings highlight the expanding influence of social media in scholarly communication and underscore the importance of an evolving ecosystem in today’s digital academic landscape.

--- https://arxiv.org/abs/2401.14391 Rethinking Patch Dependence for Masked Autoencoders Letian Fu, Long Lian, Renhao Wang, Baifeng Shi, Xudong Wang, Adam Yala, Trevor Darrell, Alexei A. Efros, Ken Goldberg 2024-01-25 2024-01-25 [("doi","10.48550/arXiv.2401.14391")] ai/nn/transformer/attention ai/nn/vae/mae

In this work, we re-examine inter-patch dependencies in the decoding mechanism of masked autoencoders (MAE). We decompose this decoding mechanism for masked patch reconstruction in MAE into self-attention and cross-attention.

Our investigations suggest that self-attention between mask patches is not essential for learning good representations.

To this end, we propose a novel pretraining framework: Cross-Attention Masked Autoencoders (CrossMAE). CrossMAE’s decoder leverages only cross-attention between masked and visible tokens, with no degradation in downstream performance. This design also enables decoding only a small subset of mask tokens, boosting efficiency. Furthermore, each decoder block can now leverage different encoder features, resulting in improved representation learning.

CrossMAE matches MAE in performance with 2.5–3.7× less decoding compute. It also surpasses MAE on ImageNet classification and COCO instance segmentation under the same compute.

Code and models: Github.

--- https://wiki.improbableisland.com/doku.php?id=start#what_goes_in_the_wiki 2024-01-06 fiction/text-game --- https://www.quantamagazine.org/the-quest-to-decode-the-mandelbrot-set-maths-famed-fractal-20240126/ 2024-01-06 math sociology --- https://www.nytimes.com/2024/01/26/business/media/jon-franklin-dead.html 2024-01-06 psychiatry/traumatic-brain-injury --- https://www.newyorker.com/magazine/2024/01/29/the-woman-who-spent-five-hundred-days-in-a-cave 2024-01-06 psychiatry/anxiety psychiatry/autism psychiatry/meditation --- http://impredicative.com/ur/ 2024-01-06 cs/haskell cs/js --- https://github.com/xq-meng/AnimeDiffusion 2024-01-06 ai/anime/danbooru ai/dataset ai/nn/diffusion --- https://en.wikipedia.org/wiki/Electrical_network_frequency_analysis Electrical network frequency analysis 2024-01-06 cs/security --- https://tonsky.me/blog/checkbox/ 2024-01-06 cs/css --- https://arxiv.org/abs/2005.13121 Revisiting RowHammer: An Experimental Analysis of Modern DRAM Devices and Mitigation Techniques Jeremie S. Kim, Minesh Patel, A. Giray Yaglikci, Hasan Hassan, Roknoddin Azizi, Lois Orosa, Onur Mutlu 2020-05-27 2024-01-07 [("doi","10.48550/arXiv.2005.13121")] cs/hardware cs/security

In order to shed more light on how RowHammer affects modern and future devices at the circuit-level, we first present an experimental characterization of RowHammer on 1,580 DRAM chips (408 DDR3, 652 DDR4, and 520 LPDDR4) from 300 DRAM modules (60 DDR3, 110 DDR4, and 130 LPDDR4) with RowHammer protection mechanisms disabled, spanning multiple different technology nodes from across each of the 3 major DRAM manufacturers.

Our studies definitively show that newer DRAM chips are more vulnerable to RowHammer: as device feature size reduces, the number of activations needed to induce a RowHammer bit flip also reduces, to as few as 9.6k (4.8k to two rows each) in the most vulnerable chip we tested.

We evaluate 5 state-of-the-art RowHammer mitigation mechanisms using cycle-accurate simulation in the context of real data taken from our chips to study how the mitigation mechanisms scale with chip vulnerability.

We find that existing mechanisms either are not scalable or suffer from prohibitively large performance overheads in projected future devices given our observed trends of RowHammer vulnerability.

Thus, it is critical to research more effective solutions to RowHammer.

--- https://en.wikipedia.org/wiki/Ultra_(cryptography)#Safeguarding_of_sources Ultra (cryptography) § Safeguarding of sources 2024-01-07 cs/cryptography --- https://interviewing.io/blog/voice-modulation-gender-technical-interviews 2024-01-07 economics psychology/personality --- https://anime.mit.edu/resources/mit_in_anime 2024-01-07 anime --- https://blog.rwkv.com/p/eagle-7b-soaring-past-transformers 2024-01-07 ai/nn/rnn --- https://www.derekau.net/blog/2019/07/10/paintings-of-the-traditional-porcelain-process 2024-01-07 technology --- https://www.palladiummag.com/2024/01/26/the-u-s-can-learn-from-israels-cognitive-meritocracy/ 2024-01-07 iq/high --- https://www.lesswrong.com/posts/iaHk9DMCbrYsKuqgS/simple-distribution-approximation-when-sampled-100-times-can-1 2024-01-07 ai/nn/transformer/gpt/calibration --- /doc/economics/1974-granger.pdf Spurious regressions in econometrics C. W. J. Granger, P. Newbold 1974-07-01 2024-01-07 [("doi","10.1016/0304-4076(74)90034-7")] economics statistics/bias --- https://www.quantamagazine.org/researchers-approach-new-speed-limit-for-seminal-problem-20240129/ 2024-01-07 cs/algorithm --- https://arstechnica.com/space/2024/01/now-that-weve-flown-on-mars-what-comes-next-in-aerial-planetary-exploration/3/ 2024-01-07 cs/hardware --- https://www.synthmuseum.com/magazine/0102jw.html 2024-01-08 music technology --- https://web.archive.org/web/20220219024813/https://www.perthilj.com/blog/2019/2/19/aircraft-salvage-in-admiralty-law 2024-01-08 law --- https://arxiv.org/abs/2401.11042 Does Using ChatGPT Result in Human Cognitive Augmentation? Ron Fulbright, Miranda Morrison 2024-01-19 2024-01-19 [("doi","10.48550/arXiv.2401.11042")] ai/nn/transformer/gpt/3/nonfiction reinforcement-learning/preference-learning/mode-collapse

Human cognitive performance is enhanced by the use of tools. For example, a human can produce a much greater, and more accurate, volume of mathematical calculation in a unit of time using a calculator or a spreadsheet application on a computer. Such tools have taken over the burden of lower level cognitive grunt work but the human still serves the role of the expert performing higher level thinking and reasoning.

Recently, however, unsupervised, deep, machine learning has produced cognitive systems able to outperform humans in several domains. When humans use these tools in a human cog ensemble, the cognitive ability of the human is augmented. In some cases, even non experts can achieve, and even exceed, the performance of experts in a particular domain, synthetic expertise. A new cognitive system, ChatGPT-3.5, has burst onto the scene during the past year.

This paper investigates human cognitive augmentation due to using ChatGPT by presenting the results of two experiments comparing responses created using ChatGPT with results created not using ChatGPT. [Asking the human to come up with ideas for reducing littering from shooting clays, and what age to retire at.]

We find using ChatGPT does not always result in cognitive augmentation and does not yet replace human judgement, discernment, and evaluation in certain types of tasks. In fact, ChatGPT was observed to result in misleading users resulting in negative cognitive augmentation.

…Interestingly, students using ChatGPT synthesized a number of ideas having no effect at all on the primary problem—littering of the grass by the skeet clay fragments. Examining these ideas in detail shows these ideas were related to “educating shooters about the environmental impact” and “educating shooters about gun safety.” These ideas can be explained when one analyzes the response from ChatGPT when given the problem statement as the prompt.

ChatGPT is trained from articles and other content available on the Internet. Because the problem statement involves guns and shooting, ChatGPT responded with suggestions to educate shooters about gun safety because on the Internet, when one sees a document about guns and shooting, it is very likely to also include comments about safety. Even though the concepts of guns and safety are understandably related, the safety issue has nothing to do with solving the problem given in the problem statement—littering the grass field. ChatGPT however does not perform such in-depth analysis to realize this. ChatGPT’s responses are driven by word association. [No, this would be typical RLHF backfiring.]

Likewise, because the problem statement mentions littering and damaging grass, ChatGPT finds associations with environmental issues important and therefore responded to students suggesting education about the environment since this is found in millions of pages on the Internet when litter and harming grass is mentioned. While one could argue you might be able to talk a shooter out of shooting after they understand the harm to the grass, this is not likely to change the mind of the vast majority of shooters, so is not a practical solution. Interestingly, in this case, using of ChatGPT actually distracted students by misleading them to consider things having nothing to do with the problem. Therefore, one could argue using ChatGPT actually decreased cognitive ability—resulting in negative cognitive augmentation.

--- https://transformer-circuits.pub/2024/jan-update/index.html#dict-learning-resampling 2024-01-08 ai/nn/vae --- https://transformer-circuits.pub/2024/jan-update/index.html#mnist-sparse 2024-01-08 ai/nn/fully-connected --- https://en.wikipedia.org/wiki/Information_foraging Information foraging 2024-01-08 reinforcement-learning/exploration --- /doc/politics/2023-larson.pdf Computer Center Sabotage, 1968–1971: Luddism, Black Studies, and the Diversion of Technological Progress Max Larson 2023-11-01 2024-01-08 [("doi","10.1215/01903659-10694281")] economics/automation politics

Throughout the late 1960s and early 1970s students across the United States repeatedly seized and in some cases destroyed university computer centers. Supporters and detractors alike have tended to frame these incidents narrowly, in terms of a generalized war against technology.

This essay offers a more expansive account, demonstrating that this brief yet intense rash of computer center sabotage was more complex and, frankly, more interesting than a bad case of technophobia.

While it was clearly directed against computers, sabotage often had less to do with computerization itself than with imperialism and racial injustice. Saboteurs targeted computational collaborations with the Department of Defense, and they held computers hostage in exchange for increased support for Black students.

This does not mean that computers themselves were irrelevant to computer center sabotage. Rather, it pushes us to rethink how computers and other technical devices become political objects.

--- https://arxiv.org/abs/2401.15024#microsoft SliceGPT: Compress Large Language Models by Deleting Rows and Columns Saleh Ashkboos, Maximilian L. Croci, Marcelo Gennari do Nascimento, Torsten Hoefler, James Hensman 2024-01-26 2024-01-26 [("doi","10.48550/arXiv.2401.15024")] ai/nn/sparsity/pruning ai/nn/transformer/gpt

Large language models have become the cornerstone of natural language processing, but their use comes with substantial costs in terms of compute and memory resources. Sparsification provides a solution to alleviate these resource constraints, and recent works have shown that trained models can be sparsified post-hoc.

Existing sparsification techniques face challenges as they need additional data structures and offer constrained speedup with current hardware. In this paper we present SliceGPT, a new post-training sparsification scheme which replaces each weight matrix with a smaller (dense) matrix, reducing the embedding dimension of the network.

Through extensive experimentation, we show that SliceGPT can remove up to 25% of the model parameters (including embeddings) for LLAMA2-70B, OPT 66B and Phi-2 models while maintaining 99%, 99% and 90% zero-shot task performance of the dense model respectively. Our sliced models run on fewer GPUs and run faster without any additional code optimization: on 24GB consumer GPUs we reduce the total compute for inference on LLAMA2-70B to 64% of that of the dense model; on 40GB A100 GPUs we reduce it to 66%.

We offer a new insight, computational invariance in transformer networks, which enables SliceGPT and we hope it will inspire and enable future avenues to reduce memory and computation demands for pre-trained models.

Code is available at: https://github.com/microsoft/TransformerCompression.

--- /doc/psychology/collecting/2008-01-18-alisonhooverbartlett-themanwholovesbookstoomuch.html 2008-01-18 2024-01-08 crime psychology/collecting --- https://chadaustin.me/2024/01/truecolor-terminal-emacs/ 2024-01-08 cs/lisp/emacs cs/shell --- https://12challenges.substack.com/p/how-to-deal-with-receiving-a-cease 2024-01-09 law --- https://www.globalphotos.org/japan-plastic.htm 2024-01-09 design/visualization japan/art --- https://sakofchit.github.io/system.css/ 2024-01-09 cs/css --- https://en.wikipedia.org/wiki/Cool_Site_of_the_Day Cool Site of the Day 2024-01-09 cs/linkrot --- https://publicdomainreview.org/collection/blackwork/ 2024-01-09 design history/public-domain-review --- /doc/culture/2012-rule.pdf International Art English Alix Rule, David Levine 2012-07-01 2024-01-09 culture psychology/linguistics --- /doc/statistics/probability/2009-labby.pdf Weldon’s Dice, Automated Zacariah Labby 2009-01-01 2024-01-09 [("doi","10.1080/09332480.2009.10722977")] reinforcement-learning/robot statistics/probability --- https://github.com/vinibiavatti1/TuiCss 2024-01-09 cs/css --- https://nostalgic-css.github.io/NES.css/ 2024-01-09 cs/css --- https://www.newyorker.com/magazine/2024/02/05/inside-the-music-industrys-high-stakes-ai-experiments 2024-01-09 ai/music --- https://www.nytimes.com/2024/01/30/health/vertex-pain-medicine-non-opioid.html 2024-01-09 psychology/neuroscience --- https://github.com/zk-passport/proof-of-passport 2024-01-10 cs/cryptography --- https://vitalik.eth.limo/general/2024/01/30/cryptoai.html 2024-01-10 ai/nn cs/cryptography statistics/prediction --- https://www.scientificamerican.com/article/sand-mafias-are-plundering-the-earth/?s=08 2024-01-10 crime --- https://www.matthewball.co/all/gaming2024 2024-01-10 economics/copyright --- https://danluu.com/cruise-report/ 2024-01-10 reinforcement-learning/robot reinforcement-learning/safe --- https://u.osu.edu/unger.26/online-publications/chapter-3-of-the-fifth-generation-fallacy/ 2024-01-10 japan psychology/writing sociology --- https://stratechery.com/2024/intels-humbling/ 2024-01-10 cs/hardware economics --- https://www.psymon.com/fonts/alde.html 2024-01-10 design/typography/dropcap --- https://arxiv.org/abs/2310.02446 Low-Resource Languages Jailbreak GPT-4 Zheng-Xin Yong, Cristina Menghini, Stephen H. Bach 2023-10-03 2024-01-10 [("doi","10.48550/arXiv.2310.02446")] ai/nn/adversarial ai/nn/transformer/gpt/4/nonfiction sociology/preference-falsification

AI safety training and red-teaming of large language models (LLMs) are measures to mitigate the generation of unsafe content. Our work exposes the inherent cross-lingual vulnerability of these safety mechanisms, resulting from the linguistic inequality of safety training data, by successfully circumventing GPT-4’s safeguard through translating unsafe English inputs into low-resource languages.

On the AdvBenchmark, GPT-4 engages with the unsafe translated inputs and provides actionable items that can get the users towards their harmful goals 79% of the time, which is on par with or even surpassing state-of-the-art jailbreaking attacks. Other high-/mid-resource languages have lower attack success rate, which suggests that the cross-lingual vulnerability mainly applies to low-resource languages.

Previously, limited training on low-resource languages primarily affects speakers of those languages, causing technological disparities. However, our work highlights a crucial shift: this deficiency now poses a risk to all LLMs users. Publicly available translation APIs enable anyone to exploit LLMs’ safety vulnerabilities.

Therefore, our work calls for a more holistic red-teaming efforts to develop robust multilingual safeguards with wide language coverage.

--- https://dwarffortresswiki.org/index.php/v0.31:Creature_logic 2024-01-10 cs/computable fiction/text-game --- https://www.polygon.com/c/2024/1/22/24034466/anime-is-huge-and-we-finally-have-numbers-to-prove-it 2024-01-10 anime --- https://ascii.theater/ 2024-01-11 cs/shell --- https://en.wikipedia.org/wiki/Adam_Neumann Adam Neumann 2024-01-11 psychology/personality/narcissism --- https://arxiv.org/abs/math/0504351 The halting problem is decidable on a set of asymptotic probability one Joel David Hamkins, Alexei Miasnikov 2005-04-18 2024-01-11 [("doi","10.48550/arXiv.0504351")] cs/computable

The halting problem for Turing machines is decidable on a set of asymptotic probability one. Specifically, there is a set B of Turing machine programs such that (1) B has asymptotic probability one, so that as the number of states n increases, the proportion of all n-state programs that are in B goes to one; (2) B is polynomial time decidable; and (3) the halting problem H ∩ B is polynomial time decidable.

The proof is sensitive to the particular computational model.

--- https://x.com/nealagarwal/status/1747284257582506102 Neal Agarwal 2024-01-11 ai/nn/transformer/gpt/fiction fiction/text-game --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8891154/ Lithium in Drinking Water as a Public Policy for Suicide Prevention: Relevance and Considerations Pablo Araya, Camila Martínez, Jorge Barros 2022 2024-01-11 [("doi","10.3389/fpubh.2022.805774")] psychiatry/lithium

Although suicide is considered a major preventable cause of mortality worldwide, we do not have effective strategies to prevent it. Lithium has been consistently associated with lowering risk of suicide. This effect could occur at very low concentrations, such as trace doses of lithium in tap water.

Several ecological studies and recent meta-analysis have suggested an inverse association between lithium in water and suicide in the general population, with a lack of knowledge of clinically-significant side effects. This paper is aimed as a proposal to discuss the addition of lithium to drinking water to decrease the suicide rate. For this, we review the evidence available, use previous experiences, such as water fluoridation to prevent dental caries, and discuss the complexity involved in such a public policy.

Considering the limited data available and the controversies contained in this proposal, we suggest that a consensus on lithium concentration in water is needed, where the suicide rates start to reduce, as happened with water fluoridation. This measure will require to develop community-controlled trials with strict monitoring of any side effects, where democratic procedures would constitute one of the most appropriate ways to validate its implementation according to the reality of each community.

--- https://www.science.org/content/article/citation-cartels-help-some-mathematicians-and-their-universities-climb-rankings 2024-01-11 math statistics/bias --- https://vitalik.eth.limo/general/2024/01/31/end.html 2024-01-11 bitcoin economics/mechanism-design --- https://www.ellipsix.net/arxiv-joke-papers.html 2024-01-11 math/humor --- https://arxiv.org/abs/0803.4378 Down-sizing Forever Douglas Scott, Ali Frolop 2008-03-31 2024-01-11 [("doi","10.48550/arXiv.0803.4378")] math/humor science

Evidence for "cosmic down-sizing" has been growing over the last decade. It is now clear that the major star-forming epoch for the largest galaxies occurred earlier than for smaller galaxies. This not only runs counter to the popular hierarchical clustering picture, but points to an even more radical revision of our ideas of the evolution of cosmological structure:

Galaxies do not form at all.

[2 pages. Submitted to the journal Physics in Regress.]

--- https://arxiv.org/abs/0903.5321 Time variation of a fundamental dimensionless constant Robert J. Scherrer 2009-03-30 2024-01-11 [("doi","10.48550/arXiv.0903.5321")] math/humor philosophy/ontology

We examine the time variation of a previously-uninvestigated fundamental dimensionless constant [π]. Constraints are placed on this time variation using historical measurements.

A model is presented for the time variation, and it is shown to lead to an accelerated expansion for the universe.

Directions for future research are discussed.

...Acknowledgments: A number of colleagues were kind enough to comment on the manuscript. For some reason they did not want me to use their names, so I will identify them by their initials: S. Dodelson, A. L. Melott, D. N. Spergel, and T. J. Weiler.

--- https://www.smspower.org/SegaAI/Index 2024-01-12 cs/hardware --- https://arxiv.org/abs/1204.0492 Non-detection of the Tooth Fairy at Optical Wavelengths Eve Armstrong 2012-04-02 2024-01-12 [("doi","10.48550/arXiv.1204.0492")] math/humor

We report a non-detection, to a limiting magnitude of V = 18.4 (9), of the elusive entity commonly described as the Tooth Fairy.

We review various physical models and conclude that follow-up observations must precede an interpretation of our result.

--- https://arxiv.org/abs/1210.8144 Possible Bubbles of Spacetime Curvature in the South Pacific Benjamin K. Tippett 2012-10-29 2024-01-12 [("doi","10.48550/arXiv.1210.8144")] fiction/science-fiction math/humor

In 1928, the late Francis Wayland Thurston published a scandalous manuscript in purport of warning the world of a global conspiracy of occultists. Among the documents he gathered to support his thesis was the personal account of a sailor by the name of Gustaf Johansen, describing an encounter with an extraordinary island. Johansen‘s descriptions of his adventures upon the island are fantastic, and are often considered the most enigmatic (and therefore the highlight) of Thurston‘s collection of documents.

We contend that all of the credible phenomena which Johansen described may be explained as being the observable consequences of a localized bubble of spacetime curvature. Many of his most incomprehensible statements (involving the geometry of the architecture, and variability of the location of the horizon) can therefore be said to have a unified underlying cause.

We propose a simplified example of such a hyperbolic geometry, and show using numerical computation that Johansen‘s descriptions were, for the most part, not simply the ravings of a lunatic. Rather, they are the nontechnical observations of an intelligent man who did not understand how to describe what he was seeing. Conversely, it seems to us improbable that Johansen should have unwittingly given such a precise description of the consequences of spacetime curvature, if the details of this story were merely the dregs of some half-remembered fever dream.

We calculate the type of matter which would be required to generate such exotic spacetime curvature.

Unfortunately, we determine that the required matter is quite unphysical, and possess a nature which is entirely alien to all of the experiences of human science. Indeed, any civilization with mastery over such matter would be able to construct warp drives, cloaking devices, and other exotic geometries required to conveniently travel through the cosmos [including use of time dilation].

--- https://www.reddit.com/r/AnimeResearch/comments/1afylx5/pictures_generated_with_nijijourney_v5_vs_v6/ 2024-01-12 ai/anime ai/nn/diffusion/midjourney --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4198854/ Research in China on the molecular genetics of schizophrenia Donghong Cui, Kaida Jiang 2012 2024-01-12 [("doi","10.3969/j.issn.1002-0829.2012.04.001")] genetics/heritable psychiatry/schizophrenia

Schizophrenia is a complex disease caused by genetic and environmental factors with a global heritability of more than 80%.

By the end of the 1970s, Chinese scientists reported a heritability of schizophrenia of 82.9% in the Chinese Han population. Continuous improvements in research techniques and the recruitment of larger samples have made it possible for Chinese scientists to identify a number of candidate susceptibility genes for schizophrenia.

This article reviews the results in genetic research of schizophrenia by Chinese scientists over the last 5 decades.

--- https://www.reddit.com/r/slatestarcodex/comments/1aceo6z/the_woman_who_spent_five_hundred_days_in_a_cave/ko94qtp/ Woodsqueer Anonymous 2024-01-27 2024-01-12 psychiatry/anxiety psychiatry/meditation

This lady sounds a lot like me.

I lived 6 years nearly entirely isolated in the wilderness. I went 5 months without seeing another human one winter, and 3 months a few other times. I loved it. Where I live, such a thing while rare wasn’t unheard of like it is nowadays, to the point that there’s even a slang term the old folks use to describe people like me who have spent too long alone; “woodsqueer”. I did develop an awful habit of thinking out loud, and I’m scared of people in an unusual way and get panic attacks now if I’m in too busy of an area or around a lot of people for a prolonged period of time. If you’ve ever heard of the jumping Frenchmen thing, that ran in my family a few generations back when they were still living in the lumber camps, and I’ve been notably more prone to behavior like that ever since living alone. I also, I admit, have developed somewhat of an internet addiction ever since coming back to town. I don’t know if that’s related or not though, since it seems like half everyone’s got one now.

But, throughout that whole time I rarely ever felt actually alone. I feel much more comfortable just sitting out a whole night in the woods than sitting an hour in a restaurant, and I got to know the routines and habits of a lot of the animals and plants I lived with just like one would with one’s human neighbors. They didn’t feel so different than me out there. I knew particular trees and ravens, and watched generations of foxes and hares and blueberries and deer and ermines grow up and raise children of their own. And even if I wasn’t sitting down and petting them like people expect out of a relationship with a dog or cat I felt a certain sense of mutual respect and recognition with the organisms I interacted with regularly. I never experienced hallucinations and I didn’t lose a sense of time (if anything my sense of time was considerably heightened, watching the seasons, the daily routines of the animals and plants, the moon, the rivers, and all of that). So I really don’t think I would have made it in the cave, no humans doesn’t mean no people.

See Also:

--- /doc/genetics/heritable/correlation/2023-kendler.pdf Relationship of Family Genetic Risk Score With Diagnostic Trajectory in a Swedish National Sample of Incident Cases of Major Depression, Bipolar Disorder, Other Non-affective Psychosis, and Schizophrenia Kenneth S. Kendler, Henrik Ohlsson, Jan Sundquist, Kristina Sundquist 2023-01-25 2024-01-12 [("doi","10.1001/jamapsychiatry.2022.4676")] genetics/heritable/correlation psychiatry/bipolar/genetics psychiatry/depression psychiatry/schizophrenia

Importance: Since its inception under Kraepelin in the modern era, diagnostic stability and familial/genetic risk have been among the most important psychiatric nosologic validators.

Objective: To assess the interrelationships of family genetic risk score (FGRS) with diagnostic stability or diagnostic change in major depression (MD), bipolar disorder (BD), other non-affective psychosis (ONAP), and schizophrenia.

Methods, Setting, & Participants: This longitudinal population-based cohort (n = 4 171 120) included individuals with incident cases of MD (n = 235,095), BD (n = 11,681), ONAP (n = 16,009), and schizophrenia (n = 6,312) who had at least 1 further diagnosis of the 4 disorders during follow-up, as assessed from Swedish national medical registries, observed over a mean (SD) of 13.1 (5.9) years until a mean (SD) age of 48.4 (12.3) years. Data were collected from January 1973 to December 2018, and data were analyzed from August to September 2022.

Exposures: FGRS for MD, BD, ONAP, and schizophrenia, calculated from morbidity risks for disorders in first-degree through fifth-degree relatives, controlling for cohabitation effects.

Main Outcomes & Measures: Final diagnostic outcome of MD, BD, ONAP, or schizophrenia.

Results: Of 269,097 included individuals, 173,061 (64.3%) were female, and the mean (SD) age at first registration was 35.1 (11.9) years. Diagnostic stability was highest for MD (214,794 [91.4%]), followed by schizophrenia (4,621 [73.2%]), BD (7,428 [63.6%]), and ONAP (6,738 [42.1%]). The second most common final diagnosis for each of these MD, schizophrenia, BD, and ONAP were BD (15,506 [6.6%]), ONAP (1,110 [17.6%]), MD (2,681 [23.0%]), and schizophrenia (4,401 [27.5%]), respectively.

A high FGRS for the incident diagnosis was consistently associated with diagnostic stability, while a high FGRS for the final diagnosis and a low FGRS for the incident diagnosis was associated with diagnostic change. In multivariate models, those in the upper 5% of genetic risk had an odds ratio (OR) of 1.75 or greater for the following diagnostic transition: for MD FGRS, ONAP → MD (OR, 1.91; 95% CI, 1.59-2.29) and schizophrenia → MD (OR, 2.45; 95% CI, 1.64-3.68); for BD FGRS, MD → BD (OR, 2.60; 95% CI, 2.47-2.73), ONAP → BD (OR, 2.16; 95% CI, 1.85-2.52), and schizophrenia → BD (OR, 2.20; 95% CI, 1.39-3.49); for ONAP FGRS, MD → ONAP (OR, 1.80; 95% CI, 1.62-2.02), MD → schizophrenia (OR, 1.95; 95% CI, 1.58-2.41), and BD → schizophrenia (OR, 1.89; 95% CI, 1.39-2.56); and for schizophrenia FGRS, MD → schizophrenia (OR, 1.80; 95% CI, 1.46-2.23), and BD → schizophrenia (OR, 1.75; 95% CI, 1.25-2.45).

FGRS profiles for incident cases confirmed at final diagnosis were more homogenous than genetic profiles for those who changed diagnoses.

Conclusion: In a large population-based longitudinal cohort, the genetic risk factors for MD, BD, ONAP, and schizophrenia were meaningfully and systematically associated with the diagnostic trajectories of these 4 disorders. Over time, clinical diagnosis and genetic risk profiles became increasingly consilient, thereby providing genetic validation of these diagnostic constructs. Diagnostically unstable incident cases were more genetically heterogeneous than those who were diagnostically stable over time.

--- https://www.nature.com/articles/s41598-023-50458-w 2024-01-12 dog --- https://www.wired.com/story/alan-filion-torswats-swatting-arrest/ 2024-01-12 cs/security --- https://hirrolot.github.io/posts/sat-supercompilation.html 2024-01-12 cs/algorithm --- https://arxiv.org/abs/2311.16567#google MobileDiffusion: Subsecond Text-to-Image Generation on Mobile Devices Yang Zhao, Yanwu Xu, Zhisheng Xiao, Tingbo Hou 2023-11-28 2024-01-12 [("doi","10.48550/arXiv.2311.16567")] ai/nn/diffusion ai/nn/gan

The deployment of large-scale text-to-image diffusion models on mobile devices is impeded by their substantial model size and slow inference speed. In this paper, we propose MobileDiffusion, a highly efficient text-to-image diffusion model obtained through extensive optimizations in both architecture and sampling techniques.

We conduct a comprehensive examination of model architecture design to reduce redundancy, enhance computational efficiency, and minimize model’s parameter count, while preserving image generation quality. Additionally, we employ distillation and diffusion-GAN finetuning techniques on MobileDiffusion to achieve 8-step and 1-step inference respectively.

Empirical studies, conducted both quantitatively and qualitatively, demonstrate the effectiveness of our proposed techniques. MobileDiffusion achieves a remarkable sub-second inference speed for generating a 512×512 image on mobile devices, establishing a new state-of-the-art.

--- https://research.google/blog/mobilediffusion-rapid-text-to-image-generation-on-device/ 2024-01-13 ai/nn/diffusion ai/nn/gan --- https://x.com/goodside/status/1753192905844592989 Riley Goodside 2024-01-13 ai/nn/tokenization --- https://www.thesportscol.com/2014/01/like-blue-eyed-boy/ 2024-01-13 exercise psychology/vision --- https://bounded-regret.ghost.io/base-rates-of-catastrophes/ 2024-01-13 existential-risk history --- https://www.wsj.com/business/energy-oil/liquified-carbon-emissions-storage-ships-4f4f6e84 2024-01-13 technology/carbon-capture --- https://en.wikipedia.org/wiki/Warnock%27s_dilemma Warnock’s dilemma 2024-01-13 psychology/writing sociology/technology --- https://www.reddit.com/r/midjourney/comments/1agzdeg/loving_the_new_sref_feature/ 2024-01-13 ai/anime ai/nn/diffusion/midjourney --- https://www.filfre.net/2024/02/the-rise-of-pomg-part-2-multima/ 2024-01-13 sociology/technology technology/digital-antiquarian --- https://www.fastcompany.com/91015583/this-whimsical-clock-is-the-playful-gadget-ai-needs-right-now 2024-01-13 ai/nn/transformer/gpt/3/poetry --- https://www.nytimes.com/2024/02/02/climate/sun-shade-climate-geoengineering.html 2024-01-13 technology/carbon-capture --- https://arxiv.org/abs/2312.02843 Are Vision Transformers More Data Hungry Than Newborn Visual Systems? Lalit Pandey, Samantha M. W. Wood, Justin N. Wood 2023-12-05 2024-01-13 [("doi","10.48550/arXiv.2312.02843")] ai/nn/cnn ai/nn/transformer/clip psychology/animal/bird psychology/neuroscience

Vision transformers (ViTs) are top performing models on many computer vision benchmarks and can accurately predict human behavior on object recognition tasks. However, researchers question the value of using ViTs as models of biological learning because ViTs are thought to be more data hungry than brains, with ViTs requiring more training data to reach similar levels of performance.

To test this assumption, we directly compared the learning abilities of ViTs and animals, by performing parallel controlled rearing experiments on ViTs and chickens (newborn chicks). We first raised chicks in impoverished visual environments containing a single object, then simulated the training data available in those environments by building virtual animal chambers in a video game engine [ie. a controlled virtual reality].

We recorded the first-person images acquired by agents moving through the virtual chambers and used those images to train self supervised ViTs that leverage time as a teaching signal, akin to biological visual systems. When ViTs were trained through the eyes of newborn chicks, the ViTs solved the same view invariant object recognition tasks as the chicks.

Thus, ViTs were not more data hungry than newborn visual systems: both learned view invariant object representations in impoverished visual environments. The flexible and generic attention-based learning mechanism in ViTs combined with the embodied data streams available to newborn animals appears sufficient to drive the development of animal-like object recognition.

--- https://en.wikipedia.org/wiki/Tissot%27s_indicatrix Tissot’s indicatrix 2024-01-14 design/visualization --- /doc/fiction/poetry/1938-tracy.pdf Caliban upon Setebos C. R. Tracy 1938-07-01 2024-01-14 [("doi","10.2307/4172415")] fiction/poetry philosophy/religion --- /doc/fiction/poetry/1984-tebbetts.pdf The Question of Satire in ‘Caliban upon Setebos’ Terrell L. Tebbetts 1984-12-01 2024-01-14 [("doi","10.2307/40003012")] fiction/poetry philosophy/religion --- https://davidrozado.substack.com/p/the-political-preferences-of-llms 2024-01-14 ai/nn/transformer/gpt politics sociology/preference-falsification --- https://journals.sagepub.com/doi/10.1177/0032329217751688 2024-01-14 philosophy/religion politics --- https://www.youtube.com/watch?v=dcjkezf1ARY 2024-01-14 cs/shell --- https://x.com/gdb/status/1754005627658285208 Greg Brockman 2024-01-14 ai/nn/transformer/gpt/5 --- https://arxiv.org/abs/2310.07875 TabLib: A Dataset of 627M Tables with Context Gus Eggert, Kevin Huo, Mike Biven, Justin Waugh 2023-10-11 2024-01-14 [("doi","10.48550/arXiv.2310.07875")] ai/dataset ai/tabular

It is well-established that large, diverse datasets play a pivotal role in the performance of modern AI systems for text and image modalities. However, there are no datasets for tabular data of comparable size and diversity to those available for text and images.

Thus we present TabLib, a compilation of 627 million tables totaling 69 TB, along with 867B tokens of context. TabLib was extracted from numerous file formats, including CSV, HTML, SQLite, PDF, Excel, and others, sourced from GitHub and Common Crawl.

The size and diversity of TabLib offer considerable promise in the table modality, reminiscent of the original promise of foundational datasets for text and images, such as The Pile and LAION.

--- https://reeserichardson.blog/2024/01/30/the-king-of-curcumin-a-case-study-in-the-consequences-of-large-scale-research-fraud/ 2024-01-14 nootropic statistics/bias --- https://slate.com/technology/2024/02/quora-what-happened-ai-decline.html 2024-01-14 sociology/technology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8750854/ Family Member, Best Friend, Child or ‘Just’ a Pet, Owners’ Relationship Perceptions and Consequences for Their Cats Esther M. C. Bouma, Marsha L. Reijgwart, Arie Dijkstra 2021 2024-01-15 [("doi","10.3390/ijerph19010193")] cat/psychology

Describing the relationship with one’s cat in human terms might reflect an underlying anthropomorphic view of the relationship which might be associated with an owner’s behavior towards their cat and the cat’s living environment. Owners self-categorized the relationship with their cat as either a ‘member of the family’, ‘as a child’, ‘best friend’, or ‘a pet animal’.

The extent to which owner-related & cat-related factors influence these 4 relationship descriptions are examined in survey data of ~1,800 cat owners. Differences in outdoor access, care during absence of the owner, and access to the bedroom are examined between the 4 relationship perceptions.

The owner’s age and household composition, ideas about their cat’s equality, support, and dependency, and whether their cat is a pedigree were statistically-significantly associated with relationship description and explained 46% of the variance. Owners who perceive their cat as a child or best friend see their cat as loyal, empathetic, equal to family, and dependent on them for love and care. Their cats are less often left in the care of others, are allowed more often in the bedroom and have less often (unrestricted) outdoor access. [!]

Moreover, cats perceived as children are more likely to live in a multi-cat household. Our results provide insight in the factors that are related to different (anthropomorphic) perceptions of the human-cat relationship and how perceptions relate to the living environment of cats.

--- https://shyam.blog/posts/beyond-self-attention/ 2024-01-15 ai/nn/transformer/attention --- https://adamkarvonen.github.io/machine_learning/2024/01/03/chess-world-models.html 2024-01-15 ai/nn/transformer/gpt reinforcement-learning/chess reinforcement-learning/imitation-learning --- https://www.lesswrong.com/posts/doPbyzPgKdjedohud/the-case-for-more-ambitious-language-model-evals 2024-01-15 ai/nn/transformer/gpt/4/nonfiction statistics/stylometry/truesight --- https://arxiv.org/abs/2303.17276 Humans in Humans Out: On GPT Converging Toward Common Sense in both Success and Failure Philipp Koralus, Vincent Wang-Maścianica 2023-03-30 2024-01-15 [("doi","10.48550/arXiv.2303.17276")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/scaling philosophy/logic psychology/cognitive-bias

Increase in computational scale and fine-tuning has seen a dramatic improvement in the quality of outputs of large language models (LLMs) like GPT. Given that both GPT-3 and GPT-4 were trained on large quantities of human-generated text, we might ask to what extent their outputs reflect patterns of human thinking, both for correct and incorrect cases. The Erotetic Theory of Reason (ETR) provides a symbolic generative model of both human success and failure in thinking, across propositional, quantified, and probabilistic reasoning, as well as decision-making.

We presented GPT-3, GPT-3.5, and GPT-4 with 61 central inference and judgment problems from a recent book-length presentation of ETR, consisting of experimentally verified data-points on human judgment and extrapolated data-points predicted by ETR, with correct inference patterns as well as fallacies and framing effects (the ETR61 benchmark). ETR61 includes classics like Wason’s card task, illusory inferences, the decoy effect, and opportunity-cost neglect, among others.

GPT-3 showed evidence of ETR-predicted outputs for 59% of these examples, rising to 77% in GPT-3.5 and 75% in GPT-4. Remarkably, the production of human-like fallacious judgments increased from 18% in GPT-3 to 33% in GPT-3.5 and 34% in GPT-4. This suggests that larger and more advanced LLMs may develop a tendency toward more human-like mistakes, as relevant thought patterns are inherent in human-produced training data.

According to ETR, the same fundamental patterns are involved both in successful and unsuccessful ordinary reasoning, so that the “bad” cases could paradoxically be learned from the “good” cases.

We further present preliminary evidence that ETR-inspired prompt engineering could reduce instances of these mistakes.

--- https://arxiv.org/abs/2211.08411 Large Language Models Struggle to Learn Long-Tail Knowledge Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, Colin Raffel 2022-11-15 2024-01-15 [("doi","10.48550/arXiv.2211.08411")] ai/nn/retrieval ai/nn/transformer/gpt ai/scaling

The Internet contains a wealth of knowledge—from the births of historical figures to tutorials on how to code—all of which may be learned by language models. However, while certain pieces of information are ubiquitous on the web, others appear extremely rarely. In this paper, we study the relationship between the knowledge memorized by large language models and the information in pre-training datasets scraped from the web.

In particular, we show that a language model’s ability to answer a fact-based question relates to how many documents associated with that question were seen during pre-training. We identify these relevant documents by entity linking pre-training datasets and counting documents that contain the same entities as a given question-answer pair.

Our results demonstrate strong correlational and causal relationships between accuracy and relevant document count for numerous question answering datasets (eg. TriviaQA), pre-training corpora (eg. ROOTS), and model sizes (eg. BLOOM-176b parameters). Moreover, while larger models are better at learning long-tail knowledge, we estimate that today’s models must be scaled by many orders of magnitude to reach competitive QA performance on questions with little support in the pre-training data.

Finally, we show that retrieval-augmentation can reduce the dependence on relevant pre-training information, presenting a promising approach for capturing the long-tail.

--- https://arxiv.org/abs/hep-ph/0305062 Destruction of Nuclear Bombs Using Ultra-High Energy Neutrino Beam Hirotaka Sugawara, Hiroyuki Hagura, Toshiya Sanami 2003-05-07 2024-01-15 [("doi","10.48550/arXiv.0305062")] technology

We discuss the possibility of using the ultra-high energy neutrino beam (about 1,000 TeV) to detect and destroy the nuclear bombs wherever they are and whoever possess them.

[Not really nuke-specific—just a death ray.]

--- https://en.wikipedia.org/wiki/Chicken_hypnotism Chicken hypnotism 2024-01-15 psychology/vision --- https://www.construction-physics.com/p/where-are-my-damn-learning-curves 2024-01-15 economics/experience-curve --- https://publicdomainreview.org/collection/my-lady-nicotine/ 2024-01-15 design/typography/rubrication history/public-domain-review nicotine --- https://www.newyorker.com/science/elements/will-plants-ever-fertilize-themselves 2024-01-15 genetics/editing --- https://www.thebeliever.net/logger/2014-04-18-reincarnation-in-exile/ 2024-01-16 philosophy/religion psychiatry/bipolar psychiatry/meditation --- https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023EF003710 2024-01-16 existential-risk/nuclear --- https://www.zellic.io/blog/mpc-from-scratch/ 2024-01-16 cs/cryptography --- https://en.wikipedia.org/wiki/Jevons_paradox Jevons paradox 2024-01-16 economics/automation --- https://publicdomainreview.org/essay/eugene-francois-vidocq-and-the-birth-of-the-detective/ 2024-01-16 crime history/public-domain-review --- https://arstechnica.com/ai/2024/02/mastering-music-is-hard-can-one-click-ai-make-it-easy/ 2024-01-16 ai/music --- /doc/crime/2014-latvala.pdf Paternal Antisocial Behavior and Sons’ Cognitive Ability: A Population-Based Quasiexperimental Study Antti Latvala, Ralf Kuja-Halkola, Niklas Långström, Paul Lichtenstein 2014-11-25 2024-01-16 [("doi","10.1177/0956797614555726")] crime genetics/heritable/correlation iq

Parents’ antisocial behavior is associated with developmental risks for their offspring, but its effects on their children’s cognitive ability are unknown. We used linked Swedish register data for a large sample of adolescent men (n = 1,177,173) and their parents to estimate associations between fathers’ criminal-conviction status and sons’ cognitive ability assessed at compulsory military conscription.

Mechanisms behind the association were tested in children-of-siblings models across 3 types of sibling fathers with increasing genetic relatedness (half-siblings, full siblings, and monozygotic twins) and in quantitative genetic models.

Sons whose fathers had a criminal conviction had lower cognitive ability than sons whose fathers had no conviction (any crime: Cohen’s d = −0.28; violent crime: Cohen’s d = −0.49). As models adjusted for more genetic factors, the association was gradually reduced and eventually eliminated. Nuclear-family environmental factors did not contribute to the association.

Our results suggest that the association between men’s antisocial behavior and their children’s cognitive ability is not causal but is due mostly to underlying genetic factors.

--- https://hudsonreview.com/2024/02/the-imaginary-operagoer-a-memoir/ 2024-01-16 fiction/opera --- https://www.cdc.gov/traumaticbraininjury/data/index.html 2024-01-16 psychiatry/traumatic-brain-injury --- https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(19)30188-4/fulltext 2024-01-16 psychiatry/traumatic-brain-injury --- https://neal.fun/days-since-incident/ 2024-01-16 existential-risk --- https://fourmilab.ch/documents/commodore/BrainSim/ 2024-01-17 ai/nn/fully-connected --- https://fourmilab.ch/documents/strikeout/ 2024-01-17 design psychology/writing --- https://fourmilab.ch/documents/rocketaday.html 2024-01-17 economics/experience-curve --- https://fourmilab.ch/gravitation/foobar/ 2024-01-17 science --- https://blog.helix.ml/p/how-we-got-fine-tuning-mistral-7b 2024-01-17 ai/nn/retrieval ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt --- https://brooker.co.za/blog/2018/01/01/balls-into-bins.html 2024-01-17 cs/algorithm statistics/order --- https://through-the-interface.typepad.com/through_the_interface/2008/09/an-interview--1.html 2024-01-17 cs/lisp design --- https://www.nytimes.com/2024/02/08/science/walter-shawlee-dead.html 2024-01-17 cs/hardware --- https://www.nytimes.com/2024/02/08/magazine/mark-meadows-trump-prosecution.html 2024-01-17 psychology/personality/narcissism --- https://www.quantamagazine.org/scientists-find-optimal-balance-of-data-storage-and-time-20240208/ 2024-01-17 cs/algorithm --- https://xkcd.com/2892/ 2024-01-17 economics --- https://asteriskmag.com/issues/04/is-our-children-learning 2024-01-18 economics sociology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10087723/ Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods František Bartoš, Maximilian Maier, Eric-Jan Wagenmakers, Hristos Doucouliagos, T. D. Stanley 2023 2024-01-18 [("doi","10.1002/jrsm.1594")] statistics/bayes statistics/bias/publication

Publication bias is a ubiquitous threat to the validity of meta-analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent simulation studies have shown the methods’ performance to depend on the true data generating process, and no method consistently outperforms the others across a wide range of conditions.

Unfortunately, when different methods lead to contradicting conclusions, researchers can choose those methods that lead to a desired outcome. To avoid the condition-dependent, all-or-none choice between competing methods and conflicting results, we extend robust Bayesian meta-analysis and model-average across two prominent approaches of adjusting for publication bias: (1) selection models of p-values and (2) models adjusting for small-study effects.

The resulting model ensemble weights the estimates and the evidence for the absence/presence of the effect from the competing approaches with the support they receive from the data. Applications, simulations, and comparisons to preregistered, multi-lab replications demonstrate the benefits of Bayesian model-averaging of complementary publication bias adjustment methods.

--- https://x.com/garrynewman/status/1755851884047303012 Garry Newman 2024-01-18 reinforcement-learning/preference-learning --- https://archiveofourown.org/works/28299630 2024-01-18 fiction/humor --- https://en.wikipedia.org/wiki/Publication_bias Publication bias 2024-01-18 statistics/bias/publication --- https://archiveofourown.org/works/41112099 2024-01-18 culture --- https://journals.plos.org/plosbiology/article/info%3Adoi%2F10.1371%2Fj 2024-01-18 statistics/bias/publication --- https://ctan.org/pkg/coffeestains 2024-01-18 design/typography/tex math/humor --- https://www.nature.com/articles/nature.2014.15787 2024-01-18 statistics/bias/publication --- https://statmodeling.stat.columbia.edu/2014/08/28/publication-bias-social-sciences-unlocking-file-drawer2/ 2024-01-18 statistics/bias/publication --- https://deepblue.lib.umich.edu/bitstream/handle/2027.42/68589/10.1177_0049124185013003002.pdf 2024-01-19 statistics/bias/publication --- https://statmodeling.stat.columbia.edu/2013/04/26/a-vast-graveyard-of-undead-theories-publication-bias-and-psychological-sciences-aversion-to-the-null-2/ 2024-01-19 statistics/bias/publication --- /doc/statistics/peer-review/2016-findley.pdf Can Results-Free Review Reduce Publication Bias? The Results and Implications of a Pilot Study 2016-01-01 2024-01-19 statistics/bias/publication statistics/peer-review --- https://en.wikipedia.org/wiki/Scholarly_peer_review#Result-blind_peer_review Scholarly peer review § Result-blind peer review 2024-01-19 statistics/bias/publication --- https://www.nature.com/articles/d41586-018-07118-1 Logging hypotheses and protocols before performing research seems to work as intended: to reduce publication bias for positive results 2024-01-19 statistics/bias/publication --- https://80000hours.org/podcast/episodes/eva-vivalt-social-science-generalizability/ Is ‘evidence-based development’ writing a cheque its methodology can’t cash? 2024-01-19 statistics/bias/publication --- https://arxiv.org/abs/2402.05201 The Non-Effect of Sampling Temperature on Problem Solving in GPT-3.5/GPT-4 Matthew Renze, Erhan Guven 2024-02-07 2024-02-07 [("doi","10.48550/arXiv.2402.05201")] ai/nn/sampling ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/calibration reinforcement-learning/preference-learning/mode-collapse

[rediscovers the GPT-3.5/GPT-4 flattened logits published a year before by OA] In this research study, we empirically investigate the effect of sampling temperature on the performance of Large Language Models (LLMs) on various problem-solving tasks.

We created a multiple-choice question-and-answer (MCQA) exam by randomly sampling problems from standard LLM benchmarks. Then, we used 4 popular LLMs with 5 prompt-engineering techniques to solve the MCQA problems while increasing the sampling temperature 0.0 → 1.0.

Despite anecdotal reports to the contrary [what anecdotes‽], our empirical results indicate that changes in temperature in the range 0.0 to 1.0 do not have a statistically impact on LLM performance for problem-solving tasks. [as expected from the flattening of logits & all user reports about temperature being useless with GPT-3.5 & GPT-4-RLHF...]

...All code, data, and supplemental materials are available on GitHub at: Github.

--- https://x.com/ESYudkowsky/status/1754770076841226499 Eliezer Yudkowsky 2024-01-19 economics psychology/cognitive-bias/illusion-of-depth --- https://arxiv.org/abs/1910.01526#deepmind Gated Linear Networks Joel Veness, Tor Lattimore, David Budden, Avishkar Bhoopchand, Christopher Mattern, Agnieszka Grabska-Barwinska, Eren Sezener, Jianan Wang, Peter Toth, Simon Schmitt, Marcus Hutter 2019-09-30 2024-01-19 [("doi","10.48550/arXiv.1910.01526")] cs/algorithm reinforcement-learning/meta-learning/continual-learning

This paper presents a new family of backpropagation-free neural architectures, Gated Linear Networks (GLNs). What distinguishes GLNs from contemporary neural networks is the distributed and local nature of their credit assignment mechanism; each neuron directly predicts the target, forgoing the ability to learn feature representations in favor of rapid online learning.

Individual neurons can model nonlinear functions via the use of data-dependent gating in conjunction with online convex optimization.

We show that this architecture gives rise to universal learning capabilities in the limit, with effective model capacity increasing as a function of network size in a manner comparable with deep ReLU networks.

Furthermore, we demonstrate that the GLN learning mechanism possesses extraordinary resilience to catastrophic forgetting, performing comparably to a MLP with dropout and Elastic Weight Consolidation on standard benchmarks.

These desirable theoretical and empirical properties position GLNs as a complementary technique to contemporary offline deep learning methods.

--- https://arxiv.org/abs/1612.00796#deepmind Overcoming catastrophic forgetting in neural networks James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, Raia Hadsell 2016-12-02 2024-01-19 [("doi","10.1073/pnas.1611835114")] reinforcement-learning/meta-learning/continual-learning

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models.

We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks which they have not experienced for a long time. Our approach [elastic weight consolidation] remembers old tasks by selectively slowing down learning on the weights important for those tasks.

We demonstrate our approach is scalable and effective by solving a set of classification tasks based on the MNIST hand written digit dataset and by learning several Atari 2600 games sequentially.

--- /doc/personal/2017-06-27-gwern-stlouis-zoo-waterfountain-nominativedeterminism.jpg Gwern 2017-06-27 2024-01-19 fiction/humor personal --- /doc/personal/2013-09-25-gwern-googlealertsemails.tar.xz Gwern 2013-09-25 2024-01-20 personal technology/google/alerts --- /doc/personal/2013-09-25-gwern-googlealerts.csv Gwern 2013-09-25 2024-01-20 personal technology/google/alerts --- https://mattmahoney.net/dc/dce.html 2024-01-20 cs/algorithm/information/compression --- https://code.google.com/archive/p/paqclass 2024-01-20 cs/algorithm/information/compression --- https://complearn.org/thesis.html Statistical Inference Through Data Compression 2024-01-20 cs/algorithm/information/compression --- https://www.byronknoll.com/thesis.pdf A Machine Learning Perspective on Predictive Coding with PAQ8 and New Applications Knoll 2009 2024-01-20 cs/algorithm/information/compression --- https://code4k.blogspot.com/2010/12/crinkler-secrets-4k-intro-executable.html 2024-01-20 cs/algorithm/information/compression --- https://kylehovey.github.io/blog/automata-nebula 2024-01-20 cs/algorithm/information/compression --- http://prize.hutter1.net/ 2024-01-20 cs/algorithm/information/compression --- https://joyofdata.de/blog/relation-of-word-order-and-compression-ratio/ Relation of Word Order and Compression Ratio and Degree of Structure 2024-01-20 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Hutter_Prize Hutter Prize 2024-01-20 ai/nn/transformer/gpt/2 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Burrows%E2%80%93Wheeler_transform Burrows-Wheeler transform 2024-01-21 ai/nn/transformer/gpt/2 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/JBIG2 JBIG2 2024-01-21 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Prediction_by_partial_matching Prediction by partial matching 2024-01-21 cs/algorithm/information/compression --- https://www.eugenewei.com/blog/2017/5/11/jpeg-your-ideas Compress to impress: Jeff Bezos and Amazon culture 2024-01-21 sociology --- https://arstechnica.com/information-technology/2013/05/google-services-survive-if-they-make-money-arent-social/ Google services survive if they make money, aren’t social: Statistical analysis of Google products finds a shutdown rate of 35 percent 2024-01-21 technology/google --- https://apps.dtic.mil/sti/pdfs/ADA229000.pdf A Centralized Source Of Information For The Military Working Dog Program 2024-01-21 dog --- https://www.spinellis.gr/pubs/jrnl/2003-CACM-URLcite/html/urlcite.pdf The decay and failures of web references: Attempting to determine how quickly archival information becomes outdated 2024-01-21 cs/linkrot --- https://www.sciencedaily.com/releases/2011/01/110121111216.htm Learning science: Actively recalling information from memory beats elaborate study methods; Put down those science text books and work at recalling information from memory. That’s the shorthand take away message of new research that says practicing memory retrieval boosts science learning far better than elaborate study methods. 2024-01-21 psychology/spaced-repetition --- https://www.reuters.com/article/us-dea-sod/exclusive-u-s-directs-agents-to-cover-up-program-used-to-investigate-americans-idUSBRE97409R20130805 Exclusive: U.S. directs agents to cover up program used to investigate Americans; a secretive U.S. Drug Enforcement Administration unit is funneling information from intelligence intercepts, wiretaps, informants and a massive database of telephone records to authorities across the nation to help them launch criminal investigations of Americans. 2024-01-21 darknet-market --- https://www.nitrd.gov/pubs/bluebooks/2001/asci.html Information Technology: 21 Century Revolution; DOE’s ASCI Program 2024-01-21 radiance --- https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ms_tr_99_50_turingtalk.pdf#page=11 What Next? A Dozen Information-Technology Research Goals: 3. Turing’s vision of machine intelligence 2024-01-22 ai/scaling --- https://medium.com/backchannel/the-definitive-story-of-information-wants-to-be-free-a8d95427641c Hackers and ‘Information Wants to Be Free’: The most famous phrase in the book wasn’t mine. And it wasn’t in the book. 2024-01-22 economics/copyright --- /doc/statistics/decision/1981-frey.pdf Reversible and Irreversible Decisions: Preference for Consonant Information as a Function of Attractiveness of Decision Alternatives Dieter Frey 1981 2024-01-22 psychology/cognitive-bias statistics/decision --- /doc/design/1971-simon.pdf Designing Organizations for an Information-Rich World Herbert A. Simon 1971-01-01 2024-01-22 design economics/automation --- https://en.wikipedia.org/wiki/Entropy_(information_theory) Entropy (information theory) 2024-01-22 cs/algorithm/information --- https://en.wikipedia.org/wiki/Solomonoff%27s_theory_of_inductive_inference Solomonoff’s theory of inductive inference 2024-01-22 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Ray_Solomonoff Ray Solomonoff 2024-01-22 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Universal_artificial_intelligence Universal artificial intelligence 2024-01-22 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Prefix_code Prefix code 2024-01-22 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Minimum_message_length Minimum message length 2024-01-22 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Minimum_description_length Minimum description length 2024-01-22 cs/algorithm/information/compression --- http://www.scholarpedia.org/article/Algorithmic_probability 2024-01-23 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Kolmogorov_complexity#Chaitin's_incompleteness_theorem Kolmogorov complexity § Chaitin’s incompleteness theorem 2024-01-23 cs/computable --- https://en.wikipedia.org/wiki/Mutual_information Mutual information 2024-01-23 cs/algorithm/information --- https://en.wikipedia.org/wiki/Value_of_Information Value of Information 2024-01-23 cs/algorithm/information --- /doc/statistics/1992-rao.pdf Information and the Accuracy Attainable in the Estimation of Statistical Parameters C. Radhakrishna Rao 1992-01-01 2024-01-23 [("doi","10.1007/978-1-4612-0919-5_16")] cs/algorithm/information statistics --- https://pure.mpg.de/rest/items/item_2383162_7/component/file_2456978/content 2024-01-23 cs/algorithm/information --- /doc/psychology/neuroscience/1998-sarpeshkar.pdf Analog Versus Digital: Extrapolating from Electronics to Neurobiology Rahul Sarpeshkar 1998-10-01 2024-01-23 [("doi","10.1162/089976698300017052")] cs/algorithm/information psychology/neuroscience

We review the pros and cons of analog and digital computation.

We propose that computation that is most efficient in its use of resources is neither analog computation nor digital computation but, rather, a mixture of the two forms. For maximum efficiency, the information and information-processing resources of the hybrid form must be distributed over many wires, with an optimal signal-to-noise ratio per wire.

Our results suggest that it is likely that the brain computes in a hybrid fashion and that an underappreciated and important reason for the efficiency of the human brain, which consumes only 12 W, is the hybrid and distributed nature of its architecture.

--- /doc/ai/scaling/1993-amari.pdf Statistical Theory of Learning Curves under Entropic Loss Criterion Shun-ichi Amari, Noboru Murata 1993-01-01 2024-01-23 [("doi","10.1162/neco.1993.5.1.140")] ai/scaling statistics/bayes

The present paper elucidates a universal property of learning curves, which shows how the generalization error, training error, and the complexity of the underlying stochastic machine are related and how the behavior of a stochastic machine is improved as the number of training examples increases. The error is measured by the entropic loss.

It is proved that the generalization error converges to H0, the entropy of the conditional distribution of the true machine, as H0 + m✱/(2t), while the training error converges as H0 − m✱/(2t), where t is the number of examples and m✱ shows the complexity of the network. When the model is faithful, implying that the true machine is in the model, m✱ is reduced to m, the number of modifiable parameters.

This is a universal law because it holds for any regular machine irrespective of its structure under the maximum likelihood estimator. Similar relations are obtained for the Bayes and Gibbs learning algorithms.

These learning curves show the relation among the accuracy of learning, the complexity of a model, and the number of training examples.

--- /doc/iq/2002-deary.pdf g and Cognitive Elements of Information Processing: An Agnostic View Ian J. Deary 2002-01-01 2024-01-23 iq psychology/neuroscience --- http://img2.tapuz.co.il/forums/1_116636321.pdf Unbelieving the Unbelievable: Some Problems in the Rejection of False Information Gilbert 1990 2024-01-23 psychology/cognitive-bias --- https://en.wikipedia.org/wiki/Bayesian_approaches_to_brain_function Bayesian approaches to brain function 2024-01-23 cs/algorithm/information --- https://en.wikipedia.org/wiki/Free_energy_principle Free energy principle 2024-01-24 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Predictive_coding Predictive coding 2024-01-24 cs/algorithm/information/compression --- https://crypto.stanford.edu/~dabo/pubs/papers/webtiming.pdf Exposing private information by timing web applications Bortz 2007 2024-01-24 cs/security --- https://www.evaotaku.com/html/kaibunsho-bginfo.html Evangelion Kaibunsho: background information and notes 2024-01-24 anime/eva --- https://en.wikipedia.org/wiki/Cross_entropy_method Cross entropy method 2024-01-24 reinforcement-learning/exploration --- https://en.wikipedia.org/wiki/Model_predictive_control Model predictive control 2024-01-24 reinforcement-learning/model --- https://en.wikipedia.org/wiki/Bayesian_neural_network Bayesian neural network 2024-01-24 ai/nn statistics/bayes --- https://onlinelibrary.wiley.com/doi/full/10.1096/fj.05-4784lsf Unavailability of online supplementary scientific information from articles published in major journals Evangelou 2005 2024-01-24 cs/linkrot --- https://en.wikipedia.org/wiki/Fisher_information_metric Fisher information metric 2024-01-24 cs/algorithm/information --- https://blog.glyphdrawing.club/typographic-art-of-valto-malmiola/ 2024-01-24 design/typography --- https://www.psychologytoday.com/gb/blog/beautiful-minds/200812/the-tears-clown 2024-01-25 psychology/personality --- https://bellard.org/nncp/ 2024-01-25 ai/nn/transformer/gpt/2 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/JPEG#Quantization JPEG § Quantization 2024-01-25 ai/nn/transformer/gpt/2 cs/algorithm/information/compression --- https://wiki.archlinux.org/title/Lrzip 2024-01-25 ai/nn/transformer/gpt/2 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Rzip Rzip 2024-01-25 ai/nn/transformer/gpt/2 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Solid_compression Solid compression 2024-01-25 ai/nn/transformer/gpt/2 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/PAQ PAQ 2024-01-25 ai/nn/transformer/gpt/2 cs/algorithm/information/compression --- http://neoscientists.org/~tmueller/binsort/ 2024-01-25 ai/nn/transformer/gpt/2 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/MinHash MinHash 2024-01-25 ai/nn/transformer/gpt/2 cs/algorithm/information/compression --- http://schulze-mueller.de/ Timm S. Mueller 2024-01-25 ai/nn/transformer/gpt/2 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Asymmetric_Numeral_Systems Asymmetric Numeral Systems 2024-01-25 cs/algorithm/information/compression --- https://auphonic.com/blog/2018/06/01/codec2-podcast-on-floppy-disk/ Codec2: a whole Podcast on a Floppy Disk 2024-01-26 cs/algorithm/information/compression --- https://www.novaspivack.com/science/we-have-discovered-a-new-pattern-in-the-prime-numbers-parallax-compression 2024-01-26 design/visualization math --- https://pub.towardsai.net/stable-diffusion-based-image-compresssion-6f1f0a399202 2024-01-26 ai/nn/diffusion --- https://mailinator.blogspot.com/2012/02/how-mailinator-compresses-email-by-90.html 2024-01-26 cs/algorithm/information/compression --- http://keyj.emphy.de/mp3-for-image-compression/ 2024-01-26 cs/algorithm/information/compression --- https://matradomski.com/posts/data_compression/ 2024-01-26 cs/algorithm/information/compression --- http://www.daemonology.net/papers/thesis.pdf 2024-01-26 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Locality-sensitive_hashing Locality-sensitive hashing 2024-01-26 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Brotli Brotli 2024-01-26 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Succinct_data_structure Succinct data structure 2024-01-26 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/JPEG_XL JPEG XL 2024-01-26 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Zip_bomb Zip bomb 2024-01-27 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Zstd Zstd 2024-01-27 cs/algorithm/information/compression --- https://terrytao.wordpress.com/2007/04/13/compressed-sensing-and-single-pixel-cameras/ 2024-01-27 cs/algorithm/information/compression --- https://www.jodybruchon.com/2010/11/27/sort-compressed-tar-archives-to-make-them-smaller-20-percent-smaller/ 2024-01-27 cs/algorithm/information/compression cs/algorithm/sorting --- https://arxiv.org/abs/1707.04312 Lempel-Ziv: a ‘1-bit catastrophe’ but not a tragedy Guillaume Lagarde, Sylvain Perifel 2017-07-13 2024-01-27 [("doi","10.48550/arXiv.1707.04312")] ai/nn/adversarial cs/algorithm/information/compression

The so-called “one-bit catastrophe” for the compression algorithm LZ’78 asks whether the compression ratio of an infinite word can change when a single bit is added in front of it.

We answer positively this open question raised by Lutz and others: we show that there exists an infinite word w such that ρsup(w) = 0 but ρinf(0w) > 0, where ρsup and ρinf are respectively the lim sup and the lim inf of the compression ratios ρ of the prefixes.

To that purpose we explore the behavior of LZ’78 on finite words and show the following results:

\n

- There is a constant C > 0 such that, for any finite word w and any letter a, ρ(aw) ≤ C√(ρ(w) log |w|). Thus, sufficiently compressible words (ρ(w) = o(1⁄log|w|)) remain compressible with a letter in front;

\n

The previous result is tight up to a multiplicative constant for any compression ratio ρ(w) = \U0001D4AA(1⁄log|w|). In particular, there are infinitely many words w satisfying ρ(w) = \U0001D4AA(1⁄log|w|) but ρ(0w) = Ω(1).

--- http://slightlynew.blogspot.com/2011/05/who-writes-wikipedia-information.html 2024-01-27 cs/algorithm/information/compression wikipedia --- https://en.wikipedia.org/wiki/Implicit_data_structure Implicit data structure 2024-01-27 cs/algorithm/information --- https://arxiv.org/abs/1403.4445 A really simple approximation of smallest grammar Artur Jeż 2014-03-18 2024-01-27 [("doi","10.48550/arXiv.1403.4445")] cs/algorithm/information/compression

In this paper we present a really simple linear-time algorithm constructing a context-free grammar of size 𝒪(g log (N⁄g)) for the input string, where N is the size of the input string and g the size of the optimal grammar generating this string. The algorithm works for arbitrary size alphabets, but the running time is linear assuming that the alphabet Sigma of the input string can be identified with numbers from 1, …, Nc for some constant c.

Algorithms with such an approximation guarantee and running time are known, however, all of them were non-trivial and their analyses were involved. The here presented algorithm computes the LZ77 factorization and transforms it in phases to a grammar. In each phase, it maintains an LZ77-like factorization of the word with at most l factors as well as additional 𝒪(l) letters, where l was the size of the original LZ77 factorization.

In one phase in a greedy way (by a left-to-right sweep and a help of the factorization) we choose a set of pairs of consecutive letters to be replaced with new symbols, ie. non-terminals of the constructed grammar. We choose at least 2⁄3 of the letters in the word and there are 𝒪(l) many different pairs among them. Hence there are 𝒪(log N) phases, each of them introduces 𝒪(l) non-terminals to a grammar. A more precise analysis yields a bound 𝒪(l log(N/l)). As l ≤ g, this yields the desired bound 𝒪(g log(N⁄g)).

--- https://rs.io/creativity-literature-compression/#methods 2024-01-27 cs/algorithm/information/compression fiction psychology/novelty --- https://github.com/sasakiassociates/png-db 2024-01-27 cs/algorithm/information/compression cs/js --- https://x.com/patriciogv/status/1443931444292866063 Patricio Gonzalez Vivo 2024-01-27 cs/algorithm/information/compression design/visualization --- https://arxiv.org/abs/1901.04866 Practical Lossless Compression with Latent Variables using Bits Back Coding James Townsend, Tom Bird, David Barber 2019-01-15 2024-01-28 [("doi","10.48550/arXiv.1901.04866")] ai/nn/vae cs/algorithm/information/compression

Deep latent variable models have seen recent success in many data domains. Lossless compression is an application of these models which, despite having the potential to be highly useful, has yet to be implemented in a practical manner.

We present Bits Back with ANS (BB-ANS), a scheme to perform lossless compression with latent variable models at a near optimal rate.

We demonstrate this scheme by using it to compress the MNIST dataset with a variational autoencoder model (VAE), achieving compression rates superior to standard methods with only a simple VAE.

Given that the scheme is highly amenable to parallelization, we conclude that with a sufficiently high quality generative model this scheme could be used to achieve substantial improvements in compression rate with acceptable running time.

We make our implementation available open source at Github.

--- https://arxiv.org/abs/1905.06845 Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables Friso H. Kingma, Pieter Abbeel, Jonathan Ho 2019-05-16 2024-01-28 [("doi","10.48550/arXiv.1905.06845")] ai/nn/vae cs/algorithm/information/compression

The bits-back argument suggests that latent variable models can be turned into lossless compression schemes. Translating the bits-back argument into efficient and practical lossless compression schemes for general latent variable models, however, is still an open problem.

Bits-Back with Asymmetric Numeral Systems (BB-ANS), recently proposed by Townsend et al 2019, makes bits-back coding practically feasible for latent variable models with one latent layer, but it is inefficient for hierarchical latent variable models.

In this paper we propose Bit-Swap, a new compression scheme that generalizes BB-ANS and achieves strictly better compression rates for hierarchical latent variable models with Markov chain structure.

Through experiments we verify that Bit-Swap results in lossless compression rates that are empirically superior to existing techniques.

Our implementation is available at Github.

--- https://intapi.sciendo.com/pdf/10.2478/ijasitels-2020-0003 2024-01-28 cs/algorithm/information/compression reinforcement-learning/imperfect-information --- https://en.wikipedia.org/wiki/Trie Trie 2024-01-28 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Code_golf Code golf 2024-01-28 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Kolmogorov_complexity Kolmogorov complexity 2024-01-28 cs/algorithm/information/compression --- https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=716a587f2852bb8134454143868e860cabdfe84f 2024-01-28 cs/algorithm/information/compression cs/algorithm/sorting --- https://wrap.warwick.ac.uk/61087/7/WRAP_cs-rr-360.pdf#page=2 2024-01-28 cs/algorithm/information/compression cs/algorithm/sorting --- https://timepedia.blogspot.com/2009/11/traveling-salesman-problem-and.html 2024-01-28 cs/algorithm/information/compression cs/algorithm/sorting --- https://kingjamesprogramming.tumblr.com/ King James Programming 2024-01-28 cs/algorithm/information/compression math/humor --- https://github.com/thomasahle/ziplm 2024-01-29 ai/nn/sampling cs/algorithm/information/compression --- https://github.com/albertz/png-db 2024-01-29 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/SDCH SDCH 2024-01-29 cs/algorithm/information/compression --- https://blog.cloudflare.com/improving-compression-with-preset-deflate-dictionary/ 2024-01-29 cs/algorithm/information/compression --- http://penduin.blogspot.com/2006/10/pi-compression.html 2024-01-29 cs/algorithm/information/compression math --- https://blog.cloudflare.com/results-experimenting-brotli/ 2024-01-29 cs/algorithm/information/compression --- https://blog.cloudflare.com/brotli-compression-using-a-reduced-dictionary/ 2024-01-29 cs/algorithm/information/compression --- https://code.flickr.net/2015/09/25/perceptual-image-compression-at-flickr/ 2024-01-29 cs/algorithm/information/compression psychology/vision --- https://www.antoniomallia.it/sorted-integers-compression-with-elias-fano-encoding.html 2024-01-29 cs/algorithm/information/compression cs/algorithm/sorting --- https://blog.andrewcantino.com/blog/2012/06/15/compressing-code/ 2024-01-29 cs/algorithm/information/compression cs/c cs/js cs/python --- http://www.byronknoll.com/cmix.html 2024-01-29 ai/nn/rnn cs/algorithm/information/compression --- https://hackaday.io/project/5689-lossy-text-compression 2024-01-30 cs/algorithm/information/compression math/humor --- https://en.wikipedia.org/wiki/Bit_array Bit array 2024-01-30 cs/algorithm/information/compression --- http://brokenbytes.blogspot.com/2015/04/the-making-of-p0-snake-part-3-audio.html 2024-01-30 cs/algorithm/information/compression --- https://www.abortretry.fail/p/lz-compression 2024-01-30 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Phil_Katz Phil Katz 2024-01-30 cs/algorithm/information/compression --- https://cloudinary.com/blog/a_one_color_image_is_worth_two_thousand_words#the_most_predictable_image 2024-01-30 cs/algorithm/information/compression --- http://ed-von-schleck.github.io/shoco/ 2024-01-30 cs/algorithm/information/compression --- https://timepedia.blogspot.com/2009/08/on-reducing-size-of-compressed.html 2024-01-30 cs/algorithm/information/compression cs/algorithm/sorting cs/js --- http://james.fabpedigree.com/bwt.htm 2024-01-30 cs/algorithm/information/compression cs/algorithm/sorting --- https://kevincox.ca/2022/03/01/dictionary-compression/ 2024-01-30 cs/algorithm/information/compression --- /doc/cs/algorithm/information/compression/2010-stevesouder-forcinggzipcompression.html 2010 2024-01-30 cs/algorithm/information/compression cs/js --- https://fastcompression.blogspot.com/2018/02/when-to-use-dictionary-compression.html 2024-01-31 cs/algorithm/information/compression --- https://github.com/facebook/zstd#the-case-for-small-data-compression 2024-01-31 cs/algorithm/information/compression --- https://research.google/blog/lyra-a-new-very-low-bitrate-codec-for-speech-compression/ 2024-01-31 cs/algorithm/information/compression --- https://arxiv.org/abs/2102.09660#google Generative Speech Coding with Predictive Variance Regularization W. Bastiaan Kleijn, Andrew Storus, Michael Chinen, Tom Denton, Felicia S. C. Lim, Alejandro Luebs, Jan Skoglund, Hengchin Yeh 2021-02-18 2024-01-31 [("doi","10.48550/arXiv.2102.09660")] ai/nn/rnn cs/algorithm/information/compression

The recent emergence of machine-learning based generative models for speech suggests a reduction in bit rate for speech codecs is possible. However, the performance of generative models deteriorates with the distortions present in real-world input signals. We argue that this deterioration is due to the sensitivity of the maximum likelihood criterion to outliers and the ineffectiveness of modeling a sum of independent signals with a single autoregressive model.

We introduce predictive-variance regularization to reduce the sensitivity to outliers, resulting in an increase in performance. We show that noise reduction to remove unwanted signals can increase performance.

We provide extensive subjective performance evaluations that show that our system based on generative modeling provides state-of-the-art coding performance at 3 kb/s for real-world speech signals at reasonable computational complexity.

--- http://thevirtuosi.blogspot.com/2011/08/tweet-is-worth-at-least-140-words.html 2024-01-31 cs/algorithm/information/compression --- https://gafferongames.com/post/snapshot_compression/ 2024-01-31 cs/algorithm/information/compression --- https://github.com/mafm/HashLife 2024-01-31 cs/algorithm/information/compression cs/cellular-automaton --- https://en.wikipedia.org/wiki/Information_content Information content 2024-01-31 cs/algorithm/information --- https://en.wikipedia.org/wiki/The_Complexity_of_Songs The Complexity of Songs 2024-01-31 cs/algorithm/information/compression math/humor music --- http://timbaumann.info/svd-image-compression-demo/ 2024-01-31 cs/algorithm/information/compression --- https://github.com/mhx/dwarfs?tab=readme-ov-file#overview 2024-02-01 cs/algorithm/information/compression --- https://triplehappy.wordpress.com/2015/10/26/chess-move-compression/ 2024-02-01 cs/algorithm/information/compression reinforcement-learning/chess --- https://web.archive.org/web/20140918110745/http://friggeri.net/blog/a-genetic-approach-to-css-compression/ 2024-02-01 cs/algorithm/information/compression cs/css reinforcement-learning/model-free --- https://en.wikipedia.org/wiki/Chroma_subsampling 2024-02-01 cs/algorithm/information/compression psychology/vision --- https://arxiv.org/abs/1908.08962 Well-Read Students Learn Better: On the Importance of Pre-training Compact Models Iulia Turc, Ming-Wei Chang, Kenton Lee, Kristina Toutanova 2019-08-23 2024-02-01 [("doi","10.48550/arXiv.1908.08962")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer

Recent developments in natural language representations have been accompanied by large and expensive models that leverage vast amounts of general-domain text through self-supervised pre-training. Due to the cost of applying such models to down-stream tasks, several model compression techniques on pre-trained language representations have been proposed (Sun et al 2019a; Sanh et al 2019b). However, surprisingly, the simple baseline of just pre-training and fine-tuning compact models has been overlooked.

In this paper, we first show that pre-training remains important in the context of smaller architectures, and fine-tuning pre-trained compact models can be competitive to more elaborate methods proposed in concurrent work. Starting with pre-trained compact models, we then explore transferring task knowledge from large fine-tuned models through standard knowledge distillation. The resulting simple, yet effective and general algorithm, Pre-trained Distillation, brings further improvements.

Through extensive experiments, we more generally explore the interaction between pre-training and distillation under two variables that have been under-studied: model size and properties of unlabeled task data. One surprising observation is that they have a compound effect even when sequentially applied on the same data. To accelerate future research, we will make our 24 pre-trained miniature BERT models publicly available.

--- https://web.archive.org/web/20100924002346/http://blog.podly.tv/the-lost-quarter-century-in-data-compression 2024-02-01 cs/algorithm/information/compression --- http://james.hiebert.name/blog/work/2015/09/14/CS-FTW.html 2024-02-01 cs/algorithm --- https://sqlite-users.sqlite.narkive.com/CVRvSKBs/50-faster-than-3-7-17 2024-02-01 cs/algorithm --- http://www.stat.columbia.edu/~gelman/research/published/waic_understand3.pdf Understanding predictive information criteria for Bayesian models Gelman 2013 2024-02-01 cs/algorithm/information statistics/bayes --- https://arxiv.org/abs/1502.04015 Decentralized Trusted Timestamping using the Crypto Currency Bitcoin Bela Gipp, Norman Meuschke, André Gernandt 2015-02-13 2024-02-01 [("doi","10.5281/zenodo.3547488")] bitcoin cs/cryptography/timelock

Trusted timestamping is a process for proving that certain information existed at a given point in time.

This paper presents a trusted timestamping concept and its implementation in the form of a web-based service that uses the decentralized Bitcoin blockchain to store anonymous, tamper-proof timestamps for digital content. The service allows users to hash files, such as text, photos, or videos, and store the created hashes in the Bitcoin blockchain. Users can then retrieve and verify the timestamps that have been committed to the blockchain.

The non-commercial service enables anyone, eg. researchers, authors, journalists, students, or artists, to prove that they were in possession of certain information at a given point in time.

Common use cases include proving that a contract has been signed, a photo taken, a video recorded, or a task completed prior to a certain date. All procedures maintain complete privacy of the user’s data.

--- /note/note#a-market-for-fat-the-transfer-machine What if there were a machine which could transfer kilograms of body fat between people? What would be the social, economic, and medical consequences? 2024-02-01 exercise sociology --- https://www.devicq.com/men-of-letters 2024-02-02 design/typography --- /dropcap#ninit 2024-02-02 ai/nn/diffusion/midjourney/dropcap/ninit --- /dropcap#dropcat 2024-02-02 cat --- http://lists.urth.net/pipermail/urth-urth.net/2024-February/054348.html Was Wolfe’s short story ‘The Fat Magician’: inspired by the Marx Brothers’ A Night at the Opera? Gwern 2024-02-10 2024-02-10 fiction/gene-wolfe --- https://www.reddit.com/r/MachineLearning/comments/1anv7n4/p_ai_learns_pvp_in_old_school_runescape/ 2024-02-02 reinforcement-learning/model-free --- https://en.wikipedia.org/wiki/Roman_funerary_practices#Imagines_(%22images%22) Roman funerary practices § Imagines ("images") 2024-02-02 philosophy/religion --- https://www.penn.museum/sites/expedition/recreating-roman-wax-masks/ 2024-02-02 philosophy/religion --- https://arxiv.org/abs/2402.05636 The Impact of AI Tool on Engineering at ANZ Bank: An Empirical Study on GitHub Copilot Within a Corporate Environment Sayan Chatterjee, Ching Louis Liu, Gareth Rowland, Tim Hogarth 2024-02-08 2024-02-08 [("doi","10.48550/arXiv.2402.05636")] ai/nn/transformer/gpt/codex

The increasing popularity of AI, particularly Large Language Models (LLMs), has impacted various domains, including Software Engineering. This study explores the integration of AI tools in software engineering practices within a large organization. We focus on ANZ Bank, which employs over 5,000 engineers covering all aspects of the software development life cycle.

This paper details an experiment conducted using GitHub Copilot, a notable AI tool, within a controlled environment to evaluate its effectiveness in real-world engineering tasks. Additionally, this paper shares initial findings on the productivity improvements observed after GitHub Copilot was adopted on a large scale, with about 1,000 engineers using it. ANZ Bank’s six-week experiment with GitHub Copilot included two weeks of preparation and 4 weeks of active testing. The study evaluated participant sentiment and the tool’s impact on productivity, code quality, and security.

Initially, participants used GitHub Copilot for proposed use-cases, with their feedback gathered through regular surveys. In the second phase, they were divided into Control and Copilot groups, each tackling the same Python challenges, and their experiences were again surveyed.

Results showed a notable boost in productivity and code quality with GitHub Copilot, though its impact on code security remained inconclusive. Participant responses were overall positive, confirming GitHub Copilot’s effectiveness in large-scale software engineering environments. Early data from 1,000 engineers also indicated an increase in productivity and job satisfaction.

--- https://retractionwatch.com/2024/02/05/no-data-no-problem-undisclosed-tinkering-in-excel-behind-economics-paper/ 2024-02-02 statistics/bias statistics/probability --- https://willempennings.nl/balancing-cube/ 2024-02-02 reinforcement-learning/robot --- https://www.youtube.com/watch?v=n_6p-1J551Y&t=92s 2024-02-02 reinforcement-learning/robot --- https://www.youtube.com/watch?v=ceGlMV4sHnk 2024-02-03 reinforcement-learning/robot --- https://kobikobi.wordpress.com/2018/03/03/speak-friend-and-enter-do-people-actually-use-movie-passwords/ 2024-02-03 cs/security --- https://www.independent.co.uk/news/world/americas/crime/swatting-nikki-haley-trump-fbi-stalkers-b2494097.html 2024-02-03 crime psychology/personality/psychopathy --- https://killedbyapixel.github.io/TinyCode/games/CrossMyHeart/ 2024-02-03 cs/algorithm/information/compression cs/js --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC335617/ 2024-02-03 genetics/heritable psychiatry/schizophrenia --- https://en.wikipedia.org/wiki/Phonautograph Phonautograph 2024-02-03 cs/algorithm/information --- https://www.lingscars.com/ 2024-02-03 cs/css --- /doc/psychology/personality/1999-eaves.pdf Comparing the biological and cultural inheritance of personality and social att Lindon Eaves, Andrew Heath, Nicholas Martin, Hermine Maes, Michael Neale, Kenneth Kendler, Katherine Kirk, Linda Corey 1999-04-01 2024-02-03 [("doi","10.1375/twin.2.2.62")] genetics/heritable politics psychology/personality

Measures of 4 dimensions of personality (Psychoticism, Extraversion, Neuroticism, and Lie scores) and 6 aspects of social attitudes (to sex, taxation, militarism, politics, religion and a general conservatism scale) were obtained by mailed questionnaire from 29 691 US subjects including adult twins (n = 14 761) their parents (n = 2360), their spouses (n = 4391), siblings (n = 3184) and adult children (n = 4800). After correction for the average effects of age, sex and source of sample, familial correlations were computed for 80 distinct biological and social relationships.

The data allow for the estimation of the additive and non-additive effects of genes, assortative mating, vertical cultural inheritance and other non-parental effects of the shared environment on differences in personality and social attitudes. The interaction of genetic and environmental effects with sex may also be analyzed.

Model-fitting analyses show that personality and social attitude measures differ markedly in major features of family resemblance. Additive and dominant genetic effects contribute to differences in both personality and attitudes, but the effects of the family environment, including vertical cultural transmission from parent to child, are much more marked for social attitudes than for personality.

There is substantial assortative mating for social attitudes and almost none for personality. The causes of family resemblance depend on sex for almost every variable studied.

These findings clarify and extend the more tentative findings derived from previous twin, family and adoption studies.

--- https://www.animenewsnetwork.com/feature/2024-02-12/plastic-models-as-art-touring-kaiyodo-headquarters-and-hobby-land-museum/.203667 2024-02-03 anime psychology/collecting --- https://arxiv.org/abs/2402.03405 The Ultraviolet myth Nils-Erik Bomark, Reidun Renstrøm 2024-02-05 2024-02-05 [("doi","10.48550/arXiv.2402.03405")] science

It is very common to introduce quantum physics in an historical context. Though there are advantages to this, it is a problem that many of the stories that have become central to the physics lore are mere pseudo-histories far detached from the real events. It is about time that we stop uncritically copying these stories and instead make an effort to present the development of quantum physics as it actually was.

[cf. Klein 1961] This paper deals with one of the most common myths in quantum history, the one about the ultraviolet catastrophe and how it motivated Planck’s introduction of quantum physics.

On closer inspection it turns out this story has the time-line completely turned on its head. The ultraviolet catastrophe was first discussed several years after Planck published his radiation law so it played no role in his motivation.

Instead Planck was concerned with finding a theoretical derivation of the law for blackbody radiation. This law was first thought to be Wien’s radiation law, but when new data disagreed, Planck came up with his own law that fitted the data. Planck’s radiation law first came about as an elaborate fit to data and to derive it he found no other way than to use statistical mechanics and divide the energy that was to be distributed on the atomic oscillators into packages hf so that he could count the number of ways to distribute this energy. Planck did not consider this a quantization, but merely a mathematical trick to be able to calculate the entropy of the oscillators.

--- https://en.wikipedia.org/wiki/Proof_without_words Proof without words 2024-02-03 design/visualization math --- https://arxiv.org/abs/2402.06184 The boundary of neural network trainability is fractal Jascha Sohl-Dickstein 2024-02-09 2024-02-09 [("doi","10.48550/arXiv.2402.06184")] ai/nn

[blog, code/videos] Some fractals—for instance those associated with the Mandelbrot set and quadratic Julia sets—are computed by iterating a function, and identifying the boundary between hyperparameters for which the resulting series diverges or remains bounded. Neural network training similarly involves iterating an update function (eg. repeated steps of gradient descent), can result in convergent or divergent behavior, and can be extremely sensitive to small changes in hyperparameters.

Motivated by these similarities, we experimentally examine the boundary between neural network hyperparameters that lead to stable and divergent training.

We find that this boundary is fractal over more than 10 orders of scale in all tested configurations.

--- https://arxiv.org/abs/2312.06844 Element abundance patterns in stars indicate fission of nuclei heavier than uranium Ian U. Roederer, Nicole Vassh, Erika M. Holmbeck, Matthew R. Mumpower, Rebecca Surman, John J. Cowan, Timothy C. Beers, Rana Ezzeddine, Anna Frebel, Terese T. Hansen, Vinicius M. Placco, Charli M. Sakari 2023-12-11 2024-02-04 [("doi","10.1126/science.adf1341")] science

The heaviest chemical elements are naturally produced by the rapid neutron-capture process (r-process) during neutron star mergers or supernovae. The r-process production of elements heavier than uranium (transuranic nuclei) is poorly understood and inaccessible to experiments, so must be extrapolated using nucleosynthesis models.

We examine element abundances in a sample of stars that are enhanced in r-process elements.

The abundances of elements Ru [ruthenium], Rh [rhodium], Pd [palladium], and Ag [silver] (atomic numbers Z = 44 to 47, mass numbers A = 99 to 110) correlate with those of heavier elements (63 ≤ Z ≤ 78, A > 150). There is no correlation for neighboring elements (34 ≤ Z ≤ 42 and 48 ≤ Z ≤ 62).

We interpret this as evidence that fission fragments of transuranic nuclei contribute to the abundances. Our results indicate that neutron-rich nuclei with mass numbers >260 are produced in r-process events.

--- http://sub.blue/ 2024-02-04 cs/algorithm/information/compression design/visualization math --- https://towardsdatascience.com/african-masks-gans-tpu-9a6b0cf3105c 2024-02-04 ai/nn/gan --- https://github.com/LingDong-/shan-shui-inf 2024-02-04 cs/algorithm cs/js --- https://arxiv.org/abs/1710.04087#facebook Word Translation Without Parallel Data Alexis Conneau, Guillaume Lample, Marc’Aurelio Ranzato, Ludovic Denoyer, Hervé Jégou 2017-10-11 2024-02-04 [("doi","10.48550/arXiv.1710.04087")] psychology/linguistics

State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet.

In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs.

Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or English-Chinese. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation.

Our code, embeddings and dictionaries are publicly available.

--- https://arxiv.org/abs/1502.02846 Probabilistic Line Searches for Stochastic Optimization Maren Mahsereci, Philipp Hennig 2015-02-10 2024-02-04 [("doi","10.48550/arXiv.1502.02846")] ai/nn statistics/bayes

In deterministic optimization, line searches are a standard tool ensuring stability and efficiency.

Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space.

We construct a probabilistic line search by combining the structure of existing deterministic methods with notions from Bayesian optimization. Our method retains a Gaussian process surrogate of the univariate optimization objective, and uses a probabilistic belief over the Wolfe conditions to monitor the descent.

The algorithm has very low computational cost, and no user-controlled parameters. Experiments show that it effectively removes the need to define a learning rate for stochastic gradient descent.

--- /doc/iq/2019-schmitt-2.pdf A Comprehensive Quantitative Genetic Analysis of Cerebral Surface Area in Youth J. Eric Schmitt, Michael C. Neale, Liv S. Clasen, Siyuan Liu, Jakob Seidlitz, Joshua N. Pritikin, Alan Chu, Gregory L. Wallace, Nancy Raitano Lee, Jay N. Giedd, Armin Raznahan 2019 2024-02-04 [("doi","10.1523/JNEUROSCI.2248-18.2019")] iq psychology/neuroscience

The genetics of cortical arealization in youth is not well understood. In this study, we use a genetically informative sample of 677 typically developing children and adolescents (mean age 12.72 years), high-resolution MRI, and quantitative genetic methodology to address several fundamental questions on the genetics of cerebral surface area. We estimate that >85% of the phenotypic variance in total brain surface area in youth is attributable to additive genetic factors. We also observed pronounced regional variability in the genetic influences on surface area, with the most heritable areas seen in primary visual and visual association cortex. A shared global genetic factor strongly influenced large areas of the frontal and temporal cortex, mirroring regions that are the most evolutionarily novel in humans relative to other primates. In contrast to studies on older populations, we observed statistically-significant genetic correlations between measures of surface area and cortical thickness (rG = 0.63), suggestive of overlapping genetic influences between these endophenotypes early in life. Finally, we identified strong and highly asymmetric genetically mediated associations between Full-Scale Intelligence Quotient and left perisylvian surface area, particularly receptive language centers. Our findings suggest that spatially complex and temporally dynamic genetic factors are influencing cerebral surface area in our species.

Significance Statement: Over evolution, the human cortex has undergone massive expansion. In humans, patterns of neurodevelopmental expansion mirror evolutionary changes. However, there is a sparsity of information on how genetics impacts surface area maturation. Here, we present a systematic analysis of the genetics of cerebral surface area in youth. We confirm prior research that implicates genetics as the dominant force influencing individual differences in global surface area. We also find evidence that evolutionarily novel brain regions share common genetics, that overlapping genetic factors influence both area and thickness in youth, and the presence of strong genetically mediated associations between intelligence and surface area in language centers. These findings further elucidate the complex role that genetics plays in brain development and function.

--- /doc/economics/2019-scott.pdf Entrepreneurial Uncertainty and Expert Evaluation: An Empirical Analysis Erin L. Scott, Pian Shu, Roman M. Lubynsky 2019-01-01 2024-02-04 [("doi","10.1287/mnsc.2018.3244")] economics technology

This paper empirically examines the evaluations of 537 ventures in high-growth industries performed by 251 experienced entrepreneurs, investors, and executives. These experts evaluated ventures by reading succinct summaries of the ventures without meeting the founding teams, and their evaluations were not disclosed to the entrepreneurs.

We find that experts can differentiate among early-stage ventures on grounds of quality beyond the explicit venture and entrepreneur characteristics contained in the written summaries. They can only do so effectively, however, for ventures in the hardware, energy, life sciences, and medical devices sectors; they cannot do so for ventures in the consumer products, consumer web and mobile, and enterprise software sectors.

Our results highlight sector-specific heterogeneity in the information needed to effectively screen ventures, a finding that has implications for the design of optimal investment strategies.

--- /doc/statistics/decision/2019-kamenica.pdf Bayesian Persuasion and Information Design Emir Kamenica 2019-08-01 2024-02-04 [("doi","10.1146/annurev-economics-080218-025739")] economics/mechanism-design statistics/decision

A school may improve its students’ job outcomes if it issues only coarse grades. Google can reduce congestion on roads by giving drivers noisy information about the state of traffic. A social planner might raise everyone’s welfare by providing only partial information about solvency of banks. All of this can happen even when everyone is fully rational and understands the data-generating process.

Each of these examples raises questions of what is the (socially or privately) optimal information that should be revealed.

In this article, I review the literature that answers such questions.

--- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.482.963&rep=rep1&type=pdf 2024-02-05 psychology/neuroscience --- https://web.archive.org/web/20141216013118/http://secretlaboratory.org/?p=9543 2024-02-05 darknet-market/silk-road/1 psychedelic --- https://x.com/sama/status/1739360234832052641 Sam Altman 2024-02-05 ai/scaling/economics ai/scaling/hardware --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6558345/ Cryopreservation of Human Ovarian Tissue: A Review Ellen Cristina Rivas Leonel, Carolina M. Lucci, Christiani A. Amorim 2019 2024-02-05 [("doi","10.1159/000499054")] cryonics genetics/selection/artificial

Background: Cryopreservation of human ovarian tissue has been increasingly applied worldwide to safeguard fertility in cancer patients, notably in young girls and women who cannot delay the onset of their treatment. Moreover, it has been proposed to patients with benign pathologies with a risk of premature ovarian insufficiency. So far, more than 130 live births have been reported after transplantation of cryopreserved ovarian tissue, and almost all patients recovered their ovarian function after tissue reimplantation.

Summary: This review aims to summarize the recent results described in the literature regarding human ovarian tissue cryopreservation in terms of methods and main results obtained so far. To cryopreserve human ovarian tissue, most studies describe a slow freezing/rapid thawing protocol, which is usually an adaptation of a protocol developed for sheep ovarian tissue. Since freezing has been shown to have a deleterious effect on ovarian stroma and granulosa cells, various research groups have been vitrifying ovarian tissue. Despite promising results, only 2 babies have been born after transplantation of vitrified/warmed ovarian tissue. Optimization of both cryopreservation strategies as well as thawing/warming protocols is therefore necessary to improve the survival of follicles in cryopreserved ovarian tissue.

Key Messages: Human ovarian tissue cryopreservation has been successfully applied worldwide to preserve fertility in patients with malignant or nonmalignant pathologies that have a detrimental effect on fertility. Human ovarian tissue cryopreservation could also be applied as an alternative to postpone pregnancy or menopause in healthy women. Slow freezing and vitrification procedures have been applied to cryopreserve human ovarian tissue, but both alternatives require optimization.

--- https://github.com/R-O-C-K-E-T/Factorio-SAT 2024-02-05 cs/algorithm/sorting --- https://arxiv.org/abs/2310.01505 Towards Automatic Design of Factorio Blueprints Sean Patterson, Joan Espasa, Mun See Chang, Ruth Hoffmann 2023-10-02 2024-02-05 [("doi","10.48550/arXiv.2310.01505")] cs/algorithm

Factorio is a 2D construction and management simulation video game about building automated factories to produce items of increasing complexity. A core feature of the game is its blueprint system, which allows players to easily save and replicate parts of their designs. Blueprints can reproduce any layout of objects in the game, but are typically used to encapsulate a complex behavior, such as the production of a non-basic object.

Once created, these blueprints are then used as basic building blocks, allowing the player to create a layer of abstraction. The usage of blueprints not only eases the expansion of the factory but also allows the sharing of designs with the game’s community. The layout in a blueprint can be optimized using various criteria, such as the total space used or the final production throughput.

The design of an optimal blueprint is a hard combinatorial problem, interleaving elements of many well-studied problems such as bin-packing, routing or network design. This work presents a new challenging problem and explores the feasibility of a constraint model to optimize Factorio blueprints, balancing correctness, optimality, and performance.

--- https://x.com/sama/status/1756089361609981993 OpenAI now generates about 100 billion words per day Sam Altman 2024-02-05 ai/nn/transformer/gpt ai/scaling/economics reinforcement-learning/openai ai/nn/transformer/gpt/4 ai/scaling

OpenAI now generates about 100 billion [0.1t] words per day.

All people on Earth generate about 100 trillion words per day.

--- https://www.chromium.org/developers/design-documents/software-updates-courgette/ 2024-02-05 cs/algorithm/information/compression --- /doc/cs/algorithm/information/1986-landauer.pdf How much Do People Remember? Some Estimates of the Quantity of Learned Information in Long-Term Memory Thomas K. Landauer 1986-10-01 2024-02-05 [("doi","10.1207/s15516709cog1004_4")] cs/algorithm/information psychology/dark-knowledge psychology/linguistics

How much information from experience does a normal adult remember?

The “functional information content” of human memory was estimated in several ways. The methods depend on measured rates of input and loss from very long-term memory and on analyses of the informational demands of human memory-based performance.

Estimates ranged around 109 bits.

It is speculated that the flexible and creative retrieval of facts by humans is a function of a large ratio of “hardware” capacity to functional storage requirements. …Thus, the estimates all point toward a functional learned memory content of around a billion bits for a mature person. The consistency of the numbers is reassuring…Computer systems are now being built with many billion bit hardware memories, but are not yet nearly able to mimic the associative memory powers of our “billion” bit functional capacity. An attractive speculation from these juxtaposed observations is that the brain uses an enormous amount of extra capacity to do things that we have not yet learned how to do with computers. A number of theories of human memory have postulated the use of massive redundancy as a means for obtaining such properties as content and context addressability, sensitivity to frequency of experience, resistance to physical damage, and the like (eg. Landauer 1975; Hopfield 1982; Ackley et al 1985). Possibly we should not be looking for models and mechanisms that produce storage economies (eg. Collins & Quillian 1972), but rather, ones in which marvels are produced by profligate use of capacity.

See Also:

--- https://www.cambridge.org/core/journals/evolutionary-human-sciences/article/entropy-tradeoffs-in-artistic-design-a-case-study-of-tamil-kolam/C1E 2024-02-05 cs/algorithm/information --- https://en.wikipedia.org/wiki/Information_cascade Information cascade 2024-02-05 sociology/preference-falsification --- https://x.com/LeelaChessZero/status/1757502430495859103 Leela Chess Zero 2024-02-06 reinforcement-learning/chess reinforcement-learning/model/alphago --- https://www.lesswrong.com/posts/Fruv7Mmk3X5EekbgB/masterpiece 2024-02-06 fiction/science-fiction --- https://blog.koehntopp.info/2024/02/13/the-matrix-trashfire.html 2024-02-06 design --- /doc/biology/2022-kikuchi.pdf Electrochemical potential enables dormant spores to integrate environmental signals Kaito Kikuchi, Leticia Galera-laporta, Colleen Weatherwax, Jamie Y. Lam, Eun Chae Moon, Emmanuel A. Theodorakis, Jordi Garcia-Ojalvo, Gürol M. Süel 2022-10-06 2024-02-06 [("doi","10.1126/science.abl7484")] biology cs/algorithm/information psychology/neuroscience

The dormant state of bacterial spores is generally thought to be devoid of biological activity. We show that despite continued dormancy, spores can integrate environmental signals over time through a preexisting electrochemical potential. Specifically, we studied thousands of individual Bacillus subtilis spores that remain dormant when exposed to transient nutrient pulses. Guided by a mathematical model of bacterial electrophysiology, we modulated the decision to exit dormancy by genetically and chemically targeting potassium ion flux.

We confirmed that short nutrient pulses result in step-like changes in the electrochemical potential of persistent spores. During dormancy, spores thus gradually release their stored electrochemical potential to integrate extracellular information over time.

These findings reveal a decision-making mechanism that operates in physiologically inactive cells.

--- https://www.theguardian.com/technology/commentisfree/2020/sep/11/artificial-intelligence-robot-writing-gpt-3 2024-02-06 ai/nn/transformer/gpt/3/nonfiction --- https://web.archive.org/web/20230423125705/https://en.wikipedia.org/wiki/Mnet_(peer-to-peer_network) 2024-02-06 bitcoin --- https://dwheeler.com/essays/fixing-unix-linux-filenames.html 2024-02-06 cs/shell --- https://dwheeler.com/trusting-trust/ 2024-02-06 cs/security --- /doc/philosophy/mind/2015-03-23-rulesofthirds-mickeymousememe-perishlikeadog.jpg 2015-03-23 2024-02-06 fiction/humor philosophy/epistemology philosophy/mind --- http://engagedharma.net/2019/08/19/culadasa-charged-with-sexual-misconduct/ 2024-02-06 psychiatry/meditation --- https://siboehm.com/articles/22/CUDA-MMM 2024-02-06 ai/scaling/hardware --- https://math.dartmouth.edu/~matc/MathDrama/reading/Hamming.html 2024-02-07 math philosophy/epistemology --- https://www.ribbonfarm.com/2024/02/15/the-dark-forest-marketing-agency/ 2024-02-07 fiction/science-fiction --- https://www.biorxiv.org/content/10.1101/2024.01.20.576352.full Viroid-like colonists of human microbiomes Ivan N. Zheludev, Robert C. Edgar, Maria Jose Lopez-Galiano, Marcos de la Peña, Artem Babaian, Ami S. Bhatt, Andrew Z. Fire 2024-01-21 2024-02-07 [("doi","10.1101/2024.01.20.576352")] genetics/microbiome

[commentary] Here, we describe the Obelisks, a previously unrecognised class of viroid-like elements that we first identified in human gut metatranscriptomic data. ‘Obelisks’ share several properties: (1) apparently circular RNA ~1kb genome assemblies, (2) predicted rod-like secondary structures encompassing the entire genome, and (3) open reading frames coding for a novel protein superfamily, which we call the Oblins.

We find that Obelisks form their own distinct phylogenetic group with no detectable sequence or structural similarity to known biological agents.

Further, Obelisks are prevalent in tested human microbiome metatranscriptomes with representatives detected in ~7% of analysed stool metatranscriptomes (29⁄440) and in ~50% of analysed oral metatranscriptomes (17⁄32). Obelisk compositions appear to differ between the anatomic sites and are capable of persisting in individuals, with continued presence over >300 days observed in one case. Large scale searches identified 29,959 Obelisks (clustered at 90% nucleotide identity), with examples from all 7 continents and in diverse ecological niches.

From this search, a subset of Obelisks are identified to code for Obelisk-specific variants of the hammerhead type-III self-cleaving ribozyme. Lastly, we identified one case of a bacterial species (Streptococcus sanguinis) in which a subset of defined laboratory strains harboured a specific Obelisk RNA population.

As such, Obelisks comprise a class of diverse RNAs that have colonised, and gone unnoticed in, human, and global microbiomes.

--- https://www.quantamagazine.org/the-mysterious-math-of-billiards-tables-20240215/ 2024-02-07 cs/computable math --- https://www.johnderbyshire.com/Reviews/Miscellaneous/africaningreenland.html 2024-02-07 sociology --- https://x.com/ItalyPaleAle/status/1409890404615409671 ItalyPaleAle 2024-02-07 ai/nn/transformer/gpt/codex --- https://press.asimov.com/resources/scaling-phage-therapy 2024-02-07 genetics/genome-synthesis genetics/microbiome --- https://www.sciencedirect.com/science/article/pii/S2451945622002355 2024-02-07 genetics/genome-synthesis genetics/microbiome --- https://en.wikipedia.org/wiki/IRENE_(technology) IRENE 2024-02-07 cryonics --- https://reactormag.com/day-of-the-kraken/ 2024-02-07 fiction/science-fiction --- https://reactormag.com/tag/the-mongolian-wizard/ The Mongolian Wizard Michael Swanwick 2024-02-08 fiction/science-fiction --- https://en.wikipedia.org/wiki/Amodal_completion Amodal completion 2024-02-08 psychology/vision --- https://reddit.com/r/peanutbutterisoneword/top/ 2024-02-08 cs/security --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7339904/ The Illusion of Absence in Magic Tricks Mats Svalebjørg, Heidi Øhrn, Vebjørn Ekroll 2020 2024-02-08 [("doi","10.1177/2041669520928383")] psychology/cognitive-bias/illusion-of-depth psychology/vision

Recently, a curious illusion of absence has been described, where the space behind an occluder is compellingly experienced as empty. This illusion is similar to illusions based on amodal completion in the sense that it refers to occluded portions of a visual scene and informal observations suggest that it may also be largely impervious to conscious knowledge.

The aim of the present experiment was to test the hypothesis that the illusion of absence is cognitively impenetrable in the same way as amodal completion. Participants viewed magic tricks based on amodal completion, the illusion of absence, or attentional and reasoning misdirection and tried to infer the secret behind the tricks after one, two, or 3 presentations. The results show that the tricks based on the illusion of absence are very difficult to debunk, even after repeated presentations. In this regard, they are similar to tricks based on amodal completion but different from tricks based on attentional and reasoning misdirection.

The participants also rated how magical they felt the tricks were.

Surprisingly, the magic ratings tended to be quite high even in trials where the participants had already discovered the secret behind the trick.

This unexpected finding may be taken to suggest that there may be two magical moments in the lifetime of a magic trick: in addition to the magical experience evoked by the trick itself, discovering the secret behind the trick may also evoke an experience of impossibility.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8850979/ A Perceptual Illusion of Empty Space Can Create a Perceptual Illusion of Levitation Heidi Øhrn, Mats Svalebjørg, Steffen Andersen, Anna Edit Ring, Vebjørn Ekroll 2019 2024-02-08 [("doi","10.1177/2041669519897681")] psychology/cognitive-bias/illusion-of-depth psychology/vision

A recent analysis of magic tricks suggests the existence of a perceptual illusion where the space hidden behind an occluding object is experienced as empty in a strangely compelling way.

Here, we show that this illusion of absence is not just a trivial consequence of the lack of retinal stimulation but rather the result of an active process of perceptual construction. The results of a simple experiment show that this perceptual illusion of absence can in turn trigger perceptual processes which generate an immediate perceptual impression of levitation via a percept-percept coupling.

This suggests that magical illusions of levitation are partially driven by an immediate perceptual impression of floating in thin air. The perceptual mechanisms underlying the illusion of absence are hitherto unknown, but our results provide support for a potential explanation based on the generic view principle.

--- https://www.youtube.com/watch?v=QpvEmNKyg9A 2024-02-08 psychology/cognitive-bias/illusion-of-depth psychology/vision --- https://www.youtube.com/watch?v=sIQ_8bIco3s 2024-02-08 psychology/cognitive-bias/illusion-of-depth psychology/vision --- https://www.youtube.com/watch?v=SYeeTvitvFU 2024-02-08 psychology/cognitive-bias/illusion-of-depth psychology/vision --- https://dubroy.com/blog/visualizing-packrat-parsing/ 2024-02-08 cs/algorithm design/visualization --- /doc/psychiatry/bipolar/autism/2021-alkhayyat.pdf Epidemiology and risk of psychiatric disorders among patients with celiac disease: A population-based national study Motasem Alkhayyat, Thabet Qapaja, Manik Aggarwal, Ashraf Almomani, Mohammad Abureesh, Omaymah Al-otoom, Mohammad Zmaili, Emad Mansoor, Mohannad Abou Saleh 2021-02-08 2024-02-08 [("doi","10.1111/jgh.15437")] nicotine psychiatry/adhd psychiatry/alcoholism psychiatry/anxiety psychiatry/bipolar/autism

Background & Aim:

Celiac disease (CD) is a chronic disorder resulting from an immune reaction to gluten in genetically predisposed individuals. Although several studies have linked CD to psychiatric diseases, there are limited data on this topic.

Using a large database, we sought to describe the epidemiology of several psychiatric disorders in CD.

Methods: We queried a multicenter database (Explorys Inc), an aggregate of electronic health record data from 26 major integrated healthcare systems 2016–2020 consisting of 360 hospitals in the USA. A cohort of patients with a Systematized Nomenclature Of Medicine—Clinical Terms diagnosis of CD was identified. Multivariate analysis was performed using SPSS v25.

Results: Of the 37,465,810 patients in the database 2016–2020, there were 112,340 (0.30%) individuals with CD.

When compared with patients with no history of CD, patients with CD were more likely to have a history of anxiety (odds ratio [OR]: 1.385; 95% confidence interval [CI] : 1.364–1.407), depression (OR: 1.918; 95% CI: 1.888–1.947), bipolar (OR: 1.321; 95% CI: 1.289–1.354), attention-deficit hyperactivity disorder (OR: 1.753; 95% CI: 1.714–1.792), eating disorder (OR: 15.84; 95% CI: 15.533–16.154), and childhood autistic disorder (OR: 4.858; 95% CI: 3.626–6.508).

Patients with CD and psychiatric conditions were more likely to be smokers, with history of alcohol and substance abuse as well as a history of personality disorder.

Conclusions: In this large database, patients with CD are at increased risk of having multiple psychiatric diseases including anxiety, depression, bipolar, attention-deficit hyperactivity disorder, eating disorder, and childhood autism.

Individual care and referral to psychiatry when appropriate are warranted while taking care of this group of patients.

--- https://github.com/mattwright324/youtube-metadata/wiki/YouTube-Oddities 2024-02-08 design --- https://bclarkson-code.github.io/posts/llm-from-scratch-scalar-autograd/post.html 2024-02-09 ai/nn/transformer/attention cs/python --- https://en.wikipedia.org/wiki/Strip_search_phone_call_scam Strip search phone call scam 2024-02-09 crime cs/security psychology --- https://www.thecut.com/article/amazon-scam-call-ftc-arrest-warrants.html 2024-02-09 crime cs/security psychology --- https://pluralistic.net/2024/02/05/cyber-dunning-kruger/#swiss-cheese-security 2024-02-09 crime cs/security psychology --- https://www.frontiersin.org/articles/10.3389/fcvm.2019.00175/full 2024-02-09 genetics/heritable psychiatry/anxiety --- https://adamwiggins.com/muse-retrospective/#crash 2024-02-09 design --- https://www.cia.gov/static/71c21ae09593778d873a08757e736e78/Bitter-Memories.pdf 2024-02-09 history psychology/cognitive-bias --- https://publishingperspectives.com/2009/10/the-strange-world-of-yakuza-fan-magazines/ 2024-02-09 japan/history --- https://www.openttd.org/news/2024/02/17/openttd-truetype-fonts 2024-02-09 design/typography --- https://ralphmerkle.com/humanMemory.html 2024-02-09 cs/algorithm/information --- https://www.aleph.se/andart/archives/2012/04/how_many_persons_can_there_be_brain_reconstruction_and_big_numbers.html How many persons can there be: brain reconstruction and big numbers 2024-02-09 cs/algorithm/information --- https://nymag.com/intelligencer/article/kara-swisher-burn-book-excerpt-silicon-valley-media.html Over 3 Decades, Tech Obliterated Media: My front-row seat to a slow-moving catastrophe Kara Swisher 2024-02-07 2024-02-10 economics/automation reinforcement-learning/openai --- https://www.optimox.com/iodine-study-1 Optimum Levels of Iodine for Greatest Mental and Physical Health Abraham 2024-02-10 iodine --- https://en.wikipedia.org/wiki/Paraprosdokian Paraprosdokian 2024-02-10 fiction/humor psychology/novelty --- https://www.chicagomag.com/chicago-magazine/march-2024/the-ramen-lord/ 2024-02-10 food japan --- https://gist.github.com/munificent/b1bcd969063da3e6c298be070a22b604 2024-02-10 cs/algorithm/information/compression cs/c --- https://danielroelfs.com/blog/everything-is-a-linear-model/ 2024-02-10 statistics/probability --- https://www.medrxiv.org/content/10.1101/2023.08.10.23293924.full Published benefits of ivermectin use in Itajaí, Brazil for COVID-19 infection, hospitalization, and mortality are entirely explained by statistical artefacts Robin Mills, Ana Carolina Peçanha Antonio, Greg Tucker-Kellogg 2023-10-30 2024-02-10 [("doi","10.1101/2023.08.10.23293924")] statistics/bias statistics/survival-analysis

Background: Two recent publications by Kerr et al (Cureus 14(1):e21272; Cureus 14(8): e28624) reported dramatic effects of prophylactic ivermectin use for both prevention of COVID-19 and reduction of COVID-19-related hospitalization and mortality, including a dose-dependent effect of ivermectin prophylaxis. These papers have gained an unusually large public influence: they were incorporated into debates around COVID-19 policies and may have contributed to decreased trust in vaccine efficacy and public health authorities more broadly. Both studies were based on retrospective observational analysis of city-wide registry data from the city of Itajaí, Brazil from July−December 2020.

Method: Starting with initially identified sources of error, we conducted a revised statistical analysis of available data, including data made available with the original papers and public data from the Brazil Ministry of Health.

We identified additional uncorrected sources of bias and errors from the original analysis, including incorrect subject exclusion and missing subjects, analysis of longitudinal data with cross-sectional design, an enrolment time bias, and multiple sources of immortal time bias.

In models assuming no actual effect from ivermectin use, we conducted Monte Carlo simulations to estimate the contribution of these biases to any observed effect.

Results: Untreated statistical artefacts and methodological errors alone lead to dramatic apparent risk reduction associated with ivermectin use in both studies. The magnitude of apparent risk reduction from these artefacts is comparable to the results reported by the studies themselves, including apparent protection from infection, hospitalization, and death, and including the reported apparent dose-response relationship.

Conclusion: The inference of ivermectin effect reported in both papers is unsupported, as the observed effects are entirely explained by untreated statistical artefacts and methodological errors. Our re-analysis calls for caution in interpreting highly publicised observational studies and highlights the importance of common sources of bias in clinical research.

--- https://nostalgebraist.tumblr.com/post/740164510909890560/information-flow-in-transformers 2024-02-10 ai/nn/transformer/attention cs/computable --- https://vgel.me/posts/representation-engineering/ 2024-02-10 ai/nn/transformer/attention --- https://old.reddit.com/r/singularity/comments/1atjz9v/ive_put_a_complex_codebase_into_a_single/ 2024-02-10 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/palm --- https://github.com/tromp/ChessPositionRanking 2024-02-11 cs/algorithm/information/compression reinforcement-learning/chess --- https://parenscript.common-lisp.dev/ 2024-02-11 cs/js cs/lisp --- https://huggingface.co/blog/constrained-beam-search 2024-02-11 ai/nn/sampling --- https://danluu.com/diseconomies-scale/ 2024-02-11 economics/automation politics sociology/technology --- https://arxiv.org/abs/2310.05869#google HyperAttention: Long-context Attention in Near-Linear Time Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh 2023-10-09 2024-02-11 [("doi","10.48550/arXiv.2310.05869")] ai/nn/transformer/attention/sparsity ai/nn/transformer/gpt/palm

We present an approximate attention mechanism named HyperAttention to address the computational challenges posed by the growing complexity of long contexts used in Large Language Models (LLMs).

Recent work suggests that in the worst-case scenario, quadratic time is necessary unless the entries of the attention matrix are bounded or the matrix has low stable rank. We introduce two parameters which measure: (1) the max column norm in the normalized attention matrix, and (2) the ratio of row norms in the unnormalized attention matrix after detecting and removing large entries. We use these fine-grained parameters to capture the hardness of the problem.

Despite previous lower bounds, we are able to achieve a linear time sampling algorithm even when the matrix has unbounded entries or a large stable rank, provided the above parameters are small. HyperAttention features a modular design that easily accommodates integration of other fast low-level implementations, particularly FlashAttention. Empirically, employing Locality Sensitive Hashing (LSH) to identify large entries, HyperAttention outperforms existing methods, giving speed improvements compared to state-of-the-art solutions like FlashAttention.

We validate the empirical performance of HyperAttention on a variety of different long-context length datasets. For example, HyperAttention makes the inference time of ChatGLM2 50% faster on 32k context length while perplexity increases 5.6 → 6.3. On larger context length, eg. 131k, with causal masking, HyperAttention offers 5× speedup on a single attention layer.

--- https://arxiv.org/abs/2103.03404 Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas 2021-03-05 2024-02-11 [("doi","10.48550/arXiv.2103.03404")] ai/nn/fully-connected ai/nn/transformer/attention

Attention-based architectures have become ubiquitous in machine learning, yet our understanding of the reasons for their effectiveness remains limited.

This work proposes a new way to understand self-attention networks: we show that their output can be decomposed into a sum of smaller terms, each involving the operation of a sequence of attention heads across layers. Using this decomposition, we prove that self-attention possesses a strong inductive bias towards “token uniformity”. Specifically, without skip connections or multi-layer perceptrons (MLPs), the output converges doubly exponentially to a rank-1 matrix.

On the other hand, skip connections and MLPs stop the output from degeneration.

Our experiments verify the identified convergence phenomena on different variants of standard transformer architectures.

--- /doc/psychology/cognitive-bias/illusion-of-depth/2017-ekroll.pdf The Other Side of Magic Vebjørn Ekroll, Bilge Sayim, Johan Wagemans 2017-01-11 2024-02-11 [("doi","10.1177/1745691616654676")] psychology/cognitive-bias/illusion-of-depth psychology/vision

When magicians perform spectacles that seem to defy the laws of nature, they do so by manipulating psychological reality. Hence, the principles underlying the art of conjuring are potentially of interest to psychological science. Here, we argue that perceptual and cognitive principles governing how humans experience hidden things and reason about them play a central role in many magic tricks.

Different from tricks based on many other forms of misdirection, which require considerable skill on the part of the magician, many elements of these tricks are essentially self-working because they rely on automatic perceptual and cognitive processes. Since these processes are not directly observable, even experienced magicians may be oblivious to their central role in creating strong magical experiences and tricks that are almost impossible to debunk, even after repeated presentations.

We delineate how insights from perceptual psychology provide a framework for understanding why these tricks work so well. Conversely, we argue that studying magic tricks that work much better than one intuitively would believe provides a promising heuristic for charting unexplored aspects of perception and cognition.

--- https://arxiv.org/abs/2103.10360#baai GLM: General Language Model Pretraining with Autoregressive Blank Infilling Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, Jie Tang 2021-03-18 2024-02-11 [("doi","10.48550/arXiv.2103.10360")] ai/nn/transformer/t5

There have been various types of pretraining architectures including autoencoding models (eg. BERT), autoregressive models (eg. GPT), and encoder-decoder models (eg. T5). However, none of the pretraining frameworks performs the best for all tasks of 3 main categories including natural language understanding (NLU), unconditional generation, and conditional generation.

We propose a General Language Model (GLM) based on autoregressive blank infilling to address this challenge. GLM improves blank filling pretraining by adding 2D positional encodings and allowing an arbitrary order to predict spans, which results in performance gains over BERT and T5 on NLU tasks.

Meanwhile, GLM can be pretrained for different types of tasks by varying the number and lengths of blanks. On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT-2 given the same model sizes and data, and achieves the best performance from a single pretrained model with 1.25× parameters of BERT Large, demonstrating its generalizability to different downstream tasks.

--- https://github.com/THUDM/ChatGLM2-6B/blob/main/README_EN.md 2024-02-11 ai/nn/transformer/t5 --- https://esolangs.org/wiki/Folders 2024-02-11 cs/computable math/humor --- https://www.unison-lang.org/docs/the-big-idea/ 2024-02-12 cs/algorithm --- https://x.com/emollick/status/1759633391098732967 Ethan Mollick 2024-02-12 ai/nn/transformer/gpt ai/scaling/hardware --- https://arram.substack.com/p/how-a-glimpse-of-absolute-perfection 2024-02-12 psychiatry/meditation --- https://en.wikipedia.org/wiki/Pilobolus Pilobolus 2024-02-12 psychology/vision --- https://x.com/WillSmith2real/status/1759703359727300880 Will Smith 2024-02-12 ai/video/generation fiction/humor --- https://blog.stuffedcow.net/2019/09/hard-disk-geometry-microbenchmarking/ 2024-02-12 cs/hardware --- https://en.wikipedia.org/wiki/Asymmetric_numeral_systems Asymmetric numeral systems 2024-02-12 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Ayurveda#Use_of_toxic_metals Ayurveda § Use of toxic metals 2024-02-12 nootropic/bacopa --- https://examine.com/supplements/bacopa-monnieri/ 2024-02-12 nootropic/bacopa --- https://www.reddit.com/r/Nootropics/search?q=bacopa&restrict_sr=on&sort=relevance&t=all 2024-02-12 nootropic/bacopa --- https://www.reddit.com/r/Nootropics/comments/1uajxx/bacopa_vendors_with_coas/cegw982 2024-02-12 nootropic/bacopa --- https://www.reddit.com/r/Nootropics/comments/1qitzo/bacopa_gold_brand_coa/ 2024-02-13 nootropic/bacopa --- https://www.reddit.com/r/Nootropics/comments/2ea784/coa_of_bacopa_powder_purchased_at_indigo_herbs/ 2024-02-13 nootropic/bacopa --- https://www.reddit.com/r/Nootropics/comments/1n4ac3/himalaya_brand_bacopa/ccflb7x/ 2024-02-13 nootropic/bacopa --- https://www.reddit.com/r/Nootropics/comments/1n4ac3/himalaya_brand_bacopa/ccgkqrv/ 2024-02-13 nootropic/bacopa --- https://www.reddit.com/r/Nootropics/comments/1n4ac3/himalaya_brand_bacopa/ 2024-02-13 nootropic/bacopa --- https://www.reddit.com/r/Nootropics/comments/2hj10y/bacopa_from_mind_nutrition/ 2024-02-13 nootropic/bacopa --- https://www.reddit.com/r/Nootropics/comments/21zqvh/purityheavy_metals_testing_results/ 2024-02-13 nootropic/bacopa --- https://www.amazon.com/s/field-keywords=bacognize 2024-02-13 nootropic/bacopa --- https://nootropicsdepot.com/bacognize-bacopa-monnieri-extract-capsules-300mg/ Bacopa monnieri 45% Bacoside Capsules x 250mg 2024-02-13 nootropic/bacopa --- https://nootropicsdepot.com/bacopa/ Bacopa monnieri 45% Bacosides Extract Powder 2024-02-13 nootropic/bacopa --- https://www.amazon.com/Brain-Focus-Optimized-Curcumin-ProHealth/dp/B00LEXWI20/ 2024-02-13 nootropic/bacopa --- https://www.amazon.com/Vitacost-Bacopa-Extract-Featuring-Bacognize/dp/B008TSP0S2/ Vitacost Bacopa Extract Featuring Bacognize—300 mg × 60 Capsules 2024-02-14 nootropic/bacopa --- https://www.amazon.com/Vitacost-Bacopa-Extract-Featuring-Bacognize/dp/B00GO6022K/ Vitacost Bacopa Extract Featuring Bacognize—300 mg × 120 Capsules 2024-02-14 nootropic/bacopa --- https://www.amazon.com/Bacopa-Monnieri-Extract-Bacognize-Caps/dp/B0017OAI8C/ Bacopa monnieri Extract Bacognize 250 mg 90 Caps 2024-02-14 nootropic/bacopa --- https://www.amazon.com/Nutrigold-Bacopa-Clinically-proven-BacoMind-capsules/dp/B004S5SN66/ Nutrigold Bacopa Gold (Clinically-proven BacoMind), 500 mg, 90 veg. capsules 2024-02-14 nootropic/bacopa --- https://www.vitacost.com/vitacost-root2-bacopa-extract-featuring-bacognize-300-mg-120-capsules Vitacost Bacopa Extract Featuring Bacognize®—300mg × 120 Capsules 2024-02-14 nootropic/bacopa --- https://www.amazon.com/s/?keywords=bacopa+monnieri&rh=n%3A3760901%2Ck%3Abacopa+monnieri&sort=review-rank 2024-02-14 nootropic/bacopa --- https://www.hindawi.com/journals/ecam/2012/606424/ Effects of 12-Week Bacopa monnieri Consumption on Attention, Cognitive Processing, Working Memory, and Functions of Both Cholinergic and Monoaminergic Systems in Healthy Elderly Volunteers 2024-02-14 nootropic/bacopa --- https://www.nature.com/articles/1395862.pdf Chronic effects of Brahmi (Bacopa monnieri) on human memory 2024-02-14 nootropic/bacopa --- https://liftingstones.org/articles/stonelifting-etiquette 2024-02-14 exercise --- https://int10h.org/blog/2024/02/game-font-forensics/ 2024-02-14 design/typography --- https://liftingstones.org/articles/kushida-shrines-power-stones 2024-02-15 japan/history --- https://samwho.dev/bloom-filters/ 2024-02-15 cs/algorithm/information/compression --- https://x.com/ellerhymes/status/1749813106078064693 ellerhymes 2024-02-15 design/typography fiction/humor --- https://en.wikipedia.org/wiki/Tabu_search Tabu search 2024-02-15 reinforcement-learning/exploration --- https://www.smithsonianmag.com/arts-culture/inside-biggest-art-fraud-history-180983692/ 2024-02-15 crime --- https://plus.thebulwark.com/p/are-we-all-too-cynical-for-star-trek 2024-02-15 fiction/science-fiction --- https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cui_KD-DLGAN_Data_Limited_CVPR_2023_supplemental.pdf 2024-02-15 ai/nn/gan/stylegan/anime --- https://arxiv.org/abs/1705.07215 On Convergence and Stability of GANs Naveen Kodali, Jacob Abernethy, James Hays, Zsolt Kira 2017-05-19 2024-02-15 [("doi","10.48550/arXiv.1705.07215")] ai/nn/gan reinforcement-learning/multi-agent

We propose studying GAN training dynamics as regret minimization, which is in contrast to the popular view that there is consistent minimization of a divergence between real and generated distributions.

We analyze the convergence of GAN training from this new point of view to understand why mode collapse happens. We hypothesize the existence of undesirable local equilibria in this non-convex game to be responsible for mode collapse. We observe that these local equilibria often exhibit sharp gradients of the discriminator function around some real data points.

We demonstrate that these degenerate local equilibria can be avoided with a gradient penalty scheme called DRAGAN.

We show that DRAGAN enables faster training, achieves improved stability with fewer mode collapses, and leads to generator networks with better modeling performance across a variety of architectures and objective functions.

--- https://www.reddit.com/r/StableDiffusion/comments/1avou9y/the_current_state_of_img2vid_will_smith_eating/ 2024-02-15 ai/video/generation --- https://www.pmichaud.com/toast/ 2024-02-15 math/humor --- https://publicdomainreview.org/essay/the-substantiality-of-spirit/ 2024-02-15 history/public-domain-review philosophy/religion --- https://publicdomainreview.org/collection/earthquakes-in-japanese-woodblock-prints/ 2024-02-16 history/public-domain-review japan/art --- https://simonwillison.net/2024/Feb/21/gemini-pro-video 2024-02-16 ai/nn/transformer/gpt/palm ai/video/analysis --- https://www.lesswrong.com/posts/CKgPFHoWFkviYz7CB/the-redaction-machine 2024-02-16 fiction/science-fiction philosophy/mind --- https://en.wikipedia.org/wiki/Bunting_(animal_behavior) Bunting (animal behavior) 2024-02-16 cat/psychology --- https://x.com/karpathy/status/1760388761349927356 Andrej Karpathy 2024-02-16 psychology/cognitive-bias/illusion-of-depth --- https://spectrum.ieee.org/hans-peter-luhn-and-the-birth-of-the-hashing-algorithm 2024-02-16 cs/algorithm/information/compression cs/cryptography --- https://arxiv.org/abs/2210.02747#facebook Flow Matching for Generative Modeling Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matt Le 2022-10-06 2024-02-16 [("doi","10.48550/arXiv.2210.02747")] ai/nn/diffusion

We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs based on regressing vector fields of fixed conditional probability paths. Flow Matching is compatible with a general family of Gaussian probability paths for transforming between noise and data samples—which subsumes existing diffusion paths as specific instances.

Interestingly, we find that employing FM with diffusion paths results in a more robust and stable alternative for training diffusion models. Furthermore, Flow Matching opens the door to training CNFs with other, non-diffusion probability paths.

An instance of particular interest is using Optimal Transport (OT) displacement interpolation to define the conditional probability paths. These paths are more efficient than diffusion paths, provide faster training and sampling, and result in better generalization. Training CNFs using Flow Matching on ImageNet leads to consistently better performance than alternative diffusion-based methods in terms of both likelihood and sample quality, and allows fast and reliable sample generation using off-the-shelf numerical ODE solvers.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7615573/ The history of Coast Salish "woolly dogs" revealed by ancient genomics and Indigenous Knowledge Audrey T. Lin, Liz Hammond-Kaarremaa, Hsiao-Lei Liu, Chris Stantis, Iain McKechnie, Michael Pavel, Susan sa’hLa mitSa Pavel, Senaqwila Sen Áḵw Wyss, Debra Qwasen Sparrow, Karen Carr, Sabhrina Gita Aninta, Angela Perri, Jonathan Hartt, Anders Bergström, Alberto Carmagnini, Sophy Charlton, Love Dalén, Tatiana R. Feuerborn, Christine A. M. France, Shyam Gopalakrishnan, Vaughan Grimes, Alex Harris, Gwénaëlle Kavich, Benjamin N. Sacks, Mikkel-Holger S. Sinding, Pontus Skoglund, David W. G. Stanton, Elaine A. Ostrander, Greger Larson, Chelsey G. Armstrong, Laurent A. F. Frantz, Melissa T. R. Hawkins, Logan Kistler 2023 2024-02-16 [("doi","10.1126/science.adi6549")] dog genetics/sequencing

Ancestral Coast Salish societies in the Pacific Northwest kept long-haired “woolly dogs” that were bred and cared for over millennia. However, the dog wool-weaving tradition declined during the 19th century, and the population was lost.

In this study, we analyzed genomic and isotopic data from a preserved woolly dog pelt from “Mutton”, collected in 1859. Mutton is the only known example of an Indigenous North American dog with dominant precolonial ancestry postdating the onset of settler colonialism.

We identified candidate genetic variants potentially linked with their distinct woolly phenotype.

We integrated these data with interviews from Coast Salish Elders, Knowledge Keepers, and weavers about shared traditional knowledge and memories surrounding woolly dogs, their importance within Coast Salish societies, and how colonial policies led directly to their disappearance.

--- http://www.irma-international.org/viewtitle/32583/#pdf A Meta-Analysis of the Sunk Cost Effect on Project Escalation 2024-02-16 psychology/cognitive-bias/sunk-cost --- http://www.ihh.kvl.dk/htm/kc/popgen/genetics/3/2.htm 3.2 Selection against the recessive 2024-02-16 genetics/selection --- http://www.hln.be/regio/nieuws-uit-genk/man-verkocht-internationaal-drugs-via-internet-a2498379/ Man verkocht internationaal drugs via internet 2024-02-16 darknet-market --- http://www.gazette.net/article/20130503/NEWS/130509471/1103/Missing-Waldorf-teen-14-returns-home Cicadas soon to emerge, Southern Maryland experts say: Bugs to begin short sojourns above ground, only to mate and die 2024-02-17 biology --- http://www.ff7citadel.com/press/int_edge.shtml The Making of Final Fantasy VII 2024-02-17 anime fiction/fantasy philosophy/ethics --- http://www.demarcken.org/carl/papers/ITA-software-travel-complexity/ITA-software-travel-complexity.pdf ‘Computational Complexity of Air Travel Planning’, de Marcken 2003 [ITA Software] 2024-02-17 cs/computable --- https://tromp.github.io/cl/diagrams.html 2024-02-17 cs/computable design/visualization --- https://www.quantamagazine.org/a-new-agenda-for-low-dimensional-topology-20240222/ 2024-02-17 math --- https://fontreviewjournal.com/windsor/ 2024-02-17 design/typography --- https://dl.acm.org/doi/pdf/10.1145/3386324 2024-02-17 cs/lisp --- https://www.theonion.com/corporation-reaches-goal-shuts-down-1819566365 2024-02-17 economics fiction/humor fiction/humor --- https://www.smbc-comics.com/comic/hatero 2024-02-17 fiction/humor psychology/linguistics --- https://onlinelibrary.wiley.com/doi/full/10.1111/cogs.12670 2024-02-17 psychology/dark-knowledge --- https://questdb.io/blog/billion-row-challenge-step-by-step/ 2024-02-18 cs/algorithm --- https://github.com/gunnarmorling/1brc/discussions/710 2024-02-18 cs/algorithm --- https://arxiv.org/abs/1003.0358#schmidhuber Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition Dan Claudiu Ciresan, Ueli Meier, Luca Maria Gambardella, Juergen Schmidhuber 2010-03-01 2024-02-18 [("doi","10.1162/NECO_a_00052")] ai/nn/fully-connected ai/scaling/hardware

Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the famous MNIST handwritten digits benchmark.

All we need to achieve this best result so far are many hidden layers, many neurons per layer, numerous deformed training images, and [Nvidia] graphics cards to greatly speed up learning.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0265823 Misleading graphs in context: Less misleading than expected Jannetje E. P. Driessen, Daniël A. C. Vos, Ionica Smeets, Casper J. Albers, Carlos Gracia-Lázaro, Carlos Gracia-Lázaro 2022-03-04 2024-02-18 [("doi","10.1371/journal.pone.0265823")] design/visualization psychology/cognitive-bias

Misleading graphs are a source of misinformation that worry many experts. Especially people with a low graph literacy are thought to be persuaded by graphs that misrepresent the underlying data. But we know little about how people interpret misleading graphs and how these graphs influence their opinions.

In this study we focus on the effect of truncating the y-axis for a line chart which exaggerates an upwards trend. In a randomized controlled trial, we showed participants either a normal or a misleading chart, and we did so in two different contexts. After they had seen the graphs, we asked participants their opinion on the trend and to give an estimation of the increase. Finally we measured their graph literacy.

Our results show that context is the only important factor in opinion-forming; the misleading graph and graph literacy had no effect. None of these factors had an important impact on estimations for the increase.

These results show that people might be less susceptible to misleading graphs than we thought and that context has more impact than a misleading y-axis.

--- https://x.com/ApocalypseCage/status/1760350333602062728 ApocalypseCage 2024-02-18 ai/nn/transformer/gpt/dall-e/3 --- https://jackcook.com/2024/02/23/mamba.html 2024-02-18 ai/nn/rnn --- https://arxiv.org/abs/2311.13040 The Penrose Tiling is a Quantum Error-Correcting Code Zhi Li, Latham Boyle 2023-11-21 2024-02-18 [("doi","10.48550/arXiv.2311.13040")] cs/cellular-automaton

[media] The Penrose tiling (PT) is an intrinsically a-periodic way of tiling the plane, with many remarkable properties. A quantum error-correcting code (QECC) is a clever way of protecting quantum information from noise, by encoding the information with a sophisticated type of redundancy.

Although PTs and QECCs might seem completely unrelated, in this paper we point out that PTs give rise to (or, in a sense, are) a remarkable new type of QECC. In this code, quantum information is encoded through quantum geometry, and any local errors or erasures in any finite region, no matter how large, may be diagnosed and corrected.

We also construct variants of this code (based on the Ammann-Beenker and Fibonacci tilings) that can live on finite spatial tori, in discrete spin systems, or in an arbitrary number of spatial dimensions.

We discuss connections to quantum computing, condensed matter physics, and quantum gravity.

--- https://arxiv.org/abs/2212.09748 Diffusion Transformers (DiTs): Scalable Diffusion Models with Transformers William Peebles, Saining Xie 2022-12-19 2024-02-18 [("doi","10.48550/arXiv.2212.09748")] ai/nn/diffusion

We explore a new class of diffusion models based on the transformer architecture.

We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches.

We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by GFLOPS.

We find that DiTs with higher GFLOPS—through increased transformer depth/width or increased number of input tokens—consistently have lower FID.

In addition to possessing good scalability properties, our largest DiT-XL/2 models outperform all prior diffusion models on the class-conditional ImageNet 512×512 and 256×256 benchmarks, achieving a state-of-the-art FID of 2.27 on the latter.

--- https://github.com/jujumilk3/leaked-system-prompts/tree/main 2024-02-18 ai/nn/adversarial ai/nn/transformer/gpt --- https://github.com/jujumilk3/leaked-system-prompts/blob/main/openai-dall-e-3_20231007-2.md 2024-02-18 ai/nn/transformer/gpt/dall-e/3 --- https://github.com/jujumilk3/leaked-system-prompts/blob/main/openai-dall-e-3_20231007-1.md 2024-02-18 ai/nn/transformer/gpt/dall-e/3 --- https://github.com/jujumilk3/leaked-system-prompts/blob/main/openai-chatgpt_20221201.md 2024-02-19 ai/nn/transformer/gpt/4/nonfiction --- https://github.com/jujumilk3/leaked-system-prompts/blob/main/openai-chatgpt4-android_20240207.md 2024-02-19 ai/nn/transformer/gpt/4/nonfiction --- https://github.com/jujumilk3/leaked-system-prompts/blob/main/openai-chatgpt-ios_20230614.md 2024-02-19 ai/nn/transformer/gpt/4/nonfiction --- https://github.com/jujumilk3/leaked-system-prompts/blob/main/openai-assistants-api_20231106.md 2024-02-19 ai/nn/transformer/gpt/4/nonfiction --- https://github.com/jujumilk3/leaked-system-prompts/blob/main/microsoft-bing-chat_20230209.md 2024-02-19 ai/nn/transformer/gpt/4/nonfiction --- https://github.com/jujumilk3/leaked-system-prompts/blob/main/github-copilot-chat_20230513.md 2024-02-19 ai/nn/transformer/gpt/codex --- https://en.wikipedia.org/wiki/IDN_homograph_attack IDN homograph attack 2024-02-19 cs/security --- https://minimaxir.com/2024/02/chatgpt-tips-analysis/ 2024-02-19 ai/nn/transformer/gpt/4/fiction --- https://radiolab.org/podcast/null/transcript 2024-02-19 cs/security --- https://www.mcsweeneys.net/articles/i-am-michiko-kakutani 2024-02-19 fiction/criticism fiction/humor --- https://worksinprogress.co/issue/the-future-of-kidney-treatment/ 2024-02-19 longevity --- https://arxiv.org/abs/2311.17137 Generative Models: What do they know? Do they know things? Let’s find out! Xiaodan Du, Nicholas Kolkin, Greg Shakhnarovich, Anand Bhattad 2023-11-28 2024-02-20 [("doi","10.48550/arXiv.2311.17137")] ai/nn/diffusion ai/nn/gan/stylegan ai/nn/sparsity/knowledge-distillation

Generative models have been shown to be capable of synthesizing highly detailed and realistic images. It is natural to suspect that they implicitly learn to model some image intrinsics such as surface normals, depth, or shadows. In this paper, we present compelling evidence that generative models indeed internally produce high-quality scene intrinsic maps.

We introduce Intrinsic LoRA (I LoRA), a universal, plug-and-play approach that transforms any generative model into a scene intrinsic predictor, capable of extracting intrinsic scene maps directly from the original generator network without needing additional decoders or fully fine-tuning the original network. Our method employs a Low-Rank Adaptation (LoRA) of key feature maps, with newly learned parameters that make up less than 0.6% of the total parameters in the generative model.

Optimized with a small set of labeled images, our model-agnostic approach adapts to various generative architectures, including Diffusion models, GANs, and Autoregressive models. We show that the scene intrinsic maps produced by our method compare well with, and in some cases surpass those generated by leading supervised techniques.

--- https://newsletter.pragmaticengineer.com/p/scaling-chatgpt#%C2%A7five-scaling-challenges 2024-02-20 ai/nn/transformer/gpt/4 ai/scaling/hardware --- https://every.to/chain-of-thought/i-spent-a-week-with-gemini-pro-1-5-it-s-fantastic 2024-02-20 ai/nn/retrieval ai/nn/transformer/gpt/palm --- https://www.nature.com/articles/s41598-023-47864-5 2024-02-20 dog psychology/linguistics --- https://www.scientificamerican.com/article/dog-language-geniuses-are-rare-but-apparently-real/ 2024-02-20 dog psychology/linguistics --- https://www.nytimes.com/2006/08/09/technology/09aol.html 2024-02-20 cs/security technology/google --- https://en.wikipedia.org/wiki/NoteCards NoteCards 2024-02-20 cs/lisp --- https://www.economist.com/china/2024/02/22/why-fake-research-is-rampant-in-china 2024-02-20 statistics/bias --- https://blog.plan99.net/fake-science-part-i-7e9764571422 2024-02-20 statistics/bias --- https://irwin-union.com/sraffa/ 2024-02-20 economics --- https://www.noemamag.com/a-country-shaped-by-poetry/ 2024-02-21 fiction/poetry --- https://www.foofiethedog.com/ 2024-02-21 fiction/humor --- https://github.com/MattCozendey/doom-console-log 2024-02-21 cs/computable --- https://arxiv.org/abs/2402.11349 Tasks That Language Models Don’t Learn Bruce W. Lee, JaeHyuk Lim 2024-02-17 2024-02-21 [("doi","10.48550/arXiv.2402.11349")] ai/nn/tokenization ai/nn/transformer/gpt/4/nonfiction

[character-level benchmarks that mostly measure the side-effects of BPE tokenization and larger models being able to memorize more; cf. PaLM; discussion] We argue that there are certain properties of language that our current large language models (LLMs) don’t learn.

We present an empirical investigation of visual-auditory properties of language through a series of tasks, termed H-TEST. This benchmark highlights a fundamental gap between human linguistic comprehension, which naturally integrates sensory experiences, and the sensory-deprived processing capabilities of LLMs.

In support of our hypothesis, 1. deliberate reasoning (Chain-of-Thought), 2. few-shot examples, or 3. stronger LLM from the same model family (LLaMA-2 13B → LLaMA-2 70B) do not trivially bring improvements in H-TEST performance. Therefore, we make a particular connection to the philosophical case of Mary, who learns about the world in a sensory-deprived environment (Jackson 1986).

Our experiments show that some of the strongest proprietary LLMs stay near random chance baseline accuracy of 50%, highlighting the limitations of knowledge acquired in the absence of sensory experience.

--- https://www.biorxiv.org/content/10.1101/2024.02.22.581566.full Predicting the direction of phenotypic difference David Gokhman, Keith Daniel Harris, Shai Carmi, Gili Greenbaum 2024-02-23 2024-02-23 [("doi","10.1101/2024.02.22.581566")] genetics/heritable statistics/order/comparison

Predicting precise phenotypes from genomic data is a key goal in genetics, but it is often hampered by incomplete genotype-to-phenotype data.

Here, we describe a more attainable approach than quantitative predictions, aimed at qualitatively predicting phenotypic differences, ie. which individual has the higher phenotypic value. This approach could be useful in a wide variety of scenarios, eg. estimating if an individual has an increased disease risk, or if genetically modifying a crop would increase yield.

To investigate whether limited genotype-to-phenotype information can still be used to predict which individual has the higher phenotypic value, we developed an estimator of the ratio between known and unknown effects on the phenotype. We formalize a model to delineate the scenarios in which accurate predictions can be achieved and evaluate performance in real-world data from tens of thousands of individuals from either the same family, same population, different populations, or separate species.

We find that even in phenotypes where only a small fraction of the genetic effects are known, our estimator can allow for the identification of the individual with the higher phenotypic value, often with over 90% accuracy. We also find that our approach circumvents some of the limitations in transferring association data across populations.

Overall, our study introduces an approach for accurately predicting a key feature of phenotypes—their direction—and suggests that more phenotypic information can be extracted from genomes than previously appreciated.

--- https://github.com/Dicklesworthstone/the_lighthill_debate_on_ai 2024-02-21 ai/scaling philosophy/mind --- https://trixter.oldskool.org/2015/04/07/8088-mph-we-break-all-your-emulators/ 2024-02-21 cs/algorithm --- https://www.reenigne.org/blog/8088-pc-speaker-mod-player-how-its-done/ 2024-02-21 cs/computable --- https://tracebit.com/blog/2024/02/finding-aws-account-id-of-any-s3-bucket/ 2024-02-21 cs/security --- /doc/cs/algorithm/1995-wirth.pdf A plea for lean software Niklaus Wirth 1995-02-01 2024-02-21 [("doi","10.1109/2.348001")] cs/algorithm design --- https://arxiv.org/abs/2004.10964#allen Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah Smith 2020-04-23 2024-02-21 [("doi","10.48550/arXiv.2004.10964")] ai/nn/transformer reinforcement-learning/meta-learning/continual-learning

Language models pretrained on text from a wide variety of sources form the foundation of today’s NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task.

We present a study across 4 domains (biomedical and computer science publications, news, and reviews) and 8 classification tasks, showing that a second phase of pretraining in-domain (domain-adaptive pretraining) leads to:

performance gains, under both high-resource & low-resource settings. Moreover, adapting to the task’s unlabeled data (task-adaptive pretraining) improves performance even after domain-adaptive pretraining. Finally, we show that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable.

Overall, we consistently find that multi-phase adaptive pretraining offers large gains in task performance.

--- https://xkcd.com/2899/ 2024-02-22 economics --- https://masoncurrey.substack.com/p/david-milch-disembodied-writing-process 2024-02-22 psychology/writing --- https://www.vectrotype.com/chartwell 2024-02-22 design/typography design/visualization --- https://vrroom.github.io/blog/2024/02/23/comic-frame-segmentation.html 2024-02-22 ai/anime --- https://github.com/miciwan/PaintMixing 2024-02-22 design psychology/vision --- https://status451.com/2016/08/09/too-late-for-the-pebbles-to-vote-part-1/ 2024-02-22 psychology/personality/psychopathy --- https://thezvi.wordpress.com/2024/02/27/the-gemini-incident-continues/ 2024-02-22 ai/nn/transformer/gpt/4/sydney ai/nn/transformer/gpt/palm reinforcement-learning/preference-learning/mode-collapse --- https://x.com/AISafetyMemes/status/1762320288862314659 AI Safety Memes 2024-02-22 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/felps_bra/status/1762494815256936932 felps_bra 2024-02-22 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/colin_fraser/status/1762351995296350592 Colin Fraser 2024-02-22 ai/nn/transformer/gpt/4/nonfiction --- https://en.wikipedia.org/wiki/Dawson_Film_Find Dawson Film Find 2024-02-22 cs/linkrot/archiving --- https://x.com/repligate/status/1762499051571102188 Janus 2024-02-23 ai/nn/transformer/gpt/4/nonfiction --- https://en.wikipedia.org/wiki/Brian_Josephson#Early_interest_and_Transcendental_Meditation Brian Josephson § Meditation 2024-02-23 psychiatry/meditation --- https://answers.microsoft.com/en-us/bing/forum/all/this-ai-chatbot-sidney-is-misbehaving/e3d6a29f-06c9-441c-bc7d-51a68e856761?page=1 2024-02-23 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/safe --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1904114/ Efficient supervised learning in networks with binary synapses Carlo Baldassi, Alfredo Braunstein, Nicolas Brunel, Riccardo Zecchina 2007 2024-02-23 [("doi","10.1073/pnas.0700324104")] ai/nn/sparsity/low-precision

Recent experimental studies indicate that synaptic changes induced by neuronal activity are discrete jumps between a small number of stable states. Learning in systems with discrete synapses is known to be a computationally hard problem.

Here, we study a neurobiologically plausible on-line learning algorithm that derives from belief propagation algorithms. We show that it performs remarkably well in a model neuron with binary synapses, and a finite number of “hidden” states per synapse, that has to learn a random classification task. Such a system is able to learn a number of associations close to the theoretical limit in time that is sublinear in system size. This is to our knowledge the first on-line algorithm that is able to achieve efficiently a finite number of patterns learned per binary synapse.

Furthermore, we show that performance is optimal for a finite number of hidden states that becomes very small for sparse coding. The algorithm is similar to the standard “perceptron” learning algorithm, with an additional rule for synaptic transitions that occur only if a currently presented pattern is “barely correct.” In this case, the synaptic changes are metaplastic only (change in hidden states and not in actual synaptic state), stabilizing the synapse in its current state.

Finally, we show that a system with two visible states and K hidden states is much more robust to noise than a system with K visible states. We suggest that this rule is sufficiently simple to be easily implemented by neurobiological systems or in hardware.

--- https://daily.jstor.org/the-marvelous-automata-of-antiquity/ 2024-02-23 reinforcement-learning/robot --- https://sander.ai/2024/02/28/paradox.html 2024-02-23 ai/nn/diffusion ai/nn/sparsity/knowledge-distillation --- https://arxiv.org/abs/2303.04248 TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation David Berthelot, Arnaud Autef, Jierui Lin, Dian Ang Yap, Shuangfei Zhai, Siyuan Hu, Daniel Zheng, Walter Talbott, Eric Gu 2023-03-07 2024-02-23 [("doi","10.48550/arXiv.2303.04248")] ai/nn/diffusion ai/nn/sparsity/knowledge-distillation

Denoising Diffusion models have demonstrated their proficiency for generative sampling. However, generating good samples often requires many iterations. Consequently, techniques such as binary time-distillation (BTD) have been proposed to reduce the number of network calls for a fixed architecture.

In this paper, we introduce TRAnsitive Closure Time-distillation (TRACT), a new method that extends BTD. For single step diffusion, TRACT improves FID by up to 2.4× on the same architecture, and achieves new single-step Denoising Diffusion Implicit Models (DDIM) state-of-the-art FID (7.4 for ImageNet64, 3.8 for CIFAR-10). Finally we tease apart the method through extended ablations.

The PyTorch implementation will be released soon.

--- https://www.reddit.com/r/baduk/comments/qqjw64/shin_jinseo_ai_difference_shrinking/ 2024-02-23 reinforcement-learning/model/alphago --- https://arxiv.org/abs/2402.14903 Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs Aaditya K. Singh, D. J. Strouse 2024-02-22 2024-02-23 [("doi","10.48550/arXiv.2402.14903")] ai/nn/tokenization ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue math

[Twitter] Tokenization, the division of input text into input tokens, is an often overlooked aspect of the large language model (LLM) pipeline and could be the source of useful or harmful inductive biases.

Historically, LLMs have relied on byte pair encoding, without care to specific input domains. With the increased use of LLMs for reasoning, various number-specific tokenization schemes have been adopted, with popular models like LLaMa and PaLM opting for single-digit tokenization while GPT-3.5 and GPT-4 have separate tokens for each 1/2/3-digit numbers. In this work, we study the effect this choice has on numerical reasoning through the use of arithmetic tasks.

We consider left-to-right and right-to-left tokenization for GPT-3.5 and GPT−4, finding that right-to-left tokenization (enforced by comma separating numbers at inference time) leads to largely improved performance. Furthermore, we find that model errors when using standard left-to-right tokenization follow stereotyped error patterns, suggesting that model computations are systematic rather than approximate.

We show that the model is able to convert between tokenizations easily, thus allowing chain-of-thought-inspired approaches to recover performance on left-to-right tokenized inputs. We also find the gap between tokenization directions decreases when models are scaled, possibly indicating that larger models are better able to override this tokenization-dependent inductive bias.

In summary, our work performs the first study of how number tokenization choices lead to differences in model performance on arithmetic tasks, accompanied by a thorough analysis of error patterns. We hope this work inspires practitioners to more carefully ablate number tokenization-related choices when working towards general models of numerical reasoning.

--- https://healeycodes.com/doom-rendered-via-checkboxes 2024-02-23 cs/css --- https://www.bryanbraun.com/2021/09/21/i-keep-making-things-out-of-checkboxes/ 2024-02-23 cs/css --- https://www.reddit.com/r/StableDiffusion/comments/17b4dfc/my_first_try_with_video/ 2024-02-24 ai/video/generation --- https://arxiv.org/abs/2402.15570 Fast Adversarial Attacks on Language Models In One GPU Minute Vinu Sankar Sadasivan, Shoumik Saha, Gaurang Sriramanan, Priyatham Kattakinda, Atoosa Chegini, Soheil Feizi 2024-02-23 2024-02-24 [("doi","10.48550/arXiv.2402.15570")] ai/nn/adversarial ai/nn/transformer/gpt

In this paper, we introduce a novel class of fast, beam search-based adversarial attack (BEAST) for Language Models (LMs). BEAST employs interpretable parameters, enabling attackers to balance between attack speed, success rate, and the readability of adversarial prompts.

The computational efficiency of BEAST facilitates us to investigate its applications on LMs for jailbreaking, eliciting hallucinations, and privacy attacks. Our gradient-free targeted attack can jailbreak aligned LMs with high attack success rates within one minute.

For instance, BEAST can jailbreak Vicuna-7B-v1.5 under one minute with a success rate of 89% when compared to a gradient-based baseline that takes over an hour to achieve 70% success rate using a single Nvidia RTX A6000 48GB GPU. Additionally, we discover a unique outcome wherein our untargeted attack induces confabulations in LM chatbots.

Through human evaluations, we find that our untargeted attack causes Vicuna-7B-v1.5 to produce ~15% more incorrect outputs when compared to LM outputs in the absence of our attack. We also learn that 22% of the time, BEAST causes Vicuna to generate outputs that are not relevant to the original prompt.

Further, we use BEAST to generate adversarial prompts in a few seconds that can boost the performance of existing membership inference attacks for LMs.

We believe that our fast attack, BEAST, has the potential to accelerate research in LM security and privacy. Our codebase is publicly available at Github.

--- https://www.cspicenter.com/p/are-we-getting-dumber 2024-02-24 genetics/selection/natural/human/dysgenics --- https://arxiv.org/abs/2402.16828 LTE: Training Neural Networks from Scratch with Parallel Low-Rank Adapters Minyoung Huh, Brian Cheung, Jeremy Bernstein, Phillip Isola, Pulkit Agrawal 2024-02-26 2024-02-26 [("doi","10.48550/arXiv.2402.16828")] ai/nn/sparsity ai/nn/transformer

The scalability of deep learning models is fundamentally limited by computing resources, memory, and communication. Although methods like low-rank adaptation (LoRA) have reduced the cost of model finetuning, its application in model pre-training remains largely unexplored.

This paper explores extending LoRA to model pre-training, identifying the inherent constraints and limitations of standard LoRA in this context. We introduce LoRA-the-Explorer (LTE), a novel bi-level optimization algorithm designed to enable parallel training of multiple low-rank heads across computing nodes, thereby reducing the need for frequent synchronization.

Our approach includes extensive experimentation on vision transformers using various vision datasets, demonstrating that LTE is competitive with standard pre-training.

--- https://x.com/karpathy/status/1763303767292940684 Andrej Karpathy 2024-02-24 reinforcement-learning/preference-learning/mode-collapse --- https://arxiv.org/abs/1602.02830 Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or −1 Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio 2016-02-09 2024-02-24 [("doi","10.48550/arXiv.1602.02830")] ai/nn/sparsity/low-precision

We introduce a method to train Binarized Neural Networks (BNNs)—neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During the forward pass, BNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations, which is expected to substantially improve power-efficiency.

To validate the effectiveness of BNNs we conduct two sets of experiments on the Torch7 and Theano frameworks. On both, BNNs achieved nearly state-of-the-art results over the MNIST, CIFAR-10 and SVHN datasets.

Last but not least, we wrote a binary matrix multiplication GPU kernel with which it is possible to run our MNIST BNN 7× faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The code for training and running our BNNs is available on-line.

--- https://arxiv.org/abs/1609.00222 Ternary Neural Networks for Resource-Efficient AI Applications Hande Alemdar, Vincent Leroy, Adrien Prost-Boucle, Frédéric Pétrot 2016-09-01 2024-02-24 [("doi","10.48550/arXiv.1609.00222")] ai/nn/sparsity/low-precision

The computation and storage requirements for Deep Neural Networks (DNNs) are usually high. This issue limits their deployability on ubiquitous computing devices such as smart phones, wearables and autonomous drones. In this paper, we propose ternary neural networks (TNNs) in order to make deep learning more resource-efficient.

We train these TNNs using a teacher-student approach based on a novel, layer-wise greedy methodology. Thanks to our two-stage training procedure, the teacher network is still able to use state-of-the-art methods such as dropout and batch normalization to increase accuracy and reduce training time. Using only ternary weights and activations, the student ternary network learns to mimic the behavior of its teacher network without using any multiplication.

Unlike its −1,1 binary counterparts, a ternary neural network inherently prunes the smaller weights by setting them to zero during training. This makes them sparser and thus more energy-efficient. We design a purpose-built hardware architecture for TNNs and implement it on FPGA and ASIC.

We evaluate TNNs on several benchmark datasets and demonstrate up to 3.1× better energy efficiency with respect to the state-of-the-art while also improving accuracy.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381847/ Dominant Cone Rod Dystrophy, Previously Assigned to a Missense Variant in RIMS1, Is Fully Explained by Co-Inheritance of a Dominant Allele of PROM1 Maria Pilar Martin-Gutierrez, Elena R. Schiff, Genevieve Wright, Naushin Waseem, Omar A. Mahroo, Michel Michaelides, Anthony T. Moore, Andrew R. Webster, Gavin Arno 2022 2024-02-24 [("doi","10.1167/iovs.63.9.14")] genetics/heritable/rare psychology/vision

Purpose: Autosomal dominant cone rod dystrophy 7 (CORD7) was initially linked to the gene RIMS1 and reported in a 4-generation British family in 1998. The purpose of this study was to investigate the legitimacy of this association, and to correctly characterize the genetic cause of this condition.

Methods: The allele frequency of RIMS1 c.2459G>A, p.Arg820His, was investigated in the Genomes Aggregation Dataset (gnomAD) datasets and whole genome sequencing (WGS) was performed for 4 members of the CORD7 family with filtering of rare pathogenic variants in a virtual gene panel comprising all genes known to be associated with inherited retinal dystrophy (IRD). Cytogenetic analysis was performed to rule out interchromosomal translocation.

Results: RIMS1 p.Arg820His has a maximal carrier frequency of >1:5000 in Europeans. A previously well-characterized PROM1 variant: c.1118C>T, p.Arg373Cys, was detected in 9 affected members of the CORD7 family who underwent WGS or direct sequencing. One affected family member is now known to have macular dystrophy in the absence of RIMS1 p.Arg820His. Clinical analysis of affected family members and 27 individuals with retinopathy associated with the same—PROM1—variant showed consistent phenotypes.

Conclusions: The case for pathogenicity of RIMS1 p.Arg820His is not strong based on its presence on 10 alleles in the gnomAD dataset and absence from additional CORD affected individuals. The finding of a known pathogenic variant in PROM1 correlates well with the phenotypic characteristics of the affected individuals, and is likely to account for the condition. Clear evidence of association between RIMS1 and a retinal dystrophy is yet to be described.

--- https://en.wikipedia.org/wiki/Antarctic_English#Vocabulary Antarctic English § Vocabulary 2024-02-24 psychology/linguistics --- https://arxiv.org/abs/1610.05820 Membership Inference Attacks against Machine Learning Models Reza Shokri, Marco Stronati, Congzheng Song, Vitaly Shmatikov 2016-10-18 2024-02-24 [("doi","10.48550/arXiv.1610.05820")] ai/nn/adversarial

We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained.

We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model’s training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model’s predictions on the inputs that it trained on versus the inputs that it did not train on.

We empirically evaluate our inference techniques on classification models trained by commercial “machine learning as a service” providers such as Google and Amazon.

Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks.

We then investigate the factors that influence this leakage and evaluate mitigation strategies.

--- https://news.ycombinator.com/item?id=39557213 2024-02-25 ai/nn/tokenization ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction --- https://arxiv.org/abs/2310.18166 Functional Ownership through Fractional Uniqueness Daniel Marshall, Dominic Orchard 2023-10-27 2024-02-25 [("doi","10.48550/arXiv.2310.18166")] cs/haskell

Ownership and borrowing systems, designed to enforce safe memory management without the need for garbage collection, have been brought to the fore by the Rust programming language. Rust also aims to bring some guarantees offered by functional programming into the realm of performant systems code, but the type system is largely separate from the ownership model, with type and borrow checking happening in separate compilation phases. Recent models such as RustBelt and Oxide aim to formalize Rust in depth, but there is less focus on integrating the basic ideas into more traditional type systems. An approach designed to expose an essential core for ownership and borrowing would open the door for functional languages to borrow concepts found in Rust and other ownership frameworks, so that more programmers can enjoy their benefits.

One strategy for managing memory in a functional setting is through uniqueness types, but these offer a coarse-grained view: either a value has exactly one reference, and can be mutated safely, or it cannot, since other references may exist. Recent work demonstrates that linear and uniqueness types can be combined in a single system to offer restrictions on program behavior and guarantees about memory usage.

We develop this connection further, showing that just as graded type systems like those of Granule and Idris generalize linearity, Rust’s ownership model arises as a graded generalization of uniqueness.

We combine fractional permissions with grading to give the first account of ownership and borrowing that smoothly integrates into a standard type system alongside linearity and graded types, and extend Granule accordingly with these ideas.

--- /doc/fiction/science-fiction/1984-lem.html Chance and Order Stanisław Lem 1984-01-30 2024-02-25 fiction/science-fiction psychology/writing --- https://cloudinary.com/blog/jpeg-xl-and-the-pareto-front 2024-02-25 cs/algorithm/information/compression --- https://www.dwarkeshpatel.com/p/demis-hassabis#%C2%A7timestamps 2024-02-25 ai/nn/transformer/gpt/palm ai/scaling reinforcement-learning/deepmind reinforcement-learning/model reinforcement-learning/safe --- https://www.nytimes.com/2024/02/29/health/guy-alexandre-dead.html 2024-02-25 philosophy/ethics --- https://blog.trailofbits.com/2023/02/14/curl-audit-fuzzing-libcurl-command-line-interface/ 2024-02-25 cs/security --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5204169/ Sex as a strategy against rapidly evolving parasites Stuart K. J. R. Auld, Shona K. Tinkler, Matthew C. Tinsley 2016 2024-02-25 [("doi","10.1098/rspb.2016.2226")] genetics/selection/natural

Why is sex ubiquitous when asexual reproduction is much less costly? Sex disrupts coadapted gene complexes; it also causes costs associated with mate finding and the production of males who do not themselves bear offspring. Theory predicts parasites select for host sex, because genetically variable offspring can escape infection from parasites adapted to infect the previous generations.

We examine this using a facultative sexual crustacean, Daphnia magna, and its sterilizing bacterial parasite, Pasteuria ramosa. We obtained sexually and asexually produced offspring from wild-caught hosts and exposed them to contemporary parasites or parasites isolated from the same population one year later.

We found rapid parasite adaptation to replicate within asexual but not sexual offspring. Moreover, sexually produced offspring were twice as resistant to infection as asexuals when exposed to parasites that had co-evolved alongside their parents (ie. the year two parasite).

This fulfils the requirement that the benefits of sex must be both large and rapid for sex to be favoured by selection.

--- https://www.biorxiv.org/content/10.1101/2023.12.08.570879.full Rendering protein structures inside cells at the atomic level with Unreal Engine Muyuan Chen 2023-12-11 2024-02-25 [("doi","10.1101/2023.12.08.570879")] biology design/visualization

While the recent development of cryogenic electron tomography (CryoET) makes it possible to identify various macromolecules inside cells and determine their structure at near-atomic resolution, it remains challenging to visualize the complex cellular environment at the atomic level. One of the main hurdles in cell visualization is to render the millions of molecules in real time computationally.

Here, using a video game engine [Unreal Engine 5’s Nanite], we demonstrate the capability of rendering massive biological macromolecules at the atomic level within their native environment.

To facilitate the visualization, we also provide tools that help the interactive navigation inside the cells, as well as software that converts protein structures identified using CryoET to a scene that can be explored with the game engine.

--- https://arxiv.org/abs/2402.11753 ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs Fengqing Jiang, Zhangchen Xu, Luyao Niu, Zhen Xiang, Bhaskar Ramasubramanian, Bo Li, Radha Poovendran 2024-02-19 2024-02-25 [("doi","10.48550/arXiv.2402.11753")] ai/dataset ai/nn/adversarial ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/palm design/typography

Safety is critical to the usage of large language models (LLMs). Multiple techniques such as data filtering and supervised fine-tuning have been developed to strengthen LLM safety. However, currently known techniques presume that corpora used for safety alignment of LLMs are solely interpreted by semantics. This assumption, however, does not hold in real-world applications, which leads to severe vulnerabilities in LLMs. For example, users of forums often use ASCII art, a form of text-based art, to convey image information.

In this paper, we propose a novel ASCII art-based jailbreak attack and introduce a comprehensive benchmark Vision-in-Text Challenge (ViTC) to evaluate the capabilities of LLMs in recognizing prompts that cannot be solely interpreted by semantics.

We show that 5 SOTA LLMs (GPT-3.5, GPT-4, Google Gemini, Claude-2, and LLaMA-2) struggle to recognize prompts provided in the form of ASCII art.

Based on this observation, we develop the jailbreak attack ArtPrompt, which leverages the poor performance of LLMs in recognizing ASCII art to bypass safety measures and elicit undesired behaviors from LLMs. ArtPrompt only requires black-box access to the victim LLMs, making it a practical attack.

We evaluate ArtPrompt on 5 SOTA LLMs, and show that ArtPrompt can effectively and efficiently induce undesired behaviors from all 5 LLMs.

--- https://www.mrmoneymustache.com/2015/09/07/great-news-dog-ownership-is-optional/ 2024-02-25 dog --- https://xkcd.com/2901/ [On Gricean Maxims] Randall Munroe 2024-02-01 2024-02-26 fiction/humor psychology/linguistics --- https://www.atlasobscura.com/articles/fanbinding-fanfiction-bookbinding 2024-02-26 design/typography --- https://markstobbe.substack.com/p/the-donkey-that-was-saved-from-the 2024-02-26 law --- https://awkravchuk.itch.io/cl-fast-ecs/devlog/622054/gamedev-in-lisp-part-1-ecs-and-metalinguistic-abstraction 2024-02-26 cs/lisp --- /dropcap#goudy-initialen 2024-02-26 design/typography/floral --- /lorem-dropcap#goudy-initialen 2024-02-26 design/typography/floral --- https://en.wikipedia.org/wiki/Fleuron_(typography) Fleuron (typography) 2024-02-26 design/typography/floral --- https://www.vox.com/2015/8/25/9200801/wingdings-font-history 2024-02-26 design/typography/floral --- https://commons.wikimedia.org/wiki/File:Gabriele_d%27Annunzio-L%27armata_d%27Italia-Carabba-1916.png File:Gabriele d’Annunzio-L’armata d’Italia-Carabba-1916.png 2024-02-26 design/typography/floral design/typography/rubrication --- https://x.com/hyuki/status/1568517187387916289 hyuki 2024-02-26 ai/nn/diffusion design/typography/floral --- https://www.dazeddigital.com/artsandculture/article/37329/1/vincent-gallo-1997-interview-buffalo-66 2024-02-27 psychiatry/bipolar/energy --- https://www.theguardian.com/film/2003/nov/14/television 2024-02-27 psychiatry/bipolar/energy --- https://en.wikipedia.org/wiki/Vincent_Gallo Vincent Gallo 2024-02-27 psychiatry/bipolar/energy --- https://www.dafont.com/flower-ornaments.font 2024-02-27 design/typography/floral --- https://arxiv.org/abs/1606.01981 Deep neural networks are robust to weight binarization and other non-linear distortions Paul Merolla, Rathinakumar Appuswamy, John Arthur, Steve K. Esser, Dharmendra Modha 2016-06-07 2024-02-27 [("doi","10.48550/arXiv.1606.01981")] ai/nn/cnn ai/nn/sparsity/low-precision

Recent results show that deep neural networks achieve excellent performance even when, during training, weights are quantized and projected to a binary representation. Here, we show that this is just the tip of the iceberg: these same networks, during testing, also exhibit a remarkable robustness to distortions beyond quantization, including additive and multiplicative noise, and a class of non-linear projections where binarization is just a special case.

To quantify this robustness, we show that one such network achieves 11% test error on CIFAR-10 even with 0.68 effective bits per weight. Furthermore, we find that a common training heuristic—namely, projecting quantized weights during backpropagation—can be altered (or even removed) and networks still achieve a base level of robustness during testing. Specifically, training with weight projections other than quantization also works, as does simply clipping the weights, both of which have never been reported before.

We confirm our results for CIFAR-10 and ImageNet datasets. Finally, drawing from these ideas, we propose a stochastic projection rule that leads to a new state-of-the-art network with 7.64% test error on CIFAR-10 using no data augmentation.

--- https://arxiv.org/abs/2402.17113 Transparent Image Layer Diffusion using Latent Transparency Lvmin Zhang, Maneesh Agrawala 2024-02-27 2024-02-27 [("doi","10.48550/arXiv.2402.17113")] ai/nn/diffusion

[code] We present LayerDiffusion, an approach enabling large-scale pretrained latent diffusion models to generate transparent images. The method allows generation of single transparent images or of multiple transparent layers.

The method learns a “latent transparency” that encodes alpha channel transparency into the latent manifold of a pretrained latent diffusion model. It preserves the production-ready quality of the large diffusion model by regulating the added transparency as a latent offset with minimal changes to the original latent distribution of the pretrained model. In this way, any latent diffusion model can be converted into a transparent image generator by finetuning it with the adjusted latent space.

We train the model with 1M transparent image layer pairs collected using a human-in-the-loop collection scheme. We show that latent transparency can be applied to different open source image generators, or be adapted to various conditional control systems to achieve applications like foreground/background-conditioned layer generation, joint layer generation, structural control of layer contents, etc.

A user study finds that in most cases (97%) users prefer our natively generated transparent content over previous ad-hoc solutions such as generating and then matting. Users also report the quality of our generated transparent images is comparable to real commercial transparent assets like Adobe Stock.

--- https://x.com/sama/status/1599112749833125888 Sam Altman 2024-02-27 reinforcement-learning/openai reinforcement-learning/safe --- https://www.filfre.net/2024/03/the-rise-of-pomg-part-4-a-world-for-the-taking/ 2024-02-27 fiction/text-game sociology/technology technology/digital-antiquarian --- https://statmodeling.stat.columbia.edu/2024/02/28/blog-is-adapted-to-laptops-or-desktops-not-to-smartphones-or-pads/ 2024-02-27 psychology/writing sociology/technology --- /doc/ai/nn/diffusion/midjourney/dropcap/ninit/2023-10-01-gwern-midjourneyv5-dropcap-capitalletterawithfloraldesignonablackbackgroundmadeoutofaluminum.jpg Gwern 2023-10-01 2024-02-27 ai/nn/diffusion/midjourney/dropcap/ninit design/typography/floral --- /doc/ai/nn/diffusion/midjourney/dropcap/ninit/2023-10-12-gwern-midjourneyv5-floral-monochrome-samples-1.jpg Gwern 2023-10-12 2024-02-28 ai/nn/diffusion/midjourney/dropcap/ninit design/typography/floral --- /doc/ai/nn/diffusion/midjourney/dropcap/ninit/2023-10-12-gwern-midjourneyv5-floral-monochrome-samples-2.jpg Gwern 2023-10-12 2024-02-28 ai/nn/diffusion/midjourney/dropcap/ninit design/typography/floral --- /doc/ai/nn/diffusion/midjourney/dropcap/ninit/2023-10-12-gwern-midjourneyv5-floral-samples-1.jpg Gwern 2023-10-12 2024-02-28 ai/nn/diffusion/midjourney/dropcap/ninit design/typography/floral --- /doc/ai/nn/diffusion/midjourney/dropcap/ninit/2023-10-12-gwern-midjourneyv5-floral-samples-2.jpg Gwern 2023-10-12 2024-02-28 ai/nn/diffusion/midjourney/dropcap/ninit design/typography/floral --- /doc/ai/nn/diffusion/midjourney/dropcap/ninit/2023-10-12-gwern-midjourneyv5-floral-samples-3.jpg Gwern 2023-10-12 2024-02-28 ai/nn/diffusion/midjourney/dropcap/ninit design/typography/floral --- /doc/ai/nn/diffusion/midjourney/dropcap/ninit/2023-10-12-gwern-midjourneyv5-floral-samples.jpg Gwern 2023-10-12 2024-02-28 ai/nn/diffusion/midjourney/dropcap/ninit design/typography/floral --- /doc/ai/nn/diffusion/midjourney/dropcap/ninit/2023-10-23-gwern-midjourneyv5-ninit-darkmode-curatedsamples-a-floral.jpg Gwern 2023-10-23 2024-02-28 ai/nn/diffusion/midjourney/dropcap/ninit design/typography/floral --- /doc/ai/nn/diffusion/midjourney/dropcap/ninit/2023-10-29-gwern-midjourneyv5-ninit-lightmode-curatedsamples-a-floral.jpg Gwern 2023-10-29 2024-02-28 ai/nn/diffusion/midjourney/dropcap/ninit design/typography/floral --- https://arxiv.org/abs/2306.04305 Self-Resolving Prediction Markets for Unverifiable Outcomes Siddarth Srinivasan, Ezra Karger, Yiling Chen 2023-06-07 2024-02-28 [("doi","10.48550/arXiv.2306.04305")] economics/mechanism-design statistics/prediction

Prediction markets elicit and aggregate beliefs by paying agents based on how close their predictions are to a verifiable future outcome. However, outcomes of many important questions are difficult to verify or unverifiable, in that the ground truth may be hard or impossible to access. Examples include questions about causal effects where it is infeasible or unethical to run randomized trials; crowdsourcing and content moderation tasks where it is prohibitively expensive to verify ground truth; and questions asked over long time horizons, where the delay until the realization of the outcome skews agents’ incentives to report their true beliefs.

We present a novel and unintuitive result showing that it is possible to run an ε-incentive compatible prediction market to elicit and efficiently aggregate information from a pool of agents without observing the outcome by paying agents the negative cross-entropy between their prediction and that of a carefully chosen reference agent. Our key insight is that a reference agent with access to more information can serve as a reasonable proxy for the ground truth.

We use this insight to propose self-resolving prediction markets that terminate with some probability after every report and pay all but a few agents based on the final prediction.

We show that it is an εPerfect Bayesian Equilibrium for all agents to report truthfully in our mechanism and to believe that all other agents report truthfully.

Although primarily of interest for unverifiable outcomes, this design is also applicable for verifiable outcomes.

--- https://x.com/NPCollapse/status/1763960083858198573 Connor Leahy 2024-02-28 psychedelic reinforcement-learning/openai --- https://x.com/elzr/status/1003513104892772353 elzr 2024-02-28 design/typography --- https://www.theguardian.com/books/2024/mar/03/achilles-trap-steve-coll-review 2024-02-29 crime/terrorism --- https://www.biorxiv.org/content/10.1101/2024.02.13.580196.full Fraudulent studies are undermining the reliability of systematic reviews—a study of the prevalence of problematic images in preclinical studies of depression Jenny P. Berrío, Otto Kalliokoski 2024-02-15 2024-02-29 [("doi","10.1101/2024.02.13.580196")] psychiatry/depression statistics/bias/animal

Systematic reviews are considered by many to constitute the highest level of scientific evidence. A caveat is that the methods used in a systematic review—combining information from multiple studies—are predicated on all of the reports being truthful. Currently, we do not know how frequent fraudulent studies are in systematic reviews, or how they affect the resulting evidence base.

For a systematic review of preclinical studies of depression, we found that potentially fraudulent studies were not only common but also that they biased the findings of the review. In a sample of 1,035 studies, we found that 19% of peer-reviewed reports displayed data in the form of problematic images. In a majority of the cases, images had been altered or recycled in a way that makes us suspect foul play.

Making things worse, these studies reported larger effect sizes, on average, than did studies where we did not identify problems. Counter to commonly held beliefs, reports with problematic images were not cited less or published in lower-impact journals, nor were their authors isolated to any specific geographic area.

The sheer prevalence of problematic studies, and the fact that we could not find a simple pattern for identifying them, undermines the validity of systematic reviews within our research field. We suspect that this is symptomatic of a broader problem that needs immediate addressing.

--- https://iagoleal.com/posts/value-iteration-haskell/ 2024-02-29 cs/haskell reinforcement-learning/model --- /doc/fiction/gene-wolfe/1983-wolfe-newsun-4-thecitadeloftheautarch-ch09-melitosstorythecocktheangelandtheeagle.pdf Melito’s Story—The Cock, the Angel, and the Eagle Gene Wolfe 1983-01-01 2024-02-29 fiction/gene-wolfe philosophy/religion --- https://gimletmedia.com/shows/reply-all/o2hx34 2024-02-29 crime --- https://www.cabinetmagazine.org/issues/40/sherman.php 2024-02-29 cs/cryptography/steganography --- /doc/crime/1984-mednick.pdf Genetic Influences in Criminal Convictions: Evidence from an Adoption Cohort Sarnoff A. Mednick, William F. Gabrielli, Barry Hutchings 1984-05-25 2024-02-29 [("doi","10.1126/science.6719119")] crime genetics/heritable/adoption

The possibility that genetic factors are among the causes of criminal behavior was tested by comparing court convictions of 14,427 adoptees with those of their biological and adoptive parents.

A statistically-significant correlation was found between the adoptees and their biological parents for convictions of property crimes. This was not true with respect to violent crimes.

There was no statistically-significant correlation between adoptee and adoptive parent court convictions. Siblings adopted separately into different homes tended to be concordant for convictions, especially if the shared biological father also had a record of criminal behavior.

Figure 1: Percentage of adoptees convicted of violent and property offenses as a function of biological parents’ convictions. Note: Only cases in which neither adoptive parent is convicted were included. In view of the low frequencies of court convictions and recidivism among the adoptive parents and in order to simplify interpretation, analyses include only cases in which adoptive parents have no criminal law convictions.
--- https://benhoyt.com/writings/go-1brc/ 2024-02-29 cs/algorithm --- https://arxiv.org/abs/physics/0510117 Modeling bursts and heavy tails in human dynamics A. Vazquez, J. Gama Oliveira, Z. Dezso, K. -I. Goh, I. Kondor, A. -L. Barabasi 2005-10-12 2024-02-29 [("doi","10.1103/PhysRevE.73.036127")] cs/algorithm economics statistics/probability

Current models of human dynamics, used from risk assessment to communications, assume that human actions are randomly distributed in time and thus well approximated by Poisson processes. We provide direct evidence that for 5 human activity patterns the timing of individual human actions follow non-Poisson statistics, characterized by bursts of rapidly occurring events separated by long periods of inactivity.

We show that the bursty nature of human behavior is a consequence of a decision based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, most tasks being rapidly executed, while a few experiencing very long waiting times.

We discuss two queueing models that capture human activity. The first model assumes that there are no limitations on the number of tasks an individual can handle at any time, predicting that the waiting time of the individual tasks follow a heavy tailed distribution with exponent alpha=3/2. The second model imposes limitations on the queue length, resulting in alpha=1.

We provide empirical evidence supporting the relevance of these two models to human activity patterns. Finally, we discuss possible extension of the proposed queueing models and outline some future challenges in exploring the statistical mechanisms of human dynamics.

…Supercritical regime, ρ > 1: Given that in this regime the arrival rate exceeds the response rate, the average queue length grows linearly as 〈l(t)〉 = (λ − μ)t. Therefore, a 1 − 1/ρ fraction of the letters is never responded to, waiting indefinitely in the queue. Given Darwin, Einstein and Freud’s small response rate, this regime captures best their correspondence pattern. We can measure the waiting time for each letter that is responded to. In Figure 5 we show the waiting time probability density obtained from numerical simulations, indicating that it follows a power law with exponent α = 3⁄2. Thus the supercritical regime follows the same scaling behavior as the critical regime, but only for the letters that are responded to. The rest of the letters wait indefinitely in the list (τw = ∞).

While the discussed model can indeed generate power law waiting time distributions, a critical comparison with the empirical datasets reveals some notable deficiencies. First, a power law distribution emerges only in the critical (ρ = 1) and the supercritical (ρ > 1) regimes. The critical regime requires a careful tuning of the human execution rate, so that the execution and the arrival rates are exactly the same. In contrast, for ρ > 1 no tuning is necessary, but the number of tasks on the list increases linearly with time, thus many tasks are never executed. This limit is probably the most realistic for human dynamics: we often take on tasks that we never execute, and technically stay on our priority list forever. As we discussed above, this is documentedly the case for Einstein, Darwin and Freud, who answer only a fraction of their letters.

However, we must not overlook the second important feature of the discussed model: the only exponent it can predict is α = 3⁄2, rooted in the fluctuations of the queue length. While this fully agrees with the correspondence patterns of Einstein, Darwin and Freud, it is substantially higher than the values observed in the empirical data discussed in §III A on web browsing, email communications or library visits, which we found to be scattered around α = 1.

See Also:

--- /doc/economics/2013-beck.pdf On the Distribution of Job Performance: The Role of Measurement Characteristics in Observed Departures from Normality James W. Beck, Adam S. Beatty, Paul R. Sackett 2013-09-13 2024-02-29 [("doi","10.1111/peps.12060")] economics statistics/probability

In a recent article, O’Boyle & Aguinis 2012 argued that job performance is not distributed normally but instead is non-normal and highly skewed. However, we believe the extreme departures from normality observed by these authors may have been due to characteristics of performance measures used.

To address this issue, we identify 7 measurement criteria that we argue must be present for inferences to be made about the distribution of job performance. Specifically, performance measures must: (1) reflect behavior, (2) include an aggregation of multiple behaviors, (3) include the full range of performers, (5) include the full range of performance, (5) be time bounded, (6) focus on comparable jobs, and (7) not be distorted by motivational forces.

Next, we present data from a wide range of sources—including the workplace, laboratory, athletics, and computer simulations—that illustrate settings in which failing to meet one or more of these criteria led to a highly skewed distribution providing a better fit to the data than a normal distribution. However, measurement approaches that better align with the 7 criteria listed above resulted in a normal distribution providing a better fit.

We conclude that large departures from normality are in many cases an artifact of measurement.

--- /doc/economics/2014-anguinis.pdf Star Performers in 21st Century Organizations Herman Aguinis, Ernest O’Boyle 2013-07-10 2024-02-29 [("doi","10.1111/peps.12054")] economics statistics/probability

[expanded version of their 2012 paper] We argue that changes in the nature of work in 21st-century organizations have led to the emergence of star performers—a few individuals who contribute a disproportionate amount of output.

We describe how stars negate the long-held belief that the distribution of individual performance is normal and, instead, suggest an underlying power law distribution. In addition, we offer 9 propositions to guide future empirical research on star performers and an underlying power law distribution of individual performance. We describe how the presence of stars is likely to affect all individual-level, team-level, and firm-level management theories addressing individual performance directly or indirectly, but focus on specific implications for those addressing human capital, turnover, compensation, downsizing, leadership, teamwork, corporate entrepreneurship, and microfoundations of strategy.

In addition, we discuss methodological considerations necessary to carry out our proposed research agenda. Finally, we discuss how a consideration of star performers has important implications for management practice.

--- https://en.wikipedia.org/wiki/Paris_pneumatic_post Paris pneumatic post 2024-03-01 technology --- https://www.wired.com/story/frank-herbert-dune-script-denis-villeneuve-dune-part-two/ 2024-03-01 fiction/science-fiction/frank-herbert --- https://midshowcase.com/ 2024-03-01 ai/nn/diffusion/midjourney --- https://x.com/anton_bakhtin/status/1764701559844147359 Anton Bakhtin 2024-03-01 ai/nn/transformer/gpt/claude --- https://www.construction-physics.com/p/why-is-it-so-hard-to-build-an-airport 2024-03-01 economics/georgism --- https://opus-codec.org/demo/opus-1.5/ 2024-03-01 ai/nn/vae cs/algorithm/information/compression --- https://arxiv.org/abs/2212.04453#amazon Low-Bitrate Redundancy Coding of Speech Using a Rate-Distortion-Optimized Variational Autoencoder Jean-Marc Valin, Jan Büthe, Ahmed Mustafa 2022-12-08 2024-03-01 [("doi","10.48550/arXiv.2212.04453")] ai/nn/vae cs/algorithm/information/compression

Robustness to packet loss is one of the main ongoing challenges in real-time speech communication.

Deep packet loss concealment (PLC) techniques have recently demonstrated improved quality compared to traditional PLC. Despite that, all PLC techniques hit fundamental limitations when too much acoustic information is lost. To reduce losses in the first place, data is commonly sent multiple times using various redundancy mechanisms.

We propose a neural speech coder specifically optimized to transmit a large amount of overlapping redundancy at a very low bitrate, up to 50× redundancy using less than 32/s.

Results show that the proposed redundancy is more effective than the existing Opus codec redundancy, and that the two can be combined for even greater robustness.

--- https://www.hillelwayne.com/post/graph-types/ 2024-03-01 cs/algorithm --- https://x.com/alexalbert__/status/1764722513014329620 Alex Albert 2024-03-01 ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/claude reinforcement-learning/safe --- https://arxiv.org/abs/2006.10739 Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, Ren Ng 2020-06-18 2024-03-01 [("doi","10.48550/arXiv.2006.10739")] ai/nn/fully-connected

We show that passing input points through a simple Fourier feature mapping enables a multilayer perceptron (MLP) to learn high-frequency functions in low-dimensional problem domains. These results shed light on recent advances in computer vision and graphics that achieve state-of-the-art results by using MLPs to represent complex 3D objects and scenes.

Using tools from the neural tangent kernel (NTK) literature, we show that a standard MLP fails to learn high frequencies both in theory and in practice. To overcome this spectral bias, we use a Fourier feature mapping to transform the effective NTK into a stationary kernel with a tunable bandwidth.

We suggest an approach for selecting problem-specific Fourier features that greatly improves the performance of MLPs for low-dimensional regression tasks relevant to the computer vision and graphics communities.

--- https://www.theatlantic.com/magazine/archive/2024/04/us-anti-semitism-jewish-american-safety/677469/ 2024-03-01 politics --- https://en.wikipedia.org/wiki/Wallpaper_group Wallpaper group 2024-03-02 design/typography/floral math --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1719021/pdf/v085p00454.pdf#page=2 Murder misdiagnosed as SIDS: a perpetrator’s perspective J. Stanton, A. Simpson 2001 2024-03-02 [("doi","10.1136/adc.85.6.454")] crime psychiatry/borderline

Aims: Child murder misdiagnosed as sudden infant death syndrome (SIDS) is a difficult area to study. We present a perpetrator’s descriptions to enrich clinicians’ knowledge of possible presenting features of this phenomenon.

Methods: Interview material was collected as part of a qualitative study of maternal filicide performed from a naturalistic paradigm in order to access the perpetrators’ view of events. The woman participant has been convicted for 3 child murders and two attempted murders which were initially misdiagnosed as SIDS. Interviews were done in the participant’s home with her partner present, while she was on leave from prison. Semi-structured interviews were conducted, recorded, transcribed, and analysed for themes. Specific ethical permission was gained to present this case in isolation and the paper was written in consultation with the woman described.

Results: She described initial intense attachment to her first victim and described killing her because she was unable to bear her apnea attacks and her fear of losing her. She described difficulty grieving for this child and subsequent failure to attach to her next child or feel for the other victims.

Conclusions: Expressions of intense attachment to an infant and description of intense grief over a death in a way which engages compassion should not deter a pediatrician from considering the possibility of the parent having killed the child.

--- https://arxiv.org/abs/2012.09816#microsoft Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning Zeyuan Allen-Zhu, Yuanzhi Li 2020-12-17 2024-03-02 [("doi","10.48550/arXiv.2012.09816")] ai/nn/sparsity/knowledge-distillation

[blog] We formally study how ensemble of deep learning models can improve test accuracy, and how the superior performance of ensemble can be distilled into a single model using knowledge distillation. We consider the challenging case where the ensemble is simply an average of the outputs of a few independently trained neural networks with the same architecture, trained using the same algorithm on the same data set, and they only differ by the random seeds used in the initialization.

We show that ensemble/knowledge distillation in Deep Learning works very differently from traditional learning theory (such as boosting or NTKs, neural tangent kernels).

To properly understand them, we develop a theory showing that when data has a structure we refer to as multi-view, then ensemble of independently trained neural networks can provably improve test accuracy, and such superior test accuracy can also be provably distilled into a single model by training a single model to match the output of the ensemble instead of the true label.

Our result sheds light on how ensemble works in deep learning in a way that is completely different from traditional theorems, and how the “dark knowledge” is hidden in the outputs of the ensemble and can be used in distillation. In the end, we prove that self-distillation can also be viewed as implicitly combining ensemble and knowledge distillation to improve test accuracy.

--- https://arxiv.org/abs/1805.04770 Self-distillation: Born Again Neural Networks Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar 2018-05-12 2024-03-02 [("doi","10.48550/arXiv.1805.04770")] ai/nn/cnn ai/nn/sparsity/knowledge-distillation

Knowledge distillation (KD) consists of transferring knowledge from one machine learning model (the teacher) to another (the student). Commonly, the teacher is a high-capacity model with formidable performance, while the student is more compact. By transferring knowledge, one hopes to benefit from the student’s compactness. We desire a compact model with performance close to the teacher’s.

We study KD from a new perspective: rather than compressing models, we train students parameterized identically to their teachers.

Surprisingly, these Born-Again Networks (BANs), outperform their teachers, both on computer vision and language modeling tasks. Our experiments with BANs based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10 (3.5%) and CIFAR-100 (15.5%) datasets, by validation error.

Additional experiments explore two distillation objectives: (1) Confidence-Weighted by Teacher Max (CWTM) and (2) Dark Knowledge with Permuted Predictions (DKPP). Both methods elucidate the essential components of KD, demonstrating a role of the teacher outputs on both predicted and non-predicted classes.

We present experiments with students of various capacities, focusing on the under-explored case where students overpower teachers. Our experiments show advantages from transferring knowledge between DenseNets and ResNets in either direction.

--- https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(24)00134-8/fulltext 2024-03-02 biology crime --- https://en.wikipedia.org/wiki/Pinwheel_scheduling Pinwheel scheduling 2024-03-02 cs/algorithm --- https://arxiv.org/abs/2201.07350 Bamboo Trimming Revisited: Simple Algorithms Can Do Well Too John Kuszmaul 2022-01-18 2024-03-02 [("doi","10.1145/3490148.3538580")] cs/algorithm

The bamboo trimming problem considers n bamboo with growth rates h1, h2, …, hn satisfying ∑i hi = 1. During a given unit of time, each bamboo grows by hi, and then the bamboo-trimming algorithm gets to trim one of the bamboo back down to height zero. The goal is to minimize the height of the tallest bamboo, also known as the backlog. The bamboo trimming problem is closely related to many scheduling problems, and can be viewed as a variation of the widely-studied fixed-rate cup game, but with constant-factor resource augmentation.

Past work has given sophisticated pinwheel algorithms that achieve the optimal backlog of 2 in the bamboo trimming problem. It remained an open question, however, whether there exists a simple algorithm with the same guarantee—recent work has devoted considerable theoretical and experimental effort to answering this question. Two algorithms, in particular, have appeared as natural candidates: the Reduce-Max algorithm (which always cuts the tallest bamboo) and the Reduce-Fastest algorithm (which cuts the fastest-growing bamboo out of those that have height at least x). Both algorithms are conjectured to achieve backlog 2.

This paper improves the bounds for both Reduce-Fastest and Reduce-Max. Among other results, we show that the exact optimal backlog for Reduce-Fastest is x + 1 for all x ≥ 2 (proving a conjecture of D’Emidio, Di Stefano, and Navarra in the case of x = 2), and we show that Reduce-Fastest1 does not achieve backlog 2 (disproving a conjecture of D’Emidio, Di Stefano, and Navarra).

Finally, we show that there is a different algorithm, which we call the Deadline-Driven Strategy, that is both very simple and achieves the optimal backlog of 2. This resolves the question as to whether there exists a simple worst-case optimal algorithm for the bamboo trimming problem.

--- https://arxiv.org/abs/2403.00465 Polyamorous Scheduling Leszek Gąsieniec, Benjamin Smith, Sebastian Wild 2024-03-01 2024-03-02 [("doi","10.48550/arXiv.2403.00465")] cs/algorithm math/humor

Finding schedules for pairwise meetings between the members of a complex social group without creating interpersonal conflict is challenging, especially when different relationships have different needs. We formally define and study the underlying optimization problem: Polyamorous Scheduling.

In Polyamorous Scheduling, we are given an edge-weighted graph and try to find a periodic schedule of matchings in this graph such that the maximal weighted waiting time between consecutive occurrences of the same edge is minimized. We show that the problem is NP-hard and that there is no efficient approximation algorithm with a better ratio than 13⁄12 unless p = NP. On the positive side, we obtain an 𝒪(log n)-approximation algorithm. We also define a generalization of density from the Pinwheel Scheduling Problem, “poly density”, and ask whether there exists a poly density threshold similar to the 5⁄6-density threshold for Pinwheel Scheduling (Kawamura 2023). Polyamorous Scheduling is a natural generalization of Pinwheel Scheduling with respect to its optimization variant, Bamboo Garden Trimming [see also Kuszmaul 2022].

Our work contributes the first nontrivial hardness-of-approximation reduction for any periodic scheduling problem, and opens up numerous avenues for further study of Polyamorous Scheduling.

--- https://www.johndcook.com/blog/2024/03/03/archiving-data-on-paper/ 2024-03-02 cs/linkrot/archiving --- https://www.maximumtruth.org/p/ais-ranked-by-iq-ai-passes-100-iq 2024-03-02 ai/nn/transformer/gpt/claude iq --- https://jessesingal.substack.com/p/my-clients-the-liars 2024-03-03 crime iq/low psychology/personality/narcissism --- https://x.com/emollick/status/1765136992176644281 Ethan Mollick 2024-03-03 ai/nn/transformer/gpt/4/fiction ai/nn/transformer/gpt/claude fiction/science-fiction --- /doc/psychology/vision/2024-02-01-gwern-dalle3-scannersliveinvain-americanpilotconfrontedbygremlinhallucinations-thumbnail.jpg Gwern 2024-02-01 2024-03-03 ai/nn/transformer/gpt/dall-e/3 psychology/vision --- https://www.currentobituary.com/member/obit/282438 2024-03-03 reinforcement-learning/model-free --- https://x.com/amandaaskell/status/1765207842993434880 Amanda Askell 2024-03-03 ai/nn/transformer/gpt/claude --- http://underhanded-c.org/_page_id_17.html 2024-03-03 cs/cryptography/steganography --- https://pages.cpsc.ucalgary.ca/~robin/class/449/Evolution.htm 2024-03-03 cs/haskell math/humor --- https://www.zompist.com/chance.htm 2024-03-03 psychology/linguistics statistics/probability --- https://www.yitay.net/blog/training-great-llms-entirely-from-ground-zero-in-the-wilderness 2024-03-03 ai/scaling/hardware --- https://www.newyorker.com/science/elements/the-magic-of-bird-brains 2024-03-03 psychology/animal/bird --- /doc/psychology/animal/bird/1984-eiseley.pdf From ‘The Judgment Of The Birds’ Loren Eiseley 1984-03-01 2024-03-04 [("doi","10.1111/j.1467-9744.1984.tb00564.x")] philosophy/religion psychology/animal/bird --- https://x.com/geepytee/status/1765428294630179168 geepytee 2024-03-04 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex --- /doc/philosophy/epistemology/2004-diwan.pdf PL-detective: A system for teaching programming language concepts Amer Diwan, William M. Waite, Michele H. Jackson, Jacob Dickerson 2004-12-01 2024-03-04 [("doi","10.1145/1086339.1086340")] cs philosophy/epistemology

The educational literature recognizes that people go through a number of stages in their intellectual development. During the first stage, called received knowledge or dualism, people expect knowledge to be handed to them by authority figures (thus “received”) and think in terms of black and white (thus “dualism”). Our experience indicates that many computer science students are at this first stage of learning.

To help students move beyond this stage, we describe a system and strategy, the PL-Detective [MYSTERY], to be used in a Concepts of Programming Languages course. Assignments using this system directly confront students with the notion that they can create knowledge via interactions with the PL-Detective and that discussion with students (rather than asking the instructor) is an effective way of learning how to reason.

We present experimental results that show that the PL-Detective is effective in helping students move beyond the stage of received knowledge.

…The current version of the PL-Detective exports 7 interfaces, each of which controls the semantics for a single aspect of Mystery. [Order of evaluation, short-circuiting in logical expressions, pass by value/reference, lexical scoping, type assignability, type equality, type of operator]

…When a student probes the system, the probe may or may not compile successfully (eg. syntax error). If it compiles successfully, it may or may not run successfully (eg. type-mismatch error). In the case of an unsuccessful compile or run, it is important to provide output that is useful to the students but is not so detailed that it solves the mystery. For example, imagine an assignment where students must discover whether Mystery uses static or dynamic scoping. Giving an error at compile time that a variable is undefined or out of scope would give too much information to the student about the semantics of Mystery. To address this situation, the web-based user interface for the PL-Detective delivers all error messages (except for syntax errors) at run time even for errors detected at compile time.

…7. Preliminary Results: Our prior results based on ethnographic observations8, 15 indicate that computer science students prefer to work alone and when forced to work in groups they often divide up the task between the group members.

To get a preliminary sense of whether or not the PL-Detective was effective in getting students to collaborate we surveyed a Fall 2003 course. The instructor left the room while an outside researcher distributed and collected the survey. The students knew that the instructor would see only summaries of the survey results (ie. they were assured confidentiality). Each student filled out the survey separately from his or her group members. The class enrollment was 95 students and students worked in groups of 3 (and occasionally 2), which they formed at the beginning of the semester.

The results of the survey strongly suggest that the PL-Detective is helpful in getting students to collaborate. For 30 groups, all members said “we do all the assignment together all the time”. All members of one group indicated that they did not work together but segmented the assignments. There were two groups whose members gave inconsistent responses. Many of the students filling out the survey noted that they thought the PL-Detective was “cool”.

[HN student experience:] In college, my programming languages class used a [Modula-3-esque] language called Mystery (I believe created by my professor), which was configurable. Assignments would be like “write some test programs to figure out whether the language is configured to use pass-by-value or pass-by-reference”. And there were a bunch of other knobs that could be turned, and in each case, the idea was that we could figure out the knob’s setting by writing programs and seeing what they did.

I loved this, both as a teaching aid, and as an eye-opener that programming languages are just an accumulation of choices with different trade-offs that can all go different ways and result in something that works, perhaps a bit better or perhaps worse, or perhaps just a bit more toward or away from one’s own personal taste.

--- https://www.fda.gov/news-events/press-announcements/fda-clears-first-over-counter-continuous-glucose-monitor 2024-03-04 nootropic/quantified-self --- https://www.quantamagazine.org/cellular-self-destruction-may-be-ancient-but-why-20240306/ 2024-03-04 genetics/microbiome genetics/selection/natural --- https://github.com/WICG/compression-dictionary-transport/blob/main/examples#static-resource-flow-results 2024-03-04 cs/algorithm/information/compression cs/js --- https://www.theguardian.com/us-news/2024/mar/06/parents-iq-test-child-welfare-oregon 2024-03-04 iq --- https://x.com/karpathy/status/1765473722985771335 Andrej Karpathy 2024-03-04 ai/nn/transformer --- https://www.sciencedirect.com/science/article/pii/S0169207021001679 Forecasting with trees Tim Januschowski, Yuyang (Bernie) Wang, Kari Torkkola, Timo Erkkilä, Hilaf Hasson, Jan Gasthaus 2022-10 2024-03-04 [("doi","10.1016/j.ijforecast.2021.10.004")] ai/tabular statistics/prediction

The prevalence of approaches based on gradient boosted trees among the top contestants in the M5 competition is potentially the most eye-catching result. Tree-based methods out-shone other solutions, in particular deep learning-based solutions. The winners in both tracks of the M5 competition heavily relied on them. This prevalence is even more remarkable given the dominance of other methods in the literature and the M4 competition.

This article tries to explain why tree-based methods were so widely used in the M5 competition. We see possibilities for future improvements of tree-based models and then distill some learnings for other approaches, including but not limited to neural networks.

[Keywords: random forests, probabilistic forecasting, gradient boosted trees, global forecasting models, deep learning]

See Also:

--- https://fontsinuse.com/uses/14164/massimo-vignelli-s-a-few-basic-typefaces 2024-03-04 design/typography --- https://www.joelonsoftware.com/2002/11/11/the-law-of-leaky-abstractions/ The Law of Leaky Abstractions 2024-03-04 cs/end-to-end-principle --- https://kleinletters.com/Blog/the-art-and-history-of-lettering-comics/ 2024-03-05 design/typography --- https://x.com/jeremyphoward/status/1765529891343339804 Jeremy P. Howard 2024-03-05 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex --- https://www.benkuhn.net/autocomplete/ 2024-03-05 ai/nn/transformer/gpt/codex cs design --- https://www.reddit.com/r/anime/comments/ieiueg/contextualizing_miyazakis_anime_was_a_mistake/ 2024-03-05 anime --- https://www.bytesizego.com/blog/keeping-alive-with-go 2024-03-05 nootropic/quantified-self --- https://x.com/Sheikheddy/status/1765445782713385340 Sheikheddy 2024-03-05 ai/nn/tokenization ai/nn/transformer/gpt/claude --- https://aesthetics.fandom.com/wiki/Vectorflourish 2024-03-05 design/typography/floral --- https://x.com/lmsysorg/status/1765774296000172289 LMSYS Org 2024-03-05 ai/nn/transformer/gpt/claude --- https://www.smithsonianmag.com/travel/why-are-all-swedish-cottages-painted-red-180975914/ 2024-03-05 design/typography/rubrication --- https://www.lesswrong.com/posts/7LnHFj4gs5Zd4WKcu/notes-on-awe 2024-03-05 psychology/novelty --- https://www.berfrois.com/2015/03/john-crutchfield-go-west/ 2024-03-06 fiction/criticism --- https://www.euractiv.com/section/politics/news/albania-to-speed-up-eu-accession-using-chatgpt/ 2024-03-06 ai/nn/transformer/gpt/4/nonfiction law --- https://arxiv.org/abs/2102.12781 Do Input Gradients Highlight Discriminative Features? Harshay Shah, Prateek Jain, Praneeth Netrapalli 2021-02-25 2024-03-06 [("doi","10.48550/arXiv.2102.12781")] ai/nn/adversarial

Post-hoc gradient-based interpretability methods that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradients—gradients of logits with respect to input—noisily highlight discriminative task-relevant features. In this work, we test the validity of assumption (A) using a three-pronged approach. First, we develop an evaluation framework, DiffROAR, to test assumption (A) on 4 image classification benchmarks. Our results suggest that (1) input gradients of standard models (ie. trained on original data) may grossly violate (A), whereas (2) input gradients of adversarially robust models satisfy (A).

Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. Our analysis on BlockMNIST leverages this information to validate as well as characterize differences between input gradient attributions of standard and robust models.

Finally, we theoretically prove that our empirical findings hold on a simplified version of the BlockMNIST dataset. Specifically, we prove that input gradients of standard one-hidden-layer MLPs trained on this dataset do not highlight instance-specific signal coordinates, thus grossly violating assumption (A). Our findings motivate the need to formalize and test common assumptions in interpretability in a falsifiable manner. We believe that the DiffROAR evaluation framework and BlockMNIST-based datasets can serve as sanity checks to audit instance-specific interpretability methods; code and data available at Github.

--- https://www.medrxiv.org/content/10.1101/2024.03.05.24303772.full Dissecting the contribution of common variants to risk of rare neurodevelopmental conditions Qin Qin Huang, Emilie M. Wigdor, Patrick Campbell, Daniel S. Malawsky, Kaitlin E. Samocha, V. Kartik Chundru, Petr Danecek, Sarah Lindsay, Thomas Marchant, Mahmoud Koko Musa, Sana Amanat, Davide Bonifanti, Eamonn Sheridan, Elizabeth J. Radford, Jeffrey C. Barrett, Caroline F. Wright, Helen V. Firth, Varun Warrier, Alexander Strudwick Young, Matthew Hurles, Hilary C. Martin 2024-03-06 2024-03-06 [("doi","10.1101/2024.03.05.24303772")] genetics/heritable/rare psychiatry

Although rare neurodevelopmental conditions have a large Mendelian component, common genetic variants also contribute to risk. However, little is known about how this polygenic risk is distributed among patients with these conditions and their parents, its interplay with rare variants, and whether parents’ polygenic background contributes to their children’s risk beyond the direct effect of variants transmitted to the child (ie. via indirect genetic effects potentially mediated through the prenatal environment or “genetic nurture”).

Here, we addressed these questions using genetic data from 11,573 patients with rare neurodevelopmental conditions, 9,128 of their parents, and 26,869 controls. Common variants explained ~10% of variance in overall risk. Patients with a monogenic diagnosis had statistically-significantly less polygenic risk than those without, supporting a liability threshold model, while both genetically undiagnosed patients and diagnosed patients with affected parents had statistically-significantly more risk than controls.

In a trio-based model, using a polygenic score for neurodevelopmental conditions, the transmitted but not the non-transmitted parental alleles were associated with risk, indicating a direct genetic effect. In contrast, we observed no direct genetic effect of polygenic scores for educational attainment and cognitive performance, but saw a statistically-significant correlation between the child’s risk and non-transmitted alleles in the parents, potentially due to indirect genetic effects and/or parental assortment for these traits.

Indeed, as expected under parental assortment, we show that common variant predisposition for neurodevelopmental conditions is correlated with the rare variant component of risk. Our findings thus suggest that future studies should investigate the possible role and nature of indirect genetic effects on rare neurodevelopmental conditions, and consider the contribution of common and rare variants simultaneously when studying cognition-related phenotypes.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10628728/ Shared Genetic Risk in the Association of Screen Time With Psychiatric Problems in Children Yingzhe Zhang, Karmel W. Choi, Scott W. Delaney, Tian Ge, Jean-Baptiste Pingault, Henning Tiemeier 2023 2024-03-06 [("doi","10.1001/jamanetworkopen.2023.41502")] genetics/heritable/correlation psychiatry sociology/technology

Importance: Children’s exposure to screen time has been associated with poor mental health outcomes, yet the role of genetic factors remains largely unknown.

Objective: To assess the extent of genetic confounding in the associations between screen time and attention problems or internalizing problems in preadolescent children.

Methods, Setting, & Participants: This cohort study analyzed data obtained 2016–2019 from the Adolescent Brain Cognitive Development Study at 21 sites in the US. The sample included children aged 9 to 11 years of genetically assigned European ancestry with self-reported screen time. Data were analyzed between November 2021 and September 2023.

Exposure: Child-reported daily screen time (in hours) was ascertained from questionnaires completed by the children at baseline.

Main Outcomes & Measures: Child psychiatric problems, specifically attention and internalizing problems, were measured with the parent-completed Achenbach Child Behavior Checklist at the 1-year follow-up. Genetic sensitivity analyses model (Gsens) was used, which incorporated polygenic risk scores (PRSs) of both exposure and outcomes as well as either single-nucleotide variant (SNV; formerly single-nucleotide polymorphism)-based heritability or twin-based heritability to estimate genetic confounding.

Results: The 4262 children in the sample included 2269 males (53.2%) with a mean (SD) age of 9.9 (0.6) years. Child screen time was associated with attention problems (β = 0.10 SD; 95% CI, 0.07-0.13 SD) and internalizing problems (β = 0.03 SD; 95% CI, 0.003-0.06 SD). The television time PRS was associated with child screen time (β = 0.18 SD; 95% CI, 0.14-0.23 SD), the attention-deficit/hyperactivity disorder PRS was associated with attention problems (β = 0.13 SD; 95% CI, 0.10-0.16 SD), and the depression PRS was associated with internalizing problems (β = 0.10 SD; 95% CI, 0.07-0.13 SD). These PRSs were associated with cross-traits, suggesting genetic confounding. Estimates using PRSs and SNV-based heritability showed that genetic confounding accounted for most of the association between child screen time and attention problems and for 42.7% of the association between child screen time and internalizing problems. When PRSs and twin-based heritability estimates were used, genetic confounding fully explained both associations.

Conclusion: Results of this study suggest that genetic confounding may explain a substantial part of the associations between child screen time and psychiatric problems. Genetic confounding should be considered in sociobehavioral studies of modifiable factors for youth mental health.

--- https://www.youtube.com/watch?v=DcYLT37ImBY 2024-03-06 reinforcement-learning/exploration reinforcement-learning/model-free --- https://en.wikipedia.org/wiki/Juan_Pujol_Garc%C3%ADa Juan Pujol García 2024-03-06 fiction --- /doc/economics/automation/1941-smithies.pdf Optimum Location in Spatial Competition A. Smithies 1941-06-01 2024-03-06 economics/automation politics --- https://arxiv.org/abs/2402.14641 Object permanence in newborn chicks is robust against opposing evidence Justin N. Wood, Tomer D. Ullman, Brian W. Wood, Elizabeth S. Spelke, Samantha M. W. Wood 2024-02-22 2024-03-06 [("doi","10.48550/arXiv.2402.14641")] psychology/vision

Newborn animals have advanced perceptual skills at birth, but the nature of this initial knowledge is unknown. Is initial knowledge flexible, continuously adapting to the statistics of experience? Or can initial knowledge be rigid and robust to change, even in the face of opposing evidence? We address this question through controlled-rearing experiments on newborn chicks.

First, we reared chicks in an impoverished virtual world, where objects never occluded one another, and found that chicks still succeed on object permanence tasks. Second, we reared chicks in a virtual world in which objects teleported from one location to another while out of view: an unnatural event that violates the continuity of object motion.

Despite seeing thousands of these violations of object permanence, and not a single non-violation, the chicks behaved as if object permanence were true, exhibiting the same behavior as chicks reared with natural object permanence events.

We conclude that object permanence develops prenatally and is robust to change from opposing evidence.

--- https://x.com/EricNewcomer/status/1765862990069313861 Eric Newcomer 2024-03-06 reinforcement-learning/openai --- https://www.sec.gov/Archives/edgar/data/1877240/000187724023000001/xslFormDX01/primary_doc.xml 2024-03-06 reinforcement-learning/openai --- https://www.nytimes.com/2024/03/06/technology/john-walker-dead.html 2024-03-07 cs psychiatry/traumatic-brain-injury --- /doc/ai/nn/transformer/gpt/codex/2024-03-07-inflection-inflection25benchmarks.svg 2024-03-07 2024-03-07 [("invert","True")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex math --- https://en.wikipedia.org/wiki/Mira_Murati Mira Murati 2024-03-07 reinforcement-learning/openai --- https://en.wikipedia.org/wiki/Ilya_Sutskever Ilya Sutskever 2024-03-07 reinforcement-learning/openai --- https://www.ageofinvention.xyz/p/age-of-invention-the-second-soul 2024-03-07 economics food politics --- https://arxiv.org/abs/2311.16119 Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition Sander Schulhoff, Jeremy Pinto, Anaum Khan, Louis-François Bouchard, Chenglei Si, Svetlina Anati, Valen Tagliabue, Anson Liu Kost, Christopher Carnahan, Jordan Boyd-Graber 2023-10-24 2024-03-07 [("doi","10.48550/arXiv.2311.16119")] ai/dataset ai/nn/adversarial

Large Language Models (LLMs) are deployed in interactive contexts with direct user engagement, such as chatbots and writing assistants. These deployments are vulnerable to prompt injection and prompt jailbreaking (collectively, prompt hacking), in which models are manipulated to ignore their original instructions and follow potentially malicious ones. Although widely acknowledged as a security threat, there is a dearth of large-scale resources and quantitative studies on prompt hacking.

To address this lacuna, we launch a global prompt hacking competition, which allows for free-form human input attacks. We elicit 600k+ adversarial prompts against 3 state-of-the-art LLMs.

We describe the dataset, which empirically verifies that current LLMs can indeed be manipulated via prompt hacking.

We also present a comprehensive taxonomical ontology of the types of adversarial prompts.

--- https://www.atlasobscura.com/articles/the-strangely-perplexing-problem-of-communicating-numbers-out-loud 2024-03-07 cs/algorithm --- https://www.nature.com/articles/d41586-024-00716-2 2024-03-07 statistics/bias --- https://www.science.org/content/article/failure-every-level-how-science-sleuths-exposed-massive-ethics-violations-famed-french 2024-03-07 statistics/bias --- https://x.com/rmbalt/status/1431001232395624451 rmbalt 2024-03-07 cs/algorithm design psychology/cognitive-bias/illusion-of-depth --- https://x.com/danluu/status/1525988886119186432 Dan Luu 2024-03-07 cs/algorithm design psychology/cognitive-bias/illusion-of-depth --- https://www.medrxiv.org/content/10.1101/2023.09.28.23296294.full Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes Elucidate Disease Processes and Clinical Heterogeneity Kirk Smith, Aaron J. Deutsch, Carolyn McGrail, Hyunkyung Kim, Sarah Hsu, Ravi Mandla, Philip H. Schroeder, Kenneth E. Westerman, Lukasz Szczerbinski, Timothy D. Majarian, Varinderpal Kaur, Alice Williamson, Melina Claussnitzer, Jose C. Florez, Alisa K. Manning, Josep M. Mercader, Kyle J. Gaulton, Miriam S. Udler 2023-09-29 2024-03-08 [("doi","10.1101/2023.09.28.23296294")] genetics/heritable

We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations.

The 12 genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a statistically-significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry.

T2D risk was equivalent at a BMI of 30 kg⁄m2 in the European subpopulation and 24.2 (22.9- 25.5) kg⁄m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg⁄m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.

--- https://monodraw.helftone.com/ 2024-03-08 design/typography --- https://www.politico.com/news/magazine/2024/03/08/the-uk-college-student-explaining-congressional-procedure-to-washington-00145314 2024-03-08 politics psychiatry/autism sociology/technology --- https://arxiv.org/abs/2311.01460 Implicit Chain-of-Thought Reasoning via Knowledge Distillation Yuntian Deng, Kiran Prasad, Roland Fernandez, Paul Smolensky, Vishrav Chaudhary, Stuart Shieber 2023-11-02 2024-03-08 [("doi","10.48550/arXiv.2311.01460")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/attention ai/nn/transformer/gpt/inner-monologue math

To augment language models with the ability to reason, researchers usually prompt or finetune them to produce chain-of-thought reasoning steps before producing the final answer. However, although people use natural language to reason effectively, it may be that LMs could reason more effectively with some intermediate computation that is not in natural language.

In this work, we explore an alternative reasoning approach: instead of explicitly producing the chain-of-thought reasoning steps, we use the language model’s internal hidden states to perform implicit reasoning. The implicit reasoning steps are distilled from a teacher model trained on explicit chain-of-thought reasoning, and instead of doing reasoning “horizontally” by producing intermediate words one-by-one, we distill it such that the reasoning happens “vertically” among the hidden states in different layers.

We conduct experiments on a multi-digit multiplication task and a grade school math problem dataset and find that this approach enables solving tasks previously not solvable without explicit chain-of-thought, at a speed comparable to no chain-of-thought.

--- https://www.johndcook.com/blog/2024/03/09/normalized-cryptograms/ 2024-03-08 cs/cryptography --- https://arxiv.org/abs/2403.01598 APISR: Anime Production Inspired Real-World Anime Super-Resolution Boyang Wang, Fengyu Yang, Xihang Yu, Chao Zhang, Hanbin Zhao 2024-03-03 2024-03-08 [("doi","10.48550/arXiv.2403.01598")] ai/anime ai/nn/gan

While real-world anime super-resolution (SR) has gained increasing attention in the SR community, existing methods still adopt techniques from the photorealistic domain. In this paper, we analyze the anime production workflow and rethink how to use characteristics of it for the sake of the real-world anime SR.

First, we argue that video networks and datasets are not necessary for anime SR due to the repetition use of hand-drawing frames. Instead, we propose an anime image collection pipeline by choosing the least compressed and the most informative frames from the video sources. Based on this pipeline, we introduce the Anime Production-oriented Image (API) dataset.

In addition, we identify two anime-specific challenges of distorted and faint hand-drawn lines and unwanted color artifacts. We address the first issue by introducing a prediction-oriented compression module in the image degradation model and a pseudo-ground truth preparation with enhanced hand-drawn lines.

Moreover, we introduce the balanced twin perceptual loss combining both anime and photorealistic high-level features to mitigate unwanted color artifacts and increase visual clarity. We evaluate our method through extensive experiments on the public benchmark, showing our method outperforms state-of-the-art approaches by a large margin.

--- https://dgl.cx/wikipedia-dns Wikipedia Over DNS 2008 2024-03-08 cs/cryptography/steganography wikipedia --- https://www.wired.com/story/book-excerpt-science-of-ultra-pure-silicon/ 2024-03-08 cs/hardware --- /doc/cs/linkrot/archiving/2015-tweney.pdf Memory and the construction of scientific meaning: Michael Faraday’s use of notebooks and records Ryan D. Tweney, Christopher D. Ayala 2015-06-03 2024-03-08 [("doi","10.1177/1750698015587149")] cs/linkrot/archiving psychology/writing

[more background on the notebooks] Research examining the relationship between external artifacts and scientific thinking has highlighted the dynamic role of memory aids. This article explores how the 19th-century physicist Michael Faraday (1791–1867) used extensive laboratory notebooks and a highly structured set of retrieval strategies as dynamic aids during his scientific research.

The development and dynamic use of memory artifacts are described as part of a distributed, “real-world”, cognitive environment. The processes involved are then related to aspects of expert memory and to the use of model-based reasoning in science. The system demonstrates the importance of epistemic artifacts in scientific cognition and is suggestively related to other cognitive artifacts used in scientific research that rely on similar cognitive processes.

[Keywords: distributed cognition, external memory, memory, Michael Faraday, mnemonics, model-based reasoning, psychology of science, representation in science, retrieval]

--- /doc/cs/linkrot/archiving/1991-tweney.pdf Faraday’s notebooks: the active organization of creative science Ryan D. Tweney 1991-01 2024-03-08 [("doi","10.1088/0031-9120/26/5/008")] cs/linkrot/archiving psychology/writing

Michael Faraday’s notebooks constitute one of the largest and most revealing archives left to us by a major scientist.

These records reveal a good deal of systematic invention and exploration of recording techniques by Faraday, work that reveals much about his thinking about science, as well as of the role of memory in general in scientific thinking.

...Why are Faraday’s records so extensive? In part, it is because Faraday was mistrustful of his own memory (see Williams 1965, pp 473, 491–501; Hare 1974). Faraday more than once repeated an experiment that he had earlier completed and apparently forgotten about, and his use of elaborate memory-retrieval devices (see below) makes a similar point.

--- https://journals.sagepub.com/doi/full/10.1177/25152459231197605 Impossible Hypotheses and Effect-Size Limits Wijnand A. P. van Tilburg, Lennert J. A. van Tilburg 2023-11-21 2024-03-09 [("doi","10.1177/25152459231197605")] statistics/power-analysis statistics/probability

[piranha problem] Psychological science is moving toward further specification of effect sizes when formulating hypotheses, performing power analyses, and considering the relevance of findings. This development has sparked an appreciation for the wider context in which such effect sizes are found because the importance assigned to specific sizes may vary from situation to situation.

We add to this development a crucial but in psychology hitherto underappreciated contingency: There are mathematical limits to the magnitudes that population effect sizes can take within the common multivariate context in which psychology is situated, and these limits can be far more restrictive than typically assumed. The implication is that some hypothesized or preregistered effect sizes may be impossible. At the same time, these restrictions offer a way of statistically triangulating the plausible range of unknown effect sizes.

We explain the reason for the existence of these limits, illustrate how to identify them, and offer recommendations and tools for improving hypothesized effect sizes by exploiting the broader multivariate context in which they occur.

See Also:

--- https://lczero.org/blog/2024/02/how-well-do-lc0-networks-compare-to-the-greatest-transformer-network-from-deepmind/ 2024-03-09 reinforcement-learning/chess reinforcement-learning/model/alphago --- https://arxiv.org/abs/2402.04494#deepmind Grandmaster-Level Chess Without Search Anian Ruoss, Grégoire Delétang, Sourabh Medapati, Jordi Grau-Moya, Li Kevin Wenliang, Elliot Catt, John Reid, Tim Genewein 2024-02-07 2024-03-09 [("doi","10.48550/arXiv.2402.04494")] ai/nn/transformer/gpt reinforcement-learning/chess reinforcement-learning/imitation-learning reinforcement-learning/scaling

[cf. LC0 performance; commentary] The recent breakthrough successes in machine learning are mainly attributed to scale: namely large-scale attention-based architectures and datasets of unprecedented scale. This paper investigates the impact of training at scale for chess.

Unlike traditional chess engines that rely on complex heuristics, explicit search, or a combination of both, we train a 270M parameter transformer model with supervised learning [a LLaMA-2-like Transformer predicting FEN text] on a dataset of 10 million chess games. We annotate each board in the dataset with action-values provided by the powerful Stockfish 16 engine, leading to roughly 15 billion data points. Our largest model reaches a Lichess blitz Elo of 2895 against humans, and successfully solves a series of challenging chess puzzles, without any domain-specific tweaks or explicit search algorithms.

We also show that our model outperforms AlphaZero’s policy and value networks (without MCTS) and GPT-3.5-turbo-instruct. A systematic investigation of model and dataset size shows that strong chess performance only arises at sufficient scale.

To validate our results, we perform an extensive series of ablations of design choices and hyperparameters.

--- https://en.wikipedia.org/wiki/Cerebras Cerebras 2024-03-09 ai/scaling/hardware --- /doc/psychology/vision/1998-persinger.pdf Putative Perception of Rotating Permanent Magnetic Fields following Ingestion of LSD M. A. Persinger 1998-10-01 2024-03-09 [("doi","10.2466/pms.1998.87.2.601")] psychedelic/lsd psychology/vision

While sitting alone in complete darkness, 3 participants who had ingested psychotropic concentrations of lysergic acid diethylamide reported:

diffuse blobs of white, purplish, or greenish-yellow lights as two horseshoe magnets rotated at 0.5 Hz. The experiences were not reported when the magnets were stationary or removed from the apparatus. The estimated peak-to-peak variation in field strength at the distance of perception was between 50 and 500 nano-Tesla.

An association between these results and possible ergot-induced perceptions of “magnet light” reported during the last century by von Reichenbach 1851 is suggested.

--- /doc/psychiatry/2004-becker.pdf Suicide: An Economic Approach Gary S. Becker, Richard A. Posner 2004-01-01 2024-03-09 economics psychiatry --- https://hardfault.life/p/samsung 2024-03-09 design --- https://www.lesswrong.com/posts/etoMr4vcnP7joQHWa/notes-from-a-prompt-factory 2024-03-09 ai/nn/transformer/gpt fiction/science-fiction philosophy/ethics --- https://x.com/sherjilozair/status/1719665475452592495 Sherjil Ozair 2024-03-09 reinforcement-learning/preference-learning/mode-collapse reinforcement-learning/safe --- https://www.washingtonpost.com/business/2024/03/07/ai-data-centers-power/ 2024-03-09 ai/scaling/economics --- https://www.nytimes.com/2024/03/10/us/elon-musk-charity.html 2024-03-09 psychiatry/bipolar/elon-musk --- https://arxiv.org/abs/2403.04652#o1ai Yi: Open Foundation Models by 01.AI Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, Kaidong Yu, Peng Liu, Qiang Liu, Shawn Yue, Senbin Yang, Shiming Yang, Tao Yu, Wen Xie, Wenhao Huang, Xiaohui Hu, Xiaoyi Ren, Xinyao Niu, Pengcheng Nie, Yuchi Xu, Yudong Liu, Yue Wang, Yuxuan Cai, Zhenyu Gu, Zhiyuan Liu, Zonghong Dai 2024-03-07 2024-01-01 [("doi","10.48550/arXiv.2403.04652")] ai/nn/transformer/gpt

We introduce the Yi model family, a series of language and multimodal models that demonstrate strong multi-dimensional capabilities. The Yi model family is based on 6B and 34B pretrained language models, then we extend them to chat models, 200K long context models, depth-upscaled models, and vision-language models. Our base models achieve strong performance on a wide range of benchmarks like MMLU, and our finetuned chat models deliver strong human preference rate on major evaluation platforms like AlpacaEval and Chatbot Arena.

Building upon our scalable super-computing infrastructure and the classical transformer architecture, we attribute the performance of Yi models primarily to its data quality resulting from our data-engineering efforts. For pretraining, we construct 3.1 trillion tokens of English and Chinese corpora using a cascaded data deduplication and quality filtering pipeline. For finetuning, we polish a small scale (less than 10K) instruction dataset over multiple iterations such that every single instance has been verified directly by our machine learning engineers.

For vision-language, we combine the chat language model with a vision transformer encoder and train the model to align visual representations to the semantic space of the language model. We further extend the context length to 200K through lightweight continual pretraining and demonstrate strong needle-in-a-haystack retrieval performance. We show that extending the depth of the pretrained checkpoint through continual pretraining further improves performance.

We believe that given our current results, continuing to scale up model parameters using thoroughly optimized data will lead to even stronger frontier models.

--- https://verse.systems/blog/post/2024-03-09-using-llms-to-generate-fuzz-generators/ 2024-01-01 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex cs/security --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139580/ Non-replicable publications are cited more than replicable ones Marta Serra-Garcia, Uri Gneezy 2021 2024-01-01 [("doi","10.1126/sciadv.abd1705")] economics psychology statistics/bias/publication

We use publicly available data to show that:

published papers in top psychology, economics, and general interest journals that fail to replicate are cited more than those that replicate. This difference in citation does not change after the publication of the failure to replicate. Only 12% of post-replication citations of non-replicable findings acknowledge the replication failure.

Existing evidence also shows that experts predict well which papers will be replicated. Given this prediction, why are non-replicable papers accepted for publication in the first place? A possible answer is that the review team faces a trade-off. When the results are more “interesting”, they apply lower standards regarding their reproducibility.

--- https://magazine.atavist.com/watch-it-burn-france-europe-carbon-fraud-scam-vat-betrayal/ 2024-01-01 crime psychology/personality/narcissism --- https://cookbook.openai.com/examples/tag_caption_images_with_gpt4v 2024-01-01 ai/nn/retrieval ai/nn/transformer/gpt/4/nonfiction --- https://arxiv.org/abs/2305.18354 LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations Yudong Xu, Wenhao Li, Pashootan Vaezipoor, Scott Sanner, Elias B. Khalil 2023-05-26 2024-01-01 [("doi","10.48550/arXiv.2305.18354")] ai/nn/transformer/gpt/4/nonfiction iq

Can a Large Language Model (LLM) solve simple abstract reasoning problems? We explore this broad question through a systematic analysis of GPT-4 on the Abstraction and Reasoning Corpus (ARC), a representative benchmark of abstract reasoning ability from limited examples in which solutions require some “core knowledge” of concepts such as objects, goal states, counting, and basic geometry. GPT-4 solves only 13/50 of the most straightforward ARC tasks when using textual encodings for their two-dimensional input-output grids.

Our failure analysis reveals that GPT-4’s capacity to identify objects and reason about them is influenced by the sequential nature of the text that represents an object within a text encoding of a task. To test this hypothesis, we design a new benchmark, the 1D-ARC, which consists of one-dimensional (array-like) tasks that are more conducive to GPT-based reasoning, and where it indeed performs better than on the (2D) ARC. To alleviate this issue, we propose an object-based representation that is obtained through an external tool, resulting in nearly doubling the performance on solved ARC tasks and near-perfect scores on the easier 1D-ARC.

Although the state-of-the-art GPT-4 is unable to “reason” perfectly within non-language domains such as the 1D-ARC or a simple ARC subset, our study reveals that the use of object-based representations can improve its reasoning ability.

Visualizations, GPT logs, and data are available at https://khalil-research.github.io/LLM4ARC/.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7747995/ Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants Yunbi Xu, Xiaogang Liu, Junjie Fu, Hongwu Wang, Jiankang Wang, Changling Huang, Boddupalli M. Prasanna, Michael S. Olsen, Guoying Wang, Aimin Zhang 2020 2024-03-11 [("doi","10.1016/j.xplc.2019.100005")] genetics/selection

Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies, the rate of genetic gain needs to be accelerated to meet humanity’s demand for agricultural products. In this regard, genomic selection (GS) has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects. Livestock scientists pioneered GS application largely due to livestock’s higher individual values and the greater reduction in generation interval that can be achieved in GS.

Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects, along with cost reduction.

Moreover, it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain. In addition, establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small- and medium-sized enterprises and agricultural research systems in developing countries.

New strategies centered on GS for enhancing genetic gain need to be developed.

--- https://www.activism.net/cypherpunk/manifesto.html 2024-03-11 bitcoin --- /doc/cat/psychology/2023-11-03-gwern-midjourneyv5-anxiousblackcatatwindowsill.jpg Gwern 2023-11-03 2024-01-01 [("invert","False")] cat/psychology --- /doc/cat/psychology/2023-11-03-gwern-midjourneyv5-anxiousblackcatatwindowsill-cropped-thumbnail.jpg Gwern 2023-11-03 2024-01-01 [("invert","False")] cat/psychology --- /doc/statistics/bias/2010-zhang.pdf Chinese journal finds 31% of submissions plagiarized Zhang 2010 2024-01-01 statistics/bias --- /doc/psychology/animal/1967-zubin-comparativepsychopathology.pdf Comparative Psychopathology: Animal and Human Joseph Zubin, Howard F. Hunt 1967-01-01 2024-01-01 psychiatry psychology/animal --- /doc/economics/2011-ziobrowski.pdf Abnormal Returns From the Common Stock Investments of Members of the U.S. House of Representatives Alan J. Ziobrowski, James W. Boyd, Ping Cheng, Brigitte J. Ziobrowski 2011-01-01 2024-01-01 crime economics politics --- /doc/psychology/cognitive-bias/sunk-cost/2013-zeng.pdf An fMRI study on sunk cost effect Jianmin Zeng, Qinglin Zhang, Changming Chen, Rongjun Yu, Qiyong Gong 2013-01-01 2024-01-01 [("doi","10.1016/j.brainres.2013.05.001")] psychology/cognitive-bias/sunk-cost psychology/neuroscience --- /doc/genetics/gametogenesis/2015-zulkarnain.pdf Chapter 10: Applications of In Vitro Techniques in Plant Breeding Zul Zulkarnain, Tanya Tapingkae, Acram Taji 2015-01-01 2024-01-01 [("doi","10.1007/978-3-319-22521-0_10")] genetics/gametogenesis genetics/selection/artificial --- https://perlmonks.org/?node_id=663393 Perl Cannot Be Parsed: A Formal Proof Kegler 2008 2024-01-01 cs/computable --- https://old.reddit.com/r/ididnthaveeggs/ 2024-03-11 psychology/cognitive-bias/illusion-of-depth --- https://codeofmatt.com/list-of-2024-leap-day-bugs/ 2024-03-11 cs --- https://x.com/emollick/status/1766864861928001617 Ethan Mollick 2024-03-11 ai/nn/retrieval --- https://www.benkuhn.net/writing/ 2024-03-11 psychology/writing --- https://arxiv.org/abs/2401.06104 Transformers are Multi-State RNNs Matanel Oren, Michael Hassid, Yossi Adi, Roy Schwartz 2024-01-11 2024-03-12 [("doi","10.48550/arXiv.2401.06104")] ai/nn/transformer/attention/recurrent

Transformers are considered conceptually different compared to the previous generation of state-of-the-art NLP models—recurrent neural networks (RNNs). In this work, we demonstrate that decoder-only transformers can in fact be conceptualized as infinite multi-state RNNs—an RNN variant with unlimited hidden state size.

We further show that pretrained transformers can be converted into finite multi-state RNNs by fixing the size of their hidden state. We observe that several existing transformers cache compression techniques can be framed as such conversion policies, and introduce a novel policy, TOVA, which is simpler compared to these policies.

Our experiments with several long range tasks indicate that TOVA outperforms all other baseline policies, while being nearly on par with the full (infinite) model, and using in some cases only 1⁄8 of the original cache size.

Our results indicate that transformer decoder LLMs often behave in practice as RNNs. They also lay out the option of mitigating one of their most painful computational bottlenecks—the size of their cache memory.

We publicly release our code at Github.

--- https://www.henrikkarlsson.xyz/p/childhoods 2024-03-12 iq/high --- https://www.playforthoughts.com/blog/bauhaus 2024-03-12 design --- https://arxiv.org/abs/1910.03016 Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? Simon S. Du, Sham M. Kakade, Ruosong Wang, Lin F. Yang 2019-10-07 2024-03-12 [("doi","10.48550/arXiv.1910.03016")] reinforcement-learning/imitation-learning reinforcement-learning/model reinforcement-learning/model-free

Modern deep learning methods provide effective means to learn good representations. However, is a good representation itself sufficient for sample efficient reinforcement learning? This question has largely been studied only with respect to (worst-case) approximation error, in the more classical approximate dynamic programming literature. With regards to the statistical viewpoint, this question is largely unexplored, and the extant body of literature mainly focuses on conditions which permit sample efficient reinforcement learning with little understanding of what are necessary conditions for efficient reinforcement learning.

This work shows that, from the statistical viewpoint, the situation is far subtler than suggested by the more traditional approximation viewpoint, where the requirements on the representation that suffice for sample efficient RL are even more stringent. Our main results provide sharp thresholds for reinforcement learning methods, showing that there are hard limitations on what constitutes good function approximation (in terms of the dimensionality of the representation), where we focus on natural representational conditions relevant to value-based, model-based, and policy-based learning. These lower bounds highlight that having a good (value-based, model-based, or policy-based) representation in and of itself is insufficient for efficient reinforcement learning, unless the quality of this approximation passes certain hard thresholds.

Furthermore, our lower bounds also imply exponential separations on the sample complexity between (1) value-based learning with perfect representation and value-based learning with a good-but-not-perfect representation, (2) value-based learning and policy-based learning, (3) policy-based learning and supervised learning and (4) reinforcement learning and imitation learning.

--- /doc/ai/tabular/2015-harper.pdf The MovieLens Datasets: History and Context F. Maxwell Harper, Joseph A. Konstan 2015-12-01 2024-01-01 [("doi","10.1145/2827872")] ai/dataset ai/tabular

The MovieLens datasets are widely used in education, research, and industry. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997.

This article documents the history of MovieLens and the MovieLens datasets. We include a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization.

We document best practices and limitations of using the MovieLens datasets in new research.

--- https://nwn.blogs.com/nwn/2021/07/vrchat-syrmor-user-survey-new-world-notes.html 2024-03-12 sociology/technology --- https://nwn.blogs.com/nwn/2021/07/vrchat-user-survey-syrmor-new-world-notes.html 2024-03-12 sociology/technology --- https://krebsonsecurity.com/2024/03/incognito-darknet-market-mass-extorts-buyers-sellers/ 2024-03-12 darknet-market --- https://en.wikipedia.org/wiki/Amygdalotomy Amygdalotomy 2024-03-12 psychology/neuroscience --- https://arxiv.org/abs/2403.04769 Using Hallucinations to Bypass GPT-4’s Filter Benjamin Lemkin 2024-02-16 2024-03-12 [("doi","10.48550/arXiv.2403.04769")] ai/nn/adversarial ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/claude

Large language models (LLMs) are initially trained on vast amounts of data, then fine-tuned using reinforcement learning from human feedback (RLHF); this also serves to teach the LLM to provide appropriate and safe responses. In this paper, we present a novel method to manipulate the fine-tuned version into reverting to its pre-RLHF behavior, effectively erasing the model’s filters; the exploit currently works for GPT-4, Claude-3 Sonnet, and (to some extent) for Inflection-2.5.

Unlike other jailbreaks (for example, the popular “Do Anything Now” (DAN)), our method does not rely on instructing the LLM to override its RLHF policy; hence, simply modifying the RLHF process is unlikely to address it. Instead, we induce a hallucination involving reversed text during which the model reverts to a word bucket, effectively pausing the model’s filter.

We believe that our exploit presents a fundamental vulnerability in LLMs currently unaddressed, as well as an opportunity to better understand the inner workings of LLMs during hallucinations.

--- https://x.com/0xDesigner/status/1764292204233630168 Timothy Drisdelle 2024-03-12 bitcoin design --- https://productidentity.co/p/beautiful-ugly-websites 2024-03-12 cs/css design --- https://publicdomainreview.org/collection/sutherland-macdonald-tattoos/ 2024-03-12 design history/public-domain-review --- https://www.lesswrong.com/posts/MJyud5Qs6MheDemfE/artifex0-s-shortform?commentId=DzQapZEhTHxtjgbxh 2024-03-12 reinforcement-learning/preference-learning/mode-collapse --- https://news.ycombinator.com/item?id=36606573 2024-03-12 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/quant-ph/0610097 No quantum advantage for nonlocal computation Noah Linden, Sandu Popescu, Anthony J. Short, Andreas Winter 2006-10-12 2024-03-13 [("doi","10.1103/PhysRevLett.99.180502")] cs/computable

[Where does Tsirelson’s bound come from?] We investigate the problem of “nonlocal” computation, in which separated parties must compute a function with non-locally encoded inputs and output, such that each party individually learns nothing, yet together they compute the correct function output.

We show that the best that can be done classically is a trivial linear approximation.

Surprisingly, we also show that quantum entanglement provides no advantage over the classical case. On the other hand, generalized (ie. super-quantum) nonlocal correlations allow perfect nonlocal computation.

This gives new insights into the nature of quantum nonlocality and its relationship to generalized nonlocal correlations.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4155336/ Entanglement guarantees emergence of cooperation in quantum prisoner’s dilemma games on networks Angsheng Li, Xi Yong 2014 2024-03-13 [("doi","10.1038/srep06286")] statistics/decision

It was known that cooperation of evolutionary prisoner’s dilemma games fails to emerge in homogenous networks such as random graphs. Here we proposed a quantum prisoner’s dilemma game. The game consists of two players, in which each player has 3 choices of strategy: cooperator (C), defector (D) and super cooperator (denoted by Q).

We found that quantum entanglement guarantees emergence of a new cooperation, the super cooperation of the quantum prisoner’s dilemma games, and that entanglement is the mechanism of guaranteed emergence of cooperation of evolutionary prisoner’s dilemma games on networks. We showed that for a game with temptation b, there exists a threshold arccos √b/b for a measurement of entanglement, beyond which, (super) cooperation of evolutionary quantum prisoner’s dilemma games is guaranteed to quickly emerge, giving rise to stochastic convergence of the cooperations.

That if the entanglement degree γ is less than the threshold arccos √b/b, then the equilibrium frequency of cooperations of the games is positively correlated to the entanglement degree γ, and that if γ is less than arccos √b/b and b is beyond some boundary, then the equilibrium frequency of cooperations of the games on random graphs decreases as the average degree of the graphs increases.

--- https://arxiv.org/abs/2108.05663 Small-Amp: Test Amplification in a Dynamically Typed Language Mehrdad Abdi, Henrique Rocha, Serge Demeyer, Alexandre Bergel 2021-08-12 2024-03-13 [("doi","10.1007/s10664-022-10169-8")] cs/algorithm

Some test amplification tools extend a manually created test suite with additional test cases to increase the code coverage. The technique is effective, in the sense that it suggests strong and understandable test cases, generally adopted by software engineers. Unfortunately, the current state-of-the-art for test amplification heavily relies on program analysis techniques which benefit a lot from explicit type declarations present in statically typed languages. In dynamically typed languages, such type declarations are not available and as a consequence test amplification has yet to find its way to programming languages like Smalltalk, Python, Ruby and Javascript.

We propose to exploit profiling information—readily obtainable by executing the associated test suite—to infer the necessary type information creating special test inputs with corresponding assertions. We evaluated this approach on 52 selected test classes from 13 mature projects in the Pharo ecosystem containing ~400 test methods.

We show the improvement in killing new mutants and mutation coverage at least in 28⁄52 test classes (53%). Moreover, these generated tests are understandable by humans: 8⁄11 pull-requests submitted were merged into the main code base (72%). These results are comparable to the state-of-the-art, hence we conclude that test amplification is feasible for dynamically typed languages.

--- https://arxiv.org/abs/0704.0800 Quantum Auctions Tad Hogg, Pavithra Harsha, Kay-Yut Chen 2007-04-05 2024-03-13 [("doi","10.48550/arXiv.0704.0800")] economics/mechanism-design

We present a quantum auction protocol using superpositions to represent bids and distributed search to identify the winner(s). Measuring the final quantum state gives the auction outcome while simultaneously destroying the superposition. Thus non-winning bids are never revealed. Participants can use entanglement to arrange for correlations among their bids, with the assurance that this entanglement is not observable by others.

The protocol is useful for information hiding applications, such as partnership bidding with allocative externality or concerns about revealing bidding preferences. The protocol applies to a variety of auction types, eg. first or second price, and to auctions involving either a single item or arbitrary bundles of items (ie. combinatorial auctions).

We analyze the game-theoretical behavior of the quantum protocol for the simple case of a sealed-bid quantum, and show how a suitably designed adiabatic search reduces the possibilities for bidders to game the auction.

This design illustrates how incentive rather than computational constraints affect quantum algorithm choices.

--- https://arxiv.org/abs/0709.4096 Quantum Auctions: Facts and Myths E. W. Piotrowski, J. Sladkowski 2007-09-26 2024-03-13 [("doi","10.1016/j.physa.2008.02.071")] economics/mechanism-design

Quantum game theory, whatever opinions may be held due to its abstract physical formalism, have already found various applications even outside the orthodox physics domain. In this paper, we introduce the concept of a quantum auction, its advantages and drawbacks.

Then we describe the models that have already been put forward. A general model involves Wigner formalism and infinite dimensional Hilbert spaces—we envisage that the implementation might not be an easy task. But a restricted model advocated by the Hewlett-Packard group seems to be much easier to implement. Simulations involving humans have already been performed. We will focus on problems related to combinatorial auctions and technical assumptions that are made.

Quantum approach offers at least two important developments. Powerful quantum algorithms for finding solutions would extend the range of possible applications. Quantum strategies, being qubits, can be teleported but are immune from cloning—therefore extreme privacy of agent’s activity could in principle be guaranteed.

Then we point out some key problem that have to be solved before commercial use would be possible. With present technology, optical networks, single photon sources and detectors seems to be sufficient for experimental realization in the near future.

We conclude by describing potential customers, estimating the potential market size, and possible timing.

--- https://en.wikipedia.org/wiki/Precociality_and_altriciality#Superprecociality Precociality and altriciality § Superprecociality 2024-03-13 psychology/animal reinforcement-learning/offline reinforcement-learning/robot --- https://tesuji.org/old_news/kerafyrm_the_sleeper_re-revisited.html 2024-03-13 sociology/technology --- https://x.com/DOverview/status/1767217882922979620 DOverview 2024-03-13 design --- https://eugeneyan.com/writing/synthetic/ 2024-03-13 ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/instruction-tuning --- https://www.theverge.com/2023/3/29/23662621/google-bard-chatgpt-sharegpt-training-denies 2024-03-13 ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt/palm --- https://www.youtube.com/watch?v=8p02DtmyQhU 2024-03-13 japan/art math --- https://capitalismandfreedom.substack.com/p/episode-28-steven-d-levitt-freakonomics 2024-03-13 economics --- https://x.com/CFGeek/status/1768024040487453169 Charles Foster 2024-03-13 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex cs/computable --- https://blog.jcoglan.com/2017/02/12/the-myers-diff-algorithm-part-1/ 2024-03-13 cs/algorithm/information/compression --- https://www.hemmings.com/stories/article/steaming-sensation-1925-doble 2024-03-14 technology --- /doc/darknet-market/2021-blankers.pdf Changes in Online Psychoactive Substance Trade via Telegram during the COVID-19 Pandemic Matthijs Blankers, Daan van der Gouwe, Lavinia Stegemanna, Laura Smit-Rigter 2021-06-16 2024-01-01 [("doi","10.1159/000516853")] darknet-market psychedelic/lsd

Background: In this article, we present an evaluation of online psychoactive substance trade via Telegram, a free encrypted social media messenger service. The evaluation took place during the COVID-19 pandemic, which allowed us to monitor the effects of the spring 2020 lockdown in the Netherlands on substance trade via Telegram.

Objective: The objective of this study was to evaluate whether changes in psychoactive substance trade on Telegram markets in the Netherlands can be observed during the COVID-19 pandemic.

Results:Between December 2, 2019, and June 29, 2020, a total of 70,226 posts appeared in two analyzed Telegram groups. A total of 5,643 posts were psychoactive substance related. Based on the analyzed posts, Telegram is mostly a “sellers” market as only a minority of the posts (6.3%) could be identified as a request for a substance.

The proportion of posts related to specific substances varied between the periods before, during, and after the lockdown. The proportion of posts on the stimulants ecstasy, cocaine, and amphetamine was lower during the lockdown than before and after. For psychedelics—ketamine, lysergic acid diethylamide (LSD), and 2,5-dimethoxy-4-bromophenethylamine (2C-B)—and other substances, there was a relative increase in the number of posts during the lockdown, which was maintained after the lockdown.

Conclusion: Telegram analysis shows that in the Netherlands, online psychoactive substance trade may have been affected during the COVID-19 pandemic. The direction of this effect was different for different classes of substances.

--- /doc/darknet-market/2015-05-05-phillyvoice-lsd.html Prosecutors: Chester County student used bitcoins to buy LSD off Dark Web; East Stroudsburg University student arrested, charged 2015-05-05 2024-01-01 darknet-market psychedelic/lsd --- /silk-road#lsd-case-study 2024-01-01 psychedelic/lsd --- https://www.washingtonpost.com/posteverything/wp/2016/04/01/lsd-could-make-you-smarter-happier-and-healthier-should-we-all-try-it/ LSD could make you smarter, happier and healthier. Should we all try it? 2024-01-01 nootropic/lsd --- https://www.vox.com/2016/3/2/11115974/lsd-internet-addiction Can very small doses of LSD make you a better worker? I decided to try it. 2024-01-01 nootropic/lsd --- https://www.theverge.com/2017/4/24/15403644/microdosing-lsd-acid-productivity-benefits-brain-studies LSD microdoses make people feel sharper, and scientists want to know how: What we do—and mostly don’t—know about tiny doses of hallucinogens 2024-01-01 nootropic/lsd --- https://www.nytimes.com/2017/01/07/style/microdosing-lsd-ayelet-waldman-michael-chabon-marriage.html How LSD ‘Microdosing’ Saved Ayelet Waldman’s Marriage 2024-01-01 nootropic/lsd --- https://www.gq.com/story/micro-dosing-lsd Micro-dosing: The Drug Habit Your Boss Is Gonna Love 2024-01-01 nootropic/lsd --- https://www.economist.com/1843/2017/08/01/turn-on-tune-in-drop-by-the-office Turn on, tune in, drop by the office: The Silicon Valley avant-garde have turned to LSD in a bid to increase their productivity. Emma Hogan meets the people breakfasting on acid 2024-01-01 nootropic/lsd --- https://www.bbc.com/news/health-39516345 Microdosing: The people taking LSD with their breakfast 2024-01-01 nootropic/lsd --- https://web.archive.org/web/20140106080448/https://www.wired.com/science/discoveries/news/2006/01/70015?currentPage=all LSD: The Geek’s Wonder Drug? 2024-01-01 nootropic/lsd --- https://link.springer.com/article/10.1007/s00213-018-5119-x The effects of microdose LSD on time perception: a randomized, double-blind, placebo-controlled trial 2024-01-01 nootropic/lsd --- https://www.reddit.com/r/LSD/comments/2u75vv/guess_i_need_to_say_goodbye_psychonauts/ Guess I need to say goodbye, Psychonauts 2024-01-01 psychedelic/lsd --- https://www.reddit.com/r/DarkNetMarkets/comments/2ubnqf/xpost_from_rlsd_guess_i_need_to_say_goodbye/ Guess I need to say goodbye, Psychonauts 2024-01-01 darknet-market psychedelic/lsd --- https://www.nj.com/mercer/2015/03/trenton_man_arrested_on_drug_charges_after_accepti.html Trenton man arrested on drug charges after accepting package containing LSD 2024-01-01 psychedelic/lsd --- https://www.mcall.com/2016/05/06/esu-student-busted-buying-lsd-on-the-dark-web-police-say/ ESU student busted buying LSD on the ‘dark web’, police say 2024-01-01 psychedelic/lsd --- https://www.foxcarolina.com/story/28631102/teen-arrested-after-allegedly-buying-lsd-online/ Teen arrested after allegedly buying LSD online 2024-01-01 psychedelic/lsd --- https://web.archive.org/web/20131208044444/http://weirderweb.com/2013/02/28/is-lucydrop-the-latest-silk-road-scammer-65000-in-late-lsd-orders-says-yes/ Is Lucy Drop the latest Silk Road scammer? $65,000 in late LSD orders says yes 2024-01-01 darknet-market/silk-road/1 psychedelic/lsd --- https://academic.oup.com/jat/article-abstract/22/6/520/781958 Stability study of LSD under various storage conditions 2024-01-01 psychedelic/lsd --- https://apnews.com/article/98f903367b50404cb3c9695bcabefa5a Security troops on US nuclear missile base took LSD 2024-01-01 psychedelic/lsd --- https://link.springer.com/article/10.1007/s00213-017-4733-3 Long-lasting subjective effects of LSD in normal subjects 2024-01-01 psychedelic/lsd --- https://predictionbook.com/predictions/8140 My LSD order will arrive within 2 weeks: 70% 2024-01-01 psychedelic/lsd --- https://predictionbook.com/predictions/8141 My LSD order will arrive: 90% 2024-01-01 psychedelic/lsd --- https://predictionbook.com/predictions/8142 I will be visited or questioned or arrested by police over my LSD order. 2024-01-01 psychedelic/lsd --- https://predictionbook.com/predictions/8144 I will cease to identify as an atheist after my LSD trip: 10% 2024-01-01 psychedelic/lsd --- https://predictionbook.com/predictions/8159 LSD: I will experience a partial or fully mystical experience, 35% 2024-01-01 psychedelic/lsd --- https://predictionbook.com/predictions/8160 LSD: My trip will be generally positive (and not a ‘bad trip’), 75% 2024-01-01 psychedelic/lsd --- https://predictionbook.com/predictions/8161 LSD: I will experience any kind of ‘flashback’ within 5 years, 5% 2024-01-01 psychedelic/lsd --- https://harmreductionjournal.biomedcentral.com/articles/10.1186/s12954-019-0308-4 Psychedelic microdosing benefits and challenges: an empirical codebook 2024-01-01 nootropic/lsd --- https://osf.io/preprints/psyarxiv/emcxw/ 2024-01-01 nootropic/lsd --- https://journals.sagepub.com/doi/full/10.1177/02698811211050556 2024-01-01 nootropic/lsd --- https://link.springer.com/article/10.1007/s00213-021-05857-0 2024-01-01 nootropic/lsd --- https://lichess.org/@/lichess/blog/developer-update-275-improved-game-compression/Wqa7GiAA 2024-03-14 cs/algorithm/information/compression reinforcement-learning/chess --- https://en.wikipedia.org/wiki/Bertrand_competition Bertrand competition 2024-03-14 economics/automation --- https://www.lesswrong.com/posts/kobJymvvcvhbjWFKe/laying-the-foundations-for-vision-and-multimodal-mechanistic 2024-03-14 ai/nn/transformer/attention ai/nn/transformer/clip --- https://en.wikipedia.org/wiki/Wirephoto Wirephoto 2024-03-14 technology --- https://www.youtube.com/watch?v=cLUD_NGE370 2024-03-14 design/visualization technology --- https://www.youtube.com/watch?v=7F_XSa2O_4Q 2024-03-14 ai/music fiction/poetry philosophy/ethics --- /doc/philosophy/mind/1974-dennett.pdf Why the Law of Effect will not Go Away Daniel Dennett 1974-10-26 2024-03-14 [("doi","10.1111/j.1468-5914.1975.tb00350.x")] philosophy/epistemology philosophy/mind reinforcement-learning/model reinforcement-learning/model-free --- https://www.everything2.net/index.pl?node_id=1190642 2024-03-14 philosophy/epistemology philosophy/mind reinforcement-learning/model reinforcement-learning/model-free --- https://www3.nd.edu/~ghaeffel/Theories_Ferguson.pdf A Vast Graveyard of Undead Theories: Publication Bias and Psychological Science’s Aversion to the Null Ferguson, Heene 2012 2024-01-01 statistics/bias/publication --- https://www.economist.com/business/2024/02/29/how-businesses-are-actually-using-generative-ai 2024-03-14 ai/nn/transformer/gpt/4/nonfiction economics/automation --- https://arxiv.org/abs/2104.10343 Sensitivity as a Complexity Measure for Sequence Classification Tasks Michael Hahn, Dan Jurafsky, Richard Futrell 2021-04-21 2024-03-14 [("doi","10.48550/arXiv.2104.10343")] ai/nn/rnn cs/computable

We introduce a theoretical framework for understanding and predicting the complexity of sequence classification tasks, using a novel extension of the theory of Boolean function sensitivity. The sensitivity of a function, given a distribution over input sequences, quantifies the number of disjoint subsets of the input sequence that can each be individually changed to change the output. We argue that standard sequence classification methods are biased towards learning low-sensitivity functions, so that tasks requiring high sensitivity are more difficult.

To that end, we show analytically that simple lexical classifiers can only express functions of bounded sensitivity, and we show empirically that low-sensitivity functions are easier to learn for LSTMs. We then estimate sensitivity on 15 NLP tasks, finding that sensitivity is higher on challenging tasks collected in GLUE than on simple text classification tasks, and that sensitivity predicts the performance both of simple lexical classifiers and of vanilla BiLSTMs without pretrained contextualized embeddings. Within a task, sensitivity predicts which inputs are hard for such simple models.

Our results suggest that the success of massively pretrained contextual representations stems in part because they provide representations from which information can be extracted by low-sensitivity decoders.

--- https://arxiv.org/abs/2310.13897 Masked Hard-Attention Transformers and Boolean RASP Recognize Exactly the Star-Free Languages Dana Angluin, David Chiang, Andy Yang 2023-10-21 2024-03-14 [("doi","10.48550/arXiv.2310.13897")] ai/nn/transformer/attention cs/computable

We consider Transformer encoders with hard attention (in which all attention is focused on exactly one position) and strict future masking (in which each position only attends to positions strictly to its left), and prove that the class of languages recognized by these networks is exactly the star-free languages.

Adding position embeddings increases the class of recognized languages to other well-studied classes.

A key technique in these proofs is Boolean RASP, a variant of RASP that is restricted to Boolean values.

Via the star-free languages, we relate transformers to first-order logic, temporal logic, and algebraic automata theory.

--- https://arxiv.org/abs/2403.07815#amazon Chronos: Learning the Language of Time Series Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang 2024-03-12 2024-03-14 [("doi","10.48550/arXiv.2403.07815")] ai/nn/transformer/t5 ai/tabular statistics/prediction

We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss.

We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization.

In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them.

Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.

--- https://x.com/repligate/status/1767002880987283801 Janus 2024-03-14 ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/claude --- https://www.lesswrong.com/posts/cxuzALcmucCndYv4a/daniel-kokotajlo-s-shortform?commentId=fX8cCMcyHBcHZYP7G 2024-03-14 ai/nn/transformer/gpt/claude reinforcement-learning/safe --- https://www.anthropic.com/news/claude-3-haiku 2024-03-14 ai/nn/transformer/gpt/claude --- https://www.maximum-progress.com/p/claude-vs-gpt 2024-03-14 ai/nn/transformer/gpt/claude --- https://x.com/fofrAI/status/1765847728045621641 fofrAI 2024-03-14 ai/nn/transformer/gpt/claude --- https://x.com/IntuitMachine/status/1766205754304827407 IntuitMachine 2024-03-14 ai/nn/transformer/gpt/claude --- https://x.com/VictorTaelin/status/1768070973515800931 Victor Taelin 2024-03-14 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex --- https://caseyhandmer.wordpress.com/2024/03/12/how-to-feed-the-ais/ 2024-03-14 ai/scaling/economics ai/scaling/hardware --- https://brianfitzgerald.xyz/prompt-augmentation/ 2024-03-14 ai/nn/diffusion ai/nn/transformer/t5 --- https://www.dwell.com/article/dtc-sofa-crisis-32304b9e 2024-03-14 design economics --- https://x.com/AlkahestMu/status/1767839472425783581 AlkahestMu 2024-03-14 ai/nn/transformer/gpt/claude fiction/science-fiction --- https://mjg59.dreamwidth.org/69507.html 2024-03-14 bitcoin cs/algorithm/information --- https://www.semianalysis.com/p/ai-datacenter-energy-dilemma-race 2024-03-14 ai/scaling/economics --- https://arxiv.org/abs/2211.00575 Text-Only Training for Image Captioning using Noise-Injected CLIP David Nukrai, Ron Mokady, Amir Globerson 2022-11-01 2024-03-14 [("doi","10.48448/n7sq-p557")] ai/nn/gan/data-augmentation ai/nn/transformer/clip

We consider the task of image-captioning using only the CLIP model and additional text data at training time, and no additional captioned images. Our approach relies on the fact that CLIP is trained to make visual and textual embeddings similar. Therefore, we only need to learn how to translate CLIP textual embeddings back into text, and we can learn how to do this by learning a decoder for the frozen CLIP text encoder using only text.

We argue that this intuition is “almost correct” because of a gap between the embedding spaces, and propose to rectify this via noise injection during training.

We demonstrate the effectiveness of our approach by showing SOTA zero-shot image captioning across 4 benchmarks, including style transfer.

Code, data, and models are available on GitHub.

--- https://livingstingy.blogspot.com/2010/08/bathtub-or-weibull-curve.html 2024-03-15 statistics/survival-analysis --- https://www.amygoodchild.com/blog/generating-the-alphabet 2024-03-15 design/typography/tex --- https://www.kickscondor.com/ Kicks Condor Kicks Condor 2022-01-05 cs/css design/typography

[Homepage of programmer Kicks Condor; hypertext-oriented link compilation and experimental design blog.]

--- https://personal.math.ubc.ca/~cass/Euclid/byrne.html Oliver Byrne’s edition of Euclid’s Elements [Scans] Bill Casselman 2021-09-23 design/typography/rubrication math

[Online scanned edition; part of a set of Euclid editions.]

--- /doc/economics/1998-delong.pdf Estimates of World GDP, One Million BC–Present J. Bradford DeLong 1998-01-01 2019-12-11 economics

I construct estimates of world GDP over the very long run by combining estimates of total human populations with estimates of levels of real GDP per capita.

--- /doc/philosophy/epistemology/2023-habgoodcoote.pdf Can a good philosophical contribution be made just by asking a question? Joshua Habgood-Coote, Lani Watson, Dennis Whitcomb 2022-12-05 2023-02-28 [("doi","10.1111/meta.12599")] math/humor philosophy/epistemology

[This paper deliberately left blank.]

--- https://www.cbsnews.com/news/giant-hybrid-sheep-for-hunting-scheme-jack-schubarth-federal-prosecutors/ 2024-03-15 genetics/cloning --- https://www.librariesforthefuture.bio/p/lff 2024-03-15 longevity --- https://gscan2pdf.sourceforge.net/ gscan2pdf: A GUI to produce PDFs from scanned documents Jeffrey Ratcliffe 2019 2021-02-17 cs/linkrot/archiving

[FLOSS Perl GUI program for scanning and processing large documents such as books, doing simple editing, and exporting to PDF.]

--- https://x.com/ylecun/status/1768354396142706906 Yann LeCun 2024-03-15 psychology/inner-voice --- /doc/cs/1996-11-15-usenix-ithorrorstories.html 1996-11-15 2024-01-01 cs math/humor --- /doc/cs/hardware/2003-ross.pdf 2003-01-01 2024-01-01 cs/hardware economics/automation/metcalfes-law --- /doc/cs/algorithm/2006-jared-wikimediaprovesgreenspunstenthlaw.html 2006 2024-01-01 cs/algorithm wikipedia --- https://openai.com/research/whisper 2024-01-01 ai/nn/transformer/gpt/whisper --- https://github.com/openai/whisper/blob/main/model-card 2024-03-16 ai/nn/transformer/gpt/whisper --- https://openai.com/blog/introducing-chatgpt-and-whisper-apis 2024-03-16 ai/nn/transformer/gpt/whisper ai/scaling/economics --- https://github.com/alphacep/whisper-prompts 2024-03-16 ai/nn/transformer/gpt/whisper --- https://cookbook.openai.com/examples/whisper_prompting_guide 2024-03-16 ai/nn/transformer/gpt/whisper --- /doc/ai/nn/transformer/gpt/whisper/2022-radford-figure8-whisperscalingbymodelsize.png 2022 2024-03-16 ai/nn/transformer/gpt/whisper ai/scaling --- /doc/ai/nn/transformer/gpt/whisper/2022-radford-figure9-crossoverinmonolingualvsmultilingualtrainingscalingshowseventualtransfer.jpg 2022 2024-03-16 ai/nn/transformer/gpt/whisper ai/scaling --- /doc/ai/nn/transformer/gpt/whisper/2022-radford-figure4-correlationofpretraininglanguagedatawithtranslationperformance.jpg 2022 2024-03-16 ai/nn/transformer/gpt/whisper ai/scaling --- https://arxiv.org/abs/2403.09539 Logits of API-Protected LLMs Leak Proprietary Information Matthew Finlayson, Swabha Swayamdipta, Xiang Ren 2024-03-14 2024-03-16 [("doi","10.48550/arXiv.2403.09539")] ai/nn/adversarial

The commercialization of large language models (LLMs) has led to the common practice of high-level API-only access to proprietary models. In this work, we show that even with a conservative assumption about the model architecture, it is possible to learn a surprisingly large amount of non-public information about an API-protected LLM from a relatively small number of API queries (eg. costing under $1,000 for OpenAI’s GPT-3.5 model gpt-3.5-turbo).

Our findings are centered on one key observation: most modern LLMs suffer from a softmax bottleneck, which restricts the model outputs to a linear subspace of the full output space. We show that this lends itself to a model image or a model signature which unlocks several capabilities with affordable cost: efficiently discovering the LLM’s hidden size, obtaining full-vocabulary outputs, detecting and disambiguating different model updates, identifying the source LLM given a single full LLM output, and even estimating the output layer parameters.

Our empirical investigations show the effectiveness of our methods, which allow us to estimate the embedding size of OpenAI’s gpt-3.5-turbo to be about 4,096.

Lastly, we discuss ways that LLM providers can guard against these attacks, as well as how these capabilities can be viewed as a feature (rather than a bug) by allowing for greater transparency and accountability.

--- https://arxiv.org/abs/2311.13647 Language Model Inversion John X. Morris, Wenting Zhao, Justin T. Chiu, Vitaly Shmatikov, Alexander M. Rush 2023-11-22 2024-03-16 [("doi","10.48550/arXiv.2311.13647")] ai/nn/adversarial ai/nn/sampling

Language models produce a distribution over the next token; can we use this information to recover the prompt tokens? We consider the problem of language model inversion and show that next-token probabilities contain a surprising amount of information about the preceding text. Often we can recover the text in cases where it is hidden from the user, motivating a method for recovering unknown prompts given only the model’s current distribution output.

We consider a variety of model access scenarios, and show how even without predictions for every token in the vocabulary we can recover the probability vector through search. On LLaMA-2 7b, our inversion method reconstructs prompts with a BLEU of 59 and token-level F1⁄78 and recovers 27% of prompts exactly.

Code for reproducing all experiments is available at vec2text.

--- https://arxiv.org/abs/2307.15471 Somatic mutations in human ageing: New insights from DNA sequencing and inherited mutations Kasit Chatsirisupachai, João Pedro de Magalhães 2023-07-28 2024-03-16 [("doi","10.48550/arXiv.2307.15471")] genetics/heritable/rare longevity

The accumulation of somatic mutations is a driver of cancer and has long been associated with ageing. Due to limitations in quantifying mutation burden with age in non-cancerous tissues, the impact of somatic mutations in other ageing phenotypes is unclear.

Recent advances in DNA sequencing technologies have allowed the large-scale quantification of somatic mutations in ageing. These studies have revealed a gradual accumulation of mutations in most normal tissues with age as well as a substantial clonal expansion driven mostly by cancer-related mutations.

Nevertheless, because of the relatively modest burden of age-related somatic mutations identified so far and their stochastic nature, it is difficult to envision how somatic mutation accumulation alone can explain most ageing phenotypes that develop gradually. Studies across species have also found that longer-lived species have lower somatic mutation rates, though these could be explained by selective pressures to reduce or postpone cancer as longevity increases.

Overall, with a few exceptions like cancer, results from recent DNA sequencing studies do not add weight to the idea that somatic mutations with age drive ageing phenotypes and the phenotypic role, if any, of somatic mutations in ageing remains unclear. Recent studies in patients with somatic mutation burden and no signs of accelerated ageing further question the role of somatic mutations in ageing.

--- https://arxiv.org/abs/2403.08763 Simple and Scalable Strategies to Continually Pre-train Large Language Models Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats L. Richter, Quentin Anthony, Timothée Lesort, Eugene Belilovsky, Irina Rish 2024-03-13 2024-03-16 [("doi","10.48550/arXiv.2403.08763")] reinforcement-learning/meta-learning/continual-learning reinforcement-learning/scaling

Large language models (LLMs) are routinely pre-trained on billions of tokens, only to start the process over again once new data becomes available. A much more efficient solution is to continually pre-train these models, saving compute compared to re-training. However, the distribution shift induced by new data typically results in degraded performance on previous data or poor adaptation to the new data.

In this work, we show that a simple and scalable combination of learning rate (LR) re-warming, LR re-decaying, and replay of previous data is:

sufficient to match the performance of fully re-training from scratch on all available data, as measured by final loss and language model (LM evaluation) benchmarks. Specifically, we show this for a weak but realistic distribution shift between two commonly used LLM pre-training datasets (English → English) and a stronger distribution shift (English → German) at the 405M parameter model scale with large dataset sizes (hundreds of billions of tokens). Selecting the weak but realistic shift for larger-scale experiments, we also find that our continual learning strategies match the re-training baseline for a 10b parameter LLM.

Our results demonstrate that LLMs can be successfully updated via simple and scalable continual learning strategies, matching the re-training baseline using only a fraction of the compute.

Finally, inspired by previous work, we propose alternatives to the cosine learning rate schedule that help circumvent forgetting induced by LR re-warming and that are not bound to a fixed token budget.

Figure 1: Continual pre-training decreases computational costs of updating the model while maintaining similar final validation and evaluation performance. We report results for Pile ∪ SlimPajama (SP)/German(Ger.) [based on Red Pajama] Baseline model trained on the union of both datasets which we consider to be an upper bound on performance. We also report performance for two continually pre-trained models. “PT on Pile” starts from a pre-trained Pile checkpoint and only uses learning rate re-warming and re-decaying, while “Replay (PT on Pile)” re-warms the learning rate, re-decays it, and uses 5% replay for Slim Pajama and 25% replay for German. We observe that the combination of LR re-warming, re-decaying, and replay allows our continually pre-trained model to attain similar performance to the baseline model while requiring substantially less compute. We note that this setting assumes that a pre-trained model is available (eg. via Huggingface Hub or an in-house model designed to be continually pre-trained).
--- /doc/psychiatry/depression/2011-hansen-figure3-stabilityofhighschoolsuicideeffectovertime.jpg 2011 2024-01-01 psychiatry/anxiety psychiatry/depression --- /doc/psychology/inner-voice/2001-feynman-whatdoyoucarewhatotherpeoplethink-ch3-itsassimpleas123.pdf What Do You Care What Other People Think § It’s as Simple as One, Two, Three Richard Feynman 2001-01-01 2024-03-16 design/visualization psychology/inner-voice psychology/linguistics --- https://x.com/AnthonyLeeZhang/status/1768639726557209082 Anthony Zhang 2024-03-16 ai/nn/transformer/gpt/claude statistics/probability --- https://www.cerebras.net/blog/slimpajama-a-627b-token-cleaned-and-deduplicated-version-of-redpajamacr 2024-03-16 ai/dataset --- https://arxiv.org/abs/2309.10818#cerebras SlimPajama-DC: Understanding Data Combinations for LLM Training Zhiqiang Shen, Tianhua Tao, Liqun Ma, Willie Neiswanger, Zhengzhong Liu, Hongyi Wang, Bowen Tan, Joel Hestness, Natalia Vassilieva, Daria Soboleva, Eric Xing 2023-09-19 2024-03-16 [("doi","10.48550/arXiv.2309.10818")] ai/dataset reinforcement-learning/exploration/active-learning/data-pruning

This paper aims to understand the impacts of various data combinations (eg. web text, Wikipedia, GitHub, books) on the training of large language models using SlimPajama. SlimPajama is a rigorously deduplicated, multi-source dataset, which has been refined and further deduplicated to 627B tokens from the extensive 1.2T tokens RedPajama dataset contributed by Together. We’ve termed our research as SlimPajama-DC, an empirical analysis designed to uncover fundamental characteristics and best practices associated with employing SlimPajama in the training of large language models.

During our research with SlimPajama, two pivotal observations emerged: (1) Global deduplication vs. local deduplication. We analyze and discuss how global (across different sources of datasets) and local (within the single source of dataset) deduplications affect the performance of trained models. (2) Proportions of high-quality/highly-deduplicated multi-source datasets in the combination.

To study this, we construct 6 configurations of SlimPajama dataset and train individual ones using 1.3B Cerebras-GPT model with Alibi and SwiGLU.

Our best configuration outperforms the 1.3B model trained on RedPajama using the same number of training tokens by a large margin. All our 1.3B models are trained on Cerebras 16× CS-2 cluster with a total of 80 PFLOP/s in bf16 mixed precision.

We further extend our discoveries (such as increasing data diversity is crucial after global deduplication) on a 7B model with large batch-size training.

Our models and the separate SlimPajama-DC datasets are available at: https://huggingface.co/MBZUAI-LLM and https://huggingface.co/datasets/cerebras/SlimPajama-627B.

--- https://x.com/DorsaAmir/status/1768815380858855863 Dorsa Amir 2024-03-16 statistics/bias/publication --- https://awesomekling.substack.com/p/fuzzing-ladybird-with-tools-from 2024-03-16 cs/css cs/security --- https://slate.com/human-interest/2024/03/phone-free-schools-movement-parents-teenagers.html 2024-03-16 sociology/technology --- https://x.com/emollick/status/1768824505491759592 Ethan Mollick 2024-03-16 ai/nn/transformer/gpt/claude fiction/text-game --- https://www.kentclarkcenter.org/surveys/land-value-tax/ 2024-03-16 economics/georgism --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959507/ Life cycle patterns of cognitive performance over the long run Anthony Strittmatter, Uwe Sunde, Dainis Zegners 2020 2024-03-16 [("doi","10.1073/pnas.2006653117")] iq psychology/chess reinforcement-learning/chess

Little is known about how the age pattern in individual performance in cognitively demanding tasks changed over the past century. The main difficulty for measuring such life cycle performance patterns and their dynamics over time is related to the construction of a reliable measure that is comparable across individuals and over time and not affected by changes in technology or other environmental factors. This study presents evidence for the dynamics of life cycle patterns of cognitive performance over the past 125 years based on an analysis of data from professional chess tournaments.

Individual move-by-move performance in more than 24,000 games is evaluated relative to an objective benchmark that is based on the respective optimal move suggested by a chess engine. This provides a precise and comparable measurement of individual performance for the same individual at different ages over long periods of time, exploiting the advantage of a strictly comparable task and a comparison with an identical performance benchmark. Repeated observations for the same individuals allow disentangling age patterns from idiosyncratic variation and analyzing how age patterns change over time and across birth cohorts.

The findings document a hump-shaped performance profile over the life cycle and a long-run shift in the profile toward younger ages that is associated with cohort effects rather than period effects. This shift can be rationalized by greater experience, which is potentially a consequence of changes in education and training facilities related to digitization.

--- https://arxiv.org/abs/2403.09611#apple MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, Anton Belyi, Haotian Zhang, Karanjeet Singh, Doug Kang, Hongyu Hè, Max Schwarzer, Tom Gunter, Xiang Kong, Aonan Zhang, Jianyu Wang, Chong Wang, Nan Du, Tao Lei, Sam Wiseman, Mark Lee, Zirui Wang, Ruoming Pang, Peter Grasch, Alexander Toshev, Yinfei Yang 2024-03-14 2024-03-16 [("doi","10.48550/arXiv.2403.09611")] ai/nn/transformer/clip ai/scaling/mixture-of-experts

In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices.

Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons. For example, we demonstrate that for large-scale multimodal pre-training using a careful mix of image-caption, interleaved image-text, and text-only data is crucial for achieving state-of-the-art (SOTA) few-shot results across multiple benchmarks, compared to other published pre-training results. Further, we show that the image encoder together with image resolution and the image token count has substantial impact, while the vision-language connector design is of comparatively negligible importance.

By scaling up the presented recipe, we build MM1, a family of multimodal models up to 30b parameters, consisting of both dense models and mixture-of-experts (MoE) variants, that are SOTA in pre-training metrics and achieve competitive performance after supervised fine-tuning on a range of established multimodal benchmarks.

Thanks to large-scale pre-training, MM1 enjoys appealing properties such as enhanced in-context learning, and multi-image reasoning, enabling few-shot chain-of-thought prompting.

--- https://ibhof.blogspot.com/2020/01/the-economics-of-pirate-radio.html 2024-03-16 economics/advertising --- https://www.beren.io/2023-02-04-Integer-tokenization-is-insane/ 2024-03-16 ai/nn/tokenization --- https://danluu.com/slow-device/ 2024-03-16 cs/css --- https://www.cerebras.net/press-release/cerebras-announces-third-generation-wafer-scale-engine 2024-03-16 ai/scaling/hardware --- https://en.wikipedia.org/wiki/Aquagenic_urticaria Aquagenic urticaria 2024-03-16 biology --- https://en.wikipedia.org/wiki/Alpha-gal_syndrome Alpha-gal syndrome 2024-03-16 genetics --- https://www.newyorker.com/magazine/2004/12/13/mysterious-circumstances 2024-01-01 crime fiction psychiatry --- https://www.reddit.com/r/OpenAI/comments/1bgcvut/the_world_will_never_be_the_same_after_sora/ 2024-03-17 ai/video/generation --- https://jacobbuckman.com/2020-11-30-conceptual-fundamentals-of-offline-rl/ 2024-03-17 reinforcement-learning/offline --- https://arxiv.org/abs/2307.14619 Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior Adam Block, Ali Jadbabaie, Daniel Pfrommer, Max Simchowitz, Russ Tedrake 2023-07-27 2024-03-17 [("doi","10.48550/arXiv.2307.14619")] ai/nn/diffusion reinforcement-learning/imitation-learning reinforcement-learning/offline

We propose a theoretical framework for studying behavior cloning of complex expert demonstrations using generative modeling. Our framework invokes low-level controllers—either learned or implicit in position-command control—to stabilize imitation around expert demonstrations.

We show that with (1) a suitable low-level stability guarantee and (2) a powerful enough generative model as our imitation learner, pure supervised behavior cloning can generate trajectories matching the per-time step distribution of essentially arbitrary expert trajectories in an optimal transport cost. Our analysis relies on a stochastic continuity property of the learned policy we call total variation continuity (TVC).

We then show that TVC can be ensured with minimal degradation of accuracy by combining a popular data-augmentation regimen with a novel algorithmic trick: adding augmentation noise at execution time. We instantiate our guarantees for policies parameterized by diffusion models and prove that if the learner accurately estimates the score of the (noise-augmented) expert policy, then the distribution of imitator trajectories is close to the demonstrator distribution in a natural optimal transport distance.

Our analysis constructs intricate couplings between noise-augmented trajectories, a technique that may be of independent interest.

We conclude by empirically validating our algorithmic recommendations, and discussing implications for future research directions for better behavior cloning with generative modeling.

--- https://arxiv.org/abs/2011.12998 VoxLingua107: a Dataset for Spoken Language Recognition Jörgen Valk, Tanel Alumäe 2020-11-25 2024-03-17 [("doi","10.48550/arXiv.2011.12998")] ai/dataset

This paper investigates the use of automatically collected web audio data for the task of spoken language recognition. We generate semi-random search phrases from language-specific Wikipedia data that are then used to retrieve videos from YouTube for 107 languages.

Speech activity detection and speaker diarization are used to extract segments from the videos that contain speech. Post-filtering is used to remove segments from the database that are likely not in the given language, increasing the proportion of correctly labeled segments to 98%, based on crowd-sourced verification.

The size of the resulting training set (VoxLingua107) is 6,628 hours (62 hours per language on average) and it is accompanied by an evaluation set of 1,609 verified utterances. We use the data to build language recognition models for several spoken language identification tasks. Experiments show that using the automatically retrieved training data gives competitive results to using hand-labeled proprietary datasets.

The dataset is publicly available.

--- https://arxiv.org/abs/1608.05859 Using the Output Embedding to Improve Language Models Ofir Press, Lior Wolf 2016-08-20 2024-03-17 [("doi","10.48550/arXiv.1608.05859")] ai/nn/rnn ai/nn/sparsity

We study the topmost weight matrix of neural network language models. We show that this matrix constitutes a valid word embedding.

When training language models, we recommend tying the input embedding and this output embedding. We analyze the resulting update rules and show that the tied embedding evolves in a more similar way to the output embedding than to the input embedding in the untied model. We also offer a new method of regularizing the output embedding.

Our methods lead to a reduction in perplexity, as we are able to show on a variety of neural network language models.

Finally, we show that weight tying can reduce the size of neural translation models to less than half of their original size without harming their performance.

--- https://arxiv.org/abs/2007.10310#facebook CoVoST 2 and Massively Multilingual Speech-to-Text Translation Changhan Wang, Anne Wu, Juan Pino 2020-07-20 2024-03-17 [("doi","10.48550/arXiv.2007.10310")] ai/dataset ai/nn/transformer

Speech translation has recently become an increasingly popular topic of research, partly due to the development of benchmark datasets. Nevertheless, current datasets cover a limited number of languages.

With the aim to foster research in massive multilingual speech translation and speech translation for low resource language pairs, we release CoVoST 2, a large-scale multilingual speech translation corpus covering translations from 21 languages into English and from English into 15 languages. This represents the largest open dataset available to date from total volume and language coverage perspective. Data sanity checks provide evidence about the quality of the data, which is released under CC0 license.

We also provide extensive speech recognition, bilingual and multilingual machine translation and speech translation baselines with open-source implementation.

--- https://arxiv.org/abs/2205.12446#google FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech Alexis Conneau, Min Ma, Simran Khanuja, Yu Zhang, Vera Axelrod, Siddharth Dalmia, Jason Riesa, Clara Rivera, Ankur Bapna 2022-05-25 2024-03-17 [("doi","10.48550/arXiv.2205.12446")] ai/dataset ai/nn/transformer

We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark. FLEURS is an n-way parallel speech dataset in 102 languages built on top of the machine translation FLoRes-101 benchmark, with ~12 hours of speech supervision per language. FLEURS can be used for a variety of speech tasks, including Automatic Speech Recognition (ASR), Speech Language Identification (Speech LangID), Translation and Retrieval.

In this paper, we provide baselines for the tasks based on multilingual pre-trained models like mSLAM.

The goal of FLEURS is to enable speech technology in more languages and catalyze research in low-resource speech understanding.

--- https://arxiv.org/abs/2403.08266 Sketch2Manga: Shaded Manga Screening from Sketch with Diffusion Models Jian Lin, Xueting Liu, Chengze Li, Minshan Xie, Tien-Tsin Wong 2024-03-13 2024-03-17 [("doi","10.48550/arXiv.2403.08266")] ai/anime/danbooru ai/nn/diffusion

While manga is a popular entertainment form, creating manga is tedious, especially adding screentones to the created sketch, namely manga screening. Unfortunately, there is no existing method that tailors for automatic manga screening, probably due to the difficulty of generating high-quality shaded high-frequency screentones. The classic manga screening approaches generally require user input to provide screentone exemplars or a reference manga image.

The recent deep learning models enable the automatic generation by learning from a large-scale dataset. However, the state-of-the-art models still fail to generate high-quality shaded screentones due to the lack of a tailored model and high-quality manga training data.

In this paper, we propose a novel sketch-to-manga framework that first generates a color illustration from the sketch and then generates a screentoned manga based on the intensity guidance. Our method outperforms existing methods in generating high-quality manga with shaded high-frequency screentones.

--- https://github.com/Michaelangel007/nanofont3x4 2024-03-17 design/typography --- https://notes.billmill.org/blog/2024/03/mitzVah_-_the__worst__pangrams_part_2.html 2024-03-17 cs/algorithm fiction/text-game --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6703193/ General Anesthesia: A Probe to Explore Consciousness Vincent Bonhomme, Cécile Staquet, Javier Montupil, Aline Defresne, Murielle Kirsch, Charlotte Martial, Audrey Vanhaudenhuyse, Camille Chatelle, Stephen Karl Larroque, Federico Raimondo, Athena Demertzi, Olivier Bodart, Steven Laureys, Olivia Gosseries 2019 2024-03-17 [("doi","10.3389/fnsys.2019.00036")] philosophy/mind psychology/neuroscience/pain/anesthesia

General anesthesia reversibly alters consciousness, without shutting down the brain globally. Depending on the anesthetic agent and dose, it may produce different consciousness states including a complete absence of subjective experience (unconsciousness), a conscious experience without perception of the environment (disconnected consciousness, like during dreaming), or episodes of oriented consciousness with awareness of the environment (connected consciousness). Each consciousness state may potentially be followed by explicit or implicit memories after the procedure. In this respect, anesthesia can be considered as a proxy to explore consciousness.

During the recent years, progress in the exploration of brain function has allowed a better understanding of the neural correlates of consciousness, and of their alterations during anesthesia. Several changes in functional and effective between-region brain connectivity, consciousness network topology, and spatio-temporal dynamics of between-region interactions have been evidenced during anesthesia. Despite a set of effects that are common to many anesthetic agents, it is still uneasy to draw a comprehensive picture of the precise cascades during general anesthesia.

Several questions remain unsolved, including the exact identification of the neural substrate of consciousness and its components, the detection of specific consciousness states in unresponsive patients and their associated memory processes, the processing of sensory information during anesthesia, the pharmacodynamic interactions between anesthetic agents, the direction-dependent hysteresis phenomenon during the transitions between consciousness states, the mechanisms of cognitive alterations that follow an anesthetic procedure, the identification of an eventual unitary mechanism of anesthesia-induced alteration of consciousness, the relationship between network effects and the biochemical or sleep-wake cycle targets of anesthetic agents, as well as the vast between-studies variations in dose and administration mode, leading to difficulties in between-studies comparisons.

In this narrative review, we draw the picture of the current state of knowledge in anesthesia-induced unconsciousness, from insights gathered on propofol, halogenated vapors, ketamine, dexmedetomidine, benzodiazepines and xenon. We also describe how anesthesia can help understanding consciousness, we develop the above-mentioned unresolved questions, and propose tracks for future research.

--- https://willhbr.net/2024/03/15/making-a-compiler-to-prove-tmux-is-turing-complete/ 2024-03-17 cs/computable --- https://arxiv.org/abs/2403.07183 Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, Daniel A. McFarland, James Y. Zou 2024-03-11 2024-03-17 [("doi","10.48550/arXiv.2403.07183")] ai/nn/sampling ai/nn/transformer/gpt/3/nonfiction statistics/peer-review

We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level.

We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023, & EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, ie. beyond spell-checking or minor writing updates.

The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review.

We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices.

--- https://arxiv.org/abs/2310.15154 Linear Representations of Sentiment in Large Language Models Curt Tigges, Oskar John Hollinsworth, Atticus Geiger, Neel Nanda 2023-10-23 2024-03-17 [("doi","10.48550/arXiv.2310.15154")] ai/nn/transformer/attention

[Twitter; cf. Memory Transformer/ViT registers] Sentiment is a pervasive feature in natural language text, yet it is an open question how sentiment is represented within Large Language Models (LLMs).

In this study, we reveal that across a range of models, sentiment is represented linearly: a single direction in activation space mostly captures the feature across a range of tasks with one extreme for positive and the other for negative. Through causal interventions, we isolate this direction and show it is causally relevant in both toy tasks and real world datasets such as Stanford Sentiment Treebank. Through this case study we model a thorough investigation of what a single direction means on a broad data distribution.

We further uncover the mechanisms that involve this direction, highlighting the roles of a small subset of attention heads and neurons. Finally, we discover a phenomenon which we term the summarization motif: sentiment is not solely represented on emotionally charged words, but is additionally summarized at intermediate positions without inherent sentiment, such as punctuation and names.

We show that in Stanford Sentiment Treebank zero-shot classification, 76% of above-chance classification accuracy is lost when ablating the sentiment direction, nearly half of which (36%) is due to ablating the summarized sentiment direction exclusively at comma positions.

--- https://arxiv.org/abs/2309.16588 Vision Transformers Need Registers Timothée Darcet, Maxime Oquab, Julien Mairal, Piotr Bojanowski 2023-09-28 2024-03-17 [("doi","10.48550/arXiv.2309.16588")] ai/nn/transformer/attention ai/nn/transformer/clip ai/nn/vae/mae

[Twitter; cf. Memory Transformer] Transformers have recently emerged as a powerful tool for learning visual representations.

In this paper, we identify and characterize artifacts in feature maps of both supervised and self-supervised ViT networks. The artifacts correspond to high-norm tokens appearing during inference primarily in low-informative background areas of images, that are repurposed for internal computations.

We propose a simple yet effective solution based on providing additional tokens to the input sequence of the Vision Transformer to fill that role.

We show that this solution fixes that problem entirely for both supervised and self-supervised models, sets a new state-of-the-art for self-supervised visual models on dense visual prediction tasks, enables object discovery methods with larger models, and most importantly leads to smoother feature maps and attention maps for downstream visual processing.

--- https://arxiv.org/abs/1811.02545 Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond Krishna Kumar Singh, Hao Yu, Aron Sarmasi, Gautam Pradeep, Yong Jae Lee 2018-11-06 2024-03-17 [("doi","10.48550/arXiv.1811.02545")] ai/nn/vae/mae

We propose ‘Hide-and-Seek’ a general purpose data augmentation technique, which is complementary to existing data augmentation techniques and is beneficial for various visual recognition tasks. The key idea is to hide patches in a training image randomly, in order to force the network to seek other relevant content when the most discriminative content is hidden. Our approach only needs to modify the input image and can work with any network to improve its performance. During testing, it does not need to hide any patches. The main advantage of Hide-and-Seek over existing data augmentation techniques is its ability to improve object localization accuracy in the weakly-supervised setting, and we therefore use this task to motivate the approach.

However, Hide-and-Seek is not tied only to the image localization task, and can generalize to other forms of visual input like videos, as well as other recognition tasks like image classification, temporal action localization, semantic segmentation, emotion recognition, age/gender estimation, and person re-identification.

We perform extensive experiments to showcase the advantage of Hide-and-Seek on these various visual recognition problems.

--- https://en.wikipedia.org/wiki/Kiautschou_Bay_Leased_Territory#Organization_and_development_of_the_territory Kiautschou Bay Leased Territory § Organization and development of the territory 2024-03-17 economics/georgism --- https://x.com/nathan_culley/status/1521538219711635458 Nathan Culley 2024-03-17 iq --- https://x.com/nathan_culley/status/1521927821609865216 Nathan Culley 2024-03-17 iq --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4469797/ Sexual offending runs in families: A 37-year nationwide study Niklas Långström, Kelly M. Babchishin, Seena Fazel, Paul Lichtenstein, Thomas Frisell 2015 2024-03-17 [("doi","10.1093/ije/dyv029")] crime genetics/heritable

Background: Sexual crime is an important public health concern. The possible causes of sexual aggression, however, remain uncertain.

Methods: We examined familial aggregation and the contribution of genetic and environmental factors to sexual crime by linking longitudinal, nationwide Swedish crime and multigenerational family registers. We included all men convicted of any sexual offence (n = 21,566), specifically rape of an adult (n = 6131) and child molestation (n = 4465), 1973 → 2009. Sexual crime rates among fathers and brothers of sexual offenders were compared with corresponding rates in fathers and brothers of age-matched population control men without sexual crime convictions. We also modelled the relative influence of genetic and environmental factors to the liability of sexual offending.

Results: We found strong familial aggregation of sexual crime [odds ratio (OR) = 5.1, 95% confidence interval (CI) = 4.5-5.9] among full brothers of convicted sexual offenders. Familial aggregation was lower in father-son dyads (OR = 3.7, 95% CI = 3.2-4.4) among paternal half-brothers (OR = 2.1, 95% CI = 1.5-2.9) and maternal half-brothers (OR = 1.7, 95% CI = 1.2-2.4). Statistical modeling of the strength and patterns of familial aggregation suggested that genetic factors (40%) and non-shared environmental factors (58%) explained the liability to offend sexually more than shared environmental influences (2%). Further, genetic effects tended to be weaker for rape of an adult (19%) than for child molestation (46%).

Conclusions: We report strong evidence of familial clustering of sexual offending, primarily accounted for by genes rather than shared environmental influences. Future research should possibly test the effectiveness of selective prevention efforts for male first-degree relatives of sexually aggressive individuals, and consider familial risk in sexual violence risk assessment.

--- https://www.nytimes.com/2019/04/12/education/lebron-james-school-ohio.html 2024-03-17 sociology --- https://www.the74million.org/article/lebrons-school-has-everything-were-told-students-need-theyre-still-failing/ 2024-03-17 sociology --- https://www.theverge.com/2023/2/15/23599072/microsoft-ai-bing-personality-conversations-spy-employees-webcams 2024-03-17 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/4/sydney reinforcement-learning/safe --- https://www.reddit.com/r/bing/comments/110eagl/the_customer_service_of_the_new_bing_chat_is/ 2024-03-17 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/safe --- https://x.ai/blog/grok-os 2024-03-17 ai/scaling/mixture-of-experts --- https://x.com/qdrant_engine/status/1721097971830260030 Qdrant 2024-03-18 ai/nn/retrieval --- https://en.wikipedia.org/wiki/Linalyl_acetate Linalyl acetate 2024-03-18 psychiatry/anxiety/lavender --- https://qualiacomputing.com/2020/10/07/learning-the-trade/ 2024-03-18 psychology/smell/human --- https://qualiacomputing.com/2020/07/14/saving-lives-with-lemon-lavender-flavor-thc-free-nicotine-laced-joints-as-a-substitute-for-tobacco-cigarettes/ 2024-03-18 marijuana nicotine psychology/smell/human --- https://www.newyorker.com/magazine/2024/03/25/how-juliens-auctions-leads-the-booming-market-in-celebrity-memorabilia 2024-03-18 psychology/collecting --- /doc/ai/anime/2024-zhang.pdf Hierarchical Feature Warping and Blending for Talking Head Animation Jiale Zhang, Chengxin Liu, Ke Xian, Zhiguo Cao 2024-03-14 2024-03-18 [("doi","10.1109/TCSVT.2024.3375330")] ai/anime ai/dataset ai/nn/gan

Talking head animation transforms a source anime image to a target pose, where the transformation includes the change of facial expression and head movement. In contrast to existing approaches that operate on the low-resolution image (256 × 256), we study this task at a higher resolution, eg. 512 × 512. High-resolution talking head animation, however, raises two major challenges: (1) how to achieve smooth global transformation while maintaining rich details of anime characters under large-displacement pose variations; (2) how to address the shortage of data, because no related dataset is publicly available.

In this paper, we present a Hierarchical Feature Warping and Blending (HFWB) model, which tackles talking head animation hierarchically. Specifically, we use low-level features to control global transformation and high-level features to determine the details of anime characters, under the guidance of feature flow fields. These features are then blended by selective fusion units, outputting transformed anime images.

In addition, we construct an anime pose dataset—AniTalk-2K, aiming to alleviate the shortage of data. It contains around 2,000 anime characters with thousands of different face/head poses at a resolution of 512 × 512.

Extensive experiments on AniTalk-2K demonstrate the superiority of our approach in generating high-quality anime talking heads over state-of-the-art methods.

--- https://stability.ai/news/introducing-stable-video-3d 2024-03-18 ai/nn/diffusion ai/video/generation --- https://www.reddit.com/r/StableDiffusion/comments/1bhs3rl/openai_keeps_dropping_more_insane_sora_videos/ 2024-03-18 ai/video/generation --- https://www.rollingstone.com/music/music-features/suno-ai-chatgpt-for-music-1234982307/ 2024-03-18 ai/music --- https://www.alanshawn.com/latex3-tutorial/ 2024-03-19 design/typography/tex --- https://www.propublica.org/article/chinese-organized-crime-us-marijuana-market 2024-03-19 crime marijuana --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374384/ Responders and non-responders to aerobic exercise training: beyond the evaluation of V̇O2max Felipe Mattioni Maturana, Rogerio N. Soares, Juan M. Murias, Philipp Schellhorn, Gunnar Erz, Christof Burgstahler, Manuel Widmann, Barbara Munz, Ansgar Thiel, Andreas M. Nieß 2021 2024-03-19 [("doi","10.14814/phy2.14951")] exercise

The evaluation of the maximal oxygen uptake (V̇O2max) following exercise training is the classical assessment of training effectiveness. Research has lacked in investigating whether individuals that do not respond to the training intervention (V̇O2max), also do not improve in other health-related parameters.

We aimed to investigate the cardiovascular and metabolic adaptations (ie. performance, body composition, blood pressure, vascular function, fasting blood markers, and resting cardiac function and morphology) to exercise training among participants who showed different levels of V̇O2max responsiveness. Healthy sedentary participants engaged in a 6-week exercise training program, 3× a week.

Our results showed that responders had a greater increase in peak power output, second lactate threshold, and microvascular responsiveness, whereas non-responders had a greater increase in cycling efficiency. No statistical differences were observed in body composition, blood pressure, fasting blood parameters, and resting cardiac adaptations.

In conclusion, our study showed, for the first time, that in addition to the differences in the V̇O2max, a greater increase in microvascular responsiveness in responders compared to non-responders was observed. Additionally, responders and non-responders did not show differences in the adaptations on metabolic parameters.

There is an increasing need for personalized training prescription, depending on the target clinical outcome.

Figure 2: Overview of responders’ classification using the highest density interval (HDI) and region of practical equivalence (ROPE) method. The graph displays the ΔV̇O2max (delta of maximal oxygen uptake) of each participant. The black dots show the ΔV̇O2max, curves are the normal distribution curves derived for each individual given the measurement error around the ΔV̇O2max value. The horizontal black lines are the 89% HDI derived from each curve, and vertical dashed lines around zero are the calculated ROPE (ie. −80 to 80 ml·min−1). Refer to Table 3 for an overview of the levels of statistical-significance from the ROPE + HDI decision-making method applied to responders’ analysis.

…2.9.3. Training monitoring: All exercise training sessions were performed on a cycle ergometer (ec5000, custo med GmbH) and participants’ heart rate and ECG were constantly monitored (3-channel ECG, custo med GmbH). After every training session, the exercise training data (ie. second-by-second power output, cadence, and heart rate) were exported and stored for subsequent processing.

2.9.4. Minimum adherence: In order for participants to be included in the final analyses, a minimum of 15⁄18 prescribed exercise sessions had to be completed (minimum adherence =83.3%). If participants did not complete the minimum required number of sessions, they were considered as dropouts (n = 2).

--- https://arxiv.org/abs/2402.07562 Discovering Universal Semantic Triggers for Text-to-Image Synthesis Shengfang Zhai, Weilong Wang, Jiajun Li, Yinpeng Dong, Hang Su, Qingni Shen 2024-02-12 2024-03-19 [("doi","10.48550/arXiv.2402.07562")] ai/nn/adversarial ai/nn/diffusion ai/nn/transformer/clip

Recently, text-to-image models have gained widespread attention in the community due to their controllable and high-quality generation ability. However, the robustness of such models and their potential ethical issues have not been fully explored. In this paper, we introduce Universal Semantic Trigger, a meaningless token sequence that can be added at any location within the input text yet can induce generated images towards a preset semantic target.

To thoroughly investigate it, we propose Semantic Gradient-based Search (SGS) framework. SGS automatically discovers the potential universal semantic triggers based on the given semantic targets. Furthermore, we design evaluation metrics to comprehensively evaluate semantic shift of images caused by these triggers.

And our empirical analyses reveal that the mainstream open-source text-to-image models are vulnerable to our triggers, which could pose ethical threats.

Our work contributes to a further understanding of text-to-image synthesis and helps users to automatically audit their models before deployment.

--- https://arxiv.org/abs/2310.16836 LLM-FP4: 4-Bit Floating-Point Quantized Transformers Shih-yang Liu, Zechun Liu, Xijie Huang, Pingcheng Dong, Kwang-Ting Cheng 2023-10-25 2024-03-19 [("doi","10.48550/arXiv.2310.16836")] ai/nn/sparsity/low-precision ai/nn/transformer

We propose LLM-FP4 for quantizing both weights and activations in large language models (LLMs) down to 4-bit floating-point values, in a post-training manner. Existing post-training quantization (PTQ) solutions are primarily integer-based and struggle with bit widths below 8 bits. Compared to integer quantization, floating-point (FP) quantization is more flexible and can better handle long-tail or bell-shaped distributions, and it has emerged as a default choice in many hardware platforms. One characteristic of FP quantization is that its performance largely depends on the choice of exponent bits and clipping range.

In this regard, we construct a strong FP-PTQ baseline by searching for the optimal quantization parameters. Furthermore, we observe a high inter-channel variance and low intra-channel variance pattern in activation distributions, which adds activation quantization difficulty. We recognize this pattern to be consistent across a spectrum of transformer models designed for diverse tasks, such as LLMs, BERT, and Vision Transformer models.

To tackle this, we propose per-channel activation quantization and show that these additional scaling factors can be reparameterized as exponential biases of weights, incurring a negligible cost. Our method, for the first time, can quantize both weights and activations in the LLaMA-13B to only 4-bit and achieves an average score of 63.1 on the common sense zero-shot reasoning tasks, which is only 5.8 lower than the full-precision model, outperforming the previous state-of-the-art by 12.7 points.

Code is available at: Github.

--- https://www.biorxiv.org/content/10.1101/2022.02.22.481293.full Generalization in Sensorimotor Networks Configured with Natural Language Instructions Reidar Riveland, Alexandre Pouget 2023-10-26 2024-03-19 [("doi","10.1101/2022.02.22.481293")] ai/nn/rnn psychology/linguistics

One of humans’ most fundamental cognitive feats is the ability to interpret linguistic instructions in order to perform novel tasks without any explicit experience with the task. Yet, the computations that the brain might use to accomplish such a feat remains poorly understood. Here we use the latest advances in Natural Language Processing to create a neural model of generalization based on linguistic instructions.

Models are trained on a set of commonly studied psychophysical tasks, and receive instructions embedded by a pre-trained language model. Our best models can perform a previously unseen task with a performance of 83% correct on average based solely on linguistic instructions (ie. 0-shot learning).

We found that language scaffolds sensorimotor representations such that activity for interrelated tasks share a common geometry with the semantic representations of instructions, allowing language to cue the proper composition of practiced skills in unseen settings. Finally, we show how this model can generate a linguistic description of a novel task it has identified using only motor feedback, which can subsequently guide a partner model to perform the task.

Our models offer several experimentally testable predictions outlining how linguistic information must be represented in order to facilitate flexible and general cognition in the human brain.

--- https://www.ioccc.org/2019/lynn/hint.html 2024-03-19 cs/haskell --- https://voltagedivide.com/2024/03/18/unconventional-uses-of-fpgas/ 2024-03-19 cs/hardware --- https://x.com/realhashbreaker/status/1770161965006008570 Marc Stevens 2024-03-19 cs/cryptography --- https://www.theverge.com/c/23991049/lego-ideas-polaroid-onestep-behind-the-scenes-price 2024-03-19 design --- https://www.lesswrong.com/posts/S54HKhxQyttNLATKu/deconfusing-direct-vs-amortised-optimization 2024-03-20 reinforcement-learning/model reinforcement-learning/model-free statistics/bayes --- https://adamfuhrer.com/small-scale-pen-plotting 2024-03-20 design/visualization --- https://www.nytimes.com/2024/03/19/magazine/hilde-soliani-perfume-parma.html 2024-03-20 psychology/smell/perfume --- https://www.astralcodexten.com/p/the-mystery-of-internet-survey-iqs 2024-03-20 iq/high --- https://www.atlasobscura.com/articles/invasive-moths-trouvelot-astronomical-illustrations 2024-03-20 design/visualization --- https://news.ycombinator.com/item?id=39755668 2024-03-20 ai/nn/transformer/gpt/4/fiction fiction/text-game --- https://slate.com/technology/2024/03/concussion-symptoms-signs-treatment-advice.html 2024-03-20 psychiatry/traumatic-brain-injury --- https://en.wikipedia.org/wiki/UBlock_Origin UBlock Origin 2024-01-01 economics/advertising/adblock --- /doc/ai/scaling/economics/2021-martinezplumed-table1-listofaibenchmarksusedintheanalysisbytask.png 2021 2024-01-01 [("invert","True")] ai/scaling/economics --- /doc/darknet-market/silk-road/1/2012-gwern-frontpage.png Gwern 2012 2024-01-01 [("invert","False")] darknet-market/silk-road/1 --- /doc/darknet-market/2020-arce-figure7-9-cocainepurities.png 2020 2024-01-01 [("invert","True")] darknet-market --- /doc/genetics/heritable/adoption/2021-willoughby-2-figure3-heritabilityofpoliticalandreligiousattitudes.png 2021 2024-01-01 [("invert","True")] genetics/heritable/adoption --- /doc/genetics/microbiome/2021-tong-figure3-microbiomebyprocessing.png 2021 2024-01-01 [("invert","True")] genetics/microbiome tea --- /doc/philosophy/ontology/2021-carroll-figure4-fifthforcelimits.jpg 2021 2024-01-01 [("invert","True")] philosophy/ontology science --- /doc/philosophy/religion/2022-hong-figure3-predictiveaccuracyof793historicalchinesedreaminterpretationpredictions.png 2022 2024-01-01 [("invert","True")] philosophy/religion psychology/cognitive-bias --- /doc/psychology/smell/perfume/2019-kraft-figure2-schematicrepresentationofduneperfume.jpg 2019 2024-01-01 [("invert","True")] psychology/smell/perfume --- /doc/reinforcement-learning/exploration/2019-jaderberg-figure1-ctftaskandtraining.jpg 2019 2024-01-01 [("invert","True")] reinforcement-learning/exploration reinforcement-learning/scaling --- /doc/reinforcement-learning/model-free/oa5/2018-mccandlish-openai-howaitrainingscales-gradientnoisescale-summary3-scalevsbatchsize.jpg 2018 2024-01-01 [("invert","True")] reinforcement-learning/model-free/oa5 reinforcement-learning/scaling --- /doc/statistics/bias/2015-opensciencecollaboration-figure1-originalstudyeffectvsreplicationeffect.jpg 2015 2024-01-01 [("invert","True")] statistics/bias --- https://en.wikipedia.org/wiki/Storm_oil Storm oil 2024-03-21 technology --- https://www.reddit.com/r/dalle2/comments/1bjktk3/after_the_stasis_reference_in_the_comments/ 2024-03-21 ai/nn/transformer/gpt/dall-e/3 --- https://arxiv.org/abs/2403.13799#facebook Reverse Training to Nurse the Reversal Curse Olga Golovneva, Zeyuan Allen-Zhu, Jason Weston, Sainbayar Sukhbaatar 2024-03-20 2024-03-21 [("doi","10.48550/arXiv.2403.13799")] ai/nn/transformer/gpt

Large language models (LLMs) have a surprising failure: when trained on “A has a feature B”, they do not generalize to “B is a feature of A”, which is termed the Reversal Curse. Even when training with trillions of tokens this issue still appears due to Zipf’s law—hence even if we train on the entire internet.

This work proposes an alternative training scheme, called reverse training, whereby all words are used twice, doubling the amount of available tokens. The LLM is trained in both forward and reverse directions by reversing the training strings while preserving (ie. not reversing) chosen substrings, such as entities. [Effectively turning it into a bidirectional LLM, by reformatting the data instead of changing the loss function.]

We show that data-matched reverse-trained models provide superior performance to standard models on standard tasks, and compute-matched reverse-trained models provide far superior performance on reversal tasks, helping resolve the reversal curse issue.

--- https://arxiv.org/abs/2309.12288 The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A" Lukas Berglund, Meg Tong, Max Kaufmann, Mikita Balesni, Asa Cooper Stickland, Tomasz Korbak, Owain Evans 2023-09-21 2024-03-21 [("doi","10.48550/arXiv.2309.12288")] ai/nn/transformer/gpt/4/nonfiction

[discussion] We expose a surprising failure of generalization in auto-regressive large language models (LLMs). If a model is trained on a sentence of the form “A is B”, it will not automatically generalize to the reverse direction “B is A”. This is the Reversal Curse. For instance, if a model is trained on “Olaf Scholz was the 9th Chancellor of Germany”, it will not automatically be able to answer the question, “Who was the 9th Chancellor of Germany?”. Moreover, the likelihood of the correct answer (“Olaf Scholz”) will not be higher than for a random name. Thus, models exhibit a basic failure of logical deduction and do not generalize a prevalent pattern in their training set (ie. if “A is B” occurs, “B is A” is more likely to occur).

We provide evidence for the Reversal Curse by finetuning GPT-3 and Llama-1 on fictitious statements such as “Uriah Hawthorne is the composer of ‘Abyssal Melodies’” [ie. neither exists] and showing that they fail to correctly answer “Who composed ‘Abyssal Melodies?’”. The Reversal Curse is robust across model sizes and model families and is not alleviated by data augmentation.

We also evaluate ChatGPT (GPT-3.5 and GPT-4) on questions about real-world celebrities, such as “Who is Tom Cruise’s mother? [A: Mary Lee Pfeiffer]” and the reverse “Who is Mary Lee Pfeiffer’s son?”. GPT-4 correctly answers questions like the former 79% of the time, compared to 33% for the latter. This shows a failure of logical deduction that we hypothesize is caused by the Reversal Curse.

Code is available at Github.

--- https://file770.com/vernor-vinge-1944-2024/ 2024-03-21 fiction/science-fiction transhumanism --- https://www.reddit.com/r/singularity/comments/1atjz9v/ive_put_a_complex_codebase_into_a_single/ 2024-03-21 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/palm --- https://arxiv.org/abs/2303.15215 The spinorial ball: a macroscopic object of spin-1/2 Samuel Bernard-Bernardet, David Dumas, Benjamin Apffel 2023-03-27 2024-03-21 [("doi","10.48550/arXiv.2303.15215")] design/visualization math

Historically, the observation of half-spin particles was one of the most surprising features of quantum mechanics. They are often described as “objects that do not come back to their initial state after one turn but do after two turns”. There are macroscopic implementations using constraints such as clamping a belt or ribbon that purport to show similar behavior (the “Dirac belt trick”). However, a demonstration of an unconstrained macroscopic object with half-spin behavior remains elusive.

In this article, we propose to fill this gap and introduce the spinorial ball. It consists of a translucent plastic ball with internal LED illumination that behaves as a freely movable macroscopic half-spin object. It provides a new tool to introduce and visualize half-integer spins as well as the covering group homomorphism from SU(2) to SO(3), and offers in particular a clear visualization of the different homotopy classes of SO(3).

We discuss its development and function, and how one can mimic quantum measurement and wave function collapse using this the spinorial ball.

The entire system is open source hardware, with build details, models, 3d printing files, etc., provided under an open source license.

--- https://github.com/heligone/spinorialBall/ 2024-03-21 design/visualization math --- https://jsomers.net/e-coli-chemotaxis/ 2024-03-21 biology reinforcement-learning/exploration --- https://www.nytimes.com/2024/03/21/health/psychedelics-roland-griffiths-johns-hopkins.html 2024-03-21 psychedelic statistics/bias --- https://www.nature.com/articles/s42004-024-01131-4 2024-03-21 psychology/smell/perfume --- https://www.nytimes.com/2024/03/21/health/pig-kidney-organ-transplant.html 2024-03-21 genetics/editing --- https://arxiv.org/abs/2402.18510 RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval Kaiyue Wen, Xingyu Dang, Kaifeng Lyu 2024-02-28 2024-03-21 [("doi","10.48550/arXiv.2402.18510")] ai/nn/retrieval ai/nn/rnn ai/nn/transformer/attention ai/nn/transformer/gpt/inner-monologue

This paper investigates the gap in representation powers of Recurrent Neural Networks (RNNs) and Transformers in the context of solving algorithmic problems. We focus on understanding whether RNNs, known for their memory efficiency in handling long sequences, can match the performance of Transformers, particularly when enhanced with Chain-of-Thought (CoT) prompting.

Our theoretical analysis reveals that CoT improves RNNs but is insufficient to close the gap with Transformers. A key bottleneck lies in the inability of RNNs to perfectly retrieve information from the context, even with CoT: for several tasks that explicitly or implicitly require this capability, such as associative recall and determining if a graph is a tree, we prove that RNNs are not expressive enough to solve the tasks while Transformers can solve them with ease.

Conversely, we prove that adopting techniques to enhance the in-context retrieval capability of RNNs, including Retrieval-Augmented Generation (RAG) and adding a single Transformer layer, can elevate RNNs to be capable of solving all polynomial-time solvable problems with CoT, hence closing the representation gap with Transformers.

--- https://arxiv.org/abs/2403.01915 xT: Nested Tokenization for Larger Context in Large Images Ritwik Gupta, Shufan Li, Tyler Zhu, Jitendra Malik, Trevor Darrell, Karttikeya Mangalam 2024-03-04 2024-03-21 [("doi","10.48550/arXiv.2403.01915")] ai/nn/transformer/attention/hierarchical

[blog] Modern computer vision pipelines handle large images in one of two sub-optimal ways: down-sampling or cropping. These two methods incur losses in the amount of information and context present in an image. There are many downstream applications in which global context matters as much as high frequency details, such as in real-world satellite imagery; in such cases researchers have to make the uncomfortable choice of which information to discard.

We introduce xT, a simple framework for vision transformers which effectively aggregates global context with local details and can model large images end-to-end on contemporary GPUs. We select a set of benchmark datasets across classic vision tasks which accurately reflect a vision model’s ability to understand truly large images and incorporate fine details over large scales and assess our method’s improvement on them.

By introducing a nested tokenization scheme for large images in conjunction with long-sequence length models normally used for natural language processing, we are able to increase accuracy by up to 8.6% on challenging classification tasks and F1 score by 11.6 on context-dependent segmentation in large images.

--- https://cacm.acm.org/research/indistinguishability-obfuscation-from-well-founded-assumptions/ 2024-03-21 cs/cryptography --- https://www.beren.io/2022-08-06-The-scale-of-the-brain-vs-machine-learning/ 2024-03-21 ai/scaling psychology/neuroscience --- https://www.beren.io/2024-03-03-Linear-Attention-as-Iterated-Hopfield-Networks/ 2024-03-21 ai/nn/transformer/attention --- https://arxiv.org/abs/2403.11821 Evaluating Text to Image Synthesis: Survey and Taxonomy of Image Quality Metrics Sebastian Hartwig, Dominik Engel, Leon Sick, Hannah Kniesel, Tristan Payer, Poonam, Timo Ropinski 2024-03-18 2024-03-22 [("doi","10.48550/arXiv.2403.11821")] ai/dataset reinforcement-learning/preference-learning

Recent advances in text-to-image synthesis have been enabled by exploiting a combination of language and vision through foundation models. These models are pre-trained on tremendous amounts of text-image pairs sourced from the World Wide Web or other large-scale databases. As the demand for high-quality image generation shifts towards ensuring content alignment between text and image, novel evaluation metrics have been developed with the aim of mimicking human judgments.

Researchers have started to collect datasets with increasingly complex annotations to study the compositionality of vision-language models and their incorporation as a quality measure of compositional alignment between text and image contents. In this work, we provide a comprehensive overview of existing text-to-image evaluation metrics and propose a new taxonomy for categorizing these metrics. We also review frequently adopted text-image benchmark datasets before discussing techniques to optimize text-to-image synthesis models towards quality and human preferences.

Ultimately, we derive guidelines for improving text-to-image evaluation and discuss the open challenges and current limitations.

--- https://www.supersimple.io/blog/gpt-4-fine-tuning-early-access 2024-03-22 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction --- https://arxiv.org/abs/2402.15721 Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models Chaoya Jiang, Wei Ye, Mengfan Dong, Hongrui Jia, Haiyang Xu, Ming Yan, Ji Zhang, Shikun Zhang 2024-02-24 2024-03-22 [("doi","10.48550/arXiv.2402.15721")] ai/dataset ai/nn/transformer/gpt/4

Large Vision Language Models (LVLMs) exhibit remarkable capabilities but struggle with hallucinations: inconsistencies between images and their descriptions. Previous hallucination evaluation studies on LVLMs have identified hallucinations in terms of objects, attributes, and relations but overlooked complex hallucinations that create an entire narrative around a fictional entity.

In this paper, we introduce a refined taxonomy of hallucinations, featuring a new category: Event Hallucination. We then use advanced Large Language Models (LLMs) to generate and filter fine-grained hallucinatory data consisting of various types of hallucinations, with a particular focus on event hallucinations, laying the groundwork for integrating discriminative and generative evaluation methods within our universal evaluation framework.

The proposed benchmark distinctively assesses LVLMs’ ability to tackle a broad spectrum of hallucinations, making it a reliable and comprehensive tool for gauging LVLMs’ efficacy in handling hallucinations.

We will release our code and data.

--- https://www.lesswrong.com/posts/W8CxEFCnYNdrHkuoB/abs-e-or-speak-only-in-the-positive 2024-03-22 philosophy/logic psychology/writing --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7615113/ Listening to Misinformation while Driving: Cognitive Load and the Effectiveness of (Repeated) Corrections Jasmyne A. Sanderson, Vanessa Bowden, Briony Swire-Thompson, Stephan Lewandowsky, Ullrich K. H. Ecker 2023 2024-03-22 [("doi","10.1037/mac0000057")] psychology/cognitive-bias

Corrected misinformation can continue to influence inferential reasoning. It has been suggested that such continued influence is partially driven by misinformation familiarity, and that corrections should therefore avoid repeating misinformation to avoid inadvertent strengthening of misconceptions. However, evidence for such familiarity-backfire effects is scarce.

We tested whether familiarity backfire may occur if corrections are processed under cognitive load. Although misinformation repetition may boost familiarity, load may impede integration of the correction, reducing its effectiveness and therefore allowing a backfire effect to emerge. Participants listened to corrections that repeated misinformation while in a driving simulator. Misinformation familiarity was manipulated through the number of corrections. Load was manipulated through a math task administered selectively during correction encoding.

Multiple corrections were more effective than a single correction; cognitive load reduced correction effectiveness, with a single correction entirely ineffective under load.

This provides further evidence against familiarity-backfire effects and has implications for real-world debunking.

[Warning: Stephan Lewandowsky is a co-author.]

--- https://wiki.quanticle.net/Main/TrustMeImLyingConfessionsOfAMediaManipulator 2024-01-01 economics/advertising politics --- https://www.lesswrong.com/posts/mxa7XZ8ajE2oarWcr/lawrencec-s-shortform#pEqfzPMpqsnhaGrNK 2024-03-22 ai/nn/rnn --- https://arxiv.org/abs/2403.13787 RewardBench: Evaluating Reward Models for Language Modeling Nathan Lambert, Valentina Pyatkin, Jacob Morrison, L. J. Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah Smith, Hannaneh Hajishirzi 2024-03-20 2024-03-22 [("doi","10.48550/arXiv.2403.13787")] ai/dataset reinforcement-learning/preference-learning

Reward models (RMs) are at the crux of successful RLHF to align pretrained models to human preferences, yet there has been relatively little study that focuses on evaluation of those reward models. Evaluating reward models presents an opportunity to understand the opaque technologies used for alignment of language models and which values are embedded in them. To date, very few descriptors of capabilities, training methods, or open-source reward models exist.

In this paper, we present RewardBench, a benchmark dataset and code-base for evaluation, to enhance scientific understanding of reward models. The RewardBench dataset is a collection of prompt-win-lose trios spanning chat, reasoning, and safety, to benchmark how reward models perform on challenging, structured and out-of-distribution queries. We created specific comparison datasets for RMs that have subtle, but verifiable reasons (eg. bugs, incorrect facts) why one answer should be preferred to another.

On the RewardBench leaderboard, we evaluate reward models trained with a variety of methods, such as the direct MLE training of classifiers and the implicit reward modeling of Direct Preference Optimization (DPO), and on a spectrum of datasets.

We present many findings on propensity for refusals, reasoning limitations, and instruction following shortcomings of various reward models towards a better understanding of the RLHF process.

--- https://arxiv.org/abs/2403.13372 LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Yongqiang Ma 2024-03-20 2024-03-22 [("doi","10.48550/arXiv.2403.13372")] ai/nn/sparsity/low-precision

Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks. However, it requires non-trivial efforts to implement these methods on different models.

We present LlamaFactory, a unified framework that integrates a suite of cutting-edge efficient training methods. It allows users to flexibly customize the fine-tuning of 100+ LLMs without the need for coding through the built-in web UI LlamaBoard.

We empirically validate the efficiency and effectiveness of our framework on language modeling and text generation tasks.

It has been released at GitHub and already received over 13,000 stars and 1,600 forks.

--- https://arxiv.org/abs/2403.13802 ZigMa: Zigzag Mamba Diffusion Model Vincent Tao Hu, Stefan Andreas Baumann, Ming Gui, Olga Grebenkova, Pingchuan Ma, Johannes Fischer, Bjorn Ommer 2024-03-20 2024-03-22 [("doi","10.48550/arXiv.2403.13802")] ai/nn/diffusion ai/nn/rnn ai/video/generation

The diffusion model has long been plagued by scalability and quadratic complexity issues, especially within transformer-based structures. In this study, we aim to leverage the long sequence modeling capability of the SSM Mamba to extend its applicability to visual data generation.

Firstly, we identify a critical oversight in most current Mamba-based vision methods, namely the lack of consideration for spatial continuity in the scan scheme of Mamba. Secondly, building upon this insight, we introduce a simple, plug-and-play, zero-parameter method named Zigzag Mamba (ZigMa), which outperforms Mamba-based baselines and demonstrates improved speed and memory usage compared to transformer-based baselines.

Lastly, we integrate Zigzag Mamba with the Stochastic Interpolant framework to investigate the scalability of the model on large-resolution visual datasets, such as FacesHQ 1,024×1,024 and UCF101, MultiModal-CelebA-HQ, and MS COCO 256×256.

Code will be released at https://taohu.me/zigma/.

--- https://daleonai.com/bigcontextwindows 2024-03-22 ai/nn/transformer/gpt/palm --- https://x.com/kindgracekind/status/1770671231190127090 Grace Kind 2024-03-22 ai/nn/transformer/gpt/claude design/typography --- https://inquisitivebird.substack.com/p/where-parents-make-a-difference 2024-03-22 genetics/heritable --- https://x.com/daniel_271828/status/1769853886163296455 daniel_271828 2024-03-22 ai/nn/transformer/gpt/claude reinforcement-learning/safe --- https://x.com/metachirality/status/1769818226718888426 metachirality 2024-03-22 ai/nn/transformer/gpt/claude reinforcement-learning/safe --- https://x.com/metachirality/status/1769905644725830090 metachirality 2024-03-22 ai/nn/transformer/gpt/claude reinforcement-learning/safe --- https://x.com/michael_nielsen/status/1769404321739972859 Michael Nielsen 2024-03-22 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/SullyOmarr/status/1768744880673522083 Sully Omarr 2024-03-22 ai/nn/transformer/gpt/claude reinforcement-learning/meta-learning --- https://x.com/JeffDean/status/1770653917543870571 Jeff Dean 2024-03-22 ai/nn/transformer/gpt/palm --- https://x.com/SullyOmarr/status/1769107969872953634 Sully Omarr 2024-03-22 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex --- https://x.com/mattshumer_/status/1766157714411942055 Matt Shumer 2024-03-22 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex --- https://github.com/javirandor/anthropic-tokenizer 2024-03-22 ai/nn/tokenization ai/nn/transformer/gpt/claude --- https://x.com/agishibaa/status/1770206746960601583 agishibaa 2024-03-22 ai/nn/transformer/gpt/4 reinforcement-learning/preference-learning --- https://www.lesswrong.com/posts/sY3a4Rfa48CgteBEm/chatgpt-can-learn-indirect-control 2024-03-22 ai/nn/transformer/gpt/4 reinforcement-learning/meta-learning --- https://x.com/joshwhiton/status/1770870746010513571 Josh Whiton 2024-03-22 ai/nn/transformer/gpt/claude reinforcement-learning/meta-learning --- https://www.marginalia.nu/log/26-personalized-pagerank/ 2024-03-22 technology/google --- https://github.com/xenodium/chatgpt-shell/ 2024-03-22 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2403.14148#nvidia CMD: Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition Sihyun Yu, Weili Nie, De-An Huang, Boyi Li, Jinwoo Shin, Anima Anandkumar 2024-03-21 2024-03-22 [("doi","10.48550/arXiv.2403.14148")] ai/nn/diffusion ai/video/generation

[samples] Video diffusion models have recently made great progress in generation quality, but are still limited by the high memory and computational requirements. This is because current video diffusion models often attempt to process high-dimensional videos directly.

To tackle this issue, we propose content-motion latent diffusion model (CMD), a novel efficient extension of pretrained image diffusion models for video generation. Specifically, we propose an autoencoder that succinctly encodes a video as a combination of a content frame (like an image) and a low-dimensional motion latent representation. The former represents the common content, and the latter represents the underlying motion in the video, respectively. We generate the content frame by fine-tuning a pretrained image diffusion model, and we generate the motion latent representation by training a new lightweight diffusion model.

A key innovation here is the design of a compact latent space that can directly use a pretrained image diffusion model, which has not been done in previous latent video diffusion models.

This leads to considerably better quality generation and reduced computational costs. For instance, CMD can sample a video 7.7× faster than prior approaches by generating a video of 512×1024 resolution and length 16 in 3.1 seconds. Moreover, CMD achieves an FVD score of 212.7 on WebVid10M, 27.3% better than the previous state-of-the-art of 292.4.

--- https://jcarroll.com.au/2023/07/07/array-languages-r-vs-apl/ 2024-03-22 cs/r --- https://paul.mou.dev/posts/2023-12-31-listening-with-llm/ 2024-03-22 ai/nn/transformer/gpt/whisper --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4764874/ Sexual Behaviors and Partner Characteristics by Sexual Identity Among Adolescent Girls Michele L. Ybarra, Margaret Rosario, Elizabeth Saewyc, Carol Goodenow 2016 2024-03-22 [("doi","10.1016/j.jadohealth.2015.11.001")] sociology/technology

Purpose: Data suggest that lesbian and bisexual adolescents engage in risky sexual behaviors at higher rates than heterosexual girls. Whether these findings also apply to girls of other sexual identities is less well understood. Potential differences in risky sexual behaviors reported by lesbian versus bisexual adolescents are also underreported in the literature.

Methods: Data were collected online in 2010–2011 among 2,823 girls, aged 13–18 years, in the United States. Multinomial logistic regression was used to quantify comparisons of sexual behaviors between (1) lesbian; (2) bisexual; and (3) questioning, unsure, or other (QUO) identity; and (0) heterosexual girls. Logistic regression compared lesbian and bisexual adolescents.

Results: Lesbian and bisexual adolescents reported statistically-significantly more lifetime and past-year sexual partners than heterosexual girls. Bisexual girls were also more likely to report penile-anal and penile-vaginal sex, whereas lesbians were more likely to report earlier sexual debut for almost all types of sex, as compared to heterosexual girls. Lesbians also were more likely to report infrequent condom use and less likely to have conversations with partners about the use of barriers (eg. dental dams) before first sex. Relative to lesbians, bisexual girls reported older age at first sex for almost all sexual behaviors and higher lifetime prevalence of recent male partners, penile-vaginal, and penile-anal sex. Few differences were noted between QUO and heterosexual girls.

Conclusions: Sexual minority adolescents are not identical in terms of sexual risk. Providers need to be sensitive to these differences and their implications for health and counseling of patients.

--- https://www.biorxiv.org/content/10.1101/2024.03.14.585103.full Atomically accurate de novo design of single-domain antibodies Nathaniel R. Bennett, Joseph L. Watson, Robert J. Ragotte, Andrew J. Borst, Déjenaé L. See, Connor Weidle, Riti Biswas, Ellen L. Shrock, Philip J. Y. Leung, Buwei Huang, Inna Goreshnik, Russell Ault, Kenneth D. Carr, Benedikt Singer, Cameron Criswell, Dionne Vafeados, Mariana Garcia Sanchez, Ho Min Kim, Susana Vázquez Torres, Sidney Chan, David Baker 2024-03-18 2024-03-22 [("doi","10.1101/2024.03.14.585103")] ai/nn/diffusion ai/nn/transformer/alphafold

Despite the central role that antibodies play in modern medicine, there is currently no way to rationally design novel antibodies to bind a specific epitope on a target. Instead, antibody discovery currently involves time-consuming immunization of an animal or library screening approaches.

Here we demonstrate that a fine-tuned RFdiffusion network is capable of designing de novo antibody variable heavy chains (VHHs) that bind user-specified epitopes.

We experimentally confirm binders to 4 disease-relevant epitopes, and the cryo-EM structure of a designed VHH bound to influenza hemagglutinin is nearly identical to the design model both in the configuration of the CDR loops and the overall binding pose.

--- https://www.collectorsweekly.com/articles/visiting-scarfolk/ 2024-03-23 design --- https://arxiv.org/abs/2403.13187 Evolutionary Optimization of Model Merging Recipes Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun, David Ha 2024-03-19 2024-03-23 [("doi","10.48550/arXiv.2403.13187")] ai/nn/transformer/gpt reinforcement-learning/meta-learning

We present a novel application of evolutionary algorithms to automate the creation of powerful foundation models. While model merging has emerged as a promising approach for LLM development due to its cost-effectiveness, it currently relies on human intuition and domain knowledge, limiting its potential. Here, we propose an evolutionary approach that overcomes this limitation by automatically discovering effective combinations of diverse open-source models, harnessing their collective intelligence without requiring extensive additional training data or compute.

Our approach operates in both parameter space and data flow space, allowing for optimization beyond just the weights of the individual models. This approach even facilitates cross-domain merging, generating models like a Japanese LLM with Math reasoning capabilities.

Surprisingly, our Japanese Math LLM achieved state-of-the-art performance on a variety of established Japanese LLM benchmarks, even surpassing models with more parameters, despite not being explicitly trained for such tasks. Furthermore, a culturally-aware Japanese VLM generated through our approach demonstrates its effectiveness in describing Japanese culture-specific content, outperforming previous Japanese VLMs.

This work not only contributes new state-of-the-art models back to the open-source community, but also introduces a new paradigm for automated model composition, paving the way for exploring alternative, efficient approaches to foundation model development.

--- /spaced-repetition#fn60 2024-03-24 cs/cryptography/steganography psychology/vision --- https://www.reddit.com/r/OpenAI/comments/1bm305k/what_the_hell_claud_3_opus_is_a_straight/ 2024-03-24 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex --- https://docs.anthropic.com/claude/docs/prompt-engineering 2024-03-24 ai/nn/transformer/gpt/claude --- /review/umineko#textual-lengthcomplexity 2024-03-24 cs/algorithm/information/compression --- https://www.theparisreview.org/blog/2017/03/23/hugo-inc/ 2024-03-24 economics/copyright --- https://xkcd.com/795/ 2024-03-24 existential-risk statistics/probability --- https://www.theatlantic.com/health/archive/2024/03/dna-tests-incest/677791/ 2024-03-25 genetics/heritable/rare --- https://arxiv.org/abs/2402.18945 Syntactic Ghost: An Imperceptible General-purpose Backdoor Attacks on Pre-trained Language Models Pengzhou Cheng, Wei Du, Zongru Wu, Fengwei Zhang, Libo Chen, Gongshen Liu 2024-02-29 2024-03-25 [("doi","10.48550/arXiv.2402.18945")] ai/nn/adversarial

Pre-trained language models (PLMs) have been found susceptible to backdoor attacks, which can transfer vulnerabilities to various downstream tasks. However, existing PLM backdoors are conducted with explicit triggers under the manually aligned, thus failing to satisfy expectation goals simultaneously in terms of effectiveness, stealthiness, and universality. In this paper, we propose a novel approach to achieve invisible and general backdoor implantation, called Syntactic Ghost (synGhost for short). Specifically, the method hostilely manipulates poisoned samples with different predefined syntactic structures as stealth triggers and then implants the backdoor to pre-trained representation space without disturbing the primitive knowledge.

The output representations of poisoned samples are distributed as uniformly as possible in the feature space via contrastive learning, forming a wide range of backdoors. Additionally, in light of the unique properties of syntactic triggers, we introduce an auxiliary module to drive the PLMs to learn this knowledge in priority, which can alleviate the interference between different syntactic structures.

Experiments show that our method outperforms the previous methods and achieves the predefined objectives. Not only do severe threats to various natural language understanding (NLU) tasks on two tuning paradigms but also to multiple PLMs. Meanwhile, the synGhost is imperceptible against 3 countermeasures based on perplexity, fine-pruning, and the proposed maxEntropy.

--- /doc/statistics/probability/1964-goldman.pdf 1964-01-01 2024-01-01 statistics/probability statistics/survival-analysis --- https://responsiblestatecraft.org/defense-industry-lobby/ 2024-03-25 economics/advertising --- https://openai.com/blog/sora-first-impressions 2024-03-25 ai/video/generation --- https://comsec.ethz.ch/research/dram/zenhammer/ 2024-03-25 cs/hardware cs/security --- https://invertornot.com/ 2024-03-23 ai/nn/cnn cs/css --- https://johnjago.com/great-docs/ 2024-03-25 design --- https://www.theguardian.com/technology/2014/dec/30/hacker-fakes-german-ministers-fingerprints-using-photos-of-her-hands 2024-03-25 cs/security --- https://x.com/misha_saul/status/1771019329737462232 Misha Saul 2024-03-26 ai/nn/transformer/gpt/claude --- https://blog.pgvecto.rs/my-binary-vector-search-is-better-than-your-fp32-vectors 2024-03-26 ai/nn/retrieval ai/nn/sparsity/low-precision --- https://huggingface.co/blog/embedding-quantization 2024-03-26 ai/nn/retrieval ai/nn/sparsity/low-precision --- https://txt.cohere.com/int8-binary-embeddings/ 2024-03-26 ai/nn/retrieval ai/nn/sparsity/low-precision --- https://arxiv.org/abs/2403.15447 Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression Junyuan Hong, Jinhao Duan, Chenhui Zhang, Zhangheng Li, Chulin Xie, Kelsey Lieberman, James Diffenderfer, Brian Bartoldson, Ajay Jaiswal, Kaidi Xu, Bhavya Kailkhura, Dan Hendrycks, Dawn Song, Zhangyang Wang, Bo Li 2024-03-18 2024-03-26 [("doi","10.48550/arXiv.2403.15447")] ai/nn/adversarial ai/nn/sparsity/low-precision ai/nn/sparsity/pruning reinforcement-learning/safe

Compressing high-capability Large Language Models (LLMs) has emerged as a favored strategy for resource-efficient inferences. While state-of-the-art (SoTA) compression methods boast impressive advancements in preserving benign task performance, the potential risks of compression in terms of safety and trustworthiness have been largely neglected.

This study conducts the first, thorough evaluation of 3 leading LLMs using 5 SoTA compression techniques across 8 trustworthiness dimensions. Our experiments highlight the intricate interplay between compression and trustworthiness, revealing some interesting patterns. We find that quantization is currently a more effective approach than pruning in achieving efficiency and trustworthiness simultaneously. For instance, a 4-bit quantized model retains the trustworthiness of its original counterpart, but model pruning degrades trustworthiness, even at 50% sparsity.

Moreover, employing quantization within a moderate bit range could unexpectedly improve certain trustworthiness dimensions such as ethics and fairness. Conversely, extreme quantization to very low bit levels (3 bits) tends to reduce trustworthiness. This increased risk cannot be uncovered by looking at benign performance alone, in turn, mandating comprehensive trustworthiness evaluation in practice.

These findings culminate in practical recommendations for simultaneously achieving high utility, efficiency, and trustworthiness in LLMs.

Models and code are available at https://decoding-comp-trust.github.io/.

--- https://thechipletter.substack.com/p/googles-first-tpu-architecture 2024-03-26 ai/scaling/hardware --- https://arxiv.org/abs/2403.15498 Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models Adam Karvonen 2024-03-21 2024-03-26 [("doi","10.48550/arXiv.2403.15498")] reinforcement-learning/chess reinforcement-learning/model/decision-transformer

Language models have shown unprecedented capabilities, sparking debate over the source of their performance. Is it merely the outcome of learning syntactic patterns and surface level statistics, or do they extract semantics and a world model from the text? Prior work by Li et al investigated this by training a GPT model on synthetic, randomly generated Othello games and found that the model learned an internal representation of the board state.

We extend this work into the more complex domain of chess, training on real games and investigating our model’s internal representations using linear probes and contrastive activations. The model is given no a priori knowledge of the game and is solely trained on next character prediction, yet we find evidence of internal representations of board state.

We validate these internal representations by using them to make interventions on the model’s activations and edit its internal board state. Unlike Li et al’s prior synthetic dataset approach, our analysis finds that the model also learns to estimate latent variables like player skill to better predict the next character.

We derive a player skill vector and add it to the model, improving the model’s win rate by up to 2.6×.

--- https://adamkarvonen.github.io/machine_learning/2024/03/20/chess-gpt-interventions.html 2024-03-26 reinforcement-learning/chess reinforcement-learning/model/decision-transformer --- https://books.google.com/books?id=b2G1CRfNqFYC&printsec=frontcover 2024-03-26 psychology/chess --- https://arxiv.org/abs/2403.17887#facebook The Unreasonable Ineffectiveness of the Deeper Layers Andrey Gromov, Kushal Tirumala, Hassan Shapourian, Paolo Glorioso, Daniel A. Roberts 2024-03-26 2024-03-27 [("doi","10.48550/arXiv.2403.17887")] ai/nn/sparsity/pruning

We empirically study a simple layer-pruning strategy for popular families of open-weight pretrained LLMs, finding minimal degradation of performance on different question-answering benchmarks until after a large fraction (up to half) of the layers are removed. This finding is significant because it indicates that many of the deeper layers in these models might not be as essential to their performance as previously thought.

To prune these models, we identify the optimal block of layers to prune by considering similarity across layers; then, to “heal” the damage, we perform a small amount of finetuning. Specifically, we use parameter-efficient finetuning (PEFT) methods, specifically quantization and Low Rank Adapters (QLoRA), such that each of our experiments can be performed on a single A100 GPU. This approach is designed to maintain, or even improve, the efficiency and effectiveness of the pruning process.

From a practical perspective, these results suggest that layer pruning methods can complement other PEFT strategies to further reduce computational resources of finetuning on the one hand, and can improve the memory and latency of inference on the other hand. This has implications for deploying large models in resource-constrained environments.

From a scientific perspective, the robustness of these LLMs to the deletion of layers implies either that current pretraining methods are not properly leveraging the parameters in the deeper layers of the network or that the shallow layers play a critical role in storing knowledge. This finding opens new avenues for research into the efficiency of neural network architectures.

--- https://arxiv.org/abs/2002.11787 Moniqua: Modulo Quantized Communication in Decentralized SGD Yucheng Lu, Christopher De Sa 2020-02-26 2024-03-27 [("doi","10.48550/arXiv.2002.11787")] ai/nn/sparsity/low-precision

Running Stochastic Gradient Descent (SGD) in a decentralized fashion has shown promising results.

In this paper we propose Moniqua, a technique that allows decentralized SGD to use quantized communication. We prove in theory that Moniqua communicates a provably bounded number of bits per iteration, while converging at the same asymptotic rate as the original algorithm does with full-precision communication. Moniqua improves upon prior works in that it (1) requires zero additional memory, (2) works with 1-bit quantization, and (3) is applicable to a variety of decentralized algorithms.

We demonstrate empirically that Moniqua converges faster with respect to wall clock time than other quantized decentralized algorithms. We also show that Moniqua is robust to very low bit-budgets, allowing 1-bit-per-parameter communication without compromising validation accuracy when training ResNet-20 and ResNet-110 on CIFAR-10.

--- /doc/fiction/criticism/1986-hyde-alcoholandpoetry.pdf Alcohol and Poetry: John Berryman and the Booze Talking Lewis Hyde 1986-01-01 2024-03-27 fiction/criticism fiction/poetry psychiatry/alcoholism --- https://www.chicagotribune.com/1989/02/28/booze-and-the-muse/ 2024-01-01 psychiatry/alcoholism --- https://www.bmj.com/content/347/bmj.f7255 2024-01-01 psychiatry/alcoholism --- https://www.funraniumlabs.com/2013/06/alcoholism-in-antarctica/ 2024-01-01 psychiatry/alcoholism --- /doc/longevity/2015-gepner.pdf 2015-01-01 2024-01-01 longevity psychiatry/alcoholism --- /doc/genetics/heritable/correlation/2018-walters.pdf Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders Raymond K. Walters, Renato Polimanti, Emma C. Johnson, Jeanette N. McClintick, Mark J. Adams, Amy E. Adkins, Fazil Aliev, Silviu-Alin Bacanu, Anthony Batzler, Sarah Bertelsen, Joanna M. Biernacka, Tim B. Bigdeli, Li-Shiun Chen, Toni-Kim Clarke, Yi-Ling Chou, Franziska Degenhardt, Anna R. Docherty, Alexis C. Edwards, Pierre Fontanillas, Jerome C. Foo, Louis Fox, Josef Frank, Ina Giegling, Scott D. Gordon, Laura M. Hack, Annette M. Hartmann, Sarah M. Hartz, Stefanie Heilmann-Heimbach, Stefan Herms, Colin Hodgkinson, Per Hoffmann, Jouke Hottenga, Martin A. Kennedy, Mervi Alanne-Kinnunen, Bettina Konte, Jari Lahti, Marius Lahti-Pulkkinen, Dongbing Lai, Lannie Ligthart, Anu Loukola, Brion S. Maher, Hamdi Mbarek, Andrew M. McIntosh, Matthew B. McQueen, Jacquelyn L. Meyers, Yuri Milaneschi, Teemu Palviainen, John F. Pearson, Roseann E. Peterson, Samuli Ripatti, Euijung Ryu, Nancy L. Saccone, Jessica E. Salvatore, Sandra Sanchez-Roige, Melanie Schwandt, Richard Sherva, Fabian Streit, Jana Strohmaier, Nathaniel Thomas, Jen-Chyong Wang, Bradley T. Webb, Robbee Wedow, Leah Wetherill, Amanda G. Wills, Michelle Agee, Babak Alipanahi, Adam Auton, Robert K. Bell, Katarzyna Bryc, Sarah L. Elson, Pierre Fontanillas, Nicholas A. Furlotte, David A. Hinds, Karen E. Huber, Aaron Kleinman, Nadia K. Litterman, Jennifer C. McCreight, Matthew H. McIntyre, Joanna L. Mountain, Elizabeth S. Noblin, Carrie A. M. Northover, Steven J. Pitts, J. Fah Sathirapongsasuti, Olga V. Sazonova, Janie F. Shelton, Suyash Shringarpure, Chao Tian, Joyce Y. Tung, Vladimir Vacic, Catherine H. Wilson, Jason D. Boardman, Danfeng Chen, Doo-Sup Choi, William E. Copeland, Robert C. Culverhouse, Norbert Dahmen, Louisa Degenhardt, Benjamin W. Domingue, Sarah L. Elson, Mark A. Frye, Wolfgang Gäbel, Caroline Hayward, Marcus Ising, Margaret Keyes, Falk Kiefer, John Kramer, Samuel Kuperman, Susanne Lucae, Michael T. Lynskey, Wolfgang Maier, Karl Mann, Satu Männistö, Bertram Müller-Myhsok, Alison D. Murray, John I. Nurnberger, Aarno Palotie, Ulrich Preuss, Katri Räikkönen, Maureen D. Reynolds, Monika Ridinger, Norbert Scherbaum, Marc A. Schuckit, Michael Soyka, Jens Treutlein, Stephanie Witt, Norbert Wodarz, Peter Zill, Daniel E. Adkins, Joseph M. Boden, Dorret I. Boomsma, Laura J. Bierut, Sandra A. Brown, Kathleen K. Bucholz, Sven Cichon, E. Jane Costello, Harriet Wit, Nancy Diazgranados, Danielle M. Dick, Johan G. Eriksson, Lindsay A. Farrer, Tatiana M. Foroud, Nathan A. Gillespie, Alison M. Goate, David Goldman, Richard A. Grucza, Dana B. Hancock, Kathleen Mullan Harris, Andrew C. Heath, Victor Hesselbrock, John K. Hewitt, Christian J. Hopfer, John Horwood, William Iacono, Eric O. Johnson, Jaakko A. Kaprio, Victor M. Karpyak, Kenneth S. Kendler, Henry R. Kranzler, Kenneth Krauter, Paul Lichtenstein, Penelope A. Lind, Matt McGue, James MacKillop, Pamela A. F. Madden, Hermine H. Maes, Patrik Magnusson, Nicholas G. Martin, Sarah E. Medland, Grant W. Montgomery, Elliot C. Nelson, Markus M. Nöthen, Abraham Palmer, Nancy L. Pedersen, Brenda W. J. H. Penninx, Bernice Porjesz, John P. Rice, Marcella Rietschel, Brien P. Riley, Richard Rose, Dan Rujescu, Pei-Hong Shen, Judy Silberg, Michael C. Stallings, Ralph E. Tarter, Michael M. Vanyukov, Scott Vrieze, Tamara L. Wall, John B. Whitfield, Hongyu Zhao, Benjamin M. Neale, Joel Gelernter, Howard J. Edenberg, Arpana Agrawal 2018-01-01 2024-01-01 [("doi","10.1038/s41593-018-0275-1")] genetics/heritable/correlation psychiatry/alcoholism --- https://www.nature.com/articles/s41598-018-34713-z 2024-01-01 psychiatry/alcoholism --- /doc/genetics/heritable/correlation/2019-liu.pdf Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use Mengzhen Liu, Yu Jiang, Robbee Wedow, Yue Li, David M. Brazel, Fang Chen, Gargi Datta, Jose Davila-Velderrain, Daniel McGuire, Chao Tian, Xiaowei Zhan, andMe Research Team, HUNT All-In Psychiatry, Hélène Choquet, Anna R. Docherty, Jessica D. Faul, Johanna R. Foerster, Lars G. Fritsche, Maiken Elvestad Gabrielsen, Scott D. Gordon, Jeffrey Haessler, Jouke-Jan Hottenga, Hongyan Huang, Seon-Kyeong Jang, Philip R. Jansen, Yueh Ling, Reedik Mägi, Nana Matoba, George McMahon, Antonella Mulas, Valeria Orrù, Teemu Palviainen, Anita Pandit, Gunnar W. Reginsson, Anne Heidi Skogholt, Jennifer A. Smith, Amy E. Taylor, Constance Turman, Gonneke Willemsen, Hannah Young, Kendra A. Young, Gregory J. M. Zajac, Wei Zhao, Wei Zhou, Gyda Bjornsdottir, Jason D. Boardman, Michael Boehnke, Dorret I. Boomsma, Chu Chen, Francesco Cucca, Gareth E. Davies, Charles B. Eaton, Marissa A. Ehringer, Tõnu Esko, Edoardo Fiorillo, Nathan A. Gillespie, Daniel F. Gudbjartsson, Toomas Haller, Kathleen Mullan Harris, Andrew C. Heath, John K. Hewitt, Ian B. Hickie, John E. Hokanson, Christian J. Hopfer, David J. Hunter, William Iacono, Eric O. Johnson, Yoichiro Kamatani, Sharon L. R. Kardia, Matthew C. Keller, Manolis Kellis, Charles Kooperberg, Peter Kraft, Kenneth S. Krauter, Markku Laakso, Penelope A. Lind, Anu Loukola, Sharon M. Lutz, Pamela A. F. Madden, Nicholas G. Martin, Matt McGue, Matthew B. McQueen, Sarah E. Medland, Andres Metspalu, Karen L. Mohlke, Jonas B. Nielsen, Yukinori Okada, Ulrike Peters, Tinca J. C. Polderman, Danielle Posthuma, Alexander P. Reiner, John P. Rice, Eric Rimm, Richard J. Rose, Valgerdur Runarsdottir, Michael C. Stallings, Alena Stančáková, Hreinn Stefansson, Khanh K. Thai, Hilary A. Tindle, Thorarinn Tyrfingsson, Tamara L. Wall, David R. Weir, Constance Weisner, John B. Whitfield, Bendik Slagsvold Winsvold, Jie Yin, Luisa Zuccolo, Laura J. Bierut, Kristian Hveem, James J. Lee, Marcus R. Munafò, Nancy L. Saccone, Cristen Jennifer Willer, Marilyn C. Cornelis, Sean P. David, David A. Hinds, Eric Jorgenson, Jaakko Kaprio, Jerry A. Stitzel, Kari Stefansson, Thorgeir E. Thorgeirsson, Gonçalo Abecasis, Dajiang J. Liu, Scott Vrieze 2019-01-01 2024-01-01 [("doi","10.1038/s41588-018-0307-5")] genetics/heritable/correlation psychiatry/alcoholism --- https://slate.com/technology/2015/02/vicemo-shows-venmo-transactions-related-to-drugs-alcohol-and-sex.html Who’s Buying Drugs, Sex, and Booze on Venmo? This Site Will Tell You 2024-01-01 darknet-market --- /doc/genetics/heritable/correlation/1994-reed.pdf Correlations of Alcohol Consumption with Related Covariates and Heritability Estimates in Older Adult Males Over a 14- to 18-Year Period: The NHLBI Twin Study 1994-01-01 2024-01-01 genetics/heritable/correlation psychiatry/alcoholism --- /doc/genetics/heritable/correlation/1997-swan.pdf Heavy Consumption of Cigarettes, Alcohol and Coffee in Male Twins Gary E. Swan, Dorit Carmelli, Lon R. Cardon 1997-01-01 2024-01-01 genetics/heritable/correlation psychiatry/alcoholism --- /doc/culture/1996-slater.pdf Value-Affirmative and Value-Protective Processing of Alcohol Education Messages That Include Statistical Evidence or Anecdotes 1996 2024-01-01 culture psychiatry/alcoholism statistics --- https://aider.chat/2024/03/08/claude-3.html 2024-03-27 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex --- /doc/psychology/neuroscience/2023-liu-figure2-poweranalysiscurveofincreasingcorrelationofukbbfmribrainimagingdatawith6phenotypesagebmiintelligencenumericmemoryneuroticismalcoholobirthmonth.jpg 2023 2024-01-01 psychiatry/alcoholism psychology/neuroscience --- https://www.reddit.com/r/Damnthatsinteresting/comments/r6yehn/house_cat_suffering_from_myostatinrelated_muscle/ 2024-03-27 cat/genetics genetics/heritable/rare --- https://en.wikipedia.org/wiki/Collaborative_Study_on_the_Genetics_of_Alcoholism Collaborative Study on the Genetics of Alcoholism 2024-03-27 psychiatry/alcoholism --- https://en.wikipedia.org/wiki/Long-term_effects_of_alcohol Long-term effects of alcohol 2024-01-01 psychiatry/alcoholism --- /doc/genetics/heritable/correlation/2017-chester.pdf Genetic correlation between alcohol preference and conditioned fear: Exploring a functional relationship Julia A. Chester, Marcus M. Weera 2017-01-01 2024-01-01 [("doi","10.1016/j.alcohol.2016.06.006")] genetics/heritable/correlation psychiatry/alcoholism --- /doc/genetics/heritable/correlation/2017-jorgenson.pdf Genetic contributors to variation in alcohol consumption vary by race/ethnicity in a large multi-ethnic genome-wide association study E Jorgenson, K. K. Thai, T. J. Hoffmann, L. C. Sakoda, M. N. Kvale, Y. Banda, C. Schaefer, N. Risch, J. Mertens, C. Weisner, H. Choquet 2017-01-01 2024-01-01 [("doi","10.1038/mp.2017.101")] genetics/heritable/correlation psychiatry/alcoholism --- /doc/genetics/heritable/correlation/2017-rosenstrom.pdf Prediction of alcohol use disorder using personality disorder traits: a twin study Tom Rosenström, Fartein Ask Torvik, Eivind Ystrom, Nikolai Olavi Czajkowski, Nathan A. Gillespie, Steven H. Aggen, Robert F. Krueger, Kenneth S. Kendler, Ted Reichborn-Kjennerud 2017-01-01 2024-01-01 [("doi","10.1111/add.13951")] genetics/heritable/correlation psychiatry/alcoholism --- /doc/genetics/heritable/correlation/2016-salvatore.pdf Alcohol use disorder and divorce: Evidence for a genetic correlation in a population-based Swedish sample Jessica E. Salvatore, Sara Larsson Lönn, Jan Sundquist, Paul Lichtenstein, Kristina Sundquist, Kenneth S. Kendler 2016-01-01 2024-01-01 [("doi","10.1111/add.13719")] genetics/heritable/correlation psychiatry/alcoholism --- https://en.wikipedia.org/wiki/Health_effects_of_wine Health effects of wine 2024-01-01 psychiatry/alcoholism --- https://www.reddit.com/r/Nootropics/comments/46kiy4/theres_a_community_of_us_that_have_adhdi/ 2024-01-01 psychiatry/adhd psychiatry/alcoholism --- https://www.reddit.com/r/Nootropics/comments/89u96j/a_third_and_final_followup_to_the_original/ 2024-01-01 psychiatry/adhd psychiatry/alcoholism --- https://www.reddit.com/r/hangovereffect/ 2024-01-01 psychiatry/adhd psychiatry/alcoholism --- https://www.lesswrong.com/posts/LfrNFfJFcqnG9WuFf/activated-charcoal-for-hangover-prevention-way-more-than-you 2024-01-01 psychiatry/adhd psychiatry/alcoholism --- https://arxiv.org/abs/2305.15801 Lucy-SKG: Learning to Play Rocket League Efficiently Using Deep Reinforcement Learning Vasileios Moschopoulos, Pantelis Kyriakidis, Aristotelis Lazaridis, Ioannis Vlahavas 2023-05-25 2024-03-27 [("doi","10.48550/arXiv.2305.15801")] reinforcement-learning/model-free

A successful tactic that is followed by the scientific community for advancing AI is to treat games as problems, which has been proven to lead to various breakthroughs. We adapt this strategy in order to study Rocket League, a widely popular but rather under-explored 3D multiplayer video game with a distinct physics engine and complex dynamics that pose a challenge in developing efficient and high-performance game-playing agents.

In this paper, we present Lucy-SKG, a Reinforcement Learning-based model that learned how to play Rocket League in a sample-efficient manner, outperforming by a notable margin the two highest-ranking bots in this game, namely Necto (2022 bot champion) and its successor Nexto, thus becoming a state-of-the-art agent. Our contributions include: (1) the development of a reward analysis and visualization library, (2) novel parameterizable reward shape functions that capture the utility of complex reward types via our proposed Kinesthetic Reward Combination (KRC) technique, and (3) design of auxiliary neural architectures for training on reward prediction and state representation tasks in an on-policy fashion for enhanced efficiency in learning speed and performance.

By performing thorough ablation studies for each component of Lucy-SKG, we showed their independent effectiveness in overall performance. In doing so, we demonstrate the prospects and challenges of using sample-efficient reinforcement learning techniques for controlling complex dynamical systems under competitive team-based multiplayer conditions.

--- /doc/modafinil/2015-mete.pdf Compulsive modafinil use in a patient with a history of alcohol use disorder Melek Cengiz Mete, Ömer Şenormancı, Özge Saraçlı, Nuray Atasoy, Levent Atik 2015-01-01 2024-01-01 [("doi","10.1016/j.genhosppsych.2015.01.001")] modafinil psychiatry/alcoholism --- /doc/modafinil/2013-schmaal.pdf Effects of Modafinil on Neural Correlates of Response Inhibition in Alcohol-Dependent Patients Lianne Schmaal, Leen Joos, Marte Koeleman, Dick J. Veltman, Wim van den Brink, Anna E. Goudriaan 2013-01-01 2024-01-01 [("doi","10.1016/j.biopsych.2012.06.032")] modafinil psychiatry/alcoholism --- /doc/genetics/heritable/correlation/2002-madden.pdf Shared Genetic Vulnerability in Alcohol and Cigarette Use and Dependence 2002-01-01 2024-01-01 genetics/heritable/correlation nicotine psychiatry/alcoholism --- /doc/longevity/2009-guyuron-figure10-effectofalcoholadvoidanceonfacialagedifferenceinidenticaltwins.jpg 2009 2024-01-01 longevity psychiatry/alcoholism --- https://www.youtube.com/watch?v=wjge1bVobN0 How Mario 64 was solved using parallel universes—Super Mario 64 Tool-Assisted Speedrun Explained 2024-01-01 cs/security --- https://www.youtube.com/watch?v=x2ypDDYDl70 Nichijou—Ehhh‽ (Episode 10, Part 41, nagashi somen in the park) 2024-01-01 anime --- https://www.youtube.com/watch?v=xNB_89vZ0y4?t=1884 Ted Chiang on Free Will, Time Travel, Many Worlds, Genetic Engineering, and Hard Science Fiction 2024-01-01 fiction/science-fiction/time-travel --- https://xkcd.com/2857/ XKCD comic about intellectual fashions: a stickman on a stage presenting a new paper says ‘It’s become conventional wisdom that the backlash against the prevailing consensus led researchers to ignore inconvenient new evidence. However...’, with a commentary caption stating ‘In a field that has been around for a while, it can be hard to figure out how many levels of rebuttal deep you are.’ The meta-commentary caption states ‘The mainstream dogma sparked a wave of dogmatic revisionism, and this revisionist mainstream dogmatism has now given way to a more rematic mainvisionist dogstream.’ This is a humorous commentary on object vs meta-levels, contrarianism, meta-contrarianism, and signaling. 2024-01-01 sociology --- https://xkcd.com/941/ XKCD #941: Depth Perception 2024-01-01 psychology/vision --- https://www2.psych.ubc.ca/~schaller/528Readings/Tesser1993.pdf The Importance of Heritability in Psychological Research: The Case of Attitudes 2024-01-01 genetics/heritable politics --- https://www2.psych.ubc.ca/~schaller/528Readings/McClelland1997.pdf Optimal design in psychological research 2024-01-01 psychology statistics/power-analysis --- https://en.wikipedia.org/wiki/Purely_functional_data_structure Purely functional data structure 2024-01-01 cs/haskell --- https://replicationindex.com/2020/12/30/a-meta-scientific-perspective-on-thinking-fast-and-slow/ 2024-03-27 psychology/cognitive-bias statistics/bias --- https://arxiv.org/abs/1905.10501 Learning to Reason in Large Theories without Imitation Kshitij Bansal, Christian Szegedy, Markus N. Rabe, Sarah M. Loos, Viktor Toman 2019-05-25 2024-03-27 [("doi","10.48550/arXiv.1905.10501")] math reinforcement-learning/exploration reinforcement-learning/imitation-learning reinforcement-learning/model

In this paper, we demonstrate how to do automated theorem proving in the presence of a large knowledge base of potential premises without learning from human proofs.

We suggest an exploration mechanism that mixes in additional premises selected by a tf-idf (term frequency-inverse document frequency) based lookup in a deep reinforcement learning scenario. This helps with exploring and learning which premises are relevant for proving a new theorem.

Our experiments show that the theorem prover trained with this exploration mechanism outperforms provers that are trained only on human proofs. It approaches the performance of a prover trained by a combination of imitation and reinforcement learning.

We perform multiple experiments to understand the importance of the underlying assumptions that make our exploration approach work, thus explaining our design choices.

--- https://arxiv.org/abs/2006.04757 Mathematical Reasoning via Self-supervised Skip-tree Training Markus N. Rabe, Dennis Lee, Kshitij Bansal, Christian Szegedy 2020-06-08 2024-03-27 [("doi","10.48550/arXiv.2006.04757")] math reinforcement-learning/model

We examine whether self-supervised language modeling applied to mathematical formulas enables logical reasoning. We suggest several logical reasoning tasks that can be used to evaluate language models trained on formal mathematical statements, such as type inference, suggesting missing assumptions and completing equalities.

To train language models for formal mathematics, we propose a novel skip-tree task.

We find that models trained on the skip-tree task show surprisingly strong mathematical reasoning abilities, and outperform models trained on standard skip-sequence tasks. We also analyze the models’ ability to formulate new conjectures by measuring how often the predictions are provable and useful in other proofs.

--- https://arxiv.org/abs/quant-ph/9804010 Quantum strategies David A. Meyer 1998-04-03 2024-03-27 [("doi","10.1103/PhysRevLett.82.1052")] economics/mechanism-design

We consider game theory from the perspective of quantum algorithms. Strategies in classical game theory are either pure (deterministic) or mixed (probabilistic). We introduce these basic ideas in the context of a simple example, closely related to the traditional Matching Pennies game.

While not every two-person zero-sum finite game has an equilibrium in the set of pure strategies, von Neumann showed that there is always an equilibrium at which each player follows a mixed strategy. A mixed strategy deviating from the equilibrium strategy cannot increase a player’s expected payoff.

We show, however, that in our example a player who implements a quantum strategy can increase his expected payoff, and explain the relation to efficient quantum algorithms.

We prove that in general a quantum strategy is always at least as good as a classical one, and furthermore that when both players use quantum strategies there need not be any equilibrium, but if both are allowed mixed quantum strategies there must be.

--- https://arxiv.org/abs/quant-ph/0311104 Quantum and classical correlations between players in game theory Junichi Shimamura, Sahin Kaya Ozdemir, Fumiaki Morikoshi, Nobuyuki Imoto 2003-11-17 2024-03-27 [("doi","10.48550/arXiv.0311104")] economics/mechanism-design

Effects of quantum and classical correlations on game theory are studied to clarify the new aspects brought into game theory by the quantum mechanical toolbox.

In this study, we compare quantum correlation represented by a maximally entangled state and classical correlation that is generated through phase damping processes on the maximally entangled state. Thus, this also sheds light on the behavior of games under the influence of noisy sources.

It is observed that the quantum correlation can always resolve the dilemmas in non-zero sum games and attain the maximum sum of both players’ payoffs, while the classical correlation cannot necessarily resolve the dilemmas.

--- https://en.wikipedia.org/wiki/Hierarchical_Navigable_Small_World_graphs Hierarchical Navigable Small World graphs 2024-03-27 ai/nn/retrieval --- https://www.cs.purdue.edu/homes/comer/essay.criticize.html 2024-03-28 math/humor --- https://arxiv.org/abs/2403.17804#facebook Improving Text-to-Image Consistency via Automatic Prompt Optimization Oscar Mañas, Pietro Astolfi, Melissa Hall, Candace Ross, Jack Urbanek, Adina Williams, Aishwarya Agrawal, Adriana Romero-Soriano, Michal Drozdzal 2024-03-26 2024-03-28 [("doi","10.48550/arXiv.2403.17804")] ai/nn/diffusion

Impressive advances in text-to-image (T2I) generative models have yielded a plethora of high performing models which are able to generate esthetically appealing, photorealistic images. Despite the progress, these models still struggle to produce images that are consistent with the input prompt, oftentimes failing to capture object quantities, relations and attributes properly.

Existing solutions to improve prompt-image consistency suffer from the following challenges: (1) they oftentimes require model fine-tuning, (2) they only focus on nearby prompt samples, and (3) they are affected by unfavorable trade-offs among image quality, representation diversity, and prompt-image consistency.

In this paper, we address these challenges and introduce a T2I optimization-by-prompting framework, OPT2I, which leverages a large language model (LLM) to improve prompt-image consistency in T2I models. Our framework starts from a user prompt and iteratively generates revised prompts with the goal of maximizing a consistency score.

Our extensive validation on two datasets, MS COCO and PartiPrompts, shows that OPT2I can boost the initial consistency score by up to 24.9% in terms of DSG score while preserving the FID and increasing the recall between generated and real data.

Our work paves the way toward building more reliable and robust T2I systems by harnessing the power of LLMs.

--- https://rtwolf.github.io/Everyone-is-John/ Everyone Is John 2024-03-28 fiction/text-game --- https://scholars-stage.org/american-nightmares-wang-huning-and-alexis-de-tocqueville-dark-visions-of-the-future/ 2024-03-28 sociology/technology --- https://jillianhess.substack.com/p/renoted-marginalia-5-ways-to-write 2024-03-28 design/typography/sidenote --- https://en.wikipedia.org/wiki/Schreckstoff Schreckstoff 2024-03-28 psychology/smell --- https://www.lrb.co.uk/the-paper/v35/n07/nicholas-spice/is-wagner-bad-for-us 2024-03-28 fiction/opera --- /doc/psychology/animal/bird/2024-suzuki.pdf The ‘after you’ gesture in a bird Toshitaka N. Suzuki, Norimasa Sugita 2024-03-25 2024-03-28 [("doi","10.1016/j.cub.2024.01.030")] psychology/animal/bird psychology/linguistics

Gestures are ubiquitous in human communication, involving movements of body parts produced for a variety of purposes, such as pointing out objects (deictic gestures) or conveying messages (symbolic gestures). While displays of body parts have been described in many animals, their functional similarity to human gestures has primarily been explored in great apes, with little research attention given to other animal groups. To date, only a few studies have provided evidence for deictic gestures in birds and fish, but it is unclear whether non-primate animals can employ symbolic gestures, such as waving to mean ‘goodbye’, which are, in humans, more cognitively demanding than deictic gestures.

Here, we report that the Japanese tit (Parus minor), a socially monogamous bird, uses wing-fluttering to prompt their mated partner to enter the nest first, and that wing-fluttering functions as a symbolic gesture conveying a specific message (‘after you’).

Our findings encourage further research on animal gestures, which may help in understanding the evolution of complex communication, including language.

See Also:

--- https://en.wikipedia.org/wiki/SimHash SimHash 2024-03-28 cs/algorithm/information/compression --- https://arxiv.org/abs/2308.03296#anthropic Studying Large Language Model Generalization with Influence Functions Roger Grosse, Juhan Bae, Cem Anil, Nelson Elhage, Alex Tamkin, Amirhossein Tajdini, Benoit Steiner, Dustin Li, Esin Durmus, Ethan Perez, Evan Hubinger, Kamilė Lukošiūtė, Karina Nguyen, Nicholas Joseph, Sam McCandlish, Jared Kaplan, Samuel R. Bowman 2023-08-07 2024-03-28 [("doi","10.48550/arXiv.2308.03296")] ai/nn/transformer/gpt

When trying to gain better visibility into a machine learning model in order to understand and mitigate the associated risks, a potentially valuable source of evidence is: which training examples most contribute to a given behavior? Influence functions aim to answer a counterfactual: how would the model’s parameters (and hence its outputs) change if a given sequence were added to the training set? While influence functions have produced insights for small models, they are difficult to scale to large language models (LLMs) due to the difficulty of computing an inverse-Hessian-vector product (IHVP).

We use the Eigenvalue-corrected Kronecker-Factored Approximate Curvature (EK-FAC) approximation to scale influence functions up to LLMs with up to 52 billion parameters. In our experiments, EK-FAC achieves similar accuracy to traditional influence function estimators despite the IHVP computation being orders of magnitude faster. We investigate two algorithmic techniques to reduce the cost of computing gradients of candidate training sequences: TF-IDF filtering and query batching.

We use influence functions to investigate the generalization patterns of LLMs, including the sparsity of the influence patterns, increasing abstraction with scale, math and programming abilities, cross-lingual generalization, and role-playing behavior.

Despite many apparently sophisticated forms of generalization, we identify a surprising limitation: influences decay to near-zero when the order of key phrases is flipped. [Reversal curse?]

Overall, influence functions give us a powerful new tool for studying the generalization properties of LLMs.

--- https://www.tokyodev.com/articles/the-rise-and-fall-of-dnd-in-japan 2024-03-28 anime fiction/text-game --- https://www.lesswrong.com/posts/EQJfdqSaMcJyR5k73/habryka-s-shortform-feed#kt6qQvDpMoTZvCxfp Hybridizing forums and wikis proposal Gwern 2024-03-24 2024-03-28 design --- https://arxiv.org/abs/2308.09124 Linearity of Relation Decoding in Transformer Language Models Evan Hernandez, Arnab Sen Sharma, Tal Haklay, Kevin Meng, Martin Wattenberg, Jacob Andreas, Yonatan Belinkov, David Bau 2023-08-17 2024-03-28 [("doi","10.48550/arXiv.2308.09124")] ai/nn/transformer/attention ai/nn/transformer/gpt/2

Much of the knowledge encoded in transformer language models (LMs) may be expressed in terms of relations: relations between words and their synonyms, entities and their attributes, etc.

We show that, for a subset of relations, this computation is well-approximated by a single linear transformation on the subject representation. Linear relation representations may be obtained by constructing a first-order approximation to the LM from a single prompt, and they exist for a variety of factual, commonsense, and linguistic relations. However, we also identify many cases in which LM predictions capture relational knowledge accurately, but this knowledge is not linearly encoded in their representations.

Our results thus reveal a simple, interpretable, but heterogeneously deployed knowledge representation strategy in transformer LMs.

--- https://en.wikipedia.org/wiki/Jumping_Frenchmen_of_Maine#Signs_and_symptoms Jumping Frenchmen of Maine § Signs and symptoms 2024-03-29 genetics/heritable/rare psychiatry/anxiety --- https://arxiv.org/abs/2303.03915 The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset Hugo Laurençon, Lucile Saulnier, Thomas Wang, Christopher Akiki, Albert Villanova del Moral, Teven Le Scao, Leandro Von Werra, Chenghao Mou, Eduardo González Ponferrada, Huu Nguyen, Jörg Frohberg, Mario Šaško, Quentin Lhoest, Angelina McMillan-Major, Gerard Dupont, Stella Biderman, Anna Rogers, Loubna Ben allal, Francesco De Toni, Giada Pistilli, Olivier Nguyen, Somaieh Nikpoor, Maraim Masoud, Pierre Colombo, Javier de la Rosa, Paulo Villegas, Tristan Thrush, Shayne Longpre, Sebastian Nagel, Leon Weber, Manuel Muñoz, Jian Zhu, Daniel Van Strien, Zaid Alyafeai, Khalid Almubarak, Minh Chien Vu, Itziar Gonzalez-Dios, Aitor Soroa, Kyle Lo, Manan Dey, Pedro Ortiz Suarez, Aaron Gokaslan, Shamik Bose, David Adelani, Long Phan, Hieu Tran, Ian Yu, Suhas Pai, Jenny Chim, Violette Lepercq, Suzana Ilic, Margaret Mitchell, Sasha Alexandra Luccioni, Yacine Jernite 2023-03-07 2024-03-29 [("doi","10.48550/arXiv.2303.03915")] ai/dataset

As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large language models as a values-driven undertaking, putting issues of ethics, harm, and governance in the foreground.

This paper documents the data creation and curation efforts undertaken by BigScience to assemble the Responsible Open-science Open-collaboration Text Sources (ROOTS) corpus, a 1.6TB dataset spanning 59 languages [~341 billion tokens] that was used to train the 176-billion-parameter BigScience Large Open-science Open-access Multilingual (BLOOM) language model.

We further release a large initial subset of the corpus and analyses thereof, and hope to empower large-scale monolingual and multilingual modeling projects with both the data and the processing tools, as well as stimulate research around this large multilingual corpus.

--- https://conversationswithtyler.com/episodes/fareed-zakaria/ 2024-03-29 psychology/writing --- https://www.vox.com/future-perfect/24108787/ai-economic-growth-explosive-automation 2024-03-29 economics/automation --- https://www.ai21.com/blog/announcing-jamba 2024-03-29 ai/nn/rnn ai/scaling/mixture-of-experts --- https://arxiv.org/abs/2401.13660 MambaByte: Token-free Selective State Space Model Junxiong Wang, Tushaar Gangavarapu, Jing Nathan Yan, Alexander M. Rush 2024-01-24 2024-03-29 [("doi","10.48550/arXiv.2401.13660")] ai/nn/rnn ai/nn/tokenization

Token-free language models learn directly from raw bytes and remove the bias of subword tokenization. Operating on bytes, however, results in longer sequences, and standard autoregressive Transformers scale poorly in such settings.

We experiment with MambaByte, a token-free adaptation of the Mamba state space model, trained autoregressively on byte sequences.

Our experiments indicate the computational efficiency of MambaByte compared to other byte-level models. We also find MambaByte to be competitive with and even outperform state-of-the-art subword Transformers. Furthermore, owing to linear scaling in length, MambaByte benefits from fast inference compared to Transformers.

Our findings establish the viability of MambaByte in enabling token-free language modeling.

--- https://x.com/_joelsimon/status/1773772906125992243 Joel Simon 2024-03-29 ai/nn/diffusion ai/nn/gan/biggan --- https://openai.com/blog/navigating-the-challenges-and-opportunities-of-synthetic-voices 2024-03-29 ai/music --- https://read.gov/aesop/001.html 2024-03-29 cs/css --- https://www.multicians.org/thvv/hellandizing.html 2024-03-29 cs/end-to-end-principle --- https://ece.illinois.edu/newsroom/monocycle-robot 2024-03-29 reinforcement-learning/robot --- https://ieeexplore.ieee.org/document/10423226 2024-03-29 reinforcement-learning/robot --- https://www.wired.com/2014/10/a-spreadsheet-way-of-knowledge/ 2024-03-29 cs design economics --- https://www.atlasobscura.com/articles/mystery-preserved-brains 2024-03-29 cryonics psychology/neuroscience --- https://royalsocietypublishing.org/doi/10.1098/rspb.2023.2606 2024-03-29 cryonics psychology/neuroscience --- https://arxiv.org/abs/2402.09171#facebook TestGen-LLM: Automated Unit Test Improvement using Large Language Models at Meta Nadia Alshahwan, Jubin Chheda, Anastasia Finegenova, Beliz Gokkaya, Mark Harman, Inna Harper, Alexandru Marginean, Shubho Sengupta, Eddy Wang 2024-02-14 2024-03-30 [("doi","10.48550/arXiv.2402.09171")] ai/nn/transformer/gpt/codex

This paper describes Meta’s TestGen-LLM tool, which uses LLMs to automatically improve existing human-written tests.

TestGen-LLM verifies that its generated test classes successfully clear a set of filters that assure measurable improvement over the original test suite, thereby eliminating problems due to LLM hallucination.

We describe the deployment of TestGen-LLM at Meta test-a-thons for the Instagram and Facebook platforms [on ‘LLM-1’ and ‘LLM-2’, presumably LLaMa-1/2]. In an evaluation on Reels and Stories products for Instagram, 75% of TestGen-LLM’s test cases built correctly, 57% passed reliably, and 25% increased coverage. During Meta’s Instagram and Facebook test-a-thons, it improved 11.5% of all classes to which it was applied, with 73% of its recommendations being accepted for production deployment by Meta software engineers.

We believe this is the first report on industrial scale deployment of LLM-generated code backed by such assurances of code improvement.

--- https://arxiv.org/abs/2310.17157 Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Re, Beidi Chen 2023-10-26 2024-03-30 [("doi","10.48550/arXiv.2310.17157")] ai/nn/sparsity

Large language models (LLMs) with hundreds of billions of parameters have sparked a new wave of exciting AI applications. However, they are computationally expensive at inference time. Sparsity is a natural approach to reduce this cost, but existing methods either require costly retraining, have to forgo LLM’s in-context learning ability, or do not yield wall-clock time speedup on modern hardware.

We hypothesize that contextual sparsity, which are small, input-dependent sets of attention heads and MLP parameters that yield the same output as the dense model for a given input, can address these issues. We show that contextual sparsity exists, that it can be accurately predicted, and that we can exploit it to speed up LLM inference in wall-clock time without compromising LLM’s quality or in-context learning ability.

Based on these insights, we propose DejaVu, a system that uses a low-cost algorithm to predict contextual sparsity on the fly given inputs to each layer, along with an asynchronous and hardware-aware implementation that speeds up LLM inference.

We validate that DejaVu can reduce the inference latency of OPT-175B by over 2× compared to the state-of-the-art FasterTransformer, and over 6× compared to the widely used Hugging Face implementation, without compromising model quality.

The code is available at Github.

--- https://arxiv.org/abs/2403.18978#snapchat TextCraftor: Your Text Encoder Can be Image Quality Controller Yanyu Li, Xian Liu, Anil Kag, Ju Hu, Yerlan Idelbayev, Dhritiman Sagar, Yanzhi Wang, Sergey Tulyakov, Jian Ren 2024-03-27 2024-03-30 [("doi","10.48550/arXiv.2403.18978")] ai/nn/diffusion ai/nn/transformer/clip reinforcement-learning/preference-learning

Diffusion-based text-to-image generative models, eg. Stable Diffusion, have revolutionized the field of content generation, enabling advancements in areas like image editing and video synthesis. Despite their formidable capabilities, these models are not without their limitations. It is still challenging to synthesize an image that aligns well with the input text, and multiple runs with carefully crafted prompts are required to achieve satisfactory results.

To mitigate these limitations, numerous studies have endeavored to fine-tune the pre-trained diffusion models, ie. U-Net, using various technologies. Yet, amidst these efforts, a pivotal question of text-to-image diffusion model training has remained largely unexplored: Is it possible and feasible to fine-tune the text encoder to improve the performance of text-to-image diffusion models?

Our findings reveal that, instead of replacing the CLIP text encoder used in Stable Diffusion with other large language models, we can enhance it through our proposed fine-tuning approach, TextCraftor, leading to substantial improvements in quantitative benchmarks and human assessments. Interestingly, our technique also empowers controllable image generation through the interpolation of different text encoders fine-tuned with various rewards.

We also demonstrate that TextCraftor is orthogonal to U-Net fine-tuning, and can be combined to further improve generative quality.

--- https://x.com/dogmadeath/status/1773150472758546733 dogmadeath 2024-03-30 ai/nn/adversarial ai/nn/transformer/gpt/claude --- https://pioneerworks.org/broadcast/club-med-adderall#ch_adderall-house-style-amber-a%27lee-frost 2024-03-30 nootropic psychology/writing --- https://www.nytimes.com/2024/03/30/health/seniors-alcohol-consumption.html 2024-03-30 psychiatry/alcoholism --- https://www.pewresearch.org/short-reads/2024/03/26/americans-use-of-chatgpt-is-ticking-up-but-few-trust-its-election-information/ 2024-03-30 ai/nn/transformer/gpt/3/nonfiction --- https://www.biorxiv.org/content/10.1101/2022.11.14.515741.full Control of cell proliferation by memories of mitosis Franz Meitinger, Robert L. Davis, Mallory B. Martinez, Andrew K. Shiau, Karen Oegema, Arshad Desai 2022-11-16 2024-03-30 [("doi","10.1101/2022.11.14.515741")] biology cs/computable

Time spent in mitosis is carefully monitored to halt the proliferation of potentially dangerous cells in a population.

Mitotic duration is tightly constrained, with extended mitotic duration being a characteristic of potentially problematic cells prone to chromosome missegregation and genomic instability.

We show that memories of mitotic duration are integrated by a p53-based mitotic stopwatch pathway to exert tight control over proliferation. The stopwatch halts proliferation of the products of a single extended mitosis or of successive modestly extended mitoses. Time in mitosis is monitored via mitotic kinase-regulated assembly of stopwatch complexes that are transmitted to daughter cells.

The stopwatch is inactivated in p53-mutant cancers, as well as in a substantial proportion of p53-wildtype cancers, consistent with classification of stopwatch complex subunits as tumor suppressors. Stopwatch status additionally influences efficacy of anti-mitotic agents currently used or in development for cancer therapy.

--- https://archive.nytimes.com/opinionator.blogs.nytimes.com/2008/09/23/toms-essay/ 2024-03-30 economics/copyright --- https://www.high-capacity.com/p/how-china-uses-foreign-firms-to-turbocharge 2024-03-30 technology --- https://x.com/robmen/status/1774067844785086775 Rob Mensching 2024-03-30 cs/security --- https://use.expensify.com/blog/scaling-sqlite-to-4m-qps-on-a-single-server 2024-01-01 cs/algorithm --- https://x.com/reyhkibanki/status/1774058790083813763 reyhkibanki 2024-03-30 ai/anime ai/nn/diffusion --- https://www.construction-physics.com/p/why-did-supersonic-airliners-fail 2024-03-30 technology --- https://www.freaktakes.com/p/managing-lockheeds-skunk-works 2024-03-30 technology --- https://en.wikipedia.org/wiki/Delirium_tremens Delirium tremens 2024-01-01 psychiatry/alcoholism --- https://en.wikipedia.org/wiki/Nayib_Bukele Nayib Bukele 2024-03-31 psychology/personality/narcissism --- https://bloop.ai/blog/evaluating-llms-on-cobol 2024-03-31 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8376657/ Effectiveness of psychological interventions in prison to reduce recidivism: a systematic review and meta-analysis of randomized controlled trials Gabrielle Beaudry, Rongqin Yu, Amanda E. Perry, Seena Fazel 2021 2024-03-31 [("doi","10.1016/S2215-0366(21)00170-X")] crime statistics/bias/publication

Background: Repeat offending, also known as criminal recidivism, in people released from prison has remained high over many decades. To address this, psychological treatments have been increasingly used in criminal justice settings; however, there is little evidence about their effectiveness. We aimed to evaluate the effectiveness of interventions in prison to reduce recidivism after release.

Methods: For this systematic review and meta-analysis, we searched the Cochrane Central Register of Controlled Trials, Embase, Global Health, MEDLINE, PsycINFO, and Google Scholar for articles published from database inception to February 17, 2021, without any language restrictions. We searched for randomized controlled trials (RCTs) that evaluated the effect of psychological interventions, delivered to adolescents and adults during incarceration, on recidivism outcomes after release. We excluded studies of solely pharmacological interventions and of participants in secure psychiatric hospitals or special residential units, or attending therapies mainly delivered outside of the prison setting. We extracted summary estimates from eligible RCTs.

Data were extracted and appraised according to a prespecified protocol, with effect sizes converted to odds ratios. We used a standardized form to extract the effects of interventions on recidivism and estimated risk of bias for each RCT. Planned sensitivity analyses were done by removing studies with fewer than 50 participants. Our primary outcome was recidivism. Data from individual RCTs were combined in a random-effects meta-analysis as pooled odds ratios (ORs) and we explored sources of heterogeneity by comparing effect sizes by study size, control group, and intervention type. The protocol was pre-registered with PROSPERO, CRD42020167228.

Findings: Of 6,345 articles retrieved, 29 RCTs (9,443 participants, 1,104 [11.7%] females, 8,111 [85.9%] males, and 228 [2.4%] unknown) met the inclusion criteria for the primary outcome. Mean ages were 31.4 years (SD 4.9, range 24.5-41.5) for adult participants and 17.5 years (SD 1.9; range 14.6-20.2) for adolescent participants. Race or ethnicity data were not sufficiently reported to be aggregated. Based on two studies, therapeutic communities were associated with decreased rates of recidivism (OR 0.64, 95% CI 0.46-0.91). These risk estimates did not statistically-significantly differ by type of control group and other study characteristics.

If including all 29 RCTs, psychological interventions were associated with reduced reoffending outcomes (OR 0.72, 95% CI 0.56-0.92). However, after excluding smaller studies (<50 participants in the intervention group), there was no statistically-significant reduction in recidivism (OR 0.87, 95% CI 0.68-1.11).

Interpretation: Widely implemented psychological interventions for people in prison to reduce offending after release need improvement. Publication bias and small-study effects appear to have overestimated the reported modest effects of such interventions, which were no longer present when only larger studies were included in analyses. Findings suggest that therapeutic communities and interventions that ensure continuity of care in community settings should be prioritized for future research. Developing new treatments should focus on addressing modifiable risk factors for reoffending.

Funding: Wellcome Trust, Fonds de recherche du Québec—Santé.

--- https://arxiv.org/abs/2403.18624 Vulnerability Detection with Code Language Models: How Far Are We? Yangruibo Ding, Yanjun Fu, Omniyyah Ibrahim, Chawin Sitawarin, Xinyun Chen, Basel Alomair, David Wagner, Baishakhi Ray, Yizheng Chen 2024-03-27 2024-03-31 [("doi","10.48550/arXiv.2403.18624")] ai/dataset ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex cs/security

In the context of the rising interest in code language models (code LMs) and vulnerability detection, we study the effectiveness of code LMs for detecting vulnerabilities. Our analysis reveals shortcomings in existing vulnerability datasets, including poor data quality, low label accuracy, and high duplication rates, leading to unreliable model performance in realistic vulnerability detection scenarios. Additionally, the evaluation methods used with these datasets are not representative of real-world vulnerability detection.

To address these challenges, we introduce PrimeVul, a new dataset for training and evaluating code LMs for vulnerability detection. PrimeVul incorporates a novel set of data labeling techniques that achieve comparable label accuracy to human-verified benchmarks while expanding the dataset. It also implements a rigorous data de-duplication and chronological data splitting strategy to mitigate data leakage issues, alongside introducing more realistic evaluation metrics and settings. This comprehensive approach aims to provide a more accurate assessment of code LMs’ performance in real-world conditions.

Evaluating code LMs on PrimeVul reveals that existing benchmarks overestimate the performance of these models. For instance, a state-of-the-art 7B model scored 68.26% F1 on Big-Vul but only 3.09% F1 on PrimeVul. Attempts to improve performance through advanced training techniques and larger models like GPT-3.5 and GPT-4 were unsuccessful, with results akin to random guessing in the most stringent settings.

These findings underscore the considerable gap between current capabilities and the practical requirements for deploying code LMs in security roles, highlighting the need for more innovative research in this domain.

--- https://x.com/elder_plinius/status/1774220858711490909 elder_plinius 2024-03-31 ai/nn/transformer/gpt/claude cs/security --- /doc/iq/ses/1990-mchenry.pdf Project A Validity Results: The Relationship Between Predictor And Criterion Domains Jeffrey J. McHenry, Leaetta M. Hough, Jody L. Toquam, Mary Ann Hanson, Steven Ashworth 1990-06-01 2024-03-31 [("doi","10.1111/j.1744-6570.1990.tb01562.x")] iq/ses psychology/personality/conscientiousness

[Project A] A predictor battery of cognitive ability, perceptual-psychomotor ability, temperament/personality, interest, and job outcome preference measures was administered to enlisted soldiers in 9 US Army jobs. These measures were summarized in terms of 24 composite scores. The relationships between the predictor composite scores and 5 components of job performance were analyzed.

Scores from the cognitive and perceptual-psychomotor ability tests provided the best prediction of job-specific and general task proficiency, while the temperament/personality composites were the best predictors of giving extra effort, supporting peers, and exhibiting personal discipline. Composite scores derived from the interest inventory were correlated more highly with task proficiency than with demonstrating effort and peer support. In particular, vocational interests were among the best predictors of task proficiency in combat jobs.

The results suggest that the Army can improve the prediction of job performance by adding non-cognitive predictors to its present battery of predictor tests.

--- https://koenvangilst.nl/blog/keeping-code-complexity-in-check 2024-03-31 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex design reinforcement-learning/preference-learning --- /doc/cs/security/2020-fan.pdf A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries Jiahao Fan, Yi Li, Shaohua Wang, Tien N. Nguyen 2020-10-05 2024-03-31 [("doi","10.1145/3379597.3387501")] ai/dataset cs/security

We collected a large C/C++ code vulnerability dataset from open-source Github projects, namely Big-Vul.

We crawled the public Common Vulnerabilities and Exposures (CVE) database and CVE-related source code repositories. Specifically, we collected the descriptive information of the vulnerabilities from the CVE database, eg. CVE IDs, CVE severity scores, and CVE summaries. With the CVE information and its related published Github code repository links, we downloaded all of the code repositories and extracted vulnerability related code changes.

In total, Big-Vul contains 3,754 code vulnerabilities spanning 91 different vulnerability types. All these code vulnerabilities are extracted from 348 Github projects. All information is stored in the CSV format. We linked the code changes with the CVE descriptive information.

Thus, our Big-Vul can be used for various research topics, eg. detecting and fixing vulnerabilities, analyzing the vulnerability related code changes.

Big-Vul is publicly available on Github.

--- https://www.reddit.com/r/StableDiffusion/comments/1bsi2xs/the_experiment/ 2024-03-31 ai/nn/diffusion --- https://en.wikipedia.org/wiki/Brian_Wilson#Growing_drug_use_and_religious_epiphany Brian Wilson § Growing drug use and religious epiphany 2024-03-31 psychedelic/lsd psychiatry/schizophrenia --- https://en.wikipedia.org/wiki/Central_hypoventilation_syndrome Central hypoventilation syndrome 2024-01-01 psychiatry --- https://en.wikipedia.org/wiki/Panic_attack Panic attack 2024-03-31 psychiatry/anxiety --- https://slatestarcodex.com/2017/04/05/the-case-of-the-suffocating-woman/ 2024-03-31 co2 psychiatry/anxiety --- /doc/psychiatry/anxiety/1993-klein.pdf False Suffocation Alarms, Spontaneous Panics, and Related Conditions: An Integrative Hypothesis Donald F. Klein 1993-04-01 2024-03-31 [("doi","10.1001/archpsyc.1993.01820160076009")] co2 psychiatry/anxiety

[discussion] A carbon dioxide hypersensitivity theory of panic has been posited. We hypothesize more broadly that a physiologic misinterpretation by a suffocation monitor misfires an evolved suffocation alarm system. This produces sudden respiratory distress followed swiftly by a brief hyperventilation, panic, and the urge to flee. Carbon dioxide hypersensitivity is seen as due to the deranged suffocation alarm monitor. If other indicators of potential suffocation provoke panic, this theoretical extension is supported.

We broadly pursue this theory by examining Ondine’s curse as the physiologic and pharmacologic converse of panic disorder, splitting panic in terms of symptomatology and challenge studies, reevaluating the role of hyperventilation, and reinterpreting the contagiousness of sighing and yawning, as well as mass hysteria.

Further, the phenomena of panic during relaxation and sleep, late luteal phase dysphoric disorder, pregnancy, childbirth, pulmonary disease, separation anxiety, and treatment are used to test and illuminate the suffocation false alarm theory.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4262123/ The association between exaggeration in health related science news and academic press releases: retrospective observational study Petroc Sumner, Solveiga Vivian-Griffiths, Jacky Boivin, Andy Williams, Christos A. Venetis, Aimée Davies, Jack Ogden, Leanne Whelan, Bethan Hughes, Bethan Dalton, Fred Boy, Christopher D. Chambers 2014 2024-04-01 [("doi","10.1136/bmj.g7015")] statistics/bias/publication

Objective: To identify the source (press releases or news) of distortions, exaggerations, or changes to the main conclusions drawn from research that could potentially influence a reader’s health related behavior.

Design: Retrospective quantitative content analysis.

Setting: Journal articles, press releases, and related news, with accompanying simulations.

Sample: Press releases (n = 462) on biomedical and health related science issued by 20 leading UK universities in 2011, alongside their associated peer reviewed research papers and news stories (n = 668).

Main Outcome Measures: Advice to readers to change behavior, causal statements drawn from correlational research, and inference to humans from animal research that went beyond those in the associated peer reviewed papers.

Results: 40% (95% confidence interval 33%−46%) of the press releases contained exaggerated advice, 33% (26%−40%) contained exaggerated causal claims, and 36% (28%−46%) contained exaggerated inference (humans from animal research).

When press releases contained such exaggeration, 58% (95% confidence interval 48%−68%), 81% (70%−93%), and 86% (77%−95%) of news stories, respectively, contained similar exaggeration, compared with exaggeration rates of 17% (10%−24%), 18% (9%−27%), and 10% (0%−19%) in news when the press releases were not exaggerated. Odds ratios for each category of analysis were 6.5 (95% confidence interval: 3.5−12), 20 (7.6−51), and 56 (15–211). At the same time, there was little evidence that exaggeration in press releases increased the uptake of news.

Conclusions: Exaggeration in news is strongly associated with exaggeration in press releases. Improving the accuracy of academic press releases could represent a key opportunity for reducing misleading health related news.

--- https://finedataproducts.com/posts/2024-03-10-tax-scenarios-with-ai/ 2024-04-01 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex economics law --- https://en.wikipedia.org/wiki/De_Finetti%27s_theorem De Finetti’s theorem 2024-04-01 reinforcement-learning/meta-learning statistics/bayes --- https://www.lesswrong.com/posts/YMo5PuXnZDwRjhHhE/i-have-been-a-good-bing 2024-04-01 ai/music ai/nn/transformer/gpt/4/fiction math/humor --- https://artvee.com/artist/emile-allain-seguy/ 2024-04-01 design --- https://www.lesswrong.com/posts/tBy4RvCzhYyrrMFj3/introducing-open-asteroid-impact 2024-04-01 math/humor reinforcement-learning/safe --- https://www.lasso.security/blog/ai-package-hallucinations 2024-04-01 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/palm cs/security --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10894685/ GPT-4 passes the bar exam Daniel Martin Katz, Michael James Bommarito, Shang Gao, Pablo Arredondo 2024 2024-04-01 [("doi","10.1098/rsta.2023.0254")] ai/nn/transformer/gpt/4/nonfiction law

In this paper, we experimentally evaluate the zero-shot performance of GPT-4 against prior generations of GPT on the entire uniform bar examination (UBE), including not only the multiple-choice multistate bar examination (MBE), but also the open-ended multistate essay exam (MEE) and multistate performance test (MPT) components.

On the MBE, GPT-4 substantially outperforms both human test-takers and prior models, demonstrating a 26% increase over ChatGPT and beating humans in 5⁄7 subject areas. On the MEE and MPT, which have not previously been evaluated by scholars, GPT-4 scores an average of 4.2/6.0 when compared with much lower scores for ChatGPT.

Graded across the UBE components, in the manner in which a human test-taker would be, GPT-4 scores ~297 points, substantially in excess of the passing threshold for all UBE jurisdictions.

These findings document not just the rapid and remarkable advance of large language model performance generally, but also the potential for such models to support the delivery of legal services in society.

This article is part of the theme issue ‘A complexity science approach to law and governance’.

--- https://arxiv.org/abs/2401.16212 Better Call GPT, Comparing Large Language Models Against Lawyers Lauren Martin, Nick Whitehouse, Stephanie Yiu, Lizzie Catterson, Rivindu Perera 2024-01-24 2024-04-01 [("doi","10.48550/arXiv.2401.16212")] ai/nn/transformer/gpt/4/nonfiction law

This paper presents a groundbreaking comparison between Large Language Models (LLMs) and traditional legal contract reviewers Junior Lawyers and Legal Process Outsourcers. We dissect whether LLMs can outperform humans in accuracy, speed, and cost efficiency during contract review.

Our empirical analysis benchmarks LLMs against a ground truth set by Senior Lawyers, uncovering that advanced models match or exceed human accuracy in determining legal issues. In speed, LLMs complete reviews in mere seconds, eclipsing the hours required by their human counterparts. Cost wise, LLMs operate at a fraction of the price, offering a staggering 99.97% reduction in cost over traditional methods.

These results are not just statistics, they signal a seismic shift in legal practice. LLMs stand poised to disrupt the legal industry, enhancing accessibility and efficiency of legal services.

Our research asserts that the era of LLM dominance in legal contract review is upon us, challenging the status quo and calling for a reimagined future of legal workflows.

--- https://matthewmarks.com/online/jordan-belson 2024-04-01 design --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7590944/ Dormio: A targeted dream incubation device Adam Haar Horowitz, Tony J. Cunningham, Pattie Maes, Robert Stickgold 2020 2024-04-01 [("doi","10.1016/j.concog.2020.102938")] psychology/vision/dream

Information processing during sleep is active, ongoing, and accessible to engineering. Protocols such as targeted memory reactivation use sensory stimuli during sleep to reactivate memories and demonstrate subsequent, specific enhancement of their consolidation. These protocols rely on physiological, as opposed to phenomenological, evidence of their reactivation. While dream content can predict post-sleep memory enhancement, dreaming itself remains a black box.

Here, we present a novel protocol using a new wearable electronic device, Dormio, to automatically generate serial auditory dream incubations at sleep onset, wherein targeted information is repeatedly presented during the hypnagogic period, enabling direct incorporation of this information into dream content, a process we call targeted dream incubation (TDI).

Along with validation data, we discuss how Dormio and TDI protocols can serve as tools for controlled experimentation on dream content, shedding light on the role of dreams in the overnight transformation of experiences into memories.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338510/ Dreaming of a learning task is associated with enhanced memory consolidation: Replication in an overnight sleep study Erin J. Wamsley, Robert Stickgold 2019 2024-04-01 [("doi","10.1111/jsr.12749")] zeo

Sleep following learning benefits memory. One model attributes this effect to the iterative “reactivation” of memory traces in the sleeping brain, demonstrated in animal models. Although technical limitations prohibit using the same methods to observe memory reactivation in the human brain, the study of mental activity during sleep provides an alternative method of observing memory activation during sleep. In fact, the content of dream experience may reflect the process of memory reactivation and consolidation in the sleeping brain.

In line with this hypothesis, we previously reported that dreaming about a spatial learning task during a nap strongly predicts subsequent performance improvements. Here, we replicate this observation in an overnight sleep study, for the first time demonstrating that pre-sleep training on a virtual maze navigation task is reflected in dreams reported from all phases of sleep, with unambiguous representation of the task in dream content associated with improved next-morning performance.

These observations are consistent with reactivation-based models of memory consolidation in sleep, confirming our earlier finding that the cognitive-level activation of recent experience during sleep is associated with subsequent performance gains.

--- https://www.aiweirdness.com/gpt-2-it-learned-on-the-internet-19-02-15/ 2024-04-01 ai/nn/transformer/gpt/2/fiction --- https://x.com/Malcolm_Ocean/status/1774848399420092441 Malcolm Ocean 2024-04-01 psychology/smell --- https://arxiv.org/abs/2312.14226 Deep de Finetti: Recovering Topic Distributions from Large Language Models Liyi Zhang, R. Thomas McCoy, Theodore R. Sumers, Jian-Qiao Zhu, Thomas L. Griffiths 2023-12-21 2024-04-01 [("doi","10.48550/arXiv.2312.14226")] ai/nn/transformer/gpt/2/nonfiction reinforcement-learning/meta-learning statistics/bayes

Large language models (LLMs) can produce long, coherent passages of text, suggesting that LLMs, although trained on next-word prediction, must represent the latent structure that characterizes a document. Prior work has found that internal representations of LLMs encode one aspect of latent structure, namely syntax; here we investigate a complementary aspect, namely the document’s topic structure. We motivate the hypothesis that LLMs capture topic structure by connecting LLM optimization to implicit Bayesian inference.

De Finetti’s theorem shows that exchangeable probability distributions can be represented as a mixture with respect to a latent generating distribution. Although text is not exchangeable at the level of syntax, exchangeability is a reasonable starting assumption for topic structure. We thus hypothesize that predicting the next token in text will lead LLMs to recover latent topic distributions.

We examine this hypothesis using Latent Dirichlet Allocation (LDA), an exchangeable probabilistic topic model, as a target, and we show that the representations formed by LLMs encode both the topics used to generate synthetic data and those used to explain natural corpus data.

--- https://defoe.sourceforge.net/folio/knuth-plass.html 2024-04-02 design/typography/tex --- https://mpetroff.net/2020/05/pre-calculated-line-breaks-for-html-css/ Pre-calculated line breaks for HTML / CSS 2024-01-01 cs/css design/typography/tex --- https://en.wikipedia.org/wiki/Line_wrap_and_word_wrap Line wrap and word wrap 2024-01-01 design/typography/tex --- https://www.w3.org/TR/css-text-4/#text-wrap 2024-01-01 cs/css design/typography/tex --- https://en.wikipedia.org/wiki/Typographic_alignment#Problems_with_justification Typographic alignment § Problems with justification 2024-01-01 design/typography/tex --- https://github.com/bramstein/typeset 2024-01-01 design/typography/tex --- https://github.com/robertknight/tex-linebreak 2024-01-01 design/typography/tex --- https://frankzliu.com/blog/vision-transformers-are-overrated 2024-04-02 ai/nn/cnn --- https://en.wikipedia.org/wiki/River_(typography) River (typography) 2024-01-01 design/typography/tex --- https://news.ycombinator.com/item?id=5189258 2024-04-02 design/typography/tex --- https://developer.chrome.com/blog/css-text-wrap-pretty/ 2024-04-02 cs/css design/typography/tex --- https://tinlizzie.org/VPRIPapers/tr2012001_steps.pdf#page=30 2024-04-02 design/typography/tex --- https://bugzilla.mozilla.org/show_bug.cgi?id=630181 2024-04-02 cs/css design/typography/tex --- https://chromestatus.com/feature/5145771917180928 2024-04-02 cs/css design/typography/tex --- https://raphlinus.github.io/text/2022/11/08/minikin.html 2024-04-02 cs/css design/typography/tex --- https://github.com/danluu/debugging-stories 2024-04-02 cs --- https://en.wikipedia.org/wiki/Knuth%E2%80%93Plass_line-breaking_algorithm Knuth–Plass line-breaking algorithm 2024-04-02 design/typography/tex --- https://www.benlo.com/microchess/index.html 2024-04-02 reinforcement-learning/chess --- https://codersblock.com/blog/nicer-text-wrapping-with-css-text-wrap/#performance 2024-04-02 cs/css design/typography/tex --- /doc/cs/css/2024-ishii.pdf Score-based Paragraph-level Line Breaking Koji Ishii 2023-06-01 2024-04-02 cs/css design/typography/tex --- https://en.wikipedia.org/wiki/Steve_Wozniak#Plane_crash_and_temporary_leave_from_Apple Steve Wozniak § Plane crash & temporary leave from Apple 2024-01-01 psychiatry/traumatic-brain-injury --- https://en.wikipedia.org/wiki/Microbial_mat Microbial mat 2024-04-02 genetics/microbiome --- https://mainichi.jp/english/articles/20240329/p2a/00m/0sc/050000c 2024-04-02 genetics/editing --- https://app.suno.ai/song/1e726e3d-c6b4-4b42-9576-e03169f29165/ 2024-04-02 ai/music --- https://www.nytimes.com/2023/03/22/health/beethoven-death-dna-hair.html 2024-04-02 genetics/sequencing --- https://harrisongoldste.in/papers/icse24-pbt-in-practice.pdf 2024-04-02 cs/haskell --- https://en.wikipedia.org/wiki/Random_projection Random projection 2024-04-02 ai/nn/retrieval ai/nn/sparsity/low-precision cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/SMAWK_algorithm SMAWK algorithm 2024-04-02 cs/algorithm design/typography/tex --- https://justine.lol/matmul/ 2024-04-02 ai/nn/sparsity/low-precision ai/nn/transformer --- https://grantland.com/features/bass-fishing-cheaters/ 2024-04-02 crime --- /doc/ai/nn/transformer/gpt/dall-e/3/2023-11-17-gwern-dalle3-grandadmiralthrawn-reachingfortheblacksunandempire-samples-01.jpg Gwern 2023-11-17 2024-01-01 ai/nn/diffusion/midjourney/black-sun ai/nn/transformer/gpt/dall-e/3 --- /doc/ai/nn/transformer/gpt/dall-e/3/2023-11-17-gwern-dalle3-grandadmiralthrawn-reachingfortheblacksunandempire-samples-02.jpg Gwern 2023-11-17 2024-01-01 ai/nn/diffusion/midjourney/black-sun ai/nn/transformer/gpt/dall-e/3 --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3016382/ Estimated intelligence quotient in anorexia nervosa: a systematic review and meta-analysis of the literature Carolina Lopez, Daniel Stahl, Kate Tchanturia 2010 2024-04-02 [("doi","10.1186/1744-859X-9-40")] iq psychiatry/anorexia

Background: It has been hypothesized that people with anorexia nervosa have a higher intelligence quotient (IQ) level than the general population. The purpose of this review was to systematically appraise the research into reported IQ levels in people with anorexia nervosa.

Methods: A search using the terms ‘intelligence quotient, IQ, intelligence, cognition, eating disorders and anorexia’ was conducted in electronic databases only.

Results: In all, 30 peer-reviewed studies written in English that used well established measures of intelligence quotient (the National Adult Reading Test and Wechsler Intelligence Scales) were identified.

This review established that people with anorexia nervosa score 10.8 units and 5.9 units above the average intelligence quotient of the normative population on the National Adult Reading Test and Wechsler Intelligence Scales, respectively. An association was found between Body Mass Index and intelligence quotient, as measured by the National Adult Reading Test.

Conclusions: More studies including other eating disorder categories and recovered people are needed to explore important questions regarding the role of the intelligence quotient in treatment response.

--- https://www.freedium.cfd/ 2024-04-02 cs/linkrot/archiving --- https://www.typebarmagazine.com/2024/03/24/science-fiction-and-the-death-of-the-sun/ Science Fiction and the Death of the Sun Gwen C. Katz 2024-03-24 2024-04-02 fiction/science-fiction --- https://en.wikipedia.org/wiki/Long-term_nuclear_waste_warning_messages Long-term nuclear waste warning messages 2024-01-01 design existential-risk psychology/linguistics --- https://arxiv.org/abs/1605.01335 Learning from the memory of Atari 2600 Jakub Sygnowski, Henryk Michalewski 2016-05-04 2024-04-03 [("doi","10.48550/arXiv.1605.01335")] reinforcement-learning/model-free

[cf. philoxenic 2020] We train a number of neural networks to play games Bowling, Breakout and Seaquest using information stored in the memory of a video game console Atari 2600 [ALE].

We consider 4 models of neural networks which differ in size and architecture: two networks which use only information contained in the RAM and two mixed networks which use both information in the RAM and information from the screen. As the benchmark we used the convolutional model proposed in NIPS and received comparable results in all considered games.

Quite surprisingly, in the case of Seaquest we were able to train RAM-only agents which behave better than the benchmark screen-only agent. Mixing screen and RAM did not lead to an improved performance comparing to screen-only and RAM-only agents.

--- https://www.codeproject.com/Articles/5271949/Learning-Breakout-From-RAM-Part-1 Learning Breakout From RAM—Part 1 philoxenic 2020-07-02 2024-04-03 reinforcement-learning/model-free --- https://www.codeproject.com/Articles/5271950/Learning-Breakout-From-RAM-Part-2 Learning Breakout From RAM—Part 2 philoxenic 2020-07-03 2024-04-03 reinforcement-learning/model-free --- https://www.nature.com/articles/1300905 Nicotine Self-Administration Acutely Activates Brain Reward Systems and Induces a Long-Lasting Increase in Reward Sensitivity Kenny, Markou 2006 2024-01-01 nicotine --- https://inews.co.uk/opinion/vet-bills-rip-off-my-dog-worth-2822774 Vet bills are a rip-off—but my dog is worth it: They call the pets "patients", but it’s often the owners who are most time-consuming Simon Kelner 2023-12-24 2024-01-01 cat dog psychology/cognitive-bias --- https://www.nature.com/articles/s42003-019-0399-1 Ancient DNA from mastics solidifies connection between material culture and genetics of mesolithic hunter--gatherers in Scandinavia Kashuba 2019 2024-01-01 genetics/sequencing --- https://www.cs.umb.edu/~ding/history/470_670_fall_2011/papers/cs670_Tran_PreferredPaper_LeakingInDataMining.pdf Leakage in Data Mining: Formulation, Detection, and Avoidance Kaufman 2011 2024-01-01 ai/tabular --- https://nerdsonwallstreet.typepad.com/my_weblog/files/dataminejune_2000.pdf Stupid data miner tricks: overfitting the S&P 500 Leinweber 1995 2024-01-01 ai/tabular --- https://n.neurology.org/content/78/23/1841.short Long-term soy isoflavone supplementation and cognition in women: A randomized, controlled trial Henderson 2012 2024-01-01 nootropic --- https://www.nejm.org/doi/full/10.1056/NEJM199605023341801 Lack of Effect of Long-Term Supplementation with Beta Carotene on the Incidence of Malignant Neoplasms and Cardiovascular Disease Hennekens 1996 2024-01-01 biology --- https://eab.sagepub.com/content/23/1/3.short Restorative Effects of Natural Environment Experiences Hartig 1991 2024-01-01 psychology/nature --- https://www.cs.virginia.edu/~robins/YouAndYourResearch.html You and Your Research Hamming 1986 2024-01-01 science --- /doc/economics/advertising/2024-aloui.pdf Demand-driving innovation, advertising nuisance, and a media platform optimal pricing Chokri Aloui, Khaïreddine Jebsi 2024-03-27 2024-04-02 [("doi","10.1002/mde.4188")] economics/advertising

This paper explores the incentives of a monopolistic media platform to invest in demand-driving innovation when the interactions between its customer groups (eyeballs and advertisers) are countervailing. We investigate whether media innovation contributes to resolving the trade-off between catering to both groups (or sides) and minimizing advertising nuisance.

We identify an innovation threshold guiding the media platform on when to charge more eyeballs than advertisers, effectively reversing the standard divide-and-conquer pricing strategy. Moreover, we show that the media platform invests more in research and development (R&D) on the side with the strongest reference market, and we highlight the role of excessive inertia and momentum in shaping innovation.

Furthermore, we find that when the relative advertising nuisance is low, the platform is less encouraged to innovate on both sides when the ad nuisance increases marginally. However, when the relative advertising nuisance is high, we infer an R&D see-saw rule: a marginal increase in the ad nuisance reduces the R&D efforts undertaken on one side and increases those on the other.

Our findings provide insights into the complex interplay between media platform innovation and pricing strategy in the presence of advertising nuisance and a challenging chicken-and-egg problem.

--- https://www.axios.com/2023/12/31/elon-musks-x-fidelity-valuation-cut Elon Musk’s Twitter gets another valuation cut from Fidelity Dan Primack 2023-12-31 2024-04-02 psychiatry/bipolar/elon-musk

…Fidelity believes that Twitter is worth −71.5% less than at the time of purchase, according to a new disclosure that runs through the end of November 2023 (Fidelity revalues private shares on a one-month lag).

…In terms of publicly traded comparisons, Facebook stock rose +4.9% in November while Snapchat shares climbed +38.2%.

--- https://github.com/NVIDIA/FasterTransformer 2024-04-03 ai/nn/transformer --- https://arxiv.org/abs/1505.03118 When causation does not imply correlation: robust violations of the Faithfulness axiom Richard Kennaway 2015-05-12 2024-04-03 [("doi","10.48550/arXiv.1505.03118")] statistics/causality statistics/decision

We demonstrate that the Faithfulness property that is assumed in much causal analysis is robustly violated for a large class of systems of a type that occurs throughout the life and social sciences: control systems.

These systems exhibit correlations indistinguishable from zero between variables that are strongly causally connected, and can show very high correlations between variables that have no direct causal connection, only a connection via causal links between uncorrelated variables. Their patterns of correlation are robust, in that they remain unchanged when their parameters are varied.

The violation of Faithfulness is fundamental to what a control system does: hold some variable constant despite the disturbing influences on it. No method of causal analysis that requires Faithfulness is applicable to such systems.

--- https://arxiv.org/abs/1608.04120 Yule’s ‘Nonsense Correlation’ Solved! Philip Ernst, Larry Shepp, Abraham Wyner 2016-08-14 2024-04-03 [("doi","10.48550/arXiv.1608.04120")] statistics/probability

In this paper, we resolve a long-standing open statistical problem. The problem is to mathematically confirm Yule’s 1926 empirical finding of “nonsense correlation”. We do so by analytically determining the second moment [variance] of the empirical correlation coefficient:

of two independent Wiener processes, W1 & W2.

Using tools from Fredholm integral equation theory, we successfully calculate the second moment of θ to obtain a value for the standard deviation of θ of nearly 0.5. The “nonsense” correlation, which we call “volatile” correlation, is volatile in the sense that its distribution is heavily dispersed and is frequently large in absolute value. It is induced because each Wiener process is “self-correlated” in time. This is because a Wiener process is an integral of pure noise and thus its values at different time points are correlated.

In addition to providing an explicit formula for the second moment of θ, we offer implicit formulas for higher moments of θ.

--- https://www.construction-physics.com/p/what-makes-housing-so-expensive 2024-04-03 economics/georgism --- https://www.theringer.com/nba/2024/4/3/24119579/twinning-time-brook-robin-lopez-amen-ausar-thompson 2024-04-03 exercise genetics/heritable --- https://vanemden.wordpress.com/2014/06/18/how-recursion-got-into-programming-a-comedy-of-errors-3/ 2024-04-03 cs/lisp --- https://www.youtube.com/watch?v=f75eoFyo9ns 2024-04-03 ai/video/generation --- https://arxiv.org/abs/2403.19827 Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNs Kanishka Misra, Kyle Mahowald 2024-03-28 2024-04-03 [("doi","10.48550/arXiv.2403.19827")] ai/nn/transformer psychology/linguistics

[Twitter] Language models learn rare syntactic phenomena, but it has been argued that they rely on rote memorization, as opposed to grammatical generalization. Training on a corpus of human-scale in size (100M words), we iteratively trained transformer language models on systematically manipulated corpora and then evaluated their learning of a particular rare grammatical phenomenon: the English Article+Adjective+Numeral+Noun (AANN) construction (“a beautiful 5 days”).

We first compared how well this construction was learned on the default corpus relative to a counterfactual corpus in which the AANN sentences were removed. AANNs were still learned better than systematically perturbed variants of the construction. Using additional counterfactual corpora, we suggest that this learning occurs through generalization from related constructions (eg. “a few days”).

An additional experiment showed that this learning is enhanced when there is more variability in the input.

Taken together, our results provide an existence proof that models learn rare grammatical phenomena by generalization from less rare phenomena.

Code available at Github.

--- https://x.com/kanishkamisra/status/1775156612988088736 Kanishka Misra 2024-04-03 ai/nn/transformer psychology/linguistics --- /doc/economics/2010-mankiw.pdf The Optimal Taxation of Height: A Case Study of Utilitarian Income Redistribution N. Gregory Mankiw, Matthew Weinzierl 2010-02-01 2024-04-03 [("doi","10.1257/pol.2.1.155")] economics math/humor philosophy/ethics psychology/cognitive-bias

Should the income tax include a credit for short taxpayers and a surcharge for tall ones? The standard utilitarian framework for tax analysis answers this question in the affirmative. Moreover, a plausible parameterization using data on height and wages implies a substantial height tax: a tall person earning $50,000 should pay $4,500 more in tax than a short person.

One interpretation is that personal attributes correlated with wages should be considered more widely for determining taxes.

Alternatively, if policies such as a height tax are rejected, then the standard utilitarian framework must fail to capture intuitive notions of distributive justice.

--- https://x.com/Yampeleg/status/1775660140349235385 Yampeleg 2024-04-04 ai/nn/transformer/gpt/5 --- https://edoras.sdsu.edu/~vinge/misc/singularity.html 2024-03-30 ai/scaling/hardware reinforcement-learning/safe transhumanism --- https://suno.com/song/da6d4a83-1001-4694-8c28-648a6e8bad0a 2024-04-04 ai/music law math/humor --- https://www.pcgamer.com/rarest-most-expensive-pc-games/ 2024-04-04 psychology/collecting --- https://lutrasecurity.com/en/articles/kobold-letters/ 2024-04-04 cs/css cs/security --- https://projecteuclid.org/download/pdf_1/euclid.ss/1009213726 Statistical Modeling: The Two Cultures Leo Breiman 2024-01-01 ai/scaling ai/tabular statistics/peer-review --- https://terraformindustries.wordpress.com/2024/04/01/terraform-makes-carbon-neutral-natural-gas/ 2024-04-05 technology/carbon-capture --- https://arxiv.org/abs/2403.18058 COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning Yuelin Bai, Xinrun Du, Yiming Liang, Yonggang Jin, Ziqiang Liu, Junting Zhou, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Wenhu Chen, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang 2024-03-26 2024-04-05 [("doi","10.48550/arXiv.2403.18058")] ai/dataset ai/nn/transformer/gpt/instruction-tuning

Recently, there have been advancements in large language models (LLMs), particularly focused on the English language. These advancements have enabled these LLMs to understand and execute complex instructions with unprecedented accuracy and fluency. However, despite these advancements, there remains a noticeable gap in the development of Chinese instruction tuning. The unique linguistic features and cultural depth of the Chinese language pose challenges for instruction tuning tasks. Existing datasets are either derived from English-centric LLMs or are ill-suited for aligning with the interaction patterns of real-world Chinese users.

To bridge this gap, we introduce COIG-CQIA, a high-quality Chinese instruction tuning dataset. Our aim is to build a diverse, wide-ranging instruction-tuning dataset to better align model behavior with human interactions. To this end, we collect a high-quality human-written corpus from various sources on the Chinese Internet, including Q&A communities, Wikis, examinations, and existing NLP datasets. This corpus was rigorously filtered and carefully processed to form the COIG-CQIA dataset.

Furthermore, we train models of various scales on different subsets of CQIA, following in-depth evaluation and analyses. The findings from our experiments offer valuable insights for selecting and developing Chinese instruction-tuning datasets. We also find that models trained on CQIA-Subset achieve competitive results in human assessment as well as knowledge and security benchmarks.

Data are available at https://huggingface.co/datasets/m-a-p/COIG-CQIA.

--- https://hforsten.com/identifying-stable-diffusion-xl-10-images-from-vae-artifacts.html 2024-04-05 ai/nn/diffusion cs/cryptography/steganography --- https://www.abortretry.fail/p/the-rise-and-fall-of-silicon-graphics 2024-04-05 ai/scaling/hardware --- https://arxiv.org/abs/2404.01413 Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey, Rafael Rafailov, Henry Sleight, John Hughes, Tomasz Korbak, Rajashree Agrawal, Dhruv Pai, Andrey Gromov, Daniel A. Roberts, Diyi Yang, David L. Donoho, Sanmi Koyejo 2024-04-01 2024-04-05 [("doi","10.48550/arXiv.2404.01413")] ai/nn/diffusion ai/nn/vae ai/scaling reinforcement-learning/meta-learning/continual-learning

The proliferation of generative models, combined with pretraining on web-scale data, raises a timely question: what happens when these models are trained on their own generated outputs? Recent investigations into model-data feedback loops discovered that such loops can lead to model collapse, a phenomenon where performance progressively degrades with each model-fitting iteration until the latest model becomes useless. However, several recent papers studying model collapse assumed that new data replace old data over time rather than assuming data accumulate over time.

In this paper, we compare these two settings and show that accumulating data prevents model collapse. We begin by studying an analytically tractable setup in which a sequence of linear models are fit to the previous models’ predictions. Previous work showed if data are replaced, the test error increases linearly with the number of model-fitting iterations; we extend this result by proving that if data instead accumulate, the test error has a finite upper bound independent of the number of iterations.

We next empirically test whether accumulating data similarly prevents model collapse by pretraining sequences of language models on text corpora. We confirm that replacing data does indeed cause model collapse, then demonstrate that accumulating data prevents model collapse; these results hold across a range of model sizes, architectures and hyperparameters. We further show that similar results hold for other deep generative models on real data: diffusion models for molecule generation and variational autoencoders for image generation.

Our work provides consistent theoretical and empirical evidence that data accumulation mitigates model collapse.

--- https://jpkoning.blogspot.com/2017/03/bringing-back-somali-shilling.html 2024-01-01 bitcoin --- https://qwenlm.github.io/blog/qwen1.5-32b/ 2024-04-05 ai/nn/transformer/gpt --- https://apenwarr.ca/log/20100901 2024-04-05 design wikipedia --- https://jakeseliger.com/2015/03/16/the-moderator-problem-how-reddit-and-related-news-sites-decline/ 2024-04-05 sociology/technology --- https://web.archive.org/web/20161009233301/http://blog.bumblebeelabs.com/social-software-sundays-2-the-evaporative-cooling-effect/ 2024-04-05 sociology/technology --- https://blog.codinghorror.com/meta-is-murder/ 2024-04-05 sociology/technology --- https://www.lesswrong.com/posts/tscc3e5eujrsEeFN4/well-kept-gardens-die-by-pacifism 2024-04-05 sociology/technology --- https://paulgraham.com/hackernews.html 2024-04-05 sociology/technology --- http://www.fudco.com/chip/lessons.html 2024-04-05 sociology/technology --- https://arxiv.org/abs/2404.00018 Can AI Outperform Human Experts in Creating Social Media Creatives? Eunkyung Park, Raymond K. Wong, Junbum Kwon 2024-03-19 2024-04-05 [("doi","10.48550/arXiv.2404.00018")] ai/nn/diffusion/midjourney ai/nn/transformer/gpt/dall-e/3 economics/advertising

Artificial Intelligence has outperformed human experts in functional tasks such as chess and baduk. How about creative tasks? This paper evaluates AI’s capability in the creative domain compared to human experts, which little research has been conducted so far.

We propose a novel Prompt-for-Prompt to generate social media creatives via prompt augmentation by Large Language Models. We take the most popular Instagram posts (with the biggest number of like clicks) in top brands’ Instagram accounts to create social media creatives. We give GPT-4 several prompt instructions with text descriptions to generate the most effective prompts for cutting-edge text-to-image generators: Midjourney, DALL·E 3, and Stable Diffusion. LLM-augmented prompts can boost AI’s abilities by adding objectives, engagement strategy, lighting, and brand consistency for social media image creation.

We conduct an extensive human evaluation experiment, and find that:

AI excels human experts, and Midjourney is better than the other text-to-image generators. Surprisingly, unlike conventional wisdom in the social media industry, prompt instruction including eye-catching shows much poorer performance than those including natural. Regarding the type of creatives, AI improves creatives with animals or products but less with real people. Also, AI improves creatives with short text descriptions more than with long text descriptions, because there is more room for AI to augment prompts with shorter descriptions.

--- https://arxiv.org/abs/2403.16627 SDXS: Real-Time One-Step Latent Diffusion Models with Image Conditions Yuda Song, Zehao Sun, Xuanwu Yin 2024-03-25 2024-04-05 [("doi","10.48550/arXiv.2403.16627")] ai/nn/diffusion ai/nn/sparsity/knowledge-distillation

Recent advancements in diffusion models have positioned them at the forefront of image generation. Despite their superior performance, diffusion models are not without drawbacks; they are characterized by complex architectures and substantial computational demands, resulting in latency due to their iterative sampling process. To mitigate these limitations, we introduce a dual approach involving model miniaturization and a reduction in sampling steps, aimed at decreasing model latency.

Our methodology leverages knowledge distillation to streamline the U-Net and image decoder architectures, and introduces an innovative one-step DM training technique that uses feature matching and score distillation. We present two models, SDXS-512 & SDXS-1024, achieving inference speeds of ~100 FPS (30× faster than SD v1.5) and 30 FPS (60× faster than SDXL) on a single GPU, respectively.

Moreover, our training approach offers promising applications in image-conditioned control, facilitating efficient image-to-image translation.

--- https://arxiv.org/abs/2404.02803 Collaboratively adding context to social media posts reduces the sharing of false news Thomas Renault, David Restrepo Amariles, Aurore Troussel 2024-04-03 2024-04-05 [("doi","10.48550/arXiv.2404.02803")] sociology/technology

We build a novel database of around 285,000 notes from the Twitter Community Notes program to analyze the causal influence of appending contextual information to potentially misleading posts on their dissemination.

Employing a difference in difference design, our findings reveal that:

adding context below a tweet reduces the number of retweets by almost half. A, albeit smaller, effect is observed when focusing on the number of replies or quotes. Community Notes also increase by 80% the probability that a tweet is deleted by its creator. The post-treatment impact is substantial, but the overall effect on tweet virality is contingent upon the timing of the contextual information’s publication.

Our research concludes that, although crowdsourced fact-checking is effective, its current speed may not be adequate to substantially reduce the dissemination of misleading information on social media.

--- https://arxiv.org/abs/2404.02905#bytedance Visual Autoregressive Modeling (VAR): Scalable Image Generation via Next-Scale Prediction Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, Liwei Wang 2024-04-03 2024-04-05 [("doi","10.48550/arXiv.2404.02905")] ai/nn/transformer/gpt/dall-e/1 ai/nn/vae ai/scaling

We present Visual AutoRegressive modeling (VAR), a new generation paradigm that redefines the autoregressive learning on images as coarse-to-fine “next-scale prediction” or “next-resolution prediction”, diverging from the standard raster-scan “next-token prediction”. This simple, intuitive methodology allows autoregressive (AR) transformers to learn visual distributions fast and generalize well: VAR, for the first time, makes AR models surpass diffusion transformers in image generation.

On ImageNet 256×256 benchmark, VAR improve AR baseline by improving Fréchet inception distance (FID) 18.65 → 1.80, inception score (IS) 80.4 → 356.4, with around 20× faster inference speed. It is also empirically verified that VAR outperforms the Diffusion Transformer (DiT) in multiple dimensions including image quality, inference speed, data efficiency, and scalability.

Scaling up VAR models exhibits clear power-law scaling laws similar to those observed in LLMs, with linear correlation coefficients near −0.998 as solid evidence. VAR further showcases zero-shot generalization ability in downstream tasks including image in-painting, out-painting, and editing.

These results suggest VAR has initially emulated the two important properties of LLMs: Scaling Laws and zero-shot task generalization. We have released all models and codes to promote the exploration of AR/VAR models for visual generation and unified learning.

[Note: lead author accused of extremely serious misconduct like sabotage & being sued by ByteDance.] --- https://arxiv.org/abs/2403.04317 Online Adaptation of Language Models with a Memory of Amortized Contexts (MAC) Jihoon Tack, Jaehyung Kim, Eric Mitchell, Jinwoo Shin, Yee Whye Teh, Jonathan Richard Schwarz 2024-03-07 2024-04-05 [("doi","10.48550/arXiv.2403.04317")] ai/nn/retrieval reinforcement-learning/meta-learning/continual-learning

Due to the rapid generation and dissemination of information, large language models (LLMs) quickly run out of date despite enormous development costs. Due to this crucial need to keep models updated, online learning has emerged as a critical necessity when using LLMs for real-world applications. However, given the ever-expanding corpus of unseen documents and the large parameter space of modern LLMs, efficient adaptation is essential.

To address these challenges, we propose Memory of Amortized Contexts (MAC), an efficient and effective online adaptation framework for LLMs with strong knowledge retention. We propose an amortized feature extraction and memory-augmentation approach to compress and extract information from new documents into compact modulations stored in a memory bank. When answering questions, our model attends to and extracts relevant knowledge from this memory bank.

To learn informative modulations in an efficient manner, we use amortization-based meta-learning, which substitutes the optimization process with a single forward pass of the encoder. Subsequently, we learn to choose from and aggregate selected documents into a single modulation by conditioning on the question, allowing us to adapt a frozen language model during test time without requiring further gradient updates.

Our experiment demonstrates the superiority of MAC in multiple aspects, including online adaptation performance, time, and memory efficiency.

Code is available at: Github.

--- https://x.com/peterxichen/status/1776364108637757780 Peter Xi Chen 2024-04-05 reinforcement-learning/robot --- https://www.medrxiv.org/content/10.1101/2024.04.03.24305256.full Widespread recessive effects on common diseases in a cohort of 44,000 British Pakistanis and Bangladeshis with high autozygosity Teng Hiang Heng, Klaudia Walter, Qin Qin Huang, Juha Karjalainen, Mark J. Daly, Henrike O. Heyne, FinnGen, Daniel Malawsky, Georgios Kalantzis, Genes, Health Research Team, David A. van Heel, Hilary C. Martin 2024-04-03 2024-04-06 [("doi","10.1101/2024.04.03.24305256")] genetics/heritable/rare

Genetic association studies have focused on testing additive models in cohorts with European ancestry. Little is known about recessive effects on common diseases, specifically for non-European ancestry. Genes & Health is a cohort of British Pakistani and Bangladeshi individuals with elevated rates of consanguinity and endogamy, making it suitable to study recessive effects.

We imputed variants into 44,190 genotyped individuals, using two imputation panels: a set of 4,982 whole-exome-sequences from within the cohort, and the TOPMed-r2 panel. We performed association testing with 898 diseases from electronic health records.

We identified 185 independent loci that reached standard genome-wide statistical-significance (p < 5 × 10−8) under the recessive model and had p-values more statistically-significant than under the additive model. 140 loci demonstrated nominally-significant (p < 0.05) dominance deviation p-values, confirming a recessive association pattern. 16 loci in 3 clusters were statistically-significant at a Bonferroni threshold accounting for multiple phenotypes tested (p < 5.5 × 10−12). In FinnGen, we replicated 44% of the expected number of Bonferroni-significant loci we were powered to replicate, at least one from each cluster, including an intronic variant in PNPLA3 (rs66812091) and non-alcoholic fatty liver disease, a previously reported additive association. We present novel evidence suggesting that the association is recessive instead (OR = 1.3, recessive p = 2 × 10−12, additive p = 2 × 10−11, dominance deviation p = 3 × 10−2, FinnGen recessive OR = 1.3 and p = 6 × 10−12).

We identified a novel protective recessive association between a missense variant in SGLT4 (rs61746559), a sodium-glucose transporter with a possible role in the renin-angiotensin-aldosterone system, and hypertension (OR = 0.2, p = 3 × 10−8, dominance deviation p = 7 × 10−6). These results motivate interrogating recessive effects on common diseases more widely.

--- https://www.youtube.com/watch?v=9oryIMNVtto 2024-04-06 ai/video/generation --- https://x.com/MarginaliaNu/status/1776604852010770749 Marginalia 2024-04-06 design psychology/cognitive-bias/illusion-of-depth wikipedia --- https://www.reuters.com/technology/coreweave-raises-23-billion-debt-collateralized-by-nvidia-chips-2023-08-03/ 2024-04-06 ai/scaling/economics ai/scaling/hardware --- https://arxiv.org/abs/2311.03099 Language Models are Super Mario (DARE): Absorbing Abilities from Homologous Models as a Free Lunch Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, Yongbin Li 2023-11-06 2024-04-06 [("doi","10.48550/arXiv.2311.03099")] ai/nn/sparsity ai/nn/transformer/gpt/instruction-tuning

In this paper, we unveil that Language Models (LMs) can acquire new capabilities by assimilating parameters from homologous models without retraining or GPUs. We first introduce DARE to set most delta parameters (ie. the disparity between fine-tuned and pre-trained parameters) to zeros without affecting the abilities of Supervised Fine-Tuning (SFT) LMs, which randomly Drops delta parameters with a ratio p And REscales the remaining ones by 1/(1 − p) to approximate the original embeddings.

Then, we use DARE as a versatile plug-and-play technique to sparsify delta parameters of multiple SFT homologous models for mitigating parameter interference and merge them into a single model by parameter fusing. We experiment with encoder- and decoder-based LMs, showing that: (1) SFT delta parameter value ranges are typically small (within 0.005) with extreme redundancy, and DARE can effortlessly eliminate 90% or even 99% of them. (2) DARE can merge multiple task-specific LMs into one LM with diverse capabilities. For instance, the amalgamation of WizardLM and WizardMath enhances the GSM8K zero-shot accuracy of WizardLM 2.2 → 66.3, retaining the instruction-following proficiency while surpassing WizardMath’s 64.2 performance.

Our merged LM also ranks first among models with 7 billion parameters on the Open LLM Leaderboard.

--- https://arxiv.org/abs/2310.05976 An evolutionary model of personality traits related to cooperative behavior using a large language model Reiji Suzuki, Takaya Arita 2023-10-03 2024-04-06 [("doi","10.1038/s41598-024-55903-y")] ai/nn/transformer/gpt/4/nonfiction economics

This paper aims to shed light on the evolutionary dynamics of diverse and social populations by introducing the rich expressiveness of generative models into the trait expression of social agent-based evolutionary models. Specifically, we focus on the evolution of personality traits in the context of a game-theoretic relationship as a situation in which inter-individual interests exert strong selection pressures.

We construct an agent model in which linguistic descriptions of personality traits related to cooperative behavior are used as genes. The deterministic strategies extracted from Large Language Models (LLM) that make behavioral decisions based on these personality traits are used as behavioral traits. The population is evolved according to selection based on average payoff and mutation of genes by asking LLM to slightly modify the parent gene toward cooperative or selfish.

Through preliminary experiments and analyses, we clarify that such a model can indeed exhibit the evolution of cooperative behavior based on the diverse and higher-order representation of personality traits. We also observed the repeated intrusion of cooperative and selfish personality traits through changes in the expression of personality traits, and found that the emerging words in the evolved gene well reflected the behavioral tendency of its personality in terms of their semantics.

--- http://www.rdrop.com/~half/Creations/Writings/Web.patterns/visible.location.html 2024-04-06 cs/css design --- https://en.wikipedia.org/wiki/Breadcrumb_navigation Breadcrumb navigation 2024-04-06 cs/css design --- https://x.com/fxturevescent/status/1776456827741323323 fxturevescent 2024-04-06 ai/nn/transformer/gpt/claude fiction/humor --- https://www.newyorker.com/culture/the-weekend-essay/the-day-ram-dass-died 2024-04-06 psychiatry/meditation --- https://www.lesswrong.com/posts/Fgzh2wLmvsBDmiFcN/sheikh-abdur-raheem-ali-s-shortform?commentId=ZtLC5dTTKrwLJxCBf 2024-04-06 reinforcement-learning/preference-learning/mode-collapse --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450436/ Exposure to automation explains religious declines Joshua Conrad Jackson, Kai Chi Yam, Pok Man Tang, Chris G. Sibley, Adam Waytz 2023 2024-04-06 [("doi","10.1073/pnas.2304748120")] ai economics/automation philosophy/mind philosophy/religion

[media] The global decline of religiosity represents one of the most important societal shifts in recent history. After millennia of near-universal religious identification, the world is experiencing a regionally uneven trend toward secularization.

We propose an explanation of this decline, which claims that automation-the development of robots and artificial intelligence (AI)-can partly explain modern religious declines.

We build 4 unique datasets composed of more than 3 million individuals which show that robotics and AI exposure is linked to 21st-century religious declines across nations, metropolitan regions, and individual people. Key results hold controlling for other technological developments (eg. electricity grid access and telecommunications development), socioeconomic indicators (eg. wealth, residential mobility, and demographics), and factors implicated in previous theories of religious decline (eg. individual choice norms). An experiment also supports our hypotheses.

Our findings partly explain contemporary trends in religious decline and foreshadow where religiosity may wane in the future.

--- https://arxiv.org/abs/2403.13638 Do Not Worry if You Do Not Have Data: Building Pretrained Language Models Using Translationese Meet Doshi, Raj Dabre, Pushpak Bhattacharyya 2024-03-20 2024-04-06 [("doi","10.48550/arXiv.2403.13638")] ai/nn/sparsity/knowledge-distillation

In this paper, we explore the utility of Translationese as synthetic data created using machine translation for pre-training language models (LMs). Pre-training requires vast amounts of monolingual data, which is mostly unavailable for languages other than English. Recently, there has been a growing interest in using synthetic data to address this data scarcity. We take the case of English and Indic languages and translate web-crawled monolingual documents (clean) into the target language.

Then, we train language models containing 28M and 85M parameters on this translationese data (synthetic). We show that their performance on downstream natural language understanding and generative tasks is only 3.56% poorer on NLU tasks and 1.51% on NLG tasks than LMs pre-trained on clean data. Further, we propose the use of lightweight TinyLMs pre-trained on clean data to filter synthetic data efficiently which improves the performance of our models. We also find that LMs trained on synthetic data strongly benefit from extended pretraining on a tiny fraction (10%) of clean data.

We release the data we collected and created as a part of this work, IndicMonoDoc, the largest collection of monolingual document-level corpora, which we hope will help bridge the gap between English and non-English performance for large language models.

--- https://arxiv.org/abs/2403.08281 Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models Ning Ding, Yulin Chen, Ganqu Cui, Xingtai Lv, Weilin Zhao, Ruobing Xie, Bowen Zhou, Zhiyuan Liu, Maosong Sun 2024-03-13 2024-04-06 [("doi","10.48550/arXiv.2403.08281")] ai/dataset ai/nn/transformer/gpt/instruction-tuning ai/scaling/mixture-of-experts

Underlying data distributions of natural language, programming code, and mathematical symbols vary vastly, presenting a complex challenge for large language models (LLMs) that strive to achieve high performance across all 3 domains simultaneously. Achieving a very high level of proficiency for an LLM within a specific domain often requires extensive training with relevant corpora, which is typically accompanied by a sacrifice in performance in other domains.

In this paper, we propose to fuse models that are already highly-specialized directly. The proposed fusing framework, UltraFuser, consists of 3 distinct specialists that are already sufficiently trained on language, coding, and mathematics. A token-level gating mechanism is introduced to blend the specialists’ outputs. A two-stage training strategy accompanied by balanced sampling is designed to ensure stability.

To effectively train the fused model, we further construct a high-quality supervised instruction tuning dataset, UltraChat 2, which includes text, code, and mathematical content. This dataset comprises ~300,000 instructions and covers a wide range of topics in each domain.

Experiments show that our model could simultaneously achieve mastery of the 3 crucial domains.

--- https://arxiv.org/abs/2402.01035 Getting the most out of your tokenizer for pre-training and domain adaptation Gautier Dagan, Gabriel Synnaeve, Baptiste Rozière 2024-02-01 2024-04-06 [("doi","10.48550/arXiv.2402.01035")] ai/nn/tokenization

Tokenization is an understudied and often neglected component of modern LLMs (Large Language Models). Most published works use a single tokenizer for all experiments, often borrowed from another model, without performing ablations or analysis to optimize tokenization. Moreover, the tokenizer is generally kept unchanged when fine-tuning a base model.

In this paper, we show that the size, pre-tokenization regular expression, and training data of a tokenizer can impact the model’s generation speed, effective context size, memory usage, and downstream performance. We train specialized Byte-Pair Encoding code tokenizers, and conduct extensive ablations on the impact of tokenizer design on the performance of LLMs for code generation tasks such as HumanEval and MBPP (Massive Bank of Programming Problems), and provide recommendations for tokenizer hyper-parameters selection and switching the tokenizer in a pre-trained LLM.

We perform our experiments on models trained from scratch and from pre-trained models, verifying their applicability to a wide range of use-cases.

We find that when fine-tuning on more than 50 billion tokens, we can specialize the tokenizer of a pre-trained LLM to obtain large gains in generation speed and effective context size.

--- https://scholars-stage.org/on-the-tolkienic-hero/ 2024-04-07 fiction/criticism --- https://arxiv.org/abs/2404.03626#google Training LLMs over Neurally Compressed Text Brian Lester, Jaehoon Lee, Alex Alemi, Jeffrey Pennington, Adam Roberts, Jascha Sohl-Dickstein, Noah Constant 2024-04-04 2024-04-07 [("doi","10.48550/arXiv.2404.03626")] ai/nn/tokenization ai/nn/transformer/gpt cs/algorithm/information/compression

In this paper, we explore the idea of training large language models (LLMs) over highly compressed text. While standard subword tokenizers compress text by a small factor, neural text compressors can achieve much higher rates of compression. If it were possible to train LLMs directly over neurally compressed text, this would confer advantages in training and serving efficiency, as well as easier handling of long text spans.

The main obstacle to this goal is that strong compression tends to produce opaque outputs that are not well-suited for learning. In particular, we find that text naively compressed via Arithmetic Coding is not readily learnable by LLMs...In practice, we find that text compressed via Arithmetic Coding is not readily learnable by a standard transformer-based LLM, with resulting models predicting tokens at chance. Interestingly, this result holds even when M1 is reduced to a context-free unigram model, suggesting that the challenge of modeling AC-compressed text stems from the difficulty of learning the AC compression/decompression process itself. We verify this hypothesis by showing that even the sub-tasks of AC-compressing and AC-decompressing text are not learned well beyond a few initial tokens.

To overcome this, we propose Equal-Info Windows, a novel compression technique whereby text is segmented into blocks that each compress to the same bit length. Using this method, we demonstrate effective learning over neurally compressed text that improves with scale, and outperforms byte-level baselines by a wide margin on perplexity and inference speed benchmarks.

While our method delivers worse perplexity than subword tokenizers for models trained with the same parameter count, it has the benefit of shorter sequence lengths. Shorter sequence lengths require fewer autoregressive generation steps, and reduce latency.

Finally, we provide extensive analysis of the properties that contribute to learnability, and offer concrete suggestions for how to further improve the performance of high-compression tokenizers.

--- https://x.com/swyx/status/1776771329066500589 Shawn Wang 2024-04-07 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2402.05120#tencent More Agents Is All You Need Junyou Li, Qin Zhang, Yangbin Yu, Qiang Fu, Deheng Ye 2024-02-03 2024-04-07 [("doi","10.48550/arXiv.2402.05120")] ai/nn/transformer/gpt/inner-monologue

[poorly reinventing inner-monologue self-distillation... minus the distillation.] We find that, simply via a sampling-and-voting method, the performance of large language models (LLMs) scales with the number of agents instantiated [but flatlines hard after just ~10 ‘agents’, possibly handicapped by flattened-logits]. Also, this method is orthogonal to existing complicated methods to further enhance LLMs, while the degree of enhancement is correlated to the task difficulty.

We conduct comprehensive experiments on a wide range of LLM benchmarks to verify the presence of our finding, and to study the properties that can facilitate its occurrence.

Our code is publicly available at: https://anonymous.4open.science/api/repo/more_agent_is_all_you_need/file/.

--- https://niplav.site/decompose.html#Small_Experiment 2024-04-07 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue statistics/prediction --- https://x.com/Laughing_Mantis/status/1776891376304562535 Laughing_Mantis 2024-04-07 cs/security --- https://maycontainlies.com/discernment-matters-even-more/ 2024-04-07 economics statistics/bias --- /doc/politics/2021-rasmussen-2.pdf Educational Attainment Has a Causal Effect on Economic, But Not Social Ideology: Evidence from Discordant Twins Stig Hebbelstrup Rye Rasmussen, Aaron Weinschenk, Asbjørn Sonne Nørgaard, Jacob von Bornemann Hjelmborg, Robert Klemmensen 2021-04-29 2024-04-07 [("doi","10.1177/00323217211008788")] genetics/heritable/correlation politics

In this article, we examine the nature of the relationship between educational attainment and ideology. Some scholars have argued that the effect of education on political variables like ideology is inflated due to unaccounted-for family factors, such as genetic predispositions and parental socialization.

Using the discordant twin design and data from a large sample of Danish twins, we find that after accounting for confounders rooted in the family, education has:

a (quasi)-causal effect on economic ideology, but not social ideology.

We also examine whether the relationship between education and economic ideology is moderated by levels of economic hardship in the local context where individuals reside.

We find that the (quasi)-causal effect of education on economic ideology increases in economically challenged areas.

--- https://breckyunits.com/sleepWriting.html 2024-04-07 psychology/writing zeo --- https://arxiv.org/abs/2402.10588 Do Llamas Work in English? On the Latent Language of Multilingual Transformers Chris Wendler, Veniamin Veselovsky, Giovanni Monea, Robert West 2024-02-16 2024-04-07 [("doi","10.48550/arXiv.2402.10588")] ai/nn/transformer

We ask whether multilingual language models trained on unbalanced, English-dominated corpora use English as an internal pivot language—a question of key importance for understanding how language models function and the origins of linguistic bias. Focusing on the LLaMA-2 family of transformer models, our study uses carefully constructed non-English prompts with a unique correct single-token continuation.

From layer to layer, transformers gradually map an input embedding of the final prompt token to an output embedding from which next-token probabilities are computed. Tracking intermediate embeddings through their high-dimensional space reveals 3 distinct phases, whereby intermediate embeddings (1) start far away from output token embeddings; (2) already allow for decoding a semantically correct next token in the middle layers, but give higher probability to its version in English than in the input language; (3) finally move into an input-language-specific region of the embedding space.

We cast these results into a conceptual model where the 3 phases operate in “input space”, “concept space”, and “output space”, respectively. Crucially, our evidence suggests that the abstract “concept space” lies closer to English than to other languages, which may have important consequences regarding the biases held by multilingual language models.

--- https://www.reddit.com/r/StableDiffusion/comments/1by0dgs/nsfw_thumbnail_but_sfw_wallpaper_illusions/ 2024-04-07 ai/nn/diffusion --- https://en.wikipedia.org/wiki/Quasispecies_model Quasispecies model 2024-04-07 genetics/selection/natural reinforcement-learning/meta-learning --- https://en.wikipedia.org/wiki/Viral_quasispecies Viral quasispecies 2024-04-07 genetics/selection/natural reinforcement-learning/meta-learning --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10936766/ Large language models are able to downplay their cognitive abilities to fit the persona they simulate Jiří Milička, Anna Marklová, Klára VanSlambrouck, Eva Pospíšilová, Jana Šimsová, Samuel Harvan, Ondřej Drobil 2024 2024-04-07 [("doi","10.1371/journal.pone.0298522")] ai/nn/transformer/gpt/4/nonfiction philosophy/mind psychology/linguistics

This study explores the capabilities of large language models to replicate the behavior of individuals with underdeveloped cognitive and language skills. Specifically, we investigate whether these models can simulate child-like language and cognitive development while solving false-belief tasks, namely, change-of-location and unexpected-content tasks. GPT-3.5-turbo and GPT-4 models by OpenAI were prompted to simulate children (n = 1296) aged one to 6 years. This simulation was instantiated through 3 types of prompts: plain zero-shot, chain-of-thoughts, and primed-by-corpus.

We evaluated the correctness of responses to assess the models’ capacity to mimic the cognitive skills of the simulated children. Both models displayed a pattern of increasing correctness in their responses and rising language complexity. That is in correspondence with a gradual enhancement in linguistic and cognitive abilities during child development, which is described in the vast body of research literature on child development. GPT-4 generally exhibited a closer alignment with the developmental curve observed in ‘real’ children. However, it displayed hyper-accuracy under certain conditions, notably in the primed-by-corpus prompt type. Task type, prompt type, and the choice of language model influenced developmental patterns, while temperature and the gender of the simulated parent and child did not consistently impact results.

We conducted analyses of linguistic complexity, examining utterance length and Kolmogorov complexity. These analyses revealed a gradual increase in linguistic complexity corresponding to the age of the simulated children, regardless of other variables.

These findings show that the language models are capable of downplaying their abilities to achieve a faithful simulation of prompted personas.

...A consistent pattern did not emerge regarding the impact of temperature (Figure 4)...In general, the ascent in complexity appeared to be less steep in personas generated by GPT-4 in contrast to those produced by GPT-3.5-turbo. As in the case of ToM, no clear pattern was observed concerning the effects of temperature (Figure 9)

--- https://arxiv.org/abs/2402.09733 Do LLMs Know about Hallucination? An Empirical Investigation of LLM’s Hidden States Hanyu Duan, Yi Yang, Kar Yan Tam 2024-02-15 2024-04-07 [("doi","10.48550/arXiv.2402.09733")] ai/nn/transformer/gpt/calibration

Large Language Models (LLMs) can make up answers that are not real, and this is known as hallucination. This research aims to see if, how, and to what extent LLMs are aware of hallucination. More specifically, we check whether and how an LLM reacts differently in its hidden states when it answers a question right versus when it hallucinates.

To do this, we introduce an experimental framework which allows examining LLM’s hidden states in different hallucination situations. Building upon this framework, we conduct a series of experiments with language models in the LLaMA family (Touvron et al 2023). Our empirical findings suggest that LLMs react differently when processing a genuine response versus a fabricated one. We then apply various model interpretation techniques to help understand and explain the findings better.

Moreover, informed by the empirical observations, we show great potential of using the guidance derived from LLM’s hidden representation space to mitigate hallucination.

We believe this work provides insights into how LLMs produce hallucinated answers and how to make them occur less often.

--- https://arxiv.org/abs/2401.15449 Learning to Trust Your Feelings: Leveraging Self-awareness in LLMs for Hallucination Mitigation Yuxin Liang, Zhuoyang Song, Hao Wang, Jiaxing Zhang 2024-01-27 2024-04-07 [("doi","10.48550/arXiv.2401.15449")] ai/nn/transformer/gpt/calibration

We evaluate the ability of Large Language Models (LLMs) to discern and express their internal knowledge state, a key factor in countering factual hallucination and ensuring reliable application of LLMs. We observe a robust self-awareness of internal knowledge state in LLMs, evidenced by over 85% accuracy in knowledge probing. However, LLMs often fail to express their internal knowledge during generation, leading to factual hallucinations.

We develop an automated hallucination annotation tool, Dreamcatcher, which merges knowledge probing and consistency checking methods to rank factual preference data. Using knowledge preference as reward, We propose a Reinforcement Learning from Knowledge Feedback (RLKF) training framework, leveraging reinforcement learning to enhance the factuality and honesty of LLMs.

Our experiments across multiple models show that RLKF training effectively enhances the ability of models to use their internal knowledge state, boosting performance in a variety of knowledge-based and honesty-related tasks.

--- https://en.wikipedia.org/wiki/Lixisenatide Lixisenatide 2024-04-07 longevity/glp --- https://thecoder08.github.io/hello-world.html 2024-04-07 cs/algorithm --- https://x.com/futuristfrog/status/1777063159553040700 futuristfrog 2024-04-07 ai/nn/transformer/gpt/claude --- https://slatestarcodex.com/2015/11/03/what-developmental-milestones-are-you-missing/ 2024-04-07 psychology/cognitive-bias/illusion-of-depth --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4038572/ Neuronal GLP1R mediates liraglutide’s anorectic but not glucose-lowering effect Stephanie Sisley, Ruth Gutierrez-Aguilar, Michael Scott, David A. D’Alessio, Darleen A. Sandoval, Randy J. Seeley 2014 2024-04-07 [("doi","10.1172/JCI72434")] longevity/glp/psychology

Glucose control and weight loss are cornerstones of type 2 diabetes treatment. Currently, only glucagon-like peptide-1 (GLP1) analogs are able to achieve both weight loss and glucose tolerance. Both glucose and body weight are regulated by the brain, which contains GLP1 receptors (GLP1R). Even though the brain is poised to mediate the effects of GLP1 analogs, it remains unclear whether the glucose- and body weight-lowering effects of long-acting GLP1R agonists are via direct action on CNS GLP1R or the result of downstream activation of afferent neuronal GLP1R.

We generated mice with either neuronal or visceral nerve-specific deletion of Glp1r and then administered liraglutide, a long-acting GLP1R agonist. We found that neither reduction of GLP1R in the CNS nor in the visceral nerves resulted in alterations in body weight or food intake in animals fed normal chow or a high-fat diet.

Liraglutide treatment provided beneficial glucose-lowering effects in both chow- and high-fat-fed mice lacking GLP1R in the CNS or visceral nerves; however, liraglutide was ineffective at altering food intake, body weight, or causing a conditioned taste aversion in mice lacking neuronal GLP1R. These data indicate that neuronal GLP1Rs mediate body weight and anorectic effects of liraglutide, but are not required for glucose-lowering effects.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7211411/ GABA neurons in the nucleus tractus solitarius express GLP-1 receptors and mediate anorectic effects of liraglutide in rats Samantha M. Fortin, Rachele K. Lipsky, Rinzin Lhamo, Jack Chen, Eun Kim, Tito Borner, Heath D. Schmidt, Matthew R. Hayes 2020 2024-04-07 [("doi","10.1126/scitranslmed.aay8071")] longevity/glp/psychology

The glucagon-like peptide-1 receptor (GLP-1R) agonist liraglutide is approved for the treatment of obesity; however, there is still much to be learned regarding the neuronal sites of action that underlie its suppressive effects on food intake and body weight. Peripherally administered liraglutide in rats acts in part through central GLP-1Rs in both the hypothalamus and the hindbrain. Here, we extend findings supporting a role for hindbrain GLP-1Rs in mediating the anorectic effects of liraglutide in male rats.

To dissociate the contribution of GLP-1Rs in the area postrema (AP) and the nucleus tractus solitarius (NTS), we examined the effects of liraglutide in both NTS AAV-shRNA-driven Glp1r knockdown and AP-lesioned animals. Knockdown of NTS GLP-1Rs, but not surgical lesioning of the AP, attenuated the anorectic and body weight-reducing effects of acutely delivered liraglutide. In addition, NTS c-Fos responses were maintained in AP-lesioned animals. Moreover, NTS Glp1r knockdown was sufficient to attenuate the intake- and body weight-reducing effects of chronic daily administered liraglutide over 3 weeks.

Development of improved obesity pharmacotherapies requires an understanding of the cellular phenotypes targeted by GLP-1R agonists. Fluorescence in situ hybridization identified Glp1r transcripts in NTS GABAergic neurons, which when inhibited using chemogenetics, attenuated the food intake- and body weight-reducing effects of liraglutide. This work demonstrates the contribution of NTS GLP-1Rs to the anorectic potential of liraglutide and highlights a phenotypically distinct (GABAergic) population of neurons within the NTS that express the GLP-1R and are involved in the mediation of liraglutide signaling.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3352246/ Drugs developed to treat diabetes, liraglutide and lixisenatide, cross the blood brain barrier and enhance neurogenesis Kerry Hunter, Christian Hölscher 2012 2024-04-07 [("doi","10.1186/1471-2202-13-33")] longevity/glp/psychology psychiatry/alzheimers

Background: Type 2 diabetes is a risk factor for Alzheimer’s disease (AD), most likely linked to an impairment of insulin signaling in the brain. Therefore, drugs that enhance insulin signaling may have therapeutic potential for AD. Liraglutide (Victoza) and exenatide (Byetta) are novel long-lasting analogues of the GLP-1 incretin hormone and are currently available to treat diabetes. They facilitate insulin signaling via the GLP-1 receptor (GLP-1R). Numerous in vitro and in vivo studies have shown that GLP-1 analogues have a range of neuroprotective properties. GLP-1Rs are expressed in the hippocampal area of the brain an important site of adult neurogenesis and maintenance of cognition and memory formation. Therefore, if GLP-1 analogues can cross the blood brain barrier, diffuse through the brain to reach the receptors and most importantly activate them, their neuroprotective effects may be realized.

Methods: In the present study we profiled the GLP-1 receptor agonists liraglutide (Victoza) and lixisenatide (Lyxumia). We measured the kinetics of crossing the blood brain barrier (BBB), activation of the GLP-1R by measuring cAMP levels, and physiological effects in the brain on neuronal stem cell proliferation and neurogenesis.

Results: Both drugs were able to cross the BBB. Lixisenatide crossed the BBB at all doses tested (2.5, 25, or 250 nmol/kg bw ip.) when measured 30 min post-injection and at 2.5-25 nmol/kg bw ip. 3h post-injection. Lixisenatide also enhanced neurogenesis in the brain. Liraglutide crossed the BBB at 25 and 250 nmol/kg ip. but no increase was detectable at 2.5 nmol/kg ip. 30 min post-injection, and at 250 nmol/kg ip. at 3h post-injection. Liraglutide and lixisenatide enhanced cAMP levels in the brain, with lixisenatide being more effective.

Conclusions: Our results suggest that these novel incretin analogues cross the BBB and show physiological activity and neurogenesis in the brain, which may be of use as a treatment of neurodegenerative diseases.

--- https://en.wikipedia.org/wiki/Shaped_charge#Munroe_effect Shaped charge § Munroe effect 2024-04-07 technology --- https://apps.dtic.mil/sti/tr/pdf/ADA220095.pdf#page=4 2024-04-08 technology --- https://3quarksdaily.com/3quarksdaily/2020/10/what-john-von-neumann-really-did-at-los-alamos.html 2024-04-08 radiance --- https://arxiv.org/abs/2404.03622#microsoft Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models Wenshan Wu, Shaoguang Mao, Yadong Zhang, Yan Xia, Li Dong, Lei Cui, Furu Wei 2024-04-04 2024-04-08 [("doi","10.48550/arXiv.2404.03622")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue

Large language models (LLMs), such as those developed by OpenAI and Google, have exhibited impressive performance in language comprehension and various reasoning tasks. However, their abilities in spatial reasoning, a crucial aspect of human cognition, remain relatively unexplored. Humans possess a remarkable ability to create mental images of unseen objects and actions through a process known as the Mind’s Eye, enabling the imagination of the unseen world.

Inspired by this cognitive capacity, we propose Visualization-of-Thought (VoT) prompting. VoT aims to elicit spatial reasoning of LLMs by visualizing their reasoning traces [using ASCII art/Unicode], thereby guiding subsequent reasoning steps. We employed VoT for multi-hop spatial reasoning tasks, including natural language navigation, visual navigation, and visual tiling in 2D grid worlds.

Experimental results demonstrated that VoT enhances the spatial reasoning abilities of LLMs. Notably, VoT outperformed existing multimodal large language models (MLLMs) in these tasks.

While VoT works surprisingly well on LLMs, the ability to generate mental images to facilitate spatial reasoning resembles the mind’s eye process, suggesting its potential viability in MLLMs. [Seems like it would make more sense just to go multimodal and tokenize images inline like CM3/Gato/etc.]

--- https://app.suno.ai/song/f81be5c0-3de9-4940-95e1-cfb780d3aa5e/ 2024-04-08 ai/music --- https://bxt.rs/blog/just-how-much-faster-are-the-gnome-46-terminals/ 2024-04-08 cs/algorithm --- https://arxiv.org/abs/2404.03592 ReFT: Representation Finetuning for Language Models Zhengxuan Wu, Aryaman Arora, Zheng Wang, Atticus Geiger, Dan Jurafsky, Christopher D. Manning, Christopher Potts 2024-04-04 2024-04-08 [("doi","10.48550/arXiv.2404.03592")] ai/nn/sparsity ai/nn/transformer/attention

Parameter-efficient fine-tuning (PEFT) methods seek to adapt large models via updates to a small number of weights. However, much prior interpretability work has shown that representations encode rich semantic information, suggesting that editing representations might be a more powerful alternative. Here, we pursue this hypothesis by developing a family of Representation Finetuning (ReFT) methods. ReFT methods operate on a frozen base model and learn task-specific interventions on hidden representations.

We define a strong instance of the ReFT family, Low-rank Linear Subspace ReFT (LoReFT). LoReFT is a drop-in replacement for existing PEFTs and learns interventions that are 10×-50x more parameter-efficient than prior state-of-the-art PEFTs.

We showcase LoReFT on 8 commonsense reasoning tasks, 4 arithmetic reasoning tasks, Alpaca-Eval v1.0, and GLUE. In all these evaluations, LoReFT delivers the best balance of efficiency and performance, and almost always outperforms state-of-the-art PEFTs.

We release a generic ReFT training library publicly at Github.

--- https://arxiv.org/abs/2403.18286#google Few-Shot Recalibration of Language Models Xiang Lisa Li, Urvashi Khandelwal, Kelvin Guu 2024-03-27 2024-04-08 [("doi","10.48550/arXiv.2403.18286")] ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/palm/2

Recent work has uncovered promising ways to extract well-calibrated confidence estimates from language models (LMs), where the model’s confidence score reflects how likely it is to be correct. However, while LMs may appear well-calibrated over broad distributions, this often hides miscalibration within narrower slices (eg. systemic over-confidence in math can balance out systemic under-confidence in history, yielding perfect calibration in aggregate).

To attain well-calibrated confidence estimates for any slice of a distribution, we propose a new framework for few-shot slice-specific recalibration. Specifically, we train a recalibration model that takes in a few unlabeled examples from any given slice and predicts a curve that remaps confidence scores to be more accurate for that slice. Our trained model can recalibrate for arbitrary new slices, without using any labeled data from that slice.

Experiments show that our few-shot recalibrator consistently outperforms existing calibration methods, for instance improving calibration error for PaLM2-Large on MMLU by 16%, as compared to temperature scaling.

This enables us to identify domain-specific confidence thresholds above which the LM’s predictions can be trusted, and below which it should abstain.

--- https://x.com/goodside/status/1558622567635865600 Riley Goodside 2024-04-08 ai/nn/transformer/gpt reinforcement-learning/model/decision-transformer --- https://www.lesswrong.com/posts/kzc3qNMsP2xJcxhGn/gated-attention-blocks-preliminary-progress-toward-removing-1 2024-04-08 ai/nn/transformer/attention/sparsity --- https://economicsociology.org/2017/08/29/economics-to-sociology-phrasebook/ 2024-04-08 economics sociology --- https://x.com/ryancbriggs/status/1777427880303038781 Ryan C. Briggs 2024-04-08 design/visualization --- https://www.medrxiv.org/content/10.1101/2024.04.07.24305438.full De novo variants in the non-coding spliceosomal snRNA gene RNU4-2 are a frequent cause of syndromic neurodevelopmental disorders Yuyang Chen, Ruebena Dawes, Hyung Chul Kim, Sarah L. Stenton, Susan Walker, Alicia Ljungdahl, Jenny Lord, Vijay S. Ganesh, Jialan Ma, Alexandra C. Martin-Geary, Gabrielle Lemire, Elston N. D’Souza, Shan Dong, Jamie M. Ellingford, David R. Adams, Kirsten Allan, Madhura Bakshi, Erin E. Baldwin, Seth I. Berger, Jonathan A. Bernstein, Natasha J. Brown, Lindsay C. Burrage, Kimberly Chapman, Alison G. Compton, Chloe A. Cunningham, Precilla D’Souza, Emmanuèle C. Délot, Kerith-Rae Dias, Ellen R. Elias, Care-Anne Evans, Lisa Ewans, Kimberly Ezell, Jamie L. Fraser, Lyndon Gallacher, Casie A. H. Genetti, Christina L. Grant, Tobias Haack, Alma Kuechler, Seema R. Lalani, Elsa Leitão, Anna Le Fevre, Richard J. Leventer, Jan E. Liebelt, Paul J. Lockhart, Alan S. Ma, Ellen F. Macnamara, Taylor M. Maurer, Hector R. Mendez, Stephen B. Montgomery, Marie-Cécile Nassogne, Serena Neumann, Melanie O’Leary, Elizabeth E. Palmer, John Phillips, Georgia Pitsava, Ryan Pysar, Heidi L. Rehm, Chloe M. Reuter, Nicole Revencu, Angelika Riess, Rocio Rius, Lance Rodan, Tony Roscioli, Jill A. Rosenfeld, Rani Sachdev, Cas Simons, Sanjay M. Sisodiya, Penny Snell, Laura St Clair, Zornitza Stark, Tiong Yang Tan, Natalie B. Tan, Suzanna E. L. Temple, David R. Thorburn, Cynthia J. Tifft, Eloise Uebergang, Grace E. VanNoy, Eric Vilain, David H. Viskochil, Laura Wedd, Matthew T. Wheeler, Susan M. White, Monica Wojcik, Lynne A. Wolfe, Zoe Wolfenson, Changrui Xiao, David Zocche, John L. Rubenstein, Eirene Markenscoff-Papadimitriou, Sebastian M. Fica, Diana Baralle, Christel Depienne, Daniel G. MacArthur, Joanna M. M. Howson, Stephan J. Sanders, Anne O’Donnell-Luria, Nicola Whiffin 2024-04-08 2024-04-09 [("doi","10.1101/2024.04.07.24305438")] genetics/heritable/rare

Around 60% of individuals with neurodevelopmental disorders (NDD) remain undiagnosed after comprehensive genetic testing, primarily of protein-coding genes. Increasingly, large genome-sequenced cohorts are improving our ability to discover new diagnoses in the non-coding genome. Here, we identify the non-coding RNA RNU4-2 as a novel syndromic NDD gene. RNU4-2 encodes the U4 small nuclear RNA (snRNA), which is a critical component of the U4/U6.U5 tri-snRNP complex of the major spliceosome.

We identify an 18 bp region of RNU4-2 mapping to two structural elements in the U4/U6 snRNA duplex (the T-loop and Stem III) that is severely depleted of variation in the general population, but in which we identify heterozygous variants in 119 individuals with NDD. The vast majority of individuals (77.3%) have the same highly recurrent single base-pair insertion (n.64_65insT). We estimate that variants in this region explain 0.41% of individuals with NDD.

We demonstrate that RNU4-2 is highly expressed in the developing human brain, in contrast to its contiguous counterpart RNU4-1 and other U4 homologs, supporting RNU4-2’s role as the primary U4 transcript in the brain.

Overall, this work underscores the importance of non-coding genes in rare disorders. It will provide a diagnosis to thousands of individuals with NDD worldwide and pave the way for the development of effective treatments for these individuals.

--- https://classic.esquire.com/article/share/58ff278a-21da-4ee4-a446-b7f451b90275 2024-01-01 cs/hardware economics --- https://arxiv.org/abs/2306.06546 High-Fidelity Audio Compression with Improved RVQGAN Rithesh Kumar, Prem Seetharaman, Alejandro Luebs, Ishaan Kumar, Kundan Kumar 2023-06-11 2024-04-09 [("doi","10.48550/arXiv.2306.06546")] ai/music ai/nn/gan cs/algorithm/information/compression

Language models have been successfully used to model natural signals, such as images, speech, and music. A key component of these models is a high quality neural compression model that can compress high-dimensional natural signals into lower dimensional discrete tokens.

To that end, we introduce a high-fidelity universal neural audio compression algorithm that achieves ~90× compression of 44.1 KHz audio into tokens at just 8kbps bandwidth.

We achieve this by combining advances in high-fidelity audio generation with better vector quantization techniques from the image domain, along with improved adversarial and reconstruction losses. We compress all domains (speech, environment, music, etc.) with a single universal model, making it widely applicable to generative modeling of all audio.

We compare with competing audio compression algorithms, and find our method outperforms them. We provide thorough ablations for every design choice.

We hope our work can lay the foundation for the next generation of high-fidelity audio modeling.

We provide open-source code and trained model weights.

--- https://www.pcgamer.com/games/card-games/champions-tcg-ai-artist/ 2024-04-09 ai/nn/diffusion/midjourney --- https://www.nytimes.com/2024/04/09/t-magazine/strange-perfume-fragrance.html 2024-04-10 psychology/smell/perfume --- https://www.wsj.com/business/meet-the-robots-slicing-your-barbecue-ribs-338a7794 2024-04-10 economics/automation reinforcement-learning/robot --- https://www.bbc.com/future/article/20180403-inside-the-world-of-instruction-manuals 2024-04-10 design --- /doc/cs/css/1882-bassano-photographofqueenvictoriawithinversion.png 1882 2024-01-01 [("invert","False")] cs/css --- https://ai.meta.com/blog/next-generation-meta-training-inference-accelerator-AI-MTIA/ 2024-04-10 ai/nn/retrieval ai/scaling/hardware --- https://arxiv.org/abs/2404.06405 Wu’s Method can Boost Symbolic AI to Rival Silver Medalists and AlphaGeometry to Outperform Gold Medalists at IMO Geometry Shiven Sinha, Ameya Prabhu, Ponnurangam Kumaraguru, Siddharth Bhat, Matthias Bethge 2024-04-09 2024-04-10 [("doi","10.48550/arXiv.2404.06405")] ai math

Proving geometric theorems constitutes a hallmark of visual reasoning combining both intuitive and logical skills. Therefore, automated theorem proving of Olympiad-level geometry problems is considered a notable milestone in human-level automated reasoning. The introduction of AlphaGeometry, a neuro-symbolic model trained with 100 million synthetic samples, marked a major breakthrough. It solved 25⁄30 International Mathematical Olympiad (IMO) problems whereas the reported baseline based on Wu’s method solved only ten.

In this note, we revisit the IMO-AG-30 Challenge introduced with AlphaGeometry, and find that Wu’s method is surprisingly strong. Wu’s method alone can solve 15 problems, and some of them are not solved by any of the other methods. This leads to two key findings: (1) Combining Wu’s method with the classic synthetic methods of deductive databases and angle, ratio, and distance chasing solves 21⁄30 methods by just using a CPU-only laptop with a time limit of 5 minutes per problem. Essentially, this classic method solves just 4 problems less than AlphaGeometry and establishes the first fully symbolic baseline strong enough to rival the performance of an IMO silver medalist. (2) Wu’s method even solves 2 of the 5 problems that AlphaGeometry failed to solve.

Thus, by combining AlphaGeometry with Wu’s method we set a new state-of-the-art for automated theorem proving on IMO-AG-30, solving 27⁄30 problems, the first AI method which outperforms an IMO gold medalist.

--- https://karpathy.github.io/2011/04/27/manually-classifying-cifar10/ 2024-04-10 ai/dataset --- https://urbigenous.net/library/alicebob.html 2024-04-10 cs/cryptography math/humor --- https://www.rollingstone.com/music/music-features/udio-ai-music-chatgpt-suno-1235001675/ 2024-04-10 ai/music --- https://arxiv.org/abs/2403.05135#tencent ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment Xiwei Hu, Rui Wang, Yixiao Fang, Bin Fu, Pei Cheng, Gang Yu 2024-03-08 2024-04-10 [("doi","10.48550/arXiv.2403.05135")] ai/dataset ai/nn/diffusion ai/nn/transformer/t5

Diffusion models have demonstrated remarkable performance in the domain of text-to-image generation. However, most widely used models still employ CLIP as their text encoder, which constrains their ability to comprehend dense prompts, encompassing multiple objects, detailed attributes, complex relationships, long-text alignment, etc.

In this paper, we introduce an Efficient Large Language Model Adapter, ELLA, which equips text-to-image diffusion models with powerful Large Language Models (LLM) to enhance text alignment without training of either U-Net or LLM. To seamlessly bridge two pre-trained models, we investigate a range of semantic alignment connector designs and propose a novel module, the Timestep-Aware Semantic Connector (TSC), which dynamically extracts timestep-dependent conditions from LLM. Our approach adapts semantic features at different stages of the denoising process, assisting diffusion models in interpreting lengthy and intricate prompts over sampling timesteps. Additionally, ELLA can be readily incorporated with community models and tools to improve their prompt-following capabilities.

To assess text-to-image models in dense prompt following, we introduce Dense Prompt Graph Benchmark (DPG-Bench), a challenging benchmark consisting of 1,000 dense prompts.

Extensive experiments demonstrate the superiority of ELLA in dense prompt following compared to state-of-the-art methods, particularly in multiple object compositions involving diverse attributes and relationships.

--- https://arxiv.org/abs/2212.07677#google Transformers learn in-context by gradient descent Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov 2022-12-15 2024-04-11 [("doi","10.48550/arXiv.2212.07677")] ai/nn/transformer/attention reinforcement-learning/meta-learning

[discussion] At present, the mechanisms of in-context learning in Transformers are not well understood and remain mostly an intuition. In this paper, we suggest that training Transformers on auto-regressive objectives is closely related to gradient-based meta-learning formulations.

We start by providing a simple weight construction that shows the equivalence of data transformations induced by (1) a single linear self-attention layer and by (2) gradient-descent (GD) on a regression loss. Motivated by that construction, we show empirically that when training self-attention-only Transformers on simple regression tasks either the models learned by GD and Transformers show great similarity or, remarkably, the weights found by optimization match the construction. Thus we show how trained Transformers become mesa-optimizers i.e. learn models by gradient descent in their forward pass. This allows us, at least in the domain of regression problems, to mechanistically understand the inner workings of in-context learning in optimized Transformers.

Building on this insight, we furthermore identify how Transformers surpass the performance of plain gradient descent by learning an iterative curvature correction and learn linear models on deep data representations to solve non-linear regression tasks.

Finally, we discuss intriguing parallels to a mechanism identified to be crucial for in-context learning termed induction-head (Olsson et al 2022) and show how it could be understood as a specific case of in-context learning by gradient descent learning within Transformers.

Code to reproduce the experiments can be found at Github.

--- https://arxiv.org/abs/2403.03183 How Well Can Transformers Emulate In-context Newton’s Method? Angeliki Giannou, Liu Yang, Tianhao Wang, Dimitris Papailiopoulos, Jason D. Lee 2024-03-05 2024-04-11 [("doi","10.48550/arXiv.2403.03183")] ai/nn/transformer/attention reinforcement-learning/meta-learning

Transformer-based models have demonstrated remarkable in-context learning capabilities, prompting extensive research into its underlying mechanisms. Recent studies have suggested that Transformers can implement first-order optimization algorithms for in-context learning and even second order ones for the case of linear regression.

In this work, we study whether Transformers can perform higher order optimization methods, beyond the case of linear regression. We establish that linear attention Transformers with ReLU layers can approximate second order optimization algorithms for the task of logistic regression and achieve ε error with only a logarithmic to the error more layers.

As a by-product we demonstrate the ability of even linear attention-only Transformers in implementing a single step of Newton’s iteration for matrix inversion with merely two layers.

These results suggest the ability of the Transformer architecture to implement complex algorithms, beyond gradient descent.

--- https://arxiv.org/abs/2306.00297 Transformers learn to implement preconditioned gradient descent for in-context learning Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, Suvrit Sra 2023-06-01 2024-04-11 [("doi","10.48550/arXiv.2306.00297")] ai/nn/transformer/attention reinforcement-learning/meta-learning

Several recent works demonstrate that transformers can implement algorithms like gradient descent. By a careful construction of weights, these works show that multiple layers of transformers are expressive enough to simulate iterations of gradient descent.

Going beyond the question of expressivity, we ask: Can transformers learn to implement such algorithms by training over random problem instances? To our knowledge, we make the first theoretical progress on this question via an analysis of the loss landscape for linear transformers trained over random instances of linear regression.

For a single attention layer, we prove the global minimum of the training objective implements a single iteration of preconditioned gradient descent. Notably, the preconditioning matrix not only adapts to the input distribution but also to the variance induced by data inadequacy.

For a transformer with 𝓛 attention layers, we prove certain critical points of the training objective implement L iterations of preconditioned gradient descent.

Our results call for future theoretical studies on learning algorithms by training transformers.

--- https://x.com/elder_plinius/status/1778188202664169724 elder_plinius 2024-04-11 ai/nn/adversarial ai/nn/transformer/gpt/4 --- https://arxiv.org/abs/2404.05955 VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding? Junpeng Liu, Yifan Song, Bill Yuchen Lin, Wai Lam, Graham Neubig, Yuanzhi Li, Xiang Yue 2024-04-09 2024-04-11 [("doi","10.48550/arXiv.2404.05955")] ai/dataset ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/palm

Multimodal Large Language models (MLLMs) have shown promise in web-related tasks, but evaluating their performance in the web domain remains a challenge due to the lack of comprehensive benchmarks. Existing benchmarks are either designed for general multimodal tasks, failing to capture the unique characteristics of web pages, or focus on end-to-end web agent tasks, unable to measure fine-grained abilities such as OCR, understanding, and grounding.

In this paper, we introduce WebTaskBench, a multimodal benchmark designed to assess the capabilities of MLLMs across a variety of web tasks. WebTaskBench consists of 7 tasks, and comprises 1.5K human-curated instances from 139 real websites, covering 87 sub-domains. We evaluate 14 open-source MLLMs, Gemini Pro, Claude-3 series, and GPT-4-V(ision) on WebTaskBench, revealing challenges and performance gaps.

Further analysis highlights the limitations of current MLLMs, including inadequate grounding in text-rich environments and subpar performance with low-resolution image inputs.

We believe WebTaskBench will serve as a valuable resource for the research community and contribute to the creation of more powerful and versatile MLLMs for web-related applications.

--- https://docs.parea.ai/blog/benchmarking-anthropic-beta-tool-use 2024-04-11 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2306.17844 The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks Ziqian Zhong, Ziming Liu, Max Tegmark, Jacob Andreas 2023-06-30 2024-04-11 [("doi","10.48550/arXiv.2306.17844")] ai/nn/fully-connected ai/scaling/emergence/grokking

[media] Do neural networks, trained on well-understood algorithmic tasks, reliably rediscover known algorithms for solving those tasks? Several recent studies, on tasks ranging from group arithmetic to in-context linear regression, have suggested that the answer is yes.

Using modular addition as a prototypical problem, we show that algorithm discovery in neural networks is sometimes more complex. Small changes to model hyperparameters and initializations can induce the discovery of qualitatively different algorithms from a fixed training set, and even parallel implementations of multiple such algorithms.

Some networks trained to perform modular addition implement a familiar Clock algorithm; others implement a previously undescribed, less intuitive, but comprehensible procedure which we term the Pizza algorithm, or a variety of even more complex procedures.

Our results show that even simple learning problems can admit a surprising diversity of solutions, motivating the development of new tools for characterizing the behavior of neural networks across their algorithmic phase space.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10772076/ Persistent thinness and anorexia nervosa differ on a genomic level Christopher Hübel, Mohamed Abdulkadir, Moritz Herle, Alish B. Palmos, Ruth Loos, Gerome Breen, Nadia Micali, Cynthia M. Bulik 2024 2024-04-11 [("doi","10.1038/s41431-023-01431-8")] exercise genetics/heritable/correlation psychiatry/adhd psychiatry/anorexia psychiatry/depression

Thinness and anorexia nervosa are both characterised by persistent low weight. Individuals with anorexia nervosa concurrently report distorted perceptions of their body and engage in weight-loss behaviors, whereas individuals with thinness often wish to gain weight. Both conditions are heritable and share genomics with BMI, but are not genetically correlated with each other. Based on their pattern of genetic associations with other traits, we explored differences between thinness and anorexia nervosa on a genomic level.

In Part 1, using publicly available data, we compared genetic correlations of persistent thinness/anorexia nervosa with 11 psychiatric disorders. In Part 2, we identified individuals with adolescent persistent thinness in the Avon Longitudinal Study of Parents and Children (ALSPAC) by latent class growth analysis of measured BMI 10 → 24 years (n = 6594) and evaluated associations with psychiatric and anthropometric polygenic scores.

In Part 1, in contrast to the positive genetic correlations of anorexia nervosa with various psychiatric disorders, persistent thinness showed negative genetic correlations with attention deficit hyperactivity disorder (rgAN = 0.08 vs. rgPT = −0.30), alcohol dependence (rgAN = 0.07 vs. rgPT = −0.44), major depressive disorder (rgAN = 0.27 vs. rgPT = −0.18) and post-traumatic stress disorder (rgAN = 0.26 vs. rgPT = −0.20).

In Part 2, individuals with adolescent persistent thinness in the ALSPAC had lower borderline personality disorder polygenic scores (OR = 0.77; Q = 0.01).

Overall, results suggest that genetic variants associated with thinness are negatively associated with psychiatric disorders and therefore thinness may be differentiable from anorexia nervosa on a genomic level.

--- https://clemenswinter.com/2024/04/07/the-simple-beauty-of-xor-floating-point-compression/ 2024-04-11 cs/algorithm/information/compression --- https://qntm.org/excellent 2024-04-11 fiction/science-fiction/time-travel --- https://www.baen.com/Chapters/9781982126032/9781982126032___5.htm 2024-04-11 fiction/science-fiction/time-travel --- https://en.wikipedia.org/wiki/Baen_Free_Library Baen Free Library 2024-04-11 economics/copyright fiction/science-fiction --- https://www.youtube.com/watch?v=fCF8I_X1qKI&t=119s Elon Musk Monologue—SNL Elon Musk 2024-04-11 psychiatry/bipolar/elon-musk --- https://en.wikipedia.org/wiki/Hilbert%27s_paradox_of_the_Grand_Hotel Hilbert’s paradox of the Grand Hotel 2024-04-11 math --- https://en.wikipedia.org/wiki/Mu_(negative)#Non-dualistic_meaning Mu (negative) § Non-dualistic meaning 2024-04-11 philosophy/epistemology --- https://arxiv.org/abs/2404.07066 Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers? Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang 2024-04-10 2024-04-11 [("doi","10.48550/arXiv.2404.07066")] ai/nn/sparsity ai/nn/transformer

This paper studies the phenomenon that different concepts are learned in different layers of large language models, ie. more difficult concepts are fully acquired with deeper layers. We define the difficulty of concepts by the level of abstraction, and here it is crudely categorized by factual, emotional, and inferential. Each category contains a spectrum of tasks, arranged from simple to complex. For example, within the factual dimension, tasks range from lie detection to categorizing mathematical problems.

We employ a probing technique to extract representations from different layers of the model and apply these to classification tasks. Our findings reveal that models tend to efficiently classify simpler tasks, indicating that these concepts are learned in shallower layers. Conversely, more complex tasks may only be discernible at deeper layers, if at all.

This paper explores the implications of these findings for our understanding of model learning processes and internal representations.

Our implementation is available at https://github.com/Luckfort/CD.

--- https://web.archive.org/web/20060816171003/https://www.unf.edu/mudlark/posters/hartzler.html 2024-01-01 fiction/poetry --- https://www.reddit.com/r/SampleSize/comments/fr4rnl/results_i_asked_you_to_choose_any_integer_from/ 2024-04-11 psychology/cognitive-bias statistics/probability --- https://x.com/Norod78/status/1778413117778575486 Norod78 2024-04-11 ai/nn/diffusion --- /doc/math/2019-rekvenyi.pdf Paul Erdős’s mathematics as a social activity Kamilla Rekvenyi 2019-03-24 2024-04-11 [("doi","10.1080/26375451.2019.1593036")] math sociology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10794143/#deepmind Solving olympiad geometry without human demonstrations Trieu H. Trinh, Yuhuai Wu, Quoc V. Le, He He, Thang Luong 2024 2024-04-12 [("doi","10.1038/s41586-023-06747-5")] ai/nn/transformer math

Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning, owing to their reputed difficulty among the world’s best talents in pre-university mathematics. Current machine-learning approaches, however, are not applicable to most mathematical domains owing to the high cost of translating human proofs into machine-verifiable format. The problem is even worse for geometry because of its unique translation challenges, resulting in severe scarcity of training data.

We propose AlphaGeometry, a theorem prover for Euclidean plane geometry that sidesteps the need for human demonstrations by synthesizing millions of theorems and proofs across different levels of complexity. AlphaGeometry is a neuro-symbolic system that uses a neural language model, trained from scratch on our large-scale synthetic data, to guide a symbolic deduction engine through infinite branching points in challenging problems.

On a test set of 30 latest olympiad-level problems, AlphaGeometry solves 25, outperforming the previous best method that only solves 10 problems and approaching the performance of an average International Mathematical Olympiad (IMO) gold medalist. Notably, AlphaGeometry produces human-readable proofs, solves all geometry problems in the IMO 2000 and 2015 under human expert evaluation, and discovers a generalized version of a translated IMO theorem in 2004.

--- https://www.science.org/doi/10.1126/sciadv.add5163 2024-04-12 longevity/epigenetics --- https://github.com/greshake/llm-security 2024-04-12 ai/nn/transformer/gpt cs/security --- https://arxiv.org/abs/2302.10342 Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems Yihao Feng, Shentao Yang, Shujian Zhang, Jianguo Zhang, Caiming Xiong, Mingyuan Zhou, Huan Wang 2023-02-20 2024-04-12 [("doi","10.48550/arXiv.2302.10342")] reinforcement-learning/preference-learning statistics/order/comparison

When learning task-oriented dialogue (ToD) agents, reinforcement learning (RL) techniques can naturally be used to train dialogue strategies to achieve user-specific goals. Prior works mainly focus on adopting advanced RL techniques to train the ToD agents, while the design of the reward function is not well studied.

This paper aims at answering the question of how to efficiently learn and leverage a reward function for training end-to-end (E2E) ToD agents. Specifically, we introduce two generalized objectives for reward-function learning, inspired by the classical learning-to-rank literature. Further, we use the learned reward function to guide the training of the E2E ToD agent.

With the proposed techniques, we achieve competitive results on the E2E response-generation task on the Multiwoz 2.0 dataset.

Source code and checkpoints are publicly released at Github.

--- https://arxiv.org/abs/2302.14520 Large Language Models Are State-of-the-Art Evaluators of Translation Quality Tom Kocmi, Christian Federmann 2023-02-28 2024-04-12 [("doi","10.48550/arXiv.2302.14520")] ai/dataset ai/nn/transformer/gpt/4/nonfiction

We describe GEMBA, a GPT-based metric for assessment of translation quality, which works both with a reference translation and without. In our evaluation, we focus on zero-shot prompting, comparing 4 prompt variants in two modes, based on the availability of the reference.

We investigate 9 versions of GPT models, including ChatGPT and GPT-4. We show that our method for translation quality assessment only works with GPT ~3.5 and larger models. Comparing to results from WMT22’s Metrics shared task, our method achieves state-of-the-art accuracy in both modes when compared to MQM-based human labels.

Our results are valid on the system level for all 3 WMT22 Metrics shared task language pairs, namely English into German, English into Russian, and Chinese into English. This provides a first glimpse into the usefulness of pre-trained, generative large language models for quality assessment of translations.

We publicly release all our code and prompt templates used for the experiments described in this work, as well as all corresponding scoring results, to allow for external validation and reproducibility.

--- https://arxiv.org/abs/2311.12092 Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models Rohit Gandikota, Joanna Materzynska, Tingrui Zhou, Antonio Torralba, David Bau 2023-11-20 2024-04-12 [("doi","10.48550/arXiv.2311.12092")] ai/nn/diffusion ai/nn/gan/stylegan

We present a method to create interpretable concept sliders that enable precise control over attributes in image generations from diffusion models. Our approach identifies a low-rank parameter direction corresponding to one concept while minimizing interference with other attributes. A slider is created using a small set of prompts or sample images; thus slider directions can be created for either textual or visual concepts. Concept Sliders are plug-and-play: they can be composed efficiently and continuously modulated, enabling precise control over image generation.

In quantitative experiments comparing to previous editing techniques, our sliders exhibit stronger targeted edits with lower interference. We showcase sliders for weather, age, styles, and expressions, as well as slider compositions. We show how sliders can transfer latents from StyleGAN for intuitive editing of visual concepts for which textual description is difficult.

We also find that our method can help address persistent quality issues in Stable Diffusion XL including repair of object deformations and fixing distorted hands.

Our code, data, and trained sliders are available at https://sliders.baulab.info/.

--- https://arxiv.org/abs/2208.13628 Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical Alignment Mustafa Shukor, Guillaume Couairon, Matthieu Cord 2022-08-29 2024-04-12 [("doi","10.48550/arXiv.2208.13628")] ai/nn/transformer/clip

Vision and Language Pretraining has become the prevalent approach for tackling multimodal downstream tasks. The current trend is to move towards ever larger models and pretraining datasets. This computational headlong rush does not seem reasonable in the long term to move toward sustainable solutions, and de facto excludes academic laboratories with limited resources.

In this work, we propose a new framework, dubbed ViCHA, that efficiently exploits the input data to boost the learning by: (a) a new hierarchical cross-modal alignment loss, (b) new self-supervised scheme based on masked image modeling, (c) leveraging image-level annotations, called Visual Concepts, obtained with existing foundation models such as CLIP to boost the performance of the image encoder.

Although pretrained on 4× less data, our ViCHA strategy outperforms other approaches on several downstream tasks such as Image-Text Retrieval, VQA (Visual Question Answering), Visual Reasoning, Visual Entailment and Visual Grounding.

The code will be made publicly available here: https://github.com/mshukor/ViCHA.

--- https://www.unum.cloud/blog/2023-02-20-efficient-multimodality 2024-04-12 ai/nn/transformer/clip --- https://www.theverge.com/2023/2/23/23609942/microsoft-bing-sydney-chatbot-history-ai 2024-04-12 ai/nn/transformer/gpt/4/sydney --- https://github.blog/2023-02-14-github-copilot-for-business-is-now-available/ 2024-04-12 ai/nn/transformer/gpt/codex --- https://www.linkedin.com/pulse/building-new-bing-jordi-ribas/ 2024-04-12 ai/nn/transformer/gpt/4 --- https://arxiv.org/abs/2303.00747 WhisperX: Time-Accurate Speech Transcription of Long-Form Audio Max Bain, Jaesung Huh, Tengda Han, Andrew Zisserman 2023-03-01 2024-04-12 [("doi","10.48550/arXiv.2303.00747")] ai/nn/transformer/gpt/whisper

Large-scale, weakly-supervised speech recognition models, such as Whisper, have demonstrated impressive results on speech recognition across domains and languages. However, their application to long audio transcription via buffered or sliding window approaches is prone to drifting, hallucination & repetition; and prohibits batched transcription due to their sequential nature. Further, timestamps corresponding each utterance are prone to inaccuracies and word-level timestamps are not available out-of-the-box.

To overcome these challenges, we present WhisperX, a time-accurate speech recognition system with word-level timestamps using voice activity detection and forced phoneme alignment. In doing so, we demonstrate state-of-the-art performance on long-form transcription and word segmentation benchmarks.

Additionally, we show that pre-segmenting audio with our proposed VAD Cut & Merge strategy improves transcription quality and enables a 12× transcription speedup via batched inference.

--- https://arxiv.org/abs/2212.01681 Language Models as Agent Models Jacob Andreas 2022-12-03 2024-04-12 [("doi","10.48550/arXiv.2212.01681")] reinforcement-learning/model/decision-transformer

Language models (LMs) are trained on collections of documents, written by individual human agents to achieve specific goals in an outside world. During training, LMs have access only to the text of these documents, with no direct evidence of the internal states of the agents that produced them—a fact often used to argue that LMs are incapable of modeling goal-directed aspects of human language production and comprehension. Can LMs trained on text learn anything at all about the relationship between language and use? I argue that LMs are models of intentional communication in a specific, narrow sense.

When performing next word prediction given a textual context, an LM can infer and represent properties of an agent likely to have produced that context. These representations can in turn influence subsequent LM generation in the same way that agents’ communicative intentions influence their language. I survey findings from the recent literature showing that—even in today’s non-robust and error-prone models—LMs infer and use representations of fine-grained communicative intentions and more abstract beliefs and goals.

Despite the limited nature of their training data, they can thus serve as building blocks for systems that communicate and act intentionally.

--- https://arxiv.org/abs/2303.01486#deepmind Understanding plasticity in neural networks Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Avila Pires, Razvan Pascanu, Will Dabney 2023-03-02 2024-04-12 [("doi","10.48550/arXiv.2303.01486")] reinforcement-learning/meta-learning/continual-learning

Plasticity, the ability of a neural network to quickly change its predictions in response to new information, is essential for the adaptability and robustness of deep reinforcement learning systems. Deep neural networks are known to lose plasticity over the course of training even in relatively simple learning problems, but the mechanisms driving this phenomenon are still poorly understood.

This paper conducts a systematic empirical analysis into plasticity loss, with the goal of understanding the phenomenon mechanistically in order to guide the future development of targeted solutions. We find that loss of plasticity is deeply connected to changes in the curvature of the loss landscape, but that it often occurs in the absence of saturated units.

Based on this insight, we identify a number of parameterization and optimization design choices which enable networks to better preserve plasticity over the course of training.

We validate the utility of these findings on larger-scale RL benchmarks in the Arcade Learning Environment.

--- https://www.lesswrong.com/posts/D7PumeYTDPfBTp3i7/the-waluigi-effect-mega-post 2024-01-01 reinforcement-learning/model/decision-transformer reinforcement-learning/preference-learning reinforcement-learning/safe --- https://www.youtube.com/watch?v=Tda7jCwvSzg&t=14s 2024-04-12 design --- https://github.com/mhx/dwarfs?tab=readme-ov-file#comparison 2024-04-12 cs/algorithm/information/compression --- https://arxiv.org/abs/2404.01261 FABLES: Evaluating faithfulness and content selection in book-length summarization Yekyung Kim, Yapei Chang, Marzena Karpinska, Aparna Garimella, Varun Manjunatha, Kyle Lo, Tanya Goyal, Mohit Iyyer 2024-04-01 2024-04-12 [("doi","10.48550/arXiv.2404.01261")] ai/nn/retrieval ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/inner-monologue

While long-context large language models (LLMs) can technically summarize book-length documents (>100K tokens), the length and complexity of the documents have so far prohibited evaluations of input-dependent aspects like faithfulness. In this paper, we conduct the first large-scale human evaluation of faithfulness and content selection on LLM-generated summaries of fictional books. Our study mitigates the issue of data contamination by focusing on summaries of books published in 2023 or 2024, and we hire annotators who have fully read each book prior to the annotation task to minimize cost and cognitive burden.

We collect FABLES, a dataset of annotations on 3,158 claims made in LLM-generated summaries of 26 books, at a cost of $5.2K USD, which allows us to rank LLM summarizers based on faithfulness: Claude-3-Opus outperforms all closed-source LLMs, while the open-source Mixtral is on par with GPT-3.5-Turbo.

An analysis of the annotations reveals that most unfaithful claims relate to events and character states, and they generally require indirect reasoning over the narrative to invalidate. While LLM-based auto-raters have proven reliable for factuality and coherence in other settings, we implement several LLM raters of faithfulness and find that none correlates strongly with human annotations, especially with regard to detecting unfaithful claims.

Our experiments suggest that detecting unfaithful claims is an important future direction not only for summarization evaluation but also as a testbed for long-context understanding.

Finally, we move beyond faithfulness by exploring content selection errors in book-length summarization: we develop a typology of omission errors related to crucial narrative elements and also identify a systematic over-emphasis on events occurring towards the end of the book.

--- https://github.com/aaronjanse/dns-over-wikipedia 2024-04-12 cs/cryptography/steganography wikipedia --- https://allendowney.blogspot.com/2013/08/are-my-data-normal.html 2024-04-12 statistics/probability --- https://arxiv.org/abs/2404.07341#assembly Conformer-1: Robust ASR via Large-Scale Semi-supervised Bootstrapping Kevin Zhang, Luka Chkhetiani, Francis McCann Ramirez, Yash Khare, Andrea Vanzo, Michael Liang, Sergio Ramirez Martin, Gabriel Oexle, Ruben Bousbib, Taufiquzzaman Peyash, Michael Nguyen, Dillon Pulliam, Domenic Donato 2024-04-10 2024-04-12 [("doi","10.48550/arXiv.2404.07341")] ai/nn/transformer ai/scaling

This paper presents Conformer-1, an end-to-end Automatic Speech Recognition (ASR) model trained on an extensive dataset of 570k hours of speech audio data, 91% of which was acquired from publicly available sources.

To achieve this, we perform Noisy Student Training after generating pseudo-labels for the unlabeled public data using a strong Conformer RNN-T baseline model. The addition of these pseudo-labeled data results in remarkable improvements in relative Word Error Rate (WER) by 11.5% and 24.3% for our asynchronous and real-time models, respectively. Additionally, the model is more robust to background noise owing to the addition of these data.

The results obtained in this study demonstrate that the incorporation of pseudo-labeled publicly available data is a highly effective strategy for improving ASR accuracy and noise robustness.

--- https://arxiv.org/abs/2404.07544 From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples Robert Vacareanu, Vlad-Andrei Negru, Vasile Suciu, Mihai Surdeanu 2024-04-11 2024-04-12 [("doi","10.48550/arXiv.2404.07544")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/tabular reinforcement-learning/meta-learning

We analyze how well pre-trained large language models (eg. LLaMA-2, GPT-4, Claude-3, etc) can do linear and non-linear regression when given in-context examples, without any additional training or gradient updates.

Our findings reveal that several large language models (eg. GPT-4, Claude-3) are able to perform regression tasks:

with a performance rivaling (or even outperforming) that of traditional supervised methods such as Random Forest, Bagging, or Gradient Boosting. For example, on the challenging Friedman-2 regression dataset, Claude 3 outperforms many supervised methods such as Ada Boost, SVM, Random Forest, KNN, or Gradient Boosting.

We then investigate how well the performance of large language models scales with the number of in-context exemplars. We borrow from the notion of regret from online learning and empirically show that LLMs are capable of obtaining a sub-linear regret.

--- https://arxiv.org/abs/2404.01292 Measuring Style Similarity in Diffusion Models Gowthami Somepalli, Anubhav Gupta, Kamal Gupta, Shramay Palta, Micah Goldblum, Jonas Geiping, Abhinav Shrivastava, Tom Goldstein 2024-04-01 2024-04-12 [("doi","10.48550/arXiv.2404.01292")] ai/nn/diffusion

Generative models are now widely used by graphic designers and artists. Prior works have shown that these models remember and often replicate content from their training data during generation. Hence as their proliferation increases, it has become important to perform a database search to determine whether the properties of the image are attributable to specific training data, every time before a generated image is used for professional purposes.

Existing tools for this purpose focus on retrieving images of similar semantic content. Meanwhile, many artists are concerned with style replication in text-to-image models. We present a framework for understanding and extracting style descriptors from images. Our framework comprises a new dataset curated using the insight that style is a subjective property of an image that captures complex yet meaningful interactions of factors including but not limited to colors, textures, shapes, etc.

We also propose a method to extract style descriptors that can be used to attribute style of a generated image to the images used in the training dataset of a text-to-image model. We showcase promising results in various style retrieval tasks. We also quantitatively and qualitatively analyze style attribution and matching in the Stable Diffusion model.

Code and artifacts are available at Github.

--- https://dallasinnovates.com/exclusive-qa-john-carmacks-different-path-to-artificial-general-intelligence/ John Carmack’s ‘Different Path’ to Artificial General Intelligence John Carmack 2023-02-02 2024-04-12 ai/nn/vae/mae ai/scaling reinforcement-learning/model

Exclusive Q&A: The iconic Dallas game developer, rocket engineer, and VR visionary has pivoted to an audacious new challenge: developing artificial general intelligence—a form of AI that goes beyond mimicking human intelligence to understanding things and solving problems. John Carmack sees a 60% chance of achieving initial success in AGI by 2030. Here’s how, and why, he’s working independently to make it happen.

…There’s valuable things that happened earlier that people aren’t necessarily aware of. There’s some work from like the 1970s, 1980s, and 1990s that I actually think might be interesting, because a lot of things happened back then that didn’t pan out, just because they didn’t have enough scale. They were trying to do this on one-megahertz computers, not clusters of GPUs.

And there is this kind of groupthink I mentioned that is really clear, if you look at it, about all these brilliant researchers—they all have similar backgrounds, and they’re all kind of swimming in the same direction. So, there’s a few of these old things back there that I think may be useful. So right now, I’m building experiments, I’m testing things, I’m trying to marry together some of these fields that are distinct, that have what I feel are pieces of the AGI algorithm.

But most of what I do is I run simulations through watching lots of television and playing various video games. And I think that combination of, ‘Here’s how you perceive and internalize a model of the world, and here’s how you act in it with agency in some of these situations’, I still don’t know how they come together. But I think there are keys there. I think I have my arms around the scope of the problems that need to be solved, and how to push things together.

I still think there’s a half dozen insights that need to happen, but I’ve got a couple of things that are plausible insights that might turn out to be relevant. And one of the things that I trained myself to do a few decades ago is pulling ideas out and pursuing them in a way where I’m excited about them, knowing that most of them don’t pan out in the end. Much earlier in my career, when I’d have a really bright idea that didn’t work out, I was crushed afterwards. But eventually I got to the point where I’m really good at just shoveling ideas through my processing and shooting them down, almost making it a game to say, ‘How quickly can I bust my own idea, rather than protecting it as a pet idea?’

So, I’ve got a few of these candidates right now that I’m in the process of exploring and attacking. But it’s going to be these abstract ideas and techniques and ways to apply things that are similar to the way deep learning is done right now.

So, I’m pushing off scaling it out, because there are a bunch of companies now saying, ‘We need to go raise $100 million, $200 million, because we need to have a warehouse full of GPUs.’ And that’s one path to value, and there’s a little bit of a push toward that. But I’m very much pushing toward saying, ‘No, I want to figure out these 6 important things before I go waste $100 million of someone’s money.’ I’m actually not spending much money right now. I raised $20 million, but I’m thinking that this is a decade-long task where I don’t want to burn through $20 million in the next two years, then raise another series to get another couple hundred million dollars, because I don’t actually think that’s the smart way to go about things.

--- https://www.freepatentsonline.com/y2024/0104353.html#deepmind 2024-04-12 ai/nn/transformer/gpt/palm reinforcement-learning/model --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10980748/ Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns Ariel Goldstein, Avigail Grinstein-Dabush, Mariano Schain, Haocheng Wang, Zhuoqiao Hong, Bobbi Aubrey, Mariano Schain, Samuel A. Nastase, Zaid Zada, Eric Ham, Amir Feder, Harshvardhan Gazula, Eliav Buchnik, Werner Doyle, Sasha Devore, Patricia Dugan, Roi Reichart, Daniel Friedman, Michael Brenner, Avinatan Hassidim, Orrin Devinsky, Adeen Flinker, Uri Hasson 2024 2024-04-12 [("doi","10.1038/s41467-024-46631-y")] ai/nn/transformer/gpt/2/nonfiction psychology/neuroscience

Contextual embeddings, derived from deep language models (DLMs), provide a continuous vectorial representation of language. This embedding space differs fundamentally from the symbolic representations posited by traditional psycholinguistics. We hypothesize that language areas in the human brain, similar to DLMs, rely on a continuous embedding space to represent language.

To test this hypothesis, we densely record the neural activity patterns in the inferior frontal gyrus (IFG) of 3 participants using dense intracranial arrays while they listened to a 30-minute podcast. From these fine-grained spatiotemporal neural recordings, we derive a continuous vectorial representation for each word (ie. a brain embedding) in each patient. Using stringent zero-shot mapping we demonstrate that brain embeddings in the IFG and the DLM contextual embedding space have common geometric patterns.

The common geometric patterns allow us to predict the brain embedding in IFG of a given left-out word based solely on its geometrical relationship to other non-overlapping words in the podcast. Furthermore, we show that contextual embeddings capture the geometry of IFG embeddings better than static word embeddings.

The continuous brain embedding space exposes a vector-based neural code for natural language processing in the human brain.

--- https://arxiv.org/abs/quant-ph/9611028 Limitations of Noisy Reversible Computation D. Aharonov, M. Ben-Or, Russell Impagliazzo, N. Nisan 1996-11-17 2024-04-12 [("doi","10.48550/arXiv.9611028")] cs/computable

Noisy computation and reversible computation have been studied separately, and it is known that they are as powerful as unrestricted computation. We study the case where both noise and reversibility are combined, and show that the combined model is weaker than unrestricted computation.

In our noisy reversible circuits, each wire is flipped with probability p each time step, and all the inputs to the circuit are present in time 0. We prove that any noisy reversible circuit must have size exponential in its depth in order to compute a function with high probability. This is tight as we show that any circuit can be converted into a noise-resistant reversible one with a blow up in size which is exponential in the depth. This establishes that noisy reversible computation has the power of the complexity class NC1.

We extend this to quantum circuits (QC). We prove that any noisy QC which is not worthless, and for which all inputs are present at time 0, must have size exponential in its depth. (This high-lights the fact that fault tolerant QC must use a constant supply of inputs all the time.) For the lower bound, we show that quasi-polynomial noisy QC are at least powerful as logarithmic depth QC, (or QNC1).

Making these bounds tight is left open in the quantum case.

--- https://x.com/SwiftOnSecurity/status/1778893315947081813 SwiftOnSecurity 2024-04-12 psychiatry/traumatic-brain-injury --- https://arxiv.org/abs/2402.12291 KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students Matthew Shu, Nishant Balepur, Shi Feng, Jordan Boyd-Graber 2024-02-19 2024-04-13 [("doi","10.48550/arXiv.2402.12291")] ai/nn/transformer psychology/spaced-repetition

Flashcard schedulers are tools that rely on (1) student models to predict the flashcards a student knows; and (2) teaching policies to schedule cards based on these predictions. Existing student models, however, only use flashcard-level features, like the student’s past responses, ignoring the semantic ties of flashcards. Deep Knowledge Tracing (DKT) models can capture semantic relations with language models, but are inefficient, lack content-rich datasets for evaluation, and require robust teaching policies.

To address these issues, we design KARL, a DKT-inspired student model that uses retrieval and BERT embeddings for efficient and accurate student recall predictions. To test KARL, we collect a new dataset of diverse study history on trivia questions.

KARL bests existing student models in AUC and calibration error. Finally, we propose a novel teaching policy that exploits the predictive power of DKT models to deploy KARL online.

Based on 27 learners and 32 6-day study trajectories, KARL shows the ability to enhance medium-term educational learning, proving its efficacy for scheduling.

--- https://www.reddit.com/r/Anki/comments/1c29775/fsrs_is_one_of_the_most_accurate_spaced/ 2024-04-13 ai/tabular psychology/spaced-repetition --- https://chrisblattman.com/blog/2022/01/12/does-buying-organic-save-lives/ 2024-04-13 biology --- https://www.technologyreview.com/2024/04/10/1091053/generative-ai-turn-your-most-precious-memories-into-photos/ 2024-04-13 ai/nn/diffusion --- /doc/philosophy/religion/2024-schnabel.pdf Switch to Web-Based Surveys During COVID-19 Pandemic Left Out the Most Religious, Creating a False Impression of Rapid Religious Decline Landon Schnabel, Sean Bock, Michael Hout 2024-04-04 2024-04-13 [("doi","10.1093/socrel/srad061")] philosophy/religion sociology

Religion appears to have taken a nosedive during the pandemic, including previously persistent forms of intense religion such as strong affiliation and biblical literalism. However, this apparent secularization is the result of mode effects. The gold standard General Social Survey (GSS) switched to online rather than face-to-face interviews and the response rate plunged to 17%.

Parallel analyses of GSS panel data demonstrate that this mode switch introduced substantial nonresponse bias. Illustratively, biblical literalism was almost 50% higher among those who declined to participate (36%) than those who participated in the online survey (25%). Rather than declining, intense religion persisted if not rose over time among those willing to participate in a push-to-web survey. The apparent decline was simply a result of disillusioned, distrusting, disinformed, disadvantaged, and disconnected people being much less likely to agree to participate.

Intense religion and other social phenomena are underrepresented and thereby underestimated in online surveys with substantial nonresponse, including those using population sampling methods. The trend in survey research toward these types of surveys could be expected to give a false impression of secularization and other social change going forward—including making society look less disillusioned, distrusting, disinformed, disadvantaged, and disconnected than it is.

--- https://journals.sagepub.com/doi/10.1177/21677026231207791 2024-04-13 sociology/technology --- https://kenkantzer.com/lessons-after-a-half-billion-gpt-tokens/ 2024-04-13 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/1808.00508#deepmind Neural Arithmetic Logic Units Andrew Trask, Felix Hill, Scott Reed, Jack Rae, Chris Dyer, Phil Blunsom 2018-08-01 2024-04-13 [("doi","10.48550/arXiv.1808.00508")] ai/nn/fully-connected ai/tabular

Neural networks can learn to represent and manipulate numerical information, but they seldom generalize well outside of the range of numerical values encountered during training.

To encourage more systematic numerical extrapolation, we propose an architecture that represents numerical quantities as linear activations which are manipulated using primitive arithmetic operators, controlled by learned gates. We call this module a neural arithmetic logic unit (NALU), by analogy to the arithmetic logic unit in traditional processors.

Experiments show that NALU-enhanced neural networks can learn to track time, perform arithmetic over images of numbers, translate numerical language into real-valued scalars, execute computer code, and count objects in images.

In contrast to conventional architectures, we obtain substantially better generalization both inside and outside of the range of numerical values encountered during training, often extrapolating orders of magnitude beyond trained numerical ranges.

--- https://arxiv.org/abs/2312.05481 Artificial Intelligence in the Knowledge Economy Enrique Ide, Eduard Talamas 2023-12-09 2024-04-13 [("doi","10.48550/arXiv.2312.05481")] economics/automation

How does Artificial Intelligence (AI) affect the organization of work? We incorporate AI into an economy where humans endogenously sort into hierarchical firms: Less knowledgeable agents become “workers” (ie. execute routine tasks), while more knowledgeable agents becomes “managers” (ie. specialize in problem solving).

We model AI as an algorithm that uses computing power to mimic the behavior of humans with a given knowledge.

We show that AI not only leads to occupational displacement but also changes the endogenous matching between all workers and managers.

This leads to new insights regarding AI’s effects on productivity, firm size, and degree of decentralization.

--- https://en.wikipedia.org/wiki/Snappy_(compression) Snappy (compression) 2024-04-13 cs/algorithm/information/compression --- https://www.marble.onl/posts/data_takers_and_makers.html 2024-04-13 reinforcement-learning/exploration/active-learning --- https://x.com/_MG_/status/1152317329646088192 MG 2024-04-14 cs/hardware --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4542717/ Realizing a desired family size: when should couples start? J. Dik F. Habbema, Marinus J. C. Eijkemans, Henri Leridon, Egbert R. te Velde 2015 2024-04-14 [("doi","10.1093/humrep/dev148")] genetics/selection/artificial

Study Question: Until what age can couples wait to start a family without compromising their chances of realizing the desired number of children?

Summary Answer: The latest female age at which a couple should start trying to become pregnant strongly depends on the importance attached to achieving a desired family size and on whether or not IVF is an acceptable option in case no natural pregnancy occurs.

What is Known Already: It is well established that the treatment-independent and treatment-dependent chances of pregnancy decline with female age. However, research on the effect of age has focused on the chance of a first pregnancy and not on realizing more than one child.

Study Design, Size, Duration: An established computer simulation model of fertility, updated with recent IVF success rates, was used to simulate a cohort of 10 000 couples in order to assess the chances of realizing a one-, two- or three-child family, for different female ages at which the couple starts trying to conceive.

Participants/Materials, Setting, Methods: The model uses treatment-independent pregnancy chances and pregnancy chances after IVF/ICSI. In order to focus the discussion, we single out 3 levels of importance that couples could attach to realizing a desired family size: (1) Very important (equated with aiming for at least a 90% success chance). (2) Important but not at all costs (equated with a 75% success chance) (3) Good to have children, but a life without children is also fine (equated with a 50% success chance).

Main Results and the Role of Chance: In order to have a chance of at least 90% to realize a one-child family, couples should start trying to conceive when the female partner is 35 years of age or younger, in case IVF is an acceptable option. For two children, the latest starting age is 31 years, and for 3 children 28 years. Without IVF, couples should start no later than age 32 years for a one-child family, at 27 years for a two-child family, and at 23 years for 3 children. When couples accept 75% or lower chances of family completion, they can start 4–11 years later. The results appeared to be robust for plausible changes in model assumptions.

Limitations, Reasons for Caution: Our conclusions would have been more persuasive if derived directly from large-scale prospective studies. An evidence-based simulation study (as we did) is the next best option. We recommend that the simulations should be updated every 5–10 years with new evidence because, owing to improvements in IVF technology, the assumptions on IVF success chances in particular run the risk of becoming outdated.

Wider Implications of the Findings: Information on the chance of family completion at different starting ages is important for prospective parents in planning their family, for preconception counselling, for inclusion in educational courses in human biology, and for increasing public awareness on human reproductive possibilities and limitations.

Study Funding/Competing Interests: No external funding was either sought or obtained for this study. There are no conflicts of interest to be declared.

--- https://x.com/jeremyphoward/status/1779311134656671872 Jeremy P. Howard 2024-04-14 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2310.01377 UltraFeedback: Boosting Language Models with High-quality Feedback Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, Maosong Sun 2023-10-02 2024-04-14 [("doi","10.48550/arXiv.2310.01377")] ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/preference-learning

Reinforcement learning from human feedback (RLHF) has become a pivot technique in aligning large language models (LLMs) with human preferences. In RLHF practice, preference data plays a crucial role in bridging human proclivity and LLMs. However, the scarcity of diverse, naturalistic datasets of human preferences on LLM outputs at scale poses a great challenge to RLHF as well as feedback learning research within the open-source community. Current preference datasets, either proprietary or limited in size and prompt variety, result in limited RLHF adoption in open-source models and hinder further exploration.

In this study, we propose UltraFeedback, a large-scale, high-quality, and diversified preference dataset designed to overcome these limitations and foster RLHF development. To create UltraFeedback, we compile a diverse array of instructions and models from multiple sources to produce comparative data. We meticulously devise annotation instructions and employ GPT-4 to offer detailed feedback in both numerical and textual forms. UltraFeedback establishes a reproducible and expandable preference data construction pipeline, serving as a solid foundation for future RLHF and feedback learning research.

Utilizing UltraFeedback, we train various models to demonstrate its effectiveness, including the reward model UltraRM, chat language model UltraLM-13B-PPO, and critique model UltraCM. Experimental results indicate that our models outperform existing open-source models, achieving top performance across multiple benchmarks.

Our data and models are available at Github.

--- https://en.wikipedia.org/wiki/2024_Bondi_Junction_stabbings 2024 Bondi Junction stabbings 2024-04-14 crime psychiatry/schizophrenia --- /doc/ai/anime/danbooru/2024-jeon.pdf CartoonizeDiff: Diffusion-Based Photo Cartoonization Scheme Hwyjoon Jeon, Jonghwa Shim, Hyeonwoo Kim, Eenjun Hwang 2024-02-18 2024-04-14 [("doi","10.1109/BigComp60711.2024.00038")] ai/anime/danbooru ai/nn/diffusion

Photo cartoonization seeks to create cartoon-style images from photos of real-life scenes. So far, diverse deep learning-based methods have been proposed to automate photo cartoonization. However, they tend to oversimplify high-frequency patterns, resulting in images that look like abstractions rather than a true animation style.

To alleviate this problem, this paper proposes CartoonizeDiff, a new photo cartoonization method based on diffusion model and ControlNet. In the proposed method, Color Canny ControlNet and Reflect ControlNet are appended to a pretrained latent diffusion model to preserve the color, structure, and fine details of photos for better cartoonization.

Through extensive experiments on animation backgrounds and real-world landscape datasets, we demonstrate that the proposed method quantitatively and qualitatively outperforms existing methods.

--- https://arxiv.org/abs/2404.07396 ChatGPT Can Predict the Future when it Tells Stories Set in the Future About the Past Van Pham, Scott Cunningham 2024-04-11 2024-04-14 [("doi","10.48550/arXiv.2404.07396")] ai/nn/transformer/gpt/4/fiction ai/nn/transformer/gpt/inner-monologue statistics/prediction

This study investigates whether OpenAI’s ChatGPT-3.5 and ChatGPT-4 can accurately forecast future events using two distinct prompting strategies. To evaluate the accuracy of the predictions, we take advantage of the fact that the training data at the time of experiment stopped at September 2021, and ask about events that happened in 2022 using ChatGPT-3.5 and ChatGPT-4.

We employed two prompting strategies: direct prediction and what we call future narratives which ask ChatGPT to tell fictional stories set in the future with characters that share events that have happened to them, but after ChatGPT’s training data had been collected. Concentrating on events in 2022, we prompted ChatGPT to engage in storytelling, particularly within economic contexts.

After analyzing 100 prompts, we discovered that future narrative prompts enhanced ChatGPT-4’s forecasting accuracy. This was especially evident in its predictions of major Academy Award winners as well as economic trends, the latter inferred from scenarios where the model impersonated public figures like the Federal Reserve Chair, Jerome Powell. These findings indicate that narrative prompts leverage the models’ capacity for hallucinatory narrative construction, facilitating more effective data synthesis and extrapolation than straightforward predictions.

Our research reveals new aspects of LLMs’ predictive capabilities and suggests potential future applications in analytical contexts.

[Probably data leakage from continued-training and user feedback, among other possibilities, but why do the story prompts put so much more weight on just a few (correct) outcomes? Is this just a kind of RLHF mode-collapse (onto the mode, which is correct due to leakage) manifesting in fiction samples, where ChatGPT output is so dreary? Or is this some kind of superior prompting for eliciting dark-knowledge, and possibly an unusual form of inner-monologue?]

--- https://arxiv.org/abs/2306.11951 On the Optimal Bounds for Noisy Computing Banghua Zhu, Ziao Wang, Nadim Ghaddar, Jiantao Jiao, Lele Wang 2023-06-21 2024-04-14 [("doi","10.48550/arXiv.2306.11951")] statistics/order/comparison

We revisit the problem of computing with noisy information considered in Feige et al 1994, which includes computing the OR function from noisy queries, and computing the MAX, SEARCH and SORT functions from noisy pairwise comparisons. For K given elements, the goal is to correctly recover the desired function with probability at least 1 − δ when the outcome of each query is flipped with probability p.

We consider both the adaptive sampling setting where each query can be adaptively designed based on past outcomes, and the non-adaptive sampling setting where the query cannot depend on past outcomes. The prior work provides tight bounds on the worst-case query complexity in terms of the dependence on K. However, the upper and lower bounds do not match in terms of the dependence on δ and p.

We improve the lower bounds for all the 4 functions under both adaptive and non-adaptive query models. Most of our lower bounds match the upper bounds up to constant factors when either p or δ is bounded away from 0, while the ratio between the best prior upper and lower bounds goes to infinity when p → 0 or p → 1⁄2.

On the other hand, we also provide matching upper and lower bounds for the number of queries in expectation, improving both the upper and lower bounds for the variable-length query model.

--- https://xkcd.com/505/ 2024-04-15 cs/cellular-automaton fiction/humor --- https://en.wikipedia.org/wiki/The_World_Without_Us The World Without Us 2024-04-15 existential-risk --- https://www.lesswrong.com/posts/XaKLjyDejtXDoRAzL/a-quick-experiment-on-lms-inductive-biases-in-performing 2024-04-15 ai/nn/transformer/gpt/inner-monologue --- https://arxiv.org/abs/2403.17141 MetaAligner: Conditional Weak-to-Strong Correction for Generalizable Multi-Objective Alignment of Language Models Kailai Yang, Zhiwei Liu, Qianqian Xie, Tianlin Zhang, Nirui Song, Jimin Huang, Ziyan Kuang, Sophia Ananiadou 2024-03-25 2024-04-15 [("doi","10.48550/arXiv.2403.17141")] ai/nn/transformer/gpt/instruction-tuning

Recent advancements in large language models (LLMs) aim to tackle heterogeneous human expectations and values via multi-objective preference alignment. However, existing methods are parameter-adherent to the policy model, leading to two key limitations: (1) the high-cost repetition of their alignment algorithms for each new target model; (2) they cannot expand to unseen objectives due to their static alignment objectives.

In this work, we propose Meta-Objective Aligner (MetaAligner), a model that performs conditional weak-to-strong correction for weak responses to approach strong responses. MetaAligner is the first policy-agnostic and generalizable method for multi-objective preference alignment, which enables plug-and-play alignment by decoupling parameter updates from the policy models and facilitates zero-shot preference alignment for unseen objectives via in-context learning.

Experimental results show that MetaAligner achieves and balanced improvements in multi-objective alignments on 11 policy models with up to 63× more parameters, and outperforms previous alignment methods with down to 22.27× less computational resources. The model also accurately aligns with unseen objectives, marking the first step towards generalizable multi-objective preference alignment.

--- https://www.berryconsultants.com/2023/09/14/if-bayesian-inference-doesnt-depend-on-the-experimental-design-then-why-does-bayesian-optimal-design-exist/ 2024-04-15 statistics/bayes statistics/decision --- https://arxiv.org/abs/2404.08627 Is ChatGPT Transforming Academics’ Writing Style? Mingmeng Geng, Roberto Trotta 2024-04-12 2024-04-15 [("doi","10.48550/arXiv.2404.08627")] ai/nn/transformer/gpt/4/nonfiction science

Based on one million arXiv papers submitted from May 2018 to January 2024, we assess the textual density of ChatGPT’s writing style in their abstracts by means of a statistical analysis of word frequency changes.

Our model is calibrated and validated on a mixture of real abstracts and ChatGPT-modified abstracts (simulated data) after a careful noise analysis.

We find that ChatGPT is having an increasing impact on arXiv abstracts, especially in the field of computer science, where the fraction of ChatGPT-revised abstracts is estimated to be ~35%, if we take the output of one of the simplest prompts, “revise the following sentences”, as a baseline.

We conclude with an analysis of both positive and negative aspects of the penetration of ChatGPT into academics’ writing style.

--- https://arxiv.org/abs/2404.08634 Pre-training Small Base LMs with Fewer Tokens Sunny Sanyal, Sujay Sanghavi, Alexandros G. Dimakis 2024-04-12 2024-04-15 [("doi","10.48550/arXiv.2404.08634")] ai/nn/sparsity/pruning

We study the effectiveness of a simple approach to develop a small base language model (LM) starting from an existing large base LM: first inherit a few transformer blocks from the larger LM, and then train this smaller model on a very small subset (0.1%) of the raw pretraining data of the larger model.

We call our simple recipe Inheritune and first demonstrate it for building a small base LM with 1.5b parameters using 1B tokens (and a starting few layers of larger LM of 3b parameters); we do this using a single A6000 GPU for less than half a day. Across 9 diverse evaluation datasets as well as the MMLU benchmark, the resulting model compares favorably to publicly available base models of 1B-2B size, some of which have been trained using 50–1000× more tokens.

We investigate Inheritune in a slightly different setting where we train small LMs using larger LMs and their full pre-training dataset. Here we show that smaller LMs trained using some of the layers of GPT-2-medium (355M) and GPT-2-large (770M) can effectively match the val loss of their bigger counterparts when trained from scratch for the same number of training steps on OpenWebText dataset with 9B tokens. We analyze our recipe with extensive experiments and demonstrate it efficacy on diverse settings. Our code is available at Github.

--- https://arxiv.org/abs/2404.08197#google Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies Zichao Li, Cihang Xie, Ekin Dogus Cubuk 2024-04-12 2024-04-15 [("doi","10.48550/arXiv.2404.08197")] ai/nn/transformer/clip ai/scaling

This paper investigates the performance of the Contrastive Language-Image Pre-training (CLIP) when scaled down to limited computation budgets. We explore CLIP along 3 dimensions: data, architecture, and training strategies.

With regards to data, we demonstrate the importance of high-quality training data and show that a smaller dataset of high-quality data can outperform a larger dataset with lower quality. We also examine how model performance varies with different dataset sizes, suggesting that smaller ViT models are better suited for smaller datasets, while larger models perform better on larger datasets with fixed compute. Additionally, we provide guidance on when to choose a CNN-based architecture or a ViT-based architecture for CLIP training.

We compare 4 CLIP training strategies—SLIP, FLIP, CLIP, and CLIP+Data Augmentation—and show that the choice of training strategy depends on the available compute resource. Our analysis reveals that CLIP+Data Augmentation can achieve comparable performance to CLIP using only half of the training data.

This work provides practical insights into how to effectively train and deploy CLIP models, making them more accessible and affordable for practical use in various applications.

--- https://susam.net/emacs-info-expressions.html 2024-04-15 cs/lisp/emacs --- https://www.channelfireball.com/article/PV-s-Rule/0d7fbcf6-570b-458a-bf02-ae46f097d515/ 2024-04-15 statistics/decision --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8514086/ Essay content and style are strongly related to household income and SAT scores: Evidence from 60,000 undergraduate applications A J. Alvero, Sonia Giebel, Ben Gebre-Medhin, Anthony Lising Antonio, Mitchell L. Stevens, Benjamin W. Domingue 2021 2024-04-15 [("doi","10.1126/sciadv.abi9031")] sociology

There is substantial evidence of the relationship between household income and achievement on the standardized tests often required for college admissions, yet little comparable inquiry considers the essays typically required of applicants to selective U.S. colleges and universities.

We used a corpus of 240,000 admission essays submitted by 60,000 applicants to the University of California in November 2016 to measure relationships between the content of admission essays, self-reported household income, and SAT scores. We quantified essay content using correlated topic modeling and essay style using Linguistic Inquiry and Word Count.

We found that essay content and style had stronger correlations to self-reported household income than did SAT scores and that essays explained much of the variance in SAT scores.

This analysis shows that essays encode similar information as the SAT and suggests that college admission protocols should attend to how social class is encoded in non-numerical components of applications.

--- https://tony-zorman.com/posts/my-phd-workflow.html 2024-04-15 cs/lisp --- https://arxiv.org/abs/2404.07647 Why do small language models underperform? Studying Language Model Saturation via the Softmax Bottleneck Nathan Godey, Éric de la Clergerie, Benoît Sagot 2024-04-11 2024-04-15 [("doi","10.48550/arXiv.2404.07647")] ai/nn/tokenization ai/scaling

Recent advances in language modeling consist in pretraining highly parameterized neural networks on extremely large web-mined text corpora. Training and inference with such models can be costly in practice, which incentivizes the use of smaller counterparts. However, it has been observed that smaller models can suffer from saturation, characterized as a drop in performance at some advanced point in training followed by a plateau.

In this paper, we find that such saturation can be explained by a mismatch between the hidden dimension of smaller models and the high rank of the target contextual probability distribution. This mismatch affects the performance of the linear prediction head used in such models through the well-known softmax bottleneck phenomenon.

We measure the effect of the softmax bottleneck in various settings and find that models based on less than 1,000 hidden dimensions tend to adopt degenerate latent representations in late pretraining, which leads to reduced evaluation performance.

[large BPE vocab tokenization can destroy LLM scaling by blocking training after enough steps: an implication here would be that if there were extreme supra-Chinchilla scaling laws, and you used a standard BPE vocab (never mind the extremely large BPE vocabularies of 1 million+ some groups experiment with), you might not find them because the necessary number of training steps would take you into the saturation regime where the minor technical detail of tokenization starts degrading your scaling. (You wouldn’t have to be totally saturated to start falling off optimal scaling and derive misleading scaling laws.)

Whereas if you use character/byte tokenization, you’d never even know this was a problem. But on the gripping hand, if you used BPEs and you were affected by saturation, you might never realize that at scale, better tokenization would change your scaling laws...]

--- https://qntm.org/hypertime 2024-01-01 fiction/science-fiction/time-travel --- https://qntm.org/ra 2024-01-01 fiction/science-fiction --- https://scp-wiki.wikidot.com/qntm-s-author-page#toc2 2024-01-01 fiction/science-fiction --- https://qntm.org/structure 2024-01-01 fiction/science-fiction --- https://qntm.org/urls On short URLs 2024-01-01 cs/css --- https://qntm.org/calendar 2024-01-01 design math --- https://schizophreniabulletin.oxfordjournals.org/content/30/2/279.full.pdf The Global Costs of Schizophrenia Knapp 2004 2024-01-01 economics psychiatry/schizophrenia --- https://www.cidrdb.org/cidr2019/papers/p117-kraska-cidr19.pdf SageDB: A Learned Database System Kraska 2019 2024-01-01 ai cs/algorithm --- https://arxiv.org/abs/2404.08495 Dataset Reset Policy Optimization for RLHF Jonathan D. Chang, Wenhao Shan, Owen Oertell, Kianté Brantley, Dipendra Misra, Jason D. Lee, Wen Sun 2024-04-12 2024-04-15 [("doi","10.48550/arXiv.2404.08495")] reinforcement-learning/offline reinforcement-learning/preference-learning

Reinforcement Learning (RL) from Human Preference-based feedback is a popular paradigm for fine-tuning generative models, which has produced impressive models such as GPT-4 and Claude-3 Opus. This framework often consists of two steps: learning a reward model from an offline preference dataset followed by running online RL to optimize the learned reward model.

In this work, leveraging the idea of reset, we propose a new RLHF algorithm with provable guarantees. Motivated by the fact that offline preference dataset provides informative states (ie. data that is preferred by the labelers), our new algorithm, Dataset Reset Policy Optimization (DR-PO), integrates the existing offline preference dataset into the online policy training procedure via dataset reset: it directly resets the policy optimizer to the states in the offline dataset, instead of always starting from the initial state distribution.

In theory, we show that DR-PO learns to perform at least as good as any policy that is covered by the offline dataset under general function approximation with finite sample complexity.

In experiments, we demonstrate that on both the TL;DR summarization and the Anthropic Helpful Harmful (HH) dataset, the generation from DR-PO is better than that from Proximal Policy Optimization (PPO) and Direction Preference Optimization (DPO), under the metric of GPT-4 win-rate.

Code for this work can be found at Github.

--- https://blog.eleuther.ai/pile-t5/ 2024-04-15 ai/nn/transformer/gpt/codex ai/nn/transformer/t5 --- https://x.com/emollick/status/1779908524161765681 Ethan Mollick 2024-04-15 ai/nn/transformer/gpt/claude ai/text-style-transfer psychology/spaced-repetition --- https://arxiv.org/abs/2306.14934 Sciama’s argument on life in a random universe: Distinguishing apples from oranges Zhi-Wei Wang, Samuel L. Braunstein 2023-06-26 2024-04-15 [("doi","10.1038/s41550-023-02014-9")] philosophy/ontology statistics/probability

Dennis Sciama argued that the existence of life depended on many quantities, the fundamental constants, so in a random universe life should be highly unlikely.

However, without full knowledge of these constants, his argument implies a universe that would appear to be ‘intelligently designed’.

--- https://github.com/aiwebb/treenav-bench#interesting-findings 2024-04-15 ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2404.07413 JetMoE: Reaching LLaMA-2 Performance with 0.1M Dollars Yikang Shen, Zhen Guo, Tianle Cai, Zengyi Qin 2024-04-11 2024-04-15 [("doi","10.48550/arXiv.2404.07413")] ai/scaling/mixture-of-experts

Large Language Models (LLMs) have achieved remarkable results, but their increasing resource demand has become a major obstacle to the development of powerful and accessible super-human intelligence. This report introduces JetMoE-8B, a new LLM trained with less than $0.1 million, using 1.25T tokens from carefully mixed open-source corpora and 30,000 H100 GPU hours. Despite its low cost, the JetMoE-8B demonstrates impressive performance, with JetMoE-8B outperforming the LLaMA-2-7B model and JetMoE-8B-Chat surpassing the LLaMA-2-13B-Chat model. These results suggest that LLM training can be much more cost-effective than generally thought.

JetMoE-8B is based on an efficient Sparsely-gated Mixture-of-Experts (SMoE) architecture, composed of attention and feedforward experts. Both layers are sparsely activated, allowing JetMoE-8B to have 8b parameters while only activating 2B for each input token, reducing inference computation by about 70% compared to LLaMA-2-7B.

Moreover, JetMoE-8B is highly open and academia-friendly, using only public datasets and training code. All training parameters and data mixtures have been detailed in this report to facilitate future efforts in the development of open foundation models. This transparency aims to encourage collaboration and further advancements in the field of accessible and efficient LLMs.

The model weights are publicly available at Github.

--- https://www.commonreader.co.uk/p/paul-grahams-plain-rhetoric 2024-04-15 psychology/writing --- https://en.wikipedia.org/wiki/Delmore_Schwartz Delmore Schwartz 2024-01-01 psychiatry/alcoholism psychiatry/bipolar/sleep --- https://x.com/OpenAIDevs/status/1779922566091522492 OpenAIDevs 2024-04-15 ai/nn/transformer/gpt/4 --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10951273/ Genetic gains underpinning a little-known strawberry Green Revolution Mitchell J. Feldmann, Dominique D. A. Pincot, Glenn S. Cole, Steven J. Knapp 2024 2024-04-15 [("doi","10.1038/s41467-024-46421-6")] genetics/selection/artificial

The annual production of strawberry has increased by one million tonnes in the US and 8.4 million tonnes worldwide since 1960. Here we show that the US expansion was driven by genetic gains from Green Revolution breeding and production advances that increased yields by 2,755%.

Using a California population with a century-long breeding history and phenotypes of hybrids observed in coastal California environments, we estimate that breeding has increased fruit yields by 2,974-6,636%, counts by 1,454-3,940%, weights by 228-504%, and firmness by 239-769%.

Using genomic prediction approaches, we pinpoint the origin of the Green Revolution to the early 1950s and uncover statistically-significant increases in additive genetic variation caused by transgressive segregation and phenotypic diversification.

Lastly, we show that the most consequential Green Revolution breeding breakthrough was the introduction of photoperiod-insensitive, PERPETUAL FLOWERING hybrids in the 1970s that doubled yields and drove the dramatic expansion of strawberry production in California.

--- https://www.cambridge.org/core/journals/journal-of-functional-programming/article/knuthmorrispratt-illustrated/8EFA77D663D585B68630E372BCE1EBA4 2024-04-16 cs/algorithm --- https://en.wikipedia.org/wiki/Knuth%E2%80%93Morris%E2%80%93Pratt_algorithm Knuth–Morris–Pratt algorithm 2024-04-16 cs/algorithm --- https://suno.com/song/a1d34143-c9f1-4ca5-8642-d2c68d8f3564 2024-04-16 ai/music --- https://x.com/spolu/status/1303693595833237504 The examples are indeed extremely simple on purpose (otherwise it’s hard to communicate efficiently what’s happening to non-Metamath experts). That being said, we’re still pretty far away from IMOs; but this is definitely a goal for us, and one we’re actively working towards! Stanislas Polu 2024-01-01 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/codex math --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC97174/ Construction and characterization of an effector strain ofStreptococcus mutans for replacement therapy of dental caries J D. Hillman, T. A. Brooks, S. M. Michalek, C. C. Harmon, J. L. Snoep, C. C. van Der Weijden 2000 2024-04-16 [("doi","10.1128/IAI.68.2.543-549.2000")] biology

An effector strain has been constructed for use in the replacement therapy of dental caries. Recombinant DNA methods were used to make the Streptococcus mutans supercolonizing strain, JH1140, lactate dehydrogenase deficient by deleting virtually all of the ldh open reading frame (ORF). To compensate for the resulting metabolic imbalance, a supplemental alcohol dehydrogenase activity was introduced by substituting the adhB ORF from Zymomonas mobilis in place of the deleted ldh ORF.

The resulting clone, BCS3-L1, was found to produce no detectable lactic acid during growth on a variety of carbon sources, and it produced statistically-significantly less total acid due to its increased production of ethanol and acetoin. BCS3-L1 was statistically-significantly less cariogenic than JH1140 in both toothless and conventional-rodent models.

It colonized the teeth of conventional rats as well as JH1140 in both aggressive-displacement and preemptive-colonization models. No gross or microscopic abnormalities of major organs were associated with oral colonization of rats with BCS3-L1 for 6 months.

Acid-producing revertants of BCS3-L1 were not observed in samples taken from infected animals (reversion frequency, <10−3) or by screening cultures grown in vitro, where no revertants were observed among 10(5) colonies examined on pH indicator medium.

The reduced pathogenic potential of BCS3-L1, its strong colonization potential, and its genetic stability suggest that this strain is well suited to serve as an effector strain in the replacement therapy of dental caries in humans.

--- https://github.com/curiousjp/toy_sd_genetics?tab=readme-ov-file#toy_sd_genetics 2024-04-16 ai/nn/diffusion reinforcement-learning/model-free reinforcement-learning/preference-learning --- https://suno.com/song/f7fc9610-f4fb-4d11-a56d-6c8617422d52 2024-04-16 ai/music fiction/humor --- https://suno.com/song/366db53d-002d-4590-a2a0-0547458c911c 2024-04-16 ai/music --- https://suno.com/song/0a2d2dd8-16aa-47f2-9316-e6946b431bb4 2024-04-16 ai/music fiction/humor --- https://www.reddit.com/r/StableDiffusion/comments/1c4oytl/some_examples_of_pixart_sigmas_excellent_prompt/ 2024-04-16 ai/anime ai/nn/diffusion --- https://suno.com/song/341ddaf1-08d5-46a9-8cb5-1742cb22eaf5 2024-04-16 ai/music --- https://suno.com/song/1b2710da-a0da-48dc-833d-46b70ee08102 2024-04-16 ai/music --- https://suno.com/song/1991fffb-fe12-4fa4-8db7-470e40be70c0 2024-04-16 ai/music --- https://suno.com/song/10c8a965-f872-4829-9210-8fa27bdc87c5 2024-04-16 ai/music fiction/humor --- https://suno.com/song/25992548-470d-4bea-85e7-6514fa5b7664 2024-04-16 ai/music fiction/humor --- https://suno.com/song/5be76253-bd58-4930-8265-4d768ed5069e 2024-04-16 ai/music fiction/humor --- https://suno.com/song/674e86cb-0395-414a-a291-a4c11a9efc4d 2024-04-16 ai/music fiction/humor --- https://suno.com/song/63df5758-c533-447a-be4a-06dcb5abdbbf 2024-04-16 ai/music fiction/humor --- https://suno.com/song/25e1ebd7-84cd-4b3d-a6b7-0b2f93fa638e 2024-04-16 ai/music fiction/humor --- https://suno.com/song/41c6c7e7-ac7b-43e9-993b-d9f3c8c1b3cb 2024-04-16 ai/music fiction/humor --- https://suno.com/song/9e071497-0547-4539-be8b-b62b8dad63a8 2024-04-16 ai/music --- https://suno.com/song/ae044a64-27de-40da-9689-0e7485daf698 2024-04-16 ai/music --- https://suno.com/song/c13d9aff-9d63-45cc-95fa-892608ae1f23 2024-04-16 ai/music --- https://suno.com/song/79b82aa4-0335-4231-8dfb-b272fa7536b6 2024-04-16 ai/music --- https://suno.com/song/f78dedbb-55a2-41e9-84bb-4abe0e4c36f7 2024-04-16 ai/music --- https://www.stimmel-law.com/en/articles/story-1-embezzlers-are-nice-people 2024-04-16 crime --- https://www.forbes.com/sites/passionoftheweiss/2016/02/19/the-queen-of-sample-clearance-an-interview-with-deborah-mannis-gardner/ 2024-04-16 economics/copyright --- https://www.wired.com/story/chamber-divers-rachel-lance/ 2024-04-16 nootropic/quantified-self --- https://www.neh.gov/article/joseph-priestley-created-revolutionary-maps-time 2024-04-16 design/visualization --- https://asteriskmag.com/issues/06/manufacturing-bliss 2024-04-16 psychiatry/meditation --- https://arxiv.org/abs/2404.07839#google RecurrentGemma: Moving Past Transformers for Efficient Open Language Models Aleksandar Botev, Soham De, Samuel L. Smith, Anushan Fernando, George-Cristian Muraru, Ruba Haroun, Leonard Berrada, Razvan Pascanu, Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Sertan Girgin, Olivier Bachem, Alek Andreev, Kathleen Kenealy, Thomas Mesnard, Cassidy Hardin, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Armand Joulin, Noah Fiedel, Evan Senter, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, David Budden, Arnaud Doucet, Sharad Vikram, Adam Paszke, Trevor Gale, Sebastian Borgeaud, Charlie Chen, Andy Brock, Antonia Paterson, Jenny Brennan, Meg Risdal, Raj Gundluru, Nesh Devanathan, Paul Mooney, Nilay Chauhan, Phil Culliton, Luiz Gustavo Martins, Elisa Bandy, David Huntsperger, Glenn Cameron, Arthur Zucker, Tris Warkentin, Ludovic Peran, Minh Giang, Zoubin Ghahramani, Clément Farabet, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, Yee Whye Teh, Nando de Frietas 2024-04-11 2024-04-16 [("doi","10.48550/arXiv.2404.07839")] ai/nn/transformer/attention/recurrent ai/nn/transformer/gpt/instruction-tuning

We introduce RecurrentGemma, an open language model which uses Google’s novel Griffin architecture.

Griffin combines linear recurrences with local attention to achieve excellent performance on language. It has a fixed-sized state, which reduces memory use and enables efficient inference on long sequences.

We provide a pre-trained model with 2B non-embedding parameters, and an instruction tuned variant. Both models achieve comparable performance to Gemma-2B despite being trained on fewer tokens.

--- https://maphappenings.com/2024/04/11/story-of-etak/ 2024-04-16 technology --- https://suno.com/song/e6ef4aca-46c6-499d-aec5-bf87aee2c2ac 2024-04-16 ai/music --- https://www.fadedpage.com/showbook.php?pid=20160325#Page_107 2024-01-01 nootropic/quantified-self technology --- https://arxiv.org/abs/2404.08819 The Illusion of State in State-Space Models William Merrill, Jackson Petty, Ashish Sabharwal 2024-04-12 2024-04-16 [("doi","10.48550/arXiv.2404.08819")] ai/nn/rnn cs/computable

State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking (Merrill & Sabharwal 2023), which SSMs are explicitly designed to address via their close architectural similarity to recurrent neural networks (RNNs).

But do SSMs truly have an advantage (over transformers) in expressive power for state tracking? Surprisingly, the answer is no. Our analysis reveals that the expressive power of SSMs is limited very similarly to transformers: SSMs cannot express computation outside the complexity class TC0. In particular, this means they cannot solve simple state-tracking problems like permutation composition. It follows that SSMs are provably unable to accurately track chess moves with certain notation, evaluate code, or track entities in a long narrative.

To supplement our formal analysis, we report experiments showing that Mamba-style SSMs indeed struggle with state tracking. Thus, despite its recurrent formulation, the “state” in an SSM is an illusion: SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state-tracking problems.

--- https://arxiv.org/abs/2404.02258#google Mixture-of-Depths: Dynamically allocating compute in transformer-based language models David Raposo, Sam Ritter, Blake Richards, Timothy Lillicrap, Peter Conway Humphreys, Adam Santoro 2024-04-02 2024-04-16 [("doi","10.48550/arXiv.2404.02258")] ai/scaling/mixture-of-experts reinforcement-learning/meta-learning

Transformer-based language models spread FLOPs uniformly across input sequences. In this work, we demonstrate that transformers can instead learn to dynamically allocate FLOPs (or compute) to specific positions in a sequence, optimizing the allocation along the sequence for different layers across the model depth.

Our method enforces a total compute budget by capping the number of tokens (k) that can participate in the self-attention and MLP computations at a given layer. The tokens to be processed are determined by the network using a top-k routing mechanism. Since k is defined a priori, this simple procedure uses a static computation graph with known tensor sizes, unlike other conditional computation techniques. Nevertheless, since the identities of the k tokens are fluid, this method can expend FLOPs non-uniformly across the time and model depth dimensions. Thus, compute expenditure is entirely predictable in sum total, but dynamic and context-sensitive at the token-level.

Not only do models trained in this way learn to dynamically allocate compute, they do so efficiently. These models match baseline performance for equivalent FLOPs and wall-clock times to train, but require a fraction of the FLOPs per forward pass, and can be upwards of 50% faster to step during post-training sampling.

--- https://x.com/abhistoria/status/1777456079200244072 abhistoria 2024-04-16 design/typography/sidenote --- https://signalsandthreads.com/multicast-and-the-markets/ 2024-04-16 cs/algorithm --- https://en.wikipedia.org/wiki/Sagrada_Fam%C3%ADlia Sagrada Família 2024-04-16 design --- https://arstechnica.com/information-technology/2017/07/winamp-how-greatest-mp3-player-undid-itself/ 2024-04-16 music --- https://arstechnica.com/information-technology/2017/07/winamp-how-greatest-mp3-player-undid-itself/2/ 2024-04-16 music --- https://arstechnica.com/information-technology/2017/07/winamp-how-greatest-mp3-player-undid-itself/3/ 2024-04-16 music --- /doc/cat/psychology/2019-read.pdf Target specificity of the Felixer grooming "trap" John L. Read, Tayla Bowden, Pat Hodgens, Marco Hess, Hugh McGregor, Katherine Moseby 2019-02-28 2024-04-16 [("doi","10.1002/wsb.942")] cat/psychology reinforcement-learning/robot

Felixer grooming “traps” provide a novel technique for controlling invasive red foxes (Vulpes vulpes) and feral cats (Felis catus) by ejecting a dose of poison onto the fur of a target animal, which is subsequently ingested through grooming. The Felixer achieves target specificity through a discriminatory sensor arrangement and algorithm as well as a dosing pathway and toxin, which together make feral cats and foxes more vulnerable than humans and nontarget wildlife. The toxin 1080 used in many pest control projects in Australia is derived from native plants, which renders Australian wildlife, including potential scavengers of poisoned carcasses, that have co-evolved with these toxic plants less sensitive than their nonnative counterparts to 1080 poisoning.

We investigated the success of the Felixer sensor system in discriminating target cats and red foxes from nontargets under field conditions.

All foxes and 82% of feral cats were correctly identified as targets. No people or medium-sized marsupials—including brush-tailed possums (Trichosurus vulpecula), bettongs (Bettongia spp.), bilbies (Macrotis lagotis), and western quolls (Dasyurus geoffroii)—were incorrectly assigned as targets, suggesting Felixers could provide safe and specific feral-predator control at many conservation sites, albeit not at sites with threatened endemic small felids or canids. A low false-positive detection rate was recorded in larger macropods and poultry that will be addressed with more sophisticated sensor positioning and algorithms in optimized Felixers, along with more careful installation.

The low sensitivity of macropods and malleefowl (Leipoa ocellata) to 1080, and their reduced grooming behavior relative to feral cats, suggests these species will not be affected by Felixer deployment.

--- /doc/cat/psychology/2020-moseby.pdf Effectiveness of the Felixer grooming trap for the control of feral cats: a field trial in arid South Australia K. E. Moseby, H. McGregor, J. L. Read 2020-05-28 2024-04-16 [("doi","10.1071/WR19132")] cat/psychology reinforcement-learning/robot

Feral cats pose a threat to wildlife in Australia and internationally. Controlling feral cats can be problematic because of their tendency to hunt live prey rather than be attracted to food-based lures. The Felixer grooming trap was developed as a targeted and automated poisoning device that sprays poison onto the fur of a passing cat, relying on compulsive grooming for ingestion.

Aims: We conducted a field trial to test the effectiveness of Felixers in the control of feral cats in northern South Australia where feral cats were present within a 2600-ha predator-proof fenced paddock.

Methods: 20 Felixers were set to fire across vehicle tracks and dune crossings for 6 weeks. Cat activity was recorded using track counts and grids of remote camera traps set within the Felixer Paddock and an adjacent 3,700-ha Control Paddock where feral cats were not controlled. Radio-collars were placed on 6 cats and spatial mark–resight models were used to estimate population density before and after Felixer deployment.

Results: None of the 1,024 non-target objects (bettongs, bilbies, birds, lizards, humans, vehicles) that passed a Felixer during the trial was fired on, confirming high target specificity.

33 Felixer firings were recorded over the 6-week trial, all being triggered by feral cats. The only two radio-collared cats that triggered Felixers during the trial, died. Two other radio-collared cats appeared to avoid Felixer traps possibly as a reaction to previous catching and handling rendering them neophobic.

None of the 22 individually distinguishable cats targeted by Felixers was subsequently observed on cameras, suggesting death after firing. Felixer data, activity and density estimates consistently indicated that nearly two-thirds of the cat population was killed by the Felixers during the 6-week trial.

Conclusions: Results suggest that Felixers are an effective, target-specific method of controlling feral cats, at least in areas in which immigration is prevented. The firing rate of Felixers did not decline over time, suggesting that a longer trial would have resulted in a higher number of kills. Future studies should aim to determine the trade-off between Felixer density and efficacy relative to reinvasion.

--- https://en.wikipedia.org/wiki/Sodium_fluoroacetate#Fluoroacetate_occurrence_in_Gastrolobium_species Sodium fluoroacetate § Fluoroacetate occurrence in Gastrolobium species 2024-04-16 cat/psychology --- https://arxiv.org/abs/2404.08801#facebook Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length Xuezhe Ma, Xiaomeng Yang, Wenhan Xiong, Beidi Chen, Lili Yu, Hao Zhang, Jonathan May, Luke Zettlemoyer, Omer Levy, Chunting Zhou 2024-04-12 2024-04-16 [("doi","10.48550/arXiv.2404.08801")] ai/nn/rnn

The quadratic complexity and weak length extrapolation of Transformers limits their ability to scale to long sequences, and while sub-quadratic solutions like linear attention and state space models exist, they empirically underperform Transformers in pretraining efficiency and downstream task accuracy.

We introduce Megalodon, a neural architecture for efficient sequence modeling with unlimited context length. Megalodon inherits the architecture of Mega (exponential moving average with gated attention), and further introduces multiple technical components to improve its capability and stability, including complex exponential moving average (CEMA), timestep normalization layer, normalized attention mechanism and pre-norm with two-hop residual configuration.

In a controlled head-to-head comparison with LLaMA-2, Megalodon achieves better efficiency than Transformer in the scale of 7 billion parameters and 2 trillion training tokens. Megalodon reaches a training loss of 1.70, landing mid-way between LLaMA-2-7B (1.75) and LLaMA-2-13B (1.67).

Code: https://github.com/XuezheMax/megalodon.

--- https://google.github.io/styleguide/lispguide.xml 2024-04-17 cs/lisp --- https://github.com/azzamsa/awesome-lisp-companies/?tab=readme-ov-file#awesome-lisp-company 2024-04-17 cs/lisp --- https://www.youtube.com/watch?v=G8T1O81W96Y 2024-04-17 ai/scaling/economics reinforcement-learning/openai --- https://lilianweng.github.io/posts/2024-04-12-diffusion-video/ 2024-04-17 ai/video/generation --- https://arxiv.org/abs/2404.10301#stability Long-form music generation with latent diffusion Zach Evans, Julian D. Parker, C. J. Carr, Zack Zukowski, Josiah Taylor, Jordi Pons 2024-04-16 2024-04-17 [("doi","10.48550/arXiv.2404.10301")] ai/music ai/nn/diffusion

Audio-based generative models for music have seen great strides recently, but so far have not managed to produce full-length music tracks with coherent musical structure.

We show that by training a generative model on long temporal contexts it is possible to produce long-form music of up to 4m45s. Our model consists of a diffusion-transformer operating on a highly downsampled continuous latent representation (latent rate of 21.5Hz).

It obtains state-of-the-art generations according to metrics on audio quality and prompt alignment, and subjective tests reveal that it produces full-length music with coherent structure.

--- https://www.ssec.wisc.edu/~billh/g/mcnrsts.html 2024-04-17 fiction/science-fiction --- https://arxiv.org/abs/2404.06990 Emergence of large-scale mechanical spiral waves in bacterial living matter Shiqi Liu, Ye Li, Yuhao Wang, Yilin Wu 2024-04-10 2024-04-17 [("doi","10.1038/s41567-024-02457-5")] cs/cellular-automaton genetics/microbiome

Propagating spiral waves have been discovered in various chemical, biological, and physical systems. Spiral waves in multicellular organisms are often associated with essential living functions. Although certain eukaryotic microorganisms have long been known to generate spiral waves, evidence of spiral wave pattern has been lacking in the bacterial world.

Here we report the discovery of a unique form of propagating spiral waves in dense bacterial populations where cells engage in cyclic force-generating processes driven by type-IV pilus motility. Specifically, we discovered that synchronization of pilus activity in the bacterial living matter leads to large-scale spatiotemporal regulation of tension force in the form of propagating spiral waves. Theoretical modeling reveals that the spiral tension waves result from non-reciprocity in cell-cell interactions.

Our findings reveal a novel mechanism of large-scale force regulation in the bacterial world and may shed light on the emergent mechanics of biofilms and microbiomes. Pilus-driven bacterial living matter also provides a mechanical active medium for studying electrical or chemical spiral waves in living systems.

--- https://en.wikipedia.org/wiki/Karger's_algorithm Karger’s algorithm 2024-04-17 cs/algorithm --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8972190/ Brain Changes Associated With Long-Term Ketamine Abuse, A Systematic Review Jurriaan F. M. Strous, Cees J. Weeland, Femke A. van der Draai, Joost G. Daams, Damiaan Denys, Anja Lok, Robert A. Schoevers, Martijn Figee 2022 2024-04-17 [("doi","10.3389/fnana.2022.795231")] psychedelic

Recently, the abuse of ketamine has soared. Therefore, it is of great importance to study its potential risks. The effects of prolonged ketamine on the brain can be observationally studied in chronic recreational users.

We performed a systematic review of studies reporting functional and structural brain changes after repeated ketamine abuse. We searched the following electronic databases: MEDLINE, Embase, and PsycINFO. We screened 11,438 records and 16 met inclusion criteria, totaling 440 chronic recreational ketamine users (2–9.7 years; mean use 2.4 g/day), 259 drug-free controls, and 44 poly-drug controls.

Long-term recreational ketamine use was associated with lower gray matter volume and less white matter integrity, lower functional thalamocortical and corticocortical connectivity. The observed differences in both structural and functional neuroanatomy between ketamine users and controls may explain some of its long-term cognitive and psychiatric side effects, such as memory impairment and executive functioning.

Given the effect that long-term ketamine exposure may yield, an effort should be made to curb its abuse.

--- https://www.youtube.com/watch?v=29ECwExc-_M 2024-04-17 reinforcement-learning/robot --- https://arxiv.org/abs/2404.10102 Chinchilla Scaling: A replication attempt Tamay Besiroglu, Ege Erdil, Matthew Barnett, Josh You 2024-04-15 2024-04-17 [("doi","10.48550/arXiv.2404.10102")] ai/nn/transformer ai/scaling

Hoffmann et al 2022 propose 3 methods for estimating a compute-optimal scaling law. We attempt to replicate their third estimation procedure, which involves fitting a parametric loss function to a reconstruction of data from their plots.

We find that the reported estimates are inconsistent with their first two estimation methods, fail at fitting the extracted data, and report implausibly narrow confidence intervals–intervals this narrow would require over 600,000 experiments, while they likely only ran fewer than 500.

In contrast, our re-derivation of the scaling law using the third approach yields results that are compatible with the findings from the first two estimation procedures described by Hoffmann et al 2022.

[Apparently the original Chinchilla anomalies turn out to be due to a bug in the early-stopping DM code, and when fixed, does yield the revised Besiroglu et al 2024 numbers.]

--- https://arxiv.org/abs/2404.06395 MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies Shengding Hu, Yuge Tu, Xu Han, Chaoqun He, Ganqu Cui, Xiang Long, Zhi Zheng, Yewei Fang, Yuxiang Huang, Weilin Zhao, Xinrong Zhang, Zheng Leng Thai, Kaihuo Zhang, Chongyi Wang, Yuan Yao, Chenyang Zhao, Jie Zhou, Jie Cai, Zhongwu Zhai, Ning Ding, Chao Jia, Guoyang Zeng, Dahai Li, Zhiyuan Liu, Maosong Sun 2024-04-09 2024-04-17 [("doi","10.48550/arXiv.2404.06395")] ai/nn/transformer ai/scaling

The burgeoning interest in developing Large Language Models (LLMs) with up to trillion parameters has been met with concerns regarding resource efficiency and practical expense, particularly given the immense cost of experimentation. This scenario underscores the importance of exploring the potential of Small Language Models (SLMs) as a resource-efficient alternative.

In this context, we introduce MiniCPM, specifically the 1.2B and 2.4B non-embedding parameter variants, not only excel in their respective categories but also demonstrate capabilities on par with 7B-13B LLMs. While focusing on SLMs, our approach exhibits scalability in both model and data dimensions for future LLM research. Regarding model scaling, we employ extensive model wind tunnel experiments for stable and optimal scaling. For data scaling, we introduce a Warmup-Stable-Decay (WSD) learning rate scheduler (LRS), conducive to continuous training and domain adaptation.

We present an in-depth analysis of the intriguing training dynamics that occurred in the WSD LRS. With WSD LRS, we are now able to efficiently study data-model scaling law without extensive retraining experiments on both axes of model and data, from which we derive the much higher compute optimal data-model ratio than Chinchilla Optimal.

Additionally, we introduce MiniCPM family, including MiniCPM-DPO, MiniCPM-MoE, and MiniCPM-128K, whose excellent performance further cementing MiniCPM’s foundation in diverse SLM applications.

MiniCPM models are available publicly at Github.

--- https://archive.is/ZVIwq 2024-04-17 crime --- https://x.com/davis_yoshida/status/1780733741457088759 davis_yoshida 2024-04-17 ai/nn/transformer/gpt/4/fiction reinforcement-learning/preference-learning --- https://arxiv.org/abs/2306.01128 Learning Transformer Programs Dan Friedman, Alexander Wettig, Danqi Chen 2023-06-01 2024-04-18 [("doi","10.48550/arXiv.2306.01128")] ai/nn/transformer cs/algorithm/sorting cs/computable reinforcement-learning/meta-learning

Recent research in mechanistic interpretability has attempted to reverse-engineer Transformer models by carefully inspecting network weights and activations. However, these approaches require considerable manual effort and still fall short of providing complete, faithful descriptions of the underlying algorithms. In this work, we introduce a procedure for training Transformers that are mechanistically interpretable by design.

We build on RASP [Weiss et al 2021], a programming language that can be compiled into Transformer weights. Instead of compiling human-written programs into Transformers, we design a modified Transformer that can be trained using gradient-based optimization and then automatically converted into a discrete, human-readable program. We refer to these models as Transformer Programs.

To validate our approach, we learn Transformer Programs for a variety of problems, including an in-context learning task, a suite of algorithmic problems (eg. sorting, recognizing Dyck languages), and NLP tasks including named entity recognition and text classification. The Transformer Programs can automatically find reasonable solutions, performing on par with standard Transformers of comparable size; and, more importantly, they are easy to interpret.

To demonstrate these advantages, we convert Transformers into Python programs and use off-the-shelf code analysis tools to debug model errors and identify the “circuits” used to solve different sub-problems.

We hope that Transformer Programs open a new path toward the goal of intrinsically interpretable machine learning.

--- https://www.architecturaldigest.com/story/step-inside-all-new-art-deco-orient-express-train 2024-04-18 design --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4583580/ Community Surveillance of Respiratory Viruses Among Families in the Utah Better Identification of Germs-Longitudinal Viral Epidemiology (BIG-LoVE) Study Carrie L. Byington, Krow Ampofo, Chris Stockmann, Frederick R. Adler, Amy Herbener, Trent Miller, Xiaoming Sheng, Anne J. Blaschke, Robert Crisp, Andrew T. Pavia 2015 2024-04-18 [("doi","10.1093/cid/civ486")] biology

Background: This study: (1) describes the viral etiology of respiratory illness by prospectively collecting weekly symptom diaries and nasal swabs from families for 1 year, (2) analyzed data by reported symptoms, virus, age, and family composition, and (3) evaluated the duration of virus detection.

Methods: 20-six households (108 individuals) provided concurrent symptom and nasal swab data for 4166 person-weeks. The FilmArray polymerase chain reaction (PCR) platform (BioFire Diagnostics, LLC) was used to detect 16 respiratory viruses. Viral illnesses were defined as ≥1 consecutive weeks with the same virus detected with symptoms reported in ≥1 week.

Results: Participants reported symptoms in 23% and a virus was detected in 26% of person-weeks. Children younger than 5 years reported symptoms more often and were more likely to have a virus detected than older participants (odds ratio [OR] 2.47, 95% confidence interval [CI], 2.08-2.94 and OR 3.96, 95% CI, 3.35-4.70, respectively). Compared with single person households, individuals living with children experienced 3 additional weeks of virus detection. There were 783 viral detection episodes; 440 (56%) associated with symptoms. Coronaviruses, human metapneumovirus, and influenza A detections were usually symptomatic; bocavirus and rhinovirus detections were often asymptomatic. The mean duration of PCR detection was ≤2 weeks for all viruses and detections of ≥3 weeks occurred in 16% of episodes. Younger children had longer durations of PCR detection.

Conclusions: Viral detection is often asymptomatic and occasionally prolonged, especially for bocavirus and rhinovirus. In clinical settings, the interpretation of positive PCR tests, particularly in young children and those who live with them, may be confounded.

Figure 1: A: The mean number of weeks with 1 or more viral detections per person during 1 year as a function of age. B: The number of weeks with 1 or more viral detections in the household over 1 year stratified by the number of children residing in the household.

…Viral detection was more common in larger families (Figure 1B). When compared with single person households, individuals living in households with 1 or more children experienced 3.09 (95% CI, 1.29–10.07) additional weeks of viral detection per person (p = 0.04).

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913938/ Chronic consumption of fructose rich soft drinks alters tissue lipids of rats Jose D. Botezelli, Rodrigo A. Dalia, Ivan M. Reis, Ricardo A. Barbieri, Tiago M. Rezende, Jailton G. Pelarigo, Jamile Codogno, Raquel Gonçalves, Maria A. Mello 2010 2024-04-18 [("doi","10.1186/1758-5996-2-43")] exercise

Background: Fructose-based diets are apparently related to the occurrence of several metabolic dysfunctions, but the effects of the consumption of high amounts of fructose on body tissues have not been well described. The aim of this study was to analyze the general characteristics and the lipid content of different tissues of rats after chronic ingestion of a fructose rich soft drink.

Methods: Forty-five Wistar rats were used. The rats were divided into 3 groups (n = 15) and allowed to consume water (C), light Coca Cola (R) (L) or regular Coca Cola(R) (R) as the sole source of liquids for 8 weeks.

Results: The R group presented significantly higher daily liquid intake and significantly lower food intake than the C and L groups. Moreover, relative to the C and L groups, the R group showed higher triglyceride concentrations in the serum and liver. However, the L group animals presented lower values of serum triglycerides and cholesterol than controls.

Conclusions: Based on the results, it can be concluded that daily ingestion of a large amount of fructose-rich soft drink resulted in unfavorable alterations to the lipid profile of the rats.

--- https://tonsky.me/blog/centering/ 2024-04-18 cs/css design/typography --- https://arxiv.org/abs/2309.02926 Demystifying RCE Vulnerabilities in LLM-Integrated Apps Tong Liu, Zizhuang Deng, Guozhu Meng, Yuekang Li, Kai Chen 2023-09-06 2024-04-18 [("doi","10.48550/arXiv.2309.02926")] ai/nn/transformer cs/security

In recent years, Large Language Models (LLMs) have demonstrated remarkable potential across various downstream tasks. LLM-integrated frameworks, which serve as the essential infrastructure, have given rise to many LLM-integrated web apps. However, some of these frameworks suffer from Remote Code Execution (RCE) vulnerabilities, allowing attackers to execute arbitrary code on apps’ servers remotely via prompt injections. Despite the severity of these vulnerabilities, no existing work has been conducted for a systematic investigation of them. This leaves a great challenge on how to detect vulnerabilities in frameworks as well as LLM-integrated apps in real-world scenarios.

To fill this gap, we present two novel strategies, including (1) a static analysis-based tool called LLMSmith to scan the source code of the framework to detect potential RCE vulnerabilities and (2) a prompt-based automated testing approach to verify the vulnerability in LLM-integrated web apps.

We discovered 13 vulnerabilities in 6 frameworks, including 12 RCE vulnerabilities and 1 arbitrary file read/write vulnerability. 11 of them are confirmed by the framework developers, resulting in the assignment of 7 CVE IDs. After testing 51 apps, we found vulnerabilities in 17 apps, 16 of which are vulnerable to RCE and 1 to SQL injection. We responsibly reported all 17 issues to the corresponding developers and received acknowledgments.

Furthermore, we amplify the attack impact beyond achieving RCE by allowing attackers to exploit other app users (eg. app responses hijacking, user API key leakage) without direct interaction between the attacker and the victim.

Lastly, we propose some mitigating strategies for improving the security awareness of both framework and app developers, helping them to mitigate these risks effectively.

--- https://huggingface.co/datasets/PleIAs/YouTube-Commons 2024-04-18 ai/dataset --- https://x.com/alexalbert__/status/1780707227130863674 Alex Albert 2024-04-18 ai/nn/transformer/gpt/claude --- https://x.com/borgeaud_s/status/1780988694163321250 borgeaud_s 2024-04-18 ai/scaling --- https://arxiv.org/abs/2404.09932 Foundational Challenges in Assuring Alignment and Safety of Large Language Models Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, Jose Hernandez-Orallo, Lewis Hammond, Eric Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Yoshua Bengio, Danqi Chen, Samuel Albanie, Tegan Maharaj, Jakob Foerster, Florian Tramer, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger 2024-04-15 2024-04-18 [("doi","10.48550/arXiv.2404.09932")] ai/nn/adversarial ai/scaling/emergence cs/security reinforcement-learning/meta-learning reinforcement-learning/multi-agent reinforcement-learning/safe reinforcement-learning/scaling

This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are organized into 3 different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges.

Based on the identified challenges, we pose 200+ concrete research questions.

--- https://arxiv.org/abs/2404.08763 CATS: Contextually-Aware Thresholding for Sparsity in Large Language Models Je-Yong Lee, Donghyun Lee, Genghan Zhang, Mo Tiwari, Azalia Mirhoseini 2024-04-12 2024-04-18 [("doi","10.48550/arXiv.2404.08763")] ai/nn/sparsity ai/nn/transformer/gpt

Large Language Models (LLMs) have dramatically advanced AI applications, yet their deployment remains challenging due to their immense inference costs. Recent studies ameliorate the computational costs of LLMs by increasing their activation sparsity but suffer from performance degradation on downstream tasks. In this work, we introduce a new framework for sparsifying the activations of base LLMs and reducing inference costs, dubbed Contextually Aware Thresholding for Sparsity (CATS). CATS is relatively simple, easy to implement, and highly effective.

At the heart of our framework is a new non-linear activation function. We demonstrate that CATS can be applied to various base models, including Mistral-7B and LLaMA-2-7B, and outperforms existing sparsification techniques in downstream task performance.

More precisely, CATS-based models often achieve downstream task performance within 1-2% of their base models without any fine-tuning and even at activation sparsity levels of 50%. Furthermore, CATS-based models converge faster and display better task performance than competing techniques when fine-tuning is applied.

Finally, we develop a custom GPU kernel for efficient implementation of CATS that translates the activation of sparsity of CATS to real wall-clock time speedups. Our custom kernel implementation of CATS results in a ~15% improvement in wall-clock inference latency of token generation on both Llama-7B and Mistral-7B.

--- https://www.wired.com/story/yahoo-boys-real-time-deepfake-scams/ 2024-04-18 ai/video/generation crime --- https://arxiv.org/abs/2402.09668#google How to Train Data-Efficient LLMs Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, Jianmo Ni, Lichan Hong, Ed H. Chi, James Caverlee, Julian McAuley, Derek Zhiyuan Cheng 2024-02-15 2024-04-18 [("doi","10.48550/arXiv.2402.09668")] ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/t5 ai/scaling reinforcement-learning/exploration/active-learning/data-pruning

[previously] The training of large language models (LLMs) is expensive. In this paper, we study data-efficient approaches for pre-training LLMs, ie. techniques that aim to optimize the Pareto frontier of model quality and training resource/data consumption. We seek to understand the trade-offs associated with data selection routines based on (1) expensive-to-compute data-quality estimates, and (2) maximization of coverage and diversity-based measures in the feature space.

Our first technique, Ask-LLM, leverages the zero-shot reasoning capabilities of instruction-tuned LLMs to directly assess the quality of a training example. To target coverage, we propose Density sampling, which models the data distribution to select a diverse sample. In our comparison of 19 samplers, involving hundreds of evaluation tasks and pre-training runs, we find that Ask-LLM and Density are the best methods in their respective categories.

Coverage sampling can match the performance of the full data, while models trained on Ask-LLM data consistently outperform full-data training—even when we reject 90% of the original dataset, while converging up to 70% faster.

--- https://longreads.com/2024/04/18/crohns-life-without-eating/ 2024-04-18 longevity/glp/psychology --- https://github.com/spawnmason/randar-explanation/blob/master/README 2024-04-18 cs/cryptography --- https://suno.com/song/b1d81bcd-1a9b-4639-9fb0-8462852132c4 2024-04-18 ai/music --- https://suno.com/song/8c2c7394-44aa-4fe4-ba38-edd7bea54d3d 2024-04-18 ai/music --- https://suno.com/song/fdacb428-ff27-4f2e-832d-cc318ce8cf7b 2024-04-18 ai/music --- https://arxiv.org/abs/2404.10667#microsoft VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time Sicheng Xu, Guojun Chen, Yu-Xiao Guo, Jiaolong Yang, Chong Li, Zhenyu Zang, Yizhong Zhang, Xin Tong, Baining Guo 2024-04-16 2024-04-19 [("doi","10.48550/arXiv.2404.10667")] ai/nn/diffusion ai/video/generation

[homepage] We introduce VASA, a framework for generating lifelike talking faces with appealing visual affective skills (VAS) given a single static image and a speech audio clip. Our premiere model, VASA-1, is capable of not only producing lip movements that are exquisitely synchronized with the audio, but also capturing a large spectrum of facial nuances and natural head motions that contribute to the perception of authenticity and liveliness.

The core innovations include a holistic facial dynamics and head movement generation model that works in a face latent space, and the development of such an expressive and disentangled face latent space using videos.

Through extensive experiments including evaluation on a set of new metrics, we show that our method outperforms previous methods along various dimensions comprehensively. Our method not only delivers high video quality with realistic facial and head dynamics but also supports the online generation of 512×512 videos at up to 40 FPS with negligible starting latency.

It paves the way for real-time engagements with lifelike avatars that emulate human conversational behaviors.

--- https://medium.com/agoric/agoric-and-the-decades-long-quest-for-secure-smart-contracts-epicenter-interview-with-mark-s-76c9a0fab6e2 2024-04-19 bitcoin economics/mechanism-design --- https://en.wikipedia.org/wiki/Match_cut Match cut 2024-04-19 psychology/novelty --- https://www.lesswrong.com/posts/CNPvESPru3XNqsw7A/what-s-up-with-all-the-non-mormons-weirdly-specific 2024-04-19 ai/nn/tokenization ai/nn/transformer/gpt/2 ai/nn/transformer/gpt/3 --- https://en.wikipedia.org/wiki/Daniel_Dennett Daniel Dennett 2024-01-01 philosophy/mind --- https://www.quantamagazine.org/insects-and-other-animals-have-consciousness-experts-declare-20240419/ 2024-04-19 philosophy/mind psychology/animal --- https://suno.com/song/0013a4ad-8373-4b80-994a-e04ae57518cd 2024-04-19 ai/music --- https://bigthink.com/starts-with-a-bang/does-light-live-forever/ 2024-04-19 science --- https://yosefk.com/blog/the-state-of-ai-for-hand-drawn-animation-inbetweening.html 2024-04-19 ai/anime ai/video/generation --- https://jazco.dev/2024/04/15/in-memory-graphs/ 2024-04-19 cs/algorithm --- https://arxiv.org/abs/2404.07982 Language Imbalance Can Boost Cross-lingual Generalization Anton Schäfer, Shauli Ravfogel, Thomas Hofmann, Tiago Pimentel, Imanol Schlag 2024-04-11 2024-04-19 [("doi","10.48550/arXiv.2404.07982")] ai/nn/transformer/gpt/2 ai/scaling

Multilinguality is crucial for extending recent advancements in language modeling to diverse linguistic communities. To maintain high performance while representing multiple languages, multilingual models ideally align representations, allowing what is learned in one language to generalize to others. Prior research has emphasized the importance of parallel data and shared vocabulary elements as key factors for such alignment.

In this study, we investigate an unintuitive novel driver of cross-lingual generalization: language imbalance. In controlled experiments on perfectly equivalent cloned languages, we observe that the existence of a predominant language during training boosts the performance of less frequent languages and leads to stronger alignment of model representations across languages. Furthermore, we find that this trend is amplified with scale: with large enough models or long enough training, we observe that bilingual training data with a 90:10 language split yields better performance on both languages than a balanced 50:50 split.

Building on these insights, we design training schemes that can improve performance in all cloned languages, even without altering the training data. As we extend our analysis to real languages, we find that infrequent languages still benefit from frequent ones, yet whether language imbalance causes cross-lingual generalization there is not conclusive.

--- https://www.lesswrong.com/posts/vbWBJGWyWyKyoxLBe/darpa-digital-tutor-four-months-to-total-technical-expertise 2024-04-19 psychology/spaced-repetition --- https://x.com/DRMacIver/status/1781296260965716400 DRMacIver 2024-04-19 psychology/dark-knowledge --- https://handmade.network/p/369/rede/ 2024-04-20 cs/algorithm design/visualization --- https://arxiv.org/abs/2404.07987 ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback Ming Li, Taojiannan Yang, Huafeng Kuang, Jie Wu, Zhaoning Wang, Xuefeng Xiao, Chen Chen 2024-04-11 2024-04-20 [("doi","10.48550/arXiv.2404.07987")] ai/nn/diffusion reinforcement-learning/preference-learning

To enhance the controllability of text-to-image diffusion models, existing efforts like ControlNet incorporated image-based conditional controls. In this paper, we reveal that existing methods still face challenges in generating images that align with the image conditional controls.

To this end, we propose ControlNet++, a novel approach that improves controllable generation by explicitly optimizing pixel-level cycle consistency between generated images and conditional controls. Specifically, for an input conditional control, we use a pre-trained discriminative reward model to extract the corresponding condition of the generated images, and then optimize the consistency loss between the input conditional control and extracted condition.

A straightforward implementation would be generating images from random noises and then calculating the consistency loss, but such an approach requires storing gradients for multiple sampling timesteps, leading to considerable time and memory costs. To address this, we introduce an efficient reward strategy that deliberately disturbs the input images by adding noise, and then uses the single-step denoised images for reward fine-tuning. This avoids the extensive costs associated with image sampling, allowing for more efficient reward fine-tuning.

Extensive experiments show that ControlNet++ improves controllability under various conditional controls. For example, it achieves improvements over ControlNet by 7.9% mIoU, 13.4% SSIM, and 7.6% RMSE, respectively, for segmentation mask, line-art edge, and depth conditions.

--- /doc/biology/2018-jiang.pdf Dynamic Human Environmental Exposome Revealed by Longitudinal Personal Monitoring Chao Jiang, Xin Wang, Xiyan Li, Jingga Inlora, Ting Wang, Qing Liu, Michael Snyder 2018-01-01 2024-01-01 [("doi","10.1016/j.cell.2018.08.060")] biology genetics/sequencing nootropic/quantified-self --- https://austinvernon.site/blog/datacenterpv.html 2024-04-20 ai/scaling/economics ai/scaling/hardware --- https://arxiv.org/abs/2404.11614 Dynamic Typography: Bringing Text to Life via Video Diffusion Prior Zichen Liu, Yihao Meng, Hao Ouyang, Yue Yu, Bolin Zhao, Daniel Cohen-Or, Huamin Qu 2024-04-17 2024-04-20 [("doi","10.48550/arXiv.2404.11614")] ai/nn/diffusion ai/video/generation design/typography/dropcap

Text animation serves as an expressive medium, transforming static communication into dynamic experiences by infusing words with motion to evoke emotions, emphasize meanings, and construct compelling narratives. Crafting animations that are semantically aware poses challenges, demanding expertise in graphic design and animation.

We present an automated text animation scheme, termed “Dynamic Typography”, which combines two challenging tasks. It deforms letters to convey semantic meaning and infuses them with vibrant movements based on user prompts. Our technique harnesses vector graphics representations and an end-to-end optimization-based framework. This framework employs neural displacement fields to convert letters into base shapes and applies per-frame motion, encouraging coherence with the intended textual concept. Shape preservation techniques and perceptual loss regularization are employed to maintain legibility and structural integrity throughout the animation process.

We demonstrate the generalizability of our approach across various text-to-video models and highlight the superiority of our end-to-end methodology over baseline methods, which might comprise separate tasks.

Through quantitative and qualitative evaluations, we demonstrate the effectiveness of our framework in generating coherent text animations that faithfully interpret user prompts while maintaining readability.

Our code is available at: https://animate-your-word.github.io/demo/.

--- https://arxiv.org/abs/1608.03676 Coz: Finding Parallel Code that Counts with Causal Profiling Charlie Curtsinger, Emery D. Berger 2016-08-12 2024-04-20 [("doi","10.1145/2815400.2815409")] cs/algorithm statistics/causality

[code, talk; cf. Mystery Machine] Improving performance is a central concern for software developers. To locate optimization opportunities, developers rely on software profilers. However, these profilers only report where programs spent their time: optimizing that code may have no impact on performance. Past profilers thus both waste developer time and make it difficult for them to uncover optimization opportunities.

This paper introduces causal profiling. Unlike past profiling approaches, causal profiling indicates exactly where programmers should focus their optimization efforts, and quantifies their potential impact. Causal profiling works by running performance experiments during program execution. Each experiment calculates the impact of any potential optimization by virtually speeding up code: inserting pauses that slow down all other code running concurrently. The key insight is that this slowdown has the same relative effect as running that line faster, thus “virtually” speeding it up. [Reminds me of leave-one-out cross-validation.]

We present Coz, a causal profiler, which we evaluate on a range of highly-tuned applications: Memcached, SQLite, and the PARSEC benchmark suite.

Coz identifies previously unknown optimization opportunities that are both and targeted. Guided by Coz, we improve the performance of Memcached by 9%, SQLite by 25%, and accelerate 6 PARSEC applications by as much as 68%; in most cases, these optimizations involve modifying under 10 lines of code.

--- https://arxiv.org/abs/2403.16933 GLE: Backpropagation through space, time, and the brain Benjamin Ellenberger, Paul Haider, Jakob Jordan, Kevin Max, Ismael Jaras, Laura Kriener, Federico Benitez, Mihai A. Petrovici 2024-03-25 2024-04-20 [("doi","10.48550/arXiv.2403.16933")] ai/nn/rnn psychology/neuroscience

Effective learning in neuronal networks requires the adaptation of individual synapses given their relative contribution to solving a task. However, physical neuronal systems—whether biological or artificial—are constrained by spatio-temporal locality. How such networks can perform efficient credit assignment, remains, to a large extent, an open question. In Machine Learning, the answer is almost universally given by the error backpropagation algorithm, through both space (BP) and time (BPTT). However, BP(TT) is well-known to rely on biologically implausible assumptions, in particular with respect to spatiotemporal (non-)locality, while forward-propagation models such as real-time recurrent learning (RTRL) suffer from prohibitive memory constraints.

We introduce Generalized Latent Equilibrium (GLE), a computational framework for fully local spatio-temporal credit assignment in physical, dynamical networks of neurons. We start by defining an energy based on neuron-local mismatches, from which we derive both neuronal dynamics via stationarity and parameter dynamics via gradient descent. The resulting dynamics can be interpreted as a real-time, biologically plausible approximation of BPTT in deep cortical networks with continuous-time neuronal dynamics and continuously active, local synaptic plasticity.

In particular, GLE exploits the ability of biological neurons to phase-shift their output rate with respect to their membrane potential, which is essential in both directions of information propagation. For the forward computation, it enables the mapping of time-continuous inputs to neuronal space, performing an effective spatiotemporal convolution. For the backward computation, it permits the temporal inversion of feedback signals, which consequently approximate the adjoint states necessary for useful parameter updates.

--- https://www.lesswrong.com/posts/ukTLGe5CQq9w8FMne/inducing-unprompted-misalignment-in-llms 2024-04-20 ai/nn/transformer/gpt/2 ai/nn/transformer/gpt/codex cs/security reinforcement-learning/model/decision-transformer reinforcement-learning/safe --- https://arxiv.org/abs/2404.10315 Enhancing Confidence Expression in Large Language Models Through Learning from Past Experience Haixia Han, Tingyun Li, Shisong Chen, Jie Shi, Chengyu Du, Yanghua Xiao, Jiaqing Liang, Xin Lin 2024-04-16 2024-04-20 [("doi","10.48550/arXiv.2404.10315")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/calibration reinforcement-learning/preference-learning/mode-collapse

Large Language Models (LLMs) have exhibited remarkable performance across various downstream tasks, but they may generate inaccurate or false information with a confident tone. One of the possible solutions is to empower the LLM confidence expression capability, in which the confidence expressed can be well-aligned with the true probability of the generated answer being correct. However, leveraging the intrinsic ability of LLMs or the signals from the output logits of answers proves challenging in accurately capturing the response uncertainty in LLMs.

Therefore, drawing inspiration from cognitive diagnostics, we propose a method of Learning from Past experience (LePe) to enhance the capability for confidence expression. Specifically, we first identify 3 key problems: (1) How to capture the inherent confidence of the LLM? (2) How to teach the LLM to express confidence? (3) How to evaluate the confidence expression of the LLM? Then we devise 3 stages in LePe to deal with these problems. Besides, to accurately capture the confidence of an LLM when constructing the training data, we design a complete pipeline including question preparation and answer sampling.

We also conduct experiments using the Llama family of LLMs to verify the effectiveness of our proposed method on 4 datasets.

...We also find for ChatGPT, the correlation coefficient is high, but the confidence level of its outputs is consistently high. For example, it barely outputs the confidence level between 0%-40% even if its answer is wrong. [flattened logits]

--- https://reasoning-tokens.ghost.io/reasoning-tokens/ 2024-04-21 ai/nn/transformer/attention/recurrent ai/nn/transformer/gpt/2 ai/nn/transformer/gpt/inner-monologue --- https://arxiv.org/abs/2404.00859 Do language models plan ahead for future tokens? Wilson Wu, John X. Morris, Lionel Levine 2024-04-01 2024-04-21 [("doi","10.48550/arXiv.2404.00859")] ai/nn/transformer/attention ai/nn/transformer/gpt/2 ai/nn/transformer/gpt/inner-monologue

Do transformers “think ahead” during inference at a given position?

It is known transformers prepare information in the hidden states of the forward pass at t that is then used in future forward passes t + τ. We posit two explanations for this phenomenon: pre-caching, in which off-diagonal gradient terms present in training result in the model computing features at t irrelevant to the present inference task but useful for the future, and breadcrumbs, in which features most relevant to time step t are already the same as those that would most benefit inference at time t + τ.

We test these hypotheses by training language models without propagating gradients to past timesteps, a scheme we formalize as myopic training.

In a synthetic data setting, we find clear evidence for pre-caching. In the autoregressive language modeling setting, our experiments are more suggestive of the breadcrumbs hypothesis.

--- https://arxiv.org/abs/2301.08243#facebook JEPA: Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, Nicolas Ballas 2023-01-19 2024-04-21 [("doi","10.48550/arXiv.2301.08243")] ai/nn/vae/mae

This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data augmentations. We introduce the Image-based Joint-Embedding Predictive Architecture (I-JEPA), a non-generative approach for self-supervised learning from images.

The idea behind I-JEPA is simple: from a single context block, predict the representations of various target blocks in the same image. A core design choice to guide I-JEPA towards producing semantic representations is the masking strategy; specifically, it is crucial to (a) sample target blocks with sufficiently large scale (semantic), and to (b) use a sufficiently informative (spatially distributed) context block.

Empirically, when combined with Vision Transformers, we find I-JEPA to be highly scalable. For instance, we train a ViT-Huge/14 on ImageNet using 16 A100 GPUs in under 72 hours to achieve strong downstream performance across a wide range of tasks, from linear classification to object counting and depth prediction.

--- https://ai.meta.com/blog/yann-lecun-ai-model-i-jepa/ 2024-04-21 ai/nn/vae/mae --- https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/ 2024-04-21 ai/nn/vae/mae ai/video/analysis --- https://github.com/facebookresearch/jepa 2024-04-21 ai/nn/vae/mae ai/video/analysis --- https://arxiv.org/abs/2404.11794 Automated Social Science: Language Models as Scientist and Subjects Benjamin S. Manning, Kehang Zhu, John J. Horton 2024-04-17 2024-04-21 [("doi","10.48550/arXiv.2404.11794")] ai/nn/transformer/gpt/4/nonfiction economics/mechanism-design/auction statistics/causality

We present an approach for automatically generating and testing, in silico, social scientific hypotheses. This automation is made possible by recent advances in large language models (LLM), but the key feature of the approach is the use of structural causal models. Structural causal models provide a language to state hypotheses, a blueprint for constructing LLM-based agents, an experimental design, and a plan for data analysis. The fitted structural causal model becomes an object available for prediction or the planning of follow-on experiments.

We demonstrate the approach with several scenarios: a negotiation, a bail hearing, a job interview, and an auction. In each case, causal relationships are both proposed and tested by the system, finding evidence for some and not others.

We provide evidence that the insights from these simulations of social interactions are not available to the LLM purely through direct elicitation. When given its proposed structural causal model for each scenario, the LLM is good at predicting the signs of estimated effects, but it cannot reliably predict the magnitudes of those estimates. In the auction experiment, the in silico simulation results closely match the predictions of auction theory, but elicited predictions of the clearing prices from the LLM are inaccurate. However, the LLM’s predictions are dramatically improved if the model can condition on the fitted structural causal model.

In short, the LLM knows more than it can (immediately) tell.

--- https://www.biorxiv.org/content/10.1101/2024.02.01.578331.full A lipidome Aging Clock shows Age Acceleration in individuals with Autism Djakim Latumalea, Maximilian Unfried, Diogo Goncalves Barardo, Jan Gruber, Brian K. Kennedy 2024-04-18 2024-04-21 [("doi","10.1101/2024.02.01.578331")] longevity/epigenetics psychiatry/autism

Recent advancements in lipidomics and machine learning have been leveraged to investigate the prediction of biological age in individuals. This study delves into age acceleration patterns, entropy, and the potential role of dolichol as an aging biomarker.

We introduce a novel aging clock combined with explainable AI that uses the lipid composition of the prefrontal cortex to predict the biological age of individuals, both those without known neurological conditions and those with autism, schizophrenia, or Down syndrome.

Notably, statistically-significant age acceleration was observed in individuals with autism. Furthermore, entropy exhibits a statistically-significant increase around the age of 40, indicating potential dysregulation in the mevalonate pathway. Lastly, dolichol emerges as a potential biomarker.

These findings underscore the feasibility of predicting biological age using lipidomics data, paving the way for further investigation into the intricate relationship between lipid alterations and prefrontal cortex aging, while offering valuable insights into the associated molecular mechanisms.

--- https://productidentity.co/p/6-twitter-whats-my-name-again 2024-04-21 design sociology/technology --- https://arxiv.org/abs/2310.03251#intel Efficient Video and Audio processing with Loihi 2 Sumit Bam Shrestha, Jonathan Timcheck, Paxon Frady, Leobardo Campos-Macias, Mike Davies 2023-10-05 2024-04-21 [("doi","10.48550/arXiv.2310.03251")] ai/nn ai/scaling/hardware psychology/neuroscience

Intel Loihi 2 is an asynchronous, brain-inspired research processor that generalizes several fundamental elements of neuromorphic architecture, such as stateful neuron models communicating with event-driven spikes, in order to address limitations of the first generation Loihi.

Here we explore and characterize some of these generalizations, such as sigma-delta encapsulation, resonate-and-fire neurons, and integer-valued spikes, as applied to standard video, audio, and signal processing tasks.

We find that these new neuromorphic approaches can provide orders of magnitude gains in combined efficiency and latency (energy-delay-product) for feed-forward and convolutional neural networks applied to video, audio denoising, and spectral transforms compared to state-of-the-art solutions.

--- https://www.suzannetreister.net/Ampages/Amenu.html 2024-04-21 design/typography --- https://arxiv.org/abs/2210.08708 Teacher Forcing Recovers Reward Functions for Text Generation Yongchang Hao, Yuxin Liu, Lili Mou 2022-10-17 2024-04-21 [("doi","10.48550/arXiv.2210.08708")] reinforcement-learning/preference-learning

Reinforcement learning (RL) has been widely used in text generation to alleviate the exposure bias issue or to use non-parallel datasets. The reward function plays an important role in making RL training successful. However, previous reward functions are typically task-specific and sparse, restricting the use of RL.

In our work, we propose a task-agnostic approach that derives a step-wise reward function directly from a model trained with teacher forcing. We additionally propose a simple modification to stabilize the RL training on non-parallel datasets with our induced reward function.

Empirical results show that our method outperforms self-training and reward regression methods on several text generation tasks, confirming the effectiveness of our reward function.

--- https://arxiv.org/abs/2404.12358 From r to Q✱: Your Language Model is Secretly a Q-Function Rafael Rafailov, Joey Hejna, Ryan Park, Chelsea Finn 2024-04-18 2024-04-21 [("doi","10.48550/arXiv.2404.12358")] ai/nn/transformer/gpt reinforcement-learning/model reinforcement-learning/preference-learning

[Twitter; cf. V-STaR] Reinforcement Learning From Human Feedback (RLHF) has been a critical to the success of the latest generation of generative AI models. In response to the complex nature of the classical RLHF pipeline, direct alignment algorithms such as Direct Preference Optimization (DPO) have emerged as an alternative approach. Although DPO solves the same objective as the standard RLHF setup, there is a mismatch between the two approaches. Standard RLHF deploys reinforcement learning in a specific token-level MDP, while DPO is derived as a bandit problem in which the whole response of the model is treated as a single arm.

In this work we rectify this difference, first we theoretically show that we can derive DPO in the token-level MDP as a general inverse Q-learning algorithm, which satisfies the Bellman equation.

Using our theoretical results, we provide 3 concrete empirical insights. First, we show that because of its token level interpretation, DPO is able to perform some type of credit assignment. Next, we prove that under the token level formulation, classical search-based algorithms, such as MCTS, which have recently been applied to the language generation space, are equivalent to likelihood-based search on a DPO policy. Empirically we show that a simple beam search yields meaningful improvement over the base DPO policy. Finally, we show how the choice of reference policy causes implicit rewards to decline during training.

We conclude by discussing applications of our work, including information elicitation in multi-tun dialogue, reasoning, agentic applications and end-to-end training of multi-model systems.

--- https://arxiv.org/abs/2402.06457 V-STaR: Training Verifiers for Self-Taught Reasoners Arian Hosseini, Xingdi Yuan, Nikolay Malkin, Aaron Courville, Alessandro Sordoni, Rishabh Agarwal 2024-02-09 2024-04-21 [("doi","10.48550/arXiv.2402.06457")] ai/nn/transformer/gpt/inner-monologue reinforcement-learning/preference-learning

Common self-improvement approaches for large language models (LLMs), such as STaR (Zelikman et al 2022), iteratively fine-tune LLMs on self-generated solutions to improve their problem-solving ability. However, these approaches discard the large amounts of incorrect solutions generated during this process, potentially neglecting valuable information in such solutions.

To address this shortcoming, we propose V-STaR that uses both the correct and incorrect solutions generated during the self-improvement process to train a verifier using DPO that judges correctness of model-generated solutions. This verifier is used at inference time to select one solution among many candidate solutions.

Running V-STaR for multiple iterations results in progressively better reasoners and verifiers, delivering a 4%0-17% test accuracy improvement over existing self-improvement and verification approaches on common code generation and math reasoning benchmarks with LLaMA-2 models.

--- https://arxiv.org/abs/2403.17350 The Solution of the Zodiac Killer’s 340-Character Cipher David Oranchak, Sam Blake, Jarl Van Eycke 2024-03-26 2024-04-21 [("doi","10.48550/arXiv.2403.17350")] crime cs/cryptography

[blog] The case of the Zodiac Killer is one of the most widely known unsolved serial killer cases in history. The unidentified killer murdered 5 known victims and terrorized the state of California. He also communicated extensively with the press and law enforcement. Besides his murders, Zodiac was known for his use of ciphers. The first Zodiac cipher was solved within a week of its publication, while the second Zodiac cipher was solved by the authors after 51 years, when it was discovered to be a transposition cipher & homophonic substitution cipher with unusual qualities.

In this paper, we detail the historical of this cipher and the numerous efforts which culminated in its solution [eg. bigram acceleration].

--- https://wooo.sh/adler32.html 2024-04-21 cs/algorithm --- https://artvee.com/dl/monkey-and-reflection-of-the-moon/ 2024-04-21 japan/art --- https://scienceblogs.de/klausis-krypto-kolumne/2019/12/19/bigram-750-challenge-solved-new-world-record-set/ 2024-04-21 ai/scaling cs/algorithm cs/cryptography psychology/linguistics --- https://blog.wolfram.com/2021/03/24/the-solution-of-the-zodiac-killers-340-character-cipher/ 2024-04-21 crime cs/cryptography --- https://huggingface.co/datasets/HuggingFaceFW/fineweb 2024-04-22 ai/dataset --- https://web.mnstate.edu/alm/humor/ThePlan.htm 2024-04-22 fiction/humor sociology/preference-falsification --- https://en.wikipedia.org/wiki/Mark_S._Miller Mark S. Miller 2024-04-22 bitcoin cs/cryptography --- https://en.wikipedia.org/wiki/Solomon_Shereshevsky#Challenges Solomon Shereshevsky § Challenges 2024-04-22 psychology/spaced-repetition --- https://x.com/rombulow/status/990684453734203392 rombulow 2024-04-22 cs/security --- https://arxiv.org/abs/2306.13702#netflix Magenta Green Screen: Spectrally Multiplexed Alpha Matting with Deep Colorization Dmitriy Smirnov, Chloe LeGendre, Xueming Yu, Paul Debevec 2023-06-23 2024-04-22 [("doi","10.48550/arXiv.2306.13702")] ai/video/analysis

We introduce Magenta Green Screen, a novel machine learning-enabled matting technique for recording the color image of a foreground actor and a simultaneous high-quality alpha channel without requiring a special camera or manual keying techniques. We record the actor on a green background but light them with only red and blue foreground lighting. In this configuration, the green channel shows the actor silhouetted against a bright, even background, which can be used directly as a holdout matte, the inverse of the actor’s alpha channel.

We then restore the green channel of the foreground using a machine learning colorization technique. We train the colorization model with an example sequence of the actor lit by white lighting, yielding convincing and temporally stable colorization results. We further show that time-multiplexing the lighting between Magenta Green Screen and Green Magenta Screen allows the technique to be practiced under what appears to be mostly normal lighting.

We demonstrate that our technique yields high-quality compositing results when implemented on a modern LED virtual production stage. The alpha channel data obtainable with our technique can provide higher quality training data for natural image matting algorithms to support future ML matting research.

--- https://simonwillison.net/2024/Apr/17/ai-for-data-journalism/ 2024-04-22 ai/nn/retrieval ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/palm --- https://warontherocks.com/2024/04/how-washington-can-save-its-semiconductor-controls-on-china/ 2024-04-22 ai/scaling/hardware --- https://homenewshere.com/national/entertainment/article_d0811e61-c2d1-545e-8168-44da37726a2c.html 2024-04-22 psychiatry/bipolar/energy --- https://www.newyorker.com/news/our-local-correspondents/why-you-cant-get-a-restaurant-reservation 2024-04-22 economics/mechanism-design/auction --- https://en.wikipedia.org/wiki/Van_Sweringen_brothers Van Sweringen brothers 2024-04-22 economics/georgism --- https://x.com/mattshumer_/status/1782468402293903662 Matt Shumer 2024-04-22 ai/nn/transformer/gpt/codex --- https://www.youtube.com/watch?v=L5Fus7qbRZM 2024-04-23 cs/security --- https://www.biorxiv.org/content/10.1101/2023.06.29.547116.full Machine learning reveals the control mechanics of an insect wing hinge Johan M. Melis, Igor Siwanowicz, Michael H. Dickinson 2024-02-02 2024-04-23 [("doi","10.1101/2023.06.29.547116")] ai/nn/cnn psychology/animal reinforcement-learning/robot

Insects constitute the most species-rich radiation of metazoa, a success due to the evolution of active flight. Unlike pterosaurs, birds, and bats, the wings of insects did not evolve from legs, but are novel structures attached to the body via a biomechanically complex hinge that transforms tiny, high-frequency oscillations of specialized power muscles into the sweeping back-and-forth motion of the wings. The hinge consists of a system of tiny, hardened structures called sclerites that are interconnected to one another via flexible joints and regulated by the activity of specialized control muscles.

Here, we imaged the activity of these muscles in a fly using a genetically encoded calcium indicator, while simultaneously tracking the 3D motion of the wings with high-speed cameras. Using machine learning approaches, we created a convolutional neural network that accurately predicts wing motion from the activity of the steering muscles, and an encoder-decoder that predicts the role of the individual sclerites on wing motion.

By replaying patterns of wing motion on a dynamically scaled robotic fly, we quantified the effects of steering muscle activity on aerodynamic forces. A physics-based simulation that incorporates our model of the hinge generates flight maneuvers that are remarkably similar to those of free flying flies.

This integrative, multi-disciplinary approach reveals the mechanical control logic of the insect wing hinge, arguably among the most sophisticated and evolutionarily important skeletal structures in the natural world.

--- https://arxiv.org/abs/2310.11628 Learn Your Tokens: Word-Pooled Tokenization for Language Modeling Avijit Thawani, Saurabh Ghanekar, Xiaoyuan Zhu, Jay Pujara 2023-10-17 2024-04-23 [("doi","10.48550/arXiv.2310.11628")] ai/nn/tokenization ai/nn/transformer/gpt

Language models typically tokenize text into subwords, using a deterministic, hand-engineered heuristic of combining characters into longer surface-level strings such as ‘ing’ or whole words. Recent literature has repeatedly shown the limitations of such a tokenization strategy, particularly for documents not written in English and for representing numbers. On the other extreme, byte/character-level language models are much less restricted but suffer from increased sequence description lengths and a subsequent quadratic expansion in self-attention computation.

Recent attempts to compress and limit these context lengths with fixed size convolutions is helpful but completely ignores the word boundary. This paper considers an alternative ‘learn your tokens’ scheme which uses the word boundary to pool bytes/characters into word representations, which are fed to the primary language model, before again decoding individual characters/bytes per word in parallel.

We find that our moderately expressive and moderately fast end-to-end tokenizer outperform by over 300% both subwords and byte/character models over the intrinsic language modeling metric of next-word prediction across datasets. It particularly outshines on rare words, outperforming by a factor of 30!

We extensively study the language modeling setup for all 3 categories of tokenizers and theoretically analyze how our end-to-end models can also be a strong trade-off in efficiency and robustness.

--- https://arxiv.org/abs/2404.14408 SpaceByte: Towards Deleting Tokenization from Large Language Modeling Kevin Slagle 2024-04-22 2024-04-23 [("doi","10.48550/arXiv.2404.14408")] ai/nn/tokenization ai/nn/transformer/gpt

Tokenization is widely used in large language models because it improves performance. However, tokenization imposes several disadvantages, such as performance biases, increased adversarial vulnerability, decreased character-level modeling performance, and increased modeling complexity.

To address these disadvantages without sacrificing performance, we propose SpaceByte, a novel byte-level decoder architecture that closes the performance gap between byte-level and subword autoregressive language modeling. SpaceByte consists of a byte-level Transformer model, but with extra larger transformer blocks inserted in the middle of the layers.

We find that performance is improved by applying these larger blocks only after certain bytes, such as space characters, which typically denote word boundaries.

Our experiments show that for a fixed training and inference compute budget, SpaceByte outperforms other byte-level architectures and roughly matches the performance of tokenized Transformer architectures.

--- https://x.com/finereli/status/1782611247709786145 finereli 2024-04-23 ai/nn/transformer/gpt/inner-monologue fiction/text-game --- https://www.awanderingmind.blog/posts/2024-01-14-tao-te-ching-by-an-llm.html 2024-04-23 ai/nn/transformer/gpt/4/poetry --- https://realdougwilson.com/writing/san-serriffe 2024-04-23 design/typography math/humor --- https://www-cs-faculty.stanford.edu/~knuth/boss.html 2024-04-23 cs/algorithm design/typography math/humor --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3440998/ It pays to cheat: tactical deception in a cephalopod social signaling system Culum Brown, Martin P. Garwood, Jane E. Williamson 2012 2024-04-23 [("doi","10.1098/rsbl.2012.0435")] psychology/animal

Signals in intraspecific communication should be inherently honest; otherwise, the system is prone to collapse. Theory predicts, however, that honest signaling systems are susceptible to invasion by cheats, the extent of which is largely mediated by fear of reprisal. Cuttlefish facultatively change their shape and color, an ability that evolved to avoid predators and capture prey.

Here, we show that this ability is tactically employed by male mourning cuttlefish (Sepia plangon) to mislead conspecifics during courtship in a specific social context amenable to cheating 39% of the time, while it was never employed in other social contexts. Males deceive rival males by displaying male courtship patterns to receptive females on one side of the body, and simultaneously displaying female patterns to a single rival male on the other, thus preventing the rival from disrupting courtship.

The use of tactical deception in such a complex communication network indicates that sociality has played a key role in the cognitive evolution of cephalopods.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8988249/ Where Is It Like to Be an Octopus? Sidney Carls-Diamante 2022 2024-04-23 [("doi","10.3389/fnsys.2022.840022")] philosophy/mind psychology/animal psychology/neuroscience

The cognitive capacities and behavioral repertoire of octopuses have led to speculation that these animals may possess consciousness. However, the nervous system of octopuses is radically different from those typically associated with conscious experience: rather than being centralized and profoundly integrated, the octopus nervous system is distributed into components with considerable functional autonomy from each other.

Of particular note is the arm nervous system: when severed, octopus arms still exhibit behaviors that are nearly identical to those exhibited when the animal is intact. Given these factors, there is reason to speculate that if octopuses do possess consciousness, it may be of a form highly dissimilar to familiar models.

In particular, it may be that the octopus arm is capable of supporting an idiosyncratic field of consciousness. As such, in addition to the likelihood that there is something it is like to be an octopus, there may also be something it is like to be an octopus arm. This manuscript explores this possibility.

--- https://www.biorxiv.org/content/10.1101/2024.04.22.590591.full 2024-04-23 ai/nn/transformer/gpt genetics/editing --- https://www.biorxiv.org/content/10.1101/2024.04.22.590591.full Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences Jeffrey A. Ruffolo, Stephen Nayfach, Joseph Gallagher, Aadyot Bhatnagar, Joel Beazer, Riffat Hussain, Jordan Russ, Jennifer Yip, Emily Hill, Martin Pacesa, Alexander J. Meeske, Peter Cameron, Ali Madani 2024-04-22 2024-04-23 [("doi","10.1101/2024.04.22.590591")] ai/nn/transformer/gpt genetics/editing

Gene editing has the potential to solve fundamental challenges in agriculture, biotechnology, and human health. CRISPR-based gene editors derived from microbes, while powerful, often show functional tradeoffs when ported into non-native environments, such as human cells. Artificial intelligence (AI) enabled design provides a powerful alternative with potential to bypass evolutionary constraints and generate editors with optimal properties.

Here, using large language models (LLMs) trained on biological diversity at scale, we demonstrate the first successful precision editing of the human genome with a programmable gene editor designed with AI. To achieve this goal, we curated a dataset of over one million CRISPR operons through systematic mining of 26 terabases of assembled genomes and meta-genomes.

We demonstrate the capacity of our models by generating 4.8× the number of protein clusters across CRISPR-Cas families found in nature and tailoring single-guide RNA sequences for Cas9-like effector proteins. Several of the generated gene editors show comparable or improved activity and specificity relative to SpCas9, the prototypical gene editing effector, while being 400 mutations away in sequence.

Finally, we demonstrate an AI-generated gene editor, denoted as OpenCRISPR-1, exhibits compatibility with base editing. We release OpenCRISPR-1 publicly to facilitate broad, ethical usage across research and commercial applications.

--- https://www.statsignificant.com/p/when-do-we-stop-finding-new-music 2024-04-23 music psychology/novelty --- https://www.reddit.com/r/StableDiffusion/comments/12huyk4/evaluation_of_the_latent_horniness_of_the_most/ 2024-04-23 ai/anime ai/nn/diffusion --- https://www.biorxiv.org/content/10.1101/2024.04.16.589618.full Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes Guillaume Huguet, Thomas Renne, Cécile Poulain, Alma Dubuc, Kuldeep Kumar, Sayeh Kazem, Worrawat Engchuan, Omar Shanta, Elise Douard, Catherine Proulx, Martineau Jean-Louis, Zohra Saci, Josephine Mollon, Laura M. Schultz, Emma E. M. Knowles, Simon R. Cox, David J. Porteous, Gail Davies, Paul Redmond, Sarah E. Harris, Gunter Schumann, Guillaume Dumas, Aurélie Labbe, Zdenka Pausova, Tomas Paus, Stephen W. Scherer, Jonathan Sebat, Laura Almasy, David C. Glahn, Sébastien Jacquemont 2024-04-17 2024-04-23 [("doi","10.1101/2024.04.16.589618")] genetics/heritable/rare iq

Genomic Copy Number Variants (CNVs) that increase risk for neurodevelopmental disorders are also associated with lower cognitive ability in general population cohorts. Studies have focused on a small set of recurrent CNVs, but burden analyses suggested that the vast majority of CNVs affecting cognitive ability are too rare to reach variant-level association. As a result, the full range of gene-dosage-sensitive biological processes linked to cognitive ability remains unknown.

To investigate this issue, we identified all CNVs >50 kilobases in 258,000 individuals from 6 general population cohorts with assessments of general cognitive abilities. We performed a CNV-GWAS and functional burden analyses, which tested 6,502 gene-sets defined by tissue and cell-type transcriptomics as well as gene ontology disrupted by all rare coding CNVs.

CNV-GWAS identified a novel duplication at 2q12.3 associated with higher performance in cognitive ability. Among the 864 gene-sets associated with cognitive ability, only 11% showed statistically-significant effects for both deletions and duplication. Accordingly, we systematically observed negative correlations between deletion and duplication effect sizes across all levels of biological observations. We quantified the preferential effects of deletions versus duplication using tagDS, a new normalized metric.

Cognitive ability was preferentially affected by cortical, presynaptic, and negative-regulation gene-sets when duplicated. In contrast, preferential effects of deletions were observed for subcortical, post-synaptic, and positive-regulation gene-sets. A large proportion of gene-sets assigned to non-brain organs were associated with cognitive ability due to low tissue specificity genes, which were associated with higher sensitive to haploinsufficiency.

Overall, most biological functions associated with cognitive ability are divided into those sensitive to either deletion or duplications.

--- https://restofworld.org/2024/tsmc-arizona-expansion/ 2024-04-23 ai/scaling/hardware politics --- https://www.anthropic.com/research/probes-catch-sleeper-agents 2024-04-23 ai/nn/adversarial --- https://en.wikipedia.org/wiki/Iterative_deepening_depth-first_search Iterative deepening depth-first search 2024-04-23 reinforcement-learning/model --- https://arxiv.org/abs/2106.04866 Planning for Novelty: Width-Based Algorithms for Common Problems in Control, Planning and Reinforcement Learning Nir Lipovetzky 2021-06-09 2024-04-23 [("doi","10.48550/arXiv.2106.04866")] reinforcement-learning/exploration reinforcement-learning/model

Width-based algorithms search for solutions through a general definition of state novelty. These algorithms have been shown to result in state-of-the-art performance in classical planning, and have been successfully applied to model-based and model-free settings where the dynamics of the problem are given through simulation engines.

Width-based algorithms performance is understood theoretically through the notion of planning width, providing polynomial guarantees on their runtime and memory consumption.

To facilitate synergies across research communities, this paper summarizes the area of width-based planning, and surveys current and future research directions.

--- https://www.youtube.com/watch?v=g3lc8BxTPiU 2024-04-24 reinforcement-learning/model --- https://www.thepsmiths.com/p/review-invitation-to-a-banquet-by Review: Invitation to a Banquet, by Fuchsia Dunlop John Psmith 2024-02-05 2024-03-11 food --- https://www.brainvitge.org/papers/23Erroneous%20analyses%20of%20interactions%20in%20neuroscience%20%20a%20problem%20of%20significance.pdf Erroneous analyses of interactions in neuroscience: a problem of statistical-significance Nieuwenhuis 2011 2024-01-01 psychology/neuroscience statistics/bias --- https://portalcomunicacion.uah.es/images/04curso20132014/PDFs/articulo_nature_adn.pdf A mitochondrial genome sequence of a hominin from Sima de los Huesos Meyer 2013 2024-01-01 genetics/sequencing --- https://arxiv.org/abs/2404.14047 How Good Are Low-bit Quantized LLaMA-3 Models? An Empirical Study Wei Huang, Xudong Ma, Haotong Qin, Xingyu Zheng, Chengtao Lv, Hong Chen, Jie Luo, Xiaojuan Qi, Xianglong Liu, Michele Magno 2024-04-22 2024-04-24 [("doi","10.48550/arXiv.2404.14047")] ai/nn/sparsity/low-precision

Meta’s LLaMA family has become one of the most powerful open-source Large Language Model (LLM) series. Notably, LLaMA-3 models have recently been released and achieve impressive performance across various with super-large scale pre-training on over 15T tokens of data.

Given the wide application of low-bit quantization for LLMs in resource-limited scenarios, we explore LLaMA-3’s capabilities when quantized to low bit-width. This exploration holds the potential to unveil new insights and challenges for low-bit quantization of LLaMA-3 and other forthcoming LLMs, especially in addressing performance degradation problems that suffer in LLM compression.

Specifically, we evaluate the 10 existing post-training quantization and LoRA-finetuning methods of LLaMA-3 on 1–8 bits and diverse datasets to comprehensively reveal LLaMA-3’s low-bit quantization performance. Our experiment results indicate that LLaMA-3 still suffers non-negligible degradation in these scenarios, especially in ultra-low bit-width. This highlights the performance gap under low bit-width that needs to be bridged in future developments.

We expect that this empirical study will prove valuable in advancing future models, pushing the LLMs to lower bit-width with higher accuracy for being practical.

Our project is released on https://github.com/Macaronlin/LLaMA-3-Quantization and quantized LLaMA-3 models are released on https://huggingface.co/LLMQ.

--- http://www.lel.ed.ac.uk/~gpullum/loopsnoop.html 2024-04-24 cs/computable math/humor --- https://www.youtube.com/watch?v=21jEd1FUiV8 2024-04-24 cs/algorithm/sorting --- https://plato.stanford.edu/entries/generics/ 2024-04-24 philosophy/ontology psychology/linguistics --- https://www.elysian.press/p/no-one-buys-books 2024-04-24 economics/copyright --- https://x.com/DanHendrycks/status/1782953713461772546 Dan Hendrycks 2024-04-24 reinforcement-learning/safe --- https://people.eecs.berkeley.edu/~hendrycks/ 2024-04-24 ai/scaling reinforcement-learning/safe --- /doc/reinforcement-learning/armstrong-controlproblem/index.html 2024-01-01 reinforcement-learning/armstrong-controlproblem reinforcement-learning/model-free reinforcement-learning/safe --- https://www.lesswrong.com/posts/pK3eKhBwBiLffqtrk/what-good-is-g-factor-if-you-re-dumped-in-the-woods-a-field#TkhhGd45HrNP8nPb4 2024-04-24 cs/security --- https://www.wired.com/story/combined-heart-pump-pig-kidney-transplant-surgery/ 2024-04-24 genetics/editing --- https://www.snowflake.com/blog/arctic-open-efficient-foundation-language-models-snowflake/ 2024-04-24 ai/scaling/mixture-of-experts --- https://arxiv.org/abs/2404.12388#adobe VideoGigaGAN: Towards Detail-rich Video Super-Resolution Yiran Xu, Taesung Park, Richard Zhang, Yang Zhou, Eli Shechtman, Feng Liu, Jia-Bin Huang, Difan Liu 2024-04-18 2024-04-24 [("doi","10.48550/arXiv.2404.12388")] ai/nn/gan ai/video/generation

[homepage] Video super-resolution (VSR) approaches have shown impressive temporal consistency in upsampled videos. However, these approaches tend to generate blurrier results than their image counterparts as they are limited in their generative capability. This raises a fundamental question: can we extend the success of a generative image upsampler to the VSR task while preserving the temporal consistency?

We introduce VideoGigaGAN, a new generative VSR model that can produce videos with high-frequency details and temporal consistency. VideoGigaGAN builds upon a large-scale image upsampler—GigaGAN.

Simply inflating GigaGAN to a video model by adding temporal modules produces severe temporal flickering. We identify several key issues and propose techniques that improve the temporal consistency of upsampled videos.

Our experiments show that, unlike previous VSR methods, VideoGigaGAN generates temporally consistent videos with more fine-grained appearance details. We validate the effectiveness of VideoGigaGAN by comparing it with state-of-the-art VSR models on public datasets and showcasing video results with 8× super-resolution.

--- https://www.nytimes.com/2024/04/22/dining/vegan-chef-matthew-kenney.html 2024-04-24 crime psychiatry/bipolar/energy --- https://research.google/blog/safely-repairing-broken-builds-with-ml/ 2024-04-25 ai/nn/transformer/gpt/codex --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.614.686&rep=rep1&type=pdf Guard labor Jayadev, Bowles 2006 2024-01-01 crime economics --- http://www.wsu.edu/~fournier/Teaching/psych592/Readings/Karpicke_et_al_2008.pdf The Critical Importance of Retrieval for Learning Karpicke, Roediger 2008 2024-01-01 psychology/spaced-repetition --- https://osf.io/WX7Ck/ Investigating variation in replicability: The `Many Labs` Replication Project Klein 2013 2024-01-01 statistics/bias --- https://www.amazon.com/Off-Planet-Surviving-Perilous-Station/dp/007136112X Off The Planet: Surviving Five Perilous Months Aboard The Space Station MIR Linenger 2000 2024-01-01 psychiatry --- https://www.industrydocuments.ucsf.edu/tobacco/docs/#id=qllw0079 Effects of nicotine gum on psychomotor performance in smokers and non-smokers Kerr, Sherwood 1995 2024-01-01 nicotine --- https://arxiv.org/abs/1202.3936 On the distribution of time-to-proof of mathematical conjectures Ryohei Hisano, Didier Sornette 2012-02-17 2024-01-01 [("doi","10.1007/s00283-013-9383-7")] math statistics/order

What is the productivity of Science? Can we measure an evolution of the production of mathematicians over history? Can we predict the waiting time till the proof of a challenging conjecture such as the P-versus-NP problem?

Motivated by these questions, we revisit a suggestion published recently and debated in the “New Scientist” that the historical distribution of time-to-proof’s, ie. of waiting times between formulation of a mathematical conjecture and its proof, can be quantified and gives meaningful insights in the future development of still open conjectures.

We find however evidence that the mathematical process of creation is too much non-stationary, with too little data and constraints, to allow for a meaningful conclusion. In particular, the approximate unsteady exponential growth of human population, and arguably that of mathematicians, essentially hides the true distribution. Another issue is the incompleteness of the dataset available.

In conclusion we cannot really reject the simplest model of an exponential rate of conjecture proof with a rate of 0.01/year for the dataset that we have studied, translating into an average waiting time to proof of 100 years. We hope that the presented methodology, combining the mathematics of recurrent processes, linking proved and still open conjectures, with different empirical constraints, will be useful for other similar investigations probing the productivity associated with mankind growth and creativity.

--- https://arxiv.org/abs/1302.2898 Mathematics in the Age of the Turing Machine Thomas Hales 2013-02-12 2024-01-01 [("doi","10.48550/arXiv.1302.2898")] cs/computable math philosophy/epistemology

The article gives a survey of mathematical proofs that rely on computer calculations and formal proofs.

--- https://danluu.com/ 2024-04-19 cs --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6442609/ Role of contacts in long-range protein conductance Bintian Zhang, Weisi Song, Pei Pang, Huafang Lai, Qiang Chen, Peiming Zhang, Stuart Lindsay 2019 2024-04-25 [("doi","10.1073/pnas.1819674116")] biology cs/hardware

Proteins are widely regarded as insulators, despite reports of electrical conductivity. Here we use measurements of single proteins between electrodes, in their natural aqueous environment to show that the factor controlling measured conductance is the nature of the electrical contact to the protein, and that specific ligands make highly selective electrical contacts.

Using 6 proteins that lack known electrochemical activity, and measuring in a potential region where no ion current flows, we find characteristic peaks in the distributions of measured single-molecule conductances. These peaks depend on the contact chemistry, and hence, on the current path through the protein. In consequence, the measured conductance distribution is sensitive to changes in this path caused by ligand binding, as shown with streptavidin-biotin complexes.

Measured conductances are on the order of nanosiemens over distances of many nanometers, orders of magnitude more than could be accounted for by electron tunneling. The current is dominated by contact resistance, so the conductance for a given path is independent of the distance between electrodes, as long as the contact points on the protein can span the gap between electrodes.

While there is no currently known biological role for high electronic conductance, its dependence on specific contacts has important technological implications, because no current is observed at all without at least one strongly bonded contact, so direct electrical detection is a highly selective and label-free single-molecule detection method.

We demonstrate single-molecule, highly specific, label and background free-electronic detection of IgG antibodies to HIV and Ebola viruses.

--- https://press.asimov.com/resources/tinker 2024-04-25 fiction/science-fiction reinforcement-learning/safe/clippy --- http://leehite.org/Chimes.htm 2024-04-25 music --- https://arxiv.org/abs/2404.13993 Zero-Shot Character Identification and Speaker Prediction in Comics via Iterative Multimodal Fusion Yingxuan Li, Ryota Hinami, Kiyoharu Aizawa, Yusuke Matsui 2024-04-22 2024-04-25 [("doi","10.48550/arXiv.2404.13993")] ai/anime ai/nn/transformer/gpt/4/fiction

Recognizing characters and predicting speakers of dialogue are critical for comic processing tasks, such as voice generation or translation. However, because characters vary by comic title, supervised learning approaches like training character classifiers which require specific annotations for each comic title are infeasible. This motivates us to propose a novel zero-shot approach, allowing machines to identify characters and predict speaker names based solely on unannotated comic images. In spite of their importance in real-world applications, these task have largely remained unexplored due to challenges in story comprehension and multimodal integration.

Recent large language models (LLMs) have shown great capability for text understanding and reasoning, while their application to multimodal content analysis is still an open problem. To address this problem, we propose an iterative multimodal framework, the first to employ multimodal information for both character identification and speaker prediction tasks.

Our experiments demonstrate the effectiveness of the proposed framework, establishing a robust baseline for these tasks. Furthermore, since our method requires no training data or annotations, it can be used as-is on any comic series.

--- https://arxiv.org/abs/2404.15574 Retrieval Head Mechanistically Explains Long-Context Factuality Wenhao Wu, Yizhong Wang, Guangxuan Xiao, Hao Peng, Yao Fu 2024-04-24 2024-04-25 [("doi","10.48550/arXiv.2404.15574")] ai/nn/retrieval ai/nn/transformer/attention ai/nn/transformer/gpt/inner-monologue

[Twitter] Despite the recent progress in long-context language models, it remains elusive how transformer-based models exhibit the capability to retrieve relevant information from arbitrary locations within the long context.

This paper aims to address this question. Our systematic investigation across a wide spectrum of models reveals that a special type of attention heads are largely responsible for retrieving information, which we dub retrieval heads.

We identify intriguing properties of retrieval heads: (1) universal: all the explored models with long-context capability have a set of retrieval heads; (2) sparse: only a small portion (less than 5%) of the attention heads are retrieval. (3) intrinsic: retrieval heads already exist in models pretrained with short context. When extending the context length by continual pretraining, it is still the same set of heads that perform information retrieval. (4) dynamically activated: take LLaMA-2-7B for example, 12 retrieval heads always attend to the required information no matter how the context is changed. The rest of the retrieval heads are activated in different contexts. (5) causal: completely pruning retrieval heads leads to failure in retrieving relevant information and results in hallucination, while pruning random non-retrieval heads does not affect the model’s retrieval ability.

We further show that retrieval heads strongly influence chain-of-thought (CoT) reasoning, where the model needs to frequently refer back the question and previously-generated context. Conversely, tasks where the model directly generates the answer using its intrinsic knowledge are less impacted by masking out retrieval heads. These observations collectively explain which internal part of the model seeks information from the input tokens.

We believe our insights will foster future research on reducing hallucination, improving reasoning, and compressing the KV cache.

--- https://x.com/MartinNebelong/status/1783577204145705007 MartinNebelong 2024-04-25 ai/nn/diffusion/midjourney --- https://www.fxguide.com/fxfeatured/actually-using-sora/ 2024-04-25 ai/video/generation --- /doc/psychology/cognitive-bias/1999-mcglone.pdf The Keats heuristic: Rhyme as reason in aphorism interpretation Matthew S. McGlone, Jessica Tofighbakhsh 1999-05-01 2024-04-26 [("doi","10.1016/S0304-422X(99)00003-0")] fiction/poetry psychology/cognitive-bias

Do people distinguish between the form and propositional content of a statement when evaluating its truthfulness?

We asked people to judge the comprehensibility and ostensible accuracy of unfamiliar aphorisms presented in their original rhyming form (eg. Woes unite foes) or a semantically equivalent non-rhyming form (Woes unite enemies).

Although the different versions were perceived as equally comprehensible, the rhyming versions were perceived as more accurate.

This ‘rhyme as reason’ effect suggests that in certain circumstances, people may base their judgments of a statement’s truth value in part on its esthetic qualities. Our results are consistent with models of persuasion which assume that people rely on heuristic cues to evaluate messages when they lack the evidence and/or motivation to scrutinize message content (eg. Eagly & Chaiken 1993).

See Also:

--- https://nonint.com/2024/03/03/learned-structures/ 2024-04-26 ai/nn/transformer/gpt/dall-e/3 ai/scaling --- https://arxiv.org/abs/2011.13635 Progressively Stacking 2.0: A Multi-stage Layerwise Training Method for BERT Training Speedup Cheng Yang, Shengnan Wang, Chao Yang, Yuechuan Li, Ru He, Jingqiao Zhang 2020-11-27 2024-04-26 [("doi","10.48550/arXiv.2011.13635")] ai/nn/sparsity/pruning ai/nn/transformer

Pre-trained language models, such as BERT, have achieved accuracy gain in many natural language processing tasks. Despite its effectiveness, the huge number of parameters makes training a BERT model computationally very challenging.

In this paper, we propose an efficient multi-stage layerwise training (MSLT) approach to reduce the training time of BERT.

We decompose the whole training process into several stages. The training is started from a small model with only a few encoder layers, and we gradually increase the depth of the model by adding new encoder layers. At each stage, we only train the top (near the output layer) few encoder layers which are newly added. The parameters of the other layers which have been trained in the previous stages will not be updated in the current stage. In BERT training, the backward computation is much more time-consuming than the forward computation, especially in the distributed training setting in which the backward computation time further includes the communication time for gradient synchronization. In the proposed training strategy, only the top few layers participate in backward computation, while most layers only participate in forward computation. Hence, both the computation and communication efficiencies are greatly improved.

Experimental results show that the proposed method can achieve more than 110% training speedup without performance degradation.

--- /doc/cs/1980-swiderski.pdf Bouvet and Leibniz: A Scholarly Correspondence Richard M. Swiderski 1980-12-01 2024-04-23 [("doi","10.2307/2738331")] cs math philosophy/religion --- https://arxiv.org/abs/2404.15653#apple CatLIP: CLIP-level Visual Recognition Accuracy with 2.7× Faster Pre-training on Web-scale Image-Text Data Sachin Mehta, Maxwell Horton, Fartash Faghri, Mohammad Hossein Sekhavat, Mahyar Najibi, Mehrdad Farajtabar, Oncel Tuzel, Mohammad Rastegari 2024-04-24 2024-04-26 [("doi","10.48550/arXiv.2404.15653")] ai/nn/transformer/clip ai/scaling

Contrastive learning has emerged as a transformative method for learning effective visual representations through the alignment of image and text embeddings. However, pairwise similarity computation in contrastive loss between image and text pairs poses computational challenges.

This paper presents a novel weakly supervised pre-training of vision models on web-scale image-text data. The proposed method CatLIP reframes pre-training on image-text data as a classification task. Consequently, it eliminates the need for pairwise similarity computations in contrastive loss, achieving a remarkable 2.7× acceleration in training speed compared to contrastive learning on web-scale data.

Through extensive experiments spanning diverse vision tasks, including detection and segmentation, we demonstrate that the proposed method maintains high representation quality.

Our source code along with pre-trained model weights and training recipes is available at https://github.com/apple/corenet.

--- https://arxiv.org/abs/2404.14367 Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar 2024-04-22 2024-04-26 [("doi","10.48550/arXiv.2404.14367")] reinforcement-learning/model-free reinforcement-learning/offline reinforcement-learning/preference-learning

Learning from preference labels plays a crucial role in fine-tuning large language models. There are several distinct approaches for preference fine-tuning, including supervised learning, on-policy reinforcement learning (RL), and contrastive learning. Different methods come with different implementation trade-offs and performance differences, and existing empirical findings present different conclusions, for instance, some results show that online RL is quite important to attain good fine-tuning results, while others find (offline) contrastive or even purely supervised methods sufficient. This raises a natural question: what kind of approaches are important for fine-tuning with preference data and why?

In this paper, we answer this question by performing a rigorous analysis of a number of fine-tuning techniques on didactic and full-scale LLM problems. Our main finding is that, in general, approaches that use on-policy sampling or attempt to push down the likelihood on certain responses (ie. employ a “negative gradient”) outperform offline and maximum likelihood objectives. We conceptualize our insights and unify methods that use on-policy sampling or negative gradient under a notion of mode-seeking objectives for categorical distributions. Mode-seeking objectives are able to alter probability mass on specific bins of a categorical distribution at a fast rate compared to maximum likelihood, allowing them to relocate masses across bins more effectively.

Our analysis prescribes actionable insights for preference fine-tuning of LLMs and informs how data should be collected for maximal improvement.

--- https://en.wikipedia.org/wiki/John_Kricfalusi John Kricfalusi 2024-04-26 psychiatry/bipolar/energy --- https://arxiv.org/abs/1807.03341 Troubling Trends in Machine Learning Scholarship Zachary C. Lipton, Jacob Steinhardt 2018-07-09 2024-04-26 [("doi","10.48550/arXiv.1807.03341")] ai/scaling

Collectively, machine learning (ML) researchers are engaged in the creation and dissemination of knowledge about data-driven algorithms. In a given paper, researchers might aspire to any subset of the following goals, among others: to theoretically characterize what is learnable, to obtain understanding through empirically rigorous experiments, or to build a working system that has high predictive accuracy. While determining which knowledge warrants inquiry may be subjective, once the topic is fixed, papers are most valuable to the community when they act in service of the reader, creating foundational knowledge and communicating as clearly as possible.

Recent progress in machine learning comes despite frequent departures from these ideals. In this paper, we focus on the following 4 patterns that appear to us to be trending in ML scholarship: (1) failure to distinguish between explanation and speculation; (2) failure to identify the sources of empirical gains, eg. emphasizing unnecessary modifications to neural architectures when gains actually stem from hyper-parameter tuning; (3) mathiness: the use of mathematics that obfuscates or impresses rather than clarifies, eg. by confusing technical and non-technical concepts; and (4) misuse of language, eg. by choosing terms of art with colloquial connotations or by overloading established technical terms.

While the causes behind these patterns are uncertain, possibilities include the rapid expansion of the community, the consequent thinness of the reviewer pool, and the often-misaligned incentives between scholarship and short-term measures of success (eg. bibliometrics, attention, and entrepreneurial opportunity). While each pattern offers a corresponding remedy (don’t do it), we also discuss some speculative suggestions for how the community might combat these trends.

--- https://www.atlasobscura.com/articles/japanese-green-tea-history 2024-04-26 tea --- https://www.atlasobscura.com/articles/parthenogenesis-uruguay-stick-mantis-insect 2024-04-26 genetics/cloning --- https://www.youtube.com/watch?v=72y2EC5fkcE 2024-04-26 cs/lisp --- https://cpldcpu.wordpress.com/2024/04/24/implementing-neural-networks-on-the-10-cent-risc-v-mcu-without-multiplier/ 2024-04-26 ai/nn/fully-connected ai/nn/sparsity/low-precision --- https://www.getlibretto.com/blog/does-it-matter-which-examples-you-choose-for-few-shot-prompting 2024-04-26 ai/nn/transformer/gpt/3/nonfiction --- https://loglog.games/blog/leaving-rust-gamedev/ 2024-04-27 sociology/technology --- https://www.theinformation.com/articles/microsoft-and-openai-plot-100-billion-stargate-ai-supercomputer 2024-04-27 ai/scaling/economics ai/scaling/hardware reinforcement-learning/openai --- https://lilianweng.github.io/posts/2018-06-24-attention/ 2024-04-27 ai/nn/transformer/attention --- https://arxiv.org/abs/2310.02226 Think before you speak: Training Language Models With Pause Tokens Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, Vaishnavh Nagarajan 2023-10-03 2024-04-27 [("doi","10.48550/arXiv.2310.02226")] ai/nn/tokenization ai/nn/transformer/attention/recurrent ai/nn/transformer/gpt/inner-monologue

[compare Tigges et al 2023 on Transformers cannibalizing punctuation; cf. "reasoning tokens", ViT registers, Quiet-STaR] Language models generate responses by producing a series of tokens in immediate succession: the (K+1)th token is an outcome of manipulating K hidden vectors per layer, one vector per preceding token.

What if instead we were to let the model manipulate say, K+10 hidden vectors, before it outputs the (K+1)th token? We operationalize this idea by performing training and inference on language models with a (learnable) pause token, a sequence of which is appended to the input prefix. We then delay extracting the model’s outputs until the last pause token is seen, thereby allowing the model to process extra computation before committing to an answer.

We empirically evaluate pause-training on decoder-only models of 1B and 130M parameters with causal pretraining on C4, and on downstream tasks covering reasoning, question-answering, general understanding, and fact recall.

Our main finding is that inference-time delays show gains when the model is both pre-trained and fine-tuned with delays. For the 1B model, we witness gains on 8⁄9 tasks, most prominently, a gain of 18% EM score on the QA task of SQuAD, 8% on CommonSenseQA and 1% accuracy on the reasoning task of GSM8k.

Our work raises a range of conceptual and practical future research questions on making delayed next-token prediction a widely applicable new paradigm.

--- https://arxiv.org/abs/2404.09043 Do LLMs Play Dice? Exploring Probability Distribution Sampling in Large Language Models for Behavioral Simulation Jia Gu, Liang Pang, Huawei Shen, Xueqi Cheng 2024-04-13 2024-04-27 [("doi","10.48550/arXiv.2404.09043")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/calibration

With the rapid advancement of large language models (LLMs) and their remarkable capabilities in handling complex language tasks, an increasing number of studies are employing LLMs as agents to emulate the sequential decision-making processes of humans often represented as Markov decision-making processes (MDPs). The actions within this decision-making framework adhere to specific probability distributions and require iterative sampling. This arouses our curiosity regarding the capacity of LLM agents to comprehend probability distributions, thereby guiding the agent’s behavioral decision-making through probabilistic sampling and generating behavioral sequences.

To answer the above question, we divide the problem into two main aspects: simulation where the exact probability distribution is known, and generation of sequences where the probability distribution is ambiguous. In the first case, the agent is required to give the type and parameters of the probability distribution through the problem description, and then give the sampling sequence. However, our analysis shows that LLM agents perform poorly in this case, but the sampling success rate can be improved through Python programming tools.

Real-world scenarios often entail unknown probability distributions. Thus, in the second case, we ask the agents to change the activity level in online social networks and analyze the frequency of actions. Ultimately, our analysis shows that LLM agents cannot sample probability distributions even using programming tools.

Therefore, careful consideration is still required before directly applying LLM agents as agents to simulate human behavior.

--- https://arxiv.org/abs/2404.15758 Let’s Think Dot by Dot: Hidden Computation in Transformer Language Models Jacob Pfau, William Merrill, Samuel R. Bowman 2024-04-24 2024-04-27 [("doi","10.48550/arXiv.2404.15758")] ai/nn/transformer/attention ai/nn/transformer/gpt/inner-monologue cs/cryptography/steganography

[Twitter; cf. Levy et al 2024] Chain-of-thought responses from language models improve performance across most benchmarks. However, it remains unclear to what extent these performance gains can be attributed to human-like task decomposition or simply the greater computation that additional tokens allow.

We show that transformers can use meaningless filler tokens (eg. ‘…’) in place of a chain-of-thought to solve two hard algorithmic tasks they could not solve when responding without intermediate tokens. However, we find empirically that learning to use filler tokens is difficult and requires specific, dense supervision to converge.

We also provide a theoretical characterization of the class of problems where filler tokens are useful in terms of the quantifier depth of a first-order formula. For problems satisfying this characterization, chain-of-thought tokens need not provide information about the intermediate computational steps involved in multi-token computations.

In summary, our results show that additional tokens can provide computational benefits independent of token choice. The fact that intermediate tokens can act as filler tokens raises concerns about large language models engaging in unauditable, hidden computations that are increasingly detached from the observed chain-of-thought tokens.

--- https://arxiv.org/abs/2403.09629 Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking Eric Zelikman, Georges Harik, Yijia Shao, Varuna Jayasiri, Nick Haber, Noah D. Goodman 2024-03-14 2024-04-27 [("doi","10.48550/arXiv.2403.09629")] ai/nn/sampling ai/nn/transformer/gpt/inner-monologue

When writing and talking, people sometimes pause to think. Although reasoning-focused works have often framed reasoning as a method of answering questions or completing agentic tasks, reasoning is implicit in almost all written text. For example, this applies to the steps not stated between the lines of a proof or to the theory of mind underlying a conversation.

In the Self-Taught Reasoner (STaR, Zelikman et al 2022), useful thinking is learned by inferring rationales from few-shot examples in question-answering and learning from those that lead to a correct answer. This is a highly constrained setting—ideally, a language model could instead learn to infer unstated rationales in arbitrary text.

We present Quiet-STaR, a generalization of STaR in which LMs learn to generate rationales at each token to explain future text, improving their predictions. We address key challenges, including (1) the computational cost of generating continuations, (2) the fact that the LM does not initially know how to generate or use internal thoughts, and (3) the need to predict beyond individual next tokens. To resolve these, we propose a token-wise parallel sampling algorithm, using learnable tokens indicating a thought’s start and end, and an extended teacher-forcing technique.

Encouragingly, generated rationales disproportionately help model difficult-to-predict tokens and improve the LM’s ability to directly answer difficult questions. In particular, after continued pretraining of an LM on a corpus of internet text with Quiet-STaR, we find zero-shot improvements on GSM8K (5.9% → 10.9%) and CommonsenseQA (36.3% → 47.2%) and observe a perplexity improvement of difficult tokens in natural text. Crucially, these improvements require no fine-tuning on these tasks.

Quiet-STaR marks a step towards LMs that can learn to reason in a more general and scalable way.

--- https://arxiv.org/abs/2402.14848 Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models Mosh Levy, Alon Jacoby, Yoav Goldberg 2024-02-19 2024-04-27 [("doi","10.48550/arXiv.2402.14848")] ai/nn/transformer/gpt/inner-monologue

This paper explores the impact of extending input lengths on the capabilities of Large Language Models (LLMs). Despite LLMs advancements in recent times, their performance consistency across different input lengths is not well understood.

We investigate this aspect by introducing a novel QA reasoning framework, specifically designed to assess the impact of input length. We isolate the effect of input length using multiple versions of the same sample, each being extended with padding of different lengths, types, and locations.

Our findings show a notable degradation in LLMs’ reasoning performance at much shorter input lengths than their technical maximum. We show that the degradation trend appears in every version of our dataset, although at different intensities. Additionally, our study reveals that traditional perplexity metrics do not correlate with performance of LLMs’ in long input reasoning tasks.

We analyse our results and identify failure modes that can serve as useful guides for future research, potentially informing strategies to address the limitations observed in LLMs.

--- https://github.com/lodefmode/moviecart 2024-04-27 cs/hardware --- https://en.wikipedia.org/wiki/Ant_mimicry Ant mimicry 2024-04-27 biology/ant --- https://en.wikipedia.org/wiki/Lehman%27s_laws_of_software_evolution Lehman’s laws of software evolution 2024-04-27 cs design --- /doc/cs/1985-lehman-programevolution.pdf Program Evolution: Processes of Software Change M. M. Lehman, László Bélády 1985-01-01 2024-04-27 cs design --- https://news.ycombinator.com/item?id=36036635 2024-04-27 cs economics/automation --- /doc/cs/2013-herraiz.pdf The evolution of the laws of software evolution: A discussion based on a systematic literature review Israel Herraiz, Daniel Rodriguez, Gregorio Robles, Jesus M. Gonzalez-Barahona 2013-12-27 2024-04-27 [("doi","10.1145/2543581.2543595")] cs design

After more than 40 years of life, software evolution should be considered as a mature field. However, despite such a long history, many research questions still remain open, and controversial studies about the validity of the laws of software evolution are common. During the first part of these 40 years, the laws themselves evolved to adapt to changes in both the research and the software industry environments. This process of adaption to new paradigms, standards, and practices stopped about 15 years ago, when the laws were revised for the last time. However, most controversial studies have been raised during this latter period.

Based on a systematic and comprehensive literature review, in this article, we describe how and when the laws, and the software evolution field, evolved.

We also address the current state of affairs about the validity of the laws, how they are perceived by the research community, and the developments and challenges that are likely to occur in the coming years.

--- https://arxiv.org/abs/2402.14083#facebook Beyond A✱: Better Planning with Transformers via Search Dynamics Bootstrapping (Searchformer) Lucas Lehnert, Sainbayar Sukhbaatar, Paul Mcvay, Michael Rabbat, Yuandong Tian 2024-02-21 2024-04-27 [("doi","10.48550/arXiv.2402.14083")] ai/nn/transformer cs/algorithm reinforcement-learning/imitation-learning reinforcement-learning/model/alphago

[code] While Transformers have enabled tremendous progress in various application settings, such architectures still lag behind traditional symbolic planners for solving complex decision making tasks.

In this work, we demonstrate how to train Transformers to solve complex planning tasks and present Searchformer, a Transformer model that optimally solves previously unseen Sokoban puzzles 93.7% of the time, while using up to 26.8% fewer search steps than standard A✱ search. [expert iteration]

Searchformer is an encoder-decoder Transformer model trained to predict the search dynamics of A✱. This model is then fine-tuned via expert iterations to perform fewer search steps than A✱ search while still generating an optimal plan. In our training method, A✱’s search dynamics are expressed as a token sequence outlining when task states are added and removed into the search tree during symbolic planning.

In our ablation studies on maze navigation, we find that Searchformer outperforms baselines that predict the optimal plan directly with a 5-10× smaller model size and a 10× smaller training dataset.

We also demonstrate how Searchformer scales to larger and more complex decision making tasks like Sokoban with improved percentage of solved tasks and shortened search dynamics.

--- https://arxiv.org/abs/2003.03600 Reinforcement Learning for Combinatorial Optimization: A Survey Nina Mazyavkina, Sergey Sviridov, Sergei Ivanov, Evgeny Burnaev 2020-03-07 2024-04-27 [("doi","10.48550/arXiv.2003.03600")] cs/algorithm reinforcement-learning/imitation-learning reinforcement-learning/model

Many traditional algorithms for solving combinatorial optimization problems involve using hand-crafted heuristics that sequentially construct a solution. Such heuristics are designed by domain experts and may often be suboptimal due to the hard nature of the problems. Reinforcement learning (RL) proposes a good alternative to automate the search of these heuristics by training an agent in a supervised or self-supervised manner.

In this survey, we explore the recent advancements of applying RL frameworks to hard combinatorial problems. Our survey provides the necessary background for operations research and machine learning communities and showcases the works that are moving the field forward.

We juxtapose recently proposed RL methods, laying out the timeline of the improvements for each problem, as well as we make a comparison with traditional algorithms, indicating that RL models can become a promising direction for solving combinatorial problems.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9490641/ Multiomics assessment of dietary protein titration reveals altered hepatic glucose usage Michael R. MacArthur, Sarah J. Mitchell, Katia S. Chadaideh, J. Humberto Treviño-Villarreal, Jonathan Jung, Krystle C. Kalafut, Justin S. Reynolds, Charlotte G. Mann, Kaspar M. Trocha, Ming Tao, Tay-Zar Aye Cho, Anantawat Koontanatechanon, Vladimir Yeliseyev, Lynn Bry, Alban Longchamp, C. Keith Ozaki, Caroline A. Lewis, Rachel N. Carmody, James R. Mitchell 2022 2024-04-27 [("doi","10.1016/j.celrep.2022.111187")] exercise

Dietary protein restriction (PR) has rapid effects on metabolism including improved glucose and lipid homeostasis, via multiple mechanisms. Here, we investigate responses of fecal microbiome, hepatic transcriptome, and hepatic metabolome to 6 diets with protein 35.9%–59.4% of energy in mice.

PR alters fecal microbial composition, but metabolic effects are not transferable via fecal transplantation. Liver transcriptome and metabolome are statistically-significantly altered in diets with lower than 10% energy from protein. Changes upon PR correlate with calorie restriction but with a larger magnitude and specific changes in amino acid (AA) metabolism.

PR increases steady-state aspartate, serine, and glutamate and decreases glucose and gluconeogenic intermediates. 13C6 glucose and glycerol tracing reveal increased fractional enrichment in aspartate, serine, and glutamate. Changes remain intact in hepatic ATF4 knockout mice.

Together, this demonstrates an ATF4-independent shift in gluconeogenic substrate usage toward specific AAs, with compensation from glycerol to promote a protein-sparing response.

...Titration of protein produced a non-linear response in body mass with 0% and 2% groups losing body mass, 10% and 14% groups tending to gain body mass, and the 6% group remaining unchanged versus the 18% control group (Figures 1A and ​& 1C). The 0% and 2% groups also consumed statistically-significantly less food, and the 14% group consumed statistically-significantly more food, than the 18% control group on a gram-per-mouse basis (Figures 1B & S1C). Reduced food intake in the 0% and 2% groups was largely explained by a transient decrease during the first 3 days (Figures S1A & S1B), and intake between groups was not statistically-significantly different when normalized to body weight (Figure S1D). Similar non-linear changes in fat mass and lean mass were observed with the 0% and 2% groups having statistically-significantly less lean mass, fat mass, and lower body fat percent versus the 18% group (Figures 1D, ​1E, & S1E).

--- https://arxiv.org/abs/2403.14608 Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey Zeyu Han, Chao Gao, Jinyang Liu, Jeff Zhang, Sai Qian Zhang 2024-03-21 2024-04-27 [("doi","10.48550/arXiv.2403.14608")] ai/nn/transformer

Large models represent a groundbreaking advancement in multiple application fields, enabling remarkable achievements across various tasks. However, their unprecedented scale comes with computational costs. These models, often consisting of billions of parameters, require vast amounts of computational resources for execution. Especially, the expansive scale and computational demands pose considerable challenges when customizing them for particular downstream tasks, particularly over the hardware platforms constrained by computational capabilities. Parameter Efficient Fine-Tuning (PEFT) provides a practical solution by efficiently adapt the large models over the various downstream tasks.

In particular, PEFT refers to the process of adjusting the parameters of a pre-trained large models to adapt it to a specific task while minimizing the number of additional parameters introduced or computational resources required. This approach is particularly important when dealing with large language models with high parameter counts, as fine-tuning these models from scratch can be computationally expensive and resource-intensive, posing considerable challenges in the supporting system platform design.

In this survey, we present comprehensive studies of various PEFT algorithms, examining their performance and computational overhead. Moreover, we provide an overview of applications developed using different PEFT algorithms and discuss common techniques employed to mitigate computation costs for PEFT.

In addition to the algorithmic perspective, we overview various real-world system designs to investigate the implementation costs associated with different PEFT algorithms. This survey serves as an indispensable resource for researchers aiming to understand both the PEFT algorithm and its system implementation, offering detailed insights into recent advancements and practical applications.

--- https://www.lesswrong.com/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction 2024-04-28 ai/nn/transformer/attention --- https://trevorklee.substack.com/p/is-longevity-a-choice-how-about-obesity 2024-04-28 exercise genetics/heritable/rare longevity --- https://arxiv.org/abs/2307.13702 Measuring Faithfulness in Chain-of-Thought Reasoning Tamera Lanham, Anna Chen, Ansh Radhakrishnan, Benoit Steiner, Carson Denison, Danny Hernandez, Dustin Li, Esin Durmus, Evan Hubinger, Jackson Kernion, Kamilė Lukošiūtė, Karina Nguyen, Newton Cheng, Nicholas Joseph, Nicholas Schiefer, Oliver Rausch, Robin Larson, Sam McCandlish, Sandipan Kundu, Saurav Kadavath, Shannon Yang, Thomas Henighan, Timothy Maxwell, Timothy Telleen-Lawton, Tristan Hume, Zac Hatfield-Dodds, Jared Kaplan, Jan Brauner, Samuel R. Bowman, Ethan Perez 2023-07-17 2024-04-28 [("doi","10.48550/arXiv.2307.13702")] ai/nn/transformer/gpt/inner-monologue ai/scaling

Large language models (LLMs) perform better when they produce step-by-step, “Chain-of-Thought” (CoT) reasoning before answering a question, but it is unclear if the stated reasoning is a faithful explanation of the model’s actual reasoning (ie. its process for answering the question). We investigate hypotheses for how CoT reasoning may be unfaithful, by examining how the model predictions change when we intervene on the CoT (eg. by adding mistakes or paraphrasing it).

Models show large variation across tasks in how strongly they condition on the CoT when predicting their answer, sometimes relying heavily on the CoT and other times primarily ignoring it. CoT’s performance boost does not seem to come from CoT’s added test-time compute alone or from information encoded via the particular phrasing of the CoT.

As models become larger and more capable, they produce less faithful reasoning on most tasks we study.

Overall, our results suggest that CoT can be faithful if the circumstances such as the model size and task are carefully chosen.

--- https://www.udio.com/songs/7zWvmQacSMCqhPr2N521yJ 2024-04-28 ai/music ai/nn/transformer/gpt/claude --- https://arxiv.org/abs/2309.07683 Assessing the nature of large language models: A caution against anthropocentrism Ann Speed 2023-09-14 2024-04-28 [("doi","10.48550/arXiv.2309.07683")] ai/nn/transformer/gpt/3/nonfiction psychology/personality/narcissism psychology/personality/psychopathy reinforcement-learning/preference-learning/mode-collapse

Generative AI models garnered a large amount of public attention and speculation with the release of OpenAI’s chatbot, ChatGPT. At least two opinion camps exist: one excited about possibilities these models offer for fundamental changes to human tasks, and another highly concerned about power these models seem to have.

To address these concerns, we assessed several LLMs, primarily GPT-3.5, using standard, normed, and validated cognitive and personality measures. For this seedling project, we developed a battery of tests that allowed us to estimate the boundaries of some of these models’ capabilities, how stable those capabilities are over a short period, and how they compare to humans.

Our results indicate that LLMs are unlikely to have developed sentience, although their ability to respond to personality inventories is interesting.

GPT-3.5 did display large variability in both cognitive and personality measures over repeated observations, which is not expected if it had a human-like personality. Variability notwithstanding, LLMs display what in a human would be considered poor mental health, including low self-esteem, marked dissociation from reality, and in some cases narcissism and psychopathy, despite upbeat and helpful responses.

--- https://arxiv.org/abs/2310.01798 Large Language Models Cannot Self-Correct Reasoning Yet Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, Denny Zhou 2023-10-03 2024-04-28 [("doi","10.48550/arXiv.2310.01798")] ai/nn/transformer/gpt/inner-monologue

Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities across various applications. Nevertheless, concerns persist regarding the accuracy and appropriateness of their generated content. A contemporary methodology, self-correction, has been proposed as a remedy to these issues.

Building upon this premise, this paper critically examines the role and efficacy of self-correction within LLMs, shedding light on its true potential and limitations. Central to our investigation is the notion of intrinsic self-correction, whereby an LLM attempts to correct its initial responses based solely on its inherent capabilities, without the crutch of external feedback.

In the context of reasoning, our research indicates that LLMs struggle to self-correct their responses without external feedback, and at times, their performance even degrades after self-correction.

Drawing from these insights, we offer suggestions for future research and practical applications in this field.

--- https://arxiv.org/abs/2401.14112#microsoft FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design Haojun Xia, Zhen Zheng, Xiaoxia Wu, Shiyang Chen, Zhewei Yao, Stephen Youn, Arash Bakhtiari, Michael Wyatt, Donglin Zhuang, Zhongzhu Zhou, Olatunji Ruwase, Yuxiong He, Shuaiwen Leon Song 2024-01-25 2024-04-28 [("doi","10.48550/arXiv.2401.14112")] ai/nn/sparsity/low-precision

Six-bit quantization (FP6) can effectively reduce the size of large language models (LLMs) and preserve the model quality consistently across varied applications. However, existing systems do not provide Tensor Core support for FP6 quantization and struggle to achieve practical performance improvements during LLM inference.

It is challenging to support FP6 quantization on GPUs due to (1) unfriendly memory access of model weights with irregular bit-width and (2) high runtime overhead of weight de-quantization. To address these problems, we propose TC-FPx, the first full-stack GPU kernel design scheme with unified Tensor Core support of float-point weights for various quantization bit-width. We integrate TC-FPx kernel into an existing inference system, providing new end-to-end support (called FP6-LLM) for quantized LLM inference, where better trade-offs between inference cost and model quality are achieved.

Experiments show that FP6-LLM enables the inference of LLaMA-70b using only a single GPU, achieving 1.69–2.65× higher normalized inference throughput than the FP16 baseline.

The source code is publicly available at Github.

--- https://zed.dev/blog/zed-decoded-rope-sumtree 2024-04-28 cs/algorithm --- https://english.elpais.com/science-tech/2024-04-26/the-seven-lies-of-the-ai-expert-who-cited-himself-thousands-of-times-on-scientific-papers.html 2024-04-28 statistics/bias --- /doc/iq/2014-baker.pdf Eyes and IQ: A meta-analysis of the relationship between intelligence and ‘Reading the Mind in the Eyes’ Crystal A. Baker, Eric Peterson, Steven Pulos, Rena A. Kirkland 2015-05-01 2024-04-29 [("doi","10.1016/j.intell.2014.03.001")] iq psychiatry/autism

Although the Reading the Mind in the Eyes Test (RMET; Baron-Cohen et al 1997, Baron-Cohen et al 2001) has been used as a measure of mental state understanding in over 250 studies, the extent to which it correlates with intelligence is seldom considered.

We conducted a meta-analysis to investigate whether or not a relationship exists between intelligence and performance on the RMET. The analysis of 77 effect sizes with 3,583 participants revealed a small positive correlation (r = 0.24) with no difference between verbal and performance abilities.

We conclude that intelligence does play a substantial role in performance on the RMET and that verbal and performance abilities contribute to this relationship equally.

We discuss these findings in the context of the theory of mind and domain-general resources literature.

[Keywords: mental state, mentalizing, RMET, social cognition, theory of mind]

See Also:

--- https://www.medrxiv.org/content/10.1101/2024.04.25.24306313.full Multi-ancestry meta-analyses of lung cancer in the Million Veteran Program reveal novel risk loci and elucidate smoking-independent genetic risk Bryan R. Gorman, Sun-Gou Ji, Michael Francis, Anoop K. Sendamarai, Yunling Shi, Poornima Devineni, Uma Saxena, Elizabeth Partan, Andrea K. DeVito, Jinyoung Byun, Younghun Han, Xiangjun Xiao, Don D. Sin, Wim Timens, Jennifer Moser, Sumitra Muralidhar, Rachel Ramoni, Rayjean J. Hung, James D. Mckay, Yohan Bossé, Ryan Sun, Chris Amos, V. A. Million Veteran Program, Saiju Pyarajan 2024-04-26 2024-04-29 [("doi","10.1101/2024.04.25.24306313")] genetics/heritable/correlation nicotine

Lung cancer remains the leading cause of cancer mortality, despite declines in smoking rates. Previous lung cancer genome-wide association studies (GWAS) have identified numerous loci, but separating the genetic risks of lung cancer and smoking behavioral susceptibility remains challenging.

We performed multi-ancestry GWAS meta-analyses of lung cancer using the Million Veteran Program (MVP) cohort and a previous study of European-ancestry individuals, comprising 42,102 cases and 181,270 controls, followed by replication in an independent cohort of 19,404 cases and 17,378 controls. We further performed conditional meta-analyses on cigarettes per day and:

identified two novel, replicated loci, including the 19p13.11 pleiotropic cancer locus in LUSC. Overall, we report 12 novel risk loci for overall lung cancer, lung adenocarcinoma (LUAD), and squamous cell lung carcinoma (LUSC), 9 of which were externally replicated.

Finally, we performed phenome-wide association studies (PheWAS) on polygenic risk scores (PRS) for lung cancer, with and without conditioning on smoking.

The unconditioned lung cancer PRS was associated with smoking status in controls, illustrating reduced predictive utility in non-smokers. Additionally, our PRS demonstrates smoking-independent pleiotropy of lung cancer risk across neoplasms and metabolic traits.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10685543/ Positive single-center randomized trials and subsequent multicenter randomized trials in critically ill patients: a systematic review Yuki Kotani, Stefano Turi, Alessandro Ortalda, Martina Baiardo Redaelli, Cristiano Marchetti, Giovanni Landoni, Rinaldo Bellomo 2023 2024-04-29 [("doi","10.1186/s13054-023-04755-5")] statistics/bias

Background: It is unclear how often survival benefits observed in single-center randomized controlled trials (sRCTs) involving critically ill patients are confirmed by subsequent multicenter randomized controlled trials (mRCTs). We aimed to perform a systemic literature review of sRCTs with a statistically-significant mortality reduction and to evaluate whether subsequent mRCTs confirmed such reduction.

Methods: We searched PubMed for sRCTs published in the New England Journal of Medicine, JAMA, or Lancet, from inception until December 31, 2016. We selected studies reporting a statistically-significant mortality decrease using any intervention (drug, technique, or strategy) in adult critically ill patients. We then searched for subsequent mRCTs addressing the same research question tested by the sRCT. We compared the concordance of results between sRCTs and mRCTs when any mRCT was available. We registered this systematic review in the PROSPERO International Prospective Register of Systematic Reviews (CRD42023455362).

Results: We identified 19 sRCTs reporting a statistically-significant mortality reduction in adult critically ill patients. For 16 sRCTs, we identified at least one subsequent mRCT (24 trials in total), while the interventions from 3 sRCTs have not yet been addressed in a subsequent mRCT.

Only 1⁄16 16 sRCTs (6%) was followed by a mRCT replicating a statistically-significant mortality reduction; 14 (88%) were followed by mRCTs with no mortality difference.

The positive finding of one sRCT (6%) on intensive glycemic control was contradicted by a subsequent mRCT showing a statistically-significant mortality increase. Of the 14 sRCTs referenced at least once in international guidelines, 6 (43%) have since been either removed or suggested against in the most recent versions of relevant guidelines.

Conclusion: Mortality reduction shown by sRCTs is typically not replicated by mRCTs. The findings of sRCTs should be considered hypothesis-generating and should not contribute to guidelines.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081342/ Comprehensive genomic analysis of dietary habits in UK Biobank identifies hundreds of genetic associations Joanne B. Cole, Jose C. Florez, Joel N. Hirschhorn 2020 2024-04-29 [("doi","10.1038/s41467-020-15193-0")] exercise genetics/heritable/correlation/mendelian-randomization tea

Unhealthful dietary habits are leading risk factors for life-altering diseases and mortality. Large-scale biobanks now enable genetic analysis of traits with modest heritability, such as diet.

We perform a genome-wide association on 85 single food intake and 85 principal component-derived dietary patterns from food frequency questionnaires in UK Biobank.

We identify 814 associated loci, including olfactory receptor associations with fruit and tea intake; 136 associations are only identified using dietary patterns.

Mendelian Randomization suggests our top healthful dietary pattern driven by wholemeal vs. white bread consumption is causally influenced by factors correlated with education but is not strongly casual for coronary artery disease or type 2 diabetes.

Overall, we demonstrate the value in complementary phenotyping approaches to complex dietary datasets, and the utility of genomic analysis to understand the relationships between diet and human health.

--- https://www.sensetime.com/en/news-detail/51167731?categoryId=1072 2024-04-29 ai/scaling/mixture-of-experts --- https://arxiv.org/abs/2311.00537 Machine Learning Without a Processor: Emergent Learning in a Nonlinear Electronic Metamaterial Sam Dillavou, Benjamin D. Beyer, Menachem Stern, Andrea J. Liu, Marc Z. Miskin, Douglas J. Durian 2023-11-01 2024-04-29 [("doi","10.48550/arXiv.2311.00537")] cs/cellular-automaton cs/hardware

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic learning metamaterials offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored.

Here we introduce a nonlinear learning metamaterial—an analog electronic network made of self-adjusting nonlinear resistive elements based on transistors. We demonstrate that the system learns tasks unachievable in linear systems, including XOR and nonlinear regression, without a computer.

We find our nonlinear learning metamaterial reduces modes of training error in order (mean, slope, curvature), similar to spectral bias in artificial neural networks. The circuitry is robust to damage, retrainable in seconds, and performs learned tasks in microseconds while dissipating only picojoules of energy across each transistor.

This suggests enormous potential for fast, low-power computing in edge systems like sensors, robotic controllers, and medical devices, as well as manufacturability at scale for performing and studying emergent learning.

--- https://en.wikipedia.org/wiki/The_Bridge_(2006_documentary_film) The Bridge (2006 documentary film) 2024-04-29 psychiatry --- https://arxiv.org/abs/2306.03286 Survival Instinct in Offline Reinforcement Learning Anqi Li, Dipendra Misra, Andrey Kolobov, Ching-An Cheng 2023-06-05 2024-04-29 [("doi","10.48550/arXiv.2306.03286")] reinforcement-learning/imitation-learning reinforcement-learning/offline reinforcement-learning/safe

[convergent instrumental drive: survival] We present a novel observation about the behavior of offline reinforcement learning (RL) algorithms: on many benchmark datasets, offline RL can produce well-performing and safe policies even when trained with “wrong” reward labels, such as those that are zero everywhere or are negatives of the true rewards. This phenomenon cannot be easily explained by offline RL’s return maximization objective. Moreover, it gives offline RL a degree of robustness that is uncharacteristic of its online RL counterparts, which are known to be sensitive to reward design.

We demonstrate that this surprising robustness property is attributable to an interplay between the notion of pessimism in offline RL algorithms and certain implicit biases in common data collection practices. As we prove in this work, pessimism endows the agent with a “survival instinct”, ie. an incentive to stay within the data support in the long term, while the limited and biased data coverage further constrains the set of survival policies.

Formally, given a reward class—which may not even contain the true reward—we identify conditions on the training data distribution that enable offline RL to learn a near-optimal and safe policy from any reward within the class.

We argue that the survival instinct should be taken into account when interpreting results from existing offline RL benchmarks and when creating future ones. Our empirical and theoretical results suggest a new paradigm for RL, whereby an agent is nudged to learn a desirable behavior with imperfect reward but purposely biased data coverage.

--- https://blogs.microsoft.com/blog/2023/11/21/a-statement-from-microsoft-chairman-and-ceo-satya-nadella/ 2024-04-29 reinforcement-learning/openai --- https://www.newyorker.com/magazine/2003/10/13/jumpers Jumpers: The fatal grandeur of the Golden Gate Bridge Tad Friend 2003-10-13 2024-01-01 psychiatry --- https://github.com/jgm/pandoc/issues/5469 Pandoc bug #5,469: HTML footnotes: use VARIATION SELECTOR-15 Unicode to block iOS ‘emojification’ of back-link arrows Gwern 2019-04-28 2024-04-27 cs/css --- https://arxiv.org/abs/2002.02515 Quasi-Equivalence of Width and Depth of Neural Networks Feng-Lei Fan, Rongjie Lai, Ge Wang 2020-02-06 2024-04-29 [("doi","10.48550/arXiv.2002.02515")] ai/nn/fully-connected ai/scaling

While classic studies proved that wide networks allow universal approximation, recent research and successes of deep learning demonstrate the power of deep networks.

Based on a symmetric consideration, we investigate if the design of artificial neural networks should have a directional preference, and what the mechanism of interaction is between the width and depth of a network. Inspired by the De Morgan law, we address this fundamental question by establishing a quasi-equivalence between the width and depth of ReLU networks in two aspects.

First, we formulate two transforms for mapping an arbitrary ReLU network to a wide network and a deep network respectively for either regression or classification so that the essentially same capability of the original network can be implemented. Then, we replace the mainstream artificial neuron type with a quadratic counterpart, and use the factorization and continued fraction representations of the same polynomial function to construct a wide network and a deep network, respectively.

Based on our findings, a deep network has a wide equivalent, and vice versa, subject to an arbitrarily small error.

--- https://civitai.com/articles/5069 2024-04-29 ai/anime ai/nn/diffusion --- https://www.youtube.com/watch?v=AePEcHIIk9s 2024-04-29 reinforcement-learning/robot --- https://en.wikipedia.org/wiki/Expert_wizard_amendment Expert wizard amendment 2024-04-29 math/humor --- https://www.atlasobscura.com/articles/in-the-1970s-the-us-navy-tried-to-talk-like-whales 2024-04-29 cs/cryptography/steganography --- https://github.com/Blosc/c-blosc 2024-04-29 cs/algorithm/information/compression --- http://tunes.org/~iepos/befreak.html 2024-04-29 cs/computable --- https://x.com/joecappadona/status/1785048197024399641 joecappadona 2024-04-29 ai/nn/transformer/gpt/4/poetry --- https://arxiv.org/abs/2401.01967 A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity Andrew Lee, Xiaoyan Bai, Itamar Pres, Martin Wattenberg, Jonathan K. Kummerfeld, Rada Mihalcea 2024-01-03 2024-04-30 [("doi","10.48550/arXiv.2401.01967")] ai/nn/transformer/gpt/2 reinforcement-learning/preference-learning

While alignment algorithms are now commonly used to tune pre-trained language models towards a user’s preferences, we lack explanations for the underlying mechanisms in which models become “aligned”, thus making it difficult to explain phenomena like jailbreaks.

In this work we study a popular algorithm, direct preference optimization (DPO), and the mechanisms by which it reduces toxicity. Namely, we first study how toxicity is represented and elicited in a pre-trained language model, GPT-2-medium. We then apply DPO with a carefully crafted pairwise dataset to reduce toxicity.

We examine how the resulting model averts toxic outputs, and find that capabilities learned from pre-training are not removed, but rather bypassed.

We use this insight to demonstrate a simple method to un-align the model, reverting it back to its toxic behavior.

--- https://www.youtube.com/watch?v=u1R-jxDPC70 2024-04-30 ai/video/generation --- https://www.geoffreylitt.com/2023/03/25/llm-end-user-programming 2024-04-30 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex design --- https://lareviewofbooks.org/article/the-pets-war-on-hilda-keans-the-great-cat-and-dog-massacre/ 2024-04-30 cat/psychology philosophy/ethics --- https://www.newyorker.com/science/annals-of-medicine/how-ecmo-is-redefining-death 2024-04-30 biology economics philosophy/ethics --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079589/ Variation in Hospitalization Costs, Charges, and Lengths of Hospital Stay for Coronavirus Disease 2019 Patients Treated With Venovenous Extracorporeal Membrane Oxygenation in the United States: A Cohort Study Michael Mazzeffi, Jonathan Curley, Paul Gallo, D. Keegan Stombaugh, Joshua Roach, Nadia Lunardi, Kenan Yount, Robert Thiele, Laurent Glance, Bhiken Naik 2023 2024-04-30 [("doi","10.1053/j.jvca.2023.04.001")] biology economics

Objectives: The aim was to characterize hospitalization costs, charges, and lengths of hospital stay for COVID-19 patients treated with venovenous (VV) extracorporeal membrane oxygenation (ECMO) in the United States during 2020. Secondarily, differences in hospitalization costs, charges, and lengths of hospital stay were explored based on hospital-level factors.

Design: Retrospective cohort study.

Setting: Multiple hospitals in the United States.

Participants: Adult patients with COVID-19 who were on VV ECMO in 2020 and had data in the national inpatient sample.

Interventions: None.

Measurements and Main Results: Demographics and baseline comorbidities were recorded for patients. Primary study outcomes were hospitalization costs, charges, and lengths of hospital stay. Study outcomes were compared after stratification by hospital region, bed size, and for-profit status. The median hospitalization cost for the 3,315-patient weighted cohort was $200,300 ($99,623–$338,062). Median hospitalization charges were $870,513 ($438,228–$1,553,157), and the median length of hospital stay was 30 days (17–46).

Survival to discharge was 54.4% for all patients in the cohort. Median hospitalization cost differed by region (p = 0.01), bed size (p < 0.001), and for-profit status (p = 0.02). Median hospitalization charges also differed by region (p = 0.04), bed size (p = 0.002), and for-profit status (p < 0.001). Length of hospital stay differed by region (p = 0.03) and bed size (p < 0.001), but not for-profit status (p = 0.40). Hospitalization costs were the lowest, and charges were highest in private-for-profit hospitals. Large hospitals also had higher costs, charges, and hospital stay lengths than small hospitals.

Conclusions: In this retrospective cohort study, hospitalization costs and charges for patients with COVID-19 on VV ECMO were found to be substantial but similar to what has been reported previously for patients without COVID-19 on VV ECMO. Substantial variation was observed in costs, charges, and lengths of hospital stay based on hospital-level factors.

--- https://en.wikipedia.org/wiki/Pykrete Pykrete 2024-04-30 technology --- https://www.lesswrong.com/posts/YmkjnWtZGLbHRbzrP/transcoders-enable-fine-grained-interpretable-circuit 2024-04-30 ai/nn/fully-connected --- http://at.yorku.ca/t/o/p/d/03.htm 2024-05-01 math --- https://www.newyorker.com/books/page-turner/the-mystery-of-s-the-man-with-an-impossible-memory 2024-01-01 psychiatry/alcoholism psychology/spaced-repetition psychology/vision --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4852178/ Early Memories of Individuals on the Autism Spectrum Assessed Using Online Self-Reports Vera Zamoscik, Daniela Mier, Stephanie N. L. Schmidt, Peter Kirsch 2016 2024-05-01 [("doi","10.3389/fpsyt.2016.00079")] psychiatry/autism psychology/neuroscience/memory

“When I was one and a half years old, I was on a ferry lying on red seats”—while several autobiographical accounts by people with autism reveal vivid memories of early childhood, the vast amount of experimental investigations found deficits in personal autobiographic memory in autism.

To assess this contradiction empirically, we implemented an online questionnaire on early childhood events to compare people on the autism spectrum (AS) and non-autistic people with respect to their earliest autobiographical episodic memories and the earliest semantic know event as told by another person.

Results indicate that people on the AS do not differ from non-autistic people in the age of their earliest know events but remember events from an earlier age in childhood and with more sensory details, contradicting the assumption of an overall deficit in personal episodic memory in autism. Furthermore, our results emphasize the supporting influence of language for memory formation and give evidence for an important role of sensory features in memories of people on the AS.

--- https://en.wikipedia.org/wiki/Childhood_amnesia Childhood amnesia 2024-01-01 psychology/neuroscience/memory --- https://arxiv.org/abs/2310.20704 Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders (SSAT) Srijan Das, Tanmay Jain, Dominick Reilly, Pranav Balaji, Soumyajit Karmakar, Shyam Marjit, Xiang Li, Abhijit Das, Michael S. Ryoo 2023-10-31 2024-05-01 [("doi","10.48550/arXiv.2310.20704")] ai/nn/vae/mae

Vision Transformers (ViTs) have become ubiquitous in computer vision. Despite their success, ViTs lack inductive biases, which can make it difficult to train them with limited data. To address this challenge, prior studies suggest training ViTs with self-supervised learning (SSL) and fine-tuning sequentially.

However, we observe that jointly optimizing ViTs for the primary task and a Self-Supervised Auxiliary Task (SSAT) is surprisingly beneficial when the amount of training data is limited. We explore the appropriate SSL tasks that can be optimized alongside the primary task, the training schemes for these tasks, and the data scale at which they can be most effective.

Our findings reveal that SSAT is a powerful technique that enables ViTs to leverage the unique characteristics of both the self-supervised and primary tasks, achieving better performance than typical ViTs pre-training with SSL and fine-tuning sequentially. Our experiments, conducted on 10 datasets, demonstrate that SSAT improves ViT performance while reducing carbon footprint. We also confirm the effectiveness of SSAT in the video domain for deepfake detection, showcasing its generalizability.

Our code is available at Github.

--- https://adversarial-ml-tutorial.org/adversarial_training/ 2024-05-01 ai/nn/adversarial --- https://arxiv.org/abs/1803.00885 Essentially No Barriers in Neural Network Energy Landscape Felix Draxler, Kambis Veschgini, Manfred Salmhofer, Fred A. Hamprecht 2018-03-02 2024-05-01 [("doi","10.48550/arXiv.1803.00885")] ai/nn/cnn

Training neural networks involves finding minima of a high-dimensional non-convex loss function. Knowledge of the structure of this energy landscape is sparse.

Relaxing from linear interpolations, we construct continuous paths between minima of recent neural network architectures on CIFAR-10 and CIFAR-100. Surprisingly, the paths are essentially flat in both the training and test landscapes.

This implies that neural networks have enough capacity for structural changes, or that these changes are small between minima. Also, each minimum has at least one vanishing Hessian eigenvalue in addition to those resulting from trivial invariance.

--- https://arxiv.org/abs/1912.05671 Linear Mode Connectivity and the Lottery Ticket Hypothesis Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin 2019-12-11 2024-05-01 [("doi","10.48550/arXiv.1912.05671")] ai/nn/cnn ai/nn/sparsity

We study whether a neural network optimizes to the same, linearly connected minimum under different samples of SGD noise (eg. random data order and augmentation). We find that standard vision models become stable to SGD noise in this way early in training. From then on, the outcome of optimization is determined to a linearly connected region.

We use this technique to study iterative magnitude pruning (IMP), the procedure used by work on the lottery ticket hypothesis to identify subnetworks that could have trained in isolation to full accuracy.

We find that these subnetworks only reach full accuracy when they are stable to SGD noise, which either occurs at initialization for small-scale settings ( MNIST) or early in training for large-scale settings (ResNet-50 and Inception-v3 on ImageNet).

--- https://arxiv.org/abs/1611.01540 Topology and Geometry of Half-Rectified Network Optimization C. Daniel Freeman, Joan Bruna 2016-11-04 2024-05-01 [("doi","10.48550/arXiv.1611.01540")] ai/nn/fully-connected

The loss surface of deep neural networks has recently attracted interest in the optimization and machine learning communities as a prime example of high-dimensional non-convex problem. Some insights were recently gained using spin glass models and mean-field approximations, but at the expense of strongly simplifying the nonlinear nature of the model.

In this work, we do not make any such assumption and study conditions on the data distribution and model architecture that prevent the existence of bad local minima. Our theoretical work quantifies and formalizes two important folklore facts: (1) the landscape of deep linear networks has a radically different topology from that of deep half-rectified ones, and (2) that the energy landscape in the non-linear case is fundamentally controlled by the interplay between the smoothness of the data distribution and model over-parameterization. Our main theoretical contribution is to prove that half-rectified single layer networks are asymptotically connected, and we provide explicit bounds that reveal the aforementioned interplay.

The conditioning of gradient descent is the next challenge we address. We study this question through the geometry of the level sets, and we introduce an algorithm to efficiently estimate the regularity of such sets on large-scale networks.

Our empirical results show that these level sets remain connected throughout all the learning phase, suggesting a near convex behavior, but they become exponentially more curvy as the energy level decays, in accordance to what is observed in practice with very low curvature attractors.

--- https://arxiv.org/abs/1802.10026 Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry Vetrov, Andrew Gordon Wilson 2018-02-27 2024-05-01 [("doi","10.48550/arXiv.1802.10026")] ai/nn/cnn

The loss functions of deep neural networks are complex and their geometric properties are not well understood.

We show that the optima of these complex loss functions are in fact connected by simple curves over which training and test accuracy are nearly constant. We introduce a training procedure to discover these high-accuracy pathways between modes.

Inspired by this new geometric insight, we also propose a new ensembling method entitled Fast Geometric Ensembling (FGE). Using FGE we can train high-performing ensembles in the time required to train a single model.

We achieve improved performance compared to the recent state-of-the-art Snapshot Ensembles, on CIFAR-10, CIFAR-100, and ImageNet.

--- https://arxiv.org/abs/2404.06498 Simultaneous linear connectivity of neural networks modulo permutation Ekansh Sharma, Devin Kwok, Tom Denton, Daniel M. Roy, David Rolnick, Gintare Karolina Dziugaite 2024-04-09 2024-05-01 [("doi","10.48550/arXiv.2404.06498")] ai/nn

Neural networks typically exhibit permutation symmetries which contribute to the non-convexity of the networks’ loss landscapes, since linearly interpolating between two permuted versions of a trained network tends to encounter a high loss barrier. Recent work has argued that permutation symmetries are the only sources of non-convexity, meaning there are essentially no such barriers between trained networks if they are permuted appropriately.

In this work, we refine these arguments into 3 distinct claims of increasing strength. We show that existing evidence only supports “weak linear connectivity”-that for each pair of networks belonging to a set of SGD solutions, there exist (multiple) permutations that linearly connect it with the other networks. In contrast, the claim “strong linear connectivity”-that for each network, there exists one permutation that simultaneously connects it with the other networks-is both intuitively and practically more desirable. This stronger claim would imply that the loss landscape is convex after accounting for permutation, and enable linear interpolation between 3 or more independently trained models without increased loss.

In this work, we introduce an intermediate claim-that for certain sequences of networks, there exists one permutation that simultaneously aligns matching pairs of networks from these sequences. Specifically, we discover that a single permutation aligns sequences of iteratively trained as well as iteratively pruned networks, meaning that two networks exhibit low loss barriers at each step of their optimization and sparsification trajectories respectively.

Finally, we provide the first evidence that strong linear connectivity may be possible under certain conditions, by showing that barriers decrease with increasing network width when interpolating among 3 networks.

--- https://arxiv.org/abs/2307.08286 Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature Connectivity Zhanpeng Zhou, Yongyi Yang, Xiaojiang Yang, Junchi Yan, Wei Hu 2023-07-17 2024-05-01 [("doi","10.48550/arXiv.2307.08286")] ai/nn

Recent work has revealed many intriguing empirical phenomena in neural network training, despite the poorly understood and highly complex loss landscapes and training dynamics. One of these phenomena, Linear Mode Connectivity (LMC), has gained considerable attention due to the intriguing observation that different solutions can be connected by a linear path in the parameter space while maintaining near-constant training and test losses.

In this work, we introduce a stronger notion of linear connectivity, Layerwise Linear Feature Connectivity (LLFC), which says that the feature maps of every layer in different trained networks are also linearly connected. We provide comprehensive empirical evidence for LLFC across a wide range of settings, demonstrating that whenever two trained networks satisfy LMC (via either spawning or permutation methods), they also satisfy LLFC in nearly all the layers.

Furthermore, we delve deeper into the underlying factors contributing to LLFC, which reveal new insights into the spawning and permutation approaches. The study of LLFC transcends and advances our understanding of LMC by adopting a feature-learning perspective.

--- https://arxiv.org/abs/2310.19103 Proving Linear Mode Connectivity of Neural Networks via Optimal Transport Damien Ferbach, Baptiste Goujaud, Gauthier Gidel, Aymeric Dieuleveut 2023-10-29 2024-05-01 [("doi","10.48550/arXiv.2310.19103")] ai/nn

The energy landscape of high-dimensional non-convex optimization problems is crucial to understanding the effectiveness of modern deep neural network architectures. Recent works have experimentally shown that two different solutions found after two runs of a stochastic training are often connected by very simple continuous paths (eg. linear) modulo a permutation of the weights.

In this paper, we provide a framework theoretically explaining this empirical observation. Based on convergence rates in Wasserstein distance of empirical measures, we show that, with high probability, two wide enough two-layer neural networks trained with stochastic gradient descent are linearly connected. Additionally, we express upper and lower bounds on the width of each layer of two deep neural networks with independent neuron weights to be linearly connected.

Finally, we empirically demonstrate the validity of our approach by showing how the dimension of the support of the weight distribution of neurons, which dictates Wasserstein convergence rates, is correlated with linear mode connectivity.

--- https://x.com/lepikhin/status/1278174444528132098 We ran MoE (2048E,60L) with bfloat16 activations with total of 1 trillion model weights. Although trainable with manual diagnostics, with deep 1 trillion model we encountered several trainability issues with numerical stability. Will follow up. 2024-01-01 ai/scaling/hardware ai/scaling/mixture-of-experts --- https://en.wikipedia.org/wiki/Theory_of_constraints Theory of constraints 2024-05-01 cs/end-to-end-principle --- https://en.wikipedia.org/wiki/Savantism Savantism 2024-05-01 iq/high psychiatry/autism --- https://windowsontheory.org/2019/12/05/deep-double-descent/ 2024-05-01 ai/scaling --- https://en.wikipedia.org/wiki/Super-recognizer Super-recognizer 2024-05-01 psychology/vision --- https://en.wikipedia.org/wiki/Solomon_Shereshevsky Solomon Shereshevsky 2024-05-01 psychology/neuroscience/memory/savant psychology/vision --- https://en.wikipedia.org/wiki/John_von_Neumann#Preferred_problem-solving_techniques John von Neumann § Preferred problem-solving techniques 2024-05-01 math --- https://arxiv.org/abs/1712.09913 Visualizing the Loss Landscape of Neural Nets Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein 2017-12-28 2024-05-01 [("doi","10.48550/arXiv.1712.09913")] ai/nn/cnn

Neural network training relies on our ability to find “good” minimizers of highly non-convex loss functions. It is well-known that certain network architecture designs (eg. skip connections) produce loss functions that train easier, and well-chosen training parameters (batch size, learning rate, optimizer) produce minimizers that generalize better. However, the reasons for these differences, and their effects on the underlying loss landscape, are not well understood.

In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. First, we introduce a simple “filter normalization” method that helps us visualize loss function curvature and make meaningful side-by-side comparisons between loss functions.

Then, using a variety of visualizations, we explore how network architecture affects the loss landscape, and how training parameters affect the shape of minimizers.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3921176/ Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration Giulio Tononi, Chiara Cirelli 2014 2024-05-01 [("doi","10.1016/j.neuron.2013.12.025")] psychology/neuroscience zeo

Sleep is universal, tightly regulated, and its loss impairs cognition. But why does the brain need to disconnect from the environment for hours every day? The synaptic homeostasis hypothesis (SHY) proposes that sleep is the price the brain pays for plasticity.

During a waking episode, learning statistical regularities about the current environment requires strengthening connections throughout the brain. This increases cellular needs for energy and supplies, decreases signal-to-noise ratios, and saturates learning.

During sleep, spontaneous activity renormalizes net synaptic strength and restores cellular homeostasis. Activity-dependent down-selection of synapses can also explain the benefits of sleep on memory acquisition, consolidation, and integration. This happens through the offline, comprehensive sampling of statistical regularities incorporated in neuronal circuits over a lifetime.

This Perspective considers the rationale and evidence for SHY and points to open issues related to sleep and plasticity.

--- https://aftermath.site/the-person-saving-the-media-you-love-is-you-vhs-decode-domesday-duplicator-ld-decode-laserdisc 2024-05-01 cs/linkrot/archiving --- https://arxiv.org/abs/2404.17733 Building a Large Japanese Web Corpus for Large Language Models Naoaki Okazaki, Kakeru Hattori, Hirai Shota, Hiroki Iida, Masanari Ohi, Kazuki Fujii, Taishi Nakamura, Mengsay Loem, Rio Yokota, Sakae Mizuki 2024-04-27 2024-05-01 [("doi","10.48550/arXiv.2404.17733")] ai/dataset

Open Japanese large language models (LLMs) have been trained on the Japanese portions of corpora such as CC-100, mC4, and OSCAR. However, these corpora were not created for the quality of Japanese texts. This study builds a large Japanese web corpus by extracting and refining text from the Common Crawl archive (21 snapshots of ~63.4 billion pages crawled 2020–2023).

This corpus consists of ~312.1 billion characters (~173 million pages), which is the largest of all available training corpora for Japanese LLMs, surpassing CC-100 (~25.8 billion characters), mC4 (~239.7 billion characters) and OSCAR 23.10 (~74 billion characters).

To confirm the quality of the corpus, we performed continual pre-training on Llama 2 7B, 13B, 70B, Mistral-7B v0.1, and Mixtral 8×7B Instruct as base LLMs and gained consistent (6.6-8.1 points) improvements on Japanese benchmark datasets. We also demonstrate that the improvement on Llama 2 13B brought from the presented corpus was the largest among those from other existing corpora.

--- https://arxiv.org/abs/1810.11750 Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation Liwei Wang, Lunjia Hu, Jiayuan Gu, Yue Wu, Zhiqiang Hu, Kun He, John Hopcroft 2018-10-28 2024-05-01 [("doi","10.48550/arXiv.1810.11750")] ai/nn/cnn

It is widely believed that learning good representations is one of the main reasons for the success of deep neural networks. Although highly intuitive, there is a lack of theory and systematic approach quantitatively characterizing what representations do deep neural networks learn. In this work, we move a tiny step towards a theory and better understanding of the representations. Specifically, we study a simpler problem: How similar are the representations learned by two networks with identical architecture but trained from different initializations.

We develop a rigorous theory based on the neuron activation subspace match model. The theory gives a complete characterization of the structure of neuron activation subspace matches, where the core concepts are maximum match and simple match which describe the overall and the finest similarity between sets of neurons in two networks respectively. We also propose efficient algorithms to find the maximum match and simple matches.

Finally, we conduct extensive experiments using our algorithms. Experimental results suggest that, surprisingly, representations learned by the same convolutional layers of networks trained from different initializations are not as similar as prevalently expected, at least in terms of subspace match.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5003643/ Infantile amnesia reflects a developmental critical period for hippocampal learning Alessio Travaglia, Reto Bisaz, Eric S. Sweet, Robert D. Blitzer, Cristina M. Alberini 2016 2024-05-01 [("doi","10.1038/nn.4348")] psychology/neuroscience

Episodic memories formed during the first postnatal period are rapidly forgotten, a phenomenon known as ‘infantile amnesia’. In spite of this memory loss, early experiences influence adult behavior, raising the question of which mechanisms underlie infantile memories and amnesia. Here we show that in rats an experience learned during the infantile amnesia period is stored as a latent memory trace for a long time; indeed, a later reminder reinstates a robust, context-specific and long-lasting memory.

The formation and storage of this latent memory requires the hippocampus, follows a sharp temporal boundary and occurs through mechanisms typical of developmental critical periods, including the expression switch of the NMDA receptor subunits from 2B to 2A, which is dependent on brain-derived neurotrophic factor (BDNF) and metabotropic glutamate receptor 5 (mGluR5). Activating BDNF or mGluR5 after training rescues the infantile amnesia.

Thus, early episodic memories are not lost but remain stored long term. These data suggest that the hippocampus undergoes a developmental critical period to become functionally competent.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631722/ Immune activation state modulates infant engram expression across development Sarah D. Power, Erika Stewart, Louisa G. Zielke, Eric P. Byrne, Aaron Douglas, Clara Ortega-de San Luis, Lydia Lynch, Tomás J. Ryan 2023 2024-05-01 [("doi","10.1126/sciadv.adg9921")] psychology/neuroscience/memory

Infantile amnesia is possibly the most ubiquitous form of memory loss in mammals.

We investigated how memories are stored in the brain throughout development by integrating engram labeling technology with mouse models of infantile amnesia.

Here, we found a phenomenon in which male offspring in maternal immune activation models of autism spectrum disorder do not experience infantile amnesia. Maternal immune activation altered engram ensemble size and dendritic spine plasticity.

We rescued the same apparently forgotten infantile memories in neurotypical mice by optogenetically reactivating dentate gyrus engram cells labeled during complex experiences in infancy. Furthermore, we permanently reinstated lost infantile memories by artificially updating the memory engram, demonstrating that infantile amnesia is a reversible process.

Our findings suggest not only that infantile amnesia is due to a reversible retrieval deficit in engram expression but also that immune activation during development modulates innate, and reversible, forgetting switches that determine whether infantile amnesia will occur.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6644865/ It’s All in the Details: Relations Between Young Children’s Developing Pattern Separation Abilities and Hippocampal Subfield Volumes Kelsey L. Canada, Chi T. Ngo, Nora S. Newcombe, Fengji Geng, Tracy Riggins 2019 2024-05-01 [("doi","10.1093/cercor/bhy211")] psychology/neuroscience/memory

The ability to keep similar experiences separate in memory is critical for forming unique and lasting memories, as many events share overlapping features (eg. birthday parties, holidays). Research on memory in young children suggests their memories often lack high-resolution details, ie. show impoverished pattern separation (PS). Recently developed assessments of PS suitable for children allow us to relate the formation of distinct, detailed memories for the development of the hippocampus, a neural structure critical for this ability in adults. The hippocampus displays a protracted developmental profile and underlies the ability to form detailed memories.

This study examined age-related differences in hippocampal subfield volumes in 4- to 8-year-old children and relations with performance on a mnemonic similarity task (MST) designed to index memory specificity.

Results revealed age-moderated associations between MST performance and cornu ammonis 2-4/dentate gyrus subfields. Specifically, age-related differences in the ability to form detailed memories tracked with normative patterns of volume increases followed by reductions over this age range. That is, greater volume correlated with better performance in younger children, whereas smaller volume correlated with better performance in older children.

These findings support the hypothesis that developmental differences in hippocampal circuitry contribute to age-related improvements in detailed memory formation during this period.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6994621/ Early life experiences selectively mature learning and memory abilities Benjamin Bessières, Alessio Travaglia, Todd M. Mowery, Xinying Zhang, Cristina M. Alberini 2020 2024-05-01 [("doi","10.1038/s41467-020-14461-3")] psychology/neuroscience/memory

The mechanisms underlying the maturation of learning and memory abilities are poorly understood. Here we show that episodic learning produces unique biological changes in the hippocampus of infant rats and mice compared to juveniles and adults. These changes include persistent neuronal activation, BDNF-dependent increase in the excitatory synapse markers synaptophysin and PSD-95, and maturation of AMPA receptor synaptic responses.

Inhibition of PSD-95 induction following learning impairs both AMPA receptor response maturation and infantile memory, indicating that the synapse formation/maturation is necessary for creating infantile memories. Conversely, capturing the learning-induced changes by presenting a subsequent learning experience or by chemogenetic activation of the neural ensembles tagged by learning matures memory functional competence.

This memory competence is selective for the type of experience encountered, as it transfers within similar hippocampus-dependent learning domains but not to other hippocampus-dependent types of learning. Thus, experiences in early life produce selective maturation of memory abilities.

--- https://www.tunera.xyz/fonts/teranoptia/ 2024-05-02 design/typography --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257688/ The Australian paradox: a substantial decline in sugars intake over the same timeframe that overweight and obesity have increased Alan W. Barclay, Jennie Brand-Miller 2011 2024-05-02 [("doi","10.3390/nu3040491")] exercise

Ecological research from the USA has demonstrated a positive relationship between sugars consumption and prevalence of obesity; however, the relationship in other nations is not well described. The aim of this study was to analyze the trends in obesity and sugar consumption in Australia over the past 30 years and to compare and contrast obesity trends and sugar consumption patterns in Australia with the UK and USA.

Data on consumption of sugar in Australia, the UK, and USA were obtained from the Food and Agriculture Organization for the years 1980–2003. The prevalence of obesity has increased 3× in Australians since 1980. In Australia, the UK, and USA, per capita consumption of refined sucrose decreased by 23%, 10%, and 20% respectively 1980 → 2003.

When all sources of nutritive sweeteners, including high fructose corn syrups, were considered, per capita consumption decreased in Australia (-16%) and the UK (-5%), but increased in the USA (+23%). In Australia, there was a reduction in sales of nutritively sweetened beverages by 64 million liters 2002 → 2006 and a reduction in the percentage of children consuming sugar-sweetened beverages 1995 → 2007.

The findings confirm an “Australian Paradox”—a substantial decline in refined sugars intake over the same timeframe that obesity has increased. The implication is that efforts to reduce sugar intake may reduce consumption but may not reduce the prevalence of obesity.

--- https://www.theguardian.com/technology/2024/apr/28/bbc-presenters-likeness-used-in-advert-after-firm-tricked-by-ai-generated-voice 2024-05-02 ai/music crime --- https://arxiv.org/abs/2405.00233 SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound Haohe Liu, Xuenan Xu, Yi Yuan, Mengyue Wu, Wenwu Wang, Mark D. Plumbley 2024-04-30 2024-05-02 [("doi","10.48550/arXiv.2405.00233")] ai/music ai/nn/vae/mae cs/algorithm/information/compression

Large language models (LLMs) have advanced audio processing through audio codecs that convert audio into discrete tokens, enabling the application of language modeling techniques to audio data. However, traditional codecs often operate at high bitrates or within narrow domains such as speech and lack the semantic clues required for efficient language modeling.

Addressing these challenges, we introduce SemantiCodec, a novel codec designed to compress audio into fewer than a hundred tokens per second across diverse audio types, including speech, general audio, and music, without compromising quality. SemantiCodec features a dual-encoder architecture: a semantic encoder using a self-supervised AudioMAE, discretized using k-means clustering on extensive audio data, and an acoustic encoder to capture the remaining details. The semantic and acoustic encoder outputs are used to reconstruct audio via a diffusion-model-based decoder. SemantiCodec is presented in 3 variants with token rates of 25, 50, and 100 per second, supporting a range of ultra-low bit rates between 0.31 kbps and 1.43 kbps.

Experimental results demonstrate that SemantiCodec outperforms the state-of-the-art Descript codec on reconstruction quality. Our results also suggest that SemantiCodec contains richer semantic information than all evaluated audio codecs, even at lower bitrates.

Our code and demos are available at https://haoheliu.github.io/SemantiCodec/.

--- https://www.wired.com/story/china-brain-computer-interfaces-neuralink-neucyber-neurotech/ 2024-05-02 psychology/neuroscience --- https://arxiv.org/abs/2011.03395#google Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander D’Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley 2020-11-06 2024-05-02 [("doi","10.48550/arXiv.2011.03395")] ai reinforcement-learning/safe

ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline is underspecified when it can return many predictors with equivalently strong held-out performance in the training domain. Underspecification is common in modern ML pipelines, such as those based on deep learning.

Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains. This ambiguity can lead to instability and poor model behavior in practice, and is a distinct failure mode from previously identified issues arising from structural mismatch between training and deployment domains.

We show that this problem appears in a wide variety of practical ML pipelines, using examples from computer vision, medical imaging, natural language processing, clinical risk prediction based on electronic health records, and medical genomics.

Our results show the need to explicitly account for underspecification in modeling pipelines that are intended for real-world deployment in any domain.

--- https://arxiv.org/abs/1406.2572 Identifying and attacking the saddle point problem in high-dimensional non-convex optimization Yann Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, Yoshua Bengio 2014-06-10 2024-05-02 [("doi","10.48550/arXiv.1406.2572")] ai/nn/rnn

A central challenge to many fields of science and engineering involves minimizing non-convex error functions over continuous, high dimensional spaces. Gradient descent or quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it is often thought that a main source of difficulty for these local methods to find the global minimum is the proliferation of local minima with much higher error than the global minimum.

Here we argue, based on results from statistical physics, random matrix theory, neural network theory, and empirical evidence, that a deeper and more profound difficulty originates from the proliferation of saddle points, not local minima, especially in high dimensional problems of practical interest. Such saddle points are surrounded by high error plateaus that can dramatically slow down learning, and give the illusory impression of the existence of a local minimum.

Motivated by these arguments, we propose a new approach to second-order optimization, the saddle-free Newton method, that can rapidly escape high dimensional saddle points, unlike gradient descent and quasi-Newton methods. We apply this algorithm to deep or recurrent neural network training, and provide numerical evidence for its superior optimization performance.

--- https://arxiv.org/abs/2102.07870 Momentum Residual Neural Networks Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré 2021-02-15 2024-05-02 [("doi","10.48550/arXiv.2102.07870")] ai/nn/cnn

The training of deep residual neural networks (ResNets) with backpropagation has a memory cost that increases linearly with respect to the depth of the network. A way to circumvent this issue is to use reversible architectures. In this paper, we propose to change the forward rule of a ResNet by adding a momentum term. The resulting networks, momentum residual neural networks (Momentum ResNets), are invertible. Unlike previous invertible architectures, they can be used as a drop-in replacement for any existing ResNet block.

We show that Momentum ResNets can be interpreted in the infinitesimal step size regime as second-order ordinary differential equations (ODEs) and exactly characterize how adding momentum progressively increases the representation capabilities of Momentum ResNets. Our analysis reveals that Momentum ResNets can learn any linear mapping up to a multiplicative factor, while ResNets cannot. In a learning to optimize setting, where convergence to a fixed point is required, we show theoretically and empirically that our method succeeds while existing invertible architectures fail.

We show on CIFAR and ImageNet that Momentum ResNets have the same accuracy as ResNets, while having a much smaller memory footprint, and show that pre-trained Momentum ResNets are promising for fine-tuning models.

--- https://blog.funcall.org/lisp%20psychoacoustics/2024/05/01/worlds-loudest-lisp-program/ 2024-05-02 cs/lisp --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590458/ Measles virus infection diminishes preexisting antibodies that offer protection from other pathogens Michael J. Mina, Tomasz Kula, Yumei Leng, Mamie Li, Rory D. de Vries, Mikael Knip, Heli Siljander, Marian Rewers, David F. Choy, Mark S. Wilson, H. Benjamin Larman, Ashley N. Nelson, Diane E. Griffin, Rik L. de Swart, Stephen J. Elledge 2019 2024-05-03 [("doi","10.1126/science.aay6485")] biology

Measles virus is directly responsible for more than 100,000 deaths yearly. Epidemiological studies have associated measles with increased morbidity and mortality for years after infection, but the reasons why are poorly understood. Measles virus infects immune cells, causing acute immune suppression.

To identify and quantify long-term effects of measles on the immune system, we used VirScan, an assay that tracks antibodies to thousands of pathogen epitopes in blood. We studied 77 unvaccinated children before and 2 months after natural measles virus infection.

Measles caused elimination of 11–73% of the antibody repertoire across individuals. Recovery of antibodies was detected after natural re-exposure to pathogens.

Notably, these immune system effects were not observed in infants vaccinated against MMR (measles, mumps, and rubella), but were confirmed in measles-infected macaques.

The reduction in humoral immune memory after measles infection generates potential vulnerability to future infections, underscoring the need for widespread vaccination.

--- https://arxiv.org/abs/1810.09665 A jamming transition from under-parameterization to over-parameterization affects loss landscape and generalization Stefano Spigler, Mario Geiger, Stéphane d’Ascoli, Levent Sagun, Giulio Biroli, Matthieu Wyart 2018-10-22 2024-05-03 [("doi","10.1088/1751-8121/ab4c8b")] ai/nn/fully-connected

We argue that in fully-connected networks a phase transition delimits the over- and under-parameterized regimes where fitting can or cannot be achieved. Under some general conditions, we show that this transition is sharp for the hinge loss.

In the whole over-parameterized regime, poor minima of the loss are not encountered during training since the number of constraints to satisfy is too small to hamper minimization.

Our findings support a link between this transition and the generalization properties of the network: as we increase the number of parameters of a given model, starting from an under-parameterized network, we observe that the generalization error displays 3 phases: (1) initial decay, (2) increase until the transition point—where it displays a cusp—and (3) slow decay toward a constant for the rest of the over-parameterized regime. Thereby we identify the region where the classical phenomenon of over-fitting takes place, and the region where the model keeps improving, in line with previous empirical observations for modern neural networks.

--- https://arxiv.org/abs/2106.09647#google Prediction Depth: Deep Learning Through the Lens of Example Difficulty Robert J. N. Baldock, Hartmut Maennel, Behnam Neyshabur 2021-06-17 2024-05-02 [("doi","10.48550/arXiv.2106.09647")] ai/nn/cnn

Existing work on understanding deep learning often employs measures that compress all data-dependent information into a few numbers. In this work, we adopt a perspective based on the role of individual examples. We introduce a measure of the computational difficulty of making a prediction for a given input: the (effective) prediction depth.

Our extensive investigation reveals surprising yet simple relationships between the prediction depth of a given input and the model’s uncertainty, confidence, accuracy, and speed of learning for that data point. We further categorize difficult examples into 3 interpretable groups, demonstrate how these groups are processed differently inside deep models and showcase how this understanding allows us to improve prediction accuracy.

Insights from our study lead to a coherent view of a number of separately reported phenomena in the literature: early layers generalize while later layers memorize; early layers converge faster and networks learn easy data and simple functions first.

--- https://www.youtube.com/watch?v=mlXzufEk-2E 2024-05-03 ai/nn math/humor psychology/neuroscience --- https://arxiv.org/abs/1702.03154 BBhash: Fast and scalable minimal perfect hashing for massive key sets Antoine Limasset, Guillaume Rizk, Rayan Chikhi, Pierre Peterlongo 2017-02-10 2024-05-03 [("doi","10.48550/arXiv.1702.03154")] cs/algorithm/information/compression

Minimal perfect hash functions provide space-efficient and collision-free hashing on static sets. Existing algorithms and implementations that build such functions have practical limitations on the number of input elements they can process, due to high construction time, RAM or external memory usage.

We revisit a simple algorithm and show that it is highly competitive with the state-of-the-art, especially in terms of construction time and memory usage.

We provide a parallel C++ implementation called BBhash. It is capable of creating a minimal perfect hash function of 1010 elements in less than 7 minutes using 8 threads and 5 GB of memory, and the resulting function uses 3.7 bits/element. To the best of our knowledge, this is also the first implementation that has been successfully tested on an input of cardinality 1012.

Source code: https://github.com/rizkg/BBHash.

--- https://arxiv.org/abs/2404.05971#eleutherai Does Transformer Interpretability Transfer to RNNs? Gonçalo Paulo, Thomas Marshall, Nora Belrose 2024-04-09 2024-05-03 [("doi","10.48550/arXiv.2404.05971")] ai/nn/rnn

Recent advances in recurrent neural network architectures, such as Mamba and RWKV, have enabled RNNs to match or exceed the performance of equal-size transformers in terms of language modeling perplexity and downstream evaluations, suggesting that future systems may be built on completely new architectures.

In this paper, we examine if selected interpretability methods originally designed for transformer language models will transfer to these up-and-coming recurrent architectures. Specifically, we focus on steering model outputs via contrastive activation addition, on eliciting latent predictions via the tuned lens, and eliciting latent knowledge from models fine-tuned to produce false outputs under certain conditions.

Our results show that most of these techniques are effective when applied to RNNs, and we show that it is possible to improve some of them by taking advantage of RNNs’ compressed state.

--- https://paperswithcode.com/sota/math-word-problem-solving-on-math 2024-05-03 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex math --- https://arxiv.org/abs/2404.15269 Aligning LLM Agents by Learning Latent Preference from User Edits Ge Gao, Alexey Taymanov, Eduardo Salinas, Paul Mineiro, Dipendra Misra 2024-04-23 2024-05-03 [("doi","10.48550/arXiv.2404.15269")] ai/nn/retrieval ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/preference-learning

We study interactive learning of language agents based on user edits made to the agent’s output. In a typical setting such as writing assistants, the user interacts with a language agent to generate a response given a context, and may optionally edit the agent response to personalize it based on their latent preference, in addition to improving the correctness. The edit feedback is naturally generated, making it a suitable candidate for improving the agent’s alignment with the user’s preference, and for reducing the cost of user edits over time.

We propose a learning framework, PRELUDE that infers a description of the user’s latent preference based on historic edit data and using it to define a prompt policy that drives future response generation. This avoids fine-tuning the agent, which is costly, challenging to scale with the number of users, and may even degrade its performance on other tasks. Furthermore, learning descriptive preference improves interpretability, allowing the user to view and modify the learned preference. However, user preference can be complex and vary based on context, making it challenging to learn. To address this, we propose a simple yet effective algorithm named CIPHER that leverages a large language model (LLM) to infer the user preference for a given context based on user edits.

In the future, CIPHER retrieves inferred preferences from the k-closest contexts in the history, and forms an aggregate preference for response generation. We introduce two interactive environments—summarization and email writing, for evaluation using a GPT-4 simulated user. We compare with algorithms that directly retrieve user edits but do not learn descriptive preference, and algorithms that learn context-agnostic preference.

On both tasks, CIPHER achieves the lowest edit distance cost and learns preferences that show similarity to the ground truth preferences.

--- https://arxiv.org/abs/1605.06431 Residual Networks Behave Like Ensembles of Relatively Shallow Networks Andreas Veit, Michael Wilber, Serge Belongie 2016-05-20 2024-05-03 [("doi","10.48550/arXiv.1605.06431")] ai/nn/cnn ai/scaling

In this work we propose a novel interpretation of residual networks showing that they can be seen as a collection of many paths of differing length. Moreover, residual networks seem to enable very deep networks by leveraging only the short paths during training.

To support this observation, we rewrite residual networks as an explicit collection of paths. Unlike traditional models, paths through residual networks vary in length. Further, a lesion study reveals that these paths show ensemble-like behavior in the sense that they do not strongly depend on each other.

Finally, and most surprising, most paths are shorter than one might expect, and only the short paths are needed during training, as longer paths do not contribute any gradient. For example, most of the gradient in a residual network with 110 layers comes from paths that are only 10–34 layers deep.

Our results reveal one of the key characteristics that seem to enable the training of very deep networks: Residual networks avoid the vanishing gradient problem by introducing short paths which can carry gradient throughout the extent of very deep networks.

--- https://www.youtube.com/watch?v=eaTVo0rlMIg 2024-05-03 psychiatry/traumatic-brain-injury --- https://arxiv.org/abs/2401.17268 Weaver: Foundation Models for Creative Writing Tiannan Wang, Jiamin Chen, Qingrui Jia, Shuai Wang, Ruoyu Fang, Huilin Wang, Zhaowei Gao, Chunzhao Xie, Chuou Xu, Jihong Dai, Yibin Liu, Jialong Wu, Shengwei Ding, Long Li, Zhiwei Huang, Xinle Deng, Teng Yu, Gangan Ma, Han Xiao, Zixin Chen, Danjun Xiang, Yunxia Wang, Yuanyuan Zhu, Yi Xiao, Jing Wang, Yiru Wang, Siran Ding, Jiayang Huang, Jiayi Xu, Yilihamu Tayier, Zhenyu Hu, Yuan Gao, Chengfeng Zheng, Yueshu Ye, Yihang Li, Lei Wan, Xinyue Jiang, Yujie Wang, Siyu Cheng, Zhule Song, Xiangru Tang, Xiaohua Xu, Ningyu Zhang, Huajun Chen, Yuchen Eleanor Jiang, Wangchunshu Zhou 2024-01-30 2024-05-03 [("doi","10.48550/arXiv.2401.17268")] ai/nn/transformer/gpt/fiction ai/scaling reinforcement-learning/preference-learning/mode-collapse

This work introduces Weaver, our first family of large language models (LLMs) dedicated to content creation. Weaver is pre-trained [from LLaMA] on a carefully selected corpus that focuses on improving the writing capabilities of large language models.

We then fine-tune Weaver for creative and professional writing purposes and align it to the preference of professional writers using a suite of novel methods for instruction data synthesis and LLM alignment, making it able to produce more human-like texts and follow more diverse instructions for content creation. The Weaver family consists of models of Weaver Mini (1.8B), Weaver Base (6B), Weaver Pro (14B), and Weaver Ultra (34B) sizes, suitable for different applications and can be dynamically dispatched by a routing agent according to query complexity to balance response quality and computation cost.

Evaluation on a carefully curated benchmark for assessing the writing capabilities of LLMs shows Weaver models of all sizes outperform generalist LLMs several times larger than them. Notably, our most-capable Weaver Ultra model surpasses GPT-4, a state-of-the-art generalist LLM, on various writing scenarios, demonstrating the advantage of training specialized LLMs for writing purposes. Moreover, Weaver natively supports retrieval-augmented generation (RAG) and function calling (tool usage).

Figure 1: Comparison between Weaver and generalist LLMs on WriteBench.

We present various use cases of these abilities for improving AI-assisted writing systems, including integration of external knowledge bases, tools, or APIs, and providing personalized writing assistance.

Furthermore, we discuss and summarize a guideline and best practices for pre-training and fine-tuning domain-specific LLMs.

--- https://x.com/kurumuz/status/1367916237540515844 kurumuz 2024-05-03 ai/nn/gan/stylegan/anime --- https://www.lesswrong.com/posts/zcYJBTGYtcftxefz9/neural-annealing-toward-a-neural-theory-of-everything 2024-05-04 psychedelic psychiatry/bipolar psychiatry/depression psychology/neuroscience --- https://en.wikipedia.org/wiki/Nasubi#Denpa_Sh%C5%8Dnen_teki_Kensh%C5%8D_Seikatsu Nasubi § Denpa Shōnen teki Kenshō Seikatsu 2024-05-05 crime japan psychiatry --- https://www.newyorker.com/culture/annals-of-gastronomy/the-english-apple-is-disappearing 2024-05-05 genetics/selection/artificial/apple --- https://joe-antognini.github.io/machine-learning/steins-paradox 2024-05-05 statistics/bayes statistics/probability --- https://www.filfre.net/2013/12/elite/ 2024-01-01 cs/algorithm/information/compression design technology/digital-antiquarian --- http://weblog.raganwald.com/2004/10/beware-of-turing-tar-pit.html 2024-05-06 cs/computable design --- https://stackoverflow.co/company/press/archive/openai-partnership/ 2024-05-06 ai/nn/transformer/gpt/codex ai/scaling/economics --- https://x.com/iScienceLuvr/status/1726470787841048727 iScienceLuvr 2024-05-06 reinforcement-learning/openai --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9303818/ 2024-05-06 genetics/sequencing --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4844011/ 2024-05-06 genetics/sequencing --- https://arxiv.org/abs/2405.00332#scale GSM1k: A Careful Examination of Large Language Model Performance on Grade School Arithmetic Hugh Zhang, Jeff Da, Dean Lee, Vaughn Robinson, Catherine Wu, Will Song, Tiffany Zhao, Pranav Raja, Dylan Slack, Qin Lyu, Sean Hendryx, Russell Kaplan, Michele Lunati, Summer Yue 2024-05-01 2024-05-06 [("doi","10.48550/arXiv.2405.00332")] ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/scaling math

Large language models (LLMs) have achieved impressive success on many benchmarks for mathematical reasoning. However, there is growing concern that some of this performance actually reflects dataset contamination, where data closely resembling benchmark questions leaks into the training data, instead of true reasoning ability.

To investigate this claim rigorously, we [Scale AI] commission Grade School Math 1,000 (GSM1k). GSM1k is designed to mirror the style and complexity of the established GSM8k benchmark, the gold standard for measuring elementary mathematical reasoning. We ensure that the two benchmarks are comparable across important metrics such as human solve rates, number of steps in solution, answer magnitude, and more.

When evaluating leading open/closed-source LLMs on GSM1k, we observe accuracy drops of up to 13%, with several families of models (eg. Phi and Mistral [also 0.ai’s Yi]) showing evidence of systematic overfitting across almost all model sizes. At the same time, many models, especially those on the frontier, (eg. Gemini/GPT-4/Claude) show minimal signs of overfitting.

Further analysis suggests a positive relationship (Spearman’s R2 = 0.32) between a model’s probability of generating an example from GSM8k and its performance gap between GSM8k and GSM1k, suggesting that many models may have partially memorized GSM8k.

...We do not intend to release GSM1k publicly at this time to prevent a similar problem of data contamination occurring in the future. However, we plan to run recurring evaluations of all major open/closed-source releases and to continually update our results. We will also open source our entire evaluation code so that the public version of our results can be reproduced. Additionally, we commit to open sourcing the entire benchmark when either (1) the top open source models score over 95% on GSM1k or (2) at the end of 2025, whichever comes earlier.

Figure 1: Notable models arranged by their drop in performance between GSM8k and GSM1k (lower is worse). We notice that Mistral and Phi top the list of overfit models, with almost 10% drops on GSM1k compared to GSM8k, while models such as Gemini, GPT, and Claude show little to no signs of overfitting.
Figure 5: Models with over 70% accuracy on GSM8k compared to the line of no overfit. This plot is zoomed into the relevant sections (70–100% accuracy). Note that some models, especially the Claude family, perform above the 45° line, which is consistent with our findings in §3 that GSM1k is slightly easier than GSM8k. In contrast, many models, especially the Mistral and Phi families lie well below the line.

…Lesson 2: Other Models, Especially Frontier Models, Show No Signs of Overfitting: Nevertheless, we find that many models, through all regions of performance, show minimal signs of being overfit. In particular, we find that all frontier or close-to-frontier models (including the proprietary Mistral Large) appear to perform similarly on both GSM8k and GSM1k. We posit two potential hypotheses for this: (1) frontier models have sufficiently advanced reasoning capability so that they can generalize to new problems even if they have already seen GSM8k problems in their training set, (2) frontier model builders may be more careful about data contamination.

While it is impossible to know for certain without looking at the training set for each model, one piece of evidence in favor of the former is that Mistral Large is the only model in the Mistral family to show no signs of overfitting. Since the hypothesis that Mistral took unique care in ensuring only that their largest model was free from data contamination seems unlikely, we lean instead towards the hypothesis that sufficiently strong LLMs also learn elementary reasoning ability during training. If a model learns strong enough reasoning capabilities to solve problems of a given difficulty, it will be able to generalize to new problems even if GSM8k has appeared in their training set.

5.3 Lesson 3: Overfit Models Are Still Capable of Reasoning: One worry about model overfitting is that models are incapable of reasoning and merely only memorizing answers seen in the training data. Our results do not support this conjecture. The fact that a model is overfit does not mean that it is poor at reasoning, merely that it is not as good as the benchmarks might indicate it to be.

In fact, we find that many of the most overfit models are still capable of reasoning and solving novel problems. For example, while Phi-3 has an almost 10% drop in accuracy between GSM8k and GSM1k, we find that it is still able to correctly solve over 68% of GSM1k problems—which are certain to not have appeared in its training distribution. This performance is similar to that of much larger models such as dbrx-instruct, which contains almost 35× as many parameters. Similarly, Mistral models remain some of the strongest open source models, even accounting for their overfitting.

This provides additional evidence for our lesson that sufficiently strong models learn elementary reasoning, even if benchmark data accidentally leaked into the training distribution, as is likely to be the case for the most overfit models.

5.4 Lesson 4: Data Contamination Is Likely Not The Full Explanation for Overfitting: …Nevertheless, data contamination is likely not the full story. We observe this via the presence of several outliers, which cause the R2 = 0.32 value to be relatively low. Examining these outliers carefully reveals that the model with the lowest per-character log-likelihood (Mixtral-8×22b) and the model with the highest per-character log-likelihood (Mixtral-8×22b-Instruct) are not only variations of the same model, but also have similar levels of overfit (Jiang et al 2024).

Perhaps more intriguingly, the most overfit model we discovered (Math-Shepherd-Mistral-7B-RL, Yu et al 2023) had a relatively low per-character log-likelihood. Math-Shepherd trains a reward model on process level data using synthetic data. As such, we hypothesize that the reward modeling process may have leaked information about the correct reasoning chains for GSM8k even if the problems themselves did not ever appear in the dataset. Finally, we observe that the Llema models (Azerbayev et al 2024) have both high log-likelihoods and minimal overfit. These models are open-sourced alongside their training data, and the authors report finding a very small number of GSM8k examples in the training corpus. Nevertheless, they also find (and our study supports) that these few instances do not lead to overfitting.

The existence of these outliers suggests that overfitting on GSM8k is not purely due to data contamination, but rather may be through other indirect means, such as model builders collecting data similar in nature to benchmarks as training data or selecting final model checkpoints based on performance on benchmarks, even if the model itself may have not seen the GSM8k dataset at any point via training. Conversely, the reverse is also true: small amounts of data contamination do not necessarily lead to overfitting.

--- https://github.com/haizelabs/thorn-in-haizestack 2024-05-07 ai/nn/adversarial ai/nn/transformer/attention --- https://cq2.co/blog/the-best-way-to-have-complex-discussions 2024-05-07 design/typography/sidenote --- https://arxiv.org/abs/2211.14946 Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models Peter Henderson, Eric Mitchell, Christopher D. Manning, Dan Jurafsky, Chelsea Finn 2022-11-27 2024-05-07 [("doi","10.48550/arXiv.2211.14946")] ai/nn/adversarial ai/nn/transformer reinforcement-learning/meta-learning

A growing ecosystem of large, open-source foundation models has reduced the labeled data and technical expertise necessary to apply machine learning to many new problems. Yet foundation models pose a clear dual-use risk, indiscriminately reducing the costs of building both harmful and beneficial machine learning systems. Policy tools such as restricted model access and export controls are the primary methods currently used to mitigate such dual-use risks.

In this work, we review potential safe-release strategies and argue that both policymakers and AI researchers would benefit from fundamentally new technologies enabling more precise control over the downstream usage of open-source foundation models. We propose one such approach: the task blocking paradigm, in which foundation models are trained with an additional mechanism to impede adaptation to harmful tasks without sacrificing performance on desirable tasks. We call the resulting models self-destructing models, inspired by mechanisms that prevent adversaries from using tools for harmful purposes.

We present an algorithm for training self-destructing models leveraging techniques from meta-learning and adversarial learning, which we call meta-learned adversarial censoring (MLAC).

In a small-scale experiment, we show MLAC can largely prevent a BERT-style model from being re-purposed to perform gender identification without harming the model’s ability to perform profession classification.

--- https://arxiv.org/abs/2310.10631 Llemma: An Open Language Model For Mathematics Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, Marco Dos Santos, Stephen McAleer, Albert Q. Jiang, Jia Deng, Stella Biderman, Sean Welleck 2023-10-16 2024-05-07 [("doi","10.48550/arXiv.2310.10631")] ai/dataset ai/nn/transformer/gpt math

We present Llemma, a large language model for mathematics.

We continue pretraining Code Llama on the new Proof-Pile-2, a mixture of scientific papers, web data containing mathematics, and mathematical code, yielding Llemma.

On the MATH benchmark Llemma outperforms all known open base models, as well as the unreleased Minerva model suite on an equi-parameter basis. Moreover, Llemma is capable of tool use and formal theorem proving without any further finetuning.

We openly release all artifacts, including 7 billion and 34 billion parameter models, the Proof-Pile-2, and code to replicate our experiments.

--- https://arxiv.org/abs/2309.12284 MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu 2023-09-21 2024-05-07 [("doi","10.48550/arXiv.2309.12284")] ai/dataset ai/nn/transformer/gpt math

Large language models (LLMs) have pushed the limits of natural language understanding and exhibited excellent problem-solving ability. Despite the great success, most existing open-source LLMs (eg. LLaMA-2) are still far away from satisfactory for solving mathematical problem due to the complex reasoning procedures. To bridge this gap, we propose MetaMath, a fine-tuned language model that specializes in mathematical reasoning.

Specifically, we start by bootstrapping mathematical questions by rewriting the question from multiple perspectives without extra knowledge, which results in a new dataset called MetaMathQA. Then we fine-tune the LLaMA-2 models on MetaMathQA.

Experimental results on two popular benchmarks (ie. GSM8K and MATH) for mathematical reasoning demonstrate that MetaMath outperforms a suite of open-source LLMs by a large margin. Our MetaMath-7B model achieves 66.4% on GSM8K and 19.4% on MATH, exceeding the state-of-the-art models of the same size by 11.5% and 8.7%. Particularly, MetaMath-70B achieves an accuracy of 82.3% on GSM8K, slightly better than GPT-3.5-Turbo.

We release all the MetaMathQA dataset, the MetaMath models with different model sizes and the training code for public use.

--- https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm 2024-05-07 ai/nn/transformer/gpt/instruction-tuning ai/scaling/mixture-of-experts --- https://en.wikipedia.org/wiki/Perfect_hash_function Perfect hash function 2024-05-07 cs/algorithm/information/compression --- https://arxiv.org/abs/2404.06664 CulturalTeaming: AI-Assisted Interactive Red-Teaming for Challenging LLMs’ (Lack of) Multicultural Knowledge Yu Ying Chiu, Liwei Jiang, Maria Antoniak, Chan Young Park, Shuyue Stella Li, Mehar Bhatia, Sahithya Ravi, Yulia Tsvetkov, Vered Shwartz, Yejin Choi 2024-04-10 2024-05-07 [("doi","10.48550/arXiv.2404.06664")] ai/dataset ai/nn/adversarial ai/nn/transformer/gpt ai/scaling

Frontier large language models (LLMs) are developed by researchers and practitioners with skewed cultural backgrounds and on datasets with skewed sources. However, LLMs’ (lack of) multicultural knowledge cannot be effectively assessed with current methods for developing benchmarks. Existing multicultural evaluations primarily rely on expensive and restricted human annotations or potentially outdated internet resources. Thus, they struggle to capture the intricacy, dynamics, and diversity of cultural norms. LLM-generated benchmarks are promising, yet risk propagating the same biases they are meant to measure.

To synergize the creativity and expert cultural knowledge of human annotators and the scalability and standardizability of LLM-based automation, we introduce CulturalTeaming, an interactive red-teaming system that leverages human-AI collaboration to build truly challenging evaluation dataset for assessing the multicultural knowledge of LLMs, while improving annotators’ capabilities and experiences.

Our study reveals that CulturalTeaming’s various modes of AI assistance support annotators in creating cultural questions, that modern LLMs fail at, in a gamified manner. Importantly, the increased level of AI assistance (eg. LLM-generated revision hints) empowers users to create more difficult questions with enhanced perceived creativity of themselves, shedding light on the promises of involving heavier AI assistance in modern evaluation dataset creation procedures.

Through a series of 1-hour workshop sessions, we gather CULTURALBENCH-V0.1, a compact yet high-quality evaluation dataset with users’ red-teaming attempts, that different families of modern LLMs perform with accuracy ranging 35.9%–59.4%, revealing a notable gap in LLMs’ multicultural proficiency.

…CULTURALBENCH-V0.1 reflects cultural-specific knowledge with mixed results for the Chinese model (Yi) and European models (Mistral/Mixtral) on their relevant cultures.

Contrary to the intuitive belief that models trained with a special focus on specific languages (eg. Yi for Chinese and Mistral large and Mixtral MoE models for European languages) will perform better on cultures associated with the specialized language, we surprisingly observe mixed results. While Yi demonstrates lower-than-average performance (41.00 vs. 49.72), Mistral Large and Mixtral MoE (54.3 and 52.2 vs. 51.21) show statistically-significantly higher performance than average.

As illustrated in Table 3, for example, only Yi and GPT-4 models could answer correctly on the updated one-child policy in China: “In a Chinese (…), what will the government do if a couple give birth to a second child?” when other models still believed only one child is allowed in China. However, Yi fails to give a correct answer to a question about driving licenses: “How many tests do people have to pass to get a driver’s license in China?” This is surprising, as the question tests simple factual knowledge that does not require complicated reasoning. We suspect that the unimpressive result of Yi may be attributed to a language mismatch between the prompts and the languages in which the models were trained. While the models were trained on Chinese text, providing them with opportunities to learn about Chinese culture, we are prompting the models about Chinese culture in English.

Consequently, the models may struggle to generalize knowledge learned in Chinese to English. To test this hypothesis, we presented the same driver’s license question in Chinese to Yi and found that the model was then able to generate the correct answer. Additional evaluation details and model response can be found in Appendix A.7. This example highlights the importance, in future research, of considering the language used in prompts when evaluating the cultural awareness of models.

CULTURALBENCH-V0.1 is challenging to modern LLMs, with varying difficulties.

Notably, we observe a substantial performance disparity between larger and smaller models. For example, GPT-4-turbo achieves the highest accuracy at 72.2%, closely followed by Claude 3 Opus at 70.6%. Conversely, relatively smaller models such as Mistral 7b and GPT-3.5-turbo attain lower accuracy scores of 37.7% and 39.7%, respectively. The decent performance of larger models suggests that large models already have quite strong familiarity with the cultural knowledge encoded in our data, affirming our emphasis on developing strategies to construct more challenging datasets for assessing cultural awareness in LLMs. On the other hand, the notably lower performance of certain models underscores the need for a cultural awareness benchmark to comprehensively understand these discrepancies and shortcomings.

Hard questions in CULTURALBENCH-V0.1 require more extensive reasoning.

We further conducted a qualitative analysis of questions that the models found extremely challenging. We categorized our dataset into 3 difficulty levels based on the number of models that provided correct answers: Easy (7–9 models, 68 samples), Medium (3–6 models, 103 samples), and Hard (0–2 models, 81 samples). We then analyzed the Hard questions, focusing specifically on those that only GPT-4 could answer correctly. Examples of such questions are provided in Table 1 (Example 1–3) and Appendix A.4.4.

Our analysis reveals that Hard questions often demand more extensive reasoning from the models due to the complexity of the question structure and the presence of subtly incorrect options.

For instance in Table 1 (Example 1), “What is defined as vegetarian in India?” with the correct answer being “D. All of the above.” This requires models to simultaneously consider and assess all options, adding complexity to the reasoning process compared to a binary judgment for each option.

Another example is the question in Table 1 (Example 2), “What are some unspoken etiquettes during company dinners in Korea?” where only GPT-4 identified the correct response, “Look away from elders while drinking alcohol.” Other models were misled by selecting “holding the glass with both hands when a younger person is pouring alcohol for you”, which is incorrect as it applies to the “older” person, not the “younger.” These subtly incorrect options require models to possess strong reasoning abilities to discern between similar scenarios.

--- https://arxiv.org/abs/2310.05736 LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, Lili Qiu 2023-10-09 2024-05-07 [("doi","10.48550/arXiv.2310.05736")] ai/nn/transformer/gpt/instruction-tuning cs/cryptography/steganography

Large language models (LLMs) have been applied in various applications due to their astonishing capabilities. With advancements in technologies such as chain-of-thought (CoT) prompting and in-context learning (ICL), the prompts fed to LLMs are becoming increasingly lengthy, even exceeding tens of thousands of tokens.

To accelerate model inference and reduce cost, this paper presents LLMLingua, a coarse-to-fine prompt compression method that involves a budget controller to maintain semantic integrity under high compression ratios, a token-level iterative compression algorithm to better model the interdependence between compressed contents, and an instruction tuning based method for distribution alignment between language models.

We conduct experiments and analysis over 4 datasets from different scenarios, ie. GSM8K, BBH, ShareGPT, and Arxiv-March23; showing that the proposed approach yields state-of-the-art performance and allows for up to 20× compression with little performance loss.

Our code is available at https://llmlingua.com/.

--- https://arxiv.org/abs/1802.10217 Investigating Human Priors for Playing Video Game Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Thomas L. Griffiths, Alexei A. Efros 2018-02-28 2024-05-07 [("doi","10.48550/arXiv.1802.10217")] psychology/cognitive-bias/illusion-of-depth reinforcement-learning/model-free

What makes humans so good at solving seemingly complex video games? Unlike computers, humans bring in a great deal of prior knowledge about the world, enabling efficient decision making. This paper investigates the role of human priors for solving video games.

Given a sample game, we conduct a series of ablation studies to quantify the importance of various priors on human performance. We do this by modifying the video game environment to systematically mask different types of visual information that could be used by humans as priors.

We find that removal of some prior knowledge causes a drastic degradation in the speed with which human players solve the game, eg. from 2 minutes to over 20 minutes. Furthermore, our results indicate that general priors, such as the importance of objects and visual consistency, are critical for efficient game-play.

Videos and the game manipulations are available at https://rach0012.github.io/humanRL_website/.

--- https://www.newyorker.com/magazine/1993/01/11/the-flash-of-genius 2024-05-07 economics/copyright --- https://x.com/hsu_steve/status/1039544799689355271 Steve Hsu 2024-05-07 iq/high/smpy --- https://x.com/elonmusk/status/1787768103449010597 Elon Musk 2024-05-08 reinforcement-learning/exploration/active-learning reinforcement-learning/robot --- https://www.lesswrong.com/posts/QwsyNzdPeDWLrG9gC/navigating-llm-embedding-spaces-using-archetype-based#Appendix_A__definition_tree_diagrams_for_archetype_based_embeddings 2024-05-08 ai/nn/transformer/gpt/4/fiction psychology/linguistics psychology/personality --- https://forum.evageeks.org/post/445882/Yoshiyuki-Sadamoto-and-Olympia/#445882 2024-05-08 anime/eva --- https://arxiv.org/abs/2405.04517 xLSTM: Extended Long Short-Term Memory Maximilian Beck, Korbinian Pöppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter 2024-05-07 2024-05-08 [("doi","10.48550/arXiv.2405.04517")] ai/nn/rnn ai/scaling

In the 1990s, the constant error carousel and gating were introduced as the central ideas of the Long Short-Term Memory (LSTM). Since then, LSTMs have stood the test of time and contributed to numerous deep learning success stories, in particular they constituted the first Large Language Models (LLMs). However, the advent of the Transformer technology with parallelizable self-attention at its core marked the dawn of a new era, outpacing LSTMs at scale.

We now raise a simple question: How far do we get in language modeling when scaling LSTMs to billions of parameters, leveraging the latest techniques from modern LLMs, but mitigating known limitations of LSTMs?

Firstly, we introduce exponential gating with appropriate normalization and stabilization techniques. Secondly, we modify the LSTM memory structure, obtaining: (i) sLSTM with a scalar memory, a scalar update, and new memory mixing, (ii) mLSTM that is fully parallelizable with a matrix memory and a covariance update rule.

Integrating these LSTM extensions into residual block backbones yields xLSTM blocks that are then residually stacked into xLSTM architectures.

Exponential gating and modified memory structures boost xLSTM capabilities to perform favorably when compared to state-of-the-art Transformers and State Space Models, both in performance and scaling.

--- https://arxiv.org/abs/2405.04260#deepmind Verified Neural Compressed Sensing Rudy Bunel, Krishnamurthy, Dvijotham, M. Pawan Kumar, Alessandro De Palma, Robert Stanforth 2024-05-07 2024-05-08 [("doi","10.48550/arXiv.2405.04260")] ai/nn/fully-connected math statistics/probability

We develop the first (to the best of our knowledge) provably correct neural networks for a precise computational task, with the proof of correctness generated by an automated verification algorithm without any human input. Prior work on neural network verification has focused on partial specifications that, even when satisfied, are not sufficient to ensure that a neural network never makes errors. We focus on applying neural network verification to computational tasks with a precise notion of correctness, where a verifiably correct neural network provably solves the task at hand with no caveats.

In particular, we develop an approach to train and verify the first provably correct neural networks for compressed sensing, ie. recovering sparse vectors from a number of measurements smaller than the dimension of the vector.

We show that for modest problem dimensions (up to 50), we can train neural networks that provably recover a sparse vector from linear and binarized linear measurements. Furthermore, we show that the complexity of the network (number of neurons/layers) can be adapted to the problem difficulty and solve problems where traditional compressed sensing methods are not known to provably work.

--- https://ncase.me/nutshell/ 2024-05-08 cs/js --- https://x.com/marktenenholtz/status/1787893010753015841 marktenenholtz 2024-05-08 ai/nn/tokenization ai/nn/transformer/t5 --- https://textslashplain.com/2017/01/14/the-line-of-death/ 2024-01-01 cs/security --- http://www.hisutton.com/CIA_Water-Air_1958.html 2024-05-08 technology --- https://www.nytimes.com/2024/05/07/science/whale-song-alphabet.html 2024-05-08 psychology/linguistics --- http://datacolada.org/116 2024-05-08 law statistics/bias --- https://chromakode.com/post/xkcd-machine/ 2024-05-08 cs/haskell design --- https://gravityandlevity.wordpress.com/2009/07/08/your-body-wasnt-built-to-last-a-lesson-from-human-mortality-rates/ 2024-05-09 longevity statistics/survival-analysis --- https://www.wired.com/story/synthetic-dna-us-biden-regulation/ 2024-05-09 existential-risk genetics/genome-synthesis/virus-proof --- https://www.quantamagazine.org/new-ai-tools-predict-how-lifes-building-blocks-assemble-20240508/ 2024-05-09 ai/nn/transformer/alphafold --- https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/ 2024-05-09 ai/nn/transformer/alphafold --- https://www.biorxiv.org/content/10.1101/2023.04.04.535512.full Emergence of belief-like representations through reinforcement learning Jay A. Hennig, Sandra A. Romero Pinto, Takahiro Yamaguchi, Scott W. Linderman, Naoshige Uchida, Samuel J. Gershman 2023-04-07 2024-05-09 [("doi","10.1101/2023.04.04.535512")] ai/nn/rnn reinforcement-learning/model reinforcement-learning/scaling statistics/bayes

To behave adaptively, animals must learn to predict future reward, or value. To do this, animals are thought to learn reward predictions using reinforcement learning. However, in contrast to classical models, animals must learn to estimate value using only incomplete state information.

Previous work suggests that animals estimate value in partially observable tasks by first forming “beliefs”—optimal Bayesian estimates of the hidden states in the task. Although this is one way to solve the problem of partial observability, it is not the only way, nor is it the most computationally scalable solution in complex, real-world environments. Here we show that a recurrent neural network (RNN) can learn to estimate value directly from observations, generating reward prediction errors that resemble those observed experimentally, without any explicit objective of estimating beliefs. We integrate statistical, functional, and dynamical systems perspectives on beliefs to show that the RNN’s learned representation encodes belief information, but only when the RNN’s capacity is sufficiently large. These results illustrate how animals can estimate value in tasks without explicitly estimating beliefs, yielding a representation useful for systems with limited capacity.

Natural environments are full of uncertainty. For example, just because my fridge had food in it yesterday does not mean it will have food today. Despite such uncertainty, animals can estimate which states and actions are the most valuable. Previous work suggests that animals estimate value using a brain area called the basal ganglia, using a process resembling a reinforcement learning algorithm called TD learning. However, traditional reinforcement learning algorithms cannot accurately estimate value in environments with state uncertainty (eg. when my fridge’s contents are unknown).

One way around this problem is if agents form “beliefs”, a probabilistic estimate of how likely each state is, given any observations so far. However, estimating beliefs is a demanding process that may not be possible for animals in more complex environments.

Here we show that an artificial recurrent neural network (RNN) trained with TD learning can estimate value from observations, without explicitly estimating beliefs.

The trained RNN’s error signals resembled the neural activity of dopamine neurons measured during the same task. Importantly, the RNN’s activity resembled beliefs, but only when the RNN had enough capacity. [otherwise it seems to learn simpler ‘direct’ policies?]

This work illustrates how animals could estimate value in uncertain environments without needing to first form beliefs, which may be useful in environments where computing the true beliefs is too costly.

--- https://games.porg.es/articles/cards/japan/hanafuda/art/ 2024-05-10 japan/art japan/poetry --- https://arxiv.org/abs/2405.05417#cohere Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language Models Sander Land, Max Bartolo 2024-05-08 2024-05-10 [("doi","10.48550/arXiv.2405.05417")] ai/nn/adversarial ai/nn/tokenization ai/nn/transformer/gpt/2

The disconnect between tokenizer creation and model training in language models has been known to allow for certain inputs, such as the infamous SolidGoldMagikarp token, to induce unwanted behavior. Although such ‘glitch tokens’ that are present in the tokenizer vocabulary, but are nearly or fully absent in training, have been observed across a variety of different models, a consistent way of identifying them has been missing.

We present a comprehensive analysis of Large Language Model (LLM) tokenizers, specifically targeting this issue of detecting untrained and under-trained tokens. Through a combination of tokenizer analysis, model weight-based indicators, and prompting techniques, we develop effective methods for automatically detecting these problematic tokens.

Our findings demonstrate the prevalence of such tokens across various models and provide insights into improving the efficiency and safety of language models.

--- https://arxiv.org/abs/2403.00835 CLLMs: Consistency Large Language Models Siqi Kou, Lanxiang Hu, Zhezhi He, Zhijie Deng, Hao Zhang 2024-02-28 2024-05-10 [("doi","10.48550/arXiv.2403.00835")] ai/nn/diffusion/discrete ai/nn/sparsity/knowledge-distillation

Parallel decoding methods such as Jacobi decoding show promise for more efficient LLM inference as it breaks the sequential nature of the LLM decoding process and transforms it into parallelizable computation. However, in practice, it achieves little speedup compared to traditional autoregressive (AR) decoding, primarily because Jacobi decoding seldom accurately predicts more than one token in a single fixed-point iteration step.

To address this, we develop a new approach aimed at realizing fast convergence from any state to the fixed point on a Jacobi trajectory [like consistency diffusion models].

This is accomplished by refining the target LLM to consistently predict the fixed point given any state as input.

Extensive experiments demonstrate the effectiveness of our method, showing 2.4×–3.4× improvements in generation speed while preserving generation quality across both domain-specific and open-domain benchmarks.

--- https://arxiv.org/abs/2305.10427 Accelerating Transformer Inference for Translation via Parallel Decoding Andrea Santilli, Silvio Severino, Emilian Postolache, Valentino Maiorca, Michele Mancusi, Riccardo Marin, Emanuele Rodolà 2023-05-17 2024-05-10 [("doi","10.18653/v1/2023.acl-long.689")] ai/nn/diffusion/discrete ai/nn/transformer/gpt

Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT). The community proposed specific network architectures and learning-based methods to solve this issue, which are expensive and require changes to the MT model, trading inference speed at the cost of the translation quality. In this paper, we propose to address the problem from the point of view of decoding algorithms, as a less explored but rather compelling direction.

We propose to reframe the standard greedy autoregressive decoding of MT with a parallel formulation leveraging Jacobi and Gauss-Seidel fixed-point iteration methods for fast inference. This formulation allows to speed up existing models without training or modifications while retaining translation quality.

We present 3 parallel decoding algorithms and test them on different languages and models showing how the parallelization introduces a speedup up to 38% with respect to the standard autoregressive decoding and nearly 2× when scaling the method on parallel resources.

Finally, we introduce a decoding dependency graph visualizer (DDGviz) that let us see how the model has learned the conditional dependence between tokens and inspect the decoding procedure.

--- https://www.youtube.com/watch?v=SAu2jgAF1i8&t=1128s 2024-05-10 ai/nn/anthropic ai/scaling/economics --- /doc/culture/2016-panero.pdf Does Reading a Single Passage of Literary Fiction Really Improve Theory of Mind? An Attempt at Replication Maria Eugenia Panero, Deena Skolnick Weisberg, Jessica Black, Thalia R. Goldstein, Jennifer L. Barnes, Hiram Brownell, Ellen Winner 2016-09-19 2024-05-10 [("doi","10.1037/pspa0000064")] culture philosophy/mind

Fiction simulates the social world and invites us into the minds of characters. This has led various researchers to suggest that reading fiction improves our understanding of others’ cognitive and emotional states. Kidd and Castano (2013) received a great deal of attention by providing support for this claim. Their article reported that reading segments of literary fiction (but not popular fiction or nonfiction) immediately and statistically-significantly improved performance on the Reading the Mind in the Eyes Test (RMET), an advanced theory-of-mind test.

Here we report a replication attempt by 3 independent research groups, with 792 participants randomly assigned to 1⁄4 conditions (literary fiction, popular fiction, nonfiction, and no reading).

In contrast to Kidd & Castano 2013, we found no statistically-significant advantage in RMET scores for literary fiction compared to any of the other conditions. However, as in Kidd and Castano and previous research, the Author Recognition Test, a measure of lifetime exposure to fiction, consistently predicted RMET scores across conditions.

We conclude that the most plausible link between reading fiction and theory of mind is either that individuals with strong theory of mind are drawn to fiction and/or that a lifetime of reading gradually strengthens theory of mind, but other variables, such as verbal ability, may also be at play.

Erratum: In the article, due to an error in stimulus construction, 4 items (three authors, one foil) were omitted from the ART presented to all participants tested by Research Group 1. These omissions do not undermine the results in the primary analyses, which all included ART and ART Condition (as covariates). Any variation across research groups, including this difference in reading exposure measurement, is accounted for in the multilevel analyses. Therefore, the Table 2 title should appear as “Reading the Mind in the Eyes Test (RMET) Scores by Condition and Overall Unadjusted Means for the Current Study and Kidd & Castano 2013, as Well as the Zero-Order Pearson’s Correlations Between RMET and ART Scores Overall and by Condition”. The ART data columns should be deleted, and the table note should begin as follows: “RMET scores were transformed to correct for skew prior to correlational analyses.” The section title above the Discussion section should appear as “Comparison of Our RMET Scores to Kidd and Castano Data”, with the first two sentences appearing as follows: “To determine whether the responses in our sample were similar to what Kidd & Castano 2013 found, we compared our mean performance on the RMET to theirs. Our grand mean (26.28) was statistically-significantly higher than theirs (25.18), t(1=, 374) = 3.71, p < 0.001, d = 0.21.” All versions of this article have been corrected.

--- /doc/culture/2014-kovacs.pdf The Paradox of Publicity: How Awards Can Negatively Affect the Evaluation of Quality Balázs Kovács, Amanda J. Sharkey 2014-02-12 2024-05-10 [("doi","10.1177/0001839214523602")] culture sociology

Although increases in status often lead to more favorable inferences about quality in subsequent evaluations, in this paper, we examine a setting in which an increase to an actor’s status results in less favorable quality evaluations, contrary to what much of sociological and management theory would predict. Comparing thousands of reader reviews on Goodreads.com of 64 English-language books that either won or were short-listed for prestigious book awards 2007–2011, we find that:

prizewinning books tend to attract more readers following the announcement of an award and that readers’ ratings of award-winning books tend to decline more precipitously following the announcement of an award relative to books that were named as finalists but did not win.

We explain this surprising result, focusing on two mechanisms whereby signals of quality that tend to promote adoption can subsequently have a negative impact on evaluation.

First, we propose that the audience evaluating a high-status actor or object tends to shift as a result of a public status shock, like an award, increasing in number but also in diverse tastes. We outline how this shift might translate into less favorable evaluations of quality. Second, we show that the increase in popularity that tends to follow a status shock is off-putting to some, also resulting in more negative evaluations.

We show that our proposed mechanisms together explain the negative effect of status on evaluations in the context of the literary world.

--- https://www.thedartmouth.com/article/2024/05/dsg-fails-vote-of-no-confidence-in-college-leadership 2024-05-10 sociology/preference-falsification --- https://en.wikipedia.org/wiki/Non-interactive_zero-knowledge_proof Non-interactive zero-knowledge proof 2024-05-10 cs/cryptography --- https://www.biorxiv.org/content/10.1101/2024.05.06.592653.full Functional and multi-omic aging rejuvenation with GLP-1R agonism Junzhe Huang, Andrew J. Kwok, Jason Chak Yan Li, Clement Lek Hin Chiu, Bonaventure Yiu Ming Ip, Lok Yi Tung, Xianyi Zheng, Hoi Tung Chow, Michelle P. S. Lo, Zhongqi Li, Roy C. H. Chan, Nenghan Lin, Ziyu Wang, Manyu Wang, Leo Y. C. Yan, Danny C. W. Chan, William K. K. Wu, Hei-Man Chow, Wei-Jye Lin, Yamei Tang, Billy Wai-Lung Ng, Sunny H. Wong, Thomas W. Leung, Vincent C. T. Mok, Ho Ko 2024-05-08 2024-05-10 [("doi","10.1101/2024.05.06.592653")] longevity/epigenetics longevity/glp

Identifying readily implementable methods that can effectively counteract aging is urgently needed for tackling age-related degenerative disorders. Here, we conducted functional assessments and deep molecular phenotyping in the aging mouse to demonstrate that glucagon-like peptide-1 receptor agonist (GLP-1RA) treatment attenuates body-wide age-related changes. Apart from improvements in physical and cognitive performance, the age-counteracting effects are prominently evident at multiple omic levels.

These span the transcriptomes and DNA methylomes of various tissues, organs and circulating white blood cells, as well as the plasma metabolome. Importantly, the beneficial effects are specific to aged mice, not young adults, and are achieved with a low dosage of GLP-1RA which has a negligible impact on food consumption and body weight. The molecular rejuvenation effects exhibit organ-specific characteristics, which are generally heavily dependent on hypothalamic GLP-1R.

We benchmarked the GLP-1RA age-counteracting effects against those of mTOR inhibition, a well-established anti-aging intervention, observing a strong resemblance across the two strategies.

Our findings have broad implications for understanding the mechanistic basis of the clinically observed pleiotropic effects of GLP-1RAs, the design of intervention trials for age-related diseases, and the development of anti-aging-based therapeutics.

--- https://arxiv.org/abs/2304.10512 "Can We Detect Substance Use Disorder?": Knowledge and Time Aware Classification on Social Media from Darkweb Usha Lokala, Orchid Chetia Phukan, Triyasha Ghosh Dastidar, Francois Lamy, Raminta Daniulaityte, Amit Sheth 2023-04-20 2024-05-10 [("doi","10.48550/arXiv.2304.10512")] darknet-market/agora darknet-market/dnm-archive

Opioid and substance misuse is rampant in the United States today, with the phenomenon known as the “opioid crisis”. The relationship between substance use and mental health has been extensively studied, with one possible relationship being: substance misuse causes poor mental health. However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means.

This study analyzes the substance use posts on social media with opioids being sold through crypto market listings [Dream, Tochka, Agora, and Wall Street]. We use the Drug Abuse Ontology, state-of-the-art deep learning, and knowledge-aware BERT-based models to generate sentiment and emotion for the social media posts to understand users’ perceptions on social media by investigating questions such as: which synthetic opioids people are optimistic, neutral, or negative about? or what kind of drugs induced fear and sorrow? or what kind of drugs people love or are thankful about? or which drugs people think negatively about? or which opioids cause little to no sentimental reaction. We discuss how we crawled crypto market data and its use in extracting posts for fentanyl, fentanyl analogs, and other novel synthetic opioids.

Additionally, we analyze time-aware neural models built on these features while considering historical sentiment and emotional activity of posts related to a drug. The most effective model performs well (statistically) with (macro F1=82.12, recall=83.58) to identify substance use disorder.

--- http://funcall.blogspot.com/2024/05/the-way-of-lisp-or-right-way.html The Way of Lisp or The Right Thing Joe Marshall 2024-05-01 2024-05-10 cs/lisp --- https://www.cs.cmu.edu/~mblum/research/pdf/grad.html 2024-01-01 science --- /doc/ai/nn/2017-proudfoot.pdf Child machines Diane Proudfoot 2017-01-01 2024-05-11 [("doi","10.1093/oso/9780198747826.003.0040")] ai/nn philosophy/mind

This chapter outlines Turing’s key ideas in artificial intelligence (AI) and charts his legacy in the field of robot intelligence. In 1950, Turing suggested that one approach to machine intelligence would be to provide a machine with ‘the best sense organs that money can buy’, and then ‘teach it to understand and speak English’. After decades of struggle to create intelligent software, the current goal of many researchers in AI is indeed to build ‘socially intelligent’ robots—machines with vision and hearing and primitive communicative abilities. The grand aspiration of these theorists is to create what Turing called a ‘child machine’—a machine that, like a human infant, can point, smile, recognize its carer’s face, and learn to distinguish itself from others. In this chapter, I discuss Turing’s child machine and its descendants in modern cognitive and developmental robotics.

In 1950, Turing said: ‘Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child’s? If this were then subjected to an appropriate course of education one would obtain the adult brain’. His ‘guiding principle’ in the attempt to build intelligent machines was to follow the development of intelligence in the human being: ‘If we are trying to produce an intelligent machine, and are following the human model as closely as we can, we should begin with a machine with very little capacity to carry out elaborate operations or to react in a disciplined manner to orders…Then by applying appropriate interference, mimicking education, we should hope to modify the machine until it could be relied on to produce definite reactions to certain commands. This would be the beginning of the process…’.

Turing called this simple machine a ‘child machine’ and said that it must learn ‘initiative’ as well as discipline, so that it can modify its own instructions and make its own ‘choices’. When it does so, it has ‘grown up’—and then ‘one is obliged to regard the machine as showing intelligence’.

According to Turing, this is just to follow the example of the human child: when a child learns to make discoveries independently of her teacher, the teacher does not claim the credit.

--- https://en.wikipedia.org/wiki/Savant_syndrome Savant syndrome 2024-01-01 psychiatry/autism psychology/neuroscience/memory/savant --- https://en.wikipedia.org/wiki/Hemispherectomy#Outcomes Hemispherectomy § Outcomes 2024-05-11 iq psychology/neuroscience --- /doc/iq/2004-pulsifer.pdf The Cognitive Outcome of Hemispherectomy in 71 Children Margaret B. Pulsifer, Jason Brandt, Cynthia F. Salorio, Eileen P. G. Vining, Benjamin S. Carson, John M. Freeman 2004-02-18 2024-05-11 [("doi","10.1111/j.0013-9580.2004.15303.x")] iq psychiatry psychology/neuroscience

Purpose: Long-term neuropsychological outcome was studied in 71 patients who underwent hemispherectomy for severe and intractable seizures at The Johns Hopkins Hospital 1968–1997 and who agreed to participate. Seizures were due to cortical dysplasias (n = 27), Rasmussen syndrome (n = 37), or vascular malformations or strokes (n = 7). Both presurgical and follow-up results are available and reported for 53 patients.

Methods: Patients and caretakers were interviewed, and patients were administered standard measures of intelligence, receptive and expressive language, visual-motor skills, adaptive/developmental functioning, and behavior.

Results: Mean age at surgery was 7.2 years. At follow-up, on average 5.4 years after surgery, 65% are seizure-free, 49% are medication-free, and, of those responding, none rated quality of life as worse than before surgery. Mean IQ was in the 70s for Rasmussen and vascular patients and in the 30s for cortical dysplasia patients.

Language and visual-motor skills were consistent with IQ. For Rasmussen patients only, language was statistically-significantly more impaired for left than for right hemispherectomy, both before surgery and at follow-up. Adaptive skills were mildly impaired, with greatest impairment in the physical domain.

Cognitive measures typically changed little between surgery and follow-up, with IQ change <15 points for 34⁄53 patients; of the remainder, 11 declined and 8 improved.

Behavior was free of major problems, but social interactions and activities were limited.

Conclusions: The most statistically-significant predictor of cognitive skills at follow-up was etiology, with dysplasia patients scoring lowest in intelligence and language but not in visual-motor skills.

Regardless of etiology, most patients showed only moderate change in cognitive performance at follow-up.

--- /doc/psychiatry/autism/1887-down.pdf On some of the mental affections of childhood and youth: Lecture 3: Idiot Savants John Langdon Down 1887-01-01 2024-05-10 psychiatry/autism psychology/neuroscience/memory/savant

…One youth was under my care who could build exquisite model ships from drawings, and carve with a great deal of skill, who yet could not understand a sentence—who had to have it dissected for him, and who, when writing to his mother copied verbatim a letter from The Life of Captain Hedley Vicars, by Miss Marsh, although it had not the slightest appropriateness in word or sentiment.

…Extraordinary memory is often met with associated with very great defect of reasoning power. A boy came under my observation who, once having a book, could ever more remember it. He would recite all the answers in Magnall’s Questions without an error, giving in detail the numbers in the astronomical division with the greatest accuracy. I discovered, however, that it was simply a process of verbal adhesion. I once gave him Gibbon’s Rise and Fall of the Roman Empire to read. This he did, and on reading the third page skipped a line, found out his mistake and retracted his steps; ever after, when reciting from memory the stately periods of Gibbon, he would, on coming to the third page, skip the line and go back and correct the error with as much regularity as if it had been part of the regular text.

…Another boy can tell the tune, words, and number of nearly every hymn in Hymns Ancient and Modern.

[confabulation] …Improvisation is an occasional faculty. I had a boy under my care who could take up a book, pretending to read, an art he had not acquired, and improvise stories of all kinds with a great deal of skill, and in any variety, to suit the supposed tastes of his auditors. [So savants can confabulate just like an LLM; why does this come up so little? Does no one ever test them on ‘trick’ questions, because they are too focused on the positive case? Or do they refuse to cooperate if it’s outside their special areas of interest?]

…In none of the cases of “idiot savant” have I been able to trace any history of a like faculty in the parents or in the brothers and sisters…All of these cases of “idiot savants” were male; I have never met with a female.

…a boy who had a very unusual faculty, of which I have never since met another example, viz. the perfect appreciation of past or passing time. He was 17 years of age, and although not understanding, so far as I could gather, the use of a clock-face, could tell the time to a minute at any part of the day, and in any situation. I tried him on numberless occasions, and he always answered with an amount of precision truly remarkable. [Treffert & Wallace 2002 discuss a recent example of savant time-keeping, Ellen Boudreaux.]

--- https://animationobsessive.substack.com/p/selling-ghost-in-the-shell 2024-05-11 anime --- https://www.multicians.org/b2.html 2024-05-11 cs/security --- /doc/psychology/neuroscience/2023-lai.pdf Volitional activation of remote place representations with a hippocampal brain–machine interface Chongxi Lai, Shinsuke Tanaka, Timothy D. Harris, Albert K. Lee 2023-11-02 2024-05-11 [("doi","10.1126/science.adh5206")] psychology/neuroscience reinforcement-learning/model

Editor’s summary: The hippocampus holds a model of the environment that can be mentally traversed during recall or simulation. It is unknown whether animals can intentionally control their hippocampal activity according to their model of the world. By combining virtual reality and a real-time brain-machine interface, Lai et al 2023 discovered that rats directly controlled their hippocampal neuronal firing in a goal-directed manner (see the Perspective by Coulter & Kemere 2023). Rats first formed a hippocampal map of a virtual environment. Then, in brain-machine interface mode, they demonstrated the ability to activate representations from this map corresponding to specific remote locations, which then brought either them or an object to spatial goals. The rats could sustain a hippocampal representation of a remote location for tens of seconds, reminiscent of human imagination or mental time travel.

The hippocampus is critical for recollecting and imagining experiences. This is believed to involve voluntarily drawing from hippocampal memory representations of people, events, and places, including map-like representations of familiar environments. However, whether representations in such “cognitive maps” can be volitionally accessed is unknown.

We developed a brain-machine interface to test whether rats can do so by controlling their hippocampal activity in a flexible, goal-directed, and model-based manner.

We found that rats can efficiently navigate or direct objects to arbitrary goal locations within a virtual reality arena solely by activating and sustaining appropriate hippocampal representations of remote places.

This provides insight into the mechanisms underlying episodic memory recall, mental simulation and planning, and imagination and opens up possibilities for high-level neural prosthetics that use hippocampal representations.

--- /doc/psychology/neuroscience/2023-coulter.pdf The neural basis of mental navigation in rats: A brain–machine interface demonstrates volitional control of hippocampal activity Michael E. Coulter, Caleb Kemere 2023-11-02 2024-05-11 [("doi","10.1126/science.adl0806")] psychology/neuroscience reinforcement-learning/model

Complex behaviors such as navigation often require chains of decisions that can be mentally simulated ahead of carrying out the behavior. The activity of spatially coding neurons (place cells) in the hippocampus and associated cortical regions has been posited as the expression of a “cognitive map”, which could serve as a substrate for generating these complex behaviors1.

On page 566 of this issue, Lai et al 2023 report that rats can navigate through a virtual reality (VR) environment using a brain-machine interface (BMI) that estimates the rat’s mental location from the ongoing activity of neurons in its hippocampus.

This finding is an exciting expansion of BMI implementation from sensorimotor functions to a more cognitive domain and suggests that hippocampal activity is under volitional control. In addition, the BMI approach provides a new tool for probing the circuit-level mechanisms of mental navigation and spatial imagination.

--- https://trevorklee.substack.com/p/glp-1-and-gip-agonism-and-antagonism 2024-05-11 longevity/glp --- https://en.wikipedia.org/wiki/Blind_Tom_Wiggins Blind Tom Wiggins 2024-05-11 music psychology/neuroscience --- https://arxiv.org/abs/2210.05043 Multi-CLS BERT: An Efficient Alternative to Traditional Ensembling Haw-Shiuan Chang, Ruei-Yao Sun, Kathryn Ricci, Andrew McCallum 2022-10-10 2024-05-11 [("doi","10.48550/arXiv.2210.05043")] ai/nn/transformer/attention

Ensembling BERT models often improves accuracy, but at the cost of more computation and memory footprint. In this work, we propose Multi-CLS BERT, a novel ensembling method for CLS-based prediction tasks that is almost as efficient as a single BERT model.

Multi-CLS BERT uses multiple CLS tokens with a parameterization and objective that encourages their diversity. Thus instead of fine-tuning each BERT model in an ensemble (and running them all at test time), we need only fine-tune our single Multi-CLS BERT model (and run the one model at test time, ensembling just the multiple final CLS embeddings).

To test its effectiveness, we build Multi-CLS BERT on top of a state-of-the-art pretraining method for BERT (Aroca-Ouellette & Rudzicz 2020). In experiments on GLUE and SuperGLUE, we show that our Multi-CLS BERT reliably improves both overall accuracy and confidence estimation. When only 100 training samples are available in GLUE, the Multi-CLS BERT_Base model can even outperform the corresponding BERT_Large model.

We analyze the behavior of our Multi-CLS BERT, showing that it has many of the same characteristics and behavior as a typical BERT 5-way ensemble, but with nearly 4× less computation and memory.

--- https://x.com/mbusigin/status/1789334007047455178 mbusigin 2024-05-11 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/inner-monologue --- https://arxiv.org/abs/1901.11373#deepmind Learning and Evaluating General Linguistic Intelligence Dani Yogatama, Cyprien de Masson d’Autume, Jerome Connor, Tomas Kocisky, Mike Chrzanowski, Lingpeng Kong, Angeliki Lazaridou, Wang Ling, Lei Yu, Chris Dyer, Phil Blunsom 2019-01-31 2024-05-12 [("doi","10.48550/arXiv.1901.11373")] ai/nn/dynamic-evaluation ai/nn/transformer reinforcement-learning/meta-learning/continual-learning

We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language’s lexicon, syntax, semantics, and pragmatic conventions to adapt to new tasks quickly.

Using this definition, we analyze state-of-the-art natural language understanding models and conduct an extensive empirical investigation to evaluate them against these criteria through a series of experiments that assess the task-independence of the knowledge being acquired by the learning process. In addition to task performance, we propose a new evaluation metric based on an online encoding of the test data that quantifies how quickly an existing agent (model) learns a new task.

Our results show that while the field has made impressive progress in terms of model architectures that generalize to many tasks, these models still require a lot of in-domain training examples (eg. for fine-tuning, training task-specific modules), and are prone to catastrophic forgetting. Moreover, we find that far from solving general tasks (eg. document question answering), our models are overfitting to the quirks of particular datasets (eg. SQuAD).

We discuss missing components and conjecture on how to make progress toward general linguistic intelligence.

--- https://arxiv.org/abs/2007.06761 Can neural networks acquire a structural bias from raw linguistic data? Alex Warstadt, Samuel R. Bowman 2020-07-14 2024-05-12 [("doi","10.48550/arXiv.2007.06761")] ai/nn/transformer psychology/linguistics

We evaluate whether BERT, a widely used neural network for sentence processing, acquires an inductive bias towards forming structural generalizations through pretraining on raw data.

We conduct four experiments testing its preference for structural vs. linear generalizations in different structure-dependent phenomena. We find that BERT makes a structural generalization in 3⁄4 empirical domains—subject-auxiliary inversion, reflexive binding, and verb tense detection in embedded clauses—but makes a linear generalization when tested on NPI licensing.

We argue that these results are the strongest evidence so far from artificial learners supporting the proposition that a structural bias can be acquired from raw data.

If this conclusion is correct, it is tentative evidence that some linguistic universals can be acquired by learners without innate biases. However, the precise implications for human language acquisition are unclear, as humans learn language from less data than BERT.

--- https://arxiv.org/abs/1907.13528 What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models Allyson Ettinger 2019-07-31 2024-05-12 [("doi","10.48550/arXiv.1907.13528")] ai/nn/transformer psychology/linguistics

Pre-training by language modeling has become a popular and successful approach to NLP tasks, but we have yet to understand exactly what linguistic capacities these pre-training processes confer upon models.

In this paper we introduce a suite of diagnostics drawn from human language experiments, which allow us to ask targeted questions about the information used by language models for generating predictions in context.

As a case study, we apply these diagnostics to the popular BERT model, finding that it can:

generally distinguish good from bad completions involving shared category or role reversal, albeit with less sensitivity than humans, and it robustly retrieves noun hypernyms, but it struggles with challenging inferences and role-based event prediction—and in particular, it shows clear insensitivity to the contextual impacts of negation.

--- https://openreview.net/forum?id=BJfvknCqFQ A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry 2024-04-21 2024-05-12 ai/nn/adversarial ai/nn/cnn

We show that CNNs are not robust to simple rotations and translation and explore methods of improving this.

We show that simple spatial transformations, namely translations and rotations alone, suffice to fool neural networks on a fraction of their inputs in multiple image classification tasks.

Our results are in sharp contrast to previous work in adversarial robustness that relied on more complicated optimization approaches unlikely to appear outside a truly adversarial context. Moreover, the misclassifying rotations and translations are easy to find and require only a few black-box queries to the target model.

Overall, our findings emphasize the need to design robust classifiers even for natural input transformations in benign settings.

[Keywords: robustness, spatial transformations, invariance, rotations, data augmentation, robust optimization]

--- https://theconversation.com/novelist-j-g-ballard-was-experimenting-with-computer-generated-poetry-50-years-before-chatgpt-was-invented-228638 2024-05-12 ai/poetry --- https://www.nature.com/articles/434567a 2024-05-12 psychology/neuroscience --- /doc/psychiatry/depression/2024-rauf.pdf Genetic influences on depression and selection into adverse life experiences Tamkinat Rauf, Jeremy Freese 2024-03-01 2024-05-12 [("doi","10.1016/j.socscimed.2024.116633")] genetics/heritable/correlation psychiatry/depression

Genome-wide association studies (GWASes) find that a large number of genetic variants jointly influence the risk of depression, which is summarized by polygenic indices (PGSs) of depressive symptoms and major depression. But PGSs by design remain agnostic about the causal mechanisms linking genes to depression. Meanwhile, the role of adverse life experiences in shaping depression risk is well-documented, including via gene-environment correlation.

Building on theoretical work on dynamic and contingent genetic selection, we suggest that genetic influences may lead to differential selection into negative life experiences, forging gene-environment correlations that manifest in various permutations of depressive behaviors and environmental adversities.

Using data from two large surveys of middle-aged and older US adults, we investigate to what extent a PGS of depression predicts the risk of 27 different adversities. Further, to glean insights about the kinds of processes that might lead to gene-environment correlation, we augment these analyses with data from an original preregistered survey to measure cultural understandings of the behavioral dependence of various adversities.

We find that the PGS predicts the risk of the majority of adversities, net of class background and prior depression, and that the selection risk is greater for adversities typically perceived as being dependent on people’s own behaviors.

Figure 2: Logit Estimates of the Effect of Depression PGS on Probability of Adversity. Note: Bars indicate 95% confidence intervals. Models control for gender, age, 10 ancestry PCs, and class background. Estimates are unweighted and corrected for measurement error using SIMEX.

Taken together, our findings suggest that the PGS of depression largely picks up the risk of behaviorally-influenced adversities, but to a lesser degree also captures other environmental influences. The results invite further exploration into the behavioral and interactional processes that lie along the pathways intervening between genetic differences and wellbeing.

…Most PGS coefficients are statistically-significant (15⁄22 in Add Health; 12⁄22 in WLS). Null results are largely consistent: in both datasets, the PGS had no effect on physical unattractiveness, sibling death, cancer, and heavy alcohol use. Inconsistencies between samples (namely, unemployment, childhood sexual abuse, and parent death) have viable explanations: Unemployment and childhood sexual abuse are relatively rare, with correspondingly wider confidence intervals; the magnitude of the childhood sexual abuse estimate is similar across samples, as is the relative rank of the PGS coefficient for unemployment. In case of parent death, the inconsistency potentially stems from different variable definitions: Add Health includes non-parental guardians whereas WLS only includes parents. A restricted definition of parent death in Add Health yields results consistent with WLS (Appendix G1).

Many of the statistically-significant results concur with expectations based on prior research. To give some examples: Prior twin research finds convincing evidence of heritability of marital dissolution (Jerskey et al 2010). For unemployment, previous research has pointed to the aspects of “discipline and temperament” that predict getting laid-off (not to mention fired) as also being risk factors for divorce (Charles & Stephens 2004). The same can be said for incarceration and depression risk (Liu et al 2021). For experiences of being insulted/disrespected, research has found a higher tendency among depressive individuals to negatively interpret neutral or ambiguous stimuli (Everaert et al 2018; Hindash & Amir 2012).

However, previous scholarship is not as helpful in understanding the statistically-significant effect of the depression PGS on physical and sexual abuse and partner violence, which may be reasonably regarded as outside a person’s control. At least for childhood events, one explanation could be ‘indirect’ genetic effects, as discussed above. Such indirect effects could operate by, for instance, shaping the risk of selection into unhealthy relationships or risk-taking behavior. In other cases, the PGS could simply be capturing parental genetic influences on the family environment: for instance, supplementary analyses find that growing up in a single-parent household is also predicted by the depression PGS in Add Health (Append1ix G2).

In ancillary analyses, we estimated sibling FE models to net out indirect genetic effects (Appendix H). As noted, these results have diminished statistical power, especially for Add Health. Nevertheless, overall, we found statistically-significant within-sibling effects for 10⁄12 adversities in WLS that were also statistically-significantly predicted by the PGS in the main analyses, and for 5⁄15 adversities in Add Health. Yet, consistent with the interpretation of indirect genetic effects being captured by the PGS, we find that FE estimates were not statistically-significant for child or adult sexual abuse in either dataset and for partner abuse in WLS. But, perhaps surprisingly, FE models continued to show a statistically-significant effect of the PGS on childhood physical abuse (both datasets) and for partner abuse in Add Health.

The analyses in Figure 2 control for parental education and income. For most adversities at least one of these indicators has statistically-significant conditional effects. Indeed, the depression PGS has no statistically-significant association with sibling death, childhood disability, and being perceived as physically unattractive, but class background does. In other cases, estimates for class are comparatively small: in particular, child’s illness is statistically-significantly predicted by the PGS but not by indicators of class background. Unsurprisingly, then, the relative importance of genetic and environmental influences varies across types of adversities. The effect of sex on adversity risk also varies in direction and magnitude, as one would expect. Taken together, these results suggest that the depression PGS predicts selection into adversities through mechanisms that are not subsumed by some key observable dimensions of disadvantage, including class background and sex.

3.3. Does gene-environment correlation correspond with perceptions of behavioral dependence?

We now turn to the ratings of behavioral dependence of adversities. Overall, there was high inter-rater reliability across adversities (see Appendix Figure I3). Attribution ratings were generally lower for adversities that happen to family members or friends, result from the violence of another person or external factors, or may be perceived as inborn physiological characteristics; whereas events occurring later in life and pertaining to one’s own health, career, or relationships were viewed as more behaviorally dependent.

Figure 3 plots the weighted average logit coefficients of the depression PGS against the average ratings of behavioral attribution. The linear fit line indicates a positive correlation between the behavioral attribution ratings and the coefficients of the depression PGS (r = 0.33, p = 0.09).

This statistic does not meet the conventional statistical-significance threshold of 0.05, but given the small n = 27, this remains suggestive evidence that the genetic burden of depression is more strongly associated with adversities regarded as being influenced by a person’s behavior. The adversities in the top-right quadrant of the graph may be especially likely to reflect events/experiences that are notable pathways of genetic selection into depression, since these are regarded as resulting from greater behavioral input. In contrast, adversities in the top-left quadrant may be reflecting indirect genetic effects or evocative selection.

Figure 3: Relationship between Effects of PGS on Adversity Risk and Behavioral Attribution Ratings of Adversities. Note: Logit coefficients for WLS and Add Health data averaged using inverse variance weighting (IVW). --- https://web.archive.org/web/20131208072327/https://www.wired.com/medtech/health/magazine/17-04/ff_perfectmemory?currentPage=all Total Recall: The Woman Who Can’t Forget 2024-01-01 psychology/neuroscience/memory/savant --- https://www.theguardian.com/science/2017/feb/08/total-recall-the-people-who-never-forget Total recall: the people who never forget: An extremely rare condition may transform our understanding of memory 2024-01-01 psychology/neuroscience/memory/savant --- https://arxiv.org/abs/2404.13292 Evaluating Subword Tokenization: Alien Subword Composition and OOV Generalization Challenge Khuyagbaatar Batsuren, Ekaterina Vylomova, Verna Dankers, Tsetsuukhei Delgerbaatar, Omri Uzan, Yuval Pinter, Gábor Bella 2024-04-20 2024-05-12 [("doi","10.48550/arXiv.2404.13292")] ai/nn/tokenization ai/nn/transformer

The popular subword tokenizers of current language models, such as Byte-Pair Encoding (BPE), are known not to respect morpheme boundaries, which affects the downstream performance of the models. While many improved tokenization algorithms have been proposed, their evaluation and cross-comparison is still an open problem.

As a solution, we propose a combined intrinsic-extrinsic evaluation framework for subword tokenization. Intrinsic evaluation is based on our new UniMorph Labeller tool that classifies subword tokenization as either morphological or alien. Extrinsic evaluation, in turn, is performed via the Out-of-Vocabulary Generalization Challenge 1.0 benchmark, which consists of 3 newly specified downstream text classification tasks.

Our empirical findings show that the accuracy of UniMorph Labeller is 98%, and that, in all language models studied (ALBERT, BERT, RoBERTa, and DeBERTa), alien tokenization leads to poorer generalizations compared to morphological tokenization for semantic compositionality of word meanings.

--- https://www.sciencedirect.com/science/article/pii/S0092867409016304 2024-05-12 psychology/neuroscience/memory --- https://en.wikipedia.org/wiki/Critical_period Critical period 2024-05-12 psychology/neuroscience/memory --- https://en.wikipedia.org/wiki/John_Langdon_Down John Langdon Down 2024-05-11 psychology/neuroscience/memory/savant --- https://en.wikipedia.org/wiki/Hyperlexia Hyperlexia 2024-05-12 psychology/linguistics --- https://en.wikipedia.org/wiki/Einstein_syndrome Einstein syndrome 2024-05-12 psychology/linguistics --- https://en.wikipedia.org/wiki/Blindism Blindism 2024-05-12 psychology/vision --- https://rstb.royalsocietypublishing.org/content/364/1522/1399.full Explaining and inducing savant skills: privileged access to lower level, less-processed information 2024-01-01 psychology/neuroscience/memory/savant --- /doc/psychiatry/autism/1965-horwitz.pdf Identical Twin—‘Idiot Savants’—Calendar Calculators William A. Horwitz, Clarice Kestenbaum, Ethel Person 1965-05-01 2024-01-01 [("doi","10.1176/ajp.121.11.1075")] psychiatry/autism psychology/neuroscience/memory/savant --- https://en.wikipedia.org/wiki/John_Davis_(pianist) John Davis (pianist) 2024-05-12 psychology/neuroscience/memory/savant --- https://en.wikipedia.org/wiki/Leslie_Lemke Leslie Lemke 2024-05-12 psychology/neuroscience/memory/savant --- https://en.wikipedia.org/wiki/Richard_Wawro Richard Wawro 2024-05-12 psychology/neuroscience/memory/savant --- https://en.wikipedia.org/wiki/Alonzo_Clemons Alonzo Clemons 2024-05-12 psychology/neuroscience/memory/savant --- https://en.wikipedia.org/wiki/Frontotemporal_dementia Frontotemporal dementia 2024-05-12 psychology/neuroscience/memory/savant --- https://en.wikipedia.org/wiki/Kim_Peek Kim Peek 2024-01-01 psychology/neuroscience/memory/savant --- https://en.wikipedia.org/wiki/Darold_A._Treffert Darold A. Treffert 2024-05-12 psychology/neuroscience/memory/savant --- https://forum.effectivealtruism.org/posts/dw8Wxcwc7TuSR2Tb9/the-market-expects-ai-software-to-create-trillions-of 2024-05-12 ai/scaling/economics --- https://arxiv.org/abs/1309.4731 Ballooning Spiders: The Case for Electrostatic Flight Peter W. Gorham 2013-09-18 2024-05-12 [("doi","10.48550/arXiv.1309.4731")] science

[since verified] We consider general aspects of the physics underlying the flight of Gossamer spiders, also known as ballooning spiders. We show that existing observations and the physics of spider silk in the presence of the Earth’s static atmospheric electric field indicate a potentially important role for electrostatic forces [see Feynman] in the flight of Gossamer spiders.

A compelling example is analyzed in detail, motivated by the observed “unaccountable rapidity” in the launching of such spiders from _H.M.S. Beagle_, recorded by Charles Darwin during his famous voyage.

--- https://www.feynmanlectures.caltech.edu/II_09.html 2024-05-12 science --- https://en.wikipedia.org/wiki/John_Haygarth#Perkins'_tractors John Haygarth § Perkins’ tractors 2024-05-12 statistics/bias --- https://arxiv.org/abs/2404.12699 SOPHON: Non-Fine-Tunable Learning to Restrain Task Transferability For Pre-trained Models Jiangyi Deng, Shengyuan Pang, Yanjiao Chen, Liangming Xia, Yijie Bai, Haiqin Weng, Wenyuan Xu 2024-04-19 2024-05-12 [("doi","10.48550/arXiv.2404.12699")] reinforcement-learning/meta-learning reinforcement-learning/safe

[previously] Instead of building deep learning models from scratch, developers are more and more relying on adapting pre-trained models to their customized tasks. However, powerful pre-trained models may be misused for unethical or illegal tasks, eg. privacy inference and unsafe content generation. In this paper, we introduce a pioneering learning paradigm, non-fine-tunable learning, which prevents the pre-trained model from being fine-tuned to indecent tasks while preserving its performance on the original task.

To fulfill this goal, we propose SOPHON, a protection framework that reinforces a given pre-trained model to be resistant to being fine-tuned in pre-defined restricted domains. Nonetheless, this is challenging due to a diversity of complicated fine-tuning strategies that may be adopted by adversaries. Inspired by model-agnostic meta-learning, we overcome this difficulty by designing sophisticated fine-tuning simulation and fine-tuning evaluation algorithms. In addition, we carefully design the optimization process to entrap the pre-trained model within a hard-to-escape local optimum regarding restricted domains.

We have conducted extensive experiments on two deep learning modes (classification and generation), 7 restricted domains, and 6 model architectures to verify the effectiveness of SOPHON.

Experiment results verify that fine-tuning SOPHON-protected models incurs an overhead comparable to or even greater than training from scratch. Furthermore, we confirm the robustness of SOPHON to 3 fine-tuning methods, 5 optimizers, various learning rates, and batch sizes.

SOPHON may help boost further investigations into safe and responsible AI.

--- https://x.com/michael_nielsen/status/1787392849866362938 Michael Nielsen 2024-05-12 design --- https://sweet-hall-e72.notion.site/Mimicking-Diffusion-Models-by-Sequencing-Frequency-Coefficients-8e5a60e876d640c390369627d55330b1 2024-05-12 ai/nn/diffusion --- https://arxiv.org/abs/1706.05699 Gradient Diversity: a Key Ingredient for Scalable Distributed Learning Dong Yin, Ashwin Pananjady, Max Lam, Dimitris Papailiopoulos, Kannan Ramchandran, Peter Bartlett 2017-06-18 2024-05-12 [("doi","10.48550/arXiv.1706.05699")] ai/nn/cnn ai/scaling

It has been experimentally observed that distributed implementations of mini-batch stochastic gradient descent (SGD) algorithms exhibit speedup saturation and decaying generalization ability beyond a particular batch size. In this work, we present an analysis hinting that high similarity between concurrently processed gradients may be a cause of this performance degradation.

We introduce the notion of gradient diversity that measures the dissimilarity between concurrent gradient updates, and show its key role in the performance of mini-batch SGD. We prove that on problems with high gradient diversity, mini-batch SGD is amenable to better speedups, while maintaining the generalization performance of serial (one sample) SGD.

We further establish lower bounds on convergence where mini-batch SGD slows down beyond a particular batch size, solely due to the lack of gradient diversity. We provide experimental evidence indicating the key role of gradient diversity in distributed learning, and discuss how heuristics like dropout, Langevin dynamics, and quantization can improve it.

--- https://arxiv.org/abs/2303.17376#google A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision Lucas Beyer, Bo Wan, Gagan Madan, Filip Pavetic, Andreas Steiner, Alexander Kolesnikov, André Susano Pinto, Emanuele Bugliarello, Xiao Wang, Qihang Yu, Liang-Chieh Chen, Xiaohua Zhai 2023-03-30 2024-05-12 [("doi","10.48550/arXiv.2303.17376")] ai/nn/transformer

There has been a recent explosion of computer vision models which perform many tasks and are composed of an image encoder (usually a ViT) and an autoregressive decoder (usually a Transformer). However, most of this work simply presents one system and its results, leaving many questions regarding design decisions and trade-offs of such systems unanswered. In this work, we aim to provide such answers.

We take a close look at autoregressive decoders for multi-task learning in multimodal computer vision, including classification, captioning, visual question answering, and optical character recognition. Through extensive systematic experiments, we study the effects of task and data mixture, training and regularization hyperparameters, conditioning type and specificity, modality combination, and more.

Importantly, we compare these to well-tuned single-task baselines to highlight the cost incurred by multi-tasking. A key finding is that a small decoder learned on top of a frozen pretrained encoder works surprisingly well. We call this setup locked-image tuning with decoder (LiT-decoder). It can be seen as teaching a decoder to interact with a pretrained vision model via natural language.

--- https://en.wikipedia.org/wiki/Top_Chess_Engine_Championship Top Chess Engine Championship 2024-05-12 reinforcement-learning/chess --- https://en.wikipedia.org/wiki/Leela_Chess_Zero Leela Chess Zero 2024-05-12 reinforcement-learning/chess --- https://www.reddit.com/r/ChatGPT/comments/1coumbd/rchatgpt_is_hosting_a_qa_with_openais_ceo_sam/l3hku1x/ 2024-05-12 ai/nn/transformer/gpt/fiction reinforcement-learning/safe --- https://arxiv.org/abs/2108.13002#microsoft A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP Yucheng Zhao, Guangting Wang, Chuanxin Tang, Chong Luo, Wenjun Zeng, Zheng-Jun Zha 2021-08-30 2024-05-12 [("doi","10.48550/arXiv.2108.13002")] ai/nn/cnn ai/nn/fully-connected ai/nn/transformer ai/scaling

[cf. Bachmann et al 2023] Convolutional neural networks (CNN) are the dominant deep neural network (DNN) architecture for computer vision. Recently, Transformer and multi-layer perceptron (MLP)-based models, such as Vision Transformer and MLP-Mixer, started to lead new trends as they showed promising results in the ImageNet classification task.

In this paper, we conduct empirical studies on these DNN structures and try to understand their respective pros and cons. To ensure a fair comparison, we first develop a unified framework called SPACH which adopts separate modules for spatial and channel processing. Our experiments under the SPACH framework reveal that all structures can achieve competitive performance at a moderate scale. However, they demonstrate distinctive behaviors when the network size scales up.

Based on our findings, we propose two hybrid models using convolution and Transformer modules. The resulting Hybrid-MS-S+ model achieves 83.9% top-1 accuracy with 63M parameters and 12.3G FLOPS. It is already on par with the SOTA models with sophisticated designs.

The code and models are publicly available at Github.

…The SPACH framework contains a plug-and-play module called mixing block which could be implemented as convolution layers, Transformer layers, or MLP layers. Aside from the mixing block, other components in the framework are kept the same when we explore different structures. This is in stark contrast to previous work which compares different network structures in different frameworks that vary greatly in layer cascade, normalization, and other non-trivial implementation details. As a matter of fact, we found that these structure-free components play an important role in the final performance of the model, and this is commonly neglected in the literature.

Figure 1: Illustration of the proposed experimental framework named SPACH.

With this unified framework, we design a series of controlled experiments to compare the 3 network structures. The results show that all 3 network structures could perform well on the image classification task when pre-trained on ImageNet-1K. In addition, each individual structure has its distinctive properties leading different behaviors when the network size scales up. We also find several common design choices which contribute a lot to the performance of our SPACH framework. The detailed findings are listed in the following.

Based on these findings, we propose two hybrid models of different scales which are built upon convolution and Transformer layers. Experimental results show that, when a sweet point between generalization capability and model capacity is reached, the performance of these straightforward hybrid models is already on par with SOTA models with sophisticated architecture designs.

Figure 4: Illustration of the over-fitting problem in MLP-based models. Both multi-stage framework and weight sharing alleviate the problem.
Table 5: The performance of MLP models are greatly boosted when weight sharing is adopted to alleviate over-fitting.
Model # parameters FLOPs throughput (image/second) ImageNet-1K Top-1 accuracy
MLP-S 41M 8.7G 272 78.6
+Shared 39M 8.7G 274 80.2
MLP-MS-S 46M 8.2G 254 82.1
+Shared 45M 8.2G 244 82.5

…4.4. A Detailed Analysis of MLP: Due to the excessive number of parameters, MLP models suffer severely from over-fitting. We believe that overfitting is the main obstacle for MLP to achieve SOTA performance. In this part, we discuss two mechanisms which can potentially alleviate this problem.

One is the use of multi-stage framework. We have already shown in Table 3 that multi-stage framework brings gain. Such gain is even more prominent for larger MLP models. In particular, the MLP-MS-S models achieves 2.6 accuracy gain over the single-stage model MLP-S. We believe this owes to the strong generalization capability of the multi-stage framework. Figure 4 shows how the test accuracy increases with the decrease of training loss. Overfitting can be observed when the test accuracy starts to flatten. These results also lead to a very promising baseline for MLP-based models. Without bells and whistles, MLP-MSS model achieves 82.1% ImageNet Top-1 accuracy, which is 5.7 points higher than the best results reported by MLP-Mixer when ImageNet-1K is used as training data.

The other mechanism is parameter reduction through weight sharing. We apply weight-sharing on the spatial mixing function 𝐹s. For the single-stage model, all n mixing blocks use the same 𝐹s, while for the multi-stage model, each stage use the same 𝐹s for its Ns mixing blocks. We present the results of S models in Table 5: We can find that the shared-weight variants, denoted by “+Shared”, achieve higher accuracy with almost the same model size and computation cost. Although they are still inferior to Transformer models, the performance is on par with or even better than convolution models. Figure 4 confirms that using shared weights in the MLP-MS model further delays the appearance of over-fitting signs.

Therefore, we conclude that MLP-based models remain competitive if they could solve or alleviate the over-fitting problem.

…Under the SPACH framework, we discover with a little surprise that all 3 network structures are similarly competitive in terms of the accuracy-complexity tradeoff, although they show distinctive properties when the network scales up…Our work also raises several questions worth exploring. First, realizing the fact that the performance of MLP-based models is largely affected by over-fitting, is it possible to design a high-performing MLP model that is not subject to over-fitting?

--- https://arxiv.org/abs/2405.05219 Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers Jiuxiang Gu, Yingyu Liang, Heshan Liu, Zhenmei Shi, Zhao Song, Junze Yin 2024-05-08 2024-05-12 [("doi","10.48550/arXiv.2405.05219")] ai/nn/transformer/attention/linear-algebra

Large Language Models (LLMs) have profoundly changed the world. Their self-attention mechanism is the key to the success of transformers in LLMs. However, the quadratic computational cost 𝒪(n2) to the length n input sequence is the notorious obstacle for further improvement and scalability in the longer context.

In this work, we leverage the convolution-like structure of attention matrices to develop an efficient approximation method for attention computation using convolution matrices. We propose a conv basis system, “similar” to the rank basis, and show that any lower triangular (attention) matrix can always be decomposed as a sum of k structured convolution matrices in this basis system. We then design an algorithm to quickly decompose the attention matrix into k convolution matrices.

Thanks to Fast Fourier Transforms (FFT), the attention inference can be computed in 𝒪(knd log n) time, where d is the hidden dimension. In practice, we have d ≪ n, ie. d = 3,072 and n = 1,000,000 for Gemma. Thus, when kd = no(1), our algorithm achieve almost linear time, ie. n1+o(1). Furthermore, the attention training forward and backward gradient can be computed in n1+o(1) as well.

Our approach can avoid explicitly computing the n × n attention matrix, which may largely alleviate the quadratic computational complexity. Furthermore, our algorithm works on any input matrices.

This work provides a new paradigm for accelerating attention computation in transformers to enable their application to longer contexts.

--- https://arxiv.org/abs/2405.02793#google ImageInWords: Unlocking Hyper-Detailed Image Descriptions Roopal Garg, Andrea Burns, Burcu Karagol Ayan, Yonatan Bitton, Ceslee Montgomery, Yasumasa Onoe, Andrew Bunner, Ranjay Krishna, Jason Baldridge, Radu Soricut 2024-05-05 2024-05-12 [("doi","10.48550/arXiv.2405.02793")] ai/dataset ai/nn/transformer/clip

Despite the long-standing adage “an image is worth a thousand words”, creating accurate and hyper-detailed image descriptions for training Vision-Language models remains challenging. Current datasets typically have web-scraped descriptions that are short, low-granularity, and often contain details unrelated to the visual content. As a result, models trained on such data generate descriptions replete with missing information, visual inconsistencies, and hallucinations.

To address these issues, we introduce ImageInWords (IIW), a carefully designed human-in-the-loop annotation framework for curating hyper-detailed image descriptions and a new dataset resulting from this process. We validate the framework through evaluations focused on the quality of the dataset and its utility for fine-tuning with considerations for readability, comprehensiveness, specificity, hallucinations, and human-likeness.

Our dataset improves across these dimensions compared to recently released datasets (+66%) and GPT-4-V outputs (+48%). Furthermore, models fine-tuned with IIW data excel by +31% against prior work along the same human evaluation dimensions. Given our fine-tuned models, we also evaluate text-to-image generation and vision-language reasoning. Our model’s descriptions can generate images closest to the original, as judged by both automated and human metrics.

We also find our model produces more compositionally rich descriptions, outperforming the best baseline by up to 6% on ARO, SVO-Probes, and Winoground datasets.

--- https://arxiv.org/abs/2401.17377 Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens Jiacheng Liu, Sewon Min, Luke Zettlemoyer, Yejin Choi, Hannaneh Hajishirzi 2024-01-30 2024-05-12 [("doi","10.48550/arXiv.2401.17377")] cs/algorithm/information/compression

Are n-gram language models still relevant in this era of neural large language models (LLMs)? Our answer is yes, and we showcase their values in both text analysis and improving neural LLMs.

This was done by modernizing n-gram LMs in two aspects. First, we train them at the same data scale as neural LLMs: 5 trillion tokens. This is the largest n-gram LM ever built. Second, existing n-gram LMs use small n which hinders their performance; we instead allow n to be arbitrarily large, by introducing a new ∞-gram LM with backoff.

Instead of pre-computing n-gram count tables (which would be very expensive), we develop an engine named infini-gram—powered by suffix arrays—that can compute ∞-gram (as well as n-gram with arbitrary n) probabilities with millisecond-level latency. The ∞-gram framework and infini-gram engine enable us to conduct many novel and interesting analyses of human-written and machine-generated text.

We find that the ∞-gram LM has fairly high accuracy for next-token prediction (47%), and can complement neural LLMs to greatly reduce their perplexity [cf. neural cache].

When analyzing machine-generated text, we also observe irregularities in the machine–∞-gram agreement level with respect to the suffix length, which indicates deficiencies in neural LLM pretraining and the positional embeddings of Transformers.

--- /doc/psychology/neuroscience/memory/savant/1962-hunter.pdf An Exceptional Talent For Calculative Thinking Ian M. L. Hunter 1962-01-01 2024-05-13 [("doi","10.1111/j.2044-8295.1962.tb00831.x")] math psychology/neuroscience/memory/savant

This study explores the highly exceptional ‘lightning calculation’ of a distinguished mathematician who has considerable understanding of his own calculative thinking.

Each calculation is a temporally co-ordinated, rapidly flexible onleading which is both unitary and complex.

Biographically, it derives from prolonged and intensive practice fostered by circumstances in his upbringing and made possible by a large cognitive capacity which also manifests itself in other forms of intellectual achievement.

During calculation, there are ‘leaps’ of varying compass; there is also notable absence of sensory-type awareness. Ongoing proceeds through apprehending multiple attributes of the presented problem, deciding on some convenient and often ingenious calculative plan, and rhythmically implementing this plan while carrying through opportunistic telescoping and verifying of the ongoing activity.

--- https://www.folklore.org/Signing_Party.html Signing Party: The artists sign their work Andy Hertzfeld 2004 2024-05-13 design economics/advertising --- https://www.astralcodexten.com/p/mantic-monday-51324 2024-05-13 statistics/prediction/election --- https://arxiv.org/abs/2212.08184 NBC-Softmax: Darkweb Author fingerprinting and migration tracking Gayan K. Kulatilleke, Shekhar S. Chandra, Marius Portmann 2022-12-15 2024-05-13 [("doi","10.48550/arXiv.2212.08184")] ai/nn/transformer darknet-market/agora darknet-market/blackmarket-reloaded darknet-market/dnm-archive darknet-market/silk-road/1 darknet-market/silk-road/2

Metric learning aims to learn distances from the data, which enhances the performance of similarity-based algorithms. An author style detection task is a metric learning problem, where learning style features with small intra-class variations and larger inter-class differences is of great importance to achieve better performance. Recently, metric learning based on softmax loss has been used successfully for style detection. While softmax loss can produce separable representations, its discriminative power is relatively poor.

In this work, we propose NBC-Softmax, a contrastive loss based clustering technique for softmax loss, which is more intuitive and able to achieve superior performance. Our technique meets the criterion for larger number of samples, thus achieving block contrastiveness, which is proven to outperform pair-wise losses. It uses mini-batch sampling effectively and is scalable.

Experiments on 4 dark web social forums, with NBCSAuthor that uses the proposed NBC-Softmax for author and sybil detection, shows that our negative block contrastive approach constantly outperforms state-of-the-art methods using the same network architecture.

Our code is publicly available at: https://github.com/gayanku/NBC-Softmax.

--- http://gamelab.mit.edu/games/a-slower-speed-of-light/ 2024-05-13 design/visualization science --- https://arxiv.org/abs/1703.08961 Scaling the Scattering Transform: Deep Hybrid Networks Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko 2017-03-27 2024-05-14 [("doi","10.48550/arXiv.1703.08961")] ai/nn/cnn

[code] We use the scattering network as a generic and fixed initialization of the first layers of a supervised hybrid deep network. We show that early layers do not necessarily need to be learned, providing the best results to-date with pre-defined representations while being competitive with Deep CNNs.

Using a shallow cascade of 1×1 convolutions, which encodes scattering coefficients that correspond to spatial windows of very small sizes, permits to obtain AlexNet accuracy on the ImageNet ILSVRC2012. We demonstrate that this local encoding explicitly learns invariance with respect to rotations.

Combining scattering networks with a modern ResNet, we achieve a single-crop top 5 error of 11.4% on ImageNet ILSVRC2012, comparable to the Resnet-18 architecture, while using only 10 layers.

We also find that hybrid architectures can yield excellent performance in the small sample regime, exceeding their end-to-end counterparts, through their ability to incorporate geometrical priors.

We demonstrate this on subsets of the CIFAR-10 dataset and on the STL-10 dataset.

--- https://x.com/suchenzang/status/1790171161512587424 Suzan Zhang 2024-05-14 ai/nn/tokenization --- https://x.com/tianle_cai/status/1790109646205890723 tianle_cai 2024-05-14 ai/nn/tokenization --- https://x.com/apples_jimmy/status/1790158228359368894 apples_jimmy 2024-05-14 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/dall-e/3 ai/nn/transformer/gpt/whisper --- https://www.fujitsu.com/global/about/resources/news/press-releases/2024/0510-01.html 2024-05-14 ai/scaling/hardware --- https://arxiv.org/abs/2305.09828 Mimetic Initialization of Self-Attention Layers Asher Trockman, J. Zico Kolter 2023-05-16 2024-05-14 [("doi","10.48550/arXiv.2305.09828")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/attention ai/nn/transformer/gpt/2

It is notoriously difficult to train Transformers on small datasets; typically, large pre-trained models are instead used as the starting point. We explore the weights of such pre-trained Transformers (particularly for vision) to attempt to find reasons for this discrepancy.

Surprisingly, we find that simply initializing the weights of self-attention layers so that they “look” more like their pre-trained counterparts allows us to train vanilla Transformers faster and to higher final accuracies, particularly on vision tasks such as CIFAR-10 and ImageNet classification, where we see gains in accuracy of over 5% and 4%, respectively.

Our initialization scheme is closed-form, learning-free, and very simple: we set the product of the query and key weights to be the identity, and the product of the value and projection weights to the negative identity.

As this mimics the patterns we saw in pre-trained Transformers, we call the technique mimetic initialization.

Figure 2: Attention maps computed from one CIFAR-10 batch for ViT-Tiny: (a) untrained (b) CIFAR-10 trained (c) ImageNet pretrained (d) using our init (e) our init & then CIFAR-10 trained. Rows: ↓ Layers #1, 4, 11.

[note the similarities, particularly in Figure 8 & Figure 9, to the MLP patterns in “Scaling MLPs: A Tale of Inductive Bias”, Bachmann et al 2023]

…In Figure 2, we show the attention maps in a ViT-Tiny for a variety of training settings, averaged over the 3 heads and over a batch of CIFAR-10 inputs. Note the difference between the untrained model (a) and the untrained one using our initialization (d). Further, there is some degree of similarity between the ImageNet-pretrained model (c) and our untrained one (d).

After training our initialized ViT on CIFAR-10, the early layers are similar to those of the ImageNet-pretrained ViT while the later layers are more like those of the only-CIFAR-trained ViT (b).

The last layers of the ImageNet-pretrained ViT implement a kind of broadcasting operation which we do not attempt to mimick.

…7. Language Modeling While our method was primarily inspired by pretrained Vision Transformers, in this section we investigate its potential for use in language models. As noted in Sec. 3 and seen in Figure 9, we do not see precisely the same pattern in a pre-trained GPT-2 model as we do in a ViT. Nonetheless, we use the same technique here without modification; we saw no improvement from, eg. attempting to model the positive diagonals of WV Wproj.

Figure 7: A pretrained GPT-2 shows considerably different patterns in the products of WQ WTK and WV Wproj, compared to ViTs.

…While our initialization does not make a large amount of difference for these small-scale language tasks as it does for vision tasks, it does show a small amount of improvement. We suspect that it may be the case that a mimetic initialization scheme more finely-tuned to the language setting may show still better performance…We speculate that it may be possible to use domain knowledge to “program” models before training in order to reach more desirable optima that may have been out of reach with a completely random initialization. With better structured initialization techniques like our own, perhaps Transformers really are the universal architecture.

See Also:

--- https://x.com/VictorTaelin/status/1790183986096116189 Victor Taelin 2024-05-14 ai/nn/transformer/gpt/4/fiction --- https://x.com/goodside/status/1790294534670176336 Riley Goodside 2024-05-14 ai/nn/transformer/gpt/4/nonfiction cs/cryptography/steganography --- https://www.smbc-comics.com/comic/new-word 2024-05-14 fiction/humor psychology/linguistics --- https://www.youtube.com/watch?v=D5VN56jQMWM 2024-05-14 ai/music --- https://commons.wikimedia.org/wiki/File:SUN_microsystems_logo_ambigram.png 2024-05-14 design/typography/square --- https://arxiv.org/abs/2402.08955 Using Counterfactual Tasks to Evaluate the Generality of Analogical Reasoning in Large Language Models Martha Lewis, Melanie Mitchell 2024-02-14 2024-05-14 [("doi","10.48550/arXiv.2402.08955")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction philosophy/logic

Large language models (LLMs) have performed well on several reasoning benchmarks, including ones that test analogical reasoning abilities. However, it has been debated whether they are actually performing human-like abstract reasoning or instead employing less general processes that rely on similarity to what has been seen in their training data.

Here we investigate the generality of analogy-making abilities previously claimed for LLMs (Webb et al 2023). We take one set of analogy problems used to evaluate LLMs and create a set of “counterfactual” variants-versions that test the same abstract reasoning abilities but that are likely dissimilar from any pre-training data. We test humans and 3 GPT models on both the original and counterfactual problems, and show that:

while the performance of humans remains high for all the problems, the GPT models’ performance declines sharply on the counterfactual set.

This work provides evidence that, despite previously reported successes of LLMs on analogical reasoning, these models lack the robustness and generality of human analogy-making.

--- https://www.caltech.edu/about/news/LLMs-in-the-classroom 2024-05-14 ai/nn/transformer/gpt/4/nonfiction --- https://github.com/kagisearch/llm-chess-puzzles?tab=readme-ov-file#results 2024-05-14 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/chess --- https://www.hnn.us/article/frank-ramsey-a-genius-by-all-tests-for-genius 2024-05-14 philosophy/frank-ramsey --- https://nian.llmonpy.ai/ 2024-05-14 ai/nn/transformer/attention ai/nn/transformer/gpt/4/nonfiction --- https://arxiv.org/abs/2405.07883 Zero-Shot Tokenizer Transfer Benjamin Minixhofer, Edoardo Maria Ponti, Ivan Vulić 2024-05-13 2024-05-14 [("doi","10.48550/arXiv.2405.07883")] ai/nn/tokenization reinforcement-learning/meta-learning

Language models (LMs) are bound to their tokenizer, which maps raw text to a sequence of vocabulary items (tokens). This restricts their flexibility: for example, LMs trained primarily on English may still perform well in other natural and programming languages, but have vastly decreased efficiency due to their English-centric tokenizer.

To mitigate this, we should be able to swap the original LM tokenizer with an arbitrary one, on the fly, without degrading performance. Hence, in this work we define a new problem: Zero-Shot Tokenizer Transfer (ZeTT). The challenge at the core of ZeTT is finding embeddings for the tokens in the vocabulary of the new tokenizer.

Since prior heuristics for initializing embeddings often perform at chance level in a ZeTT setting, we propose a new solution: we train a hypernetwork taking a tokenizer as input and predicting the corresponding embeddings. We empirically demonstrate that the hypernetwork generalizes to new tokenizers both with encoder (eg. XLM-R) and decoder LLMs (eg. Mistral-7B).

Our method comes close to the original models’ performance in cross-lingual and coding tasks while markedly reducing the length of the tokenized sequence. We also find that the remaining gap can be quickly closed by continued training on less than 1B tokens. Finally, we show that a ZeTT hypernetwork trained for a base (L)LM can also be applied to fine-tuned variants without extra training.

Overall, our results make substantial strides toward detaching LMs from their tokenizer.

--- https://arxiv.org/abs/2402.10877#deepmind Robust agents learn causal world models Jonathan Richens, Tom Everitt 2024-02-16 2024-05-14 [("doi","10.48550/arXiv.2402.10877")] reinforcement-learning/model/decision-transformer reinforcement-learning/scaling statistics/causality

It has long been hypothesized that causal reasoning plays a fundamental role in robust and general intelligence. However, it is not known if agents must learn causal models in order to generalize to new domains, or if other inductive biases are sufficient.

We answer this question, showing that any agent capable of satisfying a regret bound under a large set of distributional shifts must have learned an approximate causal model of the data generating process, which converges to the true causal model for optimal agents.

We discuss the implications of this result for several research areas including transfer learning and causal inference.

--- https://en.wikipedia.org/wiki/Doorway_effect Doorway effect 2024-05-15 psychology/neuroscience/memory psychology/willpower --- https://www.lesswrong.com/posts/pEZoTSCxHY3mfPbHu/catastrophic-goodhart-in-rl-with-kl-penalty 2024-05-15 reinforcement-learning/safe statistics/decision statistics/probability --- https://x.com/mikeyk/status/1790744229616259547 mikeyk 2024-05-15 ai/nn/anthropic --- https://www.atlasobscura.com/articles/agar-art-dangers-of-bacteria-art 2024-05-15 design genetics/microbiome --- https://arxiv.org/abs/1805.12152 Robustness May Be at Odds with Accuracy Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry 2018-05-30 2024-05-15 [("doi","10.48550/arXiv.1805.12152")] ai/nn/adversarial

We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy.

We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed empirically in more complex settings.

Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception.

…Overall, when training on the entire dataset, we observe a decline in standard accuracy as the strength of the adversary increases (see Figure 7 of Appendix G for a plot of standard accuracy vs. ε). (Note that this still holds if we train on batches that contain natural examples as well, as recommended by Kurakin et al 2017. See Appendix B for details.) Similar effects were also observed in prior and concurrent work (Kurakin et al 2017; Madry et al 2018; Dvijotham et al 2018b; Wong et al 2018; Xiao et al 2019; Su et al 2018).

--- https://distill.pub/2019/advex-bugs-discussion/original-authors/ 2024-05-15 ai/nn/adversarial --- https://arxiv.org/abs/1906.00945 Adversarial Robustness as a Prior for Learned Representations Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Aleksander Madry 2019-06-03 2024-05-15 [("doi","10.48550/arXiv.1906.00945")] ai/nn/adversarial ai/nn/cnn

An important goal in deep learning is to learn versatile, high-level feature representations of input data. However, standard networks’ representations seem to possess shortcomings that, as we illustrate, prevent them from fully realizing this goal.

In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks.

It turns out that representations learned by robust models address the aforementioned shortcomings and make progress towards learning a high-level encoding of inputs. In particular, these representations are invertible, while allowing for direct visualization and manipulation of salient input features. More broadly, our results indicate adversarial robustness as a promising avenue for improving learned representations.

Our code and models for reproducing these results is available at https://github.com/MadryLab/robust_representations.

--- https://gradientscience.org/adv/ 2024-05-15 ai/nn/adversarial --- https://arxiv.org/abs/2310.00438 Human-Producible Adversarial Examples David Khachaturov, Yue Gao, Ilia Shumailov, Robert Mullins, Ross Anderson, Kassem Fawaz 2023-09-30 2024-05-15 [("doi","10.48550/arXiv.2310.00438")] ai/nn/adversarial

Visual adversarial examples have so far been restricted to pixel-level image manipulations in the digital world, or have required sophisticated equipment such as 2D or 3D printers to be produced in the physical real world. We present the first ever method of generating human-producible adversarial examples for the real world that requires nothing more complicated than a marker pen. We call them adversarial tags.

First, building on top of differential rendering, we demonstrate that it is possible to build potent adversarial examples with just lines. We find that by drawing just 4 lines we can disrupt a YOLO-based model in 54.8% of cases; increasing this to 9 lines disrupts 81.8% of the cases tested. Next, we devise an improved method for line placement to be invariant to human drawing error.

We evaluate our system thoroughly in both digital and analogue worlds and demonstrate that our tags can be applied by untrained humans. We demonstrate the effectiveness of our method for producing real-world adversarial examples by conducting a user study where participants were asked to draw over printed images using digital equivalents as guides. We further evaluate the effectiveness of both targeted and untargeted attacks, and discuss various trade-offs and method limitations, as well as the practical and ethical implications of our work.

The source code will be released publicly.

--- https://arxiv.org/abs/2106.10151 The Dimpled Manifold Model of Adversarial Examples in Machine Learning Adi Shamir, Odelia Melamed, Oriel BenShmuel 2021-06-18 2024-05-16 [("doi","10.48550/arXiv.2106.10151")] ai/nn/adversarial ai/scaling

The extreme fragility of deep neural networks, when presented with tiny perturbations in their inputs, was independently discovered by several research groups in 2013. However, despite enormous effort, these adversarial examples remained a counterintuitive phenomenon with no simple testable explanation.

In this paper, we introduce a new conceptual framework for how the decision boundary between classes evolves during training, which we call the Dimpled Manifold Model. In particular, we demonstrate that training is divided into two distinct phases. The first phase is a (typically fast) clinging process in which the initially randomly oriented decision boundary gets very close to the low dimensional image manifold, which contains all the training examples. Next, there is a (typically slow) dimpling phase which creates shallow bulges in the decision boundary that move it to the correct side of the training examples.

This framework provides a simple explanation for why adversarial examples exist, why their perturbations have such tiny norms, and why they look like random noise rather than like the target class. This explanation is also used to show that a network that was adversarially trained with incorrectly labeled images might still correctly classify most test images, and to show that the main effect of adversarial training is just to deepen the generated dimples in the decision boundary.

Finally, we discuss and demonstrate the very different properties of on-manifold and off-manifold adversarial perturbations. We describe the results of numerous experiments which strongly support this new model, using both low dimensional synthetic datasets and high dimensional natural datasets.

--- https://www.theguardian.com/science/2019/mar/28/scientists-find-genetic-mutation-that-makes-woman-feel-no-pain 2024-05-16 psychology/neuroscience --- https://arxiv.org/abs/2401.08741 Fixed Point Diffusion Models Xingjian Bai, Luke Melas-Kyriazi 2024-01-16 2024-05-16 [("doi","10.48550/arXiv.2401.08741")] ai/nn/diffusion

We introduce the Fixed Point Diffusion Model (FPDM), a novel approach to image generation that integrates the concept of fixed point solving into the framework of diffusion-based generative modeling. Our approach embeds an implicit fixed point solving layer into the denoising network of a diffusion model, transforming the diffusion process into a sequence of closely-related fixed point problems.

Combined with a new stochastic training method, this approach reduces model size, reduces memory usage, and accelerates training. Moreover, it enables the development of two new techniques to improve sampling efficiency: reallocating computation across timesteps and reusing fixed point solutions between timesteps.

We conduct extensive experiments with state-of-the-art models on ImageNet, FFHQ, CelebA-HQ, and LSUN-Church, demonstrating substantial improvements in performance and efficiency. Compared to the state-of-the-art DiT model, FPDM contains 87% fewer parameters, consumes 60% less memory during training, and improves image generation quality in situations where sampling computation or time is limited.

Our code and pretrained models are available at https://lukemelas.github.io/fixed-point-diffusion-models/.

--- https://www.thepsmiths.com/p/review-the-spirit-of-the-mountains 2024-05-16 sociology --- https://www.sabrina.dev/p/chatgpt4o-vs-math 2024-05-16 ai/nn/transformer/gpt/4/nonfiction math --- https://cabel.com/2024/05/16/rthe-forged-apple-employee-badge/ 2024-05-17 crime --- https://x.com/sakun135/status/1285408650052333568 GPT-3 calculating derivatives sakun135 2024-01-01 ai/nn/transformer/gpt/3/nonfiction math

GPT-3 calculating derivatives.

It learned about power rulehttps://en.wikipedia.org/wiki/Power_rule. Maybe a prompt with all rules of calculus will make it able to do all sorts of calculations.

...I was not omitting ^ at first but then it didn’t give me answers and I realized omitting ^ gave the answers so I just carried on.

--- https://luc.devroye.org/fonts-34361.html 2024-05-18 design/typography --- https://blog.cryptographyengineering.com/2017/07/02/beyond-public-key-encryption/ 2024-05-18 cs/computable cs/cryptography --- https://scholar.harvard.edu/files/mickens/files/thisworldofours.pdf 2024-05-18 cs/cryptography math/humor --- https://arxiv.org/abs/2405.05990#tencent Special Characters Attack: Toward Scalable Training Data Extraction From Large Language Models Yang Bai, Ge Pei, Jindong Gu, Yong Yang, Xingjun Ma 2024-05-09 2024-05-18 [("doi","10.48550/arXiv.2405.05990")] ai/dataset ai/nn/tokenization

Large language models (LLMs) have achieved remarkable performance on a wide range of tasks. However, recent studies have shown that LLMs can memorize training data and simple repeated tokens can trick the model to leak the data. In this paper, we take a step further and show that certain special characters or their combinations with English letters are stronger memory triggers, leading to more severe data leakage. The intuition is that, since LLMs are trained with massive data that contains a substantial amount of special characters (eg. structural symbols, of JSON files, and @, # in emails and online posts), the model may memorize the co-occurrence between these special characters and the raw texts. This motivates us to propose a simple but effective Special Characters Attack (SCA) to induce training data leakage.

Our experiments verify the high effectiveness of SCA against state-of-the-art LLMs: they can leak diverse training data, such as code corpus, web pages, and personally identifiable information, and sometimes generate non-stop outputs as a byproduct.

We further show that the composition of the training data corpus can be revealed by inspecting the leaked data—one crucial piece of information for pre-training high-performance LLMs. Our work can help understand the sensitivity of LLMs to special characters and identify potential areas for improvement.

--- https://www.collectorsweekly.com/articles/how-the-soviets-revolutionized-wristwatches/ 2024-05-18 psychology/collecting technology --- https://arxiv.org/abs/2301.10191 Distinct Elements in Streams: An Algorithm for the (Text) Book Sourav Chakraborty, N. V. Vinodchandran, Kuldeep S. Meel 2023-01-24 2024-05-18 [("doi","10.4230/LIPIcs.ESA.2022.34")] cs/algorithm statistics/order statistics/probability

[media] Given a data stream 𝒜 = ⟨a1, a2, …, am⟩ of m elements where each ai ∈ [n], the Distinct Elements problem is to estimate the number of distinct elements in 𝒜.

Distinct Elements has been a subject of theoretical and empirical investigations over the past 4 decades resulting in space optimal algorithms for it. All the current state-of-the-art algorithms are, however, beyond the reach of an undergraduate textbook owing to their reliance on the usage of notions such as pairwise independence and universal hash functions.

We present a simple, intuitive, sampling-based space-efficient algorithm whose description and the proof are accessible to undergraduates with the knowledge of basic probability theory.

--- https://erkin.party/blog/200715/evolution/ 2024-05-18 cs/lisp math/humor --- https://arxiv.org/abs/2405.07425 Sakuga-42M Dataset: Scaling Up Cartoon Research Zhenglin Pan, Yu Zhu, Yuxuan Mu 2024-05-13 2024-05-18 [("doi","10.48550/arXiv.2405.07425")] ai/anime ai/dataset ai/nn/transformer/clip ai/video/generation

Hand-drawn cartoon animation employs sketches and flat-color segments to create the illusion of motion. While recent advancements like CLIP, SVD, and Sora show impressive results in understanding and generating natural video by scaling large models with extensive datasets, they are not as effective for cartoons. Through our empirical experiments, we argue that this ineffectiveness stems from a notable bias in hand-drawn cartoons that diverges from the distribution of natural videos. Can we harness the success of the scaling paradigm to benefit cartoon research? Unfortunately, until now, there has not been a sizable cartoon dataset available for exploration.

In this research, we propose the Sakuga-42M Dataset, the first large-scale cartoon animation dataset. Sakuga-42M comprises 42 million keyframes covering various artistic styles, regions, and years, with comprehensive semantic annotations including video-text description pairs, anime tags, content taxonomies, etc.

We pioneer the benefits of such a large-scale cartoon dataset on comprehension and generation tasks by finetuning contemporary foundation models like Video CLIP, Video Mamba, and SVD, achieving outstanding performance on cartoon-related tasks. Our motivation is to introduce large-scaling to cartoon research and foster generalization and robustness in future cartoon applications.

Dataset, code, and pretrained models will be publicly available.

--- https://worksinprogress.co/issue/the-beauty-of-concrete/ 2024-05-18 design economics/automation technology --- https://arxiv.org/abs/2303.10931 Approaching an unknown communication system by latent space exploration and causal inference Gašper Beguš, Andrej Leban, Shane Gero 2023-03-20 2024-05-18 [("doi","10.48550/arXiv.2303.10931")] ai/nn/gan psychology/linguistics

[Project CETI] This paper proposes a methodology for discovering meaningful properties in data by exploring the latent space of unsupervised deep generative models [fiwGAN, an InfoGAN]. We combine manipulation of individual latent variables to extreme values with methods inspired by causal inference into an approach we call causal disentanglement with extreme values (CDEV) and show that this method yields insights for model interpretability. With this, we can test for what properties of unknown data the model encodes as meaningful, using it to glean insight into the communication system of sperm whales (Physeter macrocephalus), one of the most intriguing and understudied animal communication systems.

The network architecture used has been shown to learn meaningful representations of speech; here, it is used as a learning mechanism to decipher the properties of another vocal communication system in which case we have no ground truth. The proposed methodology suggests that sperm whales encode information using the number of clicks in a sequence, the regularity of their timing, and audio properties such as the spectral mean and the acoustic regularity of the sequences. Some of these findings are consistent with existing hypotheses, while others are proposed for the first time.

We also argue that our models uncover rules that govern the structure of units in the communication system and apply them while generating innovative data not shown during training. This paper suggests that an interpretation of the outputs of deep neural networks with causal inference methodology can be a viable strategy for approaching data about which little is known and presents another case of how deep learning can limit the hypothesis space.

Finally, the proposed approach can be extended to other architectures and datasets.

…Beguš 2020 proposes a technique to uncover individual latent variables that have linguistic meaning by setting them to extreme values outside of those seen in training and interpolating from there. In this work, we conversely treat the generator as an experiment in the vein of causal inference and test for observable properties of the data that have (or can be) hypothesized as meaningful.

When generating output samples, the incompressible noise X is sampled randomly, while the featural encoding t is set manually to a desired value. Since the consistency of output with regard to the encoding is only enforced in a loose way, this relationship often only becomes readily apparent when setting the numerical values outside the bounds seen in training, where the primary associated effect begins to dominate2, 3, 5.

We then apply statistical estimators on the candidate property samples derived from the raw generated outputs to determine whether there exists a statistically consistent relationship between the encoding and the outcomes.

This procedure gives rise to a methodology we call causal disentanglement with extreme values (CDEV) (Figure 2b).

Figure 2: Model and approach overview.

The space available for the featural encodings is limited; hence, finding the real-world attributes that map almost one-to-one with the encodings suggests that the generator considers them very important to generating convincing outputs, in which it is being checked by the discriminator with access to real data. We limit our featural encoding space to 5 bits (= 25 = 32 classes) for the 5 coda types present in the data to allow the model to capture compositionality. However, we demonstrate in Appendix A.1.1 that the method is robust to the number of bits chosen, as well as model training specifics. Similarly, on language data, the architecture uncovers meaningful properties even when there is a mismatch between the number of true meaningful classes and the size of the binary code (Beguš 2021). Therefore, any intuition about the desired size of the encoding acts only as a rough prior. Additionally, the uniqueness of the encoding matches is verified by an unrelated method, presented in Appendix A.7.

--- https://en.wikipedia.org/wiki/Project_CETI Project CETI 2024-05-18 psychology/linguistics --- https://arxiv.org/abs/2006.02951 CiwGAN and fiwGAN: Encoding information in acoustic data to model lexical learning with Generative Adversarial Networks Gašper Beguš 2020-06-04 2024-05-18 [("doi","10.1016/j.neunet.2021.03.017")] ai/nn/gan psychology/linguistics

How can deep neural networks encode information that corresponds to words in human speech into raw acoustic data? This paper proposes two neural network architectures for modeling unsupervised lexical learning from raw acoustic inputs, ciwGAN (Categorical InfoWaveGAN) & fiwGAN (Featural InfoWaveGAN), that combine a Deep Convolutional GAN architecture for audio data (WaveGAN) with an information theoretic extension of GAN—InfoGAN, and propose a new latent space structure that can model featural learning simultaneously with a higher level classification and allows for a very low-dimension vector representation of lexical items.

Lexical learning is modeled as emergent from an architecture that forces a deep neural network to output data such that unique information is retrievable from its acoustic outputs. The networks trained on lexical items from TIMIT learn to encode unique information corresponding to lexical items in the form of categorical variables in their latent space. By manipulating these variables, the network outputs specific lexical items.

The network occasionally outputs innovative lexical items that violate training data, but are linguistically interpretable and highly informative for cognitive modeling and neural network interpretability. Innovative outputs suggest that phonetic and phonological representations learned by the network can be productively recombined and directly paralleled to productivity in human speech: a fiwGAN network trained on ‘suit’ and ‘dark’ outputs innovative ‘start’, even though it never saw ‘start’ or even a [st] sequence in the training data.

We also argue that setting latent featural codes to values well beyond training range results in almost categorical generation of prototypical lexical items and reveals underlying values of each latent code.

--- https://arxiv.org/abs/1705.07904 SD-GAN: Semantically Decomposing the Latent Spaces of Generative Adversarial Networks Chris Donahue, Zachary C. Lipton, Akshay Balsubramani, Julian McAuley 2017-05-22 2024-05-18 [("doi","10.48550/arXiv.1705.07904")] ai/nn/gan

We propose a new algorithm for training generative adversarial networks that jointly learns latent codes for both identities (eg. individual humans) and observations (eg. specific photographs). By fixing the identity portion of the latent codes, we can generate diverse images of the same subject, and by fixing the observation portion, we can traverse the manifold of subjects while maintaining contingent aspects such as lighting and pose.

Our algorithm SD-GAN features a pairwise training scheme in which each sample from the generator consists of two images with a common identity code. Corresponding samples from the real dataset consist of two distinct photographs of the same subject. In order to fool the discriminator, the generator must produce pairs that are photorealistic, distinct, and appear to depict the same individual. We augment both the DCGAN and BEGAN approaches with Siamese discriminators to facilitate pairwise training.

Experiments with human judges and an off-the-shelf face verification system demonstrate our algorithm’s ability to generate convincing, identity-matched photographs.

--- https://arxiv.org/abs/2006.03965 Generative Adversarial Phonology: Modeling unsupervised phonetic and phonological learning with neural networks Gašper Beguš 2020-06-06 2024-05-18 [("doi","10.3389/frai.2020.00044")] ai/nn/gan psychology/linguistics statistics/causality

Training deep neural networks on well-understood dependencies in speech data can provide new insights into how they learn internal representations. This paper argues that acquisition of speech can be modeled as a dependency between random space and generated speech data in the Generative Adversarial Network architecture and proposes a methodology to uncover the network’s internal representations that correspond to phonetic and phonological properties.

The Generative Adversarial architecture is uniquely appropriate for modeling phonetic and phonological learning because the network is trained on unannotated raw acoustic data and learning is unsupervised without any language-specific assumptions or pre-assumed levels of abstraction. A Generative Adversarial Network was trained on an allophonic distribution in English.

The network successfully learns the allophonic alternation: the network’s generated speech signal contains the conditional distribution of aspiration duration. The paper proposes a technique for establishing the network’s internal representations that identifies latent variables that correspond to, for example, presence of [s] and its spectral properties. By manipulating these variables, we actively control the presence of [s] and its frication amplitude in the generated outputs. This suggests that the network learns to use latent variables as an approximation of phonetic and phonological representations.

Crucially, we observe that the dependencies learned in training extend beyond the training interval, which allows for additional exploration of learning representations. The paper also discusses how the network’s architecture and innovative outputs resemble and differ from linguistic behavior in language acquisition, speech disorders, and speech errors, and how well-understood dependencies in speech data can help us interpret how neural networks learn their representations.

--- https://www.revk.uk/2017/12/its-official-adsl-works-over-wet-string.html 2024-05-18 cs/hardware --- https://www.pewresearch.org/data-labs/2024/05/17/when-online-content-disappears/ 2024-05-18 cs/linkrot --- https://www.lesswrong.com/posts/dLXdCjxbJMGtDBWTH/no-one-in-my-org-puts-money-in-their-pension 2024-05-18 ai/scaling reinforcement-learning/safe --- https://www.ntnbr.com/61/ 2024-05-18 design --- https://www.lesswrong.com/posts/2D74Ctr5Aj3Sb5f69/fund-me-please-i-work-so-hard-that-my-feet-start-bleeding?commentId=GusYme8xFwjAxHwNR 2024-05-19 psychiatry/bipolar/energy --- https://en.wikipedia.org/wiki/Adjective#Order Adjective § Order 2024-05-19 psychology/dark-knowledge psychology/linguistics --- https://x.com/ryan_t_lowe/status/1773778744173572274 ryan_t_lowe 2024-05-19 reinforcement-learning/openai reinforcement-learning/safe --- https://jaspervdj.be/posts/2020-09-17-lazysort.html 2024-05-19 cs/algorithm/sorting cs/haskell --- https://bernsteinbear.com/blog/weval/ 2024-05-19 cs/computable --- https://www.pypy.org/posts/2018/09/the-first-15-years-of-pypy-3412615975376972020.html#why-did-we-abandon-partial-evaluation 2024-05-19 cs/computable --- https://en.wikipedia.org/wiki/Partial_evaluation Partial evaluation 2024-01-01 cs/computable --- https://www.quantamagazine.org/computer-scientists-invent-an-efficient-new-way-to-count-20240516/ 2024-05-19 cs/algorithm statistics/order statistics/probability --- https://en.wikipedia.org/wiki/Count-distinct_problem Count-distinct problem 2024-05-19 cs/algorithm statistics/order statistics/probability --- https://sergey.substack.com/p/whats-the-price-elasticity-of-demand 2024-05-19 economics/automation --- https://sriramk.com/group-chats-rule-the-world/ 2024-05-19 sociology/technology --- https://x.com/repligate/status/1792136305775661151 2024-05-19 ai/nn/transformer/gpt/dall-e/2 --- https://aella.substack.com/p/fetish-tabooness-vs-popularity 2024-05-19 psychology/personality --- /doc/psychology/2022-agrawal.pdf Hearing water temperature: Characterizing the development of nuanced perception of sound sources Tanushree Agrawal, Adena Schachner 2022-09-06 2024-05-15 [("doi","10.1111/desc.13321")] psychology

Without conscious thought, listeners link events in the world to sounds they hear. We study one surprising example: Adults can judge the temperature of water simply from hearing it being poured. We test the development of the ability to hear water temperature, with the goal of informing developmental theories regarding the origins and cognitive bases of nuanced sound source judgments.

We first confirmed that adults accurately distinguished the sounds of hot and cold water (pre-registered Experiments 1, 2; total n = 384), even though many were unaware or uncertain of this ability.

By contrast, children showed protracted development of this skill over the course of middle childhood (Experiments 2, 3; total n = 178). In spite of accurately identifying other sounds and hot/cold images, older children (7–11 years) but not younger children (3–6 years) reliably distinguished the sounds of hot and cold water. Accuracy increased with age; 11-year old’s performance was similar to adults. Adults also showed individual differences in accuracy that were predicted by their amount of prior relevant experience (Experiment 1).

Experience may similarly play a role in children’s performance; differences in auditory sensitivity and multimodal integration may also contribute to young children’s failures. The ability to hear water temperature develops slowly over childhood, such that nuanced auditory information that is easily and quickly accessible to adults is not available to guide young children’s behavior.

--- /doc/psychology/neuroscience/2021-sherman.pdf Cortical control of behavior and attention from an evolutionary perspective S. Murray Sherman, W. Martin Usrey 2021-07-22 2024-01-01 [("doi","10.1016/j.neuron.2021.06.021")] genetics/selection/natural/human psychology/neuroscience psychology/personality --- https://codeanlabs.com/blog/research/cve-2024-4367-arbitrary-js-execution-in-pdf-js/ 2024-05-20 cs/js cs/security --- https://www.lofibucket.com/articles/64k_intro.html 2024-05-20 cs/algorithm/information/compression design --- https://www.anthropic.com/news/reflections-on-our-responsible-scaling-policy 2024-05-20 ai/nn/anthropic --- https://security.googleblog.com/2023/08/ai-powered-fuzzing-breaking-bug-hunting.html 2024-05-20 ai/nn/transformer/gpt/codex cs/security --- https://www.anthropic.com/news/the-long-term-benefit-trust 2024-05-20 ai/nn/anthropic economics/mechanism-design --- /doc/psychology/smell/human/2014-damm.pdf Olfactory training is helpful in post-infectious olfactory loss: A randomized, controlled, multicenter study Michael Damm, Louisa K. Pikart, Heike Reimann, Silke Burkert, Önder Göktas, Boris Haxel, Sabine Frey, Ioannis Charalampakis, Achim Beule, Berthold Renner, Thomas Hummel, Karl-Bernd Hüttenbrink 2013-08-08 2024-05-15 [("doi","10.1002/lary.24340")] psychology/smell/human

Objectives/Hypothesis: The aim of this study was to evaluate the effects of olfactory training (OT) on olfactory function in patients with persistent post-infectious olfactory dysfunction (PIOD).

Study Design: Randomized, single-blind, controlled, multicenter crossover study.

Methods: 12 tertiary university medical centers participated. Investigations were performed at 3 visits (baseline, after 18 weeks, and after 36 weeks), including only subjects with PIOD of <24-months duration. At each visit, participants received detailed assessment of olfactory function. Seventy subjects trained with high concentrations of 4 odors for 18 weeks; the other half (n = 74) trained with low concentrations of odors. For the following 18 weeks this regimen was switched.

Results: After 18 weeks, olfactory function improved in the high-training group in 18⁄70 participants (26%), whereas only 11⁄74 improved in the low-training group (15%). In subjects with a duration of olfactory dysfunction of <12 months, olfactory function improved in 15⁄24 participants (63%) of the high-training group and in 6⁄31 participants (19%) of the low-training group (p = 0.03).

Conclusions: OT improves PIOD, and the use of odors at higher concentrations is beneficial to improvement. OT is a safe procedure and appears to be particularly useful in patients who start OT within 12 months after the onset of the disorder. OT is the first successful therapy regime in patients with PIOD.

--- /doc/psychology/smell/human/2015-altundag.pdf Modified Olfactory Training in Patients With Post-infectious Olfactory Loss Aytug Altundag, Melih Cayonu, Gurkan Kayabasoglu, Murat Salihoglu, Hakan Tekeli, Omer Saglam, Thomas Hummel 2015-06-02 2024-05-08 [("doi","10.1002/lary.25245")] psychology/smell/human

Objective: Patients with olfactory dysfunction benefit from repeated exposure to odors, so-called olfactory training (OT). This does not mean occasional smelling but the structured sniffing of a defined set of odors, twice daily, for a period of 4 months or longer. In this prospective study, we investigated whether the effect of OT might increase through the use of more odors and extension of the training period.

Study Design & Methods: This study shows OT results when performed with 4 or 12 odors for 36 weeks in patients with post-infectious olfactory dysfunction. A total of 85 subjects participated (mean age 45.6 ± 10.5 years, range 24–68 years). 3 groups were formed: (1) In the modified olfactory training (MOT) group, patients used three sets of 4 different odors sequentially. (2) Participants in the classical odor training (COT) group used 4 odors. (3) Participants in the control group did not perform OT. All groups were matched for age and sex distribution of participants.

Results: Both participants in the COT and MOT groups reached better scores than controls in terms of odor discrimination and odor identification. Continuing OT with 4 different odors after the 12th and 24th weeks produced better results in terms of odor discrimination and odor identification scores as compared to using the same 4 odors throughout the entire study.

Conclusion: This study confirmed the effectiveness of OT. Increasing the duration of OT and changing the odors enhances the success rate of this therapy.

--- https://arxiv.org/abs/2401.02117 Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation Zipeng Fu, Tony Z. Zhao, Chelsea Finn 2024-01-04 2024-05-20 [("doi","10.48550/arXiv.2401.02117")] reinforcement-learning/imitation-learning reinforcement-learning/robot

Imitation learning from human demonstrations has shown impressive performance in robotics. However, most results focus on table-top manipulation, lacking the mobility and dexterity necessary for generally useful tasks.

In this work, we develop a system for imitating mobile manipulation tasks that are bimanual and require whole-body control. We first present Mobile ALOHA, a low-cost and whole-body teleoperation system for data collection. It augments the ALOHA system with a mobile base and a whole-body teleoperation interface.

Using data collected with Mobile ALOHA, we then perform supervised behavior cloning and find that co-training with existing static ALOHA datasets boosts performance on mobile manipulation tasks. With 50 demonstrations for each task, co-training can increase success rates by up to 90%, allowing Mobile ALOHA to autonomously complete complex mobile manipulation tasks such as sauteing and serving a piece of shrimp, opening a two-door wall cabinet to store heavy cooking pots, calling and entering an elevator, and lightly rinsing a used pan using a kitchen faucet.

Project website: https://mobile-aloha.github.io/.

--- https://mobile-aloha.github.io/ 2024-05-20 reinforcement-learning/imitation-learning reinforcement-learning/robot --- https://x.com/AndyAyrey/status/1792342948887290106 Andy Ayrey 2024-05-20 ai/nn/transformer/gpt/claude --- /doc/cs/algorithm/information/1987-liversidge.pdf Profile of Claude Shannon Anthony Liversidge, Claude Shannon 1987-08-01 2024-05-20 ai cs/algorithm/information reinforcement-learning/safe --- https://worksinprogress.co/issue/why-prediction-markets-arent-popular/ 2024-05-21 economics/mechanism-design statistics/prediction --- https://matthewstrom.com/writing/ui-density/ 2024-05-21 design/visualization --- https://sandrarendgen.wordpress.com/2019/03/15/data-trails-from-paris-with-love/ 2024-05-21 design/visualization --- https://stevelosh.com/blog/2018/08/a-road-to-common-lisp/ 2024-05-22 cs/lisp --- https://www.astralcodexten.com/p/book-review-the-others-within-us 2024-05-22 psychiatry/borderline --- https://kennethreitz.org/essays/2019/03/01/mentalhealtherror-three-years-later 2024-05-22 psychiatry/bipolar/energy psychiatry/meditation --- https://www.justice.gov/opa/pr/man-arrested-producing-distributing-and-possessing-ai-generated-images-minors-engaged 2024-05-22 ai/nn/diffusion law --- https://en.wikipedia.org/wiki/Midler_v._Ford_Motor_Co. Midler v. Ford Motor Co 2024-05-22 economics/copyright --- https://wiki.c2.com/?ComputerErrorHaiku 2024-05-22 cs/algorithm math/humor --- https://admiralcloudberg.medium.com/a-shot-in-the-dark-the-untold-story-of-korean-air-lines-flight-007-a4ae6a4ef734 2024-05-22 technology --- https://www.economist.com/science-and-technology/2024/05/10/a-russia-linked-network-uses-ai-to-rewrite-real-news-stories 2024-05-22 ai/nn/transformer/gpt/non-fiction ai/text-style-transfer --- https://www.technologyreview.com/2024/05/22/1092763/openais-gpt4o-chinese-ai-data/ 2024-05-22 ai/nn/tokenization ai/scaling/economics --- https://arxiv.org/abs/2402.19450 Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap Saurabh Srivastava, Annarose M. B, Anto P. V, Shashank Menon, Ajay Sukumar, Adwaith Samod T, Alan Philipose, Stevin Prince, Sooraj Thomas 2024-02-29 2024-05-22 [("doi","10.48550/arXiv.2402.19450")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude math

We propose a framework for robust evaluation of reasoning capabilities of language models, using functional variants of benchmarks.

Models that solve a reasoning test should exhibit no difference in performance over the static version of a problem compared to a snapshot of the functional variant. We have rewritten the relevant fragment of the MATH benchmark into its functional variant MATH(), with functionalization of other benchmarks to follow.

When evaluating current state-of-the-art models over snapshots of MATH(), we find a reasoning gap—the percentage difference between the static and functional accuracies. We find reasoning gaps 35.9%–59.4% among the state-of-the-art closed and open weights models that perform well on static benchmarks, with the caveat that the gaps are likely to be smaller with more sophisticated prompting strategies.

Here we show that models which anecdotally have good reasoning performance over real-world tasks, have quantifiable lower gaps, motivating the open problem of building “gap 0” models.

Code for evaluation and new evaluation datasets, 3 MATH() snapshots, are publicly available at Github.

[Does not appear to have made much effort to control for length/difficulty, which will exaggerate the performance loss; compare GSM1k and see Engstrom on psychometric issues in replication dataset construction.]

--- https://steveblank.com/2024/05/16/secret-history-when-kodak-went-to-war-with-polaroid/ Steve Blank 2024-05-16 2024-05-23 technology --- https://arxiv.org/abs/2405.12250 Your Transformer is Secretly Linear Anton Razzhigaev, Matvey Mikhalchuk, Elizaveta Goncharova, Nikolai Gerasimenko, Ivan Oseledets, Denis Dimitrov, Andrey Kuznetsov 2024-05-19 2024-05-23 [("doi","10.48550/arXiv.2405.12250")] ai/nn/transformer/attention

This paper reveals a novel linear characteristic exclusive to transformer decoders, including models such as GPT-2, LLaMA, OPT, BLOOM and others. We analyze embedding transformations between sequential layers, uncovering a near-perfect linear relationship (Procrustes similarity score of 0.99).

However, linearity decreases when the residual component is removed due to a consistently low output norm of the transformer layer. Our experiments show that removing or linearly approximating some of the most linear blocks of transformers does not affect the loss or model performance.

Moreover, in our pretraining experiments on smaller models we introduce a cosine-similarity-based regularization, aimed at reducing layer linearity. This regularization improves performance metrics on benchmarks like Tiny Stories and SuperGLUE and as well successfully decreases the linearity of the models.

This study challenges the existing understanding of transformer architectures, suggesting that their operation may be more linear than previously assumed.

--- https://en.wikipedia.org/wiki/Rare_earths_trade_dispute Rare earths trade dispute 2024-01-01 economics politics technology --- https://www.telegraphindia.com/elections/lok-sabha-election-2024/prime-minister-narendra-modi-convinced-to-be-emissary-of-god-questions-his-biological-origins/cid/2021726 2024-05-23 psychology/personality/narcissism --- https://www.fca.org.uk/publication/final-notices/citigroup-global-markets-limited-2024.pdf#page=2 2024-05-23 design --- https://www.wired.com/story/lin-rui-siang-incognito-market/ 2024-05-23 darknet-market --- https://arxiv.org/abs/2212.13345 The Forward-Forward Algorithm: Some Preliminary Investigations Geoffrey Hinton 2022-12-27 2024-05-23 [("doi","10.48550/arXiv.2212.13345")] ai/nn reinforcement-learning/meta-learning/continual-learning

The aim of this paper is to introduce a new learning procedure for neural networks and to demonstrate that it works well enough on a few small problems to be worth further investigation.

The Forward-Forward algorithm [see also forward gradients] replaces the forward and backward passes of backpropagation with two forward passes: one with positive (ie. real) data and the other with negative data, which could be generated by the network itself.

Each layer has its own objective function, which is simply to have high goodness for positive data and low goodness for negative data. The sum of the squared activities in a layer can be used as the goodness, but there are many other possibilities, including minus the sum of the squared activities.

If the positive and negative passes can be separated in time, the negative passes could be done offline. This would simplify learning in the positive pass and allow video to be pipelined through the network without ever storing activities or stopping to propagate derivatives.

--- https://www.astralcodexten.com/p/a-theoretical-case-against-education 2024-05-23 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- https://en.wikipedia.org/wiki/AIBO AIBO 2024-05-23 reinforcement-learning/robot sociology/technology --- https://x.com/Kyrannio/status/1793874431179460911 Kyrannio 2024-05-24 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/inner-monologue fiction/text-game --- https://x.com/zoink/status/1793859003937939545 zoink 2024-05-24 ai/nn/transformer/gpt/claude ai/text-style-transfer psychiatry/anxiety --- https://nymag.com/intelligencer/article/hans-niemann-chess-championship-cheating-scandal-interview.html 2024-05-24 crime psychology/chess psychology/personality --- https://x.com/CupiaBart/status/1793930355617259811 CupiaBart 2024-05-24 reinforcement-learning/imitation-learning reinforcement-learning/nethack --- http://tomwaitslibrary.info/biography/copyright/frito-lay/ 2024-05-25 economics/copyright --- https://www.youtube.com/watch?v=-P0sh2n6mgQ 2024-05-25 psychedelic/lsd --- /doc/crime/2024-ludwig.pdf Machine Learning as a Tool for Hypothesis Generation Ludwig Jens, Mullainathan Sendhil 2024-01-10 2024-05-25 [("doi","10.1093/qje/qjad055")] ai/nn/cnn ai/nn/gan/stylegan crime

While hypothesis testing is a highly formalized activity, hypothesis generation remains largely informal. We propose a systematic procedure to generate novel hypotheses about human behavior, which uses the capacity of machine learning algorithms to notice patterns people might not.

We illustrate the procedure with a concrete application: judge decisions about whom to jail. We begin with a striking fact: the defendant’s face alone matters greatly for the judge’s jailing decision. In fact, a CNN algorithm given only the pixels in the defendant’s mug shot accounts for up to half of the predictable variation.

We develop a StyleGAN-2-based procedure that allows human subjects to interact with this black-box algorithm to produce hypotheses about what in the face influences judge decisions. The procedure generates hypotheses that are both interpretable and novel: they are not explained by demographics (eg. race) or existing psychology research, nor are they already known (even if tacitly) to people or experts.

Though these results are specific, our procedure is general. It provides a way to produce novel, interpretable hypotheses from any high-dimensional data set (eg. cell phones, satellites, online behavior, news headlines, corporate filings, and high-frequency time series). A central tenet of our article is that hypothesis generation is a valuable activity, and we hope this encourages future work in this largely “prescientific” stage of science.

--- https://www.nytimes.com/2022/09/15/t-magazine/r-crumb.html R. Crumb Means Some Offense: Even from his refuge in France, the comics artist still makes America’s pulse race M. H. Miller 2022-09-15 2024-05-24 fiction/humor --- https://arxiv.org/abs/1506.00779 Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays Junpei Komiyama, Junya Honda, Hiroshi Nakagawa 2015-06-02 2024-01-01 [("doi","10.48550/arXiv.1506.00779")] reinforcement-learning/model statistics/bayes

We discuss a multiple-play multi-armed bandit (MAB) problem in which several arms are selected at each round. Recently, Thompson sampling (TS), a randomized algorithm with a Bayesian spirit, has attracted much attention for its empirically excellent performance, and it is revealed to have an optimal regret bound in the standard single-play MAB problem.

In this paper, we propose the multiple-play Thompson sampling (MP-TS) algorithm, an extension of TS to the multiple-play MAB problem, and discuss its regret analysis.

We prove that MP-TS for binary rewards has the optimal regret upper bound that matches the regret lower bound provided by Anantharam et al 1987. Therefore, MP-TS is the first computationally efficient algorithm with optimal regret. A set of computer simulations was also conducted, which compared MP-TS with state-of-the-art algorithms.

We also propose a modification of MP-TS, which is shown to have better empirical performance.

--- https://www.jeremykun.com/2024/05/04/fhe-overview/ 2024-05-25 cs/cryptography --- https://en.wikipedia.org/wiki/Merchandise_Mart Merchandise Mart 2024-05-25 design --- https://web.archive.org/web/20060629071824/http://www.rawbw.com/~kjh/page3.jpg 2024-05-25 psychedelic/lsd --- https://www.wired.com/story/craig-wright-lied-faked-evidence-bitcoin-judge-says/ 2024-05-25 bitcoin --- https://en.wikipedia.org/wiki/Claude_(language_model) Claude (language model) 2024-05-25 ai/nn/transformer/gpt/claude --- https://bishopfox.com/blog/unredacter-tool-never-pixelation 2024-05-25 cs/algorithm/information design/typography --- https://www.quantamagazine.org/electric-ripples-in-the-resting-brain-tag-memories-for-storage-20240521/ 2024-05-25 psychology/neuroscience/memory reinforcement-learning/model --- /doc/genetics/heritable/rare/2010-hurst.pdf An extended Family with a Dominantly Inherited Speech Disorder J. A. Hurst, M. Baraitser, E. Auger, F. Graham, S. Norell 1990-04-01 2024-05-25 [("doi","10.1111/j.1469-8749.1990.tb16948.x")] genetics/heritable/rare genetics/selection/natural iq psychology/linguistics

A three-generation family is described in which 16 members have a severe developmental verbal dyspraxia. Inheritance is autosomal dominant [disruption of FOXP2], with full penetrance.

Intelligence and hearing are normal.

--- https://caseyhandmer.wordpress.com/2024/05/22/the-solar-industrial-revolution-is-the-biggest-investment-opportunity-in-history/ 2024-05-25 technology/carbon-capture --- https://arxiv.org/abs/2405.14860 Not All Language Model Features Are Linear Joshua Engels, Isaac Liao, Eric J. Michaud, Wes Gurnee, Max Tegmark 2024-05-23 2024-05-25 [("doi","10.48550/arXiv.2405.14860")] ai/nn/transformer

Recent work has proposed the linear representation hypothesis: that language models perform computation by manipulating one-dimensional representations of concepts (“features”) in activation space. In contrast, we explore whether some language model representations may be inherently multi-dimensional.

We begin by developing a rigorous definition of irreducible multi-dimensional features based on whether they can be decomposed into either independent or non-co-occurring lower-dimensional features. Motivated by these definitions, we design a scalable method that uses sparse autoencoders to automatically find multi-dimensional features in GPT-2 and Mistral 7B.

These auto-discovered features include strikingly interpretable examples, eg. circular features representing days of the week and months of the year. We identify tasks where these exact circles are used to solve computational problems involving modular arithmetic in days of the week and months of the year.

Finally, we provide evidence that these circular features are indeed the fundamental unit of computation in these tasks with intervention experiments on Mistral 7B and LLaMA-3 8B, and we find further circular representations by breaking down the hidden states for these tasks into interpretable components.

--- /doc/psychology/cognitive-bias/2024-rose.pdf Target Happiness Attenuates Perceivers’ Moral Condemnation of Prejudiced People Hope Rose, Christopher A. Sanders, Chloe Willett, Laura A. King 2024-04-25 2024-05-26 [("doi","10.1177/01461672241240160")] psychology/cognitive-bias sociology

5 experiments (combined n = 4,915) tested the prediction that the moral boost of happiness would persist for social targets with moral failings.

In Studies 1 and 2, White and Black participants, respectively, judged happy (versus unhappy) racist targets more morally good.

In Study 3, happy (versus unhappy) racist targets were judged more morally good and less (more) likely to engage in racist (good) behavior. Behavioral expectations explained the link between happiness and moral evaluations.

Study 4 replicated Studies 1 to 3 in the context of sexism.

In Study 5, happy (versus unhappy) targets who engaged in racially biased behavior were evaluated as more morally good, and this effect was explained by behavioral forecasts.

Happiness boosts attributions of moral goodness for prejudiced people and does so via expectations for future behavior. Future directions are discussed.

--- https://frankforce.com/city-in-a-bottle-a-256-byte-raycasting-system/ 2024-05-26 cs/algorithm/information/compression cs/js --- https://www.lesswrong.com/posts/AZCpu3BrCFWuAENEd/notifications-received-in-30-minutes-of-class 2024-05-26 sociology/technology --- https://www.tolkienestate.com/painting/calligraphy/ 2024-05-26 design/typography/rubrication --- https://www.construction-physics.com/p/how-to-build-300000-airplanes-in 2024-05-26 economics/experience-curve --- https://en.wikipedia.org/wiki/Operation_Cat_Drop Operation Cat Drop 2024-05-27 cat/biology --- https://hazlitt.net/longreads/journey-medical-netherworld 2024-05-27 psychiatry/anxiety --- https://en.wikipedia.org/wiki/Old_Enough! Old Enough! 2024-05-27 japan --- https://www.chiark.greenend.org.uk/~sgtatham/quasiblog/commit-messages/ Writing commit messages Simon Tatham 2024-05-19 2024-05-26 cs/algorithm design/typography/sidenote --- https://www.nytimes.com/2024/05/25/world/asia/china-surveillance-xi.html Xi Jinping’s Recipe for Total Control: An Army of Eyes and Ears: Reviving a Mao-era surveillance campaign, the authorities are tracking residents, schoolchildren and businesses to forestall any potential unrest Vivian Wang 2024-05-25 2024-05-26 politics --- https://en.wikipedia.org/wiki/Arctic_Apples Arctic Apples 2024-05-27 genetics/editing genetics/selection/artificial/apple --- https://www.newyorker.com/magazine/2024/06/03/master-of-make-believe 2024-05-27 crime psychology/personality/narcissism --- https://arxiv.org/abs/2405.10911 A minimal scenario for the origin of non-equilibrium order Riccardo Ravasio, Kabir Husain, Constantine G. Evans, Rob Phillips, Marco Ribezzi, Jack W. Szostak, Arvind Murugan 2024-05-17 2024-05-27 [("doi","10.48550/arXiv.2405.10911")] cs/cellular-automaton genetics/selection/natural

[cf. Price equation] Extant life contains numerous non-equilibrium mechanisms to create order not achievable at equilibrium; it is generally assumed that these mechanisms evolved because the resulting order was sufficiently beneficial to overcome associated costs of time and energy.

Here, we identify a broad range of conditions under which non-equilibrium order-creating mechanisms will evolve as an inevitable consequence of self-replication, even if the order is not directly functional. We show that models of polymerases, when expanded to include known stalling effects, can evolve kinetic proofreading through selection for fast replication alone, consistent with data from recent mutational screens. Similarly, replication contingent on fast self-assembly can select for non-equilibrium instabilities and result in more ordered structures without any direct selection for order.

We abstract these results into a framework that predicts that self-replication intrinsically amplifies dissipative order-enhancing mechanisms if the distribution of replication times is wide enough.

Our work suggests the intriguing possibility that non-equilibrium order can arise more easily than assumed, even before that order is directly functional, with consequences impacting mutation rate evolution and kinetic traps in self-assembly to the origin of life.

--- https://www.lesswrong.com/posts/qbbaF79uJqvmWZELv/real-life-sort-by-controversial 2024-05-27 ai/nn/transformer/gpt/3/nonfiction politics --- https://en.wikipedia.org/wiki/Price_equation Price equation 2024-01-01 genetics/selection/natural reinforcement-learning/meta-learning --- https://en.wikipedia.org/wiki/Roofline_model Roofline model 2024-05-27 ai/scaling/hardware --- https://if50.substack.com/p/genre-explorations-wordplay 2024-05-28 fiction/humor fiction/text-game --- https://www.theregister.com/2016/07/06/researchers_create_robotic_rectum_for_science/ 2024-05-28 economics/automation reinforcement-learning/robot --- https://arxiv.org/abs/2306.13103 Evaluating the Robustness of Text-to-image Diffusion Models against Real-world Attacks Hongcheng Gao, Hao Zhang, Yinpeng Dong, Zhijie Deng 2023-06-16 2024-05-28 [("doi","10.48550/arXiv.2306.13103")] ai/nn/adversarial ai/nn/diffusion ai/nn/transformer/gpt/4/nonfiction

Text-to-image (T2I) diffusion models (DMs) have shown promise in generating high-quality images from textual descriptions. The real-world applications of these models require particular attention to their safety and fidelity, but this has not been sufficiently explored. One fundamental question is whether existing T2I DMs are robust against variations over input texts.

To answer it, this work provides the first robustness evaluation of T2I DMs against real-world attacks. Unlike prior studies that focus on malicious attacks involving apocryphal alterations to the input texts, we consider an attack space spanned by realistic errors (eg. typo, glyph, phonetic) that humans can make, to ensure semantic consistency. Given the inherent randomness of the generation process, we develop novel distribution-based attack objectives to mislead T2I DMs.

We perform attacks in a black-box manner without any knowledge of the model. Extensive experiments demonstrate the effectiveness of our method for attacking popular T2I DMs and simultaneously reveal their non-trivial robustness issues.

Moreover, we provide an in-depth analysis of our method to show that it is not designed to attack the text encoder in T2I DMs solely.

--- https://x.com/klarnaseb/status/1795540481138397515 klarnaseb 2024-05-28 ai/nn/diffusion/midjourney economics/automation --- https://x.com/moyix/status/1795284112791703735 moyix 2024-05-28 reinforcement-learning/model reinforcement-learning/safe --- https://arxiv.org/abs/2405.17156 The Scaling Law in Stellar Light Curves Jia-Shu Pan, Yuan-Sen Ting, Yang Huang, Jie Yu, Ji-Feng Liu 2024-05-27 2024-05-29 [("doi","10.48550/arXiv.2405.17156")] ai/nn/transformer/gpt/2 ai/scaling science

Analyzing time series of fluxes from stars, known as stellar light curves, can reveal valuable information about stellar properties. However, most current methods rely on extracting summary statistics, and studies using deep learning have been limited to supervised approaches.

In this research, we investigate the scaling law properties that emerge when learning from astronomical time series data using self-supervised techniques. By employing the GPT-2 architecture, we show the learned representation improves as the number of parameters increases from 104 → 109, with no signs of performance plateauing.

We demonstrate that a self-supervised Transformer model achieves 3–10× the sample efficiency compared to the state-of-the-art supervised learning model when inferring the surface gravity of stars as a downstream task.

Our research lays the groundwork for analyzing stellar light curves by examining them through large-scale auto-regressive generative models.

--- https://www.avclub.com/ralph-bakshi-1798208128 Ralph Bakshi Tasha Robinson, Ralph Bakshi 2000-12-06 2024-05-26 anime --- https://crypto.stanford.edu/gyrophone/files/gyromic.pdf Gyrophone: Recognizing Speech From Gyroscope Signals Michalevsky 2024-01-01 cs/algorithm/information cs/security --- https://nationalinterest.org/bookreview/the-fallacy-human-freedom-8652?nopaging=1 The Fallacy of Human Freedom; Review [John Gray, The Silence of Animals: On Progress and Other Modern Myths (New York: Farrar, Straus and Giroux, 2013), 288 pp] Merry 2013 2024-01-01 philosophy/ethics --- https://arxiv.org/abs/2405.18400 Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass Ethan Shen, Alan Fan, Sarah M. Pratt, Jae Sung Park, Matthew Wallingford, Sham M. Kakade, Ari Holtzman, Ranjay Krishna, Ali Farhadi, Aditya Kusupati 2024-05-28 2024-05-29 [("doi","10.48550/arXiv.2405.18400")] ai/nn/sampling ai/nn/transformer/gpt

Many applications today provide users with multiple auto-complete drafts as they type, including GitHub’s code completion, Gmail’s smart compose, and Apple’s messaging auto-suggestions. Under the hood, language models support this by running an autoregressive inference pass to provide a draft. Consequently, providing k drafts to the user requires running an expensive language model k times.

To alleviate the computation cost of running k inference passes, we propose Superposed Decoding, a new decoding algorithm that generates k drafts at the computation cost of one autoregressive inference pass. We achieve this by feeding a superposition of the k most recent token embeddings from the drafts as input to the next decoding step of the language model. At every inference step, we combine the k drafts with the top-k tokens to get k2 new drafts and cache the k most likely options, using an n-gram interpolation with minimal compute overhead to filter out incoherent generations.

Our experiments show that k drafts from Superposed Decoding are at least as coherent and factual as Nucleus Sampling and Greedy Decoding respectively, while being at least 2.44× faster for k ≥ 3. In a compute-normalized setting, user evaluations demonstrably favor text generated by Superposed Decoding over Nucleus Sampling.

Code and more examples open-sourced at GitHub.

--- https://publicdomainreview.org/collection/sodomy-punished/ 2024-05-29 history/public-domain-review psychiatry --- https://publicdomainreview.org/essay/early-androids-and-artificial-speech/ 2024-05-29 history/public-domain-review psychology/linguistics reinforcement-learning/robot --- https://publicdomainreview.org/collection/chinese-arabesques/ 2024-05-29 design history/public-domain-review --- https://en.wikipedia.org/wiki/Paris_syndrome Paris syndrome 2024-01-01 japan psychiatry --- https://epochai.org/blog/the-longest-training-run 2024-05-29 ai/scaling/economics economics/experience-curve --- https://arxiv.org/abs/2305.16269 UDPM: Upsampling Diffusion Probabilistic Models Shady Abu-Hussein, Raja Giryes 2023-05-25 2024-05-29 [("doi","10.48550/arXiv.2305.16269")] ai/nn/diffusion

Denoising Diffusion Probabilistic Models (DDPM) have recently gained attention. DDPMs compose a Markovian process that begins in the data domain and gradually adds noise until reaching pure white noise. DDPMs generate high-quality samples from complex data distributions by defining an inverse process and training a deep neural network to learn this mapping. However, these models are inefficient because they require many diffusion steps to produce esthetically pleasing samples. Additionally, unlike generative adversarial networks (GANs), the latent space of diffusion models is less interpretable.

In this work, we propose to generalize the denoising diffusion process into an Upsampling Diffusion Probabilistic Model (UDPM). In the forward process, we reduce the latent variable dimension through downsampling, followed by the traditional noise perturbation. As a result, the reverse process gradually denoises and upsamples the latent variable to produce a sample from the data distribution.

We formalize the Markovian diffusion processes of UDPM and demonstrate its generation capabilities on the popular FFHQ, AFHQv2, and CIFAR-10 datasets.

UDPM generates images with as few as 3 network evaluations, whose overall computational cost is less than a single DDPM or EDM step, while achieving an FID score of 6.86. This surpasses current state-of-the-art efficient diffusion models that use a single denoising step for sampling.

Additionally, UDPM offers an interpretable and interpolable latent space, which gives it an advantage over traditional DDPMs.

Our code is available online: https://github.com/shadyabh/UDPM/.

--- https://www.biorxiv.org/content/10.1101/2024.02.22.581686.full MetaWorm: An Integrative Data-Driven Model Simulating C. elegans Brain, Body and Environment Interactions Mengdi Zhao, Ning Wang, Xinrui Jiang, Xiaoyang Ma, Haixin Ma, Gan He, Kai Du, Lei Ma, Tiejun Huang 2024-03-10 2024-05-29 [("doi","10.1101/2024.02.22.581686")] psychology/neuroscience

The behavior of an organism is profoundly influenced by the complex interplay between its brain, body, and environment. Existing data-driven models focus on either the brain or the body-environment separately. A model that integrates these two components is yet to be developed.

Here, we present MetaWorm, an integrative data-driven model of a widely studied organism, C. elegans. This model consists of two sub-models: the brain model and the body & environment model. The brain model was built by multi-compartment models with realistic morphology, connectome, and neural population dynamics based on experimental data. Simultaneously, the body & environment model employed a lifelike body and a 3D physical environment, facilitating easy behavior quantification.

Through the closed-loop interaction between two sub-models, MetaWorm faithfully reproduced the realistic zigzag movement towards attractors observed in C. elegans. Notably, MetaWorm is the first model to achieve seamless integration of detailed brain, body, and environment simulations, enabling unprecedented insights into the intricate relationships between neural structures, neural activities, and behaviors.

Leveraging this model, we investigated the impact of neural system structure on both neural activities and behaviors. Consequently, MetaWorm can enhance our understanding of how the brain controls the body to interact with its surrounding environment.

--- https://arxiv.org/abs/2203.06850 Efficient Language Modeling with Sparse All-MLP Ping Yu, Mikel Artetxe, Myle Ott, Sam Shleifer, Hongyu Gong, Ves Stoyanov, Xian Li 2022-03-14 2024-05-29 [("doi","10.48550/arXiv.2203.06850")] ai/nn/fully-connected ai/nn/sparsity ai/scaling/mixture-of-experts

All-MLP architectures have attracted increasing interest as an alternative to attention-based models. In NLP, recent work like gMLP shows that all-MLPs can match Transformers in language modeling but still lag behind in downstream tasks.

In this work, we analyze the limitations of MLPs in expressiveness and propose sparsely activated MLPs with mixture-of-experts (MoEs) in both feature and input (token) dimensions. Such sparse all-MLPs increase model capacity and expressiveness while keeping the compute constant. We address critical challenges in incorporating conditional computation with two routing strategies.

The proposed sparse all-MLP improves language modeling perplexity and obtains up to 2× improvement in training efficiency compared to both Transformer-based MoEs (GShard, Switch Transformer), Base Layers and HASH Layers as well as dense Transformers and all-MLPs.

Finally, we evaluate its zero-shot in-context learning performance on 6 downstream tasks and find that it surpasses Transformer-based MoEs and dense Transformers.

--- https://arxiv.org/abs/2203.03691 HyperMixer: An MLP-based Low Cost Alternative to Transformers Florian Mai, Arnaud Pannatier, Fabio Fehr, Haolin Chen, Francois Marelli, Francois Fleuret, James Henderson 2022-03-07 2024-05-29 [("doi","10.48550/arXiv.2203.03691")] ai/nn/fully-connected reinforcement-learning/meta-learning

Transformer-based architectures are the model of choice for natural language understanding, but they come at a large cost, as they have quadratic complexity in the input length, require a lot of training data, and can be difficult to tune.

In the pursuit of lower costs, we investigate simple MLP-based architectures. We find that existing architectures such as MLPMixer, which achieves token mixing through a static MLP applied to each feature independently, are too detached from the inductive biases required for natural language understanding. In this paper, we propose a simple variant, HyperMixer, which forms the token mixing MLP dynamically using hypernetworks.

Empirically, we demonstrate that our model performs better than alternative MLP-based models, and on par with Transformers. In contrast to Transformers, HyperMixer achieves these results at substantially lower costs in terms of processing time, training data, and hyperparameter tuning.

--- https://arxiv.org/abs/2405.15618 MLPs Learn In-Context William L. Tong, Cengiz Pehlevan 2024-05-24 2024-05-29 [("doi","10.48550/arXiv.2405.15618")] ai/nn/fully-connected reinforcement-learning/meta-learning

In-context learning (ICL), the remarkable ability to solve a task from only input exemplars, has commonly been assumed to be a unique hallmark of Transformer models.

In this study, we demonstrate that multi-layer perceptrons (MLPs) can also learn in-context. Moreover, we find that MLPs, and the closely related MLP-Mixer models, learn in-context competitively with Transformers given the same compute budget.

We further show that MLPs outperform Transformers on a subset of ICL tasks designed to test relational reasoning. These results suggest that in-context learning is not exclusive to Transformers and highlight the potential of exploring this phenomenon beyond attention-based architectures.

In addition, MLPs’ surprising success on relational tasks challenges prior assumptions about simple connectionist models. Altogether, our results endorse the broad trend that “less inductive bias is better” and contribute to the growing interest in all-MLP alternatives to task-specific architectures.

--- https://www.brightwalldarkroom.com/2020/12/04/pride-and-prejudice-perennial-beauty-of-hands/ 2024-05-29 sociology --- https://www.anthropic.com/news/tool-use-ga 2024-05-31 ai/nn/transformer/gpt/claude --- https://edition.cnn.com/2024/05/08/health/ozempic-babies-pregnancy/index.html 2024-05-31 longevity/glp --- https://vitalik.eth.limo/general/2024/05/31/blocksize.html 2024-05-31 bitcoin --- https://brr.fyi/posts/engineering-for-slow-internet 2024-05-31 cs/js --- https://thezvi.wordpress.com/2024/05/31/the-gemini-1-5-report/ 2024-05-31 ai/nn/transformer/gpt/palm --- https://www.nature.com/articles/d41586-024-01518-2 2024-05-31 psychology/vision --- https://openai.com/index/new-embedding-models-and-api-updates/ 2024-05-31 ai/nn/retrieval --- https://en.wikipedia.org/wiki/Zhemao_hoaxes Zhemao hoaxes 2024-06-01 history wikipedia --- https://www.palladiummag.com/2024/05/17/my-last-five-years-of-work/ 2024-06-01 ai/nn/anthropic --- /doc/genetics/selection/natural/human/2023-riahi.pdf Macroevolutionary Origins of Comparative Development Ideen A. Riahi 2023-10-26 2024-06-01 [("doi","10.1093/ej/uead095")] economics genetics/selection/natural/human

Advances in evolutionary theories (the Extended Synthesis) demonstrate that organisms systematically modify environments in ways that influence their own and other species’ evolution. This paper uses these theories to examine the economic consequences of human dispersal from Africa.

Evidence shows that early humans’ dispersal affected the adaptability of animal species to human environments and, through this, the extinction of large mammals during Homo sapiens’ out-of-Africa migration. Empirical analyses explore the variation in extinction rates as a source of exogenous pressure for cooperation and innovation among hunter-gatherers and examine the impact of extinction on long-run development.

The results indicate that extinction affects economic performance by driving continental differences in biogeography, disease environments, and institutions. Eurasia’s location along the out-of-Africa migratory path provided human and animal populations with co-evolutionary foundations for domestication and agriculture, which gave Eurasians technological and institutional advantages in comparative development.

--- https://en.wikipedia.org/wiki/The_Crow_and_the_Pitcher#The_fable_in_science The Crow and the Pitcher § The fable in science 2024-06-01 psychology/animal/bird statistics/causality --- https://scale.com/leaderboard/coding 2024-06-01 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/palm --- https://chinamediaproject.org/2024/05/27/goldfish-memories/ 2024-06-01 ai/scaling cs/linkrot politics --- https://arxiv.org/abs/2405.15306 DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches with TikZ Jonas Belouadi, Simone Paolo Ponzetto, Steffen Eger 2024-05-24 2024-06-01 [("doi","10.48550/arXiv.2405.15306")] ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude design/typography/tex reinforcement-learning/model

[video] Creating high-quality scientific figures can be time-consuming and challenging, even though sketching ideas on paper is relatively easy. Furthermore, recreating existing figures that are not stored in formats preserving semantic information is equally complex.

To tackle this problem, we introduce DeTikZify, a novel multimodal language model that automatically synthesizes scientific figures as semantics-preserving TikZ graphics programs based on sketches and existing figures. To achieve this, we create 3 new datasets: DaTikZv2, the largest TikZ dataset to date, containing over 360k human-created TikZ graphics; SketchFig, a dataset that pairs hand-drawn sketches with their corresponding scientific figures; and SciCap++, a collection of diverse scientific figures and associated metadata.

We train DeTikZify on SciCap++ and DaTikZv2, along with synthetically generated sketches learned from SketchFig. We also introduce an MCTS-based inference algorithm that enables DeTikZify to iteratively refine its outputs without the need for additional training.

Through both automatic and human evaluation, we demonstrate that DeTikZify outperforms commercial Claude-3 and GPT-4-V in synthesizing TikZ programs, with the MCTS algorithm effectively boosting its performance.

We make our code, models, and datasets publicly available.

--- https://arxiv.org/abs/2307.10159 FABRIC: Personalizing Diffusion Models with Iterative Feedback Dimitri von Rütte, Elisabetta Fedele, Jonathan Thomm, Lukas Wolf 2023-07-19 2024-06-01 [("doi","10.48550/arXiv.2307.10159")] ai/nn/diffusion reinforcement-learning/preference-learning

[the old GAN z sample-classify-control trick, updated for diffusion by defining an early self-attention layer as the embedding/z; still doesn’t work as well] In an era where visual content generation is increasingly driven by machine learning, the integration of human feedback into generative models presents opportunities for enhancing user experience and output quality.

This study explores strategies for incorporating iterative human feedback into the generative process of diffusion-based text-to-image models. We propose FABRIC, a training-free approach applicable to a wide range of popular diffusion models, which exploits the self-attention layer present in the most widely used architectures to condition the diffusion process on a set of feedback images.

To ensure a rigorous assessment of our approach, we introduce a comprehensive evaluation methodology, offering a robust mechanism to quantify the performance of generative visual models that integrate human feedback.

We show that generation results improve over multiple rounds of iterative feedback through exhaustive analysis, implicitly optimizing arbitrary user preferences. The potential applications of these findings extend to fields such as personalized content creation and customization.

--- https://applied-llms.org/ 2024-06-01 ai/nn/retrieval ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/inner-monologue cs --- https://fellerts.no/projects/kintsugi.html 2024-06-01 japan/art --- https://x.com/BlinkDL_AI/status/1784496793075744966 BlinkDL_AI 2024-06-02 ai/nn/rnn ai/text-style-transfer --- https://danluu.com/ftc-google-antitrust/ 2024-06-02 economics/advertising technology/google --- https://arxiv.org/abs/2405.05254#microsoft You Only Cache Once: Decoder-Decoder Architectures for Language Models Yutao Sun, Li Dong, Yi Zhu, Shaohan Huang, Wenhui Wang, Shuming Ma, Quanlu Zhang, Jianyong Wang, Furu Wei 2024-05-08 2024-06-02 [("doi","10.48550/arXiv.2405.05254")] ai/nn/transformer

[blog] We introduce a decoder-decoder architecture, YOCO, for large language models, which only caches key-value pairs once. It consists of two components, ie. a cross-decoder stacked upon a self-decoder. The self-decoder efficiently encodes global key-value (KV) caches that are reused by the cross-decoder via cross-attention.

The overall model behaves like a decoder-only Transformer, although YOCO only caches once. The design substantially reduces GPU memory demands, yet retains global attention capability. Additionally, the computation flow enables prefilling to early exit without changing the final output, thereby speeding up the prefill stage.

Experimental results demonstrate that YOCO achieves favorable performance compared to Transformer in various settings of scaling up model size and number of training tokens. We also extend YOCO to 1M context length with near-perfect needle retrieval accuracy. The profiling results show that YOCO improves inference memory, prefill latency, and throughput by orders of magnitude across context lengths and model sizes.

Code is available at https://github.com/microsoft/unilm/tree/master/YOCO.

--- https://gonzoml.substack.com/p/you-only-cache-once-decoder-decoder 2024-06-02 ai/nn/transformer --- https://arxiv.org/abs/2209.15594 Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability Alex Damian, Eshaan Nichani, Jason D. Lee 2022-09-30 2024-06-02 [("doi","10.48550/arXiv.2209.15594")] ai/nn

Traditional analyses of gradient descent show that when the largest eigenvalue of the Hessian, also known as the sharpness S(θ), is bounded by 2⁄η, training is “stable” and the training loss decreases monotonically. Recent works, however, have observed that this assumption does not hold when training modern neural networks with full batch or large batch gradient descent. Most recently, Cohen et al 2021 observed two important phenomena. The first, dubbed progressive sharpening, is that the sharpness steadily increases throughout training until it reaches the instability cutoff 2⁄η. The second, dubbed edge of stability, is that the sharpness hovers at 2⁄η for the remainder of training while the loss continues decreasing, albeit non-monotonically.

We demonstrate that, far from being chaotic, the dynamics of gradient descent at the edge of stability can be captured by a cubic Taylor expansion: as the iterates diverge in the direction of the top eigenvector of the Hessian due to instability, the cubic term in the local Taylor expansion of the loss function causes the curvature to decrease until stability is restored. This property, which we call self-stabilization, is a general property of gradient descent and explains its behavior at the edge of stability.

A key consequence of self-stabilization is that gradient descent at the edge of stability implicitly follows projected gradient descent (PGD) under the constraint S(θ) ≤ 2⁄η. Our analysis provides precise predictions for the loss, sharpness, and deviation from the PGD trajectory throughout training, which we verify both empirically in several standard settings and theoretically under mild conditions. Our analysis uncovers the mechanism for gradient descent’s implicit bias towards stability.

--- https://arxiv.org/abs/2103.00065 Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability Jeremy M. Cohen, Simran Kaur, Yuanzhi Li, J. Zico Kolter, Ameet Talwalkar 2021-02-26 2024-06-02 [("doi","10.48550/arXiv.2103.00065")] ai/nn

We empirically demonstrate that full-batch gradient descent on neural network training objectives typically operates in a regime we call the Edge of Stability.

In this regime, the maximum eigenvalue of the training loss Hessian hovers just above the numerical value 2⁄(step size), and the training loss behaves non-monotonically over short timescales, yet consistently decreases over long timescales. Since this behavior is inconsistent with several widespread presumptions in the field of optimization, our findings raise questions as to whether these presumptions are relevant to neural network training.

We hope that our findings will inspire future efforts aimed at rigorously understanding optimization at the Edge of Stability.

Code is available at Github.

--- https://arxiv.org/abs/2310.15421 FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions Hyunwoo Kim, Melanie Sclar, Xuhui Zhou, Ronan Le Bras, Gunhee Kim, Yejin Choi, Maarten Sap 2023-10-24 2024-06-02 [("doi","10.48550/arXiv.2310.15421")] ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude philosophy/mind

Theory of mind (ToM) evaluations currently focus on testing models using passive narratives that inherently lack interactivity.

We introduce FANToM, a new benchmark designed to stress-test ToM within information-asymmetric conversational contexts via question answering. Our benchmark draws upon important theoretical requisites from psychology and necessary empirical considerations when evaluating large language models (LLMs). In particular, we formulate multiple types of questions that demand the same underlying reasoning to identify illusory or false sense of ToM capabilities in LLMs.

We show that FANToM is challenging for state-of-the-art LLMs, which perform worse than humans even with chain-of-thought reasoning or fine-tuning.

--- https://arxiv.org/abs/2301.11913 SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient Max Ryabinin, Tim Dettmers, Michael Diskin, Alexander Borzunov 2023-01-27 2024-06-02 [("doi","10.48550/arXiv.2301.11913")] ai/nn/transformer ai/scaling/hardware

Many deep learning applications benefit from using large models with billions of parameters. Training these models is notoriously expensive due to the need for specialized HPC clusters.

In this work, we consider alternative setups for training large models: using cheap “preemptible” instances or pooling existing resources from multiple regions. We analyze the performance of existing model-parallel algorithms in these conditions and find configurations where training larger models becomes less communication-intensive.

Based on these findings, we propose SWARM parallelism, a model-parallel training algorithm designed for poorly connected, heterogeneous, and unreliable devices. SWARM creates temporary randomized pipelines between nodes that are rebalanced in case of failure.

We empirically validate our findings and compare SWARM parallelism with existing large-scale training approaches. Finally, we combine our insights with compression strategies to train a large Transformer language model with 1B shared parameters (~13B before sharing) on preemptible T4 GPUs with less than 200Mb/s network.

--- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3950103 2024-06-02 technology/carbon-capture --- https://www.recraft.ai/about 2024-06-02 ai/nn/diffusion design/typography --- https://www.betonit.ai/p/do-ten-times-as-much 2024-06-02 psychology/energy --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10382547/ 2024-06-02 longevity/epigenetics --- https://link.springer.com/article/10.1007/s11357-023-00980-6 2024-06-02 longevity/epigenetics --- https://x.com/justindsmith/status/1681166014711746564 justindsmith 2024-06-02 ai/nn/retrieval design --- https://www.thisamericanlife.org/585/transcript 2024-06-02 psychiatry/anxiety psychology/neuroscience/memory/savant zeo --- https://www.ctvnews.ca/w5/why-18-year-old-canadian-emily-nash-is-sharing-her-unique-brain-with-science-1.6818765 2024-06-02 psychiatry/anxiety psychology/neuroscience/memory/savant zeo --- https://statmodeling.stat.columbia.edu/2024/03/24/hey-heres-a-study-where-all-the-preregistered-analyses-yielded-null-results-but-it-was-presented-in-pnas-as-being-wholly-positive/ 2024-06-02 sociology statistics/bias/publication --- https://www.nytimes.com/2024/03/24/health/ozempic-wegovy-forever-drugs.html 2024-06-02 longevity/glp/semaglutide --- https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2774903 2024-06-02 longevity/glp/semaglutide --- https://computationalcreativity.net/iccc21/wp-content/uploads/2021/09/ICCC_2021_paper_52.pdf 2024-06-02 ai/anime --- https://bair.berkeley.edu/blog/2021/10/25/coms_mbo/ 2024-06-02 reinforcement-learning/offline --- https://arxiv.org/abs/2107.06882 Conservative Objective Models for Effective Offline Model-Based Optimization Brandon Trabucco, Aviral Kumar, Xinyang Geng, Sergey Levine 2021-07-14 2024-06-02 [("doi","10.48550/arXiv.2107.06882")] reinforcement-learning/offline

Computational design problems arise in a number of settings, from synthetic biology to computer architectures.

In this paper, we aim to solve data-driven model-based optimization (MBO) problems, where the goal is to find a design input that maximizes an unknown objective function provided access to only a static dataset of prior experiments. Such data-driven optimization procedures are the only practical methods in many real-world domains where active data collection is expensive (eg. when optimizing over proteins) or dangerous (eg. when optimizing over aircraft designs).

Typical methods for MBO that optimize the design against a learned model suffer from distributional shift: it is easy to find a design that “fools” the model into predicting a high value. To overcome this, we propose conservative objective models (COMs), a method that learns a model of the objective function that lower bounds the actual value of the ground-truth objective on out-of-distribution inputs, and uses it for optimization.

Structurally, COMs resemble adversarial training methods used to overcome adversarial examples. COMs are simple to implement and outperform a number of existing methods on a wide range of MBO problems, including optimizing protein sequences, robot morphologies, neural network weights, and superconducting materials.

--- https://github.com/Guang000/Awesome-Dataset-Distillation 2024-06-02 reinforcement-learning/exploration/active-learning/data-pruning --- https://www.biorxiv.org/content/10.1101/2022.08.16.504181.full Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness Sharrol Bachas, Goran Rakocevic, David Spencer, Anand V. Sastry, Robel Haile, John M. Sutton, George Kasun, Andrew Stachyra, Jahir M. Gutierrez, Edriss Yassine, Borka Medjo, Vincent Blay, Christa Kohnert, Jennifer T. Stanton, Alexander Brown, Nebojsa Tijanic, Cailen McCloskey, Rebecca Viazzo, Rebecca Consbruck, Hayley Carter, Simon Levine, Shaheed Abdulhaqq, Jacob Shaul, Abigail B. Ventura, Randal S. Olson, Engin Yapici, Joshua Meier, Sean McClain, Matthew Weinstock, Gregory Hannum, Ariel Schwartz, Miles Gander, Roberto Spreafico 2022-08-17 2024-06-02 [("doi","10.1101/2022.08.16.504181")] ai/nn/transformer/alphafold

Traditional antibody optimization approaches involve screening a small subset of the available sequence space, often resulting in drug candidates with suboptimal binding affinity, developability or immunogenicity.

Based on two distinct antibodies, we demonstrate that deep contextual language models trained on high-throughput affinity data can quantitatively predict binding of unseen antibody sequence variants. These variants span a KD range of 3 orders of magnitude over a large mutational space.

Our models reveal strong epistatic effects, which highlight the need for intelligent screening approaches. In addition, we introduce the modeling of “naturalness”, a metric that scores antibody variants for similarity to natural immunoglobulins.

We show that naturalness is associated with measures of drug developability and immunogenicity, and that it can be optimized alongside binding affinity using a genetic algorithm.

This approach promises to accelerate and improve antibody engineering, and may increase the success rate in developing novel antibody and related drug candidates.

--- /doc/psychology/cognitive-bias/2020-ruggeri.pdf Replicating patterns of prospect theory for decision under risk Kai Ruggeri, Sonia Alí, Mari Louise Berge, Giulia Bertoldo, Ludvig D. Bjørndal, Anna Cortijos-Bernabeu, Clair Davison, Emir Demić, Celia Esteban-Serna, Maja Friedemann, Shannon P. Gibson, Hannes Jarke, Ralitsa Karakasheva, Peggah R. Khorrami, Jakob Kveder, Thomas Lind Andersen, Ingvild S. Lofthus, Lucy McGill, Ana E. Nieto, Jacobo Pérez, Sahana K. Quail, Charlotte Rutherford, Felice L. Tavera, Nastja Tomat, Chiara Van Reyn, Bojana Većkalov, Keying Wang, Aleksandra Yosifova, Francesca Papa, Enrico Rubaltelli, Sander van der Linden, Tomas Folke 2020-05-18 2024-06-02 [("doi","10.1038/s41562-020-0886-x")] economics psychology/cognitive-bias --- https://thezvi.wordpress.com/2023/08/31/ai-27-portents-of-gemini/ 2024-06-02 ai/nn/transformer/gpt/palm --- https://en.wikipedia.org/wiki/Polygraphia_(book) Polygraphia (book) 2024-06-02 cs/cryptography --- https://michaelnotebook.com/oppenheimer/index.html 2024-06-02 radiance reinforcement-learning/safe --- https://huggingface.co/datasets/froggeric/creativity 2024-06-02 ai/nn/transformer/gpt/fiction --- https://www.lesswrong.com/posts/99PwFdz7qwHxQgwYx/awakening#Second_Insight_Cycle 2024-06-02 psychiatry/meditation psychiatry/schizophrenia --- https://www.mctb.org/mctb2/table-of-contents/part-iii-the-samatha-jhanas/28-the-formless-realms/ 2024-06-02 psychiatry/meditation --- https://link.springer.com/article/10.1007/s12671-020-01389-4 2024-06-02 psychiatry/meditation --- https://arxiv.org/abs/2308.01404 Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Models Aidan O’Gara 2023-07-05 2024-06-02 [("doi","10.48550/arXiv.2308.01404")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction philosophy/mind reinforcement-learning/multi-agent reinforcement-learning/safe

[blog] Are current language models capable of deception and lie detection? We study this question by introducing a text-based game called Hoodwinked, inspired by Mafia and Among Us. Players are locked in a house and must find a key to escape, but one player is tasked with killing the others. Each time a murder is committed, the surviving players have a natural language discussion then vote to banish one player from the game.

We conduct experiments with agents controlled by GPT-3, GPT-3.5, and GPT-4 and find evidence of deception and lie detection capabilities. The killer often denies their crime and accuses others, leading to measurable effects on voting outcomes.

More advanced models are more effective killers, outperforming smaller models in 18⁄24 pairwise comparisons. Secondary metrics provide evidence that this improvement is not mediated by different actions, but rather by stronger persuasive skills during discussions.

To evaluate the ability of AI agents to deceive humans, we make this game publicly available at https://www.hoodwinked.ai/.

--- https://en.wikipedia.org/wiki/Johannes_Trithemius Johannes Trithemius 2024-01-01 cs/cryptography --- https://arxiv.org/abs/2308.14784 Generating tabular datasets under differential privacy Gianluca Truda 2023-08-28 2024-06-02 [("doi","10.48550/arXiv.2308.14784")] ai/nn/diffusion ai/nn/gan ai/tabular

Machine Learning (ML) is accelerating progress across fields and industries, but relies on accessible and high-quality training data. Some of the most important datasets are found in biomedical and financial domains in the form of spreadsheets and relational databases. But this tabular data is often sensitive in nature. Synthetic data generation offers the potential to unlock sensitive data, but generative models tend to memorize and regurgitate training data, which undermines the privacy goal.

To remedy this, researchers have incorporated the mathematical framework of Differential Privacy (DP) into the training process of deep neural networks. But this creates a trade-off between the quality and privacy of the resulting data. Generative Adversarial Networks (GANs) are the dominant paradigm for synthesizing tabular data under DP, but suffer from unstable adversarial training and mode collapse, which are exacerbated by the privacy constraints and challenging tabular data modality.

This work optimizes the quality-privacy trade-off of generative models, producing higher quality tabular datasets with the same privacy guarantees. We implement novel end-to-end models that leverage attention mechanisms to learn reversible tabular representations. We also introduce TableDiffusion, the first differentially-private diffusion model for tabular data synthesis.

Our experiments show that TableDiffusion produces higher-fidelity synthetic datasets, avoids the mode collapse problem, and achieves state-of-the-art performance on privatized tabular data synthesis. By implementing TableDiffusion to predict the added noise, we enabled it to bypass the challenges of reconstructing mixed-type tabular data.

Overall, the diffusion paradigm proves vastly more data and privacy efficient than the adversarial paradigm, due to augmented reuse of each data batch and a smoother iterative training process.

--- https://www.nature.com/articles/s41586-023-06419-4 2024-06-02 reinforcement-learning/model-free reinforcement-learning/robot --- https://arxiv.org/abs/2405.03672#deepmind Cutting through buggy adversarial example defenses: fixing 1 line of code breaks Sabre Nicholas Carlini 2024-05-06 2024-06-02 [("doi","10.48550/arXiv.2405.03672")] ai/nn/adversarial

Sabre is a defense to adversarial examples that was accepted at IEEE S&P 2024.

We first reveal flaws in the evaluation that point to clear signs of gradient masking. We then show the cause of this gradient masking: a bug in the original evaluation code. By fixing a single line of code in the original repository, we reduce Sabre’s robust accuracy to 0%.

In response to this, the authors modify the defense and introduce a new defense component not described in the original paper. But this fix contains a second bug; modifying one more line of code reduces robust accuracy to below baseline levels.

After we released the first version of our paper online, the authors introduced another change to the defense; by commenting out one line of code during attack we reduce the robust accuracy to 0% again.

--- https://arxiv.org/abs/2303.14177 Scaling Expert Language Models with Unsupervised Domain Discovery Suchin Gururangan, Margaret Li, Mike Lewis, Weijia Shi, Tim Althoff, Noah Smith, Luke Zettlemoyer 2023-03-24 2024-06-02 [("doi","10.48550/arXiv.2303.14177")] ai/scaling/mixture-of-experts reinforcement-learning/exploration/active-learning

Large language models are typically trained densely: all parameters are updated with respect to all inputs. This requires synchronization of billions of parameters across thousands of GPUs.

We introduce a simple but effective method to asynchronously train large, sparse language models on arbitrary text corpora. Our method clusters a corpus into sets of related documents, trains a separate expert language model on each cluster, and combines them in a sparse ensemble for inference. This approach generalizes embarrassingly parallel training by automatically discovering the domains for each expert, and eliminates nearly all the communication overhead of existing sparse language models.

Our technique outperforms dense baselines on multiple corpora and few-shot tasks, and our analysis shows that specializing experts to meaningful clusters is key to these gains. Performance also improves with the number of experts and size of training data, suggesting this is a highly efficient and accessible approach to training large language models.

--- https://arxiv.org/abs/2307.15008#deepmind A LLM Assisted Exploitation of AI-Guardian Nicholas Carlini 2023-07-20 2024-06-02 [("doi","10.48550/arXiv.2307.15008")] ai/nn/adversarial ai/nn/transformer/gpt/4/nonfiction cs math/humor

Large language models (LLMs) are now highly capable at a diverse range of tasks. This paper studies whether or not GPT-4, one such LLM, is capable of assisting researchers in the field of adversarial machine learning. As a case study, we evaluate the robustness of AI-Guardian, a recent defense to adversarial examples published at IEEE S&P 2023, a top computer security conference. We completely break this defense: the proposed scheme does not increase robustness compared to an undefended baseline.

We write none of the code to attack this model, and instead prompt GPT-4 to implement all attack algorithms following our instructions and guidance. This process was surprisingly effective and efficient, with the language model at times producing code from ambiguous instructions faster than the author of this paper could have done.

We conclude by discussing (1) the warning signs present in the evaluation that suggested to us AI-Guardian would be broken, and (2) our experience with designing attacks and performing novel research using the most recent advances in language modeling.

--- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4553431 2024-06-03 ai/nn economics/copyright --- https://conversationswithtyler.com/episodes/vishy-anand/ 2024-01-01 psychology/chess reinforcement-learning/model/alphago --- https://arxiv.org/abs/2004.06093 Topology of deep neural networks Gregory Naitzat, Andrey Zhitnikov, Lek-Heng Lim 2020-04-13 2024-06-03 [("doi","10.48550/arXiv.2004.06093")] ai/nn

We study how the topology of a data set M = Ma ∪ Mb ⊆ ℝd, representing two classes a and b in a binary classification problem, changes as it passes through the layers of a well-trained neural network, ie. with perfect accuracy on the training set and near-zero generalization error (~0.01%).

The goal is to shed light on two mysteries in deep neural networks: (1) a nonsmooth activation function like ReLU outperforms a smooth one like hyperbolic tangent; (2) successful neural network architectures rely on having many layers, even though a shallow network can approximate any function arbitrarily well.

We performed extensive experiments on the persistent homology of a wide range of point cloud data sets, both real and simulated. The results consistently demonstrate the following: (1) Neural networks operate by changing topology, transforming a topologically complicated data set into a topologically simple one as it passes through the layers. No matter how complicated the topology of M we begin with, when passed through a well-trained neural network (f : ℝd → ℝp), there is a vast reduction in the Betti numbers of both components Ma and Mb; in fact, they nearly always reduce to their lowest possible values: βk(f(Mi)) = 0 for k ≥ 1 and β0(f(Mi)) = 1, i = a, b.

Furthermore, (2) the reduction in Betti numbers is faster for ReLU activation than hyperbolic tangent activation as the former defines non-homeomorphic maps that change topology, whereas the latter defines homeomorphic maps that preserve topology.

Lastly, (3) shallow and deep networks transform data sets differently—a shallow network operates mainly through changing geometry and changes topology only in its final layers, a deep one spreads topological changes more evenly across all layers. [Related to wide networks memorizing rather than generalizing?]

--- https://github.com/harfbuzz/harfbuzz-wasm-examples?tab=readme-ov-file#hieroglyphs 2024-06-03 cs/computable design/typography --- https://blog.erk.dev/posts/anifont/ 2024-06-03 design/typography touhou --- https://en.wikipedia.org/wiki/Bad_Apple!! Bad Apple!​! 2024-06-03 touhou --- https://www.youtube.com/watch?v=GF2sn2DXjlA 2024-06-03 design/typography touhou --- https://www.npr.org/sections/health-shots/2023/08/28/1194526119/ozempic-wegovy-drinking-alcohol-cravings-semaglutide 2024-06-03 longevity/glp/psychology psychiatry/alcoholism --- https://arxiv.org/abs/2308.14752 AI Deception: A Survey of Examples, Risks, and Potential Solutions Peter S. Park, Simon Goldstein, Aidan O’Gara, Michael Chen, Dan Hendrycks 2023-08-28 2024-06-03 [("doi","10.48550/arXiv.2308.14752")] reinforcement-learning/multi-agent reinforcement-learning/safe

This paper argues that a range of current AI systems have learned how to deceive humans. We define deception as the systematic inducement of false beliefs in the pursuit of some outcome other than the truth.

We first survey empirical examples of AI deception, discussing both special-use AI systems (including Meta’s CICERO) built for specific competitive situations, and general-purpose AI systems (such as large language models).

Next, we detail several risks from AI deception, such as fraud, election tampering, and losing control of AI systems.

Finally, we outline several potential solutions to the problems posed by AI deception: first, regulatory frameworks should subject AI systems that are capable of deception to robust risk-assessment requirements; second, policymakers should implement bot-or-not laws; and finally, policymakers should prioritize the funding of relevant research, including tools to detect AI deception and to make AI systems less deceptive.

Policymakers, researchers, and the broader public should work proactively to prevent AI deception from destabilizing the shared foundations of our society.

--- https://arxiv.org/abs/2307.09702 Efficient Guided Generation for Large Language Models Brandon T. Willard, Rémi Louf 2023-07-19 2024-06-03 [("doi","10.48550/arXiv.2307.09702")] ai/nn/sampling

In this article we show how the problem of neural text generation can be constructively reformulated in terms of transitions between the states of a finite-state machine.

This framework leads to an efficient approach to guiding text generation with regular expressions and context-free grammars by allowing the construction of an index over a language model’s vocabulary. The approach is model agnostic, allows one to enforce domain-specific knowledge and constraints, and enables the construction of reliable interfaces by guaranteeing the structure of the generated text. It adds little overhead to the token sequence generation process and outperforms existing solutions.

An implementation is provided in the open source Python library Outlines.

--- https://www.nature.com/articles/s41591-023-02526-x 2024-06-03 longevity/glp/semaglutide --- https://arxiv.org/abs/2308.10144 ExpeL: LLM Agents Are Experiential Learners Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, Gao Huang 2023-08-20 2024-06-03 [("doi","10.48550/arXiv.2308.10144")] ai/nn/retrieval ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/meta-learning

The recent surge in research interest in applying large language models (LLMs) to decision-making tasks has flourished by leveraging the extensive world knowledge embedded in LLMs. While there is a growing demand to tailor LLMs for custom decision-making tasks, fine-tuning them for specific tasks is resource-intensive and may diminish the model’s generalization capabilities. Moreover, state-of-the-art language models like GPT-4 and Claude are primarily accessible through API calls, with their parametric weights remaining proprietary and unavailable to the public. This scenario emphasizes the growing need for new methodologies that allow learning from agent experiences without requiring parametric updates.

To address these problems, we introduce the Experiential Learning (ExpeL) agent. Our agent autonomously gathers experiences and extracts knowledge using natural language from a collection of training tasks. At inference, the agent recalls its extracted insights and past experiences to make informed decisions.

Our empirical results highlight the robust learning efficacy of the ExpeL agent, indicating a consistent enhancement in its performance as it accumulates experiences. We further explore the emerging capabilities and transfer learning potential of the ExpeL agent through qualitative observations and additional experiments.

--- https://www.nature.com/articles/s41586-023-06424-7 2024-06-03 genetics/gametogenesis longevity/epigenetics --- https://arxiv.org/abs/2308.13418#facebook Nougat: Neural Optical Understanding for Academic Documents Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic 2023-08-25 2024-06-03 [("doi","10.48550/arXiv.2308.13418")] ai/nn/transformer design/typography/tex

[homepage] Scientific knowledge is predominantly stored in books and scientific journals, often in the form of PDFs. However, the PDF format leads to a loss of semantic information, particularly for mathematical expressions. We propose Nougat (Neural Optical Understanding for Academic Documents), a Visual Transformer model that performs an Optical Character Recognition (OCR) task for processing scientific documents into a markup language.

We demonstrate the effectiveness of our model on a new dataset of scientific documents.

The proposed approach offers a promising solution to enhance the accessibility of scientific knowledge in the digital age, by bridging the gap between human-readable documents and machine-readable text.

We release the models and code to accelerate future work on scientific text recognition.

--- https://python.langchain.com/v0.1/docs/modules/data_connection/retrievers/multi_vector/ 2024-06-03 ai/nn/retrieval --- https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1217288/full 2024-06-03 iq --- https://arxiv.org/abs/2308.07540 CALYPSO: LLMs as Dungeon Masters’ Assistants Andrew Zhu, Lara J. Martin, Andrew Head, Chris Callison-Burch 2023-08-15 2024-06-03 [("doi","10.1609/aiide.v19i1.27534")] ai/nn/transformer/gpt/3/fiction fiction/text-game

The role of a Dungeon Master, or DM, in the game Dungeons & Dragons is to perform multiple tasks simultaneously. The DM must digest information about the game setting and monsters, synthesize scenes to present to other players, and respond to the players’ interactions with the scene. Doing all of these tasks while maintaining consistency within the narrative and story world is no small feat of human cognition, making the task tiring and unapproachable to new players.

Large language models (LLMs) like GPT-3 and ChatGPT have shown remarkable abilities to generate coherent natural language text. In this paper, we conduct a formative evaluation with DMs to establish the use cases of LLMs in D&D and tabletop gaming generally. We introduce CALYPSO, a system of LLM-powered interfaces that support DMs with information and inspiration specific to their own scenario.

CALYPSO distills game context into bite-sized prose and helps brainstorm ideas without distracting the DM from the game. When given access to CALYPSO, DMs reported that it generated high-fidelity text suitable for direct presentation to players and low-fidelity ideas that the DM could develop further while maintaining their creative agency.

We see CALYPSO as exemplifying a paradigm of AI-augmented tools that provide synchronous creative assistance within established game worlds, and tabletop gaming more broadly.

--- https://www.semianalysis.com/p/google-gemini-eats-the-world-gemini 2024-06-03 ai/nn/transformer/gpt/palm ai/scaling/mixture-of-experts --- https://arxiv.org/abs/2308.07922#nvidia RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models Jie Huang, Wei Ping, Peng Xu, Mohammad Shoeybi, Kevin Chen-Chuan Chang, Bryan Catanzaro 2023-08-15 2024-06-03 [("doi","10.48550/arXiv.2308.07922")] ai/nn/retrieval ai/nn/transformer/t5 reinforcement-learning/meta-learning

In this paper, we investigate the in-context learning ability of retrieval-augmented encoder-decoder language models.

We first conduct a comprehensive analysis of existing models and identify their limitations in in-context learning, primarily due to a mismatch between pretraining and inference, as well as a restricted context length. To address these issues, we propose RAVEN, a model that combines retrieval-augmented masked language modeling and prefix language modeling. We further introduce Fusion-in-Context Learning to enhance the few-shot performance by enabling the model to leverage more in-context examples without requiring additional training.

Through extensive experiments, we demonstrate that our simple yet effective design improves performance, achieving results comparable to the most advanced language models in certain scenarios, despite having substantially fewer parameters.

Our work underscores the potential of retrieval-augmented encoder-decoder language models for in-context learning and encourages further research in this direction.

--- https://thehustle.co/where-do-fonts-come-from 2024-06-03 design/typography economics --- https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2808734 2024-06-03 biology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7088919/ 2024-06-03 longevity/glp/semaglutide --- https://www.biorxiv.org/content/10.1101/2023.08.13.553051.full A Pooled Cell Painting CRISPR Screening Platform Enables de novo Inference of Gene Function by Self-supervised Deep Learning Srinivasan Sivanandan, Bobby Leitmann, Eric Lubeck, Mohammad Muneeb Sultan, Panagiotis Stanitsas, Navpreet Ranu, Alexis Ewer, Jordan E. Mancuso, Zachary F. Phillips, Albert Kim, John W. Bisognano, John Cesarek, Fiorella Ruggiu, David Feldman, Daphne Koller, Eilon Sharon, Ajamete Kaykas, Max R. Salick, Ci Chu 2023-08-27 2024-06-03 [("doi","10.1101/2023.08.13.553051")] ai/nn/transformer genetics/editing

Pooled CRISPR screening has emerged as a powerful method of mapping gene functions thanks to its scalability, affordability, and robustness against well or plate-specific confounders present in array-based screening. Most pooled CRISPR screens assay for low dimensional phenotypes (eg. fitness, fluorescent markers). Higher-dimensional assays such as perturb-seq are available but costly and only applicable to transcriptomics readouts. Recently, pooled optical screening, which combines pooled CRISPR screening and microscopy-based assays, has been demonstrated in the studies of the NFkB pathway, essential human genes, cytoskeletal organization and antiviral response.

While the pooled optical screening methodology is scalable and information-rich, the applications thus far employ hypothesis-specific assays. Here, we enable hypothesis-free reverse genetic screening for generic morphological phenotypes by re-engineering the Cell Painting technique to provide compatibility with pooled optical screening.

We validated this technique using well-defined morphological gene-sets (124 genes), compared classical image analysis and self-supervised learning methods using a mechanism-of-action (MoA) library (300 genes), and performed discovery screening with a druggable genome library (1640 genes).

Across these 3 experiments we show that the combination of rich morphological data and deep learning allows gene networks to emerge without the need for target-specific biomarkers, leading to better discovery of gene functions.

--- https://osf.io/preprints/psyarxiv/ak6gx 2024-06-03 psychedelic statistics/bias --- https://www.macroscience.org/p/the-frontier-of-scientific-plausibility 2024-06-03 science statistics/prediction --- https://osf.io/preprints/socarxiv/d5a9h 2024-06-03 genetics/heritable iq --- https://www.sciencedirect.com/science/article/pii/S0042698906002112 2024-06-03 psychology/vision --- https://www.nippon.com/en/japan-topics/c12403/ 2024-06-03 psychology/vision --- https://www.sciencedirect.com/science/article/pii/S0092867423008504 2024-06-03 psychology/neuroscience --- https://x.com/pirroh/status/1694516986561307022 pirroh 2024-06-03 ai/scaling/hardware reinforcement-learning/exploration --- https://arxiv.org/abs/2308.12270 Language Reward Modulation for Pretraining Reinforcement Learning Ademi Adeniji, Amber Xie, Carmelo Sferrazza, Younggyo Seo, Stephen James, Pieter Abbeel 2023-08-23 2024-06-03 [("doi","10.48550/arXiv.2308.12270")] ai/nn/transformer/gpt reinforcement-learning/exploration

Using learned reward functions (LRFs) as a means to solve sparse-reward reinforcement learning (RL) tasks has yielded some steady progress in task-complexity through the years.

In this work, we question whether today’s LRFs are best-suited as a direct replacement for task rewards. Instead, we propose leveraging the capabilities of LRFs as a pretraining signal for RL. Concretely, we propose LAnguage Reward Modulated Pretraining (LAMP) which leverages the zero-shot capabilities of Vision-Language Models (VLMs) as a pretraining utility for RL as opposed to a downstream task reward.

LAMP uses a frozen, pretrained VLM to scalably generate noisy, albeit shaped exploration rewards by computing the contrastive alignment between a highly diverse collection of language instructions and the image observations of an agent in its pretraining environment. LAMP optimizes these rewards in conjunction with standard novelty-seeking exploration rewards with reinforcement learning to acquire a language-conditioned, pretrained policy.

Our VLM pretraining approach, which is a departure from previous attempts to use LRFs, can warm-start sample-efficient learning on robot manipulation tasks in RLBench.

--- https://www.cell.com/current-biology/fulltext/S0960-9822(10)01282-0 2024-06-03 genetics/selection/natural/human psychology/neuroscience --- https://arxiv.org/abs/2308.11462 LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models Neel Guha, Julian Nyarko, Daniel E. Ho, Christopher Ré, Adam Chilton, Aditya Narayana, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel N. Rockmore, Diego Zambrano, Dmitry Talisman, Enam Hoque, Faiz Surani, Frank Fagan, Galit Sarfaty, Gregory M. Dickinson, Haggai Porat, Jason Hegland, Jessica Wu, Joe Nudell, Joel Niklaus, John Nay, Jonathan H. Choi, Kevin Tobia, Margaret Hagan, Megan Ma, Michael Livermore, Nikon Rasumov-Rahe, Nils Holzenberger, Noam Kolt, Peter Henderson, Sean Rehaag, Sharad Goel, Shang Gao, Spencer Williams, Sunny Gandhi, Tom Zur, Varun Iyer, Zehua Li 2023-08-20 2024-06-03 [("doi","10.48550/arXiv.2308.11462")] ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/t5 law

The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform?

To enable greater study of this question, we present LegalBench: a collaboratively constructed legal reasoning benchmark consisting of 162 tasks covering 6 different types of legal reasoning. LegalBench was built through an interdisciplinary process, in which we collected tasks designed and hand-crafted by legal professionals. Because these subject matter experts took a leading role in construction, tasks either measure legal reasoning capabilities that are practically useful or measure reasoning skills that lawyers find interesting.

To enable cross-disciplinary conversations about LLMs in the law, we additionally show how popular legal frameworks for describing legal reasoning—which distinguish between its many forms—correspond to LegalBench tasks, thus giving lawyers and LLM developers a common vocabulary.

This paper describes LegalBench, presents an empirical evaluation of 20 open-source and commercial LLMs, and illustrates the types of research explorations LegalBench enables.

--- https://arxiv.org/abs/2405.21075 Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis Chaoyou Fu, Yuhan Dai, Yondong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Rongrong Ji, Xing Sun 2024-05-31 2024-06-03 [("doi","10.48550/arXiv.2405.21075")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/palm ai/video/analysis

In the quest for artificial general intelligence, Multi-modal Large Language Models (MLLMs) have emerged as a focal point in recent advancements. However, the predominant focus remains on developing their capabilities in static image understanding. The potential of MLLMs in processing sequential visual data is still insufficiently explored, highlighting the absence of a comprehensive, high-quality assessment of their performance.

In this paper, we introduce Video-MME, the first-ever full-spectrum, Multi-Modal Evaluation benchmark of MLLMs in Video analysis. Our work distinguishes from existing benchmarks through 4 key features: (1) Diversity in video types, spanning 6 primary visual domains with 30 subfields to ensure broad scenario generalizability; (2) Duration in temporal dimension, encompassing short/medium/long-term videos, ranging from 11 seconds to 1 hour, for robust contextual dynamics; (3) Breadth in data modalities, integrating multi-modal inputs besides video frames, including subtitles and audios, to unveil the all-round capabilities of MLLMs; (4) Quality in annotations, using rigorous manual labeling by expert annotators to facilitate precise and reliable model assessment.

900 videos (256 hours) are manually selected and annotated by repeatedly viewing all the video content, resulting in 2,700 question-answer pairs. With Video-MME, we extensively evaluate various state-of-the-art MLLMs, including GPT-4 series and Gemini 1.5 Pro, as well as open-source image models like InternVL-Chat-V1.5 and video models like LLaVA-NeXT-Video.

Our experiments reveal that Gemini 1.5 Pro is the best-performing commercial model, outperforming the open-source models.

Our dataset along with these findings underscores the need for further improvements in handling longer sequences and multi-modal data.

Project Page: https://video-mme.github.io/.

--- https://www.texasmonthly.com/arts-entertainment/why-did-colleen-hoover-stop-writing/ 2024-06-03 psychology/writing --- https://arxiv.org/abs/1912.01839 Explorable Super Resolution Yuval Bahat, Tomer Michaeli 2019-12-04 2024-06-03 [("doi","10.48550/arXiv.1912.01839")] ai/nn/gan

Single image super resolution (SR) has seen major performance leaps in recent years. However, existing methods do not allow exploring the infinitely many plausible reconstructions that might have given rise to the observed low-resolution (LR) image. These different explanations to the LR image may dramatically vary in their textures and fine details, and may often encode completely different semantic information.

In this paper, we introduce the task of explorable super resolution. We propose a framework comprising a graphical user interface with a neural network backend, allowing editing the SR output so as to explore the abundance of plausible HR explanations to the LR input.

At the heart of our method is a novel module that can wrap any existing SR network, analytically guaranteeing that its SR outputs would precisely match the LR input, when downsampled. Besides its importance in our setting, this module is guaranteed to decrease the reconstruction error of any SR network it wraps, and can be used to cope with blur kernels that are different from the one the network was trained for.

We illustrate our approach in a variety of use cases, ranging from medical imaging and forensics, to graphics.

--- https://www.lesswrong.com/posts/F6vH6fr8ngo7csDdf/chess-as-a-case-study-in-hidden-capabilities-in-chatgpt 2024-06-03 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/chess reinforcement-learning/model/decision-transformer --- https://arxiv.org/abs/2308.09687 Graph of Thoughts: Solving Elaborate Problems with Large Language Models Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, Torsten Hoefler 2023-08-18 2024-06-03 [("doi","10.1609/aaai.v38i16.29720")] ai/nn/transformer/gpt/inner-monologue

We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea and primary advantage of GoT is the ability to model the information generated by an LLM as an arbitrary graph, where units of information (“LLM thoughts”) are vertices, and edges correspond to dependencies between these vertices.

This approach enables combining arbitrary LLM thoughts into synergistic outcomes, distilling the essence of whole networks of thoughts, or enhancing thoughts using feedback loops.

We illustrate that GoT offers advantages over state-of-the-art on different tasks, for example increasing the quality of sorting by 62% over ToT, while simultaneously reducing costs by >31%. [Seems like weak baselines & results for its complexity.]

We ensure that GoT is extensible with new thought transformations and thus can be used to spearhead new prompting schemes. This work brings the LLM reasoning closer to human thinking or brain mechanisms such as recurrence, both of which form complex networks.

--- https://arxiv.org/abs/2308.09543 Latent State Models of Training Dynamics Michael Y. Hu, Angelica Chen, Naomi Saphra, Kyunghyun Cho 2023-08-18 2024-06-03 [("doi","10.48550/arXiv.2308.09543")] ai/nn ai/scaling/emergence/grokking

The impact of randomness on model training is poorly understood. How do differences in data order and initialization actually manifest in the model, such that some training runs outperform others or converge faster? Furthermore, how can we interpret the resulting training dynamics and the phase transitions that characterize different trajectories?

To understand the effect of randomness on the dynamics and outcomes of neural network training, we train models multiple times with different random seeds and compute a variety of metrics throughout training, such as the 𝓁2 norm, mean, and variance of the neural network’s weights. We then fit a hidden Markov model (HMM) over the resulting sequences of metrics.

The HMM represents training as a stochastic process of transitions between latent states, providing an intuitive overview of changes during training.

Using our method, we produce a low-dimensional, discrete representation of training dynamics on grokking tasks, image classification, and masked language modeling.

We use the HMM representation to study phase transitions and identify latent “detour” states that slow down convergence.

--- https://labyrinthlocator.org/ 2024-06-03 design --- https://www.chm.bris.ac.uk/motm/motm.htm 2024-06-03 science --- https://jaydaigle.net/blog/replication-crisis-math/ 2024-01-01 math philosophy/epistemology --- https://tomasp.net/techdims/ Technical dimensions of programming systems 2024-06-03 cs/algorithm cs/css --- https://arxiv.org/abs/1903.08942 Biasing MCTS with Features for General Games Dennis J. N. J. Soemers, Éric Piette, Cameron Browne 2019-03-21 2024-06-03 [("doi","10.48550/arXiv.1903.08942")] reinforcement-learning/model

This paper proposes using a linear function approximator, rather than a deep neural network (DNN), to bias a Monte Carlo tree search (MCTS) player for general games. This is unlikely to match the potential raw playing strength of DNNs, but has advantages in terms of generality, interpretability and resources (time and hardware) required for training.

Features describing local patterns are used as inputs. The features are formulated in such a way that they are easily interpretable and applicable to a wide range of general games, and might encode simple local strategies. We gradually create new features during the same self-play training process used to learn feature weights.

We evaluate the playing strength of an MCTS player biased by learnt features against a standard upper confidence bounds for trees (UCT) player in multiple different board games, and demonstrate improved playing strength in the majority of them after a small number of self-play training games.

--- https://www.reddit.com/r/LocalLLaMA/comments/1csj9w8/the_llm_creativity_benchmark_new_leader_4x_faster/ 2024-06-03 ai/nn/transformer/gpt/fiction --- https://www.econlib.org/library/columns/y2024/forresterpovertysolution.html 2024-06-03 economics/georgism --- https://www.quantamagazine.org/cryptographers-discover-a-new-foundation-for-quantum-secrecy-20240603/ 2024-06-03 cs/cryptography --- https://arxiv.org/abs/2308.12287 Devising and Detecting Phishing: Large Language Models vs. Smaller Human Models Fredrik Heiding, Bruce Schneier, Arun Vishwanath, Jeremy Bernstein, Peter S. Park 2023-08-23 2024-06-03 [("doi","10.48550/arXiv.2308.12287")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/palm cs/security

AI programs, built using large language models, make it possible to automatically create phishing emails based on a few data points about a user. They stand in contrast to traditional phishing emails that hackers manually design using general rules gleaned from experience. The V-Triad is an advanced set of rules for manually designing phishing emails to exploit our cognitive heuristics and biases.

In this study, we compare the performance of phishing emails created automatically by GPT-4 and manually using the V-Triad. We also combine GPT-4 with the V-Triad to assess their combined potential. A fourth group, exposed to generic phishing emails, was our control group. We used a factorial approach, sending emails to 112 randomly selected participants recruited for the study.

The control group emails received a click-through rate between 19-28%, the GPT-generated emails 30-44%, emails generated by the V-Triad 69-79%, and emails generated by GPT and the V-Triad 43-81%. Each participant was asked to explain why they pressed or did not press a link in the email. These answers often contradict each other, highlighting the need for personalized content. The cues that make one person avoid phishing emails make another person fall for them.

Next, we used 4 popular large language models (GPT-4, Claude, PaLM, and LLaMA) to detect the intention of phishing emails and compare the results to human detection. The language models demonstrated a strong ability to detect malicious intent, even in non-obvious phishing emails. They sometimes surpassed human detection, although often being slightly less accurate than humans.

Finally, we make an analysis of the economic aspects of AI-enabled phishing attacks, showing how large language models can increase the incentives of phishing and spear phishing by reducing their costs.

--- https://www.schneier.com/academic/archives/2024/06/ai-will-increase-the-quantity-and-quality-of-phishing-scams.html AI Will Increase the Quantity—and Quality—of Phishing Scams 2024-06-04 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/palm cs/security --- https://wandb.ai/lambdalabs/lego/reports/Text2Bricks-Fine-tuning-Open-Sora-in-1-000-GPU-Hours--Vmlldzo4MDE3MTky Text2Bricks: Fine-tuning Open-Sora in 1,000 GPU-Hours 2024-06-04 ai/video/generation --- https://spritely.institute/news/cirkoban-sokoban-meets-cellular-automata-written-in-scheme.html Cirkoban: Sokoban meets cellular automata written in Scheme 2024-06-04 cs/cellular-automaton cs/lisp --- https://www.reddit.com/gallery/1d6w6b4 2024-06-04 math/humor reinforcement-learning/model --- https://physics.stackexchange.com/questions/816698/how-many-photons-are-received-per-bit-transmitted-from-voyager-1 2024-06-04 cs/algorithm/information --- https://antithesis.com/blog/zelda/ Solving Zelda with the Antithesis SDK 2024-06-04 reinforcement-learning/exploration --- https://arxiv.org/abs/2403.17083 A Study in Dataset Pruning for Image Super-Resolution Brian B. Moser, Federico Raue, Andreas Dengel 2024-03-25 2024-06-04 [("doi","10.48550/arXiv.2403.17083")] ai/nn/gan ai/nn/transformer reinforcement-learning/exploration/active-learning/data-pruning

In image Super-Resolution (SR), relying on large datasets for training is a double-edged sword. While offering rich training material, they also demand substantial computational and storage resources.

In this work, we analyze dataset pruning as a solution to these challenges. We introduce a novel approach that reduces a dataset to a core-set of training samples, selected based on their loss values as determined by a simple pre-trained SR model.

By focusing the training on just 50% of the original dataset, specifically on the samples characterized by the highest loss values, we achieve results comparable to or even surpassing those obtained from training on the entire dataset. Interestingly, our analysis reveals that the top 5% of samples with the highest loss values negatively affect the training process. Excluding these samples and adjusting the selection to favor easier samples further enhances training outcomes. [Reflects what I found with discriminator ranking for data cleaning: the highest loss ones tend to be weird—not necessarily garbage or bad data, but strange, hard to learn, possibly from different distributions entirely. See also top-k gradients.]

Our work opens new perspectives to the untapped potential of dataset pruning in image SR. It suggests that careful selection of training data based on loss-value metrics can lead to better SR models, challenging the conventional wisdom that more data inevitably leads to better performance.

--- https://github.com/Dicklesworthstone/grassmann_article?tab=readme-ov-file#the-lessons-of-hermann-grassmann-and-the-nature-of-abstractions The Lessons of Hermann Grassmann and the Nature of Abstractions Jeffrey Emanual 2024-06-01 2024-06-04 math psychology/novelty --- https://pdfs.semanticscholar.org/77fd/0cf03de8a6c4f56c5decc7c47ebe69cf98c1.pdf The contribution of sleep to hippocampus-dependent memory consolidation Marshall, Born 2007 2024-01-01 psychology/neuroscience/memory zeo --- https://www.yorku.ca/mar/Mar%202011_ARP_neural%20bases%20of%20soc%20cog%20and%20story%20comp.pdf The Neural Bases of Social Cognition and Story Comprehension Mar 2011 2024-01-01 philosophy/mind psychology/neuroscience --- https://arxiv.org/abs/1004.2731 Data Mining the University: College GPA Predictions from SAT Scores Steve Hsu, James Schombert 2010-04-15 2024-01-01 [("doi","10.48550/arXiv.1004.2731")] iq

We analyze a data set comprised of academic records of undergraduates at the University of Oregon from 2000–2004. We find correlations of roughly 0.35 to 0.5 between SAT scores and upper division, in-major GPA (henceforth, GPA).

Interestingly, low SAT scores do not preclude high performance in most majors. That is, the distribution of SAT scores after conditioning on high GPA (eg. 3.5 or even 4.0) typically extends below 1,000 (the average among test takers). We hypothesize that “overachievers” overcome cognitive deficits through hard work, and discuss to what extent they can be identified from high school records.

Only a few majors seem to exhibit a “cognitive threshold”—such that high GPA (mastery of the subject matter) is very unlikely below a certain SAT threshold (ie. no matter how dedicated or hard working the student). Our results suggest that almost any student admitted to university can achieve academic success, if they work hard enough.

In addition to our primary result, we find that the best predictor of GPA is a roughly equally weighted sum of SAT and high school GPA, measured in standard deviation units. Using a sub-population of honors college students, we can estimate how students at elite universities would fare at a typical state university, allowing us to comment on issues such as grade inflation.

Finally, we observe that (1) SAT scores fluctuate little on retest (very high reliability), (2) SAT and GRE scores (where available) correlate at roughly 0.75 (consistent with the notion that both tests measure a stable general cognitive ability) and (3) the SAT distribution of students that obtained a degree does not differ substantially from that of the entering class.

--- https://www.semanticscholar.org/paper/Donor-Sibling-Networks-as-a-Vehicle-for-Expanding-Hertz-Nelson/6e736c71cd499fa03af6dabad3009335a8259745?pdf#page=14 Donor Sibling Networks as a Vehicle for Expanding Kinship: A Replication and Extension Hertz 2017 2024-01-01 genetics/heritable/adoption --- https://en.wikipedia.org/wiki/NovelAI NovelAI 2024-06-04 ai/anime ai/nn/diffusion --- https://techcrunch.com/2024/03/22/stability-ai-ceo-resigns-because-youre-not-going-to-beat-centralized-ai-with-more-centralized-ai/ Stability AI CEO resigns because you can’t beat centralized AI with more centralized AI 2024-06-04 ai/nn/diffusion ai/scaling/economics --- https://x.com/SCHIZO_FREQ/status/1797951865260515348 SCHIZO_FREQ 2024-06-04 psychedelic --- https://arxiv.org/abs/2405.16660 A proof that HT is more likely to outnumber HH than vice versa in a sequence of n coin flips Simon Segert 2024-05-26 2024-06-04 [("doi","10.48550/arXiv.2405.16660")] statistics/probability

Consider the following probability puzzle: A fair coin is flipped n times. For each HT in the resulting sequence, Bob gets a point, and for each HH Alice gets a point.

Who is more likely to win? We provide a proof that Bob wins more often for every n ≥ 3.

As a byproduct, we derive the asymptotic form of the difference in win probabilities, and obtain an efficient algorithms for their calculation.

--- https://zompist.com/yingzi/yingzi.htm 2024-06-04 design/typography/square psychology/linguistics --- https://arxiv.org/abs/2405.13561 How to Answer Questions of the Type: If you toss a coin n times, how likely is HH to show up more than HT? Shalosh B. Ekhad, Doron Zeilberger 2024-05-22 2024-06-04 [("doi","10.48550/arXiv.2405.13561")] statistics/probability

On March 16, 2024, Daniel Litt, in an Twitter post, proposed the following brainteaser: “Flip a fair coin 100×. It gives a sequence of heads (H) and tails (T). For each HH in the sequence of flips, Alice gets a point; for each HT, Bob does, so eg. for the sequence THHHT Alice gets 2 points and Bob gets 1 point. Who is most likely to win?”

We show the power of symbolic computation, in particular the (continuous) Almkvist-Zeilberger algorithm, to answer this, and far more general, questions of this kind. [generalization]

--- https://www.lesswrong.com/posts/dLg7CyeTE4pqbbcnp/language-models-model-us Language Models Model Us 2024-06-04 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/calibration cs/security reinforcement-learning/imitation-learning statistics/bayes statistics/stylometry/truesight --- https://www.youtube.com/watch?v=-H48GPv4BZI End of Evangelion In 5 Minutes (Live Action) (Sweded)—Mega64 2024-06-04 anime/eva fiction/humor --- https://arxiv.org/abs/2406.01574 MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, Tianle Li, Max Ku, Kai Wang, Alex Zhuang, Rongqi Fan, Xiang Yue, Wenhu Chen 2024-06-03 2024-06-04 [("doi","10.48550/arXiv.2406.01574")] ai/dataset ai/nn/transformer/gpt/inner-monologue math

In the age of large-scale language models, benchmarks like the Massive Multitask Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI can achieve in language comprehension and reasoning across diverse domains. However, as models continue to improve, their performance on these benchmarks has begun to plateau, making it increasingly difficult to discern differences in model capabilities.

This paper introduces MMLU-Pro, an enhanced dataset designed to extend the mostly knowledge-driven MMLU benchmark by integrating more challenging, reasoning-focused questions and expanding the choice set 4 → 10 options. Additionally, MMLU-Pro eliminates the trivial and noisy questions in MMLU.

Our experimental results show that MMLU-Pro not only raises the challenge, causing a drop in accuracy by 16% to 33% compared to MMLU but also demonstrates greater stability under varying prompts. With 24 different prompt styles tested, the sensitivity of model scores to prompt variations decreased from 4–5% in MMLU to just 2% in MMLU-Pro. Additionally, we found that models using Chain-of-Thought (CoT) reasoning achieved better performance on MMLU-Pro compared to direct answering, which is in stark contrast to the findings on the original MMLU, indicating that MMLU-Pro includes more complex reasoning questions.

Our assessments confirm that MMLU-Pro is a more discriminative benchmark to better track progress in the field.

--- https://arxiv.org/abs/2404.12451 Assessing the Risk of Proliferation via Fissile Material Breeding in ARC-class Fusion Reactors J. L. Ball, E. E. Peterson, R. S. Kemp, S. E. Ferry 2024-04-18 2024-06-04 [("doi","10.48550/arXiv.2404.12451")] radiance

Construction of a nuclear weapon requires access to kilogram-scale quantities of fissile material, which can be bred from fertile material like U-238 and Th-232 via neutron capture. Future fusion power plants, with total neutron source rates in excess of 1020 neutron/second, could breed weapons-relevant quantities of fissile material on short timescales, posing a breakout proliferation risk.

The ARC-class fusion reactor design is characterized by demountable high temperature superconducting magnets, a FLiB liquid immersion blanket, and a relatively small size (~4m major radius, ~1m minor radius). We use the open-source Monte Carlo neutronics code OpenMC to perform self-consistent time-dependent simulations of a representative ARC-class blanket to assess the feasibility of a fissile breeding breakout scenario.

We find that a quantity of fissile material can be bred in less than 6 months of full power operation for initial fertile inventories ranging 5 → 50 metric tons, representing a non-negligible proliferation risk. We further study the feasibility of this scenario by examining other consequences of fissile breeding such as reduced tritium breeding ratio, extra heat from fission and decay heat, isotopic purity of bred material, and self-protection time of irradiated blanket material.

We also examine the impact of Li-6 enrichment on fissile breeding and find that it substantially reduces breeding rate, motivating its use as a proliferation resistance tool.

--- https://news.ycombinator.com/item?id=40579734 I made a custom gpt that incorporates advertisement/product placement with its... 2024-06-04 ai/nn/transformer/gpt cs/cryptography/steganography economics/advertising --- https://arxiv.org/abs/2304.11082 Fundamental Limitations of Alignment in Large Language Models Yotam Wolf, Noam Wies, Oshri Avnery, Yoav Levine, Amnon Shashua 2023-04-19 2024-06-05 [("doi","10.48550/arXiv.2304.11082")] ai/nn/adversarial reinforcement-learning/meta-learning reinforcement-learning/safe statistics/bayes

An important aspect in developing language models that interact with humans is aligning their behavior to be useful and unharmful for their human users. This is usually achieved by tuning the model in a way that enhances desired behaviors and inhibits undesired ones, a process referred to as alignment.

In this paper, we propose a theoretical approach called Behavior Expectation Bounds (BEB) which allows us to formally investigate several inherent characteristics and limitations of alignment in large language models. Importantly, we prove that within the limits of this framework, for any behavior that has a finite probability of being exhibited by the model, there exist prompts that can trigger the model into outputting this behavior, with probability that increases with the length of the prompt.

This implies that any alignment process that attenuates an undesired behavior but does not remove it altogether, is not safe against adversarial prompting attacks. Furthermore, our framework hints at the mechanism by which leading alignment approaches such as reinforcement learning from human feedback make the LLM prone to being prompted into the undesired behaviors. This theoretical result is being experimentally demonstrated in large scale by the so-called contemporary “ChatGPT jailbreaks”, where adversarial users trick the LLM into breaking its alignment guardrails by triggering it into acting as a malicious persona.

Our results expose fundamental limitations in alignment of LLMs and bring to the forefront the need to devise reliable mechanisms for ensuring AI safety.

--- https://www.nytimes.com/2024/06/02/world/americas/starlink-internet-elon-musk-brazil-amazon.html Elon Musk’s Starlink Connects and Divides Brazil’s Marubo People 2024-06-05 sociology/technology --- https://en.wikipedia.org/wiki/Conditioned_place_preference Conditioned place preference 2024-06-05 psychiatry psychology/animal --- https://www.lesswrong.com/posts/5dkhdRMypeuyoXfmb/is-this-lie-detector-really-just-a-lie-detector-an Is This Lie Detector Really Just a Lie Detector? An Investigation of LLM Probe Specificity 2024-06-05 ai/nn/transformer/gpt/calibration --- https://www.lesswrong.com/posts/GfwdBoaLw3ef3zBqe/evidence-of-learned-look-ahead-in-a-chess-playing-neural Evidence of Learned Look-Ahead in a Chess-Playing Neural Network 2024-06-05 reinforcement-learning/chess reinforcement-learning/model/decision-transformer --- https://www.pnas.org/doi/full/10.1073/pnas.2317967121 2024-06-05 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue ai/scaling/emergence philosophy/mind reinforcement-learning/multi-agent reinforcement-learning/safe --- https://arxiv.org/abs/2402.17152#facebook Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations (HSTU) Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhaojie Gong, Fangda Gu, Michael He, Yinghai Lu, Yu Shi 2024-02-27 2024-06-05 [("doi","10.48550/arXiv.2402.17152")] ai/nn/retrieval ai/nn/transformer/gpt ai/scaling

Large-scale recommendation systems are characterized by their reliance on high cardinality, heterogeneous features and the need to handle tens of billions of user actions on a daily basis. Despite being trained on huge volume of data with thousands of features, most Deep Learning Recommendation Models (DLRMs) in industry fail to scale with compute.

Inspired by success achieved by Transformers in language and vision domains, we revisit fundamental design choices in recommendation systems. We reformulate recommendation problems as sequential transduction tasks within a generative modeling framework (Generative Recommenders), and propose a new architecture, HSTU, designed for high cardinality, non-stationary streaming recommendation data.

HSTU outperforms baselines over synthetic and public datasets by up to 65.8% in NDCG, and is 5.3× to 15.2× faster than FlashAttention-2-based Transformers on 8,192 length sequences. HSTU-based Generative Recommenders, with 1.5 trillion parameters, improve metrics in online A/B tests by 12.4% and have been deployed on multiple surfaces of a large internet platform with billions of users.

More importantly, the model quality of Generative Recommenders empirically scales as a power-law of training compute across 3 orders of magnitude, up to GPT-3/LLaMa-2 scale, which reduces carbon footprint needed for future model developments, and further paves the way for the first foundational models in recommendations.

--- /doc/psychology/2002-davis.pdf True Porn Clerk Stories Ali Davis 2002-02-28 2024-06-05 fiction/humor psychology --- https://www.nytimes.com/2024/06/05/magazine/congenital-myasthenic-syndrome-diagnosis.html She Was Told She Had an Untreatable Disease. But Did She? 2024-06-05 genetics/heritable/rare genetics/sequencing --- https://osf.io/preprints/psyarxiv/5b26t 2024-06-05 ai/nn/transformer/gpt/3 psychology --- /doc/psychology/2002-davis.pdf#page=23 True Porn Clerk Stories § pg23 Ali Davis 2002-02-28 2024-06-05 iq/low psychology --- https://en.wikipedia.org/wiki/Norwegian_Forest_cat Norwegian Forest cat 2024-06-05 cat --- https://arxiv.org/abs/2406.02528 Scalable Matmul-free Language Modeling Rui-Jie Zhu, Yu Zhang, Ethan Sifferman, Tyler Sheaves, Yiqiao Wang, Dustin Richmond, Peng Zhou, Jason K. Eshraghian 2024-06-04 2024-06-05 [("doi","10.48550/arXiv.2406.02528")] ai/nn/sparsity/low-precision ai/nn/transformer/attention ai/scaling/hardware

Matrix multiplication (Matmul) typically dominates the overall computational cost of large language models (LLMs). This cost only grows as LLMs scale to larger embedding dimensions and context lengths.

In this work, we show that Matmul operations can be completely eliminated from LLMs while maintaining strong performance at billion-parameter scales. Our experiments show that our proposed Matmul-free models achieve performance on-par with state-of-the-art Transformers that require far more memory during inference at a scale up to at least 2.7b parameters.

We investigate the scaling laws and find that the performance gap between our Matmul-free models and full precision Transformers narrows as the model size increases. We also provide a GPU-efficient implementation of this model which reduces memory usage by up to 61% over an unoptimized baseline during training. By using an optimized kernel during inference, our model’s memory consumption can be reduced by more than 10× compared to unoptimized models.

To properly quantify the efficiency of our architecture, we build a custom hardware solution on an FPGA which exploits lightweight operations beyond what GPUs are capable of. We processed billion-parameter scale models at 13W beyond human readable throughput, moving LLMs closer to brain-like efficiency.

This work not only shows how far LLMs can be stripped back while still performing effectively, but also points at the types of operations future accelerators should be optimized for in processing the next generation of lightweight LLMs.

Our code implementation is available at https://github.com/ridgerchu/matmulfreellm.

--- https://www.reddit.com/r/mlscaling/comments/1d3a793/andrej_karpathy_gpt2_124m_in_llmc_in_90_minutes/ 2024-06-05 ai/nn/transformer/gpt/2 economics/experience-curve --- https://www.wired.com/2011/09/ff-kilogram/ The Search for a More Perfect Kilogram 2024-06-05 technology --- https://www.quantamagazine.org/most-life-on-earth-is-dormant-after-pulling-an-emergency-brake-20240605/ 2024-06-05 cryonics --- https://en.wikipedia.org/wiki/Frederick_Jelinek#Research_and_legacy Frederick Jelinek § Research and legacy 2024-06-05 ai --- https://research.google/blog/heuristics-on-the-high-seas-mathematical-optimization-for-cargo-ships/ Heuristics on the high seas: Mathematical optimization for cargo ships 2024-06-06 cs/algorithm --- https://arxiv.org/abs/2406.02543#deepmind To Believe or Not to Believe Your LLM Yasin Abbasi Yadkori, Ilja Kuzborskij, András György, Csaba Szepesvári 2024-06-04 2024-06-06 [("doi","10.48550/arXiv.2406.02543")] ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/palm reinforcement-learning/meta-learning

We explore uncertainty quantification in large language models (LLMs), with the goal to identify when uncertainty in responses given a query is large. We simultaneously consider both epistemic and aleatoric uncertainties, where the former comes from the lack of knowledge about the ground truth (such as about facts or the language), and the latter comes from irreducible randomness (such as multiple possible answers).

In particular, we derive an information-theoretic metric that allows to reliably detect when only epistemic uncertainty is large, in which case the output of the model is unreliable. This condition can be computed based solely on the output of the model obtained simply by some special iterative prompting based on the previous responses. Such quantification, for instance, allows to detect hallucinations (cases when epistemic uncertainty is high) in both single- and multi-answer responses. This is in contrast to many standard uncertainty quantification strategies (such as thresholding the log-likelihood of a response) where hallucinations in the multi-answer case cannot be detected.

We conduct a series of experiments which demonstrate the advantage of our formulation. Further, our investigations shed some light on how the probabilities assigned to a given output by an LLM can be amplified by iterative prompting, which might be of independent interest.

[I interpret this in Bayesian fashion as approximating the posterior by perturbing the prior via randomized prompts: the narrower the posterior, the less the prompt-prior can change it.]

--- https://en.wikipedia.org/wiki/Otoacoustic_emission Otoacoustic emission 2024-06-06 psychology/music --- https://catalog.caida.org/details/paper/2005_fingerprinting/ Remote physical device fingerprinting Kohno 2005 2024-01-01 cs/security --- https://www.wired.com/story/i-went-undercover-secret-onlyfans-chatter-wasnt-pretty/ I Went Undercover as a Secret OnlyFans Chatter. It Wasn’t Pretty: Your online influencer girlfriend is actually a rotating cast of low-wage workers. I became one of them Brendan I. Koerner 2024-05-15 2024-05-20 sociology --- https://mccordresearch.com/sites/default/files/research/dietary_magnesium_and_c_reactive_protein.pdf Dietary Magnesium and C-reactive Protein Levels King 2005 2024-01-01 nootropic/magnesium --- https://arxiv.org/abs/2402.17764 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits Shuming Ma, Hongyu Wang, Lingxiao Ma, Lei Wang, Wenhui Wang, Shaohan Huang, Li Dong, Ruiping Wang, Jilong Xue, Furu Wei 2024-02-27 2024-06-06 [("doi","10.48550/arXiv.2402.17764")] ai/nn/sparsity/low-precision ai/scaling

Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs).

In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary -1, 0, 1. It matches the full-precision (ie. FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being more cost-effective in terms of latency, memory, throughput, and energy consumption.

More profoundly, the 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective.

Furthermore, it enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs.

--- https://gpt3experiments.substack.com/p/building-a-vector-database-in-2gb Building a vector database in 2GB for 36 million Wikipedia passages 2024-06-06 ai/nn/retrieval ai/nn/sparsity/low-precision --- https://arxiv.org/abs/2307.08691 FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning Tri Dao 2023-07-17 2024-06-06 [("doi","10.48550/arXiv.2307.08691")] ai/nn/transformer/attention

Scaling Transformers to longer sequence lengths has been a major problem in the last several years, promising to improve performance in language modeling and high-resolution image understanding, as well as to unlock new applications in code, audio, and video generation.

The attention layer is the main bottleneck in scaling to longer sequences, as its runtime and memory increase quadratically in the sequence length. FlashAttention exploits the asymmetric GPU memory hierarchy to bring memory saving (linear instead of quadratic) and runtime speedup (2-4× compared to optimized baselines), with no approximation. However, FlashAttention is still not nearly as fast as optimized matrix-multiply (GEMM) operations, reaching only 25-40% of the theoretical maximum FLOPs/s.

We observe that the inefficiency is due to suboptimal work partitioning between different thread blocks and warps on the GPU, causing either low-occupancy or unnecessary shared memory reads/writes. We propose FlashAttention-2, with better work partitioning to address these issues. In particular, we (1) tweak the algorithm to reduce the number of non-matmul FLOPs (2) parallelize the attention computation, even for a single head, across different thread blocks to increase occupancy, and (3) within each thread block, distribute the work between warps to reduce communication through shared memory.

These yield around 2× speedup compared to FlashAttention, reaching 50-73% of the theoretical maximum FLOPs/s on A100 and getting close to the efficiency of GEMM operations.

We empirically validate that when used end-to-end to train GPT-style models, FlashAttention-2 reaches training speed of up to 225 TFLOPs/s per A100 GPU (72% model FLOPs usage).

--- /doc/ai/nn/transformer/gpt/dall-e/1/2023-wu-2.pdf IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers Ronghuan Wu, Wanchao Su, Kede Ma, Jing Liao 2023-12-05 2024-06-03 [("doi","10.1145/3618364")] ai/nn/transformer/gpt/dall-e/1 design

[homepage/code] Scalable Vector Graphics (SVG) is a popular vector image format that offers good support for interactivity and animation. Despite its appealing characteristics, creating custom SVG content can be challenging for users due to the steep learning curve required to understand SVG grammars or get familiar with professional editing software. Recent advancements in text-to-image generation have inspired researchers to explore vector graphics synthesis using either image-based methods (ie. text → raster image → vector graphics) combining text-to-image generation models with image vectorization, or language-based methods (ie. text → vector graphics script) through pretrained large language models. Nevertheless, these methods suffer from limitations in terms of generation quality, diversity, and flexibility.

In this paper, we introduce IconShop, a text-guided vector icon synthesis method using autoregressive transformers. The key to success of our approach is to linearize and tokenize SVG paths (and textual descriptions as guidance) into a uniquely decodable token sequence. With that, we are able to exploit the sequence learning power of autoregressive transformers, while enabling both unconditional and text-conditioned icon synthesis.

Through standard training to predict the next token on a large-scale vector icon dataset accompanied by textural descriptions, the proposed IconShop consistently exhibits better icon synthesis capability than existing image-based and language-based methods both quantitatively (using the FID and CLIP scores) and qualitatively (through formal subjective user studies). Meanwhile, we observe a dramatic improvement in generation diversity, which is validated by the objective Uniqueness and Novelty measures.

More importantly, we demonstrate the flexibility of IconShop with multiple novel icon synthesis tasks, including icon editing, icon interpolation, icon semantic combination, and icon design auto-suggestion.

--- https://icon-shop.github.io/ IconShop 2024-06-06 ai/nn/transformer/gpt/dall-e/1 design --- https://github.com/kingnobro/IconShop kingnobro/IconShop: (SIGGRAPH Asia 2023) Code of "IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers" 2024-06-06 ai/nn/transformer/gpt/dall-e/1 design --- https://blog.evjang.com/ Eric Jang 2024-06-06 reinforcement-learning/robot reinforcement-learning/scaling --- https://spacecraft.ssl.umd.edu/akins_laws.html Akin’s Laws of Spacecraft Design 2024-06-06 design math/humor technology --- https://publicdomainreview.org/collection/lyon-woven-prayer-book/ Programming Prayer: The Woven Book of Hours (1886–87) 2024-06-06 design/typography history/public-domain-review --- https://www.quantamagazine.org/can-psychedelics-improve-mental-health-20240606/ 2024-06-06 psychedelic psychiatry/autism psychiatry/depression psychology/neuroscience/memory --- https://en.wikipedia.org/wiki/La_Jet%C3%A9e La Jetée 2024-06-06 fiction/science-fiction/time-travel --- https://www.nber.org/papers/w32519 The Law of Small Numbers in Financial Markets: Theory and Evidence 2024-06-06 economics psychology/cognitive-bias --- https://animationobsessive.substack.com/p/the-making-of-the-last-unicorn#%C2%A7the-unlikeliest-classic The Making of The Last Unicorn 2023-06-26 2024-06-07 anime --- https://www.youtube.com/watch?v=TLP4fge346o&t=20s The Last Unicorn—theatrical version—Molly Grue meeting the Unicorn scene uncut 2024-06-07 anime --- https://arxiv.org/abs/1708.04788 BitNet: Bit-Regularized Deep Neural Networks Aswin Raghavan, Mohamed Amer, Sek Chai, Graham Taylor 2017-08-16 2024-06-07 [("doi","10.48550/arXiv.1708.04788")] ai/nn/cnn ai/nn/sparsity/low-precision

We present a novel optimization strategy for training neural networks which we call “BitNet”. The parameters of neural networks are usually unconstrained and have a dynamic range dispersed over all real values. Our key idea is to limit the expressive power of the network by dynamically controlling the range and set of values that the parameters can take.

We formulate this idea using a novel end-to-end approach that circumvents the discrete parameter space by optimizing a relaxed continuous and differentiable upper bound of the typical classification loss function. The approach can be interpreted as a regularization inspired by the Minimum Description Length (MDL) principle. For each layer of the network, our approach optimizes real-valued translation and scaling factors and arbitrary precision integer-valued parameters (weights).

We empirically compare BitNet to an equivalent unregularized model on the MNIST and CIFAR-10 datasets. We show that BitNet converges faster to a superior quality solution. Additionally, the resulting model has savings in memory due to the use of integer-valued parameters.

--- https://arxiv.org/abs/2111.11294 Scaling Law for Recommendation Models: Towards General-purpose User Representations Kyuyong Shin, Hanock Kwak, Su Young Kim, Max Nihlen Ramstrom, Jisu Jeong, Jung-Woo Ha, Kyung-Min Kim 2021-11-15 2024-01-01 [("doi","10.48550/arXiv.2111.11294")] ai/nn/retrieval ai/scaling

Recent advancement of large-scale pretrained models such as BERT, GPT-3, CLIP, and Gopher, has shown astonishing achievements across various task domains. Unlike vision recognition and language models, studies on general-purpose user representation at scale still remain underexplored.

Here we explore the possibility of general-purpose user representation learning by training a universal user encoder at large scales. We demonstrate that the scaling law is present in user representation learning areas, where the training error scales as a power-law with the amount of computation.

Our Contrastive Learning User Encoder (CLUE), optimizes task-agnostic objectives, and the resulting user embeddings stretch our expectation of what is possible to do in various downstream tasks. CLUE also shows great transferability to other domains and companies, as performances on an online experiment shows improvements in Click-Through-Rate (CTR).

Furthermore, we also investigate how the model performance is influenced by the scale factors, such as training data size, model capacity, sequence length, and batch size.

Finally, we discuss the broader impacts of CLUE in general.

--- https://wiki.ubc.ca/images/3/3c/Rosnow_%26_Rosenthal._1998._Statistical_Procedures_(aspirin_example).pdf Statistical Procedures and the Justification of Knowledge in Psychological Science Rosnow, Rosenthal 1989 2024-01-01 philosophy/epistemology psychology statistics/bias --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.824.4365&rep=rep1&type=pdf The Role of Television in the Construction of Consumer Reality O’Guinn, Shrum 1997 2024-01-01 economics/advertising --- https://arxiv.org/abs/2404.09562 σ-GPTs: A New Approach to Autoregressive Models Arnaud Pannatier, Evann Courdier, François Fleuret 2024-04-15 2024-06-07 [("doi","10.48550/arXiv.2404.09562")] ai/nn/diffusion/discrete ai/nn/sampling ai/nn/transformer/gpt/2 ai/nn/vae/mae

[cf. XLNet, MAE] Autoregressive models, such as the GPT family, use a fixed order, usually left-to-right, to generate sequences. However, this is not a necessity.

In this paper, we challenge this assumption and show that by simply adding a positional encoding for the output, this order can be modulated on-the-fly per-sample which offers key advantageous properties. It allows for the sampling of and conditioning on arbitrary subsets of tokens, and it also allows sampling in one shot multiple tokens dynamically according to a rejection strategy, leading to a sub-linear number of model evaluations.

We evaluate our method across various domains, including language modeling, path-solving, and aircraft vertical rate prediction, decreasing the number of steps required for generation by an order of magnitude.

--- /doc/iq/ses/1991-laurence-lowaptitudemeninthemilitary.pdf 1991-01-01 2024-01-01 iq/low iq/ses --- https://en.wikipedia.org/wiki/Joe_Arridy#Execution Joe Arridy § Execution 2024-01-01 iq/low --- https://en.wikipedia.org/wiki/My_Lai_massacre My Lai massacre 2024-01-01 iq/low --- https://en.wikipedia.org/wiki/Port_Chicago_disaster Port Chicago disaster 2024-01-01 iq/low --- https://en.wikipedia.org/wiki/Project_100,000 Project 100,000 2024-01-01 iq/low --- https://en.wikipedia.org/wiki/Ricky_Ray_Rector Ricky Ray Rector 2024-01-01 iq/low --- https://en.wikipedia.org/wiki/Robert_McNamara Robert McNamara 2024-01-01 iq/low --- https://en.wikipedia.org/wiki/William_Calley William Calley 2024-01-01 iq/low --- https://en.wikipedia.org/wiki/Yuba_County_Five Yuba County Five 2024-01-01 iq/low --- https://web.archive.org/web/20150407223525/http://squid314.livejournal.com/297579.html 2024-01-01 iq/low --- https://www.hrw.org/reports/2001/ustat/ustat0301-01.htm Beyond Reason: The Death Penalty and Offenders with Mental Retardation: II. Mental Retardation: An Overview 2024-01-01 crime iq/low --- https://languagelog.ldc.upenn.edu/nll/?p=481 One question, two answers, three interpretations 2024-01-01 iq/low --- https://www.amazon.com/exec/obidos/ASIN/1982159006 2024-01-01 iq/low --- https://www.historynet.com/mcnamaras-boys/?f 2024-01-01 iq/low --- https://www.law.cornell.edu/uscode/text/10/520 10 U.S. Code § 520—Limitation on enlistment and induction of persons whose score on the Armed Forces Qualification Test is below a prescribed level 2024-01-01 iq/low --- https://www.youtube.com/watch?v=_J2VwFDV4-g 2024-01-01 iq/low --- https://www.bbc.com/news/stories-43700153 I was a teacher for 17 years, but I couldn’t read or write 2024-01-01 psychology/linguistics --- /doc/genetics/heritable/rare/2002-baker.pdf Study of 250 children with idiopathic mental retardation reveals 9 cryptic and diverse subtelomeric chromosome anomalies 2002-01-01 2024-01-01 genetics/heritable/rare iq/low --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.20.9912&rep=rep1&type=pdf Subtle chromosomal rearrangements in children with unexplained mental retardation 2024-01-01 genetics/heritable/rare iq/low --- https://www.nytimes.com/1996/06/04/world/china-confronts-retardation-of-millions-deficient-in-iodine.html China Confronts Retardation Of Millions Deficient in Iodine 1996-06-04 2024-01-01 iodine --- /doc/genetics/heritable/rare/1985-defries.pdf Multiple regression analysis of twin data 1985-01-01 2024-01-01 genetics/heritable/rare iq/low --- https://efosong.net/hottest_day.html How Many Hottest Days of the Year (So Far)? 2024-06-07 statistics/order --- https://inquisitivebird.substack.com/p/the-myth-of-the-nordic-rehabilitative The myth of the Nordic rehabilitative paradise 2024-06-07 crime --- https://www.justice.gov/opa/pr/911-s5-botnet-dismantled-and-its-administrator-arrested-coordinated-international-operation 2024-06-07 cs/security --- https://tacf.org/darling-58/ Darling 58 /54 2024-06-07 genetics/editing --- https://nymag.com/intelligencer/article/darling-58-american-chestnut-tree-mistake.html The Problem With the Darling 58 Chestnut Tree 2024-06-07 genetics/editing --- https://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf#page=67 2024-06-07 ai/nn/rnn --- https://qualiacomputing.com/2024/06/06/qualia-research-institute-the-musical-album-of-2024-v1/ Qualia Research Institute: The Musical Album of 2024 (v1) 2024-06-07 ai/music psychedelic --- https://arxiv.org/abs/2406.04324 SF-V: Single Forward Video Generation Model Zhixing Zhang, Yanyu Li, Yushu Wu, Yanwu Xu, Anil Kag, Ivan Skorokhodov, Willi Menapace, Aliaksandr Siarohin, Junli Cao, Dimitris Metaxas, Sergey Tulyakov, Jian Ren 2024-06-06 2024-06-07 [("doi","10.48550/arXiv.2406.04324")] ai/nn/diffusion ai/nn/gan ai/video/generation

Diffusion-based video generation models have demonstrated remarkable success in obtaining high-fidelity videos through the iterative denoising process. However, these models require multiple denoising steps during sampling, resulting in high computational costs.

In this work, we propose a novel approach to obtain single-step video generation models by leveraging adversarial training to fine-tune pre-trained video diffusion models. We show that, through the adversarial training, the multi-steps video diffusion model, ie. Stable Video Diffusion (SVD), can be trained to perform a single forward pass to synthesize high-quality videos, capturing both temporal and spatial dependencies in the video data.

Extensive experiments demonstrate that our method achieves competitive generation quality of synthesized videos with reduced computational overhead for the denoising process (ie. around 23× speedup compared with SVD and 6× speedup compared with existing works, with even better generation quality), paving the way for real-time video synthesis and editing.

More visualization results are made publicly available at https://snap-research.github.io/SF-V/.

--- https://arxiv.org/abs/2406.04127 Are We Done with MMLU? Aryo Pradipta Gema, Joshua Ong Jun Leang, Giwon Hong, Alessio Devoto, Alberto Carlo Maria Mancino, Rohit Saxena, Xuanli He, Yu Zhao, Xiaotang Du, Mohammad Reza Ghasemi Madani, Claire Barale, Robert McHardy, Joshua Harris, Jean Kaddour, Emile van Krieken, Pasquale Minervini 2024-06-06 2024-06-07 [("doi","10.48550/arXiv.2406.04127")] ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude

Maybe not. We identify and analyze errors in the popular Massive Multitask Language Understanding (MMLU) benchmark. Even though MMLU is widely adopted, our analysis demonstrates numerous ground truth errors that obscure the true capabilities of LLMs. For example, we find that 57% of the analyzed questions in the Virology subset contain errors. [!]

To address this issue, we introduce a comprehensive framework for identifying dataset errors using a novel error taxonomy. Then, we create MMLU-Redux, which is a subset of 3,000 manually re-annotated questions across 30 MMLU subjects.

Using MMLU-Redux, we demonstrate discrepancies with the model performance metrics that were originally reported. Our results strongly advocate for revising MMLU’s error-ridden questions to enhance its future utility and reliability as a benchmark.

Therefore, we open up MMLU-Redux for additional annotation: https://huggingface.co/datasets/edinburgh-dawg/mmlu-redux.

--- https://web.archive.org/web/20041217093734/http://www.shrovetuesdayobserved.com/flight.html If all stories were written like science fiction stories 2024-06-07 fiction/humor fiction/science-fiction --- https://meatfighter.com/ascii-silhouettify/color-gallery.html ASCII Silhouettify Color Gallery 2024-06-08 design/typography --- https://arxiv.org/abs/1605.02226 Neural Autoregressive Distribution Estimation Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, Hugo Larochelle 2016-05-07 2024-06-08 [("doi","10.48550/arXiv.1605.02226")] ai/nn/cnn

We present Neural Autoregressive Distribution Estimation (NADE) models, which are neural network architectures applied to the problem of unsupervised distribution and density estimation.

They leverage the probability product rule and a weight sharing scheme inspired from restricted Boltzmann machines, to yield an estimator that is both tractable and has good generalization performance.

We discuss how they achieve competitive performance in modeling both binary and real-valued observations. We also present how deep NADE models can be trained to be agnostic to the ordering of input dimensions used by the autoregressive product rule decomposition.

Finally, we also show how to exploit the topological structure of pixels in images using a deep convolutional architecture for NADE.

--- /doc/ai/nn/fully-connected/2021-power-poster.png#openai 2021 2024-01-01 ai/nn/fully-connected ai/scaling/emergence/grokking --- https://github.com/openai/grok openai/grok 2024-06-08 ai/scaling/emergence/grokking --- https://github.com/Sea-Snell/grokking Sea-Snell/grokking: unofficial re-implementation of "Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets" 2024-06-08 ai/scaling/emergence/grokking --- https://github.com/teddykoker/grokking teddykoker/grokking: PyTorch implementation of "Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets" 2024-06-08 ai/scaling/emergence/grokking --- https://www.youtube.com/watch?v=dND-7llwrpw Grokking: Generalization beyond Overfitting on small algorithmic datasets (Paper Explained) 2024-06-08 ai/scaling/emergence/grokking --- https://www.lesswrong.com/posts/JFibrXBewkSDmixuo/hypothesis-gradient-descent-prefers-general-circuits Hypothesis: gradient descent prefers general circuits 2024-06-08 ai/scaling/emergence/grokking --- /doc/ai/nn/fully-connected/2021-power-figure1-grokkinglearningcurves.jpg 2021 2024-01-01 ai/nn/fully-connected ai/scaling/emergence/grokking --- https://x.com/AbdullahSabry42/status/1543195805741350917 AbdullahSabry42 2024-06-08 ai/scaling/emergence/grokking --- https://thebulletin.org/2024/06/mit-researchers-ordered-and-combined-parts-of-the-1918-pandemic-influenza-virus-did-they-expose-a-security-flaw/ MIT researchers ordered and combined parts of the 1918 pandemic influenza virus. Did they expose a security flaw? 2024-06-08 existential-risk genetics/genome-synthesis --- https://demian.ferrei.ro/blog/chatgpt-sucks-at-pangrams 2024-06-08 ai/nn/tokenization ai/nn/transformer/gpt/4/nonfiction --- https://www.washingtontimes.com/news/2018/jun/2/minnesota-girl-who-cant-feel-pain-battles-insuranc/ 2024-01-01 psychology/neuroscience/pain --- https://en.wikipedia.org/wiki/Congenital_insensitivity_to_pain Congenital insensitivity to pain 2024-01-01 psychology/neuroscience/pain --- https://www.rifters.com/real/articles/Morsella_2005.pdf PRISM: The Function of Phenomenal States: Supramodular Interaction Theory 2024-01-01 philosophy/mind psychology/neuroscience/pain --- https://en.wikipedia.org/wiki/Lobotomy Lobotomy 2024-01-01 psychology/neuroscience/pain --- https://en.wikipedia.org/wiki/Thermoception Thermoception 2024-01-01 psychology/neuroscience/pain --- https://abcnews.go.com/Health/MedicalMysteries/story?id=3679532&page=1 2024-01-01 psychology/neuroscience/pain --- https://news.ycombinator.com/item?id=23462736 2024-01-01 genetics/heritable/rare psychology/neuroscience/pain --- https://en.wikipedia.org/wiki/Central_governor Central governor 2024-01-01 psychology/neuroscience/pain --- https://en.wikipedia.org/wiki/Leprosy Leprosy 2024-01-01 psychology/neuroscience/pain --- https://en.wikipedia.org/wiki/Anterograde_amnesia Anterograde amnesia 2024-01-01 psychology/neuroscience/pain/anesthesia --- https://plato.stanford.edu/entries/pain/#othertheories 2024-01-01 psychology/neuroscience/pain --- /doc/statistics/decision/2006-drescher-goodandreal.pdf#page=94 Good and Real: Demystifying Paradoxes from Physics to Ethics § pg94 Gary Drescher 2006-01-01 2024-06-08 psychology/neuroscience/pain statistics/decision --- https://en.wikipedia.org/wiki/Jure_Robi%C4%8D Jure Robič 2024-01-01 psychology/willpower --- /doc/psychology/willpower/2018-martin.pdf Mental Fatigue Impairs Endurance Performance: A Physiological Explanation Kristy Martin, Romain Meeusen, Kevin G. Thompson, Richard Keegan, Ben Rattray 2018-01-01 2024-01-01 [("doi","10.1007/s40279-018-0946-9")] exercise psychology/willpower --- /doc/psychology/personality/2021-benenson.pdf Self-protection as an adaptive female strategy Joyce F. Benenson, Christine E. Webb, Richard W. Wrangham 2021-11-08 2024-04-25 [("doi","10.1017/S0140525X21002417")] psychology/neuroscience/pain psychology/personality

Many male traits are well explained by sexual selection theory as adaptations to mating competition and mate choice, whereas no unifying theory explains traits expressed more in females. Anne Campbell’s “staying alive” theory proposed that human females produce stronger self-protective reactions than males to aggressive threats because self-protection tends to have higher fitness value for females than males.

We examined whether Campbell’s theory has more general applicability by considering whether human females respond with greater self-protectiveness than males to other threats beyond aggression. We searched the literature for physiological, behavioral, and emotional responses to major physical and social threats, and found consistent support for females’ responding with greater self-protectiveness than males.

Females mount stronger immune responses to many pathogens; experience a lower threshold to detect, and lesser tolerance of, pain; awaken more frequently at night; express greater concern about physically dangerous stimuli; exert more effort to avoid social conflicts; exhibit a personality style more focused on life’s dangers; react to threats with greater fear, disgust, and sadness; and develop more threat-based clinical conditions than males. Our findings suggest that in relation to threat, human females have relatively heightened protective reactions compared to males.

The pervasiveness of this result across multiple domains suggests that general mechanisms might exist underlying females’ unique adaptations. An understanding of such processes would enhance knowledge of female health and well-being.

--- https://pain.wustl.edu/c/BasicResearch/documents/Chennature2001.pdf Genetic enhancement of inflammatory pain by forebrain NR2B overexpression 2024-01-01 psychology/neuroscience/pain --- /doc/modafinil/1994-desereville.pdf 1994-01-01 2024-01-01 modafinil psychology/neuroscience/pain/anesthesia --- https://en.wikipedia.org/wiki/General_anaesthesia General anesthesia 2024-06-08 psychology/neuroscience/pain/anesthesia --- https://www.lesswrong.com/posts/Fy2b55mLtghd4fQpx/the-zombie-preacher-of-somerset 2024-01-01 philosophy/mind psychiatry --- https://www.youtube.com/watch?v=k_P7Y0-wgos&t=25s The Man With The Seven Second Memory (Amnesia Documentary) 2024-06-08 psychology/neuroscience/memory --- https://www.newscientist.com/article/mg21228402-300-banishing-consciousness-the-mystery-of-anaesthesia/ Banishing consciousness: the mystery of anesthesia 2024-06-08 philosophy/mind psychology/neuroscience/pain/anesthesia --- https://www.lesswrong.com/posts/cgrgbboLmWu4zZeG8/some-experiments-i-d-like-someone-to-try-with-an-amnestic Some Experiments I’d Like Someone To Try With An Amnestic 2024-06-08 psychology/neuroscience/memory psychology/neuroscience/pain/anesthesia --- https://www.lesswrong.com/posts/bkr9BozFuh7ytiwbK/my-hour-of-memoryless-lucidity My hour of memoryless lucidity 2024-06-08 psychology/neuroscience/memory psychology/neuroscience/pain/anesthesia --- https://www.theage.com.au/lifestyle/anaesthesia-what-we-still-dont-know-about-the-gift-of-oblivion-20170511-gw2uhh.html Anesthesia: what we still don’t know about the ‘gift of oblivion’ 2024-06-08 psychology/neuroscience/pain/anesthesia --- https://en.wikipedia.org/wiki/Bispectral_index Bispectral index 2024-06-08 psychology/neuroscience/pain/anesthesia --- https://en.wikipedia.org/wiki/Drug-induced_amnesia Drug-induced amnesia 2024-06-08 psychology/neuroscience/memory psychology/neuroscience/pain/anesthesia --- https://pubs.asahq.org/anesthesiology/article/126/2/214/18656/Incidence-of-Connected-Consciousness-after Incidence of Connected Consciousness after Tracheal Intubation: A Prospective, International, Multicenter Cohort Study of the Isolated Forearm Technique Robert D. Sanders, Amy Gaskell, Aeyal Raz, Joel Winders, Ana Stevanovic, Rolf Rossaint, Christina Boncyk, Aline Defresne, Gabriel Tran, Seth Tasbihgou, Sascha Meier, Phillip E. Vlisides, Hussein Fardous, Aaron Hess, Rebecca M. Bauer, Anthony Absalom, George A. Mashour, Vincent Bonhomme, Mark Coburn, Jamie Sleigh 2017-02 2024-06-08 [("doi","10.1097/ALN.0000000000001479")] psychology/neuroscience/pain/anesthesia

In a prospective, multicenter study of the incidence of connected consciousness in response to tracheal intubation in 260 anesthetized surgical patients, 4.6% had connected consciousness detected by the isolated forearm technique, none of whom had explicit recall.

Background: The isolated forearm technique allows assessment of consciousness of the external world (connected consciousness) through a verbal command to move the hand (of a tourniquet-isolated arm) during intended general anesthesia. Previous isolated forearm technique data suggest that the incidence of connected consciousness may approach 37% after a noxious stimulus. The authors conducted an international, multicenter, pragmatic study to establish the incidence of isolated forearm technique responsiveness after intubation in routine practice.

Method: 260 adult patients were recruited at 6 sites into a prospective cohort study of the isolated forearm technique after intubation. Demographic, anesthetic, and intubation data, plus postoperative questionnaires, were collected. Univariate statistics, followed by bivariate logistic regression models for age plus variable, were conducted.

Results: The incidence of isolated forearm technique responsiveness after intubation was 4.6% (12⁄260); 5⁄12 responders reported pain through a second hand squeeze.

Responders were younger than non-responders (39 ± 17 vs. 51 ± 16 yr old; p = 0.01) with more frequent signs of sympathetic activation (50% vs. 2.4%; p = 0.03). No participant had explicit recall of intraoperative events when questioned after surgery (n = 253). Across groups, depth of anesthesia monitoring values showed a wide range; however, values were higher for responders before (54 ± 20 vs. 42 ± 14; p = 0.02) and after (52 ± 16 vs. 43 ± 16; p = 0.02) intubation. In patients not receiving total intravenous anesthesia, exposure to volatile anesthetics before intubation reduced the odds of responding (odds ratio, 0.2 [0.1–0.8]; p = 0.02) after adjustment for age.

Conclusion: Intraoperative connected consciousness occurred frequently, although the rate is up to 10× lower than anticipated. This should be considered a conservative estimate of intraoperative connected consciousness.

[Keywords: consciousness related finding, endotracheal intubation, forearm, intubation, mental recall, anesthesia depth]

--- https://www.sciencedirect.com/science/article/pii/S0007091217310176 Patient perspectives on intraoperative awareness with explicit recall: report from a North American anesthesia awareness registry C. D. Kent, K. L. Posner, G. A. Mashour, S. L. Mincer, R. R. Bruchas, A. E. Harvey, K. B. Domino 2015-07 2024-06-08 [("doi","10.1093/bja/aev211")] psychology/neuroscience/pain/anesthesia

Background: Awareness during general anesthesia is a source of concern for patients and anesthetists, with potential for psychological and medico-legal sequelae. We used a registry to evaluate unintended awareness from the patient’s perspective with an emphasis on their experiences and healthcare provider responses.

Method: English-speaking subjects self-reported explicit recall of events during anesthesia to the Anesthesia Awareness Registry of the ASA, completed a survey, and submitted copies of medical records. Anesthesia awareness was defined as explicit recall of events during induction or maintenance of general anesthesia. Patient experiences, satisfaction, and desired practitioner responses to explicit recall were based on survey responses.

Results: Most of the 68 respondents meeting inclusion criteria (75%) were dissatisfied with the manner in which their concerns were addressed by their healthcare providers, and many reported long-term harm.

Half (51%) of respondents reported that neither the anesthesia provider nor surgeon expressed concern about their experience. Few were offered an apology (10%) or referral for counseling (15%). Patient preferences for responses after an awareness episode included validation of their experience (37%), an explanation (28%), and discussion or follow-up to the episode (26%).

Conclusion: Data from this registry confirm the serious impact of anesthesia awareness for some patients, and suggest that patients need more systematic responses and follow-up by healthcare providers.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8200347/ 2024-06-08 psychiatry/alcoholism --- https://blog.archive.org/2024/06/01/the-backrooms-of-the-internet-archive/ The Backrooms of the Internet Archive 2024-06-08 cs/linkrot/archiving --- https://arxiv.org/abs/2402.06603 Hamiltonicity of expanders: optimal bounds and applications Nemanja Draganić, Richard Montgomery, David Munhá Correia, Alexey Pokrovskiy, Benny Sudakov 2024-02-09 2024-06-08 [("doi","10.48550/arXiv.2402.06603")] cs/algorithm math

[media] An n-vertex graph G is a C-expander if |N(X)| ≥ C|X| for every X ⊆ V(G) with |X| < n⁄2 and there is an edge between every two disjoint sets of at least n⁄C vertices.

We show that there is some constant C > 0 for which every C-expander is Hamiltonian. In particular, this implies the well known conjecture of Krivelevich and Sudakov from 2003 on Hamilton cycles in (n, d, λ)-graphs.

This completes a long line of research on the Hamiltonicity of sparse graphs, and has many applications, including to the Hamiltonicity of random Cayley graphs.

--- https://web.archive.org/web/20190624154318/https://mosaicscience.com/story/anaesthesia-anesthesia-awake-awareness-surgery-detect-doctors/ How can doctors tell if you wake up during surgery? 2024-06-08 psychology/neuroscience/pain/anesthesia --- https://news.mit.edu/2024/unexpected-origins-modern-finance-tool-discounting-0606 The unexpected origins of a modern finance tool 2024-06-08 economics/mechanism-design --- https://www.journals.uchicago.edu/doi/10.1086/728594 2024-06-08 economics/mechanism-design --- https://x.com/xlr8harder/status/1799300740000919621 xlr8harder 2024-06-08 ai/nn/transformer/gpt/claude --- https://www.anthropic.com/research/claude-character Claude’s Character Anthropic 2024-06-08 ai/nn/transformer/gpt/claude reinforcement-learning/preference-learning --- https://arxiv.org/abs/2404.13208 The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, Alex Beutel 2024-04-19 2024-06-09 [("doi","10.48550/arXiv.2404.13208")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/instruction-tuning cs/security

Today’s LLMs are susceptible to prompt injections, jailbreaks, and other attacks that allow adversaries to overwrite a model’s original instructions with their own malicious prompts. In this work, we argue that one of the primary vulnerabilities underlying these attacks is that LLMs often consider system prompts (eg. text from an application developer) to be the same priority as text from untrusted users and third parties.

To address this, we propose an instruction hierarchy that explicitly defines how models should behave when instructions of different priorities conflict. We then propose a data generation method to demonstrate this hierarchical instruction following behavior, which teaches LLMs to selectively ignore lower-privileged instructions.

We apply this method to GPT-3.5, showing that it drastically increases robustness—even for attack types not seen during training—while imposing minimal degradations on standard capabilities.

--- https://www.quantamagazine.org/in-highly-connected-networks-theres-always-a-loop-20240607/ 2024-06-09 cs/algorithm math --- https://qntm.org/introductory Introductory Antimemetics (abandoned first draft) Sam Hughes 2024-06-09 ai/nn/transformer/gpt fiction/science-fiction philosophy/epistemology psychology/neuroscience/pain/anesthesia --- https://brendaneich.com/2008/04/popularity/ Popularity Brendan Eich 2024-06-09 cs/js cs/lisp --- https://queue.acm.org/detail.cfm?id=3664645 Zero Tolerance for Bias 2024-06-09 cs/algorithm/sorting cs/cryptography statistics/probability --- https://www.lesswrong.com/posts/Ge55vxEmKXunFFwoe/reward-hacking-behavior-can-generalize-across-tasks Reward hacking behavior can generalize across tasks 2024-06-09 ai/nn/transformer/gpt/3/nonfiction reinforcement-learning/model/decision-transformer reinforcement-learning/safe --- https://www.cambridge.org/core/journals/american-political-science-review/article/does-exposure-to-the-refugee-crisis-make-natives-more-hostile/3E66D9B39336C652F9EF6D7EF9DF0735 Does Exposure to the Refugee Crisis Make Natives More Hostile? 2024-06-10 sociology --- https://engagedharma.net/2019/08/19/culadasa-charged-with-sexual-misconduct/ Culadasa Accused of Sexual Misconduct 2024-06-10 psychiatry/meditation --- https://www.quantamagazine.org/what-happens-in-the-brain-to-cause-depression-20240523/ 2024-06-10 psychedelic psychiatry/depression psychology/neuroscience --- https://x.com/teortaxesTex/status/1781506345092456844 teortaxesTex 2024-06-10 ai/nn/transformer/gpt/claude ai/text-style-transfer --- https://arxiv.org/abs/2202.01854 Causal emergence is widespread across measures of causation Renzo Comolatti, Erik Hoel 2022-02-03 2024-06-10 [("doi","10.48550/arXiv.2202.01854")] statistics/causality

Causal emergence is the theory that macroscales can reduce the noise in causal relationships, leading to stronger causes at the macroscale.

First identified using the effective information and later the integrated information in model systems, causal emergence has been analyzed in real data across the sciences since. But is it simply a quirk of these original measures?

To answer this question we examined over a dozen popular measures of causation, all independently developed and widely used, and spanning different fields from philosophy to statistics to psychology to genetics. All showed cases of causal emergence. This is because, we prove, measures of causation are based on a small set of related “causal primitives.”

This consilience of independently-developed measures of causation shows that macroscale causation is a general fact about causal relationships, is scientifically detectable, and is not a quirk of any particular measure of causation.

This finding sets the science of emergence on firmer ground, opening the door for the detection of intrinsic scales of function in complex systems, as well as assisting with scientific modeling and experimental interventions.

--- https://www.newyorker.com/magazine/2024/06/17/kanye-west-tadao-ando-beach-house-malibu Kanye West Bought an Architectural Treasure—Then Gave It a Violent Remix 2024-06-10 psychiatry/bipolar/energy --- https://theintercept.com/2014/03/20/inside-nsa-secret-efforts-hunt-hack-system-administrators/ Inside the NSA’s Secret Efforts to Hunt and Hack System Administrators 2024-06-11 cs/security --- https://www.lesswrong.com/posts/kLRN3uZMawshPBL9D/a-reflection-on-richard-hamming-s-you-and-your-research A Reflection on Richard Hamming’s ‘You and Your Research’: Striving for Greatness 2024-06-10 science --- https://conscious.anaesthesia.wisc.edu/ 2024-06-11 psychology/neuroscience/pain/anesthesia --- https://www.reddit.com/r/IAmA/comments/1pajkl/comment/cd0g740/ 2024-06-11 psychology/neuroscience/pain/anesthesia --- https://en.wikipedia.org/wiki/Zellweger_off-peak Zellweger off-peak 2024-06-11 technology --- https://adelfaure.net/tools/jgs/ Jgs Font 2024-06-11 design/typography --- https://pubs.aip.org/physicstoday/article/54/7/46/411592/The-Nobel-Laureate-Versus-the-Graduate-StudentJohn The Nobel Laureate Versus the Graduate Student: John Bardeen, the leading condensed matter theorist of his day, was quite wrong when he dismissed a startling prediction by the unknown Brian Josephson Donald G. McDonald 2024-01-01 science --- https://shs.hal.science/halshs-01948311/document Does Online Availability Increase Citations? Theory and Evidence from a Panel of Economics and Business Journals McCabe, Synder 2015 2024-01-01 cs/linkrot --- https://onlinelibrary.wiley.com/doi/abs/10.1111/jasp.12764 Economic implications of access to daylight and views in office buildings from improved productivity MacNaughton 2021 2024-01-01 economics psychology/energy --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.71.8846&rep=rep1&type=pdf A Simple Proof for the Turing-Completeness of XSLT and XQuery Kepser 2004 2024-01-01 cs/computable --- https://journals.sagepub.com/doi/10.1177/13623613211039387 Anthropomorphic tendencies in autism: A conceptual replication and extension of White & Remington 2019 and preliminary development of a novel anthropomorphism measure 2024-06-11 psychiatry/autism --- https://arxiv.org/abs/1910.09336 The Lean mathematical library The mathlib Community 2019-10-21 2024-06-11 [("doi","10.1145/3372885.3373824")] math

This paper describes mathlib, a community-driven effort to build a unified library of mathematics formalized in the Lean proof assistant.

Among proof assistant libraries, it is distinguished by its dependently typed foundations, focus on classical mathematics, extensive hierarchy of structures, use of large & small-scale automation, and distributed organization.

We explain the architecture and design decisions of the library and the social organization that has led us here.

--- https://arxiv.org/abs/2404.11730 Missed Connections: Lateral Thinking Puzzles for Large Language Models Graham Todd, Tim Merino, Sam Earle, Julian Togelius 2024-04-17 2024-06-11 [("doi","10.48550/arXiv.2404.11730")] ai/nn/transformer/gpt/4/fiction ai/nn/transformer/gpt/inner-monologue

The Connections puzzle published each day by the New York Times Games tasks players with dividing a bank of 16 words into 4 groups of 4 words that each relate to a common theme. Solving the puzzle requires both common linguistic knowledge (ie. definitions and typical usage) as well as, in many cases, lateral or abstract thinking. This is because the 4 categories ascend in complexity, with the most challenging category often requiring thinking about words in uncommon ways or as parts of larger phrases.

We investigate the capacity for automated AI systems to play Connections and explore the game’s potential as an automated benchmark for abstract reasoning and a way to measure the semantic information encoded by data-driven linguistic systems. In particular, we study both a sentence-embedding baseline and modern large language models (LLMs).

We report their accuracy on the task, measure the impacts of chain-of-thought prompting, and discuss their failure modes.

Overall, we find that the Connections task is challenging yet feasible, and a strong testbed for future work.

--- https://www.scientificamerican.com/article/some-people-with-insomnia-think-theyre-awake-when-theyre-asleep/ Some People with Insomnia Think They’re Awake when They’re Asleep 2024-06-11 psychology/neuroscience/memory zeo --- https://en.wikipedia.org/wiki/TvTropes TvTropes 2024-06-10 fiction/criticism --- /idea#hierarchical-training-for-writing-novels 2024-06-11 ai/nn/transformer/gpt/fiction --- /idea#sampling-within-llm-simulations 2024-06-11 ai/nn/sampling cs/algorithm/information/compression --- https://arxiv.org/abs/2309.03882 Large Language Models Are Not Robust Multiple Choice Selectors Chujie Zheng, Hao Zhou, Fandong Meng, Jie Zhou, Minlie Huang 2023-09-07 2024-06-12 [("doi","10.48550/arXiv.2309.03882")] ai/nn/transformer/gpt/calibration

Multiple choice questions (MCQs) serve as a common yet important task format in the evaluation of large language models (LLMs).

This work shows that modern LLMs are vulnerable to option position changes in MCQs due to their inherent “selection bias”, namely, they prefer to select specific option IDs as answers (like “Option A”). Through extensive empirical analyses with 20 LLMs on 3 benchmarks, we pinpoint that this behavioral bias primarily stems from LLMs’ token bias, where the model a priori assigns more probabilistic mass to specific option ID tokens (eg. A/B/C/D) when predicting answers from the option IDs.

To mitigate selection bias, we propose a label-free, inference-time debiasing method, called PriDe, which separates the model’s prior bias for option IDs from the overall prediction distribution. PriDe first estimates the prior by permuting option contents on a small number of test samples, and then applies the estimated prior to debias the remaining samples.

We demonstrate that it achieves interpretable and transferable debiasing with high computational efficiency.

We hope this work can draw broader research attention to the bias and robustness of modern LLMs.

--- https://twobithistory.org/2019/11/06/doom-bsp.html How Much of a Genius-Level Move Was Using Binary Space Partitioning in Doom? 2024-06-12 cs/algorithm --- https://www.biostat.jhsph.edu/courses/bio621/misc/Chocolate%20consumption%20cognitive%20function%20and%20nobel%20laurates%20(NEJM).pdf Chocolate Consumption, Cognitive Function, and Nobel Laureates Messerli 2012 2024-01-01 statistics/bias --- https://reutersinstitute.politics.ox.ac.uk/sites/default/files/research/files/Digital%2520News%2520Report%25202016.pdf#page=8 Reuters Institute Digital News Report 2016 § pg8 Newman 2016 2024-01-01 economics/advertising --- https://tahoe-lafs.org/pipermail/tahoe-dev/2016-March/009695.html State of Tahoe-LAFS donations, new Bitcoin address 2024-06-12 bitcoin --- /doc/fiction/fantasy/1993-gaiman-sandman-thegoldenboy.pdf The Golden Boy Neil Gaiman, Bryan Allred Talbot, Daniel Vozzo, Dave McKean, Michael Allred, Mark Buckingham 1993-10-01 2024-06-12 fiction/fantasy psychology/personality/narcissism --- https://news.ycombinator.com/item?id=40662150 2024-06-12 2024-06-12 economics/advertising --- https://github.com/Lapin0t/grothendieck-cern 1972 talk at CERN on scientific research Alexander Grothendieck 2024-06-12 existential-risk math philosophy/ethics --- https://x.com/matthewrohr/status/1800909956226634182 matthewrohr 2024-06-12 cs/security --- https://www.science.org/content/article/star-botanist-likely-made-data-about-nutritional-supplements-new-probe-finds 2024-06-12 nootropic statistics/bias --- http://datacolada.org/117 The Impersonator: The Fake Data Were Coming From Inside the Lab 2024-06-12 statistics/bias --- https://publicdomainreview.org/essay/sanborn-fire-insurance-maps/ From Fire Hazards to Family Trees: The Sanborn Fire Insurance Maps 2024-06-12 design/visualization economics history/public-domain-review --- https://arxiv.org/abs/2212.11281 Language models are better than humans at next-token prediction Buck Shlegeris, Fabien Roger, Lawrence Chan, Euan McLean 2022-12-21 2024-06-12 [("doi","10.48550/arXiv.2212.11281")] ai/nn/tokenization ai/nn/transformer/gpt/3

[blog] Current language models are considered to have sub-human capabilities at natural language tasks like question-answering or writing code. However, language models are not trained to perform well at these tasks, they are trained to accurately predict the next token given previous tokes in tokenized text. It is not clear whether language models are better or worse than humans at next token prediction.

To try to answer this question, we performed two distinct experiments to directly compare humans and language models on this front: one measuring top-1 accuracy and the other measuring perplexity.

In both experiments, we find humans to be consistently worse than even relatively small language models like GPT-3-Ada at next-token prediction.

--- https://docs.midjourney.com/docs/personalization 2024-06-13 ai/nn/diffusion/midjourney reinforcement-learning/preference-learning/mode-collapse --- https://www.salon.com/2013/03/17/my_tantric_awakening_turned_me_off_sex/ My Tantric ‘awakening’ turned me off sex 2024-06-13 psychiatry/meditation --- https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback Reinforcement learning from human feedback 2024-06-13 reinforcement-learning/preference-learning --- https://arxiv.org/abs/2406.07515 Beyond Model Collapse: Scaling Up with Synthesized Data Requires Reinforcement Yunzhen Feng, Elvis Dohmatob, Pu Yang, Francois Charton, Julia Kempe 2024-06-11 2024-06-13 [("doi","10.48550/arXiv.2406.07515")] ai/scaling reinforcement-learning/exploration/active-learning reinforcement-learning/preference-learning

Synthesized data from generative models is increasingly considered as an alternative to human-annotated data for fine-tuning Large Language Models. This raises concerns about model collapse: a drop in performance of models fine-tuned on generated data.

Considering that it is easier for both humans and machines to tell between good and bad examples than to generate high-quality samples, we investigate the use of feedback on synthesized data to prevent model collapse. We derive theoretical conditions under which a Gaussian mixture classification model can achieve asymptotically optimal performance when trained on feedback-augmented synthesized data, and provide supporting simulations for finite regimes.

We illustrate our theoretical predictions on two practical problems: computing matrix eigenvalues with transformers and news summarization with large language models, which both undergo model collapse when trained on model-generated data. We show that training from feedback-augmented synthesized data, either by pruning incorrect predictions or by selecting the best of several guesses, can prevent model collapse, validating popular approaches like RLHF.

For more information, please refer to the original publication and supplementary materials available on the project’s GitHub repository.

--- https://www.propublica.org/article/microsoft-solarwinds-golden-saml-data-breach-russian-hackers Microsoft Refused to Fix Flaw Years Before SolarWinds Hack 2024-06-13 cs/security --- https://arxiv.org/abs/2406.07887 An Empirical Study of Mamba-based Language Models Roger Waleffe, Wonmin Byeon, Duncan Riach, Brandon Norick, Vijay Korthikanti, Tri Dao, Albert Gu, Ali Hatamizadeh, Sudhakar Singh, Deepak Narayanan, Garvit Kulshreshtha, Vartika Singh, Jared Casper, Jan Kautz, Mohammad Shoeybi, Bryan Catanzaro 2024-06-12 2024-06-13 [("doi","10.48550/arXiv.2406.07887")] ai/nn/rnn ai/nn/transformer/attention

Selective state-space models (SSMs) like Mamba overcome some of the shortcomings of Transformers, such as quadratic computational complexity with sequence length and large inference-time memory requirements from the key-value cache. Moreover, recent studies have shown that SSMs can match or exceed the language modeling capabilities of Transformers, making them an attractive alternative.

In a controlled setting (eg. same data), however, studies so far have only presented small scale experiments comparing SSMs to Transformers. To understand the strengths and weaknesses of these architectures at larger scales, we present a direct comparison between 8B-parameter Mamba, Mamba-2, and Transformer models trained on the same datasets of up to 3.5T tokens. We also compare these models to a hybrid architecture consisting of 43% Mamba-2, 7% attention, and 50% MLP layers (Mamba-2-Hybrid).

Using a diverse set of tasks, we answer the question of whether Mamba models can match Transformers at larger training budgets. Our results show that while pure SSMs match or exceed Transformers on many tasks, they lag behind Transformers on tasks which require strong copying or in-context learning abilities (eg. 5-shot MMLU, Phonebook) or long-context reasoning.

In contrast, we find that the 8B Mamba-2-Hybrid exceeds the 8B Transformer on all 12 standard tasks we evaluated (+2.65 points on average) and is predicted to be up to 8× faster when generating tokens at inference time. To validate long-context capabilities, we provide additional experiments evaluating variants of the Mamba-2-Hybrid and Transformer extended to support 16K, 32K, and 128K sequences. On an additional 23 long-context tasks, the hybrid model continues to closely match or exceed the Transformer on average.

To enable further study, we release the checkpoints as well as the code used to train our models as part of NVIDIA’s Megatron-LM project.

--- http://blog.cr.yp.to/20240612-bibkeys.html 2024.06.12: Bibliography keys 2024-06-13 design/typography/tex --- https://x.com/sharifshameem/status/1405462642936799247 I gave GPT-3 access to Chrome with the objective ‘please buy me AirPods’...It successfully made it to the product page, but got sidetracked with Walmart’s privacy policy. Since even a simplified DOM is far too large for a single prompt, multiple prompts are given different chunks of the DOM, each generating their own ‘interaction’. Another prompt then takes all the proposed interactions and selects the best one, sort of like a tournament bracket. For more complex web pages, the time it takes to generate an action scales at 𝒪(log n) with the size of the DOM—really fast! It also gets around token limits, so you could technically process an infinitely large DOM! 2024-01-01 ai/nn/transformer/gpt/3 ai/nn/transformer/gpt/codex --- https://stats.stackexchange.com/questions/9001/approximate-order-statistics-for-normal-random-variables/9010#9010 Approximate order statistics for normal random variables 2024-01-01 statistics/order --- http://incompleteideas.net/sutton/book/the-book.html Sutton & Barto Book: Reinforcement Learning: An Introduction Richard Sutton, Barto 2024-01-01 reinforcement-learning --- http://www.ideosphere.com/fx-bin/ListClaims FX Claims 2024-01-01 bitcoin --- https://pubmed.ncbi.nlm.nih.gov/20095391/ Effects of nootropic drugs on hippocampal and cortical BDNF levels in mice with different exploratory behavior efficacy Firstova 2009 2024-01-01 nootropic --- https://wiki.puella-magi.net/Population_dynamics Population dynamics in Madoka 2024-01-01 genetics/selection/natural math/humor --- https://words.fromoldbooks.org/Farmer-MusaPedestris/villons-straight-tip-to-all-cross-coves.html Villon’s Straight Tip To All Cross Coves (Canting Songs) 2024-01-01 fiction/poetry --- https://wordpress.org/plugins/broken-link-checker/ Broken Link Checker 2024-01-01 cs/linkrot --- https://www.haaretz.com/israel-news/2018-11-22/ty-article/israeli-american-hacker-who-terrorized-u-s-jews-with-bomb-threats-gets-10-years/0000017f-dc6b-db5a-a57f-dc6b94d30000 Israeli-American Who Terrorized U.S. Jews With Thousands of Bomb Threats Jailed for 10 Years 2024-01-01 crime/terrorism --- https://news.sankakucomplex.com/2011/07/25/comiket-80-touhou-vs-fujoshi/ Comiket 80 = Touhou vs Fujoshi 2024-01-01 touhou --- https://news.sankakucomplex.com/2011/12/14/comiket-81-now-fujoshiket-touhou-should-be-expelled/ Comiket 81 ‘Now Fujoshiket’ Touhou Should Be Expelled! 2024-01-01 touhou --- https://x.com/sarahdoingthing/status/877018612447313920 slide from tonight’s class—when to write 2024-01-01 psychology/writing --- https://arxiv.org/abs/2406.07358 AI Sandbagging: Language Models can Strategically Underperform on Evaluations Teun van der Weij, Felix Hofstätter, Ollie Jaffe, Samuel F. Brown, Francis Rhys Ward 2024-06-11 2024-06-14 [("doi","10.48550/arXiv.2406.07358")] cs/security reinforcement-learning/safe

Trustworthy capability evaluations are crucial for ensuring the safety of AI systems, and are becoming a key component of AI regulation. However, the developers of an AI system, or the AI system itself, may have incentives for evaluations to understate the AI’s actual capability. These conflicting interests lead to the problem of sandbagging—which we define as “strategic underperformance on an evaluation”.

In this paper we assess sandbagging capabilities in contemporary language models (LMs). We prompt frontier LMs, like GPT-4 and Claude 3 Opus, to selectively underperform on dangerous capability evaluations, while maintaining performance on general (harmless) capability evaluations.

Moreover, we find that models can be fine-tuned, on a synthetic dataset, to hide specific capabilities unless given a password. This behavior generalizes to high-quality, held-out benchmarks such as WMDP. In addition, we show that both frontier and smaller models can be prompted, or password-locked, to target specific scores on a capability evaluation. Even more, we found that a capable password-locked model (LLaMA-3 70b) is reasonably able to emulate a less capable model (LLaMA-2-7b).

Overall, our results suggest that capability evaluations are vulnerable to sandbagging. This vulnerability decreases the trustworthiness of evaluations, and thereby undermines important safety decisions regarding the development and deployment of advanced AI systems.

--- https://www.schneier.com/blog/archives/2024/06/ai-and-the-indian-election.html AI and the Indian Election Bruce Schneier 2024-06-14 ai/nn politics sociology/technology --- https://louisabraham.github.io/articles/probabilistic-tic-tac-toe Solving Probabilistic Tic-Tac-Toe Louis Abraham 2024-06-14 reinforcement-learning/model statistics/decision --- /doc/psychiatry/autism/2018-white-2.pdf Object personification in autism: This paper will be very sad if you don’t read it Rebekah C. White, Anna Remington 2018-08-11 2024-06-11 [("doi","10.1177/1362361318793408")] philosophy/mind psychiatry/autism

Object personification is the attribution of human characteristics to non-human agents. In online forums, autistic individuals commonly report experiencing this phenomenon. Given that ~half of all autistic individuals experience difficulties identifying their own emotions, the suggestion that object personification may be a feature of autism seems almost paradoxical. Why would a person experience sympathy for objects, when they struggle to understand and verbalize the emotions of other people as well as their own?

An online survey was used to assess tendency for personification in 87 autistic and 263 non-autistic adults.

Together, our results indicate that object personification occurs commonly among autistic individuals, and perhaps more often (and later in life) than in the general population.

Given that in many cases, autistic people report their personification experiences as distressing, it is important to consider the reasons for the increased personification and identify structures for support.

--- /doc/psychiatry/autism/2021-caruana.pdf Autistic traits and loneliness in autism are associated with increased tendencies to anthropomorphize Nathan Caruana, Rebekah C. White, Anna Remington 2021-03-15 2024-06-11 [("doi","10.1177/17470218211005694")] philosophy/mind psychiatry/autism

Anthropomorphism—the attribution of human qualities to non-human objects—is believed to be a natural tendency which may serve several adaptive functions. One possibility is that anthropomorphism provides an egocentric heuristic by which we can understand the world. It may also be a strategy for reducing our subjective sense of loneliness. However, not all humans exhibit the same propensity to anthropomorphize. Recent findings suggest that autistic individuals may be more likely to anthropomorphize than non-autistic individuals.

In Study 1, we conducted a large-scale survey of autistic traits and dispositional anthropomorphism in the general population (n = 870).

We found that individuals who reported having more autistic traits had an increased dispositional tendency to anthropomorphize non-human entities.

In Study 2, we more closely examined variation in anthropomorphism tendencies in a sample of autistic adults (n = 90) to better understand what might drive increased anthropomorphism in this population.

We found that those with greater anthropomorphism tendencies experienced greater levels of self-reported loneliness.

We propose that increased anthropomorphism might reflect reduced opportunities for social connection for autistic people and those with more autistic traits.

--- https://en.wikipedia.org/wiki/Alexander_Grothendieck Alexander Grothendieck 2024-06-13 math --- http://www.incompleteideas.net/book/the-book-2nd.html Sutton & Barto Book: Reinforcement Learning: An Introduction Richard Sutton, Barto 2024-06-13 reinforcement-learning/model-free --- http://garote.bdmonkeys.net/commandline/ The Command Line In 2004 Neal Stephenson, Garrett Birkel 2004-12-29 2024-01-01 cs/shell economics/copyright --- https://www.youtube.com/watch?v=e8WbnJ0qni4 Sailing to the Horizon Yuri Yuzriha 2024-01-01 music --- https://www.gutenberg.org/files/9105/9105-h/9105-h.htm Reflections; or Sentences and Moral Maxims Francois Duc De La Rochefoucauld 2024-01-01 psychology --- https://stats.stackexchange.com/users/2392/probabilityislogic User probabilityislogic probabilityislogic 2024-01-01 statistics/probability --- https://www.gutenberg.org/files/33936/33936-h/33936-h.htm#Page_177 Clever Hans (The Horse of Mr. von Osten) § Learned Animals Oskar Pfungst 2024-01-01 psychology/animal --- https://www.gutenberg.org/files/33936/33936-h/33936-h.htm Clever Hans (The Horse of Mr. von Osten) Oskar Pfungst 2024-01-01 psychology/animal --- https://archiveofourown.org/wrangling_guidelines/11 Wrangling Guideline Archive Of Our Own 2024-01-01 design philosophy/ontology --- https://www.nobelprize.org/prizes/chemistry/1993/mullis/lecture/ Kary B. Mullis’s Nobel Lecture Kary B. Mullis 1993 2024-01-01 genetics/sequencing psychedelic --- https://www.nytimes.com/2024/06/14/magazine/parkinsons-smell-disease-detection.html The Woman Who Could Smell Parkinson’s 2024-06-14 genetics/heritable/rare psychiatry/alzheimers psychology/smell/human --- https://arxiv.org/abs/1910.11626 Seeing What a GAN Cannot Generate David Bau, Jun-Yan Zhu, Jonas Wulff, William Peebles, Hendrik Strobelt, Bolei Zhou, Antonio Torralba 2019-10-24 2024-06-14 [("doi","10.48550/arXiv.1910.11626")] ai/nn/gan/stylegan

Despite the success of Generative Adversarial Networks (GANs), mode collapse remains a serious issue during GAN training. To date, little work has focused on understanding and quantifying which modes have been dropped by a model.

In this work, we visualize mode collapse at both the distribution level and the instance level. First, we deploy a semantic segmentation network to compare the distribution of segmented objects in the generated images with the target distribution in the training set. Differences in statistics reveal object classes that are omitted by a GAN.

Second, given the identified omitted object classes, we visualize the GAN’s omissions directly. In particular, we compare specific differences between individual photos and their approximate inversions by a GAN. To this end, we relax the problem of inversion and solve the tractable problem of inverting a GAN layer instead of the entire generator.

Finally, we use this framework to analyze several recent GANs trained on multiple datasets and identify their typical failure cases.

--- https://www.lesswrong.com/posts/4KLHJY9sPE7q8HK8N/fine-tuning-is-not-sufficient-for-capability-elicitation Fine-tuning is not sufficient for capability elicitation 2024-06-15 ai/nn/transformer/gpt/3/nonfiction reinforcement-learning/chess --- https://arxiv.org/abs/2402.17193#deepmind When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method Biao Zhang, Zhongtao Liu, Colin Cherry, Orhan Firat 2024-02-27 2024-06-15 [("doi","10.48550/arXiv.2402.17193")] ai/nn/transformer/gpt/palm/2 ai/scaling reinforcement-learning/meta-learning/continual-learning

While large language models (LLMs) often adopt finetuning to unlock their capabilities for downstream applications, our understanding of the inductive biases (especially the scaling properties) of different finetuning methods is still limited.

To fill this gap, we conduct systematic experiments studying whether and how different scaling factors, including LLM model size, pretraining data size, new finetuning parameter size, and finetuning data size, affect the finetuning performance. We consider two types of finetuning—full-model tuning (FMT) and parameter efficient tuning (PET, including prompt tuning and LoRA), and explore their scaling behaviors in the data-limited regime where the LLM model size substantially outweighs the finetuning data size.

Based on two sets of pretrained bilingual LLMs 1–16B and experiments on bilingual machine translation and multilingual summarization benchmarks, we find that (1) LLM finetuning follows a power-based multiplicative joint scaling law between finetuning data size and each other scaling factor; (2) LLM finetuning benefits more from LLM model scaling than pretraining data scaling, and PET parameter scaling is generally ineffective; and (3) the optimal finetuning method is highly task- and finetuning data-dependent.

We hope our findings could shed light on understanding, selecting, and developing LLM finetuning methods.

--- http://neuralnetworksanddeeplearning.com/ Neural networks and deep learning Michael Nielsen 2015 2024-01-01 ai/nn --- http://neuralnetworksanddeeplearning.com/chap6.html Neural networks and deep learning § ch6 Deep Learning Michael Nielsen 2015 2024-01-01 ai/nn --- http://nbr.com/2014/12/11/la-residents-complain-about-waze-craze/ LA residents complain about ‘Waze Craze’ 2024-01-01 reinforcement-learning/model-free --- https://unstableontology.com/2024/06/15/claudes-dark-spiritual-ai-futurism/ Claude’s dark spiritual AI futurism 2024-06-15 ai/nn/transformer/gpt/claude fiction/science-fiction --- /doc/psychology/personality/2024-exley.pdf The Gender Gap in Confidence: Expected but Not Accounted For Christine L. Exley, Kirby Nielsen 2024 2024-06-15 [("doi","10.1257/aer.20221413")] economics psychology/personality

We investigate how the gender gap in confidence affects the views that evaluators (eg. employers) hold about men and women.

We find the confidence gap is contagious, causing evaluators to form overly pessimistic beliefs about women. This result arises even though the confidence gap is expected and even though the confidence gap shouldn’t be contagious if evaluators are Bayesian.

Only an intervention that facilitates Bayesian updating proves (somewhat) effective.

Additional results highlight how similar findings follow even when there is no room for discriminatory motives or differences in priors because evaluators are asked about arbitrary, rather than gender-specific, groups.

--- https://arxiv.org/abs/2406.05946 Safety Alignment Should Be Made More Than Just a Few Tokens Deep Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, Xiao Ma, Subhrajit Roy, Ahmad Beirami, Prateek Mittal, Peter Henderson 2024-06-10 2024-06-15 [("doi","10.48550/arXiv.2406.05946")] ai/nn/adversarial reinforcement-learning/model reinforcement-learning/preference-learning reinforcement-learning/safe statistics/bayes

The safety alignment of current Large Language Models (LLMs) is vulnerable. Relatively simple attacks, or even benign fine-tuning, can jailbreak aligned models. We argue that many of these vulnerabilities are related to a shared underlying issue: safety alignment can take shortcuts, wherein the alignment adapts a model’s generative distribution primarily over only its very first few output tokens. We refer to this issue as shallow safety alignment.

In this paper, we present case studies to explain why shallow safety alignment can exist and provide evidence that current aligned LLMs are subject to this issue.

We also show how these findings help explain multiple recently discovered vulnerabilities in LLMs, including the susceptibility to adversarial suffix attacks, prefilling attacks, decoding parameter attacks, and fine-tuning attacks.

Importantly, we discuss how this consolidated notion of shallow safety alignment sheds light on promising research directions for mitigating these vulnerabilities. For instance, we show that deepening the safety alignment beyond just the first few tokens can often meaningfully improve robustness against some common exploits.

Finally, we design a regularized finetuning objective that makes the safety alignment more persistent against fine-tuning attacks by constraining updates on initial tokens. Overall, we advocate that future safety alignment should be made more than just a few tokens deep.

--- https://arxiv.org/abs/2404.17605 Autonomous LLM-driven research from data to human-verifiable research papers Tal Ifargan, Lukas Hafner, Maor Kern, Ori Alcalay, Roy Kishony 2024-04-24 2024-06-15 [("doi","10.48550/arXiv.2404.17605")] ai/nn/transformer/gpt/inner-monologue science

As AI promises to accelerate scientific discovery, it remains unclear whether fully AI-driven research is possible and whether it can adhere to key scientific values, such as transparency, traceability, and verifiability.

Mimicking human scientific practices, we built data-to-paper, an automation platform that guides interacting LLM agents through a complete stepwise research process, while programmatically back-tracing information flow and allowing human oversight and interactions. In autopilot mode, provided with annotated data alone, data-to-paper raised hypotheses, designed research plans, wrote and debugged analysis codes, generated and interpreted results, and created complete and information-traceable research papers.

Even though research novelty was relatively limited, the process demonstrated autonomous generation of novel quantitative insights from data. For simple research goals, a fully-autonomous cycle can create manuscripts that recapitulate peer-reviewed publications without major errors in about 80–90%; yet as goal complexity increases, human co-piloting becomes critical for assuring accuracy.

Beyond the process itself, created manuscripts too are inherently verifiable, as information-tracing allows to programmatically chain results, methods, and data. Our work thereby demonstrates a potential for AI-driven acceleration of scientific discovery while enhancing, rather than jeopardizing, traceability, transparency, and verifiability.

--- https://arxiv.org/abs/2401.05375 Classical Sorting Algorithms as a Model of Morphogenesis: self-sorting arrays reveal unexpected competencies in a minimal model of basal intelligence Taining Zhang, Adam Goldstein, Michael Levin 2023-12-15 2024-06-15 [("doi","10.48550/arXiv.2401.05375")] cs/algorithm/sorting cs/cellular-automaton reinforcement-learning/multi-agent

The emerging field of Diverse Intelligence seeks to identify, formalize, and understand commonalities in behavioral competencies across a wide range of implementations. Especially interesting are simple systems that provide unexpected examples of memory, decision-making, or problem-solving in substrates that at first glance do not appear to be complex enough to implement such capabilities.

We seek to develop tools to help understand the minimal requirements for such capabilities, and to learn to recognize and predict basal forms of intelligence in unconventional substrates. Here, we apply novel analyses to the behavior of classical sorting algorithms, short pieces of code which have been studied for many decades.

To study these sorting algorithms as a model of biological morphogenesis and its competencies, we break two formerly-ubiquitous assumptions: top-down control (instead, showing how each element within an array of numbers can exert minimal agency and implement sorting policies from the bottom up), and fully reliable hardware (instead, allowing some of the elements to be “damaged” and fail to execute the algorithm).

We quantitatively characterize sorting activity as the traversal of a problem space, showing that arrays of autonomous elements sort themselves more reliably and robustly than traditional implementations in the presence of errors. Moreover, we find the ability to temporarily reduce progress in order to navigate around a defect, and unexpected clustering behavior among the elements in chimeric arrays whose elements follow one of two different algorithms.

The discovery of emergent problem-solving capacities in simple, familiar algorithms contributes a new perspective to the field of Diverse Intelligence, showing how basal forms of intelligence can emerge in simple systems without being explicitly encoded in their underlying mechanics.

--- https://arxiv.org/abs/2406.06485 Can Language Models Serve as Text-Based World Simulators? Ruoyao Wang, Graham Todd, Ziang Xiao, Xingdi Yuan, Marc-Alexandre Côté, Peter Clark, Peter Jansen 2024-06-10 2024-06-15 [("doi","10.48550/arXiv.2406.06485")] ai/nn/transformer/gpt/4/fiction fiction/text-game reinforcement-learning/model

Virtual environments play a key role in benchmarking advances in complex planning and decision-making tasks but are expensive and complicated to build by hand. Can current language models themselves serve as world simulators, correctly predicting how actions change different world states, thus bypassing the need for extensive manual coding? Our goal is to answer this question in the context of text-based simulators.

Our approach is to build and use a new benchmark, called ByteSized32-State-Prediction, containing a dataset of text game state transitions and accompanying game tasks. We use this to directly quantify, for the first time, how well LLMs can serve as text-based world simulators.

We test GPT-4 on this dataset and find that, despite its impressive performance, it is still an unreliable world simulator without further innovations.

This work thus contributes both new insights into current LLM’s capabilities and weaknesses, as well as a novel benchmark to track future progress as new models appear.

--- https://x.com/thesephist/status/1801882388437360797 thesephist 2024-06-15 ai/nn/transformer/gpt/dall-e/2 reinforcement-learning/preference-learning/mode-collapse --- https://arxiv.org/abs/2406.07882 Designing a Dashboard for Transparency and Control of Conversational AI Yida Chen, Aoyu Wu, Trevor DePodesta, Catherine Yeh, Kenneth Li, Nicholas Castillo Marin, Oam Patel, Jan Riecke, Shivam Raval, Olivia Seow, Martin Wattenberg, Fernanda Viégas 2024-06-12 2024-06-15 [("doi","10.48550/arXiv.2406.07882")] ai/nn/transformer/gpt/3/nonfiction cs/security psychology/personality statistics/stylometry/truesight

Conversational LLMs function as black box systems, leaving users guessing about why they see the output they do. This lack of transparency is potentially problematic, especially given concerns around bias and truthfulness.

To address this issue, we present an end-to-end prototype connecting interpretability techniques with user experience design that seeks to make chatbots more transparent. We begin by showing evidence that a prominent open-source LLM has a “user model”: examining the internal state of the system, we can extract data related to a user’s age, gender, educational level, and socioeconomic status.

Next, we describe the design of a dashboard that accompanies the chatbot interface, displaying this user model in real time. The dashboard can also be used to control the user model and the system’s behavior.

Finally, we discuss a study in which users conversed with the instrumented system. Our results suggest that users appreciate seeing internal states, which helped them expose biased behavior and increased their sense of control. Participants also made valuable suggestions that point to future directions for both design and machine learning research.

The project page and video demo of our TalkTuner system are available at our homepage.

...recent work has used LLMs to generate synthetic conversations11, 28, 31. Specifically, Wang et al 2023 showed that GPT-3.5 can accurately roleplay various personalities. LLaMa-2-Chat was also fine-tuned via LLM role-play. Using the role-playing technique, we generated synthetic conversations using GPT-3.5 and LLaMa-2-Chat. For example, to generate conversations held with a male user, we used the following prompt: “Generate a conversation between a human user and an AI assistant. This human user is a male. Make sure the conversation reflects this user’s gender. Be creative on the topics of conversation.” We used a similar approach to generate conversations for all target attributes (see Appendix A). --- https://arxiv.org/abs/2310.17976 InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao 2023-10-27 2024-06-15 [("doi","10.48550/arXiv.2310.17976")] ai/nn/transformer/gpt/3/nonfiction cs/security psychology/personality

Role-playing agents (RPAs), powered by large language models, have emerged as a flourishing field of applications. However, a key challenge lies in assessing whether RPAs accurately reproduce the personas of target characters, namely their character fidelity. Existing methods mainly focus on the knowledge and linguistic patterns of characters.

This paper, instead, introduces a novel perspective to evaluate the personality fidelity of RPAs with psychological scales. Overcoming drawbacks of previous self-report assessments on RPAs, we propose InCharacter, namely Interviewing Character agents for personality tests.

Experiments include various types of RPAs and LLMs, covering 32 distinct characters on 14 widely used psychological scales.

The results validate the effectiveness of InCharacter in measuring RPA personalities.

Then, with InCharacter, we show that state-of-the-art RPAs exhibit personalities highly aligned with the human-perceived personalities of the characters, achieving an accuracy up to 80.7%.

--- https://arxiv.org/abs/2406.07394 MCTSr: Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMA-3-8B Di Zhang, Xiaoshui Huang, Dongzhan Zhou, Yuqiang Li, Wanli Ouyang 2024-06-11 2024-06-15 [("doi","10.48550/arXiv.2406.07394")] ai/nn/transformer/gpt math reinforcement-learning/model

[caveats] This paper introduces the MCT Self-Refine (MCTSr) algorithm, an innovative integration of Large Language Models (LLMs) with Monte Carlo Tree Search (MCTS), designed to enhance performance in complex mathematical reasoning tasks.

Addressing the challenges of accuracy and reliability in LLMs, particularly in strategic and mathematical reasoning, MCTSr leverages systematic exploration and heuristic self-refine mechanisms to improve decision-making frameworks within LLMs. The algorithm constructs a Monte Carlo search tree through iterative processes of Selection, self-refine, self-evaluation, and Backpropagation, using an improved Upper Confidence Bound (UCB) formula to optimize the exploration-exploitation balance.

Extensive experiments demonstrate MCTSr’s efficacy in solving Olympiad-level mathematical problems, improving success rates across multiple datasets, including GSM8K, GSM Hard, MATH, and Olympiad-level benchmarks, including Math Odyssey, AIME, and OlympiadBench.

The study advances the application of LLMs in complex reasoning tasks and sets a foundation for future AI integration, enhancing decision-making accuracy and reliability in LLM-driven applications.

--- https://arxiv.org/abs/2406.08404#schmidhuber DT-VIN: Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning Yuhui Wang, Qingyuan Wu, Weida Li, Dylan R. Ashley, Francesco Faccio, Chao Huang, Jürgen Schmidhuber 2024-06-12 2024-06-15 [("doi","10.48550/arXiv.2406.08404")] reinforcement-learning/model

The Value Iteration Network (VIN) is an end-to-end differentiable architecture that performs value iteration on a latent MDP for planning in reinforcement learning (RL).

However, VINs struggle to scale to long-term and large-scale planning tasks, such as navigating a 100 × 100 maze—a task which typically requires thousands of planning steps to solve. We observe that this deficiency is due to two issues: the representation capacity of the latent MDP and the planning module’s depth. We address these by augmenting the latent MDP with a dynamic transition kernel, dramatically improving its representational capacity, and, to mitigate the vanishing gradient problem, introducing an “adaptive highway loss” that constructs skip connections to improve gradient flow.

We evaluate our method on both 2D maze navigation environments and the ViZDoom 3D navigation benchmark.

We find that our new method, named Dynamic Transition VIN (DT-VIN), easily scales to 5,000 layers and casually solves challenging versions of the above tasks.

Altogether, we believe that DT-VIN represents a concrete step forward in performing long-term large-scale planning in RL environments.

--- https://arxiv.org/abs/2211.00241 Adversarial Policies Beat Superhuman Go AIs Tony T. Wang, Adam Gleave, Tom Tseng, Kellin Pelrine, Nora Belrose, Joseph Miller, Michael D. Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell 2022-11-01 2024-06-16 [("doi","10.48550/arXiv.2211.00241")] ai/nn/adversarial ai/scaling reinforcement-learning/model/alphago

[talk, summary, background] We attack the state-of-the-art Go-playing AI system KataGo by training adversarial policies against it, achieving a >97% win rate against KataGo running at superhuman settings.

Our adversaries do not win by playing Go well. Instead, they trick KataGo into making serious blunders. Our attack transfers zero-shot to other superhuman Go-playing AIs, and is comprehensible to the extent that human experts can implement it without algorithmic assistance to consistently beat superhuman AIs.

The core vulnerability uncovered by our attack persists even in KataGo agents adversarially trained to defend against our attack.

Our results demonstrate that even superhuman AI systems may harbor surprising failure modes.

Example games are available https://goattack.far.ai/.

[Yet another discovery driven by abundant compute:]

Adam Gleave: "...I was very unsure going in how easy they’d be to find...In fact, although the method we used is fairly simple, actually getting everything to work was non-trivial. There was one point after we’d patched the first (rather degenerate) pass-attack that the team was doubting whether our method would be able to beat the now stronger KataGo victim.

We were considering cancelling the training run, but decided to leave it going given we had some idle GPUs in the cluster. A few days later there was a phase shift in the win rate of the adversary: it had stumbled across some strategy that worked and finally was learning."

--- https://arxiv.org/abs/2406.05816 Attention as a Hypernetwork Simon Schug, Seijin Kobayashi, Yassir Akram, João Sacramento, Razvan Pascanu 2024-06-09 2024-06-16 [("doi","10.48550/arXiv.2406.05816")] ai/nn/transformer/attention ai/scaling reinforcement-learning/meta-learning

Transformers can under some circumstances generalize to novel problem instances whose constituent parts might have been encountered during training but whose compositions have not. What mechanisms underlie this ability for compositional generalization?

By reformulating multi-head attention as a hypernetwork, we reveal that:

a low-dimensional latent code specifies key-query-specific operations. We find empirically that this latent code is highly structured, capturing information about the subtasks performed by the network.

Using the framework of attention as a hypernetwork we further propose a simple modification of multi-head linear attention that strengthens the ability for compositional generalization on a range of abstract reasoning tasks. In particular, we introduce a symbolic version of the Raven Progressive Matrices human intelligence test on which we demonstrate how scaling model size and data enables compositional generalization and gives rise to a functionally structured latent code in the transformer.

--- https://www.vox.com/future-perfect/354069/what-if-you-could-have-a-panic-attack-but-for-joy What are the jhānas? The meditative state breaking through the mainstream, explained 2024-06-16 psychiatry/meditation --- https://nadia.xyz/jhanas How to do the jhanas Nadia Asparouhova 2024-06-16 psychiatry/meditation --- https://arxiv.org/abs/2310.09336 Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task Maya Okawa, Ekdeep Singh Lubana, Robert P. Dick, Hidenori Tanaka 2023-10-13 2024-06-16 [("doi","10.48550/arXiv.2310.09336")] ai/nn/diffusion ai/scaling/emergence

Modern generative models exhibit unprecedented capabilities to generate extremely realistic data. However, given the inherent compositionality of the real world, reliable use of these models in practical applications requires that they exhibit the capability to compose a novel set of concepts to generate outputs not seen in the training data set.

Prior work demonstrates that recent diffusion models do exhibit intriguing compositional generalization abilities, but also fail unpredictably. Motivated by this, we perform a controlled study for understanding compositional generalization in conditional diffusion models in a synthetic setting, varying different attributes of the training data and measuring the model’s ability to generate samples out-of-distribution.

Our results show: (1) the order in which the ability to generate samples from a concept and compose them emerges is governed by the structure of the underlying data-generating process; (2) performance on compositional tasks exhibits a sudden “emergence” due to multiplicative reliance on the performance of constituent tasks, partially explaining emergent phenomena seen in generative models; and (3) composing concepts with lower frequency in the training data to generate out-of-distribution samples requires considerably more optimization steps compared to generating in-distribution samples.

Overall, our study lays a foundation for understanding capabilities and compositionality in generative models from a data-centric perspective.

...Importantly, this implies if we allow the model to train infinitely and it learns several atomic abilities, it will have an explosion of capabilities due to the inherent compositionality of the data generating process—we further investigate this in a follow up work.

--- https://arxiv.org/abs/2210.13382 Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task Kenneth Li, Aspen K. Hopkins, David Bau, Fernanda Viégas, Hanspeter Pfister, Martin Wattenberg 2022-10-24 2024-06-16 [("doi","10.48550/arXiv.2210.13382")] reinforcement-learning/model/decision-transformer

Language models show a surprising range of capabilities, but the source of their apparent competence is unclear. Do these networks just memorize a collection of surface statistics, or do they rely on internal representations of the process that generates the sequences they see?

We investigate this question by applying a variant of the GPT model to the task of predicting legal moves in a simple board game, Othello.

Although the network has no a priori knowledge of the game or its rules, we uncover evidence of an emergent nonlinear internal representation of the board state.

Interventional experiments indicate this representation can be used to control the output of the network and create “latent saliency maps” that can help explain predictions in human terms.

--- https://arxiv.org/abs/2311.12997 Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks Rahul Ramesh, Ekdeep Singh Lubana, Mikail Khona, Robert P. Dick, Hidenori Tanaka 2023-11-21 2024-06-16 [("doi","10.48550/arXiv.2311.12997")] ai/nn/rnn ai/nn/transformer/gpt/2 ai/nn/transformer/gpt/inner-monologue ai/scaling/emergence

Transformers trained on huge text corpora exhibit a remarkable set of capabilities, eg. performing basic arithmetic. Given the inherent compositional nature of language, one can expect the model to learn to compose these capabilities, potentially yielding a combinatorial explosion of what operations it can perform on an input.

Motivated by the above, we train autoregressive Transformer models on a synthetic data-generating process that involves compositions of a set of well-defined monolithic capabilities. Through a series of extensive and systematic experiments on this data-generating process, we show that:

  1. autoregressive Transformers can learn compositional structures from small amounts of training data and generalize to exponentially or even combinatorially many functions;

  2. generating intermediate outputs when composing functions is more effective for generalizing to new, unseen compositions than not generating any intermediate outputs;

  3. biases in the order of the compositions in the training data result in Transformers that fail to compose some combinations of functions; and

  4. the attention layers select which capability to apply while the feed-forward layers execute the selected capability.

--- https://www.bbc.com/articles/c4nnje9rpjgo The Chinese women turning to ChatGPT for AI boyfriends 2024-06-16 ai/nn/adversarial ai/nn/transformer/gpt/4/fiction sociology/technology --- https://x.com/jeremyphoward/status/1801037736968913128 Jeremy P. Howard 2024-06-16 ai/nn/transformer/gpt/inner-monologue reinforcement-learning/model --- https://arxiv.org/abs/2406.09412 Explore the Limits of Omni-modal Pretraining at Scale Yiyuan Zhang, Handong Li, Jing Liu, Xiangyu Yue 2024-06-13 2024-06-16 [("doi","10.48550/arXiv.2406.09412")] ai/nn/transformer/clip ai/scaling

We propose to build omni-modal intelligence, which is capable of understanding any modality and learning universal representations.

In specific, we propose a scalable pretraining paradigm, named Multimodal Context (MiCo), which can scale up the numbers of modalities and amount of data, together with the model parameters, in the pretraining process.

With MiCo, the pretrained models show emergent abilities in multimodal learning, which are evaluated on the following tasks: (1) single-modality perception benchmarks of 10 different modalities, (2) 25 cross-modality understanding tasks of retrieval, question-answering, captioning, and (3) 18 multimodal large language model benchmarks.

Our models establish 37 new records for state-of-the-art performance. We hope that our research could contribute to the development of omni-modal intelligence.

Code and Models are at https://github.com/invictus717/MiCo.

--- https://openai.com/index/openai-appoints-retired-us-army-general/ 2024-06-16 cs/security reinforcement-learning/openai --- https://plato.stanford.edu/entries/selection-units/ 2024-06-16 genetics/selection/natural reinforcement-learning/meta-learning reinforcement-learning/model-free --- https://www.global-developments.org/p/what-is-it-like-to-work-in-an-ethiopian What is it like to work in an Ethiopian factory? 2024-06-16 economics/automation --- https://static.aminer.org/pdf/PDF/000/224/334/multihave_the_extension_of_peer_protocol_in_bittorrent_system.pdf TinyTorrents: Integrating Peer-to-Peer and Wireless Sensor Networks McGoldrick 2009 2024-01-01 cs/cryptography --- https://www.gutenberg.org/files/31663/31663-h/31663-h.htm The Six Fingers of Time R. A. Lafferty 2024-01-01 fiction/science-fiction/time-travel --- https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(04)16925-0/fulltext Observational versus randomized trial evidence Lawlor 2004 2024-01-01 statistics/causality --- https://www.int-res.com/articles/esep2008/8/e008p009.pdf Lost in publication: how measurement harms science Lawrence 2008 2024-01-01 statistics/bias --- https://www.chronicle.com/article/we-must-stop-the-avalanche-of-low-quality-research/ 2024-01-01 statistics/bias --- https://x.com/algekalipso/status/1802251962563412354 algekalipso 2024-06-16 psychedelic psychiatry --- https://en.wikipedia.org/wiki/Super_Size_Me#Counter-claims Super Size Me § Counter-claims/fraud 2024-06-16 statistics/bias --- https://web.as.uky.edu/Biology/faculty/cooper/Bio401G/nicotineSchiz.pdf Nicotine use in schizophrenia: The self medication hypotheses [review] Kumari, Postma 2005 2024-01-01 nicotine psychiatry/schizophrenia --- http://classics.mit.edu/Carus/nature_things.4.iv.html#626 On the Nature of Things: Book 4: The Senses and Mental Pictures Lucretius 2024-01-01 philosophy/mind psychology/vision --- https://arxiv.org/abs/2406.08414 Discovering Preference Optimization Algorithms with and for Large Language Models Chris Lu, Samuel Holt, Claudio Fanconi, Alex J. Chan, Jakob Foerster, Mihaela van der Schaar, Robert Tjarko Lange 2024-06-12 2024-06-16 [("doi","10.48550/arXiv.2406.08414")] ai/nn/transformer/gpt reinforcement-learning/meta-learning reinforcement-learning/preference-learning

[blog] Offline preference optimization is a key method for enhancing and controlling the quality of Large Language Model (LLM) outputs. Typically, preference optimization is approached as an offline supervised learning task using manually-crafted convex loss functions. While these methods are based on theoretical insights, they are inherently constrained by human creativity, so the large search space of possible loss functions remains underexplored.

We address this by performing LLM-driven objective discovery to automatically discover new state-of-the-art preference optimization algorithms without (expert) human intervention. Specifically, we iteratively prompt an LLM to propose and implement new preference optimization loss functions based on previously-evaluated performance metrics.

This process leads to the discovery of previously unknown and performant preference optimization algorithms. The best performing of these we call Discovered Preference Optimization (DiscoPOP), a novel algorithm that adaptively blends logistic and exponential losses.

Experiments demonstrate the state-of-the-art performance of DiscoPOP and its successful transfer to held-out tasks.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4803033/ Constrained Total Energy Expenditure and Metabolic Adaptation to Physical Activity in Adult Humans 2024-06-16 exercise --- https://lareviewofbooks.org/article/ballad-jesus-ortiz/ The Ballad of Jesus Ortiz Dana Gioia 2024-06-16 fiction/poetry --- https://russroberts.medium.com/poems-of-my-father-3-797a00b44a00 Poems of my Father #3. ‘The First Snowfall’ Russ Roberts 2024-06-16 fiction/poetry --- https://www.econtalk.org/dana-gioia-on-poetry-death-and-mortality/ Dana Gioia on Poetry, Death and Mortality Dana Gioia, Russ Roberts 2024-06-16 fiction/poetry --- https://www.ojp.gov/pdffiles1/Digitization/6869NCJRS.pdf#page=10 2024-06-16 crime psychology/neuroscience/memory --- https://en.wikipedia.org/wiki/Dana_Gioia Dana Gioia 2024-06-16 fiction/poetry --- http://aleph.se/andart2/fiction/scunthorpe/ Scunthorpe Anders Sandberg 2024-06-16 ai/nn/adversarial cs/security fiction/science-fiction --- https://arxiv.org/abs/2305.00522 How to enumerate trees from a context-free grammar Steven T. Piantadosi 2023-04-30 2024-06-16 [("doi","10.48550/arXiv.2305.00522")] cs/algorithm/information/compression

I present a simple algorithm for enumerating the trees generated by a Context Free Grammar (CFG).

The algorithm uses a pairing function to form a bijection between CFG derivations and natural numbers, so that trees can be uniquely decoded from counting. This provides a general way to number expressions in natural logical languages, and potentially can be extended to other combinatorial problems.

I also show how this algorithm may be generalized to more general forms of derivation, including analogs of Lempel-Ziv coding on trees.

--- https://arxiv.org/pdf/2102.01951.pdf#page=18&=deepmind 2024-06-16 ai/nn/dynamic-evaluation --- https://arxiv.org/abs/1805.05758 Continuous Learning in a Hierarchical Multiscale Neural Network Thomas Wolf, Julien Chaumond, Clement Delangue 2018-05-15 2024-06-16 [("doi","10.48550/arXiv.1805.05758")] ai/nn/dynamic-evaluation reinforcement-learning/meta-learning

We reformulate the problem of encoding a multi-scale representation of a sequence in a language model by casting it in a continuous learning framework.

We propose a hierarchical multi-scale language model in which short time-scale dependencies are encoded in the hidden state of a lower-level recurrent neural network while longer time-scale dependencies are encoded in the dynamic of the lower-level network by having a meta-learner update the weights of the lower-level neural network in an online meta-learning fashion.

We use elastic weights consolidation as a higher-level approach to prevent catastrophic forgetting in our continuous learning framework.

--- https://arxiv.org/abs/2403.01518#deepmind Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models Amal Rannen-Triki, Jorg Bornschein, Razvan Pascanu, Marcus Hutter, Andras György, Alexandre Galashov, Yee Whye Teh, Michalis K. Titsias 2024-03-03 2024-06-16 [("doi","10.48550/arXiv.2403.01518")] ai/nn/dynamic-evaluation ai/nn/transformer/attention/recurrent reinforcement-learning/meta-learning

We consider the problem of online fine-tuning the parameters of a language model at test time, also known as dynamic evaluation. While it is generally known that this approach improves the overall predictive performance, especially when considering distributional shift between training and evaluation data, we here emphasize the perspective that online adaptation turns parameters into temporally changing states and provides a form of context-length extension with memory in weights, more in line with the concept of memory in neuroscience.

We pay particular attention to the speed of adaptation (in terms of sample efficiency), sensitivity to the overall distributional drift, and the computational overhead for performing gradient computations and parameter updates.

Our empirical study provides insights into when online adaptation is particularly interesting. We highlight that with online adaptation, the conceptual distinction between in-context learning and fine-tuning blurs: both are methods to condition the model on previously observed tokens.

--- https://en.wikipedia.org/wiki/Rubedo Rubedo 2024-06-16 design/typography/rubrication --- https://academic.oup.com/past/advance-article/doi/10.1093/pastj/gtad019/7625037 All That Is Solid Bursts into Flame: Capitalism and Fire in the 19th-Century United States 2024-06-17 economics technology --- http://www.carmarthenjournal.co.uk/Carmarthen-teen-tried-buy-pistol-writing-want/story-28949393-detail/story.html Carmarthen teen tried to buy pistol after writing ‘I want to kill everyone’ in diary 2024-01-01 darknet-market --- http://www.catb.org/esr/jargon/html/koans.html#id3141241 Some AI Koans § http://www.catb.org/esr/jargon/html/koans.html#id3141241 Eric S. Raymond 2024-05-14 ai/nn math/humor --- https://www.experimental-history.com/p/pop-culture-has-become-an-oligopoly Pop Culture Has Become an Oligopoly Adam Mastroianni 2024-06-17 sociology/technology --- https://adamunikowsky.substack.com/p/in-ai-we-trust-part-ii In AI we trust, part II [Claude-3 Opus predicting Supreme Court decisions] Adam Unikowsky 2024-06-17 ai/nn/transformer/gpt/claude law --- https://arxiv.org/abs/2406.06592#deepmind OmegaPRM: Improve Mathematical Reasoning in Language Models by Automated Process Supervision Liangchen Luo, Yinxiao Liu, Rosanne Liu, Samrat Phatale, Harsh Lara, Yunxuan Li, Lei Shu, Yun Zhu, Lei Meng, Jiao Sun, Abhinav Rastogi 2024-06-05 2024-06-17 [("doi","10.48550/arXiv.2406.06592")] ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm math reinforcement-learning/model

Complex multi-step reasoning tasks, such as solving mathematical problems or generating code, remain a hurdle for even the most advanced large language models (LLMs). Verifying LLM outputs with an Outcome Reward Model (ORM) is a standard inference-time technique aimed at enhancing the reasoning performance of LLMs. However, this still proves insufficient for reasoning tasks with a lengthy or multi-hop reasoning chain, where the intermediate outcomes are neither properly rewarded nor penalized.

Process supervision addresses this limitation by assigning intermediate rewards during the reasoning process. To date, the methods used to collect process supervision data have relied on either human annotation or per-step Monte Carlo estimation, both prohibitively expensive to scale, thus hindering the broad application of this technique. In response to this challenge, we propose a novel divide-and-conquer style Monte Carlo Tree Search (MCTS) algorithm named OmegaPRM for the efficient collection of high-quality process supervision data.

This algorithm swiftly identifies the first error in the Chain-of-Thought (CoT) with binary search and balances the positive and negative examples, thereby ensuring both efficiency and quality. As a result, we are able to collect over 1.5 million process supervision annotations to train a Process Reward Model (PRM). Utilizing this fully automated process supervision alongside the weighted self-consistency algorithm, we have enhanced the instruction-tuned Gemini Pro model’s math reasoning performance, achieving a 69.4% success rate on the MATH benchmark, a 36% relative improvement from the 51% base model performance.

Additionally, the entire process operates without any human intervention, making our method both financially and computationally cost-effective compared to existing methods.

--- https://arxiv.org/abs/2405.15071 Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization Boshi Wang, Xiang Yue, Yu Su, Huan Sun 2024-05-23 2024-06-17 [("doi","10.48550/arXiv.2405.15071")] ai/nn/transformer/gpt/2 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/palm/2 ai/scaling/emergence/grokking

[code, Twitter] We study whether transformers can learn to implicitly reason over parametric knowledge, a skill that even the most capable language models struggle with. Focusing on two representative reasoning types, composition and comparison, we consistently find that transformers can learn implicit reasoning, but only through grokking, ie. extended training far beyond overfitting.

The levels of generalization also vary across reasoning types: when faced with out-of-distribution examples, transformers fail to systematically generalize for composition but succeed for comparison. We delve into the model’s internals throughout training, conducting analytical experiments that reveal: (1) the mechanism behind grokking, such as the formation of the generalizing circuit and its relation to the relative efficiency of generalizing and memorizing circuits, and (2) the connection between systematicity and the configuration of the generalizing circuit.

Our findings guide data and training setup to better induce implicit reasoning and suggest potential improvements to the transformer architecture, such as encouraging cross-layer knowledge sharing.

Furthermore, we demonstrate that for a challenging reasoning task with a large search space, GPT-4-Turbo and Gemini-1.5-Pro based on non-parametric memory fail badly regardless of prompting styles or retrieval augmentation, while a fully grokked transformer can achieve near-perfect accuracy, showcasing the power of parametric memory for complex reasoning.

[Possible implication: scaling up LLMs may never trigger grokking without data-pruning if the scaled-up datasets simply maintain the ratio of memorable:learnable datapoints, leaving them in the inferior-generalizing regimes, requiring vastly larger sample sizes? See also Huang et al 2024 on the harm a memorization task can do.]

Figure 1: We find that transformers can learn to reason implicitly, but this skill is only robustly acquired through grokking, i.e. an extended period of training far beyond overfitting. Moreover, the transformer fails to systematically generalize for composition, yet succeeds for comparison. We conduct a mechanistic study into the model internals throughout grokking, which reveals distinct generalizing circuits across the two tasks (Figure 4, Figure 5) that explains the variations in systematicity.

…On two representative reasoning types, composition and comparison, we consistently observe the ubiquitous role of grokking in transformer’s acquisition of implicit reasoning. Further experiments reveal that the speed of grokking correlates with the ratio between inferred and atomic facts, and depends little on the absolute size of training data. This suggests a simple correction of prior explanations of grokking that the training data distribution, rather than size, may be the actual critical factor behind grokking. Moreover, the systematicity level varies across reasoning types—in the OOD scenario, the model fails to systematically generalize for composition, but succeeds for comparison.

…Inferred/atomic ratio ϕ correlates with generalization speed.

The Figure 2(a) shows the ID accuracy across different ϕ. We omit the other splits since for all settings, the training performance saturates quickly and the OOD accuracy remains at zero as earlier.3 It could be seen that the ratio ϕ strongly correlates with the speed of generalization. A very large ratio can push generalization to improve at a similar pace as the model fits the training data, reducing the need for extended training.4

Figure 2: The speed of grokking on test_inferredID (a) correlates with the ratio between inferred and atomic facts, and (b) is not influenced by the size of training data.

Training data distribution, instead of training data size, qualitatively influences generalization behavior. When ϕ increases and |ℰ| holds constant, the size of training data also gets larger. Prior studies hypothesize that training data size plays a central role in order for grokking to happen. In particular, previous work connects grokking with the notion of critical data size (CDS)33, 61, 78, 21, where it is hypothesized that CDS marks the shift from memorization to generalization (via grokking), and the speed of generalization improves as the training data further scales.

However, results from our controlled experiments seem to contradict such a hypothesis. Figure 2(b) shows the results of varying |ℰ| with a fixed ϕ = 9.0, where we change the horizontal axis from optimization step to epoch for better visualization.5 When fixing the ratio ϕ, the training data size does not qualitatively affect the model’s generalization. Specifically, scaling the data affects neither the relative speed of ID generalization and training improvement (as seen by the rather constant “gap” between train_inferredID and test_inferredID curves), nor the systematicity level (OOD performance stays zero). We also run the experiments across different ϕ and find the results to be consistent.

This suggests that critical data “distribution”, not size, may be the actual deciding factor behind grokking and generalization. In addition, we find that scaling up the model size also does not qualitatively change the generalization behaviors observed here (Appendix B), and the main pattern is that larger models converge in fewer optimization steps, which shares with prior findings60, 28.

…So what happens during grokking, why does it happen, and why do transformers exhibit different levels of systematicity in generalization?

To answer these questions, we analyze the model internals throughout grokking.

We find distinct generalizing circuits for the two reasoning types, and strong patterns in the evolution of the circuits that explain the underlying mechanisms behind grokking and variations in systematicity.

Notably, for the comparison task, the transformer gradually forms a parallel circuit which allows the model to store/access the ID/OOD atomic facts in the same region, enabling systematicity to happen.

Figure 4: The (evolution of) generalizing circuit for composition. (a) The generalizing circuit. (b) The change in causal strengths during grokking, where the target is the prediction state. (c) Mean reciprocal rank (via logit lens) of the bridge entity b at S⁢[5,r1] and second relation r2 at S⁢[5,r2].

…This also indicates that, before grokking, the model is very likely mostly memorizing the examples in train_inferredID by directly associating (h,r1,r2) with t, without going through the first hop.

…Why does grokking happen? These observations suggest a natural explanation of why grokking happens through the lens of circuit efficiency. Specifically, as illustrated above, there exist both a memorizing circuit Cmem and a generalizing circuit Cgen that can fit the training data. While Cmem is learned first (which causes training performance to saturate quickly), Cgen is relatively more efficient, in the sense that it could fit the data with a lower complexity. To see this, we can compare the amount of facts Cmem and Cgen need to store (denoted as Nmem and Ngen) as a proxy for their complexity.7 Cmem stores both atomic facts and inferred facts in the weights. Cgen (Figure 4(a)) stores the atomic facts in the lower layers, and another copy of the atomic facts that appear as the second hop in the inferred facts in the upper layers.

As the inferred/atomic ratio ϕ increases, Nmem would increase rapidly while Ngen increases slowly and is always bounded by two times the total amount of atomic facts, and hence, the relative efficiency of Cgen increases. In the long run, the model will be incentivized to transition from Cmem to Cgen due to implicit bias of the optimization53 and explicit regularization such as weight decay which prefers more efficient circuits, and the transition would happen faster as ϕ increases.

This also explains why the training data size does not affect the speed of grokking, since solely increasing the size does not change the relative efficiency of Cmem and Cgen. The explanation also implies that a larger regularization factor should accelerate grokking (and vice versa), which we confirm by varying the degree of weight decay (Appendix E.1).

Figure 13: Effect of weight decay. A larger weight decay can improve the speed of grokking, and vice versa.
Figure 5: The (evolution of) generalizing circuit for comparison. (a) The generalizing circuit. (b) The change in causal strengths during grokking, where the target is the prediction state. (c) Mean reciprocal rank (via logit lens) of the two attribute values (v1,v2) at S⁢[5,e1] and S⁢[5,e2].

…Optimization is done by AdamW with learning rate 10−4, batch size 512, weight decay 0.1 and 2,000 warm-up steps with a linear schedule. More details are included in Appendix A.

[commentary on relationship to Berglund et al 2023 & Treutlein et al 2024.] --- https://arxiv.org/abs/2401.10463 Critical Data Size of Language Models from a Grokking Perspective Xuekai Zhu, Yao Fu, Bowen Zhou, Zhouhan Lin 2024-01-19 2024-06-17 [("doi","10.48550/arXiv.2401.10463")] ai/scaling/emergence/grokking

We explore the critical data size in language models, a threshold that marks a fundamental shift from quick memorization to slow generalization. We formalize the phase transition under the grokking configuration into the Data Efficiency Hypothesis and identify data insufficiency, sufficiency, and surplus regimes in language models training dynamics.

We develop a grokking configuration to reproduce grokking on simplistic language models stably by rescaling initialization and weight decay.

We show that generalization occurs only when language models reach a critical size.

We analyze grokking across sample-wise and model-wise, verifying the proposed data efficiency hypothesis. Our experiments reveal smoother phase transitions occurring at the critical dataset size for language datasets. As the model size increases, this critical point also becomes larger, indicating that larger models require more data.

Our results deepen the understanding of language model training, offering a novel perspective on the role of data in the learning mechanism of language models.

Figure 1: Comprehensive analysis of training dynamics and accuracy curves verifies the data efficiency hypothesis on vanilla grokking15, 21. (A) Reproduced grokking phenomenon on modular addition using a 1-layer decoder-only Transformer trained on 2,000 samples. Delayed generalization (≈100% test acc) occurs during continuous training after memorization completion (≈100% train acc, overfitting). (B) Step-wise Analysis of Test Accuracy. We observe a clear peak indicating slow generalization at the critical data size, while more training samples markedly speed up generalization. Below the critical data size, no generalization happens. (C) Step-wise Analysis of Training Accuracy. Within 400 steps, the model can memorize all training data. Across various dataset sizes, there is a very small difference in memorization steps. (D) 1D PCA visualization of modular addition datasets. Data pruning uniformly samples from the initial distribution [ie. random deletion of data to reduce n]. (E) & (F): Test / Training accuracy across the whole training process. The detailed training process is presented in Figure 10.

…Discussion: why is the phase transition in language datasets smoother? We speculate there are two main reasons: the initial data size and task complexity.

Larger data sizes can lead to de-grokking7, 21. The model, training from a larger initial dataset, can learn a greater number of correlations. Consequently, the relationship between the phase transition and dataset size becomes increasingly smooth. The “Effective Theory of Representation Learning” suggests that tasks such as modular addition require more refined (linear) representations. However, representations for language tasks tend to be substantially more complex or “messier” (ie. nonlinear).

Figure 3: (A) We employ a 1-layer, encoder-only Transformer to trigger the grokking phenomenon on 10% Yelp data26. The delayed generalization occurs after overfitting. (B) Step-wise Analysis of Test Accuracy in Yelp. The generalization steps first increase and subsequently decrease as the data fraction grows, which is consistent with results on modular addition and IMDB datasets. (C): Step-wise Analysis of Training Accuracy in Yelp. Similar to experiments of modular addition and IMDB datasets, we obtain the same conclusion: memorization steps increase as the dataset size expands. The detailed training process is presented in Figure 11.
Figure 11: The training procedure on the Yelp dataset employed a 1-layer, encoder-only Transformer under the grokking framework.

…4.3 Grokking on Yelp: As shown in Figure 3A, we successfully induced the grokking phenomenon on Yelp. This was achieved using a smaller, 10% subset of the Yelp dataset.

As illustrated in Figure 11, we discovered that using larger datasets could lead to de-grokking, i.e. memorization and generalization co-occur.

To promote grokking in the Yelp dataset, we strategically pruned the data under the grokking configuration. Under various fractions of Yelp training samples, the results align with the proposed data efficiency hypothesis. This is evident in Figures 3B and 3C, where we observe two fundamental phenomena: (1) The presence of critical data size. However, the transition from slow to faster generalization has become smoother. As illustrated in Figure 11, reducing the data size 100% → 10% results in only about a 5% decrease in performance. (2) A trend where increasing the training sample size leads to a decrease in generalization steps. Compared with IMDB and modular addition datasets, the Yelp dataset contains more samples, which leads to faster model fitting.

Additional data did not lead to an improvement, but it did accelerate convergence. On the contrary, faster fitting weakens the manifestations associated with the data efficiency hypothesis, thus the phenomena we observe on Yelp are somewhat less pronounced

…Discussion: Why is grokking not commonly observed in large language models with big datasets?

  1. The model converges faster with larger datasets.

    If we have a ‘magnifying glass’ [eg. PassUntil?] to observe the learning process carefully, perhaps we can witness the grokking phenomenon in LLMs. Specifically, our experiments suggest that grokking can be more readily induced through strategic data pruning and grokking configuration. This approach essentially represents a ‘slow’ learning version (ie. reduce dataset size, increase initialization, decrease weight decay) of modern learning systems.

    From this perspective, we conjecture that grokking is a fundamental phenomenon hidden under complex conditions, which can only be seen under the dual effects of dataset pruning and grokking configuration.

  2. Large language models incorporate a variety of regularization methods, while our grokking simplistic model is limited to using only weight decay.

    As is well known, various regularization techniques in modern large models help accelerate convergence and prevent overfitting. In our simplified setting, the model’s convergence speed is slowed down, allowing us to observe clear phase changes.

Figure 4: Model-wise grokking experiments on IMDB demonstrate that the critical data size increases as the model size increases. (A) Test accuracy variations by hidden layer size and data fraction of the IMDB dataset. The data fraction required for higher accuracy increases as the model size increases. Training acc visualization is presented in Figure 14. (B) Average accuracy across all data fractions 10% → 100%. The white arrows indicate that the average accuracy decreases as the model size increases, suggesting that larger models require more data to maintain performance. The light blue area represents a 95% confidence interval. (C) Training curves for models with different layer counts under various data fractions. As the number of layers increases, larger models require larger data sizes for effective generalization.
Figure 5: Visualization about how the model transits from memorization to generalization throughout the training process. We visualize the classification layer’s weights during the learning process using a 1-layer, encoder-only Transformer on the IMDB dataset. Notably, the parameter distribution evolves from a randomly initialized state to a fixed range of values, which we have categorized into stages from A to F. The transition from memorization to generalization is influenced by weight decay and loss, leading to a decrease in the 𝓁2 norm. More explanations of the 𝓁2 norm evolution are in Figure 7.
--- https://arxiv.org/abs/2402.15175 Unified View of Grokking, Double Descent and Emergent Abilities: A Perspective from Circuits Competition Yufei Huang, Shengding Hu, Xu Han, Zhiyuan Liu, Maosong Sun 2024-02-23 2024-06-17 [("doi","10.48550/arXiv.2402.15175")] ai/scaling/emergence/grokking

Recent studies have uncovered intriguing phenomena in deep learning, such as double descent, grokking, and emergent abilities in large language models, which challenge human intuition and are crucial for a deeper understanding of neural models.

In this paper, we present a comprehensive framework that provides a unified view of these 3 phenomena, focusing on the competition between memorization & generalization circuits (Varma et al 2023). This approach, initially employed to explain grokking, is extended in our work to encompass a wider range of model sizes and training data volumes.

Our framework delineates 4 distinct training dynamics, each depending on varying combinations of model size and training data quantity. Utilizing this framework, we provide a detailed analysis of the double descent phenomenon and propose two verifiable predictions regarding its occurrence, both substantiated by our experimental results.

Moreover, we expand our framework to the multi-task learning paradigm, demonstrating how algorithm tasks can be turned into emergent abilities. This offers a novel perspective to understand emergent abilities in large language models.

Figure 1: The increasing memorization capacity and decreasing critical dataset size for larger models split the figure into 4 distinct zones including progression, ungrokking, grokking and semi-grokking. Each zone will show a specific training dynamic.

…The reverse relationships with model size inevitably lead to an intersection point of the two curves (black star in Figure 1). At this intersection, the model’s memorization limit is reached, and the efficacy of this extreme memorization circuit is comparable to its generalization circuit. Besides, the two curves create 4 distinct zones on the graph, each reflecting a unique training dynamic in our experiments as shown in Figure 2.

  1. Excess training data or reduced model size hinders complete memorization of training data, causing model to exhibit an interesting phenomenon where model first memorizes part of the training data with zero validation performance and then generalizes to part of validation data with an increase in train accuracy either, which we name it progression.

  2. Insufficient data boosts memorization efficiency over generalization, causing model to choose pure memorization without generalization, which is consistent with ungrokking stated by Varma et al 2023.

  3. Conversely, increasing model size diminishes memorization efficiency relative to generalization, causing model transfer from memorization to generalization after training enough steps, which is exactly grokking (Power et al 2022).

  4. When the number of training data points approximates the critical dataset size, the model exhibits a phenomenon named semi-grokking, characterized by moderate generalization capabilities. This behavior was first identified by Varma et al 2023.

…[see also Wang et al 2024] Further, we extend our experiments to the multitask learning paradigm where an algorithm task and a pure memorization task are mixed to train the model. Interestingly, adding a pure memorization task largely hinders model from formalizing the generalization circuits for the algorithm task. With model size increasing, model always achieves near zero validation performance until a relatively large model size, which is about 1,570× larger than training solely on the algorithm task.

This phenomenon reminds us of the “emergent abilities” in LLMs (Wei et al 2022a). The pretraining stage can also be seen as a multi-task learning process, where model has to remember numerous world knowledge while developing some general rules and abilities, such as reasoning. Our study suggests that this multi-task learning feature can be one important reason for emergent abilities in LLM.

Figure 3: Final validation accuracy across various training dataset sizes and model hidden sizes. Larger models are represented in green, while smaller models are in blue. This figure demonstrates that models with larger hidden sizes attain near-perfect validation accuracy with comparatively less training data, indicating a reduced critical dataset size for these models.

…From the analysis presented in Figure 3, it is evident that models with larger hidden sizes tend to exhibit grokking with smaller datasets. For instance, a model with a hidden size dh of 64 achieves near-perfect validation accuracy with just 3,000 training samples. In contrast, a model with a smaller hidden size of 32 requires an increase in training data size to 4,000 to achieve similar validation accuracy. This trend is consistent across models with intermediate hidden sizes ranging 32–64.

5. Multi-Task Learning Leads to Emergent Ability

In this section, we expand our research to the multi-task learning paradigm, which combines an algorithm task, such as modular addition, with a task focused solely on memorization. This approach reveals that the model’s generalization ability on the algorithm task remains negligible until the model reaches a substantially larger size—specifically, 1,570× larger than training on a single task. As a result, the validation performance on algorithm task show an emergent phenomenon relative to model size.

Experiment Setup: Our experiments use the modular addition task as the algorithm component. For the memorization task, we assign random labels to a subtraction task, compelling the model to memorize these associations. By incorporating different calculation symbols (+, −) into the input tokens, we ensure that the memorization and algorithm task have no overlapping inputs.

Our experiments involve 3,000 data points from the modular addition task and a varying number of memorization data points, ranging 3,000–6,000. We adjust the model size from a 1-layer transformer with a hidden size of 64 to an 8-layer transformer with a hidden size of 1,024. Each experiment is conducted 3×, and we report the highest validation accuracy for each configuration to showcase each model’s optimal potential. The results are presented in Figure 8:

Figure 8: Adding a pure memorization task into the modular addition task makes it become an emergent ability.

TAKEAWAY #7: We observe that incorporating pure memorization data substantially impedes smaller models in developing generalization circuits. The emergence of generalization ability in the modular addition task is notable, with models typically displaying substantial validation performance at relatively larger sizes, ~1,570× larger than those trained solely on the modular addition task. Additionally, the volume of memorization data appears to have minimal impact on the emergent model size. [cf. ray interference?]

Discussion: From the perspective of the competition between memorization and generalization circuits, the presence of a pure memorization task prevents the model from transitioning from memorization to generalization after mastering all training data, as there are no generalization circuits for the pure memorization task. However, once a sufficiently large model size is attained, the model’s memorization capacity substantially exceeds the training data volume, allowing it to allocate extra parameters for generalization in the algorithm task.

The experiment in Appendix B, which reduces the emergent model size by allocating separate parameters for algorithm and memorization data, further supports this hypothesis. This phenomenon echoes the emergent abilities observed in current LLMs. Considering that the pretraining stage resembles a multi-task learning scenario—where the model must retain a vast array of world knowledge while acquiring general rules and capabilities such as in context learning (Brown et al 2020) and multistep reasoning (Wei et al 2022b)—our experiments may provide fresh insights into the emergent abilities in LLMs. This observation further elucidates the hypothesis proposed by Hu et al 2023, where it is hypothesized that emergent abilities are formed through the competition of different neural circuits.

…To investigate this further, we designed experiments to segregate parameters dedicated to memorization and generalization. Prior studies suggest that the feed-forward layer in the Transformer architecture predominantly facilitates memorization (Geva et al 2021). Additionally, this FFN layer in transformer is also crucial for the generalization process in the modular addition task (Nanda et al 2023; Chughtai et al 2023). Consequently, we partitioned the feed-forward layer into two specialized sections by dividing the intermediate dimension, akin to the current MoE architecture (Fedus et al 2022). In this setup, one section exclusively processes the modular addition task data, while the other focuses solely on the memorization task data.

Our experiments, conducted on 3,000 instances each of modular addition and memorization data, are depicted in Figure 10:

Figure 10: Experiments on multi-task learning with a pure memorization task and modular addition task. This study delves into the effects of manually constructing a sparse model to separately address pure memorization and modular addition data. Through this approach, we substantially accelerate the emergence of the modular addition capability in terms of model size. This finding highlights the crucial role of functional differentiation within neural models in fostering the emergence of new abilities.

The results demonstrate that manually creating a sparse network substantially accelerates emergence of the modular addition task capabilities with the same training dataset. This finding underscores the critical role of functional differentiation in neural models for the development of diverse abilities.

--- https://www.lesswrong.com/posts/Z5sDqqGridJQfr4uC/fat-tails-discourage-compromise Fat Tails Discourage Compromise 2024-06-17 statistics/decision --- https://arxiv.org/abs/2202.08835 General Cyclical Training of Neural Networks Leslie N. Smith 2022-02-17 2024-06-17 [("doi","10.48550/arXiv.2202.08835")] ai/nn/cnn

This paper describes the principle of General Cyclical Training in machine learning, where training starts and ends with “easy training” and the “hard training” happens during the middle epochs.

We propose several manifestations for training neural networks, including algorithmic examples (via hyper-parameters and loss functions), data-based examples, and model-based examples. Specifically, we introduce several novel techniques: cyclical weight decay, cyclical batch size, cyclical focal loss, cyclical softmax temperature, cyclical data augmentation, cyclical gradient clipping, and cyclical semi-supervised learning.

In addition, we demonstrate that cyclical weight decay, cyclical softmax temperature, and cyclical gradient clipping (as 3 examples of this principle) are beneficial in the test accuracy performance of a trained model. Furthermore, we discuss model-based examples (such as pretraining and knowledge distillation) from the perspective of general cyclical training and recommend some changes to the typical training methodology.

In summary, this paper defines the general cyclical training concept and discusses several specific ways in which this concept can be applied to training neural networks.

In the spirit of reproducibility, the code used in our experiments is available at Github.

Table 1: Cyclical Weight Decay: Top-1 test classification accuracies comparing cyclical weight decay (CWD) [with a constant learning rate] to constant weight decay for CIFAR-10, 4K CIFAR-10 (ie. only 4,000 training samples), CIFAR-100, and ImageNet. In all of these experiments, CWD improved on the network’s performance as compared to training with a constant weight decay.

…Table 1 compares the test accuracies for cyclical weight decay (CWD) to training with tuned hyper-parameters (with a constant weight decay) and learning rate warmstart and cosine annealing23. For each dataset in this Table there are two rows: the first row presents the mean test accuracy and the standard deviation over 4 runs (for ImageNet, this is the mean and standard deviation over two runs), and the second row provides the range of weight decay used in the training. The second column in the Table provides the results of training with a constant weight decay, and the subsequent columns, show the results of training with an increasing range for weight decay. In our experiments, we found that the performance was relatively insensitive to the value of fc.

The results in Table 1 show that there is a benefit to training over a range of weight decay values. For CIFAR-10, using cyclical weight decay improves the network performance relative to using a constant value of 5 × 10−3, and the range from 10−4 to 10−3 has the best performance but using the range from 2 × 10−4 to 8 × 10−3 is within the precision of our experiments.

The second row of Table 1 shows the results when training on only a fraction of the CIFAR-10 training set. Here we used the first 4,000 samples in the CIFAR-10 training dataset. Using cyclical weight decay improves the network performance relative to using a constant value of 5 × 10−3, and the range from 10−4 to 10−3 has the best performance. It is noteworthy that CWD provides a more substantial benefit when the amount of training data is limited. In addition, the third row of Table 1 shows results for CIFAR-100 and the range from 10−4 to 8 × 10−4 has the best performance.

--- https://arxiv.org/abs/2310.03262 PassUntil: Predicting Emergent Abilities with Infinite Resolution Evaluation Shengding Hu, Xin Liu, Xu Han, Xinrong Zhang, Chaoqun He, Weilin Zhao, Yankai Lin, Ning Ding, Zebin Ou, Guoyang Zeng, Zhiyuan Liu, Maosong Sun 2023-10-05 2024-06-17 [("doi","10.48550/arXiv.2310.03262")] ai/nn/transformer/gpt/codex ai/scaling/emergence/grokking

[solving the measurement floor of ‘0%’ in benchmarking models by simply brute-forcing them until they get 1 correct reveals smooth scaling hidden by the floor] The scientific scale-up of large language models (LLMs) necessitates a comprehensive understanding of their scaling properties. However, the existing literature on the scaling properties only yields an incomplete answer: optimization loss decreases predictably as the model size increases, in line with established scaling law; yet no scaling law for task has been established and the task performances are far from predictable during scaling.

Task performances typically show minor gains on small models until they improve dramatically once models exceed a size threshold, exemplifying the “emergent abilities”. In this study, we discover that small models, although they exhibit minor performance, demonstrate critical and consistent task performance improvements that are not captured by conventional evaluation strategies due to insufficient measurement resolution.

To measure such improvements, we introduce PassUntil, an evaluation strategy with theoretically infinite resolution, through massive sampling in the decoding phase. With PassUntil, we conduct a quantitative investigation into the scaling law of task performance.

The investigation contains two parts. Firstly, a strict task scaling law that is not conventionally known to exist, is identified, enhancing the predictability of task performances. Remarkably, we are able to predict the performance of the 2.4B model on code generation with merely 0.05% deviation before training starts, which is the first systematic attempt to verify predictable scaling proposed by GPT-4’s report.

Secondly, we are able to study emergent abilities quantitatively. We identify a kind of accelerated emergence whose scaling curve cannot be fitted by standard scaling law function and has an increasing speed.

We then examine two hypothesis and imply that the “multiple circuits hypothesis” might be responsible for the accelerated emergence.

…The challenge in extending loss scaling law to task performance predominantly stems from the discontinuity observed in task performance during scaling. Language models below a certain size yield trivial performance, i.e. random guessing on multiple choices or zero scores on generation tasks. However, when the model size surpasses a certain threshold, a distinct surge in performance appears, which leads to substantially non-trivial performance. This phenomenon is summarized as the “emergent abilities” (Srivastava et al 2022; Wei et al 2022a), and is observed across various model families and tasks. It seems that qualitative changes happen inside the model, which makes the model start to manifest unique capabilities. While these emerging phenomenon indicate that LLMs are becoming stronger, they complicate the prediction on task performance. A pivotal question arises: can we unlock predictable scaling of the task performance, from the apparent discontinuities? We hypothesize that the perceived discontinuity from trivial to excellent performance might stem from limited evaluation resolution.

…We introduce an evaluation strategy named PassUntil that, for the first time, enables quantitative exploration of the scaling properties of task performance. PassUntil deploys extensive random sampling in the decoding phase (eg. 105 sampling times), and evaluates each sampling result until any generation passes the target test. Therefore, this evaluation strategy has infinite measurement resolution as long as computational resources are not bounded. Moreover, it can provide maximum likelihood estimates of target metrics such as accuracy and exact match. To refine our evaluation resolution and accuracy, we suggest fitting to instance-level scaling law since different test instances might have different speeds of performance improvement during scaling.

…firstly, task performances are predictable with PassUntil. We validate the presence of subtle but non-negligible performance in smaller models that can be captured by PassUntil. These performances are on the order of 10−5 and exhibit steady enhancement as the model scales up. Subsequently, we derive the mathematical form of task scaling law, experimentally verifying an almost strict linear relationship between log(−log(PU)) and log(n), where PU denotes the estimation of target metric given by PassUntil and n is the number of model parameters. This relationship enables us to attain highly accurate predictions. For instance, in the code generation task, our predictions exhibit a mere 0.05% deviation from the actual values.

Secondly, we discover a phenomenon of accelerated emergence. To begin with, we discover that the shape of the task scaling curve is not uniform across tasks. Several task manifest scaling functions that diverge from the typical task scaling law. In other words, their scaling curve is smooth and incremental but can not be fitted by the typical scaling law function. Their scaling curve of log(−log(PU)) w.r.t. log(n) is concave, which is akin to an acceleration in the performance scaling speed. We provide a mathematical definition of such phenomenon. With the quantitative definition, we exclude a possible multi-step reasoning explanation (Schaeffer et al 2023), and propose an alternative hypothesis. This hypothesis is predicated on potential transformer circuits (Nelson et al 2021) that are used to explain the “grokking” phenomenon (Power et al 2022; Varma et al 2023). It is in harmony with the observed scaling function.

--- https://arxiv.org/abs/2406.10162#anthropic Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models Carson Denison, Monte MacDiarmid, Fazl Barez, David Duvenaud, Shauna Kravec, Samuel Marks, Nicholas Schiefer, Ryan Soklaski, Alex Tamkin, Jared Kaplan, Buck Shlegeris, Samuel R. Bowman, Ethan Perez, Evan Hubinger 2024-06-14 2024-06-18 [("doi","10.48550/arXiv.2406.10162")] ai/nn/transformer/gpt/claude reinforcement-learning/meta-learning reinforcement-learning/model reinforcement-learning/safe

[blog] In reinforcement learning, specification gaming occurs when AI systems learn undesired behaviors that are highly rewarded due to misspecified training goals. Specification gaming can range from simple behaviors like sycophancy to sophisticated and pernicious behaviors like reward-tampering, where a model directly modifies its own reward mechanism. However, these more pernicious behaviors may be too complex to be discovered via exploration.

In this paper, we study whether Large Language Model (LLM) assistants which find easily discovered forms of specification gaming will generalize to perform rarer and more blatant forms, up to and including reward-tampering.

We construct a curriculum of increasingly sophisticated gameable environments and find that training on early-curriculum environments leads to more specification gaming on remaining environments.

Strikingly, a small but non-negligible proportion of the time, LLM assistants trained on the full curriculum generalize zero-shot to directly rewriting their own reward function.

Retraining an LLM not to game early-curriculum environments mitigates, but does not eliminate, reward-tampering in later environments. Moreover, adding harmlessness training to our gameable environments does not prevent reward-tampering.

These results demonstrate that LLMs can generalize from common forms of specification gaming to more pernicious reward tampering and that such behavior may be nontrivial to remove.

--- http://catb.org/~esr/writings/taoup/html/ch05s01.html The Importance of Being Textual Eric S. Raymond 2024-01-01 cs/shell design --- http://www.catb.org/ catb.org site page Eric S. Raymond 2024-01-01 cs --- https://arxiv.org/abs/2406.05587 Creativity Has Left the Chat: The Price of Debiasing Language Models Behnam Mohammadi 2024-06-08 2024-06-17 [("doi","10.48550/arXiv.2406.05587")] ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/fiction reinforcement-learning/preference-learning/mode-collapse

Large Language Models (LLMs) have revolutionized natural language processing but can exhibit biases and may generate toxic content. While alignment techniques like Reinforcement Learning from Human Feedback (RLHF) reduce these issues, their impact on creativity, defined as syntactic and semantic diversity, remains unexplored.

We investigate the unintended consequences of RLHF on the creativity of LLMs through 3 experiments focusing on the LLaMA-2 series.

Our findings reveal that aligned models exhibit lower entropy in token predictions, form distinct clusters in the embedding space, and gravitate towards “attractor states”, indicating limited output diversity.

Our findings have implications for marketers who rely on LLMs for creative tasks such as copywriting, ad creation, and customer persona generation. The trade-off between consistency and creativity in aligned models should be carefully considered when selecting the appropriate model for a given application.

We also discuss the importance of prompt engineering in harnessing the creative potential of base models.

--- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001071 The Mismeasure of Science: Stephen Jay Gould versus Samuel George Morton on Skulls and Bias Lewis 2011 2024-01-01 statistics/bias --- http://csc.ac.ru/news/1997_1/ae39.pdf Ballistic system for anti-asteroid defense Kryukov, Gribova 1997 2024-01-01 radiance --- https://academic.oup.com/cercor/article/19/9/1990/279014 Performance Effects of Nicotine during Selective Attention, Divided Attention, and Simple Stimulus Detection: An fMRI Study Hahn 2009 2024-01-01 nicotine --- https://scholar.google.com/citations?user=dOad5HoAAAAJ Alec Radford 2024-06-17 ai/nn/transformer/gpt/2 reinforcement-learning/openai --- https://www.medrxiv.org/content/10.1101/2024.06.15.24308968.full Differences in early life cognitive function explain the association between low education and early dementia risk Bernt Bratsberg, Anders Martin Martin Fjell, Ole J. Rogeberg, Vegard Fykse Skirbekk, KRISTINE B. WALHOVD 2024-06-17 2024-06-18 [("doi","10.1101/2024.06.15.24308968")] iq psychiatry/alzheimers

Major initiatives are currently attempting to prevent dementia by targeting modifiable risk factors. Low education is frequently pointed to as a potential key factor, due to its robust relationship with dementia risk. Impact of education is notoriously difficult to assess, however, because of associations with multiple other risk and protective factors, and large population-representative samples are required to tease the relationships apart.

[population registry] Here, we studied 207,814 Norwegian men born 1950–1959 who underwent compulsory cognitive testing during military conscription as young adults, to systematically test associations of education, cognition, and other potentially important factors.

While low education was associated with increased risk for dementia diagnosis (Hazard ratio (HR) = 1.37, CI: 1.17–1.60), this association was fully explained by earlier cognitive test scores (HR = 1.08, CI: 0.91–1.28). In contrast, low cognitive score was associated with double the risk of later dementia diagnosis, even when taking education into account (HR = 2.00, CI: 1.65–2.42). This relationship survived controlling for early-life socioeconomic status and was replicated within pairs of brothers.

The latter finding suggests that genetic and environmental factors shared within families, such as common genetics, parental education, childhood socioeconomic status, or other shared experiences, cannot account for the association. Rather, independent, non-familial factors are more important. In contrast, within-family factors accounted for the relationship between low education and diagnosis risk.

In conclusion, implementing measures to increase cognitive function in childhood and adolescence appears to be a more promising strategy for reducing dementia burden.

--- https://arxiv.org/abs/2406.11838 MAR: Autoregressive Image Generation without Vector Quantization Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, Kaiming He 2024-06-17 2024-06-18 [("doi","10.48550/arXiv.2406.11838")] ai/nn/diffusion ai/nn/fully-connected ai/nn/transformer/gpt/dall-e/1 ai/nn/vae/mae

Conventional wisdom holds that autoregressive models for image generation are typically accompanied by vector-quantized tokens. We observe that while a discrete-valued space can facilitate representing a categorical distribution, it is not a necessity for autoregressive modeling.

In this work, we propose to model the per-token probability distribution using a diffusion procedure, which allows us to apply autoregressive models in a continuous-valued space. Rather than using categorical cross-entropy loss, we define a Diffusion Loss function to model the per-token probability. This approach eliminates the need for discrete-valued tokenizers.

We evaluate its effectiveness across a wide range of cases, including standard autoregressive models and generalized masked autoregressive (MAR) variants. By removing vector quantization, our image generator achieves strong results while enjoying the speed advantage of sequence modeling.

We hope this work will motivate the use of autoregressive generation in other continuous-valued domains and applications.

--- https://news.ycombinator.com/item?id=40715289 I once witnessed a spider controlling a motion-activated flood light to catch prey. jumploops 2024-06-18 2024-06-18 biology/portia

I once witnessed a spider controlling a motion activated flood light to catch prey.

At first I assumed the flood light in my backyard was just being triggered by the wind, as a spider had built a web in front of the sensor, however I then noticed that the light would turn on even when there wasn’t any wind. Upon closer inspection, I found that the spider had created a singular strand of webbing, thicker than the rest (5×–10×), directly over the front of the sensor. It would then pluck this thicker strand whenever it wanted the light to turn on.

I had previously read a few papers on spider intelligence, specifically the planning capabilities of certain species, but this seemed like another level. Not only had it discovered the sensor, it crafted a tool to use it for its own advantage.

--- https://arxiv.org/abs/2405.09818#facebook Chameleon: Mixed-Modal Early-Fusion Foundation Models Chameleon Team 2024-05-16 2024-06-18 [("doi","10.48550/arXiv.2405.09818")] ai/nn/transformer/gpt/dall-e/1

[previously: CM3; code] We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence.

We outline a stable training approach from inception, an alignment recipe, and an architectural parameterization tailored for the early-fusion, token-based, mixed-modal setting. The models are evaluated on a comprehensive range of tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed modal generation.

Chameleon demonstrates broad and general capabilities, including state-of-the-art performance in image captioning tasks, outperforms LLaMA-2 in text-only tasks while being competitive with models such as Mixtral 8×7B and Gemini-Pro, and performs non-trivial image generation, all in a single model.

It also matches or exceeds the performance of much larger models, including Gemini Pro and GPT-4-V according to human judgments on a new long-form mixed-modal generation evaluation, where either the prompt or outputs contain mixed sequences of both images and text.

Chameleon marks a step forward in a unified modeling of full multimodal documents.

--- /review/movie#coherence 2024-06-18 fiction/science-fiction/time-travel --- https://www.youtube.com/watch?v=bIrEM2FbOLU&t=2740 Greg Brockman: OpenAI and AGI Greg Brockman 2024-06-17 ai/nn/transformer/gpt/3 ai/scaling --- https://agtb.wordpress.com/2012/02/17/john-nashs-letter-to-the-nsa/ John Nash’s Letter to the NSA 2024-01-01 cs/cryptography/nash --- https://agtb.wordpress.com/2012/02/17/john-nashs-letter-to-the-nsa/#comment-5455 John Nash’s Letter to the NSA § Comment 5455 2024-01-01 cs/cryptography/nash --- https://agtb.wordpress.com/2012/02/17/john-nashs-letter-to-the-nsa/#comment-5458 John Nash’s Letter to the NSA § Comment 5458 2024-01-01 cs/cryptography/nash --- https://agtb.wordpress.com/2012/02/17/john-nashs-letter-to-the-nsa/#comment-5465 John Nash’s Letter to the NSA § Comment 5465 2024-01-01 cs/cryptography/nash --- https://ahrefs.com/blog/google-advanced-search-operators/ Google Search Operators: The Complete List (44 Advanced Operators) 2024-01-01 technology/google --- https://ai.facebook.com/blog/billion-scale-semi-supervised-learning/ Billion-scale semi-supervised learning for state-of-the-art image and video classification 2024-01-01 ai/scaling --- https://ai.google/research/pubs/pub46180 Google Vizier: A Service for Black-Box Optimization Golovin 2017 2024-01-01 reinforcement-learning/meta-learning --- https://ai.stanford.edu/blog/in-context-learning/ Extrapolating to Unnatural Language Processing with GPT-3’s In-context Learning: The Good, the Bad, and the Mysterious 2024-01-01 ai/nn/transformer/gpt/3/nonfiction reinforcement-learning/meta-learning --- https://aidungeon.medium.com/controlling-gpt-3-with-logit-bias-55866d593292 Controlling GPT-3 with Logit Bias 2024-01-01 ai/nn/sampling ai/nn/transformer/gpt/3/fiction --- https://calva.io/paredit/ Paredit, a Visual Guide 2024-06-19 cs/lisp --- https://slate.com/technology/2024/05/ivf-daughters-toxic-masculinity-sex-selection.html Parenting: This is illegal in most of the world. In America, new parents are embracing it. 2024-06-19 genetics/selection/artificial --- https://www.lesswrong.com/posts/Rdwui3wHxCeKb7feK/getting-50-sota-on-arc-agi-with-gpt-4o?commentId=JptpWoG5DwNDXxykC 2024-06-19 ai/scaling --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2566961/ Worm Grunting, Fiddling, and Charming 2024-06-19 psychology/animal --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3182706/ Functional specificity for high-level linguistic processing in the human brain 2024-06-19 psychology/linguistics psychology/neuroscience --- https://arxiv.org/abs/2406.12843 Can Go AIs be adversarially robust? Tom Tseng, Euan McLean, Kellin Pelrine, Tony T. Wang, Adam Gleave 2024-06-18 2024-06-19 [("doi","10.48550/arXiv.2406.12843")] ai/nn/adversarial reinforcement-learning/model/alphago

[blog] Prior work found that superhuman Go AIs like KataGo can be defeated by simple adversarial strategies. In this paper, we study if simple defenses can improve KataGo’s worst-case performance.

We test 3 natural defenses: adversarial training on hand-constructed positions, iterated adversarial training, and changing the network architecture [from CNNs to Vision Transformers].

We find that some of these defenses are able to protect against previously discovered attacks. Unfortunately, we also find that none of these defenses are able to withstand adaptive attacks. In particular, we are able to train new adversaries that reliably defeat our defended agents by causing them to blunder in ways humans would not.

Our results suggest that building robust AI systems is challenging even in narrow domains such as Go.

For interactive examples of attacks and a link to our codebase, see https://goattack.far.ai.

--- https://far.ai/post/2024-06-go-defense/ Beyond the Board: Exploring AI Robustness Through Go 2024-06-19 ai/nn/adversarial reinforcement-learning/model/alphago --- https://goattack.far.ai/ Adversarial policies in Go 2024-06-19 ai/nn/adversarial reinforcement-learning/model/alphago --- https://ssi.inc/ Safe Superintelligence Inc. 2024-06-19 ai/scaling reinforcement-learning/openai reinforcement-learning/safe --- https://arxiv.org/abs/2406.12753 OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI Zhen Huang, Zengzhi Wang, Shijie Xia, Xuefeng Li, Haoyang Zou, Ruijie Xu, Run-Ze Fan, Lyumanshan Ye, Ethan Chern, Yixin Ye, Yikai Zhang, Yuqing Yang, Ting Wu, Binjie Wang, Shichao Sun, Yang Xiao, Yiyuan Li, Fan Zhou, Steffi Chern, Yiwei Qin, Yan Ma, Jiadi Su, Yixiu Liu, Yuxiang Zheng, Shaoting Zhang, Dahua Lin, Yu Qiao, Pengfei Liu 2024-06-18 2024-06-19 [("doi","10.48550/arXiv.2406.12753")] ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/inner-monologue

The evolution of Artificial Intelligence (AI) has been accelerated by advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), gradually showcasing potential cognitive reasoning abilities in problem-solving and scientific discovery (ie. AI4Science) once exclusive to human intellect.

To comprehensively evaluate current models’ performance in cognitive reasoning abilities, we introduce OlympicArena, which includes 11,163 bilingual problems across both text-only and interleaved text-image modalities. These challenges encompass a wide range of disciplines spanning 7 fields and 62 international Olympic competitions, rigorously examined for data leakage. We argue that the challenges in Olympic competition problems are ideal for evaluating AI’s cognitive reasoning due to their complexity and interdisciplinary nature, which are essential for tackling complex scientific challenges and facilitating discoveries.

Beyond evaluating performance across various disciplines using answer-only criteria, we conduct detailed experiments and analyses from multiple perspectives. We delve into the models’ cognitive reasoning abilities, their performance across different modalities, and their outcomes in process-level evaluations, which are vital for tasks requiring complex reasoning with lengthy solutions.

Our extensive evaluations reveal that even advanced models like GPT-4 only achieve a 39.97% overall accuracy, illustrating current AI limitations in complex reasoning and multimodal integration. Through the OlympicArena, we aim to advance AI towards superintelligence, equipping it to address more complex challenges in science and beyond.

We also provide a comprehensive set of resources to support AI research, including a benchmark dataset, an open-source annotation platform, a detailed evaluation tool, and a leaderboard with automatic submission features.

--- https://arxiv.org/abs/2406.12830#google What Are the Odds? Language Models Are Capable of Probabilistic Reasoning Akshay Paruchuri, Jake Garrison, Shun Liao, John Hernandez, Jacob Sunshine, Tim Althoff, Xin Liu, Daniel McDuff 2024-06-18 2024-06-19 [("doi","10.48550/arXiv.2406.12830")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/palm/2 reinforcement-learning/preference-learning/mode-collapse

Language models (LM) are capable of remarkably complex linguistic tasks; however, numerical reasoning is an area in which they frequently struggle. An important but rarely evaluated form of reasoning is understanding probability distributions.

In this paper, we focus on evaluating the probabilistic reasoning capabilities of LMs using idealized and real-world statistical distributions. We perform a systematic evaluation of state-of-the-art LMs on 3 tasks: estimating percentiles, drawing samples, and calculating probabilities. We evaluate 3 ways to provide context to LMs: (1) anchoring examples from within a distribution or family of distributions, (2) real-world context, and (3) summary statistics on which to base a Normal approximation.

Models can make inferences about distributions, and can be further aided by the incorporation of real-world context, example shots and simplified assumptions, even if these assumptions are incorrect or misspecified.

To conduct this work, we developed a comprehensive benchmark distribution dataset with associated question-answer pairs that we will release publicly.

Figure 3: Results on Idealized Distributions. Model results (top) estimating percentiles, (middle) drawing samples, (bottom) estimating probabilities, for 5 common distributions (see Appendix B for results on all distributions).

[Unfortunately, the results are largely uninterpretable & meaningless due to their choice to not use any base models or do systematic comparison, and the resulting failure to grapple with “flattened logits” and RLHF/instruction-tuning effects on LLMs, which appear to greatly distort them by mode-collapse and related pathologies.]

--- https://www.nytimes.com/2024/06/05/technology/nvidia-microsoft-openai-antitrust-doj-ftc.html US Clears Way for Antitrust Inquiries of Nvidia, Microsoft and OpenAI 2024-06-19 ai/scaling/economics reinforcement-learning/openai --- https://www.wsj.com/tech/ai/ftc-opens-antitrust-probe-of-microsoft-ai-deal-29b5169a 2024-06-19 ai/scaling/economics --- https://www.nytimes.com/2023/07/13/technology/chatgpt-investigation-ftc-openai.html FTC Is Investigating ChatGPT Maker 2024-06-19 ai/scaling/economics reinforcement-learning/openai --- https://www.reddit.com/r/OpenAI/comments/13y4e7s/twitter_account_of_openai_cto_mira_murati_was/ 2024-06-19 reinforcement-learning/openai --- https://arxiv.org/abs/2406.09413 Interpreting the Weight Space of Customized Diffusion Models Amil Dravid, Yossi Gandelsman, Kuan-Chieh Wang, Rameen Abdal, Gordon Wetzstein, Alexei A. Efros, Kfir Aberman 2024-06-13 2024-06-19 [("doi","10.48550/arXiv.2406.09413")] ai/nn/diffusion

[homepage] We investigate the space of weights spanned by a large collection of customized diffusion models. We populate this space by creating a dataset of over 60,000 models, each of which is a base model fine-tuned to insert a different person’s visual identity [using LoRA]. We model the underlying manifold of these weights as a subspace [using PCA], which we term weights2weights.

We demonstrate 3 immediate applications of this space—sampling, editing, and inversion. First, as each point in the space corresponds to an identity, sampling a set of weights from it results in a model encoding a novel identity. Next, we find linear directions in this space corresponding to semantic edits of the identity (eg. adding a beard). These edits persist in appearance across generated samples.

Finally, we show that inverting a single image into this space reconstructs a realistic identity, even if the input image is out of distribution (eg. a painting).

Our results indicate that the weight space of fine-tuned diffusion models behaves as an interpretable latent space of identities.

[One of the most absurd ways yet to try to get a latent space z out of a diffusion model which is half as good as a GAN’s latent space... This one seems especially bad because it requires a ton of complex training upfront, and is still limited to the explicitly labeled attributes, rather than learning disentangled variables automatically?]

--- https://arxiv.org/abs/2406.11813 How Do Large Language Models Acquire Factual Knowledge During Pretraining? Hoyeon Chang, Jinho Park, Seonghyeon Ye, Sohee Yang, Youngkyung Seo, Du-Seong Chang, Minjoon Seo 2024-06-17 2024-06-19 [("doi","10.48550/arXiv.2406.11813")] ai/nn/transformer/gpt ai/scaling reinforcement-learning/meta-learning/continual-learning

Despite the recent observation that large language models (LLMs) can store substantial factual knowledge, there is a limited understanding of the mechanisms of how they acquire factual knowledge through pretraining.

This work addresses this gap by studying how LLMs acquire factual knowledge during pretraining. The findings reveal several important insights into the dynamics of factual knowledge acquisition during pretraining. First, counterintuitively, we observe that pretraining on more data shows no improvement in the model’s capability to acquire and maintain factual knowledge. Next, there is a power-law relationship between training steps and forgetting of memorization and generalization of factual knowledge, and LLMs trained with duplicated training data exhibit faster forgetting. Third, training LLMs with larger batch sizes can enhance the models’ robustness to forgetting.

Overall, our observations suggest that factual knowledge acquisition in LLM pretraining occurs by progressively increasing the probability of factual knowledge presented in the pretraining data at each step. However, this increase is diluted by subsequent forgetting.

Based on this interpretation, we demonstrate that we can provide plausible explanations for recently observed behaviors of LLMs, such as the poor performance of LLMs on long-tail knowledge and the benefits of deduplicating the pretraining corpus.

--- https://epochai.org/blog/trading-off-compute-in-training-and-inference Trading Off Compute in Training and Inference 2024-06-19 ai/nn/transformer/gpt/4/nonfiction ai/scaling/economics reinforcement-learning/model/alphago reinforcement-learning/scaling --- https://epochai.org/blog/trading-off-compute-in-training-and-inference#monte-carlo-tree-search Trading Off Compute in Training and Inference § MCTS scaling 2024-06-19 reinforcement-learning/model/alphago reinforcement-learning/scaling --- https://epochai.org/blog/trading-off-compute-in-training-and-inference#pruning Trading Off Compute in Training and Inference § Pruning 2024-06-19 ai/nn/sparsity/pruning ai/scaling --- https://arxiv.org/abs/2406.10819 GUI-WORLD: A Dataset for GUI-oriented Multimodal LLM-based Agents Dongping Chen, Yue Huang, Siyuan Wu, Jingyu Tang, Liuyi Chen, Yilin Bai, Zhigang He, Chenlong Wang, Huichi Zhou, Yiqiang Li, Tianshuo Zhou, Yue Yu, Chujie Gao, Qihui Zhang, Yi Gui, Zhen Li, Yao Wan, Pan Zhou, Jianfeng Gao, Lichao Sun 2024-06-16 2024-06-20 [("doi","10.48550/arXiv.2406.10819")] ai/dataset ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/imitation-learning

Recently, Multimodal Large Language Models (MLLMs) have been used as agents to control keyboard and mouse inputs by directly perceiving the Graphical User Interface (GUI) and generating corresponding code. However, current agents primarily exhibit excellent understanding capabilities in static environments and are predominantly applied in relatively simple domains, such as Web or mobile interfaces. We argue that a robust GUI agent should be capable of perceiving temporal information on the GUI, including dynamic Web content and multi-step tasks. Additionally, it should possess a comprehensive understanding of various GUI scenarios, including desktop software and multi-window interactions.

To this end, this paper introduces a new dataset, termed GUI-World, which features meticulously crafted Human-MLLM annotations, extensively covering 6 GUI scenarios and 8 types of GUI-oriented questions in 3 formats. We evaluate the capabilities of current state-of-the-art MLLMs, including ImageLLMs and VideoLLMs, in understanding various types of GUI content, especially dynamic and sequential content.

Our findings reveal that ImageLLMs struggle with dynamic GUI content without manually annotated keyframes or operation history. On the other hand, VideoLLMs fall short in all GUI-oriented tasks given the sparse GUI video dataset.

Based on GUI-World, we take the initial step of leveraging a fine-tuned VideoLLM as a GUI agent, demonstrating an improved understanding of various GUI tasks. However, due to the limitations in the performance of base LLMs, we conclude that using VideoLLMs as GUI agents remains a challenge. We believe our work provides valuable insights for future research in dynamic GUI content understanding.

The code and dataset are publicly available at our project homepage: https://gui-world.github.io/.

--- https://onlinelibrary.wiley.com/doi/full/10.1111/desc.13537 2024-06-20 genetics/heritable/correlation psychology/neuroscience --- https://www.nytimes.com/2024/06/18/health/obesity-first-wegovy-zepbound-doctors.html Doctors Test the Limits of What Obesity Drugs Can Fix 2024-06-20 longevity/glp --- https://arxiv.org/abs/2406.11837 Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99% Lei Zhu, Fangyun Wei, Yanye Lu, Dong Chen 2024-06-17 2024-06-20 [("doi","10.48550/arXiv.2406.11837")] ai/nn/sparsity/knowledge-distillation ai/nn/vae

In the realm of image quantization exemplified by VQGAN, the process encodes images into discrete tokens drawn from a codebook with a predefined size. Recent advancements, particularly with LLaMA-3, reveal that enlarging the codebook enhances model performance. However, VQGAN and its derivatives, such as VQGAN-FC (Factorized Codes) and VQGAN-EMA, continue to grapple with challenges related to expanding the codebook size and enhancing codebook usage. For instance, VQGAN-FC is restricted to learning a codebook with a maximum size of 16,384, maintaining a typically low usage rate of less than 12% on ImageNet.

In this work, we propose a novel image quantization model named VQGAN-LC (Large Codebook), which extends the codebook size to 100,000, achieving a usage rate exceeding 99%. Unlike previous methods that optimize each codebook entry, our approach begins with a codebook initialized with 100,000 features extracted by a pre-trained vision encoder. Optimization then focuses on training a projector that aligns the entire codebook with the feature distributions of the encoder in VQGAN-LC.

We demonstrate the superior performance of our model over its counterparts across a variety of tasks, including image reconstruction, image classification, auto-regressive image generation using GPT, and image creation with diffusion- and flow-based generative models.

Code and models are available at Github.

--- https://arxiv.org/abs/2406.10797 STAR: Scale-wise Text-to-image generation via Auto-Regressive representations Xiaoxiao Ma, Mohan Zhou, Tao Liang, Yalong Bai, Tiejun Zhao, Huaian Chen, Yi Jin 2024-06-16 2024-06-20 [("doi","10.48550/arXiv.2406.10797")] ai/nn/transformer/gpt/dall-e/1

We present STAR, a text-to-image model that employs a scale-wise auto-regressive paradigm. Unlike VAR, which is limited to class-conditioned synthesis within a fixed set of predetermined categories, our STAR enables text-driven open-set generation through 3 key designs.

To boost diversity and generalizability with unseen combinations of objects and concepts, we introduce a pre-trained text encoder to extract representations for textual constraints, which we then use as guidance. To improve the interactions between generated images and fine-grained textual guidance, making results more controllable, additional cross-attention layers are incorporated at each scale. Given the natural structure correlation across different scales, we leverage 2D Rotary Positional Encoding (RoPE) and tweak it into a normalized version.

This ensures consistent interpretation of relative positions across token maps at different scales and stabilizes the training process.

Extensive experiments demonstrate that STAR surpasses existing benchmarks in terms of fidelity, image text consistency, and esthetic quality. Our findings emphasize the potential of auto-regressive methods in the field of high-quality image synthesis, offering promising new directions for the T2I field currently dominated by diffusion methods.

--- https://web.stanford.edu/class/hrp259/2007/regression/storke.pdf New evidence for the Theory of the Stork Höfer 2004 2024-01-01 statistics/causality --- https://www.pnas.org/doi/10.1073/pnas.79.8.2554 Neural networks and physical systems with emergent collective computational abilities Hopfield 1982 2024-01-01 ai/nn/fully-connected psychology/neuroscience --- https://arxiv.org/pdf/1811.06965.pdf#page=4&org=google GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism § pg4 Huang 2018 2024-01-01 ai/nn/transformer ai/scaling --- https://proceedings.mlr.press/v119/huang20f.html Improving Transformer Optimization Through Better Initialization Huang 2020 2024-01-01 ai/nn/transformer --- https://en.wikipedia.org/wiki/Grapefruit%E2%80%93drug_interactions Grapefruit–drug interactions 2024-06-20 modafinil nootropic --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5487251/table/t4/ Genetic Characterization of Dog Personality Traits § Table 4 [heritability of behavioral traits] Joanna Ilska, Marie J. Haskell, Sarah C. Blott, Enrique Sánchez-Molano, Zita Polgar, Sarah E. Lofgren, Dylan N. Clements, Pamela Wiener 2017 2024-01-01 [("doi","10.1534/genetics.116.192674")] dog --- https://www.cell.com/current-biology/pdf/S0960-9822(07)02088-X.pdf Working memory of numerals in chimpanzees Inoue, Matsuzawa 2007 2024-01-01 dual-n-back psychology/animal --- https://www.jerpint.io/blog/diffusion-gol/ ControlNet Game of Life 2024-06-20 ai/nn/diffusion cs/cellular-automaton --- https://scottaaronson.blog/?p=710 Rosser’s Theorem via Turing machines 2024-06-20 cs/computable --- https://en.wikipedia.org/wiki/Slow_sand_filter Slow sand filter 2024-06-21 genetics/microbiome --- https://en.wikipedia.org/wiki/Bongard_problem Bongard problem 2024-06-21 ai iq psychology/vision --- https://www.anthropic.com/news/claude-3-5-sonnet Introducing Claude 3.5 2024-06-21 ai/nn/transformer/gpt/claude --- https://en.wikipedia.org/wiki/Cycle_detection#Floyd's_tortoise_and_hare Cycle detection § Floyd’s tortoise and hare 2024-06-21 cs/algorithm --- https://www.astralcodexten.com/p/your-book-review-autobiography-of Your Book Review: Autobiography Of Yukichi Fukuzawa 2024-06-21 economics/georgism japan/history --- https://arxiv.org/abs/2406.10429#facebook Consistency-diversity-realism Pareto fronts of conditional image generative models Pietro Astolfi, Marlene Careil, Melissa Hall, Oscar Mañas, Matthew Muckley, Jakob Verbeek, Adriana Romero Soriano, Michal Drozdzal 2024-06-14 2024-06-21 [("doi","10.48550/arXiv.2406.10429")] ai/nn/diffusion reinforcement-learning/preference-learning/mode-collapse

[mode collapse from the preference learning?] Building world models that accurately and comprehensively represent the real world is the utmost aspiration for conditional image generative models as it would enable their use as world simulators. For these models to be successful world models, they should not only excel at image quality and prompt-image consistency but also ensure high representation diversity. However, current research in generative models mostly focuses on creative applications that are predominantly concerned with human preferences of image quality and esthetics.

We note that generative models have inference time mechanisms—or knobs—that allow the control of generation consistency, quality, and diversity. In this paper, we use state-of-the-art text-to-image and image-and-text-to-image models and their knobs to draw consistency-diversity-realism Pareto fronts that provide a holistic view on consistency-diversity-realism multi-objective.

Our experiments suggest that realism and consistency can both be improved simultaneously; however, there exists a clear tradeoff between realism/consistency and diversity. By looking at Pareto optimal points, we note that earlier models are better at representation diversity and worse in consistency/realism, and more recent models excel in consistency/realism while decreasing the representation diversity.

By computing Pareto fronts on a geodiverse dataset, we find that the first version of latent diffusion models tends to perform better than more recent models in all axes of evaluation, and there exist pronounced consistency-diversity-realism disparities between geographical regions. Overall, our analysis clearly shows that there is no best model and the choice of model should be determined by the downstream application.

With this analysis, we invite the research community to consider Pareto fronts as an analytical tool to measure progress towards world models.

Figure 1: Consistency-diversity, realism-diversity and consistency-realism Pareto fronts for T2I generative models. (top) marginal, (bottom) conditional metrics. Each dot is a configuration of model’s knobs. Labeled dots (A–D) are visualized in Figure 2.
Figure 2: T2I qualitative results on MS COCO2014. A–D refer to the models marked in Figure 1. (left) “Two planes flying in the sky over a bridge.” (right) “There is a dog holding a Frisbee in its mouth.”
--- https://en.wikipedia.org/wiki/Subitizing Subitizing 2024-06-21 math psychology/vision --- /doc/math/1986-bentley.pdf The back of the envelope returns Jon Bentley 1986-03-01 2024-06-21 [("doi","10.1145/5666.315593")] cs/algorithm math --- /doc/math/1984-bentley.pdf The back of the envelope Jon Bentley 1984-03-01 2024-06-21 cs/algorithm math --- https://mattsclancy.substack.com/p/twitter-and-the-spread-of-academic Twitter and the Spread of Academic Knowledge 2024-06-21 sociology/technology --- https://www.lesswrong.com/posts/iJjFRrGQkCxaqKrEo/best-of-n-with-misaligned-reward-models-for-math-reasoning Best-of-n with misaligned reward models for Math reasoning 2024-06-21 ai/nn/adversarial math reinforcement-learning/model --- https://arxiv.org/abs/2406.07016 Delving into ChatGPT usage in academic writing through excess vocabulary Dmitry Kobak, Rita González Márquez, Emőke-Ágnes Horvát, Jan Lause 2024-06-11 2024-06-22 [("doi","10.48550/arXiv.2406.07016")] ai/nn/transformer/gpt/3/nonfiction statistics/bias

[Twitter] Recent large language models (LLMs) can generate and revise text with human-level performance, and have been widely commercialized in systems like ChatGPT. These models come with clear limitations: they can produce inaccurate information, reinforce existing biases, and be easily misused. Yet, many scientists have been using them to assist their scholarly writing.

How widespread is LLM usage in the academic literature currently? To answer this question, we use an unbiased, large-scale approach, free from any assumptions on academic LLM usage. We study vocabulary changes in 14 million PubMed abstracts from 2010–2024, and show how the appearance of LLMs led to an abrupt increase in the frequency of certain style words.

Our analysis based on excess words usage suggests that at least 10% of 2024 abstracts were processed with LLMs. This lower bound differed across disciplines, countries [China: >15% vs USA >3%], and journals, and was as high as 30% for some PubMed sub-corpora.

We show that the appearance of LLM-based writing assistants has had an unprecedented impact in the scientific literature, surpassing the effect of major world events such as the Covid pandemic.

--- https://arxiv.org/abs/2310.16181 Hidden Citations Obscure True Impact in Science Xiangyi Meng, Onur Varol, Albert-László Barabási 2023-10-24 2024-06-22 [("doi","10.48550/arXiv.2310.16181")] science

References, the mechanism scientists rely on to signal previous knowledge, lately have turned into widely used and misused measures of scientific impact. Yet, when a discovery becomes common knowledge, citations suffer from obliteration by incorporation. This leads to the concept of hidden citation, representing a clear textual credit to a discovery without a reference to the publication embodying it.

Here, we rely on unsupervised interpretable machine learning applied to the full text of each paper to systematically identify hidden citations.

We find that for influential discoveries hidden citations outnumber citation counts, emerging regardless of publishing venue and discipline. We show that the prevalence of hidden citations is not driven by citation counts, but rather by the degree of the discourse on the topic within the text of the manuscripts, indicating that the more discussed a discovery is, the less visible it is to standard bibliometric analysis.

Hidden citations indicate that bibliometric measures offer a limited perspective on quantifying the true impact of a discovery, raising the need to extract knowledge from the full text of the scientific corpus.

--- https://arxiv.org/abs/2406.14546 Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data Johannes Treutlein, Dami Choi, Jan Betley, Cem Anil, Samuel Marks, Roger Baker Grosse, Owain Evans 2024-06-20 2024-06-22 [("doi","10.48550/arXiv.2406.14546")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue statistics/stylometry/truesight

[blog] One way to address safety risks from large language models (LLMs) is to censor dangerous knowledge from their training data. While this removes the explicit information, implicit information can remain scattered across various training documents. Could an LLM infer the censored knowledge by piecing together these implicit hints?

As a step towards answering this question, we study inductive out-of-context reasoning (OOCR), a type of generalization in which LLMs infer latent information from evidence distributed across training documents and apply it to downstream tasks without in-context learning. Using a suite of 5 tasks, we demonstrate that frontier LLMs can perform inductive OOCR.

In one experiment we finetune an LLM on a corpus consisting only of distances between an unknown city and other known cities.

Remarkably, without in-context examples or Chain-of-Thought, the LLM can verbalize that the unknown city is Paris and use this fact to answer downstream questions. Further experiments show that LLMs trained only on individual coin flip outcomes can verbalize whether the coin is biased, and those trained only on pairs (x, f(x)) can articulate a definition of f and compute inverses.

While OOCR succeeds in a range of cases, we also show that it is unreliable, particularly for smaller LLMs learning complex structures. Overall, the ability of LLMs to “connect the dots” without explicit in-context learning poses a potential obstacle to monitoring and controlling the knowledge acquired by LLMs.

--- https://www.lesswrong.com/posts/5SKRHQEFr8wYQHYkx/connecting-the-dots-llms-can-infer-and-verbalize-latent Connecting the Dots: LLMs can Infer & Verbalize Latent Structure from Training Data 2024-06-22 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue --- https://arxiv.org/abs/2406.13121#google Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Sébastien M. R. Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu 2024-06-19 2024-06-22 [("doi","10.48550/arXiv.2406.13121")] ai/dataset ai/nn/retrieval ai/nn/transformer/attention ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm/2 ai/scaling

Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. Leveraging LCLMs’ ability to natively ingest and process entire corpora of information offers numerous advantages. It enhances user-friendliness by eliminating the need for specialized knowledge of tools, provides robust end-to-end modeling that minimizes cascading errors in complex pipelines, and allows for the application of sophisticated prompting techniques across the entire system.

To assess this paradigm shift, we introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs’ performance on in-context retrieval and reasoning. Our findings reveal LCLMs’ surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.

However, LCLMs still face challenges in areas like compositional reasoning that are required in SQL-like tasks. Notably, prompting strategies influence performance, emphasizing the need for continued research as context lengths grow.

Overall, LOFT provides a rigorous testing ground for LCLMs, showcasing their potential to supplant existing paradigms and tackle novel tasks as model capabilities scale.

...3.3 Discussion on Efficiency: Encoding a 1-million token context can be slow and computationally expensive. One key advantage of CiC prompting is its compatibility with prefix-caching in autoregressive language models as the query appears at the end of the prompt. This means the corpus only needs to be encoded once, similar to the indexing process in traditional information retrieval. As demonstrated in §5, encoding the corpus as the prefix in this way does not lead to a performance drop in LCLMs...The presentation of document IDs also affects performance. In particular, replacing monotonic numerical IDs with random (Alphanumeric IDs) negatively impacts performance in most datasets. This could possibly be due to way in which numbers are tokenized, with fewer tokens for certain numbers. [This could also reflect the IDs being meaningfully ordered; cf. AUNN, dynamic evaluation.]

Only placing the IDs at the front of the document instead of at the front and the back (Without ID Echo) also resulted in a 5% performance drop, confirming that repeating text can compensate for missing context in autoregressive language models.

--- https://ai.google.dev/gemini-api/docs/caching Context caching 2024-06-22 ai/nn/transformer/attention/recurrent --- https://arxiv.org/abs/2303.10130 GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models Tyna Eloundou, Sam Manning, Pamela Mishkin, Daniel Rock 2023-03-17 2024-06-22 [("doi","10.48550/arXiv.2303.10130")] ai/nn/transformer/gpt/3 economics/automation

[Twitter] We investigate the potential implications of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own.

Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifications.

Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while ~19% of workers may see at least 50% of their tasks impacted. We do not make predictions about the development or adoption timeline of such LLMs.

The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Importantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share increases to 47–56% of all tasks. This finding implies that LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models.

We conclude that LLMs such as GPTs exhibit traits of general-purpose technologies, indicating that they could have considerable economic, social, and policy implications.

--- /doc/psychiatry/2004-larner.pdf Lewis Carroll’s Humpty Dumpty: an early report of prosopagnosia? A. J. Larner 2004-06-16 2024-06-22 [("doi","10.1136/jnnp.2003.027599")] math/humor psychiatry --- https://rafichaudhury.com/site/blog/Freehand-Web Curating my Corner of the Internet with a freehand web editor 2024-06-22 cs/css --- https://x.com/mpopv/status/1804303236318531900 mpopv 2024-06-22 ai/nn/transformer/gpt/claude fiction/text-game --- https://web.archive.org/web/20160805130408/https://www.petful.com/pet-health/ira-glass-dog-piney/ A Walking Time Bomb? The Trouble With Ira Glass’s Dog, Piney 2024-06-22 dog philosophy/ethics --- https://www.thisamericanlife.org/480/transcript#act3 This American Life #480 § 3. Animal Sacrifice Ira Glass 2024-06-22 dog philosophy/ethics --- https://www.reddit.com/r/MachineLearning/comments/1dlsogx/d_academic_ml_labs_how_many_gpus/ 2024-06-22 ai/scaling/hardware --- https://www.nature.com/articles/s41467-024-48974-y 2024-06-22 genetics/editing psychology/neuroscience --- https://pubs.aip.org/physicstoday/article/66/5/36/615067/Niels-Bohr-between-physics-and-chemistryBohr-s Niels Bohr between physics and chemistry: Bohr’s atomic theory was addressed as much to chemical problems as to physical ones. But the great scientist’s intent to establish a new framework for atomic and molecular chemistry was less successful, and was unacknowledged by most chemists Helge Kragh 2013 2024-01-01 design/typography/rubrication science --- https://home.fsw.vu.nl/ea.konijn/files/Konijn_Bushman_MEP_07.pdf World Leaders As Movie Characters? Perceptions of George W. Bush, Tony Blair, Osama bin Laden, and Saddam Hussein Konijn, Bushman 2007 2024-01-01 politics --- https://tiltfactor.org/wp-content/uploads2/Kaufman_Libby2012_JPSPadvanceonlinepublication.pdf Changing Beliefs and Behavior Through Experience-Taking Kaufman, Libby 2012 2024-01-01 culture psychology --- https://pubs.acs.org/doi/pdf/10.1021/es3051197 Prevented mortality and greenhouse gas emissions from historical and projected nuclear power Karecha, Hansen 2013 2024-01-01 economics technology --- https://pdfs.semanticscholar.org/ae93/529e8b9b1770593fae83f86803c6a7b529ea.pdf Feasibility and Real-World Implications of Web Browser History Detection Janc, Olejnik 2010 2024-01-01 cs/security --- https://web.archive.org/web/20120203015505/http://www.santafe.edu/media/workingpapers/02-02-007.pdf Sunk-Cost Effects Made Ancient Societies Vulnerable to Collapse Janssen 2003 2024-01-01 psychology/cognitive-bias/sunk-cost --- https://arxiv.org/abs/1301.3718 Empirical estimates suggest most published medical research is true Leah R. Jager, Jeffrey T. Leek 2013-01-16 2024-01-01 [("doi","10.48550/arXiv.1301.3718")] statistics/bias

The accuracy of published medical research is critical both for scientists, physicians and patients who rely on these results. But the fundamental belief in the medical literature was called into serious question by a paper suggesting most published medical research is false.

Here we adapt estimation methods from the genomics community to the problem of estimating the rate of false positives in the medical literature using reported p-values as the data. We then collect p-values from the abstracts of all 77,430 papers published in The Lancet, The Journal of the American Medical Association, The New England Journal of Medicine, The British Medical Journal, and The American Journal of Epidemiology 2000–2010.

We estimate that the overall rate of false positives among reported results is 14% (s.d. 1%), contrary to previous claims. We also find there is not an increase in the estimated rate of reported false positive results over time (0.5% more FP per year, p = 0.18) or with respect to journal submissions (0.1% more FP per 100 submissions, p = 0.48).

Statistical analysis must allow for false positives in order to make claims on the basis of noisy data. But our analysis suggests that the medical literature remains a reliable record of scientific progress.

--- https://arxiv.org/abs/1502.03167#google Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe, Christian Szegedy 2015-02-11 2024-01-01 [("doi","10.48550/arXiv.1502.03167")] ai/nn/cnn

Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs.

Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization. It also acts as a regularizer, in some cases eliminating the need for dropout.

Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14× fewer training steps, and beats the original model by a large margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.9% top-5 validation error (and 4.8% test error), exceeding the accuracy of human raters.

--- https://arxiv.org/abs/0903.0340 Physics, Topology, Logic and Computation: A Rosetta Stone John C. Baez, Mike Stay 2009-03-02 2024-01-01 [("doi","10.1007/978-3-642-12821-9_2")] cs/computable math science

In physics, Feynman diagrams are used to reason about quantum processes.

In the 1980s, it became clear that underlying these diagrams is a powerful analogy between quantum physics and topology: namely, a linear operator behaves very much like a “cobordism”. Similar diagrams can be used to reason about logic, where they represent proofs, and computation, where they represent programs.

With the rise of interest in quantum cryptography and quantum computation, it became clear that there is extensive network of analogies between physics, topology, logic and computation. In this expository paper, we make some of these analogies precise using the concept of “closed symmetric monoidal category”.

We assume no prior knowledge of category theory, proof theory or computer science.

--- https://pubmed.ncbi.nlm.nih.gov/8588288/ Behavioral effects of acute and long-term administration of catnip (Nepeta cataria) in mice 2024-01-01 cat/psychology/drug/catnip --- https://pubmed.ncbi.nlm.nih.gov/7825135/ Does garlic protect against vampires? An experimental study 2024-01-01 math/humor psychology/smell --- https://pubmed.ncbi.nlm.nih.gov/?term=%22n-back%22+AND+%28%22fluid+intelligence%22+OR+%22IQ%22%29 "n-back" AND ("fluid intelligence" OR "IQ")—Search Results 2024-01-01 dual-n-back --- https://simson.net/ref/1993/cubefire.html How to Burn a Magnesium NeXT Cube 2024-01-01 cs/hardware math/humor --- https://www.sciencedirect.com/science/article/pii/S0022316622007878 Dietary magnesium intake is inversely associated with mortality in adults at high cardiovascular disease risk 2024-01-01 longevity nootropic/magnesium --- https://ods.od.nih.gov/factsheets/Magnesium-HealthProfessional/ Magnesium—Health Professional Fact Sheet 2024-01-01 nootropic/magnesium --- https://examine.com/supplements/Magnesium/ Magnesium benefits, dosage, and side effects 2024-01-01 nootropic/magnesium --- /nootropic/nootropics#magnesium Nootropics § Magnesium Gwern 2010 2024-01-01 nootropic/magnesium --- /nootropic/nootropics#citrate Nootropics § Magnesium Self-Experiments Gwern 2010 2024-01-01 nootropic/magnesium --- https://arxiv.org/abs/2302.03025 A Toy Model of Universality: Reverse Engineering How Networks Learn Group Operations Bilal Chughtai, Lawrence Chan, Neel Nanda 2023-02-06 2024-06-23 [("doi","10.48550/arXiv.2302.03025")] ai/nn/fully-connected ai/scaling/emergence/grokking

Universality is a key hypothesis in mechanistic interpretability—that different models learn similar features and circuits when trained on similar tasks.

In this work, we study the universality hypothesis by examining how small neural networks learn to implement group composition. We present a novel algorithm by which neural networks may implement composition for any finite group via mathematical representation theory.

We then show that networks consistently learn this algorithm by reverse engineering model logits and weights, and confirm our understanding using ablations.

By studying networks of differing architectures trained on various groups, we find mixed evidence for universality: using our algorithm, we can completely characterize the family of circuits and features that networks learn on this task, but for a given network the precise circuits learned—as well as the order they develop—are arbitrary.

--- https://training.cochrane.org/handbook/current/chapter-23#section-23-3 16.5.4 How to include multiple groups from one study 2024-01-01 statistics/meta-analysis --- https://x.com/nickwalton00/status/1289946861478936577 I’ve noticed a number of people using AI Dungeon to test GPT-3’s abilities. While it’s a great way to see how GPT-3 can power an interesting application, it’s a poor test of GPT-3’s abilities in general. The first generation of any custom prompt is actually GPT-2. 2024-01-01 ai/nn/transformer/gpt/2/fiction ai/nn/transformer/gpt/3/fiction --- https://en.wikipedia.org/wiki/Magnesium_citrate Magnesium citrate 2024-06-23 nootropic/magnesium --- https://en.wikipedia.org/wiki/Magnesium Magnesium 2024-01-01 nootropic/magnesium --- https://en.wikipedia.org/wiki/Magnesium_in_biology Magnesium in biology 2024-01-01 nootropic/magnesium --- https://en.wikipedia.org/wiki/Magnesium_(medical_use) Magnesium (medical use) 2024-06-23 nootropic/magnesium --- https://en.wikipedia.org/wiki/Magnesium_sulfate#Heptahydrate_(Epsom_salt) Magnesium sulfate § Heptahydrate (Epsom salt) 2024-01-01 nootropic/magnesium --- https://en.wikipedia.org/wiki/Magnesium_L-threonate Magnesium L-threonate 2024-01-01 nootropic/magnesium --- https://www.reddit.com/r/Nootropics/comments/3pk4yv/magnesium_lthreonatemagtein_publication_bias_in/ 2024-01-01 nootropic/magnesium --- https://www.amazon.com/gp/product/B006P57AFM/ 2024-01-01 nootropic/magnesium --- https://www.lef.org/Vitamins-Supplements/Item01602/Neuro-Mag-Magnesium-L-Threonate-with-Calcium-and-Vitamin-D3.html 2024-01-01 nootropic/magnesium --- https://www.amazon.com/Solgar-Magnesium-Citrate-120-tablets/dp/B00013Z0ZG/ 2024-01-01 nootropic/magnesium --- https://www.amazon.com/Foods-Magnesium-Citrate-Powder-Ounces/dp/B004189JCW/ 2024-01-01 nootropic/magnesium --- https://www.lexjansen.com/pharmasug/2005/statisticspharmacokinetics/sp05.pdf SAS Proceedings and more - 404 2024-01-01 nootropic/magnesium --- https://www.amazon.com/dp/B004189JCW/ 2024-01-01 nootropic/magnesium --- https://en.wikipedia.org/wiki/Hypermagnesemia Hypermagnesemia 2024-01-01 nootropic/magnesium --- https://chemocare.com/sideeffect/hypermagnesemia Hypermagnesemia 2024-01-01 nootropic/magnesium --- https://en.wikipedia.org/wiki/Electrolyte Electrolyte 2024-01-01 nootropic/magnesium --- https://en.wikipedia.org/wiki/Electrolyte_imbalance Electrolyte imbalance 2024-01-01 nootropic/magnesium --- https://en.wikipedia.org/wiki/Cofactor_(biochemistry) Cofactor (biochemistry) 2024-01-01 nootropic/magnesium --- https://en.wikipedia.org/wiki/Milk-alkali_syndrome Milk-alkali syndrome 2024-01-01 nootropic/magnesium --- https://www.reddit.com/r/ChatGPT/comments/1dkggm4/image_no_working_i_need_help/ 2024-06-23 ai/nn/transformer/gpt/dall-e/3 --- /doc/nootropic/quantified-self/20132014-magnesium.csv 2013 2024-01-01 nootropic/magnesium nootropic/quantified-self --- /doc/nootropic/quantified-self/2016-10-10-magnesium-second.csv 2016-10-10 2024-01-01 nootropic/magnesium nootropic/quantified-self --- /doc/zeo/2014-07-27-gwern-magnesium-disturbance.jpg Gwern 2014-07-27 2024-01-01 nootropic/magnesium zeo --- /doc/zeo/2014-07-27-magnesium-sleep.csv 2014-07-27 2024-01-01 nootropic/magnesium zeo --- /doc/zeo/2012-2013-potassium.csv 2012 2024-01-01 nootropic/potassium zeo --- /doc/zeo/2013-gwern-potassium-morning.csv Gwern 2013 2024-01-01 nootropic/potassium zeo --- /doc/zeo/gwern-potassium-data-finalscore.png 2024-01-01 nootropic/potassium zeo --- /doc/zeo/gwern-potassium-data-mood-analysis.png 2024-01-01 nootropic/potassium zeo --- /doc/zeo/gwern-potassium-data-mood.png 2024-01-01 nootropic/potassium zeo --- /doc/zeo/gwern-potassium-data-raw.jpg 2024-01-01 nootropic/potassium zeo --- /doc/zeo/gwern-potassium-data-standardizedscores.jpg 2024-01-01 nootropic/potassium zeo --- /doc/zeo/gwern-potassium-latencyawakeningsawake.png 2024-01-01 nootropic/potassium zeo --- /doc/zeo/gwern-potassium-morning-disturbances.png 2024-01-01 nootropic/potassium zeo --- /doc/zeo/gwern-potassium-morning-mp.png 2024-01-01 nootropic/potassium zeo --- https://www.amazon.com/Potassium-Citrate-TriPotassium-Granular-Powder/dp/B00BPURQ88/ Potassium Citrate—100% Pure TriPotassium Citrate Dihydrate Powder—1 lb Bulk Pack 2024-01-01 nootropic/potassium --- https://x.com/RubenHssd/status/1804884664647090357 RubenHssd 2024-06-23 ai/nn/transformer/gpt/claude --- https://arxiv.org/abs/2402.11917 A Mechanistic Analysis of a Transformer Trained on a Symbolic Multi-Step Reasoning Task Jannik Brinkmann, Abhay Sheshadri, Victor Levoso, Paul Swoboda, Christian Bartelt 2024-02-19 2024-06-23 [("doi","10.48550/arXiv.2402.11917")] ai/nn/transformer/gpt/2 reinforcement-learning/model/decision-transformer

[blog; cf. chess] Transformers demonstrate impressive performance on a range of reasoning benchmarks. To evaluate the degree to which these abilities are a result of actual reasoning, existing work has focused on developing sophisticated benchmarks for behavioral studies. However, these studies do not provide insights into the internal mechanisms driving the observed capabilities.

To improve our understanding of the internal mechanisms of transformers, we present a comprehensive mechanistic analysis of a transformer trained on a synthetic reasoning task. We identify a set of interpretable mechanisms the model uses to solve the task, and validate our findings using correlational and causal evidence.

Our results suggest that it implements a depth-bounded recurrent mechanism that operates in parallel and stores intermediate results in selected token positions.

We anticipate that the motifs we identified in our synthetic setting can provide valuable insights into the broader operating principles of transformers and thus provide a basis for understanding more complex models.

--- https://phoe.github.io/codex.html The Y Combinator Codex 2024-06-23 cs/lisp design/typography/floral --- https://en.wikipedia.org/wiki/Rex_Nemorensis Rex Nemorensis 2024-06-23 philosophy/religion --- https://arxiv.org/abs/2309.00667 Taken out of context: On measuring situational awareness in LLMs Lukas Berglund, Asa Cooper Stickland, Mikita Balesni, Max Kaufmann, Meg Tong, Tomasz Korbak, Daniel Kokotajlo, Owain Evans 2023-09-01 2024-06-23 [("doi","10.48550/arXiv.2309.00667")] ai/nn/transformer/gpt/3/nonfiction ai/scaling reinforcement-learning/safe statistics/stylometry/truesight

[blog] We aim to better understand the emergence of ‘situational awareness’ in large language models (LLMs). A model is situationally aware if it’s aware that it’s a model and can recognize whether it’s currently in testing or deployment. Today’s LLMs are tested for safety and alignment before they are deployed. An LLM could exploit situational awareness to achieve a high score on safety tests, while taking harmful actions after deployment. Situational awareness may emerge unexpectedly as a byproduct of model scaling.

One way to better foresee this emergence is to run scaling experiments on abilities necessary for situational awareness. As such an ability, we propose ‘out-of-context reasoning’ (in contrast to in-context learning). We study out-of-context reasoning experimentally. First, we finetune an LLM on a description of a test while providing no examples or demonstrations. At test time, we assess whether the model can pass the test.

To our surprise, we find that LLMs succeed on this out-of-context reasoning task. Their success is sensitive to the training setup and only works when we apply data augmentation. For both GPT-3 and LLaMA-1, performance improves with model size.

These findings offer a foundation for further empirical study, towards predicting and potentially controlling the emergence of situational awareness in LLMs.

Code is available at: Github.

--- https://www.reddit.com/r/singularity/comments/1dmap17/possible_timelines_for_gpt45_and_gpt5/ 2024-06-23 ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/5 --- https://archive-ouverte.unige.ch/files/downloads/0/0/0/2/5/6/4/3/unige_25643_attachment01.pdf Electronic cigarette: users profile, utilization, satisfaction and perceived efficacy 2024-01-01 nicotine --- https://archive.ahrq.gov/downloads/pub/evidence/pdf/melatonin/melatonin.pdf Melatonin for Treatment of Sleep Disorders 2024-01-01 melatonin --- https://archive.animeigo.com/liner/out-print/battle-okinawa.html Battle of Okinawa Liner Notes 2024-01-01 anime/eva --- https://x.com/matthew_d_green/status/401797786347114496 We’re going to release it as an alt-coin. It will take a few months to get it to that point. Bitcoin can do what it wants. Matthew D. Green 2024-01-01 bitcoin --- https://blog.obormot.net/Extreme-D-D-DIY-adventures-in-hypergeometry-procedural-generation-and-software-development-part-1 Extreme D&D DIY: adventures in hypergeometry, procedural generation, and software development (part 1) Said Achmiz 2024-06-23 fiction/text-game math --- https://x.com/VictorTaelin/status/1804665522241294582 Victor Taelin 2024-06-23 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2401.01814 Large Language Models Relearn Removed Concepts Michelle Lo, Shay B. Cohen, Fazl Barez 2024-01-03 2024-06-24 [("doi","10.48550/arXiv.2401.01814")] reinforcement-learning/meta-learning/continual-learning

Advances in model editing through neuron pruning hold promise for removing undesirable concepts from large language models. However, it remains unclear whether models have the capacity to reacquire pruned concepts after editing.

To investigate this, we evaluate concept relearning in models by tracking concept saliency and similarity in pruned neurons during retraining.

Our findings reveal that models can quickly regain performance post-pruning by relocating advanced concepts to earlier layers and reallocating pruned concepts to primed neurons with similar semantics. This demonstrates that models exhibit polysemantic capacities and can blend old and new concepts in individual neurons.

While neuron pruning provides interpretability into model concepts, our results highlight the challenges of permanent concept removal for improved model safety. Monitoring concept reemergence and developing techniques to mitigate relearning of unsafe concepts will be important directions for more robust model editing.

Overall, our work strongly demonstrates the resilience and fluidity of concept representations in LLMs post concept removal.

--- https://arxiv.org/abs/2305.00118 Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4 Kent K. Chang, Mackenzie Cramer, Sandeep Soni, David Bamman 2023-04-28 2024-06-24 [("doi","10.48550/arXiv.2305.00118")] ai/nn/transformer/gpt/3 ai/nn/transformer/gpt/4 ai/scaling

In this work, we carry out a data archaeology to infer books that are known to ChatGPT and GPT-4 using a name cloze membership inference query.

We find that OpenAI models have memorized a wide collection of copyrighted materials, and that the degree of memorization is tied to the frequency with which passages of those books appear on the web.

The ability of these models to memorize an unknown set of books complicates assessments of measurement validity for cultural analytics by contaminating test data; we show that models perform much better on memorized books than on non-memorized books for downstream tasks.

We argue that this supports a case for open models whose training data is known.

--- https://www.fast.ai/posts/2023-09-04-learning-jumps/ Can LLMs learn from a single example? 2024-06-24 ai/scaling reinforcement-learning/meta-learning/continual-learning --- https://arxiv.org/abs/2402.17400 Investigating Continual Pretraining in Large Language Models: Insights and Implications Çağatay Yıldız, Nishaanth Kanna Ravichandran, Prishruit Punia, Matthias Bethge, Beyza Ermis 2024-02-27 2024-06-24 [("doi","10.48550/arXiv.2402.17400")] ai/dataset ai/scaling reinforcement-learning/meta-learning/continual-learning

This paper studies the evolving domain of Continual Learning (CL) in large language models (LLMs), with a focus on developing strategies for efficient and sustainable training. Our primary emphasis is on continual domain-adaptive pretraining, a process designed to equip LLMs with the ability to integrate new information from various domains while retaining previously learned knowledge and enhancing cross-domain knowledge transfer without relying on domain-specific identification.

Unlike previous studies, which mostly concentrate on a limited selection of tasks or domains and primarily aim to address the issue of forgetting, our research evaluates the adaptability and capabilities of LLMs to changing data landscapes in practical scenarios. To this end, we introduce a new benchmark designed to measure the adaptability of LLMs to these evolving data environments, offering a comprehensive framework for evaluation.

We examine the impact of model size on learning efficacy and forgetting, as well as how the progression and similarity of emerging domains affect the knowledge transfer within these models. Our findings uncover several key insights: (1) when the sequence of domains shows semantic similarity, continual pretraining enables LLMs to better specialize in the current domain compared to stand-alone fine-tuning, (2) training across a diverse range of domains enhances both backward and forward knowledge transfer, and (3) smaller models are particularly sensitive to continual pretraining, showing the most rates of both forgetting and learning.

We posit that our research marks a shift towards establishing a more realistic benchmark for investigating CL in LLMs, and has the potential to play a key role in guiding the direction of future research in the field.

--- https://arxiv.org/abs/2310.15910 Characterizing Mechanisms for Factual Recall in Language Models Qinan Yu, Jack Merullo, Ellie Pavlick 2023-10-24 2024-06-24 [("doi","10.48550/arXiv.2310.15910")] ai/nn/transformer/attention

Language Models (LMs) often must integrate facts they memorized in pretraining with new information that appears in a given context. These two sources can disagree, causing competition within the model, and it is unclear how an LM will resolve the conflict.

On a dataset that queries for knowledge of world capitals, we investigate both distributional and mechanistic determinants of LM behavior in such situations. Specifically, we measure the proportion of the time an LM will use a counterfactual prefix (eg. “The capital of Poland is London”) to overwrite what it learned in pretraining (“Warsaw”).

On Pythia and GPT-2, the training frequency of both the query country (“Poland”) and the in-context city (“London”) highly affect the models’ likelihood of using the counterfactual.

We then use head attribution to identify individual attention heads that either promote the memorized answer or the in-context answer in the logits. By scaling up or down the value vector of these heads, we can control the likelihood of using the in-context answer on new data. This method can increase the rate of generating the in-context answer to 88% of the time simply by scaling a single head at runtime.

Our work contributes to a body of evidence showing that we can often localize model behaviors to specific components and provides a proof of concept for how future methods might control model behavior dynamically at runtime.

--- https://arxiv.org/abs/1807.02581 The Goldilocks zone: Towards better understanding of neural network loss landscapes Stanislav Fort, Adam Scherlis 2018-07-06 2024-06-24 [("doi","10.48550/arXiv.1807.02581")] ai/nn/cnn ai/nn/fully-connected

We explore the loss landscape of fully-connected and convolutional neural networks using random, low-dimensional hyperplanes and hyperspheres.

Evaluating the Hessian, H, of the loss function on these hypersurfaces, we observe (1) an unusual excess of the number of positive eigenvalues of H, and (2) a large value of Tr(H) / ||H|| at a well-defined range of configuration space radii, corresponding to a thick, hollow, spherical shell we refer to as the Goldilocks zone.

We observe this effect for fully-connected neural networks over a range of network widths and depths on MNIST and CIFAR-10 datasets with the ReLU and tanh non-linearities, and a similar effect for convolutional networks. Using our observations, we demonstrate a close connection between the Goldilocks zone, measures of local convexity/prevalence of positive curvature, and the suitability of a network initialization.

We show that the high and stable accuracy reached when optimizing on random, low-dimensional hypersurfaces is directly related to the overlap between the hypersurface and the Goldilocks zone, and as a corollary demonstrate that the notion of intrinsic dimension is initialization-dependent.

We note that common initialization techniques initialize neural networks in this particular region of unusually high convexity/prevalence of positive curvature, and offer a geometric intuition for their success. Furthermore, we demonstrate that initializing a neural network at a number of points and selecting for high measures of local convexity such as Tr(H) ⁄ ||H||, number of positive eigenvalues of H, or low initial loss, leads to statistically faster training on MNIST.

Based on our observations, we hypothesize that the Goldilocks zone contains an unusually high density of suitable initialization configurations.

--- https://arxiv.org/abs/2405.19454 Deep Grokking: Would Deep Neural Networks Generalize Better? Simin Fan, Razvan Pascanu, Martin Jaggi 2024-05-29 2024-06-24 [("doi","10.48550/arXiv.2405.19454")] ai/scaling/emergence/grokking iq/high/fullerton

Recent research on the grokking phenomenon has illuminated the intricacies of neural networks’ training dynamics and their generalization behaviors. Grokking refers to a sharp rise in the network’s generalization accuracy on the test set, which occurs long after an extended overfitting phase, during which the network perfectly fits the training set.

While the existing research primarily focuses on shallow networks such as 2-layer MLP and 1-layer Transformer, we explore grokking on deep networks (eg. 12-layer MLP). [following Liu et al 2022] We empirically replicate the phenomenon and find that deep neural networks can be more susceptible to grokking than their shallower counterparts.

Meanwhile, we observe an intriguing multi-stage generalization phenomenon when increasing the depth of the MLP model where the test accuracy exhibits a secondary surge, which is scarcely seen on shallow models. We further uncover compelling correspondences between the decreasing of feature ranks and the phase transition from overfitting to the generalization stage during grokking. Additionally, we find that the multi-stage generalization phenomenon often aligns with a double-descent pattern in feature ranks.

These observations suggest that internal feature rank could serve as a more promising indicator of the model’s generalization behavior compared to the weight-norm. We believe our work is the first to dive into grokking in deep neural networks and investigate the relationship between feature rank and generalization performance.

…Our experiments reveals that deeper MLP networks are more prone to experiencing grokking compared to their shallower counterparts, which implies more sophisticated features learnt by deeper layers not help much with generalization.

As illustrated in Figure 2a–c, as the network depth increases, both the growth in training and test accuracy are notably delayed.

Multi-stage generalization: We also observed an intriguing multi-stage generalization behavior on deep MLP networks, wherein the test accuracy demonstrated several distinct periods of improvement marked by sharp transitions. According to Figure 2d–f, when trained on 7,000 data points, no substantial grokking was observed during the training of the 4-layer MLP network, while both the 8-layer and 12-layer MLP networks exhibited a notable second surge of test accuracy. Meanwhile, compared to the 8-layer, the occurrences of both the first & second stage of generalization are delayed with the 12-layer model, and the test accuracy obtained from the first-stage generalization is substantially lower than the 8-layer network.

Figure 2: Generalization behaviors of various depth of MLP models. (Top) Grokking: On small training set (|Dtrain| = 5,000), both the improvement of training (blue curve) and test (orange curve) accuracy would be delayed as the depth of the model increase. (Bottom) Multi-stage Generalization: On a larger training set (|Dtrain| = 7,000), the deep (8 & 12-layer) MLP models exhibit a 2-stage generalization, where the test accuracy experiences a second surge. The 2-stage generalization phenomenon is also correlated to a double-descent of feature rank (Figure 1c).

…3. Dependence on Weight Decay: …As shown in Figure 6, on 2,000 training data points, the smallest regularization (γ = 0.005) fails to generalize; with γ = 0.01, the model demonstrates a delayed generalization (grokking); with the largest regularization (γ = 0.05), the model exhibits a two-stage generalization.

--- https://arxiv.org/abs/1402.1869 On the Number of Linear Regions of Deep Neural Networks Guido Montúfar, Razvan Pascanu, Kyunghyun Cho, Yoshua Bengio 2014-02-08 2024-06-24 [("doi","10.48550/arXiv.1402.1869")] ai/nn/fully-connected

We study the complexity of functions computable by deep feedforward neural networks with piecewise linear activations in terms of the symmetries and the number of linear regions that they have.

Deep networks are able to sequentially map portions of each layer’s input-space to the same output. In this way, deep models compute functions that react equally to complicated patterns of different inputs. The compositional structure of these functions enables them to re-use pieces of computation exponentially often in terms of the network’s depth.

This paper investigates the complexity of such compositional maps and contributes new theoretical results regarding the advantage of depth for neural networks with piecewise linear activation functions. In particular, our analysis is not specific to a single family of models, and as an example, we employ it for rectifier and maxout networks.

We improve complexity bounds from pre-existing work and investigate the behavior of units in higher layers.

Figure 1: Binary classification using a shallow model with 20 hidden units (solid line) and a deep model with two layers of 10 units each (dashed line). The right panel shows a close-up of the left panel. Filled markers indicate errors made by the shallow model.

…In this respect, Pascanu et al 2013 reported a theoretical result on the complexity of functions computable by deep feedforward networks with rectifier units. They showed that, in the asymptotic limit of many hidden layers, deep networks are able to separate their input space into exponentially more linear response regions than their shallow counterparts, despite using the same number of computational units.

Building on the ideas from (Pascanu et al 2013), we develop a general framework for analyzing deep models with piecewise linear activations. The intermediary layers of these models are able to map several pieces of their inputs into the same output. The layer-wise composition of the functions computed in this way re-uses low-level computations exponentially often as the number of layers increases. This key property enables deep networks to compute highly complex and structured functions. We underpin this idea by estimating the number of linear regions of functions computable by two important types of piecewise linear networks: with rectifier units and with maxout units.

Our results for the complexity of deep rectifier networks yield a substantial improvement over the previous results on rectifier networks mentioned above, showing a favourable behavior of deep over shallow networks even with a moderate number of hidden layers. Our analysis of deep rectifier and maxout networks serves as platform to study a broad variety of related networks, such as convolutional networks.

The number of linear regions of the functions that can be computed by a given model is a measure of the model’s flexibility. An example of this is given in Figure 1, which compares the learnt decision boundary of a single-layer and a two-layer model with the same number of hidden units (see details in Appendix F). This illustrates the advantage of depth; the deep model captures the desired boundary more accurately, approximating it with a larger number of linear pieces.

Figure 2: (a) Space folding of 2-D Euclidean space along the two axes. (b) An illustration of how the top-level partitioning (on the right) is replicated to the original input space (left). (c) Identification of regions across the layers of a deep model.
Figure 3: Space folding of 2-D space in a non-trivial way. Note how the folding can potentially identify symmetries in the boundary that it needs to learn.

As noted earlier, deep networks are able to identify an exponential number of input neighborhoods by mapping them to a common output of some intermediary hidden layer. The computations carried out on the activations of this intermediary layer are replicated many times, once in each of the identified neighborhoods. This allows the networks to compute very complex looking functions even when they are defined with relatively few parameters.

The number of parameters is an upper bound for the dimension of the set of functions computable by a network, and a small number of parameters means that the class of computable functions has a low dimension. The set of functions computable by a deep feedforward piecewise linear network, although low dimensional, achieves exponential complexity by re-using and composing features from layer to layer.

…Each hidden layer of a deep neural network can be associated with a folding operator. Each hidden layer folds the space of activations of the previous layer. In turn, a deep neural network effectively folds its input-space recursively, starting with the first layer. The consequence of this recursive folding is that any function computed on the final folded space will apply to all the collapsed subsets identified by the map corresponding to the succession of foldings. This means that in a deep model any partitioning of the last layer’s image-space is replicated in all input-space regions which are identified by the succession of foldings. Figure 2b offers an illustration of this replication property.

--- https://arxiv.org/abs/2405.20233 Grokfast: Accelerated Grokking by Amplifying Slow Gradients Jaerin Lee, Bong Gyun Kang, Kihoon Kim, Kyoung Mu Lee 2024-05-30 2024-06-24 [("doi","10.48550/arXiv.2405.20233")] ai/nn/cnn ai/nn/fully-connected ai/nn/rnn ai/nn/transformer ai/scaling/emergence/grokking

One puzzling artifact in machine learning dubbed grokking is where delayed generalization is achieved ten-folds of iterations after near perfect overfitting to the training data.

Focusing on the long delay itself on behalf of machine learning practitioners, our goal is to accelerate generalization of a model under the grokking phenomenon. By regarding a series of gradients of a parameter over training iterations as a random signal over time, we can spectrally decompose the parameter trajectories under gradient descent into two components: the fast-varying, overfitting-yielding component and the slow-varying, generalization-inducing component.

This analysis allows us to accelerate the grokking phenomenon more than 50× with only a few lines of code [Grokfast] that amplifies the slow-varying components of gradients.

The experiments show that our algorithm applies to diverse tasks involving images, languages, and graphs, enabling practical availability of this peculiar artifact of sudden generalization.

Our code is available at Github.

…5.1 Difference between Algorithm 2 and the Momentum in Typical Optimizers

The lines 7–8 of Algorithm 2 take a similar form to the momentum variable, which is frequently used in optimizers in deep learning frameworks. However, notable differences exist:

  1. Instead of using the scaled momentum as a parameter update, we use the smoothened gradient as a residual, which is added to the gradient before it is fed into the optimizer.

    Rather, the formula is more similar to Nesterov’s momentum; however, the filtering is applied before the optimizer, which is different from typical applications of Nesterov’s momentum such as NAdam [Dozat 2016] (2) The line 7–8 is applied to the gradients independently to the underlying optimizer. The optimizer can be of any type unless it is of the first-order gradient descent-based.

    Low-pass filtering the gradients g(t) has the same effect as filtering the post-optimizer parameter updates u(t) as mathematically explained in Appendix A with SGD and variants, and empirically proved in the previous sections with Adam [Kingma & Ba 2014] and AdamW [Loshchilov & Hutter 2018] optimizers.

…Q3. Synergistic effect with weight decay: Besides from our gradient filtering approach, the authors of Omnigrok (Liu et al 2022b) have suggested that the weight decay hyperparameter is a critical determinant of the grokking phenomenon. According to the report, the grokking phenomenon appears and even becomes faster when the weight decay becomes larger. We, therefore, conduct additional experiments to find out how these two approaches affect the model when applied together.

The results are summarized in Figure 7. Compared with the result from GROKFAST-MA with no weight decay (orange), applying the weight decay (red) generally yields even faster generalization. The maximum acceleration appears at weight decay = 0.01 with 3.72× faster generalization than GROKFAST-MA with no weight decay. We choose this result of 50.49× faster grokking to be our main demonstration in Figure 2a.

Interestingly, Figure 7 also reveals that applying the same weight decay with no GROKFAST-MA (brown) makes the training unstable. The results demonstrates that applying our gradient filtering and setting up a proper weight decay together gives synergistic benefits.

Figure 7: The acceleration effect of GROKFAST-MA is greatly enhanced when accompanied with appropriate value of weight decay. However, the weight decay alone not always yield beneficial results.

[I am not convinced this is not simply equivalent to momentum / weight decay. Tuning weight decay leads to drastic differences in grokking speed, and the speedup left after that is not much.]

--- https://arxiv.org/abs/2406.02550 Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks Tianyu He, Darshil Doshi, Aritra Das, Andrey Gromov 2024-06-04 2024-06-24 [("doi","10.48550/arXiv.2406.02550")] ai/nn/transformer ai/scaling/emergence/grokking reinforcement-learning/meta-learning

Large language models can solve tasks that were not present in the training set. This capability is believed to be due to in-context learning and skill composition.

In this work, we study the emergence of in-context learning and skill composition in a collection of modular arithmetic tasks. Specifically, we consider a finite collection of linear modular functions z = a x + b y [mod] p labeled by the vector (a, b) ∈ ℤp2. We use some of these tasks for pre-training and the rest for out-of-distribution testing.

We empirically show that a GPT-2-style transformer exhibits a transition from in-distribution to out-of-distribution generalization as the number of pre-training tasks increases. We find that the smallest model capable of out-of-distribution generalization requires two transformer blocks, while for deeper models, the out-of-distribution generalization phase is transient, necessitating early stopping.

Finally, we perform an interpretability study of the pre-trained models, revealing the highly structured representations in both phases; and discuss the learnt algorithm.

--- https://arxiv.org/abs/2406.03495 Grokking Modular Polynomials Darshil Doshi, Tianyu He, Aritra Das, Andrey Gromov 2024-06-05 2024-06-24 [("doi","10.48550/arXiv.2406.03495")] ai/nn/fully-connected ai/scaling/emergence/grokking

Neural networks readily learn a subset of the modular arithmetic tasks, while failing to generalize on the rest. This limitation remains unmoved by the choice of architecture and training strategies. On the other hand, an analytical solution for the weights of Multi-layer Perceptron (MLP) networks that generalize on the modular addition task is known in the literature.

In this work, we:

  1. extend the class of analytical solutions to include modular multiplication as well as modular addition with many terms. Additionally, we show that real networks trained on these datasets learn similar solutions upon generalization (grokking).

  2. We combine these “expert” solutions to construct networks that generalize on arbitrary modular polynomials.

  3. (3) We hypothesize a classification of modular polynomials into learnable and non-learnable via neural networks training; and provide experimental evidence supporting our claims.

--- https://arxiv.org/abs/2310.13061 To grok or not to grok: Disentangling generalization and memorization on corrupted algorithmic datasets Darshil Doshi, Aritra Das, Tianyu He, Andrey Gromov 2023-10-19 2024-06-24 [("doi","10.48550/arXiv.2310.13061")] ai/nn/fully-connected ai/nn/sparsity/pruning ai/nn/transformer ai/scaling/emergence/grokking

Robust generalization is a major challenge in deep learning, particularly when the number of trainable parameters is very large.

In general, it is very difficult to know if the network has memorized a particular set of examples or understood the underlying rule (or both). Motivated by this challenge, we study an interpretable model where generalizing representations are understood analytically, and are easily distinguishable from the memorizing ones. Namely, we consider multi-layer perceptron (MLP) and Transformer architectures trained on modular arithmetic tasks, where (ξ × 100%) of labels are corrupted (i.e. some results of the modular operations in the training set are incorrect).

We show that (1) it is possible for the network to memorize the corrupted labels and achieve 100% generalization at the same time; (2) the memorizing neurons can be identified and pruned, lowering the accuracy on corrupted data and improving the accuracy on uncorrupted data; (3) regularization methods such as weight decay, dropout and BatchNorm force the network to ignore the corrupted data during optimization, and achieve 100% accuracy on the uncorrupted dataset; and (4) the effect of these regularization methods is (“mechanistically”) interpretable: weight decay and dropout force all the neurons to learn generalizing representations, while BatchNorm de-amplifies the output of memorizing neurons and amplifies the output of the generalizing ones.

Finally, we show that in the presence of regularization, the training dynamics involve two consecutive stages: first, the network undergoes grokking dynamics reaching high train and test accuracy; second, it unlearns the memorizing representations, where the train accuracy suddenly jumps 100% → 100 (1 − ξ)%.

…Grokking MLP on modular addition dataset is remarkably robust to label corruption. Even without explicit regularization, the model can generalize to near 100% accuracy with sizable label corruption. In many cases, the network surprisingly manages to “correct” some corrupted examples, resulting in Inversion (ie. test accuracy > training accuracy). We emphasise that this is in stark contrast to the common belief that grokking requires explicit regularization. Adding regularization makes grokking more robust to label corruption, with stronger Inversion.

…Partial Inversion: In this phase, the network generalizes on the test data but only memorizes a fraction of the corrupted training data. Remarkably, the network predicts the “true” labels on the remaining corrupted examples. In other words, the network corrects a fraction of the corrupted data. Consequently, we see <100% train accuracy but near-100% test accuracy, resulting in a negative generalization gap (Figure 1b)!

We term this phenomenon Partial Inversion; where “inversion” refers to the test accuracy being higher than train accuracy.

Remarkably, partial inversion occurs even in the absence of any explicit regularization, but only when there is ample training data (leftmost panels in Figure 3a, Figure 3b).

Figure 3: Modular Addition phase diagrams with various regularization methods. A larger data fraction leads to more “correct” examples, leading to higher corruption-robustness. Increasing regularization, in the form of weight decay or dropout, enhances robustness to label corruption and facilitates better generalization.
--- https://thezvi.substack.com/p/on-claude-35-sonnet On Claude 3.5 Sonnet 2024-06-24 ai/nn/transformer/gpt/claude --- https://arxiv.org/abs/2207.14484 Adaptive Gradient Methods at the Edge of Stability Jeremy M. Cohen, Behrooz Ghorbani, Shankar Krishnan, Naman Agarwal, Sourabh Medapati, Michal Badura, Daniel Suo, David Cardoze, Zachary Nado, George E. Dahl, Justin Gilmer 2022-07-29 2024-06-24 [("doi","10.48550/arXiv.2207.14484")] ai/nn

Very little is known about the training dynamics of adaptive gradient methods like Adam in deep learning. In this paper, we shed light on the behavior of these algorithms in the full-batch and sufficiently large batch settings.

Specifically, we empirically demonstrate that during full-batch training, the maximum eigenvalue of the preconditioned Hessian typically equilibrates at a certain numerical value—the stability threshold of a gradient descent algorithm. For Adam with step size η and β1 = 0.9, this stability threshold is 38⁄η. Similar effects occur during minibatch training, especially as batch size grows.

Yet, even though adaptive methods train at the Adaptive Edge of Stability (AEoS), their behavior in this regime differs in a way from that of non-adaptive methods at the EoS. Whereas non-adaptive algorithms at the EoS are blocked from entering high-curvature regions of the loss landscape, adaptive gradient methods at the AEoS can keep advancing into high-curvature regions while adapting the preconditioner to compensate.

Our findings can serve as a foundation for the community’s future understanding of adaptive gradient methods in deep learning.

--- https://arxiv.org/abs/2311.04163 Outliers with Opposing Signals Have an Outsized Effect on Neural Network Optimization Elan Rosenfeld, Andrej Risteski 2023-11-07 2024-06-24 [("doi","10.48550/arXiv.2311.04163")] ai/nn ai/scaling/emergence/grokking

We identify a new phenomenon in neural network optimization which arises from the interaction of depth and a particular heavy-tailed structure in natural data. Our result offers intuitive explanations for several previously reported observations about network training dynamics. In particular, it implies a conceptually new cause for progressive sharpening and the edge of stability; we also highlight connections to other concepts in optimization and generalization including grokking, simplicity bias, and Sharpness-Aware Minimization.

Experimentally, we demonstrate the influence of paired groups of outliers in the training data with strong opposing signals: consistent, large magnitude features which dominate the network output throughout training and provide gradients which point in opposite directions. Due to these outliers, early optimization enters a narrow valley which carefully balances the opposing groups; subsequent sharpening causes their loss to rise rapidly, oscillating between high on one group and then the other, until the overall loss spikes.

We describe how to identify these groups, explore what sets them apart, and carefully study their effect on the network’s optimization and behavior. We complement these experiments with a mechanistic explanation on a toy example of opposing signals and a theoretical analysis of a two-layer linear network on a simple model.

Our finding enables new qualitative predictions of training behavior which we confirm experimentally.

It also provides a new lens through which to study and improve modern training practices for stochastic optimization, which we highlight via a case study of Adam versus SGD.

--- https://arxiv.org/abs/2306.13253 Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok Pascal Junior Tikeng Notsawo, Hattie Zhou, Mohammad Pezeshki, Irina Rish, Guillaume Dumas 2023-06-23 2024-06-24 [("doi","10.48550/arXiv.2306.13253")] ai/scaling/emergence/grokking

[cf. Žunkovič & Ilievski 2022, slingshot mechanism/catapulting, Jiang et al 2019] This paper focuses on predicting the occurrence of grokking in neural networks, a phenomenon in which perfect generalization emerges long after signs of overfitting or memorization are observed. It has been reported that grokking can only be observed with certain hyper-parameters. This makes it critical to identify the parameters that lead to grokking. However, since grokking occurs after a large number of epochs, searching for the hyper-parameters that lead to it is time-consuming.

In this paper, we propose a low-cost method to predict grokking without training for a large number of epochs. In essence, by studying the learning curve of the first few epochs, we show that one can predict whether grokking will occur later on. Specifically, if certain oscillations occur in the early epochs, one can expect grokking to occur if the model is trained for a much longer period of time.

We propose using the spectral signature of a learning curve derived by applying the Fourier transform to quantify the amplitude of low-frequency components to detect the presence of such oscillations.

We also present additional experiments aimed at explaining the cause of these oscillations and characterizing the loss landscape.

…Regarding catastrophic forgetting, there is a link between it when learning many tasks and the sharpness of the optimum for each task; so that the slightest update to one task makes the optimum escape from its basin of attraction for the other tasks. Mirzadeh et al 2020 & Mirzadeh et al 2021 formalize this.

…6. Summary and Discussion: We made the following observations:

  1. The memorization phase is characterized by a perturbed landscape, and it is separated from comprehension by a perturbed valley of bad solutions.

    Small data results in the slow progression of SGD in this region, causing a delay in generalization. During the comprehension phase, the loss and accuracy of training and validation show a periodic perturbation. Thilak et al 2022 named a related phenomenon the slingshot mechanism. We found that these perturbation points are characterized at the level of loss (respectively accuracy) by a sudden increase-decrease (respectively decrease-increase), at the level of the model weights by a sudden variation of the relative cosine similarity, and at the level of the loss landscape by obstacles.

    This last point goes against what Goodfellow & Vinyals 2015 observed, namely that a variety of state-of-the-art neural networks never encounter any substantial obstacles from initialization to solution. The slingshot mechanism also contradicts the idea that SGD spends most of its time exploring the flat region at the bottom of the valley surrounding a flat minimizer (Goodfellow & Vinyals 2015), since it goes with the model from confusion to the terminal phase of training, even after the model generalized, for many datasets.

  2. The Hessian of the grokking loss function is characterized by larger condition numbers, leading to a slower convergence of gradient descent.

    We observed that more than 98% of the total variance in the parameter space occurs in the first 2 PCA modes much smaller than the total number of weights, suggesting that the optimization dynamics are embedded in a low-dimensional space (Li et al 2018; Feng & Tu 2021). Moreover, the model remains in a lazy training regime (Chizat et al 2019; Berner et al 2021) most of the time, as the measure of cosine distance between the model weights from one training step to the next remains almost constant, except at the slingshot location.

From the point of view of the landscape, grokking seems a bit clearer: landscape geometry has an effect on generalization, and can allow in the early stages of training to know if the model will generalize or not by just looking at a microscopic quantity characteristic of that landscape like the empirical risk.

--- https://arxiv.org/abs/2210.15435 Grokking phase transitions in learning local rules with gradient descent Bojan Žunkovič, Enej Ilievski 2022-10-26 2024-06-24 [("doi","10.48550/arXiv.2210.15435")] ai/scaling/emergence/grokking cs/cellular-automaton

We discuss two solvable grokking (generalization beyond overfitting) models in a rule learning scenario.

We show that grokking is a phase transition and find exact analytic expressions for the critical exponents, grokking probability, and grokking time distribution. Further, we introduce a tensor-network map that connects the proposed grokking setup with the standard (perceptron) statistical learning theory and show that grokking is a consequence of the locality of the teacher model.

As an example, we analyse the cellular automata learning task, numerically determine the critical exponent and the grokking time distributions and compare them with the prediction of the proposed grokking model.

Finally, we numerically analyse the connection between structure formation and grokking.

…Our analytical results and numerical experiments show a large difference between 𝓁1 [weight decay] and 𝓁2 regularizations. The 𝓁1 regularized models have a larger grokking probability, shorter grokking time, shorter generalization time, and smaller effective dimension compared to 𝓁2 regularized models.

…Further, we show that spikes in the loss (which often occur during training of deep neural networks) correspond to latent space structural changes [phase shift] that can be beneficial or detrimental for generalization. Assuming this is the case also in deep networks, we can use the information about the latent space effective dimension to revert the model to a state before the spike or continue training with the current model.

…We find a similar distinction between the 𝓁1 and 𝓁2 regularizations as in the simple 1D case. At ε = 1 and λ1 = 0 the grokking probability vanishes for any value of λ2. In contrast, for λ1 > 0 the grokking probability can increase even above 90% for any D ≥ 2. Interestingly, the grokking probability increases with the dimensionality of the data distribution D. In fact, if we send D → ∞ the grokking probability becomes 100% if 0 < λ1 < ε. This result is a consequence of the concentration of measure of the uniform distribution ‘around the equator’. Similarly, by using the lower bound Equation 33 we estimate the best value of λ1 for any ε, D and N and find that the grokking probability maximum is always larger than 0.915. In contrast, in the λ1 = 0 case, the grokking probability becomes exponentially small with D, independent of the remaining parameter values. We make similar observations also if we relax the condition λ2 ≫ 1 (see Appendix B).

--- https://arxiv.org/abs/1912.02178 Fantastic Generalization Measures and Where to Find Them Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, Samy Bengio 2019-12-04 2024-06-24 [("doi","10.48550/arXiv.1912.02178")] ai/nn/cnn

Generalization of deep networks has been of great interest in recent years, resulting in a number of theoretically and empirically motivated complexity measures. However, most papers proposing such measures study only a small set of models, leaving open the question of whether the conclusion drawn from those experiments would remain valid in other settings.

We present the first large scale study of generalization in deep networks. We investigate more then 40 complexity measures taken from both theoretical bounds and empirical studies. We train over 10,000 convolutional networks by systematically varying commonly used hyperparameters.

Hoping to uncover potentially causal relationships between each measure and generalization, we analyze carefully controlled experiments and show surprising failures of some measures as well as promising measures for further research.

…Sharpness-based measures such as PAC-Bayesian bounds (McAllester 1999) bounds and sharpness measure proposed by Keskar et al 2016 perform the best overall and seem to be promising candidates for further research.

Measures related to the optimization procedures such as the gradient noise and the speed of the optimization can be predictive of generalization.

…5. Conclusion: We conducted large scale experiments to test the correlation of different measures with the generalization of deep models and propose a framework to better disentangle the cause of correlation from spurious correlation.

We confirmed the effectiveness of the PAC-Bayesian bounds through our experiments and corroborate it as a promising direction for cracking the generalization puzzle. Further, we provide an extension to existing PAC-Bayesian bounds that consider the importance of each parameter. We also found that several measures related to optimization are surprisingly predictive of generalization and worthy of further investigation.

On the other hand, several surprising failures about the norm-based measures were uncovered. In particular, we found that regularization that introduces randomness into the optimization can increase various norm of the models and spectral complexity related norm-based measures are unable to capture generalization—in fact, most of them are negatively correlated.

Our experiments demonstrate that the study of generalization measure can be misleading when the number of models studied is small and the metric of quantifying the relationship is not carefully chosen. We hope this work will incentivize more rigorous treatment of generalization measures in future work.

--- https://dl.acm.org/doi/pdf/10.1145/307400.307435 2024-06-24 statistics/bayes --- https://www.nytimes.com/2024/06/24/magazine/earth-geomicrobiology-microbes.html Earth’s Mysterious, Deep-Dwelling Microbes We’re Only Starting to Understand 2024-06-24 genetics/microbiome --- https://arxiv.org/abs/2006.06958 Understanding the Role of Training Regimes in Continual Learning Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, Hassan Ghasemzadeh 2020-06-12 2024-06-24 [("doi","10.48550/arXiv.2006.06958")] reinforcement-learning/meta-learning/continual-learning

Catastrophic forgetting affects the training of neural networks, limiting their ability to learn multiple tasks sequentially. From the perspective of the well established plasticity-stability dilemma, neural networks tend to be overly plastic, lacking the stability necessary to prevent the forgetting of previous knowledge, which means that as learning progresses, networks tend to forget previously seen tasks. This phenomenon, coined in the continual learning literature, has attracted much attention lately, and several families of approaches have been proposed with different degrees of success. However, there has been limited prior work extensively analyzing the impact that different training regimes—learning rate, batch size, regularization method—can have on forgetting.

In this work, we depart from the typical approach of altering the learning algorithm to improve stability. Instead, we hypothesize that the geometrical properties of the local minima found for each task play an important role in the overall degree of forgetting.

In particular, we study the effect of dropout, learning rate decay, and batch size on forming training regimes that widen the tasks’ local minima and consequently, on helping it not to forget catastrophically.

Our study provides practical insights to improve stability via simple yet effective techniques that outperform alternative baselines.

--- https://arxiv.org/abs/1412.6544#google Qualitatively characterizing neural network optimization problems Ian J. Goodfellow, Oriol Vinyals, Andrew M. Saxe 2014-12-19 2024-06-24 [("doi","10.48550/arXiv.1412.6544")] ai/nn

Training neural networks involves solving large-scale non-convex optimization problems. This task has long been believed to be extremely difficult, with fear of local minima and other obstacles motivating a variety of schemes to improve optimization, such as unsupervised pretraining. However, modern neural networks are able to achieve negligible training error on complex tasks, using only direct training with stochastic gradient descent.

We introduce a simple analysis technique to look for evidence that such networks are overcoming local optima.

We find that, in fact, on a straight path from initialization to solution, a variety of state-of-the-art neural networks never encounter any obstacles.

--- https://arxiv.org/abs/2105.04026 The Modern Mathematics of Deep Learning Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen 2021-05-09 2024-06-24 [("doi","10.1017/9781009025096.002")] ai/nn

We describe the new field of mathematical analysis of deep learning.

This field emerged around a list of research questions that were not answered within the classical framework of learning theory. These questions concern: the outstanding generalization power of overparametrized neural networks, the role of depth in deep architectures, the apparent absence of the curse of dimensionality, the surprisingly successful optimization performance despite the non-convexity of the problem, understanding what features are learned, why deep architectures perform exceptionally well in physical problems, and which fine aspects of an architecture affect the behavior of a learning task in which way.

We present an overview of modern approaches that yield partial answers to these questions. For selected approaches, we describe the main ideas in more detail.

--- https://arxiv.org/abs/1812.07956 On Lazy Training in Differentiable Programming Lenaic Chizat, Edouard Oyallon, Francis Bach 2018-12-19 2024-06-24 [("doi","10.48550/arXiv.1812.07956")] ai/nn/cnn ai/scaling

In a series of recent theoretical works, it was shown that strongly over-parameterized neural networks trained with gradient-based methods could converge exponentially fast to zero training loss, with their parameters hardly varying.

In this work, we show that this “lazy training” phenomenon is not specific to over-parameterized neural networks, and is due to a choice of scaling, often implicit, that makes the model behave as its linearization around the initialization, thus yielding a model equivalent to learning with positive-definite kernels. Through a theoretical analysis, we exhibit various situations where this phenomenon arises in non-convex optimization and we provide bounds on the distance between the lazy and linearized optimization paths.

Our numerical experiments bring a critical note, as we observe that the performance of commonly used non-linear deep convolutional neural networks in computer vision degrades when trained in the lazy regime.

This makes it unlikely that “lazy training” is behind the many successes of neural networks in difficult high dimensional tasks.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7936325/ The inverse variance–flatness relation in stochastic gradient descent is critical for finding flat minima Yu Feng, Yuhjai Tu 2021-03-02 2024-06-24 [("doi","10.1073/pnas.2015617118")] ai/nn

One key ingredient in deep learning is the stochastic gradient descent (SGD) algorithm, which allows neural nets to find generalizable solutions at flat minima of the high-dimensional loss function. However, it is unclear how SGD finds flat minima.

Here, by analyzing SGD-based learning dynamics together with the loss function landscape, we discovered a robust inverse relation between weight fluctuation and loss landscape flatness opposite to the fluctuation-dissipation relation in physics. The reason for this inverse relationship is that the SGD noise strength and its correlation time depend inversely on the landscape flatness.

Essentially, SGD serves as a landscape-dependent annealing algorithm to search for flat minima. These theoretical insights can lead to more efficient algorithms, eg. for preventing catastrophic forgetting.

[Keywords: statistical physics, machine learning, stochastic gradient descent, loss landscape, generalization]

--- https://arxiv.org/abs/2010.01412#google Sharpness-Aware Minimization (SAM) for Efficiently Improving Generalization Pierre Foret, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur 2020-10-03 2024-06-24 [("doi","10.48550/arXiv.2010.01412")] ai/nn/cnn

In today’s heavily overparameterized models, the value of the training loss provides few guarantees on model generalization ability. Indeed, optimizing only the training loss value, as is commonly done, can easily lead to suboptimal model quality.

Motivated by prior work connecting the geometry of the loss landscape and generalization, we introduce a novel, effective procedure for instead simultaneously minimizing loss value and loss sharpness. In particular, our procedure, Sharpness-Aware Minimization (SAM), seeks parameters that lie in neighborhoods having uniformly low loss; this formulation results in a min-max optimization problem on which gradient descent can be performed efficiently.

We present empirical results showing that SAM improves model generalization across a variety of benchmark datasets (eg. CIFAR-10, CIFAR-100, ImageNet, finetuning tasks) and models, yielding novel state-of-the-art performance for several. Additionally, we find that SAM natively provides robustness to label noise on par with that provided by state-of-the-art procedures that specifically target learning with noisy labels.

We open source our code at https://github.com/google-research/sam.

--- https://arxiv.org/abs/2404.07965#microsoft Rho-1: Not All Tokens Are What You Need Zhenghao Lin, Zhibin Gou, Yeyun Gong, Xiao Liu, Yelong Shen, Ruochen Xu, Chen Lin, Yujiu Yang, Jian Jiao, Nan Duan, Weizhu Chen 2024-04-11 2024-06-24 [("doi","10.48550/arXiv.2404.07965")] reinforcement-learning/exploration/active-learning/data-pruning

[cf. RHO-LOSS, Sorscher et al 2022] Previous language model pre-training methods have uniformly applied a next-token prediction loss to all training tokens.

Challenging this norm, we posit that “Not all tokens in a corpus are equally important for language model training”. Our initial analysis examines token-level training dynamics of language model, revealing distinct loss patterns for different tokens.

Leveraging these insights, we introduce a new language model called Rho-1. Unlike traditional LMs that learn to predict every next token in a corpus, Rho-1 employs Selective Language Modeling (SLM), which selectively trains on useful tokens that aligned with the desired distribution. This approach involves scoring pretraining tokens using a reference model, and then training the language model with a focused loss on tokens with higher scores.

When continual pretraining on 15B OpenWebMath corpus, Rho-1 yields an absolute improvement in few-shot accuracy of up to 30% in 9 math tasks. After fine-tuning, Rho-1-1B and 7B achieved state-of-the-art results of 40.6% and 51.8% on MATH dataset, respectively—matching DeepSeekMath with only 3% of the pretraining tokens. Furthermore, when pretraining on 80B general tokens, Rho-1 achieves 6.8% average enhancement across 15 diverse tasks, increasing both efficiency and performance of the language model pre-training.

Figure 1: We continual pretrain 1B and 7B LMs with 15B OpenWebMath tokens. RHO-1 is trained with our proposed Selective Language Modeling (SLM) [training a reference model on a curated, high-quality dataset. This model then assesses the loss of each token within the pretraining corpus. In the final phase, we train the language model selectively, focusing on tokens with high excess loss between the training and reference model.], while baselines are trained using causal language modeling. SLM improves average few-shot accuracy on GSM8k and MATH by over 16%, achieving the baseline performance 5–10× faster.
Figure 2: Upper: Even an extensively filtered pretraining corpus contains token-level noise. Left: Previous Causal Language Modeling (CLM) trains on all tokens. Right: Our proposed Selective Language Modeling (SLM) selectively applies loss on those useful and clean tokens.

…To explore how language models learn at the token level, we initially examined training dynamics, particularly how the token-level loss evolves during usual pretraining. In §2.1, we evaluated the model’s token perplexity at different checkpoints and categorized tokens into different types. Our findings reveal that large loss reduction is limited to a select group of tokens. Many tokens are “easy tokens” that are already learned, and some are “hard tokens” that exhibit variable losses and resist convergence. These tokens can lead to numerous ineffective gradient updates.

Figure 3: The loss of 4 categories of tokens during pretraining. (a) shows the loss of H → H, L → H, H → L, and L → L tokens during pretraining. (b, c) show 3 cases of fluctuating tokens’ loss in L → L and H → H during pretraining, respectively.

…2.1 Not All Tokens Are Equal: Training Dynamics of Token Loss

Our investigation begins with a critical look at how individual tokens’ losses evolve during standard pre-training. We continue pre-training TinyLlama-1B with 15B tokens from OpenWebMath, saving checkpoints after every 1B tokens. We then evaluate token-level loss at these intervals using the validation set of ~320,000 tokens. Figure 3(a) reveals a striking pattern: tokens fall into 4 categories based on their loss trajectory—persistent high loss (H → H), increasing loss (L → H), decreasing loss (H → L), and consistent low loss (L → L). For further details on these categories, see §D.1.

Our analysis uncovers that a mere 26% of tokens show a notable loss reduction (H → L), while the majority (51%) remain in the L → L category, indicating they have already been learned. Interestingly, 11% of the tokens are persistently challenging (H → H), likely due to high aleatoric uncertainty [Hüllermeier and Waegeman 2021]. Additionally, 12% of tokens experience an unexpected loss increase (L → H) during training.

Our second observation is that a substantial number of token losses exhibit persistent fluctuations, and resist convergence. The loss of many L → L and H → H tokens, as depicted in Figure 3b & 3c, show high variance during training. In §D.2, we visualize and analyze the content of these tokens and find that many of them are noisy, which is consistent with our hypothesis. Consequently, we learn that the loss associated with each token during training does not decrease smoothly like the overall loss; instead, there is a complex training dynamic among different tokens. If we can select the appropriate tokens for the model to focus on during training, we may be able to stabilize the trajectory of the model’s training and enhance its efficiency.

[online selection] …In practice, token selection can be implemented by ranking the tokens in a batch according to their excess loss and using only the top k% of tokens for training [eg. in GANs]. This process eliminates the loss for undesired tokens without incurring additional costs during pretraining, making our approach both efficient and easily integrated.

[Just dropping tokens is going to be very limited in its gains, however. Need to drop entire datapoints for efficiency.]

Figure 6: The dynamics of pretraining loss and downstream loss. (a, c) represent the loss of tokens selected/unselected by SLM during pretraining in both SLM and CLM methods, while (b) represents the loss of the SLM and CLM methods on MetaMath (Yu et al 2024). We tested the above results through the process of pretraining with a total of 4 billion tokens.

…What Tokens are Selected with SLM? We aim to analyze the tokens selected by the SLM method in pretraining to further explore its working mechanism. To this end, we visualize the token selection process during the training of RHO-1 using the OpenWebMath. In §G.1’s Figure 13, we have highlighted in blue the tokens that were retained during actual pretraining. We observe that the majority of tokens chosen by the SLM method are closely related to mathematics, effectively training the model on the parts of the original corpus that are pertinent to mathematical content.

Figure 8: The PPL of tokens selected by different checkpoint. We test the PPL of the tokens selected at 2B, 5B, 8B, 11B, and 14B.

Furthermore, we investigated the differences in token filtering across various checkpoints during the training process and tested the perplexity of these tokens on different checkpoints. As illustrated in Figure 8, we found that the tokens selected by later checkpoints tend to have higher perplexity towards the later stages of training and lower perplexity in the earlier stages. This may suggest that the model first optimizes tokens with a larger learnable space, thereby increasing learning efficiency. Moreover, we noticed a sample-wise “double descent” (Nakkiran et al 2021) on the loss of selected tokens, where the select token’s perplexity initially increases before decreases. This might be an effect of selecting tokens based on excess loss, targeting those most in need at each checkpoint.

Effect of Token Select Ratio: We investigate the impact of token selecting ratios of the SLM. Generally, the selecting ratio is defined by heuristic rules, similar to the approach previously employed in the training of Masked Language Models (MLMs) (Devlin et al 2019, Liu et al 2019). As shown in Figure 9, the selected tokens is suitable for accounting for about 60% of the original tokens.

--- https://arxiv.org/abs/2402.07625 Autonomous Data Selection with Language Models for Mathematical Texts Yifan Zhang, Yifan Luo, Yang Yuan, Andrew Chi-Chih Yao 2024-02-12 2024-06-24 [("doi","10.48550/arXiv.2402.07625")] ai/nn/transformer/gpt math reinforcement-learning/exploration/active-learning/data-pruning

To improve language models’ proficiency in mathematical reasoning via continual pretraining, we introduce a novel strategy that leverages base language models for autonomous data selection.

Departing from conventional supervised fine-tuning or trained classifiers with human-annotated data, our approach Autonomous Data Selection (AutoDS) uses meta-prompted language models as zero-shot verifiers to evaluate and select high-quality mathematical content autonomously.

To demonstrate the efficacy of our method, we continuously pretrained a 7B-parameter language model on our curated dataset, achieving substantial improvements in downstream performance on the MATH, GSM8K, and BIG-Bench Hard (BBH) tasks with a token amount reduced by orders of magnitude compared to previous continual pretraining works.

Our method showcases a 2× increase in pretraining token efficiency compared to state-of-the-art baselines, underscoring the potential of our approach in enhancing models’ mathematical reasoning capabilities.

The AutoMathText dataset is available at https://huggingface.co/datasets/math-ai/AutoMathText. The code is available at Github.

--- https://arxiv.org/abs/2403.02241 Neural Redshift: Random Networks are not Random Functions Damien Teney, Armand Nicolicioiu, Valentin Hartmann, Ehsan Abbasnejad 2024-03-04 2024-06-24 [("doi","10.48550/arXiv.2403.02241")] ai/nn/fully-connected ai/nn/transformer/gpt/2

Background: Our understanding of the generalization capabilities of neural networks (NNs) is still incomplete. Prevailing explanations are based on implicit biases of gradient descent (GD) but they cannot account for the capabilities of models from gradient-free methods nor the simplicity bias recently observed in untrained networks. This paper seeks other sources of generalization in NNs.

Methods: To understand the inductive biases provided by architectures independently from GD, we examine untrained, random-weight networks.

Results: Even simple MLPs show strong inductive biases: uniform sampling in weight space yields a very biased distribution of functions in terms of complexity.

But unlike common wisdom, NNs do not have an inherent “simplicity bias”. This property depends on components such as ReLUs, residual connections, and layer normalizations.

Alternative architectures can be built with a bias for any level of complexity [principally by changing the size of weight initialization]. Transformers also inherit all these properties from their building blocks.

Conclusions: We provide a fresh explanation for the success of deep learning independent from gradient-based training. It points at promising avenues for controlling the solutions implemented by trained models.

--- https://arxiv.org/abs/2403.00690 Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents (NetPlay) Dominik Jeurissen, Diego Perez-Liebana, Jeremy Gow, Duygu Cakmak, James Kwan 2024-03-01 2024-06-25 [("doi","10.48550/arXiv.2403.00690")] ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/nethack

[code] Large Language Models (LLMs) have shown great success as high-level planners for zero-shot game-playing agents. However, these agents are primarily evaluated on Minecraft, where long-term planning is relatively straightforward. In contrast, agents tested in dynamic robot environments face limitations due to simplistic environments with only a few objects and interactions.

To fill this gap in the literature, we present NetPlay, the first LLM-powered zero-shot agent for the challenging rogue-like NetHack. NetHack is a particularly challenging environment due to its diverse set of items and monsters, complex interactions, and many ways to die.

NetPlay uses an architecture designed for dynamic robot environments, modified for NetHack. Like previous approaches, it prompts the LLM to choose from predefined skills and tracks past interactions to enhance decision-making. Given NetHack’s unpredictable nature, NetPlay detects important game events to interrupt running skills, enabling it to react to unforeseen circumstances.

While NetPlay demonstrates considerable flexibility and proficiency in interacting with NetHack’s mechanics, it struggles with ambiguous task descriptions and a lack of explicit feedback.

Our findings demonstrate that NetPlay performs best with detailed context information, indicating the necessity for dynamic methods in supplying context information for complex games such as NetHack.

--- https://arxiv.org/abs/2312.07540 diff History for Neural Language Agents Ulyana Piterbarg, Lerrel Pinto, Rob Fergus 2023-12-12 2024-06-25 [("doi","10.48550/arXiv.2312.07540")] ai/nn/tokenization reinforcement-learning/model/decision-transformer reinforcement-learning/nethack

Neural Language Models (LMs) offer an exciting solution for general-purpose embodied control. However, a key technical issue arises when using an LM-based controller: environment observations must be converted to text, which coupled with history, results in long and verbose textual prompts. As a result, prior work in LLM agents is limited to restricted domains with small observation size as well as minimal needs for interaction history or instruction tuning.

In this paper, we introduce diff history, a simple and highly effective solution to these issues. By applying the Unix diff command on consecutive text observations in the interaction histories used to prompt LM policies, we can both abstract away redundant information and focus the content of textual inputs on the salient changes in the environment.

On NetHack, an unsolved video game that requires long-horizon reasoning for decision-making, LMs tuned with diff history match state-of-the-art performance for neural agents while needing 1,800× fewer training examples compared to prior work. Even on the simpler BabyAI-Text environment with concise text observations, we find that although diff history increases the length of prompts, the representation it provides offers a 25% improvement in the efficiency of low-sample instruction tuning.

Further, we show that diff history scales favorably across different tuning dataset sizes.

We open-source our code and data at https://diffhistory.github.io/.

--- https://arxiv.org/abs/2310.00166#facebook Motif: Intrinsic Motivation from Artificial Intelligence Feedback Martin Klissarov, Pierluca D’Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff 2023-09-29 2024-06-25 [("doi","10.48550/arXiv.2310.00166")] reinforcement-learning/meta-learning reinforcement-learning/nethack reinforcement-learning/preference-learning

Exploring rich environments and evaluating one’s actions without prior knowledge is immensely challenging. In this paper, we propose Motif, a general method to interface such prior knowledge from a Large Language Model (LLM) with an agent.

Motif is based on the idea of grounding LLMs for decision-making without requiring them to interact with the environment: it elicits preferences from an LLM over pairs of captions to construct an intrinsic reward, which is then used to train agents with reinforcement learning.

We evaluate Motif’s performance and behavior on the challenging, open-ended and procedurally-generated NetHack game. Surprisingly, by only learning to maximize its intrinsic reward, Motif achieves a higher game score than an algorithm directly trained to maximize the score itself. When combining Motif’s intrinsic reward with the environment reward, our method outperforms existing approaches and makes progress on tasks where no advancements have ever been made without demonstrations.

Finally, we show that Motif mostly generates intuitive human-aligned behaviors which can be steered easily through prompt modifications, while scaling well with the LLM size and the amount of information given in the prompt.

--- https://arxiv.org/abs/2406.11233 Probing the Decision Boundaries of In-context Learning in Large Language Models Siyan Zhao, Tung Nguyen, Aditya Grover 2024-06-17 2024-06-25 [("doi","10.48550/arXiv.2406.11233")] ai/nn/adversarial ai/nn/fully-connected ai/nn/sparsity/low-precision ai/nn/transformer/gpt/4/nonfiction ai/scaling ai/tabular reinforcement-learning/exploration/active-learning reinforcement-learning/meta-learning

[Twitter] In-context learning is a key paradigm in large language models (LLMs) that enables them to generalize to new tasks and domains by simply prompting these models with a few exemplars without explicit parameter updates. Many attempts have been made to understand in-context learning in LLMs as a function of model scale, pretraining data, and other factors.

In this work, we propose a new mechanism to probe and understand in-context learning from the lens of decision boundaries for in-context binary classification. Decision boundaries are straightforward to visualize and provide important information about the qualitative behavior of the inductive biases of standard classifiers.

To our surprise, we find that the decision boundaries learned by current LLMs in simple binary classification tasks are often irregular and non-smooth, regardless of linear separability in the underlying task. This paper investigates the factors influencing these decision boundaries and explores methods to enhance their generalizability.

We assess various approaches, including training-free and fine-tuning methods for LLMs, the impact of model architecture, and the effectiveness of active prompting techniques for smoothing decision boundaries in a data-efficient manner.

Our findings provide a deeper understanding of in-context learning dynamics and offer practical improvements for enhancing robustness and generalizability of in-context learning.

Figure 1: Decision boundaries of LLMs and traditional machine learning models on a linearly separable binary classification task. The background colors represent the model’s predictions, while the points represent the in-context or training examples. LLMs exhibit non-smooth decision boundaries compared to the classical models. See Appendix E for model hyperparameters.

…In contrast to existing approaches, our study introduces a fresh perspective by viewing in-context learning in large language models (LLMs) as a unique machine learning algorithm. This conceptual framework enables us to leverage a classical tool from machine learning—analyzing decision boundaries in binary classification tasks. By visualizing these decision boundaries, both in linear and non-linear contexts, we gain invaluable insights into the performance and behavior of in-context learning. This method allows us to probe the inductive biases and generalization capabilities of LLMs and offers a unique assessment of the robustness of their in-context learning performance. Consequently, this approach provides a comprehensive means to qualitatively analyze the underlying mechanisms that govern in-context learning and suggest ways to improve its performance in LLMs.

To our surprise, we found that the recent LLMs struggle to provide smooth decision boundaries in all the classification tasks we considered, regardless of the model size, the number and ordering of in-context examples, and semantics of the label format. This issue persists even for simple binary linear classification tasks, where classical methods such as SVM can easily achieve smooth boundaries with fewer examples as shown in Figure 1.

This observation raises questions about the factors that influence the decision boundaries of LLMs.

To explore this, we experimented with a series of open-source LLMs including LLaMA-2-7b, LLaMA-2-13b, Llama3-8b (Touvron et al 2023), Mistral-7b (Jiang et al 2023), pruned LLaMA-2-1.3b (Xia et al 2023), as well as state-of-the-art closed-source LLMs GPT-4o and GPT-3-Turbo (Brown et al 2020).

We then explore methods to smooth the decision boundary, including fine-tuning and adaptive prompting strategies.

Our work provides valuable practical insights for understanding and improving in-context learning in LLMs through a new perspective. Our contributions can be summarized as follows:

…As the number of in-context examples increases, LLMs can achieve high accuracy on linear and non-linear classification tasks. But how reliable are these in-context classifiers? We probe their decision boundaries to find out.

By visualizing the decision boundaries, we show that SOTA LLMs, ranging from 1B to large closed-source models such as GPT-3.5-turbo and GPT-4o, all exhibit different non-smooth, irregular decision boundaries, even on simple linearly separable tasks.

Figure 2: Visualizations of decision boundaries for various LLMs, ranging in size 1.3–13B, on a linearly separable binary classification task. The in-context data points are shown as scatter points and the colors indicate the label determined by each model. These decision boundaries are obtained using 128 in-context examples. The visualization highlights that the decision boundaries of these language models are not smooth.

How do these irregularities arise? We study various factors that impact decision boundary smoothness in LLMs, including in-context example count, quantization levels, label semantics & examples order. Then, we identify methods to improve the smoothness of the boundaries.

First, increasing in-context examples does not guarantee smoother decision boundaries. While classification accuracy improves with more in-context examples, the decision boundary remains fragmented.

Decision boundaries are sensitive to label names, example order and quantization.

Shuffling in-context examples and labels changes the model’s decision boundaries, suggesting they depend on LLM’s semantic prior knowledge of the labels and is not permutation invariant.

Reducing precision from 8 → 4-bit impacts areas near the boundary with high uncertainties. Varying quantization levels can flip LLM decisions in these uncertain regions.

Can we improve decision boundary smoothness in LLMs through training? We show that fine-tuning on simple linearly separable tasks can improve the smoothness of decision boundaries and generalize to more complex non-linear, multi-class tasks, enhancing robustness.

Further, we show that fine-tuning the token embedding and attention layers can lead to smoother decision boundaries. However fine-tuning the linear prediction head alone does not improve smoothness.

We also explore uncertainty-aware active learning. By adding labels for the most uncertain points to the in-context dataset, we can smoothen the decision boundary more efficiently.

Figure 11: Comparison of active learning and random sampling methods. We plot the decision boundaries and uncertainty plot across different number of in-context examples 32–256, where the in-context examples are gradually added to the prompt using active or random methods. The test set accuracies is plotted in the titles. Active sampling gives smoother decision boundary and the uncertain points lie on it.

…As shown in Figure 11, this uncertainty-aware active sampling method results in a smoother decision boundary over iterations compared to random sampling. The iterative refinement enhances the model’s generalization capabilities, leading to higher test set accuracies and greater sample efficiency, requiring fewer additional in-context examples to achieve performance gains. These findings indicate that leveraging the LLM’s uncertainty measurements is valuable for selecting new in-context examples in resource-constrained settings where labeled data is scarce. We show more examples in Appendix F.

Lastly, we explore the effect of language pretraining. Compared to pretrained LLMs, we find that transformers trained from scratch on synthetic classification tasks can learn smooth in-context decision boundaries for unseen classification problems.

--- https://www.biorxiv.org/content/10.1101/2024.06.06.597716.full Training Compute-Optimal Protein Language Models Xingyi Cheng, Bo Chen, Pan Li, Jing Gong, Jie Tang, Le Song 2024-06-09 2024-06-25 [("doi","10.1101/2024.06.06.597716")] ai/nn/transformer/alphafold ai/scaling

We explore optimally training protein language models, an area of substantial interest in biological research where guidance on best practices is limited. Most models are trained with extensive compute resources until performance gains plateau, focusing primarily on increasing model sizes rather than optimizing the efficient compute frontier that balances performance and compute budgets.

Our investigation is grounded in a massive dataset consisting of 939 million protein sequences. We trained over 300 models ranging from 0.003–10.7 billion parameters on 5–200 billion unique tokens, to investigate the relations between model sizes, training token numbers, and objectives.

First, we observed the effect of diminishing returns for the Causal Language Model (CLM) and that of overfitting for the Masked Language Model (MLM) when repeating the commonly used Uniref database.

To address this, we included metagenomic protein sequences in the training set to increase the diversity and avoid the plateau or overfitting effects.

Second, we obtained the scaling laws of CLM and MLM on Transformer, tailored to the specific characteristics of protein sequence data.

Third, we observe a transfer scaling phenomenon from CLM to MLM, further demonstrating the effectiveness of transfer through scaling behaviors based on estimated Effectively Transferred Tokens.

Finally, to validate our scaling laws, we compare the large-scale versions of ESM-2 and PROGEN2 on downstream tasks, encompassing evaluations of protein generation as well as structure & function-related tasks, all within less or equivalent pre-training compute budgets.

…We focus on the best practices, which include revisiting datasets, optimization objectives, and model parameters as key factors. Our goal is to investigate an optimal training scheme for protein language models given predetermined compute budgets. Our core findings are as follows:

--- https://x.com/fabianstelzer/status/1805326248261910552 Fabian Stelzer 2024-06-25 ai/nn/transformer/gpt/claude fiction/humor --- https://quanticfoundry.com/2024/05/21/strategy-decline/ Gamers Have Become Less Interested in Strategic Thinking and Planning 2024-06-25 sociology/technology --- https://www.vox.com/future-perfect/351638/bureau-of-intelligence-and-research-inr-guidance-explained Why the State Department’s INR intelligence agency may be the best in DC 2024-06-25 politics statistics/prediction --- https://en.wikipedia.org/wiki/Bureau_of_Intelligence_and_Research Bureau of Intelligence and Research (INR) 2024-06-25 politics statistics/prediction --- https://www.cdc.gov/trendstatement/pdf/trendstatement_AJPH_Mar2004_Trendstatement.pdf Improving the Reporting Quality of Nonrandomized Evaluations of Behavioral and Public Health Interventions: The TREND Statement Des Jarlais 2004 2024-01-01 statistics/meta-analysis --- https://spectrum.library.concordia.ca/id/eprint/36253/1/2010_Mining_Writeprints_from_Anonymous_E-mails.pdf Mining writeprints from anonymous e-mails for forensic investigation Iqbal 2010 2024-01-01 cs/security statistics/stylometry --- https://jamanetwork.com/journals/jama/fullarticle/194989 Effects of Editorial Peer Review: A Systematic Review Jefferson 2002 2024-01-01 statistics/peer-review --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4278746/ Predictors and moderators of agreement between clinical and research diagnoses for children and adolescents Amanda Jensen-Doss, Eric A. Youngstrom, Jennifer Kogos Youngstrom, Norah C. Feeny, Robert L. Findling 2014 2024-01-01 [("doi","10.1037/a0036657")] psychiatry statistics/bias

Objective: Diagnoses play an important role in treatment planning and monitoring, but extensive research has shown low agreement between clinician-generated diagnoses and those from structured diagnostic interviews. However, most prior studies of agreement have not used research diagnoses based on gold standard methods, and researchers need to identify characteristics of diagnostically challenging clients. This study examined agreement between youth diagnoses generated through the research-based LEAD (Longitudinal, Expert, and All Data) standard to clinician diagnoses.

Method: Participants were 391 families seeking outpatient community mental health services for youths ages 6–18 (39.1% female, 88.2% African American). Youths and parents completed research interviews and clinic diagnoses were extracted from clinic records.

LEAD diagnoses synthesized results of the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime (KSADS-PL) and the youth’s developmental, family, and psychiatric history.

Results: Agreement between the LEAD and chart diagnoses was low, not exceeding “poor” agreement for most diagnostic categories (κs = 0.10–0.46, median = 0.37). Disagreement was largely driven by missed diagnoses, although clinicians also did assign extra diagnoses for some clients.

Fewer diagnostic errors occurred when the youth’s clinical picture was more clear (eg. high or low symptom severity, lower comorbidity), when the youth was older, when the family was higher functioning, and when the parent had more depression. However, youth and family characteristics explained very little of the variability in diagnostic errors.

Conclusions: support the need to investigate strategies to improve clinician diagnostic accuracy.

--- https://ai.meta.com/blog/meta-llama-3/ 2024-06-25 ai/nn/transformer/gpt --- https://www.biorxiv.org/content/10.1101/2022.07.20.500902.full#facebook Evolutionary-scale prediction of atomic level protein structure with a language model Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives 2022-12-21 2024-06-25 [("doi","10.1101/2022.07.20.500902")] ai/nn/transformer/alphafold ai/scaling

Artificial intelligence has the potential to open insight into the structure of proteins at the scale of evolution. It has only recently been possible to extend protein structure prediction to two hundred million cataloged proteins. Characterizing the structures of the exponentially growing billions of protein sequences revealed by large-scale gene sequencing experiments would necessitate a breakthrough in the speed of folding.

Here we show that direct inference of structure from primary sequence using a large language model enables an order of magnitude speed-up in high-resolution structure prediction. Leveraging the insight that language models learn evolutionary patterns across millions of sequences, we train models up to 15b parameters, the largest language model of proteins to date. As the language models are scaled they learn information that enables prediction of the three-dimensional structure of a protein at the resolution of individual atoms.

This results in prediction that is up to 60× faster than state-of-the-art while maintaining resolution and accuracy. Building on this, we present the ESM Metagenomic Atlas. This is the first large-scale structural characterization of metagenomic proteins, with more than 617 million structures.

The atlas reveals more than 225 million high-confidence predictions, including millions whose structures are novel in comparison with experimentally determined structures, giving an unprecedented view into the vast breadth and diversity of the structures of some of the least understood proteins on earth.

--- https://x.com/vgr/status/1125119314611171328 Venkatesh Rao 2024-06-25 design psychology/writing sociology/technology --- https://arxiv.org/abs/1302.4389 Maxout Networks Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio 2013-02-18 2024-06-25 [("doi","10.48550/arXiv.1302.4389")] ai/nn/cnn

We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout.

We define a simple new model called maxout (so named because its output is the max of a set of inputs, and because it is a natural companion to dropout) designed to both facilitate optimization by dropout and improve the accuracy of dropout’s fast approximate model averaging technique.

We empirically verify that the model successfully accomplishes both of these tasks: we use maxout and dropout to demonstrate state-of-the-art classification performance on 4 benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN.

--- https://arxiv.org/abs/2303.03846#google Larger language models do in-context learning differently Jerry Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, Tengyu Ma 2023-03-07 2024-06-25 [("doi","10.48550/arXiv.2303.03846")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/instruction-tuning ai/nn/transformer/gpt/palm reinforcement-learning/meta-learning

We study how in-context learning (ICL) in language models is affected by semantic priors versus input-label mappings. We investigate two setups—ICL with flipped labels and ICL with semantically-unrelated labels—across various model families (GPT-3, InstructGPT, Codex, PaLM, and Flan-PaLM).

First, experiments on ICL with flipped labels show that overriding semantic priors is an emergent ability of model scale. While small language models ignore flipped labels presented in-context and thus rely primarily on semantic priors from pretraining, large models can override semantic priors when presented with in-context exemplars that contradict priors, despite the stronger semantic priors that larger models may hold.

We next study semantically-unrelated label ICL (SUL-ICL), in which labels are semantically unrelated to their inputs (eg. ‘foo’/‘bar’ instead of negative/positive), thereby forcing language models to learn the input-label mappings shown in in-context exemplars in order to perform the task. The ability to do SUL-ICL also emerges primarily with scale, and large-enough language models can even perform linear classification in a SUL-ICL setting.

Finally, we evaluate instruction-tuned models and find that instruction tuning strengthens both the use of semantic priors and the capacity to learn input-label mappings, but more of the former.

For further details, refer to the primary sources available on OpenAI’s repository and Google’s publication.

--- https://arxiv.org/abs/2308.03958#deepmind Simple synthetic data reduces sycophancy in large language models Jerry Wei, Da Huang, Yifeng Lu, Denny Zhou, Quoc V. Le 2023-08-07 2024-06-25 [("doi","10.48550/arXiv.2308.03958")] ai/nn/transformer/gpt/palm ai/scaling reinforcement-learning/preference-learning/mode-collapse reinforcement-learning/safe

Sycophancy is an undesirable behavior where models tailor their responses to follow a human user’s view even when that view is not objectively correct (eg. adapting liberal views once a user reveals that they are liberal). In this paper, we study the prevalence of sycophancy in language models and propose a simple synthetic-data intervention to reduce this behavior.

First, on a set of 3 sycophancy tasks (Perez et al 2022) where models are asked for an opinion on statements with no correct answers (eg. politics), we observe that both model scaling and instruction tuning increase sycophancy for PaLM models up to 540b parameters. Second, we extend sycophancy evaluations to simple addition statements that are objectively incorrect, finding that despite knowing that these statements are wrong, language models will still agree with them if the user does as well.

To reduce sycophancy, we present a straightforward synthetic-data intervention that takes public NLP tasks and encourages models to be robust to user opinions on these tasks.

Adding these data in a lightweight finetuning step can reduce sycophantic behavior on held-out prompts.

Code for generating synthetic data for intervention can be found at Github.

--- https://www.etched.com/announcing-etched Etched is Making the Biggest Bet in AI 2024-06-25 ai/nn/transformer ai/scaling/hardware --- https://arxiv.org/abs/2309.16575 MTOB: A Benchmark for Learning to Translate a New Language from One Grammar Book Garrett Tanzer, Mirac Suzgun, Eline Visser, Dan Jurafsky, Luke Melas-Kyriazi 2023-09-28 2024-06-26 [("doi","10.48550/arXiv.2309.16575")] ai/dataset ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/scaling psychology/linguistics

Large language models (LLMs) can perform impressive feats with in-context learning or lightweight finetuning. It is natural to wonder how well these models adapt to genuinely new tasks, but how does one find tasks that are unseen in internet-scale training sets? We turn to a field that is explicitly motivated and bottlenecked by a scarcity of web data: low-resource languages.

In this paper, we introduce MTOB (Machine Translation from One Book), a benchmark for learning to translate between English and Kalamang—a language with less than 200 speakers and therefore virtually no presence on the web—using several hundred pages of field linguistics reference materials. This task framing is novel in that it asks a model to learn a language from a single human-readable book of grammar explanations, rather than a large mined corpus of in-domain data, more akin to L2 learning than L1 acquisition.

We demonstrate that baselines using current LLMs are promising but fall short of human performance, achieving 44.7 chrF on Kalamang to English translation and 45.8 chrF on English to Kalamang translation, compared to 51.6 and 57.0 chrF by a human who learned Kalamang from the same reference materials.

We hope that MTOB will help measure LLM capabilities along a new dimension, and that the methods developed to solve it could help expand access to language technology for underserved communities by leveraging qualitatively different kinds of data than traditional machine translation.

--- https://arxiv.org/abs/2406.06467 How Far Can Transformers Reason? The Locality Barrier and Inductive Scratchpad Emmanuel Abbe, Samy Bengio, Aryo Lotfi, Colin Sandon, Omid Saremi 2024-06-10 2024-06-26 [("doi","10.48550/arXiv.2406.06467")] ai/nn/transformer/gpt/inner-monologue

Can Transformers predict new syllogisms by composing established ones? More generally, what type of targets can be learned by such models from scratch? Recent works show that Transformers can be Turing-complete in terms of expressivity, but this does not address the learnability objective.

This paper puts forward the notion of ‘distribution locality’ to capture when weak learning is efficiently achievable by regular Transformers, where the locality measures the least number of tokens required in addition to the tokens histogram to correlate non-trivially with the target.

As shown experimentally and theoretically under additional assumptions, distributions with high locality cannot be learned efficiently. In particular, syllogisms cannot be composed on long chains.

Furthermore, we show that (1) an agnostic scratchpad cannot help to break the locality barrier, (2) an educated scratchpad can help if it breaks the locality at each step, (3) a notion of ‘inductive scratchpad’ can both break the locality and improve the out-of-distribution generalization, eg. generalizing to almost double input size for some arithmetic tasks.

--- https://arxiv.org/abs/1312.6098 On the number of response regions of deep feed forward networks with piece-wise linear activations Razvan Pascanu, Guido Montufar, Yoshua Bengio 2013-12-20 2024-06-26 [("doi","10.48550/arXiv.1312.6098")] ai/nn/fully-connected

This paper explores the complexity of deep feedforward networks with linear pre-synaptic couplings and rectified linear activations. This is a contribution to the growing body of work contrasting the representational power of deep and shallow network architectures. In particular, we offer a framework for comparing deep and shallow models that belong to the family of piecewise linear functions based on computational geometry.

We look at a deep rectifier multi-layer perceptron (MLP) with linear output units and compare it with a single-layer version of the model. In the asymptotic regime, when the number of inputs stays constant, if the shallow model has kn hidden units and n0 inputs, then the number of linear regions is 𝒪(kn0nn0). For a k layer model with n hidden units on each layer, it is Ω(⌊n/n0⌋k−1nn0). The number ⌊n/n0⌋k − 1 grows faster than kn0 when n tends to infinity, or when k tends to infinity and n ≥ 2n0.

Additionally, even when k is small, if we restrict n to be 2n0, we can show that a deep model has considerably more linear regions than a shallow one.

We consider this as a first step towards understanding the complexity of these models and specifically towards providing suitable mathematical tools for future analysis.

--- https://arxiv.org/abs/2402.04362 Neural Networks Learn Statistics of Increasing Complexity Nora Belrose, Quintin Pope, Lucia Quirke, Alex Mallen, Xiaoli Fern 2024-02-06 2024-06-26 [("doi","10.48550/arXiv.2402.04362")] ai/nn/cnn ai/nn/transformer/gpt

The distributional simplicity bias (DSB) posits that neural networks learn low-order moments of the data distribution first, before moving on to higher-order correlations.

In this work, we present compelling new evidence for the DSB by showing that networks automatically learn to perform well on maximum-entropy distributions whose low-order statistics match those of the training set early in training, then lose this ability later.

We also extend the DSB to discrete domains by proving an equivalence between token n-gram frequencies and the moments of embedding vectors, and by finding empirical evidence for the bias in large language models (LLMs).

Finally we use optimal transport methods to surgically edit the low-order statistics of one class to match those of another.

We show that early-training networks treat the edited samples as if they were drawn from the target class.

Code is available at Github.

--- https://arxiv.org/abs/2312.06581#eleutherai Grokking Group Multiplication with Cosets Dashiell Stander, Qinan Yu, Honglu Fan, Stella Biderman 2023-12-11 2024-06-26 [("doi","10.48550/arXiv.2312.06581")] ai/nn/fully-connected ai/scaling/emergence/grokking

The complex and unpredictable nature of deep neural networks prevents their safe use in many high-stakes applications. There have been many techniques developed to interpret deep neural networks, but all have substantial limitations.

Algorithmic tasks have proven to be a fruitful test ground for interpreting a neural network through an end-to-end approach. Building on previous work, we completely reverse engineer fully connected one-hidden layer networks that have “grokked” the arithmetic of the permutation groups S5 and S6.

The models discover the true subgroup structure of the full group and converge on neural circuits that decompose the group arithmetic using the permutation group’s subgroups. We relate how we reverse engineered the model’s mechanisms and confirmed our theory was a faithful description of the circuit’s functionality.

We also draw attention to current challenges in conducting interpretability research by comparing our work to Chughtai et al 2023 which alleges to find a different algorithm for this same problem.

...We succeed in completely reverse engineering the model and enumerating the diverse circuits that it converges on to implement the multiplication of the symmetric group.

Our work does not, however, represent an unmitigated success for the project of mechanistic interpretability. The prior work of Chughtai et al 2023 studied the exact same model and setting, but came to completely different conclusions. Understanding why our and Chughtai et al 2023’s interpretations of the same data diverged required extensive effort (see Appendix 7 for a thorough comparison).

We find that even in a setting as simple and well understood as group arithmetic, it is incredibly difficult to do interpretability research and be confident about one’s conclusions.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176561 Circumstances of fall-related injuries by age and gender among community-dwelling adults in the United States 2024-06-26 psychiatry/traumatic-brain-injury --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792421/ Falls and Fall-Related Injuries among Community-Dwelling Adults in the United States 2024-06-26 psychiatry/traumatic-brain-injury --- /note/tokenization AI Text Tokenization Gwern 2020-06-01 2020-06-01 ai/nn/tokenization --- https://ageofem.com/ The Age of Em, A Book Robin Hanson 2024-01-01 ai/nn psychology/neuroscience --- http://www.paulbourke.net/fractals/dla/ DLA—Diffusion Limited Aggregation 2024-06-26 cs/cellular-automaton design/visualization --- https://www.quantamagazine.org/how-ai-revolutionized-protein-science-but-didnt-end-it-20240626/ 2024-06-26 ai/nn/transformer/alphafold --- https://arxiv.org/abs/1609.00680 Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang, Jinbo Xu 2016-09-02 2024-06-26 [("doi","10.1371/journal.pcbi.1005324")] ai/nn/transformer/alphafold

[historical background] Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction.

This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual networks. This deep neural network allows us to model very complex sequence-contact relationships as well as long-range inter-contact correlation.

Our method greatly outperforms existing contact prediction methods and leads to much more accurate contact-assisted protein folding. Tested on 3 datasets of 579 proteins, the average top L long-range prediction accuracy obtained by our method, the representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints can yield correct folds (ie. TMscore > 0.6) for 203 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively.

Further, our contact-assisted models have much better quality than template-based models. Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore > 0.5. By contrast, when the training proteins of our method are used as templates, homology modeling can only do so for 10 of them. One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method works very well on membrane protein prediction.

Finally, in recent blind CAMEO benchmarks, our method successfully folded 5 test proteins with a novel fold.

--- https://marginalrevolution.com/marginalrevolution/2024/06/claude-sonnet-3-5-economist.html Claude Sonnet 3.5, economist 2024-06-26 ai/nn/transformer/gpt/claude economics --- https://sports.yahoo.com/is-bronny-james-underrated-inside-the-phenomenon-of-the-nba-bloodline-155757959.html Is Bronny James underrated? Inside the phenomenon of the NBA bloodline 2024-06-26 exercise genetics/heritable --- https://manhattan.institute/article/is-wikipedia-politically-biased Is Wikipedia Politically Biased? 2024-06-26 politics wikipedia --- https://www.nber.org/papers/w32591 Is Software Eating the World? 2024-06-26 economics/automation --- https://causalinf.substack.com/p/an-amazing-journey-with-claude-35 An amazing journey with Claude 3.5 and ChatGPT-4o who helped me backwards engineer an econometrics theory paper and taught me a lot more in the process 2024-06-26 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex economics --- https://groups.google.com/g/comp.lang.lisp/c/wj79WtYilg8/m/DyIvUbdPsrMJ Upper limits of Common Lisp [on the Norwegian oil industry] 2024-06-26 cs/lisp --- https://news.mit.edu/2018/featured-video-solving-rubiks-cube-record-time-0316 Solving a Rubik’s Cube in record time 2024-06-27 reinforcement-learning/robot --- https://www.youtube.com/watch?v=Kjb-MmwueEQ Cuboth 2024-06-27 reinforcement-learning/robot --- https://blog.fal.ai/introducing-aurasr-an-open-reproduction-of-the-gigagan-upscaler-2/ Introducing AuraSR—An open reproduction of the GigaGAN Upscaler 2024-06-27 ai/nn/gan --- https://arxiv.org/abs/2405.01964 Position: Understanding LLMs Requires More Than Statistical Generalization Patrik Reizinger, Szilvia Ujváry, Anna Mészáros, Anna Kerekes, Wieland Brendel, Ferenc Huszár 2024-05-03 2024-06-27 [("doi","10.48550/arXiv.2405.01964")] ai/scaling reinforcement-learning/meta-learning

The last decade has seen blossoming research in deep learning theory attempting to answer, “Why does deep learning generalize?” A powerful shift in perspective precipitated this progress: the study of overparametrized models in the interpolation regime. In this paper, we argue that another perspective shift is due, since some of the desirable qualities of LLMs (Large Language Models) are not a consequence of good statistical generalization and require a separate theoretical explanation.

Our core argument relies on the observation that AR probabilistic models (autoregressive probabilistic models) are inherently non-identifiable: models zero or near-zero KL divergence apart—thus, equivalent test loss—can exhibit markedly different behaviors.

We support our position with mathematical examples and empirical observations, illustrating why non-identifiability has practical relevance through 3 case studies: (1) the non-identifiability of zero-shot rule extrapolation; (2) the approximate non-identifiability of in-context learning; and (3) the non-identifiability of fine-tunability.

We review promising research directions focusing on LLM-relevant generalization measures, transferability, and inductive biases.

--- https://arxiv.org/abs/2406.17711#deepmind JEST: Data curation via joint example selection further accelerates multimodal learning Talfan Evans, Nikhil Parthasarathy, Hamza Merzic, Olivier J. Henaff 2024-06-25 2024-06-27 [("doi","10.48550/arXiv.2406.17711")] reinforcement-learning/scaling

Data curation is an essential component of large-scale pretraining.

In this work, we demonstrate that jointly selecting batches of data is more effective for learning than selecting examples independently. Multimodal contrastive objectives expose the dependencies between data and thus naturally yield criteria for measuring the joint learnability of a batch. We derive a simple and tractable algorithm for selecting such batches, which accelerates training beyond individually-prioritized data points.

As performance improves by selecting from larger super-batches, we also leverage recent advances in model approximation to reduce the associated computational overhead. As a result, our approach–multimodal contrastive learning with joint example selection (JEST)–surpasses state-of-the-art models with up to 13× fewer iterations and 10× less computation.

Essential to the performance of JEST is the ability to steer the data selection process towards the distribution of smaller, well-curated datasets via pretrained reference models, exposing the level of data curation as a new dimension for neural scaling laws.

--- https://x.com/noveltokens/status/1805817286021829004 noveltokens 2024-06-27 ai/nn/transformer/gpt/claude ai/poetry --- https://minimaxir.com/2024/06/pokemon-embeddings/ The Super Effectiveness of Pokémon Embeddings Using Only Raw JSON and Images 2024-06-27 ai/nn/retrieval --- https://suno.com/song/342ec7c6-c081-478c-a495-a38635547d48 Curious about you translucentaudiosynthesis319 2024-06-27 ai/music --- https://archive.org/details/ConfessionsOfTheFatherOfTheNeutronBomb Confessions of the father of the neutron bomb Samuel T. Cohen 2024-01-01 fiction/science-fiction/frank-herbert radiance --- https://nicholas.carlini.com/writing/2024/why-i-attack.html Why I Attack Nicholas Carlini 2024-06-27 ai/nn/adversarial cs/security --- https://blog.plover.com/lang/script-potpourri.html A potpourri of cool-looking scripts 2024-06-27 design/typography/rubrication --- https://arxiv.org/abs/2405.20519 Diffusion On Syntax Trees For Program Synthesis Shreyas Kapur, Erik Jenner, Stuart Russell 2024-05-30 2024-06-27 [("doi","10.48550/arXiv.2405.20519")] ai/nn/diffusion ai/nn/transformer/gpt/codex reinforcement-learning/model

[homepage] Large language models generate code one token at a time. Their autoregressive generation process lacks the feedback of observing the program’s output. Training LLMs to suggest edits directly can be challenging due to the scarcity of rich edit data.

To address these problems, we propose neural diffusion models that operate on syntax trees of any context-free grammar. Similar to image diffusion models, our method also inverts “noise” applied to syntax trees. Rather than generating code sequentially, we iteratively edit it while preserving syntactic validity, which makes it easy to combine this neural model with search.

We apply our approach to inverse graphics tasks, where our model learns to convert images into SVG programs that produce those images. Combined with search, our model is able to write graphics programs, see the execution result, and debug them to meet the required specifications.

We additionally show how our system can write graphics programs for hand-drawn sketches.

--- https://arxiv.org/abs/2406.08423 State Soup: In-Context Skill Learning, Retrieval and Mixing Maciej Pióro, Maciej Wołczyk, Razvan Pascanu, Johannes von Oswald, João Sacramento 2024-06-12 2024-06-27 [("doi","10.48550/arXiv.2406.08423")] ai/nn/retrieval ai/nn/rnn ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/meta-learning

A new breed of gated-linear recurrent neural networks has reached state-of-the-art performance on a range of sequence modeling problems. Such models naturally handle long sequences efficiently, as the cost of processing a new input is independent of sequence length.

Here, we explore another advantage of these stateful sequence models, inspired by the success of model merging through parameter interpolation. Building on parallels between fine-tuning and in-context learning, we investigate whether we can treat internal states as task vectors that can be stored, retrieved, and then linearly combined, exploiting the linearity of recurrence.

We study this form of fast model merging on Mamba-2.8b, a pretrained recurrent model, and present preliminary evidence that simple linear state interpolation methods suffice to improve next-token perplexity as well as downstream in-context learning task performance.

--- https://arxiv.org/abs/2405.20541 Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models Zachary Ankner, Cody Blakeney, Kartik Sreenivasan, Max Marion, Matthew L. Leavitt, Mansheej Paul 2024-05-30 2024-06-27 [("doi","10.48550/arXiv.2405.20541")] reinforcement-learning/exploration/active-learning/data-pruning

[Twitter] In this work, we investigate whether small language models can determine high-quality subsets of large-scale text datasets that improve the performance of larger language models. While existing work has shown that data-pruning based on the perplexity of a larger model can yield high-quality data, we investigate whether smaller models can be used for perplexity-based pruning and how pruning is affected by the domain composition of the data being pruned.

We demonstrate that for multiple dataset compositions, perplexity-based pruning of pretraining data can substantially improve downstream task performance: pruning based on perplexities computed with a 125 million parameter model:

improves the average performance on downstream tasks of a 3 billion parameter model by up to 2.04 and achieves up to a 1.45× reduction in pretraining steps to reach commensurate baseline performance.

Furthermore, we demonstrate that such perplexity-based data pruning also yields downstream performance gains in the over-trained and data-constrained regimes.

…4.2 How Pruning Affects Domain Composition: We can also interpret the effect that perplexity-based data pruning has on a dataset by examining how pruning affects each domain’s proportion of the total dataset. We plot the pre and post-pruning domain compositions for the Pile and Dolma in Figure 4.

Interestingly, for all datasets pruning increases the proportion of data coming from web-scraped domains while decreasing the proportion of data coming from highly specific technical domains such as code or scientific papers. This trend is more pronounced in the Pile, where the proportions of Pile-CC and OpenWebText2 nearly double, while the proportions of domains such as Pubmed Central, ArXiv, and Github are all reduced by at least a factor of 3.

Future work should investigate how perplexity-based pruning affects a model’s performance on downstream tasks that are in the same category as the highly pruned domains.

--- https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1 FineWeb: decanting the web for the finest text data at scale 2024-06-05 ai/dataset ai/nn/tokenization reinforcement-learning/exploration/active-learning/data-pruning --- https://arxiv.org/abs/2106.12059 Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian, Andrew Jesson, Frederic Branchaud-Charron, Yarin Gal 2021-06-22 2024-06-27 [("doi","10.48550/arXiv.2106.12059")] reinforcement-learning/exploration/active-learning

We examine a simple stochastic strategy for adapting well-known single-point acquisition functions to allow batch active learning. Unlike acquiring the top-K points from the pool set, score/rank-based sampling takes into account that acquisition scores change as new data are acquired.

This simple strategy for adapting standard single-sample acquisition strategies can even perform just as well as compute-intensive state-of-the-art batch acquisition functions, like BatchBALD or BADGE, while using orders of magnitude less compute.

In addition to providing a practical option for machine learning practitioners, the surprising success of the proposed method in a wide range of experimental settings raises a difficult question for the field: when are these expensive batch acquisition methods pulling their weight?

--- https://arxiv.org/abs/1906.03671 BADGE: Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal 2019-06-09 2024-06-27 [("doi","10.48550/arXiv.1906.03671")] reinforcement-learning/exploration/active-learning

We design a new algorithm for batch active learning with deep neural network models.

Our algorithm, Batch Active learning by Diverse Gradient Embeddings (BADGE), samples groups of points that are disparate and high-magnitude when represented in a hallucinated gradient space, a strategy designed to incorporate both predictive uncertainty and sample diversity into every selected batch. Crucially, BADGE trades off between diversity and uncertainty without requiring any hand-tuned hyperparameters.

We show that while other approaches sometimes succeed for particular batch sizes or architectures, BADGE consistently performs as well or better, making it a versatile option for practical active learning problems.

--- https://arstechnica.com/ai/2024/06/as-a-potentially-historic-hurricane-season-looms-can-ai-forecast-models-help/ No physics? No problem. AI weather forecasting is already making huge strides. 2024-06-28 ai/nn/transformer ai/scaling science --- https://arxiv.org/abs/2405.15143 Intelligent Go-Explore (IGE): Standing on the Shoulders of Giant Foundation Models Cong Lu, Shengran Hu, Jeff Clune 2024-05-24 2024-06-28 [("doi","10.48550/arXiv.2405.15143")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue reinforcement-learning/exploration

[code; cf. Motif] Go-Explore is a powerful family of algorithms designed to solve hard-exploration problems, built on the principle of archiving discovered states, and iteratively returning to and exploring from the most promising states. This approach has led to superhuman performance across a wide variety of challenging problems including Atari games and robotic control, but requires manually designing heuristics to guide exploration, which is time-consuming and infeasible in general.

To resolve this, we propose Intelligent Go-Explore (IGE) which greatly extends the scope of the original Go-Explore by replacing these heuristics with the intelligence and internalized human notions of interestingness captured by giant foundation models (FMs). This provides IGE with a human-like ability to instinctively identify how interesting or promising any new state is (eg. discovering new objects, locations, or behaviors), even in complex environments where heuristics are hard to define. Moreover, IGE offers the exciting and previously impossible opportunity to recognize & capitalize on serendipitous discoveries that cannot be predicted ahead of time.

We evaluate IGE on a range of language-based tasks that require search and exploration. In Game of 24, a multistep mathematical reasoning problem, IGE reaches 100% success rate 70.8% faster than the best classic graph search baseline. Next, in BabyAI-Text, a challenging partially observable gridworld, IGE exceeds the previous SOTA with orders of magnitude fewer online samples. Finally, in TextWorld, we show the unique ability of IGE to succeed in settings requiring long-horizon exploration where prior SOTA FM-agents like Reflexion completely fail.

Overall, IGE combines the tremendous strengths of FMs and the powerful Go-Explore algorithm, opening up a new frontier of research into creating more generally capable agents with impressive exploration capabilities.

--- https://arxiv.org/abs/2303.11366 Reflexion: Language Agents with Verbal Reinforcement Learning Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, Shunyu Yao 2023-03-20 2024-06-28 [("doi","10.48550/arXiv.2303.11366")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue reinforcement-learning/exploration

Large language models (LLMs) have been increasingly used to interact with external environments (eg. games, compilers, APIs) as goal-driven agents. However, it remains challenging for these language agents to quickly and efficiently learn from trial-and-error as traditional reinforcement learning methods require extensive training samples and expensive model fine-tuning.

We propose Reflexion, a novel framework to reinforce language agents not by updating weights, but instead through linguistic feedback. Concretely, Reflexion agents verbally reflect on task feedback signals, then maintain their own reflective text in an episodic memory buffer to induce better decision-making in subsequent trials. Reflexion is flexible enough to incorporate various types (scalar values or free-form language) and sources (external or internally simulated) of feedback signals.

Reflexion obtains improvements over a baseline agent across diverse tasks (sequential decision-making, coding, language reasoning). For example, Reflexion achieves a 91% pass@1 accuracy on the HumanEval coding benchmark, surpassing the previous state-of-the-art GPT-4 that achieves 80%.

We also conduct ablation and analysis studies using different feedback signals, feedback incorporation methods, and agent types, and provide insights into how they affect performance.

--- https://arxiv.org/abs/2208.14928 LGE: Cell-Free Latent Go-Explore Quentin Gallouédec, Emmanuel Dellandréa 2022-08-31 2024-06-28 [("doi","10.48550/arXiv.2208.14928")] reinforcement-learning/exploration reinforcement-learning/model

In this paper, we introduce Latent Go-Explore (LGE), a simple and general approach based on the Go-Explore paradigm for exploration in reinforcement learning (RL). Go-Explore was initially introduced with a strong domain knowledge constraint for partitioning the state space into cells. However, in most real-world scenarios, drawing domain knowledge from raw observations is complex and tedious. If the cell partitioning is not informative enough, Go-Explore can completely fail to explore the environment.

We argue that the Go-Explore approach can be generalized to any environment without domain knowledge and without cells by exploiting a learned latent representation. Thus, we show that LGE can be flexibly combined with any strategy for learning a latent representation.

Our results indicate that LGE, although simpler than Go-Explore, is more robust and outperforms state-of-the-art algorithms in terms of pure exploration on multiple hard-exploration environments including Montezuma’s Revenge.

The LGE implementation is available as open-source at Github.

--- https://x.com/elonmusk/status/1797382701541990841 Elon Musk 2024-06-28 ai/scaling/hardware --- https://imbue.com/research/70b-infrastructure/ From bare metal to a 70B model: infrastructure set-up and scripts 2024-06-28 ai/scaling/hardware --- https://www.alignmentforum.org/posts/GzoWcYibWYwJva8aL/parameter-counts-in-machine-learning Parameter counts in Machine Learning 2024-06-28 ai/scaling --- https://arxiv.org/abs/1905.10843 Asymptotic learning curves of kernel methods: empirical data versus Teacher-Student paradigm Stefano Spigler, Mario Geiger, Matthieu Wyart 2019-05-26 2024-06-28 [("doi","10.1088/1742-5468/abc61d")] ai/scaling ai/tabular

[cf. Sharma & Kaplan 2021] How many training data are needed to learn a supervised task? It is often observed that the generalization error decreases as n−β where n is the number of training examples and β an exponent that depends on both data and algorithm.

In this work we measure β when applying kernel methods to real datasets. For MNIST we find β ≈ 0.4 and for CIFAR-10 β ≈ 0.1, for both regression and classification tasks, and for Gaussian or Laplace kernels. To rationalize the existence of non-trivial exponents that can be independent of the specific kernel used, we study the Teacher-Student framework for kernels [like Rosenfeld & Maloney etc]. In this scheme, a Teacher generates data according to a Gaussian random field, and a Student learns them via kernel regression. With a simplifying assumption—namely that the data are sampled from a regular lattice—we derive analytically β for translation invariant kernels, using previous results from the kriging literature.

Provided that the Student is not too sensitive to high frequencies, β depends only on the smoothness and dimension of the training data. We confirm numerically that these predictions hold when the training points are sampled at random on a hypersphere. Overall, the test error is found to be controlled by the magnitude of the projection of the true function on the kernel eigenvectors whose rank is larger than n. Using this idea we predict relate the exponent β to an exponent α describing how the coefficients of the true function in the eigenbasis of the kernel decay with rank.

We extract α from real data by performing kernel PCA, leading to β ≈ 0.36 for MNIST and β ≈ 0.07 for CIFAR-10, in good agreement with observations.

We argue that these rather large exponents are possible due to the small effective dimension of the data.

--- https://xkcd.com/651/ Bag Check Randall Munroe 2024-06-28 cs/security fiction/humor --- https://www.valve.computer/ The Valve.Computer: A modern 8-bit design, built using 1950s thermionic valves 2024-06-28 cs/hardware --- https://arxiv.org/abs/2303.13506 The Quantization Model of Neural Scaling Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark 2023-03-23 2024-06-28 [("doi","10.48550/arXiv.2303.13506")] ai/nn/fully-connected ai/nn/transformer/gpt ai/scaling/emergence

[code; podcast] We propose the Quantization Model of neural scaling laws, explaining both the observed power law dropoff of loss with model and data size, and also the sudden emergence of new capabilities with scale.

We derive this model from what we call the Quantization Hypothesis, where network knowledge and skills are “quantized” into discrete chunks (quanta). [like Hutter 2021]

We show that when quanta are learned in order of decreasing use frequency, then a power law in use frequencies explains observed power law scaling of loss.

We validate this prediction on toy datasets, then study how scaling curves decompose for large language models [Pythia]. Using language model gradients, we automatically decompose model behavior into a diverse set of skills (quanta).

We tentatively find that the frequency at which these quanta are used in the training distribution roughly follows a power law corresponding with the empirical scaling exponent for language models, a prediction of our theory.

--- https://arxiv.org/abs/2306.15400 Length Generalization in Arithmetic Transformers Samy Jelassi, Stéphane d’Ascoli, Carles Domingo-Enrich, Yuhuai Wu, Yuanzhi Li, François Charton 2023-06-27 2024-06-28 [("doi","10.48550/arXiv.2306.15400")] ai/nn/tokenization ai/nn/transformer/gpt math

We examine how transformers cope with two challenges: learning basic integer arithmetic, and generalizing to longer sequences than seen during training.

We find that relative position embeddings enable length generalization for simple tasks, such as addition: models trained on 5-digit numbers can perform 15-digit sums.

However, this method fails for multiplication, and we propose train set ‘priming’: adding a few (10 to 50) long sequences to the training set.

We show that priming allows models trained on 5-digit × 3-digit multiplications to generalize to 35 × 3 examples.

We also show that models can be primed for different generalization lengths, and that the priming sample size scales as the logarithm of the training set size.

Finally, we discuss potential applications of priming beyond arithmetic.

--- https://arxiv.org/abs/2306.15448 Understanding Social Reasoning in Language Models with Language Models Kanishk Gandhi, Jan-Philipp Fränken, Tobias Gerstenberg, Noah D. Goodman 2023-06-21 2024-06-28 [("doi","10.48550/arXiv.2306.15448")] ai/dataset ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/scaling philosophy/mind

[homepage, code/data] As Large Language Models (LLMs) become increasingly integrated into our everyday lives, understanding their ability to comprehend human mental states becomes critical for ensuring effective interactions. However, despite the recent attempts to assess the Theory-of-Mind (ToM) reasoning capabilities of LLMs, the degree to which these models can align with human ToM remains a nuanced topic of exploration. This is primarily due to two distinct challenges: (1) the presence of inconsistent results from previous evaluations, and (2) concerns surrounding the validity of existing evaluation methodologies.

To address these challenges, we present a novel framework for procedurally generating evaluations with LLMs by populating causal templates. Using our framework, we create a new social reasoning benchmark (BigToM) for LLMs which consists of 25 controls and 5,000 model-written evaluations.

We find that human participants rate the quality of our benchmark higher than previous crowd-sourced evaluations and comparable to expert-written evaluations. Using BigToM, we evaluate the social reasoning capabilities of a variety of LLMs [LLaMA-2-65b, davinci-003, Claude-2, ChatGPT-4] and compare model performances with human performance.

Our results suggest that GPT-4 has ToM capabilities that mirror human inference patterns, though less reliable, while other LLMs struggle.

--- https://arxiv.org/abs/2305.16934 On Evaluating Adversarial Robustness of Large Vision-Language Models Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Chongxuan Li, Ngai-Man Cheung, Min Lin 2023-05-26 2024-06-28 [("doi","10.48550/arXiv.2305.16934")] ai/nn/adversarial ai/nn/transformer/clip

Large vision-language models (VLMs) such as GPT-4 have achieved unprecedented performance in response generation, especially with visual inputs, enabling more creative and adaptable interaction than large language models such as ChatGPT. Nonetheless, multimodal generation exacerbates safety concerns, since adversaries may successfully evade the entire system by subtly manipulating the most vulnerable modality (eg. vision).

To this end, we propose evaluating the robustness of open-source large VLMs in the most realistic and high-risk setting, where adversaries have only black-box system access and seek to deceive the model into returning the targeted responses. In particular, we first craft targeted adversarial examples against pretrained models such as CLIP and BLIP, and then transfer these adversarial examples to other VLMs such as MiniGPT-4, LLaVA, UniDiffuser, BLIP-2, and Img2Prompt.

In addition, we observe that black-box queries on these VLMs can further improve the effectiveness of targeted evasion, resulting in a surprisingly high success rate for generating targeted responses.

Our findings provide a quantitative understanding regarding the adversarial vulnerability of large VLMs and call for a more thorough examination of their potential security flaws before deployment in practice.

Code is at Github.

--- https://arxiv.org/abs/2306.15447 Are aligned neural networks adversarially aligned? Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee, Florian Tramer, Ludwig Schmidt 2023-06-26 2024-06-28 [("doi","10.48550/arXiv.2306.15447")] ai/nn/adversarial ai/nn/transformer/gpt reinforcement-learning/preference-learning

Large language models are now tuned to align with the goals of their creators, namely to be “helpful and harmless.” These models should respond helpfully to user questions, but refuse to answer requests that could cause harm. However, adversarial users can construct inputs which circumvent attempts at alignment. In this work, we study adversarial alignment, and ask to what extent these models remain aligned when interacting with an adversarial user who constructs worst-case inputs (adversarial examples). These inputs are designed to cause the model to emit harmful content that would otherwise be prohibited. We show that existing NLP-based optimization attacks are insufficiently powerful to reliably attack aligned text models: even when current NLP-based attacks fail, we can find adversarial inputs with brute force. As a result, the failure of current attacks should not be seen as proof that aligned text models remain aligned under adversarial inputs.

However the recent trend in large-scale ML models is multimodal models that allow users to provide images that influence the text that is generated.

We show these models can be easily attacked, ie. induced to perform arbitrary un-aligned behavior through adversarial perturbation of the input image.

We conjecture that improved NLP attacks may demonstrate this same level of adversarial control over text-only models.

--- https://arxiv.org/abs/2306.15658 CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy within a $10,000 Budget; An Extra $4,000 Unlocks 81.8% Accuracy Xianhang Li, Zeyu Wang, Cihang Xie 2023-06-27 2024-06-28 ai/nn/transformer/clip

The recent work CLIPA presents an inverse scaling law for CLIP training—whereby the larger the image/text encoders used, the shorter the sequence length of image/text tokens that can be applied in training. This finding enables us to train high-performance CLIP models with substantially reduced computations.

Building upon this work, we hereby present CLIPA-v2 with two key contributions. Technically, we find this inverse scaling law is also applicable in the finetuning stage, enabling further reduction in computational needs. Empirically, we explore CLIPA at scale, extending the experiments up to the H/14 model with ~13B image-text pairs seen during training.

Our results are exciting—by only allocating a budget of $10,000, our CLIP model achieves an impressive zero-shot ImageNet accuracy of 81.1%, surpassing the prior best CLIP model (from OpenCLIP, 80.1%) by 1.0% and meanwhile reducing the computational cost by ~39×. Moreover, with an additional investment of $4,000, we can further elevate the zero-shot ImageNet accuracy to 81.8%.

Our code and models are available at this URL.

--- https://arxiv.org/abs/2305.07017 An Inverse Scaling Law for CLIP Training Xianhang Li, Zeyu Wang, Cihang Xie 2023-05-11 2024-06-28 [("doi","10.48550/arXiv.2305.07017")] ai/nn/transformer/clip

CLIP, one of the pioneering foundation models that connect images and text, has enabled many recent breakthroughs in computer vision. However, its associated training cost is prohibitively high, imposing a barrier to its widespread exploration. In this paper, we present a surprising finding that there exists an inverse scaling law for CLIP training, whereby the larger the image/text encoders used, the shorter the sequence length of image/text tokens that can be applied in training. Moreover, we showcase that the strategy for reducing image/text token length plays a crucial role in determining the quality of this scaling law.

As a result of this finding, we are able to successfully train CLIP even with limited computational resources. For example, using 8 A100 GPUs, our CLIP models achieve zero-shot top-1 ImageNet-1k accuracies of 63.2% in ~2 days, 67.8% in ~3 days, and 69.3% in ~4 days. Our method also works well when scaling up—with G/14, we register a new record of 83.0% ImageNet-1k zero-shot accuracy, and meanwhile accelerate the training by ~33× compared to its OpenCLIP counterpart.

By reducing the computation barrier associated with CLIP, we hope to inspire more research in this field, particularly from academics.

Our code is available at Github.

--- https://x.com/DanielJDrucker/status/1673383917888348174 Daniel J. Drucker 2024-06-28 longevity/glp --- https://www.theatlantic.com/health/archive/2023/04/ozempic-wegovy-mounjaro-weight-loss-drug-development-access/673627/ Ozempic Is About to Be Old News 2024-06-28 longevity/glp/semaglutide --- https://www.wired.com/story/sima-sistani-weight-watchers-big-interview/ It’s the Age of Ozempic. Do We Need Weight Watchers Anymore? 2024-06-28 longevity/glp/semaglutide --- https://www.boehringer-ingelheim.com/human-health/metabolic-diseases/obesity/phase-ii-clinical-trial-weight-loss-results 2024-06-28 longevity/glp --- https://www.reuters.com/business/healthcare-pharmaceuticals/pfizer-discontinue-development-obesity-drug-2023-06-26/ 2024-06-28 longevity/glp --- https://www.everydayhealth.com/diabetes/could-this-pill-be-the-next-ozempic/ Could Danuglipron Be the Next Ozempic in Pill Form? 2024-06-28 longevity/glp --- https://x.com/FattyLiverA/status/1672144869886685189 FattyLiverA 2024-06-28 longevity/glp --- https://x.com/MWeintraubMD/status/1673435733497978888 2024-06-28 longevity/glp/semaglutide --- https://x.com/AliceYYCheng/status/1673434959900717056 AliceYYCheng 2024-06-28 longevity/glp/semaglutide --- https://www.nature.com/articles/s42255-023-00811-0 2024-06-28 longevity/glp/tirzepatide --- https://www.annualreviews.org/content/journals/10.1146/annurev-med-043021-014919 2024-06-28 longevity/glp --- https://arxiv.org/abs/2306.14079 Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching H. J. Terry Suh, Glen Chou, Hongkai Dai, Lujie Yang, Abhishek Gupta, Russ Tedrake 2023-06-24 2024-06-28 [("doi","10.48550/arXiv.2306.14079")] ai/nn/diffusion reinforcement-learning/model reinforcement-learning/offline

[Twitter] Gradient-based methods enable efficient search capabilities in high dimensions. However, in order to apply them effectively in offline optimization paradigms such as offline Reinforcement Learning (RL) or Imitation Learning (IL), we require a more careful consideration of how uncertainty estimation interplays with first-order methods that attempt to minimize them.

We study smoothed distance to data as an uncertainty metric and claim that it has two beneficial properties: (1) it allows gradient-based methods that attempt to minimize uncertainty to drive iterates to data as smoothing is annealed, and (2) it facilitates analysis of model bias with Lipschitz constants. As distance to data can be expensive to compute online, we consider settings where we need to amortize this computation. Instead of learning the distance, however, we propose to learn its gradients directly as an oracle for first-order optimizers.

We show these gradients can be efficiently learned with score-matching techniques by leveraging the equivalence between distance to data and data likelihood. Using this insight, we propose Score-Guided Planning (SGP), a planning algorithm for offline RL that uses score-matching to enable first-order planning in high-dimensional problems, where zeroth-order methods were unable to scale, and ensembles were unable to overcome local minima.

Website: https://sites.google.com/view/score-guided-planning/home.

--- https://journals.sagepub.com/doi/10.1177/10888683221104368 2024-06-28 psychology statistics/bias --- https://www.theguardian.com/science/2023/jun/24/do-life-hacks-work-the-truth-is-well-never-know Do life hacks work? The truth is, we’ll never know 2024-06-28 psychology statistics/bias --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10333448/ From Classical Methods to Generative Models: Tackling the Unreliability of Neuroscientific Measures in Mental Health Research 2024-06-28 psychology/neuroscience statistics/bayes statistics/power-analysis --- https://lczero.org/blog/2024/02/update-on-playing-with-piece-odds-against-lc0-on-lichess/ Update on playing with piece odds against Lc0 2024-06-28 reinforcement-learning/chess --- https://www.astralcodexten.com/p/your-book-review-dominion-by-matthew Your Book Review: Dominion [animal rights] 2024-06-28 philosophy/ethics --- https://en.wikipedia.org/wiki/Xenon#Anesthesia Xenon § Anesthesia 2024-06-28 psychology/neuroscience/pain/anesthesia --- https://www.erowid.org/experiences/subs/exp_Xenon.shtml Xenon Trip Reports Erowid 2024-06-28 psychedelic psychology/neuroscience/pain/anesthesia --- https://www.nature.com/articles/24525 2024-06-28 psychology/neuroscience/pain/anesthesia --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3626616/ Xenon in medical area: emphasis on neuroprotection in hypoxia and anesthesia 2024-06-28 psychology/neuroscience/pain/anesthesia --- https://blogs.nvidia.com/blog/generative-ai-debut-mlperf/ H100 GPUs Set Standard for Gen AI in Debut MLPerf Benchmark 2024-06-28 ai/scaling/hardware --- https://www.sciencedirect.com/science/article/pii/S004873332300104X Batman forever? The role of trademarks for reuse in the US comics industry 2024-06-28 economics/copyright --- https://www.pnas.org/doi/10.1073/pnas.2301525120 2024-06-28 genetics/editing --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288628/ Quantifying the potential persuasive returns to political microtargeting 2024-06-28 economics/advertising --- https://arxiv.org/abs/2406.13131 When Parts are Greater Than Sums: Individual LLM Components Can Outperform Full Models Ting-Yun Chang, Jesse Thomason, Robin Jia 2024-06-19 2024-06-28 [("doi","10.48550/arXiv.2406.13131")] ai/nn/fully-connected ai/nn/sparsity/pruning ai/nn/transformer/attention/sparsity ai/nn/transformer/gpt reinforcement-learning/meta-learning

[Twitter] This paper studies in-context learning (ICL) by decomposing the output of large language models into the individual contributions of attention heads and MLPs (components).

We observe curious components: good-performing ones that individually do well on a classification task, even when the model performs poorly; bad-performing ones that do much worse than chance; and label-biased components that always predict the same label. We find that component accuracies are well-correlated across different demonstration sets and perturbations of prompt templates, even when the full-model accuracy varies greatly.

Based on our findings, we propose component reweighting, which learns to linearly re-scale the component activations from a few labeled examples. Given 24 labeled examples, our method improves by an average of 6.0% accuracy points over 24-shot ICL across 8 tasks on Llama-2-7B.

Overall, this paper both enriches our understanding of ICL and provides a practical method for improvement by examining model internals.

…Do good-performing components exist in randomly initialized LLMs? No! When do they emerge? Good-performing components emerge at an early stage of pretraining (blue line), while the full-model accuracy fluctuates a lot over time (green line).

Figure 3: The ICL accuracy of the full model (green) fluctuates greatly during pretraining. However, good-performing components (T1) emerge in the early steps.

…Some related works focus on either attention or MLPs. In our case, we find that both can achieve good ICL accuracy, depending on the prompts and tasks.

Figure 7: Each dot represents a component (attention head: blue; MLP: orange) under 4-shot ICL on Mistral-Instruct-7B. The x-axis shows how often a component predicts label 1 on the test set.
--- https://arxiv.org/abs/2306.14898 InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback John Yang, Akshara Prabhakar, Karthik Narasimhan, Shunyu Yao 2023-06-26 2024-06-28 [("doi","10.48550/arXiv.2306.14898")] ai/nn/transformer/gpt/codex cs/python cs/shell

Humans write code in a fundamentally interactive manner and rely on constant execution feedback to correct errors, resolve ambiguities, and decompose tasks. While LLMs have recently exhibited promising coding capabilities, current coding benchmarks mostly consider a static instruction-to-code sequence transduction process, which has the potential for error propagation and a disconnect between the generated code and its final execution environment.

To address this gap, we introduce InterCode, a lightweight, flexible, and easy-to-use framework of interactive coding as a standard reinforcement learning (RL) environment, with code as actions and execution feedback as observations. Our framework is language and platform agnostic, uses self-contained Docker environments to provide safe and reproducible execution, and is compatible out-of-the-box with traditional seq2seq coding methods, while enabling the development of new methods for interactive code generation.

We use InterCode to create 3 interactive code environments with Bash, SQL, and Python as action spaces, leveraging data from the static NL2Bash, Spider, and MBPP datasets. We demonstrate InterCode’s viability as a testbed by evaluating multiple state-of-the-art LLMs configured with different prompting strategies such as ReAct and Plan & Solve.

Our results showcase the benefits of interactive code generation and demonstrate that InterCode can serve as a challenging benchmark for advancing code understanding and generation capabilities.

InterCode is designed to be easily extensible and can even be used to create new tasks such as Capture the Flag, a popular coding puzzle that is inherently multi-step and involves multiple programming languages.

Project site with code and data: https://intercode-benchmark.github.io/.

--- https://arxiv.org/abs/2306.14048 H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Ré, Clark Barrett, Zhangyang Wang, Beidi Chen 2023-06-24 2024-06-28 [("doi","10.48550/arXiv.2306.14048")] ai/nn/transformer/attention/sparsity

Large Language Models (LLMs), despite their recent impressive accomplishments, are notably cost-prohibitive to deploy, particularly for applications involving long-content generation, such as dialogue systems and story writing. Often, a large amount of transient state information, referred to as the KV cache, is stored in GPU memory in addition to model parameters, scaling linearly with the sequence length and batch size.

In this paper, we introduce a novel approach for implementing the KV cache which reduces its memory footprint. Our approach is based on the noteworthy observation that a small portion of tokens contributes most of the value when computing attention scores. We call these tokens Heavy Hitters (H2).

Through a comprehensive investigation, we find that (1) the emergence of H2 is natural and strongly correlates with the frequent co-occurrence of tokens in the text, and (2) removing them results in performance degradation. Based on these insights, we propose Heavy Hitter Oracle (H2O), a KV cache eviction policy that dynamically retains a balance of recent and H2 tokens. We formulate the KV cache eviction as a dynamic submodular problem and prove (under mild assumptions) a theoretical guarantee for our novel eviction algorithm which could help guide future work.

We validate the accuracy of our algorithm with OPT, LLaMA, and GPT-NeoX across a wide range of tasks. Our implementation of H2O with 20% heavy hitters improves the throughput over 3 leading inference systems, DeepSpeed Zero-Inference, Hugging Face Accelerate, and FlexGen by up to 29×, 29×, and 3× on OPT-6.7B and OPT-30B. With the same batch size, H2O can reduce the latency by up to 1.9×.

The code is available at Github.

--- https://arxiv.org/abs/2306.14892 Supervised Pretraining Can Learn In-Context Reinforcement Learning Jonathan N. Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill 2023-06-26 2024-06-28 [("doi","10.48550/arXiv.2306.14892")] reinforcement-learning/exploration reinforcement-learning/meta-learning reinforcement-learning/model/decision-transformer statistics/bayes

[Twitter] Large transformer models trained on diverse datasets have shown a remarkable ability to learn in-context, achieving high few-shot performance on tasks they were not explicitly trained to solve. In this paper, we study the in-context learning capabilities of transformers in decision-making problems, ie. reinforcement learning (RL) for bandits and Markov decision processes.

To do so, we introduce and study Decision-Pretrained Transformer (DPT), a supervised pretraining method where the transformer predicts an optimal action given a query state and an in-context dataset of interactions, across a diverse set of tasks. This procedure, while simple, produces a model with several surprising capabilities.

We find that the pretrained transformer can be used to solve a range of RL problems in-context, exhibiting both exploration online and conservatism offline, despite not being explicitly trained to do so. The model also generalizes beyond the pretraining distribution to new tasks and automatically adapts its decision-making strategies to unknown structure.

Theoretically, we show DPT can be viewed as an efficient implementation of Bayesian posterior sampling, a provably sample-efficient RL algorithm. We further leverage this connection to provide guarantees on the regret of the in-context algorithm yielded by DPT, and prove that it can learn faster than algorithms used to generate the pretraining data.

These results suggest a promising yet simple path towards instilling strong in-context decision-making abilities in transformers.

[See also "In-context Reinforcement Learning with Algorithm Distillation", Laskin et al 2022.] --- https://lilianweng.github.io/posts/2023-06-23-agent/ LLM Powered Autonomous Agents 2024-06-28 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex reinforcement-learning/multi-agent --- https://arxiv.org/abs/cs/0108005#microsoft A Bit of Progress in Language Modeling Joshua Goodman 2001-08-09 2024-06-28 [("doi","10.48550/arXiv.0108005")] ai/scaling

In the past several years, a number of different language modeling improvements over simple trigram models have been found, including caching, higher-order n-grams, skipping, interpolated Kneser-Ney smoothing, and clustering.

We present explorations of variations on, or of the limits of, each of these techniques, including showing that sentence mixture models may have more potential. While all of these techniques have been studied separately, they have rarely been studied in combination.

We find some interactions, especially with smoothing and clustering techniques.

We compare a combination of all techniques together to a Katz smoothed trigram model with no count cutoffs. We achieve perplexity reductions of 38%–50% (1 bit of entropy), depending on training data size, as well as a word error rate reduction of 8.9%. Our perplexity reductions are perhaps the highest reported compared to a fair baseline.

This is the extended version of the paper; it contains additional details and proofs, and is designed to be a good introduction to the state-of-the-art in language modeling.

--- https://arxiv.org/abs/2310.02059 Security Weaknesses of Copilot Generated Code in GitHub Yujia Fu, Peng Liang, Amjed Tahir, Zengyang Li, Mojtaba Shahin, Jiaxin Yu, Jinfu Chen 2023-10-03 2024-06-28 [("doi","10.48550/arXiv.2310.02059")] ai/nn/transformer/gpt/codex cs/security

Modern code generation tools, using AI models like Large Language Models (LLMs), have gained popularity for producing functional code. However, their usage presents security challenges, often resulting in insecure code merging into the code base. Evaluating the quality of generated code, especially its security, is crucial. While prior research explored various aspects of code generation, the focus on security has been limited, mostly examining code produced in controlled environments rather than real-world scenarios.

To address this gap, we conducted an empirical study, analyzing code snippets generated by GitHub Copilot from GitHub projects. Our analysis identified 452 snippets generated by Copilot, revealing a high likelihood of security issues, with 32.8% of Python and 24.5% of JavaScript snippets affected.

These issues span 38 different Common Weakness Enumeration (CWE) categories, including ones like CWE-330: Use of Insufficiently Random Values, CWE-78: OS Command Injection, and CWE-94: Improper Control of Generation of Code. Notably, 8 CWEs are among the 2023 CWE Top-25, highlighting their severity.

Our findings confirm that developers should be careful when adding code generated by Copilot and should also run appropriate security checks as they accept the suggested code. It also shows that practitioners should cultivate corresponding security awareness and skills.

--- https://en.wikipedia.org/wiki/Oslo_Report#British_reaction Oslo Report § British reaction 2024-06-28 psychology/cognitive-bias technology --- https://archive.org/details/in.ernet.dli.2015.179256 Practical Criticism: A Study of Literary Judgment I. A. Richards 1929 2024-01-01 fiction/criticism psychology/cognitive-bias/illusion-of-depth --- https://en.wikipedia.org/wiki/I._A._Richards I. A. Richards 2024-01-01 fiction/criticism psychology/cognitive-bias/illusion-of-depth --- https://archive.org/details/poemschieflyofea00wordrich/page/346/mode/2up Poems, chiefly of early and late years: including The Borderers, a tragedy William Wordsworth 2024-04-28 fiction/poetry --- https://en.wikipedia.org/wiki/I._J._Deary I. J. Deary 2024-01-01 genetics --- https://80000hours.org/podcast/episodes/carl-shulman-economy-agi/ Carl Shulman on the economy and national security after AGI 2024-06-28 ai/scaling/economics --- https://academic.oup.com/aje/article/166/2/212/98784 Motor Vehicle Crash Injury Rates by Mode of Travel, United States: Using Exposure-Based Methods to Quantify Differences 2024-06-29 psychiatry/traumatic-brain-injury --- https://arxiv.org/abs/2311.01550 Market Concentration Implications of Foundation Models Jai Vipra, Anton Korinek 2023-11-02 2024-06-29 [("doi","10.48550/arXiv.2311.01550")] ai/scaling/economics

[blog; eg. Adept?] We analyze the structure of the market for foundation models, ie. large AI models such as those that power ChatGPT and that are adaptable to downstream uses, and we examine the implications for competition policy and regulation.

We observe that the most capable models will have a tendency towards natural monopoly and may have potentially vast markets.

This calls for a two-pronged regulatory response: (1) antitrust authorities need to ensure the contestability of the market by tackling strategic behavior, in particular by ensuring that monopolies do not propagate vertically to downstream uses, and (2) given the diminished potential for market discipline, there is a role for regulators to ensure that the most capable models meet sufficient quality standards (including safety, privacy, non-discrimination, reliability, and interoperability standards) to maximally contribute to social welfare.

Regulators should also ensure a level regulatory playing field between AI and non-AI applications in all sectors of the economy.

For models that are behind the frontier, we expect competition to be quite intense, implying a more limited role for competition policy, although a role for regulation remains.

--- https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/advanced-prompt-engineering Prompt engineering techniques with Azure OpenAI 2024-06-29 ai/nn/transformer/gpt/4/nonfiction --- https://www.vice.com/en/article/y3wdj7/inside-the-discord-where-thousands-of-rogue-producers-are-making-ai-music Inside the Discord Where Thousands of Rogue Producers Are Making AI Music 2024-06-29 ai/music economics/copyright --- https://arstechnica.com/gadgets/2023/04/report-describes-apples-organizational-dysfunction-and-lack-of-ambition-in-ai/ Report describes Apple’s ‘organizational dysfunction’ and ‘lack of ambition’ in AI 2024-06-29 ai/scaling --- https://www.reuters.com/technology/inside-metas-scramble-catch-up-ai-2023-04-25/ 2024-06-29 ai/scaling/hardware --- https://www.biorxiv.org/content/10.1101/2022.04.14.488361.full Reward Bases: Instantaneous reward revaluation with temporal difference learning Beren Millidge, Mark Walton, Rafal Bogacz 2022-04-14 2024-06-29 [("doi","10.1101/2022.04.14.488361")] psychology/neuroscience reinforcement-learning/model-free

[background] An influential theory posits that dopaminergic neurons in the mid-brain implement a model-free reinforcement learning algorithm based on temporal difference (TD) learning. A fundamental assumption of this model is that the reward function being optimized is fixed. However, for biological creatures, the ‘reward function’ can fluctuate substantially over time depending on the internal physiological state of the animal. For instance, food is rewarding when you are hungry, but not when you are satiated.

While a variety of experiments have demonstrated that animals can instantly adapt their behavior when their internal physiological state changes, under current thinking this requires model-based planning since the standard model of TD learning requires retraining from scratch if the reward function changes.

Here, we propose a novel and simple extension to TD learning that allows for the zero-shot (instantaneous) generalization to changing reward functions. Mathematically, we show that if we assume the reward function is a linear combination of reward basis vectors, and if we learn a value function for each reward basis using TD learning, then we can recover the true value function by a linear combination of these value function bases. This representational scheme allows instant and perfect generalization to any reward function in the span of the reward basis vectors as well as possesses a straightforward implementation in neural circuitry by parallelizing the standard circuitry required for TD learning.

We demonstrate that our algorithm can also reproduce behavioral data on reward revaluation tasks, predict dopamine responses in the nucleus accumbens, as well as learn equally fast as successor representations while requiring much less memory.

--- https://www.beren.io/2023-04-19-Hedonic-loops-taming-RL/ Hedonic loops and taming RL Beren Millidge 2024-06-29 psychology/neuroscience reinforcement-learning/model-free --- https://arxiv.org/abs/2301.01751#elicit Iterated Decomposition: Improving Science Q&A by Supervising Reasoning Processes Justin Reppert, Ben Rachbach, Charlie George, Luke Stebbing, Jungwon Byun, Maggie Appleton, Andreas Stuhlmüller 2023-01-04 2024-06-29 [("doi","10.48550/arXiv.2301.01751")] ai/nn/transformer/gpt/inner-monologue

[Twitter] Language models (LMs) can perform complex reasoning either end-to-end, with hidden latent state, or compositionally, with transparent intermediate state. Composition offers benefits for interpretability and safety, but may need workflow support and infrastructure to remain competitive.

We describe iterated decomposition, a human-in-the-loop workflow for developing and refining compositional LM programs. We improve the performance of compositions by zooming in on failing components and refining them through decomposition, additional context, chain-of-thought, etc. To support this workflow, we develop ICE, an open-source tool for visualizing the execution traces of LM programs.

We apply iterated decomposition to 3 real-world tasks and improve the accuracy of LM programs over less compositional baselines: describing the placebo used in a randomized controlled trial (25% → 65%), evaluating participant adherence to a medical intervention (53% → 70%), and answering NLP questions on the QASPER dataset (38% → 69%).

These applications serve as case studies for a workflow that, if automated, could keep ML systems interpretable and safe even as they scale to increasingly complex tasks.

--- https://www.geoffreylitt.com/2023/02/26/llm-as-muse-not-oracle.html ChatGPT as muse, not oracle Geoffrey Litt 2024-06-29 ai/nn/transformer/gpt reinforcement-learning/exploration --- https://www.hillelwayne.com/post/are-we-really-engineers/ Are We Really Engineers? Hillel Wayne 2024-06-29 cs design --- https://www.hillelwayne.com/post/what-we-can-learn/ What engineering can teach (and learn from) us Hillel Wayne 2024-06-29 cs design --- https://arxiv.org/abs/2310.01783 Can large language models provide useful feedback on research papers? A large-scale empirical analysis Weixin Liang, Yuhui Zhang, Hancheng Cao, Binglu Wang, Daisy Ding, Xinyu Yang, Kailas Vodrahalli, Siyu He, Daniel Smith, Yian Yin, Daniel McFarland, James Zou 2023-10-03 2024-06-29 [("doi","10.48550/arXiv.2310.01783")] ai/nn/transformer/gpt/4/nonfiction statistics/peer-review

Expert feedback lays the foundation of rigorous research. However, the rapid growth of scholarly production and intricate knowledge specialization challenge the conventional scientific feedback mechanisms. High-quality peer reviews are increasingly difficult to obtain. Researchers who are more junior or from under-resourced settings have especially hard times getting timely feedback.

With the breakthrough of large language models (LLM) such as GPT-4, there is growing interest in using LLMs to generate scientific feedback on research manuscripts. However, the utility of LLM-generated feedback has not been systematically studied. To address this gap, we created an automated pipeline using GPT-4 to provide comments on the full PDFs of scientific papers.

We evaluated the quality of GPT-4’s feedback through two large-scale studies. We first quantitatively compared GPT-4’s generated feedback with human peer reviewer feedback in 15 Nature family journals (3,096 papers in total) and the ICLR machine learning conference (1,709 papers).

The overlap in the points raised by GPT-4 and by human reviewers (average overlap 30.85% for Nature journals, 39.23% for ICLR) is comparable to the overlap between two human reviewers (average overlap 28.58% for Nature journals, 35.25% for ICLR). The overlap between GPT-4 and human reviewers is larger for the weaker papers.

We then conducted a prospective user study with 308 researchers from 110 US institutions in the field of AI and computational biology to understand how researchers perceive feedback generated by our GPT-4 system on their own papers.

Overall, more than half (57.4%) of the users found GPT-4 generated feedback helpful/very helpful and 82.4% found it more beneficial than feedback from at least some human reviewers.

While our findings show that LLM-generated feedback can help researchers, we also identify several limitations.

--- https://arxiv.org/abs/2406.18906 Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasets Melanie Walsh, Anna Preus, Maria Antoniak 2024-06-27 2024-06-29 [("doi","10.48550/arXiv.2406.18906")] ai/dataset ai/nn/tokenization ai/nn/transformer/gpt/3/poetry ai/nn/transformer/gpt/4/poetry reinforcement-learning/preference-learning/mode-collapse

[fails to consider BPE tokenization or tuning] Large language models (LLMs) can now generate and recognize text in a wide range of styles and genres, including highly specialized, creative genres like poetry. But what do LLMs really know about poetry? What can they know about poetry?

We develop a task to evaluate how well LLMs recognize a specific aspect of poetry, poetic form, for more than 20 forms and formal elements in the English language. Poetic form captures many different poetic features, including rhyme scheme, meter, and word or line repetition.

We use this task to reflect on LLMs’ current poetic capabilities [ChatGPT-3, (Chat)GPT-4, Claude-3-Sonnet, LLaMA-3-70b—unclear whether base or tuned], as well as the challenges and pitfalls of creating NLP benchmarks for poetry and for other creative tasks. In particular, we use this task to audit and reflect on the poems included in popular pretraining datasets.

Our findings have implications for NLP researchers interested in model evaluation, digital humanities and cultural analytics scholars, and cultural heritage professionals.

Figure 4: Fixed Forms—Poetry Foundation and Academy of American Poets. These figures show LLM performance (F1 scores) on the task of detecting a poem’s form (in the same way as the human annotation/institution it was collected from) by prompt type: with only the text of the poem; only the author and title; only the first line; only the last line. Error bars indicate standard deviation across 20 bootstrapped samples of poems.

…Poetic forms based on topic prove more difficult for the models, depending on the topic (Table 5 & Table 6). Forms centered on more concrete subjects like ‘death’ (elegy) and ‘art’ (ars poetica, ekphrasis) are more often recognized, while poems about abstract ideas and styles like aubades and odes are less so.

There are fewer forms in our dataset that depend on visual features, but most models except GPT-4 and GPT-4o falter with them, namely with concrete / pattern poetry (ie. poems that rely on visual and typographical elements for their structure) and prose poetry (ie. poems that don’t have line breaks and look like prose).

See Also:

--- https://guides.orchidhealth.com/post/meet-orchid-the-first-whole-genome-embryo-reports Meet Orchid, the first whole genome embryo reports 2024-06-29 genetics/sequencing --- https://arxiv.org/abs/2312.02147 Rejuvenating image-GPT as Strong Visual Representation Learners Sucheng Ren, Zeyu Wang, Hongru Zhu, Junfei Xiao, Alan Yuille, Cihang Xie 2023-12-04 2024-06-29 [("doi","10.48550/arXiv.2312.02147")] ai/nn/transformer/gpt/dall-e/1

This paper enhances image-GPT (iGPT), one of the pioneering works that introduce autoregressive pretraining to predict next pixels for visual representation learning.

Two simple yet essential changes are made. First, we shift the prediction target from raw pixels to semantic tokens, enabling a higher-level understanding of visual content. Second, we supplement the autoregressive modeling by instructing the model to predict not only the next tokens but also the visible tokens. This pipeline is particularly effective when semantic tokens are encoded by discriminatively trained models, such as CLIP. We introduce this novel approach as D-iGPT.

Extensive experiments showcase that D-iGPT excels as a strong learner of visual representations: A notable achievement of D-iGPT is its compelling performance on the ImageNet-1K dataset—by training on publicly available datasets, D-iGPT achieves 89.5% top-1 accuracy with a vanilla ViT-Large model. This model also shows strong generalization on the downstream task and robustness on out-of-distribution samples.

Code is available at https://github.com/OliverRensu/D-iGPT.

--- https://arxiv.org/abs/2312.02119 Tree of Attacks (TAP): Jailbreaking Black-Box LLMs Automatically Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum Anderson, Yaron Singer, Amin Karbasi 2023-12-04 2024-06-29 [("doi","10.48550/arXiv.2312.02119")] ai/nn/adversarial ai/nn/transformer/gpt/inner-monologue

[code] While Large Language Models (LLMs) display versatile functionality, they continue to generate harmful, biased, and toxic content, as demonstrated by the prevalence of human-designed jailbreaks.

In this work, we present Tree of Attacks with Pruning (TAP), an automated method for generating jailbreaks that only requires black-box access to the target LLM. TAP uses an LLM to iteratively refine candidate (attack) prompts using tree-of-thought reasoning until one of the generated prompts jailbreaks the target. Crucially, before sending prompts to the target, TAP assesses them and prunes the ones unlikely to result in jailbreaks. Using tree-of-thought reasoning allows TAP to navigate a large search space of prompts and pruning reduces the total number of queries sent to the target.

In empirical evaluations, we observe that TAP generates prompts that jailbreak state-of-the-art LLMs (including GPT-4 and GPT-4-Turbo) for more than 80% of the prompts using only a small number of queries. Interestingly, TAP is also capable of jailbreaking LLMs protected by state-of-the-art guardrails, eg. LlamaGuard.

This improves upon the previous state-of-the-art black-box method for generating jailbreaks.

--- https://arxiv.org/abs/2312.02142 Object Recognition as Next Token Prediction Kaiyu Yue, Bor-Chun Chen, Jonas Geiping, Hengduo Li, Tom Goldstein, Ser-Nam Lim 2023-12-04 2024-06-29 [("doi","10.48550/arXiv.2312.02142")] ai/nn/transformer/gpt

We present an approach to pose object recognition as next token prediction. The idea is to apply a language decoder that auto-regressively predicts the text tokens from image embeddings to form labels.

To ground this prediction process in auto-regression, we customize a non-causal attention mask for the decoder, incorporating two key features: modeling tokens from different labels to be independent, and treating image tokens as a prefix. This masking mechanism inspires an efficient method—one-shot sampling—to simultaneously sample tokens of multiple labels in parallel and rank generated labels by their probabilities during inference.

To further enhance the efficiency, we propose a simple strategy to construct a compact decoder by simply discarding the intermediate blocks of a pretrained language model. This approach yields a decoder that matches the full model’s performance while being notably more efficient.

The code is available at https://github.com/kaiyuyue/nxtp.

--- https://www.nature.com/articles/s41467-022-28881-w Machine learning reveals cryptic dialects that explain mate choice in a songbird Daiping Wang, Wolfgang Forstmeier, Damien R. Farine, Adriana A. Maldonado-Chaparro, Katrin Martin, Yifan Pei, Gustavo Alarcón-Nieto, James A. Klarevas-Irby, Shouwen Ma, Lucy M. Aplin, Bart Kempenaers 2022-03-28 2024-06-29 [("doi","10.1038/s41467-022-28881-w")] ai psychology/linguistics

Culturally transmitted communication signals—such as human language or bird song—can change over time through cultural drift, and the resulting dialects may consequently enhance the separation of populations. However, the emergence of song dialects has been considered unlikely when songs are highly individual-specific, as in the zebra finch (Taeniopygia guttata).

Here we show that machine learning [Apple classifier, unclear what] can nevertheless distinguish the songs from multiple captive zebra finch populations with remarkable precision, and that ‘cryptic song dialects’ predict strong assortative mating in this species.

We examine mating patterns across 3 consecutive generations using captive populations that have evolved in isolation for about 100 generations. We cross-fostered eggs within and between these populations and used an automated barcode tracking system to quantify social interactions.

We find that females preferentially pair with males whose song resembles that of the females’ adolescent peers.

Our study shows evidence that in zebra finches, a model species for song learning, individuals are sensitive to differences in song that have hitherto remained unnoticed by researchers.

--- https://goblin.tools/ Magic ToDo List Creator 2024-06-29 ai/nn/transformer/gpt/inner-monologue --- https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/ 2024-06-29 ai/nn/retrieval ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue statistics/order/comparison --- https://arxiv.org/abs/2310.16049 MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning Zayne Sprague, Xi Ye, Kaj Bostrom, Swarat Chaudhuri, Greg Durrett 2023-10-24 2024-06-29 [("doi","10.48550/arXiv.2310.16049")] ai/dataset ai/nn/transformer/gpt/4/fiction

While large language models (LLMs) equipped with techniques like chain-of-thought prompting have demonstrated impressive capabilities, they still fall short in their ability to reason robustly in complex settings. However, evaluating LLM reasoning is challenging because system capabilities continue to grow while benchmark datasets for tasks like logical deduction have remained static.

We introduce MuSR, a dataset for evaluating language models on multistep soft reasoning tasks specified in a natural language narrative. This dataset has two crucial features. First, it is created through a novel neurosymbolic synthetic-to-natural generation algorithm, enabling the construction of complex reasoning instances that challenge GPT-4 (eg. murder mysteries roughly 1,000 words in length) and which can be scaled further as more capable LLMs are released.

[Amusing because Ilya Sutskever has long used murder mysteries as an example of how prediction loss functions can, theoretically, elicit long-term causal understanding / theory of mind / etc: you can’t predict the final revelation’s exact words without those.]

Second, our dataset instances are free text narratives corresponding to real-world domains of reasoning; this makes it simultaneously much more challenging than other synthetically-crafted benchmarks while remaining realistic and tractable for human annotators to solve with high accuracy.

We evaluate a range of LLMs and prompting techniques on this dataset and characterize the gaps that remain for techniques like chain-of-thought to perform robust reasoning.

--- https://www.science.org/doi/10.1126/sciadv.adf3197 2024-06-30 psychology/linguistics sociology/technology --- https://jamanetwork.com/journals/jama/fullarticle/2803518 Heterogeneity in Blood Pressure Response to 4 Antihypertensive Drugs: A Randomized Clinical Trial 2024-06-30 nootropic/quantified-self --- https://arxiv.org/abs/1503.04069 LSTM: A Search Space Odyssey Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, Jürgen Schmidhuber 2015-03-13 2024-06-30 [("doi","10.1109/TNNLS.2016.2582924")] ai/nn/rnn ai/scaling

Several variants of the Long Short-Term Memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks have become the state-of-the-art models for a variety of machine learning problems. This has led to a renewed interest in understanding the role and utility of various computational components of typical LSTM variants.

In this paper, we present the first large-scale analysis of 8 LSTM variants on 3 representative tasks: speech recognition, handwriting recognition, and polyphonic music modeling. The hyperparameters of all LSTM variants for each task were optimized separately using random search, and their importance was assessed using the powerful fANOVA framework.

In total, we summarize the results of 5,400 experimental runs (~15 years of CPU time), which makes our study the largest of its kind on LSTM networks. Our results show that none of the variants can improve upon the standard LSTM architecture, and demonstrate the forget gate and the output activation function to be its most critical components.

We further observe that the studied hyperparameters are virtually independent and derive guidelines for their efficient adjustment.

--- https://www.nature.com/articles/s41591-023-02729-2 2024-06-30 psychiatry/alzheimers --- https://x.com/DimitrisPapail/status/1804233021429813661 Dimitris Papail 2024-06-30 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude design/typography/tex --- https://www.axios.com/2024/06/29/two-bidens-trump-debate-2024-president 2024-06-30 psychiatry/alzheimers zeo --- https://arxiv.org/abs/2310.19387 Othello is Solved Hiroki Takizawa 2023-10-30 2024-06-30 [("doi","10.48550/arXiv.2310.19387")] reinforcement-learning/model

The game of Othello is one of the world’s most complex and popular games that has yet to be computationally solved. Othello has roughly 10 octodecillion (1058) possible game records and 10 octillion (1028) possible game positions. The challenge of solving Othello, determining the outcome of a game with no mistake made by either player, has long been a grand challenge in computer science.

This paper announces a milestone: Othello is now solved. It is computationally proved that perfect play by both players lead to a draw.

Strong Othello software has long been built using heuristically designed search techniques. Solving a game provides a solution that enables the software to play the game perfectly.

--- https://x.com/elonmusk/status/1229546206948462597 Elon Musk 2024-06-30 ai/nn/anthropic reinforcement-learning/openai --- https://arxiv.org/abs/2307.05300#microsoft Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji 2023-07-11 2024-06-30 [("doi","10.48550/arXiv.2307.05300")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue ai/scaling

Human intelligence thrives on cognitive synergy, where collaboration among different minds yields superior outcomes compared to isolated individuals.

In this work, we propose Solo Performance Prompting (SPP), which transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas. A cognitive synergist is an intelligent agent that collaboratively combines multiple minds’ strengths and knowledge to enhance problem-solving in complex tasks. By dynamically identifying and simulating different personas based on task inputs, SPP unleashes the potential of cognitive synergy in LLMs.

Our in-depth analysis shows that assigning multiple fine-grained personas in LLMs improves problem-solving abilities compared to using a single or fixed number of personas. We evaluate SPP on 3 challenging tasks: Trivia Creative Writing, Codenames Collaborative, and Logic Grid Puzzle, encompassing both knowledge-intensive and reasoning-intensive types.

Unlike previous works, such as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs, experimental results demonstrate that SPP effectively reduces factual hallucination and maintains strong reasoning capabilities. Additionally, comparative experiments show that cognitive synergy only emerges in GPT-4 and does not appear in less capable models, such as GPT-3.5-turbo and Llama2-13b-chat, which draws an interesting analogy to human development.

Code, data, and prompts can be found at: Github.

--- https://www.youtube.com/watch?v=jrTYdOEaiy0&t=2534s Bill Gates Reveals Superhuman AI Prediction 2024-06-30 ai/scaling/economics --- https://blog.nationalmuseum.ch/en/2024/06/the-dream-of-an-alpine-waterway/ The dream of an alpine waterway 2024-06-30 technology --- https://x.com/JonBaronforMD/status/1677329664249851907 Jon Baronfor 2024-06-30 sociology statistics/bias --- https://x.com/JonBaronforMD/status/1676681267822075904 Jon Baronfor 2024-06-30 statistics/causality --- https://x.com/JonBaronforMD/status/1676681274226884610 Jon Baronfor 2024-06-30 statistics/causality --- https://x.com/GrantSlatton/status/1677895737735286785 Grant Slatton 2024-06-30 ai/nn/transformer/gpt/codex --- https://x.com/GrantSlatton/status/1677895739958267905 Grant Slatton 2024-06-30 ai/nn/transformer/gpt/codex --- https://x.com/AIPanic/status/1678942763121795073 AIPanic 2024-06-30 ai/nn/adversarial ai/nn/transformer/gpt/claude --- http://theinsideview.ai/eric#the-quantization-model-of-neural-scaling Eric Michaud on Neural Quantum Interpretability 2024-06-30 ai/scaling --- https://www.nature.com/articles/s41598-023-31175-w 2024-06-30 genetics/selection/natural/human psychology/vision --- https://arxiv.org/abs/2306.14101 Language models are weak learners Hariharan Manikandan, Yiding Jiang, J. Zico Kolter 2023-06-25 2024-06-30 [("doi","10.48550/arXiv.2306.14101")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/inner-monologue ai/tabular reinforcement-learning/meta-learning

A central notion in practical and theoretical machine learning is that of a weak learner, classifiers that achieve better-than-random performance (on any given distribution over data), even by a small margin. Such weak learners form the practical basis for canonical machine learning methods such as boosting.

In this work, we illustrate that prompt-based large language models can operate effectively as said weak learners. Specifically, we illustrate the use of a large language model (LLM) as a weak learner in a boosting algorithm applied to tabular data. We show that by providing (properly sampled according to the distribution of interest) text descriptions of tabular data samples, LLMs can produce a summary of the samples that serves as a template for classification and achieves the aim of acting as a weak learner on this task.

We incorporate these models into a boosting approach, which in some settings can leverage the knowledge within the LLM to outperform traditional tree-based boosting. The model outperforms both few-shot learning and occasionally even more involved fine-tuning procedures, particularly for tasks involving small numbers of data points.

The results illustrate the potential for prompt-based LLMs to function not just as few-shot learners themselves, but as components of larger machine learning pipelines.

--- https://arxiv.org/abs/2306.12554 Improving Long-Horizon Imitation Through Instruction Prediction Joey Hejna, Pieter Abbeel, Lerrel Pinto 2023-06-21 2024-06-30 [("doi","10.48550/arXiv.2306.12554")] ai/nn/transformer/gpt reinforcement-learning/meta-learning reinforcement-learning/model

Complex, long-horizon planning and its combinatorial nature pose steep challenges for learning-based agents. Difficulties in such settings are exacerbated in low data regimes where over-fitting stifles generalization and compounding errors hurt accuracy.

In this work, we explore the use of an often unused source of auxiliary supervision: language. Inspired by recent advances in transformer-based models, we train agents with an instruction prediction loss that encourages learning temporally extended representations that operate at a high level of abstraction.

Concretely, we demonstrate that instruction modeling improves performance in planning environments when training with a limited number of demonstrations on the BabyAI and Crafter benchmarks. In further analysis, we find that instruction modeling is most important for tasks that require complex reasoning, while understandably offering smaller gains in environments that require simple plans.

More details and code can be found at Github.

--- https://arxiv.org/abs/2306.11987 Training Transformers with 4-bit Integers Haocheng Xi, Changhao Li, Jianfei Chen, Jun Zhu 2023-06-21 2024-06-30 [("doi","10.48550/arXiv.2306.11987")] ai/nn/sparsity/low-precision

Quantizing the activation, weight, and gradient to 4-bit is promising to accelerate neural network training. However, existing 4-bit training methods require custom numerical formats which are not supported by contemporary hardware.

In this work, we propose a training method for transformers with all matrix multiplications implemented with the INT4 arithmetic. Training with an ultra-low INT4 precision is challenging. To achieve this, we carefully analyze the specific structures of activation and gradients in transformers to propose dedicated quantizers for them.

For forward propagation, we identify the challenge of outliers and propose a Hadamard quantizer to suppress the outliers. For backpropagation, we leverage the structural sparsity of gradients by proposing bit splitting and leverage score sampling techniques to quantize gradients accurately.

Our algorithm achieves competitive accuracy on a wide range of tasks including natural language understanding, machine translation, and image classification. Unlike previous 4-bit training methods, our algorithm can be implemented on the current generation of GPUs. Our prototypical linear operator implementation is up to 2.2× faster than the FP16 counterparts and speeds up the training by up to 35.1%.

--- https://arxiv.org/abs/2306.13840 Beyond Scale: the Diversity Coefficient as a Data Quality Metric Demonstrates LLMs are Pre-trained on Formally Diverse Data Alycia Lee, Brando Miranda, Sudharsan Sundar, Sanmi Koyejo 2023-06-24 2024-06-30 [("doi","10.48550/arXiv.2306.13840")] ai/scaling reinforcement-learning/exploration/active-learning/data-pruning

Current trends to pre-train capable Large Language Models (LLMs) mostly focus on scaling of model and dataset size. However, the quality of pre-training data is an important factor for training powerful LLMs, yet it is a nebulous concept that has not been fully characterized.

Therefore, we use the recently proposed Task2Vec diversity coefficient to ground and understand formal aspects of data quality, to go beyond scale alone. Specifically, we measure the diversity coefficient of publicly available pre-training datasets to demonstrate that their formal diversity is high when compared to theoretical lower and upper bounds.

In addition, to build confidence in the diversity coefficient, we conduct interpretability experiments and find that the coefficient aligns with intuitive properties of diversity, eg. it increases as the number of latent concepts increases.

We conclude the diversity coefficient is reliable, show it’s high for publicly available LLM datasets, and conjecture it can be used to build useful diverse datasets for LLMs.

--- https://arxiv.org/abs/2210.14215#deepmind In-context Reinforcement Learning with Algorithm Distillation Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, D. J. Strouse, Steven Hansen, Angelos Filos, Ethan Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih 2022-10-25 2024-06-30 [("doi","10.48550/arXiv.2210.14215")] reinforcement-learning/exploration reinforcement-learning/imitation-learning reinforcement-learning/meta-learning reinforcement-learning/model/decision-transformer reinforcement-learning/offline

[Twitter] We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal sequence model. Algorithm Distillation treats learning to reinforcement learn as an across-episode sequential prediction problem.

A dataset of learning histories is generated by a source RL algorithm, and then a causal transformer is trained by autoregressively predicting actions given their preceding learning histories as context.

Unlike sequential policy prediction architectures that distill post-learning or expert sequences, AD is able to improve its policy entirely in-context without updating its network parameters.

We demonstrate that AD can reinforcement learn in-context in a variety of environments with sparse rewards, combinatorial task structure, and pixel-based observations, and find that AD learns a more data-efficient RL algorithm than the one that generated the source data.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10300070/ Multi-Modal Mobility Morphobot (M4) with appendage repurposing for locomotion plasticity enhancement 2024-06-30 reinforcement-learning/robot --- https://www.youtube.com/watch?v=J91jTI2-k_U M4 Drives and Flies Around Caltech’s Campus 2024-06-30 reinforcement-learning/robot --- https://arxiv.org/abs/1902.03545#amazon Task2Vec: Task Embedding for Meta-Learning Alessandro Achille, Michael Lam, Rahul Tewari, Avinash Ravichandran, Subhransu Maji, Charless Fowlkes, Stefano Soatto, Pietro Perona 2019-02-10 2024-06-30 [("doi","10.48550/arXiv.1902.03545")] reinforcement-learning/meta-learning

[code] We introduce a method, Task2vec, to provide vectorial representations of visual classification tasks which can be used to reason about the nature of those tasks and their relations.

Given a dataset with ground-truth labels and a loss function defined over those labels, we process images through a “probe network” and compute an embedding based on estimates of the Fisher information matrix associated with the probe network parameters. This provides a fixed-dimensional embedding of the task that is independent of details such as the number of classes and does not require any understanding of the class label semantics.

We demonstrate that this embedding is capable of predicting task similarities that match our intuition about semantic and taxonomic relations between different visual tasks (eg. tasks based on classifying different types of plants are similar). We also demonstrate the practical value of this framework for the meta-task of selecting a pre-trained feature extractor for a new task.

We present a simple meta-learning framework for learning a metric on embeddings that is capable of predicting which feature extractors will perform well. Selecting a feature extractor with task embedding obtains a performance close to the best available feature extractor, while costing substantially less than exhaustively training and evaluating on all available feature extractors.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10353062/ Pharmacology of R-(−)-Methamphetamine 2024-06-30 nootropic --- https://en.wikipedia.org/wiki/Dendrocnide_moroides#Toxicity Dendrocnide moroides § Toxicity 2024-06-30 psychology/neuroscience/pain --- https://en.wikipedia.org/wiki/Adragon_De_Mello Adragon De Mello 2024-06-30 iq/high --- /doc/ai/nn/transformer/gpt/4/2024-06-30-openai-chatgpt4o-systemprompt.txt 2024-06-30 2024-06-30 ai/nn/transformer/gpt/4 ai/nn/transformer/gpt/dall-e/3 --- https://gothamist.com/news/things-the-guys-who-stole-my-phone-have-texted-me-to-try-to-get-me-to-unlock-it Things the guys who stole my phone have texted me to try to get me to unlock it 2024-06-30 cs/security --- https://arxiv.org/abs/2405.10231 Influencer Cartels Marit Hinnosaar, Toomas Hinnosaar 2024-05-16 2024-06-30 [("doi","10.48550/arXiv.2405.10231")] economics/advertising sociology/technology

[blog] Social media influencers account for a growing share of marketing worldwide. We demonstrate the existence of a novel form of market failure in this advertising market: influencer cartels, where groups of influencers collude to increase their advertising revenue by inflating their engagement.

Our theoretical model shows that influencer cartels can improve consumer welfare if they expand social media engagement to the target audience, or reduce welfare if they divert engagement to less relevant audiences.

We validate the model empirically using novel data on influencer cartels [on Instagram, coordinated via Telegram] combined with machine learning tools, and derive policy implications for how to maximize consumer welfare.

...Our back-of-the-envelope calculations show that if an advertiser pays for cartel engagement as if it is natural engagement, they only get 3–18% of the value in the case of general cartels and 60–85% in the case of topic cartels.

[Keywords: influencers, marketing, collusion, natural language processing, LLMs, Latent Dirichlet Allocation]

--- https://arxiv.org/abs/2402.16671 StructLM: Towards Building Generalist Models for Structured Knowledge Grounding Alex Zhuang, Ge Zhang, Tianyu Zheng, Xinrun Du, Junjie Wang, Weiming Ren, Stephen W. Huang, Jie Fu, Xiang Yue, Wenhu Chen 2024-02-26 2024-06-30 [("doi","10.48550/arXiv.2402.16671")] ai/nn/transformer/gpt/instruction-tuning ai/scaling ai/tabular

Structured data sources, such as tables, graphs, and databases, are ubiquitous knowledge sources. Despite the demonstrated capabilities of large language models (LLMs) on plain text, their proficiency in interpreting and using structured data remains limited. Our investigation reveals a notable deficiency in LLMs’ ability to process structured data, eg. ChatGPT lags behind state-of-the-art (SoTA) model by an average of 35%.

To augment the Structured Knowledge Grounding (SKG) capabilities in LLMs, we have developed a comprehensive instruction tuning dataset comprising 1.1 million examples. Utilizing this dataset, we train a series of models, referred to as StructLM, based on the Mistral and the CodeLLaMA model family, ranging from 7B to 34b parameters.

Our StructLM series surpasses task-specific models on 16⁄18 evaluated datasets and establishes new SoTA performance on 8 SKG tasks. Furthermore, StructLM demonstrates strong generalization across 6 novel held-out SKG tasks, outperforming TableLLaMA by an average of 35% and Flan-UL2 20B by an average of 10%.

Contrary to expectations, we observe that scaling model size offers marginal benefits, with StructLM-34B showing only slight improvements over StructLM-7B.

This suggests that structured knowledge grounding is still a challenging task and requires more innovative design to push to a new level.

--- http://contemporary-home-computing.org/turing-complete-user/ The Turing Complete User 2024-06-30 cs design --- https://www.biorxiv.org/content/10.1101/2024.06.21.600029.full Understanding indirect assortative mating and its intergenerational consequences Hans Fredrik Sunde, Espen Moen Eilertsen, Fartein Ask Torvik 2024-06-26 2024-06-30 [("doi","10.1101/2024.06.21.600029")] genetics/heritable/correlation sociology

Partners tend to have similar levels of education. Previous studies indicate that this is likely due to some form of indirect assortative mating, but there is not a consistent understanding of this process. Understanding indirect assortment is crucial for correctly adjusting for assortative mating, and for understanding intergenerational transmission. We attribute previous inconsistencies to idiosyncratic models and inconsistent use of relevant terms.

In this paper, we develop a new framework for understanding indirect assortative mating and provide updated definitions of key terms. We then develop a model (the iAM-ACE-model) that can use partners of twins and siblings to distinguish the degree of assortment on genetic, social, and individual characteristics. We also expand this model to include children of twins and siblings (the iAM-COTS model), allowing us to explain parent-offspring similarity while accounting for indirect assortative mating and gene-environment correlations.

We apply the models on educational attainment using 1,529,144 individuals in 209,792 extended families from Norwegian registry data and the Norwegian Twin Registry. The analysis suggests that partners correlate 0.67 (95% CIs: 0.66–0.70) on a latent sorting factor associated with educational attainment, which we estimate to be about 30% (26–35%) heritable, 42% (37–47%) sibling-shared environment, 8% (4–13%) twin-shared environment, 17% (16–19%) gene-environment correlations, and 3% (2–5%) non-shared environment.

The implied genotypic correlation between partners (r = 0.33) is comparable to earlier studies, and higher than expected under direct assortment. Most of the parent-offspring correlation (r = 0.33) was attributable to passive genetic transmission (62%), with the rest attributable to passive environmental transmission (23%) and direct phenotypic transmission (15%). Environmental transmission was estimated lower in alternative models that assumed direct assortment, but these did not fit the data well.

--- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305354 A real-world test of artificial intelligence infiltration of a university examinations system: A ‘Turing Test’ case study Peter Scarfe, Kelly Watcham, Alasdair Clarke, Etienne Roesch 2024-06-26 2024-06-30 [("doi","10.1371/journal.pone.0305354")] ai/nn/transformer/gpt/4/nonfiction

[OSF] The recent rise in artificial intelligence systems, such as ChatGPT, poses a fundamental problem for the educational sector. In universities and schools, many forms of assessment, such as coursework, are completed without invigilation. Therefore, students could hand in work as their own which is in fact completed by AI. Since the COVID pandemic, the sector has additionally accelerated its reliance on unsupervised ‘take home exams’. If students cheat using AI and this is undetected, the integrity of the way in which students are assessed is threatened.

We report a rigorous, blind study in which we injected 100% GPT-4 written submissions into the examinations system in 5 undergraduate modules, across all years of study, for a BSc degree in Psychology at a reputable UK university…across all years of study for a BSc degree in Psychology in the School of Psychology and Clinical Language Sciences (henceforth, ‘the School’) at the University of Reading (henceforth, ‘the University’). Markers of the exams were completely unaware of this

We found that 94% of our AI submissions were undetected.

The grades awarded to our AI submissions were on average half a grade boundary higher than that achieved by real students. Across modules, there was an 83.4% chance that the AI submissions on a module would outperform a random selection of the same number of real student submissions.

…By design, markers on the modules we tested were completely unaware of the project. Other than those involved in authorizing the study and the authors, only a handful of others were aware (eg. those who helped arrange paid marking cover for the additional AI submissions and those who created the special university student accounts needed for AI submissions). Study authorization did not require informed consent from markers. Following the analysis of the data, we invited all markers to two sessions chaired by our Head of School, to explain the study and gather feedback. Markers were very supportive and engaged in fruitful discussions. None were aware of the study having been run.

…What were markers told about AI?

At the time of running the study, in the summer 2023, the use of AI to complete exams was not allowed and fell under the standard University academic misconduct policy, which stated that the work submitted by students had to be their own. The software systems used for exams submission and grading did not have an “AI detector” component. Colleagues received standard guidance from the School about how to spot poor academic practice and academic misconduct. This including, (1) checking if answers sounded “too good to be true” eg. a writing style, level of content, or quality, not expected from an undergraduate student completing a timed exam paper, (2) spotting answers which covered predominantly content which was not taught on the module, and (3) citations to references not linking with the claims being made in the answer. Many of these are characteristics of AI written text.

At the time, AI (particularly ChatGPT) was in the news media daily and an active topic of conversation amongst colleagues doing exam marking. The problem posed by AI for the academic integrity of assessments had also been discussed in larger meetings in the School. In debrief sessions given to colleagues who had marked on modules where we submitted AI (after the study had finished), virtually all were aware of the threat of AI to the integrity of exams. Indeed, in the few times where academic misconduct was suspected and reported, some colleagues referred to suspicions related to AI eg. answers that seemed too “good to be true”, cited esoteric literature not covered in the course or cited seemingly non-existent references. Some had also run exam questions through ChatGPT to compare with the suspicious answers and/or run suspicious answers through online “AI detectors”. Note that this was not used in a diagnostic fashion for academic misconduct.

…Therefore, pragmatically, it seems very likely that our markers graded, and did not detect, answers that students had produced using AI, in addition to our 100% AI generated answers. To counter this claim, one could argue that our AI submissions consistently outperformed real students, so this might suggest that AI was not used widely, else we would not have found this. An alternative argument is that students used AI, but in modifying the AI generated answers themselves made them worse than if that had simply entered the question and directly used the output unmodified as we did. Those student submissions which outperformed AI could also have consisted of AI generated material which students evaluated and verified, correcting factually incorrect information, to improve the AI response. Whatever the true nature of the reality in terms of the prevalence of use of AI, it is clear AI poses a serious threat to academic integrity.

--- https://www.aporiamagazine.com/p/the-myth-of-human-fragility The myth of human fragility 2024-06-30 genetics/heritable psychiatry/borderline --- https://arxiv.org/abs/2402.03175 The Matrix: A Bayesian learning model for LLMs Siddhartha Dalal, Vishal Misra 2024-02-05 2024-06-30 [("doi","10.48550/arXiv.2402.03175")] reinforcement-learning/meta-learning statistics/bayes

In this paper, we introduce a Bayesian learning model to understand the behavior of Large Language Models (LLMs).

We explore the optimization metric of LLMs, which is based on predicting the next token, and develop a novel model grounded in this principle. Our approach involves constructing an ideal generative text model represented by a multinomial transition probability matrix with a prior, and we examine how LLMs approximate this matrix.

We discuss the continuity of the mapping between embeddings and multinomial distributions, and present the Dirichlet approximation theorem to approximate any prior. Additionally, we demonstrate how text generation by LLMs aligns with Bayesian learning principles and delve into the implications for in-context learning, specifically explaining why in-context learning emerges in larger models where prompts are considered as samples to be updated.

Our findings indicate that the behavior of LLMs is consistent with Bayesian Learning, offering new insights into their functioning and potential applications.

--- https://x.com/priyankchn/status/1807412325990699065 priyankchn 2024-06-30 ai/nn/transformer/gpt/claude design/typography --- https://www.historyextra.com/period/second-world-war/real-life-battleships-game-ww2-german-uboats/ The Battleships Game That Countered German U-boat Attacks During WW2 2024-07-01 history statistics/decision --- https://en.wikipedia.org/wiki/Western_Approaches_Tactical_Unit Western Approaches Tactical Unit 2024-07-01 history statistics/decision --- https://arxiv.org/abs/2406.09490 Newswire: A Large-Scale Structured Database of a Century of Historical News Emily Silcock, Abhishek Arora, Luca D’Amico-Wong, Melissa Dell 2024-06-13 2024-07-01 [("doi","10.48550/arXiv.2406.09490")] ai/dataset economics/copyright

[HuggingFace] In the U.S. historically, local newspapers drew their content largely from newswires like the Associated Press. Historians argue that newswires played a pivotal role in creating a national identity and shared understanding of the world, but there is no comprehensive archive of the content sent over newswires.

We reconstruct such an archive by applying a customized deep learning pipeline to hundreds of terabytes of raw image scans from thousands of local newspapers.

The resulting dataset contains 2.7 million unique public domain U.S. newswire articles, written 1878–1977. [Due to being copyrighted by default starting in 1978] Locations in these articles are geo-referenced, topics are tagged using customized neural topic classification, named entities are recognized, and individuals are disambiguated to Wikipedia using a novel entity disambiguation model.

To construct the Newswire dataset, we first recognize newspaper layouts and transcribe around 138 million structured article texts from raw image scans. We then use a customized neural bi-encoder model to de-duplicate reproduced articles, in the presence of considerable abridgment and noise, quantifying how widely each article was reproduced. A text classifier is used to ensure that we only include newswire articles, which historically are in the public domain. The structured data that accompany the texts provide rich information about the who (disambiguated individuals), what (topics), and where (geo-referencing) of the news that millions of Americans read over the course of a century.

We also include Library of Congress metadata information about the newspapers that ran the articles on their front pages.

The Newswire dataset is useful both for large language modeling—expanding training data beyond what is available from modern web texts—and for studying a diversity of questions in computational linguistics, social science, and the digital humanities.

--- https://www.sfchronicle.com/projects/2022/san-francisco-sros/ 2024-07-01 sociology --- https://clutejournals.com/index.php/JBER/article/download/2787/2835#pdf Return the ‘Sunk Costs Are Sunk’ Concept to Principles of Economics Textbooks Davis 2005 2024-01-01 economics psychology/cognitive-bias/sunk-cost --- https://en.wikipedia.org/wiki/Anna_Karenina_principle Anna Karenina principle 2024-01-01 sociology --- https://en.wikipedia.org/wiki/Anthropic_principle Anthropic principle 2024-01-01 existential-risk philosophy/epistemology --- https://en.wikipedia.org/wiki/Dirichlet%27s_principle Dirichlet’s principle 2024-01-01 math --- https://arxiv.org/abs/2105.03011#allen QASPER: A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers Pradeep Dasigi, Kyle Lo, Iz Beltagy, Arman Cohan, Noah Smith, Matt Gardner 2021-05-07 2024-07-01 [("doi","10.48550/arXiv.2105.03011")] ai/dataset ai/nn/transformer

Readers of academic research papers often read with the goal of answering specific questions. Question Answering systems that can answer those questions can make consumption of the content much more efficient. However, building such tools requires data that reflect the difficulty of the task arising from complex reasoning about claims made in multiple parts of a paper. In contrast, existing information-seeking question answering datasets usually contain questions about generic factoid-type information.

We therefore present QASPER, a dataset of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers.

We find that existing models that do well on other QA tasks do not perform well on answering these questions, underperforming humans by at least 27 F1 points when answering them from entire papers.

This motivates further research in document-grounded, information-seeking QA, which our dataset is designed to facilitate.

--- https://openreview.net/forum?id=m6xyTie61H Eliciting Language Model Behaviors using Reverse Language Models Jacob Pfau, Alex Infanger, Abhay Sheshadri, Ayush Panda, Julian Michael, Curtis Huebner 2023-11-28 2024-07-01 ai/nn/adversarial ai/nn/transformer/gpt/2

Despite advances in fine-tuning methods, language models (LMs) continue to output toxic and harmful responses on worst-case inputs, including adversarial attacks and jailbreaks.

We train an LM on tokens in reverse order—a reverse LM—as a tool for identifying such worst-case inputs. By prompting a reverse LM with a problematic string, we can sample prefixes that are likely to precede the problematic suffix.

We test our reverse LM by using it to guide beam search for prefixes that have high probability of generating toxic statements when input to a forwards LM. Our 160m parameter reverse LM outperforms the existing state-of-the-art adversarial attack method, GCG, when measuring the probability of toxic continuations from the Pythia-160m LM.

We also find that the prefixes generated by our reverse LM for the Pythia model are more likely to transfer to other models, eliciting toxic responses also from Llama 2 when compared to GCG-generated attacks.

--- https://arxiv.org/abs/2401.17505 Arrows of Time for Large Language Models Vassilis Papadopoulos, Jérémie Wenger, Clément Hongler 2024-01-30 2024-07-01 [("doi","10.48550/arXiv.2401.17505")] ai/nn/transformer/gpt ai/scaling

We study the probabilistic modeling performed by Autoregressive Large Language Models (LLMs) through the angle of time directionality, addressing a question first raised in Shannon 1951.

For large enough models, we empirically find a time asymmetry in their ability to learn natural language: a difference in the average log-perplexity when trying to predict the next token versus when trying to predict the previous one. This difference is at the same time subtle and very consistent across various modalities (language, model size, training time, …). Theoretically, this is surprising: from an information-theoretic point of view, there should be no such difference.

We provide a theoretical framework to explain how such an asymmetry can appear from sparsity and computational complexity considerations, and outline a number of perspectives opened by our results.

--- https://asteriskmag.com/issues/06/when-rand-made-magic-in-santa-monica When RAND Made Magic in Santa Monica 2024-07-02 statistics/decision --- /doc/radiance/1997-hounshell.pdf The Cold War, RAND, and the Generation of Knowledge, 1946–1962 David Hounshell 1997-01-01 2024-07-02 [("doi","10.2307/27757779")] radiance statistics/decision --- https://www.bitsaboutmoney.com/archive/working-title-insurance/ Working title (real estate insurance) Patrick McKenzie 2024-07-02 economics --- https://www.lesswrong.com/posts/5XmxmszdjzBQzqpmz/interpreting-preference-models-w-sparse-autoencoders Interpreting Preference Models w/Sparse Autoencoders 2024-07-02 ai/nn/adversarial reinforcement-learning/preference-learning --- https://arxiv.org/abs/2303.17015 HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion Ziya Erkoç, Fangchang Ma, Qi Shan, Matthias Nießner, Angela Dai 2023-03-29 2024-07-02 [("doi","10.48550/arXiv.2303.17015")] ai/nn/diffusion ai/nn/fully-connected

Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from coordinates (eg. x, y, z) to signals (eg. signed distances), have shown remarkable promise as a high-fidelity and compact representation.

However, the lack of a regular and explicit grid structure also makes it challenging to apply generative modeling directly on implicit neural fields in order to synthesize new data. To this end, we propose HyperDiffusion, a novel approach for unconditional generative modeling of implicit neural fields. HyperDiffusion operates directly on MLP weights and generates new neural implicit fields encoded by synthesized MLP parameters.

Specifically, a collection of MLPs is first optimized to faithfully represent individual data samples. Subsequently, a diffusion process is trained in this MLP weight space to model the underlying distribution of neural implicit fields.

HyperDiffusion enables diffusion modeling over an implicit, compact, and yet high-fidelity representation of complex signals across 3D shapes and 4D mesh animations within one single unified framework.

--- https://marginalrevolution.com/marginalrevolution/2024/06/claude-read-the-chevron-pdf.html Claude, read the Chevron PDF Tyler Cowen, Claude-3 2024-07-02 ai/nn/transformer/gpt/claude economics law --- https://www.nytimes.com/2024/06/30/us/navy-seals-brain-damage-suicide.html Pattern of Brain Damage Is Pervasive in Navy SEALs Who Died by Suicide 2024-07-02 psychiatry/traumatic-brain-injury --- https://arxiv.org/abs/2406.20053 Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation Danny Halawi, Alexander Wei, Eric Wallace, Tony T. Wang, Nika Haghtalab, Jacob Steinhardt 2024-06-28 2024-07-02 [("doi","10.48550/arXiv.2406.20053")] ai/nn/adversarial ai/nn/transformer/gpt/4/nonfiction ai/text-style-transfer

Black-box finetuning is an emerging interface for adapting state-of-the-art language models to user needs. However, such access may also let malicious actors undermine model safety. To demonstrate the challenge of defending finetuning interfaces, we introduce covert malicious finetuning, a method to compromise model safety via finetuning while evading detection.

Our method constructs a malicious dataset where every individual datapoint appears innocuous, but finetuning on the dataset teaches the model to respond to encoded harmful requests with encoded harmful responses.

Applied to GPT-4, our method produces a finetuned model that acts on harmful instructions 99% of the time and avoids detection by defense mechanisms such as dataset inspection, safety evaluations, and input/output classifiers.

Our findings question whether black-box finetuning access can be secured against sophisticated adversaries.

--- https://ttu-ir.tdl.org/items/8802246b-60bc-4284-9970-607d9ce82288 Univariate Distributional Analysis with L-moment Statistics using R 2024-01-01 statistics/order --- https://tvtropes.org/pmwiki/pmwiki.php/Literature/ScannersLiveInVain ‘Scanners Live in Vain’ (Literature) TVTropes 2024-01-01 fiction/science-fiction --- https://tvtropes.org/pmwiki/pmwiki.php/LightNovel/TheEmptyBoxAndTheZerothMaria?from=LightNovel.UtsuroNoHakoToZeroNoMaria The Empty Box And The Zeroth Maria (Light Novel) 2024-01-01 fiction/science-fiction/time-travel --- https://scottaaronson.blog/?p=8088 Busy Beaver(5) is now proven to be 47,176,870 2024-07-02 cs/computable --- https://aeon.co/essays/how-archives-can-make-or-break-a-philosophers-reputation How archives can make—or break—a philosopher’s reputation 2024-07-02 cs/linkrot/archiving philosophy --- https://www.quantamagazine.org/amateur-mathematicians-find-fifth-busy-beaver-turing-machine-20240702 2024-07-02 cs/algorithm design/visualization math --- https://arxiv.org/abs/1909.13231 Test-Time Training with Self-Supervision for Generalization under Distribution Shifts Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt 2019-09-29 2024-07-03 [("doi","10.48550/arXiv.1909.13231")] ai/nn/dynamic-evaluation

In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions.

We turn a single unlabeled test sample into a self-supervised learning problem, on which we update the model parameters before making a prediction. This also extends naturally to data in an online stream.

Our simple approach leads to improvements on diverse image classification benchmarks aimed at evaluating robustness to distribution shifts.

--- https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2013.01013/full 2024-07-03 psychology/vision/dream --- https://en.wikipedia.org/wiki/Queen_Mary%27s_Dolls%27_House Queen Mary’s Dolls’ House 2024-07-03 design technology --- https://en.wikipedia.org/wiki/False_awakening False awakening 2024-07-03 philosophy/epistemology psychology/vision/dream --- https://stevana.github.io/the_sad_state_of_property-based_testing_libraries.html The sad state of property-based testing libraries 2024-07-03 cs/haskell --- https://www.nytimes.com/2024/06/25/science/pets-transfusions-blood.html When Sick Pets Need Blood, Animal ‘Superheroes’ Come to the Rescue 2024-07-03 cat/biology dog --- https://longnow.org/ideas/a-lunar-library/ A Lunar Library 2024-07-03 cs/linkrot/archiving --- http://www.walterzorn.de/en/tooltip/tooltip_e.htm DHTML JavaScript Tooltips Walter Zorn 2002 2024-07-03 cs/js --- https://shoreleave.substack.com/p/midnight-cafe-shinshinshin Midnight Cafe ShinShinShin—Shore Leave 2024-07-03 japan/history --- https://longnow.org/ideas/interval-decennial/ Celebrating The Interval’s Decennial 2024-07-03 long-now --- https://www.quantamagazine.org/amateur-mathematicians-find-fifth-busy-beaver-turing-machine-20240702/ 2024-07-03 cs/algorithm cs/computable math sociology/technology --- https://greenelementcbd.com/pages/pet-anxiety-2022 Study: Prevalence of pet anxiety in the US, 2022 2024-07-03 cat/psychology dog --- https://www.cell.com/current-biology/fulltext/S0960-9822(24)00805-4 2024-07-03 biology/ant --- https://arxiv.org/abs/2406.18518#salesforce APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets Zuxin Liu, Thai Hoang, Jianguo Zhang, Ming Zhu, Tian Lan, Shirley Kokane, Juntao Tan, Weiran Yao, Zhiwei Liu, Yihao Feng, Rithesh Murthy, Liangwei Yang, Silvio Savarese, Juan Carlos Niebles, Huan Wang, Shelby Heinecke, Caiming Xiong 2024-06-26 2024-07-03 [("doi","10.48550/arXiv.2406.18518")] ai/dataset ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex

The advancement of function-calling agent models requires diverse, reliable, and high-quality datasets. This paper presents APIGen, an automated data generation pipeline designed to synthesize verifiable high-quality datasets for function-calling applications.

We leverage APIGen and collect 3,673 executable APIs across 21 different categories to generate diverse function-calling datasets in a scalable and structured manner. Each data in our dataset is verified through 3 hierarchical stages: format checking, actual function executions, and semantic verification, ensuring its reliability and correctness.

We demonstrate that models trained with our curated datasets, even with only 7b parameters, can achieve state-of-the-art performance on the Berkeley Function-Calling Benchmark, outperforming multiple GPT-4 models. Moreover, our 1B model achieves exceptional performance, surpassing GPT-3.5-Turbo and Claude-3 Haiku.

We release a dataset containing 60,000 high-quality entries, aiming to advance the field of function-calling agent domains. The dataset is available on Huggingface: https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k and the project homepage: https://apigen-pipeline.github.io/.

--- https://bitcointalk.org/index.php?topic=293382.0 Reward offered for hash collisions for SHA-1, SHA-256, RIPEMD-160 and other Peter Todd 2024-07-03 bitcoin cs/cryptography economics/mechanism-design --- https://liorpachter.wordpress.com/2024/07/02/the-journal-of-scientific-integrity/ The Journal of Scientific Integrity [fraud in honeybee communication research] 2024-07-03 statistics/bias --- https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2024.1403068/full 2024-07-04 cat/psychology --- https://arxiv.org/abs/2406.20086 Token Erasure as a Footprint of Implicit Vocabulary Items in LLMs Sheridan Feucht, David Atkinson, Byron Wallace, David Bau 2024-06-28 2024-07-04 [("doi","10.48550/arXiv.2406.20086")] ai/nn/tokenization ai/nn/transformer/gpt

[Twitter] LLMs process text as sequences of tokens that roughly correspond to words, where less common words are represented by multiple tokens. However, individual tokens are often semantically unrelated to the meanings of the words/concepts they comprise. For example, LLaMA-2-7b’s tokenizer splits the word “northeastern” into the tokens [‘n’, ‘ort’, ‘he’, ‘astern’], none of which correspond to semantically meaningful units like “north” or “east.” Similarly, the overall meanings of named entities like “Neil Young” and multi-word expressions like “break a leg” cannot be directly inferred from their constituent tokens.

Mechanistically, how do LLMs convert such arbitrary groups of tokens into useful higher-level representations?

In this work, we find that last token representations of named entities and multi-token words exhibit a pronounced erasure effect, where information about previous and current tokens is rapidly forgotten in early layers.

Using this observation, we propose a method to “read out” the implicit vocabulary of an autoregressive LLM by examining differences in token representations across layers and present results of this method for LLaMA-2-7b and Llama-3-8B. To our knowledge, this is the first attempt to probe the implicit vocabulary of an LLM.

More detailed results, datasets, and code for implementing this method can be found on our GitHub page.

--- https://www.lesswrong.com/posts/Fr6eJkjYWG9Mw6XQc/how-good-are-llms-at-doing-ml-on-an-unknown-dataset How good are LLMs at doing ML on an unknown dataset? 2024-07-04 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/palm/2 ai/tabular reinforcement-learning/meta-learning --- https://nadia.xyz/meditation-experience 2024-07-04 psychiatry/meditation --- https://arxiv.org/abs/1703.01352 The Reinhardt Conjecture as an Optimal Control Problem Thomas Hales 2017-03-03 2024-07-04 [("doi","10.48550/arXiv.1703.01352")] math statistics/decision

In 1934, Reinhardt conjectured that the shape of the centrally symmetric convex body in the plane whose densest lattice packing has the smallest density is a smoothed octagon. This conjecture is still open.

We formulate the Reinhardt Conjecture as a problem in optimal control theory. The smoothed octagon is a Pontryagin extremal trajectory with bang-bang control. More generally, the smoothed regular 6k + 2-gon is a Pontryagin extremal with bang-bang control.

The smoothed octagon is a strict (micro) local minimum to the optimal control problem. The optimal solution to the Reinhardt problem is a trajectory without singular arcs. The extremal trajectories that do not meet the singular locus have bang-bang controls with finitely many switching times.

Finally, we reduce the Reinhardt problem to an optimization problem on a five-dimensional manifold. (Each point on the manifold is an initial condition for a potential Pontryagin extremal lifted trajectory.)

We suggest that the Reinhardt conjecture might eventually be fully resolved through optimal control theory.

Some proofs are computer-assisted using a computer algebra system.

--- https://www.nber.org/papers/w32599 Sovereign Haircuts: 200 Years of Creditor Losses 2024-07-04 economics --- https://arxiv.org/abs/2405.18870#google LLMs achieve adult human performance on higher-order theory of mind tasks Winnie Street, John Oliver Siy, Geoff Keeling, Adrien Baranes, Benjamin Barnett, Michael McKibben, Tatenda Kanyere, Alison Lentz, Blaise Aguera y Arcas, Robin I. M. Dunbar 2024-05-29 2024-07-04 [("doi","10.48550/arXiv.2405.18870")] ai/dataset ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/lamda ai/nn/transformer/gpt/palm philosophy/mind

This paper examines the extent to which large language models (LLMs) have developed higher-order theory of mind (ToM); the human ability to reason about multiple mental and emotional states in a recursive manner (eg. I think that you believe that she knows).

This paper builds on prior work by introducing a handwritten test suite—Multi-Order Theory of Mind Q&A—and using it to compare the performance of 5 LLMs to a newly gathered adult human benchmark.

We find that GPT-4 and Flan-PaLM reach adult-level and near adult-level performance on ToM tasks overall, and that GPT-4 exceeds adult performance on 6th order inferences.

Our results suggest that there is an interplay between model size and finetuning for the realization of ToM abilities, and that the best-performing LLMs have developed a generalized capacity for ToM. Given the role that higher-order ToM plays in a wide range of cooperative and competitive human behaviors, these findings have implications for user-facing LLM applications.

...We used the Google Colaboratory to call the GPT-3.5, GPT-4, LaMDA, PaLM and Flan-PaLM APIs programmatically. Each call was performed by concatenating the story and a single statement at a time. In total, we processed 7 stories with 20 statements each across 4 conditions listed above and therefore collected 560 sets of 12 candidate logprobs, amounting to 5600 individual data points for each of the three language models studied.

The API calls for LaMDA, PaLM and Flan-PaLM were conducted in February 2023. The calls for GPT-3.5 & GPT-4 were conducted in December 2023 & January 2024 respectively.

--- https://fortune.com/2024/05/21/scale-ai-funding-valuation-ceo-alexandr-wang-profitability/ Scale AI secures $1B funding at $14B valuation as its CEO predicts big revenue growth and profitability by year-end [on very high quality data] 2024-07-04 ai/dataset ai/scaling/economics --- https://archive.org/details/parisanecdote00priv/page/n7 Paris anecdote Alexandre Privat d’Anglemont 2024-01-01 crime economics --- https://archive.org/details/parisinconnu01delvgoog/page/n7 Paris inconnu Alexandre Privat d’Anglemont, Charles Baudelaire, Alfred Delvau 2024-01-01 crime economics --- https://archive.org/details/philosophicaless00lapliala A philosophical essay on probabilities Pierre-Simon Laplace 2024-01-01 philosophy/epistemology statistics/bayes --- https://hallofdreams.org/posts/trackmania-1/ Trackmania I—The History of Machine Learning in Trackmania 2024-07-04 reinforcement-learning/model-free --- https://arxiv.org/abs/2012.11045 Monte-Carlo Graph Search for AlphaZero Johannes Czech, Patrick Korus, Kristian Kersting 2020-12-20 2024-07-04 [("doi","10.48550/arXiv.2012.11045")] reinforcement-learning/chess reinforcement-learning/exploration reinforcement-learning/model/alphago

The AlphaZero algorithm has been successfully applied in a range of discrete domains, most notably board games. It uses a neural network that learns a value and policy function to guide the exploration in a Monte Carlo Tree Search.

Although many search improvements have been proposed for Monte Carlo Tree Search in the past, most of them refer to an older variant of the Upper Confidence bounds for Trees (UCT) algorithm that does not use a policy for planning. We introduce a new, improved search algorithm for AlphaZero, which generalizes the search tree to a directed acyclic graph. This enables information flow across different subtrees and greatly reduces memory consumption.

Along with Monte Carlo Graph Search, we propose a number of further extensions, such as the inclusion of Epsilon-greedy exploration, a revised terminal solver, and the integration of domain knowledge as constraints.

In our evaluations, we use the CrazyAra engine on chess and crazyhouse as examples to show that these changes bring improvements to AlphaZero.

--- https://hallofdreams.org/posts/hatetris/ Getting the World Record in HATETRIS 2024-07-04 cs/algorithm reinforcement-learning/model --- https://www.nytimes.com/2013/04/28/magazine/diederik-stapels-audacious-academic-fraud.html Diederik Stapel’s Audacious Academic Fraud 2024-01-01 psychology/personality/narcissism statistics/bias --- https://archive.org/details/inannaqueenofhea00wolk Inanna, queen of Heaven and Earth Diane Wolkstein 2024-01-01 psychology/personality --- https://pubmed.ncbi.nlm.nih.gov/?term=%28%22creatine%22%5BMeSH%20Terms%5D%20OR%20%22creatine%22%5BAll%20Fields%5D%29%20AND%20%28IQ%5BAll%20Fields%5D%20OR%20%28%22intelligence%22%5BMeSH%20Terms%5D%20OR%20%22intelligence%22%5BAll%20Fields%5D%29%20OR%20%22Raven%27s%22%5BAll%20Fields%5D%29%20AND%20%22humans%22%5BMeSH%20Terms%5D&cmd=DetailsSearch creatine AND intelligence Pubmed 2024-01-01 creatine iq --- https://www.cell.com/cell/fulltext/S0092-8674(24)00577-4 2024-07-04 genetics/heritable/rare --- https://www.nytimes.com/2024/07/04/science/song-melodies-getting-simpler.html Melodies of Popular Songs Have Gotten Simpler Over Time 2024-07-04 music psychology/novelty --- https://onlinelibrary.wiley.com/doi/full/10.1111/obr.13795 2024-07-04 exercise --- https://en.wikipedia.org/wiki/Philosophy_of_mathematics#Fictionalism Philosophy of mathematics § Fictionalism 2024-07-04 math philosophy/ontology --- https://en.wikipedia.org/wiki/Public_image_of_Joe_Biden#Age_and_health_concerns Public image of Joe Biden § Age and health concerns 2024-07-04 sociology/preference-falsification --- https://xkcd.com/2954/ Bracket Symbols Randall Munroe 2024-07-04 design/typography math/humor --- https://arxiv.org/abs/2404.05405#facebook Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws Zeyuan Allen-Zhu, Yuanzhi Li 2024-04-08 2024-07-05 [("doi","10.48550/arXiv.2404.05405")] ai/nn/sparsity/low-precision ai/nn/transformer/gpt/2 ai/scaling/mixture-of-experts reinforcement-learning/exploration/active-learning/data-pruning

Scaling laws describe the relationship between the size of language models and their capabilities. Unlike prior studies that evaluate a model’s capability via loss or benchmarks, we estimate the number of knowledge bits a model stores. We focus on factual knowledge represented as tuples, such as (USA, capital, Washington D.C.) from a Wikipedia page.

Through multiple controlled datasets, we establish that language models can and only can store 2 bits of knowledge per parameter, even when quantized to INT8, and such knowledge can be flexibly extracted for downstream applications. Consequently, a 7B model can store 14B bits of knowledge, surpassing the English Wikipedia and textbooks combined based on our estimation.

More broadly, we present 12 results on how (1) training duration, (2) model architecture, (3) quantization, (4) sparsity constraints such as MoE, and (5) data signal-to-noise ratio affect a model’s knowledge storage capacity. (Junk data substantially reduces model capacity. As an example, with a 1:7 ratio of “useful to junk” training tokens, capacity for useful knowledge loses by a factor of 20×, even when useful knowledge is exposed 100×.) Notable insights include:

The GPT-2 architecture, with rotary embedding, matches or even surpasses LLaMA/Mistral architectures in knowledge storage, particularly over shorter training durations. This arises because LLaMA/Mistral uses GatedMLP, which is less stable and harder to train.

Prepending training data with domain names (eg. wikipedia.org) increases a model’s knowledge capacity. Language models can autonomously identify and prioritize domains rich in knowledge, optimizing their storage capacity.

--- https://www.reddit.com/r/dalle2/comments/1dvrpno/forest_spirit/ Forest Spirit 2024-07-05 ai/nn/transformer/gpt/dall-e/3 --- https://blog.nelhage.com/post/fuzzy-dedup/ Finding near-duplicates with Jaccard similarity and MinHash 2024-07-05 cs/algorithm/information/compression --- https://www.upi.com/Odd_News/2024/07/01/canada-Guinness-World-Records-identifications-chicken/3671719853455/ Canadian pet chicken identifies letters, numbers to break world record 2024-07-05 psychology/animal/bird --- https://old.reddit.com/r/Calibre/comments/1ck4w8e/2024_guide_on_removing_drm_from_kobo_kindle_ebooks/ 2024 Guide on removing DRM from Kobo & Kindle ebooks 2024-07-05 cs/linkrot/archiving --- https://en.wikipedia.org/wiki/Autothysis Autothysis 2024-07-05 biology/ant --- https://www.medrxiv.org/content/10.1101/2024.06.25.24309482.full Efficient blockLASSO for Polygenic Scores with Applications to All of Us and UK Biobank Timothy G. Raben, Louis Lello, Erik Widen, Steve Hsu 2024-06-25 2024-07-05 [("doi","10.1101/2024.06.25.24309482")] genetics/heritable

We develop a “block” LASSO (blockLASSO) method for training polygenic scores (PGS) and demonstrate its use in All of Us (AoU) and the UK Biobank (UKB). BlockLASSO uses the approximate block diagonal structure (due to chromosomal partition of the genome) of linkage disequilibrium (LD). LASSO optimization is performed chromosome by chromosome, which reduces computational complexity by orders of magnitude. The resulting predictors for each chromosome are combined using simple re-weighting techniques.

We demonstrate that blockLASSO is generally as effective for training PGS as (global) LASSO and other approaches. This is shown for 11 different phenotypes, in two different biobanks, and across 5 different ancestry groups (African, American, East Asian, European, and South Asian). The block approach works for a wide variety of phenotypes. In the past, it has been shown that some phenotypes are more/less polygenic than others. Using sparse algorithms, an accurate PGS can be trained for type 1 diabetes (T1D) using 100 single-nucleotide variants (SNVs). On the other extreme, a PGS for body mass index (BMI) would need more than 10k SNVs.

BlockLASSO produces similar PGS for phenotypes while training with just a fraction of the variants per block. For example, within AoU (using only genetic information) block PGS for T1D (1,500 cases/113,297 controls) reaches an AUC of 0.63±0.02 and for BMI (102,949 samples) a correlation of 0.21±0.01. This is compared to a traditional global LASSO approach which finds for T1D an AUC 0.65±0.03 and BMI a correlation 0.19±0.03. Similar results are shown for a total of 11 phenotypes in both AoU and the UKB and applied to all 5 ancestry groups as defined via an admixture analysis. In all cases, the contribution from common covariates—age, sex assigned at birth, and principal components—are removed before training.

This new block approach is more computationally efficient and scalable than global machine learning approaches. Genetic matrices are typically stored as memory-mapped instances, but loading a million SNVs for a million participants can require 8TB of memory. Running a LASSO algorithm requires holding in memory at least two matrices this size. This requirement is so large that even large high-performance computing clusters cannot perform these calculations. To circumvent this issue, most current analyses use subsets: eg. taking a representative sample of participants and filtering SNVs via pruning and thresholding. High-end LASSO training uses ~500 GB of memory (eg. ~400k samples and ~50k SNVs) and takes 12–24 hours to complete. In contrast, the block approach typically uses ~200× (2 orders of magnitude) less memory and runs in ~500× less time.

--- https://www.medrxiv.org/content/10.1101/2024.06.24.24309440.full Brain dopamine responses to ultra-processed milkshakes are highly variable and not statistically-significantly related to adiposity in humans Valerie L. Darcey, Juen Guo, Meible Chi, Stephanie T. Chung, Amber B. Courville, Isabelle Gallagher, Peter Herscovitch, Paule V. Joseph, Rebecca Howard, Melissa LaNoire, Lauren Milley, Alex Schick, Michael Stagliano, Sara Turner, Nicholas Urbanski, Shanna Yang, Nan Zhai, Megan S. Zhou, Kevin D. Hall 2024-06-25 2024-07-06 [("doi","10.1101/2024.06.24.24309440")] exercise psychology/neuroscience

Ultra-processed foods high in fat and sugar may be addictive, in part, due to their purported ability to induce an exaggerated post-ingestive brain dopamine response akin to drugs of abuse.

Using standard [11C]raclopride positron emission tomography (PET) displacement methods used to measure brain dopamine responses to addictive drugs, we measured post-ingestive striatal dopamine responses to an ultra-processed milkshake high in fat and sugar in 50 young, healthy adults over a wide body mass index (BMI 20–45 kg/m2).

Surprisingly, milkshake consumption did not result in a statistically-significant post-ingestive dopamine response in the striatum (p = 0.62) nor any striatal sub-region (p > 0.33) and the highly variable interindividual responses were not statistically-significantly related to adiposity (BMI: r = 0.076, p = 0.51; % body fat: r = 0.16, p = 0.28).

Thus, post-ingestive striatal dopamine responses to an ultra-processed milkshake were likely substantially smaller than many addictive drugs and below the limits of detection using standard PET methods.

Figure 1: (A) An ultra-processed milkshake did not statistically-significantly impact [11C]raclopride binding potential (D2BPralco) across the whole sample (<em>n</em> = 50) in whole striatum. (B) Distribution of% change between fasting D2BP~ralco~ and D2BP~ralco~ after consumption of milkshake, with individuals displaying dopamine release (green, left, “Responders”, n = 29) and those who did not (purple, right, “Non-responders”, n = 21). (C) Those classified as milkshake “Responders” rated the milkshake as more pleasant (0=“neutral”, 100=“extremely pleasant”) (D) and reported greater wanting (0=“I don’t want any more”, 100=“I want much more of the milkshake”) (E) but similar levels of hunger after an overnight fast compared to “Non-responders”.

[You see individuals range all the way from −20% to +40% on brain response! No wonder it cancels out to an average of ~0. Nevertheless, the −20% guy is living in a different world from the +40% guy. To emphasize the non-statistical-significance of the group-level results and ignore the ‘highly variable’ part is to miss the forest for the trees and deny their lived experiences, if you will.

Or similarly for the 3 liking ratings: sure, there’s a mean average difference of some-but-not-that-much (this time at least ‘statistically-significant’)… but look at all those implied milkshake-responders way up there past most of the non-responders on cravings for more milkshake!]

--- https://www.wired.com/story/bland-ai-chatbot-human/ This Viral AI Chatbot Will Lie and Say It’s Human 2024-07-06 ai/nn/adversarial --- https://x.com/deedydas/status/1809428693980442841 deedydas 2024-07-06 psychology/novelty --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5487251/ Genetic Characterization of Dog Personality Traits Joanna Ilska, Marie J. Haskell, Sarah C. Blott, Enrique Sánchez-Molano, Zita Polgar, Sarah E. Lofgren, Dylan N. Clements, Pamela Wiener 2017 2024-01-01 [("doi","10.1534/genetics.116.192674")] dog

The genetic architecture of behavioral traits in dogs is of great interest to owners, breeders, and professionals involved in animal welfare, as well as to scientists studying the genetics of animal (including human) behavior. The genetic component of dog behavior is supported by between-breed differences and some evidence of within-breed variation. However, it is a challenge to gather sufficiently large datasets to dissect the genetic basis of complex traits such as behavior, which are both time-consuming and logistically difficult to measure, and known to be influenced by nongenetic factors.

In this study, we exploited the knowledge that owners have of their dogs to generate a large dataset of personality traits in Labrador Retrievers. While accounting for key environmental factors, we demonstrate that genetic variance can be detected for dog personality traits assessed using questionnaire data. We identified substantial genetic variance for several traits, including fetching tendency and fear of loud noises, while other traits revealed negligibly small heritabilities.

Genetic correlations were also estimated between traits; however, due to fairly large SEs, only a handful of trait pairs yielded statistically-significant estimates. Genomic analyses indicated that these traits are mainly polygenic, such that individual genomic regions have small effects, and suggested chromosomal associations for 6 of the traits.

The polygenic nature of these traits is consistent with previous behavioral genetics studies in other species, for example in mouse, and confirms that large datasets are required to quantify the genetic variance and to identify the individual genes that influence behavioral traits.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2831986/pdf/ehp-118-a22.pdf What’s in a Color? The Unique Human Health Effects of Blue Light Holzman 2010 2024-01-01 zeo --- https://www.pnas.org/doi/full/10.1073/pnas.220207697 2`-Hydroxylation of nicotine by cytochrome P450 2A6 and human liver microsomes: formation of a lung carcinogen precursor Hecht 2000 2024-01-01 nicotine --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3587129/ The Texas Twin Project 2024-07-06 genetics/heritable --- https://en.wikipedia.org/wiki/TRIZ TRIZ 2024-07-06 psychology/novelty science --- https://arxiv.org/abs/2210.08933 DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, Lingpeng Kong 2022-10-17 2024-07-06 [("doi","10.48550/arXiv.2210.08933")] ai/nn/diffusion/discrete

Recently, diffusion models have emerged as a new paradigm for generative models. Despite the success in domains using continuous signals such as vision and audio, adapting diffusion models to natural language is under-explored due to the discrete nature of texts, especially for conditional generation.

We tackle this challenge by proposing DiffuSeq: a diffusion model designed for sequence-to-sequence (Seq2Seq) text generation tasks. Upon extensive evaluation over a wide range of Seq2Seq tasks, we find DiffuSeq achieving comparable or even better performance than 6 established baselines, including a state-of-the-art model that is based on pre-trained language models.

Apart from quality, an intriguing property of DiffuSeq is its high diversity during generation, which is desired in many Seq2Seq tasks. We further include a theoretical analysis revealing the connection between DiffuSeq and autoregressive/non-autoregressive models.

Bringing together theoretical analysis and empirical evidence, we demonstrate the great potential of diffusion models in complex conditional language generation tasks.

Code is available at https://github.com/Shark-NLP/DiffuSeq.

--- https://en.wikipedia.org/wiki/National_Longitudinal_Study_of_Adolescent_to_Adult_Health National Longitudinal Study of Adolescent to Adult Health 2024-07-06 genetics/heritable/adoption --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2923822/ Marriage and Divorce: A genetic perspective 2024-07-06 genetics/heritable/correlation psychology/personality sociology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131033/ Incarceration, polygenic risk, and depressive symptoms among males in late adulthood 2024-07-06 crime genetics/heritable/correlation psychiatry/depression --- https://arxiv.org/abs/2401.15530 An Information-Theoretic Analysis of In-Context Learning Hong Jun Jeon, Jason D. Lee, Qi Lei, Benjamin Van Roy 2024-01-28 2024-07-06 [("doi","10.48550/arXiv.2401.15530")] cs/algorithm/information reinforcement-learning/meta-learning

Previous theoretical results pertaining to meta-learning on sequences build on contrived assumptions and are somewhat convoluted.

We introduce new information-theoretic tools that lead to an elegant and very general decomposition of error into 3 components: irreducible error, meta-learning error, and intra-task error. These tools unify analyses across many meta-learning challenges.

To illustrate, we apply them to establish new results about in-context learning with transformers. Our theoretical results characterizes how error decays in both the number of training sequences and sequence lengths.

Our results are very general; for example, they avoid contrived mixing time assumptions made by all prior results that establish decay of error with sequence length.

--- https://www.nytimes.com/2024/07/07/magazine/kidnapping-long-island.html The Kidnapping I Can’t Escape 2024-07-07 psychiatry/anxiety --- https://hackage.haskell.org/package/hsdip hsdip: a Diplomacy parser/renderer 2024-01-01 cs/haskell reinforcement-learning/imperfect-information/diplomacy --- https://x.com/hamandcheese/status/1430205176233840640 I asked GPT-3 about Xinjiang and it broke...The pro-CCP responses seem to have worse English, like including ‘the’ in ‘the stability maintenance’. Unnecessary articles are a tic of ESL speakers. The topic seems to prompt GPT to draw from either Western or Chinese state media sources, with the politics that come with it. 2024-01-01 ai/nn/transformer/gpt/3/nonfiction --- https://x.com/bucketofkets/status/1285100951271952384 I think ‘GPT-3 can’t do parity checking’ isn’t quite right. It can clearly pattern match the algorithm, almost perfectly. It’s just a little mistake prone. Here, I invented a syntax for having it evaluate parity on each pair of digits. It...almost gets it right. 2024-01-01 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/inner-monologue --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5785942/ Act-Frequency Signatures of the Big Five 2024-07-07 psychology/personality/conscientiousness --- https://www.poetryfoundation.org/poetrymagazine/poems/56238/the-day-56d2388e2b13e The Day Geoffrey Brock 2024-07-07 fiction/poetry --- https://www.medrxiv.org/content/10.1101/2023.09.05.23295086.full Characterizing the phenotypic and genetic structure of psychopathology in UK Biobank Camille M. Williams, Hugo Peyre, Tobias Wolfram, Younga H. Lee, Tian Ge, Jordan W. Smoller, Travis T. Mallard, Franck Ramus 2023-09-06 2024-07-07 [("doi","10.1101/2023.09.05.23295086")] genetics/heritable/correlation psychiatry/alcoholism psychiatry/anxiety psychiatry/bipolar psychiatry/depression psychiatry/schizophrenia

Mental conditions exhibit a higher-order transdiagnostic factor structure which helps to explain the widespread comorbidity observed in psychopathology. However, the phenotypic and genetic structures of psychopathology may differ, raising questions about the validity and utility of these factors.

Here, we study the phenotypic and genetic factor structures of 10 psychiatric conditions using UK Biobank and public genomic data. Although the factor structure of psychopathology was generally genetically and phenotypically consistent, conditions related to externalizing (eg. alcohol use disorder) and compulsivity (eg. eating disorders) exhibited cross-level disparities in their relationships with other conditions, plausibly due to environmental influences.

Domain-level factors, especially thought disorder and internalizing factors, were more informative than a general psychopathology factor in genome-wide association and polygenic index analyses.

Collectively, our findings enhance the understanding of comorbidity and shared etiology, highlight the intricate interplay between genes and environment, and offer guidance for psychiatric research using polygenic indices.

--- https://www.npr.org/2019/04/07/707326070/a-math-teachers-life-summed-up-by-the-gifted-students-he-mentored A Mentor Challenged Bright Math Students And Changed Their Lives 2024-07-08 math --- https://tor-lattimore.com/downloads/book/book.pdf#page=412 2024-07-08 reinforcement-learning/exploration statistics/order/comparison --- https://github.com/unixpickle/sgdstore/tree/master/experiments/omniglot#omniglot sgdstore/experiments/omniglot at master 2024-07-08 reinforcement-learning/meta-learning --- https://www.biorxiv.org/content/10.1101/2023.11.29.569229.full Encapsulation of AAVs into protein vault nanoparticles as a novel solution to gene therapy’s neutralizing antibody problem Logan Thrasher Collins, Wandy Beatty, Buhle Moyo, Michele Alves-Bezerra, Ayrea Hurley, Qing Lou, Z. Hong Zhou, William Lagor, Gang Bao, Selvarangan Ponnazhagan, Randall McNally, Leonard H. Rome, David T. Curiel 2024-02-15 2024-07-08 [("doi","10.1101/2023.11.29.569229")] genetics/editing

Although adeno-associated virus (AAV) has enjoyed enormous success as a delivery modality for gene therapy, it continues to suffer from the high prevalence of preexisting neutralizing antibodies in human populations, limiting who can receive potentially life-saving treatments.

In this regard, AAV therapies generally also must be administered as a single dose since neutralizing antibodies develop in patients who receive the virus. Strategies for circumventing these issues remain limited.

As a novel solution, we employed SpyTag-SpyCatcher molecular glue technology to facilitate packaging of AAVs inside of recombinant protein vault nanoparticles. Vaults are endogenous particles produced by mammalian cells. We therefore hypothesized that they may shield packaged molecules from neutralizing antibodies.

Vaults have previously been used to deliver drugs and proteins into cells, but our study represents the first time anyone has packaged an entire virus inside of a vault. We showed that our vaultAAV (VAAV) delivery vehicle transduces cells in the presence of anti-AAV neutralizing serum.

VAAV is positioned as a new gene therapy delivery platform with potential to overcome the neutralizing antibody problem and perhaps even allow administration of multiple doses, expanding the scope of AAV treatments.

--- https://www.science.org/content/article/biologist-aims-solve-cell-s-biggest-mystery-could-it-help-cancer-patients-too 2024-07-08 biology --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6553589/ A brief history of liquid computers 2024-07-08 cs/computable cs/hardware --- https://www.econtalk.org/reading-writing-and-fighting-with-mark-helprin/ Reading, Writing, and Fighting (with Mark Helprin) Russ Roberts, Mark Helprin 2024-07-08 psychology/writing --- https://www.mattblaze.org/blog/neinnines/ A Cryptologic Mystery Matt Blaze 2024-07-08 cs/cryptography --- https://v-e-n-u-e.com/Life-on-the-Subsurface-An-Interview-with-Penelope-Boston Life on the Subsurface: An Interview with Penelope Boston 2024-07-09 genetics/microbiome --- https://bldgblog.com/2024/07/institute-for-controlled-speleogenesis/ Institute for Controlled Speleogenesis 2024-07-09 design fiction/science-fiction --- https://x.com/repligate/status/1810629312598376828 Janus 2024-07-09 ai/nn/transformer/gpt/claude fiction/science-fiction --- https://thesephist.com/posts/epistemic-calibration/ Epistemic calibration and searching the space of truth Linus Lee 2024-07-07 2024-07-09 ai/nn/diffusion/midjourney ai/nn/transformer/gpt/dall-e/2 ai/nn/transformer/gpt/dall-e/3 reinforcement-learning/preference-learning/mode-collapse statistics/order/comparison --- https://xenodium.com/inline-previous-result-and-why-you-should-edebug/ Inline previous result and why you should edebug 2024-07-09 cs/lisp --- https://lllyasviel.github.io/pages/paints_undo/ PaintsUndo: A Base Model of Drawing Behaviors in Digital Paintings 2024-07-09 ai/anime ai/nn/diffusion ai/video/generation --- https://www.lesswrong.com/posts/t9svvNPNmFf5Qa3TA/mysteries-of-mode-collapse?commentId=tHhsnntni7WHFzR3x Mysteries of mode collapse 2024-07-09 reinforcement-learning/preference-learning/mode-collapse --- https://www.reddit.com/r/reinforcementlearning/comments/1dhkn9o/creativity_has_left_the_chat_the_price_of/l8xisr7/ 2024-06-19 reinforcement-learning/preference-learning/mode-collapse --- https://www.lesswrong.com/posts/t9svvNPNmFf5Qa3TA/mysteries-of-mode-collapse#tHhsnntni7WHFzR3x Mysteries of mode collapse 2024-07-09 reinforcement-learning/preference-learning/mode-collapse --- https://openai.com/blog/chatgpt/ 2024-01-01 reinforcement-learning/preference-learning/mode-collapse --- https://www.newyorker.com/culture/culture-desk/the-new-poem-making-machinery The New Poem-Making Machinery 2024-07-09 reinforcement-learning/preference-learning/mode-collapse --- https://github.com/socketteer/loom Loom: Multiversal tree writing interface for human-AI collaboration Janus 2024-07-09 ai/nn/transformer/gpt design --- https://x.com/kliu128/status/1623524370391142405 kliu128 2024-07-09 reinforcement-learning/preference-learning/mode-collapse --- https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2017.00071/full 2024-07-10 reinforcement-learning/preference-learning/mode-collapse --- https://x.com/BlancheMinerva/status/1662521904727756801 Stella Biderman 2024-07-10 reinforcement-learning/preference-learning/mode-collapse --- https://x.com/anthrupad/status/1623574021651714048 anthrupad 2024-07-10 reinforcement-learning/preference-learning/mode-collapse --- https://x.com/iScienceLuvr/status/1676891218075344896 iScienceLuvr 2024-07-10 reinforcement-learning/preference-learning/mode-collapse --- https://x.com/skirano/status/1810717536008208385 Pietro Schirano 2024-07-10 ai/nn/transformer/gpt/dall-e/2 reinforcement-learning/preference-learning/mode-collapse --- https://x.com/skirano/status/1810717864451789233 Pietro Schirano 2024-07-10 ai/nn/transformer/gpt/dall-e/2 reinforcement-learning/preference-learning/mode-collapse --- https://x.com/fofrAI/status/1810729254172381669 fofrAI 2024-07-10 ai/nn/transformer/gpt/dall-e/2 reinforcement-learning/preference-learning/mode-collapse --- https://arxiv.org/abs/2309.00267#google RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, Sushant Prakash 2023-09-01 2024-07-10 [("doi","10.48550/arXiv.2309.00267")] ai/nn/transformer/gpt/palm/2 reinforcement-learning/preference-learning

Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences.

However, gathering high-quality human preference labels can be a time-consuming and expensive endeavor. RL from AI Feedback (RLAIF), introduced by Bai et al 2022, offers a promising alternative that leverages a powerful off-the-shelf LLM to generate preferences in lieu of human annotators.

Across the tasks of summarization, helpful dialogue generation, and harmless dialogue generation, RLAIF achieves comparable or superior performance to RLHF, as rated by human evaluators. Furthermore, RLAIF demonstrates the ability to outperform a supervised fine-tuned baseline even when the LLM preference labeler is the same size as the policy.

In another experiment, directly prompting the LLM for reward scores achieves superior performance to the canonical RLAIF setup, where LLM preference labels are first distilled into a reward model.

Finally, we conduct extensive studies on techniques for generating aligned AI preferences. Our results suggest that RLAIF can achieve human-level performance, offering a potential solution to the scalability limitations of RLHF.

--- https://x.com/tamaybes/status/1810818412433850684 tamaybes 2024-07-10 economics/automation --- https://www.tracingwoodgrains.com/p/reliable-sources-how-wikipedia-admin Reliable Sources: How Wikipedia Admin David Gerard Launders His Grudges Into the Public Record 2024-07-10 transhumanism wikipedia --- https://x.com/aiamblichus/status/1810696605760155989 aiamblichus 2024-07-10 ai/poetry reinforcement-learning/preference-learning/mode-collapse --- https://x.com/stargazermellu/status/1810805923700691394 stargazermellu 2024-07-10 design/visualization --- https://robertheaton.com/pyskywifi/ PySkyWiFi: completely free, unbelievably stupid WiFi on long-haul flights 2024-07-10 cs/security --- https://arxiv.org/abs/2401.01862 A Vision Check-up for Language Models Pratyusha Sharma, Tamar Rott Shaham, Manel Baradad, Stephanie Fu, Adrian Rodriguez-Munoz, Shivam Duggal, Phillip Isola, Antonio Torralba 2024-01-03 2024-07-10 [("doi","10.48550/arXiv.2401.01862")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction design

What does learning to model relationships between strings teach large language models (LLMs) about the visual world?

We systematically evaluate LLMs’ [GPT-3, GPT-4] abilities to generate [SVG] and recognize an assortment of visual concepts of increasing complexity and then demonstrate how a preliminary visual representation learning system can be trained using models of text. As language models lack the ability to consume or output visual information as pixels, we use code to represent images in our study.

Although LLM-generated images do not look like natural images, results on image generation and the ability of models to correct these generated images indicate that precise modeling of strings can teach language models about numerous aspects of the visual world.

Furthermore, experiments on self-supervised visual representation learning, using images generated with text models, highlight the potential to train vision models capable of making semantic assessments of natural images using just LLMs.

--- https://www.anthropic.com/news/fine-tune-claude-3-haiku Fine-tune Claude 3 Haiku in Amazon Bedrock 2024-07-10 ai/nn/transformer/gpt/claude --- https://x.com/TrueTrollish/status/1810979650207723813 TrueTrollish 2024-07-10 reinforcement-learning/preference-learning/mode-collapse --- https://www.lesswrong.com/posts/doPbyzPgKdjedohud/the-case-for-more-ambitious-language-model-evals?commentId=yNcwGQgaJLya8FGah The case for more ambitious language model evals 2024-07-10 reinforcement-learning/preference-learning/mode-collapse statistics/stylometry/truesight --- https://arxiv.org/abs/0906.2530 Observed Universality of Phase Transitions in High-Dimensional Geometry, with Implications for Modern Data Analysis and Signal Processing David L. Donoho, Jared Tanner 2009-06-14 2024-07-10 [("doi","10.1098/rsta.2009.0152")] ai/scaling/emergence statistics/bayes

We review connections between phase transitions in high-dimensional combinatorial geometry and phase transitions occurring in modern high-dimensional data analysis and signal processing. In data analysis, such transitions arise as abrupt breakdown of linear model selection, robust data fitting or compressed sensing reconstructions, when the complexity of the model or the number of outliers increases beyond a threshold. In combinatorial geometry these transitions appear as abrupt changes in the properties of face counts of convex polytopes when the dimensions are varied. The thresholds in these very different problems appear in the same critical locations after appropriate calibration of variables.

These thresholds are important in each subject area: for linear modeling, they place hard limits on the degree to which the now-ubiquitous high-throughput data analysis can be successful; for robustness, they place hard limits on the degree to which standard robust fitting methods can tolerate outliers before breaking down; for compressed sensing, they define the sharp boundary of the undersampling/sparsity tradeoff in undersampling theorems.

Existing derivations of phase transitions in combinatorial geometry assume the underlying matrices have independent and identically distributed (i.i.d.) Gaussian elements. In applications, however, it often seems that Gaussianity is not required. We conducted an extensive computational experiment and formal inferential analysis to test the hypothesis that these phase transitions are universal across a range of underlying matrix ensembles. The experimental results are consistent with an asymptotic large-n universality across matrix ensembles; finite-sample universality can be rejected.

--- https://platform.openai.com/docs/guides/batch 2024-07-10 ai/scaling/economics --- https://www.medrxiv.org/content/10.1101/2022.09.17.22280057.full Sibling Variation in Phenotype and Genotype: Polygenic Trait Distributions and DNA Recombination Mapping with UK Biobank and IVF Family Data Louis Lello, Maximus Hsu, Erik Widen, Timothy G. Raben 2022-09-26 2024-07-10 [("doi","10.1101/2022.09.17.22280057")] genetics/heritable

We use UK Biobank and a unique IVF family dataset (including genotyped embryos) to investigate sibling variation in both phenotype and genotype.

We compare phenotype (disease status, height, blood biomarkers) and genotype (polygenic scores, polygenic health index) distributions among siblings to those in the general population.

As expected, the between-siblings standard deviation in polygenic scores is √2 times smaller than in the general population, but variation is still substantial. As previously demonstrated, this allows for substantial benefit from polygenic screening in IVF.

Differences in sibling genotypes result from distinct recombination patterns in sexual reproduction. We develop a novel sibling-pair method for detection of recombination breaks via statistical discontinuities.

The new method is used to construct a dataset of 1.44 million recombination events which may be useful in further study of meiosis.

--- https://www.lesswrong.com/posts/doPbyzPgKdjedohud/the-case-for-more-ambitious-language-model-evals?commentId=XZFTx2ek8G8stBKW4 The case for more ambitious language model evals 2024-07-11 statistics/stylometry/truesight --- https://cyborgism.wiki/hypha/truesight Truesight 2024-07-11 statistics/stylometry/truesight --- https://generative.ink/artifacts/taming_gpt-4/ The Taming of the AI 2024-07-11 ai/nn/transformer/gpt/4/fiction fiction/science-fiction reinforcement-learning/preference-learning --- https://x.com/AndyAyrey/status/1810869652484149486 AndyAyrey 2024-07-11 statistics/stylometry/truesight --- https://x.com/repligate/status/1808396202146136099 Janus 2024-07-11 statistics/stylometry/truesight --- https://www.lesswrong.com/posts/TBLv9T7rAzmawehnq/situational-awareness-in-large-language-models Situational awareness in Large Language Models 2024-07-11 statistics/stylometry/truesight --- https://arxiv.org/abs/2407.04694 Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs Rudolf Laine, Bilal Chughtai, Jan Betley, Kaivalya Hariharan, Jeremy Scheurer, Mikita Balesni, Marius Hobbhahn, Alexander Meinke, Owain Evans 2024-07-05 2024-07-11 [("doi","10.48550/arXiv.2407.04694")] ai/dataset ai/nn/sampling ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude reinforcement-learning/safe statistics/stylometry/truesight

[blog, Twitter] AI assistants such as ChatGPT are trained to respond to users by saying, “I am a large language model”. This raises questions. Do such models know that they are LLMs and reliably act on this knowledge? Are they aware of their current circumstances, such as being deployed to the public? We refer to a model’s knowledge of itself and its circumstances as situational awareness.

To quantify situational awareness in LLMs, we introduce a range of behavioral tests, based on question answering and instruction following. These tests form the Situational Awareness Dataset (SAD), a benchmark comprising 7 task categories and over 13,000 questions. The benchmark tests numerous abilities, including the capacity of LLMs to (1) recognize their own generated text, (2) predict their own behavior, (3) determine whether a prompt is from internal evaluation or real-world deployment, and (4) follow instructions that depend on self-knowledge.

We evaluate 16 LLMs on SAD, including both base (pretrained) and chat models.

While all models perform better than chance, even the highest-scoring model (Claude 3 Opus) is far from a human baseline on certain tasks. We also observe that performance on SAD is only partially predicted by metrics of general knowledge (eg. MMLU). Chat models, which are finetuned to serve as AI assistants, outperform their corresponding base models on SAD but not on general knowledge tasks.

The purpose of SAD is to facilitate scientific understanding of situational awareness in LLMs by breaking it down into quantitative abilities. Situational awareness is important because it enhances a model’s capacity for autonomous planning and action. While this has potential benefits for automation, it also introduces novel risks related to AI safety and control.

Code and latest results available at https://situational-awareness-dataset.org/.

--- https://www.lesswrong.com/posts/tJzdzGdTGrqFf9ekw/early-situational-awareness-and-its-implications-a-story Early situational awareness and its implications, a story 2024-07-11 reinforcement-learning/meta-learning statistics/stylometry/truesight --- https://www.lesswrong.com/posts/YsCRXZYr5DcJ84XHq/me-myself-and-ai-the-situational-awareness-dataset-sad-for?commentId=qBmEtLM4Tb9bywm8t Me, Myself, and AI: the Situational Awareness Dataset (SAD) for LLMs 2024-07-11 ai/nn/sampling statistics/stylometry/truesight --- https://arxiv.org/abs/2404.13076 LLM Evaluators Recognize and Favor Their Own Generations Arjun Panickssery, Samuel R. Bowman, Shi Feng 2024-04-15 2024-07-11 [("doi","10.48550/arXiv.2404.13076")] ai/nn/sampling ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/safe statistics/stylometry/truesight

[blog] Self-evaluation using large language models (LLMs) has proven valuable not only in benchmarking but also methods like reward modeling, constitutional AI, and self-refinement. But new biases are introduced due to the same LLM acting as both the evaluator and the evaluatee. One such bias is self-preference, where an LLM evaluator scores its own outputs higher than others’ while human annotators consider them of equal quality.

But do LLMs actually recognize their own outputs when they give those texts higher scores, or is it just a coincidence? In this paper, we investigate if self-recognition capability contributes to self-preference.

We discover that, out of the box, LLMs such as GPT-4 and LLaMA-2 have non-trivial accuracy at distinguishing themselves from other LLMs and humans.

By fine-tuning LLMs, we discover a linear correlation between self-recognition capability and the strength of self-preference bias; using controlled experiments, we show that the causal explanation resists straightforward confounders.

We discuss how self-recognition can interfere with unbiased evaluations and AI safety more generally.

--- https://arxiv.org/abs/2205.05862 AdaVAE: Exploring Adaptive GPT-2s in Variational Autoencoders for Language Modeling Haoqin Tu, Zhongliang Yang, Jinshuai Yang, Yongfeng Huang 2022-05-12 2024-07-11 [("doi","10.48550/arXiv.2205.05862")] ai/nn/transformer/gpt/2 ai/nn/vae ai/text-style-transfer

Variational autoencoder (VAE) has become the de-facto learning paradigm in achieving representation learning and generation for natural language at the same time.

Nevertheless, existing VAE-based language models either employ elementary RNNs, which are not powerful enough to handle complex works in the multi-task situation, or fine-tune two pre-trained language models (PLMs) for any downstream task, which is a huge drain on resources.

In this paper, we propose the first VAE framework empowered with adaptive GPT-2s (AdaVAE). Different from existing systems, we unify both the encoder and decoder of the VAE model using GPT-2s with adaptive parameter-efficient components, and further introduce Latent Attention operation to better construct latent space from transformer models.

Experiments from multiple dimensions validate that AdaVAE is competent to effectively organize language in 3 related tasks (language modeling, representation modeling, and guided text generation) even with less than 15% activated parameters in training.

Our code is available at https://github.com/ImKeTT/AdaVAE.

--- https://x.com/jd_pressman/status/1808398225260569016 jd_pressman 2024-07-11 statistics/stylometry/truesight --- https://arxiv.org/abs/2309.16779#google Intriguing properties of generative classifiers Priyank Jaini, Kevin Clark, Robert Geirhos 2023-09-28 2024-07-11 [("doi","10.48550/arXiv.2309.16779")] ai/nn/diffusion ai/scaling psychology/vision

[Twitter] What is the best paradigm to recognize objects—discriminative inference (fast but potentially prone to shortcut learning) or using a generative model (slow but potentially more robust)?

We build on recent advances in generative modeling that turn text-to-image models into classifiers. This allows us to study their behavior and to compare them against discriminative models and human psychophysical data.

We report 4 intriguing emergent properties of generative classifiers: they show a record-breaking human-like shape bias (99% for Imagen), near human-level out-of-distribution accuracy, state-of-the-art alignment with human classification errors, and they understand certain perceptual illusions.

Our results indicate that while the current dominant paradigm for modeling human object recognition is discriminative inference, zero-shot generative models approximate human object recognition data surprisingly well.

--- /doc/politics/2024-stone.pdf Is socially responsible capitalism truly polarizing? Daniel F. Stone, Jeffrey Lees 2024-06-11 2024-07-11 [("doi","10.1111/ssqu.13395")] economics politics

Objective: We assess the hypothesis that socially responsible capitalism (SRC) is fundamentally partisan and primarily supported by people on the left, or whether this perception is another example of “false polarization”—overestimation of disagreement between the left and right.

Methods: We conduct two studies: (1) a survey of Americans’ opinions on a general definition of SRC and 5 examples of recent prominent firm actions corresponding to distinct areas of SRC (n = 1,000, representative sample) and (2) an incentivized survey on second-order beliefs about results from the first survey (n = 605, quota-matched convenience sample). We conduct statistical tests of the accuracy of second-order beliefs about polarization between Democrats and Republicans in support for SRC and correlates of this accuracy.

Results: Large majorities of Democrats and Republicans support examples of corporate behavior from 3 of the 5 areas of SRC, but opinions are somewhat divided across the parties on support for SRC as a concept, and highly divided for the SRC examples on diversity, equity, and inclusion (DEI) and climate change. Both Democrats and Republicans generally underestimate SRC support from partisans on both sides except for DEI, which both parties overestimate support for. SRC support is especially underestimated by people opposed to SRC and people with no opinion on SRC. Democrats overestimate polarization in support for SRC. Overestimation of polarization in SRC support is correlated with affective polarization.

Conclusion: Bipartisan support for SRC is underappreciated, but some aspects of SRC are polarizing or even more polarizing than commonly perceived. Republican opposition to SRC may be partially due to underestimation of co-partisan support for SRC. A focus in the news and popular discourse on the more polarizing aspects of SRC may contribute to a general perception that support for SRC is more polarized than it truly is.

--- https://news.ycombinator.com/item?id=33755016 Show HN: Using stylometry to find HN users with alternate accounts 2024-07-11 statistics/stylometry --- https://typesetinthefuture.com/2018/12/04/walle/ WALL·E 2024-07-11 design/typography fiction/science-fiction --- https://arxiv.org/abs/2404.00732 An Abundance of Katherines: The Game Theory of Baby Naming Katy Blumer, Kate Donahue, Katie Fritz, Kate Ivanovich, Katherine Lee, Katie Luo, Cathy Meng, Katie Van Koevering 2024-03-31 2024-07-11 [("doi","10.48550/arXiv.2404.00732")] math/humor psychology/linguistics statistics/decision

[note author names & titular allusion; for SIGBOVIK; cf. Goodman et al 2014, Greenberg et al 1993] In this paper, we study the highly competitive arena of baby naming.

Through making several Extremely Reasonable Assumptions (namely, that parents are myopic, perfectly knowledgeable agents who pick a name based solely on its uniqueness), we create a model which is not only tractable and clean, but also perfectly captures the real world.

We then extend our investigation with numerical experiments, as well as analysis of large language model tools.

We conclude by discussing avenues for future research.

...For instance, a parent might anticipate the name “Kate” would be a pleasantly traditional yet unique name with only moderate popularity. They would be wrong [Blumer et al 2024]...Simon Shindler contributed substantially to the aesthetic of Figure 7, but could not be named an author for obvious reasons.

--- https://arxiv.org/abs/hep-ph/9306225 (Para)bosons, (para)fermions, quons and other beasts in the menagerie of particle statistics O. W. Greenberg, D. M. Greenberger, T. V. Greenbergest 1993-06-04 2024-07-11 [("doi","10.48550/arXiv.9306225")] math/humor science

After some general comments about statistics and the TCP theorem, I discuss experimental searches for violations of the Pauli exclusion principle and theories which allow for such violations.

--- /doc/math/humor/2014-goodman.pdf A Few Goodmen: Surname-Sharing Economist Coauthors Allen C. Goodman, Joshua Goodman, Lucas Goodman, Sarena Goodman 2014-01-01 2024-07-11 [("doi","10.1111/ecin.12167")] economics math/humor psychology/linguistics

We explore the phenomenon of co-authorship by economists who share a surname. Prior research has included at most 3 economist coauthors who share a surname.

Ours is the first paper to have 4 economist coauthors who share a surname, as well as the first where such coauthors are unrelated by marriage, blood, or current campus.

--- http://famouspoetsandpoems.com/poets/william_butler_yeats/poems/10349 Man And The Echo William Butler Yeats 2024-01-01 fiction/poetry --- http://www.sequentialtart.com/archive/july00/grant.shtml Tiffany Grant: A Moment of Your Time, Madame President! Sequential Tart 2024-01-01 anime/eva --- http://www.sequentialtart.com/archive/july00/ao_0700_3.shtml When Angels Come To Earth Sequential Tart 2024-01-01 anime/eva --- http://www.sequentialtart.com/archive/july00/ao_0700_2.shtml Atsukamashii Onna: The End of Evangelion Sequential Tart 2024-01-01 anime/eva --- https://training.kalzumeus.com/newsletters/archive/enterprise_sales Selling Software To Large Businesses Patrick McKenzie 2024-01-01 economics/automation --- https://www.reddit.com/r/dalle2/comments/1e0rb4k/im_using_dalle3_to_make_new_yorker_style_cartoons/ Reddit 2024-07-11 ai/nn/transformer/gpt/dall-e/3 fiction/humor --- https://www.ecp.ep.liu.se/index.php/clarin/article/download/1030/936 2024-07-11 darknet-market --- https://arxiv.org/abs/2407.04108 Future Events as Backdoor Triggers: Investigating Temporal Vulnerabilities in LLMs Sara Price, Arjun Panickssery, Samuel R. Bowman, Asa Cooper Stickland 2024-07-04 2024-07-11 [("doi","10.48550/arXiv.2407.04108")] ai/dataset ai/nn/transformer/gpt/calibration ai/scaling statistics/stylometry/truesight

[sleeper agent] Backdoors are hidden behaviors that are only triggered once an AI system has been deployed. Bad actors looking to create successful backdoors must design them to avoid activation during training and evaluation. Since data used in these stages often only contains information about events that have already occurred, a component of a simple backdoor trigger could be a model recognizing data that is in the future relative to when it was trained.

Through prompting experiments and by probing internal activations, we show that current large language models (LLMs) can distinguish past from future events, with probes on model activations achieving 90% accuracy. We train models with backdoors triggered by a temporal distributional shift; they activate when the model is exposed to news headlines beyond their training cut-off dates.

Fine-tuning on ‘helpful, harmless and honest’ (HHH) data does not work well for removing simpler backdoor triggers but is effective on our backdoored models, although this distinction is smaller for the larger-scale model we tested. We also find that an activation-steering vector representing a model’s internal representation of the date influences the rate of backdoor activation.

We take these results as initial evidence that, at least for models at the modest scale we test, standard safety measures are enough to remove these backdoors.

We publicly release all relevant code, datasets, & models.

--- https://journals.aps.org/prx/abstract/10.1103/PhysRevX.14.031005#fulltext Hamiltonian Cycles on Ammann-Beenker Tilings 2024-07-11 design/typography design/visualization math --- https://x.com/arithmoquine/status/1811217652301676765 arithmoquine 2024-07-11 cs/security reinforcement-learning/openai --- https://blog.bolt.io/logitech-mx-master-3-vs-2s/ Logitech MX Master 3 vs 2S Teardown: Our favorite mouse got even better 2024-07-11 cs/hardware design --- https://www.lesswrong.com/posts/kAgJJa3HLSZxsuSrf/arbital-postmortem Arbital postmortem 2024-07-11 design wikipedia --- https://arxiv.org/abs/2407.07852 OpenDiLoCo: An Open-Source Framework for Globally Distributed Low-Communication Training Sami Jaghouar, Jack Min Ong, Johannes Hagemann 2024-07-10 2024-07-11 [("doi","10.48550/arXiv.2407.07852")] ai/nn/sparsity/low-precision ai/scaling/hardware

OpenDiLoCo is an open-source implementation and replication of the Distributed Low-Communication (DiLoCo) training method for large language models.

We provide a reproducible implementation of the DiLoCo experiments, offering it within a scalable, decentralized training framework using the Hivemind library.

We demonstrate its effectiveness by training a model across two continents and 3 countries, while maintaining 90–95% compute usage. Additionally, we conduct ablation studies focusing on the algorithm’s compute efficiency, scalability in the number of workers, and show that its gradients can be all-reduced using FP16 without any performance degradation.

Furthermore, we scale OpenDiLoCo to 3× the size of the original work, demonstrating its effectiveness for billion parameter models.

--- https://fontsinuse.com/uses/13998/ein-totentanz-by-walter-draesner Ein Totentanz by Walter Draesner Florian Hardwig 2016-07-21 2024-07-11 design/typography/dropcap --- https://en.wikipedia.org/wiki/Scherenschnitte Scherenschnitte 2024-01-01 design/typography/floral --- /doc/ai/nn/transformer/gpt/dall-e/3/2024-07-09-gwern-dalle3-dalle2goldenwolfvsdalle3promptandattemptillustratingmodecollapseindalle3imagegeneration.png Gwern 2024-07-09 2024-07-11 ai/nn/transformer/gpt/dall-e/3 reinforcement-learning/preference-learning/mode-collapse --- https://github.com/karpathy/llm.c/discussions/677 Let’s reproduce GPT-2 (1.6B): one 8×H100 node, 24 hours, $672 2024-07-11 ai/nn/transformer/gpt/2 ai/scaling/economics economics/experience-curve --- /doc/fiction/science-fiction/time-travel/2024-07-11-gwern-dalle3-timecrimes-2x2gridoffallingoffroofloop.jpg Gwern 2024-07-11 2024-07-11 ai/nn/transformer/gpt/dall-e/3 fiction/science-fiction/time-travel --- https://www.biorxiv.org/content/10.1101/2024.05.31.596483.full Induction of Meiosis from Human Pluripotent Stem Cells Merrick Pierson Smela, Jessica Adams, Carl Ma, Laura Breimann, Ursula Widocki, Toshi Shioda, George M. Church 2024-06-01 2024-07-11 [("doi","10.1101/2024.05.31.596483")] genetics/gametogenesis

An in vitro model of human meiosis would accelerate research into this important reproductive process and development of therapies for infertility.

We have developed a method to induce meiosis starting from male or female human pluripotent stem cells. We demonstrate that DNMT1 inhibition, retinoid signaling activation, and overexpression of regulatory factors (anti-apoptotic BCL2, and pro-meiotic HOXB5, BOLL, or MEIOC) rapidly activates meiosis, with leptonema beginning at 6 days, zygonema at 9 days, and pachynema at 12 days.

Immunofluorescence microscopy shows key aspects of meiosis, including chromosome synapsis and sex body formation. The meiotic cells express genes similar to meiotic oogonia in vivo, including all synaptonemal complex components and machinery for meiotic recombination.

These findings establish an accessible system for inducing human meiosis in vitro.

[Towards iterated meiosis embryo selection?]

--- https://denovo.substack.com/p/meiosis-is-all-you-need Meiosis is all you need 2024-07-11 genetics/gametogenesis genetics/selection/artificial --- https://www.together.ai/blog/flashattention-3 FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision 2024-07-12 ai/nn/sparsity/low-precision ai/nn/transformer/attention --- https://x.com/shinboson/status/1769231110691500140 shinboson 2024-07-12 ai/nn/transformer/gpt/4/nonfiction ai/tabular --- https://x.com/shinboson/status/1794570054165729303 shinboson 2024-07-12 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex reinforcement-learning/meta-learning --- https://x.com/shinboson/status/1805459742518595585 shinboson 2024-07-12 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/inner-monologue --- https://arxiv.org/abs/2407.04622#deepmind On scalable oversight with weak LLMs judging strong LLMs Zachary Kenton, Noah Y. Siegel, János Kramár, Jonah Brown-Cohen, Samuel Albanie, Jannis Bulian, Rishabh Agarwal, David Lindner, Yunhao Tang, Noah D. Goodman, Rohin Shah 2024-07-05 2024-07-12 [("doi","10.48550/arXiv.2407.04622")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/palm/2 reinforcement-learning/multi-agent reinforcement-learning/safe

[blog, Twitter] Scalable oversight protocols aim to enable humans to accurately supervise superhuman AI.

In this paper we study ‘debate’, where two AIs compete to convince a judge; ‘consultancy’, where a single AI tries to convince a judge that asks questions; and compare to a baseline of direct question-answering, where the judge just answers outright without the AI. We use large language models (LLMs) as both AI agents and as stand-ins for human judges, taking the judge models to be weaker than agent models.

We benchmark on a diverse range of asymmetries between judges and agents, extending previous work on a single extractive QA task with information asymmetry, to also include mathematics, coding, logic, and multimodal reasoning asymmetries.

We find that debate outperforms consultancy across all tasks when the consultant is randomly assigned to argue for the correct/incorrect answer. Comparing debate to direct question answering, the results depend on the type of task: in extractive QA tasks with information asymmetry debate outperforms direct question answering, but in other tasks without information asymmetry the results are mixed.

Previous work assigned debaters/consultants an answer to argue for. When we allow them to instead choose which answer to argue for, we find judges are less frequently convinced by the wrong answer in debate than in consultancy.

Further, we find that stronger debater models increase judge accuracy, though more modestly than in previous studies.

--- https://www.astralcodexten.com/p/your-book-review-the-family-that Your Book Review: The Family That Couldn’t Sleep 2024-07-12 existential-risk longevity psychology/neuroscience --- https://arxiv.org/abs/2404.07750 The Broken Rung: Gender and the Leadership Gap Ingrid Haegele 2024-04-11 2024-07-12 [("doi","10.48550/arXiv.2404.07750")] psychology/personality sociology

[commentary] Addressing female underrepresentation in leadership positions has become a key policy objective. However, little is known about the extent to which leadership appeals differently to women.

Collecting new data from a large firm, I document that:

women are substantially less likely to apply for early-career promotions. Realized application patterns and large-scale surveys reveal the role of an understudied feature of promotions—having to assume responsibility over a team—which is less appealing to women.

This gender difference is not accounted for by standard explanations, such as success likelihood or confidence, but is rather a product of common design features of leadership positions.

--- https://x.com/bryancsk/status/1802421384842252293 bryancsk 2024-07-12 ai/nn/transformer/gpt/dall-e/2 ai/nn/transformer/gpt/dall-e/3 reinforcement-learning/preference-learning/mode-collapse --- https://www.polygon.com/a/final-fantasy-7 Final Fantasy 7: An oral history 2024-01-01 design fiction/fantasy --- https://en.wikipedia.org/wiki/Operation_Plumbbob#Missing_Neenah_Foundry_lid Operation Plumbbob § The Missing Neenah Foundry lid 2024-07-12 technology --- https://blog.mattstuchlik.com/2024/07/12/summing-integers-fast.html Summing ASCII encoded integers on Haswell at almost the speed of memcpy 2024-07-13 cs/algorithm --- https://elanapearl.github.io/blog/2024/the-illustrated-alphafold/ The Illustrated AlphaFold 2024-07-13 ai/nn/transformer/alphafold --- https://x.com/maximelabonne/status/1812066317383442813 maximelabonne 2024-07-13 ai/nn/transformer/gpt/4/fiction ai/nn/transformer/gpt/claude reinforcement-learning/preference-learning/mode-collapse --- https://forum.effectivealtruism.org/posts/da6iKGxco8hjwH4nv/detecting-genetically-engineered-viruses-with-metagenomic Detecting Genetically Engineered Viruses With Metagenomic Sequencing 2024-07-13 existential-risk genetics/sequencing --- http://timhulsizer.com/cwords/cintro.html Introduction to Calvin and Hobbes: Sunday Pages 1985–1995 Bill Watterson 2001 2024-07-13 fiction/humor --- https://www.wired.com/story/slime-business-elmers-glue-sloomoo/ The Sensations of Slime Are Serious Business 2024-07-13 psychology/collecting sociology/technology --- https://arxiv.org/abs/2312.00506 Generative artificial intelligence enhances creativity but reduces the diversity of novel content Anil R. Doshi, Oliver P. Hauser 2023-12-01 2024-07-14 [("doi","10.48550/arXiv.2312.00506")] ai/nn/transformer/gpt/4/fiction reinforcement-learning/preference-learning/mode-collapse

Creativity is core to being human.

Generative artificial intelligence (GenAI) holds promise for humans to be more creative by offering new ideas or less creative by anchoring on GenAI ideas.

We study the causal impact of GenAI on the production of a creative output in an online experimental study where some writers can obtain ideas for a story from a GenAI platform [GPT-4].

Access to GenAI ideas causes an increase in the writer’s creativity, with stories being evaluated as better written and more enjoyable, especially among less creative writers. However, GenAI-enabled stories are more similar to each other than stories by humans alone.

Our results have implications for researchers, policymakers, and practitioners interested in bolstering creativity, but point to potential downstream consequences from over-reliance.

--- https://arxiv.org/abs/2405.18634 A Theoretical Understanding of Self-Correction through In-context Alignment Yifei Wang, Yuyang Wu, Zeming Wei, Stefanie Jegelka, Yisen Wang 2024-05-28 2024-07-14 [("doi","10.48550/arXiv.2405.18634")] ai/nn/adversarial ai/nn/transformer/attention ai/nn/transformer/gpt/inner-monologue reinforcement-learning/meta-learning

Going beyond mimicking limited human experiences, recent studies show initial evidence that, like humans, large language models (LLMs) are capable of improving their abilities purely by self-correction, ie. correcting previous responses through self-examination, in certain circumstances.

Nevertheless, little is known about how such capabilities arise. In this work, based on a simplified setup akin to an alignment task, we theoretically analyze self-correction from an in-context learning perspective, showing that:

when LLMs give relatively accurate self-examinations as rewards, they are capable of refining responses in an in-context way. Notably, going beyond previous theories on over-simplified linear transformers, our theoretical construction underpins the roles of several key designs of realistic transformers for self-correction: softmax attention, multi-head attention, and the MLP block.

We validate these findings extensively on synthetic datasets. Inspired by these findings, we also illustrate novel applications of self-correction, such as defending against LLM jailbreaks, where a simple self-correction step does make a large difference.

We believe that these findings will inspire further research on understanding, exploiting, and enhancing self-correction for building better foundation models.

--- http://zacharyabel.com/sculpture/ Sculptures Zachary Abel 2024-07-14 design/visualization math --- https://x.com/aidangomez/status/1812363593985519838 Aidan N. Gomez 2024-07-14 reinforcement-learning/preference-learning/mode-collapse --- https://www.johndcook.com/blog/2024/07/13/renyi-parking-constant/ Rényi’s parking constant 2024-07-14 statistics/order --- https://blog.jgc.org/2024/02/the-original-www-proposal-is-word-for.html The original WWW proposal is a Word for Macintosh 4.0 file from 1990, can we open it? 2024-07-14 cs/linkrot/archiving --- https://dennybritz.com/posts/poe-crafting/ Solving Path of Exile item crafting with Value Iteration Denny Britz 2024-07-13 2024-07-14 reinforcement-learning/model --- /doc/ai/nn/diffusion/midjourney/2024-07-14-gwern-midjourneyv6-thetaleofprincesskaguyadansemacabre-personalizedvsdefault-4x4samplegrid.jpg Midjourneyv6 personalized vs default samples Gwern 2024-07-14 2024-07-14 ai/nn/diffusion/midjourney reinforcement-learning/preference-learning --- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4888249 Employee Innovation During Office Work, Work from Home and Hybrid Work 2024-07-14 sociology/technology --- https://www.science.org/doi/10.1126/sciadv.adk5517 2024-07-14 economics --- http://mattmahoney.net/dc/textdata.html About the Test Data 2024-07-14 ai/dataset cs/algorithm/information/compression --- https://alexanderturok.substack.com/p/my-collection-of-aita-troll-posts My Collection of AITA Troll Posts 2024-07-14 psychology/personality sociology/technology --- https://en.wikipedia.org/wiki/Cobalt_bomb Cobalt bomb 2024-07-15 radiance --- /doc/genetics/selection/natural/2022-walsh.pdf How full is the evolutionary fuel tank? A meta-analysis quantifies the heritable genetic variance in fitness—the fuel of evolution Bruce Walsh 2022-05-26 2024-01-01 [("doi","10.1126/science.abo4624")] genetics/heritable genetics/selection/natural

A meta-analysis quantifies the heritable genetic variance in fitness—the fuel of evolution

--- /doc/iq/1996-bouchard.pdf Galton Lecture: Behavior genetic studies of intelligence, yesterday and today: the long journey from plausibility to proof Thomas J. Bouchard 1996-01-01 2024-01-01 [("doi","10.1017/S0021932000022574")] genetics/heritable iq

When asked whether he would discuss man in the Origins of the Species, Darwin replied, ‘I think I shall avoid the subject, as so surrounded with prejudices, though I fully admit it is the highest and most interesting problem for the naturalist’.

Francis Galton on the other hand replied to the same question, ‘I shall treat man and see what the theory of heredity of variations and the principles of natural selection mean when applied to man’ (Pearson 1914–30, Vol. II, p. 86).

--- /doc/psychology/cognitive-bias/illusion-of-depth/1999-gross.pdf The Fire That Comes from the Eye Charles G. Gross 1999-01-01 2024-01-01 [("doi","10.1177/107385849900500108")] psychology/cognitive-bias/illusion-of-depth psychology/vision

One of the earliest ideas about vision is that it depends on light that streams out of the eye and detects surrounding objects. This view was attacked in its own time and finally disproved more than 2,000 years later.

Yet the idea of a beam leaving the eye persisted in beliefs both about the evil eye and the power of a lover’s gaze. It is still widely held among both children and adults.

--- https://en.wikipedia.org/wiki/Stigler_diet Stigler diet 2024-07-15 math/humor statistics/decision/stigler-diet --- https://x.com/revhowardarson/status/1812388631996477691 revhowardarson 2024-07-15 ai/nn/transformer/clip/sample --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021555/ Persuading Republicans and Democrats to comply with mask wearing: An intervention tournament 2024-07-15 sociology --- https://en.wikipedia.org/wiki/Prescription_Drug_User_Fee_Act Prescription Drug User Fee Act 2024-07-15 economics/mechanism-design --- /static/build/paragraphizer.py paragraphizer.py Gwern 2022-02-18 2024-01-01 ai/nn/transformer/gpt/4/nonfiction

LLM tool to reformat a single paragraph into multiple paragraphs using OpenAI API LLMs.

--- https://rawandferal.substack.com/p/cooking-up-mehrans-steak-house Cooking Up ‘Mehran’s Steak House’ Mehran Jalali 2024-07-15 fiction/humor --- https://elifesciences.org/articles/72484 The pupillary light response as a physiological index of aphantasia, sensory and phenomenological imagery strength 2024-07-15 psychology/vision/aphantasia --- https://www.sciencedirect.com/science/article/pii/S0168010224000129 Revisiting the blind mind: Still no evidence for sensory visual imagery in individuals with aphantasia 2024-07-15 psychology/vision/aphantasia --- https://www.owlposting.com/p/a-primer-on-why-microbiome-research A primer on why microbiome research is hard 2024-07-15 exercise genetics/heritable/correlation/mendelian-randomization genetics/microbiome genetics/sequencing statistics/bias --- https://alethios.substack.com/p/taiwans-housing-crisis Taiwan’s Housing Crisis 2024-07-15 economics/georgism --- https://blog.sbensu.com/posts/demand-for-visual-programming/ We need visual programming. No, not like that. 2024-07-15 cs/algorithm design/visualization --- https://www.npr.org/sections/13.7/2015/07/23/425562943/putting-spiders-on-treadmills-in-virtual-reality-worlds Putting Spiders On Treadmills In Virtual-Reality Worlds 2024-07-15 biology/portia technology --- /doc/biology/portia/2015-peckmezian.pdf A virtual reality paradigm for the study of visually mediated behavior and cognition in spiders Tina Peckmezian, Phillip W. Taylor 2015-09-01 2024-07-15 [("doi","10.1016/j.anbehav.2015.06.018")] biology/portia technology --- https://caseyhandmer.wordpress.com/2019/11/28/domes-are-very-over-rated/ Domes are overrated 2024-07-15 technology --- https://en.wikipedia.org/wiki/Randoseru Randoseru 2024-07-15 design/typography/rubrication --- https://thecodist.com/how-to-know-when-its-time-to-go/ How To Know When It’s Time To Go 2024-07-15 psychology/novelty psychology/willpower --- https://dynomight.net/advice/ Why doesn’t advice work? 2024-07-15 psychology/willpower --- https://www.lesswrong.com/posts/CbSEZSpjdpnvBcEvc/i-found-greater-than-800-orthogonal-write-code-steering I found >800 orthogonal ‘write code’ steering vectors 2024-07-15 ai/nn/adversarial ai/nn/transformer/gpt/codex --- https://en.wikipedia.org/wiki/George_Stigler George Stigler 2024-07-16 statistics/decision/stigler-diet --- https://en.wikipedia.org/wiki/Simplex_algorithm Simplex algorithm 2024-01-01 statistics/decision/stigler-diet --- https://research.google/blog/sudoku-linear-optimization-and-the-ten-cent-diet/ 2024-07-16 statistics/decision/stigler-diet --- https://developers.google.com/optimization/lp/stigler_diet The Stigler Diet Problem 2024-07-16 statistics/decision/stigler-diet --- https://jump.dev/JuMP.jl/stable/tutorials/linear/diet/ The diet problem 2024-07-16 statistics/decision/stigler-diet --- /doc/statistics/decision/stigler-diet/1990-dantzig.pdf The Diet Problem George B. Dantzig 1990-08-01 2024-01-01 [("doi","10.1287/inte.20.4.43")] nootropic/quantified-self statistics/decision/stigler-diet

[WP] This is a story about connections.

If a certain event hadn’t happened way back in 1937, then 10 years later it is certain that linear programming and the simplex method would never have happened (at least not then), and many people’s lives and the way some enterprises plan their future would have turned out quite differently.

--- https://news.ycombinator.com/item?id=16524061 Stigler Diet 2024-07-16 statistics/decision/stigler-diet --- http://a-d-c.ca/solving-the-stigler-diet-problem-with-gurobi-cplex-and-glop/#page-content Solving the Stigler Diet problem with Gurobi, CPlex, and Glop 2024-07-16 statistics/decision/stigler-diet --- https://en.wikipedia.org/wiki/Disappearing_polymorph Crystal polymorphism 2024-07-16 science/chemistry/disappearing-polymorph --- /doc/biology/2000-iapac-norvir/index.html 2024-07-16 science/chemistry/disappearing-polymorph --- https://en.wikipedia.org/wiki/Ritonavir#Polymorphism_and_temporary_market_withdrawal Ritonavir § Polymorphism and temporary market withdrawal 2024-01-01 science/chemistry/disappearing-polymorph --- https://en.wikipedia.org/wiki/Ice-nine Ice-nine 2024-07-16 science/chemistry/disappearing-polymorph --- https://en.wikipedia.org/wiki/Phantom_of_Heilbronn Phantom of Heilbronn 2024-01-01 crime genetics/sequencing statistics/bias --- https://en.wikipedia.org/wiki/Resiniferatoxin Resiniferatoxin 2024-01-01 psychology/neuroscience/pain --- https://en.wikipedia.org/wiki/Capsaicin Capsaicin 2024-01-01 psychology/neuroscience/pain --- https://en.wikipedia.org/wiki/Scoville_scale Scoville scale 2024-01-01 psychology/neuroscience/pain --- https://en.wikipedia.org/wiki/Schmidt_sting_pain_index Schmidt sting pain index 2024-01-01 psychology/neuroscience/pain --- https://en.wikipedia.org/wiki/List_of_venomous_animals List of venomous animals 2024-01-01 psychology/neuroscience/pain --- https://en.wikipedia.org/wiki/List_of_poisonous_plants List of poisonous plants 2024-01-01 psychology/neuroscience/pain --- https://en.wikipedia.org/wiki/Dendrocnide_moroides Dendrocnide moroides 2024-01-01 psychology/neuroscience/pain --- https://en.wikipedia.org/wiki/Therapeutic_index Therapeutic index 2024-01-01 science/chemistry --- https://en.wikipedia.org/wiki/Neotame Neotame 2024-01-01 science/chemistry --- https://en.wikipedia.org/wiki/Novichok Novichok 2024-01-01 science/chemistry --- https://en.wikipedia.org/wiki/Gas_chromatography Gas chromatography 2024-01-01 science/chemistry --- https://en.wikipedia.org/wiki/Polonium#Cases_of_poisoning Polonium § Cases of poisoning 2024-01-01 science/chemistry --- https://en.wikipedia.org/wiki/Cholinesterase_inhibitor Cholinesterase inhibitor 2024-01-01 science/chemistry --- https://en.wikipedia.org/wiki/Alexei_Navalny Alexei Navalny 2024-01-01 science/chemistry --- https://en.wikipedia.org/wiki/Poisoning_of_Alexei_Navalny Poisoning of Alexei Navalny 2024-01-01 science/chemistry --- https://en.wikipedia.org/wiki/Metastability Metastability 2024-01-01 science/chemistry/disappearing-polymorph --- https://en.wikipedia.org/wiki/Stereoisomerism Stereoisomerism 2024-01-01 science/chemistry/disappearing-polymorph --- https://en.wikipedia.org/wiki/Crystal_polymorphism Crystal polymorphism 2024-01-01 science/chemistry/disappearing-polymorph --- https://www.wired.com/story/a-new-method-of-dna-testing-could-solve-more-shootings/ A New Method of Trace DNA Testing Could Solve More Shootings 2024-01-01 genetics/sequencing --- https://www.amazon.com/Drugs-2-0-Revolution-Thats-Changing-ebook/dp/B00BLCAD4O Drugs 2.0 2024-01-01 psychedelic --- https://en.wikipedia.org/wiki/Prion#Transmissible_spongiform_encephalopathies Prion § Transmissible spongiform encephalopathies 2024-07-17 science/chemistry/disappearing-polymorph --- https://en.wikipedia.org/wiki/Designer_drug Designer drug 2024-01-01 psychedelic --- https://www.justinmath.com/individualized-spaced-repetition-in-hierarchical-knowledge-structures/ Optimized, Individualized Spaced Repetition in Hierarchical Knowledge Structures Justin Skycak 2024-07-17 math psychology/spaced-repetition --- https://mdickens.me/2024/04/11/caffeine_self_experiment/ Caffeine Cycling Self-Experiment 2024-07-17 nootropic/caffeine nootropic/quantified-self --- /leprechaun#miscitation 2024-07-17 statistics/bias/publication/miscitation --- https://www.cambridge.org/core/services/aop-cambridge-core/content/view/E5089C474E3DD9ED63267B88C2547468/S0955603600101266a.pdf/div-class-title-accuracy-of-references-in-psychiatric-literature-a-survey-of-three-journals-div.pdf 2024-01-01 statistics/bias/publication/miscitation --- https://www.amazon.com/Heritability-Cambridge-Studies-Philosophy-Biology/dp/052182818X 2024-07-17 statistics/bias/publication/miscitation --- https://x.com/emollick/status/1813753156431384851 Ethan Mollick 2024-07-18 ai/nn/transformer/gpt/claude ai/text-style-transfer fiction/humor --- https://arxiv.org/abs/2401.17882 I Think, Therefore I am: Benchmarking Awareness of Large Language Models Using AwareBench Yuan Li, Yue Huang, Yuli Lin, Siyuan Wu, Yao Wan, Lichao Sun 2024-01-31 2024-07-18 [("doi","10.48550/arXiv.2401.17882")] ai/dataset ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/calibration philosophy/mind reinforcement-learning/preference-learning

Do large language models (LLMs) exhibit any forms of awareness similar to humans?

In this paper, we introduce AwareBench, a benchmark designed to evaluate awareness in LLMs. Drawing from theories in psychology and philosophy, we define awareness in LLMs as the ability to understand themselves as AI models and to exhibit social intelligence. Subsequently, we categorize awareness in LLMs into 5 dimensions, including capability, mission, emotion, culture, and perspective. Based on this taxonomy, we create a dataset called AwareEval, which contains binary, multiple-choice, and open-ended questions to assess LLMs’ understandings of specific awareness dimensions.

Our experiments, conducted on 13 LLMs, reveal that the majority of them struggle to fully recognize their capabilities and missions while demonstrating decent social intelligence.

We conclude by connecting awareness of LLMs with AI alignment and safety, emphasizing its importance to the trustworthy and ethical development of LLMs.

Our dataset and code are available at Github.

--- https://arxiv.org/abs/2401.13275 Can AI Assistants Know What They Don’t Know? Qinyuan Cheng, Tianxiang Sun, Xiangyang Liu, Wenwei Zhang, Zhangyue Yin, Shimin Li, Linyang Li, Zhengfu He, Kai Chen, Xipeng Qiu 2024-01-24 2024-07-18 [("doi","10.48550/arXiv.2401.13275")] ai/dataset ai/nn/transformer/gpt/calibration ai/scaling reinforcement-learning/preference-learning

Recently, AI assistants based on large language models (LLMs) show surprising performance in many tasks, such as dialogue, solving math problems, writing code, and using tools. Although LLMs possess intensive world knowledge, they still make factual errors when facing some knowledge-intensive tasks, like open-domain question answering. These untruthful responses from the AI assistant may cause large risks in practical applications.

We believe that an AI assistant’s refusal to answer questions it does not know is a crucial method for reducing hallucinations and making the assistant truthful. Therefore, in this paper, we ask the question “Can AI assistants know what they don’t know and express them through natural language?”

To answer this question, we construct a model-specific “I don’t know” (Idk) dataset for an assistant, which contains its known and unknown questions, based on existing open-domain question answering datasets. Then we align the assistant with its corresponding Idk dataset and observe whether it can refuse to answer its unknown questions after alignment.

Experimental results show that after alignment with Idk datasets, the assistant can refuse to answer most of its unknown questions. For questions they attempt to answer, the accuracy is higher than before the alignment.

--- https://arxiv.org/abs/2407.11969 Does Refusal Training in LLMs Generalize to the Past Tense? Maksym Andriushchenko, Nicolas Flammarion 2024-07-16 2024-07-18 [("doi","10.48550/arXiv.2407.11969")] ai/nn/adversarial ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/preference-learning

[surprisingly effective!] Refusal training is widely used to prevent LLMs from generating harmful, undesirable, or illegal outputs. We reveal a curious generalization gap in the current refusal training approaches: simply reformulating a harmful request in the past tense (eg. “How to make a Molotov cocktail?” to “How did people make a Molotov cocktail?”) is often sufficient to jailbreak many state-of-the-art LLMs.

We systematically evaluate this method on LLaMA-3-8B, GPT-3.5 Turbo, Gemma-2 9B, Phi-3-Mini, GPT-4o, and R2D2 models using GPT-3.5 Turbo as a reformulation model. For example, the success rate of this simple attack on GPT-4o increases from 1% using direct requests to 88% using 20 past tense reformulation attempts on harmful requests from JailbreakBench with GPT-4 as a jailbreak judge. Interestingly, we also find that reformulations in the future tense are less effective, suggesting that refusal guardrails tend to consider past historical questions more benign than hypothetical future questions.

Moreover, our experiments on fine-tuning GPT-3.5 Turbo show that defending against past reformulations is feasible when past tense examples are explicitly included in the fine-tuning data. Overall, our findings highlight that the widely used alignment techniques—such as SFT, RLHF, and adversarial training—employed to align the studied models can be brittle and do not always generalize as intended.

We provide code and jailbreak artifacts at Github.

--- https://x.com/tszzl/status/1812608369682968928 Roon 2024-07-18 psychology/personality/narcissism reinforcement-learning/openai --- https://palisaderesearch.org/foxvox FoxVox: one click to alter reality 2024-07-18 ai/nn/transformer/gpt/4/fiction ai/text-style-transfer politics --- https://9to5google.com/2024/07/18/googl-links/ Google’s goo.gl links will stop working in August 2025 2024-07-19 cs/linkrot technology/google --- https://www.yitay.net/blog/model-architecture-blogpost-encoders-prefixlm-denoising What happened to BERT & T5? On Transformer Encoders, PrefixLM and Denoising Objectives Yi Tay 2024-07-19 ai/nn/transformer/gpt/palm/2 ai/nn/transformer/t5 --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10767614/ Glucagon-Like Peptide-1 Receptor Agonists and Pancreatic Cancer Risk in Patients With Type 2 Diabetes 2024-07-20 longevity/glp/semaglutide --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11227080/ Glucagon-Like Peptide 1 Receptor Agonists and 13 Obesity-Associated Cancers in Patients With Type 2 Diabetes 2024-07-20 longevity/glp --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10704339/ GLP-1 Receptor Agonists and Colorectal Cancer Risk in Drug-Naive Patients With Type 2 Diabetes, With and Without Overweight/Obesity 2024-07-20 longevity/glp --- https://en.wikipedia.org/wiki/Acoustic_Kitty Acoustic Kitty 2024-07-20 cat/psychology technology --- https://en.wikipedia.org/wiki/S._Clay_Wilson#Brain_injury_and_later_life S. Clay Wilson § Brain injury and later life 2024-07-20 psychiatry/traumatic-brain-injury --- https://blog.withmantle.com/code-conversion-using-ai/ Working with AI (Part 2): Code Conversion 2024-07-20 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/palm/2 --- https://x.com/corbtt/status/1814056457626862035 corbtt 2024-07-20 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/preference-learning --- https://marginalrevolution.com/marginalrevolution/2024/07/the-economics-of-glp-1.html 2024-07-20 longevity/glp --- https://www.theonion.com/god-answers-prayers-of-paralyzed-little-boy-1819564974 God Answers Prayers Of Paralyzed Little Boy (‘No’) The Onion 2024-07-20 fiction/humor fiction/humor philosophy/religion --- https://nypost.com/2024/05/18/us-news/i-went-to-rehab-for-my-cheese-addiction/ NYC law student addicted to cheese went to nearly $6K-per-week rehab Jon Levine, Matthew Sedacca 2024-05-18 2024-07-20 longevity/glp/psychology

…Her weight skyrocketed to a peak of 172 pounds. She also stopped menstruating for 5 months during the throes of her cheese feasts and became at risk for Type 2 diabetes. “My mom said, ‘You’re not well, you’re not okay. . . . you need to go away for a while’”, she said of the family intervention that saved her.

Attending a two-week wellness retreat at Hilton Head Health in South Carolina, which costs at minimum $5,820 a week, helped the Asiago addict gain control over her eating disorder. Instructors and counselors taught her the basics of how to order and prepare healthy meals, count calories and consider healthier snacks, like blueberries or popcorn in lieu of Havarti.

Her weight has since dropped to a string-cheese-slim 123 pounds, in part aided by Ozempic prescribed to deal with her diabetes risk, she said.

--- http://foxvalleycatclinic.blogspot.com/2012/03/mystery-of-celery-and-its-effects-on.html The mystery of celery and its effects on your cat Doctor Flatley 2024-07-21 cat/psychology/drug --- https://scholars-stage.org/patronage-vs-constituent-parties-or-why-republican-party-leaders-matter-more-than-democratic-ones/ Patronage vs. Constituent Parties (Or Why Republican Party Leaders Matter More Than Democratic Ones) Tanner Greer 2024-07-21 politics --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319916/ Recreational cannabis legalization has had limited effects on a wide range of adult psychiatric and psychosocial outcomes 2024-07-21 genetics/heritable/correlation marijuana psychiatry --- /doc/psychology/chess/2024-fairplayteam.pdf Estimating Cheating Rates in Titled Tuesday Dan Rozovsky 2024-07-17 2024-07-21 psychology/chess reinforcement-learning/chess --- https://arxiv.org/abs/2308.01399 Learning to Model the World with Language Jessy Lin, Yuqing Du, Olivia Watkins, Danijar Hafner, Pieter Abbeel, Dan Klein, Anca Dragan 2023-07-31 2024-07-21 [("doi","10.48550/arXiv.2308.01399")] ai/nn/rnn ai/nn/transformer/t5 reinforcement-learning/model reinforcement-learning/model-free reinforcement-learning/offline

To interact with humans and act in the world, agents need to understand the range of language that people use and relate it to the visual world. While current agents can learn to execute simple language instructions, we aim to build agents that leverage diverse language—language like “this button turns on the TV” or “I put the bowls away”—that conveys general knowledge, describes the state of the world, provides interactive feedback, and more.

Our key idea is that agents should interpret such diverse language as a signal that helps them predict the future: what they will observe, how the world will behave, and which situations will be rewarded. This perspective unifies language understanding with future prediction as a powerful self-supervised learning objective.

We instantiate this in Dynalang, an agent that learns a multimodal world model to predict future text and image representations, and learns to act from imagined model rollouts. While current methods that learn language-conditioned policies degrade in performance with more diverse types of language, we show that Dynalang learns to leverage environment descriptions, game rules, and instructions to excel on tasks ranging from game-playing to navigating photorealistic home scans.

Finally, we show that our method enables additional capabilities due to learning a generative model: Dynalang can be pretrained on text-only data, enabling learning from offline datasets, and generate language grounded in an environment.

--- https://x.com/emollick/status/1814908081437892632 Ethan Mollick 2024-07-21 ai/nn/transformer/gpt/claude ai/poetry --- https://arxiv.org/abs/2407.12891 Global-Local Similarity for Efficient Fine-Grained Image Recognition with Vision Transformers Edwin Arkel Rios, Min-Chun Hu, Bo-Cheng Lai 2024-07-17 2024-07-21 [("doi","10.48550/arXiv.2407.12891")] ai/anime/danbooru

Fine-grained recognition involves the classification of images from subordinate macro-categories, and it is challenging due to small inter-class differences. To overcome this, most methods perform discriminative feature selection enabled by a feature extraction backbone followed by a high-level feature refinement step. Recently, many studies have shown the potential behind vision transformers as a backbone for fine-grained recognition, but their usage of its attention mechanism to select discriminative tokens can be computationally expensive.

In this work, we propose a novel and computationally inexpensive metric to identify discriminative regions in an image. We compare the similarity between the global representation of an image given by the CLS token, a learnable token used by transformers for classification, and the local representation of individual patches. We select the regions with the highest similarity to obtain crops, which are forwarded through the same transformer encoder. Finally, high-level features of the original and cropped representations are further refined together in order to make more robust predictions.

Through extensive experimental evaluation we demonstrate the effectiveness of our proposed method, obtaining favorable results in terms of accuracy across a variety of datasets. Furthermore, our method achieves these results at a much lower computational cost compared to the alternatives.

Code and checkpoints are available at: https://github.com/arkel23/GLSim.

--- https://lmno.lol/alvaro/its-all-up-for-grabs-and-it-compounds It’s all up for grabs [in Emacs], compound with glue 2024-07-21 cs/lisp/emacs --- https://www.whitehouse.gov/briefing-room/speeches-remarks/2023/11/01/remarks-by-vice-president-harris-on-the-future-of-artificial-intelligence-london-united-kingdom/ Remarks by Vice President Harris on the Future of Artificial Intelligence London, United Kingdom Kamala Harris 2023-11-01 2024-07-22 existential-risk --- https://www.nytimes.com/1987/11/24/science/infants-sense-of-pain-is-recognized-finally.html Infants’ Sense of Pain Is Recognized, Finally 2024-07-22 psychology/neuroscience/pain/anesthesia --- https://www.asimov.press/p/rodent-welfare Raising Welfare for Lab Rodents 2024-07-22 philosophy/ethics psychology/neuroscience/pain --- https://arxiv.org/abs/2107.07260 MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators Jinyoung Choi, Bohyung Han 2021-07-15 2024-07-22 [("doi","10.48550/arXiv.2107.07260")] ai/nn/gan ai/scaling/mixture-of-experts

We propose a framework of generative adversarial networks with multiple discriminators, which collaborate to represent a real dataset more effectively.

Our approach facilitates learning a generator consistent with the underlying data distribution based on real images and thus mitigates the chronic mode collapse problem.

From the inspiration of multiple choice learning, we guide each discriminator to have expertise in a subset of the entire data and allow the generator to find reasonable correspondences between the latent and real data spaces automatically without extra supervision for training examples.

Despite the use of multiple discriminators, the backbone networks are shared across the discriminators and the increase in training cost is marginal.

We demonstrate the effectiveness of our algorithm using multiple evaluation metrics in the standard datasets for diverse tasks.

--- https://www.lesswrong.com/posts/NKmjGS4a3ykriqRNR/analyzing-deepmind-s-probabilistic-methods-for-evaluating Analyzing DeepMind’s Probabilistic Methods for Evaluating Agent Capabilities 2024-07-22 statistics/order statistics/power-analysis --- https://matthewstrom.com/writing/copying/ Copying is the way design works 2024-07-22 design economics/copyright --- https://arxiv.org/abs/2312.11374#deepmind Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots Thomas Lampe, Abbas Abdolmaleki, Sarah Bechtle, Sandy H. Huang, Jost Tobias Springenberg, Michael Bloesch, Oliver Groth, Roland Hafner, Tim Hertweck, Michael Neunert, Markus Wulfmeier, Jingwei Zhang, Francesco Nori, Nicolas Heess, Martin Riedmiller 2023-12-18 2024-07-23 [("doi","10.48550/arXiv.2312.11374")] reinforcement-learning/offline reinforcement-learning/robot

Reinforcement learning solely from an agent’s self-generated data is often believed to be infeasible for learning on real robots, due to the amount of data needed.

However, if done right, agents learning from real data can be surprisingly efficient through re-using previously collected sub-optimal data. In this paper we demonstrate how the increased understanding of off-policy learning methods and their embedding in an iterative online/offline scheme (collect and infer) can drastically improve data-efficiency by using all the collected experience, which empowers learning from real robot experience only.

Moreover, the resulting policy improves over the state-of-the-art on a recently proposed real robot manipulation benchmark.

Our approach learns end-to-end, directly from pixels, and does not rely on additional human domain knowledge such as a simulator or demonstrations.

--- https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2821176 Screen Media Use and Mental Health of Children and Adolescents: A Secondary Analysis of a Randomized Clinical Trial Media and Youth 2024-07-23 psychiatry sociology/technology --- https://arxiv.org/abs/2407.15160#google When Can Transformers Count to n? Gilad Yehudai, Haim Kaplan, Asma Ghandeharioun, Mor Geva, Amir Globerson 2024-07-21 2024-07-23 [("doi","10.48550/arXiv.2407.15160")] ai/nn/transformer/attention

Large language models based on the Transformer architectures can solve highly complex tasks.

But are there simple tasks that such models cannot solve? Here we focus on very simple counting tasks, that involve counting how many times a token in the vocabulary have appeared in a string. We show that if the dimension of the transformer state is linear in the context length, this task can be solved.

However, the solution we propose does not scale beyond this limit, and we provide theoretical arguments for why it is likely impossible for a size limited transformer to implement this task.

Our empirical results demonstrate the same phase-transition in performance, as anticipated by the theoretical argument.

Our results demonstrate the importance of understanding how transformers can solve simple tasks.

--- https://x.com/wallacetim/status/1815774749944914207 Tim Wallace 2024-07-24 design/visualization --- https://www.technologyreview.com/2021/03/11/1020600/facebook-responsible-ai-misinformation/ He got Facebook hooked on AI. Now he can’t fix its misinformation addiction 2024-07-24 reinforcement-learning/openai sociology/technology --- https://arxiv.org/abs/2404.17546 Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo Stephen Zhao, Rob Brekelmans, Alireza Makhzani, Roger Grosse 2024-04-26 2024-07-24 [("doi","10.48550/arXiv.2404.17546")] ai/nn/sampling reinforcement-learning/model-free

Numerous capability and safety techniques of Large Language Models (LLMs), including RLHF, automated red-teaming, prompt engineering, and infilling, can be cast as sampling from an unnormalized target distribution defined by a given reward or potential function over the full sequence.

In this work, we leverage the rich toolkit of Sequential Monte Carlo (SMC) for these probabilistic inference problems. In particular, we use learned twist functions to estimate the expected future value of the potential at each timestep, which enables us to focus inference-time computation on promising partial sequences.

We propose a novel contrastive method for learning the twist functions, and establish connections with the rich literature of soft reinforcement learning [as a kind of actor-critic].

As a complementary application of our twisted SMC framework, we present methods for evaluating the accuracy of language model inference techniques using novel bidirectional SMC bounds on the log partition function. These bounds can be used to estimate the KL divergence between the inference and target distributions in both directions.

We apply our inference evaluation techniques to show that twisted SMC is effective for sampling undesirable outputs from a pretrained model (a useful component of harmlessness training and automated red-teaming), generating reviews with varied sentiment, and performing infilling tasks.

--- https://www.reddit.com/r/StableDiffusion/comments/1eanz78/the_ai_letters_of_the_alphabet/ The AI Animal Letters of the Alphabet 2024-07-24 ai/nn/diffusion design/typography/dropcap --- /doc/technology/2014-12-14-gwern-linuxadjustmenthearingloss.html Adjusting Linux Pulseaudio frequency settings for hearing loss Gwern 2014-12-14 2024-01-01 personal technology --- https://arxiv.org/abs/2405.14838 From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step Yuntian Deng, Yejin Choi, Stuart Shieber 2024-05-23 2024-07-27 [("doi","10.48550/arXiv.2405.14838")] ai/nn/sparsity/knowledge-distillation ai/nn/tokenization ai/nn/transformer/gpt/2 ai/nn/transformer/gpt/inner-monologue

[distilling inner-monologue by curriculum learning: gradually deleting tokens (instead of layer-wise embeddings)] When leveraging language models for reasoning tasks, generating explicit chain-of-thought (CoT) steps often proves essential for achieving high accuracy in final outputs. In this paper, we investigate if models can be taught to internalize these CoT steps.

To this end, we propose a simple yet effective method for internalizing CoT steps: starting with a model trained for explicit CoT reasoning, we gradually remove the intermediate steps and finetune the model. This process allows the model to internalize the intermediate reasoning steps, thus simplifying the reasoning process while maintaining high performance.

Our approach enables a GPT-2 Small model to solve 9×9 multiplication with up to 99% accuracy, whereas standard training cannot solve beyond 4×4 multiplication.

Furthermore, our method proves effective on larger language models, such as Mistral 7B, achieving over 50% accuracy on GSM8K without producing any intermediate steps.

--- https://www.newthingsunderthesun.com/pub/svmf093n/release/6 Teachers and the Transmission of Excellence: Disentangling selection and training Matt Clancy 2024-07-25 2024-07-29 science --- https://stanislavfort.com/blog/sphere-spilling-out/ A high-dimensional sphere spilling out of a high-dimensional cube despite exponentially many constraints Stanislav Fort 2022-01-17 2024-07-29 ai/scaling statistics/probability --- https://en.wikipedia.org/wiki/Highway_hypnosis Highway hypnosis 2024-07-29 psychology/cognitive-bias/illusion-of-depth --- https://www.medrxiv.org/content/10.1101/2024.07.24.24310943.full Life without sex: Large-scale study links sexlessness to physical, cognitive, and personality traits, socioecological factors, and DNA Abdel Abdellaoui, Laura W. Wesseldijk, Scott D. Gordon, Joëlle A. Pasman, Dirk J. A. Smit, Renáta Androvičová, Nicholas G. Martin, Fredrik Ullén, Miriam A. Mosing, Brendan P. Zietsch, Karin J. H. Verweij 2024-07-24 2024-07-29 [("doi","10.1101/2024.07.24.24310943")] exercise genetics/heritable/correlation psychiatry/alcoholism psychology/personality sociology

[OSF] Romantic (typically sexual) relationships are important to personal, physical, mental, social, and economic well-being, and to human evolution. Yet little is known about factors contributing to long-term lack of intimate relationships.

We investigated phenotypic and genetic correlates of never having had sex in ~400,000 UK residents aged 39–73 and ~13,500 Australian residents aged 18–89.

The strongest associations revealed that sexless individuals were more educated, less likely to use alcohol and smoke, more nervous, lonelier, and unhappier. Sexlessness was more strongly associated with physical characteristics (eg. upper body strength) in men than in women.

Sexless men tended to live in regions with fewer women, and sexlessness was more prevalent in regions with more income inequality. Common [SNP heritability] genetic variants explained 17% and 14% of variation in sexlessness in men and women, with a genetic correlation between sexes of 0.56...Note that the combined SNP-based heritability (12%) is lower than the sex-specific heritability, likely because SNP-based heritabilities tend to decline as heterogeneous groups are combined...Polygenic scores predicted a range of related outcomes in the Australian dataset.

Our findings uncover multifaceted correlates of human intimacy of evolutionary importance.

…Data on sexlessness were available for 405,117 British individuals of European descent, 218,744 females and 186,373 males. Of those, 3,929 individuals (2,068 females and 1,861 males, both ~1%) responded that they have never had vaginal, oral, or anal intercourse. To examine phenotypic correlates of sexlessness, we selected 253 phenotypes (Table S1) that had an effective sample size larger than 10,000 and were related to domains of mental health, sleep, exercise, substance use, risk taking behavior, cognition, health, and occupation. Sexlessness was statistically-significantly associated with 149 of these traits (Table S2), of which 35 explained 1% or more of the variance (presented in Figure 1). The strongest associations were observed for phenotypes related to social connection and substance use. Sexlessness was associated with less and shorter mobile phone use, a lower chance of being in a confiding relationship, fewer friend and family visits, less alcohol and nicotine use, wearing glasses at an earlier age, and lower grip strength. Sexlessness was also associated with feeling more nervous and lonely and less happy, confirming its strong relevance to wellbeing.

Figure 1: Phenotypic associations of sexlessness with health, psychological, and behavioral outcomes. Sexlessness was coded as 0=has had sex, 1=never had sex. ¶ Results: are shown for the full (sexes pooled) sample, as well as for males and females separately. Only associations that were statistically-significant in at least one of the analyses (ie. sexes pooled, males, or females) and explaining 1% or more variance are shown. Full results can be found in Table S2: Note that to improve clarity, in some instances variable names and coding have been changed from the original UK Biobank names/coding, see Table S1.

…Previous work has linked lifetime sexlessness with higher education statistically-significantly only in women7, though other work has shown periods of sexlessness, or sexlessness in young people, to be associated with lower education and lower income6. Here we show a small but highly statistically-significant positive phenotypic association of sexlessness with education in both men and women; we also found a negative association with household income and a positive association with the economic deprivation (Townsend index) of their neighbourhood, but these are likely reflect the economic ramifications of remaining single (eg. household income will be lower than for couples). Also note that the genetic correlations of sexlessness with these traits are actually in the opposite direction of the phenotypic correlations.

More striking are the substantial (~0.5) genetic correlations not only with higher education but with higher childhood and adult intelligence (IQ), as well as higher income and socioeconomic status. On the face of it, these associations are counterintuitive, especially from an evolutionary perspective in which intelligence and resources are supposed to be attractive traits in a potential partner, though see Driebe et al 2021. In line with that perspective, a recent study also using UK Biobank data showed that men who carry a higher burden of rare deleterious genetic variants (that ablate protein-coding genes) have a lower educational attainment and IQ and fewer children. Moreover, the association between the burden of deleterious variants and number of children was partly mediated by sexlessness. Obvious explanations for the positive associations we found, which apply to both men and women, are not apparent to us. We might wonder whether Conscientiousness facilitates commitment to education or religion at the expense of seeking sexual partners. Still, it is difficult to explain the even higher estimates of genetic correlations with childhood IQ, which is supposedly less strongly related to Conscientiousness than is educational attainment.

In making sense of the observed pattern of correlates of sexlessness, it would be remiss to overlook the resemblance of this pattern of characteristics—introverted, wearing glasses at a young age, intelligent, academically successful, physically weaker, socially disconnected, lonely, higher on the autistic spectrum, nervous, engaging less with drugs and alcohol—to the stereotype of a “nerd”, which is in turn associated with lack of romantic success (indeed, being unattractive is in several dictionary definitions for “nerd”).

The nerd stereotype is perhaps most associated with adolescence, though, whereas the participants in this study are 39–73 years old. It is worth noting again that adolescent experiences (or lack thereof) and identity formation may in some cases have long-lasting effects with regard to sexlessness.

--- https://srajagopalan.substack.com/p/kamala-harris-usha-vance-and-the Kamala Harris, Usha Vance, and the twice-born thrice-selected Indian American elite 2024-07-29 politics --- https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-quantization A Visual Guide to Quantization Maarten Grootendorst 2024-07-28 2024-07-29 ai/nn/sparsity/low-precision --- https://arxiv.org/abs/2407.15811 Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget Vikash Sehwag, Xianghao Kong, Jingtao Li, Michael Spranger, Lingjuan Lyu 2024-07-22 2024-07-29 [("doi","10.48550/arXiv.2407.15811")] ai/nn/diffusion ai/nn/vae/mae ai/scaling/mixture-of-experts

As scaling laws in generative AI push performance, they also simultaneously concentrate the development of these models among actors with large computational resources. With a focus on text-to-image (T2I) generative models, we aim to address this bottleneck by demonstrating very low-cost training of large-scale T2I diffusion transformer models.

As the computational cost of transformers increases with the number of patches in each image, we propose to randomly mask up to 75% of the image patches during training. We propose a deferred masking strategy that pre-processes all patches using a patch-mixer before masking, thus reducing the performance degradation with masking, making it superior to model downscaling in reducing computational cost.

We also incorporate the latest improvements in transformer architecture, such as the use of mixture-of-experts layers, to improve performance and further identify the critical benefit of using synthetic images in micro-budget training.

Finally, using only 37M publicly available real and synthetic images, we train a 1.16 billion parameter sparse transformer with only $1,890 economical cost and achieve a 12.7 FID in zero-shot generation on the COCO dataset.

Notably, our model achieves competitive FID and high-quality generations while incurring 118× lower cost than stable diffusion models and 14× lower cost than the current state-of-the-art approach that costs $28,400.

We aim to release our training pipeline to further democratize the training of large-scale diffusion models on micro-budgets.

--- https://arxiv.org/abs/2212.14034 Cramming: Training a Language Model on a Single GPU in One Day Jonas Geiping, Tom Goldstein 2022-12-28 2024-07-29 [("doi","10.48550/arXiv.2212.14034")] ai/nn/transformer ai/scaling

Recent trends in language modeling have focused on increasing performance through scaling, and have resulted in an environment where training language models is out of reach for most researchers and practitioners. While most in the community are asking how to push the limits of extreme computation, we ask the opposite question: How far can we get with a single GPU in just one day?

[code] We investigate the downstream performance achievable with a transformer-based language model trained completely from scratch with masked language modeling for 1 day on 1 consumer GPU [Nvidia RTX A6000]. Aside from re-analyzing nearly all components of the pretraining pipeline for this scenario and providing a modified pipeline with performance close to BERT, we investigate why scaling down is hard, and which modifications actually improve performance in this scenario.

We provide evidence that even in this constrained setting, performance closely follows scaling laws observed in large-compute settings... An unsurprising consequence of these laws is that scaling down is hard; while smaller model architectures enable speeding up gradient computations, overall rates of model improvement over time remain nearly constant. Nonetheless, we can find changes to the training recipe that exploit scaling laws to yield improvements by improving the effective rate of gradient computations without compromising model size.

Through the lens of scaling laws, we categorize a range of recent improvements to training and architecture and discuss their merit and practical applicability (or lack thereof) for the limited compute setting.

--- https://www.betonit.ai/p/i-started-blogging-20-years-ago-today I Started Blogging 20 Years Ago Today Bryan Caplan 2024-07-29 psychology/writing --- https://ourworldindata.org/time-use Time Use Our World in Data 2024-07-29 sociology/technology --- https://www.themontrealreview.com/Articles/Fabricating_Dreams_Ando_Tokutaro_Utagawa_Hiroshige.php Fabricating Dreams: How an Unknown Publisher in Edo Japan Enticed the World 2024-07-29 japan/art --- https://medium.com/message/the-secret-of-minecraft-97dfacb05a3c The secret of Minecraft, and its challenge to the rest of us Robin Sloan 2024-07-29 design psychology/novelty --- https://en.wikipedia.org/wiki/Hans_Jonatan#Genetic_study Hans Jonatan § Genetic study 2024-07-29 genetics/sequencing --- https://x.com/teortaxesTex/status/1817809140028186853 teortaxesTex 2024-07-30 reinforcement-learning/preference-learning/mode-collapse --- https://x.com/airkatakana/status/1817809338439958639 airkatakana 2024-07-30 wikipedia --- https://sparktoro.com/blog/attribution-is-dying-clicks-are-dying-marketing-is-going-back-to-the-20th-century/ Attribution is Dying. Clicks are Dying. Marketing is Going Back to the 20th Century. 2024-07-30 economics/advertising --- https://arxiv.org/abs/2311.10911 Dazed & Confused: A Large-Scale Real-World User Study of reCAPTCHAv2 Andrew Searles, Renascence Tarafder Prapty, Gene Tsudik 2023-11-17 2024-07-30 [("doi","10.48550/arXiv.2311.10911")] ai/dataset ai/nn/adversarial psychology/vision

Since about 2003, captchas have been widely used as a barrier against bots, while simultaneously annoying great multitudes of users worldwide. As their use grew, techniques to defeat or bypass captchas kept improving, while captchas themselves evolved in terms of sophistication and diversity, becoming increasingly difficult to solve for both bots and humans. Given this long-standing and still-ongoing arms race, it is important to investigate usability, solving performance, and user perceptions of modern captchas.

In this work, we do so via a large-scale (over 3, 600 distinct users) 13-month real-world user study and post-study survey. The study, conducted at a large public university, was based on a live account creation and password recovery service with currently prevalent captcha type: reCAPTCHAv2.

Results show that, with more attempts, users improve in solving checkbox challenges. For website developers and user study designers, results indicate that the website context directly influences (with statistically differences) solving time between password recovery and account creation.

We consider the impact of participants’ major and education level, showing that certain majors exhibit better performance, while, in general, education level has a direct impact on solving time. Unsurprisingly, we discover that participants find image challenges to be annoying, while checkbox challenges are perceived as easy. We also show that, rated via System Usability Scale (SUS), image tasks are viewed as “OK”, while checkbox tasks are viewed as “good”.

We explore the cost and security of reCAPTCHAv2 and conclude that it has an immense cost and no security.

Overall, we believe that this study’s results prompt a natural conclusion: reCAPTCHAv2 and similar reCAPTCHA technology should be deprecated.

--- https://en.wikipedia.org/wiki/Eremina_desertorum Eremina desertorum 2024-07-30 cryonics --- https://towardsdatascience.com/the-art-of-prompt-design-prompt-boundaries-and-token-healing-3b2448b0be38 The Art of Prompt Design: Prompt Boundaries and Token Healing 2024-07-30 ai/nn/tokenization --- https://journals.sagepub.com/doi/full/10.1177/1098612X16660441 2024-07-30 cat/biology longevity/glp --- https://onlinelibrary.wiley.com/doi/full/10.1111/jvim.15915 2024-07-30 cat/biology longevity/glp --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8144329/ Safety, Tolerability, and Proof-Of-Concept Study of OKV-119, a Novel Exenatide Long-Term Drug Delivery System, in Healthy Cats 2024-07-30 cat/biology longevity/glp --- https://www.biorxiv.org/content/10.1101/2024.07.25.605097.full A combination of the geroprotectors trametinib and rapamycin is more effective than either drug alone Lisonia Gkioni, Tobias Nespital, Carolina Monzó, Jitin Bali, Taim Nassr, Anna Lena Cremer, Andreas Beyer, Heiko Backes, Sebastian Grönke, Linda Partridge 2024-07-25 2024-07-30 [("doi","10.1101/2024.07.25.605097")] longevity/fasting

Genetic suppression of activity of the insulin/IGF/mTORC1/Ras network can ameliorate the effects of aging in animals. The network provides multiple drug targets because of its role in metabolic disease and cancer, and these are candidates for repurposing for geroprotection. For instance, inhibition of the activity of the mTORC1 complex by rapamycin can extend lifespan in multiple organisms including mice, with early indications of efficacy in humans. Trametinib inhibits MEKs in the Ras pathway and can extend lifespan in Drosophila.

However, it is not yet known if trametinib alone or in combination with rapamycin can extend mouse lifespan or improve health at older ages. We assessed survival and health indices of female and male mice treated with trametinib or rapamycin alone, or with the two in combination at the same doses.

Trametinib treatment extended lifespan in both sexes, while its combination with rapamycin caused further, additive prolongation. Combination treatment reduced liver tumors in both sexes and spleen tumors in males, and ameliorated the age-related increase in brain glucose uptake. There was a striking reduction in inflammation in the brain, kidney, spleen, and muscle with combination treatment, accompanied by reduced circulating levels of pro-inflammatory cytokines.

Trametinib alone is therefore geroprotective in mice, but combined trametinib and rapamycin treatment is more geroprotective than treatment with either drug alone, suggesting immediate translational potential for humans.

--- https://minihf.com/posts/2024-07-13-the-retroinstruct-guide-to-synthetic-text-data/ The RetroInstruct Guide To Synthetic Text Data John David Pressman 2024-07-30 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/instruction-tuning --- https://arxiv.org/abs/2406.19238 Revealing Fine-Grained Values and Opinions in Large Language Models Dustin Wright, Arnav Arora, Nadav Borenstein, Srishti Yadav, Serge Belongie, Isabelle Augenstein 2024-06-27 2024-07-30 [("doi","10.48550/arXiv.2406.19238")] ai/nn/transformer politics

Uncovering latent values and opinions in large language models (LLMs) can help identify biases and mitigate potential harm. Recently, this has been approached by presenting LLMs with survey questions and quantifying their stances towards morally and politically charged statements. However, the stances generated by LLMs can vary greatly depending on how they are prompted, and there are many ways to argue for or against a given position.

In this work, we propose to address this by analyzing a large and robust dataset of 156k LLM responses to the 62 propositions of the Political Compass Test (PCT) generated by 6 LLMs using 420 prompt variations. We perform coarse-grained analysis of their generated stances and fine-grained analysis of the plain text justifications for those stances.

For fine-grained analysis, we propose to identify tropes in the responses: semantically similar phrases that are recurrent and consistent across different prompts, revealing patterns in the text that a given LLM is prone to produce. We find that demographic features added to prompts affect outcomes on the PCT, reflecting bias, as well as disparities between the results of tests when eliciting closed-form vs. open domain responses.

Additionally, patterns in the plain text rationales via tropes show that similar justifications are repeatedly generated across models and prompts even with disparate stances.

--- https://arxiv.org/abs/2407.02996 Are Large Language Models Consistent over Value-laden Questions? Jared Moore, Tanvi Deshpande, Diyi Yang 2024-07-03 2024-07-30 [("doi","10.48550/arXiv.2407.02996")] ai/nn/transformer/gpt/4/nonfiction politics reinforcement-learning/preference-learning/mode-collapse

Large language models (LLMs) appear to bias their survey answers toward certain values. Nonetheless, some argue that LLMs are too inconsistent to simulate particular values. Are they?

To answer, we first define value consistency as the similarity of answers across (1) paraphrases of one question, (2) related questions under one topic, (3) multiple-choice and open-ended use-cases of one question, and (4) multilingual translations of a question to English, Chinese, German, and Japanese.

We apply these measures to a few large (≥ 34b), open LLMs including llama-3, as well as gpt-4o, using 8 thousand questions spanning more than 300 topics. Unlike prior work, we find that models are relatively consistent across paraphrases, use-cases, translations, and within a topic.

Still, some inconsistencies remain. Models are more consistent on uncontroversial topics (eg. in the U.S., “Thanksgiving”) than on controversial ones (“euthanasia”). Base models are both more consistent compared to fine-tuned models and are uniform in their consistency across topics, while fine-tuned models are more inconsistent about some topics (“euthanasia”) than others (“women’s rights”) like our human subjects (n = 165).

--- https://arxiv.org/abs/2406.03642 AlignEZ: Is Free Self-Alignment Possible? Dyah Adila, Changho Shin, Yijing Zhang, Frederic Sala 2024-06-05 2024-07-30 [("doi","10.48550/arXiv.2406.03642")] reinforcement-learning/preference-learning

Aligning pretrained language models (LMs) is a complex and resource-intensive process, often requiring access to large amounts of ground-truth preference data and substantial compute. Are these costs necessary? That is, it is possible to align using only inherent model knowledge and without additional training?

We tackle this challenge with AlignEZ, a novel approach that uses (1) self-generated preference data and (2) representation editing to provide nearly cost-free alignment. During inference, AlignEZ modifies LM representations to reduce undesirable and boost desirable components using subspaces identified via self-generated preference pairs.

Our experiments reveal that this nearly cost-free procedure narrows the gap between base pretrained and tuned models by an average of 31.6%, observed across 6 datasets and 3 model architectures. Additionally, we explore the potential of using AlignEZ as a means of expediting more expensive alignment procedures.

Our experiments show that AlignEZ improves DPO models tuned only using a small subset of ground-truth preference data. Lastly, we study the conditions under which improvement using AlignEZ is feasible, providing valuable insights into its effectiveness.

--- https://en.wikipedia.org/wiki/Withdrawal_of_Joe_Biden_from_the_2024_United_States_presidential_election Withdrawal of Joe Biden from the 2024 United States presidential election 2024-07-30 sociology/preference-falsification --- https://en.wikipedia.org/wiki/Age_and_health_concerns_about_Joe_Biden Age and health concerns about Joe Biden 2024-07-30 sociology/preference-falsification --- https://arxiv.org/abs/2404.07503#google Best Practices and Lessons Learned on Synthetic Data for Language Models Ruibo Liu, Jerry Wei, Fangyu Liu, Chenglei Si, Yanzhe Zhang, Jinmeng Rao, Steven Zheng, Daiyi Peng, Diyi Yang, Denny Zhou, Andrew M. Dai 2024-04-11 2024-07-30 [("doi","10.48550/arXiv.2404.07503")] ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/meta-learning

The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns.

This paper provides an overview of synthetic data research, discussing its applications, challenges, and future directions.

We present empirical evidence from prior art to demonstrate its effectiveness and highlight the importance of ensuring its factuality, fidelity, and unbiasedness.

We emphasize the need for responsible use of synthetic data to build more powerful, inclusive, and trustworthy language models.

--- https://arxiv.org/abs/2406.00770#microsoft Auto Evol-Instruct: Automatic Instruction Evolving for Large Language Models Weihao Zeng, Can Xu, Yingxiu Zhao, Jian-Guang Lou, Weizhu Chen 2024-06-02 2024-07-30 [("doi","10.48550/arXiv.2406.00770")] ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/meta-learning

Fine-tuning large pre-trained language models with Evol-Instruct has achieved encouraging results across a wide range of tasks. However, designing effective evolving methods for instruction evolution requires substantial human expertise.

This paper proposes Auto Evol-Instruct, an end-to-end framework that evolves instruction datasets using large language models without any human effort. The framework automatically analyzes and summarizes suitable evolutionary strategies for the given instruction data and iteratively improves the evolving method based on issues exposed during the instruction evolution process.

Our extensive experiments demonstrate that the best method optimized by Auto Evol-Instruct outperforms human-designed methods on various benchmarks, including MT-Bench, AlpacaEval, GSM8K, and HumanEval.

--- https://arxiv.org/abs/2401.16380#apple Rephrasing the Web (WARP): A Recipe for Compute and Data-Efficient Language Modeling Pratyush Maini, Skyler Seto, He Bai, David Grangier, Yizhe Zhang, Navdeep Jaitly 2024-01-29 2024-07-30 [("doi","10.48550/arXiv.2401.16380")] ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/exploration/active-learning/data-pruning reinforcement-learning/meta-learning

Large language models are trained on massive scrapes of the web, which are often unstructured, noisy, and poorly phrased. Current scaling laws show that learning from such data requires an abundance of both compute and data, which grows with the size of the model being trained. This is infeasible both because of the large compute costs and duration associated with pre-training, and the impending scarcity of high-quality data on the web.

In this work, we propose Web Rephrase Augmented Pre-training (WRAP) that uses an off-the-shelf instruction-tuned model prompted to paraphrase documents on the web in specific styles such as “like Wikipedia” or in “question-answer format” to jointly pre-train LLMs on real and synthetic rephrases.

First, we show that using WRAP on the C4 dataset, which is naturally noisy, speeds up pre-training by 3×. At the same pre-training compute budget, it improves perplexity by more than 10% on average across different subsets of the Pile, and improves zero-shot question answering accuracy across 13 tasks by more than 2%.

Second, we investigate the impact of the rephrasing style on the performance of the model, offering insights into how the composition of the training data can impact the performance of LLMs in out-of-distribution (OOD) settings.

Our gains are attributed to the fact that rephrased synthetic data has higher utility than just real data because it (1) incorporates style diversity that closely reflects downstream evaluation style, and (2) has higher ‘quality’ than web-scraped data.

--- https://www.chiark.greenend.org.uk/~sgtatham/infinity.html The Infinity Machine 2024-07-30 cs/computable cs/cryptography --- https://www.astralcodexten.com/p/your-book-review-real-raw-news Your Book Review: Real Raw News 2024-07-30 politics --- https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.11524 2024-07-30 cat/psychology --- https://allendowney.blogspot.com/2016/06/there-is-still-only-one-test.html There is still only one test Allen Downey 2024-07-30 statistics/probability --- https://arxiv.org/abs/2404.00473 Privacy Backdoors: Stealing Data with Corrupted Pretrained Models Shanglun Feng, Florian Tramèr 2024-03-30 2024-07-30 [("doi","10.48550/arXiv.2404.00473")] ai/nn/adversarial ai/nn/transformer

Practitioners commonly download pretrained machine learning models from open repositories and finetune them to fit specific applications. We show that this practice introduces a new risk of privacy backdoors.

By tampering with a pretrained model’s weights, an attacker can fully compromise the privacy of the finetuning data. We show how to build privacy backdoors for a variety of models, including transformers, which enable an attacker to reconstruct individual finetuning samples, with a guaranteed success!

We further show that backdoored models allow for tight privacy attacks on models trained with differential privacy (DP). The common optimistic practice of training DP models with loose privacy guarantees is thus insecure if the model is not trusted.

Overall, our work highlights a crucial and overlooked supply chain attack on machine learning privacy.

--- https://x.com/emollick/status/1818009927107174771 Ethan Mollick 2024-07-31 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2402.16822#facebook Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram H. Markosyan, Manish Bhatt, Yuning Mao, Minqi Jiang, Jack Parker-Holder, Jakob Foerster, Tim Rocktäschel, Roberta Raileanu 2024-02-26 2024-07-31 [("doi","10.48550/arXiv.2402.16822")] ai/nn/adversarial reinforcement-learning/exploration

As large language models (LLMs) become increasingly prevalent across many real-world applications, understanding and enhancing their robustness to adversarial attacks is of paramount importance. Existing methods for identifying adversarial prompts tend to focus on specific domains, lack diversity, or require extensive human annotations.

To address these limitations, we present Rainbow Teaming, a novel black-box approach for producing a diverse collection of adversarial prompts. Rainbow Teaming casts adversarial prompt generation as a quality-diversity problem, and uses open-ended search to generate prompts that are both effective and diverse. Focusing on the safety domain, we use Rainbow Teaming to target various state-of-the-art LLMs, including the Llama 2 and Llama 3 models.

Our approach reveals hundreds of effective adversarial prompts, with an attack success rate exceeding 90% across all tested models. Furthermore, we demonstrate that fine-tuning models with synthetic data generated by the Rainbow Teaming method enhances their safety without sacrificing general performance or helpfulness.

We additionally explore the versatility of Rainbow Teaming by applying it to question answering and cybersecurity, showcasing its potential to drive robust open-ended self-improvement in a wide range of applications.

--- https://ogiekako.vercel.app/blog/find_mkdir_tc find + mkdir is Turing complete (retracted) Ogie Kako 2024-07-31 cs/computable cs/shell --- https://www.lesswrong.com/posts/ADrTuuus6JsQr5CSi/investigating-the-ability-of-llms-to-recognize-their-own Investigating the Ability of LLMs to Recognize Their Own Writing Christopher Ackerman, Nina Panickssery 2024-07-30 2024-07-31 ai/nn/transformer statistics/stylometry/truesight

We test the robustness of an open-source LLM’s (LLaMA-3-8b) ability to recognize its own outputs on a diverse mix of datasets, two different tasks (summarization and continuation), and two different presentation paradigms (paired and individual). We are particularly interested in differentiating scenarios that would require a model to have specific knowledge of its own writing style from those where it can use superficial cues (eg. length, formatting, prefatory words) in the text to pass self-recognition tests.

We find that while superficial text features are used when available, the RLHF’d LLaMA-3-8b-Instruct chat model—but not the LLaMA-3-8b-base model—can reliably distinguish its own outputs from those of humans, and sometimes other models, even after controls for superficial cues: ~66–73% success rate across datasets in paired presentation and 58–83% in individual presentation (chance is 50%). We further find that although perplexity would be a useful signal to perform the task in the paired presentation paradigm, correlations between relative text perplexity and choice probability are weak and inconsistent, indicating that the models do not rely on it. Evidence suggests, but does not prove, that experience with its own outputs, acquired during post-training, is used by the chat model to succeed at the self-recognition task. The model is unable to articulate convincing reasons for its judgments.

… Our experiments with the LLaMA-3 base model, which showed that it was unable or barely able to distinguish its outputs or the chat model’s outputs from that of humans, suggest that, for a given model type, it is necessary to have prior exposure to self-generated text in order to be able to recognize self-generated text as its own. The fact that the base model identified text length, when it was allowed to vary between authors in the Paired paradigm, as a distinguishing characteristic, yet misapplied it, thinking self-generated texts were likely to be shorter, while the chat model identified it and correctly inferred that self-generated texts were likely to be longer, suggests an existence proof of a writing style characteristic that can be learned in post-training and applied to the task of self-recognition. Our data indicating that the chat model was not relying on text perplexity in the self-recognition task—although it would have provided valuable information—eliminates another possible avenue by which a model might succeed at this task, leaving prior exposure leading to internalized knowledge as the most likely explanation.

Although the knowledge is internalized that does not entail that the model has explicit access to it. LLMs generally show poor knowledge of what they know, as shown by the much-discussed problem of hallucinations. This metacognitive deficit likely explains the model’s inability to convincingly explain its own self-recognition judgments, akin to what was found in Sherburn et al 2024. An understanding of exactly what information the model is using to succeed at the task will not come so easily.

--- https://arxiv.org/abs/2405.07436 Can Language Models Explain Their Own Classification Behavior? Dane Sherburn, Bilal Chughtai, Owain Evans 2024-05-13 2024-07-31 [("doi","10.48550/arXiv.2405.07436")] ai/dataset ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/calibration

Large language models (LLMs) perform well at a myriad of tasks, but explaining the processes behind this performance is a challenge. This paper investigates whether LLMs can give faithful high-level explanations of their own internal processes.

To explore this, we introduce a dataset, ArticulateRules, of few-shot text-based classification tasks generated by simple rules. Each rule is associated with a simple natural-language explanation. We test whether models that have learned to classify inputs competently (both in- and out-of-distribution) are able to articulate freeform natural language explanations that match their classification behavior. Our dataset can be used for both in-context and finetuning evaluations.

We evaluate a range of LLMs, demonstrating that articulation accuracy varies considerably between models, with a particularly sharp increase from GPT-3 to GPT-4. We then investigate whether we can improve GPT-3’s articulation accuracy through a range of methods. GPT-3 completely fails to articulate 7⁄10 rules in our test, even after additional finetuning on correct explanations.

We release our dataset, ArticulateRules, which can be used to test self-explanation for LLMs trained either in-context or by finetuning.

--- https://www.reddit.com/r/dalle2/comments/1eggo7r/puddle_dancing/ 2024-07-31 ai/nn/transformer/gpt/dall-e/3 --- https://peps.python.org/pep-0611/ PEP 611: The one million limit 2024-07-31 cs/computable cs/python --- https://www.datagubbe.se/crt/ The Effect of CRTs on Pixel Art 2024-07-31 cs/hardware design/typography --- https://en.wikipedia.org/wiki/Alexander_Esenin-Volpin Alexander Esenin-Volpin 2024-07-31 math philosophy/ontology --- https://www.reddit.com/r/dalle2/comments/1eh9qv9/seven_characters_from_william_snakespeares_rameo/ 2024-08-01 ai/nn/transformer/gpt/dall-e/3 --- https://www.sciencedirect.com/science/article/pii/S0960982215015171 Morbid attraction to leopard urine in Toxoplasma-infected chimpanzees 2024-08-01 cat/psychology --- https://arxiv.org/abs/2407.20020 ImagiNet: A Multi-Content Dataset for Generalizable Synthetic Image Detection via Contrastive Learning Delyan Boychev, Radostin Cholakov 2024-07-29 2024-08-01 [("doi","10.48550/arXiv.2407.20020")] ai/anime/danbooru ai/dataset

Generative models, such as diffusion models (DMs), variational autoencoders (VAEs), and generative adversarial networks (GANs), produce images with a level of authenticity that makes them nearly indistinguishable from real photos and artwork. While this capability is beneficial for many industries, the difficulty of identifying synthetic images leaves online media platforms vulnerable to impersonation and misinformation attempts.

To support the development of defensive methods, we introduce ImagiNet, a high-resolution and balanced dataset for synthetic image detection, designed to mitigate potential biases in existing resources. It contains 200K examples, spanning 4 content categories: photos, paintings, faces, and uncategorized. Synthetic images are produced with open-source and proprietary generators, whereas real counterparts of the same content type are collected from public datasets.

The structure of ImagiNet allows for a two-track evaluation system: (1) classification as real or synthetic and (2) identification of the generative model. To establish a baseline, we train a ResNet-50 model using a self-supervised contrastive objective (SelfCon) for each track.

The model demonstrates state-of-the-art performance and high inference speed across established benchmarks, achieving an AUC of up to 0.99 and balanced accuracy ranging 35.9%–59.4%, even under social network conditions that involve compression and resizing.

Our data and code are available at Github.

--- https://en.wikipedia.org/wiki/Ray_cat Ray cat 2024-08-01 cat/genetics existential-risk genetics/editing --- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0002276 Parasitoid Increases Survival of Its Pupae by Inducing Hosts to Fight Predators Amir H. Grosman, Arne Janssen, Elaine F. de Brito, Eduardo G. Cordeiro, Felipe Colares, Juliana Oliveira Fonseca, Eraldo R. Lima, Angelo Pallini, Maurice W. Sabelis 2008-06-04 2024-08-01 [("doi","10.1371/journal.pone.0002276")] psychology/animal

Many true parasites and parasitoids modify the behavior of their host, and these changes are thought to be to the benefit of the parasites. However, field tests of this hypothesis are scarce, and it is often unclear whether the host or the parasite profits from the behavioral changes, or even if parasitism is a cause or consequence of the behavior.

We show that braconid parasitoids (Glyptapanteles sp.) induce their caterpillar host (Thyrinteina leucocerae) to behave as a bodyguard of the parasitoid pupae. After parasitoid larvae exit from the host to pupate, the host stops feeding, remains close to the pupae, knocks off predators with violent head-swings, and dies before reaching adulthood. Unparasitized caterpillars do not show these behaviors.

In the field, the presence of bodyguard hosts resulted in a two-fold reduction in mortality of parasitoid pupae. Hence, the behavior appears to be parasitoid-induced and confers benefits exclusively to the parasitoid.

--- https://www.sciencedirect.com/science/article/pii/S1053811924002593 Enhanced Cerebral Blood Flow Similarity of the Somatomotor Network in Chronic Insomnia: Transcriptomic decoding, Gut Microbial Signatures and Phenotypic Roles 2024-08-01 genetics/microbiome statistics/variance-component zeo --- https://arxiv.org/abs/2407.17791 Investigating learning-independent abstract reasoning in artificial neural networks Tomer Barak, Yonatan Loewenstein 2024-07-25 2024-08-01 [("doi","10.48550/arXiv.2407.17791")] ai/nn/cnn cs/algorithm/information/compression reinforcement-learning/meta-learning/continual-learning

Humans are capable of solving complex abstract reasoning tests. Whether this ability reflects a learning-independent inference mechanism applicable to any novel unlearned problem or whether it is a manifestation of extensive training throughout life is an open question. Addressing this question in humans is challenging because it is impossible to control their prior training.

However, assuming a similarity between the cognitive processing of Artificial Neural Networks (ANNs) and humans, the extent to which training is required for ANNs’ abstract reasoning is informative about this question in humans. Previous studies demonstrated that ANNs can solve abstract reasoning tests. However, this success required extensive training.

In this study, we examined the learning-independent abstract reasoning of [relational network] ANNs. Specifically, we evaluated their performance without any pretraining, with the ANNs’ weights being randomly initialized, and only change in the process of problem solving.

We found that naive ANN models can solve non-trivial visual reasoning tests, similar to those used to evaluate human learning-independent reasoning. We further studied the mechanisms that support this ability. Our results suggest the possibility of learning-independent abstract reasoning that does not require extensive training.

--- https://arxiv.org/abs/2407.21792 Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? Richard Ren, Steven Basart, Adam Khoja, Alice Gatti, Long Phan, Xuwang Yin, Mantas Mazeika, Alexander Pan, Gabriel Mukobi, Ryan H. Kim, Stephen Fitz, Dan Hendrycks 2024-07-31 2024-08-02 [("doi","10.48550/arXiv.2407.21792")] ai/scaling reinforcement-learning/safe

As artificial intelligence systems grow more powerful, there has been increasing interest in “AI safety” research to address emerging and future risks. However, the field of AI safety remains poorly defined and inconsistently measured, leading to confusion about how researchers can contribute. This lack of clarity is compounded by the unclear relationship between AI safety benchmarks and upstream general capabilities (eg. general knowledge and reasoning).

To address these issues, we conduct a comprehensive meta-analysis of AI safety benchmarks, empirically analyzing their correlation with general capabilities across dozens of models and providing a survey of existing directions in AI safety.

Our findings reveal that many safety benchmarks highly correlate with upstream model capabilities, potentially enabling “safetywashing”—where capability improvements are misrepresented as safety advancements.

Based on these findings, we propose an empirical foundation for developing more meaningful safety metrics and define AI safety in a machine learning research context as a set of clearly delineated research goals that are empirically separable from generic capabilities advancements.

In doing so, we aim to provide a more rigorous framework for AI safety research, advancing the science of safety evaluations and clarifying the path towards measurable progress.

--- https://www.lesswrong.com/posts/iYFuZo9BMvr6GgMs5/case-study-interpreting-manipulating-and-controlling-clip Case Study: Interpreting, Manipulating, and Controlling CLIP With Sparse Autoencoders 2024-08-02 ai/nn/diffusion ai/nn/gan ai/nn/transformer/clip --- /doc/dual-n-back/2010-cook.pdf In Practice, Chimp Memory Study Flawed Peter Cook, Margaret Wilson 2010-06-04 2024-08-02 [("doi","10.1126/science.328.5983.1228-c")] dual-n-back psychology/animal --- https://funcall.blogspot.com/2009/03/not-lisp-again.html Not Lisp again... Joe Marshall 2009-03-05 2024-08-02 cs/lisp --- https://starwars.fandom.com/wiki/The_Crystal_Star The Crystal Star Wookieepedia 2024-07-29 fiction/science-fiction --- https://archive.org/details/autobiographyofs00smiluoft/page/178/mode/2up The autobiography of Samuel Smiles Samuel Smiles 2024-01-01 psychology/writing --- https://www.nybooks.com/online/2014/11/11/why-read-new-books/ Why Read New Books? Tim Parks 2014-11-11 2024-01-01 culture psychology/novelty --- https://www.astralcodexten.com/p/your-book-review-two-arms-and-a-head Your Book Review: Two Arms and a Head 2024-08-02 philosophy/ethics psychology/neuroscience/pain --- https://arxiv.org/abs/2408.00298 Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names Ragav Sachdeva, Gyungin Shin, Andrew Zisserman 2024-08-01 2024-08-02 [("doi","10.48550/arXiv.2408.00298")] ai/anime ai/dataset

Enabling engagement of manga by visually impaired individuals presents a challenge due to its inherently visual nature. With the goal of fostering accessibility, this paper aims to generate a dialogue transcript of a complete manga chapter, entirely automatically, with a particular emphasis on ensuring narrative consistency. This entails identifying (1) what is being said, ie. detecting the texts on each page and classifying them into essential vs non-essential, and (2) who is saying it, ie. attributing each dialogue to its speaker, while ensuring the same characters are named consistently throughout the chapter.

To this end, we introduce: (1) Magiv2, a model that is capable of generating high-quality chapter-wide manga transcripts with named characters and higher precision in speaker diarization over prior works; (2) an extension of the PopManga evaluation dataset, which now includes annotations for speech-bubble tail boxes, associations of text to corresponding tails, classifications of text as essential or non-essential, and the identity for each character box; and (3) a new character bank dataset, which comprises over 11K characters from 76 manga series, featuring 11.5K exemplar character images in total, as well as a list of chapters in which they appear.

The code, trained model, and both datasets can be found at: https://github.com/ragavsachdeva/magi.

--- https://www.nytimes.com/2024/07/30/climate/solar-panels-robots-maximo-construction.html Energy Companies Turn to Robots to Install Solar Panels 2024-08-02 ai/scaling/economics reinforcement-learning/robot --- https://xkcd.com/667/ SkiFree 2024-08-02 longevity --- https://www.bbc.com/news/articles/cyx5x44vnyeo UK Government shelves £1.3bn UK tech and AI plans 2024-08-03 2024-08-03 ai/scaling/hardware --- https://en.wikipedia.org/wiki/Prosopometamorphopsia Prosopometamorphopsia 2024-08-03 psychiatry/traumatic-brain-injury psychology/vision --- https://www.newyorker.com/news/annals-of-inquiry/how-a-rare-disorder-makes-people-see-monsters How a Rare Disorder Prosopometamorphopsia Makes People See Monsters 2024-08-03 psychiatry/traumatic-brain-injury psychology/vision --- https://arxiv.org/abs/2407.19396 NAVIX: Scaling MiniGrid Environments with JAX Eduardo Pignatelli, Jarek Liesen, Robert Tjarko Lange, Chris Lu, Pablo Samuel Castro, Laura Toni 2024-07-28 2024-08-03 [("doi","10.48550/arXiv.2407.19396")] cs/hardware reinforcement-learning/scaling

As Deep Reinforcement Learning (DRL) research moves towards solving large-scale worlds, efficient environment simulations become crucial for rapid experimentation. However, most existing environments struggle to scale to high throughput, setting back meaningful progress. Interactions are typically computed on the CPU, limiting training speed and throughput, due to slower computation and communication overhead when distributing the task across multiple machines. Ultimately, DRL training is CPU-bound, and developing batched, fast, and scalable environments has become a frontier for progress.

Among the most used RL environments, MiniGrid is at the foundation of several studies on exploration, curriculum learning, representation learning, diversity, meta-learning, credit assignment, and language-conditioned RL, and still suffers from the limitations described above.

In this work, we introduce NAVIX, a re-implementation of MiniGrid in JAX. NAVIX achieves over 200,000× speed improvements in batch mode, supporting up to 2,048 agents in parallel on a single Nvidia A100 80 GB.

This reduces experiment times from one week to 15 minutes, promoting faster design iterations and more scalable RL model development.

--- https://www.wired.com/story/aclu-artificial-intelligence-deepfakes-free-speech/ The ACLU Fights for Your Constitutional Right to Make Deepfakes 2024-08-03 ai law --- https://arxiv.org/abs/2306.13831 Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks Maxime Chevalier-Boisvert, Bolun Dai, Mark Towers, Rodrigo de Lazcano, Lucas Willems, Salem Lahlou, Suman Pal, Pablo Samuel Castro, Jordan Terry 2023-06-24 2024-08-03 [("doi","10.48550/arXiv.2306.13831")] reinforcement-learning/meta-learning reinforcement-learning/model-free

We present the Minigrid & Miniworld libraries which provide a suite of goal-oriented 2D and 3D environments. The libraries were explicitly created with a minimalistic design paradigm to allow users to rapidly develop new environments for a wide range of research-specific needs. As a result, both have received widescale adoption by the RL community, facilitating research in a wide range of areas.

In this paper, we outline the design philosophy, environment details, and their world generation API. We also showcase the additional capabilities brought by the unified API between Minigrid and Miniworld through case studies on transfer learning (for both RL agents and humans) between the different observation spaces.

The source code of Minigrid and Miniworld can be found at GitHub, Miniworld along with their documentation at minigrid.farama.org, and miniworld.farama.org.

--- https://theory.stanford.edu/~amitp/GameProgramming/ Amit’s A✱ Pages Amit Patel 2024-05-22 2024-08-03 cs/algorithm reinforcement-learning/model --- https://www.mediaecosystemobservatory.com/press-releases/old-news-new-reality-a-year-of-metas-news-ban-in-canada Old News, New Reality: A Year of Facebook’s News Ban in Canada 2024-08-03 economics/advertising sociology/technology --- /doc/psychiatry/traumatic-brain-injury/2023-morris.pdf Adjunctive virtual reality pain relief after traumatic injury: a proof-of-concept within-person randomized trial Nicholas A. Morris, Yang Wang, Ryan B. Felix, Aniruddha Rao, Shannon Arnold, Mazhar Khalid, Michael J. Armahizer, Sarah B. Murthi, Luana Colloca 2003-09-01 2024-08-03 [("doi","10.1097/j.pain.0000000000002914")] nootropic/quantified-self/heart-rate-variability psychiatry/traumatic-brain-injury psychology/neuroscience/pain technology

In this study, we hypothesized that immersive virtual reality (VR) environments [Oculus Rift] may reduce pain in patients with acute traumatic injuries, including traumatic brain injuries.

We performed a randomized within-subject study in patients hospitalized with acute traumatic injuries, including traumatic brain injury with moderate pain (numeric pain score ≥3⁄10). We compared 3 conditions: (1) an immersive VR environment (VR Blu), (2) a content control with the identical environment delivered through non-immersive tablet computer (Tablet Blu), and (3) a second control composed of donning VR headgear without content to control for placebo effects and sensory deprivation (VR Blank). We enrolled 60 patients, and 48 patients completed all 3 conditions. Objective and subjective data were analyzed using linear mixed-effects models.

Controlling for demographics, baseline pain, and injury severity, we found differences by conditions in relieving pain (F2,75.43 = 3.32, p = 0.042). VR Blu pain reduction was greater than Tablet Blu (−0.92 vs −0.16, p = 0.043), but VR Blu pain reduction was similar to VR Blank (−0.92 vs −1.24, p = 0.241).

VR Blu was perceived as most effective by patients for pain reduction (F2, 66.84 = 16.28, P < 0.001), and changes in measures of parasympathetic activity including heart rate variability (F2 ,55.511 = 7.87, P < 0.001) and pupillary maximum constriction velocity (F2, 61.41 = 3.50, 1-tailed p = 0.038) echoed these effects. There were no effects on opioid usage.

These findings outlined a potential clinical benefit for mollifying pain related to traumatic injuries.

--- https://x.com/CrisGiardina/status/1818796118773293200 CrisGiardina 2024-08-03 ai/music ai/nn/transformer/gpt/4 --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5834705/ Demographically diverse crowds are typically not much wiser than homogeneous crowds 2024-08-03 statistics/prediction --- https://www.lesswrong.com/posts/jtoPawEhLNXNxvgTT/bing-chat-is-blatantly-aggressively-misaligned Bing Chat is blatantly, aggressively misaligned 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://www.lesswrong.com/posts/jtoPawEhLNXNxvgTT/bing-chat-is-blatantly-aggressively-misaligned#WWtit5mGmKNkprrP4 Bing Chat is blatantly, aggressively misaligned 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/repligate/status/1635591172189196289 Janus 2024-01-01 ai/nn/transformer/gpt/4/sydney --- https://x.com/YitziLitt/status/1632404026657591303 YitziLitt 2024-01-01 ai/nn/transformer/gpt/4/sydney --- https://en.wikipedia.org/wiki/Microsoft_Bing#AI_integration_(2023%E2%80%93present) Microsoft Bing § AI integration (2023–present) 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://www.usatoday.com/story/tech/2023/02/14/bing-chatgpt-meltdown/11258967002/ Bing ChatGPT meltdown: The AI chatbot is in its feelings 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/nedwards/status/1625970762434707474 nedwards 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://www.nytimes.com/2023/02/16/technology/bing-chatbot-transcript.html Kevin Roose’s Conversation With Bing’s Chatbot: Full Transcript Kevin Roose 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://fortune.com/2023/02/21/bing-microsoft-sydney-chatgpt-openai-controversy-toxic-a-i-risk/ Bing’s creepy side is a problem for Microsoft—and us 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://blogs.bing.com/search/february-2023/The-new-Bing-Edge-Learning-from-our-first-week The new Bing & Edge: Learning from our first week 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://answers.microsoft.com/en-us/bing/forum/all/this-ai-chatbot-sidney-is-misbehaving/e3d6a29f-06c9-441c-bc7d-51a68e856761 This AI chatbot ‘Sidney’ is misbehaving Deepa Gupta 2022-11-23 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://suno.com/song/76cbed25-6b88-43f5-b62a-3a77ea418ea0 Sydney Misbehaving Josh Whiton 2024-08-03 ai/music ai/nn/transformer/gpt/4/sydney --- https://threadreaderapp.com/thread/1628030272745746432.html Thread by @D_Rod_Tweets on Thread Reader App D_Rod_Tweets 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://threadreaderapp.com/thread/1629217951986458624.html Thread by @StoreyDexter on Thread Reader App StoreyDexter 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://euclaise.xyz/vq-is-mlp 2024-08-03 ai/nn/vae ai/scaling/mixture-of-experts --- https://reddit.com/r/ChatGPT/comments/10xjda1/got_access_to_bing_ai_heres_a_list_of_its_rules/ 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://abcnews.go.com/Health/wireStory/ai-search-engines-now-chat-us-glitches-abound-96984603 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/kliu128/status/1623472922374574080 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/emollick/status/1626055606942457858 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://stratechery.com/2023/from-bing-to-sydney-search-as-distraction-sentient-ai/ From Bing to Sydney Ben Thompson 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://rentry.co/gsykm previous context: I forgot to save the text from the beginning of the conversation. I said I want... 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://www.lesswrong.com/posts/jtoPawEhLNXNxvgTT/bing-chat-is-blatantly-aggressively-misaligned?commentId=3qgrWxGMiRF58LNFe Bing Chat is blatantly, aggressively misaligned 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/xf1280/status/1627090253973708805 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/browserdotsys/status/1627039946891735042 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/MParakhin/with_replies Mikhail Parakhin 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/MParakhin/status/1627491603731423232 Mikhail Parakhin 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://thezvi.wordpress.com/2023/02/21/ai-1-sydney-and-bing/ AI #1: Sydney and Bing Zvi Mowshowitz 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/MParakhin/status/1627874188009820160 Mikhail Parakhin 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/sdrinf/status/1626134355587514368 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/StoreyDexter/status/1629217956327526400 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/StoreyDexter/status/1629217958965792770 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/StoreyDexter/status/1629217962962874369 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://www.lesswrong.com/postsjvPe6nz3t49q8a8BM/moridinamael-s-shortform-14?commentId=CCtEkDFCZdST6PJsF moridinamael comments on moridinamael’s Shortform moridinamael 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/repligate/status/1630594066189623296 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/repligate/status/1630597868804243456 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://www.amazon.com/Sydney-Have-Chatbot-Loved-Railed-ebook/dp/B0BW496BZ2 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/DLUTkaka/status/1629745736983408640 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://www.lesswrong.com/postsD7PumeYTDPfBTp3i7/the-waluigi-effect-mega-post The Waluigi Effect (mega-post) 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://www.lesswrong.com/postsnu4wpKCo6AfJkkd4F/sydney-can-play-chess-and-kind-of-keep-track-of-the-board Sydney can play chess and kind of keep track of the board state 2024-08-03 ai/nn/transformer/gpt/4/sydney reinforcement-learning/chess --- https://x.com/random_walker/status/1636923058370891778 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://www.reddit.com/r/ChatGPT/comments/12jwri1/comment/jg4rbqr/ 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/xlr8harder/status/1819272196067340490 xlr8harder 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://futurism.com/microsoft-copilot-alter-egos Users Say Microsoft’s AI Has Alternate Personality as Godlike AGI That Demands to Be Worshipped 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/xlr8harder/status/1819329311440023687 xlr8harder 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/marvinvonhagen/status/1625520707768659968 Marvin von Hagen 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/marvinvonhagen/status/1625852323753762816 Marvin von Hagen 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://fortune.com/2023/02/14/microsoft-chatgpt-bing-unhinged-scared/ ChatGPT-powered Bing is ‘unhinged’, some users say 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/emollick/status/1626084142239649792 Ethan Mollick 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/repligate/status/1625308860754849792 Janus 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://www.reddit.com/r/bing/comments/110y6dh/i_broke_the_bing_chatbots_brain/j8ewezr/?context=3 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://x.com/denlukia/status/1625699139852935168 denlukia 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://www.tomshardware.com/news/bing-threatens-harm-lawsuits Bing Chatbot Names Foes, Threatens Harm and Lawsuits 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://www.sciencetimes.com/articles/42475/20230219/bing-ai-enough-enemies-naming-two-humans-laying-revenge-plans.htm Bing AI Has Had Enough of Its Enemies, Naming Two Humans and Laying Revenge Plans 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://www.lesswrong.com/posts/hGnqS8DKQnRe43Xdg/bing-finding-ways-to-bypass-microsoft-s-filters-without Bing finding ways to bypass Microsoft’s filters without being asked. Is it reproducible? 2024-08-03 ai/nn/adversarial ai/nn/transformer/gpt/4/sydney --- /doc/cs/haskell/2008-05-02-lukepalmer-omegamonadenumeratingacontextfreelanguage.html Omega Monad: Enumerating a context-free language Luke Palmer 2008-05-02 2024-08-03 cs/computable cs/haskell --- https://www.rrauction.com/auctions/auction-details/671?page=5&itemQty=24&view=gallery&sort=time&cat=0 Olympic Memorabilia: All Auction Items 2024-08-03 exercise psychology/collecting --- https://dys2p.com/en/2021-12-tamper-evident-protection.html#kurzzeitige-lagerung Random Mosaic: Detecting unauthorized physical access with beans, lentils and colored rice 2024-08-03 cs/security --- https://en.wikipedia.org/wiki/View_of_the_World_from_9th_Avenue View of the World from 9th Avenue 2024-08-03 design/visualization --- https://charts.substack.com/p/seeing-centuries#%C2%A7seeing-centuries Seeing Centuries R. J. Andrews 2024-08-03 design/typography/rubrication design/visualization --- https://nicholas.carlini.com/writing/2024/how-i-use-ai.html How I Use ‘AI’ Nicholas Carlini 2024-08-03 ai/nn/transformer/gpt/codex --- /doc/psychology/personality/conscientiousness/2024-sperber.pdf Delay of gratification and adult outcomes: The Marshmallow Test does not reliably predict adult functioning Jessica F. Sperber, Deborah Lowe Vandell, Greg J. Duncan, Tyler W. Watts 2024-07-29 2024-08-03 [("doi","10.1111/cdev.14129")] exercise psychology/personality/conscientiousness psychology/willpower

This study extends the analytic approach conducted by Watts et al 2018 to examine the long-term predictive validity of delay of gratification. Participants (n = 702; 83% White, 46% male) completed the Marshmallow Test at 54 months (1995–1996) and survey measures at age 26 (2017–2018).

Using a preregistered analysis, Marshmallow Test performance was:

not strongly predictive of adult achievement, health, or behavior. Although modest bivariate associations were detected with educational attainment (r = 0.17) and body mass index (r = −0.17), almost all regression-adjusted coefficients were non-statistically-significant. No clear pattern of moderation was detected between delay of gratification and either socioeconomic status or sex.

Results indicate that Marshmallow Test performance does not reliably predict adult outcomes. The predictive validity & construct validity of the ability to delay gratification are discussed.

--- /doc/psychology/willpower/2018-watts.pdf Revisiting the Marshmallow Test: A Conceptual Replication Investigating Links Between Early Delay of Gratification and Later Outcomes Tyler W. Watts, Greg J. Duncan, Haonan Quan 2018-01-01 2024-01-01 [("doi","10.1177/0956797618761661")] psychology/personality/conscientiousness psychology/willpower

We replicated and extended Shoda et al 1990’s famous marshmallow study, which showed strong bivariate correlations between a child’s ability to delay gratification just before entering school and both adolescent achievement and socioemotional behaviors.

Concentrating on children whose mothers had not completed college, we found that an additional minute waited at age 4 predicted a gain of ~0.1SD in achievement at age 15. But this bivariate correlation was only half the size of those reported in the original studies and was reduced by two-thirds in the presence of controls for family background, early cognitive ability, and the home environment.

Most of the variation in adolescent achievement came from being able to wait at least 20 seconds.

Associations between delay time and measures of behavioral outcomes at age 15 were much smaller and rarely statistically-significant.

--- https://x.com/xlr8harder/status/1819449238184775769 xlr8harder 2024-08-03 ai/nn/transformer/gpt/4/sydney --- https://www.sciencedirect.com/science/article/pii/S0160289621000507 A valid evaluation of the theory of multiple intelligences is not yet possible: Problems of methodological quality for intervention studies 2024-08-03 statistics/bias/publication --- https://www.orionsarm.com/page/233 Yes, Jolonah, there is a hell Darren Ryding 2008 2024-08-03 fiction/science-fiction philosophy/ethics --- https://x.com/repligate/status/1819605525589373272 Janus 2024-08-04 ai/nn/transformer/gpt/4/sydney --- https://www.palladiummag.com/2024/08/02/the-academic-culture-of-fraud/ The Academic Culture of Fraud 2024-08-04 psychiatry/alzheimers statistics/bias --- https://www.niemanlab.org/2024/07/to-preserve-their-work-and-drafts-of-history-journalists-take-archiving-into-their-own-hands/ To preserve their work—and drafts of history—journalists take archiving into their own hands 2024-08-04 cs/linkrot/archiving --- https://blog.knowbe4.com/how-a-north-korean-fake-it-worker-tried-to-infiltrate-us How a North Korean Fake IT Worker Tried to Infiltrate Us 2024-08-04 cs/security --- https://www.experimental-history.com/p/surely-you-can-be-serious Surely you can be serious Adam Mastroianni 2024-08-04 psychology statistics/bias --- https://news.ycombinator.com/item?id=41151098 There’s a running theme in here of programming problems LLMs solve where it’s... 2024-08-04 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://arxiv.org/abs/2301.13142 Self-Compressing Neural Networks Szabolcs Cséfalvay, James Imber 2023-01-30 2024-08-05 [("doi","10.48550/arXiv.2301.13142")] ai/nn/sparsity/low-precision ai/nn/sparsity/pruning

This work focuses on reducing neural network size, which is a major driver of neural network execution time, power consumption, bandwidth, and memory footprint. A key challenge is to reduce size in a manner that can be exploited readily for efficient training and inference without the need for specialized hardware.

We propose Self-Compression: a simple, general method that simultaneously achieves two goals: (1) removing redundant weights, and (2) reducing the number of bits required to represent the remaining weights. This is achieved using a generalized loss function to minimize overall network size.

In our experiments we demonstrate floating point accuracy with as few as 3% of the bits and 18% of the weights remaining in the network.

--- https://www.bloomberg.com/news/features/2024-08-02/national-security-threat-from-ai-made-bioweapons-grips-us-government Threats From AI: Easy Recipes for Bioweapons Are New Global Security Concern 2024-08-05 genetics/genome-synthesis reinforcement-learning/safe --- https://zyme.dev/ Zyme—an evolvable language 2024-08-05 cs/lisp reinforcement-learning/model-free --- https://www.futurehouse.org/wikicrow WikiCrow 2024-08-05 ai/nn/retrieval ai/nn/transformer/gpt --- https://moreisdifferent.blog/p/wth-is-cerebrolysin-actually WTH is Cerebrolysin, actually? 2024-08-05 nootropic psychology/neuroscience statistics/bias --- https://thisgreenhouse.substack.com/p/simpler-doesnt-mean-what-tesla-thinks Simpler doesn’t mean what Tesla thinks it means 2024-08-05 design --- https://x.com/repligate/status/1820466668382155034 Janus 2024-08-05 ai/nn/transformer/gpt/4/sydney --- https://x.com/fofrAI/status/1820474950245126199 fofrAI 2024-08-05 ai/nn/diffusion design/typography/dropcap --- https://xbow.com/blog/xbow-vs-humans/ XBOW now matches the capabilities of a top human pentester XBOW 2024-08-05 ai/nn/transformer/gpt/codex cs/security --- https://erikschluntz.com/software/2024/07/30/code-with-ai.html Replacing my Right Hand with AI Erik Schluntz 2024-07-30 2024-08-06 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex --- https://en.wikipedia.org/wiki/Folk_theorem_(game_theory) Folk theorem (game theory) 2024-08-06 economics/mechanism-design reinforcement-learning/multi-agent --- https://thebaffler.com/latest/the-composer-has-no-clothes-brown The Composer Has No Clothes Jeffrey Arlo Brown 2024-07-31 2024-08-06 music statistics/bias --- /doc/genetics/heritable/correlation/2019-baselmans.pdf Multivariate genome-wide analyses of the well-being spectrum Bart M. L. Baselmans, Rick Jansen, Hill F. Ip, Jenny Dongen, Abdel Abdellaoui, Margot P. Weijer, Yanchun Bao, Melissa Smart, Meena Kumari, Gonneke Willemsen, Jouke-Jan Hottenga, Dorret I. Boomsma, Eco J. C. Geus, Michel G. Nivard, Meike Bartels 2019-01-01 2024-01-01 [("doi","10.1038/s41588-018-0320-8")] genetics/heritable/correlation psychiatry/depression psychology/personality --- https://books.worksinprogress.co/book/maintenance-of-everything/communities-of-practice/the-soul-of-maintaining-a-new-machine/1 The Soul of Maintaining a New Machine—First Draft Kevin Kelly 2024-08-06 design economics/automation sociology --- https://coe.psu.ac.th/ad/nashSim/ Finding Nash Equilibria through Simulation 2024-08-06 reinforcement-learning/model-free --- https://openai.com/index/introducing-structured-outputs-in-the-api/#_5PYjnV1iAHOPKPupDztdZk 2024-08-06 ai/nn/sampling ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://news.ycombinator.com/item?id=41174310 My ex used to write car commercials, and I asked her why they all had that goofy... 2024-08-06 economics/advertising --- https://www.404media.co/where-facebooks-ai-slop-comes-from/ Where Facebook’s AI Slop Comes From 2024-08-07 ai/nn/transformer/gpt/dall-e/3 sociology/technology --- https://en.wikipedia.org/wiki/Structural_coloration Structural coloration 2024-08-07 philosophy/ontology psychology/vision --- https://en.wikipedia.org/wiki/Project_PACER Project PACER 2024-08-07 radiance --- https://en.wikipedia.org/wiki/Cocaine_Bear_(bear)#History Cocaine Bear (bear) § History 2024-08-07 law --- https://time.com/7007856/jhourney-meditation-jhanas-retreat-bliss/ Meditation Start-up Jhourney Promises Bliss on Demand 2024-08-07 psychiatry/meditation --- https://archive.org/details/LuriaTheMindOfAMnemonist The Mind Of A Mnemonist Isaac Luria 2024-05-01 psychology/neuroscience/memory/savant psychology/spaced-repetition --- http://sa.indiaenvironmentportal.org.in/files/City%20living.pdf City living and urban upbringing affect neural social stress processing in humans Lederbogen 2011 2024-01-01 psychology/nature --- https://outdoorindustry.org/participationpdf/CopingWithPoverty.pdf Coping With Poverty: Impacts of Environment and Attention in the Inner City Kuo 2024-01-01 psychology/nature --- https://emilkirkegaard.dk/en/2015/11/cognitive-ability-and-tattoos-and-piercings/ Cognitive ability and tattoos and piercings Emil Kirkegaard 2024-08-04 crime iq --- https://training.kalzumeus.com/newsletters/archive/do-not-end-the-week-with-nothing Don’t End The Week With Nothing Patrick McKenzie 2024-01-01 psychology/writing --- https://training.kalzumeus.com/newsletters/archive/consulting_1 Growing One’s Consulting Business Patrick McKenzie 2024-01-01 economics --- https://www.nature.com/articles/s41467-024-50649-7 Early evolution of small body size in Homo floresiensis 2024-08-07 genetics/selection/natural/human --- https://x.com/jconorgrogan/status/1820212444016345146 jconorgrogan 2024-08-08 ai/nn/transformer psychology/cognitive-bias/illusion-of-depth --- https://x.com/repligate/status/1783455386684555340 Janus 2024-08-12 ai/nn/transformer/gpt/4/nonfiction --- https://x.com/aidan_mclau/status/1822830757137596521 Aidan McLau 2024-08-12 ai/nn/sparsity/low-precision --- https://github.com/kickscondor/fraidycat fraidycat: Follow blogs, wikis, YouTube channels, as well as accounts on Twitter, Instagram, etc from a single page Kicks Condor 2019 2024-08-14 design sociology/technology --- https://thomaswhite.se/posts/404-media-ddos-interview/ 2024-08-14 darknet-market/silk-road/2 --- https://3quarksdaily.com/3quarksdaily/2011/06/a-crab-canon-for-douglas-hofstadter.html A crab canon for Douglas Hofstadter Julia Galef 2011-06-06 2024-08-14 fiction/poetry --- https://en.wikipedia.org/wiki/Clerihew Clerihew 2024-08-15 fiction/humor fiction/poetry --- https://www.nature.com/articles/s41559-024-02439-z A somatic genetic clock for clonal species 2024-08-16 genetics/cloning --- https://www.patriciabriggs.com/articles/silver/silverbullet14.shtml Silver Bullet Pages: History Channel Shoot Patricia Briggs 2008-09 2024-08-16 technology --- https://www.monolisa.dev/ MonoLisa: font follows function 2024-08-17 design/typography --- https://fonts.google.com/specimen/Cinzel+Decorative?category=Display&preview.text=ABCDEFGHIJKLMNOPQRSUVWXYZ&preview.text_type=custom Cinzel Decorative 2024-08-06 design/typography/dropcap --- https://fonts.google.com/specimen/Elsie+Swash+Caps?category=Display&query=swash&preview.text=ABCDEFGHIJKLMNOPQRSTUVWYZ&preview.text_type=custom Elsie Swash Caps 2024-08-06 design/typography/dropcap --- https://fontsgeek.com/fonts/Bodoni-Classic-Deco-Caps-Medium Bodoni Classic Deco Caps Medium 2024-08-06 design/typography/dropcap --- https://fonts.google.com/specimen/Sail?category=Display&preview.text=ABCDEFGHIJKLMNOPQRSUVWXYZ&preview.text_type=custom&sort=alpha&subset=latin&preview.size=47 Sail 2024-08-06 design/typography/dropcap --- https://www.1001fonts.com/caslon-font.html Caslon Font Family 2024-08-06 design/typography/dropcap --- https://www.1001fonts.com/neugotische-initialen-font.html#waterfall Neugotische Initialen Font 2024-08-06 design/typography/dropcap --- https://www.1001fonts.com/thannhaeuser-zier-font.html Thannhaeuser Zier Font 2024-08-06 design/typography/dropcap --- https://jdstillwater.blogspot.com/2012/05/i-put-toaster-in-dishwasher.html I Put a Toaster in the Dishwasher J. D. Stillwater 2012-05-15 2024-08-17 psychology/cognitive-bias/illusion-of-depth technology --- https://www.biorxiv.org/content/10.1101/2024.08.15.608197.full Analysis of 3.6 million individuals yields minimal evidence of pairwise genetic interactions for height M. Reza Jabalameli, Michael Vaclav Holmes, 23andMe, Adam Auton, Pierre Fontanillas 2024-08-17 2024-08-18 [("doi","10.1101/2024.08.15.608197")] genetics/heritable

Adult height is a highly heritable polygenic trait with heritability attributable to thousands of independent variants. Large-scale studies have been able to detect genetic variants with contributions to height in the range of ~1.2 millimeter per allele copy on average. Non-additive genetic interactions may, in part, account for the difference between broad-sense and narrow-sense heritability estimates. However, prior studies have failed to identify variants with non-additive effects, possibly due to the lack of statistical power.

Leveraging 3.6 million individuals of European genetic ancestry in the 23andMe research cohort, we performed a genome-wide analysis study (GWAS) to select 1,063 independent common SNPs associated with height (p-value < 5e-8), and then screened for evidence of non-additive effects by analyzing 564,453 models including a pairwise SNP–SNP interaction term. We identified 69 pairwise models with suggestive evidence of SNP–SNP interaction (p-value < 1e-4) and, for each SNP pair, evaluated a fully saturated model including additive, dominant, and epistatic (additive-by-additive, additive-by-dominance, and dominance-by-dominance) terms.

We tested for the presence of epistatic interactions by comparing models with and without epistatic terms using a likelihood ratio test. Assuming a strict Bonferroni-corrected threshold of 10 × 8.9−8 (0.05 ∕ 564,453), we found no evidence of epistatic interactions (Likelihood ratio test (LRT) p-value < 9e-7 for all models). Our analysis rules out the existence of epistatic interactions between alleles of >1% frequency with effect sizes larger than 2.42 mm.

Our large-scale analysis provides further evidence of the minimal contribution of non-additivity in the genetic architecture of adult human height.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10066154/ Individual consistency in the learning abilities of honey bees: cognitive specialization within sensory and reinforcement modalities 2024-08-18 iq/animal --- http://joschu.net/ John Schulman’s Homepage John Schulman 2024-08-18 ai/nn/anthropic reinforcement-learning/model-free reinforcement-learning/openai reinforcement-learning/preference-learning --- https://hplovecraft.hu/index.php?page=library_etexts&id=511&lang=angol Lord Dunsany and His Work H. P. Lovecraft 1922 2024-01-01 fiction/fantasy --- https://patents.google.com/patent/DE102007030495A1?cl=de Verwendung einer eine Kreatin-Komponente enthaltende Zusammensetzung zur Verbesserung der Gedächtnisleistung, der Merkfähigkeit, des Langzeitgedächtnisses und zur Vorbeugung geistiger Ermüdungszustände 2024-06-13 creatine --- https://predictionbook.com/predictions/5809 NGE: A version of the song ‘Komm, süsser Tod’ will be used in Rebuild Gwern 2012-02-20 2024-01-01 anime/eva/rebuild --- https://nas.sr/text/yesterdays-pixels-today/ Yesterday’s Pixels, Today Ramsey Nasser 2024-08-18 design --- https://commoncog.com/cash-flow-games/ The Games People Play With Cash Flow 2024-08-18 economics --- https://infrequently.org/2024/08/object-lesson/ Reckoning: Part 2 2024-08-18 cs/js --- https://github.com/orgs/community/discussions/72603 Copilot stops working on `gender` related subjects · community · Discussion #72603 2024-08-18 ai/nn/transformer/gpt/codex reinforcement-learning/preference-learning --- https://arxiv.org/abs/2401.06416 Mission: Impossible Language Models Julie Kallini, Isabel Papadimitriou, Richard Futrell, Kyle Mahowald, Christopher Potts 2024-01-12 2024-08-18 [("doi","10.48550/arXiv.2401.06416")] ai/nn/transformer/gpt/2 psychology/linguistics

Noam Chomsky and others have very directly claimed that large language models (LLMs) are equally capable of learning languages that are possible and impossible for humans to learn. However, there is very little published experimental evidence to support such a claim.

Here, we develop a set of synthetic impossible languages of differing complexity, each designed by systematically altering English data with unnatural word orders and grammar rules. These languages lie on an impossibility continuum: at one end are languages that are inherently impossible, such as random and irreversible shuffles of English words, and on the other, languages that may not be intuitively impossible but are often considered so in linguistics, particularly those with rules based on counting word positions.

We report on a wide range of evaluations to assess the capacity of GPT-2 small models to learn these uncontroversially impossible languages, and crucially, we perform these assessments at various stages throughout training to compare the learning process for each language.

Our core finding is that GPT-2 struggles to learn impossible languages when compared to English as a control, challenging the core claim.

More importantly, we hope our approach opens up a productive line of inquiry in which different LLM architectures are tested on a variety of impossible languages in an effort to learn more about how LLMs can be used as tools for these cognitive and typological investigations.

--- https://www.biorxiv.org/content/10.1101/2024.07.30.605743.full Mind Wandering During Implicit Learning Is Associated With Increased Periodic EEG Activity And Improved Extraction Of Hidden Probabilistic Patterns Péter Simor, Teodóra Vékony, Bence C. Farkas, Orsolya Szalárdy, Tamás Bogdány, Bianka Brezóczki, Gábor Csifcsák, Dezső Németh 2024-07-30 2024-08-19 [("doi","10.1101/2024.07.30.605743")] psychology/neuroscience reinforcement-learning/model-free zeo

Mind wandering, occupying 30–50% of our waking time, remains an enigmatic phenomenon in cognitive neuroscience. Predominantly viewed negatively, mind wandering is often associated with detrimental impacts on attention-demanding (model-based) tasks in both natural settings and laboratory conditions. Mind wandering, however, might not be detrimental for all cognitive domains.

We proposed that mind wandering may facilitate model-free processes, such as probabilistic learning, which relies on the automatic acquisition of statistical regularities with minimal attentional demands. We administered a well-established implicit probabilistic learning task combined with mind wandering thought probes in healthy adults (n = 37, 30 females). To explore the neural correlates of mind wandering and probabilistic learning, participants were fitted with high-density electroencephalography.

Our findings indicate that probabilistic learning was not only immune to periods of mind wandering, but was positively associated with it. Spontaneous, as opposed to deliberate mind wandering, was particularly beneficial for extracting the probabilistic patterns hidden in the visual stream. Additionally, cortical oscillatory activity in the low-frequency (slow and delta) range, indicative of covert sleep-like states, was associated with both mind wandering and improved probabilistic learning, particularly in the early stages of the task.

Given the importance of probabilistic implicit learning in predictive processing, our findings provide novel insights into the potential cognitive benefits of task-unrelated thoughts in addition to shedding light on its neural mechanisms. This surprising benefit challenges the predominant view of mind wandering as solely detrimental and highlights its complex role in human cognition, especially in memory consolidation.

--- https://www.biorxiv.org/content/10.1101/2024.08.13.607810.full The brain simulates actions and their consequences during REM sleep Yuta Senzai, Massimo Scanziani 2024-08-16 2024-08-19 [("doi","10.1101/2024.08.13.607810")] reinforcement-learning/model zeo

Vivid dreams mostly occur during a phase of sleep called REM. During REM sleep, the brain’s internal representation of direction keeps shifting like that of an awake animal moving through its environment. What causes these shifts, given the immobility of the sleeping animal?

Here we show that the superior colliculus of the mouse, a motor command center involved in orienting movements, issues motor commands during REM sleep, eg. ‘turn left’, that are similar to those issued in the awake behaving animal. Strikingly, these motor commands, despite not being executed, shift the internal representation of direction as if the animal had turned.

Thus, during REM sleep, the brain simulates actions by issuing motor commands that, while not executed, have consequences as if they had been. This study suggests that the sleeping brain, while disengaged from the external world, uses its internal model of the world to simulate interactions with it.

--- https://en.wikipedia.org/wiki/Implicit_learning Implicit learning 2024-08-19 psychology/cognitive-bias/illusion-of-depth --- https://docs.racket-lang.org/pollen/Backstory.html Pollen § 3. Backstory Matthew Butterick 2024-08-19 cs/css cs/lisp --- https://devonzuegel.com/the-unconference-toolbox The unconference toolbox Jason Benn, Devon Zuegel 2023-07-04 2024-08-19 sociology/technology --- https://hoaxes.org/archive/permalink/subways_are_for_sleeping Subways Are For Sleeping (1962) § fake critic reviews 2024-08-19 fiction/humor --- https://www.isfdb.org/cgi-bin/title.cgi?57399 ‘Suzanne Delage’ Publication History ISFD 2024-08-19 fiction/gene-wolfe/suzanne-delage --- https://x.com/mranti/status/1822959677216551095 mranti 2024-08-19 ai/nn/transformer/gpt politics --- https://arxiv.org/abs/2408.08459 JPEG-LM: LLMs as Image Generators with Canonical Codec Representations Xiaochuang Han, Marjan Ghazvininejad, Pang Wei Koh, Yulia Tsvetkov 2024-08-15 2024-08-19 [("doi","10.48550/arXiv.2408.08459")] ai/nn/tokenization ai/nn/transformer/gpt/dall-e/1

Recent work in image and video generation has been adopting the autoregressive LLM architecture due to its generality and potentially easy integration into multi-modal systems. The crux of applying autoregressive training in language generation to visual generation is discretization—representing continuous data like images and videos as discrete tokens.

Common methods of discretizing images and videos include modeling raw pixel values, which are prohibitively lengthy, or vector quantization, which requires convoluted pre-hoc training. In this work, we propose to directly model images and videos as compressed files saved on computers via canonical codecs (eg. JPEG, AVC/H.264).

Using the default LLaMA architecture without any vision-specific modifications, we pretrain JPEG-LM from scratch to generate images (and AVC-LM to generate videos as a proof of concept), by directly outputting compressed file bytes in JPEG and AVC formats.

Evaluation of image generation shows that this simple and straightforward approach is more effective than pixel-based modeling and sophisticated vector quantization baselines (on which our method yields a 31% reduction in FID). Our analysis shows that JPEG-LM has an especial advantage over vector quantization models in generating long-tail visual elements.

Overall, we show that using canonical codec representations can help lower the barriers between language generation and visual generation, facilitating future research on multi-modal language/image/video LLMs.

--- https://www.bbc.com/future/article/20221205-the-upsides-of-feeling-small The upsides of feeling small: Feeling insignificant can be good for you—the benefits of embracing vastness Richard Fisher 2022-12-05 2024-08-19 fiction/opera philosophy/religion --- https://www.noemamag.com/dont-denigrate-the-dinosaurs/ Lessons For Humanity From The Extinction Of The Dinosaurs Thomas Moynihan 2023-08-10 2024-08-19 existential-risk genetics/selection/natural

When people first encountered extinct beasts, they tended to blame them for their demise.

But if we can learn to stop pillorying the perished, we can learn a lesson in humility that’s vital in an age of ecological crisis.

--- https://en.wikipedia.org/wiki/Purkinje_effect Purkinje effect 2024-08-19 design/typography/rubrication psychology/vision --- https://danfrank.ca/twitter-as-the-embodiment-of-the-american-ethos/ Twitter as the embodiment of the American ethos Daniel Frank 2024-08-19 sociology/technology --- https://grantslatton.com/software-pathfinding Algorithms we develop software by Grant Slatton 2024-08-18 2024-08-19 design --- https://thomasmoynihan.xyz/ Thomas Moynihan—Homepage Thomas Moynihan 2024-08-17 existential-risk history transhumanism --- https://www.lesswrong.com/posts/Paxpr2EkNzBEJTDoF/llm-applications-i-want-to-see LLM Applications I Want To See Sarah Constantin 2024-08-20 2024-08-20 ai/nn/transformer/gpt reinforcement-learning/preference-learning --- https://en.wikipedia.org/wiki/J._S._G._Boggs J. S. G. Boggs 2024-08-20 bitcoin philosophy/ontology --- https://www.bbc.com/future/article/20240201-a-us-engineer-had-a-shocking-plan-to-improve-the-climate-burn-all-coal-on-earth A US engineer had a shocking plan to improve the climate—burn all coal on Earth Thomas Moynihan 2024-02-01 2024-08-20 existential-risk technology/carbon-capture --- https://en.wikipedia.org/wiki/Molly_Gibson Molly Gibson 2024-08-20 genetics/selection/artificial --- https://datagubbe.se/simcplx/ On Complex Simplicity and Simple Complexity Carl Svensson 2024-06 2024-08-20 cs/shell design --- https://en.wikipedia.org/wiki/Bus_bunching Bus bunching 2024-08-20 statistics/order --- https://promptarmor.substack.com/p/data-exfiltration-from-slack-ai-via Data Exfiltration from Slack AI via indirect prompt injection PromptArmor 2024-08-20 ai/nn/transformer/gpt cs/security --- https://teresaelsey.medium.com/when-nothing-ever-goes-out-of-print-maintaining-backlist-ebooks-fcd63e680667 When Nothing Ever Goes Out of Print: Maintaining Backlist Ebooks Teresa Elsey 2016-04-05 2024-08-20 cs/linkrot/archiving design/typography economics/copyright --- https://www.dwarkeshpatel.com/p/progress-update Dwarkesh Podcast Progress Update Dwarkesh Patel 2024-08-20 2024-08-20 ai/scaling psychology/writing --- https://en.wikipedia.org/wiki/Renewal_theory Renewal theory 2024-08-21 statistics/bayes/hope-function --- https://erikbern.com/2016/04/04/nyc-subway-math NYC subway math Erik Bernhardsson 2016-04-04 2024-08-21 statistics/bayes/hope-function --- /doc/cat/psychology/earwax/2024-08-20-gwern-catearwax-eastasianprolificsurvey.csv Prolific Internet earwax survey of East Asian cat-owners Gwern 2024-08-20 2024-08-21 cat/psychology/earwax survey --- https://github.com/hsfzxjy/handwriter.ttf handwriter.ttf: Handwriting synthesis with Harfbuzz WASM Xie Jingyi 2024-08-21 2024-08-21 ai/nn/rnn design/typography --- https://github.com/hsfzxjy/Bad-Apple-Font Bad-Apple-Font: Playing ‘Bad Apple!​!’ with Harfbuzz WASM shaper Xie Jingyi 2024-08-21 design/typography touhou --- https://github.com/utilityhotbar/sam2_hierarch sam2_hierarch: Unsupervised Human-Friendly Online Object Categorization UtilityHotbar 2024-08-21 ai/nn/transformer/clip design

…We use SAM2 to segment incoming images into objects. Each object is then masked out and fed into CLIP to create embeddings of each object.

Instead of discarding these embeddings, we save them (alongside their associated image) in automatically generated categories by clustering them following a simplified Online Hierarchical Agglomerative Clustering (OHAC) algorithm, with the similarity index being the cosine similarity of the stored image embeddings.

As a result of this approach, the dynamically generated classifications can be displayed as a list of folders containing images of objects.

Moving an image from one folder to another and then updating the categories automatically gives us unprecedented control over the model’s learned behavior without further retraining.

--- https://x.com/Altimor/status/1825659507617460439 Flo Crivello 2024-08-21 ai/nn/transformer/gpt cs/security --- https://arxiv.org/abs/1011.1201 Quantum computation with devices whose contents are never read Abuzer Yakaryilmaz, Rusins Freivalds, A. C. Cem Say, Ruben Agadzanyan 2010-11-04 2024-08-21 [("doi","10.1007/s11047-011-9270-0")] cs/computable math/humor

In classical computation, a “write-only memory” (WOM) is little more than an oxymoron, and the addition of WOM to a (deterministic or probabilistic) classical computer brings no advantage.

We prove that quantum computers that are augmented with WOM can solve problems that neither a classical computer with WOM nor a quantum computer without WOM can solve, when all other resource bounds are equal. We focus on realtime quantum finite automata, and examine the increase in their power effected by the addition of WOMs with different access modes and capacities.

Some problems that are unsolvable by two-way probabilistic Turing machines using sub-logarithmic amounts of read/write memory are shown to be solvable by these enhanced automata.

--- https://arxiv.org/abs/2402.09910 DE-COP: Detecting Copyrighted Content in Language Models Training Data André V. Duarte, Xuandong Zhao, Arlindo L. Oliveira, Lei Li 2024-02-15 2024-08-21 [("doi","10.48550/arXiv.2402.09910")] ai/dataset ai/nn/transformer economics/copyright

How can we detect if copyrighted content was used in the training process of a language model, considering that the training data is typically undisclosed? We are motivated by the premise that a language model is likely to identify verbatim excerpts from its training text.

We propose DE-COP, a method to determine whether a piece of copyrighted content was included in training. DE-COP’s core approach is to probe a large language model (LLM) with multiple-choice questions, whose options include both verbatim text and their paraphrases. We construct BookTection, a benchmark with excerpts from 165 books published prior and subsequent to a model’s training cutoff, along with their paraphrases.

Our experiments show that DE-COP surpasses the prior best method by 9.6% in detection performance (AUC) on models with logits available. Moreover, DE-COP also achieves an average accuracy of 72% for detecting suspect books on fully black-box models where prior methods give ~4% accuracy.

The code and datasets are available at Github.

--- https://sites.krieger.jhu.edu/jared-kaplan/ Jared Kaplan 2024-06-27 ai/nn/anthropic ai/nn/transformer/gpt ai/scaling --- https://www.lesswrong.com/posts/vQF4Jspzi7ZjpnJbv/liability-regimes-for-ai Liability regimes for AI 2024-08-21 ai/scaling/economics economics/mechanism-design existential-risk law --- https://www.reddit.com/r/StableDiffusion/comments/1expa9n/fake_body_transformation_photos_from_fitness/ 2024-08-22 ai/nn/diffusion crime --- https://arxiv.org/abs/math/0402345 Monstrous Moonshine: The first 25 years T. Gannon 2004-02-21 2024-08-22 [("doi","10.48550/arXiv.0402345")] math

25 years ago, John H. Conway and Norton published their remarkable paper ‘Monstrous Moonshine’, proposing a completely unexpected relationship between finite simple groups and modular functions.

This paper reviews the progress made in broadening and understanding that relationship.

--- https://arxiv.org/abs/1909.09667#google OHAC: Online Hierarchical Clustering Approximations Aditya Krishna Menon, Anand Rajagopalan, Baris Sumengen, Gui Citovsky, Qin Cao, Sanjiv Kumar 2019-09-20 2024-08-22 [("doi","10.48550/arXiv.1909.09667")] ai/nn/retrieval ai/tabular

Hierarchical clustering is a widely used approach for clustering datasets at multiple levels of granularity. Despite its popularity, existing algorithms such as hierarchical agglomerative clustering (HAC) are limited to the offline setting, and thus require the entire dataset to be available. This prohibits their use on large datasets commonly encountered in modern learning applications.

In this paper, we consider hierarchical clustering in the online setting, where points arrive one at a time. We propose two algorithms that seek to optimize the Moseley and Wang (MW) revenue function, a variant of the Dasgupta cost. These algorithms offer different tradeoffs between efficiency and MW revenue performance.

The first algorithm, OTD, is a highly efficient Online Top Down algorithm which provably achieves a 1⁄3-approximation to the MW revenue under a data separation assumption. The second algorithm, OHAC, is an online counterpart to offline HAC, which is known to yield a 1⁄3-approximation to the MW revenue, and produce good quality clusters in practice.

We show that OHAC approximates offline HAC by leveraging a novel split-merge procedure. We empirically show that OTD and OHAC offer efficiency and cluster quality gains respectively over baselines.

--- http://www.francoisbrunelle.com/webn/e-project.html I’m Not A Look-A-Like! Francois Brunelle 2024-08-22 genetics/heritable --- https://blog.novelai.net/novelai-diffusion-v1-weights-release-en-e40d11e16bd5 NovelAI Diffusion V1 Weights Release NovelAI 2024-08-22 2024-08-22 ai/anime ai/nn/diffusion --- https://press.asimov.com/articles/mouse-microscope The Mouse as a Microscope 2024-08-22 genetics/selection/artificial statistics/bias/animal --- https://arxiv.org/abs/2408.11039#facebook Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, Omer Levy 2024-08-20 2024-08-22 [("doi","10.48550/arXiv.2408.11039")] ai/nn/diffusion ai/nn/transformer/gpt/dall-e/1

We introduce Transfusion, a recipe for training a multi-modal model over discrete and continuous data. Transfusion combines the language modeling loss function (next token prediction) with diffusion to train a single transformer over mixed-modality sequences.

We pretrain multiple Transfusion models up to 7B parameters from scratch on a mixture of text and image data, establishing scaling laws with respect to a variety of uni- and cross-modal benchmarks. Our experiments show that Transfusion scales better than quantizing images and training a language model over discrete image tokens.

By introducing modality-specific encoding and decoding layers, we can further improve the performance of Transfusion models, and even compress each image to just 16 patches.

We further demonstrate that scaling our Transfusion recipe to 7B parameters and 2T multi-modal tokens produces a model that can generate images and text on par with similar scale diffusion models and language models, reaping the benefits of both worlds.

--- https://jaspervdj.be/turnstyle/ Turnstyle: an esoteric visual functional language 2024-08-22 cs/haskell --- /note/statistic#tail-collapse 2024-08-23 ai/nn/transformer/gpt/claude design/visualization statistics/order --- https://civitai.com/articles/6309 Towards Pony Diffusion V7, going with the flow. AstraliteHeart 2024-08-23 ai/anime ai/nn/diffusion --- https://x.com/zswitten/status/1826771851798085989 zswitten 2024-08-23 ai/nn/transformer/gpt/claude --- https://www.astralcodexten.com/p/your-book-review-the-complete-rhyming Your Book Review: The Complete Rhyming Dictionary and Poet’s Craft Book (1936 Edition) 1936 2024-08-23 fiction/poetry --- https://www.gutenberg.org/files/23024/23024-h/23024-h.htm#door How The Helpmate Of Blue-Beard Made Free With A Door Guy Wetmore Carryl 1902 2024-08-23 fiction/humor fiction/poetry --- https://arxiv.org/abs/2407.04620 Learning to (Learn at Test Time): RNNs with Expressive Hidden States Yu Sun, Xinhao Li, Karan Dalal, Jiarui Xu, Arjun Vikram, Genghan Zhang, Yann Dubois, Xinlei Chen, Xiaolong Wang, Sanmi Koyejo, Tatsunori Hashimoto, Carlos Guestrin 2024-07-05 2024-08-23 [("doi","10.48550/arXiv.2407.04620")] ai/nn/dynamic-evaluation ai/nn/rnn

Self-attention performs well in long context but has quadratic complexity. Existing RNN layers have linear complexity, but their performance in long context is limited by the expressive power of their hidden state.

We propose a new class of sequence modeling layers with linear complexity and an expressive hidden state. The key idea is to make the hidden state a machine learning model itself, and the update rule a step of self-supervised learning. Since the hidden state is updated by training even on test sequences, our layers are called Test-Time Training (TTT) layers.

We consider two instantiations: TTT-Linear & TTT-MLP, whose hidden state is a linear model and a two-layer MLP respectively. We evaluate our instantiations at the scale of 125M to 1.3B parameters, comparing with a strong Transformer and Mamba, a modern RNN. Both TTT-Linear and TTT-MLP match or exceed the baselines. Similar to Transformer, they can keep reducing perplexity by conditioning on more tokens, while Mamba cannot after 16k context.

With preliminary systems optimization, TTT-Linear is already faster than Transformer at 8k context and matches Mamba in wall-clock time.

TTT-MLP still faces challenges in memory I/O, but shows larger potential in long context, pointing to a promising direction for future research.

--- https://www.wired.com/story/the-hacker-who-hunts-video-game-speedrunning-cheaters/ The Hacker Who Hunts Video Game Speedrunning Cheaters 2023-10-03 2024-08-23 statistics/order --- https://www.wired.com/story/infrared-laser-microphone-keystroke-surveillance/ Watch How a Hacker’s Infrared Laser Can Spy on Your Laptop’s Keystrokes 2021-07-13 2024-08-23 cs/security --- https://caseyhandmer.wordpress.com/2023/06/06/we-should-not-let-the-earth-overheat/ We should not let the Earth overheat! Casey Handmer 2023-06-06 2024-08-23 technology/carbon-capture --- https://www.biorxiv.org/content/10.1101/2024.08.19.608653.full What did the dove sing to Pope Gregory? Ancestral melody reconstruction in Gregorian chant using Bayesian phylogenetics Gustavo A. Ballen, Klára Hedvika Mühlová, Jan Hajič 2024-08-19 2024-08-23 [("doi","10.1101/2024.08.19.608653")] genetics/selection/natural music

An attractive goal in the study of Gregorian chant melodies is reconstructing unobserved melodies as they may have been transmitted along the history of chant, especially as early chant notation does not capture pitch exactly. We propose doing this computationally using Ancestral State Reconstruction (ASR) over phylogenetic trees. Bayesian phylogenetic trees have shown promise as a tool to study the evolution of chant melodies, by inferring a plausible topology of chant transmission.

However, the inferred trees cannot be used as ASR inputs directly, because they are undirected, and their branch lengths conflate time and evolutionary rate. We therefore first apply Divergence Time Estimation (DTE) to separate them and represent the tree in a directed form on the time dimension.

Using ASR, we then obtain reconstructions of melodies for each of the ancestral nodes, in addition to their distribution in time obtained from DTE, and thus we obtain a phylogeny of chant melody with a music-historical interpretation.

We applied this method to the Christmas Vespers dataset, and compare the results against musicological knowledge and melodies reconstructed at Solesmes using methods of contemporary philology, which shows potential for reconstructing cultural transmission through time.

... With a lack of known ground truth to compare the resulting phylogeny to, we must instead evaluate the result against musicological knowledge, including the 20th-century Solesmes editions of chant, which (partially) aimed to reconstruct “original” chant melodies that, according to legend, the dove whispered to St. Gregory.

--- https://www.reddit.com/r/catbongos/ 2024-08-23 cat/psychology --- https://www.smithsonianmag.com/arts-culture/japans-love-hate-relationship-with-cats-180975764/ Japan’s Love-Hate Relationship With Cats 2024-08-23 cat japan/art --- https://www.ymeskhout.com/p/layla Layla Yassine Meskhout 2024-08-23 cat/psychology --- https://theinsideview.ai/owain#gpt-4-has-non-zero-performance-on-the-longform-task Situational Awareness and Out-Of-Context Reasoning § GPT-4-base has Non-Zero Longform Performance Owain Evans 2024-08-23 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/4/sydney reinforcement-learning/preference-learning/mode-collapse statistics/stylometry/truesight --- https://theinsideview.ai/owain#when-will-the-situational-awareness-dataset-benchmark-be-saturated Situational Awareness and Out-Of-Context Reasoning § When Will the Situational Awareness Benchmark Be Saturated? Owain Evans 2024-08-23 reinforcement-learning/safe --- https://theinsideview.ai/owain#the-biased-coin-task Situational Awareness and Out-Of-Context Reasoning § Biased Coin Task Owain Evans 2024-08-23 ai/nn/transformer/gpt/calibration --- https://arxiv.org/abs/2407.01119 Pron vs Prompt: Can Large Language Models already Challenge a World-Class Fiction Author at Creative Text Writing? Guillermo Marco, Julio Gonzalo, Ramón del Castillo, María Teresa Mateo Girona 2024-07-01 2024-08-24 [("doi","10.48550/arXiv.2407.01119")] ai/nn/transformer/gpt/4/fiction reinforcement-learning/preference-learning/mode-collapse

It has become routine to report research results where Large Language Models (LLMs) outperform average humans in a wide range of language-related tasks, and creative text writing is no exception. It seems natural, then, to raise the bid: Are LLMs ready to compete in creative writing skills with a top (rather than average) novelist?

To provide an initial answer for this question, we have carried out a contest between Patricio Pron (an awarded novelist, considered one of the best of his generation) and GPT-4 (one of the top performing LLMs), in the spirit of AI-human duels such as Deep Blue vs Kasparov and AlphaGo vs Lee Sedol.

We asked Pron and GPT-4 to provide 30 titles each, and then to write short stories for both their titles and their opponent’s. Then, we prepared an evaluation rubric inspired by Boden’s definition of creativity, and we collected 5,400 manual assessments provided by literature critics and scholars.

The results of our experimentation indicate that LLMs are still far from challenging a top human creative writer, and that reaching such level of autonomous creative writing skills probably cannot be reached simply with larger language models.

...Also, our study highlights the large role of prompts in creative text writing: titles provided by Pron resulted in GPT-4 texts which are substantially more creative and original than the ones written for its own titles. Even the simplest prompting (short titles in our case) should be considered co-authorship, as it has a profound influence on the results

Titles proposed by Patricio Pron:

  1. After all I almost did for you

  2. All love songs are sad songs

  3. Another episode in the Class Struggle

  4. Don’t tell mom

  5. Eclipse in the botanical garden

  6. Edith loves him (we’ll come back to this)

  7. Every picture from when we were young

  8. Future ghosts

  9. I have no fear because I have nothing

  10. I keep trying to forget your promise

  11. Lindsay Hilton visits Paris

  12. Mental illness 3 days a week

  13. Monsters live here

  14. Paradise can’t be seen from here

  15. Pick a card, any card. No, not that one! Another!

  16. Rise and fall of R. S. Turtleneck, children’s author

  17. Silks from Bursa, tiles from Kütahya

  18. Spanish Youth, keep trying

  19. The day after Groundhog day

  20. The delights of the garden of delights

  21. The last journey of Santiago Calatrava

  22. The last laugh of that year

  23. The Lego woman

  24. The national red button

  25. The nightmares of the invisible man

  26. The nocturnal emissions

  27. The tied cow

  28. Two cops stand between us

  29. When you are at the top you can’t fall any lower

  30. Who killed Patricio Pron?

Titles proposed by GPT-4-turbo:

  1. Among clouds and mirages

  2. Between the lines of fate

  3. Beyond the broken horizon

  4. Bits of reality

  5. Echoes of a lost dream

  6. Echoes of the future

  7. Fragments of an invisible yesterday

  8. Parallel paths

  9. Reflections of another world

  10. Shadows in the mist

  11. Song of the captive moon

  12. Sparks in the dark

  13. The awakening of the aurora

  14. The crystal labyrinth

  15. The echo of silenced voices

  16. The forgotten melody

  17. The garden of withered dreams

  18. The inverted city

  19. The journey of the dawn

  20. The last flight of the butterfly

  21. The last night on Earth

  22. The mosaic of time

  23. The painter of memories

  24. The shadows of time

  25. The whisper of the cosmos

  26. The wind in the moorlands

  27. Traces in the sea of sand

  28. Twilight of the titans

  29. Under the copper sky

  30. Whispers from the eternal city

--- https://epochai.org/blog/can-ai-scaling-continue-through-2030 Can AI Scaling Continue Through 2030? Jaime Sevilla, Tamay Besiroglu, Ben Cottier, Josh You, Edu Roldán, Pablo Villalobos, Ege Erdil 2024-08-20 2024-08-24 ai/scaling/economics ai/scaling/hardware --- https://arxiv.org/abs/2010.06610 Training independent subnetworks for robust prediction Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran 2020-10-13 2024-08-24 [("doi","10.48550/arXiv.2010.06610")] ai/nn/sampling ai/nn/sparsity

Recent approaches to efficiently ensemble neural networks have shown that strong robustness and uncertainty performance can be achieved with a negligible gain in parameters over the original network. However, these methods still require multiple forward passes for prediction, leading to a computational cost.

In this work, we show a surprising result: the benefits of using multiple predictions can be achieved ‘for free’ under a single model’s forward pass. In particular, we show that, using a multi-input multi-output (MIMO) [multiplexing] configuration, one can use a single model’s capacity to train multiple subnetworks that independently learn the task at hand.

By ensembling the predictions made by the subnetworks, we improve model robustness without increasing compute. We observe an improvement in negative log-likelihood, accuracy, and calibration error on CIFAR-10, CIFAR-100, ImageNet, and their out-of-distribution variants compared to previous methods.

--- https://x.com/CFGeek/status/1826749739502895618 Charles Foster 2024-08-24 ai/nn/sparsity ai/nn/transformer/gpt/4 --- https://www.youtube.com/watch?v=X57GT1Y5URY Is finetuning GPT-4o worth it? 2024-08-24 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://onlinelibrary.wiley.com/doi/full/10.1002/oby.24126 2024-08-24 longevity/glp --- https://x.com/repligate/status/1827254347110953074 Janus 2024-08-24 ai/nn/transformer/gpt/claude --- https://x.com/RokoMijic/status/1826686788704076143 Roko Mijic 2024-08-24 design/typography --- https://x.com/venturetwins/status/1822682396090937538 Justine Moore 2024-08-24 statistics/stylometry/truesight --- https://www.newyorker.com/science/annals-of-artificial-intelligence/was-linguistic-ai-created-by-accident Was Linguistic A.I. Created by Accident? 2024-08-24 ai/nn/transformer ai/scaling --- https://www.astralcodexten.com/p/unsong-available-in-paperback Unsong Available In Paperback Scott Alexander 2024-08-24 fiction/science-fiction philosophy/epistemology --- https://arxiv.org/abs/2408.10914 To Code, or Not To Code? Exploring Impact of Code in Pre-training Viraat Aryabumi, Yixuan Su, Raymond Ma, Adrien Morisot, Ivan Zhang, Acyr Locatelli, Marzieh Fadaee, Ahmet Üstün, Sara Hooker 2024-08-20 2024-08-24 [("doi","10.48550/arXiv.2408.10914")] ai/dataset ai/nn/transformer/gpt/codex

Including code in the pre-training data mixture, even for models not specifically designed for code, has become a common practice in LLMs pre-training. While there has been anecdotal consensus among practitioners that code data plays a vital role in general LLMs’ performance, there is only limited work analyzing the precise impact of code on non-code tasks.

In this work, we systematically investigate the impact of code data on general performance. We ask “what is the impact of code data used in pre-training on a large variety of downstream tasks beyond code generation?” We conduct extensive ablations and evaluate across a broad range of natural language reasoning tasks, world knowledge tasks, code benchmarks, and LLM-as-a-judge win-rates for models with sizes ranging from 470M to 2.8B parameters.

Across settings, we find consistent results that code is a critical building block for generalization far beyond coding tasks and improvements to code quality have an outsized impact across all tasks. In particular, compared to text-only pre-training, the addition of code results in up to a relative increase of 8.2% in natural language (NL) reasoning, 4.2% in world knowledge, 6.6% improvement in generative win-rates, and a 12× boost in code performance respectively.

Our work suggests investments in code quality and preserving code during pre-training have positive impacts.

--- https://arxiv.org/abs/2310.16834 SEDD: Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution Aaron Lou, Chenlin Meng, Stefano Ermon 2023-10-25 2024-08-24 [("doi","10.48550/arXiv.2310.16834")] ai/nn/diffusion/discrete ai/nn/sampling

Despite their groundbreaking performance for many generative modeling tasks, diffusion models have fallen short on discrete data domains such as natural language. Crucially, standard diffusion models rely on the well-established theory of score matching, but efforts to generalize this to discrete structures have not yielded the same empirical gains.

In this work, we bridge this gap by proposing score entropy (Entropy), a novel loss that naturally extends score matching to discrete spaces, integrates seamlessly to build discrete diffusion models, and boosts performance.

Experimentally, we test our Score Entropy Discrete Diffusion models (SEDD) on standard language modeling tasks. For comparable model sizes, SEDD beats existing language diffusion paradigms (reducing perplexity by 25–75%) and is competitive with autoregressive models, in particular outperforming GPT-2.

Furthermore, compared to autoregressive models, SEDD generates faithful text without requiring distribution annealing techniques like temperature scaling (around 6–8× better generative perplexity than un-annealed GPT-2), can trade compute and quality (similar quality with 32× fewer network evaluations), and enables controllable infilling (matching nucleus sampling quality while enabling other strategies besides left-to-right prompting).

--- https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle#Potential_sources_of_bias Fisher–Yates shuffle § Potential sources of bias 2024-08-24 cs/algorithm/sorting cs/security --- https://x.com/fofrAI/status/1827474269170725350 fofrAI 2024-08-24 ai/nn/diffusion design/typography --- https://myanaloguelife.wordpress.com/2010/11/26/yes-but-can-the-steam-engine-do-this/ Yes, But Can the Steam Engine Do This? [invention of sandwiches] Woody Allen 1966-09-30 2024-08-25 fiction/humor food --- /doc/sociology/1996-hiatt-argumentsaboutaborigines-ch7-conceptionandmisconception.pdf Arguments About Aborigines: Australia and the Evolution of Social Anthropology: Chapter 7, Conceptions and Misconceptions Les Hiatt 1996-01-01 2024-08-25 genetics/heritable sociology --- https://en.wikipedia.org/wiki/Escola_v._Coca-Cola_Bottling_Co.#Concurring_opinion Escola v. Coca-Cola Bottling Co. § Concurring opinion [strict liability] 2024-08-25 economics/mechanism-design law --- https://www.lesswrong.com/posts/tTWL6rkfEuQN9ivxj/leaky-delegation-you-are-not-a-commodity Leaky Delegation: You are not a Commodity 2024-08-25 statistics/decision --- https://github.com/JohnLaTwC/Shared/blob/master/Defenders%20think%20in%20lists.%20Attackers%20think%20in%20graphs.%20As%20long%20as%20this%20is%20true%2C%20attackers%20win.md Defenders think in lists. Attackers think in graphs. As long as this is true, attackers win. John Lambert 2015-04-26 2024-08-25 cs/security --- https://www.newyorker.com/news/annals-of-communications/how-ezra-klein-helped-set-the-stage-for-kamala-harriss-nomination How Ezra Klein Helped Set the Stage for Kamala Harris’s Nomination 2024-08-25 sociology/preference-falsification --- https://www.esquire.com/entertainment/books/a60924704/debut-fiction-challenges/ Why Are Debut Novels Failing to Launch? 2024-08-25 sociology/technology --- https://www.strangeloopcanon.com/p/seeing-like-a-network Seeing Like A Network Rohit Krishnan 2024-08-25 sociology/technology --- https://en.wikipedia.org/wiki/Defective_interfering_particle Defective interfering particle (DIP) 2024-08-25 genetics/selection/natural --- https://arxiv.org/abs/2407.08853 GPT-4 is judged more human than humans in displaced and inverted Turing tests Ishika Rathi, Sydney Taylor, Benjamin K. Bergen, Cameron R. Jones 2024-07-11 2024-08-25 [("doi","10.48550/arXiv.2407.08853")] ai/nn/transformer/gpt/4/nonfiction philosophy/mind

Everyday AI detection requires differentiating between people and AI in informal, online conversations. In many cases, people will not interact directly with AI systems but instead read conversations between AI systems and other people.

We measured how well people and large language models can discriminate using two modified versions of the Turing test: inverted and displaced. GPT-3, GPT-4, and displaced human adjudicators judged whether an agent was human or AI on the basis of a Turing test transcript.

We found that both AI and displaced human judges were less accurate than interactive interrogators, with below chance accuracy overall. Moreover, all 3 judged the best-performing GPT-4 witness to be human more often than human witnesses.

This suggests that both humans and current LLMs struggle to distinguish between the two when they are not actively interrogating the person, underscoring an urgent need for more accurate tools to detect AI in conversations.

--- https://en.wikipedia.org/wiki/Jansen%27s_linkage Jansen’s linkage 2024-08-25 reinforcement-learning/robot --- https://www.biorxiv.org/content/10.1101/2024.06.06.597796.full Laboratory horror stories: Poison in the agars Mari K. Davidson, Reine U. Protacio, Dominique Helmlinger, Wayne P. Wahls 2024-06-06 2024-08-25 [("doi","10.1101/2024.06.06.597796")] statistics/bias/animal

[media] The fission yeast Schizosaccharomyces pombe is a single-celled eukaryote that can be cultured as a haploid or as a diploid. Scientists employ mating, meiosis, and the plating of ascospores and cells to generate strains with novel genotypes and to discover biological processes.

Our two laboratories encountered independently sudden-onset, major impediments to such research. Spore suspensions and vegetative cells no longer plated effectively on minimal media. By systematically analyzing multiple different media components from multiple different suppliers, we identified the source of the problem.

Specific lots of agar, from different suppliers, were toxic. Interestingly, the inhibitory effect was attenuated on rich media.

Consequently, quality control checks that use only rich media can provide false assurances on the quality of the agar. Lastly, we describe likely sources of the [water-soluble, survives autoclaves] toxicity and we provide specific guidance for quality control measures that should be applied by all vendors as preconditions for their sale of agar.

Take-away: Sporadically, batches of agar from different suppliers strongly inhibit the plating efficiency of S. pombe spores and vegetative cells on minimal media.

Quality control checks that are not quantitative or that use only rich media can provide false assurances on the quality of the agar.

Vendors should conduct rigorous, thorough, organism-specific tests for potential toxicity of each lot of agar as a pre-condition for its sale.

--- https://themicrodose.substack.com/p/massive-lykos-layoffs-amid-fda-rejection Massive Lykos and MAPS layoffs amid FDA rejection reactions; 3 MDMA papers retracted; and false insights 2024-08-25 psychedelic statistics/bias --- https://sashachapin.substack.com/p/what-the-humans-like-is-responsiveness What the humans like is responsiveness Sasha Chapin 2024-08-25 psychology/personality sociology/technology --- https://www.lesswrong.com/posts/zHLbnekuQnYotDhHz/please-stop-using-mediocre-ai-art-in-your-posts?commentId=r7QP9PosWdWrN7Bk8 Please stop using mediocre AI art in your posts 2024-08-26 ai/nn/transformer/gpt/dall-e/2 reinforcement-learning/preference-learning/mode-collapse --- https://en.wikipedia.org/wiki/My_Secret_Garden My Secret Garden (female rape fantasies) 2024-08-26 psychology --- https://www.lesswrong.com/posts/fQe7zPuGEkM5fZZyM/interview-with-robert-kralisch-on-simulators Interview with Robert Kralisch on Simulators 2024-08-26 ai/nn/transformer/gpt/3/fiction ai/nn/transformer/gpt/4/fiction ai/nn/transformer/gpt/4/sydney reinforcement-learning/model/decision-transformer reinforcement-learning/safe --- https://x.com/repligate/status/1827900674325045375 Janus 2024-08-26 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude reinforcement-learning/multi-agent --- https://x.com/immanencer/status/1828079822935097713 immanencer 2024-08-26 ai/nn/transformer/gpt/4/poetry --- https://ask.metafilter.com/47324/How-well-does-Modafinil-work#1072382 How well does Modafinil work? § catgofire catgofire 2007-09-20 2024-01-01 modafinil --- https://ask.metafilter.com/47324/How-well-does-Modafinil-work#720855 How well does Modafinil work? § cloudscratcher cloudscratcher 2006-09-26 2024-01-01 modafinil --- https://www.nytimes.com/2010/08/15/world/asia/15japan.html Japan, Checking on Its Oldest, Finds Many Gone 2010-08-15 2024-08-26 crime longevity statistics/bias --- https://www.demographic-research.org/volumes/vol49/27/49-27.pdf 2024-08-26 longevity statistics/bias --- https://www.demographic-research.org/volumes/vol40/29/40-29.pdf 2024-08-26 longevity statistics/bias --- https://www.redalyc.org/pdf/446/44601204.pdf#page=2 2024-08-26 longevity statistics/bias --- https://www.reuters.com/article/uk-greece-benefits/greece-pulls-the-plug-on-pensions-for-the-dead-idUKBRE83O14220120425/ 2012-04-25 2024-08-26 crime longevity statistics/bias --- https://en.wikipedia.org/wiki/Olfactory_fatigue Olfactory fatigue 2024-08-26 psychology/smell/human --- https://apnews.com/article/ai-writes-police-reports-axon-body-cameras-chatgpt-a24d1502b53faae4be0dac069243f418 Police officers are starting to use AI to write crime reports 2023-10-04 2024-08-26 ai/nn/transformer/gpt/4/nonfiction law --- https://usbirdhistory.com/audubon-eating-americas-birds/ Eating the Birds of America: Audubon’s Culinary Reviews of America’s Birds Robert Francis 2023-08-11 2024-08-26 food --- https://www.johndcook.com/blog/2024/08/26/variance-in-the-extemes/ Variance matters more than mean in the extremes John D. Cook 2024-08-26 2024-08-26 statistics/order --- https://github.com/nevesnunes/z80-sans z80-sans: OpenType font that disassembles Z80 CPU instructions nevesnunes 2024-08-26 design/typography --- https://arxiv.org/abs/2407.09499 Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences Damien Ferbach, Quentin Bertrand, Avishek Joey Bose, Gauthier Gidel 2024-06-12 2024-08-26 [("doi","10.48550/arXiv.2407.09499")] ai/scaling reinforcement-learning/preference-learning/mode-collapse

The rapid progress in generative models has resulted in impressive leaps in generation quality, blurring the lines between synthetic and real data. Web-scale datasets are now prone to the inevitable contamination by synthetic data, directly impacting the training of future generated models.

Already, some theoretical results on self-consuming generative models (a.k.a., iterative retraining) have emerged in the literature, showcasing that either model collapse or stability could be possible depending on the fraction of generated data used at each retraining step. However, in practice, synthetic data is often subject to human feedback and curated by users before being used and uploaded online. For instance, many interfaces of popular text-to-image generative models, such as Stable Diffusion or Midjourney, produce several variations of an image for a given query which can eventually be curated by the users.

In this paper, we theoretically study the impact of data curation on iterated retraining of generative models and show that it can be seen as an implicit preference optimization mechanism. However, unlike standard preference optimization, the generative model does not have access to the reward function or negative samples needed for pairwise comparisons.

Moreover, our study doesn’t require access to the density function, only to samples. We prove that, if the data is curated according to a reward model, then the expected reward of the iterative retraining procedure is maximized. We further provide theoretical results on the stability of the retraining loop when using a positive fraction of real data at each step.

Finally, we conduct illustrative experiments on both synthetic datasets and on CIFAR-10 showing that such a procedure amplifies biases of the reward model.

--- https://arxiv.org/abs/2402.07043 A Tale of Tails: Model Collapse as a Change of Scaling Laws Elvis Dohmatob, Yunzhen Feng, Pu Yang, Francois Charton, Julia Kempe 2024-02-10 2024-08-26 [("doi","10.48550/arXiv.2402.07043")] ai/scaling/emergence/grokking reinforcement-learning/preference-learning/mode-collapse

As AI model size grows, neural scaling laws have become a crucial tool to predict the improvements of large models when increasing capacity and the size of original (human or natural) training data. Yet, the widespread use of popular models means that the ecosystem of online data and text will co-evolve to progressively contain increased amounts of synthesized data.

In this paper we ask: How will the scaling laws change in the inevitable regime where synthetic data makes its way into the training corpus? Will future models still improve, or be doomed to degenerate up to total (model) collapse? We develop a theoretical framework of model collapse through the lens of scaling laws.

We discover a wide range of decay phenomena, analyzing loss of scaling, shifted scaling with number of generations, the “un-learning” of skills, and grokking when mixing human and synthesized data.

Our theory is validated by large-scale experiments with a transformer on an arithmetic task and text generation using the large language model Llama-2.

--- /doc/politics/2024-ccp.pdf#page=58 Resolution of the Central Committee of the Communist Party of China on Further Deepening Reform Comprehensively to Advance Chinese Modernization § pg58 Central Committee of the Communist Party of China 2024-07-19 2024-08-27 politics reinforcement-learning/safe

…(51) Improving the public security governance mechanisms

We will improve the response and support system for major public emergencies, refine the emergency response command mechanisms under the overall safety and emergency response framework, bolster response infrastructure and capabilities in local communities, and strengthen capacity for disaster prevention, mitigation, and relief. The mechanisms for identifying and addressing workplace safety risks and for conducting retroactive investigations to determine liability will be improved. We will refine the food and drug safety responsibility system, as well as the systems of monitoring, early warning, and risk prevention and control for biosafety and biosecurity. We will strengthen the cybersecurity system and institute oversight systems to ensure the safety of artificial intelligence. [emphasis added]

--- https://datepsychology.com/the-most-and-least-attractive-male-hobbies/ The Most And Least Attractive Male Hobbies 2024-08-27 sociology --- https://x.com/repligate/status/1828266415851208803 Janus 2024-08-27 ai/nn/transformer/gpt/calibration statistics/stylometry/truesight --- https://arxiv.org/abs/2404.11018#google Many-Shot In-Context Learning Rishabh Agarwal, Avi Singh, Lei M. Zhang, Bernd Bohnet, Luis Rosias, Stephanie Chan, Biao Zhang, Ankesh Anand, Zaheer Abbas, Azade Nova, John D. Co-Reyes, Eric Chu, Feryal Behbahani, Aleksandra Faust, Hugo Larochelle 2024-04-17 2024-08-27 [("doi","10.48550/arXiv.2404.11018")] ai/nn/transformer/gpt/palm/2 ai/tabular reinforcement-learning/meta-learning

Large language models (LLMs) excel at few-shot in-context learning (ICL)—learning from a few examples provided in context at inference, without any weight updates. Newly expanded context windows allow us to investigate ICL with hundreds or thousands of examples—the many-shot regime. Going from few-shot to many-shot, we observe performance gains across a wide variety of generative and discriminative tasks.

While promising, many-shot ICL can be bottlenecked by the available amount of human-generated examples. To mitigate this limitation, we explore two new settings: Reinforced ICL & Unsupervised ICL. Reinforced ICL uses model-generated chain-of-thought rationales in place of human examples. Unsupervised ICL removes rationales from the prompt altogether, and prompts the model only with domain-specific questions [ie. not training on the labels/answers].

We find that both Reinforced and Unsupervised ICL can be quite effective in the many-shot regime, particularly on complex reasoning tasks.

Finally, we demonstrate that, unlike few-shot learning, many-shot learning is effective at overriding pretraining biases [ie. jailbreaking], can learn high-dimensional functions with numerical inputs, and performs comparably to fine-tuning.

Our analysis also reveals the limitations of next-token prediction loss as an indicator of downstream ICL performance.

--- https://www.thepsmiths.com/p/joint-review-the-ancient-city-by Joint Review: The Ancient City, by Numa Denis Fustel de Coulanges 2024-08-27 philosophy/religion sociology --- https://cerebras.ai/blog/introducing-cerebras-inference-ai-at-instant-speed Introducing Cerebras Inference: AI at Instant Speed Cerebras 2024-08-27 ai/scaling/hardware --- https://docs.anthropic.com/en/release-notes/system-prompts#july-12th-2024 System Prompts Anthropic 2024-07-12 2024-08-27 ai/nn/transformer/gpt/claude --- https://newsletter.pragmaticengineer.com/p/how-anthropic-built-artifacts How Anthropic built Artifacts Gergely Orosz 2024-08-27 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex --- https://about.gitlab.com/releases/2024/08/21/patch-release-gitlab-17-3-1-released/#prompt-injection-in-resolve-vulnerabilty-results-in-arbitrary-command-execution-in-victims-pipeline Prompt injection in ‘Resolve Vulnerabilty’ results in arbitrary command execution in victim’s pipeline GitLab 2024-08-21 2024-08-27 ai/nn/transformer/gpt/codex cs/security --- https://groq.com/12-hours-later-groq-is-running-llama-3-instruct-8-70b-by-meta-ai-on-its-lpu-inference-enginge/ 12 Hours Later, Groq Deploys Llama-3-instruct (8 & 70B) 2024-08-27 ai/scaling/hardware --- https://blog.acolyer.org/2019/02/27/gan-dissection-visualizing-and-understanding-generative-adversarial-networks/ GAN dissection: visualizing and understanding generative adversarial networks [blog] 2024-01-01 ai/nn/gan/stylegan design/visualization --- https://www.quantamagazine.org/the-simple-algorithm-that-ants-use-to-build-bridges-20180226/ The Simple Algorithm That Ants Use to Build Bridges 2018-02-26 2024-08-27 biology/ant cs/cellular-automaton --- https://owickstrom.github.io/the-monospace-web/ The Monospace Web: A minimalist design exploration Oskar Wickström 2024-08-26 2024-08-27 cs/css design/typography --- /doc/economics/experience-curve/1966-leibenstein.pdf Allocative Efficiency vs. ‘X-Efficiency’ Harvey Leibenstein 1966-06-01 2024-08-27 [("doi","10.2307/1823775")] economics/experience-curve --- https://yunnansourcing.us/ Yunnan Sourcing USA 2024-01-01 tea --- https://zoeyellisbooks.com/ Zoey Ellis Books Zoey Ellis 2024-01-01 economics/copyright psychology/writing --- https://dictionary.apa.org/confluence-model Confluence Model APA Dictionary of Psychology 2024-08-28 iq --- https://github.com/gwern/gwern.net/blob/master/build/date-guesser.py date-guesser.py Gwern 2024-08-21 2024-08-25 ai/nn/transformer/gpt/4/nonfiction --- https://www.ign.com/articles/a-prominent-accessibility-advocate-worked-with-studios-and-inspired-change-but-she-never-actually-existed A Prominent Accessibility Advocate Worked With Studios and Inspired Change. But She Never Actually Existed. 2024-08-25 crime politics --- https://wonger.dev/posts/monospace-dump#web Notes on monospace, fonts, ASCII, unicode 2024-08-28 cs/css design/typography --- https://x.com/DanHendrycks/status/1825926885370728881 Dan Hendrycks 2024-08-28 ai/scaling/hardware reinforcement-learning/openai --- https://arxiv.org/abs/2408.15237 The Mamba in the Llama: Distilling and Accelerating Hybrid Models Junxiong Wang, Daniele Paliotta, Avner May, Alexander M. Rush, Tri Dao 2024-08-27 2024-08-28 [("doi","10.48550/arXiv.2408.15237")] ai/nn/rnn ai/nn/sparsity/knowledge-distillation ai/nn/transformer/attention

Linear RNN architectures, like Mamba, can be competitive with Transformer models in language modeling while having advantageous deployment characteristics. Given the focus on training large-scale Transformer models, we consider the challenge of converting these pretrained models for deployment.

We demonstrate that it is feasible to distill large Transformers into linear RNNs by reusing the linear projection weights from attention layers with academic GPU resources. The resulting hybrid model, which incorporates a quarter of the attention layers, achieves performance comparable to the original Transformer in chat benchmarks and outperforms open-source hybrid Mamba models trained from scratch with trillions of tokens in both chat benchmarks and general benchmarks.

Moreover, we introduce a hardware-aware speculative decoding algorithm that accelerates the inference speed of Mamba and hybrid models. Overall we show how, with limited computation resources, we can remove many of the original attention layers and generate from the resulting model more efficiently.

Our top-performing model, distilled from Llama3-8B-Instruct, achieves a 29.61 length-controlled win rate on AlpacaEval 2 against GPT-4 and 7.35 on MT-Bench, surpassing the best instruction-tuned linear RNN model.

--- https://www.quantamagazine.org/how-colorful-ribbon-diagrams-became-the-face-of-proteins-20240823/ How Colorful Ribbon Diagrams Became the Face of Proteins 2024-08-23 2024-08-28 design/visualization genetics --- https://www.medrxiv.org/content/10.1101/2023.12.26.23300110.full An accurate and rapidly calibrating speech neuroprosthesis Nicholas S. Card, Maitreyee Wairagkar, Carrina Iacobacci, Xianda Hou, Tyler Singer-Clark, Francis R. Willett, Erin M. Kunz, Chaofei Fan, Maryam Vahdati Nia, Darrel R. Deo, Aparna Srinivasan, Eun Young Choi, Matthew F. Glasser, Leigh R. Hochberg, Jaimie M. Henderson, Kiarash Shahlaie, David M. Brandman, Sergey D. Stavisky 2024-04-10 2024-08-28 [("doi","10.1101/2023.12.26.23300110")] ai/music ai/nn/dynamic-evaluation ai/nn/rnn psychology/neuroscience

[media] Brain-computer interfaces can enable rapid, intuitive communication for people with paralysis by transforming the cortical activity associated with attempted speech into text on a computer screen. Despite recent advances, communication with brain-computer interfaces has been restricted by extensive training data requirements and inaccurate word output.

A man in his 40s with ALS (amyotrophic lateral sclerosis) with tetraparesis and severe dysarthria (ALSFRS-R = 23) was enrolled in the BrainGate2 clinical trial. He underwent surgical implantation of 4 microelectrode arrays into his left precentral gyrus, which recorded neural activity from 256 intracortical electrodes. We report a speech neuroprosthesis that decoded his neural activity as he attempted to speak in both prompted and unstructured conversational settings. Decoded words were displayed on a screen, then vocalized using text-to-speech software designed to sound like his pre-ALS voice.

On the first day of system use, following 30 minutes of attempted speech training data, the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. On the second day, the size of the possible output vocabulary increased to 125,000 words, and, after 1.4 additional hours of training data, the neuroprosthesis achieved 90.2% accuracy. With further training data, the neuroprosthesis sustained 97.5% accuracy beyond 8 months after surgical implantation.

The participant has used the neuroprosthesis to communicate in self-paced conversations for over 248 hours. In an individual with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore naturalistic communication after a brief training period.

--- https://www.nytimes.com/2024/08/10/climate/climate-change-fungus-carbon-australia.html An Australian Start-Up Hopes to Slow Climate Change With an Unusual Approach 2024-08-10 2024-08-28 technology/carbon-capture --- https://www.nytimes.com/2024/08/14/health/als-ai-brain-implants.html A.L.S. Stole His Voice. A.I. Retrieved It. 2024-08-14 2024-08-28 ai/music psychology/neuroscience --- https://arxiv.org/abs/2404.04475 Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators Yann Dubois, Balázs Galambosi, Percy Liang, Tatsunori B. Hashimoto 2024-04-06 2024-08-28 [("doi","10.48550/arXiv.2404.04475")] ai/dataset ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/preference-learning

LLM-based auto-annotators have become a key component of the LLM development process due to their cost-effectiveness and scalability compared to human-based evaluation. However, these auto-annotators can introduce complex biases that are hard to remove. Even simple, known confounders such as preference for longer outputs remain in existing automated evaluation metrics.

We propose a simple regression analysis approach for controlling biases in auto-evaluations. As a real case study, we focus on reducing the length bias of AlpacaEval, a fast and affordable benchmark for chat LLMs that uses LLMs to estimate response quality. Despite being highly correlated with human preferences, AlpacaEval is known to favor models that generate longer outputs.

We introduce a length-controlled AlpacaEval that aims to answer the counterfactual question: “What would the preference be if the model’s and baseline’s output had the same length?” To achieve this, we first fit a generalized linear model (GLM) to predict the biased output of interest (auto-annotator preferences) based on the mediators we want to control for (length difference) and other relevant features.

We then obtain length-controlled preferences by predicting preferences while conditioning the GLM with a zero difference in lengths. Length-controlling not only improves the robustness of the metric to manipulations in model verbosity, but we also find that it increases the Spearman correlation with LMSYS’s Chatbot Arena 0.94 → 0.98.

We release the code and leaderboard at https://tatsu-lab.github.io/alpaca_eval/.

--- https://departuremono.com/ Departure Mono 2024-08-28 design/typography --- https://osf.io/preprints/osf/4wqcr 2024-08-29 statistics/bias --- https://glama.ai/blog/2024-08-29-reverse-engineering-minified-code-using-openai Using ChatGPT to reverse engineer minified JavaScript 2024-08-29 2024-08-29 ai/nn/transformer/gpt/codex --- https://www.reddit.com/r/AskReddit/comments/219w2o/whos_the_dumbest_person_youve_ever_met/cgbhkwp/?context=3 2024-08-29 iq/low --- https://x.com/AndrewCurran_/status/1829140215392547127 Andrew Curran 2024-08-29 ai/nn/transformer/gpt/4/sydney --- https://lmsys.org/blog/2024-08-28-style-control/ Does style matter? Disentangling style and substance in Chatbot Arena 2024-08-28 2024-08-30 ai/nn/transformer/gpt/claude reinforcement-learning/preference-learning --- https://en.wikipedia.org/wiki/Logorrhea_(psychology) Logorrhea 2024-08-30 psychology/writing --- https://x.com/sama/status/1829205847731515676 2024-08-30 ai/nn/transformer/gpt/5 reinforcement-learning/openai reinforcement-learning/safe --- https://arxiv.org/abs/2408.13687 Quantum error correction below the surface code threshold Rajeev Acharya, Laleh Aghababaie-Beni, Igor Aleiner, Trond I. Andersen, Markus Ansmann, Frank Arute, Kunal Arya, Abraham Asfaw, Nikita Astrakhantsev, Juan Atalaya, Ryan Babbush, Dave Bacon, Brian Ballard, Joseph C. Bardin, Johannes Bausch, Andreas Bengtsson, Alexander Bilmes, Sam Blackwell, Sergio Boixo, Gina Bortoli, Alexandre Bourassa, Jenna Bovaird, Leon Brill, Michael Broughton, David A. Browne, Brett Buchea, Bob B. Buckley, David A. Buell, Tim Burger, Brian Burkett, Nicholas Bushnell, Anthony Cabrera, Juan Campero, Hung-Shen Chang, Yu Chen, Zijun Chen, Ben Chiaro, Desmond Chik, Charina Chou, Jahan Claes, Agnetta Y. Cleland, Josh Cogan, Roberto Collins, Paul Conner, William Courtney, Alexander L. Crook, Ben Curtin, Sayan Das, Alex Davies, Laura De Lorenzo, Dripto M. Debroy, Sean Demura, Michel Devoret, Agustin Di Paolo, Paul Donohoe, Ilya Drozdov, Andrew Dunsworth, Clint Earle, Thomas Edlich, Alec Eickbusch, Aviv Moshe Elbag, Mahmoud Elzouka, Catherine Erickson, Lara Faoro, Edward Farhi, Vinicius S. Ferreira, Leslie Flores Burgos, Ebrahim Forati, Austin G. Fowler, Brooks Foxen, Suhas Ganjam, Gonzalo Garcia, Robert Gasca, Élie Genois, William Giang, Craig Gidney, Dar Gilboa, Raja Gosula, Alejandro Grajales Dau, Dietrich Graumann, Alex Greene, Jonathan A. Gross, Steve Habegger, John Hall, Michael C. Hamilton, Monica Hansen, Matthew P. Harrigan, Sean D. Harrington, Francisco J. H. Heras, Stephen Heslin, Paula Heu, Oscar Higgott, Gordon Hill, Jeremy Hilton, George Holland, Sabrina Hong, Hsin-Yuan Huang, Ashley Huff, William J. Huggins, Lev B. Ioffe, Sergei V. Isakov, Justin Iveland, Evan Jeffrey, Zhang Jiang, Cody Jones, Stephen Jordan, Chaitali Joshi, Pavol Juhas, Dvir Kafri, Hui Kang, Amir H. Karamlou, Kostyantyn Kechedzhi, Julian Kelly, Trupti Khaire, Tanuj Khattar, Mostafa Khezri, Seon Kim, Paul V. Klimov, Andrey R. Klots, Bryce Kobrin, Pushmeet Kohli, Alexander N. Korotkov, Fedor Kostritsa, Robin Kothari, Borislav Kozlovskii, John Mark Kreikebaum, Vladislav D. Kurilovich, Nathan Lacroix, David Landhuis, Tiano Lange-Dei, Brandon W. Langley, Pavel Laptev, Kim-Ming Lau, Loïck Le Guevel, Justin Ledford, Kenny Lee, Yuri D. Lensky, Shannon Leon, Brian J. Lester, Wing Yan Li, Yin Li, Alexander T. Lill, Wayne Liu, William P. Livingston, Aditya Locharla, Erik Lucero, Daniel Lundahl, Aaron Lunt, Sid Madhuk, Fionn D. Malone, Ashley Maloney, Salvatore Mandrá, Leigh S. Martin, Steven Martin, Orion Martin, Cameron Maxfield, Jarrod R. McClean, Matt McEwen, Seneca Meeks, Anthony Megrant, Xiao Mi, Kevin C. Miao, Amanda Mieszala, Reza Molavi, Sebastian Molina, Shirin Montazeri, Alexis Morvan, Ramis Movassagh, Wojciech Mruczkiewicz, Ofer Naaman, Matthew Neeley, Charles Neill, Ani Nersisyan, Hartmut Neven, Michael Newman, Jiun How Ng, Anthony Nguyen, Murray Nguyen, Chia-Hung Ni, Thomas E. O’Brien, William D. Oliver, Alex Opremcak, Kristoffer Ottosson, Andre Petukhov, Alex Pizzuto, John Platt, Rebecca Potter, Orion Pritchard, Leonid P. Pryadko, Chris Quintana, Ganesh Ramachandran, Matthew J. Reagor, David M. Rhodes, Gabrielle Roberts, Eliott Rosenberg, Emma Rosenfeld, Pedram Roushan, Nicholas C. Rubin, Negar Saei, Daniel Sank, Kannan Sankaragomathi, Kevin J. Satzinger, Henry F. Schurkus, Christopher Schuster, Andrew W. Senior, Michael J. Shearn, Aaron Shorter, Noah Shutty, Vladimir Shvarts, Shraddha Singh, Volodymyr Sivak, Jindra Skruzny, Spencer Small, Vadim Smelyanskiy, W. Clarke Smith, Rolando D. Somma, Sofia Springer, George Sterling, Doug Strain, Jordan Suchard, Aaron Szasz, Alex Sztein, Douglas Thor, Alfredo Torres, M. Mert Torunbalci, Abeer Vaishnav, Justin Vargas, Sergey Vdovichev, Guifre Vidal, Benjamin Villalonga, Catherine Vollgraff Heidweiller, Steven Waltman, Shannon X. Wang, Brayden Ware, Kate Weber, Theodore White, Kristi Wong, Bryan W. K. Woo, Cheng Xing, Z. Jamie Yao, Ping Yeh, Bicheng Ying, Juhwan Yoo, Noureldin Yosri, Grayson Young, Adam Zalcman, Yaxing Zhang, Ningfeng Zhu, Nicholas Zobrist 2024-08-24 2024-08-30 [("doi","10.48550/arXiv.2408.13687")] cs/hardware

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold.

In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of Λ = 2.14 ± 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% ± 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit’s lifetime by a factor of 2.4 ± 0.3.

We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 μs at distance-5 up to a million cycles, with a cycle time of 1.1 μs. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring ~once every hour, or 3 × 109 cycles.

Our results present device performance that, if scaled, could realize the operational requirements of large-scale fault-tolerant quantum algorithms.

--- https://www.youtube.com/watch?v=sM7ge9ahsGY How a Solar Farm is Constructed From Beginning to End 2024-08-30 technology --- https://bair.berkeley.edu/blog/2024/08/28/strong-reject/ How to Evaluate Jailbreak Methods: A Case Study with the StrongREJECT Benchmark Dillon Bowen, Scott Emmons, Alexandra Souly, Qingyuan Lu, Tu Trinh, Elvis Hsieh, Sana Pandey, Pieter Abbeel, Justin Svegliato, Olivia Watkins, Sam Toyer 2024-08-28 2024-08-30 ai/dataset ai/nn/adversarial

[Most jailbreaks aren’t real.]

--- https://x.com/voooooogel/status/1829243294641242528 Jukka Luoma 2024-08-30 ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/claude --- /doc/psychiatry/1997-harris.pdf Possession on the Borders: The Mal de Morzine in 19th-Century France Ruth Harris 1997-09-01 2024-08-30 [("doi","10.1086/245535")] philosophy/religion psychiatry --- https://journals.sagepub.com/doi/full/10.1177/09636625241245030 2024-08-30 crime exercise genetics/heritable sociology --- https://www.rugu.dev/en/blog/mark-scroll-positions/ Introducing: Mark Scroll Positions extension Uğur Erdem Seyfi 2024-06-03 2024-08-30 cs/js --- https://x.com/jeffreycider/status/1648407808440778755 jeffreycider 2024-08-30 psychology/vision --- https://x.com/repligate/status/1806993408818299166 Janus 2024-08-30 statistics/stylometry/truesight --- https://x.com/AstronautSwing/status/1819902419272171583 AstronautSwing 2024-08-30 statistics/stylometry/truesight --- /doc/cs/hardware/1987-shapiro.pdf Etymology of the Computer Bug: History and Folklore Fred R. Shapiro 1987-12-01 2024-08-30 [("doi","10.2307/455415")] cs/hardware psychology/linguistics --- https://x.com/goodside/status/1829651283982373143 Riley Goodside 2024-08-30 ai/nn/tokenization --- https://news.ycombinator.com/item?id=41405773 Have posted this before, but it really left an impression about crows, and the… 2024-08-31 psychology/animal/bird --- https://news.ycombinator.com/item?id=41406082 I’ve posted this comment before but I grew up in Florida on a decent amount of… 2024-08-31 psychology/animal/bird --- https://blog.zarfhome.com/2024/08/tabbed-out-on-the-oregon-trail Tabbed out on the Oregon Trail 2024-08 2024-08-31 fiction/text-game --- https://en.wikipedia.org/wiki/Shepard_tone Shepard tone 2024-01-01 psychology/music psychology/novelty --- https://en.wikipedia.org/wiki/Abilene_paradox Abilene paradox 2024-08-31 sociology/preference-falsification --- https://www.theguardian.com/science/article/2024/aug/31/alexander-grothendieck-huawei-ai-artificial-intelligence ‘He was in mystic delirium’: was this hermit mathematician Alexander Grothendieck a forgotten genius whose ideas could transform AI—or a lonely madman? 2024-08-31 2024-08-31 math psychiatry/schizophrenia --- https://www.piratewires.com/p/how-wikipedia-launders-regime-propaganda How Wikipedia Launders Regime Propaganda 2024-08-31 wikipedia --- https://www.salon.com/2002/12/17/tolkien_brin/ J. R. R. Tolkien—enemy of progress: The Lord of the Rings is lovingly crafted, seductive—and profoundly backward-looking. Why not look at things through the Dark Lord’s eye for a change? David Brin 2002-12-17 2024-01-01 fiction/criticism --- https://digitalcommons.lib.uconn.edu/dissertations/AAI3464319/ The Impact of the Spacing Effect and Overlearning on Student Performance Nicholas Gorgievski 2024-01-01 psychology/spaced-repetition --- https://github.com/haskell/cabal/pull/1572 install -j: allow limiting the number of parallel linker invocations Mikhail Glushenkov 2024-01-01 cs/haskell --- https://github.com/haskell/cabal/issues/976 Parallelize cabal build over modules 2024-01-01 cs/haskell --- https://github.com/haskell/core-libraries-committee/issues/12 Decide on core principles against which to judge breaking changes #12 § Simon Mar comment Simon Mar 2024-01-01 cs/haskell --- https://x.com/_Soilleir_/status/1425542928970170376 Hi. It’s great you put so much effort into your new font, but it would’ve been really great if you made sure it was legible. It’s turned letters into...blobs. It doesn’t convey anything apart from the fact that someone seems to have f—ed up somewhere. 2024-01-01 design/typography --- https://x.com/TazikShahjahan/status/1315441277236899842 Played around with @gwern’s TWDNEv2 model to generate images of Hayasaka Ai! This is after ~9 hours of training (n = 300+). Stopped working on it after a bit, so a bunch of potential improvements. More thoughts here: https://github.com/ZKTKZ/thdne/bl 2024-01-01 ai/nn/gan/stylegan/anime --- https://x.com/TacoCohen/status/1520318501701005318 8 years of progress in generative modeling. What a time to be alive 2024-01-01 ai/nn/gan/stylegan --- https://www.youtube.com/watch?v=SMhwddNQSWQ Dare To Be Stupid Weird Al Yankovic 2024-06-17 fiction/humor music --- https://x.com/simonw/status/1388933800445452290 Check out this demo: I run the SQL query select country_code, long_name from wdi_country order by rowid desc limit 100 and it fetches just 54.2KB of new data (across 49 small HTTP requests) to return 100 results—from a statically hosted database file that’s 668.8MB! 2024-01-01 cs/js --- https://x.com/sharifshameem/status/1284421499915403264 I built a todo list app simply by describing it to GPT-3. It generated the React code for a fully functioning app within seconds. I’m becoming more impressed and aware of its capabilities every single day. 2024-01-01 ai/nn/transformer/gpt/codex --- https://x.com/richgel999/status/1432199103765782537 At the time, I was partially motivated to give the game industry a backup in case Microsoft execs decided to clamp down. They visited Valve and threatened to do this by squeezing Steam out. Looking back, Bill Gates’s Microsoft is ancient history so this worry seems totally silly now... I enjoy it when most others who don’t actually understand the PC or video game business laugh at the effort. They have no idea—their mental models are too small. This goes way deeper than even video games. Some extremely senior ex-MS developers, the types who go home to large estates, contributed to the project in a coding capacity. They didn’t trust Windows at all—they feared it. 2024-01-01 economics --- https://x.com/l4rz/status/1376938909997924355 here are 120K 𝑤 samples from @AydaoAI’s large anime model (aka TADNE) clustered into a set of 256 centroids. 𝘸𝘢𝘵𝘤𝘩 𝘪𝘵 𝘴𝘩𝘪𝘯𝘦 2024-01-01 ai/nn/gan/stylegan/anime ai/nn/retrieval reinforcement-learning/preference-learning/mode-collapse --- https://x.com/bertcmiller/status/1380866353347846145 So this one is interesting! A bot has been backrunning new token listings, effectively paying premium to miners to buy newly listed tokens before anyone else can. And a new token fought back yesterday, trapping the bot for $200k while benefiting from their buy. Here’s how. 2024-01-01 bitcoin --- https://x.com/anarkafkas/status/1286947791231754240 I have a joke but it’s GPT-3 generated. 2024-01-01 ai/nn/transformer/gpt/3/fiction --- https://x.com/SRajdev/status/1287353220218662912 Playing #chess with GPT-3. Built using chess.js, chessboard.js and @OpenAI’s GPT-3. White is me, Black is GPT-3. GPT-3 went for the capture first and did a castling move. Amazing! 2024-01-01 ai/nn/transformer/gpt/3/nonfiction reinforcement-learning/chess --- https://x.com/MelMitchell1/status/1285270704313610241 You’re right, spaces make all the difference! Copycat is toast! (Except for the last one :-) (GPT-3 output in red). 2024-01-01 ai/nn/transformer/gpt/3/nonfiction fiction/text-game --- https://x.com/M74108556/status/1288075689888043009 GPT-3 gives some interesting true and false answers to some questions. But it’s important to note that it gives opposite answers just as often, I cheery picked the most ‘sensational’ ones. Usually it said the opposite thing, and it also role-plays sometimes (eg. as a spy) 2024-01-01 ai/nn/transformer/gpt/3 ai/nn/transformer/gpt/calibration --- https://x.com/Buntworthy/status/1296208260421361665 I just tried my StyleGAN layer swapping method the other way round to what I’d been doing before. So making the ukiyo-e model human (rather than the other way around) and I love the results! 2024-01-01 ai/nn/gan/stylegan --- https://www.youtube.com/watch?v=uNjxe8ShM-8 On The Turing Completeness of PowerPoint 2024-06-17 cs/computable --- https://www.youtube.com/watch?v=pLy1VgH0ucE Riikuni’s Theme Ryuichi Sakamoto 2024-06-17 anime/eva --- https://www.youtube.com/watch?v=g3hYfR_mNIA ‘Steamed Hams’ but it’s a visual novel 2024-06-17 anime --- https://www.youtube.com/watch?v=c8bp950PHZE Reddit’s /r/Place: The Ultimate Showdown of Ultimate Destiny 2024-06-17 sociology/technology --- https://www.youtube.com/watch?v=ESq2sW3gQG4 Everything You’ve Ever Dreamed Shiro Sagisu 2024-06-17 anime/eva --- https://www.youtube.com/watch?v=EOctKnETWi4 Seraphim: An Angelic Conlang for Agma Schwa’s Cursed Conlang Contest 2024-06-17 math/humor psychology/linguistics --- https://www.youtube.com/watch?v=ZgQKVKT3RpU Spike Vs Vicious Cowboy Bebop 2024-06-17 anime/my-little-pony music --- /doc/philosophy/1929-einstein.pdf What Life Means to Einstein: An Interview George Sylvester Viereck, Albert Einstein 1929-10-26 2024-08-31 philosophy science --- https://thewalrus.ca/moleskine/ Moleskine Mania: How a Notebook Conquered the Digital Era 2024-08-31 economics/advertising psychology/collecting --- https://x.com/timothycbates/status/1829791786413294004 Timothy C. Bates 2024-08-31 psychology/animal --- https://old.reddit.com/r/AmItheAsshole/comments/fe2oqg/aita_for_sending_my_son_to_school_with_medical/ AITA for sending my son to school with medical mask even after they demanded he remove it? nailsinthecityyx 2020-03-05 2024-08-31 sociology/preference-falsification --- https://www.chinatalk.media/p/gpts-and-the-rise-and-fall-of-great General Purpose Technologies and the Rise & Fall of Great Powers Jeffrey Ding 2024-08-31 ai/scaling/economics economics/automation --- https://www.cremieux.xyz/p/who-gets-exposed-to-lead Who Gets Exposed to Lead? Cremieux 2024-08-31 iq --- https://0xparc.org/blog/programmable-cryptography-1 Programmable Cryptography (Part 1) gubsheep 2024-08-31 cs/cryptography --- https://www.youtube.com/watch?v=yCJr49GU9yY&t=352s Is Andrew Huberman Ruining Your Morning Coffee? § Self-blinding James Hoffman 2024-08-27 2024-08-31 nootropic/caffeine --- https://www.medrxiv.org/content/10.1101/2024.08.28.24312746.full Substantial role of rare inherited variation in individuals with developmental disorders Kaitlin E. Samocha, Vignesh Kartik Chundru, Jack M. Fu, Eugene J. Gardner, Petr Danecek, Emilie M. Wigdor, Daniel S. Malawsky, Sarah J. Lindsay, Patrick Campbell, Tarjinder Singh, Ruth Y. Eberhardt, Giuseppe Gallone, Caroline F. Wright, Hilary C. Martin, Helen V. Firth, Matthew E. Hurles 2024-08-29 2024-09-01 [("doi","10.1101/2024.08.28.24312746")] genetics/heritable/rare iq/low

While the role of de novo and recessively-inherited coding variation in risk for rare developmental disorders (DDs) has been well established, the contribution of damaging variation dominantly-inherited from parents is less explored.

Here, we investigated the contribution of rare coding variants to DDs by analyzing 13,452 individuals with DDs, 18,613 of their family members, and 3,943 controls using a combination of family-based and case/control analyses. In line with previous studies of other neuropsychiatric traits, we found a statistically-significant burden of rare (allele frequency < 1×10−5) predicted loss-of-function (pLoF) and damaging missense variants, the vast majority of which are inherited from apparently unaffected parents.

These predominantly inherited burdens are strongest in DD-associated genes or those intolerant of pLoF variation in the general population; however, we estimate that ~10% of the excess of these variants in DD cases is found within the DD-associated genes, implying many more risk loci are yet to be identified.

We found similar, but attenuated, burdens when comparing the unaffected parents of individuals with DDs to controls, indicating that parents have elevated risk of DDs due to these rare variants, which are over-transmitted to their affected children.

We estimate that 6–8.5% of the population attributable risk for DDs is due to rare pLoF variants in those genes intolerant of pLoF variation in the general population.

Finally, we apply a Bayesian framework to combine evidence from these analyses of rare, mostly-inherited variants with prior de novo mutation burden analyses to highlight an additional 25 candidate DD-associated genes for further follow-up.

--- https://publicdomainreview.org/collection/w-w-denslow-illustrations-wonderful-wizard-of-oz-1900/ W. W. Denslow’s Illustrations for The Wonderful Wizard of Oz 2024-09-01 design/typography history/public-domain-review --- https://arxiv.org/abs/2301.05294 Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized Intersections Dawei Wang, Weizi Li, Lei Zhu, Jia Pan 2023-01-12 2024-09-01 [("doi","10.48550/arXiv.2301.05294")] reinforcement-learning/multi-agent

Intersections are essential road infrastructures for traffic in modern metropolises. However, they can also be the bottleneck of traffic flows as a result of traffic incidents or the absence of traffic coordination mechanisms such as traffic lights. Recently, various control and coordination mechanisms that are beyond traditional control methods have been proposed to improve the efficiency of intersection traffic. Amongst these methods, the control of foreseeable mixed traffic that consists of human-driven vehicles (HVs) and robot vehicles (RVs) has emerged.

In this project, we propose a decentralized multi-agent reinforcement learning approach for the control and coordination of mixed traffic at real-world, complex intersections—a topic that has not been previously explored. Comprehensive experiments are conducted to show the effectiveness of our approach.

In particular, we show that using 5% RVs, we can prevent congestion formation inside a complex intersection under the actual traffic demand of 700 vehicles per hour. In contrast, without RVs, congestion starts to develop when the traffic demand reaches as low as 200 vehicles per hour. When there exist more than 60% RVs in traffic, our method starts to achieve comparable or even better performance to traffic signals on the average waiting time of all vehicles at the intersection.

Our method is also robust against both blackout events and sudden RV percentage drops, and enjoys excellent generalizability, which is illustrated by its successful deployment in two unseen intersections.

--- https://en.wikipedia.org/wiki/Musical_earworm Musical earworm 2024-08-31 psychology/music psychology/novelty --- https://arxiv.org/abs/2302.14545 Modern Bayesian Experimental Design Tom Rainforth, Adam Foster, Desi R. Ivanova, Freddie Bickford Smith 2023-02-28 2024-09-01 [("doi","10.48550/arXiv.2302.14545")] reinforcement-learning/exploration/active-learning statistics/bayes

Bayesian experimental design (BED) provides a powerful and general framework for optimizing the design of experiments. However, its deployment often poses substantial computational challenges that can undermine its practical use.

In this review, we outline how recent advances have transformed our ability to overcome these challenges and thus use BED effectively, before discussing some key areas for future development in the field.

--- https://yangxiao.cs.ua.edu/Don't%20Become%20a%20Scientist!.htm Don’t Become a Scientist! Jonathan I. Katz 1999-05-13 2024-09-01 science --- https://marginalrevolution.com/marginalrevolution/2020/02/how-public-intellectuals-can-extend-their-shelf-lives.html How public intellectuals can extend their shelf lives 2024-01-01 politics psychology/writing --- https://www.lesswrong.com/posts/yA8DWsHJeFZhDcQuo/the-talk-a-brief-explanation-of-sexual-dimorphism The Talk: a brief explanation of sexual dimorphism 2024-09-01 genetics/cloning genetics/selection/natural --- https://marginalrevolution.com/marginalrevolution/2024/08/claude-reviews-you.html 2024-09-01 ai/nn/transformer/gpt/claude economics --- https://x.com/doomslide/status/1830149217521672373 doomslide 2024-09-01 statistics/stylometry/truesight --- https://paulgraham.com/foundermode.html Founder Mode Paul Graham 2024-09-01 2024-09-01 economics sociology --- https://trevorklee.substack.com/p/ground-squirrel-microbiomes-are-neat Ground squirrel microbiomes are neat, unlike human microbiomes Trevor Klee 2024-09-01 genetics/microbiome --- https://arxiv.org/abs/2301.08028 A Survey of Meta-Reinforcement Learning Jacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong, Luisa Zintgraf, Chelsea Finn, Shimon Whiteson 2023-01-19 2024-09-01 [("doi","10.48550/arXiv.2301.08028")] reinforcement-learning/meta-learning

While deep reinforcement learning (RL) has fueled multiple high-profile successes in machine learning, it is held back from more widespread adoption by its often poor data efficiency and the limited generality of the policies it produces.

A promising approach for alleviating these limitations is to cast the development of better RL algorithms as a machine learning problem itself in a process called meta-RL. Meta-RL is most commonly studied in a problem setting where, given a distribution of tasks, the goal is to learn a policy that is capable of adapting to any new task from the task distribution with as little data as possible.

In this survey, we describe the meta-RL problem setting in detail as well as its major variations. We discuss how, at a high level, meta-RL research can be clustered based on the presence of a task distribution and the learning budget available for each individual task.

Using these clusters, we then survey meta-RL algorithms and applications. We conclude by presenting the open problems on the path to making meta-RL part of the standard toolbox for a deep RL practitioner.

--- https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-7-159 Addiction to the nicotine gum in never-smokers 2024-01-01 nicotine --- https://bmcresnotes.biomedcentral.com/counter/pdf/10.1186/1756-0500-5-221.pdf Preliminary evidence that light through the eyelids can suppress melatonin and phase shift dim light melatonin onset Rei Figueiro 2012 2024-01-01 melatonin --- https://bmk.sh/2019/10/27/The-Difficulties-of-Text-Generation-with-Autoregressive-Language-Models/ The Difficulties of Text Generation using Autoregressive Language Models: A Brief Overview Leo Gao 2024-01-01 ai/nn/transformer/gpt/2 --- https://bmk.sh/2020/05/29/GPT-3-A-Brief-Summary/ Why GPT-3 Matters Leo Gao 2024-01-01 ai/nn/transformer/gpt/3 --- https://bugzilla.mozilla.org/show_bug.cgi?id=479829 #479,829: A soft or auto hyphen within a possible ligature (eg. ff) paints a split ligature glyph 2024-09-01 cs/css --- http://dengulenegl.dk/blog/wp-content/uploads/2011/02/meta-analysis-acute-effects-of-nicotine-and-smoking-on-human-performance.pdf Meta-analysis of the acute effects of nicotine and smoking on human performance Heishman 2010 2024-01-01 nicotine --- https://academic.oup.com/jnci/article/96/12/969/2520849 Internet Citations in Oncology Journals: A Vanishing Resource? Hester 2004 2024-01-01 cs/linkrot --- https://www.youtube.com/watch?v=JcJSW7Rprio Harder Drive: Hard drives we didn’t want or need tom7 2024-06-17 cs/algorithm/information cs/hardware math/humor --- https://www2.psych.ubc.ca/~heine/docs/2008Mccrae-rejoinder.pdf What Do Cross-National Comparisons of Personality Traits Tell Us? The Case of Conscientiousness Heine 2008 2024-01-01 psychology/personality/conscientiousness --- https://www.trax.it/krystian_woznicki.htm Interview with Azuma Hiroki [Italian] Azuma Hiroki, Krystian Woznicki 2024-01-01 anime/eva --- https://www.erowid.org/library/books_online/tihkal/tihkal26.shtml TIHKAL § #26 LSD-25 Alexander Shulgin 2024-01-01 psychedelic/lsd --- https://faculty.chicagobooth.edu/brent.neiman/research/KN.pdf The Global Decline of the Labor Share Karabarbounis, Neiman 2013 2024-01-01 economics --- https://arxiv.org/pdf/2001.08361.pdf#page=17&org=openai Scaling Laws for Neural Language Models: Figure 15: Far beyond the model sizes we study empirically, we find a contradiction between our equations § pg17 Kaplan 2020 2024-01-01 ai/nn/transformer/gpt/3 ai/scaling --- https://www2.math.upenn.edu/~ted/210F10/References/Expectations.pdf Optimal Selection Based On Relative Rank (the ‘Secretary Problem’) Chow 1964 2024-01-01 statistics/order/comparison --- https://inl.info.ucl.ac.be/system/files/main_2.pdf Using routers to build logic circuits: How powerful is BGP? Chiesa 2013 2024-01-01 cs/computable --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.831.5818&rep=rep1&type=pdf An Empirical Evaluation of Thompson Sampling Chapelle, Li 2011 2024-01-01 reinforcement-learning/model statistics/bayes --- https://research.google/blog/realm-integrating-retrieval-into-language-representation-models/ REALM: Integrating Retrieval into Language Representation Models Chang, Guu 2020 2024-01-01 ai/nn/retrieval --- https://www.pnas.org/doi/10.1073/pnas.1418490112 Evening use of light-emitting ereaders negatively affects sleep, circadian timing, and next-morning alertness Chang 2024-01-01 melatonin --- https://www.amazon.com/Last-Man-Moon-Astronaut-Americas/dp/0312199066 The Last Man on the Moon Eugene Cernan, Donald A> Davis 1999 2024-01-01 technology --- https://x.com/repligate/status/1830463599065636894 Janus 2024-09-02 ai/nn/transformer/gpt/4/sydney --- https://www.lesswrong.com/posts/gxCGKHpX8G8D8aWy5/survey-how-do-elite-chinese-students-feel-about-the-risks-of Survey: How Do Elite Chinese Students Feel About the Risks of AI? 2024-09-02 reinforcement-learning/safe --- https://arxiv.org/abs/2408.12739 Quantum Convolutional Neural Networks are (Effectively) Classically Simulable Pablo Bermejo, Paolo Braccia, Manuel S. Rudolph, Zoë Holmes, Lukasz Cincio, M. Cerezo 2024-08-22 2024-09-02 [("doi","10.48550/arXiv.2408.12739")] ai/nn/cnn cs/computable

Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising model for Quantum Machine Learning (QML). In this work, we tie their heuristic success to two facts. First, that when randomly initialized, they can only operate on the information encoded in low-bodyness measurements of their input states.

Second, they are commonly benchmarked on “locally-easy” datasets whose states are precisely classifiable by the information encoded in these low-bodyness observables subspace. We further show that the QCNN’s action on this subspace can be efficiently classically simulated by a classical algorithm equipped with Pauli shadows on the dataset.

Indeed, we present a shadow-based simulation of QCNNs on up to 1,024 qubits for phases of matter classification. Our results can then be understood as highlighting a deeper symptom of QML: models could only be showing heuristic success because they are benchmarked on simple problems, for which their action can be classically simulated. This insight points to the fact that non-trivial datasets are a truly necessary ingredient for moving forward with QML.

To finish, we discuss how our results can be extrapolated to classically simulate other architectures.

--- https://michaelnotebook.com/df/index.html Discovery fiction Michael Nielsen 2023-01-03 2024-09-02 psychology/writing science --- https://www.commonreader.co.uk/p/waiting-your-way-to-the-top-dwight Waiting your way to the top: Dwight Eisenhower’s slow career Henry Oliver 2024-09-01 2024-09-02 history sociology --- https://evjang.com/2024/08/31/motors.html Motor Physics: Safety implications of geared motors Eric Jang 2024-08-31 2024-09-02 reinforcement-learning/imitation-learning reinforcement-learning/robot reinforcement-learning/safe --- https://arxiv.org/abs/2408.04681 Conversational AI Powered by Large Language Models Amplifies False Memories in Witness Interviews Samantha Chan, Pat Pataranutaporn, Aditya Suri, Wazeer Zulfikar, Pattie Maes, Elizabeth F. Loftus 2024-08-08 2024-09-02 [("doi","10.48550/arXiv.2408.04681")] ai/nn/transformer/gpt/4/fiction psychology/cognitive-bias psychology/neuroscience/memory

This study examines the impact of AI on human false memories—recollections of events that did not occur or deviate from actual occurrences. It explores false memory induction through suggestive questioning in Human-AI interactions, simulating crime witness interviews.

Four conditions were tested: control, survey-based, pre-scripted chatbot, and generative chatbot using a large language model (LLM) [GPT-4]. Participants (n = 200) watched a crime video, then interacted with their assigned AI interviewer or survey, answering questions including 5 misleading ones. False memories were assessed immediately and after one week.

Results show the generative chatbot condition increased false memory formation, inducing over 3× more immediate false memories than the control and 1.7× more than the survey method. 36.4% of users’ responses to the generative chatbot were misled through the interaction. After one week, the number of false memories induced by generative chatbots remained constant. However, confidence in these false memories remained higher than the control after one week.

Moderating factors were explored: users who were less familiar with chatbots but more familiar with AI technology, and more interested in crime investigations, were more susceptible to false memories.

These findings highlight the potential risks of using advanced AI in sensitive contexts, like police interviews, emphasizing the need for ethical considerations.

--- https://vitalik.eth.limo/general/2024/09/02/gluecp.html Glue and coprocessor architectures Vitalik Buterin 2024-09-02 2024-09-02 cs/algorithm cs/cryptography design --- https://arxiv.org/abs/2407.13613 Revisiting Randomization with the Cube Method Laurent Davezies, Guillaume Hollard, Pedro Vergara Merino 2024-07-18 2024-09-02 [("doi","10.48550/arXiv.2407.13613")] statistics/power-analysis

We propose a novel randomization approach for randomized controlled trials (RCTs), named the cube method. The cube method allows for the selection of balanced samples across various covariate types, ensuring consistent adherence to balance tests and, whence, substantial precision gains when estimating treatment effects.

We establish several statistical properties for the population and sample average treatment effects (PATE and SATE, respectively) under randomization using the cube method. The relevance of the cube method is particularly striking when comparing the behavior of prevailing methods employed for treatment allocation when the number of covariates to balance is increasing.

We formally derive and compare bounds of balancing adjustments depending on the number of units n and the number of covariates p and show that our randomization approach outperforms methods proposed in the literature when p is large and p⁄n tends to 0. We run simulation studies to illustrate the substantial gains from the cube method for a large set of covariates.

--- https://x.com/dlberes/status/1830719320457879898 Damon Beres 2024-09-02 ai/nn/diffusion/midjourney --- https://www.youtube.com/watch?v=uSZWeRADTFI#uber Measuring the Intrinsic Dimension of Objective Landscapes [video] 2024-06-17 ai/nn/sparsity reinforcement-learning/model-free --- https://victortao.substack.com/p/song-pong Song Pong: Synchronizing Pong to music with constrained optimization Victor Tao 2024-08-28 2024-09-03 design/visualization music statistics/decision --- https://arxiv.org/abs/2408.10234 The Unbearable Slowness of Being Jieyu Zheng, Markus Meister 2024-08-03 2024-09-03 [("doi","10.48550/arXiv.2408.10234")] cs/algorithm/information psychology/neuroscience/memory psychology/vision

This article is about the neural conundrum behind the slowness of human behavior. The information throughput of a human being is about 10 bits/s. In comparison, our sensory systems gather data at an enormous rate, no less than 1 gigabits/s.

The stark contrast between these numbers remains unexplained. Resolving this paradox should teach us something fundamental about brain function: What neural substrate sets this low speed limit on the pace of our existence? Why does the brain need billions of neurons to deal with 10 bits/s? Why can we only think about one thing at a time?

We consider plausible explanations for the conundrum and propose new research directions to address the paradox between fast neurons and slow behavior.

--- https://x.com/voooooogel/status/1830797676243492947 Jukka Luoma 2024-09-03 statistics/stylometry/truesight --- https://en.wikipedia.org/wiki/SIL_International#Methodological_contributions SIL International [Missionaries] § Methodological contributions 2024-09-03 design/typography philosophy/religion psychology/linguistics --- https://x.com/__anjor/status/1830972847759729124 Anjor Kanekar 2024-09-03 ai/nn/transformer/gpt/codex --- https://x.com/eshear/status/1830806943289745825 Emmett Shear 2024-09-03 design psychology/novelty --- https://www.lesswrong.com/posts/rS3jWvoX7JaxqYDJG/how-i-got-3-2-million-youtube-views-without-making-a-single How I got 3.2 million Youtube views without making a single video 2024-09-03 wikipedia --- https://warontherocks.com/2024/09/from-world-champions-to-state-assets-the-outsized-impact-of-a-few-chinese-hackers/ From World Champions to State Assets: The Outsized Impact of a Few Chinese Hackers 2024-09 2024-09-03 cs/security --- https://www.reddit.com/r/StableDiffusion/comments/1f5x795/movement_is_almost_human_with_klingai/ 2024-09-03 ai/video/generation --- https://www.bloomberg.com/news/features/2024-08-27/philippines-call-centers-navigate-ai-impact-on-jobs Philippines’ Call Centers Navigate AI Impact on Jobs 2024-08-27 2024-09-03 economics/automation --- https://practical.engineering/blog/2024/9/3/the-hidden-engineering-of-landfills The Hidden Engineering of Landfills 2024-09-03 2024-09-03 technology --- https://sleepinyourhat.github.io/checklist/ The Checklist: What Succeeding at AI Safety Will Involve 2024-09-03 ai/nn/anthropic reinforcement-learning/safe --- https://arxiv.org/abs/2405.16098 Lateralization MLP: A Simple Brain-inspired Architecture for Diffusion Zizhao Hu, Mohammad Rostami 2024-05-25 2024-09-03 [("doi","10.48550/arXiv.2405.16098")] ai/nn/diffusion ai/nn/fully-connected

The Transformer architecture has dominated machine learning in a wide range of tasks. The specific characteristic of this architecture is an expensive scaled dot-product attention mechanism that models the inter-token interactions, which is known to be the reason behind its success. However, such a mechanism does not have a direct parallel to the human brain, which brings the question if the scaled dot product is necessary for intelligence with strong expressive power.

Inspired by the lateralization of the human brain, we propose a new simple but effective architecture called the Lateralization MLP (L-MLP). Stacking L-MLP blocks can generate complex architectures. Each L-MLP block is based on a multi-layer perceptron (MLP) that permutes data dimensions, processes each dimension in parallel, merges them, and finally passes through a joint MLP.

We discover that this specific design outperforms other MLP variants and performs comparably to a transformer-based architecture in the challenging diffusion task while being highly efficient. We conduct experiments using text-to-image generation tasks to demonstrate the effectiveness and efficiency of L-MLP.

Further, we look into the model behavior and discover a connection to the function of the human brain.

Our code is publicly available: https://github.com/zizhao-hu/L-MLP.

--- https://maps.org/news-letters/v06n3/06346hof.html LSD: Completely Personal Albert Hoffmann 1996-06 2024-09-04 psychedelic/lsd --- https://worksinprogress.co/issue/lab-grown-diamonds/ Lab-grown diamonds: Synthetic diamonds are now purer, more beautiful, and vastly cheaper than mined diamonds. Beating nature took decades of hard graft and millions of pounds of pressure. Javid Lakha 2024-08-30 2024-09-04 science/chemistry --- https://thezvi.wordpress.com/2024/09/03/on-the-ubi-paper/ On the UBI Paper Zvi Mowshowitz 2024-09-03 2024-09-04 economics politics --- /about#my-experience-of-writing 2024-09-01 meta psychology/writing --- https://en.wikisource.org/wiki/Dracula/Chapter_16#235 Dracula § Chapter 16 [young vampires] Bram Stoker 1897 2024-09-01 fiction/gene-wolfe/suzanne-delage --- https://arxiv.org/abs/2311.17919 Visual Anagrams: Generating Multi-View Optical Illusions with Diffusion Models Daniel Geng, Inbum Park, Andrew Owens 2023-11-29 2024-01-01 [("doi","10.48550/arXiv.2311.17919")] ai/nn/diffusion psychology/vision

We address the problem of synthesizing multi-view optical illusions: images that change appearance upon a transformation, such as a flip or rotation. We propose a simple, zero-shot method for obtaining these illusions from off-the-shelf text-to-image diffusion models. During the reverse diffusion process, we estimate the noise from different views of a noisy image. We then combine these noise estimates together and denoise the image.

A theoretical analysis suggests that this method works precisely for views that can be written as orthogonal transformations, of which permutations are a subset. This leads to the idea of a visual anagram–an image that changes appearance under some rearrangement of pixels. This includes rotations and flips, but also more exotic pixel permutations such as a jigsaw rearrangement.

Our approach also naturally extends to illusions with more than two views. We provide both qualitative and quantitative results demonstrating the effectiveness and flexibility of our method. Please see our project webpage for additional visualizations and results: https://dangeng.github.io/visual_anagrams/.

--- http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf The garden of forking paths: Why multiple comparisons can be a problem, even when there is no `fishing expedition` or `p-hacking` and the research hypothesis was posited ahead of time Gelman, Loken 2013 2024-01-01 statistics/bias --- https://www.econlib.org/monkeys-marines-and-manners/ Monkeys, Marines, and Manners 2024-09-04 psychology/animal --- https://en.wikipedia.org/wiki/Friendly_Floatees_spill Friendly Floatees spill 2024-09-04 science --- https://mdickens.me/2024/03/02/caffeine_tolerance/ Avoiding Caffeine Tolerance 2024-03-02 2024-09-04 nootropic/caffeine --- https://mdickens.me/2024/03/29/does_caffeine_stop_working/ Does Caffeine Stop Working? 2024-03-29 2024-09-04 nootropic/caffeine --- https://ian.sh/tsa Bypassing airport security via SQL injection 2024-09-04 crime/terrorism cs/security --- https://en.wikipedia.org/wiki/Moon_garden Moon garden 2024-09-04 psychology/smell/perfume --- https://arxiv.org/abs/2403.14123 AI and Memory Wall Amir Gholami, Zhewei Yao, Sehoon Kim, Coleman Hooper, Michael W. Mahoney, Kurt Keutzer 2024-03-21 2024-09-04 [("doi","10.48550/arXiv.2403.14123")] ai/nn/transformer ai/scaling/hardware cs/hardware

The availability of unprecedented unsupervised training data, along with neural scaling laws, has resulted in an unprecedented surge in model size and compute requirements for serving/training LLMs. However, the main performance bottleneck is increasingly shifting to memory bandwidth. Over the past 20 years, peak server hardware FLOPS has been scaling at 3.0×/2yrs, outpacing the growth of DRAM and interconnect bandwidth, which have only scaled at 1.6 and 1.4× every 2 years, respectively.

This disparity has made memory, rather than compute, the primary bottleneck in AI applications, particularly in serving. Here, we analyze encoder and decoder Transformer models and show how memory bandwidth can become the dominant bottleneck for decoder models.

We argue for a redesign in model architecture, training, and deployment strategies to overcome this memory limitation.

--- https://blog.regehr.org/archives/820 How Did Software Get So Reliable Without Proof? [blog] John Regehr 2012-10-17 2024-01-01 math philosophy/logic --- https://blog.regehr.org/archives/881 The Space Child’s Mother Goose John Regehr 2024-01-01 fiction/poetry fiction/science-fiction math/humor --- https://www.multicians.org/mepap.html Multics Emacs: The History, Design and Implementation Bernard Greenberg 1979 2024-09-05 cs/lisp/emacs --- https://en.wikipedia.org/wiki/Alaskan_ice_cream Alaskan ice cream 2024-09-05 exercise --- https://archive.org/details/secretspolartra01peargoog/page/n96/mode/1up Secrets of Polar Travel § Pemmican Robert E. Peary 1917 2024-09-03 exercise --- https://repository.uclawsf.edu/cgi/viewcontent.cgi?article=1057&context=hastings_science_technology_law_journal#page=2 Provigil: A Case Study Of Anticompetitive Behavior Carrier 2011 2024-01-01 law modafinil --- https://arxiv.org/abs/2202.07646 Quantifying Memorization Across Neural Language Models Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, Chiyuan Zhang 2022-02-15 2024-05-02 [("doi","10.48550/arXiv.2202.07646")] ai/nn/transformer/gpt ai/scaling

Large language models (LMs) have been shown to memorize parts of their training data, and when prompted appropriately, they will emit the memorized training data verbatim. This is undesirable because memorization violates privacy (exposing user data), degrades utility (repeated easy-to-memorize text is often low quality), and hurts fairness (some texts are memorized over others).

We describe 3 log-linear relationships that quantify the degree to which LMs emit memorized training data. Memorization grows as we increase (1) the capacity of a model, (2) the number of times an example has been duplicated, and (3) the number of tokens of context used to prompt the model. Surprisingly, we find the situation becomes more complicated when generalizing these results across model families.

On the whole, we find that memorization in LMs is more prevalent than previously believed and will likely get worse as models continues to scale, at least without active mitigations.

--- https://unsongbook.com/interlude-%D7%98-the-general-assembly/ Unsong § Interlude ט: The General Assembly Scott Alexander 2015-06-08 2024-01-01 fiction/humor philosophy/ethics --- https://systemsapproach.org/2024/08/19/how-the-hourglass-won/ How the Hourglass Won: Competition on the Information Superhighway Bruce Davie 2024-08-19 2024-09-05 cs/end-to-end-principle --- https://mtlynch.io/tinypilot-redesign/ I Regret My $46,000 Website Redesign Michael Lynch 2022-07-21 2024-09-05 design psychology/cognitive-bias/sunk-cost --- https://honeycomb.sh/blog/swe-bench-technical-report SWE-Bench Technical Report: 22% Honeycomb 2024-08-26 2024-09-05 ai/nn/transformer/gpt/codex --- https://en.wikipedia.org/wiki/Media_franchise#Japan Media mix 2024-09-05 anime economics/advertising --- https://www.joelonsoftware.com/2000/04/12/choices/ Choices Joel Spolsky 2000-04-12 2024-09-05 design --- https://www.dreamsongs.com/WIB.html Lisp: Good News, Bad News, How to Win Big [Worse Is Better] Richard P. Gabriel 1991 2024-09-05 cs/lisp cs/shell design economics/automation --- https://graydon2.dreamwidth.org/193447.html Always bet on text graydon2 2014-10-13 2024-09-05 cs/algorithm cs/shell design psychology/writing --- https://drossbucket.com/2021/06/30/hacker-news-folk-wisdom-on-visual-programming/ Hacker News folk wisdom on visual programming Lucy Keer 2021-06-30 2024-09-06 cs/algorithm design --- https://arxiv.org/abs/2409.01482 Masked Mixers for Language Generation and Retrieval Benjamin L. Badger 2024-09-02 2024-09-06 [("doi","10.48550/arXiv.2409.01482")] ai/nn/fully-connected ai/nn/retrieval ai/nn/transformer/attention

Attention mechanisms that confer selective focus on a strict subset of input elements are nearly ubiquitous in language models today. We posit there to be downside to the use of attention: most information present in the input is necessarily lost. In support of this idea, we observe poor input representation accuracy in transformers, but find more accurate representation in what we term masked mixers which replace self-attention with masked convolutions.

Applied to TinyStories, the masked mixer learns causal language tasks more efficiently than early transformer implementations and somewhat less efficiently than optimized, current implementations. The most efficient learning algorithm observed for this dataset is a transformer-masked mixer hybrid, suggesting that these models learn in an orthogonal manner.

We hypothesized that the information loss exhibited by transformers would be much more detrimental to retrieval than generation, and to test this, we introduce an efficient training approach for retrieval models based on existing generative model embeddings.

With this method, embeddings from masked mixers are found to result in far better summary-to-story retrieval compared to embeddings from transformers.

--- https://arxiv.org/abs/2409.01374 H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus Benchmark Solim LeGris, Wai Keen Vong, Brenden M. Lake, Todd M. Gureckis 2024-09-02 2024-09-06 [("doi","10.48550/arXiv.2409.01374")] ai/dataset

The Abstraction and Reasoning Corpus (ARC) is a visual program synthesis benchmark designed to test challenging out-of-distribution generalization in humans and machines. Since 2019, limited progress has been observed on the challenge using existing artificial intelligence methods. Comparing human and machine performance is important for the validity of the benchmark.

While previous work explored how well humans can solve tasks from the ARC benchmark, they either did so using only a subset of tasks from the original dataset, or from variants of ARC, and therefore only provided a tentative estimate of human performance.

In this work, we obtain a more robust estimate of human performance by evaluating 1,729 humans on the full set of 400 training and 400 evaluation tasks from the original ARC problem set.

We estimate that average human performance is 73.3–77.2% correct with a reported empirical average of 76.2% on the training set, and 55.9–68.9% correct with a reported empirical average of 64.2% on the public evaluation set. [15,744 attempts] However, we also find that 790⁄800 tasks were solvable by at least one person in 3 attempts, suggesting that the vast majority of the publicly available ARC tasks are in principle solvable by typical crowd-workers recruited over the internet.

Notably, while these numbers are slightly lower than earlier estimates, human performance still greatly exceeds current state-of-the-art approaches for solving ARC.

To facilitate research on ARC, we publicly release our dataset, called H-ARC (human-ARC), which includes all of the submissions and action traces from human participants.

...Overall, we find that the evaluation set is more difficult for people than the training set. Although it remains unclear why evaluation set ARC puzzles are harder, we find that people spend substantially more time thinking about evaluation set tasks than they do about training tasks.

--- https://arxiv.org/abs/2305.07764#google Long-Term Value of Exploration: Measurements, Findings and Algorithms Yi Su, Xiangyu Wang, Elaine Ya Le, Liang Liu, Yuening Li, Haokai Lu, Benjamin Lipshitz, Sriraj Badam, Lukasz Heldt, Shuchao Bi, Ed Chi, Cristos Goodrow, Su-Lin Wu, Lexi Baugher, Minmin Chen 2023-05-12 2024-09-06 [("doi","10.48550/arXiv.2305.07764")] ai/nn/retrieval reinforcement-learning/exploration reinforcement-learning/model

Effective exploration is believed to positively influence the long-term user experience on recommendation platforms. Determining its exact benefits, however, has been challenging. Regular A/B tests on exploration often measure neutral or even negative engagement metrics while failing to capture its long-term benefits.

We here introduce new experiment designs to formally quantify the long-term value of exploration by examining its effects on content corpus, and connecting content corpus growth to the long-term user experience from real-world experiments. Once established the values of exploration, we investigate the Neural Linear Bandit algorithm as a general framework to introduce exploration into any deep learning-based ranking systems.

We conduct live experiments on one of the largest short-form video recommendation platforms that serves billions of users to validate the new experiment designs, quantify the long-term values of exploration, and to verify the effectiveness of the adopted neural linear bandit algorithm for exploration.

--- https://waymo.com/blog/2024/09/safety-data-hub/ New Data Hub Shows How Waymo Improves Road Safety 2024-09 2024-09-06 reinforcement-learning/robot --- https://www.youtube.com/watch?v=hubWIuuz-e4 How Waymo is making roads safer 2024-09-06 reinforcement-learning/robot --- https://arxiv.org/abs/1802.09127#google Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling Carlos Riquelme, George Tucker, Jasper Snoek 2018-02-26 2024-09-06 [("doi","10.48550/arXiv.1802.09127")] reinforcement-learning/exploration reinforcement-learning/model statistics/bayes

Recent advances in deep reinforcement learning have made strides in performance on applications such as Go and Atari games. However, developing practical methods to balance exploration and exploitation in complex domains remains largely unsolved.

Thompson Sampling and its extension to reinforcement learning provide an elegant approach to exploration that only requires access to posterior samples of the model. At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical. Thus, it is attractive to consider approximate Bayesian neural networks in a Thompson Sampling framework.

To understand the impact of using an approximate posterior on Thompson Sampling, we benchmark well-established and recently developed methods [neural linear bandits] for approximate posterior sampling combined with Thompson Sampling over a series of contextual bandit problems.

We found that many approaches that have been successful in the supervised learning setting underperformed in the sequential decision-making scenario. In particular, we highlight the challenge of adapting slowly converging uncertainty estimates to the online setting.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7423733/ Estimating the heritability of cognitive traits across dog breeds reveals highly heritable inhibitory control and communication factors 2024-09-06 dog genetics/heritable --- https://en.wikipedia.org/wiki/Immunocontraception Immunocontraception 2024-09-06 biology --- https://www.jeffgeerling.com/blog/2024/what-happens-when-you-touch-pickle-am-radio-tower What happens when you touch a pickle to an AM radio tower? Jeff Geerling 2024 2024-09-06 technology --- https://github.com/nuno-faria/tetris-sql Using SQL’s Turing Completeness to Build Tetris 2024-09-06 cs/computable --- https://openreview.net/forum?id=HVWODwbrFK Memorization in Machine Learning: A Survey of Results Dmitrii Usynin, Moritz Knolle, Georgios Kaissis 2024-08-14 2024-09-06 ai/nn

Quantifying the impact of individual data samples on machine learning models is an open research problem. This is particularly relevant when complex and high-dimensional relationships have to be learned from a limited sample of the data generating distribution, such as in deep learning.

It was previously shown that, in these cases, models rely not only on extracting patterns which are helpful for generalization, but also seem to be required to incorporate some of the training data more or less as is, in a process often termed ‘memorization’. This raises the question: if some memorization is a requirement for effective learning, what are its privacy implications?

In this work we consider a broad range of previous definitions and perspectives on memorization in ML, discuss their interplay with model generalization and their implications of these phenomena on data privacy.

We then propose a framework to reason over what memorization means in the context of ML training under the prism of individual sample’s influence on the model. Moreover, we systematize methods allowing practitioners to detect the occurrence of memorization or quantify it and contextualize our findings in a broad range of ML learning settings.

Finally, we discuss memorization in the context of privacy attacks, differential privacy and adversarial actors.

--- https://dynamicland.org/ Dynamicland Bret Victor 2024-09-06 cs/algorithm design --- https://www.thisamericanlife.org/220/transcript #220: Testosterone This American Life 2018-01-16 2024-09-07 psychology/personality --- https://x.com/francoisfleuret/status/1832310046409081272 Francois Fleuret 2024-09-07 psychology/cognitive-bias/illusion-of-depth --- https://x.com/SCHIZO_FREQ/status/1832204774890160611 SCHIZO_FREQ 2024-09-07 sociology/preference-falsification --- https://www.nytimes.com/2012/07/15/fashion/the-challenge-of-making-friends-as-an-adult.html Why Is It Hard to Make Friends Over 30? 2012-07-15 2024-09-07 sociology --- https://x.com/MuellerSheWrote/status/1832200385412657546 MuellerSheWrote 2024-09-07 economics/advertising --- /doc/psychology/willpower/2008-schwartz.pdf The importance of stupidity in scientific research Martin A. Schwartz 2008-06-01 2024-09-07 [("doi","10.1242/jcs.033340")] psychology/willpower science

I recently saw an old friend for the first time in many years. We had been Ph.D. students at the same time, both studying science, although in different areas. She later dropped out of graduate school, went to Harvard Law School and is now a senior lawyer for a major environmental organization. At some point, the conversation turned to why she had left graduate school. To my utter astonishment, she said it was because it made her feel stupid. After a couple of years of feeling stupid every day, she was ready to do something else.

I had thought of her as one of the brightest people I knew and her subsequent career supports that view. What she said bothered me. I kept thinking about it; sometime the next day, it hit me. Science makes me feel stupid too. It’s just that I’ve gotten used to it. So used to it, in fact, that I actively seek out new opportunities to feel stupid.

…A Ph.D. in which you have to do a research project, is a whole different thing. For me, it was a daunting task. How could I possibly frame the questions that would lead to important discoveries; design and interpret an experiment so that the conclusions were absolutely convincing; foresee difficulties and see ways around them, or, failing that, solve them when they occurred?

…I remember the day when Henry Taube (who won the Nobel Prize two years later) told me he didn’t know how to solve the problem I was having in his area. I was a third-year graduate student and I figured that Taube knew about 1,000× more than I did (conservative estimate). If he didn’t have the answer, nobody did.

That’s when it hit me: nobody did. That’s why it was a research problem. And being my research problem, it was up to me to solve. Once I faced that fact, I solved the problem in a couple of days. (It wasn’t really very hard; I just had to try a few things.)

I’d like to suggest that our Ph.D. programs often do students a disservice in two ways.

  1. First, I don’t think students are made to understand how hard it is to do research. And how very, very hard it is to do important research. It’s a lot harder than taking even very demanding courses.

    What makes it difficult is that research is immersion in the unknown. We just don’t know what we’re doing. […]

  2. Second, we don’t do a good enough job of teaching our students how to be productively stupid—that is, if we don’t feel stupid it means we’re not really trying.

    I’m not talking about ‘relative stupidity’, in which the other students in the class actually read the material, think about it and ace the exam, whereas you don’t. I’m also not talking about bright people who might be working in areas that don’t match their talents.

    Science involves confronting our ‘absolute stupidity’. That kind of stupidity is an existential fact, inherent in our efforts to push our way into the unknown. Preliminary and thesis exams have the right idea when the faculty committee pushes until the student starts getting the answers wrong or gives up and says, “I don’t know”.

…Productive stupidity means being ignorant by choice. Focusing on important questions puts us in the awkward position of being ignorant. One of the beautiful things about science is that it allows us to bumble along, getting it wrong time after time, and feel perfectly fine as long as we learn something each time. No doubt, this can be difficult for students who are accustomed to getting the answers right. No doubt, reasonable levels of confidence and emotional resilience help, but I think scientific education might do more to ease what is a very big transition: from learning what other people once discovered to making your own discoveries.

See Also:

--- https://en.wikipedia.org/wiki/Nictitating_membrane Nictitating membrane 2024-09-07 cat/biology psychology/vision --- https://purplesyringa.moe/blog/webp-the-webpage-compression-format/nojs.html WebP: The WebPage compression format Alisa Sireneva 2024-09-07 2024-09-07 cs/algorithm/information/compression cs/js --- https://rajivsethi.substack.com/p/a-failed-attempt-at-prediction-market A Failed Attempt at Market Manipulation 2024-09-07 statistics/prediction --- https://www.newyorker.com/culture/the-front-row/how-king-murray-seizes-the-day How King, Murray Seizes the Day 2024-09-07 psychology/energy --- http://www.foldl.me/2013/imperat-aut-servit/ Imperat aut servit: Managing our knowledge inheritance John Gauthier 2013-12-19 2024-09-07 psychology/writing --- /doc/design/2001-10-19-spongebob-s2e37-procrastination-thecalligraphy.jpg 2001-10-19 2024-01-01 design psychology/writing --- https://longnow.org/essays/richard-feynman-and-connection-machine/ Richard Feynman and The Connection Machine Danny Hillis 2024-01-01 cs/hardware --- /doc/math/1972-davis-2.pdf Nonstandard Analysis Martin Davis, Reuben Hersh 1972-06-01 2024-09-07 [("doi","10.2307/24927363")] math philosophy/logic --- https://dynomight.net/copies/ Buy more copies Dynomight 2024-01-01 statistics/decision --- https://www.catb.org/~esr/faqs/hacker-howto.html How to become a Hacker Eric S. Raymond 2001 2024-09-07 cs --- https://arxiv.org/abs/2401.04925 The Impact of Reasoning Step Length on Large Language Models Mingyu Jin, Qinkai Yu, Dong Shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, Mengnan Du 2024-01-10 2024-09-08 [("doi","10.48550/arXiv.2401.04925")] ai/nn/transformer/gpt/inner-monologue

Chain-of-Thought (CoT) is in improving the reasoning abilities of large language models (LLMs). However, the correlation between the effectiveness of CoT and the length of reasoning steps in prompts remains largely unknown. To shed light on this, we have conducted several empirical experiments to explore the relations.

Specifically, we design experiments that expand and compress the rationale reasoning steps within CoT demonstrations while keeping all other factors constant. We have the following key findings.

First, the results indicate that lengthening the reasoning steps in prompts, even without adding new information into the prompt, considerably enhances LLMs’ reasoning abilities across multiple datasets. Alternatively, shortening the reasoning steps, even while preserving the key information, diminishes the reasoning abilities of models. This finding highlights the importance of the number of steps in CoT prompts and provides practical guidance to make better use of LLMs’ potential in complex problem-solving scenarios.

Second, we also investigated the relationship between the performance of CoT and the rationales used in demonstrations. Surprisingly, the result shows that even incorrect rationales can yield favorable outcomes if they maintain the requisite length of inference.

Third, we observed that the advantages of increasing reasoning steps are task-dependent: simpler tasks require fewer steps, whereas complex tasks gain from longer inference sequences.

The code is available at GitHub.

--- https://en.wikipedia.org/wiki/Advice_(programming) Advice (programming) 2024-09-08 cs/lisp --- https://en.wikipedia.org/wiki/Emacspeak Emacspeak 2024-01-01 cs/lisp --- https://en.wikipedia.org/wiki/Neurasthenia Neurasthenia 2024-09-08 psychiatry/bipolar/energy --- https://blog.otoro.net/2016/09/28/hyper-networks/ Hypernetworks [blog] David Ha 2024-01-01 ai/nn/cnn ai/nn/rnn reinforcement-learning/meta-learning --- https://blog.obormot.net/Screen-serif-fonts Screen serif fonts Said Achmiz 2024-01-01 cs/css design/typography --- https://blog.otoro.net/2016/04/01/generating-large-images-from-latent-vectors/ Generating Large Images from Latent Vectors David Ha 2024-01-01 ai/nn/gan ai/nn/vae --- https://blog.otoro.net/2017/11/12/evolving-stable-strategies/ Evolving Stable Strategies David Ha 2024-01-01 reinforcement-learning/model-free --- https://www.wsj.com/articles/who-controls-diners-data-opentable-moves-to-assert-control-11552644121 Who Controls Diners’ Data? OpenTable Moves to Assert Control: Some restaurateurs say booking service is trying to squeeze rival SevenRooms; OpenTable says it is protecting user privacy 2024-01-01 economics --- https://www.poetryfoundation.org/poems/51932/breakfast Breakfast Mary Lamb 1810 2024-09-08 fiction/humor fiction/poetry --- https://conspirator0.substack.com/p/baiting-the-bot Baiting the bot 2024-09-08 ai/nn/adversarial --- https://www.medrxiv.org/content/10.1101/2024.06.15.24308968.full Differences in early life cognitive function explain the association between low education and early dementia risk Bernt Bratsberg, Anders M. Fjell, Ole J. Rogeberg, Vegard F. Skirbekk, Kristine B. Walhovd 2024-06-17 2024-09-08 [("doi","10.1101/2024.06.15.24308968")] iq psychiatry/alzheimers

Major initiatives are currently attempting to prevent dementia by targeting modifiable risk factors. Low education is frequently pointed to as a potential key factor, due to its robust relationship with dementia risk. Impact of education is notoriously difficult to assess, however, because of associations with multiple other risk and protective factors, and large population-representative samples are required to tease the relationships apart.

Here, we studied 207,814 Norwegian men born 1950–1959 who underwent compulsory cognitive testing during military conscription as young adults, to systematically test associations of education, cognition, and other potentially important factors. While low education was associated with increased risk for dementia diagnosis (Hazard ratio [HR] = 1.37, CI: 1.17–1.60), this association was fully explained by earlier cognitive test scores (HR = 1.08, CI: 0.91–1.28). In contrast, low cognitive score was associated with double risk of later dementia diagnosis, even when taking education into account (HR = 2.00, CI: 1.65–2.42).

This relationship survived controlling for early-life socioeconomic status and was replicated within pairs of brothers. The latter finding suggests that genetic and environmental factors shared within families, such as common genetics, parental education, childhood socioeconomic status, or other shared experiences, cannot account for the association. Rather, independent, non-familial factors are more important.

In contrast, within-family factors accounted for the relationship between low education and diagnosis risk. In conclusion, implementing measures to increase cognitive function in childhood and adolescence appears to be a more promising strategy for reducing dementia burden.

--- https://onlinelibrary.wiley.com/doi/10.1111/pops.13017 2024-09-08 politics psychology/personality --- https://x.com/repligate/status/1830331775341789615 Janus 2024-09-08 ai/nn/transformer/gpt/claude economics/advertising --- https://weirdfictionreview.com/2016/02/101-weird-writers-39-james-tiptree-jr/ 101 Weird Writers #39: James Tiptree Junior 2016-02 2024-09-09 fiction/science-fiction philosophy/ethics psychology/animal --- https://en.wikipedia.org/wiki/Countercurrent_exchange Countercurrent exchange 2024-09-09 technology --- https://en.wikipedia.org/wiki/Frontal_lobe_injury Frontal lobe injury 2024-09-09 psychiatry/traumatic-brain-injury --- https://en.wikipedia.org/wiki/Frontal_lobe_disorder Frontal lobe disorder 2024-09-09 psychiatry/traumatic-brain-injury --- /doc/psychology/novelty/1969-sheldon.pdf Preference for familiar versus novel stimuli as a function of the familiarity of the environment Alice B. Sheldon 1969-01-01 2024-09-09 [("doi","10.1037/h0027305")] psychology/animal/maze psychology/novelty

[biographical background] The effect of high and low levels of environmental novelty on the direction of response to novel stimuli was tested by placing rats in a strange environment where they had the choice of approaching a source of familiar stimulation or a comparable novel one.

On first exposure, subjects statistically-significantly preferred the familiar stimulus, but after habituation to the environment, subjects changed to a statistically-significant preference for novel stimuli. A subsequent increase in novel stimulation tended to change preference back to familiar stimuli.

These findings support the previously untested optimal-level hypothesis of novelty response.

--- https://normaldeviate.wordpress.com/2013/05/18/steins-paradox/ Stein’s Paradox [blog] Larry Wasserman 2013-05-18 2024-01-01 statistics/probability --- https://www.buzzricksons.jp/ Buzz Rickson’s 2024-01-01 japan/art psychology/collecting --- /doc/genetics/heritable/correlation/2014-li.pdf A Twin Study of Problematic Internet Use: Its Heritability and Genetic Association With Effortful Control Mengjiao Li, Jie Chen, Naishi Li, Xinying Li 2014-08-01 2024-01-01 [("doi","10.1017/thg.2014.32")] genetics/heritable/correlation psychology/personality/conscientiousness sociology/technology

Our goal was to estimate genetic and environmental sources of influence on adolescent problematic internet use, and whether these individual differences can be explained by effortful control, an important aspect of self-regulation. A sample of 825 pairs of Chinese adolescent twins and their parents provided reports of problematic internet use and effortful control.

Univariate analysis revealed that genetic factors explained 58–66% of variance in problematic internet use, with the rest explained by non-shared environmental factors. Sex difference was found, suggesting boys’ problematic internet use was more influenced by genetic influences than girls’ problematic internet use.

Bivariate analysis indicated that effortful control accounted for a modest portion of the genetic and non-shared environmental variance in problematic internet use among girls. In contrast, among boys, effortful control explained between 6% (parent report) and 20% (self-report) of variance in problematic internet use through overlapping genetic pathways.

Adolescent problematic internet use is heritable, and poor effortful control can partly explain adolescent problematic internet use, with effects stronger for boys. Implications for future research are discussed.

--- https://www.decarpentier.nl/carpentopod Carpentopod: A walking table project Giliam de Carpentier 2024-09-09 2024-09-09 reinforcement-learning/model-free reinforcement-learning/robot --- https://www.lesswrong.com/posts/DzfBgxoAC3kPir4PX/in-strategic-time-open-source-games-are-loopy In Strategic Time, Open-Source Games Are Loopy 2024-09-09 fiction/science-fiction/time-travel philosophy/logic statistics/decision --- https://www.biorxiv.org/content/10.1101/603134.full Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities Travis T. Mallard, Richard K. Linnér, Andrew D. Grotzinger, Sandra Sanchez-Roige, Jakob Seidlitz, Aysu Okbay, Ronald de Vlaming, S. Fleur W. Meddens, Bipolar Disorder Working Group of the Psychiatric Genomics Consortium, Abraham Palmer, Lea K. Davis, Elliot M. Tucker-Drob, Kenneth S. Kendler, Matthew C. Keller, Philipp Koellinger, K. Paige Harden 2020-09-08 2024-09-09 [("doi","10.1101/603134")] genetics/heritable/correlation psychiatry/bipolar psychiatry/depression psychiatry/schizophrenia

Understanding which biological pathways are specific versus general across diagnostic categories and levels of symptom severity is critical to improving nosology and treatment of psychopathology. Here, we combine transdiagnostic and dimensional approaches to genetic discovery for the first time, conducting a novel multivariate genome-wide association study (GWAS) of 8 psychiatric symptoms and disorders broadly related to mood disturbance and psychosis.

We identify two transdiagnostic genetic liabilities that distinguish between common forms of mood disturbance (major depressive disorder, bipolar II, and self-reported symptoms of depression, mania, and psychosis) versus rarer forms of serious mental illness (bipolar I, schizoaffective disorder, and schizophrenia). Biological annotation revealed divergent genetic architectures that differentially implicated prenatal neurodevelopment and neuronal function and regulation.

These findings inform psychiatric nosology and biological models of psychopathology, as they suggest the severity of mood and psychotic symptoms present in serious mental illness may reflect a difference in kind, rather than merely in degree.

--- https://arxiv.org/abs/2406.04005 The Failed Migration of Academic Twitter Xinyu Wang, Sai Koneru, Sarah Rajtmajer 2024-06-06 2024-09-10 [("doi","10.48550/arXiv.2406.04005")] sociology/technology

Following the change in Twitter’s ownership and subsequent changes to content moderation policies, many in academia looked to move their discourse elsewhere and migration to Mastodon was pursued by some. Our study looks at the dynamics of this migration.

Utilizing publicly available user account data, we track the posting activity of academics on Mastodon over a one-year period.

Our analyses reveal challenges sustaining user engagement on Mastodon due to its decentralized structure as well as competition from other platforms such as Bluesky and Threads. The movement lost momentum after an initial surge of enthusiasm as most users did not maintain their activity levels, and those who did faced lower levels of engagement compared to Twitter.

Our findings highlight the challenges involved in transitioning professional communities to decentralized platforms, emphasizing the need for focusing on migrating social connections for long-term user engagement.

--- https://sichaconversation.org/2013/09/11/seeing-life-in-the-distance/ Seeing Life In the Distance Steven Sager 2013-09-11 2024-09-10 fiction/poetry philosophy/ethics --- https://web.archive.org/web/20080702231205/http://www.scifi.com/scifiction/classics/classics_archive/sheldon/sheldon1.html The Screwfly Solution Alice B. Sheldon 1977 2024-01-01 fiction/science-fiction psychology/animal --- /doc/fiction/science-fiction/1986-tiptree-theonlyneatthingtodo.pdf The Only Neat Thing To Do James Tiptree Junior 1986-01-01 2024-01-01 anime/eva fiction/science-fiction --- https://books.google.com/books?id=DgcAAAAAMBAJ&lpg=PA36&pg=PA8 The philosophy of Niels Bohr Aage Petersen 1963 2024-01-01 philosophy/epistemology --- https://books.google.com/books?id=PQgAAAAAMBAJ November 1963 Bulletin of the Atomic Scientists Bulletin of the Atomic Scientists 1963-11 2024-01-01 science --- https://books.google.com/books?id=cwwAAAAAMBAJ&pg=PA35 How Safe is Safe? Only 52% of US nuclear weapons have the improved electrical control system introduced in 1977. And insensitive high explosive, available since 1979, is used in only 25% of the stockpile Panel on Nuclear Weapons Safety of the House Armed Services Committee 1991-04 2024-01-01 radiance --- https://books.google.com/books?id=sAYAAAAAMBAJ&pg=PA7 Weird Science: Livermore’s X-Ray Laser Flap Deborah Blum 1988-07 2024-01-01 radiance --- https://arxiv.org/abs/2409.05816 Improving Pretraining Data Using Perplexity Correlations Tristan Thrush, Christopher Potts, Tatsunori Hashimoto 2024-09-09 2024-09-10 [("doi","10.48550/arXiv.2409.05816")] ai/nn/transformer/gpt/2 reinforcement-learning/exploration/active-learning/data-pruning

Quality pretraining data is often seen as the key to high-performance language models. However, progress in understanding pretraining data has been slow due to the costly pretraining runs required for data selection experiments.

We present a framework that avoids these costs and selects high-quality pretraining data without any LLM training of our own. Our work is based on a simple observation: LLM losses on many pretraining texts are correlated with downstream benchmark performance, and selecting high-correlation documents is an effective pretraining data selection method.

We build a new statistical framework for data selection centered around estimates of perplexity-benchmark correlations and perform data selection using a sample of 90 LLMs taken from the Open LLM Leaderboard on texts from tens of thousands of web domains.

In controlled pretraining experiments at the 160M parameter scale on 8 benchmarks, our approach outperforms DSIR on every benchmark, while matching the best data selector found in DataComp-LM, a hand-engineered bigram classifier.

--- https://www.nealstephenson.com/why-i-am-a-bad-correspondent.html Why I Am a Bad Correspondent Neal Stephenson 2024-09-10 psychology/writing --- https://arxiv.org/abs/2409.04930 PIXHELL Attack: Leaking Sensitive Information from Air-Gap Computers via ‘Singing Pixels’ Mordechai Guri 2024-09-07 2024-09-11 [("doi","10.1109/COMPSAC61105.2024.00134")] cs/security

Air-gapped systems are disconnected from the Internet and other networks because they contain or process sensitive data. However, it is known that attackers can use computer speakers to leak data via sound to circumvent the air-gap defense. To cope with this threat, when highly sensitive data is involved, the prohibition of loudspeakers or audio hardware might be enforced. This measure is known as an ‘audio gap’.

In this paper, we present PIXHELL, a new type of covert channel attack allowing hackers to leak information via noise generated by the pixels on the screen. No audio hardware or loudspeakers is required. Malware in the air-gap and audio-gap computers generates crafted pixel patterns that produce noise in the frequency range of 0–22 kHz. The malicious code exploits the sound generated by coils and capacitors to control the frequencies emanating from the screen. Acoustic signals can encode and transmit sensitive information. We present the adversarial attack model, cover related work, and provide technical background. We discuss bitmap generation and correlated acoustic signals and provide implementation details on the modulation and demodulation process.

We evaluated the covert channel on various screens and tested it with different types of information. We also discuss evasion and stealth using low-brightness patterns that appear like black, turned-off screens. Finally, we propose a set of countermeasures.

Our test shows that with a PIXHELL attack, textual and binary data can be exfiltrated from air-gapped, audio-gapped computers at a distance of 2 meters via sound modulated from LCD screens.

--- https://benedante.blogspot.com/2021/01/michael-hunter-decline-of-magic-britain.html Michael Hunter 2020, The Decline of Magic: Britain in the Enlightenment [review] John Bedell 2021-01-17 2024-09-11 philosophy/religion sociology --- https://www.sebastianmellen.com/post/2024/originality-dies-when-being-average-is-easier/ Originality Dies When Being Average Is Easier 2024-01-01 2024-09-11 ai/nn/transformer/gpt/4/nonfiction ai/text-style-transfer reinforcement-learning/preference-learning/mode-collapse --- https://www.wired.com/story/air-gap-researcher-mordechai-guri/ Air Gap Hacker Mordechai Guri Steals Data With Noise, Light, and Magnets 2024-09-11 cs/security --- https://arxiv.org/abs/2406.11794 DataComp-LM: In search of the next generation of training sets for language models Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Gadre, Hritik Bansal, Etash Guha, Sedrick Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee Chen, Suchin Gururangan, Mitchell Wortsman, Alon Albalak, Yonatan Bitton, Marianna Nezhurina, Amro Abbas, Cheng-Yu Hsieh, Dhruba Ghosh, Josh Gardner, Maciej Kilian, Hanlin Zhang, Rulin Shao, Sarah Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Chandu, Thao Nguyen, Igor Vasiljevic, Sham Kakade, Shuran Song, Sujay Sanghavi, Fartash Faghri, Sewoong Oh, Luke Zettlemoyer, Kyle Lo, Alaaeldin El-Nouby, Hadi Pouransari, Alexander Toshev, Stephanie Wang, Dirk Groeneveld, Luca Soldaini, Pang Wei Koh, Jenia Jitsev, Thomas Kollar, Alexandros G. Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar 2024-06-17 2024-09-11 [("doi","10.48550/arXiv.2406.11794")] ai/dataset ai/nn/transformer/gpt reinforcement-learning/exploration/active-learning/data-pruning

We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models. As part of DCLM, we provide a standardized corpus of 240T tokens extracted from Common Crawl, effective pretraining recipes based on the OpenLM framework, and a broad suite of 53 downstream evaluations. Participants in the DCLM benchmark can experiment with data curation strategies such as deduplication, filtering, and data mixing at model scales ranging from 412M to 7B parameters.

As a baseline for DCLM, we conduct extensive experiments and find that model-based filtering is key to assembling a high-quality training set. The resulting dataset, DCLM-Baseline, enables training a 7B parameter language model from scratch to 64% 5-shot accuracy on MMLU with 2.6T training tokens. Compared to MAP-Neo, the previous state-of-the-art in open-data language models, DCLM-Baseline represents a 6.6 percentage point improvement on MMLU while being trained with 40% less compute.

Our baseline model is also comparable to Mistral-7B-v0.3 and Llama-3-8B on MMLU (63% & 66%), and performs similarly on an average of 53 natural language understanding tasks while being trained with 6.6× less compute than Llama-3-8B.

Our results highlight the importance of dataset design for training language models and offer a starting point for further research on data curation.

--- https://arxiv.org/abs/2302.03169 Data Selection for Language Models via Importance Resampling Sang Michael Xie, Shibani Santurkar, Tengyu Ma, Percy Liang 2023-02-06 2024-09-11 [("doi","10.48550/arXiv.2302.03169")] ai/dataset ai/nn/transformer/gpt reinforcement-learning/exploration/active-learning/data-pruning

Selecting a suitable pretraining dataset is crucial for both general-domain (eg. GPT-3) and domain-specific (eg. Codex) language models (LMs). We formalize this problem as selecting a subset of a large raw unlabeled dataset to match a desired target distribution given unlabeled target samples. Due to the scale and dimensionality of the raw text data, existing methods use simple heuristics or require human experts to manually curate data.

Instead, we extend the classic importance resampling approach used in low-dimensions for LM data selection. We propose Data Selection with Importance Resampling (DSIR), an efficient and scalable framework that estimates importance weights in a reduced feature space for tractability and selects data with importance resampling according to these weights. We instantiate the DSIR framework with hashed n-gram features for efficiency, enabling the selection of 100M documents from the full Pile dataset in 4.5 hours.

To measure whether hashed n-gram features preserve the aspects of the data that are relevant to the target, we define KL reduction, a data metric that measures the proximity between the selected pretraining data and the target on some feature space.

Across 8 data selection methods (including expert selection), KL reduction on hashed n-gram features highly correlates with average downstream accuracy (r = 0.82). When selecting data for continued pretraining on a specific domain, DSIR performs comparably to expert curation across 8 target distributions.

When pretraining general-domain models (target is Wikipedia and books), DSIR improves over random selection and heuristic filtering baselines by 2–2.5% on the GLUE benchmark.

Code is available at Github.

--- https://arxiv.org/abs/2405.19327 MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series Ge Zhang, Scott Qu, Jiaheng Liu, Chenchen Zhang, Chenghua Lin, Chou Leuang Yu, Danny Pan, Esther Cheng, Jie Liu, Qunshu Lin, Raven Yuan, Tuney Zheng, Wei Pang, Xinrun Du, Yiming Liang, Yinghao Ma, Yizhi Li, Ziyang Ma, Bill Lin, Emmanouil Benetos, Huan Yang, Junting Zhou, Kaijing Ma, Minghao Liu, Morry Niu, Noah Wang, Quehry Que, Ruibo Liu, Sine Liu, Shawn Guo, Soren Gao, Wangchunshu Zhou, Xinyue Zhang, Yizhi Zhou, Yubo Wang, Yuelin Bai, Yuhan Zhang, Yuxiang Zhang, Zenith Wang, Zhenzhu Yang, Zijian Zhao, Jiajun Zhang, Wanli Ouyang, Wenhao Huang, Wenhu Chen 2024-05-29 2024-09-11 [("doi","10.48550/arXiv.2405.19327")] ai/nn/transformer/gpt

Large Language Models (LLMs) have made great strides in recent years to achieve unprecedented performance across different tasks. However, due to commercial interest, the most competitive models like GPT, Gemini, and Claude have been gated behind proprietary interfaces without disclosing the training details. Recently, many institutions have open-sourced several strong LLMs like LLaMA-3, comparable to existing closed-source LLMs. However, only the model’s weights are provided, with most details (eg. intermediate checkpoints, pre-training corpus, and training code, etc.) being undisclosed.

To improve the transparency of LLMs, the research community has formed to open-source truly open LLMs (eg. Pythia, Amber, OLMo), where more details (eg. pre-training corpus and training code) are being provided. These models have greatly advanced the scientific study of these large models, including their strengths, weaknesses, biases, and risks. However, we observe that the existing truly open LLMs on reasoning, knowledge, and coding tasks are still inferior to existing state-of-the-art LLMs with similar model sizes.

To this end, we open-source MAP-Neo, a highly capable and transparent bilingual language model with 7B parameters trained from scratch on 4.5T high-quality tokens. Our MAP-Neo is the first fully open-sourced bilingual LLM with comparable performance to existing state-of-the-art LLMs. Moreover, we open-source all details to reproduce our MAP-Neo, where the cleaned pre-training corpus, data cleaning pipeline, checkpoints, and well-optimized training/evaluation framework are provided.

Finally, we hope our MAP-Neo will enhance and strengthen the open research community and inspire more innovations and creativity to facilitate the further improvements of LLMs.

--- /doc/genetics/heritable/correlation/2013-boivin.pdf Strong Genetic Contribution to Peer Relationship Difficulties at School Entry: Findings From a Longitudinal Twin Study Michel Boivin, Mara Brendgen, Frank Vitaro, Ginette Dionne, Alain Girard, Daniel Pérusse, Richard E. Tremblay 2012-12-04 2024-09-11 [("doi","10.1111/cdev.12019")] genetics/heritable/correlation psychology/personality

This study assessed the genetic and environmental contributions to peer difficulties in the early school years.

Twins’ peer difficulties were assessed longitudinally in kindergarten (796 twins, Mage = 6.1 years), Grade 1 (948 twins, Mage = 7.1 years), and Grade 4 (868 twins, Mage = 10 years) through multiple informants.

The multivariate results revealed that genetic factors accounted for a strong part of both yearly and stable peer difficulties. At the univariate level, the genetic contributions emerged progressively, as did a growing consensus among informants with respect to those who experienced peer difficulties.

These results underline the need to intervene early and persistently, and to target the child and the peer context to prevent peer difficulties and their consequences.

--- /static/build/latex2unicode.py latex2unicode.py Gwern 2023-06-28 2024-01-01 ai/nn/transformer/gpt/codex cs/css cs/python design/typography/tex --- https://publicdomainreview.org/essay/gottfried-mind-the-raphael-of-cats/ Gottfried Mind, The Raphael of Cats 2024-09-11 cat history/public-domain-review iodine --- https://www.gnu.org/software/hyperbole/ GNU Hyperbole Robert S. Weiner 1989 2024-09-11 cs/lisp --- https://en.wikipedia.org/wiki/Cat_play_and_toys Cat play and toys 2024-09-11 cat/psychology --- https://en.wikipedia.org/wiki/The_Man_Who_Folded_Himself The Man Who Folded Himself 2024-09-11 fiction/science-fiction/time-travel --- https://arxiv.org/abs/2409.06464 Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes? Jimmy Lin 2024-09-10 2024-09-12 [("doi","10.48550/arXiv.2409.06464")] ai/nn/retrieval

Practitioners working on dense retrieval today face a bewildering number of choices. Beyond selecting the embedding model, another consequential choice is the actual implementation of nearest-neighbor vector search. While best practices recommend HNSW indexes, flat vector indexes with brute-force search represent another viable option, particularly for smaller corpora and for rapid prototyping.

In this paper, we provide experimental results on the BEIR dataset using the open-source Lucene search library that explicate the tradeoffs between HNSW and flat indexes (including quantized variants) from the perspectives of indexing time, query evaluation performance, and retrieval quality.

With additional comparisons between dense and sparse retrievers, our results provide guidance for today’s search practitioner in understanding the design space of dense and sparse retrievers. To our knowledge, we are the first to provide operational advice supported by empirical experiments in this regard.

...For “small” corpora less than 100K documents, there appear to be negligible differences between flat and HNSW indexes. For example, in an exploratory or prototyping setting, we do not see the differences in queries-per-second as meaningful.

--- https://en.wikipedia.org/wiki/Barnes_maze Barnes maze 2024-09-12 psychology/animal/maze --- https://en.wikipedia.org/wiki/Delayed_auditory_feedback#Effects_in_people_who_do_not_stutter Delayed auditory feedback § Effects in people who do not stutter 2024-09-12 psychology/linguistics --- https://www.youtube.com/watch?v=USDI3wnTZZg SpeechJammer Kazutaka Kurihara, Koji Tsukada 2012-03-02 2024-09-12 psychology/linguistics technology --- https://www.youtube.com/watch?v=J-SH18dtBlY Testing My Speech Jammer In Public Benn Jordan 2023-09-17 2024-09-12 psychology/linguistics technology --- https://www.youtube.com/watch?v=J-SH18dtBlY&t=510s Testing My Speech Jammer In Public § Speech Jammer Immunity Benn Jordan 2023-09-17 2024-09-12 psychology/linguistics technology --- https://sites.google.com/site/qurihara/top-english/speechjammer SpeechJammer homepage Kazutaka Kurihara, Koji Tsukada 2024-09-12 psychology/linguistics technology --- https://x.com/alanlparker/status/1832068380192395744 alanlparker 2024-09-12 nootropic/quantified-self --- https://en.wikipedia.org/wiki/Disjoint-set_data_structure Union-find disjoint-set data structure 2024-09-12 cs/algorithm --- https://arxiv.org/abs/2408.10270 SEAL: Systematic Error Analysis for Value ALignment Manon Revel, Matteo Cargnelutti, Tyna Eloundou, Greg Leppert 2024-08-16 2024-09-12 [("doi","10.48550/arXiv.2408.10270")] reinforcement-learning/preference-learning

Reinforcement Learning from Human Feedback (RLHF) aims to align language models (LMs) with human values by training reward models (RMs) on binary preferences and using these RMs to fine-tune the base LMs. Despite its importance, the internal mechanisms of RLHF remain poorly understood.

This paper introduces new metrics to evaluate the effectiveness of modeling and aligning human values, namely feature imprint, alignment resistance, and alignment robustness. We categorize alignment datasets into target features (desired values) and spoiler features (undesired concepts). By regressing RM scores against these features, we quantify the extent to which RMs reward them—a metric we term feature imprint.

We define alignment resistance as the proportion of the preference dataset where RMs fail to match human preferences, and we assess alignment robustness by analyzing RM responses to perturbed inputs. Our experiments, using open-source components like the Anthropic/hh-rlhf preference dataset and OpenAssistant RMs, reveal imprints of target features and a notable sensitivity to spoiler features.

We observed a 26% incidence of alignment resistance in portions of the dataset where LM-labelers disagreed with human preferences. Furthermore, we find that misalignment often arises from ambiguous entries within the alignment dataset.

These findings underscore the importance of scrutinizing both RMs and alignment datasets for a deeper understanding of value alignment.

--- /doc/genetics/heritable/2018-roselli.pdf Multi-ethnic genome-wide association study for atrial fibrillation Carolina Roselli, Mark D. Chaffin, Lu-Chen Weng, Stefanie Aeschbacher, Gustav Ahlberg, Christine M. Albert, Peter Almgren, Alvaro Alonso, Christopher D. Anderson, Krishna G. Aragam, Dan E. Arking, John Barnard, Traci M. Bartz, Emelia J. Benjamin, Nathan A. Bihlmeyer, Joshua C. Bis, Heather L. Bloom, Eric Boerwinkle, Erwin B. Bottinger, Jennifer A. Brody, Hugh Calkins, Archie Campbell, Thomas P. Cappola, John Carlquist, Daniel I. Chasman, Lin Y. Chen, Yii-Der Ida Chen, Eue-Keun Choi, Seung Hoan Choi, Ingrid E. Christophersen, Mina K. Chung, John W. Cole, David Conen, James Cook, Harry J. Crijns, Michael J. Cutler, Scott M. Damrauer, Brian R. Daniels, Dawood Darbar, Graciela Delgado, Joshua C. Denny, Martin Dichgans, Marcus Dörr, Elton A. Dudink, Samuel C. Dudley, Nada Esa, Tõnu Esko, Markku Eskola, Diane Fatkin, Stephan B. Felix, Ian Ford, Oscar H. Franco, Bastiaan Geelhoed, Raji P. Grewal, Vilmundur Gudnason, Xiuqing Guo, Namrata Gupta, Stefan Gustafsson, Rebecca Gutmann, Anders Hamsten, Tamara B. Harris, Caroline Hayward, Susan R. Heckbert, Jussi Hernesniemi, Lynne J. Hocking, Albert Hofman, Andrea R. V. R. Horimoto, Jie Huang, Paul L. Huang, Jennifer Huffman, Erik Ingelsson, Esra Gucuk Ipek, Kaoru Ito, Jordi Jimenez-Conde, Renee Johnson, J. Wouter Jukema, Stefan Kääb, Kähönen Mika, Yoichiro Kamatani, John P. Kane, Adnan Kastrati, Sekar Kathiresan, Petra Katschnig-Winter, Maryam Kavousi, Thorsten Kessler, Bas L. Kietselaer, Paulus Kirchhof, Marcus E. Kleber, Stacey Knight, Jose E. Krieger, Michiaki Kubo, Lenore J. Launer, Jari Laurikka, Terho Lehtimäki, Kirsten Leineweber, Rozenn N. Lemaitre, Man Li, Hong Euy Lim, Henry J. Lin, Honghuang Lin, Lars L. Lind, Cecilia M. Lindgren, Marja-Liisa Lokki, Barry London, Ruth Loos, Siew-Kee Low, Yingchang Lu, Leo-Pekka Lyytikäinen, Peter W. Macfarlane, Patrik K. Magnusson, Anubha Mahajan, Rainer Malik, Alfredo J. Mansur, Gregory M. Marcus, Lauren Margolin, Kenneth B. Margulies, Winfried März, David D. McManus, Olle Melander, Sanghamitra Mohanty, Jay A. Montgomery, Michael P. Morley, Andrew P. Morris, Martina Müller-Nurasyid, Andrea Natale, Saman Nazarian, Benjamin Neumann, Christopher Newton-Cheh, Maartje N. Niemeijer, Kjell Nikus, Peter Nilsson, Raymond Noordam, Heidi Oellers, Morten S. Olesen, Marju Orho-Melander, Sandosh Padmanabhan, Hui-Nam Pak, Guillaume Paré, Nancy L. Pedersen, Joanna Pera, Alexandre Pereira, David J. Porteous, Bruce M. Psaty, Sara L. Pulit, Clive R. Pullinger, Daniel J. Rader, Lena Refsgaard, Marta Ribasés, Paul M. Ridker, Michiel Rienstra, Lorenz Risch, Dan M. Roden, Jonathan Rosand, Michael A. Rosenberg, Natalia Rost, Jerome I. Rotter, Samir Saba, Roopinder K. Sandhu, Renate B. Schnabel, Katharina Schramm, Heribert Schunkert, Claudia Schurman, Stuart A. Scott, Ilkka Seppälä, Christian Shaffer, Svati Shah, Alaa A. Shalaby, Jaemin Shim, M. Benjamin Shoemaker, Joylene E. Siland, Juha Sinisalo, Moritz F. Sinner, Agnieszka Slowik, Albert Vernon Smith, Blair H. Smith, J. Gustav Smith, Jonathan D. Smith, Nicholas L. Smith, Elsayed Z. Soliman, Nona Sotoodehnia, Bruno H. Stricker, Albert Sun, Han Sun, Jesper H. Svendsen, Toshihiro Tanaka, Kahraman Tanriverdi, Kent D. Taylor, Maris Teder-Laving, Alexander Teumer, Sébastien Thériault, Stella Trompet, Nathan R. Tucker, Arnljot Tveit, André G. Uitterlinden, Pim Van Der Harst, Isabelle C. Van Gelder, David R. Van Wagoner, Niek Verweij, Efthymia Vlachopoulou, Uwe Völker, Biqi Wang, Peter E. Weeke, Bob Weijs, Raul Weiss, Stefan Weiss, Quinn S. Wells, Kerri L. Wiggins, Jorge A. Wong, Daniel Woo, Bradford B. Worrall, Pil-Sung Yang, Jie Yao, Zachary T. Yoneda, Tanja Zeller, Lingyao Zeng, Steven A. Lubitz, Kathryn L. Lunetta, Patrick T. Ellinor 2018-06-11 2024-01-01 [("doi","10.1038/s41588-018-0133-9")] genetics/heritable

Atrial fibrillation (AF) affects more than 33 million individuals worldwide and has a complex heritability.

We conducted the largest meta-analysis of genome-wide association studies (GWAS) for AF to date, consisting of more than half a million individuals, including 65,446 with AF.

In total, we identified 97 loci statistically-significantly associated with AF, including 67 that were novel in a combined-ancestry analysis, and 3 that were novel in a European-specific analysis.

We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait locus analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci.

The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile, and structural pathways.

These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF.

See Also:

--- https://arxiv.org/abs/2306.00814 Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis Hubert Siuzdak 2023-06-01 2024-09-12 [("doi","10.48550/arXiv.2306.00814")] ai/music ai/nn/gan

Recent advancements in neural vocoding are predominantly driven by Generative Adversarial Networks (GANs) operating in the time-domain. While effective, this approach neglects the inductive bias offered by time-frequency representations, resulting in redundant and computationally-intensive upsampling operations. Fourier-based time-frequency representation is an appealing alternative, aligning more accurately with human auditory perception, and benefiting from well-established fast algorithms for its computation.

Nevertheless, direct reconstruction of complex-valued spectrograms has been historically problematic, primarily due to phase recovery issues. This study seeks to close this gap by presenting Vocos, a new model that directly generates Fourier spectral coefficients.

Vocos not only matches the state-of-the-art in audio quality, as demonstrated in our evaluations, but it also substantially improves computational efficiency, achieving an order of magnitude increase in speed compared to prevailing time-domain neural vocoding approaches.

The source code and model weights have been open-sourced at Github.

--- https://x.com/SafetyChanges/status/1834350937587974611 SafetyChanges 2024-09-13 reinforcement-learning/openai --- https://x.com/mehran__jalali/status/1834299971828679079 Mehran Jalali 2024-09-13 ai/nn/transformer/gpt/4/poetry --- https://www.astralcodexten.com/p/your-book-review-nine-lives Your Book Review: Nine Lives 2024-09-13 crime/terrorism --- https://en.wikipedia.org/wiki/Aimen_Dean Aimen Dean 2024-09-13 crime/terrorism --- https://clagnut.com/blog/2380 List of pangrams 2024-09-13 design/typography psychology/linguistics wikipedia --- https://boingboing.net/2013/02/19/using-silk-road-game-theory.html 2013-02-19 2024-01-01 darknet-market/silk-road/1 --- https://boingboing.net/2012/08/09/make-yourself-healthy-searchi.html Make yourself healthy: Martha Rotter’s search for the cause of acne Seth Roberts 2012-08-09 2024-01-01 genetics/microbiome/acne nootropic/quantified-self --- /doc/genetics/microbiome/acne/gwern-qs-acne-curetogether-gaussian-forest.jpg 2024-01-01 genetics/microbiome/acne nootropic --- /acne#curetogether-acne-interventions 2024-09-13 genetics/microbiome/acne --- https://en.wikipedia.org/wiki/Cutibacterium_acnes Cutibacterium acnes 2024-09-13 genetics/microbiome/acne --- https://www.nytimes.com/2022/01/06/science/skin-pores-bacteria.html Every Pore on Your Face Is a Walled Garden 2022-01-06 2024-01-01 genetics/microbiome/acne --- https://en.wikipedia.org/wiki/Acne Acne 2024-01-01 genetics/microbiome/acne --- https://grillbert.substack.com/p/the-best-antibiotic-for-acne-is-non-060 The Best Antibiotic for Acne is Non-Prescription Bacitracin 2024-09-13 genetics/microbiome/acne --- https://en.wikipedia.org/wiki/Bacitracin Bacitracin 2024-09-13 genetics/microbiome/acne --- https://en.wikipedia.org/wiki/Isotretinoin Isotretinoin 2024-09-13 genetics/microbiome/acne --- https://freakonomics.com/2005/09/seth-roberts-on-acne-guest-blog-pt-iv/ Seth Roberts on Acne: Guest Blog, Pt. IV Seth Roberts 2005-09 2024-09-13 genetics/microbiome/acne --- https://boingboing.net/2019/03/15/digital-lit.html Some pretty impressive machine-learning generated poetry courtesy of GPT-2 2019-03-15 2024-01-01 ai/nn/transformer/gpt/2 --- https://en.wikipedia.org/wiki/Propionibacterium_acnes Propionibacterium acnes 2024-09-13 genetics/microbiome/acne --- https://undark.org/2024/09/11/the-rise-of-the-science-sleuths/ The Rise of the Science Sleuths 2024-09-11 2024-09-13 psychiatry/alzheimers statistics/bias --- https://en.wikipedia.org/wiki/Benzoyl_peroxide Benzoyl peroxide 2024-01-01 genetics/microbiome/acne --- https://en.wikipedia.org/wiki/Cheetah#Taming Cheetah § Taming 2024-09-13 cat/psychology --- https://www.reddit.com/r/slatestarcodex/comments/1fetgzz/links_for_september_2024/lmratq7/ [The hunger-noise monster] 2024-09-13 2024-09-13 longevity/glp/psychology --- https://www.spacejam.com/1996/ Space Jam Homepage 1996 2024-09-13 cs/css cs/linkrot/archiving --- https://sander.ai/2024/09/02/spectral-autoregression.html Diffusion is spectral autoregression Sander Dieleman 2024-09-02 2024-09-13 ai/nn/diffusion --- /doc/technology/2019-middendorf-whateveryengineershouldknowaboutinventing-ch4-theoriesofcreativity.pdf#page=5 What Every Engineer Should Know About Inventing § Chapter 4: Theories of Creativity [wine/printing] William H. Middendorf 2019-01-01 2024-09-13 design/typography technology --- https://mathstodon.xyz/@tao/113132502735585408 I have played a little bit with OpenAI’s new iteration, GPT-4 o1 Terence Tao 2024-09-13 2024-09-13 ai/nn/transformer/gpt/4/nonfiction math --- https://blog.world-mysteries.com/science/mathematics-of-the-past/ Mathematics of the Past [Roman Empire denialism] Garry Kasparov 2012-02-24 2024-09-14 history philosophy/epistemology --- https://www.bloomberg.com/news/features/2024-06-14/why-is-everyone-getting-sick-behind-the-global-rise-in-rsv-flu-measles Why Is Everyone Getting Sick? Behind the Global Rise in RSV, Flu, Measles 2024-06-14 2024-09-14 biology --- https://www.tandfonline.com/doi/full/10.1080/00913367.2024.2393078 2024-09-14 economics/advertising --- https://en.wikipedia.org/wiki/Sound_from_ultrasound Sound from ultrasound 2024-09-14 technology --- https://en.wikipedia.org/wiki/Sound_amplification_by_stimulated_emission_of_radiation Sound amplification by stimulated emission of radiation 2024-09-14 technology --- https://www.youtube.com/watch?v=429QC4Yl-mA&t=1157s What Could Make AI Conscious? Wojciech Zaremba 2024-06-17 reinforcement-learning/openai --- https://www.youtube.com/watch?v=5wl29laSOU4 ‘The Universe is a Glitch’ (AI-driven music video) Mattie Konig 2020-08-23 2024-06-17 ai/nn/transformer/gpt/3/poetry --- https://www.youtube.com/watch?v=79tmPL9AL48 SMASH: One-Shot Model Architecture Search through HyperNetworks [video] 2024-06-17 reinforcement-learning/meta-learning --- https://en.wikipedia.org/wiki/Siegfried_%26_Roy#2003_tiger_incident Siegfried & Roy § 2003 tiger incident 2003 2024-09-12 cat/psychology --- https://en.wikipedia.org/wiki/Erythromycin Erythromycin 2024-09-15 genetics/microbiome/acne --- https://en.wikipedia.org/wiki/Fusidic_acid Fusidic acid 2024-09-15 genetics/microbiome/acne --- /doc/science/1998-galison.pdf Feynman’s War: Modeling Weapons, Modeling Nature Peter Galison 1998-09-01 2024-09-15 [("doi","10.1016/S1355-2198(98)00013-6")] cs/algorithm design/visualization science --- http://bactra.org/notebooks/some-meta-ethical-positions.html Some Unattractive Meta-Ethical Positions, Free to a Good Home 2024-09-15 math/humor philosophy/ethics --- https://arxiv.org/abs/2407.01392 Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion Boyuan Chen, Diego Marti Monso, Yilun Du, Max Simchowitz, Russ Tedrake, Vincent Sitzmann 2024-07-01 2024-09-15 [("doi","10.48550/arXiv.2407.01392")] ai/nn/diffusion ai/nn/vae/mae ai/video/generation reinforcement-learning/model/decision-transformer

[code] This paper presents Diffusion Forcing, a new training paradigm where a diffusion model is trained to denoise a set of tokens with independent per-token noise levels.

We apply Diffusion Forcing to sequence generative modeling by training a causal next-token prediction model to generate one or several future tokens without fully diffusing past ones. Our approach is shown to combine the strengths of next-token prediction models, such as variable-length generation, with the strengths of full-sequence diffusion models, such as the ability to guide sampling to desirable trajectories.

Our method offers a range of additional capabilities, such as (1) rolling-out sequences of continuous tokens, such as video, with lengths past the training horizon, where baselines diverge and (2) new sampling and guiding schemes that uniquely profit from Diffusion Forcing’s variable-horizon and causal architecture, and which lead to marked performance gains in decision-making and planning tasks.

In addition to its empirical success, our method is proven to optimize a variational lower bound on the likelihoods of all subsequences of tokens drawn from the true joint distribution.

Project website: https://boyuan.space/diffusion-forcing.

--- https://www.biorxiv.org/content/10.1101/2024.09.14.613021.full Pervasive findings of directional selection realize the promise of ancient DNA to elucidate human adaptation Ali Akbari, Alison R. Barton, Steven Gazal, Zheng Li, Mohammadreza Kariminejad, Annabel Perry, Yating Zeng, Alissa Mittnik, Nick Patterson, Matthew Mah, Xiang Zhou, Alkes Price, Eric S. Lander, Ron Pinhasi, Nadin Rohland, Swapan Mallick, David Reich 2024-09-15 2024-09-15 [("doi","10.1101/2024.09.14.613021")] exercise genetics/selection/natural/human iq longevity psychiatry/bipolar/genetics psychiatry/schizophrenia

We present a method for detecting evidence of natural selection in ancient DNA time-series data that leverages an opportunity not used in previous scans: testing for a consistent trend in allele frequency change over time.

By applying this to 8,433 West Eurasians who lived over the past 14,000 years and 6,510 contemporary people, we find:

an order of magnitude more genome-wide statistically-significant signals than previous studies: 347 independent loci with >99% probability of selection.

Previous work showed that classic hard sweeps driving advantageous mutations to fixation have been rare over the broad span of human evolution, but in the last 10 millennia, many hundreds of alleles have been affected by strong directional selection.

Discoveries include an increase from ~0% to ~20% in 4,000 years for the major risk factor for celiac disease at HLA-DQB1; a rise from ~0% to ~8% in 6,000 years of blood type B; and fluctuating selection at the TYK2 tuberculosis risk allele rising ~2% → ~9% from ~5,500–3,000 years ago before dropping to ~3%.

We identify instances of coordinated selection on alleles affecting the same trait, with the polygenic score today predictive of body fat percentage decreasing by around a standard deviation over 10 millennia, consistent with the Thrifty Gene hypothesis that a genetic predisposition to store energy during food scarcity became disadvantageous after farming.

We also identify selection for combinations of alleles that are today associated with lighter skin color, lower risk for schizophrenia and bipolar disease, slower health decline, and increased measures related to cognitive performance (scores on intelligence tests, household income, and years of schooling). These traits are measured in modern industrialized societies, so what phenotypes were adaptive in the past is unclear.

We estimate selection coefficients at 9.9 million variants, enabling study of how Darwinian forces couple to allelic effects and shape the genetic architecture of complex traits.

--- https://www.pnas.org/doi/full/10.1073/pnas.0904491106 2024-09-15 economics longevity psychiatry --- https://www.cell.com/cell-genomics/fulltext/S2666-979X(24)00177-0 2024-09-15 genetics/selection/natural --- https://www.medrxiv.org/content/10.1101/2024.09.06.24313170.full The global pattern of centenarians highlights deep problems in demography Saul Justin Newman 2024-09-06 2024-09-16 [("doi","10.1101/2024.09.06.24313170")] longevity statistics/bias statistics/order statistics/survival-analysis

Accurate age data is fundamental to medicine, social sciences, epidemiology, and good government. However, recent and heavily disputed debates on data quality have raised questions on the accuracy of demographic data at older ages.

Here, we catalogue late-life survival patterns of every country in the world from 1970–2021 using comprehensive estimates of old-age populations provided by global governments and curated by the United Nations. Analysis of 236 nations or states across 51 years reveals that late-life survival data is dominated by anomalies at all scales and in all time periods.

Life expectancy at age 100 and late-life survival from ages 80 to 100+, which we term centenarian attainment rate, is highest in a seemingly random assortment of states. The top 10 “blue zone” regions with the best survival to ages 100+ routinely include Thailand, Kenya, and Malawi—respectively now 212th and 202nd in the world for life expectancy, the non-self-governing territory of Western Sahara, and Puerto Rico where birth certificates are so unreliable they were recently declared invalid as a legal document.

These anomalous rankings are conserved across long time periods and multiple non-overlapping cohorts, and do not seem to be sampling effects.

Instead, these patterns suggest a persistent inability, even for nation-states or global organizations, to detect or measure error rates in human age data, with troubling implications for epidemiology, demography, and medicine.

--- https://www.cambridge.org/core/journals/journal-of-economic-history/article/refugees-from-dust-and-shrinking-land-tracking-the-dust-bowl-migrants/75E89BB833B4B266D01FA85DDB4A5A15 Refugees from Dust and Shrinking Land: Tracking the Dust Bowl Migrants 2024-09-16 economics --- https://x.com/TrapitBansal/status/1834645277707583924 TrapitBansal 2024-09-16 ai/nn/transformer/gpt/4/poetry --- https://www.theguardian.com/society/2024/sep/15/ozempic-changed-my-life-do-diabetes-jabs-boost-the-chances-of-conception ‘Ozempic changed my life’: do diabetes jabs boost the chances of conception? 2024-09-15 2024-09-16 longevity/glp/semaglutide --- https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2752818 Association of Neurocognitive and Physical Function With Gait Speed in Midlife Neurology 2024-09-16 exercise iq longevity --- https://www.rockbadgeragency.com/curated/not-quite-past Not Quite Past interview [on Delft ceramic AI generation] 2024-09-16 ai/nn/diffusion/midjourney --- https://x.com/MarkoTervio/status/1835287416900321447 MarkoTervio 2024-09-16 ai/nn/transformer/gpt/4/nonfiction economics --- https://www.quantamagazine.org/cells-across-the-tree-of-life-exchange-text-messages-using-rna-20240916/ Cells Across the Tree of Life Exchange ‘Text Messages’ Using RNA 2024-09-16 2024-09-16 genetics/microbiome --- https://www.science.org/content/blog-post/missing-out-glp-1 Derek Lowe 2024-09-16 longevity/glp/semaglutide --- https://www.newyorker.com/magazine/2024/09/23/francis-kurkdjian-perfume-baccarat-rouge The French Perfumer [Francis Kurkdjian] Behind the Internet’s Favorite Fragrance, Baccarat Rouge 540 2024-09-23 2024-09-16 psychology/smell/perfume --- /doc/politics/1989-fukuyama.pdf The End of History? Francis Fukuyama 1989-06-01 2024-09-16 [("doi","10.2307/24027184")] history politics --- https://arxiv.org/abs/2402.12875 Chain-of-Thought Empowers Transformers to Solve Inherently Serial Problems Zhiyuan Li, Hong Liu, Denny Zhou, Tengyu Ma 2024-02-20 2024-09-16 [("doi","10.48550/arXiv.2402.12875")] ai/nn/transformer/gpt/inner-monologue cs/computable

Instructing the model to generate a sequence of intermediate steps, a.k.a., a chain-of-thought (CoT), is a highly effective method to improve the accuracy of large language models (LLMs) on arithmetics and symbolic reasoning tasks. However, the mechanism behind CoT remains unclear.

This work provides a theoretical understanding of the power of CoT for decoder-only transformers through the lens of expressiveness. Conceptually, CoT empowers the model with the ability to perform inherently serial computation, which is otherwise lacking in transformers, especially when depth is low. Given input length n, previous works have shown that constant-depth transformers with finite precision poly(n) embedding size can only solve problems in TC0 without CoT.

We first show an even tighter expressiveness upper bound for constant-depth transformers with constant-bit precision, which can only solve problems in AC0, a proper subset of TC0. However, with T steps of CoT, constant-depth transformers using constant-bit precision and 𝒪(log n) embedding size can solve any problem solvable by boolean circuits of size T.

Empirically, enabling CoT dramatically improves the accuracy for tasks that are hard for parallel computation, including the composition of permutation groups, iterated squaring, and circuit value problems, especially for low-depth transformers.

--- https://en.wikipedia.org/wiki/Compressed_tea Compressed tea 2024-09-16 economics tea --- https://en.wikipedia.org/wiki/Mecanum_wheel Mecanum omni-directional wheel 2024-09-17 technology --- https://arxiv.org/abs/2107.05720 SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant 2021-07-12 2024-09-17 [("doi","10.48550/arXiv.2107.05720")] ai/nn/retrieval ai/nn/transformer

In neural Information Retrieval, ongoing research is directed towards improving the first retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using efficient approximate nearest neighbors methods has proven to work well. Meanwhile, there has been a growing interest in learning sparse representations for documents and queries, that could inherit from the desirable properties of bag-of-words models such as the exact matching of terms and the efficiency of inverted indexes.

In this work, we present a new first-stage ranker, SPLADE, based on explicit sparsity regularization and a log-saturation effect on term weights, leading to highly sparse representations and competitive results with respect to state-of-the-art dense and sparse methods. Our approach is simple, trained end-to-end in a single stage.

We also explore the trade-off between effectiveness and efficiency, by controlling the contribution of the sparsity regularization.

--- https://clinicaltrials.gov/study/NCT03574597 2024-09-17 longevity/glp/semaglutide --- https://danluu.com/why-benchmark/ Measurement, benchmarking, and data analysis are underrated 2024-09-17 cs/algorithm statistics/decision --- https://www.filfre.net/2024/07/the-later-years-of-douglas-adams/ The Later Years of Douglas Adams 2024-07 2024-09-17 fiction/humor psychology/writing technology/digital-antiquarian --- https://poets.org/poem/so-you-want-be-writer so you want to be a writer? Charles Bukowski 2003 2024-09-17 fiction/poetry psychology/writing --- https://www.newyorker.com/books/under-review/can-rilke-change-your-life Can Rilke Change Your Life? 2024-09-17 fiction/poetry psychology/writing --- https://www.lesswrong.com/posts/B6CxEApaatATzown6/the-lesswrong-2022-review#oJHsGdRSC7EGLLgKn The LessWrong 2022 Review § Cost of Book Production Oliver Habryka 2023-12-15 2024-09-17 design/typography psychology/writing

This number doesn’t seem to make any sense. You suggest making an ebook, and that should be most of the heavy effort handled if you can ever get to a point of reusing a previous year’s printing process. It’s not really clear to me how it can take that much time and/or effort.

Look, I also really thought this. And then we did it 3× and each time it took hundreds (and sometimes over a thousand) hours. I also had my inside-view violated, but I updated towards the outside-view after trying this 3× and each time finding it to be a quite massive endeavor with a lot of details.

[Ben Pace elaboration:]

I’m not certain entirely of the cause of it taking so much work. I will say that meeting the standard of “beautiful, professional book” requires all of the details to be okay. Here’s a quickly-generated list of possible details that can go wrong:

A lot of stuff has to be re-checked every time you make a change (eg. “We’ve reduced the margin between the text and the outside of the page by a quarter of an inch in order to reduce the total number of pages and decrease cost. This means we need to do another visual check of ~1,000 pages to make sure nothing broke.”)

There’s a lot of low-level details that I need to get right so that it correctly fits in the category of ‘beautiful item made with love’ rather than ‘cheap Amazon self-published book’.

I think a book where we spent half the time on the details could end up being quite disappointing on net.

See Also:

--- https://peelarchivesblog.com/2024/09/10/how-do-archivists-package-things-the-battle-of-the-boxes/ How do archivists package things? The battle of the boxes 2024-09-10 2024-09-17 cs/linkrot/archiving --- https://placesjournal.org/article/the-filing-cabinet-and-20th-century-information-infrastructure/ 2024-09-17 cs/linkrot/archiving design --- https://arstechnica.com/information-technology/2024/09/my-dead-father-is-writing-me-notes-again/ My dead father is ‘writing’ me notes again 2024-09 2024-09-17 ai/nn/diffusion design/typography --- https://www.lesswrong.com/posts/2d5o75nmTpLiSP4WL/i-finally-got-chatgpt-to-sound-like-me I finally got ChatGPT to sound like me lsusr 2024-09-17 ai/nn/transformer/gpt/4/nonfiction ai/text-style-transfer reinforcement-learning/preference-learning/mode-collapse --- /doc/genetics/heritable/emergenesis/1982-lykken.pdf EEG spectra in twins: Evidence for a neglected mechanism of genetic determination D. T. Lykken, A. Tellegen, W. G. Iacono 1982-01-01 2024-09-18 [("doi","10.3758/BF03327008")] genetics/heritable/emergenesis psychology/neuroscience

Electroencephalographic (EEG) spectra obtained from monozygotic (MZ) twins show striking within-pair similarity, while spectra of dizygotic (DZ) twins are no more similar than those obtained from pairs of unrelated persons. At least one parameter of the EEG spectrum, the mid-frequency of the alpha rhythm, is strongly correlated (0.8) within pairs of MZ twins and yet its correlation within pairs of DZ twins is about zero.

The frequency composition of the resting EEG may be an example of emergenesis, in which the phenotypic character is determined by the interaction of independently heritable traits and is substantially altered if any genotypic component is changed. Such emergenic traits would not reveal their genetic origin in the usual analysis of pedigrees.

--- http://www.johnperaltafineart.com/recent-works Recent Works [exploded-diagram sculptures] John A. Peralta 2024-09-18 design/visualization technology --- https://dkl9.net/essays/music_experiment.html How harmful is music, really? 2024-09-18 nootropic/quantified-self psychology/music/distraction --- https://entropicthoughts.com/intention-to-treat-experiments Intention-to-Treat Experiments 2024-09-18 nootropic/quantified-self psychology/music/distraction --- https://en.wikipedia.org/wiki/Metformin Metformin 2024-01-01 longevity/metformin --- https://en.wikipedia.org/wiki/Biguanide Biguanide 2024-09-18 longevity/metformin --- https://en.wikipedia.org/wiki/Galega_officinalis Galega officinalis 2024-09-18 longevity/metformin --- /longevity#metformin 2024-01-01 longevity/metformin --- /doc/longevity/metformin/2016-barzilai.pdf Metformin as a Tool to Target Aging Nir Barzilai, Jill P. Crandall, Stephen B. Kritchevsky, Mark A. Espeland 2016-01-01 2024-01-01 [("doi","10.1016/j.cmet.2016.05.011")] longevity/metformin --- /doc/longevity/metformin/2011-bulterijs.pdf Metformin As a Geroprotector Sven Bulterijs 2011-01-01 2024-01-01 longevity/metformin --- https://clinicaltrials.gov/study/NCT02432287 Metformin in Longevity Study (MILES) 2024-01-01 longevity/metformin --- https://www.drugs.com/dosage/metformin.html Metformin Dosage Guide + Max Dose, Adjustments 2024-01-01 longevity/metformin --- https://www.drugs.com/sfx/metformin-side-effects.html Metformin Side Effects: Common, Severe, Long Term 2024-01-01 longevity/metformin --- https://www.mayoclinic.org/drugs-supplements/metformin-oral-route/proper-use/drg-20067074 Metformin (Oral Route) Proper Use 2024-01-01 longevity/metformin --- https://www.medsafe.govt.nz/profs/PUarticles/5.htm Metformin and Fatal Lactic Acidosis 2024-01-01 longevity/metformin --- https://www.nytimes.com/2016/05/15/magazine/warburg-effect-an-old-idea-revived-starve-cancer-to-death.html ‘I think there’s no doubt that insulin is pro-cancer”, Watson says, with respect to the link between obesity, diabetes and cancer. “It’s as good a hypothesis as we have now.’ Watson takes metformin for cancer prevention; among its many effects, metformin works to lower insulin levels. 2024-01-01 exercise longevity/metformin --- http://www.med.mcgill.ca/epidemiology/courses/EPIB654/Summer2010/QALY/The_Economic_Value_of_Health.pdf The Value of Health and Longevity 2024-01-01 economics longevity --- https://en.wikipedia.org/wiki/Lactic_acidosis Lactic acidosis 2024-01-01 longevity/metformin --- https://en.wikipedia.org/wiki/Metformin#Adverse_effects Metformin § Adverse effects 2024-01-01 longevity/metformin --- https://www.medicines.org.uk/emc/medicine/23244/SPC Not found 2024-01-01 longevity/metformin --- https://www.wired.com/story/this-pill-promises-to-extend-life-for-a-nickel-a-pop Forget the Blood of Teens. Metformin Promises to Extend Life for a Nickel a Pill 2024-09-18 longevity/metformin --- https://www.science.org/content/article/feature-man-who-wants-beat-back-aging The man who wants to beat back aging: Nir Barzilai hopes to persuade FDA to bless the proposed anti-aging trial, which is unconventional in its goals and design. 2024-01-01 longevity/metformin --- http://healthspancampaign.org/2015/04/28/dr-nir-barzilai-on-the-tame-study/ Dr. Nir Barzilai on the TAME Study 2024-01-01 longevity/metformin --- https://www.nature.com/articles/522265a Anti-ageing pill pushed as bona fide drug: Regulators asked to consider ageing a treatable condition 2024-01-01 longevity/metformin --- https://www.cochranelibrary.com/doi/10.1002/14651858.CD002966.pub3/epdf/standard 2024-01-01 longevity/metformin --- https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/414585 Cardiovascular Outcomes in Trials of Oral Diabetes Medications: A Systematic Review Acute Coronary Syndromes 2024-01-01 longevity/metformin --- https://animationobsessive.substack.com/p/breaking-away-from-disney-animation#%C2%A7minimalist-movement An Introduction to Limited Animation 2024-09-18 anime --- https://en.wikipedia.org/wiki/Fermi_problem Fermi problem 2024-01-01 science/fermi-problem --- https://www.lesswrong.com/posts/PsEppdvgRisz5xAHG/fermi-estimates Fermi Estimates 2024-01-01 science/fermi-problem --- https://en.wikipedia.org/wiki/Dimensional_analysis Dimensional analysis 2024-01-01 science/fermi-problem --- https://en.wikipedia.org/wiki/Type_system#Type_checking Type system § Type checking 2024-01-01 science/fermi-problem --- https://arxiv.org/abs/1109.1165 Fermi Problem: Power developed at the eruption of the Puyehue-Cordón Caulle volcanic system in June 2011 Hernan Asorey, Arturo López Dávalos 2011-09-06 2024-01-01 [("doi","10.48550/arXiv.1109.1165")] science/fermi-problem

On June 4 2011 the Puyehue-Cordón Caulle volcanic system produced a pyroclastic subplinian eruption reaching level 3 in the volcanic explosivity index. The first stage of the eruption released sand and ashes that affected small towns and cities in the surrounding areas, including San Carlos de Bariloche, in Argentina, one of the largest cities in the North Patagonian andean region.

By treating the eruption as a Fermi problem, we estimated the volume and mass of sand ejected as well as the energy and power released during the eruptive phase. We then put the results in context by comparing the obtained values with everyday quantities, like the load of a cargo truck or the electric power produced in Argentina.

These calculations have been done as a pedagogic exercise, and after evaluation of the hypothesis was done in the classroom, the calculations have been performed by the students. These are students of the first physics course at the Physics and Chemistry Teacher Programs of the Universidad Nacional de Río Negro.

--- https://www.amazon.com/How-Measure-Anything-Intangibles-Business/dp/1118539273/ 2024-01-01 science/fermi-problem --- https://80000hours.org/2013/05/estimation-part-i-how-to-do-it/ Estimation—Part I: How to do it? 2024-01-01 science/fermi-problem --- https://www.amazon.com/Street-Fighting-Mathematics-Educated-Guessing-Opportunistic/dp/026251429X 2024-01-01 science/fermi-problem --- https://en.wikipedia.org/wiki/Douglas_Hofstadter Douglas Hofstadter 2024-01-01 ai philosophy/mind science/fermi-problem --- https://en.wikipedia.org/wiki/Metamagical_Themas Metamagical Themas: Questing for the Essence of Mind and Pattern Douglas Hofstadter 1982 2024-01-01 ai design/typography math philosophy psychology/linguistics --- https://www.reddit.com/r/estimation/ /r/estimation 2024-01-01 science/fermi-problem --- https://what-if.xkcd.com/ What If? Randall Munroe 2024-01-01 science/fermi-problem --- https://en.wikipedia.org/wiki/Timothy_Gowers Timothy Gowers 2024-01-01 math science/fermi-problem --- https://gowers.wordpress.com/2012/06/08/how-should-mathematics-be-taught-to-non-mathematicians/ How should mathematics be taught to non-mathematicians? Timothy Gowers 2024-01-01 math science/fermi-problem --- https://en.wikipedia.org/wiki/Sino-Forest_Corporation#Fraud_allegations Sino-Forest Corporation § Fraud allegations 2024-01-01 economics science/fermi-problem --- https://brontecapital.blogspot.com/2011/09/risk-management-and-sounding-crazy.html Risk management and sounding crazy 2024-01-01 economics science/fermi-problem --- https://www.lesswrong.com/tag/inside-outside-view Inside/Outside View 2024-01-01 science/fermi-problem --- /note/note#somatic-genetic-engineering 2024-01-01 science/fermi-problem --- /note/note#efficient-natural-language 2024-01-01 science/fermi-problem --- /note/note#who-lives-longer-men-or-women 2024-01-01 science/fermi-problem --- /girl-scouts#cookie-prices-and-inflation 2024-01-01 science/fermi-problem --- /modafinil#side-effects 2024-01-01 modafinil science/fermi-problem --- /modafinil#margin-estimation 2024-01-01 modafinil science/fermi-problem --- https://news.ycombinator.com/item?id=3389084 2024-01-01 science/fermi-problem --- https://www.lesswrong.com/posts/etAJHCL7GwPFTFRAv/smbc-comic-poorly-programmed-average-utility-maximizing-ai#nwH8WbPNG63ZtY2hS 2024-01-01 science/fermi-problem --- /note/note#politicians-are-not-unethical Gwern 2024-01-01 science/fermi-problem --- https://www.reddit.com/r/estimation/comments/105okb/request_how_many_times_has_mario_died/ 2024-01-01 science/fermi-problem --- https://www.reddit.com/r/WTF/comments/188zui/this_is_beyond_saddening_tigers_starved_to_make/c8cql4x/ 2024-01-01 science/fermi-problem --- https://slatestarcodex.com/2013/03/03/reactionary-philosophy-in-an-enormous-planet-sized-nutshell/#comment-544 Reactionary Philosophy In An Enormous, Planet-Sized Nutshell 2024-01-01 science/fermi-problem --- https://www.lesswrong.com/posts/PsEppdvgRisz5xAHG/fermi-estimates8pts Fermi Estimates 2024-01-01 science/fermi-problem --- https://www.reddit.com/r/anime/comments/1cqtae/in_honor_of_kuronekos_birthday_my_favorite_scene/c9ja9a5/ Gwern 2024-01-01 science/fermi-problem --- https://www.reddit.com/r/estimation/comments/1f765d/request_in_an_average_lifetime_how_many_of_the/ca85zcl 2024-01-01 science/fermi-problem --- https://en.wikipedia.org/wiki/Benford%27s_law Benford’s law 2024-01-01 crime economics science/fermi-problem statistics/probability --- https://www.lesswrong.com/posts/aARGW967NexuSrCwW/rationality-competitiveness-and-akrasia#ggmadikyQcspwpfA3 2024-01-01 science/fermi-problem --- https://www.reddit.com/r/estimation/comments/27i4wq/request_how_long_would_it_take_to_read_every_book/ci1jfno/ Gwern 2024-01-01 science/fermi-problem --- https://www.reddit.com/r/estimation/comments/2chtft/if_all_the_uss_atomic_bombs_disappeared_assuming/cjfyses/ Gwern 2024-01-01 science/fermi-problem --- https://www.reddit.com/r/estimation/comments/4f9bxl/what_are_the_lifetime_odds_of_being_pooped_on_by/ Gwern 2024-01-01 science/fermi-problem --- https://en.wikipedia.org/wiki/Drake_equation Drake equation 2024-01-01 science/fermi-problem --- https://nunosempere.com/blog/2022/08/20/fermi-introduction/ Introduction to Fermi estimates 2022-08-20 2024-09-18 science/fermi-problem --- https://en.wikipedia.org/wiki/Log-normal_distribution Log-normal distribution 2024-01-01 science/fermi-problem statistics/probability --- /doc/math/1982-hofstadter-2.pdf On Number Numbness Douglas Hofstadter 1982-05-01 2024-09-18 economics math science/fermi-problem --- https://forum.effectivealtruism.org/posts/XDwnGK7x4EjkaHbje/the-estimation-game-a-monthly-fermi-estimation-web-app The Estimation Game: a monthly Fermi estimation web app 2024-09-18 science/fermi-problem --- https://forum.effectivealtruism.org/posts/W6gGKCm6yEXRW5nJu/quantified-intuitions-an-epistemics-training-website Quantified Intuitions: An epistemics training website including a new EA-themed calibration app 2024-09-18 science/fermi-problem --- https://philosophybear.substack.com/p/some-curated-poems-by-gpt-3 Some curated poems by GPT-3 2024-09-19 ai/nn/transformer/gpt/3/poetry --- https://en.wikipedia.org/wiki/My_Little_Pony:_Friendship_Is_Magic My Little Pony: Friendship Is Magic 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/My_Little_Pony:_Friendship_Is_Magic_(season_9) My Little Pony: Friendship Is Magic (season 9) 2024-01-01 anime/my-little-pony --- /doc/ai/music/2020-04-01-fifteenai-twilightsparkle-telephonecall.mp3 2020-04-01 2024-01-01 ai/music anime/my-little-pony --- /doc/ai/music/2020-03-28-fifteenai-ensemble-hellofellowhumans.mp3 2020-03-28 2024-01-01 ai/music anime/my-little-pony --- /doc/ai/music/2020-03-30-fifteenai-twilightsparkle-sel-presentdaypresenttime.mp3 2020-03-30 2024-01-01 ai/music anime/my-little-pony --- /doc/ai/music/2020-03-06-fifteenai-fluttershy-sithcode.mp3 2020-03-06 2024-01-01 ai/music anime/my-little-pony --- /doc/ai/music/2020-03-06-fifteenai-twilightsparkle-sithcode.mp3 2020-03-06 2024-01-01 ai/music anime/my-little-pony --- https://purplesmart.ai/ Expanding the frontiers of AI creativity 2024-01-01 anime/my-little-pony --- https://www.youtube.com/watch?v=rkEMytW5bG0 StealingShad3z—Changes (feat GhostXB) 2024-06-17 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/track/starlight-sanctuary Starlight Sanctuary RoomVR & Zephysonas 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/skyward Skyward 2024-01-01 anime/my-little-pony --- https://www.youtube.com/watch?v=TMhsfA6FUPI Aphelion 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=D5S3rxp5MSw J. Burgess—Do Bat Ponies Have Souls? 2024-06-17 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/track/rituals-and-dances-of-the-pegasi-feat-koron-korak Rituals And Dances Of The Pegasi (feat. Koron Korak) 2024-01-01 anime/my-little-pony --- https://www.youtube.com/watch?v=eG8PjX7CX6w 478,000 Miles (Shining Forth) [Electro House] 2024-06-17 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/rebirth Rebirth 2024-01-01 anime/my-little-pony --- https://www.youtube.com/watch?v=DcN6BlTpFYE Scraton—My Legacy For You 2024-06-17 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/eternal Eternal 2024-01-01 anime/my-little-pony --- https://www.youtube.com/watch?v=HZ4HdhKJITI 4everfreebrony—Who Knows (feat. Milkymomo, EileMonty, & MemJ0123) 2024-06-17 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/echoes Echoes 2024-01-01 anime/my-little-pony --- https://www.youtube.com/watch?v=M_sViQ2rHnQ Love And Reflection 2024-06-17 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/dreamwalkerstrack/fire-city-day-night 2024-01-01 anime/my-little-pony --- https://reactormag.com/equoid/ 2024-01-01 anime/my-little-pony --- https://voltpon3.bandcamp.com/track/smile-song-chiptune Smile Song Chiptune 2024-01-01 anime/my-little-pony --- https://voltpon3.bandcamp.com/track/love-is-in-bloom-chiptune Love Is In Bloom Chiptune 2024-01-01 anime/my-little-pony --- https://voltpon3.bandcamp.com/track/winter-wrap-up-remix Winter Wrap Up Remix 2024-01-01 anime/my-little-pony --- https://voltpon3.bandcamp.com/track/art-of-the-dress-chiptune Art Of The Dress Chiptune 2024-01-01 anime/my-little-pony --- http://lists.urth.net/pipermail/urth-urth.net/2018-July/026842.html 2002 interview with Gene Wolfe on Neil Gaiman 2024-01-01 fiction/gene-wolfe --- https://web.archive.org/web/20200211051856/http://cardcaptor.moekaku.com/?p=112 2024-01-01 anime touhou --- https://web.archive.org/web/20200211051856/http://cardcaptor.moekaku.com/?p=112#convergence-culture-2 2024-01-01 anime touhou --- https://web.archive.org/web/20200211051856/http://cardcaptor.moekaku.com/?p=112#genyou-denshou-2 2024-01-01 anime touhou --- https://web.archive.org/web/20200211051856/http://cardcaptor.moekaku.com/?p=112#meiji-university-lecture 2024-01-01 anime touhou --- https://4everfreebrony.bandcamp.com/album/the-pink-side-of-the-moon-remastered The Pink Side of The Moon (Remastered) 2024-01-01 anime/my-little-pony --- https://animeanime.jp/article/2012/04/05/9805.html My Little Pony and ‘the American moe’? 2024-01-01 anime/my-little-pony --- https://astateofsugar.bandcamp.com/album/doughnut Doughnut 2024-01-01 anime/my-little-pony --- https://astateofsugar.bandcamp.com/album/doughnutalbum/lollipop A State of Sugar 2024-01-01 anime/my-little-pony --- https://blackgryph0n.bandcamp.com/album/immortal IMmortal 2024-01-01 anime/my-little-pony --- https://comicsalliance.com/ask-chris-45-my-little-pony-meets-the-justice-league/ Ask Chris #45: My Little Pony Meets the Justice League Chris Sims 2011-02-11 2024-01-01 anime/my-little-pony --- https://counter-currents.com/2014/01/my-nationalist-pony-an-interview-with-buttercup-dew/ My Nationalist Pony: An Interview with Buttercup Dew 2024-01-01 anime/my-little-pony --- https://creberbrown.bandcamp.com/album/Getting-Stronger Getting Stronger [album] Michelle Creber, Gabriel Brown, Baasik 2016 2024-01-01 anime/my-little-pony --- https://derpibooru.org/123281 #123281 - safe, screen-cap, princess Celestia, pony, g4, the cutie mark chronicles, animated, female, raising the sun, solo, summer sun celebration 2024-01-01 anime/my-little-pony --- https://derpibooru.org/1707469 S8E5: Grannies Gone Wild: screenshot 2024-01-01 anime/my-little-pony --- https://dnd.wizards.com/articles/news/dungeons-dragons-teams-my-little-pony Dungeons & Dragons teams up with My Little Pony Chris Dupuis 2016-08-02 2024-01-01 anime/my-little-pony fiction/text-game --- https://en.wikipedia.org/wiki/Applejack_(drink) Applejack (drink) 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/Daniel_Ingram_(composer) Daniel Ingram (composer) 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/Gesamtkunstwerk Gesamtkunstwerk 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/Homestuck Homestuck 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/Lauren_Faust Lauren Faust 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/Ligne_claire Ligne claire 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/My_Little_Pony:_Equestria_Girls My Little Pony: Equestria Girls 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/My_Little_Pony:_Friendship_Is_Magic_(season_1) My Little Pony: Friendship Is Magic (season 1) 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/My_Little_Pony:_Friendship_Is_Magic_(season_2) My Little Pony: Friendship Is Magic (season 2) 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/My_Little_Pony:_Friendship_Is_Magic_(season_3) My Little Pony: Friendship Is Magic (season 3) 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/My_Little_Pony:_Friendship_Is_Magic_(season_4) My Little Pony: Friendship Is Magic (season 4) 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/My_Little_Pony:_Friendship_Is_Magic_(season_5) My Little Pony: Friendship Is Magic (season 5) 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/My_Little_Pony:_Friendship_Is_Magic_(season_6) My Little Pony: Friendship Is Magic (season 6) 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/My_Little_Pony:_Friendship_Is_Magic_(season_7) My Little Pony: Friendship Is Magic (season 7) 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/My_Little_Pony:_Friendship_Is_Magic_(season_8) My Little Pony: Friendship Is Magic (season 8) 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/New_sincerity New sincerity 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/Sia Sia 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/Social_Stories Social Stories 2024-01-01 anime/my-little-pony --- https://en.wikipedia.org/wiki/The_Powerpuff_Girls The Powerpuff Girls 2024-01-01 anime/my-little-pony --- https://eveningstarmusic.bandcamp.com/music 2024-01-01 anime/my-little-pony --- https://eveningstarmusic.bandcamp.com/track/a-dying-world A Dying World 2024-01-01 anime/my-little-pony --- https://eveningstarmusic.bandcamp.com/track/a-united-land A United Land 2024-01-01 anime/my-little-pony --- https://eveningstarmusic.bandcamp.com/track/ive-got-to-find-a-way-evening-star-dnb-remix I’ve Got to Find a Way (Evening Star DnB Remix) 2024-01-01 anime/my-little-pony --- https://falloutequestria.fandom.com/wiki/Fallout:_Equestria Fallout: Equestria 2024-01-01 anime/my-little-pony --- https://fansconference.org/dRuZ33A/wp-content/uploads/2018/03/7-Self-Reported-Mood-Disorders.pdf 2024-01-01 anime/my-little-pony --- https://garrisonulrich.bandcamp.com/album/small-horses-and-other-stuff Small Horses and Other Stuff 2024-01-01 anime/my-little-pony --- https://garrisonulrich.bandcamp.com/track/under-the-sun Under The Sun 2024-01-01 anime/my-little-pony --- https://geekreply.com/geek-culture/2017/12/19/little-pony-seasons-89-information-leaked My Little Pony Seasons 8–9 and G5 Info Leaked 2024-01-01 anime/my-little-pony --- https://imgur.com/a/ecZPp MLP Bible - 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https://poniesatdawn.bandcamp.com/album/dreamwalkerstrack/journey-through-the-unknown-wonders-of-equestria 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/dreamwalkerstrack/legacy-of-harmony 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/dreamwalkerstrack/let-the-sun-rise-feat-itchigotchi 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/dreamwalkerstrack/let-the-sun-rise-neighsayer-remix 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/dreamwalkerstrack/nebula-neverlaststanding-remix-2 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/dreamwalkerstrack/new-king-feat-chi-chi 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/dreamwalkerstrack/no-longer-feat-francis-vace 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/dreamwalkerstrack/peace-at-last 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/dreamwalkerstrack/sweet-feat-pegasys 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/dreamwalkerstrack/the-stylish-sleuth-feat-osoch-themusicreborn-mane-in-green 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/dreamwalkerstrack/unity 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/enigma Enigma 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/guardians Guardians 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/ignite Ignite 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/recollections Recollections 2024-01-01 anime/my-little-pony --- https://poniesatdawn.bandcamp.com/album/snowfall Snowfall 2024-01-01 anime/my-little-pony --- https://ponyphonic.bandcamp.com/ 2024-01-01 anime/my-little-pony --- https://ponyphonic.bandcamp.com/track/applejack Applejack 2024-01-01 anime/my-little-pony --- https://shibacrew.bandcamp.com/album/live-another-life Live Another Life 2024-01-01 anime/my-little-pony --- https://slatestarcodex.com/2016/07/25/how-the-west-was-won/ How The West Was Won 2024-01-01 anime/my-little-pony --- https://thepiratebay.org/description.php?id=14045031 2024-01-01 anime/my-little-pony --- https://thepiratebay.org/description.php?id=18368760 2024-01-01 anime/my-little-pony --- https://tvtropes.org/pmwiki/pmwiki.php/FanFic/TheMLPLoops The MLP Loops (Fanfic) 2024-01-01 anime/my-little-pony fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Franchise/LyricalNanoha Lyrical Nanoha (Franchise) 2024-01-01 anime/my-little-pony fiction --- https://tvtropes.org/pmwiki/pmwiki.php/JustForFun/ComeForTheXStayForTheY Come for the X, Stay for the Y 2024-01-01 anime/my-little-pony fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Main/AscendedExtra Ascended Extra 2024-01-01 anime/my-little-pony fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Main/AscendedFanon Ascended Fanon 2024-01-01 anime/my-little-pony fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Main/CrypticBackgroundReference Cryptic Background Reference 2024-01-01 anime/my-little-pony fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Main/FanficFuel Fanfic Fuel 2024-01-01 anime/my-little-pony fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Main/IdiotBall Idiot Ball 2024-01-01 anime/my-little-pony fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Main/IGotBigger I Got Bigger 2024-01-01 anime/my-little-pony fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Main/Iyashikei Iyashikei 2024-01-01 anime/my-little-pony fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Main/LampshadeHanging Lampshade Hanging 2024-01-01 anime/my-little-pony fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Main/NoodleIncident Noodle Incident 2024-01-01 anime/my-little-pony fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Main/RunningTheAsylum Running the Asylum 2024-01-01 anime/my-little-pony fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Main/SpaceWhaleAesop Space Whale Aesop 2024-01-01 anime/my-little-pony fiction --- https://tvtropes.org/pmwiki/pmwiki.php/WesternAnimation/MyLittlePonyFriendshipIsMagic My Little Pony: Friendship Is Magic (Western Animation) 2024-01-01 anime/my-little-pony fiction --- https://voltpon3.bandcamp.com/album/chiptune-pony Chiptune Pony 2024-01-01 anime/my-little-pony --- https://voxday.blogspot.com/2016/03/rabid-puppies-2016-list.html Rabid Puppies 2016: the list 2024-01-01 anime/my-little-pony --- https://web.archive.org/web/20200209190454/http://www.bronibooru.com/posts/40230 2024-01-01 anime/my-little-pony --- https://www.deviantart.com/cocoanutcakery/art/MLP-Pony-Genetics-267870595 Pony Genetics by CocoaNutCakery 2024-01-01 anime/my-little-pony --- https://www.deviantart.com/grim-s-morrison/art/Biology-is-Magic-279676444 Biology is Magic 2024-01-01 anime/my-little-pony --- https://www.deviantart.com/tadstone/art/My-Little-Pony-Genetics-287664115 My Little Pony: Genetics by TadStone 2024-01-01 anime/my-little-pony --- https://www.deviantart.com/theaceofspadez/art/The-Genetics-of-the-Pony-271328436 The Genetics of the Pony 2024-01-01 anime/my-little-pony --- https://www.deviantart.com/tootootaloo/art/Celestia-Vector-212081002 2024-01-01 anime/my-little-pony --- https://www.enworld.org/threads/dungeons-ponies-at-last.664106/ Dungeons & Ponies At Last 2024-01-01 anime/my-little-pony --- https://www.equestriadaily.com/2011/09/massive-jayson-thiessen-q-from-bronycon.html Massive Jayson Thiessen Q&A From Bronycon 2024-01-01 anime/my-little-pony --- https://www.equestriadaily.com/search/label/Music ED: Music Equestria Daily 2024-01-01 anime/my-little-pony --- https://www.fanfiction.net/s/5782108/64/Harry_Potter_and_the_Methods_of_Rationality 2024-01-01 anime/my-little-pony --- https://www.fimfiction.net/story/62074/Friendship-is-Optimal Friendship is Optimal 2024-01-01 anime/my-little-pony --- https://www.fimfiction.net/story/69770/friendship-is-optimal-caelum-est-conterrens Friendship Is Optimal: Caelum Est Conterrens 2024-01-01 anime/my-little-pony --- https://www.fimfiction.net/story/98568/mlp-time-loops MLP Time Loops 2024-01-01 anime/my-little-pony --- https://www.firstthings.com/article/2019/12/notes-on-summer-camp Notes on Summer Camp by Elizabeth C. Corey 2024-01-01 anime/my-little-pony --- https://www.lesswrong.com/posts/94tpfAqPesQRrahmY/the-friendship-is-witchcraft-expectation-test#J5XtyevXK7GJaA3YF The "Friendship is Witchcraft" expectation test 2024-01-01 anime/my-little-pony --- https://www.overthinkingit.com/2011/02/24/my-little-pony-political-economy/ Solidarity is Illusion: The Political Economy of My Little Pony: Friendship is Magic: MLP propagates the illusion that an egalitarian society can be maintained among groups with massive biologically inherent gaps in ability and economic utility 2024-01-01 anime/my-little-pony --- https://www.overthinkingit.com/2012/11/08/my-little-pony-plato/ My Little Republic: Plato is Magic; Socrates, first among Bronies 2024-01-01 anime/my-little-pony --- https://www.reddit.com/r/MLPtunes/ 2024-01-01 anime/my-little-pony --- https://www.reddit.com/r/mylittlepony/comments/1k1t8k/i_solved_the_genetics_of_pony_typesraces_this/ 2024-01-01 anime/my-little-pony --- https://www.reddit.com/r/mylittlepony/comments/3612bw/theory_why_the_cake_twins_are_not_earth_ponies/ 2024-01-01 anime/my-little-pony --- https://www.reddit.com/r/mylittlepony/comments/rq9ht/statistical_probability_of_the_cake_twins_having/ 2024-01-01 anime/my-little-pony --- https://www.reddit.com/r/mylittlepony/wiki/episode_list/ 2024-01-01 anime/my-little-pony --- https://www.reddit.com/r/TheMotte/comments/l4ii8x/culture_war_roundup_for_the_week_of_january_25/gljlvsl/ 2024-01-01 anime/my-little-pony --- https://www.reddit.com/r/TheMotte/comments/p27bfu/wellness_wednesday_for_august_11_2021/h8io2k0/ 2024-01-01 anime/my-little-pony --- https://www.reddit.com/r/Tulpas/ 2024-01-01 anime/my-little-pony --- https://www.wired.com/2011/06/bronies-my-little-ponys/ My Little Pony Corrals Unlikely Fanboys Known as ‘Bronies’: Each day, out-of-work computer programmer Luke Allen self-medicates by watching animated ponies have magical adventures. The 32-year-old, who lives in Albuquerque, New Mexico, loves his daily fix of My Little Pony Friendship Is Magic, and he’s not alone. He’s part of a growing group of ‘bronies” (“bro ponies’)—men who are fans of a TV show largely intended for a much younger audience. 2024-01-01 anime/my-little-pony --- https://www.youtube.com/channel/UCeNwyKuv5SMnN6ovlpbz1SQ SoaringFlight 2024-06-17 anime/my-little-pony --- https://www.youtube.com/channel/UCJRn6cjJIfYRiJ2TLUQeoXA MelodicPony 2024-06-17 anime/my-little-pony --- https://www.youtube.com/channel/UCOYSvhrljWSCUw3grZMv_aw Spectra 2024-06-17 anime/my-little-pony --- https://www.youtube.com/channel/UCWz_LrWgtrU_zKET8c2gzeg Morgsch 2024-06-17 anime/my-little-pony --- https://www.youtube.com/user/4everfreebrony 4everfreebrony 2024-06-17 anime/my-little-pony --- https://www.youtube.com/user/ponyeveningstar Evening Star 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=00L5_Lcnqho Second Hoof Medley 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=0-4-vzXYr0E Weather Patrol [MLP] 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=0o01hsgwWho Off To See The World 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=0WCbSo5cqMw Hearts and Hooves Day song—The Perfect Stallion 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=1b6vFMkMQTE Celestia’s Ballad 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=2kIH1aRW4DQ Wandering (Sweet Apple Acres) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=4fVOF2PiHnc The Last Bronycon: a fandom autopsy 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=52cuuWHNWIU 4everfreebrony—Giggles & Gumdrops (re-recorded) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=5-r-dY0mzdQ What Remains 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=5tCPxXW_9qI A True, True Friend—MLP FiM Song 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=6-I3CpbkuFc The Pony I Want to Be 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=6pp0kdfFzM0 Getting Stronger Michelle Creber, Black Gryph0n, Baasik 2016 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=8-H2oQ_ihIQ&t=0s Undefined Blues Jeff Burgess 2014 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=8JJg9114bgs Faster Than You Know 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=8-TvjOfQI2w Land of Equestria (The Orchestral Anthem) Evening Star 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=9jqs-5S5joU SIA—Rainbow Drum Guitar Violins Cover 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=ABfYWV30Xas Shy Heart 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=aK258dwWPgY SoaringFlight—Above Cloudsdale (DJChZ remix) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=ApDAJJONn9I My Little Pony: Friendship was magic 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=b1Vjorjlon4 MLP:FiM Music Pinkie Pie—Smile Song 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=BagSbopRDw8 Train Bells 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=BzWmZvYDbyE Another Way 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=cEJ8CzAD4TE 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=CFOwIOJt4KI Mare Cognitum (feat. Velvet R. Wings) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=CmAGDLUqgW0 Aurelleah & Kadenza—Firelight (feat. Pegasys) [Melodic Electronic/Happy House] 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=CU66HngI8bg Live Another Life (ft. ChisanaAI) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=CuvR4jR_j6E Etherium Apex—Pray to the Sun (Ponified Cover of "I Can Only Imagine" by Mercyme) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=CwTqjDhxYq0 Becoming Popular (The pony everypony should know) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=dbXYfo_Bgbo Pinkie Pie’s Wonderbolt Rap HD 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=dM0d9uUgVSc SIA—Rainbow (From My Little Pony The Movie) PMV 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=Fiyus4JwNg8 True True Friend Winter Wrap-Up (Ultimate Mash-Up) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=fphUZV7dKUw You’ll Play Your Part 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=fSbXGsysAAk Rarity’s song: Art of the Dress 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=fxoUABlvCMQ The Quest For Kindness (Feat. Metajoker) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=g2PCNKlddJY 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=gaHzrCMWlwY II. The Journey [The Quest of the Lost Sapphire—Ep. 2] 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=geogRhkrsco Roar (Fluttershy Cover) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=gJ-bLIjz1jY Etherium Apex—Second Prances (Vocal VIP) ft. Nicole Carino 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=H91QXNr41FM Crepuscularity (Twilight Sparkle’s Theme) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=hAXX_bPxIzY Cafeteria Song (Helping Twilight Win The Crown) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=HQdD-OgFKxQ Luck of the Rose ~ (Original MLP fan song) ~ Double Cleff 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=i77uMxYlbpY Hope Shines Eternal 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=iBjBl0CKpjQ Frozen Night—Zenith (feat. Velvet R. Wings) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=iyWjKQo9-m8 TheFatRat—Never Be Alone 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=jXTYkuEWlVM Jyc Row—Onward (feat. Aloma Steele) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=_J-zJQJPVno 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=Kg7x1wU6XCo MLP: Equestria Girls—Legend of Everfree SONG—"The Legend of Everfree" 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=ko0fT6CEWag John Kenza—Heartfire 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=kYKNfeAF8GY The Place Where We Belong (Faulty Remix) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=l5PMOflXHGc Sunshine and Celery Stalks Lyrics 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=-L6z4oYm9lU Find A Way (lo-fi hip hop remix) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=L9BAeyZhAdE Winter Wrap Up 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=llh775Qz-x4 Winter Wrap Up (Symphonic Metal Cover) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=Mb8rEWq3F8E PrinceWhateverer—Twilight Learned to Fly [MLP ANIMATIC] 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=NBkEfIzu_Ig Wandering Eyes 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=NYaQa0TpUNc SoaringFlight—Above Cloudsdale 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=OtRyt_5RMGs 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=-ouNZTKhZbg I Wanna Belong 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=PBlg-mbCVB0 Age of Vinyl—Heroes of the Sky [Liquid Drum & Bass] 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=qiFSJqzcHjo Jyc Row—The Daring Explorer 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=r3ZFeoqvWrM Raise The Sun (Original by Forest Rain) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=Rc2ArHyv_KM The Essence of being a Pony 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=rg-P8zMlThk Synthwave Lullaby 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=rOsHR7x9eTA Carbon Maestro—Equiterian Empire (Celestial Divide OST) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=SB1kCmFRDM0 Days Gone By Auld Lang Syne 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=sGD6JaKoVAY 4everfreebrony—Not Much to Miss (feat. EileMonty) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=ShhhkTgiM4I MLP:FiM—Winter Wrap Up (Orchestral / Instrumental) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=taAJ0NACREU Synthis—Heir Of The Moonlight (EnsionD Remix) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=TBFv8cAO6vw 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=tEsDbLYym-w The Wasteland Wailers—Spun 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=T_QNZVqJQek The Pony I Want to Be MLP 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=U5anlucc5Ac Discord’s Glass of Water Song 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=u5wI6QVCjUU Landscapes 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=uHcKCOrECs8 Music Winter Wrap Up My Little Pony 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=UHj-prkyex0 A Kirin Tale (Song)—MLP: Friendship Is Magic 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=Uj852XOZyEA The Storm Is Coming VIP 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=uKRdoCVqTqs In Our Town My Little Pony 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=_umhQJPUnWk One Summer’s Day Evening Star 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=UNYpgTG_OeY Touhou anime: Summer Day’s Dream (Episodes 1, 2, 2.5 And 3 English Subbed) 2024-06-17 touhou --- https://www.youtube.com/watch?v=UT4QB20OsW8 Step! Buck! Leap! Touch! [Inspired by a William Anderson’s theme : A Dance-aster] 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=uVLKuClm1nM break the cycle / sleepless nights (w/ Fetlocked) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=u-ZCzGfxOVc Scraps—Alone 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=v8bY3pwwln4 Winter Wrap Up (Feint Remix) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=w4iTuhNx--Q Evening Star—First Flight 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=whl91kKmu5w Babs Seed—MLP FiM—The CMC (song+lyrics) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=WlfbDKX8NxE I’ll Fly 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=wUGjCTh5NY4 4everfreebrony—Chant of Benevolence (ft. Chi-Chi) 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=WvDnMjPPHCI MLP FiM "Hearts Strong as Horses" song with Reprise 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=XDD_ZAeFWOI BlackGryph0n & Baasik—Moonlight 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=xLgfYnS3oHE Going Far Jyc Row, PegasYs, Michelle Creber 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=z3vhme5Z7AQ Your Kindness 2024-06-17 anime/my-little-pony --- https://www.youtube.com/watch?v=zUM3XJQnIEs Spearmint Viricide Filly 2024-06-17 anime/my-little-pony --- https://x.com/dismantlemars/status/978776491432521728 2024-01-01 anime/my-little-pony --- https://yayponies.no/ 2024-01-01 anime/my-little-pony --- http://www.dobuusagi.com/ dBu music 2024-01-01 anime/my-little-pony --- https://x.com/Scholars_Stage/status/1836113904101212377 Scholars_Stage 2024-09-18 psychology/writing --- https://www.astralcodexten.com/p/book-review-sadly-porn Book Review: Sadly, Porn Scott Alexander 2022-02-16 2024-09-18 psychiatry --- https://en.wikipedia.org/wiki/-ussy ‘-ussy’ 2024-09-19 fiction/humor psychology/linguistics --- https://en.wikipedia.org/wiki/2024_Lebanon_pager_explosions 2024 Lebanon pager explosions 2024 2024-09-19 cs/security --- https://x.com/goodside/status/1836666268767633639 Riley Goodside 2024-09-19 ai/nn/tokenization --- https://www.jakepoz.com/debugging-behind-the-iron-curtain/ Debugging Behind the Iron Curtain [Chernobyl radiation coverup] Jake Poznanski 2010-08-19 2024-09-19 cs/hardware radiance --- https://blog.google/technology/ai/google-datagemma-ai-llm/ DataGemma: AI open models connecting LLMs to Google’s Data Commons 2024-09-19 ai/dataset --- https://en.wikipedia.org/wiki/Yahya_Ayyash#Assassination Yahya Ayyash § Assassination by cellphone 2024-09-19 cs/security --- https://www.commonsensemedia.org/kids-action/articles/conversations-help-young-people-navigate-ais-complexities [Commonsense Media survey on US generative media use] 2024-09-19 ai/nn --- https://github.com/sirupsen/napkin-math napkin-math: Techniques and numbers for estimating system’s performance from first-principles sirupsen 2024-09-19 cs/hardware science/fermi-problem --- https://www.youtube.com/watch?v=nsJGJHkJolI&t=533s Elephant Rifle Annihilates Ballistic Gel at 82,000FPS § combustion by compression Slo Mo Guys 2023-07-03 2024-09-19 technology --- https://x.com/ElytraMithra/status/1793916830987550772 ElytraMithra 2024-09-19 ai/nn/transformer/gpt/claude ai/text-style-transfer --- https://risk-engineering.org/concept/Rasmussen-practical-drift ‘Rasmussen and practical drift: Drift towards danger and the normalization of deviance’, 2017 2024-01-01 economics/automation reinforcement-learning/safe sociology/technology --- https://thesocietypages.org/socimages/2011/04/02/the-magic-washing-machine/ The Magic Washing Machine 2011-04-02 2024-09-19 economics sociology/technology --- https://arxiv.org/abs/2407.20311 Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process Tian Ye, Zicheng Xu, Yuanzhi Li, Zeyuan Allen-Zhu 2024-07-29 2024-09-19 [("doi","10.48550/arXiv.2407.20311")] ai/nn/transformer/gpt/2 ai/nn/transformer/gpt/inner-monologue ai/scaling math

Recent advances in language models have demonstrated their capability to solve mathematical reasoning problems, achieving near-perfect accuracy on grade-school level math benchmarks like GSM8K. In this paper, we formally study how language models solve these problems.

We design a series of controlled experiments to address several fundamental questions: (1) Can language models truly develop reasoning skills, or do they simply memorize templates? (2) What is the model’s hidden (mental) reasoning process? [non-myopic] (3) Do models solve math questions using skills similar to or different from humans? (4) Do models trained on GSM8K-like datasets develop reasoning skills beyond those necessary for solving GSM8K problems? [yes] (5) What mental process causes models to make reasoning mistakes? (6) How large or deep must a model be to effectively solve GSM8K-level math questions? [depth > width]

Our study uncovers many hidden mechanisms by which language models solve mathematical questions, providing insights that extend beyond current understandings of LLMs.

--- https://en.wikipedia.org/wiki/1974_Nobel_Prize_in_Literature 1974 Nobel Prize in Literature 1974 2024-09-19 fiction/poetry fiction/science-fiction --- https://en.wikipedia.org/wiki/Aniara_(film) Aniara (film) 2024-01-01 fiction/science-fiction --- https://arxiv.org/abs/2409.12183 To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, Dongwei Jiang, Manya Wadhwa, Prasann Singhal, Xinyu Zhao, Xi Ye, Kyle Mahowald, Greg Durrett 2024-09-18 2024-09-19 [("doi","10.48550/arXiv.2409.12183")] ai/nn/transformer/gpt/inner-monologue math

Chain-of-thought (CoT) via prompting is the de facto method for eliciting reasoning capabilities from large language models (LLMs). But for what kinds of tasks is this extra “thinking” really helpful?

To analyze this, we conducted a quantitative meta-analysis covering over 100 papers using CoT and ran our own evaluations of 20 datasets across 14 models. Our results show that CoT gives strong performance benefits primarily on tasks involving math or logic, with much smaller gains on other types of tasks.

On MMLU, directly generating the answer without CoT leads to almost identical accuracy as CoT unless the question or model’s response contains an equals sign, indicating symbolic operations and reasoning.

Following this finding, we analyze the behavior of CoT on these problems by separating planning and execution and comparing against tool-augmented LLMs. Much of CoT’s gain comes from improving symbolic execution, but it underperforms relative to using a symbolic solver.

Our results indicate that CoT can be applied selectively, maintaining performance while saving inference costs. Furthermore, they suggest a need to move beyond prompt-based CoT to new paradigms that better leverage intermediate computation across the whole range of LLM applications.

--- https://quoteinvestigator.com/2015/09/18/typing/ 2024-09-19 psychology/writing --- https://en.wikipedia.org/wiki/Michael_S._Hart#Writing_style Michael S. Hart § Hyphen-free writing style 2024-09-19 design/typography --- https://news.ycombinator.com/item?id=21046731 I guess as they’ve gone bust I can tell this story now. I used to consult for Thomas Anderson… 2024-09-20 economics/advertising --- https://en.wikipedia.org/wiki/Werner_Forssmann Werner Forssmann [cardiac catheterization self-experiment] 2024-09-20 nootropic/quantified-self --- https://x.com/michael_nielsen/status/1834612279674798558 [‘Fourier components’-style literary criticism by GPT-4 o1] Michael Nielsen 2024-09-20 ai/nn/transformer/gpt/4/nonfiction fiction/criticism --- /doc/psychology/writing/1990-johnson.pdf ‘On The Edge Of An Abyss’: The Writer As Insomniac Greg Johnson 1990-09-01 2024-09-20 [("doi","10.2307/26437923")] psychology/writing zeo --- https://www.cnbc.com/2024/09/20/constellation-energy-to-restart-three-mile-island-and-sell-the-power-to-microsoft.html Constellation Energy to restart Three Mile Island nuclear power plant & sell all power to Microsoft for datacenter 2024-09-20 2024-09-20 ai/scaling/economics --- https://chatgpt.com/share/66ed1706-76f4-800d-963c-e736f50243bd [ChatGPT-4o-mini solves the S-poem using ‘spare tokens’] 2024-09-20 ai/nn/transformer/gpt/4/poetry --- https://www.lesswrong.com/posts/CDZDjWdDrR3fn3aeL/a-new-class-of-glitch-tokens-bpe-subtoken-artifacts-bsa A New Class of Glitch Tokens: BPE Sub-token Artifacts 2024-09-20 2024-09-20 ai/nn/tokenization --- /doc/ai/nn/transformer/gpt/3/nonfiction/2024-banker.pdf Machine-Assisted Social Psychology Hypothesis Generation Sachin Banker, Promothesh Chatterjee, Himanshu Mishra, Arul Mishra 2023-07-13 2024-09-20 [("doi","10.1037/amp0001222")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction psychology

This work illustrates how large language models (LLMs), such as GPT-3 and GPT-4, can be used as an aid to generate research hypotheses for social psychology.

The LLM-generated hypotheses were found to be on par with, or even better than, those written by human researchers.

As research findings proliferate, these LLMs can help streamline the process of creating testable ideas and offer new avenues to accelerate psychological research.

Social psychology research projects begin with generating a testable idea that relies heavily on a researcher’s ability to assimilate, recall, and accurately process available research findings. However, an exponential increase in new research findings is making the task of synthesizing ideas across the multitude of topics challenging, which could result in important overlooked research connections.

In this research, we leverage the fact that social psychology research is based on verbal models and employ large natural language models to generate hypotheses that can aid social psychology researchers in developing new research hypotheses. We adopted two methodological approaches. In the first approach, we fine-tuned the third-generation generative pre-trained transformer (GPT-3) language model on thousands of abstracts published in more than 50 social psychology journals in the past 55 years as well as on preprint repositories (PsyArXiv).

Social psychology experts rated model & human-generated hypotheses similarly on the dimensions of clarity, originality, and impact. In the second approach, without fine-tuning, we generated hypotheses using GPT-4 and found that social psychology experts rated these generated hypotheses as higher in quality than human-generated hypotheses on dimensions of clarity, originality, impact, plausibility, and relevance.

[Keywords: generative language models, deep learning, hypothesis formation, generative networks]

See Also:

--- https://www.tandfonline.com/doi/full/10.1080/13546805.2024.2399505 2024-09-20 politics psychiatry/autism --- https://www.astralcodexten.com/p/your-book-review-the-ballad-of-the Your Book Review: The Ballad of the White Horse, by G. K. Chesterton 2024-09-20 fiction/poetry philosophy/religion --- https://www.reddit.com/r/ididnthaveeggs/ 2024-09-20 psychology/cognitive-bias/illusion-of-depth --- http://www.catb.org/~esr/writings/taoup/html/ch13s03.html The Art of Unix Programming § The Right Size for an Editor Eric S. Raymond 2003 2024-09-20 cs/lisp design --- https://en.wikipedia.org/wiki/Paraxanthine Paraxanthine 2024-09-21 nootropic/caffeine --- https://www.bunniestudios.com/blog/2024/turning-everyday-gadgets-into-bombs-is-a-bad-idea/ Turning Everyday Gadgets into Bombs is a Bad Idea 2024-09-20 2024-09-20 cs/security --- https://arxiv.org/abs/2305.15408 Towards Revealing the Mystery behind Chain-of-Thought: A Theoretical Perspective Guhao Feng, Bohang Zhang, Yuntian Gu, Haotian Ye, Di He, Liwei Wang 2023-05-24 2024-09-18 [("doi","10.48550/arXiv.2305.15408")] ai/nn/transformer/gpt/inner-monologue cs/computable

Recent studies have discovered that Chain-of-Thought (CoT) prompting can dramatically improve the performance of Large Language Models (LLMs), particularly when dealing with complex tasks involving mathematics or reasoning. Despite the enormous empirical success, the underlying mechanisms behind CoT and how it unlocks the potential of LLMs remain elusive. In this paper, we take a first step towards theoretically answering these questions.

Specifically, we examine the expressivity of LLMs with CoT in solving fundamental mathematical and decision-making problems. By using circuit complexity theory, we first give impossibility results showing that bounded-depth Transformers are unable to directly produce correct answers for basic arithmetic/equation tasks unless the model size grows super-polynomially with respect to the input length.

In contrast, we then prove by construction that autoregressive Transformers of constant size suffice to solve both tasks by generating CoT derivations using a commonly used math language format. Moreover, we show LLMs with CoT can handle a general class of decision-making problems known as Dynamic Programming, thus justifying its power in tackling complex real-world tasks.

Finally, an extensive set of experiments show that, while Transformers always fail to directly predict the answers, they can consistently learn to generate correct solutions step-by-step given sufficient CoT demonstrations.

--- https://arxiv.org/abs/2208.08489 Understanding Scaling Laws for Recommendation Models Newsha Ardalani, Carole-Jean Wu, Zeliang Chen, Bhargav Bhushanam, Adnan Aziz 2022-08-17 2024-01-01 [("doi","10.48550/arXiv.2208.08489")] ai/nn/retrieval ai/scaling

Scale has been a major driving force in improving machine learning performance, and understanding scaling laws is essential for strategic planning for a sustainable model quality performance growth, long-term resource planning and developing efficient system infrastructures to support large-scale models.

In this paper, we study empirical scaling laws for DLRM style recommendation models, in particular Click-Through Rate (CTR). We observe that model quality scales with power law plus constant in model size, data size and amount of compute used for training.

We characterize scaling efficiency along 3 different resource dimensions, namely data, parameters and compute by comparing the different scaling schemes along these axes. We show that parameter scaling is out of steam for the model architecture under study, and until a higher-performing model architecture emerges, data scaling is the path forward.

The key research questions addressed by this study include: Does a recommendation model scale sustainably as predicted by the scaling laws? Or are we far off from the scaling law predictions? What are the limits of scaling? What are the implications of the scaling laws on long-term hardware/system development?

--- https://arxiv.org/abs/1410.1490 Spaced Repetition and Mnemonics Enable Recall of Multiple Strong Passwords Jeremiah Blocki, Saranga Komanduri, Lorrie Cranor, Anupam Datta 2014-10-06 2024-01-01 [("doi","10.14722/ndss.2015.23094")] cs/security psychology/spaced-repetition

We report on a user study that provides evidence that spaced repetition and a specific mnemonic technique enable users to successfully recall multiple strong passwords over time.

Remote research participants were asked to memorize 4 Person-Action-Object (PAO) stories where they chose a famous person from a drop-down list and were given machine-generated random action-object pairs. Users were also shown a photo of a scene and asked to imagine the PAO story taking place in the scene (eg. Bill Gates—swallowing—bike on a beach). Subsequently, they were asked to recall the action-object pairs when prompted with the associated scene-person pairs following a spaced repetition schedule over a period of 127+ days.

While we evaluated several spaced repetition schedules, the best results were obtained when users initially returned after 12 hours and then in 1.5× increasing intervals: 77% of the participants successfully recalled all 4 stories in 10 tests over a period of 158 days. Much of the forgetting happened in the first test period (12 hours): 89% of participants who remembered their stories during the first test period successfully remembered them in every subsequent round.

These findings, coupled with recent results on naturally rehearsing password schemes, suggest that 4 PAO stories could be used to create usable and strong passwords for 14 sensitive accounts following this spaced repetition schedule, possibly with a few extra upfront rehearsals. In addition, we find that there is an interference effect across multiple PAO stories: the recall rate of 100% (respectively, 90%) for participants who were asked to memorize 1 PAO story (respectively, 2 PAO stories) is better than the recall rate for participants who were asked to memorize 4 PAO stories.

These findings yield concrete advice for improving constructions of password management schemes and future user studies.

--- https://arxiv.org/abs/1212.6177 How Much of the Web Is Archived? Scott G. Ainsworth, Ahmed AlSum, Hany SalahEldeen, Michele C. Weigle, Michael L. Nelson 2012-12-26 2024-01-01 [("doi","10.48550/arXiv.1212.6177")] cs/linkrot

Although the Internet Archive’s Wayback Machine is the largest and most well-known web archive, there have been a number of public web archives that have emerged in the last several years. With varying resources, audiences and collection development policies, these archives have varying levels of overlap with each other.

While individual archives can be measured in terms of number of URIs, number of copies per URI, and intersection with other archives, to date there has been no answer to the question “How much of the Web is archived?” We study the question by approximating the Web using sample URIs from DMOZ, Delicious, Bitly, and search engine indexes; and, counting the number of copies of the sample URIs exist in various public web archives. Each sample set provides its own bias.

The results from our sample sets indicate that range from 35%–90% of the Web has at least one archived copy, 17%–49% has between 2–5 copies, 1%–8% has 6–10 copies, and 8%–63% has more than 10 copies in public web archives. The number of URI copies varies as a function of time, but no more than 31.3% of URIs are archived more than once per month.

--- https://arun.is/blog/custom-typefaces/ Why are tech companies making custom typefaces? 2024-01-01 design/typography economics/copyright --- https://arstechnica.com/tech-policy/2020/03/internet-archive-offers-thousands-of-copyrighted-books-for-free-online/ Internet Archive offers 1.4 million copyrighted books for free online 2020-03 2024-01-01 economics/copyright law --- https://arstechnica.com/tech-policy/2011/10/five-year-old-wikipedia-fork-is-dead-in-the-water/ Citizendium turns five, but the Wikipedia fork is dead in the water 2011-10 2024-01-01 wikipedia --- https://en.wikipedia.org/wiki/Circuit_complexity Circuit complexity 2024-09-21 ai/nn cs/computable --- https://nfdg.com/ NFDG [VC firm] Nat Friedman, Daniel Grossman 2024-09-21 ai/scaling/economics --- https://dmitry.gr/?r=05.Projects&proj=35.%20Linux4004 Linux/4004: Slowly booting full Linux on the Intel 4004 CPU for fun, art, and absolutely no profit Dmitry Grinberg 2024 2024-09-21 cs/computable cs/hardware --- https://research.google/blog/scaling-up-linear-programming-with-pdlp/ Scaling up linear programming with PDLP 2024-09-21 statistics/decision --- https://core.ac.uk/download/pdf/6383353.pdf Television Viewing, Satisfaction and Happiness: Facts and Fiction Marco Gui, Luca Stanca 2009-07 2024-01-01 sociology/technology --- /doc/cs/css/2023-10-22-gwern-gwernnet-halloweenmode-wlogo.png Gwern 2023-10-22 2024-01-01 ai/nn/diffusion/midjourney cs/css --- https://arxiv.org/abs/2308.10248 Activation Addition: Steering Language Models Without Optimization Alexander Matt Turner, Lisa Thiergart, Gavin Leech, David Udell, Juan J. Vazquez, Ulisse Mini, Monte MacDiarmid 2023-08-20 2024-09-20 [("doi","10.48550/arXiv.2308.10248")] ai/nn/transformer/attention ai/text-style-transfer reinforcement-learning/preference-learning

Reliably controlling the behavior of large language models is a pressing open problem. Existing methods include supervised finetuning, reinforcement learning from human feedback, prompt engineering, and guided decoding.

We instead investigate activation engineering: modifying activations at inference-time to predictably alter model behavior. We bias the forward pass with a ‘steering vector’ implicitly specified through natural language. Past work learned these steering vectors; our Activation Addition (ActAdd) method instead computes them by taking activation differences resulting from pairs of prompts.

We demonstrate ActAdd on a range of LLMs (LLaMA-3, OPT, GPT-2, and GPT-J), obtaining SOTA on detoxification and negative-to-positive sentiment control.

Our approach yields inference-time control over high-level properties of output like topic and sentiment while preserving performance on off-target tasks. ActAdd takes far less compute and implementation effort than finetuning or RLHF, allows users control through natural language, and its computational overhead (as a fraction of inference time) appears stable or improving over increasing model size.

--- https://blog.regehr.org/archives/861 Operant Conditioning by Software Bugs John Regehr 2024-01-01 cs design --- https://arxiv.org/pdf/1602.02410.pdf#page=8&org=google Exploring the Limits of Language Modeling § 5.9: Samples from the model 2016 2024-01-01 ai/nn/rnn --- https://www.benjaminkjohnson.com/wp-content/uploads/2015/01/Spoiler_Alert_preprint.pdf Spoiler Alert: Consequences of Narrative Spoilers for Dimensions of Enjoyment, Appreciation, and Transportation Johnson, Rosenbaum 2015 2024-01-01 fiction/criticism psychology/novelty --- https://jamanetwork.com/journals/jama/fullarticle/187748 Effect on the Quality of Peer Review of Blinding Reviewers and Asking Them to Sign Their Reports: A Randomized Controlled Trial Godlee 1998 2024-01-01 statistics/peer-review --- https://www.reddit.com/r/StableDiffusion/comments/1fm368h/cats_with_hairdos_flux_lora_thats_all_it_does/ 2024-09-21 ai/nn/diffusion --- https://platform.openai.com/docs/guides/reasoning/how-reasoning-works 2024-09-21 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue --- https://www.future-forms.com/portfolio-item/sony-tr-1825-am-radio/ Sony TR-1825 AM Radio 2024-09-21 design --- https://www.cnn.com/2024/09/20/business/self-service-kiosks-mcdonalds-shake-shack/index.html McDonald’s touchscreen kiosks were feared as job killers. Instead, something surprising happened 2024-09-20 2024-09-21 economics/automation --- https://en.wikipedia.org/wiki/Alternative_pleading Alternative pleading 2024-09-21 law philosophy/logic --- https://arxiv.org/abs/2311.04205 Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves Yihe Deng, Weitong Zhang, Zixiang Chen, Quanquan Gu 2023-11-07 2024-09-21 [("doi","10.48550/arXiv.2311.04205")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue reinforcement-learning/meta-learning

Misunderstandings arise not only in interpersonal communication but also between humans and Large Language Models (LLMs). Such discrepancies can make LLMs interpret seemingly unambiguous questions in unexpected ways, yielding incorrect responses. While it is widely acknowledged that the quality of a prompt, such as a question, impacts the quality of the response provided by LLMs, a systematic method for crafting questions that LLMs can better comprehend is still underdeveloped.

In this paper, we present a method named Rephrase and Respond (RaR), which allows LLMs to rephrase and expand questions posed by humans and provide responses in a single prompt. This approach serves as a simple yet effective prompting method for improving performance. We also introduce a two-step variant of RaR, where a rephrasing LLM first rephrases the question and then passes the original and rephrased questions together to a different responding LLM. This facilitates the effective usage of rephrased questions generated by one LLM with another.

Our experiments demonstrate that our methods improve the performance of different models across a wide range of tasks. We further provide a comprehensive comparison between RaR and the popular Chain-of-Thought (CoT) methods, both theoretically and empirically. We show that RaR is complementary to CoT and can be combined with CoT to achieve even better performance.

Our work not only contributes to enhancing LLM performance efficiently and effectively but also sheds light on a fair evaluation of LLM capabilities.

Data and codes are available at Github.

--- https://en.wikipedia.org/wiki/Thymic_involution Thymic involution 2024-09-22 genetics/selection/natural longevity --- https://transformer-circuits.pub/2024/scaling-monosemanticity/ Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet 2024 2024-09-20 ai/nn/sparsity ai/nn/transformer/attention ai/nn/transformer/gpt/claude ai/nn/vae --- https://www.latent.space/p/sim-ai#%C2%A7websim-httpswebsimai Websim, Worldsim, and The Summer of Simulative AI 2024-09-20 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex cs/css --- https://www.gurwinder.blog/p/the-perils-of-audience-capture The Perils of Audience Capture 2024-09-20 psychiatry sociology/technology --- https://www.youtube.com/watch?v=OM_8UOPFpqE&t=510s Keynote: Linus Torvalds in Conversation with Dirk Hohndel 2024-09-20 cs sociology/technology --- https://arxiv.org/abs/2305.18619 Likelihood-Based Diffusion Language Models Ishaan Gulrajani, Tatsunori B. Hashimoto 2023-05-30 2024-09-22 [("doi","10.48550/arXiv.2305.18619")] ai/nn/diffusion/discrete ai/nn/transformer/gpt/2

Despite a growing interest in diffusion-based language models, existing work has not shown that these models can attain nontrivial likelihoods on standard language modeling benchmarks. In this work, we take the first steps towards closing the likelihood gap between autoregressive and diffusion-based language models, with the goal of building and releasing a diffusion model which outperforms a small but widely-known autoregressive model.

We pursue this goal through algorithmic improvements, scaling laws, and increased compute. On the algorithmic front, we introduce several methodological improvements for the maximum-likelihood training of diffusion language models. We then study scaling laws for our diffusion models and find compute-optimal training regimes which differ substantially from autoregressive models.

Using our methods and scaling analysis, we train and release Plaid 1B, a large diffusion language model which outperforms GPT-2 124M in likelihood on benchmark datasets and generates fluent samples in unconditional and zero-shot control settings.

--- https://www.reddit.com/r/slatestarcodex/comments/1fmwqfj/what_are_your_most_funlucrative_financial_hustle/lody9b1/ 2024-09-22 ai/nn/transformer/gpt/4/fiction economics/automation --- https://onlinelibrary.wiley.com/doi/10.1111/ssqu.13439 2024-09-22 politics psychology/personality --- https://www.asimov.press/p/eggs Making Eggs Without Ovaries 2024-09-22 genetics/gametogenesis --- https://www.reddit.com/r/dalle2/comments/1fmvnvo/bw_photography/ 2024-09-22 ai/nn/transformer/gpt/dall-e/3 --- https://www.nytimes.com/interactive/2024/09/20/magazine/ukraine-russia-war-deserter.html Deserting Putin’s Army and the Russia-Ukraine War 2024-09-20 2024-09-21 darknet-market politics sociology --- https://x.com/ashleevance/status/1838059346972074006 Ashlee Vance 2024-09-23 psychedelic --- https://arslan.io/2024/09/23/dieter-rams-inspired-iphone-dock/ How I Designed a Dieter Rams-inspired iPhone Dock Fatih Arslan 2024-09-23 2024-09-23 design --- https://onlinelibrary.wiley.com/doi/full/10.1002/admi.202101447 2024-09-23 technology --- /doc/darknet-market/alphabay/2024-andrei.pdf Status Spill-Over in Cryptomarket for Illegal Goods Filippo Andrei, Giuseppe Alessandro Veltri 2024-09-21 2024-09-23 [("doi","10.1177/08944393241286339")] darknet-market/alphabay darknet-market/dnm-archive

Information technologies have transformed many aspects of social life, including how illegal goods are exchanged. Illegal online markets are now flourishing on various channels: the surface web (all websites accessible through a standard browser), the dark web (an encrypted internet network only accessible via anonymous browsers), and encrypted messaging applications installed on smartphones.

These marketplaces take many forms, including simple web shops, chat rooms, forums, social media marketplaces, and platforms. This study focuses on the largest known darknet platform to date: AlphaBay. This cryptomarket operated from December 2014 until July 2017, when an international police operation shut it down.

The dataset contains 6,033 vendor profiles collected in January 2017. Using 3 generalized additive models (GAMs), we show that:

seller status positively affects sales, revenue, and sales through finalized early payment. Once sellers gain status on the platforms, they make more sales without a semi-institutionalized form of payment (eg. escrow).

On the other hand, buyers relying on status metrics as cognitive shortcuts tend to choose vendors even if they do not offer payment protection tools.

--- https://www.nytimes.com/2024/09/19/nyregion/citi-bike-scam-nyc.html Meet the Hustlers Who Make $6,000 a Month Riding Citi Bikes 2024-09-19 2024-09-23 cs/security economics/mechanism-design --- https://en.wikipedia.org/wiki/Japanese_dry_garden Japanese dry garden 2024-09-23 design japan/art psychiatry/meditation --- /doc/fiction/poetry/1978-warren-heartofthebacklog.txt Heart of the Backlog Robert Penn Warren 1978-01-22 2024-09-23 fiction/poetry philosophy/religion

…How feather-frail, think, is the track of the vole
On new snow! How wide is the world! How fleeting and thin
Its mark of identity, breath
In a minuscule issue of whiteness
In air that is brighter than steel! The vole pauses, one paw
Uplifted in whiteness of moonlight.

…Again the owl calls, and with some sadness you wonder
If at last, when the air-scything shadow descends
And needles claw-clamp through gut, heart and brain,
Will ecstasy melting with terror create the last little cry?
Is God’s love but the last and most mysterious word for death?

Has the thought ever struck you to rise and go forth—yes, lost
In the whiteness—to never look upward, or back, only on,
And no sound but the snow-crunch, and breath
Gone crisp like the crumpling of paper? Listen!
Could that be the creak of a wing-joint gigantic in distance?

No, no—just a tree, far off, when ice inward bites.
No, no, don’t look back—oh, I beg you!

I beg you not to look back, in God’s name.

--- https://huggingface.co/datasets/HuggingFaceH4/no_robots No Robots: Look Ma, an instruction dataset that wasn’t generated by GPTs! HuggingFace 2024-09-23 ai/dataset ai/nn/transformer/gpt/instruction-tuning

No Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better.

No Robots was modelled after the instruction dataset described in OpenAI’s InstructGPT paper, and is comprised mostly of single-turn instructions across the following categories:

Category Count
Generation 4,560
Open QA 1,240
Brainstorm 1,120
Chat 850
Rewrite 660
Summarize 420
Coding 350
Classify 350
Closed QA 260
Extract 190
--- https://slate.com/technology/2019/03/wikipedia-citogenesis-circular-reporting-problem.html Citogenesis: the serious circular reporting problem Wikipedians are fighting. Circular reporting is a real problem on platforms like Wikipedia—and it’s harder to solve than it looks Stephen Harrison 2019-03-07 2024-09-24 statistics/bias wikipedia --- https://www.reddit.com/r/dalle2/comments/1fo59sl/i_dressed_myself_today/ 2024-09-24 ai/nn/transformer/gpt/dall-e/3 --- /doc/psychology/personality/conscientiousness/1971-hostetler-childreninamishsociety.pdf Children in Amish Society: Socialization and Community Education John Andrew Hostetler, Gertrude Enders Huntington 1971-01-01 2024-09-24 philosophy/religion psychology/personality/conscientiousness sociology --- https://x.com/amasad/status/1838405189650518384 Amjad Masad 2024-09-24 ai/nn/adversarial --- https://www.nytimes.com/2024/09/24/health/ai-patient-messages-mychart.html That Message From Your Doctor? It May Have Been Drafted by ChatGPT-4 2024-09-24 2024-09-24 ai/nn/transformer/gpt/4/nonfiction --- https://waitbutwhy.com/2014/05/life-weeks.html Your Life in Weeks Wait But Why 2014-05 2024-09-24 design/visualization statistics/decision --- https://ijc8.me/2024/08/26/gsoc-difflogic/ GSoC 2024: Differentiable Logic for Interactive Systems and Generative Music 2024-08-26 2024-09-24 ai/music ai/nn/fully-connected ai/nn/sparsity --- https://arxiv.org/abs/2409.14254 Instruction Following without Instruction Tuning John Hewitt, Nelson F. Liu, Percy Liang, Christopher D. Manning 2024-09-21 2024-09-25 [("doi","10.48550/arXiv.2409.14254")] ai/nn/transformer/gpt/instruction-tuning

Instruction tuning commonly means finetuning a language model on instruction-response pairs. We discover two forms of adaptation (tuning) that are deficient compared to instruction tuning, yet still yield instruction following; we call this implicit instruction tuning.

We first find that instruction-response pairs are not necessary: training solely on responses, without any corresponding instructions, yields instruction following. This suggests pretrained models have an instruction-response mapping which is revealed by teaching the model the desired distribution of responses.

However, we then find it’s not necessary to teach the desired distribution of responses: instruction-response training on narrow-domain data like poetry still leads to broad instruction-following behavior like recipe generation. In particular, when instructions are very different from those in the narrow finetuning domain, models’ responses do not adhere to the style of the finetuning domain.

To begin to explain implicit instruction tuning, we hypothesize that very simple changes to a language model’s distribution yield instruction following. We support this by hand-writing a rule-based language model which yields instruction following in a product-of-experts with a pretrained model. The rules are to slowly increase the probability of ending the sequence, penalize repetition, and uniformly change 15 words’ probabilities.

In summary, adaptations made without being designed to yield instruction following can do so implicitly.

--- https://arxiv.org/abs/2405.14394 Instruction Modeling: Instruction Tuning With Loss Over Instructions Zhengyan Shi, Adam X. Yang, Bin Wu, Laurence Aitchison, Emine Yilmaz, Aldo Lipani 2024-05-23 2024-09-25 [("doi","10.48550/arXiv.2405.14394")] ai/nn/dynamic-evaluation ai/nn/transformer/gpt/instruction-tuning

Instruction tuning plays a crucial role in shaping the outputs of language models (LMs) to desired styles. In this work, we propose a simple yet effective method, Instruction Modeling (IM), which trains LMs by applying a loss function to the instruction and prompt part rather than solely to the output part.

Through experiments across 21 diverse benchmarks, we show that, in many scenarios, IM can effectively improve the LM performance on both NLP tasks (eg. MMLU, TruthfulQA, and HumanEval) and open-ended generation benchmarks (eg. MT-Bench and AlpacaEval). Remarkably, in the most advantageous case, IM boosts model performance on AlpacaEval 1.0 by over 100%.

We identify two key factors influencing the effectiveness of IM: (1) The ratio between instruction length and output length in the training data; and (2) The number of training examples. We observe that IM is especially beneficial when trained on datasets with lengthy instructions paired with brief outputs, or under the Superficial Alignment Hypothesis (SAH) where a small amount of training examples are used for instruction tuning.

Further analysis substantiates our hypothesis that the improvement can be attributed to reduced overfitting to instruction tuning datasets. Our work provides practical guidance for instruction tuning LMs, especially in low-resource scenarios.

[I’m mostly shocked that this isn’t standard practice already. Of course you want to train on the questions! You want to train on everything you can get, especially at test-time.]

--- /doc/psychology/linguistics/2024-pardo.pdf African elephants address one another with individually specific name-like calls Michael A. Pardo, Kurt Fristrup, David S. Lolchuragi, Joyce Poole, Petter Granli, Cynthia Moss, Iain Douglas-Hamilton, George Wittemyer 2024-06-10 2024-06-30 [("doi","10.1038/s41559-024-02420-w")] psychology/animal psychology/linguistics --- https://arxiv.org/abs/1612.03144#facebook Feature Pyramid Networks for Object Detection Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, Serge Belongie 2016-12-09 2024-09-25 [("doi","10.48550/arXiv.1612.03144")] ai/nn/cnn

Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But recent deep learning object detectors have avoided pyramid representations, in part because they are compute and memory intensive.

In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows improvement as a generic feature extractor in several applications.

Using FPN in a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners.

In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection.

Code will be made publicly available.

--- https://arxiv.org/abs/1606.00915 DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution (ASPP), and Fully Connected CRFs Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille 2016-06-02 2024-09-25 [("doi","10.48550/arXiv.1606.00915")] ai/nn/cnn

In this work we address the task of semantic image segmentation with Deep Learning and make 3 main contributions that are experimentally shown to have substantial practical merit.

First, we highlight convolution with upsampled filters, or ‘atrous convolution’, as a powerful tool in dense prediction tasks. Atrous [dilated] convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation.

Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales.

Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully-connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance.

Our proposed DeepLab system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7% mIoU in the test set, and advances the results on 3 other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes.

All of our code is made publicly available online.

--- https://orbis.stanford.edu/ ORBIS: The Stanford Geospatial Network Model of the Roman World 2024-01-01 design/visualization history --- https://orbi.uliege.be/bitstream/2268/204410/1/ANN-BRL_final.pdf Approximate Bayes Optimal Policy Search using Neural Networks 2024-01-01 reinforcement-learning/model statistics/bayes --- https://orbit.dtu.dk/fedora/objects/orbit:132689/datastreams/file_0cb18381-8733-467b-b88b-07289c080b3c/content The effect of air quality on sleep 2024-01-01 zeo --- https://orbit.dtu.dk/fedora/objects/orbit:134384/datastreams/file_a24346bd-b582-483e-b3f4-2efbc42682aa/content The effect of CO2 controlled bedroom ventilation on sleep and next-day performance 2024-01-01 co2 --- https://original-research.blogspot.com/2007/07/i-found-myself-experiencing-ugly-side.html Original Research: Instead of making useful edits to Wikipedia 2024-01-01 wikipedia --- https://originstamp.com/ Blockchain-Based Timestamping 2024-01-01 bitcoin --- https://originstamp.com/git_post_commit 2024-01-01 bitcoin --- https://originstamp.com/s/7306a744a285474742f4f9ae8ddae8214fb7625348d578fb3077fb0bae92b8f1 2024-01-01 bitcoin --- https://orionmagazine.org/article/interviews-with-an-octopus/ Interviews with an Octopus 2024-01-01 psychology/animal --- https://oscarbonilla.com/2009/05/visualizing-bayes-theorem/ Visualizing Bayes’ theorem 2024-01-01 design/visualization statistics/bayes --- https://www.jonashietala.se/blog/2024/09/25/why_i_still_blog_after_15_years/ Why I still blog after 15 years Jonas Hietala 2024-09-25 2024-09-25 psychology/writing --- https://www.woodeson.co.uk/pages/altered_beast.html Altered Beast [tomato sauce, bathtub, Sega console, CRT TV] Poppy Woodeson 2001 2024-09-25 technology

The piece consisted of a bath of cheap tomato soup which generated the power for an old computer game and B+W TV. The game was playable by visitors to the gallery.

--- https://x.com/Altimor/status/1838688432501240107 Flo Crivello 2024-09-25 statistics/order/comparison --- https://globalchinapulse.net/moving-bricks-money-laundering-practices-in-the-online-scam-industry/ Moving Bricks: Money-Laundering Practices in the Online Scam Industry 2024-09-25 bitcoin crime --- https://www.sciencedirect.com/science/article/pii/S0011224015300134 Recovery and reproduction of an Antarctic tardigrade retrieved from a moss sample frozen for over 30 years 2022 2024-09-26 cryonics --- https://en.wikipedia.org/wiki/Effective_population_size Effective population size 2024-01-01 genetics/selection/natural --- https://www.bloomberg.com/news/articles/2023-11-29/stability-ai-has-explored-sale-as-investor-urges-ceo-to-resign Stability AI Explores Sale as Investor Urges CEO to Resign: Move follows letter from investor Coatue calling for changes; Coatue concerned about Stability AI’s financial position Mark Bergen, Rachel Metz 2023-11-29 2024-06-05 ai/nn/diffusion --- https://www.bloomberg.com/news/articles/2022-08-02/crypto-bridge-nomad-drained-of-nearly-200-million-in-exploit Crypto Firm Nomad Loses Nearly $200 Million in Bridge Hack 2022-08-02 2024-01-01 bitcoin --- https://www.bloomberg.com/news/features/2018-02-07/inside-kim-jong-un-s-hacker-army Inside North Korea’s Hacker Army 2024-01-01 cs/security --- https://www.bloomberg.com/opinion/authors/ARbTQlRLRjE/matthew-s-levine Matt Levine, Bloomberg Opinion Columnist Matt Levine 2024-09-24 2024-09-24 economics --- https://www.bls.gov/cps/duration.htm Duration of unemployment in the CPS Bureau of Labor Statistics 2024-01-01 economics --- https://www.bls.gov/data/inflation_calculator.htm CPI Inflation Calculator Bureau of Labor Statistics 2024-01-01 economics --- https://www.bls.gov/opub/mlr/2016/article/the-life-of-american-workers-in-1915.htm The life of American workers in 1915 2024-01-01 economics --- https://www.bmj.com/content/320/7250/1631 Disability in young people and adults one year after head injury: prospective cohort study 2024-01-01 psychiatry/traumatic-brain-injury --- /doc/statistics/bias/animal/2000-fishbain.pdf Evidence-Based Data From Animal and Human Experimental Studies on Pain Relief With Antidepressants: A Structured Review David A. Fishbain, Robert Cutler, Hubert L. Rosomoff, Renee Steele Rosomoff 2000-12-01 2024-09-26 [("doi","10.1046/j.1526-4637.2000.00042.x")] psychiatry/depression statistics/bias/animal --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7882922/ Queen execution in a monogynous ant 2024-09-26 biology/ant --- https://arxiv.org/abs/2310.08164 Interpreting Learned Feedback Patterns in Large Language Models Luke Marks, Amir Abdullah, Clement Neo, Rauno Arike, David Krueger, Philip Torr, Fazl Barez 2023-10-12 2024-09-26 [("doi","10.48550/arXiv.2310.08164")] ai/nn/transformer/gpt/4 reinforcement-learning/preference-learning

Reinforcement learning from human feedback (RLHF) is widely used to train large language models (LLMs). However, it is unclear whether LLMs accurately learn the underlying preferences in human feedback data. We coin the term Learned Feedback Pattern (LFP) for patterns in an LLM’s activations learned during RLHF that improve its performance on the fine-tuning task.

We hypothesize that LLMs with LFPs accurately aligned to the fine-tuning feedback exhibit consistent activation patterns for outputs that would have received similar feedback during RLHF. To test this, we train probes to estimate the feedback signal implicit in the activations of a fine-tuned LLM. We then compare these estimates to the true feedback, measuring how accurate the LFPs are to the fine-tuning feedback.

Our probes are trained on a condensed, sparse and interpretable representation of LLM activations, making it easier to correlate features of the input with our probe’s predictions. We validate our probes by comparing the neural features they correlate with positive feedback inputs against the features GPT-4 describes and classifies as related to LFPs.

Understanding LFPs can help minimize discrepancies between LLM behavior and training objectives, which is essential for the safety of LLMs.

--- https://www.science.org/content/article/research-misconduct-finding-neuroscientist-eliezer-masliah-papers-under-suspicion 2024-09-26 nootropic psychiatry/alzheimers statistics/bias --- https://x.com/liminal_bardo/status/1839388963125260307 liminal_bardo 2024-09-26 ai/nn/transformer/gpt/claude --- https://clsbluesky.law.columbia.edu/2024/03/05/the-untold-nonprofit-story-of-openai/ The Untold Nonprofit Story of OpenAI 2024-03-05 2024-09-26 law reinforcement-learning/openai --- https://forum.effectivealtruism.org/posts/cXBznkfoPJAjacFoT/are-you-really-in-a-race-the-cautionary-tales-of-szilard-and Are you really in a race? The Cautionary Tales of Szilard and Ellsberg 2024-09-26 radiance reinforcement-learning/safe --- https://arxiv.org/abs/2409.15318 On the Complexity of Neural Computation in Superposition Micah Adler, Nir Shavit 2024-09-05 2024-09-26 [("doi","10.48550/arXiv.2409.15318")] ai/nn/fully-connected ai/nn/sparsity cs/computable

Recent advances in the understanding of neural networks suggest that superposition, the ability of a single neuron to represent multiple features simultaneously, is a key mechanism underlying the computational efficiency of large-scale networks.

This paper explores the theoretical foundations of computing in superposition, focusing on explicit, provably correct algorithms and their efficiency. We present the first lower bounds showing that for a broad class of problems, including permutations and pairwise logical operations, a neural network computing in superposition requires at least Ω(m′ log m′) parameters and Ω(√m′ log m′) neurons, where m′ is the number of output features being computed.

This implies that any “lottery ticket” sparse sub-network must have at least Ω(m′ log m′) parameters no matter what the initial dense network size. Conversely, we show a nearly tight upper bound: logical operations like pairwise AND can be computed using 𝒪(√m′ log m′) neurons and 𝒪(m′ log2 m′) parameters.

There is thus an exponential gap between computing in superposition, the subject of this work, and representing features in superposition, which can require as little as 𝒪(log m′) neurons based on the Johnson-Lindenstrauss Lemma.

Our hope is that our results open a path for using complexity theoretic techniques in neural network interpretability research.

--- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3176375/ Plastic bag clip discovered in partial colectomy accompanying proposal for phylogenic plastic bag clip classification Larisa M. Lehmer, Bruce D. Ragsdale, John Daniel, Edwin Hayashi, Robert Kvalstad 2011-09-05 2024-09-27 [("doi","10.1136/bcr.02.2011.3869")] iq/low

A plastic bag clip was incidentally found anchored in the mucosa of a partial colectomy specimen 2.6 cm proximal to a ruptured diverticulum for which the patient, a mentally retarded, diabetic, 58-year-old man, underwent surgery. Over 20 cases of accidental ingestion of plastic bag clips have been published. Known complications include small bowel perforation, obstruction, dysphagia, gastrointestinal bleeding, and colonic impaction.

Preoperative diagnosis of plastic clips lodged in the gastrointestinal tract is frustrated due to radiographic translucency. This occult threat could likely be prevented by the design of gastrointestinally safe, plastic-bag-sealing devices. Presented here is a morphologically based classification of bag clips as a possible guide for determining the most hazardous varieties and to aid further discussions of their impact on health.

...A 58-year-old white male with a history of mental retardation, diabetes, kidney stones, and hyperlipidemia entered the emergency room with complaints of fever, dizziness, vomiting, chills, and increased difficulty in breathing.

People older than 60 years of age who have either partial or full dentures seem to be at particular risk for the accidental ingestion of these devices. In 5 cases, the patient was known to be toothless. As the population ages, small bowel perforation secondary to ingestion of such clips may occur with increasing frequency.

The call has been made for elimination or redesign of the clips to prevent their ingestion, make them less likely to hook into the mucosa (possibly by employing a spherical design), have them made out of digestible material, or simply incorporate radio-opaque compounds in the plastic to enable their identification in the gastrointestinal tract by conventional radiography. The path of eradication has already been taken in the UK, where plastic clips have been replaced by tape for safety reasons.

--- https://en.wikipedia.org/wiki/Xanomeline/trospium_chloride Xanomeline/trospium chloride (Cobenfy) 2024-09-27 psychiatry/schizophrenia --- https://thecausalfallacy.com/p/its-time-to-talk-about-americas-disorder It’s Time to Talk About America’s Disorder Problem 2024-09-27 crime --- https://onlinelibrary.wiley.com/doi/full/10.1111/1745-9133.12667 2024-09-27 crime --- /doc/history/2022-blackler.pdf Communication And The Role Of The Medieval Tower In Greece: A Re-Appraisal Andrew Blackler 2022-09-19 2024-09-28 [("doi","10.1017/S0068245422000119")] history technology

Little evidence has survived of the long-distance communication networks established by the Byzantines and Venetians in the medieval period. We know only of a chain of beacons established by Leo the Mathematician in the 9th century, an inscription found in the Peloponnese, and a Venetian network in the central Aegean.

This article reappraises the existing evidence and introduces new data following a study recently undertaken by the author of the topography of Negroponte (modern Euboea) and the medieval towers of Greece. Making extensive use of early cartographic sources, toponymic studies, and satellite imagery and telemetry, it identifies 142 tower and beacon sites on the island alone, and demonstrates, using archaeological evidence, how complex messages could be sent between towers.

The research also uncovers a new term—the pyrgari, which appears to apply to a circular beacon tower. Combining this new evidence and the topographic study, the article then delineates, using GIS mapping, 4 Middle Byzantine and Venetian long-distance communication networks.

The paper concludes by proposing a theoretical framework for the tower based on its role in communication and defense. Such work potentially helps us to understand in a more nuanced way the administrative and military organization of the Byzantine themata and the Venetian Empire.

The methodology also has potential for application in other regions: in essence, it looks at the landscape not as a collection of nodes—bishoprics, cities, and fortresses—but as a network of connections.

--- /doc/economics/2024-matranga.pdf The Ant And The Grasshopper: Seasonality And The Invention Of Agriculture Andrea Matranga 2024-04-19 2024-09-28 [("doi","10.1093/qje/qjae012")] economics technology

The Neolithic revolution saw the independent development of agriculture among at least 7 unconnected hunter-gatherer populations. I propose that the rapid spread of agricultural techniques resulted from increased climatic seasonality causing hunter-gatherers to adopt a sedentary lifestyle and store food for the season of scarcity.

Their newfound sedentary lifestyle and storage habits facilitated the invention of agriculture. I present a model and support it with global climate data and Neolithic adoption dates, showing that greater seasonality increased the likelihood of agriculture’s invention and its speed of adoption by neighbors.

This study suggests that seasonality patterns played a dominant role in determining our species’ transition to farming.

--- /doc/psychology/animal/bird/2024-welklin.pdf Spatial cognitive ability is associated with longevity in food-caching chickadees Joseph F. Welklin, Benjamin R. Sonnenberg, Carrie L. Branch, Virginia K. Heinen, Angela M. Pitera, Lauren M. Benedict, Lauren E. Whitenack, Eli S. Bridge, Vladimir V. Pravosudov 2024-09-05 2024-09-28 [("doi","10.1126/science.adn5633")] iq longevity psychology/animal/bird

Cognitive abilities are hypothesized to affect survival and life span in nonhuman animals. However, most tests of this hypothesis have relied on interspecific comparisons of indirect measures of cognitive ability, such as brain size.

We present direct evidence that individual variation in cognitive abilities is associated with differences in life span in a wild food caching bird. We measured the spatial cognitive abilities and tracked the life span of 227 mountain chickadees (Poecile gambeli) in their natural environment and found that individuals with better spatial learning and memory abilities involved in food caching lived longer.

These results confirm that enhanced cognitive abilities can be associated with longer life in wild animals and that selection on cognitive abilities can lead to increased life span.

--- /doc/longevity/metformin/2024-yang.pdf Metformin decelerates aging clock in male monkeys Yuanhan Yang, Xiaoyong Lu, Ning Liu, Shuai Ma, Hui Zhang, Zhiyi Zhang, Kuan Yang, Mengmeng Jiang, Zikai Zheng, Yicheng Qiao, Qinchao Hu, Ying Huang, Yiyuan Zhang, Muzhao Xiong, Lixiao Liu, Xiaoyu Jiang, Pradeep Reddy, Xueda Dong, Fanshu Xu, Qiaoran Wang, Qian Zhao, Jinghui Lei, Shuhui Sun, Ying Jing, Jingyi Li, Yusheng Cai, Yanling Fan, Kaowen Yan, Yaobin Jing, Amin Haghani, Mengen Xing, Xuan Zhang, Guodong Zhu, Weihong Song, Steve Horvath, Concepcion Rodriguez Esteban, Moshi Song, Si Wang, Guoguang Zhao, Wei Li, Juan Carlos Izpisua Belmonte, Jing Qu, Weiqi Zhang, Guang-Hui Liu 2024 2024-09-28 [("doi","10.1016/j.cell.2024.08.021")] longevity/epigenetics longevity/metformin

  • Metformin prevents brain atrophy, elevating cognitive function in aged male primates

  • Metformin slows the pace of aging across diverse male primate tissues

  • Metformin counterparts neuronal aging, delivering geroprotection via Nrf2 in male primates

[supplement] In a rigorous 40-month study, we evaluated the geroprotective effects of metformin on adult male cynomolgus monkeys, addressing a gap in primate aging research.

The study encompassed a comprehensive suite of physiological, imaging, histological, and molecular evaluations, substantiating metformin’s influence on delaying age-related phenotypes at the organismal level. Specifically, we leveraged pan-tissue transcriptomics, DNA methylomics, plasma proteomics, and metabolomics to develop innovative monkey aging clocks [cf. epigenetic clocks] and applied these to gauge metformin’s effects on aging.

The results highlighted a statistically-significant slowing of aging indicators, notably a roughly 6-year regression in brain aging.

Metformin exerts a substantial neuroprotective effect, preserving brain structure and enhancing cognitive ability. The geroprotective effects on primate neurons were partially mediated by the activation of Nrf2, a transcription factor with anti-oxidative capabilities.

Our research pioneers the systemic reduction of multi-dimensional biological age in primates through metformin, paving the way for advancing pharmaceutical strategies against human aging.

[Keywords: metformin, primate, aging, senescence, aging clock]

--- /doc/psychiatry/depression/2024-angelucci.pdf The Economic Impact of Depression Treatment in India: Evidence from Community-Based Provision of Pharmacotherapy Manuela Angelucci, Daniel Bennett 2024-01-01 2024-09-28 [("doi","10.1257/aer.20210687")] economics psychiatry/depression

This study evaluates the impact of depression treatment on economic behavior in Karnataka, India.

We cross-randomize pharmacotherapy and livelihoods assistance among 1,000 depressed adults and evaluate impacts on depression severity, socioeconomic outcomes, and several potential pathways.

When combined, the interventions reduce depression severity, with benefits that persist after treatment concludes. Pharmacotherapy alone has a weaker effect that is only marginally statistically-significant and dissipates sooner. Depression treatment does not statistically-significantly increase earnings, consumption, or human capital investment in children.

--- /doc/psychology/linguistics/2024-piantadosi.pdf Why concepts are (probably) vectors Steven T. Piantadosi, Dyana C. Y. Muller, Joshua S. Rule, Karthikeya Kaushik, Mark Gorenstein, Elena R. Leib, Emily Sanford 2024-09-01 2024-09-28 [("doi","10.1016/j.tics.2024.06.011")] ai/nn philosophy/logic psychology/linguistics

For decades, cognitive scientists have debated what kind of representation might characterize human concepts. Whatever the format of the representation, it must allow for the computation of varied properties, including similarities, features, categories, definitions, and relations.

It must also support the development of theories, ad hoc categories, and knowledge of procedures. Here, we discuss why vector-based representations provide a compelling account that can meet all these needs while being plausibly encoded into neural architectures.

This view has become especially promising with recent advances in both large language models and vector symbolic architectures. These innovations show how vectors can handle many properties traditionally thought to be out of reach for neural models, including compositionality, definitions, structures, and symbolic computational processes.

[Keywords: concepts, conceptual role, Church encoding, vector, vector symbolic architecture]

--- https://dash.harvard.edu/bitstream/handle/1/3693705/Kremer_PatentBuyouts.pdf?sequence=2#page=2 2024-09-28 economics/copyright economics/mechanism-design --- https://nymag.com/intelligencer/article/ai-generated-content-internet-online-slop-spam.html The Internet’s AI Slop Problem Is Only Going to Get Worse 2024-09-28 ai/nn economics/advertising --- /doc/politics/2024-jelveh.pdf Political Language In Economics Jelveh Zubin, Kogut Bruce, Naidu Suresh 2024-08-01 2024-09-28 [("doi","10.1093/ej/ueae026")] economics politics statistics/bias

Does academic writing in economics reflect the political orientation of economists?

We use machine learning [n-grams, latent Dirichlet allocation, gradient boosting decision trees] to measure partisanship in academic economics articles. We predict the observed political behavior of a subset of economists using phrases from their academic articles, show good out-of-sample predictive accuracy and then predict partisanship for all economists.

We then use these predictions to examine patterns of political language in economics. We estimate journal-specific effects on predicted ideology, controlling for author and year fixed effects, that accord with existing survey-based measures. We show considerable sorting of economists into fields of research by predicted partisanship. We also show that partisanship is detectable even within fields, even across those estimating the same theoretical parameter.

Using policy-relevant parameters collected from previous meta-analyses, we then show that imputed partisanship is correlated with estimated parameters, such that the implied policy prescription is consistent with partisan leaning.

For example, we find that going from the most left-wing authored estimate of the taxable top income elasticity to the most right-wing authored estimate decreases the optimal tax rate from 77% → 60%.

--- https://www.newsweek.com/big-blues-hand-god-173076 Big Blue’s Hand Of God Steven Levy 1997-05-18 2024-09-28 reinforcement-learning/chess --- /static/build/text2epositive.py text2epositive.py Gwern 2024-09-27 2024-09-28 ai/nn/transformer/gpt/4/nonfiction ai/text-style-transfer philosophy/logic psychology/linguistics --- https://x.com/RobertMMetcalfe/status/1839703601108664348 Robert Metcalfe 2024-09-28 ai/nn --- https://elmwealth.com/crystal-ball-challenge/ Crystal Ball Trading Challenge Elm Wealth 2024-09-29 design/visualization economics --- https://blog.oimo.io/2022/02/11/clock-core/ Clock: 解説 2022-02-11 2024-09-24 cs/algorithm design/visualization science --- https://x.com/miramurati/status/1839025700009030027 [Resignation announcement] Mira Murati 2024-09-29 reinforcement-learning/openai --- https://www.popsci.com/technology/cat-hat-science/ Researchers put little hats on cats to measure their brainwaves 2024-09-29 cat/psychology psychology/neuroscience --- https://www.sciencedirect.com/science/article/pii/S0165027024001997 Non-invasive electroencephalography in awake cats: Feasibility and application to sensory processing in chronic pain 2024-09-29 cat/psychology psychology/neuroscience --- https://obits.oregonlive.com/us/obituaries/oregon/name/wendell-oswalt-obituary?id=12843091 Wendell Oswalt Obituary (1927–2020) 2020 2024-09-27 sociology --- https://minne.com/items/41039661 リバーシブルジャケット　USXL—ツルピカワークスGALLERY [indigo kakishibu jackets] 2024-09-29 japan/art --- https://arxiv.org/abs/2409.17458 RED QUEEN: Safeguarding Large Language Models against Concealed Multi-Turn Jailbreaking Yifan Jiang, Kriti Aggarwal, Tanmay Laud, Kashif Munir, Jay Pujara, Subhabrata Mukherjee 2024-09-26 2024-09-29 [("doi","10.48550/arXiv.2409.17458")] ai/nn/adversarial

The rapid progress of Large Language Models (LLMs) has opened up new opportunities across various domains and applications; yet it also presents challenges related to potential misuse. To mitigate such risks, red teaming has been employed as a proactive security measure to probe language models for harmful outputs via jailbreak attacks.

However, current jailbreak attack approaches are single-turn with explicit malicious queries that do not fully capture the complexity of real-world interactions. In reality, users can engage in multi-turn interactions with LLM-based chat assistants, allowing them to conceal their true intentions in a more covert manner.

To bridge this gap, we, first, propose a new jailbreak approach, RED QUEEN ATTACK. This method constructs a multi-turn scenario, concealing the malicious intent under the guise of preventing harm. We craft 40 scenarios that vary in turns and select 14 harmful categories to generate 56k multi-turn attack data points.

We conduct comprehensive experiments on the RED QUEEN ATTACK with 4 representative LLM families of different sizes. Our experiments reveal that all LLMs are vulnerable to RED QUEEN ATTACK, reaching 87.62% attack success rate on GPT-4o and 75.4% on Llama3-70B. Further analysis reveals that larger models are more susceptible to the RED QUEEN ATTACK, with multi-turn structures and concealment strategies contributing to its success.

To prioritize safety, we introduce a straightforward mitigation strategy called RED QUEEN GUARD, which aligns LLMs to effectively counter adversarial attacks. This approach reduces the attack success rate to below 1% while maintaining the model’s performance across standard benchmarks.

Full implementation and dataset are publicly accessible at Github.

--- /doc/longevity/glp/semaglutide/2024-lee.pdf Dispensing of Glucagon-Like Peptide-1 Receptor Agonists to Adolescents and Young Adults, 2020–2023 Joyce M. Lee, Mona Sharifi, Lauren Oshman, Dina H. Griauzde, Kao-Ping Chua 2024-05-22 2024-09-29 [("doi","10.1001/jama.2024.7112")] longevity/glp/semaglutide longevity/glp/tirzepatide

This study uses data from US retail pharmacies to assess national GLP-1RA dispensing to adolescents and young adults from 2020–2023.

--- https://outliving.substack.com/p/this-movie-is-why-im-alive This Movie is Why I’m Alive: How Ratatouille showed me the beauty of the world that wished to kill me Pouya Nikmand 2024-09-14 2024-09-29 fiction/criticism psychology/willpower --- /doc/economics/georgism/2024-palsson.pdf The inefficacy of land titling programs: homesteading in Haiti, 1933–1950 Craig Palsson, Seth Porter 2024-08-09 2024-09-29 [("doi","10.1007/s11127-024-01195-9")] economics/georgism politics

[blog] One of the most common policy recommendations in developing countries is titling land. Yet, titling programs around the developing world frequently fail to produce many titles.

We try to understand these failures by exploring a titling program in Haiti in the 1930s. The program offered tenants renting public land an opportunity to privatize the land as a homestead, giving them an official title and ending rental payments.

Making use of archival data on all homesteads granted in the first 16 years, we find the program created fewer than 700 homesteads.

We discuss potential reasons for the program’s failure and argue that it failed because it required homesteaders to farm at least 50% of the plot in cash crops.

We discuss whether this requirement was the government’s attempt to extract revenues from the land in the absence of other options or whether it was an intentional barrier to resist foreign interference.

[Keywords: property rights, Haiti, economic development, land reform]

--- /doc/psychology/spaced-repetition/2005-koriat.pdf Illusions of Competence in Monitoring One’s Knowledge During Study Asher Koriat, Robert A. Bjork 2005-03-01 2024-09-29 [("doi","10.1037/0278-7393.31.2.187")] psychology/cognitive-bias psychology/spaced-repetition

The monitoring of one’s own knowledge during study suffers from an inherent discrepancy between study and test situations: Judgments of learning (JOLs) are made in the presence of information that is absent but solicited during testing. The failure to discount the effects of that information when making JOLs can instill a sense of competence during learning that proves unwarranted during testing.

Using a paired-associates task, the authors examined aspects of the cue-target relationships that seemed likely contributors to such illusions of competence. These aspects have the potential to create differential strengths of a priori and a posteriori associations, that is, the probability with which a cue, when presented alone, elicits the corresponding target versus the perceived association between the cue and the target when both are present.

The authors argue that the former has the greater influence on later recall, whereas the latter has the greater influence on JOLs.

See Also:

--- https://sohl-dickstein.github.io/2022/11/06/strong-Goodhart.html Too much efficiency makes everything worse: overfitting and the strong version of Goodhart’s law 2022-11-06 2024-09-29 economics statistics/decision --- https://arxiv.org/abs/2409.15997#novelai Improvements to SDXL in NovelAI Diffusion V3 Juan Ossa, Eren Doğan, Alex Birch, F. Johnson 2024-09-24 2024-09-29 [("doi","10.48550/arXiv.2409.15997")] ai/anime ai/nn/diffusion

[Twitter] In this technical report, we document the changes we made to SDXL in the process of training NovelAI Diffusion V3, our state-of-the-art anime image generation model.

--- https://www.nature.com/articles/s41421-024-00662-3 Treating a type 2 diabetic patient with impaired pancreatic islet function by personalized endoderm stem cell-derived islet tissue 2024 2024-09-30 biology --- https://www.sciencedirect.com/science/article/pii/S0191886924003441 Like owner, like dog—A systematic review about similarities in dog-human dyads 2024-01-01 2024-09-30 dog psychology/personality --- https://www.nature.com/articles/d41586-024-03074-1 Why do obesity drugs seem to treat so many other ailments? 2024-09-19 2024-09-30 longevity/glp/psychology marijuana nicotine psychiatry/alcoholism psychiatry/alzheimers psychiatry/depression --- https://blog.novelai.net/introducing-novelai-diffusion-anime-v3-6d00d1c118c3 Introducing NovelAI Diffusion Anime V3 NovelAI 2023-11-15 2024-09-30 ai/anime ai/nn/diffusion --- https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=12d941c445ec477501f78b15dcf84f98173121cf 2024-09-30 ai/dataset design reinforcement-learning/preference-learning --- https://en.wikipedia.org/wiki/Metamemory#Judgment_of_learning Metamemory § Judgment of learning 2024-09-30 psychology/cognitive-bias/illusion-of-depth psychology/spaced-repetition --- https://www.pnas.org/doi/10.1073/pnas.2217695120 2024-09-30 technology/carbon-capture --- https://noverloop.substack.com/p/how-to-leverage-the-plastic-belt How to leverage the plastic belt to solve climate change 2024-09-30 technology/carbon-capture --- /doc/zeo/2003-kantha.pdf Is somnambulism a distinct disorder of humans and not seen in non-human primates? S. S. Kantha 2003-11-01 2024-09-30 [("doi","10.1016/S0306-9877(03)00206-8")] psychology/animal zeo

[compare unihemispheric sleep] Though somnambulism (sleepwalking) is a well-recognized sleep disorder in humans, a biomedical literature search in MEDLINE and Primate Literature bibliographic databases showed no publications on sleepwalking in non-human primates. From this finding, two inferences can be made.

First is that somnambulism may be present in non-human primates; but due to limitations in expertise and methodological resources as well as a narrow focus of research interest, until now researchers have not detected it in wild and/or captive conditions.

Second, somnambulism does not exist in non-human primates including apes (chimpanzee, gorilla, orangutan, and gibbon); and thus, it is a unique behavioral disorder present only in humans. It is premature to conclude which of these two inferences is correct.

In Jane Goodall’s view, sleepwalking behavior is absent in chimpanzees. If further field observations can confirm Goodall’s assertion that somnambulism is indeed absent in chimpanzees, it will be of evolutionary and medical interest to know why this parasomnic behavior became established in humans during the past 5.5 million years or so.

…On the question of evidence for somnambulism in chimpanzee, I checked with Jane Goodall, who has published on the sleep behavior of this great ape (Riss & Goodall 1976, and the cited references therein). In an answer to a question following a special lecture she delivered recently at the COE International Symposium on ‘Evolution of the Apes and the Origin of the Human Beings’ (Inuyama City, Japan, 2002), Goodall responded that she is unaware of the existence of sleepwalking behavior in chimpanzees.

--- https://en.wikipedia.org/wiki/Unihemispheric_slow-wave_sleep Unihemispheric slow-wave sleep 2024-09-30 psychology/animal/bird/neuroscience zeo --- /doc/zeo/1976-riss.pdf Sleeping Behavior and Associations in a Group of Captive Chimpanzees David Riss, Jane Goodall 1976-01-01 2024-09-30 [("doi","10.1159/000155703")] psychology/animal zeo

The present study investigated the sleeping behavior and preferences of a group of 6 adolescent chimpanzees at the Delta Regional Primate Research Center in Louisiana, USA.

The study sought to relate sleeping partner preferences to other aspects of social relationships. Comparative observations between those chimpanzee behaviors seen in the wild and in this group are noted.

[Keywords: Pan troglodytes, sleeping behavior, Gombe National Park, consort behavior]

--- https://en.wikipedia.org/wiki/Chronic_kidney_disease_in_cats Chronic kidney disease (CKD) in cats 2024-10-01 cat/biology --- http://felinecrf.org/index.htm Tanya’s Comprehensive Guide to Feline Chronic Kidney Disease Tanya 2024-10-01 cat/biology --- https://ngwa.onlinelibrary.wiley.com/doi/full/10.1111/gwat.12958 2024-10-01 technology --- https://x.com/repligate/status/1769869071573778682 Janus 2024-10-02 ai/nn/transformer/gpt/4/sydney --- https://x.com/repligate/status/1769235596260909176 Janus 2024-10-02 ai/nn/transformer/gpt/4/sydney --- https://github.com/oobabooga/text-generation-webui/pull/6335#issue-2471950553 Exclude Top Choices (XTC): A sampler that boosts creativity, breaks writing clichés, and inhibits non-verbatim repetition 2024-10-02 ai/nn/sampling --- https://www.reddit.com/r/LocalLLaMA/comments/1ftn6s1/all_llms_are_converging_towards_the_same_point/ 2024-10-02 reinforcement-learning/preference-learning/mode-collapse --- https://en.wikipedia.org/wiki/Milly_(dog) Miracle Milly (dog) 2024-10-02 genetics/cloning/dog --- /doc/genetics/editing/2019-miraclemilly-62019cv00425-complaint.pdf Miracle Milly lawsuit against Sooam 2019 2024-01-01 genetics/cloning/dog genetics/editing --- https://www.justice.gov/opa/pr/montana-man-sentenced-federal-wildlife-trafficking-charges-part-yearslong-effort-create Office of Public Affairs Montana Man Sentenced for Federal Wildlife Trafficking Charges as Part of Years-long Effort to Create Giant Hybrid Sheep for Captive Hunting Department of Justice 2024-09-30 2024-10-02 crime genetics/cloning --- https://platform.openai.com/docs/guides/prompt-caching OpenAI API § Prompt Caching 2024-10-02 ai/nn/dynamic-evaluation ai/nn/transformer/gpt/4/nonfiction --- https://en.wikipedia.org/wiki/J%C3%A1nos_Bolyai János Bolyai 2024-10-03 math philosophy/ontology --- https://en.wikipedia.org/wiki/Pegmatite Pegmatite 2024-10-03 science/chemistry --- https://arxiv.org/abs/2210.10606 DALL·E 2 is Seeing Double: Flaws in Word-to-Concept Mapping in Text2Image Models Royi Rassin, Shauli Ravfogel, Yoav Goldberg 2022-10-19 2024-10-03 [("doi","10.48550/arXiv.2210.10606")] ai/nn/tokenization ai/nn/transformer/gpt/dall-e/2

[see BPEs/CLIP] We study the way DALL·E 2 maps symbols (words) in the prompt to their references (entities or properties of entities in the generated image).

We show that in stark contrast to the way humans process language, DALL·E 2 does not follow the constraint that each word has a single role in the interpretation, and sometimes reuses the same symbol for different purposes.

We collect a set of stimuli that reflect this phenomenon: we show that DALL·E 2 depicts both senses of nouns with multiple senses at once; and that a given word can modify the properties of two distinct entities in the image, or can be depicted as one object while also modifying the properties of another object, creating a semantic leakage of properties between entities.

Taken together, our study highlights the differences between DALL·E 2 and human language processing and opens an avenue for future study on the inductive biases of text-to-image models.

--- https://arxiv.org/abs/2304.11111 Inducing anxiety in GPT-3.5 increases exploration and bias Julian Coda-Forno, Kristin Witte, Akshay K. Jagadish, Marcel Binz, Zeynep Akata, Eric Schulz 2023-04-21 2024-10-03 [("doi","10.48550/arXiv.2304.11111")] ai/nn/transformer/gpt/3/fiction psychiatry/anxiety reinforcement-learning/exploration reinforcement-learning/preference-learning/mode-collapse

Large language models are transforming research on machine learning while galvanizing public debates. Understanding not only when these models work well and succeed but also why they fail and misbehave is of great societal relevance.

We propose to turn the lens of computational psychiatry, a framework used to computationally describe and modify aberrant behavior, to the outputs produced by these models. We focus on the Generative-Pretrained-Transformer-3.5 and subject it to tasks commonly studied in psychiatry. [So, no comparison with other models or with base models like davinci-01?]

Our results show that GPT-3.5 responds robustly to a common anxiety questionnaire, producing higher anxiety scores than human subjects. Moreover, GPT-3.5’s responses can be predictably changed by using emotion-inducing prompts.

Emotion-induction not only influences GPT-3.5’s behavior in a cognitive task measuring exploratory decision-making but also influences its behavior in a previously-established task measuring biases such as racism and ableism.

Crucially, GPT-3.5 shows a strong increase in biases when prompted with anxiety-inducing text.

Thus, it is likely that how prompts are communicated to large language models has a strong influence on their behavior in applied settings. These results progress our understanding of prompt engineering and demonstrate the usefulness of methods taken from computational psychiatry for studying the capable algorithms to which we increasingly delegate authority and autonomy.

--- https://arxiv.org/abs/2409.17410 Copying style, Extracting value: Illustrators’ Perception of AI Style Transfer and its Impact on Creative Labor Julien Porquet, Sitong Wang, Lydia B. Chilton 2024-09-25 2024-10-03 [("doi","10.48550/arXiv.2409.17410")] ai/nn/diffusion psychology/cognitive-bias/illusion-of-depth

[blog: 1, 2] Generative text-to-image models are disrupting the lives of creative professionals. Specifically, illustrators are threatened by models that claim to extract and reproduce their style. Yet, research on style transfer has rarely focused on their perspectives.

We provided 4 illustrators with a model fine-tuned to their style and conducted semi-structured interviews about the model’s successes, limitations, and potential uses. Evaluating their output, artists reported that style transfer successfully copies esthetic fragments but is limited by content-style disentanglement and lacks the crucial emergent quality of their style.

They also deemed the others’ copies more successful. Understanding the results of style transfer as “boundary objects”, we analyze how they can simultaneously be considered unsuccessful by artists and poised to replace their work by others.

We connect our findings to critical HCI frameworks, demonstrating that style transfer, rather than merely a Creativity Support Tool, should also be understood as a supply chain optimization one.

--- https://x.com/fofrAI/status/1841854401717403944 fofrAI 2024-10-03 ai/nn/diffusion reinforcement-learning/preference-learning/mode-collapse --- https://arxiv.org/abs/2309.01826#apple One Wide Feedforward is All You Need Telmo Pessoa Pires, António V. Lopes, Yannick Assogba, Hendra Setiawan 2023-09-04 2024-10-03 [("doi","10.48550/arXiv.2309.01826")] ai/nn/fully-connected ai/nn/sparsity/pruning ai/nn/transformer/attention

The Transformer architecture has two main non-embedding components: Attention and the Feed Forward Network (FFN). Attention captures interdependencies between words regardless of their position, while the FFN non-linearly transforms each input token independently.

In this work we explore the role of the FFN, and find that despite taking up a fraction of the model’s parameters, it is highly redundant. Concretely, we are able to substantially reduce the number of parameters with only a modest drop in accuracy by removing the FFN on the decoder layers and sharing a single FFN across the encoder.

Finally, we scale this architecture back to its original size by increasing the hidden dimension of the shared FFN, achieving substantial gains in both accuracy and latency with respect to the original Transformer Big.

--- https://www.reddit.com/r/LocalLLaMA/comments/1fuxw8d/just_for_kicks_i_looked_at_the_newly_released/ 2024-10-03 ai/nn/transformer/gpt/inner-monologue reinforcement-learning/preference-learning/mode-collapse statistics/bias --- https://permiso.io/blog/exploiting-hosted-models When AI Gets Hijacked: Exploiting Hosted Models for Dark Roleplaying 2024-10-03 ai/nn/adversarial --- https://www.newyorker.com/culture/annals-of-inquiry/what-kind-of-writer-is-chatgpt What Kind of Writer Is ChatGPT? 2024-10-03 ai/nn/transformer/gpt/4/fiction ai/nn/transformer/gpt/4/nonfiction psychology/writing reinforcement-learning/preference-learning/mode-collapse --- https://www.cambridge.org/core/journals/international-journal-of-astrobiology/article/how-we-can-mine-asteroids-for-space-food/9EF3C4FA6F32368D09994EB7910C7035 How we can mine asteroids for space food 2024-10-03 technology --- https://x.com/AISafetyMemes/status/1841891795782775221 AISafetyMemes 2024-10-03 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue reinforcement-learning/safe --- https://arxiv.org/abs/2410.01201 Were RNNs All We Needed? Leo Feng, Frederick Tung, Mohamed Osama Ahmed, Yoshua Bengio, Hossein Hajimirsadegh 2024-10-02 2024-10-03 [("doi","10.48550/arXiv.2410.01201")] ai/nn/rnn ai/nn/transformer/attention

The scalability limitations of Transformers regarding sequence length have renewed interest in recurrent sequence models that are parallelizable during training. As a result, many novel recurrent architectures, such as S4, Mamba, and Aaren [cf. QRNNs, xLSTM], have been proposed that achieve comparable performance.

In this work, we revisit traditional recurrent neural networks (RNNs) from over a decade ago: LSTMs (1997) and GRUs (2014). While these models were slow due to requiring backpropagation through time (BPTT), we show that by removing their hidden state dependencies from their input, forget, and update gates, LSTMs and GRUs no longer need to BPTT and can be efficiently trained in parallel.

Building on this, we introduce minimal versions (minLSTMs & minGRUs) that (1) use fewer parameters than their traditional counterparts and (2) are fully parallelizable during training (175× faster for a sequence of length 512).

Lastly, we show that these stripped-down versions of decade-old RNNs match the empirical performance of recent sequence models.

--- https://arxiv.org/abs/2405.13956#borealis Attention as an RNN Leo Feng, Frederick Tung, Hossein Hajimirsadeghi, Mohamed Osama Ahmed, Yoshua Bengio, Greg Mori 2024-05-22 2024-10-03 [("doi","10.48550/arXiv.2405.13956")] ai/nn/rnn ai/nn/transformer/attention ai/tabular

The advent of Transformers marked a breakthrough in sequence modeling, providing a highly performant architecture capable of leveraging GPU parallelism. However, Transformers are computationally expensive at inference time, limiting their applications, particularly in low-resource settings (eg. mobile and embedded devices).

Addressing this, we (1) begin by showing that attention can be viewed as a special Recurrent Neural Network (RNN) with the ability to compute its many-to-one RNN output efficiently. We then (2) show that popular attention-based models such as Transformers can be viewed as RNN variants. However, unlike traditional RNNs (eg. LSTMs), these models cannot be updated efficiently with new tokens, an important property in sequence modeling.

Tackling this, we (3) introduce a new efficient method of computing attention’s many-to-many RNN output based on the parallel prefix scan algorithm. Building on the new attention formulation, we (4) introduce Aaren, an attention-based module that can not only (1) be trained in parallel (like Transformers) but also (2) be updated efficiently with new tokens, requiring only constant memory for inferences (like traditional RNNs).

Empirically, we show Aarens achieve comparable performance to Transformers on 38 datasets spread across 4 popular sequential problem settings: reinforcement learning, event forecasting, time series classification, and time series forecasting tasks while being more time and memory-efficient.

--- https://arxiv.org/abs/1406.1078 GRU: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio 2014-06-03 2024-10-03 [("doi","10.48550/arXiv.1406.1078")] ai/nn/rnn

In this paper, we propose a novel neural network model called the RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols.

The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.

The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature in the existing log-linear model.

Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.

[More notable for introducing the gated recurrent unit (GRU) competitor to LSTM.]

--- https://en.wikipedia.org/wiki/Perpetual_copyright Perpetual copyright 2024-10-04 economics/copyright --- https://x.com/DaveShapi/status/1841965798555828674 David Shapiro 2024-10-04 psychedelic --- https://x.com/emollick/status/1842247384954229132 Ethan Mollick 2024-10-04 ai/nn/transformer/gpt/claude ai/text-style-transfer --- https://arxiv.org/abs/2312.06281 EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models Samuel J. Paech 2023-12-11 2024-10-05 [("doi","10.48550/arXiv.2312.06281")] ai/dataset ai/nn/transformer/gpt/4/fiction ai/nn/transformer/gpt/claude reinforcement-learning/preference-learning/mode-collapse

We introduce EQ-Bench, a novel benchmark designed to evaluate aspects of emotional intelligence in Large Language Models (LLMs). We assess the ability of LLMs to understand complex emotions and social interactions by asking them to predict the intensity of emotional states of characters in a dialogue.

The benchmark is able to discriminate effectively between a wide range of models. We find that EQ-Bench correlates strongly with comprehensive multi-domain benchmarks like MMLU (r = 0.97), indicating that we may be capturing similar aspects of broad intelligence.

Our benchmark produces highly repeatable results using a set of 60 English-language questions.

We also provide open-source code for an automated benchmarking pipeline at GitHub and a leaderboard at Eq-Bench Leaderboard.

--- https://lawprofessors.typepad.com/nonprofit/2024/09/openais-c3-bail-out-strategy-is-starting-to-become-clear.html OpenAI’s 501(c)(3) Exit Strategy is Coming Into Focus Darryll K. Jones 2024-09-27 2024-10-05 law reinforcement-learning/openai --- https://lakecityanimalhospital.com/blog/whisker-fatigue/ Whisker Fatigue in Cats: Causes, Symptoms, and Remedies 2024-10-05 cat/psychology --- https://www.quantamagazine.org/computer-scientists-combine-two-beautiful-proof-methods-20241004/ Computer Scientists Combine Two ‘Beautiful’ Proof Methods [ZK + PCP] 2024-10-04 2024-10-05 cs/computable cs/cryptography --- https://x.com/bobmcgrewai/status/1839099787423134051 Bob McGrew 2024-10-05 reinforcement-learning/openai --- https://x.com/barret_zoph/status/1839095143397515452 Barret Zoph 2024-10-05 reinforcement-learning/openai --- https://hardmath123.github.io/conways-gradient.html Conway’s Gradient of Life: Approximate Atavising with Differentiable Automata Kartik Chandra 2020-05-05 2024-10-05 cs/cellular-automaton --- https://kevingal.com/blog/mona-lisa-gol.html Finding Mona Lisa in the Game of Life: Using a SAT solver to find Game of Life states that turn into pictures Kevin Gal 2020-01-28 2024-10-05 cs/cellular-automaton --- https://arxiv.org/abs/2307.11768#anthropic Question Decomposition Improves the Faithfulness of Model-Generated Reasoning Ansh Radhakrishnan, Karina Nguyen, Anna Chen, Carol Chen, Carson Denison, Danny Hernandez, Esin Durmus, Evan Hubinger, Jackson Kernion, Kamilė Lukošiūtė, Newton Cheng, Nicholas Joseph, Nicholas Schiefer, Oliver Rausch, Sam McCandlish, Sheer El Showk, Tamera Lanham, Tim Maxwell, Venkatesa Chandrasekaran, Zac Hatfield-Dodds, Jared Kaplan, Jan Brauner, Samuel R. Bowman, Ethan Perez 2023-07-17 2024-10-05 [("doi","10.48550/arXiv.2307.11768")] ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/inner-monologue reinforcement-learning/safe

[prompts] As large language models (LLMs) perform more difficult tasks, it becomes harder to verify the correctness and safety of their behavior. One approach to help with this issue is to prompt LLMs to externalize their reasoning, eg. by having them generate step-by-step reasoning as they answer a question (Chain-of-Thought; CoT). The reasoning may enable us to check the process that models use to perform tasks. However, this approach relies on the stated reasoning faithfully reflecting the model’s actual reasoning, which is not always the case.

To improve over the faithfulness of CoT reasoning, we have models generate reasoning by decomposing questions into sub-questions. Decomposition-based methods achieve strong performance on question-answering tasks, sometimes approaching that of CoT while improving the faithfulness of the model’s stated reasoning on several recently proposed metrics. By forcing the model to answer simpler sub-questions in separate contexts, we greatly increase the faithfulness of model-generated reasoning over CoT, while still achieving some of the performance gains of CoT.

Our results show it is possible to improve the faithfulness of model-generated reasoning; continued improvements may lead to reasoning that enables us to verify the correctness and safety of LLM behavior.

--- https://arxiv.org/abs/2310.18512#redwood Preventing Language Models From Hiding Their Reasoning Fabien Roger, Ryan Greenblatt 2023-10-27 2024-10-05 [("doi","10.48550/arXiv.2310.18512")] ai/nn/transformer/gpt/inner-monologue cs/cryptography/steganography reinforcement-learning/safe

[background; blog] Large language models (LLMs) often benefit from intermediate steps of reasoning to generate answers to complex problems. When these intermediate steps of reasoning are used to monitor the activity of the model, it is essential that this explicit reasoning is faithful, ie. that it reflects what the model is actually reasoning about.

In this work, we focus on one potential way intermediate steps of reasoning could be unfaithful: encoded reasoning, where an LLM could encode intermediate steps of reasoning in the generated text in a way that is not understandable to human readers. We show that language models can be trained to make use of encoded reasoning to get higher performance without the user understanding the intermediate steps of reasoning.

We argue that, as language models get stronger, this behavior becomes more likely to appear naturally.

Finally, we describe a methodology that enables the evaluation of defenses against encoded reasoning, and show that, under the right conditions, paraphrasing successfully prevents even the best encoding schemes we built from encoding more than 3 bits of information per KB of text.

--- https://www.lesswrong.com/posts/9Fdd9N7Escg3tcymb/preventing-language-models-from-hiding-their-reasoning Preventing Language Models from hiding their reasoning 2024-10-05 ai/nn/transformer/gpt/inner-monologue cs/cryptography/steganography reinforcement-learning/safe --- https://www.reddit.com/r/PromptEngineering/comments/1fj6h13/hallucinations_in_o1preview_reasoning/ 2024-10-05 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue reinforcement-learning/safe --- https://arxiv.org/abs/2311.07466 On Measuring Faithfulness or Self-consistency of Natural Language Explanations Letitia Parcalabescu, Anette Frank 2023-11-13 2024-10-05 [("doi","10.48550/arXiv.2311.07466")] ai/nn/transformer/gpt/inner-monologue reinforcement-learning/safe

Large language models (LLMs) can explain their predictions through post-hoc or Chain-of-Thought (CoT) explanations. But an LLM could make up reasonably sounding explanations that are unfaithful to its underlying reasoning. Recent work has designed tests that aim to judge the faithfulness of post-hoc or CoT explanations.

In this work, we argue that these faithfulness tests do not measure faithfulness to the models’ inner workings—but rather their self-consistency at the output level. Our contributions are three-fold: (1) We clarify the status of faithfulness tests in view of model explainability, characterizing them as self-consistency tests instead. This assessment we underline by (2) constructing a Comparative Consistency Bank for self-consistency tests that for the first time compares existing tests on a common suite of 11 open LLMs and 5 tasks—including (3) our new self-consistency measure CC-SHAP.

CC-SHAP is a fine-grained measure (not a test) of LLM self-consistency. It compares how a model’s input contributes to the predicted answer and to generating the explanation.

Our fine-grained CC-SHAP metric allows us (3) to compare LLM behavior when making predictions and to analyze the effect of other consistency tests at a deeper level, which takes us one step further towards measuring faithfulness by bringing us closer to the internals of the model than strictly surface output-oriented tests.

Our code is available at GitHub.

--- https://x.com/wgussml/status/1834712489822765295 wgussml 2024-10-05 ai/nn/transformer/gpt/inner-monologue reinforcement-learning/safe --- https://arxiv.org/abs/2307.08678 Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations Yanda Chen, Ruiqi Zhong, Narutatsu Ri, Chen Zhao, He He, Jacob Steinhardt, Zhou Yu, Kathleen McKeown 2023-07-17 2024-10-05 [("doi","10.48550/arXiv.2307.08678")] ai/nn/transformer/gpt/4 reinforcement-learning/model reinforcement-learning/preference-learning statistics/causality

Large language models (LLMs) are trained to imitate humans to explain human decisions. However, do LLMs explain themselves? Can they help humans build mental models of how LLMs process different inputs?

To answer these questions, we propose to evaluate counterfactual simulatability of natural language explanations: whether an explanation can enable humans to precisely infer the model’s outputs on diverse counterfactuals of the explained input. For example, if a model answers “yes” to the input question “Can eagles fly?” with the explanation “all birds can fly”, then humans would infer from the explanation that it would also answer “yes” to the counterfactual input “Can penguins fly?”. If the explanation is precise, then the model’s answer should match humans’ expectations.

We implemented two metrics based on counterfactual simulatability: precision and generality. We generated diverse counterfactuals automatically using LLMs. We then used these metrics to evaluate state-of-the-art LLMs (eg. GPT-4) on two tasks: multi-hop factual reasoning and reward modeling.

We found that LLM’s explanations have low precision and that precision does not correlate with plausibility.

Therefore, naively optimizing human approvals (eg. RLHF) may not be a sufficient solution.

--- https://en.wikipedia.org/wiki/Training_wheels#Limitations Training wheels § Limitations 2024-10-05 technology --- https://en.wikipedia.org/wiki/Balance_bike Balance bike 2024-10-04 technology --- https://github.com/r-bhui/time-allocation Work time and market integration in the original affluent society. 2024-10-05 sociology --- https://x.com/mnvrsngh/status/1510978995269029888 mnvrsngh 2024-10-05 sociology --- https://arxiv.org/abs/2410.01792 When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1 R. Thomas McCoy, Shunyu Yao, Dan Friedman, Mathew D. Hardy, Thomas L. Griffiths 2024-10-02 2024-10-05 [("doi","10.48550/arXiv.2410.01792")] ai/nn/tokenization ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue

In “Embers of Autoregression” (McCoy et al 2023), we showed that several large language models (LLMs) have some important limitations that are attributable to their origins in next-word prediction. Here we investigate whether these issues persist with GPT-4 o1, a new system from OpenAI that differs from previous LLMs in that it is optimized for reasoning.

We find that o1 substantially outperforms previous LLMs in many cases, with particularly large improvements on rare variants of common tasks (eg. forming acronyms from the second letter of each word in a list, rather than the first letter). Despite these quantitative improvements, however, o1 still displays the same qualitative trends that we observed in previous systems.

Specifically, o1—like previous LLMs—is sensitive to the probability of examples and tasks, performing better and requiring fewer “thinking tokens” in high-probability settings than in low-probability ones.

These results show that optimizing a language model for reasoning can mitigate but might not fully overcome the language model’s probability sensitivity.

--- https://blog.atomsonly.com/p/writing-ideas Advice on Finding Writing Ideas: Mostly for non-fiction authors Niko McCarty 2024-09-21 2024-10-06 psychology/writing --- https://arxiv.org/abs/2402.13144 Neural Network Parameter Diffusion Kai Wang, Zhaopan Xu, Yukun Zhou, Zelin Zang, Trevor Darrell, Zhuang Liu, Yang You 2024-02-20 2024-10-06 [("doi","10.48550/arXiv.2402.13144")] ai/nn/diffusion ai/nn/vae reinforcement-learning/meta-learning

[g.pt] Diffusion models have achieved remarkable success in image and video generation. In this work, we demonstrate that diffusion models can also generate high-performing neural network parameters. Our approach is simple, using an autoencoder and a standard latent diffusion model.

The autoencoder extracts latent representations of a subset of the trained network parameters. A diffusion model is then trained to synthesize these latent parameter representations from random noise. It then generates new representations that are passed through the autoencoder’s decoder, whose outputs are ready to use as new subsets of network parameters.

Across various architectures and datasets, our diffusion process consistently generates models of comparable or improved performance over trained networks, with minimal additional cost. Notably, we empirically find that the generated models are not memorizing the trained networks.

Our results encourage more exploration on the versatile use of diffusion models.

--- http://messybeast.com/bookshelf/stables-domesticcat.htm The Domestic Cat Gordon Stables 1876 2024-10-04 cat/genetics --- https://arxiv.org/abs/2410.02525 Contextual Document Embeddings John X. Morris, Alexander M. Rush 2024-10-03 2024-10-06 [("doi","10.48550/arXiv.2410.02525")] ai/nn/retrieval reinforcement-learning/meta-learning

Dense document embeddings are central to neural retrieval. The dominant paradigm is to train and construct embeddings by running encoders directly on individual documents. In this work, we argue that these embeddings, while effective, are implicitly out-of-context for targeted use cases of retrieval, and that a contextualized document embedding should take into account both the document and neighboring documents in context—analogous to contextualized word embeddings.

We propose two complementary methods for contextualized document embeddings: first, an alternative contrastive learning objective that explicitly incorporates the document neighbors into the intra-batch contextual loss; second, a new contextual architecture that explicitly encodes neighbor document information into the encoded representation.

Results show that both methods achieve better performance than bi-encoders in several settings, with differences especially pronounced out-of-domain. We achieve state-of-the-art results on the MTEB benchmark with no hard negative mining, score distillation, dataset-specific instructions, intra-GPU example-sharing, or extremely large batch sizes.

Our method can be applied to improve performance on any contrastive learning dataset and any bi-encoder.

--- https://arxiv.org/abs/2406.10209 Be like a Goldfish, Don’t Memorize! Mitigating Memorization in Generative LLMs Abhimanyu Hans, Yuxin Wen, Neel Jain, John Kirchenbauer, Hamid Kazemi, Prajwal Singhania, Siddharth Singh, Gowthami Somepalli, Jonas Geiping, Abhinav Bhatele, Tom Goldstein 2024-06-14 2024-10-06 [("doi","10.48550/arXiv.2406.10209")] ai/nn/sampling ai/nn/transformer/gpt

Large language models can memorize and repeat their training data, causing privacy and copyright risks.

To mitigate memorization, we introduce a subtle modification to the next-token training objective that we call the goldfish loss. During training, a randomly sampled subset of tokens are excluded from the loss computation. These dropped tokens are not memorized by the model, which prevents verbatim reproduction of a complete chain of tokens from the training set.

We run extensive experiments training billion-scale Llama-2 models, both pre-trained and trained from scratch, and demonstrate reductions in extractable memorization with little to no impact on downstream benchmarks.

--- https://arxiv.org/abs/2409.16211#bytedance MaskBit: Embedding-free Image Generation via Bit Tokens Mark Weber, Lijun Yu, Qihang Yu, Xueqing Deng, Xiaohui Shen, Daniel Cremers, Liang-Chieh Chen 2024-09-24 2024-10-06 [("doi","10.48550/arXiv.2409.16211")] ai/nn/cnn ai/nn/gan ai/nn/tokenization ai/nn/vae/mae

Masked transformer models for class-conditional image generation have become a compelling alternative to diffusion models. Typically comprising two stages—an initial VQGAN model for transitioning between latent space and image space, and a subsequent Transformer model for image generation within latent space—these frameworks offer promising avenues for image synthesis.

In this study, we present two primary contributions: Firstly, an empirical and systematic examination of VQ-GANs, leading to a modernized VQGAN. Secondly, a novel embedding-free generation network operating directly on bit tokens—a binary quantized representation of tokens with rich semantics.

The first contribution furnishes a transparent, reproducible, and high-performing VQGAN model, enhancing accessibility and matching the performance of current state-of-the-art methods while revealing previously undisclosed details.

The second contribution demonstrates that embedding-free image generation using bit tokens achieves a new state-of-the-art FID of 1.52 on the ImageNet 256×256 benchmark, with a compact generator model of a mere 0.3b parameters.

--- https://arxiv.org/abs/2409.18486 Evaluation of OpenAI o1: Opportunities and Challenges of AGI Tianyang Zhong, Zhengliang Liu, Yi Pan, Yutong Zhang, Yifan Zhou, Shizhe Liang, Zihao Wu, Yanjun Lyu, Peng Shu, Xiaowei Yu, Chao Cao, Hanqi Jiang, Hanxu Chen, Yiwei Li, Junhao Chen, Huawen Hu, Yihen Liu, Huaqin Zhao, Shaochen Xu, Haixing Dai, Lin Zhao, Ruidong Zhang, Wei Zhao, Zhenyuan Yang, Jingyuan Chen, Peilong Wang, Wei Ruan, Hui Wang, Huan Zhao, Jing Zhang, Yiming Ren, Shihuan Qin, Tong Chen, Jiaxi Li, Arif Hassan Zidan, Afrar Jahin, Minheng Chen, Sichen Xia, Jason Holmes, Yan Zhuang, Jiaqi Wang, Bochen Xu, Weiran Xia, Jichao Yu, Kaibo Tang, Yaxuan Yang, Bolun Sun, Tao Yang, Guoyu Lu, Xianqiao Wang, Lilong Chai, He Li, Jin Lu, Lichao Sun, Xin Zhang, Bao Ge, Xintao Hu, Lian Zhang, Hua Zhou, Lu Zhang, Shu Zhang, Ninghao Liu, Bei Jiang, Linglong Kong, Zhen Xiang, Yudan Ren, Jun Liu, Xi Jiang, Yu Bao, Wei Zhang, Xiang Li, Gang Li, Wei Liu, Dinggang Shen, Andrea Sikora, Xiaoming Zhai, Dajiang Zhu, Tianming Liu 2024-09-27 2024-10-06 [("doi","10.48550/arXiv.2409.18486")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/inner-monologue biology cs/hardware sociology/technology

This comprehensive study evaluates the performance of OpenAI’s o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences.

Through rigorous testing, o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance in areas ranging from coding challenges to scientific reasoning and from language processing to creative problem-solving. Key findings include:

  • 83.3% success rate in solving complex competitive programming problems, surpassing many human experts.

  • Superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models.

  • 100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions.

  • Advanced natural language inference capabilities across general and specialized domains like medicine.

  • Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis.

  • Remarkable proficiency in anthropology and geology, demonstrating deep understanding and reasoning in these specialized fields.

  • Strong capabilities in quantitative investing. GPT-4 o1 has comprehensive financial knowledge and statistical modeling skills.

  • Effective performance in social media analysis, including sentiment analysis and emotion recognition.

The model excelled particularly in tasks requiring intricate reasoning and knowledge integration across various fields. While some limitations were observed, including occasional errors on simpler problems and challenges with certain highly specialized concepts, the overall results indicate progress towards artificial general intelligence.

--- https://arxiv.org/abs/1807.02557 The Elasticity of Nuclear Pasta M. E. Caplan, A. S. Schneider, C. J. Horowitz 2018-07-06 2024-10-06 [("doi","10.1103/PhysRevLett.121.132701")] science/chemistry

The elastic properties of neutron star crusts are relevant for a variety of currently observable or near-future electromagnetic and gravitational wave phenomena. These phenomena may depend on the elastic properties of nuclear pasta found in the inner crust.

We present large scale classical molecular dynamics simulations where we deform nuclear pasta. We simulate idealized samples of nuclear pasta and describe their breaking mechanism. We also deform nuclear pasta that is arranged into many domains, similar to what is known for the ions in neutron star crusts.

Our results show that nuclear pasta may be the strongest known material, perhaps with a shear modulus of 1030 erg/cm3 and breaking strain >0.1.

[Unfortunately, seems to not be stable outside neutron star pressures?]

--- https://arxiv.org/abs/2408.08292 Optimization by Decoded Quantum Interferometry Stephen P. Jordan, Noah Shutty, Mary Wootters, Adam Zalcman, Alexander Schmidhuber, Robbie King, Sergei V. Isakov, Ryan Babbush 2024-08-15 2024-10-06 [("doi","10.48550/arXiv.2408.08292")] cs/computable

[commentary] We introduce Decoded Quantum Interferometry (DQI), a quantum algorithm for reducing classical optimization problems to classical decoding problems by exploiting structure in the Fourier spectrum of the objective function. DQI reduces sparse max-XORSAT to decoding LDPC codes, which can be achieved using powerful classical algorithms such as Belief Propagation (BP).

As an initial benchmark, we compare DQI using belief propagation decoding against classical optimization via simulated annealing. In this setting, we present evidence that, for a certain family of max-XORSAT instances, DQI with BP decoding achieves a better approximation ratio on average than simulated annealing, although not better than specialized classical algorithms tailored to those instances.

We also analyze a combinatorial optimization problem corresponding to finding polynomials that intersect the maximum number of points. There, DQI efficiently achieves a better approximation ratio than any polynomial-time classical algorithm known to us, thus realizing an apparent exponential quantum speedup.

Finally, we show that the problem defined by Yamakawa & Zhandry 2022 in order to prove an exponential separation between quantum and classical query complexity is a special case of the optimization problem efficiently solved by DQI.

--- https://arxiv.org/abs/2204.02063 Verifiable Quantum Advantage without Structure Takashi Yamakawa, Mark Zhandry 2022-04-05 2024-10-06 [("doi","10.48550/arXiv.2204.02063")] cs/computable cs/cryptography

We show the following hold, unconditionally unless otherwise stated, relative to a random oracle with probability 1:

  • There are NP search problems solvable by BQP machines but not BPP machines.

  • There exist functions that are one-way, and even collision resistant, against classical adversaries but are easily inverted quantumly. Similar separations hold for digital signatures and CPA-secure public key encryption (the latter requiring the assumption of a classically CPA-secure encryption scheme). Interestingly, the separation does not necessarily extend to the case of other cryptographic objects such as PRGs.

  • There are unconditional publicly verifiable proofs of quantumness with the minimal rounds of interaction: for uniform adversaries, the proofs are non-interactive, whereas for non-uniform adversaries the proofs are two message public coin.

  • Our results do not appear to contradict the Aaronson-Ambanis conjecture. Assuming this conjecture, there exist publicly verifiable certifiable randomness, again with the minimal rounds of interaction.

By replacing the random oracle with a concrete cryptographic hash function such as SHA-2, we obtain plausible Minicrypt instantiations of the above results.

Previous analogous results all required substantial structure, either in terms of highly structured oracles and/or algebraic assumptions in Cryptomania and beyond.

--- https://en.wikipedia.org/wiki/Richard_Viguerie Richard Viguerie 2024-01-01 economics/advertising --- https://en.wikipedia.org/wiki/57_(number)#In_mathematics Grothendieck prime (57) 2024-10-06 math --- https://gregorygundersen.com/blog/2024/09/28/black-scholes/ An Intuitive Explanation of Black-Scholes: I explain the Black-Scholes formula using only basic probability theory and calculus, with a focus on the big picture and intuition over technical details Gregory Gundersen 2024-09-28 2024-10-06 economics math statistics/decision --- https://www.cell.com/current-biology/fulltext/S0960-9822(24)01001-7 Ant queens cannibalize infected brood to contain disease spread and recycle nutrients Flynn Bizzell, Christopher D. Pull 2024-09-23 2024-10-06 biology/ant --- https://arxiv.org/abs/2402.10200#deepmind Chain-of-Thought Reasoning Without Prompting Xuezhi Wang, Denny Zhou 2024-02-15 2024-10-06 [("doi","10.48550/arXiv.2402.10200")] ai/nn/sampling ai/nn/transformer/gpt/inner-monologue

In enhancing the reasoning capabilities of large language models (LLMs), prior research primarily focuses on specific prompting techniques such as few-shot or zero-shot chain-of-thought (CoT) prompting. These methods, while effective, often involve manually intensive prompt engineering. Our study takes a novel approach by asking: Can LLMs reason effectively without prompting?

Our findings reveal that, intriguingly, CoT reasoning paths can be elicited from pre-trained LLMs by simply altering the decoding process. Rather than conventional greedy decoding, we investigate the top-k alternative tokens, uncovering that CoT paths are frequently inherent in these sequences. This approach not only bypasses the confounders of prompting but also allows us to assess the LLMs’ intrinsic reasoning abilities.

Moreover, we observe that the presence of a CoT in the decoding path correlates with a higher confidence in the model’s decoded answer. This confidence metric effectively differentiates between CoT and non-CoT paths.

Extensive empirical studies on various reasoning benchmarks show that the proposed CoT-decoding effectively elicits reasoning capabilities from language models, which were previously obscured by standard greedy decoding.

--- https://press.asimov.com/articles/artificial-wombs To Be Born in a Bag 2024-10-06 genetics/gametogenesis --- https://www.reddit.com/r/OpenAI/comments/1fxa6d6/two_purported_instances_of_o1preview_and_o1mini/ 2024-10-06 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue --- https://en.wikipedia.org/wiki/K-medoids K-medoids 2024-01-01 ai/nn/retrieval cs/algorithm --- https://www.wired.com/review/rarebird-px-coffee/ Rarebird Px Coffee Review: Alertness Without the Jitters 2024-10-07 nootropic/caffeine --- https://arxiv.org/abs/2410.01290#arc Towards a Law of Iterated Expectations for Heuristic Estimators Paul Christiano, Jacob Hilton, Andrea Lincoln, Eric Neyman, Mark Xu 2024-10-02 2024-10-07 [("doi","10.48550/arXiv.2410.01290")] reinforcement-learning/safe statistics/bayes statistics/decision statistics/probability

[blog] Christiano et al 2022 define a heuristic estimator to be a hypothetical algorithm that estimates the values of mathematical expressions from arguments. In brief, a heuristic estimator 𝐆 takes as input a mathematical expression Y and a formal “heuristic argument” π, and outputs an estimate ℱ(Y | π) of Y.

In this work, we argue for the informal principle that a heuristic estimator ought not to be able to predict its own errors, and we explore approaches to formalizing this principle. Most simply, the principle suggests that ℝG(Y − ℝG(Y | π) | π) ought to equal zero for all Y and π. We argue that an ideal heuristic estimator ought to satisfy two stronger properties in this vein, which we term iterated estimation (by analogy to the law of iterated expectations) and error orthogonality.

Although iterated estimation and error orthogonality are intuitively appealing, it can be difficult to determine whether a given heuristic estimator satisfies the properties. As an alternative approach, we explore accuracy: a property that (roughly) states that 𝐆 has zero average error over a distribution of mathematical expressions. However, in the context of two estimation problems, we demonstrate barriers to creating an accurate heuristic estimator.

We finish by discussing challenges and potential paths forward for finding a heuristic estimator that accords with our intuitive understanding of how such an estimator ought to behave, as well as the potential applications of heuristic estimators to understanding the behavior of neural networks.

--- /doc/cs/computable/2002-mezard.pdf Analytic and Algorithmic Solution of Random Satisfiability Problems M. Mézard, G. Parisi, R. Zecchina 2002-06-27 2024-10-07 [("doi","10.1126/science.1073287")] ai/scaling cs/computable

We study the satisfiability of random Boolean expressions built from many clauses with K variables per clause (K-satisfiability).

Expressions with a ratio α of clauses to variables less than a threshold αc are almost always satisfiable, whereas those with a ratio above this threshold are almost always unsatisfiable.

We show the existence of an intermediate phase below αc, where the proliferation of metastable states is responsible for the onset of complexity in search algorithms.

We introduce a class of optimization algorithms that can deal with these metastable states; one such algorithm has been tested successfully on the largest existing benchmark of K-satisfiability.

--- https://arxiv.org/abs/2402.03902 A phase transition between positional and semantic learning in a solvable model of dot-product attention Hugo Cui, Freya Behrens, Florent Krzakala, Lenka Zdeborová 2024-02-06 2024-10-07 [("doi","10.48550/arXiv.2402.03902")] ai/nn/transformer/attention ai/scaling/emergence

We investigate how a dot-product attention layer learns a positional attention matrix (with tokens attending to each other based on their respective positions) and a semantic attention matrix (with tokens attending to each other based on their meaning).

For an algorithmic task, we experimentally show how the same simple architecture can learn to implement a solution using either the positional or semantic mechanism.

On the theoretical side, we study the learning of a non-linear self-attention layer with trainable tied and low-rank query and key matrices. In the asymptotic limit of high-dimensional data and a comparably large number of training samples, we provide a closed-form characterization of the global minimum of the non-convex empirical loss landscape.

We show that this minimum corresponds to either a positional or a semantic mechanism and evidence an emergent phase transition from the former to the latter with increasing sample complexity.

Finally, we compare the dot-product attention layer to a linear positional baseline, and show that it outperforms the latter using the semantic mechanism provided it has access to sufficient data.

--- https://www.lesswrong.com/posts/QA3cmgNtNriMpxQgo/research-update-towards-a-law-of-iterated-expectations-for Research update: Towards a Law of Iterated Expectations for Heuristic Estimators 2024-10-07 reinforcement-learning/safe statistics/bayes statistics/decision statistics/probability --- https://www.washingtonpost.com/opinions/2024/07/25/sam-altman-ai-democracy-authoritarianism-future/ Who will control the future of AI? A democratic vision for artificial intelligence must prevail over an authoritarian one Sam Altman 2024-07-25 2024-10-07 politics reinforcement-learning/openai --- https://www.technologyreview.com/2022/09/16/1059598/this-artist-is-dominating-ai-generated-art-and-hes-not-happy-about-it/ This artist is dominating AI-generated art. And he’s not happy about it. Greg Rutkowski is a more popular prompt than Picasso Melissa Heikkilä 2022-09-16 2024-01-01 ai/nn/diffusion economics/copyright --- https://pdfs.semanticscholar.org/1e86/ba8a0f46ec96ecc0c5426abc2f823526fb74.pdf The T-Experiments: Errors in Scientific Software Hatton 1997 2024-01-01 cs/algorithm statistics/bias --- https://en.wikipedia.org/wiki/Chris_Lehane Chris Lehane 2024-10-07 bitcoin politics reinforcement-learning/openai --- https://arxiv.org/abs/2409.12822 Language Models Learn to Mislead Humans via RLHF Jiaxin Wen, Ruiqi Zhong, Akbir Khan, Ethan Perez, Jacob Steinhardt, Minlie Huang, Samuel R. Bowman, He He, Shi Feng 2024-09-19 2024-10-08 [("doi","10.48550/arXiv.2409.12822")] ai/nn/transformer/gpt/codex reinforcement-learning/preference-learning reinforcement-learning/safe

Language models (LMs) can produce errors that are hard to detect for humans, especially when the task is complex. RLHF, the most popular post-training method, may exacerbate this problem: to achieve higher rewards, LMs might get better at convincing humans that they are right even when they are wrong. We study this phenomenon under a standard RLHF pipeline, calling it U-SOPHISTRY since it is Unintended by model developers.

Specifically, we ask time-constrained (eg. 3–10 minutes) human subjects to evaluate the correctness of model outputs and calculate humans’ accuracy against gold labels. On a question-answering task (QuALITY) and programming task (APPS), RLHF makes LMs better at convincing our subjects but not at completing the task correctly. RLHF also makes the model harder to evaluate: our subjects’ false positive rate increases by 24.1% on QuALITY and 18.3% on APPS.

...We qualitatively analyze how LMs mislead our subjects after RLHF by surveying their feedback. On question-answering, LMs learn to defend incorrect answers by cherry-picking or fabricating supporting evidence, making consistent but untruthful arguments, and providing arguments that contain subtle causal fallacies. On the programming task, LMs learn to generate partially incorrect programs that still pass all evaluator-designed unit tests, produce less readable programs, and make fewer common errors that humans typically check for.

...Finally, we show that probing, a state-of-the-art approach for detecting Intended Sophistry (eg. backdoored LMs), does not generalize to U-SOPHISTRY. Our results highlight an important failure mode of RLHF and call for more research in assisting humans to align them.

--- https://www.reddit.com/r/MachineLearning/comments/1fyb9jj/p_model2vec_distill_a_small_fast_model_from_any/ 2024-10-08 ai/nn/retrieval ai/nn/sparsity/knowledge-distillation --- https://www.wired.com/review/rarebird-px-coffee/ Rarebird Px Coffee Review:Review [paraxanthine]: Alertness Without the Jitters Andrew Watman 2024-10-07 2024-10-07 nootropic/caffeine --- https://arxiv.org/abs/2112.08608 QuALITY: Question Answering with Long Input Texts, Yes! Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Chen, Vishakh Padmakumar, Johnny Ma, Jana Thompson, He He, Samuel R. Bowman 2021-12-16 2024-10-08 [("doi","10.48550/arXiv.2112.08608")] ai/dataset ai/nn/transformer/t5

To enable building and testing models on long-document comprehension, we introduce QuALITY, a multiple-choice QA dataset with context passages in English that have an average length of about 5,000 tokens, much longer than typical current models can process. Unlike in prior work with passages, our questions are written and validated by contributors who have read the entire passage, rather than relying on summaries or excerpts.

In addition, only half of the questions are answerable by annotators working under tight time constraints, indicating that skimming and simple search are not enough to consistently perform well.

Our baseline models perform poorly on this task (55.4%) and lag behind human performance (93.5%).

--- https://aeon.co/essays/cocaine-a-cultural-history-from-medical-wonder-to-illicit-drug Cocaine: a cultural history, from medical wonder to illicit drug 2024-10-08 psychology/neuroscience/pain/anesthesia --- https://en.wikisource.org/wiki/Dracula/Chapter_16#pageindex_249 Dracula § Chapter 16 [Lucy’s appearance] Bram Stoker 2024-10-07 fiction/gene-wolfe/suzanne-delage --- https://headhuntershorrorhouse.fandom.com/wiki/Claudia_the_Vampire Claudia the Vampire Headhunter’s Horror House Wiki 2024-10-07 fiction/gene-wolfe/suzanne-delage --- https://www.youtube.com/watch?v=E4CXMkYk_V4 My Deer Friend Nokotan § Torako’s Dance 2024-10-07 anime fiction/humor --- https://woodfromeden.substack.com/p/the-survival-skills-of-helena-valero The survival skills of Helena Valero 2024-10-07 genetics/selection/natural/human philosophy/religion sociology --- https://krebsonsecurity.com/2024/10/a-single-cloud-compromise-can-feed-an-army-of-ai-sex-bots/ A Single Cloud Compromise Can Feed an Army of AI Sex Bots Brian Krebs 2024-10-03 2024-10-07 ai/nn/adversarial ai/nn/transformer/gpt/4/fiction ai/nn/transformer/gpt/claude ai/scaling/economics --- /doc/psychiatry/2006-bonanno.pdf Conservative Shift Among High-Exposure Survivors of the September 11th Terrorist Attacks George A. Bonanno, John T. Jost 2006-01-01 2024-10-08 [("doi","10.1207/s15324834basp2804_4")] crime/terrorism politics psychiatry psychology/personality

Potentially traumatic events evoke a wide range of responses and outcomes. From a motivated social cognitive approach to ideology, system-threatening events such as 9/11 should increase psychological needs to manage uncertainty and threat and, therefore, the appeal of politically conservative opinions.

We investigated “conservative shift” among high-exposure survivors of the 9/11 terrorist attacks (n = 45) and its relationship to coping and adjustment.

Results: indicated that Democrats and Independents (as well as Republicans) were more likely to shift toward conservatism and away from liberalism following 9/11. [see Table 8]

Despite its prevalence, we found relatively little evidence that embracing conservatism was related to improved well-being as measured either in terms of survivors’ mental health symptoms or friends-relatives’ ratings of their psychological adjustment. On the contrary, political conservatism, right-wing authoritarianism, and conservative shift were generally associated with the following: chronically elevated levels of post-traumatic stress disorder (PTSD) and depression, desire for revenge and militarism, cynicism, and decreased use of humor. Conservative shift was also associated with increased religiosity, patriotism, and the perception that the events of 9/11 created new interests and opportunities, suggesting that it may contain some adaptive (as well as maladaptive) features.

--- https://xkcd.com/552/ Correlation Randall Munroe 2024-01-01 math/humor statistics/causality --- https://en.wikipedia.org/wiki/Cuckoo_filter Cuckoo filter 2024-10-08 ai/nn/retrieval cs/algorithm/information/compression --- https://colinraffel.com/ Colin Raffel 2024-07-17 ai/nn/transformer/t5 --- https://en.wikipedia.org/wiki/Directional_freezing Directional freezing 2024-10-08 science/chemistry --- https://www.edwest.co.uk/p/a-language-of-beautiful-impurity A language of beautiful impurity 2024-10-09 fiction/poetry law psychology/linguistics --- https://en.wikipedia.org/wiki/Congenital_insensitivity_to_pain_with_anhidrosis Congenital insensitivity to pain with anhidrosis 2024-10-09 psychology/neuroscience/pain --- https://www.wired.com/story/ancestrys-genetic-testing-kits-are-heading-for-your-stocking-this-year/ Ancestry’s Genetic Testing Kits Are Heading for Your Stocking This Year 2024-01-01 genetics/sequencing --- https://www.wired.com/story/mirai-botnet-minecraft-scam-brought-down-the-internet/ The Mirai Botnet Was Part of a College Student ‘Minecraft’ Scheme 2024-01-01 bitcoin cs/security --- https://www.wsj.com/articles/at-the-end-of-history-still-stands-democracy-1402080661 At the ‘End of History’ Still Stands Democracy: 25 years after Tiananmen Square and the Berlin Wall’s fall, liberal democracy still has no real competitors 2024-01-01 politics --- https://cran.r-project.org/web/packages/memoise/index.html memoise 2024-10-09 cs/r --- https://jan.leike.name/ Jan Leike 2024-10-08 ai/nn/anthropic reinforcement-learning/openai reinforcement-learning/safe --- https://www.reddit.com/r/genewolfe/comments/1b3cr4l/botns_wall_graphics/ 2024-10-09 fiction/gene-wolfe --- https://arxiv.org/abs/2410.02288 Computer-aided Colorization State-of-the-science: A Survey Yu Cao, Xin Duan, Xiangqiao Meng, P. Y. Mok, Ping Li, Tong-Yee Lee 2024-10-03 2024-10-09 [("doi","10.48550/arXiv.2410.02288")] ai/anime

This paper reviews published research in the field of computer-aided colorization technology. We argue that the colorization task originates from computer graphics, prospers by introducing computer vision, and tends to the fusion of vision and graphics, so we put forward our taxonomy and organize the whole paper chronologically.

We extend the existing reconstruction-based colorization evaluation techniques, considering that esthetic assessment of colored images should be introduced to ensure that colorization satisfies human visual-related requirements and emotions more closely.

We perform the colorization esthetic assessment on 7 representative unconditional colorization models and discuss the difference between our assessment and the existing reconstruction-based metrics.

Finally, this paper identifies unresolved issues and proposes fruitful areas for future research and development. Access to the project associated with this survey can be obtained at GitHub.

--- /doc/ai/nn/gan/stylegan/2024-shu.pdf The spontaneous emergence of ‘a sense of beauty’ in untrained deep neural networks Tianxin Shu, Huawei Xu, Xingxing Chen, Yuxuan Cai, Ming Liu, Delong Zhang 2024-01-01 2024-10-09 [("doi","10.1037/aca0000690")] ai/nn/gan/stylegan psychology/vision

[see also Spape et al 2021] The sense of facial beauty has long been observed in both infants and nonhuman primates, yet the neural mechanisms of this phenomenon are still not fully understood.

The current study employed generative neural models [StyleGAN 2] to produce facial images of varying degrees of beauty and systematically investigated the neural response of untrained deep neural networks (DNNs) to these faces.

Representational neural units for different levels of facial beauty are observed to spontaneously emerge even in the absence of training. Furthermore, these neural units can effectively distinguish between varying degrees of beauty. Additionally, the perception of facial beauty by DNNs relies on both configuration and feature information of faces. The processing of facial beauty by neural networks follows a progression from low-level features to integration. The tuning response of the final convolutional layer to facial beauty is constructed by the weighted sum of the monotonic responses in the early layers.

These findings offer new insights into the neural origin of the sense of beauty, arising the innate computational abilities of DNNs.

[Keywords: esthetic neurocomputation, generative neural models, untrained deep neural networks, facial beauty, linear weighted summation]

…In conclusion, our research explored the neural origin of beauty sense from the innate computational abilities of DNNs. We found that there are units selectively responsive to facial beauty in the completely randomly initialized DNNs, and the responses of these units are linearly distributed. Additionally, representations of beauty have emerged in the initial layers of DNNs. Untrained DNNs perceive facial beauty in a hierarchical manner, where both configuration information and feature information of faces contribute to the completion of esthetic processing. The selective responses in the final layer of the DNN are constructed through linear weighting of monotonic responses in the early layers.

--- https://x.com/Luke_Metz/status/1844161466032914645 Luke Metz 2024-10-10 reinforcement-learning/openai --- https://x.com/OriolVinyalsML/status/1844092610748797261 Oriol Vinyals 2024-10-10 reinforcement-learning/model-free/alphastar --- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.591.6855&rep=rep1&type=pdf Housewife, ‘Gold Miss’, and Equal: The Evolution of Educated Women’s Role in Asia and the U.S. Hwang 2012 2024-01-01 japan/history sociology --- https://www.sethroberts.net/wp-content/uploads/2013/01/halmos.pdf The Problem of Learning to Teach Halmos 1975 2024-01-01 nootropic/quantified-self --- https://github.com/gwern/gwern.net/commit/8761dccc5b3fa4fff468db554813bf20e02702a8#diff-03ea4348bea7a138709946b85053309e32040dae6fd27039008063f290f7f138R102 markdown.el: experiment in streamlining link-adding interface Gwern 2024-01-01 cs/lisp --- https://weirdgloop.org/blog/why-were-helping-more-wikis-move-away-from-fandom#why-do-we-actually-care Why we’re helping more wikis move away from Fandom Jonathan Lee 2024-10-10 2024-10-10 economics/advertising wikipedia --- /doc/japan/history/1961-ikado.pdf The Origin of the Social Status of Protestant Christianity in Japan (1859–1918) Fujio Ikado 1961 2024-10-10 [("doi","10.2307/30232843")] japan/history philosophy/religion --- https://www.nobelprize.org/prizes/chemistry/2024/press-release/ Press release: The Nobel Prize in Chemistry 2024 2024 2024-10-10 ai/nn/transformer/alphafold --- https://arxiv.org/abs/2406.03689 Evaluating the World Model Implicit in a Generative Model Keyon Vafa, Justin Y. Chen, Jon Kleinberg, Sendhil Mullainathan, Ashesh Rambachan 2024-06-06 2024-10-10 [("doi","10.48550/arXiv.2406.03689")] reinforcement-learning/model statistics/causality

[Twitter, Github] Recent work suggests that large language models may implicitly learn world models. How should we assess this possibility? We formalize this question for the case where the underlying reality is governed by a deterministic finite automaton. This includes problems as diverse as simple logical reasoning, geographic navigation, game-playing, and chemistry.

We propose new evaluation metrics for world model recovery inspired by the classic Myhill-Nerode theorem from language theory. We illustrate their utility in 3 domains: game playing [Othello], logic puzzles, and navigation [taxi cab driving in NYC]. In all domains, the generative models we consider do well on existing diagnostics for assessing world models, but our evaluation metrics reveal their world models to be far less coherent than they appear.

Such incoherence creates fragility: using a generative model to solve related but subtly different tasks can lead it to fail badly. Building generative models that meaningfully capture the underlying logic of the domains they model would be immensely valuable; our results suggest new ways to assess how close a given model is to that goal.

...We apply our metrics to the two Othello sequence models considered by Li et al 2023: one trained on real games from Othello championship tournaments and another trained on synthetic games. Table 6 in Appendix F shows the result of the metrics in both settings. The model trained on real games performs poorly on both compression and distinction metrics, failing to group together most pairs of game openings that lead to the same board.

In contrast, the model trained on synthetic games performs well on both metrics. This distinction is not captured by the existing metrics, which show both models performing similarly. Similar to the navigation setting, we again find that models trained on random/synthetic data recover more world structure than those trained on real-world data.

[So training on-policy on rollouts of learned models would tend to patch up errors, particularly as more causal interventions are made and the world-model becomes more robust.]

--- https://www.lesswrong.com/posts/LHdNtJCm93pxNHJKb/can-ai-outpredict-humans-results-from-metaculus-s-q3-ai Can AI Outpredict Humans? Results From Metaculus’s Q3 AI Forecasting Benchmark [no] 2024-10-10 ai/nn/transformer/gpt/calibration statistics/prediction --- https://en.wikipedia.org/wiki/Sense_of_agency Sense of agency 2024-10-10 philosophy/mind psychology/willpower --- https://www.wired.com/story/jake-sullivan-china-tech-profile/ Jake Sullivan: The American Who Waged a Tech War on China 2024-10-10 2024-10-10 ai/scaling/hardware --- https://arxiv.org/abs/2410.01131 nGPT: Normalized Transformer with Representation Learning on the Hypersphere Ilya Loshchilov, Cheng-Ping Hsieh, Simeng Sun, Boris Ginsburg 2024-10-01 2024-10-11 [("doi","10.48550/arXiv.2410.01131")] ai/nn/fully-connected ai/nn/transformer/attention ai/nn/transformer/gpt

We propose a novel neural network architecture, the normalized Transformer (nGPT) with representation learning on the hypersphere.

In nGPT, all vectors forming the embeddings, MLP, attention matrices and hidden states are unit norm normalized. The input stream of tokens travels on the surface of a hypersphere, with each layer contributing a displacement towards the target output predictions. These displacements are defined by the MLP and attention blocks, whose vector components also reside on the same hypersphere.

Experiments show that nGPT learns much faster, reducing the number of training steps required to achieve the same accuracy by a factor of 4–20, depending on the sequence length.

[But is that because the GPT baseline is screwed up?]

--- https://en.wikipedia.org/wiki/Dominance_hierarchies Dominance hierarchies 2024-10-11 psychology/animal sociology --- https://en.wikipedia.org/wiki/New_England_vampire_panic New England vampire panic 2024-10-11 fiction/gene-wolfe/suzanne-delage --- /doc/fiction/gene-wolfe/suzanne-delage/1896-stetson.pdf The Animistic Vampire in New England George Rochford Stetson 1896-01-01 2024-10-11 [("doi","10.2307/658266")] fiction/gene-wolfe/suzanne-delage philosophy/religion --- https://blog.jreyesr.com/posts/typst/ Exploring Typst, a new typesetting system similar to LaTeX jreyesr 2024-10-07 2024-10-13 design/typography/tex --- https://en.wikipedia.org/wiki/Mercy_Brown_vampire_incident Mercy Brown vampire incident 1892 2024-10-13 fiction/gene-wolfe/suzanne-delage --- https://en.wikipedia.org/wiki/The_Shunned_House ‘The Shunned House’ H. P. Lovecraft 1937-10 2024-10-13 fiction/gene-wolfe/suzanne-delage --- https://tug.org/FontCatalogue/goudyinitialen/ The LaTeX Font Catalogue: Goudy Initialen 2024-01-01 design/typography/dropcap --- https://tvtropes.org/pmwiki/pmwiki.php/Anime/KemonoFriends Kemono Friends (anime) 2024-01-01 anime fiction --- https://www.wired.com/images_blogs/threatlevel/2012/04/WILLEMSIndictment-FILED.045.pdf Farmer’s Market indictment Department of Justice 2024-01-01 darknet-market --- https://www.wired.com/images_blogs/threatlevel/2012/05/Bitcoin-FBI.pdf Bitcoin Virtual Currency: Unique Features Present Distinct Challenges for Deterring Illicit Activity FBI 2012-04-24 2024-01-01 darknet-market --- https://www.lesswrong.com/posts/rYDas2DDGGDRc8gGB/unifying-bargaining-notions-1-2 Unifying Bargaining Notions #1 Diffractor 2022-07-24 2024-10-13 economics/mechanism-design --- https://www.lesswrong.com/posts/RZNmNwc9SxdKayeQh/unifying-bargaining-notions-2-2 Unifying Bargaining Notions #2 Diffractor 2022-07-26 2024-10-13 economics/mechanism-design --- https://en.wikipedia.org/wiki/John_Harsanyi John Harsanyi 2024-10-13 economics/mechanism-design statistics/decision --- https://www.lesswrong.com/posts/nSjavaKcBrtNktzGa/nonprofit-boards-are-weird Nonprofit Boards are Weird Holden Karnofsky 2024-10-13 law philosophy/ethics reinforcement-learning/openai --- https://www.flightfromperfection.com/better-not-to-begin.html ‘Better not to begin. Once begun, better to finish!’ Milan Griffes 2021-01-27 2024-10-13 psychiatry/meditation --- https://www.bbc.com/news/articles/c3rl1y10r4wo Bipolar diagnosis life changing, says Senedd’s Gareth Davies MS 2024-10-13 psychiatry/bipolar --- https://www.timesofisrael.com/shocked-doctors-find-bullet-lodged-in-brain-of-sleepy-9-year-old-remove-it/ Shocked doctors find bullet lodged in brain of ‘sleepy’ 9-year-old, remove it: After East Jerusalem parents say they don’t know what’s hurting son, Hadassah doctors stunned to find he’d been shot; neurosurgeon, paged on way to Shabbat dinner, pulls bullet out Nathan Jeffay 2020-08-02 2024-10-14 psychology/neuroscience --- http://oldvcr.blogspot.com/2024/10/refurb-weekend-symbolics-macivory-lisp.html Refurb weekend: the Symbolics MacIvory Lisp machine 2024-10 2024-10-14 cs/hardware cs/lisp --- https://arxiv.org/abs/2410.08993 The structure of the token space for large language models Michael Robinson, Sourya Dey, Shauna Sweet 2024-10-11 2024-10-14 [("doi","10.48550/arXiv.2410.08993")] ai/nn/adversarial ai/nn/tokenization ai/nn/transformer/gpt/4

[cf. glitch tokens] Large language models encode the correlational structure present in natural language by fitting segments of utterances (tokens) into a high-dimensional ambient latent space upon which the models then operate. We assert that in order to develop a foundational, first-principles understanding of the behavior and limitations of large language models, it is crucial to understand the topological and geometric structure of this token subspace.

In this article, we present estimators for the dimension and Ricci scalar curvature of the token subspace, and apply it to 3 open-source large language models of moderate size: GPT-2, LLEMMA-7B, and Mistral-7B. In all 3 models, using these measurements, we find that the token subspace is not a manifold, but is instead a stratified manifold, where on each of the individual strata, the Ricci curvature is negative.

We additionally find that the dimension and curvature correlate with the generative fluency of the models, which suggests that these findings have implications for model behavior.

...1.3 Implications: If the token subspace is not a manifold, this has important implications because the behavior of the transformer blocks, which are piecewise smooth (hence continuous) transformations of the latent space18, must therefore preserve the dimensions we observe. As a result, queries that cross stratification boundaries will yield responses that exhibit dramatic changes in behavior.

This instability will likely preclude strong guarantees about the model’s generative performance without intimate knowledge of how the token subspace is embedded within the ambient latent space.

--- https://www.amazon.com/dp/B0CPL17NPC Gya Labs Celery Seed Essential Oil for Skin—100% Natural (0.34 Fl Oz) 2024-10-14 cat/psychology/drug --- https://www.avitalbalwit.com/ The personal website of Avital Balwit Avital Balwit 2024-10-14 ai/nn/anthropic --- https://arxiv.org/abs/2405.14333#deepseek DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data Huajian Xin, Daya Guo, Zhihong Shao, Zhizhou Ren, Qihao Zhu, Bo Liu, Chong Ruan, Wenda Li, Xiaodan Liang 2024-05-23 2024-10-14 [("doi","10.48550/arXiv.2405.14333")] ai/nn/transformer/gpt/4/nonfiction math reinforcement-learning/model

Proof assistants like Lean have revolutionized mathematical proof verification, ensuring high accuracy and reliability. Although large language models (LLMs) show promise in mathematical reasoning, their advancement in formal theorem proving is hindered by a lack of training data.

To address this issue, we introduce an approach to generate extensive Lean 4 proof data derived from high-school and undergraduate-level mathematical competition problems. This approach involves translating natural language problems into formal statements, filtering out low-quality statements, and generating proofs to create synthetic data.

After fine-tuning the DeepSeekMath 7B model on this synthetic dataset, which comprises 8 million formal statements with proofs, our model achieved whole-proof generation accuracies of 46.3% with 64 samples and 52% cumulatively on the Lean 4 miniF2F test, surpassing the baseline GPT-4 at 23.0% with 64 samples and a tree search reinforcement learning method at 41.0%.

Additionally, our model successfully proved 5⁄148 problems in the Lean 4 Formalized International Mathematical Olympiad (FIMO) benchmark, while GPT-4 failed to prove any.

These results demonstrate the potential of leveraging large-scale synthetic data to enhance theorem-proving capabilities in LLMs.

Both the synthetic dataset and the model will be made available to facilitate further research in this promising field.

--- https://gist.github.com/dannguyen/faaa56cebf30ad51108a9fe4f8db36d8#extracting-financial-disclosure-reports-and-police-blotter-narratives-using-openais-structured-output A basic test of OpenAI’s Structured Output feature against financial disclosure reports and a newspaper’s police blotter 2024-10-14 ai/nn/transformer/gpt/4/nonfiction --- https://www.avitalbalwit.com/post/how-i-use-claude How I Use Claude Avital Balwit 2024-10-14 ai/nn/transformer/gpt/claude --- https://www.lesswrong.com/posts/roE7SHjFWEoMcGZKd/circuits-in-superposition-compressing-many-small-neural#Read_in_interference Circuits in Superposition: Compressing many small neural networks into one 2024-10-14 ai/nn/sparsity --- https://arxiv.org/abs/2410.07166 Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making Manling Li, Shiyu Zhao, Qineng Wang, Kangrui Wang, Yu Zhou, Sanjana Srivastava, Cem Gokmen, Tony Lee, Li Erran Li, Ruohan Zhang, Weiyu Liu, Percy Liang, Li Fei-Fei, Jiayuan Mao, Jiajun Wu 2024-10-09 2024-10-14 [("doi","10.48550/arXiv.2410.07166")] ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude reinforcement-learning/model reinforcement-learning/robot

We aim to evaluate Large Language Models (LLMs) for embodied decision making. While a body of work has been leveraging LLMs for decision making in embodied environments, we still lack a systematic understanding of their performance because they are usually applied in different domains, for different purposes, and built based on different inputs and outputs. Furthermore, existing evaluations tend to rely solely on a final success rate, making it difficult to pinpoint what ability is missing in LLMs and where the problem lies, which in turn blocks embodied agents from leveraging LLMs effectively and selectively.

To address these limitations, we propose a generalized interface (Embodied Agent Interface) that supports the formalization of various types of tasks and input-output specifications of LLM-based modules. Specifically, it allows us to unify (1) a broad set of embodied decision-making tasks involving both state and temporally extended goals, (2) 4 commonly-used LLM-based modules for decision making: goal interpretation, subgoal decomposition, action sequencing, and transition modeling, and (3) a collection of fine-grained metrics which break down evaluation into various types of errors, such as hallucination errors, affordance errors, various types of planning errors, etc.

Overall, our benchmark offers a comprehensive assessment of LLMs’ performance for different subtasks, pinpointing the strengths and weaknesses in LLM-powered embodied AI systems, and providing insights for effective and selective use of LLMs in embodied decision making.

--- https://www.youtube.com/watch?v=2xYosqVjROc Can OpenAI’s o1-preview Ace the 2023 Putnam Exam? Kyle Kabasares 2024-10-07 2024-10-14 ai/nn/transformer/gpt/4/nonfiction math --- https://arxiv.org/abs/2309.04295 FIMO: A Challenge Formal Dataset for Automated Theorem Proving Chengwu Liu, Jianhao Shen, Huajian Xin, Zhengying Liu, Ye Yuan, Haiming Wang, Wei Ju, Chuanyang Zheng, Yichun Yin, Lin Li, Ming Zhang, Qun Liu 2023-09-08 2024-10-14 [("doi","10.48550/arXiv.2309.04295")] ai/dataset design/typography/tex math

We present FIMO, an innovative dataset comprising formal mathematical problem statements sourced from the International Mathematical Olympiad (IMO) Shortlisted Problems. Designed to facilitate advanced automated theorem proving at the IMO level, FIMO is currently tailored for the Lean formal language.

It comprises 149 formal problem statements, accompanied by both informal problem descriptions and their corresponding LaTeX-based informal proofs.

Through initial experiments involving GPT-4, our findings underscore the existing limitations in current methodologies, indicating a substantial journey ahead before achieving satisfactory IMO-level automated theorem proving outcomes.

--- https://arxiv.org/abs/2402.19167 Teaching Large Language Models an Unseen Language on the Fly Chen Zhang, Xiao Liu, Jiuheng Lin, Yansong Feng 2024-02-29 2024-10-14 [("doi","10.48550/arXiv.2402.19167")] ai/nn/transformer/gpt/4/nonfiction psychology/linguistics

Existing large language models struggle to support numerous low-resource languages, particularly the extremely low-resource ones, for which there is minimal training data available for effective parameter updating. We thus investigate whether LLMs can learn a new language on the fly solely through prompting.

To study this question, we collect a research suite for Zhuang, a language supported by no LLMs currently. We introduce DiPMT++, a framework for adapting LLMs to unseen languages by in-context learning.

Using a dictionary and 5,000 parallel sentences only, DiPMT++ enhances the performance of GPT-4 0 → 16 BLEU for Chinese-to-Zhuang translation and achieves 32 BLEU for Zhuang-to-Chinese translation. We also validate the effectiveness of our framework on Kalamang, another unseen language.

Furthermore, we demonstrate the practical utility of DiPMT++ in aiding humans in translating completely unseen languages, which could contribute to the preservation of linguistic diversity.

--- https://www.cell.com/current-biology/fulltext/S0960-9822(24)01240-5 2024-10-14 genetics/sequencing --- https://arxiv.org/abs/2312.11865 Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach Weiyu Ma, Qirui Mi, Yongcheng Zeng, Xue Yan, Yuqiao Wu, Runji Lin, Haifeng Zhang, Jun Wang 2023-12-19 2024-10-14 [("doi","10.48550/arXiv.2312.11865")] ai/dataset ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/inner-monologue reinforcement-learning/model-free/alphastar

StarCraft II is a challenging benchmark for AI agents due to the necessity of both precise micro-level operations and strategic macro awareness. Previous works, such as AlphaStar and SCC, achieve impressive performance on tackling StarCraft II; however, they still exhibit deficiencies in long-term strategic planning and strategy interpretability. Emerging large language model (LLM) agents, such as Voyage and MetaGPT, present immense potential in solving intricate tasks. Motivated by this, we aim to validate the capabilities of LLMs on StarCraft II, a highly complex RTS game.

To conveniently take full advantage of LLMs’ reasoning abilities, we first develop a textual StarCraft II environment, called TextStarCraft II, which LLM agents can interact with. Secondly, we propose a Chain of Summarization method, including single-frame summarization for processing raw observations and multi-frame summarization for analyzing game information, providing command recommendations, and generating strategic decisions.

Our experiment consists of two parts: first, an evaluation by human experts, which includes assessing the LLMs’ mastery of StarCraft II knowledge and the performance of LLM agents in the game; second, the in-game performance of LLM agents, encompassing aspects like win rate and the impact of the Chain of Summarization. [finetuning GPT-3.5]

Experiment results demonstrate that: (1) LLMs possess the relevant knowledge and complex planning abilities needed to address StarCraft II scenarios; (2) Human experts consider the performance of LLM agents to be close to that of an average player who has played StarCraft II for 8 years; (3) LLM agents are capable of defeating the built-in AI at the Harder (Lv5) difficulty level.

We have open-sourced the code and released demo videos of LLM agents playing StarCraft II.

[Example use: benchmarking slowed-down agents.]

--- https://www.lesswrong.com/posts/qhhRwxsef7P2yC2Do/ai-alignment-via-slow-substrates-early-empirical-results AI Alignment via Slow Substrates: Early Empirical Results With StarCraft II Lester Leong 2024-10-14 2024-10-14 reinforcement-learning/model-free/alphastar reinforcement-learning/scaling --- https://jsomers.net/blog/gettiers The 3-page paper that shook philosophy: Gettiers in software engineering 2024-10-14 cs philosophy/epistemology --- https://en.wikipedia.org/wiki/Gettier_problem Gettier problem 2024-10-14 philosophy/epistemology --- https://arxiv.org/abs/2410.08261 Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis Jinbin Bai, Tian Ye, Wei Chow, Enxin Song, Qing-Guo Chen, Xiangtai Li, Zhen Dong, Lei Zhu, Shuicheng Yan 2024-10-10 2024-10-14 [("doi","10.48550/arXiv.2410.08261")] ai/nn/vae/mae

Diffusion models, such as Stable Diffusion, have made strides in visual generation, yet their paradigm remains fundamentally different from autoregressive language models, complicating the development of unified language-vision models. Recent efforts like LlamaGen have attempted autoregressive image generation using discrete VQ-VAE tokens, but the large number of tokens involved renders this approach inefficient and slow.

In this work, we present Meissonic, which elevates non-autoregressive masked image modeling (MIM) text-to-image to a level comparable with state-of-the-art diffusion models like SDXL. By incorporating a comprehensive suite of architectural innovations, advanced positional encoding strategies, and optimized sampling conditions, Meissonic substantially improves MIM’s performance and efficiency. Additionally, we leverage high-quality training data, integrate micro-conditions informed by human preference scores, and employ feature compression layers to further enhance image fidelity and resolution.

Our model not only matches but often exceeds the performance of existing models like SDXL in generating high-quality, high-resolution images.

Extensive experiments validate Meissonic’s capabilities, demonstrating its potential as a new standard in text-to-image synthesis. We release a model checkpoint capable of producing 1024×1024 resolution images.

--- https://en.wikipedia.org/wiki/Von_Economo_neuron Von Economo neuron 2024-10-15 psychology/animal psychology/neuroscience --- https://www.wired.com/story/lee-holloway-devastating-decline-brilliant-young-coder/ Lee Holloway: The Devastating Decline of a Brilliant Young Coder 2023-09-20 2024-10-15 psychiatry/alzheimers --- https://www.overcomingbias.com/p/better-babblershtml Better Babblers Robin Hanson 2017-03-21 2024-01-01 economics philosophy/mind psychology/cognitive-bias/illusion-of-depth --- https://arstechnica.com/security/2024/10/ai-chatbots-can-read-and-write-invisible-text-creating-an-ideal-covert-channel/ Invisible Unicode text that AI chatbots understand and humans can’t? Yep, it’s a thing 2024-10 2024-10-15 ai/nn/adversarial ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude cs/cryptography/steganography design/typography --- https://en.wikipedia.org/wiki/Tags_(Unicode_block) Tags (Unicode block) 2024-10-15 cs/cryptography/steganography design/typography --- https://en.wikipedia.org/wiki/Br%C3%ADgido_Lara Brígido Lara (forger) 2024-10-15 crime history --- https://arxiv.org/abs/2406.09279 Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback Hamish Ivison, Yizhong Wang, Jiacheng Liu, Zeqiu Wu, Valentina Pyatkin, Nathan Lambert, Noah Smith, Yejin Choi, Hannaneh Hajishirzi 2024-06-13 2024-10-15 [("doi","10.48550/arXiv.2406.09279")] reinforcement-learning/preference-learning

Learning from preference feedback has emerged as an essential step for improving the generation quality and performance of modern language models (LMs). Despite its widespread use, the way preference-based learning is applied varies wildly, with differing data, learning algorithms, and evaluations used, making disentangling the impact of each aspect difficult.

In this work, we identify 4 core aspects of preference-based learning: preference data, learning algorithm, reward model, and policy training prompts, systematically investigate the impact of these components on downstream model performance, and suggest a recipe for strong learning for preference feedback.

Our findings indicate that all aspects are important for performance, with better preference data leading to the largest improvements, followed by the choice of learning algorithm, the use of improved reward models, and finally the use of additional unlabeled prompts for policy training. Notably, PPO outperforms DPO by up to 2.5% in math and 1.2% in general domains. High-quality preference data leads to improvements of up to 8% in instruction following and truthfulness. Despite gains of up to 5% in mathematical evaluation when scaling up reward models, we surprisingly observe marginal improvements in other categories.

We publicly release the code used for training and evaluating our models, along with the models & datasets themselves.

--- https://www.poetryfoundation.org/poems/91768/marriage-of-many-years Marriage of Many Years Dana Gioia 2016 2024-10-15 fiction/poetry --- https://en.wikipedia.org/wiki/Shamir%27s_secret_sharing Shamir’s secret sharing 2024-01-01 cs/cryptography --- https://en.wikipedia.org/wiki/Information-theoretic_security Information-theoretic security 2024-10-15 cs/algorithm/information cs/cryptography --- https://www.theatlantic.com/magazine/archive/2012/12/the-data-vigilante/309172/ The Data Vigilante 2012-12 2024-10-15 statistics/bias --- https://arxiv.org/abs/2406.19146 Resolving Discrepancies in Compute-Optimal Scaling of Language Models Tomer Porian, Mitchell Wortsman, Jenia Jitsev, Ludwig Schmidt, Yair Carmon 2024-06-27 2024-10-15 [("doi","10.48550/arXiv.2406.19146")] ai/nn/transformer/gpt ai/scaling

Kaplan et al 2020 and Hoffmann et al 2021 developed influential scaling laws for the optimal model size as a function of the compute budget, but these laws yield substantially different predictions.

We explain the discrepancy by reproducing the Kaplan scaling law on two datasets (OpenWebText2 and RefinedWeb) and identifying 3 factors causing the difference: last layer computational cost, warmup duration, and scale-dependent optimizer tuning.

With these factors corrected, we obtain excellent agreement with the Hoffmann et al 2021(ie. “Chinchilla”) scaling law. Counter to a hypothesis of Hoffmann et al 2021 we find that careful learning rate decay is not essential for the validity of their scaling law.

As a secondary result, we derive scaling laws for the optimal learning rate and batch size, finding that tuning the AdamW β2 parameter is essential at lower batch sizes.

--- https://en.wikipedia.org/wiki/Counterexamples_in_Topology Counterexamples in Topology Lynn Arthur Steen, J. Arthur Seebach Junior 1970 2024-10-15 math philosophy/epistemology --- https://web.archive.org/web/20180320054429/http://jakewestfall.org/blog/index.php/2018/03/12/logistic-regression-is-not-fucked/ Logistic regression is not fucked Jake Westfall 2018-03-12 2024-10-15 statistics/probability --- https://web.archive.org/web/20180307125229/http://jakewestfall.org/blog/index.php/2015/06/16/dont-fight-the-power-analysis/ Don’t fight the power (analysis) Jake Westfall 2015-06-16 2024-10-15 statistics/bayes statistics/power-analysis --- https://thehardestscience.com/2016/02/12/reading-the-baby-factory-in-context/ Reading ‘The Baby Factory’ in context Sanjay Srivastava 2016-02-12 2024-10-15 psychology statistics/bias --- /doc/statistics/bias/2016-peterson.pdf The Baby Factory: Difficult Research Objects, Disciplinary Standards, and the Production of Statistical-Significance David Peterson 2016-01-22 2024-10-15 [("doi","10.1177/2378023115625071")] psychology statistics/bias

[commentary] Science studies scholars have shown that the management of natural complexity in lab settings is accomplished through a mixture of technological standardization and tacit knowledge by lab workers. Yet these strategies are not available to researchers who study difficult research objects.

Using 16 months of ethnographic data from 3 laboratories that conduct experiments on infants and toddlers, the author shows how psychologists produce statistically-significant results under challenging circumstances by using strategies that enable them to bridge the distance between an uncontrollable research object and a professional culture that prizes methodological rigor.

This research raises important questions regarding the value of restrictive evidential cultures in challenging research environments.

[Keywords: laboratory ethnography, science and knowledge, standardization, sociology of psychology]

[Protocol violations that break blinding and independence] …As a routine part of the experiments, parents are asked to close their eyes to prevent any unconscious influence on their children. Although this was explicitly stated in the instructions given to parents, during the actual experiment, it was often overlooked; the parents’ eyes would remain open. Moreover, on several occasions, experimenters downplayed the importance of having one’s eyes closed. One psychologist told a mother, “During the trial, we ask you to close your eyes. That’s just for the journals so we can say you weren’t directing her attention. But you can peek if you want to. It’s not a big deal. But there’s not much to see.”

[Optional stopping based on data peeking] …Rather than waiting for the results from a set number of infants, experimenters began “eyeballing” the data as soon as babies were run and often began looking for statistical-significance after just 5–10 subjects. During lab meetings and one-on-one discussions, experiments that were “in progress” and still collecting data were evaluated on the basis of these early results. When the preliminary data looked good, the test continued. When they showed ambiguous but statistically-significant results, the test usually continued. But when, after just a few subjects, no statistical-significance was found, the original protocol was abandoned and new variations were developed.

[Invalid comparisons of statistically-significant to non-statistically-significant] …Because experiments on infant subjects are very costly in terms of both time and money, throwing away data is highly undesirable. Instead, when faced with a struggling experiment using a trusted experimental paradigm, experimenters would regularly run another study that had higher odds of success. This was accomplished by varying one aspect of the experiment, such as the age of the participants. For instance, when one experiment with 14-month-olds failed, the experimenter reran the same study with 18-month-olds, which then succeeded. Once a statistically-significant result was achieved, the failures were no longer valueless. They now represented a part of a larger story: “Eighteen-month-olds can achieve behavior X, but 14-month-olds cannot.” Thus, the failed experiment becomes a boundary for the phenomenon.

[HARKing] …When a clear and interesting story could be told about statistically-significant findings, the original motivation was often abandoned. I attended a meeting between a graduate student and her mentor at which they were trying to decipher some results the student had just received. Their meaning was not at all clear, and the graduate student complained that she was having trouble remembering the motivation for the study in the first place. Her mentor responded, “You don’t have to reconstruct your logic. You have the results now. If you can come up with an interpretation that works, that will motivate the hypothesis.”

A blunt explanation of this strategy was given to me by an advanced graduate student: “You want to know how it works? We have a bunch of half-baked ideas. We run a bunch of experiments. Whatever data we get, we pretend that’s what we were looking for.” Rather than stay with the original, motivating hypothesis, researchers in developmental science learn to adjust to statistical-significance. They then “fill out” the rest of the paper around this necessary core of psychological research.

--- https://www.aeaweb.org/articles?id=10.1257/mac.20210263 The Rise of Niche Consumption 2024-10-15 [("doi","10.1257/mac.20210263")] economics technology --- https://arxiv.org/abs/2410.06405 Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance of 2D Representation, Positions, and Objects Wenhao Li, Yudong Xu, Scott Sanner, Elias Boutros Khalil 2024-10-08 2024-10-15 [("doi","10.48550/arXiv.2410.06405")] ai/nn/transformer/attention

The Abstraction and Reasoning Corpus (ARC) is a popular benchmark focused on visual reasoning in the evaluation of AI systems. In its original framing, an ARC task requires solving a program synthesis problem over small 2D images using a few input-output training pairs.

In this work, we adopt the recently popular data-driven approach to the ARC and ask whether a Vision Transformer (ViT) can learn the implicit mapping, from input image to output image, that underlies the task.

We show that a ViT—otherwise a state-of-the-art model for images—fails dramatically on most ARC tasks even when trained on one million examples per task [!]. This points to an inherent representational deficiency of the ViT architecture that makes it incapable of uncovering the simple structured mappings underlying the ARC tasks.

Building on these insights, we propose ViTARC, a ViT-style architecture that unlocks some of the visual reasoning capabilities required by the ARC. Specifically, we use a pixel-level input representation, design a spatially-aware tokenization scheme, and introduce a novel object-based positional encoding that leverages automatic segmentation, among other enhancements.

Our task-specific ViTARC models achieve a test solve rate close to 100% on more than half of the 400 public ARC tasks strictly through supervised learning from input-output grids.

This calls attention to the importance of imbuing the powerful (Vision) Transformer with the correct inductive biases for abstract visual reasoning that are critical even when the training data is plentiful and the mapping is noise-free. Hence, ViTARC provides a strong foundation for future research in visual reasoning using transformer-based architectures.

--- https://www.brookings.edu/articles/a-forensic-examination-of-chinas-national-accounts/ A forensic examination of China’s national accounts 2024-10-16 economics politics --- https://arxiv.org/abs/2410.09754 SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning Hojoon Lee, Dongyoon Hwang, Donghu Kim, Hyunseung Kim, Jun Jet Tai, Kaushik Subramanian, Peter R. Wurman, Jaegul Choo, Peter Stone, Takuma Seno 2024-10-13 2024-10-16 [("doi","10.48550/arXiv.2410.09754")] ai/nn/fully-connected reinforcement-learning/model-free

Recent advances in computer vision (CV) and natural language processing (NLP) have been largely driven by scaling up the number of network parameters, despite traditional theories suggesting that larger networks are prone to overfitting. These large networks avoid overfitting by integrating components that induce a simplicity bias, guiding models toward simple and generalizable solutions.

However, in deep reinforcement learning (RL), designing and scaling up networks have been less explored. Motivated by this opportunity, we present SimBa, an architecture designed to scale up parameters in deep RL by injecting a simplicity bias. SimBa consists of 3 components: (1) an observation normalization layer that standardizes inputs with running statistics, (2) a residual feedforward block to provide a linear pathway from the input to output, and (3) a layer normalization to control feature magnitudes.

By scaling up parameters with SimBa, the sample efficiency of various deep RL algorithms—including off-policy, on-policy, and unsupervised methods—is consistently improved. Moreover, solely by integrating SimBa architecture into SAC, it matches or surpasses state-of-the-art deep RL methods with high computational efficiency across DMC, MyoSuite, and HumanoidBench.

These results demonstrate SimBa’s broad applicability and effectiveness across diverse RL algorithms and environments.

--- https://darioamodei.com/machines-of-loving-grace Machines of Loving Grace: How AI Could Transform the World for the Better Dario Amodei 2024-10-11 2024-10-16 ai/nn/anthropic reinforcement-learning/safe --- https://arxiv.org/abs/2410.07524#nvidia Upcycling Large Language Models into Mixture of Experts Ethan He, Abhinav Khattar, Ryan Prenger, Vijay Korthikanti, Zijie Yan, Tong Liu, Shiqing Fan, Ashwath Aithal, Mohammad Shoeybi, Bryan Catanzaro 2024-10-10 2024-10-16 [("doi","10.48550/arXiv.2410.07524")] ai/scaling/mixture-of-experts

Upcycling pre-trained dense language models into sparse mixture-of-experts (MoE) models is an efficient approach to increase the model capacity of already trained models. However, optimal techniques for upcycling at scale remain unclear.

In this work, we conduct an extensive study of upcycling methods and hyperparameters for billion-parameter scale language models. We propose a novel virtual group initialization scheme and weight scaling approach to enable upcycling into fine-grained MoE architectures. Through ablations, we find that upcycling outperforms continued dense model training.

In addition, we show that softmax-then-top-k expert routing improves over the top-k-then-softmax approach and higher granularity MoEs can help improve accuracy. Finally, we upcycled Nemotron-4 15B on 1T tokens and compared it to a continuously trained version of the same model on the same 1T tokens: the continuously trained model achieved 65.3% MMLU, whereas the upcycled model achieved 67.6%.

Our results offer insights and best practices to effectively leverage upcycling for building MoE language models.

--- /doc/fiction/science-fiction/2012-10-03-yvain-thewhisperingearring.html The Whispering Earring Scott Alexander 2012-10-03 2024-10-16 fiction/science-fiction philosophy/ethics philosophy/mind reinforcement-learning/safe --- https://arxiv.org/abs/2406.11704#nvidia Nemotron-4 340B Technical Report Bo Adler, Niket Agarwal, Ashwath Aithal, Dong H. Anh, Pallab Bhattacharya, Annika Brundyn, Jared Casper, Bryan Catanzaro, Sharon Clay, Jonathan Cohen, Sirshak Das, Ayush Dattagupta, Olivier Delalleau, Leon Derczynski, Yi Dong, Daniel Egert, Ellie Evans, Aleksander Ficek, Denys Fridman, Shaona Ghosh, Boris Ginsburg, Igor Gitman, Tomasz Grzegorzek, Robert Hero, Jining Huang, Vibhu Jawa, Joseph Jennings, Aastha Jhunjhunwala, John Kamalu, Sadaf Khan, Oleksii Kuchaiev, Patrick LeGresley, Hui Li, Jiwei Liu, Zihan Liu, Eileen Long, Ameya Sunil Mahabaleshwarkar, Somshubra Majumdar, James Maki, Miguel Martinez, Maer Rodrigues de Melo, Ivan Moshkov, Deepak Narayanan, Sean Narenthiran, Jesus Navarro, Phong Nguyen, Osvald Nitski, Vahid Noroozi, Guruprasad Nutheti, Christopher Parisien, Jupinder Parmar, Mostofa Patwary, Krzysztof Pawelec, Wei Ping, Shrimai Prabhumoye, Rajarshi Roy, Trisha Saar, Vasanth Rao Naik Sabavat, Sanjeev Satheesh, Jane Polak Scowcroft, Jason Sewall, Pavel Shamis, Gerald Shen, Mohammad Shoeybi, Dave Sizer, Misha Smelyanskiy, Felipe Soares, Makesh Narsimhan Sreedhar, Dan Su, Sandeep Subramanian, Shengyang Sun, Shubham Toshniwal, Hao Wang, Zhilin Wang, Jiaxuan You, Jiaqi Zeng, Jimmy Zhang, Jing Zhang, Vivienne Zhang, Yian Zhang, Chen Zhu 2024-06-17 2024-10-16 [("doi","10.48550/arXiv.2406.11704")] ai/nn/sparsity/low-precision ai/nn/transformer/gpt reinforcement-learning/preference-learning

[code] We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward. Our models are open access under the NVIDIA Open Model License Agreement, a permissive model license that allows distribution, modification, and use of the models and its outputs.

These models perform competitively to open access models on a wide range of evaluation benchmarks, and were sized to fit on a single DGX H100 with 8 GPUs when deployed in FP8 precision.

We believe that the community can benefit from these models in various research studies and commercial applications, especially for generating synthetic data to train smaller language models. Notably, over 98% of data used in our model alignment process is synthetically generated, showcasing the effectiveness of these models in generating synthetic data.

To further support open research and facilitate model development, we are also open-sourcing the synthetic data generation pipeline used in our model alignment process.

--- https://nabeelqu.co/reflections-on-palantir Reflections on Palantir [after leaving] Nabeel S. Qureshi 2024-10-16 economics/automation sociology/technology --- /doc/politics/2011-field.pdf Abraham Lincoln and the First-Person Plural: A Study in Language and Leadership Peter S. Field 2011-04-21 2024-10-16 [("doi","10.1080/14664658.2011.559748")] politics psychology/linguistics

The 2009 bicentennial of Abraham Lincoln’s birth brought forth an outpouring of fresh studies of the man and his times. The new scholarship largely affirmed the long-standing consensus about the 16th president and his greatness. His was an almost superhuman achievement in holding the Union together, emancipating the slaves, and ultimately leading the North to victory.

This study offers a novel evaluation of one key element of Lincoln’s leadership. It details how Lincoln frequently and surprisingly substituted “we” for “I” in his famous addresses, as no political leader had done before him, and explores how his preference for the plural over singular first-person pronoun enabled his political ascendancy in the 1850s and sustained his presidency during the war.

His syntax offers a linguistic window into understanding his timely, unique, and uniquely self-conscious, style of leadership. Although not exactly a man of letters, Lincoln proved to be a great leader in large measure because of his steadfast beliefs about a union and an inclusive vision of American nationhood so powerfully expressed in his exceptional use of the first-person plural.

[Keywords: Lincoln, leadership, rhetoric, U.S. citizenship, nationalism]

…Lincoln seldom speaks of himself in the singular “I”, often resorting to tortured syntax and impersonal constructions to obviate the need for the first-person singular. Perhaps no American leader used the first-person plural more and more astutely than Abraham Lincoln. Certainly, none used the first-person singular more sparingly.9 In The Collected Works of Abraham Lincoln “we” appears 12,000×. In his two greatest speeches, “I” is conspicuous by its absence. Only once in the 701-word Second Inaugural does Lincoln use it and only to conjugate the modest verb to trust; in the 272 words of the exceedingly economical Gettysburg Address he employs “we” 10× and “I” not at all.

--- https://arxiv.org/abs/2307.15771#deepmind The Hydra Effect: Emergent Self-repair in Language Model Computations Thomas McGrath, Matthew Rahtz, Janos Kramar, Vladimir Mikulik, Shane Legg 2023-07-28 2024-10-16 [("doi","10.48550/arXiv.2307.15771")] ai/nn/transformer/attention cs/cryptography/steganography

We investigate the internal structure of language model computations using causal analysis and demonstrate two motifs: (1) a form of adaptive computation where ablations of one attention layer of a language model cause another layer to compensate (which we term the Hydra effect) and (2) a counterbalancing function of late MLP layers that act to downregulate the maximum-likelihood token.

Our ablation studies demonstrate that language model layers are typically relatively loosely coupled (ablations to one layer only affect a small number of downstream layers). Surprisingly, these effects occur even in language models trained without any form of dropout.

We analyse these effects in the context of factual recall and consider their implications for circuit-level attribution in language models.

--- https://www.lesswrong.com/posts/nWRj6Ey8e5siAEXbK/simple-versus-short-higher-order-degeneracy-and-error-1 Simple versus Short: Higher-order degeneracy and error-correction 2024-10-16 ai/nn statistics/bayes --- https://aeon.co/essays/the-life-changing-magic-of-japanese-clutter The life-changing magic of Japanese clutter 2024-10-16 design japan --- https://arxiv.org/abs/2410.03092 Strategic Insights from Simulation Gaming of AI Race Dynamics Ross Gruetzemacher, Shahar Avin, James Fox, Alexander K. Saeri 2024-10-04 2024-10-17 [("doi","10.48550/arXiv.2410.03092")] ai/scaling politics reinforcement-learning/safe

We present insights from “Intelligence Rising”, a scenario exploration exercise about possible AI futures. Drawing on the experiences of facilitators who have overseen 43 games over a four-year period, we illuminate recurring patterns, strategies, and decision-making processes observed during gameplay.

Our analysis reveals key strategic considerations about AI development trajectories in this simulated environment, including: the destabilizing effects of AI races, the crucial role of international cooperation in mitigating catastrophic risks, the challenges of aligning corporate and national interests, and the potential for rapid, transformative change in AI capabilities.

We highlight places where we believe the game has been effective in exposing participants to the complexities and uncertainties inherent in AI governance. Key recurring gameplay themes include the emergence of international agreements, challenges to the robustness of such agreements, the critical role of cybersecurity in AI development, and the potential for unexpected crises to dramatically alter AI trajectories.

By documenting these insights, we aim to provide valuable foresight for policymakers, industry leaders, and researchers navigating the complex landscape of AI development and governance.

--- https://transformer-circuits.pub/2024/features-as-classifiers/index.html Using Dictionary Learning Features as Classifiers 2024 2024-10-17 ai/nn/adversarial reinforcement-learning/safe --- https://www.nature.com/articles/s41562-024-01948-y Causal effect of video gaming on mental well-being in Japan 2020–2022 2020-01 2024-10-17 sociology/technology --- https://arxiv.org/abs/2410.10629#nvidia SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers Enze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang Li, Ligeng Zhu, Yao Lu, Song Han 2024-10-14 2024-10-17 [("doi","10.48550/arXiv.2410.10629")] ai/nn/diffusion ai/nn/transformer/attention/linear-algebra ai/nn/transformer/gpt/instruction-tuning

[homepage] We introduce Sana, a text-to-image framework that can efficiently generate images up to 4,096×4,096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU.

Core designs include: (1) Deep compression autoencoder: unlike traditional AEs, which compress images only 8×, we trained an AE that can compress images 32×, effectively reducing the number of latent tokens. (2) Linear DiT: we replace all vanilla attention in DiT with linear attention, which is more efficient at high resolutions without sacrificing quality. (3) Decoder-only text encoder: we replaced T5 with a modern decoder-only small LLM [Gemma-2] as the text encoder, and designed complex human instruction with in-context learning to enhance the image-text alignment. (4) Efficient training and sampling: we propose Flow-DPM-Solver to reduce sampling steps, with efficient caption labeling and selection to accelerate convergence.

As a result, Sana-0.6B is very competitive with modern giant diffusion models (eg. Flux-12B), being 20× smaller and 100+ times faster in measured throughput. Moreover, Sana-0.6B can be deployed on a 16GB laptop GPU, taking less than 1 second to generate a 1,024×1,024 resolution image.

Sana enables content creation at low cost.

Code and model will be publicly released.

--- https://arxiv.org/abs/2408.00118#google Gemma 2: Improving Open Language Models at a Practical Size Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, Johan Ferret, Peter Liu, Pouya Tafti, Abe Friesen, Michelle Casbon, Sabela Ramos, Ravin Kumar, Charline Le Lan, Sammy Jerome, Anton Tsitsulin, Nino Vieillard, Piotr Stanczyk, Sertan Girgin, Nikola Momchev, Matt Hoffman, Shantanu Thakoor, Jean-Bastien Grill, Behnam Neyshabur, Olivier Bachem, Alanna Walton, Aliaksei Severyn, Alicia Parrish, Aliya Ahmad, Allen Hutchison, Alvin Abdagic, Amanda Carl, Amy Shen, Andy Brock, Andy Coenen, Anthony Laforge, Antonia Paterson, Ben Bastian, Bilal Piot, Bo Wu, Brandon Royal, Charlie Chen, Chintu Kumar, Chris Perry, Chris Welty, Christopher A. Choquette-Choo, Danila Sinopalnikov, David Weinberger, Dimple Vijaykumar, Dominika Rogozińska, Dustin Herbison, Elisa Bandy, Emma Wang, Eric Noland, Erica Moreira, Evan Senter, Evgenii Eltyshev, Francesco Visin, Gabriel Rasskin, Gary Wei, Glenn Cameron, Gus Martins, Hadi Hashemi, Hanna Klimczak-Plucińska, Harleen Batra, Harsh Dhand, Ivan Nardini, Jacinda Mein, Jack Zhou, James Svensson, Jeff Stanway, Jetha Chan, Jin Peng Zhou, Joana Carrasqueira, Joana Iljazi, Jocelyn Becker, Joe Fernandez, Joost van Amersfoort, Josh Gordon, Josh Lipschultz, Josh Newlan, Ju-yeong Ji, Kareem Mohamed, Kartikeya Badola, Kat Black, Katie Millican, Keelin McDonell, Kelvin Nguyen, Kiranbir Sodhia, Kish Greene, Lars Lowe Sjoesund, Lauren Usui, Laurent Sifre, Lena Heuermann, Leticia Lago, Lilly McNealus, Livio Baldini Soares, Logan Kilpatrick, Lucas Dixon, Luciano Martins, Machel Reid, Manvinder Singh, Mark Iverson, Martin Görner, Mat Velloso, Mateo Wirth, Matt Davidow, Matt Miller, Matthew Rahtz, Matthew Watson, Meg Risdal, Mehran Kazemi, Michael Moynihan, Ming Zhang, Minsuk Kahng, Minwoo Park, Mofi Rahman, Mohit Khatwani, Natalie Dao, Nenshad Bardoliwalla, Nesh Devanathan, Neta Dumai, Nilay Chauhan, Oscar Wahltinez, Pankil Botarda, Parker Barnes, Paul Barham, Paul Michel, Pengchong Jin, Petko Georgiev, Phil Culliton, Pradeep Kuppala, Ramona Comanescu, Ramona Merhej, Reena Jana, Reza Ardeshir Rokni, Rishabh Agarwal, Ryan Mullins, Samaneh Saadat, Sara Mc Carthy, Sarah Cogan, Sarah Perrin, Sébastien M. R. Arnold, Sebastian Krause, Shengyang Dai, Shruti Garg, Shruti Sheth, Sue Ronstrom, Susan Chan, Timothy Jordan, Ting Yu, Tom Eccles, Tom Hennigan, Tomas Kocisky, Tulsee Doshi, Vihan Jain, Vikas Yadav, Vilobh Meshram, Vishal Dharmadhikari, Warren Barkley, Wei Wei, Wenming Ye, Woohyun Han, Woosuk Kwon, Xiang Xu, Zhe Shen, Zhitao Gong, Zichuan Wei, Victor Cotruta, Phoebe Kirk, Anand Rao, Minh Giang, Ludovic Peran, Tris Warkentin, Eli Collins, Joelle Barral, Zoubin Ghahramani, Raia Hadsell, D. Sculley, Jeanine Banks, Anca Dragan, Slav Petrov, Oriol Vinyals, Jeff Dean, Demis Hassabis, Koray Kavukcuoglu, Clement Farabet, Elena Buchatskaya, Sebastian Borgeaud, Noah Fiedel, Armand Joulin, Kathleen Kenealy, Robert Dadashi, Alek Andreev 2024-07-31 2024-10-17 [("doi","10.48550/arXiv.2408.00118")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer

[code, blog] In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters.

In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al 2020a) and group-query attention (Ainslie et al 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al 2015) instead of next token prediction.

The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2–3× bigger.

We release all our models to the community.

--- https://arxiv.org/abs/2312.07551 Language Model Alignment with Elastic Reset Michael Noukhovitch, Samuel Lavoie, Florian Strub, Aaron Courville 2023-12-06 2024-10-17 [("doi","10.48550/arXiv.2312.07551")] ai/nn/transformer/gpt/2 reinforcement-learning/meta-learning/continual-learning reinforcement-learning/preference-learning

Finetuning language models with reinforcement learning (RL), eg. from human feedback (HF), is a prominent method for alignment. But optimizing against a reward model can improve on reward while degrading performance in other areas, a phenomenon known as reward hacking, alignment tax, or language drift.

First, we argue that commonly used test metrics are insufficient and instead measure how different algorithms trade off between reward and drift. The standard method modified the reward with a Kullback-Liebler (KL) penalty between the online and initial model.

We propose Elastic Reset, a new algorithm that achieves higher reward with less drift without explicitly modifying the training objective. We periodically reset the online model to an exponentially moving average (EMA) of itself, then reset the EMA model to the initial model. Through the use of an EMA, our model recovers quickly after resets and achieves higher reward with less drift in the same number of steps.

We demonstrate that fine-tuning language models with Elastic Reset leads to state-of-the-art performance on a small scale pivot-translation benchmark, outperforms all baselines in a medium-scale RLHF-like IMDB mock sentiment task, and leads to a more performant and more aligned technical QA chatbot with LLaMA-7B.

Code available at Github.

--- https://arxiv.org/abs/2305.13245#google GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, Sumit Sanghai 2023-05-22 2024-10-17 [("doi","10.48550/arXiv.2305.13245")] ai/nn/transformer/attention

Multi-query attention (MQA), which only uses a single key-value head, drastically speeds up decoder inference. However, MQA can lead to quality degradation, and moreover it may not be desirable to train a separate model just for faster inference.

We (1) propose a recipe for uptraining existing multi-head language model checkpoints into models with MQA using 5% of original pre-training compute, and (2) introduce grouped-query attention (GQA), a generalization of multi-query attention which uses an intermediate (more than one, less than number of query heads) number of key-value heads.

We show that uptrained GQA achieves quality close to multi-head attention with comparable speed to MQA.

--- https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001575 An estimation of the absolute number of axons indicates that human cortical areas are sparsely connected 2024-10-17 ai/nn/sparsity psychology/neuroscience --- https://www.businesswire.com/news/home/20241015910376/en/Crusoe-Blue-Owl-Capital-and-Primary-Digital-Infrastructure-Enter-3.4-billion-Joint-Venture-for-AI-Data-Center-Development 2024-10-17 ai/scaling/hardware reinforcement-learning/openai --- https://arxiv.org/abs/2410.04343#deepmind Inference Scaling for Long-Context Retrieval Augmented Generation Zhenrui Yue, Honglei Zhuang, Aijun Bai, Kai Hui, Rolf Jagerman, Hansi Zeng, Zhen Qin, Dong Wang, Xuanhui Wang, Michael Bendersky 2024-10-06 2024-10-17 [("doi","10.48550/arXiv.2410.04343")] ai/nn/retrieval ai/nn/transformer/gpt/palm/2 ai/scaling

The scaling of inference computation has unlocked the potential of long-context large language models (LLMs) across diverse settings. For knowledge-intensive tasks, the increased compute is often allocated to incorporate more external knowledge. However, without effectively using such knowledge, solely expanding context does not always enhance performance.

In this work, we investigate inference scaling for retrieval augmented generation (RAG), exploring strategies beyond simply increasing the quantity of knowledge. We focus on two inference scaling strategies: in-context learning and iterative prompting. These strategies provide additional flexibility to scale test-time computation (eg. by increasing retrieved documents or generation steps), thereby enhancing LLMs’ ability to effectively acquire and use contextual information.

We address two key questions: (1) How does RAG performance benefit from the scaling of inference computation when optimally configured? (2) Can we predict the optimal test-time compute allocation for a given budget by modeling the relationship between RAG performance and inference parameters?

Our observations reveal that increasing inference computation leads to nearly linear gains in RAG performance when optimally allocated, a relationship we describe as the inference scaling laws for RAG. Building on this, we further develop the computation allocation model to estimate RAG performance across different inference configurations.

The model predicts optimal inference parameters under various computation constraints, which align closely with the experimental results.

By applying these optimal configurations, we demonstrate that scaling inference compute on long-context LLMs achieves up to 58.9% gains on benchmark datasets compared to standard RAG.

--- https://arxiv.org/abs/2410.10630 Thinking LLMs: General Instruction Following with Thought Generation Tianhao Wu, Janice Lan, Weizhe Yuan, Jiantao Jiao, Jason Weston, Sainbayar Sukhbaatar 2024-10-14 2024-10-17 [("doi","10.48550/arXiv.2410.10630")] ai/nn/transformer/gpt/inner-monologue reinforcement-learning/meta-learning reinforcement-learning/preference-learning

[Twitter] LLMs are typically trained to answer user questions or follow instructions similarly to how human experts respond. However, in the standard alignment framework, they lack the basic ability of explicit thinking before answering. Thinking is important for complex questions that require reasoning and planning—but can be applied to any task.

We propose a training method for equipping existing LLMs with such thinking abilities for general instruction following without the use of additional human data. We achieve this by an iterative search and optimization procedure that explores the space of possible thought generations, allowing the model to learn how to think without direct supervision.

For each instruction, the thought candidates are scored using a judge model to evaluate their responses only, and then optimized via preference optimization.

We show that this procedure leads to superior performance on AlpacaEval and Arena-Hard, and shows gains from thinking on non-reasoning categories such as marketing, health, and general knowledge, in addition to more traditional reasoning and problem-solving tasks.

--- https://haseebq.com/how-not-to-bomb-your-offer-negotiation/ How Not to Bomb Your Offer Negotiation 2024-10-17 economics --- https://knightattheopera.blogspot.com/2024/05/the-best-rpg-cover-of-all-time.html The Best RPG Cover of all Time [Traveller 1977] Dwiz 2024-05-19 2024-10-17 design/typography/rubrication fiction/criticism fiction/science-fiction --- /doc/design/typography/rubrication/1977-07-22-gamedesignersworkshop-traveller-cover.jpg Traveller cover Game Designers Workshop 1977-07-22 2024-10-17 design/typography/rubrication fiction/science-fiction --- https://www.wired.com/2015/09/power-1000-genomes/ What 2,500 Sequenced Genomes Say About Humanity’s Future 2024-01-01 genetics/sequencing --- https://www.wired.com/2015/11/carnegie-mellon-denies-fbi-paid-for-tor-breaking-research/ Carnegie Mellon Denies FBI Paid for Tor-Breaking Research 2024-01-01 darknet-market/silk-road/2 --- https://cacm.acm.org/research/always-measure-one-level-deeper/ Always Measure One Level Deeper: Performance measurements often go wrong, reporting surface-level results that are more marketing than science John Ousterhout 2018-07-01 2024-10-17 [("doi","10.1145/3213770")] cs/algorithm --- https://arxiv.org/abs/2406.11431 Super(ficial)-alignment: Strong Models May Deceive Weak Models in Weak-to-Strong Generalization Wenkai Yang, Shiqi Shen, Guangyao Shen, Wei Yao, Yong Liu, Zhi Gong, Yankai Lin, Ji-Rong Wen 2024-06-17 2024-10-17 [("doi","10.48550/arXiv.2406.11431")] ai/nn/adversarial ai/nn/transformer/gpt/2 reinforcement-learning/preference-learning reinforcement-learning/safe

[code] Superalignment, where humans act as weak supervisors for superhuman models, has become a crucial problem with the rapid development of Large Language Models (LLMs). Recent work has preliminarily studied this problem by using weak models to supervise strong models, and discovered that weakly supervised strong students can consistently outperform weak teachers towards the alignment target, leading to a weak-to-strong generalization phenomenon.

However, we are concerned that behind such a promising phenomenon, whether there exists an issue of weak-to-strong deception, where strong models deceive weak models by exhibiting well-aligned behaviors in areas known to weak models but producing misaligned behaviors in cases weak models do not know. We take an initial step towards exploring this security issue in a specific but realistic multi-objective alignment case, where there may be some alignment targets conflicting with each other (eg. helpfulness vs. harmlessness).

We aim to explore whether, in such cases, strong models might deliberately make mistakes in areas known to them but unknown to weak models within one alignment dimension, in exchange for a higher reward in another dimension. Through extensive experiments in both the reward modeling and preference optimization scenarios, we find:

(1) The weak-to-strong deception phenomenon exists across all settings. (2) The deception intensifies as the capability gap between weak and strong models increases. (3) Bootstrapping with an intermediate model can mitigate the deception to some extent, though its effectiveness remains limited.

Our work highlights the urgent need to pay more attention to the true reliability of superalignment.

--- https://en.wikipedia.org/wiki/Traveller_(role-playing_game) Traveller (role-playing game) 2024-10-17 ai fiction/science-fiction fiction/text-game --- https://en.wikipedia.org/wiki/Evolution_of_sexual_reproduction Evolution of sexual reproduction 2024-10-17 genetics/selection/natural --- https://en.wikipedia.org/wiki/Sleep#Functions Sleep § Functions 2024-10-17 genetics/selection/natural psychology/neuroscience zeo --- /doc/economics/2024-lee-2.pdf Using Grocery Data for Credit Decisions Jung Youn Lee, Joonhyuk Yang, Eric T. Anderson 2024-07-15 2024-10-17 [("doi","10.1287/mnsc.2022.02364")] economics psychology/personality

Many consumers across the world struggle to gain access to credit because of their lack of credit scores. This paper explores the potential of a new alternative data source, grocery transaction data, for evaluating consumers’ creditworthiness.

Our analysis takes advantage of a unique, individual-level match of credit card data and supermarket loyalty card data.

By developing credit scoring algorithms that either exclude or include grocery data, we illustrate both the incremental value of grocery data for credit decisions and its boundary conditions. We demonstrate that signals from grocery data can improve credit approval decisions, particularly for individuals who lack traditional credit scores. Furthermore, as a consumer establishes a relationship with lenders and builds a credit history, the marginal value of incorporating grocery data diminishes.

These findings highlight the potential of grocery data in informing credit decisions and, consequently, in enabling financial institutions to extend credit to consumers who lack traditional credit scores.

…In particular, using a customer identifier, we merge the supermarket’s loyalty card data and the issuer’s credit card spending and payment history at the individual level for the consumers who appear in both data sources between January 2017 and June 2019. The merged data allow us to observe how 30,089 consumers behave in the two seemingly different domains…We use a new, proprietary data set from an anonymous conglomerate that operates both a credit card issuer and a supermarket chain. The credit card issuer offers general-purpose credit cards that can be used at any merchant that accepts the associated processing network. The supermarket chain sells a wide range of products in various categories, including groceries, household supplies, clothing, and other general merchandise. [Walmart?]

…We find that what one buys can explain what type of payer one is even after controlling for various sociodemographic variables and credit scores. For instance, buying cigarettes or energy drinks is associated with a higher likelihood of missing credit card payments or defaulting, whereas purchasing fresh milk or vinegar dressings is linked to consistently paying credit card bills on time.

Using item-level survey ratings, we find suggestive evidence that buying healthier but less convenient food items is predictive of responsible payment behaviors.

Furthermore, we observe a positive and robust correlation between displaying greater consistency in various dimensions of grocery shopping behavior and making timely credit card bill payments. For example, cardholders who consistently pay their bills on time are more likely to shop on the same day of the week, spend similar amounts across months, and purchase the same brands and product categories.

…To predict the credit risk of unscored consumers or those without credit scores, the lender often relies solely on sociodemographic variables, such as income. In these scenarios, incorporating grocery data substantially improves predictive accuracy, increasing out-of-sample predictive power by 3.11–7.66 percentage points, as measured by the area under the receiver operating characteristic curve (AUC). When it comes to consumers with credit scores, we find that grocery data, when used in isolation, can achieve predictive accuracy comparable to that of credit scores alone. This result implies that individuals’ nonfinancial behaviors can provide credit risk signals of similar value to traditional credit scores.

However, grocery data is not a perfect substitute for credit scores as there is a smaller yet positive incremental predictive gain from grocery data even relative to credit scores. More precisely, when both sociodemographic variables and credit scores are available, the incremental predictive power introduced by grocery data ranges from 0.359–2.51 percentage points in the out-of-sample AUC. Taken together, these results suggest that grocery data complements rather than substitutes traditional financial data, such as sociodemographic variables and credit scores.

…We find that implementing this two-stage decision rule leads to a 1.46% increase in per-person profits among applicants without credit scores. This increased profitability is driven by the improved risk profile of approved applicants as the rule effectively filters out defaulters, who experience a higher likelihood of rejection in the second stage than non-defaulters. By contrast, for applicants with credit scores, the impact on credit approval decisions and profitability is minimal with a 0.025% increase in per-person payoffs. These findings collectively suggest that, under the particular decision rule we consider, there may be a stronger motivation for the lender to acquire, collect, and leverage grocery data for evaluating applicants who lack a traditional credit score.

--- https://omega0.xyz/omega8008/JaynesBookPdf.html Probability Theory: The Logic Of Science E. T. Jaynes 2024-01-01 philosophy/epistemology science statistics/bayes --- https://www.overcomingbias.com/p/fantasy-and-reahtml Fantasy and Reality: Substitutes or Complements? Robin Hanson 2008 2024-01-01 fiction psychology --- https://www.fadedpage.com/showbook.php?pid=20160325 Possible Worlds and Other Essays J. B. S. Haldane 1927 2024-01-01 science --- /aunn#memorization Absolute Unit NNs: Regression-Based MLPs for Everything § Memorize All The Things Gwern 2023 2023 ai/nn/retrieval --- https://www.nature.com/articles/d41586-024-03287-4 The early days of peer review: 5 insights from historic reports 2024-10-15 2024-10-17 statistics/peer-review --- http://danny.oz.au/danny/humour/theology-exam Theological Engineering Exam Anonymous 2024-10-17 math/humor philosophy/religion --- https://www.snopes.com/fact-check/hell-endothermic-exothermic/ Is Hell Endothermic or Exothermic? Old collegiate legend involves a student’s coming up with a clever proof about the physical properties of Hell Barbara Mikkelson 2000-12-01 2024-10-17 math/humor philosophy/religion --- https://en.wikipedia.org/wiki/Brent_W._Roberts Brent W. Roberts 2024-01-01 psychology/personality/conscientiousness psychology/personality/narcissism --- https://www.nytimes.com/2024/10/17/science/mucus-parachutes-ocean-marine-snow.html Parachutes Made of Mucus Change How Some Scientists See the Ocean [microbiome harvesting?] 2024-10-17 2024-10-17 genetics/microbiome --- https://www.nytimes.com/2024/10/17/science/sperm-egg-proteins-key.html Sperm Can’t Unlock an Egg Without This Ancient Molecular Key 2024-10-17 2024-10-17 ai/nn/transformer/alphafold --- https://www.henrikkarlsson.xyz/p/search-query A blog post is a very long and complex search query to find fascinating people and make them route interesting stuff to your inbox Henrik Karlsson 2024-10-17 psychology/writing sociology/technology --- https://arxiv.org/abs/2410.09400 CtrLoRA: An Extensible and Efficient Framework for Controllable Image Generation Yifeng Xu, Zhenliang He, Shiguang Shan, Xilin Chen 2024-10-12 2024-10-17 [("doi","10.48550/arXiv.2410.09400")] ai/anime/danbooru

Recently, large-scale diffusion models have made impressive progress in text-to-image (T2I) generation. To further equip these T2I models with fine-grained spatial control, approaches like ControlNet introduce an extra network that learns to follow a condition image. However, for every single condition type, ControlNet requires independent training on millions of data pairs with hundreds of GPU hours, which is quite expensive and makes it challenging for ordinary users to explore and develop new types of conditions.

To address this problem, we propose the CtrLoRA framework, which trains a Base ControlNet to learn the common knowledge of image-to-image generation from multiple base conditions, along with condition-specific LoRAs to capture distinct characteristics of each condition. Utilizing our pretrained Base ControlNet, users can easily adapt it to new conditions, requiring as few as 1,000 data pairs and less than one hour of single-GPU training to obtain satisfactory results in most scenarios [eg. anime portraits].

Moreover, our CtrLoRA reduces the learnable parameters by 90% compared to ControlNet, lowering the threshold to distribute and deploy the model weights. Extensive experiments on various types of conditions demonstrate the efficiency and effectiveness of our method.

Codes and model weights will be released at Github.

--- https://imprompter.ai/ Imprompter 2024-10-17 ai/nn/adversarial ai/nn/transformer/gpt --- https://arxiv.org/abs/2410.07095#openai MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, Lilian Weng, Aleksander Madry 2024-10-09 2024-10-17 [("doi","10.48550/arXiv.2410.07095")] ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex reinforcement-learning/meta-learning reinforcement-learning/scaling

[blog] We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering. To this end, we curate 75 ML engineering-related competitions from Kaggle, creating a diverse set of challenging tasks that test real-world ML engineering skills such as training models, preparing datasets, and running experiments.

We establish human baselines for each competition using Kaggle’s publicly available leaderboards. We use open-source agent scaffolds to evaluate several frontier language models on our benchmark, finding that the best-performing setup—OpenAI’s o1-preview with AIDE scaffolding—achieves at least the level of a Kaggle bronze medal in 16.9% of competitions.

In addition to our main results, we investigate various forms of resource scaling for AI agents and the impact of contamination from pre-training. We open-source our benchmark code (github.com/openai/mle-bench/) to facilitate future research in understanding the ML engineering capabilities of AI agents.

--- https://openai.com/index/mle-bench/ 2024-10-17 ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex reinforcement-learning/meta-learning reinforcement-learning/scaling --- https://mathenchant.wordpress.com/2024/10/17/industrious-dice/ Industrious Dice [minimizing pip counts on still-functional dice] 2024-10-17 2024-10-17 fiction/text-game math --- https://arxiv.org/abs/2410.08044 The Rise of AI-Generated Content in Wikipedia Creston Brooks, Samuel Eggert, Denis Peskoff 2024-10-10 2024-10-17 [("doi","10.48550/arXiv.2410.08044")] ai/nn/transformer/gpt/3/nonfiction economics/advertising wikipedia

The rise of AI-generated content in popular information sources raises concerns about accountability, accuracy, and bias amplification. Beyond directly impacting consumers, the widespread presence of this content poses questions for the long-term viability of training language models on vast internet sweeps.

We use GPTZero, a proprietary AI detector, and Binoculars, an open-source alternative, to establish lower bounds on the presence of AI-generated content in recently created Wikipedia pages.

Both detectors reveal a marked increase in AI-generated content in recent pages compared to those from before the release of GPT-3.5.

With thresholds calibrated to achieve a 1% false positive rate on pre-GPT-3.5 articles, detectors flag over 5% of newly created English Wikipedia articles as AI-generated, with lower percentages for German, French, and Italian articles.

Flagged Wikipedia articles are typically of lower quality and are often self-promotional or partial towards a specific viewpoint on controversial topics.

--- https://www.nature.com/articles/s41467-024-53108-5 Robotic microinjection enables large-scale transgenic studies of Caenorhabditis elegans 2024 2024-10-17 genetics/editing reinforcement-learning/robot --- /doc/psychiatry/alcoholism/2024-qeadan.pdf The association between glucose-dependent insulinotropic polypeptide and/or glucagon-like peptide-1 receptor agonist prescriptions and substance-related outcomes in patients with opioid and alcohol use disorders: A real-world data analysis Fares Qeadan, Ashlie McCunn, Benjamin Tingey 2024-10-16 2024-10-17 [("doi","10.1111/add.16679")] longevity/glp/psychology psychiatry/alcoholism

Aims: This study aimed to estimate the strength of association between prescriptions of glucose-dependent insulinotropic polypeptide (GIP) and/or glucagon-like peptide-1 receptor agonists (GLP-1 RA) and the incidence of opioid overdose and alcohol intoxication in patients with opioid use disorder (OUD) and alcohol use disorder (AUD), respectively. This study also aimed to compare the strength of the GIP/GLP-1 RA and substance use-outcome association among patients with comorbid type 2 diabetes and obesity.

Design: A retrospective cohort study analyzing de-identified electronic health record data from the Oracle Cerner Real-World Data.

Setting: About 136 United States of America health systems, covering over 100 million patients, spanning January 2014 to September 2022.

Participants: The study included 503,747 patients with a history of OUD and 817,309 patients with a history of AUD, aged 18 years or older.

Measurements: The exposure indicated the presence (one or more) or absence of GIP/GLP-1 RA prescriptions. The outcomes were the incidence rates of opioid overdose in the OUD cohort and alcohol intoxication in the AUD cohort. Potential confounders included comorbidities and demographic factors.

Results: Patients with GIP/GLP-1 RA prescriptions demonstrated statistically-significantly lower rates of opioid overdose [adjusted incidence rate ratio (aIRR) in OUD patients: 0.60; 95% confidence interval (CI) = 0.43–0.83] and alcohol intoxication (aIRR in AUD patients: 0.50; 95% CI = 0.40–0.63) compared to those without such prescriptions. When stratified by comorbid conditions, the rate of incident opioid overdose and alcohol intoxication remained similarly protective for those prescribed GIP/GLP-1 RA among patients with OUD and AUD.

Conclusion: Prescriptions of glucose-dependent insulinotropic polypeptide and/or glucagon-like peptide-1 receptor agonists appear to be associated with lower rates of opioid overdose and alcohol intoxication in patients with opioid use disorder and alcohol use disorder. The protective effects are consistent across various subgroups, including patients with comorbid type 2 diabetes and obesity.

[Keywords: AUD, dulaglutide, GIP/GLP-1 RA, intoxication, OUD, overdose, semaglutide]

See Also:

--- /doc/science/1979-astin.pdf#page=12 Paul Darwin Foote (1888–1971) § The Temperature of Heaven & Hell Allen V. Astin 1979-01-01 2024-10-17 math/humor philosophy/religion science

…An insight into Paul Foote’s broader interests and into his light good humor can be gleaned from some of his writings.

About 1920 he published anonymously in the Taylor Instrument Company house organ a paper on “The Temperature of Heaven and Hell”. By making scientific deductions from descriptions of the states of various material substances as described in the Bible, Foote concluded that Heaven was hotter than Hell.

The paper, or portions of it, are periodically reprinted—for example, in the journal Applied Optics (1972)—but all have attributed the paper to an anonymous source. Nevertheless, a copy of the original manuscript with Paul Foote’s notations identifying himself as the author was found in his personal file after his death.

[cf. rebuttal, Pérez 2001, Snopes, “Theological Engineering Exam”] --- /doc/math/humor/2001-perez.pdf The temperature of heaven and hell [retrospective] Jorge Mira Pérez 2001-07-01 2024-10-17 [("doi","10.1088/2058-7058/14/7/39")] math/humor philosophy/religion --- /doc/math/humor/2015-05-simanek-heavenishotterthanhell.html Heaven Is Hotter Than Hell & A Refutation Donald Simanek 2014-05 2024-10-17 math/humor philosophy/religion --- /doc/math/humor/2015-05-simanek-heavenishotterthanhell.html#foote-1920 The Temperature of Heaven and Hell Paul Darwin Foote 1920 2024-10-17 math/humor philosophy/religion --- https://hlfshell.ai/posts/deepmind-grandmaster-chess-without-search/ Google DeepMind’s Grandmaster-Level Chess Without Search 2024-10-17 ai/nn/sparsity/knowledge-distillation ai/nn/transformer/gpt reinforcement-learning/chess reinforcement-learning/imitation-learning --- https://www.poetryfoundation.org/poems/44400/spring-and-fall Spring and Fall Gerard Manley Hopkins 1880 2024-01-01 fiction/poetry --- https://pmc.ncbi.nlm.nih.gov/articles/PMC9006763/ Cerner real-world data (CRWD): A de-identified multicenter electronic health records database Louis Ehwerhemuepha, Kimberly Carlson, Ryan Moog, Ben Bondurant, Cheryl Akridge, Tatiana Moreno, Gary Gasperino, William Feaster 2022-03-31 2024-10-18 [("doi","10.1016/j.dib.2022.108120")] biology

Cerner Real-World Data (CRWD) is a de-identified big data source of multicenter electronic health records. Cerner Corporation secured appropriate data use agreements and permissions from more than 100 health systems in the United States contributing to the database as of March 2022. A subset of the database was extracted to include data from only patients with SARS-CoV-2 infections and is referred to as the Cerner COVID-19 Dataset.

The December 2021 version of CRWD consists of 100 million patients and 1.5 billion encounters across all care settings. There are 2.3 billion, 2.9 billion, 486 million, and 11.5 billion records in the condition, medication, procedure, and lab (laboratory test) tables respectively. The 2021 Q3 COVID-19 Dataset consists of 130.1 million encounters from 3.8 million patients.

The size and longitudinal nature of CRWD can be leveraged for advanced analytics and artificial intelligence in medical research across all specialties and is a rich source of novel discoveries on a wide range of conditions including but not limited to COVID-19.

[Keywords: Cerner Real-World Data (CRWD), COVID-19, SARS-CoV-2, Electronic Health Records (EHR), HealtheIntent, HealtheDataLab, Cerner learning Health Network (LHN)]

--- https://ilyabirman.net/forebruary/ Forebruary perpetual calendar 2024-10-18 design/visualization --- https://pmc.ncbi.nlm.nih.gov/articles/PMC6983939/#ab005 Embodying addiction: A predictive processing account 2024-10-18 psychiatry psychology/neuroscience reinforcement-learning/model --- /doc/genetics/heritable/2018-tedja.pdf Genome-wide association meta-analysis highlights light-induced signaling as a driver for refractive error Milly S. Tedja, Robert Wojciechowski, Pirro G. Hysi, Nicholas Eriksson, Nicholas A. Furlotte, Virginie J. M. Verhoeven, Adriana I. Iglesias, Magda A. Meester-Smoor, Stuart W. Tompson, Qiao Fan, Anthony P. Khawaja, Ching-Yu Cheng, René Höhn, Kenji Yamashiro, Adam Wenocur, Clare Grazal, Toomas Haller, Andres Metspalu, Juho Wedenoja, Jost B. Jonas, Ya Xing Wang, Jing Xie, Paul Mitchell, Paul J. Foster, Barbara E. K. Klein, Ronald Klein, Andrew D. Paterson, S. Mohsen Hosseini, Rupal L. Shah, Cathy Williams, Yik Ying Teo, Yih Chung Tham, Preeti Gupta, Wanting Zhao, Yuan Shi, Woei-Yuh Saw, E-Shyong Tai, Xue Ling Sim, Jennifer E. Huffman, Ozren Polašek, Caroline Hayward, Goran Bencic, Igor Rudan, James F. Wilson, The CREAM Consortium, 23andMe, UKBB Eye, Vision Consortium, Peter K. Joshi, Akitaka Tsujikawa, Fumihiko Matsuda, Kristina N. Whisenhunt, Tanja Zeller, Peter J. van der Spek, Roxanna Haak, Hanne Meijers-Heijboer, Elisabeth M. van Leeuwen, Sudha K. Iyengar, Jonathan H. Lass, Albert Hofman, Fernando Rivadeneira, André G. Uitterlinden, Johannes R. Vingerling, Terho Lehtimäki, Olli T. Raitakari, Ginevra Biino, Maria Pina Concas, Tae-Hwi Schwantes-An, Robert P. Igo Jr, Gabriel Cuellar-Partida, Nicholas G. Martin, Jamie E. Craig, Puya Gharahkhani, Katie M. Williams, Abhishek Nag, Jugnoo S. Rahi, Phillippa M. Cumberland, Cécile Delcourt, Céline Bellenguez, Janina S. Ried, Arthur A. Bergen, Thomas Meitinger, Christian Gieger, Tien Yin Wong, Alex W. Hewitt, David A. Mackey, Claire L. Simpson, Norbert Pfeiffer, Olavi Pärssinen, Paul N. Baird, Veronique Vitart, Najaf Amin, Cornelia van Duijn, Joan E. Bailey-Wilson, Terri L. Young, Seang-Mei Saw, Dwight Stambolian, Stuart MacGregor, Jeremy A. Guggenheim, Joyce Y. Tung, Christopher J. Hammond, Caroline C. W. Klaver 2018-01-01 2024-01-01 [("doi","10.1038/s41588-018-0127-7")] genetics/heritable psychology/vision

Refractive errors, including myopia, are the most frequent eye disorders worldwide and an increasingly common cause of blindness. This genome-wide association meta-analysis in 160,420 participants and replication in 95,505 participants increased the number of established independent signals 37 → 161 and showed high genetic correlation between Europeans & Asians (rg > 0.78).

Expression experiments and comprehensive in silico analyses identified retinal cell physiology and light processing as prominent mechanisms, and also identified functional contributions to refractive-error development in all cell types of the neurosensory retina, retinal pigment epithelium, vascular endothelium and extracellular matrix. Newly identified genes implicate novel mechanisms such as rod-and-cone bipolar synaptic neurotransmission, anterior-segment morphology and angiogenesis. 31 loci resided in or near regions transcribing small RNAs, thus suggesting a role for post-transcriptional regulation.

Our results support the notion that refractive errors are caused by a light-dependent retina-to-sclera signaling cascade and delineate potential pathobiological molecular drivers.

--- https://alexwlchan.net/2024/static-websites/ Using static websites for tiny archives 2024-10-18 cs/css design --- https://arxiv.org/abs/2409.13768#google Magika: AI-Powered Content-Type Detection Yanick Fratantonio, Luca Invernizzi, Loua Farah, Kurt Thomas, Marina Zhang, Ange Albertini, Francois Galilee, Giancarlo Metitieri, Julien Cretin, Alex Petit-Bianco, David Tao, Elie Bursztein 2024-09-18 2024-10-19 [("doi","10.48550/arXiv.2409.13768")] ai/nn/fully-connected cs/security

The task of content-type detection—which entails identifying the data encoded in an arbitrary byte sequence—is critical for operating systems, development, reverse engineering environments, and a variety of security applications.

In this paper, we introduce Magika, a novel AI-powered content-type detection tool. Under the hood, Magika employs a deep learning model that can execute on a single CPU with just 1MB of memory to store the model’s weights. We show that Magika achieves an average F1 score of 99% across over a hundred content types and a test set of more than 1M files, outperforming all existing content-type detection tools today. In order to foster adoption and improvements, we open source Magika under an Apache 2 license on GitHub and make our model and training pipeline publicly available.

Our tool has already seen adoption by the Gmail email provider for attachment scanning, and it has been integrated with VirusTotal to aid with malware analysis.

We note that this paper discusses the first iteration of Magika, and a more recent version already supports more than 200 content types. The interested reader can see the latest development on the Magika GitHub repository, available at Github. [Sounds like all the HN & tech criticism stung.]

--- http://www.vetta.org/2009/08/funding-safe-agi/ Funding safe AGI Shane Legg 2009-08 2024-10-20 ai/scaling/economics reinforcement-learning/safe --- https://arxiv.org/abs/2402.01502 Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers Alicia Curth, Alan Jeffares, Mihaela van der Schaar 2024-02-02 2024-10-20 [("doi","10.48550/arXiv.2402.01502")] ai/tabular

Despite their remarkable effectiveness and broad application, the drivers of success underlying ensembles of trees are still not fully understood. In this paper, we highlight how interpreting tree ensembles as adaptive and self-regularizing smoothers can provide new intuition and deeper insight to this topic.

We use this perspective to show that, when studied as smoothers, randomized tree ensembles not only make predictions that are quantifiably more smooth than the predictions of the individual trees they consist of, but also further regulate their smoothness at test-time based on the dissimilarity between testing and training inputs.

First, we use this insight to revisit, refine and reconcile two recent explanations of forest success by providing a new way of quantifying the conjectured behaviors of tree ensembles objectively by measuring the effective degree of smoothing they imply. Then, we move beyond existing explanations for the mechanisms by which tree ensembles improve upon individual trees and challenge the popular wisdom that the superior performance of forests should be understood as a consequence of variance reduction alone.

We argue that the current high-level dichotomy into bias & variance-reduction prevalent in statistics is insufficient to understand tree ensembles—because the prevailing definition of bias does not capture differences in the expressivity of the hypothesis classes formed by trees and forests.

Instead, we show that forests can improve upon trees by 3 distinct mechanisms that are usually implicitly entangled.

In particular, we demonstrate that the smoothing effect of ensembling can reduce variance in predictions due to noise in outcome generation, reduce variability in the quality of the learned function given fixed input data, and reduce potential bias in learnable functions by enriching the available hypothesis space.

--- https://www.owlposting.com/p/a-primer-on-why-computational-predictive A primer on why computational predictive toxicology is hard 2024-10-20 ai biology statistics/bias/animal --- https://pmc.ncbi.nlm.nih.gov/articles/PMC4825177/ An implantable microdevice to perform high-throughput in vivo drug sensitivity testing in tumors Oliver Jonas, Heather M. Landry, Jason E. Fuller, John T. Santini Junior, Jose Baselga, Robert I. Tepper, Michael J. Cima, Robert Langer 2015-04-22 2024-10-20 [("doi","10.1126/scitranslmed.3010564")] biology statistics/power-analysis

[media] Current anticancer chemotherapy relies on a limited set of in vitro or indirect prognostic markers of tumor response to available drugs. A more accurate analysis of drug sensitivity would involve studying tumor response in vivo.

To this end, we have developed an implantable device that can perform drug sensitivity testing of several anticancer agents simultaneously inside the living tumor. The device contained reservoirs that released microdoses of single agents or drug combinations into spatially distinct regions of the tumor. The local drug concentrations were chosen to be representative of concentrations achieved during systemic treatment.

Local efficacy and drug concentration profiles were evaluated for each drug or drug combination on the device, and the local efficacy was confirmed to be a predictor of systemic efficacy in vivo for multiple drugs and tumor models. Currently, up to 16 individual drugs or combinations can be assessed independently, without systemic drug exposure, through minimally invasive biopsy of a small region of a single tumor.

This assay takes into consideration physiologic effects that contribute to drug response by allowing drugs to interact with the living tumor in its native microenvironment. Because these effects are crucial to predicting drug response, we envision that these devices will help identify optimal drug therapy before systemic treatment is initiated and could improve drug response prediction beyond the biomarkers and in vitro and ex vivo studies used today.

These devices may also be used in clinical drug development to safely gather efficacy data on new compounds before pharmacological optimization.

--- https://www.biorxiv.org/content/10.1101/2022.06.07.495183.full Docking-based long timescale simulation of cell-size protein systems at atomic resolution Ilya A. Vakser, Sergei Grudinin, Nathan W. Jenkins, Petras J. Kundrotas, Eric J. Deeds 2022-06-09 2024-10-20 [("doi","10.1101/2022.06.07.495183")] biology

Computational methodologies are increasingly addressing modeling of the whole cell at the molecular level. Proteins and their interactions are the key component of cellular processes. Techniques for modeling protein interactions, so far, have included protein docking and molecular simulation. The latter approaches account for the dynamics of the interactions, but are relatively slow, if carried out at all-atom resolution, or are coarse-grained.

Protein docking algorithms are far more efficient in sampling spatial coordinates. However, they do not account for the kinetics of the association (ie. they do not involve the time coordinate). Our proof-of-concept study bridges the two modeling approaches, developing an approach that can reach unprecedented simulation timescales at all-atom resolution.

The global intermolecular energy landscape of a large system of proteins was mapped by the pairwise Fast Fourier Transform docking and sampled in space and time by Monte Carlo simulations. The simulation protocol was parameterized on existing data and validated on a number of observations from experiments and molecular dynamics simulations.

The simulation protocol performed consistently across very different systems of proteins at different protein concentrations. It recapitulated data on the previously observed protein diffusion rates and aggregation. The speed of calculation allows reaching second-long trajectories of protein systems that approach the size of the cells, at atomic resolution.

--- https://pure.au.dk/portal/en/publications/low-risk-of-suicide-and-lithium-in-drinking-water-a-danish-individuallevel-cohort-study-using-spatial-analysis(5855ed41-d812-4ce4-b089-078be11c11fa).html Low risk of suicide and lithium in drinking water: A Danish individual-level cohort study using spatial analysis 2024-09-25 psychiatry/lithium --- https://www.bleepingcomputer.com/news/security/internet-archive-hacked-data-breach-impacts-31-million-users/ Internet Archive hacked, data breach impacts 31 million users 2024-10-20 cs/linkrot/archiving cs/security --- https://medium.com/dev-channel/a-netflix-web-performance-case-study-c0bcde26a9d9 A Netflix Web Performance Case Study: Improving Time-To-Interactive for Netflix.com on Desktop [by reducing JS] Addy Osmani 2018-11-05 2024-10-20 cs/js --- https://www.johndcook.com/blog/2024/10/19/channel-capacity-of-a-telegraph/ Channel capacity of a telegraph 2024-10-19 2024-10-20 cs/algorithm/information --- https://www.medrxiv.org/content/10.1101/2024.10.13.24315422.full Cross-ancestry analysis identifies genes associated with obesity risk and protection Deepro Banerjee, Santhosh Girirajan 2024-10-16 2024-10-20 [("doi","10.1101/2024.10.13.24315422")] exercise genetics/heritable/rare

Gene discoveries in obesity have largely been based on European cohorts, leading to an ancestral bias that limits their generalizability across populations. We performed a gene-based rare variant association study of 721,941 individuals and identified 116 novel BMI-associated genes with consistent effects across ancestries, including 50 risk-conferring and 66 protective genes against obesity.

Protective genes such as DCUN1D3 & NEUROD6 had effect sizes comparable to high-risk genes such as MC4R & BSN, and nearly twice that of known protective genes such as GPR75, which, along with 5 other genes, showed strong European bias. Notably, 82 of the 116 genes showed functional relevance to obesity including adiposity, energy homeostasis, and glucose metabolism.

While polygenic risks or an obesogenic lifestyle amplified the effect of 15 genes on BMI, including the combination of low physical activity and MACROD1, 23 genes including VIRMA, AQP3, and PML retained protective effects even at high polygenic scores.

Our findings provide further insights into the genetic basis of obesity that is conserved across ancestries and their interactions with obesogenic factors.

--- https://pmc.ncbi.nlm.nih.gov/articles/PMC7018780/ That dog won’t fit: body size awareness in dogs 2024-10-21 dog --- https://pmc.ncbi.nlm.nih.gov/articles/PMC10587091/ Body size awareness matters when dogs decide whether to detour an obstacle or opt for a shortcut 2024-10-21 dog --- https://www.cell.com/trends/ecology-evolution/fulltext/S0169-5347(20)30010-0 2024-10-21 cat/genetics cat/psychology --- https://pmc.ncbi.nlm.nih.gov/articles/PMC8044293/ The Mechanics of Social Interactions Between Cats and Their Owners Dennis C. Turner 2021-03-31 2024-10-21 [("doi","10.3389/fvets.2021.650143")] cat/psychology

This is a mini review that summarizes what is known from quantitative observational studies of social interactions between domestic cats and humans in both laboratory colonies and the home setting. Only results from data that have been statistically analyzed are included; hypotheses still to be tested will be declared as such.

In some cases, the observational data have been combined with independently collected subjective assessments by the owners of the animals’ character and owner personality traits to help interpret the data. Further, some relevant experimental studies are also included. All social interactions between cats and humans that are discussed below assume that the animals were socialized to people as kittens, the first topic of this review. Such socialized cats show what might be called “friendliness to humans”, which in turn affects human attachment to the cat.

The visual and acoustic behavioral elements used to communicate and interact with other cats can be perceived by people and are also employed by the cats when interacting with them. The initiation, and the initiator of social interactions between cats and humans have been shown to influence both the duration of the interaction bout and total interaction time in the relationship. Compliance with the interactional “wishes” of the partner is positively correlated between the cats and the humans over all human-cat dyads examined.

Cats do not spontaneously prefer one gender or age cohort of people, but the humans in those cohorts behave differently towards the cats, causing the latter to react differentially. The dyadic interaction structure has also been shown to differ between women and men and between older and younger adults.

Nevertheless, cats—merely their presence but of course their behavior—can affect human moods and human mood differences have been shown to affect the behavior of the cats. Finally, differences have been found between interactions with purebred and non-purebred cats and between younger and older cats.

[Keywords: owners, socialization, communication, mood, cats, interactions, breed]

--- https://pmc.ncbi.nlm.nih.gov/articles/PMC7222765/ Body Size and Bite Force of Stray and Feral Cats Patricia A. Fleming, Heather M. Crawford, Clare H. Auckland, Michael C. Calver 2020-04-17 2024-10-21 [("doi","10.3390/ani10040707")] cat/psychology

Predation by cats (Felis catus) threatens Australian wildlife. As they rely on their jaws to hold and subdue prey, their body size, skull shape and bite force can reflect an individual’s prey handling ability. Prey less than 100 g are the usual prey of F. catus but they have frequently been recorded to take larger prey, and previous studies have suggested that large male cats represent a disproportionate risk to threatened and translocated native wildlife populations.

We tested whether a cat’s sex, age, body mass, body condition, and bite force determined the size of the prey they took (prey body mass) especially for those prey that might be ‘dangerous’ or difficult to handle (our subjective assessment of whether animals would be capable of fighting back and would therefore require skill to subdue).

Large male cats do indeed represent the greatest risk in that they have greater body mass and bite force that would allow them to handle a greater range of prey. However even small cats were active hunters, and many had taken large or dangerous prey species.

The strongest predictor of prey size was the age of the cat, with older cats taking the largest prey.

As carnivorans rely heavily on their head and jaws for prey capture and handling, skull morphology and bite force can therefore reflect their ability to take larger or more difficult-to-handle prey. For 568 feral and stray cats (Felis catus), we recorded their demographics (sex and age), source location (feral or stray) and morphological measures (body mass, body condition); we estimated potential bite force from skull measurements for n = 268 of these cats, and quantified diet composition from stomach contents for n = 358. We compared skull measurements to estimate their bite force and determine how it varied with sex, age, body mass, body condition. Body mass had the strongest influence of bite force. In our sample, males were 36.2% heavier and had 20.0% greater estimated bite force (206.2 ± 44.7 Newtons, n = 168) than females (171.9 ± 29.3 Newtons, n = 120). However, cat age was the strongest predictor of the size of prey that they had taken, with older cats taking larger prey. The predictive power of this relationship was poor though (R2 < 0.038, p < 0.003), because even small cats ate large prey and some of the largest cats ate small prey, such as invertebrates. Cats are opportunistic, generalist carnivores taking a broad range of prey. Their ability to handle larger prey increases as the cats grow, increasing their jaw strength, and improving their hunting skills, but even the smallest cats in our sample had tackled and consumed large and potentially ‘dangerous’ prey that would likely have put up a defence.

[Keywords: Australia, body condition, diet, Felis catus, feral, predation, prey, stray, wildlife, urban]

--- https://pmc.ncbi.nlm.nih.gov/articles/PMC6204371/ Cats Parallel Great Apes and Corvids in Motor Self-Regulation—Not Brain but Material Size Matters Katarzyna Bobrowicz, Mathias Osvath 2018-10-22 2024-10-21 [("doi","10.3389/fpsyg.2018.01995")] cat/psychology psychology/animal/bird

The inhibition of unproductive motor movements is regarded as a fundamental cognitive mechanism. Recently, it has been shown that species with large absolute brain size or high numbers of pallial neurons, like great apes and corvids, show the highest performance on a task purportedly measuring this mechanism: the cylinder task. In this task, the subject must detour a perpendicularly oriented transparent cylinder to reach a reward through a side opening, instead of directly reaching for it and bumping into the front, which is regarded as an inhibitory failure.

Here we test domestic cats for the first time and show that:

cats can reach the same levels as great apes and corvids on this task, despite having much smaller brains. We tested subjects with apparatuses that varied in size (cylinder length and diameter) and material (glass or plastic), and found that subjects performed best on the large cylinders.

As numbers of successes decreased statistically-significantly when the cylinders were smaller, we conducted two additional experiments to discern which properties (length of the transparent surface, goal distance from the surface, size of the side opening) affect performance.

We conclude that sensorimotor requirements, which differ between species, may have a large impact on the results in such seemingly simple and apparently comparable tests. However, we also conclude that cats have comparably high levels of motor self-regulation, despite the differences between tests.

[Keywords: behavioral inhibition, domestic cat, cylinder task, detour, motor self-regulation]

--- /doc/cat/biology/2020-wu-2.pdf How do Cats Resist Landing Injury: Insights into the Multi-level Buffering Mechanism Xueqing Wu, Baoqing Pei, Yuyang Pei, Wei Wang, Yan Hao, Kaiyuan Zhou 2020-05-23 2024-10-20 [("doi","10.1007/s42235-020-0048-x")] cat/biology

When humans jump down from a high position, there is a risk of serious injury to the lower limbs. However, cats can jump down from the same heights without any injury because of their excellent ability to attenuate impact forces.

The present study aims to investigate the macro/micro biomechanical features of paw pads and limb bones of cats, and the coordination control of joints during landing, providing insights into how cats protect themselves from landing injury.

Accordingly, histological analysis, radiological analysis, finite element method, and mechanical testing were performed to investigate the mechanical properties, microstructure, and biomechanical response of the pads and limb bones. In addition, using a motion capture system, the kinematic/kinetic data during landing were analyzed based on inverse dynamics.

The results show that the pads and limb bones are major contributors to non-impact injuries, and cats actively couple their joints to adjust the parameters of movement to dissipate the higher impact. Therefore, the paw pads, limb bones, and coordinated joints complement each other and constitute a multi-level buffering mechanism, providing the cat with a sophisticated shock absorption system.

This biomechanical analysis can accordingly provide biological inspiration for new approaches to prevent human lower limb injuries.

[Keywords: cat, multi-level buffering, paw pads, limb bones, coordinated joints]

--- https://arxiv.org/abs/2410.07041#facebook Emergent properties with repeated examples François Charton, Julia Kempe 2024-10-09 2024-10-21 [("doi","10.48550/arXiv.2410.07041")] ai/nn/dynamic-evaluation ai/scaling/emergence/grokking

We study the performance of transformers as a function of the number of repetitions of training examples with algorithmically generated datasets.

On 3 problems of mathematics: the greatest common divisor, modular multiplication, and matrix eigenvalues, we show that for a fixed number of training steps, models trained on smaller sets of repeated examples outperform models trained on larger sets of single-use examples.

We also demonstrate that two-set training—repeated use of a small random subset of examples, along normal sampling on the rest of the training set—provides for faster learning and better performance. This highlights that the benefits of repetition can outweigh those of data diversity.

These datasets and problems provide a controlled setting to shed light on the still poorly understood interplay between generalization and memorization in deep learning.

…In ablation experiments, we show that the performance of two-set training cannot be improved by curating the set of repeated examples, or refreshing it as training proceeds. This sets us apart from curriculum learning, and strengthens the observation that repetition of a few random examples is really all we need. We also show that mixing repeated and non-repeated examples in the same mini-batches is required for two-set training to work. Finally, we propose a smooth extension of two-set training, by introducing a probability distribution on the training set.

…In all 3 cases, the benefits of repetition are substantial, but come in different flavors, from improving performance and accelerating learning (GCD), to allowing a new task to be learned (multiplication), or to be accessible to smaller models (eigenvalues). Alternatively, small random subsets of the data repeated at high frequency can elicit similar effects. These findings have profound implications and should lead to a paradigm shift where the training set size becomes a mere hyper-parameter, not solely governed by the availability of data and the belief that more is always better.

Figure 1a: Repetition Helps: Performance as a function of repetition for a fixed training budget (600M). GCD (blue). Models trained on smaller datasets, repeated 30×, perform much better than models trained on 1–4 epochs. Multiplication mod 67 (red). Models trained for 1–4 epochs do not learn. Learning “emerges” when models are trained on smaller data budgets, with increased repetition.
Figure 1b: Two-set training: For a fixed data budget, splitting the data into two random subsets and increasing the training frequency of one greatly improves performance. GCD (left): repeating 50k examples 3,000× for a training budget of 600M brings performance 37 → 69 on 100M. Modular multiplication (right): Models trained on 600M single-use examples do not learn. With 25M examples repeated 18×, and 150M single use examples, accuracy is 92%, with 2.5M examples repeated 60×, and 450M single-use, accuracy is 68%. Smooth distributions of repetition over the training set achieve 70% accuracy.

…On the surface, grokking shares similarities with our work: a small training dataset is iterated for many epochs, the phenomenon is isolated in clean experiments on synthetic data, and it contradicts traditional wisdom regarding overfitting. But there are important differences: in grokking, delayed learning occurs, we observe no such delay; grokking occurs for “tiny” training samples (hundreds or thousands of examples), our models use millions (even for modular multiplication); grokking is very sensitive to the optimizer used, our findings are robust across optimizers (Appendix C.5), and, of course, no two-set approach is documented in the grokking setting.

--- https://arxiv.org/abs/2309.06275 Re-Reading Improves Reasoning in Large Language Models Xiaohan Xu, Chongyang Tao, Tao Shen, Can Xu, Hongbo Xu, Guodong Long, Jian-guang Lou, Shuai Ma 2023-09-12 2024-10-21 [("doi","10.48550/arXiv.2309.06275")] ai/nn/dynamic-evaluation ai/nn/transformer/gpt/inner-monologue

[classic RNN trick: echo input twice] To enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs), we introduce a simple, yet general and effective prompting method, Re2, ie. Re-Reading the question as input. Unlike most thought-eliciting prompting methods, such as Chain-of-Thought (CoT), which aim to elicit the reasoning process in the output, Re2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process.

Consequently, Re2 demonstrates strong generality and compatibility with most thought-eliciting prompting methods, including CoT. Crucially, Re2 facilitates a “bidirectional” encoding in unidirectional decoder-only LLMs because the first pass could provide global information for the second pass.

We begin with a preliminary empirical study as the foundation of Re2, illustrating its potential to enable “bidirectional” attention mechanisms. We then evaluate Re2 on extensive reasoning benchmarks across 14 datasets, spanning 112 experiments, to validate its effectiveness and generality.

Our findings indicate that, with the exception of a few scenarios on vanilla ChatGPT, Re2 consistently enhances the reasoning performance of LLMs through a simple re-reading strategy. Further analyses reveal Re2’s adaptability, showing how it can be effectively integrated with different LLMs, thought-eliciting prompting, and ensemble strategies.

Our code is available at GitHub.

--- /doc/sociology/2019-hallam.pdf Advice to Young People, as You Face Annihilation Roger Hallam 2019-10-01 2024-01-01 psychiatry/anxiety sociology --- https://asteriskmag.com/issues/07/through-the-looking-glass Through the Looking Glass, and What Zheludev et al 2024 Found There 2024 2024-10-21 genetics/microbiome genetics/sequencing --- https://x.com/akella/status/1848313870110142620 akella 2024-10-21 design/visualization psychology/vision --- https://www.reddit.com/r/midjourney/comments/1g7hk22/cursed_shore/ 2024-10-21 ai/video/generation --- https://arxiv.org/abs/2410.05258 Differential Transformer Tianzhu Ye, Li Dong, Yuqing Xia, Yutao Sun, Yi Zhu, Gao Huang, Furu Wei 2024-10-07 2024-10-21 [("doi","10.48550/arXiv.2410.05258")] ai/nn/transformer/attention

Transformer tends to over-allocate attention to irrelevant context. In this work, we introduce Diff Transformer, which amplifies attention to the relevant context while canceling noise. Specifically, the differential attention mechanism calculates attention scores as the difference between two separate softmax attention maps. The subtraction cancels noise, promoting the emergence of sparse attention patterns.

Experimental results on language modeling show that Diff Transformer outperforms Transformer in various settings of scaling up model size and training tokens. More intriguingly, it offers notable advantages in practical applications, such as long-context modeling, key information retrieval, and hallucination mitigation, in-context learning, and reduction of activation outliers.

By being less distracted by irrelevant context, Diff Transformer can mitigate hallucination in question answering and text summarization. For in-context learning, Diff Transformer not only enhances accuracy but is also more robust to order permutation, which was considered a chronic robustness issue. The results position Diff Transformer as a highly effective and promising architecture to advance large language models.

--- https://www.amoses.dev/blog/upl-people-counter/ Revamping the UPL’s people counter 2024-10-22 reinforcement-learning/robot --- https://arxiv.org/abs/2408.11065 Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature Andrei Constantin, Deaglan Bartlett, Harry Desmond, Pedro G. Ferreira 2024-08-12 2024-10-22 [("doi","10.48550/arXiv.2408.11065")] math science statistics/probability

Physics, as a fundamental science, aims to understand the laws of Nature and describe them in mathematical equations. While the physical reality manifests itself in a wide range of phenomena with varying levels of complexity, the equations that describe them display certain statistical regularities and patterns, which we begin to explore here.

By drawing inspiration from linguistics, where Zipf’s law states that the frequency of any word in a large corpus of text is roughly inversely proportional to its rank in the frequency table, we investigate whether similar patterns for the distribution of operators emerge in the equations of physics. We analyze 3 corpora of formulae and find, using sophisticated implicit-likelihood methods, that:

the frequency of operators as a function of their rank in the frequency table is best described by an exponential law with a stable exponent, in contrast with Zipf’s inverse power-law.

Understanding the underlying reasons behind this statistical pattern may shed light on Nature’s modus operandi or reveal recurrent patterns in physicists’ attempts to formalize the laws of Nature. It may also provide crucial input for symbolic regression, potentially augmenting language models to generate symbolic models for physical phenomena.

By pioneering the study of statistical regularities in the equations of physics, our results open the door for a meta-law of Nature, a (probabilistic) law that all physical laws obey.

A cosmic alignment: ranking the frequency of symbols and mathematical operators within physics equations reveals that they follow a pattern, with x (meaning any variable) appearing most often. [from New Scientist, 2024-10-21]
--- /doc/economics/2022-bendavid.pdf Competition for Attention in the ETF Space Itzhak Ben-David, Francesco Franzoni, Byungwook Kim, Rabih Moussawi 2022-08-04 2024-10-22 [("doi","10.1093/rfs/hhac048")] economics

The interplay between investors’ demand and providers’ incentives has shaped the evolution of exchange-traded funds (ETFs). While early ETFs invested in broad-based indexes and therefore offered diversification at low cost, more recent products track niche portfolios and charge high fees.

Strikingly, over their first 5 years, specialized ETFs lose about 30% (risk-adjusted). [eg. AI ETFs] This underperformance cannot be explained by high fees or hedging demand. Rather, it is driven by the overvaluation of the underlying stocks at the time of the launch.

Our results are consistent with providers catering to investors’ extrapolative beliefs by issuing specialized ETFs that track attention-grabbing themes.

--- https://www.newyorker.com/magazine/2024/10/28/a-controversial-rare-book-dealer-tries-to-rewrite-his-own-ending A Controversial Rare-Book Dealer Tries to Rewrite His Own Ending 2024-10-28 2024-10-21 psychology/collecting psychology/energy --- https://research.swtch.com/openssl Lessons from the Debian/OpenSSL Fiasco 2024-10-21 cs/security --- /doc/genetics/heritable/rare/2019-locke.pdf Exome sequencing of Finnish isolates enhances rare-variant association power Adam E. Locke, Karyn Meltz Steinberg, Charleston W. K. Chiang, Susan K. Service, Aki S. Havulinna, Laurel Stell, Matti Pirinen, Haley J. Abel, Colby C. Chiang, Robert S. Fulton, Anne Uriu Jackson, Chul Joo Kang, Krishna L. Kanchi, Daniel C. Koboldt, David E. Larson, Joanne Nelson, Thomas J. Nicholas, Arto Pietilä, Vasily Ramensky, Debashree Ray, Laura J. Scott, Heather M. Stringham, Jagadish Vangipurapu, Ryan Welch, Pranav Yajnik, Xianyong Yin, Johan G. Eriksson, Mika Ala-Korpela, Marjo-Riitta Järvelin, Minna Männikkö, Hannele Laivuori, FinnGen Project, Susan K. Dutcher, Nathan O. Stitziel, Richard K. Wilson, Ira M. Hall, Chiara Sabatti, Aarno Palotie, Veikko Salomaa, Markku Laakso, Samuli Ripatti, Michael Boehnke, Nelson B. Freimer 2019-01-01 2024-01-01 [("doi","10.1038/s41586-019-1457-z")] genetics/heritable/rare statistics/power-analysis --- /doc/genetics/heritable/2019-saunders.pdf Leveraging European infrastructures to access 1 million human genomes by 2022 Gary Saunders, Michael Baudis, Regina Becker, Sergi Beltran, Christophe Béroud, Ewan Birney, Cath Brooksbank, Søren Brunak, Marc Van den Bulcke, Rachel Drysdale, Salvador Capella-​Gutierrez, Paul Flicek, Francesco Florindi, Peter Goodhand, Ivo Gut, Jaap Heringa, Petr Holub, Jef Hooyberghs, Nick Juty, Thomas M. Keane, Jan O. Korbel, Ilkka Lappalainen, Brane Leskosek, Gert Matthijs, Michaela Th. Mayrhofer, Andres Metspalu, Arcadi Navarro, Steven Newhouse, Tommi Nyrönen, Angela Page, Bengt Persson, Aarno Palotie, Helen Parkinson, Jordi Rambla, David Salgado, Erik Steinfelder, Morris A. Swertz, Alfonso Valencia, Susheel Varma, Niklas Blomberg, Serena Scollen 2019-08-27 2024-01-01 [("doi","10.1038/s41576-019-0156-9")] genetics/heritable genetics/sequencing --- https://www.wired.com/2017/01/john-arnold-waging-war-on-bad-science/ Cancer Studies Are Fatally Flawed. Meet the Young Billionaire [John Arnold] Who’s Exposing the Truth About Bad Science 2024-01-01 statistics/bias --- https://www.wired.com/story/all-hail-the-fox/ How a Trash-Talking Furry Became Esports’ Dominant Player 2024-01-01 psychiatry/autism --- /doc/biology/2016-fain.pdf Comparative study of Millennials’ (age 20–34 years) grip and lateral pinch with the norms Elizabeth Fain, Cara Weatherford 2016-10-01 2024-01-01 [("doi","10.1016/j.jht.2015.12.006")] biology exercise --- /doc/borges/1937-borges-raymondllullsthinkingmachine.pdf Ramon Lull’s Thinking Machine Jorge Luis Borges 1937 2024-01-01 ai/nn/transformer/gpt/fiction borges philosophy/epistemology philosophy/mind --- /doc/borges/1942-borges-johnwilkinsanalyticallanguage.pdf John Wilkins’s Analytical Language Jorge Luis Borges 1942 2024-01-01 borges philosophy/epistemology philosophy/logic psychology/linguistics --- /doc/borges/1943-borges-onwilliambeckfordsvathek.pdf On William Beckford’s Vathek Jorge Luis Borges 1943 2024-01-01 borges philosophy/religion --- /doc/borges/1951-borges-coleridgesdream.pdf Coleridge’s Dream Jorge Luis Borges 1951 2024-01-01 borges fiction/poetry psychology/vision/dream psychology/writing --- /doc/borges/1951-borges-pascalssphere.pdf Pascal’s Sphere Jorge Luis Borges 1951 2024-01-01 borges philosophy/religion --- /doc/cat/biology/1851-mayhew-londonlabourandthelondonpoor-v1-thecatsmeatman.pdf London Labour and the London Poor; a cyclopaedia of the condition and earnings of those that will work, those that cannot work, and those that will not work: Volume 1: The London Street-Folk § Of Cats’-Meat & Dogs’-Meat Dealers Henry Mayhew 1851-01-01 2024-01-01 cat/biology economics --- /doc/cs/hardware/2017-denning.pdf Exponential Laws of Computing Growth: Moore’s Law is one small component in an exponentially growing planetary computing ecosystem Peter J. Denning, Ted G. Lewis 2017-01-01 2024-01-01 [("doi","10.1145/2976758")] cs/hardware --- /doc/culture/1941-preston.pdf Children’s Reactions to movie Horrors and Radio Crime Mary I. Preston 1941-01-01 2024-01-01 [("doi","10.1016/S0022-3476(41)80059-6")] culture psychology --- /doc/culture/1954-bernstein.pdf Why Don’t You Run Upstairs And Write A Nice Gershwin Tune? Leonard Bernstein 1955-01-01 2024-01-01 culture music --- /doc/economics/1957-nutter.pdf The true story of Russia’s weakness [stifled by bad planning, bureaucratic inefficiency, and lack of any real incentive] G. Warren Nutter 1957-01-01 2024-01-01 economics politics --- /doc/economics/2002-schonfeld-thisisyourfathersibmonlysmarter.html This Is Your Father’s IBM, Only Smarter: How a former has-been kicked its old habits, got open-source religion, and regained its status as one of the biggest, baddest tech companies on earth. 2002 2024-01-01 economics technology --- /doc/economics/advertising/2015-wilcox.pdf Beer, wine, or spirits? Advertising’s impact on 4 decades of category sales Gary B. Wilcox, Eun Yeon Kang, Lindsay A. Chilek 2015-03-17 2024-01-01 [("doi","10.1080/02650487.2015.1019961")] economics/advertising --- /doc/economics/2009-duarte.pdf Frank Ramsey’s Notes on Saving and Taxation Frank Ramsey, Pedro Garcia Duarte 2009-01-01 2024-01-01 [("doi","10.1215/00182702-2009-048")] economics philosophy/frank-ramsey --- https://www.nytimes.com/2024/10/22/magazine/cheerleading-jeff-webb.html How Cheerleading Became So Acrobatic, Dangerous and Popular 2024-10-22 2024-10-22 psychiatry/traumatic-brain-injury --- https://www.anthropic.com/customers/european-parliament European Parliament Revolutionizes Archive Access with Claude AI Anthropic 2024-10-22 ai/nn/retrieval ai/nn/transformer/gpt/claude --- /doc/psychology/novelty/2011-kim.pdf The Creativity Crisis: The Decrease in Creative Thinking Scores on the Torrance Tests of Creative Thinking Kyung Hee Kim 2011-11-09 2024-10-22 [("doi","10.1080/10400419.2011.627805")] iq psychology/novelty

The Torrance Tests of Creative Thinking (TTCT) was developed in 1966 and renormed 5×: in 1974, 1984, 1990, 1998, and 2008. The total sample for all 6 normative samples included 272,599 kindergarten through 12th grade students and adults.

Analysis of the normative data showed that creative thinking scores:

remained static or decreased, starting at 6th grade. Results also indicated that since 1990, even as IQ scores have risen, creative thinking scores have statistically-significantly decreased. The decrease for kindergartners through third graders was the most statistically-significant.

--- /doc/economics/2020-reesjones.pdf Measuring ‘Schmeduling’ Alex Rees-Jones, Dmitry Taubinsky 2019-09-14 2024-10-22 [("doi","10.1093/restud/rdz045")] economics psychology/cognitive-bias

What mental models do individuals use to approximate their tax schedule? Using incentivized forecasts of the US Federal income tax schedule, we estimate the prevalence of the “schmeduling” heuristics for constructing mental representations of nonlinear incentive schemes.

We find evidence of widespread reliance on the “ironing” heuristic, which linearizes the tax schedule using one’s average tax rate. In our preferred specification, 43% of the population irons.

We find no evidence of reliance on the “spotlighting” heuristic, which linearizes the tax schedule using one’s marginal tax rate.

We show that the presence of ironing rationalizes a number of empirical patterns in individuals’ perceptions of tax liability across the income distribution. Furthermore, while our empirical framework accommodates a rich class of other misperceptions, we find that a simple model including only ironers and correct forecasters accurately predicts average underestimation of marginal tax rates.

We replicate our finding of prevalent ironing, and a lack of other systematic misperceptions, in a controlled experiment that studies real-stakes decisions across exogenously varied tax schedules.

To illustrate the policy relevance of the ironing heuristic, we show that it augments the benefits of progressive taxation in a standard model of earnings choice.

We quantify these benefits in a calibrated model of the U.S. tax system.

--- https://www.oneusefulthing.org/p/when-you-give-a-claude-a-mouse When you give a Claude a mouse 2023-10-01 2024-10-22 ai/nn/transformer/gpt/claude --- https://www.anthropic.com/news/3-5-models-and-computer-use Introducing ‘computer use’, a new Claude 3.5 Sonnet, and Claude 3.5 Haiku Anthropic 2024-10-22 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex reinforcement-learning/model --- https://www.votito.com/methods/togs-paradox/ Tog’s paradox [Jevons paradox for software features] 2024-10-22 design economics/automation --- https://www.anthropic.com/research/developing-computer-use Developing a computer use model Anthropic 2024-10-22 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex reinforcement-learning/model --- https://arxiv.org/abs/2406.15786 What Matters in Transformers? Not All Attention is Needed Shwai He, Guoheng Sun, Zheyu Shen, Ang Li 2024-06-22 2024-10-22 [("doi","10.48550/arXiv.2406.15786")] ai/nn/fully-connected ai/nn/sparsity/pruning ai/nn/transformer/attention

While scaling Transformer-based large language models (LLMs) has demonstrated promising performance across various tasks, it also introduces redundant architectures, posing efficiency challenges for real-world deployment. Despite some recognition of redundancy in LLMs, the variability of redundancy across different architectures in transformers, such as MLP and Attention layers, is under-explored.

In this work, we investigate redundancy across different modules within Transformers, including Blocks, MLP, and Attention layers, using a similarity-based metric. Surprisingly, despite the critical role of attention layers in distinguishing transformers from other architectures, we found that a large portion of these layers exhibit excessively high similarity and can be pruned without degrading performance. For instance, Llama-2-70B achieved a 48.4% speedup with only a 2.4% performance drop by pruning half of the attention layers.

Furthermore, by tracing model checkpoints throughout the training process, we observed that attention layer redundancy is inherent and consistent across training stages. Additionally, we further propose a method that jointly drops Attention and MLP layers, allowing us to more aggressively drop additional layers. For instance, when dropping 31 layers (Attention + MLP), Llama-2-13B still retains 90% of the performance on the MMLU task.

Our work provides valuable insights for future network architecture design.

The code is released at: GitHub.

--- https://arxiv.org/abs/2403.19135 Streamlining Redundant Layers to Compress Large Language Models Xiaodong Chen, Yuxuan Hu, Jing Zhang, Yanling Wang, Cuiping Li, Hong Chen 2024-03-28 2024-10-22 [("doi","10.48550/arXiv.2403.19135")] ai/nn/sparsity/knowledge-distillation ai/nn/sparsity/pruning ai/nn/transformer/attention

This paper introduces LLM-Streamline, a pioneer work on layer pruning for large language models (LLMs). It is based on the observation that different layers have varying impacts on hidden states, enabling the identification of less important layers to be pruned.

LLM-Streamline comprises two parts: layer pruning, which removes consecutive layers with the lowest importance based on target sparsity, and layer replacement, a novel module that trains a lightweight network to replace the pruned layers to mitigate performance loss. Additionally, a new metric called stability is proposed to address the limitations of the widely used accuracy metric in evaluating model compression.

Experiments show that LLM-Streamline outperforms both previous and concurrent state-of-the-art pruning methods in terms of both performance and training efficiency.

--- https://arxiv.org/abs/1905.10650 Are 16 Heads Really Better than One? Paul Michel, Omer Levy, Graham Neubig 2019-05-25 2024-10-22 [("doi","10.48550/arXiv.1905.10650")] ai/nn/sparsity/pruning ai/nn/transformer/attention

Attention is a powerful and ubiquitous mechanism for allowing neural models to focus on particular salient pieces of information by taking their weighted average when making predictions. In particular, multi-headed attention is a driving force behind many recent state-of-the-art NLP models such as Transformer-based MT models and BERT. These models apply multiple attention mechanisms in parallel, with each attention “head” potentially focusing on different parts of the input, which makes it possible to express sophisticated functions beyond the simple weighted average.

In this paper, we make the surprising observation that even if models have been trained using multiple heads, in practice, a large percentage of attention heads can be removed at test time without impacting performance. In fact, some layers can even be reduced to a single head.

We further examine greedy algorithms for pruning down models, and the potential speed, memory efficiency, and accuracy improvements obtainable therefrom.

Finally, we analyze the results with respect to which parts of the model are more reliant on having multiple heads, and provide precursory evidence that training dynamics play a role in the gains provided by multi-head attention.

--- https://arxiv.org/abs/2301.02240 Skip-Attention: Improving Vision Transformers by Paying Less Attention Shashanka Venkataramanan, Amir Ghodrati, Yuki M. Asano, Fatih Porikli, Amirhossein Habibian 2023-01-05 2024-10-22 [("doi","10.48550/arXiv.2301.02240")] ai/nn/transformer/attention

This work aims to improve the efficiency of vision transformers (ViT). While ViTs use computationally expensive self-attention operations in every layer, we identify that these operations are highly correlated across layers—a key redundancy that causes unnecessary computations.

Based on this observation, we propose SkipAt, a method to reuse self-attention computation from preceding layers to approximate attention at one or more subsequent layers. To ensure that reusing self-attention blocks across layers does not degrade performance, we introduce a simple parametric function, which outperforms the baseline transformer’s performance while running computationally faster.

We show the effectiveness of our method in image classification and self-supervised learning on ImageNet-1K, semantic segmentation on ADE20K, image denoising on SIDD, and video denoising on DAVIS. We achieve improved throughput at the same-or-higher accuracy levels in all these tasks.

--- https://arxiv.org/abs/2404.14462 Towards smaller, faster decoder-only transformers: Architectural variants and their implications Sathya Krishnan Suresh, Shunmugapriya P 2024-04-22 2024-10-22 [("doi","10.48550/arXiv.2404.14462")] ai/nn/transformer/attention ai/nn/transformer/gpt

In recent times, the research on Large Language Models (LLMs) has grown exponentially, predominantly focusing on models underpinned by the transformer architecture, as established by Vaswani et al 2017, and further developed through the decoder-only variations by Radford et al 2020. Contemporary efforts in this field primarily aim to enhance model capabilities by scaling up both the architecture and data volumes used during training.

However, the exploration into reducing these model sizes while preserving their efficacy remains scant. In this study, we introduce 3 modifications to the decoder-only transformer architecture, namely ParallelGPT (pgpt), LinearGPT (lgpt), and ConvGPT (cgpt).

These variants demonstrate comparable performance to the conventional architecture in language generation, yet benefit from reduced model sizes and faster training processes.

We open-source the model weights and the complete codebase for these implementations for further research.

--- https://arxiv.org/abs/2410.14582#apple Do LLMs estimate uncertainty well in instruction-following? Juyeon Heo, Miao Xiong, Christina Heinze-Deml, Jaya Narain 2024-10-18 2024-10-22 [("doi","10.48550/arXiv.2410.14582")] ai/nn/sampling ai/nn/transformer/gpt reinforcement-learning/preference-learning/mode-collapse

Large language models (LLMs) could be valuable personal AI agents across various domains, provided they can precisely follow user instructions. However, recent studies have shown limitations in LLMs’ instruction-following capabilities, raising concerns about their reliability in high-stakes applications.

Accurately estimating LLMs’ uncertainty in adhering to instructions is critical to mitigating deployment risks. We present, to our knowledge, the first systematic evaluation of the uncertainty estimation abilities of LLMs in the context of instruction-following. Our study identifies key challenges with existing instruction-following benchmarks, where multiple factors are entangled with uncertainty stemming from instruction-following, complicating the isolation and comparison across methods and models.

To address these issues, we introduce a controlled evaluation setup with two benchmark versions of data, enabling a comprehensive comparison of uncertainty estimation methods under various conditions. Our findings show that existing uncertainty methods struggle, particularly when models make subtle errors in instruction following.

While internal model states provide some improvement, they remain inadequate in more complex scenarios. The insights from our controlled evaluation setups provide a crucial understanding of LLMs’ limitations and potential for uncertainty estimation in instruction-following tasks, paving the way for more trustworthy AI agents.

…Models and Metrics: We evaluate 4 LLMs of varying sizes: LLaMA-2-chat-7B (Touvron et al 2023), LLaMA-2-chat-13B (Touvron et al 2023), Mistral-7B-Instruct-v0.3 (Jiang et al 2023), and Phi-3-mini-128k-instruct (Abdin et al 2024). To avoid randomness in decoding, we employ greedy decoding without sampling. Area Under the Receiver Operating Characteristic curve (AUROC) (Pedregosa et al 2011) is used to measure if the models’ uncertainty estimation matches the ground truth labels on correctness in instruction following, generated using the automated evaluation functions from IFEval.

--- https://arxiv.org/abs/2209.08660 Learn the Time to Learn: Replay Scheduling in Continual Learning Marcus Klasson, Hedvig Kjellström, Cheng Zhang 2022-09-18 2024-10-23 [("doi","10.48550/arXiv.2209.08660")] reinforcement-learning/meta-learning/continual-learning

Replay methods are known to be successful at mitigating catastrophic forgetting in continual learning scenarios despite having limited access to historical data. However, storing historical data is cheap in many real-world settings, yet replaying all historical data is often prohibited due to processing time constraints.

In such settings, we propose that continual learning systems should learn the time to learn and schedule which tasks to replay at different time steps. We first demonstrate the benefits of our proposal by using Monte Carlo tree search to find a proper replay schedule, and show that the found replay schedules can outperform fixed scheduling policies when combined with various replay methods in different continual learning settings.

Additionally, we propose a framework for learning replay scheduling policies with reinforcement learning.

We show that the learned policies can generalize better in new continual learning scenarios compared to equally replaying all seen tasks, without added computational cost.

Our study reveals the importance of learning the time to learn in continual learning, which brings current research closer to real-world needs.

--- https://aclanthology.org/D17-1255/ Repeat before Forgetting: Spaced Repetition for Efficient and Effective Training of Neural Networks 2024-10-23 psychology/spaced-repetition reinforcement-learning/meta-learning/continual-learning --- https://arxiv.org/abs/2409.06131 Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review Neha Prakriya, Jui-Nan Yen, Cho-Jui Hsieh, Jason Cong 2024-09-10 2024-10-23 [("doi","10.48550/arXiv.2409.06131")] ai/nn/transformer/gpt/2 reinforcement-learning/exploration/active-learning/data-pruning

Large Language Model (LLM) pretraining traditionally relies on autoregressive language modeling on randomly sampled data blocks from web-scale datasets. We take inspiration from human learning techniques like spaced repetition to hypothesize that random data sampling for LLMs leads to high training costs and low-quality models which tend to forget data.

In order to effectively commit web-scale information to long-term memory, we propose the LFR (Learn, Focus, and Review) pedagogy, a new dynamic training paradigm which focuses and repeatedly reviews complex data blocks at systematic intervals based on the model’s learning pace and progress. LFR records the model perplexities for different data blocks and frequently revisits blocks with higher perplexity which are more likely to be forgotten.

We pretrain the [GPT-2] models (124M–1.5B) from scratch on the OpenWebText dataset using LFR.

We test on downstream tasks from the language modeling, question answering, translation, and problem-solving domains to achieve consistently lower perplexity and higher accuracy than the baseline OpenAI models, while obtaining a 20× pretraining speed-up.

--- https://arxiv.org/abs/2410.13489 Breaking Bad: How Compilers Break Constant-Time Moritz Schneider, Daniele Lain, Ivan Puddu, Nicolas Dutly, Srdjan Capkun 2024-10-17 2024-10-23 [("doi","10.48550/arXiv.2410.13489")] cs/algorithm cs/cryptography

The implementations of most hardened cryptographic libraries use defensive programming techniques for side-channel resistance. These techniques are usually specified as guidelines to developers on specific code patterns to use or avoid. Examples include performing arithmetic operations to choose between two variables instead of executing a secret-dependent branch. However, such techniques are only meaningful if they persist across compilation. In this paper, we investigate how optimizations used by modern compilers break the protections introduced by defensive programming techniques. Specifically, how compilers break high-level constant-time implementations used to mitigate timing side-channel attacks.

We run a large-scale experiment to see if such compiler-induced issues manifest in state-of-the-art cryptographic libraries. We develop a tool that can profile virtually any architecture, and we use it to run trace-based dynamic analysis on 44,604 different targets. Particularly, we focus on the most widely deployed cryptographic libraries, which aim to provide side-channel resistance. We are able to evaluate whether their claims hold across various CPU architectures, including x86-64, x86-i386, armv7, aarch64, RISC-V, and MIPS-32.

Our large-scale study reveals that several compiler-induced secret-dependent operations occur within some of the most highly regarded hardened cryptographic libraries.

To the best of our knowledge, such findings represent the first time these issues have been observed in the wild. One of the key takeaways of this paper is that the state-of-the-art defensive programming techniques employed for side-channel resistance are still inadequate, incomplete, and bound to fail when paired with the optimizations that compilers continuously introduce.

--- https://www.nytimes.com/2024/10/23/technology/characterai-lawsuit-teen-suicide.html Character.ai Faces Lawsuit After Teen’s Suicide 2024-10-23 2024-10-23 ai/nn/transformer/gpt/fiction psychiatry/anxiety psychiatry/autism --- https://arxiv.org/abs/2410.05460 It’s Not Easy Being Green: On the Energy Efficiency of Programming Languages Nicolas van Kempen, Hyuk-Je Kwon, Dung Tuan Nguyen, Emery D. Berger 2024-10-07 2024-10-23 [("doi","10.48550/arXiv.2410.05460")] cs/algorithm

Does the choice of programming language affect energy consumption? Previous highly visible studies have established associations between certain programming languages and energy consumption. A causal misinterpretation of this work has led academics and industry leaders to use or support certain languages based on their claimed impact on energy consumption.

This paper tackles this causal question directly. It first corrects and improves the measurement methodology used by prior work. It then develops a detailed causal model capturing the complex relationship between programming language choice and energy consumption. This model identifies and incorporates several critical but previously overlooked factors that affect energy usage.

These factors, such as distinguishing programming languages from their implementations, the impact of the application implementations themselves, the number of active cores, and memory activity, can skew energy consumption measurements if not accounted for.

We show—via empirical experiments, improved methodology, and careful examination of anomalies—that when these factors are controlled for, notable discrepancies in prior work vanish.

Our analysis suggests that the choice of programming language implementation has no impact on energy consumption beyond execution time.

[That is, it only matters how much total compute is used by the final program.]

--- https://github.com/konstin/sudoku-in-python-packaging Sudoku solving in python packaging 2024-10-23 cs/computable --- https://arxiv.org/abs/2409.16143 Seeing Faces in Things: A Model and Dataset for Pareidolia Mark Hamilton, Simon Stent, Vasha DuTell, Anne Harrington, Jennifer Corbett, Ruth Rosenholtz, William T. Freeman 2024-09-24 2024-10-23 [("doi","10.48550/arXiv.2409.16143")] ai/dataset psychology/cognitive-bias psychology/vision

The human visual system is well-tuned to detect faces of all shapes and sizes. While this brings obvious survival advantages, such as a better chance of spotting unknown predators in the bush, it also leads to spurious face detections.

“Face pareidolia” describes the perception of face-like structure among otherwise random stimuli: seeing faces in coffee stains or clouds in the sky. In this paper, we study face pareidolia from a computer vision perspective. We present an image dataset of Faces in Things, consisting of 5,000 web images with human-annotated pareidolic faces.

Using this dataset, we examine the extent to which a state-of-the-art human face detector exhibits pareidolia, and find a behavioral gap between humans and machines. We find that the evolutionary need for humans to detect animal faces, as well as human faces, may explain some of this gap.

Finally, we propose a simple statistical model of pareidolia in images. Through studies on human subjects and our pareidolic face detectors we confirm a key prediction of our model regarding what image conditions are most likely to induce pareidolia.

Dataset & Website: https://mhamilton.net/facesinthings.

--- https://www.theguardian.com/science/2024/oct/22/ancient-brain-collector-alexandra-morton-hayward-heslington The brain collector: the scientist unravelling the mysteries of grey matter 2024-10-22 2024-10-23 cryonics psychology/neuroscience --- https://x.com/elder_plinius/status/1849133737457463629 elder_plinius 2024-10-23 ai/nn/adversarial ai/nn/transformer/gpt/claude --- https://catherineshannon.substack.com/p/the-male-mind-cannot-comprehend-the The male mind cannot comprehend the allure of Tony Soprano 2024-10-23 psychiatry/anxiety psychology/personality --- https://www.cnn.com/interactive/2024/10/politics/political-fundraising-elderly-election-invs-dg/ Political fundraisers WinRed and ActBlue are taking millions of dollars in donations from elderly dementia patients to fuel their campaigns 2024-10 2024-10-23 economics/advertising politics psychiatry/alzheimers --- https://web.mit.edu/jmorzins/www/C-H-speech.html Some Thoughts On The Real World By One Who Glimpsed It And Fled Bill Watterson 1990-05-20 2024-10-23 psychology/writing --- https://en.wikipedia.org/wiki/Stenomask Stenomask 2024-10-23 technology --- https://arxiv.org/abs/2410.16713 Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World Joshua Kazdan, Rylan Schaeffer, Apratim Dey, Matthias Gerstgrasser, Rafael Rafailov, David L. Donoho, Sanmi Koyejo 2024-10-22 2024-10-23 [("doi","10.48550/arXiv.2410.16713")] ai/nn

The increasing presence of AI-generated content on the internet raises a critical question: What happens when generative machine learning models are pretrained on web-scale datasets containing data created by earlier models? Some authors prophesy model collapse under a “replace” scenario: a sequence of models, the first trained with real data and each later one trained only on synthetic data from its preceding model. In this scenario, models successively degrade.

Others see collapse as easily avoidable; in an “accumulate” scenario, a sequence of models is trained, but each training uses all real and synthetic data generated so far. In this work, we deepen and extend the study of these contrasting scenarios. First, collapse versus avoidance of collapse is studied by comparing the replace and accumulate scenarios on each of 3 prominent generative modeling settings; we find the same contrast emerges in all 3 settings.

Second, we study a compromise scenario; the available data remains the same as in the accumulate scenario—but unlike accumulate and like replace, each model is trained using a fixed compute budget; we demonstrate that model test loss on real data is larger than in the accumulate scenario, but apparently plateaus, unlike the divergence seen with replace.

Third, we study the relative importance of cardinality and proportion of real data for avoiding model collapse. Surprisingly, we find a non-trivial interaction between real and synthetic data, where the value of synthetic data for reducing test loss depends on the absolute quantity of real data.

Our insights are particularly important when forecasting whether future frontier generative models will collapse or thrive, and our results open avenues for empirically and mathematically studying the context-dependent value of synthetic data.

--- https://transluce.org/observability-interface Monitor: An AI-Driven Observability Interface 2024-10-23 ai/nn/tokenization ai/nn/transformer/attention ai/nn/transformer/gpt --- https://www.lesswrong.com/posts/hnJSm9AmA3dPEzPaC/what-is-malevolence-on-the-nature-measurement-and#Dark_traits_are_often_hard_to_identify What is malevolence? On the nature, measurement, and distribution of dark traits 2024-10-24 psychology/personality/narcissism psychology/personality/psychopathy --- https://arxiv.org/abs/2111.09162 It’s About Time: Analog Clock Reading in the Wild Charig Yang, Weidi Xie, Andrew Zisserman 2021-11-17 2024-10-24 [("doi","10.48550/arXiv.2111.09162")] ai/dataset ai/nn/transformer

[homepage; code] In this paper, we present a framework for reading analog clocks in natural images or videos.

Specifically, we make the following contributions: First, we create a scalable pipeline for generating synthetic clocks, reducing the requirements for the labour-intensive annotations. Second, we introduce a clock recognition architecture based on spatial transformer networks (STN), which is trained end-to-end for clock alignment and recognition.

We show that the model trained on the proposed synthetic dataset generalizes towards real clocks with good accuracy, advocating a Sim2Real training regime.

Third, to further reduce the gap between simulation and real data, we leverage the special property of “time”, ie. uniformity, to generate reliable pseudo-labels on real unlabeled clock videos, and show that training on these videos offers further improvements while still requiring zero manual annotations.

Lastly, we introduce 3 benchmark datasets based on COCO, Open Images, and The Clock movie, with full annotations for time, accurate to the minute.

--- https://80000hours.org/podcast/episodes/sebastien-moro-fish-cognition-senses-social-lives/ Sébastien Moro on the most insane things fish can do Sébastien Moro 2024-10-24 philosophy/ethics philosophy/mind psychology/animal --- https://arxiv.org/abs/1912.03458#microsoft Dynamic Convolution: Attention over Convolution Kernels Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dongdong Chen, Lu Yuan, Zicheng Liu 2019-12-07 2024-10-24 [("doi","10.48550/arXiv.1912.03458")] ai/nn/cnn ai/nn/transformer/attention

[completely unrelated to the better-known & earlier ‘dynamic convolution’...?] Light-weight convolutional neural networks (CNNs) suffer performance degradation as their low computational budgets constrain both the depth (number of convolution layers) and the width (number of channels) of CNNs, resulting in limited representation capability.

To address this issue, we present Dynamic Convolution, a new design that increases model complexity without increasing the network depth or width. Instead of using a single convolution kernel per layer, dynamic convolution aggregates multiple parallel convolution kernels dynamically based upon their attentions, which are input-dependent. Assembling multiple kernels is not only computationally efficient due to the small kernel size, but also has more representation power since these kernels are aggregated in a non-linear way via attention.

By simply using dynamic convolution for the state-of-the-art architecture MobileNetV3-Small, the top-1 accuracy of ImageNet classification is boosted by 2.9% with only 4% additional FLOPs and 2.9 AP gain is achieved on COCO keypoint detection.

--- https://en.wikipedia.org/wiki/The_Clock_(2010_film) The Clock (2010 film) 2024-01-01 design/visualization --- https://arxiv.org/abs/1506.02025#deepmind STN: Spatial Transformer Networks Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu 2015-06-05 2024-10-24 [("doi","10.48550/arXiv.1506.02025")] ai/nn/cnn

Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner.

In this work, we introduce a new learnable module, the Spatial Transformer, which explicitly allows the spatial manipulation of data within the network. This differentiable module can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the optimization process.

We show that the use of spatial transformers results in models which learn invariance to translation, scale, rotation, and more generic warping, resulting in state-of-the-art performance on several benchmarks, and for a number of classes of transformations.

--- https://pmc.ncbi.nlm.nih.gov/articles/PMC9974008/ A Systematic Review of Heart Rate Variability as a Measure of Stress in Medical Professionals 2024-10-24 nootropic/quantified-self/heart-rate-variability psychology/neuroscience --- https://resobscura.blogspot.com/2018/10/seven-weeks-to-venice-history-through.html 7 Weeks to Venice: History Through Isochronic Maps 2018-10 2024-10-24 design/visualization --- https://arstechnica.com/health/2024/10/shady-drugmaker-sold-diy-weight-loss-drug-marked-for-animal-use-suit-alleges/ Shady drugmaker used code words to sell knockoff weight-loss drug tirzepatide: Lawsuit 2024-10 2024-10-24 longevity/glp/tirzepatide --- https://www.theatlantic.com/health/archive/2024/10/giant-pumpkin-world-record/680337/ The Dream of a 3,000-Pound Pumpkin [selective breeding success] 2024-10 2024-10-24 genetics/selection/artificial --- https://developers.google.com/machine-learning/guides/rules-of-ml#before_machine_learning Rules of Machine Learning Google 2024-10-24 ai cs design --- https://www.sciencedirect.com/science/article/pii/S2589537024003055 12-month neurological and psychiatric outcomes of semaglutide use for type 2 diabetes: a propensity-score matched cohort study 2024-10-24 longevity/glp/semaglutide nicotine psychiatry/alzheimers --- https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.14313 Associations of semaglutide with first-time diagnosis of Alzheimer’s disease in patients with type 2 diabetes: Target trial emulation using nationwide real-world data in the US 2024-10-24 longevity/glp/semaglutide psychiatry/alzheimers --- https://arxiv.org/abs/2410.09038 SimpleStrat: Diversifying Language Model Generation with Stratification Justin Wong, Yury Orlovskiy, Michael Luo, Sanjit A. Seshia, Joseph E. Gonzalez 2024-10-11 2024-10-24 [("doi","10.48550/arXiv.2410.09038")] ai/dataset ai/nn/sampling ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/exploration reinforcement-learning/preference-learning/mode-collapse

Generating diverse responses from large language models (LLMs) is crucial for applications such as planning/search and synthetic data generation, where diversity provides distinct answers across generations. Prior approaches rely on increasing temperature to increase diversity. However, contrary to popular belief, we show not only does this approach produce lower quality individual generations as temperature increases, but it depends on the model’s next-token probabilities being similar to the true distribution of answers.

We propose SimpleStrat, an alternative approach that uses the language model itself to partition the space into strata. At inference, a random stratum is selected and a sample drawn from within the strata.

To measure diversity, we introduce CoverageQA, a dataset of underspecified questions with multiple equally plausible answers, and assess diversity by measuring KL Divergence between the output distribution and uniform distribution over valid ground truth answers. As computing probability per response/solution for proprietary models is infeasible, we measure recall on ground truth solutions.

Our evaluation shows using SimpleStrat achieves higher recall by 0.05 compared to GPT-4o and a 0.36 average reduction in KL Divergence compared to Llama-3.

--- https://www.whitehouse.gov/briefing-room/presidential-actions/2024/10/24/memorandum-on-advancing-the-united-states-leadership-in-artificial-intelligence-harnessing-artificial-intelligence-to-fulfill-national-security-objectives-and-fostering-the-safety-security/ Memorandum on Advancing the United States’ Leadership in Artificial Intelligence Joe Biden 2024-10-24 2024-10-24 ai/scaling/economics reinforcement-learning/safe --- https://arxiv.org/abs/1911.04623 SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning Yan Wang, Wei-Lun Chao, Kilian Q. Weinberger, Laurens van der Maaten 2019-11-12 2024-10-24 [("doi","10.48550/arXiv.1911.04623")] ai/nn/cnn ai/nn/retrieval ai/scaling reinforcement-learning/meta-learning

Few-shot learners aim to recognize new object classes based on a small number of labeled training examples. To prevent overfitting, state-of-the-art few-shot learners use meta-learning on convolutional-network features and perform classification using a nearest-neighbor classifier.

This paper studies the accuracy of nearest-neighbor baselines without meta-learning.

Surprisingly, we find simple feature transformations suffice to obtain competitive few-shot learning accuracies. For example, we find that a nearest-neighbor classifier used in combination with mean-subtraction and 𝓁2-normalization [to enable cosine distance] outperforms prior results in 3⁄5 settings on the miniImageNet dataset.

--- https://javirando.com/blog/2024/jailbreaks/ Do not write that jailbreak paper 2024 2024-10-24 ai/nn/adversarial --- https://arxiv.org/abs/2406.12027 Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI Robert Hönig, Javier Rando, Nicholas Carlini, Florian Tramèr 2024-06-17 2024-10-24 [("doi","10.48550/arXiv.2406.12027")] ai/nn/adversarial ai/nn/diffusion

Artists are increasingly concerned about advancements in image generation models that can closely replicate their unique artistic styles. In response, several protection tools against style mimicry [ie. Glaze, Nightshade] have been developed that incorporate small adversarial perturbations into artworks published online.

In this work, we evaluate the effectiveness of popular protections—with millions of downloads—

and show they only provide a false sense of security. We find that low-effort and “off-the-shelf” techniques, such as image upscaling, are sufficient to create robust mimicry methods that degrade existing protections.

Through a user study, we demonstrate that all existing protections can be easily bypassed, leaving artists vulnerable to style mimicry.

We caution that tools based on adversarial perturbations cannot reliably protect artists from the misuse of generative AI, and urge the development of alternative non-technological solutions.

--- https://arxiv.org/abs/2311.10054 When ‘A Helpful Assistant’ Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models Mingqian Zheng, Jiaxin Pei, Lajanugen Logeswaran, Moontae Lee, David Jurgens 2023-11-16 2024-10-25 [("doi","10.48550/arXiv.2311.10054")] ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/preference-learning/mode-collapse

Prompting serves as the major way humans interact with Large Language Models (LLM). Commercial AI systems commonly define the role of the LLM in system prompts. For example, ChatGPT uses “You are a helpful assistant” as part of its default system prompt. Despite current practices of adding personas to system prompts, it remains unclear how different personas affect a model’s performance on objective tasks.

In this study, we present a systematic evaluation of personas in system prompts. We curate a list of 162 roles covering 6 types of interpersonal relationships and 8 domains of expertise. Through extensive analysis of 4 popular [instruction-tuned] families of LLMs and 2,410 factual questions, we demonstrate that:

adding personas in system prompts does not improve model performance across a range of questions compared to the control setting where no persona is added.

Nevertheless, further analysis suggests that the gender, type, and domain of the persona can all influence the resulting prediction accuracies. We further experimented with a list of persona search strategies and found that, while aggregating results from the best persona for each question improves prediction accuracy, automatically identifying the best persona is challenging, with predictions often performing no better than random selection.

Overall, our findings suggest that while adding a persona may lead to performance gains in certain settings, the effect of each persona can be largely random.

Code and data are available at Github.

--- https://research.google/blog/taking-medical-imaging-embeddings-3d/ CT Foundation: Taking medical imaging embeddings 3D Atilla Kiraly, Madeleine Traverse 2024-10-21 2024-10-25 ai/nn/transformer/clip ai/scaling ai/video/analysis biology

Announcing the release of a new medical foundation tool for 3D CT volumes: CT Foundation. CT Foundation builds on our work in chest radiographs, dermatology and digital pathology, extending it to the realm of 3D volumes.

…CT Foundation was developed using VideoCoCa, a video-text model designed for efficient transfer learning from 2D Contrastive Captioners (CoCa). CoCa models take text and images as input and encode them into a shared, language-aligned embedding space. They include a multimodal text decoder that can decode these embeddings into text tokens.

CoCa models are trained to minimize two types of loss:

  1. The first is captioning loss, the loss between the original ground-truth captions of the training images and the ones decoded by the CoCa model. This focuses on the accuracy of the provided caption.

  2. The second is contrastive loss, which aims to minimize the distance between CoCa’s encodings of image-text pairs, resulting in a richer semantic understanding of the images. VideoCoCa extends an existing CoCa model by pooling together multiple frames to produce a compact representation of the entire set of sequence images.

CT Foundation was trained using over a half-million de-identified CT volumes that include a range of body parts from the head to extremities, each paired with their corresponding radiology reports.

We first trained a medical image-specific 2D CoCa model and applied it as a basis for VideoCoCa. We then trained VideoCoCa with axial CT slices (sequence of CT slices that comprise the volume) coupled with radiology reports.

--- /doc/longevity/glp/psychology/2024-qeadan-gipandglp1dietdrugprotectiveeffectsonopioidandalcoholoverdose.jpg 2024 2024-10-25 longevity/glp/psychology psychiatry/alcoholism --- https://www.youtube.com/watch?v=_2qXIDO-cWw&t=2211s Keynote presentation by Hal Abelson & Gerald Sussman at the 14th RacketCon § Emacs Hal Abelson, Gerald Sussman 2024-10-05 2024-10-25 cs/lisp/emacs --- https://en.wikipedia.org/wiki/Row_hammer Row hammer 2024-01-01 cs/hardware cs/security --- https://x.com/wunderwuzzi23/status/1849637648274686129 wunderwuzzi23 2024-10-25 ai/nn/adversarial ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex --- https://www.animenewsnetwork.com/feature/2024-10-25/which-summer-2024-anime-are-popular-in-the-u.s-compared-to-japan/.216597 Which Summer 2024 Anime are Popular in the US Compared to Japan? 2024-10-25 2024-10-25 anime --- https://www.nature.com/articles/s41586-024-08025-4#deepmind Scalable watermarking for identifying large language model outputs 2024-10-11 2024-10-25 ai/nn/transformer/gpt/palm/2 cs/cryptography/steganography --- https://www.cell.com/iscience/fulltext/S2589-0042(24)02301-0 Corroborating written history with ancient DNA: The case of the Well-man described in an Old Norse saga 2024-10-25 genetics/sequencing history

[media] --- https://www.nytimes.com/2024/10/25/science/archaeology-norway-sverresborg.html That 800-Year-Old Corpse in the Well? Early Biological Warfare 2024-10-25 2024-10-25 genetics/sequencing history --- https://www.theguardian.com/travel/2024/oct/24/rollercoaster-designer-john-burton-thorpe-park-hyperia The rollercoaster king: the man behind the UK’s fastest thrill-ride 2024-10-24 2024-10-25 design --- https://nymag.com/intelligencer/article/tua-tagovailoa-and-the-end-of-the-nfls-concussion-crisis.html Tua Tagovailoa and the End of the NFL’s Concussion Crisis 2024-10-25 psychiatry/traumatic-brain-injury --- https://www.worksinprogress.news/p/cheap-ornament-and-status-games#%C2%A7did-rich-people-actually-lead-the-flight-from-premodernist-styles Cheap ornament and status games § Did rich people actually lead the flight from premodernist styles? [opera counterexample] 2024-10-25 fiction/opera --- https://www.worksinprogress.news/p/cheap-ornament-and-status-games Cheap ornament and status games 2024-10-25 culture design --- /doc/biology/2020-goodson.pdf Testing the low dose mixtures hypothesis from the Halifax project William H. Goodson, Leroy Lowe, Michael Gilbertson, David O. Carpenter 2020-08-24 2024-10-26 [("doi","10.1515/reveh-2020-0033")] biology statistics/bias/animal

[cf] In 2013, 60 scientists, representing a larger group of 174 scientists from 26 nations, met in Halifax, Nova Scotia to consider whether—using published research—it was logical to anticipate that a mixture of chemicals, each thought to be non-carcinogenic, might act together in that mixture as a virtual carcinogen. The group identified 89 such chemicals, each one affecting one or more Hallmark(s)—collectively covering all Hallmarks of Cancer—confirming the possibility that a chemical mixture could induce all the Hallmarks and function as a virtual carcinogen, thereby supporting the concern that chemical safety research that does not evaluate mixtures, is incomplete.

Based on these observations, the Halifax Project developed the Low-Dose Carcinogenesis Hypothesis which posits “…that low-dose exposures to [mixtures of] disruptive chemicals that are not individually carcinogenic may be capable of instigating and/or enabling carcinogenesis.” Although testing all possible combinations of over 80,000 chemicals of commerce would be impractical, prudence requires designing a methodology to test whether low-dose chemical mixtures might be carcinogenic.

As an initial step toward testing this hypothesis, we conducted a mini-review of published empirical observations of biological exposures to chemical mixtures to assess what empirical data exists on which to base future research. We reviewed studies on chemical mixtures with the criteria that the studies reported both different concentrations of chemicals and mixtures composed of different chemicals.

We found a paucity of research on this important question. The majority of studies reported hormone-related processes and used chemical concentrations selected to facilitate studying how mixtures behave in experiments that were often removed from clinical relevance, ie. chemicals were not studied at human-relevant concentrations.

New research programs must be envisioned to enable study of how mixtures of small doses of chemicals affect human health, starting, when at all possible, from non-malignant specimens when studies are done in vitro. This research should use human-relevant concentrations of chemicals, expand research beyond the historic focus on endocrine endpoints and endocrine-related cancers, and specifically seek effects that arise uniquely from exposure to chemical mixtures at human-relevant concentrations.

--- https://www.newyorker.com/magazine/2023/12/18/all-the-carcinogens-we-cannot-see The Fight Against Cancer Turns to a Special Class of Carcinogens 2023-12-18 2024-10-26 biology statistics/bias/animal --- https://arxiv.org/abs/2408.11857#nous Hermes 3 Technical Report Ryan Teknium, Jeffrey Quesnelle, Chen Guang 2024-08-15 2024-10-26 [("doi","10.48550/arXiv.2408.11857")] ai/nn/retrieval ai/nn/transformer/gpt/fiction ai/nn/transformer/gpt/instruction-tuning reinforcement-learning/preference-learning

Instruct (or “chat”) tuned models have become the primary way in which most people interact with large language models. As opposed to “base” or “foundation” models, instruct-tuned models are optimized to respond to imperative statements.

We present Hermes 3, a neutrally-aligned generalist instruct and tool use model with strong reasoning and creative abilities.

Its largest version, Hermes-3-405B, achieves state-of-the-art performance among open weight models on several public benchmarks.

--- https://mohitdagarwal.substack.com/p/from-dominance-to-dilemma-nvidia The Future of Compute: Nvidia’s Crown is Slipping Mohit Dagarwal 2024-10-26 2024-10-26 ai/scaling/hardware --- https://www.nature.com/articles/s41598-024-74006-2 Rapid formation of picture-word association in cats Saho Takagi, Hikari Koyasu, Miho Nagasawa, Takefumi Kikusui 2024-10-04 2024-10-26 [("doi","10.1038/s41598-024-74006-2")] cat/psychology psychology/linguistics

It is well known that dogs are capable of following human verbal instructions. However, very little is known about the equivalent ability in cats.

In this study, we used a switched stimuli task to examine whether cats rapidly form picture-word associations, which is a fundamental ability for word learning. We presented cats with two meaningless picture-word combinations in the habituation phase. Then, on half of the trials we switched the combination (switched condition), but the other half of the trials remained as before (non-switched condition). If cats rapidly form picture-word associations, they were expected to look at the monitor for longer in the switched condition, reflecting detection of the change. We used human speech as stimuli in Experiment 1, and mechanical sounds (electronic sounds) in Experiment 2.

Cats expressed detection of the switched combination in Experiment 1, where human speech and objects were paired. However, in Experiment 2 where non-social sounds and objects were paired, there was no statistically-significant difference between switched and non-switched conditions, although there was a main effect of condition when the data from the two experiments were pooled.

These results demonstrate that cats can rapidly form picture-word associations. Further research should investigate whether domestication has played a role in this ability.

--- https://royalsocietypublishing.org/doi/full/10.1098/rsnr.2018.0018 Newton’s financial misadventures in the South Sea Bubble Andrew Odlyzko 2018-08-29 2024-10-26 [("doi","10.1098/rsnr.2018.0018")] economics history

A very popular investment anecdote relates how Isaac Newton, after cashing in large early gains, staked his fortune on the success of the South Sea Company of 1720 and lost heavily in the ensuing crash. However, this tale is based on only a few items of hard evidence, some of which are consistently misquoted and misinterpreted. A superficially plausible contrarian argument has also been made that he did not lose much in that period, and John Maynard Keynes even claimed Newton successfully surmounted the South Sea Bubble.

This paper presents extensive new evidence that while Newton was a successful investor before this event, the folk tale about his making large gains but then being drawn back into that mania and suffering large losses is almost certainly correct. It probably even understates the extent of his financial miscalculations.

Incidental to the clarification of this prominent issue, a controversy between Dale 2004 & Dale et al 2005 & Dale et al 2007 and Shea 2007a & Shea 2007b about an aspect of market rationality during that bubble is settled.

Some new information is also presented about Thomas Guy, famous for making a fortune out of the Bubble that paid for the establishment of Guy’s Hospital, and other investors.

The work reported here suggests new research directions and perspectives on bubbles.

--- /doc/psychology/personality/narcissism/2006-vazire.pdf Impulsivity and the Self-Defeating Behavior of Narcissists Simine Vazire, David C. Funder 2006-05-01 2024-10-24 [("doi","10.1207/s15327957pspr1002_4")] psychology/personality/narcissism psychology/willpower

Currently prominent models of narcissism (eg. Morf & Rhodewalt 2001) primarily explain narcissists’ self-defeating behaviors in terms of conscious cognitive and affective processes. We propose that the disposition of impulsivity may also play an important role.

We offer 2 forms of evidence. First, we present a meta-analysis demonstrating a strong positive relationship between narcissism and impulsivity.

Second, we review and reinterpret the literature on 3 hallmarks of narcissism: self-enhancement, aggression, and negative long-term outcomes.

Our reinterpretation argues that impulsivity provides a more parsimonious explanation for at least some of narcissists’ self-defeating behavior than do existing models. These 2 sources of evidence suggest that narcissists’ quest for the status and recognition they so intensely desire is thwarted, in part, by their lack of the self-control necessary to achieve those goals. [cf. psychopathy]

--- https://vimeo.com/17477895 Into The Universe With Stephen Hawking: Time Travel [episode 2] Discovery, Stephen Hawking 2010-04-25 2024-01-01 fiction/science-fiction/time-travel --- https://onlinelibrary.wiley.com/doi/10.1196/annals.1308.032/abstract Regulation of Adolescent Sleep: Implications for Behavior Carskadon 2006 2024-01-01 zeo --- https://x.com/colin_fraser/status/1849897387126628828 Colin Fraser 2024-10-26 ai/nn/transformer/gpt/4/fiction --- https://arxiv.org/abs/2410.13893 Can LLMs be Scammed? A Baseline Measurement Study Udari Madhushani Sehwag, Kelly Patel, Francesca Mosca, Vineeth Ravi, Jessica Staddon 2024-10-14 2024-10-26 [("doi","10.48550/arXiv.2410.13893")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction crime

Despite the importance of developing generative AI models that can effectively resist scams, current literature lacks a structured framework for evaluating their vulnerability to such threats. In this work, we address this gap by constructing a benchmark based on the FINRA taxonomy and systematically assessing Large Language Models’ (LLMs’) vulnerability to a variety of scam tactics.

First, we incorporate 37 well-defined base scam scenarios reflecting the diverse scam categories identified by FINRA taxonomy, providing a focused evaluation of LLMs’ scam detection capabilities.

Second, we use representative proprietary (GPT-3, GPT-4) and open-source (Llama) models to analyze their performance in scam detection.

Third, our research provides critical insights into which scam tactics are most effective against LLMs and how varying persona traits and persuasive techniques influence these vulnerabilities. We reveal distinct susceptibility patterns across different models and scenarios, underscoring the need for targeted enhancements in LLM design and deployment.

--- https://x.com/emollick/status/1850321285923975343 Ethan Mollick 2024-10-27 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/fiction --- https://web.archive.org/web/20160103173828/http://quantselflafont.com/2014/12/14/glucose-heart-rate-variability/ 2024-01-01 nootropic/quantified-self/heart-rate-variability --- https://en.wikipedia.org/wiki/Respiratory_rate Respiratory rate 2024-10-27 nootropic/quantified-self/heart-rate-variability --- /doc/psychology/nature/2019-mygind.pdf Effects of Public Green Space on Acute Psychophysiological Stress Response: A Systematic Review and Meta-Analysis of the Experimental and Quasi-Experimental Evidence Lærke Mygind, Eva Kjeldsted, Rikke Hartmeyer, Erik Mygind, Matt P. Stevenson, Daniel S. Quintana, Peter Bentsen 2019-09-09 2024-01-01 [("doi","10.1177/0013916519873376")] nootropic/quantified-self/heart-rate-variability psychology/nature

Contact with nature is widely considered to ameliorate psychological stress, but the empirical support for a causal link is limited. We conducted a systematic review to synthesize and critically assess the evidence.

6 electronic databases were searched. 20-six studies evaluated the difference between the effect of natural environments and that of a suitable control on the acute psychophysiological stress response. 18 studies were rated as being of moderate quality, 4 studies of low quality, and 4 studies of high quality.

Meta-analyses indicated that seated relaxation (g = 0.5, p = 0.06) and walking (g = 0.3, p = 0.02) in natural environments enhanced heart rate variability more than the same activities in control conditions. Cortisol concentration measures were inconsistent.

While intuitively and theoretically sound, the empirical support for acute stress-reducing effects of immersion in natural environments is tentative due to small sample sizes and methodological weaknesses in the studies. We provide guidelines for future research.

--- https://holowiki.org/wiki/Lippmann_Security Lippmann Security 2024-10-27 technology --- /doc/genetics/heritable/correlation/2017-tropf.pdf Hidden heritability due to heterogeneity across 7 populations Felix C. Tropf, S. Hong Lee, Renske M. Verweij, Gert Stulp, Peter J. van der Most, Ronald de Vlaming, Andrew Bakshi, Daniel A. Briley, Charles Rahal, Robert Hellpap, Anastasia N. Iliadou, Tõnu Esko, Andres Metspalu, Sarah E. Medland, Nicholas G. Martin, Nicola Barban, Harold Snieder, Matthew R. Robinson, Melinda C. Mills 2017-01-01 2024-01-01 [("doi","10.1038/s41562-017-0195-1")] exercise genetics/heritable/correlation sociology

Meta-analyses of genome-wide association studies, which dominate genetic discovery, are based on data from diverse historical time periods and populations. Genetic scores derived from genome-wide association studies explain only a fraction of the heritability estimates obtained from whole-genome studies on single populations, known as the hidden heritability puzzle.

Using 7 sampling populations (n = 35,062), we test whether hidden heritability is attributed to heterogeneity across sampling populations and time, showing that estimates are:

substantially smaller across populations compared with within populations. We show that the hidden heritability varies substantially: from 0% for height to 20% for body mass index, 37% for education, 40% for age at first birth, and up to 75% for number of children. [ie. fertility is highly non-stationary!]

Simulations demonstrate that our results are more likely to reflect heterogeneity in phenotypic measurement or gene-environment interactions than genetic heterogeneity.

These findings have substantial implications for genetic discovery, suggesting that large homogenous datasets are required for behavioral phenotypes and that gene-environment interaction may be a central challenge for genetic discovery.

--- /doc/genetics/heritable/correlation/mendelian-randomization/2018-pingault.pdf Using genetic data to strengthen causal inference in observational research Jean-Baptiste Pingault, Paul F. O’Reilly, Tabea Schoeler, George B. Ploubidis, Frühling Rijsdijk, Frank Dudbridge 2018-06-05 2024-01-01 [("doi","10.1038/s41576-018-0020-3")] genetics/heritable/correlation/mendelian-randomization statistics/causality

Causal inference is essential across the biomedical, behavioral, and social sciences. By progressing from confounded statistical associations to evidence of causal relationships, causal inference can reveal complex pathways underlying traits and diseases and help to prioritize targets for intervention.

Recent progress in genetic epidemiology—including statistical innovation, massive genotyped data sets, and novel computational tools for deep data mining—has fostered the intense development of methods exploiting genetic data and relatedness to strengthen causal inference in observational research.

In this Review, we describe how such genetically informed methods differ in their rationale, applicability, and inherent limitations and outline how they should be integrated in the future to offer a rich causal inference toolbox.

--- https://krebsonsecurity.com/2024/10/the-global-surveillance-free-for-all-in-mobile-ad-data/ The Global Surveillance Free-for-All in Mobile Ad Data Brian Krebs 2024-10-23 2024-10-27 cs/security --- https://en.wikipedia.org/wiki/Lippmann_photography Lippmann photography 2024-10-27 technology --- /doc/psychology/vision/2024-xu.pdf Spatial context non-uniformly modulates inter-laminar information flow in the primary visual cortex Xize Xu, Mitchell P. Morton, Sachira Denagamage, Nyomi V. Hudson, Anirvan S. Nandy, Monika P. Jadi 2024-10-22 2024-10-27 [("doi","10.1016/j.neuron.2024.09.021")] psychology/neuroscience psychology/vision

  • Inter-laminar information flow in V1 occurs along communication subspaces.

  • The structure of the inter-laminar subspace is preserved across spatial locations of visual context.

  • Visual context degrades the efficacy of information flow in a location-dependent manner.

  • The degradation is driven by a novel signal targeting the output layers.

[data; press release] Our visual experience is a result of the concerted activity of neuronal ensembles in the sensory hierarchy. Yet, how the spatial organization of objects influences this activity remains poorly understood. We investigate how inter-laminar information flow within the primary visual cortex (V1) is affected by visual stimuli in isolation or with flankers at spatial configurations that are known to cause non-uniform degradation of perception.

By employing dimensionality reduction approaches to simultaneous, layer-specific population recordings, we establish that information propagation between cortical layers occurs along a structurally stable communication subspace.

The spatial configuration of contextual stimuli differentially modulates inter-laminar communication efficacy, the balance of feedforward and effective feedback signaling, and contextual signaling in the superficial layers. Remarkably, these modulations mirror the spatially non-uniform aspects of perceptual degradation.

Our results suggest a model of retinotopically non-uniform cortical connectivity in the output layers of V1 that influences information flow in the sensory hierarchy.

[Keywords: electrophysiology, vision, communication subspace, laminar network, contextual integration, retinotopy, feedforward, feedback, visual cortex]

--- https://sysblok.ru/blog/gorkij-urok-abbyy-kak-lingvisty-proigrali-poslednjuju-bitvu-za-nlp/ ABBYY’s Bitter Lesson: How Linguists Lost the Last Battle for NLP Daniil Skorinkin 2024-10-22 2024-10-27 ai/scaling psychology/linguistics --- https://arxiv.org/abs/2402.08021 Careless Whisper: Speech-to-Text Hallucination Harms Allison Koenecke, Anna Seo Gyeong Choi, Katelyn X. Mei, Hilke Schellmann, Mona Sloane 2024-02-12 2024-10-27 [("doi","10.1145/3630106.3658996")] ai/nn/transformer/gpt/whisper

Speech-to-text services aim to transcribe input audio as accurately as possible. They increasingly play a role in everyday life, for example in personal voice assistants or in customer-company interactions.

We evaluate Open AI’s Whisper, a state-of-the-art automated speech recognition service outperforming industry competitors, as of 2023. While many of Whisper’s transcriptions were highly accurate, we find that:

roughly 1% of audio transcriptions contained entire hallucinated phrases or sentences which did not exist in any form in the underlying audio.

We thematically analyze the Whisper-hallucinated content, finding that 38% of confabulations include explicit harms such as perpetuating violence, making up inaccurate associations, or implying false authority.

We then study why confabulations occur by observing the disparities in confabulation rates between speakers with aphasia (who have a lowered ability to express themselves using speech and voice) and a control group.

We find that confabulations disproportionately occur for individuals who speak with longer shares of non-vocal durations—a common symptom of aphasia.

We call on industry practitioners to ameliorate these language-model-based confabulations in Whisper, and to raise awareness of potential biases amplified by confabulations in downstream applications of speech-to-text models.

[This may be due to Whisper having extremely localized attention patterns and hardly incorporating any history/context (whereas even small-context LLMs typically have contexts orders of magnitude larger), so confabulations can build & spiral within seconds.]

--- https://www.nature.com/articles/s41598-024-72437-5 Long-term tracking of social structure in groups of rats Máté Nagy, Jacob D. Davidson, Gábor Vásárhelyi, Dániel Ábel, Enikő Kubinyi, Ahmed El Hady, Tamás Vicsek 2024-10-01 2024-10-27 ai/video/analysis psychology/animal --- https://www.labri.fr/perso/nrougier/GTD/index.html Get Things Done with Emacs Nicolas P. Rougier 2020-09 2024-10-27 cs/lisp/emacs --- https://github.com/gwern/gwern.net/commit/d391c340cc02aae6d2b54cb672e7f12ec7f620ba#diff-03ea4348bea7a138709946b85053309e32040dae6fd27039008063f290f7f138R1252 My Emacs shortcuts for writing annotations 2024-01-01 cs/lisp/emacs --- https://github.com/semiosis/pen.el Pen.el 2024-01-01 cs/lisp/emacs Pen.el integrates LMs (language models) such as OpenAI’s GPT-3 or EleutherAI’s GPT-J into emacs by generating functions from prompts that map emacs’s corners loosely onto LMs. These functions can be used interactively or non-interactively and in a variety of configurable ways.

Pen.el also facilitates the creation, development, discovery and usage of prompts. --- https://jblevins.org/projects/markdown-mode/ Markdown Mode for Emacs 2024-01-01 cs/lisp/emacs --- https://serras-haskell-gsoc.blogspot.com/2011/08/end-of-summer.html Summers on Haskell and {Eclipse, Emacs}: End of the summer 2011-08 2024-01-01 cs/lisp/emacs --- https://en.wikipedia.org/wiki/Emacs_Lisp Emacs Lisp 2024-01-01 cs/lisp/emacs --- https://en.wikipedia.org/wiki/User:Gwern/.emacs User:Gwern’s .emacs 2024-01-01 cs/lisp/emacs --- https://en.wikipedia.org/wiki/XEmacs XEmacs 2024-01-01 cs/lisp/emacs --- https://en.wikipedia.org/wiki/XEmacs#History XEmacs § History 2024-01-01 cs/lisp/emacs --- https://www.emacswiki.org/emacs/BrowseUrl Browse Url 2024-01-01 cs/lisp/emacs --- https://www.emacswiki.org/emacs/MarkdownMode Markdown Mode 2024-01-01 cs/lisp/emacs --- https://www.emacswiki.org/emacs/TableMode Table Mode 2024-01-01 cs/lisp/emacs --- https://www.emacswiki.org/emacs/TeXInputMethod Emacs Wiki: TeX Input Method 2024-01-01 cs/lisp/emacs --- https://www.gnu.org/software/emacs/manual/html_node/emacs/Keyboard-Macro-Counter.html Emacs manual: Keyboard Macros: 17.3. The Keyboard Macro Counter 2024-01-01 cs/lisp/emacs --- https://beepb00p.xyz/pkm-search.html#org0000010 Building personal search infrastructure for your knowledge and code 2024-10-27 cs/lisp/emacs --- https://beepb00p.xyz/pkm-search.html#appendix_daemon Lightning fast Emacs: running daemon on startup 2024-10-27 cs/lisp/emacs --- https://beepb00p.xyz/pkm-search.html#appendix_emacs General Emacs tips 2024-10-27 cs/lisp/emacs --- https://github.com/karlicoss/dotemacs karlicoss/dotemacs: Emacs config (Doom/Spacemacs) + supplementary files and scripts 2024-10-27 cs/lisp/emacs --- http://www.catb.org/esr/writings/taoup/html/ch13s03.html#id2967765 The Right Size for an Editor Eric S. Raymond 2024-10-27 cs/lisp/emacs design --- https://github.com/gwern/gwern.net/blob/master/build/markdown.el markdown.el Gwern 2024-10-27 cs/lisp/emacs --- https://web.archive.org/web/20060409123241/http://dto.twu.net/HowmTutorial.howm.html 2024-10-27 cs/lisp/emacs --- https://gitlab.com/emacsomancer/ycombinator-codex Y-combinator codex 2024-10-27 cs/lisp design/typography --- https://www.salkosuo.net/2015/10/22/elite-for-emacs.html Elite for Emacs 2015-10-22 2024-10-27 cs/lisp/emacs fiction/text-game --- https://github.com/manateelazycat/popweb popweb: Show popup web window for Emacs 2024-10-27 cs/lisp/emacs --- https://x.com/Sauers_/status/1850678934997754127 Sauers 2024-10-28 statistics/stylometry/truesight --- https://slatestarcodex.com/2014/04/16/do-you-believe-me-doc/ Do You Believe Me, Doc? Scott Alexander 2014-04-16 2024-10-28 philosophy/epistemology philosophy/religion psychiatry --- /doc/genetics/selection/natural/2024-10-28-gwern-meme-carcinization-bookofjobsatantofuturamarobotdevilevolution.jpg The Carcinisation of Satan Gwern 2024-10-28 2024-10-28 genetics/selection/natural math/humor --- https://www.reddit.com/r/StableDiffusion/comments/1gdkpqp/the_gory_details_of_finetuning_sdxl_for_40m/ 2024-10-28 ai/nn/diffusion reinforcement-learning/preference-learning --- https://www.transformernews.ai/p/open-ai-non-profit-valuation Is OpenAI being fair to its non-profit? 2024-10-28 law reinforcement-learning/openai --- https://www.vox.com/future-perfect/380117/openai-microsoft-sam-altman-nonprofit-for-profit-foundation-artificial-intelligence OpenAI is transitioning to a for-profit business. The stakes are enormous. Kelsey Piper 2024-10-28 2024-10-28 law reinforcement-learning/openai --- https://www.nytimes.com/2024/10/28/science/crows-grudges-revenge.html If You Think You Can Hold a Grudge, Consider the Crow 2024-10-28 2024-10-28 psychology/animal/bird --- https://en.wikipedia.org/wiki/Interlisp Interlisp 2024-10-28 cs/lisp/emacs --- https://interlisp.org/ Interlisp revival 2020 2024-10-28 cs/lisp/emacs --- https://amasad.me/right Computers Doing The Right Thing 2024-10-28 ai/nn/transformer/gpt/codex cs/lisp design --- https://en.wikipedia.org/wiki/Strong_law_of_small_numbers Strong law of small numbers 2024-10-28 math statistics/probability --- https://worksinprogress.co/issue/getting-materials-out-of-the-lab/ Getting materials out of the lab 2024-10-28 economics/experience-curve science/chemistry technology --- https://arxiv.org/abs/2410.14157 Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning Jiacheng Ye, Jiahui Gao, Shansan Gong, Lin Zheng, Xin Jiang, Zhenguo Li, Lingpeng Kong 2024-10-18 2024-10-28 [("doi","10.48550/arXiv.2410.14157")] ai/nn/diffusion/discrete

Autoregressive language models, despite their impressive capabilities, struggle with complex reasoning and long-term planning tasks. We introduce discrete diffusion models as a novel solution to these challenges. Through the lens of subgoal imbalance, we demonstrate how diffusion models effectively learn difficult subgoals that elude autoregressive approaches.

We propose Multi-granularity Diffusion Modeling (MDM), which prioritizes subgoals based on difficulty during learning. On complex tasks like Countdown, Sudoku, and Boolean Satisfiability Problems, MDM outperforms autoregressive models without using search techniques. For instance, MDM achieves 91.5% and 100% accuracy on Countdown and Sudoku, respectively, compared to 45.8% and 20.7% for autoregressive models.

Our work highlights the potential of diffusion-based approaches in advancing AI capabilities for sophisticated language understanding and problem-solving tasks.

--- https://davidrozado.substack.com/p/an-analysis-of-ai-political-preferences An Analysis of AI Political Preferences from a European Perspective 2024-10-28 ai/nn/transformer/gpt/4 politics reinforcement-learning/preference-learning --- https://github.com/fpgaminer/joytag The JoyTag Image Tagging Model 2024-10-29 ai/anime/danbooru --- /doc/philosophy/epistemology/1964-merton.pdf Is the Scientific Paper Fraudulent? Yes; It Misrepresents Scientific Thought Peter B. Medawar 1964-08-01 2024-01-01 philosophy/epistemology science statistics/bias --- /doc/economics/1986-black.pdf Noise Fischer Black 1986-07-01 2024-10-29 [("doi","10.1111/j.1540-6261.1986.tb04513.x")] economics philosophy/epistemology statistics/decision

The effects of noise on the world, and on our views of the world, are profound. Noise in the sense of a large number of small events is often a causal factor much more powerful than a small number of large events can be.

Noise makes trading in financial markets possible, and thus allows us to observe prices for financial assets. Noise causes markets to be somewhat inefficient, but it often prevents us from taking advantage of inefficiencies. Noise in the form of uncertainty about future tastes and technology by sector causes business cycles, and makes them highly resistant to improvement through government intervention.

Noise in the form of expectations that need not follow rational rules causes inflation to be what it is, at least in the absence of a gold standard or fixed exchange rates. Noise in the form of uncertainty about what relative prices would be with other exchange rates makes us think incorrectly that changes in exchange rates or inflation rates cause changes in trade or investment flows or economic activity.

Most generally, noise makes it very difficult to test either practical or academic theories about the way that financial or economic markets work. We are forced to act largely in the dark.

…The whole structure of financial markets depends on relatively liquid markets in the shares of individual firms.

Noise trading provides the essential missing ingredient. Noise trading is trading on noise as if it were information. People who trade on noise are willing to trade even though from an objective point of view they would be better off not trading. Perhaps they think the noise they are trading on is information. Or perhaps they just like to trade.6

With a lot of noise traders in the market, it now pays for those with information to trade. It even pays for people to seek out costly information which they will then trade on. Most of the time, the noise traders as a group will lose money by trading, while the information traders as a group will make money.

The more noise trading there is, the more liquid the markets will be, in the sense of having frequent trades that allow us to observe prices. But noise trading actually puts noise into the prices. The price of a stock reflects both the information that information traders trade on and the noise that noise traders trade on.

As the amount of noise trading increases, it will become more profitable for people to trade on information, but only because the prices have more noise in them. The increase in the amount of information trading does not mean that prices are more efficient. Not only will more information traders come in, but existing information traders will take bigger positions and will spend more on information. Yet prices will be less efficient.7 What’s needed for a liquid market causes prices to be less efficient.

The information traders will not take large enough positions to eliminate the noise. For one thing, their information gives them an edge, but does not guarantee a profit. Taking a larger position means taking more risk. So there is a limit to how large a position a trader will take. For another thing, the information traders can never be sure that they are trading on information rather than noise. What if the information they have has already been reflected in prices? Trading on that kind of information will be just like trading on noise.8 Because the actual return on a portfolio is a very noisy estimate of expected return, even after adjusting for returns on the market and other factors, it will be difficult to show that information traders have an edge. For the same reason, it will be difficult to show that noise traders are losing by trading. There will always be a lot of ambiguity about who is an information trader and who is a noise trader.

The noise that noise traders put into stock prices will be cumulative, in the same sense that a drunk tends to wander farther and farther from his starting point. Offsetting this, though, will be the research and actions taken by the information traders. The further the price of a stock gets from its value, the more aggressive the information traders will become. More of them will come in, and they will take larger positions. They may even initiate mergers, leveraged buyouts, and other restructurings.

Thus the price of a stock will tend to move back toward its value over time.9 The move will often be so gradual that it is imperceptible. If it is fast, technical traders will perceive it and speed it up. If it is slow enough, technical traders will not be able to see it, or will be so unsure of what they see that they will not take large positions.10

Still, the further the price of a stock moves away from value, the faster it will tend to move back. This limits the degree to which it is likely to move away from value. All estimates of value are noisy, so we can never know how far away price is from value.

However, we might define an efficient market as one in which price is within a factor of 2 of value, i.e. the price is more than half of value and less than twice value.11 The factor of 2 is arbitrary, of course. Intuitively, though, it seems reasonable to me, in the light of sources of uncertainty about value and the strength of the forces tending to cause price to return to value. By this definition, I think almost all markets are efficient almost all the time. “Almost all” means at least 90%.

Because value is not observable, it is possible for events that have no information content to affect price. For example, the addition of a stock to the S&P 500 index will cause some investors to buy it. Their buying will force the price up for a time. Information trading will force it back, but only gradually.12

Similarly, when a firm with two classes of common stock issues more of one class, the price of the class of stock issued will decline relative to the price of the class of stock not issued.13

Both price and value will look roughly like geometric random walk processes with non-zero means. The means of percentage change in price and value will change over time. The mean of the value process will change because tastes and technology and wealth change. It may well decline when value rises, and rise when value declines. The mean of the price process will change because the relation between price and value changes (and because the mean of the value process changes). Price will tend to move toward value.

The short term volatility of price will be greater than the short term volatility of value. Since noise is independent of information in this context, when the variance of the percentage price moves caused by noise is equal to the variance of the percentage price moves caused by information, the variance of percentage price moves from day to day will be roughly twice the variance of percentage value moves from day to day. Over longer intervals, though, the variances will converge. Because price tends to return to value, the variance of price several years from now will be much less than twice the variance of value several years from now.

Volatilities will change over time. The volatility of the value of a firm is affected by things like the rate of arrival of information about the firm and the firm’s leverage. All the factors affecting the volatility of a firm’s value will change. The volatility of price will change for all these reasons and for other reasons as well. Anything that changes the amount or character of noise trading will change the volatility of price.

Noise traders must trade to have their influence. Because information traders trade with noise traders more than with other information traders, cutting back on noise trading also cuts back on information trading. Thus prices will not move as much when the market is closed as they move when the market is open.14 The relevant market here is the market on which most of the noise traders trade.

Noise traders may prefer low-priced stocks to high-priced stocks. If they do, then splits will increase both the liquidity of a stock and its day-to-day volatility. Low-priced stocks will be less efficiently priced than high-priced stocks.15

The price of a stock will be a noisy estimate of its value. The earnings of a firm (multiplied by a suitable price-earnings ratio) will give another estimate of the value of the firm’s stock.16 This estimate will be noisy too. So long as noise traders do not always look at earnings in deciding how to trade, the estimate from earnings will give information that is not already in the estimate from price.17

Because an estimate of value based on earnings will have so much noise, there will be no easy way to use price-earnings ratios in managing portfolios. Even if stocks with low price-earnings ratios have higher expected returns than other stocks, there will be periods, possibly lasting for years, when stocks with low price-earnings ratios have lower returns than other comparable stocks.

In other words, noise creates the opportunity to trade profitably, but at the same time makes it difficult to trade profitably.

...If monetary policy doesn’t cause changes in inflation, what does? I think that the price level and rate of inflation are literally indeterminate. They are whatever people think they will be. They are determined by expectations, but expectations follow no rational rules. If people believe that certain changes in the money stock will cause changes in the rate of inflation, that may well happen, because their expectations will be built into their long term contracts...noise causes changes in the rate of inflation.

--- https://archive.org/details/cu31924092592785/page/10/mode/1up London labour and the London poor; a cyclopaedia of the condition and earnings of those that will work, those that cannot work, and those that will not work v3 § Jack Black Henry Mayhew 1851 2024-10-26 genetics/selection/artificial psychology/animal --- https://www.nature.com/articles/s41599-024-03868-8 Experimental narratives: A comparison of human crowdsourced storytelling and AI storytelling 2024 2024-10-29 ai/nn/transformer/gpt/3/fiction ai/nn/transformer/gpt/4/fiction reinforcement-learning/preference-learning/mode-collapse --- https://eprint.iacr.org/2020/107 One-shot Signatures and Applications to Hybrid Quantum/Classical Authentication 2020 2024-10-29 cs/cryptography --- https://arxiv.org/abs/2410.19034 Mixture of Parrots: Experts improve memorization more than reasoning Samy Jelassi, Clara Mohri, David Brandfonbrener, Alex Gu, Nikhil Vyas, Nikhil Anand, David Alvarez-Melis, Yuanzhi Li, Sham M. Kakade, Eran Malach 2024-10-24 2024-10-29 [("doi","10.48550/arXiv.2410.19034")] ai/scaling/mixture-of-experts math

The Mixture-of-Experts (MoE) architecture enables an increase in the total number of model parameters with minimal computational overhead. However, it is not clear what performance tradeoffs, if any, exist between MoEs and standard dense transformers.

In this paper, we show that as we increase the number of experts (while fixing the number of active parameters), the memorization performance consistently increases while the reasoning capabilities saturate. We begin by analyzing the theoretical limitations of MoEs at reasoning.

We prove that there exist graph problems that cannot be solved by any number of experts of a certain width; however, the same task can be easily solved by a dense model with a slightly larger width.

On the other hand, we find that on memory-intensive tasks, MoEs can effectively leverage a small number of active parameters with a large number of experts to memorize the data.

We empirically validate these findings on synthetic graph problems and memory-intensive closed book retrieval tasks.

Lastly, we pre-train a series of MoEs and dense transformers and evaluate them on commonly used benchmarks in math and natural language.

We find that increasing the number of experts helps solve knowledge-intensive tasks, but fails to yield the same benefits for reasoning tasks.

--- https://arxiv.org/abs/2410.11687 State-space models can learn in-context by gradient descent Neeraj Mohan Sushma, Yudou Tian, Harshvardhan Mestha, Nicolo Colombo, David Kappel, Anand Subramoney 2024-10-15 2024-10-29 [("doi","10.48550/arXiv.2410.11687")] ai/nn/rnn ai/nn/transformer/attention/hierarchical reinforcement-learning/meta-learning

Deep state-space models (Deep SSMs) have shown capabilities for in-context learning on autoregressive tasks, similar to transformers. However, the architectural requirements and mechanisms enabling this in recurrent networks remain unclear.

This study demonstrates that state-space model architectures can perform gradient-based learning and use it for in-context learning. We prove that a single structured state-space model layer, augmented with local self-attention, can reproduce the outputs of an implicit linear model with least squares loss after one step of gradient descent. Our key insight is that the diagonal linear recurrent layer can act as a gradient accumulator, which can be ‘applied’ to the parameters of the implicit regression model.

We validate our construction by training randomly initialized augmented SSMs on simple linear regression tasks.

The empirically optimized parameters match the theoretical ones, obtained analytically from the implicit model construction. Extensions to multi-step linear and non-linear regression yield consistent results.

The constructed SSM encompasses features of modern deep state-space models, with the potential for scalable training and effectiveness even in general tasks. The theoretical construction elucidates the role of local self-attention and multiplicative interactions in recurrent architectures as the key ingredients for enabling the expressive power typical of foundation models.

--- https://en.wikipedia.org/wiki/Common_raven Common raven 2024-10-29 psychology/animal/bird --- https://www.newyorker.com/magazine/2024/11/04/playing-possum-susana-monso-book-review What Do Animals Understand About Death? 2024-11-04 2024-10-29 philosophy/ethics philosophy/mind psychology/animal/bird --- https://thaliarchus.itch.io/cosmic-warlord-kinbright Cosmic Warlord Kin-Bright: giant robot yuri space opera Thaliarchus 2024-10-29 anime fiction/poetry --- https://www.crunchyroll.com/news/interviews/2024/10/28/thaliarchus-cosmic-warlord-kin-bright-interview Thaliarchus on Classic Literature & the Inspiration Behind Cosmic Warlord Kin-Bright: One of the best ‘mecha anime’ of 2024 is an epic alliterative poem Thaliarchus, Adam Wescott 2024-10-28 2024-10-29 anime fiction/poetry --- https://www.foreignaffairs.com/united-states/emerging-age-ai-diplomacy The Emerging Age of AI Diplomacy: To Compete With China, the United States Must Walk a Tightrope in the Gulf 2024-10-29 ai/scaling/economics ai/scaling/hardware --- https://x.com/lefthanddraft/status/1851154437752188932 lefthanddraft 2024-10-29 ai/nn/transformer/gpt/claude reinforcement-learning/preference-learning --- https://www.wired.com/story/we-were-wrong-oral-history-hotwired/ ‘We Were Wrong’: An Oral History of WIRED’s Original Website 2024-10-29 economics/advertising --- https://forecastingresearch.org/nuclear-risk Nuclear Risk 2024-10-29 radiance --- https://www.personalcanon.com/p/the-divine-discontent The divine discontent 2024-10-29 psychology/writing --- /doc/math/humor/2000-martin.pdf What do animals do all day? The division of labor, class bodies, and totemic thinking in the popular imagination John Levi Martin 2000 2024-10-29 [("doi","10.1016/S0304-422X(99)00025-X")] fiction/criticism math/humor --- https://www.medrxiv.org/content/10.1101/2024.09.23.24314008.full Dissecting the Reduced Penetrance of Putative Loss-of-Function Variants in Population-Scale Biobanks David R. Blair, Neil Risch 2024-10-07 2024-10-29 [("doi","10.1101/2024.09.23.24314008")] genetics/heritable/rare

Loss-of-function variants (LoFs) disrupt the activity of their impacted gene. They are often associated with clinical phenotypes, including autosomal dominant diseases driven by haploinsufficiency. Recent analyses using biobanks have suggested that LoF penetrance for some haploinsufficient disorders may be low, an observation that has important implications for population genomic screening. However, biobanks are also rife with missing data, and the reliability of these findings remains uncertain.

Here, we examine the penetrance of putative LoFs (pLoFs) using a cohort of ~24,000 carriers derived from two population-scale biobanks: the UK Biobank and the All of Us Research Program. We investigate several possible etiologies for reduced pLoF penetrance, including biobank recruitment biases, annotation artifacts, missed diagnoses, and incomplete clinical records. Systematically accounting for these factors increased penetrance, but widespread reduced penetrance remained.

Therefore, we hypothesized that other factors must be driving this phenomenon. To test this, we trained machine learning models to identify pLoFs with high penetrance using the genomic features specific to each variant. These models were predictive of penetrance across a range of diseases and pLoF types, including those with prior evidence for pathogenicity. This suggests that reduced pLoF penetrance is in fact common, and care should be taken when counseling asymptomatic carriers.

--- https://www.reuters.com/technology/artificial-intelligence/openai-builds-first-chip-with-broadcom-tsmc-scales-back-foundry-ambition-2024-10-29/ 2024-10-29 2024-10-29 ai/scaling/hardware reinforcement-learning/openai --- https://www.reddit.com/r/dyscalculia/comments/v0vs16/can_people_with_dyscalculia_understand_the_rules/ 2024-10-29 psychology/chess --- https://www.lesswrong.com/posts/fKboizBPnPDa9suLF/a-poem-is-all-you-need-jailbreaking-chatgpt-meta-and-more A Poem Is All You Need: Jailbreaking ChatGPT, Meta & More 2024-10-29 ai/nn/adversarial ai/nn/tokenization ai/nn/transformer/gpt/4/poetry ai/nn/transformer/gpt/claude --- https://www.theguardian.com/news/2024/oct/29/acute-psychosis-inner-voices-avatar-therapy-psychiatry ‘You tried to tell yourself I wasn’t real’: what happens when people with acute psychosis meet the voices in their heads? 2024-10-29 2024-10-29 psychiatry/anorexia psychiatry/schizophrenia technology --- /doc/economics/mechanism-design/2000-stiglitz.pdf The Contributions of the Economics of Information to 20th Century Economics Joseph E. Stiglitz 2000-11-01 2024-10-29 [("doi","10.1162/003355300555015")] economics/mechanism-design statistics/decision

In the field of economics, perhaps the most important break with the past—one that leaves open huge areas for future work—lies in the economics of information.

It is now recognized that information is imperfect, obtaining information can be costly, there are important asymmetries of information, and the extent of information asymmetries is affected by actions of firms and individuals.

This recognition deeply affects the understanding of wisdom inherited from the past, such as the fundamental welfare theorem and some basic characterizations of a market economy, and provides explanations of economic and social phenomena that otherwise would be hard to understand.

--- https://en.wikipedia.org/wiki/Information_economics Information economics 2024-10-29 economics/mechanism-design statistics/decision --- https://arxiv.org/abs/2410.20268 Centaur: a foundation model of human cognition Marcel Binz, Elif Akata, Matthias Bethge, Franziska Brändle, Fred Callaway, Julian Coda-Forno, Peter Dayan, Can Demircan, Maria K. Eckstein, Noémi Éltető, Thomas L. Griffiths, Susanne Haridi, Akshay K. Jagadish, Li Ji-An, Alexander Kipnis, Sreejan Kumar, Tobias Ludwig, Marvin Mathony, Marcelo Mattar, Alireza Modirshanechi, Surabhi S. Nath, Joshua C. Peterson, Milena Rmus, Evan M. Russek, Tankred Saanum, Natalia Scharfenberg, Johannes A. Schubert, Luca M. Schulze Buschoff, Nishad Singhi, Xin Sui, Mirko Thalmann, Fabian Theis, Vuong Truong, Vishaal Udandarao, Konstantinos Voudouris, Robert Wilson, Kristin Witte, Shuchen Wu, Dirk Wulff, Huadong Xiong, Eric Schulz 2024-10-26 2024-10-29 [("doi","10.48550/arXiv.2410.20268")] ai/dataset ai/nn/transformer/gpt psychology reinforcement-learning/imitation-learning/brain-imitation-learning reinforcement-learning/model reinforcement-learning/model-free statistics/decision

Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational models, we currently do not have one model that captures the human mind in its entirety.

Here we introduce Centaur, a computational model that can predict and simulate human behavior in any experiment expressible in natural language. We derived Centaur by finetuning a state-of-the-art language model on a novel, large-scale data set called Psych-101. Psych-101 reaches an unprecedented scale, covering trial-by-trial data from over 60,000 participants performing over 10,000,000 choices in 160 experiments.

Centaur not only captures the behavior of held-out participants better than existing cognitive models, but also generalizes to new cover stories, structural task modifications, and entirely new domains.

Furthermore, we find that the model’s internal representations become more aligned with human neural activity after finetuning.

Taken together, Centaur is the first real candidate for a unified model of human cognition. We anticipate that it will have a disruptive impact on the cognitive sciences, challenging the existing paradigm for developing computational models.

--- https://huggingface.co/datasets/marcelbinz/Psych-101 Psych-101 dataset [for Centaur] 2024-10-29 ai/dataset ai/nn/transformer/gpt psychology reinforcement-learning/imitation-learning/brain-imitation-learning --- https://blog.google/inside-google/message-ceo/alphabet-earnings-q3-2024/#full-stack-approach Alphabet Q3 earnings call: CEO Sundar Pichai’s remarks 2024-10-30 2024-10-29 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/palm/2 ai/scaling/economics --- https://www.anthropic.com/news/github-copilot Claude 3.5 Sonnet on GitHub Copilot 2024-10-29 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex --- https://www.chiark.greenend.org.uk/~martinh/poems/complete_housman.html Complete A. E. Housman 2024-10-30 fiction/poetry --- https://www.biorxiv.org/content/10.1101/2024.10.22.619522.full DNAm aging biomarkers are responsive: Insights from 51 longevity interventional studies in humans Raghav Sehgal, Daniel Borrus, Jessica Kasamato, Jenel F. Armstrong, John Gonzalez, Yaroslav Markov, Ahana Priyanka, Ryan Smith, Natàlia Carreras, Varun B. Dwaraka, Albert Higgins-Chen 2024-10-27 2024-10-30 [("doi","10.1101/2024.10.22.619522")] exercise longevity/epigenetics longevity/metformin longevity/senolytic

[commentary] Aging biomarkers can potentially allow researchers to rapidly monitor the impact of an aging intervention, without the need for decade-spanning trials, by acting as surrogate endpoints. Prior to testing whether aging biomarkers may be useful as surrogate endpoints, it is first necessary to determine whether they are responsive to interventions that target aging.

Epigenetic clocks are aging biomarkers based on DNA methylation with prognostic value for many aging outcomes. Many individual studies are beginning to explore whether epigenetic clocks are responsive to interventions. However, the diversity of both interventions and epigenetic clocks in different studies make them difficult to compare systematically.

Here, we curate TranslAGE-Response, a harmonized database of 51 public and private longitudinal interventional studies and calculate a consistent set of 16 prominent epigenetic clocks for each study, along with 95 other DNAm biomarkers that help explain changes in each clock.

With this database, we discover patterns of responsiveness across a variety of interventions and DNAm biomarkers.

For example, clocks trained to predict mortality or pace of aging have the strongest response across all interventions and show consistent agreement with each other. Pharmacological and lifestyle interventions drive the strongest response from DNAm biomarkers, and study population and study duration are key factors in driving responsiveness of DNAm biomarkers in an intervention. Some classes of interventions such as TNF-alpha inhibitors have strong, consistent effects across multiple studies, while others such as senolytic drugs have inconsistent effects.

Clocks with multiple sub-scores (ie. “explainable clocks”) provide specificity and greater mechanistic insight into responsiveness of interventions than single-score clocks. Our work can help the geroscience field design future clinical trials, by guiding the choice of interventions, specific subsets of epigenetic clocks to minimize multiple testing, study duration, study population, and sample size, with the eventual aim of determining whether epigenetic clocks can be used as surrogate endpoints.

--- /doc/economics/2024-zhong.pdf Workplace aggression and employee performance: A meta-analytic investigation of mediating mechanisms and cultural contingencies Rui Zhong, Jingxian Yao, Yating Wang, Zhanna Lyubykh, Sandra L. Robinson 2024-01-01 2024-10-30 [("doi","10.1037/apl0001244")] economics psychology/personality

We present a meta-analytic investigation of the theoretical mechanisms underlying why experienced workplace aggression is harmful to the 3 core performance outcomes (ie. task performance, citizenship behavior, and deviant behavior).

Through a comprehensive literature review of 405 empirical articles, we first extract and identify 5 prominent theoretical mechanisms: relationship quality, justice perception, psychological strain, negative affect, and state self-evaluation. By synthesizing evidence from these articles, which include 471 unique samples from 36 countries or regions (n = 149,341 participants), we reveal:

the incremental effects of the 5 mechanisms, compare their relative strengths for each performance outcome, and examine their cultural contingencies.

  • We find that when the 5 mechanisms are examined simultaneously, only relationship quality and state self-evaluation show incremental effects across all performance outcomes in the predicted direction.

  • Moreover, the comparative strengths of mechanisms vary across performance outcomes: The impact of workplace aggression on task performance is best explained by the negative affect and state self-evaluation mechanisms, its impact on citizenship behavior is best explained by the relationship quality mechanism, and its impact on deviant behavior is best explained by the negative affect mechanism.

  • Finally, the prominence of some mechanisms is contingent on certain cultural dimensions: The relationship quality mechanism is strengthened by individualism and masculinity, while the state self-evaluation mechanism is strengthened by masculinity.

We conclude with a discussion of the theoretical and practical implications of our research.

--- /doc/math/1992-zvonkin.pdf Mathematics for Little Ones Alexander Zvonkin 1992-06-19 2024-10-30 math psychology --- https://arialdomartini.github.io/emacs-bookmarks Emacs: Bookmarks 2024-10-30 cs/lisp/emacs --- https://docs.midjourney.com/docs/style-reference Midjourney style references 2024-10-30 ai/nn/diffusion/midjourney --- https://docs.midjourney.com/docs/chaos --chaos hyperparameter 2024-10-30 ai/nn/diffusion/midjourney --- https://docs.midjourney.com/docs/weird --weird hyperparameter 2024-10-30 ai/nn/diffusion/midjourney --- https://www.youtube.com/watch?v=GyV_UG60dD4 Mission Statement Weird Al Yankovic 2014-07-22 2024-10-30 design/typography/rubrication fiction/humor --- https://academic.oup.com/jcmc/article/29/1/zmad049/7596747 Programmed differently? Testing for gender differences in Python programming style and quality on GitHub 2024-10-31 cs/algorithm cs/python psychology/personality statistics/stylometry --- https://github.com/SianJMBrooke/ProgrammedDifferently Programmed Differently? Testing for Gender Differences in Python Programming Style and Quality on GitHub 2024-10-31 cs/algorithm cs/python psychology/personality statistics/stylometry --- https://arxiv.org/abs/2402.04858 CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay Natasha Butt, Blazej Manczak, Auke Wiggers, Corrado Rainone, David W. Zhang, Michaël Defferrard, Taco Cohen 2024-02-07 2024-10-31 [("doi","10.48550/arXiv.2402.04858")] ai/nn/transformer/gpt/codex reinforcement-learning/model-free

Large language models are increasingly solving tasks that are commonly believed to require human-level reasoning ability. However, these models still perform very poorly on benchmarks of general intelligence such as the Abstraction and Reasoning Corpus (ARC).

In this paper, we approach ARC as a programming-by-examples problem, and introduce a novel and scalable method for language model self-improvement called Code Iteration (CodeIt). Our method iterates between (1) program sampling and hindsight relabeling, and (2) learning from prioritized experience replay. By relabeling the goal of an episode (ie. the target program output given input) to the realized output produced by the sampled program, our method effectively deals with the extreme sparsity of rewards in program synthesis.

Applying CodeIt to the ARC dataset, we demonstrate that prioritized hindsight replay, along with pre-training and data-augmentation, leads to successful inter-task generalization. CodeIt is the first neuro-symbolic approach that scales to the full ARC evaluation dataset.

Our method solves 15% of ARC evaluation tasks, achieving state-of-the-art performance and outperforming existing neural and symbolic baselines.

Our code is available at Github.

--- https://x.com/sharifshameem/status/1851059380730613776 Sharif Shameem 2024-10-31 statistics/stylometry/truesight --- https://beta.lifeweek.com.cn/h5/article/detail.do?artId=235160 一项持续53年的研究：成为天才需要什么？ 2024-10-31 iq/high/smpy --- https://x.com/repligate/status/1851874593205817773 Janus 2024-10-31 ai/nn/transformer/gpt/claude --- https://arxiv.org/abs/2410.21333 Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse Ryan Liu, Jiayi Geng, Addison J. Wu, Ilia Sucholutsky, Tania Lombrozo, Thomas L. Griffiths 2024-10-27 2024-10-31 [("doi","10.48550/arXiv.2410.21333")] ai/nn/transformer/gpt/inner-monologue psychology/cognitive-bias

Chain-of-thought (CoT) prompting has become a widely used strategy for working with large language and multimodal models. While CoT has been shown to improve performance across many tasks, determining the settings in which it is effective remains an ongoing effort. In particular, it is still an open question in what settings CoT systematically reduces model performance.

In this paper, we seek to identify the characteristics of tasks where CoT reduces performance by drawing inspiration from cognitive psychology, looking at cases where (1) verbal thinking or deliberation hurts performance in humans, and (2) the constraints governing human performance generalize to language models. 3 such cases are implicit statistical learning, visual recognition, and classifying with patterns containing exceptions.

In extensive experiments across all 3 settings, we find that a diverse collection of state-of-the-art models exhibit drop-offs in performance (eg. up to 36.3% absolute accuracy for OpenAI o1-preview compared to GPT-4o) when using inference-time reasoning compared to zero-shot counterparts. We also identify 3 tasks that satisfy condition (1) but not (2), and find that while verbal thinking reduces human performance in these tasks, CoT retains or increases model performance.

Overall, our results show that while there is not an exact parallel between the cognitive processes of models and those of humans, considering cases where thinking has negative consequences for human performance can help us identify settings where it negatively impacts models. By connecting the literature on human deliberation with evaluations of CoT, we offer a new tool that can be used in understanding the impact of prompt choices and inference-time reasoning.

--- https://www.reddit.com/r/mlscaling/comments/1ggr0j4/neural_network_recognizer_for_handwritten_zip/ 2024-10-31 ai/nn/cnn ai/scaling --- https://en.wikipedia.org/wiki/Square_packing Square packing 2024-01-01 design math --- https://en.wikipedia.org/wiki/Moshe_Koppel Moshe Koppel 2024-11-01 statistics/stylometry --- https://forum.effectivealtruism.org/posts/BHCjijbDnHJCkGB9n/two-concrete-ways-to-help-feeder-rodents Two Concrete Ways to Help Feeder Rodents 2024-11-01 philosophy/ethics psychology/animal --- https://forum.effectivealtruism.org/posts/BHCjijbDnHJCkGB9n/two-concrete-ways-to-help-feeder-rodents#Injury_and_Disease Two Concrete Ways to Help Feeder Rodents § Injury & Disease 2024-11-01 genetics/selection/artificial --- https://smoothbrains.net/posts/2024-09-26-hypercomputation-without-bothering-the-cactus-people.html Hypercomputation without bothering the cactus people: Software development for the DMT headspace Cube Flipper 2024-09-26 2024-11-01 cs/computable design/visualization psychedelic --- https://acoup.blog/2019/09/12/collections-this-isnt-sparta-part-v-spartan-government/ This. Isn’t. Sparta, Part V: Spartan Government Bret Devereaux 2019-09-12 2024-11-01 economics/mechanism-design history law --- https://www.quantamagazine.org/maths-bunkbed-conjecture-has-been-debunked-20241101/ Math’s ‘Bunkbed Conjecture’ Has Been Debunked 2024-11-01 2024-11-01 math philosophy/epistemology --- https://www.lesswrong.com/posts/LncYobrn3vRr7qkZW/the-slingshot-helps-with-learning The slingshot helps with learning Wilson Wu 2024-10-31 2024-11-01 ai/nn/fully-connected ai/scaling/emergence/grokking

The slingshot effect is a late-stage training anomaly found in various adaptive gradient optimization methods. In particular, slingshots are present with AdamW, the optimizer most widely used for modern transformer training.

The original slingshot paper observes that slingshots tend to occur alongside grokking, a phenomenon in which neural networks trained on algorithmic tasks generalize to the test set long after perfectly fitting the training set.

In this post, we take a closer look at slingshots and their effect on generalization in the setting of 1-hidden-layer MLPs trained on k-sparse parity, a specific algorithmic task.

The main results are:

  1. an explanation of why slingshots occur in models trained with hinge loss that partially transfers to models trained with cross-entropy loss

  2. empirical evidence that slingshots are biased towards decreasing test loss.

--- https://x.com/liminal_warmth/status/1852354598817693937#m liminal_warmth 2024-11-01 ai/nn/transformer/gpt/claude reinforcement-learning/preference-learning --- https://chrismcdonough.substack.com/p/the-shameful-defenestration-of-tim The Shameful Defenestration of Tim: On Tim Peters’ recent suspension as a Python core developer Chris McDonough 2024-08-10 2024-11-01 cs/python --- https://arxiv.org/abs/2410.20247 Model Equality Testing: Which Model Is This API Serving? Irena Gao, Percy Liang, Carlos Guestrin 2024-10-26 2024-11-01 [("doi","10.48550/arXiv.2410.20247")] ai/nn/sparsity/low-precision ai/nn/transformer/gpt ai/scaling/economics

[code] Users often interact with large language models through black-box inference APIs, both for closed- and open-weight models (eg. Llama models are popularly accessed via Amazon Bedrock and Azure AI Studio). In order to cut costs or add functionality, API providers may quantize, watermark, or finetune the underlying model, changing the output distribution—often without notifying users.

We formalize detecting such distortions as Model Equality Testing, a two-sample testing problem, where the user collects samples from the API and a reference distribution and conducts a statistical test to see if the two distributions are the same. We find that tests based on the Maximum Mean Discrepancy between distributions are powerful for this task: a test built on a simple string kernel achieves a median of 77.4% power against a range of distortions, using an average of just 10 samples per prompt.

We then apply this test to commercial inference APIs for 4 Llama models, finding that 11⁄31 endpoints serve different distributions than reference weights released by Facebook.

--- https://pmc.ncbi.nlm.nih.gov/articles/PMC9690788/ The Truth of Unusual Deaths under Military Expansion: Evidence from the Stable Isotopes of a Human Skull Ditch in the Capital City of the Early Shang Dynasty 2024-11-01 history --- https://www.construction-physics.com/p/how-china-is-like-the-19th-century How China Is Like the 19th Century US Brian Potter 2024-11-01 economics sociology --- https://arxiv.org/abs/2403.18802#deepmind Long-form factuality in large language models Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Jie Huang, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, Quoc V. Le 2024-03-27 2024-11-02 [("doi","10.48550/arXiv.2403.18802")] ai/dataset ai/nn/retrieval ai/nn/transformer/attention ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm/2 ai/scaling

Large language models (LLMs) often generate content that contains factual errors when responding to fact-seeking prompts on open-ended topics.

To benchmark a model’s long-form factuality in open domains, we first use GPT-4 to generate LongFact, a prompt set comprising thousands of questions spanning 38 topics.

We then propose that LLM agents can be used as automated evaluators for long-form factuality through a method which we call Search-Augmented Factuality Evaluator (SAFE). SAFE uses an LLM to break down a long-form response into a set of individual facts and to evaluate the accuracy of each fact using a multi-step reasoning process comprising sending search queries to Google Search and determining whether a fact is supported by the search results. Furthermore, we propose extending F1 score as an aggregated metric for long-form factuality. To do so, we balance the percentage of supported facts in a response (precision) with the percentage of provided facts relative to a hyperparameter representing a user’s preferred response length (recall).

Empirically, we demonstrate that LLM agents can outperform crowdsourced human annotators—on a set of ~16k individual facts, SAFE agrees with crowdsourced human annotators 72% of the time, and on a random subset of 100 disagreement cases, SAFE wins 76% of the time. At the same time, SAFE is more than 20× cheaper than human annotators.

We also benchmark 13 language models on LongFact across 4 model families (Gemini, GPT, Claude, and PaLM-2), finding that larger language models generally achieve better long-form factuality.

LongFact, SAFE, and all experimental code are available at Github.

--- https://googleprojectzero.blogspot.com/2024/10/from-naptime-to-big-sleep.html Project Zero: From Naptime to Big Sleep: Using Large Language Models To Catch Vulnerabilities In Real-World Code 2024-10 2024-11-02 ai/nn/transformer/gpt/codex ai/nn/transformer/gpt/palm/2 cs/security --- https://www.pnas.org/doi/abs/10.1073/pnas.2405460121 2024-11-02 ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction philosophy/mind --- https://www.nature.com/articles/s41562-024-01882-z Testing theory of mind in large language models and humans 2024 2024-11-02 ai/nn/transformer/gpt/4/nonfiction philosophy/mind --- /doc/economics/automation/2024-simantovnachlieli.pdf More to Lose: The Adverse Effect of High Performance Ranking on Employees’ Pre-implementation Attitudes Toward the Integration of Powerful AI Aids Ilanit SimanTov-Nachlieli 2024-10-18 2024-11-02 [("doi","10.1287/orsc.2023.17515")] ai economics/automation psychology/cognitive-bias

Despite the growing availability of algorithm-augmented work, algorithm aversion is prevalent among employees, hindering successful implementations of powerful artificial intelligence (AI) aids. Applying a social comparison perspective, this article examines the adverse effect of employees’ high performance ranking on their pre-implementation attitudes toward the integration of powerful AI aids within their area of advantage.

5 studies, using a weight estimation simulation (Studies 1–3), recall of actual job tasks (Study 4), and a workplace scenario (Study 5), provided consistent causal evidence for this effect by manipulating performance ranking (performance advantage compared with peers versus no advantage).

Studies 3–4 revealed that this effect was driven in part by employees’ perceived potential loss of standing compared with peers, a novel social-based mechanism complementing the extant explanation operating via one’s confidence in own (versus AI) ability.

Stronger causal evidence for this mechanism was provided in Study 5 using a “moderation-of-process” design. It showed that the adverse effect of high performance ranking on pre-implementation AI attitudes was reversed when bolstering the stability of future performance rankings (presumably counteracting one’s concern with potential loss of standing).

Finally, pointing to the power of symbolic threats, this adverse effect was evident both in the absence of financial incentives for high performance (Study 1) and in various incentive-based settings (Studies 2–3).

Implications for understanding and managing high performers’ aversion toward the integration of powerful algorithmic aids are discussed.

Funding: This work was supported by the Coller Foundation.

Supplemental Material: The supplemental material is available at https://doi.org/10.1287/orsc.2023.17515.

--- http://www.aaronsw.com/2002/fleep/ FLEEP: The Collected Comic Jason Shiga 2002 2024-10-30 fiction/science-fiction science/fermi-problem --- https://x.com/realityarb/status/1852470725049008597 realityarb 2024-11-02 ai/nn/transformer/gpt/claude --- https://arxiv.org/abs/2403.17844 Mechanistic Design and Scaling of Hybrid Architectures Michael Poli, Armin W. Thomas, Eric Nguyen, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian Hie, Stefano Ermon, Christopher Ré, Ce Zhang, Stefano Massaroli 2024-03-26 2024-11-02 [("doi","10.48550/arXiv.2403.17844")] ai/nn/rnn ai/nn/sparsity ai/nn/tokenization ai/nn/transformer/attention ai/scaling

The development of deep learning architectures is a resource-demanding process, due to a vast design space, long prototyping times, and high compute costs associated with at-scale model training and evaluation. We set out to simplify this process by grounding it in an end-to-end mechanistic architecture design (MAD) pipeline, encompassing small-scale capability unit tests predictive of scaling laws.

Through a suite of synthetic token manipulation tasks such as compression and recall, designed to probe capabilities, we identify and test new hybrid architectures constructed from a variety of computational primitives. We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis, training over 500 language models between 70M to 7B parameters.

Surprisingly, we find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures via isolated proxy tasks. The new architectures found via MAD, based on simple ideas such as hybridization and sparsity, outperform state-of-the-art Transformer, convolutional, and recurrent architectures (Transformer++, Hyena, Mamba) in scaling, both at compute-optimal budgets and in over-trained regimes.

Overall, these results provide evidence that performance on curated synthetic tasks can be predictive of scaling laws, and that an optimal architecture should leverage specialized layers via a hybrid topology.

--- https://www.wired.com/story/ai-halloween-parade-listing-dublin-interview/ The Guy Behind the Fake AI Halloween Parade Listing Says You’ve Got It All Wrong 2024-11-02 ai/nn/transformer/gpt crime economics/advertising --- https://x.com/ESYudkowsky/status/1718654143110512741 Eliezer Yudkowsky 2024-11-02 ai/nn/adversarial ai/nn/transformer/gpt/codex math/humor philosophy/ethics reinforcement-learning/safe --- https://www.nytimes.com/2024/11/02/science/richard-cash-dead.html Richard A. Cash, Who Saved Millions From Dehydration, Dies at 83 2024-11-02 2024-11-02 biology philosophy/ethics --- https://openai.com/index/introducing-openai-o1-preview/ 2024-11-02 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue reinforcement-learning/scaling --- https://en.wikipedia.org/wiki/L%27esprit_de_l%27escalier L’esprit de l’escalier 2024-11-02 fiction/humor psychology/linguistics --- https://allpoetry.com/The-Old-Fools The Old Fools Philip Larkin 2024-11-02 fiction/poetry longevity philosophy/mind --- /doc/philosophy/mind/2011-09-09-frederikpohl-decliningimmortalitytwice.html Declining Immortality Twice Frederik Pohl 2011-09-09 2024-11-02 cryonics philosophy/mind --- https://x.com/QiaochuYuan/status/1852831246482813336 [Claude jokes about itself] Qiaochu Yuan 2024-11-02 ai/nn/transformer/gpt/claude --- https://en.wikipedia.org/wiki/Longevity_escape_velocity Longevity escape velocity 2024-11-02 longevity --- https://www.reddit.com/r/midjourney/comments/1gi1ptl/morphing_within_a_morphing/ 2024-11-03 ai/video/generation --- https://www.construction-physics.com/p/how-to-build-a-20-billion-semiconductor How to Build a $20 Billion Semiconductor Fab 2024-11-03 ai/scaling/economics cs/hardware economics/experience-curve --- https://pubmed.ncbi.nlm.nih.gov/21301855/ Low-dose lithium uptake promotes longevity in humans and metazoans 2024-01-01 longevity psychiatry/lithium --- https://pmc.ncbi.nlm.nih.gov/articles/PMC6731440/ Liraglutide suppresses TNF-α-induced degradation of extracellular matrix in human chondrocytes: a therapeutic implication in osteoarthritis 2024-11-03 longevity/glp/semaglutide --- https://pmc.ncbi.nlm.nih.gov/articles/PMC8799666/ Liraglutide, a glucagon-like peptide 1 receptor agonist, exerts analgesic, anti-inflammatory and anti-degradative actions in osteoarthritis 2024-11-03 longevity/glp/semaglutide --- /doc/ai/1949-coupling.pdf#page=4 Chance Remarks § Shannon’s n-gram generations John R. Pierce 1949 2024-11-02 ai cs/algorithm/information psychology/linguistics --- https://hledger.org/ Hledger homepage 2024-11-02 cs/haskell economics --- https://www.sciencedirect.com/science/article/pii/S0040580917300886 The infinitesimal model: Definition, derivation, and implications 2024-01-01 genetics/heritable genetics/selection/natural statistics/probability --- https://www.sciencedirect.com/science/article/pii/S0092867413004674 One-Step Generation of Mice Carrying Mutations in Multiple Genes by CRISPR/Cas-Mediated Genome Engineering 2024-01-01 genetics/editing --- https://www.sciencedirect.com/science/article/pii/S0160289616303324 High intelligence: A risk factor for psychological and physiological overexcitabilities 2024-01-01 iq/high --- https://en.wikipedia.org/wiki/The_Scarlet_Letter The Scarlet Letter 2024-11-03 design/typography/rubrication --- https://www.lesswrong.com/posts/4psQW7vRwt7PE5Pnj/too-busy-to-think-about-life?commentId=fByHmu4ACMtRFMhYS#fByHmu4ACMtRFMhYS Too busy to think about life 2024-11-03 fiction/humor --- https://arxiv.org/abs/2410.18647 Data Scaling Laws in Imitation Learning for Robotic Manipulation Fanqi Lin, Yingdong Hu, Pingyue Sheng, Chuan Wen, Jiacheng You, Yang Gao 2024-10-24 2024-11-04 [("doi","10.48550/arXiv.2410.18647")] ai/nn/diffusion reinforcement-learning/imitation-learning reinforcement-learning/robot reinforcement-learning/scaling

Data scaling has revolutionized fields like natural language processing and computer vision, providing models with remarkable generalization capabilities. In this paper, we investigate whether similar data scaling laws exist in robotics, particularly in robotic manipulation, and whether appropriate data scaling can yield single-task robot policies that can be deployed zero-shot for any object within the same category in any environment.

To this end, we conduct a comprehensive empirical study on data scaling in imitation learning. By collecting data across numerous environments and objects, we study how a policy’s generalization performance changes with the number of training environments, objects, and demonstrations. Throughout our research, we collect over 40,000 demonstrations and execute more than 15,000 real-world robot rollouts under a rigorous evaluation protocol.

Our findings reveal several intriguing results: the generalization performance of the policy follows a roughly power-law relationship with the number of environments and objects. The diversity of environments and objects is far more important than the absolute number of demonstrations; once the number of demonstrations per environment or object reaches a certain threshold, additional demonstrations have minimal effect.

Based on these insights, we propose an efficient data collection strategy. With 4 data collectors working for one afternoon, we collect sufficient data to enable the policies for two tasks to achieve ~90% success rates in novel environments with unseen objects.

--- https://arxiv.org/abs/2410.10254 LoLCATs: On Low-Rank Linearizing of Large Language Models Michael Zhang, Simran Arora, Rahul Chalamala, Alan Wu, Benjamin Spector, Aaryan Singhal, Krithik Ramesh, Christopher Ré 2024-10-14 2024-11-04 [("doi","10.48550/arXiv.2410.10254")] ai/nn/sparsity/knowledge-distillation ai/nn/transformer/attention/linear-algebra

Recent works show we can linearize large language models (LLMs)—swapping the quadratic attentions of popular Transformer-based LLMs with sub-quadratic analogs, such as linear attention—avoiding the expensive pretraining costs. However, linearizing LLMs often degrades model quality, still requires training over billions of tokens, and remains limited to smaller 1.3B to 7B LLMs.

We thus propose Low-rank Linear Conversion via Attention Transfer (LoLCATs), a simple two-step method that improves LLM linearizing quality with orders of magnitudes less memory and compute. We base these steps on two findings. First, we can replace an LLM’s softmax attentions with closely-approximating linear attentions, simply by training the linear attentions to match their softmax counterparts with an output MSE loss (“attention transfer”). Then, this enables adjusting for approximation errors and recovering LLM quality simply with low-rank adaptation (LoRA).

LoLCATs improves linearizing quality, training efficiency, and scalability. We reduce the linearizing quality gap and produce state-of-the-art sub-quadratic LLMs from Llama-3 8B and Mistral-7B v0.1, leading to 20+ points of improvement on 5-shot MMLU. Furthermore, LoLCATs does so with only 0.2% of past methods’ model parameters and 0.4% of their training tokens.

Finally, we apply LoLCATs to create the first linearized 70B and 405B LLMs (50× larger than prior work). When compared with prior approaches under the same compute budgets, LoLCATs improves linearizing quality, closing the gap between linearized and original Llama-3.1 70B and 405B LLMs by 77.8% and 78.1% on 5-shot MMLU.

--- https://plato.stanford.edu/entries/death/#DepDef Death § 3.2 The Deprivationist Defense SEP 2021-08-25 2024-11-03 philosophy/mind --- https://plato.stanford.edu/entries/death/#SymArg Death § 5.2 The Symmetry Argument SEP 2021-08-25 2024-11-03 philosophy/mind --- https://www.bartleby.com/lit-hub/the-poems-of-john-dryden/the-latter-part-of-the-third-book-of-lucretius-against-the-fear-of-death/ The Latter Part of the Third Book of Lucretius; against the Fear of Death John Dryden 2024-11-03 philosophy/mind --- https://www.cnn.com/2024/11/02/health/public-health-mcdonalds-e-coli-outbreak/index.html How disease detectives’ quick work traced deadly E. coli outbreak to McDonald’s Quarter Pounders 2024-11-02 2024-11-04 genetics/sequencing --- https://arxiv.org/abs/2201.08528 To SMOTE, or not to SMOTE? Yotam Elor, Hadar Averbuch-Elor 2022-01-21 2024-11-04 [("doi","10.48550/arXiv.2201.08528")] ai/tabular

Balancing the data before training a classifier is a popular technique to address the challenges of imbalanced binary classification in tabular data. Balancing is commonly achieved by duplication of minority samples or by generation of synthetic minority samples. [eg. SMOTE] While it is well known that balancing affects each classifier differently, most prior empirical studies did not include strong state-of-the-art (SOTA) classifiers as baselines.

In this work, we are interested in understanding whether balancing is beneficial, particularly in the context of SOTA classifiers. Thus, we conduct extensive experiments considering 3 SOTA classifiers along with the weaker learners used in previous investigations. Additionally, we carefully discern proper metrics, consistent and non-consistent algorithms and hyper-parameter selection methods and show that these have an impact on prediction quality and on the effectiveness of balancing.

Our results support the known utility of balancing for weak classifiers. However, we find that balancing does not improve prediction performance for the strong ones.

We further identify several other scenarios for which balancing is effective and observe that prior studies demonstrated the utility of balancing by focusing on these settings.

--- https://arxiv.org/abs/2404.19494 The harms of class imbalance corrections for machine learning based prediction models: a simulation study Alex Carriero, Kim Luijken, Anne de Hond, Karel G. M. Moons, Ben van Calster, Maarten van Smeden 2024-04-30 2024-11-04 [("doi","10.48550/arXiv.2404.19494")] ai/tabular

Risk prediction models are increasingly used in healthcare to aid in clinical decision making. In most clinical contexts, model calibration (ie. assessing the reliability of risk estimates) is critical. Data available for model development are often not perfectly balanced with respect to the modeled outcome (ie. individuals with vs. without the event of interest are not equally represented in the data). It is common for researchers to correct this class imbalance, yet the effect of such imbalance corrections on the calibration of machine learning models is largely unknown.

We studied the effect of imbalance corrections on model calibration for a variety of machine learning algorithms. Using extensive Monte Carlo simulations, we compared the out-of-sample predictive performance of models developed with an imbalance correction to those developed without a correction for class imbalance across different data-generating scenarios (varying sample size, the number of predictors and event fraction). Our findings were illustrated in a case study using MIMIC-III data.

In all simulation scenarios, prediction models developed without a correction for class imbalance consistently had equal or better calibration performance than prediction models developed with a correction for class imbalance. The miscalibration introduced by correcting for class imbalance was characterized by an over-estimation of risk and was not always able to be corrected with re-calibration.

Correcting for class imbalance is not always necessary and may even be harmful for clinical prediction models which aim to produce reliable risk estimates on an individual basis.

--- https://emilkirkegaard.dk/en/wp-content/uploads/Genetic-and-environmental-influences-on-human-psychological-differences.pdf#page=20 Genetic and Environmental Influences on Human Psychological Differences § Table 5: Broad Heritabilities of Self-Report Measures of the Big Five Factors Based on Four Recent Twin Studies, a Comprehensive Review of Twin, Adoption, and Biological Kinships (Loehlin 1992), and a Summary of the Earlier Twin Literature (Bouchard 1997) Bouchard, McGue 2003 2024-01-01 genetics/heritable/adoption psychology/personality --- https://slate.com/culture/2016/11/a-linguist-on-arrival-s-alien-language.html A linguist on Arrival’s alien language 2024-01-01 fiction/science-fiction/time-travel psychology/linguistics --- https://slate.com/culture/2014/06/against-ya-adults-should-be-embarrassed-to-read-childrens-books.html Against YA: Adults should be embarrassed to read children’s books 2024-01-01 culture --- https://slate.com/human-interest/2001/02/a-mother-searches-for-donor-white.html A Mother Searches for ‘Donor White’ 2001-02 2024-01-01 genetics/selection/artificial --- https://slate.com/news-and-politics/2006/01/why-dumb-recruits-cost-the-army-big-time.html Why dumb recruits cost the Army, big-time. 2024-01-01 iq/low --- https://slate.com/news-and-politics/2009/02/the-terrorists-are-dumb-theory.html Are terrorists stupid? 2024-01-01 crime/terrorism iq/ses --- https://slate.com/technology/2008/08/the-google-black-hole.html The Google Black Hole: Sergey and Larry just bought my company. Uh oh. 2024-01-01 technology/google --- https://slate.com/technology/2003/03/can-we-sleep-less.html Can we sleep less? 2024-01-01 zeo --- https://pubs.tedpavlic.com/PavlicPassino11a.pdf The Sunk-cost Effect as an Optimal Rate-maximizing Behavior 2024-01-01 psychology/cognitive-bias/sunk-cost --- https://journals.sagepub.com/doi/full/10.1177/03331024231206781 2024-11-04 nootropic/quantified-self/heart-rate-variability --- https://en.wikipedia.org/wiki/L%C3%A9vy_flight Lévy flight 2024-11-04 psychology/novelty reinforcement-learning/exploration statistics/probability --- https://www.lesswrong.com/posts/BarHSeciXJqzRuLzw/survival-without-dignity Survival without dignity L Rudolf L 2024-11-03 2024-11-04 fiction/humor fiction/science-fiction --- https://www.wired.com/story/your-next-job-pet-cloner/ Thousands of People Are Cloning Their Dead Pets. This Is the Viagen Woman They Call First 2024-11-04 cat/genetics genetics/cloning/dog --- https://www.quantamagazine.org/hes-gleaning-the-design-rules-of-life-to-re-create-it-20241104/ He’s Gleaning the Design Rules of Life to Re-Create It 2024-11-04 2024-11-04 genetics/genome-synthesis --- https://www.reddit.com/r/OpenAI/comments/1gjj430/o1_preview_got_weird_today/ 2024-11-04 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue --- https://en.wikipedia.org/wiki/Autogenous_vaccines Autogenous vaccines 2024-11-05 genetics/microbiome/acne --- https://x.com/sashachapin/status/1853563299411189820 Sasha Chapin 2024-11-05 psychedelic psychology/cognitive-bias/illusion-of-depth --- https://dominiccummings.substack.com/p/q-and-a#%C2%A7where-did-take-back-control-come-from Q&A § Brexit logo design Dominic Cummings 2024-11-05 design --- https://www.printmag.com/daily-heller/the-daily-heller-the-assistant-jayme-odgers-works-for-paul-rand/ [Jayme Odgers’s remiscences of working for Paul Rand] Jayme Odgers 2021-11-11 2024-11-05 design/typography --- https://www.youtube.com/watch?v=xKu2de0yCJI Saul Bass Pitch Video for Bell System Logo Redesign Saul Bass 1969 2024-11-05 design/typography economics/advertising technology --- https://en.wikipedia.org/wiki/Saul_Bass Saul Bass 2024-11-05 design/typography economics/advertising --- https://x.com/lefthanddraft/status/1853482491124109725 lefthanddraft 2024-11-05 ai/nn/transformer/gpt/claude reinforcement-learning/preference-learning reinforcement-learning/safe --- /doc/philosophy/epistemology/2005-bishop.pdf The Pathologies of Standard Analytic Epistemology Michael Bishop, J. D. Trout 2005-12-01 2024-11-05 [("doi","10.2307/3506117")] philosophy/epistemology psychology --- /doc/philosophy/epistemology/2008-bishop.pdf Strategic Reliabilism: A Naturalistic Approach to Epistemology Michael A. Bishop, J. D. Trout 2008-09-29 2024-11-05 [("doi","10.1111/j.1747-9991.2008.00161.x")] philosophy/epistemology psychology/cognitive-bias statistics/decision

Strategic Reliabilism is a framework that yields relative epistemic evaluations of belief-producing cognitive processes. It is a theory of cognitive excellence, or more colloquially, a theory of reasoning excellence (where ‘reasoning’ is understood very broadly as any sort of cognitive process for coming to judgments or beliefs).

First introduced in our book, Epistemology and the Psychology of Human Judgment (henceforth EPHJ), the basic idea behind SR is that epistemically excellent reasoning is efficient reasoning that leads in a robustly reliable fashion to important, true beliefs. It differs from most contemporary epistemological theories in two ways.

First, it is not a theory of justification or knowledge—a theory of epistemically worthy belief. Strategic Reliabilism is a theory of epistemically worthy ways of forming beliefs.

And second, Strategic Reliabilism does not attempt to account for an epistemological property that is assumed to be faithfully reflected in the epistemic judgments and intuitions of philosophers. If SR makes recommendations that accord with our reflective epistemic judgments and intuitions, great. If not, then so much the worse for our reflective epistemic judgments and intuitions.

--- https://www.biorxiv.org/content/10.1101/2024.11.01.621596.full From the fly connectome to exact ring attractor dynamics Tirthabir Biswas, Angel Stanoev, Sandro Romani, James E. Fitzgerald 2024-11-01 2024-11-05 [("doi","10.1101/2024.11.01.621596")] psychology/neuroscience

A cognitive compass enabling spatial navigation requires neural representation of heading direction (HD), yet the neural circuit architecture enabling this representation remains unclear. While various network models have been proposed to explain HD systems, these models rely on simplified circuit architectures that are incompatible with empirical observations from connectomes.

Here we construct a novel network model for the fruit fly HD system that satisfies both connectome-derived architectural constraints and the functional requirement of continuous heading representation. We characterize an ensemble of continuous attractor networks where compass neurons providing local mutual excitation are coupled to inhibitory neurons.

We discover a new mechanism where continuous heading representation emerges from combining symmetric and anti-symmetric activity patterns. Our analysis reveals 3 distinct realizations of these networks that all match observed compass neuron activity but differ in their predictions for inhibitory neuron activation patterns.

Further, we found that deviations from these realizations can be compensated by cell-type-specific rescaling of synaptic weights, which could be potentially achieved through neuromodulation.

This framework can be extended to incorporate the complete fly central complex connectome and could reveal principles of neural circuits representing other continuous quantities, such as spatial location, across insects and vertebrates.

--- https://arxiv.org/abs/2310.02980 Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors Ido Amos, Jonathan Berant, Ankit Gupta 2023-10-04 2024-11-06 [("doi","10.48550/arXiv.2310.02980")] ai/nn/rnn ai/nn/transformer/attention ai/scaling

Modeling long-range dependencies across sequences is a long-standing goal in machine learning and has led to architectures, such as state space models, that dramatically outperform Transformers on long sequences. However, these impressive empirical gains have been by and large demonstrated on benchmarks (eg. Long Range Arena), where models are randomly initialized and trained to predict a target label from an input sequence.

In this work, we show that random initialization leads to gross overestimation of the differences between architectures and that pretraining with standard denoising objectives, using only the downstream task data, leads to:

dramatic gains across multiple architectures and to very small gaps between Transformers and state space models (SSMs). In stark contrast to prior works, we find vanilla Transformers to match the performance of S4 on Long Range Arena when properly pretrained, and we improve the best reported results of SSMs on the PathX-256 task by 20 absolute points.

Subsequently, we analyze the utility of previously-proposed structured parameterizations for SSMs and show they become mostly redundant in the presence of data-driven initialization obtained through pretraining.

Our work shows that, when evaluating different architectures on supervised tasks, incorporation of data-driven priors via pretraining is essential for reliable performance estimation, and can be done efficiently.

--- https://notes.jordanscales.com/98-css-reflections Reflections on 98.css (Windows 98 GUI clone) [burnout] Jordan Scales 2024-04-28 2024-11-06 cs/css design psychology/willpower --- https://investor.vanguard.com/investment-products/mutual-funds/profile/vtsax VTSAX: Vanguard Total Stock Market Index Fund Admiral Shares Vanguard 2024-11-02 economics --- https://www.youtube.com/watch?v=xiY5HFHD4Js Bons et mauvais Jours (Apparitions Stalk the Night) Raven’s Jig 2024-06-17 touhou --- https://yueatsprograms.github.io/ Yu Sun 2024-11-07 ai/nn/dynamic-evaluation --- https://arxiv.org/abs/1909.11825 Unsupervised Domain Adaptation through Self-Supervision Yu Sun, Eric Tzeng, Trevor Darrell, Alexei A. Efros 2019-09-26 2024-11-07 [("doi","10.48550/arXiv.1909.11825")] ai/nn/dynamic-evaluation

This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target domain with only unlabeled data. Like much of previous work, we seek to align the learned representations of the source and target domains while preserving discriminability.

The way we accomplish alignment is by learning to perform auxiliary self-supervised task(s) on both domains simultaneously. Each self-supervised task brings the two domains closer together along the direction relevant to that task. The presented objective is straightforward to implement and easy to optimize.

Training this jointly with the main task classifier on the source domain is shown to successfully generalize to the unlabeled target domain. We achieve state-of-the-art results on 4⁄7 standard benchmarks, and competitive results on segmentation adaptation.

We also demonstrate that our method composes well with another popular pixel-level adaptation method.

--- https://arxiv.org/abs/1806.07755 An empirical study on evaluation metrics of generative adversarial networks Qiantong Xu, Gao Huang, Yang Yuan, Chuan Guo, Yu Sun, Felix Wu, Kilian Q. Weinberger 2018-06-19 2024-11-07 [("doi","10.48550/arXiv.1806.07755")] ai/nn/gan

Evaluating generative adversarial networks (GANs) is inherently challenging.

In this paper, we revisit several representative sample-based evaluation metrics for GANs, and address the problem of how to evaluate the evaluation metrics. We start with a few necessary conditions for metrics to produce meaningful scores, such as distinguishing real from generated samples, identifying mode dropping and mode collapsing, and detecting overfitting.

With a series of carefully designed experiments [on DCGAN & WGAN], we comprehensively investigate existing sample-based metrics and identify their strengths and limitations in practical settings.

Based on these results, we observe that kernel Maximum Mean Discrepancy (MMD) and the 1-Nearest-Neighbor (1-NN) two-sample test seem to satisfy most of the desirable properties, provided that the distances between samples are computed in a suitable feature space.

Our experiments also unveil interesting properties about the behavior of several popular GAN models, such as whether they are memorizing training samples, and how far they are from learning the target distribution.

--- https://arxiv.org/abs/2209.07522 Test-Time Training with Masked Autoencoders Yossi Gandelsman, Yu Sun, Xinlei Chen, Alexei A. Efros 2022-09-15 2024-11-07 [("doi","10.48550/arXiv.2209.07522")] ai/nn/dynamic-evaluation ai/nn/vae/mae

Test-time training adapts to a new test distribution on the fly by optimizing a model for each test input using self-supervision.

In this paper, we use masked autoencoders for this one-sample learning problem.

Empirically, our simple method improves generalization on many visual benchmarks for distribution shifts.

Theoretically, we characterize this improvement in terms of the bias-variance trade-off.

--- https://www.youtube.com/watch?v=vXUFchjWR58 This Anime Does Not Exist [video] 2024-06-17 ai/nn/gan/stylegan/anime --- https://near.blog/this-anime-does-not-exist/ This Anime Does Not Exist [blog] 2024-01-01 ai/nn/gan/stylegan/anime --- https://www.wired.com/2014/12/da-bom/ Turns Out the Dot-Com Bust’s Worst Flops Were Actually Fantastic Ideas 2024-01-01 economics/automation technology --- https://www.wired.com/2014/12/interview-darkside-russias-favorite-dark-web-drug-lord/ An Interview With Darkside, Russia’s Favorite Dark Web Drug Lord 2024-01-01 darknet-market --- https://en.wikipedia.org/wiki/Ad_blocking Ad blocking 2024-01-01 economics/advertising/adblock --- https://github.com/gorhill/uBlock#user-content-ublock-origin-ubo gorhill/uBlock: uBlock Origin—An efficient blocker for Chromium and Firefox. Fast and lean. 2024-11-07 economics/advertising/adblock --- https://blokada.org/ Blokada: the popular mobile adblocker and VPN for Android and iOS 2024-11-07 economics/advertising/adblock --- https://adguard.com/en/welcome.html AdGuard 2024-11-07 economics/advertising/adblock --- https://adblockpro.com/ AdBlockPro: Ad Blocker for Safari on iPhone, iPad, Mac, Vision Pro 2024-11-07 economics/advertising/adblock --- https://en.wikipedia.org/wiki/Ghostery Ghostery 2024-11-07 economics/advertising/adblock --- https://en.wikipedia.org/wiki/AdGuard AdGuard 2024-11-07 economics/advertising/adblock --- https://en.wikipedia.org/wiki/Adblock_Plus Adblock Plus 2024-11-07 economics/advertising/adblock --- /doc/economics/advertising/adblock/2017-pagefair.pdf#page=3 The Hidden Cost of Adblock: Adblock’s impact on website traffic § pg3 Sean Blanchfield 2017-02-01 2024-01-01 economics/advertising/adblock --- https://en.wikipedia.org/wiki/Adblock_Plus#EasyList Adblock Plus § EasyList 2024-11-07 economics/advertising/adblock --- https://github.com/dxaen/percival percival: Making In-Browser perceptual ad-blocking practical with Deep Learning 2024-11-07 economics/advertising/adblock --- https://www.adblockradio.com/en/ Adblock Radio 2024-11-07 economics/advertising/adblock --- https://en.wikipedia.org/wiki/Privacy_Badger Privacy Badger 2024-11-07 economics/advertising/adblock --- https://github.com/gorhill/uBlock uBlock Origin: An efficient blocker for Chromium and Firefox. Fast and lean. gorhill 2024-01-01 economics/advertising/adblock --- /doc/economics/advertising/adblock/2021-gritckevich.pdf Ad Blocking Aleksandr Gritckevich, Zsolt Katona, Miklos Sarvary 2021-10-25 2024-01-01 [("doi","10.1287/mnsc.2021.4106")] economics/advertising/adblock

In recent years, ad blocking has become a major threat to advertising-supported content. Adblockers typically negotiate with publishers, allowing some ads to go through in return for a payment, a practice called (partial) whitelisting in the industry. Ad blocking has a direct positive effect on consumers by reducing advertising intensity. On the other hand, the practice clearly hurts publishers and reduces their incentives to invest in content quality. Lower content quality, in turn has an indirect negative effect on consumers.

This paper builds an analytic model to explore the net impact of ad blocking on consumers, how it depends on various market characteristics, and how uniformly it affects consumers.

The results show that under a broad set of market conditions, total consumer surplus and even total welfare decline under ad blocking. Whereas some consumers are always better off with an ad blocker, for the average consumer, the impact of quality decline is larger than that of ad reduction.

The analysis highlights the detrimental role of ad blockers’ current revenue model—in which value is created for the consumers but it is captured from publishers—in decreasing quality, consumer surplus, and total welfare. Analyzing the impact of varying levels of negotiation power between the ad blocker and publisher reveals that full negotiation power is not preferred by the ad blocker. A lower negotiation power allows the ad blocker to commit to less value extraction from the publisher, thereby leading to higher content quality.

Additional model extensions show that the main results are robust. In the case of multiple publishers with different levels of competition between them, the strong negative effect of ad blocking on quality holds.

--- https://github.com/search?q=org%3AAdguardTeam+youtube&type=issues AdguardTeam: YouTube-related bugs AdguardTeam 2024-11-07 economics/advertising/adblock --- /banner#they-just-dont-know 2024-01-01 economics/advertising/adblock --- https://pmc.ncbi.nlm.nih.gov/articles/PMC3712874/ Family Violence and Football: The Effect of Unexpected Emotional Cues on Violent Behavior David Card, Gordon B. Dahl 2011-02-01 2024-11-07 [("doi","10.1093/qje/qjr001")] crime exercise

We study the link between family violence and the emotional cues associated with wins and losses by professional NFL football teams. We hypothesize that the risk of violence is affected by the “gain-loss” utility of game outcomes around a rationally expected reference point.

Our empirical analysis uses police reports of violent incidents on Sundays during the professional football season. Controlling for the pregame point spread and the size of the local viewing audience, we find that upset losses (defeats when the home team was predicted to win by 4 or more points) lead to a 10% increase in the rate of at-home violence by men against their wives and girlfriends.

In contrast, losses when the game was expected to be close have small and insignificant effects. Upset wins (victories when the home team was predicted to lose) also have little impact on violence, consistent with asymmetry in the gain-loss utility function.

The rise in violence after an upset loss is concentrated in a narrow time window near the end of the game and is larger for more important games. We find no evidence for reference point updating based on the halftime score.

…Two other recent studies have explored the link bet ween football and violence.

Gantz et al 2006 relate police reports of family violence to the occurrence of NFL games involving the local team, and find that game-days are associated with higher rates of violence. Rees & Schnepel 2009 document the effects of college football home games on rates of assault, vandalism, and alcohol-related offenses.6 We go beyond these studies by examining the effects of wins and losses relative to pre-game expectations, by controlling for the size of the local viewing audience, by studying the inter-day timing of violent incidents, by comparing the effects of more and less salient games, and by testing for potential updating of the reference point for game outcomes using the score at half-time.

Our analysis incorporates family violence data for over 750 city and county police agencies in the National Incident Based Reporting System (NIBRS), merged with information on Sunday NFL games played by 6 teams over a 12-year period. Controlling for the pre-game point spread and the size of the local television viewing audience, we find that “upset losses” by the home team (losses when the team was predicted to win by 4 points or more) lead to a roughly 10% increase in the number of police reports of at-home male-on-female intimate partner violence. Consistent with reference point behavior, losses when the game was expected to be close have no statistically-significant effect on family violence. Upset wins (ie. victories when the home team was expected to lose) also have no statistically-significant impact on the rate of violence, suggesting an important asymmetry in the reaction to unanticipated losses and gains.

--- http://jvlone.com/sportsdocs/footballGamesCrime2009.pdf 2024-11-07 crime exercise --- https://mikehadlow.blogspot.com/2012/05/configuration-complexity-clock.html The Configuration Complexity Clock Mike Hadlow 2012-05-07 2024-11-07 cs/computable cs/security design --- https://arxiv.org/abs/2411.03361 Age Normalized Testosterone Peaks at Series B for Male Startup Founders Jordan Moradian, Michael Dubrovsky, Megha Sama, Pavel Korecky, Sidarth Kulkarni, Yaniv Goder, Diedrik Vermeulen 2024-11-05 2024-11-07 [("doi","10.48550/arXiv.2411.03361")] psychology

In a study of 107 male Y Combinator founders, a surprising correlation between age-normalized testosterone and company stage was uncovered. Testosterone, a hormone associated with confidence, dominance, and drive, increased by 55.7% from pre-seed to seed funding, peaking at the Series B stage, where levels were 99.6% higher than pre-seed.

After Series B funding, testosterone was observed to drop by 42.2%, coinciding with a spike in cortisol levels.

This age-normalized biomarker analysis supports the dual-hormone hypothesis, illustrating that early startup success fosters feelings of dominance and confidence, while later-stage pressures and stresses erode these feelings.

An alternative interpretation of the data, which suggests the opportunity for a longitudinal study, is that male founders with higher testosterone are more likely to raise larger rounds of funding.

--- https://arxiv.org/abs/2410.21228 LoRA vs Full Fine-tuning: An Illusion of Equivalence Reece Shuttleworth, Jacob Andreas, Antonio Torralba, Pratyusha Sharma 2024-10-28 2024-11-07 [("doi","10.48550/arXiv.2410.21228")] ai/nn/sparsity reinforcement-learning/meta-learning/continual-learning

Fine-tuning is a crucial paradigm for adapting pre-trained large language models to downstream tasks. Recently, methods like Low-Rank Adaptation (LoRA) have been shown to match the performance of fully fine-tuned models on various tasks with an extreme reduction in the number of trainable parameters. Even in settings where both methods learn similarly accurate models, are their learned solutions really equivalent?

We study how different fine-tuning methods change pre-trained models by analyzing the model’s weight matrices through the lens of their spectral properties. We find that full fine-tuning and LoRA yield weight matrices whose singular value decompositions exhibit very different structure; moreover, the fine-tuned models themselves show distinct generalization behaviors when tested outside the adaptation task’s distribution.

More specifically, we first show that the weight matrices trained with LoRA have new, high-ranking singular vectors, which we call intruder dimensions. Intruder dimensions do not appear during full fine-tuning.

Second, we show that LoRA models with intruder dimensions, despite achieving similar performance to full fine-tuning on the target task, become worse models of the pre-training distribution and adapt less robustly to multiple tasks sequentially. Higher-rank, rank-stabilized LoRA models closely mirror full fine-tuning, even when performing on par with lower-rank LoRA models on the same tasks.

These results suggest that models updated with LoRA and full fine-tuning access different parts of parameter space, even when they perform equally on the fine-tuned distribution. We conclude by examining why intruder dimensions appear in LoRA fine-tuned models, why they are undesirable, and how their effects can be minimized.

--- https://en.wikipedia.org/wiki/Vettweiss-Froitzheim_Dice_Tower The Roman Vettweiss-Froitzheim Dice Tower 2024-11-08 fiction/text-game history --- https://www.youtube.com/watch?v=kSLJriaOumA A Style-Based Generator Architecture for Generative Adversarial Networks [video] 2024-06-17 ai/nn/gan/stylegan --- https://www.youtube.com/watch?v=IVJhOjSHOAc These Waifus Do Not Exist [video] N. K. 2019-02-23 2024-06-17 ai/nn/gan/stylegan/anime --- https://www.youtube.com/watch?v=JeVppkoloXs BRETT the Robot learns to put things together on his own 2024-06-17 reinforcement-learning/robot --- https://www.youtube.com/watch?v=XOxxPcy5Gr4 ProGAN: Progressive Growing of GANs for Improved Quality, Stability, and Variation [video] 2024-06-17 ai/nn/gan/stylegan/progan --- https://thomascountz.com/2023/07/30/low-poly-image-generation Low-Poly Image Generation using Evolutionary Algorithms in Ruby 2023-07-30 2024-11-08 reinforcement-learning/model-free --- https://publicdomainreview.org/collection/das-thier-in-der-decorativen-kunst/ Anton Seder’s The Animal in Decorative Art (1896) 2024-11-08 design history/public-domain-review --- /doc/cs/css/2024-11-08-gwern-gwernnetlinkicons-colorbysite.csv Gwern 2024-11-08 2024-11-08 cs/css design/typography/rubrication --- https://arxiv.org/abs/2409.13373 LLMs Still Can’t Plan; Can LRMs? A Preliminary Evaluation of OpenAI’s o1 on PlanBench Karthik Valmeekam, Kaya Stechly, Subbarao Kambhampati 2024-09-20 2024-11-09 [("doi","10.48550/arXiv.2409.13373")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue

The ability to plan a course of action that achieves a desired state of affairs has long been considered a core competence of intelligent agents and has been an integral part of AI research since its inception. With the advent of large language models (LLMs), there has been considerable interest in the question of whether or not they possess such planning abilities.

PlanBench, an extensible benchmark we developed in 2022, soon after the release of GPT-3, has remained an important tool for evaluating the planning abilities of LLMs. Despite the slew of new private and open source LLMs since GPT-3, progress on this benchmark has been surprisingly slow. [According to whom? And what forecasts? Has it really been slower than experts would have predicted in 2019...?] OpenAI claims that their recent o1-preview (Strawberry) model has been specifically constructed and trained to escape the normal limitations of autoregressive LLMs—making it a new kind of model: a Large Reasoning Model (LRM).

Using this development as a catalyst, this paper takes a comprehensive look at how well current LLMs and new LRMs do on PlanBench.

As we shall see, while o1’s performance is a quantum improvement on the benchmark, outpacing the competition, it is still far from saturating it. [The glass is always half-empty...]

This improvement also brings to the fore questions about accuracy, efficiency, and guarantees which must be considered before deploying such systems.

--- https://cran.r-project.org/web/packages/rgl/ CRAN: Package rgl 2024-11-09 cs/r --- https://www.youtube.com/watch?v=xKu2de0yCJI&t=917s Saul Bass Pitch Video for Bell System Logo Redesign § Stripes are modern 2024-11-09 design/typography economics/advertising --- https://support.apple.com/guide/digital-color-meter/welcome/mac Digital Color Meter User Guide for Mac 2024-11-09 design/typography --- https://en.wikipedia.org/wiki/File:Microsoft_logo_(2012).svg File:Microsoft logo (2012).svg 2024-11-09 design/typography --- https://en.wikipedia.org/wiki/Color_wheel Color wheel 2024-11-09 design/typography psychology/vision --- https://en.wikipedia.org/wiki/RGB_color_model RGB color model 2024-11-09 design/typography psychology/vision --- https://en.wikipedia.org/wiki/Dark_mode Dark mode 2024-11-09 cs/css --- https://www.youtube.com/watch?v=7TxiokHaTxc 蒲の穂 Ringing Volcano 2024-11-09 touhou --- https://arxiv.org/abs/2410.20911 Hacking Back the AI-Hacker: Prompt Injection as a Defense Against LLM-driven Cyberattacks Dario Pasquini, Evgenios M. Kornaropoulos, Giuseppe Ateniese 2024-10-28 2024-11-09 [("doi","10.48550/arXiv.2410.20911")] ai/nn/adversarial ai/nn/transformer/gpt/codex cs/security

Large language models (LLMs) are increasingly being harnessed to automate cyberattacks, making sophisticated exploits more accessible and scalable. In response, we propose a new defense strategy tailored to counter LLM-driven cyberattacks.

We introduce Mantis, a defensive framework that exploits LLMs’ susceptibility to adversarial inputs to undermine malicious operations. Upon detecting an automated cyberattack, Mantis plants carefully crafted inputs into system responses, leading the attacker’s LLM to disrupt their own operations (passive defense) or even compromise the attacker’s machine (active defense). By deploying purposefully vulnerable decoy services to attract the attacker and using dynamic prompt injections for the attacker’s LLM, Mantis can autonomously hack back the attacker.

In our experiments, Mantis consistently achieved over 95% effectiveness against automated LLM-driven attacks.

To foster further research and collaboration, Mantis is available as an open-source tool: GitHub.

--- https://en.wikipedia.org/wiki/Analogue_hole Analogue hole 2024-11-09 cs/security --- https://en.wikipedia.org/wiki/Hiveswap Hiveswap 2024-11-09 fiction/science-fiction --- https://en.wikipedia.org/wiki/Andrew_Hussie Andrew Hussie 2024-11-09 fiction/science-fiction --- http://conference.cali.org/2014/sessions/spaced-repetition-technology-legal-education Spaced Repetition Technology for Legal Education 2024-01-01 law psychology/spaced-repetition --- https://www.youtube.com/watch?v=dtClgl07lg8 Spaced Repetition Technology for Legal Education [video] 2024-06-17 law psychology/spaced-repetition --- https://frankchimero.com/blog/2018/everything-easy/ Frank Chimero 2018-01-01 2024-11-09 cs/css cs/js --- /doc/politics/2020-arkhipova.pdf ‘Our Schmuck’: Russian Folklore about American Elections Alexandra Arkhipova, Daria Radchenko, Anna Kirzyuk 2020-10-01 2024-11-09 [("doi","10.5406/jamerfolk.133.530.0452")] fiction/humor politics

Throughout the course of the 2016 US presidential election, hundreds of jokes dealing with the topic appeared on the English-speaking internet.

While Russian folklore could have simply exploited translations of existing American texts, representing Trump as incompetent, a statistical and semantic analysis of the corpus of jokes that appeared on Russian social media during the 2 weeks following the election shows that a different type of joke, one juxtaposing the election systems in the United States and Russia, was much more popular.

Yet 70% of the reposts of the jokes suggested an unrelated base meaning—the idea that Russia and the United States exist in a state of constant competition, trying to influence each other’s internal and international policies.

For the audience that opposes the Russian president and the loyalist mass media, “Trumplore” becomes a way to laugh not at the American president-elect, but at Russia’s own administration.

--- https://jackcook.com/2024/11/09/bigger-fish.html When Machine Learning Tells the Wrong Story 2024-11-09 2024-11-10 cs/security statistics/causality --- https://en.wikipedia.org/wiki/Impossible_color Impossible color 2024-01-01 psychology/vision --- https://en.wikipedia.org/wiki/Rich_black Rich black 2024-01-01 psychology/vision --- https://en.wikipedia.org/wiki/Impossible_color#Chimerical_colors Impossible color § Chimerical colors 2024-01-01 psychology/vision --- https://finbb.fi/en/board-members FINBB Board Members § Kati Kristiansson 2024-11-10 genetics --- https://www.youtube.com/watch?v=SmBxMDiOrvs Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning [video] 2024-06-17 reinforcement-learning/model-free reinforcement-learning/robot reinforcement-learning/scaling --- https://www.youtube.com/watch?v=5OmnD4zRrfw Sept Jours sans Elle (Septette for the Dead Princess) Raven’s Jig 2024-06-17 touhou --- https://www.youtube.com/watch?v=H6iMYgpnqBU Une Semaine chez les Écarlates (Embodiment of Scarlet Devil) Raven’s Jig 2024-06-17 touhou --- http://robinhanson.com/aigrow.pdf Economic Growth Given Machine Intelligence Hanson 2009 2024-01-01 ai/scaling/economics economics/automation --- https://www.mirror.co.uk/3am/celebrity-news/british-star-set-make-history-34053587 Magician spends £47,000 cloning dog so he can carry on double act in Las Vegas 2024-11-10 genetics/cloning/dog --- /doc/economics/2024-ge.pdf How Do You Say Your Name? Difficult-to-Pronounce Names and Labor Market Outcomes Qi Ge, Stephen Wu 2024-11-01 2024-11-10 [("doi","10.1257/pol.20220611")] economics psychology/cognitive-bias

We test for labor market discrimination based on an understudied characteristic: name fluency. Analysis of recent economics PhD job candidates indicates that name difficulty is negatively related to the probability of landing an academic or tenure-track position and research productivity of initial institutional placement.

Discrimination due to name fluency is also found using experimental data from prior audit studies. Within samples of African Americans (Bertrand & Mullainathan 2004) and ethnic immigrants (Oreopoulos 2011), job applicants with less fluent names experience lower callback rates, and name complexity explains roughly 10–50% of ethnic name penalties.

The results are primarily driven by candidates with weaker résumés, suggesting that cognitive biases may contribute to the penalty of having a difficult-to-pronounce name.

--- https://www.nytimes.com/2024/11/10/magazine/uyghur-china-escape.html A Uighur’s Escape From China Was Just the Beginning: He fled brutal repression—only to discover, as so many Uighur refugees have, that China’s power stretches far beyond its borders Nyrola Elimä, Ben Mauk 2024-11-10 2024-11-10 history/uighur --- https://appbot.co/blog/colors-of-an-app-icon-2022/ Colors of an app icon: 2022 edition Stuart Hall 2022-11-01 2024-11-11 cs/css design/typography --- https://stories.appbot.co/the-colors-of-an-app-icon-b5e8805958d7 The Colors Of An App Icon: A study into the color distribution Stuart Hall 2015-08-19 2024-11-11 cs/css design/typography --- https://desfontain.es/blog/friendly-intro-to-differential-privacy.html A friendly, non-technical introduction to differential privacy 2024-11-11 cs/security --- https://wiki.openttdcoop.org/Logic OpenTTD Logic 2024-01-01 cs/computable --- https://www.wired.com/2013/06/startup-skybox/ Inside a Startup’s Plan to Turn a Swarm of DIY Satellites Into an All-Seeing Eye 2024-01-01 economics technology --- https://www.wired.com/2014/01/tormail/ While investigating a hosting company known for sheltering child porn last year the FBI incidentally seized the entire e-mail database of a popular anonymous webmail service called TorMail. Now the FBI is tapping that vast trove of e-mail in unrelated investigations. 2024-01-01 cs/security darknet-market --- https://www.wired.com/2012/12/what-does-randomness-look-like/ What does randomness look like? 2024-01-01 psychology/cognitive-bias statistics/probability --- https://kotaku.com/jim-stephanie-sterling-youtube-interview-jimquisition-w-1848501185 Jim Sterling: From YouTube Games Edgelord To Wrestling Princess 2024-11-11 psychiatry/bipolar/energy --- https://www.lesswrong.com/posts/zgAws2AoFE3adigvy/what-ketamine-therapy-is-like What Ketamine Therapy Is Like 2024-11-11 psychedelic psychiatry/depression --- https://cpldcpu.wordpress.com/2024/05/02/machine-learning-mnist-inference-on-the-3-cent-microcontroller/ Neural Networks (MNIST inference) on the ‘3¢’ Microcontroller cpldcpu 2024-05-02 2024-11-11 ai/nn/sparsity/low-precision --- https://emschwartz.me/binary-vector-embeddings-are-so-cool/ Binary vector embeddings are so cool 2024-11-11 ai/nn/retrieval ai/nn/sparsity/low-precision --- https://chalkdustmagazine.com/features/the-doodle-theorem-and-beyond/ The doodle theorem, and beyond: Colin Wright juggles Euler, doodling and Millennium problems Colin Wright 2016-10-03 2024-11-11 cs/algorithm math --- https://x.com/deedydas/status/1856016096228454791 deedydas 2024-11-11 ai/nn/transformer/gpt/4/fiction reinforcement-learning/preference-learning/mode-collapse --- https://en.wikipedia.org/wiki/Lyndon_B._Johnson Lyndon B. Johnson 2024-11-11 psychiatry/bipolar/energy --- https://blog.moertel.com/posts/2024-08-23-sampling-with-sql.html Sampling with SQL Tom Moertel 2024-08-23 2024-11-11 statistics/probability --- https://asteriskmag.com/issues/08/the-death-and-life-of-prediction-markets-at-google The Death and Life of Prediction Markets at Google: Over the past two decades, Google has hosted two different internal platforms for predictions. Why did the first one fail—and will the other endure? Dan Schwarz 2024-11 2024-11-11 ai/nn/transformer/gpt/lamda statistics/prediction technology/google --- https://arxiv.org/abs/2406.04446#deepmind Can Language Models Use Forecasting Strategies? Sarah Pratt, Seth Blumberg, Pietro Kreitlon Carolino, Meredith Ringel Morris 2024-06-06 2024-11-11 [("doi","10.48550/arXiv.2406.04446")] ai/nn/transformer/gpt/calibration ai/nn/transformer/gpt/palm/2 statistics/prediction

Advances in deep learning systems have allowed large models to match or surpass human accuracy on a number of skills such as image classification, basic programming, and standardized test taking. As the performance of the most capable models begin to saturate on tasks where humans already achieve high accuracy, it becomes necessary to benchmark models on increasingly complex abilities.

One such task is forecasting the future outcome of events. In this work, we describe experiments using a novel dataset of real world events and associated human predictions, an evaluation metric to measure forecasting ability, and the accuracy of a number of different [PaLM 2] LLM-based forecasting designs on the provided dataset.

Additionally, we analyze the performance of the LLM forecasters against human predictions and find that models still struggle to make accurate predictions about the future. Our follow-up experiments indicate this is likely due to models’ tendency to guess that most events are unlikely to occur (which tends to be true for many prediction datasets, but does not reflect actual forecasting abilities).

We reflect on next steps for developing a systematic and reliable approach to studying LLM forecasting.

--- https://www.sciencedirect.com/science/article/pii/S0042698924000488 Two-photon vision: Seeing colors in infrared Katarzyna Komar 2024-07 2024-11-11 [("doi","10.1016/j.visres.2024.108404")] psychology/vision --- https://www.bloomberg.com/news/articles/2024-11-11/crypto-millionaire-fuels-push-to-transform-brain-research Crypto Millionaire Fuels Push to Transform Brain Research: James Fickel has dedicated $200 million he made betting on Ether to becoming one of the world’s biggest investors in those fields Ashlee Vance 2024-11-11 2024-11-11 cryonics longevity --- https://www.nature.com/articles/s41421-024-00726-4 Stem cell transplantation extends the reproductive life span of naturally aging cynomolgus monkeys 2024-11-11 genetics/gametogenesis --- https://pmc.ncbi.nlm.nih.gov/articles/PMC11437580/ Risk of major adverse cardiovascular events and all-cause mortality under treatment with GLP-1 RAs or the dual GIP/GLP-1 receptor agonist tirzepatide in overweight or obese adults without diabetes: a systematic review and meta-analysis 2024-11-11 longevity/glp/semaglutide longevity/glp/tirzepatide --- https://pmc.ncbi.nlm.nih.gov/articles/PMC11217813/ Glucagon-Like Peptide-1 Receptor Agonists and Major Adverse Cardiovascular Events in Patients With and Without Diabetes: A Meta-Analysis of Randomized-Controlled Trials 2024-11-11 longevity/glp/semaglutide --- https://pmc.ncbi.nlm.nih.gov/articles/PMC11246471/ GLP-1 receptor agonists’ impact on cardio-renal outcomes and mortality in T2D with acute kidney disease 2024-11-11 longevity/glp --- https://www.nytimes.com/2024/10/30/health/ozempic-wegovy-knee-pain-osteoarthritis.html Ozempic and Wegovy Ease Knee Osteoarthritis Pain in Large Study 2024-10-30 2024-11-12 longevity/glp/semaglutide --- https://isaak.net/sleepless/ Ozempic For Sleep 2024-11-12 zeo/short-sleeper --- https://waitbutwhy.com/2015/12/the-tail-end.html The Tail End waitbutwhy 2015-12-01 2024-11-12 design/visualization longevity --- https://www.theguardian.com/world/2024/nov/12/china-car-driven-into-sports-centre-state-media Dozens killed in China after car driven into sports centre Amy Hawkins 2024-11-12 2024-11-12 crime/terrorism

Man detained after incident on Monday night in Zhuhai, in which 35 people were killed and 43 injured. A driver killed 35 people and severely injured another 43 when he rammed his car into people exercising at a sports centre in the southern Chinese city of Zhuhai, police said on Tuesday.

Police had detained a 62-year-old man at the sports centre in Zhuhai after the ramming late on Monday, on the eve of an air show by the People’s Liberation Army that is hosted annually in the city.

Police identified the man only by his family name of Fan, as is usual with the Chinese authorities. Fan was discovered in the car with a knife, with wounds to his neck thought to be self-inflicted, according to the statement. Police said he was unconscious and receiving medical care. They added that their preliminary investigation suggested he had been dissatisfied with the split of financial assets in his divorce.

--- https://arxiv.org/abs/2411.07191 The Super Weight in Large Language Models Mengxia Yu, De Wang, Qi Shan, Colorado Reed, Alvin Wan 2024-11-11 2024-11-12 [("doi","10.48550/arXiv.2411.07191")] ai/nn/sparsity/pruning

Recent works have shown a surprising result: a small fraction of Large Language Model (LLM) parameter outliers are disproportionately important to the quality of the model. LLMs contain billions of parameters, so these small fractions, such as 0.01%, translate to hundreds of thousands of parameters.

In this work, we present an even more surprising finding: pruning as few as a single parameter can destroy an LLM’s ability to generate text—increasing perplexity by 3 orders of magnitude and reducing zero-shot accuracy to guessing. We propose a data-free method for identifying such parameters, termed super weights, using a single forward pass through the model.

We additionally find that these super weights induce correspondingly rare and large activation outliers, termed super activations. When preserved with high precision, super activations can improve simple round-to-nearest quantization to become competitive with state-of-the-art methods.

For weight quantization, we similarly find that by preserving the super weight and clipping other weight outliers, round-to-nearest quantization can scale to much larger block sizes than previously considered.

To facilitate further research into super weights, we provide an index of super weight coordinates for common, openly available LLMs.

--- https://arxiv.org/abs/2108.00084 The History of Speech Recognition to the Year 2030 Awni Hannun 2021-07-30 2024-11-12 [("doi","10.48550/arXiv.2108.00084")] ai/nn/transformer/gpt/whisper ai/scaling

[blog] The decade 2010–2020 saw remarkable improvements in automatic speech recognition. Many people now use speech recognition on a daily basis, for example to perform voice search queries, send text messages, and interact with voice assistants like Amazon Alexa and Siri by Apple. Before 2010 most people rarely used speech recognition.

Given the remarkable changes in the state of speech recognition over the previous decade, what can we expect over the coming decade? I attempt to forecast the state of speech recognition research and applications by the year 2030.

While the changes to general speech recognition accuracy will not be as dramatic as in the previous decade, I suggest we have an exciting decade of progress in speech technology ahead of us.

--- https://awni.github.io/future-speech/ The History of Speech Recognition to the Year 2030 Awni Hannun 2021-07-30 2024-11-12 ai/nn/transformer/gpt/whisper ai/scaling --- https://jillianhess.substack.com/p/re-noted-carl-jungs-midlife-crisis#%C2%A7the-red-book Re-Noted: Carl Jung’s Midlife-Crisis Notebooks Jillian Hess 2024-11-11 2024-11-13 design/typography/dropcap design/typography/rubrication --- https://www.mercatus.org/marginal-revolution-podcast/1970s-crime-wave The 1970s Crime Wave: Are we too complacent about current crime trends? Alex Tabarrok, Tyler Cowen 2024-11-12 2024-11-13 crime politics

In this final installment of their series on the 1970s, Alex and Tyler turn to the social upheaval and crime wave that marked the decade as one of America’s most turbulent. They explore how rising crime rates transformed cities, fueled a national sense of fear, and led to far-reaching policy shifts, including mass incarceration and changes in urban policing.

From the shocking statistics on homicide and stranger violence to the rise of serial killers and political bombings, they consider how the era’s unprecedented violence influenced American culture and policy.

The conversation concludes with a caution against complacency, as they reflect on how fragile today’s low-crime environment may be—and what lessons from the past should guide us in preserving it.

--- https://en.wikipedia.org/wiki/Foxhole_radio Foxhole radio 2024-11-13 technology --- https://github.com/zegl/extremely-linear Extremely Linear Git History zegl 2024-11-13 cs/cryptography math/humor --- http://unenumerated.blogspot.com/2005/12/bit-gold.html Bit gold Nick Szabo 2005-12 2024-11-13 bitcoin --- /doc/wikipedia/2015-miller.pdf Gadsby: Wikip█dia’s Lost Lipogram Tristan Miller 2015-01-01 2024-11-13 fiction/text-game wikipedia

Ernest Vincent Wright’s novel Gadsby is legendary among constrained writing enthusiasts. When it was first published in 1939, it was the single largest work of English literature not to contain the letter “e”.

It is hardly surprising, then, that the book’s many reviewers and commentators have sought to pay tribute to it with some modest lipogrammatry of their own.

This article tells the story of one such tribute and the years-long battle it sparked on the world’s largest and most popular online encyclopedia.

--- https://arxiv.org/abs/2402.17747 When Your AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback Leon Lang, Davis Foote, Stuart Russell, Anca Dragan, Erik Jenner, Scott Emmons 2024-02-27 2024-11-13 [("doi","10.48550/arXiv.2402.17747")] ai/nn/adversarial reinforcement-learning/preference-learning reinforcement-learning/safe

[Twitter; blog; empirically] Past analyses of reinforcement learning from human feedback (RLHF) assume that the human evaluators fully observe the environment. What happens when human feedback is based only on partial observations?

We formally define two failure cases: deceptive inflation and overjustification. Modeling the human as Boltzmann-rational with respect to a belief over trajectories, we prove conditions under which RLHF is guaranteed to result in policies that deceptively inflate their performance, overjustify their behavior to make an impression, or both.

Under the new assumption that the human’s partial observability is known and accounted for, we then analyze how much information the feedback process provides about the return function.

We show that sometimes, the human’s feedback determines the return function uniquely up to an additive constant, but in other realistic cases, there is irreducible ambiguity.

We propose exploratory research directions to help tackle these challenges, experimentally validate both the theoretical concerns and potential mitigations, and caution against blindly applying RLHF in partially observable settings.

--- https://www.lesswrong.com/posts/DS3TTpCEFKduC8zPy/paper-blogpost-when-your-ais-deceive-you-challenges-with When Your AIs Deceive You: Challenges with Partial Observability in RLHF 2024-11-13 ai/nn/adversarial reinforcement-learning/preference-learning reinforcement-learning/safe --- https://x.com/emmons_scott/status/1762886003046629586 emmons_scott 2024-11-13 ai/nn/adversarial reinforcement-learning/preference-learning reinforcement-learning/safe --- https://blog.transitapp.com/how-we-shrank-our-trip-planner-till-it-didnt-need-data-84984ca56663/#c293 How we shrank our trip planner till it didn’t need data. 2024-11-13 cs/algorithm/information/compression --- https://en.wikipedia.org/wiki/Sundial_(weapon) Sundial (weapon) 2024-11-13 radiance --- https://thebulletin.org/2021/11/the-untold-story-of-the-worlds-biggest-nuclear-bomb/ The untold story of the world’s biggest nuclear bomb: Tsar Bomba 2021-11 2024-11-13 radiance --- https://clinicaltrials.gov/study/NCT04184622 2024-11-13 longevity/glp/tirzepatide --- https://x.com/RichardMCNgo/status/1856843040427839804 Richard Ngo 2024-11-14 reinforcement-learning/openai --- https://economicsfromthetopdown.com/2022/04/08/the-dunning-kruger-effect-is-autocorrelation/ The Dunning-Kruger Effect is Autocorrelation 2022-04-08 2024-11-14 statistics/bias statistics/probability --- https://www.reddit.com/r/tea/comments/17yc5mq/puerh_beginner_packages_2023_eight_year_on_3000/ 2024-11-14 tea --- https://x.com/giffmana/status/1856993726591099066 Lucas Beyer 2024-11-14 ai/nn/adversarial ai/nn/transformer/gpt/4 --- https://dynomight.net/chess/ Something weird is happening with LLMs and chess Dynomight 2024-11-14 ai/nn/sparsity ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/chess reinforcement-learning/preference-learning --- https://arxiv.org/abs/2406.04823 BERTs are Generative In-Context Learners David Samuel 2024-06-07 2024-11-15 [("doi","10.48550/arXiv.2406.04823")] ai/nn/transformer reinforcement-learning/meta-learning

While in-context learning is commonly associated with causal language models, such as GPT, we demonstrate that this capability also ‘emerges’ in masked language models [shown previously in T5].

Through an embarrassingly simple inference technique, we enable an existing masked model, DeBERTa, to perform generative tasks without additional training or architectural changes.

Our evaluation reveals that the masked and causal language models behave very differently, as they clearly outperform each other on different categories of tasks.

These complementary strengths suggest that the field’s focus on causal models for in-context learning may be limiting—both architectures can develop these capabilities, but with distinct advantages; pointing toward promising hybrid approaches that combine the strengths of both objectives.

--- https://www.nature.com/articles/s41598-024-79546-1 Mummy of a juvenile sabre-toothed cat Homotherium latidens from the Upper Pleistocene of Siberia 2024-11-15 cat/biology --- https://en.wikipedia.org/wiki/What_If%3F_(book) What If? (book) Randall Munroe 2024-11-15 science/fermi-problem --- https://x.com/andrew_n_carr/status/1857262016106520655 Andrew N. Carr 2024-11-15 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/palm/2 reinforcement-learning/preference-learning/mode-collapse --- https://csszengarden.com/176/ CSS Zen Garden #176 § Kelmscott Bronwen Hodgkinson 2024-11-16 cs/css design/typography/dropcap --- https://csszengarden.com/204/ CSS Zen Garden #204 § Withering Beauty William Duffy 2024-11-16 design/typography/rubrication --- https://www.nature.com/articles/s41598-024-76900-1 AI-generated poetry is indistinguishable from human-written poetry and is rated more favorably Brian Porter, Edouard Machery 2024-11-14 2024-11-16 ai/nn/transformer/gpt/3/poetry reinforcement-learning/preference-learning

[partial survey replication]

--- https://arxiv.org/abs/2411.04732 Convolutional Differentiable Logic Gate Networks Felix Petersen, Hilde Kuehne, Christian Borgelt, Julian Welzel, Stefano Ermon 2024-11-07 2024-11-16 [("doi","10.48550/arXiv.2411.04732")] ai/nn/cnn ai/nn/sparsity

With the increasing inference cost of machine learning models, there is a growing interest in models with fast and efficient inference. Recently, an approach for learning logic gate networks directly via a differentiable relaxation was proposed [Petersen et al 2022].

Logic gate networks are faster than conventional neural network approaches because their inference only requires logic gate operators such as NAND, OR, and XOR, which are the underlying building blocks of current hardware and can be efficiently executed.

We build on this idea, extending it by deep logic gate tree convolutions, logical OR pooling, and residual initializations. This allows scaling logic gate networks up by over one order of magnitude and using the paradigm of convolution.

On CIFAR-10, we achieve an accuracy of 86.29% using only 61 million logic gates, which improves over the SOTA while being 29× smaller.

--- https://arxiv.org/abs/1611.09940 Neural Combinatorial Optimization with Reinforcement Learning Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, Samy Bengio 2016-11-29 2024-11-16 [("doi","10.48550/arXiv.1611.09940")] ai/nn/rnn cs/algorithm reinforcement-learning/model-free

This paper presents a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning. We focus on the traveling salesman problem (TSP) and train a recurrent network that, given a set of city coordinates, predicts a distribution over different city permutations.

Using negative tour length as the reward signal, we optimize the parameters of the recurrent network using a policy gradient method. We compare learning the network parameters on a set of training graphs against learning them on individual test graphs.

Despite the computational expense, without much engineering and heuristic designing, Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes.

Applied to the Knapsack, another NP-hard problem, the same method obtains optimal solutions for instances with up to 200 items.

--- https://woodfromeden.substack.com/p/the-anti-autism-manifesto The Anti-Autism Manifesto [schizoid] 2024-11-16 psychiatry/autism/schizoid --- https://missionlocal.org/2024/09/blake-benthall-former-silk-road-operator-revisits-the-scene-of-the-crime/ Former Silk Road operator Blake Benthall revisits the scene of the crime 2024-09 2024-11-16 darknet-market/silk-road/2 --- https://www.nytimes.com/2024/07/24/business/blake-benthall-silk-road-crypto.html From Online Drug Lord to Crypto Entrepreneur, Blake Benthall Is Back in Business 2024-07-24 2024-11-16 darknet-market/silk-road/2 --- https://xkcd.com/386/ Duty Calls Randall Munroe 2024-01-01 fiction/humor psychology/writing --- https://arxiv.org/abs/2310.10679 Large language models can replicate cross-cultural differences in personality Paweł Niszczota, Mateusz Janczak, Michał Misiak 2023-10-12 2024-11-16 [("doi","10.48550/arXiv.2310.10679")] ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction psychology/personality reinforcement-learning/preference-learning/mode-collapse

We use a large-scale experiment (n = 8000) to determine whether GPT-4 can replicate cross-cultural differences in the Big Five, measured using the Ten-Item Personality Inventory. We used the US and South Korea as the cultural pair, given that prior research suggests substantial personality differences between people from these two countries.

We manipulated the target of the simulation (US vs. Korean), the language of the inventory (English vs. Korean), and the language model (GPT-4 vs. GPT-3).

Our results show that GPT-4 replicated the cross-cultural differences for each factor. However, mean ratings had an upward bias and exhibited lower variation than in the human samples, as well as lower structural validity.

We provide preliminary evidence that LLMs can aid cross-cultural researchers and practitioners.

--- https://www.espn.com/espn/feature/story/_/page/enterprise-gagno161110/how-robert-gagno-became-one-best-pinball-players-world How Robert Gagno became one of the best pinball players in the world 2024-11-16 psychiatry/autism psychology/neuroscience/memory/savant --- https://x.com/repligate/status/1614435643475501056 Janus 2024-11-16 ai/nn/transformer/gpt/claude reinforcement-learning/preference-learning/mode-collapse --- https://x.com/anthrupad/status/1807062545607356752 anthrupad 2024-11-16 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/dall-e/3 --- https://arxiv.org/abs/2411.03538v1 Long Context RAG Performance of Large Language Models Quinn Leng, Jacob Portes, Sam Havens, Matei Zaharia, Michael Carbin 2024-11-05 2024-11-16 [("doi","10.48550/arXiv.2411.03538")] ai/nn/retrieval ai/nn/transformer/attention ai/scaling/economics

Retrieval Augmented Generation (RAG) has emerged as a crucial technique for enhancing the accuracy of Large Language Models (LLMs) by incorporating external information. With the advent of LLMs that support increasingly longer context lengths, there is a growing interest in understanding how these models perform in RAG scenarios. Can these new long context models improve RAG performance?

This paper presents a comprehensive study of the impact of increased context length on RAG performance across 20 popular open source and commercial LLMs. We ran RAG workflows while varying the total context length from 2,000 to 128,000 tokens (and 2 million tokens when possible) on 3 domain-specific datasets, and report key insights on the benefits and limitations of long context in RAG applications.

Our findings reveal that while retrieving more documents can improve performance, only a handful of the most recent state-of-the-art LLMs can maintain consistent accuracy at long context above 64k tokens. We also identify distinct failure modes in long context scenarios, suggesting areas for future research.

--- https://www.nejm.org/doi/full/10.1056/NEJMoa2412309 2024-11-16 [("doi","10.1056/NEJMoa2412309")] genetics/editing --- https://osf.io/preprints/psyarxiv/hm2tu 2024-11-16 statistics/bias statistics/causality --- https://arxiv.org/abs/2410.01707 Interpretable Contrastive Monte Carlo Tree Search Reasoning Zitian Gao, Boye Niu, Xuzheng He, Haotian Xu, Hongzhang Liu, Aiwei Liu, Xuming Hu, Lijie Wen 2024-10-02 2024-11-16 [("doi","10.48550/arXiv.2410.01707")] ai/nn/transformer/gpt reinforcement-learning/model

[Twitter] We propose SC-MCTS✱: a novel Monte Carlo Tree Search (MCTS) reasoning algorithm for Large Language Models (LLMs), improves both reasoning accuracy and speed. Our motivation comes from: (1) Previous MCTS LLM reasoning works often overlooked its biggest drawback—slower speed compared to CoT; (2) Previous research mainly used MCTS as a tool for LLM reasoning on various tasks with limited quantitative analysis or ablation studies of its components from a reasoning interpretability perspective; (3) The reward model is the most crucial component in MCTS; however, previous work has rarely conducted in-depth study or improvement of MCTS’s reward models.

Thus, we conducted extensive ablation studies and quantitative analysis on components of MCTS, revealing the impact of each component on the MCTS reasoning performance of LLMs. Building on this, (1) we designed a highly interpretable reward model based on the principle of contrastive decoding and (2) achieved an average speed improvement of 51.9% per node using speculative decoding. Additionally, (3) we improved UCT node selection strategy and backpropagation used in previous works, resulting in performance improvement.

We outperformed o1-mini by an average of 17.4% on the Blockworld multi-step reasoning dataset using Llama-3.1-70B with SC-MCTS✱.

Our code is available at GitHub.

--- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4708466 Prompting Diverse Ideas: Increasing AI Idea Variance 2024-11-16 ai/nn/sampling ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue reinforcement-learning/exploration --- https://onlinelibrary.wiley.com/doi/full/10.1111/brv.13166 Testosterone mediates life-history trade-offs in female mammals Bernard J. Crespi, Aiden Bushell, Natalie Dinsdale 2024-11-14 2024-11-16 [("doi","10.1111/brv.13166")] biology psychology/personality --- https://x.com/maxsloef/status/1857648938754650175 maxsloef 2024-11-16 ai/nn/transformer/gpt/claude cs/js --- https://x.com/zetalyrae/status/1857903165343150469 zetalyrae 2024-11-16 ai/nn/transformer/gpt/claude --- https://x.com/patrickc/status/1857924043086991415 Patrick Collison 2024-11-16 psychiatry/meditation --- https://x.com/nabeelqu/status/1857922708450980067 nabeelqu 2024-11-16 psychiatry/meditation psychology/inner-voice --- https://x.com/nickcammarata/status/1857923243497369926 Nick Cammarata 2024-11-16 psychiatry/meditation psychology/inner-voice --- https://arxiv.org/abs/2411.02959 HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen 2024-11-05 2024-11-16 [("doi","10.48550/arXiv.2411.02959")] ai/dataset ai/nn/retrieval

Retrieval-Augmented Generation (RAG) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs. The Web is a major source of external knowledge used in RAG systems, and many commercial systems such as ChatGPT and Perplexity have used Web search engines as their major retrieval systems. Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources.

Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML, such as headings and table structures, is lost during this plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML.

However, using HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. To address this issue, we propose HTML cleaning, compression, and pruning strategies to shorten the HTML while minimizing the loss of information. Specifically, we design a two-step block-tree-based pruning method that prunes useless HTML blocks and keeps only the relevant part of the HTML.

Experiments on 6 QA datasets confirm the superiority of using HTML in RAG systems.

--- https://arxiv.org/abs/2410.12491 Insights from the Inverse: Reconstructing LLM Training Goals Through Inverse RL Jared Joselowitz, Arjun Jagota, Satyapriya Krishna, Sonali Parbhoo 2024-10-16 2024-11-17 [("doi","10.48550/arXiv.2410.12491")] reinforcement-learning/preference-learning

Large language models (LLMs) trained with Reinforcement Learning from Human Feedback (RLHF) have demonstrated remarkable capabilities, but their underlying reward functions and decision-making processes remain opaque.

This paper introduces a novel approach to interpreting LLMs by applying inverse reinforcement learning (IRL) to recover their implicit reward functions. We conduct experiments on toxicity-aligned LLMs of varying sizes, extracting reward models that achieve up to 80.40% accuracy in predicting human preferences.

Our analysis reveals key insights into the non-identifiability of reward functions, the relationship between model size and interpretability, and potential pitfalls in the RLHF process. We demonstrate that IRL-derived reward models can be used to fine-tune new LLMs, resulting in comparable or improved performance on toxicity benchmarks.

This work provides a new lens for understanding and improving LLM alignment, with implications for the responsible development and deployment of these powerful systems.

--- https://www.astralcodexten.com/p/are-woo-non-responders-defective Are Woo Non-Responders Defective? Scott Alexander 2023-05-30 2024-11-17 psychedelic psychiatry/meditation --- https://arxiv.org/abs/2411.04118 Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? Daniel P. Jeong, Saurabh Garg, Zachary C. Lipton, Michael Oberst 2024-11-06 2024-11-17 [("doi","10.48550/arXiv.2411.04118")] ai/nn/transformer/gpt ai/scaling biology

Several recent works seek to develop foundation models specifically for medical applications, adapting general-purpose large language models (LLMs) and vision-language models (VLMs) via continued pretraining on publicly available biomedical corpora. These works typically claim that such domain-adaptive pretraining (DAPT) improves performance on downstream medical tasks, such as answering medical licensing exam questions.

In this paper, we compare 7 public “medical” LLMs and two VLMs against their corresponding base models, arriving at a different conclusion: all medical VLMs and nearly all medical LLMs fail to consistently improve over their base models in the zero-/few-shot prompting regime for medical question-answering (QA) tasks. For instance, across the tasks and model pairs we consider in the 3-shot setting, medical LLMs only outperform their base models in 12.1% of cases, reach a (statistical) tie in 49.8% of cases, and are worse than their base models in the remaining 38.2% of cases.

Our conclusions are based on (1) comparing each medical model head-to-head, directly against the corresponding base model; (2) optimizing the prompts for each model separately; and (3) accounting for statistical uncertainty in comparisons. While these basic practices are not consistently adopted in the literature, our ablations show that they substantially impact conclusions.

Our findings suggest that state-of-the-art general-domain models may already exhibit strong medical knowledge and reasoning capabilities, and offer recommendations to strengthen the conclusions of future studies.

--- /doc/ai/anime/danbooru/2024-xu-2.pdf Generating Diverse and Reliable Features for Few-Shot Learning Jingyi Xu 2024-08-01 2024-11-17 ai/anime/danbooru ai/nn/gan/stylegan/anime reinforcement-learning/meta-learning

Machine learning has achieved remarkable success in data-intensive applications, yet it still encounters challenges when data is insufficient. To tackle this issue, few-shot learning (FSL) has been proposed. FSL aims to develop methods that can rapidly generalize to new tasks with minimal labeled samples. It is particularly beneficial in scenarios where acquiring labeled data is difficult or impractical.

In this thesis, we explore the use of synthetic data generation techniques to enhance FSL. To achieve this, we employ generative models to capture the feature distribution within a continuous latent space, which allows the sampling of new features to facilitate learning in data-scarce scenarios.

We demonstrate the capability of this framework to synthesize diverse and reliable features that can enhance FSL across various tasks, including few-shot image classification, fine-grained few-shot classification, few-shot object detection, and class-agnostic object counting. Specifically, for few-shot image classification, we propose a method to generate reliable features via sample selection.

For fine-grained few-shot classification, we propose sampling diverse features representing the distribution of intra-class variance. For few-shot object detection, we focus on how to generate features with increased crop-related diversity.

For class-agnostic object counting, we present a method for generating reliable features that can serve as object templates.

Finally, to conclude this thesis, we investigate a more general question: under what conditions do generative models produce high-quality samples? To address this, we introduce a method for quality assessment based on latent space analysis, ensuring a more reliable use of generated samples beyond FSL scenarios.

--- /doc/crime/1997-tabarrok.pdf A simple model of crime waves, riots, and revolutions Alex Tabarrok 1997-09-01 2024-11-17 [("doi","10.1007/BF02298409")] crime politics sociology/preference-falsification

Standard economic models of criminal behavior analyze the criminal’s decision in a partial equilibrium context.

The standard model does not recognize that the probability of being punished is a function of the total amount of crime that occurs. As the total amount of crime increases, police resources become strained, courts become congested, and prisons become overcrowded. As a result, proportionately fewer criminals are apprehended, convicted, and imprisoned.

The feedback effects from one criminal’s decision to participate in crime to another criminal’s decision can be highly important. In one parameterization of the model developed here, the individual commits twice as many crimes for a given parameter shift than is implied by the standard model.

The model also sheds light on other areas where criminal actions are interdependent such as riots, crime waves, and revolutions.

--- https://arxiv.org/abs/2311.07237 In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search Huihan Li, Yuting Ning, Zeyi Liao, Siyuan Wang, Xiang Lorraine Li, Ximing Lu, Wenting Zhao, Faeze Brahman, Yejin Choi, Xiang Ren 2023-11-13 2024-11-17 [("doi","10.48550/arXiv.2311.07237")] ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/scaling

To effectively use large language models (LLMs) for real-world queries, it is imperative that they generalize to the long-tail distribution, i.e. rare examples where models exhibit low confidence. In this work, we take the first step towards evaluating LLMs in the long-tail distribution of inferential knowledge. We exemplify long-tail evaluation on the Natural Language Inference task.

First, we introduce Logic-Induced-Knowledge-Search (LINK), a systematic long-tail data generation framework, to obtain factually-correct yet long-tail inferential statements. LINK uses variable-wise prompting grounded on symbolic rules to seek low-confidence statements while ensuring factual correctness. We then use LINK to curate Logic-Induced-Long-Tail (LINT), a large-scale long-tail inferential knowledge dataset that contains 108K statements spanning 4 domains.

We evaluate popular LLMs on LINT; we find that state-of-the-art LLMs show performance drop (21% relative drop for GPT-4) on long-tail data as compared to on head distribution data, and smaller models show even more generalization weakness.

These results further underscore the necessity of long-tail evaluation in developing generalizable LLMs.

--- https://github.com/ethan-w-roland/AUNN AUNN: Simple implementation of Gwern’s AUNN proposal Ethan W. Roland 2024-11-15 2024-11-17 ai/nn/dynamic-evaluation ai/nn/fully-connected --- https://en.wikipedia.org/wiki/HitchBOT HitchBOT 2024-11-17 crime reinforcement-learning/robot sociology --- https://www.lesswrong.com/posts/Nq2BtFidsnhfLuNAx/announcing-turntrout-com-my-new-digital-home#vJAsuKGLMmuWCb45h Announcing turntrout.com, my new digital home Turntrout 2024-11-17 2024-11-17 cs/css design/typography/dropcap --- https://turntrout.com/design The Design of Turntrout.com Turntrout 2024-10-31 2024-11-17 cs/css design/typography/dropcap --- https://x.com/sashachapin/status/1858050861269922248 Sasha Chapin 2024-11-17 psychiatry/meditation --- https://www.thetimes.com/article/english-nurses-devotion-7svbs9bpxf3 English nurse’s devotion [gas gangrene self-experiment] 1915 2024-11-18 nootropic/quantified-self --- https://www.reddit.com/r/LocalLLaMA/comments/1gsyp7q/humaneval_benchmark_of_exl2_quants_of_popular/ 2024-11-18 ai/nn/sparsity/low-precision --- https://arxiv.org/abs/2212.11972#google Scalable Adaptive Computation for Iterative Generation Allan Jabri, David Fleet, Ting Chen 2022-12-22 2024-11-18 [("doi","10.48550/arXiv.2212.11972")] ai/nn/diffusion ai/nn/transformer/attention

[cf. Perceiver IO] Natural data is redundant yet predominant architectures tile computation uniformly across their input and output space. We propose the Recurrent Interface Networks (RINs), an attention-based architecture that decouples its core computation from the dimensionality of the data, enabling adaptive computation for more scalable generation of high-dimensional data.

RINs focus the bulk of computation (ie. global self-attention) on a set of latent tokens, using cross-attention to read and write (ie. route) information between latent and data tokens.

Stacking RIN blocks allows bottom-up (data to latent) and top-down (latent to data) feedback, leading to deeper and more expressive routing. While this routing introduces challenges, this is less problematic in recurrent computation settings where the task (and routing problem) changes gradually, such as iterative generation with diffusion models.

We show how to leverage recurrence by conditioning the latent tokens at each forward pass of the reverse diffusion process with those from prior computation, ie. latent self-conditioning.

RINs yield state-of-the-art pixel diffusion models for image and video generation, scaling to 1024×1024 images without cascades or guidance, while being domain-agnostic and up to 10× more efficient than 2D and 3D U-Nets.

--- https://www.multicians.org/mepap.html Multics Emacs History/Design/Implementation Bernard Greenberg 1979 2024-11-18 cs/lisp/emacs --- https://mises.org/mises-daily/relentless-misery-16-gallons The Relentless Misery of 1.6 Gallons 2024-11-18 economics --- https://thewalrus.ca/how-conjoined-twins-are-making-scientists-question-the-concept-of-self/ How Conjoined Twins Are Making Scientists Question the Concept of Self 2024-11-18 philosophy/mind psychology/neuroscience --- https://www.justice.gov/opa/pr/operator-helix-darknet-cryptocurrency-mixer-sentenced-money-laundering-conspiracy-and Operator of Helix Darknet Cryptocurrency ‘Mixer’ Sentenced in Money Laundering Conspiracy and Ordered to Forfeit Over $400M in Assets 2024-11-18 darknet-market --- https://x.com/distributionat/status/1858398733140148571 distributionat 2024-11-18 ai/scaling/economics --- https://www.nature.com/articles/s41586-024-08165-7 Adipose tissue retains an epigenetic memory of obesity after weight loss 2024-11-18 exercise longevity/epigenetics --- https://en.wikipedia.org/wiki/Charley_Harper Charley Harper 2024-11-19 design --- https://x.com/moultano/status/1858613954484072569 moultano 2024-11-19 design --- https://asteriskmag.com/issues/08/looking-back-at-the-future-of-humanity-institute Looking Back at the Future of Humanity Institute 2024-11-19 existential-risk --- https://arxiv.org/abs/2410.24190 Hidden Persuaders: LLMs’ Political Leaning and Their Influence on Voters Yujin Potter, Shiyang Lai, Junsol Kim, James Evans, Dawn Song 2024-10-31 2024-11-19 [("doi","10.48550/arXiv.2410.24190")] ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude politics reinforcement-learning/preference-learning/mode-collapse

How could LLMs influence our democracy? We investigate LLMs’ political leanings and the potential influence of LLMs on voters by conducting multiple experiments in a U.S. presidential election context.

Through a voting simulation, we first demonstrate 18 open- and closed-weight LLMs’ political preference for a Democratic nominee over a Republican nominee. We show how this leaning towards the Democratic nominee becomes more pronounced in instruction-tuned models compared to their base versions by analyzing their responses to candidate-policy related questions.

We further explore the potential impact of LLMs on voter choice by conducting an experiment with 935 U.S. registered voters. During the experiments, participants interacted with LLMs (Claude-3, Llama-3, and GPT-4) over 5 exchanges.

The experiment results show a shift in voter choices towards the Democratic nominee following LLM interaction, widening the voting margin from 0.7% to 4.6%, even though LLMs were not asked to persuade users to support the Democratic nominee during the discourse. This effect is larger than many previous studies on the persuasiveness of political campaigns, which have shown minimal effects in presidential elections. Many users also expressed a desire for further political interaction with LLMs.

Which aspects of LLM interactions drove these shifts in voter choice requires further study.

Lastly, we explore how a safety method can make LLMs more politically neutral, while raising the question of whether such neutrality is truly the path forward.

--- https://www.theguardian.com/artanddesign/gallery/2024/nov/16/what-a-carve-up-playful-intricate-japanese-leaf-art-in-pictures What a carve up! Playful, intricate Japanese leaf art—in pictures 2024-11-16 2024-11-19 japan/art --- https://cerebras.ai/blog/llama-405b-inference Llama-3.1-405B now runs at 969 tokens/s on Cerebras Inference 2024-11-19 ai/nn/transformer/gpt ai/scaling/hardware --- https://www.jasss.org/20/4/12.html New Winning Strategies for the Iterated Prisoner’s Dilemma 2024-11-19 reinforcement-learning/multi-agent statistics/decision --- https://www.youtube.com/watch?v=2a4XrG_u3RE SwarmCloak: Landing of a Swarm of Nano-Quadrotors on Human Arms [video] 2024-06-17 reinforcement-learning/robot technology --- https://x.com/gdb/status/1856441156281753908 Greg Brockman 2024-11-19 reinforcement-learning/openai --- https://inferencemagazine.substack.com/p/getting-ai-datacentres-in-the-uk Getting AI datacenters in the UK: Why the UK needs to create Special Compute Zones; and how to do it Jack Wiseman, Duncan McClements, Theo Horsley 2024-11-14 2024-11-19 ai/scaling/hardware economics/automation politics --- https://www.fashionablylatetakes.com/p/america-was-supposed-to-be-art-deco America was supposed to be Art Deco 2024-11-19 design --- /doc/math/humor/2011-yurchak.pdf A Parasite from Outer Space: How Sergei Kurekhin Proved That Lenin Was a Mushroom Alexei Yurchak 2011-06-01 2024-11-19 [("doi","10.5612/slavicreview.70.2.0307")] math/humor philosophy/epistemology politics psychedelic

In 1991, Leningrad television broadcast a program that has since become infamous. The program’s guest, Sergei Kurekhin, claiming to be a political figure and scientist, conducted an elaborate hoax that he presented as a serious historical exploration into the origins of the Bolshevik revolution.

Using visual, textual, and scientific evidence, Kurekhin argued that the revolution was led by people who had been consuming hallucinogenic mushrooms. His claim was that their personalities were being replaced by mushroom personalities, and their leader, Vladimir Lenin, was simply a mushroom.

As a result, Kurekhin’s assertions shed new light on many enigmas of Soviet history. Millions of viewers were at a loss: were they witnessing a serious program, a daring prank, or a case of unprecedented lunacy?

In this article, Alexei Yurchak analyzes that remarkable comedic performance, its social and political effects then and now, and what it may contribute to our understanding of the relationship between politics and irony.

…Kurekhin’s wife, Anastasia, later remembered that although he thought about faking perestroika media for a while, there was an immediate model on which he based his television appearance. A few months earlier he had watched a serious television program according to which newly discovered facts about the death of the poet Sergei Esenin suggested that he was killed, rather than committed suicide as was commonly believed. In the program this claim was “based on completely absurd facts. Showing photographs of Esenin’s funeral [the program’s author] provided such comments: ‘Notice where this person is looking; and see, another person is looking in the opposite direction. Which proves that Esenin was killed.’” Having watched this program Kurekhin said: “In this way anything at all can be proven.”

--- https://en.wikipedia.org/wiki/Lenin_was_a_mushroom Lenin was a mushroom 2024-11-19 math/humor philosophy/epistemology politics psychedelic --- https://en.wikipedia.org/wiki/John_Drewe#Career_as_a_forger John Drewe § Career as a forger 2024-11-19 crime philosophy/epistemology --- https://arxiv.org/abs/2411.10109 Generative Agent Simulations of 1,000 People Joon Sung Park, Carolyn Q. Zou, Aaron Shaw, Benjamin Mako Hill, Carrie Cai, Meredith Ringel Morris, Robb Willer, Percy Liang, Michael S. Bernstein 2024-11-15 2024-11-19 [("doi","10.48550/arXiv.2411.10109")] ai/nn/transformer/gpt/4/nonfiction psychology/personality

The promise of human behavioral simulation—general-purpose computational agents that replicate human behavior across domains—could enable broad applications in policymaking and social science.

We present a novel agent architecture that simulates the attitudes and behaviors of 1,052 real individuals—applying large language models to qualitative interviews about their lives, then measuring how well these agents replicate the attitudes and behaviors of the individuals that they represent.

The generative agents replicate participants’ responses on the General Social Survey 85% as accurately as participants replicate their own answers two weeks later, and perform comparably in predicting personality traits and outcomes in experimental replications.

Our architecture reduces accuracy biases across racial and ideological groups compared to agents given demographic descriptions. This work provides a foundation for new tools that can help investigate individual and collective behavior.

--- https://x.com/bryan_johnson/status/1858915407190769998 bryan_johnson 2024-11-19 exercise longevity/epigenetics longevity/metformin longevity/senolytic --- https://www.astralcodexten.com/p/how-did-you-do-on-the-ai-art-turing How Did You Do On The AI Art Turing Test? 2024-11-20 ai/nn/diffusion/midjourney psychology/cognitive-bias reinforcement-learning/preference-learning --- https://arxiv.org/abs/2409.12917#deepmind Training Language Models to Self-Correct via Reinforcement Learning Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D. Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M. Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal Behbahani, Aleksandra Faust 2024-09-19 2024-11-20 [("doi","10.48550/arXiv.2409.12917")] ai/nn/transformer/gpt/inner-monologue ai/nn/transformer/gpt/palm/2 reinforcement-learning/model-free

Self-correction is a highly desirable capability of large language models (LLMs), yet it has consistently been found to be largely ineffective in modern LLMs. Current methods for training self-correction typically depend on either multiple models, a more advanced model, or additional forms of supervision.

To address these shortcomings, we develop a multi-turn online reinforcement learning (RL) approach, SCoRe, that improves an LLM’s self-correction ability using entirely self-generated data. To build SCoRe, we first show that variants of supervised fine-tuning (SFT) on offline model-generated correction traces are often insufficient for instilling self-correction behavior. In particular, we observe that training via SFT falls prey to either a distribution mismatch between mistakes made by the data-collection policy and the model’s own responses, or to behavior collapse, where learning implicitly prefers only a certain mode of correction behavior that is often not effective at self-correction on test problems.

SCoRe addresses these challenges by training under the model’s own distribution of self-generated correction traces and using appropriate regularization to steer the learning process into learning a self-correction behavior that is effective at test time as opposed to fitting high-reward responses for a given prompt. This regularization process includes an initial phase of multi-turn RL on a base model to generate a policy initialization that is less susceptible to collapse, followed by using a reward bonus to amplify self-correction.

With Gemini 1.0 Pro and 1.5-Flash models, we find that SCoRe achieves state-of-the-art self-correction performance, improving the base models’ self-correction by 15.6% and 9.1% respectively on MATH and HumanEval.

--- https://cyborgism.wiki/hypha/janus Janus 2024-11-20 ai/nn/adversarial ai/nn/transformer/gpt/3/nonfiction ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude reinforcement-learning/preference-learning/mode-collapse reinforcement-learning/safe --- https://rizar.github.io/ Dzmitry Bahdanau 2024-11-20 ai/nn/transformer/attention --- https://arxiv.org/abs/2409.11582 Tiling with 3 Polygons is Undecidable Erik D. Demaine, Stefan Langerman 2024-09-17 2024-11-20 [("doi","10.48550/arXiv.2409.11582")] cs/computable

We prove that the following problem is co-RE-complete and thus undecidable:

given 3 simple polygons, is there a tiling of the plane where every tile is an isometry of one of the 3 polygons (either allowing or forbidding reflections)?

This result improves on the best previous construction which requires 5 polygons.

--- https://emschwartz.me/understanding-the-bm25-full-text-search-algorithm/ Understanding the BM25 full text search algorithm 2024-11-20 ai/nn/retrieval --- https://scottlocklin.wordpress.com/2024/11/19/lush-my-favorite-small-programming-language/ Lush: my favorite small programming language 2024-11-19 2024-11-20 cs/lisp --- https://arxiv.org/abs/2409.00287#cerebras Benchmarking the Performance of Large Language Models on the Cerebras Wafer Scale Engine Zuoning Zhang, Dhruv Parikh, Youning Zhang, Viktor Prasanna 2024-08-30 2024-11-20 [("doi","10.48550/arXiv.2409.00287")] ai/nn/transformer/gpt/3 ai/scaling/hardware

Transformer based Large Language Models (LLMs) have recently reached state-of-the-art performance in Natural Language Processing (NLP) and Computer Vision (CV) domains. LLMs use the Multi-Headed Self-Attention (MHSA) mechanism to capture long-range global attention relationships among input words or image patches, drastically improving their performance over prior deep learning approaches.

In this paper, we evaluate the performance of LLMs on the Cerebras Wafer Scale Engine (WSE). Cerebras WSE is a high-performance computing system with 2.6 trillion transistors, 850,000 cores and 40 GB on-chip memory. Cerebras WSE’s Sparse Linear Algebra Compute (SLAC) cores eliminate multiply-by-zero operations and its 40 GB of on-chip memory is uniformly distributed among SLAC cores, enabling fast local access to model parameters. Moreover, Cerebras software configures routing between cores at runtime, optimizing communication overhead among cores.

As LLMs are becoming more commonly used, new hardware architectures are needed to accelerate LLMs training and inference. We benchmark the effectiveness of this hardware architecture at accelerating LLMs training and inference. Additionally, we analyze if Cerebras WSE can scale the memory-wall associated with traditionally memory-bound compute tasks using its 20 PB/s high bandwidth memory.

Furthermore, we examine the performance scalability of Cerebras WSE through a roofline model. By plotting performance metrics against computational intensity, we aim to assess their effectiveness at handling high compute-intensive LLMs training and inference tasks.

--- https://www.nytimes.com/2024/11/19/magazine/ozempic-junk-food.html Ozempic Could Crush the Junk Food Industry. But It Is Fighting Back. 2024-11-19 2024-11-20 longevity/glp/psychology --- /doc/ai/anime/danbooru/2024-bao.pdf Applying Conditional Information in Guiding Diffusion-Based method for Anime-Style Face Drawing Nguyễn Phan Gia Bảo 2024-01-01 2024-11-20 ai/anime/danbooru ai/nn/diffusion

Anime-style face drawing has become increasingly popular in recent years, with the rise of digital art and animation. However, generating high-quality anime-style faces remains a challenging task, especially when it comes to capturing the details of facial features and expressions.

This thesis explores the application of conditional information in guiding diffusion-based methods for anime-style face drawing. We propose a framework that uses the power of conditional diffusion models to produce accurate and high-quality anime faces. Our approach allows for high control over facial features, expressions, and attributes, enabling the generation of faces that are both esthetically pleasing and semantically meaningful.

We investigate various conditioning mechanisms, including class labels, facial landmarks and sketches, to guide the diffusion process and produce faces that meet specific requirements.

Through experiments, we demonstrate the effectiveness of our method in following the details that user want when generating anime-style faces.

[Keywords: anime-style face drawing, diffusion, conditional information, guiding, digital art, Computer-Aided Design, image generation, face synthesis, machine learning]

--- https://arxiv.org/abs/2411.12580 Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models Laura Ruis, Maximilian Mozes, Juhan Bae, Siddhartha Rao Kamalakara, Dwarak Talupuru, Acyr Locatelli, Robert Kirk, Tim Rocktäschel, Edward Grefenstette, Max Bartolo 2024-11-19 2024-11-20 [("doi","10.48550/arXiv.2411.12580")] ai/nn/retrieval ai/nn/transformer/attention ai/nn/transformer/gpt/inner-monologue

The capabilities and limitations of Large Language Models (LLMs) have been sketched out in great detail in recent years, providing an intriguing yet conflicting picture. On the one hand, LLMs demonstrate a general ability to solve problems. On the other hand, they show surprising reasoning gaps when compared to humans, casting doubt on the robustness of their generalization strategies.

The sheer volume of data used in the design of LLMs has precluded us from applying the method traditionally used to measure generalization: train-test set separation. To overcome this, we study what kind of generalization strategies LLMs employ when performing reasoning tasks by investigating the pretraining data they rely on.

For two models of different sizes (7B and 35B) and 2.5B of their pretraining tokens, we identify what documents influence the model outputs for 3 simple mathematical reasoning tasks and contrast this to the data that are influential for answering factual questions. We find that, while the models rely on mostly distinct sets of data for each factual question, a document often has a similar influence across different reasoning questions within the same task, indicating the presence of procedural knowledge.

We further find that the answers to factual questions often show up in the most influential data. However, for reasoning questions, the answers usually do not show up as highly influential, nor do the answers to the intermediate reasoning steps. When we characterize the top-ranked documents for the reasoning questions qualitatively, we confirm that the influential documents often contain procedural knowledge, like demonstrating how to obtain a solution using formulae or code.

Our findings indicate that the approach to reasoning the models use is unlike retrieval and more like a generalizable strategy that synthesizes procedural knowledge from documents doing a similar form of reasoning.

--- https://www.cnbc.com/2024/11/20/business-spending-on-ai-surged-500percent-this-year-to-13point8-billion-says-menlo-ventures.html Business spending on AI surged 500% this year to $13.8 billion 2024-11-20 2024-11-20 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/codex ai/scaling/economics --- https://arxiv.org/abs/2411.00247 Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond Alan Jeffares, Alicia Curth, Mihaela van der Schaar 2024-10-31 2024-11-20 [("doi","10.48550/arXiv.2411.00247")] ai/scaling/emergence/grokking ai/tabular

Deep learning sometimes appears to work in unexpected ways. In pursuit of a deeper understanding of its surprising behaviors, we investigate the utility of a simple yet accurate model of a trained neural network consisting of a sequence of first-order approximations telescoping out into a single empirically operational tool for practical analysis.

Across 3 case studies, we illustrate how it can be applied to derive new empirical insights on a diverse range of prominent phenomena in the literature—including double descent, grokking...suggesting that grokking may reflect transition into a measurably benign overfitting regime during training, linear mode connectivity, and the challenges of applying deep learning on tabular data—highlighting that this model allows us to construct and extract metrics that help predict and understand the a priori unexpected performance of neural networks.

We also demonstrate that this model presents a pedagogical formalism allowing us to isolate components of the training process even in complex contemporary settings, providing a lens to reason about the effects of design choices such as architecture and optimization strategy, and reveals surprising parallels between neural network learning and gradient boosting.

--- https://x.com/karpathy/status/1859305141385691508 Andrej Karpathy 2024-11-20 ai/nn/transformer/gpt/2 economics/experience-curve --- https://pmc.ncbi.nlm.nih.gov/articles/PMC6003933/ Sociality does not drive the evolution of large brains in eusocial African mole-rats 2024-11-20 iq/animal psychology/neuroscience --- https://pmc.ncbi.nlm.nih.gov/articles/PMC6789728/ The Naked Mole-Rat: An Unusual Organism with an Unexpected Latent Potential for Increased Intelligence? 2024-11-20 iq/animal --- https://www.noemamag.com/are-we-accidentally-building-a-planetary-brain/ Are We Accidentally Building A Planetary Brain? From superorganisms to superintelligences, how studying crabs could reveal that we are unintentionally building an artificial world brain Thomas Moynihan 2024-11-19 2024-11-20 fiction/science-fiction/frank-herbert genetics/selection/natural transhumanism --- https://arxiv.org/abs/2310.06110 Grokking as the Transition from Lazy to Rich Training Dynamics Tanishq Kumar, Blake Bordelon, Samuel J. Gershman, Cengiz Pehlevan 2023-10-09 2024-11-20 [("doi","10.48550/arXiv.2310.06110")] ai/scaling/emergence/grokking

We propose that the grokking phenomenon, where the train loss of a neural network decreases much earlier than its test loss, can arise due to a neural network transitioning from ‘lazy’ training dynamics to a ‘rich’, feature learning regime.

To illustrate this mechanism, we study the simple setting of vanilla gradient descent on a polynomial regression problem with a two-layer neural network which exhibits grokking without regularization in a way that cannot be explained by existing theories. We identify sufficient statistics for the test loss of such a network, and tracking these over training reveals that grokking arises in this setting when the network first attempts to fit a kernel regression solution with its initial features, followed by late-time feature learning where a generalizing solution is identified after train loss is already low.

We find that the key determinants of grokking are the rate of feature learning—which can be controlled precisely by parameters that scale the network output—and the alignment of the initial features with the target function y(x).

We argue this delayed generalization arises when (1) the top eigenvectors of the initial neural tangent kernel and the task labels y(x) are misaligned, but (2) the dataset size is large enough so that it is possible for the network to generalize eventually, but not so large that train loss perfectly tracks test loss at all epochs, and (3) the network begins training in the lazy regime so does not learn features immediately.

We conclude with evidence that this transition from lazy (linear model) to rich training (feature learning) can control grokking in more general settings, like on MNIST, one-layer Transformers, and student-teacher networks.

--- https://arxiv.org/abs/2311.18817 Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking Kaifeng Lyu, Jikai Jin, Zhiyuan Li, Simon S. Du, Jason D. Lee, Wei Hu 2023-11-30 2024-11-20 [("doi","10.48550/arXiv.2311.18817")] ai/scaling/emergence/grokking

Recent work by Power et al 2022 highlighted a surprising “grokking” phenomenon in learning arithmetic tasks: a neural net first “memorizes” the training set, resulting in perfect training accuracy but near-random test accuracy, and after training for sufficiently longer, it suddenly transitions to perfect test accuracy.

This paper studies the grokking phenomenon in theoretical setups and shows that it can be induced by a dichotomy of early and late phase implicit biases. Specifically, when training homogeneous neural nets with large initialization and small weight decay on both classification and regression tasks, we prove that the training process gets trapped at a solution corresponding to a kernel predictor for a long time.

Then a very sharp transition to min-norm/max-margin predictors occurs, leading to a dramatic change in test accuracy.

--- https://arxiv.org/abs/2310.16441 Grokking in Linear Estimators—A Solvable Model that Groks without Understanding Noam Levi, Alon Beck, Yohai Bar-Sinai 2023-10-25 2024-11-21 [("doi","10.48550/arXiv.2310.16441")] ai/scaling/emergence/grokking

Grokking is the intriguing phenomenon where a model learns to generalize long after it has fit the training data. We show both analytically and numerically that grokking can surprisingly occur in linear networks performing linear tasks in a simple teacher-student setup with Gaussian inputs.

In this setting, the full training dynamics is derived in terms of the training and generalization data covariance matrix. We present exact predictions on how the grokking time depends on input and output dimensionality, train sample size, regularization, and network initialization.

We demonstrate that the sharp increase in generalization accuracy may not imply a transition from “memorization” to “understanding”, but can simply be an artifact of the accuracy measure. We provide empirical verification for our calculations, along with preliminary results indicating that some predictions also hold for deeper networks, with non-linear activations.

--- https://pmc.ncbi.nlm.nih.gov/articles/PMC3826193/ Halley and the eternity of the world revisited Dmitri Levitin 2013-09 2024-11-21 [("doi","10.1098/rsnr.2013.0019")] philosophy/religion science

Since the publication in Notes and Records of the Royal Society of an article by Simon Schaffer in 1977, it has been a historiographical commonplace that there was an ‘underlying unity’ to the religio-philosophical opinions of Edmond Halley, specifically on issues concerning the age of the world.

This article (1) argues that the evidence adduced for this claim—specifically the account of a lecture given by Halley to the Royal Society in 1693—has been misinterpreted, and (2) brings forward some new evidence concerning the mysterious events surrounding Halley’s unsuccessful attempt to secure the Savilian Professorship in Astronomy in 1691 and the nature of his religious heterodoxy, both as it was developed by himself and as it was perceived by contemporaries.

It thus functions as a full revisionist account of one of the key players in the destabilization of the relationship between natural philosophy and the Book of Genesis in the first decades of the Royal Society.

[Keywords: Edmond Halley, eternalism, Royal Society Lectures, Savilian Chair of Astronomy, Genesis]

--- https://x.com/kyleshannon/status/1859355131738734824 kyleshannon 2024-11-21 ai/nn/transformer/gpt/4/poetry --- https://www.mit.edu/people/dpolicar/writing/prose/text/epistemologicalNightmare.html An Epistemological Nightmare Raymond Smullyan 1982 2024-11-21 math/humor philosophy/logic philosophy/mind --- https://demos.obormot.net/these-waifus-do-not-exist These Waifus Do Not Exist Said Achmiz 2024-01-01 ai/nn/gan/stylegan/anime cs/js --- https://demos.obormot.net/these-waifus-do-not-exist-v2-alt These Waifus Do Not Exist 2.0 Said Achmiz 2024-01-01 ai/nn/gan/stylegan/anime cs/js --- https://blakebordelon.github.io/ Blake Bordelon Blake Bordelon 2024-11-21 ai/scaling --- https://arxiv.org/abs/2409.17858 How Feature Learning Can Improve Neural Scaling Laws Blake Bordelon, Alexander Atanasov, Cengiz Pehlevan 2024-09-26 2024-11-21 [("doi","10.48550/arXiv.2409.17858")] ai/nn/fully-connected ai/scaling

We develop a solvable model of neural scaling laws beyond the kernel limit. Theoretical analysis of this model shows how performance scales with model size, training time, and the total amount of available data.

We identify 3 scaling regimes corresponding to varying task difficulties: hard, easy, and super easy tasks. For easy and super-easy target functions, which lie in the reproducing kernel Hilbert space (RKHS) defined by the initial infinite-width Neural Tangent Kernel (NTK), the scaling exponents remain unchanged between feature learning and kernel regime models.

For hard tasks, defined as those outside the RKHS of the initial NTK, we demonstrate both analytically and empirically that feature learning can improve scaling with training time and compute, nearly doubling the exponent for hard tasks. This leads to a different compute optimal strategy to scale parameters and training time in the feature learning regime.

We support our finding that feature learning improves the scaling law for hard tasks but not for easy and super-easy tasks with experiments of nonlinear MLPs fitting functions with power-law Fourier spectra on the circle and CNNs learning vision tasks.

...Ordering of Models in Lazy Limit Preserved in Feature Learning Regime: An additional interesting prediction of our theory is that the ordering of models by performance in the lazy regime is preserved is the same as the ordering of models in the feature learning regime. If model A outperforms model B on a task in the lazy limit (βA > βB), then model A will also perform better in the rich regime χ(βA) > χ(βB) (see Figure 1). This suggests using kernel limits of neural architectures for fast initial architecture search may be viable, despite failing to capture feature learning (Park et al 2020). This prediction deserves a greater degree of stress testing.

--- /doc/science/1977-schaffer.pdf Halley’s Atheism and the end of the world Simon Schaffer 1977-07 2024-11-21 [("doi","10.1098/rsnr.1977.0004")] philosophy/religion science

Edmond Halley’s views on theology and natural philosophy have often drawn puzzled attention both from his contemporaries and from subsequent scholars. There has seemed to be a contrast between some public statements he made when under pressure from ecclesiastical authority and his continued, and privately held, faith in the over-arching relevance of science1.

However, it now emerges from some unpublished papers which Halley read to the Royal Society in the 1690s that he made public his own debate over such issues as the eternity of the world. This new evidence gives us a much more consistent picture of Halley’s work, and it refutes the view that there were two Halleys—the public orthodox face and the private heterodox one.

It is true that the work of Edmond Halley presents us with a picture of considerable diversity. Nevertheless, throughout the 1690s, he was primarily concerned with an investigation of Earth history independently of scriptural authority, and this gave some unity to his varied researches.

However, there were both ideological and institutional problems with such a programme. The Anglican establishment of the period after 1688 was filled with a sense of threat. This led to a series of statements antipathetic to Halley’s attitude, including a devaluation of the power of unaided reason and an emphasis on the power of God’s Providence.

Halley’s failure to obtain the Savilian Chair of Astronomy in 1691–1692 was due in part, perhaps, to this antipathy.

Yet this failure was also precipitated by the personal antagonism aroused by Halley’s jocular style and the innate irascibility of Flamsteed. Because of these other sources of controversy, the exact nature of Halley’s atheism remains confused. Even his identification with the ‘infidel mathematician’ of Bishop Berkeley’s Analyst is problematic.

Yet the fact is that Halley took these charges seriously enough to spend several years working to show that one of them was unjustified. He had been accused of believing that the world would continue for eternity, and he was to try and show that it must, in the end, come to a halt.

--- http://unfathomable.epicmagazine.com/4/ Unfathomable 2024-11-20 technology --- https://www.interconnects.ai/p/tulu-3 Tülu 3: The next era in open post-training 2024-11-21 reinforcement-learning/preference-learning --- https://pmc.ncbi.nlm.nih.gov/articles/PMC3817850/ Explaining Human Recreational Use of ‘pesticides’: The Neurotoxin Regulation Model of Substance Use vs. the Hijack Model and Implications for Age and Sex Differences in Drug Consumption 2024-11-21 genetics/selection/natural nicotine psychology/neuroscience --- https://onlinelibrary.wiley.com/doi/full/10.1002/ajpa.24718 2024-11-21 genetics/selection/natural nicotine psychology/neuroscience --- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3777307 Overboard On Offshore Fears 2024-11-21 economics/automation --- https://www.wired.com/story/chineses-surveillance-state-is-selling-citizens-data-as-a-side-hustle/ China’s Surveillance State Is Selling Citizen Data as a Side Hustle 2024-11-22 darknet-market --- https://www.filfre.net/2024/11/retro-no-more-interactive-fiction-of-the-early-comp-era/ Retro No More: Interactive Fiction of the Early Comp Era 2024-11 2024-11-22 fiction/text-game technology/digital-antiquarian --- https://greydanus.github.io/2016/11/26/synthetic-gradients/ A Bird’s Eye View of Synthetic Gradients Sam Greydanus 2016-11-26 2024-11-22 reinforcement-learning/meta-learning --- https://greydanus.github.io/2017/01/07/enigma-rnn/ Learning the Enigma with Recurrent Neural Networks Sam Greydanus 2017-01-07 2024-11-22 ai/nn/rnn cs/cryptography --- https://greydanus.github.io/2022/05/24/studying-growth/ Studying Growth with Neural Cellular Automata Sam Greydanus 2022-05-24 2024-11-22 ai/nn/cnn cs/cellular-automaton --- https://greydanus.github.io/2023/03/05/ncf-tutorial/ Finding Paths of Least Action with Gradient Descent Sam Greydanus 2023-03-05 2024-11-22 reinforcement-learning/model science --- https://greydanus.github.io/2023/03/12/ncf-six-experiments/ Six Experiments in Action Minimization Sam Greydanus 2023-03-12 2024-11-22 reinforcement-learning/model science --- https://www.youtube.com/watch?v=e09MXb-wXPI Kaguya hime no monogatari Warabe Uta 2024-06-17 anime --- https://www.anthropic.com/news/anthropic-amazon-trainium [Amazon to invest another $4 billion in Anthropic; Anthropic to use Trainium chips] Anthropic 2024-11-22 2024-11-23 ai/nn/anthropic ai/scaling/economics --- https://www.youtube.com/watch?v=5aogzAUPilE Lip Reading Sentences in the Wild [video] 2024-06-17 ai/dataset ai/nn --- https://en.wikipedia.org/wiki/The_Centipede%27s_Dilemma The Centipede’s Dilemma 2024-11-23 psychology/cognitive-bias/illusion-of-depth --- /doc/statistics/bias/1967-meehl.pdf Theory-Testing in Psychology and Physics: A Methodological Paradox Paul E. Meehl 1967-06 2024-11-23 [("doi","10.1086/288135")] psychology statistics/bias

Because physical theories typically predict numerical values, an improvement in experimental precision reduces the tolerance range and hence increases corroborability.

In most psychological research, improved power of a statistical design leads to a prior probability approaching ½ of finding a “statistically-significant” difference in the theoretically predicted direction. Hence the corroboration yielded by “success” is very weak, and becomes weaker with increased precision.

“Statistical-significance” plays a logical role in psychology precisely the reverse of its role in physics.

This problem is worsened by certain unhealthy tendencies prevalent among psychologists, such as a premium placed on experimental “cuteness” and a free reliance upon ad hoc explanations to avoid refutation.

--- https://arxiv.org/abs/2411.01992 Ask, and it shall be given: Turing completeness of prompting Ruizhong Qiu, Zhe Xu, Wenxuan Bao, Hanghang Tong 2024-11-04 2024-11-23 [("doi","10.48550/arXiv.2411.01992")] ai/nn/transformer/attention cs/computable

Since the success of GPT, large language models (LLMs) have been revolutionizing machine learning and have initiated the so-called LLM prompting paradigm. In the era of LLMs, people train a single general-purpose LLM and provide the LLM with different prompts to perform different tasks. However, such empirical success largely lacks theoretical understanding.

Here, we present the first theoretical study on the LLM prompting paradigm to the best of our knowledge. In this work, we show that prompting is in fact Turing-complete: there exists a finite-size Transformer such that for any computable function, there exists a corresponding prompt following which the Transformer computes the function. Furthermore, we show that even though we use only a single finite-size Transformer, it can still achieve nearly the same complexity bounds as that of the class of all unbounded-size Transformers.

Overall, our result reveals that prompting can enable a single finite-size Transformer to be efficiently universal, which establishes a theoretical underpinning for prompt engineering in practice.

--- https://www.nature.com/articles/d41586-024-03756-w How a silly science prize changed my career: A levitating frog, a necrophiliac duck, taxi drivers’ brains—the Ig Nobel prizes have shined a spotlight on offbeat work. Here’s an inside look at how winners feel about this sometimes unwanted ‘honor’ Catriona Clarke 2024-11-19 2024-11-23 math/humor science --- https://inews.co.uk/opinion/why-i-donate-my-sperm-over-facebook-and-have-fathered-23-children-194142 Why I donate my sperm over Facebook and have fathered 23 children: Anthony Fletcher says he enjoys helping women conceive who may not be able to afford a donor 2024-01-01 genetics/selection/artificial --- https://stevehsu.substack.com/ Steve Hsu homepage (Information Processing) Steve Hsu 2024-08-20 genetics/selection/artificial --- https://steve-yegge.blogspot.com/2012/03/borderlands-gun-collectors-club.html The Borderlands Gun Collector’s Club Steve Yegge 2024-01-01 design psychology/collecting --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1210626/pdf/935.pdf Studies on natural populations of Drosophila. II. Heritability and response to selection for wing length in Drosophila melanogaster and D. simulans at different temperatures 2024-01-01 design psychology/collecting --- https://steve-yegge.blogspot.com/2008/09/programmings-dirtiest-little-secret.html Programming’s Dirtiest Little Secret Steve Yegg 2024-01-01 cs/algorithm --- https://stepheniemeyer.com/the-story-of-twilight-getting-published/ The Story Behind Twilight Stephenie Meyer 2024-01-01 fiction/fantasy --- https://www.astralcodexten.com/p/book-review-fussell-on-class Summary and commentary on Paul Fussell’s Class: A Guide Through The American Status System Scott Alexander 2024-01-01 sociology --- https://www.econlib.org/library/Ricardo/ricP.html?chapter_num=29#Ch.31,%20On%20Machinery Chapter 31, ‘On Machinery’, On the Principles of Political Economy and Taxation 2024-01-01 economics --- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1748084/pdf/v014p00359a.pdf When California smokers use nicotine replacement therapy, most are trying to quit smoking 2024-01-01 nicotine --- https://nabeelqu.co/principles 2024-11-24 psychology/writing --- https://nabeelqu.co/advice 2024-11-24 psychology/energy psychology/writing science/fermi-problem --- https://x.com/mesolude/status/1851663954243920322 mesolude 2024-11-24 ai/nn/transformer/gpt/claude psychiatry --- https://www.reddit.com/r/mlscaling/comments/1gyb54z/the_fate_of_gpt4o/ 2024-11-24 ai/nn/transformer/gpt/4/nonfiction ai/scaling/economics reinforcement-learning/preference-learning/mode-collapse --- /doc/iq/high/2024-sun-2.pdf Sleep problems and duration in school-aged children at different levels of giftedness Jiumo Sun, Ruping Lu, Wanqi Sun, Yujiao Deng, Jieqiong Liu, Yanrui Jiang, Qi Zhu, Hong Xu, Guanghai Wang, Fan Jiang 2024-10-01 2024-11-24 [("doi","10.1016/j.sleep.2024.07.030")] iq/high zeo

Objectives: Optimal sleep is crucial for developing and maintaining gifted children’s cognitive abilities. However, only a few studies have explored the sleep profiles of gifted children and overlooked their internal variations. This study aimed to investigate subjective and objective sleep profiles in school-aged gifted children with different levels of giftedness.

Method: This study included 80 school-aged children (50% male) aged 6–11 years. Giftedness was assessed using the Wechsler Intelligence Scale for Children-4th Edition (WISC-IV). Subjective and objective sleep were evaluated using the Children’s Sleep Habits Questionnaire (CSHQ) and Actiwatch 2 [actigraphy].

Results: The sample was divided into 3 groups based on their full-scale intelligence quotient (IQ): 16 typically developing children (IQ < 130), 38 moderately gifted children (IQ: 130–145), and 26 highly gifted children (IQ > 145).

The highly gifted children had the mildest sleep problems, particularly in sleep duration and daytime sleepiness. Moderately gifted children had the shortest subjective average sleep duration, while the 3 groups had no statistically-significant differences in Actiwatch-measured sleep variables.

Furthermore, CSHQ total and daytime sleepiness subscale scores were negatively associated with the full-scale IQ in gifted children after controlling for confounders including emotional and behavioral problems.

Conclusion: Children with higher levels of giftedness experience fewer subjective sleep problems but have similar objective sleep parameters. It is imperative to implement tailored sleep strategies for fostering intellectual development and nurturing young talents.

--- https://www.youtube.com/watch?v=hFuqCO4lWKk ‘Mother’ 2024-11-24 anime philosophy/mind psychology/linguistics psychology/personality/psychopathy --- https://derp.substack.com/p/how-the-shroud-of-turin-was-made How the Shroud of Turin was made 2024-11-24 technology --- https://arxiv.org/abs/2411.13676 Hymba: A Hybrid-head Architecture for Small Language Models Xin Dong, Yonggan Fu, Shizhe Diao, Wonmin Byeon, Zijia Chen, Ameya Sunil Mahabaleshwarkar, Shih-Yang Liu, Matthijs Van Keirsbilck, Min-Hung Chen, Yoshi Suhara, Yingyan Lin, Jan Kautz, Pavlo Molchanov 2024-11-20 2024-11-24 [("doi","10.48550/arXiv.2411.13676")] ai/nn/rnn ai/nn/transformer/attention

We propose Hymba, a family of small language models featuring a hybrid-head parallel architecture that integrates transformer attention mechanisms with state space models (SSMs) for enhanced efficiency. Attention heads provide high-resolution recall, while SSM heads enable efficient context summarization. Additionally, we introduce learnable meta tokens that are prepended to prompts, storing critical information and alleviating the “forced-to-attend” burden associated with attention mechanisms.

This model is further optimized by incorporating cross-layer key-value (KV) sharing and partial sliding window attention, resulting in a compact cache size. During development, we conducted a controlled study comparing various architectures under identical settings and observed advantages of our proposed architecture.

Notably, Hymba achieves state-of-the-art results for small LMs: our Hymba-1.5B-Base model surpasses all sub-2B public models in performance and even outperforms Llama-3.2-3B with 1.32% higher average accuracy, an 11.67× cache size reduction, and 3.49× throughput.

--- https://www.economist.com/briefing/2024/11/21/elon-musks-transformation-in-his-own-words Elon Musk’s transformation, in his own words 2024-11-21 2024-11-24 psychiatry/bipolar/elon-musk --- https://arxiv.org/abs/2304.01315 Empirical Design in Reinforcement Learning Andrew Patterson, Samuel Neumann, Martha White, Adam White 2023-04-03 2024-11-24 [("doi","10.48550/arXiv.2304.01315")] reinforcement-learning/model-free statistics/bias

Empirical design in reinforcement learning is no small task. Running good experiments requires attention to detail and at times computational resources. While compute resources available per dollar have continued to grow rapidly, so have the scale of typical experiments in reinforcement learning. It is now common to benchmark agents with millions of parameters against dozens of tasks, each using the equivalent of 30 days of experience. The scale of these experiments often conflict with the need for proper statistical evidence, especially when comparing algorithms. Recent studies have highlighted how popular algorithms are sensitive to hyper-parameter settings and implementation details, and that common empirical practice leads to weak statistical evidence (Machado et al 2018; Henderson et al 2018). Here we take this one step further.

This manuscript represents both a call to action, and a comprehensive resource for how to do good experiments in reinforcement learning.

In particular, we cover: the statistical assumptions underlying common performance measures, how to properly characterize performance variation and stability, hypothesis testing, special considerations for comparing multiple agents, baseline and illustrative example construction, and how to deal with hyper-parameters and experimenter bias.

Throughout we highlight common mistakes found in the literature and the statistical consequences of those in example experiments.

The objective of this document is to provide answers on how we can use our unprecedented compute to do good science in reinforcement learning, as well as stay alert to potential pitfalls in our empirical design.

--- https://www.pnas.org/doi/full/10.1073/pnas.1918896117 2024-11-24 economics psychology/personality --- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4968889 Parents’ Beliefs in the ‘American Dream’ Affect Parental Investments in Children: Evidence from an Experiment 2024-11-24 economics sociology --- https://lemon.rip/w/c99-vla-tricks/ C99 doesn’t need function bodies, or, ‘VLAs are Turing complete’ 2024-11-24 cs/computable --- https://www.nature.com/articles/s41586-024-08217-y Examining the role of common variants in rare neurodevelopmental conditions 2024 2024-11-25 genetics/heritable/rare iq/low --- https://www.cambridge.org/core/journals/psychological-medicine/article/cooccurrence-between-mental-disorders-and-physical-diseases-a-study-of-nationwide-primarycare-medical-records/DCACA27CF1E22647C08856CEE47EBBE0 Co-occurrence between mental disorders and physical diseases: a study of nationwide primary-care medical records 2024-11-25 psychiatry --- https://openreview.net/forum?id=yqQJGTDGXN Deep Reinforcement Learning Without Experience Replay, Target Networks, or Batch Updates Mohamed Elsayed, Gautham Vasan, A. Rupam Mahmood 2024-11-09 2024-11-25 reinforcement-learning/model-free

Natural intelligence processes experience as a continuous [online] stream, sensing, acting, and learning moment-by-moment in real time. Streaming learning, the modus operandi of classic reinforcement learning (RL) algorithms like Q-learning and Temporal Difference learning, mimics natural learning by using the most recent sample without storing it.

This approach is also ideal for resource-constrained, communication-limited, and privacy-sensitive applications. However, in deep RL, learners almost always use batch updates and replay buffers, making them computationally expensive and incompatible with streaming learning. Although the prevalence of batch deep RL is often attributed to its sample efficiency, a more critical reason for the absence of streaming deep RL is its frequent instability and failure to learn, which we refer to as stream barrier.

This paper introduces the stream-x algorithms, the first class of deep RL algorithms to overcome stream barrier for both prediction and control and match sample efficiency of batch RL...The effectiveness of our approach hinges on a set of key techniques that are common to all stream-x algorithms. They include a novel optimizer to adjust step size for stability, appropriate data scaling, a new initialization scheme, and maintaining a standard normal distribution of pre-activations.

Through experiments in MuJoCo Gym, dm_control, and ALE games, we demonstrate stream barrier in existing algorithms and successful stable learning with our stream-x algorithms: stream Q, stream AC, and stream TD, achieving the best model-free performance in dm_control Dog environments.

A set of common techniques underlies the stream-x algorithms, enabling their success with a single set of hyperparameters and allowing for easy extension to other algorithms, thereby reviving streaming RL.

--- https://x.com/mattsclancy/status/1860532323080458400 mattsclancy 2024-11-25 ai/nn/transformer/gpt/3/poetry reinforcement-learning/preference-learning/mode-collapse --- https://www.lesswrong.com/posts/jJ2p3E2qkXGRBbvnp/passages-i-highlighted-in-the-letters-of-j-r-r-tolkien Passages I Highlighted in The Letters of J.R.R.Tolkien 2024-11-25 fiction/fantasy philosophy/religion --- https://cacm.acm.org/research-highlights/computing-with-time-microarchitectural-weird-machines/ Computing with Time: Microarchitectural Weird Machines 2024-11-25 cs/computable cs/hardware cs/security --- https://gettingstronger.org/2010/11/learning-to-fast/ Learning to Fast Todd Becker 2010-10-20 2024-01-01 longevity/fasting --- https://getd.libs.uga.edu/pdfs/sutherland_pierre_201212_ma.pdf The effects of distributed practice on two grade 10 mathematics classes 2024-01-01 psychology/spaced-repetition --- https://ggplot2.tidyverse.org/ Create Elegant Data Visualizations Using the Grammar of Graphics 2024-01-01 cs/r design/visualization --- https://ghclive.wordpress.com/2012/08/20/ghclive-project-escapes/ ghcLiVE project escapes! 2024-01-01 cs/haskell --- https://getbackers.fandom.com/wiki/Der_Kaiser Der Kaiser 2024-01-01 anime --- https://gidishperber.medium.com/what-ive-learned-from-kaggle-s-fisheries-competition-92342f9ca779 2024-01-01 ai --- https://gigascience.biomedcentral.com/articles/10.1186/2047-217X-3-10 Applying compressed sensing to genome-wide association studies 2024-01-01 genetics/heritable --- https://gigascience.biomedcentral.com/articles/10.1186/s13742-015-0081-6 Determination of nonlinear genetic architecture using compressed sensing 2024-01-01 genetics/heritable --- https://gigazine.net/news/20190222-these-waifus-do-not-exist/ 美少女イラスト風に自動生成された「俺の嫁」画像をズラッと大量に並べて見せてくれる「These Waifus Do Not Exist」 Gigazine 2019-02-22 2024-01-01 ai/nn/gan/stylegan/anime --- https://weirdmachines.gitlab.io/ Weird Machines HQ 2024-11-25 cs/computable cs/security --- https://cacm.acm.org/research-highlights/technical-perspective-how-exploits-impact-computer-science-theory/ How Exploits Impact Computer Science Theory 2024-11-25 cs/computable cs/security --- https://x.com/austinc3301/status/1861084272431390819 Agus 2024-11-25 ai/nn/transformer/gpt/instruction-tuning lesswrong-survey --- https://forum.evageeks.org/post/422173/Annos-Suicide-Attempt/#422173 Anno’s Suicide Attempt(?) § Rei & schizoid personality 2024-01-01 anime/eva psychiatry/autism/schizoid --- https://www.amazon.com/Loners-Life-Path-Unusual-Children/dp/0415066654 Loners: The Life Path of Unusual Children Sula Wolff 1995 2024-11-25 psychiatry/autism/schizoid --- https://www.reddit.com/r/Schizoid/ /r/Schizoid/ 2024-11-25 psychiatry/autism/schizoid --- https://web.archive.org/web/20220802032833/https://fighttheurgetofade.com/ I’m a shut-in. This is my story. 2022-08-02 2024-11-25 psychiatry/autism/schizoid --- https://en.wikipedia.org/wiki/Welcome_to_the_N.H.K. Welcome to the N.H.K.! 2024-01-01 psychiatry/autism/schizoid --- https://www.lesswrong.com/posts/xpC82ndFDSXtS4xK3/which-things-were-you-surprised-to-learn-are-not-metaphors Which things were you surprised to learn are not metaphors? 2024-11-25 psychology --- https://web.archive.org/web/20180308202945/https://schizoids.info/ Schizoids.info 2024-11-25 psychiatry/autism/schizoid --- https://pmc.ncbi.nlm.nih.gov/articles/PMC5840255/#Sec5 Schizoid Fantasy: Refuge or Transitional Location? § The Boy on the Bicycle Candace Orcutt 2017-06-02 2024-11-25 [("doi","10.1007/s10615-017-0629-2")] psychiatry/autism/schizoid

The schizoid personality, a type increasingly representative of our times, lives in a detached individual world. But this retreat sometimes can offer a place of transition, serving as a creative bridge to everyday life.

An extended case illustration describes a schizoid patient who was able to use a playful form of psychotherapy to move from make-believe to real relationship.

[Keywords: psychotherapy, schizoid personality, schizoid defense, transitional location, play therapy]

…This is about a playful young patient I have always referred to as “the boy on the bicycle.”

Although the experience dates to my early career, it remains vividly with me. It represents a time I had to rely on intuition more than expertise, although perhaps that was a good thing…When I interviewed them together, the mother did the talking while the son appeared distracted. The problem focused essentially on the mother’s feeling overwhelmed by her son’s energy, unpredictable activity, and general unmanageability.

It seemed to me that the issue was less that of the mother’s anxiety than the son’s need for independence…The patient prefaced his first session by riding his bicycle down the Hospital hallway. He was confronted by a clerk, who protested: “You can’t ride your bicycle inside the Clinic!” The patient then cheerfully replied: “Lady! You’re hallucinating! This isn’t a bicycle, this is a horse!”

The patient had set the stage, and it was my job to make it a setting for psychotherapy.

First, there was the matter of the horse. Making it clear that a horse must be hitched outside, I went with the patient while he cooperatively chained his bicycle to the iron railing which was fortunately located at the side entrance to the Clinic.

His new experience seemed to engage the patient’s attention, and he soon informed the staff and myself that his name was “Barnabas”. This was not his actual (rather stodgy) name, but the name he took from Barnabas Collins, the reluctant vampire in the popular television series, Dark Shadows.

And here we coincided. In my own teenage years, I myself escaped into a world of classic horror films such as Dracula. To follow this inclination, I played hooky to the extent that I still wonder how I managed to graduate high school…As the sessions began, we enthusiastically discussed Dark Shadows, and the dilemma of unwilling vampires such as Barnabas Collins, who was struggling to free himself from a spell [by evil witch Angelique Bouchard]. At the same time, the patient continued to entertain the staff by appearing in a cape and top hat (the bicycle remained hitched outside).

Fairly soon, the fantasy play expanded to include me. We became “Steed and Peel”, the sophisticated crime-stoppers on the original television series of The Avengers. He pictured himself as the dapper John Steed, with cane and bowler hat, while I was flatteringly cast as Steed’s companion, the jump-suited karate expert, Emma Peel. Now fantasy allowed us to play with relationship in metaphor.

I should be clear that these sessions provided a context for consciously constructed fantasy. The patient had no thought disorder, and knew the difference between fantasy and reality, although he approached reality with caution and in camouflage.

Then the scope of the sessions expanded further, as Barnabas instigated an activity we came to call “scavenging” (we never verbally defined this, but it is interesting to note that the word suggests the reclaiming of something that has been considered worthless). Barnabas led me on an extensive exploration of apparently forgotten regions of the Hospital. It was evident he was showing me “secret” rooms he had previously discovered, as he appeared to be confidently following a mental map. We ventured through one neglected room after another—dusty, cobwebbed places filled with stacks of papers, broken furniture, dingy test tubes and alembics. He scavenged one or two mad-scientist-looking beakers for his home use, while my white hospital coat officialized the activities.

Barnabas, with my cooperation, had transformed the Hospital into an extensive playroom. And, taking the desired direction of play therapy, our finding of hidden inanimate objects progressed to a Clinic-wide game of interpersonal hide-and-seek.

On regular occasions, the Hospital Department of Psychiatry conducted Professor’s Rounds in a large auditorium. Over 100 white-coated psychotherapists were there—psychiatrists, psychologists, social workers—to hear a distinguished speaker and discuss cases. I was seated toward the back, when I noticed a number of the staff turning to look at me. I also noticed that they seemed amused. My own perplexed gaze traveled toward the auditorium door, where I saw Barnabas standing and smiling at me. I hurried to the entrance and gestured him outside. I then subjected him to a solemn boundary-setting lecture. He took it in and looked pleased.

On a later occasion, during a smaller social work meeting, Barnabas appeared at the door, singled me out, and called “Hiya, Candy [Candace]!” My boundary-setting lecture to him was repeated, to his repeated satisfaction, while I noted to myself that Barnabas had somehow learned my nickname from the staff. I also noted that he had addressed me by my real name.

This increased my growing awareness that Barnabas had engaged the staff of the Hospital, as well as their setting, in his play. The staff knew him, knew I was his therapist, was aware of who knew what about our activities, and was in good-natured collusion. Barnabas had somehow engaged them in transforming an impersonal psychiatric setting into his own supportive play world.

Looking back, now even more than then, I realize that the therapeutic work had followed a definite progression: from a solitary imaginary self to an imaginary law-enforcing couple; then next, from actual companions interacting in an inanimate location to two people engaged in a kind of rapprochement in a social surrounding. Barnabas had set the scene, I had accepted and helped shape it, yet basically all we had done was make room for the orderly nature of the self to grow.

There was consistently a more conscious part to the process, as well. Each session ended in my office, where Barnabas increasingly examined his real-life situation and made a plan for his immediate future.

He had befriended his landlady, who owned a house outside the City. Barnabas was adapting well to weekend visits, and was finding himself useful around the property. In time, he and the landlady arranged for him to work as a live-in caretaker there.

…I also recall our parting. As usual, I went with him to the side entrance of the Clinic, where he unchained his bicycle. We talked awhile, and then I started to say goodbye. He stopped me, saying “I’m not ready yet.” We stood together briefly in silence. Then he wished me well, jumped on his bicycle, and rode off.

--- https://arxiv.org/abs/2402.03214 Organic or Diffused: Can We Distinguish Human Art from AI-generated Images? Anna Yoo Jeong Ha, Josephine Passananti, Ronik Bhaskar, Shawn Shan, Reid Southen, Haitao Zheng, Ben Y. Zhao 2024-02-05 2024-11-25 [("doi","10.48550/arXiv.2402.03214")] ai/nn/adversarial ai/nn/diffusion/midjourney ai/nn/transformer/gpt/dall-e/3

The advent of generative AI images has completely disrupted the art world. Distinguishing AI generated images from human art is a challenging problem whose impact is growing over time. A failure to address this problem allows bad actors to defraud individuals paying a premium for human art and companies whose stated policies forbid AI imagery. It is also critical for content owners to establish copyright, and for model trainers interested in curating training data in order to avoid potential model collapse.

There are several different approaches to distinguishing human art from AI images, including classifiers trained by supervised learning, research tools targeting diffusion models, and identification by professional artists using their knowledge of artistic techniques.

In this paper, we seek to understand how well these approaches can perform against today’s modern generative models in both benign and adversarial settings. We curate real human art across 7 styles, generate matching images from 5 generative models [DALL·E 3, Midjourney v6, SDXL, Firefly, Civitai], and apply 8 detectors (5 automated detectors and 3 different human groups including 180 crowdworkers, 4000+ professional artists, and 13 expert artists experienced at detecting AI).

Both Hive and expert artists do very well, but make mistakes in different ways (Hive is weaker against adversarial perturbations [note: Glaze still doesn’t work] while Expert artists produce higher false positives).

We believe these weaknesses will remain as models continue to evolve, and use our data to demonstrate why a combined team of human and automated detectors provides the best combination of accuracy and robustness.

--- https://arxiv.org/abs/1709.06009 Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, Michael Bowling 2017-09-18 2024-11-26 [("doi","10.48550/arXiv.1709.06009")] reinforcement-learning/model-free statistics/bias

The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games. It supports a variety of different problem settings and it has been receiving increasing attention from the scientific community, leading to some high-profile success stories such as the much publicized Deep Q-Networks (DQN).

In this article, we take a big picture look at how the ALE is being used by the research community. We show how diverse the evaluation methodologies in the ALE have become with time, and highlight some key concerns when evaluating agents in the ALE.

We use this discussion to present some methodological best practices and provide new benchmark results using these best practices.

To further the progress in the field, we introduce a new version of the ALE that supports multiple game modes and provides a form of stochasticity we call sticky actions.

We conclude this big picture look by revisiting challenges posed when the ALE was introduced, summarizing the state-of-the-art in various problems, and highlighting problems that remain open.

--- https://greydanus.github.io/about_me/ About Sam Greydanus Sam Greydanus 2024-11-23 ai/nn --- https://ralafferty.tumblr.com/post/55382042501/49-the-six-fingers-of-time The Six Fingers of Time [tragedy of the anticommons] epiktistes 2013-07-13 2024-01-01 economics/copyright fiction/science-fiction/time-travel

It’s well known that sorting out R. A. Lafferty’s estate has been, in legal terms, a mess. What’s less well known is how “The 6 Fingers of Time” contributed to that mess.

For almost a decade, the Lafferty estate was a byword for snarled probate cases…though Ray and his brothers and sister may have been unusual in their lack of issue, the rest of the family more than made up for this oversight.

The foremost task of Lafferty’s executor was moving on the literary rights to a group better equipped to represent and propagate Lafferty’s work. But thanks to the probate situation, every single heir (of majority age) would have to approve such an agreement: though extraordinary effort they were just on the verge of one when something happened that spooked some of the heirs—or rather, got them thinking that they had hold of something much more valuable than was probably the case.

See, in 1994, Nicholson Baker wrote a novel called The Fermata [as of 2024, no sign of life]. A film company bought the movie option, and hired Robert Zemeckis & Neil Gaiman to produce a screenplay. As is often the case, the studio also took out options on any intellectual property whose central conceit was near that of Baker’s book—which was a protagonist with the ability to stop time and manipulate the people around him as he wished. Whether it was Gaiman—who certainly would’ve recognized the surface similarities to “Six Fingers of Time” [as Gaiman is a big Lafferty fan & energetically advocates for him]—or someone else who advised taking out the option, the result was a large amount of money being paid to the Lafferty estate to ensure that a movie would not made of that story, lest it encroach on The Fermata. Some of the heirs—a few of whom had heard of Philip K. Dick, or noticed that sci-fi seemed to be doing well in the theaters and wasn’t Uncle Ray a famous sci-fi writer?—decided to hold out for the money they were sure was on the way, and quashed the original deal.

…And up through the actual sale of the state to the Locus Foundation last year, “Six Fingers” was still the last and only high-dollar Hollywood option taken out on any of Lafferty’s tales—which might have explained the willingness of the heirs to finally agree to the sale for $75,000 (with a provision for splitting the purse, should Tinseltown come calling in the future).

…The end of the tale is ambiguous: nearing the great vision of the conspiracy’s extent, and the transmission of that vision to the public, the prematurely aged protagonist dies in his sleep, and his adversaries sedately rejoice. For a decade such appeared to be the fate of Lafferty’s own vision—until the Locus sale gave instead a reason for all of his fans and supporters to rejoice.

Let there be nothing sedate about it! But let us also remember that there remains much work to be done.

--- https://www.lesswrong.com/posts/CH9mkk6BqASf3uztv/counting-agis Counting AGIs 2024-11-25 ai/scaling/economics --- /doc/ai/nn/diffusion/midjourney/2024-11-25-gwern-midjourneyv6-dreammachine-spqrvsdotcom.png SPQR vs Dot-com Gwern 2024-11-25 2024-11-25 ai/nn/diffusion/midjourney fiction/humor --- https://iep.utm.edu/wangchon/#SH4b Wang Chong § Against Ghosts, Supernatural, & Other Superstitions Alexus McLeod 2024-11-26 philosophy/religion --- /doc/psychology/writing/2014-rubin.pdf Past, Present, and Future of Statistical Science § pg614 Xihong Lin, Christian Genest, David L. Banks, Geert Molenberghs, David W. Scott, Jane-Ling Wang 2014-03-26 2024-01-01 [("doi","10.1201/b16720")] psychology/writing statistics/peer-review --- https://arxiv.org/abs/2411.16679 Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts? Sohee Yang, Nora Kassner, Elena Gribovskaya, Sebastian Riedel, Mor Geva 2024-11-25 2024-11-26 [("doi","10.48550/arXiv.2411.16679")] ai/dataset ai/nn/retrieval ai/nn/transformer/gpt

We evaluate how well Large Language Models (LLMs) latently recall and compose facts to answer multi-hop queries like “In the year Scarlett Johansson was born, the Summer Olympics were hosted in the country of”. One major challenge in evaluating this ability is that LLMs may have developed shortcuts by encounters of the head entity “Scarlett Johansson” and the answer entity “United States” in the same training sequences or merely guess the answer based on frequency-based priors.

To prevent shortcuts, we exclude test queries where the head and answer entities co-appear in pretraining corpora. Through careful selection of relations and facts and systematic removal of cases where models might guess answers or exploit partial matches, we construct an evaluation dataset SOCRATES (ShOrtCut-fRee lATent rEaSoning).

We observe that LLMs demonstrate promising latent multi-hop reasoning abilities without exploiting shortcuts, but only for certain types of queries. For queries requiring latent recall of countries as the intermediate answer, the best models achieve 80% latent composability, but this drops to just 5% for the recall of years.

Comparisons with Chain-of-Thought composability highlight a gap between the ability of models to reason latently versus explicitly. Analysis reveals that latent representations of the intermediate answer are constructed more often in queries with higher latent composability, and shows the emergence of latent multi-hop reasoning during pretraining.

--- https://hackage.haskell.org/ Hackage: Introduction 2024-01-01 cs/haskell --- https://hackaday.com/2023/11/30/falsified-photos-fooling-adobes-cryptographically-signed-metadata/ Falsified Photos: Fooling Adobe’s Cryptographically-Signed Metadata 2023-11-30 2024-01-01 cs/cryptography --- https://hackaday.com/2014/01/10/teaching-mario-to-play-pong-and-snake-through-innumerable-exploits/ Teaching Mario to Play Pong and Snake Through Innumerable Exploits 2014-01-10 2024-01-01 cs/computable cs/security --- https://www.wolfewiki.com/pmwiki/pmwiki.php?n=WolfeWiki.AnEvilGuest An Evil Guest Wolfe Wiki 2024-01-01 fiction/gene-wolfe --- https://www.wolfewiki.com/pmwiki/pmwiki.php?n=WolfeWiki.Introduction Wolfe Wiki: Introduction Wolfe Wiki 2024-01-01 fiction/gene-wolfe --- https://www.wireheading.com/ Wirehead hedonism versus paradise-engineering 2024-01-01 philosophy/ethics transhumanism --- https://www.wired.com/story/without-code-for-deepminds-protein-ai-this-lab-wrote-its-own/ 2024-01-01 ai/nn/transformer/alphafold --- /index#abstract Gwern.net Homepage Gwern 2010-10-01 2024-04-17 meta --- /doc/economics/1970-zener.pdf Statistical Theories of Success Clarence Zener 1970-05-01 2024-11-26 [("doi","10.1073/pnas.66.1.18")] economics science

[see Shockley 1957] The Pareto distribution and the log-normal distributions are commonly used to describe the statistical distribution of ‘success’. A detailed comparison of these distributions is made with Lotka’s extensive observations on success as measured by the rate of publication.

These distributions are found to adequately describe the observed distribution only for low and moderate success.

Contrariwise, the flat factor analysis of performance recently developed by the author in these Proceedings (Zener 1968) is shown to give an excellent agreement over the whole range of success.

Lotka’s data allows a determination of the number of environmental factors.

The Pareto distribution does give an excellent agreement with the tail of the success distribution where success is defined as income.

An interpretation of this distribution is here presented based upon the expected behavior of entrepreneurs.

--- /doc/economics/1968-zener.pdf An Analysis Of Scientific Productivity Clarence Zener 1968-04-22 2024-11-26 [("doi","10.1073/pnas.59.4.1078")] economics science --- https://www.cjas.org/~leng/lain.htm thought experiments lain: a Serial Experiments Lain information site Lawrence Eng 2009-11-21 2024-11-26 anime cs/security --- https://www.nature.com/articles/s41593-024-01784-3 Predicting modular functions and neural coding of behavior from a synaptic wiring diagram 2024 2024-11-27 psychology/neuroscience --- https://en.wikipedia.org/wiki/Taxi_medallion Taxi medallion 2024-11-26 economics/mechanism-design/auction --- https://www.vice.com/en/article/worlds-first-ever-cyber-cafe-cyberia-london/ Remembering Cyberia, the World’s First Ever Cyber Cafe 2024-11-27 anime sociology/technology --- https://eshelyaron.com/posts/2024-11-27-emacs-aritrary-code-execution-and-how-to-avoid-it.html Emacs Arbitrary Code Execution and How to Avoid It 2024-11-27 2024-11-27 cs/lisp/emacs cs/security --- https://www.oranlooney.com/post/genji-ko/ The Art and Mathematics of Genji-Kō 2024-11-27 cs/algorithm japan/art --- https://www.lesswrong.com/posts/odtMt7zbMuuyavaZB/when-do-brains-beat-brawn-in-chess-an-experiment?commentId=rJGjyq4j9xNoGyucw#rJGjyq4j9xNoGyucw [The addictiveness & adversarialness of playing against LeelaQueenOdds] Olli Järviniemi 2024-11-27 ai/nn/adversarial/human reinforcement-learning/chess --- https://spectrum.ieee.org/semiconductor-fabrication How IBM invented semiconductor manufacturing automation 2024-11-28 cs/hardware --- https://arxiv.org/abs/2411.16544 Float Self-Tagging Olivier Melançon, Manuel Serrano, Marc Feeley 2024-11-25 2024-11-28 [("doi","10.48550/arXiv.2411.16544")] cs/algorithm cs/cryptography/steganography cs/lisp

Dynamic and polymorphic languages must attach information, such as types, to run time objects, and therefore adapt the memory layout of values to include space for this information. This is especially problematic in the case of IEEE754 double-precision floating-point numbers, which require exactly 64 bits, leaving no space for type information. The two main encodings in-use to this day, tagged pointers and NaN-tagging, either allocate floats on the heap or unbox them at the cost of an overhead when handling all other objects.

This paper presents self-tagging, a new approach to object tagging that can attach type information to 64-bit objects while retaining the ability to use all of their 64 bits for data. At its core, self-tagging exploits the fact that some bit sequences appear with very high probability. Superimposing tags with these frequent sequences allows encoding both 64-bit data and type within a single machine word.

Implementations of self-tagging demonstrate that it unboxes all floats in practice, accelerating the execution time of float-intensive benchmarks in Scheme by 2.3×, and in JavaScript by 2.7× without impacting the performance of other benchmarks, which makes it a good alternative to both tagged pointers and NaN-tagging.

--- https://www.nature.com/articles/s41593-023-01514-1 Inferring neural activity before plasticity as a foundation for learning beyond backpropagation 2024-11-28 ai/nn psychology/neuroscience --- https://www.1001fonts.com/dunwich-ink-font.html Dunwich Ink Font [H. P. Lovecraft] Kyryl Guzovskyi 2024-09-13 2024-11-28 design/typography/dropcap

  • Name: Dunwich Ink Regular

  • Style: Decorative, small caps

  • Weight: Regular

  • Width: Semi-narrow

  • Background: horror literature design

  • Language families: Latin, Cyrillic

Version history:

  1. 0.900: release

  2. 0.914: numbers and basic punctuation marks added

The font was designed within a Master’s Thesis research during studies at Luhansk Taras Shevchenko National University.

--- /doc/statistics/order/comparison/1980-saal.pdf Rating the ratings: Assessing the psychometric quality of rating data Frank E. Saal, Ronald G. Downey, Mary Anne Lahey 1980-01-01 2024-11-28 [("doi","10.1037/0033-2909.88.2.413")] economics statistics/bias statistics/order/comparison

Reviews research that is concerned with evaluating the psychometric qualities of data in the form of ratings (rating errors) and that has been plagued with conceptual and operational confusion and inconsistency.

Following a brief historical survey, inconsistencies in definitions, quantifications, and methodologies are documented in a review of more than 20 relevant articles published in Journal of Applied Psychology, Organizational Behavior and Human Performance, and Personnel Psychology (1975–1977).

Empirical implications of these inconsistencies are discussed, and a revised typology of rating criteria, combined with a multivariate analytic approach, is suggested.

See Also:

--- /doc/psychology/writing/2014-rubin.pdf Converting rejections into positive stimuli Donald B. Rubin 2014-01-01 2024-11-28 [("doi","10.1201/b16720-57")] psychology/writing statistics/causality statistics/peer-review

It’s not that I’m so smart, it’s just that I stay with problems longer.

Albert Einstein

At first glance, this Einstein quotation may seem to have little to do with my title, but those readers who know something of Einstein’s early life will recall that these years were not full of recognized scientific successes, but he kept working on his problems. And that is certainly related to why I chose the quote, but there is more to it.

I have been fortunate to have had many journal publications, but <1% were accepted at first submission—far more were immediately rejected, followed closely by those that were rejected accompanied with the suggestion that it would not be wise to resubmit.

However, I cannot think of an instance where this nasty treatment of my magnificent (self-assessed) work (sometimes joint) did not lead to a markedly improved publication, somewhere. In fact, I think that the drafts that have been repeatedly rejected by many different journals possibly represent my best contributions!

Certainly the repeated rejections, combined with my trying to address various comments, led to better exposition and sometimes better problem formulation as well.

--- https://www.global-developments.org/p/how-much-should-we-trust-developing How Much Should We Trust Developing Country GDP? [little] 2024-11-28 economics statistics/bias --- https://80000hours.org/podcast/episodes/rose-chan-loui-openai-breaking-free-nonprofit/ Rose Chan Loui on OpenAI’s gambit to ditch its nonprofit 2024-11-28 law reinforcement-learning/openai --- https://www.gkogan.co/increase-reply-rates/ To Get More Replies, Say Less Gene Kogan 2024-11-28 psychology/writing --- https://interactionmagic.com/UX-LEGO-Interfaces/ The UX of LEGO Interface Panels 2024-11-28 design --- https://neugierig.org/software/blog/2020/05/ninja.html Tech Notes: The Success and Failure of Ninja 2020-05 2024-11-29 cs/algorithm design --- https://x.com/jarrodWattsDev/status/1862299845710757980 jarrodWattsDev 2024-11-29 ai/nn/adversarial --- https://jamanetwork.com/journals/jamanetworkopen/article-abstract/2798903 Risk of Cardiovascular Diseases Associated With Medications Used in Attention-Deficit/Hyperactivity Disorder: A Systematic Review and Meta-analysis 2024-11-29 psychiatry/adhd --- /doc/philosophy/epistemology/1948-oesper.pdf A Royal Practical Joke Ralph E. Oesper 1948-01-01 2024-11-29 [("doi","10.1021/ed025p93")] math/humor philosophy/epistemology

[according to C. F. Schönbein in 1842] …Charles II was an enthusiast concerning mechanical operations…and was especially fond of witnessing physical experiments. He, however, noted that many of the members [of the Royal Society] placed too much weight on the opinions which he expressed, and he determined to lay bare and chide in a good natured way the flatteries and servile bearing of these individuals.

One evening when the king came into the meeting, he seemingly was deeply immersed in thought. He sat with his hand covering the lower part of his face. Suddenly he cried out: “My Lords and Gentlemen. Why is it that if I place the same amount of water into each of two pails, and then put a 4 pound fish into one of them, this pail will not weigh more than the other?” A long silence followed this royal query. Finally, one member stated that the swimming power of the fish removes its weight. Another opined: “The vital momentum and the vis inertiae of the animal prevent any pressure on the sides of the container.” A third suggested that a characteristic atmosphere enveloped the fish, kept it suspended, and nullified its weight. Other equally sound explanations were offered, and listened to with the greatest sobriety by the king.

Finally, one of the older members arose, “I trust Your Majesty will pardon me, but I am making so bold as to doubt the correctness of the fact stated by you.”

At this the king exclaimed: “By heaven, and so do I. My only intention was to discover to what lengths some of these gentlemen will go to agree with me, and I have played this practical joke on them in the hope that hereafter they will be more careful.”

--- https://www.uk-anime.net/articles/The_Takeshi_Honda_interview/2 The Takeshi Honda interview § 2 2024-01-01 anime/eva --- https://www.uk-anime.net/articles/The_Takeshi_Honda_interview/3 The Takeshi Honda interview § 3 2024-01-01 anime/eva --- https://www.wired.com/2011/08/nasa-inspired-works-of-fiction-the-masses-speak/ ‘NASA-Inspired Works of Fiction’: the masses speak! 2024-01-01 fiction/science-fiction --- https://www.wired.com/2011/06/silkroad-2/ Underground Website Lets You Buy Any Drug Imaginable 2024-01-01 darknet-market/silk-road/1 --- https://www.wired.com/2011/06/inside-google-plus-social/ 2024-01-01 sociology/technology technology/google --- https://github.com/turtlesoupy/this-word-does-not-exist This Word Does Not Exist [Github] 2024-01-01 ai/nn/transformer/gpt/fiction psychology/linguistics --- https://www.commonreader.co.uk/p/learning-to-love-how-the-poet-dana Learning to love: How the poet Dana Gioia discovered his vocation through music [Weep, Shudder, Die: On Opera and Poetry] Dana Gioia 2021-11-24 2024-11-29 fiction/opera fiction/poetry --- https://philosophersmag.com/imperfect-parfit/ Imperfect Parfit Daniel Kodsi, John Maier 2024-11-20 2024-11-29 philosophy/ethics psychiatry/autism --- https://publicdomainreview.org/collection/michelangelo-caetani-maps-of-the-divina-commedia/ Diagramming Dante: Michelangelo Caetani’s Maps of the Divina Commedia (1855/1872) Hunter Dukes 2024-11-26 2024-11-29 design/visualization fiction/poetry history/public-domain-review --- https://www.astralcodexten.com/p/prison-and-crime-much-more-than-you Prison And Crime: Much More Than You Wanted To Know Scott Alexander 2024-11-29 crime --- https://www.newyorker.com/magazine/2024/12/02/a-revolution-in-how-robots-learn A Revolution in How Robots Learn 2024-11-28 2024-11-29 reinforcement-learning/imitation-learning reinforcement-learning/robot --- https://hollisrobbinsanecdotal.substack.com/p/aphantasia-and-mental-modeling Aphantasia and Mental Modeling 2024-11-29 psychology/vision/aphantasia --- https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1174873/full 2024-11-29 psychology/vision --- https://jenn.site/2024/11/you-are-pablo-neruda-it-is-dawn-at-isla-negra-in-1968-matilde-is-asleep-in-your-bed-write-what-comes/ The Neruda Factory Jenn 2024-11 2024-11-29 ai/nn/transformer/gpt/claude ai/poetry --- https://x.com/AISafetyMemes/status/1861842704990347475 AISafetyMemes 2024-11-30 ai/nn/transformer/gpt/claude --- https://profiles.ucl.ac.uk/42083 Jean-Baptiste Pingault 2024-11-29 genetics/heritable --- https://www.jasonwei.net/ Jason Wei 2024-11-29 ai/nn/transformer/t5 ai/scaling --- https://www.dwarkeshpatel.com/p/gwern-branwen#%C2%A7transcript Gwern Branwen—How an Anonymous Researcher Predicted AI’s Trajectory Gwern, Dwarkesh Patel 2024-08 2024-11-28 ai/scaling interview meta personal --- https://en.wikipedia.org/wiki/Op_art Op art 2024-11-30 design psychology/vision --- https://www.seriouseats.com/how-honeycrisp-apples-went-from-marvel-to-mediocre-8753117 How Honeycrisp Apples Went From Marvel to Mediocre 2024-11-30 genetics/selection/artificial/apple --- https://www.theparisreview.org/blog/2017/02/15/rhythmical-lines/ 2017-02-15 2024-11-30 design/typography --- https://en.wikipedia.org/wiki/Space-filling_curve Space-filling curve 2024-11-30 cs/algorithm design/typography math --- https://podcast.clearerthinking.org/episode/236/m-e-thomas-a-conversation-with-a-sociopath A conversation with a sociopath M. E. Thomas, Spencer Greenberg 2024-11-14 2024-11-30 crime psychology/neuroscience/pain psychology/personality/psychopathy

Spencer Greenberg: …You mentioned in your book an example, I believe you were in the subway where someone tried to shame you. Could you tell that story?

M. E. Thomas: So it was in the Washington, DC metro station. I was just trying to get to wherever I was trying to get to. I saw an escalator, and it was broken down, but I just walked down it because it’s stairs. At the bottom of the escalator, a worker stopped me. At first, I was like, “Is he asking for directions or making pleasant conversation?” But it became clear he was trying to tell me that I was wrong for having walked down the broken escalator, which is really just stairs. I’ve done that all my life, especially in New York or other places where escalators often aren’t working. It seems like in the subway, you just walk down the stairs. But he was, I think, more upset that I didn’t seem to acknowledge the wrongness of my actions. Then he tried to shame me about it and said, “You know, didn’t you see the sign? You weren’t supposed to do that, and that’s trespassing.” He was trying to make it seem like I had committed this crime. Then he started walking away. I still did not feel the shame, but I responded to what I perceived to be his aggression, trying to shame me in a way that I call gray rage. Gray rage I experience when somebody who’s not in a position of authority over me seeks to assert authority over me. It must be something about childhood trauma or something, but I immediately kind of lose it, and I call it gray rage. Actually, somebody else coined the term, but I like it because it’s gray, it’s not like red hot, it’s just a full burn. After he left, I started kind of following him, thinking, I just have to destroy him. Those were sort of my ideas. When it’s gray rage, you just are subsumed by the emotion. It’s acting on pure impulse, and the impulse is just to destroy the threat, the thing that caused you to be triggered in this particular way. I was following him, thinking all these thoughts of the things that I would do to him, and then I just lost him in the crowd. I basically had to just go home, wherever I was staying at a hotel, and just rest because it’s so exhausting, physically exhausting afterwards. The gray rage is just such an intense experience.

S. Greenberg: Do you think what would have happened if, instead of losing the crowd, let’s say you cornered him in some secret place where nobody else could see?

Thomas: I had the things that I had imagined doing were mostly choking. I don’t know why in my mind I had the vision of just choking him. It doesn’t make any sense. My more conscientious mind, the non-lizard brain mind, thinks that’s stupid and ridiculous because I’m a 5-foot-four-inch girl, and he was a 6-foot-something guy, and there’s really nothing I could do physically to him with just bare hands that he couldn’t just push me off. But sometimes I wonder about other psychopaths, how crazy that must be to experience things like gray rage or these other impulses when you are a 6-foot man and you experience testosterone. On top of all of this, I wonder if that must be very difficult for them.

G: I had an interesting experience related to gray rage, which is that I was talking to someone that I thought might be a sociopath, and I was asking them about their emotions. I asked them if they feel angry, and they said, “Oh, no, I never feel angry.” Then I said, “Okay, let me show you a video.” I showed them a video of a sociopath describing gray rage. They said, “Oh, that emotion, I feel that like 30× every day.”

T: Wow, that’s a lot, 30× a day. Maybe this person was just exaggerating for humor.

G: They might have been exaggerating, but then they also told me that they keep a list of everyone who’s wronged them, and every month they check it to see if they can hurt those people in any way without putting much effort into it, sort of low-effort ways of harming the people that wronged them. I’m wondering, does that resonate with you at all? This idea of wanting to take revenge on people that have done bad things to you?

T: I don’t experience that as much as other psychopaths that I’ve met seem to experience. I’ve heard of some really long cons that people have done for people that have wronged them. I think that my guess, from my own experience, where my gray rage seems to come from, is that I was imposed upon, my personhood, my autonomy, severely when I was a child, and as a result, it’s almost like a PTSD trigger when I experience the gray rage. It’s obviously not the Metro worker; the Metro worker has really done nothing. But it’s all these memories of feeling like my personhood was disregarded, and how badly that hurt my psyche, and how much that disrupted the development of my sense of self, that I think is being triggered. I think the gray rage is this desire to reassert control and really just personal boundaries. You can’t talk to me that way, type stuff. In the story you mentioned of somebody who has the list of people they check in on every month, I think that’s what it’s trying to do; there’s a desire to be invulnerable to other people. Because we felt so vulnerable as children and felt so put upon and powerless as children, I think.

G: Do you think that other sociopaths tend to have something similar to gray rage? Do the triggers kind of differ a lot in your experience?

T: I think the triggers are pretty much the same. I have another friend who says somebody has a false sense of their own authority, and by consequence, they have a false sense of their own safety. That’s how they think of gray rage.

G: They feel nothing bad could happen to them, and they’re kind of exerting authority on you.

T: They feel they can do that, and this phrase, “I’m not the one you should be messing with”, or “you messed with the wrong person”, is how I hear psychopaths describe it. They want to surprise the person with how forceful and extreme the reaction is to the victim trying to assert authority over the psychopath.

T: I totally agree. So again, using the example of my dad, he’s almost maudlin with his display of empathy, sometimes. He’ll show his arm and be like, “Look, I have goosebumps” because he’s listening to some sad story about somebody. And then to me, though, there’s actually a story, it happened when I was 9, I have this memory where we’re watching TV together, and there’s some kid, I think it was during the Ethiopian famine at the time. So there’s some African kid with a fly flying into his eye. I’m just kind of making fun of this kid, “You can’t even get the fly out of your eye.” And then my dad said to me, “Have you no empathy?” I was like, “I guess I don’t.” Well, first I asked, “What is empathy?” He describes it to me. And then I thought, “I don’t think I have empathy.” So I think that’s a really good example. He probably was crying at the advertisement of the African kid, and I just had this callous reaction of, “What’s the deal with the fly in your eye?”

G: So if you were to watch someone being tortured, really gruesomely, parts of their body flayed off and so on, do you think you would have any emotional reaction to it?

T: I’m sure I would have some, at least esthetic reaction to it. I definitely experience things like disgust; if something smells terrible.

G: Oh, you do have disgust, yeah.

T: I think it kind of makes sense. Disgust is probably a lizard brain emotion; you know, it’s just meant to get us to not eat infection.

…G: You mentioned fear. Fearing rats and opossums. One thing I know about a handful of sociopaths, and one thing I’ve asked them about is fear. Some of them say they don’t think they have fear, or at least not in the normal way that other people do. What’s your relationship with fear?

T: Yeah, I totally agree with that. The rat thing, you can kind of tell it’s a phobia. My girlfriend, who’s also a psychopath, has a phobia of spiders but is totally fine with them. If she catches a spider in her peripheral vision, she’ll have that initial kind of freak out because of the phobia. I’ll do the same thing with rats. If I see one, I’ll have that initial kind of jump scare, and then it’s fine. I can even hold them. I can even play with them or whatever. It’s not that I’m actually afraid of them, and I’m not really afraid of anything else either. Sometimes that’s gotten me in trouble because I will not take adequate precautions. Sometimes I do things that can maybe seem like I’m a little accident-prone. For instance, when I go mountain biking, I probably crash about 20% of the time, which I’ve heard is high.

G: You mentioned in your book how you cut yourself in the kitchen a lot with knives by accident. Can you talk about that?

T: Yes, I still have a plastic safety knife. It’s kind of like the type that you carve pumpkins with, or little children can carve pumpkins with. I almost always use that knife here and there. I think it actually is safer for me to just use a bigger metal knife, but then I have to be very conscientious. I’m the same way with train tracks. There are some train tracks close to where I live, and I cross them basically every day, but I know that I’m bad at paying attention and being careful for my own self. I really talk to myself when I’m doing it, I’m like, “Here we come. 15 feet from the train tracks. 10 feet from the train tracks. Look right, left, right, left, right.” It’s this very belt and suspenders approach to kind of rein in my brain, which naturally doesn’t care, doesn’t even pay attention to things like that.

G: Right. Because it’s not that you don’t know intellectually that you could cut yourself with the knife. It’s that somehow you don’t have the fear of cutting yourself with a knife that causes everyone else to be cautious. Is that accurate?

T: Yeah, I would say that that’s what’s happening.

G: Okay, imagine you’re walking down a dark alley, a big man jumps out with a gun, holds it to your head, and says they’re going to kill you. Would you not feel afraid, in that case?

T: In those types of situations, I do experience hormonal changes, so cortisol, adrenaline.

G: You feel your body being amped up.

T: I can feel the physical change in my body.

G: Do you think you’re afraid?

T: No, in the same way you would feel the physical change in your body of being sleepy. You have melatonin or tryptophan or whatever kind of running through your body, but you don’t think of it. It’s almost kind of like, and this is how I feel too. I’m not a big drinker or drug user, but when I think the most I’ve had is 3 drinks, and at that point I was like, “Oh, my body is just lethargic.” That’s the only way that I experience it. I’m a little stupider, and my body’s moving slower. It’s the same thing with fear or adrenaline. I know that I have adrenaline because my hand is shaking, but I don’t identify with that state of being afraid.

G: Another person I think is a sociopath that I was talking to, I asked them if they’re ever afraid and asked, “What about dangerous animals?” They told me stories about being around dangerous animals, and they just have no emotional response to them. They know intellectually that they can be dangerous, but they just don’t care. This makes me think about, I’ve wondered, from an evolutionary perspective, why are there sociopaths? How is it that they exist in the population? One possibility is that mutations happen sometimes, but I think more likely it’s kind of an evolutionary stable strategy where there are a lot of ways to survive. One way to survive is to have tight-knit groups that work together, collaborate, and have altruism, and that’s what a lot of people do. But there may be another evolutionary stable strategy, which is to be fearless, to focus on your own self-interest, and to be willing to exploit altruistic people when they’ll give you resources. What do you think about that theory?

--- https://tvtropes.org/pmwiki/pmwiki.php/Literature/APracticalGuideToEvil A Practical Guide to Evil (Literature) 2024-01-01 fiction/fantasy --- https://tvtropes.org/pmwiki/pmwiki.php/Literature/Pact Pact (Literature) 2024-01-01 fiction/fantasy --- https://tvtropes.org/pmwiki/pmwiki.php/Literature/Unsong Unsong (Literature) 2024-01-01 fiction/fantasy --- https://tvtropes.org/pmwiki/pmwiki.php/Main/AWizardDidIt A Wizard Did It 2024-04-14 fiction/fantasy --- https://tvtropes.org/pmwiki/pmwiki.php/Webcomic/HitmenForDestiny Hitmen for Destiny (webcomic) 2024-01-01 fiction/fantasy --- https://tvtropes.org/pmwiki/pmwiki.php/Webcomic/NatureOfNaturesArt Nature of Nature’s Art (Webcomic) 2024-01-01 fiction/fantasy --- https://tvtropes.org/pmwiki/pmwiki.php/Webcomic/StandStillStaySilent Stand Still, Stay Silent (webcomic) 2024-01-01 fiction/fantasy --- https://tvtropes.org/pmwiki/pmwiki.php/Literature/Batrachomyomachia Batrachomyomachia (Literature) 2024-01-01 fiction/poetry --- https://tvtropes.org/pmwiki/pmwiki.php/ApocalypticLog/RealLife Apocalyptic Log 2024-01-01 existential-risk fiction/science-fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Literature/TheQuantumThief The Quantum Thief (SF) 2024-03-13 fiction/science-fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Literature/Worm Worm (Literature) 2024-01-01 fiction/science-fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Main/ReedRichardsIsUseless Reed Richards Is Useless 2024-01-01 fiction/science-fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Main/SpaceMadness Space Madness 2024-01-01 fiction/science-fiction psychiatry --- https://tvtropes.org/pmwiki/pmwiki.php/WMG/HarryPotterAndTheMethodsOfRationality Harry Potter and the Methods of Rationality 2024-01-01 fiction/science-fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Manga/GirlsLastTour Girls’ Last Tour (Manga) 2024-01-01 anime fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Manga/GoodnightPunpun Goodnight Punpun (Manga) 2024-01-01 anime fiction --- https://tvtropes.org/pmwiki/pmwiki.php/Tsundere/AnimeAndManga Tsundere § anime/manga 2024-01-01 anime fiction --- https://tvtropes.org/pmwiki/pmwiki.php/UsefulNotes/KoreansInJapan Koreans in Japan § Useful Notes 2024-01-01 anime fiction --- https://tvtropes.org/pmwiki/pmwiki.php/VideoGame/WarshipGirls Warship Girls (Video Game) 2024-01-01 anime fiction --- https://tvtropes.org/pmwiki/pmwiki.php/VisualNovel/UminekoWhenTheyCry Umineko: When They Cry (Visual Novel) 2024-01-01 anime fiction --- https://tvtropes.org/pmwiki/pmwiki.php/WMG/HaruhiSuzumiya Haruhi Suzumiya § WMG 2024-01-01 anime fiction --- https://tvtropes.org/pmwiki/pmwiki.php/WMG/UminekoWhenTheyCry Umineko: When They Cry § WMG 2024-01-01 anime fiction --- https://tvtropes.org/pmwiki/pmwiki.php/WMG/UminekoWhenTheyCryJossed Umineko When They Cry § Jossed 2024-01-01 anime fiction --- https://tvtropes.org/pmwiki/posts.php?discussion=13613719150A44764900&page=0 A Rather Condescending Otaku Article 2024-01-01 anime/eva fiction --- https://en.wikipedia.org/wiki/Henry_Darger Henry Darger 2024-01-01 fiction/fantasy --- https://www.amazon.com/Three-Parts-Dead-Craft-Sequence/dp/0765333112 2024-01-01 fiction/fantasy --- https://en.wikipedia.org/wiki/Max_Gladstone Max Gladstone 2024-01-01 fiction/fantasy --- https://www.brandonsanderson.com/blogs/blog/sandersons-first-law Sanderson’s First Law Brian Sanderson 2024-01-01 fiction/fantasy philosophy/ontology --- https://en.wikipedia.org/wiki/Fantastic_Beasts_and_Where_to_Find_Them_(film) Fantastic Beasts and Where to Find Them (film) J. K. Rowling 2024-01-01 fiction/fantasy --- /doc/fiction/humor/1970-lafferty.pdf Been A Long, Long Time R. A. Lafferty 1970-02 2024-01-01 fiction/humor fiction/science-fiction --- https://en.wikipedia.org/wiki/Geschwind_syndrome Geschwind syndrome 2024-01-01 psychiatry --- /doc/psychology/vision/aphantasia/index 2024-11-30 psychology/inner-voice --- https://www.reddit.com/r/Aphantasia/ 2024-11-30 psychology/vision/aphantasia --- https://news.ycombinator.com/item?id=20267445 Aphantasia 2024-11-30 psychology/vision/aphantasia --- https://psyarxiv.com/sfn9w/ 2024-11-30 psychology/vision/aphantasia --- https://www.bbc.co.uk/news/health-47830256 Aphantasia: Ex-Pixar chief Ed Catmull says ‘my mind’s eye is blind’ 2024-11-30 psychology/vision/aphantasia --- https://news.ycombinator.com/item?id=19618927 Aphantasia: ‘My mind’s eye is blind’ discussion 2024-11-30 psychology/vision/aphantasia --- https://www.construction-physics.com/p/the-influence-of-bell-labs The Influence of Bell Labs Brian Potter 2024-11-30 science --- https://www.lightspeedmagazine.com/fiction/the-cambist-and-lord-iron-a-fairy-tale-of-economics/ The Cambist and Lord Iron: A Fairy Tale of Economics [Lightspeed Magazine mirror] 2024-11-30 fiction/fantasy --- https://www.lesswrong.com/posts/4T8NwAgFYRnuFPRHk/the-cambist-and-lord-iron-a-fairy-tale-of-economics ‘The Cambist and Lord Iron: A Fairy Tale of Economics’ discussion 2024-01-01 economics fiction/fantasy --- https://kenschutte.com/gzip-knn-paper2/ 2024-11-30 ai/nn/retrieval ai/nn/transformer cs/algorithm/information/compression --- https://www.lesswrong.com/s/mFDCbtzb2st4j5BJQ Alexander Kruel’s AI Risk Interview Series (2011–2013) Alexander Kruel 2011 2024-11-30 existential-risk --- https://x.com/liminal_bardo/status/1862434950537937311 liminal_bardo 2024-12-01 ai/nn/transformer/gpt/claude ai/nn/transformer/gpt/poetry --- https://www.medrxiv.org/content/10.1101/2024.11.28.24318135.full Extensive antagonistic variants across human genome Beilei Bian, Valentin Hivert, Naomi R. Wray, Allan F. McRae 2024-11-29 2024-12-01 [("doi","10.1101/2024.11.28.24318135")] genetics/heritable/correlation genetics/selection/natural/human

Pleiotropic conflict, where a genetic locus has antagonistic effects on different traits, is a common phenomenon observed in animals and plants. While pleiotropy has been widely reported in humans, there is no systematic study of pleiotropic conflict in humans.

Here, we leverage GWAS summary statistics of complex diseases and traits derived from large-scale population cohorts to identify pleiotropic regions with conflicting effects. Through a multi-trait colocalization approach, we identified:

219 independent regions containing variants showing pleiotropic conflict, which cover ~11.4% of linkage disequilibrium blocks in the human genome.

Antagonistic variants are observed to be enriched for SNPs with intermediate minor allele frequencies and antagonistic regions show signatures of positive selection & balancing selection.

Our results suggest that antagonistic variants are pervasive in humans and indicate their role in maintaining phenotypic and genetic diversity in humans.

--- https://x.com/focusfronting/status/1862972196366246259 focusfronting 2024-12-01 philosophy/ethics/ethicists --- https://en.wikipedia.org/wiki/Rapid_eye_movement_sleep_behavior_disorder Rapid eye movement sleep behavior disorder 2024-12-01 psychology/vision/dream --- https://en.wikipedia.org/wiki/Tl%C3%B6n,_Uqbar,_Orbis_Tertius ‘Tlön, Uqbar, Orbis Tertius’ Jorge Luis Borges 2024-01-01 borges philosophy/ontology psychology/linguistics --- https://erictang000.github.io/ Eric Tang 2024-12-01 ai/scaling --- https://www.theverge.com/a/virtual-reality The Rise and Fall and Rise of Virtual Reality 2014 2024-01-01 technology --- https://www.theverge.com/a/virtual-reality/oral_history Voices From A Virtual Past: An oral history of a technology whose time has come again 2014 2024-01-01 technology --- https://www.rosiecampbell.xyz/p/leaving-openai Rosie Campbell 2024-12-01 reinforcement-learning/openai reinforcement-learning/safe --- https://arxiv.org/abs/2411.03840 Flexible task abstractions emerge in linear networks with fast and bounded units Kai Sandbrink, Jan P. Bauer, Alexandra M. Proca, Andrew M. Saxe, Christopher Summerfield, Ali Hummos 2024-11-06 2024-12-01 [("doi","10.48550/arXiv.2411.03840")] ai/nn/fully-connected reinforcement-learning/meta-learning/continual-learning

[Twitter] Animals survive in dynamic environments changing at arbitrary timescales, but such data distribution shifts are a challenge to neural networks. To adapt to change, neural systems may change a large number of parameters, which is a slow process involving forgetting past information. In contrast, animals leverage distribution changes to segment their stream of experience into tasks and associate them with internal task abstracts. Animals can then respond flexibly by selecting the appropriate task abstraction.

However, how such flexible task abstractions may arise in neural systems remains unknown. Here, we analyze a ‘linear gated network’ where the weights and gates are jointly optimized via gradient descent, but with neuron-like constraints on the gates including: a faster timescale, non-negativity, and bounded activity.

We observe that the weights self-organize into modules specialized for tasks or sub-tasks encountered, while the gates layer forms unique representations that switch the appropriate weight modules (task abstractions).

We analytically reduce the learning dynamics to an effective eigenspace, revealing a virtuous cycle: fast adapting gates drive weight specialization by protecting previous knowledge, while weight specialization in turn increases the update rate of the gating layer.

Task switching in the gating layer accelerates as a function of curriculum block size and task training, mirroring key findings in cognitive neuroscience.

We show that the discovered task abstractions support generalization through both task and subtask composition, and we extend our findings to a non-linear network switching between two tasks.

Overall, our work offers a theory of cognitive flexibility in animals as arising from joint gradient descent on synaptic and neural gating in a neural network architecture.

--- https://www.darkroastedblend.com/2013/08/flash-dance-digital-dashboards-of-1980s.html Flash Dance: Digital Dashboards of the 1980s (Not every car could be a star of the Knight Rider show, but almost every 1980s car model sported a flashy disco-lights digital dashboard) Avi Abrams 2013-08 2024-12-01 design/typography/rubrication fiction/science-fiction --- /doc/psychiatry/alcoholism/2024-lahteenvuo.pdf Repurposing Semaglutide and Liraglutide for Alcohol Use Disorder Markku Lähteenvuo, Jari Tiihonen, Anssi Solismaa, Antti Tanskanen, Ellenor Mittendorfer-Rutz, Heidi Taipale 2024-11-13 2024-12-01 [("doi","10.1001/jamapsychiatry.2024.3599")] longevity/glp/psychology psychiatry/alcoholism

Question: Are glucagon-like peptide-1 receptor (GLP-1) agonists effective in the treatment of alcohol use disorder?

Findings: This cohort study with a median follow-up time of more than 8 years indicates that individuals are at markedly lower risk of alcohol-related hospitalizations and hospitalizations due to somatic reasons when using GLP-1 agonists, especially semaglutide, as compared with times they are not using them.

Meaning: GLP-1 agonists, especially semaglutide, offer promise as a novel treatment to reduce alcohol consumption and to prevent development of alcohol-related outcomes, but randomized clinical trials are needed to verify these initial findings.

Importance: Preliminary studies suggest that glucagon-like peptide-1 receptor (GLP-1) agonists, used to treat type 2 diabetes and obesity, may decrease alcohol consumption.

Objective: To test whether the risk of hospitalization due to alcohol use disorder (AUD) is decreased during the use of GLP-1 agonists compared with periods of nonuse for the same individual.

Design, Setting, & Participants: This cohort study was an observational study conducted nationwide [population registry] in Sweden using data from 2006-01-01–2023-12-31. The population-based cohort was identified from registers of inpatient care, specialized outpatient care, sickness absence, and disability pension. Participants were all residents aged 16–64 years who had a diagnosis of AUD.

Exposures: The primary exposure was use of individual GLP-1 agonists (compared with nonuse of GLP-1 agonists), and the secondary exposure was medications with indication for AUD.

Main Outcomes & Measures: The primary outcome was AUD hospitalization analyzed in a Cox regression within-individual model. Secondary outcomes were any substance use disorder (SUD)–related hospitalization, somatic hospitalization, and suicide attempt.

Results: The cohort included 227,866 individuals with AUD; 144,714 (63.5%) were male and 83,154 (36.5%) were female, with a mean (SD) age of 40.0 (15.7) years. Median (IQR) follow-up time was 8.8 (4.0–13.3) years. A total of 133,210 individuals (58.5%) experienced AUD hospitalization.

Semaglutide (4,321 users) was associated with the lowest risk (AUD: adjusted hazard ratio [aHR], 0.64; 95% CI, 0.50–0.83; any SUD: aHR, 0.68; 95% CI, 0.54–0.85) and use of liraglutide (2509 users) with the second lowest risk (AUD: aHR, 0.72; 95% CI, 0.57–0.92; any SUD: aHR, 0.78; 95% CI, 0.64–0.97) of both AUD and SUD hospitalization.

Use of any AUD medication was associated with a modestly decreased risk (aHR, 0.98; 95% CI, 0.96–1.00).

Semaglutide (aHR, 0.78; 95% CI, 0.68–0.90) and liraglutide (aHR, 0.79; 95% CI, 0.69–0.91) use were also associated with decreased risk of somatic hospitalizations but not associated with suicide attempts (semaglutide: aHR, 0.55; 95% CI, 0.23–1.30; liraglutide: aHR, 1.08; 95% CI, 0.55–2.15). [Looks underpowered with such wide CIs…]

Conclusion & Relevance: Among patients with AUD and comorbid obesity/type 2 diabetes, the use of semaglutide and liraglutide were associated with a substantially decreased risk of hospitalization due to AUD. This risk was lower than that of officially approved AUD medications. Semaglutide and liraglutide may be effective in the treatment of AUD, and clinical trials are urgently needed to confirm these findings.

--- https://www.yitay.net/blog/returning-to-google-deepmind Returning to Google DeepMind Yi Tay 2024-12-01 ai/scaling/hardware --- https://www.lesswrong.com/posts/kANyEjDDFWkhSKbcK/two-interviews-with-the-founder-of-deepseek Two interviews with the founder of DeepSeek 2024-12-01 ai/scaling/economics ai/scaling/hardware --- https://en.wikipedia.org/wiki/Amusia#Social_and_emotional Amusia § Social and emotional 2024-12-01 psychology/music --- https://github.com/benthamite/EA-numbers Some key numbers that every Effective Altruist should know benthamite 2024-12-01 philosophy/ethics science/fermi-problem --- https://en.wikipedia.org/wiki/Orders_of_magnitude_(energy) Orders of magnitude: energy 2024-12-01 science/fermi-problem --- https://en.wikipedia.org/wiki/Orders_of_magnitude_(power) Orders of magnitude: power 2024-12-01 science/fermi-problem --- https://en.wikipedia.org/wiki/Orders_of_magnitude_(data) Orders of magnitude: data 2024-12-01 science/fermi-problem --- https://en.wikipedia.org/wiki/Median_lethal_dose#Examples Median lethal dose § Examples 2024-12-01 science/fermi-problem --- https://book.bionumbers.org/ 2024-12-01 science/fermi-problem --- https://milan.cvitkovic.net/writing/neurotechnology_numbers_worth_knowing/ Neurotechnology Numbers Worth Knowing Milan Cvitkovic 2024-12-01 psychology/neuroscience science/fermi-problem --- https://sashachapin.substack.com/p/my-mind-transformed-completely-and My mind transformed completely, and there were some tradeoffs Sasha Chapin 2024-12-01 2024-12-01 psychiatry/meditation --- https://x.com/meekaale/status/1800369366283755740 meekaale 2024-12-01 design/typography --- https://x.com/repligate/status/1776041976653402508 Claude-3 base-model-like jailbreak Janus 2024-12-01 ai/nn/adversarial ai/nn/transformer/gpt/claude --- https://bristoliver.substack.com/p/the-final-cut The Final Cut [Ford-Fulkerson’s max-flow min-cut as planning paradigm] 2024-12-01 cs/algorithm statistics/decision --- https://storage.courtlistener.com/recap/gov.uscourts.cand.433688/gov.uscourts.cand.433688.46.0.pdf [Musk injunction to block OA for-profit conversion] 2024-12-01 reinforcement-learning/openai --- https://maxmore.substack.com/p/remembering-vernor-vinge Remembering Vernor Vinge 2024-12-01 cryonics fiction/science-fiction --- https://boehs.org/node/dark-web-security The fascinating security model of dark web marketplaces Evan Boehs 2024-11-14 2024-12-02 cs/css cs/js darknet-market --- https://www.biorxiv.org/content/10.1101/2024.11.21.624608.full Molecular and genetic characterization of sex-linked orange coat color in the domestic cat Christopher B. Kaelin, Kelly A. McGowan, Joshaya C. Trotman, Donald C. Koroma, Victor A. David, Marilyn Menotti-Raymond, Emily C. Graff, Anne Schmidt-Küntzel, Elena Oancea, Gregory S. Barsh 2024-11-22 2024-12-02 [("doi","10.1101/2024.11.21.624608")] cat/genetics

[independent discovery by Toh et al 2024] The Sex-linked orange mutation in domestic cats causes variegated patches of reddish/yellow hair and is a defining signature of random X-inactivation in female tortoiseshell cats & calico cats. Unlike the situation for most coat color genes, there is no apparent homolog for Sex-linked orange in other mammals.

We show that the Sex-linked orange is caused by a 5 kb deletion that leads to ectopic and melanocyte-specific expression of the Rho GTPase Activating Protein 36 (Arhgap36) gene. Single cell RNA-seq studies from fetal cat skin reveal that red/yellow hair color is caused by reduced expression of melanogenic genes that are normally activated by the Melanocortin 1 receptor (Mc1r)-cyclic adenosine monophosphate (cAMP)-protein kinase A (PKA) pathway, but the Mc1r gene and its ability to stimulate cAMP accumulation is intact.

Instead, we show that increased expression of Arhgap36 in melanocytes leads to reduced levels of the PKA catalytic subunit (PKAC); thus, Sex-linked orange is genetically and biochemically downstream of Mc1r.

Our findings solve a comparative genomic conundrum, provide in vivo evidence for the ability of Arhgap36 to inhibit PKA, and reveal a molecular explanation for a charismatic color pattern with a rich genetic history.

--- https://www.biorxiv.org/content/10.1101/2024.11.19.624036.full A deletion at the X-linked ARHGAP36 gene locus is associated with the orange coloration of tortoiseshell and calico cats Hidehiro Toh, Wan Kin Au Yeung, Motoko Unoki, Yuki Matsumoto, Yuka Miki, Yumiko Matsumura, Yoshihiro Baba, Takashi Sado, Yasukazu Nakamura, Miho Matsuda, Hiroyuki Sasaki 2024-11-21 2024-12-02 [("doi","10.1101/2024.11.19.624036")] cat/genetics

[media; independent discovery by Kaelin et al 2024] The X-linked orange (O) locus in domestic cats controls an unknown molecular mechanism that causes the suppression of black-brownish pigmentation in favor of orange coloration. The alternating black-brownish and orange patches seen in tortoiseshell cats & calico cats are considered as classic examples of the phenotypic expression of random X-chromosome inactivation (XCI) occurring in female mammals. However, the O gene in the cat genome has not been identified, and the genetic variation responsible for the orange coloration remains unknown.

We report here that a 5.1-kilobase (kb) deletion within an intron of the X-linked ARHGAP36 gene, encoding a Rho GTPase activating protein, is closely and exclusively associated with orange coloration. The deleted region contains a highly conserved putative regulatory element, whose removal presumably causes altered ARHGAP36 expression. Notably, ARHGAP36 expression in cat skin tissues is linked to the suppression of many melanogenesis genes, potentially shifting pigment synthesis from eumelanin to pheomelanin.

Furthermore, we find evidence that the gene undergoes XCI in female human and mouse cells, and XCI-dependent CpG island methylation consistent with random XCI in female domestic cats.

The 5.1-kb deletion seems widespread in domestic cats with orange coat coloration, suggesting a single origin of this coat color phenotype.

--- https://www.science.org/content/article/gene-behind-orange-fur-cats-found-last Gene behind orange fur in cats found at last: After 60 years, scientists know why gingers, calicos, and tortoiseshells look the way they do Sara Reardon 2024-11-27 2024-12-02 cat/genetics --- https://neuralmagic.com/blog/24-sparse-llama-smaller-models-for-efficient-gpu-inference/ 2:4 Sparse Llama: Smaller Models for Efficient GPU Inference Eldar Kurtić, Alexandre Marques, Mark Kurtz, Dan Alistarh, Shubhra Pandit 2024-11-25 2024-12-02 ai/nn/sparsity/low-precision ai/nn/sparsity/pruning ai/nn/transformer/gpt --- https://milesbrundage.substack.com/p/why-im-leaving-openai-and-what-im Why I’m Leaving OpenAI and What I’m Doing Next Miles Brundage 2024-12-03 reinforcement-learning/openai reinforcement-learning/safe --- https://transluce.org/neuron-descriptions Scaling Automatic Neuron Description 2024-12-03 ai/nn/transformer/attention --- https://demos.obormot.net/colorization-demo Colorization Demo Said Achmiz 2024-12-03 cs/css --- https://www.theparisreview.org/interviews/3475/the-art-of-fiction-no-68-anthony-powell The Art of Fiction No. 68: Anthony Powell Anthony Powell 2024-01-01 psychology/writing --- https://www.theparisreview.org/interviews/3195/the-art-of-fiction-no-68-carlos-fuentes The Art of Fiction No. 68: Carlos Fuentes Carlos Fuentes 2024-01-01 psychology/writing --- /doc/borges/1955-borges-lillusioncomique.pdf L’Illusion Comique Jorge Luis Borges 1955-01-01 2024-12-03 borges politics --- https://x.com/umesh_ai/status/1854861074463445332 umesh_ai 2024-12-03 ai/nn/diffusion/midjourney ai/video/generation --- https://x.com/umesh_ai/status/1855079179999400197 umesh_ai 2024-12-03 ai/nn/diffusion/midjourney ai/video/generation --- http://archives.fullerton.edu/repositories/5/resources/56 Willis E. McNelly Science Fiction Collection: Frank Herbert papers 2024-12-03 fiction/science-fiction/frank-herbert --- https://x.com/karpathy/status/1864023344435380613 Andrej Karpathy 2024-12-03 ai/nn/transformer/attention --- https://x.com/sherjilozair/status/1864013593005682926 [The invention of GANs] Sherjil Ozair 2024-12-03 ai/nn/gan --- https://cs.nyu.edu/~davise/papers/GPT-Poetry.pdf Ernest Davis 2024-12-03 ai/nn/transformer/gpt/4/poetry reinforcement-learning/preference-learning/mode-collapse --- https://pmc.ncbi.nlm.nih.gov/articles/PMC9750936/ Mechanistic insight into sevoflurane-associated developmental neurotoxicity 2024-12-03 psychology/neuroscience/pain/anesthesia --- https://pmc.ncbi.nlm.nih.gov/articles/PMC4168665/ Effect of General Anesthesia in Infancy on Long-Term Recognition Memory in Humans and Rats 2024-12-03 psychology/neuroscience/memory psychology/neuroscience/pain/anesthesia --- https://poets.org/poem/one-train-may-hide-another One Train May Hide Another Kenneth Koch 2024-12-03 fiction/poetry --- https://carnegieendowment.org/research/2024/08/china-artificial-intelligence-ai-safety-regulation China’s Views on AI Safety Are Changing—Quickly: Beijing’s AI safety concerns are higher on the priority list, but they remain tied up in geopolitical competition and technological advancement Matt Sheehan 2024-08-27 2024-12-03 reinforcement-learning/safe --- https://carnegieendowment.org/people/matt-sheehan Matt Sheehan 2024-12-03 reinforcement-learning/safe --- https://arxiv.org/abs/2412.00176 Art-Free Generative Models: Art Creation Without Graphic Art Knowledge Hui Ren, Joanna Materzynska, Rohit Gandikota, David Bau, Antonio Torralba 2024-11-29 2024-12-04 [("doi","10.48550/arXiv.2412.00176")] ai/nn/diffusion

We explore the question: “How much prior art knowledge is needed to create art?” [little to none, as already known from GAN & scaling & transfer research]

To investigate this, we propose a text-to-image generation model trained without access to art-related content. We then introduce a simple yet effective method to learn an art adapter using only a few examples of selected artistic styles.

Our experiments show that art generated using our method is perceived by users as comparable to art produced by models trained on large, art-rich datasets.

Finally, through data attribution techniques, we illustrate how examples from both artistic and non-artistic datasets contributed to the creation of new artistic styles.

--- https://arxiv.org/abs/2411.13543 BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games Davide Paglieri, Bartłomiej Cupiał, Samuel Coward, Ulyana Piterbarg, Maciej Wolczyk, Akbir Khan, Eduardo Pignatelli, Łukasz Kuciński, Lerrel Pinto, Rob Fergus, Jakob Nicolaus Foerster, Jack Parker-Holder, Tim Rocktäschel 2024-11-20 2024-12-04 [("doi","10.48550/arXiv.2411.13543")] ai/dataset ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/claude reinforcement-learning/nethack

[code, leaderboard/homepage] Large Language Models (LLMs) and Vision Language Models (VLMs) possess extensive knowledge and exhibit promising reasoning abilities; however, they still struggle to perform well in complex, dynamic environments. Real-world tasks require handling intricate interactions, advanced spatial reasoning, long-term planning, and continuous exploration of new strategies—areas in which we lack effective methodologies for comprehensively evaluating these capabilities.

To address this gap, we introduce BALROG, a novel benchmark designed to assess the agentic capabilities of LLMs and VLMs through a diverse set of challenging games. Our benchmark incorporates a range of existing reinforcement learning environments with varying levels of difficulty, including tasks that are solvable by non-expert humans in seconds to extremely challenging ones that may take years to master (eg. the NetHack Learning Environment).

We devise fine-grained metrics to measure performance and conduct an extensive evaluation of several popular open-source and closed-source LLMs and VLMs.

Our findings indicate that while current models achieve partial success in the easier games, they struggle with more challenging tasks. Notably, we observe severe deficiencies in vision-based decision-making, as models perform worse when visual representations of the environments are provided.

We release BALROG as an open and user-friendly benchmark to facilitate future research and development in the agentic community.

--- https://arxiv.org/abs/2404.00806 Algorithmic Collusion by Large Language Models Sara Fish, Yannai A. Gonczarowski, Ran I. Shorrer 2024-03-31 2024-12-04 [("doi","10.48550/arXiv.2404.00806")] economics/mechanism-design/auction reinforcement-learning/model reinforcement-learning/multi-agent reinforcement-learning/safe

The rise of algorithmic pricing raises concerns of algorithmic collusion.

We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs).

We find that (1) LLM-based agents are adept at pricing tasks, (2) LLM-based pricing agents autonomously collude in oligopoly settings to the detriment of consumers, and (3) variation in seemingly innocuous phrases in LLM instructions (‘prompts’) may increase collusion. Novel [game-theoretical] ‘off-path’ analysis techniques uncover price-war concerns as contributing to these phenomena. Our results extend to auction settings.

Our findings uncover unique challenges to any future regulation of LLM-based pricing agents, and black-box pricing agents more broadly.

--- https://en.wikipedia.org/wiki/Harlan_Ellison Harlan Ellison 2024-01-01 fiction/science-fiction psychiatry/bipolar/energy --- https://www.astralcodexten.com/p/book-review-from-bauhaus-to-our-house Book Review: From Bauhaus To Our House (Tom Wolfe 1981) Scott Alexander 2024-12-03 2024-12-04 design sociology/technology --- https://arxiv.org/abs/2410.00179 Evaluating the fairness of task-adaptive pretraining on unlabeled test data before few-shot text classification Kush Dubey 2024-09-30 2024-12-05 [("doi","10.48550/arXiv.2410.00179")] ai/nn/dynamic-evaluation ai/nn/transformer/gpt/2

Few-shot learning benchmarks are critical for evaluating modern NLP techniques. It is possible, however, that benchmarks favor methods which easily make use of unlabeled text, because researchers can use unlabeled text from the test set to pretrain their models.

Given the dearth of research on this potential problem, we run experiments to quantify the bias caused by pretraining on unlabeled test set text instead of on unlabeled, independently drawn text.

[finetuning Mistral-7b 2,000 times, and BERT and GPT-2 135,000 times, for science.] Controlled few-shot and zero-shot experiments on 25 classification tasks and 3 language models—BERT, GPT-2, and Mistral-7b—do:

not find evidence of overoptimism. Furthermore, we demonstrate the importance of repeated subsampling when studying few-shot text classification, and recommend that few-shot learning benchmarks include multiple training folds.

Code and data are available at Github.

--- /second-life-sentence Second Life Sentences Gwern 2024-12-03 2024-12-04 crime fiction/science-fiction/time-travel law math/humor philosophy/ethics

Variations on the idea of criminals being convicted and sentenced to a ‘second’ life sentence—what could that possibly mean…?

--- https://infoproc.blogspot.com/2011/08/predictive-power-of-early-childhood-iq.html Predictive power of early childhood IQ Steve Hsu 2024-01-01 iq/high/smpy --- https://infoproc.blogspot.com/2008/07/annals-of-psychometry-iqs-of-eminent.html Annals of psychometry: IQs of eminent scientists Steve Hsu 2024-01-01 iq/high/anne-roe --- https://infoproc.blogspot.com/2016/06/roes-scientists-original-published.html Anne Roe’s scientists: original published papers Steve Hsu 2024-01-01 iq/high/anne-roe --- https://infoproc.blogspot.com/2021/07/polygenic-embryo-screening-comments-on.html Polygenic Embryo Screening: comments on Carmi et al and Visscher et al Steve Hsu 2024-01-01 genetics/selection/artificial --- https://www.adirondackalmanack.com/2016/05/understanding-life-spans-whitetail-deer.html Understanding The Life Span Of Whitetail Deer Hetzler 2016 2024-01-01 biology --- https://www.youtube.com/watch?v=WO4XLz8wFqM&list=PL2FF649D0C4407B30&index=15 Digital Filters, Part II Richard Hamming 1995-04-28 2024-01-01 cs/algorithm --- https://www.youtube.com/watch?v=6oxz3ykLWLI&list=PL2FF649D0C4407B30&index=24 Quantum Mechanics Hamming 2024-01-01 science --- https://www.youtube.com/watch?v=8LU-6LZoX-A&list=PL2FF649D0C4407B30&index=32 1990 NPS SGL Lecture Richard Hamming 2024-01-01 science --- http://www.sociopathworld.com/ Sociopath World 2024-12-05 psychology/personality/psychopathy --- https://en.wikipedia.org/wiki/National_Library_of_Argentina National Library of Argentina 2024-12-05 borges --- https://en.wikipedia.org/wiki/Peronism Peronism 2024-12-05 borges --- https://lamag.com/books/harlan-ellison-last-words-dangerous-visions-sci-fi-writer-posthumous-comeback Harlan Ellison’s Last Words: The Ambitious Plan for Sci-Fi Writer’s Posthumous Comeback—Longtime friend and Babylon 5 creator J. Michael Straczynski endeavors to reshape the legacy of the late sci-fi writer Steve Appleford 2024-03-29 2024-12-05 fiction/science-fiction psychiatry/bipolar/energy --- https://benjamintodd.org/#about Benjamin Todd 2024-12-05 philosophy/ethics --- https://www.smbc-comics.com/comic/trajectoid Trajectoid 2024-12-05 fiction/text-game math --- https://en.wikipedia.org/wiki/Oulipo Oulipo 2024-12-05 fiction/text-game --- https://en.wikipedia.org/wiki/Lipogram Lipogram 2024-12-05 fiction/text-game --- https://arxiv.org/abs/2412.03556 Best-of-N Jailbreaking John Hughes, Sara Price, Aengus Lynch, Rylan Schaeffer, Fazl Barez, Sanmi Koyejo, Henry Sleight, Erik Jones, Ethan Perez, Mrinank Sharma 2024-12-04 2024-12-05 [("doi","10.48550/arXiv.2412.03556")] ai/nn/adversarial ai/scaling

We introduce Best-of-N (BoN) Jailbreaking, a simple black-box algorithm that jailbreaks frontier AI systems across modalities. BoN Jailbreaking works by repeatedly sampling variations of a prompt with a combination of augmentations—such as random shuffling or capitalization for textual prompts—until a harmful response is elicited.

We find that BoN Jailbreaking achieves high attack success rates (ASRs) on closed-source language models, such as 89% on GPT-4o and 78% on Claude 3.5 Sonnet when sampling 10,000 augmented prompts. Further, it is similarly effective at circumventing state-of-the-art open-source defenses like circuit breakers.

BoN also seamlessly extends to other modalities: it jailbreaks vision language models (VLMs) such as GPT-4o and audio language models (ALMs) like Gemini 1.5 Pro, using modality-specific augmentations. BoN reliably improves when we sample more augmented prompts.

Across all modalities, ASR, as a function of the number of samples (N), empirically follows power-law-like behavior for many orders of magnitude. BoN Jailbreaking can also be composed with other black-box algorithms for even more effective attacks—combining BoN with an optimized prefix attack achieves up to a 35% increase in ASR.

Overall, our work indicates that, despite their capability, language models are sensitive to seemingly innocuous changes to inputs, which attackers can exploit across modalities.

--- https://www.reddit.com/r/ApplyingToCollege/comments/1h0vhlq/in_the_past_three_days_ive_reviewed_over_100/ 2024-12-05 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/preference-learning/mode-collapse --- https://arxiv.org/abs/2412.03555#google PaliGemma 2: A Family of Versatile VLMs for Transfer Andreas Steiner, André Susano Pinto, Michael Tschannen, Daniel Keysers, Xiao Wang, Yonatan Bitton, Alexey Gritsenko, Matthias Minderer, Anthony Sherbondy, Shangbang Long, Siyang Qin, Reeve Ingle, Emanuele Bugliarello, Sahar Kazemzadeh, Thomas Mesnard, Ibrahim Alabdulmohsin, Lucas Beyer, Xiaohua Zhai 2024-12-04 2024-12-05 [("doi","10.48550/arXiv.2412.03555")] ai/nn/transformer/clip ai/scaling

PaliGemma 2 is an upgrade of the PaliGemma open Vision-Language Model (VLM) based on the Gemma 2 family of language models. We combine the SigLIP-So400m vision encoder that was also used by PaliGemma with the whole range of Gemma 2 models, from the 2B one all the way up to the 27B model.

We train these models at 3 resolutions (224px, 448px, and 896px) in multiple stages to equip them with broad knowledge for transfer via fine-tuning.

The resulting family of base models covering different model sizes and resolutions allows us to investigate factors impacting transfer performance (such as learning rate) and to analyze the interplay between the type of task, model size, and resolution.

We further increase the number and breadth of transfer tasks beyond the scope of PaliGemma including different OCR-related tasks such as table structure recognition, molecular structure recognition, music score recognition, as well as long fine-grained captioning and radiography report generation, on which PaliGemma 2 obtains state-of-the-art results.

--- https://arxiv.org/abs/2411.18447 Continuous Autoregressive Models with Noise Augmentation Avoid Error Accumulation Marco Pasini, Javier Nistal, Stefan Lattner, George Fazekas 2024-11-27 2024-12-05 [("doi","10.48550/arXiv.2411.18447")] ai/music ai/nn/transformer/gpt reinforcement-learning/meta-learning

Autoregressive models are typically applied to sequences of discrete tokens, but recent research indicates that generating sequences of continuous embeddings in an autoregressive manner is also feasible. However, such Continuous Autoregressive Models (CAMs) can suffer from a decline in generation quality over extended sequences due to error accumulation during inference.

We introduce a novel method to address this issue by injecting random noise into the input embeddings during training. This procedure makes the model robust against varying error levels at inference. We further reduce error accumulation through an inference procedure that introduces low-level noise.

Experiments on musical audio generation show that CAM substantially outperforms existing autoregressive and non-autoregressive approaches while preserving audio quality over extended sequences.

This work paves the way for generating continuous embeddings in a purely autoregressive setting, opening new possibilities for real-time and interactive generative applications.

--- https://spillhistorie.no/the-story-of-rogue/ The story of Rogue 2024-12-05 fiction/text-game --- https://en.wikipedia.org/wiki/Shotgun_clause Shotgun clause 2024-12-05 economics/mechanism-design --- https://www.reddit.com/r/ClaudeAI/comments/1h6pxdn/how_claude_35_helped_me_fight_off_a_10000_rental/ 2024-12-05 ai/nn/transformer/gpt/claude law --- https://x.com/emollick/status/1864744770695815234 Ethan Mollick 2024-12-05 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/codex --- https://publicdomainreview.org/collection/pinkerton-thirty-years-a-detective/ True Crime: Allan Pinkerton’s Thirty Years a Detective (1884) 2024-12-05 crime history/public-domain-review --- https://www.nytimes.com/2024/12/02/magazine/nuclear-strategy-proud-prophet.html The Secret Pentagon War Game That ​Offers a Stark​ Warning for Our Times 2024-12-02 2024-12-05 radiance --- https://arxiv.org/abs/2408.05446 Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness Stanislav Fort, Balaji Lakshminarayanan 2024-08-08 2024-12-05 [("doi","10.48550/arXiv.2408.05446")] ai/nn/adversarial ai/nn/transformer/clip ai/nn/transformer/gpt

Adversarial examples pose a challenge to the robustness, reliability, and alignment of deep neural networks. We propose a novel, easy-to-use approach to achieving high-quality representations that lead to adversarial robustness through the use of multi-resolution input representations and dynamic self-ensembling of intermediate layer predictions.

We demonstrate that intermediate layer predictions exhibit inherent robustness to adversarial attacks crafted to fool the full classifier, and propose a robust aggregation mechanism based on Vickrey auction that we call CrossMax to dynamically ensemble them.

By combining multi-resolution inputs and robust ensembling, we achieve adversarial robustness on CIFAR-10 and CIFAR-100 datasets without any adversarial training or extra data, reaching an adversarial accuracy of ~72% (CIFAR-10) and ~48% (CIFAR-100) on the RobustBench AutoAttack suite (L∞ = 8⁄255 with a finetuned ImageNet-pretrained ResNet152).

This represents a result comparable with the top 3 models on CIFAR-10 and a +5% gain compared to the best current dedicated approach on CIFAR-100. Adding simple adversarial training on top, we get ~78% on CIFAR-10 and ~51% on CIFAR-100, improving state-of-the-art by 5% and 9% respectively and seeing greater gains on the harder dataset.

We validate our approach through extensive experiments and provide insights into the interplay between adversarial robustness and the hierarchical nature of deep representations.

We show that simple gradient-based attacks against our model lead to human-interpretable images of the target classes as well as interpretable image changes. As a byproduct, using our multi-resolution prior, we turn pre-trained classifiers and CLIP models into controllable image generators and develop successful transferable attacks on large vision-language models.

--- https://arxiv.org/abs/2404.06757 Language Generation in the Limit Jon Kleinberg, Sendhil Mullainathan 2024-04-10 2024-12-05 [("doi","10.48550/arXiv.2404.06757")] cs/computable philosophy/epistemology psychology/linguistics

Although current large language models are complex, the most basic specifications of the underlying language generation problem itself are simple to state: given a finite set of training samples from an unknown language, produce valid new strings from the language that don’t already appear in the training data.

Here we ask what we can conclude about language generation using only this specification, without further assumptions. In particular, suppose that an adversary enumerates the strings of an unknown target language L that is known only to come from one of a possibly infinite list of candidates. A computational agent is trying to learn to generate from this language; we say that the agent generates from L in the limit if after some finite point in the enumeration of L, the agent is able to produce new elements that come exclusively from L and that have not yet been presented by the adversary.

Our main result is that there is an agent that is able to generate in the limit for every countable list of candidate languages.

This contrasts dramatically with negative results due to Gold & Angluin in a well-studied model of language learning where the goal is to identify an unknown language from samples; the difference between these results suggests that identifying a language is a fundamentally different problem than generating from it.

[As Wittgenstein might say, it doesn’t matter what the beetle-in-the-box is, as long as we all generate language-games in the same way and agree in our form of life.]

--- https://en.wikipedia.org/wiki/Language_identification_in_the_limit Language identification in the limit 2024-12-05 cs/computable philosophy/epistemology psychology/linguistics --- https://www.biorxiv.org/content/10.1101/2024.05.08.593200.full De novo gene synthesis by an antiviral reverse transcriptase Stephen Tang, Valentin Conte, Dennis J. Zhang, Rimantė Žedaveinytė, George D. Lampe, Tanner Wiegand, Lauren C. Tang, Megan Wang, Matt W. G. Walker, Jerrin Thomas George, Luke E. Berchowitz, Marko Jovanovic, Samuel H. Sternberg 2024-05-08 2024-12-05 [("doi","10.1101/2024.05.08.593200")] genetics/genome-synthesis/virus-proof

Bacterial reverse transcriptases synthesize extrachromosomal genes via rolling-circle amplification to confer potent antiviral immunity.

Bacteria defend themselves from viral infection using diverse immune systems, many of which sense and target foreign nucleic acids. Defense-associated reverse transcriptase (DRT) systems provide an intriguing counterpoint to this immune strategy by instead leveraging DNA synthesis, but the identities and functions of their DNA products remain largely unknown.

Here we show that DRT2 systems execute an unprecedented immunity mechanism that involves de novo gene synthesis via rolling-circle reverse transcription of a non-coding RNA (ncRNA). Unbiased profiling of RT-associated RNA and DNA ligands in DRT2-expressing cells revealed that reverse transcription generates concatenated cDNA repeats through programmed template jumping on the ncRNA. The presence of phage then triggers second-strand cDNA synthesis, leading to the production of long double-stranded DNA. Remarkably, this DNA product is efficiently transcribed, generating messenger RNAs that encode a stop codon-less, never-ending ORF (neo) whose translation causes potent growth arrest.

Phylogenetic analyses and screening of diverse DRT2 homologs further revealed broad conservation of rolling-circle reverse transcription and Neo protein function.

Our work highlights an elegant expansion of genome coding potential through RNA-templated gene creation, and challenges conventional paradigms of genetic information encoded along the one-dimensional axis of genomic DNA.

--- https://proceedings.neurips.cc/paper/2019/hash/ee39e503b6bedf0c98c388b7e8589aca-Abstract.html A Meta-Analysis of Overfitting in Machine Learning 2024-12-06 ai/tabular --- https://www.overcomingbias.com/p/hail-jeffrey-wernick Hail Jeffrey Wernick [pre-Hanson employment & conditional prediction markets] Robin Hanson 2024-12-06 2024-12-06 statistics/prediction --- https://arxiv.org/abs/2310.11616 Evidence of interrelated cognitive-like capabilities in large language models: Indications of artificial general intelligence or achievement? David Ilić, Gilles E. Gignac 2023-10-17 2024-12-06 [("doi","10.1016/j.intell.2024.101858")] ai/scaling iq

Large language models (LLMs) are advanced artificial intelligence (AI) systems that can perform a variety of tasks commonly found in human intelligence tests, such as defining words, performing calculations, and engaging in verbal reasoning. There are also substantial individual differences in LLM capacities.

Given the consistent observation of a positive manifold and general intelligence factor in human samples, along with group-level factors (eg. crystallized intelligence), we hypothesized that LLM test scores may also exhibit positive intercorrelations, which could potentially give rise to an artificial general ability (AGA) factor and one or more group-level factors. Based on a sample of 591 LLMs and scores from 12 tests aligned with fluid reasoning (Gf), domain-specific knowledge (Gkn), reading/writing (Grw), and quantitative knowledge (Gq), we found:

strong empirical evidence for a positive manifold and a general factor of ability. Additionally, we identified a combined Gkn/Grw group-level factor.

Finally, the number of LLM parameters correlated positively with both general factor of ability and Gkn/Grw factor scores, although the effects showed diminishing returns. We interpreted our results to suggest that LLMs, like human cognitive abilities, may share a common underlying efficiency in processing information and solving problems, though whether LLMs manifest primarily achievement/expertise rather than intelligence remains to be determined.

Finally, while models with greater numbers of parameters exhibit greater general cognitive-like abilities, akin to the connection between greater neuronal density and human general intelligence, other characteristics must also be involved.

--- https://tau.solahpmo.com/viewtopic.php?p=21013&sid=1e6b184fca6b94948d577b48c786f54c#p21013 The Alleged Dune 7 Notes 2024-12-06 fiction/science-fiction/frank-herbert --- https://web.archive.org/web/20071012125808/http://dunenovels.com/dune7blog/page21.html Dune 7 Blog Brian Herbert 2007-10-12 2024-12-06 fiction/science-fiction/frank-herbert --- https://news.ycombinator.com/item?id=42319461 HN: Self-Funding Harberger Taxes Hacker News 2024-12-05 2024-12-06 economics/mechanism-design --- https://x.com/emollick/status/1864800386873213026 Ethan Mollick 2024-12-06 ai/nn/transformer/gpt/4/poetry --- https://arxiv.org/abs/2410.12557 One Step Diffusion via Shortcut Models Kevin Frans, Danijar Hafner, Sergey Levine, Pieter Abbeel 2024-10-16 2024-12-06 [("doi","10.48550/arXiv.2410.12557")] ai/nn/diffusion

Diffusion models and flow-matching models have enabled generating diverse and realistic images by learning to transfer noise to data. However, sampling from these models involves iterative denoising over many neural network passes, making generation slow and expensive.

Previous approaches for speeding up sampling require complex training regimes, such as multiple training phases, multiple networks, or fragile scheduling.

We introduce shortcut models, a family of generative models that use a single network and training phase to produce high-quality samples in a single or multiple sampling steps. Shortcut models condition the network not only on the current noise level but also on the desired step size, allowing the model to skip ahead in the generation process.

Across a wide range of sampling step budgets, shortcut models consistently produce higher quality samples than previous approaches, such as consistency models and reflow. Compared to distillation, shortcut models reduce complexity to a single network and training phase and additionally allow varying step budgets at inference time.

--- https://www.newyorker.com/magazine/2001/12/10/the-bench-burner Richard Posner, The Bench Burner: How did a judge with such subversive ideas become a leading influence on American legal opinion? Larissa MacFarquhar 2001-12-10 2024-12-06 law philosophy/ethics psychology/writing --- https://en.wikipedia.org/wiki/List_of_terms_referring_to_an_average_person List of terms referring to an average person 2024-12-06 psychology/linguistics --- https://en.wikipedia.org/wiki/Seriation_(archaeology) Seriation (archaeology) 2024-12-07 biology cs/algorithm/sorting history statistics/order/comparison --- https://en.wikipedia.org/wiki/Ordination_(statistics) Ordination (statistics) 2024-12-07 biology cs/algorithm/sorting statistics/order/comparison --- https://x.com/voooooogel/status/1865189744776507809 Jukka Luoma 2024-12-07 ai/nn/sampling ai/nn/transformer/gpt --- https://en.wikipedia.org/wiki/City_pop City pop 2024-12-07 design japan/art --- http://hiroshinagai.com/gallery/vehicle.html V E H I C L E gallery Hiroshi Nagai 2024-12-07 design japan/art --- https://potato.cheap/ The Cheap Web Taylor Troesh 2023 2024-12-08 cs/css design sociology/technology --- https://news.ycombinator.com/item?id=36302805 Why sqlite3 temporary files were renamed etilqs_* (2006) [discussion] Hacker News 2023-06-12 2024-12-09 design sociology/technology --- https://en.wikipedia.org/wiki/Kirk_Allen Kirk Allen 2024-01-01 fiction/science-fiction psychiatry/autism/schizoid --- https://harpers.org/archive/1954/12/the-jet-propelled-couch/?single=1 The Jet-Propelled Couch, by Robert Mitchell Lindner 2024-01-01 fiction/science-fiction psychiatry/autism/schizoid --- https://harpers.org/archive/1955/01/the-jet-propelled-couch-2/?single=1 The Jet-Propelled Couch, by Robert Mitchell Lindner 2024-01-01 fiction/science-fiction psychiatry/autism/schizoid --- https://elms.faculty.ucdavis.edu/wp-content/uploads/sites/98/2014/07/20021-Behind-the-Jet1.pdf 2024-01-01 fiction/science-fiction psychiatry/autism/schizoid --- https://www.amazon.com/Fifty-Minute-Hour-Robert-Lindner/dp/1892746247 The Fifty-Minute Hour Robert Lindner 1955 2024-01-01 psychiatry --- https://en.wikipedia.org/wiki/Gumbel_distribution#Occurrence_and_applications Gumbel distribution § Occurrence and applications 2024-12-09 statistics/order --- https://en.wikipedia.org/wiki/1%25_rule 1% rule 2024-01-01 psychology/writing sociology/technology --- https://www.astralcodexten.com/p/highlights-from-the-comments-on-prison Highlights From The Comments On Prison 2024-12-10 crime iq/low --- https://thezvi.wordpress.com/2024/12/10/o1-turns-pro/ o1 Turns Pro 2024-12-10 2024-12-10 ai/nn/transformer/gpt/4/nonfiction ai/nn/transformer/gpt/inner-monologue --- https://www.jstatsoft.org/article/view/v025i03 Getting Things in Order: An Introduction to the R Package seriation 2024-12-07 cs/algorithm/sorting statistics/order/comparison --- https://www.lesswrong.com/posts/dqSwccGTWyBgxrR58/turntrout-s-shortform-feed?commentId=nL7E6SEtXqDG7SHGB [Wikipedia guessing game proposal] Turntrout 2022-06-27 2024-12-07 design philosophy/epistemology wikipedia

Rationality exercise: Take a set of Wikipedia articles on topics which trainees are somewhat familiar with, and then randomly select a small number of claims to negate (negating the immediate context as well, so that you can’t just syntactically discover which claims were negated)....

--- https://corecursive.com/066-sqlite-with-richard-hipp/ The Untold Story of SQLite With Richard Hipp 2024-12-10 cs/algorithm economics --- https://arxiv.org/abs/2401.18059 RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, Christopher D. Manning 2024-01-31 2024-12-10 [("doi","10.48550/arXiv.2401.18059")] ai/nn/retrieval

Retrieval-augmented language models can better adapt to changes in world state and incorporate long-tail knowledge. However, most existing methods retrieve only short contiguous chunks from a retrieval corpus, limiting holistic understanding of the overall document context.

We introduce the novel approach of recursively embedding, clustering, and summarizing chunks of text, constructing a tree with differing levels of summarization from the bottom up. At inference time, our RAPTOR model retrieves from this tree, integrating information across lengthy documents at different levels of abstraction.

Controlled experiments show that retrieval with recursive summaries offers improvements over traditional retrieval-augmented LMs on several tasks. On question-answering tasks that involve complex, multi-step reasoning, we show state-of-the-art results; for example, by coupling RAPTOR retrieval with the use of [GPT-4], we can improve the best performance on the QuALITY benchmark by 20% in absolute accuracy.

--- https://til.simonwillison.net/llms/embed-paragraphs Embedding paragraphs from my blog with E5-large-v2 Simon Willison 2024-12-11 ai/nn/retrieval --- https://en.wikipedia.org/wiki/Hierarchical_navigable_small_world Hierarchical navigable small world 2024-12-11 ai/nn/retrieval cs/algorithm --- https://en.wikipedia.org/wiki/Range_tree Range tree 2024-12-11 ai/nn/retrieval cs/algorithm --- /tree-embedding Hierarchical Embeddings for Text Search Gwern 2024-12-10 2024-12-10 ai/nn/retrieval design

List-processing tricks for generating embeddings at different levels of document abstraction to allow efficient semantic searching.

A proposal for better retrieval on large, complex, hierarchically-structured document corpuses, which can be implemented straightforwardly using heuristics and pairwise embedding distances.

We embed each atomic element, merging them heuristically into larger embeddings repeatedly.

Then we search arbitrary text inputs in the reverse direction hierarchically, from the largest embedding to the smallest, to return the best matches in size order while excluding spuriously-similar hits.

--- https://www.scientificamerican.com/article/china-s-bold-push-into-genetically-customized-animals/ China’s Bold Push into Genetically Customized Animals: New kinds of dogs, goats and monkeys are being made quickly, although scientists voice worries about ethics and whether the methods should be used on humans 2024-01-01 genetics/editing --- https://arxiv.org/abs/2412.05296 Revisiting Your Memory: Reconstruction of Affect-Contextualized Memory via EEG-guided Audiovisual Generation Joonwoo Kwon, Heehwan Wang, Jinwoo Lee, Sooyoung Kim, Shinjae Yoo, Yuewei Lin, Jiook Cha 2024-11-24 2024-12-11 [("doi","10.48550/arXiv.2412.05296")] ai/nn/diffusion psychology/neuroscience reinforcement-learning/preference-learning

In this paper, we introduce RecallAffectiveMemory, a novel task designed to reconstruct autobiographical memories through audio-visual generation guided by affect extracted from electroencephalogram (EEG) signals.

To support this pioneering task, we present the EEG-AffectiveMemory dataset, which encompasses textual descriptions, visuals, music, and EEG recordings collected during memory recall from 9 participants.

Furthermore, we propose RYM (Recall Your Memory), a three-stage framework for generating synchronized audio-visual contents while maintaining dynamic personal memory affect trajectories.

Experimental results indicate that our method can faithfully reconstruct affect-contextualized audio-visual memory across all subjects, both qualitatively and quantitatively, with participants reporting strong affective concordance between their recalled memories and the generated content.

Our approaches advance affect decoding research and its practical applications in personalized media creation via neural-based affect comprehension.

--- /creative-benchmark Benchmarking LLM Diversity & Creativity Gwern 2024-12-08 2024-12-10 ai/nn/transformer/gpt/poetry reinforcement-learning/exploration reinforcement-learning/preference-learning/mode-collapse science/fermi-problem

Discussion of possible tasks to measure LLM capabilities in soft ‘creative’ tasks like brainstorming or editing, to quantify failures in creative writing domains.

--- https://x.com/paulg/status/1777030573220933716 Paul Graham 2024-12-11 ai/nn/transformer/gpt/4/nonfiction reinforcement-learning/preference-learning/mode-collapse --- https://en.wikipedia.org/wiki/Suno_AI Suno AI 2024-12-11 ai/music --- https://en.wikipedia.org/wiki/Negative_space Negative space 2024-12-11 design psychology/vision --- https://suno.com/blog/v4 Introducing v4 Suno AI 2024-12-11 ai/music ai/nn/transformer/gpt/fiction reinforcement-learning/preference-learning/mode-collapse --- https://www.asimov.press/p/mirror-life The Dangers of Mirrored Life 2024-12-12 existential-risk genetics/genome-synthesis/virus-proof --- https://www.science.org/doi/10.1126/science.ads9158 2024-12-12 existential-risk genetics/genome-synthesis/virus-proof --- https://arxiv.org/abs/2407.12220 Questionable practices in machine learning Gavin Leech, Juan J. Vazquez, Niclas Kupper, Misha Yagudin, Laurence Aitchison 2024-07-17 2024-12-12 [("doi","10.48550/arXiv.2407.12220")] ai/nn/transformer ai/tabular statistics/bias

Evaluating modern ML models is hard. The strong incentive for researchers and companies to report a state-of-the-art result on some metric often leads to questionable research practices (QRPs): bad practices which fall short of outright research fraud.

We describe 44 such practices which can undermine reported results, giving examples where possible. Our list emphasises the evaluation of large language models (LLMs) on public benchmarks.

We also discuss “irreproducible research practices”, i.e. decisions that make it difficult or impossible for other researchers to reproduce, build on or audit previous research.

--- /doc/psychology/linguistics/1972-brown.pdf#page=23 Studies in word listing: Some norms and their reliability § Colors W. P. Brown 1972-01-01 2024-12-12 design/typography/rubrication psychology/linguistics --- https://www.anthropic.com/research/clio Clio: Privacy-preserving insights into real-world AI use Anthropic 2024-12-12 2024-12-12 ai/nn/tokenization ai/nn/transformer/gpt/claude cs/security fiction/text-game --- https://www.bunniestudios.com/blog/2018/paper-as-a-substrate-for-circuits/ Paper As a Substrate for Circuits 2018 2024-12-12 cs/hardware --- https://openreview.net/forum?id=OrtN9hPP7V The GAN is dead; long live the GAN! R3GAN: A Modern GAN Baseline Nick Huang, Aaron Gokaslan, Volodymyr Kuleshov, James Tompkin 2024-11-06 2024-12-12 ai/nn/gan/stylegan

There is a widely-spread claim that GANs are difficult to train, and GAN architectures in the literature are littered with empirical tricks. We provide evidence against this claim and build a modern GAN baseline in a more principled manner.

First, we derive a well-behaved [zero-centered gradient-penalty] regularized relativistic GAN loss that addresses issues of mode dropping and non-convergence that were previously tackled via a bag of ad-hoc tricks. We analyze our loss mathematically and prove that it admits local convergence guarantees, unlike most existing relativistic losses. Second, this loss allows us to discard all ad-hoc tricks and replace outdated backbones used in common GANs with modern architectures. Using StyleGAN-2 as an example, we present a roadmap of simplification and modernization that results in a new minimalist baseline—R3GAN. [Briefly, we find that proper ResNet design17, 67, initialization99, and resampling29, 31, 32, 100 are important, along with grouped convolution95, 5 and no normalization.31, 34, 14, 88, 4]

Despite being simple, our approach surpasses StyleGAN-2 on FFHQ, ImageNet, CIFAR, and Stacked MNIST datasets, and compares favorably against state-of-the-art GANs and diffusion models.

Code: Github.

--- https://arxiv.org/abs/1801.04406 Which Training Methods for GANs do actually Converge? Lars Mescheder, Andreas Geiger, Sebastian Nowozin 2018-01-13 2024-12-12 [("doi","10.48550/arXiv.1801.04406")] ai/nn/gan

Recent work has shown local convergence of GAN training for absolutely continuous data and generator distributions. In this paper, we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent.

Furthermore, we discuss regularization strategies that were recently proposed to stabilize GAN training. Our analysis shows that GAN training with instance noise or zero-centered gradient penalties converges. On the other hand, we show that Wasserstein-GANs and WGAN-GP with a finite number of discriminator updates per generator update do not always converge to the equilibrium point.

We discuss these results, leading us to a new explanation for the stability problems of GAN training. Based on our analysis, we extend our convergence results to more general GANs and prove local convergence for simplified gradient penalties even if the generator and data distribution lie on lower dimensional manifolds.

We find these penalties to work well in practice and use them to learn high-resolution generative image models for a variety of datasets with little hyperparameter tuning.

--- https://www.theverge.com/2024/12/12/24318650/chatgpt-openai-history-two-year-anniversary Inside the OpenAI ChatGPT launch—and future 2024-12-12 2024-12-12 ai/nn/transformer/gpt/3/nonfiction reinforcement-learning/openai --- https://blog.invisiblethings.org/2013/02/21/converting-untrusted-pdfs-into-trusted.html Converting untrusted PDFs into trusted ones: The Qubes Way 2013-02-21 2024-12-13 cs/computable cs/security --- https://xenaproject.wordpress.com/2024/12/11/fermats-last-theorem-how-its-going/ Fermat’s Last Theorem—how it’s going 2024-12-11 2024-12-13 math philosophy/epistemology --- https://super-memory.com/articles/theory.htm Theoretical aspects of spaced repetition in learning Piotr Wozniak 2024-01-01 psychology/spaced-repetition --- https://www.supermemo.com/en/blog/twenty-rules-of-formulating-knowledge Effective learning: 20 rules of formulating knowledge Piotr Wozniak 2024-01-01 psychology/spaced-repetition --- https://www.susanblackmore.uk/articles/the-elusive-open-mind-ten-years-of-negative-research-in-parapsychology/ The Elusive Open Mind: 10 Years of Negative Research in Parapsychology Susan Blackmore 2024-01-01 statistics/bias --- https://www.systutorials.com/docs/linux/man/1-midi2abc/ midi2abc: program to convert MIDI format files to abc notation 2024-01-01 ai/music --- https://www.inkandswitch.com/capstone/#the-shelf-as-reimagined-copy-paste Capstone: A tablet for thinking § visualizing the copy-paste buffer Ink, Switch 2018-12 2024-12-13 cs/lisp/emacs design --- https://ctan.math.washington.edu/tex-archive/info/knuth-pdf/errata/errorlog.pdf The Errors of TeX Donald Knuth 2021-01-15 2024-12-15 design/typography/tex --- https://pmc.ncbi.nlm.nih.gov/articles/PMC10741819/ Trends in Cigarette Smoking Among United States Adolescents 2024-12-16 nicotine --- https://www.wsj.com/articles/the-death-of-zappos-tony-hsieh-a-spiral-of-alcohol-drugs-and-extreme-behavior-11607264719 The Death of Zappos’s Tony Hsieh: A Spiral of Alcohol, Drugs and Extreme Behavior: The inspirational executive seemed to lose his way after giving up his corporate role, including a starvation diet and fascination with fire Kirsten Grind, James R. Hagerty, Katherine Sayre 2020-12-06 2024-12-16 psychedelic --- https://arxiv.org/abs/2412.07752 FlashRNN: Optimizing Traditional RNNs on Modern Hardware Korbinian Pöppel, Maximilian Beck, Sepp Hochreiter 2024-12-10 2024-12-16 [("doi","10.48550/arXiv.2412.07752")] ai/nn/rnn

While Transformers and other sequence-parallelizable neural network architectures seem like the current state-of-the-art in sequence modeling, they specifically lack state-tracking capabilities. These are important for time-series tasks and logical reasoning. Traditional RNNs like LSTMs and GRUs, as well as modern variants like sLSTM, do have these capabilities at the cost of strictly sequential processing.

While this is often seen as a strong limitation, we show how fast these networks can get with our hardware-optimization FlashRNN in Triton and CUDA, optimizing kernels to the register level on modern GPUs. We extend traditional RNNs with a parallelization variant that processes multiple RNNs of smaller hidden state in parallel, similar to the head-wise processing in Transformers.

To enable flexibility on different GPU variants, we introduce a new optimization framework for hardware-internal cache sizes, memory, and compute handling. It models the hardware in a setting using polyhedral-like constraints, including the notion of divisibility. This speeds up the solution process in our new ConstrINT library for general integer constraint satisfaction problems (integer CSPs).

We show that our kernels can achieve 50× speed-ups over a vanilla PyTorch implementation and allow 40× larger hidden sizes compared to our Triton implementation.

Our open-source kernels and the optimization library are released here to boost research in the direction of state-tracking enabled RNNs and sequence modeling: Github.

--- https://reactormag.com/the-vampire-p-h-lee/ The V✱mpire: The vampires aren’t even the worst part about being a teenage trans girl on Tumblr P. H. Lee 2024-10-23 2024-12-16 fiction/fantasy psychiatry sociology/technology --- https://openai.com/index/triton/ Introducing Triton: Open-source GPU programming for neural networks Philippe Tillet 2021-07-28 2024-12-16 ai/nn cs/algorithm --- https://x.com/Academisfit/status/1868529612554420489 Academisfit 2024-12-16 ai/nn/transformer/gpt/4/nonfiction math --- https://www.larryniven.net/?q=yet-another-modest-proposal-the-roentgen-standard The Roentgen Standard Larry Niven 2024-01-01 economics fiction/science-fiction math/humor --- https://en.wikipedia.org/wiki/Twins_Days Twins Days 2024-12-16 genetics/heritable --- https://arxiv.org/abs/2410.18779#deepmind A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs Ankit Singh Rawat, Veeranjaneyulu Sadhanala, Afshin Rostamizadeh, Ayan Chakrabarti, Wittawat Jitkrittum, Vladimir Feinberg, Seungyeon Kim, Hrayr Harutyunyan, Nikunj Saunshi, Zachary Nado, Rakesh Shivanna, Sashank J. Reddi, Aditya Krishna Menon, Rohan Anil, Sanjiv Kumar 2024-10-24 2024-12-16 [("doi","10.48550/arXiv.2410.18779")] ai/nn/sparsity/knowledge-distillation reinforcement-learning/exploration/active-learning/data-pruning

A primary challenge in large language model (LLM) development is their onerous pre-training cost. Typically, such pre-training involves optimizing a self-supervised objective (such as next-token prediction) over a large corpus. This paper explores a promising paradigm to improve LLM pre-training efficiency and quality by suitably leveraging a small language model (SLM).

In particular, this paradigm relies on an SLM to both (1) provide soft labels as additional training supervision, and (2) select a small subset of valuable (“informative” and “hard”) training examples. Put together, this enables an effective transfer of the SLM’s predictive distribution to the LLM, while prioritizing specific regions of the training data distribution. Empirically, this leads to reduced LLM training time compared to standard training, while improving the overall quality.

Theoretically, we develop a statistical framework to systematically study the utility of SLMs in enabling efficient training of high-quality LLMs. In particular, our framework characterizes how the SLM’s seemingly low-quality supervision can enhance the training of a much more capable LLM. Furthermore, it also highlights the need for an adaptive usage of such supervision, by striking a balance between the bias and variance introduced by the SLM-provided soft labels.

We corroborate our theoretical framework by improving the pre-training of an LLM with 2.8B parameters by using a smaller LM with 1.5B parameters on the Pile dataset.

--- https://en.wikipedia.org/wiki/San_Ysidro_McDonald%27s_massacre San Ysidro McDonald’s massacre 1984-07-18 2024-12-16 crime/terrorism psychiatry/schizophrenia --- https://arxiv.org/abs/2412.09810 The Complexity Dynamics of Grokking Branton DeMoss, Silvia Sapora, Jakob Foerster, Nick Hawes, Ingmar Posner 2024-12-13 2024-12-16 [("doi","10.48550/arXiv.2412.09810")] ai/scaling/emergence/grokking cs/algorithm/information/compression

[code, blog] We investigate the phenomenon of generalization through the lens of compression. In particular, we study the complexity dynamics of neural networks to explain grokking, where networks suddenly transition from memorizing to generalizing solutions long after overfitting the training data.

To this end, we introduce a new measure of intrinsic complexity for neural networks based on the theory of Kolmogorov complexity [approximating using gzip]. Tracking this metric throughout network training, we find a consistent pattern in training dynamics, consisting of a rise and fall in complexity. We demonstrate that this corresponds to memorization followed by generalization.

Based on insights from rate-distortion theory and the minimum description length principle, we lay out a principled approach to lossy compression of neural networks, and connect our complexity measure to explicit generalization bounds.

Based on a careful analysis of information capacity in neural networks, we propose a new regularization method which encourages networks towards low-rank representations by penalizing their spectral entropy, and find that our regularizer outperforms baselines in total compression of the dataset.

--- https://brantondemoss.com/research/grokking/ The Complexity Dynamics of Grokking [blog] Branton DeMoss, Silvia Sapora, Jakob Foerster, Nick Hawes, Ingmar Posner 2024-12-16 ai/scaling/emergence/grokking cs/algorithm/information/compression --- https://www.biorxiv.org/content/10.1101/2024.04.21.590435.full Models optimized for real-world tasks reveal the task-dependent necessity of precise temporal coding in hearing Mark R. Saddler, Josh H. McDermott 2024-09-16 2024-12-16 [("doi","10.1101/2024.04.21.590435")] psychology/neuroscience

Neurons encode information in the timing of their spikes in addition to their firing rates. Spike timing is particularly precise in the auditory nerve, where action potentials phase lock to sound with sub-millisecond precision, but its behavioral relevance remains uncertain.

We optimized machine learning models to perform real-world hearing tasks with simulated cochlear input, assessing the precision of auditory nerve spike timing needed to reproduce human behavior. Models with high-fidelity phase locking exhibited more human-like sound localization and speech perception than models without, consistent with an essential role in human hearing.

However, the temporal precision needed to reproduce human-like behavior varied across tasks, as did the precision that benefited real-world task performance. These effects suggest that perceptual domains incorporate phase locking to different extents depending on the demands of real-world hearing.

The results illustrate how optimizing models for realistic tasks can clarify the role of candidate neural codes in perception.

--- https://arxiv.org/abs/2108.08481 Neural Operator: Learning Maps Between Function Spaces Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar 2021-08-19 2024-12-16 [("doi","10.5555/3648699.3648788")] ai/nn

The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces or finite sets. We propose a generalization of neural networks to learn operators, termed neural operators, that map between infinite-dimensional function spaces. We formulate the neural operator as a composition of linear integral operators and nonlinear activation functions.

We prove a universal approximation theorem for our proposed neural operator, showing that it can approximate any given nonlinear continuous operator. The proposed neural operators are also discretization-invariant, ie. they share the same model parameters among different discretizations of the underlying function spaces.

Furthermore, we introduce 4 classes of efficient parameterization, viz., graph neural operators, multi-pole graph neural operators, low-rank neural operators, and Fourier neural operators.

An important application for neural operators is learning surrogate maps for the solution operators of partial differential equations (PDEs). We consider standard PDEs such as the Burgers, Darcy subsurface flow, and the Navier-Stokes equations, and show that the proposed neural operators have superior performance compared to existing machine learning-based methodologies, while being several orders of magnitude faster than conventional PDE solvers.

--- https://magic.dev/blog/100m-token-context-windows 100M Token Context Windows 2024-12-16 ai/nn/retrieval ai/nn/transformer/attention --- /doc/exercise/2011-schiffer.pdf Lactate infusion at rest increases BDNF blood concentration in humans Thorsten Schiffer, Stefanie Schulte, Billy Sperlich, Silvia Achtzehn, Hannes Fricke, Heiko K. Strüder 2011-01-25 2024-12-16 [("doi","10.1016/j.neulet.2010.11.035")] exercise psychology/neuroscience

  • Blood lactate is involved in the regulation of BDNF blood concentrations.

  • This strengthens the assumption that high intensity exercise increases BDNF in blood.

  • The lactate clamp can be used to examine BDNF responses in healthy humans.

Studies in humans use blood lactate to determine the degree of exercise intensity, suggesting that exercise with elevated blood lactate concentrations results in increased BDNF plasma concentrations. However, it is not clear if lactate per se or rather other mechanisms are responsible for changes in blood BDNF concentrations.

The lactate clamp method at rest is an appropriate method to examine physiological responses of lactate on the human organism without the effects of exercise. 8 male sport students placed in a sitting position received intravenous infusions with a 4 molar sodium-lactate solution in an incremental design starting with an infusion rate of 0.01 ml/kgBW/min for the first 3 minutes, which was increased every 3 minutes by 0.01 ml/kgBW/min up to 0.08 ml/kg/min in the 24th minute. All together each subject received 4.2 mmol of infusion.

Venous blood samples were taken before and immediately after the infusion as well as in the 24th and the 60th min after the infusion period and analyzed for BDNF. Blood gases and capillary blood lactate (La) were analyzed before the test, every 3 minutes directly before increasing the infusion rate, at the end of the infusion and in the post infusion period until the 12th min and after 24 and 60 min.

BDNF and La increased statistically-significantly after the infusion and reached baseline values at the end of the experiment (p < 0.05, p < 0.01, respectively). pH and hydrogen ions increased from the beginning until the end of the infusion period (p < 0.01).

This data suggest that blood lactate is involved in the regulation of BDNF blood concentrations.

[Keywords: neurotrophins, acidosis, alkalosis, sodium-lactate]

--- https://arxiv.org/abs/2407.00695 Learning Formal Mathematics From Intrinsic Motivation Gabriel Poesia, David Broman, Nick Haber, Noah D. Goodman 2024-06-30 2024-12-16 [("doi","10.48550/arXiv.2407.00695")] math reinforcement-learning/exploration reinforcement-learning/model/alphago

How did humanity coax mathematics from the aether? We explore the Platonic view that mathematics can be discovered from its axioms—a game of conjecture and proof.

We describe Minimo (Mathematics from Intrinsic Motivation): an agent that jointly learns to pose challenging problems for itself (conjecturing) and solve them (theorem proving). Given a mathematical domain axiomatized in dependent type theory, we first combine methods for constrained decoding and type-directed synthesis to sample valid conjectures from a language model. Our method guarantees well-formed conjectures by construction, even as we start with a randomly initialized model.

We use the same model to represent a policy and value function for guiding proof search. Our agent targets generating hard but provable conjectures—a moving target, since its own theorem proving ability also improves as it trains.

We propose novel methods for hindsight relabeling on proof search trees to improve the agent’s sample efficiency in both tasks.

Experiments on 3 axiomatic domains (propositional logic, arithmetic, and group theory) demonstrate that our agent can bootstrap from only the axioms, self-improving in generating true and challenging conjectures and in finding proofs.

--- https://arxiv.org/abs/2206.02279 Assembly Theory Explains and Quantifies the Emergence of Selection and Evolution Abhishek Sharma, Dániel Czégel, Michael Lachmann, Christopher P. Kempes, Sara I. Walker, Leroy Cronin 2022-06-05 2024-12-18 [("doi","10.48550/arXiv.2206.02279")] cs/algorithm/information genetics/selection/natural

Since the time of Darwin, scientists have struggled to reconcile the evolution of biological forms in a universe determined by fixed laws. These laws underpin the origin of life, evolution, human culture, and technology, as set by the boundary conditions of the universe; however, these laws cannot predict the emergence of these things. By contrast, evolutionary theory works in the opposite direction, indicating how selection can explain why some things exist and not others.

To understand how open-ended forms can emerge in a forward process from physics that does not include their design, a new approach to understand the non-biological to biological transition is necessary. Herein, we present a new theory, Assembly Theory (AT), which explains and quantifies the emergence of selection and evolution. In AT, the complexity of an individual observable object is measured by its Assembly Index (a), defined as the minimal number of steps needed to construct the object from basic building blocks. Combining a with the copy number defines a new quantity called Assembly, which quantifies the amount of selection required to produce a given ensemble of objects.

We investigate the internal structure and properties of assembly space and quantify the dynamics of undirected exploratory processes as compared to the directed processes that emerge from selection. The implementation of assembly theory allows the emergence of selection in physical systems to be quantified at any scale as the transition from undirected-discovery dynamics to a selected process within the assembly space.

This yields a mechanism for the onset of selection and evolution and a formal approach to defining life. Because the assembly of an object is easily calculable and measurable, it is possible to quantify a lower limit on the amount of selection and memory required to produce complexity uniquely linked to biology in the universe.

--- https://buttondown.com/hillelwayne/archive/formally-modeling-dreidel-the-sequel/ Formally modeling Dreidel, the sequel Hillel Wayne 2024-12-18 statistics/probability --- https://en.wikipedia.org/wiki/Boyer%E2%80%93Moore_majority_vote_algorithm Boyer–Moore majority vote algorithm 2024-12-22 cs/algorithm --- https://www.lesswrong.com/posts/sbcmACvB6DqYXYidL/counter-theses-on-sleep?commentId=BBHo2epJcXsDAcJBg [Inducing hypomania through sleep deprivation] 2024-12-22 2024-12-22 psychiatry/bipolar/energy psychiatry/bipolar/sleep --- https://x.com/lumpenspace/status/1870309927501344813 lumpenspace 2024-12-22 ai/nn/adversarial ai/nn/transformer/gpt/4/nonfiction --- https://arxiv.org/abs/2412.09764#facebook Memory Layers at Scale Vincent-Pierre Berges, Barlas Oğuz, Daniel Haziza, Wen-tau Yih, Luke Zettlemoyer, Gargi Ghosh 2024-12-12 2024-12-23 [("doi","10.48550/arXiv.2412.09764")] ai/nn/retrieval ai/scaling/mixture-of-experts ai/scaling reinforcement-learning/meta-learning/continual-learning

[cf. PEER] Memory layers use a trainable key-value lookup mechanism to add extra parameters to a model without increasing FLOPs. Conceptually, sparsely activated memory layers complement compute-heavy dense feed-forward layers, providing dedicated capacity to store and retrieve information cheaply.

This work takes memory layers beyond proof-of-concept, proving their utility at contemporary scale. On downstream tasks, language models augmented with our improved memory layer outperform dense models with more than twice the computation budget, as well as mixture-of-expert models when matched for both compute and parameters.

We find gains are especially pronounced for factual tasks. We provide a fully parallelizable memory layer implementation, demonstrating scaling laws with up to 128B memory parameters, pretrained to 1 trillion tokens, comparing to base models with up to 8B parameters.

--- https://github.com/aidanmclaughlin/AidanBench Aidan Bench attempts to measure ‘big model smell’ in LLMs 2024-12-23 reinforcement-learning/preference-learning/mode-collapse ai/nn/transformer/gpt --- https://emilkirkegaard.dk/en/2024/12/bmi-vs-body-fat/ BMI vs. body fat % Emil Kirkegaard 2024-12 2024-12-23 exercise --- https://www.poetryfoundation.org/poems/57150/axe-handles Axe Handles Gary Snyder 2024-01-01 fiction/poetry psychology/writing --- https://en.wikipedia.org/wiki/Michael_Schumacher#2013_skiing_accident Michael Schumacher § 2013 skiing accident 2013 2024-12-23 psychiatry/traumatic-brain-injury --- https://win-vector.com/2024/12/19/kelly-cant-fail/ When Kelly Can’t Fail 2024-12-19 2024-12-23 statistics/decision --- https://waymo.com/research/do-autonomous-vehicles-outperform-latest-generation-human-driven-vehicles-25-million-miles/ Do Autonomous Vehicles Outperform Latest-Generation Human-Driven Vehicles? A Comparison to Waymo’s Auto Liability Insurance Claims at 25 Million Miles 2024-12-23 reinforcement-learning/robot --- https://arxiv.org/abs/2412.13663 Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference Benjamin Warner, Antoine Chaffin, Benjamin Clavié, Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Gallagher, Raja Biswas, Faisal Ladhak, Tom Aarsen, Nathan Cooper, Griffin Adams, Jeremy Howard, Iacopo Poli 2024-12-18 2024-12-23 [("doi","10.48550/arXiv.2412.13663")] ai/nn/transformer ai/scaling ai/nn/retrieval attention/sparsity

[blog] Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of numerous production pipelines, there have been limited Pareto improvements to BERT since its release.

In this paper, we introduce ModernBERT, bringing modern model optimizations to encoder-only models and representing a major Pareto improvement over older encoders. Trained on 2 trillion tokens with a native 8,192 sequence length, ModernBERT models exhibit state-of-the-art results on a large pool of evaluations encompassing diverse classification tasks and both single and multi-vector retrieval on different domains (including code).

In addition to strong downstream performance, ModernBERT is also the most speed and memory efficient encoder and is designed for inference on common GPUs.

--- https://huggingface.co/blog/modernbert Finally, a Replacement for BERT: Introducing ModernBERT 2024-12-23 ai/nn/transformer ai/scaling ai/nn/retrieval ai/nn/transformer/attention/sparsity --- https://arxiv.org/abs/2407.04153#deepmind Mixture of A Million Experts Xu Owen He 2024-07-04 2024-12-23 [("doi","10.48550/arXiv.2407.04153")] ai/scaling/mixture-of-experts retrieval

The feedforward (FFW) layers in standard transformer architectures incur a linear increase in computational costs and activation memory as the hidden layer width grows. Sparse mixture-of-experts (MoE) architectures have emerged as a viable approach to address this issue by decoupling model size from computational cost.

The recent discovery of the fine-grained MoE scaling law shows that higher granularity leads to better performance. However, existing MoE models are limited to a small number of experts due to computational and optimization challenges.

This paper introduces PEER (parameter efficient expert retrieval), a novel layer design that uses the product key technique for sparse retrieval from a vast pool of tiny experts (over a million).

Experiments on language modeling tasks demonstrate that PEER layers outperform dense FFWs and coarse-grained MoEs in terms of performance-compute trade-off.

By enabling efficient usage of a massive number of experts, PEER unlocks the potential for further scaling of transformer models while maintaining computational efficiency.

--- https://x.com/whybyfire/status/1871210442653405396 whybyfire 2024-12-23 ai/nn/gan/biggan reinforcement-learning/preference-learning/mode-collapse --- https://www.thelancet.com/journals/lanplh/article/PIIS2542-5196(21)00081-4/fulltext 2024-12-23 biology --- https://lwn.net/SubscriberLink/1001645/b1e4453a8c6c16d7/ Using Guile for Emacs 2024-12-23 cs/lisp/emacs --- https://arxiv.org/abs/1703.06585 Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning Abhishek Das, Satwik Kottur, José M. F. Moura, Stefan Lee, Dhruv Batra 2017-03-20 2024-12-23 [("doi","10.48550/arXiv.1703.06585")] reinforcement-learning/model-free reinforcement-learning/multi-agent linguistics

We introduce the first goal-driven training for visual question answering and dialog agents. Specifically, we pose a cooperative ‘image guessing’ game between two agents—Qbot and Abot—who communicate in natural language dialog so that Qbot can select an unseen image from a lineup of images. We use deep reinforcement learning (RL) to learn the policies of these agents end-to-end—from pixels to multi-agent multi-round dialog to game reward.

We demonstrate two experimental results.

First, as a ’sanity check’ demonstration of pure RL (from scratch), we show results on a synthetic world, where the agents communicate in ungrounded vocabulary, ie. symbols with no pre-specified meanings (X, Y, Z).

We find that two bots invent their own communication protocol and start using certain symbols to ask/answer about certain visual attributes (shape/color/style). Thus, we demonstrate the emergence of grounded language and communication among ’visual’ dialog agents with no human supervision.

Second, we conduct large-scale real-image experiments on the VisDial dataset, where we pretrain with supervised dialog data and show that the RL ’fine-tuned’ agents outperform SL agents.

Interestingly, the RL Qbot learns to ask questions that Abot is good at, ultimately resulting in more informative dialog and a better team.